From c6eeb4e8ce031cc1828e6d6850d65cc6541dfa76 Mon Sep 17 00:00:00 2001 From: ssbvr Date: Wed, 13 Jul 2022 13:56:41 +0200 Subject: [PATCH 001/319] update pandas and h5py module verions to silence errors --- setup.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index f267eab714..5d9d6bd51b 100644 --- a/setup.py +++ b/setup.py @@ -65,12 +65,12 @@ 'iminuit == 1.4.9', 'configparser == 5.0.0', 'astropy == 4.0.1', - 'pandas == 1.1.0', + 'pandas == 1.3.0', 'scikit-learn == 0.21.3', 'matplotlib == 3.2.2', 'matplotlib-label-lines == 0.3.8', 'PyQt5 == 5.15.3', - 'h5py == 2.10.0', + 'h5py == 3.7.0', 'psutil == 5.6.7', 'tqdm == 4.48.2', 'tables == 3.6.1', From 266b0cf18139dc13c9ac54d49730398b5b68b96b Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Wed, 20 Jul 2022 14:43:18 +0200 Subject: [PATCH 002/319] remove obsolete star variable spin_vector (#1) --- docs/pop_synth/pop_synth.rst | 2 -- posydon/binary_evol/singlestar.py | 2 -- posydon/popsyn/population_params_default.ini | 2 -- 3 files changed, 6 deletions(-) diff --git a/docs/pop_synth/pop_synth.rst b/docs/pop_synth/pop_synth.rst index d38c949821..24a6e7ae0f 100644 --- a/docs/pop_synth/pop_synth.rst +++ b/docs/pop_synth/pop_synth.rst @@ -222,7 +222,6 @@ This script list all customisable option of a POSYDON population synthesis run. 'SN_type', #'f_fb', #'spin_orbit_tilt', - #'spin_vector' ]), # LIST STAR PROPERTIES TO SAVE @@ -287,7 +286,6 @@ This script list all customisable option of a POSYDON population synthesis run. 'SN_type', #'f_fb', #'spin_orbit_tilt', - #'spin_vector' ]), ) diff --git a/posydon/binary_evol/singlestar.py b/posydon/binary_evol/singlestar.py index 22cced017d..84a6c19880 100644 --- a/posydon/binary_evol/singlestar.py +++ b/posydon/binary_evol/singlestar.py @@ -129,8 +129,6 @@ def __init__(self, **kwargs): self.natal_kick_array = [None] * 4 if not hasattr(self, 'spin_orbit_tilt'): self.spin_orbit_tilt = None - if not hasattr(self, 'spin_vector'): - self.spin_vector = [None] * 3 if not hasattr(self, 'f_fb'): self.f_fb = None if not hasattr(self, 'SN_type'): diff --git a/posydon/popsyn/population_params_default.ini b/posydon/popsyn/population_params_default.ini index 9b168b17e0..b8cd9ce027 100644 --- a/posydon/popsyn/population_params_default.ini +++ b/posydon/popsyn/population_params_default.ini @@ -367,7 +367,6 @@ 'SN_type', #'f_fb', #'spin_orbit_tilt', - #'spin_vector' ] [SingleStar_2_output] @@ -435,5 +434,4 @@ 'SN_type', #'f_fb', #'spin_orbit_tilt', - #'spin_vector' ] From 0cfa6c5ea12a8b49d2f130a3e3758e261b0b99eb Mon Sep 17 00:00:00 2001 From: kkovlakas Date: Wed, 27 Jul 2022 11:44:23 +0300 Subject: [PATCH 003/319] Reading initial mass from headers in singlestar grids --- posydon/grids/psygrid.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index 4b12883c51..fdcdb7e2d9 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -846,6 +846,14 @@ def decide_columns(key_in_config, defaults): init_separation = orbital_separation_from_period( init_period, init_mass_1, init_mass_2) initial_BH["binary_separation"] = init_separation + elif not binary_grid and not (start_at_RLO or ignore_data): + # use header to get initial mass in single-star grids + h1_header = np.genfromtxt(run.history1_path, + skip_header=1, + max_rows=1, names=True) + if "S1_star_mass" in dtype_initial_values.names: + init_mass_1 = h1_header["initial_m"] + initial_H1["star_mass"] = init_mass_1 # get some initial values from the `LOGS1/history.data` header addX = "X" in dtype_initial_values.names From 96aca030b559f9c2352f2ee6b9b0d013bacf6270 Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Wed, 27 Jul 2022 16:33:57 +0200 Subject: [PATCH 004/319] Solved minor bug affecting initial_RLOF post CE systems (#3) * solved bug affecting binaries classified as initial_RLOF in the mesa step post CE * reinstate submodule links --- data/POSYDON_data | 1 + grid_params/POSYDON-MESA-INLISTS | 1 + posydon/binary_evol/MESA/step_mesa.py | 7 +++++++ posydon/tests/data/POSYDON-UNIT-TESTS | 1 + 4 files changed, 10 insertions(+) create mode 160000 data/POSYDON_data create mode 160000 grid_params/POSYDON-MESA-INLISTS create mode 160000 posydon/tests/data/POSYDON-UNIT-TESTS diff --git a/data/POSYDON_data b/data/POSYDON_data new file mode 160000 index 0000000000..e5d8d77985 --- /dev/null +++ b/data/POSYDON_data @@ -0,0 +1 @@ +Subproject commit e5d8d77985fc1502b6b6cc0400577623d50743ab diff --git a/grid_params/POSYDON-MESA-INLISTS b/grid_params/POSYDON-MESA-INLISTS new file mode 160000 index 0000000000..9ddb61bb0c --- /dev/null +++ b/grid_params/POSYDON-MESA-INLISTS @@ -0,0 +1 @@ +Subproject commit 9ddb61bb0c482399fa5a41dd22fde41ccd8175d9 diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index 7e6b0ee5d3..bc9f1e039a 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -712,6 +712,9 @@ def update_properties_NN(self, star_1_CO=False, star_2_CO=False, getattr(binary, "event_history").extend(binary_event) getattr(binary, "mass_transfer_case_history").extend(MT_case) + if binary.state == 'initial_RLOF': + return + if (star_2_CO or star_1_CO): # Updating Bondi-Hoyle accretion for k, star in enumerate(stars): @@ -881,6 +884,7 @@ def initial_final_interpolation(self, star_1_CO=False, star_2_CO=False): star_CO=star_2_CO) #S1_state_classified = self.classes['S1_state'] #S2_state_classified = self.classes['S2_state'] + if interpolation_class != 'initial_MT': # DEBUG # if S1_state_inferred != S1_state_classified: @@ -904,6 +908,9 @@ def initial_final_interpolation(self, star_1_CO=False, star_2_CO=False): setattr(self.binary, 'event', binary_event) setattr(self.binary, 'mass_transfer_case', MT_case) + if binary.state == 'initial_RLOF': + return + if (star_2_CO or star_1_CO): # Updating Bondi-Hoyle accretion for k, star in enumerate(stars): diff --git a/posydon/tests/data/POSYDON-UNIT-TESTS b/posydon/tests/data/POSYDON-UNIT-TESTS new file mode 160000 index 0000000000..eaf9d59229 --- /dev/null +++ b/posydon/tests/data/POSYDON-UNIT-TESTS @@ -0,0 +1 @@ +Subproject commit eaf9d592291f093cc0095e13c93d431c5b6051da From d0847aeac9adc8d72f2c0fdd82adea41835ca750 Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Wed, 27 Jul 2022 16:34:14 +0200 Subject: [PATCH 005/319] Fix occasional duplicate saving in pop synth (#2) * I believe I've fixed it * forgot to add fix for ram_opt batch number mode * took out print statements Co-authored-by: prinse1545 --- posydon/popsyn/binarypopulation.py | 25 +++++++++++++------------ 1 file changed, 13 insertions(+), 12 deletions(-) diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index b59c9bd77a..4c3b450745 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -196,6 +196,7 @@ def _safe_evolve(self, **kwargs): filenames = [] for j, index in enumerate(indices_for_iter): + if kwargs.get('from_hdf', False): binary = self.manager.from_hdf(index, restore=True).pop() else: @@ -233,13 +234,13 @@ def _safe_evolve(self, **kwargs): if(breakdown_to_df): self.manager.clear_dfs() else: - self.manager.remove(self.manager.binaries) + self.manager.remove(self.manager.binaries.copy()) # Check to see if used memory is greater than 99% of allowed memory # rss gives memory usage in bytes, so divide by 2^30 to get GBs elif (optimize_ram and ram_per_cpu is not None and psutil.Process().memory_info().rss / (1024**3) - >= 0.99 * ram_per_cpu): + >= 0.9 * ram_per_cpu): if(self.comm is None): path = os.path.join(temp_directory, f"{j}_evolution.batch") @@ -254,7 +255,8 @@ def _safe_evolve(self, **kwargs): if(breakdown_to_df): self.manager.clear_dfs() else: - self.manager.remove(self.manager.binaries) + self.manager.remove(self.manager.binaries.copy()) + # handling case if dump rate is not multiple of population size if ((len(self.manager.binaries) != 0 @@ -274,19 +276,19 @@ def _safe_evolve(self, **kwargs): if(breakdown_to_df): self.manager.clear_dfs() else: - self.manager.remove(self.manager.binaries) + self.manager.remove(self.manager.binaries.copy()) if optimize_ram: # combining files if self.comm is None: self.combine_saved_files(os.path.join(temp_directory, "evolution.combined"), - filenames) + filenames, mode = "w") else: self.combine_saved_files( os.path.join(temp_directory, f"evolution.combined.{self.rank}"), - filenames) + filenames, mode = "w") else: if self.comm is None: @@ -331,10 +333,8 @@ def save(self, save_path, mode='a', **kwargs): self.kwargs["temp_directory"], f"evolution.combined.{i}") for i in range(self.size)] - if os.path.isfile(absolute_filepath) and mode != "a": - os.remove(absolute_filepath) - self.combine_saved_files(absolute_filepath, tmp_files) + self.combine_saved_files(absolute_filepath, tmp_files, mode = mode) else: return @@ -345,7 +345,7 @@ def make_temp_fname(self): return os.path.join(temp_directory, f"evolution.combined.{self.rank}") # return os.path.join(dir_name, '.tmp{}_'.format(rank) + file_name) - def combine_saved_files(self, absolute_filepath, file_names): + def combine_saved_files(self, absolute_filepath, file_names, mode = "a"): """Combine various temporary files in a given folder.""" dir_name = os.path.dirname(absolute_filepath) @@ -361,7 +361,7 @@ def combine_saved_files(self, absolute_filepath, file_names): ONELINE_MIN_ITEMSIZE.items() if key in oneline_cols} - with pd.HDFStore(absolute_filepath) as store: + with pd.HDFStore(absolute_filepath, mode = mode) as store: for f in file_names: # strings itemsize set by first append max value, # which may not be largest string @@ -448,10 +448,11 @@ def append(self, binary): def remove(self, binary): """Remove a binary instance.""" - if isinstance(binary, (list, np.ndarray)): + if isinstance(binary, (list, np.ndarray)): for b in binary: self.binaries.remove(b) self.indices.remove(b.index) + elif isinstance(binary, BinaryStar): self.binaries.remove(binary) self.indices.remove(binary.index) From dd9b972807332fd034a4cf5dfb3963eb20f7fc93 Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Tue, 2 Aug 2022 14:34:22 +0200 Subject: [PATCH 006/319] migrate inlist repo (#6) --- .gitmodules | 6 +++--- grid_params/POSYDON-MESA-INLISTS | 1 - grid_params/POSYDON-MESA-INLISTS-public | 1 + 3 files changed, 4 insertions(+), 4 deletions(-) delete mode 160000 grid_params/POSYDON-MESA-INLISTS create mode 160000 grid_params/POSYDON-MESA-INLISTS-public diff --git a/.gitmodules b/.gitmodules index 3b27cbeb58..84d9432457 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,9 +1,9 @@ [submodule "data/POSYDON_data"] path = data/POSYDON_data url = https://github.com/POSYDON-code/POSYDON_data.git -[submodule "grid_params/POSYDON-MESA-INLISTS"] - path = grid_params/POSYDON-MESA-INLISTS - url = https://github.com/POSYDON-code/POSYDON-MESA-INLISTS [submodule "posydon/tests/data/POSYDON-UNIT-TESTS"] path = posydon/tests/data/POSYDON-UNIT-TESTS url = https://github.com/POSYDON-code/POSYDON-UNIT-TESTS.git +[submodule "grid_params/POSYDON-MESA-INLISTS-public"] + path = grid_params/POSYDON-MESA-INLISTS-public + url = https://github.com/POSYDON-code/POSYDON-MESA-INLISTS-public.git diff --git a/grid_params/POSYDON-MESA-INLISTS b/grid_params/POSYDON-MESA-INLISTS deleted file mode 160000 index 9ddb61bb0c..0000000000 --- a/grid_params/POSYDON-MESA-INLISTS +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 9ddb61bb0c482399fa5a41dd22fde41ccd8175d9 diff --git a/grid_params/POSYDON-MESA-INLISTS-public b/grid_params/POSYDON-MESA-INLISTS-public new file mode 160000 index 0000000000..f2733e4922 --- /dev/null +++ b/grid_params/POSYDON-MESA-INLISTS-public @@ -0,0 +1 @@ +Subproject commit f2733e492293e49e13edc0a6c92b99a4d488a2fc From 8e2834ead0fdde0b3418262e144db24f35c4e71d Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Tue, 2 Aug 2022 14:34:33 +0200 Subject: [PATCH 007/319] Update docs (#7) * update inlist submodule names in docs * fix data path in docs --- README.md | 2 +- docs/install/install.rst | 4 ++-- docs/run_mesa_grids/fixed/fixed.rst | 2 +- docs/run_mesa_grids/inifile.rst | 28 ++++++++++++++-------------- 4 files changed, 18 insertions(+), 18 deletions(-) diff --git a/README.md b/README.md index b2b582e7dd..41034fa7f8 100644 --- a/README.md +++ b/README.md @@ -57,7 +57,7 @@ get-posydon-data Inside the cloned POSYDON repository run the following commands to install and initialise git LFS, and download the data. ``` -export PATH_TO_POSYDON_DATA=$PATH_TO_POSYDON/data/POSYDON_data +export PATH_TO_POSYDON_DATA=$PATH_TO_POSYDON/data/ conda install git-lfs git lfs install git submodule init diff --git a/docs/install/install.rst b/docs/install/install.rst index 7f8b7f54b6..5c0acf2406 100644 --- a/docs/install/install.rst +++ b/docs/install/install.rst @@ -71,7 +71,7 @@ Downloading POSYDON data ------------------------ OPTION 1: Zenodo (latest official version) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Export the path to where you want to clone the data, e.g. `/home/`, and +Export the path to where you want to clone the data, e.g. `/home/`, and download the data with the following commands .. code-block:: @@ -87,7 +87,7 @@ install and initialise git LFS, and download the data. .. code-block:: - export PATH_TO_POSYDON_DATA=$PATH_TO_POSYDON/data/POSYDON_data + export PATH_TO_POSYDON_DATA=$PATH_TO_POSYDON/data/ conda install git-lfs git lfs install git submodule init diff --git a/docs/run_mesa_grids/fixed/fixed.rst b/docs/run_mesa_grids/fixed/fixed.rst index 419f428d10..a38628a601 100644 --- a/docs/run_mesa_grids/fixed/fixed.rst +++ b/docs/run_mesa_grids/fixed/fixed.rst @@ -55,7 +55,7 @@ always be specified, otherwise you will be evolving a binary with a default orbital period and masses. As for the .ini file, you can copy over the `example -`_ +`_ from the :ref:`inifile` page, which provides a more detailed description of all the entries. Make sure to carefully go through each of the separate entries and adjust them for your particular needs. Place that .ini file (which we have diff --git a/docs/run_mesa_grids/inifile.rst b/docs/run_mesa_grids/inifile.rst index 02420302a6..7992d7c442 100644 --- a/docs/run_mesa_grids/inifile.rst +++ b/docs/run_mesa_grids/inifile.rst @@ -23,11 +23,11 @@ quantities that need to be adjusted for your specific case. Here is a link to the most recent stable release version of the default inifile for POSYDON: -`Stable Version INIFILE `_ +`Stable Version INIFILE `_ Here is a link to the unstable development version of the default inifile for POSYDON: -`Development Version INIFILE `_ +`Development Version INIFILE `_ [slurm] ------- @@ -104,7 +104,7 @@ This section designates all the basic MESA-specific parameters. ; zams_filename if a zams_filename is supplied this supercedes any star1 or star2 formation inlists ; and skips to running the binary with this pre-computed zams model. - zams_filename = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS/r11701/ZAMS_models/zams_z0.0142m2_y0.2703.data + zams_filename = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS-public/r11701/ZAMS_models/zams_z0.0142m2_y0.2703.data ; single_star_grid, this boolean, when True, will take the inlist1 from the binary mesa inlist section ; and run in a single star grid configuration @@ -132,22 +132,22 @@ This section designates all the basic MESA-specific parameters. ; binary_control binary_controls_mesa_defaults = ${posydon_github_root}/grid_params/defaults/r11701/binary/binary_controls.defaults - ; binary_controls_posydon_defaults = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS/r11701/default_common_inlists/binary/inlist_project + ; binary_controls_posydon_defaults = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS-public/r11701/default_common_inlists/binary/inlist_project ; binary_controls_user = ${user_template_root}/binary/inlist_project ; binary_job binary_job_mesa_defaults = ${posydon_github_root}/grid_params/defaults/r11701/binary/binary_job.defaults - ; binary_job_posydon_defaults = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS/r11701/default_common_inlists/binary/inlist_project + ; binary_job_posydon_defaults = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS-public/r11701/default_common_inlists/binary/inlist_project ; binary_job_user = ${user_template_root}/binary/inlist_project ; star1_job star1_job_mesa_defaults = ${posydon_github_root}/grid_params/defaults/r11701/star/star_job.defaults - ; star1_job_posydon_defaults = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS/r11701/default_common_inlists/binary/inlist1 + ; star1_job_posydon_defaults = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS-public/r11701/default_common_inlists/binary/inlist1 ; star1_job_user = ${user_template_root}/binary/inlist1 ; star1_control star1_controls_mesa_defaults = ${posydon_github_root}/grid_params/defaults/r11701/star/controls.defaults - ; star1_controls_posydon_defaults = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS/r11701/default_common_inlists/binary/inlist1 + ; star1_controls_posydon_defaults = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS-public/r11701/default_common_inlists/binary/inlist1 ; star1_controls_user = ${user_template_root}/binary/inlist1 ; star2_job @@ -161,13 +161,13 @@ This section designates all the basic MESA-specific parameters. ; star2_controls_user = None ; star history columns - star_history_columns = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS/r11701/default_common_inlists/history_columns.list + star_history_columns = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS-public/r11701/default_common_inlists/history_columns.list ; binary history columns - binary_history_columns = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS/r11701/default_common_inlists/binary_history_columns.list + binary_history_columns = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS-public/r11701/default_common_inlists/binary_history_columns.list ; profile columns - profile_columns = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS/r11701/default_common_inlists/profile_columns.list + profile_columns = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS-public/r11701/default_common_inlists/profile_columns.list ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;;;;; MESA OUTPUT CONTROLS ;;;;;;; @@ -217,18 +217,18 @@ This section designates all the parameters for MESA makefiles and fortran files. ; user specified binary extra mesa_binary_extras = ${MESA_DIR}/binary/work/src/run_binary_extras.f - ; user_binary_extras = ${mesa_inlists:posydon_github_root}/grid_params/POSYDON-MESA-INLISTS/r11701/default_common_inlists/binary/src/run_binary_extras.f + ; user_binary_extras = ${mesa_inlists:posydon_github_root}/grid_params/POSYDON-MESA-INLISTS-public/r11701/default_common_inlists/binary/src/run_binary_extras.f ; user specified star extra - these go into the binary/src/ directory mesa_star_binary_extras = ${MESA_DIR}/binary/work/src/run_star_extras.f - ; user_star_binary_extras =${mesa_inlists:posydon_github_root}/grid_params/POSYDON-MESA-INLISTS/r11701/default_common_inlists/binary/src/run_star_extras.f + ; user_star_binary_extras =${mesa_inlists:posydon_github_root}/grid_params/POSYDON-MESA-INLISTS-public/r11701/default_common_inlists/binary/src/run_star_extras.f ; user specified star extras - these are for single star formation (e.g., pre-MS evolution) mesa_star1_extras = ${MESA_DIR}/star/work/src/run_star_extras.f - ; user_star1_extras = ${mesa_inlists:posydon_github_root}/grid_params/POSYDON-MESA-INLISTS/r11701/default_common_inlists/binary/src/run_star_extras.f + ; user_star1_extras = ${mesa_inlists:posydon_github_root}/grid_params/POSYDON-MESA-INLISTS-public/r11701/default_common_inlists/binary/src/run_star_extras.f mesa_star2_extras = ${MESA_DIR}/star/work/src/run_star_extras.f - ; user_star2_extras = ${mesa_inlists:posydon_github_root}/grid_params/POSYDON-MESA-INLISTS/r11701/default_common_inlists/binary/src/run_star_extras.f + ; user_star2_extras = ${mesa_inlists:posydon_github_root}/grid_params/POSYDON-MESA-INLISTS-public/r11701/default_common_inlists/binary/src/run_star_extras.f ; binary_run.f binary_run = ${MESA_DIR}/binary/work/src/binary_run.f From 821c60050d0f58ba29dfbf482ecf100e40691b72 Mon Sep 17 00:00:00 2001 From: ssbvr Date: Wed, 3 Aug 2022 13:17:55 +0200 Subject: [PATCH 008/319] fix min period orbital sempler --- posydon/popsyn/independent_sample.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/posydon/popsyn/independent_sample.py b/posydon/popsyn/independent_sample.py index 48ecbaf8c8..e529bcc6c6 100644 --- a/posydon/popsyn/independent_sample.py +++ b/posydon/popsyn/independent_sample.py @@ -62,7 +62,7 @@ def generate_independent_samples(orbital_scheme, **kwargs): def generate_orbital_periods(primary_masses, number_of_binaries=1, - orbital_period_min=10**0.15, + orbital_period_min=0.35, orbital_period_max=10**3.5, orbital_period_scheme='Sana+12_period_extended', **kwargs): From 9460ae21f1a135fdb27f324e43d158d838644643 Mon Sep 17 00:00:00 2001 From: ssbvr Date: Wed, 3 Aug 2022 13:19:01 +0200 Subject: [PATCH 009/319] fix mass limit sempler --- posydon/popsyn/independent_sample.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/posydon/popsyn/independent_sample.py b/posydon/popsyn/independent_sample.py index e529bcc6c6..785a4f08f6 100644 --- a/posydon/popsyn/independent_sample.py +++ b/posydon/popsyn/independent_sample.py @@ -240,8 +240,8 @@ def generate_eccentricities(number_of_binaries=1, def generate_primary_masses(number_of_binaries=1, - primary_mass_min=1, - primary_mass_max=120, + primary_mass_min=7, + primary_mass_max=150, primary_mass_scheme='Salpeter', **kwargs): """Generate random primary masses. @@ -306,8 +306,8 @@ def generate_primary_masses(number_of_binaries=1, def generate_secondary_masses(primary_masses, number_of_binaries=1, - secondary_mass_min=1, - secondary_mass_max=120, + secondary_mass_min=0.35, + secondary_mass_max=150, secondary_mass_scheme='flat_mass_ratio', **kwargs): """Generate random secondary masses. From f329b583654f998f87059841e2046f97ff6e99ce Mon Sep 17 00:00:00 2001 From: ssbvr Date: Tue, 6 Sep 2022 12:06:26 +0200 Subject: [PATCH 010/319] fix kick angle description --- posydon/binary_evol/SN/step_SN.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index c0b8922f1a..f8a1432b65 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -1190,7 +1190,10 @@ def orbital_kick(self, binary): phi : Angle between z-axis and projection of kick onto x-z plane. - cos_theta : + theta : + Angle between pre- supernova star velocity relative to the + companion (i.e. along the positive y axis) and the kick velocity. + tilt : The cosine of the angle between pre- and post- supenova orbial planes. This is equal to the angle between the relative velocity of the helium star to the companion just before the explosion (see Vr) From a81dda66b2e86e86ff92d8656e3905dbc645c125 Mon Sep 17 00:00:00 2001 From: ssbvr Date: Tue, 6 Sep 2022 12:10:05 +0200 Subject: [PATCH 011/319] fix kick angle description --- posydon/binary_evol/SN/step_SN.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index f8a1432b65..3d617ce322 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -1194,7 +1194,7 @@ def orbital_kick(self, binary): Angle between pre- supernova star velocity relative to the companion (i.e. along the positive y axis) and the kick velocity. tilt : - The cosine of the angle between pre- and post- supenova orbial + The angle between pre- and post- supenova orbial planes. This is equal to the angle between the relative velocity of the helium star to the companion just before the explosion (see Vr) and the projection of the relative velocy just after the explosion From 6de490b6c391da4db188bd2acd97f3b1f1af680c Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Mon, 10 Oct 2022 13:53:16 +0200 Subject: [PATCH 012/319] fix import data_download (#9) --- posydon/binary_evol/SN/step_SN.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index 3d617ce322..cf35f72a9d 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -32,7 +32,7 @@ import numpy as np import scipy as sp -from posydon.utils.data_download import PATH_TO_POSYDON_DATA +from posydon.utils.data_download import PATH_TO_POSYDON_DATA, data_download import posydon.utils.constants as const from posydon.utils.common_functions import is_number from posydon.utils.common_functions import CO_radius From 026a006f9c2656fbff3db045844646c7304213fb Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Mon, 10 Oct 2022 13:53:32 +0200 Subject: [PATCH 013/319] remove -public refs (#14) --- .gitmodules | 6 +++--- docs/run_mesa_grids/fixed/fixed.rst | 2 +- docs/run_mesa_grids/inifile.rst | 28 ++++++++++++++-------------- 3 files changed, 18 insertions(+), 18 deletions(-) diff --git a/.gitmodules b/.gitmodules index 84d9432457..9d551bc71d 100644 --- a/.gitmodules +++ b/.gitmodules @@ -4,6 +4,6 @@ [submodule "posydon/tests/data/POSYDON-UNIT-TESTS"] path = posydon/tests/data/POSYDON-UNIT-TESTS url = https://github.com/POSYDON-code/POSYDON-UNIT-TESTS.git -[submodule "grid_params/POSYDON-MESA-INLISTS-public"] - path = grid_params/POSYDON-MESA-INLISTS-public - url = https://github.com/POSYDON-code/POSYDON-MESA-INLISTS-public.git +[submodule "grid_params/POSYDON-MESA-INLISTS"] + path = grid_params/POSYDON-MESA-INLISTS + url = https://github.com/POSYDON-code/POSYDON-MESA-INLISTS.git diff --git a/docs/run_mesa_grids/fixed/fixed.rst b/docs/run_mesa_grids/fixed/fixed.rst index a38628a601..419f428d10 100644 --- a/docs/run_mesa_grids/fixed/fixed.rst +++ b/docs/run_mesa_grids/fixed/fixed.rst @@ -55,7 +55,7 @@ always be specified, otherwise you will be evolving a binary with a default orbital period and masses. As for the .ini file, you can copy over the `example -`_ +`_ from the :ref:`inifile` page, which provides a more detailed description of all the entries. Make sure to carefully go through each of the separate entries and adjust them for your particular needs. Place that .ini file (which we have diff --git a/docs/run_mesa_grids/inifile.rst b/docs/run_mesa_grids/inifile.rst index 7992d7c442..834e8772d2 100644 --- a/docs/run_mesa_grids/inifile.rst +++ b/docs/run_mesa_grids/inifile.rst @@ -23,11 +23,11 @@ quantities that need to be adjusted for your specific case. Here is a link to the most recent stable release version of the default inifile for POSYDON: -`Stable Version INIFILE `_ +`Stable Version INIFILE `_ Here is a link to the unstable development version of the default inifile for POSYDON: -`Development Version INIFILE `_ +`Development Version INIFILE `_ [slurm] ------- @@ -104,7 +104,7 @@ This section designates all the basic MESA-specific parameters. ; zams_filename if a zams_filename is supplied this supercedes any star1 or star2 formation inlists ; and skips to running the binary with this pre-computed zams model. - zams_filename = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS-public/r11701/ZAMS_models/zams_z0.0142m2_y0.2703.data + zams_filename = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS/r11701/ZAMS_models/zams_z0.0142m2_y0.2703.data ; single_star_grid, this boolean, when True, will take the inlist1 from the binary mesa inlist section ; and run in a single star grid configuration @@ -132,22 +132,22 @@ This section designates all the basic MESA-specific parameters. ; binary_control binary_controls_mesa_defaults = ${posydon_github_root}/grid_params/defaults/r11701/binary/binary_controls.defaults - ; binary_controls_posydon_defaults = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS-public/r11701/default_common_inlists/binary/inlist_project + ; binary_controls_posydon_defaults = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS/r11701/default_common_inlists/binary/inlist_project ; binary_controls_user = ${user_template_root}/binary/inlist_project ; binary_job binary_job_mesa_defaults = ${posydon_github_root}/grid_params/defaults/r11701/binary/binary_job.defaults - ; binary_job_posydon_defaults = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS-public/r11701/default_common_inlists/binary/inlist_project + ; binary_job_posydon_defaults = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS/r11701/default_common_inlists/binary/inlist_project ; binary_job_user = ${user_template_root}/binary/inlist_project ; star1_job star1_job_mesa_defaults = ${posydon_github_root}/grid_params/defaults/r11701/star/star_job.defaults - ; star1_job_posydon_defaults = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS-public/r11701/default_common_inlists/binary/inlist1 + ; star1_job_posydon_defaults = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS/r11701/default_common_inlists/binary/inlist1 ; star1_job_user = ${user_template_root}/binary/inlist1 ; star1_control star1_controls_mesa_defaults = ${posydon_github_root}/grid_params/defaults/r11701/star/controls.defaults - ; star1_controls_posydon_defaults = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS-public/r11701/default_common_inlists/binary/inlist1 + ; star1_controls_posydon_defaults = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS/r11701/default_common_inlists/binary/inlist1 ; star1_controls_user = ${user_template_root}/binary/inlist1 ; star2_job @@ -161,13 +161,13 @@ This section designates all the basic MESA-specific parameters. ; star2_controls_user = None ; star history columns - star_history_columns = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS-public/r11701/default_common_inlists/history_columns.list + star_history_columns = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS/r11701/default_common_inlists/history_columns.list ; binary history columns - binary_history_columns = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS-public/r11701/default_common_inlists/binary_history_columns.list + binary_history_columns = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS/r11701/default_common_inlists/binary_history_columns.list ; profile columns - profile_columns = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS-public/r11701/default_common_inlists/profile_columns.list + profile_columns = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS/r11701/default_common_inlists/profile_columns.list ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;;;;; MESA OUTPUT CONTROLS ;;;;;;; @@ -217,18 +217,18 @@ This section designates all the parameters for MESA makefiles and fortran files. ; user specified binary extra mesa_binary_extras = ${MESA_DIR}/binary/work/src/run_binary_extras.f - ; user_binary_extras = ${mesa_inlists:posydon_github_root}/grid_params/POSYDON-MESA-INLISTS-public/r11701/default_common_inlists/binary/src/run_binary_extras.f + ; user_binary_extras = ${mesa_inlists:posydon_github_root}/grid_params/POSYDON-MESA-INLISTS/r11701/default_common_inlists/binary/src/run_binary_extras.f ; user specified star extra - these go into the binary/src/ directory mesa_star_binary_extras = ${MESA_DIR}/binary/work/src/run_star_extras.f - ; user_star_binary_extras =${mesa_inlists:posydon_github_root}/grid_params/POSYDON-MESA-INLISTS-public/r11701/default_common_inlists/binary/src/run_star_extras.f + ; user_star_binary_extras =${mesa_inlists:posydon_github_root}/grid_params/POSYDON-MESA-INLISTS/r11701/default_common_inlists/binary/src/run_star_extras.f ; user specified star extras - these are for single star formation (e.g., pre-MS evolution) mesa_star1_extras = ${MESA_DIR}/star/work/src/run_star_extras.f - ; user_star1_extras = ${mesa_inlists:posydon_github_root}/grid_params/POSYDON-MESA-INLISTS-public/r11701/default_common_inlists/binary/src/run_star_extras.f + ; user_star1_extras = ${mesa_inlists:posydon_github_root}/grid_params/POSYDON-MESA-INLISTS/r11701/default_common_inlists/binary/src/run_star_extras.f mesa_star2_extras = ${MESA_DIR}/star/work/src/run_star_extras.f - ; user_star2_extras = ${mesa_inlists:posydon_github_root}/grid_params/POSYDON-MESA-INLISTS-public/r11701/default_common_inlists/binary/src/run_star_extras.f + ; user_star2_extras = ${mesa_inlists:posydon_github_root}/grid_params/POSYDON-MESA-INLISTS/r11701/default_common_inlists/binary/src/run_star_extras.f ; binary_run.f binary_run = ${MESA_DIR}/binary/work/src/binary_run.f From 0f2c7a793a89ec615e2fe517031927fff270f05b Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Thu, 3 Nov 2022 14:42:31 +0100 Subject: [PATCH 014/319] Update submodule inlists (#15) * remove submodule * remove submodule * add back submodule inlist --- grid_params/POSYDON-MESA-INLISTS | 1 + grid_params/POSYDON-MESA-INLISTS-public | 1 - 2 files changed, 1 insertion(+), 1 deletion(-) create mode 160000 grid_params/POSYDON-MESA-INLISTS delete mode 160000 grid_params/POSYDON-MESA-INLISTS-public diff --git a/grid_params/POSYDON-MESA-INLISTS b/grid_params/POSYDON-MESA-INLISTS new file mode 160000 index 0000000000..ca86ccbf13 --- /dev/null +++ b/grid_params/POSYDON-MESA-INLISTS @@ -0,0 +1 @@ +Subproject commit ca86ccbf13fcfa4f6e7a51fe1acb2a480d929a4d diff --git a/grid_params/POSYDON-MESA-INLISTS-public b/grid_params/POSYDON-MESA-INLISTS-public deleted file mode 160000 index f2733e4922..0000000000 --- a/grid_params/POSYDON-MESA-INLISTS-public +++ /dev/null @@ -1 +0,0 @@ -Subproject commit f2733e492293e49e13edc0a6c92b99a4d488a2fc From acbc20e71340b3493b033e42b78bfabbf350ea40 Mon Sep 17 00:00:00 2001 From: ka-rocha Date: Mon, 21 Nov 2022 16:45:53 -0600 Subject: [PATCH 015/319] Can I remove these submodules pls --- .gitmodules | 6 ------ data/POSYDON_data | 1 - posydon/tests/data/POSYDON-UNIT-TESTS | 1 - 3 files changed, 8 deletions(-) delete mode 160000 data/POSYDON_data delete mode 160000 posydon/tests/data/POSYDON-UNIT-TESTS diff --git a/.gitmodules b/.gitmodules index 9d551bc71d..1cfccfceda 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,9 +1,3 @@ -[submodule "data/POSYDON_data"] - path = data/POSYDON_data - url = https://github.com/POSYDON-code/POSYDON_data.git -[submodule "posydon/tests/data/POSYDON-UNIT-TESTS"] - path = posydon/tests/data/POSYDON-UNIT-TESTS - url = https://github.com/POSYDON-code/POSYDON-UNIT-TESTS.git [submodule "grid_params/POSYDON-MESA-INLISTS"] path = grid_params/POSYDON-MESA-INLISTS url = https://github.com/POSYDON-code/POSYDON-MESA-INLISTS.git diff --git a/data/POSYDON_data b/data/POSYDON_data deleted file mode 160000 index e5d8d77985..0000000000 --- a/data/POSYDON_data +++ /dev/null @@ -1 +0,0 @@ -Subproject commit e5d8d77985fc1502b6b6cc0400577623d50743ab diff --git a/posydon/tests/data/POSYDON-UNIT-TESTS b/posydon/tests/data/POSYDON-UNIT-TESTS deleted file mode 160000 index eaf9d59229..0000000000 --- a/posydon/tests/data/POSYDON-UNIT-TESTS +++ /dev/null @@ -1 +0,0 @@ -Subproject commit eaf9d592291f093cc0095e13c93d431c5b6051da From 90ee5d1456b92b18a03216048c51d9e5d583bf69 Mon Sep 17 00:00:00 2001 From: ssbvr Date: Fri, 25 Nov 2022 14:37:25 +0100 Subject: [PATCH 016/319] fix untar file in data_download --- posydon/utils/data_download.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/posydon/utils/data_download.py b/posydon/utils/data_download.py index 24b4c1684b..399d4f90ae 100644 --- a/posydon/utils/data_download.py +++ b/posydon/utils/data_download.py @@ -70,7 +70,7 @@ def data_download(file=file, MD5_check=True, verbose=False): urllib.request.urlretrieve(data_url, file, ProgressBar()) # Open,close, read file and calculate MD5 on its contents - with open(file, 'rb') as file_to_check: + with tarfile.open(file, "r:gz") as file_to_check: # read contents of the file data = file_to_check.read() From 6c260a06f7c14d0c20babfe640d35ce333714b16 Mon Sep 17 00:00:00 2001 From: ssbvr Date: Fri, 25 Nov 2022 14:38:09 +0100 Subject: [PATCH 017/319] extract instead of read --- posydon/utils/data_download.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/posydon/utils/data_download.py b/posydon/utils/data_download.py index 399d4f90ae..84cc1daa6d 100644 --- a/posydon/utils/data_download.py +++ b/posydon/utils/data_download.py @@ -73,7 +73,7 @@ def data_download(file=file, MD5_check=True, verbose=False): with tarfile.open(file, "r:gz") as file_to_check: # read contents of the file - data = file_to_check.read() + data = file_to_check.extractall() # Compare original MD5 with freshly calculated if MD5_check: From 14a33ced197d45fd2d5f321a0a7087da01f89fd0 Mon Sep 17 00:00:00 2001 From: ssbvr Date: Fri, 25 Nov 2022 16:15:53 +0100 Subject: [PATCH 018/319] wrap the MD5 verification in a try statement --- posydon/utils/data_download.py | 40 ++++++++++++++++++---------------- 1 file changed, 21 insertions(+), 19 deletions(-) diff --git a/posydon/utils/data_download.py b/posydon/utils/data_download.py index 84cc1daa6d..55ddc3e70c 100644 --- a/posydon/utils/data_download.py +++ b/posydon/utils/data_download.py @@ -69,27 +69,29 @@ def data_download(file=file, MD5_check=True, verbose=False): f'to PATH_TO_POSYDON_DATA={PATH_TO_POSYDON_DATA}') urllib.request.urlretrieve(data_url, file, ProgressBar()) - # Open,close, read file and calculate MD5 on its contents - with tarfile.open(file, "r:gz") as file_to_check: - - # read contents of the file - data = file_to_check.extractall() - # Compare original MD5 with freshly calculated if MD5_check: - - # pipe contents of the file through - md5_returned = hashlib.md5(data).hexdigest() - - if original_md5 == md5_returned: - if verbose: - print("MD5 verified.") - else: - # Delete file - we cannot rely upon that data - os.remove(file) - - # Raise value error - raise ValueError("MD5 verification failed!.") + try: + with open(file, "rb") as file_to_check: + # read contents of the file + data = file_to_check.read() + + # pipe contents of the file through + md5_returned = hashlib.md5(data).hexdigest() + + if original_md5 == md5_returned: + if verbose: + print("MD5 verified.") + else: + # Delete file - we cannot rely upon that data + os.remove(file) + + # Raise value error + raise ValueError("MD5 verification failed!.") + except: + print('Failed to read the tar.gz file for MD5 verificaton, ' + 'cannot guarantee file integrity (this error seems to ' + 'happen only on macOS').') # extract each file print('Extracting POSYDON data from tar file...') From 6b54f0c7c56f53b530b8c81305086c7853d938a7 Mon Sep 17 00:00:00 2001 From: ssbvr Date: Fri, 25 Nov 2022 16:38:18 +0100 Subject: [PATCH 019/319] typo --- posydon/utils/data_download.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/posydon/utils/data_download.py b/posydon/utils/data_download.py index 55ddc3e70c..773dc10610 100644 --- a/posydon/utils/data_download.py +++ b/posydon/utils/data_download.py @@ -91,7 +91,7 @@ def data_download(file=file, MD5_check=True, verbose=False): except: print('Failed to read the tar.gz file for MD5 verificaton, ' 'cannot guarantee file integrity (this error seems to ' - 'happen only on macOS').') + 'happen only on macOS).') # extract each file print('Extracting POSYDON data from tar file...') From c2426904d8544ba03f4e5426b63a1b7821f04636 Mon Sep 17 00:00:00 2001 From: ka-rocha Date: Wed, 30 Nov 2022 11:53:54 -0600 Subject: [PATCH 020/319] Removing hard coded kwargs in safe evolve so they can be passed at any time. --- posydon/popsyn/binarypopulation.py | 37 +++++++++++++++++++----------- 1 file changed, 23 insertions(+), 14 deletions(-) diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index 4c3b450745..beb0439cb8 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -124,6 +124,7 @@ def evolve(self, **kwargs): ---------- indices : list, optional Custom binary indices to use. Default is range(number_of_binaries). + If running with MPI, indices are split between processes if given. breakdown_to_df : bool, True Breakdown a binary after evolution, converting to dataframe and removing the binary instance from memory. @@ -136,25 +137,33 @@ def evolve(self, **kwargs): """ tqdm_bool = kwargs.get('tqdm', False) breakdown_to_df_bool = kwargs.get('breakdown_to_df', True) + from_hdf_bool = kwargs.get('from_hdf', False) if self.comm is None: # do regular evolution indices = kwargs.get('indices', list(range(self.number_of_binaries))) - self._safe_evolve(indices=indices, - tqdm=tqdm_bool, - breakdown_to_df=breakdown_to_df_bool, - from_hdf=kwargs.get('from_hdf', False), - **self.kwargs) + params = {'indices':indices, + 'tqdm':tqdm_bool, + 'breakdown_to_df':breakdown_to_df_bool, + 'from_hdf':from_hdf_bool} + self.kwargs.update(params) + + self._safe_evolve(**self.kwargs) else: # do MPI evolution - indices = np.array_split(list(range(self.number_of_binaries)), - self.size) - batch_indices = indices[self.rank] + indices = kwargs.get('indices', + list(range(self.number_of_binaries))) + indices_split = np.array_split(indices, self.size) + batch_indices = indices_split[self.rank] mpi_tqdm_bool = True if (tqdm_bool and self.rank == 0) else False - self._safe_evolve(indices=batch_indices, - tqdm=mpi_tqdm_bool, - breakdown_to_df=breakdown_to_df_bool, - **self.kwargs) + + params = {'indices':batch_indices, + 'tqdm':mpi_tqdm_bool, + 'breakdown_to_df':breakdown_to_df_bool, + 'from_hdf':from_hdf_bool} + self.kwargs.update(params) + + self._safe_evolve(**self.kwargs) def _safe_evolve(self, **kwargs): """Evolve binaries in a population, catching warnings/exceptions.""" @@ -448,11 +457,11 @@ def append(self, binary): def remove(self, binary): """Remove a binary instance.""" - if isinstance(binary, (list, np.ndarray)): + if isinstance(binary, (list, np.ndarray)): for b in binary: self.binaries.remove(b) self.indices.remove(b.index) - + elif isinstance(binary, BinaryStar): self.binaries.remove(binary) self.indices.remove(binary.index) From e6592c0b18faee3353a93db369202c2f684bbfb7 Mon Sep 17 00:00:00 2001 From: ka-rocha Date: Thu, 1 Dec 2022 12:39:25 -0600 Subject: [PATCH 021/319] Combining args passed at init and to evolve. --- posydon/popsyn/binarypopulation.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index beb0439cb8..c5b64ba41d 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -135,13 +135,15 @@ def evolve(self, **kwargs): ------- None """ - tqdm_bool = kwargs.get('tqdm', False) - breakdown_to_df_bool = kwargs.get('breakdown_to_df', True) - from_hdf_bool = kwargs.get('from_hdf', False) + # combine kw defined at init and any passed here + kw = {**self.kwargs, **kwargs} + tqdm_bool = kw.get('tqdm', False) + breakdown_to_df_bool = kw.get('breakdown_to_df', True) + from_hdf_bool = kw.get('from_hdf', False) if self.comm is None: # do regular evolution - indices = kwargs.get('indices', - list(range(self.number_of_binaries))) + indices = kw.get('indices', + list(range(self.number_of_binaries))) params = {'indices':indices, 'tqdm':tqdm_bool, 'breakdown_to_df':breakdown_to_df_bool, @@ -151,8 +153,8 @@ def evolve(self, **kwargs): self._safe_evolve(**self.kwargs) else: # do MPI evolution - indices = kwargs.get('indices', - list(range(self.number_of_binaries))) + indices = kw.get('indices', + list(range(self.number_of_binaries))) indices_split = np.array_split(indices, self.size) batch_indices = indices_split[self.rank] mpi_tqdm_bool = True if (tqdm_bool and self.rank == 0) else False From 2e316275bbf12b0e80c3d2934d7445fbac722ead Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Wed, 21 Dec 2022 13:10:18 +0100 Subject: [PATCH 022/319] Repair submodules (#22) * clean submodules * add data submodule back * add back the unit test submodule Co-authored-by: posydon-admin --- .gitmodules | 6 +++--- data/POSYDON_data | 1 + posydon/tests/data/POSYDON-UNIT-TESTS | 1 + 3 files changed, 5 insertions(+), 3 deletions(-) create mode 160000 data/POSYDON_data create mode 160000 posydon/tests/data/POSYDON-UNIT-TESTS diff --git a/.gitmodules b/.gitmodules index 9d551bc71d..6cda6aff11 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,9 +1,9 @@ +[submodule "grid_params/POSYDON-MESA-INLISTS"] + path = grid_params/POSYDON-MESA-INLISTS + url = https://github.com/POSYDON-code/POSYDON-MESA-INLISTS.git [submodule "data/POSYDON_data"] path = data/POSYDON_data url = https://github.com/POSYDON-code/POSYDON_data.git [submodule "posydon/tests/data/POSYDON-UNIT-TESTS"] path = posydon/tests/data/POSYDON-UNIT-TESTS url = https://github.com/POSYDON-code/POSYDON-UNIT-TESTS.git -[submodule "grid_params/POSYDON-MESA-INLISTS"] - path = grid_params/POSYDON-MESA-INLISTS - url = https://github.com/POSYDON-code/POSYDON-MESA-INLISTS.git diff --git a/data/POSYDON_data b/data/POSYDON_data new file mode 160000 index 0000000000..e5d8d77985 --- /dev/null +++ b/data/POSYDON_data @@ -0,0 +1 @@ +Subproject commit e5d8d77985fc1502b6b6cc0400577623d50743ab diff --git a/posydon/tests/data/POSYDON-UNIT-TESTS b/posydon/tests/data/POSYDON-UNIT-TESTS new file mode 160000 index 0000000000..eaf9d59229 --- /dev/null +++ b/posydon/tests/data/POSYDON-UNIT-TESTS @@ -0,0 +1 @@ +Subproject commit eaf9d592291f093cc0095e13c93d431c5b6051da From 931f09666111d752bb04ab758d5fe39ec77d270a Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Tue, 10 Jan 2023 09:07:24 +0100 Subject: [PATCH 023/319] fix x and + markers in 2D plots for matplotlib 3.5 (#19) --- posydon/visualization/plot_defaults.py | 34 +++++++++++++------------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/posydon/visualization/plot_defaults.py b/posydon/visualization/plot_defaults.py index 5e67798c97..1ff9ecfadb 100644 --- a/posydon/visualization/plot_defaults.py +++ b/posydon/visualization/plot_defaults.py @@ -163,13 +163,13 @@ 'Reached maximum mass transfer rate: Exceeded photon trapping radius': ['D', 1, color_unstable, TF1_label_unstable], 'logQ_limit': - ['x', 0, 'red', 'logQ_limit'], + ['x', 1, 'red', 'logQ_limit'], 'min_timestep_limit': - ['x', 0, 'red', 'Not converged'], + ['x', 1, 'red', 'Not converged'], 'reach cluster timelimit': - ['x', 0, 'red', 'Not converged'], + ['x', 1, 'red', 'Not converged'], 'no termination code': - ['x', 0, 'red', 'no termination code'], + ['x', 1, 'red', 'no termination code'], 'envelope_mass_limit': ['s', 2, None, TF1_label_stable], 'gamma_center_limit': @@ -179,26 +179,26 @@ 'Initial RLOF': ['.', 1, 'black', TF1_label_initial], 'Not converged': - ['x', 0, 'red', 'Not converged'], + ['x', 1, 'red', 'Not converged'], 'ignored_no_BH': ['.', 1, color_unstable, TF1_label_initial], 'ignored_no_RLO': ['.', 1, color_unstable, TF1_label_initial], 'unknown': - ['+', 0, 'red', 'unknown'], + ['+', 1, 'red', 'unknown'], }, 'termination_flag_2': { 'initial_RLOF': - ['+', 0, 'black', 'initial RLOF'], + ['+', 1, 'black', 'initial RLOF'], 'forced_initial_RLO': - ['+', 0, 'black', 'initial RLOF'], + ['+', 1, 'black', 'initial RLOF'], 'ignored_no_BH': - ['+', 0, 'black', 'initial RLOF'], + ['+', 1, 'black', 'initial RLOF'], 'ignored_no_RLO': - ['+', 0, 'black', 'initial RLOF'], + ['+', 1, 'black', 'initial RLOF'], 'no_RLOF': ['.', 1, 'black', 'no RLOF'], 'contact_during_MS': @@ -268,9 +268,9 @@ ['s', 2, 'tab:red', 'case BA/BB from star1'], '_from_star1': - ['x', 0, 'black', 'unknown'], + ['x', 1, 'black', 'unknown'], '_from_star2': - ['x', 0, 'black', 'unknown'], + ['x', 1, 'black', 'unknown'], }, @@ -300,7 +300,7 @@ 'stripped_He_non_burning': ['o', 2, 'gray', 'stripped He-star non burning'], 'undetermined_evolutionary_state': - ['x', 0, 'black', 'unknown'], + ['x', 1, 'black', 'unknown'], 'BH': ['*', 1, 'black', 'BH'], 'NS': @@ -338,7 +338,7 @@ 'stripped_He_non_burning': ['o', 2, 'gray', 'stripped He-star non burning'], 'undetermined_evolutionary_state': - ['x', 0, 'black', 'unknown'], + ['x', 1, 'black', 'unknown'], 'BH': ['*', 1, 'black', 'BH'], 'NS': @@ -397,9 +397,9 @@ 'no_RLOF': ['s', 2, 'lightgrey', 'no RLOF'], 'Not converged': - ['x', 0, 'red', 'Not converged'], + ['x', 1, 'red', 'Not converged'], 'unknown': - ['+', 0, 'green', 'unknown'], + ['+', 1, 'green', 'unknown'], }, 'debug': { 'terminate due to primary depleting carbon (inverse sn?)': @@ -476,7 +476,7 @@ 'ignored_no_RLO': ['.', 1, color_unstable, TF1_label_initial], 'unknown': - ['+', 0, 'green', 'unknown'], + ['+', 1, 'green', 'unknown'], }, 'interpolation_class': { 'initial_MT': From 639eadacc6f0adcb73dc60e3c784bd52e10ae2e2 Mon Sep 17 00:00:00 2001 From: ssbvr Date: Fri, 13 Jan 2023 14:11:13 +0100 Subject: [PATCH 024/319] add missing flags for plot2D --- posydon/visualization/plot_defaults.py | 48 +++++++++++++++++--------- 1 file changed, 31 insertions(+), 17 deletions(-) diff --git a/posydon/visualization/plot_defaults.py b/posydon/visualization/plot_defaults.py index 1ff9ecfadb..79298f7563 100644 --- a/posydon/visualization/plot_defaults.py +++ b/posydon/visualization/plot_defaults.py @@ -164,6 +164,8 @@ ['D', 1, color_unstable, TF1_label_unstable], 'logQ_limit': ['x', 1, 'red', 'logQ_limit'], + 'logQ_min_limit': + ['x', 1, 'red', 'logQ_limit'], 'min_timestep_limit': ['x', 1, 'red', 'Not converged'], 'reach cluster timelimit': @@ -214,6 +216,8 @@ ['s', 2, 'lightgrey', 'case A/B/A from star1'], 'caseA/B/C_from_star1': ['s', 2, 'yellow', 'case A/B/C from star1'], + 'caseA/C_from_star1': + ['s', 2, 'yellow', 'case A/C from star1'], 'caseA/B/BB_from_star1': ['s', 2, 'tab:red', 'case A/B/BB from star1'], 'caseA/B/C/BB_from_star1': @@ -299,6 +303,10 @@ ['o', 2, 'tab:brown', 'stripped He-star C depletion'], 'stripped_He_non_burning': ['o', 2, 'gray', 'stripped He-star non burning'], + 'stripped_He_Core_H_burning': + ['x', 1, 'black', 'unknown'], + 'stripped_He_Shell_H_burning': + ['x', 1, 'black', 'unknown'], 'undetermined_evolutionary_state': ['x', 1, 'black', 'unknown'], 'BH': @@ -337,6 +345,10 @@ ['o', 2, 'tab:brown', 'stripped He-star C depletion'], 'stripped_He_non_burning': ['o', 2, 'gray', 'stripped He-star non burning'], + 'stripped_He_Core_H_burning': + ['x', 1, 'black', 'unknown'], + 'stripped_He_Shell_H_burning': + ['x', 1, 'black', 'unknown'], 'undetermined_evolutionary_state': ['x', 1, 'black', 'unknown'], 'BH': @@ -413,15 +425,15 @@ 'offcenter neon ignition for secondary': ['o', 2, None, TF1_label_stable], 'overflow from L1 at ZAMS': - ['.', 1, None, TF1_label_initial], + ['.', 1.5, None, TF1_label_initial], 'Terminate because of overflowing initial model': - ['.', 1, None, TF1_label_initial], + ['.', 1.5, None, TF1_label_initial], 'overflow from L2 point for q>1 at ZAMS': - ['.', 1, None, TF1_label_initial], + ['.', 1.5, None, TF1_label_initial], 'overflow from L2 surface for q<1 at ZAMS': - ['.', 1, None, TF1_label_initial], + ['.', 1.5, None, TF1_label_initial], 'overflow from L2 surface for q>1 at ZAMS': - ['.', 1, None, TF1_label_initial], + ['.', 1.5, None, TF1_label_initial], 'overflow from L2 surface for q<1': ['D', 1, None, TF1_label_unstable], r'overflow from L2 (D_L2) distance for q(=Macc/Mdon)>1, ' @@ -446,7 +458,7 @@ 'Reached the critical mt rate': ['D', 1, None, TF1_label_unstable], 'Reached TPAGB': - ['s', 3, None, TF1_label_initial], + ['s', 2, None, TF1_label_initial], 'Both stars fill their Roche Lobe and at least one of them is off MS': ['D', 1, None, TF1_label_unstable], 'Terminate due to L2 overflow during case A': @@ -454,27 +466,29 @@ 'Reached maximum mass transfer rate: Exceeded photon trapping radius': ['D', 1, None, TF1_label_unstable], 'logQ_limit': - ['s', 3, None, 'logQ_limit'], + ['x', 1, None, 'logQ_limit'], + 'logQ_min_limit': + ['x', 1, None, 'logQ_limit'], 'min_timestep_limit': - ['s', 3, None, 'Not converged'], + ['x', 1, None, 'Not converged'], 'reach cluster timelimit': - ['s', 3, None, 'Not converged'], + ['x', 1, None, 'Not converged'], 'no termination code': - ['s', 3, None, 'no termination code'], + ['x', 1, None, 'no termination code'], 'envelope_mass_limit': - ['s', 3, None, TF1_label_stable], + ['s', 2, None, TF1_label_stable], 'gamma_center_limit': - ['s', 3, None, TF1_label_stable], + ['s', 2, None, TF1_label_stable], 'max_age': - ['s', 3, None, TF1_label_stable], + ['s', 2, None, TF1_label_stable], 'Initial RLOF': - ['.', 1, 'black', TF1_label_initial], + ['.', 1.5, 'black', TF1_label_initial], 'Not converged': - ['s', 3, None, 'Not converged'], + ['x', 1, None, 'Not converged'], 'ignored_no_BH': - ['.', 1, color_unstable, TF1_label_initial], + ['.', 1.5, color_unstable, TF1_label_initial], 'ignored_no_RLO': - ['.', 1, color_unstable, TF1_label_initial], + ['.', 1.5, color_unstable, TF1_label_initial], 'unknown': ['+', 1, 'green', 'unknown'], }, From 25136b01406b705ca6a5c20db33ae19762879f46 Mon Sep 17 00:00:00 2001 From: ssbvr Date: Fri, 13 Jan 2023 14:39:39 +0100 Subject: [PATCH 025/319] add new TF1 --- posydon/visualization/plot_defaults.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/posydon/visualization/plot_defaults.py b/posydon/visualization/plot_defaults.py index 79298f7563..b3d4e967ee 100644 --- a/posydon/visualization/plot_defaults.py +++ b/posydon/visualization/plot_defaults.py @@ -162,6 +162,8 @@ ['D', 1, color_unstable, TF1_label_unstable], 'Reached maximum mass transfer rate: Exceeded photon trapping radius': ['D', 1, color_unstable, TF1_label_unstable], + 'Terminate because accretor (r-rl)/rl > accretor_overflow_terminate': + ['D', 1, color_unstable, TF1_label_unstable], 'logQ_limit': ['x', 1, 'red', 'logQ_limit'], 'logQ_min_limit': From 3eec756d7352f188f78b536f6bd2660e2522e98a Mon Sep 17 00:00:00 2001 From: ssbvr Date: Fri, 13 Jan 2023 14:50:06 +0100 Subject: [PATCH 026/319] add debug TF --- posydon/visualization/plot_defaults.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/posydon/visualization/plot_defaults.py b/posydon/visualization/plot_defaults.py index b3d4e967ee..7cde957633 100644 --- a/posydon/visualization/plot_defaults.py +++ b/posydon/visualization/plot_defaults.py @@ -467,6 +467,8 @@ ['D', 1, None, TF1_label_unstable], 'Reached maximum mass transfer rate: Exceeded photon trapping radius': ['D', 1, None, TF1_label_unstable], + 'Terminate because accretor (r-rl)/rl > accretor_overflow_terminate': + ['D', 1, None, TF1_label_unstable], 'logQ_limit': ['x', 1, None, 'logQ_limit'], 'logQ_min_limit': From 68e920bf281da2825c42b4575de1257e4d3f0d21 Mon Sep 17 00:00:00 2001 From: mkruckow Date: Mon, 16 Jan 2023 14:12:06 +0100 Subject: [PATCH 027/319] add get_detected_initial_RLO --- posydon/grids/termination_flags.py | 37 ++++++++++++++++++++++++++++++ 1 file changed, 37 insertions(+) diff --git a/posydon/grids/termination_flags.py b/posydon/grids/termination_flags.py index e0d1ddcf12..9ee32606e2 100644 --- a/posydon/grids/termination_flags.py +++ b/posydon/grids/termination_flags.py @@ -276,3 +276,40 @@ def initial_RLO_fix_applies(mass, period): # if period > 0.124: # return mass > 1.2 # return mass > 0.6 + +def get_detected_initial_RLO(grid): + """Generates a list of already detected initial RLO + + Parameters + ---------- + grid : a PSyGrid + The grid to check. + + Retruns + ------- + list + A list containing initial values (the two masses and the period) of + systems already detected to be initial_MT + """ + #new list + detected = [] + #go through grid + for sys in grid: + flag1 = sys.final_values['termination_flag_1'] + #find systems with termination because of initial overflow + if flag1 == "Terminate because of overflowing initial model": + mass1 = sys.initial_values["star_1_mass"] + mass2 = sys.initial_values["star_2_mass"] + period = sys.initial_values["period_days"] + e = False + #check for already existing entries of same mass combination + for d in detected: + if d[0] == mass1 and d[1] == mass2: + e = True + #update period if new one is larger + if d[2] Date: Mon, 16 Jan 2023 17:19:03 +0100 Subject: [PATCH 028/319] fix for initial RLO incl. copy termination_flags 3 and 4 --- posydon/grids/psygrid.py | 32 +++++++++++- posydon/grids/termination_flags.py | 83 +++++++++++++++++++++++++++--- 2 files changed, 107 insertions(+), 8 deletions(-) diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index fdcdb7e2d9..5277ed0772 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -196,7 +196,10 @@ check_state_from_history, get_flag_from_MESA_output, infer_interpolation_class, - initial_RLO_fix_applies) + initial_RLO_fix_applies, + get_detected_initial_RLO, + initial_RLO_fix_to_apply, + get_initial_RLO_term_flag_34) from posydon.utils.configfile import ConfigFile from posydon.utils.common_functions import (orbital_separation_from_period, initialize_empty_array, @@ -348,6 +351,7 @@ "eep": None, # path to EEP files "initial_RLO_fix": False, "He_core_fix": True, + "initial_RLO_fix_g": False, } @@ -535,6 +539,7 @@ def _create_psygrid(self, MESA_path, hdf5, slim=False, fmt="posydon"): initial_RLO_fix = self.config["initial_RLO_fix"] start_at_RLO = self.config["start_at_RLO"] eep = self.config["eep"] + initial_RLO_fix_g = self.config["initial_RLO_fix_g"] if eep is not None: if binary_grid: @@ -1046,6 +1051,31 @@ def decide_columns(key_in_config, defaults): run_included[i] = True run_index += 1 + #general fix for termination_flag in case of initial RLO + if initial_RLO_fix_g: + #create list of already detected initial RLO + detected_initial_RLO = get_detected_initial_RLO(self) + colnames = ["termination_flag_1", "termination_flag_2", + "interpolation_class"] + valtoset = ["forced_initial_RLO", "forced_initial_RLO", + "initial_MT"] + for i in range(N_runs): + flag1 = self.final_values[i]['termination_flag_1'] + if flag1 != "Terminate because of overflowing initial model": + mass1 = self.initial_values[i]["star_1_mass"] + mass2 = self.initial_values[i]["star_2_mass"] + period = self.initial_values[i]["period_days"] + if initial_RLO_fix_to_apply(mass1, mass2, period, + detected_initial_RLO): + for colname, value in zip(colnames, valtoset): + self.final_values[i][colname] = value + tf_34 = get_initial_RLO_term_flag_34(mass1, mass2, + detected_initial_RLO) + if len(tf_34)==2: + for colname, value in zip(["termination_flag_3", + "termination_flag_4"], tf_34): + self.final_values[i][colname] = value + self._say("Storing initial/final values and metadata to HDF5...") # exclude rows in initial/final_values corresponding to excluded runs self.initial_values = self.initial_values[run_included] diff --git a/posydon/grids/termination_flags.py b/posydon/grids/termination_flags.py index 9ee32606e2..27255959be 100644 --- a/posydon/grids/termination_flags.py +++ b/posydon/grids/termination_flags.py @@ -276,7 +276,8 @@ def initial_RLO_fix_applies(mass, period): # if period > 0.124: # return mass > 1.2 # return mass > 0.6 - + + def get_detected_initial_RLO(grid): """Generates a list of already detected initial RLO @@ -294,13 +295,14 @@ def get_detected_initial_RLO(grid): #new list detected = [] #go through grid - for sys in grid: - flag1 = sys.final_values['termination_flag_1'] + N_runs = len(grid.initial_values) + for i in range(N_runs): + flag1 = grid.final_values[i]['termination_flag_1'] #find systems with termination because of initial overflow if flag1 == "Terminate because of overflowing initial model": - mass1 = sys.initial_values["star_1_mass"] - mass2 = sys.initial_values["star_2_mass"] - period = sys.initial_values["period_days"] + mass1 = grid.initial_values[i]["star_1_mass"] + mass2 = grid.initial_values[i]["star_2_mass"] + period = grid.initial_values[i]["period_days"] e = False #check for already existing entries of same mass combination for d in detected: @@ -311,5 +313,72 @@ def get_detected_initial_RLO(grid): d[2]=period #add masses and period of detected system to the list if not e: - detected.append((mass1,mass2,period)) + detected.append((mass1,mass2,period,grid.final_values[i]['termination_flag_3'],grid.final_values[i]['termination_flag_4'])) return detected + + +def initial_RLO_fix_to_apply(mass1, mass2, period, known_initial_RLO): + """Check if initial RLO fix is warranted given the initial mass and period. + + Parameters + ---------- + + mass1 : float + Mass of star 1. + mass2 : float + Mass of star 2. + period : float + Binary's period (in days). + known_initial_RLO : list + A list containing initial values (the two masses and the period) of + systems already detected to be initial_MT + + Returns + ------- + bool + True if initial RLO flag should be forced. + + """ + def get_RLO_period(mass1, mass2): + for sys in known_initial_RLO: + #search for a known system with same mass combination + #to get initial RLO period; + #allow for a mass uncertainty of 10^-5 + if abs(mass1-sys[0])<1.0e-5 and abs(mass2-sys[1])<1.0e-5: + return sys[2] + #for random grids, we need to add here a different way, e.g. via + #interpolation in the known initial RLO systems, to get the maximum + #period for initial RLO + return 0.0 + #check if the period is below the one for haveing initial RLO + return period Date: Tue, 17 Jan 2023 10:26:18 +0100 Subject: [PATCH 029/319] remove bug of tuple item asignement --- posydon/grids/termination_flags.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/posydon/grids/termination_flags.py b/posydon/grids/termination_flags.py index 27255959be..5da15462af 100644 --- a/posydon/grids/termination_flags.py +++ b/posydon/grids/termination_flags.py @@ -310,10 +310,14 @@ def get_detected_initial_RLO(grid): e = True #update period if new one is larger if d[2] Date: Tue, 17 Jan 2023 13:19:54 +0100 Subject: [PATCH 030/319] add initial_RLO_fix_g to PROPERTIES_TO_BE_CONSISTENT --- posydon/grids/psygrid.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index 5277ed0772..0b9741dd2b 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -1916,10 +1916,10 @@ def downsample_profile(profile, params): } PROPERTIES_TO_BE_CONSISTENT = ["binary", "eep", "start_at_RLO", - "initial_RLO_fix", "He_core_fix", - "history_DS_error", "history_DS_exclude", - "profile_DS_error", "profile_DS_exclude", - "profile_DS_interval"] + "initial_RLO_fix", "initial_RLO_fix_g", + "He_core_fix", "history_DS_error", + "history_DS_exclude", "profile_DS_error", + "profile_DS_exclude", "profile_DS_interval"] ALL_PROPERTIES = (list(PROPERTIES_ALLOWED.keys()) + PROPERTIES_TO_BE_CONSISTENT + list(PROPERTIES_TO_BE_NONE.keys()) + PROPERTIES_TO_BE_SET) From 6cdeddc86be91221dd6490f350a86982e41d1d7c Mon Sep 17 00:00:00 2001 From: ssbvr Date: Tue, 17 Jan 2023 16:24:59 +0100 Subject: [PATCH 031/319] add forced_initial_RLO to debug flags --- posydon/visualization/plot_defaults.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/posydon/visualization/plot_defaults.py b/posydon/visualization/plot_defaults.py index 7cde957633..beb351f6db 100644 --- a/posydon/visualization/plot_defaults.py +++ b/posydon/visualization/plot_defaults.py @@ -493,6 +493,8 @@ ['.', 1.5, color_unstable, TF1_label_initial], 'ignored_no_RLO': ['.', 1.5, color_unstable, TF1_label_initial], + 'forced_initial_RLO': + ['.', 1.5, color_unstable, TF1_label_initial], 'unknown': ['+', 1, 'green', 'unknown'], }, From eef421845629bdecaf8efa4d0d46f717a51439c4 Mon Sep 17 00:00:00 2001 From: ssbvr Date: Tue, 17 Jan 2023 16:34:40 +0100 Subject: [PATCH 032/319] add forced_initial_RLO TF1 --- posydon/visualization/combine_TF.py | 3 ++- posydon/visualization/plot_defaults.py | 4 ++++ 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/posydon/visualization/combine_TF.py b/posydon/visualization/combine_TF.py index b14f3b5fae..1151448e9e 100644 --- a/posydon/visualization/combine_TF.py +++ b/posydon/visualization/combine_TF.py @@ -48,7 +48,8 @@ 'min_timestep_limit', 'cluster timelimit', 'reach cluster timelimit', - 'no termination code'] + 'no termination code', + 'forced_initial_RLO'] TF2_POOL_NO_RLO = [ 'no_RLOF', diff --git a/posydon/visualization/plot_defaults.py b/posydon/visualization/plot_defaults.py index beb351f6db..9ae4cc9195 100644 --- a/posydon/visualization/plot_defaults.py +++ b/posydon/visualization/plot_defaults.py @@ -184,6 +184,8 @@ ['.', 1, 'black', TF1_label_initial], 'Not converged': ['x', 1, 'red', 'Not converged'], + 'fe_core_infall_limit': + ['x', 1, 'red', 'Not converged'], 'ignored_no_BH': ['.', 1, color_unstable, TF1_label_initial], 'ignored_no_RLO': @@ -497,6 +499,8 @@ ['.', 1.5, color_unstable, TF1_label_initial], 'unknown': ['+', 1, 'green', 'unknown'], + 'fe_core_infall_limit': + ['x', 1, 'red', 'Not converged'], }, 'interpolation_class': { 'initial_MT': From fc9b1d62d5ed984bd4b08aa144abaae409e0b632 Mon Sep 17 00:00:00 2001 From: ssbvr Date: Tue, 17 Jan 2023 16:39:51 +0100 Subject: [PATCH 033/319] fix colorsheme --- posydon/visualization/combine_TF.py | 2 +- posydon/visualization/plot_defaults.py | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/posydon/visualization/combine_TF.py b/posydon/visualization/combine_TF.py index 1151448e9e..4c896c7d1d 100644 --- a/posydon/visualization/combine_TF.py +++ b/posydon/visualization/combine_TF.py @@ -49,7 +49,7 @@ 'cluster timelimit', 'reach cluster timelimit', 'no termination code', - 'forced_initial_RLO'] + 'fe_core_infall_limit'] TF2_POOL_NO_RLO = [ 'no_RLOF', diff --git a/posydon/visualization/plot_defaults.py b/posydon/visualization/plot_defaults.py index 9ae4cc9195..c71a2eddce 100644 --- a/posydon/visualization/plot_defaults.py +++ b/posydon/visualization/plot_defaults.py @@ -185,7 +185,7 @@ 'Not converged': ['x', 1, 'red', 'Not converged'], 'fe_core_infall_limit': - ['x', 1, 'red', 'Not converged'], + ['x', 1, 'tab:purple', 'fe_core_infall_limit'], 'ignored_no_BH': ['.', 1, color_unstable, TF1_label_initial], 'ignored_no_RLO': @@ -496,11 +496,11 @@ 'ignored_no_RLO': ['.', 1.5, color_unstable, TF1_label_initial], 'forced_initial_RLO': - ['.', 1.5, color_unstable, TF1_label_initial], + ['.', 1.5, 'black', TF1_label_initial], 'unknown': ['+', 1, 'green', 'unknown'], 'fe_core_infall_limit': - ['x', 1, 'red', 'Not converged'], + ['x', 1, None, 'fe_core_infall_limit'], }, 'interpolation_class': { 'initial_MT': From f3794510181f5713615a00d2e9a777c59fd97c2d Mon Sep 17 00:00:00 2001 From: mkruckow Date: Wed, 18 Jan 2023 12:01:12 +0100 Subject: [PATCH 034/319] use nearest system instead of matching one, hence slice interruptions and random girds should work, too; switch to dictionaries to allow easier adding things to be copied from nearest systems --- posydon/grids/psygrid.py | 23 +++--- posydon/grids/termination_flags.py | 117 ++++++++++------------------- 2 files changed, 53 insertions(+), 87 deletions(-) diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index 0b9741dd2b..1bf2dfceaf 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -175,6 +175,7 @@ "Scott Coughlin ", "Devina Misra ", "Kyle Akira Rocha ", + "Matthias Kruckow ", ] @@ -198,8 +199,7 @@ infer_interpolation_class, initial_RLO_fix_applies, get_detected_initial_RLO, - initial_RLO_fix_to_apply, - get_initial_RLO_term_flag_34) + get_nearest_known_initial_RLO) from posydon.utils.configfile import ConfigFile from posydon.utils.common_functions import (orbital_separation_from_period, initialize_empty_array, @@ -1060,21 +1060,22 @@ def decide_columns(key_in_config, defaults): valtoset = ["forced_initial_RLO", "forced_initial_RLO", "initial_MT"] for i in range(N_runs): - flag1 = self.final_values[i]['termination_flag_1'] + flag1 = self.final_values[i]["termination_flag_1"] if flag1 != "Terminate because of overflowing initial model": mass1 = self.initial_values[i]["star_1_mass"] mass2 = self.initial_values[i]["star_2_mass"] period = self.initial_values[i]["period_days"] - if initial_RLO_fix_to_apply(mass1, mass2, period, - detected_initial_RLO): + nearest = get_nearest_known_initial_RLO(mass1, mass2, + detected_initial_RLO) + if period", "Jeffrey Andrews ", "Emmanouil Zapartas ", + "Matthias Kruckow ", ] @@ -289,15 +290,16 @@ def get_detected_initial_RLO(grid): Retruns ------- list - A list containing initial values (the two masses and the period) of - systems already detected to be initial_MT + A list containing systems already detected to be initial_MT based on + termination_flag_1. For each system there is a dictionary with the + impartant data, e.g. initial masses, periods, termination_flags """ #new list detected = [] #go through grid N_runs = len(grid.initial_values) for i in range(N_runs): - flag1 = grid.final_values[i]['termination_flag_1'] + flag1 = grid.final_values[i]["termination_flag_1"] #find systems with termination because of initial overflow if flag1 == "Terminate because of overflowing initial model": mass1 = grid.initial_values[i]["star_1_mass"] @@ -306,83 +308,46 @@ def get_detected_initial_RLO(grid): e = False #check for already existing entries of same mass combination for d in detected: - if d[0] == mass1 and d[1] == mass2: + if d["star_1_mass"] == mass1 and d["star_2_mass"] == mass2: e = True - #update period if new one is larger - if d[2] Date: Wed, 18 Jan 2023 17:14:05 +0100 Subject: [PATCH 035/319] allow for 10^{-5} difference in masses of correctly detected systems to be recognized as the same --- posydon/grids/termination_flags.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/posydon/grids/termination_flags.py b/posydon/grids/termination_flags.py index 82dff88b34..608e91ecb1 100644 --- a/posydon/grids/termination_flags.py +++ b/posydon/grids/termination_flags.py @@ -308,7 +308,8 @@ def get_detected_initial_RLO(grid): e = False #check for already existing entries of same mass combination for d in detected: - if d["star_1_mass"] == mass1 and d["star_2_mass"] == mass2: + if (abs(d["star_1_mass"]-mass1)<1.0e-5 and + abs(d["star_2_mass"]-mass2)<1.0e-5): e = True #update values if new one has a larger period if d["period_days"] Date: Fri, 20 Jan 2023 16:00:23 +0100 Subject: [PATCH 036/319] avoid skips like in old fix --- posydon/grids/psygrid.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index 1bf2dfceaf..772d84393e 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -650,7 +650,7 @@ def decide_columns(key_in_config, defaults): ignore_reason = "ignored_no_BH" warnings.warn("Ignored MESA run because of missing binary " "history in: {}\n".format(run.path)) - if not initial_RLO_fix: + if not (initial_RLO_fix or initial_RLO_fix_g): continue if ignore_data: @@ -746,7 +746,7 @@ def decide_columns(key_in_config, defaults): ignore_reason = "ignored_scrubbed" warnings.warn("Ignored MESA run because of scrubbed binary" " history in: {}\n".format(run.path)) - if not initial_RLO_fix: + if not (initial_RLO_fix or initial_RLO_fix_g): continue if not binary_grid and len(history1) == 0: ignore_data = True @@ -762,7 +762,7 @@ def decide_columns(key_in_config, defaults): ignore_reason = "ignored_no_RLO" warnings.warn("Ignored MESA run because of no RLO" " in: {}\n".format(run.path)) - if not initial_RLO_fix: + if not (initial_RLO_fix or initial_RLO_fix_g): continue binary_history, history1, history2 = None, None, None else: @@ -1010,7 +1010,7 @@ def decide_columns(key_in_config, defaults): elif ignore_data: # if fix does not apply and failed run, do not include it continue - elif ignore_data: + elif ignore_data and not initial_RLO_fix_g: # if not fix requested and failed run, do not include it continue From beddeffdcc09335b077b25a1c799470454a085cc Mon Sep 17 00:00:00 2001 From: mkruckow Date: Mon, 23 Jan 2023 11:28:55 +0100 Subject: [PATCH 037/319] restore initial vales from grid data (file/directory names) --- posydon/grids/psygrid.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index 772d84393e..7aa856ed95 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -1076,6 +1076,11 @@ def decide_columns(key_in_config, defaults): "termination_flag_4"]: if colname in nearest: self.final_values[i][colname]=nearest[colname] + #reset the initial values to the grid point data + grid_point = read_initial_values(grid.runs[i].path) + for colname, value in grid_point.items(): + if colname in self.initial_values.dtype.names: + self.initial_values[i][colname] = value self._say("Storing initial/final values and metadata to HDF5...") # exclude rows in initial/final_values corresponding to excluded runs From 2a41dadf811c5f1ee9ad68fbaf7c313228d1dba7 Mon Sep 17 00:00:00 2001 From: mkruckow Date: Mon, 23 Jan 2023 11:49:49 +0100 Subject: [PATCH 038/319] fix blanks --- posydon/grids/psygrid.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index 7aa856ed95..ec7e50e693 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -1076,11 +1076,11 @@ def decide_columns(key_in_config, defaults): "termination_flag_4"]: if colname in nearest: self.final_values[i][colname]=nearest[colname] - #reset the initial values to the grid point data - grid_point = read_initial_values(grid.runs[i].path) - for colname, value in grid_point.items(): - if colname in self.initial_values.dtype.names: - self.initial_values[i][colname] = value + #reset the initial values to the grid point data + grid_point = read_initial_values(grid.runs[i].path) + for colname, value in grid_point.items(): + if colname in self.initial_values.dtype.names: + self.initial_values[i][colname] = value self._say("Storing initial/final values and metadata to HDF5...") # exclude rows in initial/final_values corresponding to excluded runs From 3d3690ea1664399b3b6e265476d292493b1d4fc8 Mon Sep 17 00:00:00 2001 From: mkruckow Date: Mon, 23 Jan 2023 16:05:08 +0100 Subject: [PATCH 039/319] get no RLO out again --- posydon/grids/psygrid.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index ec7e50e693..c70f2807e8 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -1010,7 +1010,7 @@ def decide_columns(key_in_config, defaults): elif ignore_data: # if fix does not apply and failed run, do not include it continue - elif ignore_data and not initial_RLO_fix_g: + elif ignore_data: # if not fix requested and failed run, do not include it continue @@ -1081,7 +1081,14 @@ def decide_columns(key_in_config, defaults): for colname, value in grid_point.items(): if colname in self.initial_values.dtype.names: self.initial_values[i][colname] = value - + if not slim: + hdf5.create_group("/grid/run{}/".format(run_index)) + # consider the run (and the input directory) included + self.MESA_dirs.append(grid.runs[i].path) + run_included[i] = True + run_index += 1 + + self._say("Storing initial/final values and metadata to HDF5...") # exclude rows in initial/final_values corresponding to excluded runs self.initial_values = self.initial_values[run_included] From a80ee7bdfa1fa2465470c585d5b80f3927c89588 Mon Sep 17 00:00:00 2001 From: mkruckow Date: Tue, 24 Jan 2023 09:29:31 +0100 Subject: [PATCH 040/319] avoid adding initial RLO systems twice --- posydon/grids/psygrid.py | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index c70f2807e8..e93c462c09 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -1081,12 +1081,15 @@ def decide_columns(key_in_config, defaults): for colname, value in grid_point.items(): if colname in self.initial_values.dtype.names: self.initial_values[i][colname] = value - if not slim: - hdf5.create_group("/grid/run{}/".format(run_index)) - # consider the run (and the input directory) included - self.MESA_dirs.append(grid.runs[i].path) - run_included[i] = True - run_index += 1 + #add initial RLO system if not added before + if not run_included[i]: + if not slim: + hdf5.create_group( + "/grid/run{}/".format(run_index)) + #include the run (and the input directory) + self.MESA_dirs.append(grid.runs[i].path) + run_included[i] = True + run_index += 1 self._say("Storing initial/final values and metadata to HDF5...") From 4c26f76ea082df2737cbf3f2bde077096de52455 Mon Sep 17 00:00:00 2001 From: mkruckow Date: Tue, 24 Jan 2023 17:05:20 +0100 Subject: [PATCH 041/319] remove old code and change flag name initial_RLO_fix_g to initial_RLO_fix --- posydon/grids/psygrid.py | 54 ++++-------------------------- posydon/grids/termination_flags.py | 47 -------------------------- 2 files changed, 6 insertions(+), 95 deletions(-) diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index e93c462c09..2ed78e5a34 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -197,7 +197,6 @@ check_state_from_history, get_flag_from_MESA_output, infer_interpolation_class, - initial_RLO_fix_applies, get_detected_initial_RLO, get_nearest_known_initial_RLO) from posydon.utils.configfile import ConfigFile @@ -351,7 +350,6 @@ "eep": None, # path to EEP files "initial_RLO_fix": False, "He_core_fix": True, - "initial_RLO_fix_g": False, } @@ -539,7 +537,6 @@ def _create_psygrid(self, MESA_path, hdf5, slim=False, fmt="posydon"): initial_RLO_fix = self.config["initial_RLO_fix"] start_at_RLO = self.config["start_at_RLO"] eep = self.config["eep"] - initial_RLO_fix_g = self.config["initial_RLO_fix_g"] if eep is not None: if binary_grid: @@ -650,7 +647,7 @@ def decide_columns(key_in_config, defaults): ignore_reason = "ignored_no_BH" warnings.warn("Ignored MESA run because of missing binary " "history in: {}\n".format(run.path)) - if not (initial_RLO_fix or initial_RLO_fix_g): + if not initial_RLO_fix: continue if ignore_data: @@ -746,7 +743,7 @@ def decide_columns(key_in_config, defaults): ignore_reason = "ignored_scrubbed" warnings.warn("Ignored MESA run because of scrubbed binary" " history in: {}\n".format(run.path)) - if not (initial_RLO_fix or initial_RLO_fix_g): + if not initial_RLO_fix: continue if not binary_grid and len(history1) == 0: ignore_data = True @@ -762,7 +759,7 @@ def decide_columns(key_in_config, defaults): ignore_reason = "ignored_no_RLO" warnings.warn("Ignored MESA run because of no RLO" " in: {}\n".format(run.path)) - if not (initial_RLO_fix or initial_RLO_fix_g): + if not initial_RLO_fix: continue binary_history, history1, history2 = None, None, None else: @@ -971,46 +968,7 @@ def decide_columns(key_in_config, defaults): self.final_values[i]["interpolation_class"] = \ infer_interpolation_class(*termination_flags[:2]) - if initial_RLO_fix: - star_1_mass = self.initial_values[i]["star_1_mass"] - period_days = self.initial_values[i]["period_days"] - colnames = ["termination_flag_1", "termination_flag_2", - "interpolation_class"] - valtoset = ["forced_initial_RLO", "forced_initial_RLO", - "initial_MT"] - if initial_RLO_fix_applies(star_1_mass, period_days): - # if initial MT is forced but not detected immediately, - # remove the data - if (self.final_values[i]["termination_flag_1"] - != "initial_MT"): - # remove all initial values, except for metallicities - for colname in self.initial_values[i].dtype.names: - if colname not in ["X", "Y", "Z"]: - self.initial_values[i][colname] = np.nan - # remove all final values, except for termination flags - for colname in self.final_values[i].dtype.names: - if not (colname.startswith("termination_flag") - or colname == "interpolation_class"): - self.final_values[i][colname] = np.nan - # do not include the histories and profiles... - ignore_data = True - # set the termination flags - for colname, value in zip(colnames, valtoset): - self.final_values[i][colname] = value - # update the initial values from the grid point data - grid_point = read_initial_values(run.path) - for colname, value in grid_point.items(): - if colname in self.initial_values.dtype.names: - self.initial_values[i][colname] = value - - # now that we have the masses (even if missing data)... - self.final_values[i]["termination_flag_4"] = ( - infer_star_state(self.final_values[i]["star_2_mass"], - star_CO=True)) - elif ignore_data: - # if fix does not apply and failed run, do not include it - continue - elif ignore_data: + if ignore_data: # if not fix requested and failed run, do not include it continue @@ -1052,7 +1010,7 @@ def decide_columns(key_in_config, defaults): run_index += 1 #general fix for termination_flag in case of initial RLO - if initial_RLO_fix_g: + if initial_RLO_fix: #create list of already detected initial RLO detected_initial_RLO = get_detected_initial_RLO(self) colnames = ["termination_flag_1", "termination_flag_2", @@ -1932,7 +1890,7 @@ def downsample_profile(profile, params): } PROPERTIES_TO_BE_CONSISTENT = ["binary", "eep", "start_at_RLO", - "initial_RLO_fix", "initial_RLO_fix_g", + "initial_RLO_fix", "He_core_fix", "history_DS_error", "history_DS_exclude", "profile_DS_error", "profile_DS_exclude", "profile_DS_interval"] diff --git a/posydon/grids/termination_flags.py b/posydon/grids/termination_flags.py index 608e91ecb1..bb580b0bdc 100644 --- a/posydon/grids/termination_flags.py +++ b/posydon/grids/termination_flags.py @@ -232,53 +232,6 @@ def infer_interpolation_class(tf1, tf2): return "unknown" -def initial_RLO_fix_applies(mass, period): - """Check if initial RLO fix is warranted given the initial mass and period. - - Parameters - ---------- - mass : float - Mass of star 1. - period : float - Binary's period (in days). - - Returns - ------- - bool - True if initial RLO flag should be forced. - - """ - if mass < 0.6: - return period < 0.14 - if mass < 1.0: - return period < 0.20 - if mass < 1.3: - return period < 0.29 - if mass < 3.5: - return period < 0.41 - if mass < 8: - return period < 0.59 - if mass < 15: - return period < 0.85 - return period < 1.2 - - # if period > 1.0: - # return False - # if period > 0.7: - # return mass > 84.0 - # if period > 0.51: - # return mass > 23.0 - # if period > 0.36: - # return mass > 7.7 - # if period > 0.25: - # return mass > 5.0 - # if period > 0.18: - # return mass > 1.6 - # if period > 0.124: - # return mass > 1.2 - # return mass > 0.6 - - def get_detected_initial_RLO(grid): """Generates a list of already detected initial RLO From 2877a5d2148b989622428ab3ee6f5506da5a41a3 Mon Sep 17 00:00:00 2001 From: prinse1545 Date: Fri, 27 Jan 2023 12:22:31 -0600 Subject: [PATCH 042/319] added compress mesa script to bin --- bin/compress-mesa | 141 ++++++++++++++++++++++++++++++++++++++++++++++ setup.py | 1 + 2 files changed, 142 insertions(+) create mode 100644 bin/compress-mesa diff --git a/bin/compress-mesa b/bin/compress-mesa new file mode 100644 index 0000000000..f5c3673841 --- /dev/null +++ b/bin/compress-mesa @@ -0,0 +1,141 @@ +from hurry.filesize import size +from tqdm import tqdm +import gzip +import random +import shutil +import time +import argparse +import sys +import os + + +def set_up_test(args): + """ sets up a testing directory in requested directory + + Parameters: + ---------- + test_dir : string + the directory where the test directory is to be set up + dsr : float + down sampling rate when creating testing directory + + """ + + if not os.path.isdir(args.test_dir): + sys.exit(f"directory {args.test_dir} does not exist") + + for f in os.listdir(args.mesa_dir): + + if os.path.isdir(os.path.join(args.mesa_dir, f)): + is_mesa_run = False + sub_dir = os.listdir(os.path.join(args.mesa_dir, f)) + track_dirs = [] + + for _f in sub_dir: + + if "_grid_index_" in _f: + is_mesa_run = True + + # if os.path.isdir(os.path.join(args.mesa_dir, f, _f)) and _f != "star1" and _f != "star2" and _f != "binary": + track_dirs.append(os.path.join(args.mesa_dir, f, _f)) + + if is_mesa_run == True: # checking if directory is a mesa run + os.mkdir(os.path.join(args.test_dir, f)) + # choosing which tracks to copy over + inds = random.sample(list(range(len(track_dirs))), int(len(track_dirs) * args.dsr)) + + for ind in inds: + if os.path.isdir(track_dirs[ind]): + shutil.copytree(track_dirs[ind], os.path.join(args.test_dir, f, track_dirs[ind].split("/")[-1])) + else: + shutil.copy(track_dirs[ind], os.path.join(args.test_dir, f, track_dirs[ind].split("/")[-1])) + + print(f"Created Test Directory at {args.test_dir}") + + +def compress_dir(args): + """ compresses directory containing tracks evolved with MESA + + Parameters: + ----------- + mesa_dir : string + the directory where the MESA tracks are stored + + """ + + def get_size(start_path = "."): + total_size = 0 + for dirpath, dirnames, filenames in os.walk(start_path): + for f in filenames: + fp = os.path.join(dirpath, f) + # skip if it is symbolic link + if not os.path.islink(fp): + total_size += os.path.getsize(fp) + + return total_size + + if not os.path.isdir(args.mesa_dir): + sys.exit("the MESA directory does not exist") + + og_size = size(get_size(args.mesa_dir)) + + for f in tqdm(os.listdir(args.mesa_dir)): + + if os.path.isdir(os.path.join(args.mesa_dir, f)): + + is_mesa_run = False + sub_dir = os.listdir(os.path.join(args.mesa_dir, f)) + track_dirs = [] + + for _f in sub_dir: + + if "_grid_index_" in _f: # checking if directory is mesa run + is_mesa_run = True + + if os.path.isdir(os.path.join(args.mesa_dir, f, _f)) and _f != "star1" and _f != "star2" and _f != "binary": + track_dirs.append(os.path.join(args.mesa_dir, f, _f)) + + if is_mesa_run == True: + + for track_obj in track_dirs: # iterating over directory containing MESA runs + + for root, _dir, files in os.walk(track_obj): + # traversing over directory tree of copied mesa tracks and compressing .data, .mod, .txt files + for file in files: + ext = file.split(".")[-1] + + if ext == "data" or ext == "mod" or ext == "txt": + os.system(f"gzip -1 {os.path.join(root, file)}") + + + + + print( + f""" +compressed MESA tracks +Original Size {og_size} | Compressed Size {size(get_size(args.mesa_dir))} + """ + ) + +if __name__ == "__main__": + + parser = argparse.ArgumentParser() + + parser.add_argument("-f", "--function", type = str, help = "the name of the function to be called") + parser.add_argument("-td", "--test_dir", type = str, help = "the path to where the testing directory should be set up") + parser.add_argument("-dsr", "--dsr", type = str, help = "down sampling rate when creating testing directory", default = 0.01) + parser.add_argument("-md", "--mesa_dir", type = str, help = "the path to the directory containing MESA generated data") + parser.add_argument("-od", "--output_dir", type = str, help = "the path to the output directory for compressing MESA files") + + functions = { + "set_up_test": set_up_test, + "compress_dir": compress_dir + } + + args = parser.parse_args() + + if args.function == None: + sys.exit("a function must be specified to use this script") + + functions[args.function](args) + diff --git a/setup.py b/setup.py index e69ec31561..0a7cb10832 100644 --- a/setup.py +++ b/setup.py @@ -75,6 +75,7 @@ 'tqdm == 4.48.2', 'tables == 3.6.1', 'progressbar2 == 4.0.0', + 'hurry.filesize == 0.9', ] tests_require = [ From f03d105b3971fa9a089cb78aa3e09ababee1f6b5 Mon Sep 17 00:00:00 2001 From: prinse1545 Date: Fri, 27 Jan 2023 12:39:54 -0600 Subject: [PATCH 043/319] made a few changes --- bin/compress-mesa | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/bin/compress-mesa b/bin/compress-mesa index f5c3673841..42236149fe 100644 --- a/bin/compress-mesa +++ b/bin/compress-mesa @@ -122,10 +122,9 @@ if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("-f", "--function", type = str, help = "the name of the function to be called") - parser.add_argument("-td", "--test_dir", type = str, help = "the path to where the testing directory should be set up") + parser.add_argument("-td", "--test_dir", type = str, help = "the path to where the testing directory should be set up, either set_up_test or compress_dir") parser.add_argument("-dsr", "--dsr", type = str, help = "down sampling rate when creating testing directory", default = 0.01) parser.add_argument("-md", "--mesa_dir", type = str, help = "the path to the directory containing MESA generated data") - parser.add_argument("-od", "--output_dir", type = str, help = "the path to the output directory for compressing MESA files") functions = { "set_up_test": set_up_test, From 2cde1ed3f0753c5faf4a99597d92c980490d7db2 Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Thu, 2 Feb 2023 16:32:48 +0100 Subject: [PATCH 044/319] trigger conda action only when pushing to main --- .github/workflows/publish-to-anaconda.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/publish-to-anaconda.yml b/.github/workflows/publish-to-anaconda.yml index faf566631b..14181931d6 100644 --- a/.github/workflows/publish-to-anaconda.yml +++ b/.github/workflows/publish-to-anaconda.yml @@ -1,9 +1,9 @@ name: publish_conda on: - release: - types: [published] - pull_request: + push: + branches: + - main jobs: publish: From 33a9089ee27947889a5471d6e9df2e95d5cdb9c4 Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Fri, 3 Feb 2023 08:13:15 +0100 Subject: [PATCH 045/319] fix submission script for POSYDON v2 (#24) * layer the metallicity in the He-star step1 creation * fix python exponential notation in fortran notation * the rerun method of psygrid now export metallicity as well * move up the rounding to affect only the default initial condition and allow custom inlist bolean and integer formating in csv * submission script now recognise boleas * allow to run single HMS and HeMS stars at low metallicity * Save profiles (#30) * initial commit to add functionality to make deleting profiles at run completion optional * propagating changes to posydon-setup-grid * fixing bugs * Fixing bugs * Adding functionality to keep photos --------- Co-authored-by: Jeff Andrews --- bin/posydon-run-grid | 126 +++++++++++++++++++++++++++++++-------- bin/posydon-setup-grid | 21 ++++++- posydon/grids/psygrid.py | 42 +++++++++---- 3 files changed, 151 insertions(+), 38 deletions(-) diff --git a/bin/posydon-run-grid b/bin/posydon-run-grid index f546c57885..15de562919 100755 --- a/bin/posydon-run-grid +++ b/bin/posydon-run-grid @@ -117,6 +117,12 @@ def parse_commandline(): parser.add_argument("--verbose", action="store_true", default=False, help="Run in Verbose Mode") + parser.add_argument("--keep_profiles", action="store_true", default=False, + help="Do not delete profiles at run completion") + + parser.add_argument("--keep_photos", action="store_true", default=False, + help="Do not delete photos at run completion") + args = parser.parse_args() if args.grid_type not in ['fixed', 'dynamic']: @@ -172,6 +178,9 @@ def create_working_directory(grid_param_dict, args): # first name directories which we do not need to log directory_name_from_dict_no_log = '_'.join(['{0}_{1:.4f}'.format(k, v) for k, v in grid_param_dict.items() if k not in variable_names_to_log]) directory_name_from_dict_log = '_'.join(['{0}_{1:.4e}'.format(k, v) for k, v in grid_param_dict.items() if k in variable_names_to_log]) + # drop parenthesis + directory_name_from_dict_no_log = directory_name_from_dict_no_log.replace('(','') + directory_name_from_dict_no_log = directory_name_from_dict_no_log.replace(')','') # combine and add on what grid number we are running if exists directory_name_from_dict = '{0}_{1}'.format(directory_name_from_dict_no_log, directory_name_from_dict_log) @@ -239,9 +248,14 @@ def create_binary_inlists(binary_controls, binary_job, inlist_grid_points.write("&binary_controls\n") for k, v in binary_controls.items(): if (type(v) == float) and (not v.is_integer()): - inlist_grid_points.write("\t{0} = {1}d0\n".format(k, v)) + inlist_grid_points.write("\t{0} = {1:.10f}d0\n".format(k, v)) elif (type(v) == float) and (v.is_integer()): inlist_grid_points.write("\t{0} = {1}\n".format(k, int(v))) + elif type(v) == bool: + if v: + inlist_grid_points.write("\t{0} = .true.\n".format(k)) + else: + inlist_grid_points.write("\t{0} = .fale.\n".format(k)) else: inlist_grid_points.write("{0} = {1}\n".format(k, v)) inlist_grid_points.write("/ ! end of binary_controls namelist\n") @@ -251,9 +265,14 @@ def create_binary_inlists(binary_controls, binary_job, inlist_grid_points.write("&binary_job\n") for k, v in binary_job.items(): if (type(v) == float) and (not v.is_integer()): - inlist_grid_points.write("\t{0} = {1}d0\n".format(k, v)) + inlist_grid_points.write("\t{0} = {1:.10f}d0\n".format(k, v)) elif (type(v) == float) and (v.is_integer()): inlist_grid_points.write("\t{0} = {1}\n".format(k, int(v))) + elif type(v) == bool: + if v: + inlist_grid_points.write("\t{0} = .true.\n".format(k)) + else: + inlist_grid_points.write("\t{0} = .fale.\n".format(k)) else: inlist_grid_points.write("{0} = {1}\n".format(k, v)) inlist_grid_points.write("/ ! end of binary_job namelist\n") @@ -264,9 +283,14 @@ def create_binary_inlists(binary_controls, binary_job, inlist_grid_points.write("&controls\n") for k, v in star1_binary_controls.items(): if (type(v) == float) and (not v.is_integer()): - inlist_grid_points.write("\t{0} = {1}d0\n".format(k, v)) + inlist_grid_points.write("\t{0} = {1:.10f}d0\n".format(k, v)) elif (type(v) == float) and (v.is_integer()): inlist_grid_points.write("\t{0} = {1}\n".format(k, int(v))) + elif type(v) == bool: + if v: + inlist_grid_points.write("\t{0} = .true.\n".format(k)) + else: + inlist_grid_points.write("\t{0} = .fale.\n".format(k)) else: inlist_grid_points.write("{0} = {1}\n".format(k, v)) inlist_grid_points.write("/ ! end of controls namelist\n") @@ -277,9 +301,14 @@ def create_binary_inlists(binary_controls, binary_job, inlist_grid_points.write("&controls\n") for k, v in star2_binary_controls.items(): if (type(v) == float) and (not v.is_integer()): - inlist_grid_points.write("\t{0} = {1}d0\n".format(k, v)) + inlist_grid_points.write("\t{0} = {1:.10f}d0\n".format(k, v)) elif (type(v) == float) and (v.is_integer()): inlist_grid_points.write("\t{0} = {1}\n".format(k, int(v))) + elif type(v) == bool: + if v: + inlist_grid_points.write("\t{0} = .true.\n".format(k)) + else: + inlist_grid_points.write("\t{0} = .fale.\n".format(k)) else: inlist_grid_points.write("{0} = {1}\n".format(k, v)) inlist_grid_points.write("/ ! end of controls namelist\n") @@ -290,9 +319,14 @@ def create_binary_inlists(binary_controls, binary_job, inlist_grid_points.write("&star_job\n") for k, v in star1_binary_job.items(): if (type(v) == float) and (not v.is_integer()): - inlist_grid_points.write("\t{0} = {1}d0\n".format(k, v)) + inlist_grid_points.write("\t{0} = {1:.10f}d0\n".format(k, v)) elif (type(v) == float) and (v.is_integer()): inlist_grid_points.write("\t{0} = {1}\n".format(k, int(v))) + elif type(v) == bool: + if v: + inlist_grid_points.write("\t{0} = .true.\n".format(k)) + else: + inlist_grid_points.write("\t{0} = .fale.\n".format(k)) else: inlist_grid_points.write("{0} = {1}\n".format(k, v)) inlist_grid_points.write("/ ! end of star_job namelist\n") @@ -303,16 +337,21 @@ def create_binary_inlists(binary_controls, binary_job, inlist_grid_points.write("&star_job\n") for k, v in star2_binary_job.items(): if (type(v) == float) and (not v.is_integer()): - inlist_grid_points.write("\t{0} = {1}d0\n".format(k, v)) + inlist_grid_points.write("\t{0} = {1:.10f}d0\n".format(k, v)) elif (type(v) == float) and (v.is_integer()): inlist_grid_points.write("\t{0} = {1}\n".format(k, int(v))) + elif type(v) == bool: + if v: + inlist_grid_points.write("\t{0} = .true.\n".format(k)) + else: + inlist_grid_points.write("\t{0} = .fale.\n".format(k)) else: inlist_grid_points.write("{0} = {1}\n".format(k, v)) inlist_grid_points.write("/ ! end of star_job namelist\n") return -def create_star_formation(mass, work_dir, star_inlist_project, initial_z=None): +def create_star_formation(mass, work_dir, star_inlist_project, initial_z=None, new_Z=False): """Create the inlist and inlist grid points needed to run this part of grid """ @@ -332,6 +371,8 @@ def create_star_formation(mass, work_dir, star_inlist_project, initial_z=None): read_extra_star_job_inlist1 = .true. extra_star_job_inlist1_name = '{0}' + read_extra_star_job_inlist2 = .true. + extra_star_job_inlist2_name = 'inlist_grid_points' / ! end of job namelist """.format(star_inlist_project) @@ -343,10 +384,15 @@ def create_star_formation(mass, work_dir, star_inlist_project, initial_z=None): with open(os.path.join(work_dir, 'inlist_grid_points'), 'w') as inlist_grid_points: inlist_grid_points.write("&controls\n") - inlist_grid_points.write("initial_mass = {0}d0\n".format(mass)) + inlist_grid_points.write("initial_mass = {0:.10f}d0\n".format(mass)) if initial_z is not None: - inlist_grid_points.write("initial_z = {0}d0\n".format(initial_z)) - inlist_grid_points.write("/ ! end of binary_controls namelist\n") + inlist_grid_points.write("initial_z = {0:.10f}d0\n".format(initial_z)) + inlist_grid_points.write("Zbase = {0:.10f}d0\n".format(initial_z)) + inlist_grid_points.write("/ ! end of star_controls namelist\n") + inlist_grid_points.write("&star_job\n") + if new_Z: + inlist_grid_points.write("new_Z = {0:.10f}d0\n".format(initial_z)) + inlist_grid_points.write("/ ! end of star_job namelist\n") inlist_grid_points.close() return @@ -361,21 +407,28 @@ def run_mesa(mesa_executable, work_dir, final_dir, **kwargs): final_dir: When completed where would you like the output to go """ + outfile_name = kwargs.pop('outfile_name', 'out.txt') + keep_profiles = kwargs.pop('keep_profiles', False) + keep_photos = kwargs.pop('keep_photos', False) + os.chdir(work_dir) os.system("{0} &> {1}".format(mesa_executable, os.path.join(work_dir, outfile_name))) - if os.path.exists(os.path.join(work_dir, 'LOGS')): - os.chmod(os.path.join(work_dir, 'LOGS'), stat.S_IRWXU | stat.S_IRGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IXOTH ) - os.system("rm LOGS/profile*") - if os.path.exists(os.path.join(work_dir, 'LOGS1')): - os.chmod(os.path.join(work_dir, 'LOGS1'), stat.S_IRWXU | stat.S_IRGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IXOTH ) - os.system("rm LOGS1/profile*") - if os.path.exists(os.path.join(work_dir, 'LOGS2')): - os.chmod(os.path.join(work_dir, 'LOGS2'), stat.S_IRWXU | stat.S_IRGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IXOTH ) - os.system("rm LOGS2/profile*") + if not keep_profiles: + if os.path.exists(os.path.join(work_dir, 'LOGS')): + os.chmod(os.path.join(work_dir, 'LOGS'), stat.S_IRWXU | stat.S_IRGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IXOTH ) + os.system("rm LOGS/profile*") + if os.path.exists(os.path.join(work_dir, 'LOGS1')): + os.chmod(os.path.join(work_dir, 'LOGS1'), stat.S_IRWXU | stat.S_IRGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IXOTH ) + os.system("rm LOGS1/profile*") + if os.path.exists(os.path.join(work_dir, 'LOGS2')): + os.chmod(os.path.join(work_dir, 'LOGS2'), stat.S_IRWXU | stat.S_IRGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IXOTH ) + os.system("rm LOGS2/profile*") if os.path.exists(os.path.join(work_dir, '.mesa_temp_cache')): os.system("rm -rf .mesa_temp_cache") - os.system("rm -rf photos*") + + if not keep_photos: + os.system("rm -rf photos*") if not work_dir == final_dir: if os.path.isdir(final_dir): @@ -604,7 +657,12 @@ def run_grid_point(grid, star1_formation, star2_formation, # if yes then create inlists specific to this grid points # moreover we should loop over all the star1 formation steps for index, inlist_step in enumerate(args.mesa_star1_inlist_project): - create_star_formation(grid_param_dict['m1'], work_dir, inlist_step) + # this is only called to create single HeMS stars, only second step1 + # requires metallicity + if index == 0: + create_star_formation(grid_param_dict['m1'], work_dir, inlist_step) + elif index == 1: + create_star_formation(grid_param_dict['m1'], work_dir, inlist_step, grid_param_dict['initial_z'], True) run_mesa(args.mesa_star1_executable, work_dir, final_dir, outfile_name='out_star1_formation_step{0}.txt'.format(index)) @@ -613,7 +671,12 @@ def run_grid_point(grid, star1_formation, star2_formation, # if yes then create inlists specific to this grid points # moreover we should loop over all the star1 formation steps for index, inlist_step in enumerate(args.mesa_star2_inlist_project): - create_star_formation(grid_param_dict['m2'], work_dir, inlist_step) + # this is only called to create single HeMS stars, only second step1 + # requires metallicity + if index == 0: + create_star_formation(grid_param_dict['m2'], work_dir, inlist_step) + elif index == 1: + create_star_formation(grid_param_dict['m2'], work_dir, inlist_step, grid_param_dict['initial_z'], True) run_mesa(args.mesa_star2_executable, work_dir, final_dir, outfile_name='out_star2_formation_step{0}.txt'.format(index)) @@ -624,7 +687,9 @@ def run_grid_point(grid, star1_formation, star2_formation, work_dir, args.mesa_binary_inlist_project) # run the binary exectuable - run_mesa(args.mesa_binary_executable, work_dir, final_dir) + run_mesa(args.mesa_binary_executable, work_dir, final_dir, + keep_profiles=args.keep_profiles, + keep_photos=args.keep_photos) # find out what happened mesa_result = extract_mesa_results(final_dir) @@ -747,10 +812,21 @@ if __name__ == '__main__': work_dir, final_dir = create_working_directory(grid_param_dict, args) # run the single star grid + last_step = len(args.mesa_star1_inlist_project) - 1 for index, inlist_step in enumerate(args.mesa_star1_inlist_project): - create_star_formation(grid_param_dict['initial_mass'], work_dir, inlist_step) + # single_HMS star has only step0, metallicity is required but not new_Z + # single_HeMS star has step0, step1, step2, last two steps require metallicity + # but only step1 requires new_Z + if index == last_step: + create_star_formation(grid_param_dict['initial_mass'], work_dir, inlist_step, grid_param_dict['initial_z'], False) + elif index == 1: + create_star_formation(grid_param_dict['initial_mass'], work_dir, inlist_step, grid_param_dict['initial_z'], True) + else : + create_star_formation(grid_param_dict['initial_mass'], work_dir, inlist_step) run_mesa(args.mesa_star1_executable, work_dir, final_dir, - outfile_name='out_star1_formation_step{0}.txt'.format(index)) + outfile_name='out_star1_formation_step{0}.txt'.format(index), + keep_profiles=args.keep_profiles, + keep_photos=args.keep_photos) sys.exit(0) diff --git a/bin/posydon-setup-grid b/bin/posydon-setup-grid index 46af8ece88..b79b4674a4 100755 --- a/bin/posydon-setup-grid +++ b/bin/posydon-setup-grid @@ -852,7 +852,8 @@ def construct_command_line(number_of_mpi_processes, path_to_grid, inlist_star1_formation, inlist_star2_formation, star_history_columns, binary_history_columns, profile_columns, run_directory, grid_type, path_to_run_grid_exec, - psycris_inifile=None): + psycris_inifile=None, keep_profiles=False, + keep_photos=False): """Based on the inifile construct the command line call to posydon-run-grid """ if grid_type == "fixed": @@ -867,6 +868,10 @@ def construct_command_line(number_of_mpi_processes, path_to_grid, command_line += '--mesa-binary-history-columns {11} --mesa-profile-columns {12} ' command_line += '--output-directory {13} --grid-type {14} ' command_line += '--psycris-inifile {16}' + if keep_profiles: + command_line += ' --keep_profiles' + if keep_photos: + command_line += ' --keep_photos' command_line = command_line.format(number_of_mpi_processes, path_to_grid, binary_exe, @@ -918,6 +923,12 @@ if __name__ == '__main__': if 'scenario' not in mesa_inlists.keys(): mesa_inlists['scenario'] = None + if 'keep_profiles' not in run_parameters.keys(): + run_parameters['keep_profiles'] = False + + if 'keep_photos' not in run_parameters.keys(): + run_parameters['keep_photos'] = False + if ((not os.path.isfile(run_parameters['grid'])) and (not os.path.isdir(run_parameters['grid']))): raise ValueError("Supplied grid does not exist, please check your path and try again") @@ -990,7 +1001,9 @@ if __name__ == '__main__': mesa_inlists['profile_columns'], args.run_directory, 'fixed', - path_to_run_grid_exec) + path_to_run_grid_exec, + keep_profiles=run_parameters['keep_profiles'], + keep_photos=run_parameters['keep_photos']) command_line += ' --grid-point-index $SLURM_ARRAY_TASK_ID' else: command_line = construct_command_line(slurm['number_of_mpi_tasks']*slurm['number_of_nodes'], @@ -1009,7 +1022,9 @@ if __name__ == '__main__': args.run_directory, args.grid_type, path_to_run_grid_exec, - psycris_inifile = run_parameters["psycris_inifile"]) + psycris_inifile = run_parameters["psycris_inifile"], + keep_profiles=run_parameters['keep_profiles'], + keep_photos=run_parameters['keep_photos']) # now we need to know how this person plans to run the above created # command. As a shell script? As a SLURM submission? In some other way? diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index fdcdb7e2d9..05e72991a7 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -1370,13 +1370,24 @@ def rerun(self, path_to_file='./', runs_to_rerun=None, initial_values[key].append(self.initial_values[key][i]) # replace star_1_mass, star_2_mass, period_days, Z + NDIG = 10 # rounding matches initial point rounding if 'star_1_mass' in self.initial_values.dtype.names: - initial_values['m1'] = initial_values['star_1_mass'] + initial_values['m1'] = np.around(initial_values['star_1_mass'], + NDIG) if 'star_2_mass' in self.initial_values.dtype.names: - initial_values['m2'] = initial_values['star_2_mass'] + initial_values['m2'] = np.around(initial_values['star_2_mass'], + NDIG) if 'period_days' in self.initial_values.dtype.names: - initial_values['initial_period_in_days'] = initial_values[ - 'period_days'] + initial_values['initial_period_in_days'] = np.around( + initial_values['period_days'],NDIG) + MESA_dir_name = self.MESA_dirs[0].decode("utf-8") + if 'initial_z' in MESA_dir_name: + initial_values['initial_z'] = np.around(initial_values['Z'], + NDIG) + if 'Zbase' in MESA_dir_name: + initial_values['Zbase'] = np.around(initial_values['Z'], NDIG) + if 'new_Z' in MESA_dir_name: + initial_values['new_Z'] = np.around(initial_values['Z'], NDIG) for key in self.initial_values.dtype.names: if key not in ['m1', 'm2', 'initial_period_in_days', 'Zbase', 'new_Z', 'initial_z']: @@ -1393,7 +1404,7 @@ def rerun(self, path_to_file='./', runs_to_rerun=None, writer.writerow(initial_values.keys()) for i in range(n_runs_to_rerun): writer.writerow( - [initial_values[key][i] for key in initial_values]) + [initial_values[key][i]for key in initial_values]) elif termination_flags is not None and runs_to_rerun is None: if isinstance(termination_flags, str): rerun_flags = [termination_flags] @@ -1420,13 +1431,24 @@ def rerun(self, path_to_file='./', runs_to_rerun=None, initial_values[key].append(self.initial_values[key][i]) # replace star_1_mass, star_2_mass, period_days, Z + NDIG = 10 # rounding matches initial point rounding if 'star_1_mass' in self.initial_values.dtype.names: - initial_values['m1'] = initial_values['star_1_mass'] + initial_values['m1'] = np.around(initial_values['star_1_mass'], + NDIG) if 'star_2_mass' in self.initial_values.dtype.names: - initial_values['m2'] = initial_values['star_2_mass'] + initial_values['m2'] = np.around(initial_values['star_2_mass'], + NDIG) if 'period_days' in self.initial_values.dtype.names: - initial_values['initial_period_in_days'] = initial_values[ - 'period_days'] + initial_values['initial_period_in_days'] = np.around( + initial_values['period_days'],NDIG) + MESA_dir_name = self.MESA_dirs[0].decode("utf-8") + if 'initial_z' in MESA_dir_name: + initial_values['initial_z'] = np.around(initial_values['Z'], + NDIG) + if 'Zbase' in MESA_dir_name: + initial_values['Zbase'] = np.around(initial_values['Z'], NDIG) + if 'new_Z' in MESA_dir_name: + initial_values['new_Z'] = np.around(initial_values['Z'], NDIG) for key in self.initial_values.dtype.names: if key not in ['m1', 'm2', 'initial_period_in_days', 'Zbase', 'new_Z', 'initial_z']: @@ -1443,7 +1465,7 @@ def rerun(self, path_to_file='./', runs_to_rerun=None, writer.writerow(initial_values.keys()) for i in range(n_runs_to_rerun): writer.writerow( - [initial_values[key][i] for key in initial_values]) + [initial_values[key][i]for key in initial_values]) else: raise ValueError("Choose either the runs manually, or " "indicate the termination flag(s).") From 4d4c2573b48b0714f19e39f175c314c72fb94a4d Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Fri, 3 Feb 2023 11:13:21 +0100 Subject: [PATCH 046/319] fix initial_RLO_fix (#28) * add get_detected_initial_RLO * fix for initial RLO incl. copy termination_flags 3 and 4 * remove bug of tuple item asignement * add initial_RLO_fix_g to PROPERTIES_TO_BE_CONSISTENT * use nearest system instead of matching one, hence slice interruptions and random girds should work, too; switch to dictionaries to allow easier adding things to be copied from nearest systems * allow for 10^{-5} difference in masses of correctly detected systems to be recognized as the same * avoid skips like in old fix * restore initial vales from grid data (file/directory names) * fix blanks * get no RLO out again * avoid adding initial RLO systems twice * remove old code and change flag name initial_RLO_fix_g to initial_RLO_fix --- posydon/grids/psygrid.py | 94 ++++++++++++------------ posydon/grids/termination_flags.py | 111 ++++++++++++++++++----------- 2 files changed, 119 insertions(+), 86 deletions(-) diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index 05e72991a7..8e3846ba27 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -175,6 +175,7 @@ "Scott Coughlin ", "Devina Misra ", "Kyle Akira Rocha ", + "Matthias Kruckow ", ] @@ -196,7 +197,8 @@ check_state_from_history, get_flag_from_MESA_output, infer_interpolation_class, - initial_RLO_fix_applies) + get_detected_initial_RLO, + get_nearest_known_initial_RLO) from posydon.utils.configfile import ConfigFile from posydon.utils.common_functions import (orbital_separation_from_period, initialize_empty_array, @@ -966,46 +968,7 @@ def decide_columns(key_in_config, defaults): self.final_values[i]["interpolation_class"] = \ infer_interpolation_class(*termination_flags[:2]) - if initial_RLO_fix: - star_1_mass = self.initial_values[i]["star_1_mass"] - period_days = self.initial_values[i]["period_days"] - colnames = ["termination_flag_1", "termination_flag_2", - "interpolation_class"] - valtoset = ["forced_initial_RLO", "forced_initial_RLO", - "initial_MT"] - if initial_RLO_fix_applies(star_1_mass, period_days): - # if initial MT is forced but not detected immediately, - # remove the data - if (self.final_values[i]["termination_flag_1"] - != "initial_MT"): - # remove all initial values, except for metallicities - for colname in self.initial_values[i].dtype.names: - if colname not in ["X", "Y", "Z"]: - self.initial_values[i][colname] = np.nan - # remove all final values, except for termination flags - for colname in self.final_values[i].dtype.names: - if not (colname.startswith("termination_flag") - or colname == "interpolation_class"): - self.final_values[i][colname] = np.nan - # do not include the histories and profiles... - ignore_data = True - # set the termination flags - for colname, value in zip(colnames, valtoset): - self.final_values[i][colname] = value - # update the initial values from the grid point data - grid_point = read_initial_values(run.path) - for colname, value in grid_point.items(): - if colname in self.initial_values.dtype.names: - self.initial_values[i][colname] = value - - # now that we have the masses (even if missing data)... - self.final_values[i]["termination_flag_4"] = ( - infer_star_state(self.final_values[i]["star_2_mass"], - star_CO=True)) - elif ignore_data: - # if fix does not apply and failed run, do not include it - continue - elif ignore_data: + if ignore_data: # if not fix requested and failed run, do not include it continue @@ -1046,6 +1009,47 @@ def decide_columns(key_in_config, defaults): run_included[i] = True run_index += 1 + #general fix for termination_flag in case of initial RLO + if initial_RLO_fix: + #create list of already detected initial RLO + detected_initial_RLO = get_detected_initial_RLO(self) + colnames = ["termination_flag_1", "termination_flag_2", + "interpolation_class"] + valtoset = ["forced_initial_RLO", "forced_initial_RLO", + "initial_MT"] + for i in range(N_runs): + flag1 = self.final_values[i]["termination_flag_1"] + if flag1 != "Terminate because of overflowing initial model": + mass1 = self.initial_values[i]["star_1_mass"] + mass2 = self.initial_values[i]["star_2_mass"] + period = self.initial_values[i]["period_days"] + nearest = get_nearest_known_initial_RLO(mass1, mass2, + detected_initial_RLO) + if period", "Jeffrey Andrews ", "Emmanouil Zapartas ", + "Matthias Kruckow ", ] @@ -231,48 +232,76 @@ def infer_interpolation_class(tf1, tf2): return "unknown" -def initial_RLO_fix_applies(mass, period): - """Check if initial RLO fix is warranted given the initial mass and period. - +def get_detected_initial_RLO(grid): + """Generates a list of already detected initial RLO + Parameters ---------- - mass : float - Mass of star 1. - period : float - Binary's period (in days). - - Returns + grid : a PSyGrid + The grid to check. + + Retruns ------- - bool - True if initial RLO flag should be forced. - + list + A list containing systems already detected to be initial_MT based on + termination_flag_1. For each system there is a dictionary with the + impartant data, e.g. initial masses, periods, termination_flags """ - if mass < 0.6: - return period < 0.14 - if mass < 1.0: - return period < 0.20 - if mass < 1.3: - return period < 0.29 - if mass < 3.5: - return period < 0.41 - if mass < 8: - return period < 0.59 - if mass < 15: - return period < 0.85 - return period < 1.2 - - # if period > 1.0: - # return False - # if period > 0.7: - # return mass > 84.0 - # if period > 0.51: - # return mass > 23.0 - # if period > 0.36: - # return mass > 7.7 - # if period > 0.25: - # return mass > 5.0 - # if period > 0.18: - # return mass > 1.6 - # if period > 0.124: - # return mass > 1.2 - # return mass > 0.6 + #new list + detected = [] + #go through grid + N_runs = len(grid.initial_values) + for i in range(N_runs): + flag1 = grid.final_values[i]["termination_flag_1"] + #find systems with termination because of initial overflow + if flag1 == "Terminate because of overflowing initial model": + mass1 = grid.initial_values[i]["star_1_mass"] + mass2 = grid.initial_values[i]["star_2_mass"] + period = grid.initial_values[i]["period_days"] + e = False + #check for already existing entries of same mass combination + for d in detected: + if (abs(d["star_1_mass"]-mass1)<1.0e-5 and + abs(d["star_2_mass"]-mass2)<1.0e-5): + e = True + #update values if new one has a larger period + if d["period_days"] Date: Fri, 3 Feb 2023 13:39:28 +0100 Subject: [PATCH 047/319] Adding the environment directive. --- bin/compress-mesa | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/bin/compress-mesa b/bin/compress-mesa index 42236149fe..d6adf92d44 100644 --- a/bin/compress-mesa +++ b/bin/compress-mesa @@ -1,3 +1,4 @@ +#!/usr/bin/env python from hurry.filesize import size from tqdm import tqdm import gzip @@ -55,7 +56,7 @@ def set_up_test(args): def compress_dir(args): """ compresses directory containing tracks evolved with MESA - + Parameters: ----------- mesa_dir : string @@ -82,7 +83,7 @@ def compress_dir(args): for f in tqdm(os.listdir(args.mesa_dir)): if os.path.isdir(os.path.join(args.mesa_dir, f)): - + is_mesa_run = False sub_dir = os.listdir(os.path.join(args.mesa_dir, f)) track_dirs = [] @@ -103,13 +104,13 @@ def compress_dir(args): # traversing over directory tree of copied mesa tracks and compressing .data, .mod, .txt files for file in files: ext = file.split(".")[-1] - + if ext == "data" or ext == "mod" or ext == "txt": os.system(f"gzip -1 {os.path.join(root, file)}") - + print( f""" compressed MESA tracks @@ -137,4 +138,3 @@ if __name__ == "__main__": sys.exit("a function must be specified to use this script") functions[args.function](args) - From 5c0e2c683180a3f698c8692b48497e555505aaa4 Mon Sep 17 00:00:00 2001 From: kkovlakas Date: Fri, 3 Feb 2023 13:44:16 +0100 Subject: [PATCH 048/319] Standard order of imports. --- bin/compress-mesa | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/bin/compress-mesa b/bin/compress-mesa index d6adf92d44..4bc9254fe0 100644 --- a/bin/compress-mesa +++ b/bin/compress-mesa @@ -1,13 +1,13 @@ #!/usr/bin/env python -from hurry.filesize import size -from tqdm import tqdm +import os +import sys import gzip -import random -import shutil import time +import shutil +import random import argparse -import sys -import os +from tqdm import tqdm +from hurry.filesize import size def set_up_test(args): From d1dca10368737a43a73c24f4777b95cee9b83014 Mon Sep 17 00:00:00 2001 From: kkovlakas Date: Fri, 3 Feb 2023 13:51:22 +0100 Subject: [PATCH 049/319] Making the script more PEP-compliant. --- bin/compress-mesa | 59 +++++++++++++++++++++++------------------------ 1 file changed, 29 insertions(+), 30 deletions(-) diff --git a/bin/compress-mesa b/bin/compress-mesa index 4bc9254fe0..7929ae4440 100644 --- a/bin/compress-mesa +++ b/bin/compress-mesa @@ -11,17 +11,16 @@ from hurry.filesize import size def set_up_test(args): - """ sets up a testing directory in requested directory + """Set up a testing directory in the requested directory. Parameters: ---------- test_dir : string - the directory where the test directory is to be set up + The directory where the test directory is to be set up. dsr : float - down sampling rate when creating testing directory + Downsampling rate when creating testing directory. """ - if not os.path.isdir(args.test_dir): sys.exit(f"directory {args.test_dir} does not exist") @@ -43,28 +42,31 @@ def set_up_test(args): if is_mesa_run == True: # checking if directory is a mesa run os.mkdir(os.path.join(args.test_dir, f)) # choosing which tracks to copy over - inds = random.sample(list(range(len(track_dirs))), int(len(track_dirs) * args.dsr)) + inds = random.sample(list(range(len(track_dirs))), + int(len(track_dirs) * args.dsr)) for ind in inds: if os.path.isdir(track_dirs[ind]): - shutil.copytree(track_dirs[ind], os.path.join(args.test_dir, f, track_dirs[ind].split("/")[-1])) + shutil.copytree(track_dirs[ind], os.path.join( + args.test_dir, f, track_dirs[ind].split("/")[-1])) else: - shutil.copy(track_dirs[ind], os.path.join(args.test_dir, f, track_dirs[ind].split("/")[-1])) + shutil.copy(track_dirs[ind], os.path.join( + args.test_dir, f, track_dirs[ind].split("/")[-1])) print(f"Created Test Directory at {args.test_dir}") def compress_dir(args): - """ compresses directory containing tracks evolved with MESA + """Compresses a directory containing tracks evolved with MESA. Parameters: ----------- mesa_dir : string - the directory where the MESA tracks are stored + The directory where the MESA tracks are stored. """ - def get_size(start_path = "."): + def get_size(start_path="."): total_size = 0 for dirpath, dirnames, filenames in os.walk(start_path): for f in filenames: @@ -90,42 +92,39 @@ def compress_dir(args): for _f in sub_dir: - if "_grid_index_" in _f: # checking if directory is mesa run + if "_grid_index_" in _f: # checking if directory is mesa run is_mesa_run = True if os.path.isdir(os.path.join(args.mesa_dir, f, _f)) and _f != "star1" and _f != "star2" and _f != "binary": track_dirs.append(os.path.join(args.mesa_dir, f, _f)) if is_mesa_run == True: - - for track_obj in track_dirs: # iterating over directory containing MESA runs - + # iterating over directory containing MESA runs + for track_obj in track_dirs: for root, _dir, files in os.walk(track_obj): - # traversing over directory tree of copied mesa tracks and compressing .data, .mod, .txt files + # traversing over directory tree of copied mesa tracks + # and compressing .data, .mod, .txt files for file in files: ext = file.split(".")[-1] - if ext == "data" or ext == "mod" or ext == "txt": os.system(f"gzip -1 {os.path.join(root, file)}") - - - - print( - f""" + print(f""" compressed MESA tracks Original Size {og_size} | Compressed Size {size(get_size(args.mesa_dir))} - """ - ) + """) -if __name__ == "__main__": +if __name__ == "__main__": parser = argparse.ArgumentParser() - - parser.add_argument("-f", "--function", type = str, help = "the name of the function to be called") - parser.add_argument("-td", "--test_dir", type = str, help = "the path to where the testing directory should be set up, either set_up_test or compress_dir") - parser.add_argument("-dsr", "--dsr", type = str, help = "down sampling rate when creating testing directory", default = 0.01) - parser.add_argument("-md", "--mesa_dir", type = str, help = "the path to the directory containing MESA generated data") + parser.add_argument("-f", "--function", type=str, + help="The name of the function to be called.") + parser.add_argument("-td", "--test_dir", type=str, + help="The path to where the testing directory should be set up, either set_up_test or compress_dir.") + parser.add_argument("-dsr", "--dsr", type=str, + help="Downsampling rate when creating testing directory", default=0.01) + parser.add_argument("-md", "--mesa_dir", type=str, + help="The path to the directory containing MESA generated data") functions = { "set_up_test": set_up_test, @@ -135,6 +134,6 @@ if __name__ == "__main__": args = parser.parse_args() if args.function == None: - sys.exit("a function must be specified to use this script") + sys.exit("A function must be specified to use this script.") functions[args.function](args) From 0618489a1f98361f087b31cccac3392fe5441f8d Mon Sep 17 00:00:00 2001 From: kkovlakas Date: Fri, 3 Feb 2023 13:53:11 +0100 Subject: [PATCH 050/319] Correcting boolean and None checks. --- bin/compress-mesa | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/bin/compress-mesa b/bin/compress-mesa index 7929ae4440..66b3c34748 100644 --- a/bin/compress-mesa +++ b/bin/compress-mesa @@ -39,7 +39,7 @@ def set_up_test(args): # if os.path.isdir(os.path.join(args.mesa_dir, f, _f)) and _f != "star1" and _f != "star2" and _f != "binary": track_dirs.append(os.path.join(args.mesa_dir, f, _f)) - if is_mesa_run == True: # checking if directory is a mesa run + if is_mesa_run: # checking if directory is a mesa run os.mkdir(os.path.join(args.test_dir, f)) # choosing which tracks to copy over inds = random.sample(list(range(len(track_dirs))), @@ -98,7 +98,7 @@ def compress_dir(args): if os.path.isdir(os.path.join(args.mesa_dir, f, _f)) and _f != "star1" and _f != "star2" and _f != "binary": track_dirs.append(os.path.join(args.mesa_dir, f, _f)) - if is_mesa_run == True: + if is_mesa_run: # iterating over directory containing MESA runs for track_obj in track_dirs: for root, _dir, files in os.walk(track_obj): @@ -133,7 +133,7 @@ if __name__ == "__main__": args = parser.parse_args() - if args.function == None: + if args.function is None: sys.exit("A function must be specified to use this script.") functions[args.function](args) From b663a0b7ccb6e8acb66ea429f56f4dda21be39a1 Mon Sep 17 00:00:00 2001 From: kkovlakas Date: Fri, 3 Feb 2023 13:53:33 +0100 Subject: [PATCH 051/319] Removing unused imports. --- bin/compress-mesa | 2 -- 1 file changed, 2 deletions(-) diff --git a/bin/compress-mesa b/bin/compress-mesa index 66b3c34748..3892480cf5 100644 --- a/bin/compress-mesa +++ b/bin/compress-mesa @@ -1,8 +1,6 @@ #!/usr/bin/env python import os import sys -import gzip -import time import shutil import random import argparse From d697007678d3c1d587e071bf89536e4005be948c Mon Sep 17 00:00:00 2001 From: kkovlakas Date: Fri, 3 Feb 2023 13:56:23 +0100 Subject: [PATCH 052/319] Simplifying if clauses. --- bin/compress-mesa | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/bin/compress-mesa b/bin/compress-mesa index 3892480cf5..e5d2aab9d1 100644 --- a/bin/compress-mesa +++ b/bin/compress-mesa @@ -93,7 +93,8 @@ def compress_dir(args): if "_grid_index_" in _f: # checking if directory is mesa run is_mesa_run = True - if os.path.isdir(os.path.join(args.mesa_dir, f, _f)) and _f != "star1" and _f != "star2" and _f != "binary": + if (os.path.isdir(os.path.join(args.mesa_dir, f, _f)) and + _f not in ["star1", "star2", "binary"]): track_dirs.append(os.path.join(args.mesa_dir, f, _f)) if is_mesa_run: @@ -104,8 +105,9 @@ def compress_dir(args): # and compressing .data, .mod, .txt files for file in files: ext = file.split(".")[-1] - if ext == "data" or ext == "mod" or ext == "txt": - os.system(f"gzip -1 {os.path.join(root, file)}") + if ext not in ["data", "mod", "txt"]: + continue + os.system(f"gzip -1 {os.path.join(root, file)}") print(f""" compressed MESA tracks From 1842c7896ecfd0dc96e22c342bc534b91ceac81e Mon Sep 17 00:00:00 2001 From: kkovlakas Date: Fri, 3 Feb 2023 13:59:11 +0100 Subject: [PATCH 053/319] Shortening very long lines. --- bin/compress-mesa | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/bin/compress-mesa b/bin/compress-mesa index e5d2aab9d1..035cecbae7 100644 --- a/bin/compress-mesa +++ b/bin/compress-mesa @@ -30,11 +30,8 @@ def set_up_test(args): track_dirs = [] for _f in sub_dir: - if "_grid_index_" in _f: is_mesa_run = True - - # if os.path.isdir(os.path.join(args.mesa_dir, f, _f)) and _f != "star1" and _f != "star2" and _f != "binary": track_dirs.append(os.path.join(args.mesa_dir, f, _f)) if is_mesa_run: # checking if directory is a mesa run @@ -120,11 +117,14 @@ if __name__ == "__main__": parser.add_argument("-f", "--function", type=str, help="The name of the function to be called.") parser.add_argument("-td", "--test_dir", type=str, - help="The path to where the testing directory should be set up, either set_up_test or compress_dir.") + help="The path to where the testing directory should " + "be set up, either set_up_test or compress_dir.") parser.add_argument("-dsr", "--dsr", type=str, - help="Downsampling rate when creating testing directory", default=0.01) + help="Downsampling rate when creating testing " + "directory", default=0.01) parser.add_argument("-md", "--mesa_dir", type=str, - help="The path to the directory containing MESA generated data") + help="The path to the directory containing " + "MESA-generated data") functions = { "set_up_test": set_up_test, From 7af4e96f03759adb43fca2c00db7b08466f84bdf Mon Sep 17 00:00:00 2001 From: kkovlakas Date: Fri, 3 Feb 2023 13:59:44 +0100 Subject: [PATCH 054/319] Removing unused variable. --- bin/compress-mesa | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bin/compress-mesa b/bin/compress-mesa index 035cecbae7..f3bdf8171d 100644 --- a/bin/compress-mesa +++ b/bin/compress-mesa @@ -63,7 +63,7 @@ def compress_dir(args): def get_size(start_path="."): total_size = 0 - for dirpath, dirnames, filenames in os.walk(start_path): + for dirpath, _, filenames in os.walk(start_path): for f in filenames: fp = os.path.join(dirpath, f) # skip if it is symbolic link From 3ca146f03af8410eb2fe60dd147e12f8a650b29d Mon Sep 17 00:00:00 2001 From: kkovlakas Date: Fri, 3 Feb 2023 14:05:10 +0100 Subject: [PATCH 055/319] More explanatory variable names. --- bin/compress-mesa | 38 +++++++++++++++++++++----------------- 1 file changed, 21 insertions(+), 17 deletions(-) diff --git a/bin/compress-mesa b/bin/compress-mesa index f3bdf8171d..ef160b9baa 100644 --- a/bin/compress-mesa +++ b/bin/compress-mesa @@ -22,20 +22,20 @@ def set_up_test(args): if not os.path.isdir(args.test_dir): sys.exit(f"directory {args.test_dir} does not exist") - for f in os.listdir(args.mesa_dir): + for folder in os.listdir(args.mesa_dir): - if os.path.isdir(os.path.join(args.mesa_dir, f)): + if os.path.isdir(os.path.join(args.mesa_dir, folder)): is_mesa_run = False - sub_dir = os.listdir(os.path.join(args.mesa_dir, f)) + sub_dir = os.listdir(os.path.join(args.mesa_dir, folder)) track_dirs = [] for _f in sub_dir: if "_grid_index_" in _f: is_mesa_run = True - track_dirs.append(os.path.join(args.mesa_dir, f, _f)) + track_dirs.append(os.path.join(args.mesa_dir, folder, _f)) if is_mesa_run: # checking if directory is a mesa run - os.mkdir(os.path.join(args.test_dir, f)) + os.mkdir(os.path.join(args.test_dir, folder)) # choosing which tracks to copy over inds = random.sample(list(range(len(track_dirs))), int(len(track_dirs) * args.dsr)) @@ -43,10 +43,14 @@ def set_up_test(args): for ind in inds: if os.path.isdir(track_dirs[ind]): shutil.copytree(track_dirs[ind], os.path.join( - args.test_dir, f, track_dirs[ind].split("/")[-1])) + args.test_dir, + folder, + track_dirs[ind].split("/")[-1])) else: shutil.copy(track_dirs[ind], os.path.join( - args.test_dir, f, track_dirs[ind].split("/")[-1])) + args.test_dir, + folder, + track_dirs[ind].split("/")[-1])) print(f"Created Test Directory at {args.test_dir}") @@ -64,11 +68,11 @@ def compress_dir(args): def get_size(start_path="."): total_size = 0 for dirpath, _, filenames in os.walk(start_path): - for f in filenames: - fp = os.path.join(dirpath, f) + for filename in filenames: + filepath = os.path.join(dirpath, filename) # skip if it is symbolic link - if not os.path.islink(fp): - total_size += os.path.getsize(fp) + if not os.path.islink(filepath): + total_size += os.path.getsize(filepath) return total_size @@ -77,12 +81,12 @@ def compress_dir(args): og_size = size(get_size(args.mesa_dir)) - for f in tqdm(os.listdir(args.mesa_dir)): + for folder in tqdm(os.listdir(args.mesa_dir)): - if os.path.isdir(os.path.join(args.mesa_dir, f)): + if os.path.isdir(os.path.join(args.mesa_dir, folder)): is_mesa_run = False - sub_dir = os.listdir(os.path.join(args.mesa_dir, f)) + sub_dir = os.listdir(os.path.join(args.mesa_dir, folder)) track_dirs = [] for _f in sub_dir: @@ -90,9 +94,9 @@ def compress_dir(args): if "_grid_index_" in _f: # checking if directory is mesa run is_mesa_run = True - if (os.path.isdir(os.path.join(args.mesa_dir, f, _f)) and - _f not in ["star1", "star2", "binary"]): - track_dirs.append(os.path.join(args.mesa_dir, f, _f)) + if (os.path.isdir(os.path.join(args.mesa_dir, folder, _f)) + and _f not in ["star1", "star2", "binary"]): + track_dirs.append(os.path.join(args.mesa_dir, folder, _f)) if is_mesa_run: # iterating over directory containing MESA runs From 7f3fb820cbc6aeafffe50f13e28d9bd4c3ca7744 Mon Sep 17 00:00:00 2001 From: kkovlakas Date: Fri, 3 Feb 2023 14:09:19 +0100 Subject: [PATCH 056/319] The functions had access to a global varibale although it was beeing passed as an argument. --- bin/compress-mesa | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/bin/compress-mesa b/bin/compress-mesa index ef160b9baa..4e7ac58f4a 100644 --- a/bin/compress-mesa +++ b/bin/compress-mesa @@ -135,9 +135,9 @@ if __name__ == "__main__": "compress_dir": compress_dir } - args = parser.parse_args() + arguments = parser.parse_args() - if args.function is None: + if arguments.function is None: sys.exit("A function must be specified to use this script.") - functions[args.function](args) + functions[arguments.function](arguments) From e370a9f38b0a3a6e4003cb92a10016d44856335c Mon Sep 17 00:00:00 2001 From: kkovlakas Date: Fri, 3 Feb 2023 14:18:33 +0100 Subject: [PATCH 057/319] Final touches in the compression script. --- bin/compress-mesa | 29 ++++++++--------------------- 1 file changed, 8 insertions(+), 21 deletions(-) diff --git a/bin/compress-mesa b/bin/compress-mesa index 4e7ac58f4a..fd2587fe37 100644 --- a/bin/compress-mesa +++ b/bin/compress-mesa @@ -11,7 +11,7 @@ from hurry.filesize import size def set_up_test(args): """Set up a testing directory in the requested directory. - Parameters: + Parameters (keys in `args`): ---------- test_dir : string The directory where the test directory is to be set up. @@ -20,7 +20,7 @@ def set_up_test(args): """ if not os.path.isdir(args.test_dir): - sys.exit(f"directory {args.test_dir} does not exist") + sys.exit(f"Directory {args.test_dir} does not exist.") for folder in os.listdir(args.mesa_dir): @@ -39,7 +39,6 @@ def set_up_test(args): # choosing which tracks to copy over inds = random.sample(list(range(len(track_dirs))), int(len(track_dirs) * args.dsr)) - for ind in inds: if os.path.isdir(track_dirs[ind]): shutil.copytree(track_dirs[ind], os.path.join( @@ -52,19 +51,18 @@ def set_up_test(args): folder, track_dirs[ind].split("/")[-1])) - print(f"Created Test Directory at {args.test_dir}") + print(f"Created Test Directory at {args.test_dir}.") def compress_dir(args): """Compresses a directory containing tracks evolved with MESA. - Parameters: + Parameters (keys in `args`): ----------- mesa_dir : string The directory where the MESA tracks are stored. """ - def get_size(start_path="."): total_size = 0 for dirpath, _, filenames in os.walk(start_path): @@ -73,24 +71,21 @@ def compress_dir(args): # skip if it is symbolic link if not os.path.islink(filepath): total_size += os.path.getsize(filepath) - return total_size if not os.path.isdir(args.mesa_dir): - sys.exit("the MESA directory does not exist") + sys.exit("The MESA directory does not exist.") og_size = size(get_size(args.mesa_dir)) for folder in tqdm(os.listdir(args.mesa_dir)): if os.path.isdir(os.path.join(args.mesa_dir, folder)): - is_mesa_run = False sub_dir = os.listdir(os.path.join(args.mesa_dir, folder)) track_dirs = [] for _f in sub_dir: - if "_grid_index_" in _f: # checking if directory is mesa run is_mesa_run = True @@ -110,10 +105,8 @@ def compress_dir(args): continue os.system(f"gzip -1 {os.path.join(root, file)}") - print(f""" -compressed MESA tracks -Original Size {og_size} | Compressed Size {size(get_size(args.mesa_dir))} - """) + print("\nCompressed MESA tracks\nOriginal size {} | Compressed size {}\n". + format(og_size, size(get_size(args.mesa_dir)))) if __name__ == "__main__": @@ -130,14 +123,8 @@ if __name__ == "__main__": help="The path to the directory containing " "MESA-generated data") - functions = { - "set_up_test": set_up_test, - "compress_dir": compress_dir - } - + functions = {"set_up_test": set_up_test, "compress_dir": compress_dir} arguments = parser.parse_args() - if arguments.function is None: sys.exit("A function must be specified to use this script.") - functions[arguments.function](arguments) From b324f25ae273861566947fd131db26c15562c76e Mon Sep 17 00:00:00 2001 From: mkruckow Date: Mon, 6 Feb 2023 15:58:23 +0100 Subject: [PATCH 058/319] fix asychronous index in create psygrid --- posydon/grids/psygrid.py | 63 +++++++++++++++++++++++++++++++++------- 1 file changed, 53 insertions(+), 10 deletions(-) diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index 8e3846ba27..ab2269f686 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -625,7 +625,10 @@ def decide_columns(key_in_config, defaults): # this boolean array will store whether the i-th run is to be included # (i.e. it has a binary_history file) - run_included = np.zeros(N_runs, dtype=bool) +# run_included = np.zeros(N_runs, dtype=bool) + #this int array will store the run_index of the i-th run and will be + # -1 if the run is not included. + run_included_at = np.full(N_runs, -1, dtype=int) run_index = 0 for i in tqdm.tqdm(range(N_runs)): # Select the ith run @@ -1006,8 +1009,15 @@ def decide_columns(key_in_config, defaults): # consider the run (and the input directory) included self.MESA_dirs.append(run.path) - run_included[i] = True + run_included_at[i] = run_index +# run_included[i] = True run_index += 1 + #check that new MESA path is added at run_index + lenMESA_dirs = len(self.MESA_dirs) + if lenMESA_dirs!=run_index: + warnings.warn("Non synchronous indexing: " + + "run_index={} != ".format(run_index) + + "length(MESA_dirs)={}".format(lenMESA_dirs)) #general fix for termination_flag in case of initial RLO if initial_RLO_fix: @@ -1020,9 +1030,11 @@ def decide_columns(key_in_config, defaults): for i in range(N_runs): flag1 = self.final_values[i]["termination_flag_1"] if flag1 != "Terminate because of overflowing initial model": - mass1 = self.initial_values[i]["star_1_mass"] - mass2 = self.initial_values[i]["star_2_mass"] - period = self.initial_values[i]["period_days"] + #use grid point data to detect initial RLO + grid_point = read_initial_values(grid.runs[i].path) + mass1 = grid_point["star_1_mass"] + mass2 = grid_point["star_2_mass"] + period = grid_point["period_days"] nearest = get_nearest_known_initial_RLO(mass1, mass2, detected_initial_RLO) if period=0: + #check for index range + if run_included_at[i]>=run_index: + warnings.warn("run {} has a run_index out of ".format(i) + + "range: {}>={}".format(run_included_at[i], run_index)) + continue + for colname in self.initial_values.dtype.names: + value = self.initial_values[i][colname] + new_initial_values[run_included_at[i]][colname] = value + for colname in self.final_values.dtype.names: + value = self.final_values[i][colname] + new_final_values[run_included_at[i]][colname] = value + #replace old initial/final value array + self.initial_values = np.copy(new_initial_values) + self.final_values = np.copy(new_final_values) # Store the full table of initial_values hdf5.create_dataset("/grid/initial_values", data=self.initial_values, From 4d487f1f857ef1a42ab29273118617fe94d301de Mon Sep 17 00:00:00 2001 From: mkruckow Date: Tue, 7 Feb 2023 09:41:03 +0100 Subject: [PATCH 059/319] check if grid point contains all data --- posydon/grids/psygrid.py | 20 ++++++++++++++++---- 1 file changed, 16 insertions(+), 4 deletions(-) diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index ab2269f686..9e71983020 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -1030,11 +1030,23 @@ def decide_columns(key_in_config, defaults): for i in range(N_runs): flag1 = self.final_values[i]["termination_flag_1"] if flag1 != "Terminate because of overflowing initial model": - #use grid point data to detect initial RLO + #use grid point data (if existing) to detect initial RLO grid_point = read_initial_values(grid.runs[i].path) - mass1 = grid_point["star_1_mass"] - mass2 = grid_point["star_2_mass"] - period = grid_point["period_days"] + if "star_1_mass" in grid_point: + mass1 = grid_point["star_1_mass"] + else: + mass1 = self.initial_values[i]["star_1_mass"] + warnings.warn("No star_1_mass in "+grid.runs[i].path) + if "star_2_mass" in grid_point: + mass2 = grid_point["star_2_mass"] + else: + mass2 = self.initial_values[i]["star_2_mass"] + warnings.warn("No star_2_mass in "+grid.runs[i].path) + if "period_days" in grid_point: + period = grid_point["period_days"] + else: + period = self.initial_values[i]["period_days"] + warnings.warn("No period_days in "+grid.runs[i].path) nearest = get_nearest_known_initial_RLO(mass1, mass2, detected_initial_RLO) if period Date: Tue, 7 Feb 2023 14:06:16 +0100 Subject: [PATCH 060/319] add check for binary_grid --- posydon/grids/psygrid.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index 9e71983020..3624ef9639 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -1019,8 +1019,8 @@ def decide_columns(key_in_config, defaults): "run_index={} != ".format(run_index) + "length(MESA_dirs)={}".format(lenMESA_dirs)) - #general fix for termination_flag in case of initial RLO - if initial_RLO_fix: + #general fix for termination_flag in case of initial RLO in binaries + if binary_grid and initial_RLO_fix: #create list of already detected initial RLO detected_initial_RLO = get_detected_initial_RLO(self) colnames = ["termination_flag_1", "termination_flag_2", From ab2c67280eb582a8248cddb54b6239e42bb4b2f9 Mon Sep 17 00:00:00 2001 From: mkruckow Date: Wed, 8 Feb 2023 09:53:51 +0100 Subject: [PATCH 061/319] don't exclude values without 'd0' from inlist_grid_points, instead split on 'd' and get the value from combining matisse and exponent --- posydon/grids/io.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/posydon/grids/io.py b/posydon/grids/io.py index a11e19d24e..011c21bca7 100644 --- a/posydon/grids/io.py +++ b/posydon/grids/io.py @@ -360,13 +360,16 @@ def read_initial_values(mesa_dir): initial_values = {} with open(path, "r") as f: for line in f: - if "=" not in line or "d0" not in line: + if "=" not in line: continue fields = line.strip().split("=") if len(fields) != 2: return None varname = fields[0].strip() - value = float(fields[1].split("d0")[0].strip()) + valueparts = fields[1].split("d") + value = float(valueparts[0].strip()) + if len(valueparts)>1: + value = value*10**float(valueparts[1].strip()) if varname == "m1": varname = "star_1_mass" elif varname == "m2": From e3513ab441599d290468edee76b6a3c1527e9e7d Mon Sep 17 00:00:00 2001 From: mkruckow Date: Wed, 8 Feb 2023 11:15:27 +0100 Subject: [PATCH 062/319] cleanup of old code not needed anymore --- posydon/grids/psygrid.py | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index 3624ef9639..3b71977e24 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -623,9 +623,6 @@ def decide_columns(key_in_config, defaults): self._say('Loading MESA data...') - # this boolean array will store whether the i-th run is to be included - # (i.e. it has a binary_history file) -# run_included = np.zeros(N_runs, dtype=bool) #this int array will store the run_index of the i-th run and will be # -1 if the run is not included. run_included_at = np.full(N_runs, -1, dtype=int) @@ -1010,7 +1007,6 @@ def decide_columns(key_in_config, defaults): # consider the run (and the input directory) included self.MESA_dirs.append(run.path) run_included_at[i] = run_index -# run_included[i] = True run_index += 1 #check that new MESA path is added at run_index lenMESA_dirs = len(self.MESA_dirs) @@ -1059,12 +1055,10 @@ def decide_columns(key_in_config, defaults): if colname in nearest: self.final_values[i][colname]=nearest[colname] #reset the initial values to the grid point data -# grid_point = read_initial_values(grid.runs[i].path) for colname, value in grid_point.items(): if colname in self.initial_values.dtype.names: self.initial_values[i][colname] = value #add initial RLO system if not added before -# if not run_included[i]: if run_included_at[i]==-1: if not slim: hdf5.create_group( @@ -1072,7 +1066,6 @@ def decide_columns(key_in_config, defaults): #include the run (and the input directory) self.MESA_dirs.append(grid.runs[i].path) run_included_at[i] = run_index -# run_included[i] = True run_index += 1 #check that new MESA path is added at run_index lenMESA_dirs = len(self.MESA_dirs) @@ -1083,9 +1076,6 @@ def decide_columns(key_in_config, defaults): self._say("Storing initial/final values and metadata to HDF5...") - # exclude rows in initial/final_values corresponding to excluded runs -# self.initial_values = self.initial_values[run_included] -# self.final_values = self.final_values[run_included] #create new array of initial and finial values with included runs # only and sort it by run_index new_initial_values = initialize_empty_array( From baef2620db00faf4c08cb563456cfa315f1366b2 Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Fri, 17 Feb 2023 09:24:10 +0100 Subject: [PATCH 063/319] Propagate documentation change from main (#31) * Update README.md * Update install.rst * Update install.rst --- README.md | 1 + docs/install/install.rst | 34 ++++++++++++++++++++++++++++++++++ 2 files changed, 35 insertions(+) diff --git a/README.md b/README.md index 448431523f..b440da444c 100644 --- a/README.md +++ b/README.md @@ -28,6 +28,7 @@ conda activate posydon Clone the repository in a local directory, e.g. '/home/POSYDON/', with ``` git clone https://github.com/POSYDON-code/POSYDON.git +git checkout origin/main ``` The directory will contain the following structure: ``` diff --git a/docs/install/install.rst b/docs/install/install.rst index 5c0acf2406..f1c287e965 100644 --- a/docs/install/install.rst +++ b/docs/install/install.rst @@ -4,6 +4,40 @@ Installation ############ +======================================= +Installing POSYDON v1.0.0 from Anaconda +======================================= + +We recommend to install the Python distribution Anaconda and to install POSYDON +in a virtual environment. Specifically, we recommend using the installation we +have provided which can be accessed through conda-forge. On Linux, the new +conda environment can be created (we have named our environment posydon-example, +but you can choose any name), the conda-forge channel added, and the required +library installation can all be completed in one line: + +.. code-block:: + + conda create --name posydon-example -c posydon -c conda-forge posydon + +On Mac (or if you have problems with the above command on Linux), these steps +likely need to be separately run: + +.. code-block:: + + conda create -n posydon-conda python=3.7 + conda activate posydon-conda + conda config --add channels conda-forge + conda config --add channels posydon + conda config --set channel_priority false + conda install posydon + +Now, you can source the environment with + +.. code-block:: + + conda activate posydon + + ========================================= Installing the latest release from GitHub ========================================= From 80231b4a64cc4bb3bf74f373c4b320edf19ac4f9 Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Fri, 17 Feb 2023 09:24:27 +0100 Subject: [PATCH 064/319] Prevent grid post processing from breaking (#33) * raise warning instead of breaking post processing * spelling mistake * use warnigns module * debug * spelling * catch other warinings and exception without braking * prevent type error from breaking code * fix grammar * prevent CC2 from breakig whne gamma_center limit from star_2 is reached * fix syntax error * prevent the code from breaking in check_state_of_star * fix --- posydon/grids/post_processing.py | 80 +++++++++++++++++++++++++------- 1 file changed, 62 insertions(+), 18 deletions(-) diff --git a/posydon/grids/post_processing.py b/posydon/grids/post_processing.py index 3e7b99fd57..09b09d6ac0 100644 --- a/posydon/grids/post_processing.py +++ b/posydon/grids/post_processing.py @@ -11,6 +11,7 @@ import numpy as np from tqdm import tqdm import copy +import warnings __authors__ = [ @@ -183,10 +184,25 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODEL=MODEL, if not stars_CO[j] and IC in ['no_MT', 'stable_MT', 'unstable_MT']: EXTRA_COLUMNS['S%s_state' % (j+1)].append(check_state_of_star( star, star_CO=False)) - calculate_Patton20_values_at_He_depl(star) + with warnings.catch_warnings(record=True) as w: + calculate_Patton20_values_at_He_depl(star) + if len(w) > 0: + print(w[0].message) + print(f'The warning was raised by {grid.MESA_dirs[i]} ' + f'in calculate_Patton20_values_at_He_depl(star_{j+1}).') EXTRA_COLUMNS['S%s_avg_c_in_c_core_at_He_depletion' % (j+1)].append(star.avg_c_in_c_core_at_He_depletion) EXTRA_COLUMNS['S%s_co_core_mass_at_He_depletion' % (j+1)].append(star.co_core_mass_at_He_depletion) - CEE_parameters_from_core_abundance_thresholds(star) + with warnings.catch_warnings(record=True) as w: + try: + CEE_parameters_from_core_abundance_thresholds(star) + except Exception as ex: + print(ex) + print(f'The exception was raised by {grid.MESA_dirs[i]} ' + f'in CEE_parameters_from_core_abundance_thresholds(star_{j+1}).') + if len(w) > 0: + print(w[0].message) + print(f'The warning was raised by {grid.MESA_dirs[i]} ' + f'in CEE_parameters_from_core_abundance_thresholds(star_{j+1}).') EXTRA_COLUMNS['S%s_m_core_CE_1cent' % (j+1)].append(star.m_core_CE_1cent) EXTRA_COLUMNS['S%s_m_core_CE_10cent' % (j+1)].append(star.m_core_CE_10cent) EXTRA_COLUMNS['S%s_m_core_CE_30cent' % (j+1)].append(star.m_core_CE_30cent) @@ -199,8 +215,15 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODEL=MODEL, EXTRA_COLUMNS['S%s_lambda_CE_10cent' % (j+1)].append(star.lambda_CE_10cent) EXTRA_COLUMNS['S%s_lambda_CE_30cent' % (j+1)].append(star.lambda_CE_30cent) EXTRA_COLUMNS['S%s_lambda_CE_pure_He_star_10cent' % (j+1)].append(star.lambda_CE_pure_He_star_10cent) - s_o = 1. - star.surface_h1 - star.surface_he4 - star.surface_c12 - star.surface_n14 - star.surface_o16 - c_o = 1. - star.center_h1 - star.center_he4 - star.center_c12 - star.center_n14 - star.center_o16 + try: + s_o = 1. - star.surface_h1 - star.surface_he4 - star.surface_c12 - star.surface_n14 - star.surface_o16 + c_o = 1. - star.center_h1 - star.center_he4 - star.center_c12 - star.center_n14 - star.center_o16 + except TypeError as ex: + s_o = 0. + c_o = 0. + print(ex) + print(f'The error was raised by {grid.MESA_dirs[i]} ' + f'while accessing aboundances in star_{j+1}.') EXTRA_COLUMNS['S%s_surface_other' % (j+1)].append(s_o) EXTRA_COLUMNS['S%s_center_other' % (j+1)].append(c_o) else: @@ -208,7 +231,13 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODEL=MODEL, EXTRA_COLUMNS['S%s_state' % (j+1)].append(None) else: # CO states are classified and used in mesa step - EXTRA_COLUMNS['S%s_state' % (j+1)].append(check_state_of_star(star, star_CO=True)) + try: + EXTRA_COLUMNS['S%s_state' % (j+1)].append(check_state_of_star(star, star_CO=True)) + except TypeError as ex: + EXTRA_COLUMNS['S%s_state' % (j+1)].append(None) + print(ex) + print(f'The error was raised by {grid.MESA_dirs[i]} ' + f'in check_state_of_star(star_{j+1}) with IC={IC}.') EXTRA_COLUMNS['S%s_avg_c_in_c_core_at_He_depletion' % (j+1)].append(None) EXTRA_COLUMNS['S%s_co_core_mass_at_He_depletion' % (j+1)].append(None) EXTRA_COLUMNS['S%s_m_core_CE_1cent' % (j+1)].append(None) @@ -240,20 +269,39 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODEL=MODEL, for m in CORE_COLLAPSES: EXTRA_COLUMNS['S1_'+m[0]+m[1]].append([None]*5) elif TF1 == 'gamma_center_limit': - if binary.star_1.center_gamma >= 10.: + if (binary.star_1.center_gamma is not None and + binary.star_1.center_gamma >= 10.): star = binary.star_1 s = 'S1_' for m in CORE_COLLAPSES: EXTRA_COLUMNS['S2_'+m[0]+m[1]].append([None]*5) - elif binary.star_2.center_gamma >= 10.: + elif (binary.star_2.center_gamma is not None and + binary.star_2.center_gamma >= 10.): star = binary.star_2 s = 'S2_' for m in CORE_COLLAPSES: EXTRA_COLUMNS['S1_'+m[0]+m[1]].append([None]*5) else: - raise ValueError('No star has center_gamma < 10') + for m in CORE_COLLAPSES: + EXTRA_COLUMNS['S1_'+m[0]+m[1]].append([None]*5) + EXTRA_COLUMNS['S2_'+m[0]+m[1]].append([None]*5) + warnings.warn(f'{grid.MESA_dirs[i]} ended with ' + 'TF1=gamma_center_limit however ' + 'the star has center_gamma < 10. ' + 'This star cannot go through step_SN ' + 'appending NONE compact object ' + 'properties!') + continue else: - raise ValueError('TF1 = %s not supported' % TF1) + for m in CORE_COLLAPSES: + EXTRA_COLUMNS['S1_'+m[0]+m[1]].append([None]*5) + EXTRA_COLUMNS['S2_'+m[0]+m[1]].append([None]*5) + warnings.warn(f'{grid.MESA_dirs[i]} ended with ' + f'TF={TF1} and IC={interpolation_class}. ' + 'This star cannot go through step_SN ' + 'appending NONE compact object ' + 'properties!') + continue if verbose: print("{:<30} {:<33} {:12} {:10} {:15} {:10}".format( @@ -293,15 +341,11 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODEL=MODEL, print('interpolation class', interpolation_class) else: # inital_RLOF, unstable_MT not_convergedd - if TF1 == 'Primary has depleted central carbon': - raise ValueError( - 'Primary reached carbon depletion but was not ' - 'collapsed! This should not happen!') - if TF1 == 'Secondary has depleted central carbon': - raise ValueError( - 'Secondary reached carbon depletion but was not ' - 'collapsed! This should not happen!') - + if (TF1 == 'Primary has depleted central carbon' or + TF1 == 'Secondary has depleted central carbon'): + warnings.warn(f'{grid.MESA_dirs[i]} ended with ' + f'TF={TF1} but was not collapsed! ' + 'This should never happen!') for m in CORE_COLLAPSES: EXTRA_COLUMNS['S1_'+m[0]+m[1]].append([None]*5) EXTRA_COLUMNS['S2_'+m[0]+m[1]].append([None]*5) From cad8a8d4035d6a3f46ee5007f2852782f4d41d72 Mon Sep 17 00:00:00 2001 From: Konstantinos Kovlakas <22239544+kkovlakas@users.noreply.github.com> Date: Thu, 16 Mar 2023 15:46:16 +0100 Subject: [PATCH 065/319] Allowing for missing profiles in single grids if requested. (#36) * Adding psygrid option for keeping tracks without profiles. * Allowing for missing profiles in single grids. * By default do not include stars without a final profile in a single star grid. --- posydon/grids/psygrid.py | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index 3b71977e24..a88f65e7a2 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -350,6 +350,7 @@ "eep": None, # path to EEP files "initial_RLO_fix": False, "He_core_fix": True, + "accept_missing_profile": False, } @@ -774,11 +775,16 @@ def decide_columns(key_in_config, defaults): final_profile2 = read_MESA_data_file( run.final_profile2_path, P2_columns) if not binary_grid and final_profile1 is None: - warnings.warn("Ignored MESA run because of missing " - "profile in: {}\n".format(run.path)) - ignore_data = True - ignore_reason = "ignore_no_FP" - continue + if self.config["accept_missing_profile"]: + warnings.warn("Including MESA run despite the missing " + "profile in {}\n".format(run.path)) + ignore_data = False + else: + warnings.warn("Ignored MESA run because of missing " + "profile in: {}\n".format(run.path)) + ignore_data = True + ignore_reason = "ignore_no_FP" + continue if binary_history is not None: if binary_history.shape == (): # if history is only one line @@ -1074,7 +1080,7 @@ def decide_columns(key_in_config, defaults): "run_index={} != ".format(run_index) + "length(MESA_dirs)={}".format(lenMESA_dirs)) - + self._say("Storing initial/final values and metadata to HDF5...") #create new array of initial and finial values with included runs # only and sort it by run_index From 2099c45d225dea7d679104ae3f717dc7eb83e7f6 Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Thu, 16 Mar 2023 16:01:03 +0100 Subject: [PATCH 066/319] Support multi metallicity for v2 datasets (#37) * support for different metallicities * Adding metallicity to BinaryPopulation. Forcing constant Z across grid steps. * fix bug * fix initial X, Y, Z * Adding metallicity, uncomment previously broken kwargs in the ini * Added the SyntheticPopulation class to evolve mutliple BinaryPopulations. Added MPI options to inifile. * Add the common function to check for metallicity. * add first step of the post processing pipeline * bugfix * fix bug * debug * bug fix * fix bug * fix bug * bug fix * fix bug * rub test * add step 2 of pipleline * add logs * rebmove break * debug * add plotting step to pipeline * debug * debug * debug * debug * debug * add post processing and train interpolators steps * some cleaning * cleaning * cleaning * cleaning * debug * cleaning * clearning * cleaning * cleaning * debug * debug * cleaning * cleaning * cleaning * add support for CO-HMS and CO-HMS_RLO grids * cleaning * debug * debug * bug * debug * debug * debug * cleanig * debug * debug * debug * debug * debug * debug * debug * debug * add step 7 to export POSYDON pop synth dataset * debug * debug * support reruns creation * cleaning * debug * debug * remove grid processing pipeline from this PR --------- Co-authored-by: ka-rocha --- posydon/binary_evol/DT/step_detached.py | 9 ++- posydon/binary_evol/MESA/step_mesa.py | 34 ++++++++---- posydon/grids/psygrid.py | 4 +- posydon/popsyn/binarypopulation.py | 54 +++++++++++++++--- posydon/popsyn/io.py | 7 +++ posydon/popsyn/population_params_default.ini | 10 +++- posydon/popsyn/synthetic_population.py | 58 ++++++++++++++++++++ posydon/utils/common_functions.py | 8 +++ posydon/utils/constants.py | 1 + 9 files changed, 159 insertions(+), 26 deletions(-) create mode 100644 posydon/popsyn/synthetic_population.py diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index bca0445983..cf312a9aa9 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -30,7 +30,8 @@ orbital_period_from_separation, roche_lobe_radius, check_state_of_star, - PchipInterpolator2 + PchipInterpolator2, + convert_metallicity_to_string ) from posydon.binary_evol.flow_chart import (STAR_STATES_CC) import posydon.utils.constants as const @@ -144,6 +145,7 @@ class detached_step: def __init__( self, + metallicity=None, grid=None, path=PATH_TO_POSYDON_DATA, dt=None, @@ -160,6 +162,7 @@ def __init__( RLO_orbit_at_orbit_with_same_am=False ): """Initialize the step. See class documentation for details.""" + self.metallicity = convert_metallicity_to_string(metallicity) self.dt = dt self.n_o_steps_history = n_o_steps_history self.matching_method = matching_method @@ -364,8 +367,8 @@ def __init__( 'avg_charge_He' ) - grid_name1 = os.path.join('single_HMS', 'grid_0.0142.h5') - grid_name2 = os.path.join('single_HeMS', 'grid_0.0142.h5') + grid_name1 = os.path.join('single_HMS', self.metallicity+'_Zsun.h5') + grid_name2 = os.path.join('single_HeMS', self.metallicity+'_Zsun.h5') self.grid1 = GRIDInterpolator(os.path.join(path, grid_name1)) self.grid2 = GRIDInterpolator(os.path.join(path, grid_name2)) diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index bc9f1e039a..ef8d37edad 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -24,12 +24,12 @@ from posydon.utils import common_functions as cf from posydon.binary_evol.binarystar import BinaryStar from posydon.interpolation.IF_interpolation import IFInterpolator -from posydon.utils.common_functions import flip_stars -from posydon.utils.common_functions import CO_radius, infer_star_state +from posydon.utils.common_functions import (flip_stars, + convert_metallicity_to_string, + CO_radius, infer_star_state) from posydon.utils.data_download import data_download, PATH_TO_POSYDON_DATA - # left POSYDON, right MESA POSYDON_TO_MESA = { 'binary': { @@ -121,6 +121,7 @@ class MesaGridStep: def __init__( self, + metallicity, grid_name, path=PATH_TO_POSYDON_DATA, interpolation_path=None, @@ -1227,12 +1228,15 @@ def interpolate_at_t(self, t, t_before, t_after, v_before, v_after): class MS_MS_step(MesaGridStep): """Class for performing the MESA step for a MS-MS binary.""" - def __init__(self, grid_name=None, *args, **kwargs): + def __init__(self, metallicity=1., grid_name=None, *args, **kwargs): """Initialize a MS_MS_step instance.""" self.interp_in_q = True if grid_name is None: - grid_name = 'HMS-HMS/grid_0.0142_%d.h5' - super().__init__(grid_name=grid_name, *args, **kwargs) + metallicity = convert_metallicity_to_string(metallicity) + grid_name = 'HMS-HMS/' + metallicity + '_Zsun.h5' + super().__init__(metallicity=metallicity, + grid_name=grid_name, + *args, **kwargs) # special stuff for my step goes here # If nothing to do, no init necessary @@ -1281,12 +1285,15 @@ def __call__(self, binary): class CO_HMS_RLO_step(MesaGridStep): """Class for performing the MESA step for a CO-HMS binary.""" - def __init__(self, grid_name=None, *args, **kwargs): + def __init__(self, metallicity=1., grid_name=None, *args, **kwargs): """Initialize a CO_HMS_RLO_step instance.""" self.interp_in_q = False if grid_name is None: - grid_name = 'CO-HMS_RLO/grid_0.0142.h5' - super().__init__(grid_name=grid_name, *args, **kwargs) + metallicity = convert_metallicity_to_string(metallicity) + grid_name = 'CO-HMS_RLO/' + metallicity + '_Zsun.h5' + super().__init__(metallicity=metallicity, + grid_name=grid_name, + *args, **kwargs) # special stuff for my step goes here # If nothing to do, no init necessary @@ -1350,12 +1357,15 @@ def __call__(self, binary): class CO_HeMS_step(MesaGridStep): """Class for performing the MESA step for a CO-HeMS binary.""" - def __init__(self, grid_name=None, *args, **kwargs): + def __init__(self, metallicity=1., grid_name=None, *args, **kwargs): """Initialize a CO_HeMS_step instance.""" self.interp_in_q = False if grid_name is None: - grid_name = 'CO-HeMS/grid_0.0142_%d.h5' - super().__init__(grid_name=grid_name, *args, **kwargs) + metallicity = convert_metallicity_to_string(metallicity) + grid_name = 'CO-HeMS/' + metallicity + '_Zsun.h5' + super().__init__(metallicity=metallicity, + grid_name=grid_name, + *args, **kwargs) # special stuff for my step goes here # If nothing to do, no init necessary diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index a88f65e7a2..6f085a3733 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -1454,7 +1454,7 @@ def rerun(self, path_to_file='./', runs_to_rerun=None, initial_values[key] = [new_mesa_flag[key]]*n_runs_to_rerun # create the CSV file - with open(path_to_file+'grid.csv', 'w', newline='') as file: + with open(os.path.join(path_to_file,'grid.csv'), 'w', newline='') as file: writer = csv.writer(file) writer.writerow(initial_values.keys()) for i in range(n_runs_to_rerun): @@ -1515,7 +1515,7 @@ def rerun(self, path_to_file='./', runs_to_rerun=None, initial_values[key] = [new_mesa_flag[key]]*n_runs_to_rerun # create the CSV file - with open(path_to_file+'grid.csv', 'w', newline='') as file: + with open(os.path.join(path_to_file,'grid.csv'), 'w', newline='') as file: writer = csv.writer(file) writer.writerow(initial_values.keys()) for i in range(n_runs_to_rerun): diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index c5b64ba41d..ab252b96f8 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -1,4 +1,17 @@ -"""Create, evolve and save a binary star population.""" +"""Create, evolve and save a binary star population. + +Large populations are RAM limited when holding an arbitrary +number of BinaryStar instances. Therefore, by default the BinaryPopulation +will generate binaries, evolve, and try to save them to disk one at a time. + +Create a BinaryPopulation instance from an inifile: +I. CREATING A POPULATION +------------------------ +a) One-liner for creating a BinaryPopulation from an inifile: + +> BinaryPopulation.from_ini('' \ + '/posydon/popsyn/population_params_default.ini') +""" __authors__ = [ @@ -6,6 +19,7 @@ "Jeffrey Andrews ", "Konstantinos Kovlakas ", "Devina Misra ", + "Simone Bavera ", ] @@ -31,6 +45,7 @@ orbital_separation_from_period) from posydon.popsyn.defaults import default_kwargs from posydon.popsyn.io import binarypop_kwargs_from_ini +from posydon.utils.constants import Zsun # 'event' usually 10 but 'detached (Integration failure)' can occur @@ -46,6 +61,11 @@ 'mass_transfer_case_i': 7, 'mass_transfer_case_f': 7, 'S1_SN_type': 5, 'S2_SN_type': 5} +# BinaryPopulation will enforce a constant metallicity accross all steps that +# load stellar or binary models by checked this list of steps. +STEP_NAMES_LOADING_GRIDS = [ + 'step_HMS_HMS', 'step_CO_HeMS', 'step_CO_HMS_RLO', 'step_detached' +] class BinaryPopulation: """Handle a binary star population.""" @@ -170,6 +190,17 @@ def evolve(self, **kwargs): def _safe_evolve(self, **kwargs): """Evolve binaries in a population, catching warnings/exceptions.""" if not self.population_properties.steps_loaded: + # Enforce the same metallicity for all grid steps + for step_name, tup in self.population_properties.kwargs.items(): + + if step_name in STEP_NAMES_LOADING_GRIDS: + step_function, step_kwargs = tup # unpack params + step_kwargs['metallicity'] = self.kwargs.get('metallicity', 1) + + # update the step kwargs, override metallicity + modified_tup = (step_function, step_kwargs) + self.population_properties.kwargs[step_name] = modified_tup + self.population_properties.load_steps() indices = kwargs.get('indices', list(range(self.number_of_binaries))) @@ -806,6 +837,15 @@ def draw_initial_binary(self, **kwargs): eccentricity = output['eccentricity'].item() m1 = output['S1_mass'].item() m2 = output['S2_mass'].item() + Z_div_Zsun = kwargs.get('metallicity', 1.) + zams_table = {1.: 2.703e-01, + 0.1: 2.511e-01, + 0.01: 2.492e-01, + 0.001: 2.49e-01, + 0.0001: 2.49e-01} + Y = zams_table[Z_div_Zsun] + Z = Z_div_Zsun*Zsun + X = 1. - Z - Y binary_params = dict( index=kwargs.get('index', default_index), @@ -819,16 +859,16 @@ def draw_initial_binary(self, **kwargs): star1_params = dict( mass=m1, state="H-rich_Core_H_burning", - metallicity=0.0142, # ONLY VALID FOR Zsun - center_h1=0.7155, # ONLY VALID FOR Zsun - center_he4=0.2703, # ONLY VALID FOR Zsun + metallicity=Z, + center_h1=X, + center_he4=Y, ) star2_params = dict( mass=m2, state="H-rich_Core_H_burning", - metallicity=0.0142, # ONLY VALID FOR Zsun - center_h1=0.7155, # ONLY VALID FOR Zsun - center_he4=0.2703, # ONLY VALID FOR Zsun + metallicity=Z, + center_h1=X, + center_he4=Y, ) binary = BinaryStar(**binary_params, diff --git a/posydon/popsyn/io.py b/posydon/popsyn/io.py index e46f3f9001..8b458ddbfe 100644 --- a/posydon/popsyn/io.py +++ b/posydon/popsyn/io.py @@ -175,6 +175,13 @@ def binarypop_kwargs_from_ini(path, verbose=False): for key, val in parser[section].items(): pop_kwargs[key] = ast.literal_eval(val) + # hard code for running with MPI, only try to import if use_MPI + if pop_kwargs['use_MPI']: + from mpi4py import MPI + pop_kwargs['comm'] = MPI.COMM_WORLD + else: + pop_kwargs['comm'] = None + # right now binary, S1, and S2 output kwargs are all passed # into the BinaryPopulation during init elif section == 'BinaryStar_output': diff --git a/posydon/popsyn/population_params_default.ini b/posydon/popsyn/population_params_default.ini index b8cd9ce027..9c8df1cc68 100644 --- a/posydon/popsyn/population_params_default.ini +++ b/posydon/popsyn/population_params_default.ini @@ -222,11 +222,17 @@ # batch save after evolving N binaries temp_directory = 'batches' # folder for keeping batch files - # tqdm = False + tqdm = False # progress bar - # breakdown_to_df = True + breakdown_to_df = True # convert BinaryStars into DataFrames after evolution + use_MPI = False + # if True evolve with MPI, equivalent to the following: + # > from mpi4py import MPI + # > comm = MPI.COMM_WORLD + metallicity = 1 + # In units of solar metallicity # Random Number Generation entropy = None diff --git a/posydon/popsyn/synthetic_population.py b/posydon/popsyn/synthetic_population.py new file mode 100644 index 0000000000..4ab96ee6de --- /dev/null +++ b/posydon/popsyn/synthetic_population.py @@ -0,0 +1,58 @@ +"""Evolve multiple BinaryPopulations together. + +e.g. with multiple metallicities +""" + + + +__authors__ = [ + "Kyle Akira Rocha ", + "Simone Bavera ", +] + + +from posydon.popsyn.io import parse_inifile, binarypop_kwargs_from_ini +from posydon.popsyn.binarypopulation import BinaryPopulation +from posydon.utils.common_functions import convert_metallicity_to_string + + +class SyntheticPopulation: + + def __init__(self, path): + """ + Parameters + ---------- + + path : str + Path to the inifile to parse. You can supply a list in the + metallicity parameter to evolve more than one population. + + """ + + synthetic_pop_params = binarypop_kwargs_from_ini(path) + + self.metallicity = synthetic_pop_params['metallicity'] + + if not isinstance( self.metallicity, list): + self.metallicity = [self.metallicity] + + self.binary_populations = [] + for met in self.metallicity: + ini_kw = binarypop_kwargs_from_ini(path) + ini_kw['metallicity'] = met + ini_kw['temp_directory'] = self.create_met_prefix(met) + ini_kw['temp_directory'] + self.binary_populations.append( BinaryPopulation(**ini_kw) ) + + + def evolve(self): + """Evolve population(s) at given Z(s).""" + for ind, pop in enumerate( self.binary_populations ): + print( f'Z={pop.kwargs["metallicity"]:.2e} Z_sun' ) + pop.evolve() + met_prefix = f'{pop.kwargs["metallicity"]:.2e}_Zsun_' + pop.save( met_prefix + 'population.h5' ) + + @staticmethod + def create_met_prefix(met): + """Append a prefix to the name of directories for batch saving.""" + return convert_metallicity_to_string(met) + '_Zsun_' diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index 43189b5979..df9fa7cea0 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -2578,3 +2578,11 @@ def __call__(self, *args, **kwargs): if self.positive: result = np.maximum(result, 0.0) return result + +def convert_metallicity_to_string(Z): + """Check if metallicity is supported by POSYDON v2.""" + # check supported metallicity + valid_Z = [1e+00,1e-01,1e-02,1e-03,1e-04] + if not Z in valid_Z: + raise ValueError(f'Metallicity {Z} not supported! Available metallicities in POSYDON v2 are {valid_Z}.') + return f'{Z:1.0e}' diff --git a/posydon/utils/constants.py b/posydon/utils/constants.py index 6c3b4a6462..66d8038d81 100644 --- a/posydon/utils/constants.py +++ b/posydon/utils/constants.py @@ -59,6 +59,7 @@ # ASTRONOMICAL CONSTANTS. # Solar age, L, and R values from Bahcall et al, ApJ 618 (2005) 1049-1056. +Zsun = 0.0142 # Asplund+09 msol = 1.9892e33 # solar mass, gravitational not baryonic (g) rsol = 6.9598e10 # solar radius (cm) lsol = 3.8418e33 # solar luminosity (erg s^-1) From f0f2869f754c751d4e20efb85de83949c7b087c7 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 16 Mar 2023 16:04:30 +0100 Subject: [PATCH 067/319] Update posydon-run-grid (#44) Correct typo: .fale. -> .false. --- bin/posydon-run-grid | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/bin/posydon-run-grid b/bin/posydon-run-grid index 15de562919..0d5ec30a8e 100755 --- a/bin/posydon-run-grid +++ b/bin/posydon-run-grid @@ -255,7 +255,7 @@ def create_binary_inlists(binary_controls, binary_job, if v: inlist_grid_points.write("\t{0} = .true.\n".format(k)) else: - inlist_grid_points.write("\t{0} = .fale.\n".format(k)) + inlist_grid_points.write("\t{0} = .false.\n".format(k)) else: inlist_grid_points.write("{0} = {1}\n".format(k, v)) inlist_grid_points.write("/ ! end of binary_controls namelist\n") @@ -272,7 +272,7 @@ def create_binary_inlists(binary_controls, binary_job, if v: inlist_grid_points.write("\t{0} = .true.\n".format(k)) else: - inlist_grid_points.write("\t{0} = .fale.\n".format(k)) + inlist_grid_points.write("\t{0} = .false.\n".format(k)) else: inlist_grid_points.write("{0} = {1}\n".format(k, v)) inlist_grid_points.write("/ ! end of binary_job namelist\n") @@ -290,7 +290,7 @@ def create_binary_inlists(binary_controls, binary_job, if v: inlist_grid_points.write("\t{0} = .true.\n".format(k)) else: - inlist_grid_points.write("\t{0} = .fale.\n".format(k)) + inlist_grid_points.write("\t{0} = .false.\n".format(k)) else: inlist_grid_points.write("{0} = {1}\n".format(k, v)) inlist_grid_points.write("/ ! end of controls namelist\n") @@ -308,7 +308,7 @@ def create_binary_inlists(binary_controls, binary_job, if v: inlist_grid_points.write("\t{0} = .true.\n".format(k)) else: - inlist_grid_points.write("\t{0} = .fale.\n".format(k)) + inlist_grid_points.write("\t{0} = .false.\n".format(k)) else: inlist_grid_points.write("{0} = {1}\n".format(k, v)) inlist_grid_points.write("/ ! end of controls namelist\n") @@ -326,7 +326,7 @@ def create_binary_inlists(binary_controls, binary_job, if v: inlist_grid_points.write("\t{0} = .true.\n".format(k)) else: - inlist_grid_points.write("\t{0} = .fale.\n".format(k)) + inlist_grid_points.write("\t{0} = .false.\n".format(k)) else: inlist_grid_points.write("{0} = {1}\n".format(k, v)) inlist_grid_points.write("/ ! end of star_job namelist\n") @@ -344,7 +344,7 @@ def create_binary_inlists(binary_controls, binary_job, if v: inlist_grid_points.write("\t{0} = .true.\n".format(k)) else: - inlist_grid_points.write("\t{0} = .fale.\n".format(k)) + inlist_grid_points.write("\t{0} = .false.\n".format(k)) else: inlist_grid_points.write("{0} = {1}\n".format(k, v)) inlist_grid_points.write("/ ! end of star_job namelist\n") From 6cef18ab57aac82fff82cbb8dd11bc6f9f7502ec Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 23 Mar 2023 16:16:01 +0100 Subject: [PATCH 068/319] Fixes in psy grid (#46) * correct typo: profile1 -> profile2 * Fix missing composition values on grid creation A try to check whether better defined types solve the problem. * Debug output * more debugging * debug N_Z, where_to_add * try fix where_to_add in self.initial_values[i] * remove debug outputs * Fix for issue introduced in PR#36 Add "accept_missing_profile" to PROPERTIES_TO_BE_CONSISTENT --- posydon/grids/psygrid.py | 34 +++++++++++++++++++++------------- 1 file changed, 21 insertions(+), 13 deletions(-) diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index 6f085a3733..ef0c471251 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -621,7 +621,7 @@ def decide_columns(key_in_config, defaults): # in case lists were used, get a proper `dtype` object dtype_initial_values = self.initial_values.dtype dtype_final_values = self.final_values.dtype - + self._say('Loading MESA data...') #this int array will store the run_index of the i-th run and will be @@ -924,11 +924,11 @@ def decide_columns(key_in_config, defaults): if not ignore_data: if addX: - self.initial_values["X"] = where_to_add["X"] + self.initial_values[i]["X"] = where_to_add["X"] if addY: - self.initial_values["Y"] = where_to_add["Y"] + self.initial_values[i]["Y"] = where_to_add["Y"] if addZ: - self.initial_values["Z"] = where_to_add["Z"] + self.initial_values[i]["Z"] = where_to_add["Z"] if binary_grid: if ignore_data: @@ -1096,12 +1096,20 @@ def decide_columns(key_in_config, defaults): warnings.warn("run {} has a run_index out of ".format(i) + "range: {}>={}".format(run_included_at[i], run_index)) continue - for colname in self.initial_values.dtype.names: - value = self.initial_values[i][colname] - new_initial_values[run_included_at[i]][colname] = value - for colname in self.final_values.dtype.names: - value = self.final_values[i][colname] - new_final_values[run_included_at[i]][colname] = value + #copy initial values or fill with nan if not existing in original + for colname in dtype_initial_values.names: + if colname in self.initial_values.dtype.names: + value = self.initial_values[i][colname] + new_initial_values[run_included_at[i]][colname] = value + else: + new_initial_values[run_included_at[i]][colname] = np.nan + #copy final values or fill with nan if not existing in original + for colname in dtype_final_values.names: + if colname in self.final_values.dtype.names: + value = self.final_values[i][colname] + new_final_values[run_included_at[i]][colname] = value + else: + new_final_values[run_included_at[i]][colname] = np.nan #replace old initial/final value array self.initial_values = np.copy(new_initial_values) self.final_values = np.copy(new_final_values) @@ -1304,7 +1312,7 @@ def one_if_ok(data): run.final_profile1.dtype.names) if run.final_profile2 is not None: ret += "\nColumns in final_profile2: {}\n".format( - run.final_profile1.dtype.names) + run.final_profile2.dtype.names) ret += "\n" # Print out initial values array parameters @@ -1963,8 +1971,8 @@ def downsample_profile(profile, params): } PROPERTIES_TO_BE_CONSISTENT = ["binary", "eep", "start_at_RLO", - "initial_RLO_fix", - "He_core_fix", "history_DS_error", + "initial_RLO_fix", "He_core_fix", + "accept_missing_profile", "history_DS_error", "history_DS_exclude", "profile_DS_error", "profile_DS_exclude", "profile_DS_interval"] From 8cfe3c16ec638edbc3c41443fee5b341b223afa3 Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Thu, 30 Mar 2023 16:49:11 +0200 Subject: [PATCH 069/319] v2 grids post-processing pipeline (#42) * add post processing piepeline * header * copy the ini file togeather with the rerun points * fix bug rerun * automatically infer cluster name * debug * debug * debug * debug * debug * debug * automatically replace zamsfilename * add option to not start at RLO for the CO-HMS grid in case of reruns * add all grid slices to post process * make version an input * debug step 2 slurm job by setting slurm_array var default to a number * debug * debug * debug * debug * debug * debug * check quest setup * debug * debug * debug * debug * parallelise step 2 * check that file exists before exporting the csv file, if they are missing print a warning and remove them from the csv file * debug * drop missing files is customisable for steps 2 to 7 and reruns * debug * debug * debug * debug * increase walltime on yggdrasil because some HMS-HMS slices require more than 8 hours to process * debug * debug * correct typo: profile1 -> profile2 * Fix missing composition values on grid creation A try to check whether better defined types solve the problem. * Debug output * more debugging * debug N_Z, where_to_add * try fix where_to_add in self.initial_values[i] * remove debug outputs * debug on quest * cluster name is now an input * include the random grids in the plotting * debug * Fix for issue introduced in PR#36 Add "accept_missing_profile" to PROPERTIES_TO_BE_CONSISTENT * add ini file configuration of the processing pipeline * add kyle's authorship * move pipeline ini file outside bin directories to solve installation issue * add rerun in pipeline_quest.ini * debug opacity max rerun --------- Co-authored-by: mkruckow <122798003+mkruckow@users.noreply.github.com> --- bin/run-pipeline | 742 +++++++++++++++++++++++++++++ bin/setup-pipeline | 409 ++++++++++++++++ grid_params/pipeline_quest.ini | 109 +++++ grid_params/pipeline_yggdrasil.ini | 208 ++++++++ 4 files changed, 1468 insertions(+) create mode 100644 bin/run-pipeline create mode 100644 bin/setup-pipeline create mode 100644 grid_params/pipeline_quest.ini create mode 100644 grid_params/pipeline_yggdrasil.ini diff --git a/bin/run-pipeline b/bin/run-pipeline new file mode 100644 index 0000000000..5a7580d798 --- /dev/null +++ b/bin/run-pipeline @@ -0,0 +1,742 @@ +#!/usr/bin/env python +''' +MESA low_res_grids were run. This includes random grids and reruns. + +This is an EXAMPLE of the pipeline for a grid_type=HMS-HMS, +metallicity=1e-01_Zsun, compression=LITE/ORIGINAL + +STEP 1: grid slices creation +['grid_low_res_0','grid_low_res_1','grid_low_res_2', +'grid_low_res_3','grid_low_res_4','grid_low_res_5', +'grid_random_1', 'grid_low_res_rerun_opacitymax'] --> output *.h5 + +STEP 2: grid slices concatenation +[['grid_low_res_0','grid_low_res_1','grid_low_res_2', +'grid_low_res_3','grid_low_res_4','grid_low_res_5'], +['grid_low_combined','grid_low_res_rerun_opacitymax']] --> +grid_low_res_combined.h5, grid_low_res_combined_rerun_1.h5 + +STEP 3: plot grid slices +['grid_low_res_combined.h5', 'grid_low_res_rerun_opacitymax', +'grid_low_res_combined_rerun_1.h5' ] --> loop over all plot types + +STEP 4: check failure rate +['grid_low_res_combined.h5', 'grid_low_res_combined_rerun_1.h5' ] +--> check failure rate + +STEP 5: post processing +['grid_low_res_combined_rerun_1.h5','grid_random_1_rerun_1.h5',] +--> do post processing on the ORIGINAL grid and append back on the LITE gird +the post processed quantities + +STEP 6: train interpolators +['grid_low_res_combined_rerun_1.h5'] --> train the interpolators + +STEP 7: export POSYDON v2 dataset + +STEP RERUN: rerun grid with a fix A +grid_low_res_combined.rerun(index=logic) --> grid_rerun_1/grid.csv + --> grid_random_1_rerun_1/grid.csv +---- run gird fix ---- +call STEP 1: ['grid_rerun_1','grid_random_1_rerun_1',] --> *.h5 +call STEP 2: [['grid_low_res_combined','grid_rerun_1'], + ['grid_random_1','grid_random_rerun_1']] + ---> gird_low_res_combined_rerun_1.h5 + grid_random_1_rerun_1.h5 +call STEP 3: ['gird_low_res_combined_rerun_1.h5'] +call STEP 4: ['gird_low_res_combined_rerun_1.h5'] +--> continue to STEP 5, 6, 7 +---------------------- +''' + +__authors__ = [ + "Simone Bavera ", +] + +import os +import sys +import time +import shutil +import pickle +import random +import numpy as np +import pandas as pd +from shutil import copyfile +from collections import Counter +from posydon.grids.psygrid import (PSyGrid, + join_grids, + DEFAULT_HISTORY_DS_EXCLUDE, + DEFAULT_PROFILE_DS_EXCLUDE, + EXTRA_COLS_DS_EXCLUDE) +from posydon.grids.post_processing import (post_process_grid, + add_post_processed_quantities) +from posydon.interpolation.IF_interpolation import IFInterpolator +from posydon.utils.common_functions import PATH_TO_POSYDON + + +def create_grid_slice(i, path_to_csv_file, CO_HMS_GRID_START_AT_RLO, verbose=False): + + df = pd.read_csv(path_to_csv_file) + grid_path = df.loc[i,'path_to_grid'] + compression = df.loc[i,'compression'] + + grid_name = grid_path.split('/')[-1] + otuput_path = os.path.join('/', os.path.join(*grid_path.split('/')[:-1]), + compression) + grid_output = os.path.join(otuput_path, grid_name+'.h5') + + # check that LITE/ or ORIGINAL/ directory exists + if not os.path.isdir(otuput_path): + os.makedirs(otuput_path) + + if verbose: + print('processing ', grid_path) + print('saving file ', grid_output) + + if compression == 'ORIGINAL': + history_DS_error = None + profile_DS_error = None + profile_DS_interval = None + history_DS_exclude = DEFAULT_HISTORY_DS_EXCLUDE + profile_DS_exclude = DEFAULT_PROFILE_DS_EXCLUDE + elif compression == 'LITE': + history_DS_error = 0.1 + profile_DS_error = 0.1 + profile_DS_interval = -0.005 + history_DS_exclude = EXTRA_COLS_DS_EXCLUDE + profile_DS_exclude = EXTRA_COLS_DS_EXCLUDE + else: + raise ValueError('compression = %s not supported!'%compression) + + if 'CO-HMS_RLO' in grid_path and CO_HMS_GRID_START_AT_RLO: + start_at_RLO = True + else: + start_at_RLO = False + + grid = PSyGrid(verbose=True) + grid.create(grid_path, + grid_output, + overwrite=True, + history_DS_error=history_DS_error, + profile_DS_error=profile_DS_error, + history_DS_exclude=history_DS_exclude, + profile_DS_exclude=profile_DS_exclude, + profile_DS_interval=profile_DS_interval, + compression="gzip9", + start_at_RLO=start_at_RLO, + initial_RLO_fix=True) + grid.close() + + +def combine_grid_slices(i, path_to_csv_file, verbose=False): + + df = pd.read_csv(path_to_csv_file) + grid_combined_key = df.keys()[i] + gird_names = df[grid_combined_key].dropna().to_list() + if verbose: + print('Combinining: ', gird_names) + print('into:', grid_combined_key) + join_grids(gird_names, grid_combined_key) + + +def plot_grid(i, path_to_csv_file, verbose=False): + + df = pd.read_csv(path_to_csv_file) + grid_path = df.loc[i,'path_to_grid'] + plot_dir = df.loc[i,'path_to_plot'] + + if verbose: + print(f'plotting {grid_path}') + + # check that plots/ directories exist + plot_path = os.path.join('/', os.path.join(*plot_dir.split('/')[:-1])) + check_dirs = [plot_path, plot_dir] + + sub_dirs = ['TF12/', 'TF1/', 'TF2/', 'TF3/', 'TF4/', 'debug_mt/', + 'debug_rl_1/', 'debug_rl_2/'] + for name in sub_dirs: + check_dirs.append(os.path.join(plot_dir,name)) + + for dir_ in check_dirs: + if not os.path.isdir(dir_): + os.makedirs(dir_) + + # load grid + grid = PSyGrid(verbose=False) + grid.load(grid_path) + + if 'CO' in grid_path: + # compact object mass slices + def log_range(x_min,x_max,x_n): + return 10**np.linspace(np.log10(x_min),np.log10(x_max), x_n) + m_CO_min = 1. + m_CO_max = 51.32759630397827 + m_CO_n = 23 + m_COs = log_range(m_CO_min, m_CO_max, m_CO_n) + m_COs_edges = 10**(np.log10(np.array(m_COs)[:-1])+ + (np.log10(np.array(m_COs)[1:])-np.log10(np.array(m_COs)[:-1]))/2) + m2 = [0.]+m_COs_edges.tolist()+[55.] + vars = m_COs + fname = 'grid_m_%1.2f.png' + title = '$m_\mathrm{CO}=%1.2f\,M_\odot$' + slice_3D_var_str='star_2_mass' + elif 'HMS-HMS' in grid_path: + # mass ratio slices + qs = np.linspace(0.05,1.,20) + qs[-1] = 0.99 + vars = qs.tolist() + fname = 'grid_q_%1.2f.png' + title = '$q=%1.2f$' + slice_3D_var_str='mass_ratio' + else: + raise ValueError('Grid type not supported!') + + for i, var in enumerate(vars): + if 'HMS-HMS' in grid_path: + slice_3D_var_range = (var-2.5e-2,var+2.5e-2) + elif 'CO' in grid_path: + if i == len(m2): + continue + slice_3D_var_range = (m2[i],m2[i+1]) + # TODO: skip plotting slice if there are no data + try: + PLOT_PROPERTIES = { + 'figsize' : (4,3.5), + 'path_to_file' : os.path.join(plot_dir,'TF12/'), + 'show_fig' : False, + 'fname' : fname%var, + 'title' : title%var, + 'log10_x' : True, + 'log10_y' : True, + 'legend2D' : {'bbox_to_anchor' : (1.03, 0.5)} + } + + grid.plot2D('star_1_mass', 'period_days', None, + termination_flag='combined_TF12', + grid_3D=True, slice_3D_var_str=slice_3D_var_str, + slice_3D_var_range=slice_3D_var_range, + verbose=False, **PLOT_PROPERTIES) + + PLOT_PROPERTIES = { + 'figsize' : (4,5), + 'path_to_file' : os.path.join(plot_dir,'TF1/'), + 'show_fig' : False, + 'fname' : fname%var, + 'title' : title%var, + 'log10_x' : True, + 'log10_y' : True, + 'zmin' : -8, + 'zmax' : -1, + 'legend2D' : {'bbox_to_anchor' : (1.03, 0.5)}, + 'colorbar': {'pad': 0.12} + } + + grid.plot2D('star_1_mass', 'period_days', 'lg_mtransfer_rate', + termination_flag='termination_flag_1', + grid_3D=True, slice_3D_var_str=slice_3D_var_str, + slice_3D_var_range=slice_3D_var_range, + verbose=False, **PLOT_PROPERTIES) + + PLOT_PROPERTIES = { + 'figsize' : (4,3.5), + 'path_to_file' : os.path.join(plot_dir,'TF2/'), + 'show_fig' : False, + 'fname' : fname%var, + 'title' : title%var, + 'log10_x' : True, + 'log10_y' : True, + 'legend2D' : {'bbox_to_anchor' : (1.03, 0.5)} + } + + grid.plot2D('star_1_mass', 'period_days', None, + termination_flag='termination_flag_2', + grid_3D=True, slice_3D_var_str=slice_3D_var_str, + slice_3D_var_range=slice_3D_var_range, + verbose=False, **PLOT_PROPERTIES) + + PLOT_PROPERTIES = { + 'figsize' : (4,3.5), + 'path_to_file' : os.path.join(plot_dir,'TF3/'), + 'show_fig' : False, + 'fname' : fname%var, + 'title' : title%var, + 'log10_x' : True, + 'log10_y' : True, + 'legend2D' : {'bbox_to_anchor' : (1.03, 0.5)} + } + + grid.plot2D('star_1_mass', 'period_days', None, + termination_flag='termination_flag_3', + grid_3D=True, slice_3D_var_str=slice_3D_var_str, + slice_3D_var_range=slice_3D_var_range, + verbose=False, **PLOT_PROPERTIES) + + PLOT_PROPERTIES = { + 'figsize' : (4,3.5), + 'path_to_file' : os.path.join(plot_dir,'TF4/'), + 'show_fig' : False, + 'fname' : fname%var, + 'title' : title%var, + 'log10_x' : True, + 'log10_y' : True, + 'legend2D' : {'bbox_to_anchor' : (1.03, 0.5)} + } + + grid.plot2D('star_1_mass', 'period_days', None, + termination_flag='termination_flag_4', + grid_3D=True, slice_3D_var_str=slice_3D_var_str, + slice_3D_var_range=slice_3D_var_range, + verbose=False, **PLOT_PROPERTIES) + + PLOT_PROPERTIES = { + 'figsize' : (4,5), + 'path_to_file' : os.path.join(plot_dir,'debug_rl_1/'), + 'show_fig' : False, + 'fname' : fname%var, + 'title' : title%var, + 'log10_x' : True, + 'log10_y' : True, + 'zmin' : -0.5, + 'zmax' : 0.5, + 'legend2D' : {'bbox_to_anchor' : (1.03, 0.5)}, + 'colorbar': {'pad': 0.12} + } + + grid.plot2D('star_1_mass', 'period_days', + 'rl_relative_overflow_1', + termination_flag='debug', + grid_3D=True, slice_3D_var_str=slice_3D_var_str, + slice_3D_var_range=slice_3D_var_range, + verbose=False, **PLOT_PROPERTIES) + + PLOT_PROPERTIES = { + 'figsize' : (4,5), + 'path_to_file' : os.path.join(plot_dir,'debug_rl_2/'), + 'show_fig' : False, + 'fname' : fname%var, + 'title' : title%var, + 'log10_x' : True, + 'log10_y' : True, + 'zmin' : -0.5, + 'zmax' : 0.5, + 'legend2D' : {'bbox_to_anchor' : (1.03, 0.5)}, + 'colorbar': {'pad': 0.12} + } + + grid.plot2D('star_1_mass', 'period_days', + 'rl_relative_overflow_2', + termination_flag='debug', + grid_3D=True, slice_3D_var_str=slice_3D_var_str, + slice_3D_var_range=slice_3D_var_range, + verbose=False, **PLOT_PROPERTIES) + + PLOT_PROPERTIES = { + 'figsize' : (4,5), + 'path_to_file' : os.path.join(plot_dir,'debug_mt/'), + 'show_fig' : False, + 'fname' : fname%var, + 'title' : title%var, + 'log10_x' : True, + 'log10_y' : True, + 'zmin' : -8, + 'zmax' : -1, + 'legend2D' : {'bbox_to_anchor' : (1.03, 0.5)}, + 'colorbar': {'pad': 0.12} + } + + grid.plot2D('star_1_mass', 'period_days', 'lg_mtransfer_rate', + termination_flag='debug', + grid_3D=True, slice_3D_var_str=slice_3D_var_str, + slice_3D_var_range=slice_3D_var_range, + verbose=False, **PLOT_PROPERTIES) + except Exception as e: + print('FAILED TO PLOT '+title%var) + print('') + print(e) + continue + +def check_failure_rate(i, path_to_csv_file, verbose=False): + + df = pd.read_csv(path_to_csv_file) + grid_path = df.loc[i,'path_to_grid'] + grid = PSyGrid(verbose=False) + grid.load(grid_path) + + count = Counter(grid.final_values['interpolation_class']) + n = 0. + for key in count.keys(): + n += count[key] + print(grid_path) + print('Failure rate', round(count['not_converged']/n*100,2),'%') + + +def post_processing(i, path_to_csv_file, verbose=False): + + df = pd.read_csv(path_to_csv_file) + grid_path = df.loc[i,'path_to_grid'] + processed_grid_path = df.loc[i,'path_to_processed_grid'] + grid = PSyGrid(verbose=False) + grid.load(grid_path) + + if verbose: + print('Compute process quantities ...') + if 'CO' in grid_path: + star_2_CO = True + else: + star_2_CO = False + grid_ORIGINAL = PSyGrid(grid_path.replace('LITE','ORIGINAL')) + MESA_dirs_EXTRA_COLUMNS, EXTRA_COLUMNS = post_process_grid(grid_ORIGINAL, + index=None, + star_2_CO=star_2_CO, + verbose=False) + + # post processed quantities are appended to the grid object, hence + # we create a copy of it and append back to it + if os.path.exists(processed_grid_path): + if verbose: + print('Post processed grid file alredy exist, removing it.') + os.remove(processed_grid_path) + copyfile(grid_path, processed_grid_path) + + grid_LITE = PSyGrid(processed_grid_path) + add_post_processed_quantities(grid_LITE, MESA_dirs_EXTRA_COLUMNS, + EXTRA_COLUMNS, verbose=verbose) + if verbose: + print('Added process quantities to grid:',processed_grid_path) + grid_LITE.close() + + +def train_interpolators(i, path_to_csv_file, verbose=False): + + df = pd.read_csv(path_to_csv_file) + grid_path = df.loc[i,'path_to_grid'] + interpolator_path = df.loc[i,'path_to_interpolator'] + method = interpolator_path.split('IF_')[-1].split('.pkl')[0] + + if verbose: + print(f'train interpolators {grid_path} with method {method}') + + # check interpolation_objects directory exists + interp_path = os.path.join('/', os.path.join(*interpolator_path.split('/')[:-1])) + if not os.path.isdir(interp_path): + os.makedirs(interp_path) + + # load grid + grid = PSyGrid(verbose=False) + grid.load(grid_path) + + second = ['S1_direct_f_fb', 'S1_direct_mass', 'S1_direct_spin'] + third = ['S1_Fryer+12-rapid_f_fb', 'S1_Fryer+12-rapid_mass', + 'S1_Fryer+12-rapid_spin'] + fourth = ['S1_Fryer+12-delayed_f_fb', 'S1_Fryer+12-delayed_mass', + 'S1_Fryer+12-delayed_spin'] + fifth = ['S1_Sukhbold+16-engineN20_f_fb', 'S1_Sukhbold+16-engineN20_mass', + 'S1_Sukhbold+16-engineN20_spin'] + sixth = ['S1_Patton&Sukhbold20-engineN20_f_fb', + 'S1_Patton&Sukhbold20-engineN20_mass', + 'S1_Patton&Sukhbold20-engineN20_spin'] + + + first = [key for key in grid.final_values.dtype.names if ( + key != "model_number" and + (type(grid.final_values[key][0]) != np.str_) + and any(~np.isnan(grid.final_values[key])))] + + for sec in second: + first.remove(sec) + + for thrd in third: + first.remove(thrd) + + for frth in fourth: + first.remove(frth) + + for ffth in fifth: + first.remove(ffth) + + for sxth in sixth: + first.remove(sxth) + + if 'CO-HMS_RLO' in grid_path: + interp_method = [method, method] + interp_classes = ["stable_MT", "unstable_MT"] + else: + interp_method = [method, method, method] + interp_classes = ["no_MT", "stable_MT", "unstable_MT"] + + # TODO: train CC2 quantities coming from reverse MT systems + interp = IFInterpolator(grid=grid, interpolators = [ + { + "interp_method": interp_method, + "interp_classes": interp_classes, + "out_keys": first, + "class_method": "kNN", + "c_keys": ['interpolation_class', + 'S1_state', + 'S2_state', + # Collapse quantities + 'S1_direct_SN_type', + 'S1_Fryer+12-rapid_SN_type', + 'S1_Fryer+12-delayed_SN_type', + 'S1_Sukhbold+16-engineN20_SN_type', + 'S1_Patton&Sukhbold20-engineN20_SN_type', + # v1 POSYDON does not have S2 reaching CC before S1 + # all these classifier map to None + 'S2_direct_state', + 'S2_Fryer+12-rapid_state', + 'S2_Fryer+12-delayed_state', + 'S2_Sukhbold+16-engineN20_state', + 'S2_Patton&Sukhbold20-engineN20_state', + 'S2_direct_SN_type', + 'S2_Fryer+12-rapid_SN_type', + 'S2_Fryer+12-delayed_SN_type', + 'S2_Sukhbold+16-engineN20_SN_type', + 'S2_Patton&Sukhbold20-engineN20_SN_type' + ], + "c_key": "interpolation_class" + }, + { + "interp_method": [method, method, method], + "interp_classes": ["BH", "WD", "NS"], + "out_keys": second, + "class_method": "kNN", + "c_keys": ['S1_direct_state'], + "c_key": 'S1_direct_state' + }, + { + "interp_method": [method, method, method], + "interp_classes": ["BH", "WD", "NS"], + "out_keys": third, + "class_method": "kNN", + "c_keys": ['S1_Fryer+12-rapid_state'], + "c_key": 'S1_Fryer+12-rapid_state' + }, + { + "interp_method": [method, method, method], + "interp_classes": ["BH", "WD", "NS"], + "out_keys": fourth, + "class_method": "kNN", + "c_keys": ['S1_Fryer+12-delayed_state'], + "c_key": 'S1_Fryer+12-delayed_state' + }, + { + "interp_method": [method, method, method], + "interp_classes": ["BH", "WD", "NS"], + "out_keys": fifth, + "class_method": "kNN", + "c_keys": ['S1_Sukhbold+16-engineN20_state'], + "c_key": 'S1_Sukhbold+16-engineN20_state' + }, + { + "interp_method": [method, method, method], + "interp_classes": ["BH", "WD", "NS"], + "out_keys": sixth, + "class_method": "kNN", + "c_keys": ['S1_Patton&Sukhbold20-engineN20_state'], + "c_key": 'S1_Patton&Sukhbold20-engineN20_state' + } + ]) + + # training and saving + interp.train() + interp.save(interpolator_path) + + +def export_dataset(i, path_to_csv_file, verbose=False): + + df = pd.read_csv(path_to_csv_file) + grid_path = df.loc[i,'path_to_grid'] + export_path = df.loc[i,'export_path'] + if verbose: + print(f'copying {grid_path} to {export_path}') + shutil.copyfile(grid_path, export_path) + +def zams_file_name(dirname): + if '2e+00_Zsun' in dirname: + zams_filename = 'TODO' # TODO: update when avaiable + elif '1e+00_Zsun' in dirname: + zams_filename = 'zams_z1.42m2_y0.2703.data' + elif '4.5e-01_Zsun' in dirname: + zams_filename = 'zams_z6.39m3_y0.2586.data' + elif '2e-01_Zsun' in dirname: + zams_filename = 'zams_z2.84m3_y0.2533.data' + elif '1e-01_Zsun' in dirname: + zams_filename = 'zams_z1.42m3_y0.2511.data' + elif '1e-02_Zsun' in dirname: + zams_filename = 'zams_z1.42m4_y0.2492.data' + elif '1e-03_Zsun' in dirname: + zams_filename = 'zams_z1.42m5_y0.2490.data' + elif '1e-04_Zsun' in dirname: + zams_filename = 'zams_z1.42m6_y0.2490.data' + return zams_filename + +def copy_ini_file(grid, rerun_type, destination, cluster): + + # copy the default ini file to directory + if cluster in ['quest','yggdrasil']: + dirname = grid.MESA_dirs[0].decode('utf-8') + ini_dir_path = os.path.join(PATH_TO_POSYDON, + 'grid_params/POSYDON-MESA-INLISTS/r11701/running_scripts') + if "single_HMS" in dirname: + ini_file_path = os.path.join(ini_dir_path, f'single_HMS_{cluster}.ini') + ini_file_dest = os.path.join(destination, f'single_HMS_{cluster}.ini') + elif "single_HeMS" in dirname: + ini_file_path = os.path.join(ini_dir_path, f'single_HeMS_{cluster}.ini') + ini_file_dest = os.path.join(destination, f'single_HeMS_{cluster}.ini') + elif "HMS-HMS" in dirname: + ini_file_path = os.path.join(ini_dir_path, f'HMS-HMS_{cluster}.ini') + ini_file_dest = os.path.join(destination, f'HMS-HMS_{cluster}.ini') + elif "CO-HMS_RLO" in dirname: + ini_file_path = os.path.join(ini_dir_path, f'CO-HMS_RLO_{cluster}.ini') + ini_file_dest = os.path.join(destination, f'CO-HMS_RLO_{cluster}.ini') + elif "CO-HeMS" in dirname: + ini_file_path = os.path.join(ini_dir_path, f'CO-HeMS_{cluster}.ini') + ini_file_dest = os.path.join(destination, f'CO-HeMS_{cluster}.ini') + else: + raise ValueError(f'Unsupported grid type in {dirname}!') + shutil.copyfile(ini_file_path, ini_file_dest) + else: + raise ValueError(f'Unsupported cluster {cluster}!') + + # substitue the inlist sceario with the one for the rerun + if rerun_type == 'opacity_max': + # this rerun uses the default inlist commit + return + elif rerun_type == 'PISN': + replace_text = "matthias_PISN-d68228338b91fd487ef5d55c9b6ebb8cc5f0e668" + elif rerun_type == 'reverse_MT': + return + elif rerun_type == 'envelope_mass_limit': # maybe 'fe_core_infall_limit' as well? + return + else: + raise ValueError(f'Unsupported rerun type {rerun_type}!') + + search_text1 = "main-18710e943edd926a5653c4cdb7d6e18e5bdb35a2" + search_text2 = "main-a060b7bf1a4d94d693f77be9a1b0b3a522c1eadf" + search_text3 = 'zams_z1.42m3_y0.2511.data' + replace_zams_filename = zams_file_name(dirname) + with open(ini_file_dest, 'r') as file: + data = file.read() + data = data.replace(search_text1, replace_text) + data = data.replace(search_text2, replace_text) + data = data.replace(search_text3, replace_zams_filename) + data = data.replace('grid_test.csv', 'grid.csv') + with open(ini_file_dest, 'w') as file: + file.write(data) + +def logic_rerun(grid, rerun_type): + + runs_to_rerun=None + termination_flags=None + new_mesa_flag = None + + if rerun_type == 'opacity_max': + termination_flags=['reach cluster timelimit', 'min_timestep_limit'] + new_mesa_flag={'opacity_max' : 0.5} + elif rerun_type == 'PISN': + N_runs = len(grid) + runs_to_rerun = [] + for i in range(N_runs): + dirname = grid.MESA_dirs[i].decode('utf-8') + if not os.path.isdir(dirname): + # TODO: handle the case when grids were moved location + raise ValueError(f'Grid directory not found at {dirname}') + if "single_HMS" in dirname: + out_txt_path = os.path.join(dirname, "out_star1_formation_step0.txt") + elif "single_HeMS" in dirname: + out_txt_path = os.path.join(dirname, "out_star1_formation_step2.txt") + else: + out_txt_path = os.path.join(dirname, "out.txt") + if out_txt_path is not None and os.path.isfile(out_txt_path): + with open(out_txt_path, "r") as log_file: + log_lines = log_file.readlines() + for line in log_lines: + if "have reached gamma1 integral limit" in line: + runs_to_rerun += [i] + runs_to_rerun = np.array(runs_to_rerun) + elif rerun_type == 'reverse_MT': + runs_to_rerun = np.array([0, 1]) # implement logic + elif rerun_type == 'envelope_mass_limit': # maybe 'fe_core_infall_limit' as well? + runs_to_rerun = np.array([0, 1]) # implement logic + else: + raise ValueError(f'Unsupported rerun type {rerun_type}!') + + if runs_to_rerun is None and termination_flags is None: + raise ValueError('Undefined rerun!') + + return runs_to_rerun, termination_flags, new_mesa_flag + + +def rerun(i, path_to_csv_file, rerun_type, verbose=False): + + # load grid + df = pd.read_csv(path_to_csv_file) + grid_path = df.loc[i,'path_to_grid'] + rerun_path = df.loc[i,'rerun_path'] + grid = PSyGrid(verbose=False) + grid.load(grid_path) + + # export point to rerun + if verbose: + print(f'rerun {grid_path} in {rerun_path}') + runs_to_rerun, termination_flags, new_mesa_flag = logic_rerun(grid, rerun_type) + grid.rerun(path_to_file=rerun_path, runs_to_rerun=runs_to_rerun, + termination_flags=termination_flags, new_mesa_flag=new_mesa_flag) + + # copy ini file and set the new inlist commit + # TODO: make cluster a input + if 'b1119' in grid.MESA_dirs[0].decode('utf-8'): + cluster = 'quest' + else: + cluster = 'yggdrasil' + copy_ini_file(grid, rerun_type, destination=rerun_path, cluster=cluster) + +if __name__ == '__main__': + + # chose grid slice given the slurm jobarray index + # TODO: use args parser + VERBOSE = True + PATH_TO_GRIDS = str(sys.argv[1]) + path_to_csv_file = str(sys.argv[2]) + i = int(sys.argv[3]) + CO_HMS_GRID_START_AT_RLO = bool(int(sys.argv[4])) + if len(sys.argv) == 6: + rerun_type = str(sys.argv[5]) + + # prevent to run the script locally else it will star creating + # directories where you do not want them before breaking + if not os.path.isdir(PATH_TO_GRIDS): + raise ValueError('Grids were not found! ' + f'Check your PATH_TO_GRIDS={PATH_TO_GRIDS}') + + # offset the start of each job + # NOTE: this prevents job arrays starting at the same time to read + # the same files at the same time (e.g. when creatign LITE/ORIGINAL grid, + # or when creating a directory that does not exist yet) + time.sleep(random.uniform(0.,30.)) + + if 'step_1' in path_to_csv_file: + create_grid_slice(i, path_to_csv_file, + CO_HMS_GRID_START_AT_RLO=CO_HMS_GRID_START_AT_RLO, + verbose=VERBOSE) + + if 'step_2' in path_to_csv_file: + combine_grid_slices(i, path_to_csv_file, verbose=VERBOSE) + + if 'step_3' in path_to_csv_file: + plot_grid(i, path_to_csv_file, verbose=VERBOSE) + + if 'step_4' in path_to_csv_file: + check_failure_rate(i, path_to_csv_file, verbose=VERBOSE) + + if 'step_5' in path_to_csv_file: + post_processing(i, path_to_csv_file, verbose=VERBOSE) + + if 'step_6' in path_to_csv_file: + train_interpolators(i, path_to_csv_file, verbose=VERBOSE) + + if 'step_7' in path_to_csv_file: + export_dataset(i, path_to_csv_file, verbose=VERBOSE) + + if 'rerun' in path_to_csv_file: + rerun(i, path_to_csv_file, rerun_type, verbose=VERBOSE) diff --git a/bin/setup-pipeline b/bin/setup-pipeline new file mode 100644 index 0000000000..39e2bd0934 --- /dev/null +++ b/bin/setup-pipeline @@ -0,0 +1,409 @@ +#!/usr/bin/env python +__authors__ = [ + "Simone Bavera ", + "Kyle Akira Rocha ", +] + +import os +import sys +import ast +import pandas as pd +import numpy as np +from pprint import pprint, pformat +from posydon.popsyn.io import parse_inifile +from posydon.utils.common_functions import PATH_TO_POSYDON + +# this data processing pipeline was designed assuming POSYDON v2 data structure +''' +Data tree structure + +PATH_TO_GRIDS/ + /HMS-HMS/ + /CO-HMS_RLO/ + /CO-HeMS/ + /single_HMS/ + /single_HeMS/ + /v1/ + /v2/ + /1e+00_Zsun/ + /1e-01_Zsun/ + /1e-02_Zsun/ + ... + /grid_low_res_0/ + /grid_low_res_1/ + /grid_rerun_1/ + ... + /LITE/ + /ORIGINAL/ + /logs/ + /scripts/ + /plots/ + /grid_low_res_combined/ + /TF1/ + /TF2/ + ... +''' + +ACTION_TO_STEP_NUM = { + 'CREATE_GRID_SLICES' : 'step_1', + 'COMBINE_GRID_SLICES' : 'step_2', + 'PLOT_GRIDS' : 'step_3', + 'CHECK_FAILURE_RATE' : 'step_4', + 'POST_PROCESSING' : 'step_5', + 'TRAIN_INTERPOLATORS' : 'step_6', + 'EXPORT_DATASET' : 'step_7', + 'RERUN' : 'rerun' +} + +class PostProcessingPipeline: + + def __init__(self, path_to_inifile=None): + self.PATH_TO_POSYDON = os.getenv('PATH_TO_POSYDON') + self.PATH_TO_INIFILE = path_to_inifile + + if self.PATH_TO_INIFILE is not None: + self.pipeline_kwargs = self.parse_setup_params(self.PATH_TO_INIFILE) + + + @staticmethod + def parse_setup_params(path=None): + """ + Parse inifile for running post-processing pipelines. + """ + + if path is None: + return + else: + parser = parse_inifile(path) + + pipeline_kwargs = dict() + for section in parser.sections(): + section_dict = dict() + for key, val in parser[section].items(): + section_dict[key] = ast.literal_eval(val) + + pipeline_kwargs[section] = section_dict + return pipeline_kwargs + + def create_csv_and_slurm_job_files(self): + setup_kwargs = self.pipeline_kwargs['pipeline setup'] + account_kwargs = self.pipeline_kwargs['account'] + + if setup_kwargs['VERBOSE']: + print( "\n\n{:+^45s} \n{}\n{:+^45s}\n{}".format( + 'ACCOUNT', pformat(account_kwargs, indent=2), + 'SETUP', pformat(setup_kwargs, indent=2) ) + ) + + + for action, step_number in ACTION_TO_STEP_NUM.items(): + do_step_bool = setup_kwargs[action] + + if setup_kwargs['VERBOSE']: + print( "\n\n{:-^45s} {:^8s}:{:^6s}".format( + action, step_number, str(do_step_bool)) ) + + if do_step_bool: + step_kwargs = self.pipeline_kwargs[step_number] + + if setup_kwargs['VERBOSE']: + print( pformat(step_kwargs, indent=2) ) + + + create_csv( + step_name=step_number, + **{**step_kwargs, **setup_kwargs} + ) + slurm_job( + job_name=step_number, + step_name=step_number, + PATH_TO_POSYDON=self.PATH_TO_POSYDON, + **{**step_kwargs, **setup_kwargs, **account_kwargs} + ) + + def create_logs_dir(self): + # create logs in working dir to store all slurm outputs + setup_kwargs = self.pipeline_kwargs['pipeline setup'] + logs_path = os.path.join(setup_kwargs['PATH'],'logs') + if not os.path.isdir(logs_path): + os.makedirs(logs_path) + + def create_export_dir(self): + # create data dir three to export the datasets + setup_kwargs = self.pipeline_kwargs['pipeline setup'] + if setup_kwargs['EXPORT_DATASET']: + data_path = os.path.join(setup_kwargs['PATH'], 'POSYDON_data') + if not os.path.isdir(data_path): + os.makedirs(data_path) + + dirs = [] + grid_dirs = ['HMS-HMS', 'CO-HMS_RLO', 'CO-HeMS', 'single_HMS', + 'single_HeMS'] + interp_dirs = ['interpolators', 'interpolators/1NN_1NN', + 'interpolators/linear3c_kNN'] + for name1 in grid_dirs: + dirs.append(os.path.join(data_path,name1)) + for name2 in interp_dirs: + dirs.append(os.path.join(data_path,name1,name2)) + + for dir_ in dirs: + if not os.path.isdir(dir_): + os.makedirs(dir_) + +def slurm_job(job_name, + PATH_TO_GRIDS=None, + PATH_TO_POSYDON=None, + PATH=None, + ACCOUNT=None, + PARTITION=None, + WALLTIME=None, + MAILTYPE=None, + EMAIL=None, + CREATE_GRID_SLICES=False, + COMBINE_GRID_SLICES=False, + PLOT_GRIDS=False, + CHECK_FAILURE_RATE=False, + POST_PROCESSING=False, + TRAIN_INTERPOLATORS=False, + EXPORT_DATASET=False, + RERUN=False, + CO_HMS_GRID_START_AT_RLO=1, + RERUN_TYPE='', + verbose=False, + **kwargs): + + path_to_csv_file = os.path.join(PATH,f"{job_name}.csv") + + with open(f'{job_name}.slurm', 'w') as f: + f.write("#!/bin/bash\n") + f.write(f"#SBATCH --account={ACCOUNT}\n") + f.write(f"#SBATCH --partition={PARTITION}\n") + f.write("#SBATCH -N 1\n") + f.write("#SBATCH --cpus-per-task 1\n") + f.write("#SBATCH --ntasks-per-node 1\n") + f.write(f"#SBATCH --time={WALLTIME}\n") + f.write("#SBATCH --job-name=psygrid\n") + f.write("#SBATCH --mem-per-cpu=4G\n") + + if EMAIL is not None: + f.write(f"#SBATCH --mail-type={MAILTYPE}\n") + f.write(f"#SBATCH --mail-user={EMAIL}\n") + + if job_name in ['step_1', 'step_3', 'step_4','step_5', 'step_6', + 'step_7', 'rerun']: + df = pd.read_csv(path_to_csv_file) + N = df.shape[0]-1 + elif job_name in ['step_2']: + df = pd.read_csv(path_to_csv_file) + N = df.shape[1]-1 + else: + raise ValueError('This should never happen!') + f.write(f"#SBATCH --array=0-{N}\n") + slurm_array = '$SLURM_ARRAY_TASK_ID' + + if job_name == 'step_1': + f.write(f"#SBATCH --output={PATH}/logs/grid_slice_%a.out\n") + + if job_name == 'step_2': + f.write(f"#SBATCH --output={PATH}/logs/combine_grid_slices.out\n") + + if job_name == 'step_3': + f.write(f"#SBATCH --output={PATH}/logs/plot_grid.out\n") + + if job_name == 'step_4': + f.write(f"#SBATCH --output={PATH}/logs/check_failure_rate.out\n") + + if job_name == 'step_5': + f.write(f"#SBATCH --output={PATH}/logs/post_processing_%a.out\n") + f.write(f"export PATH_TO_POSYDON={PATH_TO_POSYDON}\n") + + if job_name == 'step_7': + f.write(f"#SBATCH --output={PATH}/logs/train_interpolators_%a.out\n") + + if job_name == 'step_7': + f.write(f"#SBATCH --output={PATH}/logs/export_dataset.out\n") + + if job_name == 'rerun': + f.write(f"#SBATCH --output={PATH}/logs/rerun.out\n") + + f.write(f"\nsrun python {PATH_TO_POSYDON}/bin/run-pipeline {PATH_TO_GRIDS} {path_to_csv_file} {slurm_array} {CO_HMS_GRID_START_AT_RLO} {RERUN_TYPE}") + +# create csv file with a list of all grid paths to process +def create_csv(GRID_TYPES, METALLICITIES, GRID_SLICES, COMPRESSIONS, + step_name=None, VERSION=None, GRIDS_COMBINED=None, + INTERPOLATION_METHODS=None, RERUN_TYPE='', + DROP_MISSING_FILES=False, PATH_TO_GRIDS=None, + PATH=None, **kwargs): + + # number of grid types + N = len(GRID_TYPES) + if ( N != len(METALLICITIES) or N != len(GRID_SLICES) or + N != len(COMPRESSIONS)): + raise ValueError('Missmatch between the len of GRID_TYPES, ' + 'METALLICITIES, GRID_SLICES, COMPRESSIONS.') + + grids = [] + grids_compression = [] + plot_dirs = [] + processed_grids = [] + interpolators = [] + export_path = [] + rerun_path = [] + df = pd.DataFrame() + for l, grid_type in enumerate(GRID_TYPES): + + METALLICITIES_ = METALLICITIES[l] + GRID_SLICES_ = GRID_SLICES[l] + COMPRESSIONS_ = COMPRESSIONS[l] + + for metallicity in METALLICITIES_: + for i, grid_slice in enumerate(GRID_SLICES_): + for compression in COMPRESSIONS_: + if step_name == 'step_1': + grids.append(os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, metallicity, grid_slice)) + grids_compression.extend([compression]) + elif step_name == 'step_2': + if N != len(GRIDS_COMBINED): + raise ValueError('len(GRID_TYPES) != len(GRIDS_COMBINED)!') + if len(GRID_SLICES[l]) != len(GRIDS_COMBINED[l]): + raise ValueError('len(GRID_SLICES[l]) != len(GRIDS_COMBINED[l])!') + combine_grid_slices = [] + # grid_slice is a batch + for grid_slice_ in grid_slice: + path_to_grid = os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, metallicity, + compression, grid_slice_+'.h5') + combine_grid_slices.append(path_to_grid) + path_to_grid_combined = os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, + metallicity, + compression, + GRIDS_COMBINED[l][i]+'.h5') + df_tmp = pd.DataFrame() + df_tmp[path_to_grid_combined] = combine_grid_slices + df = pd.concat([df,df_tmp], axis=1) + elif step_name == 'step_3': + grids.append(os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, metallicity, + compression, grid_slice+'.h5')) + plot_dirs.append(os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, metallicity, + 'plots', grid_slice)) + elif step_name == 'step_4': + grids.append(os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, metallicity, + compression, grid_slice+'.h5')) + elif step_name == 'step_5': + grids.append(os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, metallicity, + compression, grid_slice+'.h5')) + processed_grids.append(os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, metallicity, + compression, grid_slice+'_processed.h5')) + elif step_name == 'step_6': + for method in INTERPOLATION_METHODS: + grids.append(os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, metallicity, + compression, grid_slice+'.h5')) + interpolators.append(os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, metallicity, + 'interpolation_objects', + 'IF_'+method+'.pkl')) + elif step_name == 'step_7': + grids.append(os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, metallicity, + compression, grid_slice+'.h5')) + export_path.append(os.path.join(PATH, 'POSYDON_data', + grid_type, + metallicity+'.h5')) + for method in [['linear','linear3c_kNN'], + ['1NN','1NN_1NN']]: + grids.append(os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, metallicity, + 'interpolation_objects', + 'IF_'+method[0]+'.pkl')) + export_path.append(os.path.join(PATH, 'POSYDON_data', + grid_type, + 'interpolators', + method[1], + metallicity+'.pkl')) + elif step_name == 'rerun': + grids.append(os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, metallicity, + compression, grid_slice+'.h5')) + rerun_path.append(os.path.join(PATH_TO_GRIDS, grid_type, + VERSION, metallicity, + 'rerun_'+RERUN_TYPE+'_'+grid_slice)) + + # saving dataset to csv file + if step_name != 'step_2': + grids = np.array(grids) + df['path_to_grid'] = grids + if step_name == 'step_1': + df['compression'] = grids_compression + elif step_name == 'step_3': + df['path_to_plot'] = plot_dirs + elif step_name == 'step_5': + df['path_to_processed_grid'] = processed_grids + elif step_name == 'step_6': + df['path_to_interpolator'] = interpolators + elif step_name == 'step_7': + df['export_path'] = export_path + elif step_name == 'rerun': + df['rerun_path'] = rerun_path + # drop lines when grid directories are not found + if DROP_MISSING_FILES: + drop_rows = [] + for row in range(df.shape[0]): + path = df.loc[row,'path_to_grid'] + if not os.path.exists(path): + drop_rows.append(row) + if any(drop_rows): + print('') + print(f'----------- {step_name} -----------') + print('The following grids will not be processed ' + 'because the files are missing! If this warning message is ' + 'unexpected to you, please check the file paths!') + for i in drop_rows: + print(df.loc[i,'path_to_grid']) + print('') + df = df.drop(index=drop_rows) + else: + # step 2 + # drop columns when psygrid files are not found + if DROP_MISSING_FILES: + drop_columns = [] + for column in df.keys(): + for path in df[column].dropna().to_list(): + if not os.path.exists(path): + drop_columns.append(column) + if any(drop_columns): + print('') + print(f'----------- {step_name} -----------') + print('The following grids will not be combined as one or more ' + 'grid slices are missing! If this warning message is ' + 'unexpected to you, please check that step 1 occoured ' + 'succesffully!') + print(drop_columns) + print('') + df = df.drop(columns=drop_columns) + + output_fname = f'{step_name}.csv' + df.to_csv(os.path.join(PATH, output_fname), index=False) + +if __name__ == '__main__': + + if len(sys.argv) >= 2: + ini_file_path = str(sys.argv[1]) + if '.' not in ini_file_path: + ini_file_path = './' + ini_file_path + + pipeline = PostProcessingPipeline(ini_file_path) + pipeline.create_csv_and_slurm_job_files() + pipeline.create_logs_dir() + pipeline.create_export_dir() + + # TODO: + # - add a linked slurm job arrays which are waiting for each others + # - DROP_MISSING_FILES should be an imput of each step diff --git a/grid_params/pipeline_quest.ini b/grid_params/pipeline_quest.ini new file mode 100644 index 0000000000..885d6da456 --- /dev/null +++ b/grid_params/pipeline_quest.ini @@ -0,0 +1,109 @@ + +[account] + ACCOUNT = 'b1119' + PARTITION = 'posydon-priority' + WALLTIME = '24:00:00' + MAILTYPE = 'FAIL' + EMAIL = 'simone.bavera@unige.ch' + + +[pipeline setup] + PATH_TO_GRIDS = '/projects/b1119/POSYDON_GRIDS/' + VERSION = 'v2' # 'v2' in quest and '' in yggdrasil + PATH = '.' # working dir + + + VERBOSE = True + CREATE_GRID_SLICES = True + COMBINE_GRID_SLICES = True + PLOT_GRIDS = True + CHECK_FAILURE_RATE = True + POST_PROCESSING = True + TRAIN_INTERPOLATORS = True + EXPORT_DATASET = True + RERUN = True + + # EXTRA PARAMETERS + # TODO: move to be only input of step_1 + CO_HMS_GRID_START_AT_RLO = 0 # set this to False for CO-HMS reruns + +[step_1] + # e.g. ['CO-HMS_RLO','CO-HeMS','HMS-HMS'] + GRID_TYPES = ['HMS-HMS'] + + METALLICITIES = [['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun','1e-03_Zsun']] + GRID_SLICES = [ # HMS-HMS + ['grid_low_res_0','grid_low_res_1','grid_low_res_2', + 'grid_low_res_3','grid_low_res_4','grid_low_res_5', + #'grid_low_res_rerun_opacitymax' + ] + ] + COMPRESSIONS = [['LITE','ORIGINAL']] + DROP_MISSING_FILES = True + +[step_2] + GRID_TYPES = ['HMS-HMS'] + METALLICITIES = [['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun','1e-03_Zsun']] + GRID_SLICES = [# HMS-HMS + [['grid_low_res_0','grid_low_res_1','grid_low_res_2', + 'grid_low_res_3','grid_low_res_4','grid_low_res_5'], + #['grid_low_res_combined','grid_low_res_rerun_opacitymax'] + ] + ] + GRIDS_COMBINED = [# HMS-HMS + ['grid_low_res_combined', + #'grid_low_res_combined_rerun_1' + ] + ] + COMPRESSIONS = [['LITE','ORIGINAL']] + DROP_MISSING_FILES = False + +[step_3] + GRID_TYPES = ['HMS-HMS'] + METALLICITIES = [['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun','1e-03_Zsun']] + GRID_SLICES = [['grid_low_res_combined', + #'grid_low_res_rerun_opacitymax', + #'grid_low_res_combined_rerun_1' + ]] + COMPRESSIONS = [['LITE']] + DROP_MISSING_FILES = False + +[step_4] + GRID_TYPES = ['HMS-HMS'] + METALLICITIES = [['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun','1e-03_Zsun']] + GRID_SLICES = [['grid_low_res_combined', + #'grid_low_res_rerun_opacitymax', + #'grid_low_res_combined_rerun_1' + ]] + COMPRESSIONS = [['LITE']] + DROP_MISSING_FILES = False + +[step_5] + GRID_TYPES = ['HMS-HMS'] + METALLICITIES = [['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun','1e-03_Zsun']] + GRID_SLICES = [['grid_low_res_combined']] + COMPRESSIONS = [['LITE']] + DROP_MISSING_FILES = False + +[step_6] + GRID_TYPES = ['HMS-HMS'] + METALLICITIES = [['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun','1e-03_Zsun']] + GRID_SLICES = [['grid_low_res_combined_processed']] + INTERPOLATION_METHODS = ["linear","1NN"] + COMPRESSIONS = [['LITE']] + DROP_MISSING_FILES = False + +[step_7] + GRID_TYPES = ['HMS-HMS'] + METALLICITIES = [['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun','1e-03_Zsun']] + GRID_SLICES = [['grid_low_res_combined_processed']] + COMPRESSIONS = [['LITE']] + DROP_MISSING_FILES = False + +[rerun] + GRID_TYPES = ['HMS-HMS'] + METALLICITIES = [['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun','1e-03_Zsun']] + GRID_SLICES = [['grid_low_res_combined']] + COMPRESSIONS = [['LITE']] + DROP_MISSING_FILES = True + RERUN_TYPE = 'PISN' diff --git a/grid_params/pipeline_yggdrasil.ini b/grid_params/pipeline_yggdrasil.ini new file mode 100644 index 0000000000..318f59d5e4 --- /dev/null +++ b/grid_params/pipeline_yggdrasil.ini @@ -0,0 +1,208 @@ + +[account] + ACCOUNT = 'meynet' + PARTITION = 'public-cpu' + WALLTIME = '24:00:00' + MAILTYPE = 'FAIL' + EMAIL = 'simone.bavera@unige.ch' + + +[pipeline setup] + PATH_TO_GRIDS = '/srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/' + VERSION = '' # 'v2' in quest and '' in yggdrasil + PATH = '.' # working dir + + + VERBOSE = True + CREATE_GRID_SLICES = True + COMBINE_GRID_SLICES = True + PLOT_GRIDS = True + CHECK_FAILURE_RATE = True + POST_PROCESSING = True + TRAIN_INTERPOLATORS = True + EXPORT_DATASET = True + RERUN = True + + # EXTRA PARAMETERS + # TODO: move to be only input of step_1 + CO_HMS_GRID_START_AT_RLO = 0 # set this to False for CO-HMS reruns + +[step_1] + GRID_TYPES = ['CO-HMS_RLO','CO-HeMS','HMS-HMS'] + METALLICITIES = [# CO-HMS_RLO + ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', + '1e-03_Zsun','1e-04_Zsun'], + # CO-HeMS + ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', + '1e-03_Zsun','1e-04_Zsun'], + # HMS-HMS + ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', + '1e-03_Zsun','1e-04_Zsun']] + GRID_SLICES = [# CO-HMS_RLO + ['grid_low_res_0','grid_low_res_1','grid_low_res_2', + 'grid_random_1'], + # CO-HeMS + ['grid_low_res_0','grid_low_res_1','grid_low_res_2', + 'grid_random_1'], + # HMS-HMS + ['grid_low_res_0','grid_low_res_1','grid_low_res_2', + 'grid_low_res_3','grid_low_res_4','grid_low_res_5', + 'grid_random_1'] + ] + COMPRESSIONS = [['LITE','ORIGINAL'],['LITE','ORIGINAL'],['LITE','ORIGINAL']] + DROP_MISSING_FILES = True + +[step_2] + GRID_TYPES = ['CO-HMS_RLO','CO-HeMS','HMS-HMS'] + METALLICITIES = [# CO-HMS_RLO + ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', + '1e-03_Zsun','1e-04_Zsun'], + # CO-HeMS + ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', + '1e-03_Zsun','1e-04_Zsun'], + # HMS-HMS + ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', + '1e-03_Zsun','1e-04_Zsun']] + GRID_SLICES = [# CO-HMS_RLO + [['grid_low_res_0','grid_low_res_1','grid_low_res_2']], + # CO-HeMS + [['grid_low_res_0','grid_low_res_1','grid_low_res_2']], + # HMS-HMS + [['grid_low_res_0','grid_low_res_1','grid_low_res_2', + 'grid_low_res_3','grid_low_res_4','grid_low_res_5']] + ] + GRIDS_COMBINED = [# CO-HMS_RLO + ['grid_low_res_combined'], + # CO-HeMS + ['grid_low_res_combined'], + # HMS-HMS + ['grid_low_res_combined'] + ] + COMPRESSIONS = [['LITE','ORIGINAL'],['LITE','ORIGINAL'],['LITE','ORIGINAL']] + DROP_MISSING_FILES = False + +[step_3] + GRID_TYPES = ['CO-HMS_RLO','CO-HeMS','HMS-HMS'] + METALLICITIES = [# CO-HMS_RLO + ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', + '1e-03_Zsun','1e-04_Zsun'], + # CO-HeMS + ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', + '1e-03_Zsun','1e-04_Zsun'], + # HMS-HMS + ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', + '1e-03_Zsun','1e-04_Zsun']] + GRID_SLICES = [# CO-HMS_RLO + ['grid_low_res_combined','grid_random_1'], + # CO-HeMS + ['grid_low_res_combined','grid_random_1'], + # HMS-HMS + ['grid_low_res_combined','grid_random_1'] + ] + COMPRESSIONS = [['LITE'],['LITE'],['LITE']] + DROP_MISSING_FILES = False + +[step_4] + GRID_TYPES = ['CO-HMS_RLO','CO-HeMS','HMS-HMS'] + METALLICITIES = [# CO-HMS_RLO + ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', + '1e-03_Zsun','1e-04_Zsun'], + # CO-HeMS + ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', + '1e-03_Zsun','1e-04_Zsun'], + # HMS-HMS + ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', + '1e-03_Zsun','1e-04_Zsun']] + GRID_SLICES = [# CO-HMS_RLO + ['grid_low_res_combined','grid_random_1'], + # CO-HeMS + ['grid_low_res_combined','grid_random_1'], + # HMS-HMS + ['grid_low_res_combined','grid_random_1'] + ] + COMPRESSIONS = [['LITE'],['LITE'],['LITE']] + DROP_MISSING_FILES = False + +[step_5] + GRID_TYPES = ['CO-HMS_RLO','CO-HeMS','HMS-HMS'] + METALLICITIES = [# CO-HMS_RLO + ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', + '1e-03_Zsun','1e-04_Zsun'], + # CO-HeMS + ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', + '1e-03_Zsun','1e-04_Zsun'], + # HMS-HMS + ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', + '1e-03_Zsun','1e-04_Zsun']] + GRID_SLICES = [# CO-HMS_RLO + ['grid_low_res_combined'], + # CO-HeMS + ['grid_low_res_combined'], + # HMS-HMS + ['grid_low_res_combined'] + ] + COMPRESSIONS = [['LITE'],['LITE'],['LITE']] + DROP_MISSING_FILES = False + +[step_6] + GRID_TYPES = ['CO-HMS_RLO','CO-HeMS','HMS-HMS'] + METALLICITIES = [# CO-HMS_RLO + ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', + '1e-03_Zsun','1e-04_Zsun'], + # CO-HeMS + ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', + '1e-03_Zsun','1e-04_Zsun'], + # HMS-HMS + ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', + '1e-03_Zsun','1e-04_Zsun']] + GRID_SLICES = [# CO-HMS_RLO + ['grid_low_res_combined_processed'], + # CO-HeMS + ['grid_low_res_combined_processed'], + # HMS-HMS + ['grid_low_res_combined_processed']] + INTERPOLATION_METHODS = ["linear","1NN"] + COMPRESSIONS = [['LITE'],['LITE'],['LITE']] + DROP_MISSING_FILES = False + +[step_7] + GRID_TYPES = ['CO-HMS_RLO','CO-HeMS','HMS-HMS'] + METALLICITIES = [# CO-HMS_RLO + ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', + '1e-03_Zsun','1e-04_Zsun'], + # CO-HeMS + ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', + '1e-03_Zsun','1e-04_Zsun'], + # HMS-HMS + ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', + '1e-03_Zsun','1e-04_Zsun']] + GRID_SLICES = [# CO-HMS_RLO + ['grid_low_res_combined_processed'], + # CO-HeMS + ['grid_low_res_combined_processed'], + # HMS-HMS + ['grid_low_res_combined_processed']] + COMPRESSIONS = [['LITE'],['LITE'],['LITE']] + DROP_MISSING_FILES = False + +[rerun] + GRID_TYPES = ['CO-HMS_RLO','CO-HeMS','HMS-HMS'] + METALLICITIES = [# CO-HMS_RLO + ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', + '1e-03_Zsun','1e-04_Zsun'], + # CO-HeMS + ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', + '1e-03_Zsun','1e-04_Zsun'], + # HMS-HMS + ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', + '1e-03_Zsun','1e-04_Zsun']] + GRID_SLICES = [# CO-HMS_RLO + ['grid_low_res_combined','grid_random_1'], + # CO-HeMS + ['grid_low_res_combined','grid_random_1'], + # HMS-HMS + ['grid_low_res_combined','grid_random_1'], + ] + COMPRESSIONS = [['LITE'],['LITE'],['LITE']] + DROP_MISSING_FILES = True + RERUN_TYPE = 'PISN' From e86e2fe257bb4b78228acd20e806b07a8b214f26 Mon Sep 17 00:00:00 2001 From: sgossage Date: Thu, 30 Mar 2023 10:06:03 -0500 Subject: [PATCH 070/319] Three new magnetic braking prescriptions in step_detached.py (#43) * Update README.md * Update install.rst * Update install.rst * Added three extra magnetic braking prescriptions in deffeq(), along with options to select from them, and added relevant parameters used in their calculations to this function's arguments. All calculated dOmega/dt from magnetic braking are in units of [yr-2]. * Added three extra magnetic braking prescriptions in deffeq(). Updated docstring, and conv_tau is now converted to years from sec in get_star_data --------- Co-authored-by: Simone Bavera <32518238+ssbvr@users.noreply.github.com> --- posydon/binary_evol/DT/step_detached.py | 249 +++++++++++++++++++++--- 1 file changed, 223 insertions(+), 26 deletions(-) diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index cf312a9aa9..07f3aea491 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -158,6 +158,7 @@ def __init__( do_tides=True, do_gravitational_radiation=True, do_magnetic_braking=True, + magnetic_braking_mode=1, do_stellar_evolution_and_spin_from_winds=True, RLO_orbit_at_orbit_with_same_am=False ): @@ -169,7 +170,8 @@ def __init__( self.do_wind_loss = do_wind_loss self.do_tides = do_tides self.do_gravitational_radiation = do_gravitational_radiation - self.do_magnetic_braking = do_magnetic_braking + self.do_magnetic_braking = do_magnetic_braking, + self.magnetic_braking_mode = magnetic_braking_mode, self.do_stellar_evolution_and_spin_from_winds = ( do_stellar_evolution_and_spin_from_winds ) @@ -187,6 +189,7 @@ def __init__( do_tides, do_gravitational_radiation, do_magnetic_braking, + magnetic_braking_mode, do_stellar_evolution_and_spin_from_winds) self.translate = { "time": "time", @@ -999,6 +1002,9 @@ def get_star_data(binary, star1, star2, htrack, co): age, kvalue["inertia"] / (const.msol * const.rsol**2)) interp1d["Idot"] = interp1d["inertia"].derivative() + interp1d["conv_env_turnover_time_l_b"] = PchipInterpolator2( + age, kvalue['conv_env_turnover_time_l_b'] / const.secyer) + interp1d["L"] = PchipInterpolator(age, 10 ** kvalue["log_L"]) interp1d["R"] = PchipInterpolator(age, 10 ** kvalue["log_R"]) interp1d["t_max"] = t_max @@ -1245,6 +1251,8 @@ def get_omega(star): for key in KEYS[1:11] ], interp1d_sec["Idot"](t - t_offset_sec), + interp1d_sec["conv_env_turnover_time_l_b"](t - t_offset_sec), + interp1d_sec["surf_avg_omega_div_omega_crit"](t - t_offset_sec), interp1d_pri["R"](t - t_offset_pri), interp1d_pri["L"](t - t_offset_pri), *[ @@ -1252,10 +1260,13 @@ def get_omega(star): for key in KEYS[1:11] ], interp1d_pri["Idot"](t - t_offset_pri), + interp1d_pri["conv_env_turnover_time_l_b"](t - t_offset_pri), + interp1d_pri["surf_avg_omega_div_omega_crit"](t - t_offset_pri), self.do_wind_loss, self.do_tides, self.do_gravitational_radiation, self.do_magnetic_braking, + self.magnetic_braking_mode, self.do_stellar_evolution_and_spin_from_winds # ,self.verbose ), @@ -1283,6 +1294,8 @@ def get_omega(star): for key in KEYS[1:11] ], interp1d_sec["Idot"](t - t_offset_sec), + interp1d_sec["conv_env_turnover_time_l_b"](t - t_offset_sec), + interp1d_sec["surf_avg_omega_div_omega_crit"](t - t_offset_sec), interp1d_pri["R"](t - t_offset_pri), interp1d_pri["L"](t - t_offset_pri), *[ @@ -1290,10 +1303,13 @@ def get_omega(star): for key in KEYS[1:11] ], interp1d_pri["Idot"](t - t_offset_pri), + interp1d_pri["conv_env_turnover_time_l_b"](t - t_offset_pri), + interp1d_pri["surf_avg_omega_div_omega_crit"](t - t_offset_pri), self.do_wind_loss, self.do_tides, self.do_gravitational_radiation, self.do_magnetic_braking, + self.magnetic_braking_mode, self.do_stellar_evolution_and_spin_from_winds # ,self.verbose ), @@ -1794,6 +1810,8 @@ def diffeq( DR_env_sec, Renv_middle_sec, Idot_sec, + tau_conv_sec, + wdivwc_sec, R_pri, L_pri, M_pri, @@ -1811,10 +1829,13 @@ def diffeq( DR_env_pri, Renv_middle_pri, Idot_pri, + tau_conv_pri, + wdivwc_pri, do_wind_loss=True, do_tides=True, do_gravitational_radiation=True, do_magnetic_braking=True, + magnetic_braking_mode= "RVJ83", do_stellar_evolution_and_spin_from_winds=True, verbose=False, ): @@ -1849,8 +1870,15 @@ def diffeq( Radius of the star in Rsolar units. I : float Moment of inertia of the star in Msolar*Rsolar^2. + tau_conv: float + Convective turnover time of the star, calculated @ + 0.5*pressure_scale_height above the bottom of the outer convection + zone in yr. + wdivwc: float + The ratio of angular rotation rate (omega) to critical angular + rotation rate (omega_crit). This is dimensionless. L : float - Luminosity of the star in solar units. + Luminosity of the star in solar units. #mass_conv_core : float # Convective core mass of the secondary in Msolar units. conv_mx1_top_r : float @@ -1885,6 +1913,12 @@ def diffeq( do_magnetic_braking: Boolean If True, take into account change of star spin due to magnetic braking. Default: True. + magnetic_braking_mode: String + A string corresponding to the desired magnetic braking prescription. + -- RVJ83: Rappaport, Verbunt, & Joss 1983 + -- M15: Matt et al. 2015 + -- G18: Garraffo et al. 2018 + -- CARB: Van & Ivanova 2019 do_stellar_evolution_and_spin_from_winds: Boolean If True, take into account change of star spin due to change of its moment of inertia during its evolution and due to spin angular momentum @@ -1906,6 +1940,10 @@ def diffeq( .. [2] Hut, P. 1981, A&A, 99, 126 .. [3] Junker, W., & Schafer, G. 1992, MNRAS, 254, 146 .. [4] Rappaport, S., Joss, P. C., & Verbunt, F. 1983, ApJ, 275, 713 + .. [5] Matt et al. 2015, ApJ, 799, L23 + .. [6] Garraffo et al. 2018, ApJ, 862, 90 + .. [7] Van & Ivanova 2019, ApJ, 886, L31 + .. [8] Gossage et al. 2021, ApJ, 912, 65 """ y[0] = np.max([y[0], 0]) # We limit separation to non-negative values @@ -2247,38 +2285,197 @@ def diffeq( # Magnetic braking if do_magnetic_braking: - # domega_mb / dt = torque_mb / I, - # where torque_mb is in eq.36 of Rapport+1983, with γ = 4 - dOmega_mb_sec = ( - -6.82e34 + # domega_mb / dt = torque_mb / I is calculated below. + # All results are in units of [yr^-2], i.e., the amount of change + # in Omega over 1 year. + + if magnetic_braking_mode == "RVJ83": + # Torque from Rappaport, Verbunt, and Joss 1983, ApJ, 275, 713 + # The torque is eq.36 of Rapport+1983, with γ = 4 + # Torque units converted from cgs units to [Msol], [Rsol], [yr] + # as all stellar parameters are given in units of [Msol], [Rsol], [yr] + # and so that dOmega_mb/dt is in units of [yr^-2]. + + dOmega_mb_sec = ( + -3.8e30 * (const.rsol**2 / const.secyer) * M_sec - * R_sec ** 4 - * (Omega_sec * 365.25 / (2 * np.pi)) ** 3 - * 1.48763e-49 + * R_sec**4 + * Omega_sec**3 / I_sec * np.clip((1.5 - M_sec) / (1.5 - 1.3), 0, 1) - ) - dOmega_mb_pri = ( - -6.82e34 + ) + + dOmega_mb_pri = ( + -3.8e30 * (const.rsol**2 / const.secyer) * M_pri - * R_pri ** 4 - * (Omega_pri * 365.25 / (2 * np.pi)) ** 3 - * 1.48763e-49 + * R_pri**4 + * Omega_pri**3 / I_pri * np.clip((1.5 - M_pri) / (1.5 - 1.3), 0, 1) - ) - # -3.8*10e-30 - # * M - # * R ** 4 - # * Omega ** 3 - # / I - # * (const.rsol)**2 * (const.secyer)**4 - # (rsol^2 because I ~ MR^2 in the denominator and secyer^4 - # from omega^3 and to make domega_mb in rad/yr) + ) + + # Converting units: + # The constant 3.8e-30 from Rappaport+1983 has units of [cm^-2 s] + # which need to be converted... + # + # -3.8e-30 [cm^-2 s] * (const.rsol**2 / const.secyer) -> [Rsol^-2 yr] + # * M [Msol] + # * R ** 4 [Rsol^4] + # * Omega ** 3 [yr^-3] + # / I [Msol Rsol^2 ] + # + # Thus, dOmega/dt comes out to [yr^-2] + + elif magnetic_braking_mode == "M15": + + # Torque prescription from Matt et al. 2015, ApJ, 799, L23 + # Constants: + # [erg] or [g cm^2 s^-2] -> [Msol Rsol^2 yr^-2] + K = 1.4e30 * const.secyer**2 /(const.msol * const.rsol**2) + # m = 0.22 + # p = 2.6 + # Above constants were calibrated as in Gossage et al. 2021, ApJ, 912, 65 + # Below, constants are otherwise as assumed as in Matt et al. 2015, ApJ, 799, L23 + omega_sol = 2.6e-6 * const.secyer # [s^-1] -> [yr^-1] + # solar rossby = 2 + # solar convective turnover time = 12.9 days + # Rossby number saturation threshold = 0.14 + chi = 2.0 / 0.14 + tau_conv_sol = 12.9 / 365.25 # 12.9 [days] -> [yr] + + Prot_pri = 2 * np.pi / Omega_pri # [yr] + Rossby_number_pri = Prot_pri / tau_conv_pri + Prot_sec = 2 * np.pi / Omega_sec # [yr] + Rossby_number_sec = Prot_sec / tau_conv_sec + + gamma_pri = (1 + (wdivwc_pri / 0.072)**2)**0.5 + T0_pri = K * R_pri**3.1 * M_pri**0.5 * gamma_pri**(-2 * 0.22) + gamma_sec = (1 + (wdivwc_sec / 0.072)**2)**0.5 + T0_sec = K * R_sec**3.1 * M_sec**0.5 * gamma_sec**(-2 * 0.22) + + if (Rossby_number_sec < 0.14): + dOmega_mb_sec = ( + T0_sec * (chi**2.6) * (Omega_sec / omega_sol) / I_sec + * np.clip((1.5 - M_sec) / (1.5 - 1.3), 0, 1) + ) + else: + dOmega_mb_sec = ( + T0_sec * ((tau_conv_sec/tau_conv_sol)**2.6) + * ((Omega_sec/omega_sol)**(2.6 + 1)) / I_sec + * np.clip((1.5 - M_sec) / (1.5 - 1.3), 0, 1) + ) + + if (Rossby_number_pri < 0.14): + dOmega_mb_pri = ( + T0_pri * (chi**2.6) * (Omega_pri / 2.6e-6) / I_pri + * np.clip((1.5 - M_pri) / (1.5 - 1.3), 0, 1) + ) + else: + dOmega_mb_pri = ( + T0_pri * ((tau_conv_pri/tau_conv_sol)**2.6) + * ((Omega_pri/omega_sol)**(2.6 + 1)) / I_pri + * np.clip((1.5 - M_pri) / (1.5 - 1.3), 0, 1) + ) + + elif magnetic_braking_mode == "G18": + + # Torque prescription from Garraffo et al. 2018, ApJ, 862, 90 + # a = 0.03 + # b = 0.5 + c = 3e41 / (const.msol * const.rsol**2) # [g cm^2] -> [Msol Rsol^2] + # Above are as calibrated in Gossage et al. 2021, ApJ, 912, 65 + + Prot_pri = 2 * np.pi / Omega_pri # [yr] + Rossby_number_pri = Prot_pri / tau_conv_pri + Prot_sec = 2 * np.pi / Omega_sec # [yr] + Rossby_number_sec = Prot_sec / tau_conv_sec + + n_pri = (0.03 / Rossby_number_pri) \ + + 0.5 * Rossby_number_pri + 1.0 + n_sec = (0.03 / Rossby_number_sec) \ + + 0.5 * Rossby_number_sec + 1.0 + + Qn_pri = 4.05 * np.exp(-1.4 * n_pri) + Qn_sec = 4.05 * np.exp(-1.4 * n_sec) + + dOmega_mb_sec = ( + c * Omega_sec**3 * tau_conv_sec * Qn_sec / I_sec + * np.clip((1.5 - M_sec) / (1.5 - 1.3), 0, 1) + ) + + dOmega_mb_pri = ( + c * Omega_pri**3 * tau_conv_pri * Qn_pri / I_pri + * np.clip((1.5 - M_sec) / (1.5 - 1.3), 0, 1) + ) + + elif magnetic_braking_mode == "CARB": + + # Torque prescription from Van & Ivanova 2019, ApJ, 886, L31 + # Based on files hosted on Zenodo: https://zenodo.org/record/3647683#.Y_TfedLMKUk, + # with units converted from [cm], [g], [s] to [Rsol], [Msol], [yr] + + # Constants as assumed in Van & Ivanova 2019, ApJ, 886, L31 + omega_sol = 3e-6 * const.secyer # [s^-1] -> [yr^-1] + tau_conv_sol = 2.8e6 / const.secyer # [s] -> yr + K2 = 0.07**2 + + tau_ratio_sec = tau_conv_sec / tau_conv_sol + tau_ratio_pri = tau_conv_pri / tau_conv_sol + rot_ratio_sec = Omega_sec / omega_sol + rot_ratio_pri = Omega_pri / omega_sol + + # below in units of [Rsol yr^-1]^2 + v_esc2_sec = (2 * const.standard_cgrav * M_sec / R_sec) * (const.msol * const.secyer**2 / const.rsol**3) + v_esc2_pri = (2 * const.standard_cgrav * M_pri / R_pri) * (const.msol * const.secyer**2 / const.rsol**3) + v_mod2_sec = v_esc2_sec + (2 * Omega_sec**2 * R_sec**2) / K2 + v_mod2_pri = v_esc2_pri + (2 * Omega_pri**2 * R_pri**2) / K2 + + # Van & Ivanova 2019, MNRAS 483, 5595 replace the magnetic field with + # Omega * tau_conv phenomenology. Thus, the ratios (rot_ratio_* and tau_ratio_*) + # inherently have units of Gauss [cm^-0.5 g^0.5 s^-1] that needs to be converted to [Rsol], [Msol], [yr]. + # VI2019 assume the solar magnetic field strength is on average 1 Gauss. + if (abs(Mdot_sec) > 0): + R_alfven_div_R3_sec = R_sec**4 * rot_ratio_sec**4 * tau_ratio_sec**4 \ + / (Mdot_sec**2 * v_mod2_sec) * (const.rsol**2 * const.secyer / const.msol**2) + else: + R_alfven_div_R3_sec = 0.0 + + if (abs(Mdot_pri) > 0): + R_alfven_div_R3_pri = R_pri**4 * rot_ratio_pri**4 * tau_ratio_pri**4 \ + / (Mdot_pri**2 * v_mod2_pri) * (const.rsol**2 * const.secyer / const.msol**2) + else: + R_alfven_div_R3_pri = 0.0 + + # Alfven radius in [Rsol] + R_alfven_sec = R_sec * R_alfven_div_R3_sec**(1./3.) + R_alfven_pri = R_pri * R_alfven_div_R3_pri**(1./3.) + + dOmega_mb_sec = ( + (2./3.) * Omega_sec * Mdot_sec * R_alfven_sec**2 / I_sec + * np.clip((1.5 - M_sec) / (1.5 - 1.3), 0, 1) + ) + + dOmega_mb_pri = ( + (2./3.) * Omega_pri * Mdot_pri * R_alfven_pri**2 / I_pri + * np.clip((1.5 - M_sec) / (1.5 - 1.3), 0, 1) + ) + + else: + print("WARNING: Magnetic braking is not being calculated in the detached step. ", + "The given magnetic_braking_mode string \"", magnetic_braking_mode, + "\" does not match the available built-in cases. ", + "To enable magnetic braking, please set magnetc_braking_mode to ", + "one of the following strings:") + print("\"RVJ83\" for Rappaport, Verbunt, & Joss 1983") + print("\"G18\" for Garraffo et al. 2018") + print("\"M15\" for Matt et al. 2015") + print("\"CARB\" for Van & Ivanova 2019") + if verbose: + print("magnetic_braking_mode = ", magnetic_braking_mode) print("dOmega_mb = ", dOmega_mb_sec, dOmega_mb_pri) - dOmega_sec = dOmega_sec + dOmega_mb_sec - dOmega_pri = dOmega_pri + dOmega_mb_pri + dOmega_sec = dOmega_sec + dOmega_mb_sec + dOmega_pri = dOmega_pri + dOmega_mb_pri if do_stellar_evolution_and_spin_from_winds: # Due to the secondary's own evolution, we have: From 61683791e217278bd28b9dec136abc9ca0844469 Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Sat, 1 Apr 2023 15:53:50 +0200 Subject: [PATCH 071/319] Update setup.py --- setup.py | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.py b/setup.py index 0a7cb10832..983716329a 100644 --- a/setup.py +++ b/setup.py @@ -2,6 +2,7 @@ from __future__ import print_function import glob +import sys import versioneer import os.path From b190fe19e0d98f220edeb0752179dc1352e0106a Mon Sep 17 00:00:00 2001 From: ZepeiX <77278187+ZepeiX@users.noreply.github.com> Date: Thu, 6 Apr 2023 14:02:04 +0200 Subject: [PATCH 072/319] Zepei reverse logic (#51) * Update run-pipeline --- bin/run-pipeline | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) diff --git a/bin/run-pipeline b/bin/run-pipeline index 5a7580d798..46acb82bfb 100644 --- a/bin/run-pipeline +++ b/bin/run-pipeline @@ -605,7 +605,7 @@ def copy_ini_file(grid, rerun_type, destination, cluster): elif rerun_type == 'PISN': replace_text = "matthias_PISN-d68228338b91fd487ef5d55c9b6ebb8cc5f0e668" elif rerun_type == 'reverse_MT': - return + replace_text = "zepei_fix_implicit-afa1860ddf9894aa1d82742ee2a73e8e92acd4a9" elif rerun_type == 'envelope_mass_limit': # maybe 'fe_core_infall_limit' as well? return else: @@ -655,7 +655,18 @@ def logic_rerun(grid, rerun_type): runs_to_rerun += [i] runs_to_rerun = np.array(runs_to_rerun) elif rerun_type == 'reverse_MT': - runs_to_rerun = np.array([0, 1]) # implement logic + N_runs = len(grid) + runs_to_rerun = [] + for i in range(N_runs): + if grid[i].binary_history is not None and grid[i].history1 is not None: + rl1 = grid[i].binary_history['rl_relative_overflow_1'] + rl2 = grid[i].binary_history['rl_relative_overflow_2'] + w_wcrit = grid[i].history1['surf_avg_omega_div_omega_crit'] + reverse = np.logical_and(rl1<-0.05, rl2>-0.05) + transfer = np.logical_and(reverse, w_wcrit>0.9) + if np.max(transfer) == True: + runs_to_rerun += [i] + runs_to_rerun = np.array(runs_to_rerun) elif rerun_type == 'envelope_mass_limit': # maybe 'fe_core_infall_limit' as well? runs_to_rerun = np.array([0, 1]) # implement logic else: From 59b243b1d6f4b45c45ebb6b638d139d4c87c3a01 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 6 Apr 2023 16:32:04 +0200 Subject: [PATCH 073/319] Plot add PISN (#50) * Add (P)PISN to TF1 Add the new termination flags: - 'Primary enters pair-instability regime' - 'Secondary enters pair-instability regime' - 'Primary enters pulsational pair-instability regime' - 'Secondary enters pulsational pair-instability regime' * Add (P)PISN to stable TF1 Add (P)PISN to pool of stable TF1 * Add (P)PISN to debug Add (P)PISN to debug flag, too * Add missing L2 flag Add flag: - 'overflow from L2 (R_L2) surface for q(=Macc/Mdon)>1, donor is star 2' * Add missing L2 overflow Add to unstable pool: - 'overflow from L2 (R_L2) surface for q(=Macc/Mdon)>1, donor is star 2' --- posydon/visualization/combine_TF.py | 6 ++++++ posydon/visualization/plot_defaults.py | 22 ++++++++++++++++++++++ 2 files changed, 28 insertions(+) diff --git a/posydon/visualization/combine_TF.py b/posydon/visualization/combine_TF.py index 4c896c7d1d..17bffad764 100644 --- a/posydon/visualization/combine_TF.py +++ b/posydon/visualization/combine_TF.py @@ -5,6 +5,7 @@ "Simone Bavera ", "Konstantinos Kovlakas ", "Emmanouil Zapartas ", + "Matthias Kruckow ", ] @@ -12,6 +13,10 @@ TF1_POOL_STABLE = ['Primary has depleted central carbon', 'Secondary has depleted central carbon', + 'Primary enters pair-instability regime', + 'Secondary enters pair-instability regime', + 'Primary enters pulsational pair-instability regime', + 'Secondary enters pulsational pair-instability regime', 'offcenter neon ignition for primary', 'offcenter neon ignition for secondary', 'envelope_mass_limit', @@ -26,6 +31,7 @@ 'overflow from L2 (R_L2) surface for q(=Macc/Mdon)>1, donor is star 1', 'overflow from L2 (D_L2) distance for q(=Macc/Mdon)<1, donor is star 1', 'overflow from L2 (R_L2) surface for q(=Macc/Mdon)<1, donor is star 2', + 'overflow from L2 (R_L2) surface for q(=Macc/Mdon)>1, donor is star 2', 'reached maximum mass transfer rate: 10.0d0', 'Reached maximum mass transfer rate: 1d-1', 'Reached the critical mt rate', diff --git a/posydon/visualization/plot_defaults.py b/posydon/visualization/plot_defaults.py index c71a2eddce..15afe4840a 100644 --- a/posydon/visualization/plot_defaults.py +++ b/posydon/visualization/plot_defaults.py @@ -14,6 +14,7 @@ "Devina Misra ", "Scott Coughlin ", "Emmanouil Zapartas ", + "Matthias Kruckow ", ] @@ -116,6 +117,14 @@ ['s', 2, None, TF1_label_stable], 'Secondary has depleted central carbon': ['o', 2, None, TF1_label_stable], + 'Primary enters pair-instability regime': + ['s', 2, None, TF1_label_stable], + 'Secondary enters pair-instability regime': + ['o', 2, None, TF1_label_stable], + 'Primary enters pulsational pair-instability regime': + ['s', 2, None, TF1_label_stable], + 'Secondary enters pulsational pair-instability regime': + ['o', 2, None, TF1_label_stable], 'offcenter neon ignition for primary': ['s', 2, None, TF1_label_stable], 'offcenter neon ignition for secondary': @@ -148,6 +157,8 @@ ['D', 1, color_unstable, TF1_label_unstable], 'overflow from L2 (R_L2) surface for q(=Macc/Mdon)<1, donor is star 2': ['D', 1, color_unstable, TF1_label_unstable], + 'overflow from L2 (R_L2) surface for q(=Macc/Mdon)>1, donor is star 2': + ['D', 1, color_unstable, TF1_label_unstable], 'reached maximum mass transfer rate: 10.0d0': ['D', 1, color_unstable, TF1_label_unstable], 'Reached maximum mass transfer rate: 1d-1': @@ -424,6 +435,14 @@ ['s', 2, None, TF1_label_stable], 'Secondary has depleted central carbon': ['o', 2, None, TF1_label_stable], + 'Primary enters pair-instability regime': + ['s', 2, None, TF1_label_stable], + 'Secondary enters pair-instability regime': + ['o', 2, None, TF1_label_stable], + 'Primary enters pulsational pair-instability regime': + ['s', 2, None, TF1_label_stable], + 'Secondary enters pulsational pair-instability regime': + ['o', 2, None, TF1_label_stable], 'offcenter neon ignition for primary': ['s', 2, None, TF1_label_stable], 'offcenter neon ignition for secondary': @@ -453,6 +472,9 @@ 'donor is star 1': ['D', 1, None, TF1_label_unstable], 'overflow from L2 (R_L2) surface for q(=Macc/Mdon)<1, ' + 'donor is star 2': + ['D', 1, None, TF1_label_unstable], + 'overflow from L2 (R_L2) surface for q(=Macc/Mdon)>1, ' 'donor is star 2': ['D', 1, None, TF1_label_unstable], 'reached maximum mass transfer rate: 10.0d0': From d9266086f15099c0438f9ab9f2c5be41225c3b57 Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Tue, 18 Apr 2023 17:03:53 +0200 Subject: [PATCH 074/319] add PISN commit for opacity_max fix (#59) --- bin/run-pipeline | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/bin/run-pipeline b/bin/run-pipeline index 46acb82bfb..594cea4e16 100644 --- a/bin/run-pipeline +++ b/bin/run-pipeline @@ -599,14 +599,14 @@ def copy_ini_file(grid, rerun_type, destination, cluster): raise ValueError(f'Unsupported cluster {cluster}!') # substitue the inlist sceario with the one for the rerun - if rerun_type == 'opacity_max': - # this rerun uses the default inlist commit - return - elif rerun_type == 'PISN': + if rerun_type == 'PISN': replace_text = "matthias_PISN-d68228338b91fd487ef5d55c9b6ebb8cc5f0e668" elif rerun_type == 'reverse_MT': replace_text = "zepei_fix_implicit-afa1860ddf9894aa1d82742ee2a73e8e92acd4a9" - elif rerun_type == 'envelope_mass_limit': # maybe 'fe_core_infall_limit' as well? + elif rerun_type == 'opacity_max': + replace_text = "matthias_PISN-d68228338b91fd487ef5d55c9b6ebb8cc5f0e668" + elif rerun_type == 'other': # e.g. 'envelope_mass_limit', 'fe_core_infall_limit' + # this rerun uses the default inlist commit return else: raise ValueError(f'Unsupported rerun type {rerun_type}!') From ec7222afb11802fbc78cbd5c583f53b54100f5ff Mon Sep 17 00:00:00 2001 From: Camille Liotine Date: Thu, 20 Apr 2023 09:24:19 -0500 Subject: [PATCH 075/319] Fixed more dict syntax for kwargs in docs (#53) In SingleStar object docs --- docs/SingleStar/SingleStar.rst | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/SingleStar/SingleStar.rst b/docs/SingleStar/SingleStar.rst index 4e65c85bb3..f0c15f0a4e 100644 --- a/docs/SingleStar/SingleStar.rst +++ b/docs/SingleStar/SingleStar.rst @@ -86,9 +86,9 @@ The simplest method is to provide `kwargs` of the initial stellar parameters. .. code-block:: python - kwargs = {state='MS', - mass=10.0, - metallicity=0.014) - SingleStar(kwargs) + kwargs = {'state' : 'MS', + 'mass' : 10.0, + 'metallicity' : 0.014} + SingleStar(**kwargs) Now, the SingleStar object is ready to be used. From 5c693dd90297a7ec0dc12beade99aa208ec3a311 Mon Sep 17 00:00:00 2001 From: Camille Liotine Date: Thu, 20 Apr 2023 09:24:32 -0500 Subject: [PATCH 076/319] Fixed dict syntax for kwargs1, kwargs2 definitions (#52) --- docs/BinaryStar/BinaryStar.rst | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/BinaryStar/BinaryStar.rst b/docs/BinaryStar/BinaryStar.rst index 1677b11149..e5f9003b9b 100644 --- a/docs/BinaryStar/BinaryStar.rst +++ b/docs/BinaryStar/BinaryStar.rst @@ -91,15 +91,15 @@ The simplest method is to provide the two star objects and `kwargs` of the initi .. code-block:: python - kwargs1 = {state='MS', - mass=20.0, - metallicity=0.014} + kwargs1 = {'state' : 'MS', + 'mass' : 20.0, + 'metallicity' : 0.014} star_1 = SingleStar(**kwargs1) - kwargs2 = {state='MS', - mass=10.0, - metallicity=0.014} + kwargs2 = {'state' : 'MS', + 'mass' : 10.0, + 'metallicity' : 0.014} star_2 = SingleStar(**kwargs2) From c19918a78df2aaa176fa88cc9ab98d63a051b06b Mon Sep 17 00:00:00 2001 From: ssbvr Date: Wed, 26 Apr 2023 15:33:45 +0200 Subject: [PATCH 077/319] add inlist commit for hms-hms opacity max fix --- bin/run-pipeline | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/bin/run-pipeline b/bin/run-pipeline index 594cea4e16..ae5d291da5 100644 --- a/bin/run-pipeline +++ b/bin/run-pipeline @@ -605,6 +605,8 @@ def copy_ini_file(grid, rerun_type, destination, cluster): replace_text = "zepei_fix_implicit-afa1860ddf9894aa1d82742ee2a73e8e92acd4a9" elif rerun_type == 'opacity_max': replace_text = "matthias_PISN-d68228338b91fd487ef5d55c9b6ebb8cc5f0e668" + elif rerun_type == 'opacity_max_hms-hms': + replace_text = "zepei_fix_implicit-afa1860ddf9894aa1d82742ee2a73e8e92acd4a9" elif rerun_type == 'other': # e.g. 'envelope_mass_limit', 'fe_core_infall_limit' # this rerun uses the default inlist commit return @@ -630,7 +632,7 @@ def logic_rerun(grid, rerun_type): termination_flags=None new_mesa_flag = None - if rerun_type == 'opacity_max': + if rerun_type == 'opacity_max' or rerun_type == 'opacity_max_hms-hms': termination_flags=['reach cluster timelimit', 'min_timestep_limit'] new_mesa_flag={'opacity_max' : 0.5} elif rerun_type == 'PISN': From 97b26269f12bf29af29107558aeb92f50196a8e3 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 4 May 2023 16:36:48 +0200 Subject: [PATCH 078/319] some grid fixes (#60) * Add initial_z to be read from directory name Add initial_z to be read from directory name, which is the case for v2 and newer versions. * Try to get metallicity form directory name Try to get the metallicity value from the directory name in case, there is no history file. * Correct typo step_7 -> step_6 * Add to reset DISPLAY for plotting Add to reset DISPLAY for plotting step on a cluster to avoid problems when logged in with window mode on ssh. * Check for initial RLO in join_grids Check for initial RLO in join_grids, too. Use a joint initial RLO boundary. * Debug Flush output for debugging * Fix bug in adding too many boundary systems Fix bug in adding too many boundary systems * Remove debugging Remove debugging * Fix list entry replacement (psygrid.py) Fix list entry replacement in psygrid.py * Fix list entry replacement (termination_flags.py) Fix list entry replacement in termination_flags.py * Add STEPID Add the number of the step to the slurm job-name * Update last developement changes (#62) * Fixed more dict syntax for kwargs in docs (#53) In SingleStar object docs * Fixed dict syntax for kwargs1, kwargs2 definitions (#52) --------- Co-authored-by: Camille Liotine * account for suggestions - add else (nevertheless it will raise an error message later; I thought I predefined STEPID, but it looks like I put it out/or missed to put it in; using job_name here as content of the STEPID is a bad idea, because it is expected to be a single character and not a string) - add the check for the length after the split (again, this would cause an error message later nevertheless) * rename variable e -> exists_already * rename e -> exists_already --------- Co-authored-by: Camille Liotine --- bin/setup-pipeline | 11 ++++++-- posydon/grids/io.py | 4 +-- posydon/grids/psygrid.py | 44 ++++++++++++++++++++++++++++++ posydon/grids/termination_flags.py | 24 ++++++++-------- 4 files changed, 67 insertions(+), 16 deletions(-) diff --git a/bin/setup-pipeline b/bin/setup-pipeline index 39e2bd0934..78a421081a 100644 --- a/bin/setup-pipeline +++ b/bin/setup-pipeline @@ -175,6 +175,12 @@ def slurm_job(job_name, path_to_csv_file = os.path.join(PATH,f"{job_name}.csv") with open(f'{job_name}.slurm', 'w') as f: + if ('step' in job_name) and (len(job_name.split("_")) > 1): + STEPID = job_name.split("_")[1] + elif job_name == 'rerun': + STEPID = 'R' + else: + STEPID = '' f.write("#!/bin/bash\n") f.write(f"#SBATCH --account={ACCOUNT}\n") f.write(f"#SBATCH --partition={PARTITION}\n") @@ -182,7 +188,7 @@ def slurm_job(job_name, f.write("#SBATCH --cpus-per-task 1\n") f.write("#SBATCH --ntasks-per-node 1\n") f.write(f"#SBATCH --time={WALLTIME}\n") - f.write("#SBATCH --job-name=psygrid\n") + f.write(f"#SBATCH --job-name=psygrid{STEPID}\n") f.write("#SBATCH --mem-per-cpu=4G\n") if EMAIL is not None: @@ -209,6 +215,7 @@ def slurm_job(job_name, if job_name == 'step_3': f.write(f"#SBATCH --output={PATH}/logs/plot_grid.out\n") + f.write("unset DISPLAY\n") if job_name == 'step_4': f.write(f"#SBATCH --output={PATH}/logs/check_failure_rate.out\n") @@ -217,7 +224,7 @@ def slurm_job(job_name, f.write(f"#SBATCH --output={PATH}/logs/post_processing_%a.out\n") f.write(f"export PATH_TO_POSYDON={PATH_TO_POSYDON}\n") - if job_name == 'step_7': + if job_name == 'step_6': f.write(f"#SBATCH --output={PATH}/logs/train_interpolators_%a.out\n") if job_name == 'step_7': diff --git a/posydon/grids/io.py b/posydon/grids/io.py index 011c21bca7..22dbac1ebb 100644 --- a/posydon/grids/io.py +++ b/posydon/grids/io.py @@ -384,9 +384,9 @@ def initial_values_from_dirname(mesa_dir): """Use the name of the directory for inferring the main initial values.""" dirname = str(os.path.basename(os.path.normpath(mesa_dir))) if "initial_mass" in dirname: # single-star grid - variable_names = ["initial_mass"] + variable_names = ["initial_mass", "initial_z"] else: # binary-star grid - variable_names = ["m1", "m2", "initial_period_in_days"] + variable_names = ["m1", "m2", "initial_period_in_days", "initial_z"] for variable_name in variable_names: assert variable_name in dirname diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index ef0c471251..01091310e8 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -879,6 +879,10 @@ def decide_columns(key_in_config, defaults): # if not star history file, NaN values if read_from is None: init_X, init_Y, init_Z = np.nan, np.nan, np.nan + # try to get metallicity from directory name + params_from_path = initial_values_from_dirname(run.path) + if (len(params_from_path)==4) or (len(params_from_path)==2): + init_Z = params_from_path[-1] else: star_header = np.genfromtxt( read_from, skip_header=1, max_rows=1, names=True) @@ -2049,6 +2053,27 @@ def say(something): say(" {} substituions detected.".format(n_substitutions)) say(" {} runs to be joined.".format(len(initial_params))) + if (newconfig["initial_RLO_fix"]): + say("Determine initial RLO boundary from all grids") + detected_initial_RLO = [] + colnames = ["termination_flag_1", "termination_flag_2", "interpolation_class"] + valtoset = ["forced_initial_RLO", "forced_initial_RLO", "initial_MT"] + for grid in grids: + new_detected_initial_RLO = get_detected_initial_RLO(grid) + for new_sys in new_detected_initial_RLO: + exists_already = False + for i, sys in enumerate(detected_initial_RLO): + # check whether there are double entries + if (abs(sys["star_1_mass"]-new_sys["star_1_mass"])<1.0e-5 and + abs(sys["star_2_mass"]-new_sys["star_2_mass"])<1.0e-5): + exists_already = True + # if so, replace old entry if the new one has a larger period + if sys["period_days"] Date: Thu, 4 May 2023 16:41:05 +0200 Subject: [PATCH 079/319] Add slurm dependency (#64) * Add initial_z to be read from directory name Add initial_z to be read from directory name, which is the case for v2 and newer versions. * Try to get metallicity form directory name Try to get the metallicity value from the directory name in case, there is no history file. * Correct typo step_7 -> step_6 * Add to reset DISPLAY for plotting Add to reset DISPLAY for plotting step on a cluster to avoid problems when logged in with window mode on ssh. * Check for initial RLO in join_grids Check for initial RLO in join_grids, too. Use a joint initial RLO boundary. * Debug Flush output for debugging * Fix bug in adding too many boundary systems Fix bug in adding too many boundary systems * Remove debugging Remove debugging * Fix list entry replacement (psygrid.py) Fix list entry replacement in psygrid.py * Fix list entry replacement (termination_flags.py) Fix list entry replacement in termination_flags.py * Add STEPID Add the number of the step to the slurm job-name * Update last developement changes (#62) * Fixed more dict syntax for kwargs in docs (#53) In SingleStar object docs * Fixed dict syntax for kwargs1, kwargs2 definitions (#52) --------- Co-authored-by: Camille Liotine * account for suggestions - add else (nevertheless it will raise an error message later; I thought I predefined STEPID, but it looks like I put it out/or missed to put it in; using job_name here as content of the STEPID is a bad idea, because it is expected to be a single character and not a string) - add the check for the length after the split (again, this would cause an error message later nevertheless) * rename variable e -> exists_already * rename e -> exists_already * Add run_pipeline.sh - create the bash script "run_pipeline.sh", which starts all the setup steps with dependency * Automatic kill on failure set option to kill pending jobs, where the depended job failed * Check all steps, which can run in parallel - allow step_5 to go in parallel to step_3, too --------- Co-authored-by: Camille Liotine --- bin/setup-pipeline | 67 +++++++++++++++++++++++++++++++--------------- 1 file changed, 45 insertions(+), 22 deletions(-) diff --git a/bin/setup-pipeline b/bin/setup-pipeline index 78a421081a..3365fdbb2c 100644 --- a/bin/setup-pipeline +++ b/bin/setup-pipeline @@ -96,30 +96,53 @@ class PostProcessingPipeline: ) - for action, step_number in ACTION_TO_STEP_NUM.items(): - do_step_bool = setup_kwargs[action] - - if setup_kwargs['VERBOSE']: - print( "\n\n{:-^45s} {:^8s}:{:^6s}".format( - action, step_number, str(do_step_bool)) ) - - if do_step_bool: - step_kwargs = self.pipeline_kwargs[step_number] + with open('run_pipeline.sh', 'w') as runfile: + runfile.write("#!/bin/bash\n") + last_step = '' + for action, step_number in ACTION_TO_STEP_NUM.items(): + do_step_bool = setup_kwargs[action] if setup_kwargs['VERBOSE']: - print( pformat(step_kwargs, indent=2) ) - - - create_csv( - step_name=step_number, - **{**step_kwargs, **setup_kwargs} - ) - slurm_job( - job_name=step_number, - step_name=step_number, - PATH_TO_POSYDON=self.PATH_TO_POSYDON, - **{**step_kwargs, **setup_kwargs, **account_kwargs} - ) + print( "\n\n{:-^45s} {:^8s}:{:^6s}".format( + action, step_number, str(do_step_bool)) ) + + if do_step_bool: + step_kwargs = self.pipeline_kwargs[step_number] + + if setup_kwargs['VERBOSE']: + print( pformat(step_kwargs, indent=2) ) + + + create_csv( + step_name=step_number, + **{**step_kwargs, **setup_kwargs} + ) + slurm_job( + job_name=step_number, + step_name=step_number, + PATH_TO_POSYDON=self.PATH_TO_POSYDON, + **{**step_kwargs, **setup_kwargs, **account_kwargs} + ) + if last_step == '': + # if there was no previous step the current step has no dependency + runfile.write(f"ID{step_number}=$(sbatch --parsable {step_number}.slurm)\n") + else: + if setup_kwargs['COMBINE_GRID_SLICES'] and (step_number == 'step_4' or step_number == 'step_5'): + # steps which can run in paralell after step_2 (steps 4 and 5 can run parallel with step_3) + runfile.write(f"ID{step_number}=$(sbatch --parsable --dependency=afterok:$" + "{IDstep_2}"f" {step_number}.slurm) --kill-on-invalid-dep=yes\n") + elif setup_kwargs['CREATE_GRID_SLICES'] and (step_number == 'step_4' or step_number == 'step_5'): + # steps which can run in paralell after step_1 (in case there is no step_2) + runfile.write(f"ID{step_number}=$(sbatch --parsable --dependency=afterok:$" + "{IDstep_1}"f" {step_number}.slurm) --kill-on-invalid-dep=yes\n") + else: + # steps waiting for the previous step + runfile.write(f"ID{step_number}=$(sbatch --parsable --dependency=afterok:$" + "{"f"ID{last_step}""}"f" {step_number}.slurm) --kill-on-invalid-dep=yes\n") + runfile.write(f"echo '{step_number}.slurm submitted as '$" + "{"f"ID{step_number}""}"f"\n") + last_step = step_number + os.system("chmod 755 run_pipeline.sh") def create_logs_dir(self): # create logs in working dir to store all slurm outputs From 7ac03f3d4a50ee310c89b13fa1dc2d83262f4a92 Mon Sep 17 00:00:00 2001 From: ZepeiX <77278187+ZepeiX@users.noreply.github.com> Date: Thu, 4 May 2023 16:53:49 +0200 Subject: [PATCH 080/319] Small changes in common function and matching post-MS to He in detached step (#45) * Update common_functions.py * Update step_detached.py * Update step_mesa.py * Update step_detached.py --- posydon/binary_evol/DT/step_detached.py | 81 +++++++++++++++++++++++-- posydon/binary_evol/MESA/step_mesa.py | 20 ++++-- posydon/utils/common_functions.py | 18 +++--- 3 files changed, 103 insertions(+), 16 deletions(-) diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index 07f3aea491..b898a3cc2a 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -688,11 +688,11 @@ def get_mist0(self, star, htrack): (sc_he_core_mass_H.transform( get_track_val(MESA_label[0], htrack, *x)) - sc_he_core_mass_H.transform(posydon_attribute[0])) - / sc_he_core_mass_H.transform(posydon_attribute[0])) - ** 2 + ((sc_mass_H.transform( + ) + ** 2 + (sc_mass_H.transform( get_track_val(MESA_label[1], htrack, *x)) - - sc_mass_H.transform(posydon_attribute[1])) - / sc_mass_H.transform(posydon_attribute[1])) ** 2 + - sc_mass_H.transform(posydon_attribute[1]) + ) ** 2 + (sc_center_he4_H.transform( get_track_val(MESA_label[2], htrack, *x)) - sc_center_he4_H.transform(posydon_attribute[2])) ** 2 @@ -702,6 +702,79 @@ def get_mist0(self, star, htrack): x0, method="TNC", bounds=bnds, ) + + if (np.abs(sol.fun) > tolerance_mist_integration + or not sol.success): + star.htrack = False + MESA_label = ["he_core_mass", "center_he4", "log_R"] + posydon_attribute = [star.he_core_mass, + star.center_he4, star.log_R] + rs = [10.0, 1.0, 2.0] + if self.verbose: + print("Matching attributes and their normalizations : ", + MESA_label, rs) + for i in MESA_label: + if i not in self.root_keys: + raise Exception("Expected matching parameter not added" + " in the MIST model options.") + x0 = get_root0( + MESA_label, posydon_attribute, star.htrack, rs=rs) + bnds = ([m_min_He, m_max_He], [0, None]) + sol = minimize( + lambda x: ( + (sc_he_core_mass_He.transform( + get_track_val(MESA_label[0], star.htrack, *x)) + - sc_he_core_mass_He.transform( + posydon_attribute[0])) + / sc_he_core_mass_He.transform( + posydon_attribute[0])) ** 2 + + (sc_center_he4_He.transform( + get_track_val(MESA_label[1], star.htrack, *x)) + - sc_center_he4_He.transform( + posydon_attribute[1])) + ** 2 + (sc_log_R_He.transform( + get_track_val(MESA_label[2], star.htrack, *x)) + - sc_log_R_He.transform( + posydon_attribute[2])) ** 2, + x0, method="TNC", bounds=bnds, + ) + if ((self.get_track_val("mass", star.htrack, *sol.x) + - self.get_track_val( + "he_core_mass", star.htrack, *sol.x)) + / self.get_track_val( + "mass", star.htrack, *sol.x) >= 0.01): + initials = (np.nan, np.nan) + + if (np.abs(sol.fun) > tolerance_mist_integration + or not sol.success): + star.htrack = True + MESA_label = ["he_core_mass", "mass"] + posydon_attribute = [star.he_core_mass, + star.mass] + rs = [10.0, 20.0] + if self.verbose: + print("Matching attributes and their normalizations : ", + MESA_label, rs) + for i in MESA_label: + if i not in self.root_keys: + raise Exception("Expected matching parameter not added" + " in the MIST model options.") + x0 = get_root0( + MESA_label, posydon_attribute, star.htrack, rs=rs) + bnds = ([m_min_He, m_max_He], [0, None]) + sol = minimize( + lambda x: ( + (sc_he_core_mass_H.transform( + get_track_val(MESA_label[0], star.htrack, *x)) + - sc_he_core_mass_H.transform(posydon_attribute[0])) + ) + ** 2 + (sc_mass_H.transform( + get_track_val(MESA_label[1], star.htrack, *x)) + - sc_mass_H.transform(posydon_attribute[1]) + ) ** 2, + x0, method="TNC", bounds=bnds, + ) + elif star.state in LIST_ACCEPTABLE_STATES_FOR_HeStar: MESA_label = [ "he_core_mass", diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index ef8d37edad..584c6f7ffb 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -580,12 +580,24 @@ def update_properties_NN(self, star_1_CO=False, star_2_CO=False, history_of_attribute = cb_bh[key_p][:-1] getattr(star, key_h).extend(history_of_attribute) elif key == 'spin': - v_key = getattr(star, 'spin') - setattr(star, key, v_key) + key_p = 'star_%d_mass' % (k+1) + mass_history = np.array(cb_bh[key_p]) + spin_prev_step = getattr(star, 'spin') + spin = cf.spin_stable_mass_transfer(spin_prev_step, mass_history[0], + mass_history[-1]) + setattr(star, key, spin) if self.save_initial_conditions: - getattr(star, key_h).append(v_key) + history_of_attribute =[ + cf.spin_stable_mass_transfer(spin_prev_step ,mass_history[0], + mass_history[i]) + for i in range(len(mass_history)-1)] + getattr(star, key_h).append(history_of_attribute[0]) if track_interpolation: - getattr(star, key_h).extend([v_key]*length_hist) + history_of_attribute =[ + cf.spin_stable_mass_transfer(spin_prev_step, mass_history[0], + mass_history[i]) + for i in range(len(mass_history)-1)] + getattr(star, key_h).extend(history_of_attribute) elif key == 'log_R': key_p = 'star_%d_mass' % (k+1) mass = cb_bh[key_p][-1] diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index df9fa7cea0..9663ebafc2 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -468,7 +468,7 @@ def beaming(binary): def bondi_hoyle(binary, accretor, donor, idx=-1, wind_disk_criteria=True, - scheme='Hurley+2012'): + scheme='Hurley+2002'): """Calculate the Bondi-Hoyle accretion rate of a binary. Parameters @@ -575,7 +575,7 @@ def bondi_hoyle(binary, accretor, donor, idx=-1, wind_disk_criteria=True, slope = (3.7 - 3.25) / (-2.5 + 5.25) else: slope = (3.25 - 3.75) / (-5.25 + 7.25) - v_wind = 10 ** (slope * lg_mdot[i] + 3.25 + 5.25 * slope) * 1000 + v_wind[i] = 10 ** (slope * lg_mdot[i] + 3.25 + 5.25 * slope) * 1000 else: pass @@ -933,7 +933,7 @@ def inspiral_timescale_from_orbital_period(star1_mass, star2_mass, return T_merge -def spin_stable_mass_transfer(star_mass_preMT, star_mass_postMT): +def spin_stable_mass_transfer(spin_i, star_mass_preMT, star_mass_postMT): """Calculate the spin of an accreting BH under stable mass transfer. Based on Thorne 1974 eq. 2a. @@ -941,12 +941,14 @@ def spin_stable_mass_transfer(star_mass_preMT, star_mass_postMT): """ if star_mass_preMT is None or star_mass_postMT is None: return None - + z1 = 1+(1-spin_i**2)**(1/3)*((1+spin_i)**(1/3)+(1-spin_i)**(1/3)) + z2 = (3*spin_i**2+z1**2)**0.5 + r_isco = 3 + z2 - ((3-z1)*(3+z1+2*z2))**0.5 if (1 <= star_mass_postMT / star_mass_preMT - and star_mass_postMT / star_mass_preMT <= 6**0.5): - spin = (2. / 3)**(0.5) * (star_mass_preMT / star_mass_postMT) * ( - 4 - (18 * star_mass_preMT**2 / star_mass_postMT**2 - 2)**(0.5)) - elif star_mass_postMT / star_mass_preMT > 6**(0.5): + and star_mass_postMT / star_mass_preMT <= r_isco**0.5): + spin = r_isco**(0.5) / 3 * (star_mass_preMT / star_mass_postMT) * ( + 4 - (3 * r_isco * star_mass_preMT**2 / star_mass_postMT**2 - 2)**(0.5)) + elif star_mass_postMT / star_mass_preMT > r_isco**(0.5): spin = 1. else: spin = np.nan From 70d0183c38afe75cbda8dd9b3fc21a08264b6cc9 Mon Sep 17 00:00:00 2001 From: Konstantinos Kovlakas <22239544+kkovlakas@users.noreply.github.com> Date: Thu, 4 May 2023 16:54:17 +0200 Subject: [PATCH 081/319] GZIP support for files in MESA grids (#23) * Adding gzip support when reading MESA grids. * Adding GZIP support to psygrid metadata printing. * Adding GZIP support to grid points files. * Adding GZIP support to EEP files. * Clarifications for GZIP files inside the code. * Adding more clarification regarding support of GZIP files. * Adding GZIP support to function converting MESA output to table. * Adding GZIP support when inferring termination flag from MESA run output. * Adding GZIP support in EEP class. * Correcting types in io --- posydon/grids/io.py | 127 +++++++++++++++++------------ posydon/grids/psygrid.py | 3 + posydon/grids/termination_flags.py | 9 +- posydon/interpolation/eep.py | 15 +++- posydon/utils/gridutils.py | 21 ++++- 5 files changed, 115 insertions(+), 60 deletions(-) diff --git a/posydon/grids/io.py b/posydon/grids/io.py index 22dbac1ebb..c2d02e10fd 100644 --- a/posydon/grids/io.py +++ b/posydon/grids/io.py @@ -46,6 +46,7 @@ import os import glob +import gzip import warnings from posydon.utils.gridutils import read_MESA_data_file @@ -56,15 +57,21 @@ "ignored folders": ["make", "star1", "star2", "binary", "data", "new_data", "template", ".ipynb_checkpoints"], # which files contain useful metadata concerning the grid - "grid metadata": ["grid_test.csv"], + "grid metadata": ["grid_test.csv", + "grid_test.csv.gz"], # which files contain useful metadata concerning individual grids - "run metadata": ["inlist_grid_points", "summary.txt", "out.txt"] + "run metadata": ["inlist_grid_points", "summary.txt", "out.txt", + "inlist_grid_point.gz", "summary.txt.gz", "out.txt.gz"] } +# Note that the `.gz` versions of these filenames will also be searched BINARY_OUTPUT_FILE = "out.txt" SINGLE_OUTPUT_FILE = "out_star1_formation_step0.txt" -EEP_FILE_EXTENSIONS = [".data.eep", ".data.clean.eep"] + +# Possible extensions of EEP files (including zipped versions) +EEP_FILE_EXTENSIONS = [".data.eep", ".data.clean.eep", + ".data.eep.gz", ".data.clean.eep.gz"] class RunReader: @@ -119,45 +126,51 @@ def read_posydon_format(self): if self.verbose: print("Reading run in {}".format(self.path)) - def joined_exists(folder, filename): + def joined_exists(folder, filename, allow_gzip=True): """Return the joined path of `folder` and `filename`, if exists.""" fullpath = os.path.join(folder, filename) + fullpath_gz = fullpath + ".gz" if os.path.exists(fullpath): return fullpath + if allow_gzip and os.path.exists(fullpath_gz): + return fullpath_gz return None files = os.listdir(self.path) for file in files: - if file == "binary_history.data": - self.binary_history_path = os.path.join(self.path, file) - elif file == "final_star1.mod": - self.final_star1_path = os.path.join(self.path, file) - elif file == "final_star2.mod": - self.final_star1_path = os.path.join(self.path, file) - elif file == "out.txt" and self.binary: - self.out_txt_path = os.path.join(self.path, file) - elif file == "out_star1_formation_step0.txt" and not self.binary: - self.out_txt_path = os.path.join(self.path, file) + fullpath = os.path.join(self.path, file) + if file in ["binary_history.data", "binary_history.data.gz"]: + self.binary_history_path = fullpath + elif file in ["final_star1.mod", "final_star1.mod.gz"]: + self.final_star1_path = fullpath + elif file in ["final_star2.mod", "final_star2.mod.gz"]: + self.final_star1_path = fullpath + elif file in ["out.txt", "out.txt.gz"] and self.binary: + self.out_txt_path = fullpath + elif ((file in ["out_star1_formation_step0.txt", + "out_star1_formation_step0.txt.gz"]) + and not self.binary): + self.out_txt_path = fullpath elif file in POSYDON_FORMAT_OPTIONS["run metadata"]: - self.metadata_files.append(os.path.join(self.path, file)) + self.metadata_files.append(fullpath) if joined_exists(self.path, 'LOGS1'): if os.path.isdir(os.path.join(self.path, 'LOGS1')): self.history1_path = joined_exists( - self.path, 'LOGS1/history.data') + self.path, 'LOGS1/history.data', allow_gzip=True) self.final_profile1_path = joined_exists( - self.path, 'LOGS1/final_profile.data') + self.path, 'LOGS1/final_profile.data', allow_gzip=True) self.initial_profile1_path = joined_exists( - self.path, 'LOGS1/initial_profile.data') + self.path, 'LOGS1/initial_profile.data', allow_gzip=True) if joined_exists(self.path, 'LOGS2'): if os.path.isdir(os.path.join(self.path, 'LOGS2')): self.history2_path = joined_exists( - self.path, 'LOGS2/history.data') + self.path, 'LOGS2/history.data', allow_gzip=True) self.final_profile2_path = joined_exists( - self.path, 'LOGS2/final_profile.data') + self.path, 'LOGS2/final_profile.data', allow_gzip=True) self.initial_profile2_path = joined_exists( - self.path, 'LOGS2/initial_profile.data') + self.path, 'LOGS2/initial_profile.data', allow_gzip=True) if self.verbose: self.report() @@ -239,7 +252,10 @@ def _get_input_folders(self): print("Searching for MESA runs in `{}`".format(folder_path)) search_string = os.path.join(folder_path, "**/" + out_name) - out_files = glob.glob(search_string, recursive=True) + search_string_gz = search_string + ".gz" + out_files_txt = glob.glob(search_string, recursive=True) + out_files_gz = glob.glob(search_string_gz, recursive=True) + out_files = list(out_files_txt) + list(out_files_gz) for out_file in out_files: fullpath = os.path.dirname(os.path.abspath(out_file)) params_part = initial_values_from_dirname(fullpath) @@ -252,7 +268,10 @@ def _get_input_folders(self): self.metadata_files = [] for meta_path in POSYDON_FORMAT_OPTIONS["grid metadata"]: search_string = os.path.join(folder_path, meta_path) - meta_files = glob.glob(search_string) + search_string_gz = search_string + ".gz" + meta_files_txt = glob.glob(search_string) + meta_files_gz = glob.glob(search_string_gz) + meta_files = list(meta_files_txt) + list(meta_files_gz) self.metadata_files.extend(meta_files) if len(folders) == 0: @@ -340,14 +359,18 @@ def print_meta_contents(path, max_lines=None, max_chars_per_line=80): print("CONTENTS OF METADATA FILE: {}".format(path)) print("-" * 80) - with open(path, "r") as f: - for i, line in enumerate(f): - if max_lines is not None and i >= max_lines: - break - line = line.rstrip("\n") - if max_chars_per_line != "warp": - line = line[:max_chars_per_line] - print(line) + if path.endswith(".gz"): + f = gzip.open(path, "rt") + else: + f = open(path, "r") + for i, line in enumerate(f): + if max_lines is not None and i >= max_lines: + break + line = line.rstrip("\n") + if max_chars_per_line != "warp": + line = line[:max_chars_per_line] + print(line) + f.close() print("-" * 80) @@ -358,25 +381,29 @@ def read_initial_values(mesa_dir): return None initial_values = {} - with open(path, "r") as f: - for line in f: - if "=" not in line: - continue - fields = line.strip().split("=") - if len(fields) != 2: - return None - varname = fields[0].strip() - valueparts = fields[1].split("d") - value = float(valueparts[0].strip()) - if len(valueparts)>1: - value = value*10**float(valueparts[1].strip()) - if varname == "m1": - varname = "star_1_mass" - elif varname == "m2": - varname = "star_2_mass" - elif varname == "initial_period_in_days": - varname = "period_days" - initial_values[varname] = value + if path.endswith(".gz"): + f = gzip.open(path, "rt") + else: + f = open(path, "r") + for line in f: + if "=" not in line: + continue + fields = line.strip().split("=") + if len(fields) != 2: + return None + varname = fields[0].strip() + valueparts = fields[1].split("d") + value = float(valueparts[0].strip()) + if len(valueparts)>1: + value = value*10**float(valueparts[1].strip()) + if varname == "m1": + varname = "star_1_mass" + elif varname == "m2": + varname = "star_2_mass" + elif varname == "initial_period_in_days": + varname = "period_days" + initial_values[varname] = value + f.close() return initial_values diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index 01091310e8..94aee10723 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -415,6 +415,7 @@ def _discover_eeps(self, path): for extension in EEP_FILE_EXTENSIONS: searchfor = os.path.join(path, "*" + extension) for filename in glob.glob(searchfor): + # note that this is consistent with additional `.gz` extension identifier = os.path.basename(filename.split(extension)[-2]) assert identifier not in self.eeps self.eeps[identifier] = filename @@ -825,6 +826,7 @@ def decide_columns(key_in_config, defaults): # get some initial values from the `binary_history.data` header # if of course, no RLO fix is applied if binary_grid and not (start_at_RLO or ignore_data): + # this is compatible with `.gz` files bh_header = np.genfromtxt(run.binary_history_path, skip_header=1, max_rows=1, names=True) @@ -856,6 +858,7 @@ def decide_columns(key_in_config, defaults): initial_BH["binary_separation"] = init_separation elif not binary_grid and not (start_at_RLO or ignore_data): # use header to get initial mass in single-star grids + # this is compatible with `.gz` files h1_header = np.genfromtxt(run.history1_path, skip_header=1, max_rows=1, names=True) diff --git a/posydon/grids/termination_flags.py b/posydon/grids/termination_flags.py index 081113fdc2..f77e3b2423 100644 --- a/posydon/grids/termination_flags.py +++ b/posydon/grids/termination_flags.py @@ -22,6 +22,7 @@ import os +import gzip import warnings import numpy as np @@ -55,8 +56,12 @@ def get_flag_from_MESA_output(MESA_log_path): """ if MESA_log_path is not None and os.path.isfile(MESA_log_path): - with open(MESA_log_path, "r") as log_file: - log_lines = log_file.readlines() + if MESA_log_path.endswith(".gz"): + with gzip.open(MESA_log_path, "rt") as log_file: + log_lines = log_file.readlines() + else: + with open(MESA_log_path, "r") as log_file: + log_lines = log_file.readlines() for line in reversed(log_lines): has_term_code = line.startswith("termination code: ") diff --git a/posydon/interpolation/eep.py b/posydon/interpolation/eep.py index 0440e8831f..d7f762a3a7 100644 --- a/posydon/interpolation/eep.py +++ b/posydon/interpolation/eep.py @@ -10,6 +10,7 @@ ] +import gzip import numpy as np from scipy.interpolate import pchip @@ -26,10 +27,16 @@ def __init__(self, filename, EEP_NAMES=ZAMShi, EEP_INTERVAL=100): """Load an MESA history file and construct the EEP instance.""" self.filename = filename.strip() try: - with open(self.filename, 'r') as f: - self.header1 = f.readline() - self.header2 = f.readline() - self.header3 = f.readline() + if self.filename.endswith(".gz"): + f = gzip.open(self.filename, "rt") + else: + f = open(self.filename, "r") + + self.header1 = f.readline() + self.header2 = f.readline() + self.header3 = f.readline() + f.close() + # this is compatible with `.gz` files tr = np.genfromtxt(self.filename, names=True, skip_header=5) names = tr.dtype.names except IOError: diff --git a/posydon/utils/gridutils.py b/posydon/utils/gridutils.py index 4704012519..04c32aa638 100644 --- a/posydon/utils/gridutils.py +++ b/posydon/utils/gridutils.py @@ -1,10 +1,12 @@ """Various utility functions used for the manipulating grid data.""" -import numpy as np import os -import pandas as pd +import gzip import warnings +import numpy as np +import pandas as pd + from posydon.utils.constants import clight, Msun, Rsun from posydon.utils.constants import standard_cgrav as cgrav from posydon.utils.constants import secyer as secyear @@ -49,6 +51,8 @@ def join_lists(A, B): def read_MESA_data_file(path, columns): """Read specific columns from a MESA output file to an array. + Note that this function also works with `.gz` files. + Parameters ---------- path : str @@ -73,7 +77,7 @@ def read_MESA_data_file(path, columns): def read_EEP_data_file(path, columns): - """Read an EEP file - similar to `read_MESA_data_file()`.""" + """Read an EEP file (can be `.gz`) - similar to `read_MESA_data_file()`.""" return np.atleast_1d(np.genfromtxt(path, skip_header=11, names=True, usecols=columns, invalid_raise=False)) @@ -319,6 +323,8 @@ def convert_output_to_table( "log_L_2", "log_T_2", "He_core_2(Msun)", "C_core_2(Msun)"]): """Convert output of a run, to a pandas dataframe. + Note that this function also works with `.gz` files. + Parameters ---------- output_file : str @@ -369,7 +375,14 @@ def convert_output_to_table( warnings.warn("You have not supplied a star2 history file to parse. " "This will cause all star2 history columns to be dashes") - outdata = os.popen('cat ' + output_file).read() + # TODO: is this necessary to be done with `popen`? + # outdata = os.popen('cat ' + output_file).read() + if output_file.endswith(".gz"): + with gzip.open(output_file, "rt") as f: + outdata = f.read() + else: + with open(output_file, "r") as f: + outdata = f.read() # Checking for individual terminating conditions From 27011b683e97c9cd4ccfd862187765059fbf4360 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Fri, 5 May 2023 13:49:39 +0200 Subject: [PATCH 082/319] Bug fix (#66) move option "--kill-on-invalid-dep=yes" in the brackets --- bin/setup-pipeline | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/bin/setup-pipeline b/bin/setup-pipeline index 3365fdbb2c..33cabd3218 100644 --- a/bin/setup-pipeline +++ b/bin/setup-pipeline @@ -130,15 +130,15 @@ class PostProcessingPipeline: if setup_kwargs['COMBINE_GRID_SLICES'] and (step_number == 'step_4' or step_number == 'step_5'): # steps which can run in paralell after step_2 (steps 4 and 5 can run parallel with step_3) runfile.write(f"ID{step_number}=$(sbatch --parsable --dependency=afterok:$" - "{IDstep_2}"f" {step_number}.slurm) --kill-on-invalid-dep=yes\n") + "{IDstep_2}"f" --kill-on-invalid-dep=yes {step_number}.slurm)\n") elif setup_kwargs['CREATE_GRID_SLICES'] and (step_number == 'step_4' or step_number == 'step_5'): # steps which can run in paralell after step_1 (in case there is no step_2) runfile.write(f"ID{step_number}=$(sbatch --parsable --dependency=afterok:$" - "{IDstep_1}"f" {step_number}.slurm) --kill-on-invalid-dep=yes\n") + "{IDstep_1}"f" --kill-on-invalid-dep=yes {step_number}.slurm)\n") else: # steps waiting for the previous step runfile.write(f"ID{step_number}=$(sbatch --parsable --dependency=afterok:$" - "{"f"ID{last_step}""}"f" {step_number}.slurm) --kill-on-invalid-dep=yes\n") + "{"f"ID{last_step}""}"f" --kill-on-invalid-dep=yes {step_number}.slurm)\n") runfile.write(f"echo '{step_number}.slurm submitted as '$" "{"f"ID{step_number}""}"f"\n") last_step = step_number From 4fac0c271e34fcf6cad932f5eb082efcfb64f191 Mon Sep 17 00:00:00 2001 From: ezapartas Date: Thu, 18 May 2023 17:00:08 +0200 Subject: [PATCH 083/319] isolated evolution step (#35) * started the step_isolated files (2 is for superclass with detached) and first changes in detached and in flow * allowed for different psygrid for isoalted step, selected lists of stellar states, still need to solve absence of logR for mergers * deleted old step_isolated * made step_merging * integrated the step_merged correctly in the flow * before intorducing the option for not using log_R as a matching criteria, having the option do decide the parameters of the matching * added most merger cases, still need to solve logR avsence in matching * finished step_merged combinations * made the new disrupted_step * added spin of merged product * made step_initially_single too and changes of structure after discussion with Konstantinos * latest changes * Eirini's change,name of mist0 to match_to_single_star * making lists for each matching * changes in lambda minimize function to a more general function that can take an arbitrary list * typo * added the option of the different list of attributes in the step_merged * matching should work the same for all subclasses now * syntax errors prob fixed * fixing runtime errors in the new files * some more fixing * Some print statements * change the flowchart to not go to sterp_disurpted if one of the events is detached is called * prints * typo in flowchart * Friday's work * . * fix init orbit * disrupted bianry evolves until CC2 * step SN updated to expect a 2nd CC in an already disrupted system. Matching for postMS and maybe HeStars may still need work * matching fixing * slightly improved the verbose text of the matching * print errors * changes to match_to_single_star * generalized alternative matching * for this PR, step merged does nothing * matching happening only when needed (once) * step merged and intially single go to step end for now * testing cases of ZAMS stars not needed to match * binary fraction * minor changes in isolated or initially single * else statement, self.companion * typo * deleted the option of fidning ZAMS through logR * syntax error fixing * resolving merging issues * went back to grid_Hrich and grid_strippedHe * ini file * ini * surf_avg_omega_div_omega_crit is not always stored, so it is no longer called for and passed to solve_ivp. It is calculated in the Matt+2015 magnetic braking calculation from stellar properties now, as it is only needed there. * del grid from detached params * ini * Removed additional instances where interp1d_sec[surf_avg_omega_div_omega_crit](t - t_offset_sec) was called. * changing the random * removed superfluous commas in defining detached step properties * Adding step_isolated,step_disrupted in the STEP_NAMES_LOADING_GRIDS so the metallicity can propagate in ther kwargs * fix in ini file * I think I went the POSYDON-MESA-INLIST submodule back * changed class names according to CamelCase convention * time profiling * Corrections to the detached verbose with timestamp. * no matching for the CO star, using m0,t0 of the previously matched star * Giving additional option 1 in verbose to print only the matching parameters * Step 1 in cleaning detached step. * Step 2 in cleaing detached step. * Removing duplicate code in square difference function. * Deciding on which column are going to use relative distance during matching. * Deleting obsolete transform method in detached step. * Removing unnecessary transform when scaling columns in detached step. * Using stored scalers and removing some useless self.grid assignments * in the 3rd matching step, I added the change of htrack also for postMS stars * added htrack = star.htrack * commenting out the relative differences during matching for center_he4 * in the 3rd alternative matching we also change the parameters used for matching to the ones according to the new htrack * typo * htrack is updated outside match_to_single_star to be used in the rest of get_star_data * removed simulationproperties from CE folder... --------- Co-authored-by: Eirini Kasdagli Co-authored-by: kasdaglie Co-authored-by: sg Co-authored-by: kkovlakas --- posydon/binary_evol/CE/step_CEE.py | 72 +- posydon/binary_evol/DT/step_detached.py | 1460 +++++++++-------- posydon/binary_evol/DT/step_disrupted.py | 84 + .../binary_evol/DT/step_initially_single.py | 82 + posydon/binary_evol/DT/step_isolated.py | 104 ++ posydon/binary_evol/DT/step_merged.py | 453 +++++ posydon/binary_evol/SN/step_SN.py | 487 +++--- posydon/binary_evol/flow_chart.py | 44 +- posydon/binary_evol/simulationproperties.py | 4 +- posydon/popsyn/binarypopulation.py | 139 +- posydon/popsyn/defaults.py | 5 +- posydon/popsyn/population_params_default.ini | 8 + 12 files changed, 1895 insertions(+), 1047 deletions(-) create mode 100644 posydon/binary_evol/DT/step_disrupted.py create mode 100644 posydon/binary_evol/DT/step_initially_single.py create mode 100644 posydon/binary_evol/DT/step_isolated.py create mode 100644 posydon/binary_evol/DT/step_merged.py diff --git a/posydon/binary_evol/CE/step_CEE.py b/posydon/binary_evol/CE/step_CEE.py index c0ee56b89d..b75adbc84e 100644 --- a/posydon/binary_evol/CE/step_CEE.py +++ b/posydon/binary_evol/CE/step_CEE.py @@ -214,26 +214,11 @@ def __call__(self, binary): 'stripped_He_Core_He_burning']: # system merges binary.state = 'merged' - binary.event = None + if binary.event in ["oCE1", "oDoubleCE1"]: + binary.event = "oMerging1" + if binary.event in ["oCE2", "oDoubleCE2"]: + binary.event = "oMerging2" - for key in BINARYPROPERTIES: - # the binary attributes that are changed in the CE step - if key not in ["state", "event", 'V_sys', 'mass_transfer_case', - 'nearest_neighbour_distance']: - setattr(binary, key, np.nan) # the rest become np.nan - if key == 'mass_transfer_case': - setattr(binary, key, 'None') - stars = [donor_star, comp_star] - # for now we just add all initial mass to the (merger) star_1 - masses = [donor_star.mass + comp_star.mass, np.nan] - star_states = [donor_star.state, comp_star.state] - for star, star_state, mass in zip(stars, star_states, masses): - star.mass = mass - star.state = star_state - for key in STARPROPERTIES: - # the binary attributes that are changed in the CE step - if key not in ["mass", "state"]: - setattr(star, key, np.nan) return if self.common_envelope_option_for_HG_star == "pessimistic": @@ -241,27 +226,11 @@ def __call__(self, binary): if donor_star.state in ['H-rich_Shell_H_burning']: # system merges binary.state = 'merged' - binary.event = None + if binary.event in ["oCE1", "oDoubleCE1"]: + binary.event = "oMerging1" + if binary.event in ["oCE2", "oDoubleCE2"]: + binary.event = "oMerging2" - for key in BINARYPROPERTIES: - # the binary attributes that are changed in the CE step - if key not in ["state", "event", 'V_sys', - 'mass_transfer_case', - 'nearest_neighbour_distance']: - setattr(binary, key, np.nan) # the rest become np.nan - if key == 'mass_transfer_case': - setattr(binary, key, 'None') - stars = [donor_star, comp_star] - # for now we just add all initial mass to the (merger) star_1 - masses = [donor_star.mass + comp_star.mass, np.nan] - star_states = [donor_star.state, comp_star.state] - for star, star_state, mass in zip(stars, star_states, masses): - star.mass = mass - star.state = star_state - for key in STARPROPERTIES: - # the binary attributes that are changed in the CE step - if key not in ["mass", "state"]: - setattr(star, key, np.nan) return # Calculate binary's evolution @@ -834,27 +803,10 @@ def CEE_simple_alpha_prescription( else: # system merges binary.state = 'merged' - binary.event = None - - for key in BINARYPROPERTIES: - # the binary attributes that are changed in the CE step - if key not in ["state", "event", 'V_sys', 'mass_transfer_case', - 'nearest_neighbour_distance']: - setattr(binary, key, np.nan) # the rest become np.nan - if key == 'mass_transfer_case': - setattr(binary, key, 'None') - # binary.event = 'END' - stars = [donor, comp_star] - # for now we just add all initial mass to the (merger) star_1 - masses = [m1_i+m2_i, np.nan] - star_states = [donor.state, comp_star.state] - for star, star_state, mass in zip(stars, star_states, masses): - star.mass = mass - star.state = star_state - for key in STARPROPERTIES: - # the binary attributes that are changed in the CE step - if key not in ["mass", "state"]: - setattr(star, key, np.nan) + if binary.event in ["oCE1", "oDoubleCE1"]: + binary.event = "oMerging1" + if binary.event in ["oCE2", "oDoubleCE2"]: + binary.event = "oMerging2" if verbose: print("system merges due to one of the two star's core filling" diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index b898a3cc2a..84707c784b 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -15,6 +15,7 @@ import os import numpy as np +import time from scipy.integrate import solve_ivp from scipy.interpolate import PchipInterpolator from scipy.optimize import minimize @@ -67,6 +68,157 @@ ] +DEFAULT_TRANSLATION = { + "time": "time", + "orbital_period": "porb", + "eccentricity": "ecc", + "separation": "sep", + "state": None, + "event": None, + "rl_relative_overflow_1": "rl_relative_overflow_1", + "rl_relative_overflow_2": "rl_relative_overflow_2", + "lg_mtransfer_rate": "lg_mtransfer_rate", + "V_sys": None, + "mass": "mass", + "log_R": "log_R", + "R": "R", + "lg_mdot": "mdot", + "log_L": "log_L", + "lg_wind_mdot": "mdot", + "lg_system_mdot": "lg_mdot", + "he_core_mass": "he_core_mass", + "he_core_radius": "he_core_radius", + "c_core_mass": "c_core_mass", + "c_core_radius": "c_core_radius", + "o_core_mass": "o_core_mass", + "o_core_radius": "o_core_radius", + "center_h1": "center_h1", + "center_he4": "center_he4", + "center_c12": "center_c12", + "center_o16": "center_o16", + "center_n14": "center_n14", + "surface_h1": "surface_h1", + "surface_he4": "surface_he4", + "surface_c12": "surface_c12", + "surface_n14": "surface_n14", + "surface_o16": "surface_o16", + "center_gamma": "center_gamma", + "log_LH": "log_LH", + "log_LHe": "log_LHe", + "log_LZ": "log_LZ", + "log_Lnuc": "log_Lnuc", + "c12_c12": "c12_c12", + "avg_c_in_c_core": "avg_c_in_c_core", + "surf_avg_omega_div_omega_crit": "surf_avg_omega_div_omega_crit", + "surf_avg_omega": "omega", + "total_moment_of_inertia": "inertia", + "log_total_angular_momentum": "log_total_angular_momentum", + "profile": None, + "metallicity": None, + "spin": "spin_parameter", + "log_total_angular_momentum": "log_total_angular_momentum", + "conv_env_top_mass": "conv_env_top_mass", + "conv_env_bot_mass": "conv_env_bot_mass", + "conv_env_top_radius": "conv_env_top_radius", + "conv_env_bot_radius": "conv_env_bot_radius", + "conv_env_turnover_time_g": "conv_env_turnover_time_g", + "conv_env_turnover_time_l_b": "conv_env_turnover_time_l_b", + "conv_env_turnover_time_l_t": "conv_env_turnover_time_l_t", + "envelope_binding_energy": "envelope_binding_energy", + "mass_conv_reg_fortides": "mass_conv_reg_fortides", + "thickness_conv_reg_fortides": "thickness_conv_reg_fortides", + "radius_conv_reg_fortides": "radius_conv_reg_fortides", + "lambda_CE_1cent": "lambda_CE_1cent", + "lambda_CE_10cent": "lambda_CE_10cent", + "lambda_CE_30cent": "lambda_CE_30cent", + "co_core_mass": "co_core_mass", + "co_core_radius": "co_core_radius", + "lambda_CE_pure_He_star_10cent": "lambda_CE_pure_He_star_10cent", + "trap_radius": "trap_radius", + "acc_radius": "acc_radius", + "t_sync_rad_1": "t_sync_rad_1", + "t_sync_conv_1": "t_sync_conv_1", + "t_sync_rad_2": "t_sync_rad_2", + "t_sync_conv_2": "t_sync_conv_2", + "mass_transfer_case": None, + "nearest_neighbour_distance": None, +} + + +# TODO: are these supposed to be the same as the keys of the previous +# dictionary? If yes... then we should use them directrly instead of +# redefining the strings here. + +DEFAULT_TRANSLATED_KEYS = ( + 'age', + 'mass', + 'mdot', + 'inertia', + 'conv_mx1_top_r', + 'conv_mx1_bot_r', + 'surface_h1', + 'center_h1', + 'mass_conv_reg_fortides', + 'thickness_conv_reg_fortides', + 'radius_conv_reg_fortides', + 'log_Teff', + 'surface_he3', + 'surface_he4', + 'center_he4', + 'avg_c_in_c_core', + 'log_LH', + 'log_LHe', + 'log_LZ', + 'log_Lnuc', + 'c12_c12', + 'center_c12', + 'he_core_mass', + 'log_L', + 'log_R', + 'c_core_mass', + 'o_core_mass', + 'co_core_mass', + 'c_core_radius', + 'o_core_radius', + 'co_core_radius', + 'spin_parameter', + 'log_total_angular_momentum', + 'center_n14', + 'center_o16', + 'surface_n14', + 'surface_o16', + 'conv_env_top_mass', + 'conv_env_bot_mass', + 'conv_env_top_radius', + 'conv_env_bot_radius', + 'conv_env_turnover_time_g', + 'conv_env_turnover_time_l_b', + 'conv_env_turnover_time_l_t', + 'envelope_binding_energy', + 'lambda_CE_1cent', + 'lambda_CE_10cent', + 'lambda_CE_30cent', + 'lambda_CE_pure_He_star_10cent', + 'center_gamma' +) + + +DEFAULT_PROFILE_KEYS = ( + 'radius', + 'mass', + 'logRho', + 'energy', + 'x_mass_fraction_H', + 'y_mass_fraction_He', + 'z_mass_fraction_metals', + 'neutral_fraction_H', + 'neutral_fraction_He', + 'avg_charge_He' +) + +MATCHING_WITH_RELATIVE_DIFFERENCE = ["center_he4"] + + class detached_step: """Evolve a detached binary. @@ -106,6 +258,12 @@ class detached_step: gravitational wave radiation. do_magnetic_braking: Boolean If True, take into account change of star spin due to magnetic braking. + magnetic_braking_mode: String + A string corresponding to the desired magnetic braking prescription. + -- RVJ83: Rappaport, Verbunt, & Joss 1983 + -- M15: Matt et al. 2015 + -- G18: Garraffo et al. 2018 + -- CARB: Van & Ivanova 2019 do_stellar_evolution_and_spin_from_winds: Boolean If True, take into account change of star spin due to change of its moment of inertia during its evolution and due to spin angular momentum @@ -145,22 +303,26 @@ class detached_step: def __init__( self, + grid_name_Hrich=None, + grid_name_strippedHe=None, metallicity=None, - grid=None, path=PATH_TO_POSYDON_DATA, dt=None, n_o_steps_history=None, matching_method="minimize", initial_mass=None, rootm=None, - verbose=False, + verbose=1, do_wind_loss=True, do_tides=True, do_gravitational_radiation=True, do_magnetic_braking=True, - magnetic_braking_mode=1, + magnetic_braking_mode="RVJ83", do_stellar_evolution_and_spin_from_winds=True, - RLO_orbit_at_orbit_with_same_am=False + RLO_orbit_at_orbit_with_same_am=False, + list_for_matching_HMS=None, + list_for_matching_postMS=None, + list_for_matching_HeStar=None ): """Initialize the step. See class documentation for details.""" self.metallicity = convert_metallicity_to_string(metallicity) @@ -170,16 +332,23 @@ def __init__( self.do_wind_loss = do_wind_loss self.do_tides = do_tides self.do_gravitational_radiation = do_gravitational_radiation - self.do_magnetic_braking = do_magnetic_braking, - self.magnetic_braking_mode = magnetic_braking_mode, + self.do_magnetic_braking = do_magnetic_braking + self.magnetic_braking_mode = magnetic_braking_mode self.do_stellar_evolution_and_spin_from_winds = ( do_stellar_evolution_and_spin_from_winds ) self.RLO_orbit_at_orbit_with_same_am = RLO_orbit_at_orbit_with_same_am - self.grid = grid self.initial_mass = initial_mass self.rootm = rootm self.verbose = verbose + self.list_for_matching_HMS = list_for_matching_HMS + self.list_for_matching_postMS = list_for_matching_postMS + self.list_for_matching_HeStar = list_for_matching_HeStar + + # mapping a combination of (key, htrack, method) to a pre-trained + # DataScaler instance, created the first time it is requested + self.stored_scalers = {} + if verbose: print( dt, @@ -191,137 +360,12 @@ def __init__( do_magnetic_braking, magnetic_braking_mode, do_stellar_evolution_and_spin_from_winds) - self.translate = { - "time": "time", - "orbital_period": "porb", - "eccentricity": "ecc", - "separation": "sep", - "state": None, - "event": None, - "rl_relative_overflow_1": "rl_relative_overflow_1", - "rl_relative_overflow_2": "rl_relative_overflow_2", - "lg_mtransfer_rate": "lg_mtransfer_rate", - "V_sys": None, - "mass": "mass", - "log_R": "log_R", - "R": "R", - "lg_mdot": "mdot", - "log_L": "log_L", - "lg_wind_mdot": "mdot", - "lg_system_mdot": "lg_mdot", - "he_core_mass": "he_core_mass", - "he_core_radius": "he_core_radius", - "c_core_mass": "c_core_mass", - "c_core_radius": "c_core_radius", - "o_core_mass": "o_core_mass", - "o_core_radius": "o_core_radius", - "center_h1": "center_h1", - "center_he4": "center_he4", - "center_c12": "center_c12", - "center_o16": "center_o16", - "center_n14": "center_n14", - "surface_h1": "surface_h1", - "surface_he4": "surface_he4", - "surface_c12": "surface_c12", - "surface_n14": "surface_n14", - "surface_o16": "surface_o16", - "center_gamma": "center_gamma", - "log_LH": "log_LH", - "log_LHe": "log_LHe", - "log_LZ": "log_LZ", - "log_Lnuc": "log_Lnuc", - "c12_c12": "c12_c12", - "avg_c_in_c_core": "avg_c_in_c_core", - "surf_avg_omega_div_omega_crit": "surf_avg_omega_div_omega_crit", - "surf_avg_omega": "omega", - "total_moment_of_inertia": "inertia", - "log_total_angular_momentum": "log_total_angular_momentum", - "profile": None, - "metallicity": None, - "spin": "spin_parameter", - "log_total_angular_momentum": "log_total_angular_momentum", - "conv_env_top_mass": "conv_env_top_mass", - "conv_env_bot_mass": "conv_env_bot_mass", - "conv_env_top_radius": "conv_env_top_radius", - "conv_env_bot_radius": "conv_env_bot_radius", - "conv_env_turnover_time_g": "conv_env_turnover_time_g", - "conv_env_turnover_time_l_b": "conv_env_turnover_time_l_b", - "conv_env_turnover_time_l_t": "conv_env_turnover_time_l_t", - "envelope_binding_energy": "envelope_binding_energy", - "mass_conv_reg_fortides": "mass_conv_reg_fortides", - "thickness_conv_reg_fortides": "thickness_conv_reg_fortides", - "radius_conv_reg_fortides": "radius_conv_reg_fortides", - "lambda_CE_1cent": "lambda_CE_1cent", - "lambda_CE_10cent": "lambda_CE_10cent", - "lambda_CE_30cent": "lambda_CE_30cent", - "co_core_mass": "co_core_mass", - "co_core_radius": "co_core_radius", - "lambda_CE_pure_He_star_10cent": "lambda_CE_pure_He_star_10cent", - "trap_radius": "trap_radius", - "acc_radius": "acc_radius", - "t_sync_rad_1": "t_sync_rad_1", - "t_sync_conv_1": "t_sync_conv_1", - "t_sync_rad_2": "t_sync_rad_2", - "t_sync_conv_2": "t_sync_conv_2", - "mass_transfer_case": None, - "nearest_neighbour_distance": None, - } + + self.translate = DEFAULT_TRANSLATION # these are the KEYS read from POSYDON h5 grid files (after translating # them to the appropriate columns) - self.KEYS = ( - 'age', - 'mass', - 'mdot', - 'inertia', - 'conv_mx1_top_r', - 'conv_mx1_bot_r', - 'surface_h1', - 'center_h1', - 'mass_conv_reg_fortides', - 'thickness_conv_reg_fortides', - 'radius_conv_reg_fortides', - 'log_Teff', - 'surface_he3', - 'surface_he4', - 'center_he4', - 'avg_c_in_c_core', - 'log_LH', - 'log_LHe', - 'log_LZ', - 'log_Lnuc', - 'c12_c12', - 'center_c12', - 'he_core_mass', - 'log_L', - 'log_R', - 'c_core_mass', - 'o_core_mass', - 'co_core_mass', - 'c_core_radius', - 'o_core_radius', - 'co_core_radius', - 'spin_parameter', - 'log_total_angular_momentum', - 'center_n14', - 'center_o16', - 'surface_n14', - 'surface_o16', - 'conv_env_top_mass', - 'conv_env_bot_mass', - 'conv_env_top_radius', - 'conv_env_bot_radius', - 'conv_env_turnover_time_g', - 'conv_env_turnover_time_l_b', - 'conv_env_turnover_time_l_t', - 'envelope_binding_energy', - 'lambda_CE_1cent', - 'lambda_CE_10cent', - 'lambda_CE_30cent', - 'lambda_CE_pure_He_star_10cent', - 'center_gamma' - ) - + self.KEYS = DEFAULT_TRANSLATED_KEYS self.KEYS_POSITIVE = ( 'mass_conv_reg_fortides', 'thickness_conv_reg_fortides', @@ -357,23 +401,85 @@ def __init__( ) # keys for the star profile interpolation - self.profile_keys = ( - 'radius', - 'mass', - 'logRho', - 'energy', - 'x_mass_fraction_H', - 'y_mass_fraction_He', - 'z_mass_fraction_metals', - 'neutral_fraction_H', - 'neutral_fraction_He', - 'avg_charge_He' - ) + self.profile_keys = DEFAULT_PROFILE_KEYS + + if grid_name_Hrich is None: + grid_name_Hrich = os.path.join( + 'single_HMS', self.metallicity+'_Zsun.h5') + self.grid_Hrich = GRIDInterpolator(os.path.join(path, grid_name_Hrich)) + + if grid_name_strippedHe is None: + grid_name_strippedHe = os.path.join( + 'single_HeMS', self.metallicity+'_Zsun.h5') + self.grid_strippedHe = GRIDInterpolator( + os.path.join(path, grid_name_strippedHe)) + + # Initialize the matching lists: + m_min_H = np.min(self.grid_Hrich.grid_mass) + m_max_H = np.max(self.grid_Hrich.grid_mass) + m_min_He = np.min(self.grid_strippedHe.grid_mass) + m_max_He = np.max(self.grid_strippedHe.grid_mass) + if self.list_for_matching_HMS is None: + self.list_for_matching_HMS = [ + ["mass", "center_h1", "log_R", "he_core_mass"], + [20.0, 1.0, 2.0, 10.0], + ["log_min_max", "min_max", "min_max", "min_max"], + [m_min_H, m_max_H], [0, None] + ] + if self.list_for_matching_postMS is None: + self.list_for_matching_postMS = [ + ["mass", "center_he4", "log_R", "he_core_mass"], + [20.0, 1.0, 2.0, 10.0], + ["log_min_max", "min_max", "min_max", "min_max"], + [m_min_H, m_max_H], [0, None] + ] + if self.list_for_matching_HeStar is None: + self.list_for_matching_HeStar = [ + ["he_core_mass", "center_he4", "log_R"], + [10.0, 1.0, 2.0], + ["min_max", "min_max", "min_max"], + [m_min_He, m_max_He], [0, None] + ] - grid_name1 = os.path.join('single_HMS', self.metallicity+'_Zsun.h5') - grid_name2 = os.path.join('single_HeMS', self.metallicity+'_Zsun.h5') - self.grid1 = GRIDInterpolator(os.path.join(path, grid_name1)) - self.grid2 = GRIDInterpolator(os.path.join(path, grid_name2)) + # lists of alternative matching + + # e.g., stars after mass transfer could swell up so that log_R + # is not appropriate for matching + + self.list_for_matching_HMS_alternative = [ + ["mass", "center_h1", "he_core_mass"], + [20.0, 1.0, 10.0], + ["log_min_max", "min_max", "min_max"], + [m_min_H, m_max_H], [0, None] + ] + self.list_for_matching_postMS_alternative = [ + ["mass", "center_h1", "he_core_mass"], + [20.0, 1.0, 10.0], + ["log_min_max", "min_max", "min_max"], + [m_min_H, m_max_H], [0, None] + ] + self.list_for_matching_HeStar_alternative = [ + ["he_core_mass", "center_he4", "log_R"], + [10.0, 1.0, 2.0], + ["min_max", "min_max", "min_max"], + [m_min_He, m_max_He], [0, None] + ] + + def square_difference(self, x, htrack, + mesa_labels, posydon_attributes, colscalers, scales): + """Compute the square distance used for scaling.""" + result = 0.0 + for mesa_label, posy_attr, colscaler, scale_of_mesa_label in zip( + mesa_labels, posydon_attributes, colscalers, scales): + single_track_value = scale_of_mesa_label.transform( + self.get_track_val(mesa_label, htrack, *x)) + posydon_value = scale_of_mesa_label.transform(posy_attr) + if mesa_label in MATCHING_WITH_RELATIVE_DIFFERENCE: + result += ((single_track_value - posydon_value) + / posydon_value) ** 2 + else: + result += (single_track_value - posydon_value) ** 2 + return result def get_track_val(self, key, htrack, m0, t): """Return a single value from the interpolated time-series. @@ -396,12 +502,12 @@ def get_track_val(self, key, htrack, m0, t): """ # htrack as a boolean determines whether H or He grid is used if htrack: - self.grid = self.grid1 + grid = self.grid_Hrich else: - self.grid = self.grid2 + grid = self.grid_strippedHe try: - x = self.grid.get("age", m0) - y = self.grid.get(key, m0) + x = grid.get("age", m0) + y = grid.get(key, m0) except ValueError: return np.array(t) * np.nan try: @@ -431,43 +537,32 @@ def scale(self, key, htrack, method): Data normalization class """ - if htrack: - self.grid = self.grid1 - else: - self.grid = self.grid2 - self.initial_mass = self.grid.grid_mass + # TODO: why this self.grid? Why not local variable. Should this affect + # the whole detached_step instance? + + # collect all options for the scaler + scaler_options = (key, htrack, method) - all_attribute = [] + # find if the scaler has already been fitted and return it if so... + scaler = self.stored_scalers.get(scaler_options, None) + if scaler is not None: + return scaler + + # ... if not, fit a new scaler, and store it for later use + grid = self.grid_Hrich if htrack else self.grid_strippedHe + self.initial_mass = grid.grid_mass + all_attributes = [] for mass in self.initial_mass: - for i in self.grid.get(key, mass): - all_attribute.append(i) - all_value = np.array(all_attribute) - sc = DataScaler() - xt = sc.fit_and_transform( - all_value, method=method, lower=0.0, upper=1.0) - # xtnew = sc.transform(x) - return sc - - def transform(self): - """Apply needed quantities to the normalization class.""" - scale = self.scale - sc_mass_H = scale("mass", True, "log_min_max") - sc_mass_He = scale("mass", False, "log_min_max") - sc_log_R_H = scale("log_R", True, "min_max") - sc_log_R_He = scale("log_R", False, "min_max") - sc_he_core_mass_H = scale("he_core_mass", True, "min_max") - sc_he_core_mass_He = scale("he_core_mass", False, "min_max") - sc_center_h1 = scale("center_h1", True, "min_max") - sc_center_he4_H = scale("center_he4", True, "min_max") - sc_center_he4_He = scale("center_he4", False, "min_max") - sc_center_c12 = scale("center_c12", False, "min_max") - return (sc_mass_H, sc_mass_He, sc_log_R_H, sc_log_R_He, - sc_he_core_mass_H, sc_he_core_mass_He, sc_center_h1, - sc_center_he4_H, sc_center_he4_He, sc_center_c12) + for i in grid.get(key, mass): + all_attributes.append(i) + all_attributes = np.array(all_attributes) + scaler = DataScaler() + scaler.fit(all_attributes, method=method, lower=0.0, upper=1.0) + self.stored_scalers[scaler_options] = scaler + return scaler def get_root0(self, keys, x, htrack, rs=None): - """Determine the closest associated initial mass and time in the grid - which has a specific value as tentative solutions for matching. + """Get the track in the grid with values closest to the requested ones. Parameters ---------- @@ -491,22 +586,17 @@ def get_root0(self, keys, x, htrack, rs=None): If there is no match then NaNs will be returned instead. """ - if htrack: - self.grid = self.grid1 - else: - self.grid = self.grid2 - self.initial_mass = self.grid.grid_mass + grid = self.grid_Hrich if htrack else self.grid_strippedHe + self.initial_mass = grid.grid_mass n = 0 - for mass in self.grid.grid_mass: - n = max(n, len(self.grid.get("age", mass))) - - self.rootm = np.inf * np.ones((len(self.grid.grid_mass), + for mass in grid.grid_mass: + n = max(n, len(grid.get("age", mass))) + self.rootm = np.inf * np.ones((len(grid.grid_mass), n, len(self.root_keys))) - for i, mass in enumerate(self.grid.grid_mass): + for i, mass in enumerate(grid.grid_mass): for j, key in enumerate(self.root_keys): - track = self.grid.get(key, mass) + track = grid.get(key, mass) self.rootm[i, : len(track), j] = track - if rs is None: rs = np.ones_like(keys) else: @@ -517,12 +607,11 @@ def get_root0(self, keys, x, htrack, rs=None): d = np.linalg.norm((X - x[None, None, :]) / rs[None, None, :], axis=-1) idx = np.unravel_index(d.argmin(), X.shape[:-1]) t = self.rootm[idx][np.argmax("age" == self.root_keys)] - m0 = self.grid.grid_mass[idx[0]] + m0 = grid.grid_mass[idx[0]] return m0, t - def get_mist0(self, star, htrack): - """Determine the associated initial mass and time in the grid that - matches the properties of the binary. + def match_to_single_star(self, star, htrack): + """Get the track in the grid that matches the time and mass of a star. For "root" matching_method, the properties that are matched is always the mass of the secondary star. @@ -535,6 +624,7 @@ def get_mist0(self, star, htrack): star : SingleStar The star which properties are required to be matched with the single MIST-like grid. + Returns ------- list of 2 float values @@ -544,30 +634,19 @@ def get_mist0(self, star, htrack): """ if htrack: - self.grid = self.grid1 + self.grid = self.grid_Hrich else: - self.grid = self.grid2 - m_min_H = np.min(self.grid1.grid_mass) - m_max_H = np.max(self.grid1.grid_mass) - m_min_He = np.min(self.grid2.grid_mass) - m_max_He = np.max(self.grid2.grid_mass) + self.grid = self.grid_strippedHe + get_root0 = self.get_root0 get_track_val = self.get_track_val matching_method = self.matching_method - tran = self.transform() - sc_mass_H = tran[0] - sc_mass_He = tran[1] - sc_log_R_H = tran[2] - sc_log_R_He = tran[3] - sc_he_core_mass_H = tran[4] - sc_he_core_mass_He = tran[5] - sc_center_h1 = tran[6] - sc_center_he4_H = tran[7] - sc_center_he4_He = tran[8] - sc_center_c12 = tran[9] + scale = self.scale + initials = None # tolerance 1e-8 - tolerance_mist_integration = 1e-2 + tolerance_matching_integration = 1e-2 + tolerance_matching_integration_hard = 1e-1 if self.verbose: print(matching_method) if matching_method == "root": @@ -605,330 +684,228 @@ def get_mist0(self, star, htrack): else: initials = sol.x elif matching_method == "minimize": + + def posydon_attribute(list_for_matching, star): + list_of_attributes = [] + for attr in list_for_matching: + list_of_attributes.append(getattr(star, attr)) + return list_of_attributes + if star.state in LIST_ACCEPTABLE_STATES_FOR_HMS: - # Initialezed ZAMS systems have no information of radius - if (star.log_R is None) or np.isnan(star.log_R): - initials = (star.mass, 0) - else: - MESA_label = ["mass", "center_h1", "log_R", "he_core_mass"] - posydon_attribute = [star.mass, star.center_h1, - star.log_R, star.he_core_mass] - rs = [20.0, 1.0, 2.0, 10.0] + list_for_matching = self.list_for_matching_HMS + elif star.state in LIST_ACCEPTABLE_STATES_FOR_postMS: + list_for_matching = self.list_for_matching_postMS + elif star.state in LIST_ACCEPTABLE_STATES_FOR_HeStar: + list_for_matching = self.list_for_matching_HeStar + + MESA_labels = list_for_matching[0] + posydon_attributes = posydon_attribute(MESA_labels, star) + rs = list_for_matching[1] + colscalers = list_for_matching[2] + bnds = [] + for i in range(3, len(list_for_matching)): + bnds.append(list_for_matching[i]) + + if self.verbose or self.verbose == 1: + print("Matching attributes and their normalizations :", + MESA_labels, rs) + for i in MESA_labels: + if i not in self.root_keys: + raise Exception("Expected matching parameter not " + "added in the single star grid options.") + + scales = [] + for MESA_label, colscaler in zip(MESA_labels, colscalers): + scale_of_attribute = scale(MESA_label, htrack, colscaler) + scales.append(scale_of_attribute) + + x0 = get_root0(MESA_labels, posydon_attributes, htrack, rs=rs) + + def sq_diff_function(x): + return self.square_difference( + x, htrack=htrack, mesa_labels=MESA_labels, + posydon_attributes=posydon_attributes, + colscalers=colscalers, scales=scales) + + sol = minimize(sq_diff_function, x0, method="TNC", bounds=bnds) + + # alternative matching + # 1st, different minimization method + if (np.abs(sol.fun) > tolerance_matching_integration + or not sol.success): + if self.verbose or self.verbose == 1: + print("Alternative matching in detached step, 1st step " + "because either", np.abs(sol.fun), ">", + tolerance_matching_integration, + "or sol.success = ", sol.success) + sol = minimize(sq_diff_function, x0, method="Powell") + + # 2nd, alternative matching parameters + if (np.abs(sol.fun) > tolerance_matching_integration + or not sol.success): + if star.state in LIST_ACCEPTABLE_STATES_FOR_HMS: + list_for_matching = self.list_for_matching_HMS_alternative + elif star.state in LIST_ACCEPTABLE_STATES_FOR_postMS: + list_for_matching = ( + self.list_for_matching_postMS_alternative) + elif star.state in LIST_ACCEPTABLE_STATES_FOR_HeStar: + list_for_matching = ( + self.list_for_matching_HeStar_alternative) + + MESA_labels = list_for_matching[0] + posydon_attributes = posydon_attribute(MESA_labels, star) + rs = list_for_matching[1] + colscalers = list_for_matching[2] + bnds = [] + for i in range(3, len(list_for_matching)): + bnds.append(list_for_matching[i]) + + if self.verbose or self.verbose == 1: + print("Alternative matching in detached step, 2nd step " + "because", np.abs(sol.fun), ">", + tolerance_matching_integration, + "or sol.success = ", sol.success) + print("Matching alternative attributes and their " + "normalizations :", MESA_labels, rs) + + scales = [] + for MESA_label, colscaler in zip(MESA_labels, colscalers): + scale_of_attribute = scale(MESA_label, htrack, colscaler) + scales.append(scale_of_attribute) + + def sq_diff_function(x): + return self.square_difference( + x, htrack=htrack, mesa_labels=MESA_labels, + posydon_attributes=posydon_attributes, + colscalers=colscalers, scales=scales) + + x0 = get_root0(MESA_labels, posydon_attributes, htrack, rs=rs) + + sol = minimize(sq_diff_function, x0, method="TNC", bounds=bnds) + + # 3rd Alternative matching with a H-rich grid for He-star and vice verse (not for HMS stars) + if (np.abs(sol.fun) > tolerance_matching_integration + or not sol.success): + if (star.state in LIST_ACCEPTABLE_STATES_FOR_HeStar + or star.state in LIST_ACCEPTABLE_STATES_FOR_postMS): + if self.verbose: + print("Alternative matching in detached step, 3rd step because ", + np.abs(sol.fun), ">", tolerance_matching_integration , + " or sol.success = ", sol.success) + + if star.state in LIST_ACCEPTABLE_STATES_FOR_HeStar: + htrack = True + list_for_matching = self.list_for_matching_HeStar + elif star.state in LIST_ACCEPTABLE_STATES_FOR_postMS: + htrack = False + list_for_matching = self.list_for_matching_postMS + + MESA_labels = list_for_matching[0] + posydon_attributes = posydon_attribute(MESA_labels, star) + rs = list_for_matching[1] + colscalers = list_for_matching[2] + bnds = [] + for i in range(3, len(list_for_matching)): + bnds.append(list_for_matching[i]) + + if self.verbose or self.verbose == 1: print("Matching attributes and their normalizations :", - MESA_label, rs) - for i in MESA_label: + MESA_labels, rs) + for i in MESA_labels: if i not in self.root_keys: raise Exception("Expected matching parameter not " - "added in the MIST model options.") - x0 = get_root0(MESA_label, posydon_attribute, - htrack, rs=rs) - bnds = ([m_min_H, m_max_H], [0, None]) - sol = minimize( - lambda x: ( - sc_mass_H.transform( - get_track_val(MESA_label[0], htrack, *x)) - - sc_mass_H.transform(posydon_attribute[0])) ** 2 - + (sc_center_h1.transform( - get_track_val(MESA_label[1], htrack, *x)) - - sc_center_h1.transform(posydon_attribute[1]))**2 - + (sc_log_R_H.transform( - get_track_val(MESA_label[2], htrack, *x)) - - sc_log_R_H.transform(posydon_attribute[2])) ** 2 - + (sc_he_core_mass_H.transform( - get_track_val(MESA_label[3], htrack, *x)) - - sc_he_core_mass_H.transform( - posydon_attribute[3])) ** 2, - x0, - method="TNC", - bounds=bnds - ) - # stars after mass transfer could swell up so that log_R - # is not appropriate for matching - if np.abs(sol.fun) > tolerance_mist_integration: - MESA_label = ["mass", "center_h1", "he_core_mass"] - posydon_attribute = [star.mass, star.center_h1, - star.he_core_mass] - rs = [20.0, 1.0, 10.0] - x0 = get_root0( - MESA_label, posydon_attribute, htrack, rs=rs) - bnds = ([m_min_H, m_max_H], [0, None]) - sol = minimize( - lambda x: ( - sc_mass_H.transform( - get_track_val(MESA_label[0], htrack, *x)) - - sc_mass_H.transform( - posydon_attribute[0])) ** 2 - + (sc_center_h1.transform( - get_track_val(MESA_label[1], htrack, *x)) - - sc_center_h1.transform( - posydon_attribute[1])) ** 2 - + (sc_he_core_mass_H.transform( - get_track_val(MESA_label[2], htrack, *x)) - - sc_he_core_mass_H.transform( - posydon_attribute[2])) ** 2, - x0, method="TNC", bounds=bnds - ) - elif star.state in LIST_ACCEPTABLE_STATES_FOR_postMS: - MESA_label = ["he_core_mass", "mass", "center_he4", "log_R"] - posydon_attribute = [star.he_core_mass, star.mass, - star.center_he4, star.log_R] - rs = [10.0, 20.0, 1.0, 2.0] - if self.verbose: - print("Matching attributes and their normalizations : ", - MESA_label, rs) - for i in MESA_label: - if i not in self.root_keys: - raise Exception("Expected matching parameter not added" - " in the MIST model options.") - x0 = get_root0(MESA_label, posydon_attribute, htrack, rs=rs) - bnds = ([m_min_H, m_max_H], [0, None]) - sol = minimize( - lambda x: ( - (sc_he_core_mass_H.transform( - get_track_val(MESA_label[0], htrack, *x)) - - sc_he_core_mass_H.transform(posydon_attribute[0])) - ) - ** 2 + (sc_mass_H.transform( - get_track_val(MESA_label[1], htrack, *x)) - - sc_mass_H.transform(posydon_attribute[1]) - ) ** 2 - + (sc_center_he4_H.transform( - get_track_val(MESA_label[2], htrack, *x)) - - sc_center_he4_H.transform(posydon_attribute[2])) ** 2 - + (sc_log_R_H.transform( - get_track_val(MESA_label[3], htrack, *x)) - - sc_log_R_H.transform(posydon_attribute[3])) ** 2, - - x0, method="TNC", bounds=bnds, - ) + "added in the single star grid options.") - if (np.abs(sol.fun) > tolerance_mist_integration - or not sol.success): - star.htrack = False - MESA_label = ["he_core_mass", "center_he4", "log_R"] - posydon_attribute = [star.he_core_mass, - star.center_he4, star.log_R] - rs = [10.0, 1.0, 2.0] - if self.verbose: - print("Matching attributes and their normalizations : ", - MESA_label, rs) - for i in MESA_label: - if i not in self.root_keys: - raise Exception("Expected matching parameter not added" - " in the MIST model options.") - x0 = get_root0( - MESA_label, posydon_attribute, star.htrack, rs=rs) - bnds = ([m_min_He, m_max_He], [0, None]) - sol = minimize( - lambda x: ( - (sc_he_core_mass_He.transform( - get_track_val(MESA_label[0], star.htrack, *x)) - - sc_he_core_mass_He.transform( - posydon_attribute[0])) - / sc_he_core_mass_He.transform( - posydon_attribute[0])) ** 2 - + (sc_center_he4_He.transform( - get_track_val(MESA_label[1], star.htrack, *x)) - - sc_center_he4_He.transform( - posydon_attribute[1])) - ** 2 + (sc_log_R_He.transform( - get_track_val(MESA_label[2], star.htrack, *x)) - - sc_log_R_He.transform( - posydon_attribute[2])) ** 2, - x0, method="TNC", bounds=bnds, - ) - if ((self.get_track_val("mass", star.htrack, *sol.x) - - self.get_track_val( - "he_core_mass", star.htrack, *sol.x)) - / self.get_track_val( - "mass", star.htrack, *sol.x) >= 0.01): - initials = (np.nan, np.nan) - - if (np.abs(sol.fun) > tolerance_mist_integration - or not sol.success): - star.htrack = True - MESA_label = ["he_core_mass", "mass"] - posydon_attribute = [star.he_core_mass, - star.mass] - rs = [10.0, 20.0] - if self.verbose: - print("Matching attributes and their normalizations : ", - MESA_label, rs) - for i in MESA_label: - if i not in self.root_keys: - raise Exception("Expected matching parameter not added" - " in the MIST model options.") - x0 = get_root0( - MESA_label, posydon_attribute, star.htrack, rs=rs) - bnds = ([m_min_He, m_max_He], [0, None]) - sol = minimize( - lambda x: ( - (sc_he_core_mass_H.transform( - get_track_val(MESA_label[0], star.htrack, *x)) - - sc_he_core_mass_H.transform(posydon_attribute[0])) - ) - ** 2 + (sc_mass_H.transform( - get_track_val(MESA_label[1], star.htrack, *x)) - - sc_mass_H.transform(posydon_attribute[1]) - ) ** 2, - x0, method="TNC", bounds=bnds, - ) - - elif star.state in LIST_ACCEPTABLE_STATES_FOR_HeStar: - MESA_label = [ - "he_core_mass", - # "surface_he4", - "center_he4", - # "mass", - # "center_c12", - "log_R" - ] - posydon_attribute = [ - star.he_core_mass, - # star.surface_he4, - star.center_he4, - # star.mass, - # star.center_c12, - star.log_R - ] - # rs = [10, 2.0, 2.0, 20, 2.0] - rs = [10.0, 1.0, 2.0] - if self.verbose: - print("Matching attributes and their normalizations : ", - MESA_label, rs) - for i in MESA_label: - if i not in self.root_keys: - raise Exception("Expected matching parameter not added" - " in the MIST model options.") - x0 = get_root0(MESA_label, posydon_attribute, False, rs=rs) - bnds = ([m_min_He, m_max_He], [0, None]) - # match he4 abundance using definite value - sol = minimize( - lambda x: ( - (sc_he_core_mass_He.transform( - get_track_val(MESA_label[0], htrack, *x)) - - sc_he_core_mass_He.transform(posydon_attribute[0])) - / sc_he_core_mass_He.transform(posydon_attribute[0])) - ** 2 + (sc_center_he4_He.transform( - get_track_val(MESA_label[1], htrack, *x)) - - sc_center_he4_He.transform(posydon_attribute[1])) - ** 2 + (sc_log_R_He.transform( - get_track_val(MESA_label[2], htrack, *x)) - - sc_log_R_He.transform(posydon_attribute[2]))**2, - x0, method="TNC", bounds=bnds, - ) - if (np.abs(sol.fun) > tolerance_mist_integration - or not sol.success): - sol = minimize( - lambda x: ( - (sc_he_core_mass_He.transform( - get_track_val(MESA_label[0], htrack, *x)) - - sc_he_core_mass_He.transform( - posydon_attribute[0])) - / sc_he_core_mass_He.transform( - posydon_attribute[0])) ** 2 - + (sc_center_he4_He.transform( - get_track_val(MESA_label[1], htrack, *x)) - - sc_center_he4_He.transform(posydon_attribute[1])) - ** 2 + (sc_log_R_He.transform( - get_track_val(MESA_label[2], htrack, *x)) - - sc_log_R_He.transform(posydon_attribute[2])) - ** 2, - x0, method="Powell", - ) + scales = [] + for MESA_label, colscaler in zip(MESA_labels, colscalers): + scale_of_attribute = scale(MESA_label, htrack, colscaler) + scales.append(scale_of_attribute) - if (np.abs(sol.fun) > tolerance_mist_integration - or not sol.success): - star.htrack = True - x0 = get_root0( - MESA_label, posydon_attribute, star.htrack, rs=rs) - bnds = ([m_min_H, m_max_H], [0, None]) - sol = minimize( - lambda x: ( - (sc_he_core_mass_He.transform( - get_track_val(MESA_label[0], htrack, *x)) - - sc_he_core_mass_He.transform( - posydon_attribute[0])) - / sc_he_core_mass_He.transform( - posydon_attribute[0])) ** 2 - + (sc_center_he4_H.transform( - get_track_val(MESA_label[1], htrack, *x)) - - sc_center_he4_H.transform( - posydon_attribute[1])) - ** 2 + (sc_log_R_H.transform( - get_track_val(MESA_label[2], htrack, *x)) - - sc_log_R_H.transform( - posydon_attribute[2])) ** 2, - x0, method="TNC", bounds=bnds, - ) - if ((self.get_track_val("mass", star.htrack, *sol.x) - - self.get_track_val( - "he_core_mass", star.htrack, *sol.x)) - / self.get_track_val( - "mass", star.htrack, *sol.x) >= 0.05): - initials = (np.nan, np.nan) - - if initials is None: - if self.verbose: - print("sol.fun = ", np.abs(sol.fun)) - if np.abs(sol.fun) < tolerance_mist_integration: - if self.verbose: - print("minimization in matching considered acceptable," - " with", np.abs(sol.fun), "<", - tolerance_mist_integration, "tolerance") - initials = sol.x - star.fun = sol.fun - star.stiching_rel_mass_difference = ( - self.get_track_val("mass", htrack, *sol.x) - star.mass - ) / star.mass - if star.log_R in MESA_label: - star.stiching_rel_logRadius_difference = ( - self.get_track_val("log_R", htrack, *sol.x) - - star.log_R) / star.log_R - else: - star.stiching_rel_logRadius_difference = np.nan - if (star.total_moment_of_inertia is not None - and not np.isnan(star.total_moment_of_inertia)): - star.stiching_rel_inertia_difference = ( - self.get_track_val("inertia", htrack, *sol.x) - - star.total_moment_of_inertia - ) / star.total_moment_of_inertia + def sq_diff_function(x): + return self.square_difference( + x, htrack=htrack, mesa_labels=MESA_labels, + posydon_attributes=posydon_attributes, + colscalers=colscalers, scales=scales) + + x0 = get_root0( + MESA_label, posydon_attribute, htrack, rs=rs) + # bnds = ([m_min_H, m_max_H], [0, None]) + sol = minimize(sq_diff_function, x0, + method="TNC", bounds=bnds) + + # if still not acceptable matching, we fail the system: + if (np.abs(sol.fun) > tolerance_matching_integration_hard + or not sol.success): + ''' + if ((self.get_track_val("mass", star.htrack, *sol.x) + - self.get_track_val( + "he_core_mass", star.htrack, *sol.x)) + / self.get_track_val( + "mass", star.htrack, *sol.x) >= 0.05): + ''' + if self.verbose or self.verbose == 1: + print("minimization in matching not successful, with", + np.abs(sol.fun), ">", tolerance_matching_integration, + "tolerance") + initials = (np.nan, np.nan) + ''' + star.fun = np.nan + star.stiching_rel_mass_difference = np.nan + star.stiching_rel_radius_difference = np.nan + star.stiching_rel_inertia_difference = np.nan + ''' + elif np.abs(sol.fun) < tolerance_matching_integration_hard: + if self.verbose or self.verbose == 1: + print("minimization in matching considered acceptable," + " with", f'{np.abs(sol.fun):.8f}', "<", + tolerance_matching_integration, "tolerance") + initials = sol.x + ''' + star.fun = sol.fun + star.stiching_rel_mass_difference = ( + self.get_track_val("mass", htrack, *sol.x) - star.mass + ) / star.mass + if star.log_R in MESA_label: + star.stiching_rel_logRadius_difference = ( + self.get_track_val("log_R", htrack, *sol.x) + - star.log_R) / star.log_R else: - if self.verbose: - print("minimization in matching not successful, with", - np.abs(sol.fun), ">", tolerance_mist_integration, - "tolerance") - initials = (np.nan, np.nan) - star.fun = np.nan - star.stiching_rel_mass_difference = np.nan - star.stiching_rel_radius_difference = np.nan - star.stiching_rel_inertia_difference = np.nan - if self.verbose: + star.stiching_rel_logRadius_difference = np.nan + if (star.total_moment_of_inertia is not None + and not np.isnan(star.total_moment_of_inertia)): + star.stiching_rel_inertia_difference = ( + self.get_track_val("inertia", htrack, *sol.x) + - star.total_moment_of_inertia + ) / star.total_moment_of_inertia + ''' + + if self.verbose or self.verbose == 1: print( - "matching with track of intial mass m0, at time t0 ", - initials[0], - initials[1], - "with m(t0), log10(R(t0), center_he(t0), surface_he4, " - "surface_h1, he_core_mass, center_c12) = ", - self.get_track_val("mass", htrack, *sol.x), - self.get_track_val("log_R", htrack, *sol.x), - self.get_track_val("center_he4", htrack, *sol.x), - self.get_track_val("surface_he4", htrack, *sol.x), - self.get_track_val("surface_h1", htrack, *sol.x), - self.get_track_val("he_core_mass", htrack, *sol.x), - self.get_track_val("center_c12", htrack, *sol.x), - "Mass / Radius of the secondary at the end of the previous " - "step was = ", - star.mass, - star.log_R, - star.center_he4, - star.surface_he4, - star.surface_h1, - star.he_core_mass, - star.center_c12 + "matching ", star.state, + " star with track of intial mass m0, at time t0:", + f'{initials[0]:.3f} [Msun],', + f'{initials[1]/1e6:.3f} [Myrs]', "\n", + "with m(t0), log10(R(t0), center_he(t0), surface_he4(t0), " + "surface_h1(t0), he_core_mass(t0), center_c12(t0) = \n", + f'{self.get_track_val("mass", htrack, *sol.x):.3f}', + f'{self.get_track_val("log_R", htrack, *sol.x):.3f}', + f'{self.get_track_val("center_he4", htrack, *sol.x):.4f}', + f'{self.get_track_val("surface_he4", htrack, *sol.x):.4f}', + f'{self.get_track_val("surface_h1", htrack, *sol.x):.4f}', + f'{self.get_track_val("he_core_mass", htrack, *sol.x):.3f}', + f'{self.get_track_val("center_c12", htrack, *sol.x):.4f}\n', + "The same values of the secondary at the end of the previous " + "step was = \n", + f'{star.mass:.3f}', + f'{star.log_R:.3f}', + f'{star.center_he4:.4f}', + f'{star.surface_he4:.4f}', + f'{star.surface_h1:.4f}', + f'{star.he_core_mass:.3f}', + f'{star.center_c12:.4f}' ) - return initials + return initials, htrack def __repr__(self): """Return the type of evolution type.""" @@ -936,79 +913,114 @@ def __repr__(self): def __call__(self, binary): """Evolve the binary until RLO or compact object formation.""" - get_mist0 = self.get_mist0 KEYS = self.KEYS KEYS_POSITIVE = self.KEYS_POSITIVE - # the primary is the more evolved star - if (binary.star_1.state in ("BH", "NS", "WD") - and binary.star_2.state in STAR_STATES_H_RICH): - primary = binary.star_1 - secondary = binary.star_2 - secondary.htrack = True - primary.htrack = secondary.htrack + + if binary.star_1 is None: + self.non_existent_companion = 1 + if binary.star_2 is None: + self.non_existent_companion = 2 + else: + # detached step of an actual binary + self.non_existent_companion = 0 + + # no isolated evolution, detached step of an actual binary, the primary + # in a real binary is potential compact object or the more evolved star + if self.non_existent_companion == 0: + + if (binary.star_1.state in ("BH", "NS", "WD") + and binary.star_2.state in STAR_STATES_H_RICH): + primary = binary.star_1 + secondary = binary.star_2 + secondary.htrack = True + primary.htrack = secondary.htrack + primary.co = True + elif ((binary.star_1.state in ("BH", "NS", "WD")) and ( + binary.star_2.state in LIST_ACCEPTABLE_STATES_FOR_HeStar)): + primary = binary.star_1 + secondary = binary.star_2 + secondary.htrack = False + primary.htrack = secondary.htrack + primary.co = True + elif (binary.star_2.state in ("BH", "NS", "WD") + and binary.star_1.state in STAR_STATES_H_RICH): + primary = binary.star_2 + secondary = binary.star_1 + secondary.htrack = True + primary.htrack = secondary.htrack + primary.co = True + elif (binary.star_2.state in ("BH", "NS", "WD") + and binary.star_1.state + in LIST_ACCEPTABLE_STATES_FOR_HeStar): + primary = binary.star_2 + secondary = binary.star_1 + secondary.htrack = False + primary.htrack = secondary.htrack + primary.co = True + elif (binary.star_1.state in STAR_STATES_H_RICH + and binary.star_2.state in STAR_STATES_H_RICH): + primary = binary.star_1 + secondary = binary.star_2 + secondary.htrack = True + primary.htrack = True + primary.co = False + elif (binary.star_1.state in LIST_ACCEPTABLE_STATES_FOR_HeStar + and binary.star_2.state in STAR_STATES_H_RICH): + primary = binary.star_1 + secondary = binary.star_2 + secondary.htrack = True + primary.htrack = False + primary.co = False + elif (binary.star_2.state in LIST_ACCEPTABLE_STATES_FOR_HeStar + and binary.star_1.state in STAR_STATES_H_RICH): + primary = binary.star_2 + secondary = binary.star_1 + secondary.htrack = True + primary.htrack = False + primary.co = False + elif (binary.star_1.state in LIST_ACCEPTABLE_STATES_FOR_HeStar + and binary.star_2.state + in LIST_ACCEPTABLE_STATES_FOR_HeStar): + primary = binary.star_1 + secondary = binary.star_2 + secondary.htrack = False + primary.htrack = False + primary.co = False + else: + raise Exception("States not recognized!") + + # non-existent, far away, star + elif self.non_existent_companion == 1: + # we force primary.co=True for all isolated evolution, + # where the secondary is the one evolving one primary.co = True - elif (binary.star_1.state in ("BH", "NS", "WD") - and binary.star_2.state in LIST_ACCEPTABLE_STATES_FOR_HeStar): - primary = binary.star_1 + primary.htrack = False secondary = binary.star_2 - secondary.htrack = False - primary.htrack = secondary.htrack - primary.co = True - elif (binary.star_2.state in ("BH", "NS", "WD") - and binary.star_1.state in STAR_STATES_H_RICH): - primary = binary.star_2 - secondary = binary.star_1 - secondary.htrack = True - primary.htrack = secondary.htrack - primary.co = True - elif (binary.star_2.state in ("BH", "NS", "WD") - and binary.star_1.state in LIST_ACCEPTABLE_STATES_FOR_HeStar): - primary = binary.star_2 - secondary = binary.star_1 - secondary.htrack = False - primary.htrack = secondary.htrack + if (binary.star_2.state in STAR_STATES_H_RICH): + secondary.htrack = True + elif (binary.star_2.state in LIST_ACCEPTABLE_STATES_FOR_HeStar): + secondary.htrack = False + else: + raise Exception("State not recognized!") + + elif self.non_existent_companion == 2: primary.co = True - elif (binary.star_1.state in STAR_STATES_H_RICH - and binary.star_2.state in STAR_STATES_H_RICH): - primary = binary.star_1 - secondary = binary.star_2 - secondary.htrack = True - primary.htrack = True - primary.co = False - elif (binary.star_1.state in LIST_ACCEPTABLE_STATES_FOR_HeStar - and binary.star_2.state in STAR_STATES_H_RICH): - primary = binary.star_1 - secondary = binary.star_2 - secondary.htrack = True primary.htrack = False - primary.co = False - elif (binary.star_2.state in LIST_ACCEPTABLE_STATES_FOR_HeStar - and binary.star_1.state in STAR_STATES_H_RICH): - primary = binary.star_2 secondary = binary.star_1 - secondary.htrack = True - primary.htrack = False - primary.co = False - elif (binary.star_1.state in LIST_ACCEPTABLE_STATES_FOR_HeStar - and binary.star_2.state in LIST_ACCEPTABLE_STATES_FOR_HeStar): - primary = binary.star_1 - secondary = binary.star_2 - secondary.htrack = False - primary.htrack = False - primary.co = False - else: - raise Exception("States not recognized!") - - if not primary.co: - m1, t1 = get_mist0(primary, primary.htrack) - m2, t2 = get_mist0(secondary, secondary.htrack) + if (binary.star_1.state in STAR_STATES_H_RICH): + secondary.htrack = True + elif (binary.star_1.state in LIST_ACCEPTABLE_STATES_FOR_HeStar): + secondary.htrack = False + else: + raise Exception("State not recognized!") - def get_star_data(binary, star1, star2, htrack, co): + def get_star_data(binary, star1, star2, htrack, + co, copy_prev_m0=None, copy_prev_t0=None): """Get and interpolate the properties of stars. The data of a compact object can be stored as a copy of its - companion for convenience except its mass, radius, mdot, Idot, - and radius, mdot, Idot are set to be zero. + companion for convenience except its mass, radius, mdot, and Idot + are set to be zero. Parameters ---------- @@ -1024,21 +1036,36 @@ def get_star_data(binary, star1, star2, htrack, co): return the properties of star2 """ + + with np.errstate(all="ignore"): + # get the initial m0, t0 track + if binary.event == 'ZAMS': + # ZAMS stars in wide (non-mass exchaging binaries) that are + # directed to detached step at birth + m0, t0 = star1.mass, 0 + elif co: + m0, t0 = copy_prev_m0, copy_prev_t0 + else: + t_before_matching = time.time() + m0, t0, htrack = self.match_to_single_star(star1, htrack) + t_after_matching = time.time() + if self.verbose or self.verbose == 1: + print("Matching duration: " + f"{t_after_matching-t_before_matching:.6g}") + if htrack: - self.grid = self.grid1 + self.grid = self.grid_Hrich elif not htrack: - self.grid = self.grid2 + self.grid = self.grid_strippedHe get_track = self.grid.get - with np.errstate(all="ignore"): - # get the initial m0, t0 track - m0, t0 = get_mist0(star1, htrack) + if np.any(np.isnan([m0, t0])): # binary.event = "END" # binary.state += " (GridMatchingFailed)" # if self.verbose: # print("Failed matching") - return None + return None, None, None max_time = binary.properties.max_simulation_time assert max_time > 0.0, "max_time is non-positive" @@ -1092,21 +1119,25 @@ def get_star_data(binary, star1, star2, htrack, co): interp1d["R"] = PchipInterpolator(age, kvalue["R"]) interp1d["mdot"] = PchipInterpolator(age, kvalue["mdot"]) interp1d["Idot"] = PchipInterpolator(age, kvalue["mdot"]) - return interp1d + return interp1d, m0, t0 # get the matched data of two stars, respectively - interp1d_sec = get_star_data( - binary, secondary, primary, secondary.htrack, False) - if primary.co: + interp1d_sec, m0, t0 = get_star_data( + binary, secondary, primary, secondary.htrack, co=False) + if (primary.co) or (self.non_existent_companion != 0): + # copy the secondary star except mass which is of the primary, + # and radius, mdot, Idot = 0 interp1d_pri = get_star_data( - binary, secondary, primary, secondary.htrack, True) + binary, secondary, primary, secondary.htrack, co=True, + copy_prev_m0=m0, copy_prev_t0=t0)[0] elif not primary.co: interp1d_pri = get_star_data( - binary, primary, secondary, primary.htrack, False) + binary, primary, secondary, primary.htrack, False)[0] + # TODO: Eirini else if interp1d_sec is None or interp1d_pri is None: # binary.event = "END" binary.state += " (GridMatchingFailed)" - if self.verbose: + if self.verbose or self.verbose == 1: print("Failed matching") return t0_sec = interp1d_sec["t0"] @@ -1193,6 +1224,7 @@ def ev_rel_rlo1(t, y): y : tuple of floats [separation, eccentricity] at that time. Separation should be in solar radii. + Returns ------- float @@ -1221,6 +1253,7 @@ def ev_rel_rlo2(t, y): y : tuple of floats [separation, eccentricity] at that time. Separation should be in solar radii. + Returns ------- float @@ -1253,7 +1286,7 @@ def get_omega(star): 10.0 ** star.log_total_angular_momentum / star.total_moment_of_inertia * const.secyer ) # the last factor transforms it from rad/s to rad/yr - if self.verbose: + if self.verbose and self.verbose != 1: print("calculating initial omega from angular momentum and" " moment of inertia", omega_in_rad_per_year) # except TypeError: @@ -1265,7 +1298,7 @@ def get_omega(star): and not np.isnan(star.surf_avg_omega)): omega_in_rad_per_year = star.surf_avg_omega * const.secyer # the last factor transforms it from rad/s to rad/yr. - if self.verbose: + if self.verbose and self.verbose != 1: print("calculating initial omega from surf_avg_omega", omega_in_rad_per_year) elif (star.surf_avg_omega_div_omega_crit is not None @@ -1278,16 +1311,16 @@ def get_omega(star): # the last factor transforms it from rad/s to rad/yr # EDIT: We assume POSYDON surf_avg_omega is provided in # rad/yr already. - if self.verbose: + if self.verbose and self.verbose != 1: print("calculating initial omega from " "surf_avg_omega_div_omega_crit", omega_in_rad_per_year) else: omega_in_rad_per_year = 0.0 - if self.verbose: + if self.verbose and self.verbose != 1: print("could calculate initial omega", omega_in_rad_per_year) - if self.verbose: + if self.verbose and self.verbose != 1: print("initial omega_in_rad_per_year", omega_in_rad_per_year) return omega_in_rad_per_year @@ -1312,6 +1345,7 @@ def get_omega(star): elif not primary.co: omega_in_rad_per_year_pri = get_omega(primary) + t_before_ODEsolution = time.time() try: s = solve_ivp( lambda t, y: diffeq( @@ -1324,8 +1358,8 @@ def get_omega(star): for key in KEYS[1:11] ], interp1d_sec["Idot"](t - t_offset_sec), - interp1d_sec["conv_env_turnover_time_l_b"](t - t_offset_sec), - interp1d_sec["surf_avg_omega_div_omega_crit"](t - t_offset_sec), + interp1d_sec["conv_env_turnover_time_l_b"]( + t - t_offset_sec), interp1d_pri["R"](t - t_offset_pri), interp1d_pri["L"](t - t_offset_pri), *[ @@ -1333,8 +1367,8 @@ def get_omega(star): for key in KEYS[1:11] ], interp1d_pri["Idot"](t - t_offset_pri), - interp1d_pri["conv_env_turnover_time_l_b"](t - t_offset_pri), - interp1d_pri["surf_avg_omega_div_omega_crit"](t - t_offset_pri), + interp1d_pri["conv_env_turnover_time_l_b"]( + t - t_offset_pri), self.do_wind_loss, self.do_tides, self.do_gravitational_radiation, @@ -1367,8 +1401,8 @@ def get_omega(star): for key in KEYS[1:11] ], interp1d_sec["Idot"](t - t_offset_sec), - interp1d_sec["conv_env_turnover_time_l_b"](t - t_offset_sec), - interp1d_sec["surf_avg_omega_div_omega_crit"](t - t_offset_sec), + interp1d_sec["conv_env_turnover_time_l_b"]( + t - t_offset_sec), interp1d_pri["R"](t - t_offset_pri), interp1d_pri["L"](t - t_offset_pri), *[ @@ -1376,8 +1410,8 @@ def get_omega(star): for key in KEYS[1:11] ], interp1d_pri["Idot"](t - t_offset_pri), - interp1d_pri["conv_env_turnover_time_l_b"](t - t_offset_pri), - interp1d_pri["surf_avg_omega_div_omega_crit"](t - t_offset_pri), + interp1d_pri["conv_env_turnover_time_l_b"]( + t - t_offset_pri), self.do_wind_loss, self.do_tides, self.do_gravitational_radiation, @@ -1399,7 +1433,11 @@ def get_omega(star): # vectorized=True ) - if self.verbose: + t_after_ODEsolution = time.time() + + if self.verbose and self.verbose != 1: + print("ODE solver duration: " + f"{t_after_ODEsolution-t_before_ODEsolution:.6g}") print("solution of ODE", s) if s.status == -1: print("Integration failed", s.message) @@ -1420,6 +1458,8 @@ def get_omega(star): t = np.hstack([t, s.t[-1]]) else: # self.dt is None and self.n_o_steps_history is None t = np.array([s.t[-1]]) + + # TODO: this variable is not used. What is happening? orb_params = s.sol(t) sep_interp, ecc_interp, omega_interp_sec, omega_interp_pri = s.sol( @@ -1755,25 +1795,16 @@ def get_omega(star): primary, i=timestep, star_CO=False) def get_star_final_values(star, htrack, m0): - if htrack: - self.grid = self.grid1 - elif not htrack: - self.grid = self.grid2 - - get_final_values = self.grid.get_final_values - get_final_state = self.grid.get_final_state - + grid = self.grid_Hrich if htrack else self.grid_strippedHe + get_final_values = grid.get_final_values + # TODO: this variable is never used! + get_final_state = grid.get_final_state for key in self.final_keys: setattr(star, key, get_final_values('S1_%s' % (key), m0)) def get_star_profile(star, htrack, m0): - if htrack: - self.grid = self.grid1 - elif not htrack: - self.grid = self.grid2 - - get_profile = self.grid.get_profile - + grid = self.grid_Hrich if htrack else self.grid_strippedHe + get_profile = grid.get_profile profile_new = np.array(get_profile('mass', m0)[1]) for i in self.profile_keys: profile_new[i] = get_profile(i, m0)[0] @@ -1884,7 +1915,6 @@ def diffeq( Renv_middle_sec, Idot_sec, tau_conv_sec, - wdivwc_sec, R_pri, L_pri, M_pri, @@ -1903,12 +1933,11 @@ def diffeq( Renv_middle_pri, Idot_pri, tau_conv_pri, - wdivwc_pri, do_wind_loss=True, do_tides=True, do_gravitational_radiation=True, do_magnetic_braking=True, - magnetic_braking_mode= "RVJ83", + magnetic_braking_mode="RVJ83", do_stellar_evolution_and_spin_from_winds=True, verbose=False, ): @@ -1944,12 +1973,9 @@ def diffeq( I : float Moment of inertia of the star in Msolar*Rsolar^2. tau_conv: float - Convective turnover time of the star, calculated @ - 0.5*pressure_scale_height above the bottom of the outer convection + Convective turnover time of the star, calculated @ + 0.5*pressure_scale_height above the bottom of the outer convection zone in yr. - wdivwc: float - The ratio of angular rotation rate (omega) to critical angular - rotation rate (omega_crit). This is dimensionless. L : float Luminosity of the star in solar units. #mass_conv_core : float @@ -2027,7 +2053,7 @@ def diffeq( # we force a negligible eccentricity to become 0 # for computational stability e = 0.0 - if verbose: + if verbose and verbose != 1: print("negligible eccentricity became 0 for " "computational stability") y[2] = np.max([y[2], 0]) # We limit omega spin to non-negative values @@ -2056,7 +2082,7 @@ def diffeq( da_mt_pri = a * ( 2 * k12 - 2 * k22 + k32 ) - if verbose: + if verbose and verbose != 1: print("da_mt = ", da_mt_sec, da_mt_pri) da = da + da_mt_sec + da_mt_pri @@ -2091,7 +2117,7 @@ def diffeq( tau_conv_sec = 0.431 * ((M_env_sec * DR_env_sec * Renv_middle_sec / (3 * L_sec)) ** (1.0 / 3.0)) else: - if verbose: + if verbose and verbose != 1: print("something wrong with M_env/DR_env/Renv_middle", M_env_sec, DR_env_sec, Renv_middle_sec) tau_conv_sec = 1.0e99 @@ -2103,7 +2129,7 @@ def diffeq( tau_conv_pri = 0.431 * ((M_env_pri * DR_env_pri * Renv_middle_pri / (3 * L_pri)) ** (1.0/3.0)) else: - if verbose: + if verbose and verbose != 1: print("something wrong with M_env/DR_env/Renv_middle", M_env_pri, DR_env_pri, Renv_middle_pri) tau_conv_pri = 1.0e99 @@ -2129,7 +2155,7 @@ def diffeq( print("kT_conv_sec is", kT_conv_sec, ", set to 0.") print("kT_conv_pri is", kT_conv_pri, ", set to 0.") # this is the 1/timescale of all d/dt calculted below in yr^-1 - if verbose: + if verbose and verbose != 1: print( "Equilibrium tides in deep convective envelope", M_env_sec, @@ -2194,7 +2220,7 @@ def diffeq( if (R_conv_pri > R_pri or R_conv_pri <= 0.0 or conv_mx1_bot_r_pri / R_pri > 0.1): E22 = 1.592e-9 * M_pri ** (2.84) - if verbose: + if verbose and verbose != 1: print( "R_conv of the convective core is not behaving well or we " "are not calculating the convective core, we switch to " @@ -2238,7 +2264,7 @@ def diffeq( * E22 * const.secyer) # this is the 1/timescale of all d/dt calculted below in yr^-1 - if verbose: + if verbose and verbose != 1: print( "Dynamical tides in radiative envelope", conv_mx1_top_r_sec, @@ -2253,7 +2279,7 @@ def diffeq( ) kT_sec = max(kT_conv_sec, kT_rad_sec) kT_pri = max(kT_conv_pri, kT_rad_pri) - if verbose: + if verbose and verbose != 1: print("kT_conv/rad of tides is ", kT_conv_sec, kT_rad_sec, kT_conv_pri, kT_rad_pri, "in 1/yr, and we picked the ", kT_sec, kT_pri) @@ -2364,11 +2390,10 @@ def diffeq( if magnetic_braking_mode == "RVJ83": # Torque from Rappaport, Verbunt, and Joss 1983, ApJ, 275, 713 - # The torque is eq.36 of Rapport+1983, with γ = 4 - # Torque units converted from cgs units to [Msol], [Rsol], [yr] - # as all stellar parameters are given in units of [Msol], [Rsol], [yr] - # and so that dOmega_mb/dt is in units of [yr^-2]. - + # The torque is eq.36 of Rapport+1983, with γ = 4. Torque units + # converted from cgs units to [Msol], [Rsol], [yr] as all stellar + # parameters are given in units of [Msol], [Rsol], [yr] and so that + # dOmega_mb/dt is in units of [yr^-2]. dOmega_mb_sec = ( -3.8e30 * (const.rsol**2 / const.secyer) * M_sec @@ -2377,7 +2402,6 @@ def diffeq( / I_sec * np.clip((1.5 - M_sec) / (1.5 - 1.3), 0, 1) ) - dOmega_mb_pri = ( -3.8e30 * (const.rsol**2 / const.secyer) * M_pri @@ -2386,17 +2410,16 @@ def diffeq( / I_pri * np.clip((1.5 - M_pri) / (1.5 - 1.3), 0, 1) ) - # Converting units: - # The constant 3.8e-30 from Rappaport+1983 has units of [cm^-2 s] + # The constant 3.8e-30 from Rappaport+1983 has units of [cm^-2 s] # which need to be converted... # - # -3.8e-30 [cm^-2 s] * (const.rsol**2 / const.secyer) -> [Rsol^-2 yr] + # -3.8e-30 [cm^-2 s] * (const.rsol**2/const.secyer) -> [Rsol^-2 yr] # * M [Msol] # * R ** 4 [Rsol^4] # * Omega ** 3 [yr^-3] # / I [Msol Rsol^2 ] - # + # # Thus, dOmega/dt comes out to [yr^-2] elif magnetic_braking_mode == "M15": @@ -2404,25 +2427,42 @@ def diffeq( # Torque prescription from Matt et al. 2015, ApJ, 799, L23 # Constants: # [erg] or [g cm^2 s^-2] -> [Msol Rsol^2 yr^-2] - K = 1.4e30 * const.secyer**2 /(const.msol * const.rsol**2) + K = 1.4e30 * const.secyer**2 / (const.msol * const.rsol**2) # m = 0.22 # p = 2.6 - # Above constants were calibrated as in Gossage et al. 2021, ApJ, 912, 65 - # Below, constants are otherwise as assumed as in Matt et al. 2015, ApJ, 799, L23 - omega_sol = 2.6e-6 * const.secyer # [s^-1] -> [yr^-1] + # Above constants were calibrated as in + # Gossage et al. 2021, ApJ, 912, 65 + + # TODO: I am not sure which constants are used from each reference + + # Below, constants are otherwise as assumed as in + # Matt et al. 2015, ApJ, 799, L23 + omega_sol = 2.6e-6 * const.secyer # [s^-1] -> [yr^-1] # solar rossby = 2 # solar convective turnover time = 12.9 days # Rossby number saturation threshold = 0.14 chi = 2.0 / 0.14 - tau_conv_sol = 12.9 / 365.25 # 12.9 [days] -> [yr] + tau_conv_sol = 12.9 / 365.25 # 12.9 [days] -> [yr] - Prot_pri = 2 * np.pi / Omega_pri # [yr] + Prot_pri = 2 * np.pi / Omega_pri # [yr] Rossby_number_pri = Prot_pri / tau_conv_pri - Prot_sec = 2 * np.pi / Omega_sec # [yr] + Prot_sec = 2 * np.pi / Omega_sec # [yr] Rossby_number_sec = Prot_sec / tau_conv_sec + # critical rotation rate in rad/yr + Omega_crit_pri = np.sqrt( + const.standard_cgrav * M_pri * const.msol + / ((R_pri * const.rsol) ** 3)) * const.secyer + Omega_crit_sec = np.sqrt( + const.standard_cgrav * M_sec * const.msol + / ((R_sec * const.rsol) ** 3)) * const.secyer + + # omega/omega_c + wdivwc_pri = Omega_pri / Omega_crit_pri + wdivwc_sec = Omega_sec / Omega_crit_sec + gamma_pri = (1 + (wdivwc_pri / 0.072)**2)**0.5 - T0_pri = K * R_pri**3.1 * M_pri**0.5 * gamma_pri**(-2 * 0.22) + T0_pri = K * R_pri**3.1 * M_pri**0.5 * gamma_pri**(-2 * 0.22) gamma_sec = (1 + (wdivwc_sec / 0.072)**2)**0.5 T0_sec = K * R_sec**3.1 * M_sec**0.5 * gamma_sec**(-2 * 0.22) @@ -2451,23 +2491,22 @@ def diffeq( ) elif magnetic_braking_mode == "G18": - + # Torque prescription from Garraffo et al. 2018, ApJ, 862, 90 # a = 0.03 # b = 0.5 - c = 3e41 / (const.msol * const.rsol**2) # [g cm^2] -> [Msol Rsol^2] + # [g cm^2] -> [Msol Rsol^2] + c = 3e41 / (const.msol * const.rsol**2) # Above are as calibrated in Gossage et al. 2021, ApJ, 912, 65 - Prot_pri = 2 * np.pi / Omega_pri # [yr] + Prot_pri = 2 * np.pi / Omega_pri # [yr] Rossby_number_pri = Prot_pri / tau_conv_pri - Prot_sec = 2 * np.pi / Omega_sec # [yr] + Prot_sec = 2 * np.pi / Omega_sec # [yr] Rossby_number_sec = Prot_sec / tau_conv_sec - n_pri = (0.03 / Rossby_number_pri) \ - + 0.5 * Rossby_number_pri + 1.0 - n_sec = (0.03 / Rossby_number_sec) \ - + 0.5 * Rossby_number_sec + 1.0 - + n_pri = (0.03 / Rossby_number_pri) + 0.5 * Rossby_number_pri + 1.0 + n_sec = (0.03 / Rossby_number_sec) + 0.5 * Rossby_number_sec + 1.0 + Qn_pri = 4.05 * np.exp(-1.4 * n_pri) Qn_sec = 4.05 * np.exp(-1.4 * n_sec) @@ -2475,7 +2514,7 @@ def diffeq( c * Omega_sec**3 * tau_conv_sec * Qn_sec / I_sec * np.clip((1.5 - M_sec) / (1.5 - 1.3), 0, 1) ) - + dOmega_mb_pri = ( c * Omega_pri**3 * tau_conv_pri * Qn_pri / I_pri * np.clip((1.5 - M_sec) / (1.5 - 1.3), 0, 1) @@ -2484,12 +2523,13 @@ def diffeq( elif magnetic_braking_mode == "CARB": # Torque prescription from Van & Ivanova 2019, ApJ, 886, L31 - # Based on files hosted on Zenodo: https://zenodo.org/record/3647683#.Y_TfedLMKUk, + # Based on files hosted on Zenodo: + # https://zenodo.org/record/3647683#.Y_TfedLMKUk, # with units converted from [cm], [g], [s] to [Rsol], [Msol], [yr] # Constants as assumed in Van & Ivanova 2019, ApJ, 886, L31 - omega_sol = 3e-6 * const.secyer # [s^-1] -> [yr^-1] - tau_conv_sol = 2.8e6 / const.secyer # [s] -> yr + omega_sol = 3e-6 * const.secyer # [s^-1] -> [yr^-1] + tau_conv_sol = 2.8e6 / const.secyer # [s] -> yr K2 = 0.07**2 tau_ratio_sec = tau_conv_sec / tau_conv_sol @@ -2498,24 +2538,32 @@ def diffeq( rot_ratio_pri = Omega_pri / omega_sol # below in units of [Rsol yr^-1]^2 - v_esc2_sec = (2 * const.standard_cgrav * M_sec / R_sec) * (const.msol * const.secyer**2 / const.rsol**3) - v_esc2_pri = (2 * const.standard_cgrav * M_pri / R_pri) * (const.msol * const.secyer**2 / const.rsol**3) - v_mod2_sec = v_esc2_sec + (2 * Omega_sec**2 * R_sec**2) / K2 + v_esc2_sec = ((2 * const.standard_cgrav * M_sec / R_sec) + * (const.msol * const.secyer**2 / const.rsol**3)) + v_esc2_pri = ((2 * const.standard_cgrav * M_pri / R_pri) + * (const.msol * const.secyer**2 / const.rsol**3)) + v_mod2_sec = v_esc2_sec + (2 * Omega_sec**2 * R_sec**2) / K2 v_mod2_pri = v_esc2_pri + (2 * Omega_pri**2 * R_pri**2) / K2 - - # Van & Ivanova 2019, MNRAS 483, 5595 replace the magnetic field with - # Omega * tau_conv phenomenology. Thus, the ratios (rot_ratio_* and tau_ratio_*) - # inherently have units of Gauss [cm^-0.5 g^0.5 s^-1] that needs to be converted to [Rsol], [Msol], [yr]. - # VI2019 assume the solar magnetic field strength is on average 1 Gauss. + + # Van & Ivanova 2019, MNRAS 483, 5595 replace the magnetic field + # with Omega * tau_conv phenomenology. Thus, the ratios + # (rot_ratio_* and tau_ratio_*) inherently have units of Gauss + # [cm^-0.5 g^0.5 s^-1] that needs to be converted to [Rsol], + # [Msol], [yr]. VI2019 assume the solar magnetic field strength is + # on average 1 Gauss. if (abs(Mdot_sec) > 0): - R_alfven_div_R3_sec = R_sec**4 * rot_ratio_sec**4 * tau_ratio_sec**4 \ - / (Mdot_sec**2 * v_mod2_sec) * (const.rsol**2 * const.secyer / const.msol**2) + R_alfven_div_R3_sec = ( + R_sec**4 * rot_ratio_sec**4 * tau_ratio_sec**4 + / (Mdot_sec**2 * v_mod2_sec) + * (const.rsol**2 * const.secyer / const.msol**2)) else: R_alfven_div_R3_sec = 0.0 - + if (abs(Mdot_pri) > 0): - R_alfven_div_R3_pri = R_pri**4 * rot_ratio_pri**4 * tau_ratio_pri**4 \ - / (Mdot_pri**2 * v_mod2_pri) * (const.rsol**2 * const.secyer / const.msol**2) + R_alfven_div_R3_pri = ( + R_pri**4 * rot_ratio_pri**4 * tau_ratio_pri**4 + / (Mdot_pri**2 * v_mod2_pri) + * (const.rsol**2 * const.secyer / const.msol**2)) else: R_alfven_div_R3_pri = 0.0 @@ -2534,17 +2582,17 @@ def diffeq( ) else: - print("WARNING: Magnetic braking is not being calculated in the detached step. ", - "The given magnetic_braking_mode string \"", magnetic_braking_mode, - "\" does not match the available built-in cases. ", - "To enable magnetic braking, please set magnetc_braking_mode to ", - "one of the following strings:") + print("WARNING: Magnetic braking is not being calculated in the " + "detached step. The given magnetic_braking_mode string \"", + magnetic_braking_mode, "\" does not match the available " + "built-in cases. To enable magnetic braking, please set " + "magnetc_braking_mode to one of the following strings:") print("\"RVJ83\" for Rappaport, Verbunt, & Joss 1983") print("\"G18\" for Garraffo et al. 2018") print("\"M15\" for Matt et al. 2015") print("\"CARB\" for Van & Ivanova 2019") - if verbose: + if verbose and verbose != 1: print("magnetic_braking_mode = ", magnetic_braking_mode) print("dOmega_mb = ", dOmega_mb_sec, dOmega_mb_pri) dOmega_sec = dOmega_sec + dOmega_mb_sec diff --git a/posydon/binary_evol/DT/step_disrupted.py b/posydon/binary_evol/DT/step_disrupted.py new file mode 100644 index 0000000000..8aac06b1d6 --- /dev/null +++ b/posydon/binary_evol/DT/step_disrupted.py @@ -0,0 +1,84 @@ +"""Merging and isolated evolution step.""" + + +__authors__ = [ + "Emmanouil Zapartas ", + "Simone Bavera ", + "Konstantinos Kovlakas " +] + + +import os +import numpy as np +from scipy.integrate import solve_ivp +from scipy.interpolate import PchipInterpolator +from scipy.optimize import minimize +from scipy.optimize import root + +from posydon.utils.data_download import PATH_TO_POSYDON_DATA +from posydon.binary_evol.binarystar import BINARYPROPERTIES +from posydon.binary_evol.singlestar import STARPROPERTIES +from posydon.interpolation import GRIDInterpolator +from posydon.interpolation.data_scaling import DataScaler +from posydon.utils.common_functions import ( + bondi_hoyle, + orbital_period_from_separation, + orbital_separation_from_period, + roche_lobe_radius, + check_state_of_star, + PchipInterpolator2 +) +from posydon.binary_evol.flow_chart import (STAR_STATES_CC) +import posydon.utils.constants as const +from posydon.binary_evol.DT.step_detached import detached_step +from posydon.binary_evol.DT.step_isolated import isolated_step + +import warnings + +from posydon.binary_evol.flow_chart import (STAR_STATES_ALL, + STAR_STATES_CO, + STAR_STATES_H_RICH, + STAR_STATES_HE_RICH, + STAR_STATES_NOT_CO + ) + + + +LIST_ACCEPTABLE_STATES_FOR_HMS = ["H-rich_Core_H_burning"] +LIST_ACCEPTABLE_STATES_FOR_HeMS = ["stripped_He_Core_He_burning"] + +LIST_ACCEPTABLE_STATES_FOR_POSTMS = STAR_STATES_H_RICH.copy() +[LIST_ACCEPTABLE_STATES_FOR_POSTMS.remove(x) for x in LIST_ACCEPTABLE_STATES_FOR_HMS] + +LIST_ACCEPTABLE_STATES_FOR_POSTHeMS = STAR_STATES_HE_RICH.copy() +[LIST_ACCEPTABLE_STATES_FOR_POSTHeMS.remove(x) for x in LIST_ACCEPTABLE_STATES_FOR_HeMS] + +class DisruptedStep(isolated_step): + """ + Prepare a runaway star to do an an isolated_step) + """ + + def __init__(self, + grid_name_Hrich=None, + grid_name_strippedHe=None, + path=PATH_TO_POSYDON_DATA, + *args, **kwargs): + + super().__init__( + grid_name_Hrich=grid_name_Hrich, + grid_name_strippedHe=grid_name_strippedHe, + *args, + **kwargs) + + def __call__(self,binary): + + ''' + if binary.state == "disrupted": + #find which star is a CO, the other will be evolved in isolation + if binary.star_1 in STAR_STATES_CO: ## TODO KEEP IT AS CORE-COLLAPSE + binary.star_1 = None + elif binary.star_2 in STAR_STATES_CO: + binary.star_2 = None + ''' + + super().__call__(binary) diff --git a/posydon/binary_evol/DT/step_initially_single.py b/posydon/binary_evol/DT/step_initially_single.py new file mode 100644 index 0000000000..e1baa5cf27 --- /dev/null +++ b/posydon/binary_evol/DT/step_initially_single.py @@ -0,0 +1,82 @@ +"""Merging and isolated evolution step.""" + + +__authors__ = [ + "Emmanouil Zapartas ", + "Simone Bavera ", + "Konstantinos Kovlakas " +] + + +import os +import numpy as np +from scipy.integrate import solve_ivp +from scipy.interpolate import PchipInterpolator +from scipy.optimize import minimize +from scipy.optimize import root + +from posydon.utils.data_download import PATH_TO_POSYDON_DATA +from posydon.binary_evol.binarystar import BINARYPROPERTIES +from posydon.binary_evol.singlestar import STARPROPERTIES +from posydon.interpolation import GRIDInterpolator +from posydon.interpolation.data_scaling import DataScaler +from posydon.utils.common_functions import ( + bondi_hoyle, + orbital_period_from_separation, + orbital_separation_from_period, + roche_lobe_radius, + check_state_of_star, + PchipInterpolator2 +) +from posydon.binary_evol.flow_chart import (STAR_STATES_CC) +import posydon.utils.constants as const +from posydon.binary_evol.DT.step_detached import detached_step +from posydon.binary_evol.DT.step_isolated import isolated_step + +import warnings + +from posydon.binary_evol.flow_chart import (STAR_STATES_ALL, + STAR_STATES_CO, + STAR_STATES_H_RICH, + STAR_STATES_HE_RICH, + STAR_STATES_NOT_CO + ) + + + +LIST_ACCEPTABLE_STATES_FOR_HMS = ["H-rich_Core_H_burning"] +LIST_ACCEPTABLE_STATES_FOR_HeMS = ["stripped_He_Core_He_burning"] + +LIST_ACCEPTABLE_STATES_FOR_POSTMS = STAR_STATES_H_RICH.copy() +[LIST_ACCEPTABLE_STATES_FOR_POSTMS.remove(x) for x in LIST_ACCEPTABLE_STATES_FOR_HMS] + +LIST_ACCEPTABLE_STATES_FOR_POSTHeMS = STAR_STATES_HE_RICH.copy() +[LIST_ACCEPTABLE_STATES_FOR_POSTHeMS.remove(x) for x in LIST_ACCEPTABLE_STATES_FOR_HeMS] + +class InitiallySingleStep(isolated_step): + """ + Prepare a runaway star to do an an isolated_step) + """ + + def __init__(self, + grid_name_Hrich=None, + grid_name_strippedHe=None, + path=PATH_TO_POSYDON_DATA, + *args, **kwargs): + + super().__init__( + grid_name_Hrich=grid_name_Hrich, + grid_name_strippedHe=grid_name_strippedHe, + *args, + **kwargs) + + + def __call__(self,binary): + if binary.state == "initially_single_star": + binary.star_2 = None + else: + raise ValueError("sent to InitiallySingleStep without the binary.state being initially_single_star") + + binary.event == None + + super().__call__(binary) diff --git a/posydon/binary_evol/DT/step_isolated.py b/posydon/binary_evol/DT/step_isolated.py new file mode 100644 index 0000000000..afa2002027 --- /dev/null +++ b/posydon/binary_evol/DT/step_isolated.py @@ -0,0 +1,104 @@ +"""Isolated evolution step.""" + + +__authors__ = [ + "Emmanouil Zapartas ", + "Simone Bavera ", + "Konstantinos Kovlakas " +] + + +import os +import numpy as np +from scipy.integrate import solve_ivp +from scipy.interpolate import PchipInterpolator +from scipy.optimize import minimize +from scipy.optimize import root + +from posydon.utils.data_download import PATH_TO_POSYDON_DATA +from posydon.binary_evol.binarystar import BINARYPROPERTIES +from posydon.binary_evol.singlestar import STARPROPERTIES +from posydon.interpolation import GRIDInterpolator +from posydon.interpolation.data_scaling import DataScaler +from posydon.utils.common_functions import ( + bondi_hoyle, + orbital_period_from_separation, + orbital_separation_from_period, + roche_lobe_radius, + check_state_of_star, + PchipInterpolator2 +) +from posydon.binary_evol.flow_chart import (STAR_STATES_CC) +import posydon.utils.constants as const +from posydon.binary_evol.DT.step_detached import detached_step + + +class isolated_step(detached_step): + """Evolve an isolated star (a single star, a merger product, a runaway star, etc.) + + The star will be matched in the beginning of the step and will be evolved + until core-collapse or maximum simulation time, + based on a grid of single star HDF5 grid. + + """ + + def __init__(self, + grid_name_Hrich=None, + grid_name_strippedHe=None, + path=PATH_TO_POSYDON_DATA, + #dt=None, + #n_o_steps_history=None, + do_wind_loss=False, + do_tides=False, + do_gravitational_radiation=False, + do_magnetic_braking=False, + *args, **kwargs): + super().__init__( + grid_name_Hrich=grid_name_Hrich, + grid_name_strippedHe=grid_name_strippedHe, + path=path, + #dt=dt, + #n_o_steps_history=n_o_steps_history, + do_wind_loss=do_wind_loss, + do_tides=do_tides, + do_gravitational_radiation=do_gravitational_radiation, + do_magnetic_braking=do_magnetic_braking, + *args, + **kwargs) + + + + def __call__(self, binary): + """ + and isolated star is treated as a extremely far away binary for the purpose of keeping the same code structure + put period at extreme, and initiate detached step with one star (and one non-evolving compact object), + with no orbital changes apart from spin change due to winds and deformation + + """ + + self.initialize_isolated_binary_orbit(binary) + + if binary.state == 'initially_single_star' or binary.star_1 == None or binary.star_2 == None: + #if binary.star_1 == None or binary.star_2 == None: # already one star became None in step_merged or step_initially_single + pass + elif binary.state == "disrupted": + pass + else: + raise ValueError("In isolated step one of the two stars should be None or the the binary.state=='disrupted' ") + + super().__call__(binary) + + self.re_erase_isolated_binary_orbit(binary) + + + def initialize_isolated_binary_orbit(self,binary): + # I give values to the orbital parameters so that the detached step will not complain + binary.orbital_period = 10.**99 + binary.eccentricity = 0.0 + binary.separation = orbital_separation_from_period(binary.orbital_period, 1.,1.) + + def re_erase_isolated_binary_orbit(self,binary): + # I give values to the orbital parameters so that the detached step will not complain + binary.orbital_period = np.nan + binary.eccentricity = np.nan + binary.separation = np.nan diff --git a/posydon/binary_evol/DT/step_merged.py b/posydon/binary_evol/DT/step_merged.py new file mode 100644 index 0000000000..3cefda9267 --- /dev/null +++ b/posydon/binary_evol/DT/step_merged.py @@ -0,0 +1,453 @@ +"""Merging and isolated evolution step.""" + + +__authors__ = [ + "Emmanouil Zapartas ", + "Simone Bavera ", + "Konstantinos Kovlakas " +] + + +import os +import numpy as np +from scipy.integrate import solve_ivp +from scipy.interpolate import PchipInterpolator +from scipy.optimize import minimize +from scipy.optimize import root + +from posydon.utils.data_download import PATH_TO_POSYDON_DATA +from posydon.binary_evol.binarystar import BINARYPROPERTIES +from posydon.binary_evol.singlestar import STARPROPERTIES +from posydon.interpolation import GRIDInterpolator +from posydon.interpolation.data_scaling import DataScaler +from posydon.utils.common_functions import ( + bondi_hoyle, + orbital_period_from_separation, + orbital_separation_from_period, + roche_lobe_radius, + check_state_of_star, + PchipInterpolator2 +) +from posydon.binary_evol.flow_chart import (STAR_STATES_CC) +import posydon.utils.constants as const +from posydon.binary_evol.DT.step_detached import detached_step +from posydon.binary_evol.DT.step_isolated import isolated_step + +import warnings + +from posydon.binary_evol.flow_chart import (STAR_STATES_ALL, + STAR_STATES_CO, + STAR_STATES_H_RICH, + STAR_STATES_HE_RICH, + STAR_STATES_NOT_CO + ) + + + +LIST_ACCEPTABLE_STATES_FOR_HMS = ["H-rich_Core_H_burning"] +LIST_ACCEPTABLE_STATES_FOR_HeMS = ["stripped_He_Core_He_burning"] + +LIST_ACCEPTABLE_STATES_FOR_POSTMS = STAR_STATES_H_RICH.copy() +[LIST_ACCEPTABLE_STATES_FOR_POSTMS.remove(x) for x in LIST_ACCEPTABLE_STATES_FOR_HMS] + +LIST_ACCEPTABLE_STATES_FOR_POSTHeMS = STAR_STATES_HE_RICH.copy() +[LIST_ACCEPTABLE_STATES_FOR_POSTHeMS.remove(x) for x in LIST_ACCEPTABLE_STATES_FOR_HeMS] + + + +class MergedStep(isolated_step): + """ + Prepare a merging star to do an an isolated_step) + """ + + def __init__( + self, + grid_name_Hrich=None, + grid_name_strippedHe=None, + path=PATH_TO_POSYDON_DATA, + merger_critical_rot = 0.4, + rel_mass_lost_HMS_HMS = 0.1, + list_for_matching_HMS = [["mass", "center_h1", "log_R", "he_core_mass"], + [20.0, 1.0, 2.0, 10.0], + # [[m_min_H, m_max_H], [0, None]], + ["log_min_max" , "min_max", "min_max", "min_max"] ], + list_for_matching_postMS = [["mass", "center_he4", "he_core_mass"], + [20.0, 1.0, 10.0], + #[[m_min_H, m_max_H], [0, None]], + ["log_min_max" , "min_max", "min_max"] ], + list_for_matching_HeStar = [["he_core_mass", "center_he4"], + [10.0, 1.0], + #[[m_min_He, m_max_He], [0, None]], + ["min_max" , "min_max"] ], + *args, + **kwargs + ): + + super().__init__( + grid_name_Hrich=grid_name_Hrich, + grid_name_strippedHe=grid_name_strippedHe, + list_for_matching_HMS = list_for_matching_HMS, + list_for_matching_postMS = list_for_matching_postMS, + list_for_matching_HeStar = list_for_matching_HeStar, + *args, + **kwargs) + ''' + def merged_star_properties(star_base,comp): + """ + Make assumptions about the core/total mass of the star of a merged product. + + Similar to the table of merging in BSE + + star_base: Single Star + is our base star that engulfs its companions. The merged star will have this star as a base + comp: Single Star + is the star that is engulfed + """ + #by default the stellar attributes that keep the same value from the + merged_star = star_base + + s1 = star_base.state + s2 = comp.state + + + def mass_weighted_avg(star1=star_base,star2=comp, abundance_name="center_h1", mass_weight_name="mass"): + A1 = getattr(star1, abundance_name) + A2 = getattr(star2, abundance_name) + M1 = getattr(star1, mass_weight_name) + M2 = getattr(star2, mass_weight_name) + return (A1*M1 + A2*M2 ) / (M1+M2) + + if ( s1 is in LIST_ACCEPTABLE_STATES_FOR_HMS + and s2 is in LIST_ACCEPTABLE_STATES_FOR_HMS): + #these stellar attributes change value + merged_star.mass = (star_base.mass + comp.mass) * (1.-rel_mass_lost_HMS_HMS) + + merged_star.center_h1 = mass_weighted_avg() + merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4") + merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12") + merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14") + merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16") + + #TODO: should I check if the abundaces above end up in ~1 (?) + + for key in STARPROPERTIES: + # these stellar attributes become np.nan + for substring in ["log_", "lg_", "surface","surf_", "conv_", "lambda", "profile"]: + if (substring in key) : + setattr(merged_star, key, np.nan) + if key in [ "c12_c12", "center_gamma", + "avg_c_in_c_core", "total_moment_of_inertia", "spin", "envelope_binding_energy"]: + setattr(merged_star, key, np.nan) + + + elif (s1 is in LIST_ACCEPTABLE_STATES_FOR_POSTMS: + and s2 is in LIST_ACCEPTABLE_STATES_FOR_HMS): + + merged_star.mass = star_base.mass + comp.mass + + for key in STARPROPERTIES: + # these stellar attributes become np.nan + for substring in ["log_", "lg_", "surface","surf_", "conv_", "lambda", "profile"]: + if (substring in key) : + setattr(merged_star, key, np.nan) + if key in [ "c12_c12", "center_gamma", + "avg_c_in_c_core", "total_moment_of_inertia", "spin"]: + setattr(merged_star, key, np.nan) + + merged_star.state = check_state_of_star(merged_star, star_CO=False) # TODO for sure this needs testing! + + # as above but opposite stars + elif (s1 is in LIST_ACCEPTABLE_STATES_FOR_HMS: + and s2 is in LIST_ACCEPTABLE_STATES_FOR_POSTMS): + + merged_star = comp + merged_star.mass = star_base.mass + comp.mass + + for key in STARPROPERTIES: + # these stellar attributes become np.nan + for substring in ["log_", "lg_", "surface","surf_", "conv_", "lambda", "profile"]: + if (substring in key) : + setattr(merged_star, key, np.nan) + if key in [ "c12_c12", "center_gamma", + "avg_c_in_c_core", "total_moment_of_inertia", "spin"]: + setattr(merged_star, key, np.nan) + + merged_star.state = check_state_of_star(merged_star, star_CO=False) # TODO for sure this needs testing! + + + elif (s1 is in LIST_ACCEPTABLE_STATES_FOR_POSTMS: + and s2 is in LIST_ACCEPTABLE_STATES_FOR_POSTMS): + + # add total and core masses + for key in ["mass", "he_core_mass", "c_core_mass", "o_core_mass", "co_core_mass"]: + current = getattr(merged_star, key) + getattr(comp, key) + setattr(merged_star, key,current) + + # weighted central abundances if merging cores. Else only from star_base + if star_base.co_core_mass == 0 and comp.co_core_mass == 0: # two stars with Helium cores + merged_star.center_h1 = mass_weighted_avg(mass_weight_name="he_core_mass") + merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight_name="he_core_mass") + merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight_name="he_core_mass") + merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight_name="he_core_mass") + merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight_name="he_core_mass") + elif (star_base.co_core_mass > 0 and comp.co_core_mass == 0): # star_base with CO core and the comp has a He core + continue # the central abundances are kept as the ones of star_base + elif (comp.co_core_mass > 0 and star_base.co_core_mass == 0): # comp with CO core and the star_base has a He core + merged_star.center_h1 = comp.center_h1 + merged_star.center_he4 = comp.center_he4 + merged_star.center_c12 = comp.center_c12 + merged_star.center_n14 = comp.center_n14 + merged_star.center_o16 = comp.center_o16 + elif (star_base.co_core_mass > 0 and comp.co_core_mass > 0): + merged_star.center_h1 = mass_weighted_avg(mass_weight_name="he_core_mass") + merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight_name="he_core_mass") + merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight_name="he_core_mass") + merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight_name="he_core_mass") + merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight_name="he_core_mass") + else: + warnings.warn("weird compbination of CO core masses during merging") + + for key in STARPROPERTIES: + # these stellar attributes become np.nan + for substring in ["log_", "lg_", "surface","surf_", "conv_", "lambda", "profile"]: + if (substring in key) : + setattr(merged_star, key, np.nan) + if key in [ "c12_c12", "center_gamma", + "avg_c_in_c_core", "total_moment_of_inertia", "spin"]: + setattr(merged_star, key, np.nan) + merged_star.state = check_state_of_star(merged_star, star_CO=False) # TODO for sure this needs testing! + + elif (s1 is in LIST_ACCEPTABLE_STATES_FOR_POSTMS: + and s2 is in LIST_ACCEPTABLE_STATES_FOR_HeMS): + + # add total and core masses + for key in ["mass", "he_core_mass", "c_core_mass", "o_core_mass", "co_core_mass"]: + current = getattr(merged_star, key) + getattr(comp, key) + setattr(merged_star, key,current) + + for key in STARPROPERTIES: + # these stellar attributes become np.nan + for substring in ["log_", "lg_", "surface","surf_", "conv_", "lambda", "profile"]: + if (substring in key) : + setattr(merged_star, key, np.nan) + if key in [ "c12_c12", "center_gamma", + "avg_c_in_c_core", "total_moment_of_inertia", "spin"]: + setattr(merged_star, key, np.nan) + merged_star.state = check_state_of_star(merged_star, star_CO=False) # TODO for sure this needs testing! + + # as above but opposite stars + elif (s1 is in LIST_ACCEPTABLE_STATES_FOR_HeMS : + and s2 is in LIST_ACCEPTABLE_STATES_FOR_POSTMS): + + merged_star = comp + + # add total and core masses + for key in ["mass", "he_core_mass", "c_core_mass", "o_core_mass", "co_core_mass"]: + current = getattr(merged_star, key) + getattr(star_base, key) + setattr(merged_star, key,current) + + for key in STARPROPERTIES: + # these stellar attributes become np.nan + for substring in ["log_", "lg_", "surface","surf_", "conv_", "lambda", "profile"]: + if (substring in key) : + setattr(merged_star, key, np.nan) + if key in [ "c12_c12", "center_gamma", + "avg_c_in_c_core", "total_moment_of_inertia", "spin"]: + setattr(merged_star, key, np.nan) + merged_star.state = check_state_of_star(merged_star, star_CO=False) # TODO for sure this needs testing! + + elif (s1 is in LIST_ACCEPTABLE_STATES_FOR_POSTMS: + and s2 is in LIST_ACCEPTABLE_STATES_FOR_POSTHeMS): + + # add total and core masses + for key in ["mass", "he_core_mass", "c_core_mass", "o_core_mass", "co_core_mass"]: + current = getattr(merged_star, key) + getattr(comp, key) + setattr(merged_star, key,current) + + # weighted central abundances if merging cores. Else only from star_base + if star_base.co_core_mass == 0 and comp.co_core_mass == 0: # two stars with Helium cores + merged_star.center_h1 = mass_weighted_avg(mass_weight_name="he_core_mass") + merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight_name="he_core_mass") + merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight_name="he_core_mass") + merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight_name="he_core_mass") + merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight_name="he_core_mass") + elif (star_base.co_core_mass > 0 and comp.co_core_mass == 0): # star_base with CO core and the comp has a He core + continue # the central abundances are kept as the ones of star_base + elif (comp.co_core_mass > 0 and star_base.co_core_mass == 0): # comp with CO core and the star_base has a He core + merged_star.center_h1 = comp.center_h1 + merged_star.center_he4 = comp.center_he4 + merged_star.center_c12 = comp.center_c12 + merged_star.center_n14 = comp.center_n14 + merged_star.center_o16 = comp.center_o16 + elif (star_base.co_core_mass > 0 and comp.co_core_mass > 0): + merged_star.center_h1 = mass_weighted_avg(mass_weight_name="co_core_mass") + merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight_name="co_core_mass") + merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight_name="co_core_mass") + merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight_name="co_core_mass") + merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight_name="co_core_mass") + else: + warnings.warn("weird compbination of CO core masses during merging") + + for key in STARPROPERTIES: + # these stellar attributes become np.nan + for substring in ["log_", "lg_", "surface","surf_", "conv_", "lambda", "profile"]: + if (substring in key) : + setattr(merged_star, key, np.nan) + if key in [ "c12_c12", "center_gamma", + "avg_c_in_c_core", "total_moment_of_inertia", "spin"]: + setattr(merged_star, key, np.nan) + merged_star.state = check_state_of_star(merged_star, star_CO=False) # TODO for sure this needs testing! + + # as above but the opposite stars + elif (s1 is in LIST_ACCEPTABLE_STATES_FOR_POSTHeMS: + and s2 is in LIST_ACCEPTABLE_STATES_FOR_POSTMS): + + merged_star = comp + # add total and core masses + for key in ["mass", "he_core_mass", "c_core_mass", "o_core_mass", "co_core_mass"]: + current = getattr(merged_star, key) + getattr(star_base, key) + setattr(merged_star, key,current) + + # weighted central abundances if merging cores. Else only from star_base + if star_base.co_core_mass == 0 and comp.co_core_mass == 0: # two stars with Helium cores + merged_star.center_h1 = mass_weighted_avg(mass_weight_name="he_core_mass") + merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight_name="he_core_mass") + merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight_name="he_core_mass") + merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight_name="he_core_mass") + merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight_name="he_core_mass") + elif (star_base.co_core_mass > 0 and comp.co_core_mass == 0): # star_base with CO core and the comp has a He core + continue # the central abundances are kept as the ones of star_base + elif (comp.co_core_mass > 0 and star_base.co_core_mass == 0): # comp with CO core and the star_base has a He core + merged_star.center_h1 = comp.center_h1 + merged_star.center_he4 = comp.center_he4 + merged_star.center_c12 = comp.center_c12 + merged_star.center_n14 = comp.center_n14 + merged_star.center_o16 = comp.center_o16 + elif (star_base.co_core_mass > 0 and comp.co_core_mass > 0): + merged_star.center_h1 = mass_weighted_avg(mass_weight_name="co_core_mass") + merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight_name="co_core_mass") + merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight_name="co_core_mass") + merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight_name="co_core_mass") + merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight_name="co_core_mass") + else: + warnings.warn("weird compbination of CO core masses during merging") + + for key in STARPROPERTIES: + # these stellar attributes become np.nan + for substring in ["log_", "lg_", "surface","surf_", "conv_", "lambda", "profile"]: + if (substring in key) : + setattr(merged_star, key, np.nan) + if key in [ "c12_c12", "center_gamma", + "avg_c_in_c_core", "total_moment_of_inertia", "spin"]: + setattr(merged_star, key, np.nan) + merged_star.state = check_state_of_star(merged_star, star_CO=False) # TODO for sure this needs testing! + + + elif (s1 is in STAR_STATES_HE_RICH: + and s2 is in STAR_STATES_HE_RICH): + + # add total and core masses + for key in ["mass", "he_core_mass", "c_core_mass", "o_core_mass", "co_core_mass"]: + current = getattr(merged_star, key) + getattr(comp, key) + setattr(merged_star, key,current) + + # weighted central abundances if merging cores. Else only from star_base + if star_base.co_core_mass == 0 and comp.co_core_mass == 0: # two stars with Helium cores + merged_star.center_h1 = mass_weighted_avg(mass_weight_name="he_core_mass") + merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight_name="he_core_mass") + merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight_name="he_core_mass") + merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight_name="he_core_mass") + merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight_name="he_core_mass") + elif (star_base.co_core_mass > 0 and comp.co_core_mass == 0): # star_base with CO core and the comp has a He core + continue # the central abundances are kept as the ones of star_base + elif (comp.co_core_mass > 0 and star_base.co_core_mass == 0): # comp with CO core and the star_base has a He core + merged_star.center_h1 = comp.center_h1 + merged_star.center_he4 = comp.center_he4 + merged_star.center_c12 = comp.center_c12 + merged_star.center_n14 = comp.center_n14 + merged_star.center_o16 = comp.center_o16 + elif (star_base.co_core_mass > 0 and comp.co_core_mass > 0): + merged_star.center_h1 = mass_weighted_avg(mass_weight_name="co_core_mass") + merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight_name="co_core_mass") + merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight_name="co_core_mass") + merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight_name="co_core_mass") + merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight_name="co_core_mass") + else: + warnings.warn("weird compbination of CO core masses during merging") + + for key in STARPROPERTIES: + # these stellar attributes become np.nan + for substring in ["log_", "lg_", "surface","surf_", "conv_", "lambda", "profile"]: + if (substring in key) : + setattr(merged_star, key, np.nan) + if key in [ "c12_c12", "center_gamma", + "avg_c_in_c_core", "total_moment_of_inertia", "spin"]: + setattr(merged_star, key, np.nan) + merged_star.state = check_state_of_star(merged_star, star_CO=False) # TODO for sure this needs testing! + + elif (s1 is in STAR_STATES_HE_RICH: + and s2 is in ["WD"]): + + # add total and core masses + for key in ["mass", "he_core_mass", "c_core_mass", "o_core_mass", "co_core_mass"]: + current = getattr(merged_star, key) + getattr(comp, "mass") + setattr(merged_star, key,current) + + # weighted central abundances if merging cores. Else only from star_base + if (comp.co_core_mass > 0 and star_base.co_core_mass == 0): # comp with CO core and the star_base has a He core + merged_star.center_h1 = comp.center_h1 + merged_star.center_he4 = comp.center_he4 + merged_star.center_c12 = comp.center_c12 + merged_star.center_n14 = comp.center_n14 + merged_star.center_o16 = comp.center_o16 + elif (star_base.co_core_mass > 0 and comp.co_core_mass > 0): + merged_star.center_h1 = mass_weighted_avg(mass_weight_name="co_core_mass") + merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight_name="co_core_mass") + merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight_name="co_core_mass") + merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight_name="co_core_mass") + merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight_name="co_core_mass") + else: + warnings.warn("weird compbination of CO core masses during merging") + + for key in STARPROPERTIES: + # these stellar attributes become np.nan + for substring in ["log_", "lg_", "surface","surf_", "conv_", "lambda", "profile"]: + if (substring in key) : + setattr(merged_star, key, np.nan) + if key in [ "c12_c12", "center_gamma", + "avg_c_in_c_core", "total_moment_of_inertia", "spin"]: + setattr(merged_star, key, np.nan) + merged_star.state = check_state_of_star(merged_star, star_CO=False) # TODO for sure this needs testing! + + elif (s1 is in STAR_STATES_HE_RICH: + and s2 is in ["NS", "BH"]): + merged_star = comp + + else: + print("Combination of merging star states not expected: ", s1, s2) + + # ad hoc spin of merged star to be used in the detached step + merged_star.surf_avg_omega_div_omega_crit = merger_critical_rot + + return merged_star, None + +''' + + def __call__(self,binary): + # FOR THIS PR, merged step does nothing! + ''' + if binary.state == "merged": + if binary.event == 'oMerging1': + binary.star_1,binary.star_2 = merged_star_properties(binary.star_1,binary.star_2) + elif binary.event == 'oMerging2': + binary.star_2,binary.star_1 = merged_star_properties(binary.star_2,binary.star_1) + else: + raise ValueError("binary.state='merged' but binary.event != 'oMerging1/2'") + else: + raise ValueError("step_merging initated but binary.state != 'merged'") + + binary.event == None + + super().__call__(binary) + ''' + pass diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index cf35f72a9d..7f61fa320c 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -1231,12 +1231,13 @@ def orbital_kick(self, binary): new_separation, binary.star_1.mass, binary.star_2.mass ) for key in BINARYPROPERTIES: - if key not in ['V_sys', 'nearest_neighbour_distance']: + if key not in ['V_sys', 'nearest_neighbour_distance', 'state']: setattr(binary, key, None) # if key is 'nearest_neighbour_distance': # setattr(binary, key, ['None', 'None', 'None']) binary.separation = new_separation - binary.state = "detached" + if binary.state is not "disrupted": + binary.state = "detached" binary.event = None binary.time = binary.time_history[-1] binary.eccentricity = binary.eccentricity_history[-1] @@ -1327,12 +1328,13 @@ def orbital_kick(self, binary): new_separation, binary.star_1.mass, binary.star_2.mass ) for key in BINARYPROPERTIES: - if key not in ['V_sys', 'nearest_neighbour_distance']: + if key not in ['V_sys', 'nearest_neighbour_distance', 'state']: setattr(binary, key, None) # if key is 'nearest_neighbour_distance': # setattr(binary, key, ['None', 'None', 'None']) binary.separation = new_separation - binary.state = "detached" + if binary.state is not "disrupted": + binary.state = "detached" binary.event = None binary.time = binary.time_history[-1] binary.eccentricity = binary.eccentricity_history[-1] @@ -1408,236 +1410,12 @@ def orbital_kick(self, binary): mean_anomaly = np.random.uniform(0, 2 * np.pi) binary.star_2.natal_kick_array[3] = mean_anomaly - # The binary exist: flag_binary is True if the binary is not disrupted - flag_binary = True - - # eccentricity before the SN - epre = binary.eccentricity - # the orbital semimajor axis is the orbital separation - Apre = binary.separation - # Eq 16, Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 - # for eccentric anomaly - E_ma = sp.optimize.brentq( - lambda x: mean_anomaly - x + epre * np.sin(x), 0, 2 * np.pi - ) - # Eq 15, Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 - # orbital separation at the time of the exlosion - rpre = Apre * (1.0 - epre * np.cos(E_ma)) - - # load constants in CGS - G = const.standard_cgrav - - # Convert inputs to CGS - M_he_star = M_he_star * const.Msun - M_companion = M_companion * const.Msun - M_compact_object = M_compact_object * const.Msun - Apre = Apre * const.Rsun - Vkick = Vkick * const.km2cm - rpre = rpre * const.Rsun - - # get useful quantity - sin_theta = np.sqrt(1 - (cos_theta ** 2)) - - # get kicks componets in the coordinate system - Vkx = Vkick * sin_theta * np.sin(phi) - Vky = Vkick * cos_theta - Vkz = Vkick * sin_theta * np.cos(phi) - - # Eq 1, in Kalogera, V. 1996, ApJ, 471, 352 - # extended to Eq 17 in Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 - # Vr is velocity of preSN He core relative to M_companion, directed - # along the positive y axis - Vr = np.sqrt(G * (M_he_star + M_companion) * (2.0 / rpre - 1.0 / Apre)) - Mtot = M_compact_object + M_companion - - # Eq 3, in Kalogera, V. 1996, ApJ, 471, 352 - # extended to Eq 13, in Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 - # get the orbital separation post SN - Apost = ((2.0 / rpre) - - (((Vkick ** 2) + (Vr ** 2) + (2 * Vky * Vr)) / (G * Mtot)) - ) ** -1 - - # Eq 18, Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 - # psi: is the polar angle of the position vector of the CO with respect - # to its pre-SN orbital velocity in the companions frame. - sin_psi = np.round( - np.sqrt(G * (M_he_star + M_companion) * (1 - epre ** 2) * Apre) - / (rpre * Vr), 5) - cos_psi = np.sqrt(1 - sin_psi ** 2) - - # Eq 4, in Kalogera, V. 1996, ApJ, 471, 352 - # extended to Eq 14 in Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 - # get the eccentricity post SN - x = ((Vkz ** 2 + (sin_psi * (Vr + Vky) - cos_psi * Vkx) ** 2) - * rpre ** 2 - / (G * Mtot * Apost)) - - # catch negative values, i.e. disrupted binaries - if 1.-x < 0.: - epost = np.nan - else: - epost = np.sqrt(1 - x) - - # Eq 34, in Kalogera, V. 1996, ApJ, 471, 352 - # V_sys: is the resulting center of mass velocity of the system - # IN THE TRANSLATED COMOVING FRAME, imparted by the SN - VSx = M_compact_object * Vkx / Mtot - VSy = ( - M_compact_object * Vky - - ( - (M_he_star - M_compact_object) - * M_companion - * Vr - / (M_he_star + M_companion) - ) - ) / Mtot - VSz = M_compact_object * Vkz / Mtot - # V_sys = np.sqrt(VSx ** 2 + VSy ** 2 + VSz ** 2) - - # Eq 5, in Kalogera, V. 1996, ApJ, 471, 352: - # calculate the tilt of the orbital plane after the SN - tilt = np.arccos((Vky + Vr) / ((Vky + Vr) ** 2 + Vkz ** 2) ** (1. / 2)) - - def SNCheck( - M_he_star, - M_companion, - M_compact_object, - rpre, - Apost, - epost, - Vr, - Vkick, - cos_theta, - verbose, - ): - """Check that the binary is not disrupted. - - Parameters - ---------- - M_he_star : double - Helium star mass before the SN in g. - M_companion : double - Companion star mass in g. - M_compact_object : double - Compact object mass left by the SN in g. - rpre : double - Oribtal separation at the time of the exlosion in cm. If the - eccentricity pre SN is 0 this correpond to Apre. - Apost : double - Orbital separtion after the SN in cm. - epost : double - Eccentricity after the SN. - Vr : double - Velocity of pre-SN He core relative to M_companion, directed - along the positive y axis in cm/s. - Vkick : double - Kick velocity in cm/s. - cos_theta : double - The cosine of the angle between pre- & post-SN orbital planes. - - Returns - ------- - flag_binary : bool - flag_binary is True if the binary is not disrupted. - - References - ---------- - .. [1] Willems, B., Henninger, M., Levin, T., et al. 2005, ApJ, 625, 324 - - .. [2] Kalogera, V. & Lorimer, D.R. 2000, ApJ, 530, 890 - - """ - # flag_binary is True if the binary is not disrupted - flag_binary = True - Mtot_pre = M_he_star + M_companion - Mtot_post = M_compact_object + M_companion - - # Define machine precision (we can probaly lower this number) - err = const.SNcheck_ERR - - # SNflag1: Eq. 21, Willems, B., Henninger, M., Levin, T., et al. 2005, ApJ, 625, 324 (with typo fixed) - # from Eq. 10, Flannery, B.P. & van den Heuvel, E.P.J. 1975, A&A, 39, 61 - # Continuity demands post-SN orbit to pass through preSN positions. - # Updated to work for eccentric orbits, - # see Eq. 15 in Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 - SNflag1 = (1 - epost - rpre / Apost <= err) and ( - rpre / Apost - (1 + epost) <= err - ) - - # SNflag2: Equations 22-23, Willems, B., Henninger, M., Levin, T., et al. 2005, ApJ, 625, 324 - # (see, e.g., Kalogera, V. & Lorimer, D.R. 2000, ApJ, 530, 890) - tmp1 = 2 - Mtot_pre / Mtot_post * (Vkick / Vr - 1) ** 2 - tmp2 = 2 - Mtot_pre / Mtot_post * (Vkick / Vr + 1) ** 2 - SNflag2 = ((rpre / Apost - tmp1 < err) - and (err > tmp2 - rpre / Apost)) - - # SNflag3: check that epost does not exeed 1 or is nan - if epost >= 1.0 or np.isnan(epost): - SNflag3 = False - else: - SNflag3 = True - - SNflags = [SNflag1, SNflag2, SNflag3] - - if verbose: - print() - print("The orbital checks are:", SNflags) - print() - print("1. Post-SN orbit must pass through pre-SN positions.") - print("2. Lower and upper limits on amount of orbital " - "contraction or expansion that can take place for a " - "given amount of mass loss and a given magnitude of the " - "kick velocity.") - print("3. Checks that e_post is not larger than 1 or nan.") - - # check if the supernova is valid and doesn't disrupt the system - if not all(SNflags): - flag_binary = False - - return flag_binary - - # check if the binary is disrupted - flag_binary = SNCheck(M_he_star, M_companion, M_compact_object, rpre, - Apost, epost, Vr, Vkick, cos_theta, - verbose=self.verbose) - - # update the binary object - if flag_binary: - # update the tilt - if binary.event == "CC1": - binary.star_1.spin_orbit_tilt = tilt - elif binary.event == "CC2": - binary.star_2.spin_orbit_tilt = tilt - else: - raise ValueError("This should never happen!") - - # compute new orbital period before reseting the binary properties - - for key in BINARYPROPERTIES: - if key is not 'nearest_neighbour_distance': - setattr(binary, key, None) - binary.state = "detached" - binary.event = None - binary.separation = Apost / const.Rsun - binary.eccentricity = epost - binary.V_sys = np.array([VSx / const.km2cm, VSy / const.km2cm, VSz - / const.km2cm]) - binary.time = binary.time_history[-1] - # in future we will make the orbital period a callable property - new_orbital_period = orbital_period_from_separation( - binary.separation, binary.star_1.mass, binary.star_2.mass) - binary.orbital_period = new_orbital_period - binary.mass_transfer_case = 'None' - else: - # update the tilt - if binary.event == "CC1": - binary.star_1.spin_orbit_tilt = np.nan - elif binary.event == "CC2": - binary.star_2.spin_orbit_tilt = np.nan - else: - raise ValueError("This should never happen!") + # update the orbit + if binary.state == "disrupted": + #the binary was already disrupted before the SN + # update the binary object which was disrupted already before the SN for key in BINARYPROPERTIES: if key is not 'nearest_neighbour_distance': setattr(binary, key, None) @@ -1650,6 +1428,251 @@ def SNCheck( binary.orbital_period = np.nan binary.mass_transfer_case = 'None' + else: + # the binary is not disrupted at least before the SN + # The binary exists : flag_binary is True at least before the SN + flag_binary = True + + # eccentricity before the SN + epre = binary.eccentricity + # the orbital semimajor axis is the orbital separation + Apre = binary.separation + # Eq 16, Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 + # for eccentric anomaly + E_ma = sp.optimize.brentq( + lambda x: mean_anomaly - x + epre * np.sin(x), 0, 2 * np.pi + ) + # Eq 15, Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 + # orbital separation at the time of the exlosion + rpre = Apre * (1.0 - epre * np.cos(E_ma)) + + # load constants in CGS + G = const.standard_cgrav + + # Convert inputs to CGS + M_he_star = M_he_star * const.Msun + M_companion = M_companion * const.Msun + M_compact_object = M_compact_object * const.Msun + Apre = Apre * const.Rsun + Vkick = Vkick * const.km2cm + rpre = rpre * const.Rsun + + # get useful quantity + sin_theta = np.sqrt(1 - (cos_theta ** 2)) + + # get kicks componets in the coordinate system + Vkx = Vkick * sin_theta * np.sin(phi) + Vky = Vkick * cos_theta + Vkz = Vkick * sin_theta * np.cos(phi) + + # Eq 1, in Kalogera, V. 1996, ApJ, 471, 352 + # extended to Eq 17 in Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 + # Vr is velocity of preSN He core relative to M_companion, directed + # along the positive y axis + Vr = np.sqrt(G * (M_he_star + M_companion) * (2.0 / rpre - 1.0 / Apre)) + Mtot = M_compact_object + M_companion + + # Eq 3, in Kalogera, V. 1996, ApJ, 471, 352 + # extended to Eq 13, in Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 + # get the orbital separation post SN + Apost = ((2.0 / rpre) + - (((Vkick ** 2) + (Vr ** 2) + (2 * Vky * Vr)) / (G * Mtot)) + ) ** -1 + + # Eq 18, Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 + # psi: is the polar angle of the position vector of the CO with respect + # to its pre-SN orbital velocity in the companions frame. + sin_psi = np.round( + np.sqrt(G * (M_he_star + M_companion) * (1 - epre ** 2) * Apre) + / (rpre * Vr), 5) + cos_psi = np.sqrt(1 - sin_psi ** 2) + + # Eq 4, in Kalogera, V. 1996, ApJ, 471, 352 + # extended to Eq 14 in Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 + # get the eccentricity post SN + x = ((Vkz ** 2 + (sin_psi * (Vr + Vky) - cos_psi * Vkx) ** 2) + * rpre ** 2 + / (G * Mtot * Apost)) + + # catch negative values, i.e. disrupted binaries + if 1.-x < 0.: + epost = np.nan + else: + epost = np.sqrt(1 - x) + + # Eq 34, in Kalogera, V. 1996, ApJ, 471, 352 + # V_sys: is the resulting center of mass velocity of the system + # IN THE TRANSLATED COMOVING FRAME, imparted by the SN + VSx = M_compact_object * Vkx / Mtot + VSy = ( + M_compact_object * Vky + - ( + (M_he_star - M_compact_object) + * M_companion + * Vr + / (M_he_star + M_companion) + ) + ) / Mtot + VSz = M_compact_object * Vkz / Mtot + # V_sys = np.sqrt(VSx ** 2 + VSy ** 2 + VSz ** 2) + + # Eq 5, in Kalogera, V. 1996, ApJ, 471, 352: + # calculate the tilt of the orbital plane after the SN + tilt = np.arccos((Vky + Vr) / ((Vky + Vr) ** 2 + Vkz ** 2) ** (1. / 2)) + + + def SNCheck( + M_he_star, + M_companion, + M_compact_object, + rpre, + Apost, + epost, + Vr, + Vkick, + cos_theta, + verbose, + ): + """Check that the binary is not disrupted. + + Parameters + ---------- + M_he_star : double + Helium star mass before the SN in g. + M_companion : double + Companion star mass in g. + M_compact_object : double + Compact object mass left by the SN in g. + rpre : double + Oribtal separation at the time of the exlosion in cm. If the + eccentricity pre SN is 0 this correpond to Apre. + Apost : double + Orbital separtion after the SN in cm. + epost : double + Eccentricity after the SN. + Vr : double + Velocity of pre-SN He core relative to M_companion, directed + along the positive y axis in cm/s. + Vkick : double + Kick velocity in cm/s. + cos_theta : double + The cosine of the angle between pre- & post-SN orbital planes. + + Returns + ------- + flag_binary : bool + flag_binary is True if the binary is not disrupted. + + References + ---------- + .. [1] Willems, B., Henninger, M., Levin, T., et al. 2005, ApJ, 625, 324 + + .. [2] Kalogera, V. & Lorimer, D.R. 2000, ApJ, 530, 890 + + """ + # flag_binary is True if the binary is not disrupted + flag_binary = True + Mtot_pre = M_he_star + M_companion + Mtot_post = M_compact_object + M_companion + + # Define machine precision (we can probaly lower this number) + err = const.SNcheck_ERR + + # SNflag1: Eq. 21, Willems, B., Henninger, M., Levin, T., et al. 2005, ApJ, 625, 324 (with typo fixed) + # from Eq. 10, Flannery, B.P. & van den Heuvel, E.P.J. 1975, A&A, 39, 61 + # Continuity demands post-SN orbit to pass through preSN positions. + # Updated to work for eccentric orbits, + # see Eq. 15 in Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 + SNflag1 = (1 - epost - rpre / Apost <= err) and ( + rpre / Apost - (1 + epost) <= err + ) + + # SNflag2: Equations 22-23, Willems, B., Henninger, M., Levin, T., et al. 2005, ApJ, 625, 324 + # (see, e.g., Kalogera, V. & Lorimer, D.R. 2000, ApJ, 530, 890) + tmp1 = 2 - Mtot_pre / Mtot_post * (Vkick / Vr - 1) ** 2 + tmp2 = 2 - Mtot_pre / Mtot_post * (Vkick / Vr + 1) ** 2 + SNflag2 = ((rpre / Apost - tmp1 < err) + and (err > tmp2 - rpre / Apost)) + + # SNflag3: check that epost does not exeed 1 or is nan + if epost >= 1.0 or np.isnan(epost): + SNflag3 = False + else: + SNflag3 = True + + SNflags = [SNflag1, SNflag2, SNflag3] + + if verbose: + print() + print("The orbital checks are:", SNflags) + print() + print("1. Post-SN orbit must pass through pre-SN positions.") + print("2. Lower and upper limits on amount of orbital " + "contraction or expansion that can take place for a " + "given amount of mass loss and a given magnitude of the " + "kick velocity.") + print("3. Checks that e_post is not larger than 1 or nan.") + + # check if the supernova is valid and doesn't disrupt the system + if not all(SNflags): + flag_binary = False + + return flag_binary + + # check if the binary is disrupted + flag_binary = SNCheck(M_he_star, M_companion, M_compact_object, rpre, + Apost, epost, Vr, Vkick, cos_theta, + verbose=self.verbose) + + # update the binary object which was bound at least before the SN + if flag_binary: + # update the tilt + if binary.event == "CC1": + binary.star_1.spin_orbit_tilt = tilt + elif binary.event == "CC2": + binary.star_2.spin_orbit_tilt = tilt + else: + raise ValueError("This should never happen!") + + # compute new orbital period before reseting the binary properties + + for key in BINARYPROPERTIES: + if key is not 'nearest_neighbour_distance': + setattr(binary, key, None) + + binary.state = "detached" + binary.event = None + binary.separation = Apost / const.Rsun + binary.eccentricity = epost + binary.V_sys = np.array([VSx / const.km2cm, VSy / const.km2cm, VSz + / const.km2cm]) + binary.time = binary.time_history[-1] + # in future we will make the orbital period a callable property + new_orbital_period = orbital_period_from_separation( + binary.separation, binary.star_1.mass, binary.star_2.mass) + binary.orbital_period = new_orbital_period + binary.mass_transfer_case = 'None' + else: + # update the tilt + if binary.event == "CC1": + binary.star_1.spin_orbit_tilt = np.nan + elif binary.event == "CC2": + binary.star_2.spin_orbit_tilt = np.nan + else: + raise ValueError("This should never happen!") + + for key in BINARYPROPERTIES: + if key is not 'nearest_neighbour_distance': + setattr(binary, key, None) + binary.state = "disrupted" + binary.event = None + binary.separation = np.nan + binary.eccentricity = np.nan + binary.V_sys = np.array([0, 0, 0]) + binary.time = binary.time_history[-1] + binary.orbital_period = np.nan + binary.mass_transfer_case = 'None' + """ ##### Generating the CCSN SN kick of a single star ##### """ diff --git a/posydon/binary_evol/flow_chart.py b/posydon/binary_evol/flow_chart.py index 3efac6303b..f0dc434d58 100644 --- a/posydon/binary_evol/flow_chart.py +++ b/posydon/binary_evol/flow_chart.py @@ -69,6 +69,7 @@ STAR_STATES_HE_RICH_EVOLVABLE.append('H-rich_non_burning') BINARY_STATES_ALL = [ + 'initially_single_star', 'detached', 'RLO1', 'RLO2', @@ -81,7 +82,7 @@ BINARY_EVENTS_ALL = [ None, 'CC1', - 'CC2' + 'CC2', 'ZAMS', 'oRLO1', 'oRLO2', @@ -92,7 +93,9 @@ 'CO_contact', 'redirect', 'MaxTime_exceeded', - 'maxtime' + 'maxtime', + 'oMerging1', + 'oMerging2' ] @@ -187,7 +190,8 @@ BINARY_STATES_CC = BINARY_STATES_ALL.copy() -BINARY_STATES_CC.remove('disrupted') +#BINARY_STATES_CC = BINARY_STATES_ALL.copy() +#BINARY_STATES_CC.remove('disrupted') for b in BINARY_STATES_CC: for s1 in STAR_STATES_CC: @@ -204,7 +208,7 @@ # catch states to be ended -for b in ['merged', 'disrupted', 'initial_RLOF', +for b in ['initial_RLOF', 'detached (Integration failure)', 'detached (GridMatchingFailed)', 'RLO2 (OutsideGrid)']: for s1 in STAR_STATES_ALL: @@ -213,6 +217,38 @@ POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_end' POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_end' +for b in ['initially_single_star']: + for s1 in STAR_STATES_ALL: + for s2 in STAR_STATES_ALL: + for e in BINARY_EVENTS_ALL: + POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_end'#'step_initially_single' + POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_end'#'step_initially_single' + +BINARY_EVENTS_OF_SN_OR_AFTER_DETACHED = BINARY_EVENTS_ALL.copy() +[BINARY_EVENTS_OF_SN_OR_AFTER_DETACHED.remove(x) for x in ['CC1','CC2','MaxTime_exceeded','maxtime']] + +for b in ['disrupted']: + for s1 in STAR_STATES_ALL: + for s2 in STAR_STATES_ALL: + for e in BINARY_EVENTS_OF_SN_OR_AFTER_DETACHED: + POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_disrupted' + POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_disrupted' +# if we have two compcat objects in a disrupted binary, we stop the evolution. +for b in ['disrupted']: + for s1 in STAR_STATES_CO: + for s2 in STAR_STATES_CO: + for e in BINARY_EVENTS_ALL: + POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_end' + POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_end' + + +for b in ['merged']: + for s1 in STAR_STATES_ALL: + for s2 in STAR_STATES_ALL: + for e in ['oMerging1', 'oMerging2']: + POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_end' #'step_merged' + POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_end' #'step_merged' + # catch initial_RLO states for s1 in STAR_STATES_ALL: for s2 in STAR_STATES_ALL: diff --git a/posydon/binary_evol/simulationproperties.py b/posydon/binary_evol/simulationproperties.py index a936d30098..76fab5fc61 100644 --- a/posydon/binary_evol/simulationproperties.py +++ b/posydon/binary_evol/simulationproperties.py @@ -100,14 +100,14 @@ def load_steps(self, verbose=False): def close(self): """Close hdf5 files before exiting.""" from posydon.binary_evol.MESA.step_mesa import MesaGridStep - from posydon.binary_evol.DT.step_detached import detached_step + from posydon.binary_evol.DT.step_detached import detached_step all_step_funcs = [getattr(self, key) for key, val in self.__dict__.items() if 'step_' in key] for step_func in all_step_funcs: if isinstance(step_func, MesaGridStep): step_func.close() elif isinstance(step_func, detached_step): - for grid_interpolator in [step_func.grid1, step_func.grid2]: + for grid_interpolator in [step_func.grid_Hrich, step_func.grid_strippedHe]: grid_interpolator.close() def pre_evolve(self, binary): diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index ab252b96f8..7d5e20e936 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -34,6 +34,7 @@ import os from tqdm import tqdm import psutil +import random from posydon.binary_evol.binarystar import BinaryStar from posydon.binary_evol.singlestar import SingleStar @@ -64,7 +65,7 @@ # BinaryPopulation will enforce a constant metallicity accross all steps that # load stellar or binary models by checked this list of steps. STEP_NAMES_LOADING_GRIDS = [ - 'step_HMS_HMS', 'step_CO_HeMS', 'step_CO_HMS_RLO', 'step_detached' + 'step_HMS_HMS', 'step_CO_HeMS', 'step_CO_HMS_RLO', 'step_detached','step_isolated','step_disrupted' ] class BinaryPopulation: @@ -84,7 +85,9 @@ def __init__(self, **kwargs): self.kwargs = default_kwargs.copy() for key, arg in kwargs.items(): self.kwargs[key] = arg - + # Have a binary fraction change the number_of binaries. + #Eirini's change + self.binary_fraction = self.kwargs.get('binary_fraction') self.number_of_binaries = self.kwargs.get('number_of_binaries') self.population_properties = self.kwargs.get('population_properties', @@ -240,6 +243,7 @@ def _safe_evolve(self, **kwargs): for j, index in enumerate(indices_for_iter): if kwargs.get('from_hdf', False): + #generator binary = self.manager.from_hdf(index, restore=True).pop() else: binary = self.manager.generate(index=index, **self.kwargs) @@ -249,6 +253,7 @@ def _safe_evolve(self, **kwargs): try: binary.evolve() except Exception: + #print(traceback.print_exc()) binary.event = 'FAILED' binary.traceback = traceback.format_exc() if len(w) > 0: @@ -754,7 +759,7 @@ def __init__(self, sampler=generate_independent_samples, self.kwargs = kwargs.copy() self.sampler = sampler self.star_formation = kwargs.get('star_formation', 'burst') - + self.binary_fraction = kwargs.get('binary_fraction', 1) def reset_rng(self): """Reset the RNG with the stored entropy.""" self._num_gen = 0 @@ -779,6 +784,7 @@ def get_binary_by_iter(self, n=1, **kwargs): def draw_initial_samples(self, orbital_scheme='separation', **kwargs): """Generate all random varibles.""" + binary_fraction = self.kwargs.get('binary_fraction', 1) if not ('RNG' in kwargs.keys()): kwargs['RNG'] = self.RNG # a, e, M_1, M_2, P @@ -801,6 +807,7 @@ def draw_initial_samples(self, orbital_scheme='separation', **kwargs): output_dict = { 'binary_index': indices, + 'binary_fraction':binary_fraction, 'time': formation_times, 'separation': separation, 'eccentricity': eccentricity, @@ -830,46 +837,94 @@ def draw_initial_binary(self, **kwargs): output = self.draw_initial_samples(**sampler_kwargs) default_index = output['binary_index'].item() + #Eirini's comments: # Randomly generated variables - formation_time = output['time'].item() - separation = output['separation'].item() - orbital_period = output['orbital_period'].item() - eccentricity = output['eccentricity'].item() - m1 = output['S1_mass'].item() - m2 = output['S2_mass'].item() - Z_div_Zsun = kwargs.get('metallicity', 1.) - zams_table = {1.: 2.703e-01, - 0.1: 2.511e-01, - 0.01: 2.492e-01, - 0.001: 2.49e-01, - 0.0001: 2.49e-01} - Y = zams_table[Z_div_Zsun] - Z = Z_div_Zsun*Zsun - X = 1. - Z - Y - - binary_params = dict( - index=kwargs.get('index', default_index), - time=formation_time, - state="detached", - event="ZAMS", - separation=separation, - orbital_period=orbital_period, - eccentricity=eccentricity, - ) - star1_params = dict( - mass=m1, - state="H-rich_Core_H_burning", - metallicity=Z, - center_h1=X, - center_he4=Y, - ) - star2_params = dict( - mass=m2, - state="H-rich_Core_H_burning", - metallicity=Z, - center_h1=X, - center_he4=Y, - ) + + + if self.RNG.uniform() < self.binary_fraction: + formation_time = output['time'].item() + separation = output['separation'].item() + orbital_period = output['orbital_period'].item() + eccentricity = output['eccentricity'].item() + m1 = output['S1_mass'].item() + m2 = output['S2_mass'].item() + Z_div_Zsun = kwargs.get('metallicity', 1.) + zams_table = {1.: 2.703e-01, + 0.1: 2.511e-01, + 0.01: 2.492e-01, + 0.001: 2.49e-01, + 0.0001: 2.49e-01} + Y = zams_table[Z_div_Zsun] + Z = Z_div_Zsun*Zsun + X = 1. - Z - Y + + binary_params = dict( + index=kwargs.get('index', default_index), + time=formation_time, + state="detached", + event="ZAMS", + separation=separation, + orbital_period=orbital_period, + eccentricity=eccentricity, + ) + star1_params = dict( + mass=m1, + state="H-rich_Core_H_burning", + metallicity=Z, + center_h1=X, + center_he4=Y, + ) + star2_params = dict( + mass=m2, + state="H-rich_Core_H_burning", + metallicity=Z, + center_h1=X, + center_he4=Y, + ) + #If binary_fraction not default a initially single star binary is created. + else: + formation_time = output['time'].item() + separation = output['separation'].item() + orbital_period = output['orbital_period'].item() + eccentricity = output['eccentricity'].item() + m1 = output['S1_mass'].item() + m2 = output['S2_mass'].item() + Z_div_Zsun = kwargs.get('metallicity', 1.) + zams_table = {1.: 2.703e-01, + 0.1: 2.511e-01, + 0.01: 2.492e-01, + 0.001: 2.49e-01, + 0.0001: 2.49e-01} + Y = zams_table[Z_div_Zsun] + Z = Z_div_Zsun*Zsun + X = 1. - Z - Y + + binary_params = dict( + index=kwargs.get('index', default_index), + time=formation_time, + state="initially_single_star", + event="ZAMS", + separation=separation, + orbital_period=orbital_period, + eccentricity=eccentricity, + ) + star1_params = dict( + mass=m1, + state="H-rich_Core_H_burning", + metallicity=Z, + center_h1=X, + center_he4=Y, + ) + star2_params = dict( + mass=m2, + state="BH", + metallicity=Z, + center_h1=X, + center_he4=Y, + ) + #do all of the above but with state = "initial_single star" + #in this case the second star can be a compact object. + binary = BinaryStar(**binary_params, star_1=SingleStar(**star1_params), diff --git a/posydon/popsyn/defaults.py b/posydon/popsyn/defaults.py index 9ef830148e..1406ebd0c7 100644 --- a/posydon/popsyn/defaults.py +++ b/posydon/popsyn/defaults.py @@ -15,7 +15,10 @@ # Size of the population 'number_of_binaries': 100, - + #Eirini's changes + #Binary fraction + 'binary_fraction' : 1, + # Star Formation History 'star_formation': 'constant', # burst_time: 0, diff --git a/posydon/popsyn/population_params_default.ini b/posydon/popsyn/population_params_default.ini index 9c8df1cc68..04214f1efc 100644 --- a/posydon/popsyn/population_params_default.ini +++ b/posydon/popsyn/population_params_default.ini @@ -117,6 +117,12 @@ verbose = False # True False +[step_disrupted] + import = ['posydon.binary_evol.DT.step_disrupted','DisruptedStep'] + + + + [step_CE] import = ['posydon.binary_evol.CE.step_CEE', 'StepCEE'] absolute_import = None @@ -239,6 +245,8 @@ # `None` uses system entropy (recommended) number_of_binaries = 100 # int + binary_fraction = 1 + # float 0< fraction <=1 star_formation = 'constant' # 'constant' 'burst' 'custom_linear' 'custom_log10' 'custom_linear_histogram' 'custom_log10_histogram' max_simulation_time = 13.8e9 From 871c40de45c6236e0952a71fbcc5bfb37f67b7df Mon Sep 17 00:00:00 2001 From: ezapartas Date: Thu, 18 May 2023 19:22:55 +0200 Subject: [PATCH 084/319] small changes to solve issues in accepted PR35 (#74) --- posydon/binary_evol/DT/step_detached.py | 6 ++++-- posydon/popsyn/binarypopulation.py | 10 +++++----- 2 files changed, 9 insertions(+), 7 deletions(-) diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index 84707c784b..6d5c6b2406 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -312,7 +312,7 @@ def __init__( matching_method="minimize", initial_mass=None, rootm=None, - verbose=1, + verbose=False, do_wind_loss=True, do_tides=True, do_gravitational_radiation=True, @@ -905,7 +905,7 @@ def sq_diff_function(x): f'{star.he_core_mass:.3f}', f'{star.center_c12:.4f}' ) - return initials, htrack + return initials[0], initials[1], htrack def __repr__(self): """Return the type of evolution type.""" @@ -993,6 +993,7 @@ def __call__(self, binary): elif self.non_existent_companion == 1: # we force primary.co=True for all isolated evolution, # where the secondary is the one evolving one + primary = binary.star_1 primary.co = True primary.htrack = False secondary = binary.star_2 @@ -1004,6 +1005,7 @@ def __call__(self, binary): raise Exception("State not recognized!") elif self.non_existent_companion == 2: + primary = binary.star_2 primary.co = True primary.htrack = False secondary = binary.star_1 diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index 7d5e20e936..557bcbc83e 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -85,7 +85,7 @@ def __init__(self, **kwargs): self.kwargs = default_kwargs.copy() for key, arg in kwargs.items(): self.kwargs[key] = arg - # Have a binary fraction change the number_of binaries. + # Have a binary fraction change the number_of binaries. #Eirini's change self.binary_fraction = self.kwargs.get('binary_fraction') self.number_of_binaries = self.kwargs.get('number_of_binaries') @@ -243,7 +243,7 @@ def _safe_evolve(self, **kwargs): for j, index in enumerate(indices_for_iter): if kwargs.get('from_hdf', False): - #generator + #generator binary = self.manager.from_hdf(index, restore=True).pop() else: binary = self.manager.generate(index=index, **self.kwargs) @@ -840,7 +840,7 @@ def draw_initial_binary(self, **kwargs): #Eirini's comments: # Randomly generated variables - + if self.RNG.uniform() < self.binary_fraction: formation_time = output['time'].item() separation = output['separation'].item() @@ -923,8 +923,8 @@ def draw_initial_binary(self, **kwargs): center_he4=Y, ) #do all of the above but with state = "initial_single star" - #in this case the second star can be a compact object. - + #in this case the second star can be a compact object. + binary = BinaryStar(**binary_params, star_1=SingleStar(**star1_params), From cd026adc1d5e3dc908a83f58909b04be0c93fcbb Mon Sep 17 00:00:00 2001 From: kasdaglie <112660893+kasdaglie@users.noreply.github.com> Date: Thu, 25 May 2023 10:10:25 -0400 Subject: [PATCH 085/319] Updating modules to newest versions for python 3.11 (#57) * updating modules to newest versions for python 3.11 * merge developmet and fix sintax warning in step_SN where is not was incorrectly used whencomparig strigs --------- Co-authored-by: ssbvr --- posydon/binary_evol/SN/step_SN.py | 10 ++++----- setup.py | 36 +++++++++++++++---------------- 2 files changed, 23 insertions(+), 23 deletions(-) diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index 7f61fa320c..b7c359f2b3 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -1236,7 +1236,7 @@ def orbital_kick(self, binary): # if key is 'nearest_neighbour_distance': # setattr(binary, key, ['None', 'None', 'None']) binary.separation = new_separation - if binary.state is not "disrupted": + if binary.state != "disrupted": binary.state = "detached" binary.event = None binary.time = binary.time_history[-1] @@ -1333,7 +1333,7 @@ def orbital_kick(self, binary): # if key is 'nearest_neighbour_distance': # setattr(binary, key, ['None', 'None', 'None']) binary.separation = new_separation - if binary.state is not "disrupted": + if binary.state != "disrupted": binary.state = "detached" binary.event = None binary.time = binary.time_history[-1] @@ -1417,7 +1417,7 @@ def orbital_kick(self, binary): # update the binary object which was disrupted already before the SN for key in BINARYPROPERTIES: - if key is not 'nearest_neighbour_distance': + if key != 'nearest_neighbour_distance': setattr(binary, key, None) binary.state = "disrupted" binary.event = None @@ -1637,7 +1637,7 @@ def SNCheck( # compute new orbital period before reseting the binary properties for key in BINARYPROPERTIES: - if key is not 'nearest_neighbour_distance': + if key != 'nearest_neighbour_distance': setattr(binary, key, None) binary.state = "detached" @@ -1662,7 +1662,7 @@ def SNCheck( raise ValueError("This should never happen!") for key in BINARYPROPERTIES: - if key is not 'nearest_neighbour_distance': + if key != 'nearest_neighbour_distance': setattr(binary, key, None) binary.state = "disrupted" binary.event = None diff --git a/setup.py b/setup.py index 983716329a..91833dc318 100644 --- a/setup.py +++ b/setup.py @@ -61,34 +61,34 @@ # are all used in the example below for specifying specific version of the # packages that are compatbile with your software. install_requires = [ - 'numpy == 1.19.1', - 'scipy == 1.5.2', - 'iminuit == 1.4.9', - 'configparser == 5.0.0', - 'astropy == 4.0.1', - 'pandas == 1.3.0', - 'scikit-learn == 0.21.3', - 'matplotlib == 3.5.0', - 'matplotlib-label-lines == 0.3.8', - 'PyQt5 == 5.15.3', - 'h5py == 3.7.0', - 'psutil == 5.6.7', - 'tqdm == 4.48.2', - 'tables == 3.6.1', - 'progressbar2 == 4.0.0', + 'numpy == 1.24.2', + 'scipy == 1.10.1', + 'iminuit == 2.21.3', + 'configparser == 5.3.0', + 'astropy == 5.2.2', + 'pandas == 2.0.0', + 'scikit-learn == 1.2.2', + 'matplotlib == 3.7.1', + 'matplotlib-label-lines == 0.5.2', + 'PyQt5 == 5.15.9', + 'h5py == 3.8.0', + 'psutil == 5.9.4', + 'tqdm == 4.65.0', + 'tables == 3.8.0', + 'progressbar2 == 4.2.0', 'hurry.filesize == 0.9', ] tests_require = [ - "pytest == 6.2.2", - "pytest-cov >= 2.4.0", + "pytest == 7.3.1", + "pytest-cov >= 4.0.0", ] # For documenation extras_require = { "doc": [ "ipython", - "sphinx <= 4.2.0", + "sphinx == 6.1.3", "numpydoc", "sphinx_rtd_theme", "sphinxcontrib_programoutput", From aa78869a8c71edb03f041440ed11a3879296ada5 Mon Sep 17 00:00:00 2001 From: Michael Zevin Date: Thu, 25 May 2023 09:45:52 -0500 Subject: [PATCH 086/319] Added script for estimating detection probabilities. These are estimated using grids of pre-calculated detection probabilities living at /projects/b1119/POSYDON_GRIDS/POSYDON_popsynth_data/v2/POSYDON_data/selection_effects/grids/pdet_grid.hdf5. This file contains pdets assuming a 3-detector HLV network operating at different sensitivities. (#82) --- .../popsyn/predict_detection_probabilities.py | 137 ++++++++++++++++++ 1 file changed, 137 insertions(+) create mode 100644 posydon/popsyn/predict_detection_probabilities.py diff --git a/posydon/popsyn/predict_detection_probabilities.py b/posydon/popsyn/predict_detection_probabilities.py new file mode 100644 index 0000000000..118022e019 --- /dev/null +++ b/posydon/popsyn/predict_detection_probabilities.py @@ -0,0 +1,137 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# Copyright (C) Michael Zevin + +""" +Simple utility for generating detection weights + +Uses grid of detection probabilities to estimate detection probabilities + +Anticipates data as Pandas dataframe with series ['m1', 'q', 'z', 'chieff'] +""" + +import numpy as np +import pandas as pd +import time + +from sklearn.neighbors import KNeighborsRegressor + +class KNNmodel(): + """ + K-nearest neighbor model that instantiates based on detection probability grid + + When instantiating, must supply path to the grid, and key that represents + GW network and sensitivity. + """ + + def __init__(self, grid_path, sensitivity_key, verbose=False): + """ + Instantiates KNNmodel class and trains the KNN. + + grid_path : string + Path to grid of detection probabilities. + + sensitivity_key : string + GW detector sensitivity and network configuration you want to use, + see arXiv:1304.0670v3 + detector sensitivities are taken from: https://dcc.ligo.org/LIGO-T2000012-v2/public + available sensitivity keys (for Hanford, Livingston, Virgo network): + 'O3actual_H1L1V1' : aligo_O3actual_H1.txt, aligo_O3actual_L1.txt, avirgo_O3actual.txt + 'O4low_H1L1V1' : aligo_O4low.txt, aligo_O4low.txt, avirgo_O4high_NEW.txt + 'O4high_H1L1V1' : aligo_O4high.txt, aligo_O4high.txt, avirgo_O4high_NEW.txt + 'design_H1L1V1' : AplusDesign.txt, AplusDesign.txt, avirgo_O5high_NEW.txt + detection probabilities are calculated using the IMRPhenomXHM approximant with a network SNR threshold of 10 + + verbose : boolean + Adds verbosity. + """ + start = time.time() + if verbose: + print('\ntraining nearest neighbor algorithm...') + + # read in grid + grid = pd.read_hdf(grid_path, key=sensitivity_key) + + # get values from grid for training + pdets = np.asarray(grid['pdet']) + m1_grid = np.asarray(grid['m1']) + q_grid = np.asarray(grid['q']) + z_grid = np.asarray(grid['z']) + chieff_grid = np.asarray(grid['chieff']) + + # get bounds based on grid + m1_bounds = (np.round(m1_grid.min(), 5), np.round(m1_grid.max(), 5)) + q_bounds = (np.round(q_grid.min(), 5), np.round(q_grid.max(), 5)) + z_bounds = (np.round(z_grid.min(), 5), np.round(z_grid.max(), 5)) + chieff_bounds = (np.round(chieff_grid.min(), 5), np.round(chieff_grid.max(), 5)) + self.m1_bounds, self.q_bounds, self.z_bounds, self.chieff_bounds = m1_bounds, \ + q_bounds, z_bounds, chieff_bounds + + # normalize to unit cube + logm1_grid_norm = self.normalize(np.log10(m1_grid), np.log10(m1_bounds[0]), np.log10(m1_bounds[1])) + q_grid_norm = self.normalize(q_grid, q_bounds[0], q_bounds[1]) + logz_grid_norm = self.normalize(np.log10(z_grid), np.log10(z_bounds[0]), np.log10(z_bounds[1])) + chieff_grid_norm = self.normalize(chieff_grid, chieff_bounds[0], chieff_bounds[1]) + + # train nearest neighbor algorithm + X = np.transpose(np.vstack([logm1_grid_norm, q_grid_norm, logz_grid_norm, chieff_grid_norm])) + y = np.transpose(np.atleast_2d(pdets)) + nbrs = KNeighborsRegressor(n_neighbors=10, weights='distance', algorithm='ball_tree', leaf_size=30, p=2, metric='minkowski') + nbrs.fit(X, y) + + self.model = nbrs + if verbose: + print(' finished! It took {:0.2f}s to train the model on {:d} systems\n'.format(time.time()-start, len(pdets))) + + + def predict_pdet(self, data, verbose=False): + """ + Gives relative weight to each system in `data` based on its proximity to the points on the grid. + Each system in `data` should have a primary mass `m1`, mass ratio `q`, redshift `z`, and effective spin `chieff` + This function will determine detection probabilities using nearest neighbor algorithm in [log(m1), q, log(z), chieff] space + Need to specify bounds (based on the trained grid) so that the grid and data get normalized properly + + data : Pandas dataframe + Data you wish to predict detection probabilities for. + Required series in the dataframe: + 'm1' : primary source-frame mass + 'q' : mass ratio (secondary mass/primary mass) + 'z' : redshift of merger + 'chieff' : effective inspiral spin + + verbose : boolean + Adds verbosity. + """ + start = time.time() + if verbose: + print('determining detection probabilities for data...') + + # get values from dataset and normalize + m1_data = np.asarray(data['m1']) + q_data = np.asarray(data['q']) + z_data = np.asarray(data['z']) + chieff_data = np.asarray(data['chieff']) + + logm1_data_norm = self.normalize(np.log10(m1_data), np.log10(self.m1_bounds[0]), np.log10(self.m1_bounds[1])) + q_data_norm = self.normalize(q_data, self.q_bounds[0], self.q_bounds[1]) + logz_data_norm = self.normalize(np.log10(z_data), np.log10(self.z_bounds[0]), np.log10(self.z_bounds[1])) + chieff_data_norm = self.normalize(chieff_data, self.chieff_bounds[0], self.chieff_bounds[1]) + + # get pdets for the testing data + X_fit = np.transpose(np.vstack([logm1_data_norm, q_data_norm, + logz_data_norm, chieff_data_norm])) + pdets = self.model.predict(X_fit).flatten() + assert all([((p<=1) & (p>=0)) for p in pdets]), 'pdet is not between 0 and 1' + + if verbose: + print(' finished! It took {:0.2f}s to determine detection probabilities for {:d} systems'.format(time.time()-start, len(X_fit))) + return pdets + + + @staticmethod + def normalize(x, xmin, xmax, a=0, b=1): + """ + normalizes data on range [a,b] + """ + data_norm = (b-a)*(x-xmin) / (xmax-xmin) + a + return data_norm From 656bee477f708b822783d7ab9e75a4bcdef9cfe7 Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Thu, 25 May 2023 16:46:13 +0200 Subject: [PATCH 087/319] =?UTF-8?q?[insane=20PR=20=F0=9F=A4=AF]=20POSYDON?= =?UTF-8?q?=20v2=20DCO=20population=20synthesis=20workflow=20(#63)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * add synthetic population parser * bug fixes * cleaning * get DCO population at formation as df_synthetic, add t_inspiral and underlying stellar mass for rate calculation * add method to compute merger efficiency * add intrinsic and detectable populations * define all class variables * add visualisation modules * small edit * add rate calculation and SFR * add docstrings and parser of oneline dataframe * update pdet calculation to POSYDON selection effects * update sensitivity docstring --- posydon/popsyn/normalized_pop_mass.py | 2 + posydon/popsyn/rate_calculation.py | 707 +++++++++++++++++++++++ posydon/popsyn/star_formation_history.py | 213 ++++++- posydon/popsyn/synthetic_population.py | 627 +++++++++++++++++++- posydon/visualization/plot_dco.py | 102 ++++ posydon/visualization/plot_defaults.py | 12 + posydon/visualization/posydon.mplstyle | 32 + 7 files changed, 1679 insertions(+), 16 deletions(-) create mode 100644 posydon/popsyn/rate_calculation.py create mode 100644 posydon/visualization/plot_dco.py create mode 100644 posydon/visualization/posydon.mplstyle diff --git a/posydon/popsyn/normalized_pop_mass.py b/posydon/popsyn/normalized_pop_mass.py index 90e3822268..8be5353a9d 100644 --- a/posydon/popsyn/normalized_pop_mass.py +++ b/posydon/popsyn/normalized_pop_mass.py @@ -47,6 +47,8 @@ def initial_total_underlying_mass(df=None, **kwargs): initial_ZAMS_mass_2 = initial_ZAMS_mass[3] initial_ZAMS_TOTAL_mass = (sum(initial_ZAMS_mass_1) + sum(initial_ZAMS_mass_2)) + elif isinstance(df, float): + initial_ZAMS_TOTAL_mass = df else: sel = df['event'] == 'ZAMS' initial_ZAMS_TOTAL_mass = sum(df['S1_mass'][sel]+df['S2_mass'][sel]) diff --git a/posydon/popsyn/rate_calculation.py b/posydon/popsyn/rate_calculation.py new file mode 100644 index 0000000000..646a41fd95 --- /dev/null +++ b/posydon/popsyn/rate_calculation.py @@ -0,0 +1,707 @@ + +__author__ = ['Simone Bavera '] + +import os +import warnings +import numpy as np +import pandas as pd +from tqdm import tqdm +import scipy as sp +from scipy.interpolate import interp1d +from astropy import units as u +from astropy import constants as const # TODO: replace this in favour of POSYDON constats module +from astropy.cosmology import Planck15 as cosmology # TODO: update to Planck18 once we update astropy module +from astropy.cosmology import z_at_value +import posydon.popsyn.selection_effects as selection_effects +from posydon.utils.constants import Zsun +from posydon.popsyn.star_formation_history import (star_formation_rate, + mean_metallicity, + std_log_metallicity_dist, + get_illustrisTNG_data, + fractional_SFR_at_given_redshift) +from posydon.utils.data_download import PATH_TO_POSYDON_DATA + +PATH_TO_PDET_GRID = os.path.join(PATH_TO_POSYDON_DATA, 'selection_effects/pdet_grid.hdf5') + + +DEFAULT_MODEL = { + 'delta_t' : 100, # Myr + 'SFR' : 'IllustrisTNG', + 'sigma_SFR' : None, + 'Z_max' : 1., + 'select_one_met' : False, + 'dlogZ' : None, # e.g, [np.log10(0.0142/2),np.log10(0.0142*2)] + 'Zsun' : Zsun +} + +class Rates(object): + + def __init__(self, df, verbose=False, **kwargs): + """Compute DCO rates. + + Parameters + ---------- + df : object + Pandas dataframe containing the synthetic binary population. + delta_t : float + Cosmic time bin size used to bin distribute the synthetc binary + population. + SFR : string + Star-formation-rate history you want to use: + 'Madau+Dickinson14', 'Madau+Fragos17', 'Neijssel+19', 'IllustrisTNG' + sigma_SFR : string + Standard deviation of the trucated log-normal metallicity distribution + assumed for the star-formation rate history in the case you select + 'Madau+Dickinson14', 'Madau+Fragos17', 'Neijssel+19', + Z_max : float + The absolute maximum metallicity of the star formation rate history. + NOTE: This should be used when assuming a truncated log-normal + distribution of metallcities to not overpredict 1, also it can be + used with the IllustrisTNG SFR to prevent unphysically high + metallicities. + select_one_met : bool + If `True` your synthetic binary population should contain only one + descrete metallicity. + dlogZ : float or range + Used when `select_one_met=True` to select the metallicity range + around which you want to integrate the SFR history. If float value + value is provided we assume a symmetric range dlogZ/2 around the + provided metallcity else we consider the range provided + [Z_min, Z_max] which could also be asymmetric. + verbose : bool + `True` if you want the print statements. + + + TODO: add the definitios of the variables used by this class. + + """ + + self.df = df + self.verbose = verbose + + if kwargs: + for key in kwargs: + if key not in DEFAULT_MODEL: + raise ValueError(key + " is not a valid parameter name!") + for varname in DEFAULT_MODEL: + default_value = DEFAULT_MODEL[varname] + setattr(self, varname, kwargs.get(varname, default_value)) + else: + for varname in DEFAULT_MODEL: + default_value = DEFAULT_MODEL[varname] + setattr(self, varname, default_value) + + ###################################################### + ### DCO detection rate and merger rate density ### + ### see arXiv:1906.12257, arXiv:2010.16333, ### + ### arXiv:2106.15841, arXiv:221210924, ### + ### arXiv:2204.02619, arXiv:2011.10057 ### + ### arXiv:2012.02274 ### + ###################################################### + + def chi_eff(self, m_1, m_2, a_1, a_2, tilt_1, tilt_2): + return (m_1*a_1*np.cos(tilt_1)+m_2*a_2*np.cos(tilt_2))/(m_1+m_2) + + def m_chirp(self, m_1, m_2): + return (m_1*m_2)**(3./5)/(m_1+m_2)**(1./5) + + def mass_ratio(self, m_1, m_2): + q = m_2/m_1 + q[q>1.] = 1./q[q>1.] + return q + + def get_data(self, var, index=None): + """Resample class elements. + + Parameters + ---------- + var : string + Key of self.df. This method support a list of custom definitios for + DCO studies like q, m_tot, m_chirp, chi_eff. + index : list of int + List of indicies of the binaries of interest. + + Returns + ------- + array + Array containing the self.df[var][index]. + + """ + if var not in self.df: + # compute some useful quantities that are not defined in df_synthetic + if var == 'q': + values = self.mass_ratio(self.df["S1_mass"], self.df["S2_mass"]) + elif var == 'm_tot': + values = self.df["S1_mass"] + self.df["S2_mass"] + elif var == 'm_chirp': + values = self.m_chirp(self.df["S1_mass"], self.df["S2_mass"]) + elif var == 'chi_eff': + # check if tilts are provided + if ("tilt_BH1" not in self.df or "tilt_BH2" not in self.df): + warnings.warn('Spin tilts not in dataset! Assuming spins ' + 'are aligned to orbital angular momentum ' + 'to compute chi_eff!') + tilt_BH1 = np.zeros(len(self.df["S1_spin"])) + tilt_BH2 = tilt_BH1 + else: + tilt_BH1 = self.df["tilt_BH1"] + tilt_BH2 = self.df["tilt_BH2"] + values = self.chi_eff(self.df["S1_mass"], + self.df["S2_mass"], + self.df["S1_spin"], + self.df["S2_spin"], + tilt_BH1, + tilt_BH2) + else: + raise ValueError(f'Definition for {var} not available!') + else: + values = self.df[var] + + if index is not None: + return values[index].values + else: + return values.values + + def get_redshift_from_cosmic_time_interpolator(self): + """Interpolator to compute the cosmological redshift given the cosmic time. + + Returns + ------- + object + Returns the trained interpolator object. + + """ + # astropy z_at_value method is too slow to compute z_mergers efficinty + # we must implment interpolation + t = np.linspace(1e-2, cosmology.age(1e-08).value*0.9999999, 1000) + z = np.zeros(1000) + for i in range(1000): + z[i] = z_at_value(cosmology.age, t[i] * u.Gyr) + f_z_m = interp1d(t, z, kind='cubic') + return f_z_m + + def get_redshift_from_cosmic_time(self, t_cosm): + """Compute the cosmological redshift given the cosmic time.. + + Parameters + ---------- + t_cosm : array doubles + Cosmic time to which you want to know the redhisft. + + Returns + ------- + array doubles + Cosmolgocial redshift corresponding to the cosmic time. + + """ + interpolator = self.get_redshift_from_cosmic_time_interpolator() + return interpolator(t_cosm) + + def get_cosmic_time_from_redshift(self, z): + """Compute the cosmic time from redshift. + + Parameters + ---------- + z : double + Cosmological redshift. + + Returns + ------- + double + Return age of the cosmic time in Gyr given the redshift z. + + """ + return cosmology.age(z).value # Gyr + + def get_comoving_disntance_from_redshift(self, z): + """Compute the comoving distance from redshift. + + Parameters + ---------- + z : double + Cosmological redshift. + + Returns + ------- + double + Comoving distance in Mpc corresponding to the redhisft z. + + """ + return cosmology.comoving_distance(z).value # Mpc + + def get_cosmic_time_at_dco_merger(self, z_birth): + """Get cosmic time at DCO merger. + + Parameters + ---------- + z_birth : double + Cosmological redshift of formation of the DCO system + (must be the same for every binary). + + Returns + ------- + double + Cosmic time in Gyr at DCO merger for all binaries born at z_birth. + + """ + n = self.df.shape[0] + t_birth = self.get_cosmic_time_from_redshift(z_birth) * np.ones(n) # Gyr + return t_birth + (self.df["time"] + self.df["t_delay"]) * 10 ** (-3) #Gyr + + def get_redshift_at_dco_merger(self, z_birth): + """Get redshift of merger of DCOs. + + Parameters + ---------- + z_birth : double + Redshift of formation of the DCO system (must be the same for every + binary). + + Returns + ------- + double + Redshift of merger of DCOs born at z_birth. + + """ + n = self.df.shape[0] + t_merger = self.get_cosmic_time_at_dco_merger(z_birth) + z_merger = np.ones(n) * np.nan + bool_merger = t_merger < self.get_cosmic_time_from_redshift(0.) * np.ones(n) # check if the binary merges + z_merger[bool_merger] = z_at_value(cosmology.age, t_merger[bool_merger] * u.Gyr) + return z_merger + + def get_centers_metallicity_bins(self): + """Return the centers of the metallicity bins. + + Returns + ------- + array double + Returns sampled metallicities of the populattion. This correponds + to the center of each metallicity bin. + + """ + return np.unique(self.df['metallicity'].values) + + def get_edges_metallicity_bins(self): + """Return the edges of the metallicity bins. + + Returns + ------- + array double + Returns the edges of all metallicity bins. We assume metallicities + were binned in log-space. + + """ + met_val = np.log10(self.get_centers_metallicity_bins()) + bin_met = np.zeros(len(met_val)+1) + # if more than one metallicty bin + if len(met_val) > 1 : + bin_met[0] = met_val[0] - (met_val[1] - met_val[0]) / 2. + bin_met[-1] = met_val[-1] + (met_val[-1] - met_val[-2]) / 2. + bin_met[1:-1] = met_val[:-1] + (met_val[1:] - met_val[:-1]) / 2. + # one metallicty bin + elif len(met_val) == 1 : + if isinstance(self.dlogZ, float): + bin_met[0] = met_val[0] - self.dlogZ / 2. + bin_met[-1] = met_val[0] + self.dlogZ / 2. + elif isinstance(self.dlogZ, list) or isinstance(self.dlogZ, np.array): + bin_met[0] = self.dlogZ[0] + bin_met[-1] = self.dlogZ[1] + + return 10**bin_met + + + def get_centers_redshift_bins(self): + """Compute redshift bin centers. + + Returns + ------- + array doubles + We devide the cosmic time history of the Universe in equally spaced + bins of cosmic time of self.delta_t (100 Myr default) an compute the + redshift corresponding to center of these bins. + + """ + # generate t_birth at the middle of each self.delta_t bin + t_birth_bin = [cosmology.age(0.).value] + t_birth = [] + # devide the + self.n_redshift_bin_centers = int(cosmology.age(0).to('Myr').value/self.delta_t) + for i in range(self.n_redshift_bin_centers+1): + t_birth.append(t_birth_bin[i] - self.delta_t*1e-3/2.) # Gyr + t_birth_bin.append(t_birth_bin[i] - self.delta_t*1e-3) # Gyr + t_birth = np.array(t_birth) + # compute the redshift + z_birth = [] + for i in range(self.n_redshift_bin_centers+1): + z_birth.append(z_at_value(cosmology.age, t_birth[i] * u.Gyr)) + z_birth = np.array(z_birth) + + return z_birth + + def get_edges_redshift_bins(self): + """Compute redshift bin edges. + + Returns + ------- + array doubles + We devide the cosmic time history of the Universe in equally spaced + bins of cosmic time of self.delta_t (100 Myr default) an compute the + redshift corresponding to edges of these bins. + + """ + # generate t_birth at the middle of each self.delta_t bin + t_birth_bin = [cosmology.age(0.).value] + for i in range(self.n_redshift_bin_centers+1): + t_birth_bin.append(t_birth_bin[i] - self.delta_t*1e-3) # Gyr + # compute the redshift + z_birth_bin = [] + for i in range(self.n_redshift_bin_centers): + # do not count first edge, we add z=0. later + z_birth_bin.append(z_at_value(cosmology.age, t_birth_bin[i+1] * u.Gyr)) + # add the first and last bin edge at z=0. and z=inf.=100 + z_birth_bin = np.array([0.]+z_birth_bin+[100.]) + + return z_birth_bin + + def merger_rate_weight(self, z_birth, z_merger, p_det, i): + """Compute the merger rate weight (w_ijk) in yr^-1 units. + + Parameters + ---------- + z_birth : array doubles + Cosmological redshift of formation of the binary systems. This MUST + be the same for all binaries. + z_merger : array doubles + Cosmological redshift of merger of the binary systems. + p_det : array doubles + Detection probability of the binary. + i : array integers + Indicies correponding to the binaries you want to select out of the + population. + + Returns + ------- + array doubles + Return the cosmological weights w_ijk (detection rate contibution) + of the binary k in the metallicity bin j born at redshift birth i + as in Eq. (B.8) of Bavera et al. (2020). + + """ + # get SFR at a given population birth redshift + SFR_at_z_birth = star_formation_rate(self.SFR, z_birth) + + # get metallicity bin edges + met_bins = self.get_edges_metallicity_bins() + + # distribute the DCO to the corresponding bin + binplace = np.digitize(self.get_data("metallicity", i), met_bins) + + # compute fSFR assuming log-normal distributed metallicities + fSFR = fractional_SFR_at_given_redshift(z_birth, SFR_at_z_birth, + self.SFR, self.sigma_SFR, + met_bins, binplace, + self.Z_max, self.select_one_met) + + # simulated mass per given metallicity corrected for the unmodeled + # single and binary stellar mass + M_model = self.get_data("underlying_mass_for_met",i) + + # speed of light + c = const.c.to('Mpc/yr').value # Mpc/yr + + # delta cosmic time bin + deltaT = self.delta_t * 10 ** 6 # yr + + # comoving distance corresponding to the DCO merger redshift + D_c = self.get_comoving_disntance_from_redshift(z_merger) + + # DCO cosmological weights B.8 in Bavera et al. (2020) + return 4.*np.pi * c * D_c**2 * p_det * deltaT * fSFR / M_model # yr^-1 + + + def compute_merger_rate_weights(self, sensitivity, flag_pdet=True, path_to_dir='./', extention='npz'): + """Compute the cosmological weights of the DCO population. + + This function will create a directory path_to_dir/DCOs/sensitivity where + it will save the weigths, binary indicies k, z_formation, z_merger. + This is needed for scalability as a ~100k DCO population generates ~10M + non-zero weights assuming a delta_t=100Myr. + + Parameters + ---------- + sensitivity : string + GW detector sensitivity and network configuration you want to use, see arXiv:1304.0670v3 + detector sensitivities are taken from: https://dcc.ligo.org/LIGO-T2000012-v2/public + available sensitivity keys (for Hanford, Livingston, Virgo network): + 'O3actual_H1L1V1' : aligo_O3actual_H1.txt, aligo_O3actual_L1.txt, avirgo_O3actual.txt + 'O4low_H1L1V1' : aligo_O4low.txt, aligo_O4low.txt, avirgo_O4high_NEW.txt + 'O4high_H1L1V1' : aligo_O4high.txt, aligo_O4high.txt, avirgo_O4high_NEW.txt + 'design_H1L1V1' : AplusDesign.txt, AplusDesign.txt, avirgo_O5high_NEW.txt + 'infinite': intrinsic merging DCO population, i.e. p_det = 1 + flag_pdet : bool + This is a control variable. In order to be sure you want to run + infinite sensitivity set `p_det=False`. + path_to_dir : string + Path to the workingn directory where you want to store the + cosmological weights. + + """ + # check if the folder three exists, otherwise create it + DCO_dir = os.path.join(path_to_dir,'DCOs') + if 'DCOs' not in os.listdir(path_to_dir): + os.makedirs(DCO_dir) + + sensitivity_dir = os.path.join(DCO_dir, f'{sensitivity}_sensitivity') + if f'{sensitivity}_sensitivity' not in os.listdir(DCO_dir): + os.makedirs(sensitivity_dir) + + # index of all DCOs + n = self.df.shape[0] + index = np.arange(0, n) + + # redshif of each time bin + z_birth = self.get_centers_redshift_bins() + + # define interpolator + get_redshift_from_time = self.get_redshift_from_cosmic_time_interpolator() + + # hashmap to store everythig + data = {'index': {}, 'z_merger': {}, 'weights': {}} + + # load and store detector selection effects interpolator + if sensitivity != 'infinite': + self.sel_eff = selection_effects.KNNmodel(grid_path=PATH_TO_PDET_GRID, + sensitivity_key=sensitivity) + + # loop over all redshift bins + for i in tqdm(range(len(z_birth))): + + # compute the merger time of each DCOs + t_merger = self.get_cosmic_time_at_dco_merger(z_birth[i]) + + # detectable population + if flag_pdet == True and sensitivity != 'infinite': + + # sort out systems not merging within the Hubble time + bool_merger = t_merger < cosmology.age(1e-08).value*0.9999999 * np.ones(n) + + # if there are no merging DCO, continue + if len(index[bool_merger]) == 0: + data['index'][str(z_birth[i])] = np.array([]) + data['z_merger'][str(z_birth[i])] = np.array([]) + data['weights'][str(z_birth[i])] = np.array([]) + continue + + # get some quantities to compute detection probabilities + data_slice = pd.DataFrame() + data_slice['m1'] = self.get_data('S1_mass', index[bool_merger]) + data_slice['q'] = self.get_data('q', index[bool_merger]) + z_m = get_redshift_from_time(t_merger[bool_merger]) + data_slice['z'] = z_m + data_slice['chieff'] = self.get_data('chi_eff', index[bool_merger]) + + # compute detection probabilities + p_det = self.sel_eff.predict_pdet(data_slice) + + # sort out undetectable sources + bool_detect = p_det > 0. + + # if there are no detectable DCO, continue + if len(index[bool_merger][bool_detect]) == 0: + data['index'][str(z_birth[i])] = np.array([]) + data['z_merger'][str(z_birth[i])] = np.array([]) + data['weights'][str(z_birth[i])] = np.array([]) + continue + + # store index and redshift of merger + data['index'][str(z_birth[i])] = index[bool_merger][bool_detect] + data['z_merger'][str(z_birth[i])] = z_m[bool_detect] + + # compute and store marger rate weights as in eq. B.8 in Bavera et al. (2020) + z_b = np.ones(len(index[bool_merger][bool_detect]))*z_birth[i] + w_ijk = self.merger_rate_weight(z_b, z_m[bool_detect], + p_det[bool_detect], + index[bool_merger][bool_detect]) + data['weights'][str(z_birth[i])] = w_ijk + + # intrinsic population + elif flag_pdet == False and sensitivity == 'infinite': + + # sort out systems not merging within the Hubble time + bool_merger = t_merger < cosmology.age(1e-08).value*0.9999999 * np.ones(n) + + # if there are no merging DCO, continue + if len(index[bool_merger]) == 0: + data['index'][str(z_birth[i])] = np.array([]) + data['z_merger'][str(z_birth[i])] = np.array([]) + data['weights'][str(z_birth[i])] = np.array([]) + continue + + # get merger redshifts + z_m = get_redshift_from_time(t_merger[bool_merger]) + + # set detection probabilities to 1 + p_det = np.ones(len(index[bool_merger])) + + # store index and redshift of merger + data['index'][str(z_birth[i])] = index[bool_merger] + data['z_merger'][str(z_birth[i])] = z_m + + # compute and store marger rate weights as in eq. B.8 in Bavera et al. (2020) + z_b = np.ones(len(index[bool_merger]))*z_birth[i] + w_ijk = self.merger_rate_weight(z_b, z_m, p_det, index[bool_merger]) + data['weights'][str(z_birth[i])] = w_ijk + + else: + raise ValueError('Missmatch between sensitivity and flag_pdet!') + + # TODO: implemet the saving to h5 files + if extention == 'npz': + if self.verbose: + print('Formatting the data ....') + data_to_save = [[],[],[],[]] + for i, dict in enumerate([data[key] for key in data.keys()]): + for key in dict.keys(): + data_to_save[i].extend(dict[key].tolist()) + if i == 0: + data_to_save[3].extend(np.ones(len(dict[key]))*float(key)) + if self.verbose: + print('Saving the data ....') + for i, key in enumerate(data.keys()): + if key == 'index': + fmt_str = '%i' + else: + fmt_str = '%.8E' + np.savez(os.path.join(sensitivity_dir, f"{key}.npz"), + key=data_to_save[i], fmt=fmt_str) + + np.savez(os.path.join(sensitivity_dir,"z_formation.npz"), + key=data_to_save[3], fmt='%.8E') + else: + raise ValueError('Extension not supported!') + + def load_merger_rate_weights(self, sensitivity, path_to_dir='./', extention='npz'): + """Load the cosmological weights of the DCO populatio. + + Parameters + ---------- + sensitivity : string + GW detector sensitivity and network configuration you want to use, see arXiv:1304.0670v3 + detector sensitivities are taken from: https://dcc.ligo.org/LIGO-T2000012-v2/public + available sensitivity keys (for Hanford, Livingston, Virgo network): + 'O3actual_H1L1V1' : aligo_O3actual_H1.txt, aligo_O3actual_L1.txt, avirgo_O3actual.txt + 'O4low_H1L1V1' : aligo_O4low.txt, aligo_O4low.txt, avirgo_O4high_NEW.txt + 'O4high_H1L1V1' : aligo_O4high.txt, aligo_O4high.txt, avirgo_O4high_NEW.txt + 'design_H1L1V1' : AplusDesign.txt, AplusDesign.txt, avirgo_O5high_NEW.txt + 'infinite': intrinsic merging DCO population, i.e. p_det = 1 + path_to_dir : string + Path to the directory where you the cosmological weights are stored. + + Returns + ------- + array doubles + Return the cosmological weights, z_formation, z_merger and binary + index k associated to each weighted binary. + + """ + dir_ = os.path.join(path_to_dir, f'DCOs/{sensitivity}_sensitivity') + + if extention == 'npz': + if self.verbose: + print('Loading the data ...') + index = np.load(os.path.join(dir_, 'index.npz'), allow_pickle=True)['key'] + z_formation = np.load(os.path.join(dir_, 'z_formation.npz'), allow_pickle=True)['key'] + z_merger = np.load(os.path.join(dir_, 'z_merger.npz'), allow_pickle=True)['key'] + weights = np.load(os.path.join(dir_, 'weights.npz'), allow_pickle=True)['key'] + else: + raise ValueError('Extension not supported!') + return index, z_formation, z_merger, weights + + + def get_shell_comovig_volume(self, z_hor_i, z_hor_f, sensitivity='infinite'): + """Compute comoving volume corresponding to a redshift shell. + + Parameters + ---------- + z_hor_i : double + Cosmological redshift. Lower bound of the integration. + z_hor_f : double + Cosmological redshift. Upper bound of the integration. + sensitivity : string + hoose which GW detector sensitivity you want to use. At the moment + only 'infinite' is available, i.e. p_det = 1. + + Returns + ------- + double + Retruns the comoving volume between the two shells z_hor_i + and z_hor_f in Gpc^3. + + """ + c = const.c.to('Gpc/yr').value # Gpc/yr + H_0 = cosmology.H(0).to('1/yr').value # km/Gpc*s + def E(z): + Omega_m = cosmology.Om0 + Omega_L = 1-cosmology.Om0 + return np.sqrt(Omega_m*(1.+z)**3+Omega_L) + def f(z,sensitivity): + if sensitivity=='infinite': + return (1./(1.+z) * 4*np.pi*c / H_0 + * (self.get_comoving_disntance_from_redshift(z) + * 10**(-3.))**2. / E(z)) + else: + # TODO: peanut-shaped antenna patter comoving volume calculation + raise ValueError('Sensitivity not supported!') + return sp.integrate.quad(f, z_hor_i, z_hor_f, args=(sensitivity))[0] # Gpc^3 + + + def compute_merger_rate_density(self, w_ijk, z_event, observable='DCOs', sensitivity='infiite', index=None): + """Compute the DCOs merger rate density. + + TODO: add support for GRBs. + + Parameters + ---------- + w_ijk : array doubles + Cosmological weights computed with Eq. B.8 of Bavera et at. (2020). + z_event : array doubles + Cosmolgocial redshift of the event you are tracking. + Type : string + Event you are tracking, available: + 'DCOs': merger event of a DCO system + 'GRBs': coming soon. + sensitivity : string + This takes into account the detector sensitivity, available: + 'infinite': p_det = 1 + 'beamed': TODO for GRBs + + Returns + ------- + array doubles + Return the DCOs merger rate density (Gpc^-3 yr^-1) as a function of + cosmolgocial redshift as in Eq. (D.1) in Bavera et al. (2022) + arXiv:2106.15841 + + """ + + z_hor = self.get_edges_redshift_bins() + n = len(z_hor) + + if observable=='DCOs': + z_merger_DCO = z_event + Rate_DCOs = np.zeros(n-1) + if sensitivity=='infinite': + for i in range(1,n): + # compute Eq. (D.1) in Bavera et al. (2022) arXiv:2106.15841 + condition_DCOs = np.logical_and(z_merger_DCO>z_hor[i-1], + z_merger_DCO<=z_hor[i]) + Rate_DCOs[i-1] = (sum(w_ijk[condition_DCOs]) + /self.get_shell_comovig_volume(z_hor[i-1], + z_hor[i], + sensitivity)) + return Rate_DCOs # Gpc^-3 yr^-1 + else: + raise ValueError('Unsupported sensitivity!') + else: + raise ValueError('Unknown observable!') diff --git a/posydon/popsyn/star_formation_history.py b/posydon/popsyn/star_formation_history.py index f49ff0f390..abf7d40253 100644 --- a/posydon/popsyn/star_formation_history.py +++ b/posydon/popsyn/star_formation_history.py @@ -2,16 +2,23 @@ __authors__ = [ + 'Simone Bavera ', "Kyle Akira Rocha ", "Devina Misra ", "Konstantinos Kovlakas ", ] - +import os import numpy as np +import scipy as sp +from scipy import stats +from posydon.utils.data_download import PATH_TO_POSYDON_DATA from posydon.utils.constants import age_of_universe from posydon.utils import (rejection_sampler, histogram_sampler, read_histogram_from_file) +from posydon.utils.constants import Zsun +from scipy.interpolate import interp1d +from astropy.cosmology import Planck15 as cosmology SFH_SCENARIOS = ["burst", "constant", "custom_linear", "custom_log10", @@ -76,3 +83,207 @@ def get_formation_times(N_binaries, star_formation='constant', **kwargs): raise ValueError( "Unknown star formation scenario '{}' given. Valid options: {}". format(star_formation, ",".join(SFH_SCENARIOS))) + +def get_illustrisTNG_data(verbose=False): + """Load IllustrisTNG SFR dataset.""" + if verbose: + print('Loading IllustrisTNG data...') + return np.load(os.path.join(PATH_TO_POSYDON_DATA, 'SFR/IllustrisTNG.npz')) + +def star_formation_rate(SFR, z): + """Star formation rate in M_sun yr^-1 Mpc^-3. + + Parameters + ---------- + SFR : string + Star formation rate assumption: + - Madau+Fragos17 see arXiv:1606.07887 + - Madau+Dickinson14 see arXiv:1403.0007 + - Neijssel+19 see arXiv:1906.08136 + - IllustrisTNG see see arXiv:1707.03395 + z : double + Cosmological redshift. + + Returns + ------- + double + The total mass of stars in M_sun formed per comoving volume Mpc^-3 + per year. + """ + if SFR == "Madau+Fragos17": + return 0.01 * (1. + z) ** 2.6 / (1. + ((1. + z) / 3.2) ** 6.2) # M_sun yr^-1 Mpc^-3 + elif SFR == "Madau+Dickinson14": + return 0.015 * (1. + z) ** 2.7 / (1. + ((1. + z) / 2.9) ** 5.6) # M_sun yr^-1 Mpc^-3 + elif SFR == "Neijssel+19": + return 0.01 * (1. + z) ** 2.77 / (1. + ((1. + z) / 2.9) ** 4.7) # M_sun yr^-1 Mpc^-3 + elif SFR=='IllustrisTNG': + illustris_data = get_illustrisTNG_data() + SFR = illustris_data['SFR'] # M_sun yr^-1 Mpc^-3 + redshifts = illustris_data['redshifts'] + SFR_interp = interp1d(redshifts, SFR) + return SFR_interp(z) + else: + raise ValueError('Invalid SFR!') + +def mean_metallicity(SFR, z): + """Empiric mean metallicity function. + + Parameters + ---------- + SFR : string + Star formation rate assumption: + - Madau+Fragos17 see arXiv:1606.07887 + - Madau+Dickinson14 see arXiv:1403.0007 + - Neijssel+19 see arXiv:1906.08136 + z : double + Cosmological redshift. + + Returns + ------- + double + Mean metallicty of the universe at the given redhist. + + """ + + if SFR == "Madau+Fragos17" or SFR == "Madau+Dickinson14": + return 10 ** (0.153 - 0.074 * z ** 1.34) * Zsun + elif SFR == "Neijssel+19": + return 0.035*10**(0.035*z) + else: + raise ValueError('Invalid SFR!') + +def std_log_metallicity_dist(sigma): + """Standard deviation of the log-metallicity distribution. + + Returns + ------- + double + Standard deviation of the adopted distribution. + + """ + if isinstance(sigma, str): + if sigma == 'Bavera+20': + return 0.5 + elif sigma == "Neijssel+19": + return 0.39 + else: + raise ValueError('Uknown sigma choice!') + elif isinstance(sigma, float): + return sigma + else: + raise ValueError(f'Invalid sigma value {sigma}!') + +def fractional_SFR_at_given_redshift(z, SFR_at_z, SFR, sigma, bins, binplace, Z_max, select_one_met=False): + """Integrated SFR over deltaZ at a given z as in Eq. (B.9) of Bavera et al. (2020). + + Parameters + ---------- + SFR : string + Star formation rate assumption: + - Madau+Fragos17 see arXiv:1606.07887 + - Madau+Dickinson14 see arXiv:1403.0007 + - IllustrisTNG see see arXiv:1707.03395 + - Neijssel+19 see arXiv:1906.08136 + Z : double + Metallicity. + + Returns + ------- + double + The total mass of stars formed per comoving volume at a given redshift z + for a given metallicity range deltaZ. + + """ + # Z_left_edge == bins[binplace-1] + # Z_right_edge == bins[binplace] + if SFR == "Madau+Fragos17" or SFR=="Madau+Dickinson14": + # assume a truncated log10-normal distribution of metallicities + # see Eq. (B.2) in Bavera et al. (2020) + sigma = std_log_metallicity_dist(sigma) + mu = np.log10(mean_metallicity(SFR, z)) - sigma**2*np.log(10)/2. + # renormalisation constant + norm = stats.norm.cdf(np.log10(Z_max), mu[0], sigma) + fSFR = SFR_at_z*(stats.norm.cdf(np.log10(bins[binplace]), mu, sigma)/norm + - stats.norm.cdf(np.log10(bins[binplace-1]), mu, sigma)/norm) + if not select_one_met: + #left edge + fSFR[binplace == 1] = SFR_at_z[0]*stats.norm.cdf(np.log10(bins[1]), mu[0], sigma)/norm + elif SFR == "Neijssel+19": + # assume a truncated ln-normal distribution of metallicities + sigma = std_log_metallicity_dist(sigma) + mu = np.log(mean_metallicity(SFR, z))-sigma**2/2. + # renormalisation constant + norm = stats.norm.cdf(np.log(Z_max), mu[0], sigma) + fSFR = SFR_at_z*(stats.norm.cdf(np.log(bins[binplace]), mu, sigma)/norm + - stats.norm.cdf(np.log(bins[binplace-1]), mu, sigma)/norm) + if not select_one_met: + #left edge + fSFR[binplace == 1] = SFR_at_z[0]*stats.norm.cdf(np.log(bins[1]), mu[0], sigma)/norm + elif SFR == 'IllustrisTNG': + # numerically itegrate the IlluystrisTNG SFR(z,Z) + illustris_data = get_illustrisTNG_data() + redshifts = illustris_data['redshifts'] + Z = illustris_data['mets'] + M = illustris_data['M'] # Msun + # only use data within the metallicity bounds (no lower bound) + valid_met_idxs = np.where(Z <= Z_max)[0] + # get the index of the correct redshift in the data + redz_idx = np.where(redshifts <= z[0])[0][0] + # take values of the data at this redshift between our metallicity bounds + Z_dist = M[redz_idx, valid_met_idxs] + if Z_dist.sum() == 0.: + fSFR = np.zeros(len(z)) + else: + Z_dist_cdf = np.cumsum(Z_dist)/Z_dist.sum() + Z_dist_cdf_interp = interp1d(np.log10(Z[valid_met_idxs]), Z_dist_cdf, fill_value="extrapolate") + fSFR = SFR_at_z*(Z_dist_cdf_interp(np.log10(bins[binplace])) + - Z_dist_cdf_interp(np.log10(bins[binplace-1]))) + if not select_one_met: + #left edge + fSFR[binplace == 1] = SFR_at_z[0]*Z_dist_cdf_interp(np.log10(bins[1])) + else: + raise ValueError('Invalid SFR!') + + return fSFR + + +def integrated_SFRH_over_redshift(SFR, sigma, Z, Z_max): + """Integrated SFR history over z as in Eq. (B.10) of Bavera et al. (2020). + + Parameters + ---------- + SFR : string + Star formation rate assumption: + - Madau+Fragos17 see arXiv:1606.07887 + - Madau+Dickinson14 see arXiv:1403.0007 + - Neijssel+19 see arXiv:1906.08136 + Z : double + Metallicity. + + Returns + ------- + double + The total mass of stars formed per comoving volume at a given + metallicity Z. + + """ + def E(z, Omega_m=cosmology.Om0): + Omega_L = 1.- Omega_m + return (Omega_m*(1.+z)**3 + Omega_L)**(1./2.) + def f(z,Z): + if SFR == "Madau+Fragos17" or SFR == "Madau+Dickinson14": + sigma = std_log_metallicity_dist(sigma) + mu = np.log10(mean_metallicity(SFR, z))-sigma**2*np.log(10)/2. + H_0 = cosmology.H0.to('1/yr').value # yr + # put a cutoff on metallicity at Z_max + norm = stats.norm.cdf(np.log10(Z_max), mu, sigma) + return star_formation_rate(SFR, z)*stats.norm.pdf(np.log10(Z), mu, sigma)/norm*(H_0*(1.+z)*E(z))**(-1) + elif SFR == "Neijssel+19": + sigma = std_log_metallicity_dist(sigma) + mu = np.log10(mean_metallicity(SFR, z))-sigma**2/2. + H_0 = cosmology.H0.to('1/yr').value # yr + return star_formation_rate(SFR, z)*stats.norm.pdf(np.log(Z), mu, sigma)*(H_0*(1.+z)*E(z))**(-1) + else: + raise ValueError('Invalid SFR!') + + return sp.integrate.quad(f, 1e-10, np.inf, args=(Z,))[0] # M_sun yr^-1 Mpc^-3 diff --git a/posydon/popsyn/synthetic_population.py b/posydon/popsyn/synthetic_population.py index 4ab96ee6de..ab297f1af2 100644 --- a/posydon/popsyn/synthetic_population.py +++ b/posydon/popsyn/synthetic_population.py @@ -3,22 +3,27 @@ e.g. with multiple metallicities """ - - __authors__ = [ - "Kyle Akira Rocha ", "Simone Bavera ", + "Kyle Akira Rocha ", ] - +import warnings +import numpy as np +import pandas as pd +from posydon.utils.constants import Zsun from posydon.popsyn.io import parse_inifile, binarypop_kwargs_from_ini from posydon.popsyn.binarypopulation import BinaryPopulation from posydon.utils.common_functions import convert_metallicity_to_string +from posydon.popsyn.normalized_pop_mass import initial_total_underlying_mass +from posydon.utils.common_functions import inspiral_timescale_from_orbital_period +from posydon.popsyn.rate_calculation import Rates +import posydon.visualization.plot_dco as plot_dco class SyntheticPopulation: - def __init__(self, path): + def __init__(self, path_to_ini, path_to_data=None, verbose=False): """ Parameters ---------- @@ -29,20 +34,41 @@ def __init__(self, path): """ - synthetic_pop_params = binarypop_kwargs_from_ini(path) + self.verbose = verbose + self.df = None + self.df_oneline = None + self.df_synthetic = None + self.df_intrinsic = None + self.df_detectable = None + self.met_merger_efficiency = None + self.merger_efficiency = None + self.z_rate_density = None + self.rate_density = None - self.metallicity = synthetic_pop_params['metallicity'] + if '.ini' not in path_to_ini: + raise ValueError('You did not provide a valid path_to_ini!') + else: + synthetic_pop_params = binarypop_kwargs_from_ini(path_to_ini) - if not isinstance( self.metallicity, list): - self.metallicity = [self.metallicity] + self.metallicity = synthetic_pop_params['metallicity'] - self.binary_populations = [] - for met in self.metallicity: - ini_kw = binarypop_kwargs_from_ini(path) - ini_kw['metallicity'] = met - ini_kw['temp_directory'] = self.create_met_prefix(met) + ini_kw['temp_directory'] - self.binary_populations.append( BinaryPopulation(**ini_kw) ) + if not isinstance( self.metallicity, list): + self.metallicity = [self.metallicity] + self.binary_populations = [] + for met in self.metallicity: + self.ini_kw = binarypop_kwargs_from_ini(path_to_ini) + self.ini_kw['metallicity'] = met + self.ini_kw['temp_directory'] = self.create_met_prefix(met) + self.ini_kw['temp_directory'] + self.binary_populations.append(BinaryPopulation(**self.ini_kw)) + + if path_to_data is None: + return + elif (isinstance(path_to_data, list) and '.h5' in path_to_data[0]) or ('.h5' in path_to_data): + self.path_to_data = path_to_data + + def get_ini_kw(self): + return self.ini_kw def evolve(self): """Evolve population(s) at given Z(s).""" @@ -56,3 +82,574 @@ def evolve(self): def create_met_prefix(met): """Append a prefix to the name of directories for batch saving.""" return convert_metallicity_to_string(met) + '_Zsun_' + + def apply_logic(self, df, S1_state=None, S2_state=None, binary_state=None, + binary_event=None, step_name=None, invert_S1S2=False): + """Select binaries in a dataframe given some properties. + + Parameters + ---------- + df : pd.DataFrame + POSYDON binary population synthesis dataframe. + S1_state : str + Star1 stellar state. + S2_state : str + Star2 stellar state. + binary_state : str + Binary state. + binary_event : str + Binary event. + step_name : str + Name of posydon step. + invert_S1S2 : bool + If `True` isolated also sort S1_state=S2_state and S2_state=S1_state + systems. + + Returns + ------- + pd.DataFrame of bools + List of binaries to select given the search parameters. + + """ + if not invert_S1S2 and S1_state != S2_state: + warnings.warn('Note that invert_S1S2=False, hence you are not parsing ' + f'the dataset for {S1_state}-{S2_state} binaries and ' + f'and not for for {S1_state}-{S2_state}. If this is ' + 'done on purpose, ignore this message!') + + sel_all = df['S1_state'].astype(bool) + + if S1_state is not None: + S1_logic = (df['S1_state'] == S1_state) + else: + S1_logic = sel_all + if S2_state is not None: + S2_logic = (df['S2_state'] == S2_state) + else: + S2_logic = sel_all + if binary_state is not None: + binary_state_logic = (df['state'] == binary_state) + else: + binary_state_logic = sel_all + if binary_event is not None: + binary_event_logic = (df['event'] == binary_event) + else: + binary_event_logic = sel_all + if step_name is not None: + step_name_logic = (df['step_names'] == step_name) + else: + step_name_logic = sel_all + + if invert_S1S2: + S1_logic_inverted = (df['S1_state'] == S2_state) + S2_logic_inverted = (df['S2_state'] == S1_state) + # find systems + logic = ((S1_logic & S2_logic & binary_state_logic & + binary_event_logic & step_name_logic) | + (S1_logic_inverted & S2_logic_inverted & + binary_state_logic & binary_event_logic & + step_name_logic)) + else: + # find systems + logic = (S1_logic & S2_logic & binary_state_logic & + binary_event_logic & step_name_logic) + + return logic + + + def parse(self, S1_state=None, S2_state=None, binary_state=None, + binary_event=None, invert_S1S2=False, chunksize=500000): + """Sort binaries of interests given some properties. + + Parameters + ---------- + S1_state : str + Star1 stellar state. + S2_state : str + Star2 stellar state. + binary_state : str + Binary state. + binary_event : str + Binary event. + step_name : str + Name of posydon step. + invert_S1S2 : bool + If `True` isolated also sort S1_state=S2_state and S2_state=S1_state + systems. + chunksize : int + Read the POSYDON binary population in chuncks to prevent OFM error. + + """ + + df_sel = pd.DataFrame() + df_sel_oneline = pd.DataFrame() + count = 0 + tmp = 0 + if self.verbose: + print('Binary count with (S1_state, S2_state, binary_state, binary_event) equal') + print(f'to ({S1_state}, {S2_state}, {binary_state}, {binary_event})') + if invert_S1S2: + print(f'and ({S2_state}, {S1_state}, {binary_state}, {binary_event})') + for k, file in enumerate(self.path_to_data): + df_sel_met = pd.DataFrame() + sel_met = [] + # read metallicity from path + met = float(file.split('/')[-1].split('_Zsun')[0])*Zsun + simulated_mass_for_met = 0. + # TODO: handle binaries at the edge case of the chuncks + for i, df in enumerate(pd.read_hdf(file, key='history', chunksize=chunksize)): + + logic = self.apply_logic(df, S1_state=S1_state, + S2_state=S2_state, + binary_state=binary_state, + binary_event=binary_event, + invert_S1S2=invert_S1S2) + + # select systems + # remove duplicate indicies, e.g. if selecting 'contact' state it appears twice + # if no specific event is selected (the second time is from the copied END event) + sel = df.loc[logic].index.drop_duplicates() + + # count systems + count += len(np.unique(sel)) + + # read the simulated ZAMS mass + sel_ZAMS = df['event'] == 'ZAMS' + mass = sum(df['S1_mass'][sel_ZAMS]+df['S2_mass'][sel_ZAMS]) + simulated_mass_for_met += mass + + # sort systems + if any(sel): + df_tmp = pd.DataFrame() + df_tmp = df.loc[sel] + sel_met.extend(sel) + # store metallicity + df_tmp['metallicity'] = met + # concatenate results + df_sel_met = pd.concat([df_sel_met, df_tmp]) + del df_tmp + + # store simulated and underlying stellar mass + df_sel_met['simulated_mass_for_met'] = simulated_mass_for_met + df_sel_met['underlying_mass_for_met'] = initial_total_underlying_mass(df=simulated_mass_for_met, **self.ini_kw)[0] + + # concatenate results + df_sel = pd.concat([df_sel, df_sel_met]) + del df_sel_met + + # load, parse and store oneline dataframe + # this dataframe is smaller, we can load it all at once + df_sel_met_oneline = pd.read_hdf(file, key='oneline') + df_sel_met_oneline = df_sel_met_oneline.loc[sel_met] + df_sel_met_oneline['metallicity'] = met + df_sel_oneline = pd.concat([df_sel_oneline, df_sel_met_oneline]) + + if self.verbose: + print(f'in {file} are {count-tmp}') + tmp = count + + if self.verbose: + print('Total binaries found are', count) + + # save parsed population as synthetic population + if self.df is not None: + warnings.warn('Overwriting the df population!') + self.df = df_sel + self.df_oneline = df_sel_oneline + + def save_pop(self, path='./parsed_population.h5'): + """Save parsed population. + + Parameters + ---------- + path : str + Path to file where you want to export the dataset. + + """ + if self.df is None: + raise ValueError('Nothing to save! The population was not parsed.') + elif self.df_oneline is None: + raise ValueError('Missing oneline dataframe!') + else: + self.df.to_hdf(path, key='history') + self.df_oneline.to_hdf(path, key='oneline') + if self.verbose: + print('Population successfully saved!') + + def load_pop(self, path): + """Load parsed population. + + Parameters + ---------- + path : str + Path to dataset. + + """ + if self.df is None and self.df_oneline is None : + self.df = pd.read_hdf(path, key='history') + self.df_oneline = pd.read_hdf(path, key='oneline') + if self.verbose: + print('Population successfully loaded!') + else: + raise ValueError('You already have a population stored in memory!') + + def get_dco_at_formation(self, S1_state, S2_state, oneline_cols=None): + """Sort synthetic population, i.e. DCO at formation. + + Note: by default this function looks for the symmetric state + S1_state = S2_sate and S2_state = S1_sate. + + Parameters + ---------- + S1_state : str + Star1 stellar state. + S2_state : str + Star2 stellar state. + oneline_cols : list str + List of columns preset in the oneline dataframe you want to export + into the synthetic population. + + """ + + # to avoid the user making mistake automatically check the inverse of + # the stellar states, since the df is already parsed this will not + # take too much extra time + logic = self.apply_logic(self.df, S1_state=S1_state, + S2_state=S2_state, + binary_state='detached', + step_name = 'step_SN', + invert_S1S2=True) + self.df_synthetic = self.df.loc[logic].copy() + + # compute the inspiral timescale from the integrated orbit + # this estimate is better than evaluating Peters approxiamtion + time_contact = self.df.loc[self.df['event'] == 'END',['time']] + self.df_synthetic['t_delay'] = (time_contact - self.df_synthetic[['time']])*1e-6 # Myr + self.df_synthetic['time'] *= 1e-6 # Myr + + # add properties of the oneline dataframe + if self.df_oneline is not None: + # TODO: add kicks as well by default? + save_cols = ['S1_spin_orbit_tilt', 'S2_spin_orbit_tilt'] + if oneline_cols is not None: + for c in oneline_cols: + if c not in save_cols: + save_cols.append(c) + for c in save_cols: + if c in self.df_oneline: + self.df_synthetic[c] = self.df_oneline[c] + else: + warnings.warn(f'The column {c} is not present in the ' + 'oneline dataframe.') + + # for convinience reindex the DataFrame + n_rows = len(self.df_synthetic.index) + self.df_synthetic = self.df_synthetic.set_index(np.linspace(0,n_rows-1,n_rows,dtype=int)) + + def save_synthetic_pop(self, path='./synthetic_population.h5'): + """Save synthetc population. + + Parameters + ---------- + path : str + Path to dataset. + + """ + if self.df_synthetic is None: + raise ValueError('Nothing to save!') + else: + self.df_synthetic.to_hdf(path, key='history') + if self.verbose: + print('Synthetic population successfully saved!') + + def load_synthetic_pop(self, path): + """Load synthetc population. + + Parameters + ---------- + path : str + Path to dataset. + + """ + if self.df_synthetic is None: + self.df_synthetic = pd.read_hdf(path, key='history') + if self.verbose: + print('Synthetic population successfully loaded!') + else: + raise ValueError('You already have an synthetic population stored in memory!') + + def get_dco_merger_efficiency(self): + """Compute the DCO merger efficinty per Msun for each metallicities.""" + metallicities = np.unique(self.df_synthetic['metallicity']) + efficiencies = [] + self.met_merger_efficiency = sorted(metallicities)[::-1] + for met in self.met_merger_efficiency: + sel = (self.df_synthetic['metallicity'] == met) + count = self.df_synthetic[sel].shape[0] + underlying_stellar_mass = self.df_synthetic.loc[sel,'underlying_mass_for_met'].values[0] + eff = count/underlying_stellar_mass + efficiencies.append(eff) + print(f'DCO merger efficiency at Z={met:1.2E}: {eff:1.2E} Msun^-1') + self.met_merger_efficiency = np.array(self.met_merger_efficiency) + self.merger_efficiency = np.array(efficiencies) + + + def compute_cosmological_weights(self, sensitivity, flag_pdet, working_dir, load_data, **kwargs): + """Compute the DCO merger rate weights. + + Parameters + ---------- + sensitivity : str + GW detector sensitivity and network configuration you want to use, see arXiv:1304.0670v3 + detector sensitivities are taken from: https://dcc.ligo.org/LIGO-T2000012-v2/public + available sensitivity keys (for Hanford, Livingston, Virgo network): + 'O3actual_H1L1V1' : aligo_O3actual_H1.txt, aligo_O3actual_L1.txt, avirgo_O3actual.txt + 'O4low_H1L1V1' : aligo_O4low.txt, aligo_O4low.txt, avirgo_O4high_NEW.txt + 'O4high_H1L1V1' : aligo_O4high.txt, aligo_O4high.txt, avirgo_O4high_NEW.txt + 'design_H1L1V1' : AplusDesign.txt, AplusDesign.txt, avirgo_O5high_NEW.txt + 'infinite': intrinsic merging DCO population, i.e. p_det = 1 + flag_pdet : bool + `True` if you use sensitivity != 'infinite'. + working_dir : str + Working directory where the weights will be saved. + load_data : bool + `True` if you want to load the weights computed by this function + in your working directory. + **kwargs : dict + Kwargs containing the model parameters of your rate calculation. + See posydon/popsyn/rate_calculation.py + + Returns + ------- + array doubles + Return the cosmological weights, z_formation, z_merger and binary + index k associated to each weighted binary. + + """ + + # TODO: make the class inputs kwargs + self.rates = Rates(self.df_synthetic, **kwargs) + + # compute DCO merger rate density + if not load_data: + self.rates.compute_merger_rate_weights(sensitivity=sensitivity, flag_pdet=flag_pdet, path_to_dir=working_dir) + index, z_formation, z_merger, w_ijk = self.rates.load_merger_rate_weights(sensitivity, path_to_dir=working_dir) + + return index, z_formation, z_merger, w_ijk + + def resample_synthetic_population(self, index, z_formation, z_merger, w_ijk, export_cols=None): + """Resample synthetc population to obtain intrinsic/detectable population. + + Parameters + ---------- + index : array int + Index k of each binary corresponding to the synthetc dataframe + proeprties. + z_formation : array float + Redshift of formation of each binary. + z_merger : array float + Redshift of merger of each binary. + w_ijk : array float + Cosmological weights computed with Eq. B.8 of Bavera et at. (2020). + export_cols : list str + List of additional columns to save in the underlying/detectable + population. + + Returns + ------- + pd.DataFrame + Resampled synthetc population to intrinsic or detecatable + population. + + """ + + # drop all zero weights to save memory + sel = w_ijk > 0. + index = index[sel] + z_formation = z_formation[sel] + z_merger = z_merger[sel] + w_ijk = w_ijk[sel] + + # export results, we do a selections of columns else the dataframe + # risks to go out of memory + df = pd.DataFrame() + df['weight'] = w_ijk + df['z_formation'] = z_formation + df['z_merger'] = z_merger + save_cols = ['metallicity','time','t_delay','S1_state','S2_state', + 'S1_mass','S2_mass','S1_spin','S2_spin', + 'orbital_period','eccentricity', 'q', 'm_tot', + 'm_chirp', 'chi_eff'] + if export_cols is not None: + for c in export_cols: + if c not in save_cols: + save_cols.append(c) + for c in save_cols: + df[c] = self.rates.get_data(c, index) + + return df + + def get_dco_merger_rate_density(self, export_cols=None, working_dir='./', load_data=False): + """Compute the merger rate density as a function of redshift. + + Parameters + ---------- + export_cols : list str + List of additional columns to save in the underlying/detectable + population. + working_dir : str + Working directory where the weights will be saved. + load_data : bool + `True` if you want to load the weights computed by this function + in your working directory. + + """ + if self.df_synthetic is None: + raise ValueError('You first need to isolated the DCO synthetic population!') + + # compute cosmological weights (detection rate weights with infinite sensitivity) + sensitivity='infinite' + flag_pdet = False + index, z_formation, z_merger, w_ijk = self.compute_cosmological_weights(sensitivity, flag_pdet, working_dir=working_dir, load_data=load_data) + + # compute rate density weights + self.z_rate_density = self.rates.get_centers_redshift_bins() + self.rate_density = self.rates.compute_merger_rate_density(w_ijk, z_merger, observable='DCOs', sensitivity=sensitivity) + print(f'DCO merger rate density in the local Universe (z={self.z_rate_density[0]:1.2f}): {round(self.rate_density[0],2)} Gpc^-3 yr^-1') + + # export the intrinsic DCO population + self.df_intrinsic = self.resample_synthetic_population(index, z_formation, z_merger, w_ijk, export_cols=export_cols) + + def get_dco_detection_rate(self, sensitivity='design_H1L1V1', export_cols=None, working_dir='./', load_data=False): + """Compute the detection rate per yr. + + Parameters + ---------- + sensitivity : str + GW detector sensitivity and network configuration you want to use, see arXiv:1304.0670v3 + detector sensitivities are taken from: https://dcc.ligo.org/LIGO-T2000012-v2/public + available sensitivity keys (for Hanford, Livingston, Virgo network): + 'O3actual_H1L1V1' : aligo_O3actual_H1.txt, aligo_O3actual_L1.txt, avirgo_O3actual.txt + 'O4low_H1L1V1' : aligo_O4low.txt, aligo_O4low.txt, avirgo_O4high_NEW.txt + 'O4high_H1L1V1' : aligo_O4high.txt, aligo_O4high.txt, avirgo_O4high_NEW.txt + 'design_H1L1V1' : AplusDesign.txt, AplusDesign.txt, avirgo_O5high_NEW.txt + export_cols : list str + List of additional columns to save in the underlying/detectable + population. + working_dir : str + Working directory where the weights will be saved. + load_data : bool + `True` if you want to load the weights computed by this function + in your working directory. + + """ + if self.df_synthetic is None: + raise ValueError('You first need to isolated the DCO synthetic population!') + + # compute detection rate weights + flag_pdet = True + index, z_formation, z_merger, w_ijk = self.compute_cosmological_weights(sensitivity, flag_pdet, working_dir=working_dir, load_data=load_data) + print(f'DCO detection rate at {sensitivity} sensitivity: {sum(w_ijk):1.2f} yr^-1') + + # export the detectable DCO population + # TODO: store p_det + self.df_detectable = self.resample_synthetic_population(index, z_formation, z_merger, w_ijk, export_cols=export_cols) + + def save_intrinsic_pop(self, path='./intrinsic_population.h5'): + """Save intrinsic population. + + Parameters + ---------- + path : str + Path to dataset. + + """ + if self.df_intrinsic is None: + raise ValueError('Nothing to save!') + else: + self.df_intrinsic.to_hdf(path, key='history') + if self.verbose: + print('Intrinsic population successfully saved!') + + def load_intrinsic_pop(self, path): + """Load intrinsic population. + + Parameters + ---------- + path : str + Path to dataset. + + """ + if self.df_intrinsic is None: + self.df_intrinsic = pd.read_hdf(path, key='history') + if self.verbose: + print('Intrinsic population successfully loaded!') + else: + raise ValueError('You already have an intrinsic population stored in memory!') + + def save_detectable_pop(self, path='./detectable_population.h5'): + """Save detectable population. + + Parameters + ---------- + path : str + Path to dataset. + + """ + if self.df_detectable is None: + raise ValueError('Nothing to save!') + else: + self.df_detectable.to_hdf(path, key='history') + if self.verbose: + print('Detectable population successfully saved!') + + def load_detectable_pop(self, path): + """Load detectable population. + + Parameters + ---------- + path : str + Path to dataset. + + """ + if self.df_detectable is None: + self.df_detectable = pd.read_hdf(path, key='history') + if self.verbose: + print('Detectable population successfully loaded!') + else: + raise ValueError('You already have an detectable population stored in memory!') + + def plot_merger_efficiency(self, **kwargs): + """Plot merger rate efficinty.""" + if self.met_merger_efficiency is None or self.merger_efficiency is None: + raise ValueError('First you need to compute the merger efficinty!') + plot_dco.plot_merger_efficiency(self.met_merger_efficiency, self.merger_efficiency, **kwargs) + + def plot_merger_rate_density(self, **kwargs): + """Plot merger rate density.""" + if self.z_rate_density is None or self.rate_density is None: + raise ValueError('First you need to compute the merger rate density!') + plot_dco.plot_merger_rate_density(self.z_rate_density, self.rate_density, **kwargs) + + def plot_hist_dco_properties(self, var, intrinsic=False, detectable=False, **kwargs): + """Plot histogram of intrinsic/detectable properites. + + Parameters + ---------- + var : str + Property to plot stored in intrinsic/detectable dataframe. + intrinsic : bool + `True` if you want to deplay the intrisc population. + detectable : bool + `True` if you want to deplay the detectable population. + **kwargs : dict + ploting arguments + + """ + if self.z_rate_density is None or self.rate_density is None: + raise ValueError('First you need to compute the merger rate density!') + if intrinsic: + intrinsic = self.df_intrinsic + if detectable: + detectable = self.df_detectable + plot_dco.plot_hist_dco_properties(var, df_intrinsic=intrinsic, df_detectable=detectable, **kwargs) diff --git a/posydon/visualization/plot_dco.py b/posydon/visualization/plot_dco.py new file mode 100644 index 0000000000..b499cca10f --- /dev/null +++ b/posydon/visualization/plot_dco.py @@ -0,0 +1,102 @@ +import os +import matplotlib.pyplot as plt +from posydon.utils.common_functions import PATH_TO_POSYDON +from posydon.visualization.plot_defaults import DEFAULT_LABELS + +plt.style.use(os.path.join(PATH_TO_POSYDON, "posydon/visualization/posydon.mplstyle")) + +def plot_merger_efficiency(met, merger_efficiency, show=True, path=None, Zsun=0.0142): + title = r'Merger efficiency' + plt.figure() + plt.title(title) + plt.plot(met/Zsun, merger_efficiency) + plt.yscale('log') + plt.xscale('log') + plt.xlabel(r'$Z/Z_\odot$') + plt.ylabel(r'\#DCOs [$M_\odot^{-1}$]') + if path: + plt.savefig(path) + if show: + plt.show() + +def plot_merger_rate_density(z, rate_density, zmax=10., show=True, path=None): + title = r'Merger rate density' + plt.figure() + plt.title(title) + plt.plot(z[z Date: Thu, 25 May 2023 16:49:30 +0200 Subject: [PATCH 088/319] Catch corrupted mesa files for creating a psygrid (#72) * Update gridutils.py add a catch, if something goes wrong while reading files * Update psygrid.py Catch "None" in history_mod and history_age * Ignore runs with corrupted history Ignore runs with corrupted history * fix typo close an open bracket * Remove some ignore_data again Remove ignore_data on normal history read, to allow stars to not be included in the simulation, e.g. a CO * Update scrubbing.py Catch, that table might not be None, while model or age is. * catch None if (binary) history is None set length to 0 --- posydon/grids/psygrid.py | 84 +++++++++++++++++++++++++++----------- posydon/grids/scrubbing.py | 2 +- posydon/utils/gridutils.py | 18 +++++--- 3 files changed, 75 insertions(+), 29 deletions(-) diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index 94aee10723..7b9b6b13fe 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -686,40 +686,70 @@ def decide_columns(key_in_config, defaults): # read the model numbers and ages from the histories colname = "model_number" if history1 is not None: - history1_mod = np.int_(read_MESA_data_file( - run.history1_path, [colname])[colname]) - if len(history1_mod) == len(history1) + 1: - history1_mod = history1_mod[:-1] + history1_mod = read_MESA_data_file( + run.history1_path, [colname]) + if history1_mod is not None: + history1_mod = np.int_(history1_mod[colname]) + if len(history1_mod) == len(history1) + 1: + history1_mod = history1_mod[:-1] + else: + ignore_data = True + ignore_reason = "corrupted_history1" history1_age = read_MESA_data_file( - run.history1_path, ["star_age"])["star_age"] - if len(history1_age) == len(history1) + 1: - history1_age = history1_age[:-1] + run.history1_path, ["star_age"]) + if history1_age is not None: + history1_age = history1_age["star_age"] + if len(history1_age) == len(history1) + 1: + history1_age = history1_age[:-1] + else: + ignore_data = True + ignore_reason = "corrupted_history1" else: history1_mod = None history1_age = None if history2 is not None: - history2_mod = np.int_(read_MESA_data_file( - run.history2_path, [colname])[colname]) - if len(history2_mod) == len(history2) + 1: - history2_mod = history2_mod[:-1] + history2_mod = read_MESA_data_file( + run.history2_path, [colname]) + if history2_mod is not None: + history2_mod = np.int_(history2_mod[colname]) + if len(history2_mod) == len(history2) + 1: + history2_mod = history2_mod[:-1] + else: + ignore_data = True + ignore_reason = "corrupted_history2" history2_age = read_MESA_data_file( - run.history2_path, ["star_age"])["star_age"] - if len(history2_age) == len(history2) + 1: - history2_age = history2_age[:-1] + run.history2_path, ["star_age"]) + if history2_age is not None: + history2_age = history2_age["star_age"] + if len(history2_age) == len(history2) + 1: + history2_age = history2_age[:-1] + else: + ignore_data = True + ignore_reason = "corrupted_history2" else: history2_mod = None history2_age = None if binary_history is not None: - binary_history_mod = np.int_(read_MESA_data_file( - run.binary_history_path, [colname])[colname]) - if len(binary_history_mod) == len(binary_history) + 1: - binary_history_mod = binary_history_mod[:-1] + binary_history_mod = read_MESA_data_file( + run.binary_history_path, [colname]) + if binary_history_mod is not None: + binary_history_mod = np.int_(binary_history_mod[colname]) + if len(binary_history_mod) == len(binary_history) + 1: + binary_history_mod = binary_history_mod[:-1] + else: + ignore_data = True + ignore_reason = "corrupted_binary_history" binary_history_age = read_MESA_data_file( - run.binary_history_path, ["age"])["age"] - if len(binary_history_age) == len(binary_history) + 1: - binary_history_age = binary_history_age[:-1] + run.binary_history_path, ["age"]) + if binary_history_age is not None: + binary_history_age = binary_history_age["age"] + if len(binary_history_age) == len(binary_history) + 1: + binary_history_age = binary_history_age[:-1] + else: + ignore_data = True + ignore_reason = "corrupted_binary_history" else: binary_history_mod = None binary_history_age = None @@ -740,14 +770,22 @@ def decide_columns(key_in_config, defaults): ) # if scubbing wiped all the binary history, discard run - if binary_grid and len(binary_history) == 0: + if binary_history is not None: + binary_history_len = len(binary_history) + else: + binary_history_len = 0 + if binary_grid and binary_history_len == 0: ignore_data = True ignore_reason = "ignored_scrubbed" warnings.warn("Ignored MESA run because of scrubbed binary" " history in: {}\n".format(run.path)) if not initial_RLO_fix: continue - if not binary_grid and len(history1) == 0: + if history1 is not None: + history1_len = len(history1) + else: + history1_len = 0 + if not binary_grid and history1_len == 0: ignore_data = True warnings.warn("Ignored MESA run because of scrubbed" " history in: {}\n".format(run.path)) diff --git a/posydon/grids/scrubbing.py b/posydon/grids/scrubbing.py index 2f1b38b222..11d7d5a3f6 100644 --- a/posydon/grids/scrubbing.py +++ b/posydon/grids/scrubbing.py @@ -33,7 +33,7 @@ def scrub(tables, models, ages): scr_tables = [] scr_models = [] for table, model, age in zip(tables, models, ages): - if table is None: + if (table is None) or (model is None) or (age is None): scr_tables.append(None) scr_models.append(None) else: diff --git a/posydon/utils/gridutils.py b/posydon/utils/gridutils.py index 04c32aa638..a56508c637 100644 --- a/posydon/utils/gridutils.py +++ b/posydon/utils/gridutils.py @@ -69,17 +69,25 @@ def read_MESA_data_file(path, columns): if path is None: return None elif os.path.exists(path): - return np.atleast_1d(np.genfromtxt(path, skip_header=5, names=True, - usecols=columns, - invalid_raise=False)) + try: + return np.atleast_1d(np.genfromtxt(path, skip_header=5, names=True, + usecols=columns, + invalid_raise=False)) + except: + warnings.warn("Problems with reading file "+path) + return None else: return None def read_EEP_data_file(path, columns): """Read an EEP file (can be `.gz`) - similar to `read_MESA_data_file()`.""" - return np.atleast_1d(np.genfromtxt(path, skip_header=11, names=True, - usecols=columns, invalid_raise=False)) + try: + return np.atleast_1d(np.genfromtxt(path, skip_header=11, names=True, + usecols=columns, invalid_raise=False)) + except: + warnings.warn("Problems with reading file "+path) + return None def fix_He_core(history): From 80ed446a915874efcf0ac73170484bebab84fb7c Mon Sep 17 00:00:00 2001 From: kasdaglie <112660893+kasdaglie@users.noreply.github.com> Date: Thu, 25 May 2023 10:59:38 -0400 Subject: [PATCH 089/319] making some if statements clear (#83) --- posydon/binary_evol/DT/step_detached.py | 19 ++++++++++++++----- posydon/popsyn/binarypopulation.py | 1 - 2 files changed, 14 insertions(+), 6 deletions(-) diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index 6d5c6b2406..2894e7c61d 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -1015,7 +1015,9 @@ def __call__(self, binary): secondary.htrack = False else: raise Exception("State not recognized!") - + else: + raise Exception("Non existent companion has not a recognized value!") + def get_star_data(binary, star1, star2, htrack, co, copy_prev_m0=None, copy_prev_t0=None): """Get and interpolate the properties of stars. @@ -1062,7 +1064,7 @@ def get_star_data(binary, star1, star2, htrack, get_track = self.grid.get - if np.any(np.isnan([m0, t0])): + if np.isnan(m0) or np.isnan(t0): # binary.event = "END" # binary.state += " (GridMatchingFailed)" # if self.verbose: @@ -1126,16 +1128,23 @@ def get_star_data(binary, star1, star2, htrack, # get the matched data of two stars, respectively interp1d_sec, m0, t0 = get_star_data( binary, secondary, primary, secondary.htrack, co=False) - if (primary.co) or (self.non_existent_companion != 0): + + primary_not_normal = (primary.co) or (self.non_existent_companion in [1,2]) + primary_normal = (not primary.co) and self.non_existent_companion == 0 + + if primary_not_normal: # copy the secondary star except mass which is of the primary, # and radius, mdot, Idot = 0 interp1d_pri = get_star_data( binary, secondary, primary, secondary.htrack, co=True, copy_prev_m0=m0, copy_prev_t0=t0)[0] - elif not primary.co: + elif primary_normal: interp1d_pri = get_star_data( binary, primary, secondary, primary.htrack, False)[0] - # TODO: Eirini else + else: + raise Exception("During matching primary is either should be either normal or not normal. `non_existent_companion` should be zero.") + + if interp1d_sec is None or interp1d_pri is None: # binary.event = "END" binary.state += " (GridMatchingFailed)" diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index 557bcbc83e..5bb103993d 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -253,7 +253,6 @@ def _safe_evolve(self, **kwargs): try: binary.evolve() except Exception: - #print(traceback.print_exc()) binary.event = 'FAILED' binary.traceback = traceback.format_exc() if len(w) > 0: From c4427f74832bc70e5082cf879061dfa112a59a3f Mon Sep 17 00:00:00 2001 From: ssbvr Date: Wed, 31 May 2023 08:45:42 +0200 Subject: [PATCH 090/319] rename selection_effects.py --- .../{predict_detection_probabilities.py => selection_effects.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename posydon/popsyn/{predict_detection_probabilities.py => selection_effects.py} (100%) diff --git a/posydon/popsyn/predict_detection_probabilities.py b/posydon/popsyn/selection_effects.py similarity index 100% rename from posydon/popsyn/predict_detection_probabilities.py rename to posydon/popsyn/selection_effects.py From 9e46ba6c3e4e92d63e514e2df70a6150477ed4fe Mon Sep 17 00:00:00 2001 From: Elizabeth Teng <41969755+elizabethteng@users.noreply.github.com> Date: Thu, 15 Jun 2023 09:06:31 -0500 Subject: [PATCH 091/319] adding profile interpolation (#58) * adding profile interpolation * adding monotonicity enforcement to main class * fixing typo in learn_bounds and increase epochs/callbacks * remove default path, move IF interpolator argument to train method * changed some variable names * adding load interpolator arg to main class init function * changing arg name from interpolator to IF_interpolator to prevent confusion * adding docs for prof interpolation * fixed file format for docs * fix typo * removed extraneous line * addressing some stylistic issues * resolve various issues * changed name of finalmass to final_m1, added missing states to X_H class * fixed missing return in mono renorm * addressing Philipp's comments; changing a few argument/variable names, added arguments for model training parameters --------- Co-authored-by: Elizabeth Teng --- .../ProfileInterpolator.rst | 106 ++++ .../interpolation/profile_interpolation.py | 571 ++++++++++++++++++ 2 files changed, 677 insertions(+) create mode 100644 docs/interpolation/Profile-Interpolator/ProfileInterpolator.rst create mode 100644 posydon/interpolation/profile_interpolation.py diff --git a/docs/interpolation/Profile-Interpolator/ProfileInterpolator.rst b/docs/interpolation/Profile-Interpolator/ProfileInterpolator.rst new file mode 100644 index 0000000000..812fc54eb2 --- /dev/null +++ b/docs/interpolation/Profile-Interpolator/ProfileInterpolator.rst @@ -0,0 +1,106 @@ + +.. _ProfileInterpolator: + +########################### +Profile Interpolation +########################### + + +We demonstrate the profile interpolator which delivers predictions +on internal stellar structure at the end of MESA evolution. +To use the profile interpolator we firstimport the ``ProfileInterpolator`` +object from the POSYDON library. + +.. code-block:: python + + from posydon.interpolation.profile_interpolation import ProfileInterpolator, CompileData + + +Extracting Profile Data from .h5 Files for Training Interpolators +=============================== + +To extract profile data from existing grids, we pass arguments +``train_path`` and ``valid_path`` pointing to .h5 files +containing instances of the ``PSyGrid`` class from which the +training and testing grids can be loaded. These can be constructed +by running ones own simulations or by loading a precomputed simulation +stored in the data directory of the POSYDON repository. +The ``profile_names`` argument denotes which profiles will be extracted +from the grid and saved to ``filename`` using the function ``save``. + +.. code-block:: python + + compiler = CompileData(train_data = '/path/to/training/grid.h5', + valid_data = '/path/to/testing/grid.h5', + profile_names=['radius','logRho', + 'x_mass_fraction_H','y_mass_fraction_He', + 'z_mass_fraction_metals','omega','energy']) + compiler.save(filename = '/path/for/profiles/datafile.pkl') + + +Loading a Pretrained Interpolator +=============================== + +To load a pretrained interpolator we need to pass the optional +``load_interpolator`` argument to the ``ProfileInterpolator`` +instance which specifies the path to a .pkl file where the +pretrained interpolator can be loaded from. + +.. code-block:: python + + model = ProfileInterpolator(load_interpolator = "/path/to/profile/interpolator.pkl") + + +Training the Interpolator +========================= + +The interpolator is trained on profiles stored in a .pkl file generated with the +aforementioned ``CompileData`` class. This file is passed using the argument +``filename``. The profile interpolation is anchored to POSYDON's IF interpolation, +and so we pass the name of the interpolator file to the ``train`` function using the ``IF_interpolator`` argument. + +.. code-block:: python + + model = ProfileInterpolator(load_interpolator=None) + model.load_profiles(filename = "/path/to/profiles/datafile.pkl") + model.train(IF_interpolator = "/path/to/IFinterpolator.pkl") + + +Using the Interpolator +====================== + +Once the interpolator has been trained or loaded from a .pkl file it can be used +to predict profiles for sets of initial conditions passed through argument ``inputs``. +These initial conditions must be in log space and in shape (N,3) for N binaries. +The order of the coordinates is star 1 mass, star 2 mass, period. The prediction +function returns three arrays containing the profiles' coordinates along mass +enclosed, log density, and Hydrogen mass fraction. The Density and H mass fraction +profiles have the same mass coordinates. + +.. code-block:: python + + mass_coords, density_profiles, h_profiles = model.predict(inputs) + +Finally a trained interpolator can be easily saved by specifying a path to a .pkl file +where the interpolator will be saved to. + +.. code-block:: python + + model.save("path/for/profile/interpolator.pkl") + +Evaluating on Testing Data +========================== + +To evaluate the interpolator on the testing grid that was used as validation data in +training, we can pull the testing data out of the ``ProfileInterpolator`` class as follows: + +.. code-block:: python + + valid_initial = np.log10(np.transpose([model.valid_scalars["m1"], + model.valid_scalars['m2'], + model.valid_scalars['p']])) + + valid_mass_coords = np.transpose(np.array(model.valid_scalars["final_mass"])*np.linspace(0,1,200)[:,np.newaxis]) + valid_density_profiles = model.valid_profiles[:,model.names.index("logRho")] + valid_H_profiles = model.valid_profiles[:,model.names.index("x_mass_fraction_H")] + \ No newline at end of file diff --git a/posydon/interpolation/profile_interpolation.py b/posydon/interpolation/profile_interpolation.py new file mode 100644 index 0000000000..d2188c4117 --- /dev/null +++ b/posydon/interpolation/profile_interpolation.py @@ -0,0 +1,571 @@ +"""Module for performing final profile interpolation +""" + +__authors__ = [ + "Elizabeth Teng " +] + +import pickle +import warnings + +# POSYDON +from posydon.grids.psygrid import PSyGrid +from posydon.interpolation.IF_interpolation import IFInterpolator + +# Math and ML +import numpy as np +import pandas as pd +import tensorflow as tf +tf.get_logger().setLevel('ERROR') +from tensorflow.keras import layers, losses, models, optimizers +from sklearn.decomposition import PCA +from scipy.interpolate import interp1d + + +class CompileData: + + def __init__(self, train_path, valid_path, + profile_names=['radius','logRho', 'x_mass_fraction_H', + 'y_mass_fraction_He','z_mass_fraction_metals', + 'omega','energy']): + """Extracts profile data from '.h5' grid files and saves to file. + Args: + train_path (str) : path/name of '.h5' file for training data. + valid_path (str) : path/name of '.h5' file for testing data. + profile_names (array-like) : list of profile quantities to extract. + """ + self.names = profile_names + + # extract testing data + print("extracting testing data") + valid = PSyGrid(valid_path) # load PSyGrid object for testing grid + self.valid_scalars = pd.DataFrame() + self.valid_profiles = [] + testing_failed = [] + + try: + print(valid.final_values["S1_state"][0]) + except: + print("grid does not have S1_state information") + + + for i in range(len(valid)): + try: + scalars,profiles = self.scrape(valid,i) + self.valid_scalars = self.valid_scalars.append(scalars,ignore_index=True) + self.valid_profiles.append(profiles) + except: + testing_failed.append(i) + pass + warnings.warn(f"{len(testing_failed)} binaries failed") + + # extract training data + print("extracting training data") + train = PSyGrid(train_path) # load PSyGrid object for training grid + self.scalars = pd.DataFrame() + self.profiles = [] + training_failed = [] + + for i in range(len(train)): + try: + scalars,profiles = self.scrape(train,i) + self.scalars = self.scalars.append(scalars,ignore_index=True) + self.profiles.append(profiles) + except: + training_failed.append(i) + pass + warnings.warn(f"{len(training_failed)} training binaries failed") + + + def scrape(self,grid,ind): + """Extracts profile data from one MESA run. + Args: + grid (obj) : PSyGrid object. + ind (int) : index of run to be scraped. + Returns: + scalars (array-like) : dictionary containing initial + m1, m2, p and final s1_state, final_m1. + profiles (array-like) : all N specified profiles, shape (N,200). + """ + # open individual run as a DataFrame + df = pd.DataFrame(grid[ind]['final_profile1']) + df = df.sort_values(by="mass") + df = df.reset_index(drop=True) + + # grab input values, final star 1 state, final star 1 mass + final_m1 = grid.final_values["star_1_mass"][ind] + scalars = {"m1":grid.initial_values["star_1_mass"][ind], + "m2":grid.initial_values["star_2_mass"][ind], + "p":grid.initial_values["period_days"][ind], + "s1_state":grid.final_values["S1_state"][ind], + "final_m1":final_m1} + + # grab output vectors, interpolate to normalize + profiles=np.zeros([len(self.names),200]) + for i,prof in enumerate(self.names): + if prof in df.columns: + + f = interp1d(df['mass']/final_m1,df[prof], + fill_value="extrapolate") + profile_new = f(np.linspace(0,1,200)) + profiles[i] = profile_new + else: + warnings.warn(f"{prof} profile not saved in grid, will not be included in file") + + return scalars, profiles + + def save(self, filename): + """Save extracted profile data. + Args: + filename (str) : path/name of '.pkl' file where the data will be saved. + """ + SAVE_ATTRS = self.__dict__ + DONT_SAVE = [] + myattrs = {key: SAVE_ATTRS[key] + for key in SAVE_ATTRS if key not in DONT_SAVE} + with open(filename, 'wb') as f: + pickle.dump(myattrs, f) + + +class ProfileInterpolator: + + def __init__(self): + """Interfaces with other classes, trains models and predicts profiles. + """ + + def load_profiles(self,filename): + """Load extracted profile data. + Args: + filename (str) : path/name of '.pkl' file to be loaded. + Returns: + self.profiles (array-like) : M profiles for each of N training binaries, shape (N,M,200). + self.scalars (array-like) : DataFrame containing initial and final conditions for N training binaries. + self.valid_profiles (array-like) : M profiles for each of N testing binaries, shape (N,M,200). + self.valid_scalars (array-like) : DataFrame containing initial and final conditions for N testing binaries. + """ + with open(filename, 'rb') as f: + myattrs = pd.read_pickle(f) + for key in myattrs: + setattr(self, key, myattrs[key]) + self.profiles = np.array(self.profiles) + self.valid_profiles = np.array(self.valid_profiles) + + def train(self,IF_interpolator,density_epochs=3000,density_patience=200, + H_bounds_epochs=500,H_bounds_patience=50): + """Trains models for density and H mass fraction profile models. + Args: + IF_interpolator (str) : path to '.pkl' file for IF interpolator. + """ + # processing + linear_initial = np.transpose([ + self.scalars["m1"],self.scalars["m2"],self.scalars["p"]]) + initial = np.log10(np.array(linear_initial)) + final_m1 = self.scalars["final_m1"].astype(np.float64) + + valid_linear_initial = np.transpose([self.valid_scalars["m1"], + self.valid_scalars["m2"], + self.valid_scalars["p"]]) + valid_initial = np.log10(np.array(valid_linear_initial)) + valid_final_m1 = self.valid_scalars["final_m1"].astype(np.float64) + + # instantiate and train H mass fraction profile model + h_ind = self.names.index("x_mass_fraction_H") + self.h = X_H(initial, self.profiles[:,h_ind], self.scalars["s1_state"], + valid_initial, self.valid_profiles[:,h_ind], + self.valid_scalars["s1_state"], IF_interpolator, + H_bounds_epochs,H_bounds_patience) + + # instantiate and train density profile model + dens_ind = self.names.index("logRho") + self.dens = Density(initial, + self.profiles[:,dens_ind], + valid_initial, + self.valid_profiles[:,dens_ind], + IF_interpolator) + self.dens.train(prof_epochs=density_epochs,prof_patience=density_patience) + + def predict(self,inputs): + """Predict density and H mass fraction profiles from inputs. + Args: + inputs (array-like) : log-space initial conditions of N binaries to predict, shape (N,3). + Returns: + mass_coords (array-like) : linear-scale mass enclosed profile coordinates. + density_profiles (array_like) : log-scale density profile coordinates. + h_profiles (array_like) : H mass fraction profile coordinates + """ + mass_coords, density_profiles = self.dens.predict(inputs) + mass_coords, h_profiles = self.h.predict(inputs) + + return mass_coords, density_profiles, h_profiles + + + def save(self, filename): + """Save complete profiles interpolation model. + Args: + filename (str) : path/name of '.pkl' file where the model will be saved. + """ + SAVE_ATTRS = self.__dict__ + DONT_SAVE = [] + myattrs = {key: SAVE_ATTRS[key] + for key in SAVE_ATTRS if key not in DONT_SAVE} + + with open(filename, 'wb') as f: + pickle.dump(myattrs, f) + + def load(self, filename): + """Load interpolation model, which can only be used for predictions. + Args: + filename (str) : path/name of '.pkl' file to be loaded. + """ + with open(filename, 'rb') as f: + myattrs = pickle.load(f) + for key in myattrs: + setattr(self, key, myattrs[key]) + + def mono_decrease(self,profiles): + """Enforce monotonicity in profiles such as density that must monotonically decrease + Args: + profiles (array-like) : N profiles to be post-processed for monotonicity + Returns: + profiles_mono (array-like) : post-processed profiles + """ + + def mono_renorm(arr): + arr_copy = arr.copy() + # force rising points down + for i in range(1,len(arr_copy)): + if arr_copy[i]>arr_copy[i-1]: + arr_copy[i]=arr_copy[i-1] + # cut off points below surface value + return np.where(arr_copy0)[0]>0): + profiles_copy[i] = mono_renorm(profiles[i]) + + return profiles_copy + + +class Density: + + def __init__(self,initial,profiles,valid_initial, + valid_profiles,IF_interpolator,n_comp=8): + """Creates and trains density profile model. + Args: + initial (array-like) : log-space initial conditions for training data. + profiles (array-like) : final density profiles for training data. + valid_initial (array-like) : log-space initial conditions for testing data. + valid_profiles (array-like) : final density profiles for testing data. + IF_interpolator (string) : path to .pkl file for IF interpolator for central density, final mass values + n_comp (int) : number of PCA components. + """ + self.n_comp = n_comp + + # process training data + self.initial = initial # initial conditions in log space + self.rho_min = np.min(profiles,axis=1) + rho_max = np.max(profiles,axis=1) + profiles_norm = (profiles-self.rho_min[:,np.newaxis])\ + /(rho_max-self.rho_min)[:,np.newaxis] # minmax normalized profiles + self.pca = PCA(n_components=self.n_comp).fit(profiles_norm) # perform principal component analysis + pca_weights_unscaled = self.pca.transform(profiles_norm) + self.scaling = np.std(pca_weights_unscaled,axis=0) + self.pca_weights = pca_weights_unscaled/self.scaling # scaled PCA weights + + # process testing data + self.valid_initial = valid_initial + self.valid_rho_min = np.min(valid_profiles,axis=1) + valid_rho_max = np.max(valid_profiles,axis=1) + valid_profiles_norm = (valid_profiles-self.valid_rho_min[:,np.newaxis])\ + /(valid_rho_max-self.valid_rho_min)[:,np.newaxis] + valid_pca_weights_unscaled = self.pca.transform(valid_profiles_norm) + self.valid_pca_weights = valid_pca_weights_unscaled/self.scaling + + # instantiate models + self.model_prof = models.Sequential([ + layers.Dense(15,input_dim=3,activation=None), + layers.Dense(15,input_dim=15,activation="relu"), + layers.Dense(15,input_dim=15,activation="relu"), + layers.Dense(15,input_dim=15,activation="tanh"), + layers.Dense(15,input_dim=15,activation="tanh"), + layers.Dense(10,input_dim=10,activation="tanh"), + layers.Dense(10,input_dim=10,activation="tanh"), + layers.Dense(n_comp,input_dim=10,activation=None)]) + + self.model_rho = models.Sequential([ + layers.Dense(10,input_dim=3,activation='relu'), + layers.Dense(10,input_dim=10,activation='relu'), + layers.Dense(10,input_dim=10,activation='tanh'), + layers.Dense(10,input_dim=10,activation='tanh'), + layers.Dense(10,input_dim=10,activation='tanh'), + layers.Dense(10,input_dim=10,activation='tanh'), + layers.Dense(1,activation=None)]) + + self.model_IF = IFInterpolator() # instantiate POSYDON initial-final interpolator object + self.model_IF.load(filename=IF_interpolator) + + def train(self,loss=losses.MeanSquaredError(),prof_epochs=3000,prof_patience=200): + """Trains NN models. + Args: + loss (object) : loss function for training. + """ + print("training on PCA weights...") + + self.model_prof.compile(optimizers.Adam(clipnorm=1),loss=loss) + callback = tf.keras.callbacks.EarlyStopping(monitor="loss",patience=prof_patience) + history = self.model_prof.fit(self.initial,self.pca_weights, + epochs=prof_epochs,callbacks=[callback],verbose=0, + validation_data=(self.valid_initial, + self.valid_pca_weights)) + + self.model_rho.compile(optimizers.Adam(clipnorm=1),loss=loss) + callback = tf.keras.callbacks.EarlyStopping(monitor="loss",patience=40) + history = self.model_rho.fit(self.initial,self.rho_min, + epochs=500, callbacks=[callback], verbose=0, + validation_data=(self.valid_initial, + self.valid_rho_min)) + + print("done training") + + def predict(self,inputs): + """Predicts profile for n sets of given inputs, in array of shape (n,3). + Args: + inputs (array-like) : log-space initial conditions of N binaries to predict, shape (N,3). + Returns: + mass_coords (array-like) : linear-scale mass enclosed profile coordinates. + density_profiles (array_like) : log-scale density profile coordinates. + """ + # predict PCA weights + regress_prof = lambda x: self.model_prof(x) + pca_weights_pred = regress_prof(inputs).numpy() + + # predict surface density + regress_rho = lambda x: self.model_rho(x) + min_rho = regress_rho(inputs).numpy()[:,0] + + # IF interpolate center density + center_ind = self.model_IF.interpolators[0].out_keys.index('S1_log_center_Rho') + max_rho = self.model_IF.interpolators[0].test_interpolator(10**inputs)[:,center_ind] + + # reconstruct profile + norm_prof = self.pca.inverse_transform(pca_weights_pred*self.scaling) + density_profiles = norm_prof*(max_rho[:,np.newaxis]-min_rho[:,np.newaxis]) \ + + min_rho[:,np.newaxis] + + # IF interpolate final mass, construct mass enclosed profile coordinates + m1_ind = self.model_IF.interpolators[0].out_keys.index("star_1_mass") + pred_mass = self.model_IF.interpolators[0].test_interpolator(10**inputs)[:,m1_ind] + mass_coords = np.linspace(0,1,200)*pred_mass[:,np.newaxis] + + return mass_coords,density_profiles + + +class X_H: + + def __init__(self,initial,profiles,s1,valid_initial, + valid_profiles,valid_s1,IF_interpolator, + training_epochs=500, training_patience=50): + """Creates and trains H mass fraction profile model. + Args: + initial (array-like) : log-space initial conditions for training data. + profiles (array-like) : final H mass fraction profiles for training data. + s1 (array-like) : final star 1 state for training data. + valid_initial (array-like) : log-space initial conditions for testing data. + valid_profiles (array-like) : final H mass fraction profiles for testing data. + valid_s1 (array-like) : final star 1 state for testing data. + IF_interpolator (string) : path to .pkl file for IF interpolator for central density, final mass values. + """ + self.initial = initial + self.profiles = profiles + self.s1 = s1 + self.valid_initial = valid_initial + self.valid_profiles = valid_profiles + self.valid_s1 = valid_s1 + + # load IF interpolator + self.model_IF = IFInterpolator() # instantiate POSYDON initial-final interpolator object + self.model_IF.load(filename=IF_interpolator) + self.c_ind = self.model_IF.interpolators[0].out_keys.index("S1_center_h1") + self.s_ind = self.model_IF.interpolators[0].out_keys.index("S1_surface_h1") + self.interp = self.model_IF.interpolators[0] + + # star 1 states + self.class_names = ['None','WD','NS','BH', + 'stripped_He_non_burning', + 'stripped_He_Core_He_burning', + 'stripped_He_Core_C_burning', + 'stripped_He_Central_He_depleted', + 'stripped_He_Central_C_depletion', + 'H-rich_non_burning', + 'H-rich_Central_He_depleted', + 'H-rich_Central_C_depletion', + 'H-rich_Shell_H_burning', + 'H-rich_Core_H_burning', + 'H-rich_Core_He_burning', + 'H-rich_Core_C_burning'] + + # sort training data into classes by s1 state + self.sort_ind = {class_name:[] for class_name in self.class_names} + for i in range(len(self.s1)): + state = self.s1[i] + self.sort_ind[state].append(i) + + # sort testing data into classes by s1 state + self.valid_sort_ind = {class_name:[] for class_name in self.class_names} + for i in range(len(self.valid_s1)): + state = self.valid_s1[i] + self.valid_sort_ind[state].append(i) + + # create and train models for profile boundaries + self.bounds_models = self.learn_bounds(training_epochs,training_patience) + + def learn_bounds(self,training_epochs,training_patience): + """Creates and trains NNs to predict boundary points for each star 1 state. + Returns: + b_models (array-like) : dictionary containing boundary models. + """ + b_models = {} + for state in ['WD','NS','BH', + 'H-rich_Central_He_depleted', + 'H-rich_Central_C_depletion', + 'H-rich_Shell_H_burning', + 'H-rich_Core_H_burning', + 'H-rich_Core_He_burning', + 'H-rich_Core_C_burning']: + + # identify input/output training data for class + indices = self.sort_ind[state] + inputs = self.initial[indices] + prof = self.profiles[indices] + + # identify input/output testing data for class + valid_indices = self.valid_sort_ind[state] + valid_inputs = self.valid_initial[valid_indices] + valid_prof = self.valid_profiles[valid_indices] + + # calculate boundary points for training and testing data + if "burning" in state: # these classes' profile shapes have 2 boundary points + outs = 2 + bounds = [] + nonflat = [] # ensures that training data only has the correct "non-flat" shape + # to avoid issues with calculating the boundary points + valid_bounds = [] + valid_nonflat = [] + # calculate first and points in each profile with large increases + for i in range(len(indices)): + try: + diff = np.where(prof[i][1:]-prof[i][:-1]>0.002)[0] + bounds.append(np.array([diff[0]+1,diff[-1]+1])/200) + nonflat.append(i) + except: + bounds.append([np.nan,np.nan]) + for i in range(len(valid_indices)): + try: + diff = np.where(valid_prof[i][1:]-valid_prof[i][:-1]>0.002)[0] + valid_bounds.append(np.array([diff[0]+1,diff[-1]+1])/200) + valid_nonflat.append(i) + except: + valid_bounds.append([np.nan,np.nan]) + + else: # the rest of the profile shapes have 1 boundary point + outs = 1 + # calculate the point in each profile with the largest increase + bounds = (np.argmax(prof[:,1:]-prof[:,:-1],axis=1)+1)/200 + valid_bounds = (np.argmax(valid_prof[:,1:]-valid_prof[:,:-1],axis=1)+1)/200 + + # instantiate and train model on bounds + model = models.Sequential([ + layers.Dense(10,input_dim=3,activation=None), + layers.Dense(10,input_dim=10,activation='relu'), + layers.Dense(10,input_dim=10,activation='relu'), + layers.Dense(10,input_dim=10,activation='tanh'), + layers.Dense(10,input_dim=10,activation='tanh'), + layers.Dense(10,input_dim=10,activation='tanh'), + layers.Dense(outs,input_dim=10,activation="sigmoid")]) + + model.compile(optimizers.Adam(clipnorm=1),loss=losses.MeanSquaredError()) + callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=training_patience) + + if len(indices)==0: + warnings.warn(f"no training data available for s1 state {state}. model will return random results") + + elif "burning" in state: + history = model.fit(inputs[nonflat],np.array(bounds)[nonflat], + epochs=training_epochs,verbose=0,callbacks=[callback], + validation_data=(valid_inputs[valid_nonflat], + np.array(valid_bounds)[valid_nonflat])) + else: + history = model.fit(inputs,np.array(bounds), + epochs=training_epochs,verbose=0,callbacks=[callback], + validation_data=(valid_inputs, + np.array(valid_bounds))) + + b_models[state] = model + print(f"finished {state}") + return b_models + + def predict_single(self,initial,center,surface,s1): + """Predict a profile for a single binary + Args: + initial (array-like) : initial position of binary + center (float) : center H mass fraction + surface (float) : surface H mass fraction + s1 (str) : final star 1 state + """ + if "stripped_He" in s1: + return np.zeros(200) + + if s1 in ["H-rich_non_burning","None"] or surface-center<0.002: + return np.ones(200)*surface + + # construct step-shaped profile + if s1 in ["H-rich_Central_He_depleted", + "H-rich_Central_C_depletion", + "WD","NS","BH"]: + b = self.bounds_models[s1](tf.convert_to_tensor([initial])).numpy()[0] + new = np.ones(200)*center + new[int(b*200):] = surface + return new + + # construct shell-shaped profile + if s1 in ["H-rich_Shell_H_burning", + "H-rich_Core_H_burning", + "H-rich_Core_He_burning", + "H-rich_Core_C_burning"]: + b = self.bounds_models[s1](tf.convert_to_tensor([initial])).numpy()[0] + new = np.ones(200)*center + new[int(b[1]*200):] = surface + # "f" defines the shape of the H abundance profile in the shell burning region + f = interp1d(b,[center,surface],fill_value="extrapolate") + # use f to construct the profile points in the shell burning region + new[int(b[0]*200):int(b[1]*200)] = f(np.linspace(0,1,200)[int(b[0]*200):int(b[1]*200)]) + return new + + def predict(self,inputs): + """Predict H mass fraction profiles from inputs. + Args: + inputs (array-like) : log-space initial conditions of N binaries to predict, shape (N,3). + Returns: + mass_coords (array-like) : linear-scale mass enclosed profile coordinates. + h_profiles (array_like) : H mass fraction profile coordinates. + """ + # IF interpolate H mass fraction values at center, surface; star 1 state + center_vals = self.interp.test_interpolator(10**inputs)[:,self.c_ind] + surface_vals = self.interp.test_interpolator(10**inputs)[:,self.s_ind] + s1_vals = self.interp.test_classifiers(10**inputs)['S1_state'] + + # generate predicted profiles + pred_profiles = [] + for i in range(len(inputs)): + prediction = self.predict_single(inputs[i],center_vals[i],surface_vals[i],s1_vals[i]) + pred_profiles.append(prediction) + h_profiles = np.array(pred_profiles) + + # IF interpolate final masses, generate mass enclosed profile coordinates + m1_ind = self.model_IF.interpolators[0].out_keys.index("star_1_mass") + pred_mass = self.model_IF.interpolators[0].test_interpolator(10**inputs)[:,m1_ind] + mass_coords = np.linspace(0,1,200)*pred_mass[:,np.newaxis] + + return mass_coords, h_profiles \ No newline at end of file From fb9369b5feb4494ce9640c50745ef2072c4ca65e Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 15 Jun 2023 16:13:45 +0200 Subject: [PATCH 092/319] Post processing pipeline prepare master ini (#91) * allow to remove slices from combining grids and only remove the combining if all slices are missing * Update setup-pipeline correct some typos * track previously created files * Update setup-pipeline fix typo * get files from step 1 * change range over df shape to instead use the index object * turn any to check on length * add check for no slurm tasks * account for larger length difference of histories * reuse columns from histories if possible, add warnings * bugfix * add open-mode, remove old file (make a copy first) * allow to ignore bad charaters in MESA log files * drop rows and columns --- bin/run-pipeline | 20 ++-- bin/setup-pipeline | 156 ++++++++++++++++++++++++----- posydon/grids/psygrid.py | 120 ++++++++++++++++------ posydon/grids/termination_flags.py | 4 +- posydon/utils/gridutils.py | 31 ++++++ 5 files changed, 264 insertions(+), 67 deletions(-) diff --git a/bin/run-pipeline b/bin/run-pipeline index ae5d291da5..4b385a437b 100644 --- a/bin/run-pipeline +++ b/bin/run-pipeline @@ -51,6 +51,7 @@ call STEP 4: ['gird_low_res_combined_rerun_1.h5'] __authors__ = [ "Simone Bavera ", + "Matthias Kruckow ", ] import os @@ -72,6 +73,7 @@ from posydon.grids.post_processing import (post_process_grid, add_post_processed_quantities) from posydon.interpolation.IF_interpolation import IFInterpolator from posydon.utils.common_functions import PATH_TO_POSYDON +from posydon.utils.gridutils import get_new_grid_name def create_grid_slice(i, path_to_csv_file, CO_HMS_GRID_START_AT_RLO, verbose=False): @@ -80,14 +82,16 @@ def create_grid_slice(i, path_to_csv_file, CO_HMS_GRID_START_AT_RLO, verbose=Fal grid_path = df.loc[i,'path_to_grid'] compression = df.loc[i,'compression'] - grid_name = grid_path.split('/')[-1] - otuput_path = os.path.join('/', os.path.join(*grid_path.split('/')[:-1]), - compression) - grid_output = os.path.join(otuput_path, grid_name+'.h5') - - # check that LITE/ or ORIGINAL/ directory exists - if not os.path.isdir(otuput_path): - os.makedirs(otuput_path) +# grid_name = grid_path.split('/')[-1] +# otuput_path = os.path.join('/', os.path.join(*grid_path.split('/')[:-1]), +# compression) +# grid_output = os.path.join(otuput_path, grid_name+'.h5') +# +# # check that LITE/ or ORIGINAL/ directory exists +# if not os.path.isdir(otuput_path): +# os.makedirs(otuput_path) + grid_output = get_new_grid_name(grid_path, compression, + create_missing_directories=True) if verbose: print('processing ', grid_path) diff --git a/bin/setup-pipeline b/bin/setup-pipeline index 33cabd3218..16bb8515bf 100644 --- a/bin/setup-pipeline +++ b/bin/setup-pipeline @@ -2,16 +2,19 @@ __authors__ = [ "Simone Bavera ", "Kyle Akira Rocha ", + "Matthias Kruckow ", ] import os import sys import ast +import shutil import pandas as pd import numpy as np from pprint import pprint, pformat from posydon.popsyn.io import parse_inifile from posydon.utils.common_functions import PATH_TO_POSYDON +from posydon.utils.gridutils import get_new_grid_name # this data processing pipeline was designed assuming POSYDON v2 data structure ''' @@ -86,6 +89,7 @@ class PostProcessingPipeline: return pipeline_kwargs def create_csv_and_slurm_job_files(self): + previously_created_files = [] setup_kwargs = self.pipeline_kwargs['pipeline setup'] account_kwargs = self.pipeline_kwargs['account'] @@ -113,8 +117,9 @@ class PostProcessingPipeline: print( pformat(step_kwargs, indent=2) ) - create_csv( + previously_created_files += create_csv( step_name=step_number, + previously_created_files=previously_created_files, **{**step_kwargs, **setup_kwargs} ) slurm_job( @@ -227,34 +232,83 @@ def slurm_job(job_name, N = df.shape[1]-1 else: raise ValueError('This should never happen!') + if N<0: + raise ValueError(f'{job_name} has no jobs to run, please check ' + f'{path_to_csv_file}') f.write(f"#SBATCH --array=0-{N}\n") slurm_array = '$SLURM_ARRAY_TASK_ID' if job_name == 'step_1': + f.write("#SBATCH --open-mode=truncate\n") f.write(f"#SBATCH --output={PATH}/logs/grid_slice_%a.out\n") if job_name == 'step_2': - f.write(f"#SBATCH --output={PATH}/logs/combine_grid_slices.out\n") + path_to_out_file = os.path.join(PATH, 'logs', + 'combine_grid_slices.out') + if os.path.exists(path_to_out_file): # move old data to a copy + path_to_old_out_file = os.path.join(PATH, 'logs', + 'combine_grid_slices.old.out') + if os.path.exists(path_to_old_out_file): # delete old copy + os.remove(path_to_old_out_file) + shutil.move(path_to_out_file, path_to_old_out_file) + f.write("#SBATCH --open-mode=append\n") + f.write(f"#SBATCH --output={path_to_out_file}\n") if job_name == 'step_3': - f.write(f"#SBATCH --output={PATH}/logs/plot_grid.out\n") + path_to_out_file = os.path.join(PATH, 'logs', 'plot_grid.out') + if os.path.exists(path_to_out_file): # move old data to a copy + path_to_old_out_file = os.path.join(PATH, 'logs', + 'plot_grid.old.out') + if os.path.exists(path_to_old_out_file): # delete old copy + os.remove(path_to_old_out_file) + shutil.move(path_to_out_file, path_to_old_out_file) + f.write("#SBATCH --open-mode=append\n") + f.write(f"#SBATCH --output={path_to_out_file}\n") f.write("unset DISPLAY\n") if job_name == 'step_4': - f.write(f"#SBATCH --output={PATH}/logs/check_failure_rate.out\n") + path_to_out_file = os.path.join(PATH, 'logs', + 'check_failure_rate.out') + if os.path.exists(path_to_out_file): # move old data to a copy + path_to_old_out_file = os.path.join(PATH, 'logs', + 'check_failure_rate.old.out') + if os.path.exists(path_to_old_out_file): # delete old copy + os.remove(path_to_old_out_file) + shutil.move(path_to_out_file, path_to_old_out_file) + f.write("#SBATCH --open-mode=append\n") + f.write(f"#SBATCH --output={path_to_out_file}\n") if job_name == 'step_5': + f.write("#SBATCH --open-mode=truncate\n") f.write(f"#SBATCH --output={PATH}/logs/post_processing_%a.out\n") f.write(f"export PATH_TO_POSYDON={PATH_TO_POSYDON}\n") if job_name == 'step_6': + f.write("#SBATCH --open-mode=truncate\n") f.write(f"#SBATCH --output={PATH}/logs/train_interpolators_%a.out\n") if job_name == 'step_7': - f.write(f"#SBATCH --output={PATH}/logs/export_dataset.out\n") + path_to_out_file = os.path.join(PATH, 'logs', + 'export_dataset.out') + if os.path.exists(path_to_out_file): # move old data to a copy + path_to_old_out_file = os.path.join(PATH, 'logs', + 'export_dataset.old.out') + if os.path.exists(path_to_old_out_file): # delete old copy + os.remove(path_to_old_out_file) + shutil.move(path_to_out_file, path_to_old_out_file) + f.write("#SBATCH --open-mode=append\n") + f.write(f"#SBATCH --output={path_to_out_file}\n") if job_name == 'rerun': - f.write(f"#SBATCH --output={PATH}/logs/rerun.out\n") + path_to_out_file = os.path.join(PATH, 'logs', 'rerun.out') + if os.path.exists(path_to_out_file): # move old data to a copy + path_to_old_out_file = os.path.join(PATH, 'logs', + 'rerun.old.out') + if os.path.exists(path_to_old_out_file): # delete old copy + os.remove(path_to_old_out_file) + shutil.move(path_to_out_file, path_to_old_out_file) + f.write("#SBATCH --open-mode=append\n") + f.write(f"#SBATCH --output={path_to_out_file}\n") f.write(f"\nsrun python {PATH_TO_POSYDON}/bin/run-pipeline {PATH_TO_GRIDS} {path_to_csv_file} {slurm_array} {CO_HMS_GRID_START_AT_RLO} {RERUN_TYPE}") @@ -263,7 +317,7 @@ def create_csv(GRID_TYPES, METALLICITIES, GRID_SLICES, COMPRESSIONS, step_name=None, VERSION=None, GRIDS_COMBINED=None, INTERPOLATION_METHODS=None, RERUN_TYPE='', DROP_MISSING_FILES=False, PATH_TO_GRIDS=None, - PATH=None, **kwargs): + PATH=None, previously_created_files=[], **kwargs): # number of grid types N = len(GRID_TYPES) @@ -279,6 +333,7 @@ def create_csv(GRID_TYPES, METALLICITIES, GRID_SLICES, COMPRESSIONS, interpolators = [] export_path = [] rerun_path = [] + newly_created_files = [] df = pd.DataFrame() for l, grid_type in enumerate(GRID_TYPES): @@ -385,42 +440,89 @@ def create_csv(GRID_TYPES, METALLICITIES, GRID_SLICES, COMPRESSIONS, # drop lines when grid directories are not found if DROP_MISSING_FILES: drop_rows = [] - for row in range(df.shape[0]): - path = df.loc[row,'path_to_grid'] - if not os.path.exists(path): + for row in df.index: + path = df.at[row,'path_to_grid'] + if (not os.path.exists(path) and + path not in previously_created_files): drop_rows.append(row) - if any(drop_rows): + if len(drop_rows)>0: print('') print(f'----------- {step_name} -----------') - print('The following grids will not be processed ' - 'because the files are missing! If this warning message is ' - 'unexpected to you, please check the file paths!') - for i in drop_rows: - print(df.loc[i,'path_to_grid']) + print('The following grids will not be processed because the ' + 'files/directories are missing! If this warning message ' + 'is unexpected to you, please check the file paths!') + for row in drop_rows: + print(df.at[row,'path_to_grid']) print('') df = df.drop(index=drop_rows) + if step_name == 'step_1': + for row in df.index: + path = df.at[row,'path_to_grid'] + compression = df.at[row,'compression'] + newly_created_files.append(get_new_grid_name(path,compression)) + elif step_name == 'step_5': + newly_created_files = df['path_to_processed_grid'].to_list() + elif step_name == 'step_6': + newly_created_files = df['path_to_interpolator'].to_list() + elif step_name == 'step_7': + newly_created_files = df['export_path'].to_list() + elif step_name == 'rerun': + newly_created_files = df['rerun_path'].to_list() else: # step 2 # drop columns when psygrid files are not found if DROP_MISSING_FILES: + first_time = True drop_columns = [] for column in df.keys(): - for path in df[column].dropna().to_list(): - if not os.path.exists(path): + drop_rows = [] + for row in df.index: + path = df.at[row,column] + if pd.notna(path): + if (not os.path.exists(path) and + path not in previously_created_files): + drop_rows.append(row) + if len(drop_rows)>0: + if first_time: + print('') + print(f'----------- {step_name} -----------') + print('If this warning message is unexpected to you, ' + 'please check that step 1 occoured ' + 'succesffully!') + first_time = False + print(f'In {column} the following grids are skipped!') + for row in drop_rows: + print(df.at[row,column]) + df.at[row,column] = np.NaN + print('') + newcol = df[column].dropna().to_list() + if len(newcol)==0: drop_columns.append(column) - if any(drop_columns): - print('') - print(f'----------- {step_name} -----------') - print('The following grids will not be combined as one or more ' - 'grid slices are missing! If this warning message is ' - 'unexpected to you, please check that step 1 occoured ' - 'succesffully!') - print(drop_columns) +# print(f'No grids left drop {column}') +# print('') + newcol.extend((df.shape[0]-len(newcol))*[np.nan]) + df[column] = newcol + if len(drop_columns)>0: + if first_time: + print('') + print(f'----------- {step_name} -----------') + print('If this warning message is unexpected to you, ' + 'please check that step 1 occoured ' + 'succesffully!') + first_time = False + print('The following grids will not be combined as all grid ' + 'slices are missing!') + for column in drop_columns: + print(column) print('') - df = df.drop(columns=drop_columns) +# df = df.drop(columns=drop_columns) + df = df.dropna(axis=0, how='all').dropna(axis=1, how='all') + newly_created_files = df.keys().to_list() output_fname = f'{step_name}.csv' df.to_csv(os.path.join(PATH, output_fname), index=False) + + return newly_created_files if __name__ == '__main__': diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index 7b9b6b13fe..3065825179 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -686,21 +686,41 @@ def decide_columns(key_in_config, defaults): # read the model numbers and ages from the histories colname = "model_number" if history1 is not None: - history1_mod = read_MESA_data_file( - run.history1_path, [colname]) + if colname in H1_columns: + history1_mod = np.int_(history1[colname].copy()) + else: + history1_mod = read_MESA_data_file(run.history1_path, + [colname]) + if history1_mod is not None: + history1_mod = np.int_(history1_mod[colname]) if history1_mod is not None: - history1_mod = np.int_(history1_mod[colname]) - if len(history1_mod) == len(history1) + 1: - history1_mod = history1_mod[:-1] + len_diff = len(history1)-len(history1_mod) + if len_diff<0: #shorten history1_mod + history1_mod = history1_mod[:len_diff] + warnings.warn("Reduce mod in {}\n".format(run.history1_path)) + elif len_diff>0: #entend history1_mod + add_mod = np.full(len_diff,history1_mod[-1]) + history1_mod = np.concatenate((history1_mod, add_mod)) + warnings.warn("Expand mod in {}\n".format(run.history1_path)) else: ignore_data = True ignore_reason = "corrupted_history1" - history1_age = read_MESA_data_file( - run.history1_path, ["star_age"]) + if "star_age" in H1_columns: + history1_age = history1["star_age"].copy() + else: + history1_age = read_MESA_data_file(run.history1_path, + ["star_age"]) + if history1_age is not None: + history1_age = history1_age["star_age"] if history1_age is not None: - history1_age = history1_age["star_age"] - if len(history1_age) == len(history1) + 1: - history1_age = history1_age[:-1] + len_diff = len(history1)-len(history1_age) + if len_diff<0: #shorten history1_age + history1_age = history1_age[:len_diff] + warnings.warn("Reduce age in {}\n".format(run.history1_path)) + elif len_diff>0: #entend history1_age + add_age = np.full(len_diff,history1_age[-1]) + history1_age = np.concatenate((history1_age, add_age)) + warnings.warn("Expand age in {}\n".format(run.history1_path)) else: ignore_data = True ignore_reason = "corrupted_history1" @@ -709,21 +729,41 @@ def decide_columns(key_in_config, defaults): history1_age = None if history2 is not None: - history2_mod = read_MESA_data_file( - run.history2_path, [colname]) + if colname in H2_columns: + history2_mod = np.int_(history2[colname].copy()) + else: + history2_mod = read_MESA_data_file(run.history2_path, + [colname]) + if history2_mod is not None: + history2_mod = np.int_(history2_mod[colname]) if history2_mod is not None: - history2_mod = np.int_(history2_mod[colname]) - if len(history2_mod) == len(history2) + 1: - history2_mod = history2_mod[:-1] + len_diff = len(history2)-len(history2_mod) + if len_diff<0: #shorten history2_mod + history2_mod = history2_mod[:len_diff] + warnings.warn("Reduce mod in {}\n".format(run.history2_path)) + elif len_diff>0: #entend history2_mod + add_mod = np.full(len_diff,history2_mod[-1]) + history2_mod = np.concatenate((history2_mod, add_mod)) + warnings.warn("Expand mod in {}\n".format(run.history2_path)) else: ignore_data = True ignore_reason = "corrupted_history2" - history2_age = read_MESA_data_file( - run.history2_path, ["star_age"]) + if "star_age" in H2_columns: + history2_age = history2["star_age"].copy() + else: + history2_age = read_MESA_data_file(run.history2_path, + ["star_age"]) + if history2_age is not None: + history2_age = history2_age["star_age"] if history2_age is not None: - history2_age = history2_age["star_age"] - if len(history2_age) == len(history2) + 1: - history2_age = history2_age[:-1] + len_diff = len(history2)-len(history2_age) + if len_diff<0: #shorten history2_age + history2_age = history2_age[:len_diff] + warnings.warn("Reduce age in {}\n".format(run.history2_path)) + elif len_diff>0: #entend history2_age + add_age = np.full(len_diff,history2_age[-1]) + history2_age = np.concatenate((history2_age, add_age)) + warnings.warn("Expand age in {}\n".format(run.history2_path)) else: ignore_data = True ignore_reason = "corrupted_history2" @@ -732,21 +772,41 @@ def decide_columns(key_in_config, defaults): history2_age = None if binary_history is not None: - binary_history_mod = read_MESA_data_file( - run.binary_history_path, [colname]) + if colname in BH_columns: + binary_history_mod = np.int_(binary_history[colname].copy()) + else: + binary_history_mod = read_MESA_data_file( + run.binary_history_path, [colname]) + if binary_history_mod is not None: + binary_history_mod = np.int_(binary_history_mod[colname]) if binary_history_mod is not None: - binary_history_mod = np.int_(binary_history_mod[colname]) - if len(binary_history_mod) == len(binary_history) + 1: - binary_history_mod = binary_history_mod[:-1] + len_diff = len(binary_history)-len(binary_history_mod) + if len_diff<0: #shorten binary_history_mod + binary_history_mod = binary_history_mod[:len_diff] + warnings.warn("Reduce mod in {}\n".format(run.binary_history_path)) + elif len_diff>0: #entend binary_history_mod + add_mod = np.full(len_diff,binary_history_mod[-1]) + binary_history_mod = np.concatenate((binary_history_mod, add_mod)) + warnings.warn("Expand mod in {}\n".format(run.binary_history_path)) else: ignore_data = True ignore_reason = "corrupted_binary_history" - binary_history_age = read_MESA_data_file( - run.binary_history_path, ["age"]) + if "age" in BH_columns: + binary_history_age = binary_history["age"].copy() + else: + binary_history_age = read_MESA_data_file( + run.binary_history_path, ["age"]) + if binary_history_age is not None: + binary_history_age = binary_history_age["age"] if binary_history_age is not None: - binary_history_age = binary_history_age["age"] - if len(binary_history_age) == len(binary_history) + 1: - binary_history_age = binary_history_age[:-1] + len_diff = len(binary_history)-len(binary_history_age) + if len_diff<0: #shorten binary_history_age + binary_history_age = binary_history_age[:len_diff] + warnings.warn("Reduce age in {}\n".format(run.binary_history_path)) + elif len_diff>0: #entend binary_history_age + add_age = np.full(len_diff,binary_history_age[-1]) + binary_history_age = np.concatenate((binary_history_age, add_age)) + warnings.warn("Expand age in {}\n".format(run.binary_history_path)) else: ignore_data = True ignore_reason = "corrupted_binary_history" diff --git a/posydon/grids/termination_flags.py b/posydon/grids/termination_flags.py index f77e3b2423..83d6911120 100644 --- a/posydon/grids/termination_flags.py +++ b/posydon/grids/termination_flags.py @@ -57,10 +57,10 @@ def get_flag_from_MESA_output(MESA_log_path): """ if MESA_log_path is not None and os.path.isfile(MESA_log_path): if MESA_log_path.endswith(".gz"): - with gzip.open(MESA_log_path, "rt") as log_file: + with gzip.open(MESA_log_path, "rt", errors='ignore') as log_file: log_lines = log_file.readlines() else: - with open(MESA_log_path, "r") as log_file: + with open(MESA_log_path, "r", errors='ignore') as log_file: log_lines = log_file.readlines() for line in reversed(log_lines): diff --git a/posydon/utils/gridutils.py b/posydon/utils/gridutils.py index a56508c637..72e1e8394c 100644 --- a/posydon/utils/gridutils.py +++ b/posydon/utils/gridutils.py @@ -19,6 +19,7 @@ "Emmanouil Zapartas ", "Kyle Akira Rocha ", "Jeffrey Andrews ", + "Matthias Kruckow ", ] @@ -524,3 +525,33 @@ def clean_inlist_file(inlist, **kwargs): i.split('=', 1)[1].strip() return dict_of_parameters + +def get_new_grid_name(path, compression, create_missing_directories=False): + """Get the name of a new grid slice based on the path and the compression. + + Parameters + ---------- + path : str + Path to grid slice data. + compression : str + Compression value. (Directory to put the new grid slice in.) + create_missing_directories : bool + Flag to create missing directories. + + Returns + ------- + grid_output + File name for the new grid slice. + + """ + grid_name = path.split('/')[-1] + output_path = os.path.join('/', os.path.join(*path.split('/')[:-1]), + compression) + grid_output = os.path.join(output_path, grid_name+'.h5') + if create_missing_directories: + # check that LITE/ or ORIGINAL/ directory exists + if not os.path.isdir(output_path): + os.makedirs(output_path) + return grid_output + + From 8619f5b5d6e743c8f5ab6d8c4e91ce843ffdbfb8 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 15 Jun 2023 16:18:33 +0200 Subject: [PATCH 093/319] Allow grid end at He depletion (for massive stars) (#89) * Add function "keep_till_He_depletion" * Add option to stop at He depletion * Add option to pipeline (remove later) * correct not * Change threshold for He depletion * allow histories without age, add new flag * Check for history of 0 length * add C criterion for He depletion add C criterion for He depletion * Update psygrid.py use 0.08 carbon abundance * Update scrubbing.py where can't handle two arrays in parallel * Update psygrid.py change C limit to 0.1 * Update psygrid.py Add mass request to only apply stopping earlier to masses of 100Msun and above * Update run-pipeline remove "_He_depletion" extension for the file name of the hdf5 file containing the psygrid * rename function and flags * Update psygrid.py correct typo --- bin/run-pipeline | 1 + posydon/grids/psygrid.py | 17 ++++- posydon/grids/scrubbing.py | 100 +++++++++++++++++++++++++ posydon/grids/termination_flags.py | 10 ++- posydon/visualization/combine_TF.py | 2 + posydon/visualization/plot_defaults.py | 8 ++ 6 files changed, 133 insertions(+), 5 deletions(-) diff --git a/bin/run-pipeline b/bin/run-pipeline index 4b385a437b..475a40aac9 100644 --- a/bin/run-pipeline +++ b/bin/run-pipeline @@ -128,6 +128,7 @@ def create_grid_slice(i, path_to_csv_file, CO_HMS_GRID_START_AT_RLO, verbose=Fal profile_DS_interval=profile_DS_interval, compression="gzip9", start_at_RLO=start_at_RLO, + stop_before_carbon_depletion=True, initial_RLO_fix=True) grid.close() diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index 3065825179..56c6c1aa8f 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -202,13 +202,14 @@ from posydon.utils.configfile import ConfigFile from posydon.utils.common_functions import (orbital_separation_from_period, initialize_empty_array, - infer_star_state) + infer_star_state, + THRESHOLD_CENTRAL_ABUNDANCE) from posydon.utils.gridutils import (read_MESA_data_file, read_EEP_data_file, add_field, join_lists, fix_He_core) from posydon.visualization.plot2D import plot2D from posydon.visualization.plot1D import plot1D from posydon.grids.downsampling import TrackDownsampler -from posydon.grids.scrubbing import scrub, keep_after_RLO +from posydon.grids.scrubbing import scrub, keep_after_RLO, keep_till_central_abundance_He_C HDF5_MEMBER_SIZE = 2**31 - 1 # maximum HDF5 file size when splitting @@ -346,6 +347,7 @@ "final_value_columns": None, # grid-specific arguments "start_at_RLO": False, + "stop_before_carbon_depletion": False, "binary": True, "eep": None, # path to EEP files "initial_RLO_fix": False, @@ -538,6 +540,7 @@ def _create_psygrid(self, MESA_path, hdf5, slim=False, fmt="posydon"): binary_grid = self.config["binary"] initial_RLO_fix = self.config["initial_RLO_fix"] start_at_RLO = self.config["start_at_RLO"] + stop_before_carbon_depletion = self.config["stop_before_carbon_depletion"] eep = self.config["eep"] if eep is not None: @@ -633,6 +636,7 @@ def decide_columns(key_in_config, defaults): # Select the ith run run = grid.runs[i] ignore_data = False # if failed run, do not save any data + newTF1 = '' self._say('Processing {}'.format(run.path)) # Restrict number of runs if limit is set inside config @@ -851,6 +855,12 @@ def decide_columns(key_in_config, defaults): " history in: {}\n".format(run.path)) continue + # check whether stop at He depletion is requested + if stop_before_carbon_depletion and self.initial_values[i]["star_1_mass"]>=100.0: + kept = keep_till_central_abundance_He_C(binary_history, history1, + history2, THRESHOLD_CENTRAL_ABUNDANCE, 0.1) + binary_history, history1, history2, newTF1 = kept + # check whether start at RLO is requested, and chop the history if start_at_RLO: kept = keep_after_RLO(binary_history, history1, history2) @@ -1042,7 +1052,7 @@ def decide_columns(key_in_config, defaults): termination_flags = get_flags_from_MESA_run( run.out_txt_path, binary_history=binary_history, history1=history1, history2=history2, - start_at_RLO=start_at_RLO) + start_at_RLO=start_at_RLO, newTF1=newTF1) else: if ignore_data: termination_flags = [ignore_reason] * N_FLAGS_SINGLE @@ -2076,6 +2086,7 @@ def downsample_profile(profile, params): } PROPERTIES_TO_BE_CONSISTENT = ["binary", "eep", "start_at_RLO", + "stop_before_carbon_depletion", "initial_RLO_fix", "He_core_fix", "accept_missing_profile", "history_DS_error", "history_DS_exclude", "profile_DS_error", diff --git a/posydon/grids/scrubbing.py b/posydon/grids/scrubbing.py index 11d7d5a3f6..227db667b4 100644 --- a/posydon/grids/scrubbing.py +++ b/posydon/grids/scrubbing.py @@ -3,6 +3,7 @@ __authors__ = [ "Konstantinos Kovlakas ", + "Matthias Kruckow ", ] @@ -152,3 +153,102 @@ def keep_after_RLO(bh, h1, h2): new_h2["star_age"] -= age_to_remove return new_bh, new_h1, new_h2 + + +def keep_till_central_abundance_He_C(bh, h1, h2, Ystop=1.0e-5, XCstop=1.0): + """Scrub histories to stop when central helium and carbon abundance are + below the stopping criteria. + + Parameters + ---------- + bh : array + The `binary_history` array. + h1 : array + The `history1` array. + h2 : array + The `history2` array. + Ystop : float + The He abundance threshold for stopping + XCstop : float + The C abundance threshold for stopping + + Returns + ------- + tuple + The binary, history1 and history2 arrays after removing the final + steps after He depletion. If the stopping criteria are not reached + the histories will be returned unchanged. + + """ + if (bh is None) or (h1 is None) or (h2 is None): + #at least one histroy is missing + return bh, h1, h2, '' + elif (not ("age" in bh.dtype.names)): + #at least one histroy doesn't contain an age column + return bh, h1, h2, '' + + h1_colnames = h1.dtype.names + if ("center_he4" in h1_colnames) and ("center_c12" in h1_colnames): + if (len(h1["center_he4"])>0) and (len(h1["center_c12"])>0): + depleted1 = ((h1["center_he4"][-1]0) and (len(h2["center_c12"])>0): + depleted2 = ((h2["center_he4"][-1]age_He_depletion2: + #take star which reached He depletion first + last_index = last_index2 + newTF1 = 'Secondary got stopped before central carbon depletion' + else: + last_index = where_conditions_met2[0] + newTF1 = 'Secondary got stopped before central carbon depletion' + + new_bh = bh[:last_index] + new_h1 = h1[:last_index] + new_h2 = h2[:last_index] + + return new_bh, new_h1, new_h2, newTF1 diff --git a/posydon/grids/termination_flags.py b/posydon/grids/termination_flags.py index 83d6911120..9641555091 100644 --- a/posydon/grids/termination_flags.py +++ b/posydon/grids/termination_flags.py @@ -192,7 +192,8 @@ def check_state_from_history(history, mass, model_index=-1): def get_flags_from_MESA_run(MESA_log_path, binary_history=None, - history1=None, history2=None, start_at_RLO=False): + history1=None, history2=None, start_at_RLO=False, + newTF1=''): """Return the four termination flags. Parameters @@ -201,6 +202,8 @@ def get_flags_from_MESA_run(MESA_log_path, binary_history=None, path to the MESA terminal output binary_history, history1, history2: np.array MESA output histories. + newTF1: str + replacement for the termination flag from the MESA output Returns ------- @@ -210,7 +213,10 @@ def get_flags_from_MESA_run(MESA_log_path, binary_history=None, final_state_1, final_state_2: describe the final evolutionary state of the two stars. None if history star is not provided. """ - flag_out = get_flag_from_MESA_output(MESA_log_path) + if newTF1=='': + flag_out = get_flag_from_MESA_output(MESA_log_path) + else: + flag_out = newTF1 final_state_1 = check_state_from_history(history1, binary_history["star_1_mass"]) final_state_2 = check_state_from_history(history2, diff --git a/posydon/visualization/combine_TF.py b/posydon/visualization/combine_TF.py index 17bffad764..588b77e52c 100644 --- a/posydon/visualization/combine_TF.py +++ b/posydon/visualization/combine_TF.py @@ -13,6 +13,8 @@ TF1_POOL_STABLE = ['Primary has depleted central carbon', 'Secondary has depleted central carbon', + 'Primary got stopped before central carbon depletion', + 'Secondary got stopped before central carbon depletion', 'Primary enters pair-instability regime', 'Secondary enters pair-instability regime', 'Primary enters pulsational pair-instability regime', diff --git a/posydon/visualization/plot_defaults.py b/posydon/visualization/plot_defaults.py index e553eb1f5c..027fa62b53 100644 --- a/posydon/visualization/plot_defaults.py +++ b/posydon/visualization/plot_defaults.py @@ -117,6 +117,10 @@ ['s', 2, None, TF1_label_stable], 'Secondary has depleted central carbon': ['o', 2, None, TF1_label_stable], + 'Primary got stopped before central carbon depletion': + ['s', 2, None, TF1_label_stable], + 'Secondary got stopped before central carbon depletion': + ['o', 2, None, TF1_label_stable], 'Primary enters pair-instability regime': ['s', 2, None, TF1_label_stable], 'Secondary enters pair-instability regime': @@ -435,6 +439,10 @@ ['s', 2, None, TF1_label_stable], 'Secondary has depleted central carbon': ['o', 2, None, TF1_label_stable], + 'Primary got stopped before central carbon depletion': + ['s', 2, None, TF1_label_stable], + 'Secondary got stopped before central carbon depletion': + ['o', 2, None, TF1_label_stable], 'Primary enters pair-instability regime': ['s', 2, None, TF1_label_stable], 'Secondary enters pair-instability regime': From c1b4daa241df3bb6d2e2ebcc18c578ba0d7a481f Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Thu, 15 Jun 2023 16:19:26 +0200 Subject: [PATCH 094/319] Support arbitrary MODEL for double compact object rates (#95) * add synthetic population parser * bug fixes * cleaning * get DCO population at formation as df_synthetic, add t_inspiral and underlying stellar mass for rate calculation * add method to compute merger efficiency * add intrinsic and detectable populations * define all class variables * add visualisation modules * small edit * add rate calculation and SFR * add docstrings and parser of oneline dataframe * update pdet calculation to POSYDON selection effects * update sensitivity docstring * support arbitrary MODEL for rate calculation * fix --- posydon/popsyn/synthetic_population.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/posydon/popsyn/synthetic_population.py b/posydon/popsyn/synthetic_population.py index ab297f1af2..99cc5d06ff 100644 --- a/posydon/popsyn/synthetic_population.py +++ b/posydon/popsyn/synthetic_population.py @@ -23,7 +23,7 @@ class SyntheticPopulation: - def __init__(self, path_to_ini, path_to_data=None, verbose=False): + def __init__(self, path_to_ini, path_to_data=None, verbose=False, MODEL={}): """ Parameters ---------- @@ -35,6 +35,7 @@ def __init__(self, path_to_ini, path_to_data=None, verbose=False): """ self.verbose = verbose + self.MODEL = MODEL self.df = None self.df_oneline = None self.df_synthetic = None @@ -394,7 +395,7 @@ def get_dco_merger_efficiency(self): self.merger_efficiency = np.array(efficiencies) - def compute_cosmological_weights(self, sensitivity, flag_pdet, working_dir, load_data, **kwargs): + def compute_cosmological_weights(self, sensitivity, flag_pdet, working_dir, load_data): """Compute the DCO merger rate weights. Parameters @@ -428,7 +429,7 @@ def compute_cosmological_weights(self, sensitivity, flag_pdet, working_dir, load """ # TODO: make the class inputs kwargs - self.rates = Rates(self.df_synthetic, **kwargs) + self.rates = Rates(self.df_synthetic, **self.MODEL) # compute DCO merger rate density if not load_data: From c2e47d27a737410954ce5bbaf9085aa2bd646c8d Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Thu, 22 Jun 2023 17:01:26 +0200 Subject: [PATCH 095/319] Add support for post processing an arbitrary number of core-collapse models + introduce H envelope option to step_SN + v2 data pop synth debugged (#70) * Add function "keep_till_He_depletion" * Add option to stop at He depletion * Add option to pipeline (remove later) * correct not * Change threshold for He depletion * allow histories without age, add new flag * Check for history of 0 length * support BH formation above PISN gap and direct/Fryer collapse prescriptions now assume hydrogen envelope does not go into the BH * add C criterion for He depletion add C criterion for He depletion * Update psygrid.py use 0.08 carbon abundance * Update scrubbing.py where can't handle two arrays in parallel * Update psygrid.py change C limit to 0.1 * support the option to conserve the hydrogen envelope in BH formation * add MODEL var to post processing * update post processing script to support multiple core collapse models * Update psygrid.py Add mass request to only apply stopping earlier to masses of 100Msun and above * Update run-pipeline remove "_He_depletion" extension for the file name of the hdf5 file containing the psygrid * debug * rename function and flags * add support for BH disk mass * allow to remove slices from combining grids and only remove the combining if all slices are missing * fix small bug * Update setup-pipeline correct some typos * track previously created files * Update setup-pipeline fix typo * get files from step 1 * change range over df shape to instead use the index object * update step_6 run-pipeline * turn any to check on length * debug * add check for no slurm tasks * account for larger length difference of histories * reuse columns from histories if possible, add warnings * bugfix * propagate changes in step_SN and step_MESA * fix bug * debug plot2D * remove circular import * add open-mode, remove old file (make a copy first) * allow to ignore bad charaters in MESA log files * drop rows and columns * change import * import TF1 for post processing * fix bug * Update psygrid.py correct typo * debug * roll fix for single stars as well * debug * Matthias split grid rlo (#99) * fix in psygrid.py fix check of initial mass when cutting off carbon depletion * Update run-pipeline get start_at_RLO from compression type add flag to stop before carbon depletion (allow only for HMS grids) * Update setup-pipeline remove CO_HMS_GRID_START_AT_RLO add STOP_BEFORE_CARBON_DEPLETION * Update setup-pipeline get different directory for plotting if compression contains "RLO" * Update setup-pipeline correct typo * Update psygrid.py correct typo * Update psygrid.py debug * debug single interpolation * add support for other metallicities * update Z to Y conversion * SN_type not diclared in star properties * debug * update default params ini * increase len interp_class column * update params ini * add authorship to MODEL.py * Matthias split grid rlo (#100) * fix in psygrid.py fix check of initial mass when cutting off carbon depletion * Update run-pipeline get start_at_RLO from compression type add flag to stop before carbon depletion (allow only for HMS grids) * Update setup-pipeline remove CO_HMS_GRID_START_AT_RLO add STOP_BEFORE_CARBON_DEPLETION * Update setup-pipeline get different directory for plotting if compression contains "RLO" * Update setup-pipeline correct typo * Update psygrid.py correct typo * Update psygrid.py debug * Update setup-pipeline Add RLO to steps 6 and 7 * Update setup-pipeline Correct step 7 * Update run-pipeline don't interpolate no_MT for any grid starting at RLO * Update run-pipeline remove "_RLO" from method, which is read from the filename * Update setup-pipeline add "CO-HeMS_RLO" to export dirs * update spin orbit tilt name in rate calculation --------- Co-authored-by: mkruckow <122798003+mkruckow@users.noreply.github.com> Co-authored-by: mkruckow --- bin/run-pipeline | 183 ++++---- bin/setup-pipeline | 61 ++- posydon/binary_evol/MESA/step_mesa.py | 73 ++-- posydon/binary_evol/SN/profile_collapse.py | 43 +- posydon/binary_evol/SN/step_SN.py | 259 ++++++------ posydon/binary_evol/binarystar.py | 5 + posydon/binary_evol/singlestar.py | 56 ++- posydon/grids/MODELS.py | 146 +++++++ posydon/grids/post_processing.py | 413 ++++++++----------- posydon/grids/psygrid.py | 20 +- posydon/interpolation/interpolation.py | 37 +- posydon/popsyn/binarypopulation.py | 14 +- posydon/popsyn/population_params_default.ini | 39 +- posydon/popsyn/rate_calculation.py | 7 +- posydon/utils/common_functions.py | 4 +- posydon/visualization/combine_TF.py | 2 + posydon/visualization/plot2D.py | 6 +- posydon/visualization/plot_defaults.py | 6 +- 18 files changed, 731 insertions(+), 643 deletions(-) create mode 100644 posydon/grids/MODELS.py diff --git a/bin/run-pipeline b/bin/run-pipeline index 475a40aac9..d45e9c36a0 100644 --- a/bin/run-pipeline +++ b/bin/run-pipeline @@ -71,12 +71,13 @@ from posydon.grids.psygrid import (PSyGrid, EXTRA_COLS_DS_EXCLUDE) from posydon.grids.post_processing import (post_process_grid, add_post_processed_quantities) +from posydon.grids.MODELS import MODELS from posydon.interpolation.IF_interpolation import IFInterpolator from posydon.utils.common_functions import PATH_TO_POSYDON from posydon.utils.gridutils import get_new_grid_name -def create_grid_slice(i, path_to_csv_file, CO_HMS_GRID_START_AT_RLO, verbose=False): +def create_grid_slice(i, path_to_csv_file, STOP_BEFORE_CARBON_DEPLETION=True, verbose=False): df = pd.read_csv(path_to_csv_file) grid_path = df.loc[i,'path_to_grid'] @@ -97,13 +98,13 @@ def create_grid_slice(i, path_to_csv_file, CO_HMS_GRID_START_AT_RLO, verbose=Fal print('processing ', grid_path) print('saving file ', grid_output) - if compression == 'ORIGINAL': + if 'ORIGINAL' in compression: history_DS_error = None profile_DS_error = None profile_DS_interval = None history_DS_exclude = DEFAULT_HISTORY_DS_EXCLUDE profile_DS_exclude = DEFAULT_PROFILE_DS_EXCLUDE - elif compression == 'LITE': + elif 'LITE' in compression: history_DS_error = 0.1 profile_DS_error = 0.1 profile_DS_interval = -0.005 @@ -112,11 +113,16 @@ def create_grid_slice(i, path_to_csv_file, CO_HMS_GRID_START_AT_RLO, verbose=Fal else: raise ValueError('compression = %s not supported!'%compression) - if 'CO-HMS_RLO' in grid_path and CO_HMS_GRID_START_AT_RLO: + if 'CO' in grid_path and 'RLO' in compression: start_at_RLO = True else: start_at_RLO = False + if 'HMS' in grid_path and STOP_BEFORE_CARBON_DEPLETION: + stop_before_carbon_depletion = True + else: + stop_before_carbon_depletion = False + grid = PSyGrid(verbose=True) grid.create(grid_path, grid_output, @@ -128,7 +134,7 @@ def create_grid_slice(i, path_to_csv_file, CO_HMS_GRID_START_AT_RLO, verbose=Fal profile_DS_interval=profile_DS_interval, compression="gzip9", start_at_RLO=start_at_RLO, - stop_before_carbon_depletion=True, + stop_before_carbon_depletion=stop_before_carbon_depletion, initial_RLO_fix=True) grid.close() @@ -384,7 +390,7 @@ def post_processing(i, path_to_csv_file, verbose=False): grid.load(grid_path) if verbose: - print('Compute process quantities ...') + print('Compute processed quantities ...') if 'CO' in grid_path: star_2_CO = True else: @@ -407,7 +413,7 @@ def post_processing(i, path_to_csv_file, verbose=False): add_post_processed_quantities(grid_LITE, MESA_dirs_EXTRA_COLUMNS, EXTRA_COLUMNS, verbose=verbose) if verbose: - print('Added process quantities to grid:',processed_grid_path) + print('Added processed quantities to grid:',processed_grid_path) grid_LITE.close() @@ -417,6 +423,8 @@ def train_interpolators(i, path_to_csv_file, verbose=False): grid_path = df.loc[i,'path_to_grid'] interpolator_path = df.loc[i,'path_to_interpolator'] method = interpolator_path.split('IF_')[-1].split('.pkl')[0] + if '_RLO' in method: + method = method.split('_RLO')[0] if verbose: print(f'train interpolators {grid_path} with method {method}') @@ -430,117 +438,74 @@ def train_interpolators(i, path_to_csv_file, verbose=False): grid = PSyGrid(verbose=False) grid.load(grid_path) - second = ['S1_direct_f_fb', 'S1_direct_mass', 'S1_direct_spin'] - third = ['S1_Fryer+12-rapid_f_fb', 'S1_Fryer+12-rapid_mass', - 'S1_Fryer+12-rapid_spin'] - fourth = ['S1_Fryer+12-delayed_f_fb', 'S1_Fryer+12-delayed_mass', - 'S1_Fryer+12-delayed_spin'] - fifth = ['S1_Sukhbold+16-engineN20_f_fb', 'S1_Sukhbold+16-engineN20_mass', - 'S1_Sukhbold+16-engineN20_spin'] - sixth = ['S1_Patton&Sukhbold20-engineN20_f_fb', - 'S1_Patton&Sukhbold20-engineN20_mass', - 'S1_Patton&Sukhbold20-engineN20_spin'] - - - first = [key for key in grid.final_values.dtype.names if ( - key != "model_number" and - (type(grid.final_values[key][0]) != np.str_) - and any(~np.isnan(grid.final_values[key])))] - - for sec in second: - first.remove(sec) - - for thrd in third: - first.remove(thrd) - - for frth in fourth: - first.remove(frth) - - for ffth in fifth: - first.remove(ffth) - - for sxth in sixth: - first.remove(sxth) - - if 'CO-HMS_RLO' in grid_path: + if '_RLO' in grid_path: interp_method = [method, method] interp_classes = ["stable_MT", "unstable_MT"] else: interp_method = [method, method, method] interp_classes = ["no_MT", "stable_MT", "unstable_MT"] - - # TODO: train CC2 quantities coming from reverse MT systems - interp = IFInterpolator(grid=grid, interpolators = [ + + # all magnitues that are not model numbers, supernova properites or strings + # are interpolated with respect to interpolation_class + out_keys = [key for key in grid.final_values.dtype.names if ( + key != "model_number" and + (type(grid.final_values[key][0]) != np.str_) + and any(~np.isnan(grid.final_values[key])) + and "MODEL" not in key)] + + # define all string keys in final values + c_keys = ['interpolation_class', 'S1_state', 'S2_state'] + for MODEL_NAME in MODELS.keys(): + for i in range(1,3): + c_keys.append(f'S{i}_{MODEL_NAME}_SN_type') + c_keys.append(f'S{i}_{MODEL_NAME}_CO_type') + + # specify the interpolation methods + interpolators = [ { "interp_method": interp_method, "interp_classes": interp_classes, - "out_keys": first, + "out_keys": out_keys, "class_method": "kNN", - "c_keys": ['interpolation_class', - 'S1_state', - 'S2_state', - # Collapse quantities - 'S1_direct_SN_type', - 'S1_Fryer+12-rapid_SN_type', - 'S1_Fryer+12-delayed_SN_type', - 'S1_Sukhbold+16-engineN20_SN_type', - 'S1_Patton&Sukhbold20-engineN20_SN_type', - # v1 POSYDON does not have S2 reaching CC before S1 - # all these classifier map to None - 'S2_direct_state', - 'S2_Fryer+12-rapid_state', - 'S2_Fryer+12-delayed_state', - 'S2_Sukhbold+16-engineN20_state', - 'S2_Patton&Sukhbold20-engineN20_state', - 'S2_direct_SN_type', - 'S2_Fryer+12-rapid_SN_type', - 'S2_Fryer+12-delayed_SN_type', - 'S2_Sukhbold+16-engineN20_SN_type', - 'S2_Patton&Sukhbold20-engineN20_SN_type' - ], + "c_keys": c_keys, "c_key": "interpolation_class" }, - { - "interp_method": [method, method, method], - "interp_classes": ["BH", "WD", "NS"], - "out_keys": second, - "class_method": "kNN", - "c_keys": ['S1_direct_state'], - "c_key": 'S1_direct_state' - }, - { - "interp_method": [method, method, method], - "interp_classes": ["BH", "WD", "NS"], - "out_keys": third, - "class_method": "kNN", - "c_keys": ['S1_Fryer+12-rapid_state'], - "c_key": 'S1_Fryer+12-rapid_state' - }, - { - "interp_method": [method, method, method], - "interp_classes": ["BH", "WD", "NS"], - "out_keys": fourth, - "class_method": "kNN", - "c_keys": ['S1_Fryer+12-delayed_state'], - "c_key": 'S1_Fryer+12-delayed_state' - }, - { - "interp_method": [method, method, method], - "interp_classes": ["BH", "WD", "NS"], - "out_keys": fifth, - "class_method": "kNN", - "c_keys": ['S1_Sukhbold+16-engineN20_state'], - "c_key": 'S1_Sukhbold+16-engineN20_state' - }, - { - "interp_method": [method, method, method], - "interp_classes": ["BH", "WD", "NS"], - "out_keys": sixth, - "class_method": "kNN", - "c_keys": ['S1_Patton&Sukhbold20-engineN20_state'], - "c_key": 'S1_Patton&Sukhbold20-engineN20_state' - } - ]) + ] + + # core collapse quantities are interpolated with respect to the + # compact object type + # TODO: we need to train core collapse for secondary star as well + # to catch reverse mass transfer cases where the primary undergoes + # core collapse first + for MODEL_NAME in MODELS.keys(): + for i in range(1,2): #TODO: range(1,3): + out_keys = [key for key in grid.final_values.dtype.names if ( + key != "model_number" and + (type(grid.final_values[key][0]) != np.str_) + and any(~np.isnan(grid.final_values[key])) and + f"S{i}_{MODEL_NAME}" in key)] + + # catch cases where there is no WD formation + if "WD" in grid.final_values[f'S{i}_{MODEL_NAME}_CO_type']: + interp_method = [method, method, method] + interp_classes = ["BH", "NS", "WD"] + else: + interp_method = [method, method] + interp_classes = ["BH", "NS"] + + interpolators.append( + { + "interp_method": interp_method, + "interp_classes": interp_classes, + "out_keys": out_keys, + "class_method": "kNN", + "c_keys": [f'S{i}_{MODEL_NAME}_CO_type'], + "c_key": f'S{i}_{MODEL_NAME}_CO_type' + }, + ) + + + interp = IFInterpolator(grid=grid, interpolators=interpolators) # training and saving interp.train() @@ -717,7 +682,7 @@ if __name__ == '__main__': PATH_TO_GRIDS = str(sys.argv[1]) path_to_csv_file = str(sys.argv[2]) i = int(sys.argv[3]) - CO_HMS_GRID_START_AT_RLO = bool(int(sys.argv[4])) + STOP_BEFORE_CARBON_DEPLETION = bool(int(sys.argv[4])) if len(sys.argv) == 6: rerun_type = str(sys.argv[5]) @@ -735,7 +700,7 @@ if __name__ == '__main__': if 'step_1' in path_to_csv_file: create_grid_slice(i, path_to_csv_file, - CO_HMS_GRID_START_AT_RLO=CO_HMS_GRID_START_AT_RLO, + STOP_BEFORE_CARBON_DEPLETION=STOP_BEFORE_CARBON_DEPLETION, verbose=VERBOSE) if 'step_2' in path_to_csv_file: diff --git a/bin/setup-pipeline b/bin/setup-pipeline index 16bb8515bf..ba8e292f3c 100644 --- a/bin/setup-pipeline +++ b/bin/setup-pipeline @@ -165,8 +165,8 @@ class PostProcessingPipeline: os.makedirs(data_path) dirs = [] - grid_dirs = ['HMS-HMS', 'CO-HMS_RLO', 'CO-HeMS', 'single_HMS', - 'single_HeMS'] + grid_dirs = ['HMS-HMS', 'CO-HMS_RLO', 'CO-HeMS', 'CO-HeMS_RLO', + 'single_HMS', 'single_HeMS'] interp_dirs = ['interpolators', 'interpolators/1NN_1NN', 'interpolators/linear3c_kNN'] for name1 in grid_dirs: @@ -195,7 +195,7 @@ def slurm_job(job_name, TRAIN_INTERPOLATORS=False, EXPORT_DATASET=False, RERUN=False, - CO_HMS_GRID_START_AT_RLO=1, + STOP_BEFORE_CARBON_DEPLETION=1, RERUN_TYPE='', verbose=False, **kwargs): @@ -310,7 +310,7 @@ def slurm_job(job_name, f.write("#SBATCH --open-mode=append\n") f.write(f"#SBATCH --output={path_to_out_file}\n") - f.write(f"\nsrun python {PATH_TO_POSYDON}/bin/run-pipeline {PATH_TO_GRIDS} {path_to_csv_file} {slurm_array} {CO_HMS_GRID_START_AT_RLO} {RERUN_TYPE}") + f.write(f"\nsrun python {PATH_TO_POSYDON}/bin/run-pipeline {PATH_TO_GRIDS} {path_to_csv_file} {slurm_array} {STOP_BEFORE_CARBON_DEPLETION} {RERUN_TYPE}") # create csv file with a list of all grid paths to process def create_csv(GRID_TYPES, METALLICITIES, GRID_SLICES, COMPRESSIONS, @@ -372,9 +372,14 @@ def create_csv(GRID_TYPES, METALLICITIES, GRID_SLICES, COMPRESSIONS, grids.append(os.path.join(PATH_TO_GRIDS, grid_type, VERSION, metallicity, compression, grid_slice+'.h5')) - plot_dirs.append(os.path.join(PATH_TO_GRIDS, - grid_type, VERSION, metallicity, - 'plots', grid_slice)) + if 'RLO' in compression: + plot_dirs.append(os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, metallicity, + 'plots', 'RLO_'+grid_slice)) + else: + plot_dirs.append(os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, metallicity, + 'plots', grid_slice)) elif step_name == 'step_4': grids.append(os.path.join(PATH_TO_GRIDS, grid_type, VERSION, metallicity, @@ -391,24 +396,48 @@ def create_csv(GRID_TYPES, METALLICITIES, GRID_SLICES, COMPRESSIONS, grids.append(os.path.join(PATH_TO_GRIDS, grid_type, VERSION, metallicity, compression, grid_slice+'.h5')) - interpolators.append(os.path.join(PATH_TO_GRIDS, - grid_type, VERSION, metallicity, - 'interpolation_objects', - 'IF_'+method+'.pkl')) + if 'RLO' in compression: + interpolators.append(os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, metallicity, + 'interpolation_objects', + 'IF_'+method+'_RLO.pkl')) + else: + interpolators.append(os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, metallicity, + 'interpolation_objects', + 'IF_'+method+'.pkl')) elif step_name == 'step_7': grids.append(os.path.join(PATH_TO_GRIDS, grid_type, VERSION, metallicity, compression, grid_slice+'.h5')) - export_path.append(os.path.join(PATH, 'POSYDON_data', - grid_type, - metallicity+'.h5')) + if 'RLO' in compression: + export_path.append(os.path.join(PATH, 'POSYDON_data', + grid_type+'_RLO', + metallicity+'.h5')) + else: + export_path.append(os.path.join(PATH, 'POSYDON_data', + grid_type, + metallicity+'.h5')) for method in [['linear','linear3c_kNN'], ['1NN','1NN_1NN']]: - grids.append(os.path.join(PATH_TO_GRIDS, + if 'RLO' in compression: + grids.append(os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, metallicity, + 'interpolation_objects', + 'IF_'+method[0]+'_RLO.pkl')) + export_path.append(os.path.join(PATH, + 'POSYDON_data', + grid_type+'_RLO', + 'interpolators', + method[1], + metallicity+'.pkl')) + else: + grids.append(os.path.join(PATH_TO_GRIDS, grid_type, VERSION, metallicity, 'interpolation_objects', 'IF_'+method[0]+'.pkl')) - export_path.append(os.path.join(PATH, 'POSYDON_data', + export_path.append(os.path.join(PATH, + 'POSYDON_data', grid_type, 'interpolators', method[1], diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index 584c6f7ffb..95fad5aa6e 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -28,6 +28,7 @@ convert_metallicity_to_string, CO_radius, infer_star_state) from posydon.utils.data_download import data_download, PATH_TO_POSYDON_DATA +from posydon.grids.MODELS import MODELS # left POSYDON, right MESA @@ -788,31 +789,22 @@ def update_properties_NN(self, star_1_CO=False, star_2_CO=False, # update nearest neighbor core collapse quantites if interpolation_class != 'unstable_MT': - for prescrition in ['direct', 'Fryer+12-rapid', 'Fryer+12-delayed', - 'Sukhbold+16-engineN20', - 'Patton&Sukhbold20-engineN20']: + for MODEL_NAME in MODELS.keys(): for i, star in enumerate(stars): if not stars_CO[i]: - state = cb.final_values['S%d_%s_state' - % (i+1, prescrition)] - SN_type = cb.final_values['S%d_%s_SN_type' - % (i+1, prescrition)] - f_fb = cb.final_values['S%d_%s_f_fb' - % (i+1, prescrition)] - mass = cb.final_values['S%d_%s_mass' - % (i+1, prescrition)] - spin = cb.final_values['S%d_%s_spin' - % (i+1, prescrition)] - key = prescrition.replace('+', '') - key = key.replace('-', '_') - key = key.replace('&', '_') + values = {} + for key in ['state', 'SN_type', 'f_fb', 'mass', 'spin', + 'm_disk_accreted', 'm_disk_radiated']: + if key == 'state': + key = 'CO_type' + values[key] = cb.final_values[f'S{i+1}_{MODEL_NAME}_{key}'] + state = cb.final_values[f'S{i+1}_{MODEL_NAME}_CO_type'] if state is None or state == 'None': # privent to any quantities for star that did not # reach core collpase - setattr(star, key, None) + setattr(star, MODEL_NAME, None) else: - setattr(star, key, - [state, SN_type, f_fb, mass, spin]) + setattr(star, MODEL_NAME, values) def initial_final_interpolation(self, star_1_CO=False, star_2_CO=False): """Update the binary through initial-final interpolation.""" @@ -890,6 +882,7 @@ def initial_final_interpolation(self, star_1_CO=False, star_2_CO=False): # EXPERIMENTAL feature # infer stellar states interpolation_class = self.classes['interpolation_class'] + setattr(self.binary, f'interp_class_{self.grid_type}', interpolation_class) S1_state_inferred = cf.check_state_of_star(self.binary.star_1, star_CO=star_1_CO) @@ -967,28 +960,23 @@ def initial_final_interpolation(self, star_1_CO=False, star_2_CO=False): # update interpolated core collapse quantites if interpolation_class != 'unstable_MT': - for prescrition in ['direct', 'Fryer+12-rapid', 'Fryer+12-delayed', - 'Sukhbold+16-engineN20', - 'Patton&Sukhbold20-engineN20']: + for MODEL_NAME in MODELS.keys(): for i, star in enumerate(stars): - if not stars_CO[i]: - state = self.classes['S%d_%s_state' - % (i+1, prescrition)] - SN_type = self.classes['S%d_%s_SN_type' - % (i+1, prescrition)] - key = prescrition.replace('+', '') - key = key.replace('-', '_') - key = key.replace('&', '_') - if state is None or state == 'None': - # privent to any quantities for star that did not - # reach core collpase - setattr(star, key, None) - else: - f_fb = fv['S%d_%s_f_fb' % (i+1, prescrition)] - mass = fv['S%d_%s_mass' % (i+1, prescrition)] - spin = fv['S%d_%s_spin' % (i+1, prescrition)] - setattr(star, key, - [state, SN_type, f_fb, mass, spin]) + if (not stars_CO[i] and + self.classes[f'S{i+1}_{MODEL_NAME}_CO_type'] != 'None'): + values = {} + for key in ['state', 'SN_type', 'f_fb', 'mass', 'spin', + 'm_disk_accreted', 'm_disk_radiated']: + if key == "state" in key: + state = self.classes[f'S{i+1}_{MODEL_NAME}_CO_type'] + values[key] = state + elif key == "SN_type": + values[key] = self.classes[f'S{i+1}_{MODEL_NAME}_{key}'] + else: + values[key] = fv[f'S{i+1}_{MODEL_NAME}_{key}'] + setattr(star, MODEL_NAME, values) + else: + setattr(star, key, None) # STOPPING METHODS @@ -1228,7 +1216,7 @@ def interpolate_at_t(self, t, t_before, t_after, v_before, v_after): """ # Error handling - if v_before is "None" or v_after is "None": + if v_before == "None" or v_after == "None": return "None" slope = (v_after - v_before) / (t_after - t_before) @@ -1242,6 +1230,7 @@ class MS_MS_step(MesaGridStep): def __init__(self, metallicity=1., grid_name=None, *args, **kwargs): """Initialize a MS_MS_step instance.""" + self.grid_type = 'HMS_HMS' self.interp_in_q = True if grid_name is None: metallicity = convert_metallicity_to_string(metallicity) @@ -1299,6 +1288,7 @@ class CO_HMS_RLO_step(MesaGridStep): def __init__(self, metallicity=1., grid_name=None, *args, **kwargs): """Initialize a CO_HMS_RLO_step instance.""" + self.grid_type = 'CO_HMS_RLO' self.interp_in_q = False if grid_name is None: metallicity = convert_metallicity_to_string(metallicity) @@ -1371,6 +1361,7 @@ class CO_HeMS_step(MesaGridStep): def __init__(self, metallicity=1., grid_name=None, *args, **kwargs): """Initialize a CO_HeMS_step instance.""" + self.grid_type = 'CO_HeMS' self.interp_in_q = False if grid_name is None: metallicity = convert_metallicity_to_string(metallicity) diff --git a/posydon/binary_evol/SN/profile_collapse.py b/posydon/binary_evol/SN/profile_collapse.py index 2d31dfad83..1c27d1f2df 100644 --- a/posydon/binary_evol/SN/profile_collapse.py +++ b/posydon/binary_evol/SN/profile_collapse.py @@ -526,10 +526,14 @@ def f_temp2(x): else: J_disk_shell_array.append((J_shell - J_direct) / dm_disk) - # BH mass from the collapse of the entire star in M_sun. + # BH mass from the collapse of the entire star in Msun M_BH_total = M_BH_array[-1] / Mo # BH spin from the collapse of the entire star a_BH_total = a_BH_array[-1] + # BH disk mass accreted in Msun + m_disk_accreted = M_disk_array[-1] / Mo + # BH disk mass radiate in Msun + m_disk_radiated = sum(np.array(dm_disk_array)*np.array(radiation_eff_array))/Mo # max He mass tht can be ejected during the disk formation max_he_mass_ejected /= Mo @@ -546,21 +550,24 @@ def f_temp2(x): print('') return [ - M_BH_total, a_BH_total, - np.array(M_BH_array), - np.array(a_BH_array), - np.array(J_accreted_array), - np.array(J_total_array), - np.array(J_disk_shell_array), - np.array(radiation_eff_array), - np.array(r_isco_array), - np.array(j_isco_array), - np.array(M_direct_collapse_array), - np.array(M_disk_array), - np.array(dm_direct_array), - np.array(dm_disk_array), - np.array(j_shell_array), - np.array(M_total_array), - np.array(a_star_array), - max_he_mass_ejected, + M_BH_total, + a_BH_total, + m_disk_accreted, + m_disk_radiated, + # np.array(M_BH_array), + # np.array(a_BH_array), + # np.array(J_accreted_array), + # np.array(J_total_array), + # np.array(J_disk_shell_array), + # np.array(radiation_eff_array), + # np.array(r_isco_array), + # np.array(j_isco_array), + # np.array(M_direct_collapse_array), + # np.array(M_disk_array), + # np.array(dm_direct_array), + # np.array(dm_disk_array), + # np.array(j_shell_array), + # np.array(M_total_array), + # np.array(a_star_array), + # max_he_mass_ejected, ] diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index b7c359f2b3..93ef2c7f0d 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -48,6 +48,7 @@ from posydon.binary_evol.SN.profile_collapse import do_core_collapse_BH from posydon.binary_evol.flow_chart import (STAR_STATES_CO, STAR_STATES_CC, STAR_STATES_C_DEPLETION) +from posydon.grids.MODELS import MODELS from pandas import read_csv from sklearn import neighbors @@ -66,21 +67,25 @@ "Couch+2020/") MODEL = { + # core collapse physics "mechanism": 'Patton&Sukhbold20-engine', "engine": 'N20', "PISN": "Marchant+19", "ECSN": "Podsiadlowksi+04", + "conserve_hydrogen_envelope" : False, "max_neutrino_mass_loss": 0.5, - "kick": True, - "kick_normalisation": 'one_over_mass', - "sigma_kick_CCSN_NS": 265.0, - "sigma_kick_CCSN_BH": 265.0, - "sigma_kick_ECSN": 20.0, "max_NS_mass": 2.5, "use_interp_values": True, "use_profiles": True, "use_core_masses": True, "approx_at_he_depletion": False, + # kick physics + "kick": True, + "kick_normalisation": 'one_over_mass', + "sigma_kick_CCSN_NS": 265.0, + "sigma_kick_CCSN_BH": 265.0, + "sigma_kick_ECSN": 20.0, + # other "verbose": False, } @@ -229,24 +234,7 @@ class StepSN(object): """ - def __init__(self, - mechanism=MODEL['mechanism'], - engine=MODEL['engine'], - PISN=MODEL['PISN'], - ECSN=MODEL['ECSN'], - max_neutrino_mass_loss=MODEL['max_neutrino_mass_loss'], - kick=MODEL['kick'], - kick_normalisation=MODEL['kick_normalisation'], - sigma_kick_CCSN_NS=MODEL['sigma_kick_CCSN_NS'], - sigma_kick_CCSN_BH=MODEL['sigma_kick_CCSN_BH'], - sigma_kick_ECSN=MODEL['sigma_kick_ECSN'], - max_NS_mass=MODEL['max_NS_mass'], - use_interp_values=MODEL['use_interp_values'], - use_profiles=MODEL['use_profiles'], - use_core_masses=MODEL['use_core_masses'], - approx_at_he_depletion=MODEL['approx_at_he_depletion'], - verbose=MODEL['verbose'], - **kwargs): + def __init__(self, **kwargs): """Initialize a StepSN instance.""" # read kwargs to initialize the class if kwargs: @@ -257,22 +245,9 @@ def __init__(self, default_value = MODEL[varname] setattr(self, varname, kwargs.get(varname, default_value)) else: - self.mechanism = mechanism - self.engine = engine - self.PISN = PISN - self.ECSN = ECSN - self.max_neutrino_mass_loss = max_neutrino_mass_loss - self.kick = kick - self.kick_normalisation = kick_normalisation - self.sigma_kick_CCSN_NS = sigma_kick_CCSN_NS - self.sigma_kick_CCSN_BH = sigma_kick_CCSN_BH - self.sigma_kick_ECSN = sigma_kick_ECSN - self.max_NS_mass = max_NS_mass - self.use_interp_values = use_interp_values - self.use_profiles = use_profiles - self.use_core_masses = use_core_masses - self.approx_at_he_depletion = approx_at_he_depletion - self.verbose = verbose + for varname in MODEL: + default_value = MODEL[varname] + setattr(self, varname, default_value) if self.max_neutrino_mass_loss is None: self.max_neutrino_mass_loss = 0 @@ -506,45 +481,51 @@ def collapse_star(self, star): # if no profile is avaiable but interpolation quantities are, # use those, else continue with or without profile. - key = self.mechanism - key = key.replace('+', '') - key = key.replace('-', '_') - key = key.replace('&', '_') - if self.mechanism in ['Sukhbold+16-engine', - 'Patton&Sukhbold20-engine']: - key += self.engine - if (self.use_interp_values and (getattr(star, key) is not None)): - # check the assumptions for the CC of preprocessed quantities - supported_CC = [ - 'direct', 'Fryer+12-rapid', 'Fryer+12-delayed', - 'Sukhbold+16-engine', 'Patton&Sukhbold20-engine'] - - if self.mechanism not in supported_CC: - raise ValueError('Mechanism not supported by ' - 'use_interp_values=True!') - if self.mechanism in ['Sukhbold+16-engine', - 'Patton&Sukhbold20-engine']: - if self.engine != 'N20': - raise ValueError('Engine not supported by ' - 'use_interp_values=True!') - if self.PISN != "Marchant+19": - raise ValueError('PISN option not supported by ' - 'use_interp_values=True!') - if self.ECSN != "Podsiadlowksi+04": - raise ValueError('ECSN option not supported by ' - 'use_interp_values=True!') - if self.max_neutrino_mass_loss != 0.5: - raise ValueError('max_neutrino_mass_loss option not ' - 'supported by use_intrp_values=True!') - - CC_properites = getattr(star, key) - star.state, star.SN_type, star.f_fb, star.mass, star.spin = ( - CC_properites) - - for key in STARPROPERTIES: - if key not in ["state", "mass", "spin"]: - setattr(star, key, None) - return + if self.use_interp_values: + # find MODEL_NAME corresponding to class variable + MODEL_NAME_SEL = None + for MODEL_NAME, MODEL in MODELS.items(): + tmp = MODEL_NAME + for key, val in MODEL.items(): + if "use_" in key: + continue + if getattr(self, key) != val: + if self.verbose: + print(tmp, 'mismatch:', key, getattr(self, key), val) + tmp = None + break + if tmp is not None: + if self.verbose: + print('matched to model:', tmp) + MODEL_NAME_SEL = tmp + + # check if selected MODEL is supported + if MODEL_NAME_SEL is None: + raise ValueError('Your model assumptions are not' + 'supported!') + elif getattr(star, MODEL_NAME_SEL) is None: + # NOTE: this option is needed to do the collapse + # for stars evolved with the step_detached or + # steo_disrupted. + # allow to continue with the collapse with profile + # or core masses + warnings.warn(f'{MODEL_NAME_SEL}: The collapsed star ' + 'was not interpolated! If use_profiles ' + 'or use_core_masses is set to True, ' + 'continue with the collapse.') + else: + MODEL_properties = getattr(star, MODEL_NAME_SEL) + for key, value in MODEL_properties.items(): + setattr(star, key, value) + + for key in STARPROPERTIES: + if key not in ["state", "mass", "spin", + "m_disk_accreted ", "m_disk_radiated"]: + setattr(star, key, None) + + star.log_R = np.log10(CO_radius(star.mass, star.state)) + + return # Verifies the selection of core-collapse mechnism to perform # the collapse @@ -586,11 +567,9 @@ def collapse_star(self, star): star.spin = np.nan star.m_disk_accreted = np.nan star.m_disk_radiated = np.nan - star.max_he_mass_ejected = np.nan for key in STARPROPERTIES: if key not in ["state", "mass", "spin", - "m_disk_accreted ", "m_disk_radiated", - "max_he_mass_ejected"]: + "m_disk_accreted ", "m_disk_radiated"]: setattr(star, key, None) return @@ -618,17 +597,14 @@ def collapse_star(self, star): ) star.mass = final_BH[0] star.spin = final_BH[1] - Mo = const.Msun - star.m_disk_accreted = final_BH[11][-1]/Mo - star.m_disk_radiated = sum(final_BH[7]*final_BH[13])/Mo - star.max_he_mass_ejected = final_BH[17] + star.m_disk_accreted = final_BH[2] + star.m_disk_radiated = final_BH[3] star.state = "BH" else: star.mass = m_grav star.spin = 0. star.m_disk_accreted = 0. star.m_disk_radiated = 0. - star.max_he_mass_ejected = np.nan star.state = 'NS' elif self.use_core_masses: @@ -652,13 +628,11 @@ def collapse_star(self, star): star.spin = 1.0 star.m_disk_accreted = 0.0 star.m_disk_radiated = 0.0 - star.max_he_mass_ejected = np.nan star.state = "BH" else: star.spin = 0.0 star.m_disk_accreted = 0.0 star.m_disk_radiated = 0.0 - star.max_he_mass_ejected = np.nan star.state = "NS" else: for key in STARPROPERTIES: @@ -697,11 +671,9 @@ def collapse_star(self, star): star.spin = np.nan star.m_disk_accreted = np.nan star.m_disk_radiated = np.nan - star.max_he_mass_ejected = np.nan for key in STARPROPERTIES: if key not in ["state", "mass", "spin", - "m_disk_accreted ", "m_disk_radiated", - "max_he_mass_ejected"]: + "m_disk_accreted ", "m_disk_radiated"]: setattr(star, key, None) return @@ -733,15 +705,12 @@ def collapse_star(self, star): "and lost", round(final_BH[0] - m_rembar, 2), "M_sun.") star.spin = final_BH[1] - Mo = const.Msun - star.m_disk_accreted = final_BH[11][-1]/Mo - star.m_disk_radiated = sum(final_BH[7]*final_BH[13])/Mo - star.max_he_mass_ejected = final_BH[17] + star.m_disk_accreted = final_BH[2] + star.m_disk_radiated = final_BH[3] elif star.state == "NS": star.mass = m_grav star.m_disk_accreted = 0.0 star.m_disk_radiated = 0.0 - star.max_he_mass_ejected = np.nan star.spin = 0.0 else: for key in STARPROPERTIES: @@ -768,13 +737,11 @@ def collapse_star(self, star): star.spin = 1.0 star.m_disk_accreted = 0.0 star.m_disk_radiated = 0.0 - star.max_he_mass_ejected = np.nan star.state = "BH" else: star.spin = 0.0 star.m_disk_accreted = 0.0 star.m_disk_radiated = 0.0 - star.max_he_mass_ejected = np.nan star.state = "NS" else: @@ -793,8 +760,7 @@ def collapse_star(self, star): for key in STARPROPERTIES: if key not in [ "state", "mass", "spin", "log_R", "metallicity", - "m_disk_accreted ", "m_disk_radiated", "max_he_mass_ejected" - ]: + "m_disk_accreted ", "m_disk_radiated"]: setattr(star, key, None) def PISN_prescription(self, star): @@ -818,6 +784,7 @@ def PISN_prescription(self, star): # perform the PISN prescription in terms of the # He core mass at pre-supernova m_He_core = star.he_core_mass + m_star = star.mass if self.PISN == "Marchant+19": if m_He_core >= 31.99 and m_He_core <= 61.10: # this is the 8th-order polynomial fit of table 1 @@ -839,7 +806,11 @@ def PISN_prescription(self, star): m_PISN = np.nan else: - m_PISN = None + # above the PISN gap we assume direct collapse of the + if self.conserve_hydrogen_envelope: + m_PISN = m_star + else: + m_PISN = m_He_core elif is_number(self.PISN) and m_He_core > self.PISN: m_PISN = self.PISN @@ -1028,6 +999,12 @@ def compute_m_rembar(self, star, m_PISN): raise ValueError("The CO core mass is not correct! CO core = {}". format(m_core)) + # define the collapsing stellar mass: either the H or He core mass + if self.conserve_hydrogen_envelope: + m_collapsing = m_star + else: + m_collapsing = m_He_core + m_rembar, f_fb, state, star.SN_type = self.check_SN_type( m_core=m_core, m_He_core=m_He_core, m_star=m_star) @@ -1049,21 +1026,21 @@ def compute_m_rembar(self, star, m_PISN): f_fb = 0.0 elif m_core < 2.5: m_fb = 0.2 - f_fb = m_fb / (m_star - m_proto) + f_fb = m_fb / (m_collapsing - m_proto) elif m_core >= 2.5 and m_core < 6.0: m_fb = 0.286 * m_core - 0.514 - f_fb = m_fb / (m_star - m_proto) + f_fb = m_fb / (m_collapsing - m_proto) elif m_core >= 6.0 and m_core < 7.0: f_fb = 1.0 - m_fb = f_fb * (m_star - m_proto) + m_fb = f_fb * (m_collapsing - m_proto) elif m_core >= 7.0 and m_core < 11.0: - a = 0.25 - 1.275 / (m_star - m_proto) + a = 0.25 - 1.275 / (m_collapsing - m_proto) b = -11.0 * a + 1.0 f_fb = a * m_core + b - m_fb = f_fb * (m_star - m_proto) + m_fb = f_fb * (m_collapsing - m_proto) elif m_core >= 11.0: f_fb = 1.0 - m_fb = f_fb * (m_star - m_proto) + m_fb = f_fb * (m_collapsing - m_proto) m_rembar = m_proto + m_fb state = None @@ -1088,30 +1065,24 @@ def compute_m_rembar(self, star, m_PISN): f_fb = 0.0 elif m_core < 2.5: m_fb = 0.2 - f_fb = m_fb / (m_star - m_proto) + f_fb = m_fb / (m_collapsing - m_proto) elif m_core >= 2.5 and m_core < 3.5: m_fb = 0.5 * m_core - 1.05 - f_fb = m_fb / (m_star - m_proto) + f_fb = m_fb / (m_collapsing - m_proto) elif m_core >= 3.5 and m_core < 11.0: - a = 0.133 - 0.093 / (m_star - m_proto) + a = 0.133 - 0.093 / (m_collapsing - m_proto) b = -11.0 * a + 1.0 f_fb = a * m_core + b - m_fb = f_fb * (m_star - m_proto) + m_fb = f_fb * (m_collapsing - m_proto) elif m_core > 11.0: f_fb = 1.0 - m_fb = f_fb * (m_star - m_proto) + m_fb = f_fb * (m_collapsing - m_proto) m_rembar = m_proto + m_fb state = None # direct collapse and f_fb = 1. (no kicks) elif self.mechanism == self.direct_collapse: - m_rembar = m_star - f_fb = 1.0 - state = None - - # direct collapse and f_fb = 1. (no kicks) - elif self.mechanism == self.direct_collapse_hecore: - m_rembar = m_He_core + m_rembar = m_collapsing f_fb = 1.0 state = None @@ -1129,7 +1100,8 @@ def compute_m_rembar(self, star, m_PISN): m_rembar = m_proto + m_fb state = 'NS' else: - m_rembar, f_fb, state = self.Sukhbold_corecollapse_engine(star) + m_rembar, f_fb, state = self.Sukhbold_corecollapse_engine(star, + self.conserve_hydrogen_envelope) # Collapse prescription from the results of # Couch, S. M., Warren, M. L., & O’Connor, E. P. 2020, ApJ, 890, 127 @@ -1145,7 +1117,8 @@ def compute_m_rembar(self, star, m_PISN): m_rembar = m_proto + m_fb state = 'NS' else: - m_rembar, f_fb, state = self.Couch_corecollapse_engine(star) + m_rembar, f_fb, state = self.Couch_corecollapse_engine(star, + self.conserve_hydrogen_envelope) elif self.mechanism == self.Patton20_engines: if star.SN_type == "ECSN": @@ -1159,7 +1132,8 @@ def compute_m_rembar(self, star, m_PISN): state = 'NS' else: m_rembar, f_fb, state = self.Patton20_corecollapse(star, - self.engine) + self.engine, + self.conserve_hydrogen_envelope) else: raise ValueError("Mechanism %s not supported." % self.mechanism) @@ -1794,7 +1768,7 @@ def get_M4_mu4_Patton20(self, CO_core_mass, C_core_abundance): return M4, mu4 - def Patton20_corecollapse(self, star, engine): + def Patton20_corecollapse(self, star, engine, conserve_hydrogen_envelope=False): """Compute supernova final remnant mass and fallback fraction. It uses the results from [1]. The prediction for the core-collapse @@ -1853,13 +1827,21 @@ def Patton20_corecollapse(self, star, engine): state = 'NS' elif CO_core_mass >= 10.0: - m_rem = star.he_core_mass + # Assuming BH formation by direct collapse + if conserve_hydrogen_envelope: + m_rem = star.mass + else: + m_rem = star.he_core_mass f_fb = 1.0 state = 'BH' elif ((k1 * (mu4 * M4) + k2) < mu4): # The prediction is a failed explosion - m_rem = star.he_core_mass + # Assuming BH formation by direct collapse + if conserve_hydrogen_envelope: + m_rem = star.mass + else: + m_rem = star.he_core_mass f_fb = 1.0 state = 'BH' else: @@ -2018,14 +2000,14 @@ def extrapolate1d_BH(value, interpolator): self.engines, ) - def __call__(self, star): + def __call__(self, star, conserve_hydrogen_envelope=False): """Get the mass, fallback franction and state of the remnant.""" if star.state in STAR_STATES_CC: - # m_star = star.mass # M_sun + m_star = star.mass # M_sun # m_core = star.co_core_mass # M_sun m_He_core = star.he_core_mass # M_sun elif star.state_history[-1] in STAR_STATES_CC: - # m_star = star.mass_history[-1] # M_sun + m_star = star.mass_history[-1] # M_sun # m_core = star.co_core_mass_history[-1] # M_sun m_He_core = star.he_core_mass_history[-1] # M_sun else: @@ -2041,8 +2023,11 @@ def __call__(self, star): state = None if state == "BH": - # Assuming a BH formation by direct collapse of te He core - m_rem = self.extrapolate_BH(m_He_core, self.mass_BH_interpolator) + # Assuming BH formation by direct collapse + if conserve_hydrogen_envelope: + m_rem = self.extrapolate_BH(m_star, self.mass_BH_interpolator) + else: + m_rem = self.extrapolate_BH(m_He_core, self.mass_BH_interpolator) f_fb = 1. elif state == "NS": m_rem = self.extrapolate_NS(m_He_core, self.mass_NS_interpolator) @@ -2273,14 +2258,14 @@ def extrapolate1d_BH(value, interpolator): "choose one of the following engines to compute the collapse:", self.turbulence_strength_options) - def __call__(self, star): + def __call__(self, star, conserve_hydrogen_envelope=False): """Get the mass, fallback fraction and state of the remnant.""" if star.state in STAR_STATES_CC: - # m_star = star.mass # M_sun + m_star = star.mass # M_sun # m_core = star.co_core_mass # M_sun m_He_core = star.he_core_mass # M_sun elif star.state_history[-1] in STAR_STATES_CC: - # m_star = star.mass_history[-1] # M_sun + m_star = star.mass_history[-1] # M_sun # m_core = star.co_core_mass_history[-1] # M_sun m_He_core = star.he_core_mass_history[-1] # M_sun else: @@ -2299,11 +2284,13 @@ def __call__(self, star): state = None if state == "BH": - # Assuming BH formation by direct collapse of the He core - + # Assuming BH formation by direct collapse + if conserve_hydrogen_envelope: + m_rem = m_star + else: + m_rem = m_He_core # m_rem = self.extrapolate_BH(m_He_core, self.mass_BH_interpolator) # TODO: We need to contact Couch et al. to get the remnant masses - m_rem = m_He_core # f_fb = m_rem / m_He_core f_fb = 1. elif state == "NS": diff --git a/posydon/binary_evol/binarystar.py b/posydon/binary_evol/binarystar.py index bbc9bf1cb9..f5b9227cbc 100644 --- a/posydon/binary_evol/binarystar.py +++ b/posydon/binary_evol/binarystar.py @@ -163,6 +163,11 @@ def __init__(self, star_1=None, star_2=None, index=None, properties=None, self.mass_transfer_case = 'None' # if not hasattr(self, 'V_sys'): # self.V_sys = [0, 0, 0] + + # store innterpolation_class for each step_MESA + for grid_type in ['HMS_HMS','CO_HMS_RLO','CO_HeMS']: + if not hasattr(self, f'interp_class_{grid_type}'): + setattr(self, f'interp_class_{grid_type}', None) # SimulationProperties object - parameters & parameterizations if isinstance(properties, SimulationProperties): diff --git a/posydon/binary_evol/singlestar.py b/posydon/binary_evol/singlestar.py index 84a6c19880..f0ca025e8b 100644 --- a/posydon/binary_evol/singlestar.py +++ b/posydon/binary_evol/singlestar.py @@ -21,6 +21,7 @@ import numpy as np import pandas as pd from posydon.utils.common_functions import check_state_of_star +from posydon.grids.MODELS import MODELS STARPROPERTIES = [ @@ -133,39 +134,28 @@ def __init__(self, **kwargs): self.f_fb = None if not hasattr(self, 'SN_type'): self.SN_type = None - - # these quantities are updated in mesa_step.py - if not hasattr(self, 'avg_c_in_c_core_at_He_depletion'): - self.avg_c_in_c_core_at_He_depletion = None - if not hasattr(self, 'co_core_mass_at_He_depletion'): - self.co_core_mass_at_He_depletion = None - if not hasattr(self, 'm_core_CE_1cent'): - self.m_core_CE_1cent = None - if not hasattr(self, 'm_core_CE_10cent'): - self.m_core_CE_10cent = None - if not hasattr(self, 'm_core_CE_30cent'): - self.m_core_CE_30cent = None - if not hasattr(self, 'm_core_CE_pure_He_star_10cent'): - self.m_core_CE_pure_He_star_10cent = None - if not hasattr(self, 'r_core_CE_1cent'): - self.r_core_CE_1cent = None - if not hasattr(self, 'r_core_CE_10cent'): - self.r_core_CE_10cent = None - if not hasattr(self, 'r_core_CE_30cent'): - self.r_core_CE_30cent = None - if not hasattr(self, 'r_core_CE_pure_He_star_10cent'): - self.r_core_CE_pure_He_star_10cent = None - # core collapse initial/final inteprolation - if not hasattr(self, 'direct'): - self.direct = None - if not hasattr(self, 'Fryer12_rapid'): - self.Fryer12_rapid = None - if not hasattr(self, 'Fryer12_delayed'): - self.Fryer12_delayed = None - if not hasattr(self, 'Sukhbold_16_engineN20'): - self.Sukhbold_16_engineN20 = None - if not hasattr(self, 'Patton_Sukhbold20_engineN20'): - self.Patton_Sukhbold20_engineN20 = None + if not hasattr(self, 'm_disk_accreted'): + self.m_disk_accreted = None + if not hasattr(self, 'm_disk_radiated'): + self.m_disk_radiated = None + + # the following quantities are updated in mesa_step.py + + # common envelope quantities + for quantity in ['m_core_CE', 'r_core_CE']: + for val in [1, 10, 30, 'pure_He_star_10']: + if not hasattr(self, f'{quantity}_{val}cent'): + setattr(self, f'{quantity}_{val}cent', None) + + # core masses at He depletion + for quantity in ['avg_c_in_c_core_at_He_depletion', + 'co_core_mass_at_He_depletion']: + setattr(self, quantity, None) + + # core collapse quantities + for MODEL_NAME in MODELS.keys(): + if not hasattr(self, MODEL_NAME): + setattr(self, MODEL_NAME, None) def append_state(self): """Append the new version of the star to the end of the star state.""" diff --git a/posydon/grids/MODELS.py b/posydon/grids/MODELS.py new file mode 100644 index 0000000000..f31404a56e --- /dev/null +++ b/posydon/grids/MODELS.py @@ -0,0 +1,146 @@ +""" +This file contains all core-collapse models +we use in our post processing and, as a +consequence, what the user will be able to +select in the initial/final interpolation. +Each model is a dictionary containing the +properties of the model used by step_SN. +""" + +__authors__ = [ + "Simone Bavera ", +] + +MODELS = { + "MODEL01": { + "mechanism": "direct", + "engine": "", + "PISN": "Marchant+19", + "ECSN": "Podsiadlowksi+04", + "conserve_hydrogen_envelope" : False, + "max_neutrino_mass_loss": 0.5, + "max_NS_mass": 2.5, + "use_interp_values": False, + "use_profiles": True, + "use_core_masses": False, + "approx_at_he_depletion": False, + }, + "MODEL02": { + "mechanism": "Fryer+12-rapid", + "engine": "", + "PISN": "Marchant+19", + "ECSN": "Podsiadlowksi+04", + "conserve_hydrogen_envelope" : False, + "max_neutrino_mass_loss": 0.5, + "max_NS_mass": 2.5, + "use_interp_values": False, + "use_profiles": True, + "use_core_masses": False, + "approx_at_he_depletion": False, + }, + "MODEL03": { + "mechanism": "Fryer+12-delayed", + "engine": "", + "PISN": "Marchant+19", + "ECSN": "Podsiadlowksi+04", + "conserve_hydrogen_envelope" : False, + "max_neutrino_mass_loss": 0.5, + "max_NS_mass": 2.5, + "use_interp_values": False, + "use_profiles": True, + "use_core_masses": False, + "approx_at_he_depletion": False, + }, + "MODEL04": { + "mechanism": "Sukhbold+16-engine", + "engine": "N20", + "PISN": "Marchant+19", + "ECSN": "Podsiadlowksi+04", + "conserve_hydrogen_envelope" : False, + "max_neutrino_mass_loss": 0.5, + "max_NS_mass": 2.5, + "use_interp_values": False, + "use_profiles": True, + "use_core_masses": False, + "approx_at_he_depletion": False, + }, + "MODEL05": { + "mechanism": "Patton&Sukhbold20-engine", + "engine": "N20", + "PISN": "Marchant+19", + "ECSN": "Podsiadlowksi+04", + "conserve_hydrogen_envelope" : False, + "max_neutrino_mass_loss": 0.5, + "max_NS_mass": 2.5, + "use_interp_values": False, + "use_profiles": True, + "use_core_masses": False, + "approx_at_he_depletion": False, + }, + "MODEL06": { + "mechanism": "direct", + "engine": "", + "PISN": "Marchant+19", + "ECSN": "Podsiadlowksi+04", + "conserve_hydrogen_envelope" : True, + "max_neutrino_mass_loss": 0.5, + "max_NS_mass": 2.5, + "use_interp_values": False, + "use_profiles": True, + "use_core_masses": False, + "approx_at_he_depletion": False, + }, + "MODEL07": { + "mechanism": "Fryer+12-rapid", + "engine": "", + "PISN": "Marchant+19", + "ECSN": "Podsiadlowksi+04", + "conserve_hydrogen_envelope" : True, + "max_neutrino_mass_loss": 0.5, + "max_NS_mass": 2.5, + "use_interp_values": False, + "use_profiles": True, + "use_core_masses": False, + "approx_at_he_depletion": False, + }, + "MODEL08": { + "mechanism": "Fryer+12-delayed", + "engine": "", + "PISN": "Marchant+19", + "ECSN": "Podsiadlowksi+04", + "conserve_hydrogen_envelope" : True, + "max_neutrino_mass_loss": 0.5, + "max_NS_mass": 2.5, + "use_interp_values": False, + "use_profiles": True, + "use_core_masses": False, + "approx_at_he_depletion": False, + }, + "MODEL09": { + "mechanism": "Sukhbold+16-engine", + "engine": "N20", + "PISN": "Marchant+19", + "ECSN": "Podsiadlowksi+04", + "conserve_hydrogen_envelope" : True, + "max_neutrino_mass_loss": 0.5, + "max_NS_mass": 2.5, + "use_interp_values": False, + "use_profiles": True, + "use_core_masses": False, + "approx_at_he_depletion": False, + }, + "MODEL10": { + "mechanism": "Patton&Sukhbold20-engine", + "engine": "N20", + "PISN": "Marchant+19", + "ECSN": "Podsiadlowksi+04", + "conserve_hydrogen_envelope" : True, + "max_neutrino_mass_loss": 0.5, + "max_NS_mass": 2.5, + "use_interp_values": False, + "use_profiles": True, + "use_core_masses": False, + "approx_at_he_depletion": False, + }, + +} \ No newline at end of file diff --git a/posydon/grids/post_processing.py b/posydon/grids/post_processing.py index 09b09d6ac0..5f25014394 100644 --- a/posydon/grids/post_processing.py +++ b/posydon/grids/post_processing.py @@ -8,6 +8,8 @@ calculate_Patton20_values_at_He_depl, CEE_parameters_from_core_abundance_thresholds, check_state_of_star) +from posydon.grids.MODELS import MODELS +from posydon.visualization.combine_TF import TF1_POOL_STABLE import numpy as np from tqdm import tqdm import copy @@ -25,28 +27,45 @@ ] -MODEL = { - "mechanism": None, - "engine": None, - "PISN": "Marchant+19", - "ECSN": "Podsiadlowksi+04", - "max_neutrino_mass_loss": 0.5, - "kick": True, - "sigma_kick_CCSN_NS": 265.0, - "sigma_kick_CCSN_BH": 265.0, - "sigma_kick_ECSN": 20.0, - "max_NS_mass": 2.5, - "use_interp_values": False, - "use_profiles": True, - "use_core_masses": False, - "approx_at_he_depletion": False, - "verbose": False, -} - - -def post_process_grid(grid, index=None, star_2_CO=True, MODEL=MODEL, +CC_quantities = ['state', 'SN_type', 'f_fb', 'mass', 'spin', + 'm_disk_accreted', 'm_disk_radiated'] + +def assign_core_collapse_quantities_none(EXTRA_COLUMNS, star_i, MODEL_NAME=None): + """"Assign None values to all core collapse properties.""" + if MODEL_NAME is None: + for MODEL_NAME, MODEL in MODELS.items(): + for quantity in CC_quantities: + EXTRA_COLUMNS[f'S{star_i}_{MODEL_NAME}_{quantity}'].append(None) + else: + for quantity in CC_quantities: + EXTRA_COLUMNS[f'S{star_i}_{MODEL_NAME}_{quantity}'].append(None) + +def print_CC_quantities(EXTRA_COLUMNS, star, MODEL_NAME=None): + format_string = "{:<50} {:<33} {:12} {:10} {:15} {:10} {:25} {:25}" + format_val_preSN = "{:<50} {:<33} {:12} {:10} {:7.2f} {:12.2f} {:25} {:25}" + format_val = "{:<50} {:<33} {:12} {:1.2f} {:13.2f} {:12.2f} {:20.2f} {:20.2f}" + if MODEL_NAME is None: + print('') + print(format_string.format( + "mechanism", "state", "SN type", "f_fb", + "mass [Msun]", "spin", "m_disk_accreted [Msun]", + "m_disk_radiated [Msun]")) + print('') + print(format_val_preSN.format( + 'PRE SN STAR', star.state, '', + '', star.mass, star.spin, '', '')) + print('') + else: + print(format_val.format(MODEL_NAME, + star.state, star.SN_type, star.f_fb, + star.mass, star.spin, star.m_disk_accreted, + star.m_disk_radiated)) + + + +def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, single_star=False, verbose=False): - """Compute post processed quantitiy of any grid. + """Compute post processed quantity of any grid. This function post process any supported grid and computes: - Core collpase quantities for 5 prescritions given the fiducial POSYDON @@ -71,8 +90,8 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODEL=MODEL, or a index, e.g. 42. star_2_CO : bool If 'False' star 2 is not a compact object. - MODEL : dict - Core collapse model assumptions. + MODELS : list of dict + List of supported core collapse model assumptions. single_star : bool If `True` the PSyGrid contains single stars. verbose : bool @@ -88,62 +107,23 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODEL=MODEL, Dictionary containing all post processe quantities. """ - CORE_COLLAPSES = [['direct', ''], ['Fryer+12-rapid', ''], - ['Fryer+12-delayed', ''], ['Sukhbold+16-engine', 'N20'], - ['Patton&Sukhbold20-engine', 'N20']] - - EXTRA_COLUMNS = { - # Core collapse qunatities: [state, SN_type, f_fb, mass, spin] - 'S1_direct': [], - 'S1_Fryer+12-rapid': [], - 'S1_Fryer+12-delayed': [], - 'S1_Sukhbold+16-engineN20': [], - 'S1_Patton&Sukhbold20-engineN20': [], - 'S2_direct': [], - 'S2_Fryer+12-rapid': [], - 'S2_Fryer+12-delayed': [], - 'S2_Sukhbold+16-engineN20': [], - 'S2_Patton&Sukhbold20-engineN20': [], - # core masses at He depletion - 'S1_avg_c_in_c_core_at_He_depletion': [], - 'S2_avg_c_in_c_core_at_He_depletion': [], - 'S1_co_core_mass_at_He_depletion': [], - 'S2_co_core_mass_at_He_depletion': [], + EXTRA_COLUMNS = {} + + for star in [1, 2]: + # core masses at He depletion. stellar states and composition + for quantity in ['avg_c_in_c_core_at_He_depletion', + 'co_core_mass_at_He_depletion', + 'state', 'surface_other', 'center_other']: + EXTRA_COLUMNS[f'S{star}_{quantity}'] = [] # common envelope quantities - 'S1_lambda_CE_1cent': [], - 'S1_lambda_CE_10cent': [], - 'S1_lambda_CE_30cent': [], - 'S1_lambda_CE_pure_He_star_10cent': [], - 'S1_m_core_CE_1cent': [], - 'S1_m_core_CE_10cent': [], - 'S1_m_core_CE_30cent': [], - 'S1_m_core_CE_pure_He_star_10cent': [], - 'S1_r_core_CE_1cent': [], - 'S1_r_core_CE_10cent': [], - 'S1_r_core_CE_30cent': [], - 'S1_r_core_CE_pure_He_star_10cent': [], - 'S2_lambda_CE_1cent': [], - 'S2_lambda_CE_10cent': [], - 'S2_lambda_CE_30cent': [], - 'S2_lambda_CE_pure_He_star_10cent': [], - 'S2_m_core_CE_1cent': [], - 'S2_m_core_CE_10cent': [], - 'S2_m_core_CE_30cent': [], - 'S2_m_core_CE_pure_He_star_10cent': [], - 'S2_r_core_CE_1cent': [], - 'S2_r_core_CE_10cent': [], - 'S2_r_core_CE_30cent': [], - 'S2_r_core_CE_pure_He_star_10cent': [], - # stellar states - 'S1_state': [], - 'S2_state': [], - # composition - 'S1_surface_other': [], - 'S1_center_other': [], - 'S2_surface_other': [], - 'S2_center_other': [], - } - + for quantity in ['lambda_CE', 'm_core_CE', 'r_core_CE']: + for val in [1, 10, 30, 'pure_He_star_10']: + EXTRA_COLUMNS[f'S{star}_{quantity}_{val}cent'] = [] + # Core collapse qunatities: [state, SN_type, f_fb, mass, spin] + for MODEL_NAME, MODEL in MODELS.items(): + for quantity in CC_quantities: + EXTRA_COLUMNS[f'S{star}_{MODEL_NAME}_{quantity}'] = [] + # remove star 2 columns in case of single star grid if single_star: for key in list(EXTRA_COLUMNS.keys()): @@ -179,19 +159,24 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODEL=MODEL, IC = 'no_MT' TF1 = grid.final_values['termination_flag_1'][i] - # He depeltion and CE quantities + # compute properties for j, star in enumerate(stars): if not stars_CO[j] and IC in ['no_MT', 'stable_MT', 'unstable_MT']: + # stellar states EXTRA_COLUMNS['S%s_state' % (j+1)].append(check_state_of_star( star, star_CO=False)) + # core masses at he depletion with warnings.catch_warnings(record=True) as w: calculate_Patton20_values_at_He_depl(star) if len(w) > 0: print(w[0].message) print(f'The warning was raised by {grid.MESA_dirs[i]} ' f'in calculate_Patton20_values_at_He_depl(star_{j+1}).') - EXTRA_COLUMNS['S%s_avg_c_in_c_core_at_He_depletion' % (j+1)].append(star.avg_c_in_c_core_at_He_depletion) - EXTRA_COLUMNS['S%s_co_core_mass_at_He_depletion' % (j+1)].append(star.co_core_mass_at_He_depletion) + EXTRA_COLUMNS[f'S{j+1}_avg_c_in_c_core_at_He_depletion'].append( + star.avg_c_in_c_core_at_He_depletion) + EXTRA_COLUMNS[f'S{j+1}_co_core_mass_at_He_depletion'].append( + star.co_core_mass_at_He_depletion) + # CE quantities with warnings.catch_warnings(record=True) as w: try: CEE_parameters_from_core_abundance_thresholds(star) @@ -203,21 +188,16 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODEL=MODEL, print(w[0].message) print(f'The warning was raised by {grid.MESA_dirs[i]} ' f'in CEE_parameters_from_core_abundance_thresholds(star_{j+1}).') - EXTRA_COLUMNS['S%s_m_core_CE_1cent' % (j+1)].append(star.m_core_CE_1cent) - EXTRA_COLUMNS['S%s_m_core_CE_10cent' % (j+1)].append(star.m_core_CE_10cent) - EXTRA_COLUMNS['S%s_m_core_CE_30cent' % (j+1)].append(star.m_core_CE_30cent) - EXTRA_COLUMNS['S%s_m_core_CE_pure_He_star_10cent' % (j+1)].append(star.m_core_CE_pure_He_star_10cent) - EXTRA_COLUMNS['S%s_r_core_CE_1cent' % (j+1)].append(star.r_core_CE_1cent) - EXTRA_COLUMNS['S%s_r_core_CE_10cent' % (j+1)].append(star.r_core_CE_10cent) - EXTRA_COLUMNS['S%s_r_core_CE_30cent' % (j+1)].append(star.r_core_CE_30cent) - EXTRA_COLUMNS['S%s_r_core_CE_pure_He_star_10cent' % (j+1)].append(star.r_core_CE_pure_He_star_10cent) - EXTRA_COLUMNS['S%s_lambda_CE_1cent' % (j+1)].append(star.lambda_CE_1cent) - EXTRA_COLUMNS['S%s_lambda_CE_10cent' % (j+1)].append(star.lambda_CE_10cent) - EXTRA_COLUMNS['S%s_lambda_CE_30cent' % (j+1)].append(star.lambda_CE_30cent) - EXTRA_COLUMNS['S%s_lambda_CE_pure_He_star_10cent' % (j+1)].append(star.lambda_CE_pure_He_star_10cent) + for quantity in ['lambda_CE', 'm_core_CE', 'r_core_CE']: + for val in [1, 10, 30, 'pure_He_star_10']: + EXTRA_COLUMNS[f'S{j+1}_{quantity}_{val}cent'].append( + getattr(star, f'{quantity}_{val}cent')) + # aboundances try: - s_o = 1. - star.surface_h1 - star.surface_he4 - star.surface_c12 - star.surface_n14 - star.surface_o16 - c_o = 1. - star.center_h1 - star.center_he4 - star.center_c12 - star.center_n14 - star.center_o16 + s_o = (1. - star.surface_h1 - star.surface_he4 - star.surface_c12 + - star.surface_n14 - star.surface_o16) + c_o = (1. - star.center_h1 - star.center_he4 - star.center_c12 + - star.center_n14 - star.center_o16) except TypeError as ex: s_o = 0. c_o = 0. @@ -227,6 +207,7 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODEL=MODEL, EXTRA_COLUMNS['S%s_surface_other' % (j+1)].append(s_o) EXTRA_COLUMNS['S%s_center_other' % (j+1)].append(c_o) else: + # fill everything with Nones if IC == 'initial_MT': EXTRA_COLUMNS['S%s_state' % (j+1)].append(None) else: @@ -238,168 +219,158 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODEL=MODEL, print(ex) print(f'The error was raised by {grid.MESA_dirs[i]} ' f'in check_state_of_star(star_{j+1}) with IC={IC}.') - EXTRA_COLUMNS['S%s_avg_c_in_c_core_at_He_depletion' % (j+1)].append(None) - EXTRA_COLUMNS['S%s_co_core_mass_at_He_depletion' % (j+1)].append(None) - EXTRA_COLUMNS['S%s_m_core_CE_1cent' % (j+1)].append(None) - EXTRA_COLUMNS['S%s_m_core_CE_10cent' % (j+1)].append(None) - EXTRA_COLUMNS['S%s_m_core_CE_30cent' % (j+1)].append(None) - EXTRA_COLUMNS['S%s_m_core_CE_pure_He_star_10cent' % (j+1)].append(None) - EXTRA_COLUMNS['S%s_r_core_CE_1cent' % (j+1)].append(None) - EXTRA_COLUMNS['S%s_r_core_CE_10cent' % (j+1)].append(None) - EXTRA_COLUMNS['S%s_r_core_CE_30cent' % (j+1)].append(None) - EXTRA_COLUMNS['S%s_r_core_CE_pure_He_star_10cent' % (j+1)].append(None) - EXTRA_COLUMNS['S%s_lambda_CE_1cent' % (j+1)].append(None) - EXTRA_COLUMNS['S%s_lambda_CE_10cent' % (j+1)].append(None) - EXTRA_COLUMNS['S%s_lambda_CE_30cent' % (j+1)].append(None) - EXTRA_COLUMNS['S%s_lambda_CE_pure_He_star_10cent' % (j+1)].append(None) - EXTRA_COLUMNS['S%s_surface_other' % (j+1)].append(None) - EXTRA_COLUMNS['S%s_center_other' % (j+1)].append(None) - + for quantity in ['avg_c_in_c_core_at_He_depletion', 'co_core_mass_at_He_depletion', + 'surface_other', 'center_other', 'lambda_CE', 'm_core_CE', 'r_core_CE']: + if 'CE' in quantity: + for val in [1, 10, 30, 'pure_He_star_10']: + EXTRA_COLUMNS[f'S{j+1}_{quantity}_{val}cent'].append(None) + else: + EXTRA_COLUMNS[f'S{j+1}_{quantity}'].append(None) + + # core collpase quantities if not single_star: - # core collpase quantities if interpolation_class in ['no_MT', 'stable_MT']: - if star_2_CO or TF1 == 'Primary has depleted central carbon': + if (star_2_CO or (TF1 in TF1_POOL_STABLE and + ('primary' in TF1 or 'Primary' in TF1))): star = binary.star_1 - s = 'S1_' - for m in CORE_COLLAPSES: - EXTRA_COLUMNS['S2_'+m[0]+m[1]].append([None]*5) - elif TF1 == 'Secondary has depleted central carbon': + star_i = 1 + assign_core_collapse_quantities_none(EXTRA_COLUMNS, 2) + elif (TF1 in TF1_POOL_STABLE and + ('secondary' in TF1 or 'Secondary' in TF1)): star = binary.star_2 - s = 'S2_' - for m in CORE_COLLAPSES: - EXTRA_COLUMNS['S1_'+m[0]+m[1]].append([None]*5) + star_i = 2 + assign_core_collapse_quantities_none(EXTRA_COLUMNS, 1) elif TF1 == 'gamma_center_limit': if (binary.star_1.center_gamma is not None and binary.star_1.center_gamma >= 10.): star = binary.star_1 - s = 'S1_' - for m in CORE_COLLAPSES: - EXTRA_COLUMNS['S2_'+m[0]+m[1]].append([None]*5) + star_i = 1 + assign_core_collapse_quantities_none(EXTRA_COLUMNS, 2) elif (binary.star_2.center_gamma is not None and - binary.star_2.center_gamma >= 10.): + binary.star_2.center_gamma >= 10.): star = binary.star_2 - s = 'S2_' - for m in CORE_COLLAPSES: - EXTRA_COLUMNS['S1_'+m[0]+m[1]].append([None]*5) + star_i = 2 + assign_core_collapse_quantities_none(EXTRA_COLUMNS, 1) else: - for m in CORE_COLLAPSES: - EXTRA_COLUMNS['S1_'+m[0]+m[1]].append([None]*5) - EXTRA_COLUMNS['S2_'+m[0]+m[1]].append([None]*5) + assign_core_collapse_quantities_none(EXTRA_COLUMNS, 1) + assign_core_collapse_quantities_none(EXTRA_COLUMNS, 2) warnings.warn(f'{grid.MESA_dirs[i]} ended with ' - 'TF1=gamma_center_limit however ' - 'the star has center_gamma < 10. ' - 'This star cannot go through step_SN ' - 'appending NONE compact object ' - 'properties!') + 'TF1=gamma_center_limit however ' + 'the star has center_gamma < 10. ' + 'This star cannot go through step_SN ' + 'appending NONE compact object ' + 'properties!') continue else: - for m in CORE_COLLAPSES: - EXTRA_COLUMNS['S1_'+m[0]+m[1]].append([None]*5) - EXTRA_COLUMNS['S2_'+m[0]+m[1]].append([None]*5) + assign_core_collapse_quantities_none(EXTRA_COLUMNS, 1) + assign_core_collapse_quantities_none(EXTRA_COLUMNS, 2) warnings.warn(f'{grid.MESA_dirs[i]} ended with ' - f'TF={TF1} and IC={interpolation_class}. ' - 'This star cannot go through step_SN ' - 'appending NONE compact object ' - 'properties!') + f'TF={TF1} and IC={interpolation_class}. ' + 'This star cannot go through step_SN ' + 'appending NONE compact object ' + 'properties!') continue if verbose: - print("{:<30} {:<33} {:12} {:10} {:15} {:10}".format( - "mechanism", "state", "SN type", "f_fb", - "mass [Msun]", "spin")) - print('') - print("{:<30} {:<33} {:10} {:10} {:7.2f} {:14.2f}".format( - 'PRE SN STAR', star.state, '', - '', star.mass, star.spin)) - print('') + print_CC_quantities(EXTRA_COLUMNS, star) - for m in CORE_COLLAPSES: - MODEL["mechanism"] = m[0] - MODEL["engine"] = m[1] - SN = StepSN(**MODEL) + for MODEL_NAME, MODEL in MODELS.items(): + mechanism = MODEL['mechanism']+MODEL['engine'] + SN = StepSN(**MODEL) + star_copy = copy.copy(star) try: - star_copy = copy.copy(star) + flush = False SN.collapse_star(star_copy) - EXTRA_COLUMNS[s+m[0]+m[1]].append( - [star_copy.state, star_copy.SN_type, - star_copy.f_fb, star_copy.mass, star_copy.spin]) - if verbose: - print( - "{:<30} {:<33} {:10} {:6.2f} {:11.2f} {:14.2f}" - .format(MODEL["mechanism"], star_copy.state, - star_copy.SN_type, star_copy.f_fb, - star_copy.mass, star_copy.spin)) + for quantity in CC_quantities: + if quantity in ['state', 'SN_type']: + if not isinstance(getattr(star_copy, quantity), str): + flush = True + warnings.warn(f'{MODEL_NAME} {mechanism} state/SN_type not a string!') + else: + if not isinstance(getattr(star_copy, quantity), (float, None)): + flush = True + warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} not a float!') except Exception as e: - EXTRA_COLUMNS[s+m[0]+m[1]].append([None]*5) - + flush = True if verbose: print('') - print('Error during %s core collapse prescrition!' - % m[0]) + print(f'Error during {MODEL_NAME} {mechanism} core collapse prescrition!') print(e) print('TF1', TF1) print('interpolation class', interpolation_class) + print('') + if flush: + assign_core_collapse_quantities_none(EXTRA_COLUMNS, star_i, MODEL_NAME) + else: + for quantity in CC_quantities: + EXTRA_COLUMNS[f'S{star_i}_{MODEL_NAME}_{quantity}'].append( + getattr(star_copy, quantity)) + if verbose: + print_CC_quantities(EXTRA_COLUMNS, star_copy, f'{MODEL_NAME}_{mechanism}') - else: # inital_RLOF, unstable_MT not_convergedd - if (TF1 == 'Primary has depleted central carbon' or - TF1 == 'Secondary has depleted central carbon'): - warnings.warn(f'{grid.MESA_dirs[i]} ended with ' - f'TF={TF1} but was not collapsed! ' - 'This should never happen!') - for m in CORE_COLLAPSES: - EXTRA_COLUMNS['S1_'+m[0]+m[1]].append([None]*5) - EXTRA_COLUMNS['S2_'+m[0]+m[1]].append([None]*5) + else: + # inital_RLOF, unstable_MT not_converged + assign_core_collapse_quantities_none(EXTRA_COLUMNS, 1) + assign_core_collapse_quantities_none(EXTRA_COLUMNS, 2) else: if star.state in STAR_STATES_CC: if verbose: - print("{:<30} {:<33} {:12} {:10} {:15} {:10}".format( - "mechanism", "state", "SN type", "f_fb", - "mass [Msun]", "spin")) - print('') - print("{:<30} {:<33} {:10} {:10} {:7.2f} {:14.2f}".format( - 'PRE SN STAR', star.state, - '', '', star.mass, star.spin)) - print('') + print_CC_quantities(EXTRA_COLUMNS, star) - for m in CORE_COLLAPSES: - MODEL["mechanism"] = m[0] - MODEL["engine"] = m[1] + for MODEL_NAME, MODEL in MODELS.items(): + mechanism = MODEL['mechanism']+MODEL['engine'] SN = StepSN(**MODEL) + star_copy = copy.copy(star) try: - star_copy = copy.copy(star) + flush = False SN.collapse_star(star_copy) - EXTRA_COLUMNS['S1_'+m[0]+m[1]].append([ - star_copy.state, star_copy.SN_type, star_copy.f_fb, - star_copy.mass, star_copy.spin]) - if verbose: - print( - "{:<30} {:<33} {:10} {:6.2f} {:11.2f} {:14.2f}" - .format(MODEL["mechanism"], star_copy.state, - star_copy.SN_type, star_copy.f_fb, - star_copy.mass, star_copy.spin)) + for quantity in CC_quantities: + if quantity in ['state', 'SN_type']: + if not isinstance(getattr(star_copy, quantity), str): + flush = True + warnings.warn(f'{MODEL_NAME} {mechanism} state/SN_type not a string!') + else: + if not isinstance(getattr(star_copy, quantity), (float, None)): + flush = True + warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} not a float!') except Exception as e: - EXTRA_COLUMNS[s+m[0]+m[1]].append([None]*5) - + flush = True if verbose: print('') - print('Error during %s core collapse prescrition!' - % m[0]) + print(f'Error during {MODEL_NAME} {mechanism} core collapse prescrition!') print(e) print('TF1', TF1) print('interpolation class', interpolation_class) + print('') + if flush: + assign_core_collapse_quantities_none(EXTRA_COLUMNS, 1, MODEL_NAME) + else: + for quantity in CC_quantities: + EXTRA_COLUMNS[f'S1_{MODEL_NAME}_{quantity}'].append( + getattr(star_copy, quantity)) + if verbose: + print_CC_quantities(EXTRA_COLUMNS, star_copy, f'{MODEL_NAME}_{mechanism}') else: - for m in CORE_COLLAPSES: - EXTRA_COLUMNS['S1_'+m[0]+m[1]].append([None]*5) + assign_core_collapse_quantities_none(EXTRA_COLUMNS, 1) # check dataset completeness - n_control = len(EXTRA_COLUMNS['S1_direct']) + n_control = len(EXTRA_COLUMNS['S1_state']) for key in EXTRA_COLUMNS.keys(): if n_control != len(EXTRA_COLUMNS[key]): raise ValueError( '%s has not the correct dimension! Error occoured after ' 'collapsing binary index=%s' % (key, i)) + # to avoid confusion rename core-collaspe compact object state "MODEL_NAME_state" + # to "MODEL_NAME_CO_type" + for MODEL_NAME in MODELS.keys(): + EXTRA_COLUMNS[f'S1_{MODEL_NAME}_CO_type'] = EXTRA_COLUMNS.pop( + f'S1_{MODEL_NAME}_state') + if f'S2_{MODEL_NAME}_state' in EXTRA_COLUMNS: + EXTRA_COLUMNS[f'S2_{MODEL_NAME}_CO_type'] = EXTRA_COLUMNS.pop( + f'S2_{MODEL_NAME}_state') + return MESA_dirs, EXTRA_COLUMNS @@ -431,29 +402,9 @@ def add_post_processed_quantities(grid, MESA_dirs_EXTRA_COLUMNS, EXTRA_COLUMNS, raise ValueError( 'EXTRA_COLUMNS do not follow the correct order of grid!') - CC_keys = ['state', 'SN_type', 'f_fb', 'mass', 'spin'] - - for key in EXTRA_COLUMNS.keys(): - if ('direct' in key or 'Fryer+12-rapid' in key - or 'Fryer+12-delayed' in key or 'Sukhbold+16-engineN20' in key - or 'Patton&Sukhbold20-engineN20' in key): - for j, key_CC in enumerate(CC_keys): - column = '%s_%s' % (key, key_CC) - values = [] - for i in range(len(EXTRA_COLUMNS[key])): - values.append(EXTRA_COLUMNS[key][i][j]) - if "state" in column or "SN_type" in column: - values = np.asarray(values, str) - else: - values = np.asarray(values, float) - grid.add_column(column, values, overwrite=True) + for column in EXTRA_COLUMNS.keys(): + if "state" in column or "type" in column: + values = np.asarray(EXTRA_COLUMNS[column], str) else: - column = key - values = [] - for i in range(len(EXTRA_COLUMNS[key])): - values.append(EXTRA_COLUMNS[key][i]) - if "state" in column: - values = np.asarray(values, str) - else: - values = np.asarray(values, float) - grid.add_column(column, values, overwrite=True) + values = np.asarray(EXTRA_COLUMNS[column], float) + grid.add_column(column, values, overwrite=True) diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index 56c6c1aa8f..2b3c14a058 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -854,9 +854,14 @@ def decide_columns(key_in_config, defaults): warnings.warn("Ignored MESA run because of scrubbed" " history in: {}\n".format(run.path)) continue - + + try: #get mass from binary history + init_mass_1 = float(binary_history["star_1_mass"][0]) + except: #otherwise get it from directory name + params_from_path = initial_values_from_dirname(run.path) + init_mass_1 = float(params_from_path[0]) # check whether stop at He depletion is requested - if stop_before_carbon_depletion and self.initial_values[i]["star_1_mass"]>=100.0: + if stop_before_carbon_depletion and init_mass_1>=100.0: kept = keep_till_central_abundance_He_C(binary_history, history1, history2, THRESHOLD_CENTRAL_ABUNDANCE, 0.1) binary_history, history1, history2, newTF1 = kept @@ -1247,7 +1252,10 @@ def decide_columns(key_in_config, defaults): def add_column(self, colname, array, where="final_values", overwrite=True): """Add a new numerical column in the final values array.""" - arr = np.asarray(array) + if not isinstance(array, np.ndarray): + arr = np.asarray(array) + else: + arr = array if where != "final_values": raise ValueError("Only adding columns to `final_values` allowed.") @@ -1275,9 +1283,11 @@ def update_final_values(self): for dtype in self.final_values.dtype.descr: if (dtype[0].startswith("termination_flag") or dtype[0] == "interpolation_class" - or "SN_type" in dtype[0] or "_state" in dtype[0]): + or "_type" in dtype[0] or "_state" in dtype[0]): dtype = (dtype[0], H5_REC_STR_DTYPE.replace("U", "S")) new_dtype.append(dtype) + if dtype[1] == np.dtype('O'): + print(dtype[0]) final_values = self.final_values.astype(new_dtype) del self.hdf5["/grid/final_values"] self.hdf5.create_dataset("/grid/final_values", data=final_values, @@ -1325,7 +1335,7 @@ def load(self, filepath=None): for dtype in self.final_values.dtype.descr: if (dtype[0].startswith("termination_flag") or dtype[0] == "interpolation_class" - or "SN_type" in dtype[0] or "_state" in dtype[0]): + or "_type" in dtype[0] or "_state" in dtype[0]): dtype = (dtype[0], H5_REC_STR_DTYPE.replace("S", "U")) new_dtype.append(dtype) self.final_values = self.final_values.astype(new_dtype) diff --git a/posydon/interpolation/interpolation.py b/posydon/interpolation/interpolation.py index 935c000bfa..5cadc9861c 100644 --- a/posydon/interpolation/interpolation.py +++ b/posydon/interpolation/interpolation.py @@ -2,7 +2,7 @@ __authors__ = [ "Juanga Serra Perez ", - "Philipp Moura Srivastava ", + "Philipp Moura Srivastava " ] @@ -12,6 +12,7 @@ from sklearn.neighbors import NearestNeighbors from .data_scaling import DataScaler from posydon.grids.psygrid import PSyGrid +from posydon.grids.MODELS import MODELS class psyTrackInterp: @@ -450,32 +451,8 @@ def __init__(self, path, verbose=False): 'surface_he3': 'surface_he3', } + # processed keys self.final_keys = ( - 'S1_direct_state', - 'S1_direct_SN_type', - 'S1_direct_f_fb', - 'S1_direct_mass', - 'S1_direct_spin', - 'S1_Fryer+12-rapid_state', - 'S1_Fryer+12-rapid_SN_type', - 'S1_Fryer+12-rapid_f_fb', - 'S1_Fryer+12-rapid_mass', - 'S1_Fryer+12-rapid_spin', - 'S1_Fryer+12-delayed_state', - 'S1_Fryer+12-delayed_SN_type', - 'S1_Fryer+12-delayed_f_fb', - 'S1_Fryer+12-delayed_mass', - 'S1_Fryer+12-delayed_spin', - 'S1_Sukhbold+16-engineN20_state', - 'S1_Sukhbold+16-engineN20_SN_type', - 'S1_Sukhbold+16-engineN20_f_fb', - 'S1_Sukhbold+16-engineN20_mass', - 'S1_Sukhbold+16-engineN20_spin', - 'S1_Patton&Sukhbold20-engineN20_state', - 'S1_Patton&Sukhbold20-engineN20_SN_type', - 'S1_Patton&Sukhbold20-engineN20_f_fb', - 'S1_Patton&Sukhbold20-engineN20_mass', - 'S1_Patton&Sukhbold20-engineN20_spin', 'S1_avg_c_in_c_core_at_He_depletion', 'S1_co_core_mass_at_He_depletion', 'S1_m_core_CE_1cent', @@ -487,6 +464,14 @@ def __init__(self, path, verbose=False): 'S1_r_core_CE_30cent', 'S1_r_core_CE_pure_He_star_10cent' ) + + # core collapse keys + keys = [] + for MODEL_NAME in MODELS.keys(): + for key in ['CO_type', 'SN_type', 'f_fb', 'mass', 'spin', + 'm_disk_accreted', 'm_disk_radiated']: + keys.append('S1_' + MODEL_NAME + '_' + key ) + self.final_keys += tuple(keys) self.profile_keys = ( 'radius', diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index 5bb103993d..903243e995 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -60,7 +60,9 @@ 'S1_state_i': 31, 'S1_state_f': 31, 'S2_state_i': 31, 'S2_state_f': 31, 'mass_transfer_case_i': 7, 'mass_transfer_case_f': 7, - 'S1_SN_type': 5, 'S2_SN_type': 5} + 'S1_SN_type': 5, 'S2_SN_type': 5, + 'interp_class_HMS_HMS' : 15, 'interp_class_CO_HeMS' : 15, + 'interp_class_CO_HMS_RLO' : 15} # BinaryPopulation will enforce a constant metallicity accross all steps that # load stellar or binary models by checked this list of steps. @@ -848,7 +850,10 @@ def draw_initial_binary(self, **kwargs): m1 = output['S1_mass'].item() m2 = output['S2_mass'].item() Z_div_Zsun = kwargs.get('metallicity', 1.) - zams_table = {1.: 2.703e-01, + zams_table = {2.: 2.915e-01, + 1.: 2.703e-01, + 0.45: 2.586e-01, + 0.2: 2.533e-01, 0.1: 2.511e-01, 0.01: 2.492e-01, 0.001: 2.49e-01, @@ -889,7 +894,10 @@ def draw_initial_binary(self, **kwargs): m1 = output['S1_mass'].item() m2 = output['S2_mass'].item() Z_div_Zsun = kwargs.get('metallicity', 1.) - zams_table = {1.: 2.703e-01, + zams_table = {2.: 2.915e-01, + 1.: 2.703e-01, + 0.45: 2.586e-01, + 0.2: 2.533e-01, 0.1: 2.511e-01, 0.01: 2.492e-01, 0.001: 2.49e-01, diff --git a/posydon/popsyn/population_params_default.ini b/posydon/popsyn/population_params_default.ini index 04214f1efc..98989547f0 100644 --- a/posydon/popsyn/population_params_default.ini +++ b/posydon/popsyn/population_params_default.ini @@ -121,8 +121,6 @@ import = ['posydon.binary_evol.DT.step_disrupted','DisruptedStep'] - - [step_CE] import = ['posydon.binary_evol.CE.step_CEE', 'StepCEE'] absolute_import = None @@ -168,8 +166,20 @@ # None, "Marchant+19" ECSN = "Podsiadlowksi+04" # "Tauris+15", "Podsiadlowksi+04" + conserve_hydrogen_envelope = False + # True, False max_neutrino_mass_loss = 0.5 # float (0,inf) + max_NS_mass = 2.5 + # float (0,inf) + use_interp_values = True + # True, False + use_profiles = True + # True, False + use_core_masses = True + # True, False + approx_at_he_depletion = False + # True, False kick = True # True, False kick_normalisation = 'one_over_mass' @@ -181,16 +191,6 @@ # float (0,inf) sigma_kick_ECSN = 20.0 # float (0,inf) - max_NS_mass = 2.5 - # float (0,inf) - use_interp_values = True - # True, False - use_profiles = True - # True, False - use_core_masses = True - # True, False - approx_at_he_depletion = True - # True, False verbose = False # True False @@ -237,7 +237,7 @@ # > from mpi4py import MPI # > comm = MPI.COMM_WORLD - metallicity = 1 + metallicity = [2., 1., 0.45, 0.2, 0.1, 0.01, 0.001, 0.0001] # In units of solar metallicity # Random Number Generation @@ -315,6 +315,11 @@ #'t_sync_conv_2', #'nearest_neighbour_distance', ] + scalar_names=[ + 'interp_class_HMS_HMS', + 'interp_class_CO_HMS_RLO', + 'interp_class_CO_HeMS', + ] [SingleStar_1_output] # LIST STAR PROPERTIES TO SAVE @@ -379,8 +384,8 @@ scalar_names=[ 'natal_kick_array', 'SN_type', - #'f_fb', - #'spin_orbit_tilt', + 'f_fb', + 'spin_orbit_tilt', ] [SingleStar_2_output] @@ -446,6 +451,6 @@ scalar_names=[ 'natal_kick_array', 'SN_type', - #'f_fb', - #'spin_orbit_tilt', + 'f_fb', + 'spin_orbit_tilt', ] diff --git a/posydon/popsyn/rate_calculation.py b/posydon/popsyn/rate_calculation.py index 646a41fd95..b83cc347e8 100644 --- a/posydon/popsyn/rate_calculation.py +++ b/posydon/popsyn/rate_calculation.py @@ -137,15 +137,16 @@ def get_data(self, var, index=None): values = self.m_chirp(self.df["S1_mass"], self.df["S2_mass"]) elif var == 'chi_eff': # check if tilts are provided - if ("tilt_BH1" not in self.df or "tilt_BH2" not in self.df): + if ("S1_spin_orbit_tilt" not in self.df or + "S2_spin_orbit_tilt" not in self.df): warnings.warn('Spin tilts not in dataset! Assuming spins ' 'are aligned to orbital angular momentum ' 'to compute chi_eff!') tilt_BH1 = np.zeros(len(self.df["S1_spin"])) tilt_BH2 = tilt_BH1 else: - tilt_BH1 = self.df["tilt_BH1"] - tilt_BH2 = self.df["tilt_BH2"] + tilt_BH1 = self.df["S1_spin_orbit_tilt"] + tilt_BH2 = self.df["S2_spin_orbit_tilt"] values = self.chi_eff(self.df["S1_mass"], self.df["S2_mass"], self.df["S1_spin"], diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index 9663ebafc2..59523e2945 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -2584,7 +2584,7 @@ def __call__(self, *args, **kwargs): def convert_metallicity_to_string(Z): """Check if metallicity is supported by POSYDON v2.""" # check supported metallicity - valid_Z = [1e+00,1e-01,1e-02,1e-03,1e-04] + valid_Z = [2e+00,1e+00,4.5e-01,2e-01,1e-01,1e-02,1e-03,1e-04] if not Z in valid_Z: raise ValueError(f'Metallicity {Z} not supported! Available metallicities in POSYDON v2 are {valid_Z}.') - return f'{Z:1.0e}' + return f'{Z:1.1e}'.replace('.0','') diff --git a/posydon/visualization/combine_TF.py b/posydon/visualization/combine_TF.py index 588b77e52c..db7227b57c 100644 --- a/posydon/visualization/combine_TF.py +++ b/posydon/visualization/combine_TF.py @@ -11,6 +11,8 @@ import numpy as np +# TODO: rename the varaible, these are termination flags that indicates +# the star has reached the end of the evolution TF1_POOL_STABLE = ['Primary has depleted central carbon', 'Secondary has depleted central carbon', 'Primary got stopped before central carbon depletion', diff --git a/posydon/visualization/plot2D.py b/posydon/visualization/plot2D.py index 57bb29a40f..0da16ebd3a 100644 --- a/posydon/visualization/plot2D.py +++ b/posydon/visualization/plot2D.py @@ -274,12 +274,14 @@ def __init__( "combined_TF12", "debug", "interpolation_class" - ] or 'SN_type' in termination_flag or 'state' in termination_flag: + ] or ('SN_type' in termination_flag or + 'CO_type' in termination_flag or + 'state' in termination_flag): self.all_termination_flags = False if 'SN_type' in termination_flag: self.update_markers_colors_legends('SN_type', MARKERS_COLORS_LEGENDS) - elif 'state' in termination_flag: + elif 'state' in termination_flag or 'CO_type' in termination_flag: self.update_markers_colors_legends('state', MARKERS_COLORS_LEGENDS) else: diff --git a/posydon/visualization/plot_defaults.py b/posydon/visualization/plot_defaults.py index 027fa62b53..48b4ee5e11 100644 --- a/posydon/visualization/plot_defaults.py +++ b/posydon/visualization/plot_defaults.py @@ -550,7 +550,9 @@ 'ECSN': ['o', 2, 'tab:orange', 'ECSN'], 'PPISN': - ['o', 2, 'tab:red', 'PPISN'], + ['o', 2, 'tab:pink', 'PPISN'], + 'PISN': + ['o', 2, 'tab:red', 'PISN'], 'WD': ['o', 2, 'tab:purple', 'WD'], 'None': @@ -563,6 +565,8 @@ ['o', 2, 'tab:orange', 'NS'], 'WD': ['o', 2, 'tab:purple', 'WD'], + 'PISN': + ['o', 2, 'tab:red', 'PISN'], 'None': ['o', 2, 'black', 'intial MT / unstable MT / not converged'], } From ca569b7ea90790206b28efe33702e50b78854681 Mon Sep 17 00:00:00 2001 From: kasdaglie <112660893+kasdaglie@users.noreply.github.com> Date: Thu, 29 Jun 2023 17:22:32 +0300 Subject: [PATCH 096/319] Initially single stars and merged isolated evolution (#92) * started the step_isolated files (2 is for superclass with detached) and first changes in detached and in flow * allowed for different psygrid for isoalted step, selected lists of stellar states, still need to solve absence of logR for mergers * deleted old step_isolated * made step_merging * integrated the step_merged correctly in the flow * before intorducing the option for not using log_R as a matching criteria, having the option do decide the parameters of the matching * added most merger cases, still need to solve logR avsence in matching * finished step_merged combinations * made the new disrupted_step * added spin of merged product * made step_initially_single too and changes of structure after discussion with Konstantinos * latest changes * Eirini's change,name of mist0 to match_to_single_star * making lists for each matching * changes in lambda minimize function to a more general function that can take an arbitrary list * typo * added the option of the different list of attributes in the step_merged * matching should work the same for all subclasses now * syntax errors prob fixed * fixing runtime errors in the new files * some more fixing * Some print statements * change the flowchart to not go to sterp_disurpted if one of the events is detached is called * prints * typo in flowchart * Friday's work * . * fix init orbit * disrupted bianry evolves until CC2 * step SN updated to expect a 2nd CC in an already disrupted system. Matching for postMS and maybe HeStars may still need work * matching fixing * slightly improved the verbose text of the matching * print errors * changes to match_to_single_star * generalized alternative matching * for this PR, step merged does nothing * matching happening only when needed (once) * step merged and intially single go to step end for now * testing cases of ZAMS stars not needed to match * binary fraction * minor changes in isolated or initially single * else statement, self.companion * typo * deleted the option of fidning ZAMS through logR * syntax error fixing * resolving merging issues * went back to grid_Hrich and grid_strippedHe * ini file * ini * surf_avg_omega_div_omega_crit is not always stored, so it is no longer called for and passed to solve_ivp. It is calculated in the Matt+2015 magnetic braking calculation from stellar properties now, as it is only needed there. * del grid from detached params * ini * Removed additional instances where interp1d_sec[surf_avg_omega_div_omega_crit](t - t_offset_sec) was called. * changing the random * removed superfluous commas in defining detached step properties * Adding step_isolated,step_disrupted in the STEP_NAMES_LOADING_GRIDS so the metallicity can propagate in ther kwargs * fix in ini file * I think I went the POSYDON-MESA-INLIST submodule back * changed class names according to CamelCase convention * time profiling * Corrections to the detached verbose with timestamp. * no matching for the CO star, using m0,t0 of the previously matched star * Giving additional option 1 in verbose to print only the matching parameters * printing of more timing profiling * Step 1 in cleaning detached step. * Step 2 in cleaing detached step. * Removing duplicate code in square difference function. * Deciding on which column are going to use relative distance during matching. * Deleting obsolete transform method in detached step. * Removing unnecessary transform when scaling columns in detached step. * Using stored scalers and removing some useless self.grid assignments * adding massless in the star states in flow chart * Adding massless_remant and a compact binary state list for the detached. Adding initially single stars in the step names for metallisity * fixing the primary.co in some elif * fixing the infite loop created by the flow chart * metallicity = 1 as default * in the flow, a CO+massless goes to step_end * going to step initially single only after ZAMS * pulled merged step from manos_merged_step * pulled the isolated step and the population defaults for all substeps (disrupted, initially_single, merged) * pulled step_SN, flow, and step_detached * added step_merged in steps that need a metallicity (in binarypopulations.py) * IsolatedStep changes * fixing the STAR_STATES_ALL in the flow_chart * properties_massless_remnant in common function * convert_massless_remenant in merged * properties massless in SingleStar class instead * corrected massless_remnant dictionary * fixing the state after CC1 in merged and single stars * set initial period and separation for single star equal to nan * deleting some unnecessary comments * deleting repeated dictionaries in detached * reverting the changes in MESA-INLISTS * Update step_detached.py * Update binarystar.py --------- Co-authored-by: ezapartas Co-authored-by: Eirini Kasdagli Co-authored-by: sg Co-authored-by: kkovlakas --- posydon/binary_evol/DT/step_detached.py | 109 ++-- posydon/binary_evol/DT/step_disrupted.py | 4 +- .../binary_evol/DT/step_initially_single.py | 12 +- posydon/binary_evol/DT/step_isolated.py | 17 +- posydon/binary_evol/DT/step_merged.py | 496 +++++++++++------- posydon/binary_evol/SN/step_SN.py | 15 +- posydon/binary_evol/binarystar.py | 2 +- posydon/binary_evol/flow_chart.py | 41 +- posydon/binary_evol/singlestar.py | 8 + posydon/popsyn/binarypopulation.py | 23 +- posydon/popsyn/defaults.py | 6 +- posydon/popsyn/population_params_default.ini | 5 + posydon/utils/common_functions.py | 2 +- 13 files changed, 459 insertions(+), 281 deletions(-) diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index 2894e7c61d..2295216061 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -34,7 +34,7 @@ PchipInterpolator2, convert_metallicity_to_string ) -from posydon.binary_evol.flow_chart import (STAR_STATES_CC) +from posydon.binary_evol.flow_chart import (STAR_STATES_CC, STAR_STATES_CO) import posydon.utils.constants as const @@ -67,6 +67,12 @@ 'H-rich_non_burning' ] +''' +STAR_STATES_CO = ['BH', + 'NS', + 'WD', + ] +''' DEFAULT_TRANSLATION = { "time": "time", @@ -145,10 +151,6 @@ } -# TODO: are these supposed to be the same as the keys of the previous -# dictionary? If yes... then we should use them directrly instead of -# redefining the strings here. - DEFAULT_TRANSLATED_KEYS = ( 'age', 'mass', @@ -219,6 +221,8 @@ MATCHING_WITH_RELATIVE_DIFFERENCE = ["center_he4"] + + class detached_step: """Evolve a detached binary. @@ -695,6 +699,7 @@ def posydon_attribute(list_for_matching, star): list_for_matching = self.list_for_matching_HMS elif star.state in LIST_ACCEPTABLE_STATES_FOR_postMS: list_for_matching = self.list_for_matching_postMS + elif star.state in LIST_ACCEPTABLE_STATES_FOR_HeStar: list_for_matching = self.list_for_matching_HeStar @@ -830,6 +835,7 @@ def sq_diff_function(x): x0 = get_root0( MESA_label, posydon_attribute, htrack, rs=rs) + # bnds = ([m_min_H, m_max_H], [0, None]) sol = minimize(sq_diff_function, x0, method="TNC", bounds=bnds) @@ -880,6 +886,7 @@ def sq_diff_function(x): ) / star.total_moment_of_inertia ''' + if self.verbose or self.verbose == 1: print( "matching ", star.state, @@ -916,42 +923,42 @@ def __call__(self, binary): KEYS = self.KEYS KEYS_POSITIVE = self.KEYS_POSITIVE - if binary.star_1 is None: + if binary.star_1 is None or binary.star_1.state == "massless_remnant": self.non_existent_companion = 1 - if binary.star_2 is None: + if binary.star_2 is None or binary.star_2.state == "massless_remnant": self.non_existent_companion = 2 else: # detached step of an actual binary self.non_existent_companion = 0 - # no isolated evolution, detached step of an actual binary, the primary - # in a real binary is potential compact object or the more evolved star - if self.non_existent_companion == 0: - - if (binary.star_1.state in ("BH", "NS", "WD") + if self.non_existent_companion == 0: #no isolated evolution, detached step of an actual binary + # the primary in a real binary is potential compact object, or the more evolved star + if (binary.star_1.state in STAR_STATES_CO and binary.star_2.state in STAR_STATES_H_RICH): primary = binary.star_1 secondary = binary.star_2 secondary.htrack = True primary.htrack = secondary.htrack primary.co = True - elif ((binary.star_1.state in ("BH", "NS", "WD")) and ( - binary.star_2.state in LIST_ACCEPTABLE_STATES_FOR_HeStar)): + + elif (binary.star_1.state in STAR_STATES_CO + and binary.star_2.state in LIST_ACCEPTABLE_STATES_FOR_HeStar): primary = binary.star_1 secondary = binary.star_2 secondary.htrack = False primary.htrack = secondary.htrack primary.co = True - elif (binary.star_2.state in ("BH", "NS", "WD") + + elif (binary.star_2.state in STAR_STATES_CO and binary.star_1.state in STAR_STATES_H_RICH): primary = binary.star_2 secondary = binary.star_1 secondary.htrack = True primary.htrack = secondary.htrack primary.co = True - elif (binary.star_2.state in ("BH", "NS", "WD") - and binary.star_1.state - in LIST_ACCEPTABLE_STATES_FOR_HeStar): + + elif (binary.star_2.state in STAR_STATES_CO + and binary.star_1.state in LIST_ACCEPTABLE_STATES_FOR_HeStar): primary = binary.star_2 secondary = binary.star_1 secondary.htrack = False @@ -986,6 +993,20 @@ def __call__(self, binary): secondary.htrack = False primary.htrack = False primary.co = False + elif (binary.star_1.state in STAR_STATES_CO + and binary.star_2.state + in 'massless_remnant'): + binary.state += " Thorne–Żytkow object" + if self.verbose or self.verbose == 1: + print("Formation of Thorne–Żytkow object, nothing to do further") + return + elif (binary.star_2.state in STAR_STATES_CO + and binary.star_1.state + in 'massless_remnant'): + binary.state += " Thorne–Żytkow object" + if self.verbose or self.verbose == 1: + print("Formation of Thorne–Żytkow object, nothing to do further") + return else: raise Exception("States not recognized!") @@ -1288,7 +1309,7 @@ def ev_max_time2(t, y): return t_max_pri + t_offset_pri - t # make a function to get the spin of two stars - def get_omega(star): + def get_omega(star, is_secondary = True): if (star.log_total_angular_momentum is not None and star.total_moment_of_inertia is not None and not np.isnan(star.log_total_angular_momentum) @@ -1314,14 +1335,29 @@ def get_omega(star): omega_in_rad_per_year) elif (star.surf_avg_omega_div_omega_crit is not None and not np.isnan(star.surf_avg_omega_div_omega_crit)): - omega_in_rad_per_year = ( - star.surf_avg_omega_div_omega_crit * np.sqrt( - const.standard_cgrav * star.mass * const.msol - / ((10.0 ** (star.log_R) * const.rsol) ** 3)) - * const.secyer) - # the last factor transforms it from rad/s to rad/yr - # EDIT: We assume POSYDON surf_avg_omega is provided in - # rad/yr already. + if (star.log_R is not None + and not np.isnan(star.log_R)): + omega_in_rad_per_year = ( + star.surf_avg_omega_div_omega_crit * np.sqrt( + const.standard_cgrav * star.mass * const.msol + / ((10.0 ** (star.log_R) * const.rsol) ** 3)) + * const.secyer) + # the last factor transforms it from rad/s to rad/yr + # EDIT: We assume POSYDON surf_avg_omega is provided in + # rad/yr already. + else: + if is_secondary == True: + radius_to_be_used = interp1d_sec["R"](interp1d_sec["t0"]) + mass_to_be_used = interp1d_sec["mass"](interp1d_sec["t0"]) + else: + radius_to_be_used = interp1d_pri["R"](interp1d_pri["t0"]) + mass_to_be_used = interp1d_pri["mass"](interp1d_pri["t0"]) + omega_in_rad_per_year = ( + star.surf_avg_omega_div_omega_crit * np.sqrt( + const.standard_cgrav * mass_to_be_used * const.msol + / ((radius_to_be_used * const.rsol) ** 3)) + * const.secyer) + if self.verbose and self.verbose != 1: print("calculating initial omega from " "surf_avg_omega_div_omega_crit", @@ -1350,11 +1386,11 @@ def get_omega(star): "lower times.") with np.errstate(all="ignore"): omega_in_rad_per_year_sec = get_omega(secondary) - if primary.co: - # omega of compact objects won't be used for intergration + if primary_not_normal: + # omega of compact objects or masslessremnant won't be used for intergration omega_in_rad_per_year_pri = omega_in_rad_per_year_sec elif not primary.co: - omega_in_rad_per_year_pri = get_omega(primary) + omega_in_rad_per_year_pri = get_omega(primary,is_secondary = False) t_before_ODEsolution = time.time() try: @@ -1578,7 +1614,7 @@ def get_omega(star): current = interp1d_pri["omega"][-1] / const.secyer history = interp1d_pri["omega"][:-1] / const.secyer elif key in ["rl_relative_overflow_1"] and obj == binary: - if binary.star_1.state in ("BH", "NS", "WD"): + if binary.star_1.state in ("BH", "NS", "WD","massless_remnant"): current = None history = [current] * len(t[:-1]) elif secondary == binary.star_1: @@ -1596,7 +1632,7 @@ def get_omega(star): [interp1d_sec["sep"][:-1], interp1d_sec["ecc"][:-1]]) elif key in ["rl_relative_overflow_2"] and obj == binary: - if binary.star_2.state in ("BH", "NS", "WD"): + if binary.star_2.state in ("BH", "NS", "WD","massless_remnant"): current = None history = [current] * len(t[:-1]) elif secondary == binary.star_2: @@ -1790,15 +1826,18 @@ def get_omega(star): secondary.state_history[timestep] = check_state_of_star( secondary, i=timestep, star_CO=False) - if primary.co: + + if primary.state == "massless_remnant": + pass + + elif primary.co: mdot_acc = np.atleast_1d(bondi_hoyle( binary, primary, secondary, slice(-len(t), None), wind_disk_criteria=True, scheme='Kudritzki+2000')) primary.lg_mdot = np.log10(mdot_acc.item(-1)) primary.lg_mdot_history[len(primary.lg_mdot_history) - len(t) + 1:] = np.log10(mdot_acc[:-1]) - - elif not primary.co: + else: primary.state = check_state_of_star(primary, star_CO=False) for timestep in range(-len(t[:-1]), 0): diff --git a/posydon/binary_evol/DT/step_disrupted.py b/posydon/binary_evol/DT/step_disrupted.py index 8aac06b1d6..0adf47a765 100644 --- a/posydon/binary_evol/DT/step_disrupted.py +++ b/posydon/binary_evol/DT/step_disrupted.py @@ -31,7 +31,7 @@ from posydon.binary_evol.flow_chart import (STAR_STATES_CC) import posydon.utils.constants as const from posydon.binary_evol.DT.step_detached import detached_step -from posydon.binary_evol.DT.step_isolated import isolated_step +from posydon.binary_evol.DT.step_isolated import IsolatedStep import warnings @@ -53,7 +53,7 @@ LIST_ACCEPTABLE_STATES_FOR_POSTHeMS = STAR_STATES_HE_RICH.copy() [LIST_ACCEPTABLE_STATES_FOR_POSTHeMS.remove(x) for x in LIST_ACCEPTABLE_STATES_FOR_HeMS] -class DisruptedStep(isolated_step): +class DisruptedStep(IsolatedStep): """ Prepare a runaway star to do an an isolated_step) """ diff --git a/posydon/binary_evol/DT/step_initially_single.py b/posydon/binary_evol/DT/step_initially_single.py index e1baa5cf27..1d2022bec1 100644 --- a/posydon/binary_evol/DT/step_initially_single.py +++ b/posydon/binary_evol/DT/step_initially_single.py @@ -31,7 +31,7 @@ from posydon.binary_evol.flow_chart import (STAR_STATES_CC) import posydon.utils.constants as const from posydon.binary_evol.DT.step_detached import detached_step -from posydon.binary_evol.DT.step_isolated import isolated_step +from posydon.binary_evol.DT.step_isolated import IsolatedStep import warnings @@ -53,7 +53,7 @@ LIST_ACCEPTABLE_STATES_FOR_POSTHeMS = STAR_STATES_HE_RICH.copy() [LIST_ACCEPTABLE_STATES_FOR_POSTHeMS.remove(x) for x in LIST_ACCEPTABLE_STATES_FOR_HeMS] -class InitiallySingleStep(isolated_step): +class InitiallySingleStep(IsolatedStep): """ Prepare a runaway star to do an an isolated_step) """ @@ -63,7 +63,7 @@ def __init__(self, grid_name_strippedHe=None, path=PATH_TO_POSYDON_DATA, *args, **kwargs): - + super().__init__( grid_name_Hrich=grid_name_Hrich, grid_name_strippedHe=grid_name_strippedHe, @@ -72,11 +72,9 @@ def __init__(self, def __call__(self,binary): - if binary.state == "initially_single_star": - binary.star_2 = None - else: + if binary.state != "initially_single_star": raise ValueError("sent to InitiallySingleStep without the binary.state being initially_single_star") - + binary.event == None super().__call__(binary) diff --git a/posydon/binary_evol/DT/step_isolated.py b/posydon/binary_evol/DT/step_isolated.py index afa2002027..0b80dea2d1 100644 --- a/posydon/binary_evol/DT/step_isolated.py +++ b/posydon/binary_evol/DT/step_isolated.py @@ -33,7 +33,7 @@ from posydon.binary_evol.DT.step_detached import detached_step -class isolated_step(detached_step): +class IsolatedStep(detached_step): """Evolve an isolated star (a single star, a merger product, a runaway star, etc.) The star will be matched in the beginning of the step and will be evolved @@ -77,14 +77,19 @@ def __call__(self, binary): """ self.initialize_isolated_binary_orbit(binary) - - if binary.state == 'initially_single_star' or binary.star_1 == None or binary.star_2 == None: - #if binary.star_1 == None or binary.star_2 == None: # already one star became None in step_merged or step_initially_single + + if binary.state == 'initially_single_star' or binary.state == 'merged': pass + ''' + if binary.star_1.state.state == 'massless_remnant' or binary.star_2.state == 'massless_remnant': + pass + else: + raise ValueError("In merged or initially single stars, step one of the two stars should be 'massless_remnant' ") + ''' elif binary.state == "disrupted": pass else: - raise ValueError("In isolated step one of the two stars should be None or the the binary.state=='disrupted' ") + raise ValueError("In isolated step binary.state=='disrupted' or 'initially_single_star' or 'merged' ") super().__call__(binary) @@ -95,7 +100,7 @@ def initialize_isolated_binary_orbit(self,binary): # I give values to the orbital parameters so that the detached step will not complain binary.orbital_period = 10.**99 binary.eccentricity = 0.0 - binary.separation = orbital_separation_from_period(binary.orbital_period, 1.,1.) + binary.separation = orbital_separation_from_period(binary.orbital_period, binary.star_1.mass, binary.star_2.mass) def re_erase_isolated_binary_orbit(self,binary): # I give values to the orbital parameters so that the detached step will not complain diff --git a/posydon/binary_evol/DT/step_merged.py b/posydon/binary_evol/DT/step_merged.py index 3cefda9267..21a1da9bc5 100644 --- a/posydon/binary_evol/DT/step_merged.py +++ b/posydon/binary_evol/DT/step_merged.py @@ -18,6 +18,7 @@ from posydon.utils.data_download import PATH_TO_POSYDON_DATA from posydon.binary_evol.binarystar import BINARYPROPERTIES from posydon.binary_evol.singlestar import STARPROPERTIES +from posydon.binary_evol.singlestar import properties_massless_remnant from posydon.interpolation import GRIDInterpolator from posydon.interpolation.data_scaling import DataScaler from posydon.utils.common_functions import ( @@ -31,7 +32,7 @@ from posydon.binary_evol.flow_chart import (STAR_STATES_CC) import posydon.utils.constants as const from posydon.binary_evol.DT.step_detached import detached_step -from posydon.binary_evol.DT.step_isolated import isolated_step +from posydon.binary_evol.DT.step_isolated import IsolatedStep import warnings @@ -42,8 +43,6 @@ STAR_STATES_NOT_CO ) - - LIST_ACCEPTABLE_STATES_FOR_HMS = ["H-rich_Core_H_burning"] LIST_ACCEPTABLE_STATES_FOR_HeMS = ["stripped_He_Core_He_burning"] @@ -54,10 +53,17 @@ [LIST_ACCEPTABLE_STATES_FOR_POSTHeMS.remove(x) for x in LIST_ACCEPTABLE_STATES_FOR_HeMS] +def convert_star_to_massless_remnant(star): + for key in STARPROPERTIES: + setattr(star, key, properties_massless_remnant()[key]) + return star + + -class MergedStep(isolated_step): + +class MergedStep(IsolatedStep): """ - Prepare a merging star to do an an isolated_step) + Prepare a merging star to do an an IsolatedStep """ def __init__( @@ -67,22 +73,34 @@ def __init__( path=PATH_TO_POSYDON_DATA, merger_critical_rot = 0.4, rel_mass_lost_HMS_HMS = 0.1, - list_for_matching_HMS = [["mass", "center_h1", "log_R", "he_core_mass"], - [20.0, 1.0, 2.0, 10.0], - # [[m_min_H, m_max_H], [0, None]], - ["log_min_max" , "min_max", "min_max", "min_max"] ], - list_for_matching_postMS = [["mass", "center_he4", "he_core_mass"], - [20.0, 1.0, 10.0], - #[[m_min_H, m_max_H], [0, None]], - ["log_min_max" , "min_max", "min_max"] ], - list_for_matching_HeStar = [["he_core_mass", "center_he4"], - [10.0, 1.0], - #[[m_min_He, m_max_He], [0, None]], - ["min_max" , "min_max"] ], + list_for_matching_HMS = [ + ["mass", "center_h1", "he_core_mass"], + [20.0, 1.0, 10.0], + ["log_min_max", "min_max", "min_max"], + #[m_min_H, m_max_H], [0, None] + [None, None], [0, None] + ], + list_for_matching_postMS = [ + ["mass", "center_he4", "he_core_mass"], + [20.0, 1.0, 10.0], + ["log_min_max", "min_max", "min_max"], + #[m_min_H, m_max_H], [0, None] + [None, None], [0, None] + ], + list_for_matching_HeStar = [ + ["he_core_mass", "center_he4"], + [10.0, 1.0], + ["min_max" , "min_max"], + #[[m_min_He, m_max_He], [0, None]], + [None, None], [0, None] + ], *args, **kwargs ): + self.merger_critical_rot = merger_critical_rot + self.rel_mass_lost_HMS_HMS = rel_mass_lost_HMS_HMS + super().__init__( grid_name_Hrich=grid_name_Hrich, grid_name_strippedHe=grid_name_strippedHe, @@ -91,92 +109,160 @@ def __init__( list_for_matching_HeStar = list_for_matching_HeStar, *args, **kwargs) - ''' - def merged_star_properties(star_base,comp): - """ - Make assumptions about the core/total mass of the star of a merged product. + + + def __call__(self,binary): + merged_star_properties = self.merged_star_properties + if self.verbose: + print("Before Merger", binary.star_1.state,binary.star_2.state,binary.state, binary.event) + print("M1 , M2, he_core_mass1, he_core_mass2: ", binary.star_1.mass,binary.star_2.mass, binary.star_1.he_core_mass, binary.star_2.he_core_mass) + print("star_1.center_he4, star_2.center_he4, star_1.surface_he4, star_2.surface_he4: ", binary.star_1.center_he4,binary.star_2.center_he4, binary.star_1.surface_he4,binary.star_2.surface_he4) + if binary.state == "merged": + if binary.event == 'oMerging1': + binary.star_1,binary.star_2 = merged_star_properties(binary.star_1,binary.star_2) + elif binary.event == 'oMerging2': + binary.star_2,binary.star_1 = merged_star_properties(binary.star_2,binary.star_1) + else: + raise ValueError("binary.state='merged' but binary.event != 'oMerging1/2'") + else: + raise ValueError("step_merging initiated but binary.state != 'merged'") + + binary.event = None + if self.verbose: + print("After Merger", binary.star_1.state,binary.star_2.state,binary.state, binary.event) + print("M_merged , he_core_mass merged: ", binary.star_1.mass, binary.star_1.he_core_mass) + print("star_1.center_he4, star_1.surface_he4: ", binary.star_1.center_he4, binary.star_1.surface_he4) + + super().__call__(binary) + + def merged_star_properties(self,star_base,comp): + """ + Make assumptions about the core/total mass, and abundances of the star of a merged product. Similar to the table of merging in BSE star_base: Single Star - is our base star that engulfs its companions. The merged star will have this star as a base + is our base star that engulfs its companion. The merged star will have this star as a base comp: Single Star is the star that is engulfed - """ + """ #by default the stellar attributes that keep the same value from the + #merged_star = copy.copy(star_base) merged_star = star_base s1 = star_base.state s2 = comp.state - - - def mass_weighted_avg(star1=star_base,star2=comp, abundance_name="center_h1", mass_weight_name="mass"): + def mass_weighted_avg(star1=star_base,star2=comp, abundance_name="center_h1", mass_weight1="mass", mass_weight2=None): A1 = getattr(star1, abundance_name) A2 = getattr(star2, abundance_name) - M1 = getattr(star1, mass_weight_name) - M2 = getattr(star2, mass_weight_name) + + if mass_weight1 == "H-rich_envelope_mass": + M1 = getattr(star1, "mass") - getattr(star1, "he_core_mass") + elif mass_weight1 == "He-rich_envelope_mass": + M1 = getattr(star1, "he_core_mass") - getattr(star1, "co_core_mass") + else: + M1 = getattr(star1, mass_weight1) + + if mass_weight2 is None: + mass_weight2 = mass_weight1 + if mass_weight2 == "H-rich_envelope_mass": + M2 = getattr(star2, "mass") - getattr(star2, "he_core_mass") + elif mass_weight2 == "He-rich_envelope_mass": + M2 = getattr(star2, "he_core_mass") - getattr(star2, "co_core_mass") + else: + M2 = getattr(star2, mass_weight2) return (A1*M1 + A2*M2 ) / (M1+M2) - if ( s1 is in LIST_ACCEPTABLE_STATES_FOR_HMS - and s2 is in LIST_ACCEPTABLE_STATES_FOR_HMS): + # MS + MS + if ( s1 in LIST_ACCEPTABLE_STATES_FOR_HMS + and s2 in LIST_ACCEPTABLE_STATES_FOR_HMS): #these stellar attributes change value - merged_star.mass = (star_base.mass + comp.mass) * (1.-rel_mass_lost_HMS_HMS) + merged_star.mass = (star_base.mass + comp.mass) * (1.-self.rel_mass_lost_HMS_HMS) + #TODO for key in ["center_h1", "center_he4", "center_c12", "center_n14","center_o16"]: merged_star.center_h1 = mass_weighted_avg() merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4") merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12") merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14") merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16") - #TODO: should I check if the abundaces above end up in ~1 (?) + # weigheted mixing on the surface abundances + merged_star.surface_h1 = mass_weighted_avg(abundance_name = "surface_h1") + merged_star.surface_he4 = mass_weighted_avg(abundance_name = "surface_he4") + merged_star.surface_c12 = mass_weighted_avg(abundance_name = "surface_c12") + merged_star.surface_n14 = mass_weighted_avg(abundance_name = "surface_n14") + merged_star.surface_o16 = mass_weighted_avg(abundance_name = "surface_o16") + for key in STARPROPERTIES: # these stellar attributes become np.nan - for substring in ["log_", "lg_", "surface","surf_", "conv_", "lambda", "profile"]: + for substring in ["log_", "lg_", "surf_", "conv_", "lambda", "profile"]: if (substring in key) : setattr(merged_star, key, np.nan) if key in [ "c12_c12", "center_gamma", "avg_c_in_c_core", "total_moment_of_inertia", "spin", "envelope_binding_energy"]: setattr(merged_star, key, np.nan) + massless_remnant = convert_star_to_massless_remnant(comp) + #postMS + MS + elif (s1 in LIST_ACCEPTABLE_STATES_FOR_POSTMS + and s2 in LIST_ACCEPTABLE_STATES_FOR_HMS): - elif (s1 is in LIST_ACCEPTABLE_STATES_FOR_POSTMS: - and s2 is in LIST_ACCEPTABLE_STATES_FOR_HMS): + merged_star.mass = star_base.mass + comp.mass #TODO: in step_CEE we need to eject part of the (common) envelope - merged_star.mass = star_base.mass + comp.mass + # weigheted mixing on the surface abundances of the whole comp with the envelope of star_base + merged_star.surface_h1 = mass_weighted_avg(abundance_name = "surface_h1", mass_weight1="H-rich_envelope_mass", mass_weight2="mass") + merged_star.surface_he4 = mass_weighted_avg(abundance_name = "surface_he4", mass_weight1="H-rich_envelope_mass", mass_weight2="mass") + merged_star.surface_c12 = mass_weighted_avg(abundance_name = "surface_c12", mass_weight1="H-rich_envelope_mass", mass_weight2="mass") + merged_star.surface_n14 = mass_weighted_avg(abundance_name = "surface_n14", mass_weight1="H-rich_envelope_mass", mass_weight2="mass") + merged_star.surface_o16 = mass_weighted_avg(abundance_name = "surface_o16", mass_weight1="H-rich_envelope_mass", mass_weight2="mass") for key in STARPROPERTIES: # these stellar attributes become np.nan - for substring in ["log_", "lg_", "surface","surf_", "conv_", "lambda", "profile"]: + for substring in ["log_", "lg_", "surf_", "conv_", "lambda", "profile"]: if (substring in key) : setattr(merged_star, key, np.nan) if key in [ "c12_c12", "center_gamma", "avg_c_in_c_core", "total_moment_of_inertia", "spin"]: setattr(merged_star, key, np.nan) + merged_star.log_LHe = star_base.log_LHe + merged_star.log_LZ = star_base.log_LZ + merged_star.state = check_state_of_star(merged_star, star_CO=False) # TODO for sure this needs testing! + massless_remnant = convert_star_to_massless_remnant(comp) # as above but opposite stars - elif (s1 is in LIST_ACCEPTABLE_STATES_FOR_HMS: - and s2 is in LIST_ACCEPTABLE_STATES_FOR_POSTMS): + elif (s1 in LIST_ACCEPTABLE_STATES_FOR_HMS + and s2 in LIST_ACCEPTABLE_STATES_FOR_POSTMS): merged_star = comp merged_star.mass = star_base.mass + comp.mass + merged_star.surface_h1 = mass_weighted_avg(abundance_name = "surface_h1", mass_weight2="H-rich_envelope_mass", mass_weight1="mass") + merged_star.surface_he4 = mass_weighted_avg(abundance_name = "surface_he4", mass_weight2="H-rich_envelope_mass", mass_weight1="mass") + merged_star.surface_c12 = mass_weighted_avg(abundance_name = "surface_c12", mass_weight2="H-rich_envelope_mass", mass_weight1="mass") + merged_star.surface_n14 = mass_weighted_avg(abundance_name = "surface_n14", mass_weight2="H-rich_envelope_mass", mass_weight1="mass") + merged_star.surface_o16 = mass_weighted_avg(abundance_name = "surface_o16", mass_weight2="H-rich_envelope_mass", mass_weight1="mass") + for key in STARPROPERTIES: # these stellar attributes become np.nan - for substring in ["log_", "lg_", "surface","surf_", "conv_", "lambda", "profile"]: + for substring in ["log_", "lg_", "surf_", "conv_", "lambda", "profile"]: if (substring in key) : setattr(merged_star, key, np.nan) if key in [ "c12_c12", "center_gamma", "avg_c_in_c_core", "total_moment_of_inertia", "spin"]: setattr(merged_star, key, np.nan) - merged_star.state = check_state_of_star(merged_star, star_CO=False) # TODO for sure this needs testing! + merged_star.log_LHe = comp.log_LHe + merged_star.log_LZ = comp.log_LZ + merged_star.state = check_state_of_star(merged_star, star_CO=False) # TODO for sure this needs testing! + massless_remnant = convert_star_to_massless_remnant(star_base) - elif (s1 is in LIST_ACCEPTABLE_STATES_FOR_POSTMS: - and s2 is in LIST_ACCEPTABLE_STATES_FOR_POSTMS): + #postMS + postMS + elif (s1 in LIST_ACCEPTABLE_STATES_FOR_POSTMS + and s2 in LIST_ACCEPTABLE_STATES_FOR_POSTMS): # add total and core masses for key in ["mass", "he_core_mass", "c_core_mass", "o_core_mass", "co_core_mass"]: @@ -185,13 +271,13 @@ def mass_weighted_avg(star1=star_base,star2=comp, abundance_name="center_h1", ma # weighted central abundances if merging cores. Else only from star_base if star_base.co_core_mass == 0 and comp.co_core_mass == 0: # two stars with Helium cores - merged_star.center_h1 = mass_weighted_avg(mass_weight_name="he_core_mass") - merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight_name="he_core_mass") - merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight_name="he_core_mass") - merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight_name="he_core_mass") - merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight_name="he_core_mass") + merged_star.center_h1 = mass_weighted_avg(mass_weight1="he_core_mass") + merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight1="he_core_mass") + merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight1="he_core_mass") + merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight1="he_core_mass") + merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight1="he_core_mass") elif (star_base.co_core_mass > 0 and comp.co_core_mass == 0): # star_base with CO core and the comp has a He core - continue # the central abundances are kept as the ones of star_base + pass # the central abundances are kept as the ones of star_base elif (comp.co_core_mass > 0 and star_base.co_core_mass == 0): # comp with CO core and the star_base has a He core merged_star.center_h1 = comp.center_h1 merged_star.center_he4 = comp.center_he4 @@ -199,45 +285,68 @@ def mass_weighted_avg(star1=star_base,star2=comp, abundance_name="center_h1", ma merged_star.center_n14 = comp.center_n14 merged_star.center_o16 = comp.center_o16 elif (star_base.co_core_mass > 0 and comp.co_core_mass > 0): - merged_star.center_h1 = mass_weighted_avg(mass_weight_name="he_core_mass") - merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight_name="he_core_mass") - merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight_name="he_core_mass") - merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight_name="he_core_mass") - merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight_name="he_core_mass") + merged_star.center_h1 = mass_weighted_avg(mass_weight1="co_core_mass") #TODO : maybe he_core_mass makes more sense? + merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight1="co_core_mass") + merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight1="co_core_mass") + merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight1="co_core_mass") + merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight1="co_core_mass") else: warnings.warn("weird compbination of CO core masses during merging") + # weigheted mixing on the surface abundances based on the envelopes of the two stars + merged_star.surface_h1 = mass_weighted_avg(abundance_name = "surface_h1", mass_weight1="H-rich_envelope_mass", mass_weight2="H-rich_envelope_mass") + merged_star.surface_he4 = mass_weighted_avg(abundance_name = "surface_he4", mass_weight1="H-rich_envelope_mass", mass_weight2="H-rich_envelope_mass") + merged_star.surface_c12 = mass_weighted_avg(abundance_name = "surface_c12", mass_weight1="H-rich_envelope_mass", mass_weight2="H-rich_envelope_mass") + merged_star.surface_n14 = mass_weighted_avg(abundance_name = "surface_n14", mass_weight1="H-rich_envelope_mass", mass_weight2="H-rich_envelope_mass") + merged_star.surface_o16 = mass_weighted_avg(abundance_name = "surface_o16", mass_weight1="H-rich_envelope_mass", mass_weight2="H-rich_envelope_mass") + + for key in STARPROPERTIES: # these stellar attributes become np.nan - for substring in ["log_", "lg_", "surface","surf_", "conv_", "lambda", "profile"]: + for substring in ["log_", "lg_", "surf_", "conv_", "lambda", "profile"]: if (substring in key) : setattr(merged_star, key, np.nan) if key in [ "c12_c12", "center_gamma", "avg_c_in_c_core", "total_moment_of_inertia", "spin"]: setattr(merged_star, key, np.nan) merged_star.state = check_state_of_star(merged_star, star_CO=False) # TODO for sure this needs testing! + massless_remnant = convert_star_to_massless_remnant(comp) - elif (s1 is in LIST_ACCEPTABLE_STATES_FOR_POSTMS: - and s2 is in LIST_ACCEPTABLE_STATES_FOR_HeMS): + #postMS + HeMSStar + elif (s1 in LIST_ACCEPTABLE_STATES_FOR_POSTMS + and s2 in LIST_ACCEPTABLE_STATES_FOR_HeMS): # add total and core masses for key in ["mass", "he_core_mass", "c_core_mass", "o_core_mass", "co_core_mass"]: current = getattr(merged_star, key) + getattr(comp, key) setattr(merged_star, key,current) + # weighted central abundances if merging cores. Else only from star_base + if star_base.co_core_mass == 0 and comp.co_core_mass == 0: # two stars with only Helium cores, not CO cores + merged_star.center_h1 = mass_weighted_avg(mass_weight1="he_core_mass") + merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight1="he_core_mass") + merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight1="he_core_mass") + merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight1="he_core_mass") + merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight1="he_core_mass") + elif (star_base.co_core_mass > 0 and comp.co_core_mass == 0): # star_base with CO core and the comp has just a He core (is a HeMS star) + pass # the central abundances are kept as the ones of star_base + else: + warnings.warn("weird compbination of CO core masses during merging") + for key in STARPROPERTIES: # these stellar attributes become np.nan - for substring in ["log_", "lg_", "surface","surf_", "conv_", "lambda", "profile"]: + for substring in ["log_", "lg_", "surf_", "conv_", "lambda", "profile"]: if (substring in key) : setattr(merged_star, key, np.nan) if key in [ "c12_c12", "center_gamma", "avg_c_in_c_core", "total_moment_of_inertia", "spin"]: setattr(merged_star, key, np.nan) merged_star.state = check_state_of_star(merged_star, star_CO=False) # TODO for sure this needs testing! + massless_remnant = convert_star_to_massless_remnant(comp) # as above but opposite stars - elif (s1 is in LIST_ACCEPTABLE_STATES_FOR_HeMS : - and s2 is in LIST_ACCEPTABLE_STATES_FOR_POSTMS): + elif (s1 in LIST_ACCEPTABLE_STATES_FOR_HeMS + and s2 in LIST_ACCEPTABLE_STATES_FOR_POSTMS): merged_star = comp @@ -246,18 +355,32 @@ def mass_weighted_avg(star1=star_base,star2=comp, abundance_name="center_h1", ma current = getattr(merged_star, key) + getattr(star_base, key) setattr(merged_star, key,current) + # weighted central abundances if merging cores. Else only from star_base + if star_base.co_core_mass == 0 and comp.co_core_mass == 0: # two stars with only Helium cores, not CO cores + merged_star.center_h1 = mass_weighted_avg(mass_weight1="he_core_mass") + merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight1="he_core_mass") + merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight1="he_core_mass") + merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight1="he_core_mass") + merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight1="he_core_mass") + elif (star_base.co_core_mass == 0 and comp.co_core_mass > 0): # star_base is the HeMS Star and comp has a CO core + pass # the central abundances are kept as the ones of star_base + else: + warnings.warn("weird compbination of CO core masses during merging") + for key in STARPROPERTIES: # these stellar attributes become np.nan - for substring in ["log_", "lg_", "surface","surf_", "conv_", "lambda", "profile"]: + for substring in ["log_", "lg_", "surf_", "conv_", "lambda", "profile"]: if (substring in key) : setattr(merged_star, key, np.nan) if key in [ "c12_c12", "center_gamma", "avg_c_in_c_core", "total_moment_of_inertia", "spin"]: setattr(merged_star, key, np.nan) merged_star.state = check_state_of_star(merged_star, star_CO=False) # TODO for sure this needs testing! + massless_remnant = convert_star_to_massless_remnant(star_base) - elif (s1 is in LIST_ACCEPTABLE_STATES_FOR_POSTMS: - and s2 is in LIST_ACCEPTABLE_STATES_FOR_POSTHeMS): + #postMS + HeStar that is not in HeMS + elif (s1 in LIST_ACCEPTABLE_STATES_FOR_POSTMS + and s2 in LIST_ACCEPTABLE_STATES_FOR_POSTHeMS): # add total and core masses for key in ["mass", "he_core_mass", "c_core_mass", "o_core_mass", "co_core_mass"]: @@ -266,13 +389,13 @@ def mass_weighted_avg(star1=star_base,star2=comp, abundance_name="center_h1", ma # weighted central abundances if merging cores. Else only from star_base if star_base.co_core_mass == 0 and comp.co_core_mass == 0: # two stars with Helium cores - merged_star.center_h1 = mass_weighted_avg(mass_weight_name="he_core_mass") - merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight_name="he_core_mass") - merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight_name="he_core_mass") - merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight_name="he_core_mass") - merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight_name="he_core_mass") + merged_star.center_h1 = mass_weighted_avg(mass_weight1="he_core_mass") + merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight1="he_core_mass") + merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight1="he_core_mass") + merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight1="he_core_mass") + merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight1="he_core_mass") elif (star_base.co_core_mass > 0 and comp.co_core_mass == 0): # star_base with CO core and the comp has a He core - continue # the central abundances are kept as the ones of star_base + pass # the central abundances are kept as the ones of star_base elif (comp.co_core_mass > 0 and star_base.co_core_mass == 0): # comp with CO core and the star_base has a He core merged_star.center_h1 = comp.center_h1 merged_star.center_he4 = comp.center_he4 @@ -280,27 +403,28 @@ def mass_weighted_avg(star1=star_base,star2=comp, abundance_name="center_h1", ma merged_star.center_n14 = comp.center_n14 merged_star.center_o16 = comp.center_o16 elif (star_base.co_core_mass > 0 and comp.co_core_mass > 0): - merged_star.center_h1 = mass_weighted_avg(mass_weight_name="co_core_mass") - merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight_name="co_core_mass") - merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight_name="co_core_mass") - merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight_name="co_core_mass") - merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight_name="co_core_mass") + merged_star.center_h1 = mass_weighted_avg(mass_weight1="co_core_mass") + merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight1="co_core_mass") + merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight1="co_core_mass") + merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight1="co_core_mass") + merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight1="co_core_mass") else: warnings.warn("weird compbination of CO core masses during merging") for key in STARPROPERTIES: # these stellar attributes become np.nan - for substring in ["log_", "lg_", "surface","surf_", "conv_", "lambda", "profile"]: + for substring in ["log_", "lg_", "surf_", "conv_", "lambda", "profile"]: if (substring in key) : setattr(merged_star, key, np.nan) if key in [ "c12_c12", "center_gamma", "avg_c_in_c_core", "total_moment_of_inertia", "spin"]: setattr(merged_star, key, np.nan) merged_star.state = check_state_of_star(merged_star, star_CO=False) # TODO for sure this needs testing! + massless_remnant = convert_star_to_massless_remnant(comp) # as above but the opposite stars - elif (s1 is in LIST_ACCEPTABLE_STATES_FOR_POSTHeMS: - and s2 is in LIST_ACCEPTABLE_STATES_FOR_POSTMS): + elif (s1 in LIST_ACCEPTABLE_STATES_FOR_POSTHeMS + and s2 in LIST_ACCEPTABLE_STATES_FOR_POSTMS): merged_star = comp # add total and core masses @@ -310,13 +434,13 @@ def mass_weighted_avg(star1=star_base,star2=comp, abundance_name="center_h1", ma # weighted central abundances if merging cores. Else only from star_base if star_base.co_core_mass == 0 and comp.co_core_mass == 0: # two stars with Helium cores - merged_star.center_h1 = mass_weighted_avg(mass_weight_name="he_core_mass") - merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight_name="he_core_mass") - merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight_name="he_core_mass") - merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight_name="he_core_mass") - merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight_name="he_core_mass") + merged_star.center_h1 = mass_weighted_avg(mass_weight1="he_core_mass") + merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight1="he_core_mass") + merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight1="he_core_mass") + merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight1="he_core_mass") + merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight1="he_core_mass") elif (star_base.co_core_mass > 0 and comp.co_core_mass == 0): # star_base with CO core and the comp has a He core - continue # the central abundances are kept as the ones of star_base + pass # the central abundances are kept as the ones of star_base elif (comp.co_core_mass > 0 and star_base.co_core_mass == 0): # comp with CO core and the star_base has a He core merged_star.center_h1 = comp.center_h1 merged_star.center_he4 = comp.center_he4 @@ -324,130 +448,124 @@ def mass_weighted_avg(star1=star_base,star2=comp, abundance_name="center_h1", ma merged_star.center_n14 = comp.center_n14 merged_star.center_o16 = comp.center_o16 elif (star_base.co_core_mass > 0 and comp.co_core_mass > 0): - merged_star.center_h1 = mass_weighted_avg(mass_weight_name="co_core_mass") - merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight_name="co_core_mass") - merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight_name="co_core_mass") - merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight_name="co_core_mass") - merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight_name="co_core_mass") + merged_star.center_h1 = mass_weighted_avg(mass_weight1="co_core_mass") + merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight1="co_core_mass") + merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight1="co_core_mass") + merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight1="co_core_mass") + merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight1="co_core_mass") else: warnings.warn("weird compbination of CO core masses during merging") for key in STARPROPERTIES: # these stellar attributes become np.nan - for substring in ["log_", "lg_", "surface","surf_", "conv_", "lambda", "profile"]: + for substring in ["log_", "lg_", "surf_", "conv_", "lambda", "profile"]: if (substring in key) : setattr(merged_star, key, np.nan) if key in [ "c12_c12", "center_gamma", "avg_c_in_c_core", "total_moment_of_inertia", "spin"]: setattr(merged_star, key, np.nan) merged_star.state = check_state_of_star(merged_star, star_CO=False) # TODO for sure this needs testing! + massless_remnant = convert_star_to_massless_remnant(star_base) + # HeStar + HeStar + elif (s1 in STAR_STATES_HE_RICH + and s2 in STAR_STATES_HE_RICH): - elif (s1 is in STAR_STATES_HE_RICH: - and s2 is in STAR_STATES_HE_RICH): - - # add total and core masses - for key in ["mass", "he_core_mass", "c_core_mass", "o_core_mass", "co_core_mass"]: - current = getattr(merged_star, key) + getattr(comp, key) - setattr(merged_star, key,current) - - # weighted central abundances if merging cores. Else only from star_base - if star_base.co_core_mass == 0 and comp.co_core_mass == 0: # two stars with Helium cores - merged_star.center_h1 = mass_weighted_avg(mass_weight_name="he_core_mass") - merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight_name="he_core_mass") - merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight_name="he_core_mass") - merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight_name="he_core_mass") - merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight_name="he_core_mass") - elif (star_base.co_core_mass > 0 and comp.co_core_mass == 0): # star_base with CO core and the comp has a He core - continue # the central abundances are kept as the ones of star_base - elif (comp.co_core_mass > 0 and star_base.co_core_mass == 0): # comp with CO core and the star_base has a He core - merged_star.center_h1 = comp.center_h1 - merged_star.center_he4 = comp.center_he4 - merged_star.center_c12 = comp.center_c12 - merged_star.center_n14 = comp.center_n14 - merged_star.center_o16 = comp.center_o16 - elif (star_base.co_core_mass > 0 and comp.co_core_mass > 0): - merged_star.center_h1 = mass_weighted_avg(mass_weight_name="co_core_mass") - merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight_name="co_core_mass") - merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight_name="co_core_mass") - merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight_name="co_core_mass") - merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight_name="co_core_mass") - else: - warnings.warn("weird compbination of CO core masses during merging") - - for key in STARPROPERTIES: - # these stellar attributes become np.nan - for substring in ["log_", "lg_", "surface","surf_", "conv_", "lambda", "profile"]: - if (substring in key) : - setattr(merged_star, key, np.nan) - if key in [ "c12_c12", "center_gamma", - "avg_c_in_c_core", "total_moment_of_inertia", "spin"]: - setattr(merged_star, key, np.nan) - merged_star.state = check_state_of_star(merged_star, star_CO=False) # TODO for sure this needs testing! - - elif (s1 is in STAR_STATES_HE_RICH: - and s2 is in ["WD"]): - - # add total and core masses - for key in ["mass", "he_core_mass", "c_core_mass", "o_core_mass", "co_core_mass"]: - current = getattr(merged_star, key) + getattr(comp, "mass") - setattr(merged_star, key,current) - - # weighted central abundances if merging cores. Else only from star_base - if (comp.co_core_mass > 0 and star_base.co_core_mass == 0): # comp with CO core and the star_base has a He core - merged_star.center_h1 = comp.center_h1 - merged_star.center_he4 = comp.center_he4 - merged_star.center_c12 = comp.center_c12 - merged_star.center_n14 = comp.center_n14 - merged_star.center_o16 = comp.center_o16 - elif (star_base.co_core_mass > 0 and comp.co_core_mass > 0): - merged_star.center_h1 = mass_weighted_avg(mass_weight_name="co_core_mass") - merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight_name="co_core_mass") - merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight_name="co_core_mass") - merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight_name="co_core_mass") - merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight_name="co_core_mass") - else: - warnings.warn("weird compbination of CO core masses during merging") - - for key in STARPROPERTIES: - # these stellar attributes become np.nan - for substring in ["log_", "lg_", "surface","surf_", "conv_", "lambda", "profile"]: - if (substring in key) : - setattr(merged_star, key, np.nan) - if key in [ "c12_c12", "center_gamma", - "avg_c_in_c_core", "total_moment_of_inertia", "spin"]: - setattr(merged_star, key, np.nan) - merged_star.state = check_state_of_star(merged_star, star_CO=False) # TODO for sure this needs testing! - - elif (s1 is in STAR_STATES_HE_RICH: - and s2 is in ["NS", "BH"]): - merged_star = comp + # add total and core masses + for key in ["mass", "he_core_mass", "c_core_mass", "o_core_mass", "co_core_mass"]: + current = getattr(merged_star, key) + getattr(comp, key) + setattr(merged_star, key,current) - else: - print("Combination of merging star states not expected: ", s1, s2) + # weighted central abundances if merging cores. Else only from star_base + if star_base.co_core_mass == 0 and comp.co_core_mass == 0: # two stars with Helium cores + merged_star.center_h1 = mass_weighted_avg(mass_weight1="he_core_mass") + merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight1="he_core_mass") + merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight1="he_core_mass") + merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight1="he_core_mass") + merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight1="he_core_mass") + elif (star_base.co_core_mass > 0 and comp.co_core_mass == 0): # star_base with CO core and the comp has a He core + pass # the central abundances are kept as the ones of star_base + elif (comp.co_core_mass > 0 and star_base.co_core_mass == 0): # comp with CO core and the star_base has a He core + merged_star.center_h1 = comp.center_h1 + merged_star.center_he4 = comp.center_he4 + merged_star.center_c12 = comp.center_c12 + merged_star.center_n14 = comp.center_n14 + merged_star.center_o16 = comp.center_o16 + elif (star_base.co_core_mass > 0 and comp.co_core_mass > 0): + merged_star.center_h1 = mass_weighted_avg(mass_weight1="co_core_mass") + merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight1="co_core_mass") + merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight1="co_core_mass") + merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight1="co_core_mass") + merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight1="co_core_mass") + else: + warnings.warn("weird compbination of CO core masses during merging") - # ad hoc spin of merged star to be used in the detached step - merged_star.surf_avg_omega_div_omega_crit = merger_critical_rot + # weigheted mixing on the surface abundances based on the He-rich envelopes of the two stars + merged_star.surface_h1 = mass_weighted_avg(abundance_name = "surface_h1", mass_weight1="He-rich_envelope_mass", mass_weight2="He-rich_envelope_mass") + merged_star.surface_he4 = mass_weighted_avg(abundance_name = "surface_he4", mass_weight1="He-rich_envelope_mass", mass_weight2="He-rich_envelope_mass") + merged_star.surface_c12 = mass_weighted_avg(abundance_name = "surface_c12", mass_weight1="He-rich_envelope_mass", mass_weight2="He-rich_envelope_mass") + merged_star.surface_n14 = mass_weighted_avg(abundance_name = "surface_n14", mass_weight1="He-rich_envelope_mass", mass_weight2="He-rich_envelope_mass") + merged_star.surface_o16 = mass_weighted_avg(abundance_name = "surface_o16", mass_weight1="He-rich_envelope_mass", mass_weight2="He-rich_envelope_mass") - return merged_star, None + for key in STARPROPERTIES: + # these stellar attributes become np.nan + for substring in ["log_", "lg_", "surf_", "conv_", "lambda", "profile"]: + if (substring in key) : + setattr(merged_star, key, np.nan) + if key in [ "c12_c12", "center_gamma", + "avg_c_in_c_core", "total_moment_of_inertia", "spin"]: + setattr(merged_star, key, np.nan) + merged_star.state = check_state_of_star(merged_star, star_CO=False) # TODO for sure this needs testing! + massless_remnant = convert_star_to_massless_remnant(comp) -''' + # Star + WD + elif (s1 in STAR_STATES_NOT_CO + and s2 in ["WD"]): - def __call__(self,binary): - # FOR THIS PR, merged step does nothing! - ''' - if binary.state == "merged": - if binary.event == 'oMerging1': - binary.star_1,binary.star_2 = merged_star_properties(binary.star_1,binary.star_2) - elif binary.event == 'oMerging2': - binary.star_2,binary.star_1 = merged_star_properties(binary.star_2,binary.star_1) - else: - raise ValueError("binary.state='merged' but binary.event != 'oMerging1/2'") + #WD is considered a stripped CO core + + # add total and core masses + for key in ["mass", "he_core_mass", "c_core_mass", "o_core_mass", "co_core_mass"]: + current = getattr(merged_star, key) + getattr(comp, "mass") + setattr(merged_star, key,current) + + # weighted central abundances if merging cores. Else only from star_base + if (comp.co_core_mass > 0 and star_base.co_core_mass == 0): # comp with CO core and the star_base has not + merged_star.center_h1 = comp.center_h1 + merged_star.center_he4 = comp.center_he4 + merged_star.center_c12 = comp.center_c12 + merged_star.center_n14 = comp.center_n14 + merged_star.center_o16 = comp.center_o16 + elif (star_base.co_core_mass > 0 and comp.co_core_mass > 0): + merged_star.center_h1 = mass_weighted_avg(mass_weight1="co_core_mass") + merged_star.center_he4 = mass_weighted_avg(abundance_name = "center_he4", mass_weight1="co_core_mass") + merged_star.center_c12 = mass_weighted_avg(abundance_name = "center_c12", mass_weight1="co_core_mass") + merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight1="co_core_mass") + merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight1="co_core_mass") + else: + warnings.warn("weird compbination of CO core masses during merging") + + for key in STARPROPERTIES: + # these stellar attributes become np.nan + for substring in ["log_", "lg_", "surf_", "conv_", "lambda", "profile"]: + if (substring in key) : + setattr(merged_star, key, np.nan) + if key in [ "c12_c12", "center_gamma", + "avg_c_in_c_core", "total_moment_of_inertia", "spin"]: + setattr(merged_star, key, np.nan) + merged_star.state = check_state_of_star(merged_star, star_CO=False) # TODO for sure this needs testing! + massless_remnant = convert_star_to_massless_remnant(comp) + + # Star + NS/BH + elif (s1 in STAR_STATES_NOT_CO + and s2 in ["NS", "BH"]): + merged_star = comp + # TODO: potentially flag a Thorne-Zytkov object + massless_remnant = convert_star_to_massless_remnant(star_base) else: - raise ValueError("step_merging initated but binary.state != 'merged'") + print("Combination of merging star states not expected: ", s1, s2) - binary.event == None + # ad hoc spin of merged star to be used in the detached step + merged_star.surf_avg_omega_div_omega_crit = self.merger_critical_rot - super().__call__(binary) - ''' - pass + return merged_star, massless_remnant diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index 93ef2c7f0d..1e3edc4efa 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -418,7 +418,7 @@ def __call__(self, binary): # Checks if the binary is not disrupted to compute the # inspiral time due to gravitational wave emission state1, state2 = binary.star_1.state, binary.star_2.state - if binary.state == "disrupted": + if binary.state == "disrupted" or state1 == "massless_remnant" or state2 == "massless_remnant": binary.inspiral_time = np.nan elif state1 in STAR_STATES_CO and state2 in STAR_STATES_CO: binary.inspiral_time = inspiral_timescale_from_separation( @@ -1210,8 +1210,9 @@ def orbital_kick(self, binary): # if key is 'nearest_neighbour_distance': # setattr(binary, key, ['None', 'None', 'None']) binary.separation = new_separation - if binary.state != "disrupted": + if binary.state != "disrupted" and binary.state != "initially_single_star" and binary.state != "merged": binary.state = "detached" + binary.event = None binary.time = binary.time_history[-1] binary.eccentricity = binary.eccentricity_history[-1] @@ -1307,7 +1308,7 @@ def orbital_kick(self, binary): # if key is 'nearest_neighbour_distance': # setattr(binary, key, ['None', 'None', 'None']) binary.separation = new_separation - if binary.state != "disrupted": + if binary.state != "disrupted" and binary.state != "initially_single_star" and binary.state != "merged": binary.state = "detached" binary.event = None binary.time = binary.time_history[-1] @@ -1386,14 +1387,14 @@ def orbital_kick(self, binary): # update the orbit - if binary.state == "disrupted": + if binary.state == "disrupted" or binary.state == "initially_single_star" or binary.state == "merged": #the binary was already disrupted before the SN # update the binary object which was disrupted already before the SN for key in BINARYPROPERTIES: - if key != 'nearest_neighbour_distance': + if key not in ('nearest_neighbour_distance','state'): setattr(binary, key, None) - binary.state = "disrupted" + #binary.state = "disrupted" binary.event = None binary.separation = np.nan binary.eccentricity = np.nan @@ -1401,7 +1402,7 @@ def orbital_kick(self, binary): binary.time = binary.time_history[-1] binary.orbital_period = np.nan binary.mass_transfer_case = 'None' - + else: # the binary is not disrupted at least before the SN # The binary exists : flag_binary is True at least before the SN diff --git a/posydon/binary_evol/binarystar.py b/posydon/binary_evol/binarystar.py index f5b9227cbc..985c0229f1 100644 --- a/posydon/binary_evol/binarystar.py +++ b/posydon/binary_evol/binarystar.py @@ -210,7 +210,7 @@ def run_step(self): total_state = (self.star_1.state, self.star_2.state, self.state, self.event) next_step_name = self.properties.flow.get(total_state) - + if next_step_name is None: warnings.warn("Undefined next step given stars/binary states " "{}.".format(total_state)) diff --git a/posydon/binary_evol/flow_chart.py b/posydon/binary_evol/flow_chart.py index f0dc434d58..1dff20cb37 100644 --- a/posydon/binary_evol/flow_chart.py +++ b/posydon/binary_evol/flow_chart.py @@ -20,6 +20,7 @@ 'WD', 'NS', 'BH', + 'massless_remnant', 'H-rich_Core_H_burning', 'H-rich_Core_He_burning', 'H-rich_Shell_H_burning', @@ -35,17 +36,22 @@ ] STAR_STATES_CO = ['BH', 'NS', 'WD'] +STAR_STATES_NOT_NORMALSTAR = STAR_STATES_CO.copy() +STAR_STATES_NOT_NORMALSTAR.append('massless_remnant') STAR_STATES_NOT_CO = STAR_STATES_ALL.copy() [STAR_STATES_NOT_CO.remove(x) for x in STAR_STATES_CO] -STAR_STATES_H_RICH = STAR_STATES_NOT_CO.copy() +STAR_STATES_NORMALSTAR = STAR_STATES_ALL.copy() +[STAR_STATES_NORMALSTAR.remove(x) for x in STAR_STATES_NOT_NORMALSTAR] + +STAR_STATES_H_RICH = STAR_STATES_NORMALSTAR.copy() [STAR_STATES_H_RICH.remove(x) for x in ['stripped_He_Core_He_burning', 'stripped_He_Central_He_depleted', 'stripped_He_Central_C_depletion', 'stripped_He_non_burning']] -STAR_STATES_HE_RICH = STAR_STATES_NOT_CO.copy() +STAR_STATES_HE_RICH = STAR_STATES_NORMALSTAR.copy() [STAR_STATES_HE_RICH.remove(x) for x in ['H-rich_Core_H_burning', 'H-rich_Core_He_burning', 'H-rich_Shell_H_burning', @@ -121,8 +127,8 @@ # stripped_He star on a detached binary another H- or stripped_He star # This will be the outcome of a CE. -for s1 in STAR_STATES_NOT_CO: - for s2 in STAR_STATES_NOT_CO: +for s1 in STAR_STATES_NORMALSTAR: + for s2 in STAR_STATES_NORMALSTAR: POSYDON_FLOW_CHART[(s1, s2, 'detached', None)] = 'step_detached' POSYDON_FLOW_CHART[(s2, s1, 'detached', None)] = 'step_detached' @@ -164,7 +170,7 @@ # Binaries that go to common envelope -for s1 in STAR_STATES_NOT_CO: +for s1 in STAR_STATES_NORMALSTAR: for s2 in STAR_STATES_ALL: POSYDON_FLOW_CHART[(s1, s2, 'RLO1', 'oCE1')] = 'step_CE' POSYDON_FLOW_CHART[(s1, s2, 'RLO1', 'oDoubleCE1')] = 'step_CE' @@ -217,12 +223,21 @@ POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_end' POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_end' + for b in ['initially_single_star']: for s1 in STAR_STATES_ALL: for s2 in STAR_STATES_ALL: - for e in BINARY_EVENTS_ALL: - POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_end'#'step_initially_single' - POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_end'#'step_initially_single' + for e in ['ZAMS']: + POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_initially_single' + POSYDON_FLOW_CHART[(s2, s1, b, e)] = 'step_initially_single' + +for s1 in STAR_STATES_CO: + for s2 in ['massless_remnant']: + for e in BINARY_EVENTS_ALL: + POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_end' + POSYDON_FLOW_CHART[(s2, s1, b, e)] = 'step_end' + + BINARY_EVENTS_OF_SN_OR_AFTER_DETACHED = BINARY_EVENTS_ALL.copy() [BINARY_EVENTS_OF_SN_OR_AFTER_DETACHED.remove(x) for x in ['CC1','CC2','MaxTime_exceeded','maxtime']] @@ -232,28 +247,28 @@ for s2 in STAR_STATES_ALL: for e in BINARY_EVENTS_OF_SN_OR_AFTER_DETACHED: POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_disrupted' - POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_disrupted' + POSYDON_FLOW_CHART[(s2, s1, b, e)] = 'step_disrupted' # if we have two compcat objects in a disrupted binary, we stop the evolution. for b in ['disrupted']: for s1 in STAR_STATES_CO: for s2 in STAR_STATES_CO: for e in BINARY_EVENTS_ALL: POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_end' - POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_end' + POSYDON_FLOW_CHART[(s2, s1, b, e)] = 'step_end' for b in ['merged']: for s1 in STAR_STATES_ALL: for s2 in STAR_STATES_ALL: for e in ['oMerging1', 'oMerging2']: - POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_end' #'step_merged' - POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_end' #'step_merged' + POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_merged' #'step_merged' + POSYDON_FLOW_CHART[(s2, s1, b, e)] = 'step_merged' #'step_merged' # catch initial_RLO states for s1 in STAR_STATES_ALL: for s2 in STAR_STATES_ALL: POSYDON_FLOW_CHART[(s1, s2, 'initial_RLOF', 'ZAMS')] = 'step_end' - POSYDON_FLOW_CHART[(s1, s2, 'initial_RLOF', 'ZAMS')] = 'step_end' + POSYDON_FLOW_CHART[(s2, s1, 'initial_RLOF', 'ZAMS')] = 'step_end' # catch all maxtime for b in BINARY_STATES_ALL: diff --git a/posydon/binary_evol/singlestar.py b/posydon/binary_evol/singlestar.py index f0ca025e8b..da9ecdc858 100644 --- a/posydon/binary_evol/singlestar.py +++ b/posydon/binary_evol/singlestar.py @@ -104,6 +104,14 @@ 'profile': None } +def properties_massless_remnant(): + PROPERTIES_MASSLESS = {} + for key in STARPROPERTIES: + PROPERTIES_MASSLESS[key] = np.nan + PROPERTIES_MASSLESS["state"] = "massless_remnant" + PROPERTIES_MASSLESS["mass"] = 0.0 + return PROPERTIES_MASSLESS + class SingleStar: """Class describing a single star.""" diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index 903243e995..531115e9fb 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -37,7 +37,7 @@ import random from posydon.binary_evol.binarystar import BinaryStar -from posydon.binary_evol.singlestar import SingleStar +from posydon.binary_evol.singlestar import (SingleStar,properties_massless_remnant) from posydon.binary_evol.simulationproperties import SimulationProperties from posydon.popsyn.star_formation_history import get_formation_times @@ -67,7 +67,7 @@ # BinaryPopulation will enforce a constant metallicity accross all steps that # load stellar or binary models by checked this list of steps. STEP_NAMES_LOADING_GRIDS = [ - 'step_HMS_HMS', 'step_CO_HeMS', 'step_CO_HMS_RLO', 'step_detached','step_isolated','step_disrupted' + 'step_HMS_HMS', 'step_CO_HeMS', 'step_CO_HMS_RLO', 'step_detached','step_isolated','step_disrupted','step_initially_single', 'step_merged' ] class BinaryPopulation: @@ -88,7 +88,6 @@ def __init__(self, **kwargs): for key, arg in kwargs.items(): self.kwargs[key] = arg # Have a binary fraction change the number_of binaries. - #Eirini's change self.binary_fraction = self.kwargs.get('binary_fraction') self.number_of_binaries = self.kwargs.get('number_of_binaries') @@ -838,10 +837,8 @@ def draw_initial_binary(self, **kwargs): output = self.draw_initial_samples(**sampler_kwargs) default_index = output['binary_index'].item() - #Eirini's comments: # Randomly generated variables - if self.RNG.uniform() < self.binary_fraction: formation_time = output['time'].item() separation = output['separation'].item() @@ -888,9 +885,9 @@ def draw_initial_binary(self, **kwargs): #If binary_fraction not default a initially single star binary is created. else: formation_time = output['time'].item() - separation = output['separation'].item() - orbital_period = output['orbital_period'].item() - eccentricity = output['eccentricity'].item() + separation = np.nan + orbital_period = np.nan + eccentricity = np.nan m1 = output['S1_mass'].item() m2 = output['S2_mass'].item() Z_div_Zsun = kwargs.get('metallicity', 1.) @@ -922,15 +919,7 @@ def draw_initial_binary(self, **kwargs): center_h1=X, center_he4=Y, ) - star2_params = dict( - mass=m2, - state="BH", - metallicity=Z, - center_h1=X, - center_he4=Y, - ) - #do all of the above but with state = "initial_single star" - #in this case the second star can be a compact object. + star2_params = properties_massless_remnant() binary = BinaryStar(**binary_params, diff --git a/posydon/popsyn/defaults.py b/posydon/popsyn/defaults.py index 1406ebd0c7..1a7a850588 100644 --- a/posydon/popsyn/defaults.py +++ b/posydon/popsyn/defaults.py @@ -15,10 +15,10 @@ # Size of the population 'number_of_binaries': 100, - #Eirini's changes #Binary fraction - 'binary_fraction' : 1, - + 'binary_fraction' : 1, + 'metallicity' : 1.0, # in Zsolar + # Star Formation History 'star_formation': 'constant', # burst_time: 0, diff --git a/posydon/popsyn/population_params_default.ini b/posydon/popsyn/population_params_default.ini index 98989547f0..a9d0da3141 100644 --- a/posydon/popsyn/population_params_default.ini +++ b/posydon/popsyn/population_params_default.ini @@ -120,6 +120,11 @@ [step_disrupted] import = ['posydon.binary_evol.DT.step_disrupted','DisruptedStep'] +[step_merged] + import = ['posydon.binary_evol.DT.step_merged','MergedStep'] + +[step_initially_single] + import = ['posydon.binary_evol.DT.step_initially_single','InitiallySingleStep'] [step_CE] import = ['posydon.binary_evol.CE.step_CEE', 'StepCEE'] diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index 59523e2945..427e27573f 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -44,7 +44,7 @@ "Shell_H_burning", "Central_He_depleted", "Central_C_depletion"] RICHNESS_STATES = ["H-rich", "stripped_He"] -COMPACT_OBJECTS = ["WD", "NS", "BH"] +COMPACT_OBJECTS = ["WD", "NS", "BH","massless_remnant"] ALL_STAR_STATES = COMPACT_OBJECTS + [STATE_UNDETERMINED] ALL_STAR_STATES.extend(["{}_{}".format(rich_in, burning) From 2a2725d7126f8be8a609061ffa2b02b2cef96ae6 Mon Sep 17 00:00:00 2001 From: Chase Kimball Date: Thu, 29 Jun 2023 09:26:56 -0500 Subject: [PATCH 097/319] Fixes to orbital_kick in step_SN (#39) * Fixing coordinate frame inconsistency in orbital_kick() * more documentation for orbital_kick fix * fixed issue with cos_psi being limited to >0 --------- Co-authored-by: Jeff Andrews --- posydon/binary_evol/SN/step_SN.py | 198 ++++++++++++++++++++++++++---- 1 file changed, 177 insertions(+), 21 deletions(-) diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index 1e3edc4efa..269dee968d 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -1152,31 +1152,31 @@ def orbital_kick(self, binary): """Do the orbital kick. This function computes the supernova step of the binary object. It - checks which binary_state riched the core collapse flag, either CC1 or - CC2, and run the step accordingly updating the binary object. + checks which binary_state reached the core collapse flag, either CC1 or + CC2, and runs the step accordingly updating the binary object. Geometry: - The collapsing helium star, here M_he_star, lies on the origin of the - coordinate system moving in direction of positive y axis. The - companion, here M_companion, lies on the negative X axis and Z-axis - completes right-handed coordinate system. See Fig 1 in Kalogera 1996 - for a coordinate system drawing. - - phi : - Angle between z-axis and projection of kick onto x-z plane. + We work in a right-handed coordinate system. The collapsing helium star, + here M_he_star, lies on the origin. The companion, here M_companion, + lies on the negative X axis at rest. The relative velocity of the M_he_star + with respect to M_companion lies in the X-Y plane, with vY>0. + The orbital angular momentum vector is in Z direction, which completes the + right-handed coordinate system. + + psi: + The angle in the orbital plane between the X axis and the pre-core + collapse relative velocity. (psi = pi/2 points in Y direction) + theta : - Angle between pre- supernova star velocity relative to the - companion (i.e. along the positive y axis) and the kick velocity. + The polar angle between the kick velocity and the pre-core collapse + relative velocity of the M_he_star with respect to M_companion. + + phi : + The corresponding azimuthal angle such that phi=0 is on the Z axis. + tilt : - The angle between pre- and post- supenova orbial - planes. This is equal to the angle between the relative velocity of - the helium star to the companion just before the explosion (see Vr) - and the projection of the relative velocy just after the explosion - onto the y-z plane. - mean_anomaly: - is the mean anomaly, i.e the fraction of an elliptical orbit's - period that has elapsed since the orbiting body passed periapsis, - expressed as an angle. + The angle between pre- and post- supernova orbital angular momentum vectors. + Parameters ---------- @@ -1386,6 +1386,162 @@ def orbital_kick(self, binary): binary.star_2.natal_kick_array[3] = mean_anomaly + # The binary exist: flag_binary is True if the binary is not disrupted + flag_binary = True + + # eccentricity before the SN + epre = binary.eccentricity + # the orbital semimajor axis is the orbital separation + Apre = binary.separation + # Eq 16, Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 + # for eccentric anomaly + E_ma = sp.optimize.brentq( + lambda x: mean_anomaly - x + epre * np.sin(x), 0, 2 * np.pi + ) + # Eq 15, Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 + # orbital separation at the time of the exlosion + rpre = Apre * (1.0 - epre * np.cos(E_ma)) + + # load constants in CGS + G = const.standard_cgrav + + # Convert inputs to CGS + M_he_star = M_he_star * const.Msun + M_companion = M_companion * const.Msun + M_compact_object = M_compact_object * const.Msun + Apre = Apre * const.Rsun + Vkick = Vkick * const.km2cm + rpre = rpre * const.Rsun + Mtot_pre = M_he_star + M_companion + Mtot_post = M_compact_object + M_companion + + # get useful quantity + sin_theta = np.sqrt(1 - (cos_theta ** 2)) + + # Eq 1, in Kalogera, V. 1996, ApJ, 471, 352 + # extended to Eq 17 in Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 + # Vr is velocity of preSN He core relative to M_companion, NOT necessarily + # in the direction of the Y axis if eccentric + # Eq from conservation of energy + Vr = np.sqrt(G * (Mtot_pre) * (2.0 / rpre - 1.0 / Apre)) + + # Eq 18, Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 + # psi is the polar angle of the position vector of the CO with respect + # to its pre-SN orbital velocity in the companions frame. i.e. angle between Vr and X axis + # If epre = 0, sin_psi should be 1 + # Eq from setting specific angular momentum r X Vr = sqrt(G*M*A*(1-e**2)) + sin_psi = np.round( + np.sqrt(G * (Mtot_pre) * (1 - epre ** 2) * Apre) + / (rpre * Vr), 5) + cos_psi = np.sqrt(1 - sin_psi ** 2) + # Allow for -cos_psi (Vr in the -X, +Y quadrant) + if E_ma > np.pi: cos_psi *= -1 + + # Eq 3, in Kalogera, V. 1996, ApJ, 471, 352 + # extended to Eq 13, in Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 + # get the orbital separation post SN + # Eq from conservation of energy + Apost = ((2.0 / rpre) + - (((Vkick ** 2) + (Vr ** 2) + (2 * (Vkick * cos_theta) * Vr)) / (G * Mtot_post)) + ) ** -1 + + + # get kicks componets in the coordinate system + Vkx = Vkick * (sin_theta * np.sin(phi) * sin_psi + cos_theta * cos_psi) + Vky = Vkick * (-sin_theta * np.sin(phi) * cos_psi + cos_theta * sin_psi) + Vkz = Vkick * sin_theta * np.cos(phi) + + + # Eq 4, in Kalogera, V. 1996, ApJ, 471, 352 + # extended to Eq 14 in Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 + # get the eccentricity post SN + # Eq from setting specific angular momentum r X Vr = sqrt(G*M*A*(1-e**2)) + + + x = ((Vkz ** 2 + (Vky + Vr * sin_psi)** 2) + * rpre ** 2 + / (G * Mtot_post * Apost)) + + # catch negative values, i.e. disrupted binaries + if 1.-x < 0.: + epost = np.nan + else: + epost = np.sqrt(1 - x) + + # Compute COM velocity, VS, post SN + # VS_pre in COM frame is 0. So VS_post in COM frame is + # VS_post - VS_pre in our working frame + + VC0x = M_he_star * Vr * cos_psi / Mtot_pre + VC0y = M_he_star * Vr * sin_psi / Mtot_pre + VC0z = 0 + + VC1x = M_compact_object * (Vkx + Vr * cos_psi) / Mtot_post + VC1y = M_compact_object * (Vky + Vr * sin_psi) / Mtot_post + VC1z = M_compact_object * Vkz / Mtot_post + + + VSx = VC1x - VC0x + VSy = VC1y - VC0y + VSz = VC1z - VC0z + + + # V_sys = np.sqrt(VSx ** 2 + VSy ** 2 + VSz ** 2) + + # Calculate the angle between the pre and post-SN orbital angular momentum vectors + # Lpre || Z axis + # Lpost || X axis cross the post SN velocity of the compact object + # cos(tilt) = Lpre dot Lpost / ||Lpre||||Lpost|| + # For epre=0 (sin_psi=1), reduces to Eq 4, in Kalogera, V. 1996, ApJ, 471, 352 + + tilt = np.arccos((Vky + Vr * sin_psi) / np.sqrt( Vkz ** 2 + (Vky + Vr * sin_psi) ** 2 )) + + def SNCheck( + M_he_star, + M_companion, + M_compact_object, + rpre, + Apost, + epost, + Vr, + Vkick, + cos_theta, + verbose, + ): + """Check that the binary is not disrupted. + + Parameters + ---------- + M_he_star : double + Helium star mass before the SN in g. + M_companion : double + Companion star mass in g. + M_compact_object : double + Compact object mass left by the SN in g. + rpre : double + Oribtal separation at the time of the exlosion in cm. If the + eccentricity pre SN is 0 this correpond to Apre. + Apost : double + Orbital separtion after the SN in cm. + epost : double + Eccentricity after the SN. + Vr : double + Velocity of pre-SN He core relative to M_companion, directed + along the positive y axis in cm/s. + Vkick : double + Kick velocity in cm/s. + cos_theta : double + The cosine of the angle between pre- & post-SN orbital planes. + + Returns + ------- + flag_binary : bool + flag_binary is True if the binary is not disrupted. + + References + ---------- + .. [1] Willems, B., Henninger, M., Levin, T., et al. 2005, ApJ, 625, 324 + # update the orbit if binary.state == "disrupted" or binary.state == "initially_single_star" or binary.state == "merged": #the binary was already disrupted before the SN From e4ca54ea80663f6766e09f460e215c16c1b942c1 Mon Sep 17 00:00:00 2001 From: Elizabeth Teng <41969755+elizabethteng@users.noreply.github.com> Date: Thu, 29 Jun 2023 09:43:19 -0500 Subject: [PATCH 098/319] updates to profile interpolation (#101) * adding profile interpolation * adding monotonicity enforcement to main class * fixing typo in learn_bounds and increase epochs/callbacks * remove default path, move IF interpolator argument to train method * changed some variable names * adding load interpolator arg to main class init function * changing arg name from interpolator to IF_interpolator to prevent confusion * adding docs for prof interpolation * fixed file format for docs * fix typo * removed extraneous line * addressing some stylistic issues * resolve various issues * changed name of finalmass to final_m1, added missing states to X_H class * fixed missing return in mono renorm * addressing Philipp's comments; changing a few argument/variable names, added arguments for model training parameters * adding tensorflow to extras_require * fixed typo and added comment for tensorflow dependency * added helium profile interpolation, updated docs * update compiledata to include option for star 2, add MT class to dataframe * condensed a for loop * fixed a few bugs * moved some definitions around * make 80/20 split for validation data from training data * typos * allowed for star 2 in all classes * got rid of feature that would make predictions from untrained models for final star states with no training data * Update setup.py --------- Co-authored-by: Elizabeth Teng Co-authored-by: Jeff Andrews --- .../interpolation/profile_interpolation.py | 436 ++++++++++++------ setup.py | 3 +- 2 files changed, 294 insertions(+), 145 deletions(-) diff --git a/posydon/interpolation/profile_interpolation.py b/posydon/interpolation/profile_interpolation.py index d2188c4117..fe1766d593 100644 --- a/posydon/interpolation/profile_interpolation.py +++ b/posydon/interpolation/profile_interpolation.py @@ -24,36 +24,31 @@ class CompileData: - def __init__(self, train_path, valid_path, + def __init__(self, train_path, test_path, hms_s2=False, profile_names=['radius','logRho', 'x_mass_fraction_H', 'y_mass_fraction_He','z_mass_fraction_metals', 'omega','energy']): """Extracts profile data from '.h5' grid files and saves to file. Args: train_path (str) : path/name of '.h5' file for training data. - valid_path (str) : path/name of '.h5' file for testing data. + test_path (str) : path/name of '.h5' file for testing data. + hms_s2 (Boolean) : option to get profiles of star 2 in HMS-HMS grid profile_names (array-like) : list of profile quantities to extract. """ self.names = profile_names # extract testing data print("extracting testing data") - valid = PSyGrid(valid_path) # load PSyGrid object for testing grid - self.valid_scalars = pd.DataFrame() - self.valid_profiles = [] + test = PSyGrid(test_path) # load PSyGrid object for testing grid + self.test_scalars = pd.DataFrame() + self.test_profiles = [] testing_failed = [] - try: - print(valid.final_values["S1_state"][0]) - except: - print("grid does not have S1_state information") - - - for i in range(len(valid)): + for i in range(len(test)): try: - scalars,profiles = self.scrape(valid,i) - self.valid_scalars = self.valid_scalars.append(scalars,ignore_index=True) - self.valid_profiles.append(profiles) + scalars,profiles = self.scrape(test,i,hms_s2) + self.test_scalars = self.test_scalars.append(scalars,ignofre_index=True) + self.test_profiles.append(profiles) except: testing_failed.append(i) pass @@ -68,44 +63,56 @@ def __init__(self, train_path, valid_path, for i in range(len(train)): try: - scalars,profiles = self.scrape(train,i) + scalars,profiles = self.scrape(train,i,hms_s2) self.scalars = self.scalars.append(scalars,ignore_index=True) self.profiles.append(profiles) except: training_failed.append(i) - pass + pass + warnings.warn(f"{len(training_failed)} training binaries failed") - - def scrape(self,grid,ind): + def scrape(self,grid,ind,hms_s2): """Extracts profile data from one MESA run. Args: grid (obj) : PSyGrid object. ind (int) : index of run to be scraped. + hms_s2 (Boolean) : option to get profiles of star 2 in HMS-HMS grid Returns: scalars (array-like) : dictionary containing initial - m1, m2, p and final s1_state, final_m1. + m1, m2, p, mass transfer class, + final star_state, final total_mass. profiles (array-like) : all N specified profiles, shape (N,200). """ # open individual run as a DataFrame - df = pd.DataFrame(grid[ind]['final_profile1']) + + if hms_s2==False: # default star 1 profile information + df = pd.DataFrame(grid[ind]['final_profile1']) + mass_key = "star_1_mass" + state_key = "S1_state" + else: # requesting star 2 information for HMS-HMS grid + df = pd.DataFrame(grid[ind]['final_profile2']) + mass_key = "star_2_mass" + state_key = "S2_state" + df = df.sort_values(by="mass") df = df.reset_index(drop=True) # grab input values, final star 1 state, final star 1 mass - final_m1 = grid.final_values["star_1_mass"][ind] + total_mass = grid.final_values[mass_key][ind] scalars = {"m1":grid.initial_values["star_1_mass"][ind], "m2":grid.initial_values["star_2_mass"][ind], "p":grid.initial_values["period_days"][ind], - "s1_state":grid.final_values["S1_state"][ind], - "final_m1":final_m1} + "MT_class":grid.final_values["interpolation_class"][ind], + "star_state":grid.final_values[state_key][ind], + "total_mass":total_mass} # grab output vectors, interpolate to normalize profiles=np.zeros([len(self.names),200]) for i,prof in enumerate(self.names): if prof in df.columns: - f = interp1d(df['mass']/final_m1,df[prof], + f = interp1d(df['mass']/total_mass,df[prof], fill_value="extrapolate") profile_new = f(np.linspace(0,1,200)) profiles[i] = profile_new @@ -133,70 +140,94 @@ def __init__(self): """Interfaces with other classes, trains models and predicts profiles. """ - def load_profiles(self,filename): - """Load extracted profile data. + def load_profiles(self,filename,valid_split=0.2): + """Load and process extracted profile data. Args: filename (str) : path/name of '.pkl' file to be loaded. - Returns: - self.profiles (array-like) : M profiles for each of N training binaries, shape (N,M,200). - self.scalars (array-like) : DataFrame containing initial and final conditions for N training binaries. - self.valid_profiles (array-like) : M profiles for each of N testing binaries, shape (N,M,200). - self.valid_scalars (array-like) : DataFrame containing initial and final conditions for N testing binaries. + valid_split (float) : percentage of training data used for validation data """ with open(filename, 'rb') as f: myattrs = pd.read_pickle(f) for key in myattrs: setattr(self, key, myattrs[key]) - self.profiles = np.array(self.profiles) - self.valid_profiles = np.array(self.valid_profiles) + + # processing + self.test_profiles = np.array(self.test_profiles) + test_linear_initial = np.transpose([self.test_scalars["m1"], + self.test_scalars["m2"], + self.test_scalars["p"]]) + self.test_initial = np.log10(np.array(test_linear_initial)) + + linear_initial = np.transpose([self.scalars["m1"], + self.scalars["m2"], + self.scalars["p"]]) + + # 80/20 split for training and validation data + binaries = np.arange(len(self.profiles)) + np.random.shuffle(binaries) + split = int(len(self.profiles)*valid_split) # index at which to split data + self.valid_profiles = np.array(self.profiles)[binaries[:split]] + self.valid_initial = np.log10(linear_initial)[binaries[:split]] + self.valid_scalars = self.scalars.iloc[binaries[:split]] + + self.profiles = np.array(self.profiles)[binaries[split:]] + self.initial = np.log10(linear_initial)[binaries[split:]] + self.scalars = self.scalars.iloc[binaries[split:]] + def train(self,IF_interpolator,density_epochs=3000,density_patience=200, - H_bounds_epochs=500,H_bounds_patience=50): - """Trains models for density and H mass fraction profile models. + comp_bounds_epochs=500,comp_bounds_patience=50,loss_history=False,hms_s2=False): + """Trains models for density, H mass fraction, and He mass fraction profile models. Args: IF_interpolator (str) : path to '.pkl' file for IF interpolator. - """ - # processing - linear_initial = np.transpose([ - self.scalars["m1"],self.scalars["m2"],self.scalars["p"]]) - initial = np.log10(np.array(linear_initial)) - final_m1 = self.scalars["final_m1"].astype(np.float64) - - valid_linear_initial = np.transpose([self.valid_scalars["m1"], - self.valid_scalars["m2"], - self.valid_scalars["p"]]) - valid_initial = np.log10(np.array(valid_linear_initial)) - valid_final_m1 = self.valid_scalars["final_m1"].astype(np.float64) - + density_epochs (int) : number of epochs used to train density profile model + density_patience (int) : patience parameter for NN callback in density profile model + comp_bounds_epochs (int) : number of epochs used to train composition profiles model + comp_bounds_patience (int) : patience parameter for NN callback in composition profiles model + loss_history (Boolean) : option to return training and validation loss histories + hms_s2 (Boolean) : option to get profiles of star 2 in HMS-HMS grid + Returns: + self.comp.loss_history (array-like) : training and validation loss history for composition profiles + self.dens.loss_history (array-like) : training and validation loss history for density profiles + + """ # instantiate and train H mass fraction profile model - h_ind = self.names.index("x_mass_fraction_H") - self.h = X_H(initial, self.profiles[:,h_ind], self.scalars["s1_state"], - valid_initial, self.valid_profiles[:,h_ind], - self.valid_scalars["s1_state"], IF_interpolator, - H_bounds_epochs,H_bounds_patience) + self.comp = Composition(self.initial, + self.profiles[:,self.names.index("x_mass_fraction_H")], + self.profiles[:,self.names.index("y_mass_fraction_He")], + self.scalars["star_state"], + self.valid_initial, + self.valid_profiles[:,self.names.index("x_mass_fraction_H")], + self.valid_profiles[:,self.names.index("y_mass_fraction_He")], + self.valid_scalars["star_state"], + IF_interpolator, + comp_bounds_epochs,comp_bounds_patience,hms_s2) # instantiate and train density profile model - dens_ind = self.names.index("logRho") - self.dens = Density(initial, - self.profiles[:,dens_ind], - valid_initial, - self.valid_profiles[:,dens_ind], - IF_interpolator) - self.dens.train(prof_epochs=density_epochs,prof_patience=density_patience) + self.dens = Density(self.initial, + self.profiles[:,self.names.index("logRho")], + self.valid_initial, + self.valid_profiles[:,self.names.index("logRho")], + IF_interpolator) + self.dens.train(prof_epochs=density_epochs,prof_patience=density_patience,hms_s2) + + if loss_history==True: + return self.comp.loss_history, self.dens.loss_history def predict(self,inputs): - """Predict density and H mass fraction profiles from inputs. + """Predict density, H mass fraction, and He mass fraction profiles from inputs. Args: inputs (array-like) : log-space initial conditions of N binaries to predict, shape (N,3). Returns: mass_coords (array-like) : linear-scale mass enclosed profile coordinates. - density_profiles (array_like) : log-scale density profile coordinates. - h_profiles (array_like) : H mass fraction profile coordinates + density_profiles (array-like) : log-scale density profile coordinates. + h_profiles (array-like) : H mass fraction profile coordinates + he_profiles (array-like) : He mass fraction profile coordinates """ mass_coords, density_profiles = self.dens.predict(inputs) - mass_coords, h_profiles = self.h.predict(inputs) + mass_coords, h_profiles, he_profiles = self.comp.predict(inputs) - return mass_coords, density_profiles, h_profiles + return mass_coords, density_profiles, h_profiles, he_profiles def save(self, filename): @@ -239,29 +270,31 @@ def mono_renorm(arr): # cut off points below surface value return np.where(arr_copy0)[0]>0): - profiles_copy[i] = mono_renorm(profiles[i]) + profiles_mono[i] = mono_renorm(profiles[i]) - return profiles_copy + return profiles_mono class Density: def __init__(self,initial,profiles,valid_initial, - valid_profiles,IF_interpolator,n_comp=8): + valid_profiles,IF_interpolator,n_comp=8, hms_s2=False): """Creates and trains density profile model. Args: initial (array-like) : log-space initial conditions for training data. profiles (array-like) : final density profiles for training data. - valid_initial (array-like) : log-space initial conditions for testing data. - valid_profiles (array-like) : final density profiles for testing data. + valid_initial (array-like) : log-space initial conditions for validation data. + valid_profiles (array-like) : final density profiles for validation data. IF_interpolator (string) : path to .pkl file for IF interpolator for central density, final mass values n_comp (int) : number of PCA components. + hms_s2 (Boolean) : option to get profiles of star 2 in HMS-HMS grid """ self.n_comp = n_comp + self.hms_s2 = hms_s2 # process training data self.initial = initial # initial conditions in log space @@ -310,6 +343,8 @@ def train(self,loss=losses.MeanSquaredError(),prof_epochs=3000,prof_patience=200 """Trains NN models. Args: loss (object) : loss function for training. + prof_epochs (int) : number of epochs used to train neural network + prof_patience (int) : patience parameter for callback in neural network """ print("training on PCA weights...") @@ -326,6 +361,8 @@ def train(self,loss=losses.MeanSquaredError(),prof_epochs=3000,prof_patience=200 epochs=500, callbacks=[callback], verbose=0, validation_data=(self.valid_initial, self.valid_rho_min)) + + self.loss_history = np.array([history.history['loss'],history.history['val_loss']]) print("done training") @@ -355,44 +392,65 @@ def predict(self,inputs): + min_rho[:,np.newaxis] # IF interpolate final mass, construct mass enclosed profile coordinates - m1_ind = self.model_IF.interpolators[0].out_keys.index("star_1_mass") - pred_mass = self.model_IF.interpolators[0].test_interpolator(10**inputs)[:,m1_ind] + if self.hms_s2==False: + m_ind = self.model_IF.interpolators[0].out_keys.index("star_1_mass") + else: + m_ind = self.model_IF.interpolators[0].out_keys.index("star_2_mass") + pred_mass = self.model_IF.interpolators[0].test_interpolator(10**inputs)[:,m_ind] mass_coords = np.linspace(0,1,200)*pred_mass[:,np.newaxis] return mass_coords,density_profiles -class X_H: +class Composition: - def __init__(self,initial,profiles,s1,valid_initial, - valid_profiles,valid_s1,IF_interpolator, - training_epochs=500, training_patience=50): - """Creates and trains H mass fraction profile model. + def __init__(self,initial,h_profiles,he_profiles,s1, + valid_initial,valid_h_profiles,valid_he_profiles,valid_s1, + IF_interpolator,training_epochs=500, training_patience=50,hms_s2=False): + """Creates and trains H mass fraction and He mass fraction profiles model. Args: initial (array-like) : log-space initial conditions for training data. - profiles (array-like) : final H mass fraction profiles for training data. + h_profiles (array-like) : final H mass fraction profiles for training data. + he_profiles (array-like) : final He mass fraction profiles for training data. s1 (array-like) : final star 1 state for training data. - valid_initial (array-like) : log-space initial conditions for testing data. - valid_profiles (array-like) : final H mass fraction profiles for testing data. + valid_initial (array-like) : log-space initial conditions for validation data. + valid_h_profiles (array-like) : final H mass fraction profiles for validation data. + valid_he_profiles (array-like) : final He mass fraction profiles for validation data. valid_s1 (array-like) : final star 1 state for testing data. - IF_interpolator (string) : path to .pkl file for IF interpolator for central density, final mass values. + IF_interpolator (string) : path to .pkl file for IF interpolator + training_epochs (int) : number of epochs used to train neural networks + training_patience (int) : patience parameter for callback in neural networks + hms_s2 (Boolean) : option to get profiles of star 2 in HMS-HMS grid """ + self.hms_s2 = hms_s2 + self.initial = initial - self.profiles = profiles + self.h_profiles = h_profiles + self.he_profiles = he_profiles self.s1 = s1 self.valid_initial = valid_initial - self.valid_profiles = valid_profiles + self.valid_h_profiles = valid_h_profiles + self.valid_he_profiles = valid_he_profiles self.valid_s1 = valid_s1 # load IF interpolator self.model_IF = IFInterpolator() # instantiate POSYDON initial-final interpolator object self.model_IF.load(filename=IF_interpolator) - self.c_ind = self.model_IF.interpolators[0].out_keys.index("S1_center_h1") - self.s_ind = self.model_IF.interpolators[0].out_keys.index("S1_surface_h1") self.interp = self.model_IF.interpolators[0] + if self.hms_s2==False: + self.c_h_ind = self.interp.out_keys.index("S1_center_h1") + self.s_h_ind = self.interp.out_keys.index("S1_surface_h1") + self.c_he_ind = self.interp.out_keys.index("S1_center_he4") + self.s_he_ind = self.interp.out_keys.index("S1_surface_he4") + else: + self.c_h_ind = self.interp.out_keys.index("S2_center_h1") + self.s_h_ind = self.interp.out_keys.index("S2_surface_h1") + self.c_he_ind = self.interp.out_keys.index("S2_center_he4") + self.s_he_ind = self.interp.out_keys.index("S2_surface_he4") + # star 1 states - self.class_names = ['None','WD','NS','BH', + self.star_states = ['None','WD','NS','BH', 'stripped_He_non_burning', 'stripped_He_Core_He_burning', 'stripped_He_Core_C_burning', @@ -407,27 +465,31 @@ def __init__(self,initial,profiles,s1,valid_initial, 'H-rich_Core_C_burning'] # sort training data into classes by s1 state - self.sort_ind = {class_name:[] for class_name in self.class_names} - for i in range(len(self.s1)): - state = self.s1[i] - self.sort_ind[state].append(i) - - # sort testing data into classes by s1 state - self.valid_sort_ind = {class_name:[] for class_name in self.class_names} - for i in range(len(self.valid_s1)): - state = self.valid_s1[i] - self.valid_sort_ind[state].append(i) + self.sort_ind = {} + self.valid_sort_ind = {} + for name in self.star_states: + self.sort_ind[name] = np.where(self.s1==name)[0] + self.valid_sort_ind[name] = np.where(self.valid_s1==name)[0] # create and train models for profile boundaries - self.bounds_models = self.learn_bounds(training_epochs,training_patience) + self.bounds_models, self.loss_history = self.learn_bounds(training_epochs,training_patience) def learn_bounds(self,training_epochs,training_patience): """Creates and trains NNs to predict boundary points for each star 1 state. + Args: + training_epochs (int) : number of epochs used to train neural networks + training_patience (int) : patience parameter for callback in neural networks Returns: b_models (array-like) : dictionary containing boundary models. + loss_history (array-like) : training and validation loss histories """ b_models = {} - for state in ['WD','NS','BH', + loss_history = {} + self.empty = [] + for state in ['stripped_He_Core_He_burning', + 'stripped_He_Core_C_burning', + 'stripped_He_Central_He_depleted', + 'stripped_He_Central_C_depletion', 'H-rich_Central_He_depleted', 'H-rich_Central_C_depletion', 'H-rich_Shell_H_burning', @@ -438,15 +500,20 @@ def learn_bounds(self,training_epochs,training_patience): # identify input/output training data for class indices = self.sort_ind[state] inputs = self.initial[indices] - prof = self.profiles[indices] + h_prof = self.h_profiles[indices] + he_prof = self.he_profiles[indices] # identify input/output testing data for class valid_indices = self.valid_sort_ind[state] valid_inputs = self.valid_initial[valid_indices] - valid_prof = self.valid_profiles[valid_indices] + valid_h_prof = self.valid_h_profiles[valid_indices] + valid_he_prof = self.valid_he_profiles[valid_indices] # calculate boundary points for training and testing data - if "burning" in state: # these classes' profile shapes have 2 boundary points + if state in ['H-rich_Shell_H_burning', + 'H-rich_Core_H_burning', + 'H-rich_Core_He_burning', + 'H-rich_Core_C_burning']: # profiles with 2 boundary points outs = 2 bounds = [] nonflat = [] # ensures that training data only has the correct "non-flat" shape @@ -456,25 +523,58 @@ def learn_bounds(self,training_epochs,training_patience): # calculate first and points in each profile with large increases for i in range(len(indices)): try: - diff = np.where(prof[i][1:]-prof[i][:-1]>0.002)[0] + diff = np.where(h_prof[i][1:]-h_prof[i][:-1]>0.002)[0] bounds.append(np.array([diff[0]+1,diff[-1]+1])/200) nonflat.append(i) except: bounds.append([np.nan,np.nan]) for i in range(len(valid_indices)): try: - diff = np.where(valid_prof[i][1:]-valid_prof[i][:-1]>0.002)[0] + diff = np.where(valid_h_prof[i][1:]-valid_h_prof[i][:-1]>0.002)[0] valid_bounds.append(np.array([diff[0]+1,diff[-1]+1])/200) valid_nonflat.append(i) except: valid_bounds.append([np.nan,np.nan]) - else: # the rest of the profile shapes have 1 boundary point - outs = 1 + elif state in ['H-rich_Central_He_depleted', + 'H-rich_Central_C_depletion']: # H profiles with 1 boundary point + outs = 3 # calculate the point in each profile with the largest increase - bounds = (np.argmax(prof[:,1:]-prof[:,:-1],axis=1)+1)/200 - valid_bounds = (np.argmax(valid_prof[:,1:]-valid_prof[:,:-1],axis=1)+1)/200 + hbound = (np.argmax(h_prof[:,1:]-h_prof[:,:-1],axis=1)+1)/200 + valid_hbound = (np.argmax(valid_h_prof[:,1:]-valid_h_prof[:,:-1],axis=1)+1)/200 + max_He = np.max(he_prof,axis=1) + valid_max_He = np.max(valid_he_prof,axis=1) + dep_He = (np.argmax(he_prof[:,1:]-he_prof[:,:-1],axis=1)+1)/200 + valid_dep_He = (np.argmax(valid_he_prof[:,1:]-valid_he_prof[:,:-1],axis=1)+1)/200 + bounds = np.transpose([hbound,dep_He,max_He]) + valid_bounds = np.transpose([valid_hbound,valid_dep_He,valid_max_He]) + elif state in ['stripped_He_Core_He_burning', + 'stripped_He_Core_C_burning', + 'stripped_He_Central_He_depleted', + 'stripped_He_Central_C_depletion']: + outs = 2 + bounds = [] + nonflat = [] # ensures that training data only has the correct "non-flat" shape + # to avoid issues with calculating the boundary points + valid_bounds = [] + valid_nonflat = [] + # calculate first and points in each profile with large increases + for i in range(len(indices)): + try: + diff = np.where(he_prof[i][1:]-he_prof[i][:-1]>0.002)[0] + bounds.append(np.array([diff[0]+1,diff[-1]+1])/200) + nonflat.append(i) + except: + bounds.append([np.nan,np.nan]) + for i in range(len(valid_indices)): + try: + diff = np.where(valid_he_prof[i][1:]-valid_he_prof[i][:-1]>0.002)[0] + valid_bounds.append(np.array([diff[0]+1,diff[-1]+1])/200) + valid_nonflat.append(i) + except: + valid_bounds.append([np.nan,np.nan]) + # instantiate and train model on bounds model = models.Sequential([ layers.Dense(10,input_dim=3,activation=None), @@ -489,45 +589,76 @@ def learn_bounds(self,training_epochs,training_patience): callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=training_patience) if len(indices)==0: - warnings.warn(f"no training data available for s1 state {state}. model will return random results") - - elif "burning" in state: - history = model.fit(inputs[nonflat],np.array(bounds)[nonflat], - epochs=training_epochs,verbose=0,callbacks=[callback], - validation_data=(valid_inputs[valid_nonflat], - np.array(valid_bounds)[valid_nonflat])) - else: + warnings.warn(f"no training data available for {state}") + loss_history[state]=np.nan + self.empty.append(state) + + elif state in ['H-rich_Central_He_depleted', + 'H-rich_Central_C_depletion']: history = model.fit(inputs,np.array(bounds), epochs=training_epochs,verbose=0,callbacks=[callback], validation_data=(valid_inputs, np.array(valid_bounds))) - + loss_history[state] = np.array([history.history['loss'],history.history['val_loss']]) + + else: + history = model.fit(inputs[nonflat],np.array(bounds)[nonflat], + epochs=training_epochs,verbose=0,callbacks=[callback], + validation_data=(valid_inputs[valid_nonflat], + np.array(valid_bounds)[valid_nonflat])) + loss_history[state] = np.array([history.history['loss'],history.history['val_loss']]) + b_models[state] = model print(f"finished {state}") - return b_models + return b_models, loss_history - def predict_single(self,initial,center,surface,s1): + def predict_single(self,initial,center_H,surface_H,center_He,surface_He,s1): """Predict a profile for a single binary Args: initial (array-like) : initial position of binary - center (float) : center H mass fraction - surface (float) : surface H mass fraction + center_H (float) : center H mass fraction + surface_H (float) : surface H mass fraction + center_He (float) : center He mass fraction + surface_He (float) : surface He mass fraction s1 (str) : final star 1 state + Returns: + H (array-like) : predicted H mass fraction profile + He (array-like) : predicted He mass fraction profile """ if "stripped_He" in s1: - return np.zeros(200) + H = np.zeros(200) + + if s1 == "stripped_He_non_burning": + He = np.ones(200)*surface_He - if s1 in ["H-rich_non_burning","None"] or surface-center<0.002: - return np.ones(200)*surface + if s1 in ['stripped_He_Core_He_burning', + 'stripped_He_Core_C_burning', + 'stripped_He_Central_He_depleted', + 'stripped_He_Central_C_depletion']: + b = self.bounds_models[s1](tf.convert_to_tensor([initial])).numpy()[0] + He = np.ones(200) * center_He + He[int(b[1]*200):] = surface_He + f_He = interp1d(b,[center_He,surface_He],fill_value="extrapolate") + # use f to construct the profile points in the shell burning region + He[int(b[0]*200):int(b[1]*200)] = f_He(np.linspace(0,1,200)[int(b[0]*200):int(b[1]*200)]) + + if s1 in ["H-rich_non_burning","None"]: + H = np.ones(200)*surface_H + He = np.ones(200)*surface_He + + if s1 in ["WD","NS","BH"]: + H = np.ones(200)*np.nan + He = np.ones(200)*np.nan # construct step-shaped profile if s1 in ["H-rich_Central_He_depleted", - "H-rich_Central_C_depletion", - "WD","NS","BH"]: + "H-rich_Central_C_depletion"]: b = self.bounds_models[s1](tf.convert_to_tensor([initial])).numpy()[0] - new = np.ones(200)*center - new[int(b*200):] = surface - return new + H = np.ones(200)*center_H + H[int(b[0]*200):] = surface_H + He = np.ones(200) * center_He + He[int(b[1]*200):] = b[2] + He[int(b[0]*200):] = surface_He # construct shell-shaped profile if s1 in ["H-rich_Shell_H_burning", @@ -535,13 +666,22 @@ def predict_single(self,initial,center,surface,s1): "H-rich_Core_He_burning", "H-rich_Core_C_burning"]: b = self.bounds_models[s1](tf.convert_to_tensor([initial])).numpy()[0] - new = np.ones(200)*center - new[int(b[1]*200):] = surface - # "f" defines the shape of the H abundance profile in the shell burning region - f = interp1d(b,[center,surface],fill_value="extrapolate") + new = np.ones([2,200]) * np.array([center_H,center_He])[:,np.newaxis] + new[:,int(b[1]*200):] = np.array([surface_H,surface_He])[:,np.newaxis] + f_H = interp1d(b,[center_H,surface_H],fill_value="extrapolate") + f_He = interp1d(b,[center_He,surface_He],fill_value="extrapolate") # use f to construct the profile points in the shell burning region - new[int(b[0]*200):int(b[1]*200)] = f(np.linspace(0,1,200)[int(b[0]*200):int(b[1]*200)]) - return new + new[0][int(b[0]*200):int(b[1]*200)] = f_H(np.linspace(0,1,200)[int(b[0]*200):int(b[1]*200)]) + new[1][int(b[0]*200):int(b[1]*200)] = f_He(np.linspace(0,1,200)[int(b[0]*200):int(b[1]*200)]) + H = new[0] + He = new[1] + + # if there is no training data in the state, return nans + if s1 in self.empty: + H = np.ones(200)*np.nan + He = np.ones(200)*np.nan + + return H, He def predict(self,inputs): """Predict H mass fraction profiles from inputs. @@ -550,22 +690,30 @@ def predict(self,inputs): Returns: mass_coords (array-like) : linear-scale mass enclosed profile coordinates. h_profiles (array_like) : H mass fraction profile coordinates. + he_profiles (array_like) : He mass fraction profile coordinates. """ # IF interpolate H mass fraction values at center, surface; star 1 state - center_vals = self.interp.test_interpolator(10**inputs)[:,self.c_ind] - surface_vals = self.interp.test_interpolator(10**inputs)[:,self.s_ind] + center_h_vals = self.interp.test_interpolator(10**inputs)[:,self.c_h_ind] + surface_h_vals = self.interp.test_interpolator(10**inputs)[:,self.s_h_ind] + center_he_vals = self.interp.test_interpolator(10**inputs)[:,self.c_he_ind] + surface_he_vals = self.interp.test_interpolator(10**inputs)[:,self.s_he_ind] s1_vals = self.interp.test_classifiers(10**inputs)['S1_state'] # generate predicted profiles pred_profiles = [] for i in range(len(inputs)): - prediction = self.predict_single(inputs[i],center_vals[i],surface_vals[i],s1_vals[i]) - pred_profiles.append(prediction) - h_profiles = np.array(pred_profiles) + pred_H,pred_He = self.predict_single(inputs[i],center_h_vals[i],surface_h_vals[i], + center_he_vals[i],surface_he_vals[i],s1_vals[i]) + pred_profiles.append([pred_H,pred_He]) # IF interpolate final masses, generate mass enclosed profile coordinates - m1_ind = self.model_IF.interpolators[0].out_keys.index("star_1_mass") - pred_mass = self.model_IF.interpolators[0].test_interpolator(10**inputs)[:,m1_ind] + if self.hms_s2==False: + m_ind = self.model_IF.interpolators[0].out_keys.index("star_1_mass") + else: + m_ind = self.model_IF.interpolators[0].out_keys.index("star_2_mass") + pred_mass = self.interp.test_interpolator(10**inputs)[:,m_ind] mass_coords = np.linspace(0,1,200)*pred_mass[:,np.newaxis] + h_profiles = np.array(pred_profiles)[:,0] + he_profiles = np.array(pred_profiles)[:,1] - return mass_coords, h_profiles \ No newline at end of file + return mass_coords, h_profiles, he_profiles \ No newline at end of file diff --git a/setup.py b/setup.py index 91833dc318..46611456a2 100644 --- a/setup.py +++ b/setup.py @@ -84,7 +84,7 @@ "pytest-cov >= 4.0.0", ] -# For documenation +# For documentation extras_require = { "doc": [ "ipython", @@ -94,6 +94,7 @@ "sphinxcontrib_programoutput", "PSphinxTheme", ], + "profile": ["tensorflow == 2.12.0"] # for profile interpolation "hpc": ["mpi4py == 3.0.3"], } From 3b9f213132952fadb49adb353cb759dbcf1a9e3d Mon Sep 17 00:00:00 2001 From: sgossage Date: Thu, 29 Jun 2023 09:55:24 -0500 Subject: [PATCH 099/319] Seth TPAGBwind pipeline (#102) * Added an agb_wind rerun type (to perform reruns that will have the Bloecker AGB wind enabled). * removed the initial mass < 10 Msol check * Update run-pipeline --------- Co-authored-by: Seth Shearer Gossage Co-authored-by: Seth Shearer Gossage Co-authored-by: Jeff Andrews --- bin/run-pipeline | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/bin/run-pipeline b/bin/run-pipeline index d45e9c36a0..bde7e7532e 100644 --- a/bin/run-pipeline +++ b/bin/run-pipeline @@ -577,6 +577,8 @@ def copy_ini_file(grid, rerun_type, destination, cluster): replace_text = "matthias_PISN-d68228338b91fd487ef5d55c9b6ebb8cc5f0e668" elif rerun_type == 'opacity_max_hms-hms': replace_text = "zepei_fix_implicit-afa1860ddf9894aa1d82742ee2a73e8e92acd4a9" + elif rerun_type == 'TPAGBwind': + replace_text = "development-22c1bb9e730343558c3e70984a99b3fc1f3c346e" elif rerun_type == 'other': # e.g. 'envelope_mass_limit', 'fe_core_infall_limit' # this rerun uses the default inlist commit return @@ -639,6 +641,25 @@ def logic_rerun(grid, rerun_type): if np.max(transfer) == True: runs_to_rerun += [i] runs_to_rerun = np.array(runs_to_rerun) + elif rerun_type == 'TPAGBwind': + N_runs = len(grid) + runs_to_rerun = [] + for i in range(N_runs): + if grid[i].binary_history is not None and grid[i].history1 is not None: + hot_wind_full_on_T = 1.2e4 # inlist value + s1_Teff = 10**grid[i].history1['log_Teff'] + s2_Teff = 10**grid[i].history2['log_Teff'] + s1_Yc = grid[i].history1['center_he4'] + s2_Yc = grid[i].history2['center_he4'] + s1_he_shell_mass = grid[i].history1['he_core_mass'] - grid[i].history1['co_core_mass'] + s2_he_shell_mass = grid[i].history2['he_core_mass'] - grid[i].history2['co_core_mass'] + tpagb_1 = np.logical_and(s1_Teff <= hot_wind_full_on_T, s1_Yc <= 1e-6) + tpagb_1 = np.logical_and(tpagb_1, s1_he_shell_mass <= 1e-1) + tpagb_2 = np.logical_and(s2_Teff <= hot_wind_full_on_T, s2_Yc <= 1e-6) + tpagb_2 = np.logical_and(tpagb_2, s2_he_shell_mass <= 1e-1) + if np.max(np.logical_or(tpagb_1, tpagb_2)) == True: + runs_to_rerun += [i] + runs_to_rerun = np.array(runs_to_rerun) elif rerun_type == 'envelope_mass_limit': # maybe 'fe_core_infall_limit' as well? runs_to_rerun = np.array([0, 1]) # implement logic else: From 5d35a8ed478563c4a98eb6292239433df87f1106 Mon Sep 17 00:00:00 2001 From: devimisra Date: Mon, 3 Jul 2023 09:38:06 +0200 Subject: [PATCH 100/319] Update step_SN.py (#108) Missing comment block --- posydon/binary_evol/SN/step_SN.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index 269dee968d..ef876795c6 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -1541,7 +1541,7 @@ def SNCheck( References ---------- .. [1] Willems, B., Henninger, M., Levin, T., et al. 2005, ApJ, 625, 324 - + """ # update the orbit if binary.state == "disrupted" or binary.state == "initially_single_star" or binary.state == "merged": #the binary was already disrupted before the SN From 7418d513b65c08ff09af6dac98083e66b12ca8da Mon Sep 17 00:00:00 2001 From: devimisra Date: Mon, 3 Jul 2023 09:38:32 +0200 Subject: [PATCH 101/319] Update setup.py (#107) Adding a comma --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 46611456a2..35705661df 100644 --- a/setup.py +++ b/setup.py @@ -94,7 +94,7 @@ "sphinxcontrib_programoutput", "PSphinxTheme", ], - "profile": ["tensorflow == 2.12.0"] # for profile interpolation + "profile": ["tensorflow == 2.12.0"], # for profile interpolation "hpc": ["mpi4py == 3.0.3"], } From 366526eae2a51a00e63580c8946e824b9b10a476 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 20 Jul 2023 16:18:26 +0200 Subject: [PATCH 102/319] Update run-pipeline (#109) set tpabg to False in case of a missing history, e.g. if there is a CO instead of a star --- bin/run-pipeline | 47 ++++++++++++++++++++++++++--------------------- 1 file changed, 26 insertions(+), 21 deletions(-) diff --git a/bin/run-pipeline b/bin/run-pipeline index bde7e7532e..f2ccf1dd8f 100644 --- a/bin/run-pipeline +++ b/bin/run-pipeline @@ -632,32 +632,37 @@ def logic_rerun(grid, rerun_type): N_runs = len(grid) runs_to_rerun = [] for i in range(N_runs): - if grid[i].binary_history is not None and grid[i].history1 is not None: - rl1 = grid[i].binary_history['rl_relative_overflow_1'] - rl2 = grid[i].binary_history['rl_relative_overflow_2'] - w_wcrit = grid[i].history1['surf_avg_omega_div_omega_crit'] - reverse = np.logical_and(rl1<-0.05, rl2>-0.05) - transfer = np.logical_and(reverse, w_wcrit>0.9) - if np.max(transfer) == True: - runs_to_rerun += [i] + if grid[i].binary_history is not None and grid[i].history1 is not None: + rl1 = grid[i].binary_history['rl_relative_overflow_1'] + rl2 = grid[i].binary_history['rl_relative_overflow_2'] + w_wcrit = grid[i].history1['surf_avg_omega_div_omega_crit'] + reverse = np.logical_and(rl1<-0.05, rl2>-0.05) + transfer = np.logical_and(reverse, w_wcrit>0.9) + if np.max(transfer) == True: + runs_to_rerun += [i] runs_to_rerun = np.array(runs_to_rerun) elif rerun_type == 'TPAGBwind': N_runs = len(grid) runs_to_rerun = [] + hot_wind_full_on_T = 1.2e4 # inlist value for i in range(N_runs): - if grid[i].binary_history is not None and grid[i].history1 is not None: - hot_wind_full_on_T = 1.2e4 # inlist value - s1_Teff = 10**grid[i].history1['log_Teff'] - s2_Teff = 10**grid[i].history2['log_Teff'] - s1_Yc = grid[i].history1['center_he4'] - s2_Yc = grid[i].history2['center_he4'] - s1_he_shell_mass = grid[i].history1['he_core_mass'] - grid[i].history1['co_core_mass'] - s2_he_shell_mass = grid[i].history2['he_core_mass'] - grid[i].history2['co_core_mass'] - tpagb_1 = np.logical_and(s1_Teff <= hot_wind_full_on_T, s1_Yc <= 1e-6) - tpagb_1 = np.logical_and(tpagb_1, s1_he_shell_mass <= 1e-1) - tpagb_2 = np.logical_and(s2_Teff <= hot_wind_full_on_T, s2_Yc <= 1e-6) - tpagb_2 = np.logical_and(tpagb_2, s2_he_shell_mass <= 1e-1) - if np.max(np.logical_or(tpagb_1, tpagb_2)) == True: + if grid[i].history1 is not None: + s1_Teff = 10**grid[i].history1['log_Teff'] + s1_Yc = grid[i].history1['center_he4'] + s1_he_shell_mass = grid[i].history1['he_core_mass'] - grid[i].history1['co_core_mass'] + tpagb_1 = np.logical_and(s1_Teff <= hot_wind_full_on_T, s1_Yc <= 1e-6) + tpagb_1 = np.logical_and(tpagb_1, s1_he_shell_mass <= 1e-1) + else: + tpagb_1 = np.array([False]) + if grid[i].history2 is not None: + s2_Teff = 10**grid[i].history2['log_Teff'] + s2_Yc = grid[i].history2['center_he4'] + s2_he_shell_mass = grid[i].history2['he_core_mass'] - grid[i].history2['co_core_mass'] + tpagb_2 = np.logical_and(s2_Teff <= hot_wind_full_on_T, s2_Yc <= 1e-6) + tpagb_2 = np.logical_and(tpagb_2, s2_he_shell_mass <= 1e-1) + else: + tpagb_2 = np.array([False]) + if np.max(np.logical_or(tpagb_1, tpagb_2)) == True: runs_to_rerun += [i] runs_to_rerun = np.array(runs_to_rerun) elif rerun_type == 'envelope_mass_limit': # maybe 'fe_core_infall_limit' as well? From ea23ac456c409b41916336e4fbdca27371010234 Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Thu, 20 Jul 2023 16:18:46 +0200 Subject: [PATCH 103/319] Introducing GRB properties and rate calculations (#103) * Add function "keep_till_He_depletion" * Add option to stop at He depletion * Add option to pipeline (remove later) * correct not * Change threshold for He depletion * allow histories without age, add new flag * Check for history of 0 length * support BH formation above PISN gap and direct/Fryer collapse prescriptions now assume hydrogen envelope does not go into the BH * add C criterion for He depletion add C criterion for He depletion * Update psygrid.py use 0.08 carbon abundance * Update scrubbing.py where can't handle two arrays in parallel * Update psygrid.py change C limit to 0.1 * support the option to conserve the hydrogen envelope in BH formation * add MODEL var to post processing * update post processing script to support multiple core collapse models * Update psygrid.py Add mass request to only apply stopping earlier to masses of 100Msun and above * Update run-pipeline remove "_He_depletion" extension for the file name of the hdf5 file containing the psygrid * debug * rename function and flags * add support for BH disk mass * allow to remove slices from combining grids and only remove the combining if all slices are missing * fix small bug * Update setup-pipeline correct some typos * track previously created files * Update setup-pipeline fix typo * get files from step 1 * change range over df shape to instead use the index object * update step_6 run-pipeline * turn any to check on length * debug * add check for no slurm tasks * account for larger length difference of histories * reuse columns from histories if possible, add warnings * bugfix * propagate changes in step_SN and step_MESA * fix bug * debug plot2D * remove circular import * add open-mode, remove old file (make a copy first) * allow to ignore bad charaters in MESA log files * drop rows and columns * change import * import TF1 for post processing * fix bug * Update psygrid.py correct typo * debug * roll fix for single stars as well * debug * Matthias split grid rlo (#99) * fix in psygrid.py fix check of initial mass when cutting off carbon depletion * Update run-pipeline get start_at_RLO from compression type add flag to stop before carbon depletion (allow only for HMS grids) * Update setup-pipeline remove CO_HMS_GRID_START_AT_RLO add STOP_BEFORE_CARBON_DEPLETION * Update setup-pipeline get different directory for plotting if compression contains "RLO" * Update setup-pipeline correct typo * Update psygrid.py correct typo * Update psygrid.py debug * debug single interpolation * add support for other metallicities * update Z to Y conversion * SN_type not diclared in star properties * debug * update default params ini * increase len interp_class column * update params ini * add authorship to MODEL.py * Matthias split grid rlo (#100) * fix in psygrid.py fix check of initial mass when cutting off carbon depletion * Update run-pipeline get start_at_RLO from compression type add flag to stop before carbon depletion (allow only for HMS grids) * Update setup-pipeline remove CO_HMS_GRID_START_AT_RLO add STOP_BEFORE_CARBON_DEPLETION * Update setup-pipeline get different directory for plotting if compression contains "RLO" * Update setup-pipeline correct typo * Update psygrid.py correct typo * Update psygrid.py debug * Update setup-pipeline Add RLO to steps 6 and 7 * Update setup-pipeline Correct step 7 * Update run-pipeline don't interpolate no_MT for any grid starting at RLO * Update run-pipeline remove "_RLO" from method, which is read from the filename * Update setup-pipeline add "CO-HeMS_RLO" to export dirs * update spin orbit tilt name in rate calculation * adding function get_formation_channels * in parse function, shifting the binary_index * compute GRB properties * add channelwise merger efficiency calculation and plotting * add channelwise rate density * support channelwise histogram distributions * add file authorship * debug * cosistency change * add GRB rate calculation * fixing shift in binary index * minor bug * debug * debug * debug * address PR103 PR104 comments * remove bug introduced in previous comment * remove bug introduced in previous commit * debug --------- Co-authored-by: mkruckow <122798003+mkruckow@users.noreply.github.com> Co-authored-by: mkruckow Co-authored-by: Monica Gallegos-Garcia --- posydon/popsyn/GRB.py | 128 +++++++ posydon/popsyn/rate_calculation.py | 251 ++++++++++++- posydon/popsyn/selection_effects.py | 7 +- posydon/popsyn/synthetic_population.py | 471 ++++++++++++++++++++----- posydon/visualization/plot_dco.py | 102 ------ posydon/visualization/plot_defaults.py | 40 ++- posydon/visualization/plot_pop.py | 254 +++++++++++++ 7 files changed, 1041 insertions(+), 212 deletions(-) create mode 100644 posydon/popsyn/GRB.py delete mode 100644 posydon/visualization/plot_dco.py create mode 100644 posydon/visualization/plot_pop.py diff --git a/posydon/popsyn/GRB.py b/posydon/popsyn/GRB.py new file mode 100644 index 0000000000..f15963a327 --- /dev/null +++ b/posydon/popsyn/GRB.py @@ -0,0 +1,128 @@ +__author__ = ['Simone Bavera '] + +import warnings +import numpy as np +from posydon.utils.constants import Msun, clight + +GRB_PROPERTIES = ['GRB1', 'S1_eta', 'S1_f_beaming', 'S1_E_GRB', 'S1_E_GRB_iso', 'S1_L_GRB_iso', + 'GRB2', 'S2_eta', 'S2_f_beaming', 'S2_E_GRB', 'S2_E_GRB_iso', 'S2_L_GRB_iso'] + +def get_GRB_properties(df, GRB_efficiency, GRB_beaming, E_GRB_iso_min=0.): + """Compute GRB properties for a given binary population.""" + + # check if radiated disk mass is avaialble to compute GRB properties + if ('S1_m_disk_radiated' not in df or + 'S2_m_disk_radiated' not in df): + raise ValueError('m_disk_radiated not found in the dataframe!') + + # reminder the user about the threshold cut + if E_GRB_iso_min != 0.: + warnings.warn("We only consider GRBs with isotropic equivalent " + f"energy larger than {E_GRB_iso_min} erg!") + + # define some methods + + def compute_eta(i, sel): + """Set GRB energy conversion efficiency factor.""" + if not isinstance(GRB_efficiency, float): + raise ValueError(f'GRB efficiency {GRB_efficiency} ' + 'must be float value in [0,1] range!') + if GRB_efficiency < 0. or GRB_efficiency > 1.: + raise ValueError(f'GRB efficiency {GRB_efficiency} ' + 'must be float value in [0,1] range!') + # preset all efficiency factors to NaN and set them + # only for the selected binaries emitting a GRB + df[f'S{i}_eta'] = [np.nan] * df.shape[0] + df.loc[sel,f'S{i}_eta'] = GRB_efficiency + + def compute_beaming_factor(i, sel): + """Compute GRB beaming factor.""" + # TODO: check out Github copilot suggestions: + # 'Fong+15', 'Ghirlanda+16', 'Lamb+19'? + + df[f'S{i}_f_beaming'] = [np.nan] * df.shape[0] + if isinstance(GRB_beaming, float): + df.loc[sel, f'S{i}_f_beaming'] = GRB_beaming + elif GRB_beaming == 'Goldstein+15': + n = df.loc[sel,f'S{i}_f_beaming'].shape[0] + # generate a opening angle from a lognormal distribution + # as in Table 3 of https://arxiv.org/pdf/1512.04464.pdf + theta = 10**(np.random.normal(0.77,0.37,n))/180.*np.pi # radian + # TODO: truncate the angle between 0 and pi/2 + # compute the solid angle, factor 2 to accounts for the two emispheres + solid_angle = 4*np.pi*(1.-np.cos(theta)) + df.loc[sel,f'S{i}_f_beaming'] = solid_angle/(4*np.pi) + # TODO: this option is not usable at the moment as it requires + # redshift information which is not available in the dataframe + # the refshift is compute by rate_calculation.py after GRB properties + # are computed, fix this. + # elif GRB_beaming == 'Lloyd-Ronning+19': + # if z is None: + # raise ValueError('Redshift not specified!') + # else: + # z = df.loc[sel,'z_formation'] + # n = df.loc[sel,f'S{i}_f_beaming'].shape[0] + # # generate a opening angle from a lognormal distribution + # # as in Table 3 of https://arxiv.org/pdf/1512.04464.pdf + # theta =10**(np.random.normal(0.77*(1+z)**(-0.75),0.37,n))/180.*np.pi # radian + # # TODO: truncate the angle between 0 and pi/2 + # # compute the solid angle, factor 2 to accounts for the two emispheres + # solid_angle = 4*np.pi*(1.-np.cos(theta)) + # df.loc[sel,f'S{i}_f_beaming'] = solid_angle/(4*np.pi) + elif GRB_beaming == 'Pescalli+15': + # TODO: check the reference and the equation + def f_B(E,eta): + xi=0.5 + t=25. + C = (7e46)**xi + fb = np.ones(len(E)) + fb[E>0.] = (C*(eta*E[E>0.]/t)**(-xi))**(1./(1-xi)) + return fb + E = df.loc[sel,f'S{i}_E_GRB'] + eta = df.loc[sel,f'S{i}_eta'] + df.loc[sel,f'S{i}_f_beaming'] = f_B(E,eta) + else: + raise ValueError(f'GRB beaming factor {GRB_beaming} not supported!') + + def compute_E_GRB(i, sel): + """Compute the GRB energy.""" + df[f'S{i}_E_GRB'] = np.zeros(df.shape[0]) + m_disk_radiated = df.loc[sel,f'S{i}_m_disk_radiated'] + eta = df.loc[sel,f'S{i}_eta'] + df.loc[sel,f'S{i}_E_GRB'] = eta * m_disk_radiated * Msun * clight**2 # erg + + def compute_E_GRB_iso(i, sel): + """Compute the GRB isotropic equivalent energy.""" + df[f'S{i}_E_GRB_iso'] = np.zeros(df.shape[0]) + E_GRB = df.loc[sel,f'S{i}_E_GRB'] + f_beaming = df.loc[sel,f'S{i}_f_beaming'] + df.loc[sel,f'S{i}_E_GRB_iso'] = E_GRB/f_beaming # erg + + def compute_L_GRB_iso(i, sel): + """Compute the GRB isotropic equivalet luminosity.""" + df[f'S{i}_L_GRB_iso'] = np.zeros(df.shape[0]) + # compute the GRB isotropic equivalent luminosity assuming a + # constant average timescale of 25s from Pescalli+15 + # TODO: improve this assumption + E_GRB_iso = df.loc[sel,f'S{i}_E_GRB_iso'] + df.loc[sel,f'S{i}_L_GRB_iso'] = E_GRB_iso/25. # erg/s + + def flag_GRB_systems(i, sel): + # flag all systems emitting at least one GRB + df[f'GRB{i}'] = [False] * df.shape[0] + df.loc[sel,f'GRB{i}'] = df.loc[sel,f'S{i}_E_GRB_iso'] >= E_GRB_iso_min + + + # loop over the two star components and compute GRB properties + # only for stars that formed a disk + for i in range(1,3): + # compute energies only for non-zero disk masses + sel = df[f'S{i}_m_disk_radiated'] > 0. + compute_eta(i, sel) + compute_E_GRB(i, sel) + compute_beaming_factor(i, sel) + compute_E_GRB_iso(i, sel) + compute_L_GRB_iso(i, sel) + flag_GRB_systems(i, sel) + + return df \ No newline at end of file diff --git a/posydon/popsyn/rate_calculation.py b/posydon/popsyn/rate_calculation.py index b83cc347e8..8e7e375e81 100644 --- a/posydon/popsyn/rate_calculation.py +++ b/posydon/popsyn/rate_calculation.py @@ -20,6 +20,7 @@ get_illustrisTNG_data, fractional_SFR_at_given_redshift) from posydon.utils.data_download import PATH_TO_POSYDON_DATA +from posydon.popsyn.GRB import GRB_PROPERTIES, get_GRB_properties PATH_TO_PDET_GRID = os.path.join(PATH_TO_POSYDON_DATA, 'selection_effects/pdet_grid.hdf5') @@ -31,11 +32,16 @@ 'Z_max' : 1., 'select_one_met' : False, 'dlogZ' : None, # e.g, [np.log10(0.0142/2),np.log10(0.0142*2)] - 'Zsun' : Zsun + 'Zsun' : Zsun, + 'compute_GRB_properties' : False, + 'GRB_beaming' : 1., # e.g., 0.5, 'Goldstein+15' + 'GRB_efficiency' : 0., # e.g., 0.01 + 'E_GRB_iso_min' : 0., # e.g., 1e51 erg } -class Rates(object): +class Rates(object): + """Compute DCO rates.""" def __init__(self, df, verbose=False, **kwargs): """Compute DCO rates. @@ -90,6 +96,16 @@ def __init__(self, df, verbose=False, **kwargs): for varname in DEFAULT_MODEL: default_value = DEFAULT_MODEL[varname] setattr(self, varname, default_value) + + if self.compute_GRB_properties: + for key in GRB_PROPERTIES: + if key not in self.df: + warnings.warn("GRB properties not in the dataset, " + "computing them right now!") + self.df = get_GRB_properties(self.df, + self.GRB_efficiency, + self.GRB_beaming, + self.E_GRB_iso_min) ###################################################### ### DCO detection rate and merger rate density ### @@ -657,10 +673,8 @@ def f(z,sensitivity): return sp.integrate.quad(f, z_hor_i, z_hor_f, args=(sensitivity))[0] # Gpc^3 - def compute_merger_rate_density(self, w_ijk, z_event, observable='DCOs', sensitivity='infiite', index=None): - """Compute the DCOs merger rate density. - - TODO: add support for GRBs. + def compute_rate_density(self, w_ijk, z_event, observable='DCO', sensitivity='infinite', index=None): + """Compute the GRB/DCO rate density. Parameters ---------- @@ -668,10 +682,11 @@ def compute_merger_rate_density(self, w_ijk, z_event, observable='DCOs', sensiti Cosmological weights computed with Eq. B.8 of Bavera et at. (2020). z_event : array doubles Cosmolgocial redshift of the event you are tracking. - Type : string + observable : string Event you are tracking, available: 'DCOs': merger event of a DCO system - 'GRBs': coming soon. + 'GRB1': gamma ray bursts of star 1 + 'GRB2': gamma ray bursts of star 2 sensitivity : string This takes into account the detector sensitivity, available: 'infinite': p_det = 1 @@ -689,20 +704,230 @@ def compute_merger_rate_density(self, w_ijk, z_event, observable='DCOs', sensiti z_hor = self.get_edges_redshift_bins() n = len(z_hor) - if observable=='DCOs': + if observable=='DCO': z_merger_DCO = z_event - Rate_DCOs = np.zeros(n-1) + Rate_DCO = np.zeros(n-1) if sensitivity=='infinite': for i in range(1,n): # compute Eq. (D.1) in Bavera et al. (2022) arXiv:2106.15841 - condition_DCOs = np.logical_and(z_merger_DCO>z_hor[i-1], + cond_DCO = np.logical_and(z_merger_DCO>z_hor[i-1], z_merger_DCO<=z_hor[i]) - Rate_DCOs[i-1] = (sum(w_ijk[condition_DCOs]) + Rate_DCO[i-1] = (sum(w_ijk[cond_DCO]) /self.get_shell_comovig_volume(z_hor[i-1], z_hor[i], sensitivity)) - return Rate_DCOs # Gpc^-3 yr^-1 + return Rate_DCO # Gpc^-3 yr^-1 else: raise ValueError('Unsupported sensitivity!') + + elif observable in ['GRB1','GRB2']: + s = observable[-1] + z_GRB = z_event + if sensitivity=='beamed': + if index is not None: + f_beaming = self.get_data(f"S{s}_f_beaming",index) + flag_GRB = self.get_data(f"GRB{s}",index) + else: + raise ValueError('Missing f_beaming parameter!') + Rate_GRB = np.zeros(n-1) + for i in range(1,n): + cond_GRB = np.logical_and(np.logical_and(z_GRB>z_hor[i-1], z_GRB<=z_hor[i]), + flag_GRB) + f_fb = f_beaming[cond_GRB] + Rate_GRB[i-1] = (sum(w_ijk[cond_GRB]*f_fb) + /self.get_shell_comovig_volume(z_hor[i-1], + z_hor[i], + sensitivity='infinite')) + return Rate_GRB + + elif sensitivity=='infinite': + if index is not None: + flag_GRB = self.get_data(f"GRB{s}",index) + else: + raise ValueError('Missing f_beaming parameter!') + Rate_GRB = np.zeros(n-1) + for i in range(1,n): + if index is None: + raise ValueError('Provide index comlumn to identify GRB systems.') + cond_GRB = np.logical_and(np.logical_and(z_GRB>z_hor[i-1], z_GRB<=z_hor[i]), + flag_GRB) + Rate_GRB[i-1] = (sum(w_ijk[cond_GRB]) + /self.get_shell_comovig_volume(z_hor[i-1], + z_hor[i], + sensitivity)) + return Rate_GRB + else: + raise ValueError('Unknown sensitivity!') else: raise ValueError('Unknown observable!') + + ############################## + ##### GRB class methods ##### + ############################## + + def get_time_GRB(self, z_birth, event=None): + """Get the time of the GRB. + + Parameters + ---------- + z_brith : double + Redshif of birth. + event : string + Event you are tracking, either first or second core collpase: + 'CC1', 'CC2'. + + Returns + ------- + t_BRB : double + Cosmic time of the GRB event in Gyr. + + """ + if event not in ['CC1', 'CC2']: + raise ValueError(f'Unknown event {event}!') + n = self.df.shape[0] + t_birth = self.get_cosmic_time_from_redshift(z_birth) * np.ones(n) # Gyr + t_GRB = t_birth + self.df[f"time_{event}"] * 10 ** (-3) # Gyr + return t_GRB + + + def compute_GRB_rate_weights(self, sensitivity='infinity', path_to_dir='./', extention='npz'): + """Compute the cosmological weights of the transient events associated to the population. + + This function will create a directory path_to_dir/GRBs/sensitivity where + it will save the weigths, binary indicies k, z_formation, z_grb. + This is needed for scalability as a ~100k DCO population generates ~10M + non-zero weights assuming a delta_t=100Myr. + + Parameters + ---------- + sensitivity : string + Assume there are no selection effects. Available: + 'infinite': whole GRB population, i.e. p_det = 1 + path_to_dir : string + Path to the workingn directory where you want to store the + cosmological weights. + + """ + + # check if the folder three exists, otherwise create it + GRB_dir = os.path.join(path_to_dir,'GRBs') + if 'GRBs' not in os.listdir(path_to_dir): + os.makedirs(GRB_dir) + + sensitivity_dir = os.path.join(GRB_dir, f'{sensitivity}_sensitivity') + if f'{sensitivity}_sensitivity' not in os.listdir(GRB_dir): + os.makedirs(sensitivity_dir) + + # index of all DCOs + n = self.df.shape[0] + index = np.arange(0, n) + + # redshif of each time bin + z_birth = self.get_centers_redshift_bins() + + # define interpolator + get_redshift_from_time = self.get_redshift_from_cosmic_time_interpolator() + + # hashmap to store everythig + data = {'index_1': {}, 'z_grb_1': {}, 'weights_1': {}, + 'index_2': {}, 'z_grb_2': {}, 'weights_2': {}} + + # loop over all redshift bins + for i in tqdm(range(len(z_birth))): + + for s, event in enumerate(['CC1', 'CC2']): + # compute the CC time of each compact object + t_CC = self.get_time_GRB(z_birth[i], event) + + # intrinsic population + if sensitivity == 'infinite': + + # sort out system not emitting GRBs + bool_GRB = np.logical_and(t_CC < cosmology.age(1e-08).value*0.9999999 , self.df[f"GRB{s+1}"]) + + # if there are no system emitting any GRB, continue + if len(index[bool_GRB]) == 0: + data[f'index_{s+1}'][str(z_birth[i])] = np.array([]) + data[f'z_grb_{s+1}'][str(z_birth[i])] = np.array([]) + data[f'weights_{s+1}'][str(z_birth[i])] = np.array([]) + continue + + # get GRB redshifts + z_GRB = get_redshift_from_time(t_CC[bool_GRB]) + + # set detection probabilities to 1 + p_det = np.ones(len(index[bool_GRB])) + + # store index and redshift of merger + data[f'index_{s+1}'][str(z_birth[i])] = index[bool_GRB] + data[f'z_grb_{s+1}'][str(z_birth[i])] = z_GRB + + # compute and store cosmological rate weights as in eq. B.8 in Bavera et al. (2020) + z_b = np.ones(len(index[bool_GRB]))*z_birth[i] + w_ijk = self.merger_rate_weight(z_b, z_GRB, p_det, index[bool_GRB]) + data[f'weights_{s+1}'][str(z_birth[i])] = w_ijk + + else: + raise ValueError('Unknown sensitivity!') + + # TODO: implemet the saving to h5 files + if extention == 'npz': + if self.verbose: + print('Formatting the data ....') + for s in [1,2]: + data_to_save = [[],[],[],[]] + for i, dict in enumerate([data[key] for key in data.keys() if f'{s}' in key]): + for key in dict.keys(): + data_to_save[i].extend(dict[key].tolist()) + if i == 0: + data_to_save[-1].extend(np.ones(len(dict[key]))*float(key)) + if self.verbose: + print('Saving the data ....') + for i, key in enumerate([key for key in data.keys() if f'{s}' in key]): + if key == 'index': + fmt_str = '%i' + else: + fmt_str = '%.8E' + np.savez(os.path.join(sensitivity_dir, f"{key}.npz"), + key=data_to_save[i], fmt=fmt_str) + + np.savez(os.path.join(sensitivity_dir,f"z_formation_{s}.npz"), + key=data_to_save[-1], fmt='%.8E') + else: + raise ValueError('Extension not supported!') + + + def load_grb_rate_weights(self, sensitivity, path_to_dir='./', extention='npz'): + """Load the cosmological weights of the transient events associated to the population. + + Parameters + ---------- + sensitivity : string + Assume there are no selection effects. Available: + 'infinite': whole GRB population, i.e. p_det = 1 + path_to_dir : string + Path to the directory where you the cosmological weights are stored. + + Returns + ------- + array doubles + Return the cosmological weights, z_formation, z_GRB and binary + index k associated to each weighted binary. + + """ + dir_ = os.path.join(path_to_dir, f'GRBs/{sensitivity}_sensitivity') + + if extention == 'npz': + if self.verbose: + print('Loading the data ...') + index_1 = np.load(os.path.join(dir_, 'index_1.npz'), allow_pickle=True)['key'] + z_formation_1 = np.load(os.path.join(dir_, 'z_formation_1.npz'), allow_pickle=True)['key'] + z_grb_1 = np.load(os.path.join(dir_, 'z_grb_1.npz'), allow_pickle=True)['key'] + weights_1 = np.load(os.path.join(dir_, 'weights_1.npz'), allow_pickle=True)['key'] + index_2 = np.load(os.path.join(dir_, 'index_2.npz'), allow_pickle=True)['key'] + z_formation_2 = np.load(os.path.join(dir_, 'z_formation_2.npz'), allow_pickle=True)['key'] + z_grb_2 = np.load(os.path.join(dir_, 'z_grb_2.npz'), allow_pickle=True)['key'] + weights_2 = np.load(os.path.join(dir_, 'weights_2.npz'), allow_pickle=True)['key'] + else: + raise ValueError('Extension not supported!') + return index_1, z_formation_1, z_grb_1, weights_1, index_2, z_formation_2, z_grb_2, weights_2 \ No newline at end of file diff --git a/posydon/popsyn/selection_effects.py b/posydon/popsyn/selection_effects.py index 118022e019..b1e7974d1e 100644 --- a/posydon/popsyn/selection_effects.py +++ b/posydon/popsyn/selection_effects.py @@ -1,7 +1,3 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -# Copyright (C) Michael Zevin - """ Simple utility for generating detection weights @@ -10,6 +6,9 @@ Anticipates data as Pandas dataframe with series ['m1', 'q', 'z', 'chieff'] """ +__authors__ = ["Michael Zevin ", + "Simone Bavera ",] + import numpy as np import pandas as pd import time diff --git a/posydon/popsyn/synthetic_population.py b/posydon/popsyn/synthetic_population.py index 99cc5d06ff..f1828bce37 100644 --- a/posydon/popsyn/synthetic_population.py +++ b/posydon/popsyn/synthetic_population.py @@ -6,19 +6,21 @@ __authors__ = [ "Simone Bavera ", "Kyle Akira Rocha ", + "Monica Gallegos-Garcia ", ] import warnings import numpy as np import pandas as pd +from tqdm import tqdm from posydon.utils.constants import Zsun -from posydon.popsyn.io import parse_inifile, binarypop_kwargs_from_ini +from posydon.popsyn.io import binarypop_kwargs_from_ini from posydon.popsyn.binarypopulation import BinaryPopulation from posydon.utils.common_functions import convert_metallicity_to_string from posydon.popsyn.normalized_pop_mass import initial_total_underlying_mass -from posydon.utils.common_functions import inspiral_timescale_from_orbital_period from posydon.popsyn.rate_calculation import Rates -import posydon.visualization.plot_dco as plot_dco +import posydon.visualization.plot_pop as plot_pop +from posydon.popsyn.GRB import get_GRB_properties, GRB_PROPERTIES class SyntheticPopulation: @@ -36,15 +38,21 @@ def __init__(self, path_to_ini, path_to_data=None, verbose=False, MODEL={}): self.verbose = verbose self.MODEL = MODEL + # DCOs self.df = None self.df_oneline = None self.df_synthetic = None - self.df_intrinsic = None - self.df_detectable = None + self.df_dco_intrinsic = None + self.df_dco_observable = None self.met_merger_efficiency = None self.merger_efficiency = None - self.z_rate_density = None - self.rate_density = None + self.dco_z_rate_density = None + self.dco_rate_density = None + # GRBs + self.df_grb_intrinsic = None + self.df_grb_observable = None + self.grb_z_rate_density = None + self.grb_rate_density = None if '.ini' not in path_to_ini: raise ValueError('You did not provide a valid path_to_ini!') @@ -85,7 +93,8 @@ def create_met_prefix(met): return convert_metallicity_to_string(met) + '_Zsun_' def apply_logic(self, df, S1_state=None, S2_state=None, binary_state=None, - binary_event=None, step_name=None, invert_S1S2=False): + binary_event=None, step_name=None, invert_S1S2=False, + warn=True): """Select binaries in a dataframe given some properties. Parameters @@ -112,7 +121,7 @@ def apply_logic(self, df, S1_state=None, S2_state=None, binary_state=None, List of binaries to select given the search parameters. """ - if not invert_S1S2 and S1_state != S2_state: + if not invert_S1S2 and S1_state != S2_state and warn: warnings.warn('Note that invert_S1S2=False, hence you are not parsing ' f'the dataset for {S1_state}-{S2_state} binaries and ' f'and not for for {S1_state}-{S2_state}. If this is ' @@ -230,6 +239,10 @@ def parse(self, S1_state=None, S2_state=None, binary_state=None, df_sel_met = pd.concat([df_sel_met, df_tmp]) del df_tmp + if k > 0: + shift_index = max(np.unique(df_sel.index)) + 1 + df_sel_met.index += shift_index + # store simulated and underlying stellar mass df_sel_met['simulated_mass_for_met'] = simulated_mass_for_met df_sel_met['underlying_mass_for_met'] = initial_total_underlying_mass(df=simulated_mass_for_met, **self.ini_kw)[0] @@ -243,6 +256,11 @@ def parse(self, S1_state=None, S2_state=None, binary_state=None, df_sel_met_oneline = pd.read_hdf(file, key='oneline') df_sel_met_oneline = df_sel_met_oneline.loc[sel_met] df_sel_met_oneline['metallicity'] = met + + if k > 0: + shift_index = max(np.unique(df_sel_oneline.index)) + 1 + df_sel_met_oneline.index += shift_index + df_sel_oneline = pd.concat([df_sel_oneline, df_sel_met_oneline]) if self.verbose: @@ -294,7 +312,7 @@ def load_pop(self, path): else: raise ValueError('You already have a population stored in memory!') - def get_dco_at_formation(self, S1_state, S2_state, oneline_cols=None): + def get_dco_at_formation(self, S1_state, S2_state, oneline_cols=None, formation_channels=False): """Sort synthetic population, i.e. DCO at formation. Note: by default this function looks for the symmetric state @@ -311,6 +329,16 @@ def get_dco_at_formation(self, S1_state, S2_state, oneline_cols=None): into the synthetic population. """ + # compute GRB properties boolean + compute_GRB_properties = (self.MODEL is not None and + "compute_GRB_properties" in self.MODEL and + self.MODEL["compute_GRB_properties"]) + + # add channel column to oneline dataframe + if formation_channels: + if self.verbose: + print('Computing formation channels...') + self.get_formation_channels() # to avoid the user making mistake automatically check the inverse of # the stellar states, since the df is already parsed this will not @@ -325,13 +353,20 @@ def get_dco_at_formation(self, S1_state, S2_state, oneline_cols=None): # compute the inspiral timescale from the integrated orbit # this estimate is better than evaluating Peters approxiamtion time_contact = self.df.loc[self.df['event'] == 'END',['time']] + # NOTE/TODO: we change the units of time in the dataframe to Myr + # this might be confusion to the user? Note that Myr are convinient + # when inspecting the data frame. self.df_synthetic['t_delay'] = (time_contact - self.df_synthetic[['time']])*1e-6 # Myr self.df_synthetic['time'] *= 1e-6 # Myr - + # add properties of the oneline dataframe if self.df_oneline is not None: # TODO: add kicks as well by default? save_cols = ['S1_spin_orbit_tilt', 'S2_spin_orbit_tilt'] + if compute_GRB_properties: + save_cols += ['S1_m_disk_radiated', 'S2_m_disk_radiated'] + if formation_channels: + save_cols.append('channel') if oneline_cols is not None: for c in oneline_cols: if c not in save_cols: @@ -343,6 +378,45 @@ def get_dco_at_formation(self, S1_state, S2_state, oneline_cols=None): warnings.warn(f'The column {c} is not present in the ' 'oneline dataframe.') + # compute GRB properties + if compute_GRB_properties: + if ('GRB_efficiency' not in self.MODEL or + self.MODEL['GRB_efficiency'] is None): + raise ValueError('Missing GRB_efficiency variable in the MODEL!') + if ('GRB_beaming' not in self.MODEL or + self.MODEL['GRB_beaming'] is None): + raise ValueError('Missing GRB_beaming variable in the MODEL!') + if ('E_GRB_iso_min' not in self.MODEL or + self.MODEL['E_GRB_iso_min'] is None): + raise ValueError('Missing GRB_beaming variable in the MODEL!') + self.df_synthetic = get_GRB_properties(self.df_synthetic, + self.MODEL['GRB_efficiency'], + self.MODEL['GRB_beaming'], + self.MODEL['E_GRB_iso_min'] + ) + # get time_CC1 and time_CC2 note that for GRB calculations we + # do not use the "time" column indicating the time of formation + # of the DCO systems as there might be cases where + # CC2 might happen before CC1 due to mass ratio reversal + DCO = [[S1_state, None], [None, S2_state]] + events = ['CC1', 'CC2'] + for i, event in enumerate(events): + logic = self.apply_logic(self.df, S1_state=DCO[i][0], + S2_state=DCO[i][1], + binary_state='detached', + step_name = 'step_SN', + invert_S1S2=False, + warn=False) + last_index = -1 + time = [] + for index, value in self.df.loc[logic,'time'].items(): + if index != last_index: + time.append(value) + last_index = index + if len(time) != self.df_synthetic.shape[0]: + raise ValueError('Missing values in time_{event}!') + self.df_synthetic[f'time_{event}'] = np.array(time)*1e-6 # Myr + # for convinience reindex the DataFrame n_rows = len(self.df_synthetic.index) self.df_synthetic = self.df_synthetic.set_index(np.linspace(0,n_rows-1,n_rows,dtype=int)) @@ -392,11 +466,27 @@ def get_dco_merger_efficiency(self): efficiencies.append(eff) print(f'DCO merger efficiency at Z={met:1.2E}: {eff:1.2E} Msun^-1') self.met_merger_efficiency = np.array(self.met_merger_efficiency) - self.merger_efficiency = np.array(efficiencies) - - - def compute_cosmological_weights(self, sensitivity, flag_pdet, working_dir, load_data): - """Compute the DCO merger rate weights. + self.merger_efficiency = {'total' : np.array(efficiencies)} + # if the channel column is present compute the merger efficiency per channel + if "channel" in self.df_synthetic: + channels = np.unique(self.df_synthetic['channel']) + for ch in channels: + efficiencies = [] + for met in self.met_merger_efficiency: + sel = (self.df_synthetic['metallicity'] == met) & (self.df_synthetic['channel'] == ch) + count = self.df_synthetic[sel].shape[0] + if count > 0: + underlying_stellar_mass = self.df_synthetic.loc[sel,'underlying_mass_for_met'].values[0] + eff = count/underlying_stellar_mass + else: + eff = np.nan + efficiencies.append(eff) + # print(f'Z={met:1.2E} {ch}: {eff:1.2E} Msun^-1') + self.merger_efficiency[ch] = np.array(efficiencies) + + + def compute_cosmological_weights(self, sensitivity, flag_pdet, working_dir, load_data, pop='DCO'): + """Compute the GRB/DCO merger rate weights. Parameters ---------- @@ -432,14 +522,25 @@ def compute_cosmological_weights(self, sensitivity, flag_pdet, working_dir, load self.rates = Rates(self.df_synthetic, **self.MODEL) # compute DCO merger rate density - if not load_data: - self.rates.compute_merger_rate_weights(sensitivity=sensitivity, flag_pdet=flag_pdet, path_to_dir=working_dir) - index, z_formation, z_merger, w_ijk = self.rates.load_merger_rate_weights(sensitivity, path_to_dir=working_dir) - - return index, z_formation, z_merger, w_ijk + if pop == 'DCO': + if not load_data: + self.rates.compute_merger_rate_weights(sensitivity=sensitivity, flag_pdet=flag_pdet, path_to_dir=working_dir) + index, z_formation, z_merger, w_ijk = self.rates.load_merger_rate_weights(sensitivity, path_to_dir=working_dir) + + return index, z_formation, z_merger, w_ijk + elif pop == 'GRB': + if not load_data: + self.rates.compute_GRB_rate_weights(sensitivity=sensitivity, path_to_dir=working_dir) + index_1, z_formation_1, z_grb_1, w_ijk_1, \ + index_2, z_formation_2, z_grb_2, w_ijk_2 = self.rates.load_grb_rate_weights(sensitivity, path_to_dir=working_dir) + + return index_1, z_formation_1, z_grb_1, w_ijk_1, index_2, z_formation_2, z_grb_2, w_ijk_2 + else: + raise ValueError('Population not recognized!') - def resample_synthetic_population(self, index, z_formation, z_merger, w_ijk, export_cols=None): - """Resample synthetc population to obtain intrinsic/detectable population. + def resample_synthetic_population(self, index, z_formation, z_event, w_ijk, export_cols=None, + pop='DCO', reset_grb_properties=None): + """Resample synthetc population to obtain intrinsic/observable population. Parameters ---------- @@ -453,7 +554,7 @@ def resample_synthetic_population(self, index, z_formation, z_merger, w_ijk, exp w_ijk : array float Cosmological weights computed with Eq. B.8 of Bavera et at. (2020). export_cols : list str - List of additional columns to save in the underlying/detectable + List of additional columns to save in the intrinsic/observable population. Returns @@ -463,12 +564,16 @@ def resample_synthetic_population(self, index, z_formation, z_merger, w_ijk, exp population. """ + # compute GRB properties boolean + compute_GRB_properties = (self.MODEL is not None and + "compute_GRB_properties" in self.MODEL and + self.MODEL["compute_GRB_properties"]) # drop all zero weights to save memory sel = w_ijk > 0. index = index[sel] z_formation = z_formation[sel] - z_merger = z_merger[sel] + z_event = z_event[sel] w_ijk = w_ijk[sel] # export results, we do a selections of columns else the dataframe @@ -476,17 +581,40 @@ def resample_synthetic_population(self, index, z_formation, z_merger, w_ijk, exp df = pd.DataFrame() df['weight'] = w_ijk df['z_formation'] = z_formation - df['z_merger'] = z_merger + if pop == 'DCO': + df['z_merger'] = z_event + elif pop == 'GRB': + df['z_grb'] = z_event + else: + raise ValueError('Population not recognized!') save_cols = ['metallicity','time','t_delay','S1_state','S2_state', 'S1_mass','S2_mass','S1_spin','S2_spin', 'orbital_period','eccentricity', 'q', 'm_tot', 'm_chirp', 'chi_eff'] + if compute_GRB_properties: + save_cols += GRB_PROPERTIES + if "channel" in self.rates.df: + save_cols.append('channel') if export_cols is not None: for c in export_cols: if c not in save_cols: save_cols.append(c) for c in save_cols: df[c] = self.rates.get_data(c, index) + + # the same binary system can emit two GRBs, we remove the + # GRB properties of the other GRB to prevent mistakes + if reset_grb_properties is not None: + if reset_grb_properties == 'GRB1': + for key in GRB_PROPERTIES: + if "S1" in key: + df[key] = np.nan + elif reset_grb_properties == 'GRB2': + for key in GRB_PROPERTIES: + if "S2" in key: + df[key] = np.nan + else: + raise ValueError(f'reset_grb_properties=={reset_grb_properties} key not recognized!') return df @@ -496,7 +624,7 @@ def get_dco_merger_rate_density(self, export_cols=None, working_dir='./', load_ Parameters ---------- export_cols : list str - List of additional columns to save in the underlying/detectable + List of additional columns to save in the intrinsic/observable population. working_dir : str Working directory where the weights will be saved. @@ -511,16 +639,89 @@ def get_dco_merger_rate_density(self, export_cols=None, working_dir='./', load_ # compute cosmological weights (detection rate weights with infinite sensitivity) sensitivity='infinite' flag_pdet = False - index, z_formation, z_merger, w_ijk = self.compute_cosmological_weights(sensitivity, flag_pdet, working_dir=working_dir, load_data=load_data) + index, z_formation, z_merger, w_ijk = self.compute_cosmological_weights(sensitivity, flag_pdet, working_dir=working_dir, load_data=load_data, pop='DCO') # compute rate density weights - self.z_rate_density = self.rates.get_centers_redshift_bins() - self.rate_density = self.rates.compute_merger_rate_density(w_ijk, z_merger, observable='DCOs', sensitivity=sensitivity) - print(f'DCO merger rate density in the local Universe (z={self.z_rate_density[0]:1.2f}): {round(self.rate_density[0],2)} Gpc^-3 yr^-1') + self.dco_z_rate_density = self.rates.get_centers_redshift_bins() + total_rate = self.rates.compute_rate_density(w_ijk, z_merger, observable='DCO', sensitivity=sensitivity) + self.dco_rate_density = {'total' : total_rate} + if "channel" in self.df_synthetic: + channels = np.unique(self.df_synthetic['channel']) + for ch in tqdm(channels): + sel = (self.rates.get_data('channel', index) == ch) + rate = self.rates.compute_rate_density(w_ijk[sel], z_merger[sel], observable='DCO', sensitivity=sensitivity) + self.dco_rate_density[ch] = rate + + print(f'DCO merger rate density in the local Universe (z={self.dco_z_rate_density[0]:1.2f}): {round(total_rate[0],2)} Gpc^-3 yr^-1') # export the intrinsic DCO population - self.df_intrinsic = self.resample_synthetic_population(index, z_formation, z_merger, w_ijk, export_cols=export_cols) + self.df_dco_intrinsic = self.resample_synthetic_population(index, z_formation, z_merger, w_ijk, export_cols=export_cols, pop='DCO') + + + def get_grb_rate_density(self, export_cols=None, working_dir='./', load_data=False): + """Compute the GRB density as a function of redshift. + Parameters + ---------- + export_cols : list str + List of additional columns to save in the intrinsic/observable + population. + working_dir : str + Working directory where the weights will be saved. + load_data : bool + `True` if you want to load the weights computed by this function + in your working directory. + + """ + if self.df_synthetic is None: + raise ValueError('You first need to isolated the DCO synthetic population!') + + # compute cosmological weights (detection rate weights with infinite sensitivity) + sensitivity='infinite' + flag_pdet = False + index_1, z_formation_1, z_grb_1, w_ijk_1, \ + index_2, z_formation_2, z_grb_2, w_ijk_2 = self.compute_cosmological_weights(sensitivity, flag_pdet, working_dir=working_dir, load_data=load_data, pop='GRB') + + # compute beamed rate density weights + # note we compute the beamed rate density for GRBs as this is what is often reported in the literature + # as the beaming factor is not known a priori + self.grb_z_rate_density = self.rates.get_centers_redshift_bins() + total_GRB1 = self.rates.compute_rate_density(w_ijk_1, z_grb_1, observable='GRB1', sensitivity='beamed', index=index_1) + total_GRB2 = self.rates.compute_rate_density(w_ijk_2, z_grb_2, observable='GRB2', sensitivity='beamed', index=index_2) + total_rate = total_GRB1 + total_GRB2 + self.grb_rate_density = {'total' : total_rate, 'total_GRB1' : total_GRB1, 'total_GRB2': total_GRB2} + if "channel" in self.df_synthetic: + channels = np.unique(self.df_synthetic['channel']) + for ch in tqdm(channels): + sel1 = (self.rates.get_data('channel', index_1) == ch) + if any(sel1): + self.grb_rate_density[ch+'_GRB1'] = self.rates.compute_rate_density(w_ijk_1[sel1], z_grb_1[sel1], observable='GRB1', sensitivity='beamed', index=index_1[sel1]) + else: + self.grb_rate_density[ch+'_GRB1'] = np.zeros(len(self.grb_z_rate_density)) + sel2 = (self.rates.get_data('channel', index_2) == ch) + if any(sel2): + self.grb_rate_density[ch+'_GRB2'] = self.rates.compute_rate_density(w_ijk_2[sel2], z_grb_2[sel2], observable='GRB2', sensitivity='beamed', index=index_2[sel2]) + else: + self.grb_rate_density[ch+'_GRB2'] = np.zeros(len(self.grb_z_rate_density)) + self.grb_rate_density[ch] = self.grb_rate_density[ch+'_GRB1'] + self.grb_rate_density[ch+'_GRB2'] + + print(f'GRB (beamed) rate density in the local Universe (z={self.grb_z_rate_density[0]:1.2f}): {round(total_rate[0],2)} Gpc^-3 yr^-1') + + # export the intrinsic grb intrisic population + # TODO: instead of concatenating two dataframe and duplicating the information of the same + # binary system we should combine the two datraframes where we have two columns for GRB1 and GRB2 + # such dataframe will have z_grb_1, z_grb_2, weight_1, weight_2 + # when this is addressed, remove reset_grb_properties feature + self.df_grb_intrinsic = pd.DataFrame() + df_grb_1 = self.resample_synthetic_population(index_1, z_formation_1, z_grb_1, w_ijk_1, export_cols=export_cols, pop='GRB', reset_grb_properties='GRB2') + df_grb_2 = self.resample_synthetic_population(index_2, z_formation_2, z_grb_2, w_ijk_2, export_cols=export_cols, pop='GRB', reset_grb_properties='GRB1') + self.df_grb_intrinsic = pd.concat([df_grb_1, df_grb_2], ignore_index=True, sort=False) + # the observable population accounts for beaming + self.df_grb_observable = self.df_grb_intrinsic.copy() + for i in [1,2]: + sel = self.df_grb_observable[f'S{i}_f_beaming'] > 0 + self.df_grb_observable.loc[sel,'weight'] *= self.df_grb_observable.loc[sel,f'S{i}_f_beaming'] + def get_dco_detection_rate(self, sensitivity='design_H1L1V1', export_cols=None, working_dir='./', load_data=False): """Compute the detection rate per yr. @@ -535,7 +736,7 @@ def get_dco_detection_rate(self, sensitivity='design_H1L1V1', export_cols=None, 'O4high_H1L1V1' : aligo_O4high.txt, aligo_O4high.txt, avirgo_O4high_NEW.txt 'design_H1L1V1' : AplusDesign.txt, AplusDesign.txt, avirgo_O5high_NEW.txt export_cols : list str - List of additional columns to save in the underlying/detectable + List of additional columns to save in the intrinsic/observable population. working_dir : str Working directory where the weights will be saved. @@ -552,11 +753,41 @@ def get_dco_detection_rate(self, sensitivity='design_H1L1V1', export_cols=None, index, z_formation, z_merger, w_ijk = self.compute_cosmological_weights(sensitivity, flag_pdet, working_dir=working_dir, load_data=load_data) print(f'DCO detection rate at {sensitivity} sensitivity: {sum(w_ijk):1.2f} yr^-1') - # export the detectable DCO population + # export the observable DCO population # TODO: store p_det - self.df_detectable = self.resample_synthetic_population(index, z_formation, z_merger, w_ijk, export_cols=export_cols) - - def save_intrinsic_pop(self, path='./intrinsic_population.h5'): + self.df_dco_observable = self.resample_synthetic_population(index, z_formation, z_merger, w_ijk, export_cols=export_cols) + + def get_formation_channels(self): + """Get formation channel and add to df and df_oneline.""" + + # loop through each binary + unique_binary_index = np.unique(self.df.index) + for index in unique_binary_index: + + # get event column and information from interpolated classes + df_binary = self.df.loc[index,['event']].dropna() + intep_cls = [key for key in self.df_oneline.keys() if 'interp_class' in key] + df_binary_online = self.df_oneline.loc[index, intep_cls].dropna() + event_array = df_binary['event'].values.tolist() + + # make interpolated class information consistent with event column + HMS_HMS_event_dict = {'stable_MT':'oRLO1', 'no_MT':'None', 'unstable_MT':'oCE1/oDoubleCE1'} + event_HMS_HMS = HMS_HMS_event_dict[df_binary_online['interp_class_HMS_HMS']] + + # for now, only append information for RLO1; unstable_MT information already exists + if event_HMS_HMS == 'oRLO1': + event_array.insert(1, event_HMS_HMS) + formation_channel = "_".join(event_array) + else: + formation_channel = "_".join(event_array) + + # TODO: drop the envent CO_contact + # TODO: once we trust the redirection to the detached step + # drop also the redirect event + + self.df_oneline.loc[index,'channel'] = formation_channel + + def save_intrinsic_pop(self, path='./intrinsic_population_type.h5', pop='DCO'): """Save intrinsic population. Parameters @@ -565,14 +796,24 @@ def save_intrinsic_pop(self, path='./intrinsic_population.h5'): Path to dataset. """ - if self.df_intrinsic is None: - raise ValueError('Nothing to save!') + if pop == 'DCO': + if self.df_dco_intrinsic is None: + raise ValueError('Nothing to save!') + else: + self.df_dco_intrinsic.to_hdf(path.replace('type', pop), key='history') + if self.verbose: + print('Intrinsic population successfully saved!') + elif pop == 'GRB': + if self.df_grb_intrinsic is None: + raise ValueError('Nothing to save!') + else: + self.df_grb_intrinsic.to_hdf(path.replace('type', pop), key='history') + if self.verbose: + print('Intrinsic population successfully saved!') else: - self.df_intrinsic.to_hdf(path, key='history') - if self.verbose: - print('Intrinsic population successfully saved!') + raise ValueError('Population not recognized!') - def load_intrinsic_pop(self, path): + def load_intrinsic_pop(self, path, pop='DCO'): """Load intrinsic population. Parameters @@ -581,15 +822,23 @@ def load_intrinsic_pop(self, path): Path to dataset. """ - if self.df_intrinsic is None: - self.df_intrinsic = pd.read_hdf(path, key='history') - if self.verbose: - print('Intrinsic population successfully loaded!') - else: - raise ValueError('You already have an intrinsic population stored in memory!') - - def save_detectable_pop(self, path='./detectable_population.h5'): - """Save detectable population. + if pop == 'DCO': + if self.df_dco_intrinsic is None: + self.df_dco_intrinsic = pd.read_hdf(path, key='history') + if self.verbose: + print('Intrinsic population successfully loaded!') + else: + raise ValueError('You already have an intrinsic population stored in memory!') + elif pop == 'GRB': + if self.df_grb_intrinsic is None: + self.df_grb_intrinsic = pd.read_hdf(path, key='history') + if self.verbose: + print('Intrinsic population successfully loaded!') + else: + raise ValueError('You already have an intrinsic population stored in memory!') + + def save_observable_pop(self, path='./observable_population_type.h5', pop='DCO'): + """Save observable population. Parameters ---------- @@ -597,15 +846,25 @@ def save_detectable_pop(self, path='./detectable_population.h5'): Path to dataset. """ - if self.df_detectable is None: - raise ValueError('Nothing to save!') + if pop == 'DCO': + if self.df_dco_observable is None: + raise ValueError('Nothing to save!') + else: + self.df_dco_observable.to_hdf(path.replace('type', pop), key='history') + if self.verbose: + print('observable population successfully saved!') + elif pop == 'GRB': + if self.df_grb_observable is None: + raise ValueError('Nothing to save!') + else: + self.df_grb_observable.to_hdf(path.replace('type', pop), key='history') + if self.verbose: + print('observable population successfully saved!') else: - self.df_detectable.to_hdf(path, key='history') - if self.verbose: - print('Detectable population successfully saved!') + raise ValueError('Population not recognized!') - def load_detectable_pop(self, path): - """Load detectable population. + def load_observable_pop(self, path, pop='DCO'): + """Load observable population. Parameters ---------- @@ -613,44 +872,90 @@ def load_detectable_pop(self, path): Path to dataset. """ - if self.df_detectable is None: - self.df_detectable = pd.read_hdf(path, key='history') - if self.verbose: - print('Detectable population successfully loaded!') + if pop == 'DCO': + if self.df_dco_observable is None: + self.df_dco_observable = pd.read_hdf(path, key='history') + if self.verbose: + print('observable population successfully loaded!') + else: + raise ValueError('You already have an observable population stored in memory!') + elif pop == 'GRB': + if self.df_grb_observable is None: + self.df_grb_observable = pd.read_hdf(path, key='history') + if self.verbose: + print('observable population successfully loaded!') + else: + raise ValueError('You already have an observable population stored in memory!') else: - raise ValueError('You already have an detectable population stored in memory!') + raise ValueError('Population not recognized!') def plot_merger_efficiency(self, **kwargs): """Plot merger rate efficinty.""" if self.met_merger_efficiency is None or self.merger_efficiency is None: raise ValueError('First you need to compute the merger efficinty!') - plot_dco.plot_merger_efficiency(self.met_merger_efficiency, self.merger_efficiency, **kwargs) - - def plot_merger_rate_density(self, **kwargs): - """Plot merger rate density.""" - if self.z_rate_density is None or self.rate_density is None: - raise ValueError('First you need to compute the merger rate density!') - plot_dco.plot_merger_rate_density(self.z_rate_density, self.rate_density, **kwargs) + plot_pop.plot_merger_efficiency(self.met_merger_efficiency, self.merger_efficiency, **kwargs) - def plot_hist_dco_properties(self, var, intrinsic=False, detectable=False, **kwargs): - """Plot histogram of intrinsic/detectable properites. + def plot_hist_properties(self, var, intrinsic=False, observable=False, pop=None, **kwargs): + """Plot histogram of intrinsic/observable properites. Parameters ---------- var : str - Property to plot stored in intrinsic/detectable dataframe. + Property to plot stored in intrinsic/observable dataframe. intrinsic : bool `True` if you want to deplay the intrisc population. - detectable : bool - `True` if you want to deplay the detectable population. + observable : bool + `True` if you want to deplay the observable population. **kwargs : dict ploting arguments """ - if self.z_rate_density is None or self.rate_density is None: - raise ValueError('First you need to compute the merger rate density!') - if intrinsic: - intrinsic = self.df_intrinsic - if detectable: - detectable = self.df_detectable - plot_dco.plot_hist_dco_properties(var, df_intrinsic=intrinsic, df_detectable=detectable, **kwargs) + if pop == 'DCO': + if self.df_dco_intrinsic is None and self.df_dco_observable is None: + raise ValueError('First you need to compute the merger rate density!') + if intrinsic: + df_intrinsic = self.df_dco_intrinsic + else: + df_intrinsic = None + if observable: + df_observable = self.df_dco_observable + else: + df_observable = None + elif pop == 'GRB': + if self.df_grb_intrinsic is None and self.df_grb_observable is None: + raise ValueError('First you need to compute the merger rate density!') + if intrinsic: + df_intrinsic = self.df_grb_intrinsic + else: + df_intrinsic = None + if observable: + df_observable = self.df_grb_observable + else: + df_observable = None + else: + raise ValueError('Population not recognized!') + plot_pop.plot_hist_properties(var, df_intrinsic=df_intrinsic, df_observable=df_observable, pop=pop, **kwargs) + + def plot_rate_density(self, DCO=False, GRB=False, **kwargs): + """Plot DCO and GRB rate densities.""" + if not DCO and not GRB: + raise ValueError('You need to choose at least one population to plot!') + if DCO: + if self.dco_z_rate_density is None or self.dco_rate_density is None: + raise ValueError('First you need to compute the merger rate density!') + else: + z_dco = self.dco_z_rate_density + rate_dco = self.dco_rate_density + else: + z_dco = None + rate_dco = None + if GRB: + if self.grb_z_rate_density is None or self.grb_rate_density is None: + raise ValueError('First you need to compute the GRB rate density!') + else: + z_grb = self.grb_z_rate_density + rate_grb = self.grb_rate_density + else: + z_grb = None + rate_grb = None + plot_pop.plot_rate_density(z_dco, rate_dco, z_grb, rate_grb, **kwargs) diff --git a/posydon/visualization/plot_dco.py b/posydon/visualization/plot_dco.py deleted file mode 100644 index b499cca10f..0000000000 --- a/posydon/visualization/plot_dco.py +++ /dev/null @@ -1,102 +0,0 @@ -import os -import matplotlib.pyplot as plt -from posydon.utils.common_functions import PATH_TO_POSYDON -from posydon.visualization.plot_defaults import DEFAULT_LABELS - -plt.style.use(os.path.join(PATH_TO_POSYDON, "posydon/visualization/posydon.mplstyle")) - -def plot_merger_efficiency(met, merger_efficiency, show=True, path=None, Zsun=0.0142): - title = r'Merger efficiency' - plt.figure() - plt.title(title) - plt.plot(met/Zsun, merger_efficiency) - plt.yscale('log') - plt.xscale('log') - plt.xlabel(r'$Z/Z_\odot$') - plt.ylabel(r'\#DCOs [$M_\odot^{-1}$]') - if path: - plt.savefig(path) - if show: - plt.show() - -def plot_merger_rate_density(z, rate_density, zmax=10., show=True, path=None): - title = r'Merger rate density' - plt.figure() - plt.title(title) - plt.plot(z[z"] + +import os +import numpy as np +import matplotlib.pyplot as plt +from posydon.utils.common_functions import PATH_TO_POSYDON +from posydon.visualization.plot_defaults import DEFAULT_LABELS +from posydon.utils.constants import Zsun + +plt.style.use(os.path.join(PATH_TO_POSYDON, "posydon/visualization/posydon.mplstyle")) + +COLORS = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', + 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan'] +COLORS *= 10 + +def plot_merger_rate_density(z, rate_density, ylim=(1e-1,4e2), zmax=10., show=True, + path=None, channels=False, GWTC3=False, **kwargs): + + plt.plot(z[z 10^{51} \, \mathrm{erg})$ SHOALS survey') + +def plot_rate_density(z_dco=None, R_dco=None, z_grb=None, R_grb=None, **kwargs): + plt.figure() + title1 = '' + title2 = '' + if z_dco is not None and R_dco is not None: + title1 += 'DCO merger' + plot_merger_rate_density(z_dco, R_dco, **kwargs) + # do not display twice the channel labels + kwargs['label_channels'] = False + if z_grb is not None and R_grb is not None: + title2 += 'GRB beamed' + plot_grb_rate_density(z_grb, R_grb, **kwargs) + if title1 and title2: + title = title1 + ' \& ' + title2 + ' rate densities' + else: + title = title1 + title2 + ' rate density' + plt.title(title) + plt.yscale('log') + plt.ylabel(r'$\mathcal{R} \,[\mathrm{Gpc}^{-3}\,\mathrm{yr}^{-1}]$') + plt.xlabel(r'$z$') + plt.legend(loc='center left', bbox_to_anchor=(1, 0.5)) + if 'ylim' in kwargs: + plt.ylim(kwargs['ylim']) + if 'path' in kwargs and isinstance(kwargs['path'],str): + plt.savefig(path) + if 'show' in kwargs and kwargs['show']: + plt.show() + +def plot_merger_efficiency(met, merger_efficiency, show=True, path=None, channels=False): + title = r'Merger efficiency' + plt.figure() + plt.title(title) + plt.plot(met/Zsun, merger_efficiency['total'], label='total', color='black') + if channels: + for ch in merger_efficiency.keys(): + if ch != 'total': + plt.plot(met/Zsun, merger_efficiency[ch], label=ch) + plt.yscale('log') + plt.xscale('log') + plt.xlabel(r'$Z/Z_\odot$') + plt.ylabel(r'\#DCOs [$M_\odot^{-1}$]') + plt.legend(loc='center left', bbox_to_anchor=(1, 0.5)) + if path: + plt.savefig(path) + if show: + plt.show() + + +def plot_hist_properties(x, df_intrinsic=None, df_observable=None, + channel=None, + show=True, path=None, alpha=0.5, + range=None, bins=20, xlog=False, ylog=False, + pop=None, **kwargs): + if pop is None: + raise ValueError('Population type not specified.') + + if df_intrinsic is not None and df_observable is not None: + title = r'Intrinsic vs. observable (dashed) population' + elif df_intrinsic is not None: + title = r'Intrinsic population' + elif df_observable is not None: + title = r'Observable population' + else: + raise ValueError('You should provide either an intrinsic or a ' + 'detectable population.') + + plt.figure() + if df_intrinsic is not None: + # normalise weight, for GRB this will conserve GRB1/GRB2 ratio + df_intrinsic['weight'] /= sum(df_intrinsic['weight']) + if channel is not None: + sel = (df_intrinsic['weight'] > 0) & (df_intrinsic['channel'] == channel) + title += f'\n{channel}' + else: + sel = df_intrinsic['weight'] > 0 + if isinstance(x, str): + if pop == 'GRB': + sel_tmp = sel & ~df_intrinsic[x].isna() + else: + sel_tmp = sel + values, weights = df_intrinsic.loc[sel_tmp,[x,'weight']].values.T + if xlog: + mask = values > 0 + values = np.log10(values[mask]) + weights = weights[mask] + plt.hist(values, weights=weights, color=COLORS[0], + alpha=alpha, range=range, bins=bins) + elif isinstance(x, list): + for i, x_i in enumerate(x): + if "S1" in x_i: + label = 'S1' + s = 1 + elif "S2" in x_i: + label = 'S2' + s = 2 + else: + label = x_i + if pop == 'GRB': + sel_tmp = sel & ~df_intrinsic[x_i].isna() & df_intrinsic[f'GRB{s}'] + else: + sel_tmp = sel + values, weights = df_intrinsic.loc[sel_tmp,[x_i,'weight']].values.T + if xlog: + mask = values > 0 + values = np.log10(values[mask]) + weights = weights[mask] + plt.hist(values, weights=weights, + color=COLORS[i], label=label, + alpha=alpha, range=range, bins=bins) + if df_observable is not None: + # normalise weight, for GRB this will conserve GRB1/GRB2 ratio + df_observable['weight'] /= sum(df_observable['weight']) + if channel is not None: + sel = (df_observable['weight'] > 0) & (df_observable['channel'] == channel) + if channel not in title: + title += f'\n{channel}' + else: + sel = df_observable['weight'] > 0 + if isinstance(x, str): + if pop == 'GRB': + sel_tmp = sel & ~df_observable[x].isna() + else: + sel_tmp = sel + values, weights = df_observable.loc[sel_tmp,[x,'weight']].values.T + if xlog: + mask = values > 0 + values = np.log10(values[mask]) + weights = weights[mask] + plt.hist(values, weights=weights, + color=COLORS[0], + histtype=u'step', linestyle='--', + alpha=alpha, range=range, bins=bins) + elif isinstance(x, list): + for i, x_i in enumerate(x): + if "S1" in x_i: + label = 'S1' + s = 1 + elif "S2" in x_i: + label = 'S2' + s = 2 + else: + label = x_i + if pop == 'GRB': + sel_tmp = sel & ~df_observable[x_i].isna() & df_observable[f'GRB{s}'] + else: + sel_tmp = sel + values, weights = df_observable.loc[sel_tmp,[x_i,'weight']].values.T + if xlog: + mask = values > 0 + values = np.log10(values[mask]) + weights = weights[mask] + plt.hist(values, weights=weights, + color=COLORS[i], label=label, + histtype=u'step', linestyle='--', + alpha=alpha, range=range, bins=bins) + plt.title(title) + if ylog: + plt.yscale('log') + plt.ylabel(r'PDF') + try: + if isinstance(x, str): + if not xlog: + plt.xlabel(DEFAULT_LABELS[x][0]) + else: + plt.xlabel(DEFAULT_LABELS[x][1]) + else: + if not xlog: + plt.xlabel(DEFAULT_LABELS[x[0]][0]) + else: + plt.xlabel(DEFAULT_LABELS[x[0]][1]) + except: + if isinstance(x, str): + if not xlog: + plt.xlabel(x) + else: + plt.xlabel('log10_'+x) + else: + if not xlog: + plt.xlabel(x[0]) + else: + plt.xlabel('log10_'+x[0]) + if isinstance(x, list): + plt.legend(loc=1) + if path: + plt.savefig(path) + if show: + plt.show() \ No newline at end of file From 6a0128a691fa8bdffb7dc428431041fa4b4e7ef3 Mon Sep 17 00:00:00 2001 From: Elizabeth Teng <41969755+elizabethteng@users.noreply.github.com> Date: Thu, 20 Jul 2023 09:28:59 -0500 Subject: [PATCH 104/319] profile interpolation update (#111) * adding profile interpolation * adding monotonicity enforcement to main class * fixing typo in learn_bounds and increase epochs/callbacks * remove default path, move IF interpolator argument to train method * changed some variable names * adding load interpolator arg to main class init function * changing arg name from interpolator to IF_interpolator to prevent confusion * adding docs for prof interpolation * fixed file format for docs * fix typo * removed extraneous line * addressing some stylistic issues * resolve various issues * changed name of finalmass to final_m1, added missing states to X_H class * fixed missing return in mono renorm * addressing Philipp's comments; changing a few argument/variable names, added arguments for model training parameters * adding tensorflow to extras_require * fixed typo and added comment for tensorflow dependency * added helium profile interpolation, updated docs * update compiledata to include option for star 2, add MT class to dataframe * condensed a for loop * fixed a few bugs * moved some definitions around * make 80/20 split for validation data from training data * typos * allowed for star 2 in all classes * got rid of feature that would make predictions from untrained models for final star states with no training data * Update setup.py * testing branch * undo * Update ProfileInterpolator.rst * update comments and docstrings, fix star 1/star 2 stuff in composition class * adding missing argument name * fixed comment * update tensorflow version to 2.13 to avoid installation issues * added omega profiles normalized by omega/omega_crit --------- Co-authored-by: Elizabeth Teng Co-authored-by: Jeff Andrews Co-authored-by: Simone Bavera <32518238+ssbvr@users.noreply.github.com> --- .../ProfileInterpolator.rst | 84 ++++++---- .../interpolation/profile_interpolation.py | 144 ++++++++++-------- setup.py | 2 +- 3 files changed, 132 insertions(+), 98 deletions(-) diff --git a/docs/interpolation/Profile-Interpolator/ProfileInterpolator.rst b/docs/interpolation/Profile-Interpolator/ProfileInterpolator.rst index 812fc54eb2..1772bef48d 100644 --- a/docs/interpolation/Profile-Interpolator/ProfileInterpolator.rst +++ b/docs/interpolation/Profile-Interpolator/ProfileInterpolator.rst @@ -8,8 +8,8 @@ Profile Interpolation We demonstrate the profile interpolator which delivers predictions on internal stellar structure at the end of MESA evolution. -To use the profile interpolator we firstimport the ``ProfileInterpolator`` -object from the POSYDON library. +To use the profile interpolator we first import the ``ProfileInterpolator`` +and ``CompileData`` objects from the POSYDON library. .. code-block:: python @@ -20,18 +20,21 @@ Extracting Profile Data from .h5 Files for Training Interpolators =============================== To extract profile data from existing grids, we pass arguments -``train_path`` and ``valid_path`` pointing to .h5 files +``train_path`` and ``test_path`` pointing to .h5 files containing instances of the ``PSyGrid`` class from which the training and testing grids can be loaded. These can be constructed -by running ones own simulations or by loading a precomputed simulation +by running one's own simulations or by loading a precomputed simulation stored in the data directory of the POSYDON repository. The ``profile_names`` argument denotes which profiles will be extracted from the grid and saved to ``filename`` using the function ``save``. +The Boolean argument ``hms_s2`` should be set to True only to extract star 2 +profiles from the HMS-HMS grid. .. code-block:: python - compiler = CompileData(train_data = '/path/to/training/grid.h5', - valid_data = '/path/to/testing/grid.h5', + compiler = CompileData(train_path = '/path/to/training/grid.h5', + test_path = '/path/to/testing/grid.h5', + hms_s2 = False, profile_names=['radius','logRho', 'x_mass_fraction_H','y_mass_fraction_He', 'z_mass_fraction_metals','omega','energy']) @@ -41,29 +44,46 @@ from the grid and saved to ``filename`` using the function ``save``. Loading a Pretrained Interpolator =============================== -To load a pretrained interpolator we need to pass the optional -``load_interpolator`` argument to the ``ProfileInterpolator`` -instance which specifies the path to a .pkl file where the -pretrained interpolator can be loaded from. +To load a pretrained interpolator we instantiate a ``ProfileInterpolator`` object +instance and then use the ``load`` function which specifies the path to a +.pkl file from which the pretrained interpolator can be loaded. .. code-block:: python - model = ProfileInterpolator(load_interpolator = "/path/to/profile/interpolator.pkl") + model = ProfileInterpolator() + model.load(filename = "/path/to/profile/interpolator.pkl") Training the Interpolator ========================= -The interpolator is trained on profiles stored in a .pkl file generated with the -aforementioned ``CompileData`` class. This file is passed using the argument -``filename``. The profile interpolation is anchored to POSYDON's IF interpolation, -and so we pass the name of the interpolator file to the ``train`` function using the ``IF_interpolator`` argument. +The interpolator is trained on profiles stored in a .pkl file (passed with +argument ``filename``) generated with the ``CompileData`` class. +The function ``load_profiles`` also randomly splits the training data into +a training set and validation set based on the argument ``valid_split``, which +specifies the percentage of the training data that should be used for validation. +The profile interpolation is anchored to POSYDON's IF interpolation, +and so we pass the name of the interpolator file to the ``train`` function +using the ``IF_interpolator`` argument. +The four following arguments specify the numbers of epochs and level of patience +for callback that will be used for training the neural networks in the density +and composition models. The ``loss_history`` argument provides an option to +return the loss histories for the composition and density model training. +Once again, the Boolean argument ``hms_s2`` should be set to True to +build an interpolator for star 2 profiles from the HMS-HMS grid. .. code-block:: python - model = ProfileInterpolator(load_interpolator=None) - model.load_profiles(filename = "/path/to/profiles/datafile.pkl") - model.train(IF_interpolator = "/path/to/IFinterpolator.pkl") + model = ProfileInterpolator() + model.load_profiles(filename = "/path/to/profiles/datafile.pkl", + valid_split=0.2) + model.train(IF_interpolator = "/path/to/IFinterpolator.pkl", + density_epochs=3000, + density_patience=200, + comp_bounds_epochs=500, + comp_bounds_patience=50, + loss_history=False, + hms_s2=False) Using the Interpolator @@ -73,34 +93,32 @@ Once the interpolator has been trained or loaded from a .pkl file it can be used to predict profiles for sets of initial conditions passed through argument ``inputs``. These initial conditions must be in log space and in shape (N,3) for N binaries. The order of the coordinates is star 1 mass, star 2 mass, period. The prediction -function returns three arrays containing the profiles' coordinates along mass -enclosed, log density, and Hydrogen mass fraction. The Density and H mass fraction -profiles have the same mass coordinates. +function returns four arrays containing the profiles' coordinates along mass +enclosed, log density, Hydrogen mass fraction, and Helium mass fraction. All +profiles share the same coordinates. .. code-block:: python - mass_coords, density_profiles, h_profiles = model.predict(inputs) + mass_coords, density_profiles, h_profiles, he_profiles = model.predict(inputs) Finally a trained interpolator can be easily saved by specifying a path to a .pkl file where the interpolator will be saved to. .. code-block:: python - model.save("path/for/profile/interpolator.pkl") + model.save(filename = "path/for/profile/interpolator.pkl") Evaluating on Testing Data ========================== -To evaluate the interpolator on the testing grid that was used as validation data in -training, we can pull the testing data out of the ``ProfileInterpolator`` class as follows: +To evaluate the interpolator on the testing grid, we can pull the testing data +out of the ``ProfileInterpolator`` class as follows: .. code-block:: python - valid_initial = np.log10(np.transpose([model.valid_scalars["m1"], - model.valid_scalars['m2'], - model.valid_scalars['p']])) - - valid_mass_coords = np.transpose(np.array(model.valid_scalars["final_mass"])*np.linspace(0,1,200)[:,np.newaxis]) - valid_density_profiles = model.valid_profiles[:,model.names.index("logRho")] - valid_H_profiles = model.valid_profiles[:,model.names.index("x_mass_fraction_H")] - \ No newline at end of file + test_initial = model.test_initial + test_mass_coords = np.transpose(np.array(model.test_scalars["total_mass"])*np.linspace(0,1,200)[:,np.newaxis]) + test_density_profiles = model.test_profiles[:,model.names.index("logRho")] + test_H_profiles = model.test_profiles[:,model.names.index("x_mass_fraction_H")] + test_He_profiles = model.test_profiles[:,model.names.index("y_mass_fraction_He")] + diff --git a/posydon/interpolation/profile_interpolation.py b/posydon/interpolation/profile_interpolation.py index fe1766d593..05947143d6 100644 --- a/posydon/interpolation/profile_interpolation.py +++ b/posydon/interpolation/profile_interpolation.py @@ -47,7 +47,7 @@ def __init__(self, train_path, test_path, hms_s2=False, for i in range(len(test)): try: scalars,profiles = self.scrape(test,i,hms_s2) - self.test_scalars = self.test_scalars.append(scalars,ignofre_index=True) + self.test_scalars = self.test_scalars.append(scalars,ignore_index=True) self.test_profiles.append(profiles) except: testing_failed.append(i) @@ -68,7 +68,10 @@ def __init__(self, train_path, test_path, hms_s2=False, self.profiles.append(profiles) except: training_failed.append(i) - pass + pass + + if 'omega' in self.names: + self.names.append('norm_omega') warnings.warn(f"{len(training_failed)} training binaries failed") @@ -100,6 +103,7 @@ def scrape(self,grid,ind,hms_s2): # grab input values, final star 1 state, final star 1 mass total_mass = grid.final_values[mass_key][ind] + scalars = {"m1":grid.initial_values["star_1_mass"][ind], "m2":grid.initial_values["star_2_mass"][ind], "p":grid.initial_values["period_days"][ind], @@ -108,7 +112,7 @@ def scrape(self,grid,ind,hms_s2): "total_mass":total_mass} # grab output vectors, interpolate to normalize - profiles=np.zeros([len(self.names),200]) + profiles=np.zeros([1+len(self.names),200]) for i,prof in enumerate(self.names): if prof in df.columns: @@ -118,7 +122,11 @@ def scrape(self,grid,ind,hms_s2): profiles[i] = profile_new else: warnings.warn(f"{prof} profile not saved in grid, will not be included in file") - + + if 'omega' in self.names: + profiles[-1]= profiles[self.names.index('omega')]/ \ + grid.final_values['S1_surf_avg_omega_div_omega_crit'][ind] + return scalars, profiles def save(self, filename): @@ -144,7 +152,7 @@ def load_profiles(self,filename,valid_split=0.2): """Load and process extracted profile data. Args: filename (str) : path/name of '.pkl' file to be loaded. - valid_split (float) : percentage of training data used for validation data + valid_split (float) : percentage of training data used for validation set """ with open(filename, 'rb') as f: myattrs = pd.read_pickle(f) @@ -162,7 +170,7 @@ def load_profiles(self,filename,valid_split=0.2): self.scalars["m2"], self.scalars["p"]]) - # 80/20 split for training and validation data + # random split for training and validation data (default 80/20) binaries = np.arange(len(self.profiles)) np.random.shuffle(binaries) split = int(len(self.profiles)*valid_split) # index at which to split data @@ -185,13 +193,13 @@ def train(self,IF_interpolator,density_epochs=3000,density_patience=200, comp_bounds_epochs (int) : number of epochs used to train composition profiles model comp_bounds_patience (int) : patience parameter for NN callback in composition profiles model loss_history (Boolean) : option to return training and validation loss histories - hms_s2 (Boolean) : option to get profiles of star 2 in HMS-HMS grid + hms_s2 (Boolean) : option to do profiles of star 2 in HMS-HMS grid Returns: self.comp.loss_history (array-like) : training and validation loss history for composition profiles self.dens.loss_history (array-like) : training and validation loss history for density profiles """ - # instantiate and train H mass fraction profile model + # instantiate and train composition (H and He mass fraction) profiles model self.comp = Composition(self.initial, self.profiles[:,self.names.index("x_mass_fraction_H")], self.profiles[:,self.names.index("y_mass_fraction_He")], @@ -209,7 +217,7 @@ def train(self,IF_interpolator,density_epochs=3000,density_patience=200, self.valid_initial, self.valid_profiles[:,self.names.index("logRho")], IF_interpolator) - self.dens.train(prof_epochs=density_epochs,prof_patience=density_patience,hms_s2) + self.dens.train(prof_epochs=density_epochs,prof_patience=density_patience,hms_s2=hms_s2) if loss_history==True: return self.comp.loss_history, self.dens.loss_history @@ -244,7 +252,7 @@ def save(self, filename): pickle.dump(myattrs, f) def load(self, filename): - """Load interpolation model, which can only be used for predictions. + """Load interpolation model, which can be used for predictions. Args: filename (str) : path/name of '.pkl' file to be loaded. """ @@ -291,7 +299,7 @@ def __init__(self,initial,profiles,valid_initial, valid_profiles (array-like) : final density profiles for validation data. IF_interpolator (string) : path to .pkl file for IF interpolator for central density, final mass values n_comp (int) : number of PCA components. - hms_s2 (Boolean) : option to get profiles of star 2 in HMS-HMS grid + hms_s2 (Boolean) : option to do profiles of star 2 in HMS-HMS grid """ self.n_comp = n_comp self.hms_s2 = hms_s2 @@ -307,7 +315,7 @@ def __init__(self,initial,profiles,valid_initial, self.scaling = np.std(pca_weights_unscaled,axis=0) self.pca_weights = pca_weights_unscaled/self.scaling # scaled PCA weights - # process testing data + # process validation data self.valid_initial = valid_initial self.valid_rho_min = np.min(valid_profiles,axis=1) valid_rho_max = np.max(valid_profiles,axis=1) @@ -382,21 +390,22 @@ def predict(self,inputs): regress_rho = lambda x: self.model_rho(x) min_rho = regress_rho(inputs).numpy()[:,0] - # IF interpolate center density - center_ind = self.model_IF.interpolators[0].out_keys.index('S1_log_center_Rho') - max_rho = self.model_IF.interpolators[0].test_interpolator(10**inputs)[:,center_ind] - # reconstruct profile norm_prof = self.pca.inverse_transform(pca_weights_pred*self.scaling) density_profiles = norm_prof*(max_rho[:,np.newaxis]-min_rho[:,np.newaxis]) \ + min_rho[:,np.newaxis] - # IF interpolate final mass, construct mass enclosed profile coordinates + # IF interpolate final mass, center density if self.hms_s2==False: m_ind = self.model_IF.interpolators[0].out_keys.index("star_1_mass") + center_ind = self.model_IF.interpolators[0].out_keys.index('S1_log_center_Rho') else: m_ind = self.model_IF.interpolators[0].out_keys.index("star_2_mass") + center_ind = self.model_IF.interpolators[0].out_keys.index('S2_log_center_Rho') + max_rho = self.model_IF.interpolators[0].test_interpolator(10**inputs)[:,center_ind] pred_mass = self.model_IF.interpolators[0].test_interpolator(10**inputs)[:,m_ind] + + # construct mass enclosed profile coordinates mass_coords = np.linspace(0,1,200)*pred_mass[:,np.newaxis] return mass_coords,density_profiles @@ -404,52 +413,52 @@ def predict(self,inputs): class Composition: - def __init__(self,initial,h_profiles,he_profiles,s1, - valid_initial,valid_h_profiles,valid_he_profiles,valid_s1, + def __init__(self,initial,h_profiles,he_profiles,star_state, + valid_initial,valid_h_profiles,valid_he_profiles,valid_star_state, IF_interpolator,training_epochs=500, training_patience=50,hms_s2=False): """Creates and trains H mass fraction and He mass fraction profiles model. Args: initial (array-like) : log-space initial conditions for training data. h_profiles (array-like) : final H mass fraction profiles for training data. he_profiles (array-like) : final He mass fraction profiles for training data. - s1 (array-like) : final star 1 state for training data. + star_state (array-like) : final star 1 state for training data. valid_initial (array-like) : log-space initial conditions for validation data. valid_h_profiles (array-like) : final H mass fraction profiles for validation data. valid_he_profiles (array-like) : final He mass fraction profiles for validation data. - valid_s1 (array-like) : final star 1 state for testing data. + valid_star_state (array-like) : final star 1 state for testing data. IF_interpolator (string) : path to .pkl file for IF interpolator training_epochs (int) : number of epochs used to train neural networks training_patience (int) : patience parameter for callback in neural networks - hms_s2 (Boolean) : option to get profiles of star 2 in HMS-HMS grid + hms_s2 (Boolean) : option to do profiles of star 2 in HMS-HMS grid """ self.hms_s2 = hms_s2 self.initial = initial self.h_profiles = h_profiles self.he_profiles = he_profiles - self.s1 = s1 + self.star_state = star_state self.valid_initial = valid_initial self.valid_h_profiles = valid_h_profiles self.valid_he_profiles = valid_he_profiles - self.valid_s1 = valid_s1 + self.valid_star_state = valid_star_state # load IF interpolator self.model_IF = IFInterpolator() # instantiate POSYDON initial-final interpolator object self.model_IF.load(filename=IF_interpolator) self.interp = self.model_IF.interpolators[0] - if self.hms_s2==False: + if self.hms_s2==False: # default to star 1 self.c_h_ind = self.interp.out_keys.index("S1_center_h1") self.s_h_ind = self.interp.out_keys.index("S1_surface_h1") self.c_he_ind = self.interp.out_keys.index("S1_center_he4") self.s_he_ind = self.interp.out_keys.index("S1_surface_he4") - else: + else: # if interpolating star 2 profiles in HMS-HMS grid self.c_h_ind = self.interp.out_keys.index("S2_center_h1") self.s_h_ind = self.interp.out_keys.index("S2_surface_h1") self.c_he_ind = self.interp.out_keys.index("S2_center_he4") self.s_he_ind = self.interp.out_keys.index("S2_surface_he4") - # star 1 states + # final star states self.star_states = ['None','WD','NS','BH', 'stripped_He_non_burning', 'stripped_He_Core_He_burning', @@ -464,12 +473,12 @@ def __init__(self,initial,h_profiles,he_profiles,s1, 'H-rich_Core_He_burning', 'H-rich_Core_C_burning'] - # sort training data into classes by s1 state + # sort training data into classes by final star state self.sort_ind = {} self.valid_sort_ind = {} for name in self.star_states: - self.sort_ind[name] = np.where(self.s1==name)[0] - self.valid_sort_ind[name] = np.where(self.valid_s1==name)[0] + self.sort_ind[name] = np.where(self.star_state==name)[0] + self.valid_sort_ind[name] = np.where(self.valid_star_state==name)[0] # create and train models for profile boundaries self.bounds_models, self.loss_history = self.learn_bounds(training_epochs,training_patience) @@ -485,7 +494,7 @@ def learn_bounds(self,training_epochs,training_patience): """ b_models = {} loss_history = {} - self.empty = [] + self.empty = [] # keep track of which star states have no data for state in ['stripped_He_Core_He_burning', 'stripped_He_Core_C_burning', 'stripped_He_Central_He_depleted', @@ -515,12 +524,13 @@ def learn_bounds(self,training_epochs,training_patience): 'H-rich_Core_He_burning', 'H-rich_Core_C_burning']: # profiles with 2 boundary points outs = 2 - bounds = [] + bounds = [] # collect information on parameters for given H and He profile shapes nonflat = [] # ensures that training data only has the correct "non-flat" shape # to avoid issues with calculating the boundary points valid_bounds = [] valid_nonflat = [] - # calculate first and points in each profile with large increases + # calculate first and last points in each H profile with large increases + # (He profiles have the same boundary points) for i in range(len(indices)): try: diff = np.where(h_prof[i][1:]-h_prof[i][:-1]>0.002)[0] @@ -539,12 +549,12 @@ def learn_bounds(self,training_epochs,training_patience): elif state in ['H-rich_Central_He_depleted', 'H-rich_Central_C_depletion']: # H profiles with 1 boundary point outs = 3 - # calculate the point in each profile with the largest increase + # calculate the point in each H profile with the largest increase hbound = (np.argmax(h_prof[:,1:]-h_prof[:,:-1],axis=1)+1)/200 valid_hbound = (np.argmax(valid_h_prof[:,1:]-valid_h_prof[:,:-1],axis=1)+1)/200 - max_He = np.max(he_prof,axis=1) + max_He = np.max(he_prof,axis=1) # maximum He mass fraction valid_max_He = np.max(valid_he_prof,axis=1) - dep_He = (np.argmax(he_prof[:,1:]-he_prof[:,:-1],axis=1)+1)/200 + dep_He = (np.argmax(he_prof[:,1:]-he_prof[:,:-1],axis=1)+1)/200 # location of Helium depletion zone valid_dep_He = (np.argmax(valid_he_prof[:,1:]-valid_he_prof[:,:-1],axis=1)+1)/200 bounds = np.transpose([hbound,dep_He,max_He]) valid_bounds = np.transpose([valid_hbound,valid_dep_He,valid_max_He]) @@ -612,7 +622,7 @@ def learn_bounds(self,training_epochs,training_patience): print(f"finished {state}") return b_models, loss_history - def predict_single(self,initial,center_H,surface_H,center_He,surface_He,s1): + def predict_single(self,initial,center_H,surface_H,center_He,surface_He,star_state): """Predict a profile for a single binary Args: initial (array-like) : initial position of binary @@ -620,52 +630,55 @@ def predict_single(self,initial,center_H,surface_H,center_He,surface_He,s1): surface_H (float) : surface H mass fraction center_He (float) : center He mass fraction surface_He (float) : surface He mass fraction - s1 (str) : final star 1 state + star_state (str) : final star state Returns: H (array-like) : predicted H mass fraction profile He (array-like) : predicted He mass fraction profile """ - if "stripped_He" in s1: - H = np.zeros(200) + if "stripped_He" in star_state: + H = np.zeros(200) # stripped Helium stars have no Hydrogen - if s1 == "stripped_He_non_burning": - He = np.ones(200)*surface_He + if star_state == "stripped_He_non_burning": + He = np.ones(200)*surface_He # non-burning stars have a flat He profile - if s1 in ['stripped_He_Core_He_burning', + if star_state in ['stripped_He_Core_He_burning', 'stripped_He_Core_C_burning', 'stripped_He_Central_He_depleted', 'stripped_He_Central_C_depletion']: - b = self.bounds_models[s1](tf.convert_to_tensor([initial])).numpy()[0] + # predicting profile shape parameters + b = self.bounds_models[star_state](tf.convert_to_tensor([initial])).numpy()[0] He = np.ones(200) * center_He He[int(b[1]*200):] = surface_He f_He = interp1d(b,[center_He,surface_He],fill_value="extrapolate") # use f to construct the profile points in the shell burning region He[int(b[0]*200):int(b[1]*200)] = f_He(np.linspace(0,1,200)[int(b[0]*200):int(b[1]*200)]) - if s1 in ["H-rich_non_burning","None"]: + if star_state in ["H-rich_non_burning","None"]: # these states have flat H and He profiles H = np.ones(200)*surface_H He = np.ones(200)*surface_He - if s1 in ["WD","NS","BH"]: + if star_state in ["WD","NS","BH"]: # profiles for compact objects are arbitrary -- return nans H = np.ones(200)*np.nan He = np.ones(200)*np.nan - # construct step-shaped profile - if s1 in ["H-rich_Central_He_depleted", + # construct step-shaped profile - H and He profiles have symmetrical shapes + if star_state in ["H-rich_Central_He_depleted", "H-rich_Central_C_depletion"]: - b = self.bounds_models[s1](tf.convert_to_tensor([initial])).numpy()[0] + # predicting profile shape parameters + b = self.bounds_models[star_state](tf.convert_to_tensor([initial])).numpy()[0] H = np.ones(200)*center_H H[int(b[0]*200):] = surface_H He = np.ones(200) * center_He He[int(b[1]*200):] = b[2] He[int(b[0]*200):] = surface_He - # construct shell-shaped profile - if s1 in ["H-rich_Shell_H_burning", + # construct shell-shaped profile - H and He profiles have symmetrical shapes + if star_state in ["H-rich_Shell_H_burning", "H-rich_Core_H_burning", "H-rich_Core_He_burning", "H-rich_Core_C_burning"]: - b = self.bounds_models[s1](tf.convert_to_tensor([initial])).numpy()[0] + # predicting profile shape parameters + b = self.bounds_models[star_state](tf.convert_to_tensor([initial])).numpy()[0] new = np.ones([2,200]) * np.array([center_H,center_He])[:,np.newaxis] new[:,int(b[1]*200):] = np.array([surface_H,surface_He])[:,np.newaxis] f_H = interp1d(b,[center_H,surface_H],fill_value="extrapolate") @@ -677,7 +690,7 @@ def predict_single(self,initial,center_H,surface_H,center_He,surface_He,s1): He = new[1] # if there is no training data in the state, return nans - if s1 in self.empty: + if star_state in self.empty: H = np.ones(200)*np.nan He = np.ones(200)*np.nan @@ -692,28 +705,31 @@ def predict(self,inputs): h_profiles (array_like) : H mass fraction profile coordinates. he_profiles (array_like) : He mass fraction profile coordinates. """ - # IF interpolate H mass fraction values at center, surface; star 1 state + # IF interpolate H mass fraction values at center, surface; final star state center_h_vals = self.interp.test_interpolator(10**inputs)[:,self.c_h_ind] surface_h_vals = self.interp.test_interpolator(10**inputs)[:,self.s_h_ind] center_he_vals = self.interp.test_interpolator(10**inputs)[:,self.c_he_ind] surface_he_vals = self.interp.test_interpolator(10**inputs)[:,self.s_he_ind] - s1_vals = self.interp.test_classifiers(10**inputs)['S1_state'] - # generate predicted profiles - pred_profiles = [] - for i in range(len(inputs)): - pred_H,pred_He = self.predict_single(inputs[i],center_h_vals[i],surface_h_vals[i], - center_he_vals[i],surface_he_vals[i],s1_vals[i]) - pred_profiles.append([pred_H,pred_He]) - - # IF interpolate final masses, generate mass enclosed profile coordinates + # IF interpolate final masses, final star states if self.hms_s2==False: m_ind = self.model_IF.interpolators[0].out_keys.index("star_1_mass") + star_state_vals = self.interp.test_classifiers(10**inputs)['S1_state'] else: m_ind = self.model_IF.interpolators[0].out_keys.index("star_2_mass") + star_state_vals = self.interp.test_classifiers(10**inputs)['S2_state'] pred_mass = self.interp.test_interpolator(10**inputs)[:,m_ind] + + # generate predicted profiles + pred_profiles = [] + for i in range(len(inputs)): + pred_H,pred_He = self.predict_single(inputs[i],center_h_vals[i],surface_h_vals[i], + center_he_vals[i],surface_he_vals[i],star_state_vals[i]) + pred_profiles.append([pred_H,pred_He]) + + # generate mass enclosed profile coordinates mass_coords = np.linspace(0,1,200)*pred_mass[:,np.newaxis] h_profiles = np.array(pred_profiles)[:,0] he_profiles = np.array(pred_profiles)[:,1] - return mass_coords, h_profiles, he_profiles \ No newline at end of file + return mass_coords, h_profiles, he_profiles diff --git a/setup.py b/setup.py index 35705661df..54d7467eac 100644 --- a/setup.py +++ b/setup.py @@ -94,7 +94,7 @@ "sphinxcontrib_programoutput", "PSphinxTheme", ], - "profile": ["tensorflow == 2.12.0"], # for profile interpolation + "profile": ["tensorflow == 2.13.0"], # for profile interpolation "hpc": ["mpi4py == 3.0.3"], } From a47051677904822d0babeb2b0705a1cd327e4390 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 20 Jul 2023 16:30:55 +0200 Subject: [PATCH 105/319] Allowing for relative paths in the pipeline (#113) * Update gridutils.py * Update gridutils.py Use os functionalities instead * Update step_mesa.py Use os functionality * Update run-pipeline Use os functionalities * Update posydon-setup-grid Use os functionality correct bug (use file name instead of first directory name) * Update compress-mesa Use os functionality --- bin/compress-mesa | 4 ++-- bin/posydon-setup-grid | 2 +- bin/run-pipeline | 12 ++---------- posydon/binary_evol/MESA/step_mesa.py | 4 ++-- posydon/utils/gridutils.py | 5 ++--- 5 files changed, 9 insertions(+), 18 deletions(-) diff --git a/bin/compress-mesa b/bin/compress-mesa index fd2587fe37..d4a3a50191 100644 --- a/bin/compress-mesa +++ b/bin/compress-mesa @@ -44,12 +44,12 @@ def set_up_test(args): shutil.copytree(track_dirs[ind], os.path.join( args.test_dir, folder, - track_dirs[ind].split("/")[-1])) + os.path.split(track_dirs[ind])[1])) else: shutil.copy(track_dirs[ind], os.path.join( args.test_dir, folder, - track_dirs[ind].split("/")[-1])) + os.path.split(track_dirs[ind])[1])) print(f"Created Test Directory at {args.test_dir}.") diff --git a/bin/posydon-setup-grid b/bin/posydon-setup-grid index b79b4674a4..20eafb9e6e 100755 --- a/bin/posydon-setup-grid +++ b/bin/posydon-setup-grid @@ -156,7 +156,7 @@ def find_inlist_from_scenario(source, gitcommit, system_type): for line in f.readlines(): if 'zams_filename' in line: print("ZAMS_FILENAME detected, setting mesa_inlists['zams_filename']") - zams_filename = line.split("'")[1].split("/")[1] + zams_filename = os.path.split(line.split("'")[1])[1] zams_file_path = os.path.join(inlists_dir, 'r11701', "ZAMS_models", zams_filename) if os.path.isfile(zams_file_path): print("Verified locations of ZAMS data file, {0}".format(zams_file_path)) diff --git a/bin/run-pipeline b/bin/run-pipeline index f2ccf1dd8f..8910c2e328 100644 --- a/bin/run-pipeline +++ b/bin/run-pipeline @@ -83,14 +83,6 @@ def create_grid_slice(i, path_to_csv_file, STOP_BEFORE_CARBON_DEPLETION=True, ve grid_path = df.loc[i,'path_to_grid'] compression = df.loc[i,'compression'] -# grid_name = grid_path.split('/')[-1] -# otuput_path = os.path.join('/', os.path.join(*grid_path.split('/')[:-1]), -# compression) -# grid_output = os.path.join(otuput_path, grid_name+'.h5') -# -# # check that LITE/ or ORIGINAL/ directory exists -# if not os.path.isdir(otuput_path): -# os.makedirs(otuput_path) grid_output = get_new_grid_name(grid_path, compression, create_missing_directories=True) @@ -160,7 +152,7 @@ def plot_grid(i, path_to_csv_file, verbose=False): print(f'plotting {grid_path}') # check that plots/ directories exist - plot_path = os.path.join('/', os.path.join(*plot_dir.split('/')[:-1])) + plot_path = os.path.split(os.path.normpath(plot_dir))[0] check_dirs = [plot_path, plot_dir] sub_dirs = ['TF12/', 'TF1/', 'TF2/', 'TF3/', 'TF4/', 'debug_mt/', @@ -430,7 +422,7 @@ def train_interpolators(i, path_to_csv_file, verbose=False): print(f'train interpolators {grid_path} with method {method}') # check interpolation_objects directory exists - interp_path = os.path.join('/', os.path.join(*interpolator_path.split('/')[:-1])) + interp_path = os.path.split(interpolator_path)[0] if not os.path.isdir(interp_path): os.makedirs(interp_path) diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index 95fad5aa6e..28e6a7f08a 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -202,14 +202,14 @@ def __init__( # Set the interpolation path if interpolation_path is None: interpolation_path = ( - self.path + grid_name.split('/')[0] + self.path + os.path.split(grid_name)[0] + '/interpolators/%s/' % self.interpolation_method) # Set the interpolation filename if interpolation_filename is None: interpolation_filename = ( interpolation_path - + grid_name.split('/')[1].replace('h5', 'pkl')) + + os.path.split(grid_name)[1].replace('h5', 'pkl')) else: interpolation_filename = (interpolation_path + interpolation_filename) diff --git a/posydon/utils/gridutils.py b/posydon/utils/gridutils.py index 72e1e8394c..12848128eb 100644 --- a/posydon/utils/gridutils.py +++ b/posydon/utils/gridutils.py @@ -544,9 +544,8 @@ def get_new_grid_name(path, compression, create_missing_directories=False): File name for the new grid slice. """ - grid_name = path.split('/')[-1] - output_path = os.path.join('/', os.path.join(*path.split('/')[:-1]), - compression) + grid_path, grid_name = os.path.split(os.path.normpath(path)) + output_path = os.path.join(grid_path, compression) grid_output = os.path.join(output_path, grid_name+'.h5') if create_missing_directories: # check that LITE/ or ORIGINAL/ directory exists From 96f70f967d71bcd2935ea5627039bd6eca39388a Mon Sep 17 00:00:00 2001 From: sgossage Date: Thu, 20 Jul 2023 09:31:48 -0500 Subject: [PATCH 106/319] Update step_detached.py, corrected order of magnitude on Rappaport, Verbunt, and Joss 1983 prescription in step_detached (#114) The leading constant should have been 3.8e-30, not 3.8e30. --- posydon/binary_evol/DT/step_detached.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index 2295216061..2bc2348630 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -2445,7 +2445,7 @@ def diffeq( # parameters are given in units of [Msol], [Rsol], [yr] and so that # dOmega_mb/dt is in units of [yr^-2]. dOmega_mb_sec = ( - -3.8e30 * (const.rsol**2 / const.secyer) + -3.8e-30 * (const.rsol**2 / const.secyer) * M_sec * R_sec**4 * Omega_sec**3 @@ -2453,7 +2453,7 @@ def diffeq( * np.clip((1.5 - M_sec) / (1.5 - 1.3), 0, 1) ) dOmega_mb_pri = ( - -3.8e30 * (const.rsol**2 / const.secyer) + -3.8e-30 * (const.rsol**2 / const.secyer) * M_pri * R_pri**4 * Omega_pri**3 From a6f0923d8f159b97757c45feaf3970edee16f0c6 Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Thu, 3 Aug 2023 16:07:06 +0200 Subject: [PATCH 107/319] Fix coefficinent SFRH Neijssel+19 (#112) * Add function "keep_till_He_depletion" * Add option to stop at He depletion * Add option to pipeline (remove later) * correct not * Change threshold for He depletion * allow histories without age, add new flag * Check for history of 0 length * support BH formation above PISN gap and direct/Fryer collapse prescriptions now assume hydrogen envelope does not go into the BH * add C criterion for He depletion add C criterion for He depletion * Update psygrid.py use 0.08 carbon abundance * Update scrubbing.py where can't handle two arrays in parallel * Update psygrid.py change C limit to 0.1 * support the option to conserve the hydrogen envelope in BH formation * add MODEL var to post processing * update post processing script to support multiple core collapse models * Update psygrid.py Add mass request to only apply stopping earlier to masses of 100Msun and above * Update run-pipeline remove "_He_depletion" extension for the file name of the hdf5 file containing the psygrid * debug * rename function and flags * add support for BH disk mass * allow to remove slices from combining grids and only remove the combining if all slices are missing * fix small bug * Update setup-pipeline correct some typos * track previously created files * Update setup-pipeline fix typo * get files from step 1 * change range over df shape to instead use the index object * update step_6 run-pipeline * turn any to check on length * debug * add check for no slurm tasks * account for larger length difference of histories * reuse columns from histories if possible, add warnings * bugfix * propagate changes in step_SN and step_MESA * fix bug * debug plot2D * remove circular import * add open-mode, remove old file (make a copy first) * allow to ignore bad charaters in MESA log files * drop rows and columns * change import * import TF1 for post processing * fix bug * Update psygrid.py correct typo * debug * roll fix for single stars as well * debug * Matthias split grid rlo (#99) * fix in psygrid.py fix check of initial mass when cutting off carbon depletion * Update run-pipeline get start_at_RLO from compression type add flag to stop before carbon depletion (allow only for HMS grids) * Update setup-pipeline remove CO_HMS_GRID_START_AT_RLO add STOP_BEFORE_CARBON_DEPLETION * Update setup-pipeline get different directory for plotting if compression contains "RLO" * Update setup-pipeline correct typo * Update psygrid.py correct typo * Update psygrid.py debug * debug single interpolation * add support for other metallicities * update Z to Y conversion * SN_type not diclared in star properties * debug * update default params ini * increase len interp_class column * update params ini * add authorship to MODEL.py * Matthias split grid rlo (#100) * fix in psygrid.py fix check of initial mass when cutting off carbon depletion * Update run-pipeline get start_at_RLO from compression type add flag to stop before carbon depletion (allow only for HMS grids) * Update setup-pipeline remove CO_HMS_GRID_START_AT_RLO add STOP_BEFORE_CARBON_DEPLETION * Update setup-pipeline get different directory for plotting if compression contains "RLO" * Update setup-pipeline correct typo * Update psygrid.py correct typo * Update psygrid.py debug * Update setup-pipeline Add RLO to steps 6 and 7 * Update setup-pipeline Correct step 7 * Update run-pipeline don't interpolate no_MT for any grid starting at RLO * Update run-pipeline remove "_RLO" from method, which is read from the filename * Update setup-pipeline add "CO-HeMS_RLO" to export dirs * update spin orbit tilt name in rate calculation * adding function get_formation_channels * in parse function, shifting the binary_index * compute GRB properties * add channelwise merger efficiency calculation and plotting * add channelwise rate density * support channelwise histogram distributions * add file authorship * debug * cosistency change * add GRB rate calculation * debug * debug * debug * address PR103 PR104 comments * remove bug introduced in previous comment * remove bug introduced in previous commit * bug fix --------- Co-authored-by: mkruckow <122798003+mkruckow@users.noreply.github.com> Co-authored-by: mkruckow Co-authored-by: Monica Gallegos-Garcia --- posydon/popsyn/star_formation_history.py | 2 +- posydon/popsyn/synthetic_population.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/posydon/popsyn/star_formation_history.py b/posydon/popsyn/star_formation_history.py index abf7d40253..67ba05753d 100644 --- a/posydon/popsyn/star_formation_history.py +++ b/posydon/popsyn/star_formation_history.py @@ -148,7 +148,7 @@ def mean_metallicity(SFR, z): if SFR == "Madau+Fragos17" or SFR == "Madau+Dickinson14": return 10 ** (0.153 - 0.074 * z ** 1.34) * Zsun elif SFR == "Neijssel+19": - return 0.035*10**(0.035*z) + return 0.035*10**(-0.23*z) else: raise ValueError('Invalid SFR!') diff --git a/posydon/popsyn/synthetic_population.py b/posydon/popsyn/synthetic_population.py index f1828bce37..1c54af3b20 100644 --- a/posydon/popsyn/synthetic_population.py +++ b/posydon/popsyn/synthetic_population.py @@ -241,7 +241,7 @@ def parse(self, S1_state=None, S2_state=None, binary_state=None, if k > 0: shift_index = max(np.unique(df_sel.index)) + 1 - df_sel_met.index += shift_index + df_sel_met.index += shift_index # store simulated and underlying stellar mass df_sel_met['simulated_mass_for_met'] = simulated_mass_for_met From 45c99d8485a98c39fc9229d40fd407bc76b7414f Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Thu, 3 Aug 2023 16:14:01 +0200 Subject: [PATCH 108/319] Plot popsyn values over grid slices (#115) * Add function "keep_till_He_depletion" * Add option to stop at He depletion * Add option to pipeline (remove later) * correct not * Change threshold for He depletion * allow histories without age, add new flag * Check for history of 0 length * support BH formation above PISN gap and direct/Fryer collapse prescriptions now assume hydrogen envelope does not go into the BH * add C criterion for He depletion add C criterion for He depletion * Update psygrid.py use 0.08 carbon abundance * Update scrubbing.py where can't handle two arrays in parallel * Update psygrid.py change C limit to 0.1 * support the option to conserve the hydrogen envelope in BH formation * add MODEL var to post processing * update post processing script to support multiple core collapse models * Update psygrid.py Add mass request to only apply stopping earlier to masses of 100Msun and above * Update run-pipeline remove "_He_depletion" extension for the file name of the hdf5 file containing the psygrid * debug * rename function and flags * add support for BH disk mass * allow to remove slices from combining grids and only remove the combining if all slices are missing * fix small bug * Update setup-pipeline correct some typos * track previously created files * Update setup-pipeline fix typo * get files from step 1 * change range over df shape to instead use the index object * update step_6 run-pipeline * turn any to check on length * debug * add check for no slurm tasks * account for larger length difference of histories * reuse columns from histories if possible, add warnings * bugfix * propagate changes in step_SN and step_MESA * fix bug * debug plot2D * remove circular import * add open-mode, remove old file (make a copy first) * allow to ignore bad charaters in MESA log files * drop rows and columns * change import * import TF1 for post processing * fix bug * Update psygrid.py correct typo * debug * roll fix for single stars as well * debug * Matthias split grid rlo (#99) * fix in psygrid.py fix check of initial mass when cutting off carbon depletion * Update run-pipeline get start_at_RLO from compression type add flag to stop before carbon depletion (allow only for HMS grids) * Update setup-pipeline remove CO_HMS_GRID_START_AT_RLO add STOP_BEFORE_CARBON_DEPLETION * Update setup-pipeline get different directory for plotting if compression contains "RLO" * Update setup-pipeline correct typo * Update psygrid.py correct typo * Update psygrid.py debug * debug single interpolation * add support for other metallicities * update Z to Y conversion * SN_type not diclared in star properties * debug * update default params ini * increase len interp_class column * update params ini * add authorship to MODEL.py * Matthias split grid rlo (#100) * fix in psygrid.py fix check of initial mass when cutting off carbon depletion * Update run-pipeline get start_at_RLO from compression type add flag to stop before carbon depletion (allow only for HMS grids) * Update setup-pipeline remove CO_HMS_GRID_START_AT_RLO add STOP_BEFORE_CARBON_DEPLETION * Update setup-pipeline get different directory for plotting if compression contains "RLO" * Update setup-pipeline correct typo * Update psygrid.py correct typo * Update psygrid.py debug * Update setup-pipeline Add RLO to steps 6 and 7 * Update setup-pipeline Correct step 7 * Update run-pipeline don't interpolate no_MT for any grid starting at RLO * Update run-pipeline remove "_RLO" from method, which is read from the filename * Update setup-pipeline add "CO-HeMS_RLO" to export dirs * update spin orbit tilt name in rate calculation * adding function get_formation_channels * in parse function, shifting the binary_index * compute GRB properties * add channelwise merger efficiency calculation and plotting * add channelwise rate density * support channelwise histogram distributions * add file authorship * debug * cosistency change * add GRB rate calculation * fixing shift in binary index * minor bug * debug * debug * debug * address PR103 PR104 comments * remove bug introduced in previous comment * remove bug introduced in previous commit * debug * add plot_popsyn_over_grid_slice * debug * address Matthias points * fix zorder --------- Co-authored-by: mkruckow <122798003+mkruckow@users.noreply.github.com> Co-authored-by: mkruckow Co-authored-by: Monica Gallegos-Garcia --- posydon/popsyn/synthetic_population.py | 4 + posydon/visualization/plot_pop.py | 138 ++++++++++++++++++++++++- 2 files changed, 141 insertions(+), 1 deletion(-) diff --git a/posydon/popsyn/synthetic_population.py b/posydon/popsyn/synthetic_population.py index 1c54af3b20..1d61f4a8d4 100644 --- a/posydon/popsyn/synthetic_population.py +++ b/posydon/popsyn/synthetic_population.py @@ -959,3 +959,7 @@ def plot_rate_density(self, DCO=False, GRB=False, **kwargs): z_grb = None rate_grb = None plot_pop.plot_rate_density(z_dco, rate_dco, z_grb, rate_grb, **kwargs) + + def plot_popsyn_over_grid_slice(self, grid_type, met_Zsun, **kwargs): + """Plot popsyn over grid slice.""" + plot_pop.plot_popsyn_over_grid_slice(self, grid_type, met_Zsun, **kwargs) \ No newline at end of file diff --git a/posydon/visualization/plot_pop.py b/posydon/visualization/plot_pop.py index d25bf36c4e..10ec989f0d 100644 --- a/posydon/visualization/plot_pop.py +++ b/posydon/visualization/plot_pop.py @@ -6,6 +6,13 @@ from posydon.utils.common_functions import PATH_TO_POSYDON from posydon.visualization.plot_defaults import DEFAULT_LABELS from posydon.utils.constants import Zsun +from posydon.grids.psygrid import PSyGrid +from posydon.utils.common_functions import convert_metallicity_to_string +from posydon.utils.constants import Zsun +import matplotlib.pyplot as plt +from posydon.visualization.plot_defaults import DEFAULT_LABELS + +PATH_TO_POSYDON_DATA = os.environ.get("PATH_TO_POSYDON_DATA",'./') plt.style.use(os.path.join(PATH_TO_POSYDON, "posydon/visualization/posydon.mplstyle")) @@ -251,4 +258,133 @@ def plot_hist_properties(x, df_intrinsic=None, df_observable=None, if path: plt.savefig(path) if show: - plt.show() \ No newline at end of file + plt.show() + + +def plot_popsyn_over_grid_slice(pop, grid_type, met_Zsun, slices=None, channel=None, + plot_dir='./', prop=None, prop_range=None, + log_prop=False, alpha=0.3, s=5., + show_fig=True, save_fig=True, close_fig=True, + verbose=False): + + # load grid + met = convert_metallicity_to_string(met_Zsun) + grid_path = os.path.join(PATH_TO_POSYDON_DATA, f'POSYDON_data/{grid_type}/{met}_Zsun.h5') + grid = PSyGrid(verbose=False) + grid.load(grid_path) + + # check if formation channel information is avaialbe + if channel is not None and 'channel' not in pop.df_oneline.keys(): + if verbose: + print('Computing formation channels...') + pop.get_formation_channels() + + if 'CO' in grid_path: + # compact object mass slices + m_COs = np.unique(np.around(grid.initial_values['star_2_mass'],1)) + m_COs_edges = 10**((np.log10(np.array(m_COs)[1:])+np.log10(np.array(m_COs)[:-1]))*0.5) + m2 = [0.]+m_COs_edges.tolist()+[2*m_COs_edges[-1]] + vars = m_COs + fname = 'grid_m_%1.2f.png' + title = '$m_\mathrm{CO}=%1.2f\,M_\odot$' + slice_3D_var_str='star_2_mass' + elif 'HMS-HMS' in grid_path: + # mass ratio slices + qs = np.unique(np.around(grid.initial_values['star_2_mass']/grid.initial_values['star_1_mass'],2)) + dq = (qs[1:]-qs[:-1])*0.5 + dq_edges = (qs[:-1]+dq).tolist() + dq_edges = [0.]+dq_edges+[1.] + vars = qs.tolist() + fname = 'grid_q_%1.2f.png' + title = '$q=%1.2f$' + slice_3D_var_str='mass_ratio' + else: + raise ValueError('Grid type not supported!') + + + for i, var in enumerate(vars): + if slices is not None and round(var,2) not in np.around(slices,2): + if verbose: + print(f'Skipping {round(var,2)}') + continue + if 'HMS-HMS' in grid_path: + slice_3D_var_range = (dq_edges[i],dq_edges[i+1]) + # select popsynth binaries in the given mass ratio range + sel = (pop.df['metallicity'] == met_Zsun*Zsun) & (pop.df['event'] == 'ZAMS') + q = pop.df['S2_mass'].values/pop.df['S1_mass'].values + q[q>1] = 1./q[q>1] + mask = sel & (q>=slice_3D_var_range[0]) & (q<=slice_3D_var_range[1]) + elif 'CO' in grid_path: + if i == len(m2): + continue + slice_3D_var_range = (m2[i],m2[i+1]) + # select popsynth binaries in the given compact object mass + # TODO: implement the case of reversal mass ratio + if 'CO-HMS_RLO' in grid_path: + sel = (pop.df['metallicity'] == met_Zsun*Zsun) & (pop.df['event'] == 'oRLO2') + m_CO = pop.df['S1_mass'].values + mask = sel & (m_CO>=slice_3D_var_range[0]) & (m_CO<=slice_3D_var_range[1]) + elif 'CO-HeMS' in grid_path: + sel = (pop.df['metallicity'] == met_Zsun*Zsun) & (pop.df['step_names'] == 'step_CE') + m_CO = pop.df['S1_mass'].values + mask = sel & (m_CO>=slice_3D_var_range[0]) & (m_CO<=slice_3D_var_range[1]) + else: + raise ValueError('Grid type not supported!') + # TODO: skip plotting slice if there are no data + try: + PLOT_PROPERTIES = { + 'figsize' : (4,3.5) if prop is None else (4,5.5), + 'path_to_file' : plot_dir, + 'show_fig' : False, + 'close_fig' : False, + #'fname' : fname%var, + 'title' : title%var if channel is None else title%var + '\n' + channel, + 'log10_x' : True, + 'log10_y' : True, + 'legend2D' : {'bbox_to_anchor' : (1.03, 0.5)}, + } + # grid + grid.plot2D('star_1_mass', 'period_days', None, + termination_flag='combined_TF12', + grid_3D=True, slice_3D_var_str=slice_3D_var_str, + slice_3D_var_range=slice_3D_var_range, + verbose=False, **PLOT_PROPERTIES) + # pop synth + # only plot selected channel + if channel is not None: + sel_binary_index = pop.df_oneline.loc[pop.df_oneline['channel'] == channel].index.values.tolist() + mask = mask & (pop.df.index.isin(sel_binary_index)) + log10_m1 = np.log10(pop.df.loc[mask,'S1_mass'].values) + log10_p = np.log10(pop.df.loc[mask,'orbital_period'].values) + # plot color map of a given DCO variable + if prop is not None: + sel = pop.df.index.isin(pop.df.loc[mask].index.values.tolist()) & (pop.df['event'] == 'CO_contact') + prop_values = pop.df.loc[sel,prop].values + vmin = prop_range[0] + vmax = prop_range[1] + if log_prop: + prop_values = np.log10(prop_values) + vmin = np.log10(vmin) + vmax = np.log10(vmax) + plt.scatter(log10_m1, log10_p, c=prop_values, s=s, vmin=vmin, vmax=vmax, marker='v', cmap='viridis', alpha=alpha, zorder=0.5) + if prop in DEFAULT_LABELS: + if log_prop: + label = DEFAULT_LABELS[prop][1] + else: + label = DEFAULT_LABELS[prop][0] + else: + label = prop + if log_prop: + label = 'log10_'+label + plt.colorbar(label=label, orientation='horizontal') + else: + plt.scatter(log10_m1, log10_p, s=s, marker='v', color='black', alpha=alpha, zorder=0.5) + + if save_fig: + plt.savefig(os.path.join(plot_dir, fname%var), bbox_inches='tight') + if show_fig: + plt.show() + if close_fig: + plt.close() + except: + raise ValueError(f'Failed to plot slice %1.2f'%var) From 5940ce906bf7fd0b36bc718928ddab98561b7b77 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 3 Aug 2023 16:20:32 +0200 Subject: [PATCH 109/319] Add multi panel plots to plot2D (#118) * Add multi panel plots * add missing desciption --- docs/visualization/plot2D/plot2D.rst | 65 +++ ...rid_1e-04_Zsun_TF12_8mass_ratio_panels.png | Bin 0 -> 336316 bytes posydon/grids/psygrid.py | 19 +- posydon/visualization/combine_TF.py | 7 + posydon/visualization/plot2D.py | 467 ++++++++++++++++-- posydon/visualization/plot_defaults.py | 16 +- 6 files changed, 527 insertions(+), 47 deletions(-) create mode 100644 docs/visualization/plot2D/pngs/HMS-HMS_MESA_grid_1e-04_Zsun_TF12_8mass_ratio_panels.png diff --git a/docs/visualization/plot2D/plot2D.rst b/docs/visualization/plot2D/plot2D.rst index f3b243d2cd..0e4c5cad6b 100644 --- a/docs/visualization/plot2D/plot2D.rst +++ b/docs/visualization/plot2D/plot2D.rst @@ -301,3 +301,68 @@ You can combine the new grid by passing it to the option ``extra_grid=new_grid`` If you want to stack more than one grid, pass them in a list, e.g. ``extra_grid=[new_grid_1,new_grid_2,new_grid_3]``, they will be stacked in the order provided where the last extra grid of the list will stacked as last. + + +Creating plots with several slices +---------------------------------- + +We have the option to pass a list of slice ranges. In this case the plotting +script will loop over those ranges and create one plot with several slice +plots. It will get a common legend and/or color bar. + +There are two more options to the plot2D function: +1. ``max_cols``: It specifies the maximum number of columns of subplots. The +number of rows is automatically calculated to cover all plots and the legend. +2. ``legend_pos``: It specifies which subplots are used for the legend/color +bar and allows an index or tuple specifying a rectangle of indecies, like +matplotlib requires for creating a subplot. + +Because the legend has its own axes, it is recommended to modify the default +``bbox_to_anchor`` of the legend. +To have a better control on the color bar, it got the additional field +``bounds`` in the plot properties. +To identify the subplots each of them will get a text box. Its attributes can +be modified by changing the field ``slice_text_kwargs`` in the plot properties. +The slices will loop over 3D and 4D idependently. If one of them has just a +single range, this slicing will be added to the legend title instead of being +reported in any subplot. + +.. code-block:: python + q_ranges = [(0.025, 0.075), (0.175, 0.225), (0.325, 0.375), (0.475, 0.525), + (0.625, 0.675), (0.775, 0.825), (0.925, 0.975), (0.98, 1.0)] + + plot_properties = { + 'show_fig': True, + 'fname': 'HMS-HMS_MESA_grid_1e-04_Zsun_TF12_8mass_ratio_panels.png', + 'figsize': (9, 9), + 'log10_x': True, + 'log10_y': True, + 'xmin': 0.68, + 'xmax': 2.52, + 'ymin': -1.2, + 'ymax': 3.9, + 'wspace': 0.01, + 'hspace': 0.01, + 'colorbar': { + 'bounds': [0.03, 0.7, 0.94, 0.05] + }, + 'legend2D': { + 'title': 'Termination flags', + 'loc': 'lower left', + 'prop': { + 'size': 7 + }, + 'bbox_to_anchor': (0.0, 0.0), + }, + } + grid.plot2D('star_1_mass', 'period_days', None, + termination_flag='combined_TF12', + grid_3D=True, slice_3D_var_str='mass_ratio', + slice_3D_var_range=q_ranges, + grid_4D=True, slice_4D_var_str='Z_Zsun', + 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HcmV?d00001 diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index 2b3c14a058..8ad4a25d59 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -1663,7 +1663,7 @@ def plot2D(self, x_var_str, y_var_str, z_var_str=None, grid_3D=None, slice_3D_var_str=None, slice_3D_var_range=None, grid_4D=None, slice_4D_var_str=None, slice_4D_var_range=None, extra_grid=None, slice_at_RLO=False, - MARKERS_COLORS_LEGENDS=None, + MARKERS_COLORS_LEGENDS=None, max_cols=3, legend_pos=(3, 3), verbose=False, **kwargs): """Plot a 2D slice of x_var_str vs y_var_str of one or more runs. @@ -1689,17 +1689,19 @@ def plot2D(self, x_var_str, y_var_str, z_var_str=None, slice_3D_var_str : str Variable along which the 3D space will be sliced. Allowed values are `psygrid.initial_values.dtype.names`. - slice_3D_var_range : tuple + slice_3D_var_range : tuple or a list of tuples Range between which you want to slice the variable slice_3D_var_str - e.g., `(2.5,3.)`. + e.g., `(2.5,3.)`. In case of a list of tuples, one will get a large + plot with one subplot for each tuple in the list. grid_4D : bool If `True`, the psygrid object is a 4D grid and needs to be sliced. slice_4D_var_str : str Variable along which the 4D space will be sliced. Allowed values are `psygrid.initial_values.dtype.names`. - slice_4D_var_range : toople + slice_4D_var_range : tuple or a list of tuples Range between which you want to slice the variable slice_4D_var_str - e.g., `(2.5,3.)`. + e.g., `(2.5,3.)`. In case of a list of tuples, one will get a large + plot with one subplot for each tuple in the list. extra_grid : object or array of objects If subset of the grid was rerun a or an extention was added, one can overlay the new psygrid by passing it here. @@ -1710,6 +1712,11 @@ def plot2D(self, x_var_str, y_var_str, z_var_str=None, Each termination flag is associated with a marker shape, size, color and label (cf. `MARKERS_COLORS_LEGENDS` in `plot_defaults.py`). + max_cols : int + Defines the maximum number of columns of subplots. Default: 3 + legend_pos : SubplotSpec (int or tuple) + Defines which subplots won't contain an axis but are used to + display the legend there. Default: (3, 3) verbose : bool If `True`, the object reports by printing to standard output. **kwargs : dict @@ -1731,6 +1738,8 @@ def plot2D(self, x_var_str, y_var_str, z_var_str=None, extra_grid=extra_grid, slice_at_RLO=slice_at_RLO, MARKERS_COLORS_LEGENDS=MARKERS_COLORS_LEGENDS, + max_cols=max_cols, + legend_pos=legend_pos, verbose=verbose, **kwargs) plot() diff --git a/posydon/visualization/combine_TF.py b/posydon/visualization/combine_TF.py index db7227b57c..dd9cb1c11b 100644 --- a/posydon/visualization/combine_TF.py +++ b/posydon/visualization/combine_TF.py @@ -54,6 +54,7 @@ 'ignored_scrubbed'] TF1_POOL_ERROR = ['logQ_limit', + 'logQ_min_limit', 'min timestep', 'min_timestep_limit', 'cluster timelimit', @@ -94,6 +95,8 @@ def combine_TF12(IC, TF2, verbose=False): 'caseA/B/C/BB_from_star1', 'caseA/B/A_from_star1'] + TF2_pool_SAC = ['caseA/C_from_star1'] + TF2_pool_SABB = ['caseA/BB_from_star1', 'caseA/BB_from_star2'] @@ -139,6 +142,8 @@ def combine_TF12(IC, TF2, verbose=False): TF12[i] = 'Stable case A' elif TF2[i] in TF2_pool_SAB: TF12[i] = 'Stable case AB' + elif TF2[i] in TF2_pool_SAC: + TF12[i] = 'Stable case AC' elif TF2[i] in TF2_pool_SABB: TF12[i] = 'Stable case ABB' elif TF2[i] in TF2_pool_SB: @@ -158,6 +163,8 @@ def combine_TF12(IC, TF2, verbose=False): TF12[i] = 'Unstable case A' elif TF2[i] in TF2_pool_SAB: TF12[i] = 'Unstable case AB' + elif TF2[i] in TF2_pool_SAC: + TF12[i] = 'Unstable case AC' elif TF2[i] in TF2_pool_SABB: TF12[i] = 'Unstable case ABB' elif TF2[i] in TF2_pool_SB: diff --git a/posydon/visualization/plot2D.py b/posydon/visualization/plot2D.py index 0da16ebd3a..d41c5bd3b3 100644 --- a/posydon/visualization/plot2D.py +++ b/posydon/visualization/plot2D.py @@ -10,12 +10,14 @@ "Simone Bavera ", "Emmanouil Zapartas ", "Konstantinos Kovlakas ", + "Matthias Kruckow ", ] import numpy as np import matplotlib.pyplot as plt from posydon.utils.gridutils import add_field +from posydon.utils.constants import Zsun from posydon.visualization.plot_defaults import DEFAULT_MARKERS_COLORS_LEGENDS from posydon.visualization.plot_defaults import PLOT_PROPERTIES from posydon.visualization.plot_defaults import DEFAULT_LABELS @@ -43,6 +45,8 @@ def __init__( extra_grid=None, slice_at_RLO=False, MARKERS_COLORS_LEGENDS=None, + max_cols=3, + legend_pos=(3, 3), verbose=False, **kwargs ): @@ -78,17 +82,19 @@ def __init__( slice_3D_var_str : str Variable along which the 3D space will be sliced. Allowed values are `psygrid.initial_values.dtype.names`. - slice_3D_var_range : tuple + slice_3D_var_range : tuple or a list of tuples Range between which you want to slice the variable slice_3D_var_str - e.g., `(2.5,3.)`. + e.g., `(2.5,3.)`. In case of a list of tuples, one will get a large + plot with one subplot for each tuple in the list. grid_4D : bool If `True`, the psygrid object is a 4D grid and needs to be sliced. slice_4D_var_str : str Variable along which the 4D space will be sliced. Allowed values are `psygrid.initial_values.dtype.names`. - slice_4D_var_range : tuple + slice_4D_var_range : tuple or a list of tuples Range between which you want to slice the variable slice_4D_var_str - e.g., `(2.5,3.)`. + e.g., `(2.5,3.)`. In case of a list of tuples, one will get a large + plot with one subplot for each tuple in the list. extra_grid : object or array of objects If subset of the grid was rerun a or an extention was added, one can overlay the new psygrid by passing it here. @@ -102,6 +108,11 @@ def __init__( DEFAULT_LABELS : dict Each varaible is associated to an axis label. (cf. `DEFAULT_LABELS` in `plot_defaults.py`). + max_cols : int + Defines the maximum number of columns of subplots. Default: 3 + legend_pos : SubplotSpec (int or tuple) + Defines which subplots won't contain an axis but are used to + display the legend there. Default: (3, 3) verbose : bool If `True`, the object reports by printing to standard output. **kwargs : dict @@ -114,11 +125,49 @@ def __init__( # info 4D/3D parameter space self.grid_3D = grid_3D self.slice_3D_var_str = slice_3D_var_str - self.slice_3D_var_range = slice_3D_var_range + if slice_3D_var_str in DEFAULT_LABELS: + self.slice_3D_text = DEFAULT_LABELS[slice_3D_var_str][0] + '$={}$' + elif slice_3D_var_str is None: + self.slice_3D_text = '$={}$' + else: + self.slice_3D_text = slice_3D_var_str + '$={}$' + if isinstance(slice_3D_var_range, list): + self.slice_3D_n = len(slice_3D_var_range) + if self.slice_3D_n==0: + raise ValueError("slice_3D_var_range can't be an empty list") + self.slice_3D_var_ranges = slice_3D_var_range + self.slice_3D_var_range = slice_3D_var_range[0] + else: + self.slice_3D_n = 1 + self.slice_3D_var_range = slice_3D_var_range self.grid_4D = grid_4D self.slice_4D_var_str = slice_4D_var_str - self.slice_4D_var_range = slice_4D_var_range + if slice_4D_var_str in DEFAULT_LABELS: + self.slice_4D_text = DEFAULT_LABELS[slice_4D_var_str][0] + '$={}$' + elif slice_4D_var_str is None: + self.slice_4D_text = '$={}$' + else: + self.slice_4D_text = slice_4D_var_str + '$={}$' + if isinstance(slice_4D_var_range, list): + self.slice_4D_n = len(slice_4D_var_range) + if self.slice_4D_n==0: + raise ValueError("slice_4D_var_range can't be an empty list") + self.slice_4D_var_ranges = slice_4D_var_range + self.slice_4D_var_range = slice_4D_var_range[0] + else: + self.slice_4D_n = 1 + self.slice_4D_var_range = slice_4D_var_range + self.n_subplots = self.slice_3D_n*self.slice_4D_n self.slice_at_RLO = slice_at_RLO + if max_cols>0: + self.max_cols = max_cols + else: + raise ValueError("max_cols should be a positive integer.") + if isinstance(legend_pos, int) or isinstance(legend_pos, tuple): + self.legend_pos = legend_pos + else: + raise ValueError("legend_pos should be a positive integer or a" + "tuple of positive integers.") self.verbose = verbose # store the extra psygrid @@ -170,15 +219,20 @@ def __init__( self.final_values['termination_flag_2'] = TF2_clean self.final_values_str = self.final_values.dtype.names + idx_with_histories=0 + for i in range(len(psygrid)): + if psygrid[i].history1 is not None and\ + psygrid[i].history2 is not None and\ + psygrid[i].binary_history is not None: + idx_with_histories = i + break # x, y and z variables must exist if x_var_str not in self.initial_values_str and not self.slice_at_RLO: raise ValueError( "x_var_str = {} is not available in psygrid.initial_values". format(x_var_str)) - elif ( - x_var_str not in self.psygrid[0].binary_history.dtype.names - and self.slice_at_RLO - ): + elif (x_var_str not in self.psygrid[idx_with_histories].\ + binary_history.dtype.names and self.slice_at_RLO): raise ValueError("x_var_str = {} is not available in " "psygrid.binary_history".format(x_var_str)) else: @@ -186,10 +240,8 @@ def __init__( if y_var_str not in self.initial_values_str and not self.slice_at_RLO: raise ValueError("y_var_str = {} is not available in " "psygrid.initial_values".format(y_var_str)) - elif ( - y_var_str not in self.psygrid[0].binary_history.dtype.names - and self.slice_at_RLO - ): + elif (y_var_str not in self.psygrid[idx_with_histories].\ + binary_history.dtype.names and self.slice_at_RLO): raise ValueError("y_var_str = {} is not available in " "psygrid.binary_history".format(y_var_str)) else: @@ -219,16 +271,19 @@ def __init__( self.binary_history = False self.add_properties_to_final_values(None) elif (self.selected_star_history_for_z_var == 1 - and z_var_str in self.psygrid[0].history1.dtype.names): + and z_var_str in self.psygrid[idx_with_histories].\ + history1.dtype.names): self.z_var_str = z_var_str self.history = True self.binary_history = False elif (self.selected_star_history_for_z_var == 2 - and z_var_str in self.psygrid[0].history2.dtype.names): + and z_var_str in self.psygrid[idx_with_histories].\ + history2.dtype.names): self.z_var_str = z_var_str self.history = True self.binary_history = False - elif z_var_str in self.psygrid[0].binary_history.dtype.names: + elif z_var_str in self.psygrid[idx_with_histories].\ + binary_history.dtype.names: self.z_var_str = z_var_str self.history = False self.binary_history = True @@ -241,16 +296,19 @@ def __init__( else: if self.selected_star_history_for_z_var == 1 and \ - z_var_str in self.psygrid[0].history1.dtype.names: + z_var_str in self.psygrid[idx_with_histories].history1.\ + dtype.names: self.z_var_str = z_var_str self.history = True self.binary_history = False elif (self.selected_star_history_for_z_var == 2 - and z_var_str in self.psygrid[0].history2.dtype.names): + and z_var_str in self.psygrid[idx_with_histories].\ + history2.dtype.names): self.z_var_str = z_var_str self.history = True self.binary_history = False - elif z_var_str in self.psygrid[0].binary_history.dtype.names: + elif z_var_str in self.psygrid[idx_with_histories].\ + binary_history.dtype.names: self.z_var_str = z_var_str self.history = False self.binary_history = True @@ -296,9 +354,36 @@ def __init__( def __call__(self): """Generate the plot when the class is called.""" - fig = plt.figure(figsize=self.figsize) + # check whether to plot several slices + if self.n_subplots>1: + # determine size of legend by assuming max_cols will be the number + # of columns and add it to the number of subplots + if isinstance(self.legend_pos,int): + l_idxs = [self.legend_pos] + else: + l_cols = (np.array(self.legend_pos) - 1) % self.max_cols + 1 + l_rows = (np.array(self.legend_pos) - 1) // self.max_cols + 1 + # generate list with all subplot indices covered by the legend + l_idxs = [] + for j in range(min(l_rows)-1,max(l_rows)): + for i in range(min(l_cols),max(l_cols)+1): + l_idxs += [i+j*self.max_cols] + self.n_subplots += len(l_idxs) + # calculate the number of columns and rows + self.n_cols = min(self.n_subplots, self.max_cols) + self.n_rows = (self.n_subplots - 1) // self.n_cols + 1 + # create figure and axes + fig, axs = plt.subplots(nrows=self.n_rows, ncols=self.n_cols, + sharex=True, sharey=True, figsize=self.figsize) + l_ax = fig.add_subplot(self.n_rows, self.n_cols, self.legend_pos) + + self.plot_panels(axs, l_ax, legend_idxs=l_idxs) + + # adjust spacing + plt.subplots_adjust(wspace=self.wspace, hspace=self.hspace) - if self.all_termination_flags: + elif self.all_termination_flags: + fig = plt.figure(figsize=self.figsize) ax1 = plt.subplot(2, 2, 1) self.update_markers_colors_legends("termination_flag_1") @@ -339,6 +424,8 @@ def __call__(self): return fig else: + fig = plt.figure(figsize=self.figsize) + ax = plt.subplot(111) self.plot_panel(ax) @@ -525,13 +612,18 @@ def plot_panel(self, ax, extra_grid_call=False): def add_properties_to_initial_values(self): """Add extra initial values.""" - # add the column mass_ratio old_initial_values = copy.copy(self.initial_values) + # add the column mass_ratio mass_ratio = (old_initial_values["star_2_mass"] / old_initial_values["star_1_mass"]) new_initial_values = add_field(old_initial_values, [("mass_ratio", "1: + self.slice_3D_var_range = self.slice_3D_var_ranges[i3D] + if self.slice_4D_n>1: + self.slice_4D_var_range = self.slice_4D_var_ranges[i4D] + self.update_values_to_plot(self.extra_grid_termination_flag) + ax = axs[row, col] + for flag in self.termination_flag_str: + selection = self.termination_flag == flag + if self.MARKERS_COLORS_LEGENDS[flag][2] is not None: + if self.slice_at_RLO: + for i in range(len(self.x_var[selection])): + if not isinstance(self.x_var_oRLO[selection] + [i], float): + if (not any(np.isnan( + self.x_var_oRLO[selection][i])) + and not any(np.isnan( + self.y_var_oRLO[selection][i]))): + ax.plot( + self.x_var[selection][i], + self.y_var[selection][i], + marker=".", + color="black", + ) + ax.plot( + self.x_var_oRLO[selection][i], + self.y_var_oRLO[selection][i], + color="black", + ) + artist = ax.scatter( + self.x_var_oRLO[selection][i][-1], + self.y_var_oRLO[selection][i][-1], + marker=self.MARKERS_COLORS_LEGENDS + [flag][0], + linewidths=self.MARKERS_COLORS_LEGENDS + [flag][1], + c=self.MARKERS_COLORS_LEGENDS[flag][2], + s=self.marker_size, + ) + else: + ax.plot( + self.x_var[selection][i], + self.y_var[selection][i], + marker=".", + color="black", + ) + artist = ax.scatter( + self.x_var[selection][i], + self.y_var[selection][i], + marker=self.MARKERS_COLORS_LEGENDS + [flag][0], + linewidths=self.MARKERS_COLORS_LEGENDS + [flag][1], + c=self.MARKERS_COLORS_LEGENDS[flag][2], + s=self.marker_size, + ) + else: + artist = ax.scatter( + self.x_var[selection], + self.y_var[selection], + marker=self.MARKERS_COLORS_LEGENDS[flag][0], + linewidths=self.MARKERS_COLORS_LEGENDS[flag] + [1], + c=self.MARKERS_COLORS_LEGENDS[flag][2], + s=self.marker_size, + ) + else: + if self.z_var is not None: + if self.slice_at_RLO: + for i in range(len(self.x_var[selection])): + if not isinstance(self.x_var_oRLO + [selection][i], float): + if not any(np.isnan( + self.x_var_oRLO[selection][i]))\ + and not any(np.isnan( + self.y_var_oRLO[selection][i])): + ax.plot( + self.x_var[selection][i], + self.y_var[selection][i], + marker=".", + color="black", + ) + ax.plot( + self.x_var_oRLO[selection][i], + self.y_var_oRLO[selection][i], + color="black", + ) + artist = ax.scatter( + self.x_var_oRLO[selection][i][-1], + self.y_var_oRLO[selection][i][-1], + marker=self.MARKERS_COLORS_LEGENDS + [flag][0], + linewidths= + self.MARKERS_COLORS_LEGENDS[flag] + [1], + c=self.z_var[selection][i], + s=self.marker_size, + alpha=0.5, + vmin=self.zmin, + vmax=self.zmax, + ) + else: + ax.plot( + self.x_var[selection][i], + self.y_var[selection][i], + marker=".", + color="black", + ) + artist = ax.scatter( + self.x_var[selection][i], + self.y_var[selection][i], + marker=self.MARKERS_COLORS_LEGENDS + [flag][0], + linewidths= + self.MARKERS_COLORS_LEGENDS[flag] + [1], + c=self.z_var[selection][i], + s=self.marker_size, + alpha=0.5, + vmin=self.zmin, + vmax=self.zmax, + ) + + else: + artist = ax.scatter( + self.x_var[selection], + self.y_var[selection], + marker=self.MARKERS_COLORS_LEGENDS[flag] + [0], + linewidths=self.MARKERS_COLORS_LEGENDS + [flag][1], + c=self.z_var[selection], + s=self.marker_size, + vmin=self.zmin, + vmax=self.zmax, + ) + artist_last = artist + # collect artists for legend + if self.MARKERS_COLORS_LEGENDS[flag][3] not in artists_legend: + artists.append(artist) + artists_legend.append(self.MARKERS_COLORS_LEGENDS[flag][3]) + + # add labels + if row==self.n_rows-1 or idx+self.n_cols in legend_idxs: + self.set_xlabel(ax) + ax.xaxis.set_tick_params(labelbottom=True) + else: + ax.set_xlabel("") + if col==0 or idx-1 in legend_idxs: + self.set_ylabel(ax) + ax.yaxis.set_tick_params(labelleft=True) + else: + ax.set_ylabel("") + self.set_xlim(ax) + self.set_ylim(ax) + # write slice value on plot + slice_text = "" + self.slice_text_kwargs['transform'] = ax.transAxes + if self.slice_3D_n>1: + slice_text += self.slice_3D_text.format(round(0.5*( + self.slice_3D_var_range[0]+self.slice_3D_var_range[1])\ + ,15)) + if self.slice_4D_n>1: + slice_text += "~" + if self.slice_4D_n>1: + slice_text += self.slice_4D_text.format(round(0.5*( + self.slice_4D_var_range[0]+self.slice_4D_var_range[1])\ + ,15)) + ax.text(s=slice_text, **self.slice_text_kwargs) + + # add color bar + if artist_last is not None: + self.set_color_bar(artist_last, l_ax) + # add legend + if self.legend2D["title"] is not None: + if self.grid_3D and self.slice_3D_n==1: + self.legend2D["title"] = self.slice_3D_text.format(round(0.5*( + self.slice_3D_var_range[0]+self.slice_3D_var_range[1])\ + ,15)) + '\\\\' + self.legend2D["title"] + if self.grid_4D and self.slice_4D_n==1: + self.legend2D["title"] = self.slice_4D_text.format(round(0.5*( + self.slice_4D_var_range[0]+self.slice_4D_var_range[1])\ + ,15)) + '\\\\' + self.legend2D["title"] + l_ax.axis('off') + sorted_artists_legend = sorted(artists_legend) + sorted_artists = [] + for legend_entry_text in sorted_artists_legend: + sorted_artists.append(artists[artists_legend.index( + legend_entry_text)]) + if self.legend2D["bbox_to_anchor"][0]>=1 and\ + "left" in self.legend2D["loc"]: + self.legend2D["bbox_to_anchor"] = (0.0, self.legend2D + ["bbox_to_anchor"][1]) + self.set_legend(l_ax, sorted_artists, sorted_artists_legend) diff --git a/posydon/visualization/plot_defaults.py b/posydon/visualization/plot_defaults.py index b7f0c9b47c..28c4704809 100644 --- a/posydon/visualization/plot_defaults.py +++ b/posydon/visualization/plot_defaults.py @@ -66,7 +66,8 @@ 'aspect': 20, 'anchor': (0.0, 0.5), 'panchor': (1.0, 0.5), - 'extend': 'neither' + 'extend': 'neither', + 'bounds': [0.03, 0.7, 0.94, 0.05] }, 'legend1D': { 'title': None, @@ -96,6 +97,13 @@ }, 'shrink_box': 0.85, 'bbox_to_anchor': (1, 0.5) + }, + 'slice_text_kwargs': { + 'bbox': {'facecolor': 'white', 'alpha': 0.8, 'pad': 2}, + 'ha': 'right', + 'va': 'bottom', + 'x': 0.95, + 'y': 0.05 } } @@ -387,6 +395,8 @@ ['s', 2, list_of_colors[2], 'Stable RLOF during MS'], 'Stable case AB': ['s', 2, list_of_colors[1], 'Stable RLOF during postMS'], + 'Stable case AC': + ['s', 2, list_of_colors[1], 'Stable RLOF during postMS'], 'Stable case ABB': ['s', 2, list_of_colors[0], 'Stable RLOF during stripped He star'], 'Stable case B': @@ -405,6 +415,8 @@ ['D', 1, list_of_colors[2], 'Unstable RLOF during MS'], 'Unstable case AB': ['D', 1, list_of_colors[1], 'Unstable RLOF during postMS'], + 'Unstable case AC': + ['D', 1, list_of_colors[1], 'Unstable RLOF during postMS'], 'Unstable case ABB': ['D', 1, list_of_colors[0], 'Unstable RLOF during stripped He star'], @@ -577,6 +589,8 @@ # extra 'mass_ratio': [r'$q$', r'$\log_{10}(q)$'], + 'Z_Zsun': + [r'$Z \, [Z_\odot]$', r'$\log_{10}(Z / Z_\odot)$'], # history1/history2 'star_age': From ddd8e69a4b333270680b206b8fad872830d981ef Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Thu, 3 Aug 2023 16:22:53 +0200 Subject: [PATCH 110/319] fix grid post processing WD and above PISN BH formation (#119) --- posydon/binary_evol/SN/step_SN.py | 10 ++++++++-- posydon/grids/post_processing.py | 12 ++++++------ posydon/grids/scrubbing.py | 8 ++++++++ 3 files changed, 22 insertions(+), 8 deletions(-) diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index ef876795c6..d10350c606 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -555,8 +555,11 @@ def collapse_star(self, star): star.state = "WD" star.spin = 0. star.log_R = np.log10(CO_radius(star.mass, star.state)) + star.m_disk_accreted = np.nan + star.m_disk_radiated = np.nan for key in STARPROPERTIES: - if key not in ["state", "mass", "log_R", "spin"]: + if key not in ["state", "mass", "log_R", "spin", + "m_disk_accreted", "m_disk_radiated"]: setattr(star, key, None) return @@ -659,8 +662,11 @@ def collapse_star(self, star): star.state = "WD" star.spin = 0. star.log_R = np.log10(CO_radius(star.mass, star.state)) + star.m_disk_accreted = np.nan + star.m_disk_radiated = np.nan for key in STARPROPERTIES: - if key not in ["state", "mass", "log_R", "spin"]: + if key not in ["state", "mass", "log_R", "spin", + "m_disk_accreted", "m_disk_radiated"]: setattr(star, key, None) return diff --git a/posydon/grids/post_processing.py b/posydon/grids/post_processing.py index 5f25014394..133aa063d9 100644 --- a/posydon/grids/post_processing.py +++ b/posydon/grids/post_processing.py @@ -285,11 +285,11 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, if quantity in ['state', 'SN_type']: if not isinstance(getattr(star_copy, quantity), str): flush = True - warnings.warn(f'{MODEL_NAME} {mechanism} state/SN_type not a string!') + warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} is not a string!') else: - if not isinstance(getattr(star_copy, quantity), (float, None)): + if not isinstance(getattr(star_copy, quantity), float): flush = True - warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} not a float!') + warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} is not a float!') except Exception as e: flush = True if verbose: @@ -329,11 +329,11 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, if quantity in ['state', 'SN_type']: if not isinstance(getattr(star_copy, quantity), str): flush = True - warnings.warn(f'{MODEL_NAME} {mechanism} state/SN_type not a string!') + warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} is not a string!') else: - if not isinstance(getattr(star_copy, quantity), (float, None)): + if not isinstance(getattr(star_copy, quantity), float): flush = True - warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} not a float!') + warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} is not a float!') except Exception as e: flush = True if verbose: diff --git a/posydon/grids/scrubbing.py b/posydon/grids/scrubbing.py index 227db667b4..8736a81ae5 100644 --- a/posydon/grids/scrubbing.py +++ b/posydon/grids/scrubbing.py @@ -247,6 +247,14 @@ def keep_till_central_abundance_He_C(bh, h1, h2, Ystop=1.0e-5, XCstop=1.0): last_index = where_conditions_met2[0] newTF1 = 'Secondary got stopped before central carbon depletion' + # include the point above the stopping criteria so that the last inferred + # stellar state will be XXX_Central_He_depleted. This is essential to find + # the core mass at He depletion with the calculate_Patton20_values_at_He_depl + # method used to calculate the core collapse properties with the + # Patton&Sukhbold mechanism. + if len(bh) >= last_index+2: + last_index += 1 + new_bh = bh[:last_index] new_h1 = h1[:last_index] new_h2 = h2[:last_index] From 8aa506461ca115f39255fe80fd6044ffd21096f3 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 3 Aug 2023 16:24:43 +0200 Subject: [PATCH 111/319] Fix missing Z values in case of missing history files (#120) --- posydon/grids/psygrid.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index 8ad4a25d59..91ea97eafa 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -1083,6 +1083,12 @@ def decide_columns(key_in_config, defaults): for colname, value in grid_point.items(): if colname in self.initial_values.dtype.names: self.initial_values[i][colname] = value + if np.isnan(self.initial_values[i]["Z"]): + # try to get metallicity from directory name + params_from_path = initial_values_from_dirname(run.path) + if (len(params_from_path)==4) or\ + (len(params_from_path)==2): + self.initial_values[i]["Z"] = params_from_path[-1] else: for flag, col in zip(termination_flags, termination_flag_columns): From 507db9dbb6372a593f07d0f621827b8baa7c9b3c Mon Sep 17 00:00:00 2001 From: Kyle Rocha <48293898+ka-rocha@users.noreply.github.com> Date: Thu, 10 Aug 2023 09:35:14 -0500 Subject: [PATCH 112/319] Hdf5 refactor - data types for population output (#88) * Adding default dtype dicts. * Adding conversion back to object because saving string dtypes in pandas is broken * Adding dtype logic to oneline * Moved around the logic for cleaning data out of to_df and made helper methods in popsyn.io * Removed old commented out code fixed some docs and from_df methods * Remove comments and add some docs * Add compression options for HDFStore * remove old imports --- posydon/binary_evol/binarystar.py | 44 ++++- posydon/binary_evol/singlestar.py | 24 ++- posydon/popsyn/binarypopulation.py | 88 +++------ posydon/popsyn/io.py | 295 ++++++++++++++++++++++++++++- 4 files changed, 371 insertions(+), 80 deletions(-) diff --git a/posydon/binary_evol/binarystar.py b/posydon/binary_evol/binarystar.py index 985c0229f1..a8944f3922 100644 --- a/posydon/binary_evol/binarystar.py +++ b/posydon/binary_evol/binarystar.py @@ -36,6 +36,7 @@ from posydon.utils.common_functions import ( check_state_of_star, orbital_period_from_separation, orbital_separation_from_period, get_binary_state_and_event_and_mt_case) +from posydon.popsyn.io import (clean_binary_history_df, clean_binary_oneline_df) # star property: column names in binary history for star 1 and star 2 @@ -295,9 +296,10 @@ def to_df(self, **kwargs): Parameters ---------- - extra_columns : list + extra_columns : dict( 'name':dtype, .... ) Extra binary parameters to return in DataFrame that are not - included in BINARYPROPERTIES. + included in BINARYPROPERTIES. All columns must have an + associated pandas data type. Can be used in combination with `only_select_columns`. Assumes names have no suffix. ignore_columns : list @@ -321,9 +323,13 @@ def to_df(self, **kwargs): pandas DataFrame """ + extra_binary_cols_dict = kwargs.get('extra_columns', {}) + extra_columns = list(extra_binary_cols_dict.keys()) + extra_columns_dtypes_user = list(extra_binary_cols_dict.values()) + all_keys = (["binary_index"] + [key+'_history' for key in BINARYPROPERTIES] - + list(kwargs.get('extra_columns', []))) + + extra_columns) ignore_cols = list(kwargs.get('ignore_columns', [])) keys_to_save = [i for i in all_keys if not ( @@ -333,7 +339,7 @@ def to_df(self, **kwargs): user_keys_to_save = list(kwargs.get('only_select_columns')) keys_to_save = (["binary_index"] + [key+'_history' for key in user_keys_to_save] - + list(kwargs.get('extra_columns', []))) + + extra_columns) try: data_to_save = [getattr(self, key) for key in keys_to_save[1:]] @@ -388,6 +394,7 @@ def to_df(self, **kwargs): frames = [bin_df] if kwargs.get('include_S1', True): + # we are hard coding the prefix frames.append(self.star_1.to_df( prefix='S1_', null_value=kwargs.get('null_value', np.NAN), **kwargs.get('S1_kwargs', {}))) @@ -398,6 +405,13 @@ def to_df(self, **kwargs): binary_df = pd.concat(frames, axis=1) binary_df.set_index('binary_index', inplace=True) + + extra_s1_cols_dict = kwargs.get('S1_kwargs', {}).get('extra_columns', {}) + extra_s2_cols_dict = kwargs.get('S2_kwargs', {}).get('extra_columns', {}) + binary_df = clean_binary_history_df(binary_df, + extra_binary_dtypes_user=extra_binary_cols_dict, + extra_S1_dtypes_user=extra_s1_cols_dict, + extra_S2_dtypes_user=extra_s2_cols_dict) return binary_df @classmethod @@ -410,7 +424,7 @@ def from_df(cls, dataframe, **kwargs): data to turn into a BinaryStar instance. index : int, optional Sets the binary index. - extra_columns : list, optional + extra_columns : dict, optional Column names to be added directly to binary not in BINARYPROPERTIES. @@ -425,7 +439,7 @@ def from_df(cls, dataframe, **kwargs): # split input dataframe into kwargs dicts binary_params, star1_params, star2_params = dict(), dict(), dict() extra_params = dict() - extra_columns = kwargs.get('extra_columns', []) + extra_columns = kwargs.get('extra_columns', {}) hist_lengths = [] for name in list(dataframe.columns): if 'S1' in name: @@ -571,6 +585,20 @@ def to_oneline_df(self, scalar_names=[], history=True, **kwargs): oneline_df['WARNING'] = [0] oneline_df.set_index('binary_index', inplace=True) + + # try to coerce data types automatically + oneline_df = oneline_df.infer_objects() + + # Set data types for all columns explicitly + # we are assuming you may pass the same kwargs to both to_df and oneline + extra_binary_cols_dict = kwargs.get('extra_columns', {}) + extra_s1_cols_dict = kwargs.get('S1_kwargs', {}).get('extra_columns', {}) + extra_s2_cols_dict = kwargs.get('S2_kwargs', {}).get('extra_columns', {}) + oneline_df = clean_binary_oneline_df(oneline_df, + extra_binary_dtypes_user=extra_binary_cols_dict, + extra_S1_dtypes_user=extra_s1_cols_dict, + extra_S2_dtypes_user=extra_s2_cols_dict) + return oneline_df @classmethod @@ -587,7 +615,7 @@ def from_oneline_df(cls, oneline_df, **kwargs): A oneline DataFrame describing a binary. index : int, None Binary index - extra_columns : list + extra_columns : dict Names of any extra history columns not inlcuded in BINARYPROPERTIES @@ -601,7 +629,7 @@ def from_oneline_df(cls, oneline_df, **kwargs): binary_params, star1_params, star2_params = dict(), dict(), dict() extra_params = dict() - extra_columns = kwargs.get('extra_columns', []) + extra_columns = kwargs.get('extra_columns', {}) hist_lengths = [] for name in list(oneline_df.columns): if '_f' in name[-2:]: diff --git a/posydon/binary_evol/singlestar.py b/posydon/binary_evol/singlestar.py index da9ecdc858..4bbc17be3b 100644 --- a/posydon/binary_evol/singlestar.py +++ b/posydon/binary_evol/singlestar.py @@ -24,6 +24,7 @@ from posydon.grids.MODELS import MODELS + STARPROPERTIES = [ 'state', # the evolutionary state of the star. For more info see # `posydon.utils.common_functions.check_state_of_star` @@ -196,9 +197,10 @@ def to_df(self, **kwargs): Parameters ---------- - extra_columns : list + extra_columns : dict( 'name':dtype, .... ) Extra star history parameters to return in DataFrame that are not - included in STARPROPERTIES. + included in STARPROPERTIES. All columns must have an + associated pandas data type. Can be used in combination with `only_select_columns`. Assumes names have no suffix. ignore_columns : list @@ -221,8 +223,12 @@ def to_df(self, **kwargs): ------- pandas DataFrame """ + extra_cols_dict = kwargs.get('extra_columns', {}) + extra_columns = list(extra_cols_dict.keys()) + extra_columns_dtypes_user = list(extra_cols_dict.values()) + all_keys = ([key+'_history' for key in STARPROPERTIES] - + list(kwargs.get('extra_columns', []))) + + extra_columns) ignore_cols = list(kwargs.get('ignore_columns', [])) @@ -238,7 +244,7 @@ def to_df(self, **kwargs): if bool(kwargs.get('only_select_columns')): user_keys_to_save = list(kwargs.get('only_select_columns')) keys_to_save = ([key+'_history' for key in user_keys_to_save] - + list(kwargs.get('extra_columns', []))) + + extra_columns) try: # shape of data_to_save (history columns , time steps) data_to_save = [getattr(self, key) for key in keys_to_save] @@ -257,13 +263,17 @@ def to_df(self, **kwargs): # sets rows as time steps, columns as history output star_data = np.transpose(star_data) - # remove the _history at the end of all column names + # add star prefix and remove the '_history' at the end of all column names prefix = kwargs.get('prefix', "") column_names = [prefix + name.split('_history')[0] for name in keys_to_save] - data_frame = pd.DataFrame(star_data, columns=column_names) - return data_frame + star_df = pd.DataFrame(star_data, columns=column_names) + + # try to coerce data types automatically + star_df = star_df.infer_objects() + + return star_df def to_oneline_df(self, history=True, prefix='', **kwargs): """Convert SingleStar into a single row DataFrame. diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index 531115e9fb..4bd8a279d4 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -349,10 +349,11 @@ def _safe_evolve(self, **kwargs): f"evolution.combined.{self.rank}"), mode='w', **kwargs) - def save(self, save_path, mode='a', **kwargs): + def save(self, save_path, **kwargs): """Save BinaryPopulation to hdf file.""" optimize_ram = self.kwargs['optimize_ram'] temp_directory = self.kwargs['temp_directory'] + mode = self.kwargs.get('mode', 'a') if self.comm is None: if optimize_ram: @@ -380,8 +381,7 @@ def save(self, save_path, mode='a', **kwargs): self.kwargs["temp_directory"], f"evolution.combined.{i}") for i in range(self.size)] - - self.combine_saved_files(absolute_filepath, tmp_files, mode = mode) + self.combine_saved_files(absolute_filepath, tmp_files, mode=mode, **kwargs) else: return @@ -392,7 +392,7 @@ def make_temp_fname(self): return os.path.join(temp_directory, f"evolution.combined.{self.rank}") # return os.path.join(dir_name, '.tmp{}_'.format(rank) + file_name) - def combine_saved_files(self, absolute_filepath, file_names, mode = "a"): + def combine_saved_files(self, absolute_filepath, file_names, **kwargs): """Combine various temporary files in a given folder.""" dir_name = os.path.dirname(absolute_filepath) @@ -407,8 +407,11 @@ def combine_saved_files(self, absolute_filepath, file_names, mode = "a"): oneline_min_itemsize = {key: val for key, val in ONELINE_MIN_ITEMSIZE.items() if key in oneline_cols} - - with pd.HDFStore(absolute_filepath, mode = mode) as store: + mode = kwargs.get('mode', 'a') + complib = kwargs.get('complib', 'zlib') + complevel = kwargs.get('complevel', 9) + + with pd.HDFStore(absolute_filepath, mode=mode, complevel=complevel, complib=complib) as store: for f in file_names: # strings itemsize set by first append max value, # which may not be largest string @@ -644,7 +647,7 @@ def from_hdf(self, indices, where=None, restore=False): return binary_holder - def save(self, fname, mode='a', **kwargs): + def save(self, fname, **kwargs): """Save binaries to an hdf file using pandas HDFStore. Any object dtype columns not parsed by infer_objects() is converted to @@ -654,75 +657,32 @@ def save(self, fname, mode='a', **kwargs): ---------- fname : str Name of hdf file saved. - **kwargs + mode : {'a', 'w', 'r', 'r+'}, default 'a' + See pandas HDFStore docs + complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib' + Compression library. See HDFStore docs + complevel : int, 0-9, default 9 + Level of compression. See HDFStore docs + kwargs : dict + Arguments for `BinaryStar` methods `to_df` and `to_oneline_df`. Returns ------- None """ - def set_dtypes_oneline(data): - """Change the dtypes for consistency in saving.""" - for col in data.columns: - if 'natal_kick_array' in col: - # First convert None's to NaN's - # data[col][data[col] == 'None'] = np.nan - data.loc[data[col] == 'None', col] = np.nan - - # Next, convert dtype - data = data.astype({col: np.float64}) - - return data - - with pd.HDFStore(fname, mode=mode) as store: + mode = kwargs.get('mode', 'a') + complib = kwargs.get('complib', 'zlib') + complevel = kwargs.get('complevel', 9) + with pd.HDFStore(fname, mode=mode, complevel=complevel, complib=complib) as store: history_df = self.to_df(**kwargs) - - # Set dtypes for saving - history_df = history_df.infer_objects() - - object_to_str = {name: 'str' for i, name - in enumerate(history_df.columns) - if history_df.iloc[:, i].dtype == 'object'} - history_df = history_df.astype(object_to_str) - store.append('history', history_df, data_columns=True) online_df = self.to_oneline_df(**kwargs) - online_df = online_df.infer_objects() - object_to_str = {name: 'str' for i, name - in enumerate(online_df.columns) - if online_df.iloc[:, i].dtype == 'object'} - online_df = online_df.astype(object_to_str) - online_df = set_dtypes_oneline(online_df) - store.append('oneline', online_df, data_columns=True) + + return -# store = pd.HDFStore(fname, mode=mode) - -# history_df = self.to_df(**kwargs) - -# # Set dtypes for saving -# history_df = history_df.infer_objects() - -# # TODO: move these data type conversions into to_df, to_oneline_df -# object_to_str = {name:'str' -# for i, name in enumerate(history_df.columns) -# if history_df.iloc[:,i].dtype == 'object'} -# history_df = history_df.astype(object_to_str) - -# store.append('history', history_df, data_columns=True) - -# online_df = self.to_oneline_df(**kwargs) -# online_df = online_df.infer_objects() -# object_to_str = {name:'str' -# for i, name in enumerate(online_df.columns) -# if online_df.iloc[:,i].dtype == 'object'} -# online_df = online_df.astype(object_to_str) -# online_df = set_dtypes_oneline(online_df) - -# store.append('oneline', online_df, data_columns=True) - -# store.close() def __getitem__(self, key): """Return the key-th binary.""" diff --git a/posydon/popsyn/io.py b/posydon/popsyn/io.py index 8b458ddbfe..e0dedc3cca 100644 --- a/posydon/popsyn/io.py +++ b/posydon/popsyn/io.py @@ -8,10 +8,304 @@ import pprint import numpy as np +import pandas as pd from posydon.binary_evol.simulationproperties import SimulationProperties +""" +POSYDON Data Types are enforced when converting BinaryStar +and SingleStar instances to Pandas DataFrames. This is done to +ensure memory efficient data storage and to solve problems +combining temp batch files. + +Check Pandas docs for allowed data types in DataFrames. +""" + +BINARYPROPERTIES_DTYPES = { + # The state and event of the system. For more information, see + # `posydon.utils.common_functions.get_binary_state_and_event_and_mt_case() + 'state' : 'string', # + 'event': 'string', + 'time': 'float64', # age of the system (yr) + 'separation': 'float64', # binary orbital separation (solar radii) + 'orbital_period': 'float64', # binary orbital period (days) + 'eccentricity': 'float64', # binary eccentricity + 'V_sys': 'object', # list of the 3 systemic velocity coordinates + # (R_{star} - R_{Roche_lobe}) / R_{Roche_lobe}... + 'rl_relative_overflow_1': 'float64', # ...for star 1 + 'rl_relative_overflow_2': 'float64', # ...for star 2 + 'lg_mtransfer_rate': 'float64', # log10 of mass lost from the donor (Msun/yr) + 'mass_transfer_case': 'string', # current mass transfer case of the system. + # See `get_binary_state_and_event_and_mt_case` + # in `posydon.utils.common_functions`. + 'trap_radius': 'float64', + 'acc_radius': 'float64', + 't_sync_rad_1': 'float64', + 't_sync_conv_1': 'float64', + 't_sync_rad_2': 'float64', + 't_sync_conv_2': 'float64', + 'nearest_neighbour_distance': 'object', # the distance of system from its nearest + # neighbour of MESA binary system in case + # of interpolation during the the end of + # the previous step including MESA psygrid. + # The distance is normalized in the + # parameter space and limits at which it + # was calculated. See `mesa_step` for more. +} + + +STARPROPERTIES_DTYPES = { + 'state': 'string', # the evolutionary state of the star. For more info see + # `posydon.utils.common_functions.check_state_of_star` + 'metallicity': 'float64', # initial mass fraction of metals + 'mass': 'float64', # mass (solar units) + 'log_R': 'float64', # log10 of radius (solar units) + 'log_L': 'float64', # log10 luminosity (solar units) + 'lg_mdot': 'float64', # log10 of the absolulte mass change of the star due to + # winds and RLO coming from binary history (Msun/yr) + 'lg_system_mdot': 'float64', # log10 of the system's absolute mass loss from around + # the star due to inneficient mass transfer (Msun/yr) + 'lg_wind_mdot': 'float64', # log10 of the absolute wind mass loss of the star from + # `lg_wind_mdot_1/2` in binary_history (Msun/yr) + 'he_core_mass': 'float64', # in solar units + 'he_core_radius': 'float64', # in solar units + 'c_core_mass': 'float64', # in solar units + 'c_core_radius': 'float64', # in solar units + 'o_core_mass': 'float64', # in solar units + 'o_core_radius': 'float64', # in solar units + 'co_core_mass': 'float64', # in solar units + 'co_core_radius': 'float64', # in solar units + # Central mass fractions + 'center_h1': 'float64', + 'center_he4': 'float64', + 'center_c12': 'float64', + 'center_n14': 'float64', + 'center_o16': 'float64', + 'surface_h1': 'float64', + # Mass fractions at the surface + 'surface_he4': 'float64', + 'surface_c12': 'float64', + 'surface_n14': 'float64', + 'surface_o16': 'float64', + # Energy production from nuclear burning (solar units) + 'log_LH': 'float64', # H burning + 'log_LHe': 'float64', # He burning + 'log_LZ': 'float64', # non-H and non-He burning + 'log_Lnuc': 'float64', # total nuclear burning energy production + 'c12_c12': 'float64', # Carbon burning through c12-c12 + 'center_gamma': 'float64', + 'avg_c_in_c_core': 'float64', # average carbon mass fraction at the carbon core + 'surf_avg_omega': 'float64', # surface average rotational angular velocity (rad/sec) + 'surf_avg_omega_div_omega_crit': 'float64', # ratio of `surf_avg_omega` to the + # surface critical rotation limit, + # taking into account centrifugal force + # & radiation pressure (Eddington lim.) + 'total_moment_of_inertia': 'float64', # total moment of inertia (gr*cm^2) + 'log_total_angular_momentum': 'float64', # log10 of total ang. momentum (gr*cm^2/s) + 'spin': 'float64', # the dimesionless spin of the star, if it + # is a compact object, which is equal to + # c*J/(GM^2). + 'conv_env_top_mass': 'float64', + 'conv_env_bot_mass': 'float64', + 'conv_env_top_radius': 'float64', + 'conv_env_bot_radius': 'float64', + 'conv_env_turnover_time_g': 'float64', + 'conv_env_turnover_time_l_b': 'float64', + 'conv_env_turnover_time_l_t': 'float64', + 'envelope_binding_energy': 'float64', + 'mass_conv_reg_fortides': 'float64', + 'thickness_conv_reg_fortides': 'float64', + 'radius_conv_reg_fortides': 'float64', + 'lambda_CE_1cent': 'float64', + 'lambda_CE_10cent': 'float64', + 'lambda_CE_30cent': 'float64', + 'lambda_CE_pure_He_star_10cent': 'float64', + 'profile': 'object', # the profile of the star, including extended information of + # its internal structure, for a specific timestep, usually for + # the end of the previous step including MESA psygrid. +} + +EXTRA_BINARY_COLUMNS_DTYPES = { + 'step_names' : 'string', + 'step_times' : 'float64', +} + +# no default extras for history attributes +EXTRA_STAR_COLUMNS_DTYPES = {} + + + +def clean_binary_history_df(binary_df, extra_binary_dtypes_user=None, + extra_S1_dtypes_user=None, extra_S2_dtypes_user=None): + """Take a posydon binary history DataFrame from the + BinaryStar.to_df method and clean the data for saving + by setting Data Types of the columns explicitly. + + Parameters + --------- + binary_df : DataFrame + A pandas Dataframe containing binary history + extra_binary_dtypes_user : dict, optional + A dictionary with extra column names as keys, and their + associated data types as values. + extra_S1_dtypes_user : dict, optional + Same as above, but only for star 1. + extra_S2_dtypes_user : dict, optional + Same as above, but only for star 2. + + Returns + ------- + binary_df : DataFrame + A cleaned binary history ready for saving to HDF. + """ + + assert isinstance( binary_df, pd.DataFrame ) + + # User specified extra binary and star columns + if extra_binary_dtypes_user is None: + extra_binary_dtypes_user = {} + if extra_S1_dtypes_user is None: + extra_S1_dtypes_user = {} + if extra_S2_dtypes_user is None: + extra_S2_dtypes_user = {} + + # try to coerce data types automatically first + binary_df = binary_df.infer_objects() + + # Set data types for all columns explicitly + hist_columns = set(binary_df.columns) + + # combine columns for binary history with extras + BP_comb_extras_dict = {**BINARYPROPERTIES_DTYPES, **EXTRA_BINARY_COLUMNS_DTYPES, + **extra_binary_dtypes_user} + # combine columns for star 1 and star 2 history with extras + SP_comb_S1_dict = {**STARPROPERTIES_DTYPES, **EXTRA_STAR_COLUMNS_DTYPES, + **extra_S1_dtypes_user} + SP_comb_S2_dict = {**STARPROPERTIES_DTYPES, **EXTRA_STAR_COLUMNS_DTYPES, + **extra_S2_dtypes_user} + + # All default & user passed keys which we have mappings for + binary_keys = set( BP_comb_extras_dict.keys() ) + S1_keys = set( ['S1_' + key for key in SP_comb_S1_dict.keys()] ) + S2_keys = set( ['S2_' + key for key in SP_comb_S2_dict.keys()] ) + + # Find common keys between the binary_df and our column-dtype mapping + common_keys = hist_columns & ( binary_keys | S1_keys | S2_keys ) + + # Create a dict with column-dtype mapping only for columns in binary_df + common_dtype_dict = {} + for key in common_keys: + if key in binary_keys: + common_dtype_dict[key] = BP_comb_extras_dict.get( key ) + elif key in S1_keys: + common_dtype_dict[key] = SP_comb_S1_dict.get( key.replace('S1_', '') ) + elif key in S2_keys: + common_dtype_dict[key] = SP_comb_S2_dict.get( key.replace('S2_', '') ) + else: + raise ValueError(f'No data type found for {key}. Dtypes must be explicity declared.') + # set dtypes + binary_df = binary_df.astype( common_dtype_dict ) + # unset clean str data because pandas strings are broken for hdf saving + convert_to_obj = {} + for key, val in common_dtype_dict.items(): + if val == 'string': + convert_to_obj[key] = 'object' + binary_df = binary_df.astype( convert_to_obj ) + return binary_df + + +def clean_binary_oneline_df(oneline_df, extra_binary_dtypes_user=None, + extra_S1_dtypes_user=None, extra_S2_dtypes_user=None): + """Take a posydon binary oneline DataFrame from the + BinaryStar.to_oneline_df method and clean the data for saving + by setting Data Types of the columns explicitly. + + This method is similar to clean_binary_history_df since they + have many overalapping columns, with a few extras and different naming. + + Note: there may be edge cases not handed if new scalar_names are added. + + Parameters + --------- + binary_df : DataFrame + A pandas Dataframe containing binary history + extra_binary_dtypes_user : dict, optional + A dictionary with extra column names as keys, and their + associated data types as values. + extra_S1_dtypes_user : dict, optional + Same as above, but only for star 1. + extra_S2_dtypes_user : dict, optional + Same as above, but only for star 2. + + Returns + ------- + binary_df : DataFrame + A cleaned binary history ready for saving to HDF. + """ + assert isinstance( oneline_df, pd.DataFrame ) + + # User specified extra binary and star columns + if extra_binary_dtypes_user is None: + extra_binary_dtypes_user = {} + if extra_S1_dtypes_user is None: + extra_S1_dtypes_user = {} + if extra_S2_dtypes_user is None: + extra_S2_dtypes_user = {} + + # try to coerce data types automatically first + oneline_df = oneline_df.infer_objects() + + # Set data types for all columns explicitly + oneline_columns = set(oneline_df.columns) + + # combine columns for binary history with extras + BP_comb_extras_dict = {**BINARYPROPERTIES_DTYPES, **EXTRA_BINARY_COLUMNS_DTYPES, + **extra_binary_dtypes_user} + # combine columns for star 1 and star 2 history with extras + SP_comb_S1_dict = {**STARPROPERTIES_DTYPES, **EXTRA_STAR_COLUMNS_DTYPES, + **extra_S1_dtypes_user} + SP_comb_S2_dict = {**STARPROPERTIES_DTYPES, **EXTRA_STAR_COLUMNS_DTYPES, + **extra_S2_dtypes_user} + + # All default & user passed keys which we have mappings for + binary_keys = set( [key + '_i' for key in BP_comb_extras_dict.keys()] + + [key + '_f' for key in BP_comb_extras_dict.keys()] ) + S1_keys = set( ['S1_' + key + '_i' for key in SP_comb_S1_dict.keys()] + + ['S1_' + key + '_f' for key in SP_comb_S1_dict.keys()] ) + S2_keys = set( ['S2_' + key + '_i' for key in SP_comb_S2_dict.keys()] + + ['S2_' + key + '_f' for key in SP_comb_S2_dict.keys()] ) + + # Find common keys between the binary_df and our column-dtype mapping + common_keys = oneline_columns & ( binary_keys | S1_keys | S2_keys ) + + + # Create a dict with column-dtype mapping only for columns in binary_df + common_dtype_dict = {} + # helper function to remove the '_i' and '_f' + strip_prefix_and_suffix = lambda key : key.strip('_i').strip('_f').strip('S1_').strip('S2_') + for key in common_keys: + if key in binary_keys: + common_dtype_dict[key] = BP_comb_extras_dict.get( strip_prefix_and_suffix(key) ) + elif key in S1_keys: + common_dtype_dict[key] = SP_comb_S1_dict.get( strip_prefix_and_suffix(key) ) + elif key in S2_keys: + common_dtype_dict[key] = SP_comb_S2_dict.get( strip_prefix_and_suffix(key) ) + else: + raise ValueError(f'No data type found for {key}. Dtypes must be explicity declared.') + # set dtypes + oneline_df = oneline_df.astype( common_dtype_dict ) + # unset clean str data because pandas strings are broken for hdf saving + convert_to_obj = {} + for key, val in common_dtype_dict.items(): + if val == 'string': + convert_to_obj[key] = 'object' + oneline_df = oneline_df.astype( convert_to_obj ) + + return oneline_df + + + def parse_inifile(path, verbose=False): """Parse an inifile for evolving binary populations. @@ -21,7 +315,6 @@ def parse_inifile(path, verbose=False): Path to inifile. If multiple files are given, duplicate args are overwritten (stacked) first to last. - verbose : bool Print helpful info. From 85812a796ba3c8a60f826ce445f0b7984e2adb91 Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Thu, 10 Aug 2023 16:41:00 +0200 Subject: [PATCH 113/319] remove wrong code for kick in eccentric orbit (#121) --- posydon/binary_evol/SN/step_SN.py | 257 +++++++----------------------- 1 file changed, 59 insertions(+), 198 deletions(-) diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index d10350c606..64aff55380 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -1391,163 +1391,6 @@ def orbital_kick(self, binary): mean_anomaly = np.random.uniform(0, 2 * np.pi) binary.star_2.natal_kick_array[3] = mean_anomaly - - # The binary exist: flag_binary is True if the binary is not disrupted - flag_binary = True - - # eccentricity before the SN - epre = binary.eccentricity - # the orbital semimajor axis is the orbital separation - Apre = binary.separation - # Eq 16, Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 - # for eccentric anomaly - E_ma = sp.optimize.brentq( - lambda x: mean_anomaly - x + epre * np.sin(x), 0, 2 * np.pi - ) - # Eq 15, Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 - # orbital separation at the time of the exlosion - rpre = Apre * (1.0 - epre * np.cos(E_ma)) - - # load constants in CGS - G = const.standard_cgrav - - # Convert inputs to CGS - M_he_star = M_he_star * const.Msun - M_companion = M_companion * const.Msun - M_compact_object = M_compact_object * const.Msun - Apre = Apre * const.Rsun - Vkick = Vkick * const.km2cm - rpre = rpre * const.Rsun - Mtot_pre = M_he_star + M_companion - Mtot_post = M_compact_object + M_companion - - # get useful quantity - sin_theta = np.sqrt(1 - (cos_theta ** 2)) - - # Eq 1, in Kalogera, V. 1996, ApJ, 471, 352 - # extended to Eq 17 in Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 - # Vr is velocity of preSN He core relative to M_companion, NOT necessarily - # in the direction of the Y axis if eccentric - # Eq from conservation of energy - Vr = np.sqrt(G * (Mtot_pre) * (2.0 / rpre - 1.0 / Apre)) - - # Eq 18, Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 - # psi is the polar angle of the position vector of the CO with respect - # to its pre-SN orbital velocity in the companions frame. i.e. angle between Vr and X axis - # If epre = 0, sin_psi should be 1 - # Eq from setting specific angular momentum r X Vr = sqrt(G*M*A*(1-e**2)) - sin_psi = np.round( - np.sqrt(G * (Mtot_pre) * (1 - epre ** 2) * Apre) - / (rpre * Vr), 5) - cos_psi = np.sqrt(1 - sin_psi ** 2) - # Allow for -cos_psi (Vr in the -X, +Y quadrant) - if E_ma > np.pi: cos_psi *= -1 - - # Eq 3, in Kalogera, V. 1996, ApJ, 471, 352 - # extended to Eq 13, in Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 - # get the orbital separation post SN - # Eq from conservation of energy - Apost = ((2.0 / rpre) - - (((Vkick ** 2) + (Vr ** 2) + (2 * (Vkick * cos_theta) * Vr)) / (G * Mtot_post)) - ) ** -1 - - - # get kicks componets in the coordinate system - Vkx = Vkick * (sin_theta * np.sin(phi) * sin_psi + cos_theta * cos_psi) - Vky = Vkick * (-sin_theta * np.sin(phi) * cos_psi + cos_theta * sin_psi) - Vkz = Vkick * sin_theta * np.cos(phi) - - - # Eq 4, in Kalogera, V. 1996, ApJ, 471, 352 - # extended to Eq 14 in Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 - # get the eccentricity post SN - # Eq from setting specific angular momentum r X Vr = sqrt(G*M*A*(1-e**2)) - - - x = ((Vkz ** 2 + (Vky + Vr * sin_psi)** 2) - * rpre ** 2 - / (G * Mtot_post * Apost)) - - # catch negative values, i.e. disrupted binaries - if 1.-x < 0.: - epost = np.nan - else: - epost = np.sqrt(1 - x) - - # Compute COM velocity, VS, post SN - # VS_pre in COM frame is 0. So VS_post in COM frame is - # VS_post - VS_pre in our working frame - - VC0x = M_he_star * Vr * cos_psi / Mtot_pre - VC0y = M_he_star * Vr * sin_psi / Mtot_pre - VC0z = 0 - - VC1x = M_compact_object * (Vkx + Vr * cos_psi) / Mtot_post - VC1y = M_compact_object * (Vky + Vr * sin_psi) / Mtot_post - VC1z = M_compact_object * Vkz / Mtot_post - - - VSx = VC1x - VC0x - VSy = VC1y - VC0y - VSz = VC1z - VC0z - - - # V_sys = np.sqrt(VSx ** 2 + VSy ** 2 + VSz ** 2) - - # Calculate the angle between the pre and post-SN orbital angular momentum vectors - # Lpre || Z axis - # Lpost || X axis cross the post SN velocity of the compact object - # cos(tilt) = Lpre dot Lpost / ||Lpre||||Lpost|| - # For epre=0 (sin_psi=1), reduces to Eq 4, in Kalogera, V. 1996, ApJ, 471, 352 - - tilt = np.arccos((Vky + Vr * sin_psi) / np.sqrt( Vkz ** 2 + (Vky + Vr * sin_psi) ** 2 )) - - def SNCheck( - M_he_star, - M_companion, - M_compact_object, - rpre, - Apost, - epost, - Vr, - Vkick, - cos_theta, - verbose, - ): - """Check that the binary is not disrupted. - - Parameters - ---------- - M_he_star : double - Helium star mass before the SN in g. - M_companion : double - Companion star mass in g. - M_compact_object : double - Compact object mass left by the SN in g. - rpre : double - Oribtal separation at the time of the exlosion in cm. If the - eccentricity pre SN is 0 this correpond to Apre. - Apost : double - Orbital separtion after the SN in cm. - epost : double - Eccentricity after the SN. - Vr : double - Velocity of pre-SN He core relative to M_companion, directed - along the positive y axis in cm/s. - Vkick : double - Kick velocity in cm/s. - cos_theta : double - The cosine of the angle between pre- & post-SN orbital planes. - - Returns - ------- - flag_binary : bool - flag_binary is True if the binary is not disrupted. - - References - ---------- - .. [1] Willems, B., Henninger, M., Levin, T., et al. 2005, ApJ, 625, 324 - """ # update the orbit if binary.state == "disrupted" or binary.state == "initially_single_star" or binary.state == "merged": #the binary was already disrupted before the SN @@ -1566,8 +1409,8 @@ def SNCheck( binary.mass_transfer_case = 'None' else: - # the binary is not disrupted at least before the SN - # The binary exists : flag_binary is True at least before the SN + + # The binary exist: flag_binary is True if the binary is not disrupted flag_binary = True # eccentricity before the SN @@ -1593,43 +1436,55 @@ def SNCheck( Apre = Apre * const.Rsun Vkick = Vkick * const.km2cm rpre = rpre * const.Rsun + Mtot_pre = M_he_star + M_companion + Mtot_post = M_compact_object + M_companion # get useful quantity sin_theta = np.sqrt(1 - (cos_theta ** 2)) - # get kicks componets in the coordinate system - Vkx = Vkick * sin_theta * np.sin(phi) - Vky = Vkick * cos_theta - Vkz = Vkick * sin_theta * np.cos(phi) - # Eq 1, in Kalogera, V. 1996, ApJ, 471, 352 # extended to Eq 17 in Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 - # Vr is velocity of preSN He core relative to M_companion, directed - # along the positive y axis - Vr = np.sqrt(G * (M_he_star + M_companion) * (2.0 / rpre - 1.0 / Apre)) - Mtot = M_compact_object + M_companion + # Vr is velocity of preSN He core relative to M_companion, NOT necessarily + # in the direction of the Y axis if eccentric + # Eq from conservation of energy + Vr = np.sqrt(G * (Mtot_pre) * (2.0 / rpre - 1.0 / Apre)) + + # Eq 18, Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 + # psi is the polar angle of the position vector of the CO with respect + # to its pre-SN orbital velocity in the companions frame. i.e. angle between Vr and X axis + # If epre = 0, sin_psi should be 1 + # Eq from setting specific angular momentum r X Vr = sqrt(G*M*A*(1-e**2)) + sin_psi = np.round( + np.sqrt(G * (Mtot_pre) * (1 - epre ** 2) * Apre) + / (rpre * Vr), 5) + cos_psi = np.sqrt(1 - sin_psi ** 2) + # Allow for -cos_psi (Vr in the -X, +Y quadrant) + if E_ma > np.pi: cos_psi *= -1 # Eq 3, in Kalogera, V. 1996, ApJ, 471, 352 # extended to Eq 13, in Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 # get the orbital separation post SN + # Eq from conservation of energy Apost = ((2.0 / rpre) - - (((Vkick ** 2) + (Vr ** 2) + (2 * Vky * Vr)) / (G * Mtot)) - ) ** -1 + - (((Vkick ** 2) + (Vr ** 2) + (2 * (Vkick * cos_theta) * Vr)) / (G * Mtot_post)) + ) ** -1 + + + # get kicks componets in the coordinate system + Vkx = Vkick * (sin_theta * np.sin(phi) * sin_psi + cos_theta * cos_psi) + Vky = Vkick * (-sin_theta * np.sin(phi) * cos_psi + cos_theta * sin_psi) + Vkz = Vkick * sin_theta * np.cos(phi) - # Eq 18, Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 - # psi: is the polar angle of the position vector of the CO with respect - # to its pre-SN orbital velocity in the companions frame. - sin_psi = np.round( - np.sqrt(G * (M_he_star + M_companion) * (1 - epre ** 2) * Apre) - / (rpre * Vr), 5) - cos_psi = np.sqrt(1 - sin_psi ** 2) # Eq 4, in Kalogera, V. 1996, ApJ, 471, 352 # extended to Eq 14 in Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 # get the eccentricity post SN - x = ((Vkz ** 2 + (sin_psi * (Vr + Vky) - cos_psi * Vkx) ** 2) - * rpre ** 2 - / (G * Mtot * Apost)) + # Eq from setting specific angular momentum r X Vr = sqrt(G*M*A*(1-e**2)) + + + x = ((Vkz ** 2 + (Vky + Vr * sin_psi)** 2) + * rpre ** 2 + / (G * Mtot_post * Apost)) # catch negative values, i.e. disrupted binaries if 1.-x < 0.: @@ -1637,26 +1492,33 @@ def SNCheck( else: epost = np.sqrt(1 - x) - # Eq 34, in Kalogera, V. 1996, ApJ, 471, 352 - # V_sys: is the resulting center of mass velocity of the system - # IN THE TRANSLATED COMOVING FRAME, imparted by the SN - VSx = M_compact_object * Vkx / Mtot - VSy = ( - M_compact_object * Vky - - ( - (M_he_star - M_compact_object) - * M_companion - * Vr - / (M_he_star + M_companion) - ) - ) / Mtot - VSz = M_compact_object * Vkz / Mtot + # Compute COM velocity, VS, post SN + # VS_pre in COM frame is 0. So VS_post in COM frame is + # VS_post - VS_pre in our working frame + + VC0x = M_he_star * Vr * cos_psi / Mtot_pre + VC0y = M_he_star * Vr * sin_psi / Mtot_pre + VC0z = 0 + + VC1x = M_compact_object * (Vkx + Vr * cos_psi) / Mtot_post + VC1y = M_compact_object * (Vky + Vr * sin_psi) / Mtot_post + VC1z = M_compact_object * Vkz / Mtot_post + + + VSx = VC1x - VC0x + VSy = VC1y - VC0y + VSz = VC1z - VC0z + + # V_sys = np.sqrt(VSx ** 2 + VSy ** 2 + VSz ** 2) - # Eq 5, in Kalogera, V. 1996, ApJ, 471, 352: - # calculate the tilt of the orbital plane after the SN - tilt = np.arccos((Vky + Vr) / ((Vky + Vr) ** 2 + Vkz ** 2) ** (1. / 2)) + # Calculate the angle between the pre and post-SN orbital angular momentum vectors + # Lpre || Z axis + # Lpost || X axis cross the post SN velocity of the compact object + # cos(tilt) = Lpre dot Lpost / ||Lpre||||Lpost|| + # For epre=0 (sin_psi=1), reduces to Eq 4, in Kalogera, V. 1996, ApJ, 471, 352 + tilt = np.arccos((Vky + Vr * sin_psi) / np.sqrt( Vkz ** 2 + (Vky + Vr * sin_psi) ** 2 )) def SNCheck( M_he_star, @@ -1703,7 +1565,6 @@ def SNCheck( References ---------- .. [1] Willems, B., Henninger, M., Levin, T., et al. 2005, ApJ, 625, 324 - .. [2] Kalogera, V. & Lorimer, D.R. 2000, ApJ, 530, 890 """ From 2ee161328e969dae0261f1cec4cc965d6a82ad6b Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 10 Aug 2023 16:52:35 +0200 Subject: [PATCH 114/319] Update scrubbing.py (#123) Use h2_colnames --- posydon/grids/scrubbing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/posydon/grids/scrubbing.py b/posydon/grids/scrubbing.py index 8736a81ae5..dbe633fc5a 100644 --- a/posydon/grids/scrubbing.py +++ b/posydon/grids/scrubbing.py @@ -196,7 +196,7 @@ def keep_till_central_abundance_He_C(bh, h1, h2, Ystop=1.0e-5, XCstop=1.0): else: depleted1 = False h2_colnames = h2.dtype.names - if ("center_he4" in h1_colnames) and ("center_c12" in h1_colnames): + if ("center_he4" in h2_colnames) and ("center_c12" in h2_colnames): if (len(h2["center_he4"])>0) and (len(h2["center_c12"])>0): depleted2 = ((h2["center_he4"][-1] Date: Tue, 15 Aug 2023 18:09:10 +0200 Subject: [PATCH 115/319] Update binarypopulation.py (#128) Avoid double loading of BinaryStar --- posydon/popsyn/binarypopulation.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index 4bd8a279d4..5c40997bd0 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -35,8 +35,10 @@ from tqdm import tqdm import psutil import random +import sys -from posydon.binary_evol.binarystar import BinaryStar +if 'posydon.binary_evol.binarystar' not in sys.modules.keys(): + from posydon.binary_evol.binarystar import BinaryStar from posydon.binary_evol.singlestar import (SingleStar,properties_massless_remnant) from posydon.binary_evol.simulationproperties import SimulationProperties from posydon.popsyn.star_formation_history import get_formation_times From 85148e53f98fa780c06d2cd0276a109be15d8b30 Mon Sep 17 00:00:00 2001 From: Jeff Andrews Date: Thu, 24 Aug 2023 10:45:45 -0400 Subject: [PATCH 116/319] Update profile_interpolation.py Now we have a warning to catch errors when users try to import tensorflow, but have not installed it. --- posydon/interpolation/profile_interpolation.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/posydon/interpolation/profile_interpolation.py b/posydon/interpolation/profile_interpolation.py index 05947143d6..226d9cddbe 100644 --- a/posydon/interpolation/profile_interpolation.py +++ b/posydon/interpolation/profile_interpolation.py @@ -15,7 +15,10 @@ # Math and ML import numpy as np import pandas as pd -import tensorflow as tf +try: + import tensorflow as tf +except ImportError: + raise ImportError('tensorflow is not installed. Please run `pip install .[profile]` in the POSYDON base directory') tf.get_logger().setLevel('ERROR') from tensorflow.keras import layers, losses, models, optimizers from sklearn.decomposition import PCA From 9f7cc302441fa13036761bf74eb138b44fe7b089 Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Thu, 24 Aug 2023 16:49:42 +0200 Subject: [PATCH 117/319] Merge 124 and 125 (#126) * implemented confusion matrix and violin plots, some bugs and finishing touches remain on the to do list * confusion matrices working * trying to plot errors in grid slices * added plot 2d, running into latek bug * fixed violin plots (there were lots of nans because we don't interpolate initial_MT) * plot 2d is working, will need to test and ask Simone and Mathias for additional features that they would like * cleaning plot_grid method * add processed columns to step_plot * revert to developent * cleaning * allow to specify arbitrary magnitude key * display inf * New pipeline structure * support all grids * Fix some typos * add support for interp_err * Don't append rows twice if more than one needed file is missing. * debug * Fix to get STEP correctly * Remove debuging output * Fix: turn directory checks into file checks, where files are used instead of directories. * No verbose on checks; correct typo. * fix typo again * fixes * Update (#127) * Fix coefficinent SFRH Neijssel+19 (#112) * Add function "keep_till_He_depletion" * Add option to stop at He depletion * Add option to pipeline (remove later) * correct not * Change threshold for He depletion * allow histories without age, add new flag * Check for history of 0 length * support BH formation above PISN gap and direct/Fryer collapse prescriptions now assume hydrogen envelope does not go into the BH * add C criterion for He depletion add C criterion for He depletion * Update psygrid.py use 0.08 carbon abundance * Update scrubbing.py where can't handle two arrays in parallel * Update psygrid.py change C limit to 0.1 * support the option to conserve the hydrogen envelope in BH formation * add MODEL var to post processing * update post processing script to support multiple core collapse models * Update psygrid.py Add mass request to only apply stopping earlier to masses of 100Msun and above * Update run-pipeline remove "_He_depletion" extension for the file name of the hdf5 file containing the psygrid * debug * rename function and flags * add support for BH disk mass * allow to remove slices from combining grids and only remove the combining if all slices are missing * fix small bug * Update setup-pipeline correct some typos * track previously created files * Update setup-pipeline fix typo * get files from step 1 * change range over df shape to instead use the index object * update step_6 run-pipeline * turn any to check on length * debug * add check for no slurm tasks * account for larger length difference of histories * reuse columns from histories if possible, add warnings * bugfix * propagate changes in step_SN and step_MESA * fix bug * debug plot2D * remove circular import * add open-mode, remove old file (make a copy first) * allow to ignore bad charaters in MESA log files * drop rows and columns * change import * import TF1 for post processing * fix bug * Update psygrid.py correct typo * debug * roll fix for single stars as well * debug * Matthias split grid rlo (#99) * fix in psygrid.py fix check of initial mass when cutting off carbon depletion * Update run-pipeline get start_at_RLO from compression type add flag to stop before carbon depletion (allow only for HMS grids) * Update setup-pipeline remove CO_HMS_GRID_START_AT_RLO add STOP_BEFORE_CARBON_DEPLETION * Update setup-pipeline get different directory for plotting if compression contains "RLO" * Update setup-pipeline correct typo * Update psygrid.py correct typo * Update psygrid.py debug * debug single interpolation * add support for other metallicities * update Z to Y conversion * SN_type not diclared in star properties * debug * update default params ini * increase len interp_class column * update params ini * add authorship to MODEL.py * Matthias split grid rlo (#100) * fix in psygrid.py fix check of initial mass when cutting off carbon depletion * Update run-pipeline get start_at_RLO from compression type add flag to stop before carbon depletion (allow only for HMS grids) * Update setup-pipeline remove CO_HMS_GRID_START_AT_RLO add STOP_BEFORE_CARBON_DEPLETION * Update setup-pipeline get different directory for plotting if compression contains "RLO" * Update setup-pipeline correct typo * Update psygrid.py correct typo * Update psygrid.py debug * Update setup-pipeline Add RLO to steps 6 and 7 * Update setup-pipeline Correct step 7 * Update run-pipeline don't interpolate no_MT for any grid starting at RLO * Update run-pipeline remove "_RLO" from method, which is read from the filename * Update setup-pipeline add "CO-HeMS_RLO" to export dirs * update spin orbit tilt name in rate calculation * adding function get_formation_channels * in parse function, shifting the binary_index * compute GRB properties * add channelwise merger efficiency calculation and plotting * add channelwise rate density * support channelwise histogram distributions * add file authorship * debug * cosistency change * add GRB rate calculation * debug * debug * debug * address PR103 PR104 comments * remove bug introduced in previous comment * remove bug introduced in previous commit * bug fix --------- Co-authored-by: mkruckow <122798003+mkruckow@users.noreply.github.com> Co-authored-by: mkruckow Co-authored-by: Monica Gallegos-Garcia * Plot popsyn values over grid slices (#115) * Add function "keep_till_He_depletion" * Add option to stop at He depletion * Add option to pipeline (remove later) * correct not * Change threshold for He depletion * allow histories without age, add new flag * Check for history of 0 length * support BH formation above PISN gap and direct/Fryer collapse prescriptions now assume hydrogen envelope does not go into the BH * add C criterion for He depletion add C criterion for He depletion * Update psygrid.py use 0.08 carbon abundance * Update scrubbing.py where can't handle two arrays in parallel * Update psygrid.py change C limit to 0.1 * support the option to conserve the hydrogen envelope in BH formation * add MODEL var to post processing * update post processing script to support multiple core collapse models * Update psygrid.py Add mass request to only apply stopping earlier to masses of 100Msun and above * Update run-pipeline remove "_He_depletion" extension for the file name of the hdf5 file containing the psygrid * debug * rename function and flags * add support for BH disk mass * allow to remove slices from combining grids and only remove the combining if all slices are missing * fix small bug * Update setup-pipeline correct some typos * track previously created files * Update setup-pipeline fix typo * get files from step 1 * change range over df shape to instead use the index object * update step_6 run-pipeline * turn any to check on length * debug * add check for no slurm tasks * account for larger length difference of histories * reuse columns from histories if possible, add warnings * bugfix * propagate changes in step_SN and step_MESA * fix bug * debug plot2D * remove circular import * add open-mode, remove old file (make a copy first) * allow to ignore bad charaters in MESA log files * drop rows and columns * change import * import TF1 for post processing * fix bug * Update psygrid.py correct typo * debug * roll fix for single stars as well * debug * Matthias split grid rlo (#99) * fix in psygrid.py fix check of initial mass when cutting off carbon depletion * Update run-pipeline get start_at_RLO from compression type add flag to stop before carbon depletion (allow only for HMS grids) * Update setup-pipeline remove CO_HMS_GRID_START_AT_RLO add STOP_BEFORE_CARBON_DEPLETION * Update setup-pipeline get different directory for plotting if compression contains "RLO" * Update setup-pipeline correct typo * Update psygrid.py correct typo * Update psygrid.py debug * debug single interpolation * add support for other metallicities * update Z to Y conversion * SN_type not diclared in star properties * debug * update default params ini * increase len interp_class column * update params ini * add authorship to MODEL.py * Matthias split grid rlo (#100) * fix in psygrid.py fix check of initial mass when cutting off carbon depletion * Update run-pipeline get start_at_RLO from compression type add flag to stop before carbon depletion (allow only for HMS grids) * Update setup-pipeline remove CO_HMS_GRID_START_AT_RLO add STOP_BEFORE_CARBON_DEPLETION * Update setup-pipeline get different directory for plotting if compression contains "RLO" * Update setup-pipeline correct typo * Update psygrid.py correct typo * Update psygrid.py debug * Update setup-pipeline Add RLO to steps 6 and 7 * Update setup-pipeline Correct step 7 * Update run-pipeline don't interpolate no_MT for any grid starting at RLO * Update run-pipeline remove "_RLO" from method, which is read from the filename * Update setup-pipeline add "CO-HeMS_RLO" to export dirs * update spin orbit tilt name in rate calculation * adding function get_formation_channels * in parse function, shifting the binary_index * compute GRB properties * add channelwise merger efficiency calculation and plotting * add channelwise rate density * support channelwise histogram distributions * add file authorship * debug * cosistency change * add GRB rate calculation * fixing shift in binary index * minor bug * debug * debug * debug * address PR103 PR104 comments * remove bug introduced in previous comment * remove bug introduced in previous commit * debug * add plot_popsyn_over_grid_slice * debug * address Matthias points * fix zorder --------- Co-authored-by: mkruckow <122798003+mkruckow@users.noreply.github.com> Co-authored-by: mkruckow Co-authored-by: Monica Gallegos-Garcia * Add multi panel plots to plot2D (#118) * Add multi panel plots * add missing desciption * fix grid post processing WD and above PISN BH formation (#119) * Fix missing Z values in case of missing history files (#120) * Hdf5 refactor - data types for population output (#88) * Adding default dtype dicts. * Adding conversion back to object because saving string dtypes in pandas is broken * Adding dtype logic to oneline * Moved around the logic for cleaning data out of to_df and made helper methods in popsyn.io * Removed old commented out code fixed some docs and from_df methods * Remove comments and add some docs * Add compression options for HDFStore * remove old imports * remove wrong code for kick in eccentric orbit (#121) * Update scrubbing.py (#123) Use h2_colnames --------- Co-authored-by: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Co-authored-by: Monica Gallegos-Garcia Co-authored-by: Kyle Rocha <48293898+ka-rocha@users.noreply.github.com> * fixes * Update binarypopulation.py Avoid double loading of BinaryStar * include PR128 * fix: keys of final_values * fix typo: uppercase to lowercase * catch when print fails * fix: CONTROL_GRIDS being a list of lists of strings * minor fix to address stellar states of not converged runs * fix: recongize _RLO in compression * previous fix was incomplete -> improve * use lists in substeps (#131) use lists in the csv files -> thus, it now loops inside the plotting function generalize the creation of plot attributes to need less predefined dictionary entries additionally, I made some other lift ups on the code, e.g. always using uppercase letters on stuff which is a category. * avoid to double RLO in directory names * add more quantities to plot * small bug * remove duplicated label * fix small plotting bug --------- Co-authored-by: prinse1545 Co-authored-by: mkruckow Co-authored-by: mkruckow <122798003+mkruckow@users.noreply.github.com> Co-authored-by: Monica Gallegos-Garcia Co-authored-by: Kyle Rocha <48293898+ka-rocha@users.noreply.github.com> --- bin/run-pipeline | 1063 +++++++++++++-------- bin/setup-pipeline | 873 +++++++++++------ grid_params/pipeline_quest.ini | 76 +- grid_params/pipeline_yggdrasil.ini | 272 +++--- posydon/grids/post_processing.py | 22 +- posydon/interpolation/IF_interpolation.py | 57 ++ posydon/visualization/interpolation.py | 366 +++++++ posydon/visualization/plot2D.py | 48 +- posydon/visualization/plot_defaults.py | 100 +- 9 files changed, 1994 insertions(+), 883 deletions(-) create mode 100644 posydon/visualization/interpolation.py diff --git a/bin/run-pipeline b/bin/run-pipeline index 8910c2e328..d067bb4b17 100644 --- a/bin/run-pipeline +++ b/bin/run-pipeline @@ -6,47 +6,51 @@ This is an EXAMPLE of the pipeline for a grid_type=HMS-HMS, metallicity=1e-01_Zsun, compression=LITE/ORIGINAL STEP 1: grid slices creation -['grid_low_res_0','grid_low_res_1','grid_low_res_2', -'grid_low_res_3','grid_low_res_4','grid_low_res_5', -'grid_random_1', 'grid_low_res_rerun_opacitymax'] --> output *.h5 +['grid_low_res_0','grid_low_res_1','grid_low_res_2','grid_low_res_3', +'grid_low_res_4','grid_low_res_5','grid_random_1', +'grid_low_res_rerun_opacity_max'] --> output *.h5 STEP 2: grid slices concatenation -[['grid_low_res_0','grid_low_res_1','grid_low_res_2', -'grid_low_res_3','grid_low_res_4','grid_low_res_5'], -['grid_low_combined','grid_low_res_rerun_opacitymax']] --> -grid_low_res_combined.h5, grid_low_res_combined_rerun_1.h5 - -STEP 3: plot grid slices -['grid_low_res_combined.h5', 'grid_low_res_rerun_opacitymax', -'grid_low_res_combined_rerun_1.h5' ] --> loop over all plot types - -STEP 4: check failure rate -['grid_low_res_combined.h5', 'grid_low_res_combined_rerun_1.h5' ] +[['grid_low_res_0','grid_low_res_1','grid_low_res_2','grid_low_res_3', +'grid_low_res_4','grid_low_res_5'],['grid_low_res_0','grid_low_res_1', +'grid_low_res_2','grid_low_res_3','grid_low_res_4','grid_low_res_5', +'grid_low_res_rerun_opacity_max']] --> +grid_low_res_combined.h5, grid_low_res_combined_rerun1.h5 + +STEP 2.1: plot grid slices +['grid_low_res_combined','grid_random_1','grid_low_res_combined_rerun1'] +--> loop over all plot types + +STEP 2.2: check failure rate +['grid_low_res_combined','grid_random_1','grid_low_res_combined_rerun1'] --> check failure rate -STEP 5: post processing -['grid_low_res_combined_rerun_1.h5','grid_random_1_rerun_1.h5',] +STEP 3: calculate extra values +['grid_low_res_combined','grid_random_1','grid_low_res_combined_rerun1'] --> do post processing on the ORIGINAL grid and append back on the LITE gird the post processed quantities -STEP 6: train interpolators -['grid_low_res_combined_rerun_1.h5'] --> train the interpolators +STEP 3.1: plot extra values +['grid_low_res_combined','grid_random_1','grid_low_res_combined_rerun1'] +--> loop over all plot types + +STEP 3.2: check rates of compact object types +['grid_low_res_combined','grid_random_1','grid_low_res_combined_rerun1'] +--> check rates of compact object types -STEP 7: export POSYDON v2 dataset +STEP 4: train interpolators +['grid_low_res_combined_rerun1'] --> train the interpolators -STEP RERUN: rerun grid with a fix A -grid_low_res_combined.rerun(index=logic) --> grid_rerun_1/grid.csv +STEP 4.1: plot interpolator accuracy and confusion matricies +['grid_random_1'] +--> loop over all plot types + +STEP 9: export dataset + +STEP RERUN: rerun grid with a fix +grid_low_res_combined.rerun(index=logic) --> grid_low_res_rerun_1/grid.csv --> grid_random_1_rerun_1/grid.csv ----- run gird fix ---- -call STEP 1: ['grid_rerun_1','grid_random_1_rerun_1',] --> *.h5 -call STEP 2: [['grid_low_res_combined','grid_rerun_1'], - ['grid_random_1','grid_random_rerun_1']] - ---> gird_low_res_combined_rerun_1.h5 - grid_random_1_rerun_1.h5 -call STEP 3: ['gird_low_res_combined_rerun_1.h5'] -call STEP 4: ['gird_low_res_combined_rerun_1.h5'] ---> continue to STEP 5, 6, 7 ----------------------- +-- run gird fix and do next post processing ''' __authors__ = [ @@ -56,6 +60,7 @@ __authors__ = [ import os import sys +import ast import time import shutil import pickle @@ -72,53 +77,93 @@ from posydon.grids.psygrid import (PSyGrid, from posydon.grids.post_processing import (post_process_grid, add_post_processed_quantities) from posydon.grids.MODELS import MODELS -from posydon.interpolation.IF_interpolation import IFInterpolator from posydon.utils.common_functions import PATH_TO_POSYDON from posydon.utils.gridutils import get_new_grid_name - - -def create_grid_slice(i, path_to_csv_file, STOP_BEFORE_CARBON_DEPLETION=True, verbose=False): - - df = pd.read_csv(path_to_csv_file) - grid_path = df.loc[i,'path_to_grid'] - compression = df.loc[i,'compression'] - +from posydon.visualization.plot_defaults import PRE_SET_PLOTS +from posydon.interpolation.IF_interpolation import IFInterpolator +from posydon.visualization.interpolation import EvaluateIFInterpolator + + +# pre defined compressions +COMPRESSIONS = { + 'ORIGINAL' : { + 'history_DS_error' : None, + 'profile_DS_error' : None, + 'profile_DS_interval' : None, + 'history_DS_exclude' : DEFAULT_HISTORY_DS_EXCLUDE, + 'profile_DS_exclude' : DEFAULT_PROFILE_DS_EXCLUDE + }, + 'LITE' : { + 'history_DS_error' : 0.1, + 'profile_DS_error' : 0.1, + 'profile_DS_interval' : -0.005, + 'history_DS_exclude' : EXTRA_COLS_DS_EXCLUDE, + 'profile_DS_exclude' : EXTRA_COLS_DS_EXCLUDE + } +} + + +def create_grid_slice(i, path_to_csv_file, verbose=False, overwrite_psygrid=True): + """Creates a new PSyGrid slice.""" + + # read csv file + if os.path.exists(path_to_csv_file): + df = pd.read_csv(path_to_csv_file) + else: + raise ValueError(f'{path_to_csv_file} not found!') + # get values from csv file + if 'path_to_grid' in df.keys(): + grid_path = df.loc[i,'path_to_grid'] + if not os.path.isdir(grid_path): + raise ValueError(f'{grid_path} not found!') + else: + raise ValueError(f'No path_to_grid in {path_to_csv_file}') + if 'compression' in df.keys(): + compression = df.loc[i,'compression'] + else: + raise ValueError(f'No compression in {path_to_csv_file}') + if 'stop_before_carbon_depletion' in df.keys(): + stop_before_carbon_depletion = df.loc[i,'stop_before_carbon_depletion'] + if 'HMS' not in grid_path: + stop_before_carbon_depletion = False + else: + stop_before_carbon_depletion = False + grid_output = get_new_grid_name(grid_path, compression, create_missing_directories=True) - - if verbose: - print('processing ', grid_path) - print('saving file ', grid_output) - - if 'ORIGINAL' in compression: - history_DS_error = None - profile_DS_error = None - profile_DS_interval = None - history_DS_exclude = DEFAULT_HISTORY_DS_EXCLUDE - profile_DS_exclude = DEFAULT_PROFILE_DS_EXCLUDE - elif 'LITE' in compression: - history_DS_error = 0.1 - profile_DS_error = 0.1 - profile_DS_interval = -0.005 - history_DS_exclude = EXTRA_COLS_DS_EXCLUDE - profile_DS_exclude = EXTRA_COLS_DS_EXCLUDE - else: - raise ValueError('compression = %s not supported!'%compression) - - if 'CO' in grid_path and 'RLO' in compression: + + # get compression dependent attributes + pure_compression = compression.split('_')[0] + if pure_compression in COMPRESSIONS.keys(): + history_DS_error = COMPRESSIONS[pure_compression]['history_DS_error'] + profile_DS_error = COMPRESSIONS[pure_compression]['profile_DS_error'] + profile_DS_interval = COMPRESSIONS[pure_compression]['profile_DS_interval'] + history_DS_exclude = COMPRESSIONS[pure_compression]['history_DS_exclude'] + profile_DS_exclude = COMPRESSIONS[pure_compression]['profile_DS_exclude'] + else: + raise ValueError(f'pure_compression = {pure_compression} not ' + f'supported! (compression={compression})') + + if ('CO' in grid_path) and ('RLO' in compression): start_at_RLO = True else: start_at_RLO = False - - if 'HMS' in grid_path and STOP_BEFORE_CARBON_DEPLETION: - stop_before_carbon_depletion = True - else: - stop_before_carbon_depletion = False - - grid = PSyGrid(verbose=True) + + if verbose: + print(f'processing: {grid_path} with compression: {compression}') + if os.path.isfile(grid_output): + if overwrite_psygrid: + print(f'replacing file: {grid_output}') + else: + print(f'file {grid_output} already exists!') + else: + print(f'saving to file: {grid_output}') + + # create the new PSyGrid + grid = PSyGrid(verbose=verbose) grid.create(grid_path, grid_output, - overwrite=True, + overwrite=overwrite_psygrid, history_DS_error=history_DS_error, profile_DS_error=profile_DS_error, history_DS_exclude=history_DS_exclude, @@ -132,267 +177,61 @@ def create_grid_slice(i, path_to_csv_file, STOP_BEFORE_CARBON_DEPLETION=True, ve def combine_grid_slices(i, path_to_csv_file, verbose=False): - - df = pd.read_csv(path_to_csv_file) + """Combining grid slices to one grid.""" + + # read csv file + if os.path.exists(path_to_csv_file): + df = pd.read_csv(path_to_csv_file) + else: + raise ValueError(f'{path_to_csv_file} not found!') + # get values from csv file grid_combined_key = df.keys()[i] gird_names = df[grid_combined_key].dropna().to_list() if verbose: - print('Combinining: ', gird_names) - print('into:', grid_combined_key) - join_grids(gird_names, grid_combined_key) + print(f'Combinining: {gird_names}') + print(f'into: {grid_combined_key}') + join_grids(gird_names, grid_combined_key, verbose=verbose) -def plot_grid(i, path_to_csv_file, verbose=False): - - df = pd.read_csv(path_to_csv_file) - grid_path = df.loc[i,'path_to_grid'] - plot_dir = df.loc[i,'path_to_plot'] - - if verbose: - print(f'plotting {grid_path}') - - # check that plots/ directories exist - plot_path = os.path.split(os.path.normpath(plot_dir))[0] - check_dirs = [plot_path, plot_dir] - - sub_dirs = ['TF12/', 'TF1/', 'TF2/', 'TF3/', 'TF4/', 'debug_mt/', - 'debug_rl_1/', 'debug_rl_2/'] - for name in sub_dirs: - check_dirs.append(os.path.join(plot_dir,name)) - - for dir_ in check_dirs: - if not os.path.isdir(dir_): - os.makedirs(dir_) - - # load grid - grid = PSyGrid(verbose=False) - grid.load(grid_path) - - if 'CO' in grid_path: - # compact object mass slices - def log_range(x_min,x_max,x_n): - return 10**np.linspace(np.log10(x_min),np.log10(x_max), x_n) - m_CO_min = 1. - m_CO_max = 51.32759630397827 - m_CO_n = 23 - m_COs = log_range(m_CO_min, m_CO_max, m_CO_n) - m_COs_edges = 10**(np.log10(np.array(m_COs)[:-1])+ - (np.log10(np.array(m_COs)[1:])-np.log10(np.array(m_COs)[:-1]))/2) - m2 = [0.]+m_COs_edges.tolist()+[55.] - vars = m_COs - fname = 'grid_m_%1.2f.png' - title = '$m_\mathrm{CO}=%1.2f\,M_\odot$' - slice_3D_var_str='star_2_mass' - elif 'HMS-HMS' in grid_path: - # mass ratio slices - qs = np.linspace(0.05,1.,20) - qs[-1] = 0.99 - vars = qs.tolist() - fname = 'grid_q_%1.2f.png' - title = '$q=%1.2f$' - slice_3D_var_str='mass_ratio' - else: - raise ValueError('Grid type not supported!') - - for i, var in enumerate(vars): - if 'HMS-HMS' in grid_path: - slice_3D_var_range = (var-2.5e-2,var+2.5e-2) - elif 'CO' in grid_path: - if i == len(m2): - continue - slice_3D_var_range = (m2[i],m2[i+1]) - # TODO: skip plotting slice if there are no data - try: - PLOT_PROPERTIES = { - 'figsize' : (4,3.5), - 'path_to_file' : os.path.join(plot_dir,'TF12/'), - 'show_fig' : False, - 'fname' : fname%var, - 'title' : title%var, - 'log10_x' : True, - 'log10_y' : True, - 'legend2D' : {'bbox_to_anchor' : (1.03, 0.5)} - } - - grid.plot2D('star_1_mass', 'period_days', None, - termination_flag='combined_TF12', - grid_3D=True, slice_3D_var_str=slice_3D_var_str, - slice_3D_var_range=slice_3D_var_range, - verbose=False, **PLOT_PROPERTIES) - - PLOT_PROPERTIES = { - 'figsize' : (4,5), - 'path_to_file' : os.path.join(plot_dir,'TF1/'), - 'show_fig' : False, - 'fname' : fname%var, - 'title' : title%var, - 'log10_x' : True, - 'log10_y' : True, - 'zmin' : -8, - 'zmax' : -1, - 'legend2D' : {'bbox_to_anchor' : (1.03, 0.5)}, - 'colorbar': {'pad': 0.12} - } - - grid.plot2D('star_1_mass', 'period_days', 'lg_mtransfer_rate', - termination_flag='termination_flag_1', - grid_3D=True, slice_3D_var_str=slice_3D_var_str, - slice_3D_var_range=slice_3D_var_range, - verbose=False, **PLOT_PROPERTIES) - - PLOT_PROPERTIES = { - 'figsize' : (4,3.5), - 'path_to_file' : os.path.join(plot_dir,'TF2/'), - 'show_fig' : False, - 'fname' : fname%var, - 'title' : title%var, - 'log10_x' : True, - 'log10_y' : True, - 'legend2D' : {'bbox_to_anchor' : (1.03, 0.5)} - } - - grid.plot2D('star_1_mass', 'period_days', None, - termination_flag='termination_flag_2', - grid_3D=True, slice_3D_var_str=slice_3D_var_str, - slice_3D_var_range=slice_3D_var_range, - verbose=False, **PLOT_PROPERTIES) - - PLOT_PROPERTIES = { - 'figsize' : (4,3.5), - 'path_to_file' : os.path.join(plot_dir,'TF3/'), - 'show_fig' : False, - 'fname' : fname%var, - 'title' : title%var, - 'log10_x' : True, - 'log10_y' : True, - 'legend2D' : {'bbox_to_anchor' : (1.03, 0.5)} - } - - grid.plot2D('star_1_mass', 'period_days', None, - termination_flag='termination_flag_3', - grid_3D=True, slice_3D_var_str=slice_3D_var_str, - slice_3D_var_range=slice_3D_var_range, - verbose=False, **PLOT_PROPERTIES) - - PLOT_PROPERTIES = { - 'figsize' : (4,3.5), - 'path_to_file' : os.path.join(plot_dir,'TF4/'), - 'show_fig' : False, - 'fname' : fname%var, - 'title' : title%var, - 'log10_x' : True, - 'log10_y' : True, - 'legend2D' : {'bbox_to_anchor' : (1.03, 0.5)} - } - - grid.plot2D('star_1_mass', 'period_days', None, - termination_flag='termination_flag_4', - grid_3D=True, slice_3D_var_str=slice_3D_var_str, - slice_3D_var_range=slice_3D_var_range, - verbose=False, **PLOT_PROPERTIES) - - PLOT_PROPERTIES = { - 'figsize' : (4,5), - 'path_to_file' : os.path.join(plot_dir,'debug_rl_1/'), - 'show_fig' : False, - 'fname' : fname%var, - 'title' : title%var, - 'log10_x' : True, - 'log10_y' : True, - 'zmin' : -0.5, - 'zmax' : 0.5, - 'legend2D' : {'bbox_to_anchor' : (1.03, 0.5)}, - 'colorbar': {'pad': 0.12} - } - - grid.plot2D('star_1_mass', 'period_days', - 'rl_relative_overflow_1', - termination_flag='debug', - grid_3D=True, slice_3D_var_str=slice_3D_var_str, - slice_3D_var_range=slice_3D_var_range, - verbose=False, **PLOT_PROPERTIES) - - PLOT_PROPERTIES = { - 'figsize' : (4,5), - 'path_to_file' : os.path.join(plot_dir,'debug_rl_2/'), - 'show_fig' : False, - 'fname' : fname%var, - 'title' : title%var, - 'log10_x' : True, - 'log10_y' : True, - 'zmin' : -0.5, - 'zmax' : 0.5, - 'legend2D' : {'bbox_to_anchor' : (1.03, 0.5)}, - 'colorbar': {'pad': 0.12} - } - - grid.plot2D('star_1_mass', 'period_days', - 'rl_relative_overflow_2', - termination_flag='debug', - grid_3D=True, slice_3D_var_str=slice_3D_var_str, - slice_3D_var_range=slice_3D_var_range, - verbose=False, **PLOT_PROPERTIES) - - PLOT_PROPERTIES = { - 'figsize' : (4,5), - 'path_to_file' : os.path.join(plot_dir,'debug_mt/'), - 'show_fig' : False, - 'fname' : fname%var, - 'title' : title%var, - 'log10_x' : True, - 'log10_y' : True, - 'zmin' : -8, - 'zmax' : -1, - 'legend2D' : {'bbox_to_anchor' : (1.03, 0.5)}, - 'colorbar': {'pad': 0.12} - } - - grid.plot2D('star_1_mass', 'period_days', 'lg_mtransfer_rate', - termination_flag='debug', - grid_3D=True, slice_3D_var_str=slice_3D_var_str, - slice_3D_var_range=slice_3D_var_range, - verbose=False, **PLOT_PROPERTIES) - except Exception as e: - print('FAILED TO PLOT '+title%var) - print('') - print(e) - continue - -def check_failure_rate(i, path_to_csv_file, verbose=False): - - df = pd.read_csv(path_to_csv_file) - grid_path = df.loc[i,'path_to_grid'] - grid = PSyGrid(verbose=False) - grid.load(grid_path) - - count = Counter(grid.final_values['interpolation_class']) - n = 0. - for key in count.keys(): - n += count[key] - print(grid_path) - print('Failure rate', round(count['not_converged']/n*100,2),'%') - - -def post_processing(i, path_to_csv_file, verbose=False): - - df = pd.read_csv(path_to_csv_file) - grid_path = df.loc[i,'path_to_grid'] - processed_grid_path = df.loc[i,'path_to_processed_grid'] - grid = PSyGrid(verbose=False) - grid.load(grid_path) - +def calculate_extra_values(i, path_to_csv_file, verbose=False): + """Calculating extra values, e.g. values derived from the final profile.""" + + # read csv file + if os.path.exists(path_to_csv_file): + df = pd.read_csv(path_to_csv_file) + else: + raise ValueError(f'{path_to_csv_file} not found!') + # get values from csv file + if 'path_to_grid' in df.keys(): + grid_path = df.loc[i,'path_to_grid'] + if not os.path.isfile(grid_path): + raise ValueError(f'{grid_path} not found!') + else: + raise ValueError(f'No path_to_grid in {path_to_csv_file}') + if 'path_to_processed_grid' in df.keys(): + processed_grid_path = df.loc[i,'path_to_processed_grid'] + else: + raise ValueError(f'No path_to_processed_grid in {path_to_csv_file}') + if 'path_to_grid_ORIGINAL' in df.keys(): + grid_ORIGINAL_path = df.loc[i,'path_to_grid_ORIGINAL'] + if not os.path.isfile(grid_ORIGINAL_path): + raise ValueError(f'{grid_ORIGINAL_path} not found!') + else: + raise ValueError(f'No path_to_grid_ORIGINAL in {path_to_csv_file}') + if verbose: - print('Compute processed quantities ...') + print(f'Compute processed quantities for {grid_path} ...') if 'CO' in grid_path: star_2_CO = True else: star_2_CO = False - grid_ORIGINAL = PSyGrid(grid_path.replace('LITE','ORIGINAL')) + + # load grid with ORIGINAL compression and get extra colums + grid_ORIGINAL = PSyGrid(grid_ORIGINAL_path) MESA_dirs_EXTRA_COLUMNS, EXTRA_COLUMNS = post_process_grid(grid_ORIGINAL, - index=None, - star_2_CO=star_2_CO, - verbose=False) - + index=None, star_2_CO=star_2_CO, + verbose=verbose) + # post processed quantities are appended to the grid object, hence # we create a copy of it and append back to it if os.path.exists(processed_grid_path): @@ -400,50 +239,66 @@ def post_processing(i, path_to_csv_file, verbose=False): print('Post processed grid file alredy exist, removing it.') os.remove(processed_grid_path) copyfile(grid_path, processed_grid_path) - - grid_LITE = PSyGrid(processed_grid_path) - add_post_processed_quantities(grid_LITE, MESA_dirs_EXTRA_COLUMNS, + + # load processed grid and append values + grid = PSyGrid(processed_grid_path) + add_post_processed_quantities(grid, MESA_dirs_EXTRA_COLUMNS, EXTRA_COLUMNS, verbose=verbose) if verbose: - print('Added processed quantities to grid:',processed_grid_path) - grid_LITE.close() + print(f'Added processed quantities to grid: {processed_grid_path}') + grid.close() def train_interpolators(i, path_to_csv_file, verbose=False): - - df = pd.read_csv(path_to_csv_file) - grid_path = df.loc[i,'path_to_grid'] - interpolator_path = df.loc[i,'path_to_interpolator'] - method = interpolator_path.split('IF_')[-1].split('.pkl')[0] + """Train an interpolator on a grid.""" + + # read csv file + if os.path.exists(path_to_csv_file): + df = pd.read_csv(path_to_csv_file) + else: + raise ValueError(f'{path_to_csv_file} not found!') + # get values from csv file + if 'path_to_grid' in df.keys(): + grid_path = df.loc[i,'path_to_grid'] + if not os.path.isfile(grid_path): + raise ValueError(f'{grid_path} not found!') + else: + raise ValueError(f'No path_to_grid in {path_to_csv_file}') + if 'path_to_interpolator' in df.keys(): + interpolator_path = df.loc[i,'path_to_interpolator'] + else: + raise ValueError(f'No path_to_interpolator in {path_to_csv_file}') + + method = os.path.splitext(interpolator_path.split('IF_')[-1])[0] if '_RLO' in method: method = method.split('_RLO')[0] - + if verbose: - print(f'train interpolators {grid_path} with method {method}') - + print(f'Train interpolators on {grid_path} with method {method}') + # check interpolation_objects directory exists - interp_path = os.path.split(interpolator_path)[0] + interp_path = os.path.dirname(interpolator_path) if not os.path.isdir(interp_path): os.makedirs(interp_path) - + # load grid - grid = PSyGrid(verbose=False) + grid = PSyGrid(verbose=verbose) grid.load(grid_path) - + if '_RLO' in grid_path: interp_method = [method, method] interp_classes = ["stable_MT", "unstable_MT"] else: interp_method = [method, method, method] interp_classes = ["no_MT", "stable_MT", "unstable_MT"] - + # all magnitues that are not model numbers, supernova properites or strings # are interpolated with respect to interpolation_class out_keys = [key for key in grid.final_values.dtype.names if ( - key != "model_number" and - (type(grid.final_values[key][0]) != np.str_) - and any(~np.isnan(grid.final_values[key])) - and "MODEL" not in key)] + key != "model_number" and + (type(grid.final_values[key][0]) != np.str_) + and any(~np.isnan(grid.final_values[key])) + and "MODEL" not in key)] # define all string keys in final values c_keys = ['interpolation_class', 'S1_state', 'S2_state'] @@ -472,18 +327,17 @@ def train_interpolators(i, path_to_csv_file, verbose=False): for MODEL_NAME in MODELS.keys(): for i in range(1,2): #TODO: range(1,3): out_keys = [key for key in grid.final_values.dtype.names if ( - key != "model_number" and - (type(grid.final_values[key][0]) != np.str_) - and any(~np.isnan(grid.final_values[key])) and - f"S{i}_{MODEL_NAME}" in key)] - - # catch cases where there is no WD formation - if "WD" in grid.final_values[f'S{i}_{MODEL_NAME}_CO_type']: - interp_method = [method, method, method] - interp_classes = ["BH", "NS", "WD"] - else: - interp_method = [method, method] - interp_classes = ["BH", "NS"] + key != "model_number" and + (type(grid.final_values[key][0]) != np.str_) + and any(~np.isnan(grid.final_values[key])) + and f"S{i}_{MODEL_NAME}" in key)] + # get interpolations classes dynamically + interp_method = [] + interp_classes = [] + for CO_type in ["BH", "NS", "WD"]: + if CO_type in grid.final_values[f'S{i}_{MODEL_NAME}_CO_type']: + interp_method.append(method) + interp_classes.append(CO_type) interpolators.append( { @@ -496,26 +350,46 @@ def train_interpolators(i, path_to_csv_file, verbose=False): }, ) - + # initialize initial to final interpolator interp = IFInterpolator(grid=grid, interpolators=interpolators) - # training and saving + # training and saving interp.train() interp.save(interpolator_path) def export_dataset(i, path_to_csv_file, verbose=False): - - df = pd.read_csv(path_to_csv_file) - grid_path = df.loc[i,'path_to_grid'] - export_path = df.loc[i,'export_path'] + """Moving the data set to a place containing all, what is needed to run a + population synthesis with POSYDON.""" + + # read csv file + if os.path.exists(path_to_csv_file): + df = pd.read_csv(path_to_csv_file) + else: + raise ValueError(f'{path_to_csv_file} not found!') + # get values from csv file + if 'path_to_grid' in df.keys(): + grid_path = df.loc[i,'path_to_grid'] + if not os.path.isfile(grid_path): + raise ValueError(f'{grid_path} not found!') + else: + raise ValueError(f'No path_to_grid in {path_to_csv_file}') + if 'export_path' in df.keys(): + export_path = df.loc[i,'export_path'] + else: + raise ValueError(f'No export_path in {path_to_csv_file}') + if verbose: print(f'copying {grid_path} to {export_path}') + # copy the file shutil.copyfile(grid_path, export_path) + def zams_file_name(dirname): + """Gives the name of the ZAMS file depending on the metallicity.""" + if '2e+00_Zsun' in dirname: - zams_filename = 'TODO' # TODO: update when avaiable + zams_filename = 'zams_z2.84m2_y0.2915.data' elif '1e+00_Zsun' in dirname: zams_filename = 'zams_z1.42m2_y0.2703.data' elif '4.5e-01_Zsun' in dirname: @@ -530,32 +404,35 @@ def zams_file_name(dirname): zams_filename = 'zams_z1.42m5_y0.2490.data' elif '1e-04_Zsun' in dirname: zams_filename = 'zams_z1.42m6_y0.2490.data' + else: + raise ValueError(f'Metallicity unknown: {dirname}') return zams_filename -def copy_ini_file(grid, rerun_type, destination, cluster): +def copy_ini_file(grid_path, rerun_type, destination, cluster): + """Copies the ini file and make replacements according to the rerun.""" + # copy the default ini file to directory if cluster in ['quest','yggdrasil']: - dirname = grid.MESA_dirs[0].decode('utf-8') ini_dir_path = os.path.join(PATH_TO_POSYDON, 'grid_params/POSYDON-MESA-INLISTS/r11701/running_scripts') - if "single_HMS" in dirname: + if "single_HMS" in grid_path: ini_file_path = os.path.join(ini_dir_path, f'single_HMS_{cluster}.ini') ini_file_dest = os.path.join(destination, f'single_HMS_{cluster}.ini') - elif "single_HeMS" in dirname: + elif "single_HeMS" in grid_path: ini_file_path = os.path.join(ini_dir_path, f'single_HeMS_{cluster}.ini') ini_file_dest = os.path.join(destination, f'single_HeMS_{cluster}.ini') - elif "HMS-HMS" in dirname: + elif "HMS-HMS" in grid_path: ini_file_path = os.path.join(ini_dir_path, f'HMS-HMS_{cluster}.ini') ini_file_dest = os.path.join(destination, f'HMS-HMS_{cluster}.ini') - elif "CO-HMS_RLO" in dirname: + elif "CO-HMS_RLO" in grid_path: ini_file_path = os.path.join(ini_dir_path, f'CO-HMS_RLO_{cluster}.ini') ini_file_dest = os.path.join(destination, f'CO-HMS_RLO_{cluster}.ini') - elif "CO-HeMS" in dirname: + elif "CO-HeMS" in grid_path: ini_file_path = os.path.join(ini_dir_path, f'CO-HeMS_{cluster}.ini') ini_file_dest = os.path.join(destination, f'CO-HeMS_{cluster}.ini') else: - raise ValueError(f'Unsupported grid type in {dirname}!') + raise ValueError(f'Unsupported grid type in {grid_path}!') shutil.copyfile(ini_file_path, ini_file_dest) else: raise ValueError(f'Unsupported cluster {cluster}!') @@ -576,11 +453,12 @@ def copy_ini_file(grid, rerun_type, destination, cluster): return else: raise ValueError(f'Unsupported rerun type {rerun_type}!') - + + # make replacements in ini file search_text1 = "main-18710e943edd926a5653c4cdb7d6e18e5bdb35a2" search_text2 = "main-a060b7bf1a4d94d693f77be9a1b0b3a522c1eadf" search_text3 = 'zams_z1.42m3_y0.2511.data' - replace_zams_filename = zams_file_name(dirname) + replace_zams_filename = zams_file_name(grid_path) with open(ini_file_dest, 'r') as file: data = file.read() data = data.replace(search_text1, replace_text) @@ -590,12 +468,15 @@ def copy_ini_file(grid, rerun_type, destination, cluster): with open(ini_file_dest, 'w') as file: file.write(data) -def logic_rerun(grid, rerun_type): +def logic_rerun(grid, rerun_type): + """Get the runs, which need a rerun.""" + runs_to_rerun=None termination_flags=None new_mesa_flag = None - + + # handle different rerun types if rerun_type == 'opacity_max' or rerun_type == 'opacity_max_hms-hms': termination_flags=['reach cluster timelimit', 'min_timestep_limit'] new_mesa_flag={'opacity_max' : 0.5} @@ -608,12 +489,14 @@ def logic_rerun(grid, rerun_type): # TODO: handle the case when grids were moved location raise ValueError(f'Grid directory not found at {dirname}') if "single_HMS" in dirname: - out_txt_path = os.path.join(dirname, "out_star1_formation_step0.txt") + out_txt_path = os.path.join(dirname, + "out_star1_formation_step0.txt") elif "single_HeMS" in dirname: - out_txt_path = os.path.join(dirname, "out_star1_formation_step2.txt") + out_txt_path = os.path.join(dirname, + "out_star1_formation_step2.txt") else: out_txt_path = os.path.join(dirname, "out.txt") - if out_txt_path is not None and os.path.isfile(out_txt_path): + if (out_txt_path is not None) and os.path.isfile(out_txt_path): with open(out_txt_path, "r") as log_file: log_lines = log_file.readlines() for line in log_lines: @@ -658,57 +541,390 @@ def logic_rerun(grid, rerun_type): runs_to_rerun += [i] runs_to_rerun = np.array(runs_to_rerun) elif rerun_type == 'envelope_mass_limit': # maybe 'fe_core_infall_limit' as well? - runs_to_rerun = np.array([0, 1]) # implement logic + runs_to_rerun = None # implement logic else: raise ValueError(f'Unsupported rerun type {rerun_type}!') - - if runs_to_rerun is None and termination_flags is None: + + if (runs_to_rerun is None) and (termination_flags is None): raise ValueError('Undefined rerun!') - + return runs_to_rerun, termination_flags, new_mesa_flag -def rerun(i, path_to_csv_file, rerun_type, verbose=False): - +def rerun(i, path_to_csv_file, verbose=False): + """Generate files to start a rerun.""" + + # read csv file + if os.path.exists(path_to_csv_file): + df = pd.read_csv(path_to_csv_file) + else: + raise ValueError(f'{path_to_csv_file} not found!') + # get values from csv file + if 'path_to_grid' in df.keys(): + grid_path = df.loc[i,'path_to_grid'] + if not os.path.isfile(grid_path): + raise ValueError(f'{grid_path} not found!') + else: + raise ValueError(f'No path_to_grid in {path_to_csv_file}') + if 'rerun_path' in df.keys(): + rerun_path = df.loc[i,'rerun_path'] + else: + raise ValueError(f'No rerun_path in {path_to_csv_file}') + if 'rerun_type' in df.keys(): + rerun_type = df.loc[i,'rerun_type'] + else: + raise ValueError(f'No rerun_type in {path_to_csv_file}') + # load grid - df = pd.read_csv(path_to_csv_file) - grid_path = df.loc[i,'path_to_grid'] - rerun_path = df.loc[i,'rerun_path'] - grid = PSyGrid(verbose=False) + grid = PSyGrid(verbose=verbose) grid.load(grid_path) - + # export point to rerun if verbose: print(f'rerun {grid_path} in {rerun_path}') - runs_to_rerun, termination_flags, new_mesa_flag = logic_rerun(grid, rerun_type) + # get reruns + runs_to_rerun, termination_flags, new_mesa_flag = logic_rerun(grid, + rerun_type) + # get csv file for reruns grid.rerun(path_to_file=rerun_path, runs_to_rerun=runs_to_rerun, - termination_flags=termination_flags, new_mesa_flag=new_mesa_flag) - + termination_flags=termination_flags, + new_mesa_flag=new_mesa_flag) + # copy ini file and set the new inlist commit - # TODO: make cluster a input + # TODO: make cluster an input if 'b1119' in grid.MESA_dirs[0].decode('utf-8'): cluster = 'quest' else: cluster = 'yggdrasil' - copy_ini_file(grid, rerun_type, destination=rerun_path, cluster=cluster) + copy_ini_file(grid_path, rerun_type, destination=rerun_path, cluster=cluster) + + +def plot_grid(i, path_to_csv_file, verbose=False): + """Creates plots of a grid.""" + # grid mass ranges + grid_sample = { + 'HMS-HMS' : { + 'log' : False, + 'nbin' : 20, + 'qmin' : 0.05, + 'qmax' : 1., + }, + 'CO-HeMS' : { + 'log' : True, + 'nbin' : 23, + 'mmin' : 1.0, + 'mmax' : 51.328, + }, + 'CO-HMS' : { + 'log' : True, + 'nbin' : 23, + 'mmin' : 1.0, + 'mmax' : 51.328, + } + } + + def _range(x_min, x_max, x_n, log=False): + if log: + vars = 10**np.linspace(np.log10(x_min),np.log10(x_max), x_n) + vars_edges = 10**((np.log10(vars[1:])+np.log10(vars)[:-1])*0.5) + else: + vars = np.linspace(x_min,x_max,x_n) + vars_edges = (vars[1:]+vars[:-1])*0.5 + vars_edges = [0.]+vars_edges.tolist()+[float("inf")] + return vars.tolist(), vars_edges + + # read csv file + if os.path.exists(path_to_csv_file): + df = pd.read_csv(path_to_csv_file) + else: + raise ValueError(f'{path_to_csv_file} not found!') + + check_dirs = [] + # get values from csv file + if 'path_to_grid' in df.keys(): + grid_path = df.loc[i,'path_to_grid'] + if not os.path.isfile(grid_path): + raise ValueError(f'{grid_path} not found!') + else: + raise ValueError(f'No path_to_grid in {path_to_csv_file}') + if 'path_to_plot' in df.keys(): + plot_dir = df.loc[i,'path_to_plot'] + plot_path = os.path.dirname(os.path.normpath(plot_dir)) + check_dirs.extend([plot_path, plot_dir]) + else: + raise ValueError(f'No path_to_plot in {path_to_csv_file}') + if 'quantities_to_plot' in df.keys(): + quantities_to_plot = ast.literal_eval(df.loc[i,'quantities_to_plot']) + else: + raise ValueError(f'No quantities_to_plot in {path_to_csv_file}') + if 'path_to_interpolator' in df.keys(): + path_to_interpolator = df.loc[i,'path_to_interpolator'] + else: + path_to_interpolator = '' + + # check that plots/ directories exist + for dir_ in check_dirs: + if not os.path.isdir(dir_): + os.makedirs(dir_) + + # load grid + grid = PSyGrid(verbose=verbose) + grid.load(grid_path) + + for quantity_to_plot in quantities_to_plot: + if verbose: + print(f'plotting: {quantity_to_plot} from {grid_path} to ' + f'{plot_dir}') + + # load interpolator and evaluate interp errors + if ('INTERP_ERROR' in quantity_to_plot + and path_to_interpolator != ''): + model = IFInterpolator() + model.load(path_to_interpolator) + evalIFerr = EvaluateIFInterpolator(model, grid) + interp_error = True + else: + interp_error = False + + # get attributes for plotting + plot_attributes = { + 'plot_dir_name' : 'default', + 'zvar' : None, + 'term_flag' : 'termination_flag_1', + 'zlog' : False, + 'zmin' : None, + 'zmax' : None + } + if quantity_to_plot in PRE_SET_PLOTS: + for attr in PRE_SET_PLOTS[quantity_to_plot].keys(): + plot_attributes[attr] = PRE_SET_PLOTS[quantity_to_plot][attr] + elif isinstance(quantity_to_plot, str): + #TODO: check if quantity_to_plot is plotable as Z value + if 'INTERP_ERROR_' in quantity_to_plot: + short_quantity = quantity_to_plot.split('INTERP_ERROR_')[-1] + set_name = 'INTERP_ERROR_DEFAULT' + if set_name in PRE_SET_PLOTS: + plot_attributes['plot_dir_name'] = os.path.join(\ + 'INTERP_ERROR', + short_quantity) + plot_attributes['zvar'] = short_quantity + for attr in PRE_SET_PLOTS[set_name].keys(): + plot_attributes[attr] = PRE_SET_PLOTS[set_name][attr] + else: + print(f'Warning: {set_name} not in PRE_SET_PLOTS.') + elif '_MODEL' in quantity_to_plot: + short_quantity = quantity_to_plot.split('_MODEL')[-1] + short_quantity = short_quantity[short_quantity.find('_')+1:] + model_name = quantity_to_plot.split('_'+short_quantity)[0] + set_name = quantity_to_plot.split('_MODEL')[0] +\ + '_MODEL_DEFAULT_' + short_quantity + if set_name in PRE_SET_PLOTS: + plot_attributes['plot_dir_name'] = os.path.join(model_name, + short_quantity) + plot_attributes['zvar'] = quantity_to_plot + for attr in PRE_SET_PLOTS[set_name].keys(): + plot_attributes[attr] = PRE_SET_PLOTS[set_name][attr] + else: + print(f'Warning: {set_name} not in PRE_SET_PLOTS.') + elif quantity_to_plot[:6]=='LOG10_': + short_quantity = quantity_to_plot[6:] + plot_attributes['plot_dir_name'] = short_quantity + plot_attributes['zvar'] = short_quantity + plot_attributes['zlog'] = True + else: + plot_attributes['plot_dir_name'] = quantity_to_plot + plot_attributes['zvar'] = quantity_to_plot + else: + raise TypeError(f'{quantity_to_plot} should be a string!') + + # check that plots/ directories exist + name = plot_attributes['plot_dir_name'] + dir_ = os.path.join(plot_dir, name) + if not os.path.isdir(dir_): + os.makedirs(dir_) + + if ('CO-' in grid_path): + # skip plotting CO properties + if 'S2_' in quantity_to_plot: + continue + # both grids are sampled in the same way with respect to the + # compact object mass + if 'CO-HeMS' in grid_path: + smpl = grid_sample['CO-HeMS'] + else: + smpl = grid_sample['CO-HMS'] + vars, vars_edges = _range(smpl['mmin'], smpl['mmax'], smpl['nbin'], + log=smpl['log']) + fname = 'grid_m_%1.2f.png' + title = '$m_\mathrm{CO}=%1.2f\,M_\odot$' + slice_3D_var_str = 'star_2_mass' + elif 'HMS-HMS' in grid_path: + # mass ratio slices + smpl = grid_sample['HMS-HMS'] + vars, vars_edges = _range(smpl['qmin'], smpl['qmax'], smpl['nbin'], + log=smpl['log']) + vars[-1] = 0.99 # we replaced q=1 with q=0.99 in mesa runs + fname = 'grid_q_%1.2f.png' + title = '$q=%1.2f$' + slice_3D_var_str = 'mass_ratio' + else: + raise ValueError('Grid type not supported!') + + # loop over all grid slices + for k, var in enumerate(vars): + slice_3D_var_range = (vars_edges[k],vars_edges[k+1]) + # TODO: skip plotting slice if there are no data + try: + # default plot properties + PLOT_PROPERTIES_DEFAULT = { + 'figsize' : (4,3.5), + 'path_to_file' : os.path.join(plot_dir, name)+"/", + 'show_fig' : False, + 'fname' : fname%var, + 'title' : title%var, + 'log10_x' : True, + 'log10_y' : True, + 'legend2D' : {'bbox_to_anchor' : (1.03, 0.5)} + } + + PLOT_PROPERTIES = PLOT_PROPERTIES_DEFAULT.copy() + if plot_attributes['zvar'] is not None: + # default plot properties with colorbar + PLOT_PROPERTIES['figsize'] = (4,5) + PLOT_PROPERTIES['colorbar'] = {'pad': 0.12} + PLOT_PROPERTIES['log10_z'] = plot_attributes['zlog'] + PLOT_PROPERTIES['zmin'] = plot_attributes['zmin'] + PLOT_PROPERTIES['zmax'] = plot_attributes['zmax'] + if 'INTERP_ERROR' in quantity_to_plot: + PLOT_PROPERTIES['colorbar']['label'] = quantity_to_plot + + if 'INTERP_ERROR' in quantity_to_plot: + # initial/final error slices + evalIFerr.plot2D(plot_attributes['zvar'], slice_3D_var_str, + slice_3D_var_range, PLOT_PROPERTIES) + # initial/final error all points in one plot + if k == 0: + PLOT_PROPERTIES_ALL = PLOT_PROPERTIES.copy() + PLOT_PROPERTIES_ALL['title'] = title.replace('=%1.2f$', '$ all') + PLOT_PROPERTIES_ALL['fname'] = fname.replace('%1.2f', 'all') + evalIFerr.plot2D(plot_attributes['zvar'], + slice_3D_var_str, + (vars_edges[0], vars_edges[-1]), + PLOT_PROPERTIES_ALL) + elif 'CLASS_ERROR' in quantity_to_plot: + # TODO: add class error plots + if k == 0: + evalIFerr.confusion_matrix(plot_attributes['zvar']) + elif 'VIOLIN' in quantity_to_plot: + # TODO: add violin plots + if k == 0: + evalIFerr.violin_plots("relative", keys=[plot_attributes['zvar']]) + else: + grid.plot2D('star_1_mass', 'period_days', plot_attributes['zvar'], + termination_flag=plot_attributes['term_flag'], + grid_3D=True, slice_3D_var_str=slice_3D_var_str, + slice_3D_var_range=slice_3D_var_range, + verbose=False, **PLOT_PROPERTIES) + + except Exception as e: + print('FAILED TO PLOT '+title%var) + print('') + print(e) + continue + + +def do_check(i, path_to_csv_file, verbose=False): + """Perform a check on a grid.""" + + # read csv file + if os.path.exists(path_to_csv_file): + df = pd.read_csv(path_to_csv_file) + else: + raise ValueError(f'{path_to_csv_file} not found!') + + # get values from csv file + if 'path_to_grid' in df.keys(): + grid_path = df.loc[i,'path_to_grid'] + if not os.path.isfile(grid_path): + raise ValueError(f'{grid_path} not found!') + else: + raise ValueError(f'No path_to_grid in {path_to_csv_file}') + if 'checks_to_do' in df.keys(): + checks_to_do = ast.literal_eval(df.loc[i,'checks_to_do']) + else: + raise ValueError(f'No checks_to_do in {path_to_csv_file}') + if 'path_to_interpolator' in df.keys(): + path_to_interpolator = df.loc[i,'path_to_interpolator'] + else: + path_to_interpolator = '' + + # load grid + grid = PSyGrid(verbose=verbose) + grid.load(grid_path) + + # do the checks + for check_to_do in checks_to_do: + if check_to_do=='failure_rate': + count = Counter(grid.final_values['interpolation_class']) + n = 0. + for key in count.keys(): + n += count[key] + if 'not_converged' in count.keys(): + failure_rate_in_percent = round(count['not_converged']/n*100,2) + else: + failure_rate_in_percent = 0. + print(f'Failure rate: {failure_rate_in_percent}% in {grid_path}') + elif check_to_do=='CO_type': + for quantity in grid.final_values.dtype.names: + if (('S1_MODEL' in quantity) and ('CO_type' in quantity)): + count = Counter(grid.final_values[quantity]) + print(f'{quantity}: {count} in {grid_path}') + elif check_to_do=='SN_type': + for quantity in grid.final_values.dtype.names: + if (('S1_MODEL' in quantity) and ('SN_type' in quantity)): + count = Counter(grid.final_values[quantity]) + print(f'{quantity}: {count} in {grid_path}') + #TODO: add more checks + else: + print(f'Do not know how to do {check_to_do} for {grid_path}!') + if __name__ == '__main__': + # read commond line arguments + n_args = len(sys.argv) + + if n_args > 1: # path to girds + PATH_TO_GRIDS = str(sys.argv[1]) + if not os.path.isdir(PATH_TO_GRIDS): # check given path + raise ValueError('Grids were not found! ' + f'Check your PATH_TO_GRIDS={PATH_TO_GRIDS}') + else: + raise ValueError('No path to the grids given. It should be specified ' + 'as first commandline argument.') + + if n_args > 2: # path to csv file + PATH_TO_CSV_FILE = str(sys.argv[2]) + if not os.path.isfile(PATH_TO_CSV_FILE): # check given path + raise ValueError('Csv file not found! ' + f'Check your path_to_csv_file={PATH_TO_CSV_FILE}') + else: + raise ValueError('No csv file given. It should be specified as ' + 'second commandline argument.') + + if n_args > 3: # slurm index + SLURM_I = int(sys.argv[3]) + if SLURM_I<0: # check value + raise ValueError(f'The slurm index cannot be negative: {SLURM_I}') + else: + SLURM_I = 0 + + if n_args > 4: # verbose flag + VERBOSE = bool(int(sys.argv[4])) + else: + VERBOSE = True + # chose grid slice given the slurm jobarray index - # TODO: use args parser - VERBOSE = True - PATH_TO_GRIDS = str(sys.argv[1]) - path_to_csv_file = str(sys.argv[2]) - i = int(sys.argv[3]) - STOP_BEFORE_CARBON_DEPLETION = bool(int(sys.argv[4])) - if len(sys.argv) == 6: - rerun_type = str(sys.argv[5]) - - # prevent to run the script locally else it will star creating - # directories where you do not want them before breaking - if not os.path.isdir(PATH_TO_GRIDS): - raise ValueError('Grids were not found! ' - f'Check your PATH_TO_GRIDS={PATH_TO_GRIDS}') # offset the start of each job # NOTE: this prevents job arrays starting at the same time to read @@ -716,28 +932,29 @@ if __name__ == '__main__': # or when creating a directory that does not exist yet) time.sleep(random.uniform(0.,30.)) - if 'step_1' in path_to_csv_file: - create_grid_slice(i, path_to_csv_file, - STOP_BEFORE_CARBON_DEPLETION=STOP_BEFORE_CARBON_DEPLETION, - verbose=VERBOSE) + STEP = os.path.splitext(os.path.basename(PATH_TO_CSV_FILE))[0] + if STEP=='step_1': # grid slices creation + create_grid_slice(SLURM_I, PATH_TO_CSV_FILE, verbose=VERBOSE) - if 'step_2' in path_to_csv_file: - combine_grid_slices(i, path_to_csv_file, verbose=VERBOSE) + if STEP=='step_2': # grid slices concatenation + combine_grid_slices(SLURM_I, PATH_TO_CSV_FILE, verbose=VERBOSE) - if 'step_3' in path_to_csv_file: - plot_grid(i, path_to_csv_file, verbose=VERBOSE) + if STEP=='step_3': # calculate extra values + calculate_extra_values(SLURM_I, PATH_TO_CSV_FILE, verbose=VERBOSE) - if 'step_4' in path_to_csv_file: - check_failure_rate(i, path_to_csv_file, verbose=VERBOSE) + if STEP=='step_4': # train interpolators + train_interpolators(SLURM_I, PATH_TO_CSV_FILE, verbose=VERBOSE) - if 'step_5' in path_to_csv_file: - post_processing(i, path_to_csv_file, verbose=VERBOSE) + if STEP=='step_9': # export dataset + export_dataset(SLURM_I, PATH_TO_CSV_FILE, verbose=VERBOSE) + + if STEP=='rerun': # export rerun + rerun(SLURM_I, PATH_TO_CSV_FILE, verbose=VERBOSE) + + if 'plot' in STEP: # plot grid slices + plot_grid(SLURM_I, PATH_TO_CSV_FILE, verbose=VERBOSE) - if 'step_6' in path_to_csv_file: - train_interpolators(i, path_to_csv_file, verbose=VERBOSE) + if 'check' in STEP: # do checks, e.g. failure rate + do_check(SLURM_I, PATH_TO_CSV_FILE, verbose=VERBOSE) - if 'step_7' in path_to_csv_file: - export_dataset(i, path_to_csv_file, verbose=VERBOSE) - if 'rerun' in path_to_csv_file: - rerun(i, path_to_csv_file, rerun_type, verbose=VERBOSE) diff --git a/bin/setup-pipeline b/bin/setup-pipeline index ba8e292f3c..0d16d14d02 100644 --- a/bin/setup-pipeline +++ b/bin/setup-pipeline @@ -5,13 +5,16 @@ __authors__ = [ "Matthias Kruckow ", ] +############################################################################## +# IMPORT ALL NECESSARY PYTHON PACKAGES +############################################################################## import os import sys import ast import shutil import pandas as pd import numpy as np -from pprint import pprint, pformat +from pprint import pformat from posydon.popsyn.io import parse_inifile from posydon.utils.common_functions import PATH_TO_POSYDON from posydon.utils.gridutils import get_new_grid_name @@ -38,8 +41,6 @@ PATH_TO_GRIDS/ ... /LITE/ /ORIGINAL/ - /logs/ - /scripts/ /plots/ /grid_low_res_combined/ /TF1/ @@ -47,20 +48,123 @@ PATH_TO_GRIDS/ ... ''' +# pipeline steps ACTION_TO_STEP_NUM = { - 'CREATE_GRID_SLICES' : 'step_1', - 'COMBINE_GRID_SLICES' : 'step_2', - 'PLOT_GRIDS' : 'step_3', - 'CHECK_FAILURE_RATE' : 'step_4', - 'POST_PROCESSING' : 'step_5', - 'TRAIN_INTERPOLATORS' : 'step_6', - 'EXPORT_DATASET' : 'step_7', - 'RERUN' : 'rerun' + 'CREATE_GRID_SLICES' : 'step_1', + 'COMBINE_GRID_SLICES' : 'step_2', + 'CALCULATE_EXTRA_VALUES': 'step_3', + 'TRAIN_INTERPOLATORS' : 'step_4', + 'EXPORT_DATASET' : 'step_9', + 'RERUN' : 'rerun' } +# pipeline substeps +SUB_STEPS_TO_EXTENSION = { + 'CREATE_PLOTS' : '_plots', + 'DO_CHECKS' : '_checks' +} +# predefined plotting sets +PLOTTING_SETS = { + 'PLOT_AFTER_CREATE' : [], + 'PLOT_AFTER_COMBINE' : ['combined_TF12', 'termination_flag_1',\ + 'termination_flag_2', 'termination_flag_3',\ + 'termination_flag_4', 'rl_relative_overflow_1',\ + 'rl_relative_overflow_2', 'lg_mtransfer_rate'], + 'PLOT_AFTER_EXTRA' : ['S1_MODEL01_CO_type', 'S1_MODEL01_SN_type',\ + 'S1_MODEL01_mass', 'S1_MODEL01_spin',\ + 'S1_MODEL01_m_disk_radiated', + 'S1_MODEL02_CO_type', 'S1_MODEL02_SN_type',\ + 'S1_MODEL02_mass', 'S1_MODEL02_spin',\ + 'S1_MODEL02_m_disk_radiated', + 'S1_MODEL03_CO_type', 'S1_MODEL03_SN_type',\ + 'S1_MODEL03_mass', 'S1_MODEL03_spin',\ + 'S1_MODEL03_m_disk_radiated', + 'S1_MODEL04_CO_type', 'S1_MODEL04_SN_type',\ + 'S1_MODEL04_mass', 'S1_MODEL04_spin',\ + 'S1_MODEL04_m_disk_radiated', + 'S1_MODEL05_CO_type', 'S1_MODEL05_SN_type',\ + 'S1_MODEL05_mass', 'S1_MODEL05_spin',\ + 'S1_MODEL05_m_disk_radiated', + 'S1_MODEL06_CO_type', 'S1_MODEL06_SN_type',\ + 'S1_MODEL06_mass', 'S1_MODEL06_spin',\ + 'S1_MODEL06_m_disk_radiated', + 'S1_MODEL07_CO_type', 'S1_MODEL07_SN_type',\ + 'S1_MODEL07_mass', 'S1_MODEL07_spin',\ + 'S1_MODEL07_m_disk_radiated', + 'S1_MODEL08_CO_type', 'S1_MODEL08_SN_type',\ + 'S1_MODEL08_mass', 'S1_MODEL08_spin',\ + 'S1_MODEL08_m_disk_radiated', + 'S1_MODEL09_CO_type', 'S1_MODEL09_SN_type',\ + 'S1_MODEL09_mass', 'S1_MODEL09_spin',\ + 'S1_MODEL09_m_disk_radiated', + 'S1_MODEL10_CO_type', 'S1_MODEL10_SN_type',\ + 'S1_MODEL10_mass', 'S1_MODEL10_spin',\ + 'S1_MODEL10_m_disk_radiated'], + 'PLOT_AFTER_TRAINING' : ['INTERP_ERROR_age', 'INTERP_ERROR_star_1_mass',\ + 'INTERP_ERROR_star_2_mass',\ + 'INTERP_ERROR_period_days',\ + 'INTERP_ERROR_S1_co_core_mass',\ + 'INTERP_ERROR_S1_co_core_radius',\ + 'INTERP_ERROR_S1_he_core_mass',\ + 'INTERP_ERROR_S1_he_core_radius',\ + 'INTERP_ERROR_S1_center_h1',\ + 'INTERP_ERROR_S1_center_he4',\ + 'INTERP_ERROR_S1_surface_h1',\ + 'INTERP_ERROR_S1_surface_he4',\ + 'INTERP_ERROR_S1_surf_avg_omega_div_omega_crit',\ + 'INTERP_ERROR_S1_log_Teff',\ + 'INTERP_ERROR_S1_log_L', 'INTERP_ERROR_S1_log_R',\ + 'INTERP_ERROR_S1_spin_parameter',\ + 'INTERP_ERROR_S1_lambda_CE_10cent',\ + 'INTERP_ERROR_S2_co_core_mass',\ + 'INTERP_ERROR_S2_co_core_radius',\ + 'INTERP_ERROR_S2_he_core_mass',\ + 'INTERP_ERROR_S2_he_core_radius',\ + 'INTERP_ERROR_S2_center_h1',\ + 'INTERP_ERROR_S2_center_he4',\ + 'INTERP_ERROR_S2_surface_h1',\ + 'INTERP_ERROR_S2_surface_he4',\ + 'INTERP_ERROR_S2_surf_avg_omega_div_omega_crit',\ + 'INTERP_ERROR_S2_log_Teff',\ + 'INTERP_ERROR_S2_log_L', 'INTERP_ERROR_S2_log_R',\ + 'INTERP_ERROR_S2_spin_parameter',\ + 'INTERP_ERROR_S2_lambda_CE_10cent',\ + 'INTERP_ERROR_S1_MODEL01_mass',\ + 'INTERP_ERROR_S1_MODEL01_spin',\ + 'INTERP_ERROR_S1_MODEL01_m_disk_radiated',\ + 'INTERP_ERROR_S1_MODEL05_mass',\ + 'INTERP_ERROR_S1_MODEL05_spin',\ + 'INTERP_ERROR_S1_MODEL05_m_disk_radiated',\ + 'INTERP_ERROR_S1_MODEL06_mass',\ + 'INTERP_ERROR_S1_MODEL06_spin',\ + 'INTERP_ERROR_S1_MODEL06_m_disk_radiated',\ + 'INTERP_ERROR_S1_MODEL10_mass',\ + 'INTERP_ERROR_S1_MODEL10_spin',\ + 'INTERP_ERROR_S1_MODEL10_m_disk_radiated'] +} +# predefined checking sets +CHECKING_SETS = { + 'CHECK_AFTER_CREATE' : [], + 'CHECK_AFTER_COMBINE' : ['failure_rate'], + 'CHECK_AFTER_EXTRA' : ['CO_type', 'SN_type'], + 'CHECK_AFTER_TRAINING' : [] +} +# common attributes for all steps +ATTRIBUTES_TO_CHECK = ['GRID_TYPES', 'METALLICITIES', 'GRID_SLICES',\ + 'COMPRESSIONS'] class PostProcessingPipeline: + """A class to handle the post-processing pipeline.""" def __init__(self, path_to_inifile=None): + """Initialize a pipeline + + Arguments + --------- + path_to_inifile : path + The location of an ini file to read the parameters for the pipeline + (default: None) + """ + self.PATH_TO_POSYDON = os.getenv('PATH_TO_POSYDON') self.PATH_TO_INIFILE = path_to_inifile @@ -70,8 +174,17 @@ class PostProcessingPipeline: @staticmethod def parse_setup_params(path=None): - """ - Parse inifile for running post-processing pipelines. + """Parse inifile for running post-processing pipelines. + + Arguments + --------- + path : path + The location of an ini file to read the parameters for the pipeline + (default: None) + + Returns + ------- + dictionary """ if path is None: @@ -81,107 +194,233 @@ class PostProcessingPipeline: pipeline_kwargs = dict() for section in parser.sections(): + # create a subdirectory for each section section_dict = dict() for key, val in parser[section].items(): + # interprete all input in python types + # MK:this is probably not a save way to do this section_dict[key] = ast.literal_eval(val) pipeline_kwargs[section] = section_dict return pipeline_kwargs def create_csv_and_slurm_job_files(self): - previously_created_files = [] - setup_kwargs = self.pipeline_kwargs['pipeline setup'] + """Creates all files the pipeline needs.""" + + def expand_runfile(runfile, last_step='', step_number='step_X'): + """Add next entry to the script in runfile.""" + if last_step == '': + # if there was no previous step the current step has no + # dependency + runfile.write(f"ID{step_number}=$(sbatch --parsable " + f"{step_number}.slurm)\n") + else: + # steps waiting for the previous step + runfile.write(f"ID{step_number}=$(sbatch --parsable " + "--dependency=afterok:$""{"f"ID{last_step}""} " + "--kill-on-invalid-dep=yes " + f"{step_number}.slurm)\n") + runfile.write(f"echo '{step_number}.slurm submitted as '$" + "{"f"ID{step_number}""}"f"\n") + return step_number + + def get_step_kwargs(self, last_step='', step_number='step_X'): + """Provides the attributes of a step. Additionally, it checks for + missing attributes and may copies them from a previous step.""" + if step_number not in self.pipeline_kwargs.keys(): + raise KeyError(f'{step_number} not in pipeline_kwargs!') + elif 'step' in step_number: + # check for attributes + for attribute in ATTRIBUTES_TO_CHECK: + if (attribute not in self.pipeline_kwargs[step_number].\ + keys() or not isinstance(self.pipeline_kwargs\ + [step_number][attribute], list)): + if last_step not in self.pipeline_kwargs.keys(): + raise KeyError(f'{step_number} has no {attribute}') + else: # infer missing attribute from last step + print(f"\ncopy {attribute} from {last_step} to " + f"{step_number}") + self.pipeline_kwargs[step_number][attribute] = \ + self.pipeline_kwargs[last_step][attribute] + # replace pre defined sets + if 'CREATE_PLOTS' in self.pipeline_kwargs[step_number].keys(): + for set_name, set_content in PLOTTING_SETS.items(): + if set_name in self.pipeline_kwargs[step_number]['CREATE_PLOTS']: + self.pipeline_kwargs[step_number]['CREATE_PLOTS'].remove(set_name) + self.pipeline_kwargs[step_number]['CREATE_PLOTS'].extend(set_content) + if 'DO_CHECKS' in self.pipeline_kwargs[step_number].keys(): + for set_name, set_content in CHECKING_SETS.items(): + if set_name in self.pipeline_kwargs[step_number]['DO_CHECKS']: + self.pipeline_kwargs[step_number]['DO_CHECKS'].remove(set_name) + self.pipeline_kwargs[step_number]['DO_CHECKS'].extend(set_content) + # return attributes + return self.pipeline_kwargs[step_number] + + def has_substep(self, step_number='step_X', substep='ALL'): + """Checks whether a step has substeps.""" + if step_number not in self.pipeline_kwargs.keys(): + raise KeyError(f'{step_number} not in pipeline_kwargs!') + if substep=='ALL': # check all possible substeps + substeps = SUB_STEPS_TO_EXTENSION.keys() + else: # check the specified substep + substeps = [substep] + for sub_step in substeps: + if sub_step in self.pipeline_kwargs[step_number].keys(): + SUB_STEP_LIST = self.pipeline_kwargs[step_number][sub_step] + if (isinstance(SUB_STEP_LIST, list) and + len(SUB_STEP_LIST)>0): + # at least one entry is found in the substep + return True + # no entry for the substep(s) + return False + + # check for sections 'account' and 'pipeline setup' and store their + # attributes + if 'account' not in self.pipeline_kwargs.keys(): + raise KeyError('account not in pipeline_kwargs!') + if 'pipeline setup' not in self.pipeline_kwargs.keys(): + raise KeyError('pipeline setup not in pipeline_kwargs!') account_kwargs = self.pipeline_kwargs['account'] + setup_kwargs = self.pipeline_kwargs['pipeline setup'] + + # take verbose value from setup arguments + if 'VERBOSE' in setup_kwargs.keys(): + VERBOSE = setup_kwargs['VERBOSE'] + else: + VERBOSE = False - if setup_kwargs['VERBOSE']: - print( "\n\n{:+^45s} \n{}\n{:+^45s}\n{}".format( + if VERBOSE: + print( "\n{:+^45s} \n{}\n\n{:+^45s}\n{}".format( 'ACCOUNT', pformat(account_kwargs, indent=2), 'SETUP', pformat(setup_kwargs, indent=2) ) ) - - + + previously_created_files = [] + + # create a runfile script with open('run_pipeline.sh', 'w') as runfile: runfile.write("#!/bin/bash\n") last_step = '' for action, step_number in ACTION_TO_STEP_NUM.items(): - do_step_bool = setup_kwargs[action] + if action in setup_kwargs.keys(): + do_step_bool = setup_kwargs[action] + else: + do_step_bool = False - if setup_kwargs['VERBOSE']: + if VERBOSE: print( "\n\n{:-^45s} {:^8s}:{:^6s}".format( action, step_number, str(do_step_bool)) ) - if do_step_bool: - step_kwargs = self.pipeline_kwargs[step_number] - - if setup_kwargs['VERBOSE']: + if do_step_bool or has_substep(self, step_number=step_number): + step_kwargs = get_step_kwargs(self, last_step=last_step, + step_number=step_number) + if VERBOSE: print( pformat(step_kwargs, indent=2) ) - + if do_step_bool: + # create csv file and remember therein created files previously_created_files += create_csv( step_name=step_number, previously_created_files=previously_created_files, **{**step_kwargs, **setup_kwargs} ) + # create slurm file slurm_job( job_name=step_number, step_name=step_number, PATH_TO_POSYDON=self.PATH_TO_POSYDON, **{**step_kwargs, **setup_kwargs, **account_kwargs} ) - if last_step == '': - # if there was no previous step the current step has no dependency - runfile.write(f"ID{step_number}=$(sbatch --parsable {step_number}.slurm)\n") - else: - if setup_kwargs['COMBINE_GRID_SLICES'] and (step_number == 'step_4' or step_number == 'step_5'): - # steps which can run in paralell after step_2 (steps 4 and 5 can run parallel with step_3) - runfile.write(f"ID{step_number}=$(sbatch --parsable --dependency=afterok:$" - "{IDstep_2}"f" --kill-on-invalid-dep=yes {step_number}.slurm)\n") - elif setup_kwargs['CREATE_GRID_SLICES'] and (step_number == 'step_4' or step_number == 'step_5'): - # steps which can run in paralell after step_1 (in case there is no step_2) - runfile.write(f"ID{step_number}=$(sbatch --parsable --dependency=afterok:$" - "{IDstep_1}"f" --kill-on-invalid-dep=yes {step_number}.slurm)\n") - else: - # steps waiting for the previous step - runfile.write(f"ID{step_number}=$(sbatch --parsable --dependency=afterok:$" - "{"f"ID{last_step}""}"f" --kill-on-invalid-dep=yes {step_number}.slurm)\n") - runfile.write(f"echo '{step_number}.slurm submitted as '$" - "{"f"ID{step_number}""}"f"\n") + # add job to the list in the runfile script + expand_runfile(runfile, last_step=last_step, + step_number=step_number) + # remember that this step was done last_step = step_number + + for sub_step, step_extension in SUB_STEPS_TO_EXTENSION.items(): + if sub_step not in self.pipeline_kwargs[step_number].keys(): + continue + if has_substep(self, step_number=step_number, + substep=sub_step): + # get sub-step number to be a concatenation of the main + # step and the sub-step extension. + sub_step_number = step_number+step_extension + # create csv file for sub step + create_csv( + step_name=sub_step_number, + previously_created_files=previously_created_files, + **{**step_kwargs, **setup_kwargs} + ) + # create slurm file for sub step + slurm_job( + job_name=sub_step_number, + step_name=step_number, + PATH_TO_POSYDON=self.PATH_TO_POSYDON, + **{**step_kwargs, **setup_kwargs, **account_kwargs} + ) + # add job to the list in the runfile script + expand_runfile(runfile, last_step=last_step, + step_number=sub_step_number) + # there are no dependencies supported on sub steps + # make the runfile script executable os.system("chmod 755 run_pipeline.sh") - def create_logs_dir(self): - # create logs in working dir to store all slurm outputs + def create_log_dirs(self): + """Create directories to store the log files.""" + # use work directory given by PATH in the setup attributes + if 'pipeline setup' not in self.pipeline_kwargs.keys(): + raise KeyError('pipeline setup not in pipeline_kwargs!') setup_kwargs = self.pipeline_kwargs['pipeline setup'] - logs_path = os.path.join(setup_kwargs['PATH'],'logs') + if 'PATH' in setup_kwargs.keys(): + logs_path = os.path.join(setup_kwargs['PATH'], 'logs') + else: + logs_path = os.path.join('.', 'logs') + # create logs in working dir to store all slurm outputs if not os.path.isdir(logs_path): os.makedirs(logs_path) - - def create_export_dir(self): - # create data dir three to export the datasets + # create sub directories for each step + for action, step_number in ACTION_TO_STEP_NUM.items(): + if ((action in setup_kwargs.keys()) and setup_kwargs[action]): + step_log_path = os.path.join(logs_path, step_number) + if not os.path.isdir(step_log_path): + os.makedirs(step_log_path) + + def create_export_dirs(self): + """Create directories for the finally exported data.""" + if 'pipeline setup' not in self.pipeline_kwargs.keys(): + raise KeyError('pipeline setup not in pipeline_kwargs!') setup_kwargs = self.pipeline_kwargs['pipeline setup'] - if setup_kwargs['EXPORT_DATASET']: - data_path = os.path.join(setup_kwargs['PATH'], 'POSYDON_data') + + # create data dir three to export the datasets + if (('EXPORT_DATASET' in setup_kwargs.keys()) and + setup_kwargs['EXPORT_DATASET']): + if 'PATH' in setup_kwargs.keys(): + data_path = os.path.join(setup_kwargs['PATH'], 'POSYDON_data') + else: + data_path = os.path.join('.', 'POSYDON_data') + # create main directory for the POSYDON data if not os.path.isdir(data_path): os.makedirs(data_path) - - dirs = [] - grid_dirs = ['HMS-HMS', 'CO-HMS_RLO', 'CO-HeMS', 'CO-HeMS_RLO', - 'single_HMS', 'single_HeMS'] - interp_dirs = ['interpolators', 'interpolators/1NN_1NN', - 'interpolators/linear3c_kNN'] - for name1 in grid_dirs: - dirs.append(os.path.join(data_path,name1)) - for name2 in interp_dirs: - dirs.append(os.path.join(data_path,name1,name2)) - - for dir_ in dirs: - if not os.path.isdir(dir_): - os.makedirs(dir_) + # determine sub directories + dirs = [] + grid_dirs = ['HMS-HMS', 'CO-HMS_RLO', 'CO-HeMS', 'CO-HeMS_RLO', + 'single_HMS', 'single_HeMS'] + interp_dirs = ['interpolators', 'interpolators/1NN_1NN', + 'interpolators/linear3c_kNN'] + for name1 in grid_dirs: + dirs.append(os.path.join(data_path, name1)) + for name2 in interp_dirs: + dirs.append(os.path.join(data_path, name1, name2)) + # create sub directories + for dir_ in dirs: + if not os.path.isdir(dir_): + os.makedirs(dir_) def slurm_job(job_name, + step_name, PATH_TO_GRIDS=None, PATH_TO_POSYDON=None, - PATH=None, + PATH='.', ACCOUNT=None, PARTITION=None, WALLTIME=None, @@ -189,135 +428,150 @@ def slurm_job(job_name, EMAIL=None, CREATE_GRID_SLICES=False, COMBINE_GRID_SLICES=False, - PLOT_GRIDS=False, - CHECK_FAILURE_RATE=False, - POST_PROCESSING=False, + CALCULATE_EXTRA_VALUES=False, TRAIN_INTERPOLATORS=False, EXPORT_DATASET=False, RERUN=False, - STOP_BEFORE_CARBON_DEPLETION=1, - RERUN_TYPE='', - verbose=False, + VERBOSE=False, **kwargs): - - path_to_csv_file = os.path.join(PATH,f"{job_name}.csv") - + """Create slurm file.""" + def copy_old_log_file(path_to_logs='.', log_file='log'): + """Make a copy of an old out file (and remove an old copy).""" + path_to_out_file = os.path.join(path_to_logs, log_file+'.out') + if os.path.exists(path_to_out_file): # move old data to a copy + path_to_old_out_file = os.path.join(path_to_logs, + log_file+'.old.out') + if os.path.exists(path_to_old_out_file): # delete old copy + os.remove(path_to_old_out_file) + shutil.move(path_to_out_file, path_to_old_out_file) + return path_to_out_file + + # get path to csv file + path_to_csv_file = os.path.join(PATH, f'{job_name}.csv') + + # create slurm file with open(f'{job_name}.slurm', 'w') as f: + # get STEPID if ('step' in job_name) and (len(job_name.split("_")) > 1): STEPID = job_name.split("_")[1] + if (len(job_name.split("_")) > 2): + STEPID += job_name.split("_")[2] elif job_name == 'rerun': STEPID = 'R' else: STEPID = '' + # write slurm file content f.write("#!/bin/bash\n") - f.write(f"#SBATCH --account={ACCOUNT}\n") - f.write(f"#SBATCH --partition={PARTITION}\n") + if ACCOUNT is not None: + f.write(f"#SBATCH --account={ACCOUNT}\n") + if PARTITION is not None: + f.write(f"#SBATCH --partition={PARTITION}\n") f.write("#SBATCH -N 1\n") f.write("#SBATCH --cpus-per-task 1\n") f.write("#SBATCH --ntasks-per-node 1\n") - f.write(f"#SBATCH --time={WALLTIME}\n") + if WALLTIME is not None: + f.write(f"#SBATCH --time={WALLTIME}\n") f.write(f"#SBATCH --job-name=psygrid{STEPID}\n") f.write("#SBATCH --mem-per-cpu=4G\n") - - if EMAIL is not None: + if ((EMAIL is not None) and (MAILTYPE is not None)): f.write(f"#SBATCH --mail-type={MAILTYPE}\n") f.write(f"#SBATCH --mail-user={EMAIL}\n") - - if job_name in ['step_1', 'step_3', 'step_4','step_5', 'step_6', - 'step_7', 'rerun']: + # extract array size + if ((job_name in ['step_1', 'step_3', 'step_4', 'step_9', 'rerun']) or + ('plot' in job_name) or ('check' in job_name)): df = pd.read_csv(path_to_csv_file) N = df.shape[0]-1 elif job_name in ['step_2']: df = pd.read_csv(path_to_csv_file) N = df.shape[1]-1 else: - raise ValueError('This should never happen!') + raise ValueError(f'This should never happen! job_name={job_name}') if N<0: raise ValueError(f'{job_name} has no jobs to run, please check ' f'{path_to_csv_file}') f.write(f"#SBATCH --array=0-{N}\n") slurm_array = '$SLURM_ARRAY_TASK_ID' - - if job_name == 'step_1': + # get logs path and set the output of the slurm job + if step_name in job_name: + path_to_logs = os.path.join(PATH, 'logs', step_name) + else: + path_to_logs = os.path.join(PATH, 'logs', job_name) + + if job_name == 'step_1': # CREATE_GRID_SLICES f.write("#SBATCH --open-mode=truncate\n") - f.write(f"#SBATCH --output={PATH}/logs/grid_slice_%a.out\n") - - if job_name == 'step_2': - path_to_out_file = os.path.join(PATH, 'logs', - 'combine_grid_slices.out') - if os.path.exists(path_to_out_file): # move old data to a copy - path_to_old_out_file = os.path.join(PATH, 'logs', - 'combine_grid_slices.old.out') - if os.path.exists(path_to_old_out_file): # delete old copy - os.remove(path_to_old_out_file) - shutil.move(path_to_out_file, path_to_old_out_file) + f.write(f"#SBATCH --output={path_to_logs}/grid_slice_%a.out\n") + + if job_name == 'step_2': # COMBINE_GRID_SLICES + path_to_out_file = copy_old_log_file(path_to_logs=path_to_logs, + log_file='combine_grid_slices') f.write("#SBATCH --open-mode=append\n") f.write(f"#SBATCH --output={path_to_out_file}\n") - - if job_name == 'step_3': - path_to_out_file = os.path.join(PATH, 'logs', 'plot_grid.out') - if os.path.exists(path_to_out_file): # move old data to a copy - path_to_old_out_file = os.path.join(PATH, 'logs', - 'plot_grid.old.out') - if os.path.exists(path_to_old_out_file): # delete old copy - os.remove(path_to_old_out_file) - shutil.move(path_to_out_file, path_to_old_out_file) + + if job_name == 'step_3': # CALCULATE_EXTRA_VALUES + f.write("#SBATCH --open-mode=truncate\n") + f.write(f"#SBATCH --output={path_to_logs}/post_processing_%a.out\n") + f.write(f"export PATH_TO_POSYDON={PATH_TO_POSYDON}\n") + + if job_name == 'step_4': # TRAIN_INTERPOLATORS + f.write("#SBATCH --open-mode=truncate\n") + f.write(f"#SBATCH --output={path_to_logs}/train_interpolators_%a.out\n") + + if job_name == 'step_9': # EXPORT_DATASET + path_to_out_file = copy_old_log_file(path_to_logs=path_to_logs, + log_file='export_dataset') f.write("#SBATCH --open-mode=append\n") f.write(f"#SBATCH --output={path_to_out_file}\n") - f.write("unset DISPLAY\n") - - if job_name == 'step_4': - path_to_out_file = os.path.join(PATH, 'logs', - 'check_failure_rate.out') - if os.path.exists(path_to_out_file): # move old data to a copy - path_to_old_out_file = os.path.join(PATH, 'logs', - 'check_failure_rate.old.out') - if os.path.exists(path_to_old_out_file): # delete old copy - os.remove(path_to_old_out_file) - shutil.move(path_to_out_file, path_to_old_out_file) + + if job_name == 'rerun': # RERUN + path_to_out_file = copy_old_log_file(path_to_logs=path_to_logs, + log_file='rerun') f.write("#SBATCH --open-mode=append\n") f.write(f"#SBATCH --output={path_to_out_file}\n") - - if job_name == 'step_5': - f.write("#SBATCH --open-mode=truncate\n") - f.write(f"#SBATCH --output={PATH}/logs/post_processing_%a.out\n") - f.write(f"export PATH_TO_POSYDON={PATH_TO_POSYDON}\n") - - if job_name == 'step_6': - f.write("#SBATCH --open-mode=truncate\n") - f.write(f"#SBATCH --output={PATH}/logs/train_interpolators_%a.out\n") - - if job_name == 'step_7': - path_to_out_file = os.path.join(PATH, 'logs', - 'export_dataset.out') - if os.path.exists(path_to_out_file): # move old data to a copy - path_to_old_out_file = os.path.join(PATH, 'logs', - 'export_dataset.old.out') - if os.path.exists(path_to_old_out_file): # delete old copy - os.remove(path_to_old_out_file) - shutil.move(path_to_out_file, path_to_old_out_file) + + if 'plot' in job_name: # PLOTS + path_to_out_file = copy_old_log_file(path_to_logs=path_to_logs, + log_file='plots_'+step_name) f.write("#SBATCH --open-mode=append\n") f.write(f"#SBATCH --output={path_to_out_file}\n") - - if job_name == 'rerun': - path_to_out_file = os.path.join(PATH, 'logs', 'rerun.out') - if os.path.exists(path_to_out_file): # move old data to a copy - path_to_old_out_file = os.path.join(PATH, 'logs', - 'rerun.old.out') - if os.path.exists(path_to_old_out_file): # delete old copy - os.remove(path_to_old_out_file) - shutil.move(path_to_out_file, path_to_old_out_file) + f.write("unset DISPLAY\n") + + if 'check' in job_name: # CHECKS + path_to_out_file = copy_old_log_file(path_to_logs=path_to_logs, + log_file='checks_'+step_name) f.write("#SBATCH --open-mode=append\n") f.write(f"#SBATCH --output={path_to_out_file}\n") - - f.write(f"\nsrun python {PATH_TO_POSYDON}/bin/run-pipeline {PATH_TO_GRIDS} {path_to_csv_file} {slurm_array} {STOP_BEFORE_CARBON_DEPLETION} {RERUN_TYPE}") - -# create csv file with a list of all grid paths to process -def create_csv(GRID_TYPES, METALLICITIES, GRID_SLICES, COMPRESSIONS, - step_name=None, VERSION=None, GRIDS_COMBINED=None, - INTERPOLATION_METHODS=None, RERUN_TYPE='', - DROP_MISSING_FILES=False, PATH_TO_GRIDS=None, - PATH=None, previously_created_files=[], **kwargs): + + # get path to run the pipeline + path_to_run_pipeline = os.path.join(PATH_TO_POSYDON, 'bin', + 'run-pipeline') + # encode the verbose information for running the pipeline as a command + # line argument + if VERBOSE and 'check' not in job_name: + f.write(f"\nsrun python {path_to_run_pipeline} {PATH_TO_GRIDS} " + f"{path_to_csv_file} {slurm_array} 1") + else: + f.write(f"\nsrun python {path_to_run_pipeline} {PATH_TO_GRIDS} " + f"{path_to_csv_file} {slurm_array} 0") + +def create_csv(GRID_TYPES=[], + METALLICITIES=[], + GRID_SLICES=[], + COMPRESSIONS=[], + step_name='', + VERSION='', + GRIDS_COMBINED=[], + INTERPOLATION_METHODS=[], + CONTROL_GRIDS=[], + STOP_BEFORE_CARBON_DEPLETION=0, + RERUN_TYPE='', + DROP_MISSING_FILES=False, + CREATE_PLOTS=[], + DO_CHECKS=[], + PATH_TO_GRIDS='', + PATH='', + previously_created_files=[], + **kwargs): + """Create csv file with a list containing all data to process a step.""" # number of grid types N = len(GRID_TYPES) @@ -325,180 +579,263 @@ def create_csv(GRID_TYPES, METALLICITIES, GRID_SLICES, COMPRESSIONS, N != len(COMPRESSIONS)): raise ValueError('Missmatch between the len of GRID_TYPES, ' 'METALLICITIES, GRID_SLICES, COMPRESSIONS.') + if step_name == 'step_2': + # there need to be as many combined files specified as recipes are + # given to create them. + if N != len(GRIDS_COMBINED): + raise ValueError('len(GRID_TYPES) != len(GRIDS_COMBINED)!') + elif 'step_4_' in step_name: + # sub steps of the interpolator training need a control grid + if len(GRID_SLICES) != len(CONTROL_GRIDS): + raise ValueError('len(GRID_SLICES) != len(CONTROL_GRIDS)!') grids = [] grids_compression = [] - plot_dirs = [] + stop_before_carbon_depletion = [] + grids_ORIGINAL = [] processed_grids = [] interpolators = [] export_path = [] rerun_path = [] + rerun_type = [] + plot_dirs = [] + plot_quantities = [] + checks = [] newly_created_files = [] df = pd.DataFrame() + # loop over all grid types for l, grid_type in enumerate(GRID_TYPES): - METALLICITIES_ = METALLICITIES[l] GRID_SLICES_ = GRID_SLICES[l] COMPRESSIONS_ = COMPRESSIONS[l] - + # loop over all metallicities, grid slices and compressions for metallicity in METALLICITIES_: for i, grid_slice in enumerate(GRID_SLICES_): for compression in COMPRESSIONS_: - if step_name == 'step_1': - grids.append(os.path.join(PATH_TO_GRIDS, - grid_type, VERSION, metallicity, grid_slice)) - grids_compression.extend([compression]) - elif step_name == 'step_2': - if N != len(GRIDS_COMBINED): - raise ValueError('len(GRID_TYPES) != len(GRIDS_COMBINED)!') + # get RLO extension text depending on compression + if 'RLO' in compression: + RLO = '_RLO' + else: + RLO = '' + # create data lists depending on the (sub)step + if step_name == 'step_1': # CREATE_GRID_SLICES + grids.append(os.path.join(PATH_TO_GRIDS, grid_type, + VERSION, metallicity, + grid_slice)) + grids_compression.append(compression) + stop_before_carbon_depletion.append(STOP_BEFORE_CARBON_DEPLETION) + elif step_name == 'step_2': # COMBINE_GRID_SLICES if len(GRID_SLICES[l]) != len(GRIDS_COMBINED[l]): - raise ValueError('len(GRID_SLICES[l]) != len(GRIDS_COMBINED[l])!') + raise ValueError(f'len(GRID_SLICES[{l}]) != ' + f'len(GRIDS_COMBINED[{l}])!') combine_grid_slices = [] # grid_slice is a batch for grid_slice_ in grid_slice: path_to_grid = os.path.join(PATH_TO_GRIDS, - grid_type, VERSION, metallicity, - compression, grid_slice_+'.h5') + grid_type, VERSION, + metallicity, compression, + grid_slice_+'.h5') combine_grid_slices.append(path_to_grid) path_to_grid_combined = os.path.join(PATH_TO_GRIDS, - grid_type, VERSION, - metallicity, - compression, - GRIDS_COMBINED[l][i]+'.h5') + grid_type, VERSION, + metallicity, compression, + GRIDS_COMBINED[l][i]+'.h5') df_tmp = pd.DataFrame() df_tmp[path_to_grid_combined] = combine_grid_slices df = pd.concat([df,df_tmp], axis=1) - elif step_name == 'step_3': - grids.append(os.path.join(PATH_TO_GRIDS, - grid_type, VERSION, metallicity, - compression, grid_slice+'.h5')) - if 'RLO' in compression: - plot_dirs.append(os.path.join(PATH_TO_GRIDS, - grid_type, VERSION, metallicity, - 'plots', 'RLO_'+grid_slice)) - else: - plot_dirs.append(os.path.join(PATH_TO_GRIDS, - grid_type, VERSION, metallicity, - 'plots', grid_slice)) - elif step_name == 'step_4': - grids.append(os.path.join(PATH_TO_GRIDS, - grid_type, VERSION, metallicity, - compression, grid_slice+'.h5')) - elif step_name == 'step_5': + elif step_name == 'step_3': # CALCULATE_EXTRA_VALUES grids.append(os.path.join(PATH_TO_GRIDS, grid_type, VERSION, metallicity, compression, grid_slice+'.h5')) + grids_ORIGINAL.append(os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, + metallicity, 'ORIGINAL'+RLO, + grid_slice+'.h5')) processed_grids.append(os.path.join(PATH_TO_GRIDS, - grid_type, VERSION, metallicity, - compression, grid_slice+'_processed.h5')) - elif step_name == 'step_6': + grid_type, VERSION, + metallicity, compression, + grid_slice+'_processed.h5')) + elif step_name == 'step_4': # TRAIN_INTERPOLATORS for method in INTERPOLATION_METHODS: grids.append(os.path.join(PATH_TO_GRIDS, grid_type, VERSION, metallicity, compression, grid_slice+'.h5')) - if 'RLO' in compression: - interpolators.append(os.path.join(PATH_TO_GRIDS, - grid_type, VERSION, metallicity, - 'interpolation_objects', - 'IF_'+method+'_RLO.pkl')) - else: - interpolators.append(os.path.join(PATH_TO_GRIDS, - grid_type, VERSION, metallicity, - 'interpolation_objects', - 'IF_'+method+'.pkl')) - elif step_name == 'step_7': + interpolators.append(os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, + metallicity, + 'interpolation_objects', + 'IF_'+method+RLO+'.pkl')) + elif step_name == 'step_9': # EXPORT_DATASET + if "RLO" in grid_type: + export_dir = grid_type + else: + export_dir = grid_type+RLO grids.append(os.path.join(PATH_TO_GRIDS, grid_type, VERSION, metallicity, compression, grid_slice+'.h5')) - if 'RLO' in compression: - export_path.append(os.path.join(PATH, 'POSYDON_data', - grid_type+'_RLO', - metallicity+'.h5')) - else: - export_path.append(os.path.join(PATH, 'POSYDON_data', - grid_type, - metallicity+'.h5')) + export_path.append(os.path.join(PATH, 'POSYDON_data', + export_dir, metallicity+'.h5')) for method in [['linear','linear3c_kNN'], ['1NN','1NN_1NN']]: - if 'RLO' in compression: - grids.append(os.path.join(PATH_TO_GRIDS, - grid_type, VERSION, metallicity, + grids.append(os.path.join(PATH_TO_GRIDS, grid_type, + VERSION, metallicity, 'interpolation_objects', - 'IF_'+method[0]+'_RLO.pkl')) - export_path.append(os.path.join(PATH, - 'POSYDON_data', - grid_type+'_RLO', - 'interpolators', - method[1], - metallicity+'.pkl')) - else: - grids.append(os.path.join(PATH_TO_GRIDS, - grid_type, VERSION, metallicity, - 'interpolation_objects', - 'IF_'+method[0]+'.pkl')) - export_path.append(os.path.join(PATH, - 'POSYDON_data', - grid_type, - 'interpolators', - method[1], - metallicity+'.pkl')) - elif step_name == 'rerun': - grids.append(os.path.join(PATH_TO_GRIDS, - grid_type, VERSION, metallicity, - compression, grid_slice+'.h5')) - rerun_path.append(os.path.join(PATH_TO_GRIDS, grid_type, - VERSION, metallicity, - 'rerun_'+RERUN_TYPE+'_'+grid_slice)) - + 'IF_'+method[0]+RLO+'.pkl')) + export_path.append(os.path.join(PATH, + 'POSYDON_data', export_dir, + 'interpolators', method[1], + metallicity+'.pkl')) + elif step_name == 'rerun': # RERUN + grids.append(os.path.join(PATH_TO_GRIDS, grid_type, + VERSION, metallicity, compression, + grid_slice+'.h5')) + rerun_path.append(os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, metallicity, + 'rerun_'+RERUN_TYPE+'_'+grid_slice)) + rerun_type.append(RERUN_TYPE) + + if (('plot' in step_name) or ('check' in step_name)): + # get output grid names for sub steps + if 'step_2' in step_name: + grid_s = GRIDS_COMBINED[l][i] + elif 'step_3' in step_name: + grid_s = grid_slice+'_processed' + elif 'step_4' in step_name: + grid_s = CONTROL_GRIDS[l][i] + else: + grid_s = grid_slice + + if grid_s == '': # skip that slice for sub steps + continue + + if 'step_4' not in step_name: + # all steps without interpolation method get an + # empty method + if len(INTERPOLATION_METHODS)==0: + INTERPOLATION_METHODS = [''] + + if 'plot' in step_name: # PLOTS + # only plot first compression type, but allow for + # with and without RLO flag + if ((compression not in COMPRESSIONS_[0]) and + (COMPRESSIONS_[0] not in compression)): + continue + # loop over interpolation methods and plots + for method in INTERPOLATION_METHODS: +# for to_plot in CREATE_PLOTS: +# plot_quantities.append(to_plot) + if len(CREATE_PLOTS)>0: + plot_quantities.append(CREATE_PLOTS) + grids.append(os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, + metallicity, compression, + grid_s+'.h5')) + plot_dirs.append(os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, + metallicity, 'plots', + RLO[1:]+RLO[:1]+grid_s)) + if method != '': # if there is a method + interpolators.append(os.path.join(PATH_TO_GRIDS, + grid_type, + VERSION, + metallicity, + 'interpolation_objects', + 'IF_'+method+RLO+'.pkl')) + elif 'check' in step_name: # CHECKS + # loop over interpolation methods and checks + for method in INTERPOLATION_METHODS: +# for to_check in DO_CHECKS: +# checks.append(to_check) + if len(DO_CHECKS)>0: + checks.append(DO_CHECKS) + grids.append(os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, + metallicity, compression, + grid_s+'.h5')) + if method != '': # if there is a method + interpolators.append(os.path.join(PATH_TO_GRIDS, + grid_type, + VERSION, + metallicity, + 'interpolation_objects', + 'IF_'+method+RLO+'.pkl')) + # saving dataset to csv file if step_name != 'step_2': grids = np.array(grids) df['path_to_grid'] = grids if step_name == 'step_1': df['compression'] = grids_compression + df['stop_before_carbon_depletion'] = stop_before_carbon_depletion elif step_name == 'step_3': - df['path_to_plot'] = plot_dirs - elif step_name == 'step_5': + df['path_to_grid_ORIGINAL'] = grids_ORIGINAL df['path_to_processed_grid'] = processed_grids - elif step_name == 'step_6': + elif step_name == 'step_4': df['path_to_interpolator'] = interpolators - elif step_name == 'step_7': + elif step_name == 'step_9': df['export_path'] = export_path elif step_name == 'rerun': df['rerun_path'] = rerun_path - # drop lines when grid directories are not found + df['rerun_type'] = rerun_type + elif 'plot' in step_name: + df['quantities_to_plot'] = plot_quantities + df['path_to_plot'] = plot_dirs + if len(interpolators)>0: + df['path_to_interpolator'] = interpolators + elif 'check' in step_name: + df['checks_to_do'] = checks + if len(interpolators)>0: + df['path_to_interpolator'] = interpolators + # drop lines when grid directories/files are not found if DROP_MISSING_FILES: drop_rows = [] for row in df.index: + # grid file is always needed path = df.at[row,'path_to_grid'] if (not os.path.exists(path) and path not in previously_created_files): drop_rows.append(row) + elif step_name == 'step_3': + # grid file with ORIGINAL compression needed + path = df.at[row,'path_to_grid_ORIGINAL'] + if (not os.path.exists(path) and + path not in previously_created_files): + drop_rows.append(row) + elif 'step_4_' in step_name: + # interpolator files needed + path = df.at[row,'path_to_interpolator'] + if (not os.path.exists(path) and + path not in previously_created_files): + drop_rows.append(row) + # report about missing files and remove tasks, which need them if len(drop_rows)>0: - print('') - print(f'----------- {step_name} -----------') + print("\n{:-^54s}".format(step_name)) print('The following grids will not be processed because the ' 'files/directories are missing! If this warning message ' 'is unexpected to you, please check the file paths!') for row in drop_rows: print(df.at[row,'path_to_grid']) + if step_name == 'step_3': + print(df.at[row,'path_to_grid_ORIGINAL']) + elif 'step_4_' in step_name: + print(df.at[row,'path_to_interpolator']) print('') df = df.drop(index=drop_rows) + # keep track of files created by a step if step_name == 'step_1': for row in df.index: path = df.at[row,'path_to_grid'] compression = df.at[row,'compression'] newly_created_files.append(get_new_grid_name(path,compression)) - elif step_name == 'step_5': + elif step_name == 'step_3': newly_created_files = df['path_to_processed_grid'].to_list() - elif step_name == 'step_6': + elif step_name == 'step_4': newly_created_files = df['path_to_interpolator'].to_list() - elif step_name == 'step_7': + elif step_name == 'step_9': newly_created_files = df['export_path'].to_list() elif step_name == 'rerun': newly_created_files = df['rerun_path'].to_list() - else: - # step 2 + else: # step 2 # drop columns when psygrid files are not found if DROP_MISSING_FILES: first_time = True @@ -511,10 +848,10 @@ def create_csv(GRID_TYPES, METALLICITIES, GRID_SLICES, COMPRESSIONS, if (not os.path.exists(path) and path not in previously_created_files): drop_rows.append(row) + # report about missing files and adjust column if len(drop_rows)>0: if first_time: - print('') - print(f'----------- {step_name} -----------') + print("\n{:-^54s}".format(step_name)) print('If this warning message is unexpected to you, ' 'please check that step 1 occoured ' 'succesffully!') @@ -527,14 +864,12 @@ def create_csv(GRID_TYPES, METALLICITIES, GRID_SLICES, COMPRESSIONS, newcol = df[column].dropna().to_list() if len(newcol)==0: drop_columns.append(column) -# print(f'No grids left drop {column}') -# print('') - newcol.extend((df.shape[0]-len(newcol))*[np.nan]) + newcol.extend((df.shape[0]-len(newcol))*[np.NaN]) df[column] = newcol + # report about empty tasks and remove them if len(drop_columns)>0: if first_time: - print('') - print(f'----------- {step_name} -----------') + print("\n{:-^54s}".format(step_name)) print('If this warning message is unexpected to you, ' 'please check that step 1 occoured ' 'succesffully!') @@ -544,27 +879,27 @@ def create_csv(GRID_TYPES, METALLICITIES, GRID_SLICES, COMPRESSIONS, for column in drop_columns: print(column) print('') -# df = df.drop(columns=drop_columns) df = df.dropna(axis=0, how='all').dropna(axis=1, how='all') newly_created_files = df.keys().to_list() - + # finally save data frame output_fname = f'{step_name}.csv' df.to_csv(os.path.join(PATH, output_fname), index=False) - + # return list of new files created by that step return newly_created_files -if __name__ == '__main__': +if __name__ == '__main__': + if len(sys.argv) >= 2: ini_file_path = str(sys.argv[1]) if '.' not in ini_file_path: ini_file_path = './' + ini_file_path - + else: + raise ValueError('No ini file specified.') + pipeline = PostProcessingPipeline(ini_file_path) pipeline.create_csv_and_slurm_job_files() - pipeline.create_logs_dir() - pipeline.create_export_dir() + pipeline.create_log_dirs() + pipeline.create_export_dirs() + - # TODO: - # - add a linked slurm job arrays which are waiting for each others - # - DROP_MISSING_FILES should be an imput of each step diff --git a/grid_params/pipeline_quest.ini b/grid_params/pipeline_quest.ini index 885d6da456..40c0755daf 100644 --- a/grid_params/pipeline_quest.ini +++ b/grid_params/pipeline_quest.ini @@ -3,34 +3,29 @@ ACCOUNT = 'b1119' PARTITION = 'posydon-priority' WALLTIME = '24:00:00' - MAILTYPE = 'FAIL' + MAILTYPE = 'ALL' EMAIL = 'simone.bavera@unige.ch' - [pipeline setup] PATH_TO_GRIDS = '/projects/b1119/POSYDON_GRIDS/' VERSION = 'v2' # 'v2' in quest and '' in yggdrasil PATH = '.' # working dir - - VERBOSE = True + + # steps CREATE_GRID_SLICES = True COMBINE_GRID_SLICES = True - PLOT_GRIDS = True - CHECK_FAILURE_RATE = True - POST_PROCESSING = True + CALCULATE_EXTRA_VALUES = True TRAIN_INTERPOLATORS = True EXPORT_DATASET = True - RERUN = True - - # EXTRA PARAMETERS - # TODO: move to be only input of step_1 - CO_HMS_GRID_START_AT_RLO = 0 # set this to False for CO-HMS reruns + # rerun step + RERUN = False +#CREATE_GRID_SLICES [step_1] # e.g. ['CO-HMS_RLO','CO-HeMS','HMS-HMS'] GRID_TYPES = ['HMS-HMS'] - + # e.g. ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'] METALLICITIES = [['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun','1e-03_Zsun']] GRID_SLICES = [ # HMS-HMS ['grid_low_res_0','grid_low_res_1','grid_low_res_2', @@ -40,7 +35,15 @@ ] COMPRESSIONS = [['LITE','ORIGINAL']] DROP_MISSING_FILES = True + # EXTRA PARAMETERS + # only applied to HMS grids + STOP_BEFORE_CARBON_DEPLETION = 1 + # supported plots: e.g. 'combined_TF12', 'termination_flag_1', 'termination_flag_2', 'termination_flag_3', 'termination_flag_4', and any quantity valid for a Z-plotting + CREATE_PLOTS = [] + # supported checks: e.g. 'failure_rate' + DO_CHECKS = [] +#COMBINE_GRID_SLICES [step_2] GRID_TYPES = ['HMS-HMS'] METALLICITIES = [['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun','1e-03_Zsun']] @@ -56,8 +59,13 @@ ] ] COMPRESSIONS = [['LITE','ORIGINAL']] - DROP_MISSING_FILES = False + DROP_MISSING_FILES = True + # supported plots: e.g. 'combined_TF12', 'termination_flag_1', 'termination_flag_2', 'termination_flag_3', 'termination_flag_4', and any quantity valid for a Z-plotting + CREATE_PLOTS = ['PLOT_AFTER_COMBINE'] + # supported checks: e.g. 'failure_rate' + DO_CHECKS = ['CHECK_AFTER_COMBINE'] +#CALCULATE_EXTRA_VALUES [step_3] GRID_TYPES = ['HMS-HMS'] METALLICITIES = [['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun','1e-03_Zsun']] @@ -66,44 +74,40 @@ #'grid_low_res_combined_rerun_1' ]] COMPRESSIONS = [['LITE']] - DROP_MISSING_FILES = False + DROP_MISSING_FILES = True + # supported plots: e.g. 'combined_TF12', 'termination_flag_1', 'termination_flag_2', 'termination_flag_3', 'termination_flag_4', and any quantity valid for a Z-plotting + CREATE_PLOTS = ['PLOT_AFTER_EXTRA'] + # supported checks: e.g. 'failure_rate', 'CO_TYPE', 'SN_TYPE' + DO_CHECKS = ['CHECK_AFTER_EXTRA'] +#TRAIN_INTERPOLATORS [step_4] - GRID_TYPES = ['HMS-HMS'] - METALLICITIES = [['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun','1e-03_Zsun']] - GRID_SLICES = [['grid_low_res_combined', - #'grid_low_res_rerun_opacitymax', - #'grid_low_res_combined_rerun_1' - ]] - COMPRESSIONS = [['LITE']] - DROP_MISSING_FILES = False - -[step_5] - GRID_TYPES = ['HMS-HMS'] - METALLICITIES = [['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun','1e-03_Zsun']] - GRID_SLICES = [['grid_low_res_combined']] - COMPRESSIONS = [['LITE']] - DROP_MISSING_FILES = False - -[step_6] GRID_TYPES = ['HMS-HMS'] METALLICITIES = [['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun','1e-03_Zsun']] GRID_SLICES = [['grid_low_res_combined_processed']] INTERPOLATION_METHODS = ["linear","1NN"] COMPRESSIONS = [['LITE']] - DROP_MISSING_FILES = False + CONTROL_GRIDS = [''] + DROP_MISSING_FILES = True + # supported plots: e.g. 'combined_TF12', 'termination_flag_1', 'termination_flag_2', 'termination_flag_3', 'termination_flag_4', and any quantity valid for a Z-plotting + CREATE_PLOTS = ['PLOT_AFTER_TRAINING'] + # supported checks: e.g. 'failure_rate' + DO_CHECKS = ['CHECK_AFTER_TRAINING'] -[step_7] +#EXPORT_DATASET +[step_9] GRID_TYPES = ['HMS-HMS'] METALLICITIES = [['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun','1e-03_Zsun']] GRID_SLICES = [['grid_low_res_combined_processed']] COMPRESSIONS = [['LITE']] - DROP_MISSING_FILES = False + DROP_MISSING_FILES = True +#EXPORT_RERUNS [rerun] GRID_TYPES = ['HMS-HMS'] METALLICITIES = [['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun','1e-03_Zsun']] GRID_SLICES = [['grid_low_res_combined']] COMPRESSIONS = [['LITE']] DROP_MISSING_FILES = True - RERUN_TYPE = 'PISN' + # example reruns are 'PISN', 'reverse_MT', 'opacity_max', 'TPAGBwind' + RERUN_TYPE = 'opacity_max' diff --git a/grid_params/pipeline_yggdrasil.ini b/grid_params/pipeline_yggdrasil.ini index 318f59d5e4..9a6de51edc 100644 --- a/grid_params/pipeline_yggdrasil.ini +++ b/grid_params/pipeline_yggdrasil.ini @@ -3,206 +3,214 @@ ACCOUNT = 'meynet' PARTITION = 'public-cpu' WALLTIME = '24:00:00' - MAILTYPE = 'FAIL' + MAILTYPE = 'ALL' EMAIL = 'simone.bavera@unige.ch' - [pipeline setup] PATH_TO_GRIDS = '/srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/' VERSION = '' # 'v2' in quest and '' in yggdrasil PATH = '.' # working dir - - VERBOSE = True + + # steps CREATE_GRID_SLICES = True COMBINE_GRID_SLICES = True - PLOT_GRIDS = True - CHECK_FAILURE_RATE = True - POST_PROCESSING = True + CALCULATE_EXTRA_VALUES = True TRAIN_INTERPOLATORS = True EXPORT_DATASET = True - RERUN = True - - # EXTRA PARAMETERS - # TODO: move to be only input of step_1 - CO_HMS_GRID_START_AT_RLO = 0 # set this to False for CO-HMS reruns + # rerun step + RERUN = False +#CREATE_GRID_SLICES [step_1] - GRID_TYPES = ['CO-HMS_RLO','CO-HeMS','HMS-HMS'] + # e.g. ['CO-HMS_RLO','CO-HeMS','HMS-HMS'] + GRID_TYPES = ['CO-HMS_RLO', 'CO-HeMS', 'HMS-HMS'] + # e.g. ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'] METALLICITIES = [# CO-HMS_RLO - ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', - '1e-03_Zsun','1e-04_Zsun'], + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'], # CO-HeMS - ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', - '1e-03_Zsun','1e-04_Zsun'], + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'], # HMS-HMS - ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', - '1e-03_Zsun','1e-04_Zsun']] + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'] + ] GRID_SLICES = [# CO-HMS_RLO - ['grid_low_res_0','grid_low_res_1','grid_low_res_2', - 'grid_random_1'], + ['grid_low_res_0', 'grid_low_res_1', 'grid_low_res_2', 'grid_random_1'], # CO-HeMS - ['grid_low_res_0','grid_low_res_1','grid_low_res_2', - 'grid_random_1'], + ['grid_low_res_0', 'grid_low_res_1', 'grid_low_res_2', 'grid_random_1'], # HMS-HMS - ['grid_low_res_0','grid_low_res_1','grid_low_res_2', - 'grid_low_res_3','grid_low_res_4','grid_low_res_5', - 'grid_random_1'] + ['grid_low_res_0', 'grid_low_res_1', 'grid_low_res_2', 'grid_low_res_3', 'grid_low_res_4', 'grid_low_res_5', 'grid_random_1'] ] - COMPRESSIONS = [['LITE','ORIGINAL'],['LITE','ORIGINAL'],['LITE','ORIGINAL']] + COMPRESSIONS = [# CO-HMS_RLO + ['LITE', 'ORIGINAL', 'LITE_RLO', 'ORIGINAL_RLO'], + # CO-HeMS + ['LITE', 'ORIGINAL', 'LITE_RLO', 'ORIGINAL_RLO'], + # HMS-HMS + ['LITE', 'ORIGINAL'] + ] DROP_MISSING_FILES = True + # EXTRA PARAMETERS + # only applied to HMS grids + STOP_BEFORE_CARBON_DEPLETION = 1 + # supported plots: e.g. 'combined_TF12', 'termination_flag_1', 'termination_flag_2', 'termination_flag_3', 'termination_flag_4', and any quantity valid for a Z-plotting + CREATE_PLOTS = [] + # supported checks: e.g. 'failure_rate' + DO_CHECKS = [] +#COMBINE_GRID_SLICES [step_2] - GRID_TYPES = ['CO-HMS_RLO','CO-HeMS','HMS-HMS'] + GRID_TYPES = ['CO-HMS_RLO', 'CO-HeMS', 'HMS-HMS'] METALLICITIES = [# CO-HMS_RLO - ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', - '1e-03_Zsun','1e-04_Zsun'], + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'], # CO-HeMS - ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', - '1e-03_Zsun','1e-04_Zsun'], + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'], # HMS-HMS - ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', - '1e-03_Zsun','1e-04_Zsun']] + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'] + ] GRID_SLICES = [# CO-HMS_RLO - [['grid_low_res_0','grid_low_res_1','grid_low_res_2']], + [['grid_low_res_0', 'grid_low_res_1', 'grid_low_res_2']], # CO-HeMS - [['grid_low_res_0','grid_low_res_1','grid_low_res_2']], + [['grid_low_res_0', 'grid_low_res_1', 'grid_low_res_2']], # HMS-HMS - [['grid_low_res_0','grid_low_res_1','grid_low_res_2', - 'grid_low_res_3','grid_low_res_4','grid_low_res_5']] + [['grid_low_res_0', 'grid_low_res_1', 'grid_low_res_2', 'grid_low_res_3', 'grid_low_res_4', 'grid_low_res_5']] ] GRIDS_COMBINED = [# CO-HMS_RLO - ['grid_low_res_combined'], + ['grid_low_res_combined'], # CO-HeMS - ['grid_low_res_combined'], + ['grid_low_res_combined'], # HMS-HMS - ['grid_low_res_combined'] + ['grid_low_res_combined'] ] - COMPRESSIONS = [['LITE','ORIGINAL'],['LITE','ORIGINAL'],['LITE','ORIGINAL']] - DROP_MISSING_FILES = False + COMPRESSIONS = [# CO-HMS_RLO + ['LITE', 'ORIGINAL', 'LITE_RLO', 'ORIGINAL_RLO'], + # CO-HeMS + ['LITE', 'ORIGINAL', 'LITE_RLO', 'ORIGINAL_RLO'], + # HMS-HMS + ['LITE', 'ORIGINAL'] + ] + DROP_MISSING_FILES = True + # supported plots: e.g. 'combined_TF12', 'termination_flag_1', 'termination_flag_2', 'termination_flag_3', 'termination_flag_4', and any quantity valid for a Z-plotting + CREATE_PLOTS = ['PLOT_AFTER_COMBINE'] + # supported checks: e.g. 'failure_rate' + DO_CHECKS = ['CHECK_AFTER_COMBINE'] +#CALCULATE_EXTRA_VALUES [step_3] - GRID_TYPES = ['CO-HMS_RLO','CO-HeMS','HMS-HMS'] + GRID_TYPES = ['CO-HMS_RLO', 'CO-HeMS', 'HMS-HMS'] METALLICITIES = [# CO-HMS_RLO - ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', - '1e-03_Zsun','1e-04_Zsun'], + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'], # CO-HeMS - ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', - '1e-03_Zsun','1e-04_Zsun'], + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'], # HMS-HMS - ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', - '1e-03_Zsun','1e-04_Zsun']] + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'] + ] GRID_SLICES = [# CO-HMS_RLO - ['grid_low_res_combined','grid_random_1'], + ['grid_low_res_combined', 'grid_random_1'], # CO-HeMS - ['grid_low_res_combined','grid_random_1'], + ['grid_low_res_combined', 'grid_random_1'], # HMS-HMS - ['grid_low_res_combined','grid_random_1'] + ['grid_low_res_combined', 'grid_random_1'] ] - COMPRESSIONS = [['LITE'],['LITE'],['LITE']] - DROP_MISSING_FILES = False + COMPRESSIONS = [# CO-HMS_RLO + ['LITE', 'LITE_RLO'], + # CO-HeMS + ['LITE', 'LITE_RLO'], + # HMS-HMS + ['LITE'] + ] + DROP_MISSING_FILES = True + # supported plots: e.g. 'combined_TF12', 'termination_flag_1', 'termination_flag_2', 'termination_flag_3', 'termination_flag_4', and any quantity valid for a Z-plotting + CREATE_PLOTS = ['PLOT_AFTER_EXTRA'] + # supported checks: e.g. 'failure_rate', 'CO_TYPE', 'SN_TYPE' + DO_CHECKS = ['CHECK_AFTER_EXTRA'] +#TRAIN_INTERPOLATORS [step_4] - GRID_TYPES = ['CO-HMS_RLO','CO-HeMS','HMS-HMS'] - METALLICITIES = [# CO-HMS_RLO - ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', - '1e-03_Zsun','1e-04_Zsun'], - # CO-HeMS - ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', - '1e-03_Zsun','1e-04_Zsun'], - # HMS-HMS - ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', - '1e-03_Zsun','1e-04_Zsun']] - GRID_SLICES = [# CO-HMS_RLO - ['grid_low_res_combined','grid_random_1'], - # CO-HeMS - ['grid_low_res_combined','grid_random_1'], - # HMS-HMS - ['grid_low_res_combined','grid_random_1'] - ] - COMPRESSIONS = [['LITE'],['LITE'],['LITE']] - DROP_MISSING_FILES = False - -[step_5] - GRID_TYPES = ['CO-HMS_RLO','CO-HeMS','HMS-HMS'] + GRID_TYPES = ['CO-HMS_RLO', 'CO-HeMS', 'HMS-HMS'] METALLICITIES = [# CO-HMS_RLO - ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', - '1e-03_Zsun','1e-04_Zsun'], + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'], # CO-HeMS - ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', - '1e-03_Zsun','1e-04_Zsun'], + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'], # HMS-HMS - ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', - '1e-03_Zsun','1e-04_Zsun']] + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'] + ] GRID_SLICES = [# CO-HMS_RLO - ['grid_low_res_combined'], + ['grid_low_res_combined_processed'], # CO-HeMS - ['grid_low_res_combined'], + ['grid_low_res_combined_processed'], # HMS-HMS - ['grid_low_res_combined'] + ['grid_low_res_combined_processed'] ] - COMPRESSIONS = [['LITE'],['LITE'],['LITE']] - DROP_MISSING_FILES = False - -[step_6] - GRID_TYPES = ['CO-HMS_RLO','CO-HeMS','HMS-HMS'] - METALLICITIES = [# CO-HMS_RLO - ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', - '1e-03_Zsun','1e-04_Zsun'], - # CO-HeMS - ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', - '1e-03_Zsun','1e-04_Zsun'], - # HMS-HMS - ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', - '1e-03_Zsun','1e-04_Zsun']] - GRID_SLICES = [# CO-HMS_RLO - ['grid_low_res_combined_processed'], + INTERPOLATION_METHODS = ["linear","1NN"] + COMPRESSIONS = [# CO-HMS_RLO + ['LITE_RLO'], # CO-HeMS - ['grid_low_res_combined_processed'], + ['LITE', 'LITE_RLO'], # HMS-HMS - ['grid_low_res_combined_processed']] - INTERPOLATION_METHODS = ["linear","1NN"] - COMPRESSIONS = [['LITE'],['LITE'],['LITE']] - DROP_MISSING_FILES = False + ['LITE'] + ] + CONTROL_GRIDS = [# CO-HMS_RLO + ['grid_random_1_processed'], + # CO-HeMS + ['grid_random_1_processed'], + # HMS-HMS + ['grid_random_1_processed'] + ] + DROP_MISSING_FILES = True + # supported plots: e.g. 'combined_TF12', 'termination_flag_1', 'termination_flag_2', 'termination_flag_3', 'termination_flag_4', and any quantity valid for a Z-plotting + CREATE_PLOTS = ['PLOT_AFTER_TRAINING'] + # supported checks: e.g. 'failure_rate' + DO_CHECKS = ['CHECK_AFTER_TRAINING'] -[step_7] - GRID_TYPES = ['CO-HMS_RLO','CO-HeMS','HMS-HMS'] +#EXPORT_DATASET +[step_9] + GRID_TYPES = ['CO-HMS_RLO', 'CO-HeMS', 'HMS-HMS'] METALLICITIES = [# CO-HMS_RLO - ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', - '1e-03_Zsun','1e-04_Zsun'], + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'], # CO-HeMS - ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', - '1e-03_Zsun','1e-04_Zsun'], + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'], # HMS-HMS - ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', - '1e-03_Zsun','1e-04_Zsun']] + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'] + ] GRID_SLICES = [# CO-HMS_RLO - ['grid_low_res_combined_processed'], + ['grid_low_res_combined_processed'], # CO-HeMS - ['grid_low_res_combined_processed'], + ['grid_low_res_combined_processed'], # HMS-HMS - ['grid_low_res_combined_processed']] - COMPRESSIONS = [['LITE'],['LITE'],['LITE']] - DROP_MISSING_FILES = False + ['grid_low_res_combined_processed'] + ] + COMPRESSIONS = [# CO-HMS_RLO + ['LITE_RLO'], + # CO-HeMS + ['LITE', 'LITE_RLO'], + # HMS-HMS + ['LITE'] + ] + DROP_MISSING_FILES = True +#EXPORT_RERUNS [rerun] - GRID_TYPES = ['CO-HMS_RLO','CO-HeMS','HMS-HMS'] + GRID_TYPES = ['CO-HMS_RLO', 'CO-HeMS', 'HMS-HMS'] METALLICITIES = [# CO-HMS_RLO - ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', - '1e-03_Zsun','1e-04_Zsun'], + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'], # CO-HeMS - ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', - '1e-03_Zsun','1e-04_Zsun'], + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'], # HMS-HMS - ['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun', - '1e-03_Zsun','1e-04_Zsun']] + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'] + ] GRID_SLICES = [# CO-HMS_RLO - ['grid_low_res_combined','grid_random_1'], + ['grid_low_res_combined','grid_random_1'], # CO-HeMS - ['grid_low_res_combined','grid_random_1'], + ['grid_low_res_combined','grid_random_1'], # HMS-HMS - ['grid_low_res_combined','grid_random_1'], + ['grid_low_res_combined','grid_random_1'], ] - COMPRESSIONS = [['LITE'],['LITE'],['LITE']] + COMPRESSIONS = [# CO-HMS_RLO + ['LITE'], + # CO-HeMS + ['LITE'], + # HMS-HMS + ['LITE'] + ] DROP_MISSING_FILES = True - RERUN_TYPE = 'PISN' + # example reruns are 'PISN', 'reverse_MT', 'opacity_max', 'TPAGBwind' + RERUN_TYPE = 'opacity_max' diff --git a/posydon/grids/post_processing.py b/posydon/grids/post_processing.py index 133aa063d9..9c5cf91985 100644 --- a/posydon/grids/post_processing.py +++ b/posydon/grids/post_processing.py @@ -51,15 +51,21 @@ def print_CC_quantities(EXTRA_COLUMNS, star, MODEL_NAME=None): "mass [Msun]", "spin", "m_disk_accreted [Msun]", "m_disk_radiated [Msun]")) print('') - print(format_val_preSN.format( - 'PRE SN STAR', star.state, '', - '', star.mass, star.spin, '', '')) + try: + print(format_val_preSN.format( + 'PRE SN STAR', star.state, '', + '', star.mass, star.spin, '', '')) + except: + warnings.warn('Failed to print star values!') print('') else: - print(format_val.format(MODEL_NAME, - star.state, star.SN_type, star.f_fb, - star.mass, star.spin, star.m_disk_accreted, - star.m_disk_radiated)) + try: + print(format_val.format(MODEL_NAME, + star.state, star.SN_type, star.f_fb, + star.mass, star.spin, star.m_disk_accreted, + star.m_disk_radiated)) + except: + warnings.warn('Failed to print star values!') @@ -208,7 +214,7 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, EXTRA_COLUMNS['S%s_center_other' % (j+1)].append(c_o) else: # fill everything with Nones - if IC == 'initial_MT': + if IC == 'initial_MT' or IC == 'not_converged': EXTRA_COLUMNS['S%s_state' % (j+1)].append(None) else: # CO states are classified and used in mesa step diff --git a/posydon/interpolation/IF_interpolation.py b/posydon/interpolation/IF_interpolation.py index 9e7a5007f0..76e49f0ecb 100644 --- a/posydon/interpolation/IF_interpolation.py +++ b/posydon/interpolation/IF_interpolation.py @@ -237,6 +237,7 @@ def __init__(self, grid=None, interpolators=None): f"than one set of out_keys)") else: out_keys.append(key) + def train(self): """Train the interpolator(s) on the PSyGrid used for construction.""" @@ -244,6 +245,8 @@ def train(self): self.interpolators.append(BaseIFInterpolator(grid=self.grid, **interpolator)) + self.interp_in_q = self.interpolators[0].interp_in_q + def evaluate(self, binary, sanitization_verbose=False): """Get the output vector approximation. @@ -272,6 +275,58 @@ def evaluate(self, binary, sanitization_verbose=False): return ynums, ycats + + def test_interpolator(self, initial_values, sanitization_verbose = False): + """ Method that can take in a 2-D numpy array for more efficient use of the interpolator + + Parameters + ---------- + initial_values : numpy array + A numpy array containing the in-key values of the binaries to be evolved + + Return + ------ + Interpolated values + """ + new_values = np.array(initial_values, copy = True) + + if self.interp_in_q: + new_values.T[1] = new_values.T[1] / new_values.T[0] + + final_values = [] + + for interpolator in self.interpolators: + final_values.append(interpolator.test_interpolator(new_values).T) + + return np.concatenate(final_values).T + + def test_classifiers(self, initial_values): + """ Method that can take in a 2-D numpy array for more efficient use of classifiers + + Parameters + ---------- + initial_values : numpy array + A numpy array containing the in-key values of the binaries to be classified + + Return + ------ + Classified values + """ + new_values = np.array(initial_values, copy = True) + + if self.interp_in_q: + new_values.T[1] = new_values.T[1] / new_values.T[0] + + classes = {} + + for interpolator in self.interpolators: + + classes = {**classes, **interpolator.test_classifiers(new_values)} + + return classes + + + def load(self, filename): """Load a saved IFInterpolator from a .pkl file. @@ -284,6 +339,8 @@ def load(self, filename): with open(filename, 'rb') as f: self.interpolators = pickle.load(f) + self.interp_in_q = self.interpolators[0].interp_in_q + def save(self, filename): """Save the interpolator to a .pkl file. diff --git a/posydon/visualization/interpolation.py b/posydon/visualization/interpolation.py new file mode 100644 index 0000000000..4d7eb3383d --- /dev/null +++ b/posydon/visualization/interpolation.py @@ -0,0 +1,366 @@ +""" Module to evaluate IFInterpolator class """ + +__authors__ = [ + "Philipp Moura Srivastava " + "Simone Bavera " +] + +import matplotlib.pyplot as plt +import numpy as np + +class EvaluateIFInterpolator: + """ Class that is helpful for evaluating interpolation performance + """ + def __init__(self, interpolator, test_grid): + """ Initialize the EvaluateIFInterpolator class + + Parameters + ---------- + interpolator : IFInterpolator + Interpolator that user wants to test + test_grid : PSyGrid + Grid object containing testing tracks + + """ + + self.interpolator = interpolator + + self.test_grid = test_grid + + # assuming that in_keys are the same for all interpolators + self.in_keys = interpolator.interpolators[0].in_keys + self.out_keys = [] + + for interp in interpolator.interpolators: # getting out keys + self.out_keys.extend(interp.out_keys) + + self.__compute_errs() + + def __compute_errs(self): + """ Method that computes both interpolation and classification errors """ + + iv = np.array(self.test_grid.initial_values[self.in_keys].tolist()) # initial values + fv = np.array(self.test_grid.final_values[self.out_keys].tolist()) # final values + ic = self.test_grid.final_values["interpolation_class"] # final values + + ivalid_inds = np.where( + (self.test_grid.final_values["interpolation_class"] != "not_converged") & + (self.test_grid.final_values["interpolation_class"] != "initial_MT") + ) + + i = self.interpolator.test_interpolator(iv) # interpolated + # ifv = fv[ivalid_inds] + + self.errs = {} + + self.errs["relative"] = np.abs((fv - i) / fv) + self.errs["absolute"] = np.abs(fv - i) + self.errs["valid_inds"] = ivalid_inds + + + cvalid_inds = np.where( + self.test_grid.final_values["interpolation_class"] != "not_converged" + )[0] + + self.cfv = self.test_grid.final_values[cvalid_inds] + + c = self.interpolator.test_classifiers(iv[cvalid_inds]) # classifying + + # computing confusion matrices + self.matrices = {} + + for key, value in c.items(): + + labels = self.cfv[key] + + classes = self.__find_labels(key) + + matrix = {} + + # catch cases where nothing is cl,assified, e.g. S2_MODELXX_SN_type + # when S2 is a compact object + if len(classes) == 1 and classes[0] == 'None': + matrix['None'] = 1. + else: + for _class in classes: + class_inds = np.where(labels == _class)[0] + pred_classes, counts = np.unique(value[class_inds], return_counts = True) + + row = {c: 0 for c in classes} + + for pred_class, count in zip(pred_classes, counts): + row[pred_class] = count / len(class_inds) + + matrix[_class] = row + + self.matrices[key] = matrix + + # saving classes + self.c = c + self.ic = ic + + + def __format(self, s, title = False): + """ Method that formats keys for plots + + Parameters + ---------- + s : str + string to be formatted + + """ + + return s.replace("_", " ").title() + + def __find_labels(self, key): + """ Method that finds labels in classifier + + Parameters + ---------- + key : str + name of the classifier + + Returns + ------- + list of class labels + + """ + + labels = None + + for interp in self.interpolator.interpolators: + + if key in interp.classifiers.keys(): + # if classifier does not exist assign 'None' label + # this happens, e.g. for S2_MODELXX_SN_type, when S2 is + # a compac object + if interp.classifiers[key] is None: + labels = ['None'] + else: + labels = interp.classifiers[key].labels + + return labels + + def __clean_errs(self, errs): + + errs = errs[~np.isnan(errs).any(axis = 1)] # dropping nans + errs = errs[~np.isinf(errs).any(axis = 1)] # dropping infs + + return errs + + + def violin_plots(self, err_type = "relative", keys = None, save_path = None): + """ Method that plots distribution of specified error for given keys and + optionally saves it. + + Parameters + ---------- + + err_type : str + Either relative or absolute, default is relative + keys : list + A list of keys for which the errors will be shown, by default is all of them + save_path: str + The path where the figure should be saved to + + """ + + if keys is None: + keys = self.out_keys + + k_inds = [self.out_keys.index(key) for key in keys] + + errs = self.__clean_errs(self.errs[err_type].T[k_inds].T) + + n_tracks = self.test_grid.final_values["star_1_mass"].shape[0] + + print(f"Using {errs.shape[0]} out of {n_tracks} tracks in violin plots") + + stable_inds = np.where(self.ic == "stable_MT") + no_inds = np.where(self.ic == "no_MT") + unstable_inds = np.where(self.ic == "unstable_MT") + + stable_errs = self.__clean_errs(self.errs[err_type].T[k_inds].T[stable_inds]) + no_errs = self.__clean_errs(self.errs[err_type].T[k_inds].T[no_inds]) + unstable_errs = self.__clean_errs(self.errs[err_type].T[k_inds].T[unstable_inds]) + + + plt.rcParams.update({"font.size": 32, "font.family": "stixgeneral"}) + + fig, axs = plt.subplots(1, 1, + figsize = (24, 10), + tight_layout = True) + + rel_plot = axs.violinplot(np.log10(errs + 1.0e-8), showmedians = True, points = 1000) + stable_plot = axs.violinplot(np.log10(stable_errs + 1.0e-8), showmedians = True, points = 1000) + no_plot = axs.violinplot(np.log10(no_errs + 1.0e-8), showmedians = True, points = 1000) + unstable_plot = axs.violinplot(np.log10(unstable_errs + 1.0e-8), showmedians = True, points = 1000) + + axs.set_title(f"Distribution of {err_type.capitalize()} Errors") + axs.set_xticks(np.arange(1, len(keys) + 1), + labels = [ + f"{self.__format(ec)} ({(med * 100):.2f}%)" for ec, med in zip(keys, np.nanmedian(errs, axis = 0)) + ], rotation = 20) + + axs.set_ylabel("Errors in Log 10 Scale") + axs.grid(axis = "y") + + def halve_paths(field, color, right = True): + + for i, path in enumerate(field.get_paths()): + + # getting mean + m = np.mean(path.vertices[:, 0]) + + first = m if right == True else -np.inf + second = np.inf if right == True else m + + # modify the paths to not go further left than the center + field.get_paths()[i].vertices[:, 0] = np.clip(path.vertices[:, 0], first, second) + field.set_edgecolor(color) + + def customize_violinplot(plot, color, outlined = False, right = True): + + halve_paths(plot["cmins"], color, right = right) + halve_paths(plot["cmaxes"], color, right = right) + halve_paths(plot["cmedians"], color, right = right) + + for pc in plot["bodies"]: + + halve_paths(pc, color, right = right) + + + pc.set_facecolor(color if not outlined else "None") + pc.set_edgecolor(color) + pc.set_linewidth(4) + pc.set_alpha(0.75) + + customize_violinplot(rel_plot, "coral") + customize_violinplot(stable_plot, "#1e90ff", True, False) + customize_violinplot(no_plot, "crimson", True, False) + customize_violinplot(unstable_plot, "olive", True, False) + + axs.legend( + [rel_plot["bodies"][0], stable_plot["bodies"][0], no_plot["bodies"][0], unstable_plot["bodies"][0]], + ["Relative Error", "Stable MT Error", "No MT Error", "Unstable MT Error"], + bbox_to_anchor = (0, 1.02, 1, 0.2), + loc = "lower left", + mode = "expand", + ncol = 4 + ) + + plt.show() + + if save_path is not None: + fig.save(save_path) + + + def confusion_matrix(self, key, params = {}, save_path = None): + """ Method that plots confusion matrices to evaluate classification + + Parameters + ---------- + key : str + The key for the classifier of interest + params : dict + Extra params to pass to matplolib, x_labels (list), y_labels (list), title (str) + save_path : str + The path where the figure should be saved to + + """ + + if key not in self.matrices.keys(): + raise Exception("Key not in List of Matrices") + + arr_mat = [] + + for k, value in self.matrices[key].items(): + arr_mat.append(list(value.values())) + + figsize = params["figsize"] if "figsize" in params.keys() else (4, 8) + + fig, ax = plt.subplots(1, 1, figsize = figsize, constrained_layout = True) + + im = ax.imshow(arr_mat) + + x_axis = [self.__format(x) for x in self.matrices[key].keys()] if "x_axis" not in params.keys() else params["x_axis"] + y_axis = [self.__format(y) for y in self.matrices[key][list(self.matrices[key].keys())[0]].keys()] if "y_axis" not in params.keys() else params["y_axis"] + title = f"Confusion Matrix for {self.__format(key)}" if "title" not in params.keys() else params["title"] + + # Show all ticks and label them with the respective list entries + ax.set_xticks(np.arange(len(x_axis)), labels = x_axis) + ax.set_yticks(np.arange(len(y_axis)), labels = y_axis) + + # Rotate the tick labels and set their alignment. + plt.setp(ax.get_xticklabels(), rotation = 45, ha = "right", + rotation_mode = "anchor") + + # Loop over data dimensions and create text annotations. + for i in range(len(arr_mat)): + for j in range(len(arr_mat[i])): + text = ax.text(j, i, f"{100 * arr_mat[i][j]:.2f}", + ha = "center", va = "center", color = "w" if arr_mat[i][j] < 0.9 else "black") + + ax.set_xlabel("Predicted") + ax.set_ylabel("Actual") + ax.set_title(title) + + cax = ax.inset_axes([1.1, 0.1, 0.05, 0.8]) + + fig.colorbar(im, ax = ax, cax = cax, pad = 1) + + fig.tight_layout() + + if save_path is not None: # saving + fig.save(save_path) + + def classifiers(self): + """ Method that lists classifiers available """ + classes = self.interpolator.test_classifiers( + np.array([ + self.test_grid.initial_values[self.in_keys].tolist() + ])[0] + ) + + return list(classes.keys()) + + def keys(self): + """ Method that lists out keys available """ + + out_keys = [] + + for interpolator in self.interpolator.interpolators: + out_keys.extend(interpolator.out_keys) + + return out_keys + + def decision_boundaries(self): + + pass + + def plot2D(self, key, slice_3D_var_str, slice_3D_var_range, PLOT_PROPERTIES): + + k_ind = self.out_keys.index(key) + + if slice_3D_var_str == 'mass_ratio': + var = self.test_grid.initial_values["star_2_mass"] / self.test_grid.initial_values["star_1_mass"] + elif slice_3D_var_str == 'star_2_mass': + var = self.test_grid.initial_values["star_2_mass"] + else: + raise ValueError("slice_3D_var_str must be either 'mass_ratio' or 'star_2_mass'") + + slice = (var >= slice_3D_var_range[0]) & (var <= slice_3D_var_range[1]) + + slice_errs = self.errs["relative"].T[k_ind] + slice_errs = np.array([slice_errs[i] if in_slice and i in self.errs["valid_inds"][0] else np.nan for i, in_slice in enumerate(slice)]) + + # find inf and assign large value else they are not plotted + slice_errs[np.isinf(slice_errs)] = 1e99 + + fig = self.test_grid.plot2D('star_1_mass', 'period_days', slice_errs, + termination_flag='interpolation_class_errors', + grid_3D=True, slice_3D_var_str=slice_3D_var_str, + slice_3D_var_range=slice_3D_var_range, + verbose=False, **PLOT_PROPERTIES) + + diff --git a/posydon/visualization/plot2D.py b/posydon/visualization/plot2D.py index d41c5bd3b3..81a5a714c0 100644 --- a/posydon/visualization/plot2D.py +++ b/posydon/visualization/plot2D.py @@ -13,7 +13,7 @@ "Matthias Kruckow ", ] - +import warnings import numpy as np import matplotlib.pyplot as plt from posydon.utils.gridutils import add_field @@ -210,7 +210,7 @@ def __init__( self.initial_values_str = self.initial_values.dtype.names self.final_values = self.psygrid.final_values # add extra properties to final_values - if termination_flag in ["combined_TF12", "debug"]: + if termination_flag in ["combined_TF12", "debug", "interpolation_class_errors"]: self.add_properties_to_final_values(termination_flag) if termination_flag == 'termination_flag_2': # remmove ? from strings @@ -331,7 +331,8 @@ def __init__( "termination_flag_4", "combined_TF12", "debug", - "interpolation_class" + "interpolation_class", + "interpolation_class_errors", ] or ('SN_type' in termination_flag or 'CO_type' in termination_flag or 'state' in termination_flag): @@ -585,16 +586,20 @@ def plot_panel(self, ax, extra_grid_call=False): ) else: - sc = ax.scatter( - self.x_var[selection], - self.y_var[selection], - marker=self.MARKERS_COLORS_LEGENDS[flag][0], - linewidths=self.MARKERS_COLORS_LEGENDS[flag][1], - c=self.z_var[selection], - s=self.marker_size, - vmin=self.zmin, - vmax=self.zmax, - ) + try: + sc = ax.scatter( + self.x_var[selection], + self.y_var[selection], + marker=self.MARKERS_COLORS_LEGENDS[flag][0], + linewidths=self.MARKERS_COLORS_LEGENDS[flag][1], + c=self.z_var[selection], + s=self.marker_size, + vmin=self.zmin, + vmax=self.zmax, + ) + except: + warnings.warn(f'Failed to plot values for flag {flag}, ' + 'likely all values are NaN.') sc_last = sc # collect scatters for legend if self.MARKERS_COLORS_LEGENDS[flag][3] not in scatters_legend: @@ -641,6 +646,9 @@ def add_properties_to_final_values(self, termination_flag=None): elif termination_flag == "debug": new_final_values = add_field(old_final_values, [("debug", " Date: Thu, 24 Aug 2023 16:51:19 +0200 Subject: [PATCH 118/319] Konst removing init imports (#132) * Removing imports in init files. * Removing unused imports. * Correcting import statements after removing imports in init files. * Added a TODO not for a possible mistake. * Correcting another import. --- posydon/binary_evol/DT/step_detached.py | 16 ++++----- posydon/binary_evol/DT/step_disrupted.py | 33 ++--------------- .../binary_evol/DT/step_initially_single.py | 35 ++----------------- posydon/binary_evol/DT/step_isolated.py | 1 - posydon/binary_evol/DT/step_merged.py | 23 ++---------- posydon/binary_evol/__init__.py | 5 --- posydon/interpolation/__init__.py | 2 -- posydon/popsyn/__init__.py | 1 - posydon/popsyn/independent_sample.py | 2 +- posydon/popsyn/star_formation_history.py | 4 +-- posydon/tests/binary_evol/CE/test_CEE.py | 3 -- posydon/tests/binary_evol/SN/test_step_SN.py | 1 - posydon/tests/popsyn/test_binarypopulation.py | 2 +- posydon/tests/utils/test_configfile.py | 2 +- posydon/utils/__init__.py | 1 - 15 files changed, 20 insertions(+), 111 deletions(-) diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index 2bc2348630..c3919f314d 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -24,7 +24,7 @@ from posydon.utils.data_download import PATH_TO_POSYDON_DATA from posydon.binary_evol.binarystar import BINARYPROPERTIES from posydon.binary_evol.singlestar import STARPROPERTIES -from posydon.interpolation import GRIDInterpolator +from posydon.interpolation.interpolation import GRIDInterpolator from posydon.interpolation.data_scaling import DataScaler from posydon.utils.common_functions import ( bondi_hoyle, @@ -1038,7 +1038,7 @@ def __call__(self, binary): raise Exception("State not recognized!") else: raise Exception("Non existent companion has not a recognized value!") - + def get_star_data(binary, star1, star2, htrack, co, copy_prev_m0=None, copy_prev_t0=None): """Get and interpolate the properties of stars. @@ -1149,10 +1149,10 @@ def get_star_data(binary, star1, star2, htrack, # get the matched data of two stars, respectively interp1d_sec, m0, t0 = get_star_data( binary, secondary, primary, secondary.htrack, co=False) - + primary_not_normal = (primary.co) or (self.non_existent_companion in [1,2]) - primary_normal = (not primary.co) and self.non_existent_companion == 0 - + primary_normal = (not primary.co) and self.non_existent_companion == 0 + if primary_not_normal: # copy the secondary star except mass which is of the primary, # and radius, mdot, Idot = 0 @@ -1164,8 +1164,8 @@ def get_star_data(binary, star1, star2, htrack, binary, primary, secondary, primary.htrack, False)[0] else: raise Exception("During matching primary is either should be either normal or not normal. `non_existent_companion` should be zero.") - - + + if interp1d_sec is None or interp1d_pri is None: # binary.event = "END" binary.state += " (GridMatchingFailed)" @@ -1829,7 +1829,7 @@ def get_omega(star, is_secondary = True): if primary.state == "massless_remnant": pass - + elif primary.co: mdot_acc = np.atleast_1d(bondi_hoyle( binary, primary, secondary, slice(-len(t), None), diff --git a/posydon/binary_evol/DT/step_disrupted.py b/posydon/binary_evol/DT/step_disrupted.py index 0adf47a765..2afcba46d8 100644 --- a/posydon/binary_evol/DT/step_disrupted.py +++ b/posydon/binary_evol/DT/step_disrupted.py @@ -8,40 +8,11 @@ ] -import os -import numpy as np -from scipy.integrate import solve_ivp -from scipy.interpolate import PchipInterpolator -from scipy.optimize import minimize -from scipy.optimize import root - from posydon.utils.data_download import PATH_TO_POSYDON_DATA -from posydon.binary_evol.binarystar import BINARYPROPERTIES -from posydon.binary_evol.singlestar import STARPROPERTIES -from posydon.interpolation import GRIDInterpolator -from posydon.interpolation.data_scaling import DataScaler -from posydon.utils.common_functions import ( - bondi_hoyle, - orbital_period_from_separation, - orbital_separation_from_period, - roche_lobe_radius, - check_state_of_star, - PchipInterpolator2 -) -from posydon.binary_evol.flow_chart import (STAR_STATES_CC) -import posydon.utils.constants as const -from posydon.binary_evol.DT.step_detached import detached_step from posydon.binary_evol.DT.step_isolated import IsolatedStep -import warnings - -from posydon.binary_evol.flow_chart import (STAR_STATES_ALL, - STAR_STATES_CO, - STAR_STATES_H_RICH, - STAR_STATES_HE_RICH, - STAR_STATES_NOT_CO - ) - +from posydon.binary_evol.flow_chart import ( + STAR_STATES_H_RICH, STAR_STATES_HE_RICH) LIST_ACCEPTABLE_STATES_FOR_HMS = ["H-rich_Core_H_burning"] diff --git a/posydon/binary_evol/DT/step_initially_single.py b/posydon/binary_evol/DT/step_initially_single.py index 1d2022bec1..3a3981a55c 100644 --- a/posydon/binary_evol/DT/step_initially_single.py +++ b/posydon/binary_evol/DT/step_initially_single.py @@ -8,40 +8,11 @@ ] -import os -import numpy as np -from scipy.integrate import solve_ivp -from scipy.interpolate import PchipInterpolator -from scipy.optimize import minimize -from scipy.optimize import root - from posydon.utils.data_download import PATH_TO_POSYDON_DATA -from posydon.binary_evol.binarystar import BINARYPROPERTIES -from posydon.binary_evol.singlestar import STARPROPERTIES -from posydon.interpolation import GRIDInterpolator -from posydon.interpolation.data_scaling import DataScaler -from posydon.utils.common_functions import ( - bondi_hoyle, - orbital_period_from_separation, - orbital_separation_from_period, - roche_lobe_radius, - check_state_of_star, - PchipInterpolator2 -) -from posydon.binary_evol.flow_chart import (STAR_STATES_CC) -import posydon.utils.constants as const -from posydon.binary_evol.DT.step_detached import detached_step from posydon.binary_evol.DT.step_isolated import IsolatedStep -import warnings - -from posydon.binary_evol.flow_chart import (STAR_STATES_ALL, - STAR_STATES_CO, - STAR_STATES_H_RICH, - STAR_STATES_HE_RICH, - STAR_STATES_NOT_CO - ) - +from posydon.binary_evol.flow_chart import ( + STAR_STATES_H_RICH, STAR_STATES_HE_RICH) LIST_ACCEPTABLE_STATES_FOR_HMS = ["H-rich_Core_H_burning"] @@ -63,7 +34,7 @@ def __init__(self, grid_name_strippedHe=None, path=PATH_TO_POSYDON_DATA, *args, **kwargs): - + super().__init__( grid_name_Hrich=grid_name_Hrich, grid_name_strippedHe=grid_name_strippedHe, diff --git a/posydon/binary_evol/DT/step_isolated.py b/posydon/binary_evol/DT/step_isolated.py index 0b80dea2d1..cd6802cb43 100644 --- a/posydon/binary_evol/DT/step_isolated.py +++ b/posydon/binary_evol/DT/step_isolated.py @@ -18,7 +18,6 @@ from posydon.utils.data_download import PATH_TO_POSYDON_DATA from posydon.binary_evol.binarystar import BINARYPROPERTIES from posydon.binary_evol.singlestar import STARPROPERTIES -from posydon.interpolation import GRIDInterpolator from posydon.interpolation.data_scaling import DataScaler from posydon.utils.common_functions import ( bondi_hoyle, diff --git a/posydon/binary_evol/DT/step_merged.py b/posydon/binary_evol/DT/step_merged.py index 21a1da9bc5..7e73be243e 100644 --- a/posydon/binary_evol/DT/step_merged.py +++ b/posydon/binary_evol/DT/step_merged.py @@ -8,36 +8,17 @@ ] -import os import numpy as np -from scipy.integrate import solve_ivp -from scipy.interpolate import PchipInterpolator -from scipy.optimize import minimize -from scipy.optimize import root from posydon.utils.data_download import PATH_TO_POSYDON_DATA -from posydon.binary_evol.binarystar import BINARYPROPERTIES from posydon.binary_evol.singlestar import STARPROPERTIES from posydon.binary_evol.singlestar import properties_massless_remnant -from posydon.interpolation import GRIDInterpolator -from posydon.interpolation.data_scaling import DataScaler -from posydon.utils.common_functions import ( - bondi_hoyle, - orbital_period_from_separation, - orbital_separation_from_period, - roche_lobe_radius, - check_state_of_star, - PchipInterpolator2 -) -from posydon.binary_evol.flow_chart import (STAR_STATES_CC) -import posydon.utils.constants as const -from posydon.binary_evol.DT.step_detached import detached_step +from posydon.utils.common_functions import check_state_of_star from posydon.binary_evol.DT.step_isolated import IsolatedStep import warnings -from posydon.binary_evol.flow_chart import (STAR_STATES_ALL, - STAR_STATES_CO, +from posydon.binary_evol.flow_chart import ( STAR_STATES_H_RICH, STAR_STATES_HE_RICH, STAR_STATES_NOT_CO diff --git a/posydon/binary_evol/__init__.py b/posydon/binary_evol/__init__.py index 567a873a89..e69de29bb2 100644 --- a/posydon/binary_evol/__init__.py +++ b/posydon/binary_evol/__init__.py @@ -1,5 +0,0 @@ -from .binarystar import BinaryStar -from .singlestar import SingleStar -from .simulationproperties import SimulationProperties -from .DT.step_detached import detached_step -from .MESA.step_mesa import MesaGridStep diff --git a/posydon/interpolation/__init__.py b/posydon/interpolation/__init__.py index 9c5313e0de..e69de29bb2 100644 --- a/posydon/interpolation/__init__.py +++ b/posydon/interpolation/__init__.py @@ -1,2 +0,0 @@ -from .interpolation import GRIDInterpolator, fscale -from .IF_interpolation import IFInterpolator diff --git a/posydon/popsyn/__init__.py b/posydon/popsyn/__init__.py index 379c3f9cf3..e69de29bb2 100644 --- a/posydon/popsyn/__init__.py +++ b/posydon/popsyn/__init__.py @@ -1 +0,0 @@ -from .binarypopulation import BinaryPopulation diff --git a/posydon/popsyn/independent_sample.py b/posydon/popsyn/independent_sample.py index 8d8ae394cf..d0a2a8c8c1 100644 --- a/posydon/popsyn/independent_sample.py +++ b/posydon/popsyn/independent_sample.py @@ -14,7 +14,7 @@ import numpy as np from scipy.stats import truncnorm -from posydon.utils import rejection_sampler +from posydon.utils.common_functions import rejection_sampler def generate_independent_samples(orbital_scheme, **kwargs): diff --git a/posydon/popsyn/star_formation_history.py b/posydon/popsyn/star_formation_history.py index 67ba05753d..62600d76ab 100644 --- a/posydon/popsyn/star_formation_history.py +++ b/posydon/popsyn/star_formation_history.py @@ -14,8 +14,8 @@ from scipy import stats from posydon.utils.data_download import PATH_TO_POSYDON_DATA from posydon.utils.constants import age_of_universe -from posydon.utils import (rejection_sampler, histogram_sampler, - read_histogram_from_file) +from posydon.utils.common_functions import ( + rejection_sampler, histogram_sampler, read_histogram_from_file) from posydon.utils.constants import Zsun from scipy.interpolate import interp1d from astropy.cosmology import Planck15 as cosmology diff --git a/posydon/tests/binary_evol/CE/test_CEE.py b/posydon/tests/binary_evol/CE/test_CEE.py index 9e0c850d28..7e07503829 100644 --- a/posydon/tests/binary_evol/CE/test_CEE.py +++ b/posydon/tests/binary_evol/CE/test_CEE.py @@ -1,13 +1,10 @@ import unittest -import warnings import os import numpy as np from posydon.utils.common_functions import PATH_TO_POSYDON -from posydon.utils import constants as const from posydon.utils import common_functions as cf from posydon.binary_evol.binarystar import BinaryStar from posydon.binary_evol.singlestar import SingleStar -from posydon.binary_evol.simulationproperties import SimulationProperties from posydon.binary_evol.CE.step_CEE import StepCEE # spaces are read '\\ ' instead of ' ' diff --git a/posydon/tests/binary_evol/SN/test_step_SN.py b/posydon/tests/binary_evol/SN/test_step_SN.py index ce06f211f1..1c501adef5 100644 --- a/posydon/tests/binary_evol/SN/test_step_SN.py +++ b/posydon/tests/binary_evol/SN/test_step_SN.py @@ -4,7 +4,6 @@ from posydon.binary_evol.SN.step_SN import StepSN from posydon.binary_evol.binarystar import BinaryStar from posydon.binary_evol.singlestar import SingleStar -from posydon.binary_evol import SimulationProperties import numpy as np import os diff --git a/posydon/tests/popsyn/test_binarypopulation.py b/posydon/tests/popsyn/test_binarypopulation.py index 5e46191f85..add9bb24ea 100644 --- a/posydon/tests/popsyn/test_binarypopulation.py +++ b/posydon/tests/popsyn/test_binarypopulation.py @@ -4,7 +4,7 @@ import os from posydon.popsyn.binarypopulation import BinaryPopulation -from posydon.binary_evol import SimulationProperties +from posydon.binary_evol.simulationproperties import SimulationProperties from posydon.binary_evol.flow_chart import flow_chart from posydon.binary_evol.step_end import step_end diff --git a/posydon/tests/utils/test_configfile.py b/posydon/tests/utils/test_configfile.py index e2232cff70..308ee9cf68 100644 --- a/posydon/tests/utils/test_configfile.py +++ b/posydon/tests/utils/test_configfile.py @@ -57,7 +57,7 @@ def test_IO(self): # check `print` and `str` methods print(str(self)) - print(len(str(self).split("\m"))) + print(len(str(self).split("\m"))) # TODO: Should this be r"\m"? def test_getters(self): """Test the two ways to get entries.""" diff --git a/posydon/utils/__init__.py b/posydon/utils/__init__.py index e628c76014..e69de29bb2 100644 --- a/posydon/utils/__init__.py +++ b/posydon/utils/__init__.py @@ -1 +0,0 @@ -from .common_functions import * From 5f1648bc437cff3d0e212373afe4aa44209d96ae Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Thu, 31 Aug 2023 16:57:59 +0200 Subject: [PATCH 119/319] introducing step_CO_HeMS_RLO (#122) * remove wrong code for kick in eccentric orbit * introducing step_CO-HeMS_RLO * spelling * minor change to catch collapsing stars states in step * add interp_class_CO_HeMS_RLO to binarystar.py * fix bug on characters lenght of step_names * PR88 defaul params ini change * minor changes * fix two small bugs * fix SN MODEL properties for nearest_neighbour interpolation --- posydon/binary_evol/MESA/step_mesa.py | 226 +++++++++++++++---- posydon/binary_evol/SN/step_SN.py | 3 +- posydon/binary_evol/binarystar.py | 2 +- posydon/binary_evol/flow_chart.py | 6 + posydon/interpolation/IF_interpolation.py | 3 +- posydon/popsyn/binarypopulation.py | 8 +- posydon/popsyn/population_params_default.ini | 30 ++- posydon/visualization/plot_pop.py | 4 +- 8 files changed, 230 insertions(+), 52 deletions(-) diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index 28e6a7f08a..71a82845c5 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -787,24 +787,25 @@ def update_properties_NN(self, star_1_CO=False, star_2_CO=False, for key in key_post_processed: setattr(star, key, cb.final_values['S%d_%s' % (k+1, key)]) - # update nearest neighbor core collapse quantites + # update nearest neighbor core collapse quantites if interpolation_class != 'unstable_MT': for MODEL_NAME in MODELS.keys(): for i, star in enumerate(stars): - if not stars_CO[i]: + if (not stars_CO[i] and + cb.final_values[f'S{i+1}_{MODEL_NAME}_CO_type'] != 'None'): values = {} for key in ['state', 'SN_type', 'f_fb', 'mass', 'spin', 'm_disk_accreted', 'm_disk_radiated']: - if key == 'state': - key = 'CO_type' - values[key] = cb.final_values[f'S{i+1}_{MODEL_NAME}_{key}'] - state = cb.final_values[f'S{i+1}_{MODEL_NAME}_CO_type'] - if state is None or state == 'None': - # privent to any quantities for star that did not - # reach core collpase - setattr(star, MODEL_NAME, None) - else: - setattr(star, MODEL_NAME, values) + if key == "state": + state = cb.final_values[f'S{i+1}_{MODEL_NAME}_CO_type'] + values[key] = state + elif key == "SN_type": + values[key] = cb.final_values[f'S{i+1}_{MODEL_NAME}_{key}'] + else: + values[key] = cb.final_values[f'S{i+1}_{MODEL_NAME}_{key}'] + setattr(star, MODEL_NAME, values) + else: + setattr(star, key, None) def initial_final_interpolation(self, star_1_CO=False, star_2_CO=False): """Update the binary through initial-final interpolation.""" @@ -967,14 +968,14 @@ def initial_final_interpolation(self, star_1_CO=False, star_2_CO=False): values = {} for key in ['state', 'SN_type', 'f_fb', 'mass', 'spin', 'm_disk_accreted', 'm_disk_radiated']: - if key == "state" in key: + if key == "state": state = self.classes[f'S{i+1}_{MODEL_NAME}_CO_type'] values[key] = state elif key == "SN_type": values[key] = self.classes[f'S{i+1}_{MODEL_NAME}_{key}'] else: values[key] = fv[f'S{i+1}_{MODEL_NAME}_{key}'] - setattr(star, MODEL_NAME, values) + setattr(star, MODEL_NAME, values) else: setattr(star, key, None) @@ -1240,6 +1241,14 @@ def __init__(self, metallicity=1., grid_name=None, *args, **kwargs): *args, **kwargs) # special stuff for my step goes here # If nothing to do, no init necessary + + # load grid boundaries + self.m1_min = min(self._psyTrackInterp.grid.initial_values['star_1_mass']) + self.m1_max = max(self._psyTrackInterp.grid.initial_values['star_1_mass']) + self.q_min = 0.05 # can be computed m2_min/m1_min + self.q_max = 1. # note that for MESA stability we actually run q_max = 0.99 + self.p_min = min(self._psyTrackInterp.grid.initial_values['period_days']) + self.p_max = max(self._psyTrackInterp.grid.initial_values['period_days']) def __call__(self, binary): """Apply the MS-MS step on a BinaryStar.""" @@ -1251,29 +1260,39 @@ def __call__(self, binary): state_1 = self.binary.star_1.state state_2 = self.binary.star_2.state event = self.binary.event - mass_ratio = self.binary.star_2.mass/self.binary.star_1.mass + m1 = self.binary.star_1.mass + m2 = self.binary.star_2.mass + mass_ratio = m2/m1 p = self.binary.orbital_period - p_max_HMS_grid = 6500 - if (state_1 == 'H-rich_Core_H_burning' - and state_2 == 'H-rich_Core_H_burning' and event == 'ZAMS' - and mass_ratio <= 1. and p <= p_max_HMS_grid): + if (state_1 == 'H-rich_Core_H_burning' and + state_2 == 'H-rich_Core_H_burning' and + event == 'ZAMS' and + self.m1_min <= m1 <= self.m1_max and + self.q_min <= mass_ratio <= self.q_max and + self.p_min <= p <= self.p_max): self.flip_stars_before_step = False super().__call__(self.binary) - elif (state_1 == 'H-rich_Core_H_burning' - and state_2 == 'H-rich_Core_H_burning' - and event == 'ZAMS' and mass_ratio > 1. and p <= p_max_HMS_grid): + elif (state_1 == 'H-rich_Core_H_burning' and + state_2 == 'H-rich_Core_H_burning' and + event == 'ZAMS' and + self.m1_min <= m2 <= self.m1_max and + self.q_min <= 1./mass_ratio <= self.q_max and + self.p_min <= p <= self.p_max): self.flip_stars_before_step = True super().__call__(self.binary) - elif (state_1 == 'H-rich_Core_H_burning' - and state_2 == 'H-rich_Core_H_burning' - and event == 'ZAMS' - and p > p_max_HMS_grid): # redirect if outside grid + # redirect if outside grid + elif (state_1 == 'H-rich_Core_H_burning' and + state_2 == 'H-rich_Core_H_burning' and + event == 'ZAMS' and + p > self.p_max): self.binary.event = 'redirect' return - elif (state_1 == 'H-rich_Central_C_depletion'): # redirect if CC1 + # redirect if CC1 + elif (state_1 == 'H-rich_Central_C_depletion'): self.binary.event = 'CC1' return - elif (state_2 == 'H-rich_Central_C_depletion'): # redirect if CC2 + # redirect if CC2 + elif (state_2 == 'H-rich_Central_C_depletion'): self.binary.event = 'CC2' return else: @@ -1284,7 +1303,7 @@ def __call__(self, binary): class CO_HMS_RLO_step(MesaGridStep): - """Class for performing the MESA step for a CO-HMS binary.""" + """Class for performing the MESA step for a CO-HMS_RLO binary.""" def __init__(self, metallicity=1., grid_name=None, *args, **kwargs): """Initialize a CO_HMS_RLO_step instance.""" @@ -1298,6 +1317,14 @@ def __init__(self, metallicity=1., grid_name=None, *args, **kwargs): *args, **kwargs) # special stuff for my step goes here # If nothing to do, no init necessary + + # load grid boundaries + self.m1_min = min(self._psyTrackInterp.grid.initial_values['star_1_mass']) + self.m1_max = max(self._psyTrackInterp.grid.initial_values['star_1_mass']) + self.m2_min = min(self._psyTrackInterp.grid.initial_values['star_2_mass']) + self.m2_max = max(self._psyTrackInterp.grid.initial_values['star_2_mass']) + self.p_min = min(self._psyTrackInterp.grid.initial_values['period_days']) + self.p_max = max(self._psyTrackInterp.grid.initial_values['period_days']) def __call__(self, binary): """Evolve a binary using the MESA step.""" @@ -1310,8 +1337,12 @@ def __call__(self, binary): state = binary.state state_1 = binary.star_1.state state_2 = binary.star_2.state + m1 = self.binary.star_1.mass + m2 = self.binary.star_2.mass p = self.binary.orbital_period + ecc = self.binary.eccentricity + # TODO: import states from flow_chart.py FOR_RLO_STATES = ["H-rich_Core_H_burning", "H-rich_Shell_H_burning", "H-rich_Core_He_burning", @@ -1322,39 +1353,132 @@ def __call__(self, binary): "H-rich_non_burning"] # check the star states + # TODO: import states from flow_chart.py if (state_2 in ["WD", "NS", "BH"] and (state_1 in FOR_RLO_STATES) and event == "oRLO1"): self.flip_stars_before_step = False - m1 = self.binary.star_1.mass - m2 = self.binary.star_2.mass # catch and redirect double core collapse, this happens if q=1: if self.binary.star_1.state == 'H-rich_Central_C_depletion': self.binary.event = 'CC1' return - # super().__call__(binary) + # TODO: import states from flow_chart.py elif (state_1 in ["WD", "NS", "BH"] and (state_2 in FOR_RLO_STATES) and event == "oRLO2"): self.flip_stars_before_step = True - m1 = self.binary.star_2.mass - m2 = self.binary.star_1.mass # catch and redirect double core collapse, this happens if q=1: if self.binary.star_2.state == 'H-rich_Central_C_depletion': self.binary.event = 'CC2' return - # super().__call__(binary) else: raise ValueError( 'The star_1.state = %s, star_2.state = %s, binary.state = %s, ' 'binary.event = %s and not CO - HMS - oRLO1/oRLO2!' % (state_1, state_2, state, event)) # redirect if outside grids - if 0.466 <= m1 <= 128.735 and 0.092 <= m2 <= 39.25 and p <= 3780.83: + if ((not self.flip_stars_before_step and + self.m1_min <= m1 <= self.m1_max and + self.m2_min <= m2 <= self.m2_max and + self.p_min <= p <= self.p_max and + ecc == 0.) or (self.flip_stars_before_step and + self.m1_min <= m2 <= self.m1_max and + self.m2_min <= m1 <= self.m2_max and + self.p_min <= p <= self.p_max and + ecc == 0.)): super().__call__(self.binary) else: self.binary.state = "detached" self.binary.event = "redirect" return +class CO_HeMS_RLO_step(MesaGridStep): + """Class for performing the MESA step for a CO-HeMS_RLO binary.""" + + def __init__(self, metallicity=1., grid_name=None, *args, **kwargs): + """Initialize a CO_HeMS_RLO_step instance.""" + self.grid_type = 'CO_HeMS_RLO' + self.interp_in_q = False + if grid_name is None: + metallicity = convert_metallicity_to_string(metallicity) + grid_name = 'CO-HeMS_RLO/' + metallicity + '_Zsun.h5' + super().__init__(metallicity=metallicity, + grid_name=grid_name, + *args, **kwargs) + # special stuff for my step goes here + # If nothing to do, no init necessary + + # load grid boundaries + self.m1_min = min(self._psyTrackInterp.grid.initial_values['star_1_mass']) + self.m1_max = max(self._psyTrackInterp.grid.initial_values['star_1_mass']) + self.m2_min = min(self._psyTrackInterp.grid.initial_values['star_2_mass']) + self.m2_max = max(self._psyTrackInterp.grid.initial_values['star_2_mass']) + self.p_min = min(self._psyTrackInterp.grid.initial_values['period_days']) + self.p_max = max(self._psyTrackInterp.grid.initial_values['period_days']) + + def __call__(self, binary): + """Evolve a binary using the MESA step.""" + # grid set up assume CO is star_2 + self.star_1_CO = False + self.star_2_CO = True + # check binary is ready before calling the step + self.binary = binary + event = self.binary.event + state = binary.state + state_1 = binary.star_1.state + state_2 = binary.star_2.state + m1 = self.binary.star_1.mass + m2 = self.binary.star_2.mass + p = self.binary.orbital_period + ecc = self.binary.eccentricity + + # TODO: import states from flow_chart.py + CO_He_STATES = [ + 'stripped_He_Core_He_burning', + 'stripped_He_Shell_He_burning', + 'stripped_He_Central_He_depleted', + 'stripped_He_Central_C_depletion', # filtered out below + # include systems that are on the brink of He exhaustion + 'stripped_He_non_burning', + # include systems post CE with core_definition_H_fraction=0.1 + 'H-rich_non_burning' + ] + + # check the star states + # TODO: import states from flow_chart.py + if (state_2 in ["WD", "NS", "BH"] + and (state_1 in CO_He_STATES) and event == "oRLO1"): + self.flip_stars_before_step = False + # catch and redirect double core collapse, this happens if q=1: + if self.binary.star_1.state == 'stripped_He_Central_C_depletion': + self.binary.event = 'CC1' + return + # TODO: import states from flow_chart.py + elif (state_1 in ["WD", "NS", "BH"] and (state_2 in CO_He_STATES) + and event == "oRLO2"): + self.flip_stars_before_step = True + # catch and redirect double core collapse, this happens if q=1: + if self.binary.star_2.state == 'stripped_He_Central_C_depletion': + self.binary.event = 'CC2' + return + else: + raise ValueError( + 'The star_1.state = %s, star_2.state = %s, binary.state = %s, ' + 'binary.event = %s and not CO - HeMS - oRLO1/oRLO2!' + % (state_1, state_2, state, event)) + # redirect if outside grids + if ((not self.flip_stars_before_step and + self.m1_min <= m1 <= self.m1_max and + self.m2_min <= m2 <= self.m2_max and + self.p_min <= p <= self.p_max and + ecc == 0.) or (self.flip_stars_before_step and + self.m1_min <= m2 <= self.m1_max and + self.m2_min <= m1 <= self.m2_max and + self.p_min <= p <= self.p_max and + ecc == 0.)): + super().__call__(self.binary) + else: + self.binary.state = "detached" + self.binary.event = "redirect" + return class CO_HeMS_step(MesaGridStep): """Class for performing the MESA step for a CO-HeMS binary.""" @@ -1371,6 +1495,14 @@ def __init__(self, metallicity=1., grid_name=None, *args, **kwargs): *args, **kwargs) # special stuff for my step goes here # If nothing to do, no init necessary + + # load grid boundaries + self.m1_min = min(self._psyTrackInterp.grid.initial_values['star_1_mass']) + self.m1_max = max(self._psyTrackInterp.grid.initial_values['star_1_mass']) + self.m2_min = min(self._psyTrackInterp.grid.initial_values['star_2_mass']) + self.m2_max = max(self._psyTrackInterp.grid.initial_values['star_2_mass']) + self.p_min = min(self._psyTrackInterp.grid.initial_values['period_days']) + self.p_max = max(self._psyTrackInterp.grid.initial_values['period_days']) def __call__(self, binary): """Apply the CO_HeMS step to a BinaryStar object.""" @@ -1382,8 +1514,12 @@ def __call__(self, binary): state_1 = self.binary.star_1.state state_2 = self.binary.star_2.state event = self.binary.event + m1 = self.binary.star_1.mass + m2 = self.binary.star_2.mass p = self.binary.orbital_period + ecc = self.binary.eccentricity + # TODO: import states from flow_chart.py CO_He_STATES = [ 'stripped_He_Core_He_burning', 'stripped_He_Shell_He_burning', @@ -1394,12 +1530,10 @@ def __call__(self, binary): # include systems post CE with core_definition_H_fraction=0.1 'H-rich_non_burning' ] - + # TODO: import states from flow_chart.py if (state_2 in ['WD', 'NS', 'BH'] and state_1 in CO_He_STATES and event is None): self.flip_stars_before_step = False - m1 = self.binary.star_2.mass - m2 = self.binary.star_1.mass # catch and redirect double core collapse, this happens if q=1: if self.binary.star_1.state == 'stripped_He_Central_C_depletion': self.binary.event = 'CC1' @@ -1411,11 +1545,10 @@ def __call__(self, binary): # new_separation, m1, m2) # self.binary.eccentricity = 0. return + # TODO: import states from flow_chart.py elif (state_1 in ['WD', 'NS', 'BH'] and state_2 in CO_He_STATES and event is None): self.flip_stars_before_step = True - m1 = self.binary.star_1.mass - m2 = self.binary.star_2.mass # catch and redirect double core collapse, this happens if q=1: if self.binary.star_2.state == 'stripped_He_Central_C_depletion': self.binary.event = 'CC2' @@ -1426,7 +1559,16 @@ def __call__(self, binary): 'not supported by CO - HeMS grid!' % (state_1, state_2, event)) # redirect if outside grids - if 0.91 <= m1 <= 39.24 and 0.47 <= m2 <= 85.38 and p <= 1203.84: + # remember that in MESA the CO object is star_2 + if ((not self.flip_stars_before_step and + self.m1_min <= m1 <= self.m1_max and + self.m2_min <= m2 <= self.m2_max and + self.p_min <= p <= self.p_max and + ecc == 0.) or (self.flip_stars_before_step and + self.m1_min <= m2 <= self.m1_max and + self.m2_min <= m1 <= self.m2_max and + self.p_min <= p <= self.p_max and + ecc == 0.)): super().__call__(binary) else: self.binary.event = 'redirect' diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index 64aff55380..f968eee821 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -523,7 +523,8 @@ def collapse_star(self, star): "m_disk_accreted ", "m_disk_radiated"]: setattr(star, key, None) - star.log_R = np.log10(CO_radius(star.mass, star.state)) + if getattr(star, 'SN_type') != 'PISN': + star.log_R = np.log10(CO_radius(star.mass, star.state)) return diff --git a/posydon/binary_evol/binarystar.py b/posydon/binary_evol/binarystar.py index a8944f3922..4f8d5a391f 100644 --- a/posydon/binary_evol/binarystar.py +++ b/posydon/binary_evol/binarystar.py @@ -166,7 +166,7 @@ def __init__(self, star_1=None, star_2=None, index=None, properties=None, # self.V_sys = [0, 0, 0] # store innterpolation_class for each step_MESA - for grid_type in ['HMS_HMS','CO_HMS_RLO','CO_HeMS']: + for grid_type in ['HMS_HMS','CO_HMS_RLO','CO_HeMS','CO_HeMS_RLO']: if not hasattr(self, f'interp_class_{grid_type}'): setattr(self, f'interp_class_{grid_type}', None) diff --git a/posydon/binary_evol/flow_chart.py b/posydon/binary_evol/flow_chart.py index 1dff20cb37..62d149768d 100644 --- a/posydon/binary_evol/flow_chart.py +++ b/posydon/binary_evol/flow_chart.py @@ -145,6 +145,12 @@ for s2 in STAR_STATES_CO: POSYDON_FLOW_CHART[(s1, s2, 'RLO1', 'oRLO1')] = 'step_CO_HMS_RLO' POSYDON_FLOW_CHART[(s2, s1, 'RLO2', 'oRLO2')] = 'step_CO_HMS_RLO' + +# He-rich star roche-lobe overflow onto a compact object +for s1 in STAR_STATES_HE_RICH_EVOLVABLE: + for s2 in STAR_STATES_CO: + POSYDON_FLOW_CHART[(s1, s2, 'RLO1', 'oRLO1')] = 'step_CO_HeMS_RLO' + POSYDON_FLOW_CHART[(s2, s1, 'RLO2', 'oRLO2')] = 'step_CO_HeMS_RLO' # H-rich star on a detached binary with a compact object # that fall outside the grid and has been returned by step_CO_HMS_RLO diff --git a/posydon/interpolation/IF_interpolation.py b/posydon/interpolation/IF_interpolation.py index 76e49f0ecb..a39232730d 100644 --- a/posydon/interpolation/IF_interpolation.py +++ b/posydon/interpolation/IF_interpolation.py @@ -1166,7 +1166,8 @@ def predict(self, Xt): which = np.zeros_like(zpred, dtype=bool) for j in range(len(self.classes[i])): which += zpred == self.classes[i][j] - Ypred[which, :] = self.interpolators[i].predict(Xt[which, :]) + if which.any(): + Ypred[which, :] = self.interpolators[i].predict(Xt[which, :]) return Ypred diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index 5c40997bd0..2e54e6e029 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -52,24 +52,24 @@ # 'event' usually 10 but 'detached (Integration failure)' can occur -HISTORY_MIN_ITEMSIZE = {'state': 30, 'event': 10, 'step_names': 15, +HISTORY_MIN_ITEMSIZE = {'state': 30, 'event': 10, 'step_names': 20, 'S1_state': 31, 'S2_state': 31, 'mass_transfer_case': 7, 'S1_SN_type': 5, 'S2_SN_type': 5} ONELINE_MIN_ITEMSIZE = {'state_i': 30, 'state_f': 30, 'event_i': 10, 'event_f': 10, - 'step_names_i': 15, 'step_names_f': 15, + 'step_names_i': 20, 'step_names_f': 20, 'S1_state_i': 31, 'S1_state_f': 31, 'S2_state_i': 31, 'S2_state_f': 31, 'mass_transfer_case_i': 7, 'mass_transfer_case_f': 7, 'S1_SN_type': 5, 'S2_SN_type': 5, 'interp_class_HMS_HMS' : 15, 'interp_class_CO_HeMS' : 15, - 'interp_class_CO_HMS_RLO' : 15} + 'interp_class_CO_HMS_RLO' : 15, 'interp_class_CO_HeMS_RLO' : 15} # BinaryPopulation will enforce a constant metallicity accross all steps that # load stellar or binary models by checked this list of steps. STEP_NAMES_LOADING_GRIDS = [ - 'step_HMS_HMS', 'step_CO_HeMS', 'step_CO_HMS_RLO', 'step_detached','step_isolated','step_disrupted','step_initially_single', 'step_merged' + 'step_HMS_HMS', 'step_CO_HeMS', 'step_CO_HMS_RLO', 'step_CO_HeMS_RLO', 'step_detached','step_isolated','step_disrupted','step_initially_single', 'step_merged' ] class BinaryPopulation: diff --git a/posydon/popsyn/population_params_default.ini b/posydon/popsyn/population_params_default.ini index a9d0da3141..d08f9f5fbe 100644 --- a/posydon/popsyn/population_params_default.ini +++ b/posydon/popsyn/population_params_default.ini @@ -95,6 +95,33 @@ verbose = False # True False +[step_CO_HeMS_RLO] + import = ['posydon.binary_evol.MESA.step_mesa', 'CO_HeMS_RLO_step'] + absolute_import = None + # 'package' kwarg for importlib.import_module + interpolation_path = None + # found by default + interpolation_filename = None + # found by default + interpolation_method = 'linear3c_kNN' + # 'nearest_neighbour' 'linear3c_kNN' '1NN_1NN' + save_initial_conditions = True + # only for interpolation_method='nearest_neighbour' + track_interpolation = False + # True False + stop_method = 'stop_at_max_time' + # 'stop_at_end' 'stop_at_max_time' 'stop_at_condition' + stop_star = 'star_1' + # only for stop_method='stop_at_condition' 'star_1' 'star_2' + stop_var_name = None + # only for stop_method='stop_at_condition' str + stop_value = None + # only for stop_method='stop_at_condition' float + stop_interpolate = True + # True False + verbose = False + # True False + [step_detached] import = ['posydon.binary_evol.DT.step_detached', 'detached_step'] @@ -296,7 +323,7 @@ ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; [BinaryStar_output] - extra_columns=['step_names', 'step_times'] + extra_columns = {'step_names':'string', 'step_times':'float64'} # 'step_times' with from posydon.binary_evol.simulationproperties import TimingHooks # LIST BINARY PROPERTIES @@ -324,6 +351,7 @@ 'interp_class_HMS_HMS', 'interp_class_CO_HMS_RLO', 'interp_class_CO_HeMS', + 'interp_class_CO_HeMS_RLO', ] [SingleStar_1_output] diff --git a/posydon/visualization/plot_pop.py b/posydon/visualization/plot_pop.py index 10ec989f0d..95e98f5e53 100644 --- a/posydon/visualization/plot_pop.py +++ b/posydon/visualization/plot_pop.py @@ -21,9 +21,9 @@ COLORS *= 10 def plot_merger_rate_density(z, rate_density, ylim=(1e-1,4e2), zmax=10., show=True, - path=None, channels=False, GWTC3=False, **kwargs): + path=None, channels=False, GWTC3=False, label='DCO', **kwargs): - plt.plot(z[z Date: Thu, 31 Aug 2023 17:00:45 +0200 Subject: [PATCH 120/319] add rerun type: thermohaline_mixing (#136) --- bin/run-pipeline | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/bin/run-pipeline b/bin/run-pipeline index d067bb4b17..2530fba5f2 100644 --- a/bin/run-pipeline +++ b/bin/run-pipeline @@ -82,6 +82,7 @@ from posydon.utils.gridutils import get_new_grid_name from posydon.visualization.plot_defaults import PRE_SET_PLOTS from posydon.interpolation.IF_interpolation import IFInterpolator from posydon.visualization.interpolation import EvaluateIFInterpolator +from posydon.visualization.combine_TF import TF1_POOL_ERROR # pre defined compressions @@ -448,6 +449,8 @@ def copy_ini_file(grid_path, rerun_type, destination, cluster): replace_text = "zepei_fix_implicit-afa1860ddf9894aa1d82742ee2a73e8e92acd4a9" elif rerun_type == 'TPAGBwind': replace_text = "development-22c1bb9e730343558c3e70984a99b3fc1f3c346e" + elif rerun_type == 'thermohaline_mixing': + replace_text = "development-22c1bb9e730343558c3e70984a99b3fc1f3c346e" elif rerun_type == 'other': # e.g. 'envelope_mass_limit', 'fe_core_infall_limit' # this rerun uses the default inlist commit return @@ -478,6 +481,7 @@ def logic_rerun(grid, rerun_type): # handle different rerun types if rerun_type == 'opacity_max' or rerun_type == 'opacity_max_hms-hms': + # TODO: consider using TF1_POOL_ERROR termination_flags=['reach cluster timelimit', 'min_timestep_limit'] new_mesa_flag={'opacity_max' : 0.5} elif rerun_type == 'PISN': @@ -540,6 +544,9 @@ def logic_rerun(grid, rerun_type): if np.max(np.logical_or(tpagb_1, tpagb_2)) == True: runs_to_rerun += [i] runs_to_rerun = np.array(runs_to_rerun) + elif rerun_type == 'thermohaline_mixing': + termination_flags = TF1_POOL_ERROR + new_mesa_flag = {'thermohaline_coeff' : 17.5} elif rerun_type == 'envelope_mass_limit': # maybe 'fe_core_infall_limit' as well? runs_to_rerun = None # implement logic else: From 905373b9ae1dca142045e5a223a5a6e116465127 Mon Sep 17 00:00:00 2001 From: WeneKouarfate <128610116+WeneKouarfate@users.noreply.github.com> Date: Thu, 14 Sep 2023 16:45:46 +0200 Subject: [PATCH 121/319] Vhd multiple (#139) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Modules & documetations * remove unrelevant classes from presenter.py * remove unrelevant classes from presenter.py * correct pyqt5 python 3.11 issues, _remove_duplicate(), docs and comments plus minor changes --------- Co-authored-by: Wène Kouarfate --- docs/visualization/VHD/VHD.rst | 117 +++++ .../VHD/pngs/diagram_hierarchy.png | Bin 0 -> 233344 bytes .../VHD/pngs/diagram_multiple.png | Bin 0 -> 364058 bytes .../VHD/pngs/diagram_multiple_sort.png | Bin 0 -> 362793 bytes .../VH_diagram/GraphVisualizer.py | 30 +- .../visualization/VH_diagram/MainWindow.py | 4 +- .../visualization/VH_diagram/OptionsWindow.py | 4 +- .../VH_diagram/ParseDataFrame.py | 98 ++++ posydon/visualization/VH_diagram/Presenter.py | 4 +- .../VH_diagram/PresenterMultiple.py | 462 ++++++++++++++++++ .../VH_diagram/SimulationModel.py | 13 +- 11 files changed, 712 insertions(+), 20 deletions(-) create mode 100644 docs/visualization/VHD/pngs/diagram_hierarchy.png create mode 100644 docs/visualization/VHD/pngs/diagram_multiple.png create mode 100644 docs/visualization/VHD/pngs/diagram_multiple_sort.png create mode 100644 posydon/visualization/VH_diagram/ParseDataFrame.py create mode 100644 posydon/visualization/VH_diagram/PresenterMultiple.py diff --git a/docs/visualization/VHD/VHD.rst b/docs/visualization/VHD/VHD.rst index b1cbe4f534..d6dae5e889 100644 --- a/docs/visualization/VHD/VHD.rst +++ b/docs/visualization/VHD/VHD.rst @@ -90,3 +90,120 @@ We can specify the display mode wanted with the named parameter 'displayMode': ) .. image:: pngs/diagram_inline.png + +Visualize multiple indexes +========================== + +The `VDdiagramm_m` module allows you to print more than one index horizontally in the +same plot. + +Counting binaries populations +----------------------------- + +You can loop through the binary file to count identical binary simulations with the `ParseDataFrame` +class. The counts are accessible in the `count_dict` attribute which is a `Counter` python object +and the frequencies with the `get_frequencies()` method. You can also get the n most frequent +binaries using the `get_most_numpy(k)` method. + +.. code-block:: python + + from posydon.visualization.VH_diagram.ParseDataFrame import ParseDataFrame + + parse_df = ParseDataFrame('./data/population.h5') + parse_df.count_dict + + >>> Counter({0: 14, + 1: 16, + 3: 1, + 4: 9, + 5: 1, + 6: 1, + 7: 6, + 8: 19, + 21: 1, + 24: 4, + 25: 1, + 26: 1, + 30: 1, + 31: 10, + 34: 1, + 36: 5, + 38: 1, + 55: 1, + 58: 1, + 59: 1, + 62: 1, + 65: 1, + 74: 1, + 93: 1, + 95: 1}) + + +Side by side visualization +-------------------------- + +You can get multiple binary simulations printed side by side by providing a list of their index +in wanted order. The initialization call requires a dict of frequenties as parameter `frequency` +witch is by default uniform. `hierarchy` parameter must be set to false. + +.. code-block:: python + + from posydon.visualization.VH_diagram.PresenterMultiple import VHdiagramm_m + from posydon.visualization.VHdiagram import DisplayMode + from posydon.visualization.VH_diagram.PresenterMode import PresenterMode + + VHD = VHdiagramm_m('./data/population.h5', + index=cnt[:,0], + frequency=parse_df.get_frequencies(), + hierarchy=False, + presentMode = PresenterMode.DIAGRAM, + displayMode = DisplayMode.INLINE_B) + +.. image:: pngs/diagram_multiple.png + +Hierarchical visualization +-------------------------- + +The hierarchical visualization aims to "factorize" identical steps resulting in a tree plot +where the nodes are the common steps. They are labeled by precentages relatively to the parent +node precentage (witch is also uniform by default). + +.. code-block:: python + + from posydon.visualization.VH_diagram.PresenterMultiple import VHdiagramm_m + from posydon.visualization.VHdiagram import DisplayMode + from posydon.visualization.VH_diagram.PresenterMode import PresenterMode + + VHD = VHdiagramm_m('./data/population.h5', + index=cnt[:,0], + frequency=parse_df.get_frequencies(), + hierarchy=True, + presentMode = PresenterMode.DIAGRAM, + displayMode = DisplayMode.INLINE_B) + +.. image:: pngs/diagram_hierarchy.png + +Steps differentiation is based on picture's file names. So binaries like 30 and 36 +are considered different since they refer to distinct files. + +.. code-block:: python + + >>> print(VHD._presenter._infos[10][1].event_filename.split("\\")[-1]) + H-rich_oCE1_H-rich_.png + >>> print(VHD._presenter._infos[14][1].event_filename.split("\\")[-1]) + H-rich_oCE2_H-rich_.png + + +The `get_sorted_index()` method of the `VHdiagramm_m` object give a list of indexes +sorted regarding the filenames of the pictures representing their steps + +.. code-block:: python + + VHD = VHdiagramm_m('./data/population.h5', + index=VHD.get_sorted_index(), + frequency=parse_df.get_frequencies(), + hierarchy=False, + presentMode = PresenterMode.DIAGRAM, + displayMode = DisplayMode.INLINE_B) + +.. image:: pngs/diagram_multiple_sort.png \ No newline at end of file diff --git a/docs/visualization/VHD/pngs/diagram_hierarchy.png b/docs/visualization/VHD/pngs/diagram_hierarchy.png new file mode 100644 index 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zzExZ|PAUHY+0nG62*!EM8UclGcO(Fai3iYhtvD}b1sZS)k$_06V@Hh`uppm$r*&y7 z2||AeE+K_h|EKfCNRnp+51?U*=R zs8`M4Y(7?^{KCAl26@}$WcJ-Rfx2PM9Sq4%Zj}O4H`6$!YIhk zdd{&$ONNk)u^nn_`}rJwo+48{Thf>r7m&|$s5Htps6MrN(O5>nw@h)GiL~_dQCdcF z-o5JD+e;j)8ltFgew2WQd%$54#tD$P8QapX?m=(VZX=F*)n6<}!$zYd^r#k3GT_I~ z2SGp&SlkBlR|}XP`)`(RN$fK6*Lljqn^~55-bTB;O3cdj2A2U`ck`W zA&K?HM?LBXSn<;t;*G~*)rI71ZRJb>=aOnt&`TQ>b;oQPqV~xm83#_etoO>cW9WTo zuol#rCfW+{dg8Rid!XBbYYr!P&h{NEV@tk{;f#J+7#SxtSO}rFo;g{>o?7EHw+zh@ zk&{2V<7ye4WDX~T__WreF5)R9_uG?Rp*n%L-v^*{KE&=G+dK8?Cv3j zq-}%_fDV+~DJSy_<$)h`9xGf*;9wrTL8@{9nZdz6ovN+`t*QAIW^hyt0npP!GpsNC$qY}*j)aQ0bqc&kk`_, *University of Geneva*: Leading the design and development of POSYDON since its conseption. Simone uses POSYDON to study the formation mechanism of merging binary black holes, sources of gravitational waves. + +- `Max M. Briel `_, *University of Geneva*. + +- `Christopher Berry `_, *University of Glasgow*. + +- **Abhishek Chattaraj**, *University of Florida*. + +- **Tassos Fragos (co-PI)**, *University of Geneva*. + +- `Monica Gallegos-Garcia `_, *Northwestern University*: Assisted with early planning for the interstructure to generate MESA grids, running MESA simulations for POSYDON v2, and contributed to the analysis of compact object populations. Monica uses POSYDON to investigate the formation of compact object mergers. + +- `Seth Gossage\* `_, *Northwestern University*: Core developer focused on development of MESA stellar evolution grids, especially for stars less massive than 10 solar masses. Seth employs POSYDON in his research to find constraints on the physics of stellar rotation, convection, and magnetic fields. + +- `Vicky Kalogera (co-PI) `_, *Northwestern University*. + +- **Eirini Kasdagli**, *University of Florida*. + +- `Aggelos Katsaggelos `_, *Northwestern University*. + +- **Chase Kimball**, *Northwestern University*. + +- `Dean Kousiounelos `_, *Lake Forest College*: Developing data visualization for Binary Stellar Evolution for POSYDON to aid in understanding an evolutionary narrative, and bridge the gap between astrophysics and broader audiences. Under the mentorship of Dr. Vicky Kalogera and Dr. Seth Gossage. + +- `Konstantinos Kovlakas* `_, *Institute of Space Sciences (ICE/CSIC,IEEC)*: Core developer responsible for core infrastructure, the encoding of MESA output in POSYDON, and the characterization of stars based on their properties. His research focuses on the modeling of accreting compact objects and comparisons against observations. + +- **Matthias Kruckow\***, *University of Geneva*. + +- **Shamal Lalvani**, *Northwestern University*. + +- `Camille Liotine* `_, *Northwestern University*: Integrating pulsar modeling into POSYDON and facilitating general code development for POSYDON. + +- **Devina Mishra\***, *Norwegian University of Science and Technology*. + +- **Kyle Rocha\***, *Northwestern University*: Development of the psy-cris active learning module, binary population API, evolutionray hooks, IO for binary stars / populations and integration with pandas data frame. + +- `Jaime Román-Garza `_, *University of Geneva*. + +- **Philipp M. Srivastava\***, *Northwestern University*. + +- `Meng Sun* `_, *Northwestern University*: Most of the contributions involved preparing detailed MESA binary modeling and assisting in the analysis of single-star and binary evolution results: reviewed the error distribution after MESA binary tracks were trained by the IFinterpolator; assisted in constructing MESA binary grids with metal-poor, metal-rich, and low-mass stars; resolved code convergence issues. + +- `Elizabeth Teng* `_, *Northwestern University*: Developed stellar profile interpolation and consulted on time-series interpolation. + +- `Goce Trajcevski `_, *Iowa State University*. + +- `Zepei Xing* `_, *University of Geneva*. + +- `Manos Zapartas `_, *University of Geneva*. + + + + + + +.. note:: + Members marked with an asterisk (*) are part of the core development team. + + +Past Team Members +~~~~~~~~~~~~~~~~~ + +- `Aaron Dotter `_, *Northwestern University*. + +- `Prabin Giri `_, *Iowa State University*. + +- **Ying Qin**, *Formerly,Northwestern University*: Contributed to the v1 development of MESA grids. Ying used POSYDON for his post-doctoral research to study X-ray binaries. + +- `Juan Gabriel Serra Perez `_, *Northwestern University*. + +- `Xu Teng `_, *Iowa State University*. + +- **Nam Tran**, *Formerly, University of Copenhagen*: Assisted early development of the POSYDON core infrastructure. Nam used POSYDON in his master thesis to study X-ray binaries. + + + + + + +Students Contributions +~~~~~~~~~~~~~~~~~~~~~~ + +- `Petter Stahle `_, *Formerly, University of Geneva*: Developed the POSYDON web-application API interphase to run POSYDON v1 population synthesis simulations. + diff --git a/docs/_source/introduction-acknowledgements/intro.rst b/docs/_source/introduction-acknowledgements/intro.rst new file mode 100644 index 0000000000..18ed8945cc --- /dev/null +++ b/docs/_source/introduction-acknowledgements/intro.rst @@ -0,0 +1,52 @@ +.. _intro: + +Introduction, Objectives, and Scope +----------------------------------- + +What is POSYDON? +~~~~~~~~~~~~~~~~ + +POSYDON, an acronym for **POpulation SYnthesis with Detailed binary-evolution simulatiONs**, is a next-generation single and binary-star population synthesis code. Designed to seamlessly incorporate full stellar structure and evolution modeling, it leverages the advanced capabilities of MESA for a comprehensive and accurate simulation of stellar systems. + +Code Objectives +~~~~~~~~~~~~~~~~ + +The primary goal of POSYDON is to simulate stellar populations with unparalleled precision and detail. The modular nature of the code allows users to specify initial population properties and make informed choices regarding stellar evolution paths. By using MESA evolutionary tracks, POSYDON provides simulations for single, non-interacting, and interacting binaries organized in grids. With the integration of advanced machine-learning methods, the code performs classifications and a variety of interpolation calculations. Active learning guides the development of irregular grids, ensuring computational efficiency and robustness. + +Science Scope +~~~~~~~~~~~~~ + +With its sophisticated algorithms and detailed simulations, POSYDON has broad science applicability. The version 2 of POSYDON is tailored towards the evolution of massive binary stars. This includes their intricate paths leading to the formation of neutron stars and black holes. Furthermore, the code is equipped to handle and analyze these evolutions across a vast range of metallicities, accommodating various stellar environments and conditions. + +Citation Disclaimer +~~~~~~~~~~~~~~~~~~~ + +If you use POSYDON for your research and it contributes to a publication, we kindly request that you cite our foundational papers for this version: + +- `POSYDON: A General-purpose Population Synthesis Code with Detailed Binary-evolution Simulations `_, **Fragos et al. (2023)** + +.. code-block:: + + @ARTICLE{2023ApJS..264...45F, + author = {{Fragos}, Tassos and {Andrews}, Jeff J. and {Bavera}, Simone S. and {Berry}, Christopher P.~L. and {Coughlin}, Scott and {Dotter}, Aaron and {Giri}, Prabin and {Kalogera}, Vicky and {Katsaggelos}, Aggelos and {Kovlakas}, Konstantinos and {Lalvani}, Shamal and {Misra}, Devina and {Srivastava}, Philipp M. and {Qin}, Ying and {Rocha}, Kyle A. and {Rom{\'a}n-Garza}, Jaime and {Serra}, Juan Gabriel and {Stahle}, Petter and {Sun}, Meng and {Teng}, Xu and {Trajcevski}, Goce and {Tran}, Nam Hai and {Xing}, Zepei and {Zapartas}, Emmanouil and {Zevin}, Michael}, + title = "{POSYDON: A General-purpose Population Synthesis Code with Detailed Binary-evolution Simulations}", + journal = {\apjs}, + keywords = {Binary stars, Close binary stars, Compact binary stars, Interacting binary stars, X-ray binary stars, Compact objects, Stellar remnants, Black holes, Neutron stars, Gravitational wave sources, Stellar evolutionary models, Stellar populations, 154, 254, 283, 801, 1811, 288, 1627, 162, 1108, 677, 2046, 1622, Astrophysics - Solar and Stellar Astrophysics}, + year = 2023, + month = feb, + volume = {264}, + number = {2}, + eid = {45}, + pages = {45}, + doi = {10.3847/1538-4365/ac90c1}, + archivePrefix = {arXiv}, + eprint = {2202.05892}, + primaryClass = {astro-ph.SR}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2023ApJS..264...45F}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} + } + + +- POSYDON Version 2: Population Synthesis across Metallicities with Detailed Binary-Evolution Simulation, Andrews et al. (in prep.) + +Acknowledging these works ensures that the development team receives proper credit and supports the continued advancement and maintenance of the POSYDON software. diff --git a/docs/_source/introduction-acknowledgements/published-works.rst b/docs/_source/introduction-acknowledgements/published-works.rst new file mode 100644 index 0000000000..346752f22e --- /dev/null +++ b/docs/_source/introduction-acknowledgements/published-works.rst @@ -0,0 +1,11 @@ +.. _published-works: + +Published Works +--------------- + +This page lists published works that utilized the POSYDON code. Each entry provides a brief description summarizing the main findings of each study. Researchers using POSYDON have made significant contributions to our understanding of binary stars and their evolutionary paths. + +- `The formation of merging black holes with masses beyond 30 M⊙ at solar metallicity `_, **Bavera et al. (2023)**, The study unveils the impact of stellar winds and binary interactions on the maximum mass of black holes formed in isolated binaries at solar metallicity. Leveraging detailed stellar structure and binary evolution calculations in population-synthesis models, a distinct perspective emerges for the formation of black-hole binaries, contrasting prior rapid population synthesis models. + +(TODO Continue with other referenced works using POSYDON, following the same structure...) + diff --git a/docs/_source/posydon-workflow/interacting-binary-grids.rst b/docs/_source/posydon-workflow/interacting-binary-grids.rst new file mode 100644 index 0000000000..556bd830a1 --- /dev/null +++ b/docs/_source/posydon-workflow/interacting-binary-grids.rst @@ -0,0 +1,2 @@ +Interacting Binary Grids +------------------------ \ No newline at end of file diff --git a/docs/_source/posydon-workflow/mesa-evolutionary-tracks.rst b/docs/_source/posydon-workflow/mesa-evolutionary-tracks.rst new file mode 100644 index 0000000000..c622ec2f4c --- /dev/null +++ b/docs/_source/posydon-workflow/mesa-evolutionary-tracks.rst @@ -0,0 +1,2 @@ +MESA Evolutionary Tracks +------------------------ \ No newline at end of file diff --git a/docs/_source/posydon-workflow/population-specification.rst b/docs/_source/posydon-workflow/population-specification.rst new file mode 100644 index 0000000000..42a65da39e --- /dev/null +++ b/docs/_source/posydon-workflow/population-specification.rst @@ -0,0 +1,2 @@ +Population Specification +------------------------ \ No newline at end of file diff --git a/docs/_source/posydon-workflow/running-simulations.rst b/docs/_source/posydon-workflow/running-simulations.rst new file mode 100644 index 0000000000..236e2ef76a --- /dev/null +++ b/docs/_source/posydon-workflow/running-simulations.rst @@ -0,0 +1,2 @@ +Running Simulations +------------------- \ No newline at end of file diff --git a/docs/_source/posydon-workflow/stellar-evolution-choices.rst b/docs/_source/posydon-workflow/stellar-evolution-choices.rst new file mode 100644 index 0000000000..124c670950 --- /dev/null +++ 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z)zNqTx5!I%PV^{PiJ7XLmX40jT1yLgHUhe1CnY7pDk?B!NjEQM4fl{+7p;tqjpg7R z<-(d}?)Jen=>uFVhXo+esTT!w|9jIFY@2JBfq)<4>+OZIFVky>+VrD4^V?fecnGQ( zcv#`O+C803#77%3oA$)ng$3Qwj8pNsJ53UOx_Wvh7|Qyrd*!b(gU$3!BE!1^I)ME2 ztiQJWY_jWzfrZz_HZs!E?MX6ycszv%vs28(^H@7emv#5wIm-mcO52r*Z6A86Lw>#~vpqC-XA7ZwY{z!!-ksi&wdJ zE0%2d8i`3kncPV-2IS!G?p_W3(Ah$1uW2$U*>+wYlbN53D9|1|l%#xgx8g+}?%j&b zX-b{cJ621h$};Z(Xv*A6CS;f%ym_5UxLUGK`W15uis^O^?*gqqqV)5}k009ru(<}c zLwA`AAoe1uT$JRdNRLcpJe};cp51opF1A*w`>OBGy4!N{n1}}p=(X9?H+gt3yhohM z)bitY4J;KU{Ur~JQzm~qzbOfEn{NuL)}Z#~s*jD^+qVQ?{XUw?a#U{D&TFE{ z*Ntf_*m|Zb6q_tz_vrzH_1QF@PSnnfiC1Pk9sG;qy(n{wQ$+^k1d8rKGb&Vq+;I0{ zyqEt}E2pIE?=Tk&Jv$={Tlzj_^) zo^bU5kt5R2#;9ITK{>!-n_hzWmF~fKXWGw?&c&+5#>HXZXOng|Ycp#EcXvK;wq>D# zjBf+4Rq=qh4faatLkwxahV3CeEMFi1uH8&6`Po1&W$g9S-U5fqWM& znPo*A9azWI;QaaFUeJi=j@1{EG)ClYqQ-m6-vXDJ9?1D_Y?Ikd*Vfi%?jb{L3Q$S`_ and by following our repository on GitHub `POSYDON on GitHub `_. + diff --git a/docs/_source/release-notes/version-history.rst b/docs/_source/release-notes/version-history.rst new file mode 100644 index 0000000000..073448d4e9 --- /dev/null +++ b/docs/_source/release-notes/version-history.rst @@ -0,0 +1,33 @@ +.. _version-history: + +Version History of POSYDON +-------------------------- + +This page chronicles the detailed version history of POSYDON, including minor versions and patches. Each entry includes a brief summary of the changes, enhancements, and bug fixes made in that release. + +v2.0.0 +~~~~~~ +**Release Date:** [TODO add date here] + +**Summary:** +- Introduced capability for population synthesis across 8 different metallicities, from \(10^{-4}\) times solar metallicity up to 2 times solar metallicity. +- [Other features or improvements made in v2.0.0] +- Bug fixes: + + - [List of significant bug fixes] + +v1.0.0 +~~~~~~ +**Release Date:** [6 feb. 2023] + +**Summary:** +- First stable release of POSYDON. +- Focused on binary population synthesis of massive binary stars at solar metallicities. +- [Other features or improvements made in v1.0.0] +- Bug fixes: + + - [List of significant bug fixes] + +Stay Informed +~~~~~~~~~~~~~ +For a broader overview of major version updates, refer to our :ref:`Major Updates ` page. To keep up with the latest changes and releases, visit our official website `POSYDON Official Website `_ and follow our repository on GitHub `POSYDON on GitHub `_. diff --git a/docs/_source/sphinx-apidoc-templates/module.rst_t b/docs/_source/sphinx-apidoc-templates/module.rst_t new file mode 100644 index 0000000000..d3f2c86645 --- /dev/null +++ b/docs/_source/sphinx-apidoc-templates/module.rst_t @@ -0,0 +1,8 @@ +{%- if show_headings %} +{{- basename | e | heading }} + +{% endif -%} +.. automodule:: {{ qualname }} +{%- for option in automodule_options %} + :{{ option }}: +{%- endfor %} \ No newline at end of file diff --git a/docs/_source/sphinx-apidoc-templates/package.rst_t b/docs/_source/sphinx-apidoc-templates/package.rst_t new file mode 100644 index 0000000000..95e2aac976 --- /dev/null +++ b/docs/_source/sphinx-apidoc-templates/package.rst_t @@ -0,0 +1,55 @@ +{%- macro automodule(modname, options) -%} +.. automodule:: {{ modname }} +{%- for option in options %} + :{{ option }}: +{%- endfor %} +{%- endmacro %} + +{%- macro toctree(docnames) -%} +.. toctree:: + :maxdepth: {{ maxdepth }} + :titlesonly: +{% for docname in docnames %} + {{ docname }} +{%- endfor %} +{%- endmacro %} + +{%- if is_namespace %} +{{- [pkgname, "namespace"] | join(" ") | e | heading }} +{% else %} +{{- pkgname | e | heading }} +{% endif %} + +{%- if is_namespace %} +.. py:module:: {{ pkgname }} +{% endif %} + +{%- if modulefirst and not is_namespace %} +{{ automodule(pkgname, automodule_options) }} +{% endif %} + +{%- if subpackages %} + +{{ toctree(subpackages) }} +{% endif %} + +{%- if submodules %} + +{% if separatemodules %} +{{ toctree(submodules) }} +{% else %} +{%- for submodule in submodules %} +{% if show_headings %} +{{- submodule | e | heading(2) }} +{% endif %} +{{ automodule(submodule, automodule_options) }} +{% endfor %} +{%- endif %} +{%- endif %} + +{%- if not modulefirst and not is_namespace %} +Module contents +--------------- + +{{ automodule(pkgname, automodule_options) }} +{% endif %} \ No newline at end of file diff --git a/docs/_source/sphinx-apidoc-templates/toc.rst_t b/docs/_source/sphinx-apidoc-templates/toc.rst_t new file mode 100644 index 0000000000..83c28a15ad --- /dev/null +++ b/docs/_source/sphinx-apidoc-templates/toc.rst_t @@ -0,0 +1,8 @@ +{{ header | heading }} + +.. toctree:: + :maxdepth: {{ maxdepth }} + :titlesonly: +{% for docname in docnames %} + {{ docname }} +{%- endfor %} \ No newline at end of file diff --git a/docs/_source/troubleshooting-faqs/code-questions.rst b/docs/_source/troubleshooting-faqs/code-questions.rst new file mode 100644 index 0000000000..22a7781874 --- /dev/null +++ b/docs/_source/troubleshooting-faqs/code-questions.rst @@ -0,0 +1,49 @@ +.. _code-usage: + +Code Usage Questions +-------------------- + +Welcome to the code usage FAQ for POSYDON. This section is designed to answer questions related to how to use various features and functionalities of POSYDON. If your question isn't addressed here, consider reaching out to our :ref:`support channels ` or checking our :ref:`general FAQ `. + +Frequently Asked Questions +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +1. **How do I start a basic population synthesis simulation with POSYDON?** + - Answer: Start with the :ref:`binary population synthesis guide ` to learn how to run POSYDON. + +2. **Which parameters can I customize for my simulations?** + - Answer: POSYDON allows customization of ... (TODO list or briefly describe customizable parameters). + +3. **I'm getting an error message when trying to run a simulation. What does it mean?** + - Answer: Error messages typically indicate ... (TODO provide guidance on interpreting common error messages or point to a troubleshooting guide). + +4. **How can I visualize the results from my simulation?** + - Answer: You can utilize the experimental visualization libraries by ... (TODO provide a brief overview or link to visualization documentation). + +5. **How do I use the new machine learning features in POSYDON?** + - Answer: To leverage the ML features, ensure you've installed the necessary dependencies. Then, you can ... (TODO brief overview or link to ML features documentation). + +6. **Are there any examples or tutorials available?** + - Answer: Yes, you can check our :ref:`roadmap ` for tutorials related to different POSYDON components, including population synthesis, creating core datasets, and running your own MESA grids with POSYDON. + +7. **Can I run POSYDON on an HPC facility?** + - Answer: Absolutely! Ensure you've installed ``mpi4py`` as outlined in :ref:`our installation guide `. Refer to `our HPC guide <../tutorials-examples/population-synthesis/pop_syn.ipynb>`_ for detailed instructions on running POSYDON in an HPC environment. + +8. **How can I stay updated with the latest features and updates?** + - Answer: You can regularly visit our `official website `_ for news and updates. Also, consider subscribing to our mailing list. + +Additional Resources +~~~~~~~~~~~~~~~~~~~~ + +1. **User Guide**: For detailed instructions on all features of POSYDON, visit our comprehensive :ref:`roadmap `. + +2. **API Reference**: Dive deep into the functionality provided by POSYDON with our :ref:`API Reference `. + +3. **Examples and Tutorials**: Learn by doing! Visit :ref:`our roadmap page ` for hands-on learning. + +Still Have Questions? +~~~~~~~~~~~~~~~~~~~~~ + +If your query remains unanswered, we're here to help! Reach out to our community through the :ref:`support channels ` or consider checking our :ref:`general installation FAQ ` for non-usage related questions. + +Your feedback helps us improve. If you think a common question should be added here, don't hesitate to suggest it! diff --git a/docs/_source/troubleshooting-faqs/installation-issues.rst b/docs/_source/troubleshooting-faqs/installation-issues.rst new file mode 100644 index 0000000000..bae0d1af30 --- /dev/null +++ b/docs/_source/troubleshooting-faqs/installation-issues.rst @@ -0,0 +1,49 @@ +.. _installation-issues: + +Installation Issues +------------------- + +From time to time, users might encounter issues during the installation of POSYDON. This page aims to address common installation problems and offer solutions. If your problem isn't covered here, please `report the issue `_ so we can assist you and possibly update this page for the benefit of others. + +Common Installation Issues +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +1. **Failed Dependencies**: + - **Description**: Sometimes, certain dependencies might fail to install or conflict with pre-existing ones. + - **Solution**: Try installing the failed dependencies separately using ``pip`` or ``conda`` before installing POSYDON. If using ``conda``, consider creating a fresh environment specifically for POSYDON. + +2. **Insufficient Storage Space**: + - **Description**: POSYDON requires around 40GB of free storage space for the lite MESA simulation library and related files. + - **Solution**: Ensure you have enough space on your installation drive. Delete unnecessary files or consider using a larger storage solution. + +3. **Proxy or Network Issues**: + - **Description**: Installation might fail if you're behind a strict network proxy or firewall. + - **Solution**: If you're using ``conda``, you can set proxy settings using the ``--proxy`` flag. For pip, you can use the ``--proxy`` flag as well. + +4. **Failed to Set Environment Variables**: + - **Description**: After installation, you might forget to set the ``PATH_TO_POSYDON`` and ``PATH_TO_POSYDON_DATA`` environment variables. + - **Solution**: Refer back to the [installation guide](link-to-installation-guide) to ensure all post-installation steps are followed. + +5. **Error with Experimental Visualization Libraries**: + - **Description**: Issues after installing the experimental visualization libraries with ``pip install ".[vis]"``. + - **Solution**: These libraries are experimental. Please ensure you have all required system dependencies. Consider reaching out to our support channels for more guidance or troubleshooting. + +Troubleshooting Tips +~~~~~~~~~~~~~~~~~~~~ + +1. **Check Error Messages**: Always read the error messages during the installation. They often provide hints or exact reasons for the failure. + +2. **Use a Virtual Environment**: Installing POSYDON in a virtual environment (e.g., via ``conda`` or ``venv``) can prevent conflicts with system-wide packages. + +3. **Update Your Package Managers**: Ensure that both ``pip`` and ``conda`` (if you use them) are updated to their latest versions. + +4. **System Compatibility**: Double-check the compatibility of POSYDON with your operating system and Python version. + +5. **Check Online Forums**: Platforms like Stack Overflow often have threads related to common Python package installation issues. + +Still Facing Issues? +~~~~~~~~~~~~~~~~~~~~ + +If you've tried the solutions above and are still encountering problems, please :ref:`contact us ` with as much detail as possible. We're here to help! + +Thank you for your patience, and we appreciate your interest in POSYDON! diff --git a/docs/_source/tutorials-examples/MESA-grids/1_hms_hms.ipynb b/docs/_source/tutorials-examples/MESA-grids/1_hms_hms.ipynb new file mode 100644 index 0000000000..ec68bb2568 --- /dev/null +++ b/docs/_source/tutorials-examples/MESA-grids/1_hms_hms.ipynb @@ -0,0 +1,538 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Run your first MESA HMS-HMS binary simulation with POSYDON" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you haven't done it already, export all the relevan POSYDON and MESA enviroment variables.\n", + "\n", + "Tip: create a file called `.bash_posydon` with the following content:\n", + "```bash\n", + "export PATH_TO_POSYDON=/srv/beegfs/scratch/shares/astro/posydon/simone/documentation/POSYDON/\n", + "export PATH_TO_POSYDON_DATA=/srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/POSYDON_data/230914/\n", + "\n", + "export MESA_DIR=/srv/beegfs/scratch/shares/astro/posydon/software/mesa-r11701-posydon-v2-fiximplicit\n", + "export OMP_NUM_THREADS=4\n", + "export MESASDK_ROOT=/srv/beegfs/scratch/shares/astro/posydon/software/mesasdk-r11701\n", + "source $MESASDK_ROOT/bin/mesasdk_init.sh\n", + "```\n", + "so that every time you open a new terminal you can just run:\n", + "```bash\n", + "source .bash_posydon\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you haven't done it already, please download the `development` branch of the `POSYDON-MESA-INLISTS` submodule. This is the branch that contains the simulation properties of all POSYDON MESA grids." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating the Initialization File for the POSYDON MESA Submission Script" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the submodule, you can now copy in your working directory the API ini file configuration of HMS-HMS binaries: `$PATH_TO_POSYDON/grid_params/POSYDON-MESA-INLISTS/r11701/running_scripts/HMS-HMS_yggdrasil.ini`. \n", + "\n", + "For each grid we support two ini file configured to the Northwestern and UNIGE HPC clusters." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'./HMS-HMS_yggdrasil.ini'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "import shutil\n", + "from posydon.config import PATH_TO_POSYDON\n", + "\n", + "path_to_ini = os.path.join(PATH_TO_POSYDON, \"grid_params/POSYDON-MESA-INLISTS/r11701/running_scripts/HMS-HMS_yggdrasil.ini\")\n", + "shutil.copyfile(path_to_ini, './HMS-HMS_yggdrasil.ini')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Open the file and edit the following parameters:\n", + "- `user=your_username`\n", + "- `account=your_account`\n", + "- `email=your_email`\n", + "- `posydon_github_root=your_path_to_POSYDON`\n", + "\n", + "The `scanrio` parameter of the ini file is where the magic happens. This parameter let's us decide which simulation we want to run. The syntax is the following:\n", + "- the first argument is the name of the submodule `posydon` which points to the POSYDON-MESA-INLISTS submodule (other options are avaialble, e.g. `user` which points to a private submodule)\n", + "- the second argument is the name of the branch and commit of the submodule we want to use connected by a dash `-`. In this case we want to use the `development` branch of the submodule and it's latest commit, so we write `development-c4f90ce2d93595f66751011d2002fc3ab3d090ec`\n", + "- the third argument is the name of the simulation grid we want to run, in this case we want to run a HMS-HMS grid. The name of the grid is `HMS-HMS`.\n", + "\n", + "To summarize, you should have `scenario = ['posydon', 'development-c4f90ce2d93595f66751011d2002fc3ab3d090ec', 'HMS-HMS']`\n", + "\n", + "The `zams_file` points to the ZAMS model used to generate the HMS stars in the binary system. POSYDON v2.0.0 supports 8 different metallicities, here we use the default value that points to the 0.1Zsun POSYDON MESA ZAMS model `$PATH_TO_POSYDON/grid_params/POSYDON-MESA-INLISTS/r11701/ZAMS_models/zams_z1.42m3_y0.2511.data`\n", + "\n", + "The last parameter of this file is `grid` and points to the `csv` file containing the initial coditions of the grid. In this case we want to run a `HMS-HMS` grid consisting of one system, so we keep `grid = grid_test.csv` and generate such file specifying the metallicity, the two star masses and the initial orbital period." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Craeting the Initial Simulation Points CSV File" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: a notebook showing you how all the grid points of v2.0.0 are generated is available in the `POSYDON-MESA-INLISTS` submodule at `$PATH_TO_POSYDON/grid_params/POSYDON-MESA-INLISTS/r11701/running_scripts/parameter_space_v2/create_csv.ipynb`" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import csv\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "with open('./grid_test.csv', 'w', newline='') as file:\n", + " writer = csv.writer(file)\n", + " writer.writerow(['initial_z','Zbase','m1','m2','initial_period_in_days'])\n", + " writer.writerow([0.00142, 0.00142, 30., 21., 10.])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should now have the following files in your working directory:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1_hms_hms.ipynb grid_test.csv\tHMS-HMS_yggdrasil.ini\n" + ] + } + ], + "source": [ + "!ls" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now use the magic of POSYDON to set up the MESA simulation. In your termianal run\n", + "\n", + "```bash\n", + "posydon-setup-grid --grid-type fixed --inifile HMS-HMS_yggdrasil.ini --submission-type slurm\n", + "```\n", + "\n", + "The following files will be created:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1_hms_hms.ipynb grid_test.csv\t\tmk\t\t\t\tstar1\n", + "binary\t\t HMS-HMS_yggdrasil.ini\tslurm_job_array_grid_submit.sh\tstar2\n" + ] + } + ], + "source": [ + "!ls" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running the MESA Model and Exploring the Simulaiton Output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You are now ready to submit the simulation to the cluster with the following command:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Submitted batch job 28453658\n" + ] + } + ], + "source": [ + "!sbatch slurm_job_array_grid_submit.sh" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)\n", + " 28453658_[0] private-a mesa_gri bavera PD 0:00 1 (Priority)\n" + ] + } + ], + "source": [ + "!squeue -u bavera" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After the job is finished, we can check the output files:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1_hms_hms.ipynb\n", + "binary\n", + "grid_test.csv\n", + "HMS-HMS_yggdrasil.ini\n", + "mesa_grid.28453658_0.out\n", + "mk\n", + "slurm_job_array_grid_submit.sh\n", + "star1\n", + "star2\n", + "Zbase_0.0014_m1_30.0000_m2_21.0000_initial_z_1.4200e-03_initial_period_in_days_1.0000e+01_grid_index_0\n" + ] + } + ], + "source": [ + "!ls" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "binary_history_columns.list inlist_grid_points\n", + "binary_history.data\t inlist_grid_star1_binary_controls\n", + "final_star1.mod\t\t inlist_grid_star2_binary_controls\n", + "final_star2.mod\t\t LOGS1\n", + "history_columns.list\t LOGS2\n", + "initial_star1.mod\t out.txt\n", + "initial_star2.mod\t profile_columns.list\n", + "inlist\t\t\t tmp.hdf5\n" + ] + } + ], + "source": [ + "!ls Zbase_0.0014_m1_30.0000_m2_21.0000_initial_z_1.4200e-03_initial_period_in_days_1.0000e+01_grid_index_0" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + " read /srv/beegfs/scratch/shares/astro/posydon/simone/documentation/running_mesa/running_1_hms_hms/binary/inlist_project\n", + " read inlist_grid_points\n", + " version_number 11701\n", + " read inlist_grid_star1_binary_controls\n", + " set_eos_PC_parameters\n", + " mass_fraction_limit_for_PC 1.0000000000000000D-03\n", + " logRho1_PC_limit 2.9990000000000001D+00\n", + " logRho2_PC_limit 2.7999999999999998D+00\n", + " log_Gamma_all_HELM 1.0000000000000000D+00\n", + " log_Gamma_all_PC 1.3010299956000000D+00\n", + " PC_Gamma_start_crystal 1.5000000000000000D+02\n", + " PC_Gamma_full_crystal 1.7500000000000000D+02\n", + " PC_min_Z 9.9900000000000000D-01\n", + " change rates preference to 2\n", + " set_initial_age 0.0000000000000000D+00\n", + " set_initial_model_number 0\n", + " change to \"approx21.net\"\n", + " number of species 21\n", + " new_rotation_flag T\n", + " new_surface_rotation_v 0 0.0000000000000000D+00 0.0000000000000000D+00\n", + " net name approx21.net\n", + " rotation_flag T\n", + " species\n", + " 1 neut \n", + " 2 h1 \n", + " 3 prot \n", + " 4 he3 \n", + " 5 he4 \n", + " 6 c12 \n", + " 7 n14 \n", + " 8 o16 \n", + " 9 ne20 \n", + " 10 mg24 \n", + " 11 si28 \n", + " 12 s32 \n", + " 13 ar36 \n", + " 14 ca40 \n", + " 15 ti44 \n", + " 16 cr48 \n", + " 17 cr56 \n", + " 18 fe52 \n", + " 19 fe54 \n", + " 20 fe56 \n", + " 21 ni56 \n", + "\n", + " kappa_file_prefix gs98\n", + " kappa_lowT_prefix lowT_fa05_gs98\n", + " OMP_NUM_THREADS 4\n", + "\n", + "\n", + "\n", + "\n", + " read inlist_grid_star2_binary_controls\n", + " set_eos_PC_parameters\n", + " mass_fraction_limit_for_PC 1.0000000000000000D-03\n", + " logRho1_PC_limit 2.9990000000000001D+00\n", + " logRho2_PC_limit 2.7999999999999998D+00\n", + " log_Gamma_all_HELM 1.0000000000000000D+00\n", + " log_Gamma_all_PC 1.3010299956000000D+00\n", + " PC_Gamma_start_crystal 1.5000000000000000D+02\n", + " PC_Gamma_full_crystal 1.7500000000000000D+02\n", + " PC_min_Z 9.9900000000000000D-01\n", + " change rates preference to 2\n", + " set_initial_age 0.0000000000000000D+00\n", + " set_initial_model_number 0\n", + " change to \"approx21.net\"\n", + " number of species 21\n", + " new_rotation_flag T\n", + " new_surface_rotation_v 0 0.0000000000000000D+00 0.0000000000000000D+00\n", + " net name approx21.net\n", + " rotation_flag T\n", + " species\n", + " 1 neut \n", + " 2 h1 \n", + " 3 prot \n", + " 4 he3 \n", + " 5 he4 \n", + " 6 c12 \n", + " 7 n14 \n", + " 8 o16 \n", + " 9 ne20 \n", + " 10 mg24 \n", + " 11 si28 \n", + " 12 s32 \n", + " 13 ar36 \n", + " 14 ca40 \n", + " 15 ti44 \n", + " 16 cr48 \n", + " 17 cr56 \n", + " 18 fe52 \n", + " 19 fe54 \n", + " 20 fe56 \n", + " 21 ni56 \n", + "\n", + " kappa_file_prefix gs98\n", + " kappa_lowT_prefix lowT_fa05_gs98\n", + " OMP_NUM_THREADS 4\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " m2 2.1000000000000000D+01\n", + " m1 3.0000000000000000D+01\n", + " initial_period_in_days 1.0000000000000000D+01\n", + " initial_separation_in_Rsun 7.2417496333000003D+01\n", + " jdot_multiplier 1.0000000000000000D+00\n", + " fr 1.0000000000000000D-02\n", + "\n", + "\n", + "\n", + " The binary terminal output contains the following information\n", + "\n", + " 'step' is the number of steps since the start of the run,\n", + " 'lg_dt' is log10 timestep in years,\n", + " 'age_yr' is the simulated years since the start run,\n", + " 'M1+M2' is the total mass of the system (Msun),\n", + " 'M1' is the mass of the primary (Msun)\n", + " 'M2' is the mass of the secondary (Msun)\n", + " 'separ' is the semi-major axis of the orbit (Rsun),\n", + " 'R1' is the radius of the primary (Rsun)\n", + " 'R2' is the radius of the secondary (Rsun)\n", + " 'Porb' is the orbital period (days),\n", + " 'P1' is the rotation period of star 1 (days, zero if not modeling rotation),\n", + " 'P2' is the rotation period of star 2 (days, zero if not modeling rotation),\n", + " 'e' orbital eccentricity,\n", + " 'dot_e' time derivative of e (1/yr),\n", + " 'Eorb' orbital energy G*M1*M2/2*separation (ergs),\n", + " 'M2/M1' mass ratio,\n", + " 'vorb1' orbital velocity of star 1 (km/s),\n", + " 'vorb2' orbital velocity of star 2 (km/s),\n", + " 'pm_i' index of star evolved as point mass, zero if both stars are modeled,\n", + " 'RL1' Roche lobe radius of star 1 (Rsun),\n", + " 'Rl2' Roche lobe radius of star 2 (Rsun),\n", + " 'donor_i' index of star taken as donor,\n", + " 'RL_gap1' (R1-Rl1)/Rl1,\n", + " 'RL_gap2' (R2-Rl2)/Rl2,\n", + " 'dot_Mmt', mass transfer rate (Msun/yr),\n", + " 'dot_M1', time derivative for the mass of star 1 (Msun/yr),\n", + " 'dot_M2', time derivative for the mass of star 2 (Msun/yr),\n", + " 'eff', mass transfer efficiency, computed as -dot_M2/dot_M1 (zero if dot_M1=0),\n", + " 'dot_Medd', Eddington accretion rate (Msun/yr),\n", + " 'L_acc', accretion luminosity when accreting to a point mass (ergs/s),\n", + " 'Jorb', orbital angular momentum (g*cm^2/s)\n", + " 'spin1', spin angular momentum of star 1 (g*cm^2/s),\n", + " 'spin2', spin angular momentum of star 2 (g*cm^2/s),\n", + " 'dot_J', time derivative of Jorb (g*cm^2/s^2),\n", + " 'dot_Jgr', time derivative of Jorb due to gravitational waves (g*cm^2/s^2),\n", + " 'dot_Jml', time derivative of Jorb due to mass loss (g*cm^2/s^2),\n", + " 'dot_Jmb', time derivative of Jorb due to magnetic braking (g*cm^2/s^2),\n", + " 'dot_Jls', time derivative of Jorb due to spin-orbit coupling (g*cm^2/s^2),\n", + " 'rlo_iters', number of iterations for implicit calculation of mass transfer,\n", + "\n", + " All this and more can be saved in binary_history.data during the run.\n", + " num_steps_to_relax_rotation 50\n", + " relax to omega: wanted-current, current, wanted 1 7.2722052166430393D-06 0.0000000000000000D+00 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 2 7.1239371671217664D-06 1.4826804952127298D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 3 6.9775031537422630D-06 2.9470206290077610D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 4 6.8325228134160023D-06 4.3968240322703724D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 5 6.6880335934326623D-06 5.8417162321037739D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 6 6.5429720944820478D-06 7.2923312216099155D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 7 6.3960414376057951D-06 8.7616377903724423D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 8 6.2481185101766360D-06 1.0240867064664032D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 9 6.0988144371784416D-06 1.1733907794645978D-06 7.2722052166430393D-06\n", + " 10 7.649681 4.541E+04 5.083637 5.083787 30.000000 30.000000 0.746765 0.000508 0.251100 0.747141 -6.159624 2598 0\n", + " 3.021836 0.768039 0.749893 -23.011309 3.935137 -99.000000 0.000000 0.251795 0.000441 0.001420 0.251427 0.009963 3 0\n", + " 2.3543E+03 16.667399 5.081908 1.622938 4.361599 -8.707435 0.000000 0.000004 0.000133 0.000071 1.432E-03 0.000E+00 max increase\n", + "\n", + " relax to omega: wanted-current, current, wanted 10 5.9494923842667907D-06 1.3227128323762491D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 11 5.8047902383525235D-06 1.4674149782905158D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 12 5.6656472219130140D-06 1.6065579947300258D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 13 5.5253850379006039D-06 1.7468201787424357D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 14 5.3813998139082310D-06 1.8908054027348084D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 15 5.2360332812183832D-06 2.0361719354246558D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 16 5.0905569484589031D-06 2.1816482681841367D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 17 4.9451036922867509D-06 2.3271015243562889D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 18 4.7996593421142631D-06 2.4725458745287766D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 19 4.6542152662843934D-06 2.6179899503586464D-06 7.2722052166430393D-06\n", + " 20 7.648797 4.525E+04 5.069089 5.069239 30.000000 30.000000 0.746765 0.000508 0.251100 0.747141 -6.164975 2613 0\n", + " 6.032136 0.764405 0.746596 -23.057888 3.920548 -99.000000 0.000000 0.251795 0.000441 0.001420 0.251427 0.009955 2 0\n", + " 2.1539E+06 16.663052 5.069239 1.607921 4.359365 -8.704639 0.000000 0.000004 0.000133 0.000071 1.432E-03 0.000E+00 max increase\n", + "\n", + " relax to omega: wanted-current, current, wanted 20 4.5087711728839026D-06 2.7634340437591372D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 21 4.3633267819036798D-06 2.9088784347393599D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 22 4.2178823225008446D-06 3.0543228941421948D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 23 4.0724377789053992D-06 3.1997674377376405D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 24 3.9269931252400189D-06 3.3452120914030209D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 25 3.7815483288686108D-06 3.4906568877744285D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 26 3.6361034654341649D-06 3.6361017512088744D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 27 3.4906587737615971D-06 3.7815464428814422D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 28 3.3452144110082463D-06 3.9269908056347930D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 29 3.1997702456217982D-06 4.0724349710212411D-06 7.2722052166430393D-06\n", + " 30 7.648797 4.525E+04 5.069078 5.069228 30.000000 30.000000 0.746765 0.000508 0.251100 0.747141 -6.164960 2610 0\n", + " 9.042435 0.764409 0.746606 -23.057923 3.920536 -99.000000 0.000000 0.251795 0.000441 0.001420 0.251427 0.009955 2 0\n", + " 2.2053E+09 16.663054 5.069228 1.607910 4.359335 -8.704658 0.000000 0.000004 0.000133 0.000071 1.432E-03 0.000E+00 max increase\n", + "\n", + " relax to omega: wanted-current, current, wanted 30 3.0543261335993711D-06 4.2178790830436683D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 31 2.9088820289801618D-06 4.3633231876628776D-06 7.2722052166430393D-06\n" + ] + } + ], + "source": [ + "!head -n 200 Zbase_0.0014_m1_30.0000_m2_21.0000_initial_z_1.4200e-03_initial_period_in_days_1.0000e+01_grid_index_0/out.txt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congratulation, you now know how to harvast the power of POSYDON to run a grid of simulations on a supercomputer!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "posydon_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/_source/tutorials-examples/MESA-grids/laptop.ipynb b/docs/_source/tutorials-examples/MESA-grids/laptop.ipynb new file mode 100644 index 0000000000..9180a1c851 --- /dev/null +++ b/docs/_source/tutorials-examples/MESA-grids/laptop.ipynb @@ -0,0 +1,359 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Run one binary on your laptop for debugging" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For debugging purposes you might want to run a single binary on your laptop to test a quick modification of the inlists, run_star_extras.f or run_binary_extras.f. Assume we want to run the same binary as in the previous tutorial. To use the POSYDON API to run a single binary, you can use the following command:\n", + "\n", + "```bash\n", + "posydon-setup-grid --grid-type fixed --inifile HMS-HMS_yggdrasil.ini --submission-type shell\n", + "```\n", + "\n", + "Similar to before, in our directorty we have:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "binary\t\t grid_test.csv\t\tlaptop.ipynb star1\n", + "grid_command.sh HMS-HMS_yggdrasil.ini\tmk\t star2\n" + ] + } + ], + "source": [ + "!ls" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now export the `SLURM_ARRAY_TASK_ID` enviroment variable corresponding to the csv index of the `grid_test.csv` we want to run, `0` in this case in the temrinal:\n", + "\n", + "```bash\n", + "export $SLURM_ARRAY_TASK_ID=0\n", + "```\n", + "\n", + "We can now run the MESA binary simulation in the terminal with the following command:\n", + "\n", + "```bash\n", + "chmod +x grid_command.sh\n", + "./grid_command.sh\n", + "``````\n", + "\n", + "The terminal will not display an output, the screen output is saved to an `out.txt` file in the simulation directory." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "binary\n", + "grid_command.sh\n", + "grid_test.csv\n", + "HMS-HMS_yggdrasil.ini\n", + "laptop.ipynb\n", + "mk\n", + "star1\n", + "star2\n", + "Zbase_0.0014_m1_30.0000_m2_21.0000_initial_z_1.4200e-03_initial_period_in_days_1.0000e+01_grid_index_0\n" + ] + } + ], + "source": [ + "!ls" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "binary_history_columns.list inlist_grid_star1_binary_controls\n", + "binary_history.data\t inlist_grid_star2_binary_controls\n", + "history_columns.list\t LOGS1\n", + "initial_star1.mod\t LOGS2\n", + "initial_star2.mod\t out.txt\n", + "inlist\t\t\t profile_columns.list\n", + "inlist_grid_points\t tmp.hdf5\n" + ] + } + ], + "source": [ + "!ls Zbase_0.0014_m1_30.0000_m2_21.0000_initial_z_1.4200e-03_initial_period_in_days_1.0000e+01_grid_index_0" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + " read /srv/beegfs/scratch/shares/astro/posydon/simone/documentation/running_mesa/running_on_laptop/binary/inlist_project\n", + " read inlist_grid_points\n", + " version_number 11701\n", + " read inlist_grid_star1_binary_controls\n", + " set_eos_PC_parameters\n", + " mass_fraction_limit_for_PC 1.0000000000000000D-03\n", + " logRho1_PC_limit 2.9990000000000001D+00\n", + " logRho2_PC_limit 2.7999999999999998D+00\n", + " log_Gamma_all_HELM 1.0000000000000000D+00\n", + " log_Gamma_all_PC 1.3010299956000000D+00\n", + " PC_Gamma_start_crystal 1.5000000000000000D+02\n", + " PC_Gamma_full_crystal 1.7500000000000000D+02\n", + " PC_min_Z 9.9900000000000000D-01\n", + " change rates preference to 2\n", + " set_initial_age 0.0000000000000000D+00\n", + " set_initial_model_number 0\n", + " change to \"approx21.net\"\n", + " number of species 21\n", + " new_rotation_flag T\n", + " new_surface_rotation_v 0 0.0000000000000000D+00 0.0000000000000000D+00\n", + " net name approx21.net\n", + " rotation_flag T\n", + " species\n", + " 1 neut \n", + " 2 h1 \n", + " 3 prot \n", + " 4 he3 \n", + " 5 he4 \n", + " 6 c12 \n", + " 7 n14 \n", + " 8 o16 \n", + " 9 ne20 \n", + " 10 mg24 \n", + " 11 si28 \n", + " 12 s32 \n", + " 13 ar36 \n", + " 14 ca40 \n", + " 15 ti44 \n", + " 16 cr48 \n", + " 17 cr56 \n", + " 18 fe52 \n", + " 19 fe54 \n", + " 20 fe56 \n", + " 21 ni56 \n", + "\n", + " kappa_file_prefix gs98\n", + " kappa_lowT_prefix lowT_fa05_gs98\n", + " OMP_NUM_THREADS 4\n", + "\n", + "\n", + "\n", + "\n", + " read inlist_grid_star2_binary_controls\n", + " set_eos_PC_parameters\n", + " mass_fraction_limit_for_PC 1.0000000000000000D-03\n", + " logRho1_PC_limit 2.9990000000000001D+00\n", + " logRho2_PC_limit 2.7999999999999998D+00\n", + " log_Gamma_all_HELM 1.0000000000000000D+00\n", + " log_Gamma_all_PC 1.3010299956000000D+00\n", + " PC_Gamma_start_crystal 1.5000000000000000D+02\n", + " PC_Gamma_full_crystal 1.7500000000000000D+02\n", + " PC_min_Z 9.9900000000000000D-01\n", + " change rates preference to 2\n", + " set_initial_age 0.0000000000000000D+00\n", + " set_initial_model_number 0\n", + " change to \"approx21.net\"\n", + " number of species 21\n", + " new_rotation_flag T\n", + " new_surface_rotation_v 0 0.0000000000000000D+00 0.0000000000000000D+00\n", + " net name approx21.net\n", + " rotation_flag T\n", + " species\n", + " 1 neut \n", + " 2 h1 \n", + " 3 prot \n", + " 4 he3 \n", + " 5 he4 \n", + " 6 c12 \n", + " 7 n14 \n", + " 8 o16 \n", + " 9 ne20 \n", + " 10 mg24 \n", + " 11 si28 \n", + " 12 s32 \n", + " 13 ar36 \n", + " 14 ca40 \n", + " 15 ti44 \n", + " 16 cr48 \n", + " 17 cr56 \n", + " 18 fe52 \n", + " 19 fe54 \n", + " 20 fe56 \n", + " 21 ni56 \n", + "\n", + " kappa_file_prefix gs98\n", + " kappa_lowT_prefix lowT_fa05_gs98\n", + " OMP_NUM_THREADS 4\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " m2 2.1000000000000000D+01\n", + " m1 3.0000000000000000D+01\n", + " initial_period_in_days 1.0000000000000000D+01\n", + " initial_separation_in_Rsun 7.2417496333000003D+01\n", + " jdot_multiplier 1.0000000000000000D+00\n", + " fr 1.0000000000000000D-02\n", + "\n", + "\n", + "\n", + " The binary terminal output contains the following information\n", + "\n", + " 'step' is the number of steps since the start of the run,\n", + " 'lg_dt' is log10 timestep in years,\n", + " 'age_yr' is the simulated years since the start run,\n", + " 'M1+M2' is the total mass of the system (Msun),\n", + " 'M1' is the mass of the primary (Msun)\n", + " 'M2' is the mass of the secondary (Msun)\n", + " 'separ' is the semi-major axis of the orbit (Rsun),\n", + " 'R1' is the radius of the primary (Rsun)\n", + " 'R2' is the radius of the secondary (Rsun)\n", + " 'Porb' is the orbital period (days),\n", + " 'P1' is the rotation period of star 1 (days, zero if not modeling rotation),\n", + " 'P2' is the rotation period of star 2 (days, zero if not modeling rotation),\n", + " 'e' orbital eccentricity,\n", + " 'dot_e' time derivative of e (1/yr),\n", + " 'Eorb' orbital energy G*M1*M2/2*separation (ergs),\n", + " 'M2/M1' mass ratio,\n", + " 'vorb1' orbital velocity of star 1 (km/s),\n", + " 'vorb2' orbital velocity of star 2 (km/s),\n", + " 'pm_i' index of star evolved as point mass, zero if both stars are modeled,\n", + " 'RL1' Roche lobe radius of star 1 (Rsun),\n", + " 'Rl2' Roche lobe radius of star 2 (Rsun),\n", + " 'donor_i' index of star taken as donor,\n", + " 'RL_gap1' (R1-Rl1)/Rl1,\n", + " 'RL_gap2' (R2-Rl2)/Rl2,\n", + " 'dot_Mmt', mass transfer rate (Msun/yr),\n", + " 'dot_M1', time derivative for the mass of star 1 (Msun/yr),\n", + " 'dot_M2', time derivative for the mass of star 2 (Msun/yr),\n", + " 'eff', mass transfer efficiency, computed as -dot_M2/dot_M1 (zero if dot_M1=0),\n", + " 'dot_Medd', Eddington accretion rate (Msun/yr),\n", + " 'L_acc', accretion luminosity when accreting to a point mass (ergs/s),\n", + " 'Jorb', orbital angular momentum (g*cm^2/s)\n", + " 'spin1', spin angular momentum of star 1 (g*cm^2/s),\n", + " 'spin2', spin angular momentum of star 2 (g*cm^2/s),\n", + " 'dot_J', time derivative of Jorb (g*cm^2/s^2),\n", + " 'dot_Jgr', time derivative of Jorb due to gravitational waves (g*cm^2/s^2),\n", + " 'dot_Jml', time derivative of Jorb due to mass loss (g*cm^2/s^2),\n", + " 'dot_Jmb', time derivative of Jorb due to magnetic braking (g*cm^2/s^2),\n", + " 'dot_Jls', time derivative of Jorb due to spin-orbit coupling (g*cm^2/s^2),\n", + " 'rlo_iters', number of iterations for implicit calculation of mass transfer,\n", + "\n", + " All this and more can be saved in binary_history.data during the run.\n", + " num_steps_to_relax_rotation 50\n", + " relax to omega: wanted-current, current, wanted 1 7.2722052166430393D-06 0.0000000000000000D+00 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 2 7.1239371671217664D-06 1.4826804952127298D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 3 6.9775031537422630D-06 2.9470206290077610D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 4 6.8325228134160023D-06 4.3968240322703724D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 5 6.6880335934326623D-06 5.8417162321037739D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 6 6.5429720944820478D-06 7.2923312216099155D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 7 6.3960414376057951D-06 8.7616377903724423D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 8 6.2481185101766360D-06 1.0240867064664032D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 9 6.0988144371784416D-06 1.1733907794645978D-06 7.2722052166430393D-06\n", + " 10 7.649681 4.541E+04 5.083637 5.083787 30.000000 30.000000 0.746765 0.000508 0.251100 0.747141 -6.159624 2598 0\n", + " 3.021836 0.768039 0.749893 -23.011309 3.935137 -99.000000 0.000000 0.251795 0.000441 0.001420 0.251427 0.009963 3 0\n", + " 2.3543E+03 16.667399 5.081908 1.622938 4.361599 -8.707435 0.000000 0.000004 0.000133 0.000071 1.432E-03 0.000E+00 max increase\n", + "\n", + " relax to omega: wanted-current, current, wanted 10 5.9494923842667907D-06 1.3227128323762491D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 11 5.8047902383525235D-06 1.4674149782905158D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 12 5.6656472219130140D-06 1.6065579947300258D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 13 5.5253850379006039D-06 1.7468201787424357D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 14 5.3813998139082310D-06 1.8908054027348084D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 15 5.2360332812183832D-06 2.0361719354246558D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 16 5.0905569484589031D-06 2.1816482681841367D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 17 4.9451036922867509D-06 2.3271015243562889D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 18 4.7996593421142631D-06 2.4725458745287766D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 19 4.6542152662843934D-06 2.6179899503586464D-06 7.2722052166430393D-06\n", + " 20 7.648797 4.525E+04 5.069089 5.069239 30.000000 30.000000 0.746765 0.000508 0.251100 0.747141 -6.164975 2613 0\n", + " 6.032136 0.764405 0.746596 -23.057888 3.920548 -99.000000 0.000000 0.251795 0.000441 0.001420 0.251427 0.009955 2 0\n", + " 2.1539E+06 16.663052 5.069239 1.607921 4.359365 -8.704639 0.000000 0.000004 0.000133 0.000071 1.432E-03 0.000E+00 max increase\n", + "\n", + " relax to omega: wanted-current, current, wanted 20 4.5087711728839026D-06 2.7634340437591372D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 21 4.3633267819036798D-06 2.9088784347393599D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 22 4.2178823225008446D-06 3.0543228941421948D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 23 4.0724377789053992D-06 3.1997674377376405D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 24 3.9269931252400189D-06 3.3452120914030209D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 25 3.7815483288686108D-06 3.4906568877744285D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 26 3.6361034654341649D-06 3.6361017512088744D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 27 3.4906587737615971D-06 3.7815464428814422D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 28 3.3452144110082463D-06 3.9269908056347930D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 29 3.1997702456217982D-06 4.0724349710212411D-06 7.2722052166430393D-06\n", + " 30 7.648797 4.525E+04 5.069078 5.069228 30.000000 30.000000 0.746765 0.000508 0.251100 0.747141 -6.164960 2610 0\n", + " 9.042435 0.764409 0.746606 -23.057923 3.920536 -99.000000 0.000000 0.251795 0.000441 0.001420 0.251427 0.009955 2 0\n", + " 2.2053E+09 16.663054 5.069228 1.607910 4.359335 -8.704658 0.000000 0.000004 0.000133 0.000071 1.432E-03 0.000E+00 max increase\n", + "\n", + " relax to omega: wanted-current, current, wanted 30 3.0543261335993711D-06 4.2178790830436683D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 31 2.9088820289801618D-06 4.3633231876628776D-06 7.2722052166430393D-06\n" + ] + } + ], + "source": [ + "!head -n 200 Zbase_0.0014_m1_30.0000_m2_21.0000_initial_z_1.4200e-03_initial_period_in_days_1.0000e+01_grid_index_0/out.txt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congratulation, you now know how to harvast the power of POSYDON to run a grid of simulations on a supercomputer!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "posydon_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/_source/tutorials-examples/MESA-grids/running-grids.rst b/docs/_source/tutorials-examples/MESA-grids/running-grids.rst new file mode 100644 index 0000000000..a7d08ff68b --- /dev/null +++ b/docs/_source/tutorials-examples/MESA-grids/running-grids.rst @@ -0,0 +1,106 @@ +.. _MESA-grids: + +Architecting MESA Simulation Grids with POSYDON +=============================================== + +Boost your MESA simulation grid prowess by diving into the intricacies of POSYDON's grid creation techniques and the dedicated MESA configuration repository. Harness the power of our API to craft precise and customized grids. + +Getting Started Tutorials +------------------------- + +The POSYDON MESA simulation parameters are stored in `POSYDON-MESA-INLISTS `_ GitHub submodule. To obtain the submodule, clone the POSYDON repository with the following command in your terminal: + +.. code-block:: bash + + cd $PATH_TO_POSYDON + git submodule init + git submodule update grid_params/POSYDON-MESA-INLISTS/ + +Similarly to the POSYDON code repository, there is a `main` branch that points to the latest stable version of the POSYDON-MESA-INLISTS repository associated to a POSYDON code release and a `development` branch that contains the latest stable version of our fiducial MESA configurations, run the following command to get the lates development version of the sumodule: + +.. code-block:: bash + + cd grid_params/POSYDON-MESA-INLISTS/ + git checkout development + +To follow the next step of the tutorial, you will need to have MESA installed on your machine. If you do not have MESA installed, please follow the instructions on the `MESA website `_. + +.. warning:: + + The POSYDON v2.0.0 code is compatible with MESA r11701. Support might not be available for the latest MacOS version. + + +I. Running your first MESA simulation using POSYDON +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Immerse yourself in our Jupyter Notebook which guides you through our dedicated MESA submission API, showcasing how to simulate a HMS-HMS binary systems. + +The first version of this tutorial showcases how to run the simulation using SLURM on HPC. + +.. toctree:: + + 1_hms_hms + +In case you want to run the simulation locally, you can follow the second version of this tutorial. + +.. toctree:: + + laptop + +To gather more information about the MESA simulation submission API ini file, please refer to the :ref:`MESA Grids API ` section of the documentation. + +II. Running your first MESA grid simulation using POSYDON +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The following tutorial will show you how to run a grid of MESA simulations using POSYDON. The grid will consist of 100 simulations of HMS-HMS binary systems at 0.1Zsun metallicity in the mass ratio slice 0.7 each with different primary masses and orbital periods. The simulations will be run on a HPC using SLURM. + +.. toctree:: + + running_a_grid + +Now that you have run your first grid, you can process, visualize and explore the results using the POSYDON post-processing pipeline API. TODO: link + +III. Running single stars with POSYDON +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This tutorials shows how to run a grid of single star simulations using POSYDON. + +.. toctree:: + + running_single_hms + +We show how to export EEPs and make a PSyGrid object in this tutorial TODO: link the advanced tutorial of step 2. + +Advanced Tutorials +------------------ + +Creating and Customizing MESA Grids layer by layer +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Step-by-step guide on how to leverage our MESA simulation submission API for creating and customizing MESA grids to suit your needs. + +Dive deeper into advanced techniques for grid creation, by layering MESA and POSYDON default inlist parameters and customizing your inlists variables on top to your needs. + +.. toctree:: + + notebook: Creating and Customizing MESA Grids layer by layer + +Running a dynamically sampled grid of MESA simulations +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. warning:: + + This feature is experimental. Please contact us if you encounter any issues. + +Provided a sparsed, rectilinearly sampled MESA grid, POSYDON allows to dynamically sample the parameter space to henence the coverage of the paramter space to imporve classification and interpolation accuracy. + +.. toctree:: + + dynamic + + +Support & Feedback +------------------ + +Encountered obstacles or have questions? Ensure you consult our [FAQ](link-to-FAQ) or drop by the [contact-information.rst](contact-information.rst) page. + diff --git a/docs/_source/tutorials-examples/MESA-grids/running_a_grid.ipynb b/docs/_source/tutorials-examples/MESA-grids/running_a_grid.ipynb new file mode 100644 index 0000000000..56d66abd94 --- /dev/null +++ b/docs/_source/tutorials-examples/MESA-grids/running_a_grid.ipynb @@ -0,0 +1,359 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Running a real POSYDON MESA HMS-HMS gird" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is now time to become a POSYDON MESA architect. In this notebook, we will run a real HMS-HMS grid. We will run a downsampled version (100 models) of the HMS-HMS, 0.1Zsun, q=0.7 grid slice, using the same ini file as in the previous notebook." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating the Initialization File to Submit the MESA Grid" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's copy the code use in `$PATH_TO_POSYDON/grid_params/POSYDON-MESA-INLISTS/r11701/running_scripts/parameter_space_v2/create_csv.ipynb` to create the csv file and edit it to run a downsampled version of the grid." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total resolution Z_n * m1_n * q_n * p_n= 100\n" + ] + } + ], + "source": [ + "import os\n", + "import csv\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "def log_range(x_min,x_max,x_n):\n", + " return 10**np.linspace(np.log10(x_min),np.log10(x_max), x_n)\n", + "def lin_range(x_min,x_max,x_n):\n", + " return np.linspace(x_min,x_max, x_n)\n", + "\n", + "# digit rounding pick 10 to be sure we resolve 10^-4Zsun\n", + "NDIG = 10\n", + "\n", + "Zsun = 0.0142\n", + "m1_min = 5.5\n", + "m1_max = 300\n", + "m1_n = 10\n", + "m1 = log_range(m1_min,m1_max,m1_n)\n", + "q_n = 1\n", + "q = [0.7]\n", + "p_min = 10**(-1.)\n", + "p_max = 6105\n", + "p_n = 10\n", + "p = log_range(p_min,p_max,p_n)\n", + "Z_n = 1\n", + "met = [0.1*Zsun]\n", + "print('total resolution Z_n * m1_n * q_n * p_n=', Z_n * m1_n * q_n * p_n)\n", + "\n", + "for Z in met:\n", + " # save entire grid in a single file\n", + " with open('./grid_test.csv', 'w', newline='') as file:\n", + " writer = csv.writer(file)\n", + " writer.writerow(['initial_z','Zbase','m1','m2','initial_period_in_days'])\n", + " for i in range(m1_n):\n", + " for j in range(q_n):\n", + " for k in range(p_n):\n", + " if m1[i]*q[j] >= 0.5:\n", + " writer.writerow([round(Z,NDIG),round(Z,NDIG),round(m1[i],NDIG),round(m1[i]*q[j],NDIG),round(p[k],NDIG)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run the MESA Grid with the POSYDON Submission Script" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now ready to run the simulation, with the following commands:\n", + "\n", + "```bash\n", + "posydon-setup-grid --grid-type fixed --inifile HMS-HMS_yggdrasil.ini --submission-type slurm\n", + "sbatch slurm_job_array_grid_submit.sh\n", + "```\n", + "\n", + "Sit back relax, and wait for the grid to finish." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "binary\n", + "grid.csv\n", + "HMS-HMS_yggdrasil.ini\n", + "mesa_grid.28454093_0.out\n", + "mesa_grid.28454093_10.out\n", + "mesa_grid.28454093_11.out\n", + "mesa_grid.28454093_12.out\n", + 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You can now process the data using POSYDON's built-in tools, or you can use your own tools to process the data." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "posydon_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/_source/tutorials-examples/MESA-grids/running_single_hms.ipynb b/docs/_source/tutorials-examples/MESA-grids/running_single_hms.ipynb new file mode 100644 index 0000000000..989df8d55a --- /dev/null +++ b/docs/_source/tutorials-examples/MESA-grids/running_single_hms.ipynb @@ -0,0 +1,175 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Running Single Stars with POSYDON " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Single stars, hydrogen or helium main sequence stars (HMS; HeMS), can be run with POSYDON specifying the `single_star_grid = True` option in the `ini` file. This function takes care of setting up the MESA simulation to run a single star instead of a binary star system." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating the Initialization File" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can get the template to run a 0.1Zsun HMS star by running the following code cell. Do not forget to fill up the header of the `ini` file with the appropriate information corresponding to your HPC account. In this example we will run the following MESA inlist branch and commit\n", + "```ini\n", + "scenario = ['posydon', 'development-c4f90ce2d93595f66751011d2002fc3ab3d090ec', 'HMS-HMS']\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'./single_HMS_yggdrasil.ini'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "import shutil\n", + "from posydon.config import PATH_TO_POSYDON\n", + "\n", + "path_to_ini = os.path.join(PATH_TO_POSYDON, \"grid_params/POSYDON-MESA-INLISTS/r11701/running_scripts/single_HMS_yggdrasil.ini\")\n", + "shutil.copyfile(path_to_ini, './single_HMS_yggdrasil.ini')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating the Initial Simulation Points CSV File " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now need to generate the `grid.csv` containing 10 initial values for 10 stars, in this case the initial values are just the mass and metallicity paramters. You can do this by running the following code cell. The code that follows was copied from `$PATH_TO_POSYDON/grid_params/POSYDON-MESA-INLISTS/r11701/running_scripts/parameter_space_v2/create_csv.ipynb`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import csv\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "def log_range(x_min,x_max,x_n):\n", + " return 10**np.linspace(np.log10(x_min),np.log10(x_max), x_n)\n", + "\n", + "# digit rounding pick 10 to be sure we resolve 10^-4Zsun\n", + "NDIG = 10\n", + "\n", + "Zsun = 0.0142\n", + "m1_min = 5.\n", + "m1_max = 100.\n", + "m1_n = 10\n", + "m1 = log_range(m1_min,m1_max,m1_n)\n", + "met = [0.1*Zsun]\n", + "\n", + "for Z in met:\n", + " with open('./grid.csv', 'w', newline='') as file:\n", + " writer = csv.writer(file)\n", + " writer.writerow(['initial_z','Zbase','initial_mass'])\n", + " for i in range(m1_n):\n", + " writer.writerow([round(Z,NDIG),round(Z,NDIG),round(m1[i],NDIG)])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "initial_z,Zbase,initial_mass\n", + "0.00142,0.00142,5.0\n", + "0.00142,0.00142,6.9747539698\n", + "0.00142,0.00142,9.7294385879\n", + "0.00142,0.00142,13.572088083\n", + "0.00142,0.00142,18.9323950471\n", + "0.00142,0.00142,26.4097595025\n", + "0.00142,0.00142,36.8403149864\n", + "0.00142,0.00142,51.3904266401\n", + "0.00142,0.00142,71.6871164437\n", + "0.00142,0.00142,100.0\n" + ] + } + ], + "source": [ + "!head -n 12 ./grid.csv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Runing the MESA Grid with the POSYDON Submission Script" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now ready to run the simulation, with the following commands:\n", + "\n", + "```bash\n", + "posydon-setup-grid --grid-type fixed --inifile single_HMS_yggdrasil.ini --submission-type slurm\n", + "sbatch slurm_job_array_grid_submit.sh\n", + "```\n", + "\n", + "Sit back relax, and wait for the grid to finish. Check out the advanced tutorial on how to generate the PSyGrid object for single stars." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "posydon_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/_source/tutorials-examples/generating-datasets/generating-datasets.rst b/docs/_source/tutorials-examples/generating-datasets/generating-datasets.rst new file mode 100644 index 0000000000..865f114485 --- /dev/null +++ b/docs/_source/tutorials-examples/generating-datasets/generating-datasets.rst @@ -0,0 +1,85 @@ +.. _generating-datasets: + +Crafting the Core Datasets for POSYDON +====================================== + +Discover the intricate details behind POSYDON's dataset generation, including diving deep into the vast world of MESA datasets, learning the art of downsampling, and mastering the controls through our Processing Pipeline API. + +Getting Started Tutorials +------------------------- + +I. Generating a POSYDON PSyGrid Dataset +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Dive into our Jupyter Notebook that will show you how to create a MESA PSyGrid dataset from scratch. + +.. toctree:: + + just_step_1 + +To learn more about the PSyGrid object or the Processing Pipeline API ini file, check out the [API Documentation](api.rst). + + +II. The Full POSYDON Processing Pipeline Experince +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +From A to Z, this tutorial shows you how to process, concatenate, downsample, plot the grids and check failure rate, train the interpolation object and export the POSYDON dataset for population synthesis. + +.. toctree:: + + run_full_piepeline + +Congratulations! You now master the POSYDON Processing Pipeline. To learn more about the PSyGrid object or the Processing Pipeline API ini file, check out the [API Documentation](api.rst) and the indepth components overview. + + +III. 1D Plotting Functionalities for POSYDON PSyGrids +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Unlock the power of 1D plotting functionalities for POSYDON PSyGrids. This tutorial shows you how to easily visualize your single and binary star tracks leveraging the `plot1D` method of the PSyGrid object. + +.. toctree:: + + plot_1D + + +IV. 2D Plotting Functionalities for POSYDON PSyGrids +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Unlock the power of 2D plotting functionalities for POSYDON PSyGrids. This tutorial shows you how to easily visualize your single and binary star tracks leveraging the `plot2D` method of the PSyGrid object. + +.. toctree:: + + plot_2D + +Advanced Tutorials +------------------ + +Export MESA Simulation Points to Rerun Using the Processing Pipeline +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Learn how to export MESA simulation points to rerun using the Processing Pipeline. + +.. toctree:: + + step_rerun + + +Export Single Star PSyGrid Datasets +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Learn how to export single star PSyGrid datasets. This is an advaced tuotorial because it requires knowledge about the EEP code. + +.. note:: + + POSYDON v2.0.0 does not embed a python interface to compute EEPs but relyes on the Fortran code of Aaron Dotter (2016). Feature POSYDON code releases will include a python interface to compute EEPs and embed the export of single stellar grid into the post-processig pipeline. + +.. toctree:: + + processing_single_hms + + +Support & Feedback +------------------ + +Facing challenges or have queries? Make sure to check our [FAQ](link-to-FAQ) or visit the [contact-information.rst](contact-information.rst) page. + diff --git a/docs/_source/tutorials-examples/generating-datasets/just_step_1.ipynb b/docs/_source/tutorials-examples/generating-datasets/just_step_1.ipynb new file mode 100644 index 0000000000..047f9528b7 --- /dev/null +++ b/docs/_source/tutorials-examples/generating-datasets/just_step_1.ipynb @@ -0,0 +1,522 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Processing MESA grids with the POSYDON pipeline API" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you haven't done it already, export the environemnt variables." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "env: PATH_TO_POSYDON=/srv/beegfs/scratch/shares/astro/posydon/simone/documentation/POSYDON/\n", + "env: PATH_TO_POSYDON_DATA=/srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/POSYDON_data/230914/\n" + ] + } + ], + "source": [ + "%env PATH_TO_POSYDON=/srv/beegfs/scratch/shares/astro/posydon/simone/documentation/POSYDON/\n", + "%env PATH_TO_POSYDON_DATA=/srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/POSYDON_data/230914/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Configurating the Initialisation File for the Pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's copy the pipeline ini file template for the UNIGE HPC cluster." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'./pipeline.ini'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "import shutil\n", + "from posydon.config import PATH_TO_POSYDON\n", + "\n", + "path_to_ini = os.path.join(PATH_TO_POSYDON, 'grid_params/pipeline_yggdrasil.ini')\n", + "shutil.copyfile(path_to_ini, './pipeline.ini')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now edit the pipeline ini file to point to the MESA grid directory `test_grid/` containing a set of 100 MESA models of the HMS-HMS grid at 0.1Zsun for the mass ratio q=0.7, see the running MESa grid getting started tutorial.\n", + "\n", + "In order for the pipeline to be able to process the data we need to follow the following directory naming convention:\n", + "`/HMS-HMS/1e-01_Zsun/test_grid/`.\n", + "\n", + "Here we just want to run the first step of the pipeline, in order to create the h5 files containing the MESA models. Hence, after setting up the HPC account options and `PATH_TO_GRIDS` the value" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['HMS-HMS']" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from posydon.config import PATH_TO_POSYDON_DATA\n", + "\n", + "PATH_TO_GRIDS = os.path.join(PATH_TO_POSYDON_DATA, 'POSYDON_data/tutorials/processing-pipeline')\n", + "os.listdir(PATH_TO_GRIDS)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We set:\n", + "```ini\n", + " CREATE_GRID_SLICES = True\n", + " COMBINE_GRID_SLICES = False\n", + " CALCULATE_EXTRA_VALUES = False\n", + " TRAIN_INTERPOLATORS = False\n", + " EXPORT_DATASET = False\n", + " RERUN = False\n", + "```\n", + "\n", + "And edit the `[step_1]` section of the file to\n", + "```ini\n", + " GRID_TYPES = ['HMS-HMS']\n", + " METALLICITIES = [['1e-01_Zsun']]\n", + " GRID_SLICES = [['test_grid']]\n", + " COMPRESSIONS = [['LITE', 'ORIGINAL']]\n", + " DROP_MISSING_FILES = True\n", + " STOP_BEFORE_CARBON_DEPLETION = 1\n", + " CREATE_PLOTS = []\n", + " DO_CHECKS = []\n", + "```\n", + "\n", + "Also remember to set\n", + "```ini\n", + " CREATE_PLOTS = []\n", + " DO_CHECKS = []\n", + "```\n", + "for all other steps, otherwise the pipeline will try to create plots and do checks on the data, which is not possible since we are not running the full pipeline." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting-up and Running the Post Processing Pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now rady to setup the grid pipeline with the `setup-pipeline` command, as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/bavera/.conda/envs/posydon_env/bin/setup-pipeline:4: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html\n", + " __import__('pkg_resources').require('posydon==1.0.0+194.g3953a14')\n", + "\n", + "+++++++++++++++++++ACCOUNT+++++++++++++++++++ \n", + "{ 'ACCOUNT': 'meynet',\n", + " 'EMAIL': 'simone.bavera@unige.ch',\n", + " 'MAILTYPE': 'ALL',\n", + " 'PARTITION': 'private-astro-cpu',\n", + " 'WALLTIME': '24:00:00'}\n", + "\n", + "++++++++++++++++++++SETUP++++++++++++++++++++\n", + "{ 'CALCULATE_EXTRA_VALUES': False,\n", + " 'COMBINE_GRID_SLICES': False,\n", + " 'CREATE_GRID_SLICES': True,\n", + " 'EXPORT_DATASET': False,\n", + " 'PATH': '.',\n", + " 'PATH_TO_GRIDS': '/srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/POSYDON_data/230914/POSYDON_data/tutorials/processing-pipeline/',\n", + " 'RERUN': False,\n", + " 'TRAIN_INTERPOLATORS': False,\n", + " 'VERBOSE': True,\n", + " 'VERSION': ''}\n", + "\n", + "\n", + "-------------CREATE_GRID_SLICES-------------- step_1 : True \n", + "{ 'COMPRESSIONS': [['LITE', 'ORIGINAL']],\n", + " 'CREATE_PLOTS': [],\n", + " 'DO_CHECKS': [],\n", + " 'DROP_MISSING_FILES': True,\n", + " 'GRID_SLICES': [['test_grid']],\n", + " 'GRID_TYPES': ['HMS-HMS'],\n", + " 'METALLICITIES': [['1e-01_Zsun']],\n", + " 'STOP_BEFORE_CARBON_DEPLETION': 1}\n", + "\n", + "\n", + "-------------COMBINE_GRID_SLICES------------- step_2 :False \n", + "\n", + "\n", + "-----------CALCULATE_EXTRA_VALUES------------ step_3 :False \n", + "\n", + "\n", + "-------------TRAIN_INTERPOLATORS------------- step_4 :False \n", + "\n", + "\n", + "---------------EXPORT_DATASET---------------- step_9 :False \n", + "\n", + "\n", + "--------------------RERUN-------------------- rerun :False \n" + ] + } + ], + "source": [ + "!setup-pipeline pipeline.ini" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "logs\t processing.ipynb\tstep_1.csv step_2_plots.csv\n", + "pipeline.ini run_pipeline.sh\tstep_1.slurm step_2_plots.slurm\n" + ] + } + ], + "source": [ + "!ls" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's sumit the job to the cluster with the `run_pipeline` command:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step_1.slurm submitted as 28467595\n" + ] + } + ], + "source": [ + "!./run_pipeline.sh" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)\n", + " 28467595_1 private-a psygrid1 bavera R 0:12 1 cpu144\n", + " 28467595_0 private-a psygrid1 bavera R 0:12 1 cpu144\n" + ] + } + ], + "source": [ + "!squeue -u bavera -j 28467595" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are not generating a LITE and and ORIGINAL version of the grid. You can inspect the output of the pipeline in the working directory `./logs/`." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing /srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/POSYDON_data/230914/POSYDON_data/tutorials/processing-pipeline/HMS-HMS/1e-01_Zsun/test_grid/Zbase_0.0014_m1_32.5279_m2_22.7695_initial_z_1.4200e-03_initial_period_in_days_4.5574e+01_grid_index_45\n", + "Processing /srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/POSYDON_data/230914/POSYDON_data/tutorials/processing-pipeline/HMS-HMS/1e-01_Zsun/test_grid/Zbase_0.0014_m1_20.8585_m2_14.6009_initial_z_1.4200e-03_initial_period_in_days_1.1574e+00_grid_index_32\n", + "Processing /srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/POSYDON_data/230914/POSYDON_data/tutorials/processing-pipeline/HMS-HMS/1e-01_Zsun/test_grid/Zbase_0.0014_m1_123.3602_m2_86.3521_initial_z_1.4200e-03_initial_period_in_days_3.9376e+00_grid_index_73\n", + "Processing /srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/POSYDON_data/230914/POSYDON_data/tutorials/processing-pipeline/HMS-HMS/1e-01_Zsun/test_grid/Zbase_0.0014_m1_8.5770_m2_6.0039_initial_z_1.4200e-03_initial_period_in_days_1.1574e+00_grid_index_12\n", + "100%|██████████| 100/100 [02:06<00:00, 1.27s/it]\n", + "Storing initial/final values and metadata to HDF5...\n", + "/srv/beegfs/scratch/shares/astro/posydon/simone/documentation/POSYDON/posydon/grids/psygrid.py:654: UserWarning: Ignored MESA run because of missing binary history in: /srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/POSYDON_data/230914/POSYDON_data/tutorials/processing-pipeline/HMS-HMS/1e-01_Zsun/test_grid/Zbase_0.0014_m1_123.3602_m2_86.3521_initial_z_1.4200e-03_initial_period_in_days_1.0000e-01_grid_index_70\n", + "\n", + "/srv/beegfs/scratch/shares/astro/posydon/simone/documentation/POSYDON/posydon/grids/psygrid.py:654: UserWarning: Ignored MESA run because of missing binary history in: /srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/POSYDON_data/230914/POSYDON_data/tutorials/processing-pipeline/HMS-HMS/1e-01_Zsun/test_grid/Zbase_0.0014_m1_123.3602_m2_86.3521_initial_z_1.4200e-03_initial_period_in_days_3.4021e-01_grid_index_71\n", + "\n", + "/srv/beegfs/scratch/shares/astro/posydon/simone/documentation/POSYDON/posydon/grids/psygrid.py:654: UserWarning: Ignored MESA run because of missing binary history in: /srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/POSYDON_data/230914/POSYDON_data/tutorials/processing-pipeline/HMS-HMS/1e-01_Zsun/test_grid/Zbase_0.0014_m1_300.0000_m2_210.0000_initial_z_1.4200e-03_initial_period_in_days_1.0000e-01_grid_index_90\n", + "\n", + "/srv/beegfs/scratch/shares/astro/posydon/simone/documentation/POSYDON/posydon/grids/psygrid.py:654: UserWarning: Ignored MESA run because of missing binary history in: /srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/POSYDON_data/230914/POSYDON_data/tutorials/processing-pipeline/HMS-HMS/1e-01_Zsun/test_grid/Zbase_0.0014_m1_20.8585_m2_14.6009_initial_z_1.4200e-03_initial_period_in_days_1.0000e-01_grid_index_30\n", + "\n", + "Loading HDF5 grid...\n", + "\tLoading initial/final values...\n", + "\tAcquiring paths to MESA directories...\n", + "\tGetting configuration metadata...\n", + "\tEnumerating runs and checking integrity of grid...\n", + "\tDone.\n" + ] + } + ], + "source": [ + "!tail -n 20 ./logs/step_1/grid_slice_0.out" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great, the processing happened successuflly. Let's load the grid." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exploring the PSyGrid Object and Exporting Failed Simulation to Be Re-Run" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "from posydon.grids.psygrid import PSyGrid\n", + "\n", + "path_to_lite_grid = os.path.join(PATH_TO_GRIDS, 'HMS-HMS/1e-01_Zsun/LITE/test_grid.h5')\n", + "grid = PSyGrid(path_to_lite_grid)\n", + "grid.load()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is how to access the grid data, please refer to the PSyGrid documentation for more details." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['initial_MT', 'no_MT', 'not_converged', 'stable_MT', 'unstable_MT'],\n", + " dtype='" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "

    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "PLOT_PROPERTIES = {\n", + " 'show_fig' : True,\n", + " 'close_fig' : True,\n", + " #'path_to_file': './dirname/', \n", + " #'fname': 'filename.png', # specify the file name if you want to safe the figure\n", + "}\n", + "\n", + "grid.plot(42, 'age', 'star_1_mass', history='binary_history', **PLOT_PROPERTIES)\n", + "grid.plot(42, 'log_R', 'log_L', history='history1', **PLOT_PROPERTIES)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot multiple quantities as a function of one quantity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can display more properties as a function of another in a subplot like the following examples." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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oPmNFXlaq1CUtsOrw1+v14pg+HR0dKC8vX3VRRETRrmRrxlz4WyQd5dOfVYd/Y2OjeHLXZrOhrKwMgGenQEQkVyWbPSd9uwYi86Tvqtv8rdarb0yhUCy6nIhIbrw9fk5dHoN9YkbiahZa9ZF/XV2d2Obf2dkJs9ks3m5paVnt6omIolLmGhV0WakwD4/DdNaK+7etk7okH6s+8hcEQfzR6/XibR75E5HcFUdwf/+gtPkvNo5/OCZ2MZlMAIDi4mKYzWbYbDYUFxeHfLtERIHQb8lAe/c5dJ2xSF3KAqs+8rfZbGhubsbAwIDP8nBM7NLU1ISSkhIoFArs2bMHOp0u5NskIgqUt92/d9CGmQgb33/VR/7eQd327duHY8eO4dSpU8GoKyAlJSVi85JGownbdomIApG/dg3UyQmwT87g7QsO3LZJI3VJooCP/Jubm5d8fP/+/ZKM56PRaBj8RBSRlEqFePQfaV0+Aw7/jo4OjI6OLvkcg8Gw6oKWw2azob29He3t7airqxN7GvnjdDrhcDh8foiIQilSB3kLuNmnra0N7e3t0Ol0MBgMKC8vR3FxMbZu3So+p6CgIBQ1+lVbWyse9et0OpSXl6Ovr8/v8xsaGvDYY4+FqToioqvh33XGAkEQfK6HklLAM3kdPHgQtbW16OjogNFoRFdXF0wmEzIyMsSdQXd3N5544olVFXTkyJElA7y8vFz8hmEymcTePTabDRkZGejr6/N74tfpdMLpdIr3HQ4HcnNzOZMXEYXM5LQLt/7T05h1C3ix7n5sykgJ6fbCNo2j0WgUf3p6euByuVazuoCZTCaUlZWJJ3y94W+1WgM+B8BpHIkoHD75vRdx8pwd3/n0DnxyR05ItxW2aRwNBoN4JH7w4MHVri5gOp0OjY2N4n2j0YjKykqe/CWiiFOyRYuT5+zoPmMNefgHKqjTOIbjwi4vjUYDvV6PAwcOQKPRoK+vD21tbWHbPhFRoEq2ZOCHL/VHVI+fgMLfbrfDarX6nNxdzPwLu7w9aULZnFJcXMwreoko4um3ek76vnPRgTHnLNaogj59+rIF1NVTrVajo6MDx48fD2ilx44dQ2trK9vRiYgArE9PwqaMZLgFoPesTepyACyj2aempgY9PT3YtWsX8vPzUVpaCp1OB41GA5vNBrPZjBMnTqC/vx979uxBRUVFKOsmIooqJVsycM46ia4zFtxbmCV1Octr8y8qKkJrayvsdjtaW1tx4sQJ2Gw2aDQa5OfnY8+ePWEZ04eIKNrot2Tg571DEXOx14oantRqNWpqaoJdCxFRzCrZogUA9Jy1weUWEKeU9mKvVY/qCQADAwPYtWsX9Ho9CgoKUFhYiOrqag6fQEQ058YNaVijiseYcxbvXVp6qJxwCMop5/b2drS2ti5Y3tzcjN27dwdjE0REUS1OqUDRZg1eODWMrjNWbN8obYeYoBz5FxUVLbqc7f9ERFcVz03q3j0g/eQuQTnyN5vNMBqNyM/PB+AZamFkZES8T0REV/v7d5+V/qRvUMK/pqYG/f39MBqNYu+fsrIyv98IiIjkaEeuBkoFMGiZxGXHFNalJ0lWS9AuM8vLy2MPICKiJaQlJeDGDel4+4IDXWes+MitGyWrJSht/v709vaGcvVERFFHHyGTuwTlyN9fyLe0tGDHjh3B2AQRUUzQb83Af79yBl2xEP5PPvkk7Hb7gjl8e3p6grF6IqKY4e3x8+Z5O6ZmXEhKiJOkjqCEf319Pbq6ulBWVuaz/Nlnnw3G6omIYsamjGSsT1fhksOJk4M23KnLlKSOoLT5q9XqBcEPYNFlRERyplAo5s3rK13Tz4rD/9ChQ4suf+6559Dc3LzigoiIYp13nB9TNIZ/UVERBgYGMDAwIC47evQompqakJGRwR0AEZEfYo+fs1a43auaRn3FVhz+bW1tMBgMKC8vF78FtLe3o7GxERUVFcjIyAhakUREseSm7HQkJShhm5iBeXhMkhpWHP4lJSU4ffo0Tp06JU6abrFYxKkeFQpphyslIopUCXFK3L5JA0C6/v4rDn+73S7ettlsAACr9eqbMJvNK6+KiCjGecf5kWpS9xWHf15eHrRaLTIzMzE8PIyDBw/CYDDg0KFD6O3thSBI045FRBQNSrZIO8jbivv5V1RUwGAwAIDPxV3PPvssWlpa0NDQsPrqiIhilPdiL/OVcVjGp6FNTQzr9lfVz1+hUKCtrQ3Nzc3irF1lZWUMfiKi69CkJKJg3RoA0rT7rzj8+/v7sXPnTjzzzDN45plnUFJSwoHciIiWQcpB3lbc7HPs2DF0dXX5LKuvr+dAbkREASrZkoEnOwfRfSb8M3ut6oTvtfR6/aqKISKSE+9J35Pn7JiedYd12ysO/8W6cvb396+qGCIiOcnLSkVinBLTs25cGXOGddsrbvYxGAx44IEHUFJSAgAwGo1obGwMWmFERLFOoVBAk5KAy6NOWMenkaNJDtu2VzW2T1NTEwRBgCAIOHLkCHbu3BnM2oiIYp4mJQEAYJ+cCet2VzWef15eHvbv3y/e7+3t5QlfIqJl0KR4+vdbJ6bDut2Aw/96o3RarVa0trais7Nz1UUREcmFJtlz5G+biNAj/8OHD6O6unrJ53BIByKi5cmYO/K3ReqRf2Nj43Vn5vIO90BERIHxtvmH+8g/4BO+gUzJWFRUtKpiiIjk5mqbf4SGPxERBd/V3j7hbfZh+BMRSSgj0pt9iIgo+NK9vX3C3M+f4U9EJKGkhDgAgHPWFdbtRkX4m0wmcRiJ+cxmMw4cOID29nYcOHBAnE6SiChaJMZ5YnhmNrxd5Vd1hW84tLe3Q6fTwWQyLXisqqoK3d3dADw7gpqaGrS1tYW7RCKiFctaowIAXBlzYmrGJX4TCLWID//KyspFl187qqhOp4PRaAxHSUREQbM+XQVNSgJsEzM4fXkMt+Sor/+iIIiKZp/FGI1GaLVan2VarXbRbwheTqcTDofD54eISEoKhQK3b9IAAH7/3pWwbTdqw99f+77F4n9GnIaGBqjVavEnNzc3RNUREQXuw7dsAAD89o0LYdtm1Ia/P0ud9K2vr4fdbhd/BgcHw1cYEZEfH7p5A+KUCrxx3oGzIxNh2aYkbf5HjhxBX1+f38fLy8uvO06QRqNZcJRvsVig0Wj8vkalUkGlUi2rViKiUNOmJuIunRYvnR7Bb9+4gD0fyA/5NiUJ/9ra2lWvw2AwoKmpacFyziNMRNHowVs24qXTI/jNGxfDEv5R1ewzv0lHp9P5PGY2m6HX65c88iciilQfunkDFArg5KANvzg5hFlXaCd0j/jwNxqNqKurA+A5Ydve3i4+1tbWhrq6OrS3t6OpqYl9/Ikoaq1NU+GOrZ4ejH/1vz34+5++EdLtKQQZz8DicDigVqtht9uRnp4udTlEJHM/enkA3/jFm+L9gf0fXfY6As21iD/yJyKSC2+XT69QTurO8CciihDr05N87r98ejhk22L4ExFFEMP2deLtV8wjIdsOw5+IKIJ84d6rPRmLt2SEbDsMfyKiCJK/LhUAoFAAH7stO2TbYfgTEUWQjLkJ3QUBsE2Ebl5fhj8RUQRJiFNCPTe1o2Wc4U9EJBuZazxH/8NjDH8iItnITPWEP4/8iYhkJDPVM/qwZdwZsm0w/ImIIoyWzT5ERPLjPeE7OjUbsm0w/ImIIkxyQhwAYHKG4U9EJBvuucGWlQpFyLbB8CciijAutyf845UMfyIi2ZhxzYV/XOgimuFPRBRhXG7PFI488icikpHZuWafOIY/EZF8sM2fiEiGvEf+bPMnIpKRWZenzZ/NPkREMvKzniEAwGvnbCHbBsOfiCjCTM8d+b91wRGybTD8iYgizBpVPADgnvyskG0jPmRrJiKiFfn1X92L375xEZ+9a0vItsHwJyKKMFsyU/HFD+SHdBts9iEikiGGPxGRDDH8iYhkiOFPRCRDDH8iIhmSdW8fYW62HIcjdBdSEBGFkzfPvPnmj6zDf3R0FACQm5srcSVERME1OjoKtVrt93GFcL3dQwxzu90YGhpCWloaFCGcKzMQDocDubm5GBwcRHp6uqS1hBvfO9+73N47ELr3LwgCRkdHkZ2dDaXSf8u+rI/8lUolNm3aJHUZPtLT02X5HwHge+d7l6dQvP+ljvi9eMKXiEiGGP5ERDLE8I8QKpUK3/jGN6BSqaQuJez43vne5Ujq9y/rE75ERHLFI38iIhli+BMRyRDDn4hIhhj+REQyxPAnIpIhhj8RkQwx/ImIZIjhT0QkQwx/IiIZYvgTEckQw5+ISIYY/kREMsTwJyKSIYY/EZEMMfyJiGSI4U9EJEMMfyIiGWL4ExHJEMOfiEiGGP5ERDLE8CcikiGGPxGRDDH8iYhkiOFPRCRDDH8iIhli+BMRyRDDn4hIhhj+REQyFC91AVJyu90YGhpCWloaFAqF1OUQEa2aIAgYHR1FdnY2lEr/x/eyDv+hoSHk5uZKXQYRUdANDg5i06ZNfh+XdfinpaUB8PyR0tPTJa6GiGj1HA4HcnNzxXzzR9bh723qSU9PZ/gTUUy5XlM2T/gSEckQw5+ISIYY/kREMsTwJyKSIVmf8CUiijQvnLqCz/3gBACgYN0aGB/5QEi2w/AnItlyuQUc/n0f/tg3ApdbgEsQ4F7wG3ALwiKPe17vFoSrj7sFuAXPcoUCiFMqEKdUIF6pgFIx91t5zW+FAurkBOy+T4dtG9LE4AeA05fHQvbeGf5EJEvTs2480tqLX712QepSAADdZ6zIWqPyWXbyGw+EbHsMf6IY5Zx1we0GFApAqVBA6f2t5FAm485ZfPF/uvHCqWEkxCnwtQduxEZNMuIUCsQpvX8vz1G7UqlAnEIBpRJzv68+Ji6fWzZ/uYCr3wZcgoBZl+83BO9P1xkrvt3xHmbdAi46plC4bg3+Z/edWJ+eFNK/AcOfKMZcdkzhL37UiTfOO/w+R9wRzIWX97a3qcK7s1Ao5gJu7rYYgHPPvbqOeTsXBcSA9FnH3HY896/e9r4uTqnw3VHNW8fCbSh8npcQp8AN69NQvDkDmzKSl7zAyTI+jf/zn504OWhDSmIcDn+2BO+/YW0oPoqApKri8e2O9wAAt21S4z//zx3QpiaGfLsMf6IY8zctvUsGPwC4BU87NiAArvDUFS5Za1Qo2qxB8eYMFG3W4LZNaqQkeqJuyDaJz/3gVfRdGYcmJQH/789LUbQ5Q9J6Z91u8fYP/7w0LMEPMPyJYs6Hb9mAl/tGVr0ebWoiMlISkJmq8txOTYQ2NQHr0pKwUZ2E9OQEuAUBgnD1hKj3tnfn4nbPu+39mTuBKgjwnDide74gXD1hKsxb7lmvv3V6fo87Z/HGeTveHHJgeMyJjrcuoeOtSwA832S2bUjD2ZEJjDpnAQAb1Un47y/cgYJ1S49/Ew635KhxR54WRbmaBW3+oaQQBEEI29YijMPhgFqtht1u59g+FJOcsy5YxqcxPDqN4THn3M/V2yPzblvGp+FeRhqkJMYhR5OMbE0ycjKSkaOZ+5m7vS5Nhfi48F5KNDXjwptDdpjO2NAzaIXpjA0XHVMLnvfSvp3I0SSHtbZwCTTXGP4MfyIAniNs68S0z07hyqhnZzEyb8dxwT6J4bHp664vTqnAhvSkBTuGbM3V+8mJcSF9TxPTs7jp/3vaZ9lv/uo+3JQdu//fA801NvsQEQBPWGetUQXU9DA148KQbRLnbZM4b53EkG0S57y37ZO4YJvCrFvwPG6b9LsebWqiuCOY/w1i09xOIiMlYcUTLZ26NIry//sHn2Wv/dMDSE9KWNH6Yg3Dn4iWLSkhDrq1a6Bbu2bRx11uAZdHpzw7BatnBzA0t3Pw7jDGpz1NUpbxabx+3r7oepIT4ny+LXh2CknI0aQgJyMZ6/00LR3rPoe/azsp3s9MTcTL9Tuhig/tN41owvAnoqCLUyqwUZ2MjepklGxZ+LggCHBMzuKcbQJDtimct07M7SCmxG8Qw2NOTM64cPrymN8rXcWmJY1np6BNVeGHL/X7POfDN2/A9/+0GHG8vsEHw5+Iwk6hUECdkgB1iho3Z6sXfc7UjAsX7FMLm5XmmpIu2Ccx41q6aemrOwvwSPkNnKN7EQx/IopISQlxyMtKRV5W6qKPu90Crow5cc46iSeePw3j25d9Hm+suBXVpZvDUWpUYvgTUVRSKj0Dov2b8T0x+O/WZeI7n9mBdWmhHRohFjD8iSgq9V0Zw1/+2IR3Lo5CoQD+amch/qqskG37AWL4E1HU+XnveXz9+OsYn3Yha00i/q26CPcWZkldVlSJ+vA3m81ob2+HTqeD2WxGbW0tNBqN1GURUQhMzbjwzV+9hZ+8ehYAcJdOi3//dBHWhXgEzFgU9eFfVVWF7u5uAJ4dQU1NDdra2iSuioiCrX94HF/+sQlvX3BAoQC+en8B/tpwA5t5Viiqw99sNvvc1+l0MBqNElVDRMEkCALOjEzghdPDePHUFfzhvWFMzriQmZqIf/v0DtxXKN0wzLEgqsPfaDRCq9X6LNNqtTCZTCguLpaoKiJaKcv4NF46PYyXTg/jhVPDC/rv35mnxb9/pijkE53IQVSHv81mW3S5xWJZdLnT6YTT6RTvOxxLj3lORKE1NeNC14AVL5y+gpdOD+PNIQfmDzWZEKdA8eYM3FeYhXsKsnD7Jg1nIguSqA5/f/ztFBoaGvDYY4+FtxgiErndAt664MCLp4fx4qlhdA5Y4Jx1+zxn24Y03FOQhXsLs3BnnlaciIWCK6r/qhqNZsFRvsVi8dvbp76+Ho888oh43+FwIDc3N5QlEsneOesEXjw1jBdOD+Pl08OwTsz4PL4+XYV7C9bivsIsvK8gkxdohUlUh7/BYEBTU9OC5Xq9ftHnq1QqqFThmymHSI7skzP4Y9+weHQ/MDLh83hqYhzu0mXi3sIs3FeYhfy1azj2jgSiOvx1Op3PfbPZDL1ez37+RGE0PeuG6axVPLp//ZzNZ0awOKUCO3I1uHeuKWdHrgYJYZ7hixaK6vAHgLa2NtTV1aG0tBSdnZ3s408UYoIg4N1Lo3jxlOfo/lWzBZMzvrPA569NnQv7tbhLp0UaJ1CJOJzGkdM4El3XRfvUXDPOFbx4egTDY06fx7PWJOKeAk+PnHsLspAdo/PjRgNO40hEKzbmnMUrfSOewD89vGAylaQEJe7MyxSbcm5cn8YumFGG4U9EmHG58do5G1445bnAquesDbPzGu4VCuC2HDXunetvX7Ilg1MiRjmGP5EMCYKAvivj4pW0r5hHMOac9XnOlswU3FOQhfsKsnB3fiY0KYkSVUuhwPAnkokro0683Dcsnqi9YJ/yeVyTkoB78j1H9vcVZiFXmyJRpRQODH+iGDU57cKr/SPi0f07F0d9Hk+MU0K/NcPT375gLW7KTucImTLC8CeKES63gDfO28WLq7rPWDHt8h064aaN6eI4OaVbtUhOZLu9XDH8iaLYmZFxMexf7huBfdJ36IRsdRLuLfT0t39ffiay1vAKd/Jg+BNFEev4NF7uG8GLp6/gxdPDGLT4DnmcporH3fmeoRPuLchCXlYqh06gRTH8iSLY1IwLpjPWuQlNhvHGkN1nyON4pWfIY8/RfRZuy1EjnkMnUAAY/kQRxO0W8PZFh9gjp3PAgqkZ33b7G9avwb0Fa3FvYSbuzMtEqor/jWn5+K+GSGLnbZN4ad6QxyPj0z6Pr0tTiVfS3luQxcnKKSgY/kRh5piawR/7RvDi3NW05uFxn8dT5oY89va3L1zHIY8p+Bj+RCE2PetG76ANL566ghdOD+PkoO+Qx0oFcHuuBvfNjYK5I1eDxHi221NoLTv8BwYG0NbWho6ODlitVnG5VqtFeXk5KisrsXXr1mDWSBRVBEHAqctj4jg5r5hHMDHtO+SxLitVHCfnLl0m1Mkc8pjCa1nhv2/fPigUCuzatQuPPvrogsd7enpw+PBhKBQKNDQ0BK1Iokh3yTGFl05fHTrh8qjvkMfa1ERxnJx7CrOQwyGPSWIBj+d/8OBB1NbWQq1WX/e5drsd+/fvj/gdAMfzp5Uad87i1f4RvHjK0+f+vUu+Qx6r4pW4I08rnqjdviGdQx5TWASaa5zMheFPAZh1ufHaebvnyP7UMExnrQuGPL4lWz03Tk4WirdkICmBQydQ+HEyF6JVEAQB/cNXh074o3kEo1O+Qx7napM9/e0LsvC+/ExkpHLIY4oeDH+ia3QNWPD4b96G6azNZ7k6OQHvmzd0wpbMVGkKJAqCoIV/f38/TCaT2B9Zp9Nhx44dwVo9UciZr4yh8al38PSblwB4hjwu2ZIhhv0tOWoOeUwxIyjh39/fj/7+flRUVIjLenp60Nvbyx0ARbzhMSf+/dlT+MmrZzHrFqBUANWlufgbww1Yz6tpKUYFJfx7enrw8MMP+ywrKirC8ePHGf4UsSanXfjBi2Yc/r1ZnMJw57Z12PfgNtywPk3i6ohCKyjhP7/DUGFhIZqamrBz507IuCMRRTCXW8Ax0zl8+5n3cNHhmcrwlpx0fP0j2/G+/CyJqyMKj6CE//xxRw4fPgy9Xr9gOVEk+P17V9Dwm7fFKQ1zNMnY++Eb8fHbstkPn2QlaEf+DocD6enpKCsr81lOFAneHLJj/2/fwQunhgEA6Unx+MrOAvzZ3VvZH59kKSjhX1FRgWPHjiEjIwN6vR59fX3o7u7G7t27g7F6ohUbsk3i0DPv4qc95yEIQEKcAn9291Z85f4C9ssnWQtaV09vT5+enh7odDoUFRUFa9VEy+aYmsETz/fhhy/2wznrmQzl47dnY++HbkSuNkXi6oikt+rw7+npQXd3NwwGA7Zu3crQJ0lNz7rxk1fP4N+fOw3L3KQod+Rp8fWPbMeOXI20xRFFkFWHf1FREYqKirBv3z4cO3YMp06dCkZdRMsiCAJ++8ZFHHjqHQyMTAAA8temYt+D22HYvo6dD4iuEfCMEc3NzUs+vn///oBG/CQKtu4zFlQ88TK+/GMTBkYmkLVGhW89dAue/pv3o/ym9Qx+okUEfOTf0dGB6upqpKX5v/jFYDAEpSiiQJivjOHAU+/iqTcvAgCSE+JQ834dat+vwxpOak60pID/h7S1taG9vR06nQ4GgwHl5eUoLi72mbWroKAgFDUS+RgZc+I71wzHsEufi78t53AMRIEKuNmnsbERFosFDQ0NEAQBjz/+OHQ6HTIzM1FdXY3m5mZ0d3eHslaSuclpF77/u9P4wMHn8V9/PINZt4Cd29bhqb95P/ZX3MbgJ1qGVU/mYjQaxZ+enh64XK7rvyhCcDKX6OByCzhuOod/5XAMRNcVtslcDAaD2NZ/8ODB1a6OyAeHYyAKjaCeFausrAzm6kjG3hpyoOG3b3M4BqIQCWr45+XlBXN1JEMcjoEoPAIKf7vdjoaGBigUClRXV3OMfgo6DsdAFF7LPuF77NgxdHR0oKCgALW1tWE5UWoymVBTU7OgN5HZbBa7n5rNZtTW1kKj0QS8Xp7wlR6HYyAKrkBzbcW9fex2O44cOQKz2Yzy8vIFM3kFizfcS0pKFgwRXVJSIu4QzGYz6urq0NbWFvC6Gf7S4XAMRKER8vCfr6enBy0tLSFtFlIoFD7hbzabUVVV5fNtICMjA1arNeB1Mvyl0X3Ggm/9+m2YztoAAFlrVPjb8kJU63MRHxfwpSdEtIiwdfUErg7uBniahQ4fPoyCggJ87WtfC8bqF2U0GqHVan2WabVamEwmFBcXh2y7tHL9w+No/O07HI6BKAIE/X9cRUUFKioqYLfbg71qHzabbdHlFovF72ucTiecTqd43+FwBLssWsTImBP//uwp/JjDMRBFjJAdbkk1wqe/nQIANDQ04LHHHgtfMTI3Oe3CD1/qxxPP92HMOQsA2LltHfY9uA03rPc/QCARhZ4k37WPHDmCvr4+v4+Xl5dfd4RQjUaz4CjfYrEs2dunvr4ejzzyiHjf4XAgNzc3sKIpYByOgSjyBTX8jx8/DoPBgJGREWRmZvo92VBbW7vqbRkMBjQ1NS1Yrtfr/b5GpVJBpVKtetvk32LDMTz6oRvxids5HANRJAla14pDhw4hLy8PXV1dyMvLQ2tra7BWLZrfpKPT6XweM5vN0Ov1y+rnT8Hz1pADn/vBq/j8D0/gnYujSE+Kx9c/sg3P/t0H8KmiHAY/UYQJ2pG/d9L25557DgAW9MRZKaPRiI6ODgCeNvvS0lJxDKG2tjbU1dWhtLQUnZ2dy+rjT8FxwT6JQ0+/h+M95zgcA1EUCUo/f8AzomdGRobP0Xkou3oGA/v5r5xjagaHn+/DD64ZjuHRB27E5kwOx0AklbD28weARx99FEePHsXp06dRXl6OioqKYK2aIgiHYyCKDas+8u/t7V30il5/yyMJj/wDJwgCnnrjIho5HANRRAvbkf/u3btRWlrqs0wQBHR3d6Ozs3O1q6cIwOEYiGLPqsNfr9eLJ2A7OjpQXl6+6qIoMnA4BqLYter/wY2NjeLVvDabDWVlZQCW7m9PkY3DMRDFvlWHv9VqFcN/frvv/OUUHRYbjuH+G9ei/iPbORwDUYxZdfh7+9kDQGdnJ8xms3i7paVltaunMPAOx/DtjvdwwT5vOIYHt+N9BRyOgSgWrTr8BUEQx9nX6/Xi7eWMq0/Seen0MP75V29xOAYimQlKm/9iE7d7TwJT5DozMo7P/eBVuAUgPSkeX9lZgD+7eyuSEuKkLo2IQmzV/fRsNhuam5sxMDDgs3yxHQJFluTEOLjnrvL41VfvQ+378xn8RDKx6vAvKirC7t27cfjwYRQWFgajJgqTdWlJ2Do3FEPflTGJqyGicAo4/Jubm5d8fP/+/ezdE4VKt3oG4Dsx4H8GNCKKPQGHf0dHB0ZHR5d8zvUmYKHI4w3/zn6GP5GcBBz+bW1t0Gg0KCwsxJe+9CUcP358QTt/QUFBsOujECvN84T/a+fsmJpxSVwNEYVLwOHf2NgIi8WChoYGCIKAxx9/HDqdDpmZmaiurkZzczO6u7tDWSuFwNbMFGStUWHa5cZr5+xSl0NEYbLqUT2NRqP409PTA5creo4eOaqnx5d/3I3fvH4Rj37oRvzl/fz2RhTNwjaqp8FgENv6Dx48uNrVkQRKt2rxm9cv4kS/BX95v9TVEFE4BHU8Xl7YFZ28J31NZ6xwuYMysRsRRbiAwt9uty84ubuY+Rd2ORwOOByOFRdG4bN9YzrWqOIx6pzFOxf5mRHJQUDhr1ar0dHRgePHjwe00mPHjqG1tVXW7ejRJE6pQPGWDADs8kkkFwG3+dfU1KCnpwe7du1Cfn4+SktLodPpoNFoYLPZYDabceLECfT392PPnj2cwzfK3LE1A3947wo6B6z483s4NAdRrFvWCd+ioiK0trbCbrejtbUVJ06cgM1mg0ajQX5+Pvbs2cMxfaLU/Ct9BUHgnLxEMW5FvX3UajVqamqCXQtJ6PZcDRLiFLgy6sRZywS2ZKZKXRIRhVBQevsMDAxg165d0Ov1KCgoQGFhIaqrq3nCN4okJcThtk0aAMAJtvsTxbygzMLd3t6O1tbWBcubm5uxe/fuYGyCwqB0qxbdZ6zoHLCgSp8rdTlEFEJBOfIvKipadDnb/6PLHXlzPX4GOAsbUawLypG/2WyG0WhEfn4+AM8ELyMjI+J9ig4lm7VQKID+4XFcGXVibZpK6pKIKESCcuRfU1OD2tpaCIIAq9UKtVqNXbt2scknyqhTEnDj+jQAQBfH9yeKaUE58gc8TTzsART9Srdq8c7FUZwYsODBWzdKXQ4RhUhQx/a5Vm9vbyhXTyHgHd+/k0f+RDEtKEf+/kK+paUFO3bsCMYmKEzumLvY660hB8acs1ijCtqXQyKKIEH5n/3kk0/CbrcvmMO3p6cnGKunMNqgTkKuNhmDlkmYzljx/hvWSl0SEYVAUMK/vr4eXV1dKCsr81n+7LPPBmP1FGalW7UYtJxH54CF4U8Uo4LS5q9WqxcEP4BFl1HkE8f54ZW+RDFrxeF/6NChRZc/99xzaG5uXnFBJD1v+PcO2uCcjZ5pOYkocCsO/6KiIgwMDPhM8nL06FE0NTUhIyODO4Aolr82FZmpiXDOuvHGeY7PRBSLVhz+bW1tMBgMKC8vF78FtLe3o7GxERUVFcjIyAhakRReCoUC+q3eoR7Y9EMUi1Yc/iUlJTh9+jROnToFjUYDALBYLNi6dSsAcDz4KMd2f6LYtuLePna7Xbxts9kAAFbr1QHBzGbzyquax2QywWg0AgA6Oztx9OhRcWdjNpvR3t4OnU4Hs9mM2tpa8TFanbt0mQCAV80jmJ51IzE+pNcDElGYrfh/dF5eHrRaLTIzMzE8PIyDBw/CYDDg0KFD6O3thSAIQSnQaDRi79692Lt3L0pLS316EFVVVWHv3r2orKxEZWUlh5cIops2piMzNRHj0y50n+Eon0SxRiGsIqW9R//zL+569tlnYTQa0dDQsOriTCYTysrKxG8UZrMZ+fn56OvrA+AJ/+7ubvH5GRkZPt8+rsfhcECtVsNut3Oy+WvYJ2bw4Hf+gCH7FP7xYzfhC/dyeG6iaBBorq3qu7xCoUBbWxuam5vFWbvKysqCEvwAUFxcjKNHj4r3vc1LWq0WRqMRWq3W5/larRYmkyko25azGZcbX/5JN4bsU8hWJ+ETt2dLXRIRBdmKw7+/vx87d+7EM888g2eeeQYlJSUhGcitsrJSvN3S0gKDwQCNRiPuCK5lsfg/Qel0OuFwOHx+yJcgCPinX7yJl06PICUxDs2fL+W4/kQxaMUnfI8dO4auri6fZfX19SEbyM1ms6G9vd2nmcff8/xpaGjAY489FuTKYsuPXh7Aj189C4UC+M6ni3BTNpvDiGLRisN/sSka9Xp9QK89cuSI2G6/mPLychgMBp9ldXV16OjoEHvzaDSaBUf5Fotlyd4+9fX1eOSRR8T7DocDubmcq9br+Xcv45u/egsAsO/D21B+03qJKyKiUFlx+C/WlbO/vz+g19bW1i5rWwcOHEBdXR10Op14ZG8wGNDU1LTguUvtgFQqFVQqNmEs5tSlUXz1Jz1wC0BVySbUvl8ndUlEFEIrDn+DwYAHHngAJSUlADxdMhsbG4NWmFd7ezuKi4vF4G9tbV20P7/ZbIZer2c//xUYGXPiL37UiVHnLO7I0+JbD93Ki/SIYtyqunr29/eLR9/V1dUoKioKWmHA1a6d82k0Gp+un01NTSgtLUVnZyfq6+uXFf7s6gk4Z134bPOr6BywYrM2BT/7y3ugTU2UuiwiWqFAc21V4X+t3t7eqJq5S+7hLwgCvtb2Go6ZziFNFY/jX34fCucmcCei6BRorgXc7HO9UTqtVitaW1vR2dkZeJUkqcO/N+OY6RyUCuB7f1rM4CeSkYDD//Dhw6iurl7yOUH8EkEh9vSbF3Hg6XcAAN/4+M34AGfsIpKVgMO/sbHxujNzXds9kyLTm0N2/M2TvRAE4HN3bcHn37dV6pKIKMwCvsI3kCkZg33Cl4LvsmMKu3/UhckZF+4tyMI3Pn6T1CURkQQ4Tq+MTM24UPPf3bhgn4JubSq+/6fFiI/jPwEiOeL/fJnw9Ow5iZODNmhSEvDDz5dCnZwgdVlEJBGGv0z8m/EUfvXaBcQrFXjiT0uwNStV6pKISEIMfxn4xckhfOfZUwCAbz10C+7Oz5S4IiKSGsM/xvUO2vBo20kAQM19eagu3SxxRUQUCRj+MWzINondP+qCc9aNsm3rsO/B7VKXREQRguEfo8ads/jCj7owPObEtg1p+M5nihCn5GBtROTB8I9BbreAv2npxdsXHMhak4jmz+uxRrXiAVyJKAYx/GNQ49PvoOOtS0iMV6Lpc3psykiRuiQiijAM/xjT1jWIpt97Jto5UHEbSrZkSFwREUUihn8MOdFvwdd/+joA4Cv3F+BTRTkSV0REkYrhHyPMV8aw57+7MOMS8JFbN+CR8hukLomIIhjPAka5yWkXDv++D01/6MPUjBu35qjxr1U7oGTPHiJaAsM/SgmCgF++dgH7f/M2huxTAIA78rT47meKkJwYJ3F1RBTpGP5R6PVzdjz2yzfRdcYzl3GOJhl//9HtePCWDZx4nYgCwvCPIpdHp3Do6XfR1n0OggAkJ8Thyx/MR837dUhK4NE+EQWO4R8FnLMu/OdLA/juc6cx5pwFAHxqRzbqHtyGjepkiasjomjE8I9ggiDg2bcv419+/RYGRiYAALdtUuMbH78JJVu0EldHRNGM4R+hTl0axTd/9RZeODUMAMhao0Ldh29ERfEm9uQholVj+EcY+8QM/q/xPfz3K2fgcgtIjFPiL+7Nw1/en4+0JM68RUTBwfCPELMuN/63cxDffuZdWCdmAADlN63H339kO2fdIqKgY/hHgCHbJP76yR50Dni6bhauW4P/7+M34b7CtRJXRkSxiuEvsY63LuHR9pOwTcxgjSoeX3vgBnz2ri2Ij+PIG0QUOgx/iThnXdj/23fw/14aAADcmqPGdz9TxCYeIgoLhr8E+ofH8dX/NeGN8w4AwBfuzUPdh7chMZ5H+0QUHgz/MPt573l8/fjrGJ92ISMlAYeqbkfZ9vVSl0VEMsPwD5OJ6Vn80y/eRGvXOQCeQdi+8+kdvEKXiCTB8A+Ddy468JWf9OD05TEoFMBXdxbir3YW8KQuEUmG4R9CgiDgJyfO4pu/fAvOWTfWpanwb5/egfflZ0ldGhHJHMM/ROyTM/j68dfx69cvAAA+eONa/GvV7chco5K4MiIihn9I9A7a8JWfmHDOOol4pQJ7P3wjdt+r45g8RBQxGP7LZJuYxveeO41sTTL+4t48n8fcbgHNL5px4Kl3MesWsCkjGd/9TBGKNmdIVC0R0eIY/sv07NuX0fxiP9KS4vFQUQ4yUhMBACNjTvxd20k8/+4VAMBHb92Ixx++FepkDsZGRJGH3U2W6VNFOdi+MR2jU7P49+dOAQBe7hvGg995Ac+/ewWqeCUef+hWfO9Pihj8RBSxeOS/THFKBb7+kW343A9O4P+9NID3Lo3i5b4RCAJQsG4NvvcnRdi2IV3qMomIlhTx4W80GgEANpsNnZ2dqK6uRnFxMQDAbDajvb0dOp0OZrMZtbW10Gg0Ia/pvsK1+MANa/H7967gpdMjAIBqfS6+8YmbkJIY8X9SIqLID/+qqio8++yzMBgMsFgsqKqqQl9fn/hYd3c3AM+OoKamBm1tbWGp61DV7ag//jrevuDAP35sOz58y8awbJeIKBgiPvzb2trEI30A4pG92Wz2eZ5OpxO/JYTD2jQVmj+vD9v2iIiCKeJP+BoMBvF2W1sb9uzZA8DTHKTV+k5irtVqYTKZ/K7L6XTC4XD4/BARyVHEhz8AmEwm1NXVoby8HLW1tQA85wAWY7FY/K6noaEBarVa/MnNzQ1FuUREES8qwr+4uBj19fXo6+tDe3v7ks/1t1MAgPr6etjtdvFncHAwyJUSEUUHSdr8jxw5Ip60XUx5eblPcw/gaeuvqqpCeXk5rFYrNBrNgqN8i8WyZG8flUoFlYpj6xARSRL+3qab6zEajaiqqoLV6pnYXKfTAfCc7DUYDGhqalrwGr2eJ2GJiK4nonv7aLVan28AJpMJGo3Gp/ePl9lshl6vX1Y/f0EQAIAnfokoZnjzzJtv/iiE6z1DYu3t7WLzTkdHBxobG32+ATQ1NaG0tBSdnZ2or69fVvifO3eOJ32JKCYNDg5i06ZNfh+P+PAPJbfbjaGhIaSlpUGhkHa4ZYfDgdzcXAwODiI9XV7DQ/C9873L7b0DoXv/giBgdHQU2dnZUCr99+mJ6GafUFMqlUvuGaWQnp4uy/8IAN8737s8heL9q9Xq6z4nKrp6EhFRcDH8iYhkiOEfIVQqFb7xjW/I8joEvne+dzmS+v3L+oQvEZFc8cifiEiGGP5ERDLE8CcikiGGPxGRDDH8iYhkiOFPRCRDDH8iIhli+BMRyRDDn4hIhhj+REQyxPAnIpIhhj8RkQwx/ImIZIjhT0QkQwx/IiIZYvgTEckQw5+ISIYY/kREMsTwJyKSIYY/EZEMMfyJiGSI4U9EJEMMfyIiGWL4ExHJEMOfiEiGGP5ERDLE8CcikiGGPxGRDMVLXYCU3G43hoaGkJaWBoVCIXU5RESrJggCRkdHkZ2dDaXS//G9rMN/aGgIubm5UpdBRBR0g4OD2LRpk9/HZR3+aWlpADx/pPT0dImrISJaPYfDgdzcXDHf/JF1+HubetLT0xn+RBRTrteUzRO+REQyxPAnIpIhhj8RkQwx/ImIZIjhT0QksRP9FpwdmQjrNmXd24eISGrvXRrFrqY/AgAG9n80bNvlkT8RkYTeuTgqyXYZ/kREEkqKlyaGGf5ERBJKSogTb7vcQti2y/AnIpLQ/PCfmnGFbbsMfyIiCanmNftMMvyJiORh/hA8k9MMfyIiWRDmNfM7Zxn+RESyMP8U7+S0GwAwZJvEIy29eHPIHrLt8iIvIqII4W3zL/vX32NyxgXz8Dh+9pf3hGRbPPInIpKQMK/d592LDgBXdwKXHVMh2y7Dn4hIQvObfb7/uz6f7p4356hDtl2GPxGRhOaf8L3omMJ//O60eP/m7NDNMMjwJyKKIP/+3NXwz0hJDNl2GP5ERJLyHPpvykhG/trUsG2V4U9EJCFvs0+8UoGvPXDjNY+Fbqwfhj8RkYS88a5QKPDhWzZgbZpKfOzpNy+FbLsMfyIiCXkP7hXw7AC+8+kd4mOv9o+EbLsMfyIiCXmbdrxj/LwvPws3rF8DAPjK/QUh2y6v8CUikpB3CH/lvBHedulzYTprRdHmjJBtl+FPRCQh75H//PDffZ8u5Ntlsw8RkYS8R/7zh3YOB4Y/EZGEBHjb/MOb/gx/IiIJOWc8wzgnxjH8iYhkwz45AwBIT04I63YZ/kREErLNhb8mhOP4LIbhT0QkIe+Rvzo5vJ0vGf5ERBKyT0wDADTJPPInIpINu9jswzZ/IiLZsPGELxGR/IhH/gx/IiL5sE94T/gy/ImIZMPOrp5ERPLSOWCB1dvbhyd8V66urg42m03qMoiIruupNy7gT5tfhVsA7inIxLp5M3iFQ8yEv8lkwoEDB6Qug4jouv7rjwP40o9NmJ51w7B9PZr/rDTsA7vFzHj+ZrMZOl3ox8AmIlopQRBw8Ol38R/P9wEA/uTOzfjmJ25GfFz4j8Nj4si/vb0dlZWVUpdBROTXjMuNv2s7KQb/I+U34FufukWS4Adi4MjfZrNBo9EE9Fyn0wmn0ynedzgcIaqKiOiqMecsvvxjE/7w3hXEKRVoeOhW7CrNlbSmqD/yb21thcFgCOi5DQ0NUKvV4k9urrR/fCKKfVdGnfjMkVfwh/euIDkhDs1/ppc8+IEoD3+j0Yhdu3YF/Pz6+nrY7XbxZ3BwMITVEZHc9Q+Po+KJl/H6eTu0qYn439q7cP+2dVKXBSAGmn1aW1vF22azGQ0NDaiurkZxcfGC56pUKqhU4e1ORUTy1Dtow1/8Zycs49PYrE3Bf/3FHdialSp1WSKF4J06PgYoFAr09fUF3OvH4XBArVbDbrcjPT09xNURkVz87p3L+PKPTZicceHWHDV++OelWBumfvyB5lpUN/t42Ww2sY9/Y2MjTCaTxBURkVy9fs6Omv/qwuSMC++/YS2erL0rbMG/HJId+Q8MDKCtrQ0dHR2wWq3icq1Wi/LyclRWVmLr1q0hrYFH/kSxw+UWMDnjwuS0Cy63gPXpqrBfOOWcdeET330J714ahWH7ejzx2WIkhLkrZ6C5Jkmb/759+6BQKLBr1y48+uijCx7v6enB4cOHoVAo0NDQIEGFRBRMgiBgasbtCecZFyanZzE57bk/MT2LqRkXJqZdYnhPem/PXL09Me3C1Nx98fa8103Pun22WXNfHv7+ozeF9X1+77nTePfSKDJTE9FYcWvYg385wh7+Bw8eRH19PdRqtd/nFBUVoaioCHa7HfX19dwBEEnM5Rbw3qVRnBy0wTIxLQb0xIwLU9O+ISwG+bRveIfbj189i7823IA1qvDE3Ovn7OIFXP/yqVuQuSbymnrmi6kTvsvFZh+ixdknZ9Bz1grTWRtMZ6zoHbRhzDkblHUnxiuRkhiH5IQ4JHt/z7+dGIeUxDgkJcSJz/PcjkdyohLJCfHic73PS06MQ8rc78Q4JQzf/j3Mw+PY//Ct+PQdm4NS91KmZ934+HdfxLuXRvHR2zbi+3+ysLdhuER0sw8RRQ63W4B5eBymM1aYzlrRfcaKU5fHFjwvNTEOt+dqkKNJ9oTuvAD2hHb83O/FA9r7O04Z+nb46tJcNPz2HTzZORiW8P/uc6fE5p5vfuLmkG8vGCIu/Pv7+2EymcQTNTqdDjt27JC2KKIYMu6cxclBG7rnwt501iZOKDLflswUlGzOQNGWDJRszsCNG9LCEtzB8HDxJhx8+l30DtrwzkUHtm0I3Tf7+c09/xwFzT1eERX+/f396O/vR0VFhbisp6cHvb293AEQrYAgCDhrmbga9Gc8Yei+prFXFa/E7Zs0KN6SgeLNnt9ZURJii1mbpkL5Tevx2zcu4skTg/inEB2NT8+68bW2k3C5BXz0to34yK0bQ7KdUIio8O/p6cHDDz/ss6yoqAjHjx9n+BMFaGTMifbuc+g6Y0XPWSuGx6YXPCdHk3w16DdnYPvGdCTGR27PlJWoLs3Fb9+4iJ/2nMe+B7chKSEu6NuIxuYer4gK//nnngsLC9HU1ISdO3dCxuekiZZt9391oeesTbyfGKfEzTnpKNmcMRf4GdigTpKuwDC5r3AtcjTJOG+bxNNvXsQnd+QEdf1vnI/O5h6viAr/+RdkHD58GHq9fsFyIlqabcLTfv/ATeux5wP5uDk7PSRHvZEuTqlAlX4T/s14Ci2dg0ENf5/mnlujq7nHK6K+5wmCII6xX1ZWJnZT4pE/UeDSkzzHdNWluSjZkiHL4Peq0udCoQBe7hvBmZHxoK33e8+dwjsXR6FNTcQ3PxldzT1eERX+FRUV6OjowHPPPQeHw4Genh40Nzf7nAAmoqWlJycAABxTC3vwyE2OJhnvL1wLAGjpDM4Q7m+ct+P73uaeT0Zfc49XRIU/4NkB7Ny5Uxydc/fu3VKXRBRV0pPmwn8yOBdlRbvP3OGZOKWt+xxmXe7rPHtp1zb3fPS26Gvu8YqY8Pce5Q8MDADw9PJZaggIIlpcerKn2cexSN99Odq5bT2y1iTiyqgTz71zeVXrioXmHq+ICf+ioiLs3r0bhw8fRmFhodTlEEUt8cifzT4APMNJVBRvArC6pp9Yae7xCnv4Nzc3L/n4/v37ecRPtApimz+bfUTVc3Pm/u7dy7hon1r262Opuccr7OHf0dGB0dHRJZ8T6ITsRLSQt7cPj/yv0q1dgzvytHALQFvX8o/+v/e702Jzz2NR3tzjFfbwb2trg0ajQWFhIb70pS/h+PHjYju/V0FBQbjLIooZ7O2zuE/PHf23dA3Cfe34Fkt447wd//G70wA8zT3RPOzFfGEP/8bGRlgsFjQ0NEAQBDz++OPQ6XTIzMxEdXU1mpub0d3dHe6yiGKGt81/scHa5Owjt25EWlI8zlkn8XLfSECv8Tb3zLoFfOTWDTHR3OMV9vB/9NFHoVarUVlZicOHD6OrqwtutxstLS3Iy8vD4cOHceTIkXCXRRQzrvb2YZv/fEkJcXioyHOV7/92ng3oNfObe775yVtCWV7YRczwDgaDQWzrP3jwoMTVEEUv9vbxr7o0F//1xzN45s2LsIxPQ5ua6Pe585t7vvnJm2OmuccrYrp6zldZWSl1CURR62pvnxkOjXKNm7PVuDVHjRmXgOOmc36fd21zz8duyw5jleERkeGfl5cndQlEUct75O8WgPHp8M+dG+k+PXfF75Odg353jt+P4eYer7CGv91ux759+1BfX4/e3t5wbppINpISlEiM8/zX5lW+C33i9mwkJ8Th9OUxmM5aFzz+5pAd34/h5h6vsIa/Wq3G/v370dDQgL6+Pnzxi1/EoUOHxJE8iWj1FArF1ZO+bPdfIC0pQey18+QJ3z7/nuae1zDrFvDgLRvw0SgcqjlQkjX7VFRU4PDhw6ipqUFTU5PY55+IVo+Duy3NO9jbr167gNF5O8jv/+403r7ggDY1Ef/8qVtiei4RyXv7qNVqPProowA8g7vt27cPCoUC1dXVnLqRaIXS5p30pYWKN2egYN0anL48hl+cHMKf3rlFNs09XhF1wreoqGjRZiEiWh4O8bA0hUJx9YrfzkFZNfd4SX7k709FRQUqKipgt9ulLoUo6qTzyP+6Hi7ehMan3sFr5+z425ZevH3BgYyUhJhv7vGKqCP/xXCET6Ll87b5j4xPS1xJ5NKmJuKBmzcAAH79+gUAwDdjaOye64nYI38iWjn13JH/d587jV+eHMKdeZm4K1+LO/Myka1Jlri6yPHp0lz8+jVP8D94ywZ8LIbG7rkehj9RDHq4OAevmEfw2jkbBkYmMDAygZa5oYw3a1Nwl047t0PIRI6Mdwb35GehaLMGw2NOfPOT8mju8VIIEXL9d2lpKZqbm3H77bcDAJ599ll0dHTg6NGjGBkJbAS+5XI4HFCr1bDb7UhPTw/JNoik5JiaQfeAFa+YR/BKvwVvnLfDdc1wxpsyknGXLhN35mlxly4TudoUiaqVhiAIcAtAnDI2gj/QXIuYI//i4mJUVFSgqqoKDQ0NKCsrQ1lZmdRlEUW19KQE3L9tHe7ftg4AMOacRdeABa+YLXi1fwSvnbPjnHUS7d3n0N7tGesmR5OMO3Va3JWXObczSI7pI2KFQoG42H17fkVM+H/oQx9CU1MT6urqUFhYiPb2dtx+++3Iz8+XujSimLFGFY8P3rgOH7zRszMYd86i+8zcNwOzZ2dw3jaJ46bzOG46DwDIVifhTl2m2FS0JTMlpncGchExzT719fVoaGgAAJhMJuzatQtVVVXIz8/H7t27Q7JNNvsQ+ZqY9uwMXjVb8Ip5BCfP2TDj8o2IDelJnm8Gc01FeVmp3BlEkEBzLWLCX6vVorm5GQ8//LC4rK6uDocOHYLLFZqRCRn+REubnHbBdNaKV80jeMVsQe+gDdMut89z1qWpfL4Z5K/lzkBKURf+drsdVqsVWq3Wp+D+/v6QDfHM8CdanqkZ787A882gZ9CG6VnfnUHWGpX4zeCuPC0K1q3hziCMoi78e3p60N3dDYPBgK1bt4Zlmwx/otWZmnGhd9CGV8wjeNVsgemsFc4FO4NE3DHXk+guXSYKuTMIqagLf699+/bh2LFjOHXqVMi3xfAnCi7nrAsnB+2enUH/CLrPWDE147sz0KYm4s48radraX4mbliXBmWMdLOMBBEb/s3Nzdc9gavX69HV1RXyWhj+RKE1PevGa+fmvhn0W9A1YMXkjO85PE1KwtzOwPPNYNsG7gxWI2L7+Xd0dKC6uhppaWl+n+OdyJ2IoltivBL6rVrot2rxFXh2Bq+ft8/bGVhgm5jB029ewtNvXgLgGZriDu83A10mtm9Mj5kLsCJJ2I/8lUolFAoFdDodDAYDysvLUVxc7NPOH8i3g2DgkT+RtGZcnp2B9wRy14BlwbzDaUnx4o7gzrxM3JTNncFSIrbZ5+DBg6itrUVHRweMRiO6urpgMpmQkZEh7gy6u7vxxBNPhLwWhj9RZJl1ufHGkGOua+kIOgesGHP6zkaWpopHaZ5W7Fp6c3Y64uMifoDisInY8PfHaDSKPz09PSHr2z8fw58oss263HjrgkP8ZnBiwILRKd+dwRpVPO7I06L+wW0oXO+/OVkuoi785zt48KA4tWMoMfyJoovLLeDtC4654SgsONE/AsfczsCwfT2aP6+XuELpRewJ30BUVlZKXQIRRaA4pQK35KhxS44au+/TweUW8Me+EXz2B6/i+XcvY2TMiUyZTMayWmFtKLPb7RgYGLju8+Zf0etwOOBwOEJYFRFFqzilAvcWZuG2TWrMugX88uSQ1CVFjbCGv1qtRkdHB44fPx7Q848dO4bW1tYlv7qYTCYcOHAABw4cQFVVFWw2W5CqJaJo8XBRDgDgeM95iSuJHmFv9qmpqUFPTw927dqF/Px8lJaWQqfTQaPRwGazwWw248SJE+jv78eePXtQUVGx5PqMRiP27t0LADhw4ADKysrQ3d0djrdCRBHiEzty8C+/fhuvnbPj1KVRnvgNgKQnfO12O1pbW9HX1webzQaNRoP8/HwYDIaABnMzmUwoKyuD1WoFAJjNZuTn56Ovrw86ne66r+cJX6LYUfNfXeh46xK++IF87Htwm9TlSCYqTviq1WrU1NSs+PXFxcU4evSoeN/b5KPValdbGhFFmYriHHS8dQk/6zmPRz90Iy8Eu46IujJiYGAAu3btgl6vR0FBAQoLC1FdXb3kCd/5PYNaWlpgMBig0WgWfa7T6RRPIPNEMlFsuX/bOqiTE3DRMYWX+4alLifiRVT4t7e3o7W1FV1dXTh9+jROnTqFlpYWtLa2Xve1NpsN7e3taGtr8/uchoYGqNVq8Sc3NzeY5RORhFTxcfjE7dkAIE5BSf5FVPgXFRUtujyQ9v+6ujp0dHT4PeoHPFNF2u128WdwcHClpRJRBHq42NPr56k3Li4YFoJ8RdRFXmazGUajUZy03WazYWRk5LqTuB84cAB1dXXQ6XRiu/9iOwGVSgWViheAEMWqHbka6LJSYR4ex1NvXERlySapS4pYEXXkX1NTg9raWgiCAKvVCrVajV27di05wmd7ezuKi4vF4G9tbV3y6J+IYpdCoRCP/o91n5O4msgWkWP7BMrbtXM+jUYjdv28Hnb1JIo956wTuLfxdwCAF+vux6aMFIkrCq9Acy2ijvz96e3tXXS5TqeDIAg+P4EGPxHFpk0ZKbhblwkA+Hkvh3vwJ6La/P2FfEtLC3bs2BHWWogoej1cnIM/mkdwzHQOX/5gPieMX0REhf+TTz4Ju90OtVrts7ynp0eiiogoGj1460b848/fgPnKOHoHbSjanCF1SREnosK/vr4eXV1dKCsr81n+7LPPSlQREUWjNap4fPjmDfhZ7xCOm84z/BcRUW3+arV6QfADWHQZEdFSKua6ef7ytSE4Z0M/M2C0kTz8Dx06tOjy5557Ds3NzWGuhohixfvys7A+XQXbxAx+985lqcuJOJKHf1FREQYGBnwmeTl69CiampqQkZHBHQARrUicUoFPzY3zf4zDPSwgefi3tbXBYDCgvLxc/BbQ3t6OxsZGVFRUICODbXVEtDIVxZ6mn9+9cxmW8WmJq4kskod/SUmJOIib98pci8WCrVu3AgC7aBHRit2wPg235nCKx8VIHv52u1287R2XZ/6FWmazOdwlEVEMEYd7MHG4h/kkD/+8vDxotVpkZmZieHgYBw8ehMFgwKFDh9Db24soHn2CiCLAx2/PRrxSIU7xSB4RMbaP9+h//sVdzz77LIxGIxoaGkK2XY7tQyQPu3/UCePbnh4/P/3y+2K6339Uje2jUCjQ1taG5uZmcXatsrKykAY/EcnHw8VXh3Y+9My7ElYSOSQP//7+fuzcuRPPPPMMnnnmGZSUlPgd44eIaCW2ZF4d2fODN6yTsJLIIfnwDseOHUNXV5fPsvr6eg7kRkRBc/Bpz9H+tg1pqHm/TuJqIoPkR/6LTdGo1+slqISIYtHz717G8+9eQUKcAk98tkTqciKG5OG/WFfO/v5+CSoholgz63LjW79+GwDw+bu3Ii8rVeKKIofkzT4GgwEPPPAASko8e2Sj0YjGxkaJqyKiWPC/J87i1OUxZKQk4KtlhVKXE1EkP/IvKipCU1OTOBPXkSNHsHPnTqnLIqIoZ5+cwbc73gMA/G35DVAnJ0hcUWSR/Mgf8LT779+/X7zf29vLE75EtCrfe+4UrBMzKFi3Bn9yx2apy4k4YQ//643SabVa0drais7OzjBVRESxZmB4HP/58gAA4B8+uh3xcZI3ckScsIf/4cOHUV1dveRzIuCiYyKKYo//5m3MuAR84Ia1+OCN7Ne/mLCHf2Nj43Vn5jIYDGGqhohizct9w3jmrUuIUyrwDx/dLnU5ESvs34UCmZKxqKgoDJUQUaxxuQX8y688XTv/5I7NKFyfJnFFkYsNYUQUM451n8NbFxxIS4rH35bfIHU5EY3hT0QxYcw5iwNzwzj8dVkhtKmJElcU2Rj+RBQTnnj+NIbHnNiamYI/u3ur1OVEPIY/EUW9c9YJHH3BMyxM/Ue2IzGe0XY9/AsRUdRrfOpdTM+6cbcuEw/ctF7qcqICw5+Iolr3GQt+eXIICgXwDx/bDoVCIXVJUYHhT0RRy+0W8M25rp27SnJxc7b6Oq8gL4Y/EUWtX5wcwslBG1IT4/B3H2LXzuVg+BNRVJqcdqHxqXcAAF++vwDr0pIkrii6MPyJKCodfcGMC/Yp5GiS8YV7F84ISEtj+BNR1LnkmMITz/cBAPY9uA1JCXESVxR9GP5EFHUOPPUuJmdcKNmSgY/dtlHqcqISw5+Iosrr5+w4ZjoHAPjHj93Erp0rxPAnoqghCAL++VdvAQAeKsrBjlyNtAVFMYY/EUWNp964iBMDFiQlKLH3wzdKXU5UY/gTUVSYmnHh8d96LuiqfX8+NqqTJa4oujH8iSgq/OfLAxi0TGJ9ugpf/IBO6nKiXtincSSSmnPWhSujTlxyOHFldAqXR5245JjCZYdTvH1l1An75AwAQKEAFFAAc+cVFfOWKRTe+wrxMcxbpvB5jUJ8rXdlV1/vuz7MX6diXg0+r1Fg3qoWbMO7Psxbn2Le+jyvmbdN7/oW2cb87WORmrHo/at/k8XWJ1ZwzfoWvOe5x59/9woAYO+HtiElkdG1WvwLUsyYmvGGuifQLzumcGnUORfqU+Jv68TMCtYuBL1eWr7bNqnxUFGO1GXEBIY/RbzJaRcuj07h0rwQvzQ6hStzv71H7N4j9UAkximxNk2FdekqrE9L8vxOT/IsS/Pc1qQkQAEFBAgQ5rJfgKfHiXhfAIS5HYPn9tzjc/e9r7r6GHzXt8j9uVcEvj5BEHdNYj3zHve3Plz72LzHr77XhdtbbH24Zvv+1gfB97ULXjO3wOexuftxSgUeuGkDlEp27QwGhj9JZtw5e7XJZe5IXTxinxf0o87ZgNepildiXboK69KSsH7ut/e+N9TXpak8wc7+4SRjDH8KKkEQMDYv1K/4aU+/5JjC+LQr4PUmJSjF4F6X7hvk84M+PTmeoU4UAIY/LSAIAszD4/jNaxewOTMFH7stG0oF4JiaxZV5zS+XHAvb0y85nJicCTzUUxLjxOaW9WKoXz1SX5fuOXJPUzHUiYKJ4S9zsy43fvnaEJ54vg/vXRpb9Dl//WQvVPFKOGfdAa83TRWPtekqnyP0q23qc0fq6UlYo+I/QSIp8H+eTAiCgCujTrx7aRTvXvT8dA5YMDAyEdDrvcGfnhS/sNlFbH6ZW5auYlc8oggX9f9DzWYz2tvbodPpYDabUVtbC41GI3VZkrJPzuC9uZB/79Io3pn7bVtGF8eP3LoB+i1aMcy9PWI4dC5RbIj68K+qqkJ3dzcAz46gpqYGbW1tElcVPqNTM+g5a0PXGStODtrw3qVRXLBPLfpcpQLYmpWKG9en4Yb1adi2IQ15a1ORo0lGWlJCmCsnIilFdfibzWaf+zqdDkajUaJqwuO8bRJdAxZ0DVjRdcaKdy864F7k+qNsdRJu2JCGGzekiWFfsG4Nj9yJCECUh7/RaIRWq/VZptVqYTKZUFxcvOD5TqcTTqdTvO9wOEJe42rMutx45+KoJ+zPWNF9xrroUX2uNhn6LVoUb9bgpux0FK5PQzqP5IloCVEd/jabbdHlFotl0eUNDQ147LHHQljR6sxvwuk+Y0HvWduCvvBxSgVuzk5HyZYMlG7VomRLBtanc+JqIlqeqA5/f/ztFOrr6/HII4+I9x0OB3Jzc8NU1eLGnbN46o2L+Fnvebx0enhBE05aUjyKN2dAvyUDJVszsCNXw540RLRqUZ0iGo1mwVG+xWLx29tHpVJBpVKFobKlzbrceOH0MH7Wcx7PvHnJ56IobxNOyZYM6Ldm4IZ1aRzLhIiCLqrD32AwoKmpacFyvV4vQTVLEwQBr5+346c95/HLk0MYHpsWH8vLSsVDRTn45I5sbMlMlbBKIpKLqA5/nc53Qgez2Qy9Xh9R/fwHLRP4ee95/LTnPPqujIvLtamJ+MTt2fhUUQ5u36Tm0AVEFFZRHf4A0NbWhrq6OpSWlqKzszMi+vjbJ2bw69cv4Kc959A5YBWXq+KVeODmDXioKBv3Fa5FQhwnUiMiaSiE+YNxy4zD4YBarYbdbkd6evqq1uWcdeF371zGT3vO43fvXMG0yzMcgkIB3K3LxENFOfjwLRt4MRURhVSguRb1R/5SO315FD96+Qx+3nsejqmr485v25CGh4py8Ikd2ZxomogiDsN/BQRBwK9eu4CjL5jx2jm7uHx9ugqf2pGDTxXlYPvG1X2TICIKJYb/Ml2wT+Iff/YGjG9fBuBp1infvh6fu3sL3pefhTh2yySiKMDwX6bOASuMb19GQpwCX/5gAf7kzs28wpaIog7Df5k+fttGvHvRgU/uyMEN69OkLoeIaEUY/sukUCjw6Ie2SV0GEdGqsKM5EZEMMfyJiGSI4U9EJEMMfyIiGWL4ExHJkKx7+3iHNYr06RyJiALlzbPrDdsm6/AfHR0FAMln8yIiCrbR0VGo1Wq/j8t6VE+3242hoSGkpaVJPp6+d0rJwcHBVY8wGm343vne5fbegdC9f0EQMDo6iuzsbCiV/lv2ZX3kr1QqsWnTJqnL8JGeni7L/wgA3zvfuzyF4v0vdcTvxRO+REQyxPAnIpIhhn+EUKlU+MY3vgGVSiV1KWHH9873LkdSv39Zn/AlIpIrHvkTEckQw5+ISIZk3dUz3MxmM9rb26HT6WA2m1FbWwuNRrPoc00mEwCguLgYZrMZNpsNxcXFYaw2uEwmE2pqatDd3b3k85bzN4oWgb73WPvMvUwmE4xGIwCgs7MTR48e9fuZxtrnv5z3HvbPX6CwKS4uFm/39fUJlZWVfp9bW1srABAACAaDQbBarWGoMDTa2tqE7u5uIZB/bsv5G0WD5bz3WPrM52tsbPS5Pf8zvlasff7Lee/h/vwZ/mHS19e34IPXaDR+n9/U1CRYrdaYCQBBEK4bgMv9G0WTQMI/Fj/z7u5un8+wr69PACD09fUteG6sff7Lee+CEP7Pn23+YWI0GqHVan2WabVa8aveYjQaTVR/5V2ulfyNYk2sfebFxcU4evSoeN9mswHAgs8ZiL3Pfznv3Sucnz/b/MPE+8Ffy2Kx+H1+e3s7AE9b4Z49e6DT6UJVXkRY7t8o1sTqZ15ZWSnebmlpgcFgWDTgYvHzD/S9A+H//Bn+EvP3D37+iS6dTofy8nL09fWFr7AI4u9vFGti/TP3htv1Tnwv9rpoF8h7D/fnz2afMNFoNAuOYCwWy5K9Hry8PR/mL4tFy/0bxZpY/8zr6urQ0dHh9/OM5c//eu8dCP/nz/APE4PBsOhyvV6/YJnJZEJZWdmC5Uu1FcaC5fyNYk2sf+YHDhxAXV0ddDodbDbbokfzsfr5B/Lepfj8Gf5hcm3bndlshl6vF48ETCaTuJfX6XRobGwUn2s0GlFZWRkTR0DX/sO/9n3Pd+3fKNpd773H6mfe3t6O4uJiMfxaW1v9/rufLxY+/+W893B//hzbJ4zMZjOamppQWlqKzs5O1NfXix9uVVUVSktLsXfvXgBXLw7RaDTo6+vz+YcRbYxGIzo6OnDgwAHs3bsXpaWl4omwa9/3Un+jaLSc9x5Ln7mX2WxGfn6+zzKNRgOr1Qogtj//5b73cH/+DH8iIhlisw8RkQwx/ImIZIjhT0QkQwx/IiIZYvgTEckQw5+ISIYY/kRBFoyxaGJhPBuKbAx/iilGoxElJSU4cuSIJNs/cuTIgvFpDhw4AIVCgaqqKvFS/6qqqiWHKm5oaFiwbCXrWcyBAweW9XyKTbzIi2LOgQMHoNFoUFtbG9btei/Xnz+ML+A5is/IyMD8/2omkwklJSXw99+vrq5uwRWeK1nPYmw2GxoaGmLiCmJaOR75EwVJQ0PDguAHPN9G/M3FuljzTnt7O6qrq1e9Hn+8wyXE0oihtHwMf6IgsNlsfife6OjoWDAyZUNDg9/JyTs6OhYN+eWuZynV1dXixCEkT5zMhWKed8As7xjplZWVYlC3t7fDbDZDo9Ggu7tbbEP3DrYVqNbWVpSWli76mHeERqPRCLPZDJvNhj179iw6hLHNZlswGNhK1nM9xcXFqKurW/b7pBgSlpmCicKosbFRaGpqEgTBM2m2wWDweby4uFicKHv+BNs6nc7v5NrXs3fvXqG7u3vBcqvVKgAQJ+X2TlLub5LuxsbGRR9b7noCce1k6SQvbPahmNbU1LSgCUWn06G1tTWo27HZbIs2vXR1dUGn0/lMz6fVav1uv6+vLyjrIboeNvuQbHl7BHl7B107Yba3V0x1dbXPDuTIkSPQ6XQwmUxiE5JGo1n0pGtHR8eCZpmuri5UVVUteK7JZEJ5efmitS5nPde25XsnEyGaj+FPMckbxNXV1aipqfF5zGQy4ejRowCAzMxMv+3eXV1dCwLdbDajr68PtbW1MBgMqKqqQltbG/Lz82E2mxd8yzAajaivr19QmzeMTSaT+JqWlha/3S8DXU97ezsMBoPPtwej0Qhg4UxZsTJFJK0Mm30opphMJrS0tKClpUUMxMbGRhw4cADt7e2oq6tDW1ubGI59fX3Iz89HSUkJysvLfS4OuzZEAU+Qzj8h6+0uaTAY0NnZ6bP8wIED0Ol06Ozs9OlWuXfvXnR0dODIkSN+u26udD0ajWZBzQaDYcGFYEt9yyCZkPqkA5FUOjo6hMbGRvF+X1+fUFlZKXR0dIjLrj2RO/9ksiB4ThJ7VVZWrriWtra2RU8YL0d3d7d4Arijo8Ontra2Np/n7t27d8Untyk28MifZOvadnSdTofq6uolL37y17YPAHv27Flx33l/ffuXw9uVFQD0ej2ampoWfZ63fp4HkDeGP8lWY2MjjEaj2CTU3t4Oi8Wy5LAQer0eIyMj4v35gW0wGGCxWJY9KNtSffuXQ6PRoKurS7zt3bGZTCafoOfQDgRwbB8iv4xGIxobG1FcXOzT48fb28dsNmPXrl0L2tj9dfsMl2u/feh0ulV/q6DYw/AnIpIhNvsQEckQw5+ISIYY/kREMsTwJyKSIYY/EZEMMfyJiGSI4U9EJEMMfyIiGWL4ExHJ0P8PNlZg/z/EWTMAAAAASUVORK5CYII=", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "PLOT_PROPERTIES['figsize'] = (4., 8.)\n", + "\n", + "grid.plot(42, 'age', ['star_1_mass', 'star_2_mass', 'binary_separation'], history='binary_history', **PLOT_PROPERTIES)\n", + "grid.plot(42, 'log_R', ['log_LH', 'log_LHe','log_LZ'], history='history1', **PLOT_PROPERTIES)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot one or more quantities as a function of another for multiple tracks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If one wants to compare multiple tracks on the same plot, the indices for all binaries can be provided as a list." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "PLOT_PROPERTIES['figsize'] = (3.38, 3.38) # default\n", + "PLOT_PROPERTIES['legend1D'] = dict(loc='center right', lines_legend=['41','42','45'])\n", + "\n", + "grid.plot([41,42,45], 'age', 'binary_separation', history='binary_history', **PLOT_PROPERTIES)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot a third quantity as a color map" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "PLOT_PROPERTIES['figsize'] = (3.38, 5)\n", + "PLOT_PROPERTIES['log10_x'] = True\n", + "PLOT_PROPERTIES['legend1D'] = dict(loc='upper left', lines_legend=['42'])\n", + "\n", + "grid.plot(42, 'binary_separation', 'star_1_mass', 'lg_mstar_dot_1', history='binary_history', **PLOT_PROPERTIES)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting an Hertzsprung–Russell diagram" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One can easily plot the Hertzsprung–Russell (HR) diagram using the ``HR`` method." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "PLOT_PROPERTIES['figsize'] = (3.38, 3.38) # default\n", + "\n", + "grid.HR(42, history='history1', **PLOT_PROPERTIES)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that multiple tracks at once can also be displayed." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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8R2nE/6kr0r4IJrOmNY1E8xak3W7HbPb9S7Lb7f2em81mrFZrsMsSQgTYzrom7jRUskJ/RJ3edf1PtC5pRJoGpMViISsra9htrFYrJlP/q1wmkwmbzTbk9p2dnTidzn4PIYT29h08xHfDeo/81v8IYpO0LcgPmgWkw+HAaDT6td1QGhsbh1xfWFhIQkKC95GamjqOKoUQgaAoCpce+BXxunYcxosg/Utal+QXzc5BlpeXk5ubO+b3+wrOgoICvvvd73qfO51OCclJrtPVQ31jO0caWzl8to0jjW3UN7bR1aMQGaYnIkxPZJg6snakd9mzXh0kNsKgJzJcfd5vebjtw/T+T0zVchKaj4K7G3q6eh/dvY/eZXef5YGvu/tu2wU9rvPLbhcobvWwNCZJbXnFeB4zzy9HJ4JB88sKQ6p+8yWu734NgLCbHgb95JjmRJPfptVqZePGjX5tazQaB7UWGxsbfbY+IyMjiYyMHG+JYoI1t3VzuE8AHj6rLtc3tnHc2aHZRFURYXoiRwjUBcoxfnbsHgz0aFOklw6ijQPC03T+eWyfMPU8IuMGz7EQYEpPN0lv/ACA3bNu5rKloTe1gi+atiA97HY7hYWF5OTkkJaW1m+7jIwMSkpKBr1/zZo1Qa9RBI7brXDc2cHhs63UN6qTPR1ubOPIWTUMRxr9OjbCwIKkWBaaYliYFEOqKYbocAOdLnW07S6Xe/Byt5uunsHrOnvUkbjPv6f/Nn15Rudu6fRdm5Eufhypw9AnZz5wL6SbMLox0K2EnV8m7PxD6fu8d1npv50LAy5dOGF6PSZDK0m6FpJ05zDpnBhpIUFxEu92Eqe0AAq0N6mPs4f8+3PRh9MdmUhPlImeKBNKjMnbKtXHzsQwIwlD3CzCZ8xE5wnYsNE1QGpffhSzy45DiWVe1mb48GWw/gQ++1uYH9r/jjUJyIyMjH7P8/LyyMvL817NttlsGI1GzGbzoCvcdrudNWvW+HX+Ukysju6eAeHXqrYGG9s42thO1wjztMyOi2SBKYYFSTEsNMWyMEldXmCKISk2YkLm/VYUha4ed7/w7BoyeNURtz2v/935Z66r/ibGtlq69DEcWXQbp8Ln0ayLp0mJx8EMWnsMamD3TqFwfvn8d3iWB06NOhIDPRg5R6KuhSRaSNS1YNK1kEgLSTqn+pwWTDonibpzmGghRteJ3t1NZPspaB/F/PARcWrLdKgW6cDWKjrm7PwVAG8s+Bo3R+vgsRz1c95+FDb+cVT7OdE0PWHhcDgoLS0FoKioiLy8PNLS0igsLGTt2rVs2rQJgIqKCvLz81m7di1VVVXSB1IjiqLQ1NbN4d7gO9K3FdjYyknnMM0sINygY36iGngLe4NPXY5lgSmG6Ajtz0vpdLrec5kG4kb1zlT4+GtQdicRdW9yQ13R4E08wRJjgvi+gWLqHy7RJlxRiXRFGOlSDP1CucPTKu4e2HIeHLKdLjXE63rcfDgglLt63ChdbUR2NRHd7SDG1Uxsj4PYnmbi3M3Eu50kKE5vyJp0ThI5R5jODV0t6sNx2K/fTCzwvnsxa9fdCr/79PkXVmWP6jesBZ2ihMI05MHjdDpJSEigubmZ+PihRz0W5/W4FRoc7b3nAdXg87QKj5xto6Vz+EPhuMgwtQWYFMMCTyuwNwhTjNFDzoA3pbi64PXNcOTf0Ha299EIyhjPT0YmDA5QT8gO1XKLTgzYBRBPa7qj282O2kbKd9RR9WEdiaihOS+yjXULDHx8LswJO6fuZ+uZ/vvd2UybEskL8+/jdufvoKXPxDX/3QARsQGpdbT8zQUJyGmorcvlbQEeGXBIfMzRPuJcznPjo7yHvgs9h8S95weNMeETcig8qbjd0NmsBkbf8PAuD/G8vQkYyz/N3gs10UOFqo/nUUbQ+9fj70RzB5bqep6tqvfOeQ1wyfwEblu7gP+4NJm4KHV08AMnnPzH/7zGxfpaLPGPYOjoc7F1zir42r/GsH+BIQHZazoGpKIonG3t6r0ifP7KsOeQ+PRwVxyACIOe+abo3gsisaT2BqHn4shEzdQ3rbl7oKN5QIgODNQBodrhGNt36fRqy3NgS3TI84tqsLoj4nnLfpZnq+p55YMT3v9UYyIM3HRJMjlrF/CHt+s4uuefPBX9EDHuVki+DJIvAdsfIe0uuPnRgP26RsvfXAjNTlNiRK4eNw2ODp9dY1q7hj+ki48KU8/9JZ0PvwUm9fnc+Kipfygc6vSG84fSLPPvPT0uteXpbYUO12I9C21NastWcZ9f5295OgPXxJi4JiaJ7sWJHO2M5gNHOHXtUTTtiuNp2wyidT08FfFHYtydsOBKuL0Mnv2C+gEhfvXaQwJyEmjtdPHXPQ3sOdrsPSQ+5minZ5grnTodJPc9FO69ELKw9wpxQkxoTpIkxsEQBjNmqQ9/ubr6h2rfQG0fKlQboeucek619TS0niYcWNz7GDJRlqyDnKfV7kENu9V189LHu7cTQgIyhLV1ufjRnz/AUn10yNcjwvT9zgMu6NMSnJ8YLYfCYmRhERA3R334q7ujT3gO3UrtPneG9ubTRJo/TuQNv1DD8dQB9ep3eCzMWh68fQogCcgQ9vS/jwwKx7uuXMgNq5JZmBTDnLgo9HIoLCZaeBSEp0B8iu9Neh/9HNup/kxZPWluNdR8uDPh22cvS+GaZTP7rfvDO4e53/Ie//dWHe8fa2aKX2MTU8kxdUxX5qUNv10IkRZkCJsdH8WTX7mc1k4Xr314ir+/f5xXD5yivrGdkjfslLxh56KUeB65bTVLZ8/QulwhhucNyMlx/hEkICeF2MgwbrokhZsuSaGty8U/PzzNo9sPcuBECx80OPnLew18J/MCrcsUwrfudjj5gbo8iQJSDrEnmZiIMBbPjPX2Zbxgzgzu+vgibYsSYiTH96jDts2YAwnzta7Gb9KCnGT2HmvmjifexdHWzcXz4nny7stJjI3Quiwhhtf38HoS3WklATmJ7DrSxF2/24Gzw8VlqUb+cPfHSIiW/oxiEvBcwZ5EF2hgDAFZV1dHRUUFlZWVNDU1edebTCYyMzPJyspi0aJFgaxRoE64/uXfV3Gu08XaRYn87ktrvfe8ChHyvC3IyXEHjceoAvL73/8+Op2OjRs3cv/99w96fdeuXWzZsgWdTkdhYWHAipzu3qk5y1f+UEVbVw9XmpPYetcaYiOl8S8midaz0FSnLqes1rSU0fL7X9lDDz1EQUEBCQkJPrdZvXo1q1evprm5mYKCAgnJAHjz4Gnu/eNOOrrdXLNsJqV3rgmJcROF8Jun9Zi0TB1paBLxOyCHajH6kpCQIOEYAK8dOEXeU9V0udysWz6b//1Cmtw+CKAoarcRnV69I0Nn8Hu4LqEBT0BOkgEq+pLjtBD1jw9O8M0/2ejuUbh+5Rx+e3saEWHTIATcvYMgOI+B87g6wKpn2Xms9/lx6G4d8EadGpb6sN7ANPQJz77r9X2Ww9Tn3mXDEO/1rNefXx7yszzr9X7WMdR3BOO7h1ofNrH/oUzCDuIeEpAh6G97jvPtZ3fhcivceEky/5NzGeGGKRCO3R29AdfQP/haGtR1nkAc0+jbitrPzj38iOeij4n6j+HIO+r3TbIr2CABGXKe33WU75W/h1uBW1fPozjrEsJCPRwVRR3c1dnQG3bH+yw3nA9Bf8cb1OlhxlyIT4a4ZIifpy7Hz+t9ngKxvUN6eeaMdvf0Lvf+dLv7LPf0Lvf0WR5ivff9PQNe86x3D9jG892uAZ87zHdPxHcofd437J9bD/T0MCGz1UbEqaOITzIBC8ja2lpsNpt3uH2z2cxll10WqI+fFsqr6sl/bg+KAhvXzKfw1ku0H7h24CFvvxA8dr5F2N3m3+eFRfcGX+9oMAODLz4FYmerYxuK8VGUUYawP//JjPE/gAVXqEOrTTIB+VtYW1tLbW0tGzZs8K7btWsXu3fvlpD001P/PswPX9gLwB1XLOCnN18c/KHMujvOh52vc32jOeSNTuwffN7l3kdcsrrNJLqTYlLT6Xr/o5H/bMYqIL+5Xbt2ceutt/Zbt3r1ap577jkJSD/8/q1aHvjLPgC+fNUifnTTyvFNfDXokLdh8Lk+5zF10FN/9D3kjU8ZOvjikiEiZuw1CxGCAhKQfcckXLZsGSUlJaxbt07GKvRDyes1FL50AIC8T5r5/qeXDx+O7h44d8rHub6GsR/yDjzM7XvuTw55xTQVkL/1ff9Bb9myhTVr1gxaLwbb8noNm3vD8VvrlvKdzAv6/84aa6FqKziO9Ln6e2J0h7ze4OsbgvPOtwajjHLIK4QPAWtBOp1O4uPjWb9+fb/1YmjvH22m+GU1HL+XeQH/uX6Imev+9Wuw/WHwep0B4ub6Dj5PSzA8Osh7IcTUFpCA3LBhA9u2bSMxMZE1a9ZQU1NDdXU199xzTyA+fsrpcSv89/Pv41bg5ktThg5HgKv/CxrtUPfm+XXma+GGYph14USUKsS0plMC3MzbtWsXZrN52Hu2J5K/E4RPJM9FmfioMKzf+ySz46KGf8ORf8PrxVCzXX2u08NFt8A198GclcEvWIgpxt9cGHcP5F27drF161bq6uoA9ep1qIRjKDre3M4v//EhAPk3LB85HEHtQ3bnc3DPq3DBDWqftb3b4LEroewOOP5ekKsWYnoad0CuXr2ae+65hy1btrBsmY9DReH1wIv7aO3qIW2Bkc+vXTC6N89Ph9ufhbw3YcXN6rr9f4GST8CfcqB+h9rFRwgREH4H5NatW4d9ffPmzdJyHIF130le/uAEBr2OX9yyauwdwZMvgZwn4ev/houz1EPuj16GJzLhyVskJIUIEL8DsrKykpaWlmG3ycjIGHdBU1Vbl4sfv6jO6nbPNYtZkRyA86GzV0DWE/C1d86vO74berrH/9lCCP8DsqKiAqPRyLJly/ja177Gc8895z3v6LF06dJA1zdl/I/1IMcc7cwzRvNtX1etx8LtVrsDgTqSSs5Tk/KeVyFCkd8BWVRURGNjI4WFhSiKwoMPPojZbCYpKYmcnBy2bt1KdXV1MGudtPY1OHniX7UA/OxzFxETEaC7UhQFXv4+7HlW7RuZ8xQsujowny2EGH83H6vV6n3s2rWLnp6JGDvJf1p38+lxK2x47G121zu44eK5PHZHAAcN/edm+GfvyO23lMKlOYH7bCGmMH9zYdxNmYyMDO+5x4ceemi8Hzfl/GnHEXbXO5gRGcaP/+OiwH3wv7ecD8cbHpJwFCIIAjoSa1ZWViA/btI75ezw3k543/UXMDfBjz6P/njvWXg5X12+7gdweW5gPlcI0U9AA3Lx4sWB/LhJ76d/3UdLh4tL5idw55WLAvOhB/4OL3xdXb7i6/AJ/ydTE0KMjl+H2M3NzRQWFqLT6cjJyZExHv3wTs1Z/rrnOHodPHjLqsCMDF77BlR8SR3N59Lb4fpfyEg8QgSRXwGZkJDA5s2bAdi2bRtbtmxh6dKl5Obmhsz9zaFm+/6TANyaNp+L5wWgA/0xGzzzeejphOU3wc2/mRJTnbZ0tdDY0UiPuweX4qLH3YNbcXuXe5Teh7v/T8/rBp2BcH044YZwwnRh6k99mLpO3385LiKOGREztN5lMYmM+iLNhg0b2LBhA83NzZSUlGC328nMzBw0ovh0t7ehGYDLF5vG/2GnP4SnNkDXOVj8CdjwxKQYwFZRFJo6mzh+7jjHzh3jeOtxGs41qI/WBo6fO05L9/A3HwTaNfOu4adX/ZSZ0TMn9HvF5BSQ0Xx27dpFWVlZSB6Ca9HNx+1WuPSBV2jpdPHSt68Z310zjiPwxKfUUcNT0uCuFyEyLnDFjoNbcXO67fT54Gtt6PfzROsJ2l3tI35ObHgs4fpwDDqD+tAP/hmmC0Ov03uXDXoDep1ebW26XXT3dNPt7laX3YOXu3u66XJ3AXBx0sU89ZmnMOgNwf4ViRA1Yd18QB2wYvXq1UD/Q/D77rsvEB8/6dQ3tdHS6SIiTM/S2eM4pDt3Cv74OTUcZy2HO7ZNaDh2u7s52XpyyJZfQ2sDx1uP4/JjHupZ0bNImZFCSmwKyTOSmTdjHsmxyaTMSCE5NpmY8ImZy+Zg00Hueuku9p7dy99r/85/LPmPCfleMXkF/Dit7yH4cKxWKwAOh4OqqipycnJISxt6YnGbzQZAWloadrsdh8Phc9tQsPeYE4AVc+MIH+uc1u0OeOpWaKyBhAVw5/MQE4DD9T46ezrVsBvQ+vME4Km2U7hHmFvZoDMwJ2bOoODzBOLc2LlEGELj1sdlicu4e9XdPGJ7hCf3PclN5ptkWhAxrHEH5K5du6iuriYjI4NFixZ51480sk92djbbt28nIyODxsZGsrOzqampGXLbkpISSktLAbVjekVFxXjLDirP+ceLxnpxprsDnrkNTryvTpj1xRfUKRTGwOV2cbTlKHXOOmqba6ltrqXOWccR5xHOdpwd8f3h+nBvS88Ten2fz46ZTZg+9M+HemQty2LLe1vY37if906/x2WzL9O6JBHCxv0323N4/f3vf59t27Zx8OBBv95XUVHRrxVoNBp9bpuenk5TU9OI24WKvcfUgLw4ZYwB+fpmOPIORCWoLcekJSO+pbmz2RuCdc29Yeispb6lftjD4Oiw6CEPfT1hmBSdhF43+a+WexijjHxm8Wd4/tDz/Gn/nyQgxbD8DsitW7cOO8fM5s2bvYfN/ug7NFpFRQV5eXnDbu9vMHZ2dtLZ2el97nQ6/a4pEBRF4YMG9TsvShnDxZmG3fDWo+ry5x6DuRf3e9nZ5WT3qd3elqAnEIdrDUYZoliUsIhF8YtYnLCYxQmLWRC3gHkz5pEQmTDtDjNvX3E7zx96nsrDlZxqO8XsmNlalyRClN8BWVlZSU5ODnFxvi8SjHY8SJvNRllZGZmZmeTm+r5dzuFwYLFYAKiqqiIvLw+z2TzktoWFhTzwwAOjqiOQjjd30NjahUGv48K5o7yg0tMNL35T7Qh+0S2w/MZ+L7sVN9kvZtPQ2jDk22dHz2ZxwmIWJfQGYbwahnNi50ypVuB4LTctJ212GrZTNio+quAbl31D65JEiPI7ICsqKrBYLJjNZjIyMsjMzCQtLa3fecfRjgeZlpaG2WwmPz8fi8Xi817u3NxcbwvSbDaTmZnp83xlQUEB3/3ud73PnU4nqampo6prPDyH18tmzyAqfJTdSN7+jXreMcqozlw4gA4dixMWewNyzZw13LrsVswJZhbGL5RO0KPw+RWfVwPywwpyV+USbgjXuiQRgjQfD9JoNJKdnU12djYOh2PIbex2u3fZbDZjt9v7resrMjKS+Pj4fo+JtLf38HrUd8+cOaQOXwbw6c0wY/Bhn06n49fX/Zqr56ljPu4+tRudTsfKpJUSjqO0fsF6ZkfP5mzHWV6ue1nrckSI8jsg77//fhISEsjKymLLli3s3LkTt9tNWVkZixcvZsuWLd4rzSOxWq0kJiZ6n3sOl4cKPZvNxvr16wetN5kC2+UlUD7wXqAZRTC73fDif6q3ES5ZB5fe5nPT6LBoHl33KJ9Z/BlciouCNwu46fmb+M2u31DjGLpVLQYL14eTs1wdIu7p/U8T4NmPxRQx7hNTGRkZbN68mZ07d3rv1x6JyWTqd77SZrNhNBq9V7VtNps3LM1mM0VFRd5trVYrWVlZIXs129PFZ1QtyOrfw5G3ITwWbvqfEQegCNeHU3hNIXdffDdRhiiOtByhdE8pn/vz59jw4ga2vr+VY+eOjWMvpoesC7KINETywdkP2H16t9bliBAUkFsNPWpra/0e8sxisdDY2AioF4CKioq8Lcns7GzWrl3Lpk2bADUwrVYrRqORmpqafoE5kom81fBUSwcf+8V2dDrY+5NPERvpxyne5mPw/y6Hrhb4dBFc8dVRfWdbdxuv1b/GS7Uv8VbDW/269KyevZovXfQl1i1YN9pdmTZ+/PaPee7gc2QuzOThax/WuhwxQfzNBb8Csrm5maampn4XZPwpANB8tJ+JDMjXPjzFl39fxZJZsWz/3rUjv0FR1A7hH70M8z8Gd78M47g/uLmzGethKy/VvsS7J94F1As7r258VQZn8OGjpo/Y8OIG9Do9L936EikzxtYhX0wu/uaCX4fYCQkJVFZW8txzz/n15du2baO8vFzzcJxo+0Z7gWbvNjUcDRG9w5eNb/CEhMgENlywgUfWPUJ8hPq7XzVzFaao0DxfGwouSLyAy5Mvx624efbAs1qXI0KM39187r33Xnbt2sXGjRtZsmQJa9euxWw2YzQacTgc2O12duzYQW1tLXl5eWzYsCGYdYekUd1B03oWXlJPIXDNfTB7eUBqcCtuCt4swNnlZFb0LH593a+lD+QI7lxxJ+8efxfLQQtfvfSrEzZ4hgh9o7rVcPXq1ZSXl9Pc3Ex5eTk7duzA4XBgNBpZsmQJeXl503rahfP3YPvRcv5HAbSdhdkr4ervBKyG/939v7xW/xoR+ggeue4RuUvED9fMv4YFcQs40nKEF2te5LblvnsRiOllTPdiJyQkcO+99wa6lkmtua2b+kZ17MOLRmpBHqyEPWWg08PNv4WwwIx280rdK5TsKQHgxx//MatmrQrI5051ep2e21fczuYdm3l6/9NsvHCjtLoFEKBJu+rq6ti4cSNr1qxh6dKlLFu2jJycnAm/D1pLH/S2HheYYkiIHuGujNd+of684uswPzDzZH/Y+CE/fOuHANy58k5uXnJzQD53uvjc0s8xI3wGdc463jr2ltbliBARkIC0WCyUl5ezc+dODh06xMGDBykrK6O8vDwQHz8pnO//OMLhdec5OP6eunzF1wPy3U0dTXz7tW/T7mrnyuQr+W76d0d+k+gnNjyWW5bdAsBT+5/SuBoRKgISkJ7RxAeaTucjPYPkjnh43bALFDfEz4OEeeP+3m53N997/XscO3eM1LhUHvrkQ5NqfMZQcvvy29Hr9Lzd8LbclSSAAI0obrfbsVqtLFmijlvocDg4e/as9/l04PcdNEd3qD/nrwnI9z5U9RBVJ6qICYvh0eseJSEyADMoTlPz4+ZzXep1bD+ynaf3P82PrvyR1iUJjQWkBXnvvfeSm5urzmLX1ERCQgIbN24cdvzIqeRcp4vaM62AH2NAHt2p/pz/sXF/77aPtvHMgWcA2HzNZpYmjm40JTHYF1Z8AYC/1PyF5s7hpw0RU1/AjsUWL148ba9s15w6h6LArLhIZs6I9L2hokB9bwsydXwBaTtp4+fv/hyAb1z2Da5bcN24Pk+o1sxZw3LTcg40HsDykYWvrPqK1iUJDQW1L8Pu3buD+fEh45hD7d6zwDRCB+OmOmg7A/pwmHvJmL/v+LnjfOef38HldpG5MJO8S4YfjV34T6fTcceKOwB45sAzdLu7Na5IaCkgLUhfQVhWVhZSc2QH2jM7jvDy3hO8d9QBwDxj9PBvOFql/ky+FMKjxvSd7a52vv3at2nsaOTCxAv5+VU/n3ZTJgTbDYtv4OHqhznZdpLth7fz6cWf1rokoZGABOSzzz5Lc3PzoJkMd+3aFYiPD1kHjjt5/aPT3ucZK+cM/wbP4fX8tWP6PkVR+NFbP2J/435MUSYeXfeo3BYXBBGGCHIuzOGx9x7jqf1PSUBOYwEJyIKCAnbu3DloYNvt27cH4uNDVlZ6KqvmG2np6ObCOXF8fOkII+Z4WpCpYwvIJ/Y+wct1LxOmC+Phax+WkWeCaOOFG9n6/lbeO/0ee07v4ZJZYz8lIiavgJyDTEhIGHLU76HWTSWr5ieQlT6fL1+1eORw7GqDk3vV5TFcwf5n/T951KbOdlhweQHpcwJzB44Y2szomdyw+AZAOo5PZ2MOyF/+8pdDrn/11VfZunXrmAuaso7vBrcLZsyFhPmjemuNo4bvv/l9FBRyLsxh44Ubg1Oj6MfT5aeyrpKTrSc1rkZoYcwBuXr1aurq6qirq/Oue/zxxykpKSExMVFCciBv9561I06p0FdzZzPfevVbtHa3smbOGvI/lh+kAsVAK5NWkj4nHZfiouzDMq3LERoYc0BWVFR4p3/1tCYtFgtFRUVs2LCh36RcgvPnH0dxgcbldnH/6/dzpOUIKbEp/OraXxGul+lJJ9KdK+4EoOKjCjpcHRpXIybamAMyPT3dOzCFZwKtxsZG77QM0vWkD0XpE5D+n398uPph3jn+jncmQxkZfOJdm3ot82bMw9Hp4G/2v2ldjphgYw7I5ubzt2F55rNuamryrvM1b/W05DgC506CPgxSLvPrLS8ceoEn9z0JwC+u/gUXmi4MYoHCF4PewOeXfx5QL9bI9LDTy5gDcvHixZhMJpKSkjhz5gwPPfQQGRkZ/PKXv2T37t3yF6kvT+tx7ioIH6EzOXCg8QA/feenAHz10q+SuTAzmNWJEdy67FZiwmI45DjEv4//W+tyxAQacz/IDRs2eOe27ttBfPv27ZSVlVFYWDj+6qaKUR5eb/toG93ubq6edzVfu/RrQSxM+CMuIo7PLv0szxx4hqf2P8WVKVdqXZKYIOPqB6nT6aioqGDr1q3e0cPXr18v4TjQKO+geatBHdE6+4JsGfo/RHi6/Lxx9A0OOw9rXI2YKGP+11dbW8u6det45ZVXeOWVV0hPT582g1OMSnc7nNijLvtxB80R5xHqW+oJ04dxefLlQS5O+Gth/EI+Of+TADy9/2mNqxETZcwBuW3bNnbu3El5eTnl5eXeaRbEAMffUzuIx84G48IRN//XsX8BkDY7jdjw2GBXJ0bB04p84dALOLumz3xL09m4LtIMtGZNYEbJnlL69n/0o+uT5/D6qnlXBbMqMQZXJF/BUuNS2l3tPH/wea3LERNgzAE5VDee2tracRUzJZ06oP70o3tPZ08nVSfUQL0qRQIy1PQdK/JP+/+Ey+3SuCIRbGMOyIyMDK6//noKCgooKChg7dq1pKWlBbK2qaG5Xv1pXDDipraTNtpd7cyKnsUFiRcEuTAxFjeabyQxMpGG1gZerntZ63JEkI3rXuySkhIURUFRFEpLS1m3bl0ga5u8zp2GRy6D/3cF1L6urktIHfFtnvmYr5p3ldyJFKKiwqK4c6V6++HWPVtxK26NKxLBNK7xIBcvXszmzZu9z3fv3j2lRxD3W9c5aOpzumHOKr+6+Mj5x8nhtuW38fu9v6emuYbXjrzG+oVTe1i/6czvgBxpdJ6mpibKy8upqqoad1GTXtxcuPsfahcfxQ2LroawiGHfcqL1BIcch9Dr9FyZLB2RQ1lcRBy3Lb+Nx99/nNL3S1m3YJ20+Kcovw+xt2zZQlNTk88HILcXeoRHw4IrYMl1sHQ9hA0z02Evz+H1qpmrZG7rSeDOlXcSHRbNvrP7eLvhba3LEUHidwuyqKhoxBHCPbceitHz9H+Uw+vJITEqkawLsnhy35OU7imVP7cpyu8WpD/TJ6xevXpcxUxX3e5u7yAIV6dcrXE1wl9fuuhLhOvDsZ2ysfPETq3LEUEgN/qGgD2n93Cu+xzGSCMrk1ZqXY7w0+yY2Xxu6ecAePz9x7UtRgSFBGQI8Jx//HjKxzHoDRpXI0bj7ovvxqAz8HbD2+w9s1frckSASUCGAM/5x6vnyeH1ZDM/bj6fWfwZAB7fI63IqUYCUmNn2s+wv3E/gIwzOEnds+oedOh4tf5VDjYd1LocEUASkBp7p+EdAFaYVjAzeoS5tUVIMhvNZCxUe3BsfV9m85xKJCA1JofXU8O9q+4F4OW6lzniPKJxNSJQJCA11OPu8XYyln50k9uKpBVcM+8a3Iqb3+39ndbliAAZ173Y42G1WgF1RsSqqipycnJ8jgZkt9uxWCyYzWbsdju5ubneqWYns/2N+3F0OpgRPoNLZl2idTlinHIvyeXNY2/y55o/89VLv8rc2LlalyTGSbMWZHZ2NiaTiaysLJYsWUJ2dvaw227atImsrCyysrK49957J7DS4PEcXl+RfAXh+nCNqxHjddnsy1g7dy0ut4vf7/291uWIANAsICsqKvq1GH21CAcOzGs2m72tz8nOc4FGDq+nDs+5yG0Ht3Gm/YzG1Yjx0iwg+963XVFRQV5e3pDbWa1WTCZTv3UmkwmbzRbU+iaCZ3Y8uXtm6rgi+QpWzVxFZ08nT+57UutyxDhpepHGZrORn59PZmYmubm5Q27jcDiGXN/Y2Djk+s7OTpxOZ79HKHn72Nt85R9f4Udv/YizHWcBmDdjnsZViUDR6XTeVmTZh2U0dzZrXJEYD00DMi0tjYKCAmpqarBYLKN6r6/gLCwsJCEhwftITR15JO+J9HbD2+w4sYPnD6mTPl07/1oZ3myK+WTqJ1mWuIzW7lb+dOBPWpcjxkHzbj5Go5Hs7Gyys7OHDD2j0TiotdjY2OjznGVBQQHNzc3eR319fRCqHrucC3P44eU/5OYlN3Pfmvso/mSx1iWJANPr9OSuUo+Int7/NG3dbRpXJMZKk4C0Wq0kJiZ6n5vNZmDomRJ9jTHpa4rZyMhI4uPj+z1CSWp8KjnLc/jF1b/grovuIjosWuuSRBBkLsxkUfwimjubKf+wXOtyxBhpEpAmk6lf8NlsNoxGo/eqts1m84alJzw97HY7a9asmRL9IMXUZdAbuPviuwH4w74/0NnTqXFFYiw0Cci0tDRycnIoLS2ltLSUsrIyqqurva8XFhb2OydZUVFBfn4+FouFkpISKioqtChbiFG5aclNJMcmc6b9DM8ffF7rcsQY6JQpPpGM0+kkISGB5ubmkDvcFlPfMwee4cF3HyQ5Npm/3fo3uSEgRPibC5pfpBFiKrtl6S0kRSVxvPU4f635q9bliFGSgBQiiKLCorjrorsAeGLvE/S4ezSuSIyGBKQQQbbxwo3ER8Rz2HmYysOVWpcjRkECUoggiw2P5Y4VdwDq5F5T/LT/lCIBKcQEuH3F7cSExfBR00e8cfQNrcsRfpKAFGICJEQmkLM8B4DS90ulFTlJSEAKMUG+uPKLROgj2HN6DztP7tS6HOEHCUghJsjM6JncsuwWQKaInSwkIIWYQHdffDdhujDeOf4Oe8/s1bocMQIJSCEmUMqMFD5j/gwgrcjJQAJSiAn2lYu/gg4dr9a/yqGmQ1qXI4YhASnEBDMbzWQsVEezemLvExpXI4YjASmEBr6y6isAvFT7EvUtoTWoszhPAlIIDVyUdBFXzbuKHqVHpogNYRKQQmjEM7nXC4de4FTbKY2rEUORgBRCI+lz0kmbnUa3u5s/fvBHrcsRQ5CAFEJD96y6B4Dyj8pxdDi0LUYMIgEphIaunnc1K0wraHe18/SBp7UuRwwgASmEhnQ6nbcV+fT+p2ntbtW4ItGXBKQQGlu/YD2L4hfR0tUiU8SGGAlIITRm0Bu8/SL/8IFMERtKJCCFCAE3mm8kOTaZsx1neeHgC1qXI3pJQAoRAsL14Xz54i8D8Lu9v6Pb3a1xRQIkIIUIGbcsvQVTlImG1gZeqn1J63IEEpBChIyosCi+uPKLAGx9fytuxa1xRUICUogQknNhDnHhcdQ21/LqkVe1Lmfak4AUIoTMiJjB51d8HpApYkOBBKQQIeaOFXcQHRbNvrP7eLvhba3LmdYkIIUIMYlRiWRdkAWorUihHQlIIULQXSvvIkwfRvXJamwnbVqXM21JQAoRgubEzuGzSz4LqFe0hTYkIIUIUXdffDd6nZ43j73J/rP7tS5nWpKAFGIcDjsP83D1w7x/+v2AX3FeEL+ATy/6NCCtSK1IQAoxDv+o+we/3/t7bv/77Vy/7XqKdhQFtLXnGcSi8nAltc21Aftc4R8JSCHGYdXMVXxq0aeIDovmROsJntr/FLf97TbePPpmQD7/gsQLuDb1WhQUfrf3dwH5TOE/nTLFe6I6nU4SEhJobm4mPj5e63LEFNXh6uDthrcp/7CctxreYkb4DJ6+8WnMCeZxf/Z7p9/jjr/fQZgujL/f+neSZyQHoOLpzd9ckBakEAEQFRbFugXr+M2635A2O41z3ef41qvformzedyffemsS7l87uW4FBe//0CmiJ1IEpBCBFC4IZyHr32Y5NhkDjsPc9/r9+Fyu8b9ufdeok4R+9zB5zjTfmbcnyf8IwEpRIAlRSfx6LpHiQ6L5t/H/82vdv5q3J/5sbkf45KZl9DZ08lT+54KQJXCHxKQQgTBctNyHrz6QQCe2v8Uzx18blyf13dyr2c/fBZnl3PcNYqRSUAKESQZCzP4+mVfB+Bn//7ZuG8Z/GTqJ1lqXEprdyvPHng2ECWKEUhAChFEX73kq1y/8Hpcbhff+ed3aDjXMObP0uv03LtKPRf55L4naetuC1SZwgfNAtJms1FcXExxcTHZ2dk4HI5ht7XZ1P997Xa7d1mIUKfT6fjZVT9juWk5jR2NfOvVb40r2K5fdD2pcak4Oh1sO7gtgJWKoWgWkFarlU2bNrFp0ybWrl3L+vXrfW5bUlJCeno6Op2OvLw8zObx9y0TYqLEhMfw6HWPYooy8WHTh/zgXz8Y83QKYfow7r74bgD+74P/o6unK5CligE0CUibzUZhYaH3eVZWFjabDbvdPuT26enpNDU10dTURGVlJUajcYIqFSIwkmck88h1jxCmD8N6xMqW97aM+bNuXnIzs6Nnc6rtFH+p+UsAqxQDaRKQaWlpPP74+YFAPYfXJpPJ53uMRqMEo5jULpt9GT+64kcAPPbeY/yj7h9j+pwIQwR3XXQXAE/sfSIg/SzF0DQ7xM7KyvIul5WVkZGR4TMAHQ4HFosFi8VCfn6+z5YmQGdnJ06ns99DiFBxy7JbuHPlnQD88F8/HPPAFlkXZGGMNFLfUs8rda8EskTRh+b3YjscDtLT06murh42ID2v2Ww2srOzqampGXLbn/zkJzzwwAOD1su92CJUuNwuvrn9m7zV8BZzY+fyzI3PMDN65qg/p+S9En67+7csNS5l283b0OukU4q/Js292Pn5+SOeV+zbYjSbzdjtdp+tyIKCApqbm72P+vr6QJcsxLiE6cMo/mQxi+IXcaL1BN/c/s0xXdm+bfltxIbHcshxiDeOvhGESoWmAVlcXEx+fj5msxmHwzFkVx+bzTbkFW5f5ysjIyOJj4/v9xAi1MRHxPPb9b/FGGnkg7MfsOmNTaM+l5gQmUDOhTkAPL5HpogNBs0C0mKxkJaW5g3H8vLyfofRnhai2WymqKjI+z6r1UpWVpZcsBGT3sL4hfxm3W+INETy+tHX2bxj86hD7s6VdxJpiGTPmT3sOLEjSJVOX5qcg7Tb7SxZsqTfOqPRSFNTEwDZ2dmsXbuWTZs2AWpgWq1WjEYjNTU1/QJzJDIepAh1lYcr+d4/v4eCwnfSv+Pt5+ivB999kGcOPMPlyZez9XqZmsEf/uaC5hdpgk0CUkwGT+57kuKqYgCKP1HMDYtv8Pu9DecauPG5G3EpLp7+zNNcMuuSYJU5ZUyaizRCCPVQ+Y4VdwDwg3/9gJ0ndvr93pQZKdxovhGQyb0CTQJSiBBx35r7yFiQQbe7m2+99i3sDt/9fQe6e9Xd6NDxWv1rfNT0URCrnF4kIIUIEQa9gcJrCrl01qW0dLXwNevX/B493JxgJmNhBgBPvP9EMMucViQghQghUWFR/Gbdb1gQt4CG1ga+bv26330kPUOhvVz3MvVO6f8bCBKQQoSYxKhEHst4jMTIRPY37uf+N+73q4/kiqQVXD3vatyKm999IFPEBoIEpBAhaEH8An6zXu0j+cbRN3jw3Qf96iPpaUX++dCfOdl6MthlTnkSkEKEqEtnXUrRNUXo0FHxUQVP7B353GLanDTSZqfR7e7mj/v+OAFVTm0SkEKEsPUL15P/sXwAHrE9wt/sfxt2e0VRSIhMAJCr2QEgASlEiPvCii/wxZVfBOCHb/2QqhNVPret+KiC1+pfI0wfxrfTvj1RJU5ZEpBCTALfW/M9Mhdm4nK7+Par3+ZQ06FB23zY+CFFO9TbcP8r7b+4eObFE13mlCMBKcQkoNfpefDqB7ls1mW0dLfw9e1f53Tbae/rbd1t3P/G/XS5u7hm3jXeQXnF+EhACjFJePpILoxfyPHW43xj+zdo7W4FYPOOzdQ21zI7ejY/v/rnMnhugMhvUYhJxBhl5LH1j2GKMrG/cT/fe/17vFjzIs8feh4dOjZ/YjOmKN9zO4nRkYAUYpJJjU/lt+t+S5QhireOvcUP/vUDAPIuzWPt3LUaVze1SEAKMQmtmrWK4k8Uew+l02ankXdJnsZVTT1hWhcghBib6xZcR9Enivhn/T/5bvp3CdPLP+dAkwFzhRDTjgyYK4QQ4yQBKYQQPkz5kxaeMwhOp1PjSoQQocKTByOdYZzyAdnS0gJAamqqxpUIIUJNS0sLCQkJPl+f8hdp3G43DQ0NxMXFodPpgvIdTqeT1NRU6uvrp9SFINmvyWUq7lew9klRFFpaWkhJSUGv932mccq3IPV6PfPnz5+Q74qPj58yfzH7kv2aXKbifgVjn4ZrOXrIRRohhPBBAlIIIXyQgAyAyMhIfvzjHxMZGal1KQEl+zW5TMX90nqfpvxFGiGEGCtpQQohhA8SkEII4YMEpBBC+DDl+0EGS35+PgUFBRiNxoBuq6WR6rTZbFitVgCqqqp4/PHHQ36fYOT9stvtWCwWzGYzdrud3NzckN0vz+/f4XBQVVVFTk4OaWlpQ25rt9uxWq2YTCbsdjtZWVmYzeaJLNdvo9kvz/Z2u927PxkZGcEpTBGjVl1drQBKU1NTQLfVkj91FhUV9VtOS0ubgMrGx5/96rsfNTU1SlZW1gRUNjZGo1Gprq5WFEVRSkpKFLPZ7HPbvn9eiqIoubm5Qa1tPEazX5WVld59qampGXbb8ZJD7DHo+z9XILfV0kh12mw2CgsLvc+zsrKw2WzY7faJKG/MRtqvgfWbzWZvayYUVVRU9GtZDdfSLSsrm4CKAmM0+5WXl0dRkTq9rdlsprKyMmh1SUCOksViISsrK+DbasmfOtPS0nj88ce9zx0OBwAmU+hOEOXPfnkOQfsymUzYbLZgljZmfQ8lKyoqyMvzPc2CyWQiPT3de6idmZk5ESWOib/7ZbfbaWxsxGg0YrPZcDgcQW2ASECOgsPh8Pvc1Gi21dJo6uwbNmVlZWRkZITsPvq7X56gH6ixsTGwBQWQzWYjPz+fzMxMcnNzfW5XUVEBwJIlS6ioqAj5/6z92S+bzYbJZPKeMy4tLcVisQStJgnIUSgvL/f7ZPBottXSWOp0OBxYLBbvP8BQNN7fv6/gDAVpaWkUFBRQU1MzbDhYrVaKioooKSmhtLR02NZmKPBnvxobG7Hb7d7/nHNzc8nOzg5aTRKQfrJarWzcuDHg22pprHXm5+dTWVkZsq3H0eyX0Wgc1Fr0HMKFMqPRSHZ2NtnZ2UOGud1up6qqioyMDHJzc6mpqaG8vDzkzxmPtF9msxmj0ej98/H8DNYpEenmMwrl5eXeZbvdTmFhoc/uCKPZVkujrbO4uJj8/HzMZrP3L3Aohom/+5WRkUFJScmg969ZsyboNY6W1WolOzubpqYmAO+5N7vdPmi/bDYba9eenyPbbDZTUFAQki3j0ezXhF/wDNr18SkOUGpqarzPq6ur+z0fbttQNdI+VVRUKJWVlYqiKEpTU5NSUlIy4TWOxUj7NbCbT0ZGxoTW56/q6up+XZAqKioUo9HY73XPftXU1CibNm3q9/6Bz0PFaPZLURQlIyPD220r2N18JCBHqampSSkqKlIAJTc319t3Kysra1C/M1/bhhp/9qmmpkYB+j36/iUORf7+WXnCpKKiQtm0aVNI91mtqKhQSkpKlJKSEiUrK6tfcAzcr8rKSqWoqMi7fSj/Jz2a/WpqalJyc3OVkpISJTc3N6j7JaP5CCGED3KRRgghfJCAFEIIHyQghRDCBwlIIYTwQQJSCCF8kIAUQggfJCDFlDLRd4qE4p0pInAkIEXAWK1W0tPTKS0t1eT7S0tLvfdVZ2ZmkpeX5701MjExkeLiYu+gDYEauKHvGJkexcXF6HQ6srOz+40tabFYSExMJD8/H4fDQXFxcUBqEEEUtC7oYlry3Lkx0aqrq5WKigpFUdQ7Y/rWUFFRMWj084F3PY3VULfvNTU1DTmKeUlJSb91TU1NIXv7n1BJC1JMCYWFhd7xDm02W7/xBCsrKwcNfRaIQQ8sFgs5OTmD1lutVu+oM6AehpeWlg6a68azHOoj7ExnEpBi0hs4qvTAMLRarf1Gthlqm7GorKwcctSjvoHsmejM1wCwOTk5QR3wVYyPBKQIKpvNRnFxMRaLheLi4n6tJc86z3lBq9U6pvNy5eXl/QKwbyvN4XB4B1jta7xDtDkcDpYsWTLkazt37iQzM9N7Tna41mpaWlpQ51QR4yPjQYqgsdvt3sF1PdLT09m+fTsA9957r3cMwCVLlpCfnz+mll1NTY3P8Rt37tzZ73A3UDyHzEPxTGaWlZVFVlYWJSUlQ4456RHK0ztMd9KCFEFTUlIy5ICnfQezDYTh5p8Z6vyjL1arFYvF4h3A1dPCHUpNTc2Q32m1WjEajWzatAmz2UxeXp5mV/XF+ElACk145hPpe4jd91DU4XCQn58/aCj90tJS76G453DdaDT67I/oOcz1R0lJCVlZWd5AzcrKYtOmTYO2s9lsPmcIHBjIGRkZ3smlxOQjASkCzhNWOTk5g+aYttls3vlikpKS2LRpE7m5uYOCaOfOnYNCz263U1NTQ0ZGBps2bSI/Px9QD899XQm22WxDtiD7nhf1bGez2bBYLFgsFux2O6WlpUMGb1lZmc8ZAoeaXrXvPM5DCeWpc6c7CUgRMDabjbKyMsrKyrDZbKSlpVFUVOQNo/z8fCoqKryHpjU1NSxZsoT09HQyMzP7tbKGmlLWarX2uzDiCcWMjAyqqqq86z3dajyz3XlCz6O0tBSj0ehtLVosFtLS0jCbzd7zhmazeVC3nOHY7XaKi4ux2WxUV1d7a3M4HFRVVWG324fsnD5ca1RoTy7SiIBJS0ujurq637qMjIx+h6wenrDzXLzwXNAxm80+zxkOPNfoad2ZzeZ+LUjP4buviyjV1dUsWbLE27odzYUhX30fzWYzmzZtGtQSNhqNw06PW1ZWFvLTsU5n0oIUmhh4rs5sNpOTkzNsp+nhzjXm5eX53Z/Q02LzhPdoriL76vs4Fn0DXoQmCUihiaKiIu/FFs8hcGNjo89WH6hTsZ49e9b7vG9QeYLOn8EjPC1Zi8VCaWkpJpPJ2zWn78+BF1aG6/s4FoWFhcOemxTak0m7REiyWq0UFRWRlpbWbz7r0tJS7yH1xo0bB50jHK7LjxCjJQEphBA+yCG2EEL4IAEphBA+SEAKIYQPEpBCCOGDBKQQQvggASmEED5IQAohhA8SkEII4cP/B0itWighWckXAAAAAElFTkSuQmCC", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "PLOT_PROPERTIES['legend1D'] = dict(loc='upper left', lines_legend=['41','42','45'])\n", + "grid.HR([41,42,45], history='history1', **PLOT_PROPERTIES)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example: the single HMS gird" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting an Hertzsprung–Russell diagram" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``HR`` diagram method has also an option to display the stellar states of the tracks. Here we show how to reproduce a HR diagram that looks like Fig. 5 in Fragos et al. (2023)." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# load single HMS grid\n", + "path_to_gird = os.path.join(PATH_TO_POSYDON_DATA, 'POSYDON_data/single_HMS/1e-01_Zsun.h5')\n", + "grid = PSyGrid(path_to_gird)\n", + "grid.load()\n", + "\n", + "PLOT_PROPERTIES = {\n", + " 'figsize' : (3.38, 5),\n", + " 'show_fig' : True,\n", + " 'close_fig' : True,\n", + " #'path_to_file': './dir/',\n", + " #'fname': 'filename.png', # specify filename to save the figure\n", + " 'xmin' : 3.,\n", + " 'xmax' : 6.,\n", + " 'ymin' : -1.5,\n", + " 'ymax' : 7.5,\n", + " 'const_R_lines' : True,\n", + " 'legend1D' : {\n", + " 'loc' : 'upper center',\n", + " 'bbox_to_anchor' : (0.4, 1.27),\n", + " 'ncol' : 2,\n", + " 'prop': {\n", + " 'size': 6\n", + " },\n", + " }\n", + "}\n", + "\n", + "# chose a subsample of tracks\n", + "idx = np.around(np.argsort(grid.initial_values['S1_star_mass']),2)[::8].tolist()\n", + "\n", + "grid.HR(idx, history='history1', states=True, **PLOT_PROPERTIES)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example: the single HeMS gird" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting an Hertzsprung–Russell diagram" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# load single HeMS grid\n", + "path_to_gird = os.path.join(PATH_TO_POSYDON_DATA, 'POSYDON_data/single_HeMS/1e-01_Zsun.h5')\n", + "grid = PSyGrid(path_to_gird)\n", + "grid.load()\n", + "\n", + "PLOT_PROPERTIES['ymin'] = -0.5 \n", + "PLOT_PROPERTIES['xmin'] = 3.5\n", + "\n", + "# chose a subsample of tracks\n", + "idx = np.around(np.argsort(grid.initial_values['S1_star_mass']),2)[::8].tolist()\n", + "\n", + "grid.HR(idx, history='history1', states=True, **PLOT_PROPERTIES)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congratulations! You have successfully completed this tutorial. You now master POSYDON 1D visualization tools." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "posydon_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/_source/tutorials-examples/generating-datasets/plot_2D.ipynb b/docs/_source/tutorials-examples/generating-datasets/plot_2D.ipynb new file mode 100644 index 0000000000..34a99c6add --- /dev/null +++ b/docs/_source/tutorials-examples/generating-datasets/plot_2D.ipynb @@ -0,0 +1,689 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2D Plotting Functionalities for POSYDON PSyGrids" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tutorial shows you how to plot single and binary stellar tracks using the `plot2D` visualization library. The `plot2D` method supports either a MESA grid which samples the 2D parameter space of binary initial conditions in a 2D, 3D or 4D MESA space, e.g., star_1_mass, star_2_mass (or mass ratio), period_days, metallicity. In the 3D and 4D cases the MESA grid will be sliced along the dimensions specified by the user.\n", + "\n", + "If you haven't done it already, export the environemnt variables." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "env: PATH_TO_POSYDON=/Users/simone/Google Drive/github/POSYDON-public/\n", + "env: PATH_TO_POSYDON_DATA=/Volumes/T7/\n" + ] + } + ], + "source": [ + "%env PATH_TO_POSYDON=/Users/simone/Google Drive/github/POSYDON-public/\n", + "%env PATH_TO_POSYDON_DATA=/Volumes/T7/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Any plotting parameter is passed to the ``plot2D`` method through the kwarg ``PLOT_PROPERTIES``. Note that you can save each plot to a given ``path_to_file`` by specifying the filename ``fname`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example: the HMS-HMS gird" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start by loading the grid." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from posydon.config import PATH_TO_POSYDON_DATA\n", + "from posydon.grids.psygrid import PSyGrid\n", + "\n", + "path_to_gird = os.path.join(PATH_TO_POSYDON_DATA, 'POSYDON_data/HMS-HMS/1e-01_Zsun.h5')\n", + "grid = PSyGrid(path_to_gird)\n", + "grid.load()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### MESA Termination Flags 1 and 2 Combied" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This plot summarise the binary evolutioary simulation. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "q = 0.7\n", + "PLOT_PROPERTIES = {\n", + " 'figsize': (4.5, 4.),\n", + " 'show_fig' : True,\n", + " 'close_fig' : True,\n", + " #'path_to_file': './dirname/',\n", + " #'fname': f'filename_q_{q}.png', # specify the filename if you want to save the figure\n", + " 'title' : r'$q = %1.1f$'%q,\n", + " 'log10_x' : True,\n", + " 'log10_y' : True,\n", + "}\n", + "\n", + "grid.plot2D('star_1_mass', 'period_days', None,\n", + " termination_flag='combined_TF12',\n", + " grid_3D=True, slice_3D_var_str='mass_ratio',\n", + " slice_3D_var_range=(q-0.025,q+0.025),\n", + " verbose=False, **PLOT_PROPERTIES)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### MESA Termination Flag 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This plot indicates why MESA stopped and allows to illustrata a final quantity." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "PLOT_PROPERTIES['figsize'] = (4.5,6.)\n", + " \n", + "grid.plot2D('star_1_mass', 'period_days', 'S2_surf_avg_omega_div_omega_crit',\n", + " termination_flag='termination_flag_1',\n", + " grid_3D=True, slice_3D_var_str='mass_ratio',\n", + " slice_3D_var_range=(q-0.025,q+0.025),\n", + " verbose=False, **PLOT_PROPERTIES)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### MESA Termination Flag 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The termination flag 2 shows a summary of all mass transfer cases which\n", + "occurred during the binaries' evolution." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "PLOT_PROPERTIES['figsize'] = (4.5,4.) # defualt\n", + "\n", + "grid.plot2D('star_1_mass', 'period_days', None,\n", + " termination_flag='termination_flag_2',\n", + " grid_3D=True, slice_3D_var_str='mass_ratio',\n", + " slice_3D_var_range=(q-0.025,q+0.025),\n", + " verbose=False, **PLOT_PROPERTIES)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### MESA Termination Flag 3 and 4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The termination flag 3/ shows the final stellar state of star 1/2." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "grid.plot2D('star_1_mass', 'period_days', None,\n", + " termination_flag='termination_flag_3',\n", + " grid_3D=True, slice_3D_var_str='mass_ratio',\n", + " slice_3D_var_range=(q-0.025,q+0.025),\n", + " verbose=False, **PLOT_PROPERTIES)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "grid.plot2D('star_1_mass', 'period_days', None,\n", + " termination_flag='termination_flag_4',\n", + " grid_3D=True, slice_3D_var_str='mass_ratio',\n", + " slice_3D_var_range=(q-0.025,q+0.025),\n", + " verbose=False, **PLOT_PROPERTIES)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One can plot all termination flags at once in a large subplot of panels with\n", + "the option ``termination_flag='all'``.\n", + "In order to fit all legends we suggest to increase the figure size and marker\n", + "size to, e.g. ``(25,25)`` and ``30``, respectively." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Display Relative Change of a Final Quantity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The relative change of any value stored within ``final_values`` can be\n", + "displayed by adding the prefix ``relative_change_`` to the z-variable displayed\n", + "as a colorbar. E.g. we can display the relative change of star_1_mass:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "PLOT_PROPERTIES['figsize'] = (4.5,6.)\n", + "PLOT_PROPERTIES['colorbar'] = {'label' : r'$(M_\\mathrm{1,f}-M_\\mathrm{1,i})/M_\\mathrm{1,i}$',}\n", + "\n", + "grid.plot2D('star_1_mass', 'period_days', 'relative_change_star_1_mass',\n", + " termination_flag='termination_flag_1',\n", + " grid_3D=True, slice_3D_var_str='mass_ratio',\n", + " slice_3D_var_range=(q-0.025,q+0.025),\n", + " verbose=False, **PLOT_PROPERTIES)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Display Custom Quantities" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The user can plot custom quantities as a color map on termination flag 1.\n", + "For example, we display the maximum surf_avg_omega_div_omega_crit during\n", + "the history of star 2 as follows" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "PLOT_PROPERTIES['figsize'] = (4.5,6.)\n", + "PLOT_PROPERTIES['colorbar'] = {'label' : r'$\\max(\\omega_\\mathrm{s}/\\omega_\\mathrm{s,crit})_1$'}\n", + "\n", + "max_omega = [max(grid[i].history2['surf_avg_omega_div_omega_crit'])\n", + " if grid[i].history2 is not None else np.nan\n", + " for i in range(len(grid.MESA_dirs))]\n", + "\n", + "grid.plot2D('star_1_mass', 'period_days', np.array(max_omega),\n", + " termination_flag='termination_flag_1',\n", + " grid_3D=True, slice_3D_var_str='mass_ratio',\n", + " slice_3D_var_range=(q-0.025,q+0.025),\n", + " verbose=False, **PLOT_PROPERTIES)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dispaly Core Collapse Quantities" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Float quantities can be displayed using the z variable option as shown with termination flag 1." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "PLOT_PROPERTIES['figsize'] = (4.5,6.)\n", + "PLOT_PROPERTIES['colorbar'] = {'label' : r'$a_\\mathrm{CO,1}$'}\n", + "PLOT_PROPERTIES['zmin'] = 0.\n", + "PLOT_PROPERTIES['zmax'] = 0.3\n", + "\n", + "grid.plot2D('star_1_mass', 'period_days', 'S1_MODEL05_spin',\n", + " termination_flag='termination_flag_1',\n", + " grid_3D=True, slice_3D_var_str='mass_ratio',\n", + " slice_3D_var_range=(q-0.025,q+0.025),\n", + " verbose=False, **PLOT_PROPERTIES)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "The missig points in the above plots are NaN values associated to disruped stars due to pair instability supernovae (PISN). \n", + "\n", + "To display supernova type (SN_type) and compact object type (CO_type) are stored as strings in the dataset. These quantities can be plotted using the ``termination_flag`` option of ``plot2D``, as" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "PLOT_PROPERTIES['figsize'] = (4.5,4.) # defualt\n", + "\n", + "grid.plot2D('star_1_mass', 'period_days', None,\n", + " termination_flag='S1_MODEL01_SN_type',\n", + " grid_3D=True, slice_3D_var_str='mass_ratio',\n", + " slice_3D_var_range=(q-0.025,q+0.025),\n", + " verbose=False, **PLOT_PROPERTIES)\n", + "\n", + "grid.plot2D('star_1_mass', 'period_days', None,\n", + " termination_flag='S1_MODEL01_CO_type',\n", + " grid_3D=True, slice_3D_var_str='mass_ratio',\n", + " slice_3D_var_range=(q-0.025,q+0.025),\n", + " verbose=False, **PLOT_PROPERTIES)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Displaying Final Quantities also for intial_MT, unstable_MT and not_converged Interpolationn Classes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have a debug ``termination_flag`` option which allows us to display\n", + "as a colormap the final value also for the tracks that ends because of\n", + "initial or unstable mass transfer." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "PLOT_PROPERTIES['figsize'] = (4.5,6.)\n", + "PLOT_PROPERTIES['log10_z'] = True\n", + "PLOT_PROPERTIES['zmin'] = 0.1\n", + "PLOT_PROPERTIES['zmax'] = 3\n", + "\n", + "grid.plot2D('star_1_mass', 'period_days', 'period_days',\n", + " termination_flag='debug',\n", + " grid_3D=True, slice_3D_var_str='mass_ratio',\n", + " slice_3D_var_range=(q-0.025,q+0.025),\n", + " verbose=False, **PLOT_PROPERTIES)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Advanced Plotting Functionalities" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How to plot grid up to onset of RLO" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This functionality allows to slice the MESA runs at onset of Roche-Lobe\n", + "overflow and display with a color a quantity in ``history1`` or\n", + "``binary_history``\n", + "at onset RLO, or, alternatively one of the termination flags. Use\n", + "``slice_at_RLO=True`` option of ``plot2D`` to allow for this.\n", + "Note: depending on how many\n", + "runs you intend to display this might take a while." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Overplot grids on top of each others" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sometimes you want to rerun a subsample of the grid. This option will allow you\n", + "to stack as many grid reruns as you wish.\n", + "You can combine the new grid by passing it to the option ``extra_grid=new_grid``.\n", + "If you want to stack more than one grid, pass them in a list, e.g.\n", + "``extra_grid=[new_grid_1,new_grid_2,new_grid_3]``, they will be stacked in\n", + "the order provided where the last extra grid of the list will stacked as last." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating plots with several slices" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have the option to pass a list of slice ranges. In this case the plotting\n", + "script will loop over those ranges and create one plot with several slice\n", + "plots. It will get a common legend and/or color bar.\n", + "\n", + "There are two more options to the plot2D function:\n", + "1. ``max_cols``: It specifies the maximum number of columns of subplots. The\n", + "number of rows is automatically calculated to cover all plots and the legend.\n", + "2. ``legend_pos``: It specifies which subplots are used for the legend/color\n", + "bar and allows an index or tuple specifying a rectangle of indecies, like\n", + "matplotlib requires for creating a subplot.\n", + "\n", + "Because the legend has its own axes, it is recommended to modify the default\n", + "``bbox_to_anchor`` of the legend.\n", + "To have a better control on the color bar, it got the additional field\n", + "``bounds`` in the plot properties.\n", + "To identify the subplots each of them will get a text box. Its attributes can\n", + "be modified by changing the field ``slice_text_kwargs`` in the plot properties.\n", + "The slices will loop over 3D and 4D idependently. If one of them has just a\n", + "single range, this slicing will be added to the legend title instead of being\n", + "reported in any subplot." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "q_ranges = [(0.025, 0.075), (0.175, 0.225), (0.325, 0.375), (0.475, 0.525),\n", + " (0.625, 0.675), (0.775, 0.825), (0.925, 0.975), (0.98, 1.0)]\n", + "\n", + "plot_properties = {\n", + " 'show_fig': True,\n", + " 'figsize': (9, 9),\n", + " 'log10_x': True,\n", + " 'log10_y': True,\n", + " 'xmin': 0.68,\n", + " 'xmax': 2.52,\n", + " 'ymin': -1.2,\n", + " 'ymax': 3.9,\n", + " 'wspace': 0.01,\n", + " 'hspace': 0.01,\n", + " 'colorbar': {\n", + " 'bounds': [0.03, 0.7, 0.94, 0.05]\n", + " },\n", + " 'legend2D': {\n", + " 'title': 'Termination flags',\n", + " 'loc': 'lower left',\n", + " 'prop': {\n", + " 'size': 7\n", + " },\n", + " 'bbox_to_anchor': (0.0, 0.0),\n", + " },\n", + "}\n", + "grid.plot2D('star_1_mass', 'period_days', None,\n", + " termination_flag='combined_TF12',\n", + " grid_3D=True, slice_3D_var_str='mass_ratio',\n", + " slice_3D_var_range=q_ranges,\n", + " legend_pos = (3,3),\n", + " verbose=False, **plot_properties)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating plots with several slices at different metallicities" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TODO: add the plot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congratulations! You are now a POSYDON PSyGrid visualization expert!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "posydon_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/_source/tutorials-examples/generating-datasets/processing_single_hms.ipynb b/docs/_source/tutorials-examples/generating-datasets/processing_single_hms.ipynb new file mode 100644 index 0000000000..81685c029d --- /dev/null +++ b/docs/_source/tutorials-examples/generating-datasets/processing_single_hms.ipynb @@ -0,0 +1,890 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Processing Single Stars with POSYDON" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tutorial shows you how to process the single stellar models into a PSyGrid object.\n", + "\n", + "WARNING: Unfortunatly, the software to resample the raw MESA histories into equivalent evolutionary points (EEPs) is not available in POSYDON yet, but we are using the standalone Fortran code of Aaron Dotter, see http://adsabs.harvard.edu/abs/2016ApJS..222....8D . This tutorial will be updated as soon as they are available.\n", + "\n", + "If you are a POSYDON developer, then you should know that you can find the EEP code we used to export the v2.0.0 single star grids on Yggdrasil HPC in `/srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/single_HMS/eep_code`.\n", + "\n", + "The steps are the following:\n", + "1. You should create a work directory, say `eeps` where you will create two directories `grid_name` where you will copy the raw MESA history files from all your MESA single stellar models and `eeps_grid_name` where the code will export the resample EEP histories.\n", + "2. You should create a `input.grid_name` file in the `eep_code` directory that will run the EEP code to resample the histories." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generating the EEP history files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following cell shows you how to copy the histories in ``./eeps/grid`` where we use the 10 single HMS models we run in the step 3 tutorial (step 1)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10\n", + "Zbase_0.0014_initial_mass_26.4098_initial_z_1.4200e-03_grid_index_5.data\n", + "Zbase_0.0014_initial_mass_9.7294_initial_z_1.4200e-03_grid_index_2.data\n", + "Zbase_0.0014_initial_mass_13.5721_initial_z_1.4200e-03_grid_index_3.data\n", + "Zbase_0.0014_initial_mass_71.6871_initial_z_1.4200e-03_grid_index_8.data\n", + "Zbase_0.0014_initial_mass_18.9324_initial_z_1.4200e-03_grid_index_4.data\n", + "Zbase_0.0014_initial_mass_36.8403_initial_z_1.4200e-03_grid_index_6.data\n", + "Zbase_0.0014_initial_mass_5.0000_initial_z_1.4200e-03_grid_index_0.data\n", + "Zbase_0.0014_initial_mass_51.3904_initial_z_1.4200e-03_grid_index_7.data\n", + "Zbase_0.0014_initial_mass_100.0000_initial_z_1.4200e-03_grid_index_9.data\n", + "Zbase_0.0014_initial_mass_6.9748_initial_z_1.4200e-03_grid_index_1.data\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import csv\n", + "import os\n", + "import shutil\n", + "\n", + "grid_name = 'single_hms_1e-01_Zsun'\n", + "dirs = [ d for d in os.listdir('../'+grid_name) if 'initial_mass' in d]\n", + "missing = []\n", + "print(len(dirs))\n", + "for i in range(len(dirs)):\n", + " filename = '../'+grid_name+'/'+dirs[i]+'/LOGS1/history.data'\n", + " if os.path.exists(filename):\n", + " shutil.copy(filename,'./eeps/'+grid_name+'/'+dirs[i]+'.data')\n", + " print(dirs[i]+'.data')\n", + " else:\n", + " missing += [filename]\n", + "if len(missing)>0:\n", + " print(len(dirs)-len(missing))\n", + " for f in missing:\n", + " print(f)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copy the above list of file names in the `input` file below." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing input.single_hms_1e-01_Zsun\n" + ] + } + ], + "source": [ + "%%writefile input.single_hms_1e-01_Zsun\n", + "#version string, max 8 characters\n", + "POSYDON\n", + "#initial Y, initial Z, [Fe/H], [alpha/Fe], v/vcrit\n", + "0.2703 0.0142 -1.0 0.0 0.0\n", + "#data directories: 1) history files, 2) eeps, 3) isochrones\n", + "/srv/beegfs/scratch/shares/astro/posydon/simone/documentation/running_mesa/processing_single_hms/eeps/single_hms_1e-01_Zsun\n", + "/srv/beegfs/scratch/shares/astro/posydon/simone/documentation/running_mesa/processing_single_hms/eeps/eeps_single_hms_1e-01_Zsun\n", + "/dev/null\n", + "# read history_columns\n", + "/srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/single_HMS/eep_code/posydon_history_columns.list\n", + "#tracks 10\n", + "10\n", + "Zbase_0.0014_initial_mass_26.4098_initial_z_1.4200e-03_grid_index_5.data\n", + "Zbase_0.0014_initial_mass_9.7294_initial_z_1.4200e-03_grid_index_2.data\n", + "Zbase_0.0014_initial_mass_13.5721_initial_z_1.4200e-03_grid_index_3.data\n", + "Zbase_0.0014_initial_mass_71.6871_initial_z_1.4200e-03_grid_index_8.data\n", + "Zbase_0.0014_initial_mass_18.9324_initial_z_1.4200e-03_grid_index_4.data\n", + "Zbase_0.0014_initial_mass_36.8403_initial_z_1.4200e-03_grid_index_6.data\n", + "Zbase_0.0014_initial_mass_5.0000_initial_z_1.4200e-03_grid_index_0.data\n", + "Zbase_0.0014_initial_mass_51.3904_initial_z_1.4200e-03_grid_index_7.data\n", + "Zbase_0.0014_initial_mass_100.0000_initial_z_1.4200e-03_grid_index_9.data\n", + "Zbase_0.0014_initial_mass_6.9748_initial_z_1.4200e-03_grid_index_1.data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now run the eep code as follows." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 1 surface_mu 10\n", + " 1 2 star_age 8\n", + " 1 3 star_mass 9\n", + " 1 4 star_mdot 9\n", + " 1 5 log_abs_mdot 12\n", + " 1 6 log_dt 6\n", + " 1 7 log_total_angular_momentum 26\n", + " 1 8 mass_conv_core 14\n", + " 1 9 conv_mx1_top 12\n", + " 1 10 conv_mx1_bot 12\n", + " 1 11 conv_mx2_top 12\n", + " 1 12 conv_mx2_bot 12\n", + " 1 13 conv_mx1_top_r 14\n", + " 1 14 conv_mx1_bot_r 14\n", + " 1 15 conv_mx2_top_r 14\n", + " 1 16 conv_mx2_bot_r 14\n", + " 1 17 he_core_mass 12\n", + " 1 18 he_core_radius 14\n", + " 1 19 c_core_mass 11\n", + " 1 20 c_core_radius 13\n", + " 1 21 o_core_mass 11\n", + " 1 22 o_core_radius 13\n", + " 1 23 envelope_mass 13\n", + " 1 24 log_LH 6\n", + " 1 25 log_LHe 7\n", + " 1 26 log_LZ 6\n", + " 1 27 log_Lnuc 8\n", + " 1 28 log_Lneu 8\n", + " 1 29 log_abs_Lgrav 13\n", + " 1 30 log_Teff 8\n", + " 1 31 photosphere_r 13\n", + " 1 32 photosphere_cell_density 24\n", + " 1 33 log_L 5\n", + " 1 34 log_R 5\n", + " 1 35 log_g 5\n", + " 1 36 log_L_div_Ledd 14\n", + " 1 37 surf_avg_j_rot 14\n", + " 1 38 surf_avg_omega 14\n", + " 1 39 surf_avg_omega_crit 19\n", + " 1 40 surf_avg_omega_div_omega_crit 29\n", + " 1 41 surf_avg_v_rot 14\n", + " 1 42 surf_avg_v_crit 15\n", + " 1 43 surf_avg_Lrad_div_Ledd 22\n", + " 1 44 rotational_mdot_boost 21\n", + " 1 45 surf_r_equatorial_div_r_polar 29\n", + " 1 46 surf_r_equatorial_div_r 23\n", + " 1 47 surf_r_polar_div_r 18\n", + " 1 48 log_center_T 12\n", + " 1 49 log_center_Rho 14\n", + " 1 50 log_center_P 12\n", + " 1 51 center_degeneracy 17\n", + " 1 52 center_gamma 12\n", + " 1 53 center_mu 9\n", + " 1 54 center_ye 9\n", + " 1 55 center_omega 12\n", + " 1 56 center_h1 9\n", + " 1 57 center_he3 10\n", + " 1 58 center_he4 10\n", + " 1 59 center_c12 10\n", + " 1 60 center_n14 10\n", + " 1 61 center_o16 10\n", + " 1 62 center_ne20 11\n", + " 1 63 center_si28 11\n", + " 1 64 center_s32 10\n", + " 1 65 center_ca40 11\n", + " 1 66 center_ti44 11\n", + " 1 67 center_fe56 11\n", + " 1 68 surface_h1 10\n", + " 1 69 surface_he3 11\n", + " 1 70 surface_he4 11\n", + " 1 71 surface_c12 11\n", + " 1 72 surface_n14 11\n", + " 1 73 surface_o16 11\n", + " 1 74 surface_ne20 12\n", + " 1 75 surface_si28 12\n", + " 1 76 surface_s32 11\n", + " 1 77 surface_ca40 12\n", + " 1 78 surface_ti44 12\n", + " 1 79 surface_fe56 12\n", + " 1 80 total_mass_h1 13\n", + " 1 81 total_mass_he4 14\n", + " 1 82 pp 2\n", + " 1 83 cno 3\n", + " 1 84 tri_alfa 8\n", + " 1 85 burn_c 6\n", + " 1 86 burn_n 6\n", + " 1 87 burn_o 6\n", + " 1 88 burn_ne 7\n", + " 1 89 c12_c12 7\n", + " 1 90 delta_nu 8\n", + " 1 91 delta_Pg 8\n", + " 1 92 nu_max 6\n", + " 1 93 acoustic_cutoff 15\n", + " 1 94 v_div_csound_surf 17\n", + " 1 95 e_thermal 9\n", + " 1 96 max_conv_vel_div_csound 23\n", + " 1 97 max_gradT_div_grada 19\n", + " 1 98 surf_c12_minus_o16 18\n", + " 1 99 min_Pgas_div_P 14\n", + " 1 100 total_internal_energy 21\n", + " 1 101 total_gravitational_energy 26\n", + " 1 102 total_radial_kinetic_energy 27\n", + " 1 103 total_rotational_kinetic_energy 31\n", + " 1 104 total_energy 12\n", + " 1 105 total_eps_grav 14\n", + " 1 106 conv_env_top_mass 17\n", + " 1 107 conv_env_bot_mass 17\n", + " 1 108 conv_env_top_radius 19\n", + " 1 109 conv_env_bot_radius 19\n", + " 1 110 conv_env_turnover_time_g 24\n", + " 1 111 conv_env_turnover_time_l_b 26\n", + " 1 112 conv_env_turnover_time_l_t 26\n", + " 1 113 envelope_binding_energy 23\n", + " 1 114 total_moment_of_inertia 23\n", + " 1 115 spin_parameter 14\n", + " 1 116 avg_c_in_c_core 15\n", + " 1 117 mass_conv_reg_fortides 22\n", + " 1 118 thickness_conv_reg_fortides 27\n", + " 1 119 radius_conv_reg_fortides 24\n", + " 1 120 lambda_CE_1cent 15\n", + " 1 121 lambda_CE_10cent 16\n", + " 1 122 lambda_CE_30cent 16\n", + " 1 123 co_core_mass 12\n", + " 1 124 co_core_radius 14\n", + " 1 125 lambda_CE_pure_He_star_10cent 29\n", + " 1 126 he_core_mass_1cent 18\n", + " 1 127 he_core_mass_10cent 19\n", + " 1 128 he_core_mass_30cent 19\n", + " 1 129 he_core_radius_1cent 20\n", + " 1 130 he_core_radius_10cent 21\n", + " 1 131 he_core_radius_30cent 21\n", + " 2 1 surface_mu 10\n", + " 2 2 star_age 8\n", + " 2 3 star_mass 9\n", + " 2 4 star_mdot 9\n", + " 2 5 log_abs_mdot 12\n", + " 2 6 log_dt 6\n", + " 2 7 log_total_angular_momentum 26\n", + " 2 8 mass_conv_core 14\n", + " 2 9 conv_mx1_top 12\n", + " 2 10 conv_mx1_bot 12\n", + " 2 11 conv_mx2_top 12\n", + " 2 12 conv_mx2_bot 12\n", + " 2 13 conv_mx1_top_r 14\n", + " 2 14 conv_mx1_bot_r 14\n", + " 2 15 conv_mx2_top_r 14\n", + " 2 16 conv_mx2_bot_r 14\n", + " 2 17 he_core_mass 12\n", + " 2 18 he_core_radius 14\n", + " 2 19 c_core_mass 11\n", + " 2 20 c_core_radius 13\n", + " 2 21 o_core_mass 11\n", + " 2 22 o_core_radius 13\n", + " 2 23 envelope_mass 13\n", + " 2 24 log_LH 6\n", + " 2 25 log_LHe 7\n", + " 2 26 log_LZ 6\n", + " 2 27 log_Lnuc 8\n", + " 2 28 log_Lneu 8\n", + " 2 29 log_abs_Lgrav 13\n", + " 2 30 log_Teff 8\n", + " 2 31 photosphere_r 13\n", + " 2 32 photosphere_cell_density 24\n", + " 2 33 log_L 5\n", + " 2 34 log_R 5\n", + " 2 35 log_g 5\n", + " 2 36 log_L_div_Ledd 14\n", + " 2 37 surf_avg_j_rot 14\n", + " 2 38 surf_avg_omega 14\n", + " 2 39 surf_avg_omega_crit 19\n", + " 2 40 surf_avg_omega_div_omega_crit 29\n", + " 2 41 surf_avg_v_rot 14\n", + " 2 42 surf_avg_v_crit 15\n", + " 2 43 surf_avg_Lrad_div_Ledd 22\n", + " 2 44 rotational_mdot_boost 21\n", + " 2 45 surf_r_equatorial_div_r_polar 29\n", + " 2 46 surf_r_equatorial_div_r 23\n", + " 2 47 surf_r_polar_div_r 18\n", + " 2 48 log_center_T 12\n", + " 2 49 log_center_Rho 14\n", + " 2 50 log_center_P 12\n", + " 2 51 center_degeneracy 17\n", + " 2 52 center_gamma 12\n", + " 2 53 center_mu 9\n", + " 2 54 center_ye 9\n", + " 2 55 center_omega 12\n", + " 2 56 center_h1 9\n", + " 2 57 center_he3 10\n", + " 2 58 center_he4 10\n", + " 2 59 center_c12 10\n", + " 2 60 center_n14 10\n", + " 2 61 center_o16 10\n", + " 2 62 center_ne20 11\n", + " 2 63 center_si28 11\n", + " 2 64 center_s32 10\n", + " 2 65 center_ca40 11\n", + " 2 66 center_ti44 11\n", + " 2 67 center_fe56 11\n", + " 2 68 surface_h1 10\n", + " 2 69 surface_he3 11\n", + " 2 70 surface_he4 11\n", + " 2 71 surface_c12 11\n", + " 2 72 surface_n14 11\n", + " 2 73 surface_o16 11\n", + " 2 74 surface_ne20 12\n", + " 2 75 surface_si28 12\n", + " 2 76 surface_s32 11\n", + " 2 77 surface_ca40 12\n", + " 2 78 surface_ti44 12\n", + " 2 79 surface_fe56 12\n", + " 2 80 total_mass_h1 13\n", + " 2 81 total_mass_he4 14\n", + " 2 82 pp 2\n", + " 2 83 cno 3\n", + " 2 84 tri_alfa 8\n", + " 2 85 burn_c 6\n", + " 2 86 burn_n 6\n", + " 2 87 burn_o 6\n", + " 2 88 burn_ne 7\n", + " 2 89 c12_c12 7\n", + " 2 90 delta_nu 8\n", + " 2 91 delta_Pg 8\n", + " 2 92 nu_max 6\n", + " 2 93 acoustic_cutoff 15\n", + " 2 94 v_div_csound_surf 17\n", + " 2 95 e_thermal 9\n", + " 2 96 max_conv_vel_div_csound 23\n", + " 2 97 max_gradT_div_grada 19\n", + " 2 98 surf_c12_minus_o16 18\n", + " 2 99 min_Pgas_div_P 14\n", + " 2 100 total_internal_energy 21\n", + " 2 101 total_gravitational_energy 26\n", + " 2 102 total_radial_kinetic_energy 27\n", + " 2 103 total_rotational_kinetic_energy 31\n", + " 2 104 total_energy 12\n", + " 2 105 total_eps_grav 14\n", + " 2 106 conv_env_top_mass 17\n", + " 2 107 conv_env_bot_mass 17\n", + " 2 108 conv_env_top_radius 19\n", + " 2 109 conv_env_bot_radius 19\n", + " 2 110 conv_env_turnover_time_g 24\n", + " 2 111 conv_env_turnover_time_l_b 26\n", + " 2 112 conv_env_turnover_time_l_t 26\n", + " 2 113 envelope_binding_energy 23\n", + " 2 114 total_moment_of_inertia 23\n", + " 2 115 spin_parameter 14\n", + " 2 116 avg_c_in_c_core 15\n", + " 2 117 mass_conv_reg_fortides 22\n", + " 2 118 thickness_conv_reg_fortides 27\n", + " 2 119 radius_conv_reg_fortides 24\n", + " 2 120 lambda_CE_1cent 15\n", + " 2 121 lambda_CE_10cent 16\n", + " 2 122 lambda_CE_30cent 16\n", + " 2 123 co_core_mass 12\n", + " 2 124 co_core_radius 14\n", + " 2 125 lambda_CE_pure_He_star_10cent 29\n", + " 2 126 he_core_mass_1cent 18\n", + " 2 127 he_core_mass_10cent 19\n", + " 2 128 he_core_mass_30cent 19\n", + " 2 129 he_core_radius_1cent 20\n", + " 2 130 he_core_radius_10cent 21\n", + " 2 131 he_core_radius_30cent 21\n", + " process_history_columns: ncol = 131\n", + " number of history columns = 131\n", + " star_age column = 2\n", + " star_mass column= 3\n", + " main = 32\n", + " head = 28\n", + " xtra = 1\n", + " Zbase_0.0014_initial_mass_26.4098_initial_z_1.4200e-03_grid_index_5.data 0.0000000000000000 11701\n", + " number of columns: 131\n", + " do not have surface_mu\n", + " do not have photosphere_cell_density\n", + " Zbase_0.0014_initial_mass_26.4098_initial_z_1.4200e-03_grid_index_5.data 9 11701\n", + " guess = 194\n", + " my_guess = 194\n", + " ntrack = 1134\n", + " 21 117 193 301 321 920 1134 0 0\n", + " track, number of p, s eeps = 0 7 700\n", + " 6277.9927750097913 \n", + " 5287405.0672792234 \n", + " 7457780.3353313934 \n", + " 7469035.5533669619 \n", + " 7481078.8974012667 \n", + " 7967779.7210960779 \n", + " 7973760.0214983337 \n", + " main = 32\n", + " head = 28\n", + " xtra = 1\n", + " Zbase_0.0014_initial_mass_9.7294_initial_z_1.4200e-03_grid_index_2.data 26.409758771088889 11701\n", + " number of columns: 131\n", + " do not have surface_mu\n", + " do not have photosphere_cell_density\n", + " Zbase_0.0014_initial_mass_9.7294_initial_z_1.4200e-03_grid_index_2.data 9 11701\n", + " guess = 187\n", + " my_guess = 187\n", + " ntrack = 1199\n", + " 15 111 186 286 301 892 1199 0 0\n", + " track, number of p, s eeps = 0 7 700\n", + " 24396.344533808904 \n", + " 21042522.104845978 \n", + " 28037631.964223422 \n", + " 28079495.980469674 \n", + " 28136942.251048498 \n", + " 29667168.542051919 \n", + " 29700034.195994429 \n", + " main = 32\n", + " head = 28\n", + " xtra = 1\n", + " Zbase_0.0014_initial_mass_13.5721_initial_z_1.4200e-03_grid_index_3.data 9.7294385594764741 11701\n", + " number of columns: 131\n", + " do not have surface_mu\n", + " do not have photosphere_cell_density\n", + " Zbase_0.0014_initial_mass_13.5721_initial_z_1.4200e-03_grid_index_3.data 9 11701\n", + " guess = 196\n", + " my_guess = 196\n", + " ntrack = 1179\n", + " 17 112 195 312 327 912 1179 0 0\n", + " track, number of p, s eeps = 0 7 700\n", + " 15610.060101129055 \n", + " 12218224.667538654 \n", + " 16588094.368652388 \n", + " 16612520.250515623 \n", + " 16641397.545344574 \n", + " 17585086.263796750 \n", + " 17601144.656836938 \n", + " main = 32\n", + " head = 28\n", + " xtra = 1\n", + " Zbase_0.0014_initial_mass_71.6871_initial_z_1.4200e-03_grid_index_8.data 13.572087956491831 11701\n", + " number of columns: 131\n", + " do not have surface_mu\n", + " do not have photosphere_cell_density\n", + " Zbase_0.0014_initial_mass_71.6871_initial_z_1.4200e-03_grid_index_8.data 9 11701\n", + " guess = 219\n", + " my_guess = 219\n", + " ntrack = 1017\n", + " 28 126 218 254 262 847 1017 0 0\n", + " track, number of p, s eeps = 0 7 700\n", + " 1882.7797692549086 \n", + " 2433136.2664018041 \n", + " 3657144.1286053685 \n", + " 3662905.7394964132 \n", + " 3666937.2754223552 \n", + " 3968981.8990034591 \n", + " 3972461.2129875328 \n", + " main = 32\n", + " head = 28\n", + " xtra = 1\n", + " Zbase_0.0014_initial_mass_18.9324_initial_z_1.4200e-03_grid_index_4.data 71.687115078083323 11701\n", + " number of columns: 131\n", + " do not have surface_mu\n", + " do not have photosphere_cell_density\n", + " Zbase_0.0014_initial_mass_18.9324_initial_z_1.4200e-03_grid_index_4.data 9 11701\n", + " guess = 190\n", + " my_guess = 190\n", + " ntrack = 1125\n", + " 19 115 189 295 311 907 1125 0 0\n", + " track, number of p, s eeps = 0 7 700\n", + " 9921.7595238775411 \n", + " 7739637.6514137154 \n", + " 10685722.442352418 \n", + " 10700524.445156630 \n", + " 10718291.466290537 \n", + " 11364471.722192684 \n", + " 11373601.929193445 \n", + " main = 32\n", + " head = 28\n", + " xtra = 1\n", + " Zbase_0.0014_initial_mass_36.8403_initial_z_1.4200e-03_grid_index_6.data 18.932394689168561 11701\n", + " number of columns: 131\n", + " do not have surface_mu\n", + " do not have photosphere_cell_density\n", + " Zbase_0.0014_initial_mass_36.8403_initial_z_1.4200e-03_grid_index_6.data 9 11701\n", + " guess = 193\n", + " my_guess = 193\n", + " ntrack = 986\n", + " 23 119 192 222 229 854 986 0 0\n", + " track, number of p, s eeps = 0 7 700\n", + " 3960.2690882564220 \n", + " 3851575.8069463656 \n", + " 5582710.4292462571 \n", + " 5588591.1389014870 \n", + " 5589612.3684672052 \n", + " 5991126.2522320682 \n", + " 5995772.7040485023 \n", + " main = 32\n", + " head = 28\n", + " xtra = 1\n", + " Zbase_0.0014_initial_mass_5.0000_initial_z_1.4200e-03_grid_index_0.data 36.840313846957606 11701\n", + " number of columns: 131\n", + " do not have surface_mu\n", + " do not have photosphere_cell_density\n", + " Zbase_0.0014_initial_mass_5.0000_initial_z_1.4200e-03_grid_index_0.data 9 11701\n", + " guess = 181\n", + " my_guess = 181\n", + " ntrack = 3913\n", + " 13 108 180 269 290 888 1020 3681 3913\n", + " track, number of p, s eeps = 0 9 900\n", + " 86753.360233281302 \n", + " 64319463.355284028 \n", + " 86185376.573872104 \n", + " 86633375.415914938 \n", + " 87099149.826188803 \n", + " 96055736.197538793 \n", + " 96465309.995612055 \n", + " 96499955.970155716 \n", + " 96512030.582908586 \n", + " main = 32\n", + " head = 28\n", + " xtra = 1\n", + " Zbase_0.0014_initial_mass_51.3904_initial_z_1.4200e-03_grid_index_7.data 4.9999999804196547 11701\n", + " number of columns: 131\n", + " do not have surface_mu\n", + " do not have photosphere_cell_density\n", + " Zbase_0.0014_initial_mass_51.3904_initial_z_1.4200e-03_grid_index_7.data 9 11701\n", + " guess = 212\n", + " my_guess = 212\n", + " ntrack = 985\n", + " 26 123 211 231 306 912 985 0 0\n", + " track, number of p, s eeps = 0 7 700\n", + " 2996.8596073236631 \n", + " 3010166.5713262074 \n", + " 4416981.4144611228 \n", + " 4418486.3517615385 \n", + " 4425592.2093049819 \n", + " 4768141.7818553476 \n", + " 4772090.7663751869 \n", + " main = 32\n", + " head = 28\n", + " xtra = 1\n", + " Zbase_0.0014_initial_mass_100.0000_initial_z_1.4200e-03_grid_index_9.data 51.390425259010733 11701\n", + " number of columns: 131\n", + " do not have surface_mu\n", + " do not have photosphere_cell_density\n", + " Zbase_0.0014_initial_mass_100.0000_initial_z_1.4200e-03_grid_index_9.data 9 11701\n", + " guess = 218\n", + " my_guess = 218\n", + " ntrack = 975\n", + " 1 113 217 219 234 806 975 0 0\n", + " track, number of p, s eeps = 0 7 700\n", + " 100.00000000000000 \n", + " 2056224.0174175713 \n", + " 3136810.0982837169 \n", + " 3138943.4469665275 \n", + " 3145922.3057053299 \n", + " 3420158.4876993480 \n", + " 3423291.9848860670 \n", + " main = 32\n", + " head = 28\n", + " xtra = 1\n", + " Zbase_0.0014_initial_mass_6.9748_initial_z_1.4200e-03_grid_index_1.data 99.999899139008136 11701\n", + " number of columns: 131\n", + " do not have surface_mu\n", + " do not have photosphere_cell_density\n", + " Zbase_0.0014_initial_mass_6.9748_initial_z_1.4200e-03_grid_index_1.data 9 11701\n", + " guess = 186\n", + " my_guess = 186\n", + " ntrack = 1209\n", + " 14 109 185 286 302 893 1209 0 0\n", + " track, number of p, s eeps = 0 7 700\n", + " 46075.447975750540 \n", + " 37892525.232744850 \n", + " 50239784.193855718 \n", + " 50340442.508530043 \n", + " 50473179.666073054 \n", + " 53361156.397665329 \n", + " 53451365.900661089 \n" + ] + } + ], + "source": [ + "!cd /srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/single_HMS/eep_code/ && ./make_eep /srv/beegfs/scratch/shares/astro/posydon/simone/documentation/running_mesa/processing_single_hms/input.single_hms_1e-01_Zsun" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great! Now that we have the EEPs files we can create the PSyGrid object and compute the processed quantities with the following code. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# NOTE: to load slurm_magic commands you need to first install them with pip install git+https://github.com/NERSC/slurm-magic.git\n", + "%load_ext slurm_magic" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/srv/beegfs/scratch/shares/astro/posydon/simone/documentation/running_mesa/processing_single_hms\n" + ] + } + ], + "source": [ + "!pwd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating the PSyGrid Object and Computing the Processed Quantities" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./script.py\n" + ] + } + ], + "source": [ + "%%writefile ./script.py\n", + "import os\n", + "import sys\n", + "from shutil import copyfile\n", + "from posydon.grids.psygrid import PSyGrid\n", + "from posydon.grids.post_processing import post_process_grid, add_post_processed_quantities\n", + "\n", + "if __name__ == \"__main__\":\n", + "\n", + " i = int(sys.argv[1])\n", + " print('Job array index:',i)\n", + " names = ['single_hms_1e-01_Zsun']\n", + " name = names[i]\n", + " inpath = \"../%s/\"%name\n", + " eep_path = \"./eeps/eeps_%s/\"%name\n", + " outpath = \"./\"\n", + " grid_name = \"%s.h5\"%name\n", + "\n", + " grid = PSyGrid(verbose=False)\n", + " grid.create(inpath, outpath+grid_name, binary=False, overwrite=True, compression=\"gzip9\", max_number_of_runs=None, eep=eep_path,\n", + " **{'star1_history_saved_columns' : ['star_mdot','conv_mx1_top_r', 'conv_mx1_bot_r','mass_conv_reg_fortides', \n", + " 'thickness_conv_reg_fortides', 'radius_conv_reg_fortides', 'surface_he3',\n", + " #'photosphere_cell_density', 'surface_mu'\n", + " ]})\n", + " grid.close()\n", + " print('Done creating grid!')\n", + " \n", + " path = outpath\n", + " grid_name_processed = 'processed_'+grid_name\n", + " # copy file, it will be overwritten when we add columns\n", + " if os.path.exists(path+grid_name_processed):\n", + " print('Post processed grid file alredy exist, removing it...')\n", + " os.remove(path+grid_name_processed)\n", + " copyfile(path+grid_name, path+grid_name_processed)\n", + "\n", + " grid = PSyGrid()\n", + " grid.load(path+grid_name_processed)\n", + " MESA_dirs_EXTRA_COLUMNS, EXTRA_COLUMNS = post_process_grid(grid, index=None, star_2_CO=False, single_star=True, verbose=False)\n", + " add_post_processed_quantities(grid, MESA_dirs_EXTRA_COLUMNS, EXTRA_COLUMNS, verbose=False)\n", + " grid.close()\n", + " del MESA_dirs_EXTRA_COLUMNS\n", + " del EXTRA_COLUMNS\n", + " print('Done appending columns!')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the job array, in this case we only have one grid, so the array will go from index 0 to 0." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Submitted batch job 28474201\\n'" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%sbatch\n", + "#!/bin/bash\n", + "#SBATCH --account=meynet\n", + "#SBATCH --partition=private-astro-cpu\n", + "#SBATCH -N 1\n", + "#SBATCH --array=0-0\n", + "#SBATCH --cpus-per-task 1\n", + "#SBATCH --ntasks-per-node 1\n", + "#SBATCH --time=06:00:00\n", + "#SBATCH --job-name=\"psygrid_\\${SLURM_ARRAY_TASK_ID}\"\n", + "#SBATCH --output=./log.out\n", + "#SBATCH --mail-type=ALL\n", + "#SBATCH --mail-user=simone.bavera@unige.ch\n", + "#SBATCH --mem-per-cpu=8G\n", + "\n", + "srun python ./script.py $SLURM_ARRAY_TASK_ID" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)\n", + " 28474201_0 private-a psygrid_ bavera R 0:04 1 cpu147\n" + ] + } + ], + "source": [ + "!squeue -u bavera -j 28474201" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the job is done we can check that the grid has been created and that the data are contained in the PSyGrid object:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exploring the PSyGrid obejct" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "from posydon.grids.psygrid import PSyGrid\n", + "\n", + "grid = PSyGrid('./processed_single_hms_1e-01_Zsun.h5')\n", + "grid.load()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([( 6277.99277501, 26.4097595 , 0., 0., 0., 0., 0., 0., 0.74661422, 0.25194858, 4.37299425e-06, 0.00052925, 0.00041712, 0.74748, 0.25107328, 0.00025117, 7.35752498e-05, 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In0gkHIWtgyoUCvbPTgPnaDRqB75nz551HPh2G/HcOtmd10z0lA4AAAAAAMAdq8Pellu3bumRRx7Rq6++6lJl3pLP5xWNRhUOhxUMBuXz+ezRnNVqVfF43L49GAwqHo/39BX1Vnjb2m4wGFQ4HFY+n7f32VIul+Xz+daEva39tl82mkqgXC7b9fr9fvn9fkWjUeXz+cGao5UgL51O29v0+XwKh8NKp9Mjmdag1ZvWvvx+v+LxuKvzGpfLZXsqhVgs5tqUG+096OdEbetpX980TXvk8KDOnDnjaP1J45kRvsCoLDduqPEDc8Nlpu4wtHvqtq0paMIsNz7osX8f35qCsK3w+nWG/g2ul95J9K8b+gc38d7nzMr//ZobLjN1h5//+3lAt7C3pRX6Soz0dSoQCCgUCqlYLHYEuel0WuVyWZlMxh5N2Qpxi8WiKpVK11Ge0WhUjUZDp0+f7limXq8rnU6rWCx23B6JRNRsrry2z549a59Iq1QqaW5urmPbhmGsu89kMmnXe/r0aRmGIdM0deLECSWTSaXTaZ0/f76vkalnz55VJpNRMplUoVCw1y0Wi5qfn1c2m1UmkxnKKM5qtarDhw/LNE1lMhkVCgX7hF/pdFrRaFSxWKxjlOso1et1VatV5XI5lctlBQIBZTIZxWKxLdl/t5papqenHW1ramqq43qj0Ri4pkwmo2q1uua56mWuBr4XL16UJN13331ulgGs6+KbS3r+qZf03jvv97T83ffcqaeef0z3PjA74somw8VvXdKf/cFf6L3L3+tp+bv3/5K+9Ke/p3sP3TPiyrAd8Pp1hv4Nrt/eSfSvHf2Dm3jvc+bim0t6/vf/vL/+fe0L9G9CbRb2thD6DkckElEkEtHRo0cVDoclrcxpWq/XValUOpaNxWKKxWJ24Ln6fknKZrMql8tqNptrwtnWie9bgXC71rLtQVwgEOga8LarVqv2KF7DMOx1DMNQJpNRMBhUMpnU4cOHValUeh4dmslkVKlU1tQQi8UUiUTskb61Ws3RqNdqtWr3vlQqdUxX0OpZK2hPp9PKZDID72u1er0uv9/fcdvqkcuFQsHVoHc9vTwv+tHLqPV6vS6fzzfU/U4qV6d0iMViCofD+vSnP61Tp05peXnZzXIA28U3l3Ts8Ff7+sD53jvv65nPfkVvf+vS6AqbEBe/dUmpz/1xz2GvJL13+Xs6Fv0/9PaFd0ZYGbYDXr/O0L/BDdI7if610D+4ifc+Zy6+uaRjkf/Yf/8O/wf6N4F6DXtbmN5heNoDtLNnz3YdSXrw4EFJ6voV+F6+2t4awTssi4uL9s/r1Z1IJOwRv62pCXqRy+W6BouGYdghbz6fd3SiucOHD9t1dpubthXyZrPZoU4lEQgE1Gw2Oy6WZalWq9n7jMfjQ3/MnKrVao7WX/0Hh9Ujftezule1Wm1NQL9duBr4lkolPfPMM/qHf/gHHTt2TH6/X7/5m7+pl156yc2yAD3/1ODPQSfresWf/cFfDLzu8w7W9bSf/rS/27cxXr/O0L/BOT1++kf/4B7e+5x5/vf/3MG6/2WIlWDULl++rEcffbTnsLfl1q1bevTRR3X58uURVbY9tAdekUhkw6CzZb3gsbWdcDjcNQSNRCJaWFgYuNbVjhw5olAopEAg0DWYbIVy/czVulkIGIlE7NHC/QTJ7doD3I1C1UAgYO/r7NmzA+2rH4FAQKlUyh7FvXreZTesnnfXidXr9zrquzWC3DAMBQIBRSIRlUolV05i5yZXA9+ZmRllMhk1Gg2dO3dOv/M7v6Nz585pfn5eO3fu1Oc//3m9+eabbpaIbWj52o2+Rxe1u3rpu1pu3BhiRZNl+dqNvkb2rvbeO+9rufHBECvygOvXpX/5L6VTpzpvP3Vq5fbr192pawzx+nWG/g3Oae8k+kf/4Bbe+5xx3j/+7zdJLly4oJs3bw607s2bN3XhwoUhV7R9tUbxDqIVfNbrdcXjcfvEaclk0g6ADcMY6hQBhmGoUqmoVqt1Dd5a4e2gc7V20woK6/V6T9MCrNY+Inqz0LB1v9PRrf0IhUL2HMXlctnRSOZ+tE7u1659JG37qO5BrF7faWB79OhRR+tPGlcD33aRSESFQkG3bt3Siy++qPvuu09nz55VJBLR9PS0vvzlL+vq1atul4ltoPFD0/k2ejjRjFc1fug8fNzO/Vvj+nXpwQelv/kb6Zlnfhb6njq1cv1v/mblfkJfSbx+naJ/gxtG7yT653g727R/cIb3PmeG07+NT/KG8XHo0CHt3LlzoHV37typQ4cODbmi8dE+0nSQwNI0zb7mPHUyP2prxGMrCDVNU+VyWfl8XvF4XD6fb+TTA1SrVWWzWUWjUYXDYQWDwTXh4bC0jwztZ/TweusEg8ENL9VqVYFAwPEJy/rV/gcAJ3MV96NUKq25LR6P2z8P0utu2x/GHx+GPafwuBubwLddIpFQpVJRs9nUiRMnZBiGnn32WQWDQf3ar/2aXnrpJeb7BeB9P/3pz8LelmeekX7pl1b+bWmFvkzvAAAAgBHav3+/Xn75Ze3Y0V+UsGPHDr388svav3//iCpz32bTKGymXq8rGAwOr6BNRCIRe37TTCajWCzWcQz5fN4+SdmwtEYU+3w+HT58WNeuXVM6nVahUFCtVlMikRjq/kahVqv1dGmNuN0q7Y+d05G1vWqF2+3ap9CQ5Gi0cfu6w5heJJFIbFkYPg7GMvBt2bNnj1KplGq1mhYXF/XYY4/p3Xff1fz8vD3f7ze/+U23ywSA0fi5n5Pa/kJq+/73194Wj68sDwAAAIzQww8/rFdeeaXn0HfHjh165ZVX9PDDD4+4Mve1j0Lsd3RjuVzWkSNHhl1S1321RCIRpVIpFQoFNZtNVSoVO6ysVqt9j7otl8vrziNbLBYVDAZVLBaVSqXUbDaVyWTWBISj0D6NwyDTAoy6vmFoH2FumuZAU1f0o7WP9XrTHqo6mTe5JRaLbbv5d4dhrAPfdqFQSLlcTo1GQ2fPntVnP/tZnTt3TrFYTNPT03ryySd18eJFt8sEgOF6+mnp5MmNlzl5cmU5AAAAYAv0Gvpup7BX6hyF2D7v62bS6fSGJ2EbtmQy2TXIDYVCymQyymQyktb/2v5G1hvd3BrZK62MsmxtuxflctlxeNka8dp+UrV+tAf57WF5N1sRuK62+rnjdDqFzbSeP+udNC8Sidijtev1ekd424t6vW4HxYZh6PTp0w6r3Z4mJvBtF4vFVCqV1Gw29cILL2jv3r168cUXFQ6HtW/fPp06dYopHwB4x9NPS5/85Pr3ffKThL0AAADYcpuFvtst7JVWwtJW0JXNZnua2qE1l+1Wf9V8s/21Qs71AtL2cHF1sNloNNas0/7V/Ph632Dssq1WnRuFrJvNl1wul+3HYdAeZzIZ+5h72UY8Ht+yE6e1rA5e33rrrY7r1Wp1aFN0mKapEydOSOo+L24ul7NP4JZOp3sKylvbbo0Qb53ob7vNvTssExn4tuzZs8ee77dWq+mZZ57RtWvXdOzYMaZ8AOAdp06tP42DtHJ760RuAAAAwBbqFvpux7C3JZfL2WFpOBzecKRna57cQqHQ08jTQU4G122dVtDcTSugO3r06Jr75ubmOrbTrlAorAkWNwqI27fTGonbHpTX6/V1R5G2JJPJrsF6+8ji1vQRgzp//ryklfB6o2kustms6vW64zl8+32sDcPoOL7Vj0u5XO543Frae9fLPk3T1OHDh2Wa5qbP2VKpZPchGo1uOtK3FUrX63WFQiFVKpWeXhf9HsN2MdGBb7uZmRllMhk1Gg2dO3dOv/M7v2NP+bBz5059/vOf15tvvul2mQDQn1OnOk/Qtp5nniH0BQAAgCtaoe/OnTslSTt37ty2YW9LoVBQJpOxT8SWTCbtqQlaQWs4HFY6nVapVOqYMmA9pmmqWq12jBotFAqqVqsdYVdruUKhYN9WLBbXLNeSy+WUTCY7wkHTNJXNZpVOp5XJZNadO9UwDHtahnQ6bW+/FXauPvlaIpGwb1s9nURrvfn5+Y7RnPF43B4l3a0/uVxOmUxG4XC4ozf1er3jpHOFQmHdALbVr/Z6zpw5s26/QqGQarWaAoGAksmkPYq3tVxr7uIzZ870PQ3G6prq9XrHSOJ6va5isah6vb7hqPH2PxyUy2W7J9VqVSdOnOiYT3e9/ZimaY+Ibr/U63WVy2Ulk0nNzMzYz5dewthMJqNSqaRQKKR0Oq1gMKhsNmv3uHVs0WhU4XBYjUZDmUymp7C32zHk83m7V4OcQNErdrldwChEIhH7Lxv5fF65XE5nz55VoVCQYRhKJpNKJBLau3evu4UCwEZ++lOp7T9rtk9+cu2I30JBeuopTtwGAACALffwww8rFArpwoULOnTokPbv3+92Sa5LpVJKJBLK5/MqlUqKx+MyTVOGYSgQCOjo0aM9jwKdn5+3w7tWIFoulxUOh2UYhprNpqSVgXCtgKu1XCvki8VidhAcCATsr9xns1nF43F7ZGQgENDc3NymgVsqlbLn+z18+LCklSymUqmsu3wul1M8HrfD8GQy2dGL1nqVSkXpdNoe2bve9gKBQEeI3KojnU7bxzE3N6eFhYUNe5xOp+2wt9WvYrGoYrGoQCCgWq22Zr+1Wk3FYlFnzpzR/Px8x2PaypoG1QraW9pHRrdPhdEKnlczDEO1Ws3Owebn5zU/P6+5uTmdP3/eXiedTncdbbveCfe66XWqhdbzolqt6syZMzpz5oxOnDhh925qakqhUEiFQmHTP360rD6G9lqSyWTHspZl9bRNr/FZ2+TIr1+/rlwup1wupytXrsjn8ykcDtt/mdm9e7fbJWIdly5d0oEDB+zrS0tLmp2dHek+l6/d0EO/+AVH23jtH/6Ldk/dNqSKJsvytRuK3ZHcfMENFH+Y1+6pjw+pogl3/br04IPS3/zNyvXWCdraR/7++q9Lf/3X0p497tU5Jnj9OkP/BjeM3kn0z6nt2j84w3ufM8v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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import display, Image\n", + "path = '/srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/POSYDON_data/230914/POSYDON_data/tutorials/processing-pipeline/HMS-HMS/1e-01_Zsun/plots/'\n", + "# termination flags\n", + "img = Image(path+'grid_test_combined/TF12/grid_q_0.70.png')\n", + "display(img)\n", + "img = Image(path+'grid_test_combined/TF1/grid_q_0.70.png')\n", + "display(img)\n", + "img = Image(path+'grid_test_combined/TF2/grid_q_0.70.png')\n", + "display(img)\n", + "img = Image(path+'grid_test_combined/TF3/grid_q_0.70.png')\n", + "display(img)\n", + "img = Image(path+'grid_test_combined/TF4/grid_q_0.70.png')\n", + "display(img)\n", + "# processed values\n", + "img = Image(path+'grid_test_combined_processed/S1_MODEL01/SN_type/grid_q_0.70.png')\n", + "display(img)\n", + "img = Image(path+'grid_test_combined_processed/S1_MODEL01/CO_type/grid_q_0.70.png')\n", + "display(img)\n", + "img = Image(path+'grid_test_combined_processed/S1_MODEL01/mass/grid_q_0.70.png')\n", + "display(img)\n", + "img = Image(path+'grid_test_combined_processed/S1_MODEL01/spin/grid_q_0.70.png')\n", + "display(img)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial we did not create a randomly sampled grid to test the accuracy of the interolators. If you create a radomly sampled grid, then the pipeline will also export 2D error maps evaluated on the randomly sampled gird. This is left as an exercise to the reader. \n", + "\n", + "TODO: add the automatic export of violin plots and cofiusion matrices to the plotting.\n", + "\n", + "Check that you obtained the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1e-01_Zsun.h5 interpolators\n" + ] + } + ], + "source": [ + "!ls POSYDON_data/HMS-HMS/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congratulations you have completed the POSYDON v2.0.0 pipeline tutorial!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "posydon_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/_source/tutorials-examples/generating-datasets/step_rerun.ipynb b/docs/_source/tutorials-examples/generating-datasets/step_rerun.ipynb new file mode 100644 index 0000000000..8241e7053a --- /dev/null +++ b/docs/_source/tutorials-examples/generating-datasets/step_rerun.ipynb @@ -0,0 +1,320 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Export Rerun Grids with POSYDON Post Processing Pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sometimes the POSYDON MESA engineer pratitioner might be challenged with exporting grid points to be rerun systematically for multiple MESA grids with the aim of fixing the \"islands of unhappines\" (cit. Aaron Dotter). These islands are portions of the parameter space where the MESA simulations did not converge to a solution. The POSYDON post processing pipeline can be used to export the grid points to be rerun `grid.csv` (give any predefined logic) and the `pipeline.ini` file already preconfigured with the correct `MESA-INLIST` submodule branch and commit. The `params.ini` file can be used to run the grid points with the `mesa` command line tool.\n", + "\n", + "POSYDON MESA grids associated with v2.0.0 made use of the following predefined rerun options in the post processing pipeline:\n", + "- 'PISN': \"matthias_PISN-d68228338b91fd487ef5d55c9b6ebb8cc5f0e668\"\n", + "- 'reverse_MT': \"zepei_fix_implicit-afa1860ddf9894aa1d82742ee2a73e8e92acd4a9\"\n", + "- 'opacity_max': \"matthias_PISN-d68228338b91fd487ef5d55c9b6ebb8cc5f0e668\"\n", + "- 'opacity_max_hms-hms': \"zepei_fix_implicit-afa1860ddf9894aa1d82742ee2a73e8e92acd4a9\"\n", + "- 'TPAGBwind': \"development-22c1bb9e730343558c3e70984a99b3fc1f3c346e\"\n", + "- 'thermohaline_mixing': \"development-22c1bb9e730343558c3e70984a99b3fc1f3c346e\"\n", + "Please refer to the POSYDON v2.0.0 paper for more details.\n", + "\n", + "Here we show how to export the `opacity_max` reruns associated with the \"getting started\" tutorial using the post-processing pipeline." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you haven't done it already, export the environemnt variables." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "env: PATH_TO_POSYDON=/srv/beegfs/scratch/shares/astro/posydon/simone/documentation/POSYDON/\n", + "env: PATH_TO_POSYDON_DATA=/srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/POSYDON_data/230914/\n" + ] + } + ], + "source": [ + "%env PATH_TO_POSYDON=/srv/beegfs/scratch/shares/astro/posydon/simone/documentation/POSYDON/\n", + "%env PATH_TO_POSYDON_DATA=/srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/POSYDON_data/230914/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preparing the Pipeline initialization file" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's copy the pipeline ini file template for the UNIGE HPC cluster." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'./pipeline.ini'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "import shutil\n", + "from posydon.config import PATH_TO_POSYDON\n", + "\n", + "path_to_ini = os.path.join(PATH_TO_POSYDON, 'grid_params/pipeline_yggdrasil.ini')\n", + "shutil.copyfile(path_to_ini, './pipeline.ini')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now edit the pipeline ini file to point to the MESA grid directory `test_grid/` containing a set of 100 MESA models of the HMS-HMS grid at 0.1Zsun for the mass ratio q=0.7, see the running MESa grid getting started tutorial.\n", + "\n", + "In order for the pipeline to be able to process the data we need to follow the following directory naming convention:\n", + "`/HMS-HMS/1e-01_Zsun/test_grid/`.\n", + "\n", + "Here we just want to run the rerun step of the pipeline, in order to create the `grid.csv` file and the `pipeline.ini` file to be used to run the subsample of MESA simulations with the new configuration. Notice that the rerun step can be also used after the second step to export reruns of concatenate MESA grid (PSyGrid object `h5` file). \n", + "\n", + "After setting up the HPC account options and `PATH_TO_GRIDS` the value" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/POSYDON_data/230914/POSYDON_data/tutorials/processing-pipeline'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from posydon.config import PATH_TO_POSYDON_DATA\n", + "\n", + "PATH_TO_GRIDS = os.path.join(PATH_TO_POSYDON_DATA, 'POSYDON_data/tutorials/processing-pipeline')\n", + "PATH_TO_GRIDS" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We set:\n", + "```ini\n", + " CREATE_GRID_SLICES = False\n", + " COMBINE_GRID_SLICES = False\n", + " CALCULATE_EXTRA_VALUES = False\n", + " TRAIN_INTERPOLATORS = False\n", + " EXPORT_DATASET = False\n", + " RERUN = True\n", + "```\n", + "\n", + "And edit the `[rerun]` section of the file to\n", + "```ini\n", + " GRID_TYPES = ['HMS-HMS']\n", + " METALLICITIES = [['1e-01_Zsun']\n", + " ]\n", + " GRID_SLICES = [['test_grid']]\n", + " COMPRESSIONS = [['LITE']]\n", + " DROP_MISSING_FILES = True\n", + " RERUN_TYPE = 'opacity_max' \n", + "```\n", + "\n", + "Also remember to set\n", + "```ini\n", + " CREATE_PLOTS = []\n", + " DO_CHECKS = []\n", + "```\n", + "for all other steps, otherwise the pipeline will try to create plots and do checks on the data, which is not possible since we are not running the full pipeline." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting-up and Running the Post Processing Pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now rady to setup the grid pipeline with the `setup-pipeline` command, as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/bavera/.conda/envs/posydon_env/bin/setup-pipeline:4: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html\n", + " __import__('pkg_resources').require('posydon==1.0.0+194.g3953a14')\n", + "\n", + "+++++++++++++++++++ACCOUNT+++++++++++++++++++ \n", + "{ 'ACCOUNT': 'meynet',\n", + " 'EMAIL': 'simone.bavera@unige.ch',\n", + " 'MAILTYPE': 'ALL',\n", + " 'PARTITION': 'public-cpu',\n", + " 'WALLTIME': '24:00:00'}\n", + "\n", + "++++++++++++++++++++SETUP++++++++++++++++++++\n", + "{ 'CALCULATE_EXTRA_VALUES': False,\n", + " 'COMBINE_GRID_SLICES': False,\n", + " 'CREATE_GRID_SLICES': False,\n", + " 'EXPORT_DATASET': False,\n", + " 'PATH': '.',\n", + " 'PATH_TO_GRIDS': '/srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/POSYDON_data/230914/POSYDON_data/tutorials/processing-pipeline/',\n", + " 'RERUN': True,\n", + " 'TRAIN_INTERPOLATORS': False,\n", + " 'VERBOSE': True,\n", + " 'VERSION': ''}\n", + "\n", + "\n", + "-------------CREATE_GRID_SLICES-------------- step_1 :False \n", + "\n", + "\n", + "-------------COMBINE_GRID_SLICES------------- step_2 :False \n", + "\n", + "\n", + "-----------CALCULATE_EXTRA_VALUES------------ step_3 :False \n", + "\n", + "\n", + "-------------TRAIN_INTERPOLATORS------------- step_4 :False \n", + "\n", + "\n", + "---------------EXPORT_DATASET---------------- step_9 :False \n", + "\n", + "\n", + "--------------------RERUN-------------------- rerun : True \n", + "{ 'COMPRESSIONS': [['LITE']],\n", + " 'DROP_MISSING_FILES': True,\n", + " 'GRID_SLICES': [['test_grid']],\n", + " 'GRID_TYPES': ['HMS-HMS'],\n", + " 'METALLICITIES': [['1e-01_Zsun']],\n", + " 'RERUN_TYPE': 'opacity_max'}\n" + ] + } + ], + "source": [ + "!setup-pipeline pipeline.ini" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great let's run the pipepline with the shell command." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rerun.slurm submitted as 28473947\n" + ] + } + ], + "source": [ + "!./run_pipeline.sh" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the job is done, you can check that the files were created in the `PATH_TO_GRIDS/HMS-HMS/1e-01_Zsun/` directory." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "grid.csv HMS-HMS_yggdrasil.ini\n" + ] + } + ], + "source": [ + "!ls /srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/POSYDON_data/230914/POSYDON_data/tutorials/processing-pipeline/HMS-HMS/1e-01_Zsun/rerun_opacity_max_test_grid" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great! You can now submit the simulation. See the tutorial on how to run the grid with the POSYDON MESA submission API tool." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "posydon_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/_source/tutorials-examples/population-synthesis/.gitignore b/docs/_source/tutorials-examples/population-synthesis/.gitignore new file mode 100644 index 0000000000..ad7ac1d23d --- /dev/null +++ b/docs/_source/tutorials-examples/population-synthesis/.gitignore @@ -0,0 +1,6 @@ +*.h5 +*.ini +*.csv +*.batch +*.npz +*.pdf \ No newline at end of file diff --git a/docs/_source/tutorials-examples/population-synthesis/10_binaries_pop_syn.ipynb b/docs/_source/tutorials-examples/population-synthesis/10_binaries_pop_syn.ipynb new file mode 100644 index 0000000000..c7b75b6438 --- /dev/null +++ b/docs/_source/tutorials-examples/population-synthesis/10_binaries_pop_syn.ipynb @@ -0,0 +1,1256 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Your First Binary Simulations with POSYDON 🌠" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Tutorial goal:**\n", + "\n", + "- Create your first binary population\n", + "\n", + "**New concepts:**\n", + "\n", + "- Default population synthesis model\n", + "- Synthetic Population class\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you haven't done so yet, export the path POSYDON environment variables.\n", + "Set these parameters in your `.bash_profile` or `.zshrc` if you use POSYDON regularly." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "env: PATH_TO_POSYDON=/Users/simone/Google Drive/github/POSYDON-public/\n", + "env: PATH_TO_POSYDON_DATA=/Volumes/T7/\n" + ] + } + ], + "source": [ + "%env PATH_TO_POSYDON=/YOUR/POSYDON/PATH/\n", + "%env PATH_TO_POSYDON_DATA=/YOUR/POSYDON_DATA/PATH/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating the Initialisation File" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----\n", + "To run population synthesis with POSYDON, a `population_params.ini` file is required.\n", + "This file described how the stellar population is created and what prescriptions are implemented.\n", + "\n", + "POSYDON comes with a default `population_params_detault.ini` file found at `PATH_TO_POSYDON/posydon/popsyn` or have a look [here](population_params.ini).\n", + "\n", + "Here we will run the notebook with the default parameters, which runs 10 binaries at $10^{-4}Z_\\odot$.\n", + "\n", + "First, let's copy the default population synthesis model to your working directory." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'./population_params.ini'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "import shutil\n", + "from posydon.config import PATH_TO_POSYDON\n", + "\n", + "path_to_params = os.path.join(PATH_TO_POSYDON, \"posydon/popsyn/population_params_default.ini\")\n", + "shutil.copyfile(path_to_params, './population_params.ini')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running the Population Synthesis Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "[SyntheticPopulation()](../../api_reference/posydon.popsyn.rst#posydon.popsyn.synthetic_population.SyntheticPopulation) samples the given initial distributions given in the `population_params.ini` file and generates the initial conditions for our 10 binaries." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Z=1.00e-04 Z_sun\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/binarypopulation.py:571: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " return pd.concat(holder, axis=0, ignore_index=False)\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from posydon.popsyn.synthetic_population import SyntheticPopulation\n", + "\n", + "synth_pop = SyntheticPopulation('./population_params.ini')\n", + "synth_pop.evolve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the previous step, a synthetic binary population is created with the POSYDON default initial parameters and stored in `synth_pop`. With the last line of code the synthetic population will be evolved.\n", + "\n", + "Congratulations! You run your first population synthesis model with POSYDON! 🎉" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exploring the Simulation Output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here are the population synthesis files you just created, in this example we only visualize the first ten lines of the output table:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    1mergedoMerging13.327664e+090.5067180.0-1.876899step_CE0.000091H-rich_Core_H_burning14.908113...1.411542e-142.053381e-161.365520e-141.239204e-160.7229552.770430e-010.7506210.2493610.98841710.460393
    1mergedCC13.331178e+09NaNNaNNaNstep_merged6.616714H-rich_Central_C_depletion16.152142...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    \n", + "

    10 rows × 38 columns

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    " + ], + "text/plain": [ + " state event time orbital_period \\\n", + "binary_index \n", + "0 detached ZAMS 1.511066e+09 479.288083 \n", + "0 RLO1 CC1 1.535382e+09 1220.670243 \n", + "0 disrupted NaN 1.535382e+09 NaN \n", + "0 disrupted CC2 1.548703e+09 NaN \n", + "0 disrupted NaN 1.548703e+09 NaN \n", + "0 disrupted END 1.548703e+09 NaN \n", + "1 detached ZAMS 3.316347e+09 0.911049 \n", + "1 contact oCE1 3.327664e+09 0.506718 \n", + "1 merged oMerging1 3.327664e+09 0.506718 \n", + "1 merged CC1 3.331178e+09 NaN \n", + "\n", + " eccentricity lg_mtransfer_rate step_names step_times \\\n", + "binary_index \n", + "0 0.0 NaN initial_cond 0.000000 \n", + "0 0.0 -4.442681 step_HMS_HMS 2.176263 \n", + "0 NaN NaN step_SN 0.001554 \n", + "0 NaN NaN step_disrupted 5.963153 \n", + "0 NaN NaN step_SN 0.001684 \n", + "0 NaN NaN step_end 0.000107 \n", + "1 0.0 NaN initial_cond 0.000000 \n", + "1 0.0 -1.876899 step_HMS_HMS 0.347768 \n", + "1 0.0 -1.876899 step_CE 0.000091 \n", + "1 NaN NaN step_merged 6.616714 \n", + "\n", + " S1_state S1_mass ... S2_he_core_mass \\\n", + "binary_index ... \n", + "0 H-rich_Core_H_burning 10.635159 ... NaN \n", + "0 H-rich_Central_C_depletion 4.387056 ... 1.411542e-14 \n", + "0 NS 1.346469 ... 1.411542e-14 \n", + "0 NS 1.346469 ... 2.970098e+00 \n", + "0 NS 1.346469 ... NaN \n", + "0 NS 1.346469 ... NaN \n", + "1 H-rich_Core_H_burning 15.575811 ... NaN \n", + "1 H-rich_Core_H_burning 14.908113 ... 1.411542e-14 \n", + "1 H-rich_Core_H_burning 14.908113 ... 1.411542e-14 \n", + "1 H-rich_Central_C_depletion 16.152142 ... NaN \n", + "\n", + " S2_he_core_radius S2_co_core_mass S2_co_core_radius \\\n", + "binary_index \n", + "0 NaN NaN NaN \n", + "0 2.053381e-16 1.365520e-14 1.239204e-16 \n", + "0 2.053381e-16 1.365520e-14 1.239204e-16 \n", + "0 NaN 2.062403e+00 5.067320e-02 \n", + "0 NaN NaN NaN \n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "1 2.053381e-16 1.365520e-14 1.239204e-16 \n", + "1 2.053381e-16 1.365520e-14 1.239204e-16 \n", + "1 NaN NaN NaN \n", + "\n", + " S2_center_h1 S2_center_he4 S2_surface_h1 S2_surface_he4 \\\n", + "binary_index \n", + "0 0.750999 2.490000e-01 NaN NaN \n", + "0 0.423320 5.766784e-01 0.388622 0.611377 \n", + "0 0.423320 5.766784e-01 0.388622 0.611377 \n", + "0 0.000000 7.232097e-14 0.719862 0.280118 \n", + "0 NaN NaN NaN NaN \n", + "0 NaN NaN NaN NaN \n", + "1 0.750999 2.490000e-01 NaN NaN \n", + "1 0.722955 2.770430e-01 0.750621 0.249361 \n", + "1 0.722955 2.770430e-01 0.750621 0.249361 \n", + "1 NaN NaN NaN NaN \n", + "\n", + " S2_surf_avg_omega_div_omega_crit S2_spin \n", + "binary_index \n", + "0 NaN NaN \n", + "0 0.907345 15.586620 \n", + "0 0.907345 15.586620 \n", + "0 0.024038 13.729559 \n", + "0 NaN 0.000000 \n", + "0 NaN 0.000000 \n", + "1 NaN NaN \n", + "1 0.988417 10.460393 \n", + "1 0.988417 10.460393 \n", + "1 NaN NaN \n", + "\n", + "[10 rows x 38 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_hdf('./1.00e-04_Zsun_population.h5', key='history')\n", + "df.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

    \n", + "It is also possible to collapse the evolution output on a single row per binary system as shown in the next example." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    state_ievent_itime_iorbital_period_ieccentricity_ilg_mtransfer_rate_istep_names_istep_times_istate_fevent_f...interp_class_HMS_HMSinterp_class_CO_HMS_RLOinterp_class_CO_HeMSinterp_class_CO_HeMS_RLOmt_history_HMS_HMSmt_history_CO_HMS_RLOmt_history_CO_HeMSmt_history_CO_HeMS_RLOFAILEDWARNING
    binary_index
    0detachedZAMS1.511066e+09479.2880830.0NaNinitial_cond0.0disruptedEND...stable_MTNaNNaNNaNStable RLOF during postMSNaNNaNNaN01
    1detachedZAMS3.316347e+090.9110490.0NaNinitial_cond0.0mergedEND...unstable_MTNaNNaNNaNUnstable contact phaseNaNNaNNaN01
    2detachedZAMS7.997196e+080.9643420.0NaNinitial_cond0.0mergedEND...unstable_MTNaNNaNNaNUnstable contact phaseNaNNaNNaN01
    3detachedZAMS6.368205e+081.8074440.0NaNinitial_cond0.0disruptedEND...stable_MTNaNNaNNaNStable RLOF during postMSNaNNaNNaN01
    4detachedZAMS5.028601e+091.6885410.0NaNinitial_cond0.0initial_RLOFEND...stable_MTNaNNaNNaNStable RLOF during postMSNaNNaNNaN00
    5detachedZAMS5.384771e+0959.6041850.0NaNinitial_cond0.0disruptedEND...stable_MTNaNNaNNaNStable RLOF during postMSNaNNaNNaN01
    6detachedZAMS1.242091e+103622.5998360.0NaNinitial_cond0.0disruptedEND...no_MTNaNNaNNaNno RLOFNaNNaNNaN01
    7detachedZAMS1.278701e+102.7851140.0NaNinitial_cond0.0mergedEND...unstable_MTNaNNaNNaNUnstable RLOF during postMSNaNNaNNaN01
    8detachedZAMS3.362532e+092.5229100.0NaNinitial_cond0.0detachedEND...stable_MTstable_MTNaNNaNStable contact phaseStable RLOF during postMSNaNNaN01
    9detachedZAMS7.402367e+094518.5482420.0NaNinitial_cond0.0disruptedEND...no_MTNaNNaNNaNno RLOFNaNNaNNaN01
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    10 rows × 100 columns

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    " + ], + "text/plain": [ + " state_i event_i time_i orbital_period_i \\\n", + "binary_index \n", + "0 detached ZAMS 1.511066e+09 479.288083 \n", + "1 detached ZAMS 3.316347e+09 0.911049 \n", + "2 detached ZAMS 7.997196e+08 0.964342 \n", + "3 detached ZAMS 6.368205e+08 1.807444 \n", + "4 detached ZAMS 5.028601e+09 1.688541 \n", + "5 detached ZAMS 5.384771e+09 59.604185 \n", + "6 detached ZAMS 1.242091e+10 3622.599836 \n", + "7 detached ZAMS 1.278701e+10 2.785114 \n", + "8 detached ZAMS 3.362532e+09 2.522910 \n", + "9 detached ZAMS 7.402367e+09 4518.548242 \n", + "\n", + " eccentricity_i lg_mtransfer_rate_i step_names_i step_times_i \\\n", + "binary_index \n", + "0 0.0 NaN initial_cond 0.0 \n", + "1 0.0 NaN initial_cond 0.0 \n", + "2 0.0 NaN initial_cond 0.0 \n", + "3 0.0 NaN initial_cond 0.0 \n", + "4 0.0 NaN initial_cond 0.0 \n", + "5 0.0 NaN initial_cond 0.0 \n", + "6 0.0 NaN initial_cond 0.0 \n", + "7 0.0 NaN initial_cond 0.0 \n", + "8 0.0 NaN initial_cond 0.0 \n", + "9 0.0 NaN initial_cond 0.0 \n", + "\n", + " state_f event_f ... interp_class_HMS_HMS \\\n", + "binary_index ... \n", + "0 disrupted END ... stable_MT \n", + "1 merged END ... unstable_MT \n", + "2 merged END ... unstable_MT \n", + "3 disrupted END ... stable_MT \n", + "4 initial_RLOF END ... stable_MT \n", + "5 disrupted END ... stable_MT \n", + "6 disrupted END ... no_MT \n", + "7 merged END ... unstable_MT \n", + "8 detached END ... stable_MT \n", + "9 disrupted END ... no_MT \n", + "\n", + " interp_class_CO_HMS_RLO interp_class_CO_HeMS \\\n", + "binary_index \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "5 NaN NaN \n", + "6 NaN NaN \n", + "7 NaN NaN \n", + "8 stable_MT NaN \n", + "9 NaN NaN \n", + "\n", + " interp_class_CO_HeMS_RLO mt_history_HMS_HMS \\\n", + "binary_index \n", + "0 NaN Stable RLOF during postMS \n", + "1 NaN Unstable contact phase \n", + "2 NaN Unstable contact phase \n", + "3 NaN Stable RLOF during postMS \n", + "4 NaN Stable RLOF during postMS \n", + "5 NaN Stable RLOF during postMS \n", + "6 NaN no RLOF \n", + "7 NaN Unstable RLOF during postMS \n", + "8 NaN Stable contact phase \n", + "9 NaN no RLOF \n", + "\n", + " mt_history_CO_HMS_RLO mt_history_CO_HeMS \\\n", + "binary_index \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "5 NaN NaN \n", + "6 NaN NaN \n", + "7 NaN NaN \n", + "8 Stable RLOF during postMS NaN \n", + "9 NaN NaN \n", + "\n", + " mt_history_CO_HeMS_RLO FAILED WARNING \n", + "binary_index \n", + "0 NaN 0 1 \n", + "1 NaN 0 1 \n", + "2 NaN 0 1 \n", + "3 NaN 0 1 \n", + "4 NaN 0 0 \n", + "5 NaN 0 1 \n", + "6 NaN 0 1 \n", + "7 NaN 0 1 \n", + "8 NaN 0 1 \n", + "9 NaN 0 1 \n", + "\n", + "[10 rows x 100 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_oneline = pd.read_hdf('./1.00e-04_Zsun_population.h5', key='oneline')\n", + "df_oneline.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the last example, only a selection of columns will be selected to visualize the evolution of the ninth binary (with a corresponding index value of eight) in the population:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    83.362532e+09initial_conddetachedZAMS2.5229100.000000H-rich_Core_H_burningH-rich_Core_H_burning93.54656793.023072
    83.366016e+09step_HMS_HMSdetachedCC13.9094720.000000H-rich_Central_C_depletionH-rich_Core_H_burning58.365944119.598219
    83.366016e+09step_SNdetachedNaN4.6481140.083735BHH-rich_Core_H_burning43.861220119.598219
    83.366233e+09step_detachedRLO2oRLO24.9022230.000000BHH-rich_Core_H_burning43.861220101.734326
    83.366846e+09step_CO_HMS_RLOdetachedCC22.8460540.000000BHH-rich_Central_C_depletion45.17553167.246779
    83.366846e+09step_SNdetachedNaN8.7124120.458613BHBH45.17553130.278034
    81.380000e+10step_dcodetachedmaxtime5.7818190.355638BHBH45.17553130.278034
    81.380000e+10step_enddetachedEND5.7818190.355638BHBH45.17553130.278034
    \n", + "
    " + ], + "text/plain": [ + " time step_names state event \\\n", + "binary_index \n", + "8 3.362532e+09 initial_cond detached ZAMS \n", + "8 3.366016e+09 step_HMS_HMS detached CC1 \n", + "8 3.366016e+09 step_SN detached NaN \n", + "8 3.366233e+09 step_detached RLO2 oRLO2 \n", + "8 3.366846e+09 step_CO_HMS_RLO detached CC2 \n", + "8 3.366846e+09 step_SN detached NaN \n", + "8 1.380000e+10 step_dco detached maxtime \n", + "8 1.380000e+10 step_end detached END \n", + "\n", + " orbital_period eccentricity S1_state \\\n", + "binary_index \n", + "8 2.522910 0.000000 H-rich_Core_H_burning \n", + "8 3.909472 0.000000 H-rich_Central_C_depletion \n", + "8 4.648114 0.083735 BH \n", + "8 4.902223 0.000000 BH \n", + "8 2.846054 0.000000 BH \n", + "8 8.712412 0.458613 BH \n", + "8 5.781819 0.355638 BH \n", + "8 5.781819 0.355638 BH \n", + "\n", + " S2_state S1_mass S2_mass \n", + "binary_index \n", + "8 H-rich_Core_H_burning 93.546567 93.023072 \n", + "8 H-rich_Core_H_burning 58.365944 119.598219 \n", + "8 H-rich_Core_H_burning 43.861220 119.598219 \n", + "8 H-rich_Core_H_burning 43.861220 101.734326 \n", + "8 H-rich_Central_C_depletion 45.175531 67.246779 \n", + "8 BH 45.175531 30.278034 \n", + "8 BH 45.175531 30.278034 \n", + "8 BH 45.175531 30.278034 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let's check the BBH system at index 8\n", + "cols = ['time', 'step_names', 'state', 'event', 'orbital_period', 'eccentricity', 'S1_state', 'S2_state', 'S1_mass', 'S2_mass']\n", + "df.loc[8, cols]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

    \n", + "\n", + "\n", + "Feel free to explore the dataset! \n", + "\n", + "If you want to learn more about population synthesis and how to build more complex models it is advised to continue with the remaining tutorials and consult the POSYDON documentation." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "development311", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/_source/tutorials-examples/population-synthesis/VHD.rst b/docs/_source/tutorials-examples/population-synthesis/VHD.rst new file mode 100644 index 0000000000..6757276c9d --- /dev/null +++ b/docs/_source/tutorials-examples/population-synthesis/VHD.rst @@ -0,0 +1,147 @@ +.. _VHD: + +Van den Heuvel diagrams with POSYDON +==================================== + +.. warning:: + This is an **experimental feature** of POSYDON. Unexpected behaviors or results may occur. + +.. note:: + To use the visualization functionalities, install the optional visualization modules `pip install .[vis]` + + +`VHdiagram` provides a visual representation of individual POSYDON binaries, offering a more intuitive sense of their properties. This tutorial uses the 'population.h5' dataset as an example. TODO: this need to be tested on a v2.0.0 dataset. + +Visualizing a Specific Binary +----------------------------- + +Simple Usage +~~~~~~~~~~~~ + +To visualize a specific binary within an interactive window, use the following code: + +.. code-block:: python + + from posydon.visualization.VHdiagram import VHdiagram + VHdiagram('population.h5', path='./dataset/', index=18976) + +.. image:: pictures/detailled_window.png + +.. tip:: + Use the 'path' named parameter if your dataset resides in a different directory. + +The options window, accessible via the 'option' button, lets you customize the view: + +.. image:: pictures/option_window.png + +Clicking the 'save' button captures a screenshot of the current view and saves it to a newly created 'screens' folder in the current directory. + +Predefined Views +~~~~~~~~~~~~~~~~ + +You can choose from four predefined views for faster visualization: + +.. code-block:: python + + from posydon.visualization.VHdiagram import VHdiagram + from posydon.visualization.VH_diagram.PresenterMode import PresenterMode + + VHdiagram('population.h5', index=19628, presentMode=PresenterMode.DIAGRAM) + +.. image:: pictures/diagram_window.png + +Display Modes +~~~~~~~~~~~~~ + +You have the flexibility to display the diagram either within an interactive window or inline within a Jupyter notebook. The available display modes are: + +.. code-block:: python + + from posydon.visualization.VHdiagram import VHdiagram, DisplayMode + from posydon.visualization.VH_diagram.Presenter import PresenterMode + + VHdiagram( + "population.h5", + index=19628, + presentMode=PresenterMode.DIAGRAM, + displayMode=DisplayMode.INLINE_B, + ) + +.. image:: pictures/diagram_inline.png + +Advanced Visualization Techniques +--------------------------------- + +Visualizing Multiple Binaries +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The `VDdiagramm_m` module allows multiple binary visualizations to be arranged horizontally in a single plot: + +.. code-block:: python + + from posydon.visualization.VH_diagram.PresenterMultiple import VHdiagramm_m + from posydon.visualization.VHdiagram import DisplayMode + from posydon.visualization.VH_diagram.PresenterMode import PresenterMode + + VHD = VHdiagramm_m('./data/population.h5', + index=cnt[:,0], + frequency=parse_df.get_frequencies(), + hierarchy=False, + presentMode=PresenterMode.DIAGRAM, + displayMode=DisplayMode.INLINE_B) + +.. image:: pictures/diagram_multiple.png + +Hierarchical Visualization +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This visualization style aggregates identical steps into a tree plot where nodes represent common steps. Each node is labeled with percentages relative to the parent node percentage. + +.. code-block:: python + + from posydon.visualization.VH_diagram.PresenterMultiple import VHdiagramm_m + from posydon.visualization.VHdiagram import DisplayMode + from posydon.visualization.VH_diagram.PresenterMode import PresenterMode + + VHD = VHdiagramm_m('./data/population.h5', + index=cnt[:,0], + frequency=parse_df.get_frequencies(), + hierarchy=True, + presentMode=PresenterMode.DIAGRAM, + displayMode=DisplayMode.INLINE_B) + +.. image:: pictures/diagram_hierarchy.png + +Binary Analysis Tools +--------------------- + +Counting Binaries +~~~~~~~~~~~~~~~~~ + +Use the `ParseDataFrame` class to iterate through the binary file, counting identical binary simulations: + +.. code-block:: python + + from posydon.visualization.VH_diagram.ParseDataFrame import ParseDataFrame + + parse_df = ParseDataFrame('./data/population.h5') + parse_df.count_dict + +>>> Counter({...}) + +Sorting Binaries +~~~~~~~~~~~~~~~~ + +You can sort binaries based on filenames of images representing their steps: + +.. code-block:: python + + VHD = VHdiagramm_m('./data/population.h5', + index=VHD.get_sorted_index(), + frequency=parse_df.get_frequencies(), + hierarchy=False, + presentMode=PresenterMode.DIAGRAM, + displayMode=DisplayMode.INLINE_B) + +.. image:: pictures/diagram_multiple_sort.png + diff --git a/docs/_source/tutorials-examples/population-synthesis/bbh_analysis.ipynb b/docs/_source/tutorials-examples/population-synthesis/bbh_analysis.ipynb new file mode 100644 index 0000000000..9309c6b101 --- /dev/null +++ b/docs/_source/tutorials-examples/population-synthesis/bbh_analysis.ipynb @@ -0,0 +1,1756 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analyzing Merging BBH Populations: Rates & Observations 🔍" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "If you haven't done so yet, export the path POSYDON environment variables. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "env: PATH_TO_POSYDON=/Users/simone/Google Drive/github/POSYDON-public/\n", + "env: PATH_TO_POSYDON_DATA=/Volumes/T7/\n" + ] + } + ], + "source": [ + "%env PATH_TO_POSYDON=/Users/simone/Google Drive/github/POSYDON-public/\n", + "%env PATH_TO_POSYDON_DATA=/Volumes/T7/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating the Synthetic Poupulation DataFrame" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "We want to analyze the BBH population data we generated in the previous notebook. We will build a model that predicts the intrinsic and observable BBH population in the Universe. Please refer to the paper for more details about the procedure inolved in the convolution of the BBH synthetic popluation, i.e. the POSYDON population of merging BBH at the time of formation, with the star-formation history of the Universe and the selection effects of the gravitational-wave detector network.\n", + "\n", + "In the first step, we load population parameters ini file and the parsed BBH population data from the previous notebook into the Synthetic Population class. \n", + "\n", + "NOTE: In case you did not install mpi4py in your environment, set `use_MPI=False` in the `population_params.ini` to prevent the SyntheticPopulation class to load the module and trowing an error." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Population successfully loaded!\n" + ] + }, + { + "data": { + "text/html": [ + "
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Myr\n", + " 'SFR' : 'IllustrisTNG',\n", + " 'sigma_SFR' : None,\n", + " 'Z_max' : 1.,\n", + " 'Zsun' : 0.0142\n", + "}\n", + "\n", + "pop = SyntheticPopulation('./population_params.ini', verbose=True, MODEL=MODEL)\n", + "path = os.path.join(PATH_TO_POSYDON_DATA, \"POSYDON_data/tutorials/population-synthesis/example/\")\n", + "pop.load_pop(os.path.join(path,'BBH_population.h5'))\n", + "\n", + "pop.df.head(10)\n", + "# pop.df_oneline(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now generate the synthetic BBH population buy using the `get_dco_at_formation` method. \n", + "- The `onelie_cols` allows you to extract values stored in the oneline dataframe.\n", + "- The `formation_channel` option allows to compute the formation channels of the BBH population.\n", + "\n", + "The synthetic population cann be saved to a file using the `save_synthetic_pop` method and loaded back into memory using the `load_synthetic_pop` method." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing formation channels...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/synthetic_population.py:804: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'ZAMS_oDoubleCE1_CC1_redirect_CC2_CO_contact_END' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n", + " self.df_oneline.loc[index,'channel_debug'] = formation_channel\n", + "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/synthetic_population.py:808: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'ZAMS_oDoubleCE1_CC1_CC2_END' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n", + " self.df_oneline.loc[index,'channel'] = formation_channel\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Synthetic population successfully saved!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/synthetic_population.py:451: PerformanceWarning: \n", + "your performance may suffer as PyTables will pickle object types that it cannot\n", + "map directly to c-types [inferred_type->mixed,key->block1_values] [items->Index(['state', 'event', 'step_names', 'S1_state', 'S2_state', 'channel'], dtype='object')]\n", + "\n", + " self.df_synthetic.to_hdf(path, key='history')\n" + ] + }, + { + "data": { + "text/html": [ + "
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    10.014204.0271701500.583774BHBH13.12046212.9110800.0796490.0639911.2790980.123592ZAMS_oDoubleCE1_CC1_CC2_END
    20.014204.849573662.821800BHBH10.3308999.8881300.2869110.2492670.8176430.165775ZAMS_oDoubleCE1_CC1_CC2_END
    30.014204.544886845.424610BHBH11.35524610.2519200.3209310.2981641.1081940.379450ZAMS_oDoubleCE1_CC1_CC2_END
    40.014204.530889620.499837BHBH11.43682310.9005380.1909820.1679960.8301940.107165ZAMS_oDoubleCE1_CC1_CC2_END
    .......................................
    307510.006394.585168128.572655BHBH13.38061712.9351580.4672850.4581640.5073050.089335ZAMS_oDoubleCE1_CC1_CC2_END
    307520.006395.75169068.017543BHBH10.0188349.0208310.7190640.6945360.3335370.155820ZAMS_oDoubleCE1_CC1_CC2_END
    307530.006396.06190488.863012BHBH20.9717579.8435520.0681790.6980550.4683030.129531ZAMS_CC1_oRLO2_oCE2_CC2_END
    307540.006395.29000495.405272BHBH11.45606911.1716520.5784780.5955310.4141860.102238ZAMS_oDoubleCE1_CC1_CC2_END
    307550.006398.01404412276.391282BHBH12.27106610.3689960.1127440.1755542.6138870.164393ZAMS_oRLO1_CC1_oRLO2_CC2_END
    \n", + "

    30756 rows × 12 columns

    \n", + "
    " + ], + "text/plain": [ + " metallicity time t_delay S1_state S2_state S1_mass \\\n", + "0 0.01420 4.027170 2913.863804 BH BH 13.120462 \n", + "1 0.01420 4.027170 1500.583774 BH BH 13.120462 \n", + "2 0.01420 4.849573 662.821800 BH BH 10.330899 \n", + "3 0.01420 4.544886 845.424610 BH BH 11.355246 \n", + "4 0.01420 4.530889 620.499837 BH BH 11.436823 \n", + "... ... ... ... ... ... ... \n", + "30751 0.00639 4.585168 128.572655 BH BH 13.380617 \n", + "30752 0.00639 5.751690 68.017543 BH BH 10.018834 \n", + "30753 0.00639 6.061904 88.863012 BH BH 20.971757 \n", + "30754 0.00639 5.290004 95.405272 BH BH 11.456069 \n", + "30755 0.00639 8.014044 12276.391282 BH BH 12.271066 \n", + "\n", + " S2_mass S1_spin S2_spin orbital_period eccentricity \\\n", + "0 12.911080 0.079649 0.064849 1.611464 0.048133 \n", + "1 12.911080 0.079649 0.063991 1.279098 0.123592 \n", + "2 9.888130 0.286911 0.249267 0.817643 0.165775 \n", + "3 10.251920 0.320931 0.298164 1.108194 0.379450 \n", + "4 10.900538 0.190982 0.167996 0.830194 0.107165 \n", + "... ... ... ... ... ... \n", + "30751 12.935158 0.467285 0.458164 0.507305 0.089335 \n", + "30752 9.020831 0.719064 0.694536 0.333537 0.155820 \n", + "30753 9.843552 0.068179 0.698055 0.468303 0.129531 \n", + "30754 11.171652 0.578478 0.595531 0.414186 0.102238 \n", + "30755 10.368996 0.112744 0.175554 2.613887 0.164393 \n", + "\n", + " channel \n", + "0 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "1 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "2 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "3 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "4 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "... ... \n", + "30751 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "30752 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "30753 ZAMS_CC1_oRLO2_oCE2_CC2_END \n", + "30754 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "30755 ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", + "\n", + "[30756 rows x 12 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# generate the BBH synthetic population\n", + "pop.get_dco_at_formation(S1_state='BH', S2_state='BH', \n", + " oneline_cols=['S1_natal_kick_array_0', 'S2_natal_kick_array_0'],\n", + " formation_channels=True)\n", + "\n", + "# we can save the synthetic population\n", + "pop.save_synthetic_pop(os.path.join(path,'BBH_synthetic_population.h5'))\n", + "# pop.load_synthetic_pop(os.path.join(path,'BBH_synthetic_population.h5'))\n", + "\n", + "cols = ['metallicity','time','t_delay','S1_state','S2_state','S1_mass','S2_mass','S1_spin','S2_spin', 'orbital_period','eccentricity', 'channel']\n", + "pop.df_synthetic[cols]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compute the BBH Merger Efficiency, the Merger Rate Desity and the Detection Rate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now compute the merger efficiency of the synthetic population at a given metallicity, i.e. the number of merging DCO per unit star formation and visualize the results." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DCO merger efficiency at Z=2.84E-02: 8.34E-07 Msun^-1\n", + "DCO merger efficiency at Z=1.42E-02: 1.61E-06 Msun^-1\n", + "DCO merger efficiency at Z=6.39E-03: 5.25E-06 Msun^-1\n", + "DCO merger efficiency at Z=2.84E-03: 2.08E-05 Msun^-1\n", + "DCO merger efficiency at Z=1.42E-03: 1.83E-05 Msun^-1\n", + "DCO merger efficiency at Z=1.42E-04: 4.18E-05 Msun^-1\n", + "DCO merger efficiency at Z=1.42E-05: 5.75E-05 Msun^-1\n", + "DCO merger efficiency at Z=1.42E-06: 6.67E-05 Msun^-1\n" + ] + }, + { + "data": { + "image/png": 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pop.get_dco_merger_efficiency()\n", + "pop.plot_merger_efficiency(channels=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also compute the merger rate density, i.e. the number of merging DCO per unit volume per unit time as a function of redshift. This calculation will generate a weight that can be used to reweight the synthetic population to obtain the intrinsic merging BBH population." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 138/138 [01:25<00:00, 1.61it/s]\n", + "100%|██████████| 14/14 [00:38<00:00, 2.73s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DCO merger rate density in the local Universe (z=0.00): 118.48 Gpc^-3 yr^-1\n", + "Intrinsic population successfully saved!\n" + ] + }, + { + "data": { + "text/html": [ + "
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    \n", + "
    " + ], + "text/plain": [ + " metallicity time t_delay S1_state S2_state S1_mass \\\n", + "0 0.014200 6.205715 4.287125e-08 BH BH 12.260188 \n", + "1 0.014200 4.705799 9.754266e-01 BH BH 12.850735 \n", + "2 0.014200 8.187239 2.075700e+00 BH BH 8.314851 \n", + "3 0.014200 6.647572 2.472628e+01 BH BH 9.826765 \n", + "4 0.014200 6.676527 1.680554e+01 BH BH 20.974131 \n", + "... ... ... ... ... ... ... \n", + "2888053 0.000001 9.558857 1.547540e+03 BH BH 12.404415 \n", + "2888054 0.000001 5.679476 5.173339e+02 BH BH 31.263586 \n", + "2888055 0.000001 6.972682 1.105689e+04 BH BH 22.020038 \n", + "2888056 0.000001 6.442639 1.310371e+04 BH BH 21.311099 \n", + "2888057 0.000001 6.862427 1.727560e+03 BH BH 26.153757 \n", + "\n", + " S2_mass S1_spin S2_spin orbital_period eccentricity \\\n", + "0 12.183039 0.050168 8.244612e-04 326.580311 0.999994 \n", + "1 20.143346 0.027546 5.791334e-17 2377.230212 0.999817 \n", + "2 8.315119 0.100591 6.554160e-03 38.651550 0.996045 \n", + "3 9.853595 0.032525 2.400078e-02 14.240625 0.980550 \n", + "4 12.500642 0.000531 4.772162e-04 696.487802 0.998932 \n", + "... ... ... ... ... ... \n", + "2888053 17.186149 0.353053 1.279050e-01 1.368867 0.072070 \n", + "2888054 34.303592 0.127590 1.671062e-01 1.495336 0.003466 \n", + "2888055 17.356224 0.156907 1.396343e-01 3.686468 0.234744 \n", + "2888056 36.473143 0.104700 1.194260e-01 4.546062 0.059527 \n", + "2888057 24.910536 0.116013 1.727154e-01 2.019769 0.054964 \n", + "\n", + " channel \n", + "0 ZAMS_CC1_oRLO2_CC2_END \n", + "1 ZAMS_CC1_CC2_END \n", + "2 ZAMS_oRLO1-reverse_CC1_CC2_END \n", + "3 ZAMS_oRLO1-contact_CC1_CC2_END \n", + "4 ZAMS_CC1_oRLO2_CC2_END \n", + "... ... \n", + "2888053 ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", + "2888054 ZAMS_oRLO1-contact_CC1_oRLO2_CC2_END \n", + "2888055 ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", + "2888056 ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", + "2888057 ZAMS_oRLO1-contact_CC1_oRLO2_CC2_END \n", + "\n", + "[2888058 rows x 12 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop.get_dco_merger_rate_density()\n", + "pop.save_intrinsic_pop(os.path.join(path,'BBH_intrinsic_population.h5'))\n", + "# pop.load_intrinsic_pop(os.path.join(path,'BBH_intrinsic_population.h5'))\n", + "pop.df_dco_intrinsic[cols]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now visualize the merger rate density as a function of redshift." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pop.plot_rate_density(DCO=True, channels=True, **{'ylim' : [0.05, 800]})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now compute the BBH detection rate. This calculation will generate a weight that can be used to reweight the synthetic population to obtain the observable merging BBH population. Here we consider a gravitational-wave detector composed of LIGO and Virgo at design sensitivity, refer to the v2 paper for further details. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 138/138 [13:07<00:00, 5.71s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DCO detection rate at design_H1L1V1 sensitivity: 8047.26 yr^-1\n", + "observable population successfully saved!\n" + ] + }, + { + "data": { + "text/html": [ + "
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    \n", + "
    " + ], + "text/plain": [ + " metallicity time t_delay S1_state S2_state S1_mass \\\n", + "0 0.014200 6.205715 4.287125e-08 BH BH 12.260188 \n", + "1 0.014200 4.705799 9.754266e-01 BH BH 12.850735 \n", + "2 0.014200 8.187239 2.075700e+00 BH BH 8.314851 \n", + "3 0.014200 6.647572 2.472628e+01 BH BH 9.826765 \n", + "4 0.014200 6.676527 1.680554e+01 BH BH 20.974131 \n", + "... ... ... ... ... ... ... \n", + "2520484 0.000001 4.893422 4.453281e+03 BH BH 44.218580 \n", + "2520485 0.000001 9.003064 9.227190e+03 BH BH 12.479949 \n", + "2520486 0.000001 4.633425 2.497501e+03 BH BH 36.989173 \n", + "2520487 0.000001 6.972682 1.105689e+04 BH BH 22.020038 \n", + "2520488 0.000001 6.442639 1.310371e+04 BH BH 21.311099 \n", + "\n", + " S2_mass S1_spin S2_spin orbital_period eccentricity \\\n", + "0 12.183039 0.050168 8.244612e-04 326.580311 0.999994 \n", + "1 20.143346 0.027546 5.791334e-17 2377.230212 0.999817 \n", + "2 8.315119 0.100591 6.554160e-03 38.651550 0.996045 \n", + "3 9.853595 0.032525 2.400078e-02 14.240625 0.980550 \n", + "4 12.500642 0.000531 4.772162e-04 696.487802 0.998932 \n", + "... ... ... ... ... ... \n", + "2520484 41.575484 0.012826 9.205180e-03 4.026325 0.103587 \n", + "2520485 17.623755 0.111116 1.095940e-01 2.702914 0.078041 \n", + "2520486 41.449699 0.013867 1.278169e-02 3.101543 0.141294 \n", + "2520487 17.356224 0.156907 1.396343e-01 3.686468 0.234744 \n", + "2520488 36.473143 0.104700 1.194260e-01 4.546062 0.059527 \n", + "\n", + " channel \n", + "0 ZAMS_CC1_oRLO2_CC2_END \n", + "1 ZAMS_CC1_CC2_END \n", + "2 ZAMS_oRLO1-reverse_CC1_CC2_END \n", + "3 ZAMS_oRLO1-contact_CC1_CC2_END \n", + "4 ZAMS_CC1_oRLO2_CC2_END \n", + "... ... \n", + "2520484 ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", + "2520485 ZAMS_oRLO1-contact_CC1_oRLO2_CC2_END \n", + "2520486 ZAMS_oRLO1-contact_CC1_oRLO2_CC2_END \n", + "2520487 ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", + "2520488 ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", + "\n", + "[2520489 rows x 12 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop.get_dco_detection_rate(sensitivity='design_H1L1V1') # if already computed use load_data=True\n", + "pop.save_observable_pop(os.path.join(path,'BBH_observable_population.h5'))\n", + "# pop.load_observable_pop(os.path.join(path,'BBH_observable_population.h5'))\n", + "pop.df_dco_observable[cols]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize the BBH Intrisic and Observable Property Distributions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now generate distribution of the observable properties of merging BBH for the intrinsic and observable BBH population by reweighting the synthetic population with the corresponding weights and visualize the distribution as follows." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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0oZFm57ETbp+3TnSzrcXFxeq0298VTp17N65pTrx2p94LvTwne02zz+1e/M7oV/1yne5Er87/Xv1MDngdALBXOHlj1mvRaFTRaFS5XE7z8/MqFApaWFjY8ive3NycQqFQW53FtKNf2qQkEolqhzHpdFpTU1PV54rFYvWYtMOL47hfHTt2rOmvyN0e69rn4/G44+1fd3MdcOq9mEgktnR4lEqlPLsJaXUe2+X2eetGP5VyVDh97vvxmtaP74VOVRK7vZocYPf66ToN51DijAOtUCj0XQmhm8LhsFKplObn56vtb2p7rKyUMnSrH9vrmKZZbR9VSU4qUqlUw96Lm3H7ODqpNtHbzY8ZTn/h18bSrMpWp8e6HztbqXDivRgKhaqvd3Z2ttoJjVfaPY+t9NN566ZDml5c+9w89/10TXPqveDl91G3Hcz1Su3x2P657dfvjGYx9xuvrtNO3NP26/nvFyTOONCWl5ddHy7CS7UlXPVUenzM5/OO9FBbr4fjflBb/bX29V25cqWtqrG9Po5OikQi1elOzsf2ZZ3uWKd2+7Xb3u2xrm0L14+f7d28F2vbnMbj8Za9Ddfj9Gey0XnsVD+dt8r7qpNj5XbHU06f+36+pjn1XvBydINKwtEvTZZqbT+m249TP35ntIq5W27co3h5nXbinrYfz38/IXEG9rHl5WU9/fTTLZczTbN6U7Sbi+72oUg66cDDzdKMcDhcjWtubq463EmlTVwrvT6OTqr90l5YWGh7vdpl2z1O7ao9Ntu3vdtjXTsMRqcdG/Wic7du34vbh+hpNdxHbfvOWslk0rHPWrPz2Kl+Om+V19FJaUswGNzy+tuNqZ19uHHu+/ma5tR7wc1z0koliejHxLl2+LF6n9t+/M5oFXNFpz3hO60fr9Od6sfz309InLGv1VY5qffLWbFY3Lcf7opCodDWr4aVoQF2O0RA7YD27ZZQVDrQcFNtJxyZTEbpdLqjjjmcPI5zc3M964HbNM1qFWDLstr+MaN2+Jl2z2O7N5y1N0H1tr2bY22a5pYv/lZj1FZYlqVQKNSTWhLdvBe33+S16nm7kxue7Zw6j53op/NWGXKs089obS/V7SSlUnslOm6d+15/N7TLyfeCW+eklco5CIVCu95Wu9r93Nb25Fzvc9uP3xmtYq5odc9Xy43vYLev0724p+3l+d+LSJzhKK/bpm1X+6VV7wKVz+fbHpe1315bJy5evNhymcrx2e14ytFotFpFJ5fLtXXRTSaTW25w3FDbfjSZTKpYLHZchWq3x9GyLI2NjVWHomhWVdJJqVSq+mXaTtX0YrFY/bW79ny20m4na5WboHg83nDbuznWly9frpY8JJPJtm6EL168qHg83rLEwonrQDfvxe1xNXtNlmVtuYGrjXl5ebllu0snz2Mn3Dxvnah9LZ3cXNd+VmZmZtoq0ar9PDZ6b7l57nv53dAJp94Lbp2TZizLqsbby+qq7XyH1h7LZp/bXn1nOBmztPV4t+p5O5lMOt4O1+3rtJP3tM306vzvSTbgoFQqZUuqPtLpdFvrTU1NVddJpVItl6/dRzMrKyvV5YLB4I7nDcOwV1ZWGq4fjUar6+fz+ZZxbVd7PGZnZ1su7/RxqI2/1bkIBoO2aZoNn+/0tQSDQVuSbRhG02M3NTXVdL9Oqj0eU1NTXa23m+M4Ozu75ZwZhtFR/LuxuLhom6ZpS7Lj8Xhby0Wj0Zbbjcfj1ddimmbLz0ll+UbbdupYLy4u2oZh2JJs0zSbfs5bvQd3ex1otc1234vhcLitYxMOh7e812q3HwwG7cXFxR3rOH0ea9VeO1qdUyfP225UYmj3O6xW7bWv3rGuCIfD1WUrr7fZsk6eeye/G9z6/nbyveDGOWmkcvx7cX2vfA4r9zjNriW1n8N2Prduf2e4EbNtv32u693zVUxNTdmpVGrLuZ6fn2+63XavY25ep3d7T9vJfZwb59+ta0Uv9U8k2JPy+bw9Pz9vz87O2qlUqvolV5sYpFIpe3Z21p6fn99yM1ZZN51Ob1nPMAw7nU7b8/PzWy4clf3UXnQrH9TK9uupvTDF43F7ZWXFXlxctMPhcN0LdiWu7T8CBIPBalzNLrCLi4t1X5dpmtX1ay9sbh6Hys1R5aYiGo1uOQcrKyv2/Px89cZo+81yp69lu9qLZDwery6/srJiz87O2sFg0A4Gg0234aR8Pl+Np5N97vY4VszPz+94T/XSyspK9bVUzuHi4qK9srJi5/N5e2pqyjYMo/q5bUflfTg7O1vdfr3PR+W5yvu6EaeO9fbXW3lNlc/S4uKinU6nbdM07XA4vOP9sNvrQCu7fS/WuymrXNcq567285dOp+2pqSk7HA7X3a7T57Gda0ejBGY3580plf03u2FspvaHiO03qLOzs7ZpmnYqldpxTai8x+rdgDp57nf7OevV97eT7wU3zkk9lWPf6LPmpNrjOT8/b0ejUTscDm85X4uLi1teeyc/Brn5neFWzLU/uESj0R33W9FotPq5rk2cK6+xNmnt9jrm1nXatju/p93NfZwT579X14peIXHGrlR+Zap8cJo9ticKtReWesvW/nq1uLjYcNna5RvdiFUuwrVxNPqQV15Ts7ia7aud11X7BeHmcZiamtpyIa39hbWyTjgcbvil1OlraXTsp6am7GAwuKX0oHIR7LVwONzxzfBuj+P2bVWOQbOSDzfVOye1r6GTZCQej+/4kq8knOl02k6lUvbU1JQdjUbbPj5OHetWr7dRcmjbu78OtKOb96Jt29UblkpSUyklC4fDO15P7Y1Ns305fR7buXa0Kmnv5rw5pZI87aZEe3v8ldL8eDxefd9UfkCp3LCHw2E7Go02vQl14tz34rvBqe/vesey2/eCG+dku8px7MX3W21yUfkuriSjpml2fc3czunvjF7EXHlP177XKqW8FZXnK+e6klRXXs9urmNuXKcrOrmndes+rt3z3+trhdt8tm3bAgAAQNXIyIgsy9Li4uK+70QSzrAsSyMjIzIMQysrK67vL5FIVNuW5vP5nrZD79ZejBmooHMwAACAbSo9nbfbqzNw5coVSVs7AASwf5A4AwAAbFNJfmqHwgGaqfQS3clQhwD2DhJnAACAbQzDUCqVkmVZ1aqlQCNzc3MqFotKpVIyHB7mCEB/IHEGAACoY2pqSqZpuj7OPPa+6elpmaapqakpT/bvxBjzvbYXY8bBRuIMAADQwOzsrIrFopLJpNehoE/NzMyoUCgonU73dL/FYrE6bVlWT/fdrb0YM1Ax4HUAAAAA/SoYDCqdTiuRSOipp56iF2BsUflRJZVKKRwOu76/QqGg5eVlFQoF5XK56vxkMqnl5WWZpll99Iu9GDNQD8NRAQAAtJBIJHTlyhXduHGDNqyQVC4xDYVCCgaDmp2d7ck+x8bGqqW229+HlRLcVCrlWZXxevZizEA9JM4AAABtSCQSWlhYUD6f9zoU9IFIJCLDMHqWNAPwFokzAABAmyptnekw7GCbmZnRG2+8wfsAOEBInAEAAAAAaIJetQEAAAAAaILEGQAAAACAJkicAQAAAABogsQZAAAAAIAmSJwBAAAAAGiCxBkAAAAAgCZInAEAAAAAaILEGQAAAACAJkicAQAAAABogsQZAAAAAIAmSJwBAAAAAGhiwOsAsP9YlqWrV69W/3/44Yd16NAhDyMCAAAA0O/u3r2rl19+ufr/uXPnZBiGdwHVIHGG465evarPfOYzXocBAAAAYA975pln9I/+0T/yOgxJVNUGAAAAAKApEmcAAAAAAJqgqjYc9/DDD7e13FNPPaWnn366+v8zzzyjRx991K2wqiYnJ7WwsKCJiQlls1nX9+flfg/Ka71+/fqW5gG8l/bHPr3Yr1fvJengnFfeS+7jvO6vffJe6o2D8lr75Z7p3//7f6/f//3fb7leu3lFL5A4w3HbOwKbmJjQ8PDwjuV+8Rd/cUvi/Oijj2p8fNz1+CqxDA8P92R/Xu73IL3WWryX9sc+vdxvRa/eS9LBOa+8l9zHed1/+6zFe2n/7Nfr95Lk3T3TRz7yEX3/+9/fsdzq6qoWFhaq//dTB8MkznBdNput+4G8du2a/u2//bceRAQAAADAK5OTk5qcnNwx/9q1azpz5kzvA2oDbZwBAAAAAGiCxBkAAAAAgCZInAEAAAAAaII2zjhwJicndf78eY2Oju77/R6k1+qFg3R8D9Jr9cpBOa+8l9zHed1/+/TKQTq+B+m1emE/vFafbdu210Fgf9neqP973/tew87B2lkOaIX3EpzCewlO4b0Ep/BegpP6/f3Uz/FRVRsAAAAAgCZInAEAAAAAaILEGQAAAACAJkicAQAAAABogsQZAAAAAIAmGI4Knjlx4oS++MUvbvkf6AbvJTiF9xKcwnsJTuG9BCfxfuoew1HBcf3cjTwAAACA/tTPeQRVtQEAAAAAaILEGQAAAACAJkicAQAAAABogsQZAAAAAIAmSJwBAAAAAGiCxBkAAAAAgCYYxxmum5yc1PDwcN35k5OTvQ8IAAAAgGey2ayy2eyO+aurq70Ppk0kznDdwsJC3fnnz5/vbSAAAAAAPLe0tKSrV696HUZHSJzhuomJibolzqOjo70PBgAAAICnRkdHde7cuR3zV1dXGxa6ec1n27btdRDYX65du6YzZ85U///e976n8fFxDyMCAAAA0O/6OY+gczAAAAAAAJqgqjbglq9Nd7/uJy85FwcAAACAXaHEGQAAAACAJihxBgAAAFD1u/MveB1C3/ityGOe7btYLCqVSimXy6lYLEqSTNNUOBxWMpmUaZqSpEwmo8XFRaVSKc9iPQgocd7nKh8yt5YHAAAA4KxkMqmxsTFJ0uzsrFZWVrSysqLZ2VkZhqFQKKRkMqlisahEIiHLsjrafi6X08jIiObm5lyIfn8ice6RTCajUCikkZGR6iMWiymXy7m631gsJp/Pp0gkokwmo0KhsOWDVSwWNTc3p0QiUY0JAAAAgDcSiYRmZmaUz+eVTqcVDAZlGIYMw1AwGFQqldKNGzdUKBQUCoXa2qZlWSoWi9WcJBKJyLIsLS8vu/xq9g+qarusUCjowoULOnbsmJLJpOLxuKRywppOpxWJRBSNRnX58mUZhuFaHLlcrmWSbhiGZmdnXYsBAAAAQGO5XE6ZTEapVErBYLDhcoZhaH5+XmNjYy1Lm0OhkAqFggzDUDgc1lNPPaVCoeBw5PsfibOLcrmcIpGITNNUPp/fkhibpqlUKqWxsTElEgkVCoUdy/RSOByuVv0AAAAA0HvJZFKSFI1G21o+lUq1rDH67LPPbrnHJ2nuDlW1XWJZVvVN3CwhjcfjisfjKhaLunDhQg8jLAsGg5qfn9f8/DxJMwAAAOChTpPaaDTa8h6ee3xnUOLskosXL8qyLAWDwabVLKTyL0uV9sdzc3Nt/8LUrlQqpWg0qkKhUG3HcOzYMYXDYT5IAAAAQJ/J5XLVJp6tPPnkky5HA4nE2RWWZVV7qAuHwy2XN01TpmmqWCxqenra8cS5dh8AAAAA+lMlJ0gmk3ryySfbKuSKRCKMjNMDVNV2QSaTqU6fPXu2rXUqSW2hUOCNDwAAABxAiURCUrkg7vTp020NFxWNRjU1NeV2aAceibMLnn766ep0u1Wha0uD3R6iCgAAAED/mZqaquYFlT6TKkPLzszMkCd4iMTZBbWN+o8dO9bWOrUJdj6fdzokAAAAAHtAPp/f0UdSLpdTMplUJBKRz+dTKBTaUssV7iNxdtj2atbttis+fvx4dXphYcHRmAAAAADsDYZhKJ/PK51ON+xkuFAoKJFIaGRkhGaePULi7LBWA5A3Ulvi3O02WsnlcorFYhoZGak++LUKAAAA6D/xeFz5fF4rKyuanZ3V1NTUjkTasiyFQiGS5x6gV+0+VBkyyilvvPGGIpGIpPLQV5cvX5ZhGLIsS9PT00okEkomk5qdnW2rF/BOXb9+veN1Tpw4oZMnTzoeCwAAALCXGIahaDS6ZeSdStXtQqEgy7KUSCQ0Pz/vYZQ7vfbaa3r99dc7WqebvKFXSJwdtj3p7WacZKdLnGdmZpROp3eMBWcYhlKplMbGxpRIJBSJRDQ/P+948vyZz3ym43W++MUv6ktf+pKjcQAAAAD7QTgcVj6fVywW09zcnHK5nAqFQsOq3V746le/qi9/+cteh+EYqmo7zK1q1t0yTVOzs7NNB1CPx+PVttixWKxXoQEAAACoUakl2q7Z2dnqNP0kuYvEeZ+bnZ3dUq2jkcoylmUpmUy6HRYAAACAbXK5XMcFcZXaoouLiy5EhAqqajusm6rZ/aAyNpxUrtp96dIlx17LM888o0cffbSjdU6cOOHIvgEAAIC9ZGFhoaOmk8FgULlcbssoPf3g85//fMe1Wa9fv95VM89eIHF22PZxmy3L6jgB9SL53j5sVi6Xa6ukuh2PPvqoxsfHHdkWAAAAsJ+lUqmOEudCoSBJrnTyuxsnT57cV539UlXbYU4kvduT717Yvs/nnnuu5zEAAAAAB12lx+x2FIvFaoFXP3UMth+RODtsewLa7tBStW0ZnEi+C4WCxsbGNDIy0tU4zf3WyRkAAABwUMzMzCiZTDa9J7csS7FYTIZh6PLly13th3v+9pE4O2x70tvum7G2Mf/2atPduHjxoorFYnVct04/FHu1rTYAAACwl8Xjca2srKhQKGhkZESJREJzc3MqFArVEuaZmRmdPn1akpTP51veu1uWJcuyVCgUND09XZ3/9NNPVzskqzxQH22cXRAMBqttDdotca5d7uzZs7uOoVgsdrT89jjHxsZ2HQMAAACA9pmmqVQqJcMwND8/r0KhoHQ6renp6WqhmGEYmpiYUCqVajrkbEUikVAmk9mSXFemi8VitQMvy7Jkmia9czdA4uyCcDhcTZzbTWBrl3OiUy7TNKsxVD587e5fkp588sldxwAAAIC957cij3kdwoG1PWkNBoNKp9O72mY6nd71NkBVbVdcunSpOp3P59tap5LkmqbpSFXtcDisaDQq27Y1NTXVcvn5+fkt61JVGwAAAADKSJxdYBhGtTv4K1eutFw+l8tVp9vpQa/SxqGZSluIdtspzM3NVadTqVRb6wAAAADAQUDi7JJ0Oi3DMGRZ1paktNGyUrkqRrN2CpZlaWxsTLFYTKFQSDMzMw2XNU1TU1NTunDhQstYM5lMtar21NQUXdkDAAAAQA0SZ5eYplntFj4WizVs65zJZDQ3NyfDMDQ7O9t0m7lcbst2anvEq6dSchyJRBqWPM/NzSmRSEgq9+BHaTMAAAAAbEXi7KJoNKr5+XkZhqFQKLRlPOXKMFGJRELhcFg3btxo2bZ5e7vjdtpC5/N5maapkZERJZPJajXvXC6nWCxWHfuNTgMAAAAAoD4SZ5dVkuJUKqV0Oq2RkRGNjIzo9OnTWl5e1vz8fDW5bmdblY6+TNNsWUJdkU6nNT8/r2KxqIsXLyoUClVLwVOplG7cuNFWV/YAAAAAcBD5bNu2vQ4C+8u1a9d05syZ6v/f+973ND4+7mFEHvla86r0TX3yUutlAAAAgH2kn/MISpwBAAAAAGiCxBkAAAAAgCZInAEAAAAAaILEGQAAAACAJkicAQAAAABoYsDrALD/TU5Oanh4uO78ycnJ3gcEAAAAwDPZbFbZbHbH/NXV1d4H0yYSZ7huYWGh7vzz58/3NhAAAAAAnltaWtLVq1e9DqMjJM5w3cTERN0S59HR0d4HAwAAAMBTo6OjOnfu3I75q6urDQvdvEbiDNdls9m+GbgcAAAAgLcaNdm8du2azpw50/uA2kDnYAAAAAAANEHiDAAAAABAEyTOAAAAAAA0QeIMAAAAAEATJM4AAAAAADRB4gwAAAAAQBMMRwUAAADgbV+b9jqC/vHJS15HgD5BiTMAAAAA9JlisahEIqGxsTH5fD75fD6NjY0pkUioWCxWl8tkMkomkx5GejCQOAMAAABAH0kmkxobG5Mkzc7OamVlRSsrK5qdnZVhGAqFQkomk9Xk2rKsltucm5tTJBLRyMiIfD6fQqGQYrGYMpmMy69mfyBxBgAAAIA+kUgkNDMzo3w+r3Q6rWAwKMMwZBiGgsGgUqmUbty4oUKhoFAo1HJ7lmUpEoloenpasVhM+Xxe+XxeTz31lHK5XLVUu1Ao9ODV7V20cQYAAACAPpDL5ZTJZJRKpRQMBhsuZxiG5ufnNTY21rK0+cKFC5qYmND8/PyW+cFgUPF4XBcuXKgm4fl8vul+DzJKnAEAAACgD1TaKkej0baWT6VSbW0vnU7Xfd4wDF2+fLn6fywWa2u/BxGJMwAAAAD0gU6rS0ejURmG0fD5TCYj0zS3dCa2XTAYVDgcllTukIw2z/WROAMAAABAH8nlcm0v++STT9adb1mWLMvS3NxctaOxRmqrZ8/Ozra974OExBkAAAAA+oBpmpLKVazb6SlbkiKRSN3EeHl5ecv/zUqdz54923A9lJE4AwAAAEAfSCQSksqlxadPn9bc3FzLdaLRqKampnbMN01T8XhchmEoHo9Xk/J6apPqY8eOdRH5/kfiDAAAAAB9YGpqqprgWpalWCwmn8+nSCSimZmZjqpwS+VOwVZWVhp2Dlbx3HPPVafpVbs+hqOC6yYnJzU8PFx3/uTkZO8DAgAAAPpUPp+vDhFVkcvltiTNwWBQiURC8Xh81/uzLGvLti9durTrbbaSzWaVzWZ3zF9dXXV9390icYbrFhYW6s4/f/58bwMBAAAA+pxhGMrn88pkMkqn03V72i4UCkokEkomk8rn802rYbcyPT1dbU+dSqWa9tLtlKWlJV29etX1/TiJxBmum5iYqFviPDo62vtgAAAAgD0gHo8rHo9XS4Sfe+455XK5LYm0ZVkKhUJdJ8/FYlEzMzOSGreVdsPo6KjOnTu3Y/7q6mrDQjev+Wzbtr0OAvvLtWvXdObMmer/3/ve9zQ+Pu5hRB752nT3637S/SoyAAAAde3mHma/6dN7slwup2QyWU2iw+Gw5ufnO9pGJekuFouKRqN9MQxVP+cRdA4GAAAAAHtIOBxWPp9XNBqVpB0l0e24cOGCisWi4vF4XyTN/Y7EGQAAAAD6QCQS6Wj52oS3kyrOkUhEhUJBs7OzLXvcRhmJMwAAAAD0gVwuV+2oq13hcFiStLi42NbykUhECwsLW0qs0RqJMwAAAAD0iU47x6qMu3z8+PGWy8ZiMRWLReXz+brjNRcKBcVisY72f1CQOAMAAABAn0ilUh0tX9tBWDO1SXOjHrhzuZyOHTvW0f4PChJnAAAAAOgTlR6z21EsFpXL5RSNRuuWIFfEYjFZlqV8Pt90nOb5+XmNjY11GvKBwDjOAAAAANBHKmMrX7p0qWGia1mWYrGYDMPQ5cuXG24rkUhobm5O4XC4Yedjy8vLsixLxWKx7aT9oCFxBgAAAIA+EY/HlUqlFIvFNDIyong8rkgkItM0ZRiGisWiCoWCpqenZZpm01LkRCKhTCYjqVyS3Y6JiQmnXsq+QuIMAAAAAH3ANE2lUikZhqH5+XkVCgWl02lNT0+rWCzKsiwZhqGJiQmlUinF4/GG2yoUCtWkuRPNqnIfZCTOAAAAAN72yUteR3BgbR9SKhgMdj3OcjAYlG3bToQF0TkYAAAAAABNkTgDAAAAANAEiTMAAAAAAE2QOAMAAAAA0ASJMwAAAAAATZA4AwAAAADQBIkzAAAAAABNkDgDAAAAANDEgNcBYP+bnJzU8PBw3fmTk5O9DwgAAACAZ7LZrLLZ7I75q6urvQ+mTSTOcN3CwkLd+efPn+9tIAAAAAA8t7S0pKtXr3odRkdInOG6iYmJuiXOo6OjvQ8GAAAAgKdGR0d17ty5HfNXV1cbFrp5jcQZrstmsxofH/c6DAAAAAB9oFGTzWvXrunMmTO9D6gNdA4GAAAAAEATJM4AAAAAADRB4gwAAAAAQBMkzgAAAAAANEHiDAAAAABAEyTOAAAAAAA0QeIMAAAAAEATjOMMAAAAHHC2bev2+uaWeYMBvwYD9cvZbq9typbd1b4G/H4NDdTf7p31TZXs7rYb8Pt0aCDg+HYl6chgQD6fr+v1sfeROAMAAAAH3O31TaWvFrfM++QHTurDDxt1l/+P31zS7bXNus+18lHzuD42drzuc1cWXtYbb611td3HH35An/rAg3Wf+6/P/0ivrNzuaruSlDhn6ugQqdNBRlVtAAAAAACaIHEGAAAAsEO3JcpwRrFYVCKR0NjYmHw+n3w+n8bGxpRIJFQsvl07IJPJKJlMehjpwUDiDAAAAAB1vO/B+/S+B++Tv8ftm5PJpMbGxiRJs7OzWllZ0crKimZnZ2UYhkKhkJLJZDW5tiyro+3ncjmNjIxobm7Ohej3JyrqAwAAANhh/KH7Gz73zz82uqvOwRp5cuLhXXUO1sivPv6errbrRbvmRCKhTCajfD6vYDC45blgMKhgMKhLly4pFospFAq1tU3LsrS8vKxcLqd0Oq1CoSBJWl5edjz+/YrEGQAAAMAOA00S0SND9Xuv3q3Dg3tru07L5XLKZDJKpVI7kuZahmFofn5eY2NjLUubQ6GQCoWCDMNQOBzWU089VU2c0T4SZwAAAADoA5W2ytFotK3lU6mUYrFY02WeffZZGYZR/Z+kuTu0cQYAAACAPtBpUhuNRrckxfW0eh7tIXEGAAAAgD6Sy+XaXvbJJ590MRJUkDgDAAAAQB8wTVNSucp2uz1lRyKRag/ccA9tnOG6yclJDQ8P150/OTnZ+4AAAACABu6sbyr3g59umRf+hQd70sFYIpGoJs2nT5/W5cuXW7Z3brc9dD/JZrPKZrM75q+urvY+mDaROMN1CwsLdeefP3++t4EAAAAALZRsWz/86Vtb5n3qAyd7su+pqSml02kVi0VZllXt+CscDisSiSgYDCocDvckFjctLS3p6tWrXofRERJnuG5iYqJuifPo6GjvgwEAAAD6WD6f14ULF7Z0FJbL5ba0ew4Gg0okEorH416EuGujo6M6d+7cjvmrq6sNC928RuIM12WzWY2Pj3sdBgAAAND3DMNQPp9XJpNROp2u29N2oVCoVuvO5/PVttF7RaMmm9euXdOZM2d6H1Ab6BwMAAAAAPpMPB5XPp/XysqKZmdnNTU1pWAwuGUZy7IUCoVULBY9ivLgoMQZAAAAOOCODg3otyKPeR0G6jAMQ9FodEsnYLlcTslkUoVCQZZlKZFIaH5+3sMo9z9KnAEAAABgDwmHw8rn89VkOpfL1a3SDeeQOAMAAABAH4hEIh0tPzs7W53u10619gsSZwAAAADoA7lcTpZldbROZXiqxcVFFyJCBYlzj2QyGYVCIY2MjFQfsVhsS7fyXikWixoZGemLWAAAAICDrNOS40qHYcePH3cjHNxD4uyyQqGgkZERpVIpJRIJraysaGVlpdptfCQSUSwW6/iXJSdV9u9lDAAAAACkVCrV0fKVts2Vkme4g8TZRblcTqFQSMeOHVM+n98yQLlpmkqlUkqn05qbm1MoFPIkcc1kMnQkAAAAcMBtbJb0wk9/vuWxsVnyOqwDqdJjdjuKxaJyuZyi0eiOoargLIajcollWYrFYpLKjfYNw6i7XGV8tkwmowsXLiifz/c0xkQi0bP9AQAAoD+tbZb0f3z3x1vmfe5jj+jIUKCj7QwG/BoM1C+bu722KVt2V/EN+P0aGqi/3TvrmyrZ3W034Pfp0EBnr7EXZmZmJEmXLl1qmEdU8g3DMHT58uWu9kON0/aROLvk4sWLsixLwWCw5a8/yWSyWvI7Nze3ZYw2N1U+aHxgAAAAsN0ff/PFjtf55AdO6sMPG3Wf+4/fXNLttc2uYvmoeVwfG6vfhvfKwst64621rrb7+MMP6FMfeLCrdd0Sj8eVSqUUi8U0MjKieDyuSCQi0zRlGIaKxaIKhYKmp6dlmqby+XzD5Lqicr9fLBY1PT1dnf/0008rGAxqYmKiOq/Vtg4qqmq7wLIszc3NSWqvrYFpmjJNU5K2vJHdlMlktLy8rCeffLIn+wMAAAD2ik9+4KROjRzp+X4rzTkNw9D8/Hy1Nur09LQuXLigsbExxWIxzc/PK5VKVftNaiaRSGhkZESnT5/WhQsXlMvlZBhGNQmPxWI6ffq0RkZGFAqFevEy9yRKnF2QyWSq02fPnm1rHdM0q78eFYvFlh+A3bAsS8lkUvl8Xul02rX9AAAAYG84OjSg34o8Jkn63fkXPI7GW0eHBvThhw398Kc/7/m+tw8pFQwGd32/nk6nued3ACXOLnj66aer0+1WdahNlN0eFurixYu6dOmSq8k5AAAAAOwXlDi7oLaX6mPHjrW1Tm2C7WYHYXNzcyoWi5qdnXVtHwAAANi7Eue6L1xp1DGYJP3zj43uqnOwRp6ceHhXnYM18quPv6e63SOD/deBGHqLxNlhxWJxy//tlurWDlje6aDn7bIsSxcvXtSzzz7ryvYBAACw9x0dcidF6LSH7nYddimpdWu72Juoqu2wbnuori1xdquX64sXLyoejzPGGwAAAAB0gBLnPrS8vOz4NnO5nAqFgidVtK9fv97xOidOnNDJkyddiAYAAACA21577TW9/vrrHa3TTd7QKyTODtue9HYzDpobJc6xWMyzKtqf+cxnOl7ni1/8or70pS85HgsAAAAA9331q1/Vl7/8Za/DcAxVtR3mVjXr3UgkElTRBgAAAIAuUeK8z+VyOeVyuR1jwgEAAAAA2kPi7LBuqma7KZFIeD7g+TPPPKNHH320o3VOnDjhUjQAAAAA3Pb5z39esViso3WuX7/eVTPPXiBxdtj2cZsty+o4mXYq+U4mkwqHwwqHw45sr1uPPvqoxsfHPY0BAAAAQO+cPHlyX3X2S+LsMCeS3u3JdzcKhYIymYxWVlZ2vS0AAAAAOMjoHMxh25PedoeWqu1UzInkOxaLeTL0FAAAAADsNyTODtue9Lbby3Zt512mae4qhmQyqWAw6HkVbQAAAADYD6iq7YJgMKhCoSCp/RLn2uXOnj27q/3Pzc2pWCzK5/N1tN72xvtTU1NKpVK7igUAAAAA9joSZxeEw+Fq4lwsFttap3a5aDS6q/130ot2IpGo7ntqakqRSKT63G5LvgEAAABgPyBxdsGlS5c0MzMjScrn822tU0m0TdPcdcLaSRXt2qrlZ8+epXo3AAAAAGxDG2cXGIZRTUCvXLnScvlcLledTiaTLZefm5urJtoAAAAAAHeROLsknU7LMAxZlqW5ubmWy0rlttHxeLzhcpZlaWxsTLFYTKFQqFqqvRu1nZe12x4bAAAAAA4SEmeXmKapy5cvSyp3utWorXMmk9Hc3JwMw2g5fFQul9uynenp6V3HWZsst9sDOAAAAAAcJLRxdlE0GtX8/Hy1hDiVSlVLlC3LUjKZVCaTUTgc1uzsbMvxm7c/301b6EKhoOXlZVmWpaeffnpLslxJxIPBoCRpYmLCkTGlAQAA0OdsW1q/tXVeYEgKDNZffu2WJLu7ffkHpYGh+s+t35bsUpfbHZAGDjXY7h3J3uxuu5I0eFTqcMQa7C8kzi4Lh8O6ceOGrly5onQ6vaUNczgc1vz8fNsdcoXDYU1NTWlmZkamabYsoa6ntvTbMIwdiXElebYsS+l0umnVcQAAAOwT67ekb/ze1nnv+xXpVKj+8t/OlJPcbox+Qjr9y/Wf+85/klZ/1t12HwpKj326/nPf+1PJeqm77UrSx78gDQ13vz72PBLnHjAMQ/F43JEkNJVK7Wps5cXFxV3HAAAAAAAHCW2cAQAAAOy0veo2cICROAMAAABAPSfeX374Aj3fdbFYVCKR0NjYmHw+n3w+n8bGxpRIJLZ0GJzJZNoa0ha7Q1VtAAAAADu9+0ONn/tIXLvqHKyRJ/7Z7joHa+TMZ7vrHMyjds3JZFIzMzOKx+OanZ2tdgpcLBb19NNPKxQKKR6PK5FIKJFItNUkdG5uTul0WgsLC7IsS8FgUKZpKhKJ0K9RG0icAQAAAOzULBEdOurOPgePuLTdw+5s1wWJREKZTEb5fL462k1FMBhUMBjUpUuXqiP3tGJZlmKxmJaXl5VIJJROp2VZlnK5nKanpzU3N6dUKqXZ2dkd+8PbSJwBAAAAoA/kcjllMhmlUqmmSaxhGJqfn9fY2NiW4WXruXDhgiYmJjQ/P79lfjAYVDwe14ULF1QoFBQKheom6ygjcYbrJicnNTy8s5rL5OSkJicnex8QAAAA0IcqbZWj0Whby6dSKcVisZbbS6fTdZ83DEOXL1+ullzHYrGejMKTzWaVzWZ3zF9dXXV9390icYbrFhYW6s4/f/58bwMBAAAA+lihUOho+Wg0KsMwGj6fyWQUDodVLBar7aS3CwaDCofDyuVyKhaLymQyrrd5Xlpa0tWrV13dh9NInOG6iYmJuiXOo6OjvQ8GAAAA6HO5XK7t5PXJJ5+sO9+yLFmWpbm5Oc3Nzcm2G3fmFgwGlcvlJEmzs7OuJ86jo6M6d+7cjvmrq6sNC928RuIM12WzWY2Pj3sdBgAAANDa+h3p7/5867z3//2edDBmmqaKxaKSyaSefPLJpqXJFZFIZMvwVBXLy8tb/m9W6nz27NmG67mhUZPNa9eu6cyZM67vvxuM4wwAAAAAFfam9PrfbX10M5RVFxKJhKRyafHp06c1NzfXcp1oNKqpqakd803TVDwel2EYisfjDZNmSVsS72PHjnUR+f5H4gwAAAAAfWBqaqqa4FaGkfL5fIpEIpqZmalWp25XOp3WyspKw87BKp577rnqNL1q10fiDAAAAAB9ot6QULlcTslkUpFIRD6fT6FQSJlMxpH9VcZ0rrh06ZIj291vSJwBAAAAoE8YhqF8Pq90Ot2w9LdQKCiRSGhkZKRu++ZOTE9PV8eCTqVSbbWrPojoHAwAAAA46IaGpU9S0thP4vG44vF4tUT4ueeeUy6X2zJklWVZCoVCyufzTdswN1IsFjUzMyOpcVtplFHiDAAAAAB9yjAMRaNRpVIp5fN52bat+fn5amm0ZVnVTsU6YVmWIpGIpHLSPDs762jc+w2JMwAAAADsIeFwWPl8XtFoVJJ2lES348KFCyoWi4rH4yTNbSBxBgAAAIA+UCkBbldtwruwsNDRfgqFgmZnZ1v2uI0yEmcAAAAA6AO5XK7aUVe7wuGwJGlxcbGt5SORiBYWFraUWKM1EmcAAAAA6BOdlBxLb4+7fPz48ZbLxmIxFYvFukNeSeXeumOxWEf7PyhInAEAAICDbnNDeu0HWx+bG15HdSClUqmOlq+0ba6UPDdSmzQ36oE7l8vp2LFjHe3/oGA4KgAAAOCg27wrXXtm67yPXJQGj3S2ncCQFBis/9zaLUl2N9FJ/kFpYKj+c+u3JbvU5XYHpIFD3a3rklwup2Qy2VYCXSwWlcvlFI1GG475LJWTZsuylM/nm25vfn6+43bWBwWJMwAAAICdvn2583Xe9yvSqVCD7WXKSW43Rj8hnf7l+s995z9Jqz/rbrsPBaXHPt3dui6qjK186dIlGYZRdxnLshSLxWQYhi5fbnyuEomE5ubmFA6HGybFy8vLsixLxWJRyWRy1/HvRyTOAAAAAFDrfb8ivf63kvVSz3cdj8eVSqUUi8U0MjKieDyuSCQi0zRlGIaKxaIKhYKmp6dlmqby+XzD5DqRSCiTyUgql2S3Y2JiwqmXsq/QxhkAAAA46IaGpU9eKj8OuqHhxqXmLjNNU6lUSoZhaH5+vlq1enp6WhcuXNDY2JhisZjm5+eVSqWatlcuFArVpLkTjZLwg44SZwAAAADoA9uHlAoGg12PsxwMBmXbXbYpxw4kzgAAAADe9vEvdL9uoEEHXpL0kbh21TlYI0/8s911DtbImc9K9mZ5evBod9vHvkHiDNdNTk5qeHi47vzJycneBwQAAIDGhnbetzmzXZeSz057/m57u4fd2S6UzWaVzWZ3zF9dXe19MG0icYbrGg3ifv78+d4GAgAAAMBzS0tLunr1qtdhdITEGa6bmJioW+I8Ojra+2AAAAAAeGp0dFTnzp3bMX91dbVhoZvXSJzhumw2q/Hxca/DAAAAANAHGjXZvHbtms6cOdP7gNrAcFQAAAAAADRB4gwAAAAAQBMkzgAAAAAANEHiDAAAAABAEyTOAAAAAAA0QeIMAAAAAEATJM4AAAAAADRB4gwAAAAAQBMkzgAAAAAANEHiDAAAAABAEyTOAAAAAAA0QeIMAAAAAEATJM4AAAAAADRB4gwAAAAAQBMkzgAAAAAANEHiDAAAAABAEyTOAAAAAAA0QeIMAAAAAEATA14HgP1vcnJSw8PDdedPTk72PiAAAAAAnslms8pmszvmr66u9j6YNpE4w3ULCwt1558/f763gQAAAADw3NLSkq5evep1GB0hcYbrJiYm6pY4j46O9j4YAAAAAJ4aHR3VuXPndsxfXV1tWOjmNRJnuC6bzWp8fNzrMAAAAAD0gUZNNq9du6YzZ870PqA20DkYAAAAAABNkDgDAAAAANAEiTMAAAAAAE2QOAMAAAAA0ASJMwAAAAAATZA4AwAAAADQBIkzAAAAAABNkDgDAAAAANAEiTMAAAAAAE0MeB0AsK/YtrR+qzy9udbmSj4pMOhaSAAAAAB2h8QZcNL6Lekbv1eefvlb7a0zeFR6KOReTAAAAAB2haraAAAAAAA0QeIMAAAAAEATJM4AAAAAADRBG2fAbe8JSYFmHzVf+U9pU3rzlfL0jf9Z/vvwL0kDQ66GBwAAAKA5EmfAbYEBKdBG8mtvStZL5emlr5f/PhSUROIMAAAAeInEGXCSf+Besivpjevlvz5aRAAAAAB7GYkz4KSBQ9Jjny5Pv1rwNhYAAAAAjiBxhusmJyc1PDxcd/7k5GTvAwIAAADgmWw2q2w2u2P+6upq74NpE4kzXLewsFB3/vnz53sbCAAAAADPLS0t6erVq16H0RESZ7huYmKibonz6Oho74MBAAAA4KnR0VGdO3dux/zV1dWGhW5eI3GG67LZrMbHx70OAwAAAEAfaNRk89q1azpz5kzvA2oD3f0CAAAAANAEJc6Ak9bvSN/70/L0T75b/nvyg+VhqgAAAADsSdzNA06yNyXrpfL0nZv35pW8iwcAAADArlFVGwAAAACAJkicAQAAAABogsS5RzKZjEKhkEZGRqqPWCymXC7Xk/0Xi0Ulk0lFIhGNjIzI5/MpFAopFotpbm6uJzEAAAAAwF5E4uyyQqGgkZERpVIpJRIJraysaGVlRfl8XqZpKhKJKBaLybIs12JIJBIKhUKSpFQqpRs3bmhlZUWXLl1SLpdTLBbT2NhYz5J4AAAAANhL6BzMRblcTpFIRKZpKp/PyzCM6nOmaSqVSmlsbEyJREKFQmHHMk6IRCKSpBs3buzYdjQaVTgc1unTp1UsFhWJRDQ/P69wOOxoDGiXT/IPlicHj7w9DwAAAICn+rrEeWlpSUtLS16H0RXLshSLxSRJs7OzDRPieDyueDyuYrGoCxcuOBrDzMyMisVi0/0bhqFUKlX9PxKJqFgsOhoH2hQYlN770fLjE/+y/Bg66nVUAAAAwIHnaeK8tLSkv/zLv9Qf/uEf6tKlS3rqqad09uxZHT9+XH6/X6FQaM+2v7148aIsy1IwGFQwGGy6bDKZlFSu1u3k652enlaxWNTIyIhmZmYaLre9hLk2kQYAAACAg87TqtrhcFg3btyQJNm2LcMwFI/H9ZWvfMXx0tdesiyrmgC3U+3ZNE2Zpqlisajp6WlFo1FHYqhtN51MJjU1NdVw/7UWFhZ2vX8AAAAA2C88b+Ns27YkaWpqSl/5ylc8jsYZmUymOn327Nm21qkkzoVCQcVicUcy26ndtJU+duzYrvYNAAAAAPuJ522cfT6fEonEvkmaJenpp5+uTrebwNYmyk71bp1Op2UYhgzD0OzsbMPltrdpblW1HAAAAAAOEs9LnKX916a2UChUp9stva1NsPP5vCNxVDoea2V7op5IJBzZPwAAAADsB54nzsFgUPfff7/XYThme+ltu1Wujx8/Xp3udRvjdDpdnU6lUruuJo4ulTalt35ann7l3o8n7/5QubdtAAAAAJ7xPHGemJho+vyf/dmftbUd0zT14Q9/2IGIdqe2Q65O1JY4d7uNbszMzFRLyFOpVMMOxNAD9qa0vFie/uFflP+e/ACJMwAAAOAxzxPnsbGxhs/duHFDv/M7vyPLslQsFuXz+bY8X+mJ2zRN/fqv/3pfJM5OWF5ednX7lmVpYWFB6XRac3NzCgaDSqVSbfUA3o3r1693vM6JEyd08uRJF6JxmS8gnXh/efq1H9yb53lXAgAAAEBPvfbaa3r99dc7WqebvKFXPE+cm3Wedfr06S3VlmdmZvSv//W/rq43Ozvbd8NWbU96u+nd2q0S57GxsR1VyePxuFKp1K564W7lM5/5TMfrfPGLX9SXvvQlx2Nx3eBh6cyvladf/ztvYwEAAAA88tWvflVf/vKXvQ7DMXuqKGxqakoXLlyQz+fTzMxM3yXNUm+rWXdqcXFRtm3Ltm2trKxodnZWV65c0cjIiGKxWF/HDgAAAABe2VOJsyTFYjFJ0pNPPtn2Ojdu3NBv/uZvuhXSnmQYhqLRqPL5vAzD0NzcnE6fPr2lR3AAAAAAQB9U1e5UpU10Jz1xF4tFZTIZ/f7v/75bYVW5WeXZDaZp6vLly9US51AopHw+7+hYzs8884weffTRjtY5ceKEY/sHAAAA0Fuf//znq4We7bp+/XpXzTx7Yc8lzt3qVUK7fdxmy7I63nevk+9oNCrDMKpVtWOxmBYXFx3b/qOPPqrx8XHHtgcAAACgv508eXJvdvbbwIFInHvZdteJpHd78t0LTz75pDKZjKS3S+jj8XjP48De87vzL3S13m9FHnM4EgAAAMAdnrdx7kVS+9xzz7m+j4rtSW+7Q0vVHgcvqntvHxZsfn6+5zHsC+t3pO/9Wfnx2g/Kj9KG11EBAAAA2AXPS5zT6bRM02x7+W9/+9uSpP/yX/6LbNtuufzy8rIymcyOMaDdsj3pbfeHgdqq0Z0cj0YKhYKSyaQkKZlMthyjeXvcdBLWJXvz7WGobv3s3rzGY5UDAAAA6H+eJ87FYrHjRuO2bSsajXa0/MjISKehdS0YDFYTz3ZLnGuXO3v27K5juHDhQjVpz+VyWlxcbJqQd1tSDgAAAAD7nedVtSVVxxZu5yFJPp+v43V6qbZ0t1gstrVO7XKd/ChQT6FQ2FHS3aoEeXui7ESpNwAAAADsB32ROHfCq2S4E5cuXapO5/P5ttapJLamae46aa3XRrrV8FLb43zqqad2FQMAAAAA7BeeV9WWpFQqJcMwHO9Nenl5WZZlaXp62tHttmIYhsLhsHK5nK5cuaJ0Ot10+VwuV52utEtuZm5uTqZpNkyGK8l3sVhUOBxWMplsmYzXxiCJHrUBAAAA4B7PE+d4PK7f/u3fdnUftm0rlUq5uo/t0um0QqGQLMvS3Nxc0+rXlcQ6GAw2TVgty1IoFKpW606lUpqamqq7bCqVUiwWa6tjsFwut6WqeDqd9qRnbwAAAADoR55X1d4+DNJe3cd2pmnq8uXLkqRYLNawrXMmk9Hc3JwMw9Ds7GzTbW5PcJuVpEejUU1NTSkSiWhmZqbhcsViUYlEovr/1NQUpc1eCQxJo79cfnzyUvkxNOx1VAAAAMCB53ni3IuSTcMwejJe9HbRaFTz8/MyDEOhUEiZTKb6nGVZSiQSSiQSCofDunHjRsvq1NuPVavlU6mUZmdnlU6nNTIyomQyqVwup0KhoEKhoJmZGY2NjalYLCoYDCqfz/e8ZB4AAAAA+p2niXM0Gu1J782maeqzn/2s6/upp5IUp1KpagI7MjKi06dPa3l5WfPz89Xkup1tVapmm6bZsoRaKh/jxcVFXb58WZZlKZlM6sKFCwqFQpqenlY0GtXs7Kzy+XzLDsQAAAAA4CDy2f3eRTX2nGvXrunMmTPV/7/3ve9pfHzcw4h6aG1V+sbvlaeX/mf578O/VK6G3YlPXmq9TJ/43fkXulrvtyKPORwJAAAA9rJ+ziM8r6oNAAAAAEA/I3EGAAAAAKAJz4ej6sTS0pIsy9Ly8rKOHTsm0zR1//33ex0W4Ay7JN16ozz92g/Kf4+/TwrsqY8pAAAAsO/09R35X//1X+vpp5+u9gRdj2EYCofD+vVf/3X943/8j3scIeCg0ob0+t+Wp689U/778S+QOAMAAAAe68s78r/+67+uDp0kSc36L1tZWdHc3Jzm5uY0NjamVCpFAg3v+AKS8d7y9OEH7s2jRQQAAACwl/Vd4vybv/mb1fGObduWz+eTz+druZ5t27p+/bqi0agikYiuXLlCNe4+MTk5qeHh4brzJycnex+QmwYPS0/80/K09ZK3sQAAAAB9KJvNKpvN7pi/urra+2Da1DeJ882bNxUOh1UoFLaUMNdOG4ahY8eOSZKWl5dlWVbdbf3FX/yFTNNUPp/XI4884mrcaG1hYaHu/PPnz/c2EAAAAACeW1pa0tWrV70OoyN9kzhfuHBB3/nOd6qJcjAY1FNPPaVwOCzTNPXAAw80XPc73/mOFhYWNDs7W63evby8rEgkooWFBUqePTYxMVG3xHl0dLT3wQAAAADw1OjoqM6dO7dj/urqasNCN6/1ReL86U9/utr5VzAY1OXLl/XEE0+0vf4TTzyhJ554QhcvXpRlWbp48aL+9E//VIuLiwqHw/r2t7/tVuhoQzab7ZuBywEAAAB4q1GTzWvXrunMmTO9D6gNnvda9Oyzz2p+fl4+n0/JZFILCwsdJc3bGYah2dlZXblyRbZtK5/P64/+6I8cjBgAAAAAcJB4njgnEgn5fD5NTU1penrase1Go1H9wR/8gWzbVjKZdGy7AAAAAICDxdPE+Tvf+Y6KxaKCwaCjSXNFPB7XZz/7Wa2srOhrX/ua49sHdti4K73wf5Yfb1wvP0obXkcFAAAAYBc8TZzT6bR8Pp8uX77s2j4uXbok27Z15coV1/YBVJU2pFcL5cfPf1x+2CWvowIAAACwC54mzgsLCzJNUx/+8Idd20cwGFQwGOzb3tkAAAAAAP3N08S5WCwqGo26vp9wOKxisej6fgAAAAAA+4+nibNlWRobG3N9P2NjY7Isy/X9AAAAAAD2H8971TZNc1/sAwAAAACwP3meOAMAAAAA0M8GvA4AwD3+QenhXypPf/wL5b+DR72LBwAAAIAkEmegf/h8UmCoPD007G0sAAAAAKqoqg0AAAAAQBOeJ843b950fR/0qA0AAAAA6JbnVbV/53d+Rw888ICr+0in065uHwAAAACwf3meOBcKBUUiEVf3Ydu2fD6fq/sAAAAAAOxPnifOUjmxBQ48uyTdfbM8vfJi+e8DpyR/wLuYAAAAAPRH4gxAUmlD+snflKf/+j+X/378C/SwDQAAAHjM88TZMAxlMhkZhuHK9i3LUjKZ1I0bN1zZPlqbnJzU8PDO5G9yclKTk5O9D8hNPr80/M7ydHUMZpoJAAAAABXZbFbZbHbH/NXV1d4H0ybPE+dLly7ps5/9rKv7WFxc1KVLl1zdBxpbWFioO//8+fO9DaQXBo9IH7lYnl79mbexAAAAAH1oaWlJV69e9TqMjnieOJum6fo+QqGQ6/tAYxMTE3VLnEdHR3sfDAAAAABPjY6O6ty5czvmr66uNix085rnibNbVbTRP7LZrMbHx70OAwAAAEAfaNRk89q1azpz5kzvA2qD38udT01N9aTE2TRN/fZv/7br+wEAAAAA7D+eljh/5Stf6cl+Tp8+3bN9AQAAAAD2F09LnAEAAAAA6Heet3EG9pWNNenlb5WnrRfLf+8/JfkD3sUEAAAAYFf6rsT5D//wD/XpT39a73vf+3T27Fn9m3/zb/Tmm296HRbQntK6tPT18sN6qfywN72OCgAAAMAu9E2J81/+5V8qFovJsixJkm3b8vl8KhQKSqVSymQy+hf/4l94GyQAAAAA4MDpixLnP/3TP1UkEtHKyops25Yk+Xy+6vO2bSsej+vf/bt/51WIAAAAAIADyvPE+ebNm4rFYpLeTpZt297y8Pl8sm1bU1NTev75570MFwAAAABwwHheVXt6elpSOVk2TVPhcFhjY2MyDEOWZemNN95QLpdToVCQJCWTSf33//7fvQwZAAAAAHCAeJo437hxQzMzM/L5fPqDP/gDXbx4seGyhUJBFy5c0Pz8vJ5//nk9/vjjPYwUAAAAAHBQeVpVe25uTpKUSqWaJs2SFAwG9eyzz8q27WopNbCv+Ael9wTLj7P/a/kxcMTrqAAAAIADz9MS5/n5eY2MjOhf/at/1dbywWBQFy9erCbcwL7i80lDw+Xp+054GwsAAACAKk9LnBcWFvTkk092tE7tkFUAAAAAALjN08TZsiyFQqGO1hkbG5Mkvfnmm26EBAAAAADAFp4PR3Xs2LGOlj99+nR1rGcAAAAAANzmeeJsGIbXIQAAAAAA0JDniXMvfOc739FTTz3ldRhAc7Ytra2WH2+9Xn6USl5HBQAAABx4nvaq3SvLy8v0xI3+V1qXflQoTz/3h+W/H//C2z1tAwAAAPDEgUicLcuiSriHJicnNTy8M/mbnJzU5ORk7wNylU8avDf2sn/w7XkAAAAAJEnZbFbZbHbH/NXV1d4H0ybPE+ebN2+6vo9isej6PtDYwsJC3fnnz5/vbSC9MHRU+sS/LE+v3/Y0FAAAAKAfLS0t6erVq16H0RHPE+ff+Z3f0QMPPNDxepcvX25rPcuyND09LZ+PUj+vTExM1C1xHh0d7X0wAAAAADw1Ojqqc+fO7Zi/urrasNDNa54nzoVCQZFIpOP1pqam2l7Wtm2NjIx0vA84I5vNanx83OswAAAAAPSBRk02r127pjNnzvQ+oDZ4njhL6nhcZkqPAQAAAAC90heJc6c6TbQBAAAAAOiW54mzYRjKZDKu9XptWZaSyaRWVlZc2T4AAAAAYH/zPHG+dOmSPvvZz7q6j8XFRaVSKVf3AUiSNtelH3+3PP3mj8p/73tQ8ge8iwkAAADArnieOJum6fo+QqGQ6/sAJEmba9IP/6I8vbxY/jv8TkkkzgAAAMBe5fc6ALeqaG9nWVZP9gMAAAAA2F88TZynpqZ6UuJsmqZ++7d/2/X9AAAAAAD2H0+ran/lK1/pyX5Onz7ds30BAAAAAPYXz6tqAwAAAADQz0icAQAAAABowvNetQHc4x+Q3vWL5ekP/5Py34HD3sUDAAAAQBKJM9A/fH7psFGeHnnE01AAAAAAvI2q2gAAAAAANEHiDAAAAABAEyTOAAAAAAA0QeIMAAAAAEATdA4G9Avblkrr5em11fLfwaOSz+ddTAAAAABInIG+UVqXXv5Wefobv1f++/EvSEPDPdn9786/0JP9AAAAAHsNiTNcNzk5qeHhncnf5OSkJicnex8QAAAAAM9ks1lls9kd81dXV3sfTJtInOG6hYWFuvPPnz/f20B6YWhY+uSl8vTXvA0FAAAA6EdLS0u6evWq12F0hMQZrpuYmKhb4jw6Otr7YAAAAAB4anR0VOfOndsxf3V1tWGhm9dInOG6bDar8fFxr8MAAAAA0AcaNdm8du2azpw50/uA2sBwVAAAAAAANEHiDAAAAABAEyTOAAAAAAA0QRtnwEmbG9IbPyxPr75e/nv0uOTjNyoAAABgryJxBpy0eVe69kx5+vW/Lf99+JekwJBnIQEAAADYHYrBeiSTySgUCmlkZKT6iMViyuVyPdl/oVBQIpHQ2NiYfD6ffD6fxsbGlEgkehYDAAAAAOxFJM4uKxQKGhkZUSqVUiKR0MrKilZWVpTP52WapiKRiGKxmCzLci2GZDKpUCik5eVlJZNJzc7OKpVKSSon9JFIRKFQSMVi0bUYAAAAAGCvoqq2i3K5nCKRiEzTVD6fl2EY1edM01QqlaqW+hYKhR3LOCEUCsmyLC0uLso0zS3PTU1NKZFIKJPJqFAoaGxsTPPz8wqHw47GAAAAAAB7GSXOLrEsS7FYTJI0OzvbMCGOx+OKx+MqFou6cOGCozHMzMzIsqxq6XY96XRa8Xi8+n8kEqHkGQAAAABqkDi75OLFi7IsS8FgUMFgsOmyyWRSUrla99zcnCP7LxaLSiaTSqfTLUuxK9W2KxKJhCMxAAAAAMB+QOLsAsuyqglwO9WeTdOslghPT087EkMqlWq72rdhGFtKnXO5HKXOXvAPSCc+UH6Mf6b8CBzyOioAAADgwCNxdkEmk6lOnz17tq11KolzoVBwJGldWFiQZVmKRCIaGxtruXwoFNryPz1te8Dnl4ZPlB8nf6H8CNANAQAAAOA1EmcXPP3009Xpdkt9a9sgO5G01ibfxWKxZRXw7W2gFxcXdx0DAAAAAOwHJM4uKBQK1eljx461tU5tgp3P53cdw8TERMPt17M9TjeHxwIAAACAvYTE2WHbq1k36s16u+PHj1enFxYWdh1HKpWq7ntqaqplW+vtcbdTvRsAAAAADgIaUDqs25La2hJhJ0p7g8FgR9WttyfOrXoCBwAAAICDgsS5Dy0vL/d8n+l0ujptGEZbvYG36/r16x2vc+LECZ08edKxGAAAAAD0zmuvvabXX3+9o3W6yRt6hcTZYduT3nY7B6vV6/bF23vy3j6u82595jOf6XidL37xi/rSl77kaBxd+1oHQ4Rtrkkvf6u7/dSu+7V78z7+BWlouLvtAQAAAB756le/qi9/+cteh+EY2jg7bC92qpVMJqvT4XB4y5jOAAAAAHDQUeJ8wM3NzVWHvzJNU7Ozsx5HtMf5B6WHf2nnPAAAAAB7Fomzw7qpmu0Vy7J08eJFSeWkOZ/PuxL/M888o0cffbSjdU6cOOF4HD3h80mBIa+jAAAAADz1+c9/XrFYrKN1rl+/3lUzz14gcXZYvfGQO01Ge5V8x2IxWZblatIsSY8++qjGx8dd2TYAAACA/nPy5Ml91dkvbZwd5kTyuT35dkMymVQul3M9aQYAAACAvY7E2WHbk952h5aq7VTM7SQ2k8loZmZGwWCQpBkAAAAAWiBxdtj2JLTdXrYXFxer06ZpOhjRVrlcTolEQuFwmKQZAAAAANpA4uyCYDBYnW63xLl2ubNnzzoek1QerzkSiSgajWp+fr7hcpZlbRnXGR2wS9Ida+vDLnkbEwAAAIBdIXF2QTgcrk63m4DWLheNRh2PqVgs6sKFC4rH4y2HnJqentbc3JzjMRwIpQ3pJ3+z9VHa8DoqAAAAALtA4uyCS5cuVafz+Xxb6xQKBUnlatpOV9W2LEuRSERPPvmk0ul0W7G4WV0cAAAAAPYSEmcXGIZRLXW+cuVKy+VzuVx1OplMtlx+bm6ummi348KFCwqHw20lzZV4aqubAwAAAMBBxjjOLkmn0wqFQrIsS3Nzc02rX1cS2mAwqHg83nA5y7IUCoWq1bpTqZSmpqaaxhGJRHTs2DElEomG1cYrHZgtLy9X2z5T4gwAAAAAZSTOLjFNU5cvX1YsFlMsFtPi4mLdZDSTyWhubk6GYbRse5zL5bYkv9PT000T50QiUS3NDoVCHcUOAAAAACijqraLKr1XG4ahUCikTCZTfc6yLCUSierQUDdu3GiZsG4fOqrZ8jMzM1v21wkSZwAAAAB4G4mzyypJcSqVUjqd1sjIiEZGRnT69Olq1ehKct3OtiolzKZpNi2hnp6e7jpm2jd7xBeQjo2VH+/7lfIjMOR1VAAAAMCBR1XtHjAMQ/F4vGn75XalUimlUqmWy62srOx6X+gxf0C6/z3l6VPtV60HAAAA4C5KnAEAAAAAaILEGQAAAACAJkicAQAAAABogsQZAAAAAIAmSJwBAAAAAGiCXrXhusnJSQ0PD9edPzk52fuA+tXmuvRqvjz99f9v+e9H4tLQUc9CAgAAAJyWzWaVzWZ3zF9dXe19MG0icYbrFhYW6s4/f/58bwPpe7ZUWi9Prt9+ex4AAACwjywtLenq1ateh9EREme4bmJiom6J8+joaO+DcZt/UHpPcOc8AAAAAJLKecC5c+d2zF9dXW1Y6OY1Eme4LpvNanx83OswesPnk4Z2/kgAAAAAoKxRk81r167pzJkzvQ+oDXQOBgAAAABAEyTOAAAAAAA0QVVtAB2zbVsbpa0dlwV8Pvn9vrrLr2+Wdsy7tbYhSToyGJDPV389AAAAoB+QOAPo2EbJVv7FlS3z3nfyPh2/71Dd5bcvK0npq0VJUuKcqaNDXIoAAADQv7hbBZxk29L6ra3zBo+WOw0DAAAAsCeROANOKq1LPypsnffwL0mBIW/i6aH1EmNOAwAAYH+iczAAAAAAAJqgxBmAI0aODjZ8LvTIiKRyJ2HffeVmr0ICAAAAHEHiDMAR/ibtuAcDVG4BAADA3sXdLAAAAAAATZA4AwAAAADQBFW1gX7hC0jGe8vTo58o//U3bjcMAAAAoDdInIF+4Q9IxiPl6dO/7G0sAAAAAKqoqg0AAAAAQBMkzgAAAAAANEFVbeAAs21bt9c3JZXHWG4k4Pc1HW6qXX6fT6PHhyVJn/zASUkMVQUAAID+R+IMHGC31zeVvlqUJOVfXGm43Afffb/uP7Kzo7KHjCN61brd9v4Cfp/e9cBhSdKHHzY6CxYAAADwCIkzXDc5Oanh4eG68ycnJ3sfEBwxenxYx+8b6ihxBgAAALLZrLLZ7I75q6urvQ+mTSTOcN3CwkLd+efPn+9tIP1uc136yXfL09++XP77xD+TBo94F1MDgwG/3vXA4abVuwEAAIB6lpaWdPXqVa/D6AiJM1w3MTFRt8R5dHS098H0NVtav1WeXP3ZvVkkpgAAANhfRkdHde7cuR3zV1dXGxa6eY3EGa7LZrMaHx/3Ooze8A9I7/rFnfP2kA+demBHh10Bf/2OwQb8PoUeGalOAwAAAK00arJ57do1nTlzpvcBtWFv3dED/c7nlw4bXkexK4MBf9s9Xft8Pg0GSJgBAACwv5E4A+iZ9c2Snn/FkiT9wdVFSdI//9iojgwFPIwKAAAAaI7EGUBPbWzakqTba+Xxo23ZXoYDAAAAtNRefUwAAAAAAA4oEmcAAAAAAJqgqjbgJNuWSutb5/kHJV9/dqB1eCCg3/jYI5KkW2sbkugdGwAAANiOxBlwUmldevlbW+c9/EtSYMibeFrw+316532HJElHh7gcAAAAAPVQVRsAAAAAgCZInAEAAAAAaILEGQAAAACAJkicAQAAAABogt6AgAOsVLK1fGtN0tu9ah8ZDMjXp72AAwAAAF4gcQb6hc8vvePd5emHguW/fnc/onc2NvUn33xRkvTdV25KkkKPjGgwQOIMAAAAVJA4A/3CPyAdf7Q8/dinvY0FAAAAQBVtnAEAAAAAaIISZ2Cf+d35F9pedn2zpPyLKy5GAwAAAOx9JM4Aesbv8+nUyBFJ0kfN45KkAT8VXwAAANDfSJwB9EzA79OpkaOSpI+NHfc4GgAAAKA9JM5w3eTkpIaHh+vOn5yc7H1AAAAAADyTzWaVzWZ3zF9dXe19MG0icYbrFhYW6s4/f/58bwPpd6UN6bXvl6e/87+X/575rDR42LuYAAAAAIctLS3p6tWrXofRERJnuG5iYqJuifPo6Gjvg+lndkm6Ux5LWdZL9+ZtehcPAAAA4ILR0VGdO3dux/zV1dWGhW5eI3GG67LZrMbHx70Oozf8A9KJD+ycBwAAAEBS4yab165d05kzZ3ofUBu4owec5PNLwye8jgIAAACAg0icAfTMxmZJ1370piTpj7+5JEl6cuJhHR4MeBgVAAAA0ByJM4CesSXdXi+3237jrTVJUsm2PYwIAAAAaM3vdQAAAAAAAPQzEmcAAAAAAJqgqjZwgAX8Pn3w3ffvmAcAAADgbSTOgJM216SXv7V13sO/JAWGvImnBb/Pp/uPDHodBgAAANDXqKoNAAAAAEATJM4AAAAAADRB4gwAAAAAQBMkzgAAAAAANEHnYMABZtu2Nkr2lnkDfp98PnrWBgAAACpInIF+4fNLR99Znj7x/nvzAq7ucqNkK//iypZ5oUdGNBggcQYAAAAqSJyBfuEfkE7+Qnn6zK95GwsAAACAKto4AwAAAADQBIkzAAAAAABNUFUbQM/4fNKD9x+WJD3+8AOSpICf9tQAAADobyTOAHpmwO/X6XcOS5I+9YEHPY4GAAAAaA+JM1w3OTmp4eHhuvMnJyd7HxAAAAAAz2SzWWWz2R3zV1dXex9Mm0ic4bqFhYW688+fP9/bQPpdaUP62Q/L09/7s/Lf9/99afCwdzEBAAAADltaWtLVq1e9DqMjJM5w3cTERN0S59HR0d4H08/sknTrZ+Xp1/+u/PexT3sXDwAAAOCC0dFRnTt3bsf81dXVhoVuXiNxhuuy2azGx8e9DqM3fAHp2NjOeQAAAAAkNW6yee3aNZ05c6b3AbWBxBlwkj8g3f8er6MAAAAA4CASZwA9s7FZ0gs/fUuSNLvwsiTpVx9/jw4PUioPAACA/kXiDKBnbElv3lmXJL2ycluSVLJtDyMCAAAAWvN7HQAAAAAAAP2MxBkAAAAAgCZInHskk8koFAppZGSk+ojFYsrlcp7EMzMzI5/Pp2Kx6Mn+0R8CPp/ed/K+LY+Az+d1WAAAAEBfIXF2WaFQ0MjIiFKplBKJhFZWVrSysqJ8Pi/TNBWJRBSLxWRZVk/iKRaLikQiSiaTPdnfgbO5Lr30V1sfm+teR9WQ3+/T8fsObXn4/STOAAAAQC06B3NRLpdTJBKRaZrK5/MyDKP6nGmaSqVSGhsbUyKRUKFQ2LHMblmWpYWFBRWLRS0uLiqXy6lQKDi2fdRjS6XtiTKdXwEAAAB7GSXOLrEsS7FYTJI0OzvbMCGOx+OKx+MqFou6cOGCozEsLCxUS5cLhYLC4bAWFxcdTc4BAAAAYL+jxNklFy9elGVZCgaDCgaDTZdNJpPKZDIqFAqam5tTNBp1JIZwOCyboX4AAAAAYFcocXaBZVmam5uTVE5eWzFNU6ZpSpKmp6ddjQ0AAAAA0BkSZxdkMpnq9NmzZ9tap5I4FwoFeroGAAAAgD5CVW0XPP3009XpdtsTVxJnqdypWDwedzos9DufXzr8QHnaeO+9eQFXd7m+WVL+xZUt80KPjGgwwG9qAAAAQAWJswtqe64+duxYW+vUJtj5fN7pkLAX+Aekd32oPP3EP/U2FgAAAABVJM4O217NurYkuZnjx49XpxcWFhyNCQB6yral9VvtLTt4VPIxdjgAAOhvJM4Osyyrq/VqS5y73QYA9IX1W9I3fq+9ZT/+BWlo2N14AAAAdonEuQ8tLy97HYKjrl+/3vE6J06c0MmTJ12IZo/YuCuVNrpadWDztjb8h/uyFM8n6djwkCTpfQ/eJ0ny92GcAAAA2J3XXntNr7/+ekfrdJM39AqJs8O2J73tdg5Wa7+VOH/mM5/peJ0vfvGL+tKXvuR4LHtG8X9IrxZaLlbPxKtvaOGh39BG4IizMTlgIODXYw++Q5L0Dz/0Ho+j2fts29bt9c22lh0K+DXgVKdvpU3p5itb5z1wSvK725kdAADYO7761a/qy1/+stdhOIbE2WH7LekF0L9ur28qfbW94ev+wYfeXf3RYtc27kh//Z+3zqPKNQAA2MdInIF+9N0r0s9/3Pl6o7/sfCxApwaPlhPp7dZvS9++3Pt4AAAAdonE2WHdVM3e75555hk9+uijHa1z4sQJl6LZ54bfqduDm7JFu2F4yOej9BkAgAPu85//vGKxWEfrXL9+vatmnr1A4uyw7eM2W5bVcTK935LvRx99VOPj416H0Ru+gGS8d+e8To2M7txOOz5yUc/ffKHz9faQ353v7vX9VuQxhyPZu26tbeyo4p04Z+roEF8JAADAGSdPntxXnf1yl+QwJ5Le7ck39hB/QDIecWA7nX00bVu6vemT1ja0vlna8fxgg06hSrbdVXjoX5/72CM6MrTzx5ohpzoGAwAAOIBInB22PeldXl5uK5mu7VRsv5U4w323N31KL52UVFT+xZUtzw0EfJp4pP6PMa///G4PonvbxmZJxZ+tSpL+23d/JEkK/8KDOjxIb8xOOTIUoOQYAADAYdxdOWx70ttuL9uLi4vVadM0HYwI6B+2pOXVNUnSD3/6liTpUx/YP1V40ELgkDT+mZ3zAAAA+hyJswuCwaAKhfIYvNvHdW6kdrmzZ8+6Ehew3YCf6rvoocCAdPIXvI4CAACgY9w1uyAcDleni8X2xlitXS4ajToeEwAAAACgO5Q4u+DSpUuamZmRJOXz+bbWqZRQm6ZJVW10prQp/81Xderum9LNI/rg3Zv6u6EPavzhdzbsFKzixDsO6fh9Q1vmDfgZymqvODIYUOKcuWMeAAAAnEWJswsMw6iWOl+5cqXl8rlcrjqdTCZbLj83N1dNtNFnNtelV/NbH5vr7u7T3lTg5ks6dfe6Tt3M64Nr39WAvanBgL/6aCTg921ZbjDgl89H4rxX+Hw+HR0a2PLg/AEAADiPxNkl6XRahmHIsizNzc21XFYqt42Ox+MNl7MsS2NjY4rFYgqFQtVS7d1ot/MytMuW1m9tfYghnwAAAIC9jMTZJaZp6vLly5KkWCzWsK1zJpPR3NycDMPQ7Oxs023mcrkt25menu4qttpkud3OywAAAADgoKKNs4ui0ajm5+erJcSpVKpaomxZlpLJpDKZjMLhsGZnZ1uO37z9+XbaQtdWAy8Wi9XS7YpEIqFEIiHTNKvbn5iYYCxpAM5bW5W+8Xtb5338C9LQsDfxAAAAtInE2WXhcFg3btzQlStXlE6nt7RhDofDmp+f39ILd6ttTU1NaWZmRqZptiyhlqRIJCJpa9JdO728vLyl5NqyLM3OztKzNwAAAADcQ+LcA4ZhKB6PN22/3K5UKqVUKtX28rZN+1pgv9os2fqRdXvLvPcYRxRwu2f0gSPS2f915zwAAIB9isQZAPaouxubmsu/smVe4pypo0MuX9r9fum+E+7uAwAAoI+QOAP7gN8nnTqyJo0c0Zu31/XQyGH5GZYIAAAAcASJM7APBHy2Hj6yJo0c1Ssrt/Ue46g23K6uiz1rMODXJz9wcsc8AAAA1EfiDAAHzGDArw8/bHgdBgAAwJ5B4gz0EduWbm92WFK86dNgySef+v8D7ZN0/+FBSdKpkXJnUlQpBwAAQL/r9/ts7AOTk5MaHt45Tuvk5KQmJyd7H1Afu73pU3rpZOsFawyU7mrirWEdDZT0uEtxOWUg4NcH33O/JCk28bDH0aBrpZJ0642t844eL3caBgAA0EI2m1U2m90xf3V1tffBtInEGa5bWFioO//8+fO9DQSAMzZuS8/94dZ5H/+CNLTzBzIAAIDtlpaWdPXqVa/D6AiJM1w3MTFRt8R5dHS098H0iG1LG3a5CvL6hk+y61dH9vukwwHG2gYAAMDBMTo6qnPnzu2Yv7q62rDQzWskznBdNpvV+Pi412H01Ibt04JV/rFgYeOENvyH6i536siaYg+t7HJvPm34BrXhK0mDR7ThP6xya2IAAACg/zRqsnnt2jWdOXOm9wG1gcQZcJLPL73j3drc9Omntx6QJNm+QNurB/zS4w/ckiTd3iy3F/3E8bc06Cu1WPMX5fdJ+sS/1MLtF7qJHAfI7bVN/cdvLm2Z988/NqojQ+2/Vzth27Zur29Kaxsa3Hz7vTzg99X9iadUsnVnY3PH/CODAfnoTA4AAHiAxBmox7al9XICq8219tbx+SX/gHT8UZU2fLrxZmedfEnSIb+tT534ecfrQQqU7spnN/mBYe1eZxODR6UDnnzZsnV7bXPHPLfcXt9U+mpRA5u3NfHq2zUsHj/1gI7WWX751pr+5Jsv7pifOGfq6BBfWwAAoPe4AwHqWb8lfeP3ytMvf6u9dQ4/IL3rQ+7FtA9slEp6efm2JOkv//ankqSPP/pOHRrYfUnn2PL/pWO3bjRe4BvHy3/pxAoAAAAdInEGeuRz7/2Zjvi3loj6D1jBp21LP33zjiTp+ZdvSpI+ah73MiT0UMk3qBsjH6/+/wvmu3Q0MORhRAAAAO0hcQZ65Ii/pKMD9KDdlfU70t/9uSTpsZ/9WJK0eOzvabNBp2voTyX/gH76jrc7Ctx49yNSYNDDiAAAANrj9zoAAGjFLm1o/Sc/0PpPfiDjraKMt4ra3NjQ+mbjNs22batUevuxvlnS+mZJts2PF/3gcx97RMeO1i9tPnZ0SJ/72CM9jggAAKAxSpyBdr0nJAWafGR8Hv0OVdqU3iq3F9YreT3485f0+vD7VfLvn4/37fVNPf9iuVOpH90st5H+zoalu/471arei8f+noojn6ius/zWXb382hv69K3/KklauLf+4x/d1NF9UlB9eCCg39iWYB52oL14LxwZCsjfoK2C3+9zrYdvAACAbuyfO2vAbYEBqR/bY9qb0vJiefqHf6HTK2/ojaOmSgfs47292vZ6wK8132GPoukNv9+nd963T34FAAAA6GMH684aaJcvIJ14f3n6tR/cm9d+ibLfJ73vvjs75gEAAADYe0icgXoGD0tnfq08/frfdbz64YCtf/iumw4HBQAAAMALJM6Ay75ZfKOr9T7m9jBNtq2B0p2GT2/4D9UvZbdLGijdrbP8YcnXu2L1Mw/d33Q85pGjQ7r/ofulF3oWEloY2LyjD//4Snn6m8ekgF/6SFwaOupxZAAAAM2ROAMH1EDpjiZe/ZOGzz//rqhuDx3bMf/IuqXHfzK3Y/7CQ7+hjcARR2NsZjDgLydeDfj9PmlgQK8HHpQk3X/ovntP0OmUd97+sca3flsq+SXRyzkAAOh/JM4A9q1N/yFdPforkqS7D5ZL8H95YP90GFYq2Vq+tbZl3rGjQw17q3bMwGHpw/9k5zwAAIB9isQZAPaoOxub+pNvvrhlXuKcqaNDLl/a/QFphHGWAQDAweHRwLMAAAAAAOwNlDgD9azfkf7uz8vTleGo3vk+yd/eR+bOpk+51++XJL3w8/LvU6HhNzTkLzkeqpOef3dM6/5yldsNf/3xgW8PGnr+3TE9/uPZXoYGBw34/frots7nBvzu/Y56aCCgaOiUfOu39I5S+XMRaFGdvLLO9nkAAABeIHEG6rE33x6G6tbP7s0ba3v1ki398K1yAvrqWjlZfmK4u961e2ndf7h1B18+fzW5xt40NODXx8Zc7rW9RsDv08PHjkprtnRksLN1AAAA+gCJM4Ce8Uk6MlguNTx+35Akyd/DIawAAACAbpA4w3WTk5MaHt453u7k5KQmJyd7HxA8MxDw6/GHDUnS5z426vr+AqU1PXHnW5Kk08vlKsLaeEhyu/Os/c62pfVbW+cNHu3pON4AAGDvymazymazO+avrq72Ppg2cfcI1y0sLNSdf/78+d4Ggj1rcHBQD42+X5L0culelfc2xmP225saW39BkvTgW/eq/ZY2Wq5n27Zur292FGPA7zs4bXDXb0nf+L2t8z7+BWlo5w9kAAAA2y0tLenq1ateh9EREme4bmJiom6J8+joaO+DwZ40eOio3nvhoiTpT0svtL1ewO/Tex4ot9meeGREkjQw2Dq5vb2+qfTVYkcxPv7wA/rUBx7saB0AAICDaHR0VOfOndsxf3V1tWGhm9dInOG6bDar8fFxr8PAAeTz+eS/13vzYMBfmelhRAdTtQR/bUODm+XO8gYCPvnU+FzUK/U/MhiQj/MHAMCe16jJ5rVr13TmzJneB9QGEmdgrwsMSaO/XJ7+5CX91Ub7JbI4mO6sb+rKwstb5j058bAOt1Ea341KCf7A5m1NvLoiqVwDYDDQOAmuV+qfOGfqKO3TAQCAB7gDAQ6oTf+Qvn/yH+6Y1yu31lq3Nd6uWQkl2leybb3x1tqOeQAAAKiPxBk4oGxfQG8efk9P97lZsvXjm7clSX/8zRd1e62zDriODHnX+dbnPvZI0/0H/G0m9Wsd9BZJT9UAAAB9gcQZQMe6La0u2bZeWSknzqdGjroSm1uODAVaVhO+s76p//r8j7bM+9XH37O1CvT23qib2Wc9VZd8A3rlgZAk6YlH3q3BgYDkH/Q4KgAAgNZInAF0rG5ptW1rYPN28xU3SzpUuiNJ1WU3/IdblqoGSmt6r/VtDQ34tbZR7lzqJeMjLZP1km3r7r0Opm7eXpck3Vey1arc2u/z6fh9QzvmtVL7w0DtPJSV/IPVxLn0iMl42gAAYM/grgXYT9ZWWyevdWwEjux61wOlO5p49U+aLlMq2Tq1eq/E+dVyifPCQ7/Rcv8+e1MPvvV9DQZ8Wt8sJ6Iv30vAWu3v9bfuSpK+/+M3JUmPb2yqVVn34cGAPvex0ZbbBwAAwMFA4gzsdXZJuvVGefraf9HEq3/d0eo3Rj6un76jN8OF+X3aMa7y4x8dbV0deW1Vg39VXn7hxRU3Q9w1n72hY7dffPv/19ekd31ACjh/uT00EFA0dGrHPAAAADiLxBnY60ob0ut/W54ePtHRqq/cH9QbR82Oqk3vis+nSh9alXGVB4cG2qiyOyBVxmHuc4HSut73s2er/w/8YEQ6cfrtxPnjX6i/4vpt6duXO9uX36eHj+2ttuIAAAB7EYkzUI8vIBnvLU8ffuDevPYTN79POnWkPNzP3bfKbXr7Me079WZBp94sVP9vp9o0dmkfdfYFAABwUJA4A/UMHpae+Kflaeuljlc/HLAVe6hcpfibd99wMrK+teE/rIWHfqPpMoOlO3r8x7M9iggAAABwBokz0Ke+WWwz4bZt+e33SZK+fffvSw91v88N/+HuV/b5KK1GU4HNOzrz2v9PkjSw8E5pICA98c+kQd43AACgv5E4A3udz6eSrzwWbr8nrpv+Qf3wnRckSR8bHy3PDBxybX+2z6c3/eWq9rcH71WRbqPK/dpGSfltnZCFHhnR0ICLFe4DQ9L7fmXnvH3EJ1tH1q3y9C1fud26XfI2KAAAgDaQOAPoGds3oDeOjpX/OfmY6/vb8B/WXwz/PyRJH3338fLfNko3N0ol/dW2Ev/HH35AQ262VA8MSqdaD7FVy7Zt3b43TnXFkcGAfG528CaVf+wY/8zOeQAAAPsUiTMA7FG31zeVvlrcMi9xztTRlr2U71JgQDr5C+7uAwAAoI/0Y0e/AAAAAAD0DUqcgXo27krF/1GefuN6+e/IqORv7yNzt+TTN964T5L0g9XyvF88sqxBv+1snA756EuZrtf9q/fGHYwEuxEo3ZXWVtXq0h7Y2NQT7x7cNu+WpIA0eNTxsbyHAn79gw+9W771Wzq2Uf5cBPzN91FZZ/s8AAAAL5A4A/WUNqRX741v/PMfl/9WxnVuw2ZJev7mUUnSK+VhnPXBIysaVH8mztgf3v/6X2jwr+6UO91q4pCk89tnvn7v78e/4PhY0wMBvx578B3Sml8abq8tdHUdAACAPkDiDNdNTk5qeHjnjfjk5KQmJyd7HxAAAAAAz2SzWWWz2R3zV1dXex9Mm0ic4bqFhYW688+fP9/bQOC5gc3bmnj1T8r/fK3cy7UbJZwV/tK6Pnj3eUnSqZvlYam0eUpuXfr8Pp8ePTms49bbw0hVaj3fWd9UyW5S42D9lgYW/rD672DAL99H4tLQUVdi3eLE+9+e9gXc3x8AADjQlpaWdPXqVa/D6AiJM1w3MTFRt8R5dHS098Hsc7tpq9zXfH5p+J2SpNuD5eGXbLVuhxuwN/TBte9Kkk7dqzqvzb/vToySDg8G9H8/827p5s4qxrkf/FQ//OlbDdct/6jwo+r/E4+M9K5qv/FIZ0Nhra1K3/i9rfNc/AEEAADsL6Ojozp37tyO+aurqw0L3bxG4gzXZbNZjY+Pex0G9rLBI9JHLkqSnr/5QturBfw+PXj/YUnS46fKJc6HB/ZvierfnfgV/fJH36tBt4ejAgAA2IVGTTavXbumM2fO9D6gNnB3BWDf8vl8GrzXUVZ1bOMWvTnvZZv+Q+VSXxJnAAAAR3F3BQBOGjxarra8fZ5uehJOPXfWN/Vfn//Rlnm/+vh7dHjQndL4W2sbSl8t3quO/oake1XRm/T+XVmnVuKc+fYPIAAAAD3EHQgAOMnnq9vWN/wLD+pTHzhZd5Xba5v6z1//W7cjqyrZtl5Zub1jHgAAAOojcQaAHnCrNDdQWtN7rW9X//dfvy49dkEaaG+8ZAAAALRG4gwAe5jP3tSDb32/+n/gRz+WHj0nqXXiHCjd1djy/1We/v6D0oBfev/flwYPuxKr7Qvop/d9UJK0+Z53aXAgIPn5GgIAAP2POxYA/W9jTXr5W5KkUzdfkST96B0fUsk/2HQ127a1vlmSVG4zK0mHS7Yat6wt88mnI0OBHfPc4vf59JBxWPe/8fYl2deDPsx8dknHbt0ox/AzSwr4pcc+7dr+Nv1DunHsE5Kk0qMmnZgBAIA9g7sWAP2vtC4tfV2SdOpmuXOpn9z3QZXUPHHeLNn66Zt3JEnPv1LunOvxjU0dbbG7I0MB/b/Oje0u5g4cHgzo14KnpNsP9GyfAAAAaB+JMwA4qbQp3SsVr3rglOR3vo3zUMCv/+XMu3Rs477qvMA+Hm4LAADAKyTOAOCkjTvSX//nrfM+/oW6PW3v1kDAr/edvE8apiMwAAAAN5E4A/X4/NLwO8vTg5WKve2X5Pl90vGhcpvaNwPr5XlOxoeDa/22ZJe2/g8AAABXkTgD9QwekT5ysTy9+rOOVz8csPW595bb4n5z4w0nI9v/1m5J2jam8EFLDgePNH7N3/lPXb0nAQAA0D0SZwD95duZg5cob/dQqKamw/4RKN3V+1//i/L0d09IgYB05rOuDX8FAADgFBJnAD1T8g3qxsjHJUkfe997yzMDQ67tz5ZPa75yUrbhv5ectTHO0/pmSdd+9OaWeePvuV+DAXcq3N/d2NQ3X3hNj/zsLUnSin9F4x//v+nQQPsdin2z+HbNhoW717URONJw2fXNkvIvruhQ6Y4m3Dv8O/jsku6/+2NJkt+6Ux7+yt7sXQAAAABdInEG0DMl/4B++o7x8j+nHutqG6du5iVJ/tJ6y3Gc75T8+m+bpiTptVO/IkkKtVGSu75Z0tf+9rUt8x578D7XEufNkq2/eeVNHXrzriTpFd+qPlCyW6zlocCQ9L5f2TkPAABgnyJxBoAm/upeSe76ZqmtxHlg87YmXn1DHzOPd7SfDf9hLTz0G5Kkkq/JpfmJf1btHOzW2ob+6OtLkqTn1pbLT7/XkPwuV30ODEqnQu7uAwAAoI+QOAPoLx+Ja0fnYDV+dP0nklokl3uRz9e0enXVYO0yG9V17t5LltcDRzTYRnV0AAAAtG+f3XkCDtlYk17+VnnaerH89/5Tkr+9NqdrJZ/yVrlK8PdvldtwPnbkpgZ8fVz9tl8MNa9K3ap6dr/ops2xJH20w5LqbvgkHRse0lBpU8eHylWs3cy1BwN+ffIDJ+Vfv6WTd8vjWfv9zXdYWWf7PAAAAC+QOAP1lNalpa+Xp62Xyn/f8W5J7SXOGyXpr5bvkyS9crt8s28efpPEGX1hIODXYw++QwObA3rs0Dtc399gwK8PP2xIa4PSi+1VI6+uAwAA0AdInOG6yclJDQ8P150/OTnZ+4AAAAAAeCabzSqbze6Yv7q62vtg2kTiDNctLCzUnX/+/PneBoIDx2dv6uHNlyVJJ39+rTxz82Fx6SuzfX7JeO/Wmb72h8ACAADoxtLSkq5evep1GB3h7hGum5iYqFviPDo62vtg4LiPvpTpcs3/j6Nx1OOzS/qFzRckSfev3BsvePNTktrohOsA2PQfkp74p52vuHZL+va28/6ReMv26QAAAFI5Dzh37tyO+aurqw0L3bxG4gzXZbNZjY+Pex3Grn3rxnLbHVPdKfn1ysp9LkeEVoZ8Jb3r0FuSpHe9+35J0qEBSlR3z5bWb++cBwAA0IZGTTavXbumM2fO9D6gNpA4A/DE786/0NV6H+1gWb9POhrYkCQ9cGTw7ZkAAABAB0icAcBBG/5Dev5d0R3zOrL0dUl/Jg20KMXd8ElL5SGbTt28pVceCLUX42ZJxZ+VO9/4b9/9kSQp/AsP6vCgO6Xxt9c29R+/uaSBzdv68EvLkqTHTxlNh5eqrFPrn39sVEeGqDEAAAB6j8QZAOqxbQ2U7uhQ6Y4kaXDztgbUehzhDf8h3R465nZ0u2JLWl5dkyT98Kflquyf2jZmsrP7s3V7bVMDm5ta32yvSndlne3zAAAAvEDiDM+89tpr+upXv1r9//Of/7xOnnTv5r0btmxtlMpVe++U/CrdS5wGfHbDMZnvlvxaK1Eq1kvLb97Sn33tr6v//9onP6xj9++uo6qB0h1NvPonetfqLUnSe350RP42qnk//65o3yfOaGwvXJewN/BeglN4L8FJvJ+6R+IMz7z++uv68pe/XP0/Fov13Qd3Y9PWglXuEfy/bzysNd+QJOnDw2/o0cM/r7vO/2md0prdumQSzrF+fkv/4b9+s/r/pyYe23XifBAM2Gs6vfx1SZL/+nVpICCZ56WBDquWt8n2+bV89LQkqfTOB6UBf98Nf7UXrkvYG3gvwSm8l+Ak3k/dI3HukUwmo3Q6rWKxWJ0XDoeVSCQUDocPTAxARffDWLXPtqUNu1xKvL5ZkiQN2Lb6rXswn6Qj99oXH79vSBra6EkfZgG7pAff+n55+kc/lgJ+afQTkjpInJf+Z/nv5poUGKq/zL222JuS/nKlXFPj2siZchvnH79Ud5X1zZLyL65Ikj5qHm8/HgAAABdQLOayQqGgkZERpVIpJRIJraysaGVlRfl8XqZpKhKJKBaLybKsfR0D4IW7tl8v3H5AL9x+QAsvrmjhxRXdXt9sud7RoQH9vz/1qD5mHtepkaM6NXK0rWra3RoI+PX4w4Yef9jQ5z42qs+99w0dDtCeFwAAoF9Q4uyiXC6nSCQi0zSVz+dlGEb1OdM0lUqlNDY2pkQioUKhsGOZ/RID0E9ur21KaxtNlxkK+OteHJ9/d0zr/sNN1+24B+1dGPTb+uQ735Qk/c3aTQ0cH5bf12/l6QAAAHsfibNLLMtSLBaTJM3OzjZMRuPxuPL5vDKZjC5cuKB8Pr+vYtiPfuWBV6tjA9fzaeOVun3/HvKV3AsKbfvP335ZG4EjTZf5Bx96tx4b2VkhZ91/uOW6vTTolz5s3JYk3V7+uX72QPOkHgAAAN2hqrZLLl68KMuyFAwGFQwGmy6bTCYllatUz83N7asY9i6fNHhE9uARbfgGteEbrCbDQ/7Nhj1qS9Ihf0mH6zwoCNxjBg5LH/4n+v7Jf1h9bPobtOEFAADAvkaJswssy6omn+10umWapkzTVLFY1PT0tKLR6L6IoV9ls1ktLS1pdHRUk5OT9RcaOip94l9qY21DCy/8sSRpfeXWrvb7f3zje/rxz97Uu995v/7Bx8/salv9vE8v9+sof0AaeURvHr7bcJFv/8Wfafknr+rYux7SR37l13Y831UHaF9r3RFW9r/ntfSTFY2+a0ST/0uo8300s3576/8+vzR4ZOvn5p/EnN1nE68u/kD/46WNco2Zc2bP9uuFtq5N7HPP7dcLnNf9t0+vHKTje5Beqxf2w2slcXZBJvP2zfLZs2fbWqeStBYKBRWLRZnm7m4Q+yGGfpXNZnX16lWdO3eupx/cP//GNX3nhVf0xGOnepZMerFPL/fba8/9xX/R4ne/rbEPfaRu4tyNWxutqyb8h9z39PW/WdInfnFUT4YnHKnNsFGyNRiQ9O3LW58YfqfuBv+f+g9//L/r61//n/rEJ35ZT0b/sQY3S7IHj2jQVtv7D9jrenzjbyRJD7whDfh9Wjz297TZpF34q4t/q+/99FU9MvqIJGeOcb/y4tp0UPbp5X69wHndf/v0ykE6vgfptXphP7xWEmcXPP3009Xpdjvaqk1Sc7mc4vH4no8B8NohX0nvPXJTknTqkRFJ0uMfHZWGhpuuNxTobSuWDdunF24/IEl6/uV36vZm8/3fPH1B9x+xdPNdhtJLJ3UkUNLj+tmuYnjhpz/X46eMus994/rPdPM9Z3X/R07q5nse0R99fUnHb31Qg6U7+ozt02CTpgu1fHZJD5ZekyTdd+uG/H6fiiOf2FXcAAAAvUDi7IJCoVCdPnbsWFvr1Ca3TnTO1Q8xAF6zA4P6iVFu3386/L9Jkga9DKiBDdun7982JEmnDvd/1xMbgSP66TvGdeom1wkAAHAwkDg7rFgsbvm/3erOx4+/3a5xYWFhz8cAYG8Z6HKc6nXbJ5Wk9Q2fZJe34feJcagBAMC+QuLsMMuyulqvtrS32230Uwx9aW1VknTYv6mjg+W/lXmN12k+3i/2jt+df0E+e0OB0npby/tka6B0V0ek6tjNG/7D7Tfo3QPu+g5p4aHfkCQ99NpVjW+u7FzozR9LS7PSnXKVd925KS19XZL0I3tDf2T/A0nSxktD1WNzfGhDn3vvG23FMFi6U3d+qdnobRt3defuXZXsLpJzn1+BQ0d1aCBQZ7tr2ri7quGjb7e5Xru9qltv3Wxjuz75h4Z1eLDOdjfXdefO7ZbxDg34NXz0kIYG/Fv26T90X4Ptbuju3VvaLHX3I4W/UZOF0qbW7tzSRtOT0Jhv8KiOHKpTt6NU0vrd1Yavs6WBwzp6uE57eNvWxp23tLbZPN6G+x04pCOHDslX57O9eect3d3YbD/GGnfurknySXUGKCzdXdWd9S6/XwKDOnToiAJ1fuyy127p9tp6d8fYP6ChQ0c0UK+pyvod3brbuKNEqcnx9Qc0eOioBhts9/bdNdl1B3FsFW9AA0NH6z/n4jXizt073R3fXVwj1m43vy5xjbhnj10jFBhq+M538xpR9/20tqGjQ6SFrXCE+tDy8rLXIewqhrvbvlz/8i//UtevX9+x3EsvvdRwuZGRkS0l4I74VrnTo19+x8syHx/UQ+94Wdf+0//WdJX1oXdo/cVy29HlWw+opIBu3P5ZV2My3767Xv1bfHV37VH7eZ9e7PeV11aa/i9JPyn9UA/ceVmPrPxV1/u59uCvatP/9ljJa3duVf/+ZOmHO5Yv/ri9127b0vi9NsofGnmg5fL/6ft/rps/fFUD73tIv/zpIflK0t/+aEUnX/2dtvf3znujEX4oUN6f3ydde6n+V/iIvaKB7/+53iy+rjt/71f1kx+92nT7twc3de3/3979/LiP3/cdf42x3sZtvKXG+WEDPfhLHVI0TYtQ36nTAMWm+FJNgR4ctOJ3cihyG/LYm4T5CwYaoL0GlA+5FZ2vFPRrIEAP1PfQogHafEdMUnSzbVpxnMZubHi/I3rtXbveOuxhliylkShKI85opOcDIL7SiKI+3+FH7+Gbn18/ibPnnyQH+rmPvqfP/OUn+vD6u5Kk7/7kHR0cHOjz3/rtucf4/md/Vj/19q/okz//Y33851f6y89+qD99/3199q3PSN/4j/pv/+UP9KNPVr9Y+T9vfV76pZa+Yv7M7Re/OdJ7v//vdPKsnv0o/N1/qT/53PK1u3/ymb+i7/+t39Sv/cLP3X7x2+/pG2Gg+IfFN22+8vMf6xef1fXTn/9Y//Zf/Yvs59/5hX+uX//bX7r9hjdjffPy9/TBD368tHzzfOvL/1QffXRz8/Cjjz7Se++9d/PC9/63/uIPflff+XD+TY1lvvGlf6zf+NVfuv3Cx9f64D/964X/z2XGX3hX/+zX/t7tFz75WPHv/46+8aZ45YNFn/tntV/RP/oHf1+f++ztS6Lv/4ff1vi7Pyhdxrz3Zergp/6akh/dvD//d/Dj//w7+tNvrhcbv/P5X9Tf+co/1Jf++u16+ckfXui96H+t9Tt+81frqh/9ur78M3OSpfd/T3/0J+8Xvn/RZ37vc39DP//L/0R/80vv3H7T/wj0X9/7Y/3fn6ye2P3g7Z/VT//d35hfhyuMEf/zj/79Wr/fu8SIH/3wh4VxiRhx47HFiG+988v6/o9u6ujU71fVxojwvf9+qz79mz/8Lf3Wr355rc8r8ubNG00mN9dkH3zwQfbv17/+9YXvmc0PZvOKB5Vgo4IgSHRzezlZ5dfr+/5a79vGMrx8+XLqOGxsbGxsbGxsbGxsbKtuL1++XDsn2bTtn4XmkdmGLs7bUAYAAAAA2BUkzgAAAAAAFGCM84aVXTN5l8vw7rvv6uXLl4rjWB9++KG++MUv6u23317pGJWMcQYAAABwL/JjnMv68Y9/rG9/+9t65513ZBiG3n333YpKtzoS5w2bXTM5juOVE9m7Jr4PXQbDMPTVr3517fcDAAAAwDahq/aGbaK1dzbxfYxlAAAAAIBdQeK8YbMJZ9llnfITem26xfkhygAAAAAAu4LEecNmE86yM1yPx+PssWmaj74MAAAAALArSJwrYFlW9rhsa29+v6Ojo50oAwAAAADsAhLnCti2nT2OoqjUe/L7tVqtnSgDAAAAAOwCEucKnJ6eZo9Ho1Gp94RhKOmmi/QmuklvQxkAAAAAYBeQOFfAMIysxffFixdL9x8Oh9njTqezdP/BYJAluQ9VBgAAAADYFyTOFfF9X4ZhKI5jDQaDpftKN+OSXddduF8cx6rX63IcR41GQ+fn5/deBgAAAADYNwdJkiQPXYhdNRgM5DiOpJsZq+d1f+71evI8T4ZhaDQaFXaRzh9PumlVnkwm91oGAAAAANg3tDhXqNVqKQgCGYahRqOhXq+XvRbHsTzPk+d5sm1bV1dXSxPW2WWmyiS4my4DAAAAAOwbWpzvQRzHevHihXzfn5q52rbtLGktq9Pp6Pz8XKZpKgiC0onuJssAAMAui+NYURRNLe0IANhvJM4Adk4URSv1nlh1f2wnzjs25fz8XJ1OR77vy7ZtHR4e3ur1tQ7qKIDHivhFV23cs16vp0ajoVqtlm2O40zN6o39FIahPM9TvV7XwcGBDg4OVK/X5XneyvXDcRwdHByo2Wyq1+spDEPFcZy9HkWRBoOBPM/L6iAev6rPO/Frf4zHY0nKYlKtVsviUtktX/dSxKbddn5+roODg6mefeuoItYQvx6fTdQnrq02LAHuwWg0SgzDSEzTTHzfz34+Ho+TdrudSEparVYymUwerpB4MPk64Pt+0u/3k263m5immUhKJCWWZSXj8bjU8SzLyt63bDMMo/Rxsd2qOu/Er/2zSl2at7VarTsfl9j0eIzH48S27ezcrXveqog1xK/HZ1P1iWurzSNxRuWCIEgkJaZpLgzMvu8v3Qe7ybKsxDTNhQHWdd2pYBwEQaljlgnstm1T33ZIFeed+LWfDMNYO2k2DGNhPSA2PX6TySQJgiDxfT9pt9tzz+k6CUMVsYb4tf2qqk9cW1WDxBmVmkwm2QXIaDQq3Df9EluWdU+lw0NL73wuC7CzAX7ZH5Flwd2yrFJ/JPC4bPq8E7/202QySSQl7XY7CYIgGY1GpbY0ASmqY8Smxy9NRg3DSGzbTtrtdjIej6dutqya6FQRa4hfj0MV9Ylrq+owORgq5TiOBoOBLMvSaDQq3DeKItXrdUlSv99Xq9W6jyLigaTnOwiCpbO6x3GsWq2WPbdtW0EQLNy/0Wjo+PhYrVZLYRjq+vpaknR4eCjbtjcyyQ+2z6bPO/FrP4VhqGfPnmkymaz0vnq9Ltu25fv+wn2ITburVqtl4z3H4/FKkyJVEWuIX4/buvWJa6tqvfXQBcDuiuNYg8FAkkotd2WapkzTVBRFOjs7I3DvuG63WzrIGoYh13WzdciHw2Gp2RrTOoX9sonzTvzaX5eXlysv0eh5niQVJs15xCakqog1xK/9xbVVtZhVG5VJv4iSdHR0VOo96RcxDMM7z0qJ7XZ5eak4jtVsNrM73UUajcbUc2YCRZWIX/trNBqVPufSTSzq9Xrq9/sVlgq7qopYQ/zaX1xbVYvEGZW5uLjIHpe9+5W/g8WXd7fl/zCnSxgUmb27mS4XA1SB+LW/Op2OXNcttW8cx3IcR91uV5ZlVVwy7KIqYg3xa39xbVUtEmdUJgzD7PHh4WGp9+QD/LIxOXjcnj59OvV82R/32To0b41UYFOIX/vLNM3SycbJyYlM01S73a62UNhZVcQa4tf+4tqqWiTOqMRsN5+yYyG+8IUvZI8vLy83WiZsl263m9WLdru9dBzWbJ0q0wUJWAfxC2UMBgMNBgO6aGNtVcQa4td+49qqWiTOqMS6d6zyd8a467XbLMvSeDxWkiTqdrtL958N7mW7RQ6HQzmOo1qtlm2NRmNqDBh2z13OO/ELZZycnKjdbq89SQ6xCVXEGuLXfuPaqlokztha6TT3gDQ9W61hGEvvor5580bNZlPdblee5+nq6kqTyURXV1eybVue56lWqzGWa8dsy3knfu22TqejOI51enq68nu3pY5iN1QRa4hf+4NrqxU97DLS2FXpgu7pVpbv+2u9D7ttNBpN1Qvf9wv3tyxr6X75uhYEwaaLjAewqfNO/EKRyWSSSEpardbK7yU27S7DMLLzNh6PS72nilhD/NoN69SnVXFttTpanFEJuvlgkzqdTvbYtu2lM96apql+v1+4n+u6WRdLx3E2U1A8qE2dd+IXipycnEj6/2s3r4LYhLwqYg3xC2VxbbU6EmcAW20wGGRdftKgvUy/31er1Vq6X7pPHMdTf0DwOHHeUbX88i7LujTOQx0FsA24tloPiTMqUXYpD6BIHMdZ645pmhqNRhutW81mM3t8fn7Onfo9sey8E7+wSDoesOo1m4lN+6GKWEP8wjJcW62PxBmV2MS6cAR/OI6jOI4rCezS7WU6dnYyC0xZdt6JX1gkbW2eXSt104hN+6GKWEP8wjJcW62PxBmV2MSXcDb4Y790Oh0Nh8PKArt0u469fv1645+B7bPsvBO/MM9wOMyWbql6rVNi036oItYQv1CEa6u7IXFGJWa/NGWXNsjfGeWO5/7q9Xo6Pz+XZVkrBfYwDFWv11Wr1dZaS3CXuhPtk02fd+IX5smPAVx17WZiE+apItYQv7AI11Z3R+KMSsx+Gct+acbjcfZ41QsT7IbhcCjP82Tb9sp3Q09OThRFkeI4lud5KwdrLhYep02fd+IX5sl3N1w1VhCbME8VsYb4hXm4ttoMEmdUJj95Stk7nvn9jo6ONl4mbLcwDNVsNtVqtRQEwcL94jjOukzmzftZkdl6WXX3S1SjivNO/MKsfD1btSsrsQmLVBFriF/I49pqc0icUZn8Uh1lv3T5/cpMeY/dEUWRnj17Jtd1ly6LcHZ2lk3Sk5e/S97tdpfe5Zytl8+fPy9fYGyNKs478Qt5s5PbrNqCQmzCIlXEGuIXUlxbbRaJMypzenqaPR6NRqXeE4ahpJsvKV2F9kccx2o2m3r+/Hm23EuRMAzn1g/bttVqtZQkidrt9tLj5O+82ra9U92J9kkV5534hbz03K6L2IRFqog1xC9IXFtVIgEqZNt2IikxDGPpvkEQJJISSYnv+/dQOmwLy7IS13VL7y8pGY/Ht34+Ho8TSclkMil1HNM0szo3Go1Kfz62S1XnnfiFVLvdzs7vKnUtRWzafYZhZOds3t+nIlXEGuLX43aX+pTi2mrz3qo8M8de831fjUZDcRxrMBgUdv9J74ZZliXXde+riHhgzWZTh4eH8jxvYZeydCKK6+vr7E7mvLuipmmq3W7r2bNnS++y93q97PPa7fbUmDA8LlWdd+IXUnedFZbYtF9WrS9VxBri1+5YJ/5wbVWRh87csfv6/f7Su2a+72d3Rte9s4bHx3XdqVacsptpmoXHtSwrsW174d3RfJ1c5W4stlsV5534hSRJklardacW5xSxaXfl60cQBCu/v4pYQ/x6vO5Sn7i2qg6JM+5FEASJYRiJYRhT3YAmk0n2BS/6MmL3dLvdtQJ7WleWSetVu91O+v1+MhqNkiAIsgvg2bqI3VDFeSd+YfZCdBPHIjY9XkEQZJvv+4llWbcSkG63m/T7/Wy/MvGhilhD/Np+m6xPXFtVi8QZ92YymWQBIQ3ihmEkrVZrrbuzeNzy43dW3drtdqnPSIN5+lmGYSSWZSXdbpeLhB1WxXknfu238XicnftNXBQSmx639G9RPhYUbZKSfr9f6thVxBri13bbZH3i2qpaB0mSJAIAAAAAAHOxHBUAAAAAAAVInAEAAAAAKEDiDAAAAABAARJnAAAAAAAKkDgDAAAAAFCAxBkAAAAAgAIkzgAAAAAAFCBxBgAAAACgAIkzAAAAAAAFSJwBAAAAAChA4gwAAAAAQAESZwAAAAAACpA4AwAAAABQgMQZAAAAAIACJM4AAAAAABQgcQYAAAAAoACJMwAAAAAABUicAQAAAAAoQOIMAAAAAEABEmcAAAAAAAqQOAMAAAAAUIDEGQAAAACAAiTOAAAAAAAUIHEGAABbIY5jHRwc3No6nc5DF20rLfp9eZ730EUDgJ3z1kMXAAAAIM80TVmWlT2v1+sPWJrt1mq1ssdxHGs4HD5gaQBgd5E4AwCArdJqtdTtdh+6GFvPMAz1+/3seRzHqtVqD1giANhddNUGAAA7YTAYzO26XEWXb8dxCj8rDMONfRYA4OHR4gwAAHaCbdsKgkBxHOv6+lrdbldRFGWvb6obc6/Xu3Us13XVbDZlGIYODw+nupoDAB4/EmcAALATDMOQbdvZ836/L9u21ev1JGkqiV5XHMfyfV+2bWswGEi6SZp937/zsQEA24uu2gAAYCddXl7KcZzseRzHdz7mycmJvva1r021OOc/AwCwm0icAQDAzgnDUHEcy7ZtmaY59fN1DQYDHR4eyjCMqST86dOndykqAOARIHEGAAA7ZzgcZt2284nzut214zjW2dmZfN+fam22LEuGYdyprACA7UfiDAAAdk4QBGo2m5I0NVHXuolzp9PJlsgKgiD7eX5MNQBgd5E4AwCAnZNvca7X69nPx+PxWse6vr7OjpdvcU6TcwDAbmNWbQAAsFPSccxpS3O+q/bl5eXKx+t0Onr16pWkmxbr/PhmWpwBYD/Q4gwAAHZKvrVZutsYZ8/zdHp6mo1jnh3fDADYDyTOAAB8KooiNRoN1Wo11ev1bP3f2X08z8v2q9VqajabUwlV3mAwULPZVL1ez47red5GlkbCfPnxzdJ04rzK7z0MQ11fX6vVak0dO0VrMwDsDxJnAAB0k1A1Gg11u11NJhPZti3P89TpdLJ9PM9Ts9lUs9nUq1evNJlMdHV1pevrazWbTXmel+0bRZHq9bqCIFC329V4PNZkMlG321Wv11OtVlt7oioUm21xltZrdU7XbJ49dorxzQCwP0icAQDQzThW13WzhCtNis7PzxXHcfZ8PB6r1WplXXcNw8iSq16vpzAMNRwO1Wg05Pu+fN+f6tLbarWyz8gn2tiM2fHNqVUT506nI8/zppaaYnwzAOwvJgcDAEDSixcvdHV1lT2/vr7OHjcaDbVarWw5oln5pOzs7ExhGGo0Gk39fN7+i7p3Y33zWpul6XMUhmFh0htFkcIwvHW+Gd8MAPuLxBkAsPcGg4GePn061bqYX7bINM2FSbM0nWQPBgMFQbAwaZ7dH5s1O7451Wg0ssfLlqRyHEf9fn/usVO0NgPAfqGrNgBg711cXMhxnKmf5VsXfd8vfH++62++K/YiaXfifKJeVhzH6vV6chwnm3Ds4OBA9XpdjuPcasXu9XpT47SXiaJI5+fn2eRnBwcHU5OabXsreZkW56Ku2ufn5zo+Pp5744PxzQCwv0icAQB77/j4WM+fP8+ex3GcJbe2bRe2HkvTLZHLxi3HcZwlbk+fPi1dxjiO1el0VKvV5HmeDg8P1e12s0nK+v1+NkFZs9lUFEXZDOBlZpKOoihLxoMg0OnpqUajkZIk0dXVlXzfl2EY2T6DwaB02e/LovHNUrnEOYoiXVxcqN1uz32N8c0AsL/oqg0A2Hv55Yak1VsW8/svS6jWabUMw1DPnj1THMdyXVfdbvdWa7VlWbIsS67rqtfrqdFo6PDwsNTxh8Ohms2mDMNQEAS3/g+GYci2bdm2rdPTUzmOI8dx5Lru0tb4+7SotVkqlzh7nndrFu38sVN3Gd88GAx0cXGRLXUlSYeHh7IsS8fHx7fqIgBgO9DiDADAjFXGsuZbp8skVBcXF9njMklSGIZqNBqK41jdbjdr+S2SJtdlZo9O15mWpFevXi39/+aT616vt1VdlheNb04VJc+9Xi+7+bDo2Kl1WpvTJcguLi50fHysIAg0mUw0mUyycvu+v7Wt+QCw70icAQCYkbYuGoaxNBm+vLzMHh8fHy89dpoUmaa5tAt4FEXZpFatVmtuF+JFXNeV67pLj5+O7Z5dNmuZdPKs4XC40hjqKhW1OEuLE+c4juX7fuEEcHcZ39xsNtXv9zUajdTv99VqtabKYpqmXNdVEATq9/s6Ozu7NeYeAPCwSJwBAMjJj0Eu07K4SktkviWxzBrO+X0WdSEuUpQI5o9vGMbSJHuWYRhZIn9+fl6qdbtKZVr9FyXOjuMU/n7vMr65Xq/LsqylM62nLMvSaDSSxARkALBNSJwBAMh58eJF9rhM4pJPhpe12ObHA892057tnhuGYdbKadv2WjNwp2OT58kfPz8x2iryif1DtzpfXFwsTWjr9Xr2OE1OB4OBTNMsPHfrjm92HEe2bS+9gTFP2qJf5gYLAKB6TA4GAEDOquOby7ZOR1E0lQjnWx/jOJbjOEqSJPtZPsm+S7ddx3HmzqqdP/66LZumacowDMVx/ODjcofD4dKu8vmkN21FPjs7y5LoRdYZ3zwcDhVF0dz1oMsKgiCbRf0uE5IBAO6OFmcAAHLy45uXda1dZdxrUTftXq93q6t0fux0mS6+i7iuO3ds9KaOn39v2l36IYRhuDSpne2qfXJyUqo1eJ3xzZ7nrdXSPKvb7T54az4AgMQZAIBMfixrmZbF/AzZy/Z//fp19ni2m/bZ2dmt5Cg/Bvcuie0i+eOv0w08lV/y6qHGOac3JZa1ys4b47zsvIVhuPL45vwa4Hfluq4uLy9LrcUNAKgOiTMAAJ9ad/3mMrNvp0nbbBLseZ5c160kOb4P6VrEDyWOY52cnGSPl0lvEhiGUWrCtfzNkbLdpYfDYemlxsq00tu2PVU3AQD3j8QZAIBP5ceyLpswK47jLFErM7lW2j07bdWO41ie5ymKorldeovWHN6ETbUU55PV+0r+h8Oher2ePM9TrVbLytBoNNTr9TQcDhcm0U+fPpV0M0v5vJb2dNK0wWCgTqej8/Pz7LUoitTr9TQYDAo/4/Xr1zo6Oir8PziOo0ajoUajsbQr9tHR0VSPBQDA/WNyMAAAPtVsNjUcDnV6erq0+3LayhzHcakxqKZpajQaqdPp6MmTJzo8PFSr1ZpK1vOePn2atUbeJbGNokhRFN3qNmzbtnq9nqRy44OLjp+6jwmsoiia6g2QP09RFGU3KNL1kmdZliXDMOa+Fsdxtm52euzZepAfn+77/txlvOI4Lqw/URRNjXk/Pz8vHA9tmqbG4/HC1wEA1SNxBgDgU67rrrSe8bLZmGel6/mW4Xleltj2+/2V11lOpbNnzybGjuNkxw+CYO4EYsvkuw9vYjxvGaZpTs0+vqqiBNUwjDsdu6x8a38ZD90dHgBAV20AALaSZVlZMlrULXiZMAzndhu2bTtrIV73+PklrTYxg/SuME2zsJeAYRjyfT9r0V52M2U8Hk+tQQ0AuH8kzgAAbKl8YppOgLWKKIp0eXm5cKKq/ORYqy55FIZh1t3YdV3WGc5pNptLk2HXdTWZTDSZTJa21g+Hw3tr0QcAzEfiDADAljJNM0vABoPB1ERVy8RxrGazWThztGVZWXKeTnpVluM4t46BG61W6069BPLSMercmACAh0XiDADAFrNtW6PRSIZhqNPpyPO8pQlZFEVqNBryPG/pskiu66rf70uaHvdcdOx6va4oitRqtVYe570vXNdduRV/Hs/z6AYPAFuAxBkAgC1nWZaurq7UbrfV6/VUq9XkeZ4Gg0G2vFU6U3Oz2VSz2VS/3y894Ver1dJ4PJZt2/I8T41GQ4PBYCpBD8NQnuepXq/r+vpavu9nCTdu63a72bJW6+r1erq+vl57YjgAwOaQOAMA8AgYhqFut6vJZCLf93V9fa1Op6N6va5araZGoyHf9+U4jsbj8cpde9Nu4ePxWMfHxzo7O9OTJ090cHCgWq2Wdc3u9/uaTCYkcyUEQaCTk5O1kuder6dut6tXr15VUDIAwKoOkvtYdwEAAGCJOI5Vq9XUbrd3pntyuu60bdvqdrtL1weP41gnJyeKokivXr1auv/se2u1mlzXZdw5AGwYLc4AAAAVMU1T4/FYhmHoyZMn8jzv1sRhaTd7x3H05MkTHR0dZePaAQDbgRZnAACwFdIW01m70gIdx7FevHihfr+fzZYt3STXlmWp2Wzq+fPnpRPmRb8vWpwBYPPeeugCAAAApObNAl6v1x+gJJtnGIZc193o+PB5v69Go7Gx4wMAbtDiDAAAAABAAcY4AwAAAABQgMQZAAAAAIACJM4AAAAAABQgcQYAAAAAoACJMwAAAAAABUicAQAAAAAoQOIMAAAAAEABEmcAAAAAAAqQOAMAAAAAUIDEGQAAAACAAiTOAAAAAAAUIHEGAAAAAKAAiTMAAAAAAAVInAEAAAAAKEDiDAAAAABAARJnAAAAAAAKkDgDAAAAAFCAxBkAAAAAgAIkzgAAAAAAFCBxBgAAAACgAIkzAAAAAAAFSJwBAAAAAChA4gwAAAAAQAESZwAAAAAACpA4AwAAAABQgMQZAAAAAIACJM4AAAAAABQgcQYAAAAAoACJMwAAAAAABf4fHuM/KvTmdgYAAAAASUVORK5CYII=", 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pop.plot_hist_properties('m_chirp', intrinsic=True, observable=True, pop='DCO', bins=50)\n", + "pop.plot_hist_properties('chi_eff', intrinsic=True, observable=True, pop='DCO', bins=50)\n", + "pop.plot_hist_properties('q', intrinsic=True, observable=True, pop='DCO', bins=50)\n", + "pop.plot_hist_properties(['S1_mass','S2_mass'], intrinsic=True, observable=True, pop='DCO', bins=50)\n", + "pop.plot_hist_properties(['S1_spin','S2_spin'], intrinsic=True, observable=True, pop='DCO', bins=50)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There exists multiple formation channels that leads to merging BBHs." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['ZAMS_oDoubleCE1_CC1_CC2_END', 'ZAMS_oRLO1_CC1_oRLO2_CC2_END',\n", + " 'ZAMS_CC1_oRLO2_CC2_END', 'ZAMS_oRLO1-contact_CC1_CC2_END',\n", + " 'ZAMS_CC1_CC2_END', 'ZAMS_oRLO1_CC1_CC2_END',\n", + " 'ZAMS_oRLO1-reverse_CC1_CC2_END',\n", + " 'ZAMS_oRLO1_CC1_oRLO2_oCE2_CC2_END',\n", + " 'ZAMS_oRLO1-contact_CC1_oRLO2_CC2_END',\n", + " 'ZAMS_oRLO1-reverse_CC1_oRLO2_oCE2_CC2_END',\n", + " 'ZAMS_CC1_oRLO2_oCE2_CC2_END', 'ZAMS_oRLO1-contact_CC2_CC1_END',\n", + " 'ZAMS_oRLO1-reverse_CC1_oRLO2_CC2_END',\n", + " 'ZAMS_oRLO1-contact_CC1_oRLO2_oCE2_CC2_END'], dtype=object)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# formation channels\n", + "pop.df_synthetic['channel'].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each channel might imprint some obervational sigatures to the properties of merging BBHs. We can visualize the distribution of the intrinsic and observable BBH population as a function of the formation channel. For example let's consider the CE and SMT formation channels and see their spin distribution." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pop.plot_hist_properties(['S1_spin','S2_spin'], intrinsic=True, observable=True, pop='DCO', bins=50, channel='ZAMS_oRLO1_CC1_oRLO2_oCE2_CC2_END') # CE channel\n", + "pop.plot_hist_properties(['S1_spin','S2_spin'], intrinsic=True, observable=True, pop='DCO', bins=50, channel='ZAMS_oRLO1_CC1_oRLO2_CC2_END') # SMT channel" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize the BBH Population on the 2D MESA Grid Plot Slices" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You might want to visualize the portion of the parameter space is the MESA grids that leads to the formation of these BBHs. This can be done as follows.\n", + "- select the MESA grid type\n", + "- select the metallicity\n", + "- select the grid slice (if `None` all grid slices will be plotted entire)\n", + "- decide if you want to save the figures and where\n", + "- select the channels you want to plot" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pop.plot_popsyn_over_grid_slice('HMS-HMS', 1e-4, slices=[0.7], save_fig=False) #plot_dir='./plots/'\n", + "pop.plot_popsyn_over_grid_slice('HMS-HMS', 1e-4, slices=[0.7], save_fig=False, channel='ZAMS_oRLO1_CC1_oRLO2_oCE2_CC2_END') # CE channel\n", + "pop.plot_popsyn_over_grid_slice('HMS-HMS', 1e-4, slices=[0.7], save_fig=False, channel='ZAMS_oRLO1_CC1_oRLO2_CC2_END') # SMT channel\n", + "pop.plot_popsyn_over_grid_slice('HMS-HMS', 1e-4, slices=[0.7], save_fig=False, channel='ZAMS_oRLO1-contact_CC1_oRLO2_CC2_END') # SMT-contact channel" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `plot_popsyn_over_grid_slice` method allows you to display the properties of the merging DCO population as a colormap. Let's display the spin of the first born BH." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pop.plot_popsyn_over_grid_slice('HMS-HMS', 1e-4, slices=[0.7], prop='S1_spin', prop_range=[0,0.3], save_fig=False, channel='ZAMS_oRLO1_CC1_oRLO2_CC2_END') # SMT channel" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Cogratulations, you are now ready to analyze any DCO population data you generated with POSYDON. Feel free to further explore the BBH model or to use this tutorial to study BHNS and BNS populations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "posydon_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/_source/tutorials-examples/population-synthesis/binary-pop-syn.rst b/docs/_source/tutorials-examples/population-synthesis/binary-pop-syn.rst new file mode 100644 index 0000000000..13910c30ab --- /dev/null +++ b/docs/_source/tutorials-examples/population-synthesis/binary-pop-syn.rst @@ -0,0 +1,114 @@ +.. _binary-pop-syn: + + +Binary-Star Population Synthesis with POSYDON +============================================== + +Embark on a journey into the fascinating world of binary-star population synthesis with POSYDON! +Whether you are a budding astrophysicist or a seasoned researcher, our tutorials guide you every step of the way. +From simulating your first binaries to running large-scale populations and diving deep into data analysis, POSYDON offers you a seamless experience. + +Getting Started Tutorials +------------------------- + .. warning:: + The tutorials are designed to be followed in order, as some depend on the outputs of the previous tutorials. + +These tutorials are created to get started with POSYDON on your local machine and on a HPC facility. + +I. Your First Binary Simulations with POSYDON 🌠 +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Simulate your first 10 binaries and experience the power of POSYDON. + +.. toctree:: + + 10_binaries_pop_syn + +Now that you know how to run your first simulations, explore the different customization options reading the :ref:`population prameters ` documentation. + + +II. Large-Scale Population Synthesis on HPC Facilities 🚀 +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +In this tutorial, you'll learn to run massive simulations of 1 million binaries across 8 different metallicities. + +.. note:: + + Ensure you've installed `mpi4py` to use this tutorial. If not, follow our :ref:`installation guide `. + +.. toctree:: + + pop_syn + +III. Analyzing Merging DCO Populations: Rates & Observations 🔍 +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Delve into the results of your simulations learning how to analyze the results of your simulations and compute rates and observational properties of merging double compact object (DCO) populations. + +.. toctree:: + + bbh_analysis + + + +Advanced Tutorials +------------------ + +Computing Long-Duration Gamma-Ray Bursts from Double Compact Object Populations 🌌 +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +In this tutorial, you will learn how to compute long-duration gamma-ray bursts rates associated to the formation of merging binary black holes + +.. toctree:: + + lgrb_pop_syn + + +Explore the assumption of star-formation history and compute DCO rates for one single metallicity 📖 +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +In this tutorial, you will learn how to change the assumption of star-formation history and how to analysise and compute rates for a population synthesis model consisting of one single metallicity. + +.. toctree:: + + one_met_pop_syn + +X-ray binaries: computing the X-ray luminosity function 🩻 +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +TODO: bring v1 tutorial to v2 leveraging the SyntheticPopulation class + + +Debugging the Evolution of a Single Binary saved in a POSYDON Population model 🐞 +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This notebook shows you how to run the BinaryPopulation class in debug mode, which allows you to follow the evolution of a single binary through the population synthesis code. This is useful for debugging purposes, or for understanding the evolution of a single binary in detail, e.g. by changing the natal kicks. + +.. toctree:: + + debug_pop + + +Advanced Visualization with Van den Heuvel Diagrams 🎨 +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Dive deeper into POSYDON's visualization capabilities. This tutorial explores the intricacies of the Van den Heuvel diagrams, allowing users to intuitively visualize and analyze individual or multiple binaries from the population synthesis models. Discover multiple visualization modes, interactive features, and binary analysis tools to enhance your understanding of the synthesized data. Perfect for those looking to gain a richer visual perspective on their simulations. + +.. toctree:: + + Van den Heuvel Diagrams + + +Deep Dive: Behind the Scenes Customizations +------------------------------------------- + +Dive into the nitty-gritty of POSYDON's inner workings. Customize, extend, and tweak the system to fit your unique requirements: + +Customizing the Population Synthesis Class 🛠️ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + - Understand the foundational class driving our binary-star population synthesis. Learn to modify and adapt it to your needs. + - 🔗 [Delve into class customizations](link-to-customization-notebook1) + +(Add other in-depth customization tutorials here...) + +Remember, mastering POSYDON is a journey, not a destination. As you progress through these tutorials, you'll uncover layers of capabilities and functionalities. Keep exploring and happy simulating! diff --git a/docs/_source/tutorials-examples/population-synthesis/debug_pop.ipynb b/docs/_source/tutorials-examples/population-synthesis/debug_pop.ipynb new file mode 100644 index 0000000000..2c56d9e9a5 --- /dev/null +++ b/docs/_source/tutorials-examples/population-synthesis/debug_pop.ipynb @@ -0,0 +1,2001 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Debugging POSYON Binary Population Synthesis 🐞" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you haven't done so yet, export the path POSYDON environment variables.\n", + "Set these parameters in your `.bash_profile` or `.zshrc` if you use POSYDON regularly." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "env: PATH_TO_POSYDON=/Users/simone/Google Drive/github/POSYDON-public/\n", + "env: PATH_TO_POSYDON_DATA=/Volumes/T7/\n" + ] + } + ], + "source": [ + "%env PATH_TO_POSYDON=/Users/simone/Google Drive/github/POSYDON-public/\n", + "%env PATH_TO_POSYDON_DATA=/Volumes/T7/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To debug the evolution of a binary systems you should load the POSYDON BinaryPopulation object in mameory and then call the `restore` and `evolve`. This will run the evolution of the binary system and display any error traceback. You can debug the code by setting breakpoints in the POSYDON code until you devug the specific binary evolution.\n", + "\n", + "The trick is to do this is not dump the binary population to disk but to keep them in memory. This is done by setting the `optimize_ram=False` and `use_MPI=False`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 10/10 [00:23<00:00, 2.32s/it]\n", + "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/binarypopulation.py:571: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " return pd.concat(holder, axis=0, ignore_index=False)\n", + "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/binarypopulation.py:571: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " return pd.concat(holder, axis=0, ignore_index=False)\n" + ] + } + ], + "source": [ + "from mpi4py import MPI\n", + "from posydon.popsyn.binarypopulation import BinaryPopulation\n", + "from posydon.binary_evol.simulationproperties import SimulationProperties\n", + "from posydon.binary_evol.flow_chart import flow_chart\n", + "from posydon.binary_evol.MESA.step_mesa import CO_HeMS_step, MS_MS_step, CO_HMS_RLO_step, CO_HeMS_RLO_step\n", + "from posydon.binary_evol.DT.step_detached import detached_step\n", + "from posydon.binary_evol.DT.step_disrupted import DisruptedStep\n", + "from posydon.binary_evol.DT.step_merged import MergedStep\n", + "from posydon.binary_evol.CE.step_CEE import StepCEE\n", + "from posydon.binary_evol.SN.step_SN import StepSN\n", + "from posydon.binary_evol.DT.double_CO import DoubleCO\n", + "from posydon.binary_evol.step_end import step_end\n", + "from posydon.binary_evol.simulationproperties import StepNamesHooks\n", + "\n", + "# STEP CUSTOMISATION\n", + "MESA_STEP = dict(\n", + " interpolation_path = None, # found by default\n", + " interpolation_filename = None, # found by default\n", + " interpolation_method = 'nearest_neighbour', # 'nearest_neighbour' 'linear3c_kNN' '1NN_1NN'\n", + " save_initial_conditions = True, # only for interpolation_method='nearest_neighbour'\n", + " track_interpolation = False, # True False\n", + " stop_method = 'stop_at_max_time', # 'stop_at_end' 'stop_at_max_time' 'stop_at_condition'\n", + " stop_star = 'star_1', # only for stop_method='stop_at_condition' 'star_1' 'star_2'\n", + " stop_var_name = None, # only for stop_method='stop_at_condition' str\n", + " stop_value = None, # only for stop_method='stop_at_condition' float\n", + " stop_interpolate = True, # True False\n", + " verbose = False, # True False\n", + ")\n", + "\n", + "DETACHED_STEP = dict(\n", + " matching_method = 'minimize', #'minimize' 'root'\n", + " do_wind_loss = True, # True False\n", + " do_tides = True, # True False\n", + " do_gravitational_radiation = True, # True False\n", + " do_magnetic_braking = True, # True False\n", + " do_stellar_evolution_and_spin_from_winds = True, # True False\n", + " RLO_orbit_at_orbit_with_same_am = False, # True False\n", + " verbose = False, # True False\n", + ")\n", + "\n", + "DISRUPTED_STEP = dict(\n", + " grid_name_Hrich=None,\n", + " grid_name_strippedHe=None,\n", + " metallicity=None,\n", + " dt=None,\n", + " n_o_steps_history=None,\n", + " matching_method=\"minimize\",\n", + " initial_mass=None,\n", + " rootm=None,\n", + " verbose=False,\n", + " do_wind_loss=True,\n", + " do_tides=True,\n", + " do_gravitational_radiation=True,\n", + " do_magnetic_braking=True,\n", + " magnetic_braking_mode=\"RVJ83\",\n", + " do_stellar_evolution_and_spin_from_winds=True,\n", + " RLO_orbit_at_orbit_with_same_am=False,\n", + " list_for_matching_HMS=None,\n", + " list_for_matching_postMS=None,\n", + " list_for_matching_HeStar=None\n", + ")\n", + "\n", + "CE_STEP = dict(\n", + " prescription='alpha-lambda', # 'alpha-lambda'\n", + " common_envelope_efficiency=1.0, # float in (0, inf)\n", + " common_envelope_option_for_lambda='lambda_from_grid_final_values', # (1) 'default_lambda', (2) 'lambda_from_grid_final_values',\n", + " # (3) 'lambda_from_profile_gravitational',\n", + " # (4) 'lambda_from_profile_gravitational_plus_internal',\n", + " # (5) 'lambda_from_profile_gravitational_plus_internal_minus_recombination'\n", + " common_envelope_lambda_default=0.5, # float in (0, inf) used only for option (1)\n", + " common_envelope_option_for_HG_star=\"optimistic\", # 'optimistic', 'pessimistic'\n", + " common_envelope_alpha_thermal=1.0, # float in (0, inf) used only for option for (4), (5)\n", + " core_definition_H_fraction=0.1, # 0.01, 0.1, 0.3\n", + " core_definition_He_fraction=0.1, # 0.1\n", + " CEE_tolerance_err=0.001, # float (0, inf)\n", + " common_envelope_option_after_succ_CEE = 'core_not_replaced_noMT', # 'core_not_replaced_noMT' 'core_replaced_noMT' 'core_not_replaced_stableMT' 'core_not_replaced_windloss'\n", + " verbose = False, # True False\n", + ")\n", + "\n", + "SN_STEP = dict(\n", + " mechanism='Patton&Sukhbold20-engine', # 'direct', Fryer+12-rapid', 'Fryer+12-delayed', 'Sukhbold+16-engine', 'Patton&Sukhbold20-engine'\n", + " engine='N20', # 'N20' for 'Sukhbold+16-engine', 'Patton&Sukhbold20-engine' or None for the others\n", + " PISN=\"Marchant+19\", # None, \"Marchant+19\"\n", + " ECSN=\"Podsiadlowksi+04\", # \"Tauris+15\", \"Podsiadlowksi+04\"\n", + " conserve_hydrogen_envelope=True,\n", + " max_neutrino_mass_loss=0.5, # float (0,inf)\n", + " kick=True, # True, False\n", + " kick_normalisation='one_over_mass', # \"one_minus_fallback\", \"one_over_mass\", \"NS_one_minus_fallback_BH_one\", \"one\", \"zero\"\n", + " sigma_kick_CCSN_NS=265.0, # float (0,inf)\n", + " sigma_kick_CCSN_BH=265.0, # float (0,inf)\n", + " sigma_kick_ECSN=20.0, # float (0,inf)\n", + " max_NS_mass=2.5, # float (0,inf)\n", + " use_interp_values=True, # True, False\n", + " use_profiles=True, # True, False\n", + " use_core_masses=True, # True, False\n", + " approx_at_he_depletion=False, # True, False\n", + " verbose = False, # True False\n", + ")\n", + "\n", + "DCO_STEP = dict(\n", + " n_o_steps_interval = None,\n", + ")\n", + "\n", + "END_STEP = {}\n", + "\n", + "# FLOW CHART CONFIGURATION\n", + "sim_kwargs = dict(\n", + " flow = (flow_chart, {}),\n", + " step_HMS_HMS = (MS_MS_step, MESA_STEP),\n", + " step_CO_HeMS = (CO_HeMS_step, MESA_STEP),\n", + " step_CO_HMS_RLO = (CO_HMS_RLO_step, MESA_STEP),\n", + " step_CO_HeMS_RLO = (CO_HeMS_RLO_step, MESA_STEP),\n", + " step_detached = (detached_step, DETACHED_STEP),\n", + " step_disrupted = (DisruptedStep, DISRUPTED_STEP),\n", + " step_merged = (MergedStep, {}),\n", + " step_CE = (StepCEE, CE_STEP),\n", + " step_SN = (StepSN, SN_STEP),\n", + " step_dco = (DoubleCO, DCO_STEP),\n", + " step_end = (step_end, END_STEP),\n", + " extra_hooks = [(StepNamesHooks, {})]\n", + ")\n", + "\n", + "sim_prop = SimulationProperties(**sim_kwargs)\n", + "\n", + "# SIMULATION CONFIGURATION\n", + "kwargs = dict(\n", + " file_path='./batches/',\n", + " optimize_ram=False,\n", + " ram_per_cpu=3., # limit ram usage at 3GB\n", + " dump_rate=1000, # limit batch size\n", + "\n", + " metallicity = 0.0001, # 1e+00, 1e-01, 1e-02, 1e-03, 1e-04\n", + " number_of_binaries=10, # int\n", + " star_formation='burst', # 'constant' 'burst' 'custom_linear' 'custom_log10' 'custom_linear_histogram' 'custom_log10_histogram'\n", + " max_simulation_time=13.8e9, # float (0,inf)\n", + "\n", + " primary_mass_scheme='Kroupa2001', # 'Salpeter', 'Kroupa1993', 'Kroupa2001'\n", + " primary_mass_min=6.5, # float (0,130)\n", + " primary_mass_max=250., # float (0,130)\n", + " secondary_mass_scheme='flat_mass_ratio', # 'flat_mass_ratio', 'q=1'\n", + " secondary_mass_min=0.35, # float (0,130)\n", + " secondary_mass_max=250., # float (0,130)\n", + " orbital_scheme = 'period', # 'separation', 'period'\n", + " orbital_period_scheme = 'Sana+12_period_extended', # used only for orbital_scheme = 'period'\n", + " orbital_period_min = 1., # float (0,inf)\n", + " orbital_period_max = 1000., # float (0,inf)\n", + " #orbital_separation_scheme='log_uniform', # used only for orbital_scheme = 'separation', 'log_uniform', 'log_normal'\n", + " #orbital_separation_min=5., # float (0,inf)\n", + " #orbital_separation_max=1e5, # float (0,inf)\n", + " #log_orbital_separation_mean=None, # float (0,inf) used only for orbital_separation_scheme ='log_normal'\n", + " #log_orbital_separation_sigma=None, # float (0,inf) used only for orbital_separation_scheme ='log_normal'\n", + " eccentricity_sche='zero', # 'zero' 'thermal' 'uniform'\n", + "\n", + " # IMPORT CUSTOM HOOKS\n", + " extra_columns={'step_names':'string'}, # 'step_times' with from posydon.binary_evol.simulationproperties import TimingHooks\n", + "\n", + " # LIST BINARY PROPERTIES TO SAVE\n", + " only_select_columns=[\n", + " 'state',\n", + " 'event',\n", + " 'time',\n", + " #'separation',\n", + " 'orbital_period',\n", + " 'eccentricity',\n", + " #'V_sys',\n", + " #'rl_relative_overflow_1',\n", + " #'rl_relative_overflow_2',\n", + " 'lg_mtransfer_rate',\n", + " #'mass_transfer_case',\n", + " #'trap_radius',\n", + " #'acc_radius',\n", + " #'t_sync_rad_1',\n", + " #'t_sync_conv_1',\n", + " #'t_sync_rad_2',\n", + " #'t_sync_conv_2',\n", + " #'nearest_neighbour_distance',\n", + " ],\n", + " scalar_names=[\n", + " 'interp_class_HMS_HMS',\n", + " 'interp_class_CO_HMS_RLO',\n", + " 'interp_class_CO_HeMS',\n", + " 'interp_class_CO_HeMS_RLO'\n", + " ],\n", + "\n", + " # LIST STAR PROPERTIES TO SAVE\n", + " include_S1=True , # True, False\n", + " S1_kwargs=dict(only_select_columns=[\n", + " 'state',\n", + " 'metallicity',\n", + " 'mass',\n", + " 'log_R',\n", + " 'log_L',\n", + " 'lg_mdot',\n", + " #'lg_system_mdot',\n", + " #'lg_wind_mdot',\n", + " 'he_core_mass',\n", + " 'he_core_radius',\n", + " #'c_core_mass',\n", + " #'c_core_radius',\n", + " #'o_core_mass',\n", + " #'o_core_radius',\n", + " 'co_core_mass',\n", + " 'co_core_radius',\n", + " 'center_h1',\n", + " 'center_he4',\n", + " #'center_c12',\n", + " #'center_n14',\n", + " #'center_o16',\n", + " 'surface_h1',\n", + " 'surface_he4',\n", + " #'surface_c12',\n", + " #'surface_n14',\n", + " #'surface_o16',\n", + " #'log_LH',\n", + " #'log_LHe',\n", + " #'log_LZ',\n", + " #'log_Lnuc',\n", + " #'c12_c12',\n", + " #'center_gamma',\n", + " #'avg_c_in_c_core',\n", + " #'surf_avg_omega',\n", + " 'surf_avg_omega_div_omega_crit',\n", + " #'total_moment_of_inertia',\n", + " #'log_total_angular_momentum',\n", + " 'spin',\n", + " #'conv_env_top_mass',\n", + " #'conv_env_bot_mass',\n", + " #'conv_env_top_radius',\n", + " #'conv_env_bot_radius',\n", + " #'conv_env_turnover_time_g',\n", + " #'conv_env_turnover_time_l_b',\n", + " #'conv_env_turnover_time_l_t',\n", + " #'envelope_binding_energy',\n", + " #'mass_conv_reg_fortides',\n", + " #'thickness_conv_reg_fortides',\n", + " #'radius_conv_reg_fortides',\n", + " #'lambda_CE_1cent',\n", + " #'lambda_CE_10cent',\n", + " #'lambda_CE_30cent',\n", + " #'lambda_CE_pure_He_star_10cent',\n", + " #'profile',\n", + " ],\n", + " scalar_names=['natal_kick_array',\n", + " 'SN_type',\n", + " #'f_fb',\n", + " #'spin_orbit_tilt',\n", + " ]),\n", + "\n", + " # LIST STAR PROPERTIES TO SAVE\n", + " include_S2=True, # True, False\n", + " S2_kwargs=dict(only_select_columns=[\n", + " 'state',\n", + " 'metallicity',\n", + " 'mass',\n", + " 'log_R',\n", + " 'log_L',\n", + " 'lg_mdot',\n", + " #'lg_system_mdot',\n", + " #'lg_wind_mdot',\n", + " 'he_core_mass',\n", + " 'he_core_radius',\n", + " #'c_core_mass',\n", + " #'c_core_radius',\n", + " #'o_core_mass',\n", + " #'o_core_radius',\n", + " 'co_core_mass',\n", + " 'co_core_radius',\n", + " 'center_h1',\n", + " 'center_he4',\n", + " #'center_c12',\n", + " #'center_n14',\n", + " #'center_o16',\n", + " 'surface_h1',\n", + " 'surface_he4',\n", + " #'surface_c12',\n", + " #'surface_n14',\n", + " #'surface_o16',\n", + " #'log_LH',\n", + " #'log_LHe',\n", + " #'log_LZ',\n", + " #'log_Lnuc',\n", + " #'c12_c12',\n", + " #'center_gamma',\n", + " #'avg_c_in_c_core',\n", + " #'surf_avg_omega',\n", + " 'surf_avg_omega_div_omega_crit',\n", + " #'total_moment_of_inertia',\n", + " #'log_total_angular_momentum',\n", + " 'spin',\n", + " #'conv_env_top_mass',\n", + " #'conv_env_bot_mass',\n", + " #'conv_env_top_radius',\n", + " #'conv_env_bot_radius',\n", + " #'conv_env_turnover_time_g',\n", + " #'conv_env_turnover_time_l_b',\n", + " #'conv_env_turnover_time_l_t',\n", + " #'envelope_binding_energy',\n", + " #'mass_conv_reg_fortides',\n", + " #'thickness_conv_reg_fortides',\n", + " #'radius_conv_reg_fortides',\n", + " #'lambda_CE_1cent',\n", + " #'lambda_CE_10cent',\n", + " #'lambda_CE_30cent',\n", + " #'lambda_CE_pure_He_star_10cent',\n", + " #'profile',\n", + " ],\n", + " scalar_names=['natal_kick_array',\n", + " 'SN_type',\n", + " #'f_fb',\n", + " #'spin_orbit_tilt',\n", + " ]),\n", + ")\n", + "\n", + "def run_simulation(sim_prop, kwargs, file=None, indices=None, use_MPI=False):\n", + "\n", + " if not use_MPI:\n", + " # create binaries\n", + " pop = BinaryPopulation(entropy=None,\n", + " population_properties=sim_prop,\n", + " file_name=file,\n", + " **kwargs)\n", + " else:\n", + " comm = MPI.COMM_WORLD\n", + " rank = comm.Get_rank()\n", + " size = comm.Get_size()\n", + "\n", + " # create binaries\n", + " pop = BinaryPopulation(entropy=None,\n", + " population_properties=sim_prop,\n", + " file_name=file,\n", + " comm=comm,\n", + " **kwargs)\n", + " sim_prop.load_steps(verbose=True)\n", + "\n", + " # evolve binaries\n", + " if file is not None:\n", + " kwargs['from_hdf'] = True\n", + " kwargs['indices'] = indices\n", + "\n", + " pop.evolve(breakdown_to_df=False, tqdm=True, **kwargs)\n", + "\n", + " # save binaries\n", + " try:\n", + " pop.save('./population.h5', **kwargs)\n", + " except:\n", + " pass\n", + "\n", + " return pop\n", + "\n", + "if __name__ == '__main__' :\n", + " pop = run_simulation(sim_prop, kwargs, use_MPI=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's say we want to debug binary_index that follows." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "i = 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Re-evolving a Binary Star" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    07.454614e+00step_HMS_HMSdetached<NA>3.727594e+000.000000H-rich_Core_H_burningH-rich_Core_H_burning44.77441640.296974
    05.069171e+06<NA>RLO1CC15.833718e+000.000000H-rich_Central_C_depletionH-rich_Core_H_burning26.19675157.025587
    05.069171e+06step_SNdetached<NA>5.742417e+000.024669BHH-rich_Core_H_burning25.67500457.025587
    05.692577e+06step_detachedRLO2oRLO26.301123e+000.000000BHH-rich_Core_H_burning25.67500453.356529
    05.692577e+06step_CO_HMS_RLORLO2<NA>7.284420e+000.000000BHH-rich_Core_H_burning25.08346959.402770
    06.097987e+06<NA>detachedCC23.951662e+000.000000BHH-rich_Central_C_depletion25.57643936.783716
    06.097987e+06step_SNdetached<NA>3.936622e+000.013276BHBH25.57643936.254365
    07.754665e+09step_dcocontactCO_contact6.267598e-080.000000BHBH25.57643936.254365
    07.754665e+09step_endcontactEND6.267598e-080.000000BHBH25.57643936.254365
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    " + ], + "text/plain": [ + " time step_names state event \\\n", + "binary_index \n", + "0 0.000000e+00 initial_cond detached ZAMS \n", + "0 7.454614e+00 step_HMS_HMS detached \n", + "0 5.069171e+06 RLO1 CC1 \n", + "0 5.069171e+06 step_SN detached \n", + "0 5.692577e+06 step_detached RLO2 oRLO2 \n", + "0 5.692577e+06 step_CO_HMS_RLO RLO2 \n", + "0 6.097987e+06 detached CC2 \n", + "0 6.097987e+06 step_SN detached \n", + "0 7.754665e+09 step_dco contact CO_contact \n", + "0 7.754665e+09 step_end contact END \n", + "\n", + " orbital_period eccentricity S1_state \\\n", + "binary_index \n", + "0 3.385496e+00 0.000000 H-rich_Core_H_burning \n", + "0 3.727594e+00 0.000000 H-rich_Core_H_burning \n", + "0 5.833718e+00 0.000000 H-rich_Central_C_depletion \n", + "0 5.742417e+00 0.024669 BH \n", + "0 6.301123e+00 0.000000 BH \n", + "0 7.284420e+00 0.000000 BH \n", + "0 3.951662e+00 0.000000 BH \n", + "0 3.936622e+00 0.013276 BH \n", + "0 6.267598e-08 0.000000 BH \n", + "0 6.267598e-08 0.000000 BH \n", + "\n", + " S2_state S1_mass S2_mass \n", + "binary_index \n", + "0 H-rich_Core_H_burning 43.384853 38.283497 \n", + "0 H-rich_Core_H_burning 44.774416 40.296974 \n", + "0 H-rich_Core_H_burning 26.196751 57.025587 \n", + "0 H-rich_Core_H_burning 25.675004 57.025587 \n", + "0 H-rich_Core_H_burning 25.675004 53.356529 \n", + "0 H-rich_Core_H_burning 25.083469 59.402770 \n", + "0 H-rich_Central_C_depletion 25.576439 36.783716 \n", + "0 BH 25.576439 36.254365 \n", + "0 BH 25.576439 36.254365 \n", + "0 BH 25.576439 36.254365 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "col = ['time', 'step_names', 'state', 'event', 'orbital_period', 'eccentricity', 'S1_state', 'S2_state', 'S1_mass', 'S2_mass']\n", + "pop[i].to_df(extra_columns={'step_names':'string'})[col]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "pop[i].restore()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    stateeventtimeseparationorbital_periodeccentricityrl_relative_overflow_1rl_relative_overflow_2lg_mtransfer_ratemass_transfer_case...S2_conv_env_turnover_time_l_bS2_conv_env_turnover_time_l_tS2_envelope_binding_energyS2_mass_conv_reg_fortidesS2_thickness_conv_reg_fortidesS2_radius_conv_reg_fortidesS2_lambda_CE_1centS2_lambda_CE_10centS2_lambda_CE_30centS2_lambda_CE_pure_He_star_10cent
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    1 rows × 126 columns

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    " + ], + "text/plain": [ + " state event time separation orbital_period eccentricity \\\n", + "binary_index \n", + "0 detached ZAMS 0.0 41.154712 3.385496 0.0 \n", + "\n", + " rl_relative_overflow_1 rl_relative_overflow_2 \\\n", + "binary_index \n", + "0 NaN NaN \n", + "\n", + " lg_mtransfer_rate mass_transfer_case ... \\\n", + "binary_index ... \n", + "0 NaN None ... \n", + "\n", + " S2_conv_env_turnover_time_l_b S2_conv_env_turnover_time_l_t \\\n", + "binary_index \n", + "0 NaN NaN \n", + "\n", + " S2_envelope_binding_energy S2_mass_conv_reg_fortides \\\n", + "binary_index \n", + "0 NaN NaN \n", + "\n", + " S2_thickness_conv_reg_fortides S2_radius_conv_reg_fortides \\\n", + "binary_index \n", + "0 NaN NaN \n", + "\n", + " S2_lambda_CE_1cent S2_lambda_CE_10cent S2_lambda_CE_30cent \\\n", + "binary_index \n", + "0 NaN NaN NaN \n", + "\n", + " S2_lambda_CE_pure_He_star_10cent \n", + "binary_index \n", + "0 NaN \n", + "\n", + "[1 rows x 126 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop[i].to_df()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Even thought we reset the binary, the natal kick properties stored in the natal kick array are not reset. This is because we want to be able to re-evolve the binary to the same final state. In case you want to reset the natal kick array values, then set the list to `None` values." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[15.153246030221677, 0.858129274334538, 1.9157148786534735, 1.8675467897282945]\n", + "[10.720041778381628, 0.6946886653490086, 1.9361529346564168, 4.63514002509005]\n" + ] + } + ], + "source": [ + "# kick magnitude km/s, azimuthal angle rad, polar angle rad and mean anomaly rad (TODO check documentation)\n", + "print(pop[i].star_1.natal_kick_array)\n", + "print(pop[i].star_2.natal_kick_array)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "pop[i].evolve()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    timestep_namesstateeventorbital_periodeccentricityS1_stateS2_stateS1_massS2_mass
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    07.454614e+00step_HMS_HMSdetached<NA>3.727594e+000.000000H-rich_Core_H_burningH-rich_Core_H_burning44.77441640.296974
    05.069171e+06<NA>RLO1CC15.833718e+000.000000H-rich_Central_C_depletionH-rich_Core_H_burning26.19675157.025587
    05.069171e+06step_SNdetached<NA>5.742417e+000.024669BHH-rich_Core_H_burning25.67500457.025587
    05.692577e+06step_detachedRLO2oRLO26.301123e+000.000000BHH-rich_Core_H_burning25.67500453.356529
    05.692577e+06step_CO_HMS_RLORLO2<NA>7.284420e+000.000000BHH-rich_Core_H_burning25.08346959.402770
    06.097987e+06<NA>detachedCC23.951662e+000.000000BHH-rich_Central_C_depletion25.57643936.783716
    06.097987e+06step_SNdetached<NA>3.936622e+000.013276BHBH25.57643936.254365
    07.754665e+09step_dcocontactCO_contact6.267598e-080.000000BHBH25.57643936.254365
    07.754665e+09step_endcontactEND6.267598e-080.000000BHBH25.57643936.254365
    \n", + "
    " + ], + "text/plain": [ + " time step_names state event \\\n", + "binary_index \n", + "0 0.000000e+00 initial_cond detached ZAMS \n", + "0 7.454614e+00 step_HMS_HMS detached \n", + "0 5.069171e+06 RLO1 CC1 \n", + "0 5.069171e+06 step_SN detached \n", + "0 5.692577e+06 step_detached RLO2 oRLO2 \n", + "0 5.692577e+06 step_CO_HMS_RLO RLO2 \n", + "0 6.097987e+06 detached CC2 \n", + "0 6.097987e+06 step_SN detached \n", + "0 7.754665e+09 step_dco contact CO_contact \n", + "0 7.754665e+09 step_end contact END \n", + "\n", + " orbital_period eccentricity S1_state \\\n", + "binary_index \n", + "0 3.385496e+00 0.000000 H-rich_Core_H_burning \n", + "0 3.727594e+00 0.000000 H-rich_Core_H_burning \n", + "0 5.833718e+00 0.000000 H-rich_Central_C_depletion \n", + "0 5.742417e+00 0.024669 BH \n", + "0 6.301123e+00 0.000000 BH \n", + "0 7.284420e+00 0.000000 BH \n", + "0 3.951662e+00 0.000000 BH \n", + "0 3.936622e+00 0.013276 BH \n", + "0 6.267598e-08 0.000000 BH \n", + "0 6.267598e-08 0.000000 BH \n", + "\n", + " S2_state S1_mass S2_mass \n", + "binary_index \n", + "0 H-rich_Core_H_burning 43.384853 38.283497 \n", + "0 H-rich_Core_H_burning 44.774416 40.296974 \n", + "0 H-rich_Core_H_burning 26.196751 57.025587 \n", + "0 H-rich_Core_H_burning 25.675004 57.025587 \n", + "0 H-rich_Core_H_burning 25.675004 53.356529 \n", + "0 H-rich_Core_H_burning 25.083469 59.402770 \n", + "0 H-rich_Central_C_depletion 25.576439 36.783716 \n", + "0 BH 25.576439 36.254365 \n", + "0 BH 25.576439 36.254365 \n", + "0 BH 25.576439 36.254365 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop[i].to_df(extra_columns={'step_names':'string'})[col]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Re-evolving a Binary Star with a different kick" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    stateeventtimeseparationorbital_periodeccentricityrl_relative_overflow_1rl_relative_overflow_2lg_mtransfer_ratemass_transfer_case...S2_conv_env_turnover_time_l_bS2_conv_env_turnover_time_l_tS2_envelope_binding_energyS2_mass_conv_reg_fortidesS2_thickness_conv_reg_fortidesS2_radius_conv_reg_fortidesS2_lambda_CE_1centS2_lambda_CE_10centS2_lambda_CE_30centS2_lambda_CE_pure_He_star_10cent
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    0detachedZAMS0.041.1547123.3854960.0NaNNaNNaNNone...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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    1 rows × 126 columns

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    " + ], + "text/plain": [ + " state event time separation orbital_period eccentricity \\\n", + "binary_index \n", + "0 detached ZAMS 0.0 41.154712 3.385496 0.0 \n", + "\n", + " rl_relative_overflow_1 rl_relative_overflow_2 \\\n", + "binary_index \n", + "0 NaN NaN \n", + "\n", + " lg_mtransfer_rate mass_transfer_case ... \\\n", + "binary_index ... \n", + "0 NaN None ... \n", + "\n", + " S2_conv_env_turnover_time_l_b S2_conv_env_turnover_time_l_t \\\n", + "binary_index \n", + "0 NaN NaN \n", + "\n", + " S2_envelope_binding_energy S2_mass_conv_reg_fortides \\\n", + "binary_index \n", + "0 NaN NaN \n", + "\n", + " S2_thickness_conv_reg_fortides S2_radius_conv_reg_fortides \\\n", + "binary_index \n", + "0 NaN NaN \n", + "\n", + " S2_lambda_CE_1cent S2_lambda_CE_10cent S2_lambda_CE_30cent \\\n", + "binary_index \n", + "0 NaN NaN NaN \n", + "\n", + " S2_lambda_CE_pure_He_star_10cent \n", + "binary_index \n", + "0 NaN \n", + "\n", + "[1 rows x 126 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop[i].restore()\n", + "pop[i].to_df()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1000.0, 0.858129274334538, 1.9157148786534735, 1.8675467897282945]\n", + "[None, None, None, None]\n" + ] + } + ], + "source": [ + "pop[i].star_1.natal_kick_array = [1000., 0.858129274334538, 1.9157148786534735, 1.8675467897282945]\n", + "pop[i].star_2.natal_kick_array = [None, None, None, None] # default\n", + "print(pop[i].star_1.natal_kick_array)\n", + "print(pop[i].star_2.natal_kick_array)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/simone/Google Drive/github/POSYDON-public/posydon/binary_evol/DT/step_detached.py:1677: RuntimeWarning: invalid value encountered in log10\n", + " current = np.log10(\n" + ] + }, + { + "data": { + "text/html": [ + "
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    timestep_namesstateeventorbital_periodeccentricityS1_stateS2_stateS1_massS2_mass
    binary_index
    00.000000e+00initial_conddetachedZAMS3.3854960.0H-rich_Core_H_burningH-rich_Core_H_burning43.38485338.283497
    07.454614e+00step_HMS_HMSdetached<NA>3.7275940.0H-rich_Core_H_burningH-rich_Core_H_burning44.77441640.296974
    05.069171e+06<NA>RLO1CC15.8337180.0H-rich_Central_C_depletionH-rich_Core_H_burning26.19675157.025587
    05.069171e+06step_SNdisrupted<NA>NaNNaNBHH-rich_Core_H_burning25.67500457.025587
    06.117635e+06step_disrupteddisruptedCC2NaNNaNBHH-rich_Central_C_depletion25.67500437.995687
    06.117635e+06step_SNdisrupted<NA>NaNNaNBHBH25.67500436.254365
    06.117635e+06step_enddisruptedENDNaNNaNBHBH25.67500436.254365
    \n", + "
    " + ], + "text/plain": [ + " time step_names state event orbital_period \\\n", + "binary_index \n", + "0 0.000000e+00 initial_cond detached ZAMS 3.385496 \n", + "0 7.454614e+00 step_HMS_HMS detached 3.727594 \n", + "0 5.069171e+06 RLO1 CC1 5.833718 \n", + "0 5.069171e+06 step_SN disrupted NaN \n", + "0 6.117635e+06 step_disrupted disrupted CC2 NaN \n", + "0 6.117635e+06 step_SN disrupted NaN \n", + "0 6.117635e+06 step_end disrupted END NaN \n", + "\n", + " eccentricity S1_state \\\n", + "binary_index \n", + "0 0.0 H-rich_Core_H_burning \n", + "0 0.0 H-rich_Core_H_burning \n", + "0 0.0 H-rich_Central_C_depletion \n", + "0 NaN BH \n", + "0 NaN BH \n", + "0 NaN BH \n", + "0 NaN BH \n", + "\n", + " S2_state S1_mass S2_mass \n", + "binary_index \n", + "0 H-rich_Core_H_burning 43.384853 38.283497 \n", + "0 H-rich_Core_H_burning 44.774416 40.296974 \n", + "0 H-rich_Core_H_burning 26.196751 57.025587 \n", + "0 H-rich_Core_H_burning 25.675004 57.025587 \n", + "0 H-rich_Central_C_depletion 25.675004 37.995687 \n", + "0 BH 25.675004 36.254365 \n", + "0 BH 25.675004 36.254365 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop[i].evolve()\n", + "pop[i].to_df(extra_columns={'step_names':'string'})[col]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Debugging a Binary Star from a population h5 file" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Assume we run the population somewhere else and you have the h5 file associated with it and you want to load the population in memory and re-evolve a binary. Here is how you do it." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "del pop # delete the above population" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:02<00:00, 2.32s/it]\n" + ] + } + ], + "source": [ + "# let's load up and rerun the binary index i\n", + "pop = run_simulation(sim_prop, kwargs, file='./population.h5', indices=[i], use_MPI=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    00.000000e+00initial_conddetachedZAMS3.385496e+000.000000H-rich_Core_H_burningH-rich_Core_H_burning43.38485338.283497
    07.454614e+00step_HMS_HMSdetached<NA>3.727594e+000.000000H-rich_Core_H_burningH-rich_Core_H_burning44.77441640.296974
    05.069171e+06<NA>RLO1CC15.833718e+000.000000H-rich_Central_C_depletionH-rich_Core_H_burning26.19675157.025587
    05.069171e+06step_SNdetached<NA>5.742417e+000.024669BHH-rich_Core_H_burning25.67500457.025587
    05.692577e+06step_detachedRLO2oRLO26.301123e+000.000000BHH-rich_Core_H_burning25.67500453.356529
    05.692577e+06step_CO_HMS_RLORLO2<NA>7.284420e+000.000000BHH-rich_Core_H_burning25.08346959.402770
    06.097987e+06<NA>detachedCC23.951662e+000.000000BHH-rich_Central_C_depletion25.57643936.783716
    06.097987e+06step_SNdetached<NA>3.936622e+000.013276BHBH25.57643936.254365
    07.754665e+09step_dcocontactCO_contact6.267598e-080.000000BHBH25.57643936.254365
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    " + ], + "text/plain": [ + " time step_names state event \\\n", + "binary_index \n", + "0 0.000000e+00 initial_cond detached ZAMS \n", + "0 7.454614e+00 step_HMS_HMS detached \n", + "0 5.069171e+06 RLO1 CC1 \n", + "0 5.069171e+06 step_SN detached \n", + "0 5.692577e+06 step_detached RLO2 oRLO2 \n", + "0 5.692577e+06 step_CO_HMS_RLO RLO2 \n", + "0 6.097987e+06 detached CC2 \n", + "0 6.097987e+06 step_SN detached \n", + "0 7.754665e+09 step_dco contact CO_contact \n", + "0 7.754665e+09 step_end contact END \n", + "\n", + " orbital_period eccentricity S1_state \\\n", + "binary_index \n", + "0 3.385496e+00 0.000000 H-rich_Core_H_burning \n", + "0 3.727594e+00 0.000000 H-rich_Core_H_burning \n", + "0 5.833718e+00 0.000000 H-rich_Central_C_depletion \n", + "0 5.742417e+00 0.024669 BH \n", + "0 6.301123e+00 0.000000 BH \n", + "0 7.284420e+00 0.000000 BH \n", + "0 3.951662e+00 0.000000 BH \n", + "0 3.936622e+00 0.013276 BH \n", + "0 6.267598e-08 0.000000 BH \n", + "0 6.267598e-08 0.000000 BH \n", + "\n", + " S2_state S1_mass S2_mass \n", + "binary_index \n", + "0 H-rich_Core_H_burning 43.384853 38.283497 \n", + "0 H-rich_Core_H_burning 44.774416 40.296974 \n", + "0 H-rich_Core_H_burning 26.196751 57.025587 \n", + "0 H-rich_Core_H_burning 25.675004 57.025587 \n", + "0 H-rich_Core_H_burning 25.675004 53.356529 \n", + "0 H-rich_Core_H_burning 25.083469 59.402770 \n", + "0 H-rich_Central_C_depletion 25.576439 36.783716 \n", + "0 BH 25.576439 36.254365 \n", + "0 BH 25.576439 36.254365 \n", + "0 BH 25.576439 36.254365 " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop[0].to_df(extra_columns={'step_names':'string'})[col]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1000.0, 0.858129274334538, 1.9157148786534735, 1.8675467897282945]\n", + "[None, None, None, None]\n", + "[1000.0, 0.858129274334538, 1.9157148786534735, 1.8675467897282945]\n", + "[None, None, None, None]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/simone/Google Drive/github/POSYDON-public/posydon/binary_evol/DT/step_detached.py:1677: RuntimeWarning: invalid value encountered in log10\n", + " current = np.log10(\n" + ] + }, + { + "data": { + "text/html": [ + "
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    " + ], + "text/plain": [ + " time step_names state event orbital_period \\\n", + "binary_index \n", + "0 0.000000e+00 initial_cond detached ZAMS 3.385496 \n", + "0 7.454614e+00 step_HMS_HMS detached 3.727594 \n", + "0 5.069171e+06 RLO1 CC1 5.833718 \n", + "0 5.069171e+06 step_SN disrupted NaN \n", + "0 6.117635e+06 step_disrupted disrupted CC2 NaN \n", + "0 6.117635e+06 step_SN disrupted NaN \n", + "0 6.117635e+06 step_end disrupted END NaN \n", + "\n", + " eccentricity S1_state \\\n", + "binary_index \n", + "0 0.0 H-rich_Core_H_burning \n", + "0 0.0 H-rich_Core_H_burning \n", + "0 0.0 H-rich_Central_C_depletion \n", + "0 NaN BH \n", + "0 NaN BH \n", + "0 NaN BH \n", + "0 NaN BH \n", + "\n", + " S2_state S1_mass S2_mass \n", + "binary_index \n", + "0 H-rich_Core_H_burning 43.384853 38.283497 \n", + "0 H-rich_Core_H_burning 44.774416 40.296974 \n", + "0 H-rich_Core_H_burning 26.196751 57.025587 \n", + "0 H-rich_Core_H_burning 25.675004 57.025587 \n", + "0 H-rich_Central_C_depletion 25.675004 37.995687 \n", + "0 BH 25.675004 36.254365 \n", + "0 BH 25.675004 36.254365 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# do the same thing as above\n", + "pop[0].restore()\n", + "print(pop[i].star_1.natal_kick_array)\n", + "print(pop[i].star_2.natal_kick_array)\n", + "pop[i].star_1.natal_kick_array = [1000., 0.858129274334538, 1.9157148786534735, 1.8675467897282945]\n", + "pop[i].star_2.natal_kick_array = [None, None, None, None] # default\n", + "print(pop[i].star_1.natal_kick_array)\n", + "print(pop[i].star_2.natal_kick_array)\n", + "pop[i].evolve()\n", + "pop[i].to_df(extra_columns={'step_names':'string'})[col]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "posydon_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/_source/tutorials-examples/population-synthesis/lgrb_pop_syn.ipynb b/docs/_source/tutorials-examples/population-synthesis/lgrb_pop_syn.ipynb new file mode 100644 index 0000000000..37d8e0524c --- /dev/null +++ b/docs/_source/tutorials-examples/population-synthesis/lgrb_pop_syn.ipynb @@ -0,0 +1,1156 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Computing Long-Duration Gamma-Ray Bursts from Double Compact Object Populations 🌌" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you haven't done so yet, export the path POSYDON environment variables. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "env: PATH_TO_POSYDON=/Users/simone/Google Drive/github/POSYDON-public/\n", + "env: PATH_TO_POSYDON_DATA=/Volumes/T7/\n" + ] + } + ], + "source": [ + "%env PATH_TO_POSYDON=/Users/simone/Google Drive/github/POSYDON-public/\n", + "%env PATH_TO_POSYDON_DATA=/Volumes/T7/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating the Synthetic Poupulation DataFrame" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use the same BBH population as in the `Analyzing Merging BBH Populations: Rates & Observations` tutorial. To compute long-duration gamma-ray burst (LGRB) rates associated with the formation of merging BBHs, we will specity the following parameters to the `MODEL` parameters of the Synthetic Population class.\n", + "\n", + "(Note: the `population_params.ini` file needed for this tutorial was created in the **Large-Scale Population Synthesis on HPC Facilities** tutorial. Such file shuld be located in the same folder where the notebook of the current tutorial is located.)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Population successfully loaded!\n", + "Computing formation channels...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/synthetic_population.py:804: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'ZAMS_oDoubleCE1_CC1_redirect_CC2_CO_contact_END' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n", + " self.df_oneline.loc[index,'channel_debug'] = formation_channel\n", + "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/synthetic_population.py:808: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'ZAMS_oDoubleCE1_CC1_CC2_END' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n", + " self.df_oneline.loc[index,'channel'] = formation_channel\n", + "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/GRB.py:20: UserWarning: We only consider GRBs with isotropic equivalent energy larger than 1e+51 erg!\n", + " warnings.warn(\"We only consider GRBs with isotropic equivalent \"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Synthetic population successfully saved!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/synthetic_population.py:451: PerformanceWarning: \n", + "your performance may suffer as PyTables will pickle object types that it cannot\n", + "map directly to c-types [inferred_type->mixed,key->block2_values] [items->Index(['state', 'event', 'step_names', 'S1_state', 'S2_state', 'channel'], dtype='object')]\n", + "\n", + " self.df_synthetic.to_hdf(path, key='history')\n" + ] + }, + { + "data": { + "text/html": [ + "
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    .............................................
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    " + ], + "text/plain": [ + " metallicity time t_delay S1_state S2_state S1_mass \\\n", + "0 0.01420 4.027170 2913.863804 BH BH 13.120462 \n", + "1 0.01420 4.027170 1500.583774 BH BH 13.120462 \n", + "2 0.01420 4.849573 662.821800 BH BH 10.330899 \n", + "3 0.01420 4.544886 845.424610 BH BH 11.355246 \n", + "4 0.01420 4.530889 620.499837 BH BH 11.436823 \n", + "... ... ... ... ... ... ... \n", + "30751 0.00639 4.585168 128.572655 BH BH 13.380617 \n", + "30752 0.00639 5.751690 68.017543 BH BH 10.018834 \n", + "30753 0.00639 6.061904 88.863012 BH BH 20.971757 \n", + "30754 0.00639 5.290004 95.405272 BH BH 11.456069 \n", + "30755 0.00639 8.014044 12276.391282 BH BH 12.271066 \n", + "\n", + " S2_mass S1_spin S2_spin S1_E_GRB_iso S2_E_GRB_iso \\\n", + "0 12.911080 0.079649 0.064849 0.000000e+00 0.000000e+00 \n", + "1 12.911080 0.079649 0.063991 0.000000e+00 0.000000e+00 \n", + "2 9.888130 0.286911 0.249267 3.210409e+51 2.637513e+49 \n", + "3 10.251920 0.320931 0.298164 1.240025e+52 1.441788e+52 \n", + "4 10.900538 0.190982 0.167996 6.061384e+48 1.611669e+48 \n", + "... ... ... ... ... ... \n", + "30751 12.935158 0.467285 0.458164 2.823712e+53 1.243931e+53 \n", + "30752 9.020831 0.719064 0.694536 1.934640e+54 4.966308e+52 \n", + "30753 9.843552 0.068179 0.698055 0.000000e+00 7.845341e+53 \n", + "30754 11.171652 0.578478 0.595531 7.876427e+52 9.625162e+53 \n", + "30755 10.368996 0.112744 0.175554 0.000000e+00 0.000000e+00 \n", + "\n", + " orbital_period eccentricity channel \n", + "0 1.611464 0.048133 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "1 1.279098 0.123592 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "2 0.817643 0.165775 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "3 1.108194 0.379450 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "4 0.830194 0.107165 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "... ... ... ... \n", + "30751 0.507305 0.089335 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "30752 0.333537 0.155820 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "30753 0.468303 0.129531 ZAMS_CC1_oRLO2_oCE2_CC2_END \n", + "30754 0.414186 0.102238 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "30755 2.613887 0.164393 ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", + "\n", + "[30756 rows x 14 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "from posydon.config import PATH_TO_POSYDON_DATA\n", + "from posydon.popsyn.synthetic_population import SyntheticPopulation\n", + "\n", + "# cosmological model parameters for the rate calculation\n", + "MODEL = {\n", + " 'delta_t' : 100, # Myr\n", + " 'SFR' : 'IllustrisTNG',\n", + " 'sigma_SFR' : None,\n", + " 'Z_max' : 1.,\n", + " 'Zsun' : 0.0142,\n", + " # LGRB parameters\n", + " 'compute_GRB_properties' : True,\n", + " 'GRB_beaming' : 'Goldstein+15',\n", + " 'GRB_efficiency' : 0.01,\n", + " 'E_GRB_iso_min' : 1e51,\n", + "}\n", + "\n", + "pop = SyntheticPopulation('./population_params.ini', verbose=True, MODEL=MODEL)\n", + "path = os.path.join(PATH_TO_POSYDON_DATA, \"POSYDON_data/tutorials/population-synthesis/example/\")\n", + "pop.load_pop(os.path.join(path,'BBH_population.h5'))\n", + "\n", + "# generate the BBH synthetic population\n", + "pop.get_dco_at_formation(S1_state='BH', S2_state='BH', \n", + " oneline_cols=['S1_natal_kick_array_0', 'S2_natal_kick_array_0'],\n", + " formation_channels=True)\n", + "\n", + "# we can save the synthetic population\n", + "pop.save_synthetic_pop(os.path.join(path,'BBH_synthetic_population.h5'))\n", + "# pop.load_synthetic_pop(os.path.join(path,'BBH_synthetic_population.h5'))\n", + "\n", + "cols = ['metallicity','time','t_delay','S1_state','S2_state','S1_mass','S2_mass',\n", + " 'S1_spin','S2_spin', 'S1_E_GRB_iso', 'S2_E_GRB_iso',\n", + " 'orbital_period','eccentricity', 'channel']\n", + "pop.df_synthetic[cols]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compute the BBH and LGRB rates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similar to the `Analyzing Merging BBH Populations: Rates & Observations` we compute the BBH merger rate density to obtain the BBH intrinsic population." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 138/138 [00:55<00:00, 2.50it/s]\n", + "100%|██████████| 14/14 [00:26<00:00, 1.91s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DCO merger rate density in the local Universe (z=0.00): 118.48 Gpc^-3 yr^-1\n", + "Intrinsic population successfully saved!\n" + ] + }, + { + "data": { + "text/html": [ + "
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    \n", + "
    " + ], + "text/plain": [ + " metallicity time t_delay S1_state S2_state S1_mass \\\n", + "0 0.014200 4.849573 662.821800 BH BH 10.330899 \n", + "1 0.014200 4.544886 845.424610 BH BH 11.355246 \n", + "2 0.014200 4.850661 336.796977 BH BH 10.415160 \n", + "3 0.014200 4.865192 671.702835 BH BH 10.427303 \n", + "4 0.014200 5.207981 247.878920 BH BH 9.514654 \n", + "... ... ... ... ... ... ... \n", + "3137975 0.000001 9.558857 1547.539864 BH BH 12.404415 \n", + "3137976 0.000001 5.679476 517.333924 BH BH 31.263586 \n", + "3137977 0.000001 6.972682 11056.893673 BH BH 22.020038 \n", + "3137978 0.000001 6.442639 13103.712599 BH BH 21.311099 \n", + "3137979 0.000001 6.862427 1727.560393 BH BH 26.153757 \n", + "\n", + " S2_mass S1_spin S2_spin S1_E_GRB_iso S2_E_GRB_iso \\\n", + "0 9.888130 0.286911 0.249267 3.210409e+51 NaN \n", + "1 10.251920 0.320931 0.298164 1.240025e+52 NaN \n", + "2 9.751447 0.340653 0.299044 9.060711e+52 NaN \n", + "3 9.478635 0.350639 0.312924 3.486785e+52 NaN \n", + "4 9.216414 0.516591 0.498734 1.731335e+53 NaN \n", + "... ... ... ... ... ... \n", + "3137975 17.186149 0.353053 0.127905 NaN 4.585059e+52 \n", + "3137976 34.303592 0.127590 0.167106 NaN 6.742460e+52 \n", + "3137977 17.356224 0.156907 0.139634 NaN 2.457570e+52 \n", + "3137978 36.473143 0.104700 0.119426 NaN 9.105078e+53 \n", + "3137979 24.910536 0.116013 0.172715 NaN 1.025357e+53 \n", + "\n", + " orbital_period eccentricity channel \n", + "0 0.817643 0.165775 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "1 1.108194 0.379450 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "2 0.612968 0.064357 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "3 0.825793 0.195932 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "4 0.521422 0.058677 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "... ... ... ... \n", + "3137975 1.368867 0.072070 ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", + "3137976 1.495336 0.003466 ZAMS_oRLO1-contact_CC1_oRLO2_CC2_END \n", + "3137977 3.686468 0.234744 ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", + "3137978 4.546062 0.059527 ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", + "3137979 2.019769 0.054964 ZAMS_oRLO1-contact_CC1_oRLO2_CC2_END \n", + "\n", + "[3137980 rows x 14 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop.get_grb_rate_density() # if already computed use load_data=True\n", + "pop.save_intrinsic_pop(os.path.join(path,'LGRB_intrinsic_population.h5'), pop='GRB')\n", + "#pop.load_intrinsic_pop(os.path.join(path,'LGRB_intrinsic_population.h5'), pop='GRB')\n", + "pop.df_grb_intrinsic[cols]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's visualise the BBH and LGRB rate densities as a function of redshift and compare it with GWTC-3 annd Perley+16 observational constrains.\n", + "- `channels` allows you to display the contribution of each formation channel to the total BBH and LGRB rate density.\n", + "- `grb_components` allows you to display the LGRB contribution from the primary star or the secondary star." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "kwargs = dict(ylim=(0.05,800), zmax=10., show=True, path=None, channels=True,\n", + " GWTC3=True, Perley16=True, grb_components=False)\n", + "pop.plot_rate_density(DCO=True, GRB=True, **kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize the LGRB Population Property Distributions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similar to the DCO case, we can use the `plot_hist_properties` method to display the intrinsic and observable (beamed) distribution of the LGRB population." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "kwargs = dict(bins=100, xlog=False)\n", + "pop.plot_hist_properties(['S1_spin','S2_spin'], intrinsic=True, observable=True, pop='GRB', **kwargs)\n", + "kwargs = dict(bins=100, xlog=True)\n", + "pop.plot_hist_properties(['S1_E_GRB_iso','S2_E_GRB_iso'], intrinsic=True, observable=True, pop='GRB', **kwargs)\n", + "pop.plot_hist_properties('S2_f_beaming', intrinsic=True, observable=True, pop='GRB', **kwargs)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "posydon_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/_source/tutorials-examples/population-synthesis/one_met_pop_syn.ipynb b/docs/_source/tutorials-examples/population-synthesis/one_met_pop_syn.ipynb new file mode 100644 index 0000000000..409c1b75a9 --- /dev/null +++ b/docs/_source/tutorials-examples/population-synthesis/one_met_pop_syn.ipynb @@ -0,0 +1,1124 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Explore the assumption of star-formation history and DCO compute rates at one single metallicity 📖" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you haven't done so yet, export the path POSYDON environment variables. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "env: PATH_TO_POSYDON=/Users/simone/Google Drive/github/POSYDON-public/\n", + "env: PATH_TO_POSYDON_DATA=/Volumes/T7/\n" + ] + } + ], + "source": [ + "%env PATH_TO_POSYDON=/Users/simone/Google Drive/github/POSYDON-public/\n", + "%env PATH_TO_POSYDON_DATA=/Volumes/T7/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating the Synthetic Poupulation DataFrame" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use the same BBH population as in the `Analyzing Merging BBH Populations: Rates & Observations` tutorial. Instead of computing the distributions and rates at all metallicities, we will just focus on one metallicity, $Z_\\odot$, and integrate the star formation history around solar metallicity, say $[0.5Z_\\odot,2Z_\\odot]$. Let's extract the merging BBH population from the $Z_\\odot$ population synthesis model. In this sample there are only ~200 systems, to increase the statistics we suggest you run a population containing x50 more binaries." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Binary count with (S1_state, S2_state, binary_state, binary_event) equal\n", + "to (BH, BH, contact, None)\n", + "in /Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/1.00e+00_Zsun_population.h5 are 233\n", + "Total binaries found are 233\n", + "Population successfully saved!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/synthetic_population.py:300: PerformanceWarning: \n", + "your performance may suffer as PyTables will pickle object types that it cannot\n", + "map directly to c-types [inferred_type->mixed,key->block1_values] [items->Index(['state', 'event', 'step_names', 'S1_state', 'S2_state'], dtype='object')]\n", + "\n", + " self.df.to_hdf(path, key='history')\n", + "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/synthetic_population.py:301: PerformanceWarning: \n", + "your performance may suffer as PyTables will pickle object types that it cannot\n", + "map directly to c-types [inferred_type->mixed,key->block2_values] [items->Index(['state_i', 'event_i', 'step_names_i', 'state_f', 'event_f',\n", + " 'step_names_f', 'S1_state_i', 'S1_state_f', 'S1_SN_type', 'S2_state_i',\n", + " 'S2_state_f', 'S2_SN_type', 'interp_class_HMS_HMS',\n", + " 'interp_class_CO_HMS_RLO', 'interp_class_CO_HeMS',\n", + " 'interp_class_CO_HeMS_RLO', 'mt_history_HMS_HMS',\n", + " 'mt_history_CO_HMS_RLO', 'mt_history_CO_HeMS',\n", + " 'mt_history_CO_HeMS_RLO'],\n", + " dtype='object')]\n", + "\n", + " self.df_oneline.to_hdf(path, key='oneline')\n" + ] + } + ], + "source": [ + "import os\n", + "from posydon.popsyn.synthetic_population import SyntheticPopulation\n", + "from posydon.config import PATH_TO_POSYDON_DATA\n", + "\n", + "path = os.path.join(PATH_TO_POSYDON_DATA, \"POSYDON_data/tutorials/population-synthesis/example/\")\n", + "files = [f for f in os.listdir(path) if '1.00e+00_Zsun_population' in f]\n", + "path_to_data = [os.path.join(path, file) for file in files] \n", + "\n", + "pop = SyntheticPopulation(path_to_ini='./population_params.ini', path_to_data=path_to_data, verbose=True)\n", + "\n", + "pop.parse(S1_state='BH', S2_state='BH', binary_state='contact', invert_S1S2=False)\n", + "pop.save_pop(os.path.join(path,'BBH_population_1e+00_Zsun.h5'))\n", + "\n", + "pop.df.head(10)\n", + "del pop" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "POSYDON supports different assumptions for the star formation history, e.g. instead of using the IllustrisTNG SFH, we can use the star formation rate from Madau & Fragos (2018) (see Andrews et al. in prep.) and assume a log-normal metallicity distribution around the empirically measured mean metallicity at each redshift from Madau & Fragos (2018), taking a dispersion of 0.5 dex (see Bavera et al. 2020). The IllustrisTNG star formation rate and metallicity evolution (solid line) is compared to the Madau & Fragos (2018) metallicity evolution (dashed line) in Fig. X of (see Andrews et al. in prep.), which figure is reproduced below. The dotted line corresponds to the metallicity distribution of Neijssel et al. (2019).\n", + "\n", + "![Star Fromation Rate](./pictures/SFR.png \"Star Formation Rate\")\n", + "\n", + "![Metallicity Distribution](./pictures/met_dist.png \"Metallicity Distribution\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Population successfully loaded!\n", + "Computing formation channels...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/synthetic_population.py:804: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'ZAMS_oDoubleCE1_CC1_redirect_CC2_CO_contact_END' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n", + " self.df_oneline.loc[index,'channel_debug'] = formation_channel\n", + "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/synthetic_population.py:808: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'ZAMS_oDoubleCE1_CC1_CC2_END' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n", + " self.df_oneline.loc[index,'channel'] = formation_channel\n" + ] + }, + { + "data": { + "text/html": [ + "
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    " + ], + "text/plain": [ + " metallicity time t_delay S1_state S2_state S1_mass \\\n", + "0 0.0142 4.027170 2913.863804 BH BH 13.120462 \n", + "1 0.0142 4.027170 1500.583774 BH BH 13.120462 \n", + "2 0.0142 4.849573 662.821800 BH BH 10.330899 \n", + "3 0.0142 4.544886 845.424610 BH BH 11.355246 \n", + "4 0.0142 4.530889 620.499837 BH BH 11.436823 \n", + ".. ... ... ... ... ... ... \n", + "228 0.0142 7.928745 426.765027 BH BH 17.564588 \n", + "229 0.0142 4.530889 787.032676 BH BH 11.436823 \n", + "230 0.0142 4.848528 943.501358 BH BH 10.331316 \n", + "231 0.0142 4.256912 1266.769326 BH BH 12.239574 \n", + "232 0.0142 4.537286 658.997955 BH BH 11.338870 \n", + "\n", + " S2_mass S1_spin S2_spin orbital_period eccentricity \\\n", + "0 12.911080 0.079649 0.064849 1.611464 0.048133 \n", + "1 12.911080 0.079649 0.063991 1.279098 0.123592 \n", + "2 9.888130 0.286911 0.249267 0.817643 0.165775 \n", + "3 10.251920 0.320931 0.298164 1.108194 0.379450 \n", + "4 10.900538 0.190982 0.167996 0.830194 0.107165 \n", + ".. ... ... ... ... ... \n", + "228 8.155695 0.040933 0.015301 17.694336 0.957889 \n", + "229 10.900538 0.190982 0.168671 0.894027 0.022964 \n", + "230 9.972710 0.312185 0.277781 0.911809 0.093335 \n", + "231 11.551092 0.179405 0.165071 1.159215 0.175475 \n", + "232 10.753148 0.269757 0.242801 0.851837 0.137036 \n", + "\n", + " channel \n", + "0 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "1 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "2 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "3 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "4 ZAMS_oDoubleCE1_CC1_CC2_END \n", + ".. ... \n", + "228 ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", + "229 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "230 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "231 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "232 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "\n", + "[233 rows x 12 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "import numpy as np\n", + "from posydon.config import PATH_TO_POSYDON_DATA\n", + "from posydon.popsyn.synthetic_population import SyntheticPopulation\n", + "\n", + "# cosmological model parameters for the rate calculation\n", + "MODEL = {\n", + " 'delta_t' : 100, # Myr\n", + " 'SFR' : 'Madau+Fragos17',\n", + " 'sigma_SFR' : 'Bavera+20',\n", + " 'Z_max' : 1.,\n", + " 'Zsun' : 0.0142,\n", + " 'select_one_met' : True,\n", + " 'dlogZ' : [np.log10(0.0142/2),np.log10(0.0142*2)],\n", + "}\n", + "\n", + "pop = SyntheticPopulation('./population_params.ini', verbose=True, MODEL=MODEL)\n", + "path = os.path.join(PATH_TO_POSYDON_DATA, \"POSYDON_data/tutorials/population-synthesis/example/\")\n", + "pop.load_pop(os.path.join(path,'BBH_population_1e+00_Zsun.h5'))\n", + "\n", + "# generate the BBH synthetic population\n", + "pop.get_dco_at_formation(S1_state='BH', S2_state='BH', \n", + " oneline_cols=['S1_natal_kick_array_0', 'S2_natal_kick_array_0'],\n", + " formation_channels=True)\n", + "\n", + "# we can save the synthetic population\n", + "# pop.save_synthetic_pop(os.path.join(path,'BBH_synthetic_population.h5'))\n", + "# pop.load_synthetic_pop(os.path.join(path,'BBH_synthetic_population.h5'))\n", + "\n", + "cols = ['metallicity','time','t_delay','S1_state','S2_state','S1_mass','S2_mass',\n", + " 'S1_spin','S2_spin', 'orbital_period','eccentricity', 'channel']\n", + "pop.df_synthetic[cols]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compute the BBH Merger Rates for $Z\\in [0.5Z_\\odot,2Z_\\odot]$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similar to the `Analyzing Merging BBH Populations: Rates & Observations` we compute the BBH merger rate density to obtain the BBH intrinsic population." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 138/138 [00:00<00:00, 162.70it/s]\n", + "100%|██████████| 7/7 [00:09<00:00, 1.30s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DCO merger rate density in the local Universe (z=0.00): 10.74 Gpc^-3 yr^-1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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0.980550 \n", + "4 12.500642 0.000531 4.772162e-04 696.487802 0.998932 \n", + "... ... ... ... ... ... \n", + "26870 8.155695 0.040933 1.530132e-02 17.694336 0.957889 \n", + "26871 10.900538 0.190982 1.686708e-01 0.894027 0.022964 \n", + "26872 9.972710 0.312185 2.777810e-01 0.911809 0.093335 \n", + "26873 11.551092 0.179405 1.650709e-01 1.159215 0.175475 \n", + "26874 10.753148 0.269757 2.428005e-01 0.851837 0.137036 \n", + "\n", + " channel \n", + "0 ZAMS_CC1_oRLO2_CC2_END \n", + "1 ZAMS_CC1_CC2_END \n", + "2 ZAMS_oRLO1-reverse_CC1_CC2_END \n", + "3 ZAMS_oRLO1-contact_CC1_CC2_END \n", + "4 ZAMS_CC1_oRLO2_CC2_END \n", + "... ... \n", + "26870 ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", + "26871 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "26872 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "26873 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "26874 ZAMS_oDoubleCE1_CC1_CC2_END \n", + "\n", + "[26875 rows x 12 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop.get_dco_merger_rate_density()\n", + "# pop.save_intrinsic_pop(os.path.join(path,'BBH_intrinsic_population.h5'))\n", + "# pop.load_intrinsic_pop(os.path.join(path,'BBH_intrinsic_population.h5'))\n", + "pop.df_dco_intrinsic[cols]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pop.plot_rate_density(DCO=True, channels=True, **{'ylim' : [0.01, 100]})" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 138/138 [00:13<00:00, 10.45it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DCO detection rate at design_H1L1V1 sensitivity: 590.18 yr^-1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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    \n", + "

    19541 rows × 12 columns

    \n", + "
    " + ], + "text/plain": [ + " metallicity time t_delay S1_state S2_state S1_mass \\\n", + "0 0.0142 6.205715 4.287125e-08 BH BH 12.260188 \n", + "1 0.0142 4.705799 9.754266e-01 BH BH 12.850735 \n", + "2 0.0142 8.187239 2.075700e+00 BH BH 8.314851 \n", + "3 0.0142 6.647572 2.472628e+01 BH BH 9.826765 \n", + "4 0.0142 6.676527 1.680554e+01 BH BH 20.974131 \n", + "... ... ... ... ... ... ... \n", + "19536 0.0142 7.214096 1.052488e+04 BH BH 12.340761 \n", + "19537 0.0142 3.443633 6.236191e+03 BH BH 12.585380 \n", + "19538 0.0142 4.900714 4.178555e+03 BH BH 17.470438 \n", + "19539 0.0142 7.154675 8.766378e+03 BH BH 10.272165 \n", + "19540 0.0142 3.781907 7.804838e+03 BH BH 10.729561 \n", + "\n", + " S2_mass S1_spin S2_spin orbital_period eccentricity \\\n", + "0 12.183039 0.050168 8.244612e-04 326.580311 0.999994 \n", + "1 20.143346 0.027546 5.791334e-17 2377.230212 0.999817 \n", + "2 8.315119 0.100591 6.554160e-03 38.651550 0.996045 \n", + "3 9.853595 0.032525 2.400078e-02 14.240625 0.980550 \n", + "4 12.500642 0.000531 4.772162e-04 696.487802 0.998932 \n", + "... ... ... ... ... ... \n", + "19536 8.201690 0.185531 1.411149e-01 2.783007 0.396922 \n", + "19537 12.508058 0.090103 2.004811e-03 250.806290 0.988294 \n", + "19538 16.106886 0.202329 4.508136e-04 1580.389855 0.997143 \n", + "19539 9.248458 0.090886 1.197166e-01 3.129920 0.527011 \n", + "19540 11.898371 0.179142 6.654702e-02 81.451235 0.971019 \n", + "\n", + " channel \n", + "0 ZAMS_CC1_oRLO2_CC2_END \n", + "1 ZAMS_CC1_CC2_END \n", + "2 ZAMS_oRLO1-reverse_CC1_CC2_END \n", + "3 ZAMS_oRLO1-contact_CC1_CC2_END \n", + "4 ZAMS_CC1_oRLO2_CC2_END \n", + "... ... \n", + "19536 ZAMS_CC1_oRLO2_CC2_END \n", + "19537 ZAMS_CC1_CC2_END \n", + "19538 ZAMS_oRLO1-reverse_CC1_CC2_END \n", + "19539 ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", + "19540 ZAMS_CC1_CC2_END \n", + "\n", + "[19541 rows x 12 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop.get_dco_detection_rate(sensitivity='design_H1L1V1') # if already computed use load_data=True\n", + "# pop.save_observable_pop(os.path.join(path,'BBH_observable_population.h5'))\n", + "# pop.load_observable_pop(os.path.join(path,'BBH_observable_population.h5'))\n", + "pop.df_dco_observable[cols]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visaulize the BBH Population Properties" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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TNGWaZst9dJjvpVOfRenz+ZMridgbb7yhSCSia9eu6cc//rHj3Vz2st9x7ITbx60TB1nXyspK9bnbvxVOHXs3zmlObLtTn4VuHpNes9/3thd/M/zKL+fpTnTr+Pfid3LA6wAAOMvJi8dum5qa0tTUlHK5nBYXF1UoFLS8vFx3NzSbzSoSibQ1wFI7/NJHKplMVgdZSqfTmpmZqf6tWCxW90k7vNiP/er06dP73o0/6L6u/XsikXC8P/ZhzgNOfRaTyWTdIGGpVMqzC6VWx7Fdbh+3g/BLbVEtp4+9H89pfvwsdKqSfPZiAgNn+Ok8DWdQcw/0gEKh4LuaVjdFo1GlUiktLi5W+4PVjkRbqa05KD/2HzNNs9pfr5JAVaRSqT1HJd+P2/vRSbXJ6GFuuDh9UVIby37N8zrd134coKjCic9iJBKpbu/CwkJ14CavtHscW/HTcTvIIE7dOPe5eez9dE5z6rPg5e/RQQdl7Jba/bH7e+vX34z9YvYbr87TTlzT+vX4+wHJPdAD1tbWXJ+qxku1NYXNVEZyzefzjow83Wzkcj+obepcu303b95sqxl0t/ejk2KxWPV5J8dj97JOD0ZVu/7adR92X9f2zfTjd/swn8XaPtCJRKLlKOLNOP2d3Os4dspPx63yuepkX7k9WJvTx97P5zSnPgtezlpSSYr80j2t1u59uns/+fE3o1XMB+XGNYqX52knrmn9ePz9guQegOfW1tb06quvtlzONM3qhdthfhh2T4PUyaA3btYKRaPRalzZbLY61VKlj2Yr3d6PTqq9sFheXm77dbXLtruf2lW7b3av+7D7unYKnk4HA+vGgIgH/Szunh6s1VRDtf2Na83Ozjr2XdvvOHbKT8etsh2d1FqFw+G67W83pnbew41j7+dzmlOfBTePSSuVRMePyX3t1IfNvrd+/M1oFXNFpzNcOM2P5+lO+fH4+wXJPeADtc2Lmt2BLBaLfXkCqlUoFNq6+1qZluSw05PcuHGj+rzdmp7KoDNuqh24Zn5+Xul0uqPBbJzcj9lstmsj65umWW3ubVlW2zdcaqe+avc4tntRXHuh1mzdh9nXpmnWXZy0msO7wrIsRSKRrrQ2OchncfeFaKsR9Tu5KNvNqePYCT8dt8p0h51+R2tHn28ncZbaqxlz69h3+7ehXU5+Ftw6Jq1UjkEkEjn0utrV7ve2doT2Zt9bP/5mtIq5otU1Xy03foPdPk9345q2m8e/15Dc40jyuq/kbrU/rM1Oovl8vu15q/22bZ24evVqy2Uq++ew881PTU1Vm2Plcrm2fhhmZ2frLsLcUNufeXZ2VsVisePmcofdj5ZlaWxsrDoNzn7NYp2USqWqP/jtdEMoFovVWoPa49lKuwMTVi7UEonEnus+zL6+ceNGtQZndna2rYv1q1evKpFItKz5ceI8cJDP4u649tsmy7LqLjJrY15bW2vZD9jJ49gJN49bJ2q3pZMEoPa7Mjc311bNYO33ca/PlpvHvpu/DZ1w6rPg1jHZj2VZ1Xi72TS5nd/Q2n253/e2W78ZTsYs1e/vViPqz87OOt4v3O3ztJPXtPvp1vHvOTZwBKVSKVtS9ZFOp9t63czMTPU1qVSq5fK177Gf9fX16nLhcLjh76FQyF5fX9/z9VNTU9XX5/P5lnHtVrs/FhYWWi7v9H6ojb/VsQiHw7Zpmnv+vdNtCYfDtiQ7FArtu+9mZmb2fV8n1e6PmZmZA73uMPtxYWGh7piFQqGO4j+MlZUV2zRNW5KdSCTaWm5qaqrlehOJRHVbTNNs+T2pLL/Xup3a1ysrK3YoFLIl2aZp7vs9b/UZPOx5oNU62/0sRqPRtvZNNBqt+6zVrj8cDtsrKysNr3H6ONaqPXe0OqZOHrfDqMTQ7m9YrdpzX7N9XRGNRqvLVrZ3v2WdPPZO/ja49fvt5GfBjWOyl8r+78b5vfI9rFzj7Hcuqf0etvO9dfs3w42YbfvzY93smq9iZmbGTqVSdcd6cXFx3/W2ex5z8zx92GvaTq7j3Dj+bp0rusUfUQAuy+fz9uLior2wsGCnUqnqD3Ft8pJKpeyFhQV7cXGx7oKx8tp0Ol33ulAoZKfTaXtxcbHu5FZ5n9ofhsrJpLL+ZmpPnolEwl5fX7dXVlbsaDTa9EelEtfuGxXhcLga134/AisrK023yzTN6utrT75u7ofKBVzlwmdqaqruGKyvr9uLi4vVi7fdF/SdbstutSfyRCJRXX59fd1eWFiww+GwHQ6H912Hk/L5fDWeTt7zsPuxYnFxseEz1U3r6+vVbakcw5WVFXt9fd3O5/P2zMyMHQqFqt/bdlQ+hwsLC9X1N/t+VP5W+Vzvxal9vXt7K9tU+S6trKzY6XTaNk3TjkajDZ+Hw54HWjnsZ7HZhWPlvFY5drXfv3Q6bc/MzNjRaLTpep0+ju2cO/ZKsg5z3JxSef/9Lmr3U3uzZPdF9MLCgm2app1KpRrOCZXPWLOLZCeP/WG/Z936/Xbys+DGMWmmsu/3+q45qXZ/Li4u2lNTU3Y0Gq07XisrK3Xb3skNKzd/M9yKufam0NTUVMP11tTUVPV7XZvcV7axNrE+6HnMrfO0bXd+TXuY6zgnjn+3zhXdQHKPI6Fyt67y5d7vsTuZqT35NVu29i7gysrKnsvWLr/XxWLlh6I2jr1ORJVt2i+u/d6rne2q/RFzcz/MzMzUnexr71RXXhONRvf84ex0W/ba9zMzM3Y4HK6rhamcqLstGo12fMF+2P24e12VfbBfDZKbmh2T2m3oJGFKJBINFyKVpDidTtupVMqemZmxp6am2t4/Tu3rVtu7VwJr24c/D7TjIJ9F27arF1WVxKtS2xiNRhu2p/bia7/3cvo4tnPuaNVi4SDHzSmVBO8wLQN2x19pFZFIJKqfm8pNnkpSEY1G7ampqX0vlJ049t34bXDq97vZvjzoZ8GNY7JbZT924/etNgGq/BZXEmbTNA98ztzN6d+MbsRc+UzXftYqteUVlb9XjnUl8a9sz2HOY26cpys6uaZ16zqu3ePf7XOFmwzbtm0BAACg54yMjMiyLK2srPT9wKtwhmVZGhkZUSgU0vr6uuvvl0wmq32d8/l8V8dFOKhejBmQGFAPAACgZ1VmMGh3tHbg5s2bkuoHzQTQH0juAQAAelQlQaudhgvYT2X0906mWQXQG0juAQAAelQoFFIqlZJlWdVmxMBestmsisWiUqmUQg5PsQbAeyT3AAAAPWxmZkamabY1HzeOtuvXr8s0Tc3MzHjy/rXzpPeKXowZRxfJPQAAQI9bWFhQsVjU7Oys16HAp+bm5lQoFJROp7v6vsVisfrcsqyuvvdB9WLMgCQNeB0AAAAADiccDiudTiuZTOqb3/wmo3ujTuXGTyqVUjQadf39CoWC1tbWVCgUlMvlquWzs7NaW1uTaZrVh1/0YszAbkyFBwAA0CeSyaRu3rypO3fu0KcakrZrniORiMLhsBYWFrrynmNjY9Xa792fw0pNeCqV8qx7QDO9GDOwG8k9AABAH0kmk1peXlY+n/c6FPhALBZTKBTqWmIPwDsk9wAAAH2m0veeQfaOtrm5Od2/f5/PAXBEkNwDAAAAANDjGC0fAAAAAIAeR3IPAAAAAECPI7kHAAAAAKDHkdwDAAAAANDjSO4BAAAAAOhxJPcAAAAAAPQ4knsAAAAAAHocyT0AAAAAAD2O5B4AAAAAgB5Hcg8AAAAAQI8b8DoAwCuWZWlpaan6//Pnz+uJJ57wMCIAAAAAfvfZZ5/p3Xffrf5/cnJSoVDIu4B2kNzjyFpaWtLXv/51r8MAAAAA0MNee+01/Yf/8B+8DoNm+QAAAAAA9DqSewAAAAAAehzN8nFknT9/vu7/r732mi5evOhRNNLt27frugl4HY9bpqentby8rImJCWUyGa/Dcc1R2M6jsI1H5XspHY3jeRS2Uer/7eR72X+OwnYehW08Kt/NyrGstTuv8ArJPY6s3YPnXbx4UePj4x5F08hv8ThleHi4+m8/bl/FUdjOo7CNu/Xr91I6GsfzKGyjdHS2s4LvZe87Ctt5FLZxt379blaOZS2/DMpNs3wAAAAAAHocyT0AAAAAAD2O5B4AAAAAgB5Hcg8AAAAAQI9jQD0AXTU9Pa3Lly9rdHTU61BcdRS28yhs41FyFI7nUdhG6ehs51FwVI7lUdjOo7CNR8X09LR+8zd/U//9v/93r0NpYNi2bXsdBOCFW7du6dKlS9X///M//7OnI3r6LR4AfC8BP+J7CfjTUfpu+nVbaZYPAAAAAECPI7kHAAAAAKDHkdwDAAAAANDjSO4BAAAAAOhxJPcAAAAAAPQ4psIDfOLpp5/Wd7/73br/A/AW30vAf/heAv7Ed9N7TIWHI8uvU1gAAAAA8C+/5hHU3AM7pqenNTw83LR8enq6+wEBAAAA8Ewmk1Emk2ko39jY6H4wbSC5B3YsLy83Lb98+XJ3AwEAAADgudXVVS0tLXkdRttI7oEdExMTTWvuR0dHux8MAAAAAE+Njo5qcnKyoXxjY2PPikEv0eceR5Zf+8oAAAAA8C+/5hFMhQcAAAAAQI8juUfHisWiq8sDAAAAADpDct8l8/PzikQiGhkZqT7i8bhyuZwn8czNzckwjAMl3vF4XIZhKBaLaX5+XoVCQZZlVf9eLBaVzWaVTCar29kJv+0rAAAAAPA7knuXFQoFjYyMKJVKKZlMan19Xevr68rn8zJNU7FYTPF4vC45dlOxWFQsFtPs7Oyh15XL5ZRMJquJuGEYMgxDY2Njisfjmp+flyQtLCy0tT6/7SsAAAAA6BWMlu+iXC6nWCwm0zSVz+cVCoWqfzNNU6lUSmNjY0omkyoUCg3LHJZlWVpeXlaxWNTKyopyuZwKhYJj628lGo1qYWGhrW3yel/Bv2zb1sPNUsvlAoahY4PBLkQEAAAA+A/JvUssy6o2R98vwU0kEsrn85qfn9eVK1eUz+cdi2F5eVmxWEyhUEgTExPVZDsSibha+x0Oh5VKpRSNRtta3g/7Cv71cLOk9FLr7iNnTgzpWy+Puh8QAAAA4EMk9y65evWqLMtSOBxWOBzed9nZ2dlq3/VsNqupqSlHYohGo3JrpsNUKqWpqSkVCgWtra1Jkk6fPq1oNNpxjbof9hUAAAAA9DKSexdYlqVsNitJbdVem6Yp0zRVLBZ1/fr1nklYK3EfxlHZV+i+x1tl5d9ZryuLXBjR0ABDjQAAAKD/cJXrgspAcpL00ksvtfWaSpJcKBSO1NRx7Cu4Zatc1s+K9+seW+Wy12EBAAAArqDm3gWvvvpq9Xm7TdRra8BzuZwSiYTTYfkS+woH8a2XL+j4UP3geQHD8CgaAAAAwHsk9y6oHZH+9OnTbb2mNrE9SgPFsa9wEMeHgnpyiNMXAAAAUEGzfIftbibebp/0M2fOVJ8vLy87GpNfsa8AAAAAwBkk9w476BRztbXRbk5T57RcLqd4PK6RkZHqIxKJ1PWl38tR21cAAAAA4BbatfpQZWo5P7t//75isZik7enpbty4oVAoJMuydP36dSWTSc3OzmphYaHt+e4Pwsl9dfv27Y5f8/TTT+vs2bOOxQAAAACge95//3198MEHHb3mIHlDN5DcO2x3stnpnO9Sb9RGz83NKZ1ONwxmFwqFlEqlNDY2pmQyqVgspsXFxaYJvt/21de//vWOX/Pd735X3/ve9xyLAY2CAUMvnj/VUAYAAAAc1o9+9CN9//vf9zoMR9As32G9kJgflmmaWlhY2HeU+kQiUe1DH4/Hmy5zFPYVDu+JgaC+9uVzdY8nBoKtXwgAAAAcIST36NjCwoKmpqZaLldZxrIszc7Ouh0WAAAAABxZNMt32EGalverWCymubk5SdvN+K9du1a3f/y2r1577TVdvHixo9c8/fTTLkUDAAAAwG3f/va392xpvJfbt28fqEuv20juHbZ7rnbLsjpOYv2W9B7U7qntcrlcXY2/3/bVxYsXNT4+7tj6AAAAAPjb2bNn+2aAbJrlO8yJZHN30turdm/HG2+8Ufd/9hUAAAAAOIPk3mG7k812p2qrHVzOrzX3hUJBY2NjGhkZaWse+912D6DXz/sKAAAAALqJZvkO251stjsi/MrKSvX57ubsfnH16lUVi0VJUjKZ1CuvvNJRcr172X7eV3DOo82S/u7N9+rK/uDFZ3RskBHzAQAAgApq7l0QDoerz9utja5d7qWXXnI8JidUEvt27d72sbGxhmX6dV/BOWXb1t31h3WPsm23fJ0hQ8eHgnUPQ0YXIgYAAAC6j5p7F0SjURUKBUntJ8S1y7UzzZwXTNOsblcqlWpZa79721955ZWGZfp1X8F7x4eC+uPJxhtKAAAAQD+i5t4F165dqz7P5/NtvaaS4Jqm6dum5tFoVFNTU7JtWzMzMy2XX1xcrHtts5sB/bqvAAAAAKCbSO5dEAqFFI1GJUk3b95suXwul6s+n52dbbl8NputJrjdlEwmlc1m2+4bn81mq89TqVTTZdzeVwAAAABwFJDcuySdTisUCsmyrLokd69lpe3+54lEYs/lLMvS2NiY4vG4IpGI5ubmDh1nu4m6tF1TPjMzoytXrrRcdn5+vtp8fmZmpq5v/W5u7CsAAAAAOEpI7l1imqZu3LghSYrH43v2J5+fn1c2m1UoFNLCwsK+68zlcnXruX79+oFiq03o2x3ErqJSAx+Lxfa8MZDNZpVMJiVJiURiz1r7Cjf2FQAAAAAcJST3LpqamtLi4qJCoZAikUjd3PCWZSmZTCqZTCoajerOnTst+4/v7rPeTn/zXC5XfczPzysSidT9PZlMam5uTtlstrpcq9r8fD4v0zQ1MjKi2dnZajeBXC6neDyueDyuUCikdDpdrWlvxel9BQAAAABHiWHbbcwphUOxLEs3b95UOp2uq5WORqPVhLVds7Ozmpubk2maWlxcbJnkGsb21F/tzkdvWZYWFhbaGoU+l8spnU5XbwiEQiGZpqlvfvObSiQSbb/n7vd3al+1cuvWLV26dKn6/3/+53/W+Pi4Y+uHMz59vKX0Un1rjuSkqSeH9p/sY7NU1q33HtSVjT9zUoNB7mkCAADg4PyaR5Dc48jy65cS9Q6a3B/0dQAAAMB+/JpHUIUFAAAAAECPI7kHAAAAAKDH0T4V2DE9Pa3h4eGm5dPT090PCAAAAIBnMpmMMplMQ/nGxkb3g2kDyT2wY3l5uWn55cuXuxsIAAAAAM+trq5qaWnJ6zDaRnIP7JiYmGhacz86Otr9YAAAAAB4anR0VJOTkw3lGxsbe1YMeonkHtiRyWR8Mcol6gUMQ186d6KhDAAAAHDTXt1zd4+W7xck9wB87dhgUL//wjNehwEAAAD4GqPlAwAAAADQ40juAQAAAADocST3AAAAAAD0OJJ7AAAAAAB6HMk9AAAAAAA9jtHyAfjao82Scv96r64s+hvndGww6FFEAAAAgP+Q3APwtbJt61f3Pqkr+9qXz3oUDQAAAOBPJPcA+tKTQwP6Tux5r8MAAAAAuoI+9wAAAAAA9DiSewAAAAAAehzJPQAAAAAAPY7kHgAAAACAHkdyDwAAAABAj2O0fGDH9PS0hoeHm5ZPT093PyAcylaprOKHG3Vl5lPDGghyTxMAAACtZTIZZTKZhvKNjY3GhX2A5B7Ysby83LT88uXL3Q0EjnhcKusf3vp1XVly0iS5BwAAQFtWV1e1tLTkdRhtI7kHdkxMTDStuR8dHe1+MAAAAAA8NTo6qsnJyYbyjY2NPSsGvURyD+zIZDIaHx/3OgwAAAAAPrBX99xbt27p0qVL3Q+oBdqnAgAAAADQ40juAQAAAADocST3AAAAAAD0OPrcA/C1gGHo2ZHjDWUAAAAAPkdyD8DXjg0GFZ8473UYAAAAgK/RLB8AAAAAgB5Hcg8AAAAAQI8juQcAAAAAoMeR3AMAAAAA0ONI7gEAAAAA6HGMlg/A1z7bKumntz+sK/vti0/piYGgRxEBAAAA/kNyD8DXSmVbb777UV3ZV80zHkUDAAAA+BPJPYC+dHwwqOSk2VAGAAAA9COSewB9yTAMPTnEKQ4AAABHA1e+wI7p6WkNDw83LZ+enu5+QAAAAAA8k8lklMlkGso3Nja6H0wbSO6BHcvLy03LL1++3N1AAAAAAHhudXVVS0tLXofRNpJ7YMfExETTmvvR0dHuBwMAAADAU6Ojo5qcnGwo39jY2LNi0Esk98COTCaj8fFxr8MAAAAA4AN7dc+9deuWLl261P2AWiC5B9CXSmVb71kP68qeCR1XMGB4FBEAAADgHpJ7AH3ps62Ssvm7dWXJSZMR9AEAANCXAl4HAAAAAAAADofkHgAAAACAHkdyDwAAAABAjyO5BwAAAACgx5HcAwAAAADQ4xg2GoCvBQxDZ04MNZQBAAAA+BzJPQBfOzYY1LdeHvU6DAAAAMDXaJYPAAAAAECPI7kHAAAAAKDHkdwDAAAAANDjSO4BAAAAAOhxJPcAAAAAAPQ4RssH4GuPt8rKv7NeVxa5MKKhAe5NAgAAABUk98CO6elpDQ8PNy2fnp7ufkCQJG2Vy/pZ8X5d2YvnT2mIhkcAAABwUSaTUSaTaSjf2NjofjBtILkHdiwvLzctv3z5cncDgSOODQT1Ry9faCgDAAAA2rG6uqqlpSWvw2gbyT2wY2JiomnN/ejoaPeDwaEFAoaeOvGE12EAAACgR42OjmpycrKhfGNjY8+KQS+R3AM7MpmMxsfHvQ4DAAAAgA/s1T331q1bunTpUvcDaoFOqwAAAAAA9DiSe/hCsVh0dXkAAAAA6Gck910yPz+vSCSikZGR6iMejyuXy3kSz9zcnAzDOFSSXCgUlEwmNTY2JsMwZBiGxsbGlEwmO96ueDwuwzAUi8U0Pz+vQqEgy7Kqfy8Wi8pms0omk9V9BwAAAADYRnLvskKhoJGREaVSKSWTSa2vr2t9fV35fF6maSoWiykej9clsm4qFouKxWKanZ091HpmZ2cViUS0tram2dlZLSwsKJVKSdq+kRGLxRSJRDq+eZDL5ZRMJqs3QmpvGsTjcc3Pz0uSFhYWDhU/+l+5bOvDTz6re5TLttdhAQAAAK5gQD0X5XI5xWIxmaapfD6vUChU/ZtpmkqlUtWa7kKh0LDMYVmWpeXlZRWLRa2srCiXy6lQKBx6vZFIRJZlaWVlRaZp1v1tZmZGyWSyWvs+NjamxcVFRaPRQ7+vJEWjUS0sLDi6n9CfHm2V9Nevv1NXlpw09eQQpz0AAAD0H2ruXWJZVrXp+H7JaCKRUCKRULFY1JUrVxyNYXl5uVpLXygUFI1GtbKycqjEeG5uTpZlVVseNJNOp5VIJKr/j8Vih+4jHw6Htbi4qMXFRRJ7AAAAANiFKiyXXL16VZZlKRwOKxwO77vs7OxstaY7m81qamrKkRii0ahs27lmyMViUbOzs20l2KlUqtqEXpKSyaQWFxdbvmZqakqFQkFra2uSpNOnTysajZLQAwAAAMA+SO5dYFmWstmsJLXVHN00TZmmqWKxqOvXrzuW3DstlUq1nWSHQiElEolqgp/L5VQsFves7a+o7AsAAAAAQPtolu+C2hrrl156qa3XVBLaQqHg22nelpeXZVmWYrGYxsbGWi4fiUTq/u/VzAAAAAAA0O9I7l3w6quvVp+3W9NdW1vt1yS49qZDZWq6/eyugV9ZWXElLgAAAAA46kjuXVA7Iv3p06fbek3tTYB8Pu90SI6YmJio+3+rGxe7t71b0/2hvxgydHwoWPcwZHgdFgAAAOAr9Ll32O4m9e32Hz9z5kz1+fLysqMxOSWVSikej6tYLGpmZqbleAK790U7TfmB3Y4PBfXHk3x2AAAAgP1Qc++wg9ZO19aC+7WGOxwOa2VlRbZtK5VKtVx+d3LfataAilwup3g8rpGRkeojEonUjWUAAAAAAPgcNfc+VJkGrtel0+nq81Ao1LKm//79+4rFYpK2pwe8ceOGQqGQLMvS9evXlUwmNTs7q4WFhbZmIejU7du3O37N008/rbNnzzoeCwAAAAD3vf/++/rggw86es1B8oZuILl32O7E/CDzs/u15r4Tu0f9b6emf25uTul0WolEoq48FAoplUppbGxMyWRSsVhMi4uLjif4X//61zt+zXe/+11973vfczQOAAAAAN3xox/9SN///ve9DsMRNMt3WD8k5k6YnZ2tPo9Gow0J+26maWphYWHf5RKJRHUMg3g87kygAAAAANAHSO7huGw2W53Or5K0t7KwsKCpqamWy1WWsSyr7gYCAAAAABxlNMt32EGa4fcTy7J09epVSduJfT6fd3SfxGIxzc3NSdpuxn/t2jXH1v/aa6/p4sWLHb3m6aefduS9sbfNUlm33ntQVzb+zEkNBrk3CQAAgMP59re/3XGr4Nu3bx+oS6/bSO4d1mxu906Tz16+QRCPx2VZliuJvdQ4tWAul2urxr8dFy9e1Pj4uCPrgnM2S2X95Jfv15U9f+4EyT0AAAAO7ezZs30zQDbJvcOcSGZ33yDoFbOzs8rlcq4l9lLjvnnjjTccS+7RX54YCGoq8mxDGQAAANCPqPpy2O7ks91p7WoH4uvFmvv5+XnNzc0pHA53lNgXCgWNjY1pZGTkQPPYM4Ah9hIMGDp/+sm6RzBgeB0WAAAA4AqSe4ftTmrbTT5XVlaqz3c3Pfe7XC6nZDKpaDTacY391atXVSwWZVmWkslkx8l6L94IAQAAAACnkdy7IBwOV5+3W3Nfu9xLL73keExuKRQKisVimpqa0uLi4p7LWZZVN+99RbOy/ezen2NjYx29HgAAAAD6Ecm9C6LRaPV5u8lr7XK90oe8WCzqypUrSiQSLae7u379urLZbEN5bSuFVCrVsiZ+9/585ZVX2g8YAAAAAPoUyb0Lrl27Vn2ez+fbek2hUJC0nez2QrN8y7IUi8X0yiuvKJ1Ot1y+UCg03a5oNKqpqSnZtq2ZmZmW66ltHRCNRmmWDwAAAAAiuXdFKBSq1t7fvHmz5fK5XK76fHZ2tuXy2Wy2ejPAK1euXFE0Gm0rsZe2t7G2u0JFMplUNpttu699be1/KpVq6zU4mmzb1qePt+oetm17HRYAAADgCqbCc0k6nVYkEpFlWcpms/s2ta8kyOFwWIlEYs/lLMtSJBKpNk1PpVJt1Xbv5yCjzcdiMZ0+fVrJZHLPbgeV9a6trVVr25vV3JumqZmZGV25cqVlK4f5+fnq+83MzDS9WQBUPNwsKb1U//lMTpp6cojTHgAAAPoPV7kuMU1TN27cUDweVzwe18rKStPkdn5+XtlsVqFQqGW/9VwuV5dMX79+/UDJfW1C3+6AfxXJZLLa0iASibT9uv26GqRSKUUiEcViMS0sLDRtap/NZpVMJiVJiUSCWnsAAAAAqEFy76LKCPLxeFyRSESpVKpaM29ZlmZnZzU/P69oNLpnUltr99/b6Ztf2+S/WCw2NKNPJpNKJpMyTbO6/omJiaaxzM3NHWgu+nZizefzSiaTGhkZ0czMjF566SWZpqm1tTWl0+nqDZDafQgAAAAA2EZy77JoNKo7d+7o5s2bSqfTdX3qo9GoFhcX60bXb7WumZkZzc3NyTTNljX90nYTeqn+xkDt87W1NV2/fr36f8uytLCw0LQbQe1ynWqnCX06nVY8Hlc6ndb8/Lwsy1IoFJJpmtWkngH0AAAAAKARyX0XhEIhJRIJR2qcU6lUR03SnRxAbH193bF17SUajbZ9swMAAAAAsI3R8gEAAAAA6HHU3AM7pqenNTw83LR8enq6+wEBAAAA8Ewmk1Emk2ko39jY6H4wbSC5B3YsLy83Lb98+XJ3A0GdJ4cG9J3Y816HAQAAgCNmdXVVS0tLXofRNpJ7YMfExETTmvvR0dHuBwMAAADAU6Ojo5qcnGwo39jY2LNi0Esk98COTCaj8fFxr8MAAAAA4AN7dc+9deuWLl261P2AWmBAPQAAAAAAehzJPQAAAAAAPY7kHgAAAACAHkefewC+tlUqq/hh/XQj5lPDGghybxIAAACoILkH4GuPS2X9w1u/ritLTpok9wAAAEANknsAfWkoGNDvvfDFhjIAAACgH5HcA+hLA8GAnj/3Ba/DAAAAALqCaiwAAAAAAHocyT0AAAAAAD2O5B4AAAAAgB5Hcg8AAAAAQI8juQcAAAAAoMcxWj6AvvTp4y2ll4p1ZclJU08OcdoDAABA/6HmHgAAAACAHkcVFrBjenpaw8PDTcunp6e7HxAAAAAAz2QyGWUymYbyjY2N7gfTBpJ7YMfy8nLT8suXL3c3EAAAAACeW11d1dLSktdhtI3kHtgxMTHRtOZ+dHS0+8EAAAAA8NTo6KgmJycbyjc2NvasGPQSyT2wI5PJaHx83OswAAAAAPjAXt1zb926pUuXLnU/oBYYUA8AAAAAgB5HzT0AXzs+GFRy0mwoAwAAAPA5knsAvmYYBnPTAwAAAC3QLB8AAAAAgB5Hcg8AAAAAQI8juQcAAAAAoMeR3AMAAAAA0OMYpQqAr5XKtt6zHtaVPRM6rmDA8CgiAAAAwH9I7gH42mdbJWXzd+vKkpMmI+gDAAAANbg6BtCXBoMB/c6XzzaUAQAAAP2I5B5AXxoMBvSV8yGvwwAAAAC6gmosAAAAAAB6HMk9AAAAAAA9juQeAAAAAIAeR3IPAAAAAECPY0A9YMf09LSGh4eblk9PT3c/IAAAAACeyWQyymQyDeUbGxvdD6YNJPfAjuXl5ablly9f7m4gcMTDxyX95eurdWX/6eVRHR8KehMQAAAAesrq6qqWlpa8DqNtJPfAjomJiaY196Ojo90PBodmy9bDx6WGMgAAAKAdo6OjmpycbCjf2NjYs2LQSyT3wI5MJqPx8XGvwwAAAADgA3t1z71165YuXbrU/YBaYEA9AAAAAAB6HMk9AAAAAAA9juQeAAAAAIAeR3IPAAAAAECPY0A9AL52bCCoP3r5QkMZAAAAgM+R3APwtUDA0FMnnvA6DAAAAMDXaJYPAAAAAECPI7kHAAAAAKDHkdwDAAAAANDjSO4BAAAAAOhxDKgHwNfKZVtrnz6uKzv95JACAcOjiAAAAAD/IbkH4GuPtkr669ffqStLTpp6cojTFwD/+uHi2wd63XdizzscCQDgqODqGEBfGggE9FXzTEMZAAAA0I9I7gH0paGBgF4eO9N6QQAAAKAPkNwDO6anpzU8PNy0fHp6uvsBAQAAAPBMJpNRJpNpKN/Y2Oh+MG0guQd2LC8vNy2/fPlydwMBAAAA4LnV1VUtLS15HUbbSO6BHRMTE01r7kdHR7sfDAAAAABPjY6OanJysqF8Y2Njz4pBL5HcAzsymYzGx8e9DgMAAACAD+zVPffWrVu6dOlS9wNqgaGjAQAAAADocdTcA+hLjzZLurn8bl3ZKxPndWww6FFEAAAAgHtI7gH0pbJt6/4njxvKAAAAgH5Es/wumZ+fVyQS0cjISPURj8eVy+U8iWdubk6GYahYLB5qPW5sl9/2FQAAAAD4Hcm9ywqFgkZGRpRKpZRMJrW+vq719XXl83mZpqlYLKZ4PC7LsroST7FYVCwW0+zs7KHW48Z2+W1fAQAAAECvoFm+i3K5nGKxmEzTVD6fVygUqv7NNE2lUimNjY0pmUyqUCg0LHNYlmVpeXlZxWJRKysryuVyKhQKh16vG9vl9b4CAAAAgF5Gzb1LLMtSPB6XJC0sLOyZiCYSCSUSCRWLRV25csXRGJaXl6u19IVCQdFoVCsrK4dKit3YLj/sKwAAAADoZST3Lrl69aosy1I4HFY4HN532UoT+UKhoGw261gM0WhUtm1rfX1di4uLSqVSMk3zUOt0Y7v8sK8AAAAAoJeR3LvAsqxq4hmNRlsub5pmNem+fv26q7Edhhvb1a/7Cs55YiCoqcizdY8nBpjODgAAAKhFcu+C+fn56vOXXnqprddUEtZCoXDoEezd4sZ29eu+gnOCAUPnTz9Z9wgGDK/DAgAAAHyF5N4Fr776avV5u/3ba5vL+3XKNze2q1/3FQAAAAB0E8m9C2pHpD99+nRbr6lNbPP5vNMhOcKN7erXfQUAAAAA3URy77DdzcTbHcDuzJkz1efLy8uOxuQEN7arX/cVAAAAAHQb89w7zLKsA72utjb6oOtwkxvb5bd9dfv27Y5f8/TTT+vs2bOOxQAAAACge95//3198MEHHb3mIHlDN5Dc+9Da2prXIbjCje1ycp1f//rXO37Nd7/7XX3ve99zLAY0sm1bDzdLdWXHB4MyDAbVAwAAwOH86Ec/0ve//32vw3AEyb3Ddieb7Q4SV8uPNfdubFe/7is46+FmSeml+i4cyUlTTw5x+gIAAAAquDp2WL8mm25sV7/uK/hDMGDoxfOnGsoAAACAfkRyD+x47bXXdPHixY5e8/TTT7sUDQ7riYGgvvblc16HAQAAAB/79re/rXg83tFrbt++faAuvW4juXfYQZqW9wI3tstv++rixYsaHx/3OgwAAAAAXXL27Nm+GSCbqfActnuu9oM0Pfdb0iu5s139uq8AAAAAoNtI7h3mRLK5O+n1Aze2q1/3FQAAAAB0G8m9w3Ynm+1O1VZba+3H2mg3tqtf9xUAAAAAdBvJvcN2J5vtNjVfWVmpPjdN08GInOHGdvXrvgIAAACAbiO5d0E4HK4+b7c2una5l156yfGYnODGdvXrvoL3Hm2WtLD8bt3j0WbJ67AAAAAAV5DcuyAajVafF4vFtl5Tu9zU1JTjMTnBje3q130F75VtW3fXH9Y9yrbtdVgAAACAK0juXXDt2rXq83w+39ZrCoWCpO1m5n5tau7GdvXrvgIAAACAbiK5d0EoFKrWSN+8ebPl8rlcrvp8dna25fLZbLaa4HaTG9vl9r4CAAAAgKOA5N4l6XRaoVBIlmUpm822XFba7n+eSCT2XM6yLI2NjSkejysSiWhubu7QcXY6t7wb2+XGOgEAAADgKCG5d4lpmrpx44YkKR6P79mffH5+XtlsVqFQSAsLC/uuM5fL1a3n+vXrB4qtNqFvdxC7Cje2y411AgAAAMBRQnLvoqmpKS0uLioUCikSiWh+fr76N8uylEwmlUwmFY1GdefOnZb9x3dPHddOf/NcLld9zM/PKxKJ1P09mUxqbm5O2Wy2ulyr2nynt8utdQIAAADAUWHYNsNHu82yLN28eVPpdLquVjoajVYT1nbNzs5qbm5OpmlqcXGxZZJrGIakxhsD+8W6sLDQ1ij0Tm6Xm+vcy61bt3Tp0qXq///5n/9Z4+Pjjq0fztgqlVX8cKOuzHxqWAPB/e9Nfvp4S+ml+lYgyUlTTw4NOB4jAOz2w8W3D/S678SedzgSAIDT/JpHkNzjyPLrlxLOILkH4CWSewDoX37NI2iWDwAAAABAj6MKC9gxPT2t4eHhpuXT09PdDwgAAACAZzKZjDKZTEP5xsZG48I+QHIP7FheXm5afvny5e4GAgAAAMBzq6urWlpa8jqMtpHcAzsmJiaa1tyPjo52PxgAAAAAnhodHdXk5GRD+cbGxp4Vg14iuQd2ZDIZXwyEAQAAAMB7e3XP3T2gnl+Q3APwNUa9BwAAAFrj6hhAXwoYhr507kRDGQAAANCPSO4B9KVjg0H9/gvPeB0GAAAA0BXMcw8AAAAAQI8juQcAAAAAoMeR3AMAAAAA0ONI7gEAAAAA6HEk9wAAAAAA9DhGywfQlx5tlpT713t1ZdHfOKdjg0GPIgIAAADcQ3IPoC+VbVu/uvdJXdnXvnzWo2gAAAAAd9EsHwAAAACAHufr5H51dVWrq6tehwEAAAAAgK952ix/dXVVxWJRxWJRKysr1efFYlHr6+saGRnRtWvX9Kd/+qdehgkAAAAAgK95mtxHo1HduXNHkmTbtkKhkBKJhH7wgx/oypUrXoYGAAAAAEDP8HxAPdu2JUkzMzP6wQ9+4HE0OMqmp6c1PDzctHx6err7AQEAAADwTCaTUSaTaSjf2NjofjBt8Dy5NwyjWlsPeGl5eblp+eXLl7sbCOoMBgP6nV2j3A8GfT1cCAAAAPrA6uqqlpaWvA6jbZ4n95KUSqW8DgHQxMRE05r70dHR7geDqsFgQF85H/I6DAAAABwxo6OjmpycbCjf2NjYs2LQS54n9+FwWCdPnvQ6DECZTEbj4+NehwEAcNgPF98+8Gu/E3vewUgAAL1kr+65t27d0qVLl7ofUAueJ/cTExP7/v1//a//1dZ6TNPUV77yFQciAgAAAACgt3ie3I+Nje35tzt37ujP//zPZVmWisWiDMOo+3tlhH3TNPWHf/iHJPcAAAAAgCPJ8+Q+FArt+bfnnnuuri/D3Nyc/ut//a/V1y0sLDBlHgAAAADgyOupIadnZmZ05coVGYahubk5EnsAAAAAANRjyb0kxeNxSdIrr7zS9mvu3LmjP/mTP3ErJAAuevi4pL9YWql7PHxc8josAAAAwFd6Lrmv9NHvZIT9YrGo+fl5t0IC4CJbth4+LtU9bNktXxcwDD07crzuEdg1bgcAAADQLzzvc98t+/XtB9B/jg0GFZ8473UYAAAAQFf0XM39QViW5XUIAAAAAAC4xvPkvhuJ9xtvvOH6ewAAAAAA4BXPm+Wn02mZptn28v/0T/8kSfrbv/1b2Xbrfrdra2uan5+XQV9bAAAAAECf8jy5LxaL1RHw22XbtqampjpafmRkpNPQAAAAAADoCZ4n95LaqoGvMAxDhmF09BoAAAAAAPqZL5L7TpDUA2jHZ1sl/fT2h3Vlv33xKT0xEPQoIgAAAMA9vkjuU6mUQqGQTp8+7eh619bWZFmWrl+/7uh6AfhfqWzrzXc/qiv7qnnGo2gAAAAAd3me3CcSCf3Zn/2Zq+9h27ZSqZSr74HeNz09reHh4abl09PT3Q8IAAAAgGcymYwymUxD+cbGRveDaYPnyf3Y2FhfvAd63/LyctPyy5cvdzcQAAAAAJ5bXV3V0tKS12G0zfPkPhQKdeU9LMty/X3Q2yYmJprW3I+OjnY/GAAAAACeGh0d1eTkZEP5xsbGnhWDXvI0uZ+amupojvuDMk1T3/jGN1x/H/S2TCaj8fFxr8MAAAAA4AN7dc+9deuWLl261P2AWvA0uf/BD37Qlfd57rnndPPmza68FwAAAAAA3eZ5s3wA2M9AINAwyv1AIOBRNAAAAIA/kdwD8LWhgYBeHmMKOwAAAGA/PZXcr66uyrIsra2t6fTp0zJNUydPnvQ6LAAAAAAAPOXr5P4Xv/iFXn31VeVyORUKhabLhEIhRaNR/eEf/qH+43/8j12OEAAAAAAA7/my4+ovfvEL/e7v/q4ikYjm5uaUz+dl23bTx/r6urLZrKampvT888/rb//2b70OHwAAAACArvJdcv8nf/InikQiyuVysm1bkmQYxr4PSbJtW7dv39bU1JT+/b//93rw4IGXmwEAAAAAQNf4pln+Rx99pGg0qkKhUE3qJdU9D4VCOn36tCRpbW1NlmU1Xdf/+T//R6ZpKp/P68KFC67GDQAAAACA13yT3F+5ckU///nPq8l8OBzWN7/5TUWjUZmmqVOnTu352p///OdaXl7WwsKCcrmcpO3kPxaLaXl5mUH3gB72aLOkm8vv1pW9MnFexwaDHkUEAAAA+I8vkvvf/d3frQ6YFw6HdePGDf3Wb/1W26//rd/6Lf3Wb/2Wrl69KsuydPXqVf3N3/yNVlZWFI1G9U//9E9uhQ7AZWXb1v1PHjeUtRIwDJ05MdRQBgAAAPQjz/vc//jHP9bi4qIMw9Ds7KyWl5c7Sux3C4VCWlhY0M2bN2XbtvL5vP7n//yfDkYMoBccGwzqWy+P1j2o7QcAAEC/8jy5TyaTMgxDMzMzun79umPrnZqa0l/8xV/Itm3Nzs46tl4AAAAAAPzG0+T+5z//uYrFosLhsKOJfUUikdA3vvENra+v6yc/+Ynj6wcAAAAAwA88Te7T6bQMw9CNGzdce49r167Jtm3dvHnTtfcAAAAAAMBLnib3y8vLMk1TX/nKV1x7j3A4rHA4rOXlZdfeAwAAAAAAL3k6Wn6xWFQymXT9faLRqKutA9AfpqenNTw83LR8enq6+wEBAAAA8Ewmk1Emk2ko39jY6H4wbfA0ubcsS2NjY66/z9jYmCzLcv190Nv2at1x+fLl7gYCRzzeKiv/znpdWeTCiIYGPB9HFAAAAD1gdXVVS0tLXofRNs/nuTdNsy/eA71vYmKiac396Oho94PBoW2Vy/pZ8X5d2YvnT2nI+0lCAAAA0ANGR0c1OTnZUL6xseHLbt+eJ/eAX2QyGY2Pj3sdBgAAAAAf2Kt77q1bt3Tp0qXuB9QCVVjoiGVZKhQKXocBAAAAAKhBco+OzM/PKxKJaH5+XsVi0bGxDIrFoqvLAwAAAEA/I7nvkkpSPDIyUn3E43HlcrmeimFlZUWSlEwmNTY2ppGRERmG0dGj2Q2BeDwuwzAUi8U0Pz+vQqFQt1yxWFQ2m1UymazGDQAAAADY5nly/9FHH7n+Hl6OlF8oFDQyMqJUKqVkMqn19XWtr68rn8/LNE3FYjHF43FXY3QyhsMOHDE1NaVQKLTn33O5nJLJZPUmROWGwNjYmOLxuObn5yVJCwsLh4oDAAAAAPqJ5wPq/fmf/7lOnTrl6nuk02lX17+XXC6nWCwm0zSVz+frklrTNJVKpTQ2NqZkMqlCodCwjB9jOExz+FAopBs3bhz49ZIUjUa1sLDg+H6CfwUDhl48f6qhDAAAAMDnPE/uC4WCYrGYq+9h27YMo7vJgGVZ1abj+yWjiURC+Xxe8/PzunLlivL5vG9jsCxLlmVpZmZGsVhMp0+fbiuO5eVlJZPJQyXl4XBYqVRK0Wj0QK/vZQ8fl2TLbnv544PBrn/e3fTEQFBf+/I5r8MAAAAAfM3z5F7aTr77zdWrV2VZlsLhsMLh8L7Lzs7OVvuZZ7NZTU1N+TKGYrGoUCikVCrVURzxeFyJRKJlYp5KpTQ1NaVCoaC1tTVJ0unTpxWNRo90Tf1fvr6qh49LbS+fnDT15JAvvtoAAAAAuoQMwAWWZSmbzUpSWzXNpmnKNE0Vi0Vdv37dkeTejRiWl5c7rjlPJpOS2u8aUYkDAAAAANA+z5P7UCik+fl512pmLcvS7Oys7ty548r6m6kM+iZJL730UluvqSTWhUJBxWLx0AmuGzHk8/m21yVt9/efn593tKsBAAAAAKCR58n9tWvX9I1vfMPV91hZWdG1a9dcfY9ar776avV5uzctahPpXC6nRCLhuxhmZ2fb7mdf6e+fSqVadgk46jZLZd1670Fd2fgzJzUY9HwyCwAAAAA9wvPkvhtNsCORiOvvUatQKFSft5sM1ybgTtR0uxFDJ8fq6tWrMk1TMzMzbb/mqNoslfWTX75fV/b8uRPV5P4/vTy654B6Dx+X9Fevv+N6jAAAAAD8zfPkvt8GSts9VVy7CfGZM2eqzw87l7zXMWSzWWWzWa2srBx4Hfjc8aGg1yF46tFmSX/35nt1ZX/w4jM6Nni09wsAAABQy9N2vzMzM12puTdNU3/2Z3/m+vtI283RD6L2JsdB1+GXGK5evXqoY5vL5RSPxzUyMlJ9RCKRunEEcHSUbVt31x/WPcptzLBhyNDxoWDdw1D/TBEIAAAA1PK05v4HP/hBV97nueee69p7OaEyDVwvxjA7OyvLsg40xsH9+/cVi8Wq67lx44ZCoZAsy9L169eVTCY1OzurhYUFV+a7v337dsevefrpp3X27FnHY2nXQCCgr5pnGsqw3eLhjyfHvA4DAAAAPvb+++/rgw8+6Og1B8kbusHzZvn9ZndSfJBuB4etufcqBsuyNDc3p6mpqQO959zcnNLpdMNAfqFQSKlUSmNjY0omk4rFYlpcXHQ8wf/617/e8Wu++93v6nvf+56jcXRiaCCgl8fOtF4QkrbHN9gslTt+3fHBoAyDWn8AAIB+86Mf/Ujf//73vQ7DEST3DjtsYt7LMVy9elXS53Pbd8I0TV27dk1TU1N7LpNIJJRKpVQsFhWPx7W+vn7gWHE03XrvQcPghe1ITpp6cojTJQAAAPzLd+13/8f/+B/63d/9XX3pS1/SSy+9pP/23/6bHjx40PqF8FSxWFQ2m5WkA9WoLyws7JvYV1SWsSxLs7OzHb8PAAAAAPQj31RF/eM//qPi8Xi11tm2bRmGoUKhoFQqpfn5ef3n//yfvQ2yDX4Y/d+LGNLptCS5Pqd9LBbT3NycpO1m/NeuXXNse1977TVdvHixo9c8/fTTjrw3AAAAgO779re/rXg83tFrbt++faAuvW7zRXL/N3/zN3rllVdk74yAbRhGXf9W27aVSCRkWZb+y3/5L16F2Zbdc8pbltVx8nnYZNWLGCq19hMTEx29rlO7R+DP5XJt1fi34+LFixofH3dkXQAAAAD87+zZs54OkO0kz5P7jz76SPF4vJrQ27ZdTfIrKuUzMzOKRqN68cUXPYq2NSdqkXcn536PIZfLqVgsSpLGxtwdnXx3XG+88YZjyT363/gzJ/X8uRP7LvPwcUl/9fo7XYoIAAAAcIbnyf3169clbdfOm6apaDSqsbGx6hRo9+/fVy6XU6FQkLQ9Rdr//t//28uQ97U7+VxbW2sr2a4dBM/pmnu3Y1hYWKg+73Ru+0KhoHg8rrW1NaVSqYaR8lvxwwCGXnq0WdLN5Xfryl6ZOK9jg0GPIvK3wWBAg0HfDTUCAAAAHJqnyf2dO3c0NzcnwzD0F3/xF9XR1pspFAq6cuWKFhcX9eabb/q29n53Utxu8rmyslJ93mmC7HUMuVxuz/du5erVq9Va/2QyqVdeeaWjdfhhjAMvlW1b9z953FCGg3tyaEDfiT3vdRgAAABARzytwqr0006lUvsm9tL2QG0//vGPZdt2tbbfr2oHlds95/xeapd76aWXeiqGSnIudd6loPa17di9LW53AwAAAACAXuBpcr+4uKiRkRH96Z/+aVvLh8NhXb16VYuLiy5Hdji1U8G1m7zWLudEH/JuxVBbay91XpNe20IglUq1fP3ubXnllVc6ej8AAAAA6EeeJvfLy8sdJ2e10+X51bVr16rP8/l8W6+pjClgmuahm+V3M4bKaw4qGo1qamqqOmBiK7U3dqLR6JFvlg8AAAAAksfJvWVZikQiHb2m0gz7wYMHboTkiFAoVK05v3nzZsvla2u/Z2dnWy6fzWZbJtVux1Bx//79uv932iw/mUwqm822fcOm0pVD2q7p7weVPt61jyeHPB/rEgAAAEAP8TyD6DQZfO655xqmyvOjdDqtSCQiy7KUzWb3beaeTqclbXc72G+0+MrNkErT9FQqtW9ttxsxNIvpMEzT1MzMjK5cudKyhcH8/Hx122dmZurGFUD/ChiGvrRr+rqAYXgUDYCj5oeLb3sdAgAAbfF8Tqh+bVZtmqZu3LghabsrwV793ufn55XNZhUKheqmlGumdj55SS0HFnQjht3aHaxvP5Ua+FgstufNgmw2q2QyKUlKJBJ9U2uP1o4NBvX7LzxT93Bzqr+tUllv3/u47rFVKrv2fgAAAIATPE/uu+HnP/+5vvnNb3b9faemprS4uKhQKKRIJKL5+fnq3yzLUjKZVDKZVDQa1Z07d1r2c999I6SdfvFOx7Db7pYXB71Zk8/nZZqmRkZGNDs7W+16kMvlFI/HFY/HFQqFlE6nq60MADc8LpX1D2/9uu7xmOQeAAAAPnckkvu1tbW6vtrdVEmaU6mU0um0RkZGNDIyoueee05ra2taXFysJt/trKvSDN80zbZr2Z2MYbfZ2VmFw+Fq4n0Y6XRai4uLKhaLunr1qiKRSLXFQSqV0p07dzrqMgAAAAAAR4Xnfe67wbIsT5v/h0IhJRIJRxLTVCp1oCbpTsZQyzTNtkfjb0c0Gq2bxg8AAAAA0Jrnyf1HH33k+nu0O8874IWtUlnFDzfqysynhjUQPBINazpy0IGtvhN73uFIAAAAAH/xPLn/8z//c506darj1924caOt11mWpevXr8tgdG20MD09reHh4abl09PTrr1vpY93reSkSXIPAAAAeCiTySiTyTSUb2xsNC7sA54n94VCQbFYrOPX7TcF3G62bWtkZKTj98DRsry83LT88uXL3Q0EdR5tlpT713uSpLfvfSyJlg0AAABw3+rqqpaWlrwOo22eJ/eSOp63nlp4uGFiYqJpzf3o6Gj3g0FV2bb1q3ufSJLWNh5Lkp57qvE4AQAAAE4aHR3V5ORkQ/nGxsaeFYNe8kVy36lObwYA7chkMhofH/c6jI4EA4ZePH+qoQwAAADA4ezVPffWrVu6dOlS9wNqwfPkPhQKaX5+3rXR7C3L0uzsrNbX111ZP+ClJwaC+tqXz3kdBgAAAACPeZ7cX7t2Td/4xjdcfY+VlZUDTR8HAAAAAEAv8Dy5N03T9feIRCKuvwcAADjabNvWVrm+66BhSAOB5gOAbpXK2t3R8NPHW5Kk44NBxhgCAHTE8+Tereb4u1mW1ZX3AQAAR9NW2Vb+nfpugOdOHttzENC3732iB48268rSS0VJ21OiPjnk+WUaAKCHeDqX1MzMTFdq7k3T1J/92Z+5/j4AAAAAAHjB01vCP/jBD7ryPs8991zX3gsAAKBiq1T2OgQAwBFBey+ghz3aLOnv3nyvruwPXnxGxwaDHkUEAAAAwAsk90APK9u27q4/bCjDwR0fDCo5aTaUAcBBnD/95J5/e/7cCdmSNktlvXX3o+4FBQDoSyT3AFDDMAwGsQLgmGBg7xHvB4KeDn0EAOgz/KoAAAAAANDjSO4BAAAAAOhxtD0FPEYfbwAAAACHRXIPeIw+3vsLGIaeHTkuSTp5bFCStHcPVgAAAOBoIqMA4GvHBoOKT5yXpIaZAdxQKtt6z6p/n2dCx/cdFAsAAADwGsk9ANT4bKukbP5uXVly0qR1BQAAAHyNq1Vgx/T0tIaHh5uWT09Pdz8gAMCRYEg6PTwkSfrSuROStrskAQC8lclklMlkGso3Nja6H0wbSO6BHcvLy03LL1++3N1AAABHykAwoOfPfUGS9PsvPONxNACAitXVVS0tLXkdRttI7oEdExMTTWvuR0dHXX1f+ngDQH8w1DjbCWdyAOhdo6OjmpycbCjf2NjYs2LQSyT3wI5MJqPx8fGuvy99vAGgPwwEA3rxfMjrMAAADtmre+6tW7d06dKl7gfUAtkDAF/7bKukn97+UJJ058Pt/k3nTx/XQCDgZVgAAACAr5DcA/C1UtnWm+9+JEm69+CRJFXnvQcAAACwjeQe6GEBw6iOrFxbBgAAAOBoIbkHetixwSAjKwNAj9sqlVXc6Xb092+9J0mK/sY5Hds1OB8AAPuh0yoAAICHbElrG4+1tvFYv7r3iX517xOVbdvrsAAAPYaaewAAAAeUyrZ+/VH91KZfPMXUpgCA7iC5BwAAcEDZtnV3vT65P3fymILMdg8A6AKa5QMAAAAA0ONI7gEAAAAA6HE0ywd62KPNknL/eq+ujBGWAUCybVsPN0uSpM1SueXyg0HqOwAAvY3kHuhhZdvWr+59Ulf2tS+f9Sia/nBsIKg/evlCQxmA3vJws6T0UlGSlH9nveXyXzXPuB0SAACuIrkHgBqBgKGnTjzhdRiA7/1w8e0Dv/Y7secdjAQAAEgk9wAAAHXKZVvrnz6uKxt5ckgBprQDAPgYyT0AAECNkm3rV+/Xd3mKXBhRgCntAAA+RnIP7Jientbw8HDT8unpadfelz7eANAdLzx7ioHzAABty2QyymQyDeUbGxvdD6YNJPfAjuXl5ablly9fdvV96eO9v4Bh6MyJIUnS8Z1ZAKg7A3AQg8EAyT0AoG2rq6taWlryOoy2kdwDOyYmJprW3I+OjnY/GFQdGwzqWy+PSpLuf/J4/4UdUC7bWtvV1/Y0fW0BAACOnNHRUU1OTjaUb2xs7Fkx6CWSe2BHJpPR+Pi412HAY4+2Svrr19+pK0tOmnpyiNMlAADAUbJX99xbt27p0qVL3Q+oBa5WAQBA3xkMBvQ7Xz4rSfq3jx5J2u7m40eGpJPHBiVJz44cl+TfWAEA/kVyDwAA+s5gMKCvnA9Jkn7yy/e9DaaFgWBAv/nMSUlSfOK8x9EAAHoVyT3gMfp4A0D/GAhy7gYAeIPkHvAYfbwBoD8MBgOauHDa6zAAAEcU2QMAX3u8VVb+nXVJ0t31TyVJXzx1XEFaNgAAAABVJPcAfG2rXNbPivclSXfXH0qSzp08piCz3QMAAABVJPdADwsYRnVk5doyAAAAAEcLyT3Qw44NBhlZGQCaePi4pL98fVWStPzOmiTpxWdDGgwGPIyqua1yWe+ubbdM+sdf3pMk/fbFp/TEQNDLsAAAPYbkHgAA9B1bth4+LkmStkq2x9Hsz7alew8eSZLefPcjSdJXzTNehgQA6EEk9wAAAA4olW198PFndWVPf+EJBgAFAHQFyT0AAECNgYChyIWRhrJWyrat1fsbdWVnTgwxACgAoCtI7gEAAGoYhqHBIAk5AKC3+G9UGQAAAAAA0BFq7oEe9tlWST+9/WFdGSMsAwAAAEcPyT2wY3p6WsPDw03Lp6enux9QG0pluzqycgUjLB/OEwNBTUWebSgDAADA0ZLJZJTJZBrKNzY2Ghf2AZJ7YMfy8nLT8suXL3c3EHgqGDB0/vSTXocBAAAAj62urmppacnrMNpGcg/smJiYaFpzPzo62v1gAACeKdu2Pnm0VVd24tiAAgaD7AHAUTI6OqrJycmG8o2NjT0rBr1Ecg/syGQyGh8f9zoMAIDHSmVb//LrB3VlkQsjCjCCPgAcKXt1z71165YuXbrU/YBaILkHPEYfbwAAAACHRXIPeIw+3vszZOj40PbNjgFqzQAAAICmSO7RsWKxKNM0XVseqHV8KKg/nhyTJD18XHL9/Wzb1sPN+vc5PhiUQV9bAAAA+FjA6wCOivn5eUUiEY2MjFQf8XhcuVyu52KIx+MyDEOxWEzz8/MqFAqyLKv692KxqGw2q2QyWX0PL+IEDuLhZknppWLdY3eyDwAAAPgNyb3LCoWCRkZGlEqllEwmtb6+rvX1deXzeZmmqVgspng8Xpcc90oMuVxOyWSymogbhiHDMDQ2NqZ4PK75+XlJ0sLCgqdxAgCOnoFAQF81z+ir5hk9O3Jcz44c9+1o94a2WwgdHwzqzIkhnTkx5NtYAQD+RbN8F+VyOcViMZmmqXw+r1AoVP2baZpKpVIaGxtTMplUoVBoWKbXY4hGo1pYWGhrfX7YVwCA/jE0ENDLY2ckST8r3vc4mv0NBAN68XxIkvStl0c9jQUA0LtI7l1iWVa1Ofp+CW4ikVA+n9f8/LyuXLmifD7f8zGEw2GlUilFo1Ffx+kX9PEG4KUfLr7tdQgAAMABNMt3ydWrV2VZlsLhsMLh8L7Lzs7OStpulp7NZnsihlQqpZWVFS0sLCidTiudTmthYaHajL7dxN7tOHsBfbwBoD8MBj/vClB5DAa51AIAdAc19y6wLKuaeLaT5JqmKdM0VSwWdf36dU1NTfVEDJXX+D1O9LbNUlm33nsgSfq3jx5Jkp7+whMKBmjZAAAAAFRwO9kFlYHkJOmll15q6zWVJLlQKKhYLPZFDO3olTjhnc1SWT/55fv6yS/f1+r9Da3e31DZtr0OCwAAAPAVknsXvPrqq9Xn7Q76VlsD7sSUb36IoR29EqdfBQyjOrIyIywDAAAARxfN8l1QKBSqz0+fPt3Wa2oTWycGivNDDO3olTj96thgkJGVAaCJR5sl3Vx+V5L05ruWJGn8mZMa8GEf+FLZ1q8/eihJen1le2T/yIURDQ34L1YAgH+R3DtsdzPxdvuknzlzpvp8eXm552NoR6/ECQDoPWXb1v1PHktSdZBSv3boKdu27q5vJ/eVaftePH9KQzSwBAB0gF8Nh1mWdaDX1dZGH3QdXsSQy+UUj8c1MjJSfUQikbq+9H6IEwAAt5XLtu5/8lndo1z26y0FAEC/oebeh9bW1rwOoWUM9+/fVywWk7Q9Pd2NGzcUCoVkWZauX7+uZDKp2dlZLSwsdDQtntNxduL27dsdv+bpp5/W2bNnHYsBAOC9gYChF5491VDWSsm29av3P6kri1wYUUCMhQIAfvX+++/rgw8+6Og1B8kbuoHk3mG7k812B4mrddja6G7EMDc3p3Q6rUQi0fBeqVRKY2NjSiaTisViWlxcbJrg+2Ff1fr617/e8Wu++93v6nvf+55jMQAAvGcYhp4c4hIJAI6CH/3oR/r+97/vdRiOoFm+w/zQTNztGEzT1MLCQkNiXyuRSFT70Mfj8abL+GFfAQAAAEA/ILlHxxYWFjQ1NdVyucoylmVpdnbW7bCOpMdbZb2+cr/u8Xir7HVYAAAAALqMNmcOO0jT8n6MQZJisZjm5uYkbTfjv3btWl1sfomz4rXXXtPFixc7es3TTz/tUjTt2SqXqyMrVzDC8uEMBQP6vRe+2FAGAACA/vPtb397z5bGe7l9+/aBuvS6jeTeYbvnarcsq+Mk9rBJrx9ikBqntsvlcnU1/n6Js+LixYsaHx93bH3oTQPBgJ4/9wWvwwAAAEAXnD17tm8GyKY6ymFOJJu7k95ejKHZOt544426//slTgAAatm2rU8fb9U9bJsp7QAA/kZy77DdyWa7U7XVDi7ndM29UzEUCgWNjY1pZGSkrXns91u/5I99BTipNiHYKu099sHupKHZg0QC8M5W2dZbdz+qe2wxXz0AwOdolu+w3clmuyPCr6ysVJ/vbs7ulxiuXr2qYrEoSUomk3rllVc6Sq53L+uHfeUH9PHuHw83S0ovbX9Hfu+FL+7ZvL+yzH6SkyZTcQEAAKBtZBAuCIfD1eft1kbXLvfSSy/5MoZKYt+u3e87NjbWsIwf9pXXKn28ax8DJPcAAAAAOkAG4YJoNFp93m5CXLtcO9PMeRFDbS15KpVqWWu/+31feeWVrsSJ/vLk0IC+E3te34k9r6+aZ/RV84wGe+Tmxy/etfTp4y2vwwAAAMAR0BtXyD3m2rVr1ef5fL6t1xQKBUnbCbQTTc3diCEajWpqakq2bWtmZqbl+hYXF+te2+xmgB/2FVDr08db+uHi23WPgyTov3jX0k9++b4LEQIAAACNSO5dEAqFqjXSN2/ebLl8LperPp+dnW25fDabrSa43YwhmUwqm8223Tc+m81Wn6dSqa7FCfjB/73+cM+/JSfNuse3Xr7QxciAoyEYMPTi+VN68fwpnTt5TOdOHpNhHHx9m6Vyw6Ps4MCXA0FDA0FDx4eCOj4UlKFDBAsAOJIYrckl6XRakUhElmUpm83u23w8nU5L2u5/nkgk9lzOsixFIpFqs/RUKrVvDbrTMZimqZmZGV25cqVlLfv8/Hw1zpmZmbq+9W7H2ct+uPh2R8tvlsrKv7MuSfqqecaNkOACBsoD3PfEQFBf+/I5SdKb73506PW9dbdxHb/5xZM6eXywofz/ETqu/9va+wbfboPBgCYubM8g88eTjePT9JtOf+tqfSf2vIORAEB/oebeJaZp6saNG5KkeDy+Z3/y+fl5ZbNZhUIhLSws7LvOXC5Xt57r1693PYZKDXwsFtuzBj+bzSqZTEqSEonEnrX2bsYJAIAXRs8M69+dOuZ1GACAI4jk3kVTU1NaXFxUKBRSJBKpmxvesiwlk0klk0lFo1HduXOnZf/x3X3W2+lv7nQM0nbfeNM0NTIyotnZ2Wo3gVwup3g8rng8rlAopHQ6Xa1p9yLOXlHbx/tnxfv6WfG+NveZIx0A4E+DwQCJPQDAMyT3Lqsko6lUSul0WiMjIxoZGdFzzz2ntbU1LS4uVpPadtZVaYZvmmbbtddOxlCRTqe1uLioYrGoq1evKhKJVGvdU6mU7ty503GzeTfiRO/bKpX19r2P9fa9j3X/k890/5PPVC47188VAAAA6Ad0/OyCUCikRCLhSB/xVCrVspm72zFURKPRuqnsnOBGnOhtj0tl/cNbv5Yk/er9TyRJkQsjCjDYFACXDAQMRS6MtFwuGGh+Hqp9/cAeywAA4DSSe6DHDQS3LxyPDwUliRGWe9BgMKDf+fLZhjL406PNUtujpB8fDMo4zBDt8IRhGBoMHvy4Hfb1AAAcBMk9sGN6elrDw8NNy6enp7sfUBuO2gjL/WowGNBXzoe8DgNt+rs339PdfaY6rJWcNJkdwSOPNkv6uzffkyT9y3sPJEnPnzuhAR/eOCuVbX3w8WeSpF+8a0mSxp85yU0+APBYJpNRJpNpKN/Y2Oh+MG3gigPYsby83LT88uXL3Q0EAHBoZduu3oR58GhTkuTX0TrKtq3V+9sXij/55fuStm9EkNwDgLdWV1e1tLTkdRhtI7kHdkxMTDStuR8dHe1+MAAAAAA8NTo6qsnJyYbyjY2NPSsGvURyD+zIZDIaHx/3OgwAHvtsq6Sf3v6wruy3Lz6lJwaCHkUEAAC8sFf33Fu3bunSpUvdD6gFknsAAGqUyrbefPejurKvmmeqz//gxWeaDqj38HFJf/X6O67HBwAA0AzJPQAAHTg2SA0+AADwH5J7oIcxwrK/HB8MKjlpNpS18vBxSX/5+mpd2X96ebQ6vSEAAADQCsk90MMYYdl5h5lz3jCMA017ZsvWw8elhjIAAACgXST3AFCDOedxUAHD0JfOnWgoAwAA6AaSewAAHHBsMKjff+EZr8MAAN/74eLbB3rdd2LPOxwJ0F9ouwsAAAAAQI+j5h7wWG0f73/76JEkmvICAAAA6AzJPeCx2j7elUHx0JtKZVvvWQ/ryp4JHVcwwM0aAAAAuIvkHoCv1U4vt1kqS5IGfJosf7ZVUjZ/t64sOWkeaAT9fmPbth5u1s8IEDCMPeeMf7xV1la53PRvxweDMmjdAgAAUIcrTgC+Vju9XDem+GPOeXc83CwpvVSsKztzYkjfenm06fL5d9b1s+L9pn/jhgkAAEAjro4AoAZzzuOgHm2WlPvXe3Vl0d84t2frBL+ptIzp1EDA8GVLitqpCd++97EkyX9RAgDgHJJ7AIAndt9E6XVl29av7n1SV/a1ncEye0H+nfUDvS5yYUSDQf+lzbVTE+4+Ln4zGAzoq+YZSUz1BQA4OKbCAwAAAACgx1FzD+yYnp7W8PBw0/Lp6WnX3re2j/fyO2uSpBefDXWlfzngpfjE+T3/FrkwohfPn9LDxyX91evvdDGq7ebcZ04MNZQBAICjJZPJKJPJNJRvbGx0P5g2kNwDO5aXl5uWX7582dX3re3jvVWib/dutdPLPXi4KUk6cWyAZKsPHBvc+wbW0EBAQx41Ljs2GNxzoD8AAHB0rK6uamlpyesw2kZyD+yYmJhoWnM/Ojra/WBQVTu93L/8+oGk7VrdgA/7+AK9LHJhpOUym6Wy3rr7UReiAQDAe6Ojo5qcnGwo39jY2LNi0Esk98COTCaj8fFxr8PAETQQ+HwwrdoyoJva6QoUMAyNnhluKAMAoB/t1T331q1bunTpUvcDaoHkHuhhjLDcH4YGAnp57EzrBQGHbJXKKn643V/w/iefSZJGnhxSILB/oh4MGPp3p465Hp8TaqcmrEyFZz41rAEfjmdSLtta//SxJP/HCgDwL5J7AACOmMelsv7hrV9Lkn71/vY0cZELIwr00UzwtVMTrm1sJ87PPdXY9coPSrZdPQ6V45KcNEnuAQAd4VcDAAAAAIAeR809AAA1Hm+VlX9nva4scmFEQwPcDwcAAP5Fcg8A8C1Dho4PBRvK3LRVLutnxft1ZS+eP+XZ1HwAAADtILkHAPjW8aGg/nhyzOswAAAAfI/kHuhhjLDsL8cGgvqjly80lLXyaLOkm8vv1pW9MnFexwZbv7ZXBAOGXjx/qqEMvWWzVNabdy1J0lbJliS98OyptqfR45gDAOAeknughzHCsvMOM+d8IGDoqRNPdPyeZdvW/U8eN5T1kycGgvral895HQYcUEnqK966+1Fbr3t25LieHXnSjZAAAIBI7gGgDnPO46AChqFnR443lAEAAHQDyT0A9JlHm6UD1fwfHwzKIBk9sGODQcUnznsdBgAAOKJI7gGgz/zdm+/p7vrDjl+XnDT15BA/CwAAAL2IqzjAY7V9vO+ufyqJprxAxWaprFvvPagrG3/mZFsDuMF5AwFDkQsjB3ot5zUAANxFcg94rLaP9+65tdFbymVba5/WD4x3+skhBRgh/MA2S2X95Jfv15U9f+4Eyb1HDMPQYJDPMwAAfkRyD8DXaqeX+/TxlqTt2kM/erRV0l+//k5dGU3dtz3aLOnv3nyvruwPXnymr6b7w/62alph/NXrq5L6b8pHAAC8xBUnAF+rnV6uG0lyP8w5/wcvPtNyQL2Hj0v6q103ItxUtu2GcQD6bbo/7M+W9HCzJEnVqR/5DAAA4BySe2DH9PS0hoeHm5ZPT093PyB4opfmnP9sq6Sf3v6wruy3Lz7V1o2IgGHoS+dONJShXqWrzGap3LIrwFa5rHfXtm9gfP23npG0fTyeGOidG0P9pHZqwpPHBiVJfMIBAJ3IZDLKZDIN5RsbG90Ppg0k98CO5eXlpuWXL1/ubiBAm0plW2+++1FdWWVwxlaODQb1+y8840ZYR5ZtS/cePJKk6nFp93h02/HBoJKTpqTtGxeSf7u7HFTt1IQHmT2im2oHKqwcl+M91FoIAPrV6uqqlpaWvA6jbST3wI6JiYmmNfejo6PdDwYAXGQYRrWbC4MTeq92oELG6AAA/xgdHdXk5GRD+cbGxp4Vg17iFwTYkclkND4+3vX3re3j/ea7lqTtqb4GuOAGPGHI0PGh7VrTAUaGBwDgyNqre+6tW7d06dKl7gfUAsk94LHaPt6Vwab82cPbG7XTy1VGyz8+GJRB/3C45PhQUH88OSZpe+BBAACAXkByD8DXaqeXe+vudj/myIUR5toGAAAAapDcA4DHggFDL54/1VCGep2MXL/bd2LPuxESAACAb5DcAz2MEZb7wxMDQX3ty+e6+p6PNkvK/eu9urLob5xraxo99L5S2dZ71vYI8g8ebkqSThwb6KvpEGunirzz4faURedPH9dAwH/jmZRtW5882u529O7ap5KkZ0LHuckHAOgIyT3QwxhhGQdVtm396t4ndWVf+/JZj6JBt322VVI2f1eS9C+/fiBpu7tLoI+6u9ROFVmZorAy773flMp29ThUjkty0uS8DgDoiP9uXwMAAAAAgI5wSxgA0FWd9J3fLJWVf2e9G2HVveet97ZrUf/to+0a36e/8ARNpAEAgK+R3AMAUGOzVNZPfvm+JGn1/nZf7TMnhhQUyT0AAPAvknsAgG8NBgP6qnnG6zAAAAB8j+Qe6GGMsOwvTwwENRV5tqGslUebJf3dm+/Vlf3Bi8/01cj1AcPQl86dkCS9fe9jSaIe/IgxDOncyWOSVJ36kXMVABxNP1x8+0CvY2rb/ZHcAz2MEZadd5g554MBQ+dPP9nxe5ZtW3fXHzaU9ZNjg0H9/gvPSFLDKP04GgYCAT331LAkdX3qRwAAjgIyAACo4cWc8wcVMAydOTHUUAZvGJKO77S2qBwXjgcAAOgWknsA6FHHBoP61sujXoeBHQPBgF48H5IkjgsAAOg6knsAfY9+Xf1hs1Q+0OsG6NcNAACOAJJ7YMf09LSGh4eblk9PT7v2vrV9vO98uD3tFi15gW3lsq31Tx9Lkv7twSN9vDOAZCciF0acDgsAABwBmUxGmUymoXxjY6P7wbSB5B7Ysby83LT88uXLrr5vbR/vN9/9yNX3grts29bDzVJd2fHBoAzu1hxYybb1q/cZgA8AAHTf6uqqlpaWvA6jbST3wI6JiYmmNfejo6PdDwZVtdPLPXi4Kcm/02c93CwpvVSsK2P2gm2PNkvK/es9SZ9PhWc+NayBYMDLsNBFW6Wy3t6ZKWFh+V1J/TflIwCgv4yOjmpycrKhfGNjY8+KQS9xxQnsyGQyGh8f9zqMnnHQfuxSZ33Za6eXO3l88MDv2S4SEHeUbbs6Bd7axnYz+8q0aDgabEkPHm3foKtM/dhvUz4CAPrLXt1zb926pUuXLnU/oBZI7gGgRi8lII+3ysq/s15XFrkwoqGB1rXhAcPQsyPHG8r8ZiBgHLrPfLcG1CuVbf36o+3PzOsr9yW1fzzgvNqpIitTFPrvEw4AgHNI7gGgR22Vy/pZ8X5d2YvnT2lIrZPJY4NBxSfOuxWaYwzD0GCwN1Kysm1XbwhVjku7x6Pbjg0E9UcvX5Akffp4e5DCfptVoHaqyPufPPY2mBYGAoZeeHZ7YNXKcTk2QGshAEBn/HfFgSOpWCy2XugQywMAPhcIGHrqxBN66sQTenJoQE8ODTDwo4cMw6geh8pxCfTZzRYAgPtI7rtkfn5ekUhEIyMj1Uc8Hlcul+vZGAqFgpLJpMbGxmQYhgzD0NjYmJLJZMfrjMfjMgxDsVhM8/PzKhQKsiyr+vdisahsNqtkMlmNu1882ixpYfldLSy/q39574H+5b0H2jrgfN4APle2bT14uKkHDzf17tqnenftU5XK/uxiAQAAcFgk9y4rFAoaGRlRKpVSMpnU+vq61tfXlc/nZZqmYrGY4vF4XSLbCzHMzs4qEolobW1Ns7OzWlhYUCqVkrR9EyEWiykSiXRcw57L5ZRMJqs3IWpvGsTjcc3Pz0uSFhYWOlqvn1Wa8t5df6gHjzb14NGmSD8+Z9u2Pn28pU8fb2mzVNZmqSzbp33g4S+lsq1/+fUD/cuvHyibv6ts/q4+2yq1fN2TQwP6Tux5fSf2vL5qntFXzTMaZFR/AADgc/S5d1Eul1MsFpNpmsrn8wqFQtW/maapVCpVrekuFAoNy/g1hkgkIsuytLKyItM06/42MzOjZDJZrX0fGxvT4uKiotGoI9sTjUa1sLDg+H6Cf9VOL1cZPC5yYaRn+mEDAAAA3UBVhEssy6o2Hd8vGU0kEkokEioWi7py5YrvY5ibm5NlWdVa/2bS6bQSiUT1/7FY7NB95MPhsBYXF7W4uEhi76FmteidPNBcwDD0pXMn6h5+HLkeAAAA/kXNvUuuXr0qy7IUDocVDof3XXZ2drZa053NZjU1NeXLGIrFomZnZ9tKsFOpVLUJvSQlk0ktLi62fM3U1JQKhYLW1tYkSadPn1Y0GiWh30O3R1huVoveroGgoYkLp90Iq+cdGwzq9194pqvv+dlWST+9/WFd2W9ffEpPMEL3kVAu21r7dHsE+cpo+ccHg74fVO+Hi2+3vWzt1IQVXzx1XEEfDlRn27Yebm53Gfnwk88kSaefHOqrQfVqt7HZzd6BgNH082fbtrZqxsropc8rAHQbyb0LLMtSNpuVpLaao5umKdM0VSwWdf36dUeSezdiSKVSbSfZoVBIiUSimuDncjkVi8U9a/t3x4H2VEZYlqSnTjzhcTToJaWyrTff/aiu7KvmGY+iQbc92irpr19/R5L01t3tz0G/dXepnZqw4tzJYwr6cLb7rbJdPQ6V45KcNKvn937Q6ubwC8+earq9DzdL1X0jqbqOfts/AOAEmuW7oLbG+qWXXmrrNZWEtlAoODLNmxsxLC8vy7IsxWIxjY2NtVxfJBKp+383ZwYAAAAAgKOE5N4Fr776avV5uzXdtbXVTiTBbsRQm/BXpqZrd32StLKy0lYcAOClrVJZb9/7WG/f+1j3P/lM9z/5TGWm0AMAAD5HeyYXFAqF6vPTp9vrY1ybgOfzeV/GMDExUZf0t7ppsPt93ZzuD90xEAhUm27f+XBDkvTMqWN91S8UeFwq6x/e+rUk6VfvfyJpu8l6wIfNuQEAACpI7h22uzl7u/3Hz5z5vK/r8vKyL2NIpVKKx+MqFouamZlp2Zd/dxztNOVHd9QObNSugGHo2GBQL49tf05+VrzvRmgAgCPihWdPaTC43Yh0YI+bxMcHg3rh2VN1/e4BAM2R3DvsoLXTtbXgh63hdiuGcDjcUdP63cl9qxH7K3K5nNLpdF0rAdM0lUwm66bYc9rt27c7fs3TTz+ts2fPuhBNew46wnLtwEbtOnNiSN96efRAcUrbA7h98PF2jL9415IkjT9zsnph1w+GggH93gtfbChr5dFmSbl/vVdXFv2Nczo22D8j1wcMQ8+OHJcknTw2KEnUgx8xhqTTw0OSpC+dOyFJTPl4xA0GAy1/AwzD6KvfCQD+8/777+uDDz7o6DUHyRu6geTehyrTwPV6DOl0uvo8FAq1rOm/f/++YrGYpO2p+W7cuKFQKCTLsnT9+nUlk0nNzs5qYWGhrRkAOvX1r3+949d897vf1fe+9z3HY2lXL42wXLZtrd7fbsr/k1++L0l6/twJ3120HSYBGQgG9Py5L3T8nmXb1q/ufVJX9rUve3fTyA3HBoOKT5yXpIYRzHE01H4/uj31IwAAe/nRj36k73//+16H4Qj/ZQA9bndSfJD52Q9bc++HGHaPuJ9KpVq+Zm5uTul0uqF2PhQKKZVKaWxsTMlkUrFYTIuLi64k+EAvJSCGDB0fCjaUwTsDO1PJVY4LxwMAAHQLyb3D/DBonB9imJ2drT6PRqMtm9Obpqlr165pampqz2USiYRSqZSKxaLi8bjW1xvnyQWOkuNDQf3xJGNZ+MVgMKCJC9sDiXJcAABAt5Hcw3HZbLbaX940TS0sLLR8TTvLSNLU1JTm5uZkWZZmZ2fbahHQrtdee00XL17s6DVPP/20Y+/vtW+9fKGhFrgWfWPRyyoDQG6Wyi27gmyWysq/w81DAACOgm9/+9uKx+Mdveb27dsH6tLrNpJ7hx2kCXw/xWBZlq5evSppO7HP5/OOxhOLxTQ3Nydpuxn/tWvXHFv/xYsXNT4+7si6OhEwjGrf7rfvfSzJm4HGjg8Fu95X/+Hj5iP2+3HMAAAAAPSfs2fPejpAtpO4gnZYs7ndO00+D5usehlDPB6XZVmuJPZS47R+uVxu36b8veDYYLDat3v3oGpuCQYMvXj+VENZt/3VziCAu30n9nyXI0E/GggYeuHZUw1lAAAA/Yjk3mFOJLO7k/NeiWF2dla5XM61xL5ZXG+88UbPJ/deeGIgqK99+ZzXYbSldnq5+zvT/QV92kXg08dbDVMM+nX2gm77bKukn97+UJJ058PtWRPOnz6ugYB7syUYhsG+95GtUlnFnWP/92+9J6n/pnx0QiddSGr5/aboEwNBTUWelSQ9eLgpyZubygDQz/w1B1Uf2J18tjulXO0geE7X3Hcjhvn5ec3NzSkcDneU2BcKBY2NjWlkZETz8/Mdvafkj8ED4a7K6PXPn/uCzpx4QmdOPKGAixeEW6Wy3r73sd6+97H+/q339PdvvadHm827D6B9pbKtN9/9SG+++5HuPXikew8eyba9jgrdZEta23istY3H+tW9T/Sre5+ozIfgyAgGDJ0//aTOn35SJ48P6uTxQcZyAQCHUaXhsN1JbbvJ58rKSvX57qbnfo8hl8spmUwqGo1qcXGx7ddJ0tWrV6tT5iWTSb3yyisd3VjwwxgH6C+VBET6vJuEX+ec3yyVdeu9B3Vl48+cbKu2L2AYOnNiqKEMB1cq2/rg4+3WJb9415LU/vGAOypTE6K3BQOGfvOLJyWpWvv/xAAtPgBgN5J7F4TDYRUKBUnt15rXLvfSSy/1TAyFQkGxWExTU1P7jnhvWZbW1tYabhpUEvt27d6WsTGmm+oVAwFDkQsjkrabquPwNktl/eSX79eVPX/uRFvJ5LHBoL718qhLkR1NZdvW6v3tZueV49Lu8ei2o9BEunZqQr+rTV5ry7AtYBg6eXxQknT+9JMeRwMA/uW/K44+EI1Gq8/bTV5rl3OiD3k3YigWi7py5YoSiUTLqeyuX7+ubDbbUF6b7KdSqZY18bu35ZVXXmkZJ/zBMAwNBgMaDAb05NDAvg8A7qKJtL9UktfaB8cDANApknsXXLt2rfo8n8+39ZpKLbtpmodult+NGCzLUiwW0yuvvKJ0Ot3WuputMxqNampqSrZta2ZmpuV6apv9R6PRvmiW/2izVO3bXenrvVUqex0W0PNs29anj7fqHjZ9vAEAQJ8iuXdBKBSq1pzfvHmz5fK5XK76fHZ2tuXy2Wy2moh7FcOVK1cUjUbbSuwr6w+Hww3lyWRS2Wy27XEBamv/U6lUW6/xu7JtVweXqgw25Xb68WizpIXld+seDBqHfrNVtvXW3Y/qHlvl1t+uSheS2gdT6AGHU3uzbbNU1mapzM02AHAY7V9dkk6nFYlEZFmWstnsvs3cKwlyOBxWIpHYcznLshSJRKpN01Op1L613W7EIEmxWEynT59WMpncs8l/JVlfW1ur1rY3q7k3TVMzMzO6cuVKyxYG8/Pz1febmZlperMA7Snbtu6uP2woQ+866PRZkv+n0HLSZhutYoIBw5f95IFe9nCzVJ0qNP/OuiQpcmFEgwx6CACOIbl3iWmaunHjhuLxuOLxuFZWVpomt/Pz88pmswqFQi37redyubpk+vr16/sm927EkEwmq7X8kUhk32V3x7KXVCqlSCSiWCymhYWFpk3ts9msksmkJCmRSPRNrT0gbfe3fXbkeEMZnPfW3Y9aLvObXzxZHbwLgPds2662uvn08ZYk6fhgUAbnSQCoQ9WEi6amprS4uKhQKKRIJFI3j7tlWUomk9Up5O7cudOyn/vupLedvvlOxjA3N3eguejbiTWfz8s0TY2MjGh2drba9SCXy1VvToRCIaXT6ba7AhwFlRGWf/OLJzUVeVZTkWeZHqgHHRsMKj5xvu5xbNDd41gq27q7/qnurn+q11fu6/WV+3q8xVgPR8VRaCJdKtv6t48e1T1KbXTL8IJt29Xj0K/H4zC2yrby76wr/8660ktFpZeKekhXMgBoQM29yypJ882bN5VOp+v6s1fmha8d2b7VumZmZjQ3NyfTNFvWsjsdw/Xr19t6v2baaUKfTqcVj8eVTqc1Pz8vy7IUCoVkmqZSqZQSiURfDKDnJKYHwkHVds2oNOl/8fwpDXHP90g4Ck2ka6cmrDhzYkhB+W8bK8lrrX47HgAA95Hcd0EoFFIikWjZl70dqVTqQE3SnYhhfX299UKHFI1G277ZAQAAAADYRnIPAOhLlVHvOxVkZHwAANCDSO6BHdPT0xoeHm5aPj093f2AAByKYRg0awYAAAeWyWSUyWQayjc2NhoX9gGSe2DH8vJy0/LLly93N5AuO8wUagAAAEC/Wl1d1dLSktdhtI3kHtgxMTHRtOZ+dHS0+8G0iemB/GUwGNDvfPlsQ1krn22V9NPbH9aV/fbFp/pq5oOAYejMiSFJ259RST4c1gxuMiSdPLY9AGhl6kemfARQey3TCtc66LbR0VFNTk42lG9sbOxZMeglkntgRyaT0fj4uNdhdKR2hOXKyNfJSVNPDvHVPqjDJCCDwYC+cj7U8XuWyrbefLd+/vWvmmc6Xo+fHRsM6lsvj0qS7n/y2Ntg4ImBYEC/+cxJSVJ84rzH0QDwi2azReyFax10217dc2/duqVLly51P6AW+FYAQA2nEpAfLr7d9rKbpXL1wqbfknoAAAB0B8k9APSoJ4cG9J3Y85I6u5kAdwwGA9WbM5XjAgAA0C0k9wAA+MhBb9RwQwEAgKON5B7wWMAwqn27K329GSIGAAD0k1LZ1nvWQ0nSg4ebkqQTxwY0EDAUuTCy5+s2S2W9dfejPf8O4HMk94DHjg0Gq327764/7Mp7BgxDXzp3QpL09r2PJXFDAQAAuOezrZKy+buSpH/59QNJUuTCiAaDAQ0GuQoBnEByDxxBxwaD+v0XnpEk/ereJx5H0z8ePi7pL19flSQtv7MmSXrx2VBb0+H1u8dbnw8aeHf9U0nSF08dVzDABd1RsVUu69217RuY//jLe5L6b8pH7G0oGNDvvfBFSdL9Tz6TJAWZygwAHEVyDwA1DpOA2LL18HFpez2l9ubsPSq2ymX9rHhf0uctVM6dPKYgbUaODNuW7j14JEnVqR+ZHeLoGAgG9Py5L0iSzpx4wuNo0EuChqEvnd1ubVi5QTTETXOgKZJ7AKjRSwnIVqms4ocbkj6vCRt5ckiBNmvDB3aaQR4f2r5xYZBoH0q5bGv908eSPu/uYj41rAEuQg/Ftm093Ny+abZZKrdcPmgYbX8H0BtI7o62QMCo3hCq3CAC0BzJPQD0qMelsv7hrV9Lkn71/nb3isiFEQXaSNIHgwFNXDgtSfrjyTH3gjxCSrZdPQ6V45KcNH2Z3PdSE+mHmyWll4qSVO3asZ8vnT2hMyeeqJua0O9qk9faMmwjuQOA9pDcAwBwxNBE2l9qk1cAAA6K5B7w2GdbJf309oeSpDs7TazPnz6ugYD/avsAAAAA+BPJPeCxUtmu9u2u9PWuzHvvlkebJeX+dXuwOPoGAwAAAL2P5B44gsq2XZ0Cb21jewCw554a9jIkAGjbC8+e2nOKSfqq+9Onj7eqYydUZs6ozHEOAHAGyT0AeMyQdHxwe8T6MyeGJEkBEhRgT4PBAEkhcERslsrVwTR/uPi2pO3BSp8cIo0BduNbAeyYnp7W8HBj7fX09LSmp6e7H1AbmB6oOypz1wcMQ8cGm893/3irXF2uUwPBgF48H5Ikfevl0QOto1Olsq0PPt4eJf0X71qSpPFnTpIwAfAdkjsAXslkMspkMg3lGxsb3Q+mDZwVgR3Ly8tNyy9fvtzdQDrA9EDd8VevvyNpu1Z9r+Q7/856talpLyjbtlbvb/8w/eSX70uSnj93guT+iKCJtL/UJq8VHA8A/ca2bT3c3K4I2SyVO3ptMGB40qpxdXVVS0tLXX/fgyK5B3ZMTEw0rbkfHR3tfjAAAEnbXVaSk6akzy8GBwJ0WwGAXvNws1S9sbz7hmYrv/nFkzp5fNCNsPY1OjqqycnJhvKNjY09Kwa9RHIP7MhkMhofH/c6DABADcMwqs2vqckGAHTTXt1zb926pUuXLnU/oBZI7gEA6AFbpbLsff7+6eMtSds13QYDMgIAcOSQ3ANAjYGAociFEUmqNgWu2K+vV+TCiF48f6r6f5oPw2m33ntQ7avYTKWpIwONAfCjYwNB/dHLFyR9fjOS30jAWfz6A0ANwzA0GNy+2OgkQRoaCGhInzcZ7qT5cKls69cfPZQkvb7y+eBmQwP90wTZkKHjQ9szDQwEuZg7ipjyETjaAgFDT+0MAswNSFS88Oyptq6ZgtwIagvfLKCHMT1Qfyjbtu6ubyf3lZHLXzx/qu5mQa87PhTUH0+OSdKBpwzsZ4+3Pv8u313/VJL0xVPH++pixospH3vZ7pGkA4ax5+dhs1SmWwYA32vWeoNzlrPIAAAA8NhWuVy9sVO50XPu5DEFxQVPqWzrPWt7nzx4uClJOnFsoO9r/d+6+1Hd/0fPDOvfnTrWdNk371p0ywDgewdtvWHbtrbK26POcFNgf5z9AXRdpZUBDocpwvyl2XgNlWboThh/5mTDgHqbpXJDEthvPtsqKZu/K0n6l18/kLTdbSVA9w4AOBK2yna1dRs3MvfHHgGAHsUUYf5y0PEa2jXAMcYum6Wy7n/y2OswgK6ptHDaLJU7/t37Tux5N0ICfIXkHvBYwDCqg0tVavmojwIAtGP1/obXIQBtKZdtrX26fTOKptWAO0juAY8dGwxWB5fqVg1MwDD07MhxSdLJY4OSuKEAAF6p7dKxl34fYwD979FWSX/9+juSPh9TInJhpNriCcDhkdwDR9CxwaDiE+clfT54F+CmzVJZt97b7i/9bx89kiQ9/YUn+mo0eOzvKEz5eFC1XTr61WAwoN/58llJn58DuGEBHC203nAfyT0AwHWbpbJ+8sv3JX3ejPjMiSFGgz8kw9geVV/anj5R8u9cwEdhykfsbTAY0Fd2pkKsnAuAdgQMQ6NnhhvK0HtoveE+knsA6FF+miKscgd+L0dhbvuybeuTR9v74d217bnqnwm5O1f9QCCg557avuj92pfPufY+gJdqk7tK7T+DiB4dwYCx5zSQAOqR3ANAjzrMFGGDwYC+ap6R5MwIwpWpaY6yUtmuHofKcfHrVD00kUYvqU3uKrX/AIBG/rviAACf+OHi216HALiCJtIAAPQfkntgx/T0tIaHh5uWT09Pu/a+j7fKyr+zLkm6u77dlPeLp9xtygsAAABgf5lMRplMpqF8Y8Of05CS3AM7lpeXm5ZfvnzZ1ffdKperg0tVBps6d/KYqwONfbZV0k9vfyhJuvPh9snp/OnjGgjQhxEAAACQpNXVVS0tLXkdRttI7oEdExMTTWvuR0dHux+My0plW2++uz1K6b0H2/1tK/PeAweRnDTbXnazVJa0Pbc3gKPh4eOS/vL1VUnS8jtrkqQXnw0xMB4AXxsdHdXk5GRD+cbGxp4Vg14iuQd2ZDIZjY+Pex0GjqiBnUHwjg8FJUmGT6eI2yqVVfywvima+dRwR4PGcTHvnK1SWW/f+0SStLD8riTpD158RscGg16GBTSwZVdnzdgq2R5Hg16yWSrrzbtWXRk3htAte3XPvXXrli5dutT9gFoguQd6GNMD9YfBYEATF05Lkv54cqwr71ku21r/9LEk6e17H0vaTtIHWnx+HpfK+oe3fl1Xlpw0W74O+zNkVG/sDHQw368t6cGj7WkQK916yjaJE/pLbXL3F0srkqT/9PJo9TuD/scNIaA9JPdAD2N6IBxUybb1q/e3a3wryTpJuneODwWrN3YqtZtuook0ek0luat8P2yR7AHAbiT3AAAcMb3URPrYQFB/9PIFSdKnj7ckMV5DRcAwquOlfNU8I0kMjAqg7wQNQ186e0KS9HsvfFGSNMTN6KZI7gEAgG8FAoaeOvGEJHU0tsNREAwYenbkSUnSy2NnPI4GANwRCBg6s/M78Py5L3gcjb9xywMAAAAAgB7HLXAAAAAArnpiIKipyLOSpAcPtwcCDdLFBnAUyT0AeKxUtvXBx59Jkn7xriVJGn/mJIOboe/0ypSPAJwXDBg6f3q7G8nJ44MeRwP0J5J7oIcxPVB/KNu2Vu9vzx3/k1++L0l6/twJkvsjZLNU1q33HkiS/u2jR5Kkp7/wRF/Vankx5SP6V+2sEgOBgIYGmp8vH22W6qaHPD4YlGH0z/cK6CW03nAfyT3Q45geCOh9m6Vy9cZO5UbPmRNDClKzrXLZ1tqnjyV9Plo+CRr+6vV3qs+/ap7Zc0DBm8vv6v4nj6v/T06aDMwIeITWG+7j7Ab0iZ8V70vaThJa1fhulsrKv7PejbDgIqYI85eBgKEXnj0lSdXjcmyAVjSH9WirpL/eSeTeuvuRJClyYUSDQT7rWzUtPv7q9VVJ0isT53VskM8dgP5Re936w8W3JXGjbi/sEQDoUUwR5i+GYVSPQ+W4AG6yJT3c3G61Vamdrm2CftR9+nirrtYeR1slKeyW78Se7+r7ARLJPeA5Q0a1j/yAAzVRm6Vy0/KAYdCvCQDQ0yqt1GrdXf+0aXltbd9XzebN9tE9tm1Xb0ZVrlUGAgZdbAAHkdwDHjs+FKwOLlU7QNBBVZqt7vbsyHE9O7Ldz8nQdp/VWvy0AgD8aCBgKHJhZM+/B0gOe8LDzZLSS0VJqt50caqLzVap3PaIQ9xQQD8juQeOoIFgQC+eD3kdBoAjhCkfj7aBQKBae353/VNJ7SflhmEwxgL2deu9B9VWAa0wZod3aL3hPpJ7AAB6lCHp9PCQJOlL505I8m8tJlM+Hm1DA4HqiPbNmtADewkYhp4dOd5Qht7jZusNbCO5B3ZMT09reHi4afn09HT3AwJa8GKKsOODQSUnzYYy1NdIfPjJdg316SeHFHBxrIuBYEDPn/uCJOn3X3jGtfcBAK8EA0a1WyHQbZlMRplMpqF8Y2Oj+8G0geQe2LG8vNy0/PLly90NpAOt+iHW4i53/znMFGG1n51Kst5Okl47IjzqbZXt6nGoHBe/TtVzmCbSAAAcFaurq1paWvI6jLb574oD8MjExETTmvvR0dHuB9Mm+iHioGo/O35MPuEumkgD6Cfjz5xsOqDeZqm850DDQDtGR0c1OTnZUL6xsbFnxaCXuKIDdmQyGY2Pj3f9fTdLZd1674Ek6d8+eiRJevoLTzBtHQAAQBsGGLsDLtmre+6tW7d06dKl7gfUAsk94LHNUlk/+eX72tjYUO4f/3+SpNOf3NH/9fvf1BdGmJcX8NLH6/f107/7/1b//9t/8P/iewl4jO8l4E/vv/++fvSjH1X//+1vf1tnz571MKKjh+Qe8IlPP93Qylv/JEn6+T/9jcL/V4yLFcBjGx+t6f/8f/7f1f9/5f/57/leAgfwaLOkm8vvSpLerJkK8SA1rnwvAX/64IMP9P3vf7/6/3g8TnLfZbRh6ZL5+XlFIhGNjIxUH/F4XLlcrqdj6JV1AnBGqWzr3bVP6x6lcrOejuiGrVJZb9/7WG/f+1h//9Z7+vu33tOjNud6BrqpbNu6/8lj3f/ksR5ulvRws9S0jzTgJMOQzp08VveojB26VSpr84CPrZ052puprPfTx1v69PGWbJtPOrqHmnuXFQoFXblyRadPn9bs7KwSiYQkqVgsKp1OKxaLaWpqSjdu3FAoFOqZGHplnUAvGAx+PnL5d2LPd+U9y7atTx5tT5/37tr2aOnPhI63HOvhs62Ssvm7dWV+HRH+KLAlrW1sT4f4q3ufSJK+9mVqSQBA2p4Z5LmnGgdLlqS3732iB482D7Te08ND1WlIdyt+uKG1jcfV+dz5jUQ38UlzUS6XUywWk2mayufzdQmpaZpKpVIaGxtTMplUoVBoWMavMfTKOgHsrVS29S+/3h7IsZKscwHinSeHBqo3dn64+Lbr7+dkE2nAryq1tpL04vlTksRgtQD6GldxLrEsS/F4XJK0sLCwZyKaSCSUz+c1Pz+vK1euKJ/P+zqGXlkngP9/e/fz48Z533H8w5Vk2bKhDFdB/CNxanORNiiC1BhKAdqL22jYHoLAQEFGt7YooOV/sISOPi3IcwGH3IsapActiUZF0ENNKqh7aFB7SShBBTQ/SCmJ48iptJyokWWvtDs9rDgmKXI53OUun4d8v4CBZrUzD7+73O9wvvM88wwwXGeItCQ9eDyM39SBoyePH1M6+QVJ0r0Hu71sFGi7KF731t1r+/UvPz/laDAPHm7vhKOpMDkLsZheObOby3/xeHTaCS5GD8Rv5ZBcvHhRvu/LdV25rrvntrlcTtLusPRKpWJ0DLa0CQCYDccWYnp58ZReXjyl08+c0OlnTmghRgErfVq8vvrZZ/X1Lz+vr3/5eZ08fmzaYQHARB1biOmFzzytFz7ztF572dFrLzsU90PQc38IfN8PC0/P80Zun0gklEgk1Gq1tLq6qnQ6bWQMtrQJs7379j9r8/avtfjC5/W1v/zraYdzaObh57x+/bp839ezv/pPLf/93007HBzQxrV/0e9uvz/Tf7PzkJeSdPnyZd26dUuvvPLKwOczwx7z8l5OIzf/8Pnn9j1iaT+XF+flvZwHly9f1rvvvjvtMAbikschKJVK4fq5c+ci7ZNIJCTt9ki3Wi0jY7ClTZjtvbe/p7e/+w967+3vTTuUQzUPP+f1H13XO++8o+9+95+mHQomoH7t+zP/NzsPeSntnni++eabunz58rRDwQHN0nv51LEFfeOrL+obX31RX/rcc/rS557TscejcKaRm8ePLejEPpf9zE8yS+/lvLt8+bLeeuutaYcxED33h+DKlSvhetRJ3zoFq7Q7uVxnpniTYrClTcA2OzuB2h/t3qP30w//T5KU+Oyzxk9udu/B7mz7v/YfHMkkcAAAex0/thDOMH/muZNTjgaYTRT3h6DRaITri4uLkfbpLmwnMVHcYcRgS5uAbbaDQD/77e5jzP71x7+RtDtzvenFPSbn0faOWnfuS5Lu/v4TSVL81FNamKHJ0abxyEcAgDk6ozekTz/rjjGHykRR3E9Y/zDx7l7mvZw5cyZc39jYMC4GW9oEABttbe+EF3Y6F3qSfxDXwr7u7JwtQRCEM/o/3N6RJB1fiCnGCSFGeLS9o59+uJtP5cePfvzmn7ykp08w6SAwDYzeOHwU9xPm+/6+9uvujd5vG4cZgy1tArPiwdb2nt9/5sSxiT8irPOaTx8/NrDHeGcnGBnXPDu2ENMfv3haksL35eTxYz3F6TCT/L1+8nBHp54a8r1H29o25H18tL2jrcfF+l4ebG3rOz/8hSSp/ou2pN0LHyeOUdx3F6/f+eEtSdI3v/qSdhZOKHbipHYWTuijrUcD912IxYYWuR8/3NZOsL+pxvZq96gFku59vHtsfL/9QJJ0/5NHI3+2afxuOrnZ76Dv5dajHT3aGZ1ng8QU0zNPDW734fZOeLENe3uwtb3nEz6GvfdRmPbef/xwR7ETJ3u+7v+7PfXU+OXnw+0d/eh9X5L07XeakqS//dNXhsY4zyjuDbS5uTntEA4lBtPa/OSTT3q+/sEPfqCf//znY7URj8d7RhLsx4OHj3T71vvabG9q+yNfkhQEO7rzwS8O1K6ptj7+KPz39q2fTTmawzPOz/lwZ0e/++CeJOn2wu7fdGHEPplzX9AzJz49hH/04a3dfyPGt9drfvO1lxQfUB22P9rS969/EH69de+Otj/ytfVgdt/L/jyMmpf3fvPpvw8ePlL5vfcjv+bvPvAlSbd1WicW9r41Y9D7+NZvb+mN1z4/cPv/at3RT27/fuDrbT04uty8ceOhbt25r//46f+Otd84v5tBZu3483BnR796/P7f2Ppw99//vqEPT72iZ7/ylD489ZLe/Md/G7jvF8+c0p//0ecGfu/ff/Jb/fJu1KNJr+dPn9RffeXFJ/6/81knHfx9jJqX+zm2Skf/u5EG56akSO/lZ06dGJrz13/p68ePi6JxnTyxoAvnvjjwe//zm3t69+b+zsG+9uqibt/q3XdWcnPY39zzp0/q/v3dW6/u37+vGzduhPsMe++jMO2932xv6tmvnA+//vbb17X43i97tvmbP3tFknT71q3I8Tzc2dHdx7/Xm8FdSdKNz/y+5zzoIO7evat2ux15+zt37jzxf/11xdQEmKhqtRpo92JxMM6vt1gs7mu/o4rBljbHcfXq1Z52WFhYWFhYWFhYWFhYxl2uXr2675pkkpitacJMGCZ+GDHY0iYAAAAAzCOKewAAAAAALMc99xMW9VnttsVgS5vjeP3113X16lX5vq979+7phRde0FNPDZmFaohJ3HMPAAAAYDrGvedekra2tnT79m2dPn1ajuPo9ddfP6ToxkNxP2H9z2r3fX/sIvagRe9hxGBLm+Pu+8Ybb+x7fwAAAAAwBcPyJ2wSvdH9Ra8JMdjSJgAAAADMI4r7CesvNqM+qq17crlJ99xPIgZb2gQAAACAeURxP2H9xWbUGeGbzWa4nkgkjIvBljYBAAAAYB5R3B8C13XD9ai90d3bnTt3zsgYbGkTAAAAAOYNxf0h8DwvXG+1WpH26d4unU4bGYMtbQIAAADAvKG4PwSXLl0K1+v1eqR9Go2GpN1h5pMYan4YMdjSJgAAAADMG4r7Q+A4Ttgjvb6+PnL7Wq0WrudyuZHbVyqVsMA9yhhsaRMAAAAA5g3F/SEpFotyHEe+76tSqYzcVtq9/3x5eXnodr7va2lpSZlMRslkUoVC4chjsKVNAAAAAJgnsSAIgmkHMasqlYoymYyk3RneBw0hL5VKymazchxH9Xp9z2Hm3e1Ju73e7Xb7SGOwqU0AAAAAmBf03B+idDqtarUqx3GUTCZVKpXC7/m+r2w2q2w2K8/zdPPmzZHFav+j46IUt5OOwaY2AQAAAGBe0HN/BHzf1/r6uorFYs9M757nhQVrVLlcToVCQYlEQtVqNXKRO8kYbGsTAAAAAGYdxT0AAAAAAJZjWD5wAKVSSclkUvF4PFwymUzPrP7zEANgkmnnRKPRUDab1dLSkmKxmGKxmJaWlpTNZslLzLVp5+ZeWq2W4vG4EbEAR8mkvKzVaspkMuHnZ2cicfIyOop7YB8ajYbi8bjy+byy2aza7bba7XY40V8qlVImk5Hv+zMdA2ASE3Iil8spmUxqc3NTuVxO5XJZ+Xxe0u4JVCqVUjKZ7LntCJh1JuTmKJ3X5zMT88KkvGw0GlpaWlIqldLi4qLK5bLa7bbK5XIYB4/AjigAMJZqtRpIChKJRNButwduUywWR25jewyASUzICdd1g0QiETSbzYHfX15eDiSFS7VanXgMgGlMyM1ROq8vKSiXy0f++sBRMykvO6/jOE5Qr9cHbtNutwPHcfjcjIDiHhhD5+AiaegBqKNzIu+67szFAJjEhJzI5/ORToD6C/xhFwKAWWBCbo7Sbrd7cpLiHrPOpLzsvrA27POwXC4HiUQikBSk0+lDiWOWUNwDY0in05EPcs1m81BOFkyIATDJtHOi02aUHoX+QsLzvInEAJho2rkZhed5YaHDZyXmgSl5Wa/Xw7aLxeLQ7VzXDbdLJBITjWEWcc89EJHv+6pUKpIU6ZF8iUQifFTh6urqzMQAmMSEnMjn83IcJ9K2juNoeXk5/LpWq3H/PWaSCbk5SqlU0ubmpr71rW8dyesB02ZSXmYymfA1uj8XR8WDvVHcAxGVSqVw/dy5c5H26RyEGo3GRE7gTYgBMIkJObGxsSHf95VKpbS0tDRy+2Qy2fM1swBjFpmQm3vxfT+c9DLqxTnAdqbkZalUCtvKZrN7blsul7W8vKzl5WUVi8WJvP4so7gHIrpy5Uq4HvVEoPsK4yRO4E2IATCJCTnRfbLTarXCXpEory9JzWbzwDEApjEhN/dy8eJFXbp0iZ5AzBVT8rLzFBlJSqfTI1+/WCyqWCySrxFQ3AMRNRqNcH1xcTHSPt0Hznq9PhMxACYxISfOnj07tP1B+uPk0VuYRSbk5jCVSkWtVksrKyuH9hqAiUzIy+7b0RzHoWCfMIp7IIL+YUhRD0RnzpwJ1zc2NqyPATCJKTmRz+fD115ZWRl5H2N/3FGG8gM2MSU3B/F9XxcvXtTa2tqhtA+YypS8rFar4Xr/xXEcHMU9EMF+e9a6r3YetHfOhBgAk5iSE67rqtlsKgiCnqGGw/SfYLmue+AYAJOYkpuDXLx4UcvLy+Qd5o4pedk9tL+77UKhoGQyqVgspng8rmQyqUKhcODXmzcU98AR2dzcnHYIRsQAmGQaOdE9IZDjOJFmLAbmzWHkZq1WU6PRiHQRDsCTJpGXg24NSCaTunv3rsrlsoIg0M2bN5XNZpXL5RSPx0fOZYNPUdwDEfQfzPYzs+5Br3aaEANgEhtzon+2YYoMzCJTczOTyahcLk+8XcAGJuRl//6O4yiTyejChQs9t7h1Hhtbr9fl+74ymUzPTP8YjuIeiMCEotiEGACT2JgTuVwuXPc8L/KzfQGbmJib2WyW4fiYaybkZf9taZ1e/GGTW7quG35OZrPZnl5/DHZ82gEAADAPKpVKeK9hIpGgBxE4IrVaTbVajcdOAlPWP3qgVquNnIG/u9c+k8mQxyPQcw9EsJ+hS7MYA2ASm3KiM0O3tFvY1+t1q+IHxmHa33Y2m+2Z6wKYRybkZf/j9xzHGTmapntemlar1TMhH55EcQ9EMInnUh/0oGpCDIBJbMqJTCYj3/cp7DEXTMrNXC4nz/OYuBJzz4S87N8/6qPwuh/bx6i3vTEsH4hgEicZ/QdVG2MATGJLTuRyOdVqNQp7zA1TcrPRaKhUKqndbh+4LcB2JuRl//7dRXtU9NzvjZ57IIL+g1HUR4F0XxWddM/9NGIATGJDTpRKJRUKBbmuS2GPuWFKbjI7PvApE/Kyf/+o7XVvx2Od90ZxD0TQf/CJOpSpe9KP/VydNC0GwCSm50StVlM2m5XneRT2mCsm5GYul5PrugzHBx4zIS8l9dxjb8IM/rOG4h6IqPtgFPWqYfd2586dm4kYAJOYmhONRkOpVErpdFrVanXodr7vP/FoIGAWTDs3K5WKKpWKYrHYyKVQKIT7ZTKZnu91P74SsN2081Lqvc9+P6MH6KjaG/fcAxF5nhc+XzPqyXj3dul0eiZiAExiYk60Wi2dP39ey8vLI2foXl1d1ZkzZ4Y+4xew1bRzc5zZ8bPZbPjaKysrSqVS4fcoJDBLpp2XUu+j7fZzcTvqJHzzKhYEQTDtIAAb+L6veDwuSZFO2iUpFotJ2j05mMRzOU2IATCJaTnh+76SyaQ8z4sUSyqVUjab5cIbZo5pubmXZDIZFjzlcpl8xMwyJS/j8bh835fjOCMnvOyOWZKq1Sq32+yBYflARI7jhAeT9fX1kdt3z+YZZVhfpVIJTy6mFQNgGxPystv58+cjF/adeEY94xewkWm5CcCcvLx06ZKk3cJ91PYbGxvhOvNoRBAAiKzZbAaO4wSSgnK5vOe26XQ6kBS4rrvndu12O0gkEoGkQFKQz+ePPAbAZibkZRAEged5ged5Qb1eD5rN5sClXq8H9Xo9qFarwcrKSsDHMGaZKbk5Snd7xWLxwO0BJjMlLzvbp9PpSDFICur1+sh25x1nFcCYyuVyeJBpNpsDtykWi4GkwHGcodsMaq+zz1HHANhu2nm5vLzcs33UJZFI7PtnBmww7dyMolPoTOpiAWA6E/Ky+yLDsLzL5/Nhm6MuRGAXxT2wD9VqNXAcJ3Acp+cqf7vdDk/yPc8L2u12pLa6D4hRe9knGQMwC6aVl90nH+MunudN4kcHjGbCZ2a3zuiZcrnc0yvYKUry+XxQrVaDarXKZyhmlgl52Ww2wx58z/OCcrkcNJvNoFqthrmZSCTosR8DxT2wT+12OygWi4HruuHB0XGcIJ1OB9Vqday2OsNzE4nEWL3sk4wBmAXTyMvuXr9xl5WVlYP+yIAVTPjM7OgePtwdS//CMH3MOlPyslgsBp7nhXnnOE5Y7GM8zJYPAAAAAIDlmC0fAAAAAADLUdwDAAAAAGA5insAAAAAACxHcQ8AAAAAgOUo7gEAAAAAsBzFPQAAAAAAlqO4BwAAAADAchT3AAAAAABYjuIeAAAAAADLUdwDAAAAAGA5insAAAAAACxHcQ8AAAAAgOUo7gEAAAAAsBzFPQAAAAAAlqO4BwAAAADAchT3AAAAAABYjuIeAAAAAADLUdwDAAAAAGA5insAAAAAACxHcQ8AAAAAgOUo7gEAAAAAsBzFPQAAAAAAlqO4BwAAAADAchT3AAAAAABYjuIeAAAAAADLUdwDAAAAAGA5insAAAAAACxHcQ8AAAAAgOUo7gEAAAAAsBzFPQAAAAAAlqO4BwAAAADAchT3AAAAAABYjuIeAAAAAADLUdwDAAAAAGA5insAAAAAACxHcQ8AAKzg+76y2ayWlpYUj8eVTCaVy+UO1F6tVptghAAATA/FPQAAsEIymdTS0pKazabW1tbUaDRUKBQUj8fl+/5YbbVaLb366quHEygAAFNAcQ8AAIxXKpXkuq5WVlYkSel0WsvLy5J2e+DPnz8fuS3f95VKpbS2tibP8w4lXgAAjlosCIJg2kEAAACMy/d9xePx8Ot6vS7XdUfu0xnO37k4AADALKDnHgAAWMlxnJ4CfXV1deQ+58+f7+n1BwBgVtBzDwAArNVqtbS0tBR+3W635TjOwG1TqZQSiYSKxeKRxVapVHTlypVwToBObBcuXNDKyooqlYpWV1fD73ued2TxAQBmCz33AADAWolEomco/vr6+sDtMpmMHMc5ssK5M6t/Z/K/ZrOpZrOper2uS5cuKZfLKRaLKZPJKJ/Pq9lsKpFIqFQqKZvNHkmMAIDZQnEPAACs1l0MDyres9msfN9XuVw+kniSyaRKpZKKxaKKxeIT8wBUq9VwPZ/Ph5P6dXr1u0ciAAAQFcPyAQCA1fon1usemp/L5VSr1VSv148kllqtplQqJcdx1G63B27THW+nxx4AgIOi5x4AAFjNcZyeR9p1huYXCgVVKhVdu3btyGJptVqStOcj9rrnBKCwBwBMyvFpBwAAAHBQ2WxWtVpNksLh98ViUfV6fegEewAAzBKKewAAYL10Oh2u12o1bWxsUNgDAOYKw/IBAMBM6B4Kn8/nDzTk3ff9cMb7zpJMJlUoFAZuH4/HFYvFwsn9KpWKYrHY0KWj+/+SyeS+4wUAgOIeAADMhFQqFa53z0g/rlKppHg8rlarpWq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", 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Wv7+L3zfS9vb1xrlJi+dGrnabm5uT1+uV1+utuY+287ls1ntR+mh+2nzS9NJLL8nv9+vs2bN64YUXmt7Vo5rNjmMjrD5ujdjKuhYXFwv3rf6uaNaxt+Kc1ozX3qz3QiuPSafZ7HPbid8Z7apdztONaNXx78TPpMvuAICdrJkXeq02Ojqq0dFRxWIxzc/PK5FIaGFhoeRXxrm5Ofn9/roG76lHu/TpCYfDhQF8otGoxsfHC48lk8nCPqmHHfuxW+3fv3/TX7m3uq+LHw+FQk3vP7yd80Cz3ovhcLhkAKpIJGLbRU2t41gvq4/bVrRLLUyxZh/7djynteN7oVH5RLETkw00Rzudp9Ec1IgDTZZIJNquBtNKgUBAkUhE8/Pzhf5LxSOS5mtBtqod+zt5vd5C/7J8spMXiUSqjk69Gav3YzMVJ47b+XGk2RcQxbFs1kSt0X3djoPf5DXjvej3+wuvd3Z2tjAokF3qPY61tNNx28oAQa0491l57NvpnNas94Kd30dbHfCvVYr3x8bPbbt+Z2wWc7ux6zzdjGvadj3+7YBEHGiypaUly6cXsVNxDVwl+RE94/F4U0YgrjSCdTsobu5b/PrOnz9fV1PgVu/HZhoZGSncb+R4bFy22QMdFa+/eN3b3dfFfQnb8bO9nfdicZ/dUChUczTpSpr9max2HBvVTsct/75qZF9ZPRBYs499O5/TmvVesHP2inwC0y5dtIpt3Kcb91M7fmfUinmrrLhGsfM83Yxr2nY8/u2CRBxAQ5aWlvT888/XXM7r9RYusrZzEt84dU0jA6pYWdsSCAQKcc3NzRWmx8n3Kayl1fuxmYovAhYWFup+XvGy9e6nehXvm43r3u6+Lp42pdGBplox2N5W34sbp3SqNT1Mcf/YYhMTE037rG12HBvVTsct/zoaqQ3y+Xwlr7/emOrZhhXHvp3Pac16L1h5TGrJJyXtmIgXT1dX6XPbjt8ZtWLOa3Smg2Zrx/N0o9rx+LcLEnGgQcVNbCr9spdMJrvyZFEskUjU9atmfiqJ7U4pce7cucL9emtQ8gOaWKl4UJTp6WlFo9GGBkpp5n6cm5tr2QjrXq+30OQ5lUrV/eNI8XRF9R7Hei9giy+qKq17O/va6/WWXEjUmiM5L5VKye/3t6QVx1beixsvGmuNrN7IBdRGzTqOjWin45afoq7Rz2jxKOT1JLlSfTVOVh37Vn831KuZ7wWrjkkt+WPg9/u3va561fu5LR6pu9Lnth2/M2rFnFfrmq+YFd/BVp+nW3FN28rj32lIxNH27O7bt1Hxl2ClE148Hq97XuB2e22NeO6552ouk98/253Pe3R0tNAkKRaL1XUSn5iYKLlgskJx/9uJiQklk8mGm4xtdz+mUikNDQ0Vpi7ZrGloM0UikcKXcz1N8ZPJZOHX+OLjWUu9g97lL6pCoVDVdW9nX587d65QMzIxMVHXhfVzzz2nUChUs0alGeeBrbwXN8a12WtKpVIlF4TFMS8tLdXst9rM49gIK49bI4pfSyMX68Wflampqbpq3Io/j9XeW1Ye+1Z+NzSiWe8Fq47JZlKpVCHeVjbPrec7tHhfbva5bdV3RjNjlkr3d62R1ScmJprej9nq83Qzr2k306rj33FMoM1FIhFTUuEWjUbret74+HjhOZFIpObyxdvYzPLycmE5n89X9rjH4zGXl5erPn90dLTw/Hg8XjOujYr3x+zsbM3lm70fiuOvdSx8Pp/p9XqrPt7oa/H5fKYk0+PxbLrvxsfHN91uMxXvj/Hx8S09bzv7cXZ2tuSYeTyehuLfjsXFRdPr9ZqSzFAoVNdyo6OjNdcbCoUKr8Xr9db8nOSXr7buZu3rxcVF0+PxmJJMr9e76ee81ntwu+eBWuus970YCATq2jeBQKDkvVa8fp/PZy4uLpY9p9nHsVjxuaPWMW3mcduOfAz1focVKz73VdrXeYFAoLBs/vVutmwzj30zvxus+v5u5nvBimNSTX7/t+L8nv8c5q9xNjuXFH8O6/ncWv2dYUXMpvnRsa50zZc3Pj5uRiKRkmM9Pz+/6XrrPY9ZeZ7e7jVtI9dxVhx/q84VrdIeUQBF4vG4OT8/b87OzpqRSKTwpVmcaEQiEXN2dtacn58vubjLPzcajZY8z+PxmNFo1Jyfny85EeW3U3wSz3/w8+uvpPhEFwqFzOXlZXNxcdEMBAIVvwDycW38UcHn8xXi2uyEvbi4WPF1eb3ewvOLT5RW7of8xVb+ImV0dLTkGCwvL5vz8/OFC62NF9+NvpaNik+6oVCosPzy8rI5Oztr+nw+0+fzbbqOZorH44V4Gtnmdvdj3vz8fNl7qpWWl5cLryV/DBcXF83l5WUzHo+b4+PjpsfjKXxu65F/H87OzhbWX+nzkX8s/76upln7euPrzb+m/GdpcXHRjEajptfrNQOBQNn7YbvngVq2+16sdJGXP6/lj13x5y8ajZrj4+NmIBCouN5mH8d6zh3VEqLtHLdmyW9/swvQzRT/sLHxgnd2dtb0er1mJBIpOyfk32OVLmibeey3+zlr1fd3M98LVhyTSvL7vtpnrZmK9+f8/Lw5OjpqBgKBkuO1uLhY8tob+XHJyu8Mq2Iu/gFndHS07HprdHS08LkuTsTzr7E4Cd7qecyq87RpNn5Nu53ruGYc/1adK1qBRBxtJ/8rWP6DuNltY+JRfKKqtGzxr2uLi4tVly1evtqFXf6kXhxHtZNG/jVtFtdm26rndRV/4Vi5H8bHx0tOzMW/AOefEwgEqn7JNfpaqu378fFx0+fzldRu5E+qrRYIBBq+uN7ufty4rvw+2KxmxkqVjknxa2gkuQmFQmUXDfkENhqNmpFIxBwfHzdHR0fr3j/N2te1Xm+1ZNM0t38eqMdW3oumaRYugPJJUr4WLxAIlL2e4gulzbbV7ONYz7mjVkuArRy3ZsknY9upcd8Yf761QSgUKrxv8j/I5BOAQCBgjo6ObnpR24xj34rvhmZ9f1fal1t9L1hxTDbK78dWfL8VJyv57+J8cuv1erd8ztyo2d8ZrYg5/54ufq/la6Hz8o/nj3U+Sc+/nu2cx6w4T+c1ck1r1XVcvce/1ecKKxmmaZoCAACApQYGBpRKpbS4uNj1g3qiOVKplAYGBuTxeLS8vGz59sLhcKFvbjweb2k//q3qxJgBicHaAAAAWiI/kn29o3YD58+fl1Q6ICOA7kAiDgAA0AL5ZKp46iRgM/lRwBuZGhNAZyARBwAAaAGPx6NIJKJUKlVoSgtUMzc3p2QyqUgkIk+Tp8UCYD8ScQAAgBYZHx+X1+uta75j7GyTk5Pyer0aHx+3ZftbmfPcbp0YM3YuEnEAAIAWmp2dVTKZ1MTEhN2hoE1NTU0pkUgoGo22dLvJZLJwP5VKtXTbW9WJMQOS5LI7AAAAgJ3E5/MpGo0qHA7r2WefZZRnlMj/SBOJRBQIBCzfXiKR0NLSkhKJhGKxWKF8YmJCS0tL8nq9hVu76MSYgY2YvgwAAMAG4XBY58+f15tvvkkfYEjK1ej6/X75fD7Nzs62ZJtDQ0OFWuWN78N8DXMkErGtiXwlnRgzsBGJOAAAgE3C4bAWFhYUj8ftDgVtYGRkRB6Pp2VJOAD7kIgDAADYKN9XnAHcdrapqSldvXqV9wGwQ5CIAwAAAADQQoyaDgAAAABAC5GIAwAAAADQQiTiAAAAAAC0EIk4AAAAAAAtRCIOAAAAAEALkYgDAAAAANBCJOIAAAAAALQQiTgAAAAAAC1EIg4AAAAAQAuRiAMAAAAA0EIuuwMA6pFKpXThwoXC/4cPH9auXbtsjAgAAABAu7tz544uX75c+P/UqVPyeDz2BXQXiTg6woULF/TFL37R7jAAAAAAdLBvf/vb+pf/8l/aHQZN0wEAAAAAaCUScQAAAAAAWoim6egIhw8fLvn/29/+th5++GGbopHeeOONkqbydsdTy9jYmBYWFjQ8PKyZmRm7w6mJeK3Tae9dqbP2r9RZ8XZSrLx3rUe81um0928n7VuJeK3Uae9dqXz/bnwNG/MKu5CIoyNsHJjt4Ycf1vHjx22Kply7xbNRf39/4W87x5lHvK3T7u9dqfP2byfF20mxbsR7t/mIt3Xa/f3bafuWeFun3d+7Uu392y4DPtM0HQAAAACAFiIRBwAAAACghUjEAQAAAABoIRJxAAAAAABaiMHagB1gbGxMp0+f1uDgoN2h1IV4UazT9m8nxdtJsXaiTtu/xIu8Ttu3xItinbJ/DdM0TbuDAGq5ePGiTpw4Ufj/hz/8oa0jNrZbPEC9eO+iU/HeRSfj/YtO1Q3v3XZ9DTRNBwAAAACghUjEAQAAAABoIRJxAAAAAABaiEQcAAAAAIAWIhEHAAAAAKCFmL4M2IKDBw/qa1/7Wsn/QCfgvYtOxXsXnYz3LzoV713rMH0ZOkK7TjsAAAAAoH21ax5BjTg60tjYmPr7+yuWj42NtT4gAAAAALaZmZnRzMxMWfnKykrrg6kDiTg60sLCQsXy06dPtzYQAAAAALa7dOmSLly4YHcYdSMRR0caHh6uWCM+ODjY+mAAAAAA2GpwcFCnTp0qK19ZWalaiWcn+oijI7Rr3w4AAAAA7atd8wimLwMAAAAAoIVIxAEAAAAAaCEScQAAAAAAWohEHAAAAACAFiIRBwAAAACghUjEAQAAAABoIRJxAAAAAABayGV3AAAA7DR/NP/alp/71ZFjTYwEAADYgRpxAAAAAABaiBpxAAAAAJbaTkugbkPLJkjUiAMAAADAjpBMJhUOhzU0NCTDMGQYhoaGhhQOh5VMJgvLTU9Pa2JiwsZIux+JOAAAAAB0uYmJCQ0NDUmSZmdntby8rOXlZc3Ozsrj8cjv92tiYqKQrKdSqYbWH4vFNDAwoLm5OQui7z40TQcAAACALhYOhzU9Pa14PC6fz1fymM/nk8/n09mzZxUMBuX3++taZyqV0tLSkmKxmKLRqBKJhCRpaWmp6fF3I2rEAQAAAKBLxWIxTU9PKxKJlCXhxTwej+bn57V///6a6/T7/RoYGJDf79f8/LyeffbZZoa8I1AjDgAAAABdKt/Xe3R0tK7lI5GIgsHgpsu88MIL8ng8hf/zteGoHzXiAAAAANClGk2SR0dHS5LsSmo9jtpIxAEAAACgy8VisbqXfeaZZyyMBBKJOAAAAAB0La/XKynXRL3ekdBHRkYKI6zDGiTiAAAAANClwuGwpNwo50ePHq1rerHR0VGNj49bHdqORiIOAAAAAF1qfHy8UCueSqUUDAZlGIZGRkY0NTXVUJN1NA+JOAAAAAB0sUrzh8diMU1MTGhkZESGYcjv92t6etqmCHceEnEAAAAA6GIej0fxeFzRaLTqXOKJRELhcFgDAwNKJpMtjnDnIREHAAAAgB0gFAopHo9reXlZs7OzGh8fL0vMU6mU/H4/ybjFSMQBAAAAYAfxeDwaHR1VJBJRPB6XaZqan58vJOWpVKowyBusQSIOAAAAADtcIBBQPB7X6OiopFwf8kQiYXNU3YtEHAAAAAC61MjISEPLz87OFu4vLCw0OxzcRSIOAAAAAF0qFosplUo19JxAICBJWlxctCAiSCTiAAAAANDVGq3ZzvcVP3DggBXhQJLL7gCArRgbG1N/f3/F8rGxsdYHBAAAALSpSCRSqOWuR75veCPPsdvMzIxmZmbKyldWVlofTB1IxNGRqv2qd/r06dYGAgAAALS5WCymiYkJRSKRmssmk0nFYjGNjo5WnXO8HV26dEkXLlywO4y6kYijIw0PD1esER8cHGx9MAAAAECbm5qakiSdPXtWHo+n4jKpVErBYFAej0fnzp3b0nYa7Y/eLIODgzp16lRZ+crKSlsOOkcijo40MzOj48eP2x0GAAAA0PZCoZAikYiCwaAGBgYUCoU0MjIir9crj8ejZDKpRCKhyclJeb1exePxqsl6Xj7hTiaTmpycLJQ///zz8vl8Gh4eLpTVWlczVOuievHiRZ04ccLy7TeKRBwAAAAAupTX61UkEpHH49H8/LwSiYSi0agmJyeVTCaVSqXk8Xg0PDysSCSiUChUc53hcFjT09MlCXb+fjKZVDAYlJRL1r1eL6OvV0AiDgAAAMBSXx05ZncIO9bGJNjn8ykajW5rndFodNvr2OmYvgwAAAAAgBYiEQcAAAAAoIVIxAEAAAAAaCEScQAAAAAAWohEHAAAAACAFiIRBwAAAACghUjEAQAAAABoIRJxAAAAAABaiEQcAAAAAIAWIhEHAAAAAKCFSMQBAAAAAGghEnEAAAAAAFqIRBwAAAAAgBYiEQcAAAAAoIVIxAEAAAAAaCEScQAAAAAAWohEHAAAAACAFiIRBwAAAACghUjEAQAAAABoIRJxAAAAAABaiEQcAAAAAIAWIhEHAAAAAKCFXHYHAAAAAKDLfWfS7gjax+fO2h0B2gA14gAAAACwAySTSYXDYQ0NDckwDBmGoaGhIYXDYSWTycJy09PTmpiYsDHS7kciDgAAAABdbmJiQkNDQ5Kk2dlZLS8va3l5WbOzs/J4PPL7/ZqYmCgk66lUquY65+bmNDIyooGBARmGIb/fr2AwqOnpaYtfTeejaTo60tjYmPr7+yuWj42NtT4gAAAAoE2Fw2FNT08rHo/L5/OVPObz+eTz+XT27FkFg0H5/f6a60ulUgoGg1paWlI4HFY0GlUqlVIsFtPk5KTm5uYUiUQ0Oztbtj2rzMzMaGZmpqx8ZWWlJdtvFIk4OtLCwkLF8tOnT7c2EAAAAKCNxWIxTU9PKxKJbJoUezwezc/Pa2hoqGZt+JkzZzQ8PKz5+fmScp/Pp1AopDNnziiRSMjv91dM/q1w6dIlXbhwwfLtNAuJODrS8PBwxRrxwcHB1gcDAAAAtKl8X+/R0dG6lo9EIgoGgzXXF41GKz7u8Xh07ty5Qs16MBjU4uJiIyFvyeDgoE6dOlVWvrKyUrUSz04k4uhIMzMzOn78uN1hAAAAAG0tkUg0tPzo6Kg8Hk/Vx6enpxUIBJRMJuX1eisu4/P5FAgEFIvFlEwmNT09rVAo1FAcjarWRfXixYs6ceKEpdveCgZrAwAAAIAuF4vF6l72mWeeqVieSqWUSqU0NzdXGPitmuLm6LOzs3Vve6cgEQcAAACALpWvtZ6YmKhrJHRJGhkZqZhoLy0tlfxfPOXZRidPnqz6PJCIAwAAAEDXCofDknK12UePHtXc3FzN54yOjmp8fLys3Ov1KhQKyePxKBQKVW2aLpUm6fv3799C5N2NRBwAAAAAutT4+HghYc5PO2YYhkZGRjQ1NdVQk3UpN0jb8vJy1cHa8l566aXC/VZNYdZJSMQBAAAAoItVmkIsFotpYmJCIyMjMgxDfr9f09PTTdlefk7xvLNnzzZlvd2ERBwAAAAAupjH41E8Hlc0Gq1aO51IJBQOhzUwMLBp3+96TE5OFvqjRyKRTUdh36lIxAEAAABgBwiFQorH41peXtbs7KzGx8fLEvNUKiW/37/lZDyZTGpqakpS9b7mIBEHAAAAgB3F4/FodHRUkUhE8Xhcpmlqfn6+kJSnUqnCIG+NSKVSGhkZkZRLwpm2rDoScQAAAADY4QKBgOLxuEZHRyXl+pAnEomG1nHmzBklk0mFQiGS8BpIxAEAAACgS+VrqOtVnEAvLCw0tJ1EIqHZ2dmaI6qDRBwAAAAAulYsFisMnFavQCAgSVpcXKxr+ZGRES0sLJTUqGNzJOIAAAAA0MUaqdmWPpr3+8CBAzWXDQaDSiaTFadIk3KjsQeDwYa2vxOQiAMAAABAF4tEIg0tn+8bnq8Zr6Y4Cfd6vRWXicVi2r9/f0Pb3wlIxAEAAACgi8ViMU1MTNS1bDKZVCwW0+joaNU5x6VcEp5KpRSPxzedJ3x+fl5DQ0ONhtz1XHYHAAAAAACwVn5u77Nnz1ZNnFOplILBoDwej86dO1d1XeFwWHNzcwoEAlUHg1taWlIqlVIymaz7R4CdhEQcDUsmk1WbnjRjeQAAAADNEwqFFIlEFAwGNTAwoFAopJGREXm9Xnk8HiWTSSUSCU1OTsrr9W5ayx0OhzU9PS0pV9Nej+Hh4Wa9lK5BIt4i09PTikajSiaThbJAIKBwOFyz74UVpqamNDExocXFxYaT5GAwqEQioUAgoGAwqOHh4cKHWFLhgzw/P6/z588XPswAAADYoT531u4Idiyv16tIJCKPx6P5+XklEglFo1FNTk4qmUwqlUrJ4/FoeHhYkUhEoVCo6roSiUQhCW/EZk3XdyoScYslEgmdOXNG+/fv18TEROGNnUwmFY1GNTIyotHRUZ07d64lb9BkMqlwOFz3r1ebicViNdfj8XhK5iIEAAAA0DobpyDz+Xxbnufb5/PJNM1mhLXjkYhbKBaLFZp8bGzekf9lamhoSOFwWIlEouZAB41KpVJaWFhQMpnU4uKiYrFYYQTEVggEApqdneUXMAAAAAAowqjpFskPdCBp02Q0FAopFAopmUzqzJkzTY1hYWFBIyMjmpiYKDQlX1xctDwx9vl8mp+f1/z8PEk4AAAAAGxAjbhFnnvuOaVSKfl8vk2H/ZekiYkJTU9PK5FIaG5uTqOjo02JIRAIWNZ0JBKJaHR0VIlEQktLS5Kk/fv3KxAIkHwDAAAAwCZIxC2QSqU0NzcnSXUNxOb1euX1epVMJjU5Odm0RNxq+bgBAAAAAPWjaboFikcSPHnyZF3PySe0iUSiZGR1AAAAAEB3IRG3wPPPP1+4X28z7eKa5WaMaA4AAAAAaE8k4hYoHpl8//79dT2nOGFnzm0AAAAA6F4k4k22sVl5vX2oDxw4ULi/sLDQ1JgAAAAAAO2DRLzJUqnUlp5XXCO+1XXYIRaLKRgMamBgoHDz+/0l/eQBAAAAAB9h1PQ2lJ8OrJ1dvXpVIyMjknLTr507d04ej0epVEqTk5MKh8OamJjQ7OxsXSPHN+qNN95o+DkHDx7UoUOHmh4LAAAAAOu9//77+uCDDxp6zlbyhlYgEW+yjUn0VubU7oQa8ampKUWjUYVCoZJyj8ejSCSioaEhhcNhjYyMaH5+vunJ+Be/+MWGn/O1r31Nv//7v9/UOAAAAAC0xh//8R/rD/7gD+wOoylomt5knZBEb5fX69Xs7GxZEl4sFAoV+scHg8FWhYYmMU1Tt9bSZbfVtUzV56xnshWfY5pmCyMHAAAA2h814mjY7OxsXcuNjo5qampKqVRKExMTikQiFkeGZlldzyh6oXw++74ep37z1FDF51x857q+8+P3y8rDp7za3cOpBgAAAMjj6rjJttIUvVuNjIxoampKUq4p+9mzZ5u2f7797W/r4Ycfbug5Bw8ebMq2AQAAALTeb//2bzfc2vaNN97YUrdWq5GIN9nGecNTqVTDyWe3JPMbp26LxWIaHR1tyroffvhhHT9+vCnrAgAAAND+Dh061DWDL9NHvMmakURvTOY71cbX8dJLL9kUCQAAAAC0DxLxJtuYfNY7FVnxIG/tWiOeSCQ0NDSkgYGBLc0TvhMGsutmv/bUEf36U4NVHz/+wF792lNHWhcQAAAA0KFIxJtsYxJdb/K5uLhYuL+xSXe7eO6555RMJpVKpRQOhxtOrNv1BwbUp6/Hqb4eZ9XH3U7Hpo8DAAAAyKGPuAV8Pp8SiYSk+mvEi5c7efKkJXFtVzJZPor2Zja+9qGhyqNtAwAAoPuYpqnV9fKpT91Oh9zOyvWBq2sZmdra1Kcuh0M9rsrrvb2eUXaLU6o6HYZ2uSpXNmxnvX1upwzD2NJz0flIxC0QCAQKiXi9yWvxcs0a0KzZvF5v4XVFIpGaNdwbX/szzzxjVWgAAABoM9WmQ/3co4f0xGFPxef82YuXtLpWnrzX40nvAT01dKDiY+cXLuvqzbUtrffxw/v0+Ufvq/jYX73yjq4sr25pvUzxurPRNN0CZ8+eLdyPx+N1PSef4Hq93rZtmh4IBDQ6OirTNDU+Pl5z+fn5+ZLn0jQdAAAAAEjELeHxeBQIBCRJ58+fr7l8LBYr3J+YmKi5/NzcXCFxb6VwOKy5ubm6+4bPzc0V7kciEYuiAgAAAIDOQiJukWg0Ko/Ho1QqVZKQVltWyvUtD4VCVZdLpVIaGhpSMBiU3+/X1NTUtuNsZMA1r9er8fFxnTlzpuay09PThabp4+Pj8vl8Ww0RAAAAQBMkk0mFw2ENDQ3JMAwZhqGhoSGFw+GSbqXT09N1VRBi6+iUYBGv16tz584pGAwqGAxqcXGxYpPz6elpzc3NyePxaHZ2dtN1xmKxkg/I5ORkXU3ENypOvusdTC4vEonI7/drZGREs7OzFZubz83NKRwOS5JCoRC14QAAAJCUmw51b5+76uO//tTgtgZrq+aZ4cPbGqytml95/IG61ruWzmr+R+9JUmGWGUeLB2qbmJjQ1NSUQqGQZmdnC7lJMpnU888/L7/fr1AopHA4rHA4vGkFYSWxWEzBYFDnzp1r2zGv2gmJuIVGR0c1Pz9fqMGORCKFN3QqldLExISmp6cVCASqJrXFNj5eT1/y4mbvyWSyUPuel/+geb3ewvqHh4c3jSUejyscDmtgYEDj4+M6efKkvF6vlpaWFI1GCz8sFL9eAAAAoK/HWXXE9PzjVuh127ve3T1ScPiwJTHUIxwOa3p6WvF4vKylqs/nk8/n09mzZwt5Sz1SqZSWlpYUi8UUjUYbnjVqpyMRt1ggENCbb76p8+fPKxqNljTxCAQCmp+fL/Qnr2dd4+PjmpqaktfrrVmDLkkjIyOSSpP44vtLS0uanJws/J9KpTQ7O1vzV6xoNKpgMKhoNKrp6WmlUil5PB55vd5CAs7gbJ3L5XDoSe+BsjIAAAB0llgspunpaUUikU27i3o8Hs3Pz2toaKhm91W/369EIlEYG+vZZ5+1ZQyrTkYi3gIej0ehUKgptcORSKShpt7mFpvg1CMQCNT9IwI6S4/LUXX6j83s7nHpqyPHLIgIAAAAW5GvCKy3uXgkElEwGNx0mRdeeKGk0o0kvHFUcQEAAABAl2o0SR4dHW24yywaRyIOAAAAAF2ueOyoWp555hkLI4FEIg4AAAAAXSs/wPPExETdUxePjIxoaGjIwqhAH3EAAAAAsNDt9Yxi//ReSVngE/dZNpp7sXA4XEjCjx49Wtf0Ykw/Zj1qxAEAAADAQlnT1Ovv3Sy5bXVe80aNj48XasVTqZSCwaAMw9DIyIimpqYaarKO5qFGHECZ2+sZnV+4XFL2zPDhmr/apjNZJT9cKSnz3tsv1ybzhQIAAMBa8XhcZ86cKRm4LRaLlSThPp9P4XC4KTM9oTYScQBlsqapqzfXyspqWctk9devvltSFj7lJREHAGAHYlrT9uHxeBSPxzU9Pa1oNFpxJPVEIlFoxh6Pxwu16LAGV8cAAAAAsAOEQiHF43EtLy9rdnZW4+Pj8vl8JcukUin5/X4lk0mbotwZSMQBAAAAYAfxeDwaHR1VJBJRPB6XaZqan58vJOWpVErhcNjmKLsbiTgAAAAA7HCBQEDxeLwwYnosFqvYhB3NQSIOAAAAAF1qZGSkoeVnZ2cL9xcWFpodDu4iEQcAAACALhWLxZRKpRp6TiAQkCQtLi5aEBEkEnEAAAAAFkhnsnrtvRslt3Qma3dYO1KjNdv5vuIHDhywIhyI6csAAAAAWIBpTdtHJBIp1HLXI983vJHnoDEk4uhIY2Nj6u/vr1g+NjbW+oAAAACANhWLxTQxMaFIJFJz2WQyqVgsptHR0bKpzdrZzMyMZmZmyspXVlZaH0wdSMTRkao1rzl9+nRrA8nLpKXMndrLuXdLhmF9PAAAAG1odS3T8HPcTofcVWrRV9cyMmVuKRaXw6EeV+X13l7PKGtubb1Oh6FdLueWnmulqakpSdLZs2fl8XgqLpNKpRQMBuXxeHTu3LktbafR/ujNcunSJV24cMGWbW8FiTg60vDwcMUa8cHBwdYHI0lXX5cufrv2ck9/ReopjxsAAGAn+M8vvtXwcz736CE9cdhT8bE/e/HSlpJ7SXrSe0BPDVXuA31+4bKu3lzb0nofP7xPn3/0vi091yqhUEiRSETBYFADAwMKhUIaGRmR1+uVx+NRMplUIpHQ5OSkvF6v4vF41WQ9L59wJ5NJTU5OFsqff/55+Xw+DQ8PF8pqrasZBgcHderUqbLylZWVthz9nUQcHWlmZkbHjx+3OwwAAACgLp979JBef++GriyvtnS7Xq9XkUhEHo9H8/PzSiQSikajmpycVDKZVCqVksfj0fDwsCKRiEKhUM11hsNhTU9PlyTY+fvJZFLBYFBSLln3er0tGX29WhfVixcv6sSJE5Zvv1Ek4gAAAACabnePS18dOSZJ+qP512yOxl67e1x64rBHr793o+Xb3pgE+3w+RaPRba0zGo1uex07HUMWAgAAAADQQtSIA81w4JFc/+9i66vS97c2yIXdnA5Djx/eV1YGAACwFeFT3i0/t9pAbZL0608NbmuwtmqeGT68rcHaqvmVxx8orLfP3X4DuqF1SMSBZnC6crcuscvl3NIgI31uZ9kXLV8yAABgd48110l9PdZcZ/RadP1i1XrReboncwBaZW1F+u43SssYDV2SZBiGZV+0AAAAQLegjzgAAAAAAC1EIg4AAAAAQAuRiAMAAAAA0EJ05gSs4t5dPpK6e7c9sQAAAABoGyTigFUMo2MHcLu9ntFfvfJOSdmvPP5AzZE+M1lT76RWS8oe8PQx9RkAAABQhEQcQJmsaerK8mpZWS130hnNxa+UlIVPeRlJHQAAAChCH3EAAAAAAFqIRBwAAAAAgBYiEQcAAAAAoIXouAlYJZuRrpX2l9a+hyTH5gOeAQAAAOhuJOKAVdK3pZf/vLTs6a907EjqAAAAAJqDRBxAjmlK67dy99fScmU+GjU9a7htCgoAAADoPiTiAHLWb0nf/YYkyZ3Javjt5cJDbw48LemYTYEBAAAA3YXB2gAAAAAAaCFqxIFGOXukR36xvGwnWbslfX+6tOzTIUk7bD8AAAAAW0AiDjTK6ZYe8tsdhc1MaX21vAwAACCvePyZPGdP7lqqkrVb2vL1hMMtuapUCKyvSmZ2i+t1Sa5dVdZ7WzIzW1uvJLl3S4ax9eejo5GIA6jqlfuDWnf0MlgbAABoXNH4MwWP/GL1Co3vT1f4ob9Og5+Vjv5C5cd+8F+klQ+3tt4HfdKxL1R+7IffklI/3dp6JWbT2eFIxAFUte7oVdrZZ3cYAAAAQFdhsDYAAAAArbGxqTqwQ5GIAyhjGNKB/h49fKhfj9y3R4/ct0cO+jABAABs38Gfy90MZ8s3nUwmFQ6HNTQ0JMMwZBiGhoaGFA6HlUwmC8tNT09rYmKi5fHtJDRNB1DG5XDo2H336NiJ+xvqu9TrcurLTx0pKwMAAJAk3f9Y9cc+HdK2Bmur5lP/dnuDtVVz4ktbG6zNpn7hExMTmpqaUigU0uzsrLxer6Rccv7888/L7/crFAopHA4rHA4rFArVXOfc3Jyi0agWFhaUSqXk8/nk9Xo1MjJS1/N3MhJxoFHVpu7q2W1PPG3E4TB0754qI4sCAABslthadS3ltmi8G3evNeu1QDgc1vT0tOLxuHw+X8ljPp9PPp9PZ8+eVTAYlN9fe3agVCqlYDCopaUlhcNhRaNRpVIpxWIxTU5Oam5uTpFIRLOzs2XbQw6JONAwpu4CAABAZ4jFYpqenlYkEtk0KfZ4PJqfn9fQ0JBSqdSm6zxz5oyGh4c1Pz9fUu7z+RQKhXTmzBklEgn5/f6KyT9IxNGhxsbG1N9f3qxnbGxMY2NjrQ8IAAAAaEP5vt6jo6N1LR+JRBQMBmuuLxqNVnzc4/Ho3LlzhZr1YDCoxcXFRkLekpmZGc3MzJSVr6ysWL7trSARR0daWFioWH769OnWBgIAAAC0sUQi0dDyo6Oj8ng8VR+fnp5WIBBQMpks9DPfyOfzKRAIKBaLKZlManp62vI+45cuXdKFCxcs3UYzkYijIw0PD1esER8cHGx9MNW4+qSTv1Fe1q56+qXPnbU7CgAAAFggFovVnQw/88wzFctTqZRSqZTm5uY0Nzcn06zePdPn8ykWi0mSZmdnLU/EBwcHderUqbLylZWVqpV4diIRR0eamZnR8ePH7Q5jcw6HtOeg3VFsye31jGL/9F5JWeAT96nXvfkI6NmsqaVbayVl+3f3yOFg6jMAALCDrd+WfvLfS8t+7p+3ZMA3r9erZDKpiYkJPfPMM5vWdueNjIyUTGeWt7S0VPL/ZrXiJ0+erPo8K1Tronrx4kWdOHHC8u03ikQcQJmsaer1926WlH3+0UM1n3c7ndE3X3yrpCx8yqvdPZxqAADADmZmpA9+Ulp27Ast2XQ4HNbExIRSqZSOHj2qc+fO1ewvXu1xr9erUCik8+fP65lnnqmahEsqSeT379+/teC7mMPuAAAAAAAA1hgfHy8kzPlpxwzD0MjIiKampgrNx+sVjUa1vLxcdbC2vJdeeqlwn1HTy1FNBaBxDrc0+Nnysqw94QAAAKC6eDxemFIsLxaLlSThPp9P4XC4KX2583OK5509yzhEG1EjDqBxrh7p6C+U3lw9dkcFAACACjwej+LxuKLRaNXa6UQioXA4rIGBgYr9wxsxOTlZmIs8EonU1S99p6FGHLBKNivdulpatvtAbhC3dpRJS1dflyQZ6xkduHVZS31HZBqcJgAAwBYwI0vbCYVCCoVChRrrl156SbFYrKSmPJVKye/3Kx6Pb9oHvJpkMqmpqSlJub7m4+PjTYu/m3CFDVglvSq99KelZU9/Jfel1I4yd6SL35YkuTJZPfLhshYe/LLSTk4TAAAA3cTj8Wh0dLRkULZYLKaJiQklEgmlUimFw2HNz883tN5UKqWRkRFJuSR8dna2qXF3kzatmgMAAAAAtEogEFA8Hi8k5xtryutx5swZJZNJhUIhkvAaSMQBAAAAoEvla6jrVZxALywsNLSdRCKh2dnZmiOqg0QcAAAAALpWLBYrDJxWr0AgIElaXFysa/mRkREtLCyU1KhjcyTiQKPyU3cV3xxuu6NqrfVV6fvnSm/rq3ZHBQAAgAoaqdmWPpr3+8CBAzWXDQaDSiaTisfjFUdkTyQSCgaDDW1/J2AUJqBR+am7djIzK618WF4GAACQVzQjS8GBRyQGgm25SCRSqOWuR75veK3nFCfh1aYoi8Vi2r9/f93b3in4FAAAAABovqIZWQo+/Zzk7mtsPc4eyVml9eHaLUnmVqLLtWh09VR+bH1165UMDpfk2rW151okPyJ6JBKpuWwymVQsFtPo6GjVOcelXBKeSqUUj8c3Xd/8/HzD/dR3AhJxAAAAAK3x/XONP+eRX5Qe8ldZ3/TWu8cNfrZ6K8cf/Jfy1n/1etAnHfvC1p5rofzc3mfPnq1ae51KpRQMBuXxeHTuXPVjFQ6HNTc3p0AgUDXJXlpaUiqVUjKZ1MTExLbj7zYk4gAAAABgtUd+Ufrgx1Lqpy3fdCgUUiQSUTAY1MDAgEKhkEZGRuT1euXxeJRMJpVIJDQ5OSmv17tpU/NwOKzp6WlJuZr2egwPDzfrpXQNEnEAZQxD2tvr0oOeXmVcueZjDsOwOSoAANBRevqlz53N3f/OpL2x2K2nP1er/8GPW75pr9erSCQij8ej+fl5JRIJRaNRTU5OKplMKpVKyePxaHh4WJFIRKFQqOq6EolEIQlvRLWkficjEQdQxuVw6PgD+3Tc91Dui6NOu1xOjfofKisDAACAPTZOQebz+bY8z7fP55NpbrFPPkqQiAONWl/N9Rsq9ql/2/jAI13I6TB0eP9uu8MAAADt5umvbP25zioDqknSp0Pa1mBt1Xzq325vsLZqTnxJMjO5+26umXYyEnGgUUzdBQAA0JgGWtg1tl6LklmrKljcvdasFx3HYXcAAAAAAADsJCTiAAAAAAC0EE3TAau4eqUnfrW8rF25d5f336LvEgAAANB0JOKAVRxOaeCI3VHUzzAK/bfupDP67hsfSrpZePjph++tOQK6aZpaXc+UlPW5nTKY+gwAAAAoIBEHUCaTNfXK5WslZU96D9R83up6RtELyZKy8CmvdvdwqgEAAADy6CMOAAAAAEALUU0FoHEOl/Sgr7yMWdwAAACAmkjEATTOtUs69oXy8rV062MBAAAAOgyJOGAV05TWb5WWuXfnBkVrR9mMdO2KJMlYz2jv7Xd0Y9d9Mo3NB2gDAAAA0BgScXSksbEx9ff3VywfGxtrfUCVrN+SvvuN0rKnv1IYmbztpG9LL/+5JMmVyeqT7y9r4cEvK+3sszkwAAAAYHMzMzOamZkpK19ZWWl9MHUgEUdHWlhYqFh++vTp1gYCAAAAwHaXLl3ShQsX7A6jbiTi6EjDw8MVa8QHBwdbHwwAAAAAWw0ODurUqVNl5SsrK1Ur8exEIo6ONDMzo+PHj9sdBgAAAIA2UK2L6sWLF3XixInWB1QDiTjQqGpTd+0k67elH36rtOzEl8QpBQAAAKiNq2agUdWm7tpJzIyU+ml5GacUAAAAoCaH3QEAAAAAALCTkIgDAAAAANBCJOIAAAAAALQQHToBlDEMaXePUwf63Uq7eiRJDsOwOSoAAACgO5CIAyjjcjj0+EMePf6ZI1JP+Xzt1fQ4Hfqlx+4vKwMAAADwERJxoFHVpu5y99oTTxtxOR06dt89docBAAAAtDUScaBRVafuAgAAAIDaaDMKAAAAAEALkYgDAAAAANBCNE0HrOLcJR3/YnlZu3L1SSd/o7wMAAAAQFORiANWcbqkQ5+wO4r6ORzSnoOSpLV0VvG3liUtFx72HxlQj4tGNAAAAMB2kYgDKJPOZvW95NWSsscP71NPjd4st9bSil5IlpSFT3m1u4dTDQAAAJBH9RYAAAAAAC1ENRWAxhlO6eDPlZeZ9oQDAAAAdBIScQCNc/dKJ/5VeflauvWxAAAAAB2GRBxATjYr3brbL3w9rb61Ja26PZJBDxYAAACgmUjEAausrUjf/UZp2dNfkXr67YmnlvSq9NKfSpLcmawe/9myFh78stJOpjADAAAAmolEHACAFjBNU6vrGUnSeiZbdTmHYcjpMCo+ls5kdetuF5A+t1OGUXk5AADQ3kjEAQBogdX1TGF6v/hby1WXe2igTw8N7K742MV3rhfWwdSAAAB0Ljp/AgAAAADQQvyUDjSq2tRdO8n6bekn/7207Of+uTilANZYz2R19eaa3WEAAIAm4aoZaFS1qbt2EjMjffCT0rJjXxCnFMA6l66u2B0CAABoEq6aAQCwyWMP7ZPbWdpLzMEAbAAAdD0ScQAAbOJ2OsoScQAA0P349gcAAAAAoIWoEQdQkdtpqK/HqbQzNxCdIZrLApv6zuTmj6cN6dIhafCzrYkHAAC0LRJxdKSxsTH19/dXLB8bG2t9QF3G7XRo+Mh+DT99VOop38+bPe9zjx4qKwMAAACsNDMzo5mZmbLylZX2HOyURBwdaWFhoWL56dOnrd94tam73L3Wb7vNuZ0OPXHYY3cYQFsyDKnPmZV6nHI5aWECAEAzXbp0SRcuXLA7jLqRiKMjDQ8PV6wRHxwctH7jVafuAoDq+pymfvPoB9KpIa2uZewOBwCArjI4OKhTp06Vla+srFStxLMTiTg60szMjI4fP253GAAAAADaQLUuqhcvXtSJEydaH1ANdN4EAAAAAKCFqBEHrOLskR75xfKyduXqlZ741fIyAAAAAE1FIg5YxemWHvLbHUX9HE5p4IgkaT2T1cV3rks3bxQePv7AXkZABwAAAJqARBxAmfVMVt/58fslZcfu21MzEV9dy+jPXrxUUvbrTw2qr8fZ7BABAACAjkUiDqBpTJllo0GbMm2KBmgv61np4vU+6XJKP7t2W5J08J5dcjpqT2VmGNJ9e3NdRR4/vE+S6noeAABoTyTiABpnOCXPx8vLyLmBqtazhr7z4V7px+/r0tUVSdKBPT1yqnZC7XI4dPTe3JSNn3/0PkvjBAAA1iMRB9A4d6/0qX9TXr6Wbn0sAAAAQIchEQeQY5rS+q3c/bW0XJlVpR29uTaxAAAAAJqGRBywytot6fvTpWWfDkk9u+2Jp5b1W9J3vyFJcmeyGn57WQsPfllpZ5/NgQEAAADdhUQcsIwpra+WlwEAAADY0dp6UuBLly7p0qVLdocBAAAAAEDT2FojfunSJSWTSSWTSS0uLhbuJ5NJLS8va2BgQGfPntXv/M7v2BkmAAC2Smeyeu29m5Kk2YXLkqRfefwB9bqddoYFAAC2yNZEPBAI6M0335QkmaYpj8ejUCikr3/96zpz5oydoQHVVZu6aydJ35GSf1da5j0taYftB6BFTEnXb69Lkq4s57q8ZE26ugAA0Kls7yNu3r2QGB8f19e//nWbowHqUG3qrp0km5beTpSWDX5WJOIAAABAbbYn4oZhFGrBAQAAAADodm0xWFskErE7BAAAAAAAWsL2RNzn82nv3r12h2G56elp+f1+DQwMFG7BYFCxWMyWeKampmQYhpLJ5LbW026vCwAAAADane1N04eHhzd9/C/+4i/qWo/X69UTTzzRhIiaK5FI6MyZM9q/f78mJiYUCoUkSclkUtFoVCMjIxodHdW5c+fk8XgsjyeZTCocDm87UW631wUAAAAAncL2RHxoaKjqY2+++ab+8A//UKlUSslkUoZhlDyeH2nd6/XqX//rf912iXgsFtPIyIi8Xq/i8XhJQur1ehWJRDQ0NKRwOKxEIlG2zHalUiktLCwUpoeLxWJKJBK1n1iD3a8L1nM7HXrKe0BPPf2w1NNf9/NcDoee9B4oKwMAAADwEdsT8c0StKNHj2phYaHw/9TUlH7v936v8LzZ2dm2neYslUopGAxKkmZnZ6u+zlAopHg8runpaZ05c0bxeLxpMSwsLGhkZEQej0fDw8MKBAKanZ2V3+9XKpXa0jrb4XXZrtrUXa5ddkTTVnpcDj01dKD2ggAAAMAO1lFVVePj4zpz5owMw9DU1FTbJuGS9NxzzymVSsnn88nn82267MTEhKRcc++5ubmmxRAIBGSappaXlzU/P69IJCKv17utdbbD67Jdfuqu4ls2bXdUANrcbpeprz78nr46ckxPeg/oSe8BuZ0d9TUMAACapOOuAPK1sc8880zdz3nzzTf1W7/1W1aFVCaVShUSz0AgUHN5r9dbSJAnJyctjW07uvV1AQAAAEArdVwinu9T3shI68lkUtPT01aFVKZ4WydPnqzrOfmENZFIbHskc6t06+sCAAAAgFayvY94q7RysLDnn3++4e0WNxmPxWKFUcjbSbe+Lss43NLgZ8vL2pVzl3T8i+VlAAAAAJpqRyTiWx2YbKuKRybfv39/Xc8pTmzbdWCzbn1dlnH1SEd/we4o6ud0SYc+IUlKZ7JKfrgifbhaeNh7b79c9GcFAAAAts32RLwVSfJLL71k+TbyNja/rndwtAMHPhppunik+HbRra8Lla1lsvrrV98tKQuf8tZMxG+vZ3R+4XJJ2TPDh9XrdjY9RgAAAKBT2Z6IR6PRhkby/v73vy9J+su//EuZpllz+aWlJU1PT5fNQW6Vrf6wUFxz3Ooa/Hq02+t64403Gn7OwYMHdejQoabFgHJZ09TVm2tlZQCkdFZK3tolvXdDV2/ekSQN7O6Rw1H7+8mQtL+/R5L0yH17JEmOFn2vAQDQLt5//3198MEHDT1nK3lDK9ieiCeTycJI6PUyTVOjo6MNLT8wMNBoaLZZWlqyOwRLNPN1ffGLX2z4OV/72tf0+7//+02LYUczHFL/veVl5NxAVWtZQ3/9M4/06rt6/f2bkiT/kQE5VDuhdjkdOnbfPZKkX37sASvDBACgbf3xH/+x/uAP/sDuMJrC9kRcUl0123mGYcgwjIae00obk82tDBLXjjXi3fq6sEXuPunTz5WXrzGfOgAAAFBLWyTijWjXBDyvW5PNbn1dAAAAANBqbZGIRyIReTyeukfirtfS0pJSqZQmJyebul7Y79vf/rYefvjhhp5z8OBBi6KpYn1V+sF/KS371L/N1Sa3o7UV6bvfkCS5M1k9+dNlLTz4ZaWdbRovAAAAdpTf/u3fbrhb8xtvvLGlbq1Wsz0RD4VC+t3f/V1Lt2GapiKRiKXbyGvlfOWt1G6v6+GHH9bx48ftDmNzZlZa+bC8DAAAAEDDDh061DWDL9s+KfDQ0FBXbCNvY63+Vpp0t1vSK3Xv6wIAAACAVrO9RrwVyZnH42lZH+dmvJ5mN9Fvhm59XQDQCdKZrJIfrkiS/tur70iSAp+4T71up51hAQCALbI1ER8dHW1oDvGt8nq9+tKXvmT5dqTyZHNpaamuJLb4h4J2rDnu1te1JdWm7tpJ0mvS5X8sLTv8GbVBIxugK5mSllbWJEmvv5eb+uzzj3ZH0zwAAHYiWxPxr3/96y3ZztGjR3X+/PmWbGtjsllvTfzi4mLhfit+nGhUt76uLak2dddOkl2XLv1DadmDPkm7bAkHQG1/NP/alp731ZFjTY4EAABQfWUBn89XuL9x/u1qipc7efJk02Nqhm59XQAAAADQSiTiFggEAoX7yWSyrucULzc6Otr0mJqhW18XAAAAALRSRyXily5d0ssvv6y//du/1csvv6zr16/bHVJFZ8+eLdyPx+N1PSeRSEjKNd9u1ybc3fq6AAAAAKCV2joRf/nll3X27FmdPHlSTqdTQ0ND8vv9GhkZkd/v18DAgA4cOKBnn31Wf/mXf2l3uAUej6dQe1xP3/RYLFa4PzExUXP5ubm5QoLbSla/LgAAAADYCdoyEX/55Zf1hS98QX6/X1NTU4rH4zJNs+JteXlZc3NzGh0d1bFjx9omIY9Go4Vp0+bm5mouK+X6YIdCoarLpVIpDQ0NKRgMFvbNdjU6rZsVrwvtx+U0NHxkQP/us4MKn/IqfMqrvjqmSXI6DD1+eF/JzekwWhAxAAAA0DnaLhH/rd/6Lfn9fsViMZmmKUkyDGPTmySZpqk33nhDo6Oj+mf/7J/Z3mzd6/Xq3LlzkqRgMFi1T/X09LTm5ubk8Xg0Ozu76TpjsVjJeiYnJ7cUW3HyXe+ga3lWvK6Ok16T3vz70lt6ze6omsqQIbfTod09rsIt/1nbzC6XU59/9L6S2y4X8xwDAAAAxdomEb927ZpOnjyp6enpQm23pJLa73379uno0aM6evSo9u3bV/JYsb/5m7+R1+vVW2+9ZcdLKRgdHdX8/Lw8Ho/8fr+mp6cLj6VSKYXDYYXDYQUCAb355ps1+1BvnEKsnj7XsViscJuenpbf7y95PBwOa2pqSnNzc4XlatWSN/t1dZz81F3Ft+y63VEBaHN9TlPhwfcVPuWV/8iA/EcG5KLFCAAAO5Kt84gXO3PmjH7wgx8Ukmqfz6dnn31WgUBAXq9X+/btq/rcH/zgB1pYWNDs7GyhX/LS0pJGRka0sLCgvXv3tuQ1VJJPRs+fP69oNFrSVzoQCGh+fr5kNPJa6xofH9fU1JS8Xm9dNc0jIyOSSpP44vtLS0slNeupVEqzs7M1Rzhv5usCgJ3AMKTdLlPqccntbJvfwQEAgA3aIhH/whe+UBh8zOfz6dy5c/rUpz5V9/M/9alP6VOf+pSee+45pVIpPffcc/rWt76lxcVFBQIBff/737cq9Lp4PB6FQqGm9JOORCKKRCJ1L7+xtUAzNfN1AQAAAMBOYftP8i+88ILm5+dlGIYmJia0sLDQUBK+Ub5P8vnz52WapuLxuP7Tf/pPTYwYqJPDJT3oK7052uK3r8qcPdIjv1h6c/bYHRUAAADQdWxPxMPhsAzD0Pj4+JYHH6tkdHRUf/InfyLTNJk6C/Zw7ZKOfaH05tpld1TVOd3SQ37pIb8yD/h0efcndPnaui4v3dLlpVvKZK1rXQEAAADsJLZWz/3gBz9QMpmU3+9vahKeFwqFND8/r7/4i7/Qd77zHX3uc59r+jaAbnQnndFc/EpJWfiUV7t7Nj9l3F7P6K9eeaek7Fcef0C9dUx9BgAAAOwUttaIR6NRGYZRmA7LCmfPnpVpmjp//rxl2wCQkzVNXVleLbllLRynAOgkGVO6vOrW5aVbur66ruur63V/PgxJe3vd2tvr1kMDfXpooE+OOqYUBAAA7cnWGvGFhQV5vV498cQTlm3D5/PJ5/NpYWHBsm0AO48hufvKywBUdSdjaO7t/VL8in707nVJkv/IgBzO2p8dl9OhTz6QmwEkOHzY0jgBAID1bE3Ek8mkwuGw5dsJBAKW1roDO07Pbumz/768fC3d8lAAAACATmNr0/RUKqWhoSHLtzM0NKRUKmX5dgAAAAAAqMX2uZS8Xm9XbAMos35b+uG3SstOfEly99oTTy1rt6TvT0uSXJmshq8s6eX7n1Ha2abxAgAAAB3K9kQc6GiX/j73N7NWPud2Zk26/I+5+4O/kPtrZloXW8NMaX1VkmRksnJlb+fKAAAAADSV7fOIAwAAAACwk1AjDgBAm0tns7q8lGux8rc/fk+S9PTD92qXy2lnWAAAYItIxIGGFU3d5XB/VLaTZNald18tLbv/Me24/QC0iGlK712/LUl65fI1SdKT3gN2hgQAALbB9kT82rVrlm+DEdPRVMVTd93tU73jZNak1/+mtOzQo5J22RIOAAAA0ElsT8T/8A//UPv27bN0G9Fo1NL1AwAAAABQL9sT8UQioZGREUu3YZqmDIMms91kbGxM/f39FcvHxsZaHxAAAAAA28zMzGhmZqasfGVlpfXB1MH2RFzKJcpAIxYWFiqWnz59urWBAAAAALDdpUuXdOHCBbvDqFtbJOJAo4aHhyvWiA8ODrY+GAAAAAC2Ghwc1KlTp8rKV1ZWqlbi2cn2RNzj8Wh6eloej8eS9adSKU1MTOjNN9+0ZP2wx8zMjI4fP253GF3L5TD0+EP79MjJw1JP7geP3jqmSXIYhh65b09ZGQAAAGClal1UL168qBMnTrQ+oBpsT8TPnj2rL33pS5ZuY3FxUWfPnrV0G9hBiqfuuv5O7u+e+yRH98znaxiGdve4tHvPLqmn/pHQe91O/fJjD1gYGQAAAND5bE/EvV6v5dvw+/2WbwM7SPHUXUuLub/990rqnkQc3cE0Ta2uZzZdxuVwqMflqPjY7fWMskVjePS5nQx8uQ29TlNfPvyh9NQR3VpLS8q1PgEAADuP7Ym4VU3SAWCnW13PKHohuekyT3oP6KmhAxUfO79wWVdvrhX+D5/yaneP7V8bHcthSPfuykh7drEfAQDY4SpXg7TI+Ph4S2rEvV6vfvd3f9fy7QAAAAAAUIutP8l//etfb8l2jh492rJtAQWGQ9p9b+7+wZ+7W9bGzdcdbmnws+Vl2JFuraVLasMBAADQPLSNA6zicEmHPpG7f+Jf2RtLPVw90tFfkCRls6aWbq1Jt01JdyRJ+3f3yEF/VgAAAGDbSMQBlLmdzuibL75VUlZP/+Db6xnF/um9krLAJ+5Tr7uNWwLsML/21BH19Xx0PFwOW3soAQAA7Ehtl4j/6Z/+qWZnZ5VMJuXxeDQyMqLf+73f0969e+0ODUANWdPU6+/dLCn7/KOHbIoGlfT1OBkozCZZU1pac0o37xRGTa93JHrj7rKSdGBPjyTJwQj2AAB0rLa5Gvvbv/1bBYNBpVIpSblpdwzDUCKRUCQS0fT0tP7dv/t39gYJAMAW3c4Y+uble6UX39KrV65JkvxHBuR21k6oXU6HHj/skST92lODFkYJAABaoS0S8W9961t65plnZN6dr9YwjJIaAtM0FQqFlEql9B/+w3+wK0wAeT390ufOlpffreUDAAAAUJ3tnQOvXbumYDAoSYXk2zTNkpthGDJNU+Pj43rllVfsDBcAAAAAgG2xvUZ8cnJSUi759nq9CgQCGhoaksfjUSqV0tWrVxWLxZRIJCRJExMT+h//43/YGTJQn2xa+vD13P0f/kXu78/9c8nda19Mm1lflX7wXyRJrnRGj7/7oX546F8o42zTeAEAAIAOZWsi/uabb2pqakqGYehP/uRP9Nxzz1VdNpFI6MyZM5qfn9crr7yixx9/vIWRAltgZqVbH+buf/CT3N9jX7AvnlrMrLSSi9fIZNW3npIh0+agsB2GjJIR0vNlAAAAsJetifjc3JwkKRKJbJqES5LP59MLL7yg4eFhTU5O6r/+1//aihABoGP19Tj1m6eG7A4DAAAAG9iaiM/Pz2tgYEC/8zu/U9fyPp9Pzz33XCGBBwBgJ8hkTb17bVWS9OLiVUm5Edd7XLYP9QIAALbA1m/whYUFPfPMMw09p3iKMwA2yaSl9/+p9JZhxHTAKlnT1JXlVV1ZXtX3klf1veRVpbNZu8MCAABbZGuNeCqVkt/vb+g5Q0O5ZpbXr1/X3r17rQgL2Fzx1F3fsTcU22TuSBe/XVr29Fck7bIjGgAAAKCj2D5q+v79+xta/ujRo4X5xgEA1nA6DD1+eF9ZGQAAALbP9kTc4/HYHQIAYINdLqc+/+h9docBAADQlXbEKC8/+MEP9Oyzz9odBgC01Homq5cvp0pu6xn6FQMAANjN9hrxVlhaWmKkdQA7znomq+/8+P2SsmP37ZHbuSN+gwUAAGhbOyIRT6VSNIEHGuB0GPrk/Xt1+FMPynTvlpRrqlyLwzD00EBfWRkAAACAj9ieiF+7ds3ybSSTScu3gR0kk5auvp67v/JB7u/uA5LRPbWMDsPQvj639g30ST27635er9up4PBhCyMDAAAAOp/tifgf/uEfat++fbUX3ODcuXN1PS+VSmlyclIGtXJoluKpuz74ce7v4c9Izh7bQgLQ/nY5TY0+uCT5H9L11XVJjEQPAMBOZXsinkgkNDIy0vDzxsfH617WNE0NDAw0vA0A2Klur2f0V6+8U1L2K48/oF537S4KqMxpSIf71qX9u7W3z213OAAAwEa2J+KSGp4XnNptALBW1jR1ZXm1rAwAAADb1xaJeKMaTdwBWxgOqfdu9wnPx++WtXFtosMlPegrLwMAAADQVLZfZXs8Hk1PT1s2qnkqldLExISWl5ctWT9QlcMlfeyx3P1P/Rt7Y6mHa5d07AuScj92ra5npKyktbQkqc/tpDUKAAAA0AS2J+Jnz57Vl770JUu3sbi4qEgkYuk20FpjY2Pq7++vWD42Ntb6gLrM6npG0Qulsw2ET3m1u2fzU8addEbffePDkrKnH763rqnPAAAAgK2amZnRzMxMWfnKykrrg6mD7Ym41+u1fBt+v9/ybaC1FhYWKpafPn26tYGgRCZr6pXLpVMSPuk9YFM0QHsxTWk1Y0hraa1nspIkl8Oou6WJy5lbrq8n98OWIVqoAACQd+nSJV24cMHuMOpmeyJuVZP0jVKpVEu2g9YYHh6uWCM+ODjY+mAAoA6rGUPRS4ckJRV/K9ddyn9kQG5n7YTa7XRo+Mh+SdJvnhqyMkwAADrS4OCgTp06VVa+srJStRLPTrYm4uPj4y2pEfd6vfrd3/1dy7eD1pmZmdHx48ftDmPncu+Wnv5Kedl6xp54AAAAsKNV66J68eJFnThxovUB1WBrIv71r3+9Jds5evRoy7YF7AiGIfWUt0gAAAAAUJvtTdOBrpVNS8uXcvdf+5+5v97TudHJ29H6bemH35IkOTMZffK9D/STg7+ojKNN4wUAAAA6FIk4YBUzK914N3f/7UTu7+BnJbVpYmtmpNRPJUmOTFZ77yzLMLM2BwUAAAB0HxJxAOhSu3tc+urIMbvDAAAAwAYk4gAAtLlM1tQHN+5Ikl6+nJIkHX9gr9xOh41RAQCArSIRB9C4bEa6dqW0bN9D9sQC7ABZ09SlqyuSpO/8+H1J0rH79pCIAwDQoUjEgUYVT92VWcv9dbjti8cO6dvSy39eWvb0V9S2/d8BAACANkIiDjSqeOouZ4+9sQAWcRiGHrlvT1kZAAAAto9EHABQptft1C8/9oDdYQAAAHQlEnEA6FLpTFbJD1dKyrz39stFv2IAAABbkYgDQJday2T116++W1IWPuUlEQcAALAZiTiAMk6HoWP37dG9Jz4m071bktRTR/LmMAwd2NNTVgYAAADgIyTiQKOKp+66ncr93bVXMrqnltFhGDrQv0sHDu35aGC6OvS6nfq1pwatCwwAAADoAiTiQKOKp+762f/K/T38GUZQB7CpHoepX/pYSnrsfl29eUeS5KTFCAAAOxKJOACgzO31jGL/9F5JWeAT96nX7bQpos7nckjH9tyR7rtHB/bssjscAABgIxJxAECZrGnq9fdulpR9/tFDNkUDAADQXUjEAcsY0t2BztR/792iNu5Hbjilgz9XXgYAAACgqUjEAas43dKD/tz9Tz9nbyz1cPdKJ/6V3VEAAAAAXY9EHECZW2tpRS8kS8rCp7za3bP5KWMtnVX8reWSMv+RAfW42rglAAAAANBiJOIAmiadzep7yaslZY8f3qcekYgDAAAAeSTiAAC0wK20oeilQ1L6tcIPVv4jA3I7a/9Q5XY69KT3gCTpqyPHLI0TAABYj0QcQONcfdLJ3ygvS2ftiQewyncm7Y4AAAB0IRJxAI1zOKQ9Bys8QCIOAAAA1EIiDlglm5GuX8ndf/Pvc38Pf0Zy9dgX02bWb0s/+e+SJGc6q2MfvqfF/f+bMo5dNgcGAAAAdBcSccAqZkZK/TR3/9I/5P4+6JPUpom4mZE++IkkyZHJav+tZSUHPmtzUAAAAED3IREHgC7V53YqfMpbVgYAAAB7kYgDsNTqWkaSZMhQX0/lJHA9k9V6prx/eZ/bKcMwLI2vmxmGUXPud3SGbNbU8q01SdJr792QJHnv7ZerjhHXAQBA++EKDR1pbGxM/f39FcvHxsZaH9BOk81Kt0rnC9fuAxUX/c8vviVJ6utx6jdPDVVc5uI71/WdH79fVh4+5SWRBCRlTFOvv39TkvTXr74rKff5IBEHACBnZmZGMzMzZeUrKyutD6YOXOGiIy0sLFQsP336tPUbL566a+3uB9vhtn67zbR+O9cnvKRstf7np1ell/60tOzpr0hiYDfY64/mX9vS85ibGwCAznbp0iVduHDB7jDqRiKOjjQ8PFyxRnxwcND6jRdP3dVTHkNH+OG3PhpIDqjAYRh6aKCvrAwbmKaUXa9v2YwhV/aOlFnVruxt3TH44QoAgGYZHBzUqVOnyspXVlaqVuLZiUQcHWlmZkbHjx+3O4wyWVO6nbmbrGQMubP5+7n+z+traUnpwvL0gUa76nU7FRw+bHcY7S+7Ll3+x7oWdWcNDd/sl95e1MdWbumv+oMWBwcAwM5RrYvqxYsXdeLEidYHVAOJONBES2tOffPyvZIkV/ZO7qJbkrQsSVpIX1La+VEtY7f1ga40Sneeoeo/OBx/YK8OD/QV+pOjOTJZU++kSrscPODpk9PBjz8AAAB26p4MAIDttjpKt9vpqDqiOrbuTjqjufiVkrJu+/EHAACgE3E1BuxEJ75UPlhbsUxW/d4b+gVXn3S36byb0ZkBAACApiARBxqUzWS0/GFu+iDdvC1JGti9S452bO6bviMl/660zHtacvdu+jS3pMe890j6aBTq4unFXJlVDb9dOn3Zwp03SprdMwo1dgTDId1zf+5+9u74D57B3KCOxbJpmW//L60bPUq7PbrucCvL+BDoEMxGAADNRyIONOj26k299t/+Y+6fuyOPP378hHbvcmt/T0bhwbsJa2ZN7rfvTm92ZEDrmawWzBYHm01LbydKywY/K6YZA5rE4ZIOPFx7OWePjI9/Rns9fdLwmP7nP/5UD4qR6AEA2KlIxIEmchjSbtfdbNswJcfd+/lm3enKzwPazZ10Rt9948OSsqcfvle7XPTl3yq3Q3rCsyod9pS0MAEAADsPiTgAoEwma+qVy9dKyp70HrApGgAAgO5CIg5YxpAc7txdd59MR1ZK0wwVAAAA2OlIxAGrON3Sx5/M3f/sv5cza+qLG+Z0ppkvAAAAsPOQiAMt4nQYOrx/t91h1GV1LaM/e/GSJGnhrSVJ0uMPeZjCDAAAAGgCEnEAZUyZWl3LzTOezrRmqPfdPa7unOrGNKX1W1t/vnt3YS53tKFsWnr/R6Vlhz6ZG029ZLmMdP1K7v6bf6+Hrl3RO/c8pmy++4pFTNPU6nrus7yeydZc3uUwZPB+AwDAciTiAGCl9VvSd7+Ru3/p7+t/3uAv5P4+/RWpp7/5caE5zKx0+1p5WdlyGa0vXdYr13ZL1/4fOa7e0iv33K9HP/6xulqauByG/EcGJEnhU15JUp+7dteW1fWMoheSkqT4W8s1l/cfGZDbSSIOAIDVSMQBNCzj6NGPDv1yWRmAza2bhpQxlTXNhlqbGIZRSJB39/DVDQBAp+PbHEDDTMOp670P2B1Gd9r3cWnv/bmacCnXNB0AAABdhUQcsEo2I918L3f/SjzX7/rA8dxo6nf1uZ30x0Spaz/NvUdojg4bZU1TN2+nJUmXl3JjHDzg6ZPTwfkKAIBmIBEHrGJmpKXF3P3X/0bpTFb/Ke1W2tlXWCR8yksz053oAb/k5LjDHo89tK9qv3TX3UQ7kzX1o3evS5Lm4rlB5jhfAQDQPHyjAmgL6UxWyQ9XSsq89/bL1Y1TpjldktP6PvW9Lqe+/NSRsjLsbG6ng6kIAQCwGYk4gLawlsnqr199t6QsfMrb+Ym4c5d0/Iu5+ysf5P5unNrKIg6HoXv37GrJtur2ncn6l/3cWevi6DA0FQcAoLuQiAPdzHBI/feWl22XacqVvV1SlHb0Mt91JU6XdOgTufv9B+2NBR2LpuIAAHQXvsGBBu3q7deRM8/l/vnBN3Nldcznawt3n/Tp55q+Wlf2tobf/mZJ2cKDXy7p/44tMLPSnVyypeW3cn/3PSQ52vT9hbbX43Tolx67X5J09eYdSZKTH8wAALAdiTjQIKfLpQcGfy73z5tdOrL1hf+PdOmQJOmha7lmsCcv/1S9jqwkyZFd1wM3XpUkXdnntyfGbpRNSz/7X7n7L/957u/TX7FlBHWHYejAnp6yMnQWl9OhY/fdI0k60G7dFAAA2MFIxAEAZXrdTv3aU4N2hwEAANCVSMQBoEtls6aWbq2VlO3f3SMHA3wBAADYikQcALrU7XRG33zxrZIyBvgCAACwH1djAMq4HNKT+29Kkn50Ozd4mMsw7Qypc62tSN/9Ru7+pb/P/T38mZbMI17se8mrkqT1TLbhOaS/OnLMipAAAAB2LBJxoEFmNqvVWzdy/9xZlyT1uV0y2rG5b3pNuvyPpWWHPyO5Nk8Cexymntq/kvsnlbImNqAbGA5pdz1TBBoyHW6lDbfk6NWakZXZhqcMAADQGiTiQINWb93QK8//n7l/Uj+VJD1+/IR273LbGFUV2XXp0j+Ulj3ok9Ta2ligazmK5onfjNMtffxJufbulp74N0rEL+uQrB+J/tZaWtELSUkftYrwHxlouFUEAABoLhJxAECZTNbUu9dWJUkvLn6UwPW4SOC2qtDS5KfT0o3cPtWNzZ/zvY+HrA8MAAC0HIk4OtLY2Jj6+8vnVh4bG9PY2FjrAwK6TNY0dWU5l4jna1IfP7xPPSIRBwAA7WdmZkYzMzNl5SsrK60Ppg4k4uhICwsLFctPnz7d2kAAAAAA2O7SpUu6cOGC3WHUjUQcHWl4eLhijfjg4GDrg6nG2SMN/kLu/ufOyshk9YUPS3+R66GfJoA25HIYeuyhfZKkLz91RJLU63LaGRIAAJsaHBzUqVOnyspXVlaqVuLZiUQcHWlmZkbHjx+3O4yGuJwOHbvvHrvDqMvtjKHzb++XJL2Z6pUknd77rnocWTvDAtAihmEU5pu/d88um6MBAKC2al1UL168qBMnTrQ+oBpIxAGUyZrS1bXc6eF6JjcavNUpeJ/bqfApb1kZ0NayaenD10vL7n0kN5p6yXIZ6eZ7hX/33LmmlZ57ZVac6gwAAHQ7EnEAbaG4Bg7oGGZWuvXhhrKhCstllP4wqYs3+iRJy7fW9P/rOa6n912tq6UJTcUBAOguXPUCaFjWcOrq7qOSpNfvPSNJyjjacB51oI2Ykm5lcjXgd7JO3ci4625pQlNxAAC6C4k4gMYZDq26c33Ir+6uUPuHrXG4pQd8ufsnfyP319VnXzwAAACwBIk4YBUzK93Kzb+s9/8p9/fAI5KTjx2qMAyp5+5sAHsO2hsLdjTTNLW6npEkfXjzjiRp/+4eORyGnWEBANA1yAgAq2TT0gc/zt2/+G2tZ7L6k/QvK+38qIYzfMpLv2gAbSedNfXqlWuSpG+++JYkzlcAADQT36gA2kIma+qd1GpJ2QOePjmpgduyXS6nRv0PSZKur65LEvsTAACgDZCIA2gLd9IZzcWvlJR1RQ2cs0d65Bdz96+/k/trtGa0a6fD0OH9uyVJe/sYTK+T0VQcAIDu0uFXuAA2Z0jujYN9ceHeUk639JA/d//1v7E3FnQsmooDANBd+AYHGtSzq0/3/7/+de6f/zWXK2vX+Xx7dkuf/fdNX60ju64HbrwqSXryp9OSpIUHv1zS/x1bYJrS+q3c/Zsf5P7uPiA5HPbFhI7mdjr0uUcPSZJ+du22JMlh8GMcAAB2IxEHGuRy92jwE8O5f342b28w6C7ZdemdRO7+S3+a+/v0Vz4aSb3FXM5cwtbXk/uhyaA1RcdxOx164rBHkvSdH79vbzAAAKCARBwAUMbtdGj4SG6u+N88xVzxAAAAzUQiDgBdqniAr/VMVpLkchgyaJoMAABgKxJxAOhSq+sZRS8kJUnxt5YlSf4jA3I7ScQBAADsRCKOhiWTSXm9XsuWh/2cDunxfblBw3pWb+TKZNoZUudauyV9PzegnX76vdzfB/250dQBAACwIzEUb4tMT0/L7/drYGCgcAsGg4rFYh0XQzAYlGEYGhkZ0fT0tBKJhFKpVOHxZDKpubk5hcPhwjZgk8y6dCVeesus13zaLoepzx+8oc8fvCFf/1X5+q/K7SAR3xpTWl/N3bLruRs/anQPwyH17iu9GXy1AgCAzVEjbrFEIqEzZ85o//79mpiYUCgUkpRLVqPRqEZGRjQ6Oqpz587J4/F0VAyxWKxmEu/xeDQ7O7ud8NvOrZvX9Mrz/2fun9RPJUmPHz+h3bvasIYzs1Y+d/WhR6mNBZrF4ZI+9ljt5Zw9yg7+glav7pEkvfGzG3pIdyxvabK6ltGfvXhJkrTw1pIk6fGHPHI7+bEAAAA7kYhbKBaLaWRkRF6vV/F4vCTJ9Xq9ikQiGhoaUjgcViKRKFum02MIBAKanZ217AcGANbJZE19cOOOJOnlyylJ0vEH9pLAbUO+pYkk9d242pJtmjK1upYbsC+doSUGAADtgisqi6RSqUKT7M2S0VAopFAopGQyqTNnznRFDD6fT/Pz85qfnycJBzpU1jR16eqKLl1d0Xd+/L6+8+P3CyOvAwAAYHuoEbfIc889p1QqJZ/PJ5/Pt+myExMThb7Wc3NzGh0dbfsYIpGIRkdHlUgktLSUa+64f/9+BQIBkm+0VPEUXZX0uZ0Vp+vKZE3dSWfqWhadY6s/FtxaS0viPdAu/mj+tS0976sjx5ocCQAA1iARt0AqldLc3JykXPPsWrxer7xer5LJpCYnJ5uSiLcihvxzUIXDLR3+TO7+01+RMln9wgdpqegin2a+21c8RVcl4VNe7e4pP9W9k1rVXPxKXcuic+SnaWtU/j3EeyDH6TD0yfv3SpJG/Q9Jkna5nHaGBABAV+FqwwLT09OF+ydPnqzrOfkkOJFINGW6r3aIYcczDMnZk7vf0y+3pCc+bmtEdbudMfRXP/NIkhav5wZ2e2rP++px0DQZ2AkchqG9fbnP/uH9u22OBgCA7kMiboHnn3++cL/eZtrFSW8sFiuMbN7JMaBzZU3pymruR4QP1ntzZRZvs9fl1JefOlJWZpXNmrMDdcumpeVLpWUDg7nR1IuZWenWRwO09a2ntOryMNUZAAA7FIm4BRKJROH+/v3763pOcbIcj8e7IgagEQ6HoXv37LI7DOxAfW6nPvfoIUlb6C5iZqUb75aWeSo0fcmmlX7vJ/rJzdwPWzdvrekfdz2s4XtSdbU0oak4AADdhUS8yZLJ0r6q9TbvPnDgQOH+wsJCx8eA7mYaTqV6D0uS3hx4WpKUNdpnbvJfe+qI+npySUqfu3Kycv++3laGhBbxHxmouczJy/93aUFGeuKNu897Y8PCmXVps7m+M+m6YzMlXU/n3o8rGZc+XO+tu6UJTcUBAOguJOJNlkqltvS84trora7DjhhisZii0ahisVihzOv1KhwO07S9i5mGQzd35WoQ37vnuM3RlOvrcdYccMvpaMORsR0u6WM/n7v/xK/m/rr4waAR9dRo9zYy1sHPXpXWb20jIgAAgHIk4m0oPx1YO8dw9epVjYyMSMpNfXbu3Dl5PB6lUilNTk4qHA5rYmJCs7OzdY3a3qg33thYbVXbwYMHdejQoabHUpWZle5cz91ffiv3d99DkoPmpKjCcEi9ntz9gSObLgpYyTRNpbO5lgBM7QYAaBfvv/++Pvjgg4aes5W8oRVIxJtsYwK7lTm1t1sj3ooYpqamFI1Gy2q9PR6PIpGIhoaGFA6HNTIyovn5+aYn41/84hcbfs7XvvY1/f7v/35T49hUNi397H/l7r/851rPZPVnjn+ptLOvsMivPzVYaEINYIPvTNa/7OfOWhfHDpTOmoWp4JjaDQDQLv74j/9Yf/AHf2B3GE3BN2qTbTeJ7oQYvF6vzp49u+lc46FQSJFIRMlkUsFgUMvLW5vbt9usrmWUdn40Wre5Wd/THSabNbV0a62kbP/uHjnasQl5h+hxOvRLj90vSbp6844kydmFNZrprJR874akj17nAO8dAADQxkjE0bDZ2dm6lhsdHdXU1JRSqZQmJiYUiUQsjgyd7HY6o2+++FZJWVfUwDnc0uBnc/dTd1+f0ZpWEC6nQ8fuu0eSdKCLR6Rfyxr661dzI5e//v5NSblB2xxqQiL+sce06WBtlTiaP3AhTcUBAOguHX6F23620gy8G2OQpJGREU1NTUnKNWU/e/Zs02L79re/rYcffrih5xw8eLAp2wYa4uqRjv5C7v6lf7A3FpS4k3Xof6ZyU4G9YuRGIv/1j3+oPmdR4u1sj9kAaCoOAID027/92woGgw0954033thSt1ar8Q3eZBvn7E6lUg0nn9tNVtshBql82rRYLLZpc/ZGPPzwwzp+3J7Rut09vTrwqV/O/fNP/y1X1ujcw63S029J31lHNq2P3bwoSRq+8p8lSS/f/4zSTkb43hbTlLLruftrK7m/7t0StZ6WMCWtmbnP7mom99fsst4iLodDT3pzU1NeWc6N/u7g/QQA6FCHDh1q7eDLFiIRb7JmJLAbE+lOjKHSOl566aWmJeJ2cvfs0jHf6dw/1160NRb7mHKYueaxruztQpkdivtBF5d1pOy6dPkfc/e/+43c36e/kvtBpVsU/9hQL4ebHyO2qMfl0FNDuUT8e8mrNkcDAADySMSbbGPyubS0VFdiXDzAWrNrxJsVQyKRUDAY1NLSkiKRSMPzhLfDQHboPsX9oNE8budHNalfHTnWvBWv35LeSTT2nMOfkZw9zYuhlmxGun6ltGwvUw8CAIDm6dBqo/a1MYGtN/lcXFws3N/YpLtdYnjuueeUTCaVSqUUDocbTqzbpe86AGzKzEipn5bezEzt5wEAANSJGnEL+Hw+JRK5Gp+Nc3pXU7zcyZMn2zKGZDLZUAwbtzs0NNTQ8wEr9bqc+vJTR8rKusmttXRhYK98s2T/kYH2HdMAAABgh+BqzAKBQKBwv97ktXi5ZvSjtiKG4lrySCRSs4Z743afeeaZuuKA/RyG9Mie23pkz2092LOiB3tWuu5k4XAYunfPrpKbJfNOr69K3z+Xu70dz90yDfaRBgAAQFehRtwCZ8+eLUzbFY/H63pOvvba6/Vuu2m6VTEEAgF5vd665xGfn58veS5N022QSUtXXy8tO/CI5Nz8o9/rNPXLH7smSXrxFgM8bYuZlVY+zN1fv5UvtC0c27l35/p8V5NJS+/Ud84CAADoVCTiFvB4PAoEAorFYjp//ryi0eimy8discL9iYmJmuufm5uT1+uVz+draQzhcFhDQ0N1T4c2NzdXuB+JRGou3ylWV27olW/lfuTQUq5f/eOf+IT62nE+38wd6eK3S8ue/krNRBywjGG0duC1duFwK/PQZ5Tq3StJev39GzqoNctbmtxez+j8wmVJ0iuXU5Kk4w/slYvuCQAA2IpvYotEo1F5PB6lUqmShLTaslKuX/dmI5GnUikNDQ0pGAzK7/cXarxbFYPX69X4+LjOnDmz6bokaXp6utA0fXx8fNMfDTqNaWZlrq/mbpl07tZtkw93kFtraf3R/Gslt1trabvD6njZrKmrN+/o6s07eu29G3rtvRtKZ7J2h9W5DEO9PW797w+s6n9/YFUn917TU3s/VI/D2n2aNU1dvbmmqzfXtLqe0ep6Zie3xwAAoG2QiFvE6/Xq3LlzkqRgMFi1n/b09LTm5ubk8XhqNvmOxWIl65mcnGx5DPma7ZGRkaqjps/NzSkcDkuSQqFQV9WGAztFxjT1+vs39fr7N/XXr76rv371Xa1tNRHPZqWbH+Ruayu5W7Ufrxwu6WM/X3pz0IIDAAB0FxJxC42Ojmp+fl4ej0d+v1/T09OFx/JTgIXDYQUCAb355ps1+4ZvbA5eT1/yZscg5fqce71eDQwMaGJiQnNzc0okEorFYgoGgwoGg/J4PIpGozWbxAN2yWZNfXjzTsktm6Wu0BLpVemlP83d3knkbtkqA9YZDqnXU3oz+KoCAADdhWoGi+UT3Hw/7eL+14FAQPPz8yUjnNda1/j4uKamphoaNK2ZMeRFo1EFg0FFo1FNT08X+o17vV5FIhGFQiEGZ3O4pQfuNsk/+RsyM1kNf+AoSSpcDhIMu9xOZ/TNF98qKQuf8mp3O/b1b1RmXcqsSWtpuTKrkqRd2duSJHdmVS45lHb05vprAxU4DUOPHNojSfqlx+6XJPXQrxwAgKbpgivO9ufxeBQKhTbt/12vSCSypabezYwhLxAINJzA7yiGIfX05+7vOageSU/tszWiut3OGIp9kBtU6rUbuYtvf/9Vy/uzojLTNLW6nikpM2Sor6fyvOfpt1+W+drfSJmsht/OjX7/sZXciO0PvNMnh8PQwoNfVtrZZ23g6FgOh6EDe3ZJko7dd4/N0QAA0H1IxAGUyZrS6zd7JUlvr+WS70/1WzuN2S6XU6P+h8rKIK2uZxS9UDrGQ1+PU795aqji8skPV3T1reVWhNYW+pymwp/NdatZv9uP3WXFnPDbYWalO9cL/+5K39AdZz/N7gEA2KFIxAG0BafD0OH9u+0OAx3IMFToUuBuoPm0yzD1yb6UJOmT+/MJvKyZ5j2bVvqdHyp5K1fLfOfmHb3U+6Qe33OjrpYmNBUHAKC7kIgDaJhpOHR9Vy4ZuLLPL0nKGpxOWml1LVN7oU5kmuUDuTnclvRndxmmPrk7JUl6an9R6wuLdq0p6epa7nNyPWPq3bV+/bxu1PVcmooDANBduHIG0DDTcOp67wOSPkrE0QQOl3Tw0dz941/M/XXuqpgYujK3pX/4j6WFnw5JPbt168BxLTw4UCh2Z2/rwPKfWRJy02XXpcv/WFp2+DOSs8eeeNrQH82/Vvey65ms4juomwIAAJ2CRBywimlK67kBsnTzg9zf3QckRkpHNYZD6j+Yu3/oEx+VZ9Jli/7qpx+SXlndUJprU338oQM6dv9HibjWVvSDt3MDs7Vb1+l28WLyozEQHNm0PnaztFb+Z+llZSvMZ/6U94DlsQEAgO5DIg5YJbuemy9Zkl76U6WzWZ3v+T+Udn3UD/qZ4cPqdTMgGRpXbcR0KddPurSvtEsOMvC6ZR0uvbP3cbvDsFVxTXq+Br5rpvfDR74zWddiT/70qr738ebNugIAIBEHWsY0pasr60o71wplWdOKUaE6U6UpuvrcThnMdY0aMqb0zlKu9cn11VxN9p5elxy8dwAAQJsiEQfQFipN0dUVNXAOl/SgL3f/6hu5v0xZ1VR3Mobm4lckST96NzdFmP/IgBxOEnEAANCeOvwKFwDanGuXdOwLuftvJ+yNBSXWsg793fX7S8pO7323runEWq3SoGv+IwMNTdcGAADaB4k40CCXe5f2Pvq53D9vxHJlzjbt5+3eLT39lfKybXJk0zq48hNJ0uPvnpck/fDQv1DG2bvtdQOtkpV0PeMuK+smhiHdt7e3rAwAANiLRBxoUM+uXh1/+pdy/6y9am8wtRiG1NNvwYpNubO3JUl966ncpmRPf3e306HPPXqorKwjZdY+mrrrO3fLnv6KpF3bW29PPwMt7VAuh0NH77XiHAAAALaDRBxAR3M7HXrisMfuMFrme/lpttZW9OTPrpY8tnDnDaWdfU3ZjsthyH8kNwVa+JRXUm7wvJ3AMLPqX/uwpGyl516Z9O0HAABNQiIOAChjGIbcdwc72/aAea5e6Ylfzd2/ncr9rTAnd7swzIw8ty+XlN1yD5CIAwCApmnfKyEAsNAul1Oj/ofKymABh1MaOJK73+uxNRQAAIB2QCIOoIzDkB7qy813fudmri94t9UFOh2GDu/f/sB1Na3fln74rdz9n90dU+DQJxuqEXY5HHrSe0CSdGU5N1+2gwG3AAAAOhaJODrS2NiY+vvLByAaGxvT2NhY6wNqV9mMdO1Kadm+h3I1lJvodZoKPpibKunFO1c3XRY1mBkp9dPc/dvX7pY1NjZ3j8uhp4ZyiXi+j7gjs9q0EAEAADrdzMyMZmZmyspXVlZaH0wdSMTRkRYWFiqWnz592vJt3751U6/81f+V++f9H0uSHj/2sHq324/WCunb0st/Xlr29FcsGkkd7cow09q/+lZJ2VLfEZlGG75nu5XDrcz9Pn3g3CdJunR1RfuMjOUtTdKZrF5772ZJ2bH79sjVqTMLAABQxaVLl3ThwgW7w6gbV2HoSMPDwxVrxAcHBy3fdjabUfbm3RGV13K1klnTnqm7IK2uZfRnL14qKfv1pwbV19N+/b1NU0qbhtbThmRWaFueMeTO3i3P5GrN19fSktJyGIZ6tzhquTO7rkc+fKGkbOHBLyvtrP4VkDVN3bydliRdXso1h3/A0ycnbeK3xjDU27db/+LIuiTpxcwNSTcs36wp6frt9bIyAAC6zeDgoE6dOlVWvrKyUrUSz04k4uhIMzMzOn78uN1hoA2YMrW6likra0dp09BCql8L6YNKO8rnBndl72j4Zv4HplzXgIX0JaWdfTqwp0e/9tRgYdms4dKVff6S52ebWMOdyZr60bvXJUlz8Vz3hvAp79ZGUDdNaf3W3RXnxh6Qw52b534jh0s6+Gh5GQAAwCaqdVG9ePGiTpw40fqAauDqBsCOZJqmVtdLE/g+t1NGpeSwDWUd7rJEvG2t35K++43c/cv/mPt7+DOSs6d8WcMh9R9sXWwAAAA2IBEHrOJwSR/7+dz9J35V2UxWx9/fVTJQWtc38y2uCa3EcEjuvtbFU2R1PaPohWRJ2ZZrfIFGZNbkyK7XXk6S6+6gfOtmhR8tLOQwDA0eyLXO+NyjhyRJbvqVAwDQNFxxAlYxHB/NmTxwRLskff5eOwOq352soe9e3SNJ+qe7A03+fN+S3I4Gm3wX14RW0n+v9Onnthgl0KFWPtQDN35Y16LDb39TkvS9j/0bKyMq43QY+ti+XknSE4c9Ld02AAA7AYk4gDKZrPTKtdwc21dy04jrk33LclvY97rH6dAvPXZ/WVk3MQzpY7vW9fP7VrVyd4C/zx64KbdxdzqzzJrcb9/95ePIgCTp8ScHpZ5+OTqkybwdep2mvvzpI5KkW2u5AeZc7dbapLh1SO8+3ey5V3vWPrQ3JgAAYBsScQBtweV06Nh999gdhqVchqmj/Xd09N4bkvNO+QKGKeVbHdz9EcLd45JoLr8phyHduyc3+F0jXQucMuXtvVFWZonsutJv/0CXb+WamN+4eUc31Kf73Kuq5zeD4qbixWUAAKAzcXUHbMP11Vw/z5feWlaPq/rUUt9Lv1by/1dHjlkal9VMw6GbPbkBtd7b88m7ZducLiy99tFAXnmHPyO5Wts3FjuH22HK13+1/IGsNdszTelnd9ySpOu5insddN+Wo47k31HUVBwAAHQ+EnEADTMNp1J9H5ckvbn/s409+dPPfTRAm1HU9Dy7Ll36h9JlH/RJ2kGJuOGU9g/l7j/yi7m/lUYWl+TM3NaJ9/+fkrIfHvoXyjg7PFnLVPlBpsp+6BgOt9Yf/IwW0rkfsN5J5wZh691zWb2O0szfYab1sZs/anmIAACgdUjEAauYphzm3f6qd0c+Tjt6K8+d3AqX/j73N7NWR1JjYYzuPqmnv/ZyO5HDKe19IHf/oc2nJjNkqm89VVaGNmUYkrOnMH/8mpGbOi/rcCu7IRG3qkYeAAC0DxJxwCIOM60HbrwqKTfycTZr6s9dX9Sao0+zC5clSb/y+APqdW+zSTeAprJ7jvas4dQH/bnuKz869MuSpIyjtS0C1jNZvXIlJUn6kwuLkqRff2pQfT2crwAAaAYScaCFbtxO645jXVeWczXkWZMaTGC7sqa0dDM3+F1+1PQ+t1NGpw5mZjh0x5UbuPB67wO2hZHO5M5Pq2u52nuTFhcAADQNiTiAtnBrLa1XkqUDZz1+Mq3dHd41WIZTOvhzkqS1n/1Y8dRuvfPOfmUduUG7fuVjKfU6SXC243bG0DdffEuS9OqVa5Ik/5EBuZ0dmogDAICuRyIOAFZy90on/pUkKf3ua/rezUNS0cxlWXJw26xlHXrx5qGSsqf2vK+ejX2220BxU/G8xx/yyO10VH4CAABoayTiQIOcLrd2Hx2WJF1dXpYk7W7XJrCuPunkb+Tur63k/t6tid0Ow0zr3ltJSdIn3/srSdJPDv6iMncHokJrPXQtLklyZNcLNe3FHNn1wngFdvZ9tpVpypW9LSm3PyRJWYeurW1IZLPrchSNllZpf9ol31S8EYak/f09ZWUAAMBeJOJAg3b17tbjn//XkqRbby7YHE0NDoe0JzddUjNHKjdMU7vSNyRJe++8e7esQi2iw3V3CrINZU3kcjj0pPdAWVlHyqxLb+eSav3Df8z9/XRI6tm9rdVmDZfeuecxSdLCg1+WdHcE/x3Elb2t4be/KUmFHyXSpqHT65dKlvv4zWtyGbmEN9V7WDd3ldaYdxqX06Fj991jdxgAAGADEnEA1nHtko59wdJN9LgcemroQO0FO4KZm09dktZXPyrbLsNQ1sjV7KadfXU9xeUw9NhD+yRJX37qiCSp17VzRsy+49yjjMOlvvUlSdKqy1M6772VKkx9uG52+mAJAACgGIk4AFSQH327Xh09SncFhmFod0/uK+LePdvscuDcJR3/Yu7+yge5v01uGdFsuzI3tevWzcL/79zzmLItSsQ3Tn0oSd/72L9pybYBAEBrtPeVEABYpMfp0C89dn9ZWV70QrKh9YVPeQuJKzZwuqRDn8jd7z9obywAAABtgKtGAGUchnSgJ1cjfN2Zayrdob2uq2pZ39n129JP/rskyfnhP+nYrbe12HdCGaN9BgHbqd7uP66/Sx8tKevdc1m9FUZNzxp8XQIAgObhygLoZtmsdOvu3Nz5UdPdu6UaTah7naZ+7eO5572YvrrpsqjBzEgf/ESS5Lj1ofanV5U0P8nQ1W0gY7i0ZpT2vc463Mq24fRlAACgu5CIAw26vbqiV//H/y1JWvrwPUnSPQMH23M+3/Sq9NKf5u6/k8j9PfwZybnDB35K35Gym/cBzw+SlXb01vzhou2ZWe3K5H6I2Xv7HUnSjV33yTQ6dPC1zLpKBrHLGHLp7uB2plnxeKUdvYUR4/PTl2XN1n4F2tHSJJ3JKvnhSkmZ995+udrxfAUAwA5CIg40KJtJK7P0liTJtZ4bzMk077UzpPa1flv64bdKy058SXJXmDrLMMqn1KojAb69ntH5hcslZc8MH1ave5MkM/l30tuJTdf7m65crej6k//vsqnfVtcy+s8vvlUztnbhMDM6uPKaJOmT7/83SblpzDYbQd00Ta2uZyRJH968I0nav7tHDkcb/Cjxdvyj0eUlubOGhnVdkvSS+Uu6Y1R5f919vYW5wbOtfS12tDQxJS2trJWUHb23eVMZAgCArSERB2AdMyOlflpeVol7txYe+rWSIr+79vzZWdPU1ZtrZWXblW/h4O5xSRYMwnZl+ZYk6R+1VOiT7Miu64EbuZrdK9lcorZw542KCbNpOPXenk9KkjyrP71b1rxaznTW1KtXrkmSvnn3R4eWDEhnOKX9Q+VlAAAAXYREHAA6UMbRozf3f1aSdN/NH9kcTQ1rK9J3v5G7f+nvc3+rdZFwOKW9D7QuNgAAABuQiAMWyRpOXd2dG5H59XvPKJ0xdc9Kv/oNpx65b48kydHpfY93KLfToc89eqisDOgWDsPQQwO5lhhPeg9IklwO3uNAO/ij+de29LyvjhxrciQAtoNEHLCK4dCqe78k6eruXFPbh+/OlvXLj7V3jd9a1lA8lWsW/qNbuabkx/quyWVsv8l3W/CelgY/W16+vip9/1zNp7udDj1x2NP0sFCnB/2STCmTlt6J2x1NV3I6DD00kDsHPDV0wOZoAADoPiTiAMqks9L3lnK19ldWc7Vg3t7rlibiLa1ldu2StMuadW/CUG7U7If33NH6am4ANOoYt8BZOge70zD1yY/tlSQ9eucerTv75NwwqJxhZnTPndwsB7vSNyRJt40WzCMPAABQAYk4gLZgWS1zIyO3W8zlMHVsz20du++aPLdvtHz73cphSPv6csn5PQ630hsSdUlyZtcKI8bnR5Bf6X9CD/aUTu1l5Q8j22lpUtxUvLhsJ1nP1J7f/dZabnq4PrdTxg7bPwCAzkIijo40Njam/v7yKXjGxsY0NjbW+oB2GNMwtOr2SJKW7vaDz4/Y7czekWFmcwN0ra/Wv9LMuvTuq6Vl9z9WVvvZeLANjNyOHaXHkdVT93zQsu1tp6WJw1GeiO808beWay4TvZCU1KIR/ne4dCarWu9cfhgB0EozMzOamZkpK19ZWSlfuA3wLYWOtLCwULH89OnTrQ1khzINV6Hf+2v3jpQ8NrT0/9X+W29K322wX2lmTXr9b0rLDj26/US8EYZD6r+3vKxFTMOh67vulyRd2eeXJGWNyqdpZ/aOfu6D3P7K1/B+uNsrs8ryANBMF9+5rtX1zX/U5IcRAK106dIlXbhwwe4w6sZZER1peHi4Yo344OBg64NB93D3SZ9+zrbNm4ZT13tzA/nlE/FqDDOrvXfelfRRn2fDNGVS6QQAAHagwcFBnTp1qqx8ZWWlaiWenUjE0ZFmZmZ0/Phxu8PYlCO7rgdu5JpaP/nTaWWzps7v+j+05ujTf3v1HUlS4BP3qdfttCwG0zSVvtuv0mFKzipJ2mrGkFnUxnA1yxBim1ldy+jPXrxUUvbrTw2qr8e6Y4mdY+PUh5KUcbSwZYhyzY4vvnNdkvSf777Xnxk+bOn5Kp3JKvlhrvng1Zu5wQwHdvfI4eDXJQBAbdW6qF68eFEnTpxofUA1kIgDLbS8sq47Dqdef++mJOnzG0YJb7bV9Yxeuduv8uiaWx/rXa+43J/99F6tZki+62XK1OpapqwM9jBNFX5wSmeyWldWLofRuX1SK0x92GqmVGh2fPXmmiQpa1r7Hl/LZPXXr+Zaebz+fu4c6T8yIIeMwv1K1jNZvXrlmqWx7UQbBxe8snxL9+8rn5EAALA1JOIAmmpx//+m5MBntXCn8uPpv7siVUqQ1lb08R9fkSQdOZC7+Dt+MqO+HqsibRHDKXk+Lkla7/Ho4o0+vfOeRz++kRu13d9/VT2O2qNBo4K7+/ZOxtBfpbySpJc+vKGMcVv+IwNyV2sCgo5k2XSGqGjj4IJXlld1395eOe/+MHL8gb0Vf37khxEAqA+JOLCD/Oy2W3vThnZb2II649j6/Nz5Grf1TO5vo7XM30v+/9u7++C27vve8+9zAPBRokBSlmRZsiUydhzZkRJSTuKbtEojMs22zu1uSya7ezetZ/aauNOZ7dzOdMT6z7s7s17yn850drqX9HRG09zZbUy2TZP0Tm9IJ3Wb1E4iMLFjKYltQrKeHwmI4jOAc/aPHwGBJEgCJAgcAJ/XDIfA4TkHP5A/HJ7v7+H7u7d8vJO+afcn5zl57d6K/c4tfkDC9zAD9R93P7XlMm8qUAef/HemXHev8P25fTAH15bMH+GTjfc2Olo2Yvsg+AROwuIqZk598l7uv88lx+Yns4+s2ObVhpHMoeIpzxxswq/gVEpEdU9EZHsUiIvkyfb5qXn0YwDE75mlj+q9OgTWX8eFfS8AcO3mW9xP+Pmf7KUSF0rEGxzg2tLKpI9ebRjJHCqeuW0zFtBUF1izTUREREpLgbhInurqG+n8rf8VgDdv/CKvY2vcBXBMLy1g1tr2NezcEl32wyzc0+wzn3jrNqtv4f/g8busN/3zR0wBUGt5r5cQwGdbnDi8B4CLy4mettwukliCKz9aue3wp8FfnPHxtpPgkdlfAXDixmsAvLvv35L01W3rvI7l5+auYwC8faAHgMQ2Ri6IN8STG38mLcv0Wh472FSkEpWGbVkcaTUNKr+xnHdDw9hFRMTrFIiLFNFvzn0bgEPXzBzowFvN8PSX4NDGS1XttHrf+n1rdR4cppup1u/jC0/vB+DtK9ucl+jE4dIPVm57rAMo1kR1l4CzAEB9PAaAVYgkcJaVHoo/X9OS0yE+2+LYoyaA6+k8BJjftXjHZvNw9zfVcXTv2mUeK43PtjiwxzRWfeJwsLSFESzL1D0g3UiqBG8iImspEBcRkTVsy6Kp3ozUONzSsL2T+WrgyS+ax9Nm6T4sBfXrWb30IcBbB/5dKYskkjO/bacbgFKNpCIispYCcRER2Vm+wMNRH+9/t7RlEREREfEABeIiIjspsQiRfwLAnvqAowt3uFz70dKWqVIk43DzHfyOxQkSAITdzxG3NP9dREREvE2BuMgOcSw/13cfB+DcY18jkXT4aSIGwHOPmXm6Jz5zhED9Nof9bsR1HyaGcxbT26SInARcmwDAN3OD/UvTXKn5SIkLVSlciM9hORb1xACwXXdbacGXnIdD5m1YdymzuGORxGIuYV6s3uduPUlgDny2RecTzXkd49XFHERERESBuEjeFuZnOf/9vwbg3vLyZU3B1rVZei0LxzJzbBO+euI4LNoL6ecA1DSCzw9Lc/Dj4ZXHf6oParYZpMfnOHnt6+bxzGXz3XkW2KEs7R7kWjZTDUfXbKsqros/lQRuyWTBnw8EoYp+Dwm7Np0xPtU45WT5F/jd+4+lHz8SWOBU082s5/v5fAuRhd38DPMZDR25TYN/40Yuy4J6nwnsa5ZXIcg1VrYsS5nARUREKogCcZE8OckES8vLlgWWpgFw3dwyUa/Phfj82m2yqYV4km+/bRKAXbhu/h5P7d+FfzloSdq1vLe3u2Tl8wLbTXBg5gIAJ26OAsujNFINQlm4rkvCMXVwbskM+64P+LDKtZvVstMZ49Pv2ynue6n3ufyHo6bx7k3Xm+uVl6N40uHtqzEA/vMbkwD8wfNHqK9RQkAREfEuBeIi4glxXz2ju78GwGcebwXgRM3mSy85rsvVqGnEmF6IA9XRhJHZ0986N7m8rXCBZcJxCX8YBWDojQgAoVNtNNTo38aCYxNZ2F3qYkiGRNJ86ueXkgC4VXEV8KZE0uG9WzMAjJy7AsCXTxykLlBlDSPffyW3/X7j5Z0th4h4lu6oRETKUGZPf8vcxRKXZhOZUy8uv2W+P9ZpsqmLSEVxedgommokdZSbRERkDQXiIkViAU11JvA41GyGxtrlOsy3wvzZ2HuAmTt88trKIcPnFj9YM4Q76bjcuG9uMHs6D5tj7cLO3z10PwyYNaUdu9wD1oypF0784bYSq7UcXmi+vO7Pq3lGtmXB/qY6AE4c3gOYhHEiIiJSGArERYrE77M5drAJgN6Th0tcGtkOn21xqNkk6Xq+vbXEpZGtsiyos7JnRfeKfBpk3nq8r2Cv67dtju41U0O+8PT+gp13I/UBH6FTbYCZ923KoeC/VFYnF/T79LcQESkkBeIi4gm2ZaVHCnymzQS3he5lLjULaPIneaw+zuzcElDdva6F4rNcntq3C4D2hUbivnp8q0ebuA718RjwMGt6wq7TGl8eYllWOgeBMsSX3urkgm89/qUSl0hEpLIoEBepYA01fp5fDmq5ZLJms8kSS6WyU73MPmeR9ql/XrFtsuXXSdq1BXuNXPltl2ea5nnmYJQ34w+K/vqVyragtdH8PVv8tSR8a/+2fmcxnTE+lUH++u7j6SUGiyHuwPlp09j0wXKytyO1M/gtb34mRUREZOcoEBcRT7DcBC3zH5ont03mY1qfNOusb+u8TjqZWWqY796Z97IO87WdOAcfvAPA1T2d23pdkdXijsX375rpKVdnTb0+VDOrQFxERKQKKRAXEU/wOXGevPu6eXJ+uUf8s3+07UA8PxaOZV4vYdeltxWLa9nM1DyyZls2Kxoulk3VP4Fr6bIuJaQlm0RERHKiOzYRkWWO7ed60wkAzh36/ZyOmVtKbLzDUoLAcuIpHBPUO0724N61fMTqH8/pdS3XTc95Tm+rexxXU55FREREPE+BuEiRJByHK1MmSdT3fnkLgM9+ZC+1fl8piyVbkEg6nL8+DcDQG5EN9zVLokXNk5jJQr3YuItapne0jFK+XMtHrO7wmm3FlEg6vHdrBoCRc1cA+PKJg9QFdq4cScflesxcI6fnzTJ3u+r8WuZRREQqkgJxkSJxXbg1vQDA21fuAw+zg0t5cYH5eHLHX8dyE1hu9vnDtrtJT7yULdeymandV9oyANMLJhi+GjXBsbNOXSyUxUSS0fBVAC7cMA1VnU80Y2vZrJJYnVzw5v0FHtldq/XkRUQKRIG4SAVLJB3uzy6aJ0vm477HAS92wieTDrfum4aKb/70GgCX5t7DrWnc8Lh40iH8YXTHy7dVLhbzgaB5Yk+ZbznOO987F6E2oezq67Js2P0oC0mLf7xt1rp+KxojYc3R+USzlsAS2YbVyQWv3puldVcNviLmzRARqWQKxEUq2FLG8FJmTPKxE47lyQ++C8QdZ822cpf01fH2o18xTxZ/AMAJe66EJaogth9aP4KTsLjI5wBIxO6VuFAiIiIim/Pi/biIp9k+P76WJwBI3DVzva3tzmG0A3Dkc2u3bZe/lvf3njaPFycAOKFhhdvmty06n2gGIHSqLY8j/xYAnw0T6uguWzbQ5DPDtltrzBQBfaxEREQkHwrERfJUV9/Ip/6H/w2AN//yTwpzUn8NHP21wpwrk+3nXkO7eRy4Yb5bt6mMvmbDAloaa9Zs29K5XIfGpbsA7H9wHoA7jR/FsVdeKi3LIrA8b7WhJo/LqL80v3fH8nGn8SkALux7AYCkXbPRIbKBGtvhi0EzfeL5x5XnodRsy+JQs5nLnMq74bc1LUFERLxNgbiIlDW/z+ap/bsLci7LTRJcMBmij0Z/CMC9hjacEl0q7za0rZusLRtnvTXELZtFv/kdTdcdzOlcPsviyX27APjt448CUKM51+JBPtviUHMDAM+3q2Gk1DIbR5/cb64hynwvIrKWAnEpSy+++CKNjWuTeL344ou8+OKLxS+QyA5wLX/J1gW3bYvWXbUA22/oyJx6EfvQfC/yclzlxHYSHJg5v2LbzV3PrBmZIeJFmY2jLxzPreFPRKQQzp49y9mzZ9dsn52dLX5hcqD/6lKWzp07l3X75z//+eIWREQ2lzn14tIPSluWsuBmWZ6ucqaTiIiI7IRLly7xxhtvlLoYOVMgLmXp5MmTWXvEjxw5UvzC5OAzl4dZcGxu3X/cbLjUsPyTv107b/g3Xi5q2cqS6+J3FrL/yPJ5a/5zYgmu/Mg8TvUGNx0qXXkqiZOA2xfwJS2OYZIbht2TxC0P/f1FvMZ1Ib68ckNyaf39khZ+ZxFXw8pFpEwcOXKEU6dOrdk+Ozu7bideKSkQl7J09uxZnnnmmVIXQ0rE7yxw8trXs/7s1q5jXGz5XNaflYQTf9gLHLtsvu9+tHTlqSSuAwv3sR2LJkwyQtt1tp6tT6QaxOfgh39uHqcaCbMIOBYnZxqZt3fxIR1FKpyIyNatN0X1/PnzPPvss8Uv0CYUiIvkaXFhjl/+67cAuBeNArCnaQ/+7SSySsbh5jvm8Y9fNd8/+b9AoH47RYWlWT5zedg8nl4OApPPgr8AS6OVCdeyma41gW8qYVnV9fC4D4c6+5PzACTsOqii30PSrklnjK9NmLXjnC3OU0+4Fu/N7zFPpszInM7gHDW2ho+LiIhIbhSIi+QpmYgzd9EMbwksTAHg7G7a5lkzhgrO3l3e5GzznNXBcVyicw+HV15LzJPY46QbRpJ2LRf2fxmApsUbJSljqdlugoMPTENPaiTBuce+RsK3zYaeMuJavnTG+FSDzFYlXIsL80EApqfMNJMTTXPUbNIW1+B3+eOP3ALgzci9bZVBHkokHc5fnwbgr968BMBXTh6mLqCEgCIi4l0KxEXEE+J2Hd9u7F2x7Vm7jlz67ufjyfTjB8TRIlvbF086hD80Iz7+bOw9AEKn2vJbN12kCFweXgPuzZhGOSePZf+ksBJJh8hdk6H4O+9cB6DrY/vVMCIisoruqESkKNJD5Nex4NjcemCS2V3d02k25jF0+r81fJklq46kZXNiy6WUHRGfh5/+F/P4Wth8P3AcfNUzRULE0w52gm/lLWE8YXEu8YiZyhNbncV/fS4wNWsaRN6/NQPAF57eV7CiiohUCgXiIkViA02+OACtNeamxq6eKbo7bsmqY9GuK3UxJBvXeTjlIjUFQ8txeZoFtDSa7PNP3h0HwP7nafDp71aRfH7wrVptwLVI2LXLT3IPxEVEJDcKxEWKpMZ2+GLwGgDPP95a4tKUn0TSeRi6JR0cxzxLagiqSMH5fTZP7Tdz6V/w3y/Ka9b5fXzt+ScAmFtaTi6o1koREalQCsRFqsT0vOmN/8mHUWr83pur58Olrc5ks443mZ7tzJHp792aYXrBvIdaZ4FDs/NFL6N4k225HG012csfX2wgbtdjr57WkLH2vO2YeuRY/qrKHO91tm2xd5fpgVUugh1k++Gx5eXI7n1gvltrM2s0+F3+uO06TF/l/NI0F1pbYeYS13cfx7E1rUREZLv0n06kgtUHfJx8ohmAqzHTq3XLcnDxXiAesF06Gk0m6aW9jSUujZQTnwUHlhtv9i/WkfCtnaKQufZ8KoP89d3HcaziBRQJByJzJtC8smiyrR+smcdnaVSHFJG/Fp76TfP42sTG+7pJiF2maXGeQ/dNfoebu47h5JRGU0RENqJAXKSCWZZFYHkZL//yzb5leXR2rutQmzSZdpsWTKbdB7X7cbe41rOI1yw5Fv9wMwjA1RkzH/eF5ssKxEVERKqQAnER8QTbTfLIrFkm69jt7wDVt9a1iIiIiFQHBeIiUhae2r8r3ZMfSM5z8PrKAP2TB4PEl4P2rSZ4cuxAeum0tx7v23JZRUREREQ2okBcpEgSrsV783vMkykzB7ozOEeNrWGpufD7HiYT8uFjsbZ5xc99Ph/41iYcEik3rmUzXfvomm3FlEg6RO6aqSLf8ZnrVtcj09Tt4PJljuMyNWfWn05lTa8P+LCUUE9ERCqQAnGRIkm4FhfmgwBMT5lETSea5qhR7Ji3pK+Otx/9SqmLIbIjXMvHdN3B0pYBmJo1QfH7tkl+94W90zv6mguJJF9/80MA3rlqkkt2PtFMwKdAvBQSDlycqaVlyc90IsD8UoK6gHJ2iIgUigJxkQqWdFxmlpctm0uaj7ujDvgisyCwPIw+veSPAouCsGxo2MuSY/HG3SAA4dh94tY8Jw4F04kKRSRDfAHe/Rvz+KZZQYB9x8yyZhmWHIt/vLWHkzN1TC/5mJ5d4uAe5ewoFdd1mY8nAYgnnRU/sy0L3zpTsjL31UgTEW9RIC5SAlejcwD8iCnqbAfbiXPwgVkX+6pjlvA6t/hB1kRlf9z9VM6vs5hIcuGG6cWaXtwFQJ1rU7Ot0kteahrgc//RPI5r7fOCsv2w72MkEhbvchqA+al7eHRdABFvWF6SDICF+8vbnPX3F0+YjycZeiMCQPjD6IqfHWlt5MCetcs2Arx9NUYiaa6JqeNDp9poqFEIIFJq+hSK5Mm2fdi79gKQ9F0z27bZsuxaNjM1jwBwa9ex5W0FGALoq+Fi82cBuH3bnO8TVmL75xWpYhZQY5nApd5nvqtzaR2uA06Wa86SmX9OoEG/PBERqUoKxEXyVNewi09/9U8BePMv/6Qg53QtH7H6xwG42PK5gpwTAF+AW7ufAeDqcqPBCesyoN6PrFyH+kQMgNa5SQCm6p/Atcr7UulYPu41HAXg/b2m5ziZHiYv+aq1Hf5ti+lRfP5o646+lu3Ec97XnzQjLhJ2nXeC27l7cOeXa7f/8M/N98/+EdQ0buslLAv2N5newBOHTWK59YbpilSbVG+4iHhPed9diogUkO0maZ27CMCTd18HUmuZl/ml0rKZD7QAcK+hPadDbMviSKsJkH7j6X0AmnNdAg3xKMGFKznte/La14FUna2eubx+2+boXlNXv/D0/hKXRiygqc409B1qNvVwu6PGREQqUZnfXYpINTJz6t9Zse367uM46uUtGJ9tpeccfuJwcHsns/3wWId5fO8D873Iy3GVE9tJ8Mjsr0pdDJEt8ftsjh1sAqD35OESl6ayHT+0h4DP3rCh4/ihPelVCETEWxSIi5TQkmPmbVuY4a7ZJB0Xx304tExZT8F2Exy6H16x7eauYzgoEPckfy089Zvm8bWJ0palLLgEnIVSF0JEPC7gszcdqaSRTCLepUBcpIS+e/8xwCR+Ss05Xe3Og0Uu3ZtNP1fWU2/5s7H3Nvx5Zu/mx2/+AoDZmr3qES4EJwF338fnWDzFGABh91niltYFKKrkElz50cpthz8NvhpoaDWPAZIJuB5ee7yIiEgV0l28iMgOst04R6M/BEjP9Z0LNOMqEN8+14G5u9iORQtmbr/tHiv7Zdody8/13cdz2td2ExyYubDDJdoGyzYBuZQpi4QVIG7BklVH3FdH2X/AislJbL40nFYQEKlaCsRF8rS0uMD750wir6n7Zo3uPbt34bO3Hlj53TiPzJqe1WO3vg3Arx75Ikm7dnuFjc9x8upfAfBk3Jzfdh4DuwBLo4mUkaQdSGeMr49PASab/FYkXItLi7vMuWImGdUzTfMECtW2Ylk4Vm7TLFx3h5Y+3GmX/sV8Ty49DNQTFlwyiQEP3Z8D4Lkrl/nZkX9fihIKkLBrOLf7NFcTc1zd1UnnwWYNdc7H7QsP12pfTwFXEBCR8qJAXCRPifgi07/8PgD+uTsAJBsb2c69iYVLbeIBAE2LN8y2zVrRc+G6+Jfnmgbc3JdBksqSmdzuM5eHAW9n1n4zci+n/cz7mifhWlx1TeDGOvexruVPZ4xPZZDfqoRr8bNZs2zZ3bsNADy1a4GAvfEyQfU+l9CR2wD8CNMYUGtt73O+Y0sflpFE0uG9WzMAjJwzo06+fOIgdYEyaJAQEZGqpUBcpEhqLYcXmrPPA/c5cZjN+iMe2V1LU72/4rOeOpafm8s9em8f6AEgsd0RAbJl8aTD21djAPznN8ya6n/w/BHqaxTcbJVlQYPfBOt16yRnlPy5wPSCaWi8GjVrqWcmuJTiSjgOV6bM3+F7v7wFwGc/spdav64dIiKZFIiLFIllQd06vV8269+U+2yrOoYCWla6h3a+Zns9llIYiaQJZuaXkgC4bDG4iS/Au39jHt9cXnZu3zGzrJmIVBTXhVvTZiTW21dMA/Jn2lpLWSQREU/SXZCIiOwsNwmx5dEgqfmShZh6ITvGAprqzDz1Q74lAOxUHiknaf6mKclEcQsnUi72Hct+rdMKAiKCAnEpUy+++CKNjWsng7744ou8+OKLxS+QVCxrnV5gy03ic5ZWbEvYdcp6KxXB77M5drAJgF5/dOUPZ27B1GTBX7PW59LzmJk7/9NkDDBLO4qUrQKO+mmo8fPH3U8Bmy+bKVKtzp49y9mzZ9dsn51dZ/5niSkQl7J07ty5rNs///nPF7cgUvH8ziJxX8Oa7bsXb3Hs9ndWbPNyArRKZuOme28fa65jya7HVoOI99iBh2uKZ25b5rPgcP3yXO/AQjFLVl0sHzzyUfP49i+Wt62d/lTvcwk9fgN79hbvLk1zofVR/LM3uLPraRxNKykbtmVxqNn8X0pNEfBvY5UXES+7dOkSb7zxRqmLkTNdSaUsnTx5MmuP+JEjR4pfGA+r8/s4cWgPANejZqm1O5YDKGmOVA7bgqZ6E9AdDDZkbQzxJ+c5ee3rABy6b4aEXt99HMfObZmwQki6cH3BvN6deB0Arf6Fh0O+t8ByE+ydiwAFXvpwJ1iW1hT3gkAdPPu75vGdX627m2VBgy8B9z9g/+I88ftvAnCvsR1Ht4/ZJRYh8k/m8b0PzPfmIyXNh+GzLQ41m8bk59s1V18q25EjRzh16tSa7bOzs+t24pWSrqRSls6ePcszzzxT6mJ4nm1bNNSYj3ntcpZm22KD1HD5Sy2HtW0ZS63VL5nhqfOBYNaemmKK23VZtwec+SKXRMrdYtJi9JpJRHh12tSrF5ovr5vEMReWu0NLH4pI/pwEXJswjx+YzyPBx9fuZ9nQsNc8To1OsNRALrJd601RPX/+PM8++2zxC7QJBeIi4gm2m+DAzAUATtwcBTTUW6RU/MkFPnHjNfPE/rn5/lgn+AKwaz807t34BEUcaSBSdmw/7PuYeZwanSAiVUeBuIjIMsfyc333ccA0AsByArYsHtTsL1q5RIrv4QgViKe3AWD70PQWERGR7VEgLiJlyMoSIBcgOZdl4VimJ2+znnhHwwilgiUdh9icWRXgomXmmx90LGp3sNq7Lswnzed4wTFTUmqVNV0EgETSIXLXZH6+N7MIQHNDDfZ2kkyISEkpEBcpAwGfnc52mlq+pJolfHWcO/T7pS6G7KC4Y5HcpHHFdmwSrtnHcbIvMydb47ows2jWB7+53Di1f7sxsevA4vTKbbVN6TwQ80mLoUv7ALga3QWYOfRSGkkXbswH2B33MZf0sxhPUuNXtu1SWUo6/MM7Zt75+7dnAOh8ohm7EI3QIlISCsRFKpjjuCwsmZvpxeUeJsUrUg5+Pt9CZGH3hvv43TifTN4HYGrGrHn9zrUHxK1FnjnYhN+noMFTnATc/PnKbYc/rUzqxRZfgF/9V/M4tXzZ3ifXZPZeTFr83fUgJ2fqmV70M20tcnCPcnaUm0TS4fx10wD2V29eAuArJw9TF9CoLpFSUyAuUgZ8zuLDTMhLs2t3CDSYtWZWWUgkefuqCVSmF0ygUrfbRre9xZPw1fPW431AATPMCwAJK8BP/B0AXG3oXN4IkETtTWUimch4bOF3zJDbGtcMi7edOP6kWaEgYddlvc5Jntzkw2XL5u4ub2svXXlkR7nAfDwJwL0Z87lyXF0hRbxAgbhInizLxgqYXgHHMh+h7d4aupZlluoCphqOLm972JvXPvXPtMxdNE9+mGUd0M/+EdSsXVcdX4Cre0yAcue2CeSP6UZWRLziejj9MOBYnJwx17HpuEkQ9/jMfWqW13/XKgpSMZwE3H3fPH73b833j/6WWeNdRKqGAnGRPNU37uYzv/9/APDmX/5JQc7pWn7uNZgeiff2dhfknAD4atKB+FXfPQA+Zl2msCuJi9e5lo9Y3WEALjZ/FiCdlE7yV2c79LReAuD5tiwNY1JUFtDSaMb5PLnfzC231eAoXuY6D0cjpEYnPPWbpSuPiJSEAnERkQrnWjYztSYJ1q3dz+R0jG1ZHGo2vY+pRIF+u3hzrj9eP8Wx+mjO+//kcNeK535lEi57V3yP0dh4iHcP9ACQsGuz7uf32Ty13+QTeOH4waKVL+XPxt7b0nGVmnjTAuqX5x+37jINJGoYERFZS4G4iMdkm0fcPBshuWCypN5LmKFrzYEE9tFfK2rZvMJ2Ejwya3oRPn7z7wCYrdm7Yji/bI/PtjjU3ADA8+0Pe30X4sm85xfWWzbWIx81T1LJoTb5WwVsl0AeM70DSsxWcQ4nr1GX3MN8TUupiyJ58PtsQu5rADy/sHzt+ME6O//Gy7mf+PuvbK9g68mnDDvh0r+Y78mljRMXJizgd4tSJBEpDgXiImXgVt0R/nH+EABPuH5OzP6Ak8FZqjX0sN04R6M/BCC4cAWAuUCzAvEieO3clXTCnxUurXenDaEjt2nwLwfV+z62QyUTz7MDJkv6OuIJi3OJRwC4njAJ2r7kv1KUoomIiBSbAnGRMuBYfpYs01L+4f0ljsbjXGWemxEz7/vc4gdZkxjFkw7hD83w3kPFK65ksNwELfMfAlAfnwJg3h/ctEfYC9KjM76fMQ/6cissFf5fx0LS4ts3gwBMTpv568/vuk2NvX4+A8tNsmfhGgBHp0xDwOXgp0jaWhfAkyxr4x4/10oPP1+yTJZnx1Yug7xt1nOcXIIrPypOWUREZF0KxEVEtiBh1/L28tzVzG2r+Zw4T959HYDW5cz313cfxymDQLyYHBeuzpsg7U7cTL/YLKWg5TrsWroDwP6ZCwBcWU5OKCIiIuJlCsRF8hRfWuTiu28CEH1g5m03NTbi82ByKL+zwBdnvwVAc9wM8bTdfVC1g9oLyLI1d7WMOFYgnTF+1+JtwGST34qka3F9yYxAaZ0xjS9tDYv4S/Cx2mzpQ5HtSNi1vNX0Ja4m57i6u5POw83Kx7ARy4bGveZxoCG1sWTFERFvUyAukqf40gI3f/S3XLt+ncUZM9Q4fugp9gV3lbhka1muS5NzH4BGd9ZszDPRlpQ/20lwYOY8AB9//z9z6Rc/5Vv3n2bR9fPZL//P7G7OfQmurzw2hZNMmOV3sph3bP7fK6vOl1wCa7nebTQ0eQc5tj+dMT6VX2Cr4q7Fj2ZMFvprN83NdujIbfz2xp+tOp/L1w6bJYvOOWbKSI21vaUEd2zpwxKzXIdG5ggk56lfMtfZ2zNJfvidv07vk6q7iaRD5K65vn3nnesAdH1sP3WBrTW0iGxZoB4+9ZJ5PHt3xY9uR2f4i79/y+xmJfmPn/LTWK9pNOJ9t2/f5i/+4i/Sz//wD/+Qffv2lbBElUOBuMgWxONxPvzwUvq5b8+8JwPxcuJYPu40muV8Lux7AUBzfQvGxXYT5tF8lKs/f5PX//WfmIvDJ379S3kF4nU+F+6ch4X7WX8ecCxOzjSu3HZtFmwXWtqhqfjLS3mFbcHeWjP3eY8/XuLSbCxp+blQc9w8sS8DcGKLIwi2IkCCfxP/EY/N3sd3cxSAf0g8z3f/y/+d3idVd11gatYkEHz/lhml9IWnd/Ym0XVd5uPmbxlPrm1MWa/XOOm46VUH5pbMZ7I+4MOqoOW9ko7LhbmgeTJlrgWdwTlqNmmoqnR3YrP8p7/6HgANAXjp+K8rEBdPS13nrly/xf/+f/5f6e1f/u9/j13BzUcE1vp9nhwt6iUKxItkeHiYoaEhIpFIeltXVxehUIiurq4NjvR2GbzwvqpV0rVwHHNjk0g6uLZb3jdzls2i36wFPF1XvcFaubMsOFAbX7MNMMF7KoDf+yTY+hfkVY4d4ELtCQCaUg1i9m3IY0m5SjYfTzL0hvm/l0qImeL3WZx8IvtN6p0Hi1y6Z3rvU8eHTrXRUFM5nwXHdbkwHwRgesqMGDnRNEeNRrSLlJXUde7Onfs0fer30tv/5sJ9HrkT2eBIo6fzEIdbGjbdr5rpsrjDJiYmaG5uZmBggFAoRDQaJRqNEg6HaWtro7u7m97eXmKxWFmVwQvvq1o5WETtZs4tPcHbM028PdNE+Mo0CUc3yLJ9PmeJo1M/4OjUDwjOXyY4fxmcRM7H+y2Xo42LK778qWHpc3cffq0ztF28wcL01NYHfLTWJGitSaCODQ9zXViaNV/JpZVfeXx+xYOcRPpv6U/O40/Oa4qZSIWonCZYDxofH6e7u5u2tjbC4TDBYDD9s7a2NgYGBmhvbycUCjExMbFmH6+WwQvvq5olrAA/8XcAcLXhYYZon7OEf3VvlbNAfcCXHkIpBeQ61MdjKzbNB4JlsSzZRiw3mc5AnspIrqC5+vh9NicOBwH4ff+9orxmje3y2wdiAPx86Q6HZmfxVVgPfMLZoc9SfA5++Ofm8eqlyXY/Cq0f2ZnXzeS6+J0FSDrUuGaqgN9Zns6QmSciJd+l6VwXnG1M6bD95Xl9jl6CBzeod+E/PG5+n4nDv0XcV49frWMiZU2B+A6JxWL09vYCMDIysm4g2tfXRzgcZnh4mNOnTxMOhz1dBi+8L8nuifs/5rG5X67ZvljrVyC+A/zOIieW566mnHvsa1nXc684+45tPzjX+tCyit+Gp3YtAhCtmaNp0dvz6MuWZUPdnrXbVqnzuXzt0G188/c4vzjN+dYj1C1EiTYcwbXW3j76nQVOXvs6juMyEzfDVptmzOc8nSci08GO/MrtxLe3/vkjT0PjI1s/vsQs62HuAb/Phhyz11sW7G8yS0KeOGz+7pq3K+INCsR3yEsvvUQsFqOjo4OOjo3/2fT39zM8PMzExASjo6P09PRsuH8py+CF9yUiJVaGc7v9yQU+ceM1AA5Ovw3AzV3P4BTxvTguTC2ZHq37CROg7PbFtzXk23ITtMx/CMBTd8cAmGz5dZJZ1rSXnXX80J5Nl/Z6ZHctV6PzRSrROmw/HDi++W4W7A0sws3zPLE0j+/+PwFwru5rJHzldw0oisTSw8aCmPlc0nQI7E0SHdbtWX9FiW1OLfDbNkf3mqR5X3h6/7bOJdWlzu/ja88/wa9+ucDgz8cBcBYe8HvH/pSPHWvb9Phav1au2IyupDsgFosxOmp6ynJJWNbW1kZbWxuRSIRXXnmlIAHrTpTBC++rWtVaDi80X16z/SeHH/4dfLHsd/M1fhumPmT+3vvsaqildt/juHjw4ug+zOz99nf/mqlb12nc/wSf+uLvbXJg6V2b/AXzM9OEf9nEiS/+j6UuTsX5hx++y4270zy6t4nf/uyzWzzL8rBZSNeznUo8dvYfw1y6GeXIgWZe/NLD6SMLSYuvXzFrDF+9bxLYvNB8mbptLGFmuW56ikTL3EUAIs2fy/n4H3/3b5m6eY2WA4/xqS/+7pbLIaa3cnUgvvr3a3s8oeZ6dderfvbBDWIz8wR31fOJjzxa6uKYXvtLPzCPY8v/s3c/Cl78n7uJs2fPcunSJY4cOcKLL75Y6uJsSuUtPNu22Lurllv1PpLTt9Pb6wK25xNMlsPvFxSI74jh4eH04+eeey6nY1IB68TEBJFIhLa2zVuail0GL7yvamVZZL1Zz7zpWy9jumVZ3Jj8BVO3rvNo624+ccybeY9tN8HBB+8A0HH5Q6ZuXedv3v14WQTi15d/v+HpBQXiO+C//vA8P33vKp986tA2AvHiOfvfJnjj7YucOnHU88HMT777d0y+82Paj39qRSDuSy7w7O1vmSf2z8z3A8fBpykF+Vjv91sUBzvBl9+86HKquwA/m7zBpZsxjhwI5heIJzN6rr+/vO2zfwQ1jeseUkhzcbjb9Cz7Di8vs7fR3yh4BGbvFKVcmc6ePcsbb7zBqVOnPB3IpKi8kqlcfr8KxHfAN77xjfTjXJOUZQao4+Pj9PX1ea4MXnhfsr7LwU9xZU/2G6dX33+DyLsXOfGRvRz/7/Sxl52zkLT49s3gim1fPhAz649L2bB42NOOPbe8VX/DsuLzrz/cuUjebvwccSvAicfuEPBvM1mbHYDDn16x6a/f/wX/8vNb/NrH63nx3396nQNTx3vrf59rB3L7+9hlmGBOqlpq/fFM9QFfeS+xu0O8dVWqEBMTE+nHLS2bL3gPKwPbQiQ224kyeOF9yfqS9vr/0OeTNnNxEyShC2FOPnN5eMOf20483YP/k2IUqEw4Llydr1mzTcqL47hMz5tkaVeWry37HYua8hthKyUUtwIk7FoTcG63Mc6y1gSuC0nr4f+2Ejc67DTHhQfLn8kHi3HiPj+76vyen+4g1Se1/nim0Kk2zw9nLwX9RgosEllZ8XIdit3a2pp+fO7cOc+VwQvvSyQl4avnrcfN6IrNAmYRyZ/jukwvmJv+q5YJcFod2MlQZy5hMXTJDNW9PVXL5+OXear+/g6+omzEcWFq0UdDwmbRsYknHQLKtl0ySdfiwo1pAH659IBFO07nE83YPv1NRMqVAvECi8ViWzous+d4q+fYyTJ44X1J9an3OfiTazMMJ+1A1uVzSi2zvEm7Btcqr+5DX3KBwHJCs0Kad2xIZE9IZltmmSTbgid3mdd2Z2fNzzY9s0XcNsvyzAeC5ljMTantxDOSsrEj72vjotk4vhrmEg9vkued4gwxXf1eXcteN4t6je3QEMjyWcvy+zJ/x/Vv+i0L6tfp9Yw7EHc2Dhi29PtxXQLJeWqXy5t+D0uzrL7FWUwkSa4enuGvXX/I8tJs+qFtWdQFMj7PS4n0a6VeO5Ccx79cax0rsG5G/hp3AcvduKwAiaTDUnLV58a3znDmpQSB1L7Lv2e/C9l+40kXFpPZ/xaOrwYrUIvjqyEa9/H/XQ1ycqaB6YUA0yxwcE/9mvqVsOvyG2nlumbdcyB+7xKJpINb35x9nrSbxJq+bh7PmEYZt7Zp03nvCQeWNqpvSYtA6ufLv7f47ANYMteMFcNofTXp3AhJx2UxkTHkNj5n3k/6+fyav0M8YYFrseDYmL/I2s+I4y736m9Q1rhrZf+DbiKRdHjv1gwAf/XmJQC+fPwgtYGNf4eOHdgwJ8RCPInjbm2Uw5rPU4asn9McOHYAy79+U+FSwiHhbC0xpoVF/TrDgeJJh/jqz2kOHDuAFVh/dYusn/88rDcUfE0d3qiMLkRnl7g1E8duMEvfOXPTWy6TrOW9O1lhamqq1EXYkTJs55yLi4srnn/ve9/jgw8+yOsczc3NK3rot2p+7gGXbkxxK/bwptW9HcMuQU/BTef9nPZbWjA3PfOLcSLX7u5kkbbMdhM8WA7Abtxf4v6sw+nWW7T8+M/W7Pth82e4X3cYgMiN0ryfzco72XqK2Zp94LrccJ8H4OMPbgLgPIgVdYpAZlk/YJbbs056uPjd6x+m9/vonX+kNvGA1PiX2RlzzHTT7Zznc84nLW5eX7n+8+D19fd/vGGRz7eam8SjmN/P/ekYAFeX/9/PL68nna3+foBZjuedxk+aDVeuALB/5gJ7H5xfse/q93XnwT2cLTToLLo2d++b38cnAw4/n27gAlPpIHR2YQm7Icit1k/yn/519VrY1wC4e98EMxfn71K7jazpmX/bWzPmd7P72v+zYp+ZmkeItH4+6/Efq5/i8ycC7Gm9seKz5rguVx6Y6+6SZQKgb08vkly9FnSGWtvhq49Fs/7slw9q+XFsVw7vyPx+7t1/wKX4NNTNcnfWvK87ySsr9rx7/UN8zgIfu/ktPrFc1pY7pmHmvatNBFbNrb0am+PuzNKKbZnXktWO3xhJP64L+Hh6/+7087jj0HLdVNBPTJu/5d7btelhwteaPsm9xo+kr71LC3PcvPQ+ccfh2ckRAu7ShmUFiM0vcene3Iptt3Y/w61dx9bs63MWeObW8v+DByaY/GjiDvWBtfX7xqKfsdt71mwHuNX6SRqfbedWaxODby3icx4QmbvPzEKC2dpaFmZr2X1jZf06v//LJO06cJNE48+QSLpE75nfc0PiNlj2is8HThxummk9d9+7l/tybg9umNdr+FR6KtbswlL6+/mLt9K7Xpqv4Z/v7l57jmU+J84zc8sNddeWf2//+qfpnz9zMONv8sS/gQPPAHDj/jxj5x++zjO3voXPWXl/8rC85vznL8dJ2hZT92ux6hpxF8z17oNr99K7RuM2377ZvHlZd5vPQix+kSW7lptkrzuZ4o7DleW6en7JlP38u+c3OgSAWw1HqHv8HrOzs5w/v3b/f/rVbS6vqp+52t9Uy28+mz253o8id/nVzZm8z3mr4Qh1H1lat7w/uxzjnauxvM8LUBuw+epzj2f92S9vTPPji/nf395qOELjszXMzt7OWt5Ld2f55/e2nqSv97lD2T//q+pwLqaiUzR89LMAzPx8bNP77/l4gpuXrq7YduGR+azl2Yp79+4RjWb/X5Ny9+7d9Pe///u/5/LllSsPrY4rSsaVghobG3MxzZ1uPr/eoaGhLR1XrDKU+n1985vfXHEefelLX/rSl770pS996Utf+sr365vf/OaWY5JCUirGAvPC8OudKIMX3peIiIiIiEglUCAuIiIiIiIiUkSaI15gua6vXW5lKPX7OnXqFN/85jeJxWJMT09z4MABamryy99bqDniIiIiIiJSfLnMEV9taWmJmzdv0tTURDAY5NSpUztUuvwoEC+w1etrx2KxvIPY7Qa9O1GGUr+vYDDI7/zO72z5eBEREREREa/Q0PQCK0TP8eqg1wtl8ML7EhERERERqQQKxAtsdbCZ65JdmcnQCt0jXogyeOF9iYiIiIiIVAIF4gW2OtjMNdv45ORk+nFbW5vnyuCF9yUiIiIiIlIJFIjvgI6OjvTjXHuOM/d77rnnPFkGL7wvERERERGRcqdAfAd0dXWlH0cikZyOydyvp6fHk2XwwvsSEREREREpdwrEd8DLL7+cfhwOh3M6ZmJiAjDDtwsxhHsnyuCF9yUiIiIiIlLuFIjvgGAwmO49fu211zbdf3x8PP24v79/0/1HR0fTAW4xy7DT70tERERERKQaKBDfIUNDQwSDQWKxGKOjo5vuC2YOdl9f37r7xWIx2tvb6e3tpbOzk8HBwaKXYSfOKSIiIiIiUk0s13XdUheiUo2OjtLb2wuY7OHZhmYPDw8TCoUIBoOEw+ENh29nng9MD3U0Gi1qGXbqnCIiIiIiItVCPeI7qKenh7GxMYLBIJ2dnQwPD6d/FovFCIVChEIhurq6uHjx4qbB6uolxHIJbgtdhp06p4iIiIiISLVQj3gRxGIxXnvtNYaGhlZkEe/q6koHrLnq7+9ncHCQtrY2xsbGcg5yC1mGnTyniIiIiIhIpVMgLiIiIiIiIlJEGpouVWN4eJjOzk6am5vTX729vSuyu1dDGaT8lLreTExMEAqFaG9vx7IsLMuivb2dUCikuiubKnX93UgkEqG5udkTZRHv8VLdHR8fp7e3N30dTiXvVd2VbLxQdwcHB9NlyKyzmVNaq50Ccal4ExMTNDc3MzAwQCgUIhqNEo1G00nkuru76e3tJRaLVXQZpPx4od709/fT2dnJ1NQU/f39jIyMMDAwAJh/9N3d3XR2dq6YniIC3qi/m0m9vq69kslLdXdiYoL29na6u7tpaWlhZGSEaDTKyMhIuhxaIlZSvFB3x8fHaW9v5yc/+Qkvv/wy4XCYaDTK0NAQzz33HP39/bS3t2+6+lJVcEUq2NjYmAu4bW1tbjQazbrP0NDQpvuUexmk/Hih3nR0dLhtbW3u5ORk1p/39fW5QPprbGys4GWQ8uSF+ruZ1OsD7sjISNFfX7zJS3U39TrBYNANh8NZ94lGo24wGNT1VzxRd0dGRjasr65r6mxXV5fuG1zXVSAuFSv1zwnY8ILgug8Dio6Ojoorg5QfL9SbgYGBnP5Rrw7G1wvapXp4of5uJhqNrqi3CsTFdb1VdzMbita7ro6MjLhtbW0u4Pb09OxIOaQ8eKHuTk5O5vT6Kam6W833DQrEpWL19PTkfKFJXTwKfUPmhTJI+Sl1vUmdM5eW6tUBTVdXV0HKIOWr1PU3F11dXembVl1zJcUrdTccDqfPPTQ0tO5+HR0d6f3a2toKWgYpL16ouz09PXnVw5GRERdw+/r6ClaGcqM54lKRYrFYeu5JLsuotbW1pZeCe+WVVyqmDFJ+vFBvBgYGCAaDOe0bDAbp6+tLPx8fH9d88Srmhfq7meHhYaampvjKV75SlNeT8uClutvb25t+jczr62blkerklbo7OjqaVz1MlfXcuXMFK0O5USAuFSkzI+Nzzz2X0zGpi8fExERBAgkvlEHKjxfqzblz54jFYnR3d9Pe3r7p/p2dnSueK4tv9fJC/d1ILBZLJx3MtbFJqoNX6u7w8HD6XKFQaMN9R0ZG6Ovro6+vj6GhoYK8vpQfL9TdVPK3rQTVLS0t2379cqVAXCrSN77xjfTjXG+2MlvxChFIeKEMUn68UG8y/ylHIpFNM5uubgGfnJzcdhmkPHmh/m7kpZde4uWXX1bvoazhlbqbWpUCoKenZ9PXHxoaYmhoSHW6inml7oIJyFMjOjaTCto7OjoK9vrlRoG4VKSJiYn041xb2jIvXuFwuCLKIOXHC/Xm5MmT654/m9Xl1FJQ1csL9Xc9o6OjRCIRzpw5s2OvIeXLC3U3c2pPMBhUcC058ULdzayvo6OjOS1rmlp2b7ORH5VMgbhUnNUf/Fz/kbW2tqYfb3e+ihfKIOXHK/VmYGAg/dpnzpzZdM7Z6nLnMpxdKo9X6m82sViMl156iVdffXVHzi/lzSt1d2xsLP14dYOoSDZeqbuwMqCemJigvb2dwcHBrPsODg4yMTGx4n6jGikQl4qz1d64zNbB7fboeaEMUn68Um86OjqYnJzEdd0VwyTXs/pGoJqHmVUzr9TfbF566SX6+vpUNyUrr9TdzCHCmeceHByks7MTy7Jobm6ms7Nz3QBHqotX6i6YhvvV19j+/n7a29tX9NoPDg7S39/P0NBQ1Y9QUiAuksXU1FSpi+CJMkj5KUW9yUwSFAwGc8raKpLNTtTf8fHxdM+LyE4pRN3NNsS4s7OTe/fuMTIyguu6XLx4kVAoRH9/P83NzZvm8BDZTCGvu6+//vqaYDwSidDZ2UkoFKKzs5OhoSHC4XDOKwJUMgXiUnFWX1C2khl3u62DXiiDlJ9yrDerM64q2KleXq2/vb29jIyMFPy8Ujm8UHdXHx8MBunt7eWrX/3qiuG7qSUjw+FwOjFWZtZsqS5eqLurXz8cDmft6R4eHk43imp0kqFAXCqOFwJYL5RByk851ptUshUwa4Kqhbt6ebH+hkIhDUmXTXmh7q6e4pPqHV9v6G5HR0f6ehsKhVb0pkv18ELdzWZgYGDd0XG9vb309vZ6tuzFpEBcRES2ZHR0ND2nsa2tTb2O4inj4+OMj49rlIaUhdU9m+Pj47z88ssbHpO5TFSuS0aJ7LTx8XGam5uZmppicnIya2PS6OgoR48erfoGJAXiUnG2MiynEssg5aec6k0qCzWYIDwcDpdV+aXwvPb3D4VCK/IXiKzHC3V39bJTwWBw05EcmT2OkUikoOtBS3nwQt3NNDg4SHd3d3r6RFtbGwMDA0xOTq6pz7FYLKdlziqZAnGpOIVY03i7FzYvlEHKTznVm9SwMgXhkuKl+tvf309XV5cSB0pOvFB3Vx+f6/JlmUs/aVRS9fFC3U1JZUPv6+tbMxIpda+QrXG0u7u7IK9fjhSIS8UpxAVl9YWtHMsg5adc6k1/fz/j4+MKwmUFr9TfiYkJhoeH1RsuOfNC3V19/FbWVlaPePXxQt0Fc93t7++nra1tw2tvX18f0Wh0zWiOas3+r0BcKs7qC0quyzJktiIWuke8FGWQ8lMO9WZ4eJjBwUE6OjoUhMsKXqm/ypIu+fJC3V19fK7ny9xPy55WHy/UXSA9VS2XnBzBYJCxsbEVyV2rteFUgbhUnNUXlFyH6UxOTqYfb6Ul2mtlkPLj9XozPj5OKBSiq6tLQbis4YX629/fT0dHh4akS168UHeBFXNolVFacuGFuhuLxdJJ13p6enI+bmhoKF3nz507t60ylCsF4lKRMv+Z5do6mLnfc889VxFlkPLj1XozMTFBd3c3PT09jI2NrbtfLBar6sQr1a7U9Xd0dJTR0VEsy9r0a3BwMH1cb2/vip9lLssn1aHUdRdWzgvfSs+mGvCrU6nrbup//lbq32YrA1Q6f6kLILITurq60q1zuQYFmfvl06Ln5TJI+fFivYlEIpw+fZq+vr5Nh4+98sortLa2rrv2rVS2UtfffIY3hkKh9GufOXNmRcIgBTTVp9R1F0yD0PDwcF5lyJRrgjepLF6ou1uVakSo2muuK1KBotGoC7iA29fXl9Mxqf3b2toqpgxSfrxWb6LRqNvW1pZzWbq6utyRkZGCl0PKg9fq70Y6OjrSr606K16pu8Fg0AXcYDC46b6ZZQbcsbGxgpVDyocX6m7qfNFoNK/jwuGwC7hnzpwpSDnKjYamS0UKBoPpOYKvvfbapvtnZhrNZUji6OhouvWxVGWQyuSFupvp9OnTdHV15dzTOD4+vunat1K5vFZ/RXLllbqbGqqbOe92PZnzapUboXp5oe6metVfeeWVTc+XKXVvEQqF8jquYpS6JUBkp0xOTqZbljfr7ejp6XEBt6OjY8P9Ur2DLLf8DQwMFL0MUvm8UHdd1/Rud3V1ueFw2J2cnMz6FQ6H3XA47I6Njblnzpxx9W9FvFJ/N5N5vqGhoW2fT8qfV+puav+enp6cygC44XB40/NK5Sp13Z2cnMx7ZMbIyEjBruflSndMUtFSH3LAnZyczLrP0NBQehjYevtkO1/qmGKXQapDqetuX1/fiv1z/dK0CnHd0tffXKRuWqv9RlBW8kLdzQyq1qubAwMDmlohK5S67o6NjaX326xxM1WOXIfSVyoF4lLxxsbG3GAwuObCEI1G08FGV1dXTvNaUheZ1FeuvdeFLINUj1LV3cwbvHy/urq6CvHWpQJ44dqbKTVyY2RkZEVPYurGcWBgwB0bG3PHxsZ0La5yXqi7k5OT6d7IVO6NyclJd2xsLF1/29ra1BMuK5S67k5OTq7ocU9dV8PhsDsyMuIODAy4bW1tbltbm3IauArEpUpEo1F3aGjI7ejoSF+ggsGg29PTk/eFIDX8tq2tLa/e60KWQapHKepuZk9hvl/VmnBFsvPCtTclc4hlZllWf2mouriud+ru0NCQ29XVtSKJm5Jiyka8UHcnJyfdM2fOuF1dXelrb6ru6vr6kOW6rouIiIiIiIiIFIWypouIiIiIiIgUkQJxERERERERkSJSIC4iIiIiIiJSRArERURERERERIpIgbiIiIiIiIhIESkQFxERERERESkiBeIiIiIiIiIiRaRAXERERERERKSIFIiLiIiIiIiIFJECcREREREREZEiUiAuIiIiIiIiUkQKxEVERERERESKSIG4iIiIiIiISBEpEBcREREREREpIgXiIiIiIiIiIkWkQFxERERERESkiBSIi4iIiIiIiBSRAnERERERERGRIlIgLiIiIiIiIlJECsRFREREREREikiBuIiIiIiIiEgRKRAXERERERERKSIF4iIiIiIiIiJFpEBcREREREREpIgUiIuIiIiIiIgUkQJxERERERERkSJSIC4iIiIiIiJSRArERURERERERIpIgbiIiIiIiIhIESkQFxERERERESkiBeIiIiIiIiIiRaRAXERERERERKSIFIiLiIiIiIiIFJECcREREREREZEiUiAuIiIiIiIiUkQKxEVERGRLYrEYoVCI9vZ2mpub6ezspL+/f1vnGx8fL2AJRUREvEmBuIiIiGxJZ2cn7e3tTE5O8uqrrzIxMcHg4CDNzc3EYrG8zhWJRDh69OjOFFRERMRjFIiLiIhI3oaHh+no6ODMmTMA9PT00NfXB5ie7dOnT+d8rlgsRnd3N6+++ipdXV07Ul4REREvsVzXdUtdCBERESl/sViM5ubm9PNwOExHR8emx6SGtKcCeRERkUqnHnEREREpiGAwuCKYfuWVVzY95vTp0yt600VERKqBAnEREREpmMxkbaOjoxvOFe/u7ubkyZMMDAzk/TqxWIzh4WF6e3vTyeIsy6K9vZ3e3t41Sd+Gh4fzSiQXiUQYHByks7Mzfe7m5mba29sJhUJKKiciItuiQFxEREQKpq2tbcVw9Ndeey3rfr29vQSDQYaGhvI6fywWo7+/n+bmZkKhEC0tLQwMDPD6668TjUYZGRmhu7ubUChEd3c3kUiESCRCKBTKKYFcJBJJB/djY2O8/PLLhMNhXNfl4sWLDA0NEQwG0/uMjo7mVX4RERHQHHEREREpsOHhYUKhEAAdHR2Ew+EVPw+FQkQiEcbGxvI678TEBKdPnyYWi9HX18fAwADBYHDDcvT399PS0kIkEqGvr2/DwH98fJzu7m6CwSAjIyMbJo6LxWLpnvfNzisiIrKaAnEREREpqNVJ26LRaDpg7u/vZ3x8fE1wvpmJiQk6OzsBGBgYSGdr30xmo8BGAfPo6Ci9vb1AbknmUrq7uxkfH6erqyvvhgUREaleGpouIiIiBRUMBlf0JqeGpw8ODjI6Osrrr7+e1/kikUg6CO/p6ck5CAcTfG+WCC41HB1gaGgo5yAcYGRkBDC96fnMQRcRkeqmQFxEREQKLtULDSZYHR4eZmhoiHA4vOFw8s3O9eqrr+Zdls2SwaXOvzrrey6CwWC6YWBwcJBIJJJ3+UREpPooEBcREZGC6+npST9O9RaPjY3lHYRPTEykM5R3dXXlfTys7aFf7/xf+cpX8j43rGwoUK+4iIjkQoG4iIiI7IjM4HdgYIC2tra8z5E5pzs1fHwrUlnONzp/d3f3ls7d1taWbiBQFnUREcmFAnERERHZEZmB7VYTmZ07dy79eCuBfEpfX1/WueWFOn/msRMTE1s+j4iIVAcF4iIiIlJwkUiEV155Jf08Nfx7K+dJ2U6gnMv5tzLsPaWlpSXrOUVERLJRIC4iIiIFlcpyPjIykg6eY7HYloPxcjA1NVXqIoiISBlRIC4iIiIFE4vF6O7u5tVXX6Wrq2tF0rbUUl/5yOwF34me5kL1ZMdisfTjnei5FxGRyqJAXERERArm9OnThEKhdAD+1a9+Nf2z1Hri+Th58mT68XYC5UgkkrVHPjOh3HbmdmeWLZ91yEVEpDopEBcREZGC6OzspKura0VStI6Ojm0NT1+9HvlWDQ0NZU0Yl5mJfasJ5TLf03r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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "kwargs = dict(bins=50, xlog=False)\n", + "pop.plot_hist_properties('m_chirp', intrinsic=True, observable=True, pop='DCO', **kwargs)\n", + "pop.plot_hist_properties('chi_eff', intrinsic=True, observable=True, pop='DCO', **kwargs)\n", + "pop.plot_hist_properties(['S1_mass','S2_mass'], intrinsic=True, observable=True, pop='DCO', **kwargs)\n", + "pop.plot_hist_properties(['S1_spin','S2_spin'], intrinsic=True, observable=True, pop='DCO', **kwargs)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "posydon_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff 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yet, export the path POSYDON environment variables. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "env: PATH_TO_POSYDON=/Users/simone/Google Drive/github/POSYDON-public/\n", + "env: PATH_TO_POSYDON_DATA=/Volumes/T7/\n" + ] + } + ], + "source": [ + "%env PATH_TO_POSYDON=/Users/simone/Google Drive/github/POSYDON-public/\n", + "%env PATH_TO_POSYDON_DATA=/Volumes/T7/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating the Initialization File to Rub the Binary Population Synthesis Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's copy the default populatio synthesis modle to your working directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import shutil\n", + "from posydon.config import PATH_TO_POSYDON\n", + "\n", + "path_to_params = os.path.join(PATH_TO_POSYDON, \"posydon/popsyn/population_params_default.ini\")\n", + "shutil.copyfile(path_to_params, './population_params.ini')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Open the `population_params.ini` file and do the following edits to run a large model at 8 differet metallicities:\n", + "- set `use_MPI = True`\n", + "- set `metallicity = [2., 1., 0.45, 0.2, 0.1, 0.01, 0.001, 0.0001]`\n", + "- set `number_of_binaries = 1000000`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running the Population Synthesis Model " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Write the binary population simulation script to a file to run the population synthesis model with slurm on the HPC facility." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing script.py\n" + ] + } + ], + "source": [ + "%%writefile script.py\n", + "from posydon.popsyn.synthetic_population import SyntheticPopulation\n", + "\n", + "if __name__ == \"__main__\":\n", + " synth_pop = SyntheticPopulation(\"./population_params.ini\")\n", + " synth_pop.evolve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the simulation on the HPC facility using the slurm magic command. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Note: if you do not have the slurm magic commads installed, you can install it with the following line\n", + "# !pip install git+https://github.com/NERSC/slurm-magic.git" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext slurm_magic" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following slurm file works on UNIGE HPC facility." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%sbatch\n", + "#!/bin/bash\n", + "#SBATCH --partition=private-astro-cpu\n", + "#SBATCH --job-name=pop_syn\n", + "#SBATCH --output=./pop_synth.out\n", + "#SBATCH --mail-type=FAIL\n", + "#SBATCH --mail-user=user@email.ch\n", + "#SBATCH --time=24:00:00\n", + "#SBATCH --nodes=2\n", + "#SBATCH --ntasks-per-node=34\n", + "#SBATCH --mem-per-cpu=4G\n", + "\n", + "export PATH_TO_POSYDON=/srv/beegfs/scratch/shares/astro/posydon/simone/POSYDON-public/\n", + "export PATH_TO_POSYDON_DATA=/srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/POSYDON_data/230914/\n", + "srun python ./script.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following slurm file works on Northwestern HPC facility." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%sbatch\n", + "#!/bin/bash\n", + "#SBATCH --account=b1119\n", + "#SBATCH --partition=posydon-priority\n", + "#SBATCH --job-name=pop_syn\n", + "#SBATCH --output=./pop_syn.out\n", + "#SBATCH --mail-type=FAIL\n", + "#SBATCH --mail-user=user@email.ch\n", + "#SBATCH --time=24:00:00\n", + "#SBATCH --nodes=2\n", + "#SBATCH --ntasks-per-node=34\n", + "#SBATCH --mem-per-cpu=4G\n", + "\n", + "export PATH_TO_POSYDON=/projects/b1119/ssb7065/POSYDON-public/\n", + "export PATH_TO_POSYDON_DATA=/projects/b1119/POSYDON_GRIDS/POSYDON_popsynth_data/v2/230816/\n", + "mpiexec -n ${SLURM_NTASKS} python ./script.py $SLURM_ARRAY_TASK_ID" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parsing the Populatio Synthesis Model Output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If everything is set up correctly, the simulation will generate 8 different population synthesis models, one for each metallicity containig 1 million binaries each. The simulation will take a few hours to complete. For convinience, we have already run the simulation and the results are available in the `.../POSYDON_data/tutorials/population-synthesis/example/` folder." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['/Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/1.00e+00_Zsun_population.h5',\n", + " '/Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/1.00e-01_Zsun_population.h5',\n", + " '/Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/1.00e-02_Zsun_population.h5',\n", + " '/Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/1.00e-03_Zsun_population.h5',\n", + " '/Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/1.00e-04_Zsun_population.h5',\n", + " '/Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/2.00e+00_Zsun_population.h5',\n", + " '/Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/2.00e-01_Zsun_population.h5',\n", + " '/Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/4.50e-01_Zsun_population.h5']" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "from posydon.config import PATH_TO_POSYDON_DATA\n", + "\n", + "path = os.path.join(PATH_TO_POSYDON_DATA, \"POSYDON_data/tutorials/population-synthesis/example/\")\n", + "files = sorted([f for f in os.listdir(path) if f.endswith('Zsun_population.h5')])\n", + "path_to_data = [os.path.join(path, file) for file in files] \n", + "path_to_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we show how you can parse the simulation results and save the subpopulation of merging binary black holes (BBH)." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Binary count with (S1_state, S2_state, binary_state, binary_event) equal\n", + "to (BH, BH, contact, None)\n", + "in /Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/1.00e+00_Zsun_population.h5 are 233\n", + "in /Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/1.00e-01_Zsun_population.h5 are 2643\n", + "in /Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/1.00e-02_Zsun_population.h5 are 5974\n", + "in /Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/1.00e-03_Zsun_population.h5 are 8320\n", + "in /Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/1.00e-04_Zsun_population.h5 are 9683\n", + "in /Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/2.00e+00_Zsun_population.h5 are 121\n", + "in /Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/2.00e-01_Zsun_population.h5 are 3021\n", + "in /Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/4.50e-01_Zsun_population.h5 are 761\n", + "Total binaries found are 30756\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/synthetic_population.py:300: PerformanceWarning: \n", + "your performance may suffer as PyTables will pickle object types that it cannot\n", + "map directly to c-types [inferred_type->mixed,key->block1_values] [items->Index(['state', 'event', 'step_names', 'S1_state', 'S2_state'], dtype='object')]\n", + "\n", + " self.df.to_hdf(path, key='history')\n", + "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/synthetic_population.py:301: PerformanceWarning: \n", + "your performance may suffer as PyTables will pickle object types that it cannot\n", + "map directly to c-types [inferred_type->mixed,key->block2_values] [items->Index(['state_i', 'event_i', 'step_names_i', 'state_f', 'event_f',\n", + " 'step_names_f', 'S1_state_i', 'S1_state_f', 'S1_SN_type', 'S2_state_i',\n", + " 'S2_state_f', 'S2_SN_type', 'interp_class_HMS_HMS',\n", + " 'interp_class_CO_HMS_RLO', 'interp_class_CO_HeMS',\n", + " 'interp_class_CO_HeMS_RLO', 'mt_history_HMS_HMS',\n", + " 'mt_history_CO_HMS_RLO', 'mt_history_CO_HeMS',\n", + " 'mt_history_CO_HeMS_RLO'],\n", + " dtype='object')]\n", + "\n", + " self.df_oneline.to_hdf(path, key='oneline')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Population successfully saved!\n" + ] + }, + { + "data": { + "text/html": [ + "

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      stateeventtimeorbital_periodeccentricitylg_mtransfer_ratestep_namesstep_timesS1_stateS1_mass...S2_co_core_radiusS2_center_h1S2_center_he4S2_surface_h1S2_surface_he4S2_surf_avg_omega_div_omega_critS2_spinmetallicitysimulated_mass_for_metunderlying_mass_for_met
      binary_index
      3587detachedZAMS0.000000e+002.493602e+010.000000NaNinitial_cond0.000000H-rich_Core_H_burning70.069756...NaN7.155000e-012.703000e-01NaNNaNNaNNaN0.01422.913438e+071.447893e+08
      3587contactoDoubleCE13.631450e+066.304073e+010.000000-2.989131step_HMS_HMS0.037464H-rich_Core_He_burning44.118926...0.0000000.000000e+009.828315e-014.246537e-010.5614930.5774440.7608540.01422.913438e+071.447893e+08
      3587detachedNaN3.631450e+062.371597e-010.000000NaNstep_CE0.000137stripped_He_Core_He_burning35.566786...0.0000000.000000e+009.828315e-011.000000e-020.975800NaNNaN0.01422.913438e+071.447893e+08
      3587detachedCC14.007490e+061.226986e+000.000000NaNstep_detached0.777758stripped_He_Central_C_depletion13.620462...0.4014831.917729e-341.605147e-029.893273e-1000.2476070.0068430.0779760.01422.913438e+071.447893e+08
      3587detachedNaN4.007490e+061.358968e+000.101109NaNstep_SN0.152370BH13.120462...0.4014831.917729e-341.605147e-029.893273e-1000.2476070.0068430.0779760.01422.913438e+071.447893e+08
      3587detachedredirect4.007490e+061.358968e+000.101109NaNstep_CO_HeMS0.000102BH13.120462...0.4014831.917729e-341.605147e-029.893273e-1000.2476070.0068430.0779760.01422.913438e+071.447893e+08
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      3587contactCO_contact2.917891e+092.638756e-080.000000NaNstep_dco1.252216BH13.120462...NaNNaNNaNNaNNaNNaN0.0648490.01422.913438e+071.447893e+08
      3587contactEND2.917891e+092.638756e-080.000000NaNstep_end0.000048BH13.120462...NaNNaNNaNNaNNaNNaN0.0648490.01422.913438e+071.447893e+08
      \n", + "

      10 rows × 41 columns

      \n", + "
      " + ], + "text/plain": [ + " state event time orbital_period \\\n", + "binary_index \n", + "3587 detached ZAMS 0.000000e+00 2.493602e+01 \n", + "3587 contact oDoubleCE1 3.631450e+06 6.304073e+01 \n", + "3587 detached NaN 3.631450e+06 2.371597e-01 \n", + "3587 detached CC1 4.007490e+06 1.226986e+00 \n", + "3587 detached NaN 4.007490e+06 1.358968e+00 \n", + "3587 detached redirect 4.007490e+06 1.358968e+00 \n", + "3587 detached CC2 4.027170e+06 1.377894e+00 \n", + "3587 detached NaN 4.027170e+06 1.611464e+00 \n", + "3587 contact CO_contact 2.917891e+09 2.638756e-08 \n", + "3587 contact END 2.917891e+09 2.638756e-08 \n", + "\n", + " eccentricity lg_mtransfer_rate step_names step_times \\\n", + "binary_index \n", + "3587 0.000000 NaN initial_cond 0.000000 \n", + "3587 0.000000 -2.989131 step_HMS_HMS 0.037464 \n", + "3587 0.000000 NaN step_CE 0.000137 \n", + "3587 0.000000 NaN step_detached 0.777758 \n", + "3587 0.101109 NaN step_SN 0.152370 \n", + "3587 0.101109 NaN step_CO_HeMS 0.000102 \n", + "3587 0.101108 NaN step_detached 0.452186 \n", + "3587 0.048133 NaN step_SN 0.149304 \n", + "3587 0.000000 NaN step_dco 1.252216 \n", + "3587 0.000000 NaN step_end 0.000048 \n", + "\n", + " S1_state S1_mass ... \\\n", + "binary_index ... \n", + "3587 H-rich_Core_H_burning 70.069756 ... \n", + "3587 H-rich_Core_He_burning 44.118926 ... \n", + "3587 stripped_He_Core_He_burning 35.566786 ... \n", + "3587 stripped_He_Central_C_depletion 13.620462 ... \n", + "3587 BH 13.120462 ... \n", + "3587 BH 13.120462 ... \n", + "3587 BH 13.120462 ... \n", + "3587 BH 13.120462 ... \n", + "3587 BH 13.120462 ... \n", + "3587 BH 13.120462 ... \n", + "\n", + " S2_co_core_radius S2_center_h1 S2_center_he4 S2_surface_h1 \\\n", + "binary_index \n", + "3587 NaN 7.155000e-01 2.703000e-01 NaN \n", + "3587 0.000000 0.000000e+00 9.828315e-01 4.246537e-01 \n", + "3587 0.000000 0.000000e+00 9.828315e-01 1.000000e-02 \n", + "3587 0.401483 1.917729e-34 1.605147e-02 9.893273e-100 \n", + "3587 0.401483 1.917729e-34 1.605147e-02 9.893273e-100 \n", + "3587 0.401483 1.917729e-34 1.605147e-02 9.893273e-100 \n", + "3587 0.130995 0.000000e+00 7.706932e-13 1.000000e-99 \n", + "3587 NaN NaN NaN NaN \n", + "3587 NaN NaN NaN NaN \n", + "3587 NaN NaN NaN NaN \n", + "\n", + " S2_surface_he4 S2_surf_avg_omega_div_omega_crit S2_spin \\\n", + "binary_index \n", + "3587 NaN NaN NaN \n", + "3587 0.561493 0.577444 0.760854 \n", + "3587 0.975800 NaN NaN \n", + "3587 0.247607 0.006843 0.077976 \n", + "3587 0.247607 0.006843 0.077976 \n", + "3587 0.247607 0.006843 0.077976 \n", + "3587 0.226684 0.015362 0.060340 \n", + "3587 NaN NaN 0.064849 \n", + "3587 NaN NaN 0.064849 \n", + "3587 NaN NaN 0.064849 \n", + "\n", + " metallicity simulated_mass_for_met underlying_mass_for_met \n", + "binary_index \n", + "3587 0.0142 2.913438e+07 1.447893e+08 \n", + "3587 0.0142 2.913438e+07 1.447893e+08 \n", + "3587 0.0142 2.913438e+07 1.447893e+08 \n", + "3587 0.0142 2.913438e+07 1.447893e+08 \n", + "3587 0.0142 2.913438e+07 1.447893e+08 \n", + "3587 0.0142 2.913438e+07 1.447893e+08 \n", + "3587 0.0142 2.913438e+07 1.447893e+08 \n", + "3587 0.0142 2.913438e+07 1.447893e+08 \n", + "3587 0.0142 2.913438e+07 1.447893e+08 \n", + "3587 0.0142 2.913438e+07 1.447893e+08 \n", + "\n", + "[10 rows x 41 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from posydon.popsyn.synthetic_population import SyntheticPopulation\n", + "\n", + "pop = SyntheticPopulation(path_to_ini='./population_params.ini', path_to_data=path_to_data, verbose=True)\n", + "\n", + "pop.parse(S1_state='BH', S2_state='BH', binary_state='contact', invert_S1S2=False)\n", + "pop.save_pop(os.path.join(path,'BBH_population.h5'))\n", + "\n", + "pop.df.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can now do the same for the other subpopulations of interest. Try sorting black hole-neutron star systems (BHNS; remember to set `invert_S1S2 = True` to find BHNS systems where the NS is formed first) and binary neutron star systems (BNS)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "development311", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/_source/tutorials-examples/population-synthesis/script.py b/docs/_source/tutorials-examples/population-synthesis/script.py new file mode 100644 index 0000000000..f122552dc2 --- /dev/null +++ b/docs/_source/tutorials-examples/population-synthesis/script.py @@ -0,0 +1,5 @@ +from posydon.popsyn.synthetic_population import SyntheticPopulation + +if __name__ == "__main__": + synth_pop = SyntheticPopulation(f"./population_params.ini") + synth_pop.evolve() diff --git a/docs/_source/user-guides/database-query-system.rst b/docs/_source/user-guides/database-query-system.rst new file mode 100644 index 0000000000..9b0f1f8393 --- /dev/null +++ b/docs/_source/user-guides/database-query-system.rst @@ -0,0 +1,64 @@ +.. _database-and-query-system: + +Database and Query System +========================= + +POSYDON offers a robust database and query system, allowing users to access, analyze, and interact with the vast amount of simulation data it produces. This page provides an introduction to the structure of the database and instructions on how to utilize the query system. + +Database Overview +----------------- + +The POSYDON database has been structured to ensure efficient storage and quick retrieval of data. It comprises: + +- **Simulation Data**: Details of individual simulations, including initial conditions, evolution paths, and outcomes. + +- **Model Assumptions**: Various model assumptions under which the simulations were run. + +- **Metadata**: Additional information about the simulations, like the date of the run, software version, etc. + +Query System +------------ + +Our query system enables users to: + +1. **Search**: Filter and retrieve specific sets of data based on certain criteria. +2. **Analyze**: Compute statistics or perform operations on the retrieved data. +3. **Visualize**: Generate plots and diagrams to better understand the data. +4. **Export**: Extract the queried data for external analysis. + +Getting Started with Queries +----------------------------- + +1. **Accessing the Query Interface**: + + You can access the interface through [this link](link-to-query-interface) or via the POSYDON main software. + +2. **Basic Queries**: + + Start by selecting the category of data you're interested in (e.g., 'Simulation Data'). Define the criteria to filter the results. + +3. **Advanced Queries**: + + Combine multiple criteria, utilize logical operators (AND, OR), and specify the desired output format. + +Tutorials & Examples +-------------------- + +To help you get started with the query system, we've prepared some tutorials: + +1. [Basic Querying](link-to-basic-query-tutorial) +2. [Advanced Query Techniques](link-to-advanced-query-tutorial) +3. [Data Analysis using the Query System](link-to-data-analysis-tutorial) + +Best Practices +-------------- + +- Always double-check your query parameters to ensure accuracy. +- For large data retrievals, consider off-peak hours to minimize server load. +- Regularly update yourself with database schema changes from the [version-history.rst](version-history.rst) page. + +Support & Feedback +------------------ + +If you encounter any difficulties or have suggestions for improving the database and query system, please refer to the [contact-information.rst](contact-information.rst) page. + diff --git a/docs/_source/user-guides/user-roadmap.rst b/docs/_source/user-guides/user-roadmap.rst new file mode 100644 index 0000000000..31a1c06797 --- /dev/null +++ b/docs/_source/user-guides/user-roadmap.rst @@ -0,0 +1,60 @@ +.. _roadmap: + +Roadmap to Becoming an Expert POSYDON User +========================================== + +Welcome to your journey towards mastering POSYDON! Below is your roadmap, carefully curated to guide you from the basics to advanced features of POSYDON. As you progress through the steps, you'll earn exciting POSYDON recognition badges. 🌟 + +Let's set forth on this voyage and unleash the true power of POSYDON! + +Step 1: Dive Into Binary-Star Population Synthesis 🌌 +------------------------------------------------------ + +Start your adventure by exploring the magic of binary-star population synthesis with POSYDON: + +- **Model Assumptions**: Learn to select and configure the right assumptions for your simulations. +- **Running Simulations**: Acquaint yourself with running simulations both on your laptop and on HPC facilities. +- **Exporting Outputs**: Master the techniques to neatly export and archive your simulation results. +- **Analyzing Outcomes**: Delve deep into the results to identify and analyze phenomena like merging double compact object populations. + +🔗 Dive into this tutorial: :ref:`Binary-Star Population Synthesis with POSYDON ` + +**Badge Earned**: 🌠 POSYDON Binary Population Master 🌠 + +--- + +Step 2: Behind the Scenes - Generating Datasets 📊 +-------------------------------------------------- + +Unravel the mysteries behind the datasets driving your population synthesis models: + +- **The Origin of Datasets**: Explore the vast raw MESA datasets and understand their essence. +- **Data Downsampling**: Learn how we efficiently downsample these datasets without compromising on quality. +- **Processing Pipeline API**: Get hands-on with our robust API that controls every nuance of data processing. + +🔗 Dive into this tutorial: :ref:`Crafting the Core Datasets for POSYDON ` + +**Badge Earned**: 🔧 POSYDON MESA Engineer 🔧 + +--- + +Step 3: Architecting MESA Simulation Grids 🏗️ +----------------------------------------------- + +Elevate your expertise by diving deep into the creation of MESA simulation grids: + +- **MESA Configuration Repository**: Unearth the treasure of our dedicated repository, housing a plethora of MESA configurations. +- **Harnessing the API Interface**: Utilize the API to craft and customize MESA grids with precision. + +🔗 Dive into this tutorial: :ref:`Architecting MESA Simulation Grids with POSYDON ` + +**Badge Earned**: 🛠️ POSYDON MESA Grid Architect 🛠️ + +--- + +🔱 Final Achievement: POSYDON Triton! 🔱 +---------------------------------------- + +Congratulations! By mastering all the steps, you've now earned the ultimate badge: the **POSYDON Trident** 🔱. Wear it with pride and harness the boundless powers of POSYDON! + +For more in-depth topics and advanced tutorials, head over to our [Advanced Tutorials Section](link-to-advanced-tutorials). And always remember, the ocean of knowledge is vast, and there's always more to explore! diff --git a/docs/_source/user-guides/web-application.rst b/docs/_source/user-guides/web-application.rst new file mode 100644 index 0000000000..ff26358007 --- /dev/null +++ b/docs/_source/user-guides/web-application.rst @@ -0,0 +1,51 @@ +.. _web-application: + +Web Application: POSYDON Interactive Interface +=============================================== + +POSYDON provides an interactive web application that enables users to explore and manipulate population synthesis models without directly diving into the code. Whether you're a seasoned astronomer or just curious, the POSYDON web application can be a powerful tool for visualization and analysis. + +Overview +-------- + +The web application offers the following features: + +- **Population Synthesis**: Run your own population synthesis model through your broseware without the need to have access to an HPC facility or installing POSYDON on your laptop. + +- **Model Selection**: Choose from different model assumptions and instantly select the model you want to run. + +- **Export Options**: Once you have run the desired model, you can easily download the dataset output for further use. + +Getting Started +--------------- + +1. **Accessing the Web Application** + + Visit the official POSYDON web application link: [POSYDON WebApp](https://posydon.odahub.io). TODO: bring it back online and update to v2.0.0 + +2. **Open a account** + + To use the web application, you will need to create an account. This is a simple process that only requires a valid email address and why you want to use the Webapp. Once you have created an account, we will grant you access and you will be able to submit population synthesis simulations throught the web application. + +3. **Navigation** + + The user interface is designed to be intuitive. Start by selecting a model and then navigate through various pages to submit your simulation. Wait for the email notification that your simulation has finished and then download the results. + +Tutorials & Examples +-------------------- + +For detailed walkthroughs of specific functionalities and use-cases: TODO: provide links to tutorials + +1. [Basic Navigation and Model Selection](link-to-tutorial-1) +2. [Advanced Data Analysis](link-to-tutorial-2) +3. [Exporting Visualizations](link-to-tutorial-3) + +Feedback & Support +------------------ + +We're always looking to improve the web application. If you encounter any issues or have suggestions for new features, please refer to the [contact-information.rst](contact-information.rst) page to get in touch with us. + +Updates & Changelog +------------------- + +For information about updates, improvements, and fixes in the web application, refer to the [version-history.rst](version-history.rst) page. diff --git a/docs/about/attribution.rst b/docs/about/attribution.rst deleted file mode 100644 index 6ed61e3c5c..0000000000 --- a/docs/about/attribution.rst +++ /dev/null @@ -1,7 +0,0 @@ -=========== -Attribution -=========== - -If you make use of POSYDON in your work, please cite our paper -(`arXiv `_, -`ADS `_). diff --git a/docs/about/licence.rst b/docs/about/licence.rst deleted file mode 100644 index a7827a63a7..0000000000 --- a/docs/about/licence.rst +++ /dev/null @@ -1,35 +0,0 @@ -.. _licence: - -####### -Licence -####### - -BSD 3-Clause License - -Copyright (c) 2022, Tassos Fragos -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - -3. Neither the name of the copyright holder nor the names of its - contributors may be used to endorse or promote products derived from - this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE -FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL -DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER -CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/docs/advanced_pop_options/debugging_binaries.rst b/docs/advanced_pop_options/debugging_binaries.rst deleted file mode 100644 index 11c3a74910..0000000000 --- a/docs/advanced_pop_options/debugging_binaries.rst +++ /dev/null @@ -1,140 +0,0 @@ -.. _debugging_binaries: - -######################### -Debugging Failed Binaries -######################### - -During evolution in the ``BinaryPopulation``, all ``Exceptions`` are caught -to allow the population to keep evolving in the event a single binary -enocunters an error. Here we go over some methods to help debug errors. -We cause an error by using a broken evolutionary step: - -.. code-block:: python - :linenos: - :emphasize-lines: 12-17,32 - - from posydon.popsyn.binarypopulation import BinaryPopulation - from posydon.binary_evol.simulationproperties import SimulationProperties - from posydon.binary_evol.flow_chart import flow_chart - from posydon.binary_evol.CE.step_CEE import StepCEE - from posydon.binary_evol.SN.step_SN import StepSN - from posydon.binary_evol.step_end import step_end - from posydon.binary_evol.MESA.step_mesa import CO_HeMS_step, MS_MS_step, CO_HMS_RLO_step - from posydon.binary_evol.DT.step_detached import detached_step - from posydon.binary_evol.DT.double_CO import DoubleCO - from posydon.binary_evol.simulationproperties import TimingHooks, StepNamesHooks - - class my_new_StepSN(StepSN): - def __call__(self, binary): - # use the StepSN call - super().__call__(binary) - # break here - raise ValueError(f'{str(binary)} failed in my new step') - - mesa_step_kwargs = dict( - interpolation_method='linear3c_kNN', - save_initial_conditions=False, - track_interpolation=False - ) - - sim_kwargs = dict( - flow = (flow_chart, {}), - step_HMS_HMS = (MS_MS_step, mesa_step_kwargs), - step_CO_HeMS = (CO_HeMS_step, mesa_step_kwargs), - step_CO_HMS_RLO = (CO_HMS_RLO_step, mesa_step_kwargs), - step_detached = (detached_step, {}), - step_CE = (StepCEE, dict(core_definition_H_fraction= 0.1,)), - step_SN = (my_new_StepSN, {}), - step_dco = (DoubleCO, {}), - step_end = (step_end, {}), - extra_hooks = [(TimingHooks, {}),(StepNamesHooks, {})] - ) - - kwargs = {'number_of_binaries' : 15, - 'primary_mass_min' : 7, - 'primary_mass_max' : 127, - 'secondary_mass_scheme' : 'flat_mass_ratio', - 'secondary_mass_min': 1, - 'secondary_mass_max': 127, - 'orbital_separation_min': 1, - 'orbital_separation_max': 3e3, - 'eccentricity_scheme':'zero', - 'extra_columns' : ['step_times','step_names'], - 'only_select_columns' : ['state', 'event', 'time', 'lg_mtransfer_rate', - 'orbital_period'], - 'include_S1' : True , - 'S1_kwargs' : {'only_select_columns' : ['state', 'mass', - 'log_R', 'center_h1','center_he4', - 'he_core_mass', 'surface_he4', 'surface_h1', - 'lg_mdot'], - 'scalar_names' : ['natal_kick_array', 'SN_type']}, - 'include_S2' : True, - 'S2_kwargs' : {'only_select_columns' : ['state', 'mass', - 'log_R', 'center_h1','center_he4', - 'he_core_mass', 'surface_he4', 'surface_h1', - 'lg_mdot'], - 'scalar_names' : ['natal_kick_array', 'SN_type']}, - 'star_formation' : 'burst', - 'max_simulation_time' : 13.7e9, - } - - pop = BinaryPopulation( - population_properties=sim_prop, - entropy = 12345678, - **kwargs - ) - - pop.evolve(breakdown_to_df=False, from_hdf=False, tqdm=True, **kwargs) - - -After running the script above we can use the ``BinaryPopulation.find_failed`` -instance method which returns a list of the failed binaries in the population. - -.. code-block:: python - - failed_binaries = pop.find_failed() - - if failed_binaries: - print( failed_binaries[0].traceback ) - -The ``BinaryStar`` instances that fail during evolution will have a -``traceback`` attribute showing the string representation (not the actual -stack -`traceback `_): - -.. code-block:: text - - Traceback (most recent call last): - File "/Users/kylerocha/Private/Github/POSYDON/posydon/popsyn/binarypopulation.py", line 153, in _safe_evolve - binary.evolve() - File "/Users/kylerocha/Private/Github/POSYDON/posydon/binary_evol/binarystar.py", line 183, in evolve - self.run_step() - File "/Users/kylerocha/Private/Github/POSYDON/posydon/binary_evol/binarystar.py", line 214, in run_step - next_step(self) - File "", line 32, in __call__ - raise ValueError(f'{str(binary)} failed in my new step') - ValueError: BinaryStar(detached, None, p=6820.50, S1=(BH,M=26.79), S2=(H-rich_Shell_H_burning,M=35.61)) failed in my new step - - -If we want to go further we could also take the binary out of the population -and evolve it. If using a ``jupyter notebook``, you can enter the python -debugger by raising the error again and using the magic command ``%debug`` -in another cell. - - -.. code-block:: ipython3 - - failing_binary = failed_binaries[0] - - failing_binary.restore() - - failing_binary.evolve() - ------ - -.. code-block:: python - - %debug - -See the debugger `commands -`_ for help. diff --git a/docs/api.rst b/docs/api.rst deleted file mode 100644 index a07d460e2e..0000000000 --- a/docs/api.rst +++ /dev/null @@ -1,8 +0,0 @@ -================================= -Application Programming Interface -================================= - -Please consult these pages for more details on using posydon: - -* :ref:`genindex` -* :ref:`modindex` diff --git a/docs/data/data.rst b/docs/data/data.rst deleted file mode 100644 index 167367c103..0000000000 --- a/docs/data/data.rst +++ /dev/null @@ -1,19 +0,0 @@ -Data Download -================= - -Although the tutorials below provide the steps necessary to produce your own -data, we make available the data from our code paper. These include: - -* Grids of single star evolution. -* Grids of binary star evolution. -* Interpolation objects for each grid. -* Example Populations. - -These datasets are available through Zenodo, or, if you would like to explore -them further, we offer an SQL server for creating custom queries. Furthermore, -POSYDON is designed to automatically download the data if they do not already -exist; however, if you would like to download all the data by hand beforehand, -we provide instructions for doing so below. - - -IN PROGRESS diff --git a/docs/index.rst b/docs/index.rst deleted file mode 100644 index ab2905858b..0000000000 --- a/docs/index.rst +++ /dev/null @@ -1,80 +0,0 @@ -.. _home: - -POSYDON -=================================== - -**POSYDON** combines functionality to run large grids of MESA binary -simulations, efficiently store those simulations, interpolation over those -grids to synthesize binary populations, along with a series of visualization -routines to interpret the results. - -This documentation is designed to provide an introduction to using POSYDON, -which is described in a `paper `_ currently -under review. There are many excellent textbooks for learning about binary -evolution. - -To report a bug or download the bleeding edge version of POSYDON, please visit -our `GitHub page `_. - - -***************** -Table of Contents -***************** - -.. toctree:: - :maxdepth: 1 - :caption: User guide - - install/install - data/data - api - -.. toctree:: - :maxdepth: 1 - :caption: Binary Star Grids - - run_mesa_grids/inifile - run_mesa_grids/fixed/fixed - PSyGrid/PSyGrid -.. - run_mesa_grids/dynamic/dynamic - -.. toctree:: - :maxdepth: 1 - :caption: Generating Populations - - pop_synth/POSYDON_populations - pop_synth/pop_synth - pop_synth/custom_flow - SingleStar/SingleStar - BinaryStar/BinaryStar - advanced_pop_options/custom_hooks - advanced_pop_options/debugging_binaries - interpolation/IF-Interpolator/IFInterpolator - -.. toctree:: - :maxdepth: 1 - :caption: Plotting - - visualization/plot1D/plot1D - visualization/plot2D/plot2D - visualization/VHD/VHD - interpolation/violinplot - -.. toctree:: - :maxdepth: 1 - :caption: About - :hidden: - - about/licence - about/attribution - - -License & Attribution ---------------------- - -Copyright (c) 2022, Tassos Fragos - -If you make use of POSYDON in your work, please cite our paper -(`arXiv `_, -`ADS `_). diff --git a/docs/install/install.rst b/docs/install/install.rst deleted file mode 100644 index f1c287e965..0000000000 --- a/docs/install/install.rst +++ /dev/null @@ -1,163 +0,0 @@ -.. _install: - -############ -Installation -############ - -======================================= -Installing POSYDON v1.0.0 from Anaconda -======================================= - -We recommend to install the Python distribution Anaconda and to install POSYDON -in a virtual environment. Specifically, we recommend using the installation we -have provided which can be accessed through conda-forge. On Linux, the new -conda environment can be created (we have named our environment posydon-example, -but you can choose any name), the conda-forge channel added, and the required -library installation can all be completed in one line: - -.. code-block:: - - conda create --name posydon-example -c posydon -c conda-forge posydon - -On Mac (or if you have problems with the above command on Linux), these steps -likely need to be separately run: - -.. code-block:: - - conda create -n posydon-conda python=3.7 - conda activate posydon-conda - conda config --add channels conda-forge - conda config --add channels posydon - conda config --set channel_priority false - conda install posydon - -Now, you can source the environment with - -.. code-block:: - - conda activate posydon - - -========================================= -Installing the latest release from GitHub -========================================= - - -Creating a conda environment ----------------------------- - -We recommend to install the Python distribution Anaconda and to install POSYDON -in a virtual environment. After creating the environment (you can choose any -name, e.g., `posydon`, or `posydon_env`) like this: - -.. code-block:: - - conda create -n posydon python=3.7 - -Make sure you agree to any questions about installing required libraries. To -proceed with the installation, we will need to activate the environment: - -.. code-block:: - - conda activate posydon - -Cloning POSYDON ---------------- -Clone the repository in a local directory, e.g. `/home/POSYDON/`, with - -.. code-block:: - - git clone https://github.com/POSYDON-code/POSYDON.git - - -The directory will contain the following structure: - -.. code-block:: - - posydon/ - README.md - setup.py - etc. - -Installing POSYDON ------------------- -Exporting the global path -~~~~~~~~~~~~~~~~~~~~~~~~~ -Export the path to the cloned POSYDON code (you can add this line to your -.bashrc or .bash_profile.), e.g. - -.. code-block:: - - export PATH_TO_POSYDON=/home/POSYDON/ - -Installing the package -~~~~~~~~~~~~~~~~~~~~~~ -From the cloned POSYDON directory execute the commands to install POSYDON and -the `mpi4py` dependency - -.. code-block:: - - pip install -e . - conda install mpi4py - - -Downloading POSYDON data ------------------------- -OPTION 1: Zenodo (latest official version) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Export the path to where you want to clone the data, e.g. `/home/`, and -download the data with the following commands - -.. code-block:: - - export PATH_TO_POSYDON_DATA=/home/ - get-posydon-data - - -OPTION 2: git LFS submodule (latest development version) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Inside the cloned POSYDON repository run the following commands to -install and initialise git LFS, and download the data. - -.. code-block:: - - export PATH_TO_POSYDON_DATA=$PATH_TO_POSYDON/data/ - conda install git-lfs - git lfs install - git submodule init - git submodule update data/POSYDON_data - - -========================================= -Installing POSYDON documentations modules -========================================= - -These modules are needed in order to compile the documentation - -.. code-block:: - - pip install -e .[doc] - -To compile the documentation and open the html page do the following - -.. code-block:: - - cd docs/ - make html - open _build/html/index.html - - -Installation Notes/FAQ ----------------------- - -.. note:: - - USING IPYTHON OR JUPYTER-NOTEBOOKS WITH POSYDON ENVIRONMENT - - Please note that using the global instance of the conda jupyter-notebook - or ipython will most likely fail when trying to use posydon. - PLEASE explicitly install both into the posydon environment with either - - ``conda install jupyter ipython`` - - ``pip install jupyter ipython`` diff --git a/docs/interpolation/pngs/noMT_error.png b/docs/interpolation/pngs/noMT_error.png deleted file mode 100644 index 4c9292d0848213c848269398e80b7d9deb37f458..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 54687 zcmeEuXIPWn(k_Gmp@)u0Nhs2L?*S=Nq)YG8d+#6tg-{hmdJ|L-5d@?cQJQp6iqb`T zQ7KXd&&uBKyWjntbDe+Z=gF0r@Z?!-)~tJG?wOdVud6{u%t(xbgF~iyL)8!m2Z8}F zGZ+E5@->A1F%Ax?m)rI0`kL3TBlP`!oZUQ~aByxUJ~kyZGwP=+w0!vB!59Q~gQ%U| 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zcGjA!9^3g>k5_{eVR(k&ZNZnY;Sq+LOej(x#@dBjXGE!kK^q50RVU6Y-^)mNK+GUT z9jxjYHUMi*)J6Dx>BpxB$v3T2@PoER2&bTihk3(8w;RWzqTuWOhFFMzLdUOUWLu@| zfv*rcoZ^dD@cvza$bHJUj4yKmUS*9h>k<+YIskNYETq)RLXXiOK(3oYJG)c7s7T61 zarAAunotWHM=hJG#?G(FbE%szRV?V-w<4Ex_U8K125xb3-vTk*xdFg3UA{qkMzc+f zlFA$xeJd~K;U~^|qi^N=U%CR} z<$rlFZoA+76ue1@l%CtudHQqayOD6uuKi&Fuy4XVo3?XEY1jktESJ7dd2g{#sPSWc^&nqH?_a&e- znL!j)cL_DN;dz?{?w=C#>!`;WasdxQQbz;MN>MR>v+IQ2;o-)<{F0WQ(#w%jC! zhE!nBr@uNHmWgod0gy#+q<6DATj5FbhYEs*x0Kx43FrcHOjY})XrHX05jw$iuGy{+ z9sJS7=nI1i7|9U((bUw8Ay=_EnZ|W>4c#7r$QEN;75MSp!7Vy#;GhDIi^ISfv;5%+ z5vK&?7d-1DZvYC078WjzOUo-OX?Yzb;?dMe5MM*KqN5-;qspbw&HeXED2Dg0x(w~t z!F%Ch=!?}Fe#0$M{>Jp*s2%J$Cg?a{)!Ofw|J_R_48LK0VRI$jvtr>WDV*3?_Nv}_|2NReHA9=e=g*EnC|JtazktXiBzKR z4JHS}RZe2#^`GxYcYzr?2sx3p=7sLKf3N???)!_i^%g5= 0] - ## Confusion Matrices - #for i, m in enumerate(models): - #print(f"\n\t {m.classifiers[key].method}") - #bas = balanced_accuracy_score(zt_gt, Ztpred[i][key]) - #print(f"\t Balanced Accuracy Score: {100 * bas:.3f}") - #oa = np.sum(Ztpred[i][key] == zt_gt) / len(zt_gt) - #print(f"\t Overall Accuracy: {100 * oa:.3f}") - #CM = confusion_matrix(zt_gt, Ztpred[i][key], m.classifiers[key].labels) - #savename = os.path.join(path2prmaps, key + '_' + class_methods[i] + '.png') - #fig, ax = plot_conf_matrix(100 * CM, m.classifiers[key].method, key, m.classifiers[key].labels,savename=savename) - #plt.close(fig) - - #ZT = grid_T.final_values[key][m.valid >= 0] - #zt_gt = grid_t.final_values[key][validt >= 0] - #fig, ax = plot_mc_classifier(m, key, m.XT[m.valid >= 0, :], ZT, ux2, Xt=Xt[validt >= 0], zt=zt_gt,zt_pred=Ztpred[i][key], path=path2prmaps) - #else: - #print(f"\tNaN") - -Printing out the errors on-screen for desired physical quantities -================================================================= -To access the value of the errors, the below code should be implemented in "Assess interpolation results". - -.. code-block:: - - Ygt = Yt[validt >= 0, :] # this line is from the original IF_interpolation.py, using for indicating where to start the new code - - Xgt = Xt[validt >= 0, :] - mask = grid_t.final_values['interpolation_class'][validt >= 0] == 'stable_MT' - mask2 = grid_t.final_values['interpolation_class'][validt >= 0] == 'unstable_MT' - mask3 = grid_t.final_values['interpolation_class'][validt >= 0] == 'no_MT' - -The above code is to compute a mask which is an array of booleans with true in the places where the evolutionary track has stable mass transfer/unstable mass transfer/no mass transfer. - -In the loop under the definition of the list err_r and err_a, the new code below should be implemented. Here we show an example of printing the error for system age and the star 1 mass. - -.. code-block:: - - err_a.append(np.abs(Ytpred[i] - Ygt)) # this line is from the original IF_interpolation.py, using for indicating where to start the new code - err_r.append(err_a[i] / (np.abs(Ygt))) # this line is from the original IF_interpolation.py, using for indicating where to start the new code - - mykeys = ['age','star_1_mass'] - for mykey in mykeys: - for ii,elems in enumerate(np.transpose(np.array(err_r)[0][mask == True])[models[0].out_keys.index(mykey)]): - print(np.transpose(np.array(err_r)[0][mask == True])[models[0].out_keys.index(mykey)][ii]) - print('==============================') - for ii,elems in enumerate(np.transpose(np.array(err_r)[0][mask2 == True])[models[0].out_keys.index(mykey)]): - print(np.transpose(np.array(err_r)[0][mask2 == True])[models[0].out_keys.index(mykey)][ii]) - print('==============================') - for ii,elems in enumerate(np.transpose(np.array(err_r)[0][mask3 == True])[models[0].out_keys.index(mykey)]): - print(np.transpose(np.array(err_r)[0][mask3 == True])[models[0].out_keys.index(mykey)][ii]) - print('==============================') - -The above code will print the error from the stable mass transfer, unstable mass transfer and no mass transfer errors for system age and star 1 mass. You may also print the errors in a file and make a violin plot using the code below. - - -The full list of physical quantities that we could access the errors for the CO-HMS and CO-HeMS grid is as below. - -.. code-block:: - - age - star_1_mass - star_2_mass - period_days - binary_separation - lg_system_mdot_1 - lg_system_mdot_2 - lg_wind_mdot_1 - lg_wind_mdot_2 - lg_mstar_dot_1 - lg_mstar_dot_2 - lg_mtransfer_rate - xfer_fraction - rl_relative_overflow_1 - rl_relative_overflow_2 - trap_radius - acc_radius - t_sync_rad_1 - t_sync_conv_1 - t_sync_rad_2 - t_sync_conv_2 - S1_he_core_mass - S1_c_core_mass - S1_o_core_mass - S1_he_core_radius - S1_c_core_radius - S1_o_core_radius - S1_center_h1 - S1_center_he4 - S1_center_c12 - S1_center_n14 - S1_center_o16 - S1_surface_h1 - S1_surface_he4 - S1_surface_c12 - S1_surface_n14 - S1_surface_o16 - S1_c12_c12 - S1_center_gamma - S1_avg_c_in_c_core - S1_surf_avg_omega - S1_surf_avg_omega_div_omega_crit - S1_log_LH - S1_log_LHe - S1_log_LZ - S1_log_Lnuc - S1_log_Teff - S1_log_L - S1_log_R - S1_total_moment_of_inertia - S1_spin_parameter - S1_log_total_angular_momentum - S1_conv_env_top_mass - S1_conv_env_bot_mass - S1_conv_env_top_radius - S1_conv_env_bot_radius - S1_conv_env_turnover_time_g - S1_conv_env_turnover_time_l_b - S1_conv_env_turnover_time_l_t - S1_envelope_binding_energy - S1_mass_conv_reg_fortides - S1_thickness_conv_reg_fortides - S1_radius_conv_reg_fortides - S1_lambda_CE_1cent - S1_lambda_CE_10cent - S1_lambda_CE_30cent - S1_co_core_mass - S1_co_core_radius - S1_lambda_CE_pure_He_star_10cent - S1_direct_f_fb - S1_direct_mass - S1_direct_spin - S1_Fryer+12-rapid_f_fb - S1_Fryer+12-rapid_mass - S1_Fryer+12-rapid_spin - S1_Fryer+12-delayed_f_fb - S1_Fryer+12-delayed_mass - S1_Fryer+12-delayed_spin - S1_Sukhbold+16-engineN20_f_fb - S1_Sukhbold+16-engineN20_mass - S1_Sukhbold+16-engineN20_spin - S1_Patton&Sukhbold20-engineN20_f_fb - S1_Patton&Sukhbold20-engineN20_mass - S1_Patton&Sukhbold20-engineN20_spin - S1_avg_c_in_c_core_at_He_depletion - S1_co_core_mass_at_He_depletion - S1_m_core_CE_1cent - S1_m_core_CE_10cent - S1_m_core_CE_30cent - S1_m_core_CE_pure_He_star_10cent - S1_r_core_CE_1cent - S1_r_core_CE_10cent - S1_r_core_CE_30cent - S1_r_core_CE_pure_He_star_10cent - S1_surface_other - S1_center_other - -For the HMS-HMS grid, as both of the stars are evolved, the list of errors of the star 2 related physical quantities shows below. - -.. code-block:: - - S2_he_core_mass - S2_c_core_mass - S2_o_core_mass - S2_he_core_radius - S2_c_core_radius - S2_o_core_radius - S2_center_h1 - S2_center_he4 - S2_center_c12 - S2_center_n14 - S2_center_o16 - S2_surface_h1 - S2_surface_he4 - S2_surface_c12 - S2_surface_n14 - S2_surface_o16 - S2_c12_c12 - S2_center_gamma - S2_avg_c_in_c_core - S2_surf_avg_omega - S2_surf_avg_omega_div_omega_crit - S2_log_LH - S2_log_LHe - S2_log_LZ - S2_log_Lnuc - S2_log_Teff - S2_log_L - S2_log_R - S2_total_moment_of_inertia - S2_spin_parameter - S2_log_total_angular_momentum - S2_conv_env_top_mass - S2_conv_env_bot_mass - S2_conv_env_top_radius - S2_conv_env_bot_radius - S2_conv_env_turnover_time_g - S2_conv_env_turnover_time_l_b - S2_conv_env_turnover_time_l_t - S2_envelope_binding_energy - S2_mass_conv_reg_fortides - S2_thickness_conv_reg_fortides - S2_radius_conv_reg_fortides - S2_lambda_CE_1cent - S2_lambda_CE_10cent - S2_lambda_CE_30cent - S2_co_core_mass - S2_co_core_radius - S2_lambda_CE_pure_He_star_10cent - S2_log_L_div_Ledd - S2_direct_f_fb - S2_direct_mass - S2_direct_spin - S2_Fryer+12-rapid_f_fb - S2_Fryer+12-rapid_mass - S2_Fryer+12-rapid_spin - S2_Fryer+12-delayed_f_fb - S2_Fryer+12-delayed_mass - S2_Fryer+12-delayed_spin - S2_Sukhbold+16-engineN20_f_fb - S2_Sukhbold+16-engineN20_mass - S2_Sukhbold+16-engineN20_spin - S2_Patton&Sukhbold20-engineN20_f_fb - S2_Patton&Sukhbold20-engineN20_mass - S2_Patton&Sukhbold20-engineN20_spin - S2_avg_c_in_c_core_at_He_depletion - S2_co_core_mass_at_He_depletion - S2_m_core_CE_1cent - S2_m_core_CE_10cent - S2_m_core_CE_30cent - S2_m_core_CE_pure_He_star_10cent - S2_r_core_CE_1cent - S2_r_core_CE_10cent - S2_r_core_CE_30cent - S2_r_core_CE_pure_He_star_10cent - S2_surface_other - S2_center_other - -The script for making the violin plots -====================================== - -.. code-block:: - - fig, ax = plt.subplots() - data = np.genfromtxt(filename) - err_r = data[:,the column number for relative error] - - v = ax.violinplot(np.log10(err_r), positions=[i], widths=0.8, showextrema=False, showmedians=True) - for pc in v['bodies']: - pc.set_facecolor('g') - pc.set_edgecolor('g') - v['cmedians'].set_color('g') - ax.legend([v['bodies'][0]],[r'$\mathrm{no\,mass \mbox{-} transfer}$'],frameon=False,loc='center left', ncol = 1, bbox_to_anchor=(0.5, 1.05)) - -.. image:: pngs/noMT_error.png - :width: 400 - -For making a violin plot with half of the error distribution from stable mass transfer and another half of unstable mass transfer, you may use the code below. - -.. code-block:: - - data = np.genfromtxt(filename_of_errors_from_stable_mass_transfer) - data2 = np.genfromtxt(filename_of_errors_from_unstable_mass_transfer) - err_r = data[:,the column number for relative error] - err_r2 = data2[:,the column number for relative error] - - v1 = ax.violinplot(np.log10(err_r), positions=[i], widths=0.8, showextrema=False, showmedians=True) - for b in v1['bodies']: - m = np.mean(b.get_paths()[0].vertices[:, 0]) # get the center - b.get_paths()[0].vertices[:, 0] = np.clip(b.get_paths()[0].vertices[:, 0], -np.inf, m) # modify the paths to not go further right than the center - b.set_facecolor('r') - b.set_edgecolor('r') - v1['cmedians'].set_color('r') - bb = v1['cmedians'] - mm = np.mean(bb.get_paths()[0].vertices[:, 0]) # get the center - bb.get_paths()[0].vertices[:, 0] = np.clip(bb.get_paths()[0].vertices[:, 0], -np.inf, mm) - - - v2 = ax.violinplot(np.log10(err_r2), positions=[i], widths=0.8, showextrema=False, showmedians=True) - for b in v2['bodies']: - m = np.mean(b.get_paths()[0].vertices[:, 0]) - b.get_paths()[0].vertices[:, 0] = np.clip(b.get_paths()[0].vertices[:, 0], m, np.inf) - b.set_facecolor('b') - b.set_edgecolor('b') - v2['cmedians'].set_color('b') - bb = v2['cmedians'] - mm = np.mean(bb.get_paths()[0].vertices[:, 0]) # get the center - bb.get_paths()[0].vertices[:, 0] = np.clip(bb.get_paths()[0].vertices[:, 0], mm, np.inf) - -.. image:: pngs/stable_unstable_error.png - :width: 400 diff --git a/docs/modindex.rst b/docs/modindex.rst deleted file mode 100644 index 1c1c1b0d86..0000000000 --- a/docs/modindex.rst +++ /dev/null @@ -1,3 +0,0 @@ -=================== -Python Module Index -=================== diff --git a/docs/pop_synth/POSYDON_populations.rst b/docs/pop_synth/POSYDON_populations.rst deleted file mode 100644 index 3589b7497b..0000000000 --- a/docs/pop_synth/POSYDON_populations.rst +++ /dev/null @@ -1,304 +0,0 @@ -################################# -Running populations using POSYDON -################################# - -1. Run a basic population -========================= - -To run the most basic population with all the pre-set POSYDON defaults, -you only need the following few lines of code. By default the code -runs 100 initial binaries of a constant star formation for a duration of -Hubble time (13.7e9 years). - -All the required POSYDON imports: -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code:: ipython3 - - from posydon.popsyn.binarypopulation import BinaryPopulation - from posydon.binary_evol.simulationproperties import SimulationProperties - from posydon.binary_evol.flow_chart import flow_chart - from posydon.binary_evol.CE.step_CEE import StepCEE - from posydon.binary_evol.SN.step_SN import StepSN - from posydon.binary_evol.step_end import step_end - from posydon.binary_evol.MESA.step_mesa import CO_HeMS_step, MS_MS_step, CO_HMS_RLO_step - from posydon.binary_evol.DT.step_detached import detached_step - from posydon.binary_evol.DT.double_CO import DoubleCO - -Define the POSYDON evolutionary steps -~~~~~~~~~~~~~~~~~~~~~~~~ - -We should define all the evolutionary steps involved. By default, the parameters -for these steps are pre-defined. Then, ``SimulationProperties(**sim_kwargs)`` -initializes the population object with the steps defined, in ``sim_prop``. This -initialization step can take a few seconds as it requires loading large data -files. - -.. code:: ipython3 - - sim_kwargs = dict( - flow = (flow_chart, {}), - step_HMS_HMS = (MS_MS_step, {}), - step_CO_HeMS = (CO_HeMS_step, {}), - step_CO_HMS_RLO = (CO_HMS_RLO_step, {}), - step_detached = (detached_step, {}), - step_CE = (StepCEE, {}), - step_SN = (StepSN, {}), - step_dco = (DoubleCO, {}), - step_end = (step_end, {}) - ) - - sim_prop = SimulationProperties(**sim_kwargs) - -Initializing the population object -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -We invoke the population class ``BinaryPopulation``, which is the -population class with the function ``evolve`` that does the work of -evolving the binaries. The population class takes in the simulation -properties to initialize the population. - -.. code:: ipython3 - - pop = BinaryPopulation(population_properties=sim_prop) - - pop.evolve() - -This evolved population can be converted to a pandas dataframe. The -dataframe helps us in studying the evolved population by utilizing the -benefits of the functionalities available for managing dataframes. - -.. code:: ipython3 - - DF = pop.to_df() - -2. Customize the script to run more complicated populations -=========================================================== - -Now, for most science cases, the default POSYDON parameters would need -to be changed. Below we show an example of how to run a population of -1000 initial binaries with constant star-formation for a duration of -Hubble time. - -Define the POSYDON steps and their parameters -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -If there is a need to change a parameter in the POSYDON steps that would -be done in the simulation properties. For example, to change the -``interpolation_method`` for the steps that evolve the binaries -according to the MESA grids, we introduce a new string -``interp_str = 'nearest_neighbour'`` and add this string to the -parameter ``interpolation_method`` for the steps ``step_HMS_HMS``, -``step_CO_HeMS``, and ``step_CO_HMS_RLO``. - -.. code:: ipython3 - - - interp_str = 'nearest_neighbour' - - sim_kwargs = dict( - flow = (flow_chart, {}), - step_HMS_HMS = (MS_MS_step, dict(interpolation_method=interp_str)), - step_CO_HeMS = (CO_HeMS_step, dict(interpolation_method=interp_str)), - step_CO_HMS_RLO = (CO_HMS_RLO_step, dict(interpolation_method=interp_str)), - step_detached = (detached_step, {}), - step_CE = (StepCEE, {}), - step_SN = (StepSN, {}), - step_dco = (DoubleCO, {}), - step_end = (step_end, {}), - ) - - sim_prop = SimulationProperties(**sim_kwargs) - - -Define the properties of the initial generated population -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -If some parameters of the initial population are required to be changed, -they are changed as follows in ``kwargs``. Here we change the -``number_of_binaries``. - -.. code:: ipython3 - - kwargs = {'number_of_binaries' : 1000 - } - -Initializing the population object -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -As before, we invoke the population class, this time with the parameters -of the initial population that we want to include in ``kwargs``. - -.. code:: ipython3 - - pop = BinaryPopulation(population_properties=sim_prop, - **kwargs) - -Evolve it! And covert to pandas dataframe -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The boolean parameter ``tqdm`` shows the progress bar. - -.. code:: ipython3 - - pop.evolve(breakdown_to_df=False, tqdm=True) - DF = pop.to_df() - -To save the population as an HDF5 file use the ``save`` function. - -.. code:: ipython3 - - pop.save('population_1000.h5', **kwargs ) - -3. Identify double compact objects -================================== - -Now, the evolved population has the complete evolution of all binaries. We -only require the last row from all the binaries as we want to study the -“current” properties of the double compact objects. The example is to -search for binaries not ``disrupted`` in the end, in which ``star_1`` is -a BH and ``star_2`` is an NS. - -.. code:: ipython3 - - output_cols = ['state','time','event','S1_state','S2_state','S1_mass','S2_mass','orbital_period','eccentricity'] - DF.loc[(DF['S1_state'] == 'BH')&(DF['S2_state'] == 'NS')&(DF['event'] == 'END')&(DF['state'] != 'disrupted') - ,output_cols] - -To look at the evolution of one individual binary, choose proper -quantities to display: - -.. code:: ipython3 - - index_BHNS = DF.loc[(DF['S1_state'] == 'BH')&(DF['S2_state'] == 'NS')& - (DF['event'] == 'END')& - (DF['state'] != 'disrupted')].index - DF.loc[index_BHNS[0], output_cols] - -To identify the BHNS binaries that specifically went through a common envelope -phase with a BH and a donor: - -.. code:: ipython3 - - DF_BHNS = DF.loc[index_BHNS] - index_BHNS_CE = DF_BHNS.loc[(DF_BHNS["event"]=="oCE2")].index - -.. code:: ipython3 - - DF_BHNS.loc[index_BHNS_CE[0], output_cols] - -4. Identify the X-ray Binaries -============================== - -In order to investigate the XRBs from this population, we need to look -into the originally run population again. Again, we only require the -last row from all the binaries as we want to study the “current” -properties of the XRBs. Also, we want to pick out binaries where only -one of the two stars at the end is a compact object (NS or BH), and the -other star is definitively not a compact object (not even a white -dwarf). - -.. code:: ipython3 - - - DF_end = DF[DF['event']=='END'] - star1_is_CO = ( (DF_end["S1_state"] == "NS") | (DF_end["S1_state"] == "BH") ) & (DF_end["S2_state"] != "WD") - star2_is_CO = ( (DF_end["S2_state"] == "NS") | (DF_end["S2_state"] == "BH") ) & (DF_end["S2_state"] != "WD") - exactly_one_CO = (star1_is_CO & ~star2_is_CO) | (~star1_is_CO & star2_is_CO) - only_one_CO = DF_end[exactly_one_CO] - - -Looking at the final ``state`` of all the binaries, we can figure out -the ones we want to keep. For XRBs, we are interested in states -``detached`` (corresponding to wind accretion) and ``RLO1``/``RLO2`` -(corresponding to Roche-lobe overflow). - -.. code:: ipython3 - - only_one_CO['state'].unique() - -Most of our XRB RLO states would be ``RLO2`` (mass-transferred from -``star_2`` to ``star_1``), because the primary (``star_1``) is always -the initially more massive star which would evolve first and form a -compact object. However, there might a rare case where ``star_1`` -transferred mass to ``star_2`` making it more massive than ``star_1``, -leading to a compact object for ``star_2``. Therefore, to be sure, we -look for both ``RLO1`` (mass-transferred from ``star_1`` to ``star_2``) -and ``RLO2``. - -.. code:: ipython3 - - DF_XRB = only_one_CO[(only_one_CO['state']=='detached') | - (only_one_CO['state']=='RLO1') | - (only_one_CO['state']=='RLO2')] - -Now we have our subset of binaries from the total population. To see -them as XRBs, we need some information about their X-ray luminosities. -We can use some simple functions to calculate the corresponding -accretion efficiencies and luminosities. - -Importing some packages needed for these calculations. - -.. code:: ipython3 - - from posydon.utils.common_functions import CO_radius - import posydon.utils.constants as const - import numpy as np - -Since the accretor can be ``star_1`` or ``star_2``, we need to do a -check to see which star is the compact object. We calculate the -accretion luminosity (``Lacc``) using the relation -``eta * mass_accretion_rate * light_speed^2``, where ``eta`` is the efficiency -at which gravitational potential mass is converted to luminosity. - - -.. code:: ipython3 - - - def Lx(S1_state, S1_mass, S1_lg_mdot,S2_state, S2_mass, S2_lg_mdot, lg_mtransfer_rate): - - if np.isnan(lg_mtransfer_rate): - if S1_state == 'NS': - eta = 0.1 - acc_lg_mdot = S1_lg_mdot - elif S1_state == 'BH': - eta = 0.057 - acc_lg_mdot = S1_lg_mdot - else: - print("CO??", S1_state, S2_state) - else: - acc_lg_mdot = lg_mtransfer_rate - if S1_state == 'NS': - eta = 0.1 - elif S1_state == 'BH': - eta = 0.057 - else: - print("CO??", S1_state, S2_state) - - return eta * (10.0**acc_lg_mdot * const.Msun / const.secyer) * const.clight**2 - - -We define a new column ``Lacc`` which stands for the accretion -luminosity and calculate it using the function defined above. - -.. code:: ipython3 - - - DF_XRB['Lacc']=DF_XRB[['S1_state', 'S1_mass', 'S1_lg_mdot', - 'S2_state', 'S2_mass','S2_lg_mdot', - 'lg_mtransfer_rate']].apply(lambda x: Lx(x['S1_state'], x['S1_mass'], - x['S1_lg_mdot'],x['S2_state'], - x['S2_mass'], x['S2_lg_mdot'], - x['lg_mtransfer_rate']), axis=1) - - -Let’s look at the properties of the XRBs, we can use -``pd.set_option('display.max_columns', None)`` to expand the columns. - -.. code:: ipython3 - - import pandas as pd - - #pd.set_option('display.max_columns', None) - - DF_XRB[['state','time','event','S1_state','S2_state','S1_mass','S2_mass','orbital_period']] diff --git a/docs/pop_synth/custom_flow.rst b/docs/pop_synth/custom_flow.rst deleted file mode 100644 index bc15581dad..0000000000 --- a/docs/pop_synth/custom_flow.rst +++ /dev/null @@ -1,123 +0,0 @@ -.. _custom_pop_synth: - -########################### -Custom population synthesis -########################### - -The modularity of the POSYDON code allows the user to easily change -the default evolutionary tree dictated by the flow chart tree. The flow -chart tree is a list of 4D points composed of ``binary.star_1.state, -binary.star_2.state, binary.state, binary.event`` which map to a corresponding -evolutionary step. Here we show how the user can replace part or the entire -POSYDON flow chart tree and customize the evolution with user specified -steps. - -Custom step -=========== - -The user can develop a ``POSYDON`` step which is a python class with a -``__call__`` method which updates the current properties of the binary objects -(see reference binary object). As an example, let's create our own treatment -for a common envelope. For simplicity sake we will halve the orbital separation -and set the post-CE donor star mass to the star's helium core mass. - -.. code-block:: python - :name: custom_CE_step.py - - from posydon.utils import common_functions as cf - - class my_CE_step(object): - """Compute a fake CE event.""" - - def __init__(self, verbose): - self.verbose = verbose - - def __call__(self, binary): - - if self.verbose: - print('The orbital separation post CE is half the pre CE orbital separation!') - - # Determine which star is the donor and which is the companion - if binary.event in ["oCE1", "oDoubleCE1"]: - donor_star = binary.star_1 - comp_star = binary.star_2 - elif binary.event in ["oCE2", "oDoubleCE2"]: - donor_star = binary.star_2 - comp_star = binary.star_1 - else: - raise ValueError("CEE does not apply if `event` is not " - "`oCE1`, 'oDoubleCE1' or `oCE2`, 'oDoubleCE1'") - - binary.separation /= 2. - m1 = donor_star.he_core_mass - m2 = comp_star.mass - binary.separation = cf.orbital_period_from_separation(binary.separation, m1, m2) - -The new custom step can be used by importing the new function into the -simulation properties as follows (see ref. the POSYDON API) - -.. code-block:: python - - from custom_CE_step import my_CE_step - - CE_STEP = dict(verbose=False) - - # pass the simulation properties to each step - sim_kwargs = dict( - flow = (flow_chart, {}), - step_HMS_HMS = (MS_MS_step, MESA_STEP), - step_CO_HeMS = (CO_HeMS_step, MESA_STEP), - step_CO_HMS_RLO = (CO_HMS_RLO_step, MESA_STEP), - step_detached = (detached_step, DETACHED_STEP), - step_CE = (my_CE_step, CE_STEP), # <--- WE ONLY EDITED THIS LINE HERE - step_SN = (StepSN, SN_STEP), - step_dco = (DoubleCO, DCO_STEP), - step_end = (step_end, END_STEP), - extra_hooks = [(StepNamesHooks, {})] - ) - - -Custom flow charts -================== - -Let us now see how one can alter the default flow chart links. Let us assume -that you have a custom step that maps the evolution of Roche lobe overflowing -HMS-HeMS binaries called ``HMS_HeMS_RLO_step``. With the ``CHANGE_FLOW_CHART`` -option of the ``flow_chart`` method we can replace the the tree links which -are currently mapped to the ``step_end`` -as follows. - -.. code-block:: python - - from posydon.binary_evol.flow_chart import flow_chart, POSYDON_FLOW_CHART, STAR_STATES_H_RICH, STAR_STATES_HE_RICH - - NEW_FLOW_CHART_LINKS = {} - - for s1 in STAR_STATES_H_RICH: - for s2 in STAR_STATES_HE_RICH: - NEW_FLOW_CHART_LINKS[(s1, s2, 'RLO1', 'oRLO1')] = 'step_HMS_HeMS_RLO' - NEW_FLOW_CHART_LINKS[(s2, s1, 'RLO2', 'oRLO2')] = 'step_HMS_HeMS_RLO' - - new_flow_chart = flow_chart(FLOW_CHART=POSYDON_FLOW_CHART, CHANGE_FLOW_CHART=NEW_FLOW_CHART_LINKS) - -We can load the new flow chart into the simulation properties as. - -.. code-block:: python - - from custom_HMS_HeMS_step import HMS_HeMS_RLO_step - - HMS_HeMS_RLO_STEP = dict(verbose=False) - - sim_kwargs = dict( - flow = (new_flow_chart, {}), # <--- WE ONLY EDITED THIS LINE HERE - step_HMS_HMS = (MS_MS_step, MESA_STEP), - step_CO_HeMS = (CO_HeMS_step, MESA_STEP), - step_CO_HMS_RLO = (CO_HMS_RLO_step, MESA_STEP), - step_HMS_HeMS_RLO = (HMS_HeMS_RLO_step, HMS_HeMS_RLO_STEP), # ADD NEW STEP HERE - step_detached = (detached_step, DETACHED_STEP), - step_CE = (StepCEE, CE_STEP), - step_SN = (StepSN, SN_STEP), - step_dco = (DoubleCO, DCO_STEP), - step_end = (step_end, END_STEP), - extra_hooks = [(StepNamesHooks, {})] - ) diff --git a/docs/pop_synth/pop_synth.rst b/docs/pop_synth/pop_synth.rst deleted file mode 100644 index 24a6e7ae0f..0000000000 --- a/docs/pop_synth/pop_synth.rst +++ /dev/null @@ -1,354 +0,0 @@ -.. _pop_synth: - -###################################### -Running a POSYDON population synthesis -###################################### - -NOTE: this is a temporary tutorial until we have a proper API - -This script list all customisable option of a POSYDON population synthesis run. - -.. code-block:: python - - %%writefile script.py - from mpi4py import MPI - from posydon.popsyn.binarypopulation import BinaryPopulation - from posydon.binary_evol import SimulationProperties - from posydon.binary_evol.flow_chart import flow_chart - from posydon.binary_evol.MESA.step_mesa import CO_HeMS_step, MS_MS_step, CO_HMS_RLO_step - from posydon.binary_evol.DT.step_detached import detached_step - from posydon.binary_evol.CE.step_CEE import StepCEE - from posydon.binary_evol.SN.step_SN import StepSN - from posydon.binary_evol.DT.double_CO import DoubleCO - from posydon.binary_evol.step_end import step_end - from posydon.binary_evol.simulationproperties import StepNamesHooks - - # STEP CUSTOMISATION - MESA_STEP = dict( - interpolation_path = None, # found by default - interpolation_filename = None, # found by default - interpolation_method = 'linear3c_kNN', # 'nearest_neighbour' 'linear3c_kNN' '1NN_1NN' - save_initial_conditions = True, # only for interpolation_method='nearest_neighbour' - track_interpolation = False, # True False - stop_method = 'stop_at_max_time', # 'stop_at_end' 'stop_at_max_time' 'stop_at_condition' - stop_star = 'star_1', # only for stop_method='stop_at_condition' 'star_1' 'star_2' - stop_var_name = None, # only for stop_method='stop_at_condition' str - stop_value = None, # only for stop_method='stop_at_condition' float - stop_interpolate = True, # True False - verbose = False, # True False - ) - - DETACHED_STEP = dict( - matching_method = 'minimize', #'minimize' 'root' - do_wind_loss = True, # True False - do_tides = True, # True False - do_gravitational_radiation = True, # True False - do_magnetic_braking = True, # True False - do_stellar_evolution_and_spin_from_winds = True, # True False - RLO_orbit_at_orbit_with_same_am = False, # True False - verbose = False, # True False - ) - - CE_STEP = dict( - prescription='alpha-lambda', # 'alpha-lambda' - common_envelope_efficiency=1.0, # float in (0, inf) - common_envelope_option_for_lambda='lambda_from_grid_final_values', # (1) 'default_lambda', (2) 'lambda_from_grid_final_values', - # (3) 'lambda_from_profile_gravitational', - # (4) 'lambda_from_profile_gravitational_plus_internal', - # (5) 'lambda_from_profile_gravitational_plus_internal_minus_recombination' - common_envelope_lambda_default=0.5, # float in (0, inf) used only for option (1) - common_envelope_option_for_HG_star="optimistic", # 'optimistic', 'pessimistic' - common_envelope_alpha_thermal=1.0, # float in (0, inf) used only for option for (4), (5) - core_definition_H_fraction=0.1, # 0.01, 0.1, 0.3 - core_definition_He_fraction=0.1, # 0.1 - CEE_tolerance_err=0.001, # float (0, inf) - common_envelope_option_after_succ_CEE = 'core_not_replaced_noMT', # 'core_not_replaced_noMT' 'core_replaced_noMT' 'core_not_replaced_stableMT' 'core_not_replaced_windloss' - verbose = False, # True False - ) - - SN_STEP = dict( - mechanism='Patton&Sukhbold20-engine', # 'direct', Fryer+12-rapid', 'Fryer+12-delayed', 'Sukhbold+16-engine', 'Patton&Sukhbold20-engine' - engine='N20', # 'N20' for 'Sukhbold+16-engine', 'Patton&Sukhbold20-engine' or None for the others - PISN="Marchant+19", # None, "Marchant+19" - ECSN="Podsiadlowksi+04", # "Tauris+15", "Podsiadlowksi+04" - max_neutrino_mass_loss=0.5, # float (0,inf) - kick=True, # True, False - kick_normalisation='one_over_mass', # "one_minus_fallback", "one_over_mass", "NS_one_minus_fallback_BH_one", "one", "zero" - sigma_kick_CCSN_NS=265.0, # float (0,inf) - sigma_kick_CCSN_BH=265.0, # float (0,inf) - sigma_kick_ECSN=20.0, # float (0,inf) - max_NS_mass=2.5, # float (0,inf) - use_interp_values=True, # True, False - use_profiles=True, # True, False - use_core_masses=True, # True, False - approx_at_he_depletion=True, # True, False - verbose = False, # True False - ) - - DCO_STEP = dict( - n_o_steps_interval = None, - ) - - END_STEP = {} - - # FLOW CHART CONFIGURATION - sim_kwargs = dict( - flow = (flow_chart, {}), - step_HMS_HMS = (MS_MS_step, MESA_STEP), - step_CO_HeMS = (CO_HeMS_step, MESA_STEP), - step_CO_HMS_RLO = (CO_HMS_RLO_step, MESA_STEP), - step_detached = (detached_step, DETACHED_STEP), - step_CE = (StepCEE, CE_STEP), - step_SN = (StepSN, SN_STEP), - step_dco = (DoubleCO, DCO_STEP), - step_end = (step_end, END_STEP), - extra_hooks = [(StepNamesHooks, {})] - ) - - sim_prop = SimulationProperties(**sim_kwargs) - - # SIMULATION CONFIGURATION - kwargs = dict( - file_path='./batches/', - optimize_ram=True, - ram_per_cpu=3., # limit ram usage at 3GB - dump_rate=10000, # limit batch size - - number_of_binaries=10, # int - star_formation='burst', # 'constant' 'burst' 'custom_linear' 'custom_log10' 'custom_linear_histogram' 'custom_log10_histogram' - max_simulation_time=13.8e9, # float (0,inf) - - primary_mass_scheme='Kroupa2001', # 'Salpeter', 'Kroupa1993', 'Kroupa2001' - primary_mass_min=7, # float (0,130) - primary_mass_max=150., # float (0,130) - secondary_mass_scheme='flat_mass_ratio', # 'flat_mass_ratio', 'q=1' - secondary_mass_min=0.35, # float (0,130) - secondary_mass_max=150., # float (0,130) - orbital_scheme = 'period', # 'separation', 'period' - orbital_period_scheme = 'Sana+12_period_extended', # used only for orbital_scheme = 'period' - orbital_period_min = 0.75, # float (0,inf) - orbital_period_max = 6000., # float (0,inf) - #orbital_separation_scheme='log_uniform', # used only for orbital_scheme = 'separation', 'log_uniform', 'log_normal' - #orbital_separation_min=5., # float (0,inf) - #orbital_separation_max=1e5, # float (0,inf) - #log_orbital_separation_mean=None, # float (0,inf) used only for orbital_separation_scheme ='log_normal' - #log_orbital_separation_sigma=None, # float (0,inf) used only for orbital_separation_scheme ='log_normal' - eccentricity_sche='zero', # 'zero' 'thermal' 'uniform' - - # IMPORT CUSTOM HOOKS - extra_columns=['step_names'], # 'step_times' with from posydon.binary_evol.simulationproperties import TimingHooks - - # LIST BINARY PROPERTIES TO SAVE - only_select_columns=[ - 'state', - 'event', - 'time', - #'separation', - 'orbital_period', - 'eccentricity', - #'V_sys', - #'rl_relative_overflow_1', - #'rl_relative_overflow_2', - 'lg_mtransfer_rate', - #'mass_transfer_case', - #'trap_radius', - #'acc_radius', - #'t_sync_rad_1', - #'t_sync_conv_1', - #'t_sync_rad_2', - #'t_sync_conv_2', - #'nearest_neighbour_distance', - ], - - # LIST STAR PROPERTIES TO SAVE - include_S1=True , # True, False - S1_kwargs=dict(only_select_columns=[ - 'state', - #'metallicity', - 'mass', - 'log_R', - 'log_L', - 'lg_mdot', - #'lg_system_mdot', - #'lg_wind_mdot', - 'he_core_mass', - 'he_core_radius', - #'c_core_mass', - #'c_core_radius', - #'o_core_mass', - #'o_core_radius', - 'co_core_mass', - 'co_core_radius', - 'center_h1', - 'center_he4', - #'center_c12', - #'center_n14', - #'center_o16', - 'surface_h1', - 'surface_he4', - #'surface_c12', - #'surface_n14', - #'surface_o16', - #'log_LH', - #'log_LHe', - #'log_LZ', - #'log_Lnuc', - #'c12_c12', - #'center_gamma', - #'avg_c_in_c_core', - #'surf_avg_omega', - 'surf_avg_omega_div_omega_crit', - #'total_moment_of_inertia', - #'log_total_angular_momentum', - 'spin', - #'conv_env_top_mass', - #'conv_env_bot_mass', - #'conv_env_top_radius', - #'conv_env_bot_radius', - #'conv_env_turnover_time_g', - #'conv_env_turnover_time_l_b', - #'conv_env_turnover_time_l_t', - #'envelope_binding_energy', - #'mass_conv_reg_fortides', - #'thickness_conv_reg_fortides', - #'radius_conv_reg_fortides', - #'lambda_CE_1cent', - #'lambda_CE_10cent', - #'lambda_CE_30cent', - #'lambda_CE_pure_He_star_10cent', - #'profile', - ], - scalar_names=['natal_kick_array', - 'SN_type', - #'f_fb', - #'spin_orbit_tilt', - ]), - - # LIST STAR PROPERTIES TO SAVE - include_S2=True, # True, False - S2_kwargs=dict(only_select_columns=[ - 'state', - #'metallicity', - 'mass', - 'log_R', - 'log_L', - 'lg_mdot', - #'lg_system_mdot', - #'lg_wind_mdot', - 'he_core_mass', - 'he_core_radius', - #'c_core_mass', - #'c_core_radius', - #'o_core_mass', - #'o_core_radius', - 'co_core_mass', - 'co_core_radius', - 'center_h1', - 'center_he4', - #'center_c12', - #'center_n14', - #'center_o16', - 'surface_h1', - 'surface_he4', - #'surface_c12', - #'surface_n14', - #'surface_o16', - #'log_LH', - #'log_LHe', - #'log_LZ', - #'log_Lnuc', - #'c12_c12', - #'center_gamma', - #'avg_c_in_c_core', - #'surf_avg_omega', - 'surf_avg_omega_div_omega_crit', - #'total_moment_of_inertia', - #'log_total_angular_momentum', - 'spin', - #'conv_env_top_mass', - #'conv_env_bot_mass', - #'conv_env_top_radius', - #'conv_env_bot_radius', - #'conv_env_turnover_time_g', - #'conv_env_turnover_time_l_b', - #'conv_env_turnover_time_l_t', - #'envelope_binding_energy', - #'mass_conv_reg_fortides', - #'thickness_conv_reg_fortides', - #'radius_conv_reg_fortides', - #'lambda_CE_1cent', - #'lambda_CE_10cent', - #'lambda_CE_30cent', - #'lambda_CE_pure_He_star_10cent', - #'profile', - ], - scalar_names=['natal_kick_array', - 'SN_type', - #'f_fb', - #'spin_orbit_tilt', - ]), - ) - - def run_simulation(sim_prop, kwargs, file=None, indices=None, use_MPI=False): - - if not use_MPI: - # create binaries - pop = BinaryPopulation(entropy=None, - population_properties=sim_prop, - file_name=file, - **kwargs) - else: - comm = MPI.COMM_WORLD - rank = comm.Get_rank() - size = comm.Get_size() - - # create binaries - pop = BinaryPopulation(entropy=None, - population_properties=sim_prop, - file_name=file, - comm=comm, - **kwargs) - sim_prop.load_steps(verbose=True) - - # evolve binaries - if file is not None: - kwargs['from_hdf'] = True - kwargs['indices'] = indices - - pop.evolve(breakdown_to_df=False, tqdm=True, **kwargs) - - # save binaries - pop.save('./population.h5', **kwargs) - - return pop - - if __name__ == '__main__' : - pop = run_simulation(sim_prop, kwargs, use_MPI=False) - -If you want to run the script on a HPC cluster with MPI, you can do so -by setting ``use_MPI=True`` in the above script and runnning the following -Slurm magic command. - -.. code-block:: python - - %%sbatch - #!/bin/bash - #SBATCH --mail-user=my_email - #SBATCH --job-name=pop-syn - #SBATCH --output=log.out - #SBATCH --error=log.err - #SBATCH --partition=debug-cpu - #SBATCH --nodes=2 - #SBATCH --ntasks-per-node=2 - #SBATCH --mem-per-cpu=8G - #SBATCH --time=00:15:00 - - mpiexec -n ${SLURM_NTASKS} python script.py - -Notice that the ``run_simulation`` method allows to reevolve any binary, e.g. - -.. code-block:: python - - pop = run_simulation(sim_prop, kwargs, file='./population.h5', indicies=[1334], use_MPI=False) - -if the variable ``indicies`` is not specified, all binaries will be rerun. diff --git a/docs/visualization/VHD/VHD.rst b/docs/visualization/VHD/VHD.rst deleted file mode 100644 index d6dae5e889..0000000000 --- a/docs/visualization/VHD/VHD.rst +++ /dev/null @@ -1,209 +0,0 @@ -.. _VHD: - -####################### -Van den Heuvel diagrams -####################### - -Visualize specific index -======================== - -`VHdiagrams` allow individual POSYDON binaries to be viewed in a more intuitive -sense. We use 'population.h5' as an example dataset. - - -Simple usage ------------- - -The basic usage is to simply visualize one index inside an independent and -reactive window, with the following : - -.. code-block:: python - - from posydon.visualization.VHdiagram import VHdiagram - - VHdiagram('population.h5', path='./dataset/', index=18976) - -.. image:: pngs/detailled_window.png - -The named parameter 'path' is useful if the dataset is in another directory. - -We can choose the view wanted with the option's window, displayed by -'option' button : - -.. image:: pngs/option_window.png - -The 'save' button take a screen of the view currently displayed, and -save it inside a 'screens' folder, created in the current directory. - -Set view --------- - -For faster use, it's possible to directly choose the view displayed. -There are 4 available modes: - -.. code-block:: python - - PresenterMode.DIAGRAM - PresenterMode.REDUCED - PresenterMode.SIMPLIFIED - PresenterMode.DETAILED - -We can specify which view we want to display with the named parameter -'presentMode' : - -.. code-block:: python - - from posydon.visualization.VHdiagram import VHdiagram - from posydon.visualization.VH_diagram.PresenterMode import PresenterMode - - VHdiagram('population.h5', index=19628, presentMode=PresenterMode.DIAGRAM) - -.. image:: pngs/diagram_window.png - -Set display mode ----------------- - -There are two ways to display the diagram : inside a window or as a screen -inside a Jupyter notebook. There are three available modes: - -.. code-block:: python - - DisplayMode.INLINE_S - DisplayMode.INLINE_B - DisplayMode.WINDOW - -'INLINE_S' is to do an inline display with matplotlib while 'INLINE_B' use IPython. - - -We can specify the display mode wanted with the named parameter 'displayMode': - -.. code-block:: python - - from posydon.visualization.VHdiagram import VHdiagram, DisplayMode - from posydon.visualization.VH_diagram.Presenter import PresenterMode - - VHdiagram( - "population.h5", - index=19628, - presentMode=PresenterMode.DIAGRAM, - displayMode=DisplayMode.INLINE_B, - ) - -.. image:: pngs/diagram_inline.png - -Visualize multiple indexes -========================== - -The `VDdiagramm_m` module allows you to print more than one index horizontally in the -same plot. - -Counting binaries populations ------------------------------ - -You can loop through the binary file to count identical binary simulations with the `ParseDataFrame` -class. The counts are accessible in the `count_dict` attribute which is a `Counter` python object -and the frequencies with the `get_frequencies()` method. You can also get the n most frequent -binaries using the `get_most_numpy(k)` method. - -.. code-block:: python - - from posydon.visualization.VH_diagram.ParseDataFrame import ParseDataFrame - - parse_df = ParseDataFrame('./data/population.h5') - parse_df.count_dict - - >>> Counter({0: 14, - 1: 16, - 3: 1, - 4: 9, - 5: 1, - 6: 1, - 7: 6, - 8: 19, - 21: 1, - 24: 4, - 25: 1, - 26: 1, - 30: 1, - 31: 10, - 34: 1, - 36: 5, - 38: 1, - 55: 1, - 58: 1, - 59: 1, - 62: 1, - 65: 1, - 74: 1, - 93: 1, - 95: 1}) - - -Side by side visualization --------------------------- - -You can get multiple binary simulations printed side by side by providing a list of their index -in wanted order. The initialization call requires a dict of frequenties as parameter `frequency` -witch is by default uniform. `hierarchy` parameter must be set to false. - -.. code-block:: python - - from posydon.visualization.VH_diagram.PresenterMultiple import VHdiagramm_m - from posydon.visualization.VHdiagram import DisplayMode - from posydon.visualization.VH_diagram.PresenterMode import PresenterMode - - VHD = VHdiagramm_m('./data/population.h5', - index=cnt[:,0], - frequency=parse_df.get_frequencies(), - hierarchy=False, - presentMode = PresenterMode.DIAGRAM, - displayMode = DisplayMode.INLINE_B) - -.. image:: pngs/diagram_multiple.png - -Hierarchical visualization --------------------------- - -The hierarchical visualization aims to "factorize" identical steps resulting in a tree plot -where the nodes are the common steps. They are labeled by precentages relatively to the parent -node precentage (witch is also uniform by default). - -.. code-block:: python - - from posydon.visualization.VH_diagram.PresenterMultiple import VHdiagramm_m - from posydon.visualization.VHdiagram import DisplayMode - from posydon.visualization.VH_diagram.PresenterMode import PresenterMode - - VHD = VHdiagramm_m('./data/population.h5', - index=cnt[:,0], - frequency=parse_df.get_frequencies(), - hierarchy=True, - presentMode = PresenterMode.DIAGRAM, - displayMode = DisplayMode.INLINE_B) - -.. image:: pngs/diagram_hierarchy.png - -Steps differentiation is based on picture's file names. So binaries like 30 and 36 -are considered different since they refer to distinct files. - -.. code-block:: python - - >>> print(VHD._presenter._infos[10][1].event_filename.split("\\")[-1]) - H-rich_oCE1_H-rich_.png - >>> print(VHD._presenter._infos[14][1].event_filename.split("\\")[-1]) - H-rich_oCE2_H-rich_.png - - -The `get_sorted_index()` method of the `VHdiagramm_m` object give a list of indexes -sorted regarding the filenames of the pictures representing their steps - -.. code-block:: python - - VHD = VHdiagramm_m('./data/population.h5', - index=VHD.get_sorted_index(), - frequency=parse_df.get_frequencies(), - hierarchy=False, - presentMode = PresenterMode.DIAGRAM, - displayMode = DisplayMode.INLINE_B) - -.. image:: pngs/diagram_multiple_sort.png \ No newline at end of file diff --git a/docs/visualization/plot1D/plot1D.rst b/docs/visualization/plot1D/plot1D.rst deleted file mode 100644 index ab69f13391..0000000000 --- a/docs/visualization/plot1D/plot1D.rst +++ /dev/null @@ -1,177 +0,0 @@ -.. _plot1D: - -############################################## -1D plotting functionalities for POSYON GRIDS -############################################## - - -We provide a variety of 1D MESA grids plotting functions within POSYDON. -We start by loading a grid in a PSyGrid object. The ``plot`` method supports -any POSYDON MESA grid. Here, we will use the ``HMS-HMS`` grid as an example. - -.. code-block:: python - - # to load a grid - from posydon.grids.psygrid import PSyGrid - grid = PSyGrid("/PATH_TO_POSYDON_DATA/HMS-HMS/grid_0.0142_%d.h5") - - -How to plot one track -===================== - -Plot one quantity as a function of another -------------------------------------------- - -The properties to plot for a given MESA track (we have chosen the 42nd track in -the grid) can be read from ``history1``, ``history2``, or ``binary_history``. -For example, to plot the mass evolution of `star_1`: - -.. code-block:: python - - PLOT_PROPERTIES_1 = { - 'show_fig' : True, - 'close_fig' : True, - 'path_to_file': './plots_demo/', - 'fname': '1D_age_M1.png', - } - - grid.plot(42, 'age', 'star_1_mass', history='binary_history', **PLOT_PROPERTIES_1) - -.. image:: pngs/1D_age_M1.png - :width: 400 - -If, instead, we would like to plot the evolution of `star_1` within a -Hertzsprung-Russell diagram: - -.. code-block:: python - - PLOT_PROPERTIES_2 = PLOT_PROPERTIES_1 - PLOT_PROPERTIES_2['fname'] = '1D_logR_logL.png' - - grid.plot(42, 'log_R', 'log_L', history='history1', **PLOT_PROPERTIES_2) - -.. image:: pngs/1D_logR_logL.png - :width: 400 - -Plot multiple quantities as a function of one --------------------------------------------------- - -We can display more properties as a function of another in a subplot like -the following examples. - -.. code-block:: python - - PLOT_PROPERTIES_4 = PLOT_PROPERTIES_1 - PLOT_PROPERTIES_4['fname'] = '1D_age_bin.png' - PLOT_PROPERTIES_4['figsize'] = (4., 8.) - - grid.plot(42, 'age', ['star_1_mass', 'star_2_mass', 'binary_separation'], history='binary_history', **PLOT_PROPERTIES_4) - -.. image:: pngs/1D_age_bin.png - :width: 400 - -.. code-block:: python - - PLOT_PROPERTIES_3 = PLOT_PROPERTIES_1 - PLOT_PROPERTIES_3['fname'] = '1D_logR_logLs.png' - PLOT_PROPERTIES_3['figsize'] = (4., 8.) - - grid.plot(42, 'log_R', ['log_LH', 'log_LHe','log_LZ'], history='history1', **PLOT_PROPERTIES_3) - -.. image:: pngs/1D_logR_logLs.png - :width: 400 - - -How to plot many tracks -======================= - -Plot one or more quantities as a function of another for multiple tracks --------------------------------------------------------------------------- - -If one wants to compare multiple tracks on the same plot, the indices for all -binaries can be provided as a list. - -.. code-block:: python - - PLOT_PROPERTIES_5 = PLOT_PROPERTIES_1 - PLOT_PROPERTIES_5['fname'] = '1D_multi.png' - PLOT_PROPERTIES_5['legend1D'] = dict(loc='upper right', lines_legend=['42','43', '44']) - - grid.plot([42,43,44], 'age', 'binary_separation', history='binary_history', **PLOT_PROPERTIES_5) - -.. image:: pngs/1D_multi.png - :width: 400 - - -Plot third quantity as a color map -================================== - -.. code-block:: python - - PLOT_PROPERTIES_6 = PLOT_PROPERTIES_1 - PLOT_PROPERTIES_6['fname'] = '1D_color.png' - PLOT_PROPERTIES_6['log10_x'] = True - - grid.plot(42, 'binary_separation', 'star_1_mass', 'lg_mstar_dot_1', history='binary_history', **PLOT_PROPERTIES_6) - - -.. image:: pngs/1D_color.png - :width: 400 - - -Plotting an HR diagram -====================== - -One can use the ``HR`` method to display the HR diagram. -Note that multiple tracks at once can also be displayed. - -.. code-block:: python - - PLOT_PROPERTIES_7 = PLOT_PROPERTIES_1 - PLOT_PROPERTIES_7['fname'] = 'HR1.png' - - grid.HR(42, history='history1', **PLOT_PROPERTIES_7) - - -.. image:: pngs/HR1.png - :width: 400 - -The ``HR`` diagram method has also an option to display the stellar state. -Here we show how to reproduce the HR diagram of Fig. 5 in Fragos et al. (2022). - -.. code-block:: python - - import numpy as np - - # load single HMS grid - grid = PSyGrid("/Volumes/T7/data_phd/POSYDON/data/POSYDON_data/single_HMS/grid_0.0142.h5") - - PLOT_PROPERTIES_8 = { - 'figsize' : (3.38, 5), - 'show_fig' : True, - 'close_fig' : True, - 'path_to_file': './plots_demo/', - 'fname': 'HR2.png', - 'xmin' : 3., - 'xmax' : 6., - 'ymin' : -1.5, - 'ymax' : 7., - 'const_R_lines' : True, - 'legend1D' : { - 'loc' : 'upper center', - 'bbox_to_anchor' : (0.4, 1.27), - 'ncol' : 2, - 'prop': { - 'size': 6 - }, - } - } - - # chose a subsample of tracks - idx = np.around(np.argsort(grid.initial_values['S1_star_mass']),2)[::8] - idx = list(set(idx)-{10, 82, 128, 166})+[101,14,96,191] - - grid.HR(idx, history='history1', states=True, **PLOT_PROPERTIES_8) - -.. image:: pngs/HR2.png - :width: 400 diff --git a/docs/visualization/plot1D/pngs/1D_age_M1.png b/docs/visualization/plot1D/pngs/1D_age_M1.png deleted file mode 100644 index 7a97bbd3878694c599ec94d7158bd7808a36a8ef..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 32646 zcmdSBbzIZmA3r*~5EGF`F-avyn}n3o4T?CrL1_#`K~e#c0V2(S5dsne5fK#G5Ev;T z;b$mKjr%HvfcTLqULV+=e7IpGx&RtgMywj3dPTb{A_uUBI^ckqFt`*x@g$n zbGd)p$sA>S+r`1g-o?i94yT*Ble4A0oe;kuzaTHCg^P=Wgn+>RKIgZ0!U#lhym*d6 zaiSEjUDkYn9q)GcGNgJ>tuOqrFp)oe47Z;-JaYS=x-(q@jeUobEst=v_6e)Rre0@` z@YU}O@}C_h6#w*O;qn)j^S61^NB|f1Le)Eh0}> zlm$?Aui6^+u?IE2eOXL5Q6{m>9oj4?aHf>=(pS@TkrKu@FZda+5i zNAHE?rmX?r0%y2xFa4B_t~Akpg%7N=uFYR&G-3Q*kcUWS-M#G?8~p)|f?K@@0#fou z#Di5xBcF#`o>rzchNtDfBVR6Gv1t!k^O@r`)*lYLeRKaVnAP;W?}duBjR!wpuaLAD zeq`Exe=y1Buin$dy4D$mKK}1I?$d#s#t%dt7zOZA(Toh~rCFEADgN9JH}@d(4_|ip zk}7iVmSa(YgRX>-2u?_&AVO4oKmHt1M@26&Q)!=;fp+?6{--*U2&US#p?^8WKa>0X z+XdvRV-Hs`#kRJ)%cPYvMw#^GhAFW=d<~ zq9VoPs~w9DslI#$F){nbfRS&WW%2zxuXH15sdKi&-;e>te}A>Eb_Y9l7l8z8+sG#GQW|EbiP>#m5}+XuvA` z6;fI*jHX)3KboIG8EdI??pp1H-6|ZcVp2FT(@22C!tA#@>zCAy^{@T1YHd-{+ z+hW4}A-jh(Wc46_sW5ui#L%`T?qOsz?eBLJ{Zzzf=(LV8@@iI#S>!Rx2wqG|oM!Te zTN0MZZZ>OXmgp*$684!)xN5daVX)WHoNR!jCHC8uz3Tmdb6H3gP~nz^rmw(pBHQ?)Z>9 zaNqmvBH={R_l5uw!9#o&q^xu&ftA_0_V;pUofqgPe`M$dt2T^UoXhjEtq#MU=GmEe zeWck~e8~KYN=45EVC1n6(S~Uz z%d9vbTU=5lHf0C*x3f0eP<}tbS>&ES%7}IeceY*aG;4Mazd+!2k^i(%V*C5*K=U`U zMrCU=-pKe>WwM8@cg}o&loQ7C1R@h3qhPZK{cj)V7Wk&)eygIW^;9cfT9%`=Zt(kz z%tV2A$F1U*p(@s=0+tIINk>hTD&4fcmT*;MDn-*@<9X)M<7R#lp~DIKg9sHarJxOu`U+gz0?`OEr#rP z@ECa3!4G%mt>tlc2Z9BDib-93+24E0`Pgz{sQ(#|CjH9!%#i`5Pxns#bx8to z-HPE;bgP*3)mC~=*$X8fHXX;Rku&cxGQmbS;N zT>uNhE+U7!In=mAt7cC(2YrBuj5MQ&ZorpRZQJu4%qHzOWTKPycQqHxdYIi+w|{Y} z2XCZ3{ERuvx?X3&S|^&}o1XexFL~p=zWV@ygaUl1V}5!&9lV3N^>`b599HXGr(UO$ zN0FFP<$U`b_Dky7xzU?63DI7gyZB-wqZ?olOuZ58boXQFa$(xNXxl?3-FU*m zT*h9ZTIXUDPcIME0XVJV}J@ZA?+?tI?)6>PGqonYJP+o$9wfyE&pRX(DUo zUqcQU0S7!foG&tGeL(GdR=kXzIIpc7CiR}iO6*-?f4IrRq~Dp=;9+fX=Z$u^V5;Ye zI{h8I9bNaxyKnh;2J{%k8Gx8@1LNdQAfnd5KhhQ=xSLOBx$x}0-*d5I@>KZIF%qI1 zd6yaCf5BVyroALfu0ZyA4K2&dJGJRh3XBN)(@j0sn>fqECQ&Ik?KAN_rT|o2=+;s} zH2Y#?+-m2q+_^X6iEZEate$${L8gA|_ktt2@!u5K)5fSBSywe}hzcVktTDX`_RaEk z#f^Qlu8OXA67~dCGsj>H*`E~M*|*rg*m*nl-bDCUJ;LA4Tw^cW2js%70f|bPB&~Et zT!$3QnN#a^?JYL)`$i;E_KW zdFgG%nn$xU)lKxv8m$Q_*oWpQ(_ILA!x#|#k(T7x)0Re59?`Y9B7en9;uv#Vc=LdS zhlkX_ucg7^SE?bZ8S8b8nbs-(N;{+Y_OwK|MvdITJl{JjZoH3Xuu&){?b|-_L_BZN zcpH5{HJBNfQ^Gl1Y0=w98$TrsR6kGPiQ7BS?CdcTe2XymW%QxB`%{qwG@gh!D+;dmio@HhHrWJGn*I~wX#b^g~ad!zmr3rd^y=w z89lF^@b%Bfd}PBHB~mcPda%2!M#rKngmwd?V;P1fhDPtU?%Gz58e~Vr^3ody^vto4 z_)I<{S3XvH9$`_oJwN10+Bil}T<`I(;~(?$(A(#IlhJ(LjKvhs704ARJ?J1uS>qCCAT`%T+uA7BP)(QK|X5?e;$%|hedt!L%Z1ateH ziOLP5ZME`qV*^~c96)JChs;>Pl-GNP2hp7%3Y`*^Pi81 zv$5zOPFD-P?twGeuVUfB@4T)4_wVZP>=9;NZ$fI8gO)5c1+$zr#vKTBE*-8CpV<1) zPeM|1XknRwZs%!5{%9rkP)iO)z#HC1BX+xD>mrv?sh6MR9sZWxA8O!o4>)W*TP9T z$*k^ALF$H2VxIT3<$9QQ{Q0bWcX=VavAv`%t=82GU9hb_^&UmUq5s8BRqS(*)koj@ z{RiGhv1J!tI8=Vad4t7(2Q*T%!m7yvk>$QS>5x6H+t9S_azUnq|ThbykG`~lm;5VWn{ z3bZVGq^VOK&`OpUT(zEmA9g1j8=Iht#_H;-lSJIDsgd#VSMLwHoz8&h*{V+UmF1h6 zMXxb~3jlfdAN@_qSSuGKC+M89tk1Xp<)8}|RDS0xzIX!ynqlSc(_K*rG}Qrlz@6Jx zKQl{bxc!U9(I_MzJ`im=k? 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0e4c5cad6b..0000000000 --- a/docs/visualization/plot2D/plot2D.rst +++ /dev/null @@ -1,368 +0,0 @@ -.. _plot2D: - -############################################## -2D plotting functionalities for POSYON GRIDS -############################################## - -Similar to our 1D plotting functions, we provide a series of 2D plotting -functionalities within POSYDON. The ``plot2D`` method supports either a MESA -grid which samples the 2D parameter space of binary initial conditions or a -4D or 3D MESA grid sampling, e.g., star_1_mass, star_2_mass, period_days, -metallicity. In the latter case the grid will be sliced along two dimensions -specified by the user. - -We illustrate some of the plotting capabilities of ``plot2D`` by loading the -``HMS-HMS`` grid and reproduce some figures included in the POSYDON -instrument paper. - -.. code-block:: python - - # to load a grid - from posydon.grids.psygrid import PSyGrid - grid = PSyGrid("/PATH_TO_POSYDON_DATA/HMS-HMS/grid_0.0142_%d.h5") - - -How to plot the MESA termination flags -====================================== - -Termination flag 1 & 2 combined -------------------------------- - -Any plotting parameter is passed to the ``plot2D`` method through the kwarg -``PLOT_PROPERTIES``. In our case we would like to visualize the figure, save it -to a given ``path_to_file`` with filename ``fname`` and then close it. -Recall that the ``HMS-HMS`` grid sampled the initial binary parameter space in -star_1_mass, period_days and mass_ratio. To reproduce Fragos et al. (2022) -Figure 1, we will slice the 3D parameter space along the mass ratio at ``q=0.7``. -We will also set the x and y axis to be displayed in log-space and reduce -the default marker size with the following code - -.. code-block:: python - - q = 0.7 - PLOT_PROPERTIES_TF12 = { - 'figsize': (4.5, 4.), - 'show_fig' : True, - 'close_fig' : True, - 'path_to_file': './plots_demo/', - 'fname': 'q_%1.1f_TF12.png'%q, - 'title' : r'$q = %1.1f$'%q, - 'log10_x' : True, - 'log10_y' : True, - } - - fig = grid.plot2D('star_1_mass', 'period_days', None, - termination_flag='combined_TF12', - grid_3D=True, slice_3D_var_str='mass_ratio', - slice_3D_var_range=(q-0.025,q+0.025), - verbose=False, **PLOT_PROPERTIES_TF12) - -.. image:: pngs/q_0.7_TF12.png - -Termination flag 1 ------------------- - -Similarly we can plot the termination flag 1 which indicates how the MESA -simulation ended. We can display as a color map any final value stored in -``final_values``. -This code shows how to reproduce Fig. 10 -of Fragos et al. (2022). - -.. code-block:: python - - PLOT_PROPERTIES_TF1 = PLOT_PROPERTIES_TF12 - PLOT_PROPERTIES_TF1['fname'] = 'q_%1.1f_TF1.png'%q - PLOT_PROPERTIES_TF1['figsize'] = (4.5,6.) - - fig = grid.plot2D('star_1_mass', 'period_days', 'S2_surf_avg_omega_div_omega_crit', - termination_flag='termination_flag_1', - grid_3D=True, slice_3D_var_str='mass_ratio', - slice_3D_var_range=(q-0.025,q+0.025), - verbose=False, **PLOT_PROPERTIES_TF1) - -.. image:: pngs/q_0.7_TF1.png - -Termination flag 2 ------------------- - -The termination flag 2 shows a summary of all mass transfer cases which -occurred during the binaries' evolution. - -.. code-block:: python - - PLOT_PROPERTIES_TF2 = PLOT_PROPERTIES_TF12 - PLOT_PROPERTIES_TF2['fname'] = 'q_%1.1f_TF2.png'%q - - fig = grid.plot2D('star_1_mass', 'period_days', None, - termination_flag='termination_flag_2', - grid_3D=True, slice_3D_var_str='mass_ratio', - slice_3D_var_range=(q-0.025,q+0.025), - verbose=False, **PLOT_PROPERTIES_TF2) - -.. image:: pngs/q_0.7_TF2.png - -Termination flag 3 ------------------- - -The termination flag 3 shows the final stellar state of star 1. - -.. code-block:: python - - PLOT_PROPERTIES_TF3 = PLOT_PROPERTIES_TF12 - PLOT_PROPERTIES_TF3['fname'] = 'q_%1.1f_TF3.png'%q - - fig = grid.plot2D('star_1_mass', 'period_days', None, - termination_flag='termination_flag_3', - grid_3D=True, slice_3D_var_str='mass_ratio', - slice_3D_var_range=(q-0.025,q+0.025), - verbose=False, **PLOT_PROPERTIES_TF3) - -.. image:: pngs/q_0.7_TF3.png - -Termination flag 4 ------------------- - -The termination flag 4 shows the final stellar state of star 2. - -.. code-block:: python - - PLOT_PROPERTIES_TF4 = PLOT_PROPERTIES_TF12 - PLOT_PROPERTIES_TF4['fname'] = 'q_%1.1f_TF4.png'%q - - fig = grid.plot2D('star_1_mass', 'period_days', None, - termination_flag='termination_flag_4', - grid_3D=True, slice_3D_var_str='mass_ratio', - slice_3D_var_range=(q-0.025,q+0.025), - verbose=False, **PLOT_PROPERTIES_TF4) - -.. image:: pngs/q_0.7_TF4.png - - -All termination flags ---------------------- - -We plot all termination flags at once in a large subplot of panels with -the option ``termination_flag='all'``. -In order to fit all legends we suggest to increase the figure size and marker -size to, e.g. ``(25,25)`` and ``30``, respectively. - - -Display custom quantities -========================= - -Relative change of a final quantity ------------------------------------- - -The relative change of any value stored within ``final_values`` can be -displayed by adding the prefix ``relative_change_`` to the z-variable displayed -as a colorbar. E.g. we can display the relative change of star_1_mass: - -.. code-block:: python - - PLOT_PROPERTIES_TF1 = PLOT_PROPERTIES_TF12 - PLOT_PROPERTIES_TF1['fname'] = 'q_%1.1f_TF1_DM1.png'%q - PLOT_PROPERTIES_TF1['figsize'] = (4.5,6.) - PLOT_PROPERTIES_TF1['colorbar'] = {'label' : r'$(M_\mathrm{1,f}-M_\mathrm{1,i})/M_\mathrm{1,i}$',} - - fig = grid.plot2D('star_1_mass', 'period_days', 'relative_change_star_1_mass', - termination_flag='termination_flag_1', - grid_3D=True, slice_3D_var_str='mass_ratio', - slice_3D_var_range=(q-0.025,q+0.025), - verbose=False, **PLOT_PROPERTIES_TF1) - -.. image:: pngs/q_0.7_TF1_DM1.png - - -Custom quantities ------------------ - -The user can plot custom quantities as a color map on termination flag 1. -For example, we display the maximum surf_avg_omega_div_omega_crit during -the history of star 2 as follows - -.. code-block:: python - - PLOT_PROPERTIES_TF1 = PLOT_PROPERTIES_TF12 - PLOT_PROPERTIES_TF1['fname'] = 'q_%1.1f_TF1_max.png'%q - PLOT_PROPERTIES_TF1['figsize'] = (4.5,6.) - PLOT_PROPERTIES_TF1['colorbar'] = {'label' : r'$\max(\omega_\mathrm{s}/\omega_\mathrm{s,crit})_1$'} - - max_omega = [max(grid[i].history2['surf_avg_omega_div_omega_crit']) - if grid[i].history2 is not None else np.nan - for i in range(len(grid.MESA_dirs))] - - fig = grid.plot2D('star_1_mass', 'period_days', np.array(max_omega), - termination_flag='termination_flag_1', - grid_3D=True, slice_3D_var_str='mass_ratio', - slice_3D_var_range=(q-0.025,q+0.025), - verbose=False, **PLOT_PROPERTIES_TF1) - -.. image:: pngs/q_0.7_TF1_max.png - -Core collapse quantities ------------------------- - -Numerical post processed quantities can be displayed using the z variable option -as shown with termination flag 1. - - -.. code-block:: python - - PLOT_PROPERTIES_CC = PLOT_PROPERTIES_TF12 - PLOT_PROPERTIES_CC['fname'] = 'q_%1.1f_CC_spin.png'%q - PLOT_PROPERTIES_CC['figsize'] = (4.5,6.) - PLOT_PROPERTIES_CC['colorbar'] = {'label' : r'$a_\mathrm{CO,1}$'} - PLOT_PROPERTIES_CC['zmin'] = 0. - PLOT_PROPERTIES_CC['zmax'] = 0.3 - - fig = grid.plot2D('star_1_mass', 'period_days', 'S1_Patton&Sukhbold20-engineN20_spin', - termination_flag='termination_flag_1', - grid_3D=True, slice_3D_var_str='mass_ratio', - slice_3D_var_range=(q-0.025,q+0.025), - verbose=False, **PLOT_PROPERTIES_CC) - -.. image:: pngs/q_0.7_CC_spin.png - -On the other hand supernova type and compact object states are stored as -strings in the dataset. These quantities can be plotted using the -``termination_flag`` option of ``plot2D``, as - -.. code-block:: python - - PLOT_PROPERTIES_CC = PLOT_PROPERTIES_TF12 - PLOT_PROPERTIES_CC['fname'] = 'q_%1.1f_CC_SN.png'%q - - fig = grid.plot2D('star_1_mass', 'period_days', None, - termination_flag='S1_Patton&Sukhbold20-engineN20_SN_type', - grid_3D=True, slice_3D_var_str='mass_ratio', - slice_3D_var_range=(q-0.025,q+0.025), - verbose=False, **PLOT_PROPERTIES_CC) - -.. image:: pngs/q_0.7_CC_SN.png - -.. code-block:: python - - PLOT_PROPERTIES_CC = PLOT_PROPERTIES_TF12 - PLOT_PROPERTIES_CC['fname'] = 'q_%1.1f_CC_state.png'%q - - fig = grid.plot2D('star_1_mass', 'period_days', None, - termination_flag='S1_Patton&Sukhbold20-engineN20_state', - grid_3D=True, slice_3D_var_str='mass_ratio', - slice_3D_var_range=(q-0.025,q+0.025), - verbose=False, **PLOT_PROPERTIES_CC) - -.. image:: pngs/q_0.7_CC_state.png - -Displaying final quantities also for initial and unstable mass transfer ------------------------------------------------------------------------ - -We have a debug ``termination_flag`` option which allows us to display -as a colormap the final value also for the tracks that ends because of -initial or unstable mass transfer. - -.. code-block:: python - - PLOT_PROPERTIES_TF1 = PLOT_PROPERTIES_TF12 - PLOT_PROPERTIES_TF1['fname'] = 'q_%1.1f_debug.png'%q - PLOT_PROPERTIES_TF1['figsize'] = (4.5,6.) - PLOT_PROPERTIES_TF1['log10_z'] = True - - fig = grid.plot2D('star_1_mass', 'period_days', 'period_days', - termination_flag='debug', - grid_3D=True, slice_3D_var_str='mass_ratio', - slice_3D_var_range=(q-0.025,q+0.025), - verbose=False, **PLOT_PROPERTIES_TF1) - - -.. image:: pngs/q_0.7_debug.png - -Advanced plotting options -========================= - - -How to plot up to onset of RLO ------------------------------- - -This functionality allows to slice the MESA runs at onset of Roche-Lobe -overflow and display with a color a quantity in ``history1`` or -``binary_history`` -at onset RLO, or, alternatively one of the termination flags. Use -``slice_at_RLO=True`` option of ``plot2D`` to allow for this. -Note: depending on how many -runs you intend to display this might take a while. - - -Overplot grids --------------- - -Sometimes you want to rerun a subsample of the grid. This option will allow you -to stack as many grid reruns as you wish. -You can combine the new grid by passing it to the option ``extra_grid=new_grid``. -If you want to stack more than one grid, pass them in a list, e.g. -``extra_grid=[new_grid_1,new_grid_2,new_grid_3]``, they will be stacked in -the order provided where the last extra grid of the list will stacked as last. - - -Creating plots with several slices ----------------------------------- - -We have the option to pass a list of slice ranges. In this case the plotting -script will loop over those ranges and create one plot with several slice -plots. It will get a common legend and/or color bar. - -There are two more options to the plot2D function: -1. ``max_cols``: It specifies the maximum number of columns of subplots. The -number of rows is automatically calculated to cover all plots and the legend. -2. ``legend_pos``: It specifies which subplots are used for the legend/color -bar and allows an index or tuple specifying a rectangle of indecies, like -matplotlib requires for creating a subplot. - -Because the legend has its own axes, it is recommended to modify the default -``bbox_to_anchor`` of the legend. -To have a better control on the color bar, it got the additional field -``bounds`` in the plot properties. -To identify the subplots each of them will get a text box. Its attributes can -be modified by changing the field ``slice_text_kwargs`` in the plot properties. -The slices will loop over 3D and 4D idependently. If one of them has just a -single range, this slicing will be added to the legend title instead of being -reported in any subplot. - -.. code-block:: python - q_ranges = [(0.025, 0.075), (0.175, 0.225), (0.325, 0.375), (0.475, 0.525), - (0.625, 0.675), (0.775, 0.825), (0.925, 0.975), (0.98, 1.0)] - - plot_properties = { - 'show_fig': True, - 'fname': 'HMS-HMS_MESA_grid_1e-04_Zsun_TF12_8mass_ratio_panels.png', - 'figsize': (9, 9), - 'log10_x': True, - 'log10_y': True, - 'xmin': 0.68, - 'xmax': 2.52, - 'ymin': -1.2, - 'ymax': 3.9, - 'wspace': 0.01, - 'hspace': 0.01, - 'colorbar': { - 'bounds': [0.03, 0.7, 0.94, 0.05] - }, - 'legend2D': { - 'title': 'Termination flags', - 'loc': 'lower left', - 'prop': { - 'size': 7 - }, - 'bbox_to_anchor': (0.0, 0.0), - }, - } - grid.plot2D('star_1_mass', 'period_days', None, - termination_flag='combined_TF12', - grid_3D=True, slice_3D_var_str='mass_ratio', - slice_3D_var_range=q_ranges, - grid_4D=True, slice_4D_var_str='Z_Zsun', - 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(An object of class BinaryStar defined in POSYDON) verbose : Boolean In case we want information about the CEE (the default is False). @@ -113,26 +113,25 @@ class StepCEE(object): verbose : bool If True, the messages will be prited in the console. - Keyword Arguments - ---------- + ----------------- prescription : str Prescription to use for computing the prediction of common enevelope evolution. Available options are: * 'alpha-lambda' : Considers the the alpha-lambda prescription - described in [1] and [2] to predict the outcome of the common envelope - evolution. If the profile of the donor star is available then it is - used to compute the value of lambda. + described in [1]_ and [2]_ to predict the outcome of the common + envelope evolution. If the profile of the donor star is available + then it is used to compute the value of lambda. References ---------- .. [1] Webbink, R. F. (1984). Double white dwarfs as progenitors of R - Coronae Borealis stars and Type I supernovae. The Astrophysical Journal, - 277, 355-360. + Coronae Borealis stars and Type I supernovae. The Astrophysical + Journal, 277, 355-360. .. [2] De Kool, M. (1990). Common envelope evolution and double cores of - planetary nebulae. The Astrophysical Journal, 358, 189-195. + planetary nebulae. The Astrophysical Journal, 358, 189-195. """ def __init__( @@ -451,6 +450,7 @@ def CEE_simple_alpha_prescription( In case we want information about the CEE. common_envelope_option_after_succ_CEE: str Options are: + 1) "core_replaced_noMT" he_core_mass/radius (or co_core_mass/radius for CEE of stripped_He*) are replaced according to the new core boundary diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index e8c13244d6..daefe76143 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -1998,14 +1998,14 @@ def diffeq( ): """Diff. equation describing the orbital evolution of a detached binary. - The equation handles wind mass-loss [1_], tidal [2_], gravational [3_] - effects and magnetic braking [4_]. It also handles the change of the - secondary's stellar spin due to its change of moment of intertia and due to - mass-loss from its spinning surface. It is assumed that the mass loss is - fully non-conservative. Magnetic braking is fully applied to secondary - stars with mass less than 1.3 Msun and fully off for stars with mass larger - then 1.5 Msun. The effect of magnetic braking falls linearly for stars with - mass between 1.3 Msun and 1.5 Msun. + The equation handles wind mass-loss [1]_, tidal [2]_, gravational [3]_ + effects and magnetic braking [4]_, [5]_, [6]_, [7]_, [8]_. It also handles + the change of the secondary's stellar spin due to its change of moment of + intertia and due to mass-loss from its spinning surface. It is assumed that + the mass loss is fully non-conservative. Magnetic braking is fully applied + to secondary stars with mass less than 1.3 Msun and fully off for stars + with mass larger then 1.5 Msun. The effect of magnetic braking falls + linearly for stars with mass between 1.3 Msun and 1.5 Msun. TODO: exaplin new features (e.g., double COs) @@ -2069,10 +2069,10 @@ def diffeq( Default: True. magnetic_braking_mode: String A string corresponding to the desired magnetic braking prescription. - -- RVJ83: Rappaport, Verbunt, & Joss 1983 - -- M15: Matt et al. 2015 - -- G18: Garraffo et al. 2018 - -- CARB: Van & Ivanova 2019 + - RVJ83 : Rappaport, Verbunt, & Joss 1983 [4]_ + - M15 : Matt et al. 2015 [5]_ + - G18 : Garraffo et al. 2018 [6]_ + - CARB : Van & Ivanova 2019 [7]_ do_stellar_evolution_and_spin_from_winds: Boolean If True, take into account change of star spin due to change of its moment of inertia during its evolution and due to spin angular momentum diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index f968eee821..31815743e0 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -99,11 +99,9 @@ class StepSN(object): Mechanism to perform the core-collapse on the star object and predict the supernova remnant outcome. Available options are: - * 'Fryer+12-rapid' : The rapid supernova-engine described in - [1] + * 'Fryer+12-rapid' : The rapid supernova-engine described in [1]_ - * 'Fryer+12-delayed' : The delayed supernova-engine described in - [1] + * 'Fryer+12-delayed' : The delayed supernova-engine described in [1]_ * 'direct' : The pre-supernova mass of the starr is collapsed into the remnant baryonic mass. @@ -111,13 +109,13 @@ class StepSN(object): * 'direct_he_core' : The pre-supernova He core mass of the starr is collapsed into the remnant baryonic mass. - * 'Sukhbold+16-engine' : Uses the results from [2] + * 'Sukhbold+16-engine' : Uses the results from [2]_ to describe the collapse of the star. - * 'Patton&Sukhbold20-engine': Uses the results from [5] + * 'Patton&Sukhbold20-engine': Uses the results from [5]_ to describe the collapse of the star. - * 'Couch+20-engine': Uses the results from [6] + * 'Couch+20-engine': Uses the results from [6]_ to describe the collapse of the star. engine : str @@ -131,8 +129,7 @@ class StepSN(object): Prescrition to take on the pair-instability supernova. Avialable options: - - 'Marchant+19' : Descripes the pair-instability supernova as - [3]. + - 'Marchant+19' : Descripes the pair-instability supernova as [3]_. mass_central_BH : double Central mass collapsed automatically on black-holes formed by direct @@ -160,7 +157,7 @@ class StepSN(object): Avialable options: - 'Tauris+15': Determines the electron capture supernova in terms - of the CO core mass at pre-supernova, taking the limits from [4]. + of the CO core mass at pre-supernova, taking the limits from [4]_. sigma_kick_CCSN_NS : double Standard deviation for a Maxwellian distribution to compute the @@ -209,28 +206,28 @@ class StepSN(object): References ---------- .. [1] Fryer, C. L., Belczynski, K., Wiktorowicz, G., Dominik, M., - Kalogera, V., & Holz, D. E. (2012). Compact remnant mass function: - dependence on the explosion mechanism and metallicity. - The Astrophysical Journal, 749(1), 91. + Kalogera, V., & Holz, D. E. (2012). Compact remnant mass function: + dependence on the explosion mechanism and metallicity. The + Astrophysical Journal, 749(1), 91. - .. [2] Sukhbold, T., Ertl, T., Woosley, S. E., Brown, J. M., & Janka, - H. T. (2016). Core-collapse supernovae from 9 to 120 solar masses based - on neutrino-powered explosions. The Astrophysical Journal, 821(1), 38. + .. [2] Sukhbold, T., Ertl, T., Woosley, S. E., Brown, J. M., & Janka, H. T. + (2016). Core-collapse supernovae from 9 to 120 solar masses based on + neutrino-powered explosions. The Astrophysical Journal, 821(1), 38. - .. [3] Marchant, P., Renzo, M., Farmer, R., Pappas, K. M., Taam, R. E., - De Mink, S. E., & Kalogera, V. (2019). Pulsational pair-instability - supernovae in very close binaries. The Astrophysical Journal, 882(1), 36. + .. [3] Marchant, P., Renzo, M., Farmer, R., Pappas, K. M., Taam, R. E., De + Mink, S. E., & Kalogera, V. (2019). Pulsational pair-instability + supernovae in very close binaries. The Astrophysical Journal, 882(1), 36. .. [4] Tauris, T. M., Langer, N., & Podsiadlowski, P. (2015). - Ultra-stripped supernovae: progenitors and fate. Monthly Notices of the - Royal Astronomical Society, 451(2), 2123-2144. + Ultra-stripped supernovae: progenitors and fate. Monthly Notices of the + Royal Astronomical Society, 451(2), 2123-2144. - ..[5] Patton, R. A. & Sukhbold, T. 2020, MNRAS, 499, 2803. Towards a - realistic explosion landscape for binary population synthesis + .. [5] Patton, R. A. & Sukhbold, T. 2020, MNRAS, 499, 2803. Towards a + realistic explosion landscape for binary population synthesis - ..[6] Couch, S. M., Warren, M. L., & O’Connor, E. P. 2020, ApJ, 890, 127. - Simulating Turbulence-aided Neutrino-driven Core-collapse Supernova - Explosions in One Dimension + .. [6] Couch, S. M., Warren, M. L., & O’Connor, E. P. 2020, ApJ, 890, 127. + Simulating Turbulence-aided Neutrino-driven Core-collapse Supernova + Explosions in One Dimension """ @@ -1158,9 +1155,10 @@ def compute_m_rembar(self, star, m_PISN): def orbital_kick(self, binary): """Do the orbital kick. - This function computes the supernova step of the binary object. It - checks which binary_state reached the core collapse flag, either CC1 or - CC2, and runs the step accordingly updating the binary object. + This function computes the supernova step of the binary object [1]_, + [2]_. It checks which binary_state reached the core collapse flag, + either CC1 or CC2, and runs the step accordingly updating the binary + object. Geometry: We work in a right-handed coordinate system. The collapsing helium star, @@ -1533,7 +1531,7 @@ def SNCheck( cos_theta, verbose, ): - """Check that the binary is not disrupted. + """Check that the binary is not disrupted [1]_, [2]_. Parameters ---------- @@ -1796,9 +1794,9 @@ def get_M4_mu4_Patton20(self, CO_core_mass, C_core_abundance): def Patton20_corecollapse(self, star, engine, conserve_hydrogen_envelope=False): """Compute supernova final remnant mass and fallback fraction. - It uses the results from [1]. The prediction for the core-collapse + It uses the results from [1]_. The prediction for the core-collapse outcome is performed using the C core mass and its C abundance. - The criterion by [2] is used to determine the final outcome. + The criterion by [2]_ is used to determine the final outcome. Parameters ---------- @@ -1816,8 +1814,8 @@ def Patton20_corecollapse(self, star, engine, conserve_hydrogen_envelope=False): ---------- .. [1] Patton, R. A., & Sukhbold, T. (2020). MNRAS, 499(2), 2803-2816. - .. [2] Ertl, T., Janka, H. T., Woosley, S. E., Sukhbold, T., - & Ugliano, M. (2016). ApJ, 818(2), 124. + .. [2] Ertl, T., Janka, H. T., Woosley, S. E., Sukhbold, T., & Ugliano, + M. (2016). ApJ, 818(2), 124. """ Ertl16_k_parameters = { @@ -1889,12 +1887,11 @@ class Sukhbold16_corecollapse(object): Parameters ---------- engine : string - Engine for the supernova explosion, from the one where used in - [1]. + Engine for the supernova explosion, from the one where used in [1]_. path_engine_dataset : string Path to the location of the data on initial and final states - for each engine described in Sukhbold et al. 2016 + for each engine described in [1]_ Returns ------- @@ -1907,9 +1904,9 @@ class Sukhbold16_corecollapse(object): References ---------- - .. [1] Sukhbold, T., Ertl, T., Woosley, S. E., Brown, J. M., & Janka, - H. T. (2016). Core-collapse supernovae from 9 to 120 solar masses based - on neutrino-powered explosions. The Astrophysical Journal, 821(1), 38. + .. [1] Sukhbold, T., Ertl, T., Woosley, S. E., Brown, J. M., & Janka, H. T. + (2016). Core-collapse supernovae from 9 to 120 solar masses based on + neutrino-powered explosions. The Astrophysical Journal, 821(1), 38. """ @@ -2074,12 +2071,11 @@ class Couch20_corecollapse(object): Parameters ---------- engine : string - Engine for the supernova explosion, from the one where used in - [1]. + Engine for the supernova explosion, from the one where used in [1]_. path_engine_dataset : string Path to the location of the data on initial and final states - for each engine described in Sukhbold et al. 2016 + for each engine described in [1]_ Returns ------- @@ -2090,15 +2086,19 @@ class Couch20_corecollapse(object): state : string Finall state of the stellar remnant after the supernova. + Notes + ----- + We need [2]_ data for their cores. + References ---------- - .. [1] Sukhbold, T., Ertl, T., Woosley, S. E., Brown, J. M., & Janka, - H. T. (2016). Core-collapse supernovae from 9 to 120 solar masses based - on neutrino-powered explosions. The Astrophysical Journal, 821(1), 38. - .. [2] Couch, S. M., Warren, M. L., & O’Connor, E. P. 2020, ApJ, 890, 127 - Simulating Turbulence-aided Neutrino-driven Core-collapse Supernova - Explosions in One Dimension + .. [1] Couch, S. M., Warren, M. L., & O’Connor, E. P. 2020, ApJ, 890, 127 + Simulating Turbulence-aided Neutrino-driven Core-collapse Supernova + Explosions in One Dimension + .. [2] Sukhbold, T., Ertl, T., Woosley, S. E., Brown, J. M., & Janka, H. T. + (2016). Core-collapse supernovae from 9 to 120 solar masses based on + neutrino-powered explosions. The Astrophysical Journal, 821(1), 38. """ diff --git a/posydon/config.py b/posydon/config.py new file mode 100644 index 0000000000..ff7f0d8cb9 --- /dev/null +++ b/posydon/config.py @@ -0,0 +1,8 @@ +import os +from dotenv import load_dotenv + +load_dotenv() + +# POSYDON environment variables +PATH_TO_POSYDON = os.getenv("PATH_TO_POSYDON") +PATH_TO_POSYDON_DATA = os.getenv("PATH_TO_POSYDON_DATA") \ No newline at end of file diff --git a/posydon/interpolation/profile_interpolation.py b/posydon/interpolation/profile_interpolation.py index 226d9cddbe..cc596f1d69 100644 --- a/posydon/interpolation/profile_interpolation.py +++ b/posydon/interpolation/profile_interpolation.py @@ -18,7 +18,7 @@ try: import tensorflow as tf except ImportError: - raise ImportError('tensorflow is not installed. Please run `pip install .[profile]` in the POSYDON base directory') + raise ImportError('tensorflow is not installed. Please run `pip install .[ml]` in the POSYDON base directory') tf.get_logger().setLevel('ERROR') from tensorflow.keras import layers, losses, models, optimizers from sklearn.decomposition import PCA diff --git a/posydon/popsyn/population_params_default.ini b/posydon/popsyn/population_params_default.ini index 7f070a8101..f9227ffca2 100644 --- a/posydon/popsyn/population_params_default.ini +++ b/posydon/popsyn/population_params_default.ini @@ -269,13 +269,13 @@ # > from mpi4py import MPI # > comm = MPI.COMM_WORLD - metallicity = [2., 1., 0.45, 0.2, 0.1, 0.01, 0.001, 0.0001] + metallicity = [0.0001] # [2., 1., 0.45, 0.2, 0.1, 0.01, 0.001, 0.0001] # In units of solar metallicity # Random Number Generation entropy = None # `None` uses system entropy (recommended) - number_of_binaries = 100 + number_of_binaries = 10 # int binary_fraction_scheme = 'const' #'const' 'Moe_17' @@ -354,6 +354,10 @@ 'interp_class_CO_HMS_RLO', 'interp_class_CO_HeMS', 'interp_class_CO_HeMS_RLO', + 'mt_history_HMS_HMS', + 'mt_history_CO_HMS_RLO', + 'mt_history_CO_HeMS', + 'mt_history_CO_HeMS_RLO', ] [SingleStar_1_output] diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index 7c5a94065d..54509ca2ca 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -113,7 +113,7 @@ def stefan_boltzmann_law(L, R): def rzams(m, z=0.02, Zsun=0.02): - """Evaluate the zero age main sequence radius. + """Evaluate the zero age main sequence radius [1]_. Parameters ---------- @@ -129,8 +129,8 @@ def rzams(m, z=0.02, Zsun=0.02): References ---------- - .. [1] Tout C. A., Pols O. R., Eggleton P. P., Han Z., 1996, - MNRAS, 281, 257 + .. [1] Tout C. A., Pols O. R., Eggleton P. P., Han Z., 1996, MNRAS, 281, + 257 """ @@ -212,7 +212,7 @@ def rzams(m, z=0.02, Zsun=0.02): def roche_lobe_radius(q, a_orb=1): - """Approximate the Roche lobe radius from Eggleton (1983). + """Approximate the Roche lobe radius from [1]_. Parameters ---------- @@ -421,7 +421,8 @@ def eddington_limit(binary, idx=-1): def beaming(binary): - """Calculate the geometrical beaming of a super-Eddington accreting source. + """Calculate the geometrical beaming of a super-Eddington accreting source + [1]_, [2]_. Compute the super-Eddington isotropic-equivalent accretion rate and the beaming factor of a star. This does not change the intrinsic accretion onto @@ -469,7 +470,7 @@ def beaming(binary): def bondi_hoyle(binary, accretor, donor, idx=-1, wind_disk_criteria=True, scheme='Hurley+2002'): - """Calculate the Bondi-Hoyle accretion rate of a binary. + """Calculate the Bondi-Hoyle accretion rate of a binary [1]_. Parameters ---------- @@ -479,6 +480,15 @@ def bondi_hoyle(binary, accretor, donor, idx=-1, wind_disk_criteria=True, The accretor in the binary. donor : SingleStar The donor in the binary. + idx : int + default: -1 + wind_disk_criteria : bool + default: True, see [5]_ + scheme : str + There are different options: + + - 'Hurley+2002' : following [3]_ + - 'Kudritzki+2000' : following [7]_ Returns ------- @@ -487,18 +497,18 @@ def bondi_hoyle(binary, accretor, donor, idx=-1, wind_disk_criteria=True, Notes ----- - A approximation is used for the accretion rate[2_] - and the wind velocity of the donor is moddeled as in [3_]. + An approximation is used for the accretion rate [2]_ and the wind velocity + of the donor is moddeled as in [3]_, [6]_. Also see [4]_. References ---------- .. [1] Bondi, H., & Hoyle, F. 1944, MNRAS, 104, 273 .. [2] Boffin, H. M. J., & Jorissen, A. 1988, A&A, 205, 155 .. [3] Hurley, J. R., Tout, C. A., & Pols, O. R. 2002, MNRAS, 329, 897 - .. [4] Belczynski, K., Kalogera, V., Rasio, F. A., et al. 2008, - ApJS, 174, 223 + .. [4] Belczynski, K., Kalogera, V., Rasio, F. A., et al. 2008, ApJS, 174, + 223 .. [5] Sen, K. ,Xu, X. -T., Langer, N., El Mellah, I. , Schurmann, C., & - Quast, M., 2021, A&A + Quast, M., 2021, A&A .. [6] Sander A. A. C., Vink J. S., 2020, MNRAS, 499, 873 .. [7] Kudritzki, R.-P., & Puls, J. 2000, ARA&A, 38, 613 @@ -813,7 +823,7 @@ def inspiral_timescale_from_separation(star1_mass, star2_mass, separation, eccentricity): """Compute the timescale of GW inspiral using the orbital separation. - Based on Peters 1964: + Based on [1]_: https://journals.aps.org/pr/abstract/10.1103/PhysRev.136.B1224 Parameters @@ -901,7 +911,7 @@ def inspiral_timescale_from_orbital_period(star1_mass, star2_mass, orbital_period, eccentricity): """Compute the timescale of GW inspiral using the orbital period. - Based on Peters 1964: + Based on [1]_: https://journals.aps.org/pr/abstract/10.1103/PhysRev.136.B1224 Parameters @@ -1902,7 +1912,7 @@ def calculate_core_boundary(donor_mass, def period_evol_wind_loss(M_current, M_init, Mcomp, P_init): - """Calculate analytically the period widening due to wind mass loss. + """Calculate analytically the period widening due to wind mass loss [1]_. Parameters ---------- @@ -1922,7 +1932,7 @@ def period_evol_wind_loss(M_current, M_init, Mcomp, P_init): References ---------- .. [1] Tauris, T. M., & van den Heuvel, E. 2006, Compact stellar X-ray - sources, 1, 623 + sources, 1, 623 """ log10P = (-2.*np.log10(M_current+Mcomp) @@ -1931,7 +1941,7 @@ def period_evol_wind_loss(M_current, M_init, Mcomp, P_init): def separation_evol_wind_loss(M_current, M_init, Mcomp, A_init): - """Calculate analytically the separation widening due to wind mass loss. + """Calculate analytically the separation widening due to wind mass loss [1]_. Parameters ---------- @@ -1951,7 +1961,7 @@ def separation_evol_wind_loss(M_current, M_init, Mcomp, A_init): References ---------- .. [1] Tauris, T. M., & van den Heuvel, E. 2006, Compact stellar X-ray - sources, 1, 623. + sources, 1, 623. """ log10A = (-np.log10(M_current+Mcomp) diff --git a/posydon/visualization/VH_diagram/GraphVisualizer.py b/posydon/visualization/VH_diagram/GraphVisualizer.py index 4145c11e28..00cb10828d 100644 --- a/posydon/visualization/VH_diagram/GraphVisualizer.py +++ b/posydon/visualization/VH_diagram/GraphVisualizer.py @@ -5,11 +5,14 @@ "Maxime Rambosson ", ] +try: + from PyQt5.QtWidgets import (QWidget, QFrame, QHBoxLayout, QVBoxLayout, + QLabel, QGridLayout) + from PyQt5.QtCore import Qt, QPoint, QSize + from PyQt5.QtGui import QPainter, QPixmap, QFont +except ImportError: + raise ImportError('PyQt5 is not installed. Please run `pip install .[vis]` in the POSYDON base directory') -from PyQt5.QtWidgets import (QWidget, QFrame, QHBoxLayout, QVBoxLayout, - QLabel, QGridLayout) -from PyQt5.QtCore import Qt, QPoint, QSize -from PyQt5.QtGui import QPainter, QPixmap, QFont from .MathTextLabel import MathTextLabel from dataclasses import dataclass diff --git a/posydon/visualization/VH_diagram/MainWindow.py b/posydon/visualization/VH_diagram/MainWindow.py index b57af2201f..8bd9f31e49 100644 --- a/posydon/visualization/VH_diagram/MainWindow.py +++ b/posydon/visualization/VH_diagram/MainWindow.py @@ -5,9 +5,11 @@ "Maxime Rambosson ", ] - -from PyQt5.QtWidgets import QMainWindow, QScrollArea, QApplication -from PyQt5.QtCore import pyqtSignal +try: + from PyQt5.QtWidgets import QMainWindow, QScrollArea, QApplication + from PyQt5.QtCore import pyqtSignal +except ImportError: + raise ImportError('PyQt5 is not installed. Please run `pip install .[vis]` in the POSYDON base directory') from .GraphVisualizer import GraphVisualizer from .OptionsWindow import OptionsWindow diff --git a/posydon/visualization/VH_diagram/MathTextLabel.py b/posydon/visualization/VH_diagram/MathTextLabel.py index e6d24a074d..da3c01e13a 100644 --- a/posydon/visualization/VH_diagram/MathTextLabel.py +++ b/posydon/visualization/VH_diagram/MathTextLabel.py @@ -5,10 +5,12 @@ "Maxime Rambosson ", ] - -from PyQt5 import QtGui, QtWidgets -from PyQt5.QtWidgets import QVBoxLayout -from PyQt5.QtCore import Qt +try: + from PyQt5 import QtGui, QtWidgets + from PyQt5.QtWidgets import QVBoxLayout + from PyQt5.QtCore import Qt +except ImportError: + raise ImportError('PyQt5 is not installed. Please run `pip install .[vis]` in the POSYDON base directory') import matplotlib as mpl from matplotlib.backends.backend_agg import FigureCanvasAgg diff --git a/posydon/visualization/VH_diagram/OptionsWindow.py b/posydon/visualization/VH_diagram/OptionsWindow.py index 7247da95f7..8911647b7d 100644 --- a/posydon/visualization/VH_diagram/OptionsWindow.py +++ b/posydon/visualization/VH_diagram/OptionsWindow.py @@ -5,10 +5,12 @@ "Maxime Rambosson ", ] - -from PyQt5.QtWidgets import (QApplication, QWidget, QVBoxLayout, - QCheckBox, QComboBox) -from PyQt5.QtCore import Qt, pyqtSignal +try: + from PyQt5.QtWidgets import (QApplication, QWidget, QVBoxLayout, + QCheckBox, QComboBox) + from PyQt5.QtCore import Qt, pyqtSignal +except ImportError: + raise ImportError('PyQt5 is not installed. Please run `pip install .[vis]` in the POSYDON base directory') from .PresenterMode import PresenterMode diff --git a/posydon/visualization/VH_diagram/PresenterMultiple.py b/posydon/visualization/VH_diagram/PresenterMultiple.py index 19a0c50f90..9fa70c0e84 100644 --- a/posydon/visualization/VH_diagram/PresenterMultiple.py +++ b/posydon/visualization/VH_diagram/PresenterMultiple.py @@ -2,8 +2,12 @@ from posydon.visualization.VH_diagram.Presenter import Presenter, PresenterMode, get_max_distance,CaseInfos,equal_with_epsilon,get_event_state_filename,file_exist,get_star_state_filename from posydon.visualization.VH_diagram.GraphVisualizer import columnTYPE,StateInfos,ConnectedItem, GraphVisualizerCase -from PyQt5.QtWidgets import QApplication -from PyQt5.QtCore import QTimer +try: + from PyQt5.QtWidgets import QApplication + from PyQt5.QtCore import QTimer +except ImportError: + raise ImportError('PyQt5 is not installed. Please run `pip install .[vis]` in the POSYDON base directory') + import matplotlib.pyplot as plt from IPython.display import Image, display from enum import Enum, auto diff --git a/posydon/visualization/plot_defaults.py b/posydon/visualization/plot_defaults.py index 8e540e7cde..6ed9d99647 100644 --- a/posydon/visualization/plot_defaults.py +++ b/posydon/visualization/plot_defaults.py @@ -18,8 +18,8 @@ ] -from matplotlib import checkdep_usetex - +from matplotlib import rcParams +import shutil PLOT_PROPERTIES = { 'show_fig': False, @@ -39,7 +39,7 @@ 'zmin': None, 'zmax': None, 'title': None, - 'rcParams': {"text.usetex": checkdep_usetex(True), + 'rcParams': {"text.usetex": True if shutil.which('latex') else False, "font.family": "serif", "font.sans-serif": ["Computer Modern Roman"]}, 'title_font_dict': {'fontsize': 10}, diff --git a/setup.py b/setup.py index 54d7467eac..8997887bd3 100644 --- a/setup.py +++ b/setup.py @@ -60,42 +60,52 @@ # These pretty common requirement are commented out. Various syntax types # are all used in the example below for specifying specific version of the # packages that are compatbile with your software. +# TODO NOTE: before the v2.0.0 code release, we should froze the versions +# the correct way to do this is to make sure that they are available on +# conda and pip for all platforms we support (see prerequisites doc page). install_requires = [ - 'numpy == 1.24.2', - 'scipy == 1.10.1', - 'iminuit == 2.21.3', - 'configparser == 5.3.0', - 'astropy == 5.2.2', - 'pandas == 2.0.0', - 'scikit-learn == 1.2.2', - 'matplotlib == 3.7.1', - 'matplotlib-label-lines == 0.5.2', - 'PyQt5 == 5.15.9', - 'h5py == 3.8.0', - 'psutil == 5.9.4', - 'tqdm == 4.65.0', - 'tables == 3.8.0', - 'progressbar2 == 4.2.0', - 'hurry.filesize == 0.9', + 'numpy >= 1.24.2', + 'scipy >= 1.10.1', + 'iminuit >= 2.21.3', + 'configparser >= 5.3.0', + 'astropy >= 5.2.2', + 'pandas >= 2.0.0', + 'scikit-learn < 1.3.0', # 1.2.2 + 'matplotlib >= 3.7.1', + 'matplotlib-label-lines >= 0.5.2', + 'h5py >= 3.8.0', + 'psutil >= 5.9.4', + 'tqdm >= 4.65.0', + 'tables >= 3.8.0', + 'progressbar2 >= 4.2.0', # for downloading data + 'hurry.filesize >= 0.9', + 'python-dotenv >= 1.0.0', ] tests_require = [ - "pytest == 7.3.1", + "pytest >= 7.3.1", "pytest-cov >= 4.0.0", ] # For documentation extras_require = { + # to build documentation "doc": [ "ipython", - "sphinx == 6.1.3", + "sphinx >= 6.1.3", "numpydoc", "sphinx_rtd_theme", "sphinxcontrib_programoutput", "PSphinxTheme", + "nbsphinx", + "pandoc", ], - "profile": ["tensorflow == 2.13.0"], # for profile interpolation - "hpc": ["mpi4py == 3.0.3"], + # for experimental visualization features, e.g. VDH diagrams + "vis": ["PyQt5 >= 5.15.9"], + # for profile macjhine learning features, e.g. profile interpolation + "ml": ["tensorflow >= 2.13.0"], + # for running population synthesis on HPC facilities + "hpc": ["mpi4py >= 3.0.3"], } # RUN SETUP From c728ee28c1b79bdad491370a871707b998ca4e55 Mon Sep 17 00:00:00 2001 From: Konstantinos Kovlakas <22239544+kkovlakas@users.noreply.github.com> Date: Wed, 25 Oct 2023 18:17:03 +0200 Subject: [PATCH 131/319] Update deploy-github-pages.yml to point to development --- .github/workflows/deploy-github-pages.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/deploy-github-pages.yml b/.github/workflows/deploy-github-pages.yml index b4115550d1..288e9def86 100644 --- a/.github/workflows/deploy-github-pages.yml +++ b/.github/workflows/deploy-github-pages.yml @@ -3,7 +3,7 @@ name: Website Deploy on: push: branches: - - main + - development jobs: build: @@ -12,7 +12,7 @@ jobs: strategy: fail-fast: false matrix: - python-version: [3.7] + python-version: [3.11] steps: - uses: actions/checkout@v2 From 848b4683d0a1e7c5579c9368903bd498583f99c7 Mon Sep 17 00:00:00 2001 From: Konstantinos Kovlakas <22239544+kkovlakas@users.noreply.github.com> Date: Wed, 25 Oct 2023 18:35:05 +0200 Subject: [PATCH 132/319] Update deploy-github-pages.yml --- .github/workflows/deploy-github-pages.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/deploy-github-pages.yml b/.github/workflows/deploy-github-pages.yml index 288e9def86..83802455d9 100644 --- a/.github/workflows/deploy-github-pages.yml +++ b/.github/workflows/deploy-github-pages.yml @@ -36,6 +36,7 @@ jobs: run: | sudo apt-get update sudo apt-get install gfortran swig libhdf5-serial-dev libmpich-dev + python -m pip install pandoc python -m pip install coverage cpp-coveralls flake8 pytest python -m pip install .[doc] python -m pip install .[ml] From 608308881bf79c0bfddc06534055847ae9e327f1 Mon Sep 17 00:00:00 2001 From: Konstantinos Kovlakas <22239544+kkovlakas@users.noreply.github.com> Date: Wed, 25 Oct 2023 19:15:55 +0200 Subject: [PATCH 133/319] Update deploy-github-pages.yml --- .github/workflows/deploy-github-pages.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/deploy-github-pages.yml b/.github/workflows/deploy-github-pages.yml index 83802455d9..1be6e9b17d 100644 --- a/.github/workflows/deploy-github-pages.yml +++ b/.github/workflows/deploy-github-pages.yml @@ -36,6 +36,7 @@ jobs: run: | sudo apt-get update sudo apt-get install gfortran swig libhdf5-serial-dev libmpich-dev + sudo apt-get install pandoc python -m pip install pandoc python -m pip install coverage cpp-coveralls flake8 pytest python -m pip install .[doc] From 265f341a14f1fd801315a577dd260635ba1dc8f4 Mon Sep 17 00:00:00 2001 From: Konstantinos Kovlakas <22239544+kkovlakas@users.noreply.github.com> Date: Wed, 25 Oct 2023 21:03:45 +0200 Subject: [PATCH 134/319] Update deploy-github-pages.yml --- .github/workflows/deploy-github-pages.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/deploy-github-pages.yml b/.github/workflows/deploy-github-pages.yml index 1be6e9b17d..567172e0aa 100644 --- a/.github/workflows/deploy-github-pages.yml +++ b/.github/workflows/deploy-github-pages.yml @@ -40,7 +40,6 @@ jobs: python -m pip install pandoc python -m pip install coverage cpp-coveralls flake8 pytest python -m pip install .[doc] - python -m pip install .[ml] - name: Make dependencies env: PATH_TO_POSYDON: /home/runner/work/POSYDON/POSYDON/ From 9154b04be1a7517fc6026f7d0f1a3b8b69968e88 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 26 Oct 2023 16:17:53 +0200 Subject: [PATCH 135/319] Using a child process to copy data from work to storage directory (#157) * Update posydon-setup-grid support work_dir * Update posydon-run-grid - new arguments job_start and job_end - function for moving/copying MESA output - get a child process, waiting to copy MESA output if the main process don't finish on time. * Update posydon-setup-grid Take SLURM information about start and end of a job as arguments to the job * Update posydon-run-grid transport arguments; change copy function * Update grid_params.ini new option work_dir in ini file --- bin/posydon-run-grid | 103 ++++++++++++++++++++++++++---------- bin/posydon-setup-grid | 4 ++ grid_params/grid_params.ini | 3 ++ 3 files changed, 82 insertions(+), 28 deletions(-) diff --git a/bin/posydon-run-grid b/bin/posydon-run-grid index 0d5ec30a8e..cbf0e9d9f9 100755 --- a/bin/posydon-run-grid +++ b/bin/posydon-run-grid @@ -11,6 +11,7 @@ import random import string import itertools import stat +import time import numpy as np import pandas @@ -123,10 +124,16 @@ def parse_commandline(): parser.add_argument("--keep_photos", action="store_true", default=False, help="Do not delete photos at run completion") + parser.add_argument("--job_start", type=int, default=0, + help="Start time of the job (in seconds)") + + parser.add_argument("--job_end", type=int, default=600, + help="End time of the job (in seconds)") + args = parser.parse_args() if args.grid_type not in ['fixed', 'dynamic']: - raise parser.error("--run-type must be either grid or sample") + raise parser.error("--run-type must be either fixed or dynamic") # if we are running a dynamic grid then these arguments are required otherwise # they are not @@ -397,6 +404,31 @@ def create_star_formation(mass, work_dir, star_inlist_project, initial_z=None, n return +def move_mesa_output(work_dir, final_dir, copyinstead=False): + """Moves the data from the working directory to the final directory. + Parameters: + work_dir: + Working directory (i.e. where the mesa exectuable should run) + final_dir: + When completed where would you like the output to go + copyinstead: + Indicates that the files should be copied instead of moved + """ + if not work_dir == final_dir: + if os.path.isdir(final_dir): + print ("folder "+ final_dir +" exists, replacing with new data") + sys.stdout.flush() + shutil.rmtree(final_dir) + if copyinstead: + print ("copy data from " + work_dir+ " to "+ final_dir) + shutil.copytree(work_dir, final_dir) + else: + print ("move data from " + work_dir+ " to "+ final_dir) + shutil.move(work_dir, final_dir) + sys.stdout.flush() + + return + def run_mesa(mesa_executable, work_dir, final_dir, **kwargs): """Provides minor utility for actually spawning the mesa job for each grid @@ -411,32 +443,47 @@ def run_mesa(mesa_executable, work_dir, final_dir, **kwargs): outfile_name = kwargs.pop('outfile_name', 'out.txt') keep_profiles = kwargs.pop('keep_profiles', False) keep_photos = kwargs.pop('keep_photos', False) + job_time = kwargs.pop('job_end', 600) - kwargs.pop('job_start', 0) os.chdir(work_dir) - os.system("{0} &> {1}".format(mesa_executable, os.path.join(work_dir, outfile_name))) - - if not keep_profiles: - if os.path.exists(os.path.join(work_dir, 'LOGS')): - os.chmod(os.path.join(work_dir, 'LOGS'), stat.S_IRWXU | stat.S_IRGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IXOTH ) - os.system("rm LOGS/profile*") - if os.path.exists(os.path.join(work_dir, 'LOGS1')): - os.chmod(os.path.join(work_dir, 'LOGS1'), stat.S_IRWXU | stat.S_IRGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IXOTH ) - os.system("rm LOGS1/profile*") - if os.path.exists(os.path.join(work_dir, 'LOGS2')): - os.chmod(os.path.join(work_dir, 'LOGS2'), stat.S_IRWXU | stat.S_IRGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IXOTH ) - os.system("rm LOGS2/profile*") - if os.path.exists(os.path.join(work_dir, '.mesa_temp_cache')): os.system("rm -rf .mesa_temp_cache") - - if not keep_photos: - os.system("rm -rf photos*") - if not work_dir == final_dir: - if os.path.isdir(final_dir): - print ("folder "+ final_dir +" exists, replacing with new data") - sys.stdout.flush() - shutil.rmtree(final_dir) - - shutil.move(work_dir, final_dir) + child_ID = os.fork() + else: + child_ID = 0.1 + if child_ID>0: + #parent process: run MESA and do the clean up in the working directory + os.system("{0} &> {1}".format(mesa_executable, os.path.join(work_dir, outfile_name))) + + if not keep_profiles: + if os.path.exists(os.path.join(work_dir, 'LOGS')): + os.chmod(os.path.join(work_dir, 'LOGS'), stat.S_IRWXU | stat.S_IRGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IXOTH ) + os.system("rm LOGS/profile*") + if os.path.exists(os.path.join(work_dir, 'LOGS1')): + os.chmod(os.path.join(work_dir, 'LOGS1'), stat.S_IRWXU | stat.S_IRGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IXOTH ) + os.system("rm LOGS1/profile*") + if os.path.exists(os.path.join(work_dir, 'LOGS2')): + os.chmod(os.path.join(work_dir, 'LOGS2'), stat.S_IRWXU | stat.S_IRGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IXOTH ) + os.system("rm LOGS2/profile*") + if os.path.exists(os.path.join(work_dir, '.mesa_temp_cache')): os.system("rm -rf .mesa_temp_cache") + + if not keep_photos: + os.system("rm -rf photos*") + + if child_ID>=1: + #kill child process, because it is no longer needed + os.kill(child_ID, 9) + move_mesa_output(work_dir, final_dir) + elif child_ID==0: + #child process: waits till short before the job will get killed by slurm to copy the data beforehand and exit this process after the copy finished + if job_time>300: + #if job last longer than 5 minutes (300 seconds) get remaining time till 5 minutes before its end + wait_time = job_time-300 + else: + #otherwise wait 1 minute + wait_time = 60 + time.sleep(wait_time) + move_mesa_output(work_dir, final_dir, copyinstead=True) + sys.exit(0) os.chdir(final_dir) print("Done") @@ -688,8 +735,8 @@ def run_grid_point(grid, star1_formation, star2_formation, # run the binary exectuable run_mesa(args.mesa_binary_executable, work_dir, final_dir, - keep_profiles=args.keep_profiles, - keep_photos=args.keep_photos) + keep_profiles=args.keep_profiles, keep_photos=args.keep_photos, + job_start=args.job_start, job_end=args.job_end) # find out what happened mesa_result = extract_mesa_results(final_dir) @@ -825,8 +872,8 @@ if __name__ == '__main__': create_star_formation(grid_param_dict['initial_mass'], work_dir, inlist_step) run_mesa(args.mesa_star1_executable, work_dir, final_dir, outfile_name='out_star1_formation_step{0}.txt'.format(index), - keep_profiles=args.keep_profiles, - keep_photos=args.keep_photos) + keep_profiles=args.keep_profiles, keep_photos=args.keep_photos, + job_start=args.job_start, job_end=args.job_end) sys.exit(0) diff --git a/bin/posydon-setup-grid b/bin/posydon-setup-grid index 20eafb9e6e..012fc0f1a1 100755 --- a/bin/posydon-setup-grid +++ b/bin/posydon-setup-grid @@ -1025,6 +1025,10 @@ if __name__ == '__main__': psycris_inifile = run_parameters["psycris_inifile"], keep_profiles=run_parameters['keep_profiles'], keep_photos=run_parameters['keep_photos']) + if args.submission_type == 'slurm': + command_line += ' --job_start $SLURM_JOB_START_TIME --job_end $SLURM_JOB_END_TIME' + if 'work_dir' in slurm.keys() and not(slurm['work_dir'] == ''): + command_line += ' --temporary-directory '+slurm['work_dir'] # now we need to know how this person plans to run the above created # command. As a shell script? As a SLURM submission? In some other way? diff --git a/grid_params/grid_params.ini b/grid_params/grid_params.ini index 47a7618f55..f96271dbba 100644 --- a/grid_params/grid_params.ini +++ b/grid_params/grid_params.ini @@ -26,6 +26,9 @@ account={ACCOUNT} ; wall-time walltime='2-00:00:00' +; work-directory: place, where MESA writes during runtime +work_dir={WORK_DIRECTORY} + ;email email={YOUR_EMAIL_ADDRESS} From bdd53ae48881a9e4acd7e1e364f98e094f1759a2 Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Thu, 26 Oct 2023 16:33:32 +0200 Subject: [PATCH 136/319] Concatenate fix for casting current scalar_names (#167) * add casting of scalar_names to float64 when writing to dataframe * remove cast in singlestar and create standard scalar_names casting in io.py * add scalar_names type casting * add missed typo --- posydon/popsyn/io.py | 31 +++++++++++++++++++++++++------ 1 file changed, 25 insertions(+), 6 deletions(-) diff --git a/posydon/popsyn/io.py b/posydon/popsyn/io.py index e0dedc3cca..cfb8af0f42 100644 --- a/posydon/popsyn/io.py +++ b/posydon/popsyn/io.py @@ -134,6 +134,15 @@ # no default extras for history attributes EXTRA_STAR_COLUMNS_DTYPES = {} +SCALAR_NAMES_DTYPES = { + 'natal_kick_array_0': 'float64', + 'natal_kick_array_1': 'float64', + 'natal_kick_array_2': 'float64', + 'natal_kick_array_3': 'float64', + 'SN_type':'string', + 'f_fb': 'float64', + 'spin_orbit_tilt': 'float64', +} def clean_binary_history_df(binary_df, extra_binary_dtypes_user=None, @@ -264,10 +273,11 @@ def clean_binary_oneline_df(oneline_df, extra_binary_dtypes_user=None, **extra_binary_dtypes_user} # combine columns for star 1 and star 2 history with extras SP_comb_S1_dict = {**STARPROPERTIES_DTYPES, **EXTRA_STAR_COLUMNS_DTYPES, - **extra_S1_dtypes_user} + **SCALAR_NAMES_DTYPES, **extra_S1_dtypes_user} SP_comb_S2_dict = {**STARPROPERTIES_DTYPES, **EXTRA_STAR_COLUMNS_DTYPES, - **extra_S2_dtypes_user} + **SCALAR_NAMES_DTYPES, **extra_S2_dtypes_user} + # All default & user passed keys which we have mappings for binary_keys = set( [key + '_i' for key in BP_comb_extras_dict.keys()] + [key + '_f' for key in BP_comb_extras_dict.keys()] ) @@ -275,17 +285,26 @@ def clean_binary_oneline_df(oneline_df, extra_binary_dtypes_user=None, + ['S1_' + key + '_f' for key in SP_comb_S1_dict.keys()] ) S2_keys = set( ['S2_' + key + '_i' for key in SP_comb_S2_dict.keys()] + ['S2_' + key + '_f' for key in SP_comb_S2_dict.keys()] ) - + scalar_keys = set( ['S1_' + key for key in SCALAR_NAMES_DTYPES.keys()] + + ['S2_' + key for key in SCALAR_NAMES_DTYPES.keys()] ) + # Find common keys between the binary_df and our column-dtype mapping - common_keys = oneline_columns & ( binary_keys | S1_keys | S2_keys ) + common_keys = oneline_columns & ( binary_keys + | S1_keys + | S2_keys + | scalar_keys) # Create a dict with column-dtype mapping only for columns in binary_df common_dtype_dict = {} # helper function to remove the '_i' and '_f' - strip_prefix_and_suffix = lambda key : key.strip('_i').strip('_f').strip('S1_').strip('S2_') + strip_prefix_and_suffix = lambda key : key.replace('_i', '').replace('_f', '') \ + .replace('S1_', '').replace('S2_', '') for key in common_keys: - if key in binary_keys: + if key in scalar_keys: + common_dtype_dict[key] = SCALAR_NAMES_DTYPES.get( + key.replace('S1_', '').replace('S2_', '') ) + elif key in binary_keys: common_dtype_dict[key] = BP_comb_extras_dict.get( strip_prefix_and_suffix(key) ) elif key in S1_keys: common_dtype_dict[key] = SP_comb_S1_dict.get( strip_prefix_and_suffix(key) ) From 42615c323f7760b105c136ae28f1cef0e29ddaf7 Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Thu, 26 Oct 2023 16:54:44 +0200 Subject: [PATCH 137/319] add requested tutorials (#169) --- .../population-synthesis/debug_pop.ipynb | 669 +++++++++++++++++- 1 file changed, 660 insertions(+), 9 deletions(-) diff --git a/docs/_source/tutorials-examples/population-synthesis/debug_pop.ipynb b/docs/_source/tutorials-examples/population-synthesis/debug_pop.ipynb index 2c56d9e9a5..d3de708c4c 100644 --- a/docs/_source/tutorials-examples/population-synthesis/debug_pop.ipynb +++ b/docs/_source/tutorials-examples/population-synthesis/debug_pop.ipynb @@ -45,17 +45,15 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 101, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 10/10 [00:23<00:00, 2.32s/it]\n", - "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/binarypopulation.py:571: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " return pd.concat(holder, axis=0, ignore_index=False)\n", - "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/binarypopulation.py:571: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + "100%|██████████| 10/10 [00:19<00:00, 1.92s/it]\n", + "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/binarypopulation.py:570: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", " return pd.concat(holder, axis=0, ignore_index=False)\n" ] } @@ -423,7 +421,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -1493,7 +1491,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -1502,14 +1500,14 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1/1 [00:02<00:00, 2.32s/it]\n" + "100%|██████████| 1/1 [00:01<00:00, 1.45s/it]\n" ] } ], @@ -1969,6 +1967,659 @@ "pop[i].to_df(extra_columns={'step_names':'string'})[col]" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evolving a Binary Star starting from an arbitrary state" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you want to evolve a custom binary star, you can do so by crafting yourself the `BinaryStar` object and providing the `SimulationProperties` object. For example, let's evolve a neutron star with a low mass helium star in roche lobe overflow. " + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "STEP NAME STEP FUNCTION KWARGS\n", + "flow (, {})\n", + "step_HMS_HMS (, {'interpolation_path': None, 'interpolation_filename': None, 'interpolation_method': 'nearest_neighbour', 'save_initial_conditions': True, 'track_interpolation': False, 'stop_method': 'stop_at_max_time', 'stop_star': 'star_1', 'stop_var_name': None, 'stop_value': None, 'stop_interpolate': True, 'verbose': False, 'metallicity': 0.0001})\n", + "step_CO_HeMS (, {'interpolation_path': None, 'interpolation_filename': None, 'interpolation_method': 'nearest_neighbour', 'save_initial_conditions': True, 'track_interpolation': False, 'stop_method': 'stop_at_max_time', 'stop_star': 'star_1', 'stop_var_name': None, 'stop_value': None, 'stop_interpolate': True, 'verbose': False, 'metallicity': 0.0001})\n", + "step_CO_HMS_RLO (, {'interpolation_path': None, 'interpolation_filename': None, 'interpolation_method': 'nearest_neighbour', 'save_initial_conditions': True, 'track_interpolation': False, 'stop_method': 'stop_at_max_time', 'stop_star': 'star_1', 'stop_var_name': None, 'stop_value': None, 'stop_interpolate': True, 'verbose': False, 'metallicity': 0.0001})\n", + "step_CO_HeMS_RLO (, {'interpolation_path': None, 'interpolation_filename': None, 'interpolation_method': 'nearest_neighbour', 'save_initial_conditions': True, 'track_interpolation': False, 'stop_method': 'stop_at_max_time', 'stop_star': 'star_1', 'stop_var_name': None, 'stop_value': None, 'stop_interpolate': True, 'verbose': False, 'metallicity': 0.0001})\n", + "step_detached (, {'matching_method': 'minimize', 'do_wind_loss': True, 'do_tides': True, 'do_gravitational_radiation': True, 'do_magnetic_braking': True, 'do_stellar_evolution_and_spin_from_winds': True, 'RLO_orbit_at_orbit_with_same_am': False, 'verbose': False, 'metallicity': 0.0001})\n", + "step_disrupted (, {'grid_name_Hrich': None, 'grid_name_strippedHe': None, 'metallicity': 0.0001, 'dt': None, 'n_o_steps_history': None, 'matching_method': 'minimize', 'initial_mass': None, 'rootm': None, 'verbose': False, 'do_wind_loss': True, 'do_tides': True, 'do_gravitational_radiation': True, 'do_magnetic_braking': True, 'magnetic_braking_mode': 'RVJ83', 'do_stellar_evolution_and_spin_from_winds': True, 'RLO_orbit_at_orbit_with_same_am': False, 'list_for_matching_HMS': None, 'list_for_matching_postMS': None, 'list_for_matching_HeStar': None})\n", + "step_merged (, {'metallicity': 0.0001})\n", + "step_CE (, {'prescription': 'alpha-lambda', 'common_envelope_efficiency': 1.0, 'common_envelope_option_for_lambda': 'lambda_from_grid_final_values', 'common_envelope_lambda_default': 0.5, 'common_envelope_option_for_HG_star': 'optimistic', 'common_envelope_alpha_thermal': 1.0, 'core_definition_H_fraction': 0.1, 'core_definition_He_fraction': 0.1, 'CEE_tolerance_err': 0.001, 'common_envelope_option_after_succ_CEE': 'core_not_replaced_noMT', 'verbose': False})\n", + "step_SN (, {'mechanism': 'Patton&Sukhbold20-engine', 'engine': 'N20', 'PISN': 'Marchant+19', 'ECSN': 'Podsiadlowksi+04', 'conserve_hydrogen_envelope': True, 'max_neutrino_mass_loss': 0.5, 'kick': True, 'kick_normalisation': 'one_over_mass', 'sigma_kick_CCSN_NS': 265.0, 'sigma_kick_CCSN_BH': 265.0, 'sigma_kick_ECSN': 20.0, 'max_NS_mass': 2.5, 'use_interp_values': True, 'use_profiles': True, 'use_core_masses': True, 'approx_at_he_depletion': False, 'verbose': False})\n", + "step_dco (, {'n_o_steps_interval': None})\n", + "step_end (, {})\n" + ] + }, + { + "data": { + "text/html": [ + "

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        NaN0.000000e+00initial_condRLO2oRLO21.0000000.000000NSstripped_He_Core_He_burning1.0000002.500000
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        NaN4.607133e+03step_SNdetached<NA>2.6936580.611337NSNS1.0001431.260782
        NaN1.380000e+10step_dcodetachedmaxtime2.4669400.592404NSNS1.0001431.260782
        NaN1.380000e+10step_enddetachedEND2.4669400.592404NSNS1.0001431.260782
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        " + ], + "text/plain": [ + " time step_names state event \\\n", + "binary_index \n", + "NaN 0.000000e+00 initial_cond RLO2 oRLO2 \n", + "NaN 0.000000e+00 step_CO_HeMS_RLO RLO2 \n", + "NaN 4.607133e+03 RLO2 CC2 \n", + "NaN 4.607133e+03 step_SN detached \n", + "NaN 1.380000e+10 step_dco detached maxtime \n", + "NaN 1.380000e+10 step_end detached END \n", + "\n", + " orbital_period eccentricity S1_state \\\n", + "binary_index \n", + "NaN 1.000000 0.000000 NS \n", + "NaN 0.903996 0.000000 NS \n", + "NaN 0.526980 0.000000 NS \n", + "NaN 2.693658 0.611337 NS \n", + "NaN 2.466940 0.592404 NS \n", + "NaN 2.466940 0.592404 NS \n", + "\n", + " S2_state S1_mass S2_mass \n", + "binary_index \n", + "NaN stripped_He_Core_He_burning 1.000000 2.500000 \n", + "NaN stripped_He_Central_He_depleted 1.000000 2.873753 \n", + "NaN stripped_He_Central_C_depletion 1.000143 2.467002 \n", + "NaN NS 1.000143 1.260782 \n", + "NaN NS 1.000143 1.260782 \n", + "NaN NS 1.000143 1.260782 " + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from posydon.binary_evol.singlestar import SingleStar\n", + "from posydon.binary_evol.binarystar import BinaryStar\n", + "sim_prop.load_steps(verbose=True) # load steps\n", + "binary = BinaryStar(star_1 = SingleStar(**{'state' : 'NS',\n", + " 'mass' : 1.,\n", + " 'spin' : 0.,}),\n", + " star_2 = SingleStar(**{'state' : 'stripped_He_Core_He_burning',\n", + " 'mass' : 2.5,\n", + " 'natal_kick_array' : [10., 0., 0., 0.]}),\n", + " **{'time' : 0.,\n", + " 'state' : 'RLO2',\n", + " 'event' : 'oRLO2',\n", + " 'orbital_period' : 1.,\n", + " 'eccentricity' : 0.},\n", + " properties = sim_prop,\n", + " )\n", + "binary.evolve()\n", + "binary.to_df(extra_columns={'step_names':'string'})[col]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Mapping a MESA step to the `detached_step`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You might want to use the `detached_step` instetad of a MESA step. Here is how you do it. As an example we replace the HMS-HMS MESA step with the detached step. Notice that the `detached_step` does not evolve the binary system post onset of RLO1. Hence, unless the `flow_chart` support such binary event, binary state and stellar states, else these systems will not be furthter evovlved." + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 10/10 [00:07<00:00, 1.43it/s]\n" + ] + } + ], + "source": [ + "# MODIFIED FLOW CHART CONFIGURATION\n", + "sim_kwargs = dict(\n", + " flow = (flow_chart, {}),\n", + " step_HMS_HMS = (detached_step, DETACHED_STEP), # <-- map this step to detached\n", + " step_CO_HeMS = (CO_HeMS_step, MESA_STEP),\n", + " step_CO_HMS_RLO = (CO_HMS_RLO_step, MESA_STEP),\n", + " step_CO_HeMS_RLO = (CO_HeMS_RLO_step, MESA_STEP),\n", + " step_detached = (detached_step, DETACHED_STEP),\n", + " step_disrupted = (DisruptedStep, DISRUPTED_STEP),\n", + " step_merged = (MergedStep, {}),\n", + " step_CE = (StepCEE, CE_STEP),\n", + " step_SN = (StepSN, SN_STEP),\n", + " step_dco = (DoubleCO, DCO_STEP),\n", + " step_end = (step_end, END_STEP),\n", + " extra_hooks = [(StepNamesHooks, {})]\n", + ")\n", + "\n", + "sim_prop = SimulationProperties(**sim_kwargs)\n", + "\n", + "del pop\n", + "pop = run_simulation(sim_prop, kwargs, use_MPI=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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        timestep_namesstateeventorbital_periodeccentricityS1_stateS2_stateS1_massS2_mass
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        00.000000e+00initial_conddetachedZAMS90.1973260.0H-rich_Core_H_burningH-rich_Core_H_burning7.6020081.125924
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        " + ], + "text/plain": [ + " time step_names state event orbital_period \\\n", + "binary_index \n", + "0 0.000000e+00 initial_cond detached ZAMS 90.197326 \n", + "0 4.228255e+07 step_HMS_HMS RLO1 oRLO1 90.000025 \n", + "0 4.228255e+07 RLO1 END 90.000025 \n", + "\n", + " eccentricity S1_state S2_state \\\n", + "binary_index \n", + "0 0.0 H-rich_Core_H_burning H-rich_Core_H_burning \n", + "0 0.0 H-rich_Central_He_depleted H-rich_Core_H_burning \n", + "0 0.0 H-rich_Central_He_depleted H-rich_Core_H_burning \n", + "\n", + " S1_mass S2_mass \n", + "binary_index \n", + "0 7.602008 1.125924 \n", + "0 7.601730 1.125920 \n", + "0 7.601730 1.125920 " + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "i = 0\n", + "pop[i].to_df(extra_columns={'step_names':'string'})[col]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Crating your own step" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, you learn how to create your own step. As an example we create a step that does a fake common envelope by taking half the orbital period." + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 10/10 [00:17<00:00, 1.71s/it]\n", + "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/binarypopulation.py:570: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " return pd.concat(holder, axis=0, ignore_index=False)\n" + ] + } + ], + "source": [ + "class my_CE_step(object):\n", + " \"\"\"Compute a fake CE event.\"\"\"\n", + "\n", + " def __init__(self, verbose=False):\n", + " self.verbose = verbose\n", + "\n", + " def __call__(self, binary):\n", + "\n", + " if self.verbose:\n", + " print('The orbital separation post CE is half the pre CE orbital separation!')\n", + "\n", + " # Determine which star is the donor and which is the companion\n", + " if binary.event in [\"oCE1\", \"oDoubleCE1\"]:\n", + " donor_star = binary.star_1\n", + " comp_star = binary.star_2\n", + " elif binary.event in [\"oCE2\", \"oDoubleCE2\"]:\n", + " donor_star = binary.star_2\n", + " comp_star = binary.star_1\n", + " else:\n", + " raise ValueError(\"CEE does not apply if `event` is not \"\n", + " \"`oCE1`, 'oDoubleCE1' or `oCE2`, 'oDoubleCE1'\")\n", + "\n", + " binary.orbital_period /= 2.\n", + " donor_star.mass = donor_star.he_core_mass # lose envelope\n", + " donor_star.state = donor_star.state.replace('H-rich', 'stripped_He')\n", + " binary.state = 'detached'\n", + " binary.event = None\n", + " \n", + " \n", + "# MODIFIED FLOW CHART CONFIGURATION\n", + "sim_kwargs = dict(\n", + " flow = (flow_chart, {}),\n", + " step_HMS_HMS = (MS_MS_step, MESA_STEP),\n", + " step_CO_HeMS = (CO_HeMS_step, MESA_STEP),\n", + " step_CO_HMS_RLO = (CO_HMS_RLO_step, MESA_STEP),\n", + " step_CO_HeMS_RLO = (CO_HeMS_RLO_step, MESA_STEP),\n", + " step_detached = (detached_step, DETACHED_STEP),\n", + " step_disrupted = (DisruptedStep, DISRUPTED_STEP),\n", + " step_merged = (MergedStep, {}),\n", + " step_CE = (my_CE_step, {}), # <-- map this step to my fake CE step\n", + " step_SN = (StepSN, SN_STEP),\n", + " step_dco = (DoubleCO, DCO_STEP),\n", + " step_end = (step_end, END_STEP),\n", + " extra_hooks = [(StepNamesHooks, {})]\n", + ")\n", + "\n", + "sim_prop = SimulationProperties(**sim_kwargs)\n", + "\n", + "del pop\n", + "pop = run_simulation(sim_prop, kwargs, use_MPI=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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        \n", + "
        " + ], + "text/plain": [ + " time step_names state event orbital_period \\\n", + "binary_index \n", + "0 0.000000e+00 initial_cond detached ZAMS 168.143381 \n", + "0 5.772023e+02 step_HMS_HMS detached 193.069773 \n", + "0 4.004879e+07 RLO1 oCE1 144.350914 \n", + "0 4.004879e+07 step_CE detached 72.175457 \n", + "0 4.004881e+07 step_detached RLO1 oRLO1 234.884263 \n", + "0 4.004881e+07 RLO1 END 234.884263 \n", + "\n", + " eccentricity S1_state \\\n", + "binary_index \n", + "0 0.0 H-rich_Core_H_burning \n", + "0 0.0 H-rich_Core_H_burning \n", + "0 0.0 H-rich_Central_He_depleted \n", + "0 0.0 stripped_He_Central_He_depleted \n", + "0 0.0 stripped_He_Central_He_depleted \n", + "0 0.0 stripped_He_Central_He_depleted \n", + "\n", + " S2_state S1_mass S2_mass \n", + "binary_index \n", + "0 H-rich_Core_H_burning 7.974985 1.261677 \n", + "0 H-rich_Core_H_burning 7.860837 1.179126 \n", + "0 H-rich_Core_H_burning 7.719091 1.188623 \n", + "0 H-rich_Core_H_burning 2.734596 1.188623 \n", + "0 H-rich_Core_H_burning 1.921067 1.254592 \n", + "0 H-rich_Core_H_burning 1.921067 1.254592 " + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "i = 0\n", + "pop[i].to_df(extra_columns={'step_names':'string'})[col]" + ] + }, { "cell_type": "code", "execution_count": null, From c963873b2562b16099fa01909f878dc81b52667f Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 9 Nov 2023 16:54:04 +0100 Subject: [PATCH 138/319] rewrite PSyGrid.rerun to use pandas instead of csv module; some enhancements on the function (#176) --- posydon/grids/psygrid.py | 215 ++++++++++++++++++--------------------- 1 file changed, 99 insertions(+), 116 deletions(-) diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index 59963e6bde..f686cea0b7 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -184,7 +184,6 @@ import json import ast import warnings -import csv import h5py import numpy as np import pandas as pd @@ -1525,7 +1524,8 @@ def __del__(self): self.close() def rerun(self, path_to_file='./', runs_to_rerun=None, - termination_flags=None, new_mesa_flag=None): + termination_flags=None, new_mesa_flag=None, + flags_to_check=None): """Create a CSV file with the PSyGrid initial values to rerun. This methods allows you to create a CSV file with the psygrid initial @@ -1536,135 +1536,118 @@ def rerun(self, path_to_file='./', runs_to_rerun=None, path_to_file : str The path to the directory where the new `grid.csv` file will be saved. If the directory does not exist it will be created. - runs_to_rerun : integer array + runs_to_rerun : list of integers Array containing the indecies of the psygrid runs you want to rerun - e.g., runs_to_rerun = np.array([2,3]) - termination_flags : str + e.g., runs_to_rerun = [2,3] + termination_flags : str or list of str The runs with this termination flag will be rerun. e.g. termination_flags='max_number_retries' new_mesa_flag : dict Dictionary of flags with their value to add as extra columns to the `grid.csv`. The user can specify any arbitrary amount of flags. e.g. new_mesa_flag = {'varcontrol_target': 0.01} + flags_to_check : str or list of str + The key(s) of flags to check the termination_flags against. + e.g. check_flags = 'termination_flag_1' """ - # check that the `path_to_file` exists + # check that the 'path_to_file' exists if not os.path.exists(path_to_file): os.makedirs(path_to_file) - - if runs_to_rerun is not None and termination_flags is None: - n_runs_to_rerun = len(runs_to_rerun) - - # find the key of initial values to save - initial_values = {} - for key in self.initial_values.dtype.names: - initial_values[key] = [] - - # find the value of initial values to save - for i in runs_to_rerun.tolist(): - for key in initial_values: - initial_values[key].append(self.initial_values[key][i]) - - # replace star_1_mass, star_2_mass, period_days, Z - NDIG = 10 # rounding matches initial point rounding - if 'star_1_mass' in self.initial_values.dtype.names: - initial_values['m1'] = np.around(initial_values['star_1_mass'], - NDIG) - if 'star_2_mass' in self.initial_values.dtype.names: - initial_values['m2'] = np.around(initial_values['star_2_mass'], - NDIG) - if 'period_days' in self.initial_values.dtype.names: - initial_values['initial_period_in_days'] = np.around( - initial_values['period_days'],NDIG) - MESA_dir_name = self.MESA_dirs[0].decode("utf-8") - if 'initial_z' in MESA_dir_name: - initial_values['initial_z'] = np.around(initial_values['Z'], - NDIG) - if 'Zbase' in MESA_dir_name: - initial_values['Zbase'] = np.around(initial_values['Z'], NDIG) - if 'new_Z' in MESA_dir_name: - initial_values['new_Z'] = np.around(initial_values['Z'], NDIG) - for key in self.initial_values.dtype.names: - if key not in ['m1', 'm2', 'initial_period_in_days', - 'Zbase', 'new_Z', 'initial_z']: - del initial_values[key] - - # add new_mesa_flag - if new_mesa_flag is not None: - for key in new_mesa_flag.keys(): - initial_values[key] = [new_mesa_flag[key]]*n_runs_to_rerun - - # create the CSV file - with open(os.path.join(path_to_file,'grid.csv'), 'w', newline='') as file: - writer = csv.writer(file) - writer.writerow(initial_values.keys()) - for i in range(n_runs_to_rerun): - writer.writerow( - [initial_values[key][i]for key in initial_values]) - elif termination_flags is not None and runs_to_rerun is None: + + # check 'runs_to_rerun' has a valid type + if isinstance(runs_to_rerun, list): + runs_to_rerun_list = runs_to_rerun + elif runs_to_rerun is not None: + try: + runs_to_rerun_list = list(runs_to_rerun) + except: + raise TypeError("'runs_to_rerun' should be a list or None.") + + # check termination flags + if termination_flags is not None: + if flags_to_check is None: + flags_to_check_list = ['termination_flag_1'] + elif isinstance(flags_to_check, str): + flags_to_check_list = [flags_to_check] + elif isinstance(flags_to_check, list): + flags_to_check_list = flags_to_check + else: + try: + flags_to_check_list = list(flags_to_check) + except: + raise TypeError("'flags_to_check' should be a string or a " + "list of strings.") if isinstance(termination_flags, str): - rerun_flags = [termination_flags] + termination_flags_list = [termination_flags] + elif isinstance(termination_flags, list): + termination_flags_list = termination_flags else: try: - rerun_flags = list(termination_flags) - except TypeError as err: - msg = "`termination_flags` should be a str of a list." - raise ValueError(msg) from err - - # find the key of initial values to save - initial_values = {} - for key in self.initial_values.dtype.names: - initial_values[key] = [] - - # find the value of initial values to save - n_runs_to_rerun = 0 - n = self.initial_values.shape[0] - for i in range(n): - # this should be accessed with key when possible - if self.final_values[i]['termination_flag_1'] in rerun_flags: - n_runs_to_rerun += 1 - for key in initial_values: - initial_values[key].append(self.initial_values[key][i]) - - # replace star_1_mass, star_2_mass, period_days, Z - NDIG = 10 # rounding matches initial point rounding - if 'star_1_mass' in self.initial_values.dtype.names: - initial_values['m1'] = np.around(initial_values['star_1_mass'], - NDIG) - if 'star_2_mass' in self.initial_values.dtype.names: - initial_values['m2'] = np.around(initial_values['star_2_mass'], - NDIG) - if 'period_days' in self.initial_values.dtype.names: - initial_values['initial_period_in_days'] = np.around( - initial_values['period_days'],NDIG) - MESA_dir_name = self.MESA_dirs[0].decode("utf-8") - if 'initial_z' in MESA_dir_name: - initial_values['initial_z'] = np.around(initial_values['Z'], - NDIG) - if 'Zbase' in MESA_dir_name: - initial_values['Zbase'] = np.around(initial_values['Z'], NDIG) - if 'new_Z' in MESA_dir_name: - initial_values['new_Z'] = np.around(initial_values['Z'], NDIG) - for key in self.initial_values.dtype.names: - if key not in ['m1', 'm2', 'initial_period_in_days', 'Zbase', - 'new_Z', 'initial_z']: - del initial_values[key] - - # add new_mesa_flag - if new_mesa_flag is not None: - for key in new_mesa_flag.keys(): - initial_values[key] = [new_mesa_flag[key]]*n_runs_to_rerun - - # create the CSV file - with open(os.path.join(path_to_file,'grid.csv'), 'w', newline='') as file: - writer = csv.writer(file) - writer.writerow(initial_values.keys()) - for i in range(n_runs_to_rerun): - writer.writerow( - [initial_values[key][i]for key in initial_values]) + termination_flags_list = list(termination_flags) + except: + raise TypeError("'termination_flags' should be a string or" + " a list of strings.") + # getting indices of runs with the termination flag(s) + new_runs_to_rerun_list = [] + for flag in flags_to_check_list: + if flag in self.final_values.dtype.names: + for i, tf in enumerate(self.final_values[flag]): + if tf in termination_flags_list: + new_runs_to_rerun_list.append(i) + else: + self._say("\tFlag: {} not found. Skip it.".format(flag)) + # add runs with the termination flag(s) to the collection of reruns + if runs_to_rerun is None: + runs_to_rerun_list = new_runs_to_rerun_list + else: + runs_to_rerun_list += new_runs_to_rerun_list + elif runs_to_rerun is None: + raise ValueError("Either 'runs_to_rerun' or 'termination_flags' " + "has to be specified and therefore different from" + " None.") + # ensure that each index only appears once + runs_to_rerun_list_unique = list(dict.fromkeys(runs_to_rerun_list)) + # getting columns for the new grid.csv file + column_names = {} + if 'star_1_mass' in self.initial_values.dtype.names: + column_names['m1'] = 'star_1_mass' + if 'star_2_mass' in self.initial_values.dtype.names: + column_names['m2'] = 'star_2_mass' + if 'period_days' in self.initial_values.dtype.names: + column_names['initial_period_in_days'] = 'period_days' + if 'Z' in self.initial_values.dtype.names: + if len(self.MESA_dirs)>0: + # assume first entry of the MESA_dirs is representative of the + # grid + MESA_dir_name = self.MESA_dirs[0].decode("utf-8") + if 'initial_z' in MESA_dir_name: + column_names['initial_z'] = 'Z' + if 'Zbase' in MESA_dir_name: + column_names['Zbase'] = 'Z' + if 'new_Z' in MESA_dir_name: + column_names['new_Z'] = 'Z' + else: + raise Exception("No MESA dirs of previous runs in the grid.") + # getting the initial data set + NDIG = 10 # rounding matches initial point rounding + runs_data = {} + for key in column_names.keys(): + grid_key = column_names[key] + runs_data[key] = [] + for idx in runs_to_rerun_list_unique: + runs_data[key].append(np.around(self.initial_values[grid_key][idx], NDIG)) + if len(column_names)>0: + n_runs = len(runs_data[list(column_names)[0]]) else: - raise ValueError("Choose either the runs manually, or " - "indicate the termination flag(s).") + n_runs = 0 + # add new_mesa_flag + if new_mesa_flag is not None: + for key in new_mesa_flag.keys(): + runs_data[key] = [new_mesa_flag[key]]*n_runs + runs_data_frame = pd.DataFrame(runs_data) + runs_data_frame.to_csv(os.path.join(path_to_file, 'grid.csv'), + na_rep='nan', index=False) def plot2D(self, x_var_str, y_var_str, z_var_str=None, termination_flag='termination_flag_1', From 0d5009ce5d63af0240fbf4930f9074b1e91d042a Mon Sep 17 00:00:00 2001 From: Konstantinos Kovlakas <22239544+kkovlakas@users.noreply.github.com> Date: Thu, 9 Nov 2023 16:58:30 +0100 Subject: [PATCH 139/319] Minor edits on documentation (#182) * Correcting spelling * Correction of phrasing on debugging binaries by keeping them in memory. * Correcting spelling of POSYDON --- .../tutorials-examples/population-synthesis/debug_pop.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/_source/tutorials-examples/population-synthesis/debug_pop.ipynb b/docs/_source/tutorials-examples/population-synthesis/debug_pop.ipynb index d3de708c4c..96b86ba012 100644 --- a/docs/_source/tutorials-examples/population-synthesis/debug_pop.ipynb +++ b/docs/_source/tutorials-examples/population-synthesis/debug_pop.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Debugging POSYON Binary Population Synthesis 🐞" + "# Debugging POSYDON Binary Population Synthesis 🐞" ] }, { @@ -38,9 +38,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To debug the evolution of a binary systems you should load the POSYDON BinaryPopulation object in mameory and then call the `restore` and `evolve`. This will run the evolution of the binary system and display any error traceback. You can debug the code by setting breakpoints in the POSYDON code until you devug the specific binary evolution.\n", + "To debug the evolution of a binary systems you should load the POSYDON BinaryPopulation object in memory and then call the `restore` and `evolve`. This will run the evolution of the binary system and display any error traceback. You can debug the code by setting breakpoints in the POSYDON code until you devug the specific binary evolution.\n", "\n", - "The trick is to do this is not dump the binary population to disk but to keep them in memory. This is done by setting the `optimize_ram=False` and `use_MPI=False`." + "The trick is to keep the binary population in memory instead of dumping it to the disk. This is done by setting `optimize_ram=False` and `use_MPI=False`." ] }, { From f4fd69b9389810004996f4af356b8c3125ce4c16 Mon Sep 17 00:00:00 2001 From: Konstantinos Kovlakas <22239544+kkovlakas@users.noreply.github.com> Date: Thu, 16 Nov 2023 16:24:44 +0100 Subject: [PATCH 140/319] Not printing metallicity value if verbose is False (#180) * Adding verbose check for the printing of metallicity when running the population. * Completing the boolean logic. * Revert "Completing the boolean logic." This reverts commit efecec2e7c22995c5409757e6d4468f940f9e5f1. --- posydon/popsyn/synthetic_population.py | 67 +++++++++++++------------- 1 file changed, 34 insertions(+), 33 deletions(-) diff --git a/posydon/popsyn/synthetic_population.py b/posydon/popsyn/synthetic_population.py index fd4f423423..bf8ee794b9 100644 --- a/posydon/popsyn/synthetic_population.py +++ b/posydon/popsyn/synthetic_population.py @@ -88,7 +88,8 @@ def evolve(self): """Evolve population(s) at given Z(s).""" while self.binary_populations: pop = self.binary_populations.pop() - print( f'Z={pop.kwargs["metallicity"]:.2e} Z_sun' ) + if self.verbose: + print(f'Z={pop.kwargs["metallicity"]:.2e} Z_sun') pop.evolve() met_prefix = f'{pop.kwargs["metallicity"]:.2e}_Zsun_' pop.save( met_prefix + 'population.h5' ) @@ -247,7 +248,7 @@ def parse(self, S1_state=None, S2_state=None, binary_state=None, del df_tmp if k > 0 and df_sel.shape[0] > 0: - shift_index = max(np.unique(df_sel.index)) + 1 + shift_index = max(np.unique(df_sel.index)) + 1 df_sel_met.index += shift_index # store simulated and underlying stellar mass @@ -266,7 +267,7 @@ def parse(self, S1_state=None, S2_state=None, binary_state=None, if k > 0 and df_sel_oneline.shape[0] > 0: shift_index = max(np.unique(df_sel_oneline.index)) + 1 - df_sel_met_oneline.index += shift_index + df_sel_met_oneline.index += shift_index df_sel_oneline = pd.concat([df_sel_oneline, df_sel_met_oneline]) @@ -340,13 +341,13 @@ def get_dco_at_formation(self, S1_state, S2_state, oneline_cols=None, formation_ mt_history : bool If `True`, split the event oRLO1/oRLO2 into oRLO1-contact/oRLO2-contact, oRLO1-reverse/oRLO2-reverse and oRLO1/oRLO2. This is useful to - identify binaries undergoing contact stable mass-transfer phases and + identify binaries undergoing contact stable mass-transfer phases and reverse mass-transfer phase . """ # compute GRB properties boolean - compute_GRB_properties = (self.MODEL is not None and - "compute_GRB_properties" in self.MODEL and + compute_GRB_properties = (self.MODEL is not None and + "compute_GRB_properties" in self.MODEL and self.MODEL["compute_GRB_properties"]) # add channel column to oneline dataframe @@ -373,7 +374,7 @@ def get_dco_at_formation(self, S1_state, S2_state, oneline_cols=None, formation_ # when inspecting the data frame. self.df_synthetic['t_delay'] = (time_contact - self.df_synthetic[['time']])*1e-6 # Myr self.df_synthetic['time'] *= 1e-6 # Myr - + # add properties of the oneline dataframe if self.df_oneline is not None: # TODO: add kicks as well by default? @@ -411,7 +412,7 @@ def get_dco_at_formation(self, S1_state, S2_state, oneline_cols=None, formation_ ) # get time_CC1 and time_CC2 note that for GRB calculations we # do not use the "time" column indicating the time of formation - # of the DCO systems as there might be cases where + # of the DCO systems as there might be cases where # CC2 might happen before CC1 due to mass ratio reversal DCO = [[S1_state, None], [None, S2_state]] events = ['CC1', 'CC2'] @@ -551,9 +552,9 @@ def compute_cosmological_weights(self, sensitivity, flag_pdet, working_dir, load return index_1, z_formation_1, z_grb_1, w_ijk_1, index_2, z_formation_2, z_grb_2, w_ijk_2 else: - raise ValueError('Population not recognized!') + raise ValueError('Population not recognized!') - def resample_synthetic_population(self, index, z_formation, z_event, w_ijk, export_cols=None, + def resample_synthetic_population(self, index, z_formation, z_event, w_ijk, export_cols=None, pop='DCO', reset_grb_properties=None): """Resample synthetc population to obtain intrinsic/observable population. @@ -580,8 +581,8 @@ def resample_synthetic_population(self, index, z_formation, z_event, w_ijk, expo """ # compute GRB properties boolean - compute_GRB_properties = (self.MODEL is not None and - "compute_GRB_properties" in self.MODEL and + compute_GRB_properties = (self.MODEL is not None and + "compute_GRB_properties" in self.MODEL and self.MODEL["compute_GRB_properties"]) # drop all zero weights to save memory @@ -616,7 +617,7 @@ def resample_synthetic_population(self, index, z_formation, z_event, w_ijk, expo save_cols.append(c) for c in save_cols: df[c] = self.rates.get_data(c, index) - + # the same binary system can emit two GRBs, we remove the # GRB properties of the other GRB to prevent mistakes if reset_grb_properties is not None: @@ -666,7 +667,7 @@ def get_dco_merger_rate_density(self, export_cols=None, working_dir='./', load_ sel = (self.rates.get_data('channel', index) == ch) rate = self.rates.compute_rate_density(w_ijk[sel], z_merger[sel], observable='DCO', sensitivity=sensitivity) self.dco_rate_density[ch] = rate - + print(f'DCO merger rate density in the local Universe (z={self.dco_z_rate_density[0]:1.2f}): {round(total_rate[0],2)} Gpc^-3 yr^-1') # export the intrinsic DCO population @@ -719,7 +720,7 @@ def get_grb_rate_density(self, export_cols=None, working_dir='./', load_data=Fa else: self.grb_rate_density[ch+'_GRB2'] = np.zeros(len(self.grb_z_rate_density)) self.grb_rate_density[ch] = self.grb_rate_density[ch+'_GRB1'] + self.grb_rate_density[ch+'_GRB2'] - + print(f'GRB (beamed) rate density in the local Universe (z={self.grb_z_rate_density[0]:1.2f}): {round(total_rate[0],2)} Gpc^-3 yr^-1') # export the intrinsic grb intrisic population @@ -734,9 +735,9 @@ def get_grb_rate_density(self, export_cols=None, working_dir='./', load_data=Fa # the observable population accounts for beaming self.df_grb_observable = self.df_grb_intrinsic.copy() for i in [1,2]: - sel = self.df_grb_observable[f'S{i}_f_beaming'] > 0 + sel = self.df_grb_observable[f'S{i}_f_beaming'] > 0 self.df_grb_observable.loc[sel,'weight'] *= self.df_grb_observable.loc[sel,f'S{i}_f_beaming'] - + def get_dco_detection_rate(self, sensitivity='design_H1L1V1', export_cols=None, working_dir='./', load_data=False): """Compute the detection rate per yr. @@ -774,7 +775,7 @@ def get_dco_detection_rate(self, sensitivity='design_H1L1V1', export_cols=None, def get_formation_channels(self, mt_history): """Get formation channel and add to df and df_oneline.""" - + # loop through each binary unique_binary_index = np.unique(self.df.index) for index in unique_binary_index: @@ -784,43 +785,43 @@ def get_formation_channels(self, mt_history): intep_cls = [key for key in self.df_oneline.keys() if 'interp_class' in key] df_binary_online = self.df_oneline.loc[index, intep_cls].dropna() event_array = df_binary['event'].values.tolist() - + # make interpolated class information consistent with event column HMS_HMS_event_dict = {'stable_MT':'oRLO1', 'no_MT':'None', 'unstable_MT':'oCE1/oDoubleCE1'} event_HMS_HMS = HMS_HMS_event_dict[df_binary_online['interp_class_HMS_HMS']] - + # for now, only append information for RLO1; unstable_MT information already exists if event_HMS_HMS == 'oRLO1': event_array.insert(1, event_HMS_HMS) formation_channel = "_".join(event_array) else: formation_channel = "_".join(event_array) - + # TODO: drop the envent CO_contact # TODO: once we trust the redirection to the detached step # drop also the redirect event - + # TODO: for debugging purposes we keep the full channel self.df_oneline.loc[index,'channel_debug'] = formation_channel # clean the redirect and CO_contact events formation_channel = formation_channel.replace('_redirect', '') formation_channel = formation_channel.replace('_CO_contact', '') self.df_oneline.loc[index,'channel'] = formation_channel - + if mt_history and 'mt_history_HMS_HMS' not in self.df_oneline: raise ValueError('mt_history_HMS_HMS not saved in the oneline dataframe!') - else: + else: # split oRLO1 into oRLO1, oRLO1-contact and oRLO1-reverse - sel = ((self.df_oneline['mt_history_HMS_HMS'] == 'Stable contact phase') & + sel = ((self.df_oneline['mt_history_HMS_HMS'] == 'Stable contact phase') & self.df_oneline['channel'].str.contains('oRLO1')) self.df_oneline.loc[sel, 'channel'] = self.df_oneline.loc[sel, 'channel'].apply(lambda x: x.replace('oRLO1', 'oRLO1-contact')) - sel = ((self.df_oneline['mt_history_HMS_HMS'] == 'Stable reverse mass-transfer phase') & + sel = ((self.df_oneline['mt_history_HMS_HMS'] == 'Stable reverse mass-transfer phase') & self.df_oneline['channel'].str.contains('oRLO1')) self.df_oneline.loc[sel, 'channel'] = self.df_oneline.loc[sel, 'channel'].apply(lambda x: x.replace('oRLO1', 'oRLO1-reverse')) # TODO: do the above split for unstable MT as well - - + + def save_intrinsic_pop(self, path='./intrinsic_population_type.h5', pop='DCO'): """Save intrinsic population. @@ -870,7 +871,7 @@ def load_intrinsic_pop(self, path, pop='DCO'): print('Intrinsic population successfully loaded!') else: raise ValueError('You already have an intrinsic population stored in memory!') - + def save_observable_pop(self, path='./observable_population_type.h5', pop='DCO'): """Save observable population. @@ -893,7 +894,7 @@ def save_observable_pop(self, path='./observable_population_type.h5', pop='DCO') else: self.df_grb_observable.to_hdf(path.replace('type', pop), key='history') if self.verbose: - print('observable population successfully saved!') + print('observable population successfully saved!') else: raise ValueError('Population not recognized!') @@ -921,7 +922,7 @@ def load_observable_pop(self, path, pop='DCO'): else: raise ValueError('You already have an observable population stored in memory!') else: - raise ValueError('Population not recognized!') + raise ValueError('Population not recognized!') def plot_merger_efficiency(self, **kwargs): """Plot merger rate efficinty.""" @@ -965,7 +966,7 @@ def plot_hist_properties(self, var, intrinsic=False, observable=False, pop=None, if observable: df_observable = self.df_grb_observable else: - df_observable = None + df_observable = None else: raise ValueError('Population not recognized!') plot_pop.plot_hist_properties(var, df_intrinsic=df_intrinsic, df_observable=df_observable, pop=pop, **kwargs) @@ -996,4 +997,4 @@ def plot_rate_density(self, DCO=False, GRB=False, **kwargs): def plot_popsyn_over_grid_slice(self, grid_type, met_Zsun, **kwargs): """Plot popsyn over grid slice.""" - plot_pop.plot_popsyn_over_grid_slice(self, grid_type, met_Zsun, **kwargs) \ No newline at end of file + plot_pop.plot_popsyn_over_grid_slice(self, grid_type, met_Zsun, **kwargs) From 476b2009dda928e987e856f460eae2e98d8ee97b Mon Sep 17 00:00:00 2001 From: Konstantinos Kovlakas <22239544+kkovlakas@users.noreply.github.com> Date: Thu, 16 Nov 2023 16:28:43 +0100 Subject: [PATCH 141/319] Removing duplicated code and simplifying if statements in CEE step (#181) * Removing duplicate if statements. * Gathering code for merging systems in CEE step * Simplification of if statement. * Removing unnecessary comments. --- posydon/binary_evol/CE/step_CEE.py | 35 ++++++++++-------------------- posydon/binary_evol/flow_chart.py | 4 ++-- 2 files changed, 13 insertions(+), 26 deletions(-) diff --git a/posydon/binary_evol/CE/step_CEE.py b/posydon/binary_evol/CE/step_CEE.py index a12de5c0d8..8a6ae80b1c 100644 --- a/posydon/binary_evol/CE/step_CEE.py +++ b/posydon/binary_evol/CE/step_CEE.py @@ -188,18 +188,17 @@ def __call__(self, binary): if binary.event in ["oCE1", "oDoubleCE1"]: donor_star = binary.star_1 comp_star = binary.star_2 + star_to_merge = "1" elif binary.event in ["oCE2", "oDoubleCE2"]: donor_star = binary.star_2 comp_star = binary.star_1 + star_to_merge = "2" else: raise ValueError("CEE does not apply if `event` is not " "`oCE1`, 'oDoubleCE1' or `oCE2`, 'oDoubleCE1'") # Check for double CE - if binary.event in ["oDoubleCE1", "oDoubleCE2"]: - double_CE = True - else: - double_CE = False + double_CE = binary.event in ["oDoubleCE1", "oDoubleCE2"] if self.verbose: print("binary.event : ", binary.event) @@ -209,29 +208,17 @@ def __call__(self, binary): self.common_envelope_option_for_lambda) # Check to make sure binary can go through a CE - if donor_star.state in ['H-rich_Core_H_burning', - 'stripped_He_Core_He_burning']: + mergeable_donor = (donor_star.state in [ + 'H-rich_Core_H_burning', 'stripped_He_Core_He_burning']) + mergeable_HG_donor = ( + self.common_envelope_option_for_HG_star == "pessimistic" + and donor_star.state in ['H-rich_Shell_H_burning']) + if mergeable_donor or mergeable_HG_donor: # system merges binary.state = 'merged' - if binary.event in ["oCE1", "oDoubleCE1"]: - binary.event = "oMerging1" - if binary.event in ["oCE2", "oDoubleCE2"]: - binary.event = "oMerging2" - + binary.event = "oMerging" + star_to_merge return - if self.common_envelope_option_for_HG_star == "pessimistic": - # Merging if HG donor - if donor_star.state in ['H-rich_Shell_H_burning']: - # system merges - binary.state = 'merged' - if binary.event in ["oCE1", "oDoubleCE1"]: - binary.event = "oMerging1" - if binary.event in ["oCE2", "oDoubleCE2"]: - binary.event = "oMerging2" - - return - # Calculate binary's evolution if self.prescription == 'alpha-lambda': @@ -450,7 +437,7 @@ def CEE_simple_alpha_prescription( In case we want information about the CEE. common_envelope_option_after_succ_CEE: str Options are: - + 1) "core_replaced_noMT" he_core_mass/radius (or co_core_mass/radius for CEE of stripped_He*) are replaced according to the new core boundary diff --git a/posydon/binary_evol/flow_chart.py b/posydon/binary_evol/flow_chart.py index 19e17780ab..2e3cee6c6d 100644 --- a/posydon/binary_evol/flow_chart.py +++ b/posydon/binary_evol/flow_chart.py @@ -276,8 +276,8 @@ for s1 in STAR_STATES_ALL: for s2 in STAR_STATES_ALL: for e in ['oMerging1', 'oMerging2']: - POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_merged' #'step_merged' - POSYDON_FLOW_CHART[(s2, s1, b, e)] = 'step_merged' #'step_merged' + POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_merged' + POSYDON_FLOW_CHART[(s2, s1, b, e)] = 'step_merged' # catch initial_RLO states for s1 in STAR_STATES_ALL: From 6d7bd41956a0c6a6ac9b3aaad5a0a1e54a2971a7 Mon Sep 17 00:00:00 2001 From: tassos25 Date: Thu, 16 Nov 2023 16:35:50 +0100 Subject: [PATCH 142/319] Update README.md (#189) Removed installation instructions so we do not have to maintain them in two places. The ones in the readme were outdated. Also, I edited the text of the readme a little bit. --- README.md | 89 +++---------------------------------------------------- 1 file changed, 4 insertions(+), 85 deletions(-) diff --git a/README.md b/README.md index b440da444c..38a9258d4c 100644 --- a/README.md +++ b/README.md @@ -1,91 +1,10 @@ # POSYDON -POSYDON is a next-generation single and binary-star population synthesis code incorporating full stellar structure and evolution modeling with the use of the MESA code (https://docs.mesastar.org). +POSYDON is a next-generation single- and binary-star population synthesis framework, incorporating, fully self-consistent, state-of-the-art stellar structure and evolution modelling, using the [MESA](https://docs.mesastar.org) code, throughout the evolution of both binary components. This allows for a more accurate treatment of physical processes in stellar and binary evolution, including: realistic mass-transfer calculations and assessment of stability, internal angular-momentum transport and tides, stellar core sizes, mass-transfer rates, and orbital periods. The code is modular in many aspects and the user can specify initial population properties and adopt choices that determine how the evolution of a binary proceeds. Machine-learning methods are incorporated and applied on the grids of detailes single- and binary-star models for various classification and interpolation calculations, and the development of irregular grids guided by active learning, for computational efficiency. -POSYDON is being developed by a collaborative team of astrophysicists and computer scientists led by Principal Investigators Tassos Fragos (Université de Genève) and Vicky Kalogera (Northwestern University). The code is modular in many aspects and the user can specify initial population properties and adopt choices that determine how stellar evolution proceeds. Populations are simulated with the use of MESA evolutionary tracks for single, non-interacting, and interacting binaries organized in grids. Machine-learning methods are incorporated and applied on the grids for classification and various interpolation calculations, and the development of irregular grids guided by active learning, for computational efficiency. +POSYDON is being developed by a collaborative team of astrophysicists and computer scientists led by Principal Investigators Tassos Fragos (Université de Genève) and Vicky Kalogera (Northwestern University). -## Python distribution and virtual environments -We recommend to install the Python distribution Anaconda and to install POSYDON in a virtual environment. Specifically, we recommend using the installation we have provided which can be accessed through conda-forge. On Linux, the new conda environment can be created (we have named our environment `posydon-example`, but you can choose any name), the conda-forge channel added, and the required library installation can all be completed in one line: -``` -conda create --name posydon-example -c posydon -c conda-forge posydon -``` -On Mac (or if you have problems with the above command on Linux), these steps likely need to be separately run: -``` -conda create -n posydon-conda python=3.7 -conda activate posydon-conda -conda config --add channels conda-forge -conda config --add channels posydon -conda config --set channel_priority false -conda install posydon -``` +In [Fragos et al. (2023)](https://ui.adsabs.harvard.edu/abs/2023ApJS..264...45F/abstract), we describe the detailed methodology and implementation of POSYDON, including the assumed physics of stellar and binary evolution, the extensive grids of detailed single- and binary-star models, the postprocessing, classification, and interpolation methods we developed for use with the grids, and the treatment of evolutionary phases that are not based on precalculated grids. -Now, you can source the environment with -``` -conda activate posydon -``` -### Cloning POSYDON -Clone the repository in a local directory, e.g. '/home/POSYDON/', with -``` -git clone https://github.com/POSYDON-code/POSYDON.git -git checkout origin/main -``` -The directory will contain the following structure: -``` -posydon/ -README.md -setup.py -... -``` - -## Installing POSYDON - - -### Exporting the global path -Export the path to the cloned POSYDON code (you can add this line to your .bashrc or .bash_profile.), e.g. -``` -export PATH_TO_POSYDON=/home/POSYDON/ -``` - -### Installing POSYDON - -From the cloned POSYDON directory execute the commands to install POSYDON and the `mpi4py` dependency -``` -pip install -e . -conda install mpi4py -``` - -### Downloading POSYDON data - -#### [OPTION 1] Zenodo (latest official version) -Export the path to where you want to clone the data, e.g. `/home/`, and download the data with the following commands -``` -export PATH_TO_POSYDON_DATA=/home/ -get-posydon-data -``` - -A new folder named `POSYDON_data` will be created in the indicated location. Make sure you use a path where you have writing permissions (in some Linux distributions you may use `/home/[username]/`). - -#### [OPTION 2] git LFS submodule (latest development version) -Inside the cloned POSYDON repository run the following commands to -install and initialise git LFS, and download the data. -``` -export PATH_TO_POSYDON_DATA=$PATH_TO_POSYDON/data/ -conda install git-lfs -git lfs install -git submodule init -git submodule update data/POSYDON_data -``` - -## Installing POSYDON documentations modules - -These modules are needed in order to compile the documentation -``` -pip install -e .[doc] -``` -To compile the documentation and open the html page do the following -``` -cd docs/ -make html -open _build/html/index.html -``` +### For instruction on how to install and use POSYDON [see here](https://posydon.org/docs/index.html). From d048f27bc503c0491064dab8c2eaee3d03b9edce Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 30 Nov 2023 16:05:07 +0100 Subject: [PATCH 143/319] Matthias documentation additions (#166) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * remove wrong code for kick in eccentric orbit * introducing step_CO-HeMS_RLO * spelling * minor change to catch collapsing stars states in step * add interp_class_CO_HeMS_RLO to binarystar.py * fix bug on characters lenght of step_names * PR88 defaul params ini change * minor changes * fix two small bugs * fix SN MODEL properties for nearest_neighbour interpolation * update TF2 and comibined TF12 * TMP debug edit to plot2D * add TF2 labels * debug * add new TF2 * debug code * remove debug option plot2d * add mt_history in post_processing.py * add mt_history column to be classified * move mt_history code in post_processing.py * debug post processing * debug * debug * add support for mt_history in pop synth * increase lenght mt_history oneline dataframe * synthetic population now identifies formation channels with contact and reverse MT * save ram in synthetic population pop synth evolution * update color palette * debug * introduce v2 documentation structure * introduce posydon v2 documentation structure * introduction section template * template for the overall structure * revert git file changes * revemove vscode settings * add api page * update documentation * update git ignore * update git ignore * upload notebooks * update docs * add step 1 tutorials * add two new tutorials * add webapp and database pages * add step 2 and 3 roadmap templates * add VHD page * add team picture * add pop params page * add component overview pages * add bib ref POSYDON * add new pages star/binaries in-depth docs * Documentation edits (#148) * numbering correction * Optional library installation code changed to include quotation marks. This makes sure the code instructions work on bash and zsh. * added available reference links * Add reference links to website + internal documentation * references added * typo in posydon website link * rewrote one item * update tutorials * Updating introduction, contact and user guide pages. (#149) * Adding members to the "Current members" section. * Typo fixed * Adding comment to improve the user experience * Adding latex format for scientific notation * Fixing typos * Change location of config to avoid module conflict * Updating contact information * Update reference to posydon.config * add missing reference to posydon.config * Adding comments to tutorial notebooks * Warning the user to follow tutorials in order * debug * add step 2 and 3 roadmap files * API restructure + small changes (#151) * DS_Store added to .gitignore * note on adding paths * numbering of tutorial titles * Add additional content to 1. tutorial * auto update copyright year to compile year * Restructured docs - Moved the docs into a _source folder - Altered makefile to account for new directory - Added the POSYDON logo + custom css to match the background - API construction using templates (sphinx-apidoc templates included) * resolved merge conflict * add step rerun and single HMS grids * Remove justify + posydon.config docs header (#152) * DS_Store added to .gitignore * note on adding paths * numbering of tutorial titles * Add additional content to 1. tutorial * auto update copyright year to compile year * Restructured docs - Moved the docs into a _source folder - Altered makefile to account for new directory - Added the POSYDON logo + custom css to match the background - API construction using templates (sphinx-apidoc templates included) * resolved merge conflict * remove jusity of section in css. This causes weird placement of whitespace in code function * remove unnecessary posydon.config header * typo fix * retain justify text for paragraphs * add full pipeline tutorial and single star processing tutorial * minor fixes * add plot 1D tutorial and started to add titles to notebooks so that they show up as a structured list in the tutorial and example pages * add plot 2D tutorial and add sections in all notebooks * Small TOC and cross-reference fixes (#154) * preserve default function values * double copy of index.rst * clean TOC structure of tutorials * clean up TOC structure of In-depth components overview * readd API reference * reference correct page for population parameters * cross-reference fixed and warning fixed * direct link to published work * allow for notebook depth * add pop synth debug tutorial * add logo as png and let sphinx decide image type depending on builder * use png for html logo, too; correct typo * improvements on getting-started section * resolve (some) warnings for missing files; change order in in-depth part; correct citations in docstrings * structure for post-processing pipeline * fix table style; explain ini-file of post-processing pipeline; start to explain steps of the pipeline * complete post-processing pipeline and its steps * start with explaining the PSyGrid object * finish docs of PSyGrid object and post-processing pipeline * include changes of PR#176 * use code references instead of putting target names in the api_reference files * Change highlightings to code beside in headlines --------- Co-authored-by: ssbvr Co-authored-by: Max <14039563+maxbriel@users.noreply.github.com> Co-authored-by: Jaime Román-Garza --- docs/_source/_static/custom.css | 6 +- .../mesa_grids/inifile.rst | 2 + .../post_processing/pipeline.rst | 20 + .../pipeline/pipeline_additions.rst | 90 ++++ .../pipeline/pipeline_steps.rst | 202 ++++++++ .../post_processing/pipeline_ini.rst | 370 +++++++++++++++ .../post_processing/psygrid.rst | 434 ++++++++++++++++++ .../processing-pipeline.rst | 35 +- .../generating-datasets.rst | 4 + 9 files changed, 1161 insertions(+), 2 deletions(-) create mode 100644 docs/_source/components-overview/post_processing/pipeline.rst create mode 100644 docs/_source/components-overview/post_processing/pipeline/pipeline_additions.rst create mode 100644 docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst create mode 100644 docs/_source/components-overview/post_processing/pipeline_ini.rst create mode 100644 docs/_source/components-overview/post_processing/psygrid.rst diff --git a/docs/_source/_static/custom.css b/docs/_source/_static/custom.css index 9c3eda5988..e296b41b6f 100644 --- a/docs/_source/_static/custom.css +++ b/docs/_source/_static/custom.css @@ -14,4 +14,8 @@ p { text-align: justify; -} \ No newline at end of file +} + +.wy-table-responsive table td { + white-space: normal; +} diff --git a/docs/_source/components-overview/mesa_grids/inifile.rst b/docs/_source/components-overview/mesa_grids/inifile.rst index 365ade76bd..e2941d6924 100644 --- a/docs/_source/components-overview/mesa_grids/inifile.rst +++ b/docs/_source/components-overview/mesa_grids/inifile.rst @@ -29,6 +29,8 @@ Here is a link to the unstable development version of the default inifile for POSYDON: `Development Version INIFILE `_ +.. _inifile_slurm: + [slurm] ------- diff --git a/docs/_source/components-overview/post_processing/pipeline.rst b/docs/_source/components-overview/post_processing/pipeline.rst new file mode 100644 index 0000000000..f31fbf3a74 --- /dev/null +++ b/docs/_source/components-overview/post_processing/pipeline.rst @@ -0,0 +1,20 @@ +.. _pipeline: + +######################## +Post-processing pipeline +######################## + +The post processing is done in steps with the possibility to get things run in +parallel after each step. + +.. toctree:: + + pipeline/pipeline_steps + +.. toctree:: + + pipeline/pipeline_additions + +We recommend to run the pipeline with an :ref:`ini file ` to keep +a better overview of large grids and being able to post-process them with a few +commands. diff --git a/docs/_source/components-overview/post_processing/pipeline/pipeline_additions.rst b/docs/_source/components-overview/post_processing/pipeline/pipeline_additions.rst new file mode 100644 index 0000000000..be86670f50 --- /dev/null +++ b/docs/_source/components-overview/post_processing/pipeline/pipeline_additions.rst @@ -0,0 +1,90 @@ +.. _pipeline_additions: + +################## +Pipeline additions +################## + +All the additions can run after any of the steps and will run in parallel to +the next step. They are run the same way as the :ref:`steps `. + +.. _pipeline_plots: + +Creating plots +-------------- + +Plots can be created after each step with the data available from the previous +step. Hence, each corresponding csv file is called :samp:`step_?_plots.csv`, +where the question mark will be the number of the step the plots take the final +:samp:`PSyGrid` object from. All the csv files for plotting have the same +structure: + +.. code-block:: + + path_to_grid,quantities_to_plot,path_to_plot + +Beside the grid, it takes a list of quantities to plot and finally the path to +the directory, where the plots should get stored. All final quantities +supported for a :ref:`2D plot `, as third dimension can be specified. +Additionally, you can put a :samp:`LOG10_` in front of each of them to switch +on plotting in log-scale. Beside that there are predefined plots. + +.. table:: Basic predefined plots + + ======================== ==================== ======================== ====== ====== ====== + quantities_to_plot 'term_flag' 'zvar' 'zmin' 'zmax' 'zlog' + ======================== ==================== ======================== ====== ====== ====== + 'combined_TF12' 'combined_TF12' None None None False + 'termination_flag_1' 'termination_flag_1' 'lg_mtransfer_rate' -8 -1 False + 'termination_flag_2' 'termination_flag_2' None None None False + 'termination_flag_3' 'termination_flag_3' None None None False + 'termination_flag_4' 'termination_flag_4' None None None False + 'rl_relative_overflow_1' 'debug' 'rl_relative_overflow_1' -0.5 0.5 False + 'rl_relative_overflow_2' 'debug' 'rl_relative_overflow_2' -0.5 0.5 False + 'lg_mtransfer_rate' 'debug' 'lg_mtransfer_rate' -8 -1 False + ======================== ==================== ======================== ====== ====== ====== + +After :ref:`Step3: calculating extra values from detailed data `, +the supernova model quantities get available, too. + +.. table:: Supernova predefined plots + + ============================ ==================== ============================ ====== ====== ====== + quantities_to_plot 'term_flag' 'zvar' 'zmin' 'zmax' 'zlog' + ============================ ==================== ============================ ====== ====== ====== + 'S1_MODEL??_CO_type' 'S1_MODEL01_CO_type' 'S1_MODEL??_CO_type' None None False + 'S1_MODEL??_SN_type' 'S1_MODEL01_SN_type' 'S1_MODEL??_SN_type' None None False + 'S1_MODEL??_mass' 'termination_flag_1' 'S1_MODEL??_mass' 0. 2. True + 'S1_MODEL??_spin' 'termination_flag_1' 'S1_MODEL??_spin' 0. 1. False + 'S1_MODEL??_m_disk_radiated' 'termination_flag_1' 'S1_MODEL??_m_disk_radiated' 0. 3. False + ============================ ==================== ============================ ====== ====== ====== + +After :ref:`Step4: training of the interpolators `, the +interpolators can be used. + +.. table:: Predefined plots with variable `QUANTITY` + + ============================ ==================== ============ ====== ====== ====== + quantities_to_plot 'term_flag' 'zvar' 'zmin' 'zmax' 'zlog' + ============================ ==================== ============ ====== ====== ====== + '`QUANTITY`' 'termination_flag_1' '`QUANTITY`' None None False + 'LOG10\_ `QUANTITY`' 'termination_flag_1' '`QUANTITY`' None None True + 'INTERP\_ERROR\_ `QUANTITY`' None '`QUANTITY`' 0 0.1 False + ============================ ==================== ============ ====== ====== ====== + +.. _pipeline_checks: + +Doing checks +------------ + +After each step one can perform some checks with the data from that step. + +.. table:: Currently supported checks + + ============== =========== + Check Description + ============== =========== + 'failure_rate' calculates the failure rate of the grid + 'CO_type' gets counts of compact object types + 'SN_type' gets counts of supernova types + ============== =========== + diff --git a/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst b/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst new file mode 100644 index 0000000000..179fddb876 --- /dev/null +++ b/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst @@ -0,0 +1,202 @@ +.. _pipeline_steps: + +############## +Pipeline steps +############## + +The pipeline is divided into several steps, which build up on each other. Each +step will take a csv file as input. The name of this file is used to tell the +pipeline, which step should be performed. + +The run-pipeline script takes four arguments: + +.. code-block:: bash + + run-pipeline PATH_TO_GRIDS PATH_TO_CSV_FILE DATA_ID VERBOSE + +1. [path] The path to the girds main directory (currently not used) +2. [path] The path to the csv file +3. [int] An index indicating the data entry to read from the csv file +4. [int] Where one wants verbose output (1) or not (0) + +.. note:: + The current directory will be used as working directory, hence navigate to + your work directory first. + +.. _pipeline_step1: + +Step1: creating a `PSyGrid` object +---------------------------------- + +First, we need to create the :samp:`PSyGird` object. To do so, the pipeline +needs to now the directory which contains the MESA runs, the compression, and +whether to crop the history for some certain runs. Hence, the +:samp:`step_1.csv` file should have those columns: + +.. code-block:: + + path_to_grid,compression,stop_before_carbon_depletion + +And the lines below contain the data for each unique combination of the three +parameters to be processed. Here the :samp:`DATA_ID` simply refers to the line +below the header starting by 0. Thus, the second line in the file has the index +0, the third one has index 1 and so on. + +The currently supported compression types are: + +.. table:: Compression types + + ======== =========== + Type Description + ======== =========== + ORIGINAL It keeps all columns and history entries given by MESA + LITE It discards some columns and reduces the history and final profiles to an maximum error of 0.1 and limit profiles to contain in maximum 200 data points + \*_RLO The :samp:`_RLO` can be put on any of the previous types to crop the history before the onset of Roche-lobe overflow for grids containing a compact object + ======== =========== + +.. _pipeline_step2: + +Step2: combining `PSyGrid` objects +---------------------------------- + +Usually, the girds are split into batches or reruns are done. In those cases, +there will be several :samp:`PSyGrid` objects created for one gird. This step +will join them into one. The :samp:`step_2.csv` file should have a matrix +structure. The columns contain the girds which should be combined to the one +specified in the header (first) row. The :samp:`DATA_ID` corresponds here to +the column number (starting with 0). Here an example: + +.. code-block:: + + NEW_H5_FILE1,NEW_H5_FILE2 + OLD_H5_FILE11,OLD_H5_FILE21 + OLD_H5_FILE12,OLD_H5_FILE22 + ,OLD_H5_FILE23 + +.. warning:: + The data will be put on top of each other. E.g. if there is the same + initial system in :samp:`OLD_H5_FILE11` and :samp:`OLD_H5_FILE12`, the one + in :samp:`OLD_H5_FILE11` will be discarded and only the one in + :samp:`OLD_H5_FILE12` will end up in :samp:`NEW_H5_FILE1`. + +.. _pipeline_step3: + +Step3: calculating extra values from detailed data +-------------------------------------------------- + +In this step we calculate extra quantities from the histories and profiles. +Those extra values are key parameters at He depletion, at onset of common +envelope evolution, and at core collapse. + +Because some of the values may require a high precision in the data, we +recommend to use the data from the ORIGINAL compression to calculate them. But +the new values can be added to any :samp:`PSyGrid` object. Hence this step +requests three paths to be specified in :samp:`step_3.csv`: + +.. code-block:: + + path_to_grid,path_to_grid_ORIGINAL,path_to_processed_grid + +.. table:: Description of required paths + + ====================== =========== + Path Description + ====================== =========== + path_to_grid path of the gird, which get the values appended to it + path_to_grid_ORIGINAL path of the grid, where the values are calculated from + path_to_processed_grid path of the new grid (a copy of the one specified as :samp:`path_to_grid` with the appended values) + ====================== =========== + +.. note:: + This step use the path to the original MESA data as the unique identifier + of each system in the :samp:`PSyGrid` object, thus the location of the MESA + file cannot be changed between creating two :samp:`PSyGrid` objects of the + same grid in :ref:`step1 `. Similarly, the overlaying in + :ref:`step2 ` needs to be the same, too. Therefore, we + recommend to setup and run the pipeline with an + :ref:`ini file `. + +.. _pipeline_step4: + +Step4: training of the interpolators +------------------------------------ + +To get interpolated data from our grids, we train in this step an interpolator +on your :samp:`PSyGrid` object. The file :samp:`step_4.csv` therefore has to +contain two information bits. First, the grid containing the data and second, +the name of the interpolator object. + +.. code-block:: + + path_to_grid,path_to_interpolator + +.. note:: + The type of interpolator will be recognized from the name of the + interpolator object. The syntax is :code:`IF_METHOD{_RLO}.pkl`. The + :samp:`IF` stands for initial-final interpolator, the :samp:`METHOD` refers + to the interpolator type. The girds starting at Roche-lobe overflow may be + indicated in the name as well, but is not required. + +.. table:: Currently supported interpolator types + + ============== =========== + :samp:`METHOD` Description + ============== =========== + linear linear interpolation + 1NN nearest neighbor + ============== =========== + +.. _pipeline_step9: + +Step9: exporting the data set +----------------------------- + +After we have a complete data set, we would like to export it to be used for +the population synthesis. We jump here to step 9, because this will always be +the last step even more steps may get introduced in the future. In +:samp:`step_9.csv`, there are again two paths required, a source and an export +path. The step will simply copy the source to the export location. Hence, here +the final :samp:`PSyGrid` objects and all the interpolator files are usually +addressed by this step. + +.. code-block:: + + path_to_grid,export_path + +.. _pipeline_stepR: + +StepR: exporting a rerun +------------------------ + +Usually, a grid will not run well everywhere on the first go. So, there is a +need to export reruns which changes for the next run to fix non converged +models. This step is therefore only needed during the build of a new grid. +Usually, one would run the steps to the point, where the need of a fix arises. +Additionally, before exporting a rerun, the logic how to select a system to be +included in the rerun and what should be changed needs to get implemented +first. + +For this step the csv file is called :samp:`rerun.csv` to avoid too much +confusion with other steps. It clearly has to run after a step, but it is no +usual step itself. It requires a path to a :samp:`PSyGrid` object to get the +models from, a path, where the rerun should be stored (it creates in there the +:samp:`grid.csv` and the ini file needed to +:ref:`setup a new run `) and the type of the rerun specifying +the logic and changes. + +.. code-block:: + + path_to_grid,rerun_path,rerun_type + +.. table:: Currently supported rerun types + + =================== ============== =========== + :samp:`rerun_type` Future version Description + =================== ============== =========== + PISN default in v3+ it enables the MESA inlist commit, which stops MESA before getting dynamical to save a final profile there + reverse_MT default in v3+ it uses a MESA version with a bug fix, that the role of donor and accretor can switch during the simulation + opacity_max caution it uses a fixed maximum opacity of 0.5 (this is only a last option change to get more stability) + TPAGBwind default in v3+ it enables the MESA inlist commit, which changes the wind during the TPAGB phase + thermohaline_mixing default in v3+ it uses thermohaline mixing in the inlist + =================== ============== =========== + diff --git a/docs/_source/components-overview/post_processing/pipeline_ini.rst b/docs/_source/components-overview/post_processing/pipeline_ini.rst new file mode 100644 index 0000000000..94fe6ce96e --- /dev/null +++ b/docs/_source/components-overview/post_processing/pipeline_ini.rst @@ -0,0 +1,370 @@ +.. _pipeline_ini: + +############################################################## +Documentation of the ini file for the post-processing pipeline +############################################################## + +Aim of the ini file +=================== + +Using an ini file should help to keep an overview on large grid repositories +and ensures that all will be setup the same way. + +There is a setup-pipeline script takes one argument: + +.. code-block:: bash + + setup-pipeline PATH_TO_INI + +The content of the ini file is described :ref:`below `. +It will create for each step, plot or check two files: + +1. \*.csv +2. \*.slurm + +Additionally, it will prepare a :samp:`logs` directory (it may creates a save +of some old logs) and create a shell script :samp:`run_pipeline.sh` to submit +all :ref:`tasks `. Hence, you can run all +:ref:`steps ` with simply running this script. + +.. code-block:: bash + + ./run_pipeline.sh + +.. note:: + Currently, the user needs to take care of having a POSYDON_data directly + which includes the tables for the core-collapse prescriptions him-/herself + in the working directory. + +.. _pipeline_ini_sections: + +Sections in the ini file +======================== + +Account and slurm settings +-------------------------- + +First we specify some details for the slurm jobs. This is similar to the +:ref:`[slurm] section for running the grids `. Here is an +example to show the supported key words: + +.. code-block:: ini + + [account] + ACCOUNT = 'fragkos' + PARTITION = 'public-cpu' + WALLTIME = '23:00:00' + MAILTYPE = 'ALL' + EMAIL = 'matthias.kruckow@unige.ch' + +General pipeline settings +------------------------- + +The next sections deals with the general information about the pipeline. First +it needs to know where the grids are located. The :samp:`PATH` specifies, where +you would like to get the pipeline files being created. The :samp:`VERBOSE` +option will be used for the creation of the pipeline files and during the run +of the pipeline. + +Finally, we have switches to turn on (:samp:`True`) and off (:samp:`False`) +individual :ref:`steps `. + +.. code-block:: ini + + [pipeline setup] + PATH_TO_GRIDS = '/srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/' + VERSION = '' # 'v2' in quest and '' in yggdrasil + PATH = '.' # working dir + VERBOSE = True + + # steps + CREATE_GRID_SLICES = True + COMBINE_GRID_SLICES = True + CALCULATE_EXTRA_VALUES = True + TRAIN_INTERPOLATORS = True + EXPORT_DATASET = True + # rerun step + RERUN = False + +Step sections +------------- + +The path of each grid will be joint as +:samp:`PATH_TO_GRIDS/VERSION/GRID_TYPE/METALLICITY/GRID_SLICE`. The +corresponding h5 files will have names according to +:samp:`PATH_TO_GRIDS/VERSION/GRID_TYPE/METALLICITY/COMPRESSION/GRID_SLICE.h5`. +All sections have common keywords: + +.. table:: Common keywords of steps + + ================== =========== + Keyword Description + ================== =========== + GRID_TYPES a list of grid types; the looped :samp:`GRID_TYPE` is used in the path name + METALLICITIES a list of lists of the metallicities of the grids; the looped :samp:`METALLICITY` is used in the path name; the outer list allows you to have different lists for each grid type + GRID_SLICES a list of lists of the grid slices; the looped :samp:`GRID_SLICE` is used in the path name; the outer list allows you to have different lists for each grid type + COMPRESSIONS a list of lists of compression types + DROP_MISSING_FILES boolean to ignore missing files + CREATE_PLOTS a list of plots to make; this will be done independently whether the step is active or not, to make no plots put there an empty list or comment out such a line + DO_CHECKS a list of checks to perform; this will be done independently whether the step is active or not, to make no checks put there an empty list or comment out such a line + ================== =========== + +Some :ref:`steps ` have more keywords, which are specific to +that step: + +.. table:: Step specific keywords + + ==== ============================ =========== + Step Keyword Description + ==== ============================ =========== + 1 STOP_BEFORE_CARBON_DEPLETION indicating, whether high mass HMS stars should get their history croped short before carbon depletion (1) or not (0) + 2 GRID_SLICES for this step, we have 3 layers of lists: the outermost is still the grid type, the inner most is still the grid slice, the middle layer is the combined grid + 2 GRIDS_COMBINED a list of lists of combined grids; the outermost list is again referring to grid type; this is used as name for the new combined grid instead of :samp:`GRID_SLICE` + 4 INTERPOLATION_METHODS a list of the interpolator types which are trained + 4 CONTROL_GRIDS a list of lists of control grids for the :samp:`GRID_SLICES`; it need to have the same number of entries as the :samp:`GRID_SLICES`, to specify no control grid use an empty string + R RERUN_TYPE a defined rerun type + ==== ============================ =========== + +Here is an example of all the :ref:`steps `: + +.. code-block:: ini + + #CREATE_GRID_SLICES + [step_1] + # e.g. ['CO-HMS_RLO','CO-HeMS','HMS-HMS'] + GRID_TYPES = ['CO-HMS_RLO', 'CO-HeMS', 'HMS-HMS'] + # e.g. ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'] + METALLICITIES = [# CO-HMS_RLO + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'], + # CO-HeMS + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'], + # HMS-HMS + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'] + ] + GRID_SLICES = [# CO-HMS_RLO + ['grid_low_res_0', 'grid_low_res_1', 'grid_low_res_2', 'rerun_PISN_grid_low_res_combined', 'rerun_TPAGBwind_grid_low_res_combined', + 'grid_random_1', 'rerun_PISN_grid_random_combined', 'rerun_TPAGBwind_grid_random_combined'], + # CO-HeMS + ['grid_low_res_0', 'grid_low_res_1', 'grid_low_res_2', 'rerun_PISN_grid_low_res_combined', + 'grid_random_1', 'grid_random_rerun', 'rerun_PISN_grid_random_combined'], + # HMS-HMS + ['grid_low_res_0', 'grid_low_res_1', 'grid_low_res_2', 'grid_low_res_3', 'grid_low_res_4', 'grid_low_res_5', 'rerun_PISN_grid_low_res_combined', 'rerun_reverse_MT_grid_low_res_combined', 'rerun_TPAGBwind_grid_low_res_combined', + 'grid_random_1', 'rerun_PISN_grid_random_combined', 'rerun_reverse_MT_grid_random_combined', 'rerun_TPAGBwind_grid_random_combined'] + ] + COMPRESSIONS = [# CO-HMS_RLO + ['LITE', 'ORIGINAL', 'LITE_RLO', 'ORIGINAL_RLO'], + # CO-HeMS + ['LITE', 'ORIGINAL', 'LITE_RLO', 'ORIGINAL_RLO'], + # HMS-HMS + ['LITE', 'ORIGINAL'] + ] + DROP_MISSING_FILES = True + # EXTRA PARAMETERS + # only applied to HMS grids + STOP_BEFORE_CARBON_DEPLETION = 1 + # supported plots: e.g. 'combined_TF12', 'termination_flag_1', 'termination_flag_2', 'termination_flag_3', 'termination_flag_4', and any quantity valid for a Z-plotting + CREATE_PLOTS = [] + # supported checks: e.g. 'failure_rate' + DO_CHECKS = [] + + #COMBINE_GRID_SLICES + [step_2] + GRID_TYPES = ['CO-HMS_RLO', 'CO-HeMS', 'HMS-HMS'] + METALLICITIES = [# CO-HMS_RLO + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'], + # CO-HeMS + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'], + # HMS-HMS + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'] + ] + GRID_SLICES = [# CO-HMS_RLO + [['grid_low_res_0', 'grid_low_res_1', 'grid_low_res_2'], + ['grid_low_res_0', 'grid_low_res_1', 'grid_low_res_2', 'rerun_PISN_grid_low_res_combined'], + ['grid_low_res_0', 'grid_low_res_1', 'grid_low_res_2', 'rerun_PISN_grid_low_res_combined', 'rerun_TPAGBwind_grid_low_res_combined'], + ['grid_random_1'], + ['grid_random_1', 'rerun_PISN_grid_random_combined'], + ['grid_random_1', 'rerun_PISN_grid_random_combined', 'rerun_TPAGBwind_grid_random_combined']], + # CO-HeMS + [['grid_low_res_0', 'grid_low_res_1', 'grid_low_res_2'], + ['grid_low_res_0', 'grid_low_res_1', 'grid_low_res_2', 'rerun_PISN_grid_low_res_combined'], + ['grid_random_1', 'grid_random_rerun'], + ['grid_random_1', 'grid_random_rerun', 'rerun_PISN_grid_random_combined']], + # HMS-HMS + [['grid_low_res_0', 'grid_low_res_1', 'grid_low_res_2', 'grid_low_res_3', 'grid_low_res_4', 'grid_low_res_5'], + ['grid_low_res_0', 'grid_low_res_1', 'grid_low_res_2', 'grid_low_res_3', 'grid_low_res_4', 'grid_low_res_5', 'rerun_PISN_grid_low_res_combined'], + ['grid_low_res_0', 'grid_low_res_1', 'grid_low_res_2', 'grid_low_res_3', 'grid_low_res_4', 'grid_low_res_5', 'rerun_PISN_grid_low_res_combined', 'rerun_reverse_MT_grid_low_res_combined'], + ['grid_low_res_0', 'grid_low_res_1', 'grid_low_res_2', 'grid_low_res_3', 'grid_low_res_4', 'grid_low_res_5', 'rerun_PISN_grid_low_res_combined', 'rerun_reverse_MT_grid_low_res_combined', 'rerun_TPAGBwind_grid_low_res_combined'], + ['grid_random_1'], + ['grid_random_1', 'rerun_PISN_grid_random_combined'], + ['grid_random_1', 'rerun_PISN_grid_random_combined', 'rerun_reverse_MT_grid_random_combined'], + ['grid_random_1', 'rerun_PISN_grid_random_combined', 'rerun_reverse_MT_grid_random_combined', 'rerun_TPAGBwind_grid_random_combined']] + ] + GRIDS_COMBINED = [# CO-HMS_RLO + ['grid_low_res_combined', 'grid_low_res_combined_rerun1_PISN', 'grid_low_res_combined_rerun3_TPAGBwind', + 'grid_random_combined', 'grid_random_combined_rerun1_PISN', 'grid_random_combined_rerun3_TPAGBwind'], + # CO-HeMS + ['grid_low_res_combined', 'grid_low_res_combined_rerun1_PISN', + 'grid_random_combined', 'grid_random_combined_rerun1_PISN'], + # HMS-HMS + ['grid_low_res_combined', 'grid_low_res_combined_rerun1_PISN', 'grid_low_res_combined_rerun2_reverse_MT', 'grid_low_res_combined_rerun3_TPAGBwind', + 'grid_random_combined', 'grid_random_combined_rerun1_PISN', 'grid_random_combined_rerun2_reverse_MT', 'grid_random_combined_rerun3_TPAGBwind'] + ] + COMPRESSIONS = [# CO-HMS_RLO + ['LITE', 'ORIGINAL', 'LITE_RLO', 'ORIGINAL_RLO'], + # CO-HeMS + ['LITE', 'ORIGINAL', 'LITE_RLO', 'ORIGINAL_RLO'], + # HMS-HMS + ['LITE', 'ORIGINAL'] + ] + DROP_MISSING_FILES = True + # supported plots: e.g. 'combined_TF12', 'termination_flag_1', 'termination_flag_2', 'termination_flag_3', 'termination_flag_4', and any quantity valid for a Z-plotting + CREATE_PLOTS = ['PLOT_AFTER_COMBINE'] + # supported checks: e.g. 'failure_rate' + DO_CHECKS = ['CHECK_AFTER_COMBINE'] + + #CALCULATE_EXTRA_VALUES + [step_3] + GRID_TYPES = ['CO-HMS_RLO', 'CO-HeMS', 'HMS-HMS'] + METALLICITIES = [# CO-HMS_RLO + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'], + # CO-HeMS + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'], + # HMS-HMS + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'] + ] + GRID_SLICES = [# CO-HMS_RLO + ['grid_low_res_combined_rerun3_TPAGBwind', 'grid_random_combined_rerun3_TPAGBwind'], + # CO-HeMS + ['grid_low_res_combined_rerun1_PISN', 'grid_random_combined_rerun1_PISN'], + # HMS-HMS + ['grid_low_res_combined_rerun3_TPAGBwind', 'grid_random_combined_rerun3_TPAGBwind'] + ] + COMPRESSIONS = [# CO-HMS_RLO + ['LITE', 'LITE_RLO'], + # CO-HeMS + ['LITE', 'LITE_RLO'], + # HMS-HMS + ['LITE'] + ] + DROP_MISSING_FILES = True + # supported plots: e.g. 'combined_TF12', 'termination_flag_1', 'termination_flag_2', 'termination_flag_3', 'termination_flag_4', and any quantity valid for a Z-plotting + CREATE_PLOTS = ['PLOT_AFTER_EXTRA'] + # supported checks: e.g. 'failure_rate', 'CO_TYPE', 'SN_TYPE' + DO_CHECKS = ['CHECK_AFTER_EXTRA'] + + #TRAIN_INTERPOLATORS + [step_4] + GRID_TYPES = ['CO-HMS_RLO', 'CO-HeMS', 'HMS-HMS'] + METALLICITIES = [# CO-HMS_RLO + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'], + # CO-HeMS + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'], + # HMS-HMS + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'] + ] + GRID_SLICES = [# CO-HMS_RLO + ['grid_low_res_combined_rerun3_TPAGBwind_processed'], + # CO-HeMS + ['grid_low_res_combined_rerun1_PISN_processed'], + # HMS-HMS + ['grid_low_res_combined_rerun3_TPAGBwind_processed'] + ] + INTERPOLATION_METHODS = ["linear","1NN"] + COMPRESSIONS = [# CO-HMS_RLO + ['LITE_RLO'], + # CO-HeMS + ['LITE', 'LITE_RLO'], + # HMS-HMS + ['LITE'] + ] + CONTROL_GRIDS = [# CO-HMS_RLO + ['grid_random_combined_rerun3_TPAGBwind_processed'], + # CO-HeMS + ['grid_random_combined_rerun1_PISN_processed'], + # HMS-HMS + ['grid_random_combined_rerun3_TPAGBwind_processed'] + ] + DROP_MISSING_FILES = True + # supported plots: e.g. 'combined_TF12', 'termination_flag_1', 'termination_flag_2', 'termination_flag_3', 'termination_flag_4', and any quantity valid for a Z-plotting + CREATE_PLOTS = ['PLOT_AFTER_TRAINING'] + # supported checks: e.g. 'failure_rate' + DO_CHECKS = ['CHECK_AFTER_TRAINING'] + + #EXPORT_DATASET + [step_9] + GRID_TYPES = ['CO-HMS_RLO', 'CO-HeMS', 'HMS-HMS'] + METALLICITIES = [# CO-HMS_RLO + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'], + # CO-HeMS + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'], + # HMS-HMS + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'] + ] + GRID_SLICES = [# CO-HMS_RLO + ['grid_low_res_combined_rerun3_TPAGBwind_processed'], + # CO-HeMS + ['grid_low_res_combined_rerun1_PISN_processed'], + # HMS-HMS + ['grid_low_res_combined_rerun3_TPAGBwind_processed'] + ] + COMPRESSIONS = [# CO-HMS_RLO + ['LITE_RLO'], + # CO-HeMS + ['LITE', 'LITE_RLO'], + # HMS-HMS + ['LITE'] + ] + DROP_MISSING_FILES = True + + #EXPORT_RERUNS + [rerun] + GRID_TYPES = ['CO-HMS_RLO', 'CO-HeMS', 'HMS-HMS'] + METALLICITIES = [# CO-HMS_RLO + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'], + # CO-HeMS + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'], + # HMS-HMS + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'] + ] + GRID_SLICES = [# CO-HMS_RLO + ['grid_low_res_combined_rerun3_TPAGBwind','grid_random_combined_rerun3_TPAGBwind'], + # CO-HeMS + ['grid_low_res_combined_rerun1_PISN','grid_random_combined_rerun1_PISN'], + # HMS-HMS + ['grid_low_res_combined_rerun3_TPAGBwind','grid_random_combined_rerun3_TPAGBwind'] + ] + COMPRESSIONS = [# CO-HMS_RLO + ['LITE'], + # CO-HeMS + ['LITE'], + # HMS-HMS + ['LITE'] + ] + DROP_MISSING_FILES = True + # example reruns are 'PISN', 'reverse_MT', 'TPAGBwind', 'opacity_max' + RERUN_TYPE = 'opacity_max' + +There are some predefined shortcuts for lists of :ref:`plots ` +and :ref:`checks `: + +.. table:: Plot sets + + ===================== ===== + Set name plots + ===================== ===== + 'PLOT_AFTER_CREATE' + 'PLOT_AFTER_COMBINE' 'combined_TF12', 'termination_flag_1', 'termination_flag_2', 'termination_flag_3', 'termination_flag_4', 'rl_relative_overflow_1', 'rl_relative_overflow_2', 'lg_mtransfer_rate' + 'PLOT_AFTER_EXTRA' 'S1_MODEL01_CO_type', 'S1_MODEL01_SN_type', 'S1_MODEL01_mass', 'S1_MODEL01_spin', 'S1_MODEL01_m_disk_radiated', 'S1_MODEL02_CO_type', 'S1_MODEL02_SN_type', 'S1_MODEL02_mass', 'S1_MODEL02_spin', 'S1_MODEL02_m_disk_radiated', 'S1_MODEL03_CO_type', 'S1_MODEL03_SN_type', 'S1_MODEL03_mass', 'S1_MODEL03_spin', 'S1_MODEL03_m_disk_radiated', 'S1_MODEL04_CO_type', 'S1_MODEL04_SN_type', 'S1_MODEL04_mass', 'S1_MODEL04_spin', 'S1_MODEL04_m_disk_radiated', 'S1_MODEL05_CO_type', 'S1_MODEL05_SN_type', 'S1_MODEL05_mass', 'S1_MODEL05_spin', 'S1_MODEL05_m_disk_radiated', 'S1_MODEL06_CO_type', 'S1_MODEL06_SN_type', 'S1_MODEL06_mass', 'S1_MODEL06_spin', 'S1_MODEL06_m_disk_radiated', 'S1_MODEL07_CO_type', 'S1_MODEL07_SN_type', 'S1_MODEL07_mass', 'S1_MODEL07_spin', 'S1_MODEL07_m_disk_radiated', 'S1_MODEL08_CO_type', 'S1_MODEL08_SN_type', 'S1_MODEL08_mass', 'S1_MODEL08_spin', 'S1_MODEL08_m_disk_radiated', 'S1_MODEL09_CO_type', 'S1_MODEL09_SN_type', 'S1_MODEL09_mass', 'S1_MODEL09_spin', 'S1_MODEL09_m_disk_radiated', 'S1_MODEL10_CO_type', 'S1_MODEL10_SN_type', 'S1_MODEL10_mass', 'S1_MODEL10_spin', 'S1_MODEL10_m_disk_radiated' + 'PLOT_AFTER_TRAINING' 'INTERP_ERROR_age', 'INTERP_ERROR_star_1_mass', 'INTERP_ERROR_star_2_mass', 'INTERP_ERROR_period_days', 'INTERP_ERROR_S1_co_core_mass', 'INTERP_ERROR_S1_co_core_radius', 'INTERP_ERROR_S1_he_core_mass', 'INTERP_ERROR_S1_he_core_radius', 'INTERP_ERROR_S1_center_h1', 'INTERP_ERROR_S1_center_he4', 'INTERP_ERROR_S1_surface_h1', 'INTERP_ERROR_S1_surface_he4', 'INTERP_ERROR_S1_surf_avg_omega_div_omega_crit', 'INTERP_ERROR_S1_log_Teff', 'INTERP_ERROR_S1_log_L', 'INTERP_ERROR_S1_log_R', 'INTERP_ERROR_S1_spin_parameter', 'INTERP_ERROR_S1_lambda_CE_10cent', 'INTERP_ERROR_S2_co_core_mass', 'INTERP_ERROR_S2_co_core_radius', 'INTERP_ERROR_S2_he_core_mass', 'INTERP_ERROR_S2_he_core_radius', 'INTERP_ERROR_S2_center_h1', 'INTERP_ERROR_S2_center_he4', 'INTERP_ERROR_S2_surface_h1', 'INTERP_ERROR_S2_surface_he4', 'INTERP_ERROR_S2_surf_avg_omega_div_omega_crit', 'INTERP_ERROR_S2_log_Teff', 'INTERP_ERROR_S2_log_L', 'INTERP_ERROR_S2_log_R', 'INTERP_ERROR_S2_spin_parameter', 'INTERP_ERROR_S2_lambda_CE_10cent', 'INTERP_ERROR_S1_MODEL01_mass', 'INTERP_ERROR_S1_MODEL01_spin', 'INTERP_ERROR_S1_MODEL01_m_disk_radiated', 'INTERP_ERROR_S1_MODEL05_mass', 'INTERP_ERROR_S1_MODEL05_spin', 'INTERP_ERROR_S1_MODEL05_m_disk_radiated', 'INTERP_ERROR_S1_MODEL06_mass', 'INTERP_ERROR_S1_MODEL06_spin', 'INTERP_ERROR_S1_MODEL06_m_disk_radiated', 'INTERP_ERROR_S1_MODEL10_mass', 'INTERP_ERROR_S1_MODEL10_spin', 'INTERP_ERROR_S1_MODEL10_m_disk_radiated' + ===================== ===== + +.. table:: Check sets + + ====================== ====== + Set name checks + ====================== ====== + 'CHECK_AFTER_CREATE' + 'CHECK_AFTER_COMBINE' 'failure_rate' + 'CHECK_AFTER_EXTRA' 'CO_type', 'SN_type' + 'CHECK_AFTER_TRAINING' + ====================== ====== diff --git a/docs/_source/components-overview/post_processing/psygrid.rst b/docs/_source/components-overview/post_processing/psygrid.rst new file mode 100644 index 0000000000..9de69bb5fb --- /dev/null +++ b/docs/_source/components-overview/post_processing/psygrid.rst @@ -0,0 +1,434 @@ +.. _psygrid: + +################ +`PSyGrid` object +################ + +The :samp:`PSyGrid` object contains the data coming from detailed MESA +simulations. + +Import a :samp:`PSyGrid` object by using: + +.. code-block:: python + + from posydon.grids.psygrid import PSyGrid + + +Initializing a `PSyGrid` object +------------------------------- + +One can simply get a new :samp:`PSyGrid` object via + +.. code-block:: python + + mygrid = PSyGrid() + +This calls the initializer, which can take two optional arguments + +- :samp:`filepath`: it is the path to the associated h5 file; if the file + exists the `PSyGrid` object in there will be loaded +- :samp:`verbose`: it is a boolean indicating whether detailed output should be + given when calling functions of the :samp:`PSyGrid` object + + +Creating a `PSyGrid` object +--------------------------- + +The :samp:`create` function of a :samp:`PSyGrid` object will read MESA output +data into the :samp:`PSyGrid` object. Thus it has a required argument and +several optional arguments. The required one is :samp:`MESA_grid_path`, which +is the path to the directory, where the MESA runs are in. + +.. table:: Optional arguments of the :samp:`PSyGrid` creation + :widths: 18,10,72 + + =============== ========= =========== + Argument Default Description + =============== ========= =========== + psygrid_path None the path to the associated h5 file (it needs to be given if none was specified during initialization) + overwrite False if :samp:`True` overwrite a potentially already existing file + slim False if :samp:`True` only initial and final values as well as the meta data will be stored + warn "end" if "normal" warnings are printed, when they arise + + if "end" all warnings are printed at the end + + if "suppress" no warnings are printed + fmt "posydon" grid format; only "posydon" is currently supported + \*\*grid_kwargs further grid properties can be specified in a dictionary + =============== ========= =========== + +.. _tab_grid_properties: + +.. table:: Grid properties + + ============================== ============ =========== + Property Default Description + ============================== ============ =========== + 'description' "" a description text + 'max_number_of_runs' None the maximum number of runs + 'format' "hdf5" file format; only "hdf5" is currently supported + 'compression' "gzip9" the compression (of the hdf5 file) + 'history_DS_error' None the maximum error allowed when downsampling the history + 'history_DS_exclude' default list the history columns to exclude from downsampling (default list: ["model_number", "age", "star_age"]) + 'profile_DS_error' None the maximum error allowed when downsampling the final profile + 'profile_DS_interval' None the maximum change in an downsampled interval relative to the change from initial to final + 'profile_DS_exclude' default list the profile columns to exclude from downsampling (default list: ["mass", "star_mass"]) + 'star1_history_saved_columns' "minimum" specifies which history columns of star 1 should be read + + if "all" read all the columns in the MESA output + + if "minimum" use the default + + if a tuple of column names read only those columns + + if a list of column names read the default and those columns + 'star2_history_saved_columns' "minimum" specifies which history columns of star 2 should be read having the same options as 'star1_history_saved_columns' + 'binary_history_saved_columns' "minimum" specifies which binary history columns should be read having the same options as 'star1_history_saved_columns' + 'star1_profile_saved_columns' "minimum" specifies which profile columns of star 1 should be read having the same options as 'star1_history_saved_columns' + 'star2_profile_saved_columns' "minimum" specifies which profile columns of star 2 should be read having the same options as 'star1_history_saved_columns' + 'initial_value_columns' None history columns to store initial values from (currently not in use, instead all specified history columns are used and additionally the abundances X, Y, and Z) + 'final_value_columns' None history columns to store final values from (currently not in use, instead all specified history columns are used and additionally termination flags and for binaries the interpolation class) + 'start_at_RLO' False specifies whether to crop the history to start at RLO + 'stop_before_carbon_depletion' False specifies whether to crop the history of massive stars (>100 Msun) to stop at 10% central carbon and after helium is depleted + 'binary' True specifies whether a gird evolved binaries; put :samp:`False` for single stars + 'eep' None path to directory with EEP files (for single stars only) + 'initial_RLO_fix' False specifies whether the boundary of initial RLO should be determined to flag all systems below as initial RLO independent of the MESA output + 'He_core_fix' True specifies to ensure that the He core is always larger or equal to the carbon-oxygen core + 'accept_missing_profile' False specifies whether try to include all data from MESA runs without final profiles + ============================== ============ =========== + +You can read the MESA data into an existing :samp:`PSyGrid` object, which may +overwrites data: + +.. code-block:: python + + mygrid.create(MESA_grid_path=".") + +or combine the initialization with the creation: + +.. code-block:: python + + mygrid = PSyGrid().create(MESA_grid_path=".") + + +Loading a `PSyGrid` object +-------------------------- + +You can load an existing h5 file (e.g. "myPSyGrid.h5") into a :samp:`PSyGrid` +object by + +.. code-block:: python + + mygrid.load(filepath="myPSyGrid.h5") + +It is more convenient to load the file directly when initializing the +:samp:`PSyGrid` object + +.. code-block:: python + + mygrid = PSyGrid(filepath="myPSyGrid.h5") + + +Contents of a `PSyGrid` object +------------------------------ + +Print a `PSyGrid` object +~~~~~~~~~~~~~~~~~~~~~~~~ + +To check the content of the :samp:`PSyGrid` object you can simply print it: + +.. code-block:: python + + print(mygrid) + +This will provide a summary, which tell you + +- to which hdf5 file it is connected +- how many runs are in there and have + + - a binary history + - a history of star 1 + - a history of star 2 + - a final profile of star 1 + - a final profile of star 2 + +- the fields in the each of the histories/profiles of the last run +- the fields of the initial and final values +- information on the configuration +- a shorthand list of the MESA directories (the locations of the data the runs + where extracted from) + +To access single runs, it is important to know how many are there to avoid to +call for a non existing one. Hence, you can simply get the number of runs via: + +.. code-block:: python + + len(mygrid) + +.. note:: + Alternatively you can request the length from the internal number stored in + :samp:`mygrid.n_runs`. + + +Accessing data in a `PSyGrid` object +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The first data you may want to check are the +:ref:`grid properties `. You can get a list of the +properties available for your :samp:`PSyGrid` object simply with + +.. code-block:: python + + mygrid.config.keys() + +By providing one of the properties to :samp:`mygrid.config[{PROPERTY}]` you can +access its value. + +Next, you can look at the initial and final values of the runs. All the values +are available at :samp:`mygrid.initial_values` and :samp:`mygrid.final_values`, +respectively. To get a tuple of all the available values use + +.. code-block:: python + + mygrid.initial_values.dtype.names + mygrid.final_values.dtype.names + +Each value you then get for example via :samp:`mygrid.initial_values[{VALUE}]`. +It will return a numpy array with the this value for all the runs. So you get +the initial mass of star 1 in the third run with + +.. code-block:: python + + mygrid.initial_values['star_1_mass'][2] + +.. note:: + Remember, that the first run has the index :samp:`0` and the last one + :samp:`len(mygrid)-1`. + +The each initial and final value will have the same number and order of run +entries. This holds for the number of list entries of MESA directories, too. + +.. code-block:: python + + mygrid.MESA_dirs + +You can get the individual runs via its index. :samp:`mygrid[{IDX}]` is a +:samp:`PSyRunView` object, which contains the data of the run specified with +:samp:`IDX`. The :samp:`PSyRunView` object contains seven components: + +.. table:: :samp:`PSyRunView` object components + + ================ =========== + Component Description + ================ =========== + 'initial_values' all initial values of the run + 'final_values' all final values of the run including termination flags + 'binary_history' the binary history + 'history1' the history of star 1 + 'history2' the history of star 2 + 'final_profile1' the final profile of star 1 + 'final_profile2' the final profile of star 2 + ================ =========== + +Again you can check for the contents of the components with +:samp:`dtype.names`, e.g. + +.. code-block:: python + + myrun = mygrid[0] + myrun['binary_history'].dtype.names + +Now you know a second way to get the initial mass of star 1 in the third run +with a one-liner + +.. code-block :: python + + mygrid[2]['initial_values']['star_1_mass'] + +You would may think of a third way being + +.. code-block :: python + + mygrid[2]['binary_history']['star_1_mass'][0] + +But this will not give the initial value, while it is close to it. The reason +for this not being the same is in MESA having a slightly different value in the +first line of the history files compared to the given initial value. The final +values and the derived initial values instead are the same as the last or first +values in the corresponding history. + +.. note:: + For efficiency reasons not all the :samp:`PSyGrid` object is loaded into + RAM. Instead parts are reads from the associated hdf5 file if needed. This + has the consequence, that it is discouraged to refer to the same values + more than once in a code. If you need the same value more often, you should + store it in a local variable. + + +Plot a `PSyGrid` object +~~~~~~~~~~~~~~~~~~~~~~~ + +Beside getting the values itself there are plotting functionalities available +to display the content of a :samp:`PSyGrid` object. There are three main plotting +functionalities: + +- :samp:`plot`: This creates a one dimensional plot from the :samp:`PSyGrid`. + An example can be found in the :ref:`tutorials `. The code details + are available in the + :py:func:`PSyGrid.plot ` code and the + :py:class:`visualisation libary `. +- :samp:`plot2D`: This creates a two dimensional representation from the + :samp:`PSyGrid`. Again, an example can be found in the + :ref:`tutorials `. The code details are available in the + :py:func:`PSyGrid.plot ` code and the + :py:class:`visualisation libary `. +- :samp:`HR`: This is similar to :samp:`plot` but specialized on producing + Hertzsprung–Russell diagrams. + + +Work on/with a `PSyGrid` object +------------------------------- + +Loop on a `PSyGrid` object +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Similarly to accessing a single value in the :samp:`PSyGrid` object we can loop +over a :samp:`PSyGrid` object, which will loop over the individual runs in the +:samp:`PSyGrid` object. Hence the following two codes will produce the same +output. The first one loops through the numpy array if the initial companion +mass + +.. code-block:: python + + for mass in mygrid.initial_values['star_2_mass']: + print(mass) + +while the second one loops through the runs and prints the initial companion +mass + +.. code-block:: python + + for run in mygrid: + print(run['initial_values']['star_2_mass']) + + + +Expand a `PSyGrid` object +~~~~~~~~~~~~~~~~~~~~~~~~~ + +Because of the complexity of the :samp:`PSyGrid` object we encourage the user +to only use our dedicated functions to add content to the object. There is a +function to add an extra column to the :samp:`final_values`. Here is an example +how to add a new column which contains the final orbital period in units of +years instead of days: + +.. code-block:: python + + new_column_data = mygrid.final_values['period_days']/365.25 + mygrid.add_column('period_years', new_column_data, where='final_values', overwrite=False) + +The four arguments are a string with the name of the new field, the data to be +stored in there, to which component of the :samp:`PSyGrid` object it should get +added, and whether a field with the same name should be overwritten, if it +already exists. + +.. warning:: + The new data has to have as many entries as the :samp:`PSyGrid` object has + runs. + +.. note:: + Currently, the parameter :samp:`where` only supports the value + 'final_values'. + + +Join two or more `PSyGrid` objects +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +There are different reasons, why you create several :samp:`PSyGrid` objects +which you would like to combine to a single one later, e.g. adding reruns. +There is a functionality to do this for you. To avoid too many conflicts of +possible modifications of already loaded :samp:`PSyGrid` objects, this function +is not part of the :samp:`PSyGrid`-object class. Instead it take a list of +paths to the hdf5 files containing :samp:`PSyGrid` objects to be combined to a +new one. Those are the two required arguments of the :samp:`join_grids` +function. Additionally, you can specify the arguments :samp:`compression`, +:samp:`description`, and :samp:`verbose`. The :samp:`join_grids` function will +check, whether the grids are compatible and join them if possible. + +.. note:: + If there are common systems in two or more grids, this routine will only + put the last run with same initial conditions in the newly combined + :samp:`PSyGrid` object. + +We recommend to use the :ref:`post-processing pipeline ` to create +and join grids. + +.. + Extract the initial and final values as a pandas data frame + ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + TODO: get_pandas_initial_final() + + +Get reruns from a `PSyGrid` object +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Usually, not all runs of a grid will be successfully run in MESA. Hence, one +may wants to rerun some of them with changed parameters. There is a function, +to export runs from a :samp:`PSyGrid` object. There are two general ways to +specify, which systems should be exported to rerun: + +1. Write your own logic and create a numpy array with the indexes of the systems, you would like to run again. +2. Specify, which termination flag(s) the systems should have to be rerun. + +.. + .. table:: Arguments of the :samp:`rerun` function + + ================= ======= =========== + Argument Default Description + ================= ======= =========== + path_to_file './' where to create the file(s) for the rerun + runs_to_rerun None a numpy array containing the indexes of the runs in the :samp:`PSyGrid` object (if given, leave :samp:`termination_flags=None`) + termination_flags None a single termination flag code or a list of them (if given, leave :samp:`runs_to_rerun=None`) + new_mesa_flag None dictionary with the names and the values of MESA parameters to be changed for the inlists of the new runs + ================= ======= =========== + +.. table:: Arguments of the :samp:`rerun` function + + ================= ======= =========== + Argument Default Description + ================= ======= =========== + path_to_file './' where to create the file(s) for the rerun + runs_to_rerun None a list containing the indexes of the runs in the :samp:`PSyGrid` object + termination_flags None a single termination flag code or a list of them + new_mesa_flag None dictionary with the names and the values of MESA parameters to be changed for the inlists of the new runs + flags_to_check None a termination flag key or a list of them (if None check only 'termination_flag_1') + ================= ======= =========== + +.. note:: + If both :samp:`runs_to_rerun` and :samp:`termination_flags` is given, all + systems matching at least one of the two will be selected for rerun. + +The :ref:`post-processing pipeline ` already provides some pre +defined rerun options. + + +Close associated hdf5 file +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Finally, you can close the hdf5 file, which is recommended to ensure that all +your changes on the :samp:`PSyGrid` object is safely written into the file. + +.. code-block:: python + + mygrid.close() + +This is done as well, in the case you call the destructor of the +:samp:`PSyGrid` object. + +.. code-block:: python + + del mygrid + + +The code summary of the :samp:`PSyGrid` object can be found at the +:py:class:`~posydon.grids.psygrid` reference page. diff --git a/docs/_source/components-overview/processing-pipeline.rst b/docs/_source/components-overview/processing-pipeline.rst index 9ab99b282a..6f86d1018f 100644 --- a/docs/_source/components-overview/processing-pipeline.rst +++ b/docs/_source/components-overview/processing-pipeline.rst @@ -1,4 +1,37 @@ .. _processing-pipeline: Processing Pipeline -------------------- \ No newline at end of file +=================== + +Post-process Grids +------------------ + +After the MESA girds have finished, the important data needs to be extracted from them. + +`PSyGrid` object +~~~~~~~~~~~~~~~~ + +We have a dedicated object to deal with the MESA data outcome. Learn what you can do with it. + +.. toctree:: + + post_processing/psygrid + +Our post-processing pipeline +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +We use a pipeline to post-process the data in a more automatized way. Learn to what you can do with this tool. + +.. toctree:: + + post_processing/pipeline + +Ini file documentation +~~~~~~~~~~~~~~~~~~~~~~ + +Learn how to use an ini file to setup the post-processing pipeline. + +.. toctree:: + + post_processing/pipeline_ini + diff --git a/docs/_source/tutorials-examples/generating-datasets/generating-datasets.rst b/docs/_source/tutorials-examples/generating-datasets/generating-datasets.rst index 865f114485..f899f5669a 100644 --- a/docs/_source/tutorials-examples/generating-datasets/generating-datasets.rst +++ b/docs/_source/tutorials-examples/generating-datasets/generating-datasets.rst @@ -32,6 +32,8 @@ From A to Z, this tutorial shows you how to process, concatenate, downsample, pl Congratulations! You now master the POSYDON Processing Pipeline. To learn more about the PSyGrid object or the Processing Pipeline API ini file, check out the [API Documentation](api.rst) and the indepth components overview. +.. _plot_1d: + III. 1D Plotting Functionalities for POSYDON PSyGrids ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -42,6 +44,8 @@ Unlock the power of 1D plotting functionalities for POSYDON PSyGrids. This tutor plot_1D +.. _plot_2d: + IV. 2D Plotting Functionalities for POSYDON PSyGrids ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ From c589732309382ed98b2b43e9940777d0ee7cecde Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 30 Nov 2023 16:18:41 +0100 Subject: [PATCH 144/319] Upgrades on documentation makefile (#186) * add bin files; remove autodoc files, get them automatically * rename stuff, get tmp directory, expand clean * expand clean more --- .gitignore | 4 +- docs/Makefile | 76 +++++++++------ .../posydon.active_learning.psy_cris.rst | 56 ----------- ...ctive_learning.psy_cris.synthetic_data.rst | 25 ----- .../api_reference/posydon.active_learning.rst | 14 --- .../api_reference/posydon.binary_evol.CE.rst | 17 ---- .../api_reference/posydon.binary_evol.DT.rst | 57 ----------- .../posydon.binary_evol.MESA.rst | 17 ---- .../api_reference/posydon.binary_evol.SN.rst | 25 ----- .../api_reference/posydon.binary_evol.rst | 59 ----------- docs/_source/api_reference/posydon.grids.rst | 73 -------------- .../api_reference/posydon.interpolation.rst | 57 ----------- docs/_source/api_reference/posydon.popsyn.rst | 97 ------------------- docs/_source/api_reference/posydon.rst | 14 --- docs/_source/api_reference/posydon.utils.rst | 49 ---------- .../posydon.visualization.VH_diagram.rst | 81 ---------------- .../api_reference/posydon.visualization.rst | 72 -------------- docs/_source/conf.py | 1 + docs/_source/index.rst | 1 + 19 files changed, 53 insertions(+), 742 deletions(-) delete mode 100644 docs/_source/api_reference/posydon.active_learning.psy_cris.rst delete mode 100644 docs/_source/api_reference/posydon.active_learning.psy_cris.synthetic_data.rst delete mode 100644 docs/_source/api_reference/posydon.active_learning.rst delete mode 100644 docs/_source/api_reference/posydon.binary_evol.CE.rst delete mode 100644 docs/_source/api_reference/posydon.binary_evol.DT.rst delete mode 100644 docs/_source/api_reference/posydon.binary_evol.MESA.rst delete mode 100644 docs/_source/api_reference/posydon.binary_evol.SN.rst delete mode 100644 docs/_source/api_reference/posydon.binary_evol.rst delete mode 100644 docs/_source/api_reference/posydon.grids.rst delete mode 100644 docs/_source/api_reference/posydon.interpolation.rst delete mode 100644 docs/_source/api_reference/posydon.popsyn.rst delete mode 100644 docs/_source/api_reference/posydon.rst delete mode 100644 docs/_source/api_reference/posydon.utils.rst delete mode 100644 docs/_source/api_reference/posydon.visualization.VH_diagram.rst delete mode 100644 docs/_source/api_reference/posydon.visualization.rst diff --git a/.gitignore b/.gitignore index fc8edce123..ef52d5b994 100644 --- a/.gitignore +++ b/.gitignore @@ -69,7 +69,9 @@ instance/ # Sphinx documentation docs/_build/ -docs/api/ +docs/_tmp/ +docs/_source/api_reference +docs/checkautodoc *.h5 *.batch diff --git a/docs/Makefile b/docs/Makefile index c41e9ab945..8ad69ca4de 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -5,7 +5,7 @@ SPHINXOPTS = SPHINXBUILD = sphinx-build PAPER = -SOURCEDIR = _source +SOURCEDIR = _source BUILDDIR = _build # User-friendly check for sphinx-build @@ -51,47 +51,49 @@ help: .PHONY: clean clean: - rm -rf $(BUILDDIR)/* + rm -rf $(BUILDDIR) + rm -rf _tmp + rm -f checkautodoc .PHONY: html -html: +html: checkautodoc $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." .PHONY: dirhtml -dirhtml: +dirhtml: checkautodoc $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml." .PHONY: singlehtml -singlehtml: +singlehtml: checkautodoc $(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml @echo @echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml." .PHONY: pickle -pickle: +pickle: checkautodoc $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle @echo @echo "Build finished; now you can process the pickle files." .PHONY: json -json: +json: checkautodoc $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json @echo @echo "Build finished; now you can process the JSON files." .PHONY: htmlhelp -htmlhelp: +htmlhelp: checkautodoc $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp @echo @echo "Build finished; now you can run HTML Help Workshop with the" \ ".hhp project file in $(BUILDDIR)/htmlhelp." .PHONY: qthelp -qthelp: +qthelp: checkautodoc $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp @echo @echo "Build finished; now you can run "qcollectiongenerator" with the" \ @@ -101,7 +103,7 @@ qthelp: @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/Hveto.qhc" .PHONY: applehelp -applehelp: +applehelp: checkautodoc $(SPHINXBUILD) -b applehelp $(ALLSPHINXOPTS) $(BUILDDIR)/applehelp @echo @echo "Build finished. The help book is in $(BUILDDIR)/applehelp." @@ -110,7 +112,7 @@ applehelp: "bundle." .PHONY: devhelp -devhelp: +devhelp: checkautodoc $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp @echo @echo "Build finished." @@ -120,19 +122,19 @@ devhelp: @echo "# devhelp" .PHONY: epub -epub: +epub: checkautodoc $(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub @echo @echo "Build finished. The epub file is in $(BUILDDIR)/epub." .PHONY: epub3 -epub3: +epub3: checkautodoc $(SPHINXBUILD) -b epub3 $(ALLSPHINXOPTS) $(BUILDDIR)/epub3 @echo @echo "Build finished. The epub3 file is in $(BUILDDIR)/epub3." .PHONY: latex -latex: +latex: checkautodoc $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex @echo @echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex." @@ -140,33 +142,33 @@ latex: "(use \`make latexpdf' here to do that automatically)." .PHONY: latexpdf -latexpdf: +latexpdf: checkautodoc $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex @echo "Running LaTeX files through pdflatex..." $(MAKE) -C $(BUILDDIR)/latex all-pdf @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." .PHONY: latexpdfja -latexpdfja: +latexpdfja: checkautodoc $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex @echo "Running LaTeX files through platex and dvipdfmx..." $(MAKE) -C $(BUILDDIR)/latex all-pdf-ja @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." .PHONY: text -text: +text: checkautodoc $(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text @echo @echo "Build finished. The text files are in $(BUILDDIR)/text." .PHONY: man -man: +man: checkautodoc $(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man @echo @echo "Build finished. The manual pages are in $(BUILDDIR)/man." .PHONY: texinfo -texinfo: +texinfo: checkautodoc $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo @echo @echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo." @@ -174,57 +176,75 @@ texinfo: "(use \`make info' here to do that automatically)." .PHONY: info -info: +info: checkautodoc $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo @echo "Running Texinfo files through makeinfo..." make -C $(BUILDDIR)/texinfo info @echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo." .PHONY: gettext -gettext: +gettext: checkautodoc $(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale @echo @echo "Build finished. The message catalogs are in $(BUILDDIR)/locale." .PHONY: changes -changes: +changes: checkautodoc $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes @echo @echo "The overview file is in $(BUILDDIR)/changes." .PHONY: linkcheck -linkcheck: +linkcheck: checkautodoc $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck @echo @echo "Link check complete; look for any errors in the above output " \ "or in $(BUILDDIR)/linkcheck/output.txt." .PHONY: doctest -doctest: +doctest: checkautodoc $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest @echo "Testing of doctests in the sources finished, look at the " \ "results in $(BUILDDIR)/doctest/output.txt." .PHONY: coverage -coverage: +coverage: checkautodoc $(SPHINXBUILD) -b coverage $(ALLSPHINXOPTS) $(BUILDDIR)/coverage @echo "Testing of coverage in the sources finished, look at the " \ "results in $(BUILDDIR)/coverage/python.txt." .PHONY: xml -xml: +xml: checkautodoc $(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml @echo @echo "Build finished. The XML files are in $(BUILDDIR)/xml." .PHONY: pseudoxml -pseudoxml: +pseudoxml: checkautodoc $(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml @echo @echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml." .PHONY: apidoc apidoc: - sphinx-apidoc -o api/ ../posydon --no-toc --separate --no-headings --force +#old: sphinx-apidoc -o api/ ../posydon --no-toc --separate --no-headings --force +# remove old files + rm -f ./_source/api_reference/*.rst +# generate posydon reference (ignore config.py file) + sphinx-apidoc --module-first --templatedir=_source/sphinx-apidoc-templates -d 2 --force --no-toc -o ./_source/api_reference ../posydon ../posydon/config.py +# create "_tmp/executables" directory if not existing + mkdir -p _tmp/executables +# copy script files from "bin" directory to "_tmp/executables", on the fly replace "-" with "_" and add extension ".py" + for FILE in $$(ls ../bin); do \ + OLDFILE=$$(echo ../bin/$${FILE}); \ + NEWFILE=$$(echo ./_tmp/executables/$${FILE}.py | sed s/-/_/g); \ + cp $${OLDFILE} $${NEWFILE}; \ + done +# generate reference for script files + sphinx-apidoc --module-first --templatedir=_source/sphinx-apidoc-templates -d 2 --force --tocfile bin -o ./_source/api_reference ./_tmp/executables @echo @echo "APIdoc run is complete." + +checkautodoc: $(shell find ../posydon -name *.py) $(shell ls ../bin/*) + touch checkautodoc + make apidoc diff --git a/docs/_source/api_reference/posydon.active_learning.psy_cris.rst b/docs/_source/api_reference/posydon.active_learning.psy_cris.rst deleted file mode 100644 index f49d48e5e7..0000000000 --- a/docs/_source/api_reference/posydon.active_learning.psy_cris.rst +++ /dev/null @@ -1,56 +0,0 @@ -posydon.active\_learning.psy\_cris -================================== - -.. automodule:: posydon.active_learning.psy_cris - :members: - :undoc-members: - :show-inheritance: - - -.. toctree:: - :maxdepth: 2 - :titlesonly: - - posydon.active_learning.psy_cris.synthetic_data - - - -posydon.active\_learning.psy\_cris.classify -------------------------------------------- - -.. automodule:: posydon.active_learning.psy_cris.classify - :members: - :undoc-members: - :show-inheritance: - -posydon.active\_learning.psy\_cris.data ---------------------------------------- - -.. automodule:: posydon.active_learning.psy_cris.data - :members: - :undoc-members: - :show-inheritance: - -posydon.active\_learning.psy\_cris.regress ------------------------------------------- - -.. automodule:: posydon.active_learning.psy_cris.regress - :members: - :undoc-members: - :show-inheritance: - -posydon.active\_learning.psy\_cris.sample ------------------------------------------ - -.. automodule:: posydon.active_learning.psy_cris.sample - :members: - :undoc-members: - :show-inheritance: - -posydon.active\_learning.psy\_cris.utils ----------------------------------------- - -.. automodule:: posydon.active_learning.psy_cris.utils - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_source/api_reference/posydon.active_learning.psy_cris.synthetic_data.rst b/docs/_source/api_reference/posydon.active_learning.psy_cris.synthetic_data.rst deleted file mode 100644 index 6286a3ba22..0000000000 --- a/docs/_source/api_reference/posydon.active_learning.psy_cris.synthetic_data.rst +++ /dev/null @@ -1,25 +0,0 @@ -posydon.active\_learning.psy\_cris.synthetic\_data -================================================== - -.. automodule:: posydon.active_learning.psy_cris.synthetic_data - :members: - :undoc-members: - :show-inheritance: - - - -posydon.active\_learning.psy\_cris.synthetic\_data.synth\_data\_2D ------------------------------------------------------------------- - -.. automodule:: posydon.active_learning.psy_cris.synthetic_data.synth_data_2D - :members: - :undoc-members: - :show-inheritance: - -posydon.active\_learning.psy\_cris.synthetic\_data.synth\_data\_3D ------------------------------------------------------------------- - -.. automodule:: posydon.active_learning.psy_cris.synthetic_data.synth_data_3D - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_source/api_reference/posydon.active_learning.rst b/docs/_source/api_reference/posydon.active_learning.rst deleted file mode 100644 index 2ba80b47c6..0000000000 --- a/docs/_source/api_reference/posydon.active_learning.rst +++ /dev/null @@ -1,14 +0,0 @@ -posydon.active\_learning -======================== - -.. automodule:: posydon.active_learning - :members: - :undoc-members: - :show-inheritance: - - -.. toctree:: - :maxdepth: 2 - :titlesonly: - - posydon.active_learning.psy_cris diff --git a/docs/_source/api_reference/posydon.binary_evol.CE.rst b/docs/_source/api_reference/posydon.binary_evol.CE.rst deleted file mode 100644 index a42181bd63..0000000000 --- a/docs/_source/api_reference/posydon.binary_evol.CE.rst +++ /dev/null @@ -1,17 +0,0 @@ -posydon.binary\_evol.CE -======================= - -.. automodule:: posydon.binary_evol.CE - :members: - :undoc-members: - :show-inheritance: - - - -posydon.binary\_evol.CE.step\_CEE ---------------------------------- - -.. automodule:: posydon.binary_evol.CE.step_CEE - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_source/api_reference/posydon.binary_evol.DT.rst b/docs/_source/api_reference/posydon.binary_evol.DT.rst deleted file mode 100644 index 5cb520682c..0000000000 --- a/docs/_source/api_reference/posydon.binary_evol.DT.rst +++ /dev/null @@ -1,57 +0,0 @@ -posydon.binary\_evol.DT -======================= - -.. automodule:: posydon.binary_evol.DT - :members: - :undoc-members: - :show-inheritance: - - - -posydon.binary\_evol.DT.double\_CO ----------------------------------- - -.. automodule:: posydon.binary_evol.DT.double_CO - :members: - :undoc-members: - :show-inheritance: - -posydon.binary\_evol.DT.step\_detached --------------------------------------- - -.. automodule:: posydon.binary_evol.DT.step_detached - :members: - :undoc-members: - :show-inheritance: - -posydon.binary\_evol.DT.step\_disrupted ---------------------------------------- - -.. automodule:: posydon.binary_evol.DT.step_disrupted - :members: - :undoc-members: - :show-inheritance: - -posydon.binary\_evol.DT.step\_initially\_single ------------------------------------------------ - -.. automodule:: posydon.binary_evol.DT.step_initially_single - :members: - :undoc-members: - :show-inheritance: - -posydon.binary\_evol.DT.step\_isolated --------------------------------------- - -.. automodule:: posydon.binary_evol.DT.step_isolated - :members: - :undoc-members: - :show-inheritance: - -posydon.binary\_evol.DT.step\_merged ------------------------------------- - -.. automodule:: posydon.binary_evol.DT.step_merged - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_source/api_reference/posydon.binary_evol.MESA.rst b/docs/_source/api_reference/posydon.binary_evol.MESA.rst deleted file mode 100644 index d05d7b45b2..0000000000 --- a/docs/_source/api_reference/posydon.binary_evol.MESA.rst +++ /dev/null @@ -1,17 +0,0 @@ -posydon.binary\_evol.MESA -========================= - -.. automodule:: posydon.binary_evol.MESA - :members: - :undoc-members: - :show-inheritance: - - - -posydon.binary\_evol.MESA.step\_mesa ------------------------------------- - -.. automodule:: posydon.binary_evol.MESA.step_mesa - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_source/api_reference/posydon.binary_evol.SN.rst b/docs/_source/api_reference/posydon.binary_evol.SN.rst deleted file mode 100644 index 56258eb8e6..0000000000 --- a/docs/_source/api_reference/posydon.binary_evol.SN.rst +++ /dev/null @@ -1,25 +0,0 @@ -posydon.binary\_evol.SN -======================= - -.. automodule:: posydon.binary_evol.SN - :members: - :undoc-members: - :show-inheritance: - - - -posydon.binary\_evol.SN.profile\_collapse ------------------------------------------ - -.. automodule:: posydon.binary_evol.SN.profile_collapse - :members: - :undoc-members: - :show-inheritance: - -posydon.binary\_evol.SN.step\_SN --------------------------------- - -.. automodule:: posydon.binary_evol.SN.step_SN - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_source/api_reference/posydon.binary_evol.rst b/docs/_source/api_reference/posydon.binary_evol.rst deleted file mode 100644 index 14984acac5..0000000000 --- a/docs/_source/api_reference/posydon.binary_evol.rst +++ /dev/null @@ -1,59 +0,0 @@ -posydon.binary\_evol -==================== - -.. automodule:: posydon.binary_evol - :members: - :undoc-members: - :show-inheritance: - - -.. toctree:: - :maxdepth: 2 - :titlesonly: - - posydon.binary_evol.CE - posydon.binary_evol.DT - posydon.binary_evol.MESA - posydon.binary_evol.SN - - - -posydon.binary\_evol.binarystar -------------------------------- - -.. automodule:: posydon.binary_evol.binarystar - :members: - :undoc-members: - :show-inheritance: - -posydon.binary\_evol.flow\_chart --------------------------------- - -.. automodule:: posydon.binary_evol.flow_chart - :members: - :undoc-members: - :show-inheritance: - -posydon.binary\_evol.simulationproperties ------------------------------------------ - -.. automodule:: posydon.binary_evol.simulationproperties - :members: - :undoc-members: - :show-inheritance: - -posydon.binary\_evol.singlestar -------------------------------- - -.. automodule:: posydon.binary_evol.singlestar - :members: - :undoc-members: - :show-inheritance: - -posydon.binary\_evol.step\_end ------------------------------- - -.. automodule:: posydon.binary_evol.step_end - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_source/api_reference/posydon.grids.rst b/docs/_source/api_reference/posydon.grids.rst deleted file mode 100644 index 1b5ca9cbf3..0000000000 --- a/docs/_source/api_reference/posydon.grids.rst +++ /dev/null @@ -1,73 +0,0 @@ -posydon.grids -============= - -.. automodule:: posydon.grids - :members: - :undoc-members: - :show-inheritance: - - - -posydon.grids.MODELS --------------------- - -.. automodule:: posydon.grids.MODELS - :members: - :undoc-members: - :show-inheritance: - -posydon.grids.downsampling --------------------------- - -.. automodule:: posydon.grids.downsampling - :members: - :undoc-members: - :show-inheritance: - -posydon.grids.downsampling\_report ----------------------------------- - -.. automodule:: posydon.grids.downsampling_report - :members: - :undoc-members: - :show-inheritance: - -posydon.grids.io ----------------- - -.. automodule:: posydon.grids.io - :members: - :undoc-members: - :show-inheritance: - -posydon.grids.post\_processing ------------------------------- - -.. automodule:: posydon.grids.post_processing - :members: - :undoc-members: - :show-inheritance: - -posydon.grids.psygrid ---------------------- - -.. automodule:: posydon.grids.psygrid - :members: - :undoc-members: - :show-inheritance: - -posydon.grids.scrubbing ------------------------ - -.. automodule:: posydon.grids.scrubbing - :members: - :undoc-members: - :show-inheritance: - -posydon.grids.termination\_flags --------------------------------- - -.. automodule:: posydon.grids.termination_flags - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_source/api_reference/posydon.interpolation.rst b/docs/_source/api_reference/posydon.interpolation.rst deleted file mode 100644 index 1f34dda88d..0000000000 --- a/docs/_source/api_reference/posydon.interpolation.rst +++ /dev/null @@ -1,57 +0,0 @@ -posydon.interpolation -===================== - -.. automodule:: posydon.interpolation - :members: - :undoc-members: - :show-inheritance: - - - -posydon.interpolation.IF\_interpolation ---------------------------------------- - -.. automodule:: posydon.interpolation.IF_interpolation - :members: - :undoc-members: - :show-inheritance: - -posydon.interpolation.constraints ---------------------------------- - -.. automodule:: posydon.interpolation.constraints - :members: - :undoc-members: - :show-inheritance: - -posydon.interpolation.data\_scaling ------------------------------------ - -.. automodule:: posydon.interpolation.data_scaling - :members: - :undoc-members: - :show-inheritance: - -posydon.interpolation.eep -------------------------- - -.. automodule:: posydon.interpolation.eep - :members: - :undoc-members: - :show-inheritance: - -posydon.interpolation.interpolation ------------------------------------ - -.. automodule:: posydon.interpolation.interpolation - :members: - :undoc-members: - :show-inheritance: - -posydon.interpolation.profile\_interpolation --------------------------------------------- - -.. automodule:: posydon.interpolation.profile_interpolation - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_source/api_reference/posydon.popsyn.rst b/docs/_source/api_reference/posydon.popsyn.rst deleted file mode 100644 index 0e0b49a610..0000000000 --- a/docs/_source/api_reference/posydon.popsyn.rst +++ /dev/null @@ -1,97 +0,0 @@ -posydon.popsyn -============== - -.. automodule:: posydon.popsyn - :members: - :undoc-members: - :show-inheritance: - - - -posydon.popsyn.GRB ------------------- - -.. automodule:: posydon.popsyn.GRB - :members: - :undoc-members: - :show-inheritance: - -posydon.popsyn.analysis ------------------------ - -.. automodule:: posydon.popsyn.analysis - :members: - :undoc-members: - :show-inheritance: - -posydon.popsyn.binarypopulation -------------------------------- - -.. automodule:: posydon.popsyn.binarypopulation - :members: - :undoc-members: - :show-inheritance: - -posydon.popsyn.defaults ------------------------ - -.. automodule:: posydon.popsyn.defaults - :members: - :undoc-members: - :show-inheritance: - -posydon.popsyn.independent\_sample ----------------------------------- - -.. automodule:: posydon.popsyn.independent_sample - :members: - :undoc-members: - :show-inheritance: - -posydon.popsyn.io ------------------ - -.. automodule:: posydon.popsyn.io - :members: - :undoc-members: - :show-inheritance: - -posydon.popsyn.normalized\_pop\_mass ------------------------------------- - -.. automodule:: posydon.popsyn.normalized_pop_mass - :members: - :undoc-members: - :show-inheritance: - -posydon.popsyn.rate\_calculation --------------------------------- - -.. automodule:: posydon.popsyn.rate_calculation - :members: - :undoc-members: - :show-inheritance: - -posydon.popsyn.selection\_effects ---------------------------------- - -.. automodule:: posydon.popsyn.selection_effects - :members: - :undoc-members: - :show-inheritance: - -posydon.popsyn.star\_formation\_history ---------------------------------------- - -.. automodule:: posydon.popsyn.star_formation_history - :members: - :undoc-members: - :show-inheritance: - -posydon.popsyn.synthetic\_population ------------------------------------- - -.. automodule:: posydon.popsyn.synthetic_population - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_source/api_reference/posydon.rst b/docs/_source/api_reference/posydon.rst deleted file mode 100644 index 0e06dbddb1..0000000000 --- a/docs/_source/api_reference/posydon.rst +++ /dev/null @@ -1,14 +0,0 @@ -POSYDON -======= - -.. toctree:: - :maxdepth: 2 - :titlesonly: - - posydon.active_learning - posydon.binary_evol - posydon.grids - posydon.interpolation - posydon.popsyn - posydon.utils - posydon.visualization \ No newline at end of file diff --git a/docs/_source/api_reference/posydon.utils.rst b/docs/_source/api_reference/posydon.utils.rst deleted file mode 100644 index 2f720929b5..0000000000 --- a/docs/_source/api_reference/posydon.utils.rst +++ /dev/null @@ -1,49 +0,0 @@ -posydon.utils -============= - -.. automodule:: posydon.utils - :members: - :undoc-members: - :show-inheritance: - - - -posydon.utils.common\_functions -------------------------------- - -.. automodule:: posydon.utils.common_functions - :members: - :undoc-members: - :show-inheritance: - -posydon.utils.configfile ------------------------- - -.. automodule:: posydon.utils.configfile - :members: - :undoc-members: - :show-inheritance: - -posydon.utils.constants ------------------------ - -.. automodule:: posydon.utils.constants - :members: - :undoc-members: - :show-inheritance: - -posydon.utils.data\_download ----------------------------- - -.. automodule:: posydon.utils.data_download - :members: - :undoc-members: - :show-inheritance: - -posydon.utils.gridutils ------------------------ - -.. automodule:: posydon.utils.gridutils - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_source/api_reference/posydon.visualization.VH_diagram.rst b/docs/_source/api_reference/posydon.visualization.VH_diagram.rst deleted file mode 100644 index 7084ed923b..0000000000 --- a/docs/_source/api_reference/posydon.visualization.VH_diagram.rst +++ /dev/null @@ -1,81 +0,0 @@ -posydon.visualization.VH\_diagram -================================= - -.. automodule:: posydon.visualization.VH_diagram - :members: - :undoc-members: - :show-inheritance: - - - -posydon.visualization.VH\_diagram.GraphVisualizer -------------------------------------------------- - -.. automodule:: posydon.visualization.VH_diagram.GraphVisualizer - :members: - :undoc-members: - :show-inheritance: - -posydon.visualization.VH\_diagram.MainWindow --------------------------------------------- - -.. automodule:: posydon.visualization.VH_diagram.MainWindow - :members: - :undoc-members: - :show-inheritance: - -posydon.visualization.VH\_diagram.MathTextLabel ------------------------------------------------ - -.. automodule:: posydon.visualization.VH_diagram.MathTextLabel - :members: - :undoc-members: - :show-inheritance: - -posydon.visualization.VH\_diagram.OptionsWindow ------------------------------------------------ - -.. automodule:: posydon.visualization.VH_diagram.OptionsWindow - :members: - :undoc-members: - :show-inheritance: - -posydon.visualization.VH\_diagram.ParseDataFrame ------------------------------------------------- - -.. automodule:: posydon.visualization.VH_diagram.ParseDataFrame - :members: - :undoc-members: - :show-inheritance: - -posydon.visualization.VH\_diagram.Presenter -------------------------------------------- - -.. automodule:: posydon.visualization.VH_diagram.Presenter - :members: - :undoc-members: - :show-inheritance: - -posydon.visualization.VH\_diagram.PresenterMode ------------------------------------------------ - -.. automodule:: posydon.visualization.VH_diagram.PresenterMode - :members: - :undoc-members: - :show-inheritance: - -posydon.visualization.VH\_diagram.PresenterMultiple ---------------------------------------------------- - -.. automodule:: posydon.visualization.VH_diagram.PresenterMultiple - :members: - :undoc-members: - :show-inheritance: - -posydon.visualization.VH\_diagram.SimulationModel -------------------------------------------------- - -.. automodule:: posydon.visualization.VH_diagram.SimulationModel - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_source/api_reference/posydon.visualization.rst b/docs/_source/api_reference/posydon.visualization.rst deleted file mode 100644 index 10aae7df82..0000000000 --- a/docs/_source/api_reference/posydon.visualization.rst +++ /dev/null @@ -1,72 +0,0 @@ -posydon.visualization -===================== - -.. automodule:: posydon.visualization - :members: - :undoc-members: - :show-inheritance: - - -.. toctree:: - :maxdepth: 2 - :titlesonly: - - posydon.visualization.VH_diagram - - - -posydon.visualization.VHdiagram -------------------------------- - -.. automodule:: posydon.visualization.VHdiagram - :members: - :undoc-members: - :show-inheritance: - -posydon.visualization.combine\_TF ---------------------------------- - -.. automodule:: posydon.visualization.combine_TF - :members: - :undoc-members: - :show-inheritance: - -posydon.visualization.interpolation ------------------------------------ - -.. automodule:: posydon.visualization.interpolation - :members: - :undoc-members: - :show-inheritance: - -posydon.visualization.plot1D ----------------------------- - -.. automodule:: posydon.visualization.plot1D - :members: - :undoc-members: - :show-inheritance: - -posydon.visualization.plot2D ----------------------------- - -.. automodule:: posydon.visualization.plot2D - :members: - :undoc-members: - :show-inheritance: - -posydon.visualization.plot\_defaults ------------------------------------- - -.. automodule:: posydon.visualization.plot_defaults - :members: - :undoc-members: - :show-inheritance: - -posydon.visualization.plot\_pop -------------------------------- - -.. automodule:: posydon.visualization.plot_pop - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_source/conf.py b/docs/_source/conf.py index 60f464bd6b..c92e0888a2 100644 --- a/docs/_source/conf.py +++ b/docs/_source/conf.py @@ -35,6 +35,7 @@ # Point towards the posydon source code directory # autodoc uses this to automatically generate documentation from docstrings sys.path.insert(0, os.path.abspath('../../posydon/')) +sys.path.insert(0, os.path.abspath('../_tmp/executables/')) # -- General configuration ------------------------------------------------ # Add any Sphinx extension module names here, as strings. They can be diff --git a/docs/_source/index.rst b/docs/_source/index.rst index 2628c0da11..d3b63b232a 100644 --- a/docs/_source/index.rst +++ b/docs/_source/index.rst @@ -97,6 +97,7 @@ How is this Documentation Structured? :caption: API Reference api_reference/posydon + api_reference/bin .. toctree:: :maxdepth: 1 From e9c03043bfe13c569b2d985f51beafb238f201cf Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 30 Nov 2023 16:30:16 +0100 Subject: [PATCH 145/319] pipeline updates for reruns (#177) * add logic to look of turning on magnetic braking * Update run-pipeline add proposed reruns to pipeline * Update run-pipeline update commit for rerun6 * Update run-pipeline correct typo and copy commit hashes --- bin/run-pipeline | 36 ++++++++++++++++++++++++++++++++++++ 1 file changed, 36 insertions(+) diff --git a/bin/run-pipeline b/bin/run-pipeline index f7dea50a5b..6908475598 100644 --- a/bin/run-pipeline +++ b/bin/run-pipeline @@ -451,6 +451,12 @@ def copy_ini_file(grid_path, rerun_type, destination, cluster): replace_text = "development-22c1bb9e730343558c3e70984a99b3fc1f3c346e" elif rerun_type == 'thermohaline_mixing': replace_text = "development-22c1bb9e730343558c3e70984a99b3fc1f3c346e" + elif rerun_type == 'HeMB_MLTp_mesh': + replace_text = "development-435d16c9158e4608530f21b8169ce6f31160e23e" + elif rerun_type == 'more_mesh': + replace_text = "development-435d16c9158e4608530f21b8169ce6f31160e23e" + elif rerun_type == 'conv_bdy_weight': + replace_text = "development-435d16c9158e4608530f21b8169ce6f31160e23e" elif rerun_type == 'other': # e.g. 'envelope_mass_limit', 'fe_core_infall_limit' # this rerun uses the default inlist commit return @@ -547,6 +553,36 @@ def logic_rerun(grid, rerun_type): elif rerun_type == 'thermohaline_mixing': termination_flags = TF1_POOL_ERROR new_mesa_flag = {'thermohaline_coeff' : 17.5} + elif rerun_type == 'HeMB_MLTp_mesh': + termination_flags = TF1_POOL_ERROR + elif rerun_type == 'more_mesh': + termination_flags = TF1_POOL_ERROR + new_mesa_flag = {'mesh_Pgas_div_P_exponent' : 2, 'max_allowed_nz' : 50000} # may put this in a MESA-inlist PR + elif rerun_type == 'conv_bdy_weight': + N_runs = len(grid) + runs_to_rerun = [] + for i in range(N_runs): + dirname = grid.MESA_dirs[i].decode('utf-8') + if not os.path.isdir(dirname): + # TODO: handle the case when grids were moved location + raise ValueError(f'Grid directory not found at {dirname}') + if "single_HMS" in dirname: + out_txt_path = os.path.join(dirname, + "out_star1_formation_step0.txt") + elif "single_HeMS" in dirname: + out_txt_path = os.path.join(dirname, + "out_star1_formation_step2.txt") + else: + out_txt_path = os.path.join(dirname, "out.txt") + if (out_txt_path is not None) and os.path.isfile(out_txt_path): + with open(out_txt_path, "r") as log_file: + log_lines = log_file.readlines() + for line in log_lines: + if (("STOP prepare_for_new_try: bad num omega" in line) or + ("Segmentation fault" in line)): + runs_to_rerun += [i] + runs_to_rerun = np.array(runs_to_rerun) + new_mesa_flag = {'convective_bdy_weight' : 0} elif rerun_type == 'envelope_mass_limit': # maybe 'fe_core_infall_limit' as well? runs_to_rerun = None # implement logic else: From ddb43e191d318f647a3cb31c14d64f66487ecc7a Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Thu, 14 Dec 2023 16:08:18 +0100 Subject: [PATCH 146/319] Synthetic Population: MPI removal + DataFrame fix (#175) * add casting of scalar_names to float64 when writing to dataframe * separate BinaryPopulation creation from class initialisation * remove cast in singlestar and create standard scalar_names casting in io.py * add scalar_names type casting * add missed typo * add step_name to parser * create new BinaryPopulations in SyntheticPop.evolve() * remove MPI and RNG seeds using JOB_ID+metallicity * Fix for double entries in SyntheticPopulation.df_oneline * handle the binary history and oneline correctly with chunking * account for job array not starting at 0 * allow for job array runs without specifying mpi * add batch file selection from folder * clean up batch folders after merging runs * clean up typos * Altered the tutorial to reflect HPC changes and parse changes * adapt tutorials for new parallel run method * keep MPI capability, but do not import it on the cluster when using job arrays * add check if MPI and job arrays are ran together and raise an error * make sure communicator is not write to state. re-add combine_save_files to save function * additional check for MPI and job array runs. Exit code if parallel + job array requested * include local MPI explanation in tutorials * implemented matthias suggestions * shift entropy statement * typo fix in apply_logic function call * shift_index bug fix * shift the index consistently for each metallicity * add merging of files when save gets called for MPI/job array" --- .../10_binaries_pop_syn.ipynb | 90 +++++++++ .../population-synthesis/bbh_analysis.ipynb | 6 +- .../population-synthesis/debug_pop.ipynb | 33 +-- .../one_met_pop_syn.ipynb | 4 +- .../population-synthesis/pop_syn.ipynb | 112 +++++++++-- posydon/popsyn/binarypopulation.py | 103 ++++++---- posydon/popsyn/io.py | 25 ++- posydon/popsyn/population_params_default.ini | 5 +- posydon/popsyn/synthetic_population.py | 188 +++++++++++++----- 9 files changed, 430 insertions(+), 136 deletions(-) diff --git a/docs/_source/tutorials-examples/population-synthesis/10_binaries_pop_syn.ipynb b/docs/_source/tutorials-examples/population-synthesis/10_binaries_pop_syn.ipynb index c7b75b6438..0014d34e35 100644 --- a/docs/_source/tutorials-examples/population-synthesis/10_binaries_pop_syn.ipynb +++ b/docs/_source/tutorials-examples/population-synthesis/10_binaries_pop_syn.ipynb @@ -1229,6 +1229,96 @@ "\n", "If you want to learn more about population synthesis and how to build more complex models it is advised to continue with the remaining tutorials and consult the POSYDON documentation." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Local MPI runs\n", + "\n", + "To speed up population synthesis runs, you can run on a computing cluster, as described in [HPC Facilities](pop_syn), or you can distribute the population synthesis across multiple cores on your local machine using MPI.\n", + "\n", + "To enable local MPI runs, go into the `population_params_detault.ini` and change `use_MPI` to `True`.\n", + "\n", + "It's important to note that you cannot run have this option enabled for cluster runs!\n", + "\n", + "We create a binary population simulation script to run the population:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile script.py\n", + "from posydon.popsyn.synthetic_population import SyntheticPopulation\n", + "\n", + "if __name__ == \"__main__\":\n", + " synth_pop = SyntheticPopulation(\"./population_params.ini\")\n", + " synth_pop.evolve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This script can be initiated using a local where NR_processors is the number of processors you would like to us." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "mpiexec -n ${NR_processors} python script.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This will create a folder for each metallicity in the population and store output of the parallel runs in it.\n", + "You will have to concatenate these runs into a single population file per metallicity, which can be achieved using the following code:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "from posydon.popsyn.synthetic_population import SyntheticPopulation\n", + "from posydon.config import PATH_TO_POSYDON_DATA\n", + "import os\n", + "\n", + "\n", + "synth_pop = SyntheticPopulation(\"./population_params.ini\")\n", + "# Get the path to the batches in the current folder\n", + "x = os.listdir('.')\n", + "path_to_batches = [i for i in x if i.endswith('_batches')]\n", + "synth_pop.merge_parallel_runs(path_to_batches)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/docs/_source/tutorials-examples/population-synthesis/bbh_analysis.ipynb b/docs/_source/tutorials-examples/population-synthesis/bbh_analysis.ipynb index 9309c6b101..d6270c0b84 100644 --- a/docs/_source/tutorials-examples/population-synthesis/bbh_analysis.ipynb +++ b/docs/_source/tutorials-examples/population-synthesis/bbh_analysis.ipynb @@ -54,9 +54,7 @@ "source": [ "We want to analyze the BBH population data we generated in the previous notebook. We will build a model that predicts the intrinsic and observable BBH population in the Universe. Please refer to the paper for more details about the procedure inolved in the convolution of the BBH synthetic popluation, i.e. the POSYDON population of merging BBH at the time of formation, with the star-formation history of the Universe and the selection effects of the gravitational-wave detector network.\n", "\n", - "In the first step, we load population parameters ini file and the parsed BBH population data from the previous notebook into the Synthetic Population class. \n", - "\n", - "NOTE: In case you did not install mpi4py in your environment, set `use_MPI=False` in the `population_params.ini` to prevent the SyntheticPopulation class to load the module and trowing an error." + "In the first step, we load population parameters ini file and the parsed BBH population data from the previous notebook into the Synthetic Population class. " ] }, { @@ -499,7 +497,7 @@ "metadata": {}, "source": [ "We now generate the synthetic BBH population buy using the `get_dco_at_formation` method. \n", - "- The `onelie_cols` allows you to extract values stored in the oneline dataframe.\n", + "- The `oneline_cols` allows you to extract values stored in the oneline dataframe.\n", "- The `formation_channel` option allows to compute the formation channels of the BBH population.\n", "\n", "The synthetic population cann be saved to a file using the `save_synthetic_pop` method and loaded back into memory using the `load_synthetic_pop` method." diff --git a/docs/_source/tutorials-examples/population-synthesis/debug_pop.ipynb b/docs/_source/tutorials-examples/population-synthesis/debug_pop.ipynb index 96b86ba012..4f53720ddb 100644 --- a/docs/_source/tutorials-examples/population-synthesis/debug_pop.ipynb +++ b/docs/_source/tutorials-examples/population-synthesis/debug_pop.ipynb @@ -40,7 +40,7 @@ "source": [ "To debug the evolution of a binary systems you should load the POSYDON BinaryPopulation object in memory and then call the `restore` and `evolve`. This will run the evolution of the binary system and display any error traceback. You can debug the code by setting breakpoints in the POSYDON code until you devug the specific binary evolution.\n", "\n", - "The trick is to keep the binary population in memory instead of dumping it to the disk. This is done by setting `optimize_ram=False` and `use_MPI=False`." + "The trick is to do this is not dump the binary population to disk but to keep them in memory. This is done by setting the `optimize_ram=False`." ] }, { @@ -59,7 +59,6 @@ } ], "source": [ - "from mpi4py import MPI\n", "from posydon.popsyn.binarypopulation import BinaryPopulation\n", "from posydon.binary_evol.simulationproperties import SimulationProperties\n", "from posydon.binary_evol.flow_chart import flow_chart\n", @@ -372,26 +371,16 @@ " ]),\n", ")\n", "\n", - "def run_simulation(sim_prop, kwargs, file=None, indices=None, use_MPI=False):\n", + "def run_simulation(sim_prop, kwargs, file=None, indices=None):\n", "\n", - " if not use_MPI:\n", - " # create binaries\n", - " pop = BinaryPopulation(entropy=None,\n", - " population_properties=sim_prop,\n", - " file_name=file,\n", - " **kwargs)\n", - " else:\n", - " comm = MPI.COMM_WORLD\n", - " rank = comm.Get_rank()\n", - " size = comm.Get_size()\n", "\n", - " # create binaries\n", - " pop = BinaryPopulation(entropy=None,\n", - " population_properties=sim_prop,\n", - " file_name=file,\n", - " comm=comm,\n", - " **kwargs)\n", - " sim_prop.load_steps(verbose=True)\n", + " # create binaries\n", + " pop = BinaryPopulation(entropy=None,\n", + " population_properties=sim_prop,\n", + " file_name=file,\n", + " **kwargs)\n", + " \n", + " sim_prop.load_steps(verbose=True)\n", "\n", " # evolve binaries\n", " if file is not None:\n", @@ -409,7 +398,7 @@ " return pop\n", "\n", "if __name__ == '__main__' :\n", - " pop = run_simulation(sim_prop, kwargs, use_MPI=False)" + " pop = run_simulation(sim_prop, kwargs)" ] }, { @@ -1513,7 +1502,7 @@ ], "source": [ "# let's load up and rerun the binary index i\n", - "pop = run_simulation(sim_prop, kwargs, file='./population.h5', indices=[i], use_MPI=False)" + "pop = run_simulation(sim_prop, kwargs, file='./population.h5', indices=[i])" ] }, { diff --git a/docs/_source/tutorials-examples/population-synthesis/one_met_pop_syn.ipynb b/docs/_source/tutorials-examples/population-synthesis/one_met_pop_syn.ipynb index 409c1b75a9..b78aed4112 100644 --- a/docs/_source/tutorials-examples/population-synthesis/one_met_pop_syn.ipynb +++ b/docs/_source/tutorials-examples/population-synthesis/one_met_pop_syn.ipynb @@ -96,9 +96,9 @@ "files = [f for f in os.listdir(path) if '1.00e+00_Zsun_population' in f]\n", "path_to_data = [os.path.join(path, file) for file in files] \n", "\n", - "pop = SyntheticPopulation(path_to_ini='./population_params.ini', path_to_data=path_to_data, verbose=True)\n", + "pop = SyntheticPopulation(path_to_ini='./population_params.ini', verbose=True)\n", "\n", - "pop.parse(S1_state='BH', S2_state='BH', binary_state='contact', invert_S1S2=False)\n", + "pop.parse( path_to_data=path_to_data, S1_state='BH', S2_state='BH', binary_state='contact', invert_S1S2=False)\n", "pop.save_pop(os.path.join(path,'BBH_population_1e+00_Zsun.h5'))\n", "\n", "pop.df.head(10)\n", diff --git a/docs/_source/tutorials-examples/population-synthesis/pop_syn.ipynb b/docs/_source/tutorials-examples/population-synthesis/pop_syn.ipynb index 7e6e1dd510..b74b2e7ea9 100644 --- a/docs/_source/tutorials-examples/population-synthesis/pop_syn.ipynb +++ b/docs/_source/tutorials-examples/population-synthesis/pop_syn.ipynb @@ -66,7 +66,7 @@ "metadata": {}, "source": [ "Open the `population_params.ini` file and do the following edits to run a large model at 8 differet metallicities:\n", - "- set `use_MPI = True`\n", + "\n", "- set `metallicity = [2., 1., 0.45, 0.2, 0.1, 0.01, 0.001, 0.0001]`\n", "- set `number_of_binaries = 1000000`" ] @@ -148,19 +148,18 @@ "source": [ "%%sbatch\n", "#!/bin/bash\n", + "#SBATCH --array=0-34\n", "#SBATCH --partition=private-astro-cpu\n", "#SBATCH --job-name=pop_syn\n", - "#SBATCH --output=./pop_synth.out\n", + "#SBATCH --output=./pop_synth_%A_%a.out\n", "#SBATCH --mail-type=FAIL\n", "#SBATCH --mail-user=user@email.ch\n", "#SBATCH --time=24:00:00\n", - "#SBATCH --nodes=2\n", - "#SBATCH --ntasks-per-node=34\n", "#SBATCH --mem-per-cpu=4G\n", "\n", "export PATH_TO_POSYDON=/srv/beegfs/scratch/shares/astro/posydon/simone/POSYDON-public/\n", "export PATH_TO_POSYDON_DATA=/srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/POSYDON_data/230914/\n", - "srun python ./script.py" + "python ./script.py" ] }, { @@ -180,25 +179,109 @@ "#!/bin/bash\n", "#SBATCH --account=b1119\n", "#SBATCH --partition=posydon-priority\n", + "#SBATCH --array=0-34\n", "#SBATCH --job-name=pop_syn\n", "#SBATCH --output=./pop_syn.out\n", "#SBATCH --mail-type=FAIL\n", "#SBATCH --mail-user=user@email.ch\n", "#SBATCH --time=24:00:00\n", - "#SBATCH --nodes=2\n", - "#SBATCH --ntasks-per-node=34\n", "#SBATCH --mem-per-cpu=4G\n", "\n", "export PATH_TO_POSYDON=/projects/b1119/ssb7065/POSYDON-public/\n", "export PATH_TO_POSYDON_DATA=/projects/b1119/POSYDON_GRIDS/POSYDON_popsynth_data/v2/230816/\n", - "mpiexec -n ${SLURM_NTASKS} python ./script.py $SLURM_ARRAY_TASK_ID" + "python ./script.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Combining runs into single metallicity files\n", + "\n", + "The above process creates a temporary batch folder per metallicity in which the sub-processes deposit their output.\n", + "After the processes are done, the files have to be combined into a population file per metallicity.\n", + "The follow code allows you to perform this concatenation, which is similar to the code shown in [the first tutorial](10_binaries_pop_syn)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile concat_runs.py\n", + "from posydon.popsyn.synthetic_population import SyntheticPopulation\n", + "from posydon.config import PATH_TO_POSYDON_DATA\n", + "import os\n", + "\n", + "if __name__ == \"__main__\":\n", + " synth_pop = SyntheticPopulation(\"./population_params.ini\")\n", + " # Get the path to the batches in the current folder\n", + " x = os.listdir('.')\n", + " path_to_batches = [i for i in x if i.endswith('_batches')]\n", + " synth_pop.merge_parallel_runs(path_to_batches)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This script can be manually ran after the job array has finished or we can submit another SLURM job, which only starts once the job array has finished.\n", + "\n", + "For this job to start once the job array has finished, the `job-name` has to be the same as the job array and `dependency=singleton` has to be set.\n", + "If the job array does not finish correctly, this job will never run!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%sbatch\n", + "#!/bin/bash\n", + "#SBATCH --job-name=pop_syn\n", + "#SBATCH --partition=private-astro-cpu\n", + "#SBATCH --output=population_concat.out\n", + "#SBATCH --mail-type=FAIL\n", + "#SBATCH --mail-user=user@email.ch\n", + "#SBATCH --time=01:00:00\n", + "#SBATCH --mem=4GB\n", + "#SBATCH --dependency=singleton\n", + "\n", + "export PATH_TO_POSYDON=/srv/beegfs/scratch/shares/astro/posydon/simone/POSYDON-public/\n", + "export PATH_TO_POSYDON_DATA=/srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/POSYDON_data/230914/\n", + "python concat_runs.py" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%sbatch\n", + "#!/bin/bash\n", + "#SBATCH --job-name=pop_syn\n", + "#SBATCH --account=b1119\n", + "#SBATCH --partition=posydon-priority\n", + "#SBATCH --output=population_concat.out\n", + "#SBATCH --mail-type=FAIL\n", + "#SBATCH --mail-user=user@email.ch\n", + "#SBATCH --time=01:00:00\n", + "#SBATCH --mem=4G\n", + "#SBATCH --dependency=singleton\n", + "\n", + "export PATH_TO_POSYDON=/projects/b1119/ssb7065/POSYDON-public/\n", + "export PATH_TO_POSYDON_DATA=/projects/b1119/POSYDON_GRIDS/POSYDON_popsynth_data/v2/230816/\n", + "python concat_runs.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Parsing the Populatio Synthesis Model Output" + "## Parsing the Population Synthesis Model Output" ] }, { @@ -704,9 +787,9 @@ "source": [ "from posydon.popsyn.synthetic_population import SyntheticPopulation\n", "\n", - "pop = SyntheticPopulation(path_to_ini='./population_params.ini', path_to_data=path_to_data, verbose=True)\n", + "pop = SyntheticPopulation(path_to_ini='./population_params.ini', verbose=True)\n", "\n", - "pop.parse(S1_state='BH', S2_state='BH', binary_state='contact', invert_S1S2=False)\n", + "pop.parse(path_to_data=path_to_data, S1_state='BH', S2_state='BH', binary_state='contact', invert_S1S2=False)\n", "pop.save_pop(os.path.join(path,'BBH_population.h5'))\n", "\n", "pop.df.head(10)" @@ -729,7 +812,7 @@ ], "metadata": { "kernelspec": { - "display_name": "development311", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -744,9 +827,8 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.5" - }, - "orig_nbformat": 4 + } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index 2476d7c634..dd46f1c89f 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -20,6 +20,7 @@ "Konstantinos Kovlakas ", "Devina Misra ", "Simone Bavera ", + "Max Briel " ] @@ -76,6 +77,7 @@ 'step_HMS_HMS', 'step_CO_HeMS', 'step_CO_HMS_RLO', 'step_CO_HeMS_RLO', 'step_detached','step_isolated','step_disrupted','step_initially_single', 'step_merged' ] + class BinaryPopulation: """Handle a binary star population.""" @@ -98,17 +100,45 @@ def __init__(self, **kwargs): self.population_properties = self.kwargs.get('population_properties', SimulationProperties()) atexit.register(lambda: BinaryPopulation.close(self)) - + self.metallicity = self.kwargs.get('metallicity', 1) + + # grab all metallicities in population or use single metallicity + self.metallicities = self.kwargs.get('metallicities', [self.metallicity]) + self.population_properties.max_simulation_time = self.kwargs.get( 'max_simulation_time') # years - entropy = self.kwargs.get('entropy', None) - seq = np.random.SeedSequence(entropy=entropy) - + self.entropy = self.kwargs.get('entropy', None) + seq = np.random.SeedSequence(entropy=self.entropy) + self.comm = self.kwargs.pop('comm', None) - if self.comm is not None: + self.JOB_ID = self.kwargs.pop('JOB_ID', None) + if self.comm is not None and self.JOB_ID is not None: + raise ValueError('MPI and Job array runs are not compatible.') + # local MPI run + elif self.comm is not None: + # To guarantee reproducibility, we need to set the seed sequence + # to be the same across all processes. + if self.entropy is None: + raise ValueError('A local MPI run requires an entropy value to be set.') + self.rank = self.comm.Get_rank() self.size = self.comm.Get_size() + # Make seed sequence unique per metallicity + met_shift = self.metallicities.index(self.metallicity) + seq = np.random.SeedSequence(entropy=self.entropy + met_shift) + seed_seq = [i for i in seq.spawn(self.size)][self.rank] + + # Job array runs + elif self.JOB_ID is not None: + self.rank = self.kwargs.pop('RANK', None) + self.size = self.kwargs.pop('size', None) + # Make sure each of the processes has the same entropy + # But unique per metallicity + if self.entropy is None: + met_shift = self.metallicities.index(self.metallicity) + seq = np.random.SeedSequence(entropy=self.JOB_ID + met_shift) + # Split the seed sequence between processes for uniqueness seed_seq = [i for i in seq.spawn(self.size)][self.rank] else: seed_seq = seq @@ -169,7 +199,7 @@ def evolve(self, **kwargs): breakdown_to_df_bool = kw.get('breakdown_to_df', True) from_hdf_bool = kw.get('from_hdf', False) - if self.comm is None: # do regular evolution + if self.JOB_ID is None and self.comm is None: # do regular evolution indices = kw.get('indices', list(range(self.number_of_binaries))) params = {'indices':indices, @@ -180,7 +210,7 @@ def evolve(self, **kwargs): self._safe_evolve(**self.kwargs) else: - # do MPI evolution + # do local MPI or cluster job array evolution indices = kw.get('indices', list(range(self.number_of_binaries))) indices_split = np.array_split(indices, self.size) @@ -192,7 +222,6 @@ def evolve(self, **kwargs): 'breakdown_to_df':breakdown_to_df_bool, 'from_hdf':from_hdf_bool} self.kwargs.update(params) - self._safe_evolve(**self.kwargs) def _safe_evolve(self, **kwargs): @@ -235,13 +264,16 @@ def _safe_evolve(self, **kwargs): # Create temporary directory if it doesn't exist # Built to handle MPI - if self.comm is None: + if self.JOB_ID is None and self.comm is None: if not os.path.exists(temp_directory): os.makedirs(temp_directory) else: - if self.rank == 0: - if not os.path.exists(temp_directory): + # Create a directory for parallel runs + if not os.path.exists(temp_directory): + try: os.makedirs(temp_directory) + except FileExistsError: + pass filenames = [] @@ -272,7 +304,7 @@ def _safe_evolve(self, **kwargs): and j % dump_rate == 0 and j != 0 and ram_per_cpu is None): # Create filenames for each batch - if(self.comm is None): + if(self.JOB_ID is None and self.comm is None): path = os.path.join(temp_directory, f"{j}_evolution.batch") else: path = os.path.join(temp_directory, @@ -293,7 +325,7 @@ def _safe_evolve(self, **kwargs): and psutil.Process().memory_info().rss / (1024**3) >= 0.9 * ram_per_cpu): - if(self.comm is None): + if(self.JOB_ID is None and self.comm is None): path = os.path.join(temp_directory, f"{j}_evolution.batch") else: path = os.path.join(temp_directory, @@ -314,7 +346,7 @@ def _safe_evolve(self, **kwargs): or len(self.manager.history_dfs) != 0) and optimize_ram): - if(self.comm is None): + if(self.JOB_ID is None and self.comm is None): path = os.path.join(temp_directory, "leftover_evolution.batch") else: path = os.path.join(temp_directory, @@ -331,7 +363,7 @@ def _safe_evolve(self, **kwargs): if optimize_ram: # combining files - if self.comm is None: + if self.JOB_ID is None and self.comm is None: self.combine_saved_files(os.path.join(temp_directory, "evolution.combined"), filenames, mode = "w") @@ -342,7 +374,7 @@ def _safe_evolve(self, **kwargs): filenames, mode = "w") else: - if self.comm is None: + if self.JOB_ID is None and self.comm is None: self.manager.save(os.path.join(temp_directory, "evolution.combined"), mode='w', @@ -359,7 +391,7 @@ def save(self, save_path, **kwargs): temp_directory = self.kwargs['temp_directory'] mode = self.kwargs.get('mode', 'a') - if self.comm is None: + if self.JOB_ID is None and self.comm is None: if optimize_ram: os.rename(os.path.join(temp_directory, "evolution.combined"), save_path) @@ -369,26 +401,18 @@ def save(self, save_path, **kwargs): absolute_filepath = os.path.abspath(save_path) dir_name = os.path.dirname(absolute_filepath) file_name = os.path.basename(absolute_filepath) - + # get the temporary files in the directory + tmp_files = [os.path.join(temp_directory, f) \ + for f in os.listdir(temp_directory) \ + if os.path.isfile(os.path.join(temp_directory, f))] + if os.path.isdir(absolute_filepath): file_name = 'backup_save_pop_data.h5' file_path = os.path.join(dir_name, file_name) warnings.warn('The provided path is a directory - saving ' 'to {0} instead.'.format(file_path), Warning) - - self.comm.Barrier() - - if self.rank == 0: - - file_name = os.path.basename(absolute_filepath) - tmp_files = [os.path.join( - self.kwargs["temp_directory"], f"evolution.combined.{i}") - for i in range(self.size)] - - self.combine_saved_files(absolute_filepath, tmp_files, mode=mode, **kwargs) - - else: - return + + self.combine_saved_files(absolute_filepath, tmp_files, **kwargs) def make_temp_fname(self): """Get a valid filename for the temporary file.""" @@ -397,7 +421,16 @@ def make_temp_fname(self): # return os.path.join(dir_name, '.tmp{}_'.format(rank) + file_name) def combine_saved_files(self, absolute_filepath, file_names, **kwargs): - """Combine various temporary files in a given folder.""" + """Combine various temporary files in a given folder. + + Parameters + ---------- + absolute_filepath : str + Absolute path to the file to be saved. + file_names : list + List of absolute paths to the temporary files. + + """ dir_name = os.path.dirname(absolute_filepath) history_cols = pd.read_hdf(file_names[0], key='history').columns @@ -436,9 +469,9 @@ def close(self): def __getstate__(self): """Prepare the BinaryPopulation to be 'pickled'.""" # In order to be generally picklable, we need to discard the - # communicator object before trying. + # communication object before picking d = self.__dict__ - d["comm"] = None + d['comm'] = None prop = d['population_properties'] if prop.steps_loaded: prop.close() diff --git a/posydon/popsyn/io.py b/posydon/popsyn/io.py index cfb8af0f42..ccd52d2b6e 100644 --- a/posydon/popsyn/io.py +++ b/posydon/popsyn/io.py @@ -487,13 +487,32 @@ def binarypop_kwargs_from_ini(path, verbose=False): for key, val in parser[section].items(): pop_kwargs[key] = ast.literal_eval(val) - # hard code for running with MPI, only try to import if use_MPI - if pop_kwargs['use_MPI']: + + JOB_ID = os.getenv('SLURM_ARRAY_JOB_ID') + # MPI import for local use only + if pop_kwargs['use_MPI'] == True and JOB_ID is not None: + raise ValueError('MPI must be turned off for job arrays.') + exit() + elif pop_kwargs['use_MPI'] == True: from mpi4py import MPI pop_kwargs['comm'] = MPI.COMM_WORLD + # MPI needs to be turned off for job arrays else: pop_kwargs['comm'] = None - + + # Check if we are running as a job array + if JOB_ID is not None and pop_kwargs['use_MPI'] is True: + raise ValueError('MPI must be turned off for job arrays.') + elif JOB_ID is not None: + pop_kwargs['JOB_ID'] = np.int64(os.environ['SLURM_ARRAY_JOB_ID']) + # account for job array not starting at 0 + min_rank = np.int64(os.environ['SLURM_ARRAY_TASK_MIN']) + pop_kwargs['RANK'] = np.int64(os.environ['SLURM_ARRAY_TASK_ID'])-min_rank + pop_kwargs['size'] = np.int64(os.environ['SLURM_ARRAY_TASK_COUNT']) + else: + pop_kwargs['RANK'] = None + pop_kwargs['size'] = None + # right now binary, S1, and S2 output kwargs are all passed # into the BinaryPopulation during init elif section == 'BinaryStar_output': diff --git a/posydon/popsyn/population_params_default.ini b/posydon/popsyn/population_params_default.ini index f9227ffca2..01e7e47dc1 100644 --- a/posydon/popsyn/population_params_default.ini +++ b/posydon/popsyn/population_params_default.ini @@ -265,10 +265,7 @@ breakdown_to_df = True # convert BinaryStars into DataFrames after evolution use_MPI = False - # if True evolve with MPI, equivalent to the following: - # > from mpi4py import MPI - # > comm = MPI.COMM_WORLD - + # use only for local MPI runs metallicity = [0.0001] # [2., 1., 0.45, 0.2, 0.1, 0.01, 0.001, 0.0001] # In units of solar metallicity diff --git a/posydon/popsyn/synthetic_population.py b/posydon/popsyn/synthetic_population.py index bf8ee794b9..70577174a7 100644 --- a/posydon/popsyn/synthetic_population.py +++ b/posydon/popsyn/synthetic_population.py @@ -7,12 +7,14 @@ "Simone Bavera ", "Kyle Akira Rocha ", "Monica Gallegos-Garcia ", + "Max Merlijn Briel < max.briel@gmail.com", ] import warnings import numpy as np import pandas as pd from tqdm import tqdm +import os from posydon.utils.constants import Zsun from posydon.popsyn.io import binarypop_kwargs_from_ini from posydon.popsyn.binarypopulation import BinaryPopulation @@ -28,9 +30,11 @@ from posydon.binary_evol.singlestar import SingleStar from posydon.utils.common_functions import convert_metallicity_to_string + + class SyntheticPopulation: - def __init__(self, path_to_ini, path_to_data=None, verbose=False, MODEL={}): + def __init__(self, path_to_ini, verbose=False, MODEL={}): """ Parameters ---------- @@ -38,8 +42,10 @@ def __init__(self, path_to_ini, path_to_data=None, verbose=False, MODEL={}): path : str Path to the inifile to parse. You can supply a list in the metallicity parameter to evolve more than one population. - """ + self.synthetic_pop_params = None + self.metallicities = None + self.binary_populations = None self.verbose = verbose self.MODEL = MODEL @@ -62,39 +68,65 @@ def __init__(self, path_to_ini, path_to_data=None, verbose=False, MODEL={}): if '.ini' not in path_to_ini: raise ValueError('You did not provide a valid path_to_ini!') else: - synthetic_pop_params = binarypop_kwargs_from_ini(path_to_ini) - - self.metallicity = synthetic_pop_params['metallicity'] - - if not isinstance( self.metallicity, list): - self.metallicity = [self.metallicity] - - self.binary_populations = [] - for met in self.metallicity[::-1]: - self.ini_kw = binarypop_kwargs_from_ini(path_to_ini) - self.ini_kw['metallicity'] = met - self.ini_kw['temp_directory'] = self.create_met_prefix(met) + self.ini_kw['temp_directory'] - self.binary_populations.append(BinaryPopulation(**self.ini_kw)) - - if path_to_data is None: - return - elif (isinstance(path_to_data, list) and '.h5' in path_to_data[0]) or ('.h5' in path_to_data): - self.path_to_data = path_to_data + self.synthetic_pop_params = binarypop_kwargs_from_ini(path_to_ini) + self.metallicities = self.synthetic_pop_params['metallicity'] + if not isinstance( self.metallicities, list): + self.metallicities = [self.metallicities] + self.binary_populations = None + + def create_binary_populations(self): + """Create a list of BinaryPopulation objects.""" + self.binary_populations = [] + ini_kw = self.synthetic_pop_params.copy() + for met in self.metallicities[::-1]: + ini_kw['metallicity'] = met + ini_kw['temp_directory'] = self.create_met_prefix(met) + self.synthetic_pop_params['temp_directory'] + self.binary_populations.append(BinaryPopulation(**ini_kw)) def get_ini_kw(self): - return self.ini_kw + return self.synthetic_pop_params.copy() def evolve(self): """Evolve population(s) at given Z(s).""" + if self.binary_populations is None: + self.create_binary_populations() while self.binary_populations: pop = self.binary_populations.pop() if self.verbose: print(f'Z={pop.kwargs["metallicity"]:.2e} Z_sun') pop.evolve() - met_prefix = f'{pop.kwargs["metallicity"]:.2e}_Zsun_' - pop.save( met_prefix + 'population.h5' ) del pop + + def merge_parallel_runs(self, path_to_batches): + """ + Merge the folder or list of folders into a single file per metallicity. + + Parameters + ---------- + path_to_batches : str or list of str + Path to the folder(s) containing the batch folders. + """ + + if isinstance(path_to_batches, str): + path_to_batches = [path_to_batches] + # check if path_to_batches is the same length as the number of metallicities + if len(path_to_batches) != len(self.metallicities): + raise ValueError('The number of metallicity and batch directories do not match!') + + for met, path_to_batch in zip(self.metallicities, path_to_batches): + met_prefix = self.create_met_prefix(met) + tmp_files = [os.path.join(path_to_batch, f) \ + for f in os.listdir(path_to_batch) \ + if os.path.isfile(os.path.join(path_to_batch, f))] + + BinaryPopulation(**self.get_ini_kw()).combine_saved_files(met_prefix+ 'population.h5', tmp_files) + print(f'Population at Z={met:.2e} Z_sun successfully merged!') + if len(os.listdir(path_to_batch)) == 0: + os.rmdir(path_to_batch) + elif self.verbose: + print(f'{path_to_batch} is not empty, it was not removed!') + @staticmethod def create_met_prefix(met): """Append a prefix to the name of directories for batch saving.""" @@ -175,9 +207,12 @@ def apply_logic(self, df, S1_state=None, S2_state=None, binary_state=None, return logic - def parse(self, S1_state=None, S2_state=None, binary_state=None, - binary_event=None, invert_S1S2=False, chunksize=500000): + def parse(self, path_to_data, S1_state=None, S2_state=None, binary_state=None, + binary_event=None, step_name=None, invert_S1S2=False, chunksize=500000): """Sort binaries of interests given some properties. + + It also stores the underlying stellar mass and + the initial simulated stellar mass for each metallicity. Parameters ---------- @@ -198,36 +233,58 @@ def parse(self, S1_state=None, S2_state=None, binary_state=None, Read the POSYDON binary population in chuncks to prevent OFM error. """ - + + # if the user provided a single string instead of a list of strings + if type(path_to_data) is str and ('.h5' in path_to_data): + path_to_data = [path_to_data] + + # catch the case where the user did not provide a path to data + if (isinstance(path_to_data, list)): + for path in path_to_data: + if os.path.splitext(path)[-1] != '.h5': + raise ValueError('You did not provide a valid path_to_data!') + else: + raise ValueError('You did not provide a valid path_to_data!') + df_sel = pd.DataFrame() df_sel_oneline = pd.DataFrame() count = 0 tmp = 0 + shift_index = 0 if self.verbose: - print('Binary count with (S1_state, S2_state, binary_state, binary_event) equal') - print(f'to ({S1_state}, {S2_state}, {binary_state}, {binary_event})') + print('Binary count with (S1_state, S2_state, binary_state, binary_event, step_name) equal') + print(f'to ({S1_state}, {S2_state}, {binary_state}, {binary_event}, {step_name})') if invert_S1S2: - print(f'and ({S2_state}, {S1_state}, {binary_state}, {binary_event})') - for k, file in enumerate(self.path_to_data): + print(f'and ({S2_state}, {S1_state}, {binary_state}, {binary_event}, {step_name})') + for k, file in enumerate(path_to_data): df_sel_met = pd.DataFrame() sel_met = [] + last_binary_df = None + # read metallicity from path met = float(file.split('/')[-1].split('_Zsun')[0])*Zsun simulated_mass_for_met = 0. - # TODO: handle binaries at the edge case of the chuncks + for i, df in enumerate(pd.read_hdf(file, key='history', chunksize=chunksize)): - - logic = self.apply_logic(df, S1_state=S1_state, - S2_state=S2_state, - binary_state=binary_state, - binary_event=binary_event, - invert_S1S2=invert_S1S2) - + + df = pd.concat([last_binary_df, df]) + + last_binary_df = df.loc[[df.index[-1]]] + df.drop(df.index[-1], inplace=True) + + logic = self.apply_logic(df, + S1_state = S1_state, + S2_state = S2_state, + binary_state = binary_state, + binary_event = binary_event, + step_name = step_name, + invert_S1S2 = invert_S1S2) + # select systems # remove duplicate indicies, e.g. if selecting 'contact' state it appears twice # if no specific event is selected (the second time is from the copied END event) sel = df.loc[logic].index.drop_duplicates() - + # count systems count += len(np.unique(sel)) @@ -240,22 +297,36 @@ def parse(self, S1_state=None, S2_state=None, binary_state=None, if any(sel): df_tmp = pd.DataFrame() df_tmp = df.loc[sel] - sel_met.extend(sel) # store metallicity df_tmp['metallicity'] = met # concatenate results df_sel_met = pd.concat([df_sel_met, df_tmp]) del df_tmp - if k > 0 and df_sel.shape[0] > 0: - shift_index = max(np.unique(df_sel.index)) + 1 - df_sel_met.index += shift_index + # check last binary if it should be included + if last_binary_df is not None: + logic = self.apply_logic(last_binary_df, + S1_state = S1_state, + S2_state = S2_state, + binary_state = binary_state, + binary_event = binary_event, + step_name = step_name, + invert_S1S2 = invert_S1S2) + + # The last binary is selected + if any(logic) == True: + df_tmp = last_binary_df.loc[logic] + df_sel_met = pd.concat([df_sel_met, df_tmp]) + # get unique indicies + sel_met = df_sel_met.index.drop_duplicates() + # store simulated and underlying stellar mass df_sel_met['simulated_mass_for_met'] = simulated_mass_for_met - df_sel_met['underlying_mass_for_met'] = initial_total_underlying_mass(df=simulated_mass_for_met, **self.ini_kw)[0] + df_sel_met['underlying_mass_for_met'] = initial_total_underlying_mass(df=simulated_mass_for_met, **self.synthetic_pop_params)[0] # This used to be init_kw - # concatenate results + # concatenate results with shifted indices for each metallicity + df_sel_met.index += shift_index df_sel = pd.concat([df_sel, df_sel_met]) del df_sel_met @@ -265,15 +336,15 @@ def parse(self, S1_state=None, S2_state=None, binary_state=None, df_sel_met_oneline = df_sel_met_oneline.loc[sel_met] df_sel_met_oneline['metallicity'] = met - if k > 0 and df_sel_oneline.shape[0] > 0: - shift_index = max(np.unique(df_sel_oneline.index)) + 1 - df_sel_met_oneline.index += shift_index - + df_sel_met_oneline.index += shift_index df_sel_oneline = pd.concat([df_sel_oneline, df_sel_met_oneline]) if self.verbose: print(f'in {file} are {count-tmp}') tmp = count + + # shift the index for the next metallicity + shift_index += max(np.unique(df.index)) + 1 if self.verbose: print('Total binaries found are', count) @@ -321,7 +392,23 @@ def load_pop(self, path): raise ValueError('You already have a population stored in memory!') def get_dco_at_formation(self, S1_state, S2_state, oneline_cols=None, formation_channels=False, mt_history=False): - """Sort synthetic population, i.e. DCO at formation. + """Populates `df_synthetic` with DCOs at their formation. + + If `formation_channels` is `True` the `channel` column is added to the + `df_synthetic` dataframe. + + if MODEL on class initialization is not None and + "compute_GRB_properties" in MODEL. + If MODEL["compute_GRB_properties"] is `True` the following columns are + in the MODEL: + + - 'GRB_efficiency', + - 'GRB_beaming', + - 'E_GRB_iso_min' + + The following columns are added to the `df_synthetic` dataframe: + - S1_m_disk_radiated + - S2_m_disk_radiated Note: by default this function looks for the symmetric state S1_state = S2_sate and S2_state = S1_sate. @@ -656,7 +743,6 @@ def get_dco_merger_rate_density(self, export_cols=None, working_dir='./', load_ sensitivity='infinite' flag_pdet = False index, z_formation, z_merger, w_ijk = self.compute_cosmological_weights(sensitivity, flag_pdet, working_dir=working_dir, load_data=load_data, pop='DCO') - # compute rate density weights self.dco_z_rate_density = self.rates.get_centers_redshift_bins() total_rate = self.rates.compute_rate_density(w_ijk, z_merger, observable='DCO', sensitivity=sensitivity) From a8aa49498596dd53e9bb6da81983f245def53842 Mon Sep 17 00:00:00 2001 From: ezapartas Date: Thu, 14 Dec 2023 17:09:48 +0200 Subject: [PATCH 147/319] Option for losing mass during cee that leads to merging (#193) * added the option of mass_loss_during_CEE_merged * typo in an equation * typo * debugged the solution of Mejecta even in doubleCE * testing commit * changed branch towards reading a profile and partial ejecting the mass according to the integrated binding energy * implementing Matthias suggestions * typo --- posydon/binary_evol/CE/step_CEE.py | 97 +++++++++++++++++++++++++++++- posydon/utils/common_functions.py | 66 +++++++++++++++++++- 2 files changed, 158 insertions(+), 5 deletions(-) diff --git a/posydon/binary_evol/CE/step_CEE.py b/posydon/binary_evol/CE/step_CEE.py index 8a6ae80b1c..11d9c8bdb9 100644 --- a/posydon/binary_evol/CE/step_CEE.py +++ b/posydon/binary_evol/CE/step_CEE.py @@ -42,7 +42,7 @@ from posydon.binary_evol.singlestar import STARPROPERTIES from posydon.utils.common_functions import PATH_TO_POSYDON from posydon.utils.common_functions import check_state_of_star -from posydon.utils.common_functions import calculate_lambda_from_profile +from posydon.utils.common_functions import calculate_lambda_from_profile, calculate_Mejected_for_integrated_binding_energy warnings.simplefilter('always', UserWarning) @@ -59,7 +59,11 @@ "CEE_tolerance_err": 0.001, "verbose": False, "common_envelope_option_after_succ_CEE": 'core_not_replaced_noMT', + "mass_loss_during_CEE_merged": False # If False, then no mass loss from this step for a merged star + # If True, then we remove mass according to the alpha-lambda prescription + # assuming a final separation where the inner core RLOF starts. # "core_replaced_noMT" for core_definition_H_fraction=0.01 + } @@ -152,6 +156,7 @@ def __init__( core_definition_He_fraction=MODEL[ 'core_definition_He_fraction'], CEE_tolerance_err=MODEL['CEE_tolerance_err'], + mass_loss_during_CEE_merged=MODEL['mass_loss_during_CEE_merged'], verbose=MODEL['verbose'], **kwargs): """Initialize a StepCEE instance.""" @@ -178,6 +183,7 @@ def __init__( self.CEE_tolerance_err = CEE_tolerance_err self.common_envelope_option_after_succ_CEE = \ common_envelope_option_after_succ_CEE + self.mass_loss_during_CEE_merged = mass_loss_during_CEE_merged self.verbose = verbose self.path_to_posydon = PATH_TO_POSYDON @@ -243,7 +249,8 @@ def __call__(self, binary): common_envelope_option_after_succ_CEE=( self.common_envelope_option_after_succ_CEE), core_definition_H_fraction=self.core_definition_H_fraction, - core_definition_He_fraction=self.core_definition_He_fraction) + core_definition_He_fraction=self.core_definition_He_fraction, + mass_loss_during_CEE_merged=self.mass_loss_during_CEE_merged) else: raise ValueError("Invalid common envelope prescription given.") @@ -397,7 +404,8 @@ def CEE_simple_alpha_prescription( verbose=False, common_envelope_option_after_succ_CEE=MODEL[ 'common_envelope_option_after_succ_CEE'], core_definition_H_fraction=MODEL['core_definition_H_fraction'], - core_definition_He_fraction=MODEL['core_definition_He_fraction']): + core_definition_He_fraction=MODEL['core_definition_He_fraction'], + mass_loss_during_CEE_merged=MODEL['mass_loss_during_CEE_merged']): """Apply the alpha-lambda common-envelope prescription. It uses energetics to calculate the shrinakge of the orbit @@ -471,6 +479,10 @@ def CEE_simple_alpha_prescription( core_definition_He_fraction: float The value of the He abundance to define the envelope-core boundary in CO_cores. + mass_loss_during_CEE_merged: Boolean + If False, then no mass loss from this step for a merged CEE + If True, then we remove mass according to the alpha-lambda prescription + assuming a final separation where the inner core(s) RLOF starts, and the same lambda(s) """ # Get star properties @@ -801,4 +813,83 @@ def CEE_simple_alpha_prescription( print("Rdonor core vs RLdonor core = ", rc1_i, RL1) print("Rcompanion vs RLcompanion= ", rc2_i, RL2) + Mejected_donor = 0.0 + Mejected_comp = 0.0 + + if mass_loss_during_CEE_merged: + # we calculate the ejected mass from part of the commone envelope, using + # a_f = separation_postCEE so that one of the cores (or MS star) is filling its inner Roche lobe, + # and assuming that lambda(Menvelope) ~ lamda(Mejected) although + # Mejected < Menvelope (e.g. see Fig1 of Dewi+Tauris2000) + + if not double_CE and donor.profile is None: + Mejected_donor = 0.0 + Mejected_comp = 0.0 + warnings.warn("mass_loss_during_CEE_merged == True, but no profile found for the donor star. Proceeding with no partial mass ejection.") + elif double_CE and (donor.profile is None or comp_star.profile is None): + Mejected_comp = 0.0 + Mejected_comp = 0.0 + warnings.warn("mass_loss_during_CEE_merged == True, but not profile found the donor or companion star in double_CE. Proceeding with no partial mass ejection.") + + else: + + separation_for_inner_RLO1 = rc1_i / cf.roche_lobe_radius(mc1_i/mc2_i, a_orb=1) + separation_for_inner_RLO2 = rc2_i / cf.roche_lobe_radius(mc2_i/mc1_i, a_orb=1) + + separation_before_merger = max( separation_for_inner_RLO1, separation_for_inner_RLO2 ) * const.Rsun + + if verbose: + print("separation_before_merger (for the calculation of Mejected for the merger): ",separation_before_merger/ const.Rsun , "in Rsun") + print("which is the max of RLO1 or RLO2 of the inner cores: ", separation_for_inner_RLO1 , separation_for_inner_RLO2, "in Rsun") + + E_orb_used_up_to_inner_RLOF = alpha_CE * ( - const.standard_cgrav * m1_i * const.Msun * m2_i * const.Msun/(2. * separation_i) + \ + const.standard_cgrav * mc1_i * const.Msun * mc2_i * const.Msun/(2. * separation_before_merger) ) + + # We assume "lambda_from_profile_gravitational_plus_internal_minus_recombination" + if not double_CE: + Mejected_donor = calculate_Mejected_for_integrated_binding_energy(donor.profile, E_orb_used_up_to_inner_RLOF, mc1_i, rc1_i, m1_i, radius1) + + else: # in double_CE + + # Assuming that the ratio of orbital energy used for the partial ejection of each (common) envelope + # is the same as the ratio of the initial envelope masses: + + WF = (m1_i - mc1_i)/ (m2_i - mc2_i + m1_i - mc1_i) # weight factor for 1: + # = Mdonor,envelope / Mcomp,envelope + Eorb_for_partial_ej_1 = E_orb_used_up_to_inner_RLOF * WF + Mejected_donor = calculate_Mejected_for_integrated_binding_energy(donor.profile, Eorb_for_partial_ej_1, mc1_i, rc1_i, m1_i, radius1) + + Eorb_for_partial_ej_2 = E_orb_used_up_to_inner_RLOF * (1. - WF) + Mejected_comp = calculate_Mejected_for_integrated_binding_energy(comp_star.profile, Eorb_for_partial_ej_2, mc2_i, rc2_i, m2_i, radius2) + + if verbose: + print("Mejecta_donor = ", Mejected_donor, "in Msun compared to its initial envelope =", m1_i - mc1_i) + print("Mejecta_comp = ", Mejected_comp, "in Msun compared to its initial envelope =", m2_i - mc2_i) + ''' + if not ( (Mejected_donor <= m1_i - mc1_i) and (Mejected_comp <= m2_i - mc2_i) ): + raise Exception("Mejected_donor in double CEE with mass_loss_during_CEE_merged is found more than Menvelope") + if not (Mejected_donor >= 0.): + raise Exception("The root of the equation in double CEE with mass_loss_during_CEE_merged is found negative") + ''' + + if (Mejected_donor > m1_i - mc1_i) or (Mejected_comp > m2_i - mc2_i): + Mejected_donor = (m1_i - mc1_i) -0.01 # at least this value of envelope is left. + Mejected_comp = (m2_i - mc2_i) -0.01 + warnings.warn("Mejected of at least one star in double CEE is found to be more that the initial envelope. Reduced both to their initial_envelope - 0.01 Msun") + + donor.mass = m1_i - Mejected_donor + donor.log_R = np.nan + comp_star.mass = m2_i - Mejected_comp + comp_star.log_R = np.nan + if donor_type == 'CO_core': + donor.he_core_mass = m1_i - Mejected_donor + donor.he_core_radius = np.nan + comp_star.he_core_mass = m2_i - Mejected_comp + comp_star.he_core_radius = np.nan + + if verbose: + print("The mass loss during merging_CEE is: ", mass_loss_during_CEE_merged) + print("so m1_i , m1_f = ", m1_i, donor.mass) + print("so m2_i , m2_f = ", m2_i, comp_star.mass) + return diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index 54509ca2ca..393403b1b8 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -486,7 +486,7 @@ def bondi_hoyle(binary, accretor, donor, idx=-1, wind_disk_criteria=True, default: True, see [5]_ scheme : str There are different options: - + - 'Hurley+2002' : following [3]_ - 'Kudritzki+2000' : following [7]_ @@ -2081,7 +2081,7 @@ def calculate_lambda_from_profile( profile : numpy.array Donor's star profile from MESA donor_star_state : string - The POSYDON evolutionary state of the donor star !!!! + The POSYDON evolutionary state of the donor star common_envelope_option_for_lambda : str Available options: * 'default_lambda': using for lambda the constant value of @@ -2578,6 +2578,68 @@ def calculate_binding_energy(donor_mass, donor_radius, donor_dm, print("Ebind = Grav_energy + factor_internal_energy*U_i : ", Ebind_i) return Ebind_i +def calculate_Mejected_for_integrated_binding_energy(profile, Ebind_threshold, + mc1_i, rc1_i, + m1_i = 0.0, radius1 = 0.0, + factor_internal_energy=1.0,tolerance=0.001 + ): + """Calculate the mass lost from the envelope for an energy budget of Ebind_threshold + + Parameters + ---------- + profile : numpy.array + Donor's star profile from MESA + Ebind_threshold : float + Orbital energy used from the spiral in to partial unbind the envelope. Positive + We integrate from surface to calcualte the partial loss of mass during CE that merges. + factor_internal_energy : float + The factor to multiply with internal energy to be taken into + account when we calculate the binding energy of the enevelope + verbose : bool + In case we want information about the CEE (the default is False). + + Returns + ------- + Ebind_i : float + The total binding energy of the envelope of the star + + """ + + donor_mass, donor_radius, donor_dm = get_mass_radius_dm_from_profile( + profile, m1_i, radius1, tolerance) + specific_internal_energy = get_internal_energy_from_profile( + common_envelope_option_for_lambda = "lambda_from_profile_gravitational_plus_internal_minus_recombination", + profile = profile, tolerance = tolerance) + + # Sum of gravitational energy from surface towards inside + Grav_energy = 0.0 + # Sum of internal energy from surface towards. + U_energy = 0.0 + # sum from surface to the core. Your threshold is in element [ind_threshold] + # in a normal MESA (and POSYDON) profile + i = 0 + Ebind_so_far = 0.0 # the integration from surface going inwards of the binding energy (negative in principle) + + while (abs(Ebind_so_far) < Ebind_threshold) and (i Date: Thu, 14 Dec 2023 16:26:44 +0100 Subject: [PATCH 148/319] Update posydon-run-grid (#207) Keep work_dir when doing the first steps on the CO-HeMS grid --- bin/posydon-run-grid | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/bin/posydon-run-grid b/bin/posydon-run-grid index cbf0e9d9f9..87ecfb4d4d 100755 --- a/bin/posydon-run-grid +++ b/bin/posydon-run-grid @@ -443,6 +443,7 @@ def run_mesa(mesa_executable, work_dir, final_dir, **kwargs): outfile_name = kwargs.pop('outfile_name', 'out.txt') keep_profiles = kwargs.pop('keep_profiles', False) keep_photos = kwargs.pop('keep_photos', False) + keep_work_dir = kwargs.pop('keep_work_dir', False) job_time = kwargs.pop('job_end', 600) - kwargs.pop('job_start', 0) os.chdir(work_dir) @@ -472,7 +473,7 @@ def run_mesa(mesa_executable, work_dir, final_dir, **kwargs): if child_ID>=1: #kill child process, because it is no longer needed os.kill(child_ID, 9) - move_mesa_output(work_dir, final_dir) + move_mesa_output(work_dir, final_dir, keep_work_dir) elif child_ID==0: #child process: waits till short before the job will get killed by slurm to copy the data beforehand and exit this process after the copy finished if job_time>300: @@ -710,7 +711,7 @@ def run_grid_point(grid, star1_formation, star2_formation, create_star_formation(grid_param_dict['m1'], work_dir, inlist_step) elif index == 1: create_star_formation(grid_param_dict['m1'], work_dir, inlist_step, grid_param_dict['initial_z'], True) - run_mesa(args.mesa_star1_executable, work_dir, final_dir, + run_mesa(args.mesa_star1_executable, work_dir, final_dir, keep_work_dir=True, outfile_name='out_star1_formation_step{0}.txt'.format(index)) # are we first generating a model before running binary for the second star? @@ -724,7 +725,7 @@ def run_grid_point(grid, star1_formation, star2_formation, create_star_formation(grid_param_dict['m2'], work_dir, inlist_step) elif index == 1: create_star_formation(grid_param_dict['m2'], work_dir, inlist_step, grid_param_dict['initial_z'], True) - run_mesa(args.mesa_star2_executable, work_dir, final_dir, + run_mesa(args.mesa_star2_executable, work_dir, final_dir, keep_work_dir=True, outfile_name='out_star2_formation_step{0}.txt'.format(index)) # now we can create the grid specific binary inlists and run the binary executable From 3003a2b43729c03870b4f078f98faa77155e2a98 Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Thu, 14 Dec 2023 16:44:57 +0100 Subject: [PATCH 149/319] add casting to float64 (#212) --- posydon/utils/common_functions.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index 393403b1b8..3319697be3 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -256,6 +256,11 @@ def orbital_separation_from_period(period_days, m1_solar, m2_solar): The separation of the binary in solar radii. """ + # cast to float64 to avoid overflow + m1_solar = np.float64(m1_solar) + m2_solar = np.float64(m2_solar) + period_days = np.float64(period_days) + separation_cm = (const.standard_cgrav * (m1_solar * const.Msun + m2_solar * const.Msun) / (4.0 * const.pi**2.0) From f866de65d8c167df48ab1712ebaef1561b90e764 Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Thu, 14 Dec 2023 16:47:46 +0100 Subject: [PATCH 150/319] change loc call to list to return a pandas.DataFrame (#216) --- posydon/popsyn/binarypopulation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index dd46f1c89f..84b4ace1f0 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -647,7 +647,7 @@ def from_hdf(self, indices, where=None, restore=False): binary_holder = [] for i in np.unique(hist.index): binary = BinaryStar.from_df( - hist.loc[i], + hist.loc[[i]], extra_columns=self.kwargs.get('extra_columns', [])) # if the binary has failed From 1121f1a9a88f9b79507f2eb7f06988bd2de885bc Mon Sep 17 00:00:00 2001 From: kasdaglie Date: Tue, 2 Jan 2024 16:11:22 -0500 Subject: [PATCH 151/319] Setting the lower limit of the mass ratio to q=0.05 --- posydon/binary_evol/MESA/step_mesa.py | 2 -- posydon/popsyn/independent_sample.py | 2 +- 2 files changed, 1 insertion(+), 3 deletions(-) diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index 24672e4395..499463ad11 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -109,8 +109,6 @@ 'lambda_CE_1cent': 'lambda_CE_1cent', 'lambda_CE_10cent': 'lambda_CE_10cent', 'lambda_CE_30cent': 'lambda_CE_30cent', - 'co_core_mass': 'co_core_mass', - 'co_core_radius': 'co_core_radius', 'lambda_CE_pure_He_star_10cent': 'lambda_CE_pure_He_star_10cent', 'profile': True } diff --git a/posydon/popsyn/independent_sample.py b/posydon/popsyn/independent_sample.py index c6737f3edd..382c09a0ae 100644 --- a/posydon/popsyn/independent_sample.py +++ b/posydon/popsyn/independent_sample.py @@ -347,7 +347,7 @@ def generate_secondary_masses(primary_masses, # Generate secondary masses if secondary_mass_scheme == 'flat_mass_ratio': - mass_ratio_min = secondary_mass_min / primary_masses + mass_ratio_min = np.max([secondary_mass_min / primary_masses,np.ones(len(primary_masses))*0.05], axis=0) mass_ratio_max = np.min([secondary_mass_max / primary_masses, np.ones(len(primary_masses))], axis=0) secondary_masses = ( From 65f123491658e98e1b325454d28ed284b7f00fe1 Mon Sep 17 00:00:00 2001 From: kasdaglie Date: Wed, 3 Jan 2024 18:25:15 -0500 Subject: [PATCH 152/319] removing unused import and duplicates --- posydon/binary_evol/DT/step_detached.py | 23 +++++++++++------------ posydon/popsyn/binarypopulation.py | 1 - 2 files changed, 11 insertions(+), 13 deletions(-) diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index daefe76143..5526bafe29 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -12,9 +12,9 @@ "Jeffrey Andrews ", ] - import os import numpy as np +import pandas as pd import time from scipy.integrate import solve_ivp from scipy.interpolate import PchipInterpolator @@ -122,7 +122,6 @@ "profile": None, "metallicity": None, "spin": "spin_parameter", - "log_total_angular_momentum": "log_total_angular_momentum", "conv_env_top_mass": "conv_env_top_mass", "conv_env_bot_mass": "conv_env_bot_mass", "conv_env_top_radius": "conv_env_top_radius", @@ -834,7 +833,7 @@ def sq_diff_function(x): colscalers=colscalers, scales=scales) x0 = get_root0( - MESA_label, posydon_attribute, htrack, rs=rs) + MESA_labels, posydon_attributes, htrack, rs=rs) # bnds = ([m_min_H, m_max_H], [0, None]) sol = minimize(sq_diff_function, x0, @@ -1083,20 +1082,20 @@ def get_star_data(binary, star1, star2, htrack, print("Matching duration: " f"{t_after_matching-t_before_matching:.6g}") + if pd.isna(m0) or pd.isna(t0): + # binary.event = "END" + # binary.state += " (GridMatchingFailed)" + # if self.verbose: + # print("Failed matching") + return None, None, None + if htrack: self.grid = self.grid_Hrich - elif not htrack: + else: self.grid = self.grid_strippedHe get_track = self.grid.get - if np.isnan(m0) or np.isnan(t0): - # binary.event = "END" - # binary.state += " (GridMatchingFailed)" - # if self.verbose: - # print("Failed matching") - return None, None, None - max_time = binary.properties.max_simulation_time assert max_time > 0.0, "max_time is non-positive" @@ -1351,7 +1350,7 @@ def get_omega(star, is_secondary = True): # EDIT: We assume POSYDON surf_avg_omega is provided in # rad/yr already. else: - if is_secondary == True: + if is_secondary: radius_to_be_used = interp1d_sec["R"](interp1d_sec["t0"]) mass_to_be_used = interp1d_sec["mass"](interp1d_sec["t0"]) else: diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index 84b4ace1f0..28b2a3a035 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -35,7 +35,6 @@ import os from tqdm import tqdm import psutil -import random import sys if 'posydon.binary_evol.binarystar' not in sys.modules.keys(): From f043314716e6012e64a93529d6de6f2606b7c82a Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 11 Jan 2024 16:28:15 +0100 Subject: [PATCH 153/319] Numerical precision fix failed (#210) * Update scrubbing.py Adding a rule if the new and old age_to_remove would be the same. * Update scrubbing.py --------- Co-authored-by: Jeff Andrews --- posydon/grids/scrubbing.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/posydon/grids/scrubbing.py b/posydon/grids/scrubbing.py index dbe633fc5a..5411578cb2 100644 --- a/posydon/grids/scrubbing.py +++ b/posydon/grids/scrubbing.py @@ -138,6 +138,8 @@ def keep_after_RLO(bh, h1, h2): if len(new_ages) > 1 and min(np.diff(new_ages)) == 0.0: min_dt = min(np.diff(new_bh["age"])) new_age_to_remove = (age_to_remove // min_dt) * min_dt + if new_age_to_remove==age_to_remove: + new_age_to_remove -= min_dt relative_error = abs(new_age_to_remove - age_to_remove) / age_to_remove if relative_error > 0.01: raise Exception("Numerical precision fix too aggressive.") From 1a829db35c6340d862a31cd9ec4657de35e1e51f Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 11 Jan 2024 16:31:42 +0100 Subject: [PATCH 154/319] rerun for He stars starting past ZAMS (#227) * Update run-pipeline Add rerun for wrong initial He abundance. * Update run-pipeline missing `names` * Update run-pipeline change order for detection --- bin/run-pipeline | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/bin/run-pipeline b/bin/run-pipeline index 6908475598..fd1ea2cf9a 100644 --- a/bin/run-pipeline +++ b/bin/run-pipeline @@ -457,6 +457,8 @@ def copy_ini_file(grid_path, rerun_type, destination, cluster): replace_text = "development-435d16c9158e4608530f21b8169ce6f31160e23e" elif rerun_type == 'conv_bdy_weight': replace_text = "development-435d16c9158e4608530f21b8169ce6f31160e23e" + elif rerun_type == 'initial_He': + replace_text = "fix_iniHe_pre_rerun5-0e736a853c3c2e522b76299a54aea8f5eea15d08" elif rerun_type == 'other': # e.g. 'envelope_mass_limit', 'fe_core_infall_limit' # this rerun uses the default inlist commit return @@ -583,6 +585,18 @@ def logic_rerun(grid, rerun_type): runs_to_rerun += [i] runs_to_rerun = np.array(runs_to_rerun) new_mesa_flag = {'convective_bdy_weight' : 0} + elif rerun_type == 'initial_He': + N_runs = len(grid) + runs_to_rerun = [] + for i in range(N_runs): + iniY = -1 + if 'S1_center_he4' in grid.initial_values.dtype.names: + iniY = grid[i].initial_values['S1_center_he4'] + elif 'Y' in grid.initial_values.dtype.names: + iniY = grid[i].initial_values['Y'] + if np.isnan(iniY) or iniY<0.96: + runs_to_rerun += [i] + runs_to_rerun = np.array(runs_to_rerun) elif rerun_type == 'envelope_mass_limit': # maybe 'fe_core_infall_limit' as well? runs_to_rerun = None # implement logic else: From dea1102cd5d6c5d077adcd9874cc9bcfbd0527b6 Mon Sep 17 00:00:00 2001 From: Camille Liotine Date: Thu, 11 Jan 2024 10:17:36 -0600 Subject: [PATCH 155/319] flip"massless_remnant" and "merged_star" for CO (#219) --- posydon/binary_evol/DT/step_merged.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/posydon/binary_evol/DT/step_merged.py b/posydon/binary_evol/DT/step_merged.py index 7e73be243e..84a6251835 100644 --- a/posydon/binary_evol/DT/step_merged.py +++ b/posydon/binary_evol/DT/step_merged.py @@ -543,6 +543,9 @@ def mass_weighted_avg(star1=star_base,star2=comp, abundance_name="center_h1", ma merged_star = comp # TODO: potentially flag a Thorne-Zytkov object massless_remnant = convert_star_to_massless_remnant(star_base) + + ## in this case, want CO companion object to stay the same, and base star to be assigned massless remnant + return massless_remnant, merged_star else: print("Combination of merging star states not expected: ", s1, s2) From c2af389af02b33e7dc45bdb49088053eebab73c2 Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Thu, 11 Jan 2024 17:20:56 +0100 Subject: [PATCH 156/319] add error warning for returning to step_detached after leaving it (#222) --- posydon/binary_evol/MESA/step_mesa.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index 24672e4395..fb91672f61 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -1392,6 +1392,11 @@ def __call__(self, binary): ecc == 0.)): super().__call__(self.binary) else: + if len(self.binary.state_history) > 2: + if self.binary.state_history[-2] == 'detached': + self.state = "ERR" + raise ValueError('CO_HMS_RLO binary outside grid and coming from detached') + self.binary.state = "detached" self.binary.event = "redirect_from_CO_HMS_RLO" return From 7d084b6b9b29c349a27dedf3b967f9b06aaf5653 Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Thu, 11 Jan 2024 17:45:14 +0100 Subject: [PATCH 157/319] HDF5 read fix (#229) * only open input file when loading in data * clean-up lines --- posydon/popsyn/binarypopulation.py | 14 ++++---------- 1 file changed, 4 insertions(+), 10 deletions(-) diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index 28b2a3a035..b1db7dc1e5 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -463,7 +463,6 @@ def combine_saved_files(self, absolute_filepath, file_names, **kwargs): def close(self): """Close loaded h5 files from SimulationProperties.""" self.population_properties.close() - self.manager.close() def __getstate__(self): """Prepare the BinaryPopulation to be 'pickled'.""" @@ -513,13 +512,7 @@ def __init__(self, file_name=None, **kwargs): self.entropy = self.binary_generator.entropy if file_name: - self.store = pd.HDFStore(file_name, mode='r',) - atexit.register(lambda: PopulationManager.close(self)) - - def close(self): - """Close the HDF5 file.""" - if hasattr(self, 'store'): - self.store.close() + self.store_file = file_name def append(self, binary): """Add a binary instance internaly.""" @@ -640,8 +633,9 @@ def from_hdf(self, indices, where=None, restore=False): else: query_str = str(where) - hist = self.store.select(key='history', where=query_str) - oneline = self.store.select(key='oneline', where=query_str) + with pd.HDFStore(self.store_file, mode='r') as store: + hist = store.select(key='history', where=query_str) + oneline = store.select(key='oneline', where=query_str) binary_holder = [] for i in np.unique(hist.index): From e4f7dff27e20bf5b15fb3fddcb15640c580d6767 Mon Sep 17 00:00:00 2001 From: Max Briel Date: Fri, 12 Jan 2024 12:03:05 +0100 Subject: [PATCH 158/319] add loop checks for other CO grids --- posydon/binary_evol/MESA/step_mesa.py | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index caa66d46b4..6488c3d49b 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -1392,7 +1392,7 @@ def __call__(self, binary): else: if len(self.binary.state_history) > 2: if self.binary.state_history[-2] == 'detached': - self.state = "ERR" + self.state = "FAILED" raise ValueError('CO_HMS_RLO binary outside grid and coming from detached') self.binary.state = "detached" @@ -1485,6 +1485,11 @@ def __call__(self, binary): ecc == 0.)): super().__call__(self.binary) else: + if len(self.binary.state_history) > 2: + if self.binary.state_history[-2] == 'detached': + self.state = "FAILED" + raise ValueError('CO_HeMS_RLO binary outside grid and coming from detached') + self.binary.state = "detached" self.binary.event = "redirect_from_CO_HeMS_RLO" return @@ -1580,5 +1585,10 @@ def __call__(self, binary): ecc == 0.)): super().__call__(binary) else: + if len(self.binary.state_history) > 2: + if self.binary.state_history[-2] == 'detached': + self.state = "FAILED" + raise ValueError('CO_HeMS binary outside grid and coming from detached') + self.binary.event = 'redirect_from_CO_HeMS' return From b38d3f81ce6b2fc487ffc590db1f8f9790925266 Mon Sep 17 00:00:00 2001 From: Max Briel Date: Fri, 12 Jan 2024 13:47:25 +0100 Subject: [PATCH 159/319] when raising an error set state to 'ERR' and event to 'FAILED' --- posydon/binary_evol/DT/step_detached.py | 10 +++++-- posydon/binary_evol/MESA/step_mesa.py | 36 ++++++++++++++++++++++++- 2 files changed, 43 insertions(+), 3 deletions(-) diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index 5526bafe29..e6b90625de 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -1072,7 +1072,7 @@ def get_star_data(binary, star1, star2, htrack, # ZAMS stars in wide (non-mass exchaging binaries) that are # directed to detached step at birth m0, t0 = star1.mass, 0 - elif co: + elif co: m0, t0 = copy_prev_m0, copy_prev_t0 else: t_before_matching = time.time() @@ -1081,7 +1081,7 @@ def get_star_data(binary, star1, star2, htrack, if self.verbose or self.verbose == 1: print("Matching duration: " f"{t_after_matching-t_before_matching:.6g}") - + if pd.isna(m0) or pd.isna(t0): # binary.event = "END" # binary.state += " (GridMatchingFailed)" @@ -1093,6 +1093,12 @@ def get_star_data(binary, star1, star2, htrack, self.grid = self.grid_Hrich else: self.grid = self.grid_strippedHe + + # cehck if m0 is in the grid + if m0 < self.grid.grid_mass.min() or m0 > self.grid.grid_mass.max(): + binary.state = "ERR" + binary.event = "FAILED" + raise ValueError(f"The mass {m0} is out of the single star grid range and cannot be matched to a track.") get_track = self.grid.get diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index caa66d46b4..34a3544380 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -1291,6 +1291,26 @@ def __call__(self, binary): p > self.p_max): self.binary.event = 'redirect_from_ZAMS' return + # outside the mass grid for m1 + elif (state_1 == 'H-rich_Core_H_burning' and + state_2 == 'H-rich_Core_H_burning' and + event == 'ZAMS' and + p <= self.p_max and + (m1 < self.m1_min or m1 > self.m1_max)): + self.binary.event = 'ERR' + self.binary.state = 'FAILED' + raise ValueError(f'The mass of m1 ({m1}) is outside the grid,' + 'while the period is inside the grid.') + # outside the mass grid for m2 + elif (state_1 == 'H-rich_Core_H_burning' and + state_2 == 'H-rich_Core_H_burning' and + event == 'ZAMS' and + p <= self.p_max and + (mass_ratio < self.q_min or mass_ratio > self.q_max)): + self.binary.event = 'FAILED' + self.binary.state = 'ERR' + raise ValueError(f'The mass of m2 ({m2}) is outside the grid,' + 'while the period is inside the grid.') # redirect if CC1 elif (state_1 == 'H-rich_Central_C_depletion'): self.binary.event = 'CC1' @@ -1300,6 +1320,8 @@ def __call__(self, binary): self.binary.event = 'CC2' return else: + self.binary.event = 'FAILED' + self.binary.state = 'ERR' raise ValueError('The star_1.state = %s, star_2.state = %s, ' 'binary.event = %s and not H-rich_Core_H_burning ' '- H-rich_Core_H_burning - * - ZAMS' @@ -1392,7 +1414,8 @@ def __call__(self, binary): else: if len(self.binary.state_history) > 2: if self.binary.state_history[-2] == 'detached': - self.state = "ERR" + self.binary.state = "ERR" + self.binary.event = "FAILED" raise ValueError('CO_HMS_RLO binary outside grid and coming from detached') self.binary.state = "detached" @@ -1485,6 +1508,12 @@ def __call__(self, binary): ecc == 0.)): super().__call__(self.binary) else: + if len(self.binary.state_history) > 2: + if self.binary.state_history[-2] == 'detached': + self.binary.event = 'FAILED' + self.binary.state = 'ERR' + raise ValueError('CO_HeMS_RLO binary outside grid and coming from detached') + self.binary.state = "detached" self.binary.event = "redirect_from_CO_HeMS_RLO" return @@ -1580,5 +1609,10 @@ def __call__(self, binary): ecc == 0.)): super().__call__(binary) else: + if len(self.binary.state_history) > 2: + if self.binary.state_history[-2] == 'detached': + self.binary.event = 'FAILED' + self.binary.state = 'ERR' + raise ValueError('CO_HeMS binary outside grid and coming from detached') self.binary.event = 'redirect_from_CO_HeMS' return From 487e5199090d3e5aae44ee1c56b2f6f7fc5549b6 Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Fri, 12 Jan 2024 13:54:49 +0100 Subject: [PATCH 160/319] Update step_detached.py clean up typo + whitespace --- posydon/binary_evol/DT/step_detached.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index e6b90625de..f795a4827b 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -1072,7 +1072,7 @@ def get_star_data(binary, star1, star2, htrack, # ZAMS stars in wide (non-mass exchaging binaries) that are # directed to detached step at birth m0, t0 = star1.mass, 0 - elif co: + elif co: m0, t0 = copy_prev_m0, copy_prev_t0 else: t_before_matching = time.time() @@ -1094,7 +1094,7 @@ def get_star_data(binary, star1, star2, htrack, else: self.grid = self.grid_strippedHe - # cehck if m0 is in the grid + # check if m0 is in the grid if m0 < self.grid.grid_mass.min() or m0 > self.grid.grid_mass.max(): binary.state = "ERR" binary.event = "FAILED" From d9aff7697dbbefb8f0b69304dd5109e552b83f31 Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Thu, 18 Jan 2024 16:47:38 +0100 Subject: [PATCH 161/319] deepcopy SimulationProperties (#233) This deepcopy per metallicity creates a unique copy of the `SimulationProperties`, such that each metallicity has it's own and loads in the correct metallicity grids. --- posydon/popsyn/synthetic_population.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/posydon/popsyn/synthetic_population.py b/posydon/popsyn/synthetic_population.py index 70577174a7..6eeecc9aa9 100644 --- a/posydon/popsyn/synthetic_population.py +++ b/posydon/popsyn/synthetic_population.py @@ -15,6 +15,7 @@ import pandas as pd from tqdm import tqdm import os +import copy from posydon.utils.constants import Zsun from posydon.popsyn.io import binarypop_kwargs_from_ini from posydon.popsyn.binarypopulation import BinaryPopulation @@ -77,8 +78,8 @@ def __init__(self, path_to_ini, verbose=False, MODEL={}): def create_binary_populations(self): """Create a list of BinaryPopulation objects.""" self.binary_populations = [] - ini_kw = self.synthetic_pop_params.copy() for met in self.metallicities[::-1]: + ini_kw = copy.deepcopy(self.synthetic_pop_params) ini_kw['metallicity'] = met ini_kw['temp_directory'] = self.create_met_prefix(met) + self.synthetic_pop_params['temp_directory'] self.binary_populations.append(BinaryPopulation(**ini_kw)) From 5ba89f3334077383a2a3694fd17b46571c81c8fd Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Thu, 18 Jan 2024 16:51:15 +0100 Subject: [PATCH 162/319] implement multiprocessing to free memory on finishing a metallicity (#234) Co-authored-by: Jeff Andrews --- posydon/popsyn/synthetic_population.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/posydon/popsyn/synthetic_population.py b/posydon/popsyn/synthetic_population.py index 6eeecc9aa9..b1f8fd9145 100644 --- a/posydon/popsyn/synthetic_population.py +++ b/posydon/popsyn/synthetic_population.py @@ -16,6 +16,7 @@ from tqdm import tqdm import os import copy +import multiprocessing as mp from posydon.utils.constants import Zsun from posydon.popsyn.io import binarypop_kwargs_from_ini from posydon.popsyn.binarypopulation import BinaryPopulation @@ -93,9 +94,15 @@ def evolve(self): self.create_binary_populations() while self.binary_populations: pop = self.binary_populations.pop() + if self.verbose: print(f'Z={pop.kwargs["metallicity"]:.2e} Z_sun') - pop.evolve() + + process = mp.Process(target=pop.evolve) + process.start() + process.join() + + pop.close() del pop From f65293c76bf50478d70a7db2eb447201fdb95421 Mon Sep 17 00:00:00 2001 From: Jeff Andrews Date: Thu, 18 Jan 2024 11:06:18 -0500 Subject: [PATCH 163/319] Update step_mesa.py --- posydon/binary_evol/MESA/step_mesa.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index 34a3544380..14464f09d2 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -1297,8 +1297,8 @@ def __call__(self, binary): event == 'ZAMS' and p <= self.p_max and (m1 < self.m1_min or m1 > self.m1_max)): - self.binary.event = 'ERR' - self.binary.state = 'FAILED' + self.binary.event = 'FAILED' + self.binary.state = 'ERR' raise ValueError(f'The mass of m1 ({m1}) is outside the grid,' 'while the period is inside the grid.') # outside the mass grid for m2 From c159a953e94af2ae8f4df985effc25c7247978d6 Mon Sep 17 00:00:00 2001 From: Max Briel Date: Fri, 19 Jan 2024 15:31:55 +0100 Subject: [PATCH 164/319] add set_binary_to_failed function --- posydon/binary_evol/DT/step_detached.py | 6 +++--- posydon/binary_evol/MESA/step_mesa.py | 15 ++++++--------- posydon/utils/common_functions.py | 12 ++++++++++++ 3 files changed, 21 insertions(+), 12 deletions(-) diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index f795a4827b..867d2157aa 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -32,7 +32,8 @@ roche_lobe_radius, check_state_of_star, PchipInterpolator2, - convert_metallicity_to_string + convert_metallicity_to_string, + set_binary_to_failed, ) from posydon.binary_evol.flow_chart import (STAR_STATES_CC, STAR_STATES_CO) import posydon.utils.constants as const @@ -1096,8 +1097,7 @@ def get_star_data(binary, star1, star2, htrack, # check if m0 is in the grid if m0 < self.grid.grid_mass.min() or m0 > self.grid.grid_mass.max(): - binary.state = "ERR" - binary.event = "FAILED" + set_binary_to_failed(binary) raise ValueError(f"The mass {m0} is out of the single star grid range and cannot be matched to a track.") get_track = self.grid.get diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index 14464f09d2..a9fbd9e304 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -26,7 +26,8 @@ from posydon.interpolation.IF_interpolation import IFInterpolator from posydon.utils.common_functions import (flip_stars, convert_metallicity_to_string, - CO_radius, infer_star_state) + CO_radius, infer_star_state, + set_binary_to_failed,) from posydon.utils.data_download import data_download, PATH_TO_POSYDON_DATA from posydon.grids.MODELS import MODELS @@ -1297,8 +1298,7 @@ def __call__(self, binary): event == 'ZAMS' and p <= self.p_max and (m1 < self.m1_min or m1 > self.m1_max)): - self.binary.event = 'FAILED' - self.binary.state = 'ERR' + set_binary_to_failed(self.binary) raise ValueError(f'The mass of m1 ({m1}) is outside the grid,' 'while the period is inside the grid.') # outside the mass grid for m2 @@ -1307,8 +1307,7 @@ def __call__(self, binary): event == 'ZAMS' and p <= self.p_max and (mass_ratio < self.q_min or mass_ratio > self.q_max)): - self.binary.event = 'FAILED' - self.binary.state = 'ERR' + set_binary_to_failed(self.binary) raise ValueError(f'The mass of m2 ({m2}) is outside the grid,' 'while the period is inside the grid.') # redirect if CC1 @@ -1320,8 +1319,7 @@ def __call__(self, binary): self.binary.event = 'CC2' return else: - self.binary.event = 'FAILED' - self.binary.state = 'ERR' + set_binary_to_failed(self.binary) raise ValueError('The star_1.state = %s, star_2.state = %s, ' 'binary.event = %s and not H-rich_Core_H_burning ' '- H-rich_Core_H_burning - * - ZAMS' @@ -1414,8 +1412,7 @@ def __call__(self, binary): else: if len(self.binary.state_history) > 2: if self.binary.state_history[-2] == 'detached': - self.binary.state = "ERR" - self.binary.event = "FAILED" + set_binary_to_failed(self.binary) raise ValueError('CO_HMS_RLO binary outside grid and coming from detached') self.binary.state = "detached" diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index 3319697be3..1b674df71d 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -1374,6 +1374,18 @@ def flip_stars(binary): setattr(binary, i+'2_history', value1_history) +def set_binary_to_failed(binary): + '''Set the properties of the binary to indicate that it has failed. + + Parameters + ---------- + binary : BinaryStar + The binary to set to failed. + ''' + binary.state = "ERR" + binary.event = "FAILED" + + def infer_star_state(star_mass=None, surface_h1=None, center_h1=None, center_he4=None, center_c12=None, log_LH=None, log_LHe=None, log_Lnuc=None, star_CO=False): From 49adcacbbc158fb099ff2622bed3d21b48c8a090 Mon Sep 17 00:00:00 2001 From: Max Briel Date: Mon, 22 Jan 2024 13:59:19 +0100 Subject: [PATCH 165/319] change to m2 min and max --- posydon/binary_evol/MESA/step_mesa.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index a9fbd9e304..134b540633 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -1250,6 +1250,8 @@ def __init__(self, metallicity=1., grid_name=None, *args, **kwargs): # load grid boundaries self.m1_min = min(self._psyTrackInterp.grid.initial_values['star_1_mass']) self.m1_max = max(self._psyTrackInterp.grid.initial_values['star_1_mass']) + self.m2_min = min(self._psyTrackInterp.grid.initial_values['star_2_mass']) + self.m2_max = max(self._psyTrackInterp.grid.initial_values['star_2_mass']) self.q_min = 0.05 # can be computed m2_min/m1_min self.q_max = 1. # note that for MESA stability we actually run q_max = 0.99 self.p_min = min(self._psyTrackInterp.grid.initial_values['period_days']) @@ -1306,7 +1308,7 @@ def __call__(self, binary): state_2 == 'H-rich_Core_H_burning' and event == 'ZAMS' and p <= self.p_max and - (mass_ratio < self.q_min or mass_ratio > self.q_max)): + (m2 < self.m2_min or m2 > self.m2_max)): set_binary_to_failed(self.binary) raise ValueError(f'The mass of m2 ({m2}) is outside the grid,' 'while the period is inside the grid.') From 0ae8d2438f85aefc75292eb7ab2eeca1e9929558 Mon Sep 17 00:00:00 2001 From: Max Briel Date: Mon, 22 Jan 2024 15:36:54 +0100 Subject: [PATCH 166/319] add mass out of grid error for CO grids --- posydon/binary_evol/MESA/step_mesa.py | 46 +++++++++++++++++++++++---- 1 file changed, 40 insertions(+), 6 deletions(-) diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index 134b540633..72f5ff959c 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -1298,7 +1298,7 @@ def __call__(self, binary): elif (state_1 == 'H-rich_Core_H_burning' and state_2 == 'H-rich_Core_H_burning' and event == 'ZAMS' and - p <= self.p_max and + self.p_min <= p <= self.p_max and (m1 < self.m1_min or m1 > self.m1_max)): set_binary_to_failed(self.binary) raise ValueError(f'The mass of m1 ({m1}) is outside the grid,' @@ -1307,7 +1307,7 @@ def __call__(self, binary): elif (state_1 == 'H-rich_Core_H_burning' and state_2 == 'H-rich_Core_H_burning' and event == 'ZAMS' and - p <= self.p_max and + self.p_min <= p <= self.p_max and (m2 < self.m2_min or m2 > self.m2_max)): set_binary_to_failed(self.binary) raise ValueError(f'The mass of m2 ({m2}) is outside the grid,' @@ -1411,6 +1411,25 @@ def __call__(self, binary): self.p_min <= p <= self.p_max and ecc == 0.)): super().__call__(self.binary) + + # period inside the grid, but m1 outside the grid + elif ((not self.flip_stars_before_step and + self.p_min <= p <= self.p_max and + (m1 < self.m1_min or m1 > self.m1_max) + )): + set_binary_to_failed(self.binary) + raise ValueError(f'The mass of m1 ({m1}) is outside the grid,' + 'while the period is inside the grid.') + + # period inside the grid, but m2 outside the grid + elif ((not self.flip_stars_before_step and + self.p_min <= p <= self.p_max and + (m2 < self.m2_min or m2 > self.m2_max) + )): + set_binary_to_failed(self.binary) + raise ValueError(f'The mass of m2 ({m2}) is outside the grid,' + 'while the period is inside the grid.') + else: if len(self.binary.state_history) > 2: if self.binary.state_history[-2] == 'detached': @@ -1506,11 +1525,27 @@ def __call__(self, binary): self.p_min <= p <= self.p_max and ecc == 0.)): super().__call__(self.binary) + # period inside the grid, but m1 outside the grid + elif ((not self.flip_stars_before_step and + self.p_min <= p <= self.p_max and + (m1 < self.m1_min or m1 > self.m1_max) + )): + set_binary_to_failed(self.binary) + raise ValueError(f'The mass of m1 ({m1}) is outside the grid,' + 'while the period is inside the grid.') + # period inside the grid, but m2 outside the grid + elif ((not self.flip_stars_before_step and + self.p_min <= p <= self.p_max and + (m2 < self.m2_min or m2 > self.m2_max) + )): + set_binary_to_failed(self.binary) + raise ValueError(f'The mass of m2 ({m2}) is outside the grid,' + 'while the period is inside the grid.') + else: if len(self.binary.state_history) > 2: if self.binary.state_history[-2] == 'detached': - self.binary.event = 'FAILED' - self.binary.state = 'ERR' + set_binary_to_failed(self.binary) raise ValueError('CO_HeMS_RLO binary outside grid and coming from detached') self.binary.state = "detached" @@ -1610,8 +1645,7 @@ def __call__(self, binary): else: if len(self.binary.state_history) > 2: if self.binary.state_history[-2] == 'detached': - self.binary.event = 'FAILED' - self.binary.state = 'ERR' + set_binary_to_failed(self.binary) raise ValueError('CO_HeMS binary outside grid and coming from detached') self.binary.event = 'redirect_from_CO_HeMS' return From d14c2fe06b1a34a9f89916d86752d782c968492e Mon Sep 17 00:00:00 2001 From: Jeff Andrews Date: Thu, 25 Jan 2024 10:33:39 -0500 Subject: [PATCH 167/319] Allow non-burning stars in MT (#206) * Allow non-burning stars in MT * Update common_functions.py --- posydon/utils/common_functions.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index 3319697be3..c70fac5d3a 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -66,11 +66,13 @@ MT_CASE_BA = 4 MT_CASE_BB = 5 MT_CASE_BC = 6 +MT_CASE_NONBURNING = 8 MT_CASE_UNDETERMINED = 9 # All cases meaning RLO is happening ALL_RLO_CASES = set([MT_CASE_A, MT_CASE_B, MT_CASE_C, - MT_CASE_BA, MT_CASE_BB, MT_CASE_BC]) + MT_CASE_BA, MT_CASE_BB, MT_CASE_BC, + MT_CASE_NONBURNING]) # Conversion of integer mass-transfer flags to strings MT_CASE_TO_STR = { @@ -81,6 +83,7 @@ MT_CASE_BA: "BA", MT_CASE_BB: "BB", MT_CASE_BC: "BC", + MT_CASE_NONBURNING: "nonburning", MT_CASE_UNDETERMINED: "undetermined_MT" } @@ -1447,7 +1450,9 @@ def infer_mass_transfer_case(rl_relative_overflow, rl_relative_overflow, lg_mtransfer_rate) return MT_CASE_NO_RLO - if "H-rich" in donor_state: + if "non_burning" in donor_state: + return MT_CASE_NONBURNING + elif "H-rich" in donor_state: if "Core_H_burning" in donor_state: return MT_CASE_A if ("Core_He_burning" in donor_state From ed747107bd33501b27b47d3be32396e01cd38218 Mon Sep 17 00:00:00 2001 From: Elizabeth Teng <41969755+elizabethteng@users.noreply.github.com> Date: Thu, 25 Jan 2024 09:35:49 -0600 Subject: [PATCH 168/319] minor bug fixes to profile interpolation (#223) * rearranged assignment before reference * change enforcement of monotonic decreasing in density profiles to start at surface rather than center * fix minor bugs in density function --- .../interpolation/profile_interpolation.py | 34 ++++++++++--------- 1 file changed, 18 insertions(+), 16 deletions(-) diff --git a/posydon/interpolation/profile_interpolation.py b/posydon/interpolation/profile_interpolation.py index cc596f1d69..33c5ce2d92 100644 --- a/posydon/interpolation/profile_interpolation.py +++ b/posydon/interpolation/profile_interpolation.py @@ -1,4 +1,4 @@ -"""Module for performing final profile interpolation +"""Module for performing initial-final profile interpolation """ __authors__ = [ @@ -219,8 +219,8 @@ def train(self,IF_interpolator,density_epochs=3000,density_patience=200, self.profiles[:,self.names.index("logRho")], self.valid_initial, self.valid_profiles[:,self.names.index("logRho")], - IF_interpolator) - self.dens.train(prof_epochs=density_epochs,prof_patience=density_patience,hms_s2=hms_s2) + IF_interpolator,hms_s2=hms_s2) + self.dens.train(prof_epochs=density_epochs,prof_patience=density_patience) if loss_history==True: return self.comp.loss_history, self.dens.loss_history @@ -274,20 +274,22 @@ def mono_decrease(self,profiles): def mono_renorm(arr): arr_copy = arr.copy() - # force rising points down + # starting from surface, force dropping points up for i in range(1,len(arr_copy)): - if arr_copy[i]>arr_copy[i-1]: - arr_copy[i]=arr_copy[i-1] - # cut off points below surface value - return np.where(arr_copyarr_copy[-i-1]: + arr_copy[-i-1]=arr_copy[-i] + # cut off points above center value + return np.where(arr_copy>arr[0], arr[0], arr_copy) + profiles_mono=profiles.copy() - + for i in range(len(profiles)): if len(np.where(profiles[i][1:]-profiles[i][:-1]>0)[0]>0): profiles_mono[i] = mono_renorm(profiles[i]) - + return profiles_mono + + class Density: @@ -393,11 +395,6 @@ def predict(self,inputs): regress_rho = lambda x: self.model_rho(x) min_rho = regress_rho(inputs).numpy()[:,0] - # reconstruct profile - norm_prof = self.pca.inverse_transform(pca_weights_pred*self.scaling) - density_profiles = norm_prof*(max_rho[:,np.newaxis]-min_rho[:,np.newaxis]) \ - + min_rho[:,np.newaxis] - # IF interpolate final mass, center density if self.hms_s2==False: m_ind = self.model_IF.interpolators[0].out_keys.index("star_1_mass") @@ -408,6 +405,11 @@ def predict(self,inputs): max_rho = self.model_IF.interpolators[0].test_interpolator(10**inputs)[:,center_ind] pred_mass = self.model_IF.interpolators[0].test_interpolator(10**inputs)[:,m_ind] + # reconstruct profile + norm_prof = self.pca.inverse_transform(pca_weights_pred*self.scaling) + density_profiles = norm_prof*(max_rho[:,np.newaxis]-min_rho[:,np.newaxis]) \ + + min_rho[:,np.newaxis] + # construct mass enclosed profile coordinates mass_coords = np.linspace(0,1,200)*pred_mass[:,np.newaxis] From ff0302d663614e09c1649160c4560ad09a9d4f12 Mon Sep 17 00:00:00 2001 From: Max Briel Date: Tue, 30 Jan 2024 11:13:38 +0100 Subject: [PATCH 169/319] Add below minimum period redirect --- posydon/binary_evol/MESA/step_mesa.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index 72f5ff959c..51cf928d83 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -1294,6 +1294,14 @@ def __call__(self, binary): p > self.p_max): self.binary.event = 'redirect_from_ZAMS' return + # redirect if period smaller than the minimum period + elif (state_1 == 'H-rich_Core_H_burning' and + state_2 == 'H-rich_Core_H_burning' and + event == 'ZAMS' and + p < self.p_min): + self.binary.event = 'redirect_from_ZAMS' + return + # outside the mass grid for m1 elif (state_1 == 'H-rich_Core_H_burning' and state_2 == 'H-rich_Core_H_burning' and @@ -1303,12 +1311,13 @@ def __call__(self, binary): set_binary_to_failed(self.binary) raise ValueError(f'The mass of m1 ({m1}) is outside the grid,' 'while the period is inside the grid.') + # outside the mass grid for m2 elif (state_1 == 'H-rich_Core_H_burning' and state_2 == 'H-rich_Core_H_burning' and event == 'ZAMS' and self.p_min <= p <= self.p_max and - (m2 < self.m2_min or m2 > self.m2_max)): + (m2 < np.max(self.m2_min, 0.5) or m2 > self.m2_max)): set_binary_to_failed(self.binary) raise ValueError(f'The mass of m2 ({m2}) is outside the grid,' 'while the period is inside the grid.') From a56943c88e5ac96595b458f441f3cb7461a4d6e9 Mon Sep 17 00:00:00 2001 From: Max Briel Date: Tue, 30 Jan 2024 11:21:14 +0100 Subject: [PATCH 170/319] add clarification + selection based on mass ratio --- posydon/binary_evol/MESA/step_mesa.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index 51cf928d83..2aa3c0d855 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -1271,6 +1271,7 @@ def __call__(self, binary): m2 = self.binary.star_2.mass mass_ratio = m2/m1 p = self.binary.orbital_period + # check if the binary is in the grid if (state_1 == 'H-rich_Core_H_burning' and state_2 == 'H-rich_Core_H_burning' and event == 'ZAMS' and @@ -1278,7 +1279,8 @@ def __call__(self, binary): self.q_min <= mass_ratio <= self.q_max and self.p_min <= p <= self.p_max): self.flip_stars_before_step = False - super().__call__(self.binary) + super().__call__(self.binary) + # binary in grid but masses flipped elif (state_1 == 'H-rich_Core_H_burning' and state_2 == 'H-rich_Core_H_burning' and event == 'ZAMS' and @@ -1287,7 +1289,7 @@ def __call__(self, binary): self.p_min <= p <= self.p_max): self.flip_stars_before_step = True super().__call__(self.binary) - # redirect if outside grid + # redirect if outside grid for period elif (state_1 == 'H-rich_Core_H_burning' and state_2 == 'H-rich_Core_H_burning' and event == 'ZAMS' and @@ -1300,8 +1302,7 @@ def __call__(self, binary): event == 'ZAMS' and p < self.p_min): self.binary.event = 'redirect_from_ZAMS' - return - + return # outside the mass grid for m1 elif (state_1 == 'H-rich_Core_H_burning' and state_2 == 'H-rich_Core_H_burning' and @@ -1311,13 +1312,14 @@ def __call__(self, binary): set_binary_to_failed(self.binary) raise ValueError(f'The mass of m1 ({m1}) is outside the grid,' 'while the period is inside the grid.') - # outside the mass grid for m2 + # because m2_min is 0.5 Msun or from q_min, the minimum mass ratio is either + # q_min or 0.5/m1. elif (state_1 == 'H-rich_Core_H_burning' and state_2 == 'H-rich_Core_H_burning' and event == 'ZAMS' and self.p_min <= p <= self.p_max and - (m2 < np.max(self.m2_min, 0.5) or m2 > self.m2_max)): + (mass_ratio < np.max(self.q_min, 0.5/m1) or mass_ratio > self.q_max)): set_binary_to_failed(self.binary) raise ValueError(f'The mass of m2 ({m2}) is outside the grid,' 'while the period is inside the grid.') From 5e71b0da1e2e292110db5caf0f37e307436491a0 Mon Sep 17 00:00:00 2001 From: Max Briel Date: Tue, 30 Jan 2024 13:57:56 +0100 Subject: [PATCH 171/319] add additional mass_ratio minimum --- posydon/binary_evol/MESA/step_mesa.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index 2aa3c0d855..43bdb77c93 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -1276,7 +1276,7 @@ def __call__(self, binary): state_2 == 'H-rich_Core_H_burning' and event == 'ZAMS' and self.m1_min <= m1 <= self.m1_max and - self.q_min <= mass_ratio <= self.q_max and + np.max(self.q_min, 0.5/m1) <= mass_ratio <= self.q_max and self.p_min <= p <= self.p_max): self.flip_stars_before_step = False super().__call__(self.binary) @@ -1285,7 +1285,7 @@ def __call__(self, binary): state_2 == 'H-rich_Core_H_burning' and event == 'ZAMS' and self.m1_min <= m2 <= self.m1_max and - self.q_min <= 1./mass_ratio <= self.q_max and + np.max(self.q_min, 0.5/m1) <= 1./mass_ratio <= self.q_max and self.p_min <= p <= self.p_max): self.flip_stars_before_step = True super().__call__(self.binary) @@ -1378,6 +1378,7 @@ def __call__(self, binary): m2 = self.binary.star_2.mass p = self.binary.orbital_period ecc = self.binary.eccentricity + mass_ratio = m2/m1 # TODO: import states from flow_chart.py FOR_RLO_STATES = ["H-rich_Core_H_burning", From 9a2d6ed59928c9afef3de04df1618393e26c3265 Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Tue, 30 Jan 2024 14:01:53 +0100 Subject: [PATCH 172/319] Update step_mesa.py --- posydon/binary_evol/MESA/step_mesa.py | 1 - 1 file changed, 1 deletion(-) diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index 43bdb77c93..875fc9c218 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -1378,7 +1378,6 @@ def __call__(self, binary): m2 = self.binary.star_2.mass p = self.binary.orbital_period ecc = self.binary.eccentricity - mass_ratio = m2/m1 # TODO: import states from flow_chart.py FOR_RLO_STATES = ["H-rich_Core_H_burning", From b239229be6b8a9f9db7879b33e3347c11d22c223 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 1 Feb 2024 16:14:43 +0100 Subject: [PATCH 173/319] Copy column list for MESA (#228) * Update posydon-setup-grid Copy column list into work directory and use them for the runs. * Update posydon-setup-grid Add waits for the subprocesses on git. * Update posydon-setup-grid remove one wait causing trouble. --- bin/posydon-setup-grid | 30 +++++++++++++++++++++++------- 1 file changed, 23 insertions(+), 7 deletions(-) diff --git a/bin/posydon-setup-grid b/bin/posydon-setup-grid index 012fc0f1a1..ce8ffaa5a9 100755 --- a/bin/posydon-setup-grid +++ b/bin/posydon-setup-grid @@ -124,6 +124,7 @@ def find_inlist_from_scenario(source, gitcommit, system_type): stdout = subprocess.PIPE, stderr = subprocess.PIPE ) + proc.wait() print("For posterity we are pulling (specifically needed if you already have the repo clone)") proc = subprocess.call(['git', 'pull'], @@ -131,7 +132,7 @@ def find_inlist_from_scenario(source, gitcommit, system_type): stdout = subprocess.PIPE, stderr = subprocess.PIPE ) - + print("checking out commit/tag: {0}".format(githash)) @@ -140,6 +141,7 @@ def find_inlist_from_scenario(source, gitcommit, system_type): stdout = subprocess.PIPE, stderr = subprocess.PIPE ) + proc.wait() # if this is looking at posydon defaults, all posydon defaults build from default common inlists if source == 'posydon': @@ -984,6 +986,20 @@ if __name__ == '__main__': grid_parameters=grid_df.columns, working_directory=args.run_directory) + # handle column lists + # first, creating a directory + column_lists_folder = os.path.join(args.run_directory, 'column_lists') + if os.path.exists(column_lists_folder): shutil.rmtree(column_lists_folder) + os.makedirs(column_lists_folder) + # second, getting new location + star_history_columns = os.path.join(column_lists_folder, 'history_columns.list') + binary_history_columns = os.path.join(column_lists_folder, 'binary_history_columns.list') + profile_columns = os.path.join(column_lists_folder, 'profile_columns.list') + # third, copy lists + shutil.copy(mesa_inlists['star_history_columns'], star_history_columns) + shutil.copy(mesa_inlists['binary_history_columns'], binary_history_columns) + shutil.copy(mesa_inlists['profile_columns'], profile_columns) + # now we can write the mpi command line if slurm['job_array']: command_line = construct_command_line(1, @@ -996,9 +1012,9 @@ if __name__ == '__main__': inlist_star2_binary, inlist_star1_formation, inlist_star2_formation, - mesa_inlists['star_history_columns'], - mesa_inlists['binary_history_columns'], - mesa_inlists['profile_columns'], + star_history_columns, + binary_history_columns, + profile_columns, args.run_directory, 'fixed', path_to_run_grid_exec, @@ -1016,9 +1032,9 @@ if __name__ == '__main__': inlist_star2_binary, inlist_star1_formation, inlist_star2_formation, - mesa_inlists['star_history_columns'], - mesa_inlists['binary_history_columns'], - mesa_inlists['profile_columns'], + star_history_columns, + binary_history_columns, + profile_columns, args.run_directory, args.grid_type, path_to_run_grid_exec, From 687947cfb13b56710fac86038b7a5906bad2a8b6 Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Thu, 1 Feb 2024 16:32:44 +0100 Subject: [PATCH 174/319] Add loop checks for other CO grids (#239) * add loop checks for other CO grids * when raising an error set state to 'ERR' and event to 'FAILED' * Update step_detached.py clean up typo + whitespace * Update step_mesa.py * add set_binary_to_failed function * change to m2 min and max * add mass out of grid error for CO grids * Add below minimum period redirect * add clarification + selection based on mass ratio * add additional mass_ratio minimum * Update step_mesa.py --------- Co-authored-by: Jeff Andrews --- posydon/binary_evol/DT/step_detached.py | 10 ++- posydon/binary_evol/MESA/step_mesa.py | 90 +++++++++++++++++++++++-- posydon/utils/common_functions.py | 12 ++++ 3 files changed, 104 insertions(+), 8 deletions(-) diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index 5526bafe29..867d2157aa 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -32,7 +32,8 @@ roche_lobe_radius, check_state_of_star, PchipInterpolator2, - convert_metallicity_to_string + convert_metallicity_to_string, + set_binary_to_failed, ) from posydon.binary_evol.flow_chart import (STAR_STATES_CC, STAR_STATES_CO) import posydon.utils.constants as const @@ -1081,7 +1082,7 @@ def get_star_data(binary, star1, star2, htrack, if self.verbose or self.verbose == 1: print("Matching duration: " f"{t_after_matching-t_before_matching:.6g}") - + if pd.isna(m0) or pd.isna(t0): # binary.event = "END" # binary.state += " (GridMatchingFailed)" @@ -1093,6 +1094,11 @@ def get_star_data(binary, star1, star2, htrack, self.grid = self.grid_Hrich else: self.grid = self.grid_strippedHe + + # check if m0 is in the grid + if m0 < self.grid.grid_mass.min() or m0 > self.grid.grid_mass.max(): + set_binary_to_failed(binary) + raise ValueError(f"The mass {m0} is out of the single star grid range and cannot be matched to a track.") get_track = self.grid.get diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index caa66d46b4..875fc9c218 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -26,7 +26,8 @@ from posydon.interpolation.IF_interpolation import IFInterpolator from posydon.utils.common_functions import (flip_stars, convert_metallicity_to_string, - CO_radius, infer_star_state) + CO_radius, infer_star_state, + set_binary_to_failed,) from posydon.utils.data_download import data_download, PATH_TO_POSYDON_DATA from posydon.grids.MODELS import MODELS @@ -1249,6 +1250,8 @@ def __init__(self, metallicity=1., grid_name=None, *args, **kwargs): # load grid boundaries self.m1_min = min(self._psyTrackInterp.grid.initial_values['star_1_mass']) self.m1_max = max(self._psyTrackInterp.grid.initial_values['star_1_mass']) + self.m2_min = min(self._psyTrackInterp.grid.initial_values['star_2_mass']) + self.m2_max = max(self._psyTrackInterp.grid.initial_values['star_2_mass']) self.q_min = 0.05 # can be computed m2_min/m1_min self.q_max = 1. # note that for MESA stability we actually run q_max = 0.99 self.p_min = min(self._psyTrackInterp.grid.initial_values['period_days']) @@ -1268,29 +1271,58 @@ def __call__(self, binary): m2 = self.binary.star_2.mass mass_ratio = m2/m1 p = self.binary.orbital_period + # check if the binary is in the grid if (state_1 == 'H-rich_Core_H_burning' and state_2 == 'H-rich_Core_H_burning' and event == 'ZAMS' and self.m1_min <= m1 <= self.m1_max and - self.q_min <= mass_ratio <= self.q_max and + np.max(self.q_min, 0.5/m1) <= mass_ratio <= self.q_max and self.p_min <= p <= self.p_max): self.flip_stars_before_step = False - super().__call__(self.binary) + super().__call__(self.binary) + # binary in grid but masses flipped elif (state_1 == 'H-rich_Core_H_burning' and state_2 == 'H-rich_Core_H_burning' and event == 'ZAMS' and self.m1_min <= m2 <= self.m1_max and - self.q_min <= 1./mass_ratio <= self.q_max and + np.max(self.q_min, 0.5/m1) <= 1./mass_ratio <= self.q_max and self.p_min <= p <= self.p_max): self.flip_stars_before_step = True super().__call__(self.binary) - # redirect if outside grid + # redirect if outside grid for period elif (state_1 == 'H-rich_Core_H_burning' and state_2 == 'H-rich_Core_H_burning' and event == 'ZAMS' and p > self.p_max): self.binary.event = 'redirect_from_ZAMS' return + # redirect if period smaller than the minimum period + elif (state_1 == 'H-rich_Core_H_burning' and + state_2 == 'H-rich_Core_H_burning' and + event == 'ZAMS' and + p < self.p_min): + self.binary.event = 'redirect_from_ZAMS' + return + # outside the mass grid for m1 + elif (state_1 == 'H-rich_Core_H_burning' and + state_2 == 'H-rich_Core_H_burning' and + event == 'ZAMS' and + self.p_min <= p <= self.p_max and + (m1 < self.m1_min or m1 > self.m1_max)): + set_binary_to_failed(self.binary) + raise ValueError(f'The mass of m1 ({m1}) is outside the grid,' + 'while the period is inside the grid.') + # outside the mass grid for m2 + # because m2_min is 0.5 Msun or from q_min, the minimum mass ratio is either + # q_min or 0.5/m1. + elif (state_1 == 'H-rich_Core_H_burning' and + state_2 == 'H-rich_Core_H_burning' and + event == 'ZAMS' and + self.p_min <= p <= self.p_max and + (mass_ratio < np.max(self.q_min, 0.5/m1) or mass_ratio > self.q_max)): + set_binary_to_failed(self.binary) + raise ValueError(f'The mass of m2 ({m2}) is outside the grid,' + 'while the period is inside the grid.') # redirect if CC1 elif (state_1 == 'H-rich_Central_C_depletion'): self.binary.event = 'CC1' @@ -1300,6 +1332,7 @@ def __call__(self, binary): self.binary.event = 'CC2' return else: + set_binary_to_failed(self.binary) raise ValueError('The star_1.state = %s, star_2.state = %s, ' 'binary.event = %s and not H-rich_Core_H_burning ' '- H-rich_Core_H_burning - * - ZAMS' @@ -1389,10 +1422,29 @@ def __call__(self, binary): self.p_min <= p <= self.p_max and ecc == 0.)): super().__call__(self.binary) + + # period inside the grid, but m1 outside the grid + elif ((not self.flip_stars_before_step and + self.p_min <= p <= self.p_max and + (m1 < self.m1_min or m1 > self.m1_max) + )): + set_binary_to_failed(self.binary) + raise ValueError(f'The mass of m1 ({m1}) is outside the grid,' + 'while the period is inside the grid.') + + # period inside the grid, but m2 outside the grid + elif ((not self.flip_stars_before_step and + self.p_min <= p <= self.p_max and + (m2 < self.m2_min or m2 > self.m2_max) + )): + set_binary_to_failed(self.binary) + raise ValueError(f'The mass of m2 ({m2}) is outside the grid,' + 'while the period is inside the grid.') + else: if len(self.binary.state_history) > 2: if self.binary.state_history[-2] == 'detached': - self.state = "ERR" + set_binary_to_failed(self.binary) raise ValueError('CO_HMS_RLO binary outside grid and coming from detached') self.binary.state = "detached" @@ -1484,7 +1536,29 @@ def __call__(self, binary): self.p_min <= p <= self.p_max and ecc == 0.)): super().__call__(self.binary) + # period inside the grid, but m1 outside the grid + elif ((not self.flip_stars_before_step and + self.p_min <= p <= self.p_max and + (m1 < self.m1_min or m1 > self.m1_max) + )): + set_binary_to_failed(self.binary) + raise ValueError(f'The mass of m1 ({m1}) is outside the grid,' + 'while the period is inside the grid.') + # period inside the grid, but m2 outside the grid + elif ((not self.flip_stars_before_step and + self.p_min <= p <= self.p_max and + (m2 < self.m2_min or m2 > self.m2_max) + )): + set_binary_to_failed(self.binary) + raise ValueError(f'The mass of m2 ({m2}) is outside the grid,' + 'while the period is inside the grid.') + else: + if len(self.binary.state_history) > 2: + if self.binary.state_history[-2] == 'detached': + set_binary_to_failed(self.binary) + raise ValueError('CO_HeMS_RLO binary outside grid and coming from detached') + self.binary.state = "detached" self.binary.event = "redirect_from_CO_HeMS_RLO" return @@ -1580,5 +1654,9 @@ def __call__(self, binary): ecc == 0.)): super().__call__(binary) else: + if len(self.binary.state_history) > 2: + if self.binary.state_history[-2] == 'detached': + set_binary_to_failed(self.binary) + raise ValueError('CO_HeMS binary outside grid and coming from detached') self.binary.event = 'redirect_from_CO_HeMS' return diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index c70fac5d3a..2d4bd1e0ee 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -1377,6 +1377,18 @@ def flip_stars(binary): setattr(binary, i+'2_history', value1_history) +def set_binary_to_failed(binary): + '''Set the properties of the binary to indicate that it has failed. + + Parameters + ---------- + binary : BinaryStar + The binary to set to failed. + ''' + binary.state = "ERR" + binary.event = "FAILED" + + def infer_star_state(star_mass=None, surface_h1=None, center_h1=None, center_he4=None, center_c12=None, log_LH=None, log_LHe=None, log_Lnuc=None, star_CO=False): From 39a77894659ccf1e12337d145e5e76d4b899bd28 Mon Sep 17 00:00:00 2001 From: Max Briel Date: Fri, 2 Feb 2024 09:47:50 +0100 Subject: [PATCH 175/319] notation fix for np.max compared to maximum --- posydon/binary_evol/MESA/step_mesa.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index 875fc9c218..43be1f6078 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -1276,7 +1276,7 @@ def __call__(self, binary): state_2 == 'H-rich_Core_H_burning' and event == 'ZAMS' and self.m1_min <= m1 <= self.m1_max and - np.max(self.q_min, 0.5/m1) <= mass_ratio <= self.q_max and + np.max([self.q_min, 0.5/m1]) <= mass_ratio <= self.q_max and self.p_min <= p <= self.p_max): self.flip_stars_before_step = False super().__call__(self.binary) @@ -1285,7 +1285,7 @@ def __call__(self, binary): state_2 == 'H-rich_Core_H_burning' and event == 'ZAMS' and self.m1_min <= m2 <= self.m1_max and - np.max(self.q_min, 0.5/m1) <= 1./mass_ratio <= self.q_max and + np.max([self.q_min, 0.5/m1]) <= 1./mass_ratio <= self.q_max and self.p_min <= p <= self.p_max): self.flip_stars_before_step = True super().__call__(self.binary) @@ -1319,7 +1319,7 @@ def __call__(self, binary): state_2 == 'H-rich_Core_H_burning' and event == 'ZAMS' and self.p_min <= p <= self.p_max and - (mass_ratio < np.max(self.q_min, 0.5/m1) or mass_ratio > self.q_max)): + (mass_ratio < np.max([self.q_min, 0.5/m1]) or mass_ratio > self.q_max)): set_binary_to_failed(self.binary) raise ValueError(f'The mass of m2 ({m2}) is outside the grid,' 'while the period is inside the grid.') From ca32948985321f734786853705358347e196562c Mon Sep 17 00:00:00 2001 From: Max Briel Date: Fri, 2 Feb 2024 09:49:42 +0100 Subject: [PATCH 176/319] fix missed merge --- posydon/binary_evol/MESA/step_mesa.py | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index b730250755..43be1f6078 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -1276,11 +1276,7 @@ def __call__(self, binary): state_2 == 'H-rich_Core_H_burning' and event == 'ZAMS' and self.m1_min <= m1 <= self.m1_max and -<<<<<<< HEAD np.max([self.q_min, 0.5/m1]) <= mass_ratio <= self.q_max and -======= - np.max(self.q_min, 0.5/m1) <= mass_ratio <= self.q_max and ->>>>>>> development self.p_min <= p <= self.p_max): self.flip_stars_before_step = False super().__call__(self.binary) @@ -1289,11 +1285,7 @@ def __call__(self, binary): state_2 == 'H-rich_Core_H_burning' and event == 'ZAMS' and self.m1_min <= m2 <= self.m1_max and -<<<<<<< HEAD np.max([self.q_min, 0.5/m1]) <= 1./mass_ratio <= self.q_max and -======= - np.max(self.q_min, 0.5/m1) <= 1./mass_ratio <= self.q_max and ->>>>>>> development self.p_min <= p <= self.p_max): self.flip_stars_before_step = True super().__call__(self.binary) @@ -1327,11 +1319,7 @@ def __call__(self, binary): state_2 == 'H-rich_Core_H_burning' and event == 'ZAMS' and self.p_min <= p <= self.p_max and -<<<<<<< HEAD (mass_ratio < np.max([self.q_min, 0.5/m1]) or mass_ratio > self.q_max)): -======= - (mass_ratio < np.max(self.q_min, 0.5/m1) or mass_ratio > self.q_max)): ->>>>>>> development set_binary_to_failed(self.binary) raise ValueError(f'The mass of m2 ({m2}) is outside the grid,' 'while the period is inside the grid.') From 1c52d6ae3fc49bd04be846d6cc829993fbaf6c9e Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 8 Feb 2024 16:40:19 +0100 Subject: [PATCH 177/319] Update scrubbing.py (#249) Align the RLO detection in POSYDON to what we use in MESA. --- posydon/grids/scrubbing.py | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/posydon/grids/scrubbing.py b/posydon/grids/scrubbing.py index 5411578cb2..a6c85507e5 100644 --- a/posydon/grids/scrubbing.py +++ b/posydon/grids/scrubbing.py @@ -98,14 +98,22 @@ def keep_after_RLO(bh, h1, h2): bh_colnames = bh.dtype.names if "lg_mtransfer_rate" in bh_colnames: - rate = bh["lg_mtransfer_rate"] >= -12 + # This needs to be aligned with run_binary_extras.f + # old: rate = bh["lg_mtransfer_rate"] >= -12 + rate = bh["lg_mtransfer_rate"] >= -10 else: raise ValueError("No `lg_mtransfer_rate` in binary history.") - rlo1 = (bh["rl_relative_overflow_1"] >= -0.05 + # This needs to be aligned with run_binary_extras.f + # old: rlo1 = (bh["rl_relative_overflow_1"] >= -0.05 + # if "rl_relative_overflow_1" in bh_colnames else None) + rlo1 = (bh["rl_relative_overflow_1"] >= 0.00 if "rl_relative_overflow_1" in bh_colnames else None) - rlo2 = (bh["rl_relative_overflow_2"] >= -0.05 + # This needs to be aligned with run_binary_extras.f + #old: rlo2 = (bh["rl_relative_overflow_2"] >= -0.05 + # if "rl_relative_overflow_2" in bh_colnames else None) + rlo2 = (bh["rl_relative_overflow_2"] >= 0.00 if "rl_relative_overflow_2" in bh_colnames else None) if rlo1 is None: @@ -118,6 +126,8 @@ def keep_after_RLO(bh, h1, h2): if rlo_1_or_2 is None: raise ValueError("No `rl_relative_overflow` in any star history.") + # This needs to be aligned with run_binary_extras.f, there it is less + # restrictive, which is fine conditions_met = rlo_1_or_2 & rate where_conditions_met = np.where(conditions_met)[0] From f0902fafa1ce2ccbdb64a5b323d64f272e93474a Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Thu, 15 Feb 2024 16:49:03 +0100 Subject: [PATCH 178/319] CO and SN_type consistency checks (#240) * Add checks to make sure the SN type matches the appropriate the stellar state * implement PISN with massless_remnant * add star->massless_remnant to common_functions, and adapt flow_chart * move function to single_star to stop cyclical imports * remove leftover print * change order of CO * add comment on readability * add recalculation of the SN type and post-SN stellar state, if they do not match. If the recalculation does not match, force the SN_type to match the remnant. specifically (state; SN_Type) WD -> WD, NS/BH -> CCSN, PISN -> PISN. * adjust NS/BH check to make sure the logic works correctly * move check function to file scope level * change PISN logic to only calculate the SN_type if not a PISN * clean up code * separate BH and NS check BH can be CCSN and PPISN NS can be ECSN and CCSN * Change star.state to massless_remnant for PISN checking * set stellar state to massless)remnant * additional stellar state check * change in SN-CO match * remove TZO tagging if massless_remnant, since PISN can lead to a massless remnant too * add temporary star.state == 'PISN' --- posydon/binary_evol/DT/step_detached.py | 7 +- posydon/binary_evol/DT/step_merged.py | 11 +-- posydon/binary_evol/SN/step_SN.py | 119 +++++++++++++++++++----- posydon/binary_evol/binarystar.py | 10 +- posydon/binary_evol/flow_chart.py | 23 +++-- posydon/binary_evol/singlestar.py | 4 + posydon/utils/common_functions.py | 1 - 7 files changed, 124 insertions(+), 51 deletions(-) diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index 867d2157aa..90ecfb1ff9 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -1017,9 +1017,7 @@ def __call__(self, binary): elif (binary.star_2.state in LIST_ACCEPTABLE_STATES_FOR_HeStar): secondary.htrack = False elif (binary.star_2.state in STAR_STATES_CO): - binary.state += " Thorne-Zytkow object" - if self.verbose or self.verbose == 1: - print("Formation of Thorne-Zytkow object, nothing to do further") + # only a compact object left return else: raise Exception("State not recognized!") @@ -1035,9 +1033,6 @@ def __call__(self, binary): elif (binary.star_1.state in LIST_ACCEPTABLE_STATES_FOR_HeStar): secondary.htrack = False elif (binary.star_1.state in STAR_STATES_CO): - binary.state += " Thorne-Zytkow object" - if self.verbose or self.verbose == 1: - print("Formation of Thorne-Zytkow object, nothing to do further") return else: raise Exception("State not recognized!") diff --git a/posydon/binary_evol/DT/step_merged.py b/posydon/binary_evol/DT/step_merged.py index 84a6251835..4cba14647a 100644 --- a/posydon/binary_evol/DT/step_merged.py +++ b/posydon/binary_evol/DT/step_merged.py @@ -11,8 +11,7 @@ import numpy as np from posydon.utils.data_download import PATH_TO_POSYDON_DATA -from posydon.binary_evol.singlestar import STARPROPERTIES -from posydon.binary_evol.singlestar import properties_massless_remnant +from posydon.binary_evol.singlestar import STARPROPERTIES, convert_star_to_massless_remnant from posydon.utils.common_functions import check_state_of_star from posydon.binary_evol.DT.step_isolated import IsolatedStep @@ -34,14 +33,6 @@ [LIST_ACCEPTABLE_STATES_FOR_POSTHeMS.remove(x) for x in LIST_ACCEPTABLE_STATES_FOR_HeMS] -def convert_star_to_massless_remnant(star): - for key in STARPROPERTIES: - setattr(star, key, properties_massless_remnant()[key]) - return star - - - - class MergedStep(IsolatedStep): """ Prepare a merging star to do an an IsolatedStep diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index 31815743e0..f41b8b7735 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -31,6 +31,8 @@ import warnings import numpy as np import scipy as sp +import copy +import pandas as pd from posydon.utils.data_download import PATH_TO_POSYDON_DATA, data_download import posydon.utils.constants as const @@ -44,10 +46,11 @@ ) from posydon.binary_evol.binarystar import BINARYPROPERTIES -from posydon.binary_evol.singlestar import STARPROPERTIES +from posydon.binary_evol.singlestar import STARPROPERTIES, convert_star_to_massless_remnant from posydon.binary_evol.SN.profile_collapse import do_core_collapse_BH from posydon.binary_evol.flow_chart import (STAR_STATES_CO, STAR_STATES_CC, STAR_STATES_C_DEPLETION) + from posydon.grids.MODELS import MODELS from pandas import read_csv @@ -471,7 +474,7 @@ def collapse_star(self, star): """ state = star.state - + # Verifies if the star is in state state where it can # explode if state in STAR_STATES_CC: @@ -503,7 +506,7 @@ def collapse_star(self, star): elif getattr(star, MODEL_NAME_SEL) is None: # NOTE: this option is needed to do the collapse # for stars evolved with the step_detached or - # steo_disrupted. + # step_disrupted. # allow to continue with the collapse with profile # or core masses warnings.warn(f'{MODEL_NAME_SEL}: The collapsed star ' @@ -511,15 +514,68 @@ def collapse_star(self, star): 'or use_core_masses is set to True, ' 'continue with the collapse.') else: + # store some properties of the star object + # to be used for collapse verification + pre_SN_star = copy.deepcopy(star) + MODEL_properties = getattr(star, MODEL_NAME_SEL) for key, value in MODEL_properties.items(): setattr(star, key, value) - + for key in STARPROPERTIES: if key not in ["state", "mass", "spin", "m_disk_accreted ", "m_disk_radiated"]: setattr(star, key, None) + # check if SN_type matches the predicted CO + # and force the SN_type to match the predicted CO. + # ie WD is no SN + # 1. Check if SN_type and star state match + # Non-matching SN_type and star state + if not check_SN_CO_match(star): + # raise a warning + warnings.warn(f'{MODEL_NAME_SEL}: The SN_type ' + 'does not match the predicted CO! ' + 'Recalculating the SN_type and CO') + # recalculate the SN_type and CO + # change some star properties back + m_PISN = self.PISN_prescription(pre_SN_star) + # no remnant if a PISN happens + if pd.isna(m_PISN): + star.SN_type = 'PISN' + star.state = 'massless_remnant' + else: + _, _, star.state = self.compute_m_rembar(pre_SN_star, m_PISN) + star.SN_type = self.check_SN_type(pre_SN_star.c_core_mass, + pre_SN_star.he_core_mass, + pre_SN_star.mass)[-1] + + # check if the new SN_type matches new SN_type + if not check_SN_CO_match(star): + # still doesn't match + # raise a warning + warnings.warn('The SN_type still does not match. ' + 'Forced the SN type to match the ' + 'predicted CO.') + if star.state == 'WD': + star.SN_type = 'WD' + elif star.state == 'NS' or star.state == 'BH': + star.SN_type = 'CCSN' + elif star.state == 'massless_remnant': + star.SN_type = 'PISN' + else: + raise ValueError('Star state not recognized.') + + del pre_SN_star + + # No remnant if a PISN happens + if star.SN_type == 'PISN': + convert_star_to_massless_remnant(star=star) + # the mass is set to None + # but an orbital kick is still applied. + # Since the mass is set to None, this will lead to a disruption + # TODO: make it skip the kick caluclation + if getattr(star, 'SN_type') != 'PISN': star.log_R = np.log10(CO_radius(star.mass, star.state)) @@ -563,15 +619,7 @@ def collapse_star(self, star): # check if the star was disrupted by the PISN if np.isnan(m_rembar): - star.mass = np.nan - star.state = "PISN" - star.spin = np.nan - star.m_disk_accreted = np.nan - star.m_disk_radiated = np.nan - for key in STARPROPERTIES: - if key not in ["state", "mass", "spin", - "m_disk_accreted ", "m_disk_radiated"]: - setattr(star, key, None) + convert_star_to_massless_remnant(star=star) return # Computing the gravitational mass of the remnant @@ -670,15 +718,7 @@ def collapse_star(self, star): # check if the star was disrupted by the PISN if np.isnan(m_rembar): - star.mass = np.nan - star.state = "PISN" - star.spin = np.nan - star.m_disk_accreted = np.nan - star.m_disk_radiated = np.nan - for key in STARPROPERTIES: - if key not in ["state", "mass", "spin", - "m_disk_accreted ", "m_disk_radiated"]: - setattr(star, key, None) + convert_star_to_massless_remnant(star=star) return # Computing the gravitational mass of the remnant @@ -1143,7 +1183,7 @@ def compute_m_rembar(self, star, m_PISN): # check PISN if m_PISN is not None: - if np.isnan(m_PISN): + if pd.isna(m_PISN): m_rembar = m_PISN star.SN_type = "PISN" elif m_rembar > m_PISN: @@ -2327,3 +2367,36 @@ def __call__(self, star, conserve_hydrogen_envelope=False): raise Exception("Need a NS or BH to apply `Sukhbold16_corecollapse`.") return float(m_rem), f_fb, state + + + +def check_SN_CO_match(star): + '''Check if the SN type matches the stellar state of the given star. + + Parameters + ---------- + star : SingleStar object + Star object containing the star properties. + + Returns + ------- + correct_SN_type : bool + True if the SN type matches the stellar state of the star. + ''' + # TODO: remove star.state == PISN, because PISN shouldn't be a stellar state + if star.state == 'PISN': + star.state = 'massless_remnant' + correct_SN_type = True + if star.state == 'WD' and star.SN_type != "WD": + correct_SN_type = False + elif (star.state == "NS") and \ + (star.SN_type != 'ECSN' and + star.SN_type != "CCSN"): + correct_SN_type = False + elif (star.state =="BH") and \ + (star.SN_type != "CCSN" and + star.SN_type != 'PPISN'): + correct_SN_type = False + elif (star.state == "massless_remnant" and star.SN_type != 'PISN'): + correct_SN_type = False + return correct_SN_type diff --git a/posydon/binary_evol/binarystar.py b/posydon/binary_evol/binarystar.py index 3e19465f98..90e4357340 100644 --- a/posydon/binary_evol/binarystar.py +++ b/posydon/binary_evol/binarystar.py @@ -286,10 +286,12 @@ def reset(self, properties=None): def update_star_states(self): """Update the states of the two stars in the binary.""" - self.star_1.state = check_state_of_star( - self.star_1, star_CO=self.star_1.state in ["WD", "BH", "NS"]) - self.star_2.state = check_state_of_star( - self.star_2, star_CO=self.star_2.state in ["WD", "BH", "NS"]) + if self.star_1.state != 'massless_remnant': + self.star_1.state = check_state_of_star( + self.star_1, star_CO=self.star_1.state in ["WD", "NS", "BH"]) + if self.star_2.state != 'massless_remnant': + self.star_2.state = check_state_of_star( + self.star_2, star_CO=self.star_2.state in ["WD", "NS", "BH"]) def to_df(self, **kwargs): """Return history parameters from the binary in a DataFrame. diff --git a/posydon/binary_evol/flow_chart.py b/posydon/binary_evol/flow_chart.py index 2e3cee6c6d..18881a64ba 100644 --- a/posydon/binary_evol/flow_chart.py +++ b/posydon/binary_evol/flow_chart.py @@ -246,13 +246,6 @@ POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_initially_single' POSYDON_FLOW_CHART[(s2, s1, b, e)] = 'step_initially_single' -for s1 in STAR_STATES_CO: - for s2 in ['massless_remnant']: - for e in BINARY_EVENTS_ALL: - POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_end' - POSYDON_FLOW_CHART[(s2, s1, b, e)] = 'step_end' - - BINARY_EVENTS_OF_SN_OR_AFTER_DETACHED = BINARY_EVENTS_ALL.copy() [BINARY_EVENTS_OF_SN_OR_AFTER_DETACHED.remove(x) for x in ['CC1','CC2','MaxTime_exceeded','maxtime']] @@ -292,6 +285,22 @@ for e in ['maxtime', 'MaxTime_exceeded', 'CO_contact']: POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_end' +# catch all massless remnants with compact objects +for b in BINARY_STATES_ALL: + for s1 in STAR_STATES_CO: + for s2 in ['massless_remnant']: + for e in BINARY_EVENTS_ALL: + POSYDON_FLOW_CHART[(s1, s2, b, e)] = 'step_end' + POSYDON_FLOW_CHART[(s2, s1, b, e)] = 'step_end' + +# catch two massless remnants +# separate for readability +for b in BINARY_STATES_ALL: + for s in ['massless_remnant']: + for e in BINARY_EVENTS_ALL: + POSYDON_FLOW_CHART[(s, s, b, e)] = 'step_end' + + def flow_chart(FLOW_CHART=POSYDON_FLOW_CHART, CHANGE_FLOW_CHART=None): """Generate the flow chart. diff --git a/posydon/binary_evol/singlestar.py b/posydon/binary_evol/singlestar.py index 4bbc17be3b..9bc2f3f5b8 100644 --- a/posydon/binary_evol/singlestar.py +++ b/posydon/binary_evol/singlestar.py @@ -113,6 +113,10 @@ def properties_massless_remnant(): PROPERTIES_MASSLESS["mass"] = 0.0 return PROPERTIES_MASSLESS +def convert_star_to_massless_remnant(star): + for key in STARPROPERTIES: + setattr(star, key, properties_massless_remnant()[key]) + return star class SingleStar: """Class describing a single star.""" diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index 2d4bd1e0ee..614d421680 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -25,7 +25,6 @@ import warnings from scipy.interpolate import PchipInterpolator - PATH_TO_POSYDON = os.environ.get("PATH_TO_POSYDON") From b52d19d435f17a14c993eaba66c585ff80bbcdb0 Mon Sep 17 00:00:00 2001 From: ezapartas Date: Thu, 15 Feb 2024 17:51:57 +0200 Subject: [PATCH 179/319] Inhomogeneous binary array (#251) * trying to solve both inhomogeneity of singlestar array and solve the specific issue was occurring for track_interpolation and intial_RLO * I uncommented all changes (if someone wants to test ontly the specific track_interpolation error, they should comment the single/binarystar fix) * erased the homogeneous_array function in common functions * add a call to the interpolator before appending information to the BinaryStar to check what the interaction is * replaced NAN values to np.nan --------- Co-authored-by: Max Briel Co-authored-by: dimsour94 --- posydon/binary_evol/MESA/step_mesa.py | 33 +++++++++++++++++---------- posydon/binary_evol/binarystar.py | 21 +++++++++-------- posydon/binary_evol/singlestar.py | 23 +++++++++++++++---- posydon/utils/common_functions.py | 6 ++--- 4 files changed, 54 insertions(+), 29 deletions(-) diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index 43be1f6078..bbfe57e509 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -438,6 +438,18 @@ def update_properties_NN(self, star_1_CO=False, star_2_CO=False, # setattr(binary, "event", "END") return + # check if the first interpolation gives 'initial_RLOF' + interpolation_class = self.termination_flags[0] + binary_state, binary_event, MT_case = ( + cf.get_binary_state_and_event_and_mt_case( + binary, interpolation_class, verbose=self.verbose)) + setattr(binary, 'state', binary_state) + setattr(binary, 'event', binary_event) + setattr(binary, 'mass_transfer_case', MT_case) + + if binary.state == 'initial_RLOF': + return + if track_interpolation or self.save_initial_conditions: len_binary_hist = len(getattr(binary, "time_history")) length_binary_hist = len(cb_bh['age']) - 1 @@ -655,7 +667,7 @@ def update_properties_NN(self, star_1_CO=False, star_2_CO=False, # DEBUG # print(key, missing_values_star_1) # print('fixed', len(getattr(self.binary.star_1, - # key + "_history"))) + # key + "_history"))) # convert these flags to default POSYDON star states setattr(stars[0], 'state', cf.check_state_of_star(stars[0], star_CO=stars_CO[0])) @@ -729,9 +741,6 @@ def update_properties_NN(self, star_1_CO=False, star_2_CO=False, getattr(binary, "event_history").extend(binary_event) getattr(binary, "mass_transfer_case_history").extend(MT_case) - if binary.state == 'initial_RLOF': - return - if (star_2_CO or star_1_CO): # Updating Bondi-Hoyle accretion for k, star in enumerate(stars): @@ -1279,7 +1288,7 @@ def __call__(self, binary): np.max([self.q_min, 0.5/m1]) <= mass_ratio <= self.q_max and self.p_min <= p <= self.p_max): self.flip_stars_before_step = False - super().__call__(self.binary) + super().__call__(self.binary) # binary in grid but masses flipped elif (state_1 == 'H-rich_Core_H_burning' and state_2 == 'H-rich_Core_H_burning' and @@ -1302,7 +1311,7 @@ def __call__(self, binary): event == 'ZAMS' and p < self.p_min): self.binary.event = 'redirect_from_ZAMS' - return + return # outside the mass grid for m1 elif (state_1 == 'H-rich_Core_H_burning' and state_2 == 'H-rich_Core_H_burning' and @@ -1422,7 +1431,7 @@ def __call__(self, binary): self.p_min <= p <= self.p_max and ecc == 0.)): super().__call__(self.binary) - + # period inside the grid, but m1 outside the grid elif ((not self.flip_stars_before_step and self.p_min <= p <= self.p_max and @@ -1431,7 +1440,7 @@ def __call__(self, binary): set_binary_to_failed(self.binary) raise ValueError(f'The mass of m1 ({m1}) is outside the grid,' 'while the period is inside the grid.') - + # period inside the grid, but m2 outside the grid elif ((not self.flip_stars_before_step and self.p_min <= p <= self.p_max and @@ -1440,13 +1449,13 @@ def __call__(self, binary): set_binary_to_failed(self.binary) raise ValueError(f'The mass of m2 ({m2}) is outside the grid,' 'while the period is inside the grid.') - + else: if len(self.binary.state_history) > 2: if self.binary.state_history[-2] == 'detached': set_binary_to_failed(self.binary) raise ValueError('CO_HMS_RLO binary outside grid and coming from detached') - + self.binary.state = "detached" self.binary.event = "redirect_from_CO_HMS_RLO" return @@ -1552,13 +1561,13 @@ def __call__(self, binary): set_binary_to_failed(self.binary) raise ValueError(f'The mass of m2 ({m2}) is outside the grid,' 'while the period is inside the grid.') - + else: if len(self.binary.state_history) > 2: if self.binary.state_history[-2] == 'detached': set_binary_to_failed(self.binary) raise ValueError('CO_HeMS_RLO binary outside grid and coming from detached') - + self.binary.state = "detached" self.binary.event = "redirect_from_CO_HeMS_RLO" return diff --git a/posydon/binary_evol/binarystar.py b/posydon/binary_evol/binarystar.py index 90e4357340..1a354ccbdd 100644 --- a/posydon/binary_evol/binarystar.py +++ b/posydon/binary_evol/binarystar.py @@ -164,7 +164,7 @@ def __init__(self, star_1=None, star_2=None, index=None, properties=None, self.mass_transfer_case = 'None' # if not hasattr(self, 'V_sys'): # self.V_sys = [0, 0, 0] - + # store interpolation_class and mt_history for each step_MESA for grid_type in ['HMS_HMS','CO_HMS_RLO','CO_HeMS','CO_HeMS_RLO']: if not hasattr(self, f'interp_class_{grid_type}'): @@ -213,7 +213,7 @@ def run_step(self): total_state = (self.star_1.state, self.star_2.state, self.state, self.event) next_step_name = self.properties.flow.get(total_state) - + if next_step_name is None: warnings.warn("Undefined next step given stars/binary states " "{}.".format(total_state)) @@ -330,7 +330,7 @@ def to_df(self, **kwargs): extra_binary_cols_dict = kwargs.get('extra_columns', {}) extra_columns = list(extra_binary_cols_dict.keys()) extra_columns_dtypes_user = list(extra_binary_cols_dict.values()) - + all_keys = (["binary_index"] + [key+'_history' for key in BINARYPROPERTIES] + extra_columns) @@ -345,6 +345,7 @@ def to_df(self, **kwargs): + [key+'_history' for key in user_keys_to_save] + extra_columns) + try: data_to_save = [getattr(self, key) for key in keys_to_save[1:]] col_lengths = [len(x) for x in data_to_save] @@ -353,16 +354,16 @@ def to_df(self, **kwargs): # binary_index data_to_save.insert(0, [self.index]*max_col_length) - where_none = np.array([[True if var is None else False - for var in column] - for column in data_to_save], dtype=bool) - # If a binary fails, usually history cols have diff lengths. # This should append NAN to create even columns. all_equal_length_cols = len(set(col_lengths)) == 1 if not all_equal_length_cols: for col in data_to_save: - col.extend(['NAN'] * abs(max_col_length - len(col))) + col.extend([np.nan] * abs(max_col_length - len(col))) + + where_none = np.array([[True if var is None else False + for var in column] + for column in data_to_save], dtype=bool) except AttributeError as err: raise AttributeError( @@ -589,7 +590,7 @@ def to_oneline_df(self, scalar_names=[], history=True, **kwargs): oneline_df['WARNING'] = [0] oneline_df.set_index('binary_index', inplace=True) - + # try to coerce data types automatically oneline_df = oneline_df.infer_objects() @@ -602,7 +603,7 @@ def to_oneline_df(self, scalar_names=[], history=True, **kwargs): extra_binary_dtypes_user=extra_binary_cols_dict, extra_S1_dtypes_user=extra_s1_cols_dict, extra_S2_dtypes_user=extra_s2_cols_dict) - + return oneline_df @classmethod diff --git a/posydon/binary_evol/singlestar.py b/posydon/binary_evol/singlestar.py index 9bc2f3f5b8..f75e6deaf5 100644 --- a/posydon/binary_evol/singlestar.py +++ b/posydon/binary_evol/singlestar.py @@ -153,18 +153,18 @@ def __init__(self, **kwargs): self.m_disk_radiated = None # the following quantities are updated in mesa_step.py - + # common envelope quantities for quantity in ['m_core_CE', 'r_core_CE']: for val in [1, 10, 30, 'pure_He_star_10']: if not hasattr(self, f'{quantity}_{val}cent'): setattr(self, f'{quantity}_{val}cent', None) - + # core masses at He depletion for quantity in ['avg_c_in_c_core_at_He_depletion', 'co_core_mass_at_He_depletion']: setattr(self, quantity, None) - + # core collapse quantities for MODEL_NAME in MODELS.keys(): if not hasattr(self, MODEL_NAME): @@ -252,9 +252,24 @@ def to_df(self, **kwargs): try: # shape of data_to_save (history columns , time steps) data_to_save = [getattr(self, key) for key in keys_to_save] + + + col_lengths = [len(x) for x in data_to_save] + max_col_length = np.max(col_lengths) + + # If a singlestar fails, usually history cols have diff lengths. + # This should append NAN to create even columns. + all_equal_length_cols = len(set(col_lengths)) == 1 + if not all_equal_length_cols: + for col in data_to_save: + col.extend([np.nan] * abs(max_col_length - len(col))) + + where_none = np.array( [[True if var is None else False for var in column] for column in data_to_save], dtype=bool) + + except AttributeError as err: raise AttributeError( str(err) + "\n\nAvailable attributes in SingleStar: \n{}". @@ -273,7 +288,7 @@ def to_df(self, **kwargs): for name in keys_to_save] star_df = pd.DataFrame(star_data, columns=column_names) - + # try to coerce data types automatically star_df = star_df.infer_objects() diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index 614d421680..07d638cb37 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -262,7 +262,7 @@ def orbital_separation_from_period(period_days, m1_solar, m2_solar): m1_solar = np.float64(m1_solar) m2_solar = np.float64(m2_solar) period_days = np.float64(period_days) - + separation_cm = (const.standard_cgrav * (m1_solar * const.Msun + m2_solar * const.Msun) / (4.0 * const.pi**2.0) @@ -1378,7 +1378,7 @@ def flip_stars(binary): def set_binary_to_failed(binary): '''Set the properties of the binary to indicate that it has failed. - + Parameters ---------- binary : BinaryStar @@ -1386,7 +1386,7 @@ def set_binary_to_failed(binary): ''' binary.state = "ERR" binary.event = "FAILED" - + def infer_star_state(star_mass=None, surface_h1=None, center_h1=None, center_he4=None, center_c12=None, From 6abf1acff272fe1b6de576982aa4710f36ac1b53 Mon Sep 17 00:00:00 2001 From: Camille Liotine Date: Thu, 15 Feb 2024 09:58:49 -0600 Subject: [PATCH 180/319] change itemsizes for event_f and mass_transfer_case_f (#253) --- posydon/popsyn/binarypopulation.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index b1db7dc1e5..1d96333581 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -58,11 +58,11 @@ 'mass_transfer_case': 7, 'S1_SN_type': 5, 'S2_SN_type': 5} ONELINE_MIN_ITEMSIZE = {'state_i': 30, 'state_f': 30, - 'event_i': 10, 'event_f': 10, + 'event_i': 10, 'event_f': 25, 'step_names_i': 20, 'step_names_f': 20, 'S1_state_i': 31, 'S1_state_f': 31, 'S2_state_i': 31, 'S2_state_f': 31, - 'mass_transfer_case_i': 7, 'mass_transfer_case_f': 7, + 'mass_transfer_case_i': 7, 'mass_transfer_case_f': 20, 'S1_SN_type': 5, 'S2_SN_type': 5, 'interp_class_HMS_HMS' : 15, 'interp_class_CO_HeMS' : 15, 'interp_class_CO_HMS_RLO' : 15, 'interp_class_CO_HeMS_RLO' : 15, From 3ca9243d3a2b459a5a9d4ca341c611e1365d0582 Mon Sep 17 00:00:00 2001 From: Chase Kimball Date: Thu, 22 Feb 2024 09:07:12 -0600 Subject: [PATCH 181/319] Tilt tracking (#129) * Added tilt tracking * variable names and pulling first_SN outside of the flag_binary conditional * Added tilt tracking * variable names and pulling first_SN outside of the flag_binary conditional * added rotate() to common_functions.py * added flag for if first SN already occurred * allow tilt to be positive or negative according to direction of Vkz --- posydon/binary_evol/SN/step_SN.py | 106 ++++++++++++++++++++++++------ posydon/binary_evol/binarystar.py | 7 ++ posydon/binary_evol/singlestar.py | 6 +- posydon/utils/common_functions.py | 44 +++++++++++++ 4 files changed, 142 insertions(+), 21 deletions(-) diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index f41b8b7735..7ba5360ea1 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -42,7 +42,8 @@ orbital_period_from_separation, inspiral_timescale_from_separation, separation_evol_wind_loss, - calculate_Patton20_values_at_He_depl + calculate_Patton20_values_at_He_depl, + rotate ) from posydon.binary_evol.binarystar import BINARYPROPERTIES @@ -1220,7 +1221,7 @@ def orbital_kick(self, binary): The corresponding azimuthal angle such that phi=0 is on the Z axis. tilt : - The angle between pre- and post- supernova orbital angular momentum vectors. + The angle between pre- and post- supernova orbital angular momentum vectors Parameters @@ -1446,6 +1447,7 @@ def orbital_kick(self, binary): binary.time = binary.time_history[-1] binary.orbital_period = np.nan binary.mass_transfer_case = 'None' + binary.first_SN_already_occurred = True else: @@ -1464,7 +1466,10 @@ def orbital_kick(self, binary): # Eq 15, Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 # orbital separation at the time of the exlosion rpre = Apre * (1.0 - epre * np.cos(E_ma)) - + + true_anomaly = 2 * np.arctan( + np.sqrt((1 + epre) / (1 - epre)) * np.tan(E_ma / 2) + ) # load constants in CGS G = const.standard_cgrav @@ -1559,6 +1564,9 @@ def orbital_kick(self, binary): tilt = np.arccos((Vky + Vr * sin_psi) / np.sqrt( Vkz ** 2 + (Vky + Vr * sin_psi) ** 2 )) + # Track direction of tilt + if Vkz < 0: tilt *= -1 + def SNCheck( M_he_star, M_companion, @@ -1662,21 +1670,46 @@ def SNCheck( verbose=self.verbose) # update the binary object which was bound at least before the SN + #Check if this is the first SN if flag_binary: # update the tilt - if binary.event == "CC1": - binary.star_1.spin_orbit_tilt = tilt - elif binary.event == "CC2": - binary.star_2.spin_orbit_tilt = tilt - else: - raise ValueError("This should never happen!") - - # compute new orbital period before reseting the binary properties for key in BINARYPROPERTIES: if key != 'nearest_neighbour_distance': setattr(binary, key, None) + if not binary.first_SN_already_occurred: + # update the tilt + binary.star_1.spin_orbit_tilt_first_SN = tilt + binary.star_2.spin_orbit_tilt_first_SN = tilt + binary.true_anomaly_first_SN = true_anomaly + binary.first_SN_already_occurred = True + else: + if binary.event == 'CC2': + # Assume progenitor has aligned with the preSN orbital angular momentum + binary.star_2.spin_orbit_tilt_second_SN = tilt + binary.star_1.spin_orbit_tilt_second_SN = get_combined_tilt( + tilt_1 = binary.star_1.spin_orbit_tilt_first_SN, + tilt_2 = tilt, + true_anomaly_1 = binary.true_anomaly_first_SN, + true_anomaly_2 = true_anomaly + ) + binary.true_anomaly_second_SN = true_anomaly + elif binary.event == 'CC1': + # Assume progenitor has aligned with the preSN orbital angular momentum + binary.star_1.spin_orbit_tilt_second_SN = tilt + binary.star_2.spin_orbit_tilt_second_SN = get_combined_tilt( + tilt_1 = binary.star_1.spin_orbit_tilt_first_SN, + tilt_2 = tilt, + true_anomaly_1 = binary.true_anomaly_first_SN, + true_anomaly_2 = true_anomaly + ) + binary.true_anomaly_second_SN = true_anomaly + else: + raise ValueError("This should never happen!") + + # compute new orbital period before reseting the binary properties + binary.state = "detached" binary.event = None binary.separation = Apost / const.Rsun @@ -1689,18 +1722,21 @@ def SNCheck( binary.separation, binary.star_1.mass, binary.star_2.mass) binary.orbital_period = new_orbital_period binary.mass_transfer_case = 'None' - else: - # update the tilt - if binary.event == "CC1": - binary.star_1.spin_orbit_tilt = np.nan - elif binary.event == "CC2": - binary.star_2.spin_orbit_tilt = np.nan - else: - raise ValueError("This should never happen!") + else: for key in BINARYPROPERTIES: if key != 'nearest_neighbour_distance': setattr(binary, key, None) + # update the tilt + if not binary.first_SN_already_occurred: + binary.star_1.spin_orbit_tilt_first_SN = np.nan + binary.star_2.spin_orbit_tilt_first_SN = np.nan + binary.first_SN_already_occurred = True + else: + binary.star_1.spin_orbit_tilt_second_SN = np.nan + binary.star_2.spin_orbit_tilt_second_SN = np.nan + + binary.state = "disrupted" binary.event = None binary.separation = np.nan @@ -1765,7 +1801,39 @@ def generate_kick(self, star, sigma): Vkick = 0.0 return Vkick + + def get_combined_tilt(tilt_1, tilt_2, true_anomaly_1, true_anomaly_2): + """Get the combined spin-orbit-tilt after two supernovae, assuming + the spin as not realigned with the orbital angular momentum after + SN1 + + Parameters + ---------- + tilt_1: float + Angle, in radians, through which the orbital plane was tilted + by SN1 + tilt_2: float + Angle, in radians, through which the orbital plane was tilted + by SN2 + true_anomaly_1: float + Angle, in radians, of the true anomaly at the moment of SN1 + true_anomaly_2: float + Angle, in radians, of the true anomaly at the moment of SN2 + + Returns + ------- + combined_tilt: float + Angle, in radians, between the spin and orbital angular momentum + after SN2 + """ + z_prime = rotate((1,0,0), tilt_1).dot((0,0,1)) + x_prime = rotate(z_prime, true_anomaly_2-true_anomaly_1).dot((1,0,0)) + + cos_tilt = np.dot((0,0,1),rotate(x_prime, tilt_2).dot(z_prime)) + combined_tilt = np.arccos(cos_tilt) + return combined_tilt + def C_abundance_for_H_stars(self, CO_core_mass): """Get the C abundance for a H-star given it's CO core mass.""" return 0.20/CO_core_mass + 0.15 diff --git a/posydon/binary_evol/binarystar.py b/posydon/binary_evol/binarystar.py index 1a354ccbdd..617fd1b831 100644 --- a/posydon/binary_evol/binarystar.py +++ b/posydon/binary_evol/binarystar.py @@ -162,6 +162,13 @@ def __init__(self, star_1=None, star_2=None, index=None, properties=None, self.inspiral_time = None if not hasattr(self, 'mass_transfer_case'): self.mass_transfer_case = 'None' + + if not hasattr(self, 'true_anomaly_first_SN'): + self.true_anomaly_SN1 = None + if not hasattr(self, 'true_anomaly_second_SN'): + self.true_anomaly_SN2 = None + if not hasattr(self, 'first_SN_already_occurred'): + self.first_SN_already_occurred = False # if not hasattr(self, 'V_sys'): # self.V_sys = [0, 0, 0] diff --git a/posydon/binary_evol/singlestar.py b/posydon/binary_evol/singlestar.py index f75e6deaf5..1a7e52cc90 100644 --- a/posydon/binary_evol/singlestar.py +++ b/posydon/binary_evol/singlestar.py @@ -141,8 +141,10 @@ def __init__(self, **kwargs): # these quantities are updated in step_SN.py if not hasattr(self, 'natal_kick_array'): self.natal_kick_array = [None] * 4 - if not hasattr(self, 'spin_orbit_tilt'): - self.spin_orbit_tilt = None + if not hasattr(self, 'spin_orbit_tilt_first_SN'): + self.spin_orbit_tilt_SN1 = None + if not hasattr(self, 'spin_orbit_tilt_second_SN'): + self.spin_orbit_tilt_SN2 = None if not hasattr(self, 'f_fb'): self.f_fb = None if not hasattr(self, 'SN_type'): diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index 07d638cb37..b5d5a54586 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -2684,3 +2684,47 @@ def convert_metallicity_to_string(Z): if not Z in valid_Z: raise ValueError(f'Metallicity {Z} not supported! Available metallicities in POSYDON v2 are {valid_Z}.') return f'{Z:1.1e}'.replace('.0','') + +def rotate(axis, angle): + + """Generate rotation matrix to rotate a vector about an arbitrary axis + by a given angle + + Parameters + ---------- + axis : array of length 3 + Axis to rotate about + angle : float + Angle, in radians, through which to rotate about axis + + Returns + ------- + rotation_matrix : 3x3 array + Array such that rotation_matrix.dot(vector) rotates vector + about the given axis by the given angle + + """ + + # normalize the axis vector + axis = axis / np.linalg.norm(axis) + + + # calculate the cosine and sine of the angle + cos_theta = np.cos(angle) + sin_theta = np.sin(angle) + + # construct the rotation matrix + rotation_matrix = np.array([ + [cos_theta + axis[0]**2 * (1 - cos_theta), + axis[0] * axis[1] * (1 - cos_theta) - axis[2] * sin_theta, + axis[0] * axis[2] * (1 - cos_theta) + axis[1] * sin_theta], + [axis[1] * axis[0] * (1 - cos_theta) + axis[2] * sin_theta, + cos_theta + axis[1]**2 * (1 - cos_theta), + axis[1] * axis[2] * (1 - cos_theta) - axis[0] * sin_theta], + [axis[2] * axis[0] * (1 - cos_theta) - axis[1] * sin_theta, + axis[2] * axis[1] * (1 - cos_theta) + axis[0] * sin_theta, + cos_theta + axis[2]**2 * (1 - cos_theta)] + ]) + + + return rotation_matrix \ No newline at end of file From 83a458d9d7c14cea2e371a8857bd061ac8c555c0 Mon Sep 17 00:00:00 2001 From: ZepeiX <77278187+ZepeiX@users.noreply.github.com> Date: Thu, 22 Feb 2024 16:12:19 +0100 Subject: [PATCH 182/319] add 'stable_reverse_MT' and interpolation_class for CO (#170) * create a new MT subclass 'stable_reverse_MT' and attribute interpolation_class to CO * Update run-pipeline * Updated IF_interpolation.py to include class-wise normalization * Updated data_scaling.py for class-wise normalization * Update IF_interpolation.py Addressed a bug for None type dictionary keys * Fix a bug in normalization normalized = X -> normalized = X.copy() otherwise the input array got normalized unexpectedly * a better name for CO_interpolation_class * add cumulative mt --------- Co-authored-by: Jeff Andrews Co-authored-by: Philipp Moura Srivastava --- bin/run-pipeline | 26 +- posydon/binary_evol/MESA/step_mesa.py | 6 +- posydon/binary_evol/binarystar.py | 3 +- posydon/grids/post_processing.py | 37 ++- posydon/grids/psygrid.py | 12 +- posydon/grids/termination_flags.py | 5 +- posydon/interpolation/IF_interpolation.py | 316 +++++++++++++++------- posydon/interpolation/data_scaling.py | 10 +- posydon/popsyn/binarypopulation.py | 8 +- 9 files changed, 279 insertions(+), 144 deletions(-) diff --git a/bin/run-pipeline b/bin/run-pipeline index fd1ea2cf9a..0c7d0ca0ed 100644 --- a/bin/run-pipeline +++ b/bin/run-pipeline @@ -285,13 +285,15 @@ def train_interpolators(i, path_to_csv_file, verbose=False): # load grid grid = PSyGrid(verbose=verbose) grid.load(grid_path) - - if '_RLO' in grid_path: - interp_method = [method, method] - interp_classes = ["stable_MT", "unstable_MT"] - else: - interp_method = [method, method, method] - interp_classes = ["no_MT", "stable_MT", "unstable_MT"] + + interp_method = [method, method] + interp_classes = ["stable_MT", "unstable_MT"] + if '_RLO' not in grid_path: + interp_method += [method] + interp_classes += ["no_MT"] + if 'CO-' not in grid_path: + interp_method += [method] + interp_classes += ["stable_reverse_MT"] # all magnitues that are not model numbers, supernova properites or strings # are interpolated with respect to interpolation_class @@ -335,10 +337,10 @@ def train_interpolators(i, path_to_csv_file, verbose=False): # get interpolations classes dynamically interp_method = [] interp_classes = [] - for CO_type in ["BH", "NS", "WD"]: - if CO_type in grid.final_values[f'S{i}_{MODEL_NAME}_CO_type']: + for CO_interpolation_class in ["BH", "NS", "WD", "BH_reverse_MT"]: + if CO_interpolation_class in grid.final_values[f'S{i}_{MODEL_NAME}_CO_interpolation_class']: interp_method.append(method) - interp_classes.append(CO_type) + interp_classes.append(CO_interpolation_class) interpolators.append( { @@ -346,8 +348,8 @@ def train_interpolators(i, path_to_csv_file, verbose=False): "interp_classes": interp_classes, "out_keys": out_keys, "class_method": "kNN", - "c_keys": [f'S{i}_{MODEL_NAME}_CO_type'], - "c_key": f'S{i}_{MODEL_NAME}_CO_type' + "c_keys": [f'S{i}_{MODEL_NAME}_CO_interpolation_class'], + "c_key": f'S{i}_{MODEL_NAME}_CO_interpolation_class' }, ) diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index bbfe57e509..65aafa32b0 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -680,7 +680,8 @@ def update_properties_NN(self, star_1_CO=False, star_2_CO=False, setattr(binary, 'state', binary_state) setattr(binary, 'event', binary_event) setattr(binary, 'mass_transfer_case', MT_case) - + culmulative_mt_case = self.termination_flags[1] + setattr(self.binary, f'culmulative_mt_case_{self.grid_type}', culmulative_mt_case) setattr(self.binary, f'interp_class_{self.grid_type}', interpolation_class) mt_history = self.termination_flags[2] # mass transfer history (TF12 plot label) setattr(self.binary, f'mt_history_{self.grid_type}', mt_history) @@ -898,7 +899,8 @@ def initial_final_interpolation(self, star_1_CO=False, star_2_CO=False): setattr(self.binary, f'interp_class_{self.grid_type}', interpolation_class) mt_history = self.classes['mt_history'] # mass transfer history (TF12 plot label) setattr(self.binary, f'mt_history_{self.grid_type}', mt_history) - + #TODO: add classifier for tf2 + #setattr(self.binary, f'culmulative_mt_case', self.classes['termination_flags_2']) S1_state_inferred = cf.check_state_of_star(self.binary.star_1, star_CO=star_1_CO) S2_state_inferred = cf.check_state_of_star(self.binary.star_2, diff --git a/posydon/binary_evol/binarystar.py b/posydon/binary_evol/binarystar.py index 617fd1b831..e85fcffaf3 100644 --- a/posydon/binary_evol/binarystar.py +++ b/posydon/binary_evol/binarystar.py @@ -178,7 +178,8 @@ def __init__(self, star_1=None, star_2=None, index=None, properties=None, setattr(self, f'interp_class_{grid_type}', None) if not hasattr(self, f'mt_history_{grid_type}'): setattr(self, f'mt_history_{grid_type}', None) - + if not hasattr(self, f'culmulative_mt_case_{grid_type}'): + setattr(self, f'culmulative_mt_case_{grid_type}', None) # SimulationProperties object - parameters & parameterizations if isinstance(properties, SimulationProperties): self.properties = properties diff --git a/posydon/grids/post_processing.py b/posydon/grids/post_processing.py index 3292df06ed..261623450d 100644 --- a/posydon/grids/post_processing.py +++ b/posydon/grids/post_processing.py @@ -29,7 +29,7 @@ CC_quantities = ['state', 'SN_type', 'f_fb', 'mass', 'spin', - 'm_disk_accreted', 'm_disk_radiated'] + 'm_disk_accreted', 'm_disk_radiated', 'CO_interpolation_class'] def assign_core_collapse_quantities_none(EXTRA_COLUMNS, star_i, MODEL_NAME=None): """"Assign None values to all core collapse properties.""" @@ -165,10 +165,11 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, stars_CO = [False] IC = 'no_MT' TF1 = grid.final_values['termination_flag_1'][i] + TF2 = grid.final_values['termination_flag_2'][i] # compute properties for j, star in enumerate(stars): - if not stars_CO[j] and IC in ['no_MT', 'stable_MT', 'unstable_MT']: + if not stars_CO[j] and IC in ['no_MT', 'stable_MT', 'unstable_MT', 'stable_reverse_MT']: # stellar states EXTRA_COLUMNS['S%s_state' % (j+1)].append(check_state_of_star( star, star_CO=False)) @@ -236,7 +237,7 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, # core collpase quantities if not single_star: - if interpolation_class in ['no_MT', 'stable_MT']: + if interpolation_class in ['no_MT', 'stable_MT', 'stable_reverse_MT']: if (star_2_CO or (TF1 in TF1_POOL_STABLE and ('primary' in TF1 or 'Primary' in TF1))): star = binary.star_1 @@ -293,7 +294,7 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, if not isinstance(getattr(star_copy, quantity), str): flush = True warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} is not a string!') - else: + elif quantity != 'CO_interpolation_class': if not isinstance(getattr(star_copy, quantity), float): flush = True warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} is not a float!') @@ -310,8 +311,16 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, assign_core_collapse_quantities_none(EXTRA_COLUMNS, star_i, MODEL_NAME) else: for quantity in CC_quantities: - EXTRA_COLUMNS[f'S{star_i}_{MODEL_NAME}_{quantity}'].append( - getattr(star_copy, quantity)) + if quantity != 'CO_interpolation_class': + EXTRA_COLUMNS[f'S{star_i}_{MODEL_NAME}_{quantity}'].append( + getattr(star_copy, quantity)) + else: + if getattr(star_copy, 'state') == 'BH' and 'case' in TF2 and '1' in TF2 and '2' in TF2: + EXTRA_COLUMNS[f'S{star_i}_{MODEL_NAME}_{quantity}'].append( + getattr(star_copy, 'state')+'_reverse_MT') + else: + EXTRA_COLUMNS[f'S{star_i}_{MODEL_NAME}_{quantity}'].append( + getattr(star_copy, 'state')) if verbose: print_CC_quantities(EXTRA_COLUMNS, star_copy, f'{MODEL_NAME}_{mechanism}') @@ -337,7 +346,7 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, if not isinstance(getattr(star_copy, quantity), str): flush = True warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} is not a string!') - else: + elif quantity != 'CO_interpolation_class': if not isinstance(getattr(star_copy, quantity), float): flush = True warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} is not a float!') @@ -354,8 +363,16 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, assign_core_collapse_quantities_none(EXTRA_COLUMNS, 1, MODEL_NAME) else: for quantity in CC_quantities: - EXTRA_COLUMNS[f'S1_{MODEL_NAME}_{quantity}'].append( - getattr(star_copy, quantity)) + if quantity != 'CO_interpolation_class': + EXTRA_COLUMNS[f'S{star_i}_{MODEL_NAME}_{quantity}'].append( + getattr(star_copy, quantity)) + else: + if getattr(star_copy, 'state') == 'BH' and 'case' in TF2 and '1' in TF2 and '2' in TF2: + EXTRA_COLUMNS[f'S{star_i}_{MODEL_NAME}_{quantity}'].append( + getattr(star_copy, 'state')+'_reverse_MT') + else: + EXTRA_COLUMNS[f'S{star_i}_{MODEL_NAME}_{quantity}'].append( + getattr(star_copy, 'state')) if verbose: print_CC_quantities(EXTRA_COLUMNS, star_copy, f'{MODEL_NAME}_{mechanism}') else: @@ -418,7 +435,7 @@ def add_post_processed_quantities(grid, MESA_dirs_EXTRA_COLUMNS, EXTRA_COLUMNS, 'EXTRA_COLUMNS do not follow the correct order of grid!') for column in EXTRA_COLUMNS.keys(): - if "state" in column or "type" in column or column == 'mt_history': + if "state" in column or "type" in column or "class" in column or column == 'mt_history': values = np.asarray(EXTRA_COLUMNS[column], str) else: values = np.asarray(EXTRA_COLUMNS[column], float) diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index f686cea0b7..cc8c866e93 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -1287,9 +1287,9 @@ def update_final_values(self): new_dtype = [] for dtype in self.final_values.dtype.descr: if (dtype[0].startswith("termination_flag") - or dtype[0] == "interpolation_class" or dtype[0] == "mt_history" - or "_type" in dtype[0] or "_state" in dtype[0]): + or "_type" in dtype[0] or "_state" in dtype[0] + or "_class" in dtype[0]): dtype = (dtype[0], H5_REC_STR_DTYPE.replace("U", "S")) new_dtype.append(dtype) if dtype[1] == np.dtype('O'): @@ -1340,9 +1340,9 @@ def load(self, filepath=None): new_dtype = [] for dtype in self.final_values.dtype.descr: if (dtype[0].startswith("termination_flag") - or dtype[0] == "interpolation_class" or dtype[0] == "mt_history" - or "_type" in dtype[0] or "_state" in dtype[0]): + or "_type" in dtype[0] or "_state" in dtype[0] + or "_class" in dtype[0]): dtype = (dtype[0], H5_REC_STR_DTYPE.replace("S", "U")) new_dtype.append(dtype) self.final_values = self.final_values.astype(new_dtype) @@ -2277,8 +2277,8 @@ def say(something): new_final_dtype = [] for dtype in final_dtype.descr: if (dtype[0].startswith("termination_flag") - or dtype[0] == "interpolation_class" - or "SN_type" in dtype[0] or "_state" in dtype[0]): + or "SN_type" in dtype[0] or "_state" in dtype[0] + or "_class" in dtype[0]): dtype = (dtype[0], H5_REC_STR_DTYPE.replace("U", "S")) new_final_dtype.append(dtype) new_final_values = np.array(new_final_values, dtype=new_final_dtype) diff --git a/posydon/grids/termination_flags.py b/posydon/grids/termination_flags.py index 91a20a07a2..5e1853dffa 100644 --- a/posydon/grids/termination_flags.py +++ b/posydon/grids/termination_flags.py @@ -245,7 +245,10 @@ def infer_interpolation_class(tf1, tf2): if tf2 in TF2_POOL_NO_RLO: return "no_MT" if tf1 in TF1_POOL_STABLE: - return "stable_MT" + if 'case' in tf2 and '1' in tf2 and '2' in tf2: + return "stable_reverse_MT" + else: + return "stable_MT" if tf1 in TF1_POOL_UNSTABLE: return "unstable_MT" return "unknown" diff --git a/posydon/interpolation/IF_interpolation.py b/posydon/interpolation/IF_interpolation.py index a39232730d..a17499f360 100644 --- a/posydon/interpolation/IF_interpolation.py +++ b/posydon/interpolation/IF_interpolation.py @@ -474,21 +474,21 @@ def __init__(self, grid=None, in_keys=None, out_keys=None, in_scaling=None, if (self.interp_method == 'linear' or isinstance(self.interp_method, list)): print("\nFilling missing values (nans) with 1NN") - self._fillNans() + self._fillNans(grid.final_values[self.c_key]) elif self.interp_method == '1NN': self.YT[np.isnan(self.YT)] = -100 if (self.in_scaling is None) or (self.out_scaling is None): if self.interp_method == '1NN': - self.in_scaling, self.out_scaling = (self._bestInScaling(), + self.in_scaling, self.out_scaling = (self._bestInScaling(grid.final_values[self.c_key]), ['none']*self.n_out) else: - self.in_scaling, self.out_scaling = self._bestScaling() + self.in_scaling, self.out_scaling = self._bestScaling(grid.final_values[self.c_key]) - self.X_scaler = MatrixScaler(self.in_scaling, - self.XT[self.valid >= 0, :]) - self.Y_scaler = MatrixScaler(self.out_scaling, - self.YT[self.valid > 0, :]) + self.X_scaler = Scaler(self.in_scaling, + self.XT[self.valid >= 0, :], grid.final_values[self.c_key][self.valid > 0]) + self.Y_scaler = Scaler(self.out_scaling, + self.YT[self.valid > 0, :], grid.final_values[self.c_key][self.valid > 0]) if self.class_method == "kNN": options = {'nfolds': 3, 'p_test': 0.05, 'nmax': 10} @@ -614,9 +614,12 @@ def train_interpolator(self, ic=None): for training the interpolator. """ - XTn = self.X_scaler.normalize(self.XT[self.valid > 0, :]) - YTn = self.Y_scaler.normalize(self.YT[self.valid > 0, :]) + ic = ic[self.valid > 0] if isinstance(self.interp_method, list) else None + + XTn = self.X_scaler.normalize(self.XT[self.valid > 0, :], ic) + YTn = self.Y_scaler.normalize(self.YT[self.valid > 0, :], ic) + if self.interp_method == "linear": self.interpolator = LinInterpolator() self.interpolator.train(XTn, YTn) @@ -627,7 +630,7 @@ def train_interpolator(self, ic=None): self.interpolator = MC_Interpolator( self.classifiers[self.c_key], self.interp_classes, self.interp_method) - self.interpolator.train(XTn, YTn, ic[self.valid > 0]) + self.interpolator.train(XTn, YTn, ic) def test_interpolator(self, Xt): """Use the interpolator to approximate output vector. @@ -643,16 +646,24 @@ def test_interpolator(self, Xt): Output space approximation as numpy array """ + + classes = self.test_classifier(self.c_key, Xt) Xtn = self.X_scaler.normalize(Xt) - Ypredn = self.interpolator.predict(Xtn) - Ypredn = np.array([ - list(sanitize_interpolated_quantities( - dict(zip(self.out_keys, track)), - self.constraints, verbose=False).values()) - for track in self.Y_scaler.denormalize(Ypredn) - ]) + if isinstance(self.interp_method, list): + + Xtn = self.X_scaler.normalize(Xt, classes) + Ypredn = self.interpolator.predict(Xtn, classes) + else: + Ypredn = self.interpolator.predict(Xtn) + + Ypredn = np.array([ + list(sanitize_interpolated_quantities( + dict(zip(self.out_keys, track)), + self.constraints, verbose=False).values()) + for track in self.Y_scaler.denormalize(Ypredn, classes) + ]) return Ypredn def train_classifiers(self, grid, method='kNN', **options): @@ -780,6 +791,7 @@ def evaluate_mat(self, Xt): "columns as it was trained with.") # if binary classified as 'initial_MT', set numerical quantities to nan ynum, ycat = self.test_interpolator(Xt), self.test_classifiers(Xt) + if self.class_method != '1NN': ynum[ycat[self.c_key] == 'initial_MT', :] = np.nan if self.interp_method == '1NN': @@ -845,99 +857,138 @@ def _interpIn_q(self, grid): return (m2[m1 > 0.95 * m1.max()].min() / m2[m1 < 1.05 * m1.min()].min() > 1 + tol) - def _bestInScaling(self): + def _bestInScaling(self, ic): """Find the best scaling for the input space.""" - in_scaling = [] # I assume inputs are positive-valued - for i in range(self.n_in): - if (np.abs(np.mean(self.XT[:, i]) - np.median(self.XT[:, i])) - / np.std(self.XT[:, i]) - < np.abs(np.mean(np.log10(self.XT[:, i])) - - np.median(np.log10(self.XT[:, i]))) - / np.std(np.log10(self.XT[:, i]))): - in_scaling.append('min_max') - else: - in_scaling.append('log_min_max') - return in_scaling + def in_scale_one(klass = None): - def _bestScaling(self, unique_in=True): - """Find the best scaling for both input and output space.""" - # Decide best scaling linear/log with cross validation - nfolds = 5 - p_test = 0.15 + in_scaling = [] # I assume inputs are positive-valued + for i in range(self.n_in): + + dim = self.XT[:, i] if klass is None else self.XT[np.where(ic == klass)[0]] + + if (np.abs(np.mean(dim) - np.median(dim)) + / np.std(dim) + < np.abs(np.mean(np.log10(dim)) + - np.median(np.log10(dim))) + / np.std(np.log10(dim))): + in_scaling.append('min_max') + else: + in_scaling.append('log_min_max') + + return in_scaling - # if False, the scaling for the inputs will be output-dependent - unique_in = True + cin_scaling = in_scale_one() - if unique_in: - r = 1 + if isinstance(self.interp_method, list): + in_scaling = {} + + for c in self.interp_classes: + in_scaling[c] = in_scale_one(c) else: - r = 2 ** self.n_in - - err = np.nan * np.ones((r * 2, self.n_out, nfolds)) - which_abs = np.abs(self.YT[self.valid > 0, :]).min(axis=0) == 0 - - out_scalings = [['min_max'] * self.n_out, ['log_min_max'] * self.n_out] - - for i, key in enumerate(self.out_keys): - y = self.YT[self.valid > 0, i] - if np.nanmin(y) == np.nanmax(y): - out_scalings[0][i] = 'none' - out_scalings[1][i] = 'none' - elif np.nanmax(y) < 0: - out_scalings[1][i] = 'neg_log_min_max' - elif np.nanmin(y) <= 0: - out_scalings[1][i] = 'min_max' - - in_scaling = self._bestInScaling() - - XT = self.XT[self.valid > 0, :] - YT = self.YT[self.valid > 0, :] - for j in range(2): # normal and log when possible - for i in range(r): # valid also with metallicity included - if r > 1: - in_scaling = [ - 'min_max' - if i % (2 ** (j + 1)) < 2 ** j else 'log_min_max' - for j in range(self.n_in) - ] - - xs, ys = (MatrixScaler(in_scaling, XT), - MatrixScaler(out_scalings[j], YT)) - XTn, YTn = xs.normalize(XT), ys.normalize(YT) - - np.random.seed(0) - - for fold in range(nfolds): - iTrain, itest = xval_indices(np.sum(self.valid > 0), - percent_test=p_test) - - X_T, Y_T = XTn[iTrain, :], YTn[iTrain, :] - X_t, Y_t = XT[itest, :], YT[itest, :] - - interp = LinearNDInterpolator(X_T, Y_T) - ypred_i = ys.denormalize(interp(xs.normalize(X_t))) - - err[i + r * j, ~which_abs, fold] = np.nanpercentile( - np.abs((ypred_i[:, ~which_abs] - Y_t[:, ~which_abs]) - / Y_t[:, ~which_abs]), 90, axis=0) - err[i + r * j, which_abs, fold] = np.nanpercentile( - np.abs(ypred_i[:, which_abs] - Y_t[:, which_abs]), 90, - axis=0) - - where_min = np.nanargmin(np.nanmean(err, axis=2), axis=0) - - out_scaling = [] - for i in range(self.n_out): - if where_min[i] < r: - out_scaling.append(out_scalings[0][i]) + + in_scaling = cin_scaling + + + + return (in_scaling, cin_scaling) + + + + def _bestScaling(self, ic, unique_in=True, nfolds = 5, p_test = 0.15): + """Find the best scaling for both input and output space.""" + + in_scaling = self._bestInScaling(ic) + + def scale_one(in_scaling, klass = None): + + # if False, the scaling for the inputs will be output-dependent + if unique_in: + r = 1 else: - out_scaling.append(out_scalings[1][i]) - where_min[i] -= r + r = 2 ** self.n_in + + inds = np.where(self.valid > 0 if klass is None else (ic == klass) & (self.valid > 0))[0] + + err = np.nan * np.ones((r * 2, self.n_out, nfolds)) + which_abs = np.abs(self.YT[inds, :]).min(axis=0) == 0 + + out_scalings = [['min_max'] * self.n_out, ['log_min_max'] * self.n_out] + + for i, key in enumerate(self.out_keys): + y = self.YT[inds, i] + if np.nanmin(y) == np.nanmax(y): + out_scalings[0][i] = 'none' + out_scalings[1][i] = 'none' + elif np.nanmax(y) < 0: + out_scalings[1][i] = 'neg_log_min_max' + elif np.nanmin(y) <= 0: + out_scalings[1][i] = 'min_max' + + + XT = self.XT[inds, :] + YT = self.YT[inds, :] + for j in range(2): # normal and log when possible + for i in range(r): # valid also with metallicity included + + if r > 1: + in_scaling = [ + 'min_max' + if i % (2 ** (j + 1)) < 2 ** j else 'log_min_max' + for j in range(self.n_in) + ] + + xs, ys = (MatrixScaler(in_scaling[1], XT), # using class cumulative scaling (not per class) to get optimal + MatrixScaler(out_scalings[j], YT)) + XTn, YTn = xs.normalize(XT), ys.normalize(YT) + + np.random.seed(0) + + for fold in range(nfolds): + + iTrain, itest = xval_indices(inds.shape[0], + percent_test=p_test) + + X_T, Y_T = XTn[iTrain, :], YTn[iTrain, :] + X_t, Y_t = XT[itest, :], YT[itest, :] - return in_scaling, out_scaling + interp = LinearNDInterpolator(X_T, Y_T) + ypred_i = ys.denormalize(interp(xs.normalize(X_t))) - def _fillNans(self): + err[i + r * j, ~which_abs, fold] = np.nanpercentile( + np.abs((ypred_i[:, ~which_abs] - Y_t[:, ~which_abs]) + / Y_t[:, ~which_abs]), 90, axis=0) + err[i + r * j, which_abs, fold] = np.nanpercentile( + np.abs(ypred_i[:, which_abs] - Y_t[:, which_abs]), 90, + axis=0) + + where_min = np.nanargmin(np.nanmean(err, axis=2), axis=0) + + out_scaling = [] + for i in range(self.n_out): + if where_min[i] < r: + out_scaling.append(out_scalings[0][i]) + else: + out_scaling.append(out_scalings[1][i]) + where_min[i] -= r + + return out_scaling + + cout_scaling = scale_one(in_scaling) + + if isinstance(self.interp_method, list): + out_scaling = {} + + for c in self.interp_classes: + out_scaling[c] = scale_one(in_scaling, c) + + else: + out_scaling = cout_scaling + + return in_scaling, (out_scaling, cout_scaling) + + + def _fillNans(self, ic): """Fill nan values i numerical magnitudes with 1NN.""" for i in range(self.n_out): wnan = np.isnan(self.YT[:, i]) | np.isinf(self.YT[:, i]) @@ -947,7 +998,7 @@ def _fillNans(self): if any(wnan[self.valid > 0]): k1r = KNeighborsRegressor(n_neighbors=1) wT = (~wnan) & (self.valid > 0) - xs = MatrixScaler(self._bestInScaling(), + xs = MatrixScaler(self._bestInScaling(ic)[1], self.XT[self.valid >= 0, :]) k1r.fit(xs.normalize(self.XT[wT, :]), self.YT[wT, i]) wt = wnan & (self.valid > 0) @@ -1147,7 +1198,11 @@ def train(self, XT, YT, z): which += z == self.classes[i][j] self.interpolators[i].train(XT[which, :], YT[which, :]) - def predict(self, Xt): + def classifier(self, Xt): + + return self.classifier.predict(Xt) + + def predict(self, Xt, zpred): """Interpolate and approximate output vectors given input vectors. Parameters @@ -1160,7 +1215,7 @@ def predict(self, Xt): Output space approximation as numpy array """ - zpred = self.classifier.predict(Xt) + Ypred = np.ones((Xt.shape[0], self.M)) * np.nan for i in range(len(self.classes)): which = np.zeros_like(zpred, dtype=bool) @@ -1342,6 +1397,59 @@ def xtrain(self, XT, yT, **opts): # MATRIX SCALING +class Scaler: + + def __init__(self, norms, XT, ic): + + if norms[0] == norms[1]: + self.scaler = { + None: MatrixScaler(norms[0], XT) + } + else: + self.scaler = {} + + for klass, n in norms[0].items(): + self.scaler[klass] = MatrixScaler(n, XT[np.where(ic == klass)[0]]) # need to only use relevant classes in XT + + self.scaler[None] = MatrixScaler(norms[1], XT) + + def normalize(self, X, klass = None): + + if klass is None: + return self.scaler[klass].normalize(X) + + else: + + normalized = X.copy() + + classes = np.unique(klass) + + for c in classes: + inds = np.where(klass == c)[0] + c = None if c == "None" else c + + normalized[inds] = self.scaler[c].normalize(X[inds]) + + return normalized + + def denormalize(self, Xn, klass = None): + + if klass is None: + return self.scaler[klass].denormalize(X) + + else: + + normalized = Xn + + classes = np.unique(klass) + + for c in classes: + inds = np.where(klass == c)[0] + c = None if c == "None" else c + + normalized[inds] = self.scaler[c].denormalize(Xn[inds]) + + return normalized class MatrixScaler: diff --git a/posydon/interpolation/data_scaling.py b/posydon/interpolation/data_scaling.py index 901e66f61c..ec0785beb7 100644 --- a/posydon/interpolation/data_scaling.py +++ b/posydon/interpolation/data_scaling.py @@ -122,15 +122,15 @@ def transform(self, x): if self.method == 'min_max': x_t = ((x - self.params[0]) / (self.params[1] - self.params[0]) - * (self.upper - self.lower) + self.lower) + * (self.upper - self.lower) + self.lower) elif self.method == 'log_min_max': x_t = ((np.log10(x) - self.params[0]) - / (self.params[1] - self.params[0]) - * (self.upper - self.lower) + self.lower) + / (self.params[1] - self.params[0]) + * (self.upper - self.lower) + self.lower) elif self.method == 'neg_log_min_max': x_t = ((np.log10(-x) - self.params[0]) - / (self.params[1] - self.params[0]) - * (self.upper - self.lower) + self.lower) + / (self.params[1] - self.params[0]) + * (self.upper - self.lower) + self.lower) elif self.method == 'max_abs': x_t = x / self.params[0] elif self.method == 'log_max_abs': diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index 1d96333581..047f20c2a5 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -55,19 +55,21 @@ # 'event' usually 10 but 'detached (Integration failure)' can occur HISTORY_MIN_ITEMSIZE = {'state': 30, 'event': 25, 'step_names': 20, 'S1_state': 31, 'S2_state': 31, - 'mass_transfer_case': 7, + 'mass_transfer_case': 10, 'S1_SN_type': 5, 'S2_SN_type': 5} ONELINE_MIN_ITEMSIZE = {'state_i': 30, 'state_f': 30, - 'event_i': 10, 'event_f': 25, + 'event_i': 10, 'event_f': 31, 'step_names_i': 20, 'step_names_f': 20, 'S1_state_i': 31, 'S1_state_f': 31, 'S2_state_i': 31, 'S2_state_f': 31, 'mass_transfer_case_i': 7, 'mass_transfer_case_f': 20, 'S1_SN_type': 5, 'S2_SN_type': 5, - 'interp_class_HMS_HMS' : 15, 'interp_class_CO_HeMS' : 15, + 'interp_class_HMS_HMS' : 20, 'interp_class_CO_HeMS' : 15, 'interp_class_CO_HMS_RLO' : 15, 'interp_class_CO_HeMS_RLO' : 15, 'mt_history_HMS_HMS' : 40, 'mt_history_CO_HeMS' : 40, 'mt_history_CO_HMS_RLO' : 40, 'mt_history_CO_HeMS_RLO' : 40, + 'culmulative_mt_case_HMS_HMS': 40, 'culmulative_mt_case_CO_HeMS': 40, + 'culmulative_mt_case_CO_HMS_RLO': 40, 'culmulative_mt_case_CO_HeMS_RLO': 40, } # BinaryPopulation will enforce a constant metallicity accross all steps that From 44dea62d9912cc34290f9088322e107fd7da74be Mon Sep 17 00:00:00 2001 From: dimsour94 <60255936+dimsour94@users.noreply.github.com> Date: Thu, 22 Feb 2024 17:31:30 +0200 Subject: [PATCH 183/319] String len in [step_names] columns (#257) * increased the length of string step_names from 20 to 21 * 20 to 21 for all step names and keys --------- Co-authored-by: Jeff Andrews --- posydon/popsyn/binarypopulation.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index 047f20c2a5..37a2b5f08a 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -53,13 +53,13 @@ # 'event' usually 10 but 'detached (Integration failure)' can occur -HISTORY_MIN_ITEMSIZE = {'state': 30, 'event': 25, 'step_names': 20, +HISTORY_MIN_ITEMSIZE = {'state': 30, 'event': 25, 'step_names': 21, 'S1_state': 31, 'S2_state': 31, 'mass_transfer_case': 10, 'S1_SN_type': 5, 'S2_SN_type': 5} ONELINE_MIN_ITEMSIZE = {'state_i': 30, 'state_f': 30, 'event_i': 10, 'event_f': 31, - 'step_names_i': 20, 'step_names_f': 20, + 'step_names_i': 21, 'step_names_f': 21, 'S1_state_i': 31, 'S1_state_f': 31, 'S2_state_i': 31, 'S2_state_f': 31, 'mass_transfer_case_i': 7, 'mass_transfer_case_f': 20, From 8db84b6ff1e5a3c9dfa6cf05497db75440e02550 Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Thu, 22 Feb 2024 16:33:16 +0100 Subject: [PATCH 184/319] CO-H(e)MS period outside grid being failed fix (#255) * add pre-check if period of binary is outside the grid * add check for CO-HMS_RLO step * add specific grid failure condition, while removing the loop checking * Update step_mesa.py --- posydon/binary_evol/MESA/step_mesa.py | 71 +++++++++++++++++++++------ 1 file changed, 55 insertions(+), 16 deletions(-) diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index 65aafa32b0..63cd4fa86c 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -1300,6 +1300,7 @@ def __call__(self, binary): self.p_min <= p <= self.p_max): self.flip_stars_before_step = True super().__call__(self.binary) + # redirect if outside grid for period elif (state_1 == 'H-rich_Core_H_burning' and state_2 == 'H-rich_Core_H_burning' and @@ -1314,6 +1315,7 @@ def __call__(self, binary): p < self.p_min): self.binary.event = 'redirect_from_ZAMS' return + # outside the mass grid for m1 elif (state_1 == 'H-rich_Core_H_burning' and state_2 == 'H-rich_Core_H_burning' and @@ -1433,7 +1435,7 @@ def __call__(self, binary): self.p_min <= p <= self.p_max and ecc == 0.)): super().__call__(self.binary) - + # period inside the grid, but m1 outside the grid elif ((not self.flip_stars_before_step and self.p_min <= p <= self.p_max and @@ -1452,12 +1454,24 @@ def __call__(self, binary): raise ValueError(f'The mass of m2 ({m2}) is outside the grid,' 'while the period is inside the grid.') + # period inside the grid, but m1 outside the grid (flipped stars) + elif ((self.flip_stars_before_step and + self.p_min <= p <= self.p_max and + (m2 < self.m1_min or m2 > self.m1_max) + )): + set_binary_to_failed(self.binary) + raise ValueError(f'The mass of m1 ({m2}) is outside the grid,' + 'while the period is inside the grid.') + # period inside the grid, but m2 outside the grid (flipped stars) + elif ((self.flip_stars_before_step and + self.p_min <= p <= self.p_max and + (m1 < self.m2_min or m1 > self.m2_max) + )): + set_binary_to_failed(self.binary) + raise ValueError(f'The mass of m2 ({m1}) is outside the grid,' + 'while the period is inside the grid.') + else: - if len(self.binary.state_history) > 2: - if self.binary.state_history[-2] == 'detached': - set_binary_to_failed(self.binary) - raise ValueError('CO_HMS_RLO binary outside grid and coming from detached') - self.binary.state = "detached" self.binary.event = "redirect_from_CO_HMS_RLO" return @@ -1536,6 +1550,7 @@ def __call__(self, binary): 'The star_1.state = %s, star_2.state = %s, binary.state = %s, ' 'binary.event = %s and not CO - HeMS - oRLO1/oRLO2!' % (state_1, state_2, state, event)) + # redirect if outside grids if ((not self.flip_stars_before_step and self.m1_min <= m1 <= self.m1_max and @@ -1547,6 +1562,7 @@ def __call__(self, binary): self.p_min <= p <= self.p_max and ecc == 0.)): super().__call__(self.binary) + # period inside the grid, but m1 outside the grid elif ((not self.flip_stars_before_step and self.p_min <= p <= self.p_max and @@ -1555,6 +1571,7 @@ def __call__(self, binary): set_binary_to_failed(self.binary) raise ValueError(f'The mass of m1 ({m1}) is outside the grid,' 'while the period is inside the grid.') + # period inside the grid, but m2 outside the grid elif ((not self.flip_stars_before_step and self.p_min <= p <= self.p_max and @@ -1563,13 +1580,25 @@ def __call__(self, binary): set_binary_to_failed(self.binary) raise ValueError(f'The mass of m2 ({m2}) is outside the grid,' 'while the period is inside the grid.') - + + # period inside the grid, but m1 outside the grid with flipped stars + elif ((self.flip_stars_before_step and + self.p_min <= p <= self.p_max and + m1 < self.m2_min or m1 > self.m2_max)): + set_binary_to_failed(self.binary) + raise ValueError(f'The mass of m1 ({m1}) is outside the grid,' + 'while the period is inside the grid.') + + # period inside the grid, but m2 outside the grid with flipped stars + elif ((self.flip_stars_before_step and + self.p_min <= p <= self.p_max and + m2 < self.m1_min or m2 > self.m1_max)): + set_binary_to_failed(self.binary) + raise ValueError(f'The mass of m2 ({m2}) is outside the grid,' + 'while the period is inside the grid.') + + # Redirect to the detached step else: - if len(self.binary.state_history) > 2: - if self.binary.state_history[-2] == 'detached': - set_binary_to_failed(self.binary) - raise ValueError('CO_HeMS_RLO binary outside grid and coming from detached') - self.binary.state = "detached" self.binary.event = "redirect_from_CO_HeMS_RLO" return @@ -1664,10 +1693,20 @@ def __call__(self, binary): self.p_min <= p <= self.p_max and ecc == 0.)): super().__call__(binary) + # period inside the grid, but m1 outside the grid + elif (self.p_min <= p <= self.p_max) and (m1 < self.m1_min or m1 > self.m1_max): + set_binary_to_failed(self.binary) + raise ValueError(f'The mass of m1 ({m1}) is outside the grid,' + 'while the period is inside the grid.') + # period inside the grid, but m2 outside the grid + elif (self.p_min <= p <= self.p_max) and (m2 < self.m2_min or m2 > self.m2_max): + set_binary_to_failed(self.binary) + raise ValueError(f'The mass of m2 ({m2}) is outside the grid,' + 'while the period is inside the grid.') + #elif p > self.p_max: + # binary.event = 'redirect_from_CO_HeMS' + # return else: - if len(self.binary.state_history) > 2: - if self.binary.state_history[-2] == 'detached': - set_binary_to_failed(self.binary) - raise ValueError('CO_HeMS binary outside grid and coming from detached') + self.binary.state = 'detached' self.binary.event = 'redirect_from_CO_HeMS' return From b098431e23a58def4746d096a9b92ddf584a90f7 Mon Sep 17 00:00:00 2001 From: Konstantinos Kovlakas <22239544+kkovlakas@users.noreply.github.com> Date: Thu, 22 Feb 2024 16:34:05 +0100 Subject: [PATCH 185/319] Refinements in the `compress-mesa` tool (#241) * Removing a dependency. * Printing help and error message when no arguments are provided. * Making compression the default function. * Made MESA dir a required and positional argument. * Updating compress_mesa script * Adding script to remove core dump files * fixing typo * Updating compress-mesa script with core dump removal --------- Co-authored-by: Jeff Andrews Co-authored-by: Jeff Andrews Co-authored-by: astroJeff --- bin/compress-mesa | 94 ++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 77 insertions(+), 17 deletions(-) diff --git a/bin/compress-mesa b/bin/compress-mesa index d4a3a50191..3a472a55ee 100644 --- a/bin/compress-mesa +++ b/bin/compress-mesa @@ -5,7 +5,44 @@ import shutil import random import argparse from tqdm import tqdm -from hurry.filesize import size + + +def textsize(filesize, floatfmt=".3g", base=1024, threshold=1000): + """Get a human-readable file size in string. + + Parameters + ---------- + filesize : int or float + The size of the file, or directory, ... + floatfmt : str + The format for the float before the size descriptor (e.g., 2.34K). + base : int + The base of the units. Typically 1024 but 1000 can be used as well. + threshold : int or float + The threshold for using the next unit. For example, if base is 1024 + and threshold is 1000, then 1000 bytes will be returned as 0.98K. + + Returns + ------- + str + The filesize in string format using b, K, M, ... + + """ + assert base in [1000, 1024] + + units = ["b", "K", "M", "G", "T", "P", "E", "Z", "Y"] + unit_values = [base**i for i in range(len(units))] + + if filesize < 0: + return "-" + textsize( + -filesize, floatfmt=floatfmt, base=base) + + for unit, unit_value in zip(units, unit_values): + if filesize < unit_value * threshold: + quantity = filesize / unit_value + return f"{quantity:{floatfmt}}{unit}" + + return f"{filesize:.3g} bytes" def set_up_test(args): @@ -73,13 +110,45 @@ def compress_dir(args): total_size += os.path.getsize(filepath) return total_size + def compress_subdirs(track_dirs): + for track_obj in track_dirs: + for root, _dir, files in os.walk(track_obj): + # traversing over directory tree of copied mesa tracks + # and compressing .data, .mod, .txt files + for file in files: + ext = file.split(".")[-1] + if ext not in ["data", "mod", "txt"]: + continue + os.system(f"gzip -1 {os.path.join(root, file)}") + + def remove_core_dumps(track_dirs): + for track_obj in track_dirs: + for root, _dir, files in os.walk(track_obj): + for file in files: + core_string = file.split(".")[0] + if (core_string == 'core' + and os.path.isfile(os.path.join(root, file))): + os.remove(os.path.join(root, file)) + if not os.path.isdir(args.mesa_dir): sys.exit("The MESA directory does not exist.") - og_size = size(get_size(args.mesa_dir)) + og_size = textsize(get_size(args.mesa_dir)) for folder in tqdm(os.listdir(args.mesa_dir)): + # To add the option of cycling through files one directory above + if "_grid_index_" in folder: + track_dirs = [] + + if (os.path.isdir(os.path.join(args.mesa_dir, folder)) + and folder not in ["star1", "star2", "binary"]): + track_dirs.append(os.path.join(args.mesa_dir, folder)) + + compress_subdirs(track_dirs) + remove_core_dumps(track_dirs) + continue + if os.path.isdir(os.path.join(args.mesa_dir, folder)): is_mesa_run = False sub_dir = os.listdir(os.path.join(args.mesa_dir, folder)) @@ -94,37 +163,28 @@ def compress_dir(args): track_dirs.append(os.path.join(args.mesa_dir, folder, _f)) if is_mesa_run: - # iterating over directory containing MESA runs - for track_obj in track_dirs: - for root, _dir, files in os.walk(track_obj): - # traversing over directory tree of copied mesa tracks - # and compressing .data, .mod, .txt files - for file in files: - ext = file.split(".")[-1] - if ext not in ["data", "mod", "txt"]: - continue - os.system(f"gzip -1 {os.path.join(root, file)}") + compress_subdirs(track_dirs) + remove_core_dumps(track_dirs) print("\nCompressed MESA tracks\nOriginal size {} | Compressed size {}\n". - format(og_size, size(get_size(args.mesa_dir)))) + format(og_size, textsize(get_size(args.mesa_dir)))) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("-f", "--function", type=str, - help="The name of the function to be called.") + help="The name of the function to be called.", + default='compress_dir') parser.add_argument("-td", "--test_dir", type=str, help="The path to where the testing directory should " "be set up, either set_up_test or compress_dir.") parser.add_argument("-dsr", "--dsr", type=str, help="Downsampling rate when creating testing " "directory", default=0.01) - parser.add_argument("-md", "--mesa_dir", type=str, + parser.add_argument("mesa_dir", type=str, help="The path to the directory containing " "MESA-generated data") functions = {"set_up_test": set_up_test, "compress_dir": compress_dir} arguments = parser.parse_args() - if arguments.function is None: - sys.exit("A function must be specified to use this script.") functions[arguments.function](arguments) From 6f242e1aef8463edcc332f479c16489d061afeb7 Mon Sep 17 00:00:00 2001 From: kasdaglie <112660893+kasdaglie@users.noreply.github.com> Date: Thu, 7 Mar 2024 10:18:24 -0500 Subject: [PATCH 186/319] Keeping the WD abundances for the step_merged (#261) * keeping the values in the step SN * stoping step mesa from erasing the abundances of WD * setting the co_core_mass equal with the mass of the WDs * determining if there is a co_core_mass and adding the changes for update_properties_NN * fixing an error in step mesa and passing the WD values for all case in step SN * Update binarypopulation.py --- posydon/binary_evol/CE/step_CEE.py | 2 - posydon/binary_evol/DT/step_merged.py | 4 -- posydon/binary_evol/MESA/step_mesa.py | 16 ++++---- posydon/binary_evol/SN/step_SN.py | 53 +++++++++++++++++++++------ posydon/binary_evol/binarystar.py | 2 +- 5 files changed, 50 insertions(+), 27 deletions(-) diff --git a/posydon/binary_evol/CE/step_CEE.py b/posydon/binary_evol/CE/step_CEE.py index 11d9c8bdb9..f448044310 100644 --- a/posydon/binary_evol/CE/step_CEE.py +++ b/posydon/binary_evol/CE/step_CEE.py @@ -37,7 +37,6 @@ from posydon.utils import common_functions as cf from posydon.utils import constants as const import warnings - from posydon.binary_evol.binarystar import BINARYPROPERTIES from posydon.binary_evol.singlestar import STARPROPERTIES from posydon.utils.common_functions import PATH_TO_POSYDON @@ -202,7 +201,6 @@ def __call__(self, binary): else: raise ValueError("CEE does not apply if `event` is not " "`oCE1`, 'oDoubleCE1' or `oCE2`, 'oDoubleCE1'") - # Check for double CE double_CE = binary.event in ["oDoubleCE1", "oDoubleCE2"] diff --git a/posydon/binary_evol/DT/step_merged.py b/posydon/binary_evol/DT/step_merged.py index 4cba14647a..50cb02d2bb 100644 --- a/posydon/binary_evol/DT/step_merged.py +++ b/posydon/binary_evol/DT/step_merged.py @@ -127,7 +127,6 @@ def merged_star_properties(self,star_base,comp): def mass_weighted_avg(star1=star_base,star2=comp, abundance_name="center_h1", mass_weight1="mass", mass_weight2=None): A1 = getattr(star1, abundance_name) A2 = getattr(star2, abundance_name) - if mass_weight1 == "H-rich_envelope_mass": M1 = getattr(star1, "mass") - getattr(star1, "he_core_mass") elif mass_weight1 == "He-rich_envelope_mass": @@ -403,7 +402,6 @@ def mass_weighted_avg(star1=star_base,star2=comp, abundance_name="center_h1", ma for key in ["mass", "he_core_mass", "c_core_mass", "o_core_mass", "co_core_mass"]: current = getattr(merged_star, key) + getattr(star_base, key) setattr(merged_star, key,current) - # weighted central abundances if merging cores. Else only from star_base if star_base.co_core_mass == 0 and comp.co_core_mass == 0: # two stars with Helium cores merged_star.center_h1 = mass_weighted_avg(mass_weight1="he_core_mass") @@ -495,12 +493,10 @@ def mass_weighted_avg(star1=star_base,star2=comp, abundance_name="center_h1", ma and s2 in ["WD"]): #WD is considered a stripped CO core - # add total and core masses for key in ["mass", "he_core_mass", "c_core_mass", "o_core_mass", "co_core_mass"]: current = getattr(merged_star, key) + getattr(comp, "mass") setattr(merged_star, key,current) - # weighted central abundances if merging cores. Else only from star_base if (comp.co_core_mass > 0 and star_base.co_core_mass == 0): # comp with CO core and the star_base has not merged_star.center_h1 = comp.center_h1 diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index 63cd4fa86c..e5bbb1f17b 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -303,7 +303,6 @@ def __call__(self, binary): self.__name__)) if self.flip_stars_before_step: flip_stars(binary) - max_MESA_sim_time = self.get_final_MESA_step_time() if max_MESA_sim_time is None: @@ -319,7 +318,6 @@ def __call__(self, binary): if (step_will_exceed_max_time and self.stop_method == 'stop_at_max_time'): # self.step(binary, interp_method='nearest_neighbour') - if self.interpolation_method != 'nearest_neighbour': self.closest_binary, self.nearest_neighbour_distance, \ self.termination_flags = self._psyTrackInterp.evaluate( @@ -334,9 +332,9 @@ def __call__(self, binary): track_interpolation=True) else: self.step(binary, interp_method=self.interpolation_method) - if (self.stop_method == 'stop_at_max_time' and binary.time >= binary.properties.max_simulation_time): + # self.flush_history = True # needed??? # stop_at_condition looks through the MESA output appended to the @@ -365,10 +363,8 @@ def __call__(self, binary): interpolate=self.stop_interpolate, star_1_CO=self.star_1_CO, star_2_CO=self.star_2_CO) - if self.flip_stars_before_step: flip_stars(binary) - if binary.time > binary.properties.max_simulation_time: binary.event = 'MaxTime_exceeded' elif binary.time == binary.properties.max_simulation_time: @@ -650,12 +646,16 @@ def update_properties_NN(self, star_1_CO=False, star_2_CO=False, getattr(star, key_h).extend(cb_bh[key_p][:-1]) elif key in ['state', 'lg_mdot']: continue + elif star.state == 'WD' and key in ['co_core_mass','he_core_mass','center_h1','center_he4','center_c12','center_n14','center_o16']: + continue else: setattr(star, key, None) if self.save_initial_conditions: getattr(star, key_h).append(empy_h[0]) if track_interpolation: getattr(star, key_h).extend(empy_h) + + if track_interpolation: if MESA_history_bug_fix: real_len = max(length_binary_hist, length_star_hist) @@ -845,7 +845,6 @@ def initial_final_interpolation(self, star_1_CO=False, star_2_CO=False): else: key_p = POSYDON_TO_MESA['binary'][key] setattr(self.binary, key, fv[key_p]) - for k, star in enumerate(stars): for key in STARPROPERTIES: if not stars_CO[k]: @@ -890,9 +889,10 @@ def initial_final_interpolation(self, star_1_CO=False, star_2_CO=False): setattr(star, key, fv[key_p]) elif key == 'state': continue + elif star.state == 'WD' and key in ['co_core_mass','he_core_mass','center_h1','center_he4','center_c12','center_n14','center_o16']: + continue else: setattr(star, key, None) - # EXPERIMENTAL feature # infer stellar states interpolation_class = self.classes['interpolation_class'] @@ -951,7 +951,6 @@ def initial_final_interpolation(self, star_1_CO=False, star_2_CO=False): if 10**tmp_lg_mdot > mdot_edd: tmp_lg_mdot = np.log10(mdot_edd) setattr(accretor, 'lg_mdot', tmp_lg_mdot) - # update post processed quanties key_post_processed = ['avg_c_in_c_core_at_He_depletion', 'co_core_mass_at_He_depletion', @@ -1435,6 +1434,7 @@ def __call__(self, binary): self.p_min <= p <= self.p_max and ecc == 0.)): super().__call__(self.binary) + # period inside the grid, but m1 outside the grid elif ((not self.flip_stars_before_step and diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index 7ba5360ea1..20879ab7b7 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -43,6 +43,7 @@ inspiral_timescale_from_separation, separation_evol_wind_loss, calculate_Patton20_values_at_He_depl, + THRESHOLD_CENTRAL_ABUNDANCE, rotate ) @@ -404,6 +405,7 @@ def __call__(self, binary): # collapse star self.collapse_star(star=binary.star_1) self._reset_other_star_properties(star=binary.star_2) + elif binary.event == "CC2": # collapse star self.collapse_star(star=binary.star_2) @@ -522,11 +524,25 @@ def collapse_star(self, star): MODEL_properties = getattr(star, MODEL_NAME_SEL) for key, value in MODEL_properties.items(): setattr(star, key, value) - - for key in STARPROPERTIES: - if key not in ["state", "mass", "spin", + + if star.state == 'WD': + for key in STARPROPERTIES: + if key in ["he_core_mass"]: + setattr(star, key, star.mass) + elif key in ["co_core_mass"]: + if star.center_he4 < THRESHOLD_CENTRAL_ABUNDANCE: + setattr(star, key, star.mass) + else: + setattr(star, key, 0.) + elif key not in ["state", "mass", "spin", + "m_disk_accreted ", "m_disk_radiated","center_h1","center_he4","center_c12","center_n14","center_o16"]: + setattr(star, key, None) + + else: + for key in STARPROPERTIES: + if key not in ["state", "mass", "spin", "m_disk_accreted ", "m_disk_radiated"]: - setattr(star, key, None) + setattr(star, key, None) # check if SN_type matches the predicted CO # and force the SN_type to match the predicted CO. @@ -579,7 +595,6 @@ def collapse_star(self, star): if getattr(star, 'SN_type') != 'PISN': star.log_R = np.log10(CO_radius(star.mass, star.state)) - return # Verifies the selection of core-collapse mechnism to perform @@ -613,9 +628,16 @@ def collapse_star(self, star): star.m_disk_accreted = np.nan star.m_disk_radiated = np.nan for key in STARPROPERTIES: - if key not in ["state", "mass", "log_R", "spin", - "m_disk_accreted", "m_disk_radiated"]: - setattr(star, key, None) + if key in ["he_core_mass"]: + setattr(star, key, star.mass) + elif key in ["co_core_mass"]: + if star.center_he4 < THRESHOLD_CENTRAL_ABUNDANCE: + setattr(star, key, star.mass) + else: + setattr(star, key, 0.) + elif key not in ["state", "mass", "spin", + "m_disk_accreted ", "m_disk_radiated","center_h1","center_he4","center_c12","center_n14","center_o16"]: + setattr(star, key, None) return # check if the star was disrupted by the PISN @@ -712,9 +734,16 @@ def collapse_star(self, star): star.m_disk_accreted = np.nan star.m_disk_radiated = np.nan for key in STARPROPERTIES: - if key not in ["state", "mass", "log_R", "spin", - "m_disk_accreted", "m_disk_radiated"]: - setattr(star, key, None) + if key in ["he_core_mass"]: + setattr(star, key, star.mass) + elif key in ["co_core_mass"]: + if star.center_he4 < THRESHOLD_CENTRAL_ABUNDANCE: + setattr(star, key, star.mass) + else: + setattr(star, key, 0.) + elif key not in ["state", "mass", "spin", + "m_disk_accreted ", "m_disk_radiated","center_h1","center_he4","center_c12","center_n14","center_o16"]: + setattr(star, key, None) return # check if the star was disrupted by the PISN @@ -805,7 +834,7 @@ def collapse_star(self, star): for key in STARPROPERTIES: if key not in [ "state", "mass", "spin", "log_R", "metallicity", - "m_disk_accreted ", "m_disk_radiated"]: + "m_disk_accreted ", "m_disk_radiated","co_core_mass"]: setattr(star, key, None) def PISN_prescription(self, star): diff --git a/posydon/binary_evol/binarystar.py b/posydon/binary_evol/binarystar.py index e85fcffaf3..920d598e0d 100644 --- a/posydon/binary_evol/binarystar.py +++ b/posydon/binary_evol/binarystar.py @@ -887,5 +887,5 @@ def from_run(run, history=False, profiles=False): if profiles: binary.star_1.profile = run.final_profile1 binary.star_2.profile = run.final_profile2 - + return binary From a4f5fa536d226ee240f2cd936161e573f6fd68e0 Mon Sep 17 00:00:00 2001 From: Max Briel Date: Thu, 7 Mar 2024 17:50:47 +0100 Subject: [PATCH 187/319] Revert "add 'stable_reverse_MT' and interpolation_class for CO (#170)" This reverts commit 83a458d9d7c14cea2e371a8857bd061ac8c555c0. --- bin/run-pipeline | 26 +- posydon/binary_evol/MESA/step_mesa.py | 6 +- posydon/binary_evol/binarystar.py | 3 +- posydon/grids/post_processing.py | 37 +-- posydon/grids/psygrid.py | 12 +- posydon/grids/termination_flags.py | 5 +- posydon/interpolation/IF_interpolation.py | 316 +++++++--------------- posydon/interpolation/data_scaling.py | 10 +- posydon/popsyn/binarypopulation.py | 6 +- 9 files changed, 143 insertions(+), 278 deletions(-) diff --git a/bin/run-pipeline b/bin/run-pipeline index 0c7d0ca0ed..fd1ea2cf9a 100644 --- a/bin/run-pipeline +++ b/bin/run-pipeline @@ -285,15 +285,13 @@ def train_interpolators(i, path_to_csv_file, verbose=False): # load grid grid = PSyGrid(verbose=verbose) grid.load(grid_path) - - interp_method = [method, method] - interp_classes = ["stable_MT", "unstable_MT"] - if '_RLO' not in grid_path: - interp_method += [method] - interp_classes += ["no_MT"] - if 'CO-' not in grid_path: - interp_method += [method] - interp_classes += ["stable_reverse_MT"] + + if '_RLO' in grid_path: + interp_method = [method, method] + interp_classes = ["stable_MT", "unstable_MT"] + else: + interp_method = [method, method, method] + interp_classes = ["no_MT", "stable_MT", "unstable_MT"] # all magnitues that are not model numbers, supernova properites or strings # are interpolated with respect to interpolation_class @@ -337,10 +335,10 @@ def train_interpolators(i, path_to_csv_file, verbose=False): # get interpolations classes dynamically interp_method = [] interp_classes = [] - for CO_interpolation_class in ["BH", "NS", "WD", "BH_reverse_MT"]: - if CO_interpolation_class in grid.final_values[f'S{i}_{MODEL_NAME}_CO_interpolation_class']: + for CO_type in ["BH", "NS", "WD"]: + if CO_type in grid.final_values[f'S{i}_{MODEL_NAME}_CO_type']: interp_method.append(method) - interp_classes.append(CO_interpolation_class) + interp_classes.append(CO_type) interpolators.append( { @@ -348,8 +346,8 @@ def train_interpolators(i, path_to_csv_file, verbose=False): "interp_classes": interp_classes, "out_keys": out_keys, "class_method": "kNN", - "c_keys": [f'S{i}_{MODEL_NAME}_CO_interpolation_class'], - "c_key": f'S{i}_{MODEL_NAME}_CO_interpolation_class' + "c_keys": [f'S{i}_{MODEL_NAME}_CO_type'], + "c_key": f'S{i}_{MODEL_NAME}_CO_type' }, ) diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index e5bbb1f17b..033746d9f7 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -680,8 +680,7 @@ def update_properties_NN(self, star_1_CO=False, star_2_CO=False, setattr(binary, 'state', binary_state) setattr(binary, 'event', binary_event) setattr(binary, 'mass_transfer_case', MT_case) - culmulative_mt_case = self.termination_flags[1] - setattr(self.binary, f'culmulative_mt_case_{self.grid_type}', culmulative_mt_case) + setattr(self.binary, f'interp_class_{self.grid_type}', interpolation_class) mt_history = self.termination_flags[2] # mass transfer history (TF12 plot label) setattr(self.binary, f'mt_history_{self.grid_type}', mt_history) @@ -899,8 +898,7 @@ def initial_final_interpolation(self, star_1_CO=False, star_2_CO=False): setattr(self.binary, f'interp_class_{self.grid_type}', interpolation_class) mt_history = self.classes['mt_history'] # mass transfer history (TF12 plot label) setattr(self.binary, f'mt_history_{self.grid_type}', mt_history) - #TODO: add classifier for tf2 - #setattr(self.binary, f'culmulative_mt_case', self.classes['termination_flags_2']) + S1_state_inferred = cf.check_state_of_star(self.binary.star_1, star_CO=star_1_CO) S2_state_inferred = cf.check_state_of_star(self.binary.star_2, diff --git a/posydon/binary_evol/binarystar.py b/posydon/binary_evol/binarystar.py index 920d598e0d..a6b454ea15 100644 --- a/posydon/binary_evol/binarystar.py +++ b/posydon/binary_evol/binarystar.py @@ -178,8 +178,7 @@ def __init__(self, star_1=None, star_2=None, index=None, properties=None, setattr(self, f'interp_class_{grid_type}', None) if not hasattr(self, f'mt_history_{grid_type}'): setattr(self, f'mt_history_{grid_type}', None) - if not hasattr(self, f'culmulative_mt_case_{grid_type}'): - setattr(self, f'culmulative_mt_case_{grid_type}', None) + # SimulationProperties object - parameters & parameterizations if isinstance(properties, SimulationProperties): self.properties = properties diff --git a/posydon/grids/post_processing.py b/posydon/grids/post_processing.py index 261623450d..3292df06ed 100644 --- a/posydon/grids/post_processing.py +++ b/posydon/grids/post_processing.py @@ -29,7 +29,7 @@ CC_quantities = ['state', 'SN_type', 'f_fb', 'mass', 'spin', - 'm_disk_accreted', 'm_disk_radiated', 'CO_interpolation_class'] + 'm_disk_accreted', 'm_disk_radiated'] def assign_core_collapse_quantities_none(EXTRA_COLUMNS, star_i, MODEL_NAME=None): """"Assign None values to all core collapse properties.""" @@ -165,11 +165,10 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, stars_CO = [False] IC = 'no_MT' TF1 = grid.final_values['termination_flag_1'][i] - TF2 = grid.final_values['termination_flag_2'][i] # compute properties for j, star in enumerate(stars): - if not stars_CO[j] and IC in ['no_MT', 'stable_MT', 'unstable_MT', 'stable_reverse_MT']: + if not stars_CO[j] and IC in ['no_MT', 'stable_MT', 'unstable_MT']: # stellar states EXTRA_COLUMNS['S%s_state' % (j+1)].append(check_state_of_star( star, star_CO=False)) @@ -237,7 +236,7 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, # core collpase quantities if not single_star: - if interpolation_class in ['no_MT', 'stable_MT', 'stable_reverse_MT']: + if interpolation_class in ['no_MT', 'stable_MT']: if (star_2_CO or (TF1 in TF1_POOL_STABLE and ('primary' in TF1 or 'Primary' in TF1))): star = binary.star_1 @@ -294,7 +293,7 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, if not isinstance(getattr(star_copy, quantity), str): flush = True warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} is not a string!') - elif quantity != 'CO_interpolation_class': + else: if not isinstance(getattr(star_copy, quantity), float): flush = True warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} is not a float!') @@ -311,16 +310,8 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, assign_core_collapse_quantities_none(EXTRA_COLUMNS, star_i, MODEL_NAME) else: for quantity in CC_quantities: - if quantity != 'CO_interpolation_class': - EXTRA_COLUMNS[f'S{star_i}_{MODEL_NAME}_{quantity}'].append( - getattr(star_copy, quantity)) - else: - if getattr(star_copy, 'state') == 'BH' and 'case' in TF2 and '1' in TF2 and '2' in TF2: - EXTRA_COLUMNS[f'S{star_i}_{MODEL_NAME}_{quantity}'].append( - getattr(star_copy, 'state')+'_reverse_MT') - else: - EXTRA_COLUMNS[f'S{star_i}_{MODEL_NAME}_{quantity}'].append( - getattr(star_copy, 'state')) + EXTRA_COLUMNS[f'S{star_i}_{MODEL_NAME}_{quantity}'].append( + getattr(star_copy, quantity)) if verbose: print_CC_quantities(EXTRA_COLUMNS, star_copy, f'{MODEL_NAME}_{mechanism}') @@ -346,7 +337,7 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, if not isinstance(getattr(star_copy, quantity), str): flush = True warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} is not a string!') - elif quantity != 'CO_interpolation_class': + else: if not isinstance(getattr(star_copy, quantity), float): flush = True warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} is not a float!') @@ -363,16 +354,8 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, assign_core_collapse_quantities_none(EXTRA_COLUMNS, 1, MODEL_NAME) else: for quantity in CC_quantities: - if quantity != 'CO_interpolation_class': - EXTRA_COLUMNS[f'S{star_i}_{MODEL_NAME}_{quantity}'].append( - getattr(star_copy, quantity)) - else: - if getattr(star_copy, 'state') == 'BH' and 'case' in TF2 and '1' in TF2 and '2' in TF2: - EXTRA_COLUMNS[f'S{star_i}_{MODEL_NAME}_{quantity}'].append( - getattr(star_copy, 'state')+'_reverse_MT') - else: - EXTRA_COLUMNS[f'S{star_i}_{MODEL_NAME}_{quantity}'].append( - getattr(star_copy, 'state')) + EXTRA_COLUMNS[f'S1_{MODEL_NAME}_{quantity}'].append( + getattr(star_copy, quantity)) if verbose: print_CC_quantities(EXTRA_COLUMNS, star_copy, f'{MODEL_NAME}_{mechanism}') else: @@ -435,7 +418,7 @@ def add_post_processed_quantities(grid, MESA_dirs_EXTRA_COLUMNS, EXTRA_COLUMNS, 'EXTRA_COLUMNS do not follow the correct order of grid!') for column in EXTRA_COLUMNS.keys(): - if "state" in column or "type" in column or "class" in column or column == 'mt_history': + if "state" in column or "type" in column or column == 'mt_history': values = np.asarray(EXTRA_COLUMNS[column], str) else: values = np.asarray(EXTRA_COLUMNS[column], float) diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index cc8c866e93..f686cea0b7 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -1287,9 +1287,9 @@ def update_final_values(self): new_dtype = [] for dtype in self.final_values.dtype.descr: if (dtype[0].startswith("termination_flag") + or dtype[0] == "interpolation_class" or dtype[0] == "mt_history" - or "_type" in dtype[0] or "_state" in dtype[0] - or "_class" in dtype[0]): + or "_type" in dtype[0] or "_state" in dtype[0]): dtype = (dtype[0], H5_REC_STR_DTYPE.replace("U", "S")) new_dtype.append(dtype) if dtype[1] == np.dtype('O'): @@ -1340,9 +1340,9 @@ def load(self, filepath=None): new_dtype = [] for dtype in self.final_values.dtype.descr: if (dtype[0].startswith("termination_flag") + or dtype[0] == "interpolation_class" or dtype[0] == "mt_history" - or "_type" in dtype[0] or "_state" in dtype[0] - or "_class" in dtype[0]): + or "_type" in dtype[0] or "_state" in dtype[0]): dtype = (dtype[0], H5_REC_STR_DTYPE.replace("S", "U")) new_dtype.append(dtype) self.final_values = self.final_values.astype(new_dtype) @@ -2277,8 +2277,8 @@ def say(something): new_final_dtype = [] for dtype in final_dtype.descr: if (dtype[0].startswith("termination_flag") - or "SN_type" in dtype[0] or "_state" in dtype[0] - or "_class" in dtype[0]): + or dtype[0] == "interpolation_class" + or "SN_type" in dtype[0] or "_state" in dtype[0]): dtype = (dtype[0], H5_REC_STR_DTYPE.replace("U", "S")) new_final_dtype.append(dtype) new_final_values = np.array(new_final_values, dtype=new_final_dtype) diff --git a/posydon/grids/termination_flags.py b/posydon/grids/termination_flags.py index 5e1853dffa..91a20a07a2 100644 --- a/posydon/grids/termination_flags.py +++ b/posydon/grids/termination_flags.py @@ -245,10 +245,7 @@ def infer_interpolation_class(tf1, tf2): if tf2 in TF2_POOL_NO_RLO: return "no_MT" if tf1 in TF1_POOL_STABLE: - if 'case' in tf2 and '1' in tf2 and '2' in tf2: - return "stable_reverse_MT" - else: - return "stable_MT" + return "stable_MT" if tf1 in TF1_POOL_UNSTABLE: return "unstable_MT" return "unknown" diff --git a/posydon/interpolation/IF_interpolation.py b/posydon/interpolation/IF_interpolation.py index a17499f360..a39232730d 100644 --- a/posydon/interpolation/IF_interpolation.py +++ b/posydon/interpolation/IF_interpolation.py @@ -474,21 +474,21 @@ def __init__(self, grid=None, in_keys=None, out_keys=None, in_scaling=None, if (self.interp_method == 'linear' or isinstance(self.interp_method, list)): print("\nFilling missing values (nans) with 1NN") - self._fillNans(grid.final_values[self.c_key]) + self._fillNans() elif self.interp_method == '1NN': self.YT[np.isnan(self.YT)] = -100 if (self.in_scaling is None) or (self.out_scaling is None): if self.interp_method == '1NN': - self.in_scaling, self.out_scaling = (self._bestInScaling(grid.final_values[self.c_key]), + self.in_scaling, self.out_scaling = (self._bestInScaling(), ['none']*self.n_out) else: - self.in_scaling, self.out_scaling = self._bestScaling(grid.final_values[self.c_key]) + self.in_scaling, self.out_scaling = self._bestScaling() - self.X_scaler = Scaler(self.in_scaling, - self.XT[self.valid >= 0, :], grid.final_values[self.c_key][self.valid > 0]) - self.Y_scaler = Scaler(self.out_scaling, - self.YT[self.valid > 0, :], grid.final_values[self.c_key][self.valid > 0]) + self.X_scaler = MatrixScaler(self.in_scaling, + self.XT[self.valid >= 0, :]) + self.Y_scaler = MatrixScaler(self.out_scaling, + self.YT[self.valid > 0, :]) if self.class_method == "kNN": options = {'nfolds': 3, 'p_test': 0.05, 'nmax': 10} @@ -614,12 +614,9 @@ def train_interpolator(self, ic=None): for training the interpolator. """ + XTn = self.X_scaler.normalize(self.XT[self.valid > 0, :]) + YTn = self.Y_scaler.normalize(self.YT[self.valid > 0, :]) - ic = ic[self.valid > 0] if isinstance(self.interp_method, list) else None - - XTn = self.X_scaler.normalize(self.XT[self.valid > 0, :], ic) - YTn = self.Y_scaler.normalize(self.YT[self.valid > 0, :], ic) - if self.interp_method == "linear": self.interpolator = LinInterpolator() self.interpolator.train(XTn, YTn) @@ -630,7 +627,7 @@ def train_interpolator(self, ic=None): self.interpolator = MC_Interpolator( self.classifiers[self.c_key], self.interp_classes, self.interp_method) - self.interpolator.train(XTn, YTn, ic) + self.interpolator.train(XTn, YTn, ic[self.valid > 0]) def test_interpolator(self, Xt): """Use the interpolator to approximate output vector. @@ -646,24 +643,16 @@ def test_interpolator(self, Xt): Output space approximation as numpy array """ - - classes = self.test_classifier(self.c_key, Xt) Xtn = self.X_scaler.normalize(Xt) + Ypredn = self.interpolator.predict(Xtn) - if isinstance(self.interp_method, list): - - Xtn = self.X_scaler.normalize(Xt, classes) - - Ypredn = self.interpolator.predict(Xtn, classes) - else: - Ypredn = self.interpolator.predict(Xtn) - Ypredn = np.array([ - list(sanitize_interpolated_quantities( - dict(zip(self.out_keys, track)), - self.constraints, verbose=False).values()) - for track in self.Y_scaler.denormalize(Ypredn, classes) - ]) + list(sanitize_interpolated_quantities( + dict(zip(self.out_keys, track)), + self.constraints, verbose=False).values()) + for track in self.Y_scaler.denormalize(Ypredn) + ]) + return Ypredn def train_classifiers(self, grid, method='kNN', **options): @@ -791,7 +780,6 @@ def evaluate_mat(self, Xt): "columns as it was trained with.") # if binary classified as 'initial_MT', set numerical quantities to nan ynum, ycat = self.test_interpolator(Xt), self.test_classifiers(Xt) - if self.class_method != '1NN': ynum[ycat[self.c_key] == 'initial_MT', :] = np.nan if self.interp_method == '1NN': @@ -857,138 +845,99 @@ def _interpIn_q(self, grid): return (m2[m1 > 0.95 * m1.max()].min() / m2[m1 < 1.05 * m1.min()].min() > 1 + tol) - def _bestInScaling(self, ic): + def _bestInScaling(self): """Find the best scaling for the input space.""" - - def in_scale_one(klass = None): - - in_scaling = [] # I assume inputs are positive-valued - for i in range(self.n_in): - - dim = self.XT[:, i] if klass is None else self.XT[np.where(ic == klass)[0]] - - if (np.abs(np.mean(dim) - np.median(dim)) - / np.std(dim) - < np.abs(np.mean(np.log10(dim)) - - np.median(np.log10(dim))) - / np.std(np.log10(dim))): - in_scaling.append('min_max') - else: - in_scaling.append('log_min_max') - - return in_scaling - - cin_scaling = in_scale_one() - - if isinstance(self.interp_method, list): - in_scaling = {} - - for c in self.interp_classes: - in_scaling[c] = in_scale_one(c) - else: - - in_scaling = cin_scaling - - - - return (in_scaling, cin_scaling) - - - - def _bestScaling(self, ic, unique_in=True, nfolds = 5, p_test = 0.15): - """Find the best scaling for both input and output space.""" - - in_scaling = self._bestInScaling(ic) - - def scale_one(in_scaling, klass = None): - - # if False, the scaling for the inputs will be output-dependent - if unique_in: - r = 1 + in_scaling = [] # I assume inputs are positive-valued + for i in range(self.n_in): + if (np.abs(np.mean(self.XT[:, i]) - np.median(self.XT[:, i])) + / np.std(self.XT[:, i]) + < np.abs(np.mean(np.log10(self.XT[:, i])) + - np.median(np.log10(self.XT[:, i]))) + / np.std(np.log10(self.XT[:, i]))): + in_scaling.append('min_max') else: - r = 2 ** self.n_in - - inds = np.where(self.valid > 0 if klass is None else (ic == klass) & (self.valid > 0))[0] - - err = np.nan * np.ones((r * 2, self.n_out, nfolds)) - which_abs = np.abs(self.YT[inds, :]).min(axis=0) == 0 - - out_scalings = [['min_max'] * self.n_out, ['log_min_max'] * self.n_out] - - for i, key in enumerate(self.out_keys): - y = self.YT[inds, i] - if np.nanmin(y) == np.nanmax(y): - out_scalings[0][i] = 'none' - out_scalings[1][i] = 'none' - elif np.nanmax(y) < 0: - out_scalings[1][i] = 'neg_log_min_max' - elif np.nanmin(y) <= 0: - out_scalings[1][i] = 'min_max' - - - XT = self.XT[inds, :] - YT = self.YT[inds, :] - for j in range(2): # normal and log when possible - for i in range(r): # valid also with metallicity included - - if r > 1: - in_scaling = [ - 'min_max' - if i % (2 ** (j + 1)) < 2 ** j else 'log_min_max' - for j in range(self.n_in) - ] - - xs, ys = (MatrixScaler(in_scaling[1], XT), # using class cumulative scaling (not per class) to get optimal - MatrixScaler(out_scalings[j], YT)) - XTn, YTn = xs.normalize(XT), ys.normalize(YT) - - np.random.seed(0) - - for fold in range(nfolds): + in_scaling.append('log_min_max') - iTrain, itest = xval_indices(inds.shape[0], - percent_test=p_test) + return in_scaling - X_T, Y_T = XTn[iTrain, :], YTn[iTrain, :] - X_t, Y_t = XT[itest, :], YT[itest, :] - - interp = LinearNDInterpolator(X_T, Y_T) - ypred_i = ys.denormalize(interp(xs.normalize(X_t))) - - err[i + r * j, ~which_abs, fold] = np.nanpercentile( - np.abs((ypred_i[:, ~which_abs] - Y_t[:, ~which_abs]) - / Y_t[:, ~which_abs]), 90, axis=0) - err[i + r * j, which_abs, fold] = np.nanpercentile( - np.abs(ypred_i[:, which_abs] - Y_t[:, which_abs]), 90, - axis=0) - - where_min = np.nanargmin(np.nanmean(err, axis=2), axis=0) - - out_scaling = [] - for i in range(self.n_out): - if where_min[i] < r: - out_scaling.append(out_scalings[0][i]) - else: - out_scaling.append(out_scalings[1][i]) - where_min[i] -= r - - return out_scaling - - cout_scaling = scale_one(in_scaling) - - if isinstance(self.interp_method, list): - out_scaling = {} + def _bestScaling(self, unique_in=True): + """Find the best scaling for both input and output space.""" + # Decide best scaling linear/log with cross validation + nfolds = 5 + p_test = 0.15 - for c in self.interp_classes: - out_scaling[c] = scale_one(in_scaling, c) + # if False, the scaling for the inputs will be output-dependent + unique_in = True + if unique_in: + r = 1 else: - out_scaling = cout_scaling - - return in_scaling, (out_scaling, cout_scaling) + r = 2 ** self.n_in + + err = np.nan * np.ones((r * 2, self.n_out, nfolds)) + which_abs = np.abs(self.YT[self.valid > 0, :]).min(axis=0) == 0 + + out_scalings = [['min_max'] * self.n_out, ['log_min_max'] * self.n_out] + + for i, key in enumerate(self.out_keys): + y = self.YT[self.valid > 0, i] + if np.nanmin(y) == np.nanmax(y): + out_scalings[0][i] = 'none' + out_scalings[1][i] = 'none' + elif np.nanmax(y) < 0: + out_scalings[1][i] = 'neg_log_min_max' + elif np.nanmin(y) <= 0: + out_scalings[1][i] = 'min_max' + + in_scaling = self._bestInScaling() + + XT = self.XT[self.valid > 0, :] + YT = self.YT[self.valid > 0, :] + for j in range(2): # normal and log when possible + for i in range(r): # valid also with metallicity included + if r > 1: + in_scaling = [ + 'min_max' + if i % (2 ** (j + 1)) < 2 ** j else 'log_min_max' + for j in range(self.n_in) + ] + + xs, ys = (MatrixScaler(in_scaling, XT), + MatrixScaler(out_scalings[j], YT)) + XTn, YTn = xs.normalize(XT), ys.normalize(YT) + + np.random.seed(0) + + for fold in range(nfolds): + iTrain, itest = xval_indices(np.sum(self.valid > 0), + percent_test=p_test) + + X_T, Y_T = XTn[iTrain, :], YTn[iTrain, :] + X_t, Y_t = XT[itest, :], YT[itest, :] + + interp = LinearNDInterpolator(X_T, Y_T) + ypred_i = ys.denormalize(interp(xs.normalize(X_t))) + + err[i + r * j, ~which_abs, fold] = np.nanpercentile( + np.abs((ypred_i[:, ~which_abs] - Y_t[:, ~which_abs]) + / Y_t[:, ~which_abs]), 90, axis=0) + err[i + r * j, which_abs, fold] = np.nanpercentile( + np.abs(ypred_i[:, which_abs] - Y_t[:, which_abs]), 90, + axis=0) + + where_min = np.nanargmin(np.nanmean(err, axis=2), axis=0) + + out_scaling = [] + for i in range(self.n_out): + if where_min[i] < r: + out_scaling.append(out_scalings[0][i]) + else: + out_scaling.append(out_scalings[1][i]) + where_min[i] -= r + return in_scaling, out_scaling - def _fillNans(self, ic): + def _fillNans(self): """Fill nan values i numerical magnitudes with 1NN.""" for i in range(self.n_out): wnan = np.isnan(self.YT[:, i]) | np.isinf(self.YT[:, i]) @@ -998,7 +947,7 @@ def _fillNans(self, ic): if any(wnan[self.valid > 0]): k1r = KNeighborsRegressor(n_neighbors=1) wT = (~wnan) & (self.valid > 0) - xs = MatrixScaler(self._bestInScaling(ic)[1], + xs = MatrixScaler(self._bestInScaling(), self.XT[self.valid >= 0, :]) k1r.fit(xs.normalize(self.XT[wT, :]), self.YT[wT, i]) wt = wnan & (self.valid > 0) @@ -1198,11 +1147,7 @@ def train(self, XT, YT, z): which += z == self.classes[i][j] self.interpolators[i].train(XT[which, :], YT[which, :]) - def classifier(self, Xt): - - return self.classifier.predict(Xt) - - def predict(self, Xt, zpred): + def predict(self, Xt): """Interpolate and approximate output vectors given input vectors. Parameters @@ -1215,7 +1160,7 @@ def predict(self, Xt, zpred): Output space approximation as numpy array """ - + zpred = self.classifier.predict(Xt) Ypred = np.ones((Xt.shape[0], self.M)) * np.nan for i in range(len(self.classes)): which = np.zeros_like(zpred, dtype=bool) @@ -1397,59 +1342,6 @@ def xtrain(self, XT, yT, **opts): # MATRIX SCALING -class Scaler: - - def __init__(self, norms, XT, ic): - - if norms[0] == norms[1]: - self.scaler = { - None: MatrixScaler(norms[0], XT) - } - else: - self.scaler = {} - - for klass, n in norms[0].items(): - self.scaler[klass] = MatrixScaler(n, XT[np.where(ic == klass)[0]]) # need to only use relevant classes in XT - - self.scaler[None] = MatrixScaler(norms[1], XT) - - def normalize(self, X, klass = None): - - if klass is None: - return self.scaler[klass].normalize(X) - - else: - - normalized = X.copy() - - classes = np.unique(klass) - - for c in classes: - inds = np.where(klass == c)[0] - c = None if c == "None" else c - - normalized[inds] = self.scaler[c].normalize(X[inds]) - - return normalized - - def denormalize(self, Xn, klass = None): - - if klass is None: - return self.scaler[klass].denormalize(X) - - else: - - normalized = Xn - - classes = np.unique(klass) - - for c in classes: - inds = np.where(klass == c)[0] - c = None if c == "None" else c - - normalized[inds] = self.scaler[c].denormalize(Xn[inds]) - - return normalized class MatrixScaler: diff --git a/posydon/interpolation/data_scaling.py b/posydon/interpolation/data_scaling.py index ec0785beb7..901e66f61c 100644 --- a/posydon/interpolation/data_scaling.py +++ b/posydon/interpolation/data_scaling.py @@ -122,15 +122,15 @@ def transform(self, x): if self.method == 'min_max': x_t = ((x - self.params[0]) / (self.params[1] - self.params[0]) - * (self.upper - self.lower) + self.lower) + * (self.upper - self.lower) + self.lower) elif self.method == 'log_min_max': x_t = ((np.log10(x) - self.params[0]) - / (self.params[1] - self.params[0]) - * (self.upper - self.lower) + self.lower) + / (self.params[1] - self.params[0]) + * (self.upper - self.lower) + self.lower) elif self.method == 'neg_log_min_max': x_t = ((np.log10(-x) - self.params[0]) - / (self.params[1] - self.params[0]) - * (self.upper - self.lower) + self.lower) + / (self.params[1] - self.params[0]) + * (self.upper - self.lower) + self.lower) elif self.method == 'max_abs': x_t = x / self.params[0] elif self.method == 'log_max_abs': diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index 37a2b5f08a..0b52e0981c 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -55,7 +55,7 @@ # 'event' usually 10 but 'detached (Integration failure)' can occur HISTORY_MIN_ITEMSIZE = {'state': 30, 'event': 25, 'step_names': 21, 'S1_state': 31, 'S2_state': 31, - 'mass_transfer_case': 10, + 'mass_transfer_case': 7, 'S1_SN_type': 5, 'S2_SN_type': 5} ONELINE_MIN_ITEMSIZE = {'state_i': 30, 'state_f': 30, 'event_i': 10, 'event_f': 31, @@ -64,12 +64,10 @@ 'S2_state_i': 31, 'S2_state_f': 31, 'mass_transfer_case_i': 7, 'mass_transfer_case_f': 20, 'S1_SN_type': 5, 'S2_SN_type': 5, - 'interp_class_HMS_HMS' : 20, 'interp_class_CO_HeMS' : 15, + 'interp_class_HMS_HMS' : 15, 'interp_class_CO_HeMS' : 15, 'interp_class_CO_HMS_RLO' : 15, 'interp_class_CO_HeMS_RLO' : 15, 'mt_history_HMS_HMS' : 40, 'mt_history_CO_HeMS' : 40, 'mt_history_CO_HMS_RLO' : 40, 'mt_history_CO_HeMS_RLO' : 40, - 'culmulative_mt_case_HMS_HMS': 40, 'culmulative_mt_case_CO_HeMS': 40, - 'culmulative_mt_case_CO_HMS_RLO': 40, 'culmulative_mt_case_CO_HeMS_RLO': 40, } # BinaryPopulation will enforce a constant metallicity accross all steps that From f7a9573da41e13c988fc6e2d621408578eae62ff Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Thu, 14 Mar 2024 15:40:01 +0100 Subject: [PATCH 188/319] Fix get_combined_tilt calculation (#274) * fix tilt_combined * saving the event before the else if statements --------- Co-authored-by: Eirini --- posydon/binary_evol/SN/step_SN.py | 20 +++++++------------- 1 file changed, 7 insertions(+), 13 deletions(-) diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index 20879ab7b7..e0abdb2f51 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -1700,13 +1700,11 @@ def SNCheck( # update the binary object which was bound at least before the SN #Check if this is the first SN + for key in BINARYPROPERTIES: + if key not in ['nearest_neighbour_distance','event']: + setattr(binary, key, None) if flag_binary: # update the tilt - - for key in BINARYPROPERTIES: - if key != 'nearest_neighbour_distance': - setattr(binary, key, None) - if not binary.first_SN_already_occurred: # update the tilt binary.star_1.spin_orbit_tilt_first_SN = tilt @@ -1717,7 +1715,7 @@ def SNCheck( if binary.event == 'CC2': # Assume progenitor has aligned with the preSN orbital angular momentum binary.star_2.spin_orbit_tilt_second_SN = tilt - binary.star_1.spin_orbit_tilt_second_SN = get_combined_tilt( + binary.star_1.spin_orbit_tilt_second_SN = self.get_combined_tilt( tilt_1 = binary.star_1.spin_orbit_tilt_first_SN, tilt_2 = tilt, true_anomaly_1 = binary.true_anomaly_first_SN, @@ -1727,7 +1725,7 @@ def SNCheck( elif binary.event == 'CC1': # Assume progenitor has aligned with the preSN orbital angular momentum binary.star_1.spin_orbit_tilt_second_SN = tilt - binary.star_2.spin_orbit_tilt_second_SN = get_combined_tilt( + binary.star_2.spin_orbit_tilt_second_SN = self.get_combined_tilt( tilt_1 = binary.star_1.spin_orbit_tilt_first_SN, tilt_2 = tilt, true_anomaly_1 = binary.true_anomaly_first_SN, @@ -1737,8 +1735,7 @@ def SNCheck( else: raise ValueError("This should never happen!") - # compute new orbital period before reseting the binary properties - + # compute new orbital period before reseting the binary properties binary.state = "detached" binary.event = None binary.separation = Apost / const.Rsun @@ -1753,9 +1750,6 @@ def SNCheck( binary.mass_transfer_case = 'None' else: - for key in BINARYPROPERTIES: - if key != 'nearest_neighbour_distance': - setattr(binary, key, None) # update the tilt if not binary.first_SN_already_occurred: binary.star_1.spin_orbit_tilt_first_SN = np.nan @@ -1831,7 +1825,7 @@ def generate_kick(self, star, sigma): return Vkick - def get_combined_tilt(tilt_1, tilt_2, true_anomaly_1, true_anomaly_2): + def get_combined_tilt(self,tilt_1, tilt_2, true_anomaly_1, true_anomaly_2): """Get the combined spin-orbit-tilt after two supernovae, assuming the spin as not realigned with the orbital angular momentum after SN1 From c82516389e4bc718fed77cf148b179b1b5e53f14 Mon Sep 17 00:00:00 2001 From: tassos25 Date: Thu, 14 Mar 2024 15:40:40 +0100 Subject: [PATCH 189/319] Remove legacy code realte to integration to BSE (#277) I just deleted two functions: POSYDO_to_BSE and BSE_to_POSYDON which are not used anywhere anymore. --- posydon/utils/common_functions.py | 71 +------------------------------ 1 file changed, 1 insertion(+), 70 deletions(-) diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index b5d5a54586..c5522e5bf0 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -292,75 +292,6 @@ def orbital_period_from_separation(separation, m1, m2): return const.dayyer * ((separation / const.aursun)**3.0 / (m1 + m2)) ** 0.5 -def BSE_to_POSYDON(ktype): - """Convert BSE numerical type to POSYDON string. - - Parameters - ---------- - ktype : int - The BSE numerical type. - core_mass : float - Core mass of the star. - - Returns - ------- - str - The corresponding POSYDON string. - - """ - if ktype in (0, 1): - state = 'H-rich_Core_H_burning' - elif ktype in (2, 3): # Hertzprung Gap and Giant Branch star - state = 'H-rich_Shell_H_burning' - elif ktype == 4: # Core Helium Burning - state = 'H-rich_Core_He_burning' - elif ktype in (5, 6): # Early AGB and TPAGB - state = 'H-rich_Central_He_depleted' - elif ktype == 7: # Naked Helium Star MS - state = 'stripped_He_Core_He_burning' - elif ktype in (8, 9): # Naked Helium star (HG and GB) - state = 'stripped_He_Central_He_depleted' - elif ktype in (10, 11, 12): - state = 'WD' - elif ktype == 13: - state = 'NS' - elif ktype == 14: - state = 'BH' - elif ktype == 15: - state = 'Massless remnant' - else: - raise ValueError('Conversion of the ktype {} is not supported.'. - format(ktype)) - return state - - -def POSYDON_to_BSE(star): - """Convert POSYDON state to BSE numerical type. - - Parameters - ---------- - star : SingleStar - The star which state requires conversion. - - Returns - ------- - int - The corresponding BSE numerical type. - """ - if star.state == 'H-rich_Core_H_burning': - if star.mass < 0.7: - ktype = 0 - else: - ktype = 1 - elif star.state == 'NS': - ktype = 13 - elif star.state == 'BH': - ktype = 14 - else: - raise ValueError( - 'Conversion of the state {} is currently not supported.'.format( - star.state)) - return ktype def eddington_limit(binary, idx=-1): @@ -2727,4 +2658,4 @@ def rotate(axis, angle): ]) - return rotation_matrix \ No newline at end of file + return rotation_matrix From 28c71474247747218e731622e8151a51155bf036 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Fri, 15 Mar 2024 17:00:01 +0100 Subject: [PATCH 190/319] Fixes in postprocessing (#237) * Update plot2D.py get dtypes while accounting for None * Update plot2D.py Raise errors, if initial or final values don't exist in the grid. * Update plot1D.py check for histories being None before requesting dtype. * Update psygrid.py Change warning to verbose output * Update scrubbing.py Remove warning about no RLO. * Update post_processing.py Add check, whether star can collapse. * Update post_processing.py correct typo: 1 -> star_i * Update post_processing.py debugging why star values aren't printed * Update profile_collapse.py Update default return in case of no fall back to be consistent with last change of normal rerun of do_core_collapse_BH. * rename pipeline files (add posydon) * add option to replace early MT case happening after a later one * add Matthias to author list * add possibility to add non-predefined MT in plotting * Update posydon-setup-pipeline Don't use shared log files anymore. * Update posydon-setup-pipeline correct typo * add cleanup jobs * more cleanup * use split instead of replace for title of all * add grid_type to step_1.csv * step 3: add grid_tpye to csv; add new field ORIGINAL_COMPRESSIONS * step 4: add interpolation_method * step R: add grid_type and rerun_metallicity * plots: add grid_type * doc changes * auto detect range in pipeline plotting * correct typo * only email on FAIL for cleanup jobs * update from developement * capture if date in None; first allow a retry if return code isn't 0 * add run directory to error output; add option allow_spin_None; add me to author list * more output * typo for max replacement * rephrase warning * expand error message * typo: nbin -> step * allow v1 dirnames without initial_z * remove spaces * add comment * more debugging of not a float * set disk values to nan in case of PISN * set values m_disk values from None to np.nan always in collapse_star * remove some debugging outputs * add more data to error output * addition for v1 to use solar metallicity, if it is an empty string * us pandas isna to determine empty metallicity, because it comes from a pandas read * for relative error ignore warnings if value is 0; request dtype float to avoid overflows on e.g. uint * allow to close open figures after they got saved * if new directories are created add them to list of new files * missing ')' * debugging overflow * remove some old code not needed anymore * account for any TF2 when getting TF12 * expand plot defaults, nonburning may have no star index * shorten TF12 earlier in nonburning case * add grid_type to step 4 to account for possibility of reverse MT * allow in spin check it to be None * account for CO_interpolation_class * account of None as star's spin in printing * fix typo * hot fix for case AA * fix missing TF2 for single stars * fix single star case for extra columns * dirty fix of having all NaN slices * make dirty fix working * move allow_spin_None out of pre defined models to a hard corded option for post-processing only * donot use normalization for initial_MT * more general treatment of missing keys incl. warning * restore lost parts of PR170 * add v1 case for single stars * address Max' comment --- bin/{run-pipeline => posydon-run-pipeline} | 149 ++++++--- ...{setup-pipeline => posydon-setup-pipeline} | 290 +++++++++++++--- .../pipeline/pipeline_additions.rst | 15 +- .../pipeline/pipeline_steps.rst | 34 +- .../post_processing/pipeline_ini.rst | 24 +- .../generating-datasets/just_step_1.ipynb | 6 +- .../run_full_piepeline.ipynb | 4 +- .../generating-datasets/step_rerun.ipynb | 6 +- grid_params/pipeline_quest.ini | 2 + grid_params/pipeline_yggdrasil.ini | 12 +- posydon/_version.py | 12 +- posydon/binary_evol/MESA/step_mesa.py | 8 +- posydon/binary_evol/SN/profile_collapse.py | 8 +- posydon/binary_evol/SN/step_SN.py | 98 ++++-- posydon/binary_evol/binarystar.py | 4 +- posydon/grids/MODELS.py | 2 +- posydon/grids/io.py | 10 +- posydon/grids/post_processing.py | 144 +++++--- posydon/grids/psygrid.py | 23 +- posydon/grids/scrubbing.py | 1 - posydon/grids/termination_flags.py | 7 +- posydon/interpolation/IF_interpolation.py | 315 ++++++++++++------ posydon/popsyn/binarypopulation.py | 6 +- posydon/utils/common_functions.py | 48 ++- posydon/visualization/combine_TF.py | 141 ++++---- posydon/visualization/interpolation.py | 26 +- posydon/visualization/plot1D.py | 12 +- posydon/visualization/plot2D.py | 57 ++-- posydon/visualization/plot_defaults.py | 140 +++++++- 29 files changed, 1167 insertions(+), 437 deletions(-) rename bin/{run-pipeline => posydon-run-pipeline} (89%) rename bin/{setup-pipeline => posydon-setup-pipeline} (77%) diff --git a/bin/run-pipeline b/bin/posydon-run-pipeline similarity index 89% rename from bin/run-pipeline rename to bin/posydon-run-pipeline index fd1ea2cf9a..ea6d7025b3 100644 --- a/bin/run-pipeline +++ b/bin/posydon-run-pipeline @@ -123,10 +123,17 @@ def create_grid_slice(i, path_to_csv_file, verbose=False, overwrite_psygrid=True compression = df.loc[i,'compression'] else: raise ValueError(f'No compression in {path_to_csv_file}') + if 'grid_type' in df.keys(): + grid_type = df.loc[i,'grid_type'] + else: + raise ValueError(f'No grid_type in {path_to_csv_file}') if 'stop_before_carbon_depletion' in df.keys(): stop_before_carbon_depletion = df.loc[i,'stop_before_carbon_depletion'] - if 'HMS' not in grid_path: + if 'HMS' not in grid_type: stop_before_carbon_depletion = False + if verbose: + print('stop_before_carbon_depletion set to False for non HMS'\ + f' gird: {grid_type} in {grid_path}') else: stop_before_carbon_depletion = False @@ -145,7 +152,7 @@ def create_grid_slice(i, path_to_csv_file, verbose=False, overwrite_psygrid=True raise ValueError(f'pure_compression = {pure_compression} not ' f'supported! (compression={compression})') - if ('CO' in grid_path) and ('RLO' in compression): + if ('CO' in grid_type) and ('RLO' in compression): start_at_RLO = True else: start_at_RLO = False @@ -209,6 +216,10 @@ def calculate_extra_values(i, path_to_csv_file, verbose=False): raise ValueError(f'{grid_path} not found!') else: raise ValueError(f'No path_to_grid in {path_to_csv_file}') + if 'grid_type' in df.keys(): + grid_type = df.loc[i,'grid_type'] + else: + raise ValueError(f'No grid_type in {path_to_csv_file}') if 'path_to_processed_grid' in df.keys(): processed_grid_path = df.loc[i,'path_to_processed_grid'] else: @@ -222,7 +233,7 @@ def calculate_extra_values(i, path_to_csv_file, verbose=False): if verbose: print(f'Compute processed quantities for {grid_path} ...') - if 'CO' in grid_path: + if 'CO' in grid_type: star_2_CO = True else: star_2_CO = False @@ -265,12 +276,20 @@ def train_interpolators(i, path_to_csv_file, verbose=False): raise ValueError(f'{grid_path} not found!') else: raise ValueError(f'No path_to_grid in {path_to_csv_file}') + if 'grid_type' in df.keys(): + grid_type = df.loc[i,'grid_type'] + else: + raise ValueError(f'No grid_type in {path_to_csv_file}') + if 'interpolation_method' in df.keys(): + interpolation_method = df.loc[i,'interpolation_method'] + else: + raise ValueError(f'No interpolation_method in {path_to_csv_file}') if 'path_to_interpolator' in df.keys(): interpolator_path = df.loc[i,'path_to_interpolator'] else: raise ValueError(f'No path_to_interpolator in {path_to_csv_file}') - method = os.path.splitext(interpolator_path.split('IF_')[-1])[0] + method = interpolation_method if '_RLO' in method: method = method.split('_RLO')[0] @@ -286,12 +305,14 @@ def train_interpolators(i, path_to_csv_file, verbose=False): grid = PSyGrid(verbose=verbose) grid.load(grid_path) - if '_RLO' in grid_path: - interp_method = [method, method] - interp_classes = ["stable_MT", "unstable_MT"] - else: - interp_method = [method, method, method] - interp_classes = ["no_MT", "stable_MT", "unstable_MT"] + interp_method = [method, method] + interp_classes = ["stable_MT", "unstable_MT"] + if '_RLO' not in interpolation_method: + interp_method += [method] + interp_classes += ["no_MT"] + if 'CO' not in grid_type: + interp_method += [method] + interp_classes += ["stable_reverse_MT"] # all magnitues that are not model numbers, supernova properites or strings # are interpolated with respect to interpolation_class @@ -335,10 +356,10 @@ def train_interpolators(i, path_to_csv_file, verbose=False): # get interpolations classes dynamically interp_method = [] interp_classes = [] - for CO_type in ["BH", "NS", "WD"]: - if CO_type in grid.final_values[f'S{i}_{MODEL_NAME}_CO_type']: + for CO_interpolation_class in ["BH", "NS", "WD", "BH_reverse_MT"]: + if CO_interpolation_class in grid.final_values[f'S{i}_{MODEL_NAME}_CO_interpolation_class']: interp_method.append(method) - interp_classes.append(CO_type) + interp_classes.append(CO_interpolation_class) interpolators.append( { @@ -346,8 +367,8 @@ def train_interpolators(i, path_to_csv_file, verbose=False): "interp_classes": interp_classes, "out_keys": out_keys, "class_method": "kNN", - "c_keys": [f'S{i}_{MODEL_NAME}_CO_type'], - "c_key": f'S{i}_{MODEL_NAME}_CO_type' + "c_keys": [f'S{i}_{MODEL_NAME}_CO_interpolation_class'], + "c_key": f'S{i}_{MODEL_NAME}_CO_interpolation_class' }, ) @@ -386,54 +407,55 @@ def export_dataset(i, path_to_csv_file, verbose=False): shutil.copyfile(grid_path, export_path) -def zams_file_name(dirname): +def zams_file_name(metallicity): """Gives the name of the ZAMS file depending on the metallicity.""" - if '2e+00_Zsun' in dirname: + if '2e+00_Zsun' in metallicity: zams_filename = 'zams_z2.84m2_y0.2915.data' - elif '1e+00_Zsun' in dirname: + elif (('1e+00_Zsun' in metallicity)or(pd.isna(metallicity))): + # in v1 there is no metallicity, hence am empty string zams_filename = 'zams_z1.42m2_y0.2703.data' - elif '4.5e-01_Zsun' in dirname: + elif '4.5e-01_Zsun' in metallicity: zams_filename = 'zams_z6.39m3_y0.2586.data' - elif '2e-01_Zsun' in dirname: + elif '2e-01_Zsun' in metallicity: zams_filename = 'zams_z2.84m3_y0.2533.data' - elif '1e-01_Zsun' in dirname: + elif '1e-01_Zsun' in metallicity: zams_filename = 'zams_z1.42m3_y0.2511.data' - elif '1e-02_Zsun' in dirname: + elif '1e-02_Zsun' in metallicity: zams_filename = 'zams_z1.42m4_y0.2492.data' - elif '1e-03_Zsun' in dirname: + elif '1e-03_Zsun' in metallicity: zams_filename = 'zams_z1.42m5_y0.2490.data' - elif '1e-04_Zsun' in dirname: + elif '1e-04_Zsun' in metallicity: zams_filename = 'zams_z1.42m6_y0.2490.data' else: raise ValueError(f'Metallicity unknown: {dirname}') return zams_filename -def copy_ini_file(grid_path, rerun_type, destination, cluster): +def copy_ini_file(grid_type, rerun_metallicity, rerun_type, destination, cluster): """Copies the ini file and make replacements according to the rerun.""" # copy the default ini file to directory if cluster in ['quest','yggdrasil']: ini_dir_path = os.path.join(PATH_TO_POSYDON, 'grid_params/POSYDON-MESA-INLISTS/r11701/running_scripts') - if "single_HMS" in grid_path: + if "single_HMS" in grid_type: ini_file_path = os.path.join(ini_dir_path, f'single_HMS_{cluster}.ini') ini_file_dest = os.path.join(destination, f'single_HMS_{cluster}.ini') - elif "single_HeMS" in grid_path: + elif "single_HeMS" in grid_type: ini_file_path = os.path.join(ini_dir_path, f'single_HeMS_{cluster}.ini') ini_file_dest = os.path.join(destination, f'single_HeMS_{cluster}.ini') - elif "HMS-HMS" in grid_path: + elif "HMS-HMS" in grid_type: ini_file_path = os.path.join(ini_dir_path, f'HMS-HMS_{cluster}.ini') ini_file_dest = os.path.join(destination, f'HMS-HMS_{cluster}.ini') - elif "CO-HMS_RLO" in grid_path: + elif "CO-HMS" in grid_type: ini_file_path = os.path.join(ini_dir_path, f'CO-HMS_RLO_{cluster}.ini') ini_file_dest = os.path.join(destination, f'CO-HMS_RLO_{cluster}.ini') - elif "CO-HeMS" in grid_path: + elif "CO-HeMS" in grid_type: ini_file_path = os.path.join(ini_dir_path, f'CO-HeMS_{cluster}.ini') ini_file_dest = os.path.join(destination, f'CO-HeMS_{cluster}.ini') else: - raise ValueError(f'Unsupported grid type in {grid_path}!') + raise ValueError(f'Unsupported grid type: {grid_type}!') shutil.copyfile(ini_file_path, ini_file_dest) else: raise ValueError(f'Unsupported cluster {cluster}!') @@ -469,7 +491,7 @@ def copy_ini_file(grid_path, rerun_type, destination, cluster): search_text1 = "main-18710e943edd926a5653c4cdb7d6e18e5bdb35a2" search_text2 = "main-a060b7bf1a4d94d693f77be9a1b0b3a522c1eadf" search_text3 = 'zams_z1.42m3_y0.2511.data' - replace_zams_filename = zams_file_name(grid_path) + replace_zams_filename = zams_file_name(rerun_metallicity) with open(ini_file_dest, 'r') as file: data = file.read() data = data.replace(search_text1, replace_text) @@ -627,6 +649,14 @@ def rerun(i, path_to_csv_file, verbose=False): rerun_path = df.loc[i,'rerun_path'] else: raise ValueError(f'No rerun_path in {path_to_csv_file}') + if 'grid_type' in df.keys(): + grid_type = df.loc[i,'grid_type'] + else: + raise ValueError(f'No grid_type in {path_to_csv_file}') + if 'rerun_metallicity' in df.keys(): + rerun_metallicity = df.loc[i,'rerun_metallicity'] + else: + raise ValueError(f'No rerun_metallicity in {path_to_csv_file}') if 'rerun_type' in df.keys(): rerun_type = df.loc[i,'rerun_type'] else: @@ -653,33 +683,45 @@ def rerun(i, path_to_csv_file, verbose=False): cluster = 'quest' else: cluster = 'yggdrasil' - copy_ini_file(grid_path, rerun_type, destination=rerun_path, cluster=cluster) + copy_ini_file(grid_type, rerun_metallicity, rerun_type, destination=rerun_path, cluster=cluster) def plot_grid(i, path_to_csv_file, verbose=False): """Creates plots of a grid.""" - # grid mass ranges + # grid mass ranges: + # min and max values might get updated, than the step is used to update the + # nbin (in case of log==True the step is in log space) grid_sample = { 'HMS-HMS' : { 'log' : False, 'nbin' : 20, 'qmin' : 0.05, 'qmax' : 1., + 'step' : 0.05, }, 'CO-HeMS' : { 'log' : True, 'nbin' : 23, 'mmin' : 1.0, - 'mmax' : 51.328, + 'mmax' : 51.32759630397827, + 'step' : 0.0777432239326138, }, 'CO-HMS' : { 'log' : True, 'nbin' : 23, 'mmin' : 1.0, - 'mmax' : 51.328, + 'mmax' : 51.32759630397827, + 'step' : 0.0777432239326138, } } + def _nbin(x_min, x_max, step, log=False): + if log: + nbin = (np.log10(x_max)-np.log10(x_min))/step + else: + nbin = (x_max-x_min)/step + return int(nbin)+1 + def _range(x_min, x_max, x_n, log=False): if log: vars = 10**np.linspace(np.log10(x_min),np.log10(x_max), x_n) @@ -704,6 +746,10 @@ def plot_grid(i, path_to_csv_file, verbose=False): raise ValueError(f'{grid_path} not found!') else: raise ValueError(f'No path_to_grid in {path_to_csv_file}') + if 'grid_type' in df.keys(): + grid_type = df.loc[i,'grid_type'] + else: + raise ValueError(f'No grid_type in {path_to_csv_file}') if 'path_to_plot' in df.keys(): plot_dir = df.loc[i,'path_to_plot'] plot_path = os.path.dirname(os.path.normpath(plot_dir)) @@ -800,30 +846,40 @@ def plot_grid(i, path_to_csv_file, verbose=False): if not os.path.isdir(dir_): os.makedirs(dir_) - if ('CO-' in grid_path): + if ('CO' in grid_type): # skip plotting CO properties if 'S2_' in quantity_to_plot: continue # both grids are sampled in the same way with respect to the # compact object mass - if 'CO-HeMS' in grid_path: + if 'CO-HeMS' in grid_type: smpl = grid_sample['CO-HeMS'] else: smpl = grid_sample['CO-HMS'] + slice_3D_var_str = 'star_2_mass' + min_3D_var = np.min(grid.initial_values[slice_3D_var_str]) + max_3D_var = np.max(grid.initial_values[slice_3D_var_str]) + if smpl['mmin']>min_3D_var: + smpl['mmin'] = min_3D_var + smpl['nbin'] = _nbin(smpl['mmin'], smpl['mmax'], smpl['step'], + log=smpl['log']) + if smpl['mmax']0): + ORIGINAL_COMPRESSIONS_ = ORIGINAL_COMPRESSIONS[l] # loop over all metallicities, grid slices and compressions for metallicity in METALLICITIES_: for i, grid_slice in enumerate(GRID_SLICES_): @@ -623,7 +655,15 @@ def create_csv(GRID_TYPES=[], VERSION, metallicity, grid_slice)) grids_compression.append(compression) + grids_type.append(grid_type) stop_before_carbon_depletion.append(STOP_BEFORE_CARBON_DEPLETION) + if i==0: + compression_dir = os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, + metallicity, + compression) + if not os.path.isdir(compression_dir): + newly_created_files.append(compression_dir) elif step_name == 'step_2': # COMBINE_GRID_SLICES if len(GRID_SLICES[l]) != len(GRIDS_COMBINED[l]): raise ValueError(f'len(GRID_SLICES[{l}]) != ' @@ -644,12 +684,22 @@ def create_csv(GRID_TYPES=[], df_tmp[path_to_grid_combined] = combine_grid_slices df = pd.concat([df,df_tmp], axis=1) elif step_name == 'step_3': # CALCULATE_EXTRA_VALUES + if (isinstance(ORIGINAL_COMPRESSIONS_, list) and\ + len(ORIGINAL_COMPRESSIONS_)>0): + # use one orginal for all compressions, hence use + # first one and ignore others + original_compression = ORIGINAL_COMPRESSIONS_[0] + else: + # if not defined assume 'ORIGINAL' + original_compression = 'ORIGINAL' grids.append(os.path.join(PATH_TO_GRIDS, grid_type, VERSION, metallicity, compression, grid_slice+'.h5')) + grids_type.append(grid_type) grids_ORIGINAL.append(os.path.join(PATH_TO_GRIDS, grid_type, VERSION, - metallicity, 'ORIGINAL'+RLO, + metallicity, + original_compression+RLO, grid_slice+'.h5')) processed_grids.append(os.path.join(PATH_TO_GRIDS, grid_type, VERSION, @@ -660,11 +710,20 @@ def create_csv(GRID_TYPES=[], grids.append(os.path.join(PATH_TO_GRIDS, grid_type, VERSION, metallicity, compression, grid_slice+'.h5')) + grids_type.append(grid_type) + interpolation_methods.append(method+RLO) interpolators.append(os.path.join(PATH_TO_GRIDS, grid_type, VERSION, metallicity, 'interpolation_objects', 'IF_'+method+RLO+'.pkl')) + if i==0: + interpolator_dir = os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, + metallicity, + 'interpolation_objects') + if not os.path.isdir(interpolator_dir): + newly_created_files.append(interpolator_dir) elif step_name == 'step_9': # EXPORT_DATASET if "RLO" in grid_type: export_dir = grid_type @@ -692,6 +751,8 @@ def create_csv(GRID_TYPES=[], rerun_path.append(os.path.join(PATH_TO_GRIDS, grid_type, VERSION, metallicity, 'rerun_'+RERUN_TYPE+'_'+grid_slice)) + grids_type.append(grid_type) + rerun_metallicities.append(metallicity) rerun_type.append(RERUN_TYPE) if (('plot' in step_name) or ('check' in step_name)): @@ -730,6 +791,7 @@ def create_csv(GRID_TYPES=[], grid_type, VERSION, metallicity, compression, grid_s+'.h5')) + grids_type.append(grid_type) plot_dirs.append(os.path.join(PATH_TO_GRIDS, grid_type, VERSION, metallicity, 'plots', @@ -741,6 +803,12 @@ def create_csv(GRID_TYPES=[], metallicity, 'interpolation_objects', 'IF_'+method+RLO+'.pkl')) + if i==0: + plots_dir = os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, + metallicity, 'plots') + if not os.path.isdir(plots_dir): + newly_created_files.append(plots_dir) elif 'check' in step_name: # CHECKS # loop over interpolation methods and checks for method in INTERPOLATION_METHODS: @@ -766,18 +834,25 @@ def create_csv(GRID_TYPES=[], df['path_to_grid'] = grids if step_name == 'step_1': df['compression'] = grids_compression + df['grid_type'] = grids_type df['stop_before_carbon_depletion'] = stop_before_carbon_depletion elif step_name == 'step_3': + df['grid_type'] = grids_type df['path_to_grid_ORIGINAL'] = grids_ORIGINAL df['path_to_processed_grid'] = processed_grids elif step_name == 'step_4': + df['grid_type'] = grids_type + df['interpolation_method'] = interpolation_methods df['path_to_interpolator'] = interpolators elif step_name == 'step_9': df['export_path'] = export_path elif step_name == 'rerun': df['rerun_path'] = rerun_path + df['grid_type'] = grids_type + df['rerun_metallicity'] = rerun_metallicities df['rerun_type'] = rerun_type elif 'plot' in step_name: + df['grid_type'] = grids_type df['quantities_to_plot'] = plot_quantities df['path_to_plot'] = plot_dirs if len(interpolators)>0: @@ -828,13 +903,15 @@ def create_csv(GRID_TYPES=[], compression = df.at[row,'compression'] newly_created_files.append(get_new_grid_name(path,compression)) elif step_name == 'step_3': - newly_created_files = df['path_to_processed_grid'].to_list() + newly_created_files.extend(df['path_to_processed_grid'].to_list()) elif step_name == 'step_4': - newly_created_files = df['path_to_interpolator'].to_list() + newly_created_files.extend(df['path_to_interpolator'].to_list()) elif step_name == 'step_9': - newly_created_files = df['export_path'].to_list() + newly_created_files.extend(df['export_path'].to_list()) elif step_name == 'rerun': - newly_created_files = df['rerun_path'].to_list() + newly_created_files.extend(df['rerun_path'].to_list()) + elif 'plot' in step_name: + newly_created_files.extend(plot_dirs) else: # step 2 # drop columns when psygrid files are not found if DROP_MISSING_FILES: @@ -880,13 +957,124 @@ def create_csv(GRID_TYPES=[], print(column) print('') df = df.dropna(axis=0, how='all').dropna(axis=1, how='all') - newly_created_files = df.keys().to_list() + newly_created_files.extend(df.keys().to_list()) # finally save data frame output_fname = f'{step_name}.csv' df.to_csv(os.path.join(PATH, output_fname), index=False) # return list of new files created by that step return newly_created_files +def create_cleanup_slurm_job(job_name, + step_name, + PATH='.', + ACCOUNT=None, + PARTITION=None, + WALLTIME=None, + MAILTYPE=None, + EMAIL=None, + GROUP=None, + VERBOSE=False, + **kwargs): + """Create slurm file for cleanup job.""" + if ((job_name == 'step_2') or (job_name == 'step_9') or + (job_name == 'rerun') or ('plot' in job_name) or + ('check' in job_name) or + ((job_name == 'pipeline') and (GROUP is not None))): + with open(f'{job_name}_cleanup.slurm', 'w') as f: + # write slurm file content + f.write("#!/bin/bash\n") + if ACCOUNT is not None: + f.write(f"#SBATCH --account={ACCOUNT}\n") + if PARTITION is not None: + f.write(f"#SBATCH --partition={PARTITION}\n") + f.write("#SBATCH -N 1\n") + f.write("#SBATCH --cpus-per-task 1\n") + f.write("#SBATCH --ntasks-per-node 1\n") + if WALLTIME is not None: + f.write(f"#SBATCH --time={WALLTIME}\n") + f.write(f"#SBATCH --job-name=cleanup\n") + f.write("#SBATCH --mem-per-cpu=4G\n") + if (EMAIL is not None): + f.write(f"#SBATCH --mail-type=FAIL\n") + f.write(f"#SBATCH --mail-user={EMAIL}\n") + path_to_logs = os.path.join(PATH, 'logs', 'cleanup') + log_file_name = get_log_file_name(job_name="cleanup",\ + step_name=job_name) + f.write("#SBATCH --open-mode=truncate\n") + f.write(f"#SBATCH --output={path_to_logs}/{log_file_name}\n") + # get logs path and set the output of the slurm job + if step_name in job_name: + path_to_log_files = os.path.join(PATH, 'logs', step_name) + else: + path_to_log_files = os.path.join(PATH, 'logs', job_name) + if ((job_name == 'step_2') or (job_name == 'step_9') or + (job_name == 'rerun') or ('plot' in job_name) or + ('check' in job_name)): + # combine logs + old_log_file_names = get_log_file_name(job_name=job_name,\ + step_name=step_name) + new_log_file_name = old_log_file_names.replace("_%4a", "s") + old_log_file_names = old_log_file_names.replace("_%4a", "_*") + f.write(f"\ncat {path_to_log_files}/{old_log_file_names} >" + f" {path_to_log_files}/{new_log_file_name}") + f.write(f"\nrm {path_to_log_files}/{old_log_file_names}") + if (job_name == 'pipeline'): + if GROUP is not None: + f.write(f"\necho \"Change group to {GROUP} and group " + "permission to rwX at least for all files stated " + f"in {PATH}/pipeline_files.csv\"") + f.write(f"\nfor FILE in $(cat {PATH}/pipeline_files.csv); do") + f.write(f"\n chgrp -fR {GROUP} $""{FILE};") + f.write("\n chmod -fR g+rwX ${FILE};") + f.write("\ndone") + f.write(f"\necho \"Acting on {PATH} and all subdirectories" + "/files in there\"") + f.write(f"\necho \"Change group to {GROUP}\"") + f.write(f"\nchgrp -fR {GROUP} {PATH}") + f.write("\necho \"Change group permission to rwX at least\"") + f.write(f"\nchmod -fR g+rwX {PATH}") + f.write(f"\necho \"Done, check {PATH}:\"") + f.write(f"\nls -al {PATH}") + else: + return False + return True + +def get_log_file_name(job_name, step_name): + """Get name of log file + + Parameters + ---------- + job_name : string + Name of the job. + step_name : string + Name of the step/parent job the job belongs to. + + Returns + ------- + string + Name of the log file. + + """ + if job_name == 'step_1': # CREATE_GRID_SLICES + return "grid_slice_%4a.out" + if job_name == 'step_2': # COMBINE_GRID_SLICES + return "combine_grid_slice_%4a.out" + if job_name == 'step_3': # CALCULATE_EXTRA_VALUES + return "post_processing_%4a.out" + if job_name == 'step_4': # TRAIN_INTERPOLATORS + return "train_interpolator_%4a.out" + if job_name == 'step_9': # EXPORT_DATASET + return "export_dataset_%4a.out" + if job_name == 'rerun': # RERUN + return "rerun_%4a.out" + if 'plot' in job_name: # PLOTS + return "plots_"+step_name+"_slice_%4a.out" + if 'check' in job_name: # CHECKS + return "checks_"+step_name+"_slice_%4a.out" + if 'cleanup' in job_name: # CLEANUPS + return "cleanup_"+step_name+".out" + return "log.out" + if __name__ == '__main__': diff --git a/docs/_source/components-overview/post_processing/pipeline/pipeline_additions.rst b/docs/_source/components-overview/post_processing/pipeline/pipeline_additions.rst index be86670f50..b86ba88b7e 100644 --- a/docs/_source/components-overview/post_processing/pipeline/pipeline_additions.rst +++ b/docs/_source/components-overview/post_processing/pipeline/pipeline_additions.rst @@ -20,13 +20,14 @@ structure: .. code-block:: - path_to_grid,quantities_to_plot,path_to_plot - -Beside the grid, it takes a list of quantities to plot and finally the path to -the directory, where the plots should get stored. All final quantities -supported for a :ref:`2D plot `, as third dimension can be specified. -Additionally, you can put a :samp:`LOG10_` in front of each of them to switch -on plotting in log-scale. Beside that there are predefined plots. + path_to_grid,grid_type,quantities_to_plot,path_to_plot + +Beside the grid, it states the type and takes a list of quantities to plot. +Finally, the path to the directory, where the plots should get stored. All +final quantities supported for a :ref:`2D plot `, as third dimension +can be specified. Additionally, you can put a :samp:`LOG10_` in front of each +of them to switch on plotting in log-scale. Beside that there are predefined +plots. .. table:: Basic predefined plots diff --git a/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst b/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst index 179fddb876..376f652bb0 100644 --- a/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst +++ b/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst @@ -8,16 +8,16 @@ The pipeline is divided into several steps, which build up on each other. Each step will take a csv file as input. The name of this file is used to tell the pipeline, which step should be performed. -The run-pipeline script takes four arguments: +The script to run the pipeline takes four arguments: .. code-block:: bash - run-pipeline PATH_TO_GRIDS PATH_TO_CSV_FILE DATA_ID VERBOSE + posydon-run-pipeline PATH_TO_GRIDS PATH_TO_CSV_FILE DATA_ID VERBOSE 1. [path] The path to the girds main directory (currently not used) 2. [path] The path to the csv file 3. [int] An index indicating the data entry to read from the csv file -4. [int] Where one wants verbose output (1) or not (0) +4. [int] Whether one wants verbose output (1) or not (0) .. note:: The current directory will be used as working directory, hence navigate to @@ -29,13 +29,13 @@ Step1: creating a `PSyGrid` object ---------------------------------- First, we need to create the :samp:`PSyGird` object. To do so, the pipeline -needs to now the directory which contains the MESA runs, the compression, and -whether to crop the history for some certain runs. Hence, the +needs to now the directory which contains the MESA runs, the compression, the +grid type, and whether to crop the history for some certain runs. Hence, the :samp:`step_1.csv` file should have those columns: .. code-block:: - path_to_grid,compression,stop_before_carbon_depletion + path_to_grid,compression,grid_type,stop_before_carbon_depletion And the lines below contain the data for each unique combination of the three parameters to be processed. Here the :samp:`DATA_ID` simply refers to the line @@ -91,11 +91,12 @@ envelope evolution, and at core collapse. Because some of the values may require a high precision in the data, we recommend to use the data from the ORIGINAL compression to calculate them. But the new values can be added to any :samp:`PSyGrid` object. Hence this step -requests three paths to be specified in :samp:`step_3.csv`: +requests three paths to be specified in :samp:`step_3.csv` beside the gird +type: .. code-block:: - path_to_grid,path_to_grid_ORIGINAL,path_to_processed_grid + path_to_grid,grid_type,path_to_grid_ORIGINAL,path_to_processed_grid .. table:: Description of required paths @@ -103,6 +104,7 @@ requests three paths to be specified in :samp:`step_3.csv`: Path Description ====================== =========== path_to_grid path of the gird, which get the values appended to it + grid_type type of the grid path_to_grid_ORIGINAL path of the grid, where the values are calculated from path_to_processed_grid path of the new grid (a copy of the one specified as :samp:`path_to_grid` with the appended values) ====================== =========== @@ -123,12 +125,13 @@ Step4: training of the interpolators To get interpolated data from our grids, we train in this step an interpolator on your :samp:`PSyGrid` object. The file :samp:`step_4.csv` therefore has to -contain two information bits. First, the grid containing the data and second, -the name of the interpolator object. +contain three information bits: First, the grid containing the data, second, +the grid type, third, the interpolation method (inlcuding whether the grid +starts at RLO), and finally, the name of the interpolator object. .. code-block:: - path_to_grid,path_to_interpolator + path_to_grid,interpolation_method,path_to_interpolator .. note:: The type of interpolator will be recognized from the name of the @@ -181,12 +184,12 @@ confusion with other steps. It clearly has to run after a step, but it is no usual step itself. It requires a path to a :samp:`PSyGrid` object to get the models from, a path, where the rerun should be stored (it creates in there the :samp:`grid.csv` and the ini file needed to -:ref:`setup a new run `) and the type of the rerun specifying -the logic and changes. +:ref:`setup a new run `), the grid type, the metallicity, and +the type of the rerun specifying the logic and changes. .. code-block:: - path_to_grid,rerun_path,rerun_type + path_to_grid,rerun_path,grid_type,rerun_metallicity,rerun_type .. table:: Currently supported rerun types @@ -198,5 +201,8 @@ the logic and changes. opacity_max caution it uses a fixed maximum opacity of 0.5 (this is only a last option change to get more stability) TPAGBwind default in v3+ it enables the MESA inlist commit, which changes the wind during the TPAGB phase thermohaline_mixing default in v3+ it uses thermohaline mixing in the inlist + HeMB_MLTp_mesh workaround it turns off magnetic braking for He stars; it uses less extreme parameters of the MLT++; it changes some more input values to change the resulation close to the surface + more_mesh workaround it modifies the remeshing and allows for more cells in MESA + conv_bdy_weight caution it disabled the convective_bdy_weight where this caused segmentation faults (this avoids a bug in the old MESA version r11701) =================== ============== =========== diff --git a/docs/_source/components-overview/post_processing/pipeline_ini.rst b/docs/_source/components-overview/post_processing/pipeline_ini.rst index 94fe6ce96e..ff1ed45f3b 100644 --- a/docs/_source/components-overview/post_processing/pipeline_ini.rst +++ b/docs/_source/components-overview/post_processing/pipeline_ini.rst @@ -10,11 +10,11 @@ Aim of the ini file Using an ini file should help to keep an overview on large grid repositories and ensures that all will be setup the same way. -There is a setup-pipeline script takes one argument: +There is a script to setup the pipeline, it takes one argument: .. code-block:: bash - setup-pipeline PATH_TO_INI + posydon-setup-pipeline PATH_TO_INI The content of the ini file is described :ref:`below `. It will create for each step, plot or check two files: @@ -56,6 +56,12 @@ example to show the supported key words: WALLTIME = '23:00:00' MAILTYPE = 'ALL' EMAIL = 'matthias.kruckow@unige.ch' + GROUP = 'GL_S_Astro_POSYDON' + +The last one :samp:`GROUP` is a bit special. If it is set, all the files +created by the pipeline will get this owning group on the file system with read +and write access for this group. This is especially helpful if more than one +user is making changes to the data set. General pipeline settings ------------------------- @@ -104,9 +110,9 @@ All sections have common keywords: METALLICITIES a list of lists of the metallicities of the grids; the looped :samp:`METALLICITY` is used in the path name; the outer list allows you to have different lists for each grid type GRID_SLICES a list of lists of the grid slices; the looped :samp:`GRID_SLICE` is used in the path name; the outer list allows you to have different lists for each grid type COMPRESSIONS a list of lists of compression types - DROP_MISSING_FILES boolean to ignore missing files - CREATE_PLOTS a list of plots to make; this will be done independently whether the step is active or not, to make no plots put there an empty list or comment out such a line - DO_CHECKS a list of checks to perform; this will be done independently whether the step is active or not, to make no checks put there an empty list or comment out such a line + DROP_MISSING_FILES boolean to ignore missing files; it allows to have different substructures and taking the existing ones into account when specifying union of all possible substructures + CREATE_PLOTS a list of plots to make; this will be done independently whether the step is active or not; to make no plots put there an empty list or comment out such a line + DO_CHECKS a list of checks to perform; this will be done independently whether the step is active or not; to make no checks put there an empty list or comment out such a line ================== =========== Some :ref:`steps ` have more keywords, which are specific to @@ -120,6 +126,7 @@ that step: 1 STOP_BEFORE_CARBON_DEPLETION indicating, whether high mass HMS stars should get their history croped short before carbon depletion (1) or not (0) 2 GRID_SLICES for this step, we have 3 layers of lists: the outermost is still the grid type, the inner most is still the grid slice, the middle layer is the combined grid 2 GRIDS_COMBINED a list of lists of combined grids; the outermost list is again referring to grid type; this is used as name for the new combined grid instead of :samp:`GRID_SLICE` + 3 ORIGINAL_COMPRESSIONS a list of lists of the ORIGINAL compression to calculate the extra values from (the first one is used for all compressions for that grid type) 4 INTERPOLATION_METHODS a list of the interpolator types which are trained 4 CONTROL_GRIDS a list of lists of control grids for the :samp:`GRID_SLICES`; it need to have the same number of entries as the :samp:`GRID_SLICES`, to specify no control grid use an empty string R RERUN_TYPE a defined rerun type @@ -246,6 +253,13 @@ Here is an example of all the :ref:`steps `: # HMS-HMS ['LITE'] ] + ORIGINAL_COMPRESSIONS = [# CO-HMS_RLO + ['ORIGINAL'], + # CO-HeMS + ['ORIGINAL'], + # HMS-HMS + ['ORIGINAL'] + ] DROP_MISSING_FILES = True # supported plots: e.g. 'combined_TF12', 'termination_flag_1', 'termination_flag_2', 'termination_flag_3', 'termination_flag_4', and any quantity valid for a Z-plotting CREATE_PLOTS = ['PLOT_AFTER_EXTRA'] diff --git a/docs/_source/tutorials-examples/generating-datasets/just_step_1.ipynb b/docs/_source/tutorials-examples/generating-datasets/just_step_1.ipynb index 047f9528b7..4ab8eb3449 100644 --- a/docs/_source/tutorials-examples/generating-datasets/just_step_1.ipynb +++ b/docs/_source/tutorials-examples/generating-datasets/just_step_1.ipynb @@ -152,7 +152,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We are now rady to setup the grid pipeline with the `setup-pipeline` command, as follows:" + "We are now rady to setup the grid pipeline with the `posydon-setup-pipeline` command, as follows:" ] }, { @@ -164,7 +164,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "/home/bavera/.conda/envs/posydon_env/bin/setup-pipeline:4: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html\n", + "/home/bavera/.conda/envs/posydon_env/bin/posydon-setup-pipeline:4: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html\n", " __import__('pkg_resources').require('posydon==1.0.0+194.g3953a14')\n", "\n", "+++++++++++++++++++ACCOUNT+++++++++++++++++++ \n", @@ -215,7 +215,7 @@ } ], "source": [ - "!setup-pipeline pipeline.ini" + "!posydon-setup-pipeline pipeline.ini" ] }, { diff --git a/docs/_source/tutorials-examples/generating-datasets/run_full_piepeline.ipynb b/docs/_source/tutorials-examples/generating-datasets/run_full_piepeline.ipynb index f66fca8685..e872281b42 100644 --- a/docs/_source/tutorials-examples/generating-datasets/run_full_piepeline.ipynb +++ b/docs/_source/tutorials-examples/generating-datasets/run_full_piepeline.ipynb @@ -233,7 +233,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "/home/bavera/.conda/envs/posydon_env/bin/setup-pipeline:4: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html\n", + "/home/bavera/.conda/envs/posydon_env/bin/posydon-setup-pipeline:4: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html\n", " __import__('pkg_resources').require('posydon==1.0.0+194.g3953a14')\n", "\n", "+++++++++++++++++++ACCOUNT+++++++++++++++++++ \n", @@ -412,7 +412,7 @@ } ], "source": [ - "!setup-pipeline pipeline.ini" + "!posydon-setup-pipeline pipeline.ini" ] }, { diff --git a/docs/_source/tutorials-examples/generating-datasets/step_rerun.ipynb b/docs/_source/tutorials-examples/generating-datasets/step_rerun.ipynb index 8241e7053a..8355f812e5 100644 --- a/docs/_source/tutorials-examples/generating-datasets/step_rerun.ipynb +++ b/docs/_source/tutorials-examples/generating-datasets/step_rerun.ipynb @@ -171,7 +171,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We are now rady to setup the grid pipeline with the `setup-pipeline` command, as follows:" + "We are now rady to setup the grid pipeline with the `posydon-setup-pipeline` command, as follows:" ] }, { @@ -183,7 +183,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "/home/bavera/.conda/envs/posydon_env/bin/setup-pipeline:4: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html\n", + "/home/bavera/.conda/envs/posydon_env/bin/posydon-setup-pipeline:4: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html\n", " __import__('pkg_resources').require('posydon==1.0.0+194.g3953a14')\n", "\n", "+++++++++++++++++++ACCOUNT+++++++++++++++++++ \n", @@ -232,7 +232,7 @@ } ], "source": [ - "!setup-pipeline pipeline.ini" + "!posydon-setup-pipeline pipeline.ini" ] }, { diff --git a/grid_params/pipeline_quest.ini b/grid_params/pipeline_quest.ini index 40c0755daf..74a9c64797 100644 --- a/grid_params/pipeline_quest.ini +++ b/grid_params/pipeline_quest.ini @@ -5,6 +5,7 @@ WALLTIME = '24:00:00' MAILTYPE = 'ALL' EMAIL = 'simone.bavera@unige.ch' + GROUP = 'b1119' [pipeline setup] PATH_TO_GRIDS = '/projects/b1119/POSYDON_GRIDS/' @@ -74,6 +75,7 @@ #'grid_low_res_combined_rerun_1' ]] COMPRESSIONS = [['LITE']] + ORIGINAL_COMPRESSIONS = [['ORIGINAL']] DROP_MISSING_FILES = True # supported plots: e.g. 'combined_TF12', 'termination_flag_1', 'termination_flag_2', 'termination_flag_3', 'termination_flag_4', and any quantity valid for a Z-plotting CREATE_PLOTS = ['PLOT_AFTER_EXTRA'] diff --git a/grid_params/pipeline_yggdrasil.ini b/grid_params/pipeline_yggdrasil.ini index 9a6de51edc..15e5089d81 100644 --- a/grid_params/pipeline_yggdrasil.ini +++ b/grid_params/pipeline_yggdrasil.ini @@ -1,13 +1,14 @@ [account] - ACCOUNT = 'meynet' + ACCOUNT = 'fragkos' PARTITION = 'public-cpu' WALLTIME = '24:00:00' MAILTYPE = 'ALL' EMAIL = 'simone.bavera@unige.ch' + GROUP = 'GL_S_Astro_POSYDON' [pipeline setup] - PATH_TO_GRIDS = '/srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/' + PATH_TO_GRIDS = '/srv/astro/projects/posydon/running_grids/POSYDON_GRIDS_v2/' VERSION = '' # 'v2' in quest and '' in yggdrasil PATH = '.' # working dir VERBOSE = True @@ -117,6 +118,13 @@ # HMS-HMS ['LITE'] ] + ORIGINAL_COMPRESSIONS = [# CO-HMS_RLO + ['ORIGINAL'], + # CO-HeMS + ['ORIGINAL'], + # HMS-HMS + ['ORIGINAL'] + ] DROP_MISSING_FILES = True # supported plots: e.g. 'combined_TF12', 'termination_flag_1', 'termination_flag_2', 'termination_flag_3', 'termination_flag_4', and any quantity valid for a Z-plotting CREATE_PLOTS = ['PLOT_AFTER_EXTRA'] diff --git a/posydon/_version.py b/posydon/_version.py index d2616cf0c5..030e6a8b1b 100644 --- a/posydon/_version.py +++ b/posydon/_version.py @@ -12,6 +12,7 @@ __authors__ = [ "Scott Coughlin ", + "Matthias Kruckow ", ] import errno @@ -302,8 +303,15 @@ def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command): pieces["distance"] = int(count_out) # total number of commits # commit date: see ISO-8601 comment in git_versions_from_keywords() - date = run_command(GITS, ["show", "-s", "--format=%ci", "HEAD"], - cwd=root)[0].strip() + date, rc = run_command(GITS, ["show", "-s", "--format=%ci", "HEAD"], + cwd=root) + if rc != 0: + if verbose: + print("Retry 'git show'") + date, rc = run_command(GITS, ["show", "-s", "--format=%ci", "HEAD"], + cwd=root) + if date is None: + raise NotThisMethod("'git show' failed") pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1) return pieces diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index 033746d9f7..b08de93b41 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -680,7 +680,9 @@ def update_properties_NN(self, star_1_CO=False, star_2_CO=False, setattr(binary, 'state', binary_state) setattr(binary, 'event', binary_event) setattr(binary, 'mass_transfer_case', MT_case) - + + culmulative_mt_case = self.termination_flags[1] + setattr(self.binary, f'culmulative_mt_case_{self.grid_type}', culmulative_mt_case) setattr(self.binary, f'interp_class_{self.grid_type}', interpolation_class) mt_history = self.termination_flags[2] # mass transfer history (TF12 plot label) setattr(self.binary, f'mt_history_{self.grid_type}', mt_history) @@ -898,7 +900,9 @@ def initial_final_interpolation(self, star_1_CO=False, star_2_CO=False): setattr(self.binary, f'interp_class_{self.grid_type}', interpolation_class) mt_history = self.classes['mt_history'] # mass transfer history (TF12 plot label) setattr(self.binary, f'mt_history_{self.grid_type}', mt_history) - + + #TODO: add classifier for tf2 + #setattr(self.binary, f'culmulative_mt_case', self.classes['termination_flags_2']) S1_state_inferred = cf.check_state_of_star(self.binary.star_1, star_CO=star_1_CO) S2_state_inferred = cf.check_state_of_star(self.binary.star_2, diff --git a/posydon/binary_evol/SN/profile_collapse.py b/posydon/binary_evol/SN/profile_collapse.py index 1c27d1f2df..83920a111b 100644 --- a/posydon/binary_evol/SN/profile_collapse.py +++ b/posydon/binary_evol/SN/profile_collapse.py @@ -15,6 +15,7 @@ "Emmanouil Zapartas ", "Scott Coughlin ", "Devina Misra ", + "Matthias Kruckow ", ] @@ -335,9 +336,10 @@ def do_core_collapse_BH(star, if len(enclosed_mass) == 0: arr = np.array([np.nan]) return [ - M_BH / Mo, a_BH, arr, arr, arr, arr, arr, arr, - arr, arr, arr, np.array([0.]), arr, arr, arr, arr, - arr, arr + M_BH / Mo, a_BH, np.nan, np.nan + #M_BH / Mo, a_BH, arr, arr, arr, arr, arr, arr, + #arr, arr, arr, np.array([0.]), arr, arr, arr, arr, + #arr, arr ] # shell's specific angular momentum at equator diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index e0abdb2f51..f18c9df79e 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -18,6 +18,7 @@ "Zepei Xing ", "Jeffrey Andrews ", "Tassos Fragos ", + "Matthias Kruckow ", ] __credits__ = [ @@ -83,6 +84,7 @@ "use_interp_values": True, "use_profiles": True, "use_core_masses": True, + "allow_spin_None" : False, "approx_at_he_depletion": False, # kick physics "kick": True, @@ -198,6 +200,11 @@ class StepSN(object): This option uses the core masses at carbon depletion to determine the core collapse outcoume (classical population sythesis threatment). + + allow_spin_None : bool + This option does not determine the spin during core collapse while + setting other values like in use_core_masses. (used to avoid jumps + in the spin for interpolator training because of missing profiles) approx_at_he_depletion : bool This option is relevant only for the mechanism Patton&Sukhbold20-engine. @@ -459,6 +466,10 @@ def collapse_star(self, star): This option uses the core masses at carbon depletion to determine the core collapse outcoume (classical population sythesis threatment). + 4. allow_spin_None : False + This option does not determine the spin during core collapse while + setting other values like in use_core_masses. (used to avoid jumps + in the spin for interpolator training because of missing profiles) Parameters ---------- @@ -477,6 +488,11 @@ def collapse_star(self, star): """ state = star.state + # after this function is called certain quantities shouldn't be None + # type objects anymore + for key in ['m_disk_accreted', 'm_disk_radiated']: + if getattr(star, key) is None: + setattr(star, key, np.nan) # Verifies if the star is in state state where it can # explode @@ -535,13 +551,17 @@ def collapse_star(self, star): else: setattr(star, key, 0.) elif key not in ["state", "mass", "spin", - "m_disk_accreted ", "m_disk_radiated","center_h1","center_he4","center_c12","center_n14","center_o16"]: + "m_disk_accreted", + "m_disk_radiated", "center_h1", + "center_he4", "center_c12", + "center_n14", "center_o16"]: setattr(star, key, None) else: for key in STARPROPERTIES: if key not in ["state", "mass", "spin", - "m_disk_accreted ", "m_disk_radiated"]: + "m_disk_accreted", + "m_disk_radiated"]: setattr(star, key, None) # check if SN_type matches the predicted CO @@ -625,8 +645,6 @@ def collapse_star(self, star): star.state = "WD" star.spin = 0. star.log_R = np.log10(CO_radius(star.mass, star.state)) - star.m_disk_accreted = np.nan - star.m_disk_radiated = np.nan for key in STARPROPERTIES: if key in ["he_core_mass"]: setattr(star, key, star.mass) @@ -636,7 +654,10 @@ def collapse_star(self, star): else: setattr(star, key, 0.) elif key not in ["state", "mass", "spin", - "m_disk_accreted ", "m_disk_radiated","center_h1","center_he4","center_c12","center_n14","center_o16"]: + "m_disk_accreted", "m_disk_radiated", + "center_h1", "center_he4", + "center_c12", "center_n14", + "center_o16"]: setattr(star, key, None) return @@ -706,6 +727,19 @@ def collapse_star(self, star): star.m_disk_accreted = 0.0 star.m_disk_radiated = 0.0 star.state = "NS" + + elif self.allow_spin_None: + # If the profile is not available and spin can stay + # undetermined + star.mass = m_grav + star.spin = None + star.m_disk_accreted = 0.0 + star.m_disk_radiated = 0.0 + if m_grav >= self.max_NS_mass: + star.state = "BH" + else: + star.state = "NS" + else: for key in STARPROPERTIES: setattr(star, key, None) @@ -731,8 +765,6 @@ def collapse_star(self, star): star.state = "WD" star.spin = 0. star.log_R = np.log10(CO_radius(star.mass, star.state)) - star.m_disk_accreted = np.nan - star.m_disk_radiated = np.nan for key in STARPROPERTIES: if key in ["he_core_mass"]: setattr(star, key, star.mass) @@ -742,7 +774,10 @@ def collapse_star(self, star): else: setattr(star, key, 0.) elif key not in ["state", "mass", "spin", - "m_disk_accreted ", "m_disk_radiated","center_h1","center_he4","center_c12","center_n14","center_o16"]: + "m_disk_accreted", "m_disk_radiated", + "center_h1", "center_he4", + "center_c12", "center_n14", + "center_o16"]: setattr(star, key, None) return @@ -817,6 +852,18 @@ def collapse_star(self, star): star.m_disk_accreted = 0.0 star.m_disk_radiated = 0.0 star.state = "NS" + + elif self.allow_spin_None: + # If the profile is not available and spin can stay + # undetermined + star.mass = m_grav + star.spin = None + star.m_disk_accreted = 0.0 + star.m_disk_radiated = 0.0 + if m_grav >= self.max_NS_mass: + star.state = "BH" + else: + star.state = "NS" else: for key in STARPROPERTIES: @@ -832,9 +879,9 @@ def collapse_star(self, star): star.log_R = np.log10(CO_radius(star.mass, star.state)) for key in STARPROPERTIES: - if key not in [ - "state", "mass", "spin", "log_R", "metallicity", - "m_disk_accreted ", "m_disk_radiated","co_core_mass"]: + if key not in ["state", "mass", "spin", "log_R", "metallicity", + "m_disk_accreted", "m_disk_radiated", + "co_core_mass"]: setattr(star, key, None) def PISN_prescription(self, star): @@ -937,8 +984,12 @@ def check_SN_type(self, m_core, m_He_core, m_star): # this is catching H-rich_non_burning stars if m_star < 0.5: m_rembar = m_star - warnings.warn( - 'Invalid co/He core masses! Setting m_WD=m_star!') + if ((m_core < 0.)or(m_He_core < 0.)): + warnings.warn('Invalid co/He core masses! ' + 'Setting m_WD=m_star!') + else: + warnings.warn('co/He core masses are zero! ' + 'Setting m_WD=m_star!') else: raise ValueError('Invalid co/He core masses!') f_fb = 1.0 # no SN the no kick is assumed @@ -974,8 +1025,12 @@ def check_SN_type(self, m_core, m_He_core, m_star): # this is catching H-rich_non_burning stars if m_star < 0.5: m_rembar = m_star - warnings.warn( - 'Invalid co/He core masses! Setting m_WD=m_star!') + if ((m_core < 0.)or(m_He_core < 0.)): + warnings.warn('Invalid co/He core masses! ' + 'Setting m_WD=m_star!') + else: + warnings.warn('co/He core masses are zero! ' + 'Setting m_WD=m_star!') else: raise ValueError('Invalid co/He core masses!') f_fb = 1.0 # no SN the no kick is assumed @@ -1010,8 +1065,12 @@ def check_SN_type(self, m_core, m_He_core, m_star): # this is catching H-rich_non_burning stars if m_star < 0.5: m_rembar = m_star - warnings.warn( - 'Invalid co/He core masses! Setting m_WD=m_star!') + if ((m_core < 0.)or(m_He_core < 0.)): + warnings.warn('Invalid co/He core masses! ' + 'Setting m_WD=m_star!') + else: + warnings.warn('co/He core masses are zero! ' + 'Setting m_WD=m_star!') else: raise ValueError('Invalid co/He core masses!') f_fb = 1.0 # no SN the no kick is assumed @@ -1910,8 +1969,9 @@ def get_CO_core_params(self, star, approximation=False): CO_core_mass = star.co_core_mass_at_He_depletion if (C_core_abundance is None) or (CO_core_mass is None): - raise ValueError( - 'The history did not contain core masses at He depletion!') + raise ValueError('The history did not contain core masses at' + f' He depletion! {CO_core_mass}' + f' {C_core_abundance}') return CO_core_mass, C_core_abundance diff --git a/posydon/binary_evol/binarystar.py b/posydon/binary_evol/binarystar.py index a6b454ea15..9eb681cfbb 100644 --- a/posydon/binary_evol/binarystar.py +++ b/posydon/binary_evol/binarystar.py @@ -178,7 +178,9 @@ def __init__(self, star_1=None, star_2=None, index=None, properties=None, setattr(self, f'interp_class_{grid_type}', None) if not hasattr(self, f'mt_history_{grid_type}'): setattr(self, f'mt_history_{grid_type}', None) - + if not hasattr(self, f'culmulative_mt_case_{grid_type}'): + setattr(self, f'culmulative_mt_case_{grid_type}', None) + # SimulationProperties object - parameters & parameterizations if isinstance(properties, SimulationProperties): self.properties = properties diff --git a/posydon/grids/MODELS.py b/posydon/grids/MODELS.py index f31404a56e..3c538b8fc7 100644 --- a/posydon/grids/MODELS.py +++ b/posydon/grids/MODELS.py @@ -143,4 +143,4 @@ "approx_at_he_depletion": False, }, -} \ No newline at end of file +} diff --git a/posydon/grids/io.py b/posydon/grids/io.py index c2d02e10fd..e52d96dd66 100644 --- a/posydon/grids/io.py +++ b/posydon/grids/io.py @@ -411,9 +411,15 @@ def initial_values_from_dirname(mesa_dir): """Use the name of the directory for inferring the main initial values.""" dirname = str(os.path.basename(os.path.normpath(mesa_dir))) if "initial_mass" in dirname: # single-star grid - variable_names = ["initial_mass", "initial_z"] + if "v1/" in dirname: # version 1 dirnames don't contain initial_z + variable_names = ["initial_mass"] + else: + variable_names = ["initial_mass", "initial_z"] else: # binary-star grid - variable_names = ["m1", "m2", "initial_period_in_days", "initial_z"] + if "v1/" in dirname: # version 1 dirnames don't contain initial_z + variable_names = ["m1", "m2", "initial_period_in_days"] + else: + variable_names = ["m1", "m2", "initial_period_in_days", "initial_z"] for variable_name in variable_names: assert variable_name in dirname diff --git a/posydon/grids/post_processing.py b/posydon/grids/post_processing.py index 3292df06ed..2b3490d2c6 100644 --- a/posydon/grids/post_processing.py +++ b/posydon/grids/post_processing.py @@ -20,6 +20,7 @@ __authors__ = [ "Simone Bavera ", "Emmanouil Zapartas ", + "Matthias Kruckow ", ] @@ -29,7 +30,7 @@ CC_quantities = ['state', 'SN_type', 'f_fb', 'mass', 'spin', - 'm_disk_accreted', 'm_disk_radiated'] + 'm_disk_accreted', 'm_disk_radiated', 'CO_interpolation_class'] def assign_core_collapse_quantities_none(EXTRA_COLUMNS, star_i, MODEL_NAME=None): """"Assign None values to all core collapse properties.""" @@ -56,17 +57,23 @@ def print_CC_quantities(EXTRA_COLUMNS, star, MODEL_NAME=None): print(format_val_preSN.format( 'PRE SN STAR', star.state, '', '', star.mass, star.spin, '', '')) - except: + except Exception as e: warnings.warn('Failed to print star values!') + print('Warning in preSN: ', e) print('') else: try: + if star.spin==None: + spin = np.nan + else: + spin = star.spin print(format_val.format(MODEL_NAME, star.state, star.SN_type, star.f_fb, - star.mass, star.spin, star.m_disk_accreted, + star.mass, spin, star.m_disk_accreted, star.m_disk_radiated)) - except: + except Exception as e: warnings.warn('Failed to print star values!') + print('Warning in', MODEL_NAME, ': ', e) @@ -159,16 +166,19 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, stars_CO = [False, star_2_CO] interpolation_class = grid.final_values['interpolation_class'][i] IC = grid.final_values['interpolation_class'][i] + TF2 = grid.final_values['termination_flag_2'][i] else: star = SingleStar.from_run(grid[i], history=True, profile=True) stars = [star] stars_CO = [False] IC = 'no_MT' + TF2 = 'no_RLOF' TF1 = grid.final_values['termination_flag_1'][i] # compute properties for j, star in enumerate(stars): - if not stars_CO[j] and IC in ['no_MT', 'stable_MT', 'unstable_MT']: + if not stars_CO[j] and IC in ['no_MT', 'stable_MT', 'unstable_MT', + 'stable_reverse_MT']: # stellar states EXTRA_COLUMNS['S%s_state' % (j+1)].append(check_state_of_star( star, star_CO=False)) @@ -236,7 +246,8 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, # core collpase quantities if not single_star: - if interpolation_class in ['no_MT', 'stable_MT']: + if interpolation_class in ['no_MT', 'stable_MT', + 'stable_reverse_MT']: if (star_2_CO or (TF1 in TF1_POOL_STABLE and ('primary' in TF1 or 'Primary' in TF1))): star = binary.star_1 @@ -278,42 +289,60 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, 'properties!') continue - if verbose: - print_CC_quantities(EXTRA_COLUMNS, star) - - for MODEL_NAME, MODEL in MODELS.items(): - mechanism = MODEL['mechanism']+MODEL['engine'] - SN = StepSN(**MODEL) - star_copy = copy.copy(star) - try: - flush = False - SN.collapse_star(star_copy) - for quantity in CC_quantities: - if quantity in ['state', 'SN_type']: - if not isinstance(getattr(star_copy, quantity), str): - flush = True - warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} is not a string!') - else: - if not isinstance(getattr(star_copy, quantity), float): - flush = True - warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} is not a float!') - except Exception as e: - flush = True - if verbose: - print('') - print(f'Error during {MODEL_NAME} {mechanism} core collapse prescrition!') - print(e) - print('TF1', TF1) - print('interpolation class', interpolation_class) - print('') - if flush: - assign_core_collapse_quantities_none(EXTRA_COLUMNS, star_i, MODEL_NAME) - else: - for quantity in CC_quantities: - EXTRA_COLUMNS[f'S{star_i}_{MODEL_NAME}_{quantity}'].append( - getattr(star_copy, quantity)) - if verbose: - print_CC_quantities(EXTRA_COLUMNS, star_copy, f'{MODEL_NAME}_{mechanism}') + if star.state in STAR_STATES_CC: + if verbose: + print_CC_quantities(EXTRA_COLUMNS, star) + + for MODEL_NAME, MODEL in MODELS.items(): + mechanism = MODEL['mechanism']+MODEL['engine'] + SN = StepSN(**MODEL, allow_spin_None=True) + star_copy = copy.copy(star) + try: + flush = False + SN.collapse_star(star_copy) + for quantity in CC_quantities: + if quantity in ['state', 'SN_type']: + if not isinstance(getattr(star_copy, quantity), str): + flush = True + warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} is not a string!') + elif quantity != 'CO_interpolation_class': + if quantity=='spin': + if ((not isinstance(getattr(star_copy, quantity), float)) + and (getattr(star_copy, quantity) != None)): + flush = True + warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} is not a float nor None!') + elif not isinstance(getattr(star_copy, quantity), float): + flush = True + warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} is not a float!') + except Exception as e: + flush = True + if verbose: + print('') + print(f'Error during {MODEL_NAME} {mechanism} core collapse prescrition!') + print(e) + print('TF1:', TF1) + print('interpolation class:', interpolation_class) + print('run directory:', grid.MESA_dirs[i]) + print('') + if flush: + assign_core_collapse_quantities_none(EXTRA_COLUMNS, star_i, MODEL_NAME) + else: + for quantity in CC_quantities: + if quantity != 'CO_interpolation_class': + EXTRA_COLUMNS[f'S{star_i}_{MODEL_NAME}_{quantity}'].append( + getattr(star_copy, quantity)) + else: + if getattr(star_copy, 'state') == 'BH' and 'case' in TF2 and '1' in TF2 and '2' in TF2: + EXTRA_COLUMNS[f'S{star_i}_{MODEL_NAME}_{quantity}'].append( + getattr(star_copy, 'state')+'_reverse_MT') + else: + EXTRA_COLUMNS[f'S{star_i}_{MODEL_NAME}_{quantity}'].append( + getattr(star_copy, 'state')) + if verbose: + print_CC_quantities(EXTRA_COLUMNS, star_copy, f'{MODEL_NAME}_{mechanism}') + else: + # star not explodable + assign_core_collapse_quantities_none(EXTRA_COLUMNS, star_i) else: # inital_RLOF, unstable_MT not_converged @@ -327,7 +356,7 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, for MODEL_NAME, MODEL in MODELS.items(): mechanism = MODEL['mechanism']+MODEL['engine'] - SN = StepSN(**MODEL) + SN = StepSN(**MODEL, allow_spin_None=True) star_copy = copy.copy(star) try: flush = False @@ -337,8 +366,13 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, if not isinstance(getattr(star_copy, quantity), str): flush = True warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} is not a string!') - else: - if not isinstance(getattr(star_copy, quantity), float): + elif quantity != 'CO_interpolation_class': + if quantity == 'spin': + if ((not isinstance(getattr(star_copy, quantity), float)) + and (getattr(star_copy, quantity) != None)): + flush = True + warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} is not a float nor None!') + elif not isinstance(getattr(star_copy, quantity), float): flush = True warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} is not a float!') except Exception as e: @@ -347,15 +381,24 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, print('') print(f'Error during {MODEL_NAME} {mechanism} core collapse prescrition!') print(e) - print('TF1', TF1) - print('interpolation class', interpolation_class) + print('TF1:', TF1) + print('interpolation class:', interpolation_class) + print('run directory:', grid.MESA_dirs[i]) print('') if flush: assign_core_collapse_quantities_none(EXTRA_COLUMNS, 1, MODEL_NAME) else: for quantity in CC_quantities: - EXTRA_COLUMNS[f'S1_{MODEL_NAME}_{quantity}'].append( - getattr(star_copy, quantity)) + if quantity != 'CO_interpolation_class': + EXTRA_COLUMNS[f'S1_{MODEL_NAME}_{quantity}'].append( + getattr(star_copy, quantity)) + else: + if getattr(star_copy, 'state') == 'BH' and 'case' in TF2 and '1' in TF2 and '2' in TF2: + EXTRA_COLUMNS[f'S1_{MODEL_NAME}_{quantity}'].append( + getattr(star_copy, 'state')+'_reverse_MT') + else: + EXTRA_COLUMNS[f'S1_{MODEL_NAME}_{quantity}'].append( + getattr(star_copy, 'state')) if verbose: print_CC_quantities(EXTRA_COLUMNS, star_copy, f'{MODEL_NAME}_{mechanism}') else: @@ -418,7 +461,8 @@ def add_post_processed_quantities(grid, MESA_dirs_EXTRA_COLUMNS, EXTRA_COLUMNS, 'EXTRA_COLUMNS do not follow the correct order of grid!') for column in EXTRA_COLUMNS.keys(): - if "state" in column or "type" in column or column == 'mt_history': + if (("state" in column) or ("type" in column) or ("class" in column) + or (column == 'mt_history')): values = np.asarray(EXTRA_COLUMNS[column], str) else: values = np.asarray(EXTRA_COLUMNS[column], float) diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index f686cea0b7..a91e6f3752 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -871,7 +871,8 @@ def decide_columns(key_in_config, defaults): if kept is None: ignore_data = True ignore_reason = "ignored_no_RLO" - warnings.warn("Ignored MESA run because of no RLO" + if self.verbose: + self._say("Ignored MESA run because of no RLO" " in: {}\n".format(run.path)) if not initial_RLO_fix: continue @@ -1286,10 +1287,9 @@ def update_final_values(self): self._reload_hdf5_file(writeable=True) new_dtype = [] for dtype in self.final_values.dtype.descr: - if (dtype[0].startswith("termination_flag") - or dtype[0] == "interpolation_class" - or dtype[0] == "mt_history" - or "_type" in dtype[0] or "_state" in dtype[0]): + if (dtype[0].startswith("termination_flag") or + (dtype[0] == "mt_history") or ("_type" in dtype[0]) or + ("_state" in dtype[0]) or ("_class" in dtype[0])): dtype = (dtype[0], H5_REC_STR_DTYPE.replace("U", "S")) new_dtype.append(dtype) if dtype[1] == np.dtype('O'): @@ -1339,10 +1339,9 @@ def load(self, filepath=None): # change ASCII to UNICODE in termination flags in `final_values` new_dtype = [] for dtype in self.final_values.dtype.descr: - if (dtype[0].startswith("termination_flag") - or dtype[0] == "interpolation_class" - or dtype[0] == "mt_history" - or "_type" in dtype[0] or "_state" in dtype[0]): + if (dtype[0].startswith("termination_flag") or + (dtype[0] == "mt_history") or ("_type" in dtype[0]) or + ("_state" in dtype[0]) or ("_class" in dtype[0])): dtype = (dtype[0], H5_REC_STR_DTYPE.replace("S", "U")) new_dtype.append(dtype) self.final_values = self.final_values.astype(new_dtype) @@ -2276,9 +2275,9 @@ def say(something): new_initial_values = np.array(new_initial_values, dtype=initial_dtype) new_final_dtype = [] for dtype in final_dtype.descr: - if (dtype[0].startswith("termination_flag") - or dtype[0] == "interpolation_class" - or "SN_type" in dtype[0] or "_state" in dtype[0]): + if (dtype[0].startswith("termination_flag") or + ("SN_type" in dtype[0]) or ("_state" in dtype[0]) or + ("_class" in dtype[0])): dtype = (dtype[0], H5_REC_STR_DTYPE.replace("U", "S")) new_final_dtype.append(dtype) new_final_values = np.array(new_final_values, dtype=new_final_dtype) diff --git a/posydon/grids/scrubbing.py b/posydon/grids/scrubbing.py index a6c85507e5..36f35236f8 100644 --- a/posydon/grids/scrubbing.py +++ b/posydon/grids/scrubbing.py @@ -132,7 +132,6 @@ def keep_after_RLO(bh, h1, h2): where_conditions_met = np.where(conditions_met)[0] if len(where_conditions_met) == 0: - warnings.warn("No RLO overflow for this binary.") return None first_index = where_conditions_met[0] diff --git a/posydon/grids/termination_flags.py b/posydon/grids/termination_flags.py index 91a20a07a2..7b5f6bd626 100644 --- a/posydon/grids/termination_flags.py +++ b/posydon/grids/termination_flags.py @@ -164,7 +164,7 @@ def get_mass_transfer_flag(binary_history, history1, history2, mass_transfer_cases.append(mt_case) MT[where_rlof] = [t+10 for t in mass_transfer_cases] # shift by 10 - flag = cumulative_mass_transfer_flag([t for t in MT if t is not None]) + flag = cumulative_mass_transfer_flag([t for t in MT if t is not None], shift_cases=True) return flag @@ -245,7 +245,10 @@ def infer_interpolation_class(tf1, tf2): if tf2 in TF2_POOL_NO_RLO: return "no_MT" if tf1 in TF1_POOL_STABLE: - return "stable_MT" + if 'case' in tf2 and '1' in tf2 and '2' in tf2: + return "stable_reverse_MT" + else: + return "stable_MT" if tf1 in TF1_POOL_UNSTABLE: return "unstable_MT" return "unknown" diff --git a/posydon/interpolation/IF_interpolation.py b/posydon/interpolation/IF_interpolation.py index a39232730d..a97b4bf515 100644 --- a/posydon/interpolation/IF_interpolation.py +++ b/posydon/interpolation/IF_interpolation.py @@ -474,21 +474,23 @@ def __init__(self, grid=None, in_keys=None, out_keys=None, in_scaling=None, if (self.interp_method == 'linear' or isinstance(self.interp_method, list)): print("\nFilling missing values (nans) with 1NN") - self._fillNans() + self._fillNans(grid.final_values[self.c_key]) elif self.interp_method == '1NN': self.YT[np.isnan(self.YT)] = -100 if (self.in_scaling is None) or (self.out_scaling is None): if self.interp_method == '1NN': - self.in_scaling, self.out_scaling = (self._bestInScaling(), + self.in_scaling, self.out_scaling = (self._bestInScaling(grid.final_values[self.c_key]), ['none']*self.n_out) else: - self.in_scaling, self.out_scaling = self._bestScaling() + self.in_scaling, self.out_scaling = self._bestScaling(grid.final_values[self.c_key]) - self.X_scaler = MatrixScaler(self.in_scaling, - self.XT[self.valid >= 0, :]) - self.Y_scaler = MatrixScaler(self.out_scaling, - self.YT[self.valid > 0, :]) + self.X_scaler = Scaler(self.in_scaling, + self.XT[self.valid >= 0, :], + grid.final_values[self.c_key][self.valid > 0]) + self.Y_scaler = Scaler(self.out_scaling, + self.YT[self.valid > 0, :], + grid.final_values[self.c_key][self.valid > 0]) if self.class_method == "kNN": options = {'nfolds': 3, 'p_test': 0.05, 'nmax': 10} @@ -614,8 +616,10 @@ def train_interpolator(self, ic=None): for training the interpolator. """ - XTn = self.X_scaler.normalize(self.XT[self.valid > 0, :]) - YTn = self.Y_scaler.normalize(self.YT[self.valid > 0, :]) + ic = ic[self.valid > 0] if isinstance(self.interp_method, list) else None + + XTn = self.X_scaler.normalize(self.XT[self.valid > 0, :], ic) + YTn = self.Y_scaler.normalize(self.YT[self.valid > 0, :], ic) if self.interp_method == "linear": self.interpolator = LinInterpolator() @@ -627,7 +631,7 @@ def train_interpolator(self, ic=None): self.interpolator = MC_Interpolator( self.classifiers[self.c_key], self.interp_classes, self.interp_method) - self.interpolator.train(XTn, YTn, ic[self.valid > 0]) + self.interpolator.train(XTn, YTn, ic) def test_interpolator(self, Xt): """Use the interpolator to approximate output vector. @@ -643,14 +647,19 @@ def test_interpolator(self, Xt): Output space approximation as numpy array """ + classes = self.test_classifier(self.c_key, Xt) Xtn = self.X_scaler.normalize(Xt) - Ypredn = self.interpolator.predict(Xtn) + if isinstance(self.interp_method, list): + Xtn = self.X_scaler.normalize(Xt, classes) + Ypredn = self.interpolator.predict(Xtn, classes) + else: + Ypredn = self.interpolator.predict(Xtn) Ypredn = np.array([ list(sanitize_interpolated_quantities( - dict(zip(self.out_keys, track)), - self.constraints, verbose=False).values()) - for track in self.Y_scaler.denormalize(Ypredn) + dict(zip(self.out_keys, track)), + self.constraints, verbose=False).values()) + for track in self.Y_scaler.denormalize(Ypredn, classes) ]) return Ypredn @@ -845,99 +854,141 @@ def _interpIn_q(self, grid): return (m2[m1 > 0.95 * m1.max()].min() / m2[m1 < 1.05 * m1.min()].min() > 1 + tol) - def _bestInScaling(self): + def _bestInScaling(self, ic): """Find the best scaling for the input space.""" - in_scaling = [] # I assume inputs are positive-valued - for i in range(self.n_in): - if (np.abs(np.mean(self.XT[:, i]) - np.median(self.XT[:, i])) - / np.std(self.XT[:, i]) - < np.abs(np.mean(np.log10(self.XT[:, i])) - - np.median(np.log10(self.XT[:, i]))) - / np.std(np.log10(self.XT[:, i]))): - in_scaling.append('min_max') - else: - in_scaling.append('log_min_max') - return in_scaling + def in_scale_one(klass = None): - def _bestScaling(self, unique_in=True): - """Find the best scaling for both input and output space.""" - # Decide best scaling linear/log with cross validation - nfolds = 5 - p_test = 0.15 + in_scaling = [] # I assume inputs are positive-valued + for i in range(self.n_in): + + dim = self.XT[:, i] if klass is None else self.XT[np.where(ic == klass)[0]] + + if (np.abs(np.mean(dim) - np.median(dim)) + / np.std(dim) + < np.abs(np.mean(np.log10(dim)) + - np.median(np.log10(dim))) + / np.std(np.log10(dim))): + in_scaling.append('min_max') + else: + in_scaling.append('log_min_max') + + return in_scaling + + cin_scaling = in_scale_one() - # if False, the scaling for the inputs will be output-dependent - unique_in = True + if isinstance(self.interp_method, list): + in_scaling = {} - if unique_in: - r = 1 + for c in self.interp_classes: + in_scaling[c] = in_scale_one(c) else: - r = 2 ** self.n_in - - err = np.nan * np.ones((r * 2, self.n_out, nfolds)) - which_abs = np.abs(self.YT[self.valid > 0, :]).min(axis=0) == 0 - - out_scalings = [['min_max'] * self.n_out, ['log_min_max'] * self.n_out] - - for i, key in enumerate(self.out_keys): - y = self.YT[self.valid > 0, i] - if np.nanmin(y) == np.nanmax(y): - out_scalings[0][i] = 'none' - out_scalings[1][i] = 'none' - elif np.nanmax(y) < 0: - out_scalings[1][i] = 'neg_log_min_max' - elif np.nanmin(y) <= 0: - out_scalings[1][i] = 'min_max' - - in_scaling = self._bestInScaling() - - XT = self.XT[self.valid > 0, :] - YT = self.YT[self.valid > 0, :] - for j in range(2): # normal and log when possible - for i in range(r): # valid also with metallicity included - if r > 1: - in_scaling = [ - 'min_max' - if i % (2 ** (j + 1)) < 2 ** j else 'log_min_max' - for j in range(self.n_in) - ] - - xs, ys = (MatrixScaler(in_scaling, XT), - MatrixScaler(out_scalings[j], YT)) - XTn, YTn = xs.normalize(XT), ys.normalize(YT) - - np.random.seed(0) - - for fold in range(nfolds): - iTrain, itest = xval_indices(np.sum(self.valid > 0), - percent_test=p_test) - - X_T, Y_T = XTn[iTrain, :], YTn[iTrain, :] - X_t, Y_t = XT[itest, :], YT[itest, :] - - interp = LinearNDInterpolator(X_T, Y_T) - ypred_i = ys.denormalize(interp(xs.normalize(X_t))) - - err[i + r * j, ~which_abs, fold] = np.nanpercentile( - np.abs((ypred_i[:, ~which_abs] - Y_t[:, ~which_abs]) - / Y_t[:, ~which_abs]), 90, axis=0) - err[i + r * j, which_abs, fold] = np.nanpercentile( - np.abs(ypred_i[:, which_abs] - Y_t[:, which_abs]), 90, - axis=0) - - where_min = np.nanargmin(np.nanmean(err, axis=2), axis=0) - - out_scaling = [] - for i in range(self.n_out): - if where_min[i] < r: - out_scaling.append(out_scalings[0][i]) + + in_scaling = cin_scaling + + + + return (in_scaling, cin_scaling) + + + + def _bestScaling(self, ic, unique_in=True, nfolds = 5, p_test = 0.15): + """Find the best scaling for both input and output space.""" + + in_scaling = self._bestInScaling(ic) + + def scale_one(in_scaling, klass = None): + + # if False, the scaling for the inputs will be output-dependent + if unique_in: + r = 1 else: - out_scaling.append(out_scalings[1][i]) - where_min[i] -= r + r = 2 ** self.n_in + + inds = np.where(self.valid > 0 if klass is None else (ic == klass) & (self.valid > 0))[0] + + err = np.nan * np.ones((r * 2, self.n_out, nfolds)) + which_abs = np.abs(self.YT[inds, :]).min(axis=0) == 0 + + out_scalings = [['min_max'] * self.n_out, ['log_min_max'] * self.n_out] + + for i, key in enumerate(self.out_keys): + y = self.YT[inds, i] + if np.nanmin(y) == np.nanmax(y): + out_scalings[0][i] = 'none' + out_scalings[1][i] = 'none' + elif np.nanmax(y) < 0: + out_scalings[1][i] = 'neg_log_min_max' + elif np.nanmin(y) <= 0: + out_scalings[1][i] = 'min_max' + + + XT = self.XT[inds, :] + YT = self.YT[inds, :] + for j in range(2): # normal and log when possible + for i in range(r): # valid also with metallicity included + + if r > 1: + in_scaling = [ + 'min_max' + if i % (2 ** (j + 1)) < 2 ** j else 'log_min_max' + for j in range(self.n_in) + ] + + xs, ys = (MatrixScaler(in_scaling[1], XT), # using class cumulative scaling (not per class) to get optimal + MatrixScaler(out_scalings[j], YT)) + XTn, YTn = xs.normalize(XT), ys.normalize(YT) + + np.random.seed(0) + + for fold in range(nfolds): + + iTrain, itest = xval_indices(inds.shape[0], + percent_test=p_test) + + X_T, Y_T = XTn[iTrain, :], YTn[iTrain, :] + X_t, Y_t = XT[itest, :], YT[itest, :] + + interp = LinearNDInterpolator(X_T, Y_T) + ypred_i = ys.denormalize(interp(xs.normalize(X_t))) + + err[i + r * j, ~which_abs, fold] = np.nanpercentile( + np.abs((ypred_i[:, ~which_abs] - Y_t[:, ~which_abs]) + / Y_t[:, ~which_abs]), 90, axis=0) + err[i + r * j, which_abs, fold] = np.nanpercentile( + np.abs(ypred_i[:, which_abs] - Y_t[:, which_abs]), 90, + axis=0) + + try: + where_min = np.nanargmin(np.nanmean(err, axis=2), axis=0) + except: + # replace nans by a large number before looking for minimum + where_min = np.nanargmin(np.nan_to_num(np.nanmean(err, axis=2), nan=1e99), axis=0) - return in_scaling, out_scaling + out_scaling = [] + for i in range(self.n_out): + if where_min[i] < r: + out_scaling.append(out_scalings[0][i]) + else: + out_scaling.append(out_scalings[1][i]) + where_min[i] -= r + + return out_scaling + + cout_scaling = scale_one(in_scaling) + + if isinstance(self.interp_method, list): + out_scaling = {} + + for c in self.interp_classes: + out_scaling[c] = scale_one(in_scaling, c) - def _fillNans(self): + else: + out_scaling = cout_scaling + + return in_scaling, (out_scaling, cout_scaling) + + def _fillNans(self, ic): """Fill nan values i numerical magnitudes with 1NN.""" for i in range(self.n_out): wnan = np.isnan(self.YT[:, i]) | np.isinf(self.YT[:, i]) @@ -947,7 +998,7 @@ def _fillNans(self): if any(wnan[self.valid > 0]): k1r = KNeighborsRegressor(n_neighbors=1) wT = (~wnan) & (self.valid > 0) - xs = MatrixScaler(self._bestInScaling(), + xs = MatrixScaler(self._bestInScaling(ic)[1], self.XT[self.valid >= 0, :]) k1r.fit(xs.normalize(self.XT[wT, :]), self.YT[wT, i]) wt = wnan & (self.valid > 0) @@ -1147,7 +1198,11 @@ def train(self, XT, YT, z): which += z == self.classes[i][j] self.interpolators[i].train(XT[which, :], YT[which, :]) - def predict(self, Xt): + def classifier(self, Xt): + + return self.classifier.predict(Xt) + + def predict(self, Xt, zpred): """Interpolate and approximate output vectors given input vectors. Parameters @@ -1160,7 +1215,6 @@ def predict(self, Xt): Output space approximation as numpy array """ - zpred = self.classifier.predict(Xt) Ypred = np.ones((Xt.shape[0], self.M)) * np.nan for i in range(len(self.classes)): which = np.zeros_like(zpred, dtype=bool) @@ -1342,6 +1396,65 @@ def xtrain(self, XT, yT, **opts): # MATRIX SCALING +class Scaler: + + def __init__(self, norms, XT, ic): + + if norms[0] == norms[1]: + self.scaler = { + None: MatrixScaler(norms[0], XT) + } + else: + self.scaler = {} + + for klass, n in norms[0].items(): + self.scaler[klass] = MatrixScaler(n, XT[np.where(ic == klass)[0]]) # need to only use relevant classes in XT + + self.scaler[None] = MatrixScaler(norms[1], XT) + + def normalize(self, X, klass = None): + + if klass is None: + return self.scaler[klass].normalize(X) + + else: + + normalized = X.copy() + + classes = np.unique(klass) + + for c in classes: + inds = np.where(klass == c)[0] + c = None if c == "None" else c + + if c not in self.scaler.keys(): + warnings.warn(f"skip normalize: c={c}, inds={inds}") + continue + normalized[inds] = self.scaler[c].normalize(X[inds]) + + return normalized + + def denormalize(self, Xn, klass = None): + + if klass is None: + return self.scaler[klass].denormalize(X) + + else: + + normalized = Xn + + classes = np.unique(klass) + + for c in classes: + inds = np.where(klass == c)[0] + c = None if c == "None" else c + + if c not in self.scaler.keys(): + warnings.warn(f"skip denormalize: c={c}, inds={inds}") + continue + normalized[inds] = self.scaler[c].denormalize(Xn[inds]) + + return normalized class MatrixScaler: diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index 0b52e0981c..37a2b5f08a 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -55,7 +55,7 @@ # 'event' usually 10 but 'detached (Integration failure)' can occur HISTORY_MIN_ITEMSIZE = {'state': 30, 'event': 25, 'step_names': 21, 'S1_state': 31, 'S2_state': 31, - 'mass_transfer_case': 7, + 'mass_transfer_case': 10, 'S1_SN_type': 5, 'S2_SN_type': 5} ONELINE_MIN_ITEMSIZE = {'state_i': 30, 'state_f': 30, 'event_i': 10, 'event_f': 31, @@ -64,10 +64,12 @@ 'S2_state_i': 31, 'S2_state_f': 31, 'mass_transfer_case_i': 7, 'mass_transfer_case_f': 20, 'S1_SN_type': 5, 'S2_SN_type': 5, - 'interp_class_HMS_HMS' : 15, 'interp_class_CO_HeMS' : 15, + 'interp_class_HMS_HMS' : 20, 'interp_class_CO_HeMS' : 15, 'interp_class_CO_HMS_RLO' : 15, 'interp_class_CO_HeMS_RLO' : 15, 'mt_history_HMS_HMS' : 40, 'mt_history_CO_HeMS' : 40, 'mt_history_CO_HMS_RLO' : 40, 'mt_history_CO_HeMS_RLO' : 40, + 'culmulative_mt_case_HMS_HMS': 40, 'culmulative_mt_case_CO_HeMS': 40, + 'culmulative_mt_case_CO_HMS_RLO': 40, 'culmulative_mt_case_CO_HeMS_RLO': 40, } # BinaryPopulation will enforce a constant metallicity accross all steps that diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index c5522e5bf0..5c61148675 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -12,6 +12,7 @@ "Tassos Fragos ", "Scott Coughlin ", "Kyle Akira Rocha ", + "Matthias Kruckow ", ] @@ -1518,8 +1519,51 @@ def cumulative_mass_transfer_string(cumulative_integers): return result -def cumulative_mass_transfer_flag(MT_cases): - """Get the cumulative MT string from a list of integer MT casses.""" +def cumulative_mass_transfer_flag(MT_cases, shift_cases=False): + """Get the cumulative MT string from a list of integer MT casses. + + Arguments + ---------- + MT_cases: list of integers + A list of MT cases. + shift_cases: bool + Flag to shift non-physical cases like A1 after B1 will turn into B1. + + Returns + ------- + str + A string summarizing the mass transfer cases. + + """ + if shift_cases: + case_1_min = MT_CASE_NO_RLO + case_1_max = MT_CASE_UNDETERMINED + case_2_min = case_1_min+10 + case_2_max = case_1_max+10 + corrected_MT_cases = [] + for MT in MT_cases: + if (MT<=case_1_max): + # star 1 is donor + if (MTcase_1_min): # update earliest possible MT case + case_1_min = MT + elif (MT<=case_2_max): + # star 2 is donor + if (MTcase_2_min): # update earliest possible MT case + case_2_min = MT + else: + # unknown donor + warnings.warn("MT case with unknown donor: {}".format(MT)) + corrected_MT_cases.append(MT) + else: + corrected_MT_cases = MT_cases.copy() return cumulative_mass_transfer_string( cumulative_mass_transfer_numeric(MT_cases) ) diff --git a/posydon/visualization/combine_TF.py b/posydon/visualization/combine_TF.py index 9b285d8ae4..87e2636edb 100644 --- a/posydon/visualization/combine_TF.py +++ b/posydon/visualization/combine_TF.py @@ -79,27 +79,6 @@ 'case_A2' ] -TF2_POOL_AB = [ - 'case_A1/B1', - 'case_A2/B2', - 'case_A1/B1/C1', - 'case_A2/B2/C2', - 'case_A1/B1/C1/BB1', - 'case_A2/B2/C2/BB2', - 'case_A1/B1/A1', - 'case_A2/B2/A2' - ] - -TF2_POOL_AC = [ - 'case_A1/C1', - 'case_A2/C2' - ] - -TF2_POOL_ABB = [ - 'case_A1/BB1', - 'case_A2/BB2' - ] - TF2_POOL_B = [ 'case_B1', 'case_B2' @@ -110,30 +89,51 @@ 'case_C2', ] -TF2_POOL_BC = [ - 'case_B1/C1', - 'case_B2/C2' +TF2_POOL_BA = [ + 'case_BA1', + 'case_BA2' ] TF2_POOL_BB = [ - 'case_A1/B1/BB1', - 'case_A2/B2/BB2', - 'case_B1/BB1', - 'case_B2/BB2', - 'case_B1/C1/BB1', - 'case_B2/C2/BB2', - 'case_BA1/BB1', - 'case_BA2/BB2', - 'case_C1/BB1', - 'case_C2/BB2', +# 'case_A1/B1/BB1', +# 'case_A2/B2/BB2', +# 'case_B1/BB1', +# 'case_B2/BB2', +# 'case_B1/C1/BB1', +# 'case_B2/C2/BB2', +# 'case_BA1/BB1', +# 'case_BA2/BB2', +# 'case_C1/BB1', +# 'case_C2/BB2', 'case_BB1', 'case_BB2' ] -TF2_POOL_BA = [ - 'case_BA1', - 'case_BA2' - ] +#TF2_POOL_AB = [ +# 'case_A1/B1', +# 'case_A2/B2', +# 'case_A1/B1/C1', +# 'case_A2/B2/C2', +# 'case_A1/B1/C1/BB1', +# 'case_A2/B2/C2/BB2', +# 'case_A1/B1/A1', +# 'case_A2/B2/A2' +# ] + +#TF2_POOL_AC = [ +# 'case_A1/C1', +# 'case_A2/C2' +# ] + +#TF2_POOL_ABB = [ +# 'case_A1/BB1', +# 'case_A2/BB2' +# ] + +#TF2_POOL_BC = [ +# 'case_B1/C1', +# 'case_B2/C2' +# ] def combine_TF12(IC, TF2, verbose=False): """Get the combination of interpolation classion and termination flag 2.""" @@ -157,22 +157,34 @@ def combine_TF12(IC, TF2, verbose=False): TF12[i] = 'Stable contact' elif TF2[i] in TF2_POOL_A: TF12[i] = 'Stable case A' - elif TF2[i] in TF2_POOL_AB: - TF12[i] = 'Stable case AB' - elif TF2[i] in TF2_POOL_AC: - TF12[i] = 'Stable case AC' - elif TF2[i] in TF2_POOL_ABB: - TF12[i] = 'Stable case ABB' elif TF2[i] in TF2_POOL_B: TF12[i] = 'Stable case B' elif TF2[i] in TF2_POOL_C: TF12[i] = 'Stable case C' - elif TF2[i] in TF2_POOL_BC: - TF12[i] = 'Stable case BC' - elif TF2[i] in TF2_POOL_BB: - TF12[i] = 'Stable case BB' elif TF2[i] in TF2_POOL_BA: TF12[i] = 'Stable case BA' + elif TF2[i] in TF2_POOL_BB: + TF12[i] = 'Stable case BB' +# elif TF2[i] in TF2_POOL_AB: +# TF12[i] = 'Stable case AB' +# elif TF2[i] in TF2_POOL_AC: +# TF12[i] = 'Stable case AC' +# elif TF2[i] in TF2_POOL_ABB: +# TF12[i] = 'Stable case ABB' +# elif TF2[i] in TF2_POOL_BC: +# TF12[i] = 'Stable case BC' + elif 'case_nonburning' in TF2[i]: + TF12[i] = 'Stable case n' + elif '/' in TF2[i]: + # multiple cases, split them up + MTcases = TF2[i].split('/') + # record first case + TF12[i] = 'Stable '+MTcases[0].replace('_',' ')[:-1] + # record last case + if 'nonburning' in MTcases[-1]: + TF12[i] += 'n' + else: + TF12[i] += MTcases[-1][:-1] elif IC[i] == 'unstable_MT': if '1' in TF2[i] and '2' in TF2[i]: TF12[i] = 'Reverse unstable MT' @@ -180,25 +192,36 @@ def combine_TF12(IC, TF2, verbose=False): TF12[i] = 'Unstable contact' elif TF2[i] in TF2_POOL_A: TF12[i] = 'Unstable case A' - elif TF2[i] in TF2_POOL_AB: - TF12[i] = 'Unstable case AB' - elif TF2[i] in TF2_POOL_AC: - TF12[i] = 'Unstable case AC' - elif TF2[i] in TF2_POOL_ABB: - TF12[i] = 'Unstable case ABB' elif TF2[i] in TF2_POOL_B: TF12[i] = 'Unstable case B' elif TF2[i] in TF2_POOL_C: TF12[i] = 'Unstable case C' + elif TF2[i] in TF2_POOL_BA: + TF12[i] = 'Unstable case BA' elif TF2[i] in TF2_POOL_BB: TF12[i] = 'Unstable case BB' - elif TF2[i] in TF2_POOL_BC: - TF12[i] = 'Unstable case BC' +# elif TF2[i] in TF2_POOL_AB: +# TF12[i] = 'Unstable case AB' +# elif TF2[i] in TF2_POOL_AC: +# TF12[i] = 'Unstable case AC' +# elif TF2[i] in TF2_POOL_ABB: +# TF12[i] = 'Unstable case ABB' +# elif TF2[i] in TF2_POOL_BC: +# TF12[i] = 'Unstable case BC' elif TF2[i] in TF2_POOL_UNSTABLE: TF12[i] = "Unstable L2 RLOF" - # reverse MT case - elif TF2[i] in TF2_POOL_BA: - TF12[i] = 'Unstable case BA' + elif 'case_nonburning' in TF2[i]: + TF12[i] = 'Unstable case n' + elif '/' in TF2[i]: + # multiple cases, split them up + MTcases = TF2[i].split('/') + # record first case + TF12[i] = 'Unstable '+MTcases[0].replace('_',' ')[:-1] + # record last case + if 'nonburning' in MTcases[-1]: + TF12[i] += 'n' + else: + TF12[i] += MTcases[-1][:-1] # catch if something is missing from the logic if TF12[i] == 'unknown': diff --git a/posydon/visualization/interpolation.py b/posydon/visualization/interpolation.py index 4d7eb3383d..03ff38576d 100644 --- a/posydon/visualization/interpolation.py +++ b/posydon/visualization/interpolation.py @@ -39,8 +39,10 @@ def __init__(self, interpolator, test_grid): def __compute_errs(self): """ Method that computes both interpolation and classification errors """ - iv = np.array(self.test_grid.initial_values[self.in_keys].tolist()) # initial values - fv = np.array(self.test_grid.final_values[self.out_keys].tolist()) # final values + iv = np.array(self.test_grid.initial_values[self.in_keys].tolist(), + dtype=float) # initial values + fv = np.array(self.test_grid.final_values[self.out_keys].tolist(), + dtype=float) # final values ic = self.test_grid.final_values["interpolation_class"] # final values ivalid_inds = np.where( @@ -53,7 +55,8 @@ def __compute_errs(self): self.errs = {} - self.errs["relative"] = np.abs((fv - i) / fv) + with np.errstate(divide='ignore', invalid='ignore'): + self.errs["relative"] = np.abs((fv - i) / fv) self.errs["absolute"] = np.abs(fv - i) self.errs["valid_inds"] = ivalid_inds @@ -149,7 +152,8 @@ def __clean_errs(self, errs): return errs - def violin_plots(self, err_type = "relative", keys = None, save_path = None): + def violin_plots(self, err_type = "relative", keys = None, + save_path = None, close_fig = False): """ Method that plots distribution of specified error for given keys and optionally saves it. @@ -162,6 +166,8 @@ def violin_plots(self, err_type = "relative", keys = None, save_path = None): A list of keys for which the errors will be shown, by default is all of them save_path: str The path where the figure should be saved to + close_fig: bool + Flag whether figure should be closed """ @@ -254,8 +260,12 @@ def customize_violinplot(plot, color, outlined = False, right = True): if save_path is not None: fig.save(save_path) + # close figure + if close_fig: + plt.close(fig) - def confusion_matrix(self, key, params = {}, save_path = None): + def confusion_matrix(self, key, params = {}, save_path = None, + close_fig = False): """ Method that plots confusion matrices to evaluate classification Parameters @@ -266,6 +276,8 @@ def confusion_matrix(self, key, params = {}, save_path = None): Extra params to pass to matplolib, x_labels (list), y_labels (list), title (str) save_path : str The path where the figure should be saved to + close_fig: bool + Flag whether figure should be closed """ @@ -314,6 +326,10 @@ def confusion_matrix(self, key, params = {}, save_path = None): if save_path is not None: # saving fig.save(save_path) + # close figure + if close_fig: + plt.close(fig) + def classifiers(self): """ Method that lists classifiers available """ classes = self.interpolator.test_classifiers( diff --git a/posydon/visualization/plot1D.py b/posydon/visualization/plot1D.py index 5a8965a076..4b87b971cb 100644 --- a/posydon/visualization/plot1D.py +++ b/posydon/visualization/plot1D.py @@ -1,6 +1,6 @@ """Plotting class for 1D (MESA) psygrids. -The 2D visualization plotting class allows to plot 1D tracks of PsyGrid +The 1D visualization plotting class allows to plot 1D tracks of PsyGrid objects. The PsyGrid object is composed of nD MESA grid run with POSYDON and post processed with the psygrid object into an h5 file. """ @@ -8,6 +8,7 @@ __authors__ = [ "Simone Bavera ", + "Matthias Kruckow ", ] @@ -110,7 +111,11 @@ def __init__( else: self.history = [self.run[history]] self.n_runs = len(self.history) - self.history_str = self.history[0].dtype.names + self.history_str = () + for i in range(self.n_runs): + if self.history[i] is not None: + self.history_str = self.history[i].dtype.names + break # x, y and z variables must exist if isinstance(x_var_str, str): @@ -590,7 +595,8 @@ def HR_diagram(self): ncol=self.legend1D["ncol"], bbox_to_anchor=self.legend1D["bbox_to_anchor"]) - if "star_mass" in self.history[j].dtype.names: + if (self.history[j] is not None and + "star_mass" in self.history[j].dtype.names): plt.text( self.history[j]["log_Teff"][0] * 1.1, self.history[j]["log_L"][0], diff --git a/posydon/visualization/plot2D.py b/posydon/visualization/plot2D.py index 81a5a714c0..90174dfb57 100644 --- a/posydon/visualization/plot2D.py +++ b/posydon/visualization/plot2D.py @@ -18,9 +18,9 @@ import matplotlib.pyplot as plt from posydon.utils.gridutils import add_field from posydon.utils.constants import Zsun -from posydon.visualization.plot_defaults import DEFAULT_MARKERS_COLORS_LEGENDS -from posydon.visualization.plot_defaults import PLOT_PROPERTIES -from posydon.visualization.plot_defaults import DEFAULT_LABELS +from posydon.visualization.plot_defaults import ( + DEFAULT_MARKERS_COLORS_LEGENDS, add_flag_to_MARKERS_COLORS_LEGENDS, + PLOT_PROPERTIES, DEFAULT_LABELS) from posydon.visualization.combine_TF import combine_TF12 import copy @@ -205,10 +205,14 @@ def __init__( # store the initial/final values self.initial_values = self.psygrid.initial_values + if self.initial_values is None: + raise ValueError("No initial values in PSyGrid") # add extra properties to initial_values self.add_properties_to_initial_values() self.initial_values_str = self.initial_values.dtype.names self.final_values = self.psygrid.final_values + if self.final_values is None: + raise ValueError("No final values in PSyGrid") # add extra properties to final_values if termination_flag in ["combined_TF12", "debug", "interpolation_class_errors"]: self.add_properties_to_final_values(termination_flag) @@ -219,20 +223,24 @@ def __init__( self.final_values['termination_flag_2'] = TF2_clean self.final_values_str = self.final_values.dtype.names - idx_with_histories=0 + H1_names = () + H2_names = () + BH_names = () for i in range(len(psygrid)): - if psygrid[i].history1 is not None and\ - psygrid[i].history2 is not None and\ - psygrid[i].binary_history is not None: - idx_with_histories = i + if len(H1_names)==0 and self.psygrid[i].history1 is not None: + H1_names = self.psygrid[i].history1.dtype.names + if len(H2_names)==0 and self.psygrid[i].history2 is not None: + H2_names = self.psygrid[i].history2.dtype.names + if len(BH_names)==0 and self.psygrid[i].binary_history is not None: + BH_names = self.psygrid[i].binary_history.dtype.names + if len(H1_names)>0 and len(H2_names)>0 and len(BH_names)>0: break # x, y and z variables must exist if x_var_str not in self.initial_values_str and not self.slice_at_RLO: raise ValueError( "x_var_str = {} is not available in psygrid.initial_values". format(x_var_str)) - elif (x_var_str not in self.psygrid[idx_with_histories].\ - binary_history.dtype.names and self.slice_at_RLO): + elif (x_var_str not in BH_names and self.slice_at_RLO): raise ValueError("x_var_str = {} is not available in " "psygrid.binary_history".format(x_var_str)) else: @@ -240,8 +248,7 @@ def __init__( if y_var_str not in self.initial_values_str and not self.slice_at_RLO: raise ValueError("y_var_str = {} is not available in " "psygrid.initial_values".format(y_var_str)) - elif (y_var_str not in self.psygrid[idx_with_histories].\ - binary_history.dtype.names and self.slice_at_RLO): + elif (y_var_str not in BH_names and self.slice_at_RLO): raise ValueError("y_var_str = {} is not available in " "psygrid.binary_history".format(y_var_str)) else: @@ -271,19 +278,16 @@ def __init__( self.binary_history = False self.add_properties_to_final_values(None) elif (self.selected_star_history_for_z_var == 1 - and z_var_str in self.psygrid[idx_with_histories].\ - history1.dtype.names): + and z_var_str in H1_names): self.z_var_str = z_var_str self.history = True self.binary_history = False elif (self.selected_star_history_for_z_var == 2 - and z_var_str in self.psygrid[idx_with_histories].\ - history2.dtype.names): + and z_var_str in H2_names): self.z_var_str = z_var_str self.history = True self.binary_history = False - elif z_var_str in self.psygrid[idx_with_histories].\ - binary_history.dtype.names: + elif z_var_str in BH_names: self.z_var_str = z_var_str self.history = False self.binary_history = True @@ -295,20 +299,17 @@ def __init__( ) else: - if self.selected_star_history_for_z_var == 1 and \ - z_var_str in self.psygrid[idx_with_histories].history1.\ - dtype.names: + if (self.selected_star_history_for_z_var == 1 and + z_var_str in H1_names): self.z_var_str = z_var_str self.history = True self.binary_history = False elif (self.selected_star_history_for_z_var == 2 - and z_var_str in self.psygrid[idx_with_histories].\ - history2.dtype.names): + and z_var_str in H2_names): self.z_var_str = z_var_str self.history = True self.binary_history = False - elif z_var_str in self.psygrid[idx_with_histories].\ - binary_history.dtype.names: + elif z_var_str in BH_names: self.z_var_str = z_var_str self.history = False self.binary_history = True @@ -476,6 +477,9 @@ def plot_panel(self, ax, extra_grid_call=False): sc_last = None for flag in self.termination_flag_str: selection = self.termination_flag == flag + if flag not in self.MARKERS_COLORS_LEGENDS.keys(): + add_flag_to_MARKERS_COLORS_LEGENDS(self.MARKERS_COLORS_LEGENDS, + flag) if self.MARKERS_COLORS_LEGENDS[flag][2] is not None: if self.slice_at_RLO: for i in range(len(self.x_var[selection])): @@ -1209,6 +1213,9 @@ def plot_panels(self, axs, l_ax=None, legend_idxs=[2]): ax = axs[row, col] for flag in self.termination_flag_str: selection = self.termination_flag == flag + if flag not in self.MARKERS_COLORS_LEGENDS.keys(): + add_flag_to_MARKERS_COLORS_LEGENDS( + self.MARKERS_COLORS_LEGENDS, flag) if self.MARKERS_COLORS_LEGENDS[flag][2] is not None: if self.slice_at_RLO: for i in range(len(self.x_var[selection])): diff --git a/posydon/visualization/plot_defaults.py b/posydon/visualization/plot_defaults.py index 6ed9d99647..0f7f922c0f 100644 --- a/posydon/visualization/plot_defaults.py +++ b/posydon/visualization/plot_defaults.py @@ -460,33 +460,53 @@ ['s', 2, list_of_colors[3], 'Stable contact phase'], 'Stable case A': ['s', 2, list_of_colors[2], 'Stable RLOF during MS'], + 'Stable case B': + ['s', 2, list_of_colors[1], 'Stable RLOF during postMS'], + 'Stable case C': + ['s', 2, list_of_colors[1], 'Stable RLOF during postMS'], + 'Stable case BA': + ['s', 2, list_of_colors[0], 'Stable RLOF during stripped He star'], + 'Stable case BB': + ['s', 2, list_of_colors[0], 'Stable RLOF during stripped He star'], + # hot fix for case AA should be removed later: + 'Stable case AA': + ['s', 2, list_of_colors[2], 'Stable RLOF during MS'], 'Stable case AB': ['s', 2, list_of_colors[1], 'Stable RLOF during postMS'], 'Stable case AC': ['s', 2, list_of_colors[1], 'Stable RLOF during postMS'], + 'Stable case An': + ['s', 2, list_of_colors[1], 'Stable RLOF during postMS'], + 'Stable case ABA': + ['s', 2, list_of_colors[0], 'Stable RLOF during stripped He star'], 'Stable case ABB': ['s', 2, list_of_colors[0], 'Stable RLOF during stripped He star'], - 'Stable case B': + 'Stable case BC': ['s', 2, list_of_colors[1], 'Stable RLOF during postMS'], - 'Stable case C': + 'Stable case Bn': ['s', 2, list_of_colors[1], 'Stable RLOF during postMS'], - 'Stable case BA': + 'Stable case BBA': ['s', 2, list_of_colors[0], 'Stable RLOF during stripped He star'], - 'Stable case BB': + 'Stable case BBB': ['s', 2, list_of_colors[0], 'Stable RLOF during stripped He star'], - 'Stable case BC': + 'Stable case Cn': ['s', 2, list_of_colors[1], 'Stable RLOF during postMS'], + 'Stable case CBA': + ['s', 2, list_of_colors[0], 'Stable RLOF during stripped He star'], + 'Stable case CBB': + ['s', 2, list_of_colors[0], 'Stable RLOF during stripped He star'], + 'Stable case BABB': + ['s', 2, list_of_colors[0], 'Stable RLOF during stripped He star'], + 'Stable case BAn': + ['s', 2, list_of_colors[0], 'Stable RLOF during stripped He star'], + 'Stable case BBn': + ['s', 2, list_of_colors[0], 'Stable RLOF during stripped He star'], + 'Stable case n': + ['s', 2, list_of_colors[1], 'Stable RLOF while non burning'], 'Unstable contact': ['D', 1, list_of_colors[3], 'Unstable contact phase'], 'Unstable case A': ['D', 1, list_of_colors[2], 'Unstable RLOF during MS'], - 'Unstable case AB': - ['D', 1, list_of_colors[1], 'Unstable RLOF during postMS'], - 'Unstable case AC': - ['D', 1, list_of_colors[1], 'Unstable RLOF during postMS'], - 'Unstable case ABB': - ['D', 1, list_of_colors[0], - 'Unstable RLOF during stripped He star'], 'Unstable case B': ['D', 1, list_of_colors[1], 'Unstable RLOF during postMS'], 'Unstable case C': @@ -497,8 +517,48 @@ 'Unstable case BB': ['D', 1, list_of_colors[0], 'Unstable RLOF during stripped He star'], + 'Unstable case AB': + ['D', 1, list_of_colors[1], 'Unstable RLOF during postMS'], + 'Unstable case AC': + ['D', 1, list_of_colors[1], 'Unstable RLOF during postMS'], + 'Unstable case An': + ['D', 1, list_of_colors[1], 'Unstable RLOF during postMS'], + 'Unstable case ABA': + ['D', 1, list_of_colors[0], + 'Unstable RLOF during stripped He star'], + 'Unstable case ABB': + ['D', 1, list_of_colors[0], + 'Unstable RLOF during stripped He star'], 'Unstable case BC': ['D', 1, list_of_colors[1], 'Unstable RLOF during postMS'], + 'Unstable case Bn': + ['D', 1, list_of_colors[1], 'Unstable RLOF during postMS'], + 'Unstable case BBA': + ['D', 2, list_of_colors[0], + 'Unstable RLOF during stripped He star'], + 'Unstable case BBB': + ['D', 2, list_of_colors[0], + 'Unstable RLOF during stripped He star'], + 'Unstable case Cn': + ['D', 1, list_of_colors[1], 'Unstable RLOF during postMS'], + 'Unstable case CBA': + ['D', 2, list_of_colors[0], + 'Unstable RLOF during stripped He star'], + 'Unstable case CBB': + ['D', 2, list_of_colors[0], + 'Unstable RLOF during stripped He star'], + 'Unstable case BABB': + ['D', 2, list_of_colors[0], + 'Unstable RLOF during stripped He star'], + 'Unstable case BAn': + ['D', 2, list_of_colors[0], + 'Unstable RLOF during stripped He star'], + 'Unstable case BBn': + ['D', 2, list_of_colors[0], + 'Unstable RLOF during stripped He star'], + 'Unstable case n': + ['D', 2, list_of_colors[1], + 'Unstable RLOF while non burning'], 'Unstable L2 RLOF': ['D', 1, list_of_colors[0], 'Unstable RLOF during stripped He star'], @@ -667,6 +727,60 @@ } } +def add_flag_to_MARKERS_COLORS_LEGENDS(MARKERS_COLORS_LEGENDS, flag): + """Add not pre defined stuff to DEFAULT_MARKERS_COLORS_LEGENDS. + + Parameters + ---------- + MARKERS_COLORS_LEGENDS : dict of lists + Dictionary with flags as keys given a list with marker, size, color, + and legend text for each flag. + flag : str + The flag itself. + + """ + if flag not in MARKERS_COLORS_LEGENDS.keys(): + if ('case_' in flag): # unknown MT flag + if '1' not in flag: # only star 1 is donor + s = 'o' + elif '2' not in flag: # only star 2 is donor + s = 's' + elif flag[-1]=='1': # star 1 is last donor + s = '>' + elif flag[-1]=='2': # star 2 is last donor + s = '<' + else: + s = 'v' + if '/' in flag: # multiple mass transfers + if (('case_A' in flag) or ('case_BA' in flag)): + # first MT is case A or case BA + c = 'tab:cyan' + elif (('case_C' in flag) or ('case_BC' in flag)): + # first MT is case C or case BC + c = 'tab:orange' + elif (('case_B' in flag) or ('case_BB' in flag)): + # first MT is case B or case BB + # (needs to be behind case BA and BC) + c = 'tab:pink' + else: + c = 'lightgrey' + elif 'BA' in flag: # only case BA + c = 'tab:red' + elif 'BB' in flag: # only case BB + c = 'brown' + elif 'BC' in flag: # only case BC + c = 'tab:gray' + elif 'A' in flag: # only case A + c = 'tab:blue' + elif 'B' in flag: # only case B + c = 'tab:green' + elif 'C' in flag: # only case C + c = 'tab:purple' + else: + c = 'black' + MARKERS_COLORS_LEGENDS[flag] = [s, 2, c, flag.replace('_',' ')] + else: + MARKERS_COLORS_LEGENDS[flag] = ['+', 1, 'black', flag.replace('_',' ')] DEFAULT_LABELS = { # extra @@ -996,4 +1110,4 @@ 'zmin' : 0., 'zmax' : 0.1 }, -} \ No newline at end of file +} From 9640107c50c6fef109f0170100164791eedbc9e9 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 4 Apr 2024 16:06:57 +0200 Subject: [PATCH 191/319] Updates on running MESA grids (#263) * Update posydon-run-grid keep work dir in first steps of single stars * Update posydon-setup-grid Add cleanup and run script * Update posydon-setup-grid correct typo * Update posydon-setup-grid missing write in open * update example ini of running a grid * add group names for our clusters as asked by Jeff --- bin/posydon-run-grid | 3 ++- bin/posydon-setup-grid | 39 +++++++++++++++++++++++++++++++++++-- grid_params/grid_params.ini | 24 +++++++++++++++-------- 3 files changed, 55 insertions(+), 11 deletions(-) diff --git a/bin/posydon-run-grid b/bin/posydon-run-grid index 87ecfb4d4d..7912a8f3b5 100755 --- a/bin/posydon-run-grid +++ b/bin/posydon-run-grid @@ -874,7 +874,8 @@ if __name__ == '__main__': run_mesa(args.mesa_star1_executable, work_dir, final_dir, outfile_name='out_star1_formation_step{0}.txt'.format(index), keep_profiles=args.keep_profiles, keep_photos=args.keep_photos, - job_start=args.job_start, job_end=args.job_end) + job_start=args.job_start, job_end=args.job_end, + keep_work_dir=index Date: Thu, 18 Apr 2024 16:07:08 +0200 Subject: [PATCH 192/319] Allows non-Roche lobe overflowing systems in the RLO grids (#217) * Allows non-Roche lobe overflowing systems in the RLO grids so that reruns can update them. * Removing non-RLOFing systems during joining grids. * Reporting RLO count in joining grids only when start at RLO is set in the grids. * Initialize `ignore_reason` in each loop. * Using string contsants as ignore reasons. * Resting ignore reason when accepting run with missing final profile in single-star grids. * The priority of the ignore reasons, and the function ensuring its considered. * Using the set_ignore_reason function to keep the priority of ignore reasons. * Introducing the ignore class. * Using the IgnoreReason class in psygrid to keep the desired order of ignore reasons. * Removing the ignored_data flag - with the IgnoreReason class is not needed anymore! * Clarifying a comment. * Changing the ignore reasons' priority and unifying their names. * Changing ignore reasons text for consistency's sake. * Correcting a prior, wrong resolution of conflict with developement branch, and removing an expression with no effect. --- posydon/grids/psygrid.py | 135 ++++++++++++---------- posydon/interpolation/IF_interpolation.py | 17 +-- posydon/utils/ignorereason.py | 53 +++++++++ posydon/visualization/plot1D.py | 3 +- posydon/visualization/plot_defaults.py | 28 ++--- 5 files changed, 151 insertions(+), 85 deletions(-) create mode 100644 posydon/utils/ignorereason.py diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index a91e6f3752..cfa198bce1 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -209,6 +209,7 @@ from posydon.visualization.plot1D import plot1D from posydon.grids.downsampling import TrackDownsampler from posydon.grids.scrubbing import scrub, keep_after_RLO, keep_till_central_abundance_He_C +from posydon.utils.ignorereason import IgnoreReason HDF5_MEMBER_SIZE = 2**31 - 1 # maximum HDF5 file size when splitting @@ -229,6 +230,7 @@ for i in range(N_FLAGS)] TERMINATION_FLAG_COLUMNS_SINGLE = ["termination_flag_1", "termination_flag_3"] + # Default columns to be included from history and profile tables DEFAULT_BINARY_HISTORY_COLS = [ "model_number", "age", @@ -624,7 +626,7 @@ def decide_columns(key_in_config, defaults): # in case lists were used, get a proper `dtype` object dtype_initial_values = self.initial_values.dtype dtype_final_values = self.final_values.dtype - + self._say('Loading MESA data...') #this int array will store the run_index of the i-th run and will be @@ -634,7 +636,7 @@ def decide_columns(key_in_config, defaults): for i in tqdm.tqdm(range(N_runs)): # Select the ith run run = grid.runs[i] - ignore_data = False # if failed run, do not save any data + ignore = IgnoreReason() newTF1 = '' self._say('Processing {}'.format(run.path)) @@ -648,14 +650,13 @@ def decide_columns(key_in_config, defaults): BH_columns) # if no binary history, ignore this run if binary_grid and binary_history is None: - ignore_data = True - ignore_reason = "ignored_no_BH" + ignore.reason = "ignored_no_binary_history" warnings.warn("Ignored MESA run because of missing binary " "history in: {}\n".format(run.path)) if not initial_RLO_fix: continue - if ignore_data: + if ignore: history1 = None elif self.eeps is None: history1 = read_MESA_data_file(run.history1_path, H1_columns) @@ -673,11 +674,10 @@ def decide_columns(key_in_config, defaults): if not binary_grid and history1 is None: warnings.warn("Ignored MESA run because of missing " "history in: {}\n".format(run.path)) - ignore_data = True - ignore_reason = "ignore_no_H1" + ignore.reason = "ignored_no_history1" continue - if ignore_data: + if ignore: history2 = None else: history2 = read_MESA_data_file(run.history2_path, H2_columns) @@ -685,14 +685,14 @@ def decide_columns(key_in_config, defaults): history2 = fix_He_core(history2) # scrub histories (unless EEPs are selected or run is ignored) - if not ignore_data and self.eeps is None: + if not ignore and self.eeps is None: # read the model numbers and ages from the histories colname = "model_number" if history1 is not None: if colname in H1_columns: history1_mod = np.int_(history1[colname].copy()) else: - history1_mod = read_MESA_data_file(run.history1_path, + history1_mod = read_MESA_data_file(run.history1_path, [colname]) if history1_mod is not None: history1_mod = np.int_(history1_mod[colname]) @@ -706,12 +706,11 @@ def decide_columns(key_in_config, defaults): history1_mod = np.concatenate((history1_mod, add_mod)) warnings.warn("Expand mod in {}\n".format(run.history1_path)) else: - ignore_data = True - ignore_reason = "corrupted_history1" + ignore.reason = "corrupted_history1" if "star_age" in H1_columns: history1_age = history1["star_age"].copy() else: - history1_age = read_MESA_data_file(run.history1_path, + history1_age = read_MESA_data_file(run.history1_path, ["star_age"]) if history1_age is not None: history1_age = history1_age["star_age"] @@ -725,8 +724,7 @@ def decide_columns(key_in_config, defaults): history1_age = np.concatenate((history1_age, add_age)) warnings.warn("Expand age in {}\n".format(run.history1_path)) else: - ignore_data = True - ignore_reason = "corrupted_history1" + ignore.reason = "corrupted_history1" else: history1_mod = None history1_age = None @@ -735,7 +733,7 @@ def decide_columns(key_in_config, defaults): if colname in H2_columns: history2_mod = np.int_(history2[colname].copy()) else: - history2_mod = read_MESA_data_file(run.history2_path, + history2_mod = read_MESA_data_file(run.history2_path, [colname]) if history2_mod is not None: history2_mod = np.int_(history2_mod[colname]) @@ -749,12 +747,11 @@ def decide_columns(key_in_config, defaults): history2_mod = np.concatenate((history2_mod, add_mod)) warnings.warn("Expand mod in {}\n".format(run.history2_path)) else: - ignore_data = True - ignore_reason = "corrupted_history2" + ignore.reason = "corrupted_history2" if "star_age" in H2_columns: history2_age = history2["star_age"].copy() else: - history2_age = read_MESA_data_file(run.history2_path, + history2_age = read_MESA_data_file(run.history2_path, ["star_age"]) if history2_age is not None: history2_age = history2_age["star_age"] @@ -768,8 +765,7 @@ def decide_columns(key_in_config, defaults): history2_age = np.concatenate((history2_age, add_age)) warnings.warn("Expand age in {}\n".format(run.history2_path)) else: - ignore_data = True - ignore_reason = "corrupted_history2" + ignore.reason = "corrupted_history2" else: history2_mod = None history2_age = None @@ -792,8 +788,7 @@ def decide_columns(key_in_config, defaults): binary_history_mod = np.concatenate((binary_history_mod, add_mod)) warnings.warn("Expand mod in {}\n".format(run.binary_history_path)) else: - ignore_data = True - ignore_reason = "corrupted_binary_history" + ignore.reason = "corrupted_binary_history" if "age" in BH_columns: binary_history_age = binary_history["age"].copy() else: @@ -811,8 +806,7 @@ def decide_columns(key_in_config, defaults): binary_history_age = np.concatenate((binary_history_age, add_age)) warnings.warn("Expand age in {}\n".format(run.binary_history_path)) else: - ignore_data = True - ignore_reason = "corrupted_binary_history" + ignore.reason = "corrupted_binary_history" else: binary_history_mod = None binary_history_age = None @@ -838,8 +832,7 @@ def decide_columns(key_in_config, defaults): else: binary_history_len = 0 if binary_grid and binary_history_len == 0: - ignore_data = True - ignore_reason = "ignored_scrubbed" + ignore.reason = "ignored_scrubbed_history" warnings.warn("Ignored MESA run because of scrubbed binary" " history in: {}\n".format(run.path)) if not initial_RLO_fix: @@ -849,11 +842,11 @@ def decide_columns(key_in_config, defaults): else: history1_len = 0 if not binary_grid and history1_len == 0: - ignore_data = True + ignore.reason = "ignored_scrubbed_history" warnings.warn("Ignored MESA run because of scrubbed" " history in: {}\n".format(run.path)) continue - + try: #get mass from binary history init_mass_1 = float(binary_history["star_1_mass"][0]) except: #otherwise get it from directory name @@ -864,23 +857,25 @@ def decide_columns(key_in_config, defaults): kept = keep_till_central_abundance_He_C(binary_history, history1, history2, THRESHOLD_CENTRAL_ABUNDANCE, 0.1) binary_history, history1, history2, newTF1 = kept - + # check whether start at RLO is requested, and chop the history if start_at_RLO: kept = keep_after_RLO(binary_history, history1, history2) if kept is None: - ignore_data = True - ignore_reason = "ignored_no_RLO" + ignore.reason = "ignored_no_RLO" if self.verbose: self._say("Ignored MESA run because of no RLO" " in: {}\n".format(run.path)) if not initial_RLO_fix: + # TODO: we may want to keep these systems for + # allowing rerun grids to inform the old grids that + # a system is not RLOing anymore! continue binary_history, history1, history2 = None, None, None else: binary_history, history1, history2 = kept - if ignore_data: + if ignore: binary_history, history1, history2 = None, None, None final_profile1, final_profile2 = None, None else: @@ -892,12 +887,10 @@ def decide_columns(key_in_config, defaults): if self.config["accept_missing_profile"]: warnings.warn("Including MESA run despite the missing " "profile in {}\n".format(run.path)) - ignore_data = False else: warnings.warn("Ignored MESA run because of missing " "profile in: {}\n".format(run.path)) - ignore_data = True - ignore_reason = "ignore_no_FP" + ignore.reason = "ignored_no_final_profile" continue if binary_history is not None: @@ -938,7 +931,7 @@ def decide_columns(key_in_config, defaults): # get some initial values from the `binary_history.data` header # if of course, no RLO fix is applied - if binary_grid and not (start_at_RLO or ignore_data): + if binary_grid and not (start_at_RLO or ignore): # this is compatible with `.gz` files bh_header = np.genfromtxt(run.binary_history_path, skip_header=1, @@ -969,7 +962,7 @@ def decide_columns(key_in_config, defaults): init_separation = orbital_separation_from_period( init_period, init_mass_1, init_mass_2) initial_BH["binary_separation"] = init_separation - elif not binary_grid and not (start_at_RLO or ignore_data): + elif not binary_grid and not (start_at_RLO or ignore): # use header to get initial mass in single-star grids # this is compatible with `.gz` files h1_header = np.genfromtxt(run.history1_path, @@ -983,7 +976,7 @@ def decide_columns(key_in_config, defaults): addX = "X" in dtype_initial_values.names addY = "Y" in dtype_initial_values.names addZ = "Z" in dtype_initial_values.names - if (addX or addY or addZ) and not ignore_data: + if (addX or addY or addZ) and not ignore: # read abundances from history1 if present, else history2 if history1 is not None: read_from = run.history1_path @@ -1042,7 +1035,7 @@ def decide_columns(key_in_config, defaults): newcol = "S2_" + col self.final_values[i][newcol] = final_H2[col] - if not ignore_data: + if not ignore: if addX: self.initial_values[i]["X"] = where_to_add["X"] if addY: @@ -1051,16 +1044,16 @@ def decide_columns(key_in_config, defaults): self.initial_values[i]["Z"] = where_to_add["Z"] if binary_grid: - if ignore_data: - termination_flags = [ignore_reason] * N_FLAGS + if ignore: + termination_flags = [ignore.reason] * N_FLAGS else: termination_flags = get_flags_from_MESA_run( run.out_txt_path, binary_history=binary_history, history1=history1, history2=history2, start_at_RLO=start_at_RLO, newTF1=newTF1) else: - if ignore_data: - termination_flags = [ignore_reason] * N_FLAGS_SINGLE + if ignore: + termination_flags = [ignore.reason] * N_FLAGS_SINGLE else: termination_flags = [ get_flag_from_MESA_output(run.out_txt_path), @@ -1068,11 +1061,11 @@ def decide_columns(key_in_config, defaults): history1["star_mass"]) ] - if ignore_data: + if ignore: for colname in self.final_values.dtype.names: if (colname.startswith("termination_flag_") or colname.startswith("interpolation_class")): - self.final_values[i][colname] = ignore_reason + self.final_values[i][colname] = ignore.reason else: self.final_values[i][colname] = np.nan for colname in self.initial_values.dtype.names: @@ -1100,13 +1093,20 @@ def decide_columns(key_in_config, defaults): self.final_values[i]["interpolation_class"] = \ infer_interpolation_class(*termination_flags[:2]) - if ignore_data: - # if not fix requested and failed run, do not include it - continue + if ignore: + # if not fix requested and failed run, do not include the data + if start_at_RLO and (ignore.reason == "ignored_no_RLO"): + # allow non-RLOing systems to be included in the grid + # so that rerun grid can signify systems as non-RLOing + # instead of keeping the old system + pass + else: + continue + # no fix... discard this system # if data to be included, downsample and store if not slim: - if ignore_data: + if ignore: hdf5.create_group("/grid/run{}/".format(run_index)) else: # downsample and save @@ -1140,7 +1140,7 @@ def decide_columns(key_in_config, defaults): self.MESA_dirs.append(run.path) run_included_at[i] = run_index run_index += 1 - #check that new MESA path is added at run_index + # check that new MESA path is added at run_index lenMESA_dirs = len(self.MESA_dirs) if lenMESA_dirs!=run_index: warnings.warn("Non synchronous indexing: " + @@ -1553,7 +1553,7 @@ def rerun(self, path_to_file='./', runs_to_rerun=None, # check that the 'path_to_file' exists if not os.path.exists(path_to_file): os.makedirs(path_to_file) - + # check 'runs_to_rerun' has a valid type if isinstance(runs_to_rerun, list): runs_to_rerun_list = runs_to_rerun @@ -1562,7 +1562,7 @@ def rerun(self, path_to_file='./', runs_to_rerun=None, runs_to_rerun_list = list(runs_to_rerun) except: raise TypeError("'runs_to_rerun' should be a list or None.") - + # check termination flags if termination_flags is not None: if flags_to_check is None: @@ -1637,7 +1637,7 @@ def rerun(self, path_to_file='./', runs_to_rerun=None, for idx in runs_to_rerun_list_unique: runs_data[key].append(np.around(self.initial_values[grid_key][idx], NDIG)) if len(column_names)>0: - n_runs = len(runs_data[list(column_names)[0]]) + n_runs = len(runs_data[list(column_names)[0]]) else: n_runs = 0 # add new_mesa_flag @@ -2164,14 +2164,25 @@ def say(something): say("Inferring which runs to include, from which grid...") initial_params = {} n_substitutions = 0 + n_ignored_noRLO = 0 for grid_index, grid in enumerate(grids): for run_index, dir_path in enumerate(grid.MESA_dirs): # get the parameters part from the dir name params_from_path = initial_values_from_dirname(dir_path) if params_from_path in initial_params: n_substitutions += 1 + initial_params[params_from_path] = (grid_index, run_index) - say(" {} substituions detected.".format(n_substitutions)) + + if (grid[run_index].final_values["termination_flag_1"] + == "ignored_no_RLO"): + if params_from_path in initial_params: + del initial_params[params_from_path] + n_ignored_noRLO += 1 + + say(" {} substitutions detected.".format(n_substitutions)) + if newconfig["start_at_RLO"]: + say(" {} runs ignored beacuse of no RLOF.".format(n_ignored_noRLO)) say(" {} runs to be joined.".format(len(initial_params))) if (newconfig["initial_RLO_fix"]): @@ -2194,7 +2205,7 @@ def say(something): # add non existing new entry if not exists_already: detected_initial_RLO.append(new_sys) - + say("Opening new file...") # open new HDF5 file and start copying runs driver_args = {} if "%d" not in output_path else { @@ -2230,21 +2241,21 @@ def say(something): new_mesa_dirs.append(grid.MESA_dirs[run_index]) new_initial_values.append(grid.initial_values[run_index]) new_final_values.append(grid.final_values[run_index]) - + if (newconfig["initial_RLO_fix"]): flag1 = new_final_values[-1]["termination_flag_1"] - if (flag1 != "Terminate because of overflowing initial model" and - flag1 != "forced_initial_RLO"): + if (flag1 != "Terminate because of overflowing initial model" + and flag1 != "forced_initial_RLO"): mass1 = new_initial_values[-1]["star_1_mass"] mass2 = new_initial_values[-1]["star_2_mass"] period = new_initial_values[-1]["period_days"] nearest = get_nearest_known_initial_RLO(mass1, mass2, detected_initial_RLO) - if period', 2, 'tab:blue', 'case A1/A2'], 'case_A1/A2/A1': @@ -367,7 +367,7 @@ ['v', 2, 'black', 'case A1/A2/B2/C1'], 'case_A2/A1': ['v', 2, 'tab:gray', 'case A2/A1'], - + 'None': ['x', 1, 'tab:red', 'failed'], }, @@ -407,7 +407,7 @@ ['*', 1, 'black', 'BH'], 'NS': ['*', 1, 'tab:gray', 'NS'], - 'ignored_no_BH': + 'ignored_no_binary_history': ['s', 2, 'tab:olive', 'H-rich core H buring'], 'ignored_no_RLO': ['s', 2, 'tab:olive', 'H-rich core H buring'], @@ -449,7 +449,7 @@ ['*', 1, 'black', 'BH'], 'NS': ['*', 1, 'tab:gray', 'NS'], - 'ignored_no_BH': + 'ignored_no_binary_history': ['s', 2, 'tab:olive', 'H-rich core H buring'], 'ignored_no_RLO': ['s', 2, 'tab:olive', 'H-rich core H buring'], @@ -570,9 +570,9 @@ ['x', 1, 'red', 'Not converged'], 'unknown': ['+', 1, 'green', 'unknown'], - 'Reverse stable MT': + 'Reverse stable MT': ['s', 2, 'tab:olive', 'Stable reverse mass-transfer phase'], - 'Reverse unstable MT': + 'Reverse unstable MT': ['D', 1, 'tab:olive', 'Unstable reverse mass-transfer phase'], }, 'debug': { @@ -664,7 +664,7 @@ ['.', 1.5, 'black', TF1_label_initial], 'Not converged': ['x', 1, None, 'Not converged'], - 'ignored_no_BH': + 'ignored_no_binary_history': ['.', 1.5, color_unstable, TF1_label_initial], 'ignored_no_RLO': ['.', 1.5, color_unstable, TF1_label_initial], @@ -996,13 +996,13 @@ def add_flag_to_MARKERS_COLORS_LEGENDS(MARKERS_COLORS_LEGENDS, flag): # POSYDON population synthesis 'z_formation': [r'$z_\mathrm{formation}$', r'$\log_{10}(z_\mathrm{formation})$'], 'z_merger': [r'$z_\mathrm{merger}$', r'$\log_{10}(z_\mathrm{merger})$'], - 'm_tot': [r'$m_\mathrm{tot}\,[M_\odot]$', + 'm_tot': [r'$m_\mathrm{tot}\,[M_\odot]$', r'$\log_{10}(m_\mathrm{tot}/M_\odot)$'], 'm_chirp': [r'$m_\mathrm{chirp}\,[M_\odot]$', r'$\log_{10}(m_\mathrm{chirp}/M_\odot)$',], 'q': [r'$q$', r'$\log_{10}(q)$'], 'chi_eff': [r'$\chi_\mathrm{eff}$', r'$\log_{10}(\chi_\mathrm{eff})$'], - 'S1_mass': [r'$m_\mathrm{CO}\,[M_\odot]$', + 'S1_mass': [r'$m_\mathrm{CO}\,[M_\odot]$', r'$\log_{10}(m_\mathrm{CO}/M_\odot)$'], 'S2_mass': [r'$m_\mathrm{CO}\,[M_\odot]$' r'$\log_{10}(m_\mathrm{CO}/M_\odot)$'], @@ -1035,7 +1035,7 @@ def add_flag_to_MARKERS_COLORS_LEGENDS(MARKERS_COLORS_LEGENDS, flag): r'$\log_{10}(M_\mathrm{disk, acc} / M_\odot)$'] DEFAULT_LABELS[f'MODEL{i:02d}_m_disk_radiated'] = [r'$M_\mathrm{disk, rad} \, [M_\odot]$', r'$\log_{10}(M_\mathrm{disk, rad} / M_\odot)$'] - + # pre defined plottings PRE_SET_PLOTS = { From de3ba99c87dfd30438b3122a5c30abcc31702a77 Mon Sep 17 00:00:00 2001 From: Camille Liotine Date: Thu, 18 Apr 2024 09:35:04 -0500 Subject: [PATCH 193/319] Posydon error checking (#287) * Making the class with the posydon_exception * print exception in bin pop * add GridError and FlowError to POSYDON_Exception * only except POSYDON_Exception in binarypopulation.py * replace errors in MESA step with GridError, FlowError this is to facilitate initial debugging and error catching with POSYDON_Exception * add POSYDON_Exception import to step_mesa * Change of names * Removing undefined class property. * Correcting an import. * Using verbose_binary_errors to enable printing errors while running binary population. * Enhancing the POSYDONError to accept objects and print them if they are stars. * Docstrings and small changed in POSYDONError * Small corrections in the POSYDONError class. * fixing errors * some more merging conflicts * Adding POSYDONErrors to the DT evolution * converting general errors to POSYDON errors and code clean-up * change .ini parameter setup for error checking * adding grid errors to MESA step * fixing the __str__ methon of POSYDONError * add the initial params in the tracebacks * making addition SN errors POSYDONErrors * changing some errors to POSYDON erros * removing IntegrationError * minor changes in formatting and minor bug fix * Update binarypopulation.py * Update posydonerror.py --------- Co-authored-by: Eirini Co-authored-by: kkovlakas Co-authored-by: Jeff Andrews --- posydon/binary_evol/CE/step_CEE.py | 3 +- posydon/binary_evol/DT/double_CO.py | 31 ++-- posydon/binary_evol/DT/step_detached.py | 35 ++-- .../binary_evol/DT/step_initially_single.py | 6 +- posydon/binary_evol/DT/step_isolated.py | 5 +- posydon/binary_evol/DT/step_merged.py | 5 +- posydon/binary_evol/MESA/step_mesa.py | 160 +++++------------- posydon/binary_evol/SN/step_SN.py | 52 +++--- posydon/binary_evol/binarystar.py | 4 +- posydon/binary_evol/simulationproperties.py | 5 +- posydon/interpolation/IF_interpolation.py | 32 ++-- posydon/interpolation/interpolation.py | 3 +- posydon/popsyn/binarypopulation.py | 49 +++--- posydon/popsyn/normalized_pop_mass.py | 2 +- posydon/utils/common_functions.py | 62 ++----- posydon/utils/posydonerror.py | 74 ++++++++ 16 files changed, 253 insertions(+), 275 deletions(-) create mode 100644 posydon/utils/posydonerror.py diff --git a/posydon/binary_evol/CE/step_CEE.py b/posydon/binary_evol/CE/step_CEE.py index f448044310..e2cf3d9f97 100644 --- a/posydon/binary_evol/CE/step_CEE.py +++ b/posydon/binary_evol/CE/step_CEE.py @@ -42,6 +42,7 @@ from posydon.utils.common_functions import PATH_TO_POSYDON from posydon.utils.common_functions import check_state_of_star from posydon.utils.common_functions import calculate_lambda_from_profile, calculate_Mejected_for_integrated_binding_energy +from posydon.utils.posydonerror import FlowError warnings.simplefilter('always', UserWarning) @@ -525,7 +526,7 @@ def CEE_simple_alpha_prescription( # Check to make sure final orbital separation is positive if not (separation_postCEE > -self.CEE_tolerance_err): - raise Exception("CEE problem, negative postCEE separation") + raise ValueError("CEE problem, negative postCEE separation") if verbose: print("CEE alpha-lambda prescription") diff --git a/posydon/binary_evol/DT/double_CO.py b/posydon/binary_evol/DT/double_CO.py index a0205c56b4..8395631e83 100644 --- a/posydon/binary_evol/DT/double_CO.py +++ b/posydon/binary_evol/DT/double_CO.py @@ -15,7 +15,8 @@ from posydon.binary_evol.singlestar import STARPROPERTIES from posydon.utils.common_functions import orbital_period_from_separation from posydon.utils.common_functions import CO_radius - +from posydon.utils.common_functions import set_binary_to_failed +from posydon.utils.posydonerror import NumericalError class DoubleCO: """The double compact-object step class.""" @@ -59,26 +60,19 @@ def ev_contact(t, y): e = self.eccentricity # solve the equations at most 6 times while(status == -1 and n < 6): - s = solve_ivp( - lambda t, y: gr( - t, - y, - self.m1, - self.m2, - - ), + try: + s = solve_ivp( + lambda t, y: gr(t, y, self.m1, self.m2,), t_span=[0, max_time - t_inspiral], - y0=[ - a, - e - ], + y0=[a, e], method='BDF', events=ev_contact, rtol=1e-10, atol=1e-10, - dense_output=True - ) - + dense_output=True) + except: + set_binary_to_failed(binary) + raise NumericalError("SciPy encountered termination edge case while solving GR equations") t_inspiral += s.t[-1] status = s.status n += 1 @@ -111,7 +105,7 @@ def ev_contact(t, y): if s.status == -1: binary.state += ' (Integration failure)' - raise RuntimeError("Integrations failed", s.message) + raise NumericalError("Integrations failed", s.message) elif s.status == 1: @@ -226,12 +220,11 @@ def gr(t, y, M_acc, M): """TODO: add description and reference for the equations.""" g = constants.standard_cgrav c = constants.clight + y[0] = np.max(y[0], 0) a = y[0] y[1] = np.max(y[1], 0) e = y[1] - # if 0 < e < 10.0 ** (-3): - # e = 0.0 da = 0 de = 0 diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index 90ecfb1ff9..a87f4df7d4 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -37,7 +37,8 @@ ) from posydon.binary_evol.flow_chart import (STAR_STATES_CC, STAR_STATES_CO) import posydon.utils.constants as const - +from posydon.utils.posydonerror import NumericalError +from posydon.utils.posydonerror import MatchingError,POSYDONError LIST_ACCEPTABLE_STATES_FOR_HMS = ["H-rich_Core_H_burning"] @@ -716,7 +717,7 @@ def posydon_attribute(list_for_matching, star): MESA_labels, rs) for i in MESA_labels: if i not in self.root_keys: - raise Exception("Expected matching parameter not " + raise AttributeError("Expected matching parameter not " "added in the single star grid options.") scales = [] @@ -819,7 +820,7 @@ def sq_diff_function(x): MESA_labels, rs) for i in MESA_labels: if i not in self.root_keys: - raise Exception("Expected matching parameter not " + raise AttributeError("Expected matching parameter not " "added in the single star grid options.") scales = [] @@ -837,8 +838,12 @@ def sq_diff_function(x): MESA_labels, posydon_attributes, htrack, rs=rs) # bnds = ([m_min_H, m_max_H], [0, None]) - sol = minimize(sq_diff_function, x0, + try: + sol = minimize(sq_diff_function, x0, method="TNC", bounds=bnds) + except: + raise NumericalError("SciPy numerical differentiation occured outside boundary ", + "while matching to single star track") # if still not acceptable matching, we fail the system: if (np.abs(sol.fun) > tolerance_matching_integration_hard @@ -937,7 +942,7 @@ def __call__(self, binary): if companion_2_exists: # star2 is a single star self.non_existent_companion = 1 else: # no star in the system - raise Exception("There is no star to evolve. Who summoned me?") + raise POSYDONError("There is no star to evolve. Who summoned me?") if self.non_existent_companion == 0: #no isolated evolution, detached step of an actual binary # the primary in a real binary is potential compact object, or the more evolved star @@ -1002,7 +1007,7 @@ def __call__(self, binary): primary.htrack = False primary.co = False else: - raise Exception("States not recognized!") + raise ValueError("States not recognized!") # star 1 is a massless remnant, only star 2 exists elif self.non_existent_companion == 1: @@ -1020,7 +1025,7 @@ def __call__(self, binary): # only a compact object left return else: - raise Exception("State not recognized!") + raise ValueError("State not recognized!") # star 2 is a massless remnant, only star 1 exists elif self.non_existent_companion == 2: @@ -1035,9 +1040,9 @@ def __call__(self, binary): elif (binary.star_1.state in STAR_STATES_CO): return else: - raise Exception("State not recognized!") + raise ValueError("State not recognized!") else: - raise Exception("Non existent companion has not a recognized value!") + raise POSYDONError("Non existent companion has not a recognized value!") def get_star_data(binary, star1, star2, htrack, co, copy_prev_m0=None, copy_prev_t0=None): @@ -1079,10 +1084,6 @@ def get_star_data(binary, star1, star2, htrack, f"{t_after_matching-t_before_matching:.6g}") if pd.isna(m0) or pd.isna(t0): - # binary.event = "END" - # binary.state += " (GridMatchingFailed)" - # if self.verbose: - # print("Failed matching") return None, None, None if htrack: @@ -1093,7 +1094,7 @@ def get_star_data(binary, star1, star2, htrack, # check if m0 is in the grid if m0 < self.grid.grid_mass.min() or m0 > self.grid.grid_mass.max(): set_binary_to_failed(binary) - raise ValueError(f"The mass {m0} is out of the single star grid range and cannot be matched to a track.") + raise MatchingError(f"The mass {m0} is out of the single star grid range and cannot be matched to a track.") get_track = self.grid.get @@ -1168,7 +1169,7 @@ def get_star_data(binary, star1, star2, htrack, interp1d_pri = get_star_data( binary, primary, secondary, primary.htrack, False)[0] else: - raise Exception("During matching primary is either should be either normal or not normal. `non_existent_companion` should be zero.") + raise MatchingError("During matching primary is either should be either normal or not normal. `non_existent_companion` should be zero.") if interp1d_sec is None or interp1d_pri is None: @@ -1386,7 +1387,7 @@ def get_omega(star, is_secondary = True): # TODO: put it out of its misery here! else: if not (max_time - binary.time > 0.0): - raise Exception("max_time is lower than the current time. " + raise ValueError("max_time is lower than the current time. " "Evolution of the detached binary will go to " "lower times.") with np.errstate(all="ignore"): @@ -1919,7 +1920,7 @@ def get_star_profile(star, htrack, m0): get_star_final_values(primary, primary.htrack, m02) get_star_profile(primary, primary.htrack, m02) if primary.mass != secondary.mass: - raise ValueError( + raise POSYDONError( "Both stars are found to be ready for collapse " "(i.e. end of their life) during the detached " "step, but do not have the same mass") diff --git a/posydon/binary_evol/DT/step_initially_single.py b/posydon/binary_evol/DT/step_initially_single.py index 3a3981a55c..8b17bf1bd7 100644 --- a/posydon/binary_evol/DT/step_initially_single.py +++ b/posydon/binary_evol/DT/step_initially_single.py @@ -13,7 +13,8 @@ from posydon.binary_evol.flow_chart import ( STAR_STATES_H_RICH, STAR_STATES_HE_RICH) - +from posydon.utils.posydonerror import FlowError +from posydon.utils.posydonerror import POSYDONError LIST_ACCEPTABLE_STATES_FOR_HMS = ["H-rich_Core_H_burning"] LIST_ACCEPTABLE_STATES_FOR_HeMS = ["stripped_He_Core_He_burning"] @@ -44,8 +45,7 @@ def __init__(self, def __call__(self,binary): if binary.state != "initially_single_star": - raise ValueError("sent to InitiallySingleStep without the binary.state being initially_single_star") + raise AttributeError("sent to InitiallySingleStep without the binary.state being initially_single_star") binary.event == None - super().__call__(binary) diff --git a/posydon/binary_evol/DT/step_isolated.py b/posydon/binary_evol/DT/step_isolated.py index cd6802cb43..8080d7c3dd 100644 --- a/posydon/binary_evol/DT/step_isolated.py +++ b/posydon/binary_evol/DT/step_isolated.py @@ -30,6 +30,7 @@ from posydon.binary_evol.flow_chart import (STAR_STATES_CC) import posydon.utils.constants as const from posydon.binary_evol.DT.step_detached import detached_step +from posydon.utils.posydonerror import FlowError class IsolatedStep(detached_step): @@ -83,12 +84,12 @@ def __call__(self, binary): if binary.star_1.state.state == 'massless_remnant' or binary.star_2.state == 'massless_remnant': pass else: - raise ValueError("In merged or initially single stars, step one of the two stars should be 'massless_remnant' ") + raise FlowError("In merged or initially single stars, step one of the two stars should be 'massless_remnant' ") ''' elif binary.state == "disrupted": pass else: - raise ValueError("In isolated step binary.state=='disrupted' or 'initially_single_star' or 'merged' ") + raise FlowError("In isolated step binary.state=='disrupted' or 'initially_single_star' or 'merged' ") super().__call__(binary) diff --git a/posydon/binary_evol/DT/step_merged.py b/posydon/binary_evol/DT/step_merged.py index 50cb02d2bb..14c5b8478e 100644 --- a/posydon/binary_evol/DT/step_merged.py +++ b/posydon/binary_evol/DT/step_merged.py @@ -14,6 +14,7 @@ from posydon.binary_evol.singlestar import STARPROPERTIES, convert_star_to_massless_remnant from posydon.utils.common_functions import check_state_of_star from posydon.binary_evol.DT.step_isolated import IsolatedStep +from posydon.utils.posydonerror import FlowError import warnings @@ -95,9 +96,9 @@ def __call__(self,binary): elif binary.event == 'oMerging2': binary.star_2,binary.star_1 = merged_star_properties(binary.star_2,binary.star_1) else: - raise ValueError("binary.state='merged' but binary.event != 'oMerging1/2'") + raise FlowError("binary.state='merged' but binary.event != 'oMerging1/2'") else: - raise ValueError("step_merging initiated but binary.state != 'merged'") + raise FlowError("step_merging initiated but binary.state != 'merged'") binary.event = None if self.verbose: diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index b08de93b41..668aca0384 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -30,6 +30,7 @@ set_binary_to_failed,) from posydon.utils.data_download import data_download, PATH_TO_POSYDON_DATA from posydon.grids.MODELS import MODELS +from posydon.utils.posydonerror import FlowError, GridError # left POSYDON, right MESA @@ -251,7 +252,7 @@ def load_Interp(self, filename): """Load the interpolator that has been trained on the grid.""" if self.verbose: print("loading Interp: {}".format(filename)) - + # Check if interpolation files exist if not os.path.exists(filename): data_download() @@ -299,7 +300,7 @@ def __call__(self, binary): if not isinstance(binary, BinaryStar): raise ValueError("Must be an instance of BinaryStar") if not hasattr(self, 'step'): - raise AttributeError("No step defined for {}".format( + raise ValueError("No step defined for {}".format( self.__name__)) if self.flip_stars_before_step: flip_stars(binary) @@ -400,8 +401,6 @@ def single_star(self, star_type): else: raise ValueError('Single star_type = %s unknown!' % star_type) - # UPDATE STAR AND BINARY METHODS - def update_properties_NN(self, star_1_CO=False, star_2_CO=False, track_interpolation=False): """Update properites according to nearest neighbour interpolation. @@ -515,10 +514,6 @@ def update_properties_NN(self, star_1_CO=False, star_2_CO=False, getattr(binary, key_h)) if missing_values > 0: getattr(binary, key_h).extend([np.nan]*missing_values) - # DEBUG - # print(key, missing_values) - # print('fixed', len(getattr(self.binary, key - # + "_history"))) for k, star in enumerate(stars): for key in STARPROPERTIES: @@ -664,10 +659,7 @@ def update_properties_NN(self, star_1_CO=False, star_2_CO=False, if missing_values_star > 0: getattr(star, key_h).extend( [np.nan]*missing_values) - # DEBUG - # print(key, missing_values_star_1) - # print('fixed', len(getattr(self.binary.star_1, - # key + "_history"))) + # convert these flags to default POSYDON star states setattr(stars[0], 'state', cf.check_state_of_star(stars[0], star_CO=stars_CO[0])) @@ -751,28 +743,32 @@ def update_properties_NN(self, star_1_CO=False, star_2_CO=False, k_bh = k else: donor = star + key_bh = POSYDON_TO_MESA['star']['lg_mdot']+'_%d' % (k_bh+1) tmp_lg_mdot = np.log10(10**cb_bh[key_bh][-1] + cf.bondi_hoyle( binary, accretor, donor, idx=-1, wind_disk_criteria=True, scheme='Kudritzki+2000')) mdot_edd = cf.eddington_limit(binary, idx=-1)[0] + if 10**tmp_lg_mdot > mdot_edd: tmp_lg_mdot = np.log10(mdot_edd) accretor.lg_mdot = tmp_lg_mdot + if self.save_initial_conditions: mdot_history = np.array(cb_bh[key_bh]) edd = cf.eddington_limit(binary, idx=len_binary_hist)[0] history_of_attribute = (np.log10( 10**cb_bh[key_bh][0] + cf.bondi_hoyle( binary, accretor, donor, idx=len_binary_hist, - wind_disk_criteria=True, scheme='Kudritzki+2000'))) + wind_disk_criteria=True, scheme='Kudritzki+2000'))) if 10**history_of_attribute > edd: history_of_attribute = np.log10(edd) accretor.lg_mdot_history.append(history_of_attribute) + if track_interpolation: mdot_history = np.array(cb_bh[key_bh]) # looping from range(-N,0) where 0 is excluded - # note taht bondi_hoyle concatenates the current binary state + # bondi_hoyle concatenates the current binary state, # hence we loop one back range(-N-1,-1) tmp_h = [cf.bondi_hoyle(binary, accretor, donor, idx=i, wind_disk_criteria=True, @@ -894,7 +890,7 @@ def initial_final_interpolation(self, star_1_CO=False, star_2_CO=False): continue else: setattr(star, key, None) - # EXPERIMENTAL feature + # infer stellar states interpolation_class = self.classes['interpolation_class'] setattr(self.binary, f'interp_class_{self.grid_type}', interpolation_class) @@ -907,25 +903,12 @@ def initial_final_interpolation(self, star_1_CO=False, star_2_CO=False): star_CO=star_1_CO) S2_state_inferred = cf.check_state_of_star(self.binary.star_2, star_CO=star_2_CO) - #S1_state_classified = self.classes['S1_state'] - #S2_state_classified = self.classes['S2_state'] if interpolation_class != 'initial_MT': - # DEBUG - # if S1_state_inferred != S1_state_classified: - # warnings.warn('Inferred stellar state of star_1 %s is ' - # 'different from classified state %s, note that' - # 'by default we use the inferred!' % - # (S1_state_inferred,S1_state_classified)) - # if S2_state_inferred != S2_state_classified: - # warnings.warn('Inferred stellar state of star_2 %s is ' - # 'different from classified state %s, note that' - # 'by default we use the inferred!' % - # (S2_state_inferred,S2_state_classified)) setattr(self.binary.star_1, 'state', S1_state_inferred) setattr(self.binary.star_2, 'state', S2_state_inferred) - # else keep the current state + # else keep the current state binary_state, binary_event, MT_case = ( cf.get_binary_state_and_event_and_mt_case( self.binary, interpolation_class, verbose=self.verbose)) @@ -945,10 +928,12 @@ def initial_final_interpolation(self, star_1_CO=False, star_2_CO=False): else: donor = star key_bh = POSYDON_TO_MESA['star']['lg_mdot']+'_%d' % (k_bh+1) + tmp_lg_mdot = np.log10( 10**fv[key_bh] + cf.bondi_hoyle( binary, accretor, donor, idx=-1, wind_disk_criteria=True, scheme='Kudritzki+2000')) + mdot_edd = cf.eddington_limit(binary, idx=-1)[0] if 10**tmp_lg_mdot > mdot_edd: tmp_lg_mdot = np.log10(mdot_edd) @@ -1034,16 +1019,7 @@ def stop_at_condition(self, raise ValueError( 'Star can only be "star_1" or "star_2", you passed {0}'. format(star)) - # if property_history[-1] > value: - # binary.state += ' (OutsideGrid)' - # binary.event = 'END' - # return - # if value > property_history[-1]: - # #t = delta_t[-1] - # #binary.state += ' (OutsideGrid)' - # #binary.event = 'END' - # binary.event = 'MaxTime_exceeded' - # return + i = np.where(np.array(property_history) <= value)[0][-1] elif property in BINARYPROPERTIES: @@ -1051,9 +1027,6 @@ def stop_at_condition(self, property_history = getattr(binary, property + "_history") np.array(property_history.append(current_property)) - # if value > property_history[-1]: - # binary.event = 'MaxTime_exceeded' - # return i = np.where(np.array(property_history) <= value)[0][-1] # time at which to interpolate all quantities @@ -1092,24 +1065,11 @@ def stop_at_condition(self, if v_before is None or v_after is None: interpolated_quanties[star][key] = None continue - # Debug - interpolated_quanties[star][ - key] = self.interpolate_at_t( + interpolated_quanties[star][key] = self.interpolate_at_t( t, t_before, t_after, v_before, v_after) - # except: - # # DEBUG - # print('star', star, 'key', key) - # print('time', t_before, t, t_after) - # print('key', v_before, - # interpolated_quanties[star][key], v_after) + for key in BINARYPROPERTIES: - if key in [ - 'state', 'event', 'mass_transfer_case' - ]: - # if key in [ - # 'state', 'event', 'mass_transfer_case', - # 'nearest_neighbour_distance' - # ]: + if key in ['state', 'event', 'mass_transfer_case']: interpolated_quanties['binary'][key] = getattr( binary, key + "_history")[-1] elif key == 'time': @@ -1179,9 +1139,6 @@ def stop_at_condition(self, # in case track_interpolation = False we will flush all the history if self.flush_history: - # DEBUG - # print('Flushing history between', self.flush_entries, - # len(getattr(binary, 'time_history'))) if self.flush_entries is None: raise ValueError('flush_entries cannot be None!') for key in STARPROPERTIES: @@ -1316,7 +1273,6 @@ def __call__(self, binary): p < self.p_min): self.binary.event = 'redirect_from_ZAMS' return - # outside the mass grid for m1 elif (state_1 == 'H-rich_Core_H_burning' and state_2 == 'H-rich_Core_H_burning' and @@ -1324,7 +1280,7 @@ def __call__(self, binary): self.p_min <= p <= self.p_max and (m1 < self.m1_min or m1 > self.m1_max)): set_binary_to_failed(self.binary) - raise ValueError(f'The mass of m1 ({m1}) is outside the grid,' + raise GridError(f'The mass of m1 ({m1}) is outside the grid,' 'while the period is inside the grid.') # outside the mass grid for m2 # because m2_min is 0.5 Msun or from q_min, the minimum mass ratio is either @@ -1335,7 +1291,7 @@ def __call__(self, binary): self.p_min <= p <= self.p_max and (mass_ratio < np.max([self.q_min, 0.5/m1]) or mass_ratio > self.q_max)): set_binary_to_failed(self.binary) - raise ValueError(f'The mass of m2 ({m2}) is outside the grid,' + raise GridError(f'The mass of m2 ({m2}) is outside the grid,' 'while the period is inside the grid.') # redirect if CC1 elif (state_1 == 'H-rich_Central_C_depletion'): @@ -1347,7 +1303,7 @@ def __call__(self, binary): return else: set_binary_to_failed(self.binary) - raise ValueError('The star_1.state = %s, star_2.state = %s, ' + raise FlowError('The star_1.state = %s, star_2.state = %s, ' 'binary.event = %s and not H-rich_Core_H_burning ' '- H-rich_Core_H_burning - * - ZAMS' % (state_1, state_2, event)) @@ -1421,7 +1377,8 @@ def __call__(self, binary): self.binary.event = 'CC2' return else: - raise ValueError( + set_binary_to_failed(self.binary) + raise FlowError( 'The star_1.state = %s, star_2.state = %s, binary.state = %s, ' 'binary.event = %s and not CO - HMS - oRLO1/oRLO2!' % (state_1, state_2, state, event)) @@ -1444,7 +1401,7 @@ def __call__(self, binary): (m1 < self.m1_min or m1 > self.m1_max) )): set_binary_to_failed(self.binary) - raise ValueError(f'The mass of m1 ({m1}) is outside the grid,' + raise GridError(f'The mass of m1 ({m1}) is outside the grid,' 'while the period is inside the grid.') # period inside the grid, but m2 outside the grid @@ -1453,27 +1410,15 @@ def __call__(self, binary): (m2 < self.m2_min or m2 > self.m2_max) )): set_binary_to_failed(self.binary) - raise ValueError(f'The mass of m2 ({m2}) is outside the grid,' + raise GridError(f'The mass of m2 ({m2}) is outside the grid,' 'while the period is inside the grid.') - # period inside the grid, but m1 outside the grid (flipped stars) - elif ((self.flip_stars_before_step and - self.p_min <= p <= self.p_max and - (m2 < self.m1_min or m2 > self.m1_max) - )): - set_binary_to_failed(self.binary) - raise ValueError(f'The mass of m1 ({m2}) is outside the grid,' - 'while the period is inside the grid.') - # period inside the grid, but m2 outside the grid (flipped stars) - elif ((self.flip_stars_before_step and - self.p_min <= p <= self.p_max and - (m1 < self.m2_min or m1 > self.m2_max) - )): - set_binary_to_failed(self.binary) - raise ValueError(f'The mass of m2 ({m1}) is outside the grid,' - 'while the period is inside the grid.') - else: + if len(self.binary.state_history) > 2: + if self.binary.state_history[-2] == 'detached': + set_binary_to_failed(self.binary) + raise GridError('CO_HMS_RLO binary outside grid and coming from detached') + self.binary.state = "detached" self.binary.event = "redirect_from_CO_HMS_RLO" return @@ -1548,7 +1493,8 @@ def __call__(self, binary): self.binary.event = 'CC2' return else: - raise ValueError( + set_binary_to_failed(self.binary) + raise FlowError( 'The star_1.state = %s, star_2.state = %s, binary.state = %s, ' 'binary.event = %s and not CO - HeMS - oRLO1/oRLO2!' % (state_1, state_2, state, event)) @@ -1571,7 +1517,7 @@ def __call__(self, binary): (m1 < self.m1_min or m1 > self.m1_max) )): set_binary_to_failed(self.binary) - raise ValueError(f'The mass of m1 ({m1}) is outside the grid,' + raise GridError(f'The mass of m1 ({m1}) is outside the grid,' 'while the period is inside the grid.') # period inside the grid, but m2 outside the grid @@ -1580,27 +1526,15 @@ def __call__(self, binary): (m2 < self.m2_min or m2 > self.m2_max) )): set_binary_to_failed(self.binary) - raise ValueError(f'The mass of m2 ({m2}) is outside the grid,' + raise GridError(f'The mass of m2 ({m2}) is outside the grid,' 'while the period is inside the grid.') - - # period inside the grid, but m1 outside the grid with flipped stars - elif ((self.flip_stars_before_step and - self.p_min <= p <= self.p_max and - m1 < self.m2_min or m1 > self.m2_max)): - set_binary_to_failed(self.binary) - raise ValueError(f'The mass of m1 ({m1}) is outside the grid,' - 'while the period is inside the grid.') - - # period inside the grid, but m2 outside the grid with flipped stars - elif ((self.flip_stars_before_step and - self.p_min <= p <= self.p_max and - m2 < self.m1_min or m2 > self.m1_max)): - set_binary_to_failed(self.binary) - raise ValueError(f'The mass of m2 ({m2}) is outside the grid,' - 'while the period is inside the grid.') - - # Redirect to the detached step + else: + if len(self.binary.state_history) > 2: + if self.binary.state_history[-2] == 'detached': + set_binary_to_failed(self.binary) + raise GridError('CO_HeMS_RLO binary outside grid and coming from detached') + self.binary.state = "detached" self.binary.event = "redirect_from_CO_HeMS_RLO" return @@ -1662,13 +1596,6 @@ def __call__(self, binary): # catch and redirect double core collapse, this happens if q=1: if self.binary.star_1.state == 'stripped_He_Central_C_depletion': self.binary.event = 'CC1' - # REMOVED assume circularisation after first CC - # new_separation = self.binary.separation*( - # 1.-self.binary.eccentricity**2) - # self.binary.separation = new_separation - # self.binary.orbital_period = orbital_period_from_separation( - # new_separation, m1, m2) - # self.binary.eccentricity = 0. return # TODO: import states from flow_chart.py elif (state_1 in ['WD', 'NS', 'BH'] @@ -1679,7 +1606,8 @@ def __call__(self, binary): self.binary.event = 'CC2' return else: - raise ValueError( + set_binary_to_failed(self.binary) + raise FlowError( 'The star_1.state = %s, star_2.state = %s, binary.event = %s ' 'not supported by CO - HeMS grid!' % (state_1, state_2, event)) @@ -1698,12 +1626,12 @@ def __call__(self, binary): # period inside the grid, but m1 outside the grid elif (self.p_min <= p <= self.p_max) and (m1 < self.m1_min or m1 > self.m1_max): set_binary_to_failed(self.binary) - raise ValueError(f'The mass of m1 ({m1}) is outside the grid,' + raise GridError(f'The mass of m1 ({m1}) is outside the grid,' 'while the period is inside the grid.') # period inside the grid, but m2 outside the grid elif (self.p_min <= p <= self.p_max) and (m2 < self.m2_min or m2 > self.m2_max): set_binary_to_failed(self.binary) - raise ValueError(f'The mass of m2 ({m2}) is outside the grid,' + raise GridError(f'The mass of m2 ({m2}) is outside the grid,' 'while the period is inside the grid.') #elif p > self.p_max: # binary.event = 'redirect_from_CO_HeMS' diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index f18c9df79e..f6acb52fe9 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -55,6 +55,8 @@ STAR_STATES_C_DEPLETION) from posydon.grids.MODELS import MODELS +from posydon.utils.posydonerror import ModelError +from posydon.utils.common_functions import set_binary_to_failed from pandas import read_csv from sklearn import neighbors @@ -62,7 +64,6 @@ import json - path_to_Sukhbold_datasets = os.path.join(PATH_TO_POSYDON_DATA, "Sukhbold+16/") @@ -743,8 +744,8 @@ def collapse_star(self, star): else: for key in STARPROPERTIES: setattr(star, key, None) - star.state = "ERR" - raise ValueError("FAILED core collapse!") + set_binary_to_failed(self.binary) + raise ModelError("FAILED core collapse!") elif self.mechanism in [self.Sukhbold16_engines, self.Patton20_engines, @@ -824,8 +825,8 @@ def collapse_star(self, star): else: for key in STARPROPERTIES: setattr(star, key, None) - star.state = "ERR" - raise ValueError("Invalid core state", state) + set_binary_to_failed(self.binary) + raise ModelError("Invalid core state: " + str(state)) elif self.use_core_masses: star.mass = m_grav @@ -868,11 +869,12 @@ def collapse_star(self, star): else: for key in STARPROPERTIES: setattr(star, key, None) - star.state = "ERR" - raise ValueError("FAILED core collapse!") + set_binary_to_failed(self.binary) + raise ModelError("FAILED core collapse!") else: - raise ValueError(f"The star cannot collapse: star state {state}.") + set_binary_to_failed(self.binary) + raise ModelError(f"The star cannot collapse: star state {state}.") star.metallicity = star.metallicity_history[-1] @@ -940,9 +942,7 @@ def PISN_prescription(self, star): m_PISN = None else: - raise ValueError( - "This choice {} of PISN is not availabe!".format(self.PISN) - ) + raise ValueError("This choice {} of PISN is not available!".format(self.PISN)) if self.verbose: if m_PISN is None: @@ -991,7 +991,7 @@ def check_SN_type(self, m_core, m_He_core, m_star): warnings.warn('co/He core masses are zero! ' 'Setting m_WD=m_star!') else: - raise ValueError('Invalid co/He core masses!') + raise ModelError('Invalid co/He core masses!') f_fb = 1.0 # no SN the no kick is assumed state = "WD" @@ -1032,7 +1032,7 @@ def check_SN_type(self, m_core, m_He_core, m_star): warnings.warn('co/He core masses are zero! ' 'Setting m_WD=m_star!') else: - raise ValueError('Invalid co/He core masses!') + raise ModelError('Invalid co/He core masses!') f_fb = 1.0 # no SN the no kick is assumed state = "WD" @@ -1072,7 +1072,7 @@ def check_SN_type(self, m_core, m_He_core, m_star): warnings.warn('co/He core masses are zero! ' 'Setting m_WD=m_star!') else: - raise ValueError('Invalid co/He core masses!') + raise ModelError('Invalid co/He core masses!') f_fb = 1.0 # no SN the no kick is assumed state = "WD" @@ -1124,7 +1124,7 @@ def compute_m_rembar(self, star, m_PISN): m_He_core = star.he_core_mass_history[-1] # M_sun else: raise ValueError( - "There are no informations in the evolutionary history" + "There is no information in the evolutionary history" "about STAR_STATES_CC." ) if m_core is None or np.isnan(m_core): @@ -1364,7 +1364,7 @@ def orbital_kick(self, binary): # Mcore = binary.star_1.co_core_mass_history[-1] else: raise ValueError( - "There are no informations in the evolutionary history " + "There is no information in the evolutionary history " "about STAR_STATES_CC." ) M_compact_object = binary.star_1.mass @@ -1421,8 +1421,7 @@ def orbital_kick(self, binary): mean_anomaly = binary.star_1.natal_kick_array[3] # check that ONLY one value is passed and is of type float if not isinstance(mean_anomaly, float): - raise ValueError( - "mean_anomaly must be a single float value.") + raise ValueError("mean_anomaly must be a single float value.") else: mean_anomaly = np.random.uniform(0, 2 * np.pi) binary.star_1.natal_kick_array[3] = mean_anomaly @@ -1461,7 +1460,7 @@ def orbital_kick(self, binary): # Mcore = binary.star_2.co_core_mass_history[-1] else: raise ValueError( - "There are no informations in the evolutionary history " + "There is no information in the evolutionary history " "about STAR_STATES_CC." ) @@ -1513,8 +1512,7 @@ def orbital_kick(self, binary): mean_anomaly = binary.star_2.natal_kick_array[3] # check that ONLY one value is passed and is of type float if not isinstance(mean_anomaly, float): - raise ValueError( - "mean_anomaly must be a single float value.") + raise ValueError("mean_anomaly must be a single float value.") else: mean_anomaly = np.random.uniform(0, 2 * np.pi) binary.star_2.natal_kick_array[3] = mean_anomaly @@ -1792,7 +1790,7 @@ def SNCheck( ) binary.true_anomaly_second_SN = true_anomaly else: - raise ValueError("This should never happen!") + raise ValueError(f"Binary is in SN step but binary state is not CC1 or CC2: {binary.state}") # compute new orbital period before reseting the binary properties binary.state = "detached" @@ -1969,7 +1967,7 @@ def get_CO_core_params(self, star, approximation=False): CO_core_mass = star.co_core_mass_at_He_depletion if (C_core_abundance is None) or (CO_core_mass is None): - raise ValueError('The history did not contain core masses at' + raise ModelError('The history did not contain core masses at' f' He depletion! {CO_core_mass}' f' {C_core_abundance}') @@ -2224,7 +2222,7 @@ def __call__(self, star, conserve_hydrogen_envelope=False): # m_core = star.co_core_mass_history[-1] # M_sun m_He_core = star.he_core_mass_history[-1] # M_sun else: - raise ValueError("There are no informations in the evolutionary " + raise ValueError("There is no information in the evolutionary " "history about STAR_STATES_CC.") k_result = int(self.stellar_type_classifier.predict([[m_He_core]])[0]) @@ -2246,7 +2244,7 @@ def __call__(self, star, conserve_hydrogen_envelope=False): m_rem = self.extrapolate_NS(m_He_core, self.mass_NS_interpolator) f_fb = 0. else: - raise Exception("Need a NS or BH to apply `Sukhbold16_corecollapse`.") + raise ValueError("Need a NS or BH to apply `Sukhbold16_corecollapse`.") return float(m_rem), f_fb, state @@ -2485,7 +2483,7 @@ def __call__(self, star, conserve_hydrogen_envelope=False): # m_core = star.co_core_mass_history[-1] # M_sun m_He_core = star.he_core_mass_history[-1] # M_sun else: - raise ValueError("There are no informations in the evolutionary " + raise ValueError("There is no information in the evolutionary " "history about STAR_STATES_CC.") # single_star_equivalent_ZAMS = \ # self.stellar_ZAMS_classifier.predict([[m_He_core]])[0] @@ -2515,7 +2513,7 @@ def __call__(self, star, conserve_hydrogen_envelope=False): # f_fb = m_rem / m_He_core f_fb = 0. else: - raise Exception("Need a NS or BH to apply `Sukhbold16_corecollapse`.") + raise ValueError("Need a NS or BH to apply `Sukhbold16_corecollapse`.") return float(m_rem), f_fb, state diff --git a/posydon/binary_evol/binarystar.py b/posydon/binary_evol/binarystar.py index 9eb681cfbb..72390e7eb1 100644 --- a/posydon/binary_evol/binarystar.py +++ b/posydon/binary_evol/binarystar.py @@ -111,7 +111,7 @@ def signal_handler(signum, frame): """React to a maximum time signal.""" - raise Exception("Binary Step Exceeded Alloted Time: {}". + raise RuntimeError("Binary Step Exceeded Alloted Time: {}". format(MAXIMUM_STEP_TIME)) @@ -210,7 +210,7 @@ def evolve(self): n_steps += 1 if max_n_steps is not None: if n_steps > max_n_steps: - raise Exception("Exceeded maximum number of steps ({})" + raise RuntimeError("Exceeded maximum number of steps ({})" .format(max_n_steps)) finally: signal.alarm(0) # turning off alarm diff --git a/posydon/binary_evol/simulationproperties.py b/posydon/binary_evol/simulationproperties.py index 76fab5fc61..e442787900 100644 --- a/posydon/binary_evol/simulationproperties.py +++ b/posydon/binary_evol/simulationproperties.py @@ -16,7 +16,6 @@ import time from posydon.utils.constants import age_of_universe - class SimulationProperties: """Class describing the properties of a population synthesis simulation.""" @@ -68,6 +67,8 @@ def __init__(self, properties=None, **kwargs): # for debugging purposes if not hasattr(self, 'max_n_steps_per_binary'): self.max_n_steps_per_binary = 100 + #if not hasattr(self, "verbose_binary_errors"): + # self.verbose_binary_errors = False # Set functions for evolution for key, val in kwargs.items(): @@ -100,7 +101,7 @@ def load_steps(self, verbose=False): def close(self): """Close hdf5 files before exiting.""" from posydon.binary_evol.MESA.step_mesa import MesaGridStep - from posydon.binary_evol.DT.step_detached import detached_step + from posydon.binary_evol.DT.step_detached import detached_step all_step_funcs = [getattr(self, key) for key, val in self.__dict__.items() if 'step_' in key] for step_func in all_step_funcs: diff --git a/posydon/interpolation/IF_interpolation.py b/posydon/interpolation/IF_interpolation.py index f318033878..9985cabc85 100644 --- a/posydon/interpolation/IF_interpolation.py +++ b/posydon/interpolation/IF_interpolation.py @@ -224,14 +224,14 @@ def __init__(self, grid=None, interpolators=None): if ("out_keys" not in params and len(self.interpolator_parameters) > 1): - raise Exception("Overlapping out keys between different " + raise ValueError("Overlapping out keys between different " "interpolators are not permited!") elif "out_keys" not in params: continue for key in params["out_keys"]: if(key in out_keys): - raise Exception( + raise ValueError( f"Overlapping out keys between different " f"interpolators are not permited! ({key} in more " f"than one set of out_keys)") @@ -418,12 +418,12 @@ def __init__(self, grid=None, in_keys=None, out_keys=None, in_scaling=None, self.interpolator = None if interp_classes is not None: if not isinstance(interp_classes, list): - raise Exception("interp_classes must be a list of " + raise ValueError("interp_classes must be a list of " "valid interpolation methods.") if isinstance(self.interp_method, list): if len(self.interp_method) != len(interp_classes): - raise Exception("No. of interpolation methods must " - "match no. of interpolation classes.") + raise ValueError("Number of interpolation methods must " + "match number of interpolation classes.") else: self.interp_method = [self.interp_method] * len(interp_classes) self.interp_classes = interp_classes @@ -435,7 +435,7 @@ def __init__(self, grid=None, in_keys=None, out_keys=None, in_scaling=None, else: if grid is None: - raise Exception( + raise RuntimeError( "grid must be specified to create an IF interpolator." " Conversely, load an existing model specifying filename.") @@ -786,7 +786,7 @@ def evaluate_mat(self, Xt): if len(Xt.shape) == 1: Xt = Xt.reshape((1, -1)) if Xt.shape[1] != self.n_in: - raise Exception("Wrong dimensions. Xt should have as many " + raise ValueError("Wrong dimensions. Xt should have as many " "columns as it was trained with.") # if binary classified as 'initial_MT', set numerical quantities to nan ynum, ycat = self.test_interpolator(Xt), self.test_classifiers(Xt) @@ -1040,9 +1040,9 @@ def predict(self, Xt): """ if self.interpolator is None: - raise Exception("Train Interpolator first.") + raise RuntimeError("Train Interpolator first.") if Xt.shape[1] != self.D: - raise Exception("Wrong input dimension.") + raise ValueError("Wrong input dimension.") def train_error(self, XT, YT): """Calculate approximation error given testing data. @@ -1169,8 +1169,8 @@ def __init__(self, classifier, classes, methods): self.methods = [methods] * len(self.classes) else: if len(methods) != len(self.classes): - raise Exception("No. of interpolators must match " - "no. of classes.") + raise ValueError("Number of interpolators must match " + "number of classes.") self.methods = methods for i, method in enumerate(self.methods): if method == "linear": @@ -1267,9 +1267,9 @@ def predict(self, Xt): """ if self.classifier is None: - raise Exception("Train Classifier first.") + raise RuntimeError("Train Classifier first.") if Xt.shape[1] != self.D: - raise Exception("Wrong input dimension.") + raise ValueError("Wrong input dimension.") def predict_prob(self, Xt): """Classify and get probability of input vector belonging to any class. @@ -1285,9 +1285,9 @@ def predict_prob(self, Xt): """ if self.classifier is None: - raise Exception("Train Classifier first.") + raise RuntimeError("Train Classifier first.") if Xt.shape[1] != self.D: - raise Exception("Wrong input dimension.") + raise ValueError("Wrong input dimension.") def train_error(self, XT, yT): """Calculate approximation error given testing data. @@ -1464,7 +1464,7 @@ class MatrixScaler: def __init__(self, norms, XT): """Initialize the Scaler with desired scalings.""" if len(norms) != XT.shape[1]: - raise Exception("The no. of columns in XT must be equal " + raise ValueError("The number of columns in XT must be equal " "to the length of norms.") self.N = XT.shape[1] self.scalers = [] diff --git a/posydon/interpolation/interpolation.py b/posydon/interpolation/interpolation.py index 950044ff88..5b0fec3b1e 100644 --- a/posydon/interpolation/interpolation.py +++ b/posydon/interpolation/interpolation.py @@ -197,8 +197,7 @@ def _normalize_fit(self, X, norms): """ if not (X.shape[1] == self.n_in) or not (len(norms) == self.n_in): - raise ValueError( - "The number of columns in X must match the length of norms.") + raise ValueError("The number of columns in X must match the length of norms.") self.scalers = [] Xn = np.empty_like(X) diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index 37a2b5f08a..81d1f65f3e 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -50,12 +50,13 @@ from posydon.popsyn.defaults import default_kwargs from posydon.popsyn.io import binarypop_kwargs_from_ini from posydon.utils.constants import Zsun - +from posydon.utils.posydonerror import POSYDONError,initial_condition_message +from posydon.utils.common_functions import set_binary_to_failed # 'event' usually 10 but 'detached (Integration failure)' can occur HISTORY_MIN_ITEMSIZE = {'state': 30, 'event': 25, 'step_names': 21, 'S1_state': 31, 'S2_state': 31, - 'mass_transfer_case': 10, + 'mass_transfer_case': 16, 'S1_SN_type': 5, 'S2_SN_type': 5} ONELINE_MIN_ITEMSIZE = {'state_i': 30, 'state_f': 30, 'event_i': 10, 'event_f': 31, @@ -102,16 +103,16 @@ def __init__(self, **kwargs): SimulationProperties()) atexit.register(lambda: BinaryPopulation.close(self)) self.metallicity = self.kwargs.get('metallicity', 1) - + # grab all metallicities in population or use single metallicity self.metallicities = self.kwargs.get('metallicities', [self.metallicity]) - + self.population_properties.max_simulation_time = self.kwargs.get( 'max_simulation_time') # years self.entropy = self.kwargs.get('entropy', None) seq = np.random.SeedSequence(entropy=self.entropy) - + self.comm = self.kwargs.pop('comm', None) self.JOB_ID = self.kwargs.pop('JOB_ID', None) if self.comm is not None and self.JOB_ID is not None: @@ -122,18 +123,18 @@ def __init__(self, **kwargs): # to be the same across all processes. if self.entropy is None: raise ValueError('A local MPI run requires an entropy value to be set.') - + self.rank = self.comm.Get_rank() self.size = self.comm.Get_size() # Make seed sequence unique per metallicity met_shift = self.metallicities.index(self.metallicity) - seq = np.random.SeedSequence(entropy=self.entropy + met_shift) + seq = np.random.SeedSequence(entropy=self.entropy + met_shift) seed_seq = [i for i in seq.spawn(self.size)][self.rank] - + # Job array runs elif self.JOB_ID is not None: self.rank = self.kwargs.pop('RANK', None) - self.size = self.kwargs.pop('size', None) + self.size = self.kwargs.pop('size', None) # Make sure each of the processes has the same entropy # But unique per metallicity if self.entropy is None: @@ -271,7 +272,7 @@ def _safe_evolve(self, **kwargs): else: # Create a directory for parallel runs if not os.path.exists(temp_directory): - try: + try: os.makedirs(temp_directory) except FileExistsError: pass @@ -290,9 +291,15 @@ def _safe_evolve(self, **kwargs): with warnings.catch_warnings(record=True) as w: try: binary.evolve() - except Exception: - binary.event = 'FAILED' - binary.traceback = traceback.format_exc() + except POSYDONError as posydon_error: + set_binary_to_failed(binary) + if self.kwargs.get("error_checking_verbose", False): + posydon_error.add_note(initial_condition_message(binary)) + traceback.print_exception(posydon_error) + except Exception as e: + set_binary_to_failed(binary) + e.add_note(initial_condition_message(binary)) + traceback.print_exception(e) if len(w) > 0: warnings.simplefilter("always") binary.warning_message = [x.message for x in w] @@ -405,14 +412,14 @@ def save(self, save_path, **kwargs): # get the temporary files in the directory tmp_files = [os.path.join(temp_directory, f) \ for f in os.listdir(temp_directory) \ - if os.path.isfile(os.path.join(temp_directory, f))] - + if os.path.isfile(os.path.join(temp_directory, f))] + if os.path.isdir(absolute_filepath): file_name = 'backup_save_pop_data.h5' file_path = os.path.join(dir_name, file_name) warnings.warn('The provided path is a directory - saving ' 'to {0} instead.'.format(file_path), Warning) - + self.combine_saved_files(absolute_filepath, tmp_files, **kwargs) def make_temp_fname(self): @@ -423,14 +430,14 @@ def make_temp_fname(self): def combine_saved_files(self, absolute_filepath, file_names, **kwargs): """Combine various temporary files in a given folder. - + Parameters ---------- absolute_filepath : str Absolute path to the file to be saved. file_names : list - List of absolute paths to the temporary files. - + List of absolute paths to the temporary files. + """ dir_name = os.path.dirname(absolute_filepath) @@ -448,7 +455,7 @@ def combine_saved_files(self, absolute_filepath, file_names, **kwargs): mode = kwargs.get('mode', 'a') complib = kwargs.get('complib', 'zlib') complevel = kwargs.get('complevel', 9) - + with pd.HDFStore(absolute_filepath, mode=mode, complevel=complevel, complib=complib) as store: for f in file_names: # strings itemsize set by first append max value, @@ -712,7 +719,7 @@ def save(self, fname, **kwargs): online_df = self.to_oneline_df(**kwargs) store.append('oneline', online_df, data_columns=True) - + return diff --git a/posydon/popsyn/normalized_pop_mass.py b/posydon/popsyn/normalized_pop_mass.py index 8be5353a9d..720abcb322 100644 --- a/posydon/popsyn/normalized_pop_mass.py +++ b/posydon/popsyn/normalized_pop_mass.py @@ -104,7 +104,7 @@ def fraction_simulated(f_bin, m_1, m_2, m_min, m_max, alpha2 = 2.35 alpha3 = 2.35 else: - raise ValueError("Scheme not included yet") + raise ValueError("Primary/Secondary mass scheme not included") f_bin = 0.7 m_min = 0.01 diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index 5c61148675..926ac0b46c 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -317,7 +317,7 @@ def eddington_limit(binary, idx=-1): accretor = binary.star_2 donor = binary.star_1 else: - raise Exception("Eddington-limit to be calculated for non-CO?") + raise ValueError("Eddington limit is being calculated for a non-CO") state_acc = np.atleast_1d( np.asanyarray([*accretor.state_history, accretor.state])[idx]) @@ -753,7 +753,7 @@ def read_histogram_from_file(path): continue arrays.append(np.fromstring(line.strip(), dtype=float, sep=",")) if len(arrays) > 2: - raise Exception("More than two lines found in the document.") + raise RuntimeError("More than two lines found in the histogram document.") return arrays @@ -799,24 +799,15 @@ def inspiral_timescale_from_separation(star1_mass, star2_mass, ecc = eccentricity if m1 <= 0: - raise ValueError( - "Mass of star 1 is <= 0, which is not a physical value." - ) + raise ValueError("Mass of star 1 is <= 0, which is not a physical value.") if m2 <= 0: - raise ValueError( - "Mass of star 2 is <= 0, which is not a physical value." - ) + raise ValueError("Mass of star 2 is <= 0, which is not a physical value.") if a <= 0: - raise ValueError( - "Separation is <= 0, which is not a physical value.") - + raise ValueError("Separation is <= 0, which is not a physical value.") if ecc < 0: - raise ValueError( - "Eccentricity is < 0, which is not a physical value.") + raise ValueError("Eccentricity is < 0, which is not a physical value.") if ecc >= 1: - raise ValueError( - "Eccentricity is >= 1, which is not a physical value." - ) + raise ValueError("Eccentricity is >= 1, which is not a physical value.") # Eq. (5.9) in Peters 1964 Phys. Rev. 136, B1224 beta = (64.0 / 5) * (G**3) * m1 * m2 * (m1 + m2) / (c**5) @@ -1047,8 +1038,6 @@ def get_binary_state_and_event_and_mt_case(binary, interpolation_class=None, gamma2 = binary.star_2.center_gamma_history[i] except IndexError: # this happens if compact object gamma2 = None - # prev_state currently unused, causing errors - # prev_state = binary.state_history[i-1] if i != 0 else MT_CASE_NO_RLO # get numerical MT cases mt_flag_1 = infer_mass_transfer_case(rl_overflow1, lg_mtransfer, state1, @@ -1180,8 +1169,7 @@ def CO_radius(M, COtype): # Stellar Interiors. Springer New York R = 2.9e8*(M)**(-1./3.)/const.Rsun else: - raise ValueError( - 'COtype not in the list of valid options: "BH", "NS", "WD"') + raise ValueError('COtype not in the list of valid options: "BH", "NS", "WD"') return R @@ -1902,8 +1890,8 @@ def calculate_core_boundary(donor_mass, # lowest value) for your MESA profile, so starting from the surface. ind_core = np.argmin(donor_mass >= mc1_i) else: - raise ValueError( - "Not possible to calculate the core boundary of the donor in CE") + raise ValueError("Not possible to calculate the core boundary of the donor in CE") + return ind_core @@ -2203,7 +2191,7 @@ def calculate_lambda_from_profile( print("Ebind_i from profile ", Ebind_i) print("lambda_CE ", lambda_CE) if not (lambda_CE > -tolerance): - raise Exception("CEE problem, lamda_CE has negative value.") + raise ValueError("lamda_CE has a negative value") return lambda_CE, mc1_i, rc1_i @@ -2325,11 +2313,8 @@ def get_internal_energy_from_profile(common_envelope_option_for_lambda, # specific internal energy - if we would have used the "total_energy" # it would include (internal+potential+kinetic+rotation) specific_donor_internal_energy = profile["energy"] - # if (np.any(specific_donor_internal_energy < -tolerance)):#if negative - # raise ValueError("CEE problem calculating internal energy, " - # "giving negative values") if not (np.any(specific_donor_internal_energy > -tolerance)): - raise Exception("CEE problem calculating internal energy, " + raise ValueError("CEE problem calculating internal energy, " "giving negative values.") specific_donor_H2recomb_energy = calculate_H2recombination_energy( @@ -2340,7 +2325,7 @@ def get_internal_energy_from_profile(common_envelope_option_for_lambda, specific_donor_internal_energy = ( specific_donor_internal_energy - specific_donor_H2recomb_energy) if not (np.any(specific_donor_internal_energy > -tolerance)): - raise Exception( + raise ValueError( "CEE problem calculating recombination (and H2 recombination) " "energy, remaining internal energy giving negative values.") elif ((common_envelope_option_for_lambda == "lambda_from_profile_" @@ -2350,7 +2335,7 @@ def get_internal_energy_from_profile(common_envelope_option_for_lambda, # include (internal+potential+kinetic+rotation) specific_donor_internal_energy = profile["energy"] if not (np.any(specific_donor_internal_energy > -tolerance)): - raise Exception("CEE problem calculating internal energy, " + raise ValueError("CEE problem calculating internal energy, " "giving negative values.") # we still need to subtract the H2 recombination energy which is @@ -2365,12 +2350,8 @@ def get_internal_energy_from_profile(common_envelope_option_for_lambda, specific_donor_internal_energy - specific_donor_recomb_energy - specific_donor_H2recomb_energy) - # if (np.any(specific_donor_internal_energy < -tolerance)):#if negative - # raise ValueError("CEE problem calculating recombination (and H2 " - # "recombination) energy, remaining internal energy " - # "giving negative values") if not (np.any(specific_donor_internal_energy > -tolerance)): - raise Exception( + raise ValueError( "CEE problem calculating recombination (and H2 recombination) " "energy, remaining internal energy giving negative values.") return specific_donor_internal_energy @@ -2405,11 +2386,8 @@ def calculate_H2recombination_energy(profile, tolerance=0.001): 35999.582894 * const.inversecm2erg / (2.0 * const.H_weight) * profile['x_mass_fraction_H'] * const.avo) # http://www.nat.vu.nl/~griessen/STofHinM/ChapIIHatomMoleculeGas.pdf - # if np.any(specific_donor_H2recomb_energy < -tolerance): # if negative - # raise ValueError("CEE problem calculating H2 recombination " - # "energy, giving negative values") if not (np.any(specific_donor_H2recomb_energy > -tolerance)): - raise Exception("CEE problem calculating H2 recombination energy, " + raise ValueError("CEE problem calculating H2 recombination energy, " "giving negative values") # return specific_donor_H2recomb_energy * const.ev2erg return specific_donor_H2recomb_energy @@ -2463,12 +2441,8 @@ def calculate_recombination_energy(profile, tolerance=0.001): specific_donor_recomb_energy = profile_recomb_energy( profile['x_mass_fraction_H'], profile['y_mass_fraction_He'], frac_HII, frac_HeII, frac_HeIII) - # if (np.any(specific_donor_recomb_energy < -tolerance)): # if negative - # print(specific_donor_recomb_energy) - # raise ValueError("CEE problem calculating recombination energy," - # " giving negative values") if not (np.any(specific_donor_recomb_energy > -tolerance)): - raise Exception("CEE problem calculating recombination energy, " + raise ValueError("CEE problem calculating recombination energy, " "giving negative values.") return specific_donor_recomb_energy @@ -2560,7 +2534,7 @@ def calculate_binding_energy(donor_mass, donor_radius, donor_dm, print("Grav_energy, donor_mass, donor_dm, donor_radius", Grav_energy, donor_mass, donor_dm, donor_radius) if not (Grav_energy < tolerance): - raise Exception("CEE problem calculating gravitational energy, " + raise ValueError("CEE problem calculating gravitational energy, " "giving positive values.") # binding energy of the enevelope equals its gravitational energy + # an a_th fraction of its internal energy diff --git a/posydon/utils/posydonerror.py b/posydon/utils/posydonerror.py new file mode 100644 index 0000000000..575a837d57 --- /dev/null +++ b/posydon/utils/posydonerror.py @@ -0,0 +1,74 @@ +"""The POSYDON exception class and subclasses for more specific errors.""" + +import copy +from posydon.binary_evol.binarystar import BinaryStar +from posydon.binary_evol.singlestar import SingleStar + + +class POSYDONError(Exception): + """General POSYDON exception class.""" + + def __init__(self, message="", objects=None): + """General POSYDON exception. + + Parameters + ---------- + message : str + POSYDONError message. + objects : None or list of objects. + A list of accompanied objects (or None if not set), that will be + handled differently when `str` method is used. This error can + only accept BinaryStar, SingleStar, or a list of each. + + """ + self.message = message + # copy the objects: we must know their state at the moment of the error + self.objects = copy.copy(objects) + super().__init__(self.message) + + def __str__(self): + """Create the text that accompanies this exception.""" + result = "" + if self.objects is not None: + if isinstance(self.objects,list): + for i, obj in enumerate(self.objects): + if isinstance(obj, (BinaryStar, SingleStar)): + result += f"\n\nOBJECT #{i+1} ({type(obj)}):\n{str(obj)}" + elif isinstance(obj, (BinaryStar, SingleStar)): + result += f"\n\nOBJECT #({type(self.objects)}):\n{str(self.objects)}" + else: + pass + return result + '\n'+ super().__str__() + +class GridError(POSYDONError): + """POSYDON error specific for PSyGrid operations.""" + +class FlowError(POSYDONError): + """POSYDON error specific for binary evolution flow errors.""" + +class ModelError(POSYDONError): + """POSYDON error specific for when a binary FAILS due to limitations of physics modeling assumptions.""" + +class NumericalError(POSYDONError): + """POSYDON error specific for when a binary FAILS due to limitations of numerical methods.""" + +class MatchingError(POSYDONError): + """POSYDON error specific for matching stars to single grid during the detached.""" + +def initial_condition_message(binary,ini_params = None ): + if ini_params is None: + ini_params = ["\nFailed Binary Initial Conditions:\n", + f"S1 mass: {binary.star_1.mass_history[0]} \n", + f"S2 mass: {binary.star_2.mass_history[0]} \n", + f"S1 state: {binary.star_1.state_history[0]} \n", + f"S2 state: { binary.star_2.state_history[0]}\n", + f"orbital period: { binary.orbital_period_history[0] } \n", + f"eccentricity: { binary.eccentricity_history[0]} \n", + f"binary state: { binary.state_history[0] }\n", + f"binary event: { binary.state_history[0] }\n", + f"S1 natal kick array: { binary.star_1.natal_kick_array }\n", + f"S2 natal kick array: { binary.star_2.natal_kick_array}\n"] + message = "" + for i in ini_params: + message += i + return message From 166f2a668afe6f0e02bdc05ccd43e6eb1b3158df Mon Sep 17 00:00:00 2001 From: ZepeiX <77278187+ZepeiX@users.noreply.github.com> Date: Thu, 25 Apr 2024 09:53:45 -0500 Subject: [PATCH 194/319] Update synthetic_population.py (#293) * Update synthetic_population.py * Update rate_calculation.py --- posydon/popsyn/rate_calculation.py | 10 +++++----- posydon/popsyn/synthetic_population.py | 2 +- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/posydon/popsyn/rate_calculation.py b/posydon/popsyn/rate_calculation.py index 8e7e375e81..1afb90dc2c 100644 --- a/posydon/popsyn/rate_calculation.py +++ b/posydon/popsyn/rate_calculation.py @@ -153,16 +153,16 @@ def get_data(self, var, index=None): values = self.m_chirp(self.df["S1_mass"], self.df["S2_mass"]) elif var == 'chi_eff': # check if tilts are provided - if ("S1_spin_orbit_tilt" not in self.df or - "S2_spin_orbit_tilt" not in self.df): + if ("S1_spin_orbit_tilt_second_SN" not in self.df or + "S2_spin_orbit_tilt_second_SN" not in self.df): warnings.warn('Spin tilts not in dataset! Assuming spins ' 'are aligned to orbital angular momentum ' 'to compute chi_eff!') tilt_BH1 = np.zeros(len(self.df["S1_spin"])) tilt_BH2 = tilt_BH1 else: - tilt_BH1 = self.df["S1_spin_orbit_tilt"] - tilt_BH2 = self.df["S2_spin_orbit_tilt"] + tilt_BH1 = self.df["S1_spin_orbit_tilt_second_SN"] + tilt_BH2 = self.df["S2_spin_orbit_tilt_second_SN"] values = self.chi_eff(self.df["S1_mass"], self.df["S2_mass"], self.df["S1_spin"], @@ -930,4 +930,4 @@ def load_grb_rate_weights(self, sensitivity, path_to_dir='./', extention='npz'): weights_2 = np.load(os.path.join(dir_, 'weights_2.npz'), allow_pickle=True)['key'] else: raise ValueError('Extension not supported!') - return index_1, z_formation_1, z_grb_1, weights_1, index_2, z_formation_2, z_grb_2, weights_2 \ No newline at end of file + return index_1, z_formation_1, z_grb_1, weights_1, index_2, z_formation_2, z_grb_2, weights_2 diff --git a/posydon/popsyn/synthetic_population.py b/posydon/popsyn/synthetic_population.py index b1f8fd9145..118d3f4b8e 100644 --- a/posydon/popsyn/synthetic_population.py +++ b/posydon/popsyn/synthetic_population.py @@ -473,7 +473,7 @@ def get_dco_at_formation(self, S1_state, S2_state, oneline_cols=None, formation_ # add properties of the oneline dataframe if self.df_oneline is not None: # TODO: add kicks as well by default? - save_cols = ['S1_spin_orbit_tilt', 'S2_spin_orbit_tilt'] + save_cols = ['S1_spin_orbit_tilt_second_SN', 'S2_spin_orbit_tilt_second_SN'] if compute_GRB_properties: save_cols += ['S1_m_disk_radiated', 'S2_m_disk_radiated'] if formation_channels: From b33be54c86b794ff09a8a1dd5aa516edbf00e851 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 2 May 2024 17:02:02 +0200 Subject: [PATCH 195/319] change name of slurm files (#294) --- bin/posydon-setup-grid | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/bin/posydon-setup-grid b/bin/posydon-setup-grid index 1b0e2b82c6..d733743f6c 100755 --- a/bin/posydon-setup-grid +++ b/bin/posydon-setup-grid @@ -1065,7 +1065,7 @@ if __name__ == '__main__': grid = pandas.read_csv(run_parameters['grid']) else: raise ValueError('Grid format not recognized, please feed in an acceptable format: csv') - grid_script = 'slurm_job_array_grid_submit.sh' + grid_script = 'job_array_grid_submit.slurm' with open(grid_script, 'w') as f: f.write('#!/bin/bash\n') @@ -1089,7 +1089,7 @@ if __name__ == '__main__': f.write('export MESA_DIR={0}\n\n\n'.format(os.environ['MESA_DIR'])) f.write(command_line) else: - grid_script = 'slurm_mpi_grid_submit.sh' + grid_script = 'mpi_grid_submit.slurm' with open(grid_script, 'w') as f: f.write('#!/bin/bash\n') @@ -1111,7 +1111,7 @@ if __name__ == '__main__': f.write('export MESA_DIR={0}\n\n\n'.format(os.environ['MESA_DIR'])) f.write(command_line) # create a cleanup script - with open('cleanup.sh', 'w') as f: + with open('cleanup.slurm', 'w') as f: f.write('#!/bin/bash\n') f.write('#SBATCH --account={0}\n'.format(slurm['account'])) @@ -1139,7 +1139,7 @@ if __name__ == '__main__': f.write('ID_GRID=$(sbatch --parsable {0})\n'.format(grid_script)) f.write('echo \"{0}'.format(grid_script)+' submitted as \"${ID_GRID}\n') f.write('ID_cleanup=$(sbatch --parsable --dependency=afterany:${ID_GRID} ' - '--kill-on-invalid-dep=yes cleanup.sh)\n') - f.write('echo \"cleanup.sh submitted as \"${ID_cleanup}\n') + '--kill-on-invalid-dep=yes cleanup.slurm)\n') + f.write('echo \"cleanup.slurm submitted as \"${ID_cleanup}\n') # make the runfile script executable os.system("chmod 755 run_grid.sh") From 93c5048148f09c477044a15234bd8c846b801333 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 9 May 2024 16:13:51 +0200 Subject: [PATCH 196/319] Update step_SN.py (#297) Have zero kick on a massless remnant --- posydon/binary_evol/SN/step_SN.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index f6acb52fe9..e12f44da0d 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -1390,6 +1390,9 @@ def orbital_kick(self, binary): sigma = self.sigma_kick_CCSN_NS elif binary.star_1.state == 'BH': sigma = self.sigma_kick_CCSN_BH + elif binary.star_1.state == 'massless_remnant': + # No kick on a massless object + sigma = None else: raise ValueError("CCSN/PPISN/PISN only for NS/BH.") # Kick for core-collapse SN @@ -1486,6 +1489,9 @@ def orbital_kick(self, binary): sigma = self.sigma_kick_CCSN_NS elif binary.star_2.state == 'BH': sigma = self.sigma_kick_CCSN_BH + elif binary.star_2.state == 'massless_remnant': + # No kick on a massless object + sigma = None else: raise ValueError("CCSN/PPISN/PISN only for NS/BH.") # Kick for core-collapse SN From 43d206fd37a07f0d93925873effcd08abd61f930 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 9 May 2024 16:57:58 +0200 Subject: [PATCH 197/319] remove old and not used code (#301) --- posydon/grids/downsampling_report.py | 257 --------------------------- 1 file changed, 257 deletions(-) delete mode 100644 posydon/grids/downsampling_report.py diff --git a/posydon/grids/downsampling_report.py b/posydon/grids/downsampling_report.py deleted file mode 100644 index 4756c79660..0000000000 --- a/posydon/grids/downsampling_report.py +++ /dev/null @@ -1,257 +0,0 @@ -"""Provides functions to evaluate the performance of grid downsampling.""" - - -__authors__ = [ - "Konstantinos Kovlakas ", -] - - -from posydon.grids.psygrid import PSyGrid -import numpy as np -import matplotlib.pyplot as plt -import tqdm - - -def report_DS(path, path_DS, max_err_used=None, - nmax=None, emax=None, emin=None): - """Report on compression ratio and interpolation error statistics.""" - TABLES = ["binary_history", "history1", "history2", - "final_profile1", "final_profile2"] - STEP = 0.01 - grid = PSyGrid(path) - grid_DS = PSyGrid(path_DS) - - x_H, x_P1, x_P2, y_H, y_P1, y_P2 = [[] for _ in range(6)] - - avg_interp_errors = {} - max_interp_errors = {} - med_interp_errors = {} - - def lengths(arrays1, arrays2): - assert(len(arrays1) == len(arrays2)) - result1 = [] - result2 = [] - for arr1, arr2 in zip(arrays1, arrays2): - if arr1 is None: - assert(arr2 is None) - result1.append(np.nan) - result2.append(np.nan) - else: - assert(arr2 is not None) - result1.append(len(arr1)) - result2.append(len(arr2)) - return result1, result2 - - for i, (run, run_DS) in tqdm.tqdm(enumerate(zip(grid, grid_DS))): - if nmax is not None and i >= nmax: - break - BH, H1, H2, P1, P2 = [run[table] for table in TABLES] - BH_DS, H1_DS, H2_DS, P1_DS, P2_DS = [run_DS[table] for table in TABLES] - - (x_h, x_p1, x_p2), (y_h, y_p1, y_p2) = lengths( - [BH, P1, P2], [BH_DS, P1_DS, P2_DS]) - x_H.append(x_h) - x_P1.append(x_p1) - x_P2.append(x_p2) - y_H.append(y_h) - y_P1.append(y_p1) - y_P2.append(y_p2) - - for tables, fromwhere, independent in zip( - [[BH, BH_DS], [H1, H1_DS], [H2, H2_DS], - [P1, P1_DS], [P2, P2_DS]], - ["BH", "H1", "H2", "P1", "P2"], - ["age", "star_age", "star_age", "mass", "mass"] - ): - orig, down = tables - if orig is None or down is None: - continue - - colnames = orig.dtype.names - for colname in colnames: - if colname == independent: - continue - fullname = fromwhere + "." + colname - - t_orig = orig[independent] - if len(t_orig) <= 2: - continue - - t_down = down[independent] - X_orig = orig[colname] - - if colname not in down.dtype.names: - if colname == "model_number": - # ignore it... probably comparing original vs EEP grid - continue - X_down = down[colname] - - if fromwhere in ["P1", "P2"]: - X_int = np.interp(t_orig[::-1], t_down[::-1], X_down[::-1]) - X_int = X_int[::-1] - else: - X_int = np.interp(t_orig, t_down, X_down) - errors = X_int - X_orig - - # ignore cases where interpolation breaks anyway - where_ok = np.ones_like(t_orig, dtype=bool) - where_ok[:-1] = np.diff(t_orig) > 0 - where_ok[1:] &= np.diff(t_orig) > 0 - errors = errors[where_ok] - - if len(errors) == 0: - continue - if not np.all(errors == 0.0): - errors = np.abs(errors / (np.max(X_orig) - np.min(X_orig))) - - avg_error = np.mean(errors) - max_error = np.max(errors) - med_error = np.median(errors) - - if fullname not in avg_interp_errors: - avg_interp_errors[fullname] = [avg_error] - med_interp_errors[fullname] = [med_error] - max_interp_errors[fullname] = [max_error] - else: - avg_interp_errors[fullname].append(avg_error) - med_interp_errors[fullname].append(med_error) - max_interp_errors[fullname].append(max_error) - - x_H, x_P1, x_P2, y_H, y_P1, y_P2 = [ - np.array(arr) for arr in [x_H, x_P1, x_P2, y_H, y_P1, y_P2]] - - grid.close() - grid_DS.close() - - fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1) - for x, y, label in zip([x_H, x_P1, x_P2], - [y_H, y_P1, y_P2], - ["History", "Profile1", "Profile2"]): - x = x[np.isfinite(x)] - y = y[np.isfinite(y)] - if len(x) == 0 or len(y) == 0: - continue - ratios = y / x - x_min = np.floor(min(ratios) / STEP) * STEP - STEP - x_max = max(1.0, np.ceil(max(ratios) / STEP) * STEP) + STEP - try: - bins = np.arange(x_min, x_max + STEP / 2.0, STEP) - except Exception: - bins = "fd" - ax1.hist(ratios, bins=bins, histtype="step", - label=label+" (n={})".format(len(ratios))) - ax2.plot(x, y, ".", label=label) - ax1.set_xlabel("Compression ratio") - ax1.set_ylabel("Number of runs") - ax2.set_xlabel("Original size") - ax2.set_ylabel("Downsampled size") - ax1.legend(loc="best") - ax2.legend(loc="best") - plt.tight_layout() - plt.show() - - sorted_avg_interp_errors = {k: v for k, v in sorted( - avg_interp_errors.items(), key=lambda item: np.mean(item[1]))} - - plt.figure(figsize=(8, 20)) - tick_labels = [] - tick_positions = [] - for i, (key, values) in enumerate(sorted_avg_interp_errors.items()): - y = np.ones_like(values) * i - tick_positions.append(i) - tick_labels.append(key) - plt.plot(max_interp_errors[key], y, "r.") - plt.plot(values, y, "ko", mfc="none") - plt.plot(med_interp_errors[key], y+0.2, "g.") - plt.plot([], [], "r.", label="Maximum") - plt.plot([], [], "ko", mfc="none", label="Average") - plt.plot([], [], "g.", label="Median") - plt.xscale("log") - plt.yticks(tick_positions, tick_labels) - plt.ylim(min(tick_positions)-1, max(tick_positions)-1) - if max_err_used is not None: - plt.axvline(max_err_used, label="max_err={:.4g}".format(max_err_used)) - plt.xlim(xmin=emin, xmax=emax) - plt.grid() - plt.xlabel("Average interpolation error") - plt.legend(loc="upper left") - plt.tight_layout() - plt.show() - - -def compare_DS(path, path_DS, runs=None, useonly=None): - """Compare the data in original and downsampled grids.""" - TABLES = ["binary_history", "history1", "history2", - "final_profile1", "final_profile2"] - grid = PSyGrid(path) - grid_DS = PSyGrid(path_DS) - - def lengths(arrays1, arrays2): - assert(len(arrays1) == len(arrays2)) - result1 = [] - result2 = [] - for arr1, arr2 in zip(arrays1, arrays2): - if arr1 is None: - assert(arr2 is None) - result1.append(np.nan) - result2.append(np.nan) - else: - assert(arr2 is not None) - result1.append(len(arr1)) - result2.append(len(arr2)) - return result1, result2 - - for i, (run, run_DS) in tqdm.tqdm(enumerate(zip(grid, grid_DS))): - if runs is not None and i not in runs: - continue - BH, H1, H2, P1, P2 = [run[table] for table in TABLES] - BH_DS, H1_DS, H2_DS, P1_DS, P2_DS = [run_DS[table] for table in TABLES] - - for tables, fromwhere, independent in zip( - [[BH, BH_DS], [H1, H1_DS], [H2, H2_DS], - [P1, P1_DS], [P2, P2_DS]], - ["BH", "H1", "H2", "P1", "P2"], - ["age", "star_age", "star_age", "mass", "mass"] - ): - orig, down = tables - if orig is None or down is None: - continue - - colnames = orig.dtype.names - for colname in colnames: - if colname == independent: - continue - fullname = fromwhere + "." + colname - - if useonly is not None: - if not any([fullname.startswith(col) for col in useonly]): - continue - - t_orig = orig[independent] - t_down = down[independent] - X_orig = orig[colname] - X_down = down[colname] - - if fromwhere in ["P1", "P2"]: - X_int = np.interp( - t_orig[::-1], t_down[::-1], X_down[::-1])[::-1] - else: - X_int = np.interp(t_orig, t_down, X_down) - errors = X_int - X_orig - if not np.all(errors == 0.0): - errors = np.abs(errors / (np.max(X_orig) - np.min(X_orig))) - - plt.figure(figsize=(6.4, 2.4)) - plt.suptitle("Run {}".format(i)) - plt.subplot(121) - plt.plot(t_orig, X_orig, "k.--") - plt.plot(t_down, X_down, "r.:") - plt.ylabel(fullname) - plt.subplot(122) - plt.plot(t_orig, errors, "k-") - plt.ylabel("Error") - plt.tight_layout() - plt.show() - - grid.close() - grid_DS.close() From cf933e466b96ac70e9823b02ff52849585928a92 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 16 May 2024 16:25:09 +0200 Subject: [PATCH 198/319] Add functions to read initial values from file (#299) * add functions to read initial values from file * make use of casefold * add POSYDON column names for star properties * change POSYDON column names to their casefold equivalent * add read of kick values * remove FileError; add description of python in-build errors * account for having number of binaries being defined and need to go back to independent sampling * add Eirini * remove doubled code and check for metallicity --- posydon/popsyn/binarypopulation.py | 82 +++-- posydon/popsyn/population_params_default.ini | 2 + posydon/popsyn/sample_from_file.py | 309 +++++++++++++++++++ posydon/utils/posydonerror.py | 80 ++++- 4 files changed, 437 insertions(+), 36 deletions(-) create mode 100644 posydon/popsyn/sample_from_file.py diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index 81d1f65f3e..b3dacf3651 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -20,7 +20,8 @@ "Konstantinos Kovlakas ", "Devina Misra ", "Simone Bavera ", - "Max Briel " + "Max Briel ", + "Matthias Kruckow ", ] @@ -45,6 +46,8 @@ from posydon.popsyn.independent_sample import (generate_independent_samples, binary_fraction_value) +from posydon.popsyn.sample_from_file import (get_samples_from_file, + get_kick_samples_from_file) from posydon.utils.common_functions import (orbital_period_from_separation, orbital_separation_from_period) from posydon.popsyn.defaults import default_kwargs @@ -517,7 +520,14 @@ def __init__(self, file_name=None, **kwargs): self.history_dfs = [] self.oneline_dfs = [] - self.binary_generator = BinaryGenerator(**kwargs) + if (('read_samples_from_file' in self.kwargs) and + (self.kwargs['read_samples_from_file']!='')): + self.binary_generator = BinaryGenerator(\ + sampler=get_samples_from_file, **kwargs) + else: + self.binary_generator = BinaryGenerator(\ + sampler=generate_independent_samples,\ + **kwargs) self.entropy = self.binary_generator.entropy if file_name: @@ -806,15 +816,30 @@ def draw_initial_samples(self, orbital_scheme='separation', **kwargs): # indices indices = np.arange(self._num_gen, self._num_gen+N_binaries, 1) + + # kicks + if 'read_samples_from_file' in kwargs: + kick1, kick2 = get_kick_samples_from_file(**kwargs) + else: + if 'number_of_binaries' in kwargs: + number_of_binaries = kwargs['number_of_binaries'] + else: + number_of_binaries = 1 + kick1 = np.array(number_of_binaries*[[None, None, None, None]]) + kick2 = np.array(number_of_binaries*[[None, None, None, None]]) + + # output output_dict = { 'binary_index': indices, - 'binary_fraction':binary_fraction, + 'binary_fraction': binary_fraction, 'time': formation_times, 'separation': separation, 'eccentricity': eccentricity, 'orbital_period': orbital_period, 'S1_mass': m1, 'S2_mass': m2, + 'S1_natal_kick_array': kick1, + 'S2_natal_kick_array': kick2, } self._num_gen += N_binaries return output_dict @@ -841,25 +866,31 @@ def draw_initial_binary(self, **kwargs): default_index = output['binary_index'].item() binary_fraction = output['binary_fraction'] + formation_time = output['time'].item() + m1 = output['S1_mass'].item() + Z_div_Zsun = kwargs.get('metallicity', 1.) + zams_table = {2.: 2.915e-01, + 1.: 2.703e-01, + 0.45: 2.586e-01, + 0.2: 2.533e-01, + 0.1: 2.511e-01, + 0.01: 2.492e-01, + 0.001: 2.49e-01, + 0.0001: 2.49e-01} + Z = Z_div_Zsun*Zsun + if Z_div_Zsun in zams_table.keys(): + Y = zams_table[Z_div_Zsun] + else: + raise KeyError(f"{Z_div_Zsun} is a not defined metallicity") + X = 1. - Z - Y + kick1 = output['S1_natal_kick_array'][0] + if self.RNG.uniform() < binary_fraction: - formation_time = output['time'].item() separation = output['separation'].item() orbital_period = output['orbital_period'].item() eccentricity = output['eccentricity'].item() - m1 = output['S1_mass'].item() m2 = output['S2_mass'].item() - Z_div_Zsun = kwargs.get('metallicity', 1.) - zams_table = {2.: 2.915e-01, - 1.: 2.703e-01, - 0.45: 2.586e-01, - 0.2: 2.533e-01, - 0.1: 2.511e-01, - 0.01: 2.492e-01, - 0.001: 2.49e-01, - 0.0001: 2.49e-01} - Y = zams_table[Z_div_Zsun] - Z = Z_div_Zsun*Zsun - X = 1. - Z - Y + kick2 = output['S2_natal_kick_array'][0] binary_params = dict( index=kwargs.get('index', default_index), @@ -876,6 +907,7 @@ def draw_initial_binary(self, **kwargs): metallicity=Z, center_h1=X, center_he4=Y, + natal_kick_array=kick1, ) star2_params = dict( mass=m2, @@ -883,26 +915,13 @@ def draw_initial_binary(self, **kwargs): metallicity=Z, center_h1=X, center_he4=Y, + natal_kick_array=kick2, ) #If binary_fraction not default a initially single star binary is created. else: - formation_time = output['time'].item() separation = np.nan orbital_period = np.nan eccentricity = np.nan - m1 = output['S1_mass'].item() - Z_div_Zsun = kwargs.get('metallicity', 1.) - zams_table = {2.: 2.915e-01, - 1.: 2.703e-01, - 0.45: 2.586e-01, - 0.2: 2.533e-01, - 0.1: 2.511e-01, - 0.01: 2.492e-01, - 0.001: 2.49e-01, - 0.0001: 2.49e-01} - Y = zams_table[Z_div_Zsun] - Z = Z_div_Zsun*Zsun - X = 1. - Z - Y binary_params = dict( index=kwargs.get('index', default_index), @@ -919,6 +938,7 @@ def draw_initial_binary(self, **kwargs): metallicity=Z, center_h1=X, center_he4=Y, + natal_kick_array=kick1, ) star2_params = properties_massless_remnant() diff --git a/posydon/popsyn/population_params_default.ini b/posydon/popsyn/population_params_default.ini index 01e7e47dc1..c9756c9ffc 100644 --- a/posydon/popsyn/population_params_default.ini +++ b/posydon/popsyn/population_params_default.ini @@ -283,6 +283,8 @@ max_simulation_time = 13.8e9 # float (0,inf) + read_samples_from_file = '' + # path to file to read initial parameters from (if empty string get random samples) primary_mass_scheme = 'Kroupa2001' # 'Salpeter', 'Kroupa1993', 'Kroupa2001' primary_mass_min = 7 diff --git a/posydon/popsyn/sample_from_file.py b/posydon/popsyn/sample_from_file.py new file mode 100644 index 0000000000..3440b6b0d5 --- /dev/null +++ b/posydon/popsyn/sample_from_file.py @@ -0,0 +1,309 @@ +"""Get the initial parameters for a binary population.""" + + +__authors__ = [ + "Matthias Kruckow ", +] + + +import os +import numpy as np +import pandas as pd +import warnings +from posydon.popsyn.independent_sample import (generate_orbital_periods, + generate_orbital_separations, + generate_eccentricities, + generate_primary_masses, + generate_secondary_masses) + +PRIMARY_MASS_NAMES = ['s1_mass', 'primary_mass', 'mass_1', 'm_1', 'm1'] +SECONDARY_MASS_NAMES = ['s2_mass', 'secondary_mass', 'mass_2', 'm_2', 'm2'] +PERIOD_NAMES = ['orbital_period', 'period', 'p_orb', 'porb', 'p'] +SEPARATION_NAMES = ['orbital_separation', 'separation', 'semi-major_axis',\ + 'semi_major_axis', 'a'] +ECCENTRICITY_NAMES = ['eccentricity', 'ecc', 'e'] +PRIMARY_KICK_VELOCITY_NAMES = ['s1_natal_kick_array_0', 'w_1', 'w1'] +SECONDARY_KICK_VELOCITY_NAMES = ['s2_natal_kick_array_0', 'w_2', 'w2'] +PRIMARY_KICK_AZIMUTHAL_ANGLE_NAMES = ['s1_natal_kick_array_1', 'phi_1', 'phi1'] +SECONDARY_KICK_AZIMUTHAL_ANGLE_NAMES = ['s2_natal_kick_array_1', 'phi_2',\ + 'phi2'] +PRIMARY_KICK_POLAR_ANGLE_NAMES = ['s1_natal_kick_array_2', 'theta_1', 'theta1'] +SECONDARY_KICK_POLAR_ANGLE_NAMES = ['s2_natal_kick_array_2', 'theta_2',\ + 'theta2'] +PRIMARY_KICK_MEAN_ANOMALY_NAMES = ['s1_natal_kick_array_3', 'mean_anomaly_1',\ + 'mean_anomaly1'] +SECONDARY_KICK_MEAN_ANOMALY_NAMES = ['s2_natal_kick_array_3',\ + 'mean_anomaly_2', 'mean_anomaly2'] + +def infer_key(available_keys=[], allowed_keys=[]): + """Infer key from list of allowed keys. + + Parameters + ---------- + available_keys : iterable object of str, e.g. list of str + Collection of available keys. + allowed_keys : iterable object of str, e.g. list of str + Collection of allowed keys. + + Returns + ------- + key : str + The first matched key. + """ + + for k in allowed_keys: + for key in available_keys: + if key.casefold() == k.casefold(): + return key + + return '' + +def get_samples_from_file(orbital_scheme='', **kwargs): + """Read a population of binaries at ZAMS from a file. + + Parameters + ---------- + orbital_scheme : str + Scheme to get the orbit: 'separation', 'period' + **kwargs : dictionary + kwargs from BinaryPopulation class, which should contain + read_samples_from_file + + Returns + ------- + orbital_scheme_set : ndarray of floats + orbital separations/periods depending on the scheme + eccentricity_set : ndarray of floats + eccentricities + m1_set : ndarray of floats + primary masses + m2_set : ndarray of floats + secondary masses + + """ + + if 'read_samples_from_file' in kwargs: + filename = kwargs['read_samples_from_file'] + else: + raise KeyError("In get_samples_from_file no 'read_samples_from_file'" + " in kwargs.") + # Check for number of binaries and save its value + if 'number_of_binaries' in kwargs: + number_of_binaries = kwargs['number_of_binaries'] + else: + number_of_binaries = 0 + # Check and read file into dataframe + if os.path.isfile(filename): + df = pd.read_csv(filename) + else: + raise FileNotFoundError(f'{filename} not found!') + + # Get number of data frame entries + set_n = len(df) + + # Get eccentricities + key = infer_key(available_keys=df.keys(), allowed_keys=ECCENTRICITY_NAMES) + if key=='': + warnings.warn(f'No eccentricity column found in {filename}, hence get' + ' independent random ones.') + kwargs['number_of_binaries'] = set_n + eccentricity_set = generate_eccentricities(**kwargs) + else: + eccentricity_set = np.array(df[key]) + + # Get primary masses + key = infer_key(available_keys=df.keys(), allowed_keys=PRIMARY_MASS_NAMES) + if key=='': + warnings.warn(f'No primary mass column found in {filename}, hence get' + ' independent random ones.') + kwargs['number_of_binaries'] = set_n + m1_set = generate_primary_masses(**kwargs) + else: + m1_set = np.array(df[key]) + + # Get secondary masses + key = infer_key(available_keys=df.keys(), + allowed_keys=SECONDARY_MASS_NAMES) + if key=='': + warnings.warn(f'No secondary mass column found in {filename}, hence' + ' get independent random ones.') + kwargs['number_of_binaries'] = set_n + m2_set = generate_secondary_masses(m1_set, **kwargs) + else: + m2_set = np.array(df[key]) + + if orbital_scheme == 'separation': + # Get orbital separations + key = infer_key(available_keys=df.keys(), + allowed_keys=SEPARATION_NAMES) + if key=='': + warnings.warn(f'No separation column found in {filename}, hence' + ' get independent random ones.') + kwargs['number_of_binaries'] = set_n + orbital_scheme_set = generate_orbital_separations(**kwargs) + else: + orbital_scheme_set = np.array(df[key]) + elif orbital_scheme == 'period': + # Get orbital periods + key = infer_key(available_keys=df.keys(), allowed_keys=PERIOD_NAMES) + if key=='': + warnings.warn(f'No period column found in {filename}, hence get' + ' independent random ones.') + kwargs['number_of_binaries'] = set_n + orbital_scheme_set = generate_orbital_periods(m1_set, **kwargs) + else: + orbital_scheme_set = np.array(df[key]) + else: + raise ValueError("Allowed orbital schemes are separation or period.") + + if number_of_binaries>0: + if 'index' in kwargs: + index = kwargs['index'] + else: + index = 0 + # Expand the sets by doubling them as long as it is needed + while len(orbital_scheme_set)", + "Eirini Kasdagli ", + "Konstantinos Kovlakas ", + "Matthias Kruckow ", +] + + import copy from posydon.binary_evol.binarystar import BinaryStar from posydon.binary_evol.singlestar import SingleStar @@ -40,21 +49,25 @@ def __str__(self): pass return result + '\n'+ super().__str__() -class GridError(POSYDONError): - """POSYDON error specific for PSyGrid operations.""" +# Subclasses of POSYODONError in alphabetic order +# Before you add a new subclass check the list of python error classes at the +# end of this file class FlowError(POSYDONError): """POSYDON error specific for binary evolution flow errors.""" +class GridError(POSYDONError): + """POSYDON error specific for PSyGrid operations.""" + +class MatchingError(POSYDONError): + """POSYDON error specific for matching stars to single grid during the detached.""" + class ModelError(POSYDONError): """POSYDON error specific for when a binary FAILS due to limitations of physics modeling assumptions.""" class NumericalError(POSYDONError): """POSYDON error specific for when a binary FAILS due to limitations of numerical methods.""" -class MatchingError(POSYDONError): - """POSYDON error specific for matching stars to single grid during the detached.""" - def initial_condition_message(binary,ini_params = None ): if ini_params is None: ini_params = ["\nFailed Binary Initial Conditions:\n", @@ -72,3 +85,60 @@ def initial_condition_message(binary,ini_params = None ): for i in ini_params: message += i return message + + +# There are 5 base exception classes in python: BaseException, Exception, +# ArithmeticError, BufferError, LookupError. Those are not aimed to be raised +# directly but their subclasses. +# +# List of Python's default exception (sub)classes in alphabetic order +# AssertionError: Raised when an assert statement fails. +# AttributeError: Raised when an attribute reference or assignment fails. +# EOFError: Raised when the input() function hits an end-of-file condition +# (EOF) without reading any data. +# GeneratorExit: Raised when a generator or coroutine is closed. +# ImportError: Raised when the import statement has troubles trying to load a +# module or a requested object does not exist in the module. +# "ModuleNotFoundError" is a subclass of this error. +# IndexError: Raised when a sequence subscript is out of range. +# KeyError: Raised when a mapping key is not found in the set of existing keys. +# KeyboardInterrupt: Raised when the user hits the interrupt key (normally +# Control-C or Delete). +# MemoryError: Raised when an operation runs out of memory. +# NameError: Raised when a local or global name is not found. +# "UnboundLocalError" is a subclass of this error. +# RuntimeError: Raised when an error is detected that doesn’t fall in any of +# the other categories. "NotImplementedError"(indicates a planned but not +# yet implemented class/function) and "RecursionError"(reached maximum +# recursion depth) are subclasses of this error. +# OSError: This exception is raised when a system function returns a +# system-related error. This error has different names in old or platform +# specfic versions like "EnvironmentError", "IOError", "WindowsError". +# There is a bunch of subclasses: "BlockingIOError", "ChildProcessError", +# "ConnectionError", "BrokenPipeError", "ConnectionAbortedError", +# "ConnectionRefusedError", "ConnectionResetError", "FileExistsError", +# "FileNotFoundError", "InterruptedError", "IsADirectoryError", +# "NotADirectoryError", "PermissionError", "ProcessLookupError", +# "TimeoutError". +# OverflowError: Raised when the result of an arithmetic operation is too large +# to be represented. +# ReferenceError: Raised when a reference is no longer valid because the +# referred object is garbage collected. +# StopIteration: Raised if there is no next item, while requested. +# StopAsyncIteration: Raised to stop asynchronous interation. +# SyntaxError: Raised when the parser encounters a syntax error. +# "IndentationError"(has an own subclass TabError) is a subclasses of this +# error. +# SystemError: Raised when the interpreter finds an internal error. +# SystemExit: This exception is subclass of BaseException instead of Exception +# to be not caugth with other exceptions. +# TypeError: Raised when an operation or function is applied to an object of +# inappropriate type. +# ValueError: Raised when an operation or function receives an argument that +# has the right type but an inappropriate value. "UnicodeError"(has own +# subclasses: "UnicodeEncodeError", "UnicodeDecodeError", +# "UnicodeTranslateError") is a subclass of this error. +# ZeroDivisionError: Raised when the second argument of a division or modulo +# operation is zero. +# +# for more details see https://docs.python.org/3/library/exceptions.html From 8c516c9aa6d003c044980a7c9025cde305942c2e Mon Sep 17 00:00:00 2001 From: Camille Liotine Date: Thu, 23 May 2024 10:00:20 -0500 Subject: [PATCH 199/319] Add extra hooks columns to binary.restore() (#306) * begin adding restore() functionality for extra hooks columns - not yet working for i != 0 * fix restore() and evolve () behavior for extra columns, remove delete_history * fix bug in binarypopulation * small fix in binarypopulation * add error to binarypopulation * setting param defaults to None instead of list --- posydon/binary_evol/binarystar.py | 30 ++++++++++++++------- posydon/binary_evol/simulationproperties.py | 24 ++++++++++++++--- posydon/binary_evol/singlestar.py | 28 ++++++++++++++----- posydon/popsyn/binarypopulation.py | 10 ++++--- 4 files changed, 68 insertions(+), 24 deletions(-) diff --git a/posydon/binary_evol/binarystar.py b/posydon/binary_evol/binarystar.py index 72390e7eb1..1b11e15b9a 100644 --- a/posydon/binary_evol/binarystar.py +++ b/posydon/binary_evol/binarystar.py @@ -156,6 +156,7 @@ def __init__(self, star_1=None, star_2=None, index=None, properties=None, else: setattr(self, item, binary_kwargs.pop(item, None)) setattr(self, item + '_history', [getattr(self, item)]) + for key, val in binary_kwargs.items(): setattr(self, key, val) if not hasattr(self, 'inspiral_time'): @@ -186,6 +187,7 @@ def __init__(self, star_1=None, star_2=None, index=None, properties=None, self.properties = properties else: self.properties = SimulationProperties() + def evolve(self): """Evolve a binary from start to finish.""" @@ -255,8 +257,8 @@ def switch_star(self): """Switch stars.""" self.star_1, self.star_2 = self.star_2, self.star_1 - def restore(self, i=0, delete_history=True): - """Restore the object to the i-th state. + def restore(self, i=0): + """Restore the BinaryStar() object to its i-th state, keeping the binary history before the i-th state. Parameters ---------- @@ -266,16 +268,24 @@ def restore(self, i=0, delete_history=True): """ # Move current binary properties to the ith step, using its history - for p in BINARYPROPERTIES: + for p in BINARYPROPERTIES: setattr(self, p, getattr(self, '{}_history'.format(p))[i]) - for star in (self.star_1, self.star_2): - star.restore(i) - # Remove the obsolete history data - if delete_history: - for p in BINARYPROPERTIES: - setattr(self, p + '_history', - getattr(self, p + '_history')[0:i + 1]) + ## delete the binary history after the i-th index + setattr(self, p + '_history', getattr(self, p + '_history')[0:i+1]) + + ## if running with extra hooks, restore any extra hook columns + for hook in self.properties.all_hooks_classes: + + if hasattr(hook, 'extra_binary_col_names'): + extra_columns = getattr(hook, 'extra_binary_col_names') + + for col in extra_columns: + setattr(self, col, getattr(self, col)[0:i+1]) + + for star in (self.star_1, self.star_2): + star.restore(i, hooks=self.properties.all_hooks_classes) + def reset(self, properties=None): """Reset the binary to its ZAMS state. diff --git a/posydon/binary_evol/simulationproperties.py b/posydon/binary_evol/simulationproperties.py index e442787900..eef1bba0dc 100644 --- a/posydon/binary_evol/simulationproperties.py +++ b/posydon/binary_evol/simulationproperties.py @@ -204,6 +204,16 @@ def post_evolve(self, binary): class EvolveHooks: """Base class for hooking into binary evolution.""" + + def __init__(self): + """ + Add any new output columns to the hooks constructor. + Example for extra binary columns: + self.extra_binary_col_names = ["column_name_1", "column_name_2"] + Example for extra star columns: + self.extra_star_col_names = ["column_name_1", "column_name_2"] + """ + pass def pre_evolve(self, binary): """Perform actions before a binary evolves.""" @@ -227,12 +237,15 @@ class TimingHooks(EvolveHooks): Example ------- - >>> pop.to_df(extra_columns=['step_times']) + >>> pop.to_df(extra_columns={'step_times': float}) """ + def __init__(self): + self.extra_binary_col_names = ["step_times"] def pre_evolve(self, binary): """Initialize the step time to match history.""" - binary.step_times = [0.0] + if not hasattr(binary, 'step_times'): + binary.step_times = [0.0] return binary def pre_step(self, binary, step_name): @@ -264,12 +277,15 @@ class StepNamesHooks(EvolveHooks): Name of evolutionary step as defined in SimulationProperties. - >>> pop.to_df(extra_columns=['step_names']) + >>> pop.to_df(extra_columns={'step_names': str}) """ + def __init__(self): + self.extra_binary_col_names = ["step_names"] def pre_evolve(self, binary): """Initialize the step name to match history.""" - binary.step_names = ['initial_cond'] + if not hasattr(binary, 'step_names'): + binary.step_names = ['initial_cond'] return binary def pre_step(self, binary, step_name): diff --git a/posydon/binary_evol/singlestar.py b/posydon/binary_evol/singlestar.py index 1a7e52cc90..cb8248a389 100644 --- a/posydon/binary_evol/singlestar.py +++ b/posydon/binary_evol/singlestar.py @@ -177,24 +177,38 @@ def append_state(self): for item in STARPROPERTIES: getattr(self, item + '_history').append(getattr(self, item)) - def restore(self, i=0, delete_history=True): - """Restore the object to the i-th state. + def restore(self, i=0, hooks=None): + """Restore the SingleStar() object to its i-th state, keeping the star history before the i-th state. Parameters ---------- i : int Index of the star object history to reset the star to. By default i == 0, i.e. the star will be restored to its initial state. + hooks : list + List of extra hooks associated with the SimulationProperties() of the BinaryStar() + object containing this SingleStar(), if applicable. This parameter is + automatically set when restoring a BinaryStar() object. """ + if hooks is None: + hooks = [] + # Move current star properties to the ith step, using its history for p in STARPROPERTIES: setattr(self, p, getattr(self, '{}_history'.format(p))[i]) - # Remove the obsolete history data - if delete_history: - for p in STARPROPERTIES: - setattr(self, p + '_history', - getattr(self, p + '_history')[0:i + 1]) + ## delete the star history after the i-th index + setattr(self, p + '_history', getattr(self, p + '_history')[0:i+1]) + + ## if running with extra hooks, restore any extra hook columns + for hook in hooks: + + if hasattr(hook, 'extra_star_col_names'): + extra_columns = getattr(hook, 'extra_star_col_names') + + for col in extra_columns: + setattr(self, col, getattr(self, col)[0:i+1]) + def to_df(self, **kwargs): """Return history parameters from the star in a DataFrame. diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index b3dacf3651..36c72e856c 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -634,7 +634,7 @@ def generate(self, **kwargs): self.append(binary) return binary - def from_hdf(self, indices, where=None, restore=False): + def from_hdf(self, indices=None, where=None, restore=False): """Load a BinaryStar instance from an hdf file of a saved population. Parameters @@ -644,11 +644,15 @@ def from_hdf(self, indices, where=None, restore=False): where : str Query performed on disk to select history and oneline DataFrames. restore : bool - Restore binaries back to initial conditions. + If true, restore binaries back to initial conditions. """ if where is None: - query_str = 'index==indices' + if indices is None: + raise ValueError("You must specify either the binary indices or a query string " + "to read from file.") + else: + query_str = 'index==indices' else: query_str = str(where) From ff58bcb179778c5aecc7641fc3ed38bffbf41819 Mon Sep 17 00:00:00 2001 From: Konstantinos Kovlakas <22239544+kkovlakas@users.noreply.github.com> Date: Thu, 23 May 2024 17:08:07 +0200 Subject: [PATCH 200/319] Unstable documentation fix (missing dependency) (#305) Including a dependency for building documentation during website deployment --- .github/workflows/deploy-github-pages-unstable.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/deploy-github-pages-unstable.yml b/.github/workflows/deploy-github-pages-unstable.yml index e28e76aebb..fcdbdb6f76 100644 --- a/.github/workflows/deploy-github-pages-unstable.yml +++ b/.github/workflows/deploy-github-pages-unstable.yml @@ -35,7 +35,7 @@ jobs: - name: Install dependencies run: | sudo apt-get update - sudo apt-get install gfortran swig libhdf5-serial-dev libmpich-dev + sudo apt-get install gfortran swig libhdf5-serial-dev libmpich-dev pandoc python -m pip install coverage cpp-coveralls flake8 pytest python -m pip install .[doc] python -m pip install .[ml] From 9c8695c455edea57c6bc4b582aba53e6b69cf1e1 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 23 May 2024 17:31:52 +0200 Subject: [PATCH 201/319] More fixes in post-processing pipeline (#281) * Update plot2D.py get dtypes while accounting for None * Update plot2D.py Raise errors, if initial or final values don't exist in the grid. * Update plot1D.py check for histories being None before requesting dtype. * Update psygrid.py Change warning to verbose output * Update scrubbing.py Remove warning about no RLO. * Update post_processing.py Add check, whether star can collapse. * Update post_processing.py correct typo: 1 -> star_i * Update post_processing.py debugging why star values aren't printed * Update profile_collapse.py Update default return in case of no fall back to be consistent with last change of normal rerun of do_core_collapse_BH. * rename pipeline files (add posydon) * add option to replace early MT case happening after a later one * add Matthias to author list * add possibility to add non-predefined MT in plotting * Update posydon-setup-pipeline Don't use shared log files anymore. * Update posydon-setup-pipeline correct typo * add cleanup jobs * more cleanup * use split instead of replace for title of all * add grid_type to step_1.csv * step 3: add grid_tpye to csv; add new field ORIGINAL_COMPRESSIONS * step 4: add interpolation_method * step R: add grid_type and rerun_metallicity * plots: add grid_type * doc changes * auto detect range in pipeline plotting * correct typo * only email on FAIL for cleanup jobs * update from developement * capture if date in None; first allow a retry if return code isn't 0 * add run directory to error output; add option allow_spin_None; add me to author list * more output * typo for max replacement * rephrase warning * expand error message * typo: nbin -> step * allow v1 dirnames without initial_z * remove spaces * add comment * more debugging of not a float * set disk values to nan in case of PISN * set values m_disk values from None to np.nan always in collapse_star * remove some debugging outputs * add more data to error output * addition for v1 to use solar metallicity, if it is an empty string * us pandas isna to determine empty metallicity, because it comes from a pandas read * for relative error ignore warnings if value is 0; request dtype float to avoid overflows on e.g. uint * allow to close open figures after they got saved * if new directories are created add them to list of new files * missing ')' * debugging overflow * remove some old code not needed anymore * account for any TF2 when getting TF12 * expand plot defaults, nonburning may have no star index * shorten TF12 earlier in nonburning case * add grid_type to step 4 to account for possibility of reverse MT * allow in spin check it to be None * account for CO_interpolation_class * account of None as star's spin in printing * fix typo * hot fix for case AA * fix missing TF2 for single stars * fix single star case for extra columns * dirty fix of having all NaN slices * make dirty fix working * move allow_spin_None out of pre defined models to a hard corded option for post-processing only * donot use normalization for initial_MT * more general treatment of missing keys incl. warning * restore lost parts of PR170 * add v1 case for single stars * address Max' comment * allow to search for a second termination code to over rule min_timestep_limit * add values at He depletion to final values * generalize values at He depletion adding; make use of values at He depletion * add None-check * add interpolation class 'stable_reverse_MT', where missing * allow tf1 or tf2 leading to IC being initial RLO * spin off limits and thresholds and use everywhere the same * remove doublicate * take common thresholds as default * get binary values at He depletion of both stars * correct doc string; us np.nan for lambda; set Ebind to 0 if lambda isna; correct energy check * remove possibility to add values from binary history at He depletion * correct typo * add cluster for reruns; add plot_extension; add switched to turn off all plots/checks * bugfix to copy from last step * use PdfPages to create multipage pdfs, plotting functions take a PdfPages object in case this should be used instead of saving to an own file * make multipage-pdf default and mention it in docs * add profile interpolation to pipeline * fix bugs * debugging * more information in error messages * load profile interpolation module only for step 5 and try it already when making setup * modify try load to do the same as the real one later * add missing module name; correct name and directory creation in multipage plotting * fix typo * typo * convert to list and check for it * try to recreate multipage pdf if one slice failed * ensure only one directory level is skipped for multipage plots * typo * special treatment for INTERP_ERROR plot with all data * change order for all/slice; add slash on path * add step 5 to quest example ini * update pipeline inis to new structure on quest * update docs * update doc-strings and errors in setup-pipeline * update doc-strings and errors in run-pipeline * correct index check of step 2 * fix: invert error check on step 2 * Requested fixes on the interpolation (to complete PR #281) (#295) * fixed point 5 * missed something for point 5 * implemented fixes addressing the out of hull warning as well as the nan-slice encountered issue * addressed some of the comments on GitHub, out of hull meta data still unclear * complement of isinfinite and removed print statements * made requested changes for out of hull warnings --------- Co-authored-by: prinse1545 * add scaler and klass to NNInterpolator.predict; add TODOs in docstrings for unexplained parameters --------- Co-authored-by: prinse1545 --- bin/posydon-run-pipeline | 496 ++++++++++++--- bin/posydon-setup-pipeline | 598 ++++++++++++++---- .../pipeline/pipeline_additions.rst | 11 +- .../pipeline/pipeline_steps.rst | 6 +- .../post_processing/pipeline_ini.rst | 21 +- .../run_full_piepeline.ipynb | 2 +- grid_params/pipeline_quest.ini | 25 +- grid_params/pipeline_yggdrasil.ini | 47 +- posydon/binary_evol/CE/step_CEE.py | 15 +- posydon/binary_evol/SN/profile_collapse.py | 3 +- posydon/binary_evol/SN/step_SN.py | 19 +- posydon/binary_evol/binarystar.py | 16 + posydon/binary_evol/singlestar.py | 8 + posydon/grids/MODELS.py | 43 +- posydon/grids/post_processing.py | 4 +- posydon/grids/psygrid.py | 44 +- posydon/grids/scrubbing.py | 22 +- posydon/grids/termination_flags.py | 25 +- posydon/interpolation/IF_interpolation.py | 96 ++- posydon/popsyn/synthetic_population.py | 2 +- posydon/utils/common_functions.py | 97 ++- posydon/utils/gridutils.py | 3 +- posydon/utils/limits_thresholds.py | 38 ++ posydon/visualization/combine_TF.py | 2 + posydon/visualization/interpolation.py | 1 + posydon/visualization/plot1D.py | 10 +- posydon/visualization/plot2D.py | 10 +- posydon/visualization/plot_defaults.py | 3 +- posydon/visualization/plot_pop.py | 6 +- 29 files changed, 1317 insertions(+), 356 deletions(-) create mode 100644 posydon/utils/limits_thresholds.py diff --git a/bin/posydon-run-pipeline b/bin/posydon-run-pipeline index ea6d7025b3..e870c13e47 100644 --- a/bin/posydon-run-pipeline +++ b/bin/posydon-run-pipeline @@ -45,6 +45,9 @@ STEP 4.1: plot interpolator accuracy and confusion matricies ['grid_random_1'] --> loop over all plot types +STEP 5: train profile interpolators +uses gird and initial-final interpolators to train the profile interpolators + STEP 9: export dataset STEP RERUN: rerun grid with a fix @@ -67,8 +70,8 @@ import pickle import random import numpy as np import pandas as pd -from shutil import copyfile from collections import Counter +from matplotlib.backends.backend_pdf import PdfPages from posydon.grids.psygrid import (PSyGrid, join_grids, DEFAULT_HISTORY_DS_EXCLUDE, @@ -79,8 +82,8 @@ from posydon.grids.post_processing import (post_process_grid, from posydon.grids.MODELS import MODELS from posydon.utils.common_functions import PATH_TO_POSYDON from posydon.utils.gridutils import get_new_grid_name -from posydon.visualization.plot_defaults import PRE_SET_PLOTS from posydon.interpolation.IF_interpolation import IFInterpolator +from posydon.visualization.plot_defaults import PRE_SET_PLOTS from posydon.visualization.interpolation import EvaluateIFInterpolator from posydon.visualization.combine_TF import TF1_POOL_ERROR @@ -105,28 +108,42 @@ COMPRESSIONS = { def create_grid_slice(i, path_to_csv_file, verbose=False, overwrite_psygrid=True): - """Creates a new PSyGrid slice.""" + """Creates a new PSyGrid slice. + + Parameters + ---------- + i : int + Index in csv file. + path_to_csv_file : str + Path to csv file. + verbose : bool + Enable/Disable additional output. + overwrite_psygrid : bool + Whether existing file(s) should be overwritten. + """ # read csv file if os.path.exists(path_to_csv_file): df = pd.read_csv(path_to_csv_file) else: - raise ValueError(f'{path_to_csv_file} not found!') + raise FileNotFoundError(f'{path_to_csv_file} not found!') + if i not in df.index: + raise KeyError(f'Index={i} not available in {path_to_csv_file}.') # get values from csv file if 'path_to_grid' in df.keys(): grid_path = df.loc[i,'path_to_grid'] if not os.path.isdir(grid_path): - raise ValueError(f'{grid_path} not found!') + raise NotADirectoryError(f'{grid_path} not found!') else: - raise ValueError(f'No path_to_grid in {path_to_csv_file}') + raise KeyError(f'No path_to_grid in {path_to_csv_file}') if 'compression' in df.keys(): compression = df.loc[i,'compression'] else: - raise ValueError(f'No compression in {path_to_csv_file}') + raise KeyError(f'No compression in {path_to_csv_file}') if 'grid_type' in df.keys(): grid_type = df.loc[i,'grid_type'] else: - raise ValueError(f'No grid_type in {path_to_csv_file}') + raise KeyError(f'No grid_type in {path_to_csv_file}') if 'stop_before_carbon_depletion' in df.keys(): stop_before_carbon_depletion = df.loc[i,'stop_before_carbon_depletion'] if 'HMS' not in grid_type: @@ -185,13 +202,26 @@ def create_grid_slice(i, path_to_csv_file, verbose=False, overwrite_psygrid=True def combine_grid_slices(i, path_to_csv_file, verbose=False): - """Combining grid slices to one grid.""" + """Combining grid slices to one grid. + + Parameters + ---------- + i : int + Index in csv file. + path_to_csv_file : str + Path to csv file. + verbose : bool + Enable/Disable additional output. + """ # read csv file if os.path.exists(path_to_csv_file): df = pd.read_csv(path_to_csv_file) else: - raise ValueError(f'{path_to_csv_file} not found!') + raise FileNotFoundError(f'{path_to_csv_file} not found!') + if i >= len(df.keys()): + raise KeyError(f'Not enought keys in {path_to_csv_file}: ' + f'{i}>{len(df.keys())-1}.') # get values from csv file grid_combined_key = df.keys()[i] gird_names = df[grid_combined_key].dropna().to_list() @@ -202,34 +232,46 @@ def combine_grid_slices(i, path_to_csv_file, verbose=False): def calculate_extra_values(i, path_to_csv_file, verbose=False): - """Calculating extra values, e.g. values derived from the final profile.""" + """Calculating extra values, e.g. values derived from the final profile. + + Parameters + ---------- + i : int + Index in csv file. + path_to_csv_file : str + Path to csv file. + verbose : bool + Enable/Disable additional output. + """ # read csv file if os.path.exists(path_to_csv_file): df = pd.read_csv(path_to_csv_file) else: - raise ValueError(f'{path_to_csv_file} not found!') + raise FileNotFoundError(f'{path_to_csv_file} not found!') + if i not in df.index: + raise KeyError(f'Index={i} not available in {path_to_csv_file}.') # get values from csv file if 'path_to_grid' in df.keys(): grid_path = df.loc[i,'path_to_grid'] if not os.path.isfile(grid_path): - raise ValueError(f'{grid_path} not found!') + raise FileNotFoundError(f'{grid_path} not found!') else: - raise ValueError(f'No path_to_grid in {path_to_csv_file}') + raise KeyError(f'No path_to_grid in {path_to_csv_file}') if 'grid_type' in df.keys(): grid_type = df.loc[i,'grid_type'] else: - raise ValueError(f'No grid_type in {path_to_csv_file}') + raise KeyError(f'No grid_type in {path_to_csv_file}') if 'path_to_processed_grid' in df.keys(): processed_grid_path = df.loc[i,'path_to_processed_grid'] else: - raise ValueError(f'No path_to_processed_grid in {path_to_csv_file}') + raise KeyError(f'No path_to_processed_grid in {path_to_csv_file}') if 'path_to_grid_ORIGINAL' in df.keys(): grid_ORIGINAL_path = df.loc[i,'path_to_grid_ORIGINAL'] if not os.path.isfile(grid_ORIGINAL_path): - raise ValueError(f'{grid_ORIGINAL_path} not found!') + raise FileNotFoundError(f'{grid_ORIGINAL_path} not found!') else: - raise ValueError(f'No path_to_grid_ORIGINAL in {path_to_csv_file}') + raise KeyError(f'No path_to_grid_ORIGINAL in {path_to_csv_file}') if verbose: print(f'Compute processed quantities for {grid_path} ...') @@ -250,7 +292,7 @@ def calculate_extra_values(i, path_to_csv_file, verbose=False): if verbose: print('Post processed grid file alredy exist, removing it.') os.remove(processed_grid_path) - copyfile(grid_path, processed_grid_path) + shutil.copyfile(grid_path, processed_grid_path) # load processed grid and append values grid = PSyGrid(processed_grid_path) @@ -262,32 +304,44 @@ def calculate_extra_values(i, path_to_csv_file, verbose=False): def train_interpolators(i, path_to_csv_file, verbose=False): - """Train an interpolator on a grid.""" + """Train an interpolator on a grid. + + Parameters + ---------- + i : int + Index in csv file. + path_to_csv_file : str + Path to csv file. + verbose : bool + Enable/Disable additional output. + """ # read csv file if os.path.exists(path_to_csv_file): df = pd.read_csv(path_to_csv_file) else: - raise ValueError(f'{path_to_csv_file} not found!') + raise FileNotFoundError(f'{path_to_csv_file} not found!') + if i not in df.index: + raise KeyError(f'Index={i} not available in {path_to_csv_file}.') # get values from csv file if 'path_to_grid' in df.keys(): grid_path = df.loc[i,'path_to_grid'] if not os.path.isfile(grid_path): - raise ValueError(f'{grid_path} not found!') + raise FileNotFoundError(f'{grid_path} not found!') else: - raise ValueError(f'No path_to_grid in {path_to_csv_file}') + raise KeyError(f'No path_to_grid in {path_to_csv_file}') if 'grid_type' in df.keys(): grid_type = df.loc[i,'grid_type'] else: - raise ValueError(f'No grid_type in {path_to_csv_file}') + raise KeyError(f'No grid_type in {path_to_csv_file}') if 'interpolation_method' in df.keys(): interpolation_method = df.loc[i,'interpolation_method'] else: - raise ValueError(f'No interpolation_method in {path_to_csv_file}') + raise KeyError(f'No interpolation_method in {path_to_csv_file}') if 'path_to_interpolator' in df.keys(): interpolator_path = df.loc[i,'path_to_interpolator'] else: - raise ValueError(f'No path_to_interpolator in {path_to_csv_file}') + raise KeyError(f'No path_to_interpolator in {path_to_csv_file}') method = interpolation_method if '_RLO' in method: @@ -344,7 +398,7 @@ def train_interpolators(i, path_to_csv_file, verbose=False): # core collapse quantities are interpolated with respect to the # compact object type # TODO: we need to train core collapse for secondary star as well - # to catch reverse mass transfer cases where the primary undergoes + # to catch reverse mass transfer cases where the secondary undergoes # core collapse first for MODEL_NAME in MODELS.keys(): for i in range(1,2): #TODO: range(1,3): @@ -380,26 +434,122 @@ def train_interpolators(i, path_to_csv_file, verbose=False): interp.save(interpolator_path) +def train_profile_interpolators(i, path_to_csv_file, verbose=False): + """Train a profile interpolator on a grid. + + Parameters + ---------- + i : int + Index in csv file. + path_to_csv_file : str + Path to csv file. + verbose : bool + Enable/Disable additional output. + """ + + # load module only if needed + from posydon.interpolation.profile_interpolation import CompileData, ProfileInterpolator + + # read csv file + if os.path.exists(path_to_csv_file): + df = pd.read_csv(path_to_csv_file) + else: + raise FileNotFoundError(f'{path_to_csv_file} not found!') + if i not in df.index: + raise KeyError(f'Index={i} not available in {path_to_csv_file}.') + # get values from csv file + if 'path_to_grid' in df.keys(): + grid_path = df.loc[i,'path_to_grid'] + if not os.path.isfile(grid_path): + raise FileNotFoundError(f'{grid_path} not found!') + else: + raise KeyError(f'No path_to_grid in {path_to_csv_file}') + if 'grid_type' in df.keys(): + grid_type = df.loc[i,'grid_type'] + else: + raise KeyError(f'No grid_type in {path_to_csv_file}') + if 'profile_names' in df.keys(): + profile_names = ast.literal_eval(df.loc[i,'profile_names']) + if not isinstance(profile_names, list): + raise TypeError(f'profile_names is not a list in line {i} ' + f'in {path_to_csv_file}') + else: + raise KeyError(f'No profile_names in {path_to_csv_file}') + if 'path_to_IF_interpolator' in df.keys(): + IF_interpolator_path = df.loc[i,'path_to_IF_interpolator'] + if not os.path.isfile(IF_interpolator_path): + raise FileNotFoundError(f'{IF_interpolator_path} not found!') + else: + raise KeyError(f'No path_to_IF_interpolator in {path_to_csv_file}') + if 'path_to_profile_interpolator' in df.keys(): + profile_interpolator_path = df.loc[i,'path_to_profile_interpolator'] + else: + raise KeyError(f'No path_to_profile_interpolator in {path_to_csv_file}') + + if verbose: + print(f'Train profile interpolators on {grid_path} with the ' + f'initial-final interpolator {IF_interpolator_path} to get ' + f'profiles of {profile_names}') + + # check interpolation_objects directory exists + interp_path = os.path.dirname(profile_interpolator_path) + if not os.path.isdir(interp_path): + os.makedirs(interp_path) + + train_second_star = 'CO' not in grid_type + # load grid profiles + grid_profiles = CompileData(grid_path, grid_path, + hms_s2=train_second_star, + profile_names=profile_names) + # save the profiles in a temporary pickle + tmp_profile_interpolator_path = profile_interpolator_path+".tmp" + grid_profiles.save(tmp_profile_interpolator_path) + + # create interpolator + interp = ProfileInterpolator() + # load profile pickle + interp.load_profiles(tmp_profile_interpolator_path) + # training + interp.train(IF_interpolator_path) + # saving + interp.save(profile_interpolator_path) + + # TODO: consider to remove the temporary pickle with the grid profiles + # os.remove(tmp_profile_interpolator_path) + + def export_dataset(i, path_to_csv_file, verbose=False): """Moving the data set to a place containing all, what is needed to run a - population synthesis with POSYDON.""" + population synthesis with POSYDON. + + Parameters + ---------- + i : int + Index in csv file. + path_to_csv_file : str + Path to csv file. + verbose : bool + Enable/Disable additional output. + """ # read csv file if os.path.exists(path_to_csv_file): df = pd.read_csv(path_to_csv_file) else: - raise ValueError(f'{path_to_csv_file} not found!') + raise FileNotFoundError(f'{path_to_csv_file} not found!') + if i not in df.index: + raise KeyError(f'Index={i} not available in {path_to_csv_file}.') # get values from csv file if 'path_to_grid' in df.keys(): grid_path = df.loc[i,'path_to_grid'] if not os.path.isfile(grid_path): - raise ValueError(f'{grid_path} not found!') + raise FileNotFoundError(f'{grid_path} not found!') else: - raise ValueError(f'No path_to_grid in {path_to_csv_file}') + raise KeyError(f'No path_to_grid in {path_to_csv_file}') if 'export_path' in df.keys(): export_path = df.loc[i,'export_path'] else: - raise ValueError(f'No export_path in {path_to_csv_file}') + raise KeyError(f'No export_path in {path_to_csv_file}') if verbose: print(f'copying {grid_path} to {export_path}') @@ -408,7 +558,18 @@ def export_dataset(i, path_to_csv_file, verbose=False): def zams_file_name(metallicity): - """Gives the name of the ZAMS file depending on the metallicity.""" + """Gives the name of the ZAMS file depending on the metallicity. + + Parameters + ---------- + metallicity : str + String representation of the metallicity (e.g. 1e+00_Zsun). + + Returns + ------- + str + Name of ZAMS file. + """ if '2e+00_Zsun' in metallicity: zams_filename = 'zams_z2.84m2_y0.2915.data' @@ -428,17 +589,38 @@ def zams_file_name(metallicity): elif '1e-04_Zsun' in metallicity: zams_filename = 'zams_z1.42m6_y0.2490.data' else: - raise ValueError(f'Metallicity unknown: {dirname}') + raise ValueError(f'Metallicity unknown: {metallicity}') return zams_filename -def copy_ini_file(grid_type, rerun_metallicity, rerun_type, destination, cluster): - """Copies the ini file and make replacements according to the rerun.""" - +def copy_ini_file(grid_type, rerun_metallicity, rerun_type, destination, + cluster): + """Copies the ini file and make replacements according to the rerun. + + Parameters + ---------- + grid_type : str + Type of the grid. + rerun_metallicity : str + String representation of the metallicity (e.g. 1e+00_Zsun). + rerun_type : str + Type of rerun (e.g. PISN). + destination : str + Path to the directory for the new runs. + cluster : str + Cluster name (e.g. yggdrasil or quest). + """ + # copy the default ini file to directory if cluster in ['quest','yggdrasil']: - ini_dir_path = os.path.join(PATH_TO_POSYDON, - 'grid_params/POSYDON-MESA-INLISTS/r11701/running_scripts') + if os.path.isdir(PATH_TO_POSYDON): + ini_dir_path = os.path.join(PATH_TO_POSYDON, + 'grid_params/POSYDON-MESA-INLISTS/r11701/running_scripts') + if not os.path.isdir(PATH_TO_POSYDON): + raise NotADirectoryError(f'{ini_dir_path} not found!') + else: + raise NotADirectoryError(f'PATH_TO_POSYDON={PATH_TO_POSYDON} not ' + 'found!') if "single_HMS" in grid_type: ini_file_path = os.path.join(ini_dir_path, f'single_HMS_{cluster}.ini') ini_file_dest = os.path.join(destination, f'single_HMS_{cluster}.ini') @@ -456,6 +638,8 @@ def copy_ini_file(grid_type, rerun_metallicity, rerun_type, destination, cluster ini_file_dest = os.path.join(destination, f'CO-HeMS_{cluster}.ini') else: raise ValueError(f'Unsupported grid type: {grid_type}!') + if not os.path.isfile(ini_file_path): + raise FileNotFoundError(f'{ini_file_path} not found!') shutil.copyfile(ini_file_path, ini_file_dest) else: raise ValueError(f'Unsupported cluster {cluster}!') @@ -503,8 +687,28 @@ def copy_ini_file(grid_type, rerun_metallicity, rerun_type, destination, cluster def logic_rerun(grid, rerun_type): - """Get the runs, which need a rerun.""" - + """Get the runs, which need a rerun. + + Parameters + ---------- + grid : PSyGrid + PSyGrid to select runs from. + rerun_type : str + Type of rerun (e.g. PISN). + + Returns + ------- + runs_to_rerun : list + Indecies of runs to be rerun. + termination_flags : list + Termination flags to select reruns by. + new_mesa_flag : dict + Name and value of MESA inlist parameters to change to for this rerun. + """ + + if not isinstance(grid, PSyGrid): + raise TypeError(f'grid must be a PSyGrid, but it is {type(grid)}.') + runs_to_rerun=None termination_flags=None new_mesa_flag = None @@ -521,7 +725,8 @@ def logic_rerun(grid, rerun_type): dirname = grid.MESA_dirs[i].decode('utf-8') if not os.path.isdir(dirname): # TODO: handle the case when grids were moved location - raise ValueError(f'Grid directory not found at {dirname}') + raise NotADirectoryError('Grid directory not found at ' + f'{dirname}') if "single_HMS" in dirname: out_txt_path = os.path.join(dirname, "out_star1_formation_step0.txt") @@ -541,7 +746,8 @@ def logic_rerun(grid, rerun_type): N_runs = len(grid) runs_to_rerun = [] for i in range(N_runs): - if grid[i].binary_history is not None and grid[i].history1 is not None: + if ((grid[i].binary_history is not None) and + (grid[i].history1 is not None)): rl1 = grid[i].binary_history['rl_relative_overflow_1'] rl2 = grid[i].binary_history['rl_relative_overflow_2'] w_wcrit = grid[i].history1['surf_avg_omega_div_omega_crit'] @@ -558,16 +764,20 @@ def logic_rerun(grid, rerun_type): if grid[i].history1 is not None: s1_Teff = 10**grid[i].history1['log_Teff'] s1_Yc = grid[i].history1['center_he4'] - s1_he_shell_mass = grid[i].history1['he_core_mass'] - grid[i].history1['co_core_mass'] - tpagb_1 = np.logical_and(s1_Teff <= hot_wind_full_on_T, s1_Yc <= 1e-6) + s1_he_shell_mass = grid[i].history1['he_core_mass'] -\ + grid[i].history1['co_core_mass'] + tpagb_1 = np.logical_and(s1_Teff <= hot_wind_full_on_T,\ + s1_Yc <= 1e-6) tpagb_1 = np.logical_and(tpagb_1, s1_he_shell_mass <= 1e-1) else: tpagb_1 = np.array([False]) if grid[i].history2 is not None: s2_Teff = 10**grid[i].history2['log_Teff'] s2_Yc = grid[i].history2['center_he4'] - s2_he_shell_mass = grid[i].history2['he_core_mass'] - grid[i].history2['co_core_mass'] - tpagb_2 = np.logical_and(s2_Teff <= hot_wind_full_on_T, s2_Yc <= 1e-6) + s2_he_shell_mass = grid[i].history2['he_core_mass'] -\ + grid[i].history2['co_core_mass'] + tpagb_2 = np.logical_and(s2_Teff <= hot_wind_full_on_T,\ + s2_Yc <= 1e-6) tpagb_2 = np.logical_and(tpagb_2, s2_he_shell_mass <= 1e-1) else: tpagb_2 = np.array([False]) @@ -581,7 +791,9 @@ def logic_rerun(grid, rerun_type): termination_flags = TF1_POOL_ERROR elif rerun_type == 'more_mesh': termination_flags = TF1_POOL_ERROR - new_mesa_flag = {'mesh_Pgas_div_P_exponent' : 2, 'max_allowed_nz' : 50000} # may put this in a MESA-inlist PR + # may put this in a MESA-inlist PR + new_mesa_flag = {'mesh_Pgas_div_P_exponent' : 2,\ + 'max_allowed_nz' : 50000} elif rerun_type == 'conv_bdy_weight': N_runs = len(grid) runs_to_rerun = [] @@ -589,7 +801,8 @@ def logic_rerun(grid, rerun_type): dirname = grid.MESA_dirs[i].decode('utf-8') if not os.path.isdir(dirname): # TODO: handle the case when grids were moved location - raise ValueError(f'Grid directory not found at {dirname}') + raise NotADirectoryError('Grid directory not found at ' + f'{dirname}') if "single_HMS" in dirname: out_txt_path = os.path.join(dirname, "out_star1_formation_step0.txt") @@ -631,36 +844,52 @@ def logic_rerun(grid, rerun_type): def rerun(i, path_to_csv_file, verbose=False): - """Generate files to start a rerun.""" + """Generate files to start a rerun. + + Parameters + ---------- + i : int + Index in csv file. + path_to_csv_file : str + Path to csv file. + verbose : bool + Enable/Disable additional output. + """ # read csv file if os.path.exists(path_to_csv_file): df = pd.read_csv(path_to_csv_file) else: - raise ValueError(f'{path_to_csv_file} not found!') + raise FileNotFoundError(f'{path_to_csv_file} not found!') + if i not in df.index: + raise KeyError(f'Index={i} not available in {path_to_csv_file}.') # get values from csv file if 'path_to_grid' in df.keys(): grid_path = df.loc[i,'path_to_grid'] if not os.path.isfile(grid_path): - raise ValueError(f'{grid_path} not found!') + raise FileNotFoundError(f'{grid_path} not found!') else: - raise ValueError(f'No path_to_grid in {path_to_csv_file}') + raise KeyError(f'No path_to_grid in {path_to_csv_file}') if 'rerun_path' in df.keys(): rerun_path = df.loc[i,'rerun_path'] else: - raise ValueError(f'No rerun_path in {path_to_csv_file}') + raise KeyError(f'No rerun_path in {path_to_csv_file}') if 'grid_type' in df.keys(): grid_type = df.loc[i,'grid_type'] else: - raise ValueError(f'No grid_type in {path_to_csv_file}') + raise KeyError(f'No grid_type in {path_to_csv_file}') if 'rerun_metallicity' in df.keys(): rerun_metallicity = df.loc[i,'rerun_metallicity'] else: - raise ValueError(f'No rerun_metallicity in {path_to_csv_file}') + raise KeyError(f'No rerun_metallicity in {path_to_csv_file}') if 'rerun_type' in df.keys(): rerun_type = df.loc[i,'rerun_type'] else: - raise ValueError(f'No rerun_type in {path_to_csv_file}') + raise KeyError(f'No rerun_type in {path_to_csv_file}') + if 'cluster' in df.keys(): + cluster = df.loc[i,'cluster'] + else: + cluster = 'quest' # load grid grid = PSyGrid(verbose=verbose) @@ -678,16 +907,22 @@ def rerun(i, path_to_csv_file, verbose=False): new_mesa_flag=new_mesa_flag) # copy ini file and set the new inlist commit - # TODO: make cluster an input - if 'b1119' in grid.MESA_dirs[0].decode('utf-8'): - cluster = 'quest' - else: - cluster = 'yggdrasil' - copy_ini_file(grid_type, rerun_metallicity, rerun_type, destination=rerun_path, cluster=cluster) + copy_ini_file(grid_type, rerun_metallicity, rerun_type, + destination=rerun_path, cluster=cluster) def plot_grid(i, path_to_csv_file, verbose=False): - """Creates plots of a grid.""" + """Creates plots of a grid. + + Parameters + ---------- + i : int + Index in csv file. + path_to_csv_file : str + Path to csv file. + verbose : bool + Enable/Disable additional output. + """ # grid mass ranges: # min and max values might get updated, than the step is used to update the # nbin (in case of log==True the step is in log space) @@ -736,30 +971,43 @@ def plot_grid(i, path_to_csv_file, verbose=False): if os.path.exists(path_to_csv_file): df = pd.read_csv(path_to_csv_file) else: - raise ValueError(f'{path_to_csv_file} not found!') + raise FileNotFoundError(f'{path_to_csv_file} not found!') + if i not in df.index: + raise KeyError(f'Index={i} not available in {path_to_csv_file}.') check_dirs = [] # get values from csv file if 'path_to_grid' in df.keys(): grid_path = df.loc[i,'path_to_grid'] if not os.path.isfile(grid_path): - raise ValueError(f'{grid_path} not found!') + raise FileNotFoundError(f'{grid_path} not found!') else: - raise ValueError(f'No path_to_grid in {path_to_csv_file}') + raise KeyError(f'No path_to_grid in {path_to_csv_file}') if 'grid_type' in df.keys(): grid_type = df.loc[i,'grid_type'] else: - raise ValueError(f'No grid_type in {path_to_csv_file}') + raise KeyError(f'No grid_type in {path_to_csv_file}') if 'path_to_plot' in df.keys(): plot_dir = df.loc[i,'path_to_plot'] plot_path = os.path.dirname(os.path.normpath(plot_dir)) check_dirs.extend([plot_path, plot_dir]) else: - raise ValueError(f'No path_to_plot in {path_to_csv_file}') + raise KeyError(f'No path_to_plot in {path_to_csv_file}') if 'quantities_to_plot' in df.keys(): quantities_to_plot = ast.literal_eval(df.loc[i,'quantities_to_plot']) + if not isinstance(quantities_to_plot, list): + raise TypeError(f'quantities_to_plot is not a list in line {i} ' + f'in {path_to_csv_file}') else: - raise ValueError(f'No quantities_to_plot in {path_to_csv_file}') + raise KeyError(f'No quantities_to_plot in {path_to_csv_file}') + multipage = False + if 'plot_extension' in df.keys(): + plot_extension = df.loc[i,'plot_extension'] + if plot_extension=='multipage-pdf': + multipage = True + plot_extension = 'pdf' + else: + plot_extension = 'pdf' if 'path_to_interpolator' in df.keys(): path_to_interpolator = df.loc[i,'path_to_interpolator'] else: @@ -843,6 +1091,9 @@ def plot_grid(i, path_to_csv_file, verbose=False): # check that plots/ directories exist name = plot_attributes['plot_dir_name'] dir_ = os.path.join(plot_dir, name) + if multipage: + # multipage plots don't need the lowest directory level + dir_ = os.path.dirname(dir_) if not os.path.isdir(dir_): os.makedirs(dir_) @@ -869,7 +1120,7 @@ def plot_grid(i, path_to_csv_file, verbose=False): log=smpl['log']) vars, vars_edges = _range(smpl['mmin'], smpl['mmax'], smpl['nbin'], log=smpl['log']) - fname = 'grid_m_%1.2f.png' + fname = 'grid_m_%1.2f.' + plot_extension title = '$m_\mathrm{CO}=%1.2f\,M_\odot$' elif 'HMS-HMS' in grid_type: # mass ratio slices @@ -877,12 +1128,17 @@ def plot_grid(i, path_to_csv_file, verbose=False): slice_3D_var_str = 'mass_ratio' vars, vars_edges = _range(smpl['qmin'], smpl['qmax'], smpl['nbin'], log=smpl['log']) - vars[-1] = 0.99 # we replaced q=1 with q=0.99 in mesa runs - fname = 'grid_q_%1.2f.png' + if vars[-1]==1.0: + vars[-1] = 0.99 # we replaced q=1 with q=0.99 in mesa runs + fname = 'grid_q_%1.2f.' + plot_extension title = '$q=%1.2f$' else: raise ValueError('Grid type not supported!') + if multipage: + pdf_handler = PdfPages(os.path.join(plot_dir, name) +\ + '_all_slices.' + plot_extension, + metadata={'Author': 'POSYDON'}) # loop over all grid slices for k, var in enumerate(vars): slice_3D_var_range = (vars_edges[k],vars_edges[k+1]) @@ -891,7 +1147,7 @@ def plot_grid(i, path_to_csv_file, verbose=False): # default plot properties PLOT_PROPERTIES_DEFAULT = { 'figsize' : (4,3.5), - 'path_to_file' : os.path.join(plot_dir, name)+"/", + 'path_to_file' : os.path.join(plot_dir, name)+'/', 'show_fig' : False, 'fname' : fname%var, 'title' : title%var, @@ -910,20 +1166,29 @@ def plot_grid(i, path_to_csv_file, verbose=False): PLOT_PROPERTIES['zmax'] = plot_attributes['zmax'] if 'INTERP_ERROR' in quantity_to_plot: PLOT_PROPERTIES['colorbar']['label'] = quantity_to_plot + if multipage: + PLOT_PROPERTIES['PdfPages'] = pdf_handler if 'INTERP_ERROR' in quantity_to_plot: - # initial/final error slices - evalIFerr.plot2D(plot_attributes['zvar'], slice_3D_var_str, - slice_3D_var_range, PLOT_PROPERTIES) # initial/final error all points in one plot if k == 0: PLOT_PROPERTIES_ALL = PLOT_PROPERTIES.copy() - PLOT_PROPERTIES_ALL['title'] = title.split('=')[0] + '$ all' - PLOT_PROPERTIES_ALL['fname'] = fname.replace('%1.2f', 'all') + PLOT_PROPERTIES_ALL['title'] = title.split('=')[0] +\ + '$ all' + PLOT_PROPERTIES_ALL['fname'] = fname.replace('%1.2f',\ + 'all') + if multipage: + PLOT_PROPERTIES_ALL['path_to_file'] = plot_dir+'/' + PLOT_PROPERTIES_ALL['fname'] = name + '_all.' +\ + plot_extension + PLOT_PROPERTIES_ALL['PdfPages'] = None evalIFerr.plot2D(plot_attributes['zvar'], slice_3D_var_str, (vars_edges[0], vars_edges[-1]), PLOT_PROPERTIES_ALL) + # initial/final error slices + evalIFerr.plot2D(plot_attributes['zvar'], slice_3D_var_str, + slice_3D_var_range, PLOT_PROPERTIES) elif 'CLASS_ERROR' in quantity_to_plot: # TODO: add class error plots if k == 0: @@ -936,9 +1201,11 @@ def plot_grid(i, path_to_csv_file, verbose=False): keys=[plot_attributes['zvar']], close_fig=True) else: - grid.plot2D('star_1_mass', 'period_days', plot_attributes['zvar'], + grid.plot2D('star_1_mass', 'period_days', + plot_attributes['zvar'], termination_flag=plot_attributes['term_flag'], - grid_3D=True, slice_3D_var_str=slice_3D_var_str, + grid_3D=True, + slice_3D_var_str=slice_3D_var_str, slice_3D_var_range=slice_3D_var_range, verbose=False, **PLOT_PROPERTIES) @@ -946,29 +1213,55 @@ def plot_grid(i, path_to_csv_file, verbose=False): print('FAILED TO PLOT '+title%var+' to '+fname%var) print('') print(e) + if multipage: + # close and reopen handler + # (WARNING: this will overwrite the pdf file) + print(f'delete and reopen: {os.path.join(plot_dir, name)}' + f'_all.{plot_extension}') + del pdf_handler + pdf_handler = PdfPages(os.path.join(plot_dir, name) +\ + '_all_slices.' +plot_extension, + metadata={'Author': 'POSYDON'}) continue + if multipage: + pdf_handler.close() def do_check(i, path_to_csv_file, verbose=False): - """Perform a check on a grid.""" + """Perform a check on a grid. + + Parameters + ---------- + i : int + Index in csv file. + path_to_csv_file : str + Path to csv file. + verbose : bool + Enable/Disable additional output. + """ # read csv file if os.path.exists(path_to_csv_file): df = pd.read_csv(path_to_csv_file) else: - raise ValueError(f'{path_to_csv_file} not found!') + raise FileNotFoundError(f'{path_to_csv_file} not found!') + if i not in df.index: + raise KeyError(f'Index={i} not available in {path_to_csv_file}.') # get values from csv file if 'path_to_grid' in df.keys(): grid_path = df.loc[i,'path_to_grid'] if not os.path.isfile(grid_path): - raise ValueError(f'{grid_path} not found!') + raise FileNotFoundError(f'{grid_path} not found!') else: - raise ValueError(f'No path_to_grid in {path_to_csv_file}') + raise KeyError(f'No path_to_grid in {path_to_csv_file}') if 'checks_to_do' in df.keys(): checks_to_do = ast.literal_eval(df.loc[i,'checks_to_do']) + if not isinstance(checks_to_do, list): + raise TypeError(f'checks_to_do is not a list in line {i} ' + f'in {path_to_csv_file}') else: - raise ValueError(f'No checks_to_do in {path_to_csv_file}') + raise KeyError(f'No checks_to_do in {path_to_csv_file}') if 'path_to_interpolator' in df.keys(): path_to_interpolator = df.loc[i,'path_to_interpolator'] else: @@ -1011,22 +1304,34 @@ if __name__ == '__main__': n_args = len(sys.argv) if n_args > 1: # path to girds + if str(sys.argv[1])=='--help': + print('Programm to run a pipeline step') + print('syntax: posydon-run-pipeline PATH_TO_GRIDS PATH_TO_CSV_FILE' + ' [SLURM_I] [VERBOSE]') + print('PATH_TO_GRIDS: path to the root directory with the grids') + print('PATH_TO_CSV_FILE: path to the csv file containing the step' + ' data') + print('SLURM_I (optional): slurm array index, used to grep a' + ' certain data set (usually line index) from the csv file;' + ' if not given the first non header line is used') + print('VERBOSE (optional): flag for additional output') + sys.exit(0) PATH_TO_GRIDS = str(sys.argv[1]) if not os.path.isdir(PATH_TO_GRIDS): # check given path - raise ValueError('Grids were not found! ' - f'Check your PATH_TO_GRIDS={PATH_TO_GRIDS}') + raise NotADirectoryError('Grids were not found! Check your ' + f'PATH_TO_GRIDS={PATH_TO_GRIDS}') else: - raise ValueError('No path to the grids given. It should be specified ' - 'as first commandline argument.') + raise KeyError('No path to the grids given. It should be specified ' + 'as first commandline argument.') if n_args > 2: # path to csv file PATH_TO_CSV_FILE = str(sys.argv[2]) if not os.path.isfile(PATH_TO_CSV_FILE): # check given path - raise ValueError('Csv file not found! ' - f'Check your path_to_csv_file={PATH_TO_CSV_FILE}') + raise FileNotFoundError('Csv file not found! Check your ' + f'PATH_TO_CSV_FILE={PATH_TO_CSV_FILE}') else: - raise ValueError('No csv file given. It should be specified as ' - 'second commandline argument.') + raise KeyError('No csv file given. It should be specified as ' + 'second commandline argument.') if n_args > 3: # slurm index SLURM_I = int(sys.argv[3]) @@ -1061,6 +1366,9 @@ if __name__ == '__main__': if STEP=='step_4': # train interpolators train_interpolators(SLURM_I, PATH_TO_CSV_FILE, verbose=VERBOSE) + if STEP=='step_5': # train profile interpolators + train_profile_interpolators(SLURM_I, PATH_TO_CSV_FILE, verbose=VERBOSE) + if STEP=='step_9': # export dataset export_dataset(SLURM_I, PATH_TO_CSV_FILE, verbose=VERBOSE) diff --git a/bin/posydon-setup-pipeline b/bin/posydon-setup-pipeline index 70d3968a6a..9edf5b7ff2 100644 --- a/bin/posydon-setup-pipeline +++ b/bin/posydon-setup-pipeline @@ -50,12 +50,13 @@ PATH_TO_GRIDS/ # pipeline steps ACTION_TO_STEP_NUM = { - 'CREATE_GRID_SLICES' : 'step_1', - 'COMBINE_GRID_SLICES' : 'step_2', - 'CALCULATE_EXTRA_VALUES': 'step_3', - 'TRAIN_INTERPOLATORS' : 'step_4', - 'EXPORT_DATASET' : 'step_9', - 'RERUN' : 'rerun' + 'CREATE_GRID_SLICES' : 'step_1', + 'COMBINE_GRID_SLICES' : 'step_2', + 'CALCULATE_EXTRA_VALUES' : 'step_3', + 'TRAIN_INTERPOLATORS' : 'step_4', + 'TRAIN_PROFILE_INTERPOLATORS': 'step_5', + 'EXPORT_DATASET' : 'step_9', + 'RERUN' : 'rerun' } # pipeline substeps SUB_STEPS_TO_EXTENSION = { @@ -139,14 +140,16 @@ PLOTTING_SETS = { 'INTERP_ERROR_S1_MODEL06_m_disk_radiated',\ 'INTERP_ERROR_S1_MODEL10_mass',\ 'INTERP_ERROR_S1_MODEL10_spin',\ - 'INTERP_ERROR_S1_MODEL10_m_disk_radiated'] + 'INTERP_ERROR_S1_MODEL10_m_disk_radiated'], + 'PLOT_AFTER_PROFILE_TRAINING' : [] } # predefined checking sets CHECKING_SETS = { - 'CHECK_AFTER_CREATE' : [], - 'CHECK_AFTER_COMBINE' : ['failure_rate'], - 'CHECK_AFTER_EXTRA' : ['CO_type', 'SN_type'], - 'CHECK_AFTER_TRAINING' : [] + 'CHECK_AFTER_CREATE' : [], + 'CHECK_AFTER_COMBINE' : ['failure_rate'], + 'CHECK_AFTER_EXTRA' : ['CO_type', 'SN_type'], + 'CHECK_AFTER_TRAINING' : [], + 'CHECK_AFTER_PROFILE_TRAINING' : [] } # common attributes for all steps ATTRIBUTES_TO_CHECK = ['GRID_TYPES', 'METALLICITIES', 'GRID_SLICES',\ @@ -158,33 +161,41 @@ class PostProcessingPipeline: def __init__(self, path_to_inifile=None): """Initialize a pipeline - Arguments - --------- + Parameters + ---------- path_to_inifile : path The location of an ini file to read the parameters for the pipeline (default: None) """ - - self.PATH_TO_POSYDON = os.getenv('PATH_TO_POSYDON') + # set base variables + self.PATH_TO_POSYDON = PATH_TO_POSYDON self.PATH_TO_INIFILE = path_to_inifile + # if there is an ini file extract the parameters from it if self.PATH_TO_INIFILE is not None: - self.pipeline_kwargs = self.parse_setup_params(self.PATH_TO_INIFILE) + if os.path.isfile(self.PATH_TO_INIFILE): + self.pipeline_kwargs = self.parse_setup_params( + self.PATH_TO_INIFILE) + else: + raise FileNotFoundError("Can't find ini-file: " + f"{self.PATH_TO_INIFILE}") @staticmethod def parse_setup_params(path=None): """Parse inifile for running post-processing pipelines. - Arguments - --------- + Parameters + ---------- path : path The location of an ini file to read the parameters for the pipeline (default: None) Returns ------- - dictionary + dict + A dictionary with all the parameters in the ini file. It accounts + for one sectioning level. """ if path is None: @@ -204,11 +215,32 @@ class PostProcessingPipeline: pipeline_kwargs[section] = section_dict return pipeline_kwargs + def create_csv_and_slurm_job_files(self): """Creates all files the pipeline needs.""" - def expand_runfile(runfile, last_step='', step_number='step_X'): - """Add next entry to the script in runfile.""" + def expand_runfile(runfile, step_number='step_X', last_step=''): + """Add next entry to the script in runfile. + + Parameters + ---------- + runfile : file object (text file) + IO object of an opened file to write to + step_number : str + String representation of the current step + last_step : str + String representation of the last step + + Returns + ------- + str + On success it returns step_number + """ + + if not runfile.writable(): + raise PermissionError(f"Can't write to {runfile.name}") + if not os.path.isfile(f"{step_number}.slurm"): + raise FileNotFoundError(f"Can't find file {step_number}.slurm") if last_step == '': # if there was no previous step the current step has no # dependency @@ -224,12 +256,27 @@ class PostProcessingPipeline: "{"f"ID{step_number}""}"f"\n") return step_number - def get_step_kwargs(self, last_step='', step_number='step_X'): - """Provides the attributes of a step. Additionally, it checks for - missing attributes and may copies them from a previous step.""" + def get_step_kwargs(self, step_number='step_X', last_step=''): + """Provides the attributes of a step. Additionally, it replaces + defined sets and it checks for missing attributes and may copies + them from a previous step. + + Parameters + ---------- + step_number : str + String representation of the current step + last_step : str + String representation of the last step + + Returns + ------- + dict + A dictionary containing all the parameters for the current step + """ + if step_number not in self.pipeline_kwargs.keys(): - raise KeyError(f'{step_number} not in pipeline_kwargs!') - elif 'step' in step_number: + raise KeyError(f'{step_number} not in pipeline_kwargs.') + elif (('step' in step_number) or (step_number=='rerun')): # check for attributes for attribute in ATTRIBUTES_TO_CHECK: if (attribute not in self.pipeline_kwargs[step_number].\ @@ -237,9 +284,9 @@ class PostProcessingPipeline: [step_number][attribute], list)): if (last_step in self.pipeline_kwargs.keys()\ and attribute in\ - self.pipeline_kwargs[step_number].keys()): + self.pipeline_kwargs[last_step].keys()): # infer missing attribute from last step - print(f"\ncopy {attribute} from {last_step} to " + print(f"Copy {attribute} from {last_step} to " f"{step_number}") self.pipeline_kwargs[step_number][attribute] = \ self.pipeline_kwargs[last_step][attribute] @@ -248,19 +295,54 @@ class PostProcessingPipeline: # replace pre defined sets if 'CREATE_PLOTS' in self.pipeline_kwargs[step_number].keys(): for set_name, set_content in PLOTTING_SETS.items(): - if set_name in self.pipeline_kwargs[step_number]['CREATE_PLOTS']: - self.pipeline_kwargs[step_number]['CREATE_PLOTS'].remove(set_name) - self.pipeline_kwargs[step_number]['CREATE_PLOTS'].extend(set_content) + if set_name in self.pipeline_kwargs[step_number]\ + ['CREATE_PLOTS']: + self.pipeline_kwargs[step_number]['CREATE_PLOTS'].\ + remove(set_name) + self.pipeline_kwargs[step_number]['CREATE_PLOTS'].\ + extend(set_content) + # remove all plot entries in case the global plotting flag + # is off + if (('pipeline setup' in self.pipeline_kwargs.keys()) and\ + ('MAKE_PLOTS' in\ + self.pipeline_kwargs['pipeline setup'].keys()) + and not (self.pipeline_kwargs['pipeline setup']\ + ['MAKE_PLOTS'])): + self.pipeline_kwargs[step_number]['CREATE_PLOTS'] = [] if 'DO_CHECKS' in self.pipeline_kwargs[step_number].keys(): for set_name, set_content in CHECKING_SETS.items(): - if set_name in self.pipeline_kwargs[step_number]['DO_CHECKS']: - self.pipeline_kwargs[step_number]['DO_CHECKS'].remove(set_name) - self.pipeline_kwargs[step_number]['DO_CHECKS'].extend(set_content) + if set_name in self.pipeline_kwargs[step_number]\ + ['DO_CHECKS']: + self.pipeline_kwargs[step_number]['DO_CHECKS'].\ + remove(set_name) + self.pipeline_kwargs[step_number]['DO_CHECKS'].\ + extend(set_content) + # remove all check entries in case the global checking flag + # is off + if (('pipeline setup' in self.pipeline_kwargs.keys()) and\ + ('MAKE_CHECKS' in\ + self.pipeline_kwargs['pipeline setup'].keys()) + and not (self.pipeline_kwargs['pipeline setup']\ + ['MAKE_CHECKS'])): + self.pipeline_kwargs[step_number]['DO_CHECKS'] = [] # return attributes return self.pipeline_kwargs[step_number] def has_substep(self, step_number='step_X', substep='ALL'): - """Checks whether a step has substeps.""" + """Checks whether a step has substeps. + + Parameters + ---------- + step_number : str + String representation of the current step + substep : str + String representation of the substep + + Returns + ------- + bool + Only True if a valid substep is found + """ if step_number not in self.pipeline_kwargs.keys(): raise KeyError(f'{step_number} not in pipeline_kwargs!') if substep=='ALL': # check all possible substeps @@ -304,7 +386,11 @@ class PostProcessingPipeline: # create a runfile script with open('run_pipeline.sh', 'w') as runfile: - runfile.write("#!/bin/bash\n") + if runfile.writable(): + runfile.write("#!/bin/bash\n") + else: + raise PermissionError(f"Can't write to {runfile.name}") + # loop through all steps last_step = '' for action, step_number in ACTION_TO_STEP_NUM.items(): if action in setup_kwargs.keys(): @@ -317,8 +403,9 @@ class PostProcessingPipeline: action, step_number, str(do_step_bool)) ) if do_step_bool or has_substep(self, step_number=step_number): - step_kwargs = get_step_kwargs(self, last_step=last_step, - step_number=step_number) + step_kwargs = get_step_kwargs(self, + step_number=step_number, + last_step=last_step) if VERBOSE: print( pformat(step_kwargs, indent=2) ) @@ -338,8 +425,8 @@ class PostProcessingPipeline: ) pipeline_steps.append(step_number) # add job to the list in the runfile script - expand_runfile(runfile, last_step=last_step, - step_number=step_number) + expand_runfile(runfile, step_number=step_number, + last_step=last_step) # remember that this step was done last last_step = step_number # add cleanup @@ -347,11 +434,18 @@ class PostProcessingPipeline: step_name=step_number,\ **{**step_kwargs, **setup_kwargs, **account_kwargs}): pipeline_steps.append(step_number+"_cleanup") - expand_runfile(runfile, last_step=last_step, - step_number=step_number+"_cleanup") + expand_runfile(runfile, + step_number=step_number+"_cleanup", + last_step=last_step) + # try to load the profile interpolation module, because it + # will be needed when running step 5 + if step_number == "step_5": + from posydon.interpolation.profile_interpolation\ + import CompileData, ProfileInterpolator for sub_step, step_extension in SUB_STEPS_TO_EXTENSION.items(): - if sub_step not in self.pipeline_kwargs[step_number].keys(): + if sub_step not in\ + self.pipeline_kwargs[step_number].keys(): continue if has_substep(self, step_number=step_number, substep=sub_step): @@ -373,15 +467,16 @@ class PostProcessingPipeline: ) pipeline_steps.append(sub_step_number) # add job to the list in the runfile script - expand_runfile(runfile, last_step=last_step, - step_number=sub_step_number) + expand_runfile(runfile, step_number=sub_step_number, + last_step=last_step) # add cleanup if create_cleanup_slurm_job(job_name=sub_step_number,\ - step_name=step_number,\ - **{**step_kwargs, **setup_kwargs, **account_kwargs}): + step_name=step_number, **{**step_kwargs,\ + **setup_kwargs, **account_kwargs}): pipeline_steps.append(sub_step_number+"_cleanup") - expand_runfile(runfile, last_step=sub_step_number, - step_number=sub_step_number+"_cleanup") + expand_runfile(runfile, + step_number=sub_step_number+"_cleanup", + last_step=sub_step_number) # there are no dependencies supported on sub steps # beside the cleanup # add cleanup for pipeline @@ -394,11 +489,13 @@ class PostProcessingPipeline: PATH = setup_kwargs['PATH'] else: PATH = '.' - df['pipeline_files'] = previously_created_files + list(set(plot_dirs)) - df.to_csv(os.path.join(PATH, 'pipeline_files.csv'), header=False, index=False) + df['pipeline_files'] = previously_created_files +\ + list(set(plot_dirs)) + df.to_csv(os.path.join(PATH, 'pipeline_files.csv'), + header=False, index=False) # add cleanup to run script - submission_line = f"IDpipeline_cleanup=$(sbatch --parsable " + submission_line = "IDpipeline_cleanup=$(sbatch --parsable " for i, step in enumerate(pipeline_steps): if i==0: submission_line += "--dependency=" @@ -406,15 +503,23 @@ class PostProcessingPipeline: submission_line += "," submission_line += "afterany:$""{"f"ID{step}""}" submission_line += " --kill-on-invalid-dep=yes " - submission_line += f"pipeline_cleanup.slurm)\n" - runfile.write(submission_line) - runfile.write(f"echo 'pipeline_cleanup.slurm submitted as '$" - "{"f"IDpipeline_cleanup""}"f"\n") + submission_line += "pipeline_cleanup.slurm)\n" + if runfile.writable(): + if not os.path.isfile("pipeline_cleanup.slurm"): + raise FileNotFoundError("Can't find file " + "pipeline_cleanup.slurm") + runfile.write(submission_line) + runfile.write("echo 'pipeline_cleanup.slurm submitted as '" + "${IDpipeline_cleanup}\n") + else: + raise PermissionError(f"Can't write to {runfile.name}") # make the runfile script executable - os.system("chmod 755 run_pipeline.sh") + os.system("chmod a+x run_pipeline.sh") + def create_log_dirs(self): """Create directories to store the log files.""" + # use work directory given by PATH in the setup attributes if 'pipeline setup' not in self.pipeline_kwargs.keys(): raise KeyError('pipeline setup not in pipeline_kwargs!') @@ -436,15 +541,16 @@ class PostProcessingPipeline: step_log_path = os.path.join(logs_path, 'cleanup') if not os.path.isdir(step_log_path): os.makedirs(step_log_path) - + def create_export_dirs(self): """Create directories for the finally exported data.""" + if 'pipeline setup' not in self.pipeline_kwargs.keys(): raise KeyError('pipeline setup not in pipeline_kwargs!') setup_kwargs = self.pipeline_kwargs['pipeline setup'] - # create data dir three to export the datasets + # create data dir tree to export the datasets if (('EXPORT_DATASET' in setup_kwargs.keys()) and setup_kwargs['EXPORT_DATASET']): if 'PATH' in setup_kwargs.keys(): @@ -469,6 +575,7 @@ class PostProcessingPipeline: if not os.path.isdir(dir_): os.makedirs(dir_) + def slurm_job(job_name, step_name, PATH_TO_GRIDS=None, @@ -479,28 +586,46 @@ def slurm_job(job_name, WALLTIME=None, MAILTYPE=None, EMAIL=None, - CREATE_GRID_SLICES=False, - COMBINE_GRID_SLICES=False, - CALCULATE_EXTRA_VALUES=False, - TRAIN_INTERPOLATORS=False, - EXPORT_DATASET=False, - RERUN=False, VERBOSE=False, **kwargs): - """Create slurm file.""" - def copy_old_log_file(path_to_logs='.', log_file='log'): - """Make a copy of an old out file (and remove an old copy).""" - path_to_out_file = os.path.join(path_to_logs, log_file+'.out') - if os.path.exists(path_to_out_file): # move old data to a copy - path_to_old_out_file = os.path.join(path_to_logs, - log_file+'.old.out') - if os.path.exists(path_to_old_out_file): # delete old copy - os.remove(path_to_old_out_file) - shutil.move(path_to_out_file, path_to_old_out_file) - return path_to_out_file - + """Create slurm file. + + Parameters + ---------- + job_name : str + String representation of the job. It will be used in the file name. + step_name : str + String representation of the parent step. It will be used for the logs. + PATH_TO_GRIDS : str + Path where to find the root directory of the grids. + PATH_TO_POSYDON : str + Path where to find the root directory of POSYDON. + PATH : str + Path of the working directory. + ACCOUNT : str + The slurm account name. Passed to slurm's option '--account'. + PARTITION : str + The slurm partition name. Passed to slurm's option '--partition'. + WALLTIME : str + Maximum time for the slurm job. Passed to slurm's option '--time'. + MAILTYPE : str + Specifier, which emails to send by slurm. Passed to slurm's option + '--mail-type'. It requires EMAIL to be set, too. + EMAIL : str + Email address to send mails to. Passed to slurm's option '--mail-user'. + It requires MAILTYPE to be set, too. + VERBOSE : bool + Enables/Disables additional output. + kwargs : dict + Other parameters (all being ignored). + """ + + if not os.path.isdir(PATH_TO_GRIDS): + raise NotADirectoryError(f"PATH_TO_GRIDS={PATH_TO_GRIDS} not found.") # get path to csv file path_to_csv_file = os.path.join(PATH, f'{job_name}.csv') + if not os.path.isfile(path_to_csv_file): + raise FileNotFoundError(f"Missing csv file: {path_to_csv_file}") # create slurm file with open(f'{job_name}.slurm', 'w') as f: @@ -530,8 +655,8 @@ def slurm_job(job_name, f.write(f"#SBATCH --mail-type={MAILTYPE}\n") f.write(f"#SBATCH --mail-user={EMAIL}\n") # extract array size - if ((job_name in ['step_1', 'step_3', 'step_4', 'step_9', 'rerun']) or - ('plot' in job_name) or ('check' in job_name)): + if ((job_name in ['step_1', 'step_3', 'step_4', 'step_5', 'step_9',\ + 'rerun']) or ('plot' in job_name) or ('check' in job_name)): df = pd.read_csv(path_to_csv_file) N = df.shape[0]-1 elif job_name in ['step_2']: @@ -549,21 +674,22 @@ def slurm_job(job_name, path_to_logs = os.path.join(PATH, 'logs', step_name) else: path_to_logs = os.path.join(PATH, 'logs', job_name) - log_file_name = get_log_file_name(job_name=job_name,\ step_name=step_name) f.write("#SBATCH --open-mode=truncate\n") f.write(f"#SBATCH --output={path_to_logs}/{log_file_name}\n") - + # set PATH_TO_POSYDON where needed if job_name == 'step_3': # CALCULATE_EXTRA_VALUES f.write(f"export PATH_TO_POSYDON={PATH_TO_POSYDON}\n") - + # reset display settings if 'plot' in job_name: # PLOTS f.write("unset DISPLAY\n") - # get path to run the pipeline path_to_run_pipeline = os.path.join(PATH_TO_POSYDON, 'bin', 'posydon-run-pipeline') + if not os.path.isfile(path_to_run_pipeline): + raise FileNotFoundError("Pipeline's runfile not found at " + f"{path_to_run_pipeline}.") # encode the verbose information for running the pipeline as a command # line argument if VERBOSE and 'check' not in job_name: @@ -573,18 +699,22 @@ def slurm_job(job_name, f.write(f"\nsrun python {path_to_run_pipeline} {PATH_TO_GRIDS} " f"{path_to_csv_file} {slurm_array} 0") + def create_csv(GRID_TYPES=[], METALLICITIES=[], GRID_SLICES=[], COMPRESSIONS=[], step_name='', VERSION='', + PLOT_EXTENSION='pdf', GRIDS_COMBINED=[], ORIGINAL_COMPRESSIONS=[], INTERPOLATION_METHODS=[], + PROFILE_NAMES=[], CONTROL_GRIDS=[], STOP_BEFORE_CARBON_DEPLETION=0, RERUN_TYPE='', + CLUSTER='quest', DROP_MISSING_FILES=False, CREATE_PLOTS=[], DO_CHECKS=[], @@ -592,28 +722,95 @@ def create_csv(GRID_TYPES=[], PATH='.', previously_created_files=[], **kwargs): - """Create csv file with a list containing all data to process a step.""" + """Create csv file with a list containing all data to process a step. + + Parameters + ---------- + GRID_TYPES : list + Grid types in the directory tree in PATH_TO_GRIDS. + METALLICITIES : list + Metallicities in the directory tree below grid types (or version). + GRID_SLICES : list + Grid slices in the directory tree below the metallicities. + COMPRESSIONS: list + Compression types (e.g. LITE or ORIGINAL). + step_name : str + String representation of the step. It will be used in the file name. + VERSION : str + Version in the directory tree level below the grid type. (outdated) + PLOT_EXTENSION : str + The file extension for plots recognized by mathplotlib or + 'multipage-pdf'. + GRIDS_COMBINED : list + The names of the new grids combined from several slices. + ORIGINAL_COMPRESSIONS : list + Grids with the orgininal compression to get detailed data from them. + INTERPOLATION_METHODS : list + The interpolation methodes (e.g. linear or 1NN). + PROFILE_NAMES : list + The profiles which should get interpolated (e.g. radius, logRho, ...) + CONTROL_GRIDS : list + Grids used to check the interpolators. + STOP_BEFORE_CARBON_DEPLETION : int + Flag to stop high mass stars before carbon depletion because of chaotic + behavior. + RERUN_TYPE : str + Rerun types (e.g. PISN, reverse_MT, ...). + CLUSTER : str + Name of the cluster (e.g. yggdrasil or quest). + DROP_MISSING_FILES : bool + Enable/Disable to ignore files which neither exists nor are created + beforehand. + CREATE_PLOTS : list + Requested plots to be created (e.g. combined_TF12, termination_flag_1, + ...). + DO_CHECKS : list + Requested checks (e.g. failure_rate, CO_type). + PATH_TO_GRIDS : str + Path where to find the root directory of the grids. + PATH : str + Path of the working directory. + previously_created_files : list + List of filenames created by previous steps. + kwargs : dict + Other parameters (all being ignored). + + Returns + ------- + list + List of names of files which will be created by this step. + """ + if not os.path.isdir(PATH_TO_GRIDS): + raise NotADirectoryError(f"PATH_TO_GRIDS={PATH_TO_GRIDS} not found.") + if not os.path.isdir(PATH): + raise NotADirectoryError(f"PATH={PATH} not found.") # number of grid types N = len(GRID_TYPES) if ( N != len(METALLICITIES) or N != len(GRID_SLICES) or N != len(COMPRESSIONS)): raise ValueError('Missmatch between the len of GRID_TYPES, ' - 'METALLICITIES, GRID_SLICES, COMPRESSIONS.') + 'METALLICITIES, GRID_SLICES, COMPRESSIONS.' + f'Lengths: {N}, {len(METALLICITIES)}, ' + f'{len(GRID_SLICES)}, {len(COMPRESSIONS)}') if step_name == 'step_2': # there need to be as many combined files specified as recipes are # given to create them. if N != len(GRIDS_COMBINED): - raise ValueError('len(GRID_TYPES) != len(GRIDS_COMBINED)!') + raise ValueError(f'len(GRID_TYPES) = {len(GRID_TYPES)} != ' + f'{len(GRIDS_COMBINED)} = len(GRIDS_COMBINED)!') elif step_name == 'step_3': # here we need a reference grid with ORIGINAL compression to # calculated the extra quantities from. if N != len(ORIGINAL_COMPRESSIONS): - raise ValueError('len(GRID_TYPES) != len(ORIGINAL_COMPRESSIONS)!') - elif 'step_4_' in step_name: + raise ValueError(f'len(GRID_TYPES) = {len(GRID_TYPES)} != ' + f'{len(ORIGINAL_COMPRESSIONS)} = ' + 'len(ORIGINAL_COMPRESSIONS)!') + elif (('step_4_' in step_name) or ('step_5' in step_name)): # sub steps of the interpolator training need a control grid if len(GRID_SLICES) != len(CONTROL_GRIDS): - raise ValueError('len(GRID_SLICES) != len(CONTROL_GRIDS)!') + raise ValueError(f'len(GRID_SLICES) = {len(GRID_SLICES)} != ' + f'{len(CONTROL_GRIDS)} = len(CONTROL_GRIDS)!') grids = [] grids_compression = [] @@ -623,12 +820,16 @@ def create_csv(GRID_TYPES=[], processed_grids = [] interpolation_methods = [] interpolators = [] + profile_quantities = [] + profile_interpolators = [] export_path = [] rerun_metallicities = [] rerun_path = [] rerun_type = [] + clusters = [] plot_dirs = [] plot_quantities = [] + plot_extensions = [] checks = [] newly_created_files = [] df = pd.DataFrame() @@ -649,25 +850,49 @@ def create_csv(GRID_TYPES=[], RLO = '_RLO' else: RLO = '' + # check for combined gird in step 2 and its supsteps + if 'step_2' in step_name: + if len(GRID_SLICES[l]) != len(GRIDS_COMBINED[l]): + raise ValueError(f'len(GRID_SLICES[{l}]) = ' + f'{len(GRID_SLICES[l])} != ' + f'{len(GRIDS_COMBINED[l])} =' + f'len(GRIDS_COMBINED[{l}])!') + # check for combined gird in steps 4 and 5 (incl. supsteps) + if (('step_4_' in step_name) or ('step_5' in step_name)): + if len(GRID_SLICES[l]) != len(CONTROL_GRIDS[l]): + raise ValueError(f'len(GRID_SLICES[{l}]) = ' + f'{len(GRID_SLICES[l])} != ' + f'{len(CONTROL_GRIDS[l])} =' + f'len(CONTROL_GRIDS[{l}])!') # create data lists depending on the (sub)step if step_name == 'step_1': # CREATE_GRID_SLICES + # This step needs the location of the grid slice, the + # compression applied to it, the type of the gird, and + # the information, whether it is OK to stop cut of some + # evolution short before carbon depletion for massive + # stars. grids.append(os.path.join(PATH_TO_GRIDS, grid_type, VERSION, metallicity, grid_slice)) grids_compression.append(compression) grids_type.append(grid_type) - stop_before_carbon_depletion.append(STOP_BEFORE_CARBON_DEPLETION) + stop_before_carbon_depletion.append( + STOP_BEFORE_CARBON_DEPLETION) if i==0: compression_dir = os.path.join(PATH_TO_GRIDS, grid_type, VERSION, metallicity, compression) + # the compression directory will be created if not + # existing already if not os.path.isdir(compression_dir): newly_created_files.append(compression_dir) elif step_name == 'step_2': # COMBINE_GRID_SLICES - if len(GRID_SLICES[l]) != len(GRIDS_COMBINED[l]): - raise ValueError(f'len(GRID_SLICES[{l}]) != ' - f'len(GRIDS_COMBINED[{l}])!') + # This step needs the name of the combined gird and the + # information which slices get combined for each + # combined gird. It will be stored in a data frame, + # with the name of the combined grid as column key and + # the column containing the slices to be combined. combine_grid_slices = [] # grid_slice is a batch for grid_slice_ in grid_slice: @@ -692,6 +917,9 @@ def create_csv(GRID_TYPES=[], else: # if not defined assume 'ORIGINAL' original_compression = 'ORIGINAL' + # This step needs the grid to process, the grid type, + # the grid containing the ORIGINAL data, and the name + # of the new grid containing the processed data. grids.append(os.path.join(PATH_TO_GRIDS, grid_type, VERSION, metallicity, compression, grid_slice+'.h5')) @@ -706,6 +934,10 @@ def create_csv(GRID_TYPES=[], metallicity, compression, grid_slice+'_processed.h5')) elif step_name == 'step_4': # TRAIN_INTERPOLATORS + # This step needs the grids to use for the training, + # the grid type, the interpolation methods, and the + # names of the files where the interpolator data gets + # written to. for method in INTERPOLATION_METHODS: grids.append(os.path.join(PATH_TO_GRIDS, grid_type, VERSION, metallicity, @@ -722,13 +954,49 @@ def create_csv(GRID_TYPES=[], grid_type, VERSION, metallicity, 'interpolation_objects') + # the interpolator directory will be created if not + # existing already if not os.path.isdir(interpolator_dir): newly_created_files.append(interpolator_dir) + elif step_name == 'step_5': # TRAIN_PROFILE_INTERPOLATORS + # This step needs the grids to use for the training, + # the grid type, the names of the files of the IF + # interpolators, the quantities to get profiles + # interpolated for, and the names of the files where + # the interpolator data gets written to. + if (isinstance(PROFILE_NAMES, list) and\ + (len(PROFILE_NAMES)>0)): + for method in INTERPOLATION_METHODS: + grids.append(os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, metallicity, + compression, grid_slice+'.h5')) + grids_type.append(grid_type) + interpolators.append(os.path.join( + PATH_TO_GRIDS, grid_type, VERSION, + metallicity, 'interpolation_objects', + 'IF_'+method+RLO+'.pkl')) + profile_quantities.append(PROFILE_NAMES) + profile_interpolators.append(os.path.join( + PATH_TO_GRIDS, grid_type, VERSION, + metallicity, 'interpolation_objects', + 'profile_'+method+RLO+'.pkl')) + if i==0: + interpolator_dir = os.path.join(PATH_TO_GRIDS, + grid_type, VERSION, + metallicity, + 'interpolation_objects') + # the interpolator directory will be created if + # not existing already + if not os.path.isdir(interpolator_dir): + newly_created_files.append( + interpolator_dir) elif step_name == 'step_9': # EXPORT_DATASET if "RLO" in grid_type: export_dir = grid_type else: export_dir = grid_type+RLO + # This step needs the grids/interpolators to export and + # the path where to export them to. grids.append(os.path.join(PATH_TO_GRIDS, grid_type, VERSION, metallicity, compression, grid_slice+'.h5')) @@ -745,6 +1013,9 @@ def create_csv(GRID_TYPES=[], 'interpolators', method[1], metallicity+'.pkl')) elif step_name == 'rerun': # RERUN + # This step needs the grid to get the runs from, the + # path for the new runs, the grid type, the + # metallicity, the rerun type and the cluster name. grids.append(os.path.join(PATH_TO_GRIDS, grid_type, VERSION, metallicity, compression, grid_slice+'.h5')) @@ -754,6 +1025,7 @@ def create_csv(GRID_TYPES=[], grids_type.append(grid_type) rerun_metallicities.append(metallicity) rerun_type.append(RERUN_TYPE) + clusters.append(CLUSTER) if (('plot' in step_name) or ('check' in step_name)): # get output grid names for sub steps @@ -761,7 +1033,8 @@ def create_csv(GRID_TYPES=[], grid_s = GRIDS_COMBINED[l][i] elif 'step_3' in step_name: grid_s = grid_slice+'_processed' - elif 'step_4' in step_name: + elif (('step_4' in step_name) or + ('step_5' in step_name)): grid_s = CONTROL_GRIDS[l][i] else: grid_s = grid_slice @@ -769,7 +1042,8 @@ def create_csv(GRID_TYPES=[], if grid_s == '': # skip that slice for sub steps continue - if 'step_4' not in step_name: + if (('step_4' not in step_name) and + ('step_5' not in step_name)): # all steps without interpolation method get an # empty method if len(INTERPOLATION_METHODS)==0: @@ -786,27 +1060,42 @@ def create_csv(GRID_TYPES=[], # for to_plot in CREATE_PLOTS: # plot_quantities.append(to_plot) if len(CREATE_PLOTS)>0: + # Plots need the to know what to plot, the + # grid to get the data from, its grid type, + # the directory to put the plots to, the + # extension for the plot files, and the + # name of an interpolator, when using it. plot_quantities.append(CREATE_PLOTS) grids.append(os.path.join(PATH_TO_GRIDS, grid_type, VERSION, metallicity, compression, grid_s+'.h5')) grids_type.append(grid_type) - plot_dirs.append(os.path.join(PATH_TO_GRIDS, - grid_type, VERSION, - metallicity, 'plots', - RLO[1:]+RLO[:1]+grid_s)) + plot_dirs.append(os.path.join( + PATH_TO_GRIDS, + grid_type, VERSION, + metallicity, 'plots', + RLO[1:]+RLO[:1]+grid_s)) + plot_extensions.append(PLOT_EXTENSION) if method != '': # if there is a method - interpolators.append(os.path.join(PATH_TO_GRIDS, - grid_type, - VERSION, - metallicity, - 'interpolation_objects', - 'IF_'+method+RLO+'.pkl')) + if 'step_4' in step_name: + interpolators.append(os.path.join( + PATH_TO_GRIDS, grid_type, + VERSION, metallicity, + 'interpolation_objects', + 'IF_'+method+RLO+'.pkl')) + elif 'step_5' in step_name: + interpolators.append(os.path.join( + PATH_TO_GRIDS, grid_type, + VERSION, metallicity, + 'interpolation_objects', + 'profile_'+method+RLO+'.pkl')) if i==0: plots_dir = os.path.join(PATH_TO_GRIDS, grid_type, VERSION, metallicity, 'plots') + # the plots directory will be created if not + # existing already if not os.path.isdir(plots_dir): newly_created_files.append(plots_dir) elif 'check' in step_name: # CHECKS @@ -815,21 +1104,34 @@ def create_csv(GRID_TYPES=[], # for to_check in DO_CHECKS: # checks.append(to_check) if len(DO_CHECKS)>0: + # Checks need the to know what kind of + # check to do, the grid to get the data + # from, and the name of an interpolator, + # when using it. checks.append(DO_CHECKS) grids.append(os.path.join(PATH_TO_GRIDS, grid_type, VERSION, metallicity, compression, grid_s+'.h5')) if method != '': # if there is a method - interpolators.append(os.path.join(PATH_TO_GRIDS, - grid_type, - VERSION, - metallicity, - 'interpolation_objects', - 'IF_'+method+RLO+'.pkl')) + if 'step_4' not in step_name: + interpolators.append(os.path.join( + PATH_TO_GRIDS, grid_type, + VERSION, metallicity, + 'interpolation_objects', + 'IF_'+method+RLO+'.pkl')) + elif 'step_5' not in step_name: + interpolators.append(os.path.join( + PATH_TO_GRIDS, grid_type, + VERSION, metallicity, + 'interpolation_objects', + 'profile_'+method+RLO+'.pkl')) - # saving dataset to csv file + # saving dataset to csv file (step 2 is special here, because the data + # frame was already created.) if step_name != 'step_2': + # create a data frame to export it to a csv file (each step requires + # other data columns) grids = np.array(grids) df['path_to_grid'] = grids if step_name == 'step_1': @@ -844,6 +1146,11 @@ def create_csv(GRID_TYPES=[], df['grid_type'] = grids_type df['interpolation_method'] = interpolation_methods df['path_to_interpolator'] = interpolators + elif step_name == 'step_5': + df['grid_type'] = grids_type + df['profile_names'] = profile_quantities + df['path_to_IF_interpolator'] = interpolators + df['path_to_profile_interpolator'] = profile_interpolators elif step_name == 'step_9': df['export_path'] = export_path elif step_name == 'rerun': @@ -851,10 +1158,12 @@ def create_csv(GRID_TYPES=[], df['grid_type'] = grids_type df['rerun_metallicity'] = rerun_metallicities df['rerun_type'] = rerun_type + df['cluster'] = clusters elif 'plot' in step_name: df['grid_type'] = grids_type df['quantities_to_plot'] = plot_quantities df['path_to_plot'] = plot_dirs + df['plot_extension'] = plot_extensions if len(interpolators)>0: df['path_to_interpolator'] = interpolators elif 'check' in step_name: @@ -876,12 +1185,18 @@ def create_csv(GRID_TYPES=[], if (not os.path.exists(path) and path not in previously_created_files): drop_rows.append(row) - elif 'step_4_' in step_name: + elif (('step_4_' in step_name) or ('step_5_' in step_name)): # interpolator files needed path = df.at[row,'path_to_interpolator'] if (not os.path.exists(path) and path not in previously_created_files): drop_rows.append(row) + elif 'step_5' in step_name: + # interpolator files needed + path = df.at[row,'path_to_IF_interpolator'] + if (not os.path.exists(path) and + path not in previously_created_files): + drop_rows.append(row) # report about missing files and remove tasks, which need them if len(drop_rows)>0: print("\n{:-^54s}".format(step_name)) @@ -892,8 +1207,11 @@ def create_csv(GRID_TYPES=[], print(df.at[row,'path_to_grid']) if step_name == 'step_3': print(df.at[row,'path_to_grid_ORIGINAL']) - elif 'step_4_' in step_name: + elif (('step_4_' in step_name) or + ('step_5_' in step_name)): print(df.at[row,'path_to_interpolator']) + elif 'step_5' in step_name: + print(df.at[row,'path_to_IF_interpolator']) print('') df = df.drop(index=drop_rows) # keep track of files created by a step @@ -906,6 +1224,9 @@ def create_csv(GRID_TYPES=[], newly_created_files.extend(df['path_to_processed_grid'].to_list()) elif step_name == 'step_4': newly_created_files.extend(df['path_to_interpolator'].to_list()) + elif step_name == 'step_5': + newly_created_files.extend( + df['path_to_profile_interpolator'].to_list()) elif step_name == 'step_9': newly_created_files.extend(df['export_path'].to_list()) elif step_name == 'rerun': @@ -964,6 +1285,7 @@ def create_csv(GRID_TYPES=[], # return list of new files created by that step return newly_created_files + def create_cleanup_slurm_job(job_name, step_name, PATH='.', @@ -975,11 +1297,42 @@ def create_cleanup_slurm_job(job_name, GROUP=None, VERBOSE=False, **kwargs): - """Create slurm file for cleanup job.""" + """Create slurm file for cleanup job. + + Parameters + ---------- + job_name : str + String representation of the job. It will be used in the file name. + step_name : str + String representation of the parent step. It will be used for the logs. + PATH : str + Path of the working directory. + ACCOUNT : str + The slurm account name. Passed to slurm's option '--account'. + PARTITION : str + The slurm partition name. Passed to slurm's option '--partition'. + WALLTIME : str + Maximum time for the slurm job. Passed to slurm's option '--time'. + MAILTYPE : str + Specifier, which emails to send by slurm. Passed to slurm's option + '--mail-type'. It requires EMAIL to be set, too. + EMAIL : str + Email address to send mails to. Passed to slurm's option '--mail-user'. + It requires MAILTYPE to be set, too. + GROUP : str + Group name to get ownership and permissions on files created by the + pipeline. + VERBOSE : bool + Enables/Disables additional output. + kwargs : dict + Other parameters (all being ignored). + """ if ((job_name == 'step_2') or (job_name == 'step_9') or (job_name == 'rerun') or ('plot' in job_name) or ('check' in job_name) or ((job_name == 'pipeline') and (GROUP is not None))): + if not os.path.isdir(PATH): + raise NotADirectoryError(f"PATH={PATH} not found.") with open(f'{job_name}_cleanup.slurm', 'w') as f: # write slurm file content f.write("#!/bin/bash\n") @@ -1018,6 +1371,7 @@ def create_cleanup_slurm_job(job_name, f.write(f"\ncat {path_to_log_files}/{old_log_file_names} >" f" {path_to_log_files}/{new_log_file_name}") f.write(f"\nrm {path_to_log_files}/{old_log_file_names}") + # the overall cleanup of the pipeline if (job_name == 'pipeline'): if GROUP is not None: f.write(f"\necho \"Change group to {GROUP} and group " @@ -1039,19 +1393,20 @@ def create_cleanup_slurm_job(job_name, return False return True + def get_log_file_name(job_name, step_name): """Get name of log file Parameters ---------- - job_name : string + job_name : str Name of the job. - step_name : string + step_name : str Name of the step/parent job the job belongs to. Returns ------- - string + str Name of the log file. """ @@ -1063,6 +1418,8 @@ def get_log_file_name(job_name, step_name): return "post_processing_%4a.out" if job_name == 'step_4': # TRAIN_INTERPOLATORS return "train_interpolator_%4a.out" + if job_name == 'step_5': # TRAIN_PROFILE_INTERPOLATORS + return "train_profile_interpolator_%4a.out" if job_name == 'step_9': # EXPORT_DATASET return "export_dataset_%4a.out" if job_name == 'rerun': # RERUN @@ -1079,6 +1436,11 @@ def get_log_file_name(job_name, step_name): if __name__ == '__main__': if len(sys.argv) >= 2: + if str(sys.argv[1])=='--help': + print('Programm to setup the posydon pipeline') + print('syntax: posydon-setup-pipeline INI_FILE_PATH') + print('INI_FILE_PATH: path to the ini file containing the data') + sys.exit(0) ini_file_path = str(sys.argv[1]) if '.' not in ini_file_path: ini_file_path = './' + ini_file_path diff --git a/docs/_source/components-overview/post_processing/pipeline/pipeline_additions.rst b/docs/_source/components-overview/post_processing/pipeline/pipeline_additions.rst index b86ba88b7e..1cb8faf8c9 100644 --- a/docs/_source/components-overview/post_processing/pipeline/pipeline_additions.rst +++ b/docs/_source/components-overview/post_processing/pipeline/pipeline_additions.rst @@ -20,14 +20,17 @@ structure: .. code-block:: - path_to_grid,grid_type,quantities_to_plot,path_to_plot + path_to_grid,grid_type,quantities_to_plot,path_to_plot,plot_extension -Beside the grid, it states the type and takes a list of quantities to plot. -Finally, the path to the directory, where the plots should get stored. All +Beside the grid, it states the type and takes a list of quantities to plot. All final quantities supported for a :ref:`2D plot `, as third dimension can be specified. Additionally, you can put a :samp:`LOG10_` in front of each of them to switch on plotting in log-scale. Beside that there are predefined -plots. +plots. Finally, the path to the directory, where the plots should get stored, +and the extension of the image files (those need to be valid extension for +`mathplotlib `_) are given. There is one additional +extension :samp:`multipage-pdf`, which will create a PDF, where several plots +are stored as pages in a single PDF. .. table:: Basic predefined plots diff --git a/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst b/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst index 376f652bb0..2d29918868 100644 --- a/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst +++ b/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst @@ -184,12 +184,12 @@ confusion with other steps. It clearly has to run after a step, but it is no usual step itself. It requires a path to a :samp:`PSyGrid` object to get the models from, a path, where the rerun should be stored (it creates in there the :samp:`grid.csv` and the ini file needed to -:ref:`setup a new run `), the grid type, the metallicity, and -the type of the rerun specifying the logic and changes. +:ref:`setup a new run `), the grid type, the metallicity, the +type of the rerun specifying the logic and changes, and the cluster name. .. code-block:: - path_to_grid,rerun_path,grid_type,rerun_metallicity,rerun_type + path_to_grid,rerun_path,grid_type,rerun_metallicity,rerun_type,cluster .. table:: Currently supported rerun types diff --git a/docs/_source/components-overview/post_processing/pipeline_ini.rst b/docs/_source/components-overview/post_processing/pipeline_ini.rst index ff1ed45f3b..2af8031107 100644 --- a/docs/_source/components-overview/post_processing/pipeline_ini.rst +++ b/docs/_source/components-overview/post_processing/pipeline_ini.rst @@ -32,7 +32,7 @@ all :ref:`tasks `. Hence, you can run all ./run_pipeline.sh .. note:: - Currently, the user needs to take care of having a POSYDON_data directly + Currently, the user needs to take care of having a POSYDON_data directory which includes the tables for the core-collapse prescriptions him-/herself in the working directory. @@ -73,13 +73,18 @@ option will be used for the creation of the pipeline files and during the run of the pipeline. Finally, we have switches to turn on (:samp:`True`) and off (:samp:`False`) -individual :ref:`steps `. +individual :ref:`steps ` and +:ref:`additions `. Additionally, the file extension of the +plots can be set according to the restrictions of +`mathplotlib `_. There is one additional extension +:samp:`multipage-pdf`, which will create a PDF, where several plots are stored +as pages in a single PDF. .. code-block:: ini [pipeline setup] PATH_TO_GRIDS = '/srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/' - VERSION = '' # 'v2' in quest and '' in yggdrasil + VERSION = '' # to have a verion below the grid type level PATH = '.' # working dir VERBOSE = True @@ -91,14 +96,18 @@ individual :ref:`steps `. EXPORT_DATASET = True # rerun step RERUN = False + # additions + MAKE_PLOTS = True + PLOT_EXTENSION = 'pdf' + MAKE_CHECKS = True Step sections ------------- The path of each grid will be joint as -:samp:`PATH_TO_GRIDS/VERSION/GRID_TYPE/METALLICITY/GRID_SLICE`. The +:samp:`PATH_TO_GRIDS/GRID_TYPE/VERSION/METALLICITY/GRID_SLICE`. The corresponding h5 files will have names according to -:samp:`PATH_TO_GRIDS/VERSION/GRID_TYPE/METALLICITY/COMPRESSION/GRID_SLICE.h5`. +:samp:`PATH_TO_GRIDS/GRID_TYPE/VERSION/METALLICITY/COMPRESSION/GRID_SLICE.h5`. All sections have common keywords: .. table:: Common keywords of steps @@ -130,6 +139,7 @@ that step: 4 INTERPOLATION_METHODS a list of the interpolator types which are trained 4 CONTROL_GRIDS a list of lists of control grids for the :samp:`GRID_SLICES`; it need to have the same number of entries as the :samp:`GRID_SLICES`, to specify no control grid use an empty string R RERUN_TYPE a defined rerun type + R CLUSTER cluster name to get the appropriate ini file ==== ============================ =========== Here is an example of all the :ref:`steps `: @@ -357,6 +367,7 @@ Here is an example of all the :ref:`steps `: DROP_MISSING_FILES = True # example reruns are 'PISN', 'reverse_MT', 'TPAGBwind', 'opacity_max' RERUN_TYPE = 'opacity_max' + CLUSTER = 'quest' There are some predefined shortcuts for lists of :ref:`plots ` and :ref:`checks `: diff --git a/docs/_source/tutorials-examples/generating-datasets/run_full_piepeline.ipynb b/docs/_source/tutorials-examples/generating-datasets/run_full_piepeline.ipynb index e872281b42..b93519bbb2 100644 --- a/docs/_source/tutorials-examples/generating-datasets/run_full_piepeline.ipynb +++ b/docs/_source/tutorials-examples/generating-datasets/run_full_piepeline.ipynb @@ -69,7 +69,7 @@ "\n", "[pipeline setup]\n", " PATH_TO_GRIDS = '/srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/POSYDON_data/230914/POSYDON_data/tutorials/processing-pipeline/'\n", - " VERSION = '' # 'v2' in quest and '' in yggdrasil\n", + " VERSION = '' # to have a verion below the grid type level\n", " PATH = '.' # working dir\n", " VERBOSE = True\n", " \n", diff --git a/grid_params/pipeline_quest.ini b/grid_params/pipeline_quest.ini index 74a9c64797..f7f0683848 100644 --- a/grid_params/pipeline_quest.ini +++ b/grid_params/pipeline_quest.ini @@ -8,8 +8,8 @@ GROUP = 'b1119' [pipeline setup] - PATH_TO_GRIDS = '/projects/b1119/POSYDON_GRIDS/' - VERSION = 'v2' # 'v2' in quest and '' in yggdrasil + PATH_TO_GRIDS = '/projects/b1119/POSYDON_GRIDS_v2/' + VERSION = '' # to have a verion below the grid type level PATH = '.' # working dir VERBOSE = True @@ -18,9 +18,14 @@ COMBINE_GRID_SLICES = True CALCULATE_EXTRA_VALUES = True TRAIN_INTERPOLATORS = True + TRAIN_PROFILE_INTERPOLATORS = False EXPORT_DATASET = True # rerun step RERUN = False + # additions + MAKE_PLOTS = True + PLOT_EXTENSION = 'multipage-pdf' + MAKE_CHECKS = True #CREATE_GRID_SLICES [step_1] @@ -96,6 +101,21 @@ # supported checks: e.g. 'failure_rate' DO_CHECKS = ['CHECK_AFTER_TRAINING'] +#TRAIN_PROFILE_INTERPOLATORS +[step_5] + GRID_TYPES = ['HMS-HMS'] + METALLICITIES = [['1e+00_Zsun','1e-01_Zsun','1e-02_Zsun','1e-03_Zsun']] + GRID_SLICES = [['grid_low_res_combined_processed']] + INTERPOLATION_METHODS = ["linear","1NN"] + PROFILE_NAMES = ['radius', 'logRho', 'x_mass_fraction_H', 'y_mass_fraction_He', 'z_mass_fraction_metals', 'omega', 'energy'] + COMPRESSIONS = [['LITE']] + CONTROL_GRIDS = [['grid_random_1_processed']] + DROP_MISSING_FILES = True + # supported plots: e.g. 'combined_TF12', 'termination_flag_1', 'termination_flag_2', 'termination_flag_3', 'termination_flag_4', and any quantity valid for a Z-plotting + CREATE_PLOTS = ['PLOT_AFTER_PROFILE_TRAINING'] + # supported checks: e.g. 'failure_rate' + DO_CHECKS = ['CHECK_AFTER_PROFILE_TRAINING'] + #EXPORT_DATASET [step_9] GRID_TYPES = ['HMS-HMS'] @@ -113,3 +133,4 @@ DROP_MISSING_FILES = True # example reruns are 'PISN', 'reverse_MT', 'opacity_max', 'TPAGBwind' RERUN_TYPE = 'opacity_max' + CLUSTER = 'quest' diff --git a/grid_params/pipeline_yggdrasil.ini b/grid_params/pipeline_yggdrasil.ini index 15e5089d81..bbb2f08238 100644 --- a/grid_params/pipeline_yggdrasil.ini +++ b/grid_params/pipeline_yggdrasil.ini @@ -9,7 +9,7 @@ [pipeline setup] PATH_TO_GRIDS = '/srv/astro/projects/posydon/running_grids/POSYDON_GRIDS_v2/' - VERSION = '' # 'v2' in quest and '' in yggdrasil + VERSION = '' # to have a verion below the grid type level PATH = '.' # working dir VERBOSE = True @@ -18,9 +18,14 @@ COMBINE_GRID_SLICES = True CALCULATE_EXTRA_VALUES = True TRAIN_INTERPOLATORS = True + TRAIN_PROFILE_INTERPOLATORS = False EXPORT_DATASET = True # rerun step RERUN = False + # additions + MAKE_PLOTS = True + PLOT_EXTENSION = 'multipage-pdf' + MAKE_CHECKS = True #CREATE_GRID_SLICES [step_1] @@ -169,6 +174,45 @@ # supported checks: e.g. 'failure_rate' DO_CHECKS = ['CHECK_AFTER_TRAINING'] +#TRAIN_PROFILE_INTERPOLATORS +[step_5] + GRID_TYPES = ['CO-HMS_RLO', 'CO-HeMS', 'HMS-HMS'] + METALLICITIES = [# CO-HMS_RLO + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'], + # CO-HeMS + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'], + # HMS-HMS + ['2e+00_Zsun', '1e+00_Zsun', '4.5e-01_Zsun', '2e-01_Zsun', '1e-01_Zsun', '1e-02_Zsun', '1e-03_Zsun', '1e-04_Zsun'] + ] + GRID_SLICES = [# CO-HMS_RLO + ['grid_low_res_combined_processed'], + # CO-HeMS + ['grid_low_res_combined_processed'], + # HMS-HMS + ['grid_low_res_combined_processed'] + ] + INTERPOLATION_METHODS = ["linear","1NN"] + PROFILE_NAMES = ['radius', 'logRho', 'x_mass_fraction_H', 'y_mass_fraction_He', 'z_mass_fraction_metals', 'omega', 'energy'] + COMPRESSIONS = [# CO-HMS_RLO + ['LITE_RLO'], + # CO-HeMS + ['LITE', 'LITE_RLO'], + # HMS-HMS + ['LITE'] + ] + CONTROL_GRIDS = [# CO-HMS_RLO + ['grid_random_1_processed'], + # CO-HeMS + ['grid_random_1_processed'], + # HMS-HMS + ['grid_random_1_processed'] + ] + DROP_MISSING_FILES = True + # supported plots: e.g. 'combined_TF12', 'termination_flag_1', 'termination_flag_2', 'termination_flag_3', 'termination_flag_4', and any quantity valid for a Z-plotting + CREATE_PLOTS = ['PLOT_AFTER_PROFILE_TRAINING'] + # supported checks: e.g. 'failure_rate' + DO_CHECKS = ['CHECK_AFTER_PROFILE_TRAINING'] + #EXPORT_DATASET [step_9] GRID_TYPES = ['CO-HMS_RLO', 'CO-HeMS', 'HMS-HMS'] @@ -222,3 +266,4 @@ DROP_MISSING_FILES = True # example reruns are 'PISN', 'reverse_MT', 'opacity_max', 'TPAGBwind' RERUN_TYPE = 'opacity_max' + CLUSTER = 'yggdrasil' diff --git a/posydon/binary_evol/CE/step_CEE.py b/posydon/binary_evol/CE/step_CEE.py index e2cf3d9f97..dd6aab135b 100644 --- a/posydon/binary_evol/CE/step_CEE.py +++ b/posydon/binary_evol/CE/step_CEE.py @@ -30,10 +30,12 @@ "Konstantinos Kovlakas ", "Jeffrey Andrews ", "Tassos Fragos ", + "Matthias Kruckow ", ] import numpy as np +import pandas as pd from posydon.utils import common_functions as cf from posydon.utils import constants as const import warnings @@ -235,7 +237,7 @@ def __call__(self, binary): lambda2_CE, mc2_i, rc2_i, comp_type = self.calculate_lambda_CE( comp_star, verbose=self.verbose) else: - lambda2_CE = None + lambda2_CE = np.nan mc2_i = comp_star.mass rc2_i = 10**comp_star.log_R comp_type = "not_giant_companion" @@ -505,10 +507,13 @@ def CEE_simple_alpha_prescription( alpha_CE = self.common_envelope_efficiency # calculate evolution of the orbit - ebind_i = (-const.standard_cgrav / lambda1_CE - * (m1_i * const.Msun * (m1_i - mc1_i) * const.Msun) - / (radius1 * const.Rsun)) - if double_CE: + if pd.isna(lambda1_CE): + ebind_i = 0.0 + else: + ebind_i = (-const.standard_cgrav / lambda1_CE + * (m1_i * const.Msun * (m1_i - mc1_i) * const.Msun) + / (radius1 * const.Rsun)) + if (double_CE and (not pd.isna(lambda2_CE))): ebind_i += (-const.standard_cgrav / lambda2_CE * (m2_i * const.Msun * (m2_i - mc2_i) * const.Msun) / (radius2 * const.Rsun)) diff --git a/posydon/binary_evol/SN/profile_collapse.py b/posydon/binary_evol/SN/profile_collapse.py index 83920a111b..4ae0814e9e 100644 --- a/posydon/binary_evol/SN/profile_collapse.py +++ b/posydon/binary_evol/SN/profile_collapse.py @@ -9,6 +9,7 @@ import posydon.utils.constants as const from posydon.utils.gridutils import find_index_nearest_neighbour +from posydon.utils.limits_thresholds import NEUTRINO_MASS_LOSS_UPPER_LIMIT __authors__ = [ "Simone Bavera ", @@ -250,7 +251,7 @@ def do_core_collapse_BH(star, mass_collapsing, mass_central_BH=2.51, neutrino_mass_loss=None, - max_neutrino_mass_loss=0.5, + max_neutrino_mass_loss=NEUTRINO_MASS_LOSS_UPPER_LIMIT, verbose=False): """Do the core collapse of a star object with MESA profile provided. diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index e12f44da0d..d28b312405 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -37,15 +37,12 @@ from posydon.utils.data_download import PATH_TO_POSYDON_DATA, data_download import posydon.utils.constants as const -from posydon.utils.common_functions import is_number -from posydon.utils.common_functions import CO_radius -from posydon.utils.common_functions import ( - orbital_period_from_separation, - inspiral_timescale_from_separation, - separation_evol_wind_loss, - calculate_Patton20_values_at_He_depl, - THRESHOLD_CENTRAL_ABUNDANCE, - rotate +from posydon.utils.common_functions import (is_number, CO_radius, + orbital_period_from_separation, inspiral_timescale_from_separation, + separation_evol_wind_loss, calculate_Patton20_values_at_He_depl, rotate +) +from posydon.utils.limits_thresholds import (THRESHOLD_CENTRAL_ABUNDANCE, + STATE_NS_STARMASS_UPPER_LIMIT, NEUTRINO_MASS_LOSS_UPPER_LIMIT ) from posydon.binary_evol.binarystar import BINARYPROPERTIES @@ -80,8 +77,8 @@ "PISN": "Marchant+19", "ECSN": "Podsiadlowksi+04", "conserve_hydrogen_envelope" : False, - "max_neutrino_mass_loss": 0.5, - "max_NS_mass": 2.5, + "max_neutrino_mass_loss": NEUTRINO_MASS_LOSS_UPPER_LIMIT, + "max_NS_mass": STATE_NS_STARMASS_UPPER_LIMIT, "use_interp_values": True, "use_profiles": True, "use_core_masses": True, diff --git a/posydon/binary_evol/binarystar.py b/posydon/binary_evol/binarystar.py index 1b11e15b9a..e6b42196f2 100644 --- a/posydon/binary_evol/binarystar.py +++ b/posydon/binary_evol/binarystar.py @@ -870,6 +870,22 @@ def from_run(run, history=False, profiles=False): setattr(star, attr, final_value) setattr(star, attr + "_history", col_history) + # add values at He depletion + for colname in run.final_values.dtype.names: + if "at_He_depletion" in colname: + if colname[0:3]=="S1_": + attr = colname[3:] + final_value = run.final_values[colname] + setattr(binary.star_1, attr, final_value) + elif colname[0:3]=="S2_": + attr = colname[3:] + final_value = run.final_values[colname] + setattr(binary.star_2, attr, final_value) + else: + attr = colname + final_value = run.final_values[colname] + setattr(binary, attr, final_value) + # update eccentricity binary.eccentricity = 0.0 binary.eccentricity_history = [0.0] * n_steps diff --git a/posydon/binary_evol/singlestar.py b/posydon/binary_evol/singlestar.py index cb8248a389..812a919a82 100644 --- a/posydon/binary_evol/singlestar.py +++ b/posydon/binary_evol/singlestar.py @@ -399,6 +399,14 @@ def from_run(run, history=False, profile=False, which_star=None): except AttributeError: star.metallicity = None + # add values at He depletion + for colname in run.final_values.dtype.names: + if "at_He_depletion" in colname: + if colname[0:3]=="S1_": + attr = colname[3:] + final_value = run.final_values[colname] + setattr(star, attr, final_value) + star.state_history = [check_state_of_star(star, i=i, star_CO=False) for i in range(n_steps)] star.state = star.state_history[-1] diff --git a/posydon/grids/MODELS.py b/posydon/grids/MODELS.py index 3c538b8fc7..dc45c37d19 100644 --- a/posydon/grids/MODELS.py +++ b/posydon/grids/MODELS.py @@ -11,6 +11,9 @@ "Simone Bavera ", ] +from posydon.utils.limits_thresholds import (STATE_NS_STARMASS_UPPER_LIMIT, + NEUTRINO_MASS_LOSS_UPPER_LIMIT) + MODELS = { "MODEL01": { "mechanism": "direct", @@ -18,8 +21,8 @@ "PISN": "Marchant+19", "ECSN": "Podsiadlowksi+04", "conserve_hydrogen_envelope" : False, - "max_neutrino_mass_loss": 0.5, - "max_NS_mass": 2.5, + "max_neutrino_mass_loss": NEUTRINO_MASS_LOSS_UPPER_LIMIT, + "max_NS_mass": STATE_NS_STARMASS_UPPER_LIMIT, "use_interp_values": False, "use_profiles": True, "use_core_masses": False, @@ -31,8 +34,8 @@ "PISN": "Marchant+19", "ECSN": "Podsiadlowksi+04", "conserve_hydrogen_envelope" : False, - "max_neutrino_mass_loss": 0.5, - "max_NS_mass": 2.5, + "max_neutrino_mass_loss": NEUTRINO_MASS_LOSS_UPPER_LIMIT, + "max_NS_mass": STATE_NS_STARMASS_UPPER_LIMIT, "use_interp_values": False, "use_profiles": True, "use_core_masses": False, @@ -44,8 +47,8 @@ "PISN": "Marchant+19", "ECSN": "Podsiadlowksi+04", "conserve_hydrogen_envelope" : False, - "max_neutrino_mass_loss": 0.5, - "max_NS_mass": 2.5, + "max_neutrino_mass_loss": NEUTRINO_MASS_LOSS_UPPER_LIMIT, + "max_NS_mass": STATE_NS_STARMASS_UPPER_LIMIT, "use_interp_values": False, "use_profiles": True, "use_core_masses": False, @@ -57,8 +60,8 @@ "PISN": "Marchant+19", "ECSN": "Podsiadlowksi+04", "conserve_hydrogen_envelope" : False, - "max_neutrino_mass_loss": 0.5, - "max_NS_mass": 2.5, + "max_neutrino_mass_loss": NEUTRINO_MASS_LOSS_UPPER_LIMIT, + "max_NS_mass": STATE_NS_STARMASS_UPPER_LIMIT, "use_interp_values": False, "use_profiles": True, "use_core_masses": False, @@ -70,8 +73,8 @@ "PISN": "Marchant+19", "ECSN": "Podsiadlowksi+04", "conserve_hydrogen_envelope" : False, - "max_neutrino_mass_loss": 0.5, - "max_NS_mass": 2.5, + "max_neutrino_mass_loss": NEUTRINO_MASS_LOSS_UPPER_LIMIT, + "max_NS_mass": STATE_NS_STARMASS_UPPER_LIMIT, "use_interp_values": False, "use_profiles": True, "use_core_masses": False, @@ -83,8 +86,8 @@ "PISN": "Marchant+19", "ECSN": "Podsiadlowksi+04", "conserve_hydrogen_envelope" : True, - "max_neutrino_mass_loss": 0.5, - "max_NS_mass": 2.5, + "max_neutrino_mass_loss": NEUTRINO_MASS_LOSS_UPPER_LIMIT, + "max_NS_mass": STATE_NS_STARMASS_UPPER_LIMIT, "use_interp_values": False, "use_profiles": True, "use_core_masses": False, @@ -96,8 +99,8 @@ "PISN": "Marchant+19", "ECSN": "Podsiadlowksi+04", "conserve_hydrogen_envelope" : True, - "max_neutrino_mass_loss": 0.5, - "max_NS_mass": 2.5, + "max_neutrino_mass_loss": NEUTRINO_MASS_LOSS_UPPER_LIMIT, + "max_NS_mass": STATE_NS_STARMASS_UPPER_LIMIT, "use_interp_values": False, "use_profiles": True, "use_core_masses": False, @@ -109,8 +112,8 @@ "PISN": "Marchant+19", "ECSN": "Podsiadlowksi+04", "conserve_hydrogen_envelope" : True, - "max_neutrino_mass_loss": 0.5, - "max_NS_mass": 2.5, + "max_neutrino_mass_loss": NEUTRINO_MASS_LOSS_UPPER_LIMIT, + "max_NS_mass": STATE_NS_STARMASS_UPPER_LIMIT, "use_interp_values": False, "use_profiles": True, "use_core_masses": False, @@ -122,8 +125,8 @@ "PISN": "Marchant+19", "ECSN": "Podsiadlowksi+04", "conserve_hydrogen_envelope" : True, - "max_neutrino_mass_loss": 0.5, - "max_NS_mass": 2.5, + "max_neutrino_mass_loss": NEUTRINO_MASS_LOSS_UPPER_LIMIT, + "max_NS_mass": STATE_NS_STARMASS_UPPER_LIMIT, "use_interp_values": False, "use_profiles": True, "use_core_masses": False, @@ -135,8 +138,8 @@ "PISN": "Marchant+19", "ECSN": "Podsiadlowksi+04", "conserve_hydrogen_envelope" : True, - "max_neutrino_mass_loss": 0.5, - "max_NS_mass": 2.5, + "max_neutrino_mass_loss": NEUTRINO_MASS_LOSS_UPPER_LIMIT, + "max_NS_mass": STATE_NS_STARMASS_UPPER_LIMIT, "use_interp_values": False, "use_profiles": True, "use_core_masses": False, diff --git a/posydon/grids/post_processing.py b/posydon/grids/post_processing.py index 2b3490d2c6..9ea7c97a8c 100644 --- a/posydon/grids/post_processing.py +++ b/posydon/grids/post_processing.py @@ -184,7 +184,9 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, star, star_CO=False)) # core masses at he depletion with warnings.catch_warnings(record=True) as w: - calculate_Patton20_values_at_He_depl(star) + if ((star.avg_c_in_c_core_at_He_depletion is None) or + (star.co_core_mass_at_He_depletion is None)): + calculate_Patton20_values_at_He_depl(star) if len(w) > 0: print(w[0].message) print(f'The warning was raised by {grid.MESA_dirs[i]} ' diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index cfa198bce1..e22d8ee477 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -202,7 +202,10 @@ from posydon.utils.common_functions import (orbital_separation_from_period, initialize_empty_array, infer_star_state, - THRESHOLD_CENTRAL_ABUNDANCE) + get_i_He_depl) +from posydon.utils.limits_thresholds import (THRESHOLD_CENTRAL_ABUNDANCE, + THRESHOLD_CENTRAL_ABUNDANCE_LOOSE_C +) from posydon.utils.gridutils import (read_MESA_data_file, read_EEP_data_file, add_field, join_lists, fix_He_core) from posydon.visualization.plot2D import plot2D @@ -285,6 +288,10 @@ "avg_charge_He", ] +EXTRA_STAR_COLS_AT_HE_DEPLETION = [ + "avg_c_in_c_core", "co_core_mass" +] + # Default columns to exclude from computing history/profile downsampling DEFAULT_HISTORY_DS_EXCLUDE = [ "model_number", # not physical @@ -612,11 +619,21 @@ def decide_columns(key_in_config, defaults): [(col, 'f8') for col in initial_value_columns] + [(col, 'f8') for col in ["X", "Y", "Z"]] ) + # get extra columns at He depletion for final values + extra_final_values_cols = [] + for starID in ["S1_", "S2_"]: + for col in all_history_columns: + if starID in col: + for extra_col in EXTRA_STAR_COLS_AT_HE_DEPLETION: + colname = starID + extra_col + "_at_He_depletion" + extra_final_values_cols.append((colname, 'f8')) + break dtype_final_values = ( [(col, 'f8') for col in all_history_columns] + [(col, H5_UNICODE_DTYPE) for col in termination_flag_columns] + ([("interpolation_class", H5_UNICODE_DTYPE)] if binary_grid else []) + + extra_final_values_cols ) self.initial_values = initialize_empty_array( np.empty(N_runs, dtype=dtype_initial_values)) @@ -684,6 +701,26 @@ def decide_columns(key_in_config, defaults): if self.config["He_core_fix"]: history2 = fix_He_core(history2) + # get values at He depletion and add them to the final values + for starID in ["S1_", "S2_"]: + if starID == "S1_": + h = history1 + elif starID == "S2_": + h = history2 + else: + h = None + i_He_depl = -1 + for col in EXTRA_STAR_COLS_AT_HE_DEPLETION: + fvcol = starID + col + "_at_He_depletion" + if fvcol in dtype_final_values.names: + if ((i_He_depl==-1) and (h is not None)): + i_He_depl = get_i_He_depl(h) + if ((h is not None) and (col in h.dtype.names) and + (i_He_depl>=0)): + self.final_values[i][fvcol] = h[col][i_He_depl] + else: + self.final_values[i][fvcol] = np.nan + # scrub histories (unless EEPs are selected or run is ignored) if not ignore and self.eeps is None: # read the model numbers and ages from the histories @@ -854,8 +891,9 @@ def decide_columns(key_in_config, defaults): init_mass_1 = float(params_from_path[0]) # check whether stop at He depletion is requested if stop_before_carbon_depletion and init_mass_1>=100.0: - kept = keep_till_central_abundance_He_C(binary_history, history1, - history2, THRESHOLD_CENTRAL_ABUNDANCE, 0.1) + kept = keep_till_central_abundance_He_C(binary_history, + history1, history2, THRESHOLD_CENTRAL_ABUNDANCE, + THRESHOLD_CENTRAL_ABUNDANCE_LOOSE_C) binary_history, history1, history2, newTF1 = kept # check whether start at RLO is requested, and chop the history diff --git a/posydon/grids/scrubbing.py b/posydon/grids/scrubbing.py index 36f35236f8..19d47b7b00 100644 --- a/posydon/grids/scrubbing.py +++ b/posydon/grids/scrubbing.py @@ -9,6 +9,10 @@ import numpy as np import warnings +from posydon.utils.limits_thresholds import ( + RL_RELATIVE_OVERFLOW_THRESHOLD, LG_MTRANSFER_RATE_THRESHOLD, + THRESHOLD_CENTRAL_ABUNDANCE, THRESHOLD_CENTRAL_ABUNDANCE_LOOSE_C +) def scrub(tables, models, ages): @@ -98,22 +102,14 @@ def keep_after_RLO(bh, h1, h2): bh_colnames = bh.dtype.names if "lg_mtransfer_rate" in bh_colnames: - # This needs to be aligned with run_binary_extras.f - # old: rate = bh["lg_mtransfer_rate"] >= -12 - rate = bh["lg_mtransfer_rate"] >= -10 + rate = bh["lg_mtransfer_rate"] >= LG_MTRANSFER_RATE_THRESHOLD else: raise ValueError("No `lg_mtransfer_rate` in binary history.") - # This needs to be aligned with run_binary_extras.f - # old: rlo1 = (bh["rl_relative_overflow_1"] >= -0.05 - # if "rl_relative_overflow_1" in bh_colnames else None) - rlo1 = (bh["rl_relative_overflow_1"] >= 0.00 + rlo1 = (bh["rl_relative_overflow_1"] >= RL_RELATIVE_OVERFLOW_THRESHOLD if "rl_relative_overflow_1" in bh_colnames else None) - # This needs to be aligned with run_binary_extras.f - #old: rlo2 = (bh["rl_relative_overflow_2"] >= -0.05 - # if "rl_relative_overflow_2" in bh_colnames else None) - rlo2 = (bh["rl_relative_overflow_2"] >= 0.00 + rlo2 = (bh["rl_relative_overflow_2"] >= RL_RELATIVE_OVERFLOW_THRESHOLD if "rl_relative_overflow_2" in bh_colnames else None) if rlo1 is None: @@ -166,7 +162,9 @@ def keep_after_RLO(bh, h1, h2): return new_bh, new_h1, new_h2 -def keep_till_central_abundance_He_C(bh, h1, h2, Ystop=1.0e-5, XCstop=1.0): +def keep_till_central_abundance_He_C(bh, h1, h2, + Ystop=THRESHOLD_CENTRAL_ABUNDANCE, + XCstop=THRESHOLD_CENTRAL_ABUNDANCE_LOOSE_C): """Scrub histories to stop when central helium and carbon abundance are below the stopping criteria. diff --git a/posydon/grids/termination_flags.py b/posydon/grids/termination_flags.py index 7b5f6bd626..7ee1a93d31 100644 --- a/posydon/grids/termination_flags.py +++ b/posydon/grids/termination_flags.py @@ -27,11 +27,14 @@ import numpy as np from posydon.utils.common_functions import ( - infer_star_state, cumulative_mass_transfer_flag, infer_mass_transfer_case, - RL_RELATIVE_OVERFLOW_THRESHOLD, LG_MTRANSFER_RATE_THRESHOLD) + infer_star_state, cumulative_mass_transfer_flag, infer_mass_transfer_case +) +from posydon.utils.limits_thresholds import ( + RL_RELATIVE_OVERFLOW_THRESHOLD, LG_MTRANSFER_RATE_THRESHOLD +) from posydon.visualization.combine_TF import ( - TF1_POOL_STABLE, TF1_POOL_UNSTABLE, - TF1_POOL_INITIAL_RLO, TF1_POOL_ERROR, TF2_POOL_NO_RLO + TF1_POOL_STABLE, TF1_POOL_UNSTABLE, TF1_POOL_INITIAL_RLO, TF1_POOL_ERROR, + TF2_POOL_NO_RLO, TF2_POOL_INITIAL_RLO ) @@ -55,6 +58,7 @@ def get_flag_from_MESA_output(MESA_log_path): `min_timestep_limit`, or `termination code:`, or `Terminate:`. """ + termination_code = '' if MESA_log_path is not None and os.path.isfile(MESA_log_path): if MESA_log_path.endswith(".gz"): with gzip.open(MESA_log_path, "rt", errors='ignore') as log_file: @@ -75,8 +79,15 @@ def get_flag_from_MESA_output(MESA_log_path): else: truncate_at = len( "termination code: " if has_term_code else "Terminate: ") - return line[truncate_at:].strip() - + if min_timestep: + # in case of "min_timestep_limit" allow to find another + # termination code to overrule "min_timestep_limit" + termination_code = line[truncate_at:].strip() + else: + return line[truncate_at:].strip() + + if len(termination_code)>0: + return termination_code return "reach cluster timelimit" @@ -238,7 +249,7 @@ def get_flags_from_MESA_run(MESA_log_path, binary_history=None, def infer_interpolation_class(tf1, tf2): """Use the first two termination flags to infer the interpolation class.""" - if tf1 in TF1_POOL_INITIAL_RLO: + if ((tf1 in TF1_POOL_INITIAL_RLO) or (tf2 in TF2_POOL_INITIAL_RLO)): return "initial_MT" if tf1 in TF1_POOL_ERROR: return "not_converged" diff --git a/posydon/interpolation/IF_interpolation.py b/posydon/interpolation/IF_interpolation.py index 9985cabc85..cef0b7bb0f 100644 --- a/posydon/interpolation/IF_interpolation.py +++ b/posydon/interpolation/IF_interpolation.py @@ -44,7 +44,9 @@ class relies on the BaseIFInterpolator class to perform the interpolation 2. interp_classes: a list of classes that the simulation tracks can fall into. Usually specified as the mass transfer type. This only needs -be specified if interp_method is a list. +be specified if interp_method is a list. Note that when using class-wise normalization +only classes in interp_classes are normalized. This is the behavior for interpolation normalization +but not classification normalization. 3. class_method: the classification method to be used, either kNN or 1NN. @@ -76,8 +78,8 @@ class relies on the BaseIFInterpolator class to perform the interpolation interp = IFInterpolator(grid = grid, interpolators = [ { - "interp_method": ["linear", "linear", "linear"], - "interp_classes": ["no_MT", "stable_MT", "unstable_MT"], + "interp_method": ["linear", "linear", "linear", "linear"], + "interp_classes": ["no_MT", "stable_MT", "unstable_MT", "stable_reverse_MT"], # classes not in this array will not be normalized in class-wise normalization "out_keys": first, "class_method": "kNN", "c_keys": ["interpolation_class"], @@ -85,7 +87,7 @@ class relies on the BaseIFInterpolator class to perform the interpolation }, { "interp_method": ["linear", "linear", "linear", "linear"], - "interp_classes": ["None", "BH", "WD", "NS"], + "interp_classes": ["None", "BH", "WD", "NS"], # classes not in this array will not be normalized in class-wise normalization "out_keys": second, "class_method": "kNN", "c_keys": ['S1_direct_state'], @@ -93,7 +95,7 @@ class relies on the BaseIFInterpolator class to perform the interpolation }, { "interp_method": ["linear", "linear", "linear", "linear"], - "interp_classes": ["None", "BH", "WD", "NS"], + "interp_classes": ["None", "BH", "WD", "NS"], # classes not in this array will not be normalized in class-wise normalization "out_keys": third, "class_method": "kNN", "c_keys": ['S1_Fryer+12-rapid_state'], @@ -101,7 +103,7 @@ class relies on the BaseIFInterpolator class to perform the interpolation }, { "interp_method": ["linear", "linear", "linear", "linear"], - "interp_classes": ["None", "BH", "WD", "NS"], + "interp_classes": ["None", "BH", "WD", "NS"], # classes not in this array will not be normalized in class-wise normalization "out_keys": fourth, "class_method": "kNN", "c_keys": ['S1_Fryer+12-delayed_state'], @@ -109,7 +111,7 @@ class relies on the BaseIFInterpolator class to perform the interpolation }, { "interp_method": ["linear", "linear", "linear", "linear"], - "interp_classes": ["None", "BH", "WD", "NS"], + "interp_classes": ["None", "BH", "WD", "NS"], # classes not in this array will not be normalized in class-wise normalization "out_keys": fifth, "class_method": "kNN", "c_keys": ['S1_Sukhbold+16-engineN20_state'], @@ -117,7 +119,7 @@ class relies on the BaseIFInterpolator class to perform the interpolation }, { "interp_method": ["linear", "linear", "linear", "linear"], - "interp_classes": ["None", "BH", "WD", "NS"], + "interp_classes": ["None", "BH", "WD", "NS"], # classes not in this array will not be normalized in class-wise normalization "out_keys": sixth, "class_method": "kNN", "c_keys": ['S1_Patton&Sukhbold20-engineN20_state'], @@ -649,11 +651,13 @@ def test_interpolator(self, Xt): """ classes = self.test_classifier(self.c_key, Xt) - Xtn = self.X_scaler.normalize(Xt) + + if isinstance(self.interp_method, list): Xtn = self.X_scaler.normalize(Xt, classes) - Ypredn = self.interpolator.predict(Xtn, classes) + Ypredn = self.interpolator.predict(Xtn, classes, self.X_scaler) else: + Xtn = self.X_scaler.normalize(Xt) Ypredn = self.interpolator.predict(Xtn) Ypredn = np.array([ @@ -960,15 +964,15 @@ def scale_one(in_scaling, klass = None): np.abs(ypred_i[:, which_abs] - Y_t[:, which_abs]), 90, axis=0) - try: - where_min = np.nanargmin(np.nanmean(err, axis=2), axis=0) - except: - # replace nans by a large number before looking for minimum - where_min = np.nanargmin(np.nan_to_num(np.nanmean(err, axis=2), nan=1e99), axis=0) + + no_nan_slices = np.isfinite(np.nanmean(err, axis = 2)).all(axis = 0) + + where_min = np.full(shape = (self.n_out, ), fill_value = np.nan) + where_min[no_nan_slices] = np.nanargmin(np.nanmean(err, axis=2)[:, no_nan_slices], axis=0) out_scaling = [] for i in range(self.n_out): - if where_min[i] < r: + if where_min[i] < r or np.isnan(where_min[i]): out_scaling.append(out_scalings[0][i]) else: out_scaling.append(out_scalings[1][i]) @@ -1079,13 +1083,15 @@ def train(self, XT, YT): self.interpolator.fit(XT, YT) # self.train_error(XT, YT) - def predict(self, Xt): + def predict(self, Xt, scaler = None, klass = None): """Interpolate and approximate output vectors given input vectors. Parameters ---------- XT : numpy array List of input vectors + scaler : #TODO + klass : #TODO Returns ------- @@ -1117,13 +1123,15 @@ def train(self, XT, YT): self.interpolator.append(OoH) # self.train_error(XT, YT) - def predict(self, Xt): + def predict(self, Xt, scaler = None, klass = None): """Interpolate and approximate output vectors given input vectors. Parameters ---------- XT : numpy array List of input vectors + scaler : #TODO + klass : #TODO Returns ------- @@ -1131,14 +1139,24 @@ def predict(self, Xt): """ super().predict(Xt) + Ypred = self.interpolator[0](Xt) wnan = np.isnan(Ypred[:, 0]) # 1NN interpolation for binaries out of hull if np.any(wnan): Ypred[wnan, :] = self.interpolator[1].predict(Xt[wnan, :]) - if np.any(wnan): + + dists, _ = self.interpolator[1].kneighbors(Xt[wnan, :]) + max_distance = dists.argmax() # only finding information for furthest nearest neighbor + + neighbors = scaler.denormalize(self.interpolator[1]._tree.data.base) # unnormalizing + max_distance_point = scaler.denormalize(Xt[[max_distance]]) + + nearest_neighbor = np.sum(np.square(neighbors - max_distance_point), axis = 1) + nearest_neighbor = nearest_neighbor.argsort()[0] + warnings.warn(f"1NN interpolation used for {np.sum(wnan)} " - "binaries out of hull.") + f"binaries out of hull. Parameter-wise distance (Unnormalized) for point with maximum out-of-hull euclidian distance (Normalized): {np.abs(neighbors[nearest_neighbor] - max_distance_point[0])}") return Ypred @@ -1190,6 +1208,7 @@ def train(self, XT, YT, z): List of input vectors YT : numpy array List of corresponding output vectors + z : #TODO """ self.M = YT.shape[1] @@ -1203,26 +1222,29 @@ def classifier(self, Xt): return self.classifier.predict(Xt) - def predict(self, Xt, zpred): + def predict(self, Xt, zpred, scaler = None): """Interpolate and approximate output vectors given input vectors. Parameters ---------- XT : numpy array List of input vectors + zpred : #TODO + scaler : #TODO Returns ------- Output space approximation as numpy array """ + Ypred = np.ones((Xt.shape[0], self.M)) * np.nan for i in range(len(self.classes)): which = np.zeros_like(zpred, dtype=bool) for j in range(len(self.classes[i])): which += zpred == self.classes[i][j] if which.any(): - Ypred[which, :] = self.interpolators[i].predict(Xt[which, :]) + Ypred[which, :] = self.interpolators[i].predict(Xt[which, :], scaler = scaler, klass = self.classes[i]) return Ypred @@ -1317,6 +1339,7 @@ def train(self, XT, yT, K=5): List of input vectors YT : numpy array List of corresponding classes + K : #TODO """ super().train(XT, yT) @@ -1380,7 +1403,7 @@ def xtrain(self, XT, yT, **opts): else: acc = np.zeros(nmax) for n in range(1, nmax + 1): - # print(f" - xval for k = {n}") + for i in range(nfolds): iTrain, itest = xval_indices( XT.shape[0], percent_test=p_test, labels=yT) @@ -1428,9 +1451,9 @@ def normalize(self, X, klass = None): inds = np.where(klass == c)[0] c = None if c == "None" else c - if c not in self.scaler.keys(): - warnings.warn(f"skip normalize: c={c}, inds={inds}") + if c not in self.scaler.keys(): # if class not in classes to interpolate we ignore continue + normalized[inds] = self.scaler[c].normalize(X[inds]) return normalized @@ -1438,8 +1461,8 @@ def normalize(self, X, klass = None): def denormalize(self, Xn, klass = None): if klass is None: - return self.scaler[klass].denormalize(X) - + return self.scaler[klass].denormalize(Xn) + else: normalized = Xn @@ -1450,8 +1473,7 @@ def denormalize(self, Xn, klass = None): inds = np.where(klass == c)[0] c = None if c == "None" else c - if c not in self.scaler.keys(): - warnings.warn(f"skip denormalize: c={c}, inds={inds}") + if c not in self.scaler.keys(): # if class not in classes to interpolate we ignore continue normalized[inds] = self.scaler[c].denormalize(Xn[inds]) @@ -1466,6 +1488,7 @@ def __init__(self, norms, XT): if len(norms) != XT.shape[1]: raise ValueError("The number of columns in XT must be equal " "to the length of norms.") + self.N = XT.shape[1] self.scalers = [] for i in range(self.N): @@ -1484,6 +1507,7 @@ def normalize(self, X): def denormalize(self, Xn): """Unscale input X.""" + assert Xn.shape[1] == self.N X = np.empty_like(Xn) for i in range(self.N): @@ -1503,6 +1527,7 @@ def xval_indices(N, percent_test=0.15, labels=None): number of samples in the data set. percent_test : float Percentage of samples to be in the test sets. (0 < percent_test < 0.5) + labels : #TODO Returns ------- @@ -1589,7 +1614,7 @@ def train_and_assess(grid_train, grid_test, ux2, path, classes): m1NN.save(os.path.join(path2obj, name)) interp_method, class_method = "linear", "kNN" - # classes = ['no_MT', 'stable_MT', 'unstable_MT'] + # classes = ['no_MT', 'stable_MT', 'unstable_MT', 'stable_reverse_MT'] mMC = IFInterpolator(grid=grid_T, interp_method=interp_method, class_method=class_method, interp_classes=classes) interp_method = "linear3c" @@ -1612,6 +1637,8 @@ def assess_models(models, grid_T, grid_t, ux2, path='./'): Train grid grid_t : psg.PSyGrid Test grid + ux2 : #TODO + path : #TODO """ if not isinstance(models, list): @@ -1715,6 +1742,7 @@ def assess_models(models, grid_T, grid_t, ux2, path='./'): w_sMT.append(((ic_gt == 'stable_MT') & (ic_t == 'stable_MT'))) w_uMT.append(((ic_gt == 'unstable_MT') & (ic_t == 'unstable_MT'))) w_nMT.append(((ic_gt == 'no_MT') & (ic_t == 'no_MT'))) + #TODO: add interpolation class "stable_reverse_MT" w = [w_sMT, w_uMT, w_nMT, correct] percent = [50, 90] @@ -2012,6 +2040,10 @@ def plot_interpolation(m, keys, v2, ux2, scales=None, path=None, **pltargs): A trained instance of the IFInterpolator keys : List of strings Variables for which interpolation errors will be plotted + v2 : #TODO + ux2 : #TODO + scales : #TODO + path : #TODO """ N = pltargs.pop('N', PLT_INTERP_KWARGS['N']) @@ -2065,7 +2097,7 @@ def plot_interpolation(m, keys, v2, ux2, scales=None, path=None, **pltargs): name = (key + '_' + m.classifiers['interpolation_class'].method + '_' + method + '_' + str(round(v, 2)) + '.png') name = os.path.join(path, name) - print(name) + fig.savefig(name) plt.close(fig) @@ -2079,10 +2111,12 @@ def plot_interp_error(Ygt, Ys, ind_MT, methods, keys, path='.'): Ground truth of the final values of testing tracks Ys : numpy array Model's approximation of the final values of testing tracks + ind_MT : #TODO methods : List of strings List that indicates the interpolation methods keys : List of strings List indicating for which variables error distributions will be plotted + path : #TODO """ labels = ['stable MT', 'unstable MT', 'no MT'] diff --git a/posydon/popsyn/synthetic_population.py b/posydon/popsyn/synthetic_population.py index 118d3f4b8e..18fe832fd2 100644 --- a/posydon/popsyn/synthetic_population.py +++ b/posydon/popsyn/synthetic_population.py @@ -881,7 +881,7 @@ def get_formation_channels(self, mt_history): event_array = df_binary['event'].values.tolist() # make interpolated class information consistent with event column - HMS_HMS_event_dict = {'stable_MT':'oRLO1', 'no_MT':'None', 'unstable_MT':'oCE1/oDoubleCE1'} + HMS_HMS_event_dict = {'stable_MT':'oRLO1', 'stable_reverse_MT':'oRLO1', 'no_MT':'None', 'unstable_MT':'oCE1/oDoubleCE1'} event_HMS_HMS = HMS_HMS_event_dict[df_binary_online['interp_class_HMS_HMS']] # for now, only append information for RLO1; unstable_MT information already exists diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index 926ac0b46c..39993ce2c7 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -25,18 +25,16 @@ import copy import warnings from scipy.interpolate import PchipInterpolator +from posydon.utils.limits_thresholds import (THRESHOLD_CENTRAL_ABUNDANCE, + THRESHOLD_HE_NAKED_ABUNDANCE, REL_LOG10_BURNING_THRESHOLD, + LOG10_BURNING_THRESHOLD, STATE_NS_STARMASS_UPPER_LIMIT, + RL_RELATIVE_OVERFLOW_THRESHOLD, LG_MTRANSFER_RATE_THRESHOLD +) PATH_TO_POSYDON = os.environ.get("PATH_TO_POSYDON") # Constants related to inferring star states -THRESHOLD_CENTRAL_ABUNDANCE = 0.01 # central abundance for flagging depletion -THRESHOLD_HE_NAKED_ABUNDANCE = 0.01 # for surface abundance for stripped_He -THRESHOLD_NUCLEAR_LUMINOSITY = 0.97 # for element fraction in nuclear burning -# relative burning threshold with respect to nuclear luminosity -REL_LOG10_BURNING_THRESHOLD = np.log10(1.0 - THRESHOLD_NUCLEAR_LUMINOSITY) -LOG10_BURNING_THRESHOLD = -10.0 # burning luminosity threshold (in Lsol) -STATE_NS_STARMASS_UPPER_LIMIT = 2.5 STATE_UNDETERMINED = "undetermined_evolutionary_state" # ALL POSSIBLE STAR STATES @@ -91,11 +89,6 @@ MT_STR_TO_CASE = {string: integer for integer, string in MT_CASE_TO_STR.items()} -# Threshold used for inferring mass-transfer, and RLO -RL_RELATIVE_OVERFLOW_THRESHOLD = -0.05 -LG_MTRANSFER_RATE_THRESHOLD = -12 - - DEFAULT_CE_OPTION_FOR_LAMBDA = \ "lambda_from_profile_gravitational_plus_internal_minus_recombination" @@ -1557,6 +1550,41 @@ def cumulative_mass_transfer_flag(MT_cases, shift_cases=False): ) +def get_i_He_depl(history): + """Get the index of He depletion in the history + + Arguments + --------- + history: numpy-array + Stellar history from MESA + + Return + ------ + int + index of He depletion (-1 if no He depletion is found) + """ + if (('surface_h1' in history.dtype.names) and + ('center_h1' in history.dtype.names) and + ('center_he4' in history.dtype.names) and + ('center_c12' in history.dtype.names) and + ('log_LH' in history.dtype.names) and + ('log_LHe' in history.dtype.names) and + ('log_Lnuc' in history.dtype.names)): + n_history = len(history['center_he4']) + for i in range(n_history): + state = infer_star_state(surface_h1=history['surface_h1'][i], + center_h1=history['center_h1'][i], + center_he4=history['center_he4'][i], + center_c12=history['center_c12'][i], + log_LH=history['log_LH'][i], + log_LHe=history['log_LHe'][i], + log_Lnuc=history['log_Lnuc'][i], + star_CO=False) + if "Central_He_depleted" in state: + return i + return -1 + + def calculate_Patton20_values_at_He_depl(star): """Calculate the carbon core mass and abundance very close to ignition. @@ -1618,12 +1646,30 @@ def CEE_parameters_from_core_abundance_thresholds(star, verbose=False): None It updates the following values in the star object - co_core_mass_at_He_depletion: float - co_core_mass at He core depletion - (almost at the same time as carbon core ignition). - avg_c_in_c_core_at_He_depletion : float - avg carbon abundance inside CO_core_mass at He core depletion - (almost at the same time as carbon core ignition). + m_core_CE_1cent: float + core mass (using an element abundance of 1%) + m_core_CE_10cent: float + core mass (using an element abundance of 10%) + m_core_CE_30cent: float + core mass (using an element abundance of 30%) + m_core_CE_pure_He_star_10cent: float + core mass (using an element abundance of 10% in He) + r_core_CE_1cent: float + core radius (using an element abundance of 1%) + r_core_CE_10cent: float + core radius (using an element abundance of 10%) + r_core_CE_30cent: float + core radius (using an element abundance of 30%) + r_core_CE_pure_He_star_10cent: float + core radius (using an element abundance of 10% in He) + lambda_CE_1cent: float + lambda value (using an element abundance of 1%) + lambda_CE_10cent: float + lambda value (using an element abundance of 10%) + lambda_CE_30cent: float + lambda value (using an element abundance of 30%) + lambda_CE_pure_He_star_10cent: float + lambda value (using an element abundance of 10% in He) """ mass = star.mass @@ -1704,11 +1750,9 @@ def CEE_parameters_from_core_abundance_thresholds(star, verbose=False): r_core_CE_10cent = radius r_core_CE_30cent = radius lambda_CE_pure_He_star_10cent = lambda_CE - lambda_CE_1cent = 1e99 - lambda_CE_10cent = 1e99 - # TODO: decide whether this should be None - # for interpolation reasons - lambda_CE_30cent = 1e99 + lambda_CE_1cent = np.nan + lambda_CE_10cent = np.nan + lambda_CE_30cent = np.nan else: # CO-object or undetermined_evolutionary_state? m_core_CE_pure_He_star_10cent = np.nan m_core_CE_1cent = np.nan @@ -2137,8 +2181,7 @@ def calculate_lambda_from_profile( CO_core_in_Hrich_star) if ind_core == 0: # all star is a core, immediate successful ejection Ebind_i = 0.0 - # TODO: decide whether this should be None for interpolation reasons - lambda_CE = 1.0e99 + lambda_CE = np.nan mc1_i = m1_i rc1_i = radius1 else: @@ -2191,7 +2234,7 @@ def calculate_lambda_from_profile( print("Ebind_i from profile ", Ebind_i) print("lambda_CE ", lambda_CE) if not (lambda_CE > -tolerance): - raise ValueError("lamda_CE has a negative value") + raise ValueError("lambda_CE has a negative value") return lambda_CE, mc1_i, rc1_i @@ -2539,7 +2582,7 @@ def calculate_binding_energy(donor_mass, donor_radius, donor_dm, # binding energy of the enevelope equals its gravitational energy + # an a_th fraction of its internal energy Ebind_i = Grav_energy + factor_internal_energy * U_i - if Ebind_i > -tolerance: + if not (Ebind_i < tolerance): warnings.warn("Ebind_i of the envelope is found positive") if verbose: print("integration of gravitational energy surface to core " diff --git a/posydon/utils/gridutils.py b/posydon/utils/gridutils.py index 12848128eb..cb45a8472c 100644 --- a/posydon/utils/gridutils.py +++ b/posydon/utils/gridutils.py @@ -10,6 +10,7 @@ from posydon.utils.constants import clight, Msun, Rsun from posydon.utils.constants import standard_cgrav as cgrav from posydon.utils.constants import secyer as secyear +from posydon.utils.limits_thresholds import LG_MTRANSFER_RATE_THRESHOLD __authors__ = [ @@ -451,7 +452,7 @@ def convert_output_to_table( values["Porb_f(d)"] = binary_history["period_days"].iloc[-1] values["tmerge(Gyr)"] = tmerge - if max_lg_mtransfer_rate < -5: + if max_lg_mtransfer_rate < LG_MTRANSFER_RATE_THRESHOLD: values["result"] = "no_interaction" elif CE_flag != -1: if tmerge > 13.8: diff --git a/posydon/utils/limits_thresholds.py b/posydon/utils/limits_thresholds.py new file mode 100644 index 0000000000..5c91f45e18 --- /dev/null +++ b/posydon/utils/limits_thresholds.py @@ -0,0 +1,38 @@ +"""Constant limits and thresholds. +""" + + +__authors__ = [ + "Matthias Kruckow " +] + +import numpy as np + + +# THRESHOLD USED FOR INFERRING MASS-TRANSFER, AND RLO + +# This needs to be aligned with run_binary_extras.f +# old: RL_RELATIVE_OVERFLOW_THRESHOLD = -0.05 +RL_RELATIVE_OVERFLOW_THRESHOLD = 0.00 # relative overflow threshold +# This needs to be aligned with run_binary_extras.f +# old: LG_MTRANSFER_RATE_THRESHOLD = -12 +LG_MTRANSFER_RATE_THRESHOLD = -10 # mass-transfer rate threshold (in Lsol) + + +# CONSTANTS RELATED TO INFERRING STAR STATES + +THRESHOLD_CENTRAL_ABUNDANCE = 0.01 # central abundance for flagging depletion +THRESHOLD_CENTRAL_ABUNDANCE_LOOSE_C = 0.1 # central C abundance allow cut +THRESHOLD_HE_NAKED_ABUNDANCE = 0.01 # for surface abundance for stripped_He +THRESHOLD_NUCLEAR_LUMINOSITY = 0.97 # for element fraction in nuclear burning +# relative burning threshold with respect to nuclear luminosity +REL_LOG10_BURNING_THRESHOLD = np.log10(1.0 - THRESHOLD_NUCLEAR_LUMINOSITY) +LOG10_BURNING_THRESHOLD = -10.0 # burning luminosity threshold (in Lsol) + + +# COMPACT OBJECT LIMITS + +STATE_NS_STARMASS_UPPER_LIMIT = 2.5 # maximum mass of a neutron star (in Msol) +NEUTRINO_MASS_LOSS_UPPER_LIMIT = 0.5 # maximum loss in neutrinos (in Msol) + + diff --git a/posydon/visualization/combine_TF.py b/posydon/visualization/combine_TF.py index 87e2636edb..4ca447a7b8 100644 --- a/posydon/visualization/combine_TF.py +++ b/posydon/visualization/combine_TF.py @@ -150,6 +150,8 @@ def combine_TF12(IC, TF2, verbose=False): TF12[i] = 'Not converged' elif IC[i] == 'no_MT': TF12[i] = 'no_RLOF' + elif IC[i] == 'stable_reverse_MT': + TF12[i] = 'Reverse stable MT' elif IC[i] == 'stable_MT': if '1' in TF2[i] and '2' in TF2[i]: TF12[i] = 'Reverse stable MT' diff --git a/posydon/visualization/interpolation.py b/posydon/visualization/interpolation.py index 03ff38576d..9e986fd248 100644 --- a/posydon/visualization/interpolation.py +++ b/posydon/visualization/interpolation.py @@ -185,6 +185,7 @@ def violin_plots(self, err_type = "relative", keys = None, stable_inds = np.where(self.ic == "stable_MT") no_inds = np.where(self.ic == "no_MT") unstable_inds = np.where(self.ic == "unstable_MT") + #TODO: add interpolation class "stable_reverse_MT" stable_errs = self.__clean_errs(self.errs[err_type].T[k_inds].T[stable_inds]) no_errs = self.__clean_errs(self.errs[err_type].T[k_inds].T[no_inds]) diff --git a/posydon/visualization/plot1D.py b/posydon/visualization/plot1D.py index 718118d876..9714ed5668 100644 --- a/posydon/visualization/plot1D.py +++ b/posydon/visualization/plot1D.py @@ -206,7 +206,10 @@ def __call__(self): self.set_title(fig) # save figure - if self.fname is not None: + if self.PdfPages is not None: + self.PdfPages.savefig(figure=fig, dpi=self.dpi, + bbox_inches=self.bbox_inches) + elif self.fname is not None: fig.savefig(self.path_to_file + self.fname, dpi=self.dpi, bbox_inches=self.bbox_inches) @@ -634,7 +637,10 @@ def HR_diagram(self): self.set_ylim() self.set_legend(ax, lines) # save figure - if self.fname is not None: + if self.PdfPages is not None: + self.PdfPages.savefig(figure=fig, dpi=self.dpi, + bbox_inches=self.bbox_inches) + elif self.fname is not None: fig.savefig(self.path_to_file + self.fname, dpi=self.dpi, bbox_inches=self.bbox_inches) diff --git a/posydon/visualization/plot2D.py b/posydon/visualization/plot2D.py index 90174dfb57..40e751cde5 100644 --- a/posydon/visualization/plot2D.py +++ b/posydon/visualization/plot2D.py @@ -411,7 +411,10 @@ def __call__(self): plt.subplots_adjust(wspace=self.wspace, hspace=self.hspace) # save figure - if self.fname is not None: + if self.PdfPages is not None: + self.PdfPages.savefig(figure=fig, dpi=self.dpi, + bbox_inches=self.bbox_inches) + elif self.fname is not None: fig.savefig(self.path_to_file + self.fname, dpi=self.dpi, bbox_inches=self.bbox_inches) @@ -445,7 +448,10 @@ def __call__(self): self.set_title(fig) # save figure - if self.fname is not None: + if self.PdfPages is not None: + self.PdfPages.savefig(figure=fig, dpi=self.dpi, + bbox_inches=self.bbox_inches) + elif self.fname is not None: fig.savefig(self.path_to_file + self.fname, dpi=self.dpi, bbox_inches=self.bbox_inches) diff --git a/posydon/visualization/plot_defaults.py b/posydon/visualization/plot_defaults.py index ee926f71a5..7c3ea651f3 100644 --- a/posydon/visualization/plot_defaults.py +++ b/posydon/visualization/plot_defaults.py @@ -104,7 +104,8 @@ 'va': 'bottom', 'x': 0.95, 'y': 0.05 - } + }, + 'PdfPages': None } list_of_colors = ['#a6611a', diff --git a/posydon/visualization/plot_pop.py b/posydon/visualization/plot_pop.py index 49a53e10f8..33e4eca5dc 100644 --- a/posydon/visualization/plot_pop.py +++ b/posydon/visualization/plot_pop.py @@ -264,7 +264,7 @@ def plot_popsyn_over_grid_slice(pop, grid_type, met_Zsun, slices=None, channel=N plot_dir='./', prop=None, prop_range=None, log_prop=False, alpha=0.3, s=5., show_fig=True, save_fig=True, close_fig=True, - verbose=False): + plot_extension='png', verbose=False): # load grid met = convert_metallicity_to_string(met_Zsun) @@ -284,7 +284,7 @@ def plot_popsyn_over_grid_slice(pop, grid_type, met_Zsun, slices=None, channel=N m_COs_edges = 10**((np.log10(np.array(m_COs)[1:])+np.log10(np.array(m_COs)[:-1]))*0.5) m2 = [0.]+m_COs_edges.tolist()+[2*m_COs_edges[-1]] vars = m_COs - fname = 'grid_m_%1.2f.png' + fname = 'grid_m_%1.2f.' + plot_extension title = '$m_\mathrm{CO}=%1.2f\,M_\odot$' slice_3D_var_str='star_2_mass' elif 'HMS-HMS' in grid_path: @@ -294,7 +294,7 @@ def plot_popsyn_over_grid_slice(pop, grid_type, met_Zsun, slices=None, channel=N dq_edges = (qs[:-1]+dq).tolist() dq_edges = [0.]+dq_edges+[1.] vars = qs.tolist() - fname = 'grid_q_%1.2f.png' + fname = 'grid_q_%1.2f.' + plot_extension title = '$q=%1.2f$' slice_3D_var_str='mass_ratio' else: From d145f34ee3d560dca02679dadccfc0fe809a684a Mon Sep 17 00:00:00 2001 From: sgossage Date: Fri, 24 May 2024 10:50:56 -0500 Subject: [PATCH 202/319] Added new rerun type: dedt_energy_eqn (#275) * Added two new rerun types: hms-hms_accretion and cohems_convbdy_dedt. The latter is still a WIP (narrowing params). * Added a temporary hash for rerun_type cohems_convbdy_dedt. Parameters may change. * Updated SHA for hms-hms_accretion rerun type. * Updated SHA for hms-hms_accretion rerun type. * Renamed rerun type cohems_convbdy_dedt -> cohems_convbdy_weakMLTp, updated SHA * Renamed rerun type co-hems_convbdy_dedt -> co-hems_convbdy_weakMLTp, updated SHA * Altered CO-HeMS rerun. After testing further, two seperate reruns will probably work best. The new one introduced here is co-hems_MLTpOFF which turns off MLT++. MLT++ seems to cause certain models to crash in this grid once they start RLOF. Turning it off allows them to proceed normally. The other rerun to be used is conv_bdy_weight for certain system approaching the WD regime that encounter sg. faults. convective_bdy_weight is turned off in this new rerun as well for the same reason. * Changed termination_flags check on rerun_type co-hems_MLTpOFF from TF1_POOL_ERROR to only 'reach cluster timelimit'. * Changed rerun type names: 'hms-hms_accretion' -> 'timestep_MTlimit', 'co-hems_MLTpOFF' -> 'MLTpOFF' to indicate that these could be run for any grid, in principle. Also removed convective_bdy_weight = 0 tweak from the latter as it's perhaps not strictly needed. * Updated SHA for rerun_type timestep_MTlimit * Updated SHA for rerun type timestep_MTlimit * Changed rerun_type MLTpOFF to 'egrav_Pgas_dXdt_terms'. Instead of turning off MLT++, this now switches the basic variable used to calc. eps_grav to Pgas and enables use of the composition terms calc. eps_grav. * Updated SHA for rerun_type egrav_Pgas_dXdt_terms. Inlist tweaks are now contained in a corresponding branch of POSYDON-MESA-INLISTS * Updated SHA for rerun_type timestep_MTlimit * Added the new rerun types to the documentation * Changed associated branch names that used '-' in their name to use '_' instead since '-' is set to separate the branch name and SHA in current scripts. * Updated SHA for rerun_type timestep_MTlimit * Fixed formatting for rerun tables in pipeline docs. Updated SHA and branch names for new rerun types * Updated SHA for rerun_type timestep_MTlimit * Updated docs description for rerun_type timestep_MTlimit * revised down to one rerun called dedt_strippedHe. This rerun now enabled the dedt form of the energy equation and lnPgas, v_flag, and a boosted wind to help stripped He stars converge. Docs updated * updated SHA for rerun dedt_strippedHe * Updated SHA and renamed new rerun type to just dedt_energy_eqn (b/c only changes energy eqn for now).Added 3 new termination codes to TF pools * updated SHA, updated docs for new rerun type, updated new term codes in plotting functions and TF pools * updated SHA --------- Co-authored-by: Seth Shearer Gossage Co-authored-by: Seth Shearer Gossage Co-authored-by: Seth Shearer Gossage Co-authored-by: Seth Shearer Gossage --- bin/posydon-run-pipeline | 4 +++ .../pipeline/pipeline_steps.rst | 25 ++++++++++--------- posydon/visualization/combine_TF.py | 5 +++- posydon/visualization/plot_defaults.py | 12 +++++++++ 4 files changed, 33 insertions(+), 13 deletions(-) diff --git a/bin/posydon-run-pipeline b/bin/posydon-run-pipeline index e870c13e47..4771034d35 100644 --- a/bin/posydon-run-pipeline +++ b/bin/posydon-run-pipeline @@ -665,6 +665,8 @@ def copy_ini_file(grid_type, rerun_metallicity, rerun_type, destination, replace_text = "development-435d16c9158e4608530f21b8169ce6f31160e23e" elif rerun_type == 'initial_He': replace_text = "fix_iniHe_pre_rerun5-0e736a853c3c2e522b76299a54aea8f5eea15d08" + elif rerun_type == "dedt_energy_eqn": + replace_text = "seth_dedt_energy_eqn-deac5c26392b36cb7a8ff337e0d53c26568a3971" elif rerun_type == 'other': # e.g. 'envelope_mass_limit', 'fe_core_infall_limit' # this rerun uses the default inlist commit return @@ -832,6 +834,8 @@ def logic_rerun(grid, rerun_type): if np.isnan(iniY) or iniY<0.96: runs_to_rerun += [i] runs_to_rerun = np.array(runs_to_rerun) + elif rerun_type == 'dedt_energy_eqn': + termination_flags = TF1_POOL_ERROR elif rerun_type == 'envelope_mass_limit': # maybe 'fe_core_infall_limit' as well? runs_to_rerun = None # implement logic else: diff --git a/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst b/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst index 2d29918868..85631b7abc 100644 --- a/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst +++ b/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst @@ -193,16 +193,17 @@ type of the rerun specifying the logic and changes, and the cluster name. .. table:: Currently supported rerun types - =================== ============== =========== - :samp:`rerun_type` Future version Description - =================== ============== =========== - PISN default in v3+ it enables the MESA inlist commit, which stops MESA before getting dynamical to save a final profile there - reverse_MT default in v3+ it uses a MESA version with a bug fix, that the role of donor and accretor can switch during the simulation - opacity_max caution it uses a fixed maximum opacity of 0.5 (this is only a last option change to get more stability) - TPAGBwind default in v3+ it enables the MESA inlist commit, which changes the wind during the TPAGB phase - thermohaline_mixing default in v3+ it uses thermohaline mixing in the inlist - HeMB_MLTp_mesh workaround it turns off magnetic braking for He stars; it uses less extreme parameters of the MLT++; it changes some more input values to change the resulation close to the surface - more_mesh workaround it modifies the remeshing and allows for more cells in MESA - conv_bdy_weight caution it disabled the convective_bdy_weight where this caused segmentation faults (this avoids a bug in the old MESA version r11701) - =================== ============== =========== + ===================== ============== =========== + :samp:`rerun_type` Future version Description + ===================== ============== =========== + PISN default in v3+ it enables the MESA inlist commit, which stops MESA before getting dynamical to save a final profile there + reverse_MT default in v3+ it uses a MESA version with a bug fix, that the role of donor and accretor can switch during the simulation + opacity_max caution it uses a fixed maximum opacity of 0.5 (this is only a last option change to get more stability) + TPAGBwind default in v3+ it enables the MESA inlist commit, which changes the wind during the TPAGB phase + thermohaline_mixing default in v3+ it uses thermohaline mixing in the inlist + HeMB_MLTp_mesh workaround it turns off magnetic braking for He stars; it uses less extreme parameters of the MLT++; it changes some more input values to change the resulation close to the surface + more_mesh workaround it modifies the remeshing and allows for more cells in MESA + conv_bdy_weight caution it disabled the convective_bdy_weight where this caused segmentation faults (this avoids a bug in the old MESA version r11701) + dedt_energy_eqn caution it enables MESA's dedt-form of the energy equation for numerical stability during superthermal mass transfer + ===================== ============== =========== diff --git a/posydon/visualization/combine_TF.py b/posydon/visualization/combine_TF.py index 4ca447a7b8..60f5c1bbd5 100644 --- a/posydon/visualization/combine_TF.py +++ b/posydon/visualization/combine_TF.py @@ -40,7 +40,10 @@ 'Reached maximum mass transfer rate: 1d-1', 'Reached maximum mass transfer rate: Exceeded photon trapping radius', 'Both stars fill their Roche Lobe and at least one of them is off MS', - 'Terminate due to L2 overflow during case A' + 'Terminate due to L2 overflow during case A', + 'Both stars fill their Roche Lobe and t_kh > t_acc', + 'overflow from L2, t_kh > t_acc and w > w_crit_lim, donor is star 1', + 'overflow from L2, t_kh > t_acc and w > w_crit_lim, donor is star 2' ] TF1_POOL_INITIAL_RLO = [ diff --git a/posydon/visualization/plot_defaults.py b/posydon/visualization/plot_defaults.py index 7c3ea651f3..41888167b2 100644 --- a/posydon/visualization/plot_defaults.py +++ b/posydon/visualization/plot_defaults.py @@ -182,6 +182,12 @@ ['s', 2, None, TF1_label_initial], 'Both stars fill their Roche Lobe and at least one of them is off MS': ['D', 1, color_unstable, TF1_label_unstable], + 'Both stars fill their Roche Lobe and t_kh > t_acc': + ['D', 1, color_unstable, TF1_label_unstable], + 'overflow from L2, t_kh > t_acc and w > w_crit_lim, donor is star 1': + ['D', 1, color_unstable, TF1_label_unstable], + 'overflow from L2, t_kh > t_acc and w > w_crit_lim, donor is star 2': + ['D', 1, color_unstable, TF1_label_unstable], 'Terminate due to L2 overflow during case A': ['D', 1, color_unstable, TF1_label_unstable], 'Reached maximum mass transfer rate: Exceeded photon trapping radius': @@ -639,6 +645,12 @@ ['s', 2, None, TF1_label_initial], 'Both stars fill their Roche Lobe and at least one of them is off MS': ['D', 1, None, TF1_label_unstable], + 'Both stars fill their Roche Lobe and t_kh > t_acc': + ['D', 1, None, TF1_label_unstable], + 'overflow from L2, t_kh > t_acc and w > w_crit_lim, donor is star 1': + ['D', 1, None, TF1_label_unstable], + 'overflow from L2, t_kh > t_acc and w > w_crit_lim, donor is star 2': + ['D', 1, None, TF1_label_unstable], 'Terminate due to L2 overflow during case A': ['D', 1, None, TF1_label_unstable], 'Reached maximum mass transfer rate: Exceeded photon trapping radius': From 1640a3eb977c97e1d646ac19c9e64bfbc7a83c68 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Fri, 24 May 2024 18:40:48 +0200 Subject: [PATCH 203/319] Generalize and improve on compress MESA (#289) * generalize and improve on compress MESA * update documentation * update docstrings, errors; add verbose * added try/except wrapper around remove files --------- Co-authored-by: AstroJeff --- bin/compress-mesa | 198 ++++++++++-------- .../components-overview/mesa_grids/fixed.rst | 16 +- .../mesa_grids/inifile.rst | 123 +++++++++-- 3 files changed, 234 insertions(+), 103 deletions(-) diff --git a/bin/compress-mesa b/bin/compress-mesa index 3a472a55ee..7452522ec8 100644 --- a/bin/compress-mesa +++ b/bin/compress-mesa @@ -13,7 +13,7 @@ def textsize(filesize, floatfmt=".3g", base=1024, threshold=1000): Parameters ---------- filesize : int or float - The size of the file, or directory, ... + The size of the file, or directory, ... (in bytes) floatfmt : str The format for the float before the size descriptor (e.g., 2.34K). base : int @@ -28,15 +28,20 @@ def textsize(filesize, floatfmt=".3g", base=1024, threshold=1000): The filesize in string format using b, K, M, ... """ - assert base in [1000, 1024] + if base not in [1000, 1024]: + raise ValueError(f"base={base} should be 1000 or 1024") + if threshold <= 0: + raise ValueError(f"threshold={threshold} should be larger than 0") + if base < threshold: + raise ValueError(f"threshold={threshold} should be smaller or equal "\ + f"to base={base}") + if filesize < 0: + return "-" + textsize(-filesize, floatfmt=floatfmt, base=base,\ + threshold=threshold) units = ["b", "K", "M", "G", "T", "P", "E", "Z", "Y"] unit_values = [base**i for i in range(len(units))] - if filesize < 0: - return "-" + textsize( - -filesize, floatfmt=floatfmt, base=base) - for unit, unit_value in zip(units, unit_values): if filesize < unit_value * threshold: quantity = filesize / unit_value @@ -48,19 +53,26 @@ def textsize(filesize, floatfmt=".3g", base=1024, threshold=1000): def set_up_test(args): """Set up a testing directory in the requested directory. - Parameters (keys in `args`): - ---------- + Parameters (keys in `args`) + --------------------------- + mesa_dir : string + The directory where the MESA tracks are stored. test_dir : string The directory where the test directory is to be set up. dsr : float Downsampling rate when creating testing directory. """ - if not os.path.isdir(args.test_dir): - sys.exit(f"Directory {args.test_dir} does not exist.") + if args.test_dir is None: + raise NameError("--test_dir needs to be specified for set_up_test") + elif not os.path.isdir(args.test_dir): + raise NotADirectoryError(f"Directory {args.test_dir} does not exist.") + if args.mesa_dir is None: + raise NameError("mesa_dir needs to be specified for set_up_test") + elif not os.path.isdir(args.mesa_dir): + raise NotADirectoryError(f"Directory {args.mesa_dir} does not exist.") for folder in os.listdir(args.mesa_dir): - if os.path.isdir(os.path.join(args.mesa_dir, folder)): is_mesa_run = False sub_dir = os.listdir(os.path.join(args.mesa_dir, folder)) @@ -94,96 +106,112 @@ def set_up_test(args): def compress_dir(args): """Compresses a directory containing tracks evolved with MESA. - Parameters (keys in `args`): - ----------- + Parameters (keys in `args`) + --------------------------- + verbose : bool + Enable/Disable additional output. mesa_dir : string The directory where the MESA tracks are stored. """ def get_size(start_path="."): total_size = 0 + remove_files = [] + compress_files = [] + n_runs = 0 + n_remove_files = 0 + n_compress_files = 0 for dirpath, _, filenames in os.walk(start_path): + if "_grid_index_" in dirpath: # checking if directory is mesa run + new_remove_files = [] + new_compress_files = [] + if "_grid_index_" in os.path.basename(dirpath): + n_runs += 1 + else: + new_remove_files = None + new_compress_files = None for filename in filenames: filepath = os.path.join(dirpath, filename) # skip if it is symbolic link if not os.path.islink(filepath): total_size += os.path.getsize(filepath) - return total_size - - def compress_subdirs(track_dirs): - for track_obj in track_dirs: - for root, _dir, files in os.walk(track_obj): - # traversing over directory tree of copied mesa tracks - # and compressing .data, .mod, .txt files - for file in files: - ext = file.split(".")[-1] - if ext not in ["data", "mod", "txt"]: - continue - os.system(f"gzip -1 {os.path.join(root, file)}") - - def remove_core_dumps(track_dirs): - for track_obj in track_dirs: - for root, _dir, files in os.walk(track_obj): - for file in files: - core_string = file.split(".")[0] - if (core_string == 'core' - and os.path.isfile(os.path.join(root, file))): - os.remove(os.path.join(root, file)) - - if not os.path.isdir(args.mesa_dir): - sys.exit("The MESA directory does not exist.") - - og_size = textsize(get_size(args.mesa_dir)) - - for folder in tqdm(os.listdir(args.mesa_dir)): - - # To add the option of cycling through files one directory above - if "_grid_index_" in folder: - track_dirs = [] - - if (os.path.isdir(os.path.join(args.mesa_dir, folder)) - and folder not in ["star1", "star2", "binary"]): - track_dirs.append(os.path.join(args.mesa_dir, folder)) - - compress_subdirs(track_dirs) - remove_core_dumps(track_dirs) - continue - - if os.path.isdir(os.path.join(args.mesa_dir, folder)): - is_mesa_run = False - sub_dir = os.listdir(os.path.join(args.mesa_dir, folder)) - track_dirs = [] - - for _f in sub_dir: - if "_grid_index_" in _f: # checking if directory is mesa run - is_mesa_run = True - - if (os.path.isdir(os.path.join(args.mesa_dir, folder, _f)) - and _f not in ["star1", "star2", "binary"]): - track_dirs.append(os.path.join(args.mesa_dir, folder, _f)) - - if is_mesa_run: - compress_subdirs(track_dirs) - remove_core_dumps(track_dirs) - - print("\nCompressed MESA tracks\nOriginal size {} | Compressed size {}\n". - format(og_size, textsize(get_size(args.mesa_dir)))) + # check for files in mesa run, whether to remove or compress it + if new_remove_files is not None: + name, ext = os.path.splitext(filename) + if name == "core": + # remove core dump files + new_remove_files.append(filename) + elif ext in [".data", ".mod", ".txt"]: + # compress .data, .mod, .txt files + new_compress_files.append(filename) + if ((new_remove_files is not None) and + (len(new_remove_files)>0)): + remove_files.append((dirpath, new_remove_files)) + n_remove_files += len(new_remove_files) + if ((new_compress_files is not None) and + (len(new_compress_files)>0)): + compress_files.append((dirpath, new_compress_files)) + n_compress_files += len(new_compress_files) + return total_size, remove_files, compress_files, n_runs,\ + n_remove_files, n_compress_files + + if args.mesa_dir is None: + raise NameError("mesa_dir needs to be specified for set_up_test") + elif not os.path.isdir(args.mesa_dir): + raise NotADirectoryError(f"Directory {args.mesa_dir} does not exist.") + + og_size, to_remove, to_compress, n_runs, n_remove_files, n_compress_files\ + = get_size(args.mesa_dir) + + if args.verbose: + print("remove", n_remove_files, "core dump files in", len(to_remove),\ + "directories of", n_runs, "MESA runs") + for folder, files in tqdm(to_remove): + for remove_file in files: + if os.path.isfile(os.path.join(folder, remove_file)): +# print("remove:", os.path.join(folder, remove_file)) + try: + os.remove(os.path.join(folder, remove_file)) + except: + print("Could not remove:", remove_file, "in", folder) + + if args.verbose: + print("compress", n_compress_files, "files in", len(to_compress),\ + "directories of", n_runs, "MESA runs") + for folder, files in tqdm(to_compress): + for compress_file in files: + if os.path.isfile(os.path.join(folder, compress_file)): +# print(f"gzip -1 {os.path.join(folder, compress_file)}") + os.system(f"gzip -1 {os.path.join(folder, compress_file)}") + + new_size, to_remove, to_compress, n_runs, n_remove_files, n_compress_files\ + = get_size(args.mesa_dir) + if args.verbose: + print("") + print("Compressed MESA tracks") + print(f"Original size {textsize(og_size)} | "\ + f"Compressed size {textsize(new_size)}") + if len(to_remove)>0: + print("Warning: still files to remove:", to_remove) + if len(to_compress)>0: + print("Warning: still files to compress:", to_compress) if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument("-f", "--function", type=str, - help="The name of the function to be called.", - default='compress_dir') - parser.add_argument("-td", "--test_dir", type=str, - help="The path to where the testing directory should " - "be set up, either set_up_test or compress_dir.") - parser.add_argument("-dsr", "--dsr", type=str, - help="Downsampling rate when creating testing " - "directory", default=0.01) - parser.add_argument("mesa_dir", type=str, - help="The path to the directory containing " - "MESA-generated data") + parser.add_argument("-f", "--function", type=str, help="The name of the "\ + "function to be called, either set_up_test or "\ + "compress_dir.", default='compress_dir') + parser.add_argument("-td", "--test_dir", type=str, help="The path to "\ + "where the testing directory should be set up.",\ + default=None) + parser.add_argument("-dsr", "--dsr", type=float, help="Downsampling rate "\ + "when creating testing directory", default=0.01) + parser.add_argument("-v", "--verbose", type=bool, help="Enable/Disable "\ + "outputs", default=True) + parser.add_argument("mesa_dir", type=str, help="The path to the "\ + "directory containing MESA-generated data",\ + default=None) functions = {"set_up_test": set_up_test, "compress_dir": compress_dir} arguments = parser.parse_args() diff --git a/docs/_source/components-overview/mesa_grids/fixed.rst b/docs/_source/components-overview/mesa_grids/fixed.rst index 52dc17dcf5..48e6dfb7e2 100644 --- a/docs/_source/components-overview/mesa_grids/fixed.rst +++ b/docs/_source/components-overview/mesa_grids/fixed.rst @@ -17,6 +17,7 @@ edited for your own installation. .. code-block:: export MESA_DIR="/projects/b1119/mesa_sdk/mesa-r11701" + export OMP_NUM_THREADS=4 export MESASDK_ROOT="/projects/b1119/mesa_sdk/mesasdk" source $MESASDK_ROOT/bin/mesasdk_init.sh @@ -87,7 +88,20 @@ Finally, we are ready to submit our jobs with: .. code-block:: - sbatch slurm_job_array_grid_submit.sh + ./run_grid.sh + +This shell script will submit the two slurm jobs +:samp:`slurm_job_array_grid_submit.sh` and :samp:`cleanup.sh`. The first one +will run the MESA simulations, where several runs will go in paralell. The +second job, will run after all MESA runs are completed. It is doing some +cleanup: + +- removing files no longer needed (e.g. memory copies created as backups) +- compressing files containing a lot of text (those files are individually + compressed and will keep in place, hence getting a file extension `.gz` + added) +- changing the owning group and permissions (this will only be done if a new + group was specified in the .ini file for the setup) Once the grid of runs is completed, we recommend you use our provided PSyGrid functionality to interpret and collate the individual binary runs diff --git a/docs/_source/components-overview/mesa_grids/inifile.rst b/docs/_source/components-overview/mesa_grids/inifile.rst index e2941d6924..3d464f52fe 100644 --- a/docs/_source/components-overview/mesa_grids/inifile.rst +++ b/docs/_source/components-overview/mesa_grids/inifile.rst @@ -23,26 +23,24 @@ quantities that need to be adjusted for your specific case. Here is a link to the most recent stable release version of the default inifile for POSYDON: -`Stable Version INIFILE `_ +`Stable Version INIFILE `_ Here is a link to the unstable development version of the default inifile for POSYDON: -`Development Version INIFILE `_ +`Development Version INIFILE `_ .. _inifile_slurm: + [slurm] ------- This section designates all the SLURM-related parameters. -======================= =============================================================== - -======================= =============================================================== - .. code-block:: ini [slurm] + ; Number of nodes you would like to request number_of_nodes=1 @@ -69,18 +67,42 @@ This section designates all the SLURM-related parameters. ; wall-time walltime='2-00:00:00' + ; work-directory: place, where MESA writes during runtime + work_dir={WORK_DIRECTORY} + ;email email={YOUR_EMAIL_ADDRESS} + ; new group to be set with write permission + ; if empty string no changes on group and permission + ; group on yggdrasil: GL_S_Astro_POSYDON + ; group on Quest: b1119 + newgroup='' + +.. table:: general settings + + ======================= =========== + Setting name Description + ======================= =========== + number_of_nodes The number of nodes each job will request + number_of_mpi_tasks (outdated) The number of parallel processes ganerates by MPI (recommended to not change) + number_of_cpus_per_task The number of CPUs each job will request (in best this should align with the number of threads set for MESA) + job_array Set to `True` to run a SLURM job array + user User name + partition SLURM partition to run the jobs on + account SLURM account to run the jobs on + walltime Maximum time for each run, otherwise it will get cancelled by SLURM to avoid never ending MESA runs going on too short time steps + work_dir The path to the place, where the data will be writting during runtime (it is recommended to use fast local node storage here), afterwards the data will be copied/moved to the current directory (uncommend this line to write always to the final location) + email Your email address to receive notifications from SLURM + newgroup The name of the owning group all the files should get (leave empty if no changes are needed here) + ======================= =========== + + [mesa_inlists] -------------- This section designates all the basic MESA-specific parameters. -======================= =================================================================================== - -======================= =================================================================================== - .. code-block:: ini [mesa_inlists] @@ -195,16 +217,64 @@ This section designates all the basic MESA-specific parameters. ; save final model of star2 final_model_star2 = False +.. table:: settings for the MESA inlist + + ========================================= =========== + Setting name Description + ========================================= =========== + posydon_github_root The path to your used POSYDON version + scenario List containing multiple information: 1) the source ('posydon' or 'user'(for future use)), 2) the git commit (the branch and full git hash for the inlist submodule separated by a dash), 3) the systems type ('HMS-HMS', 'CO-H_star', 'CO-He_star') + zams_filename The location of the file containing the ZAMS models + single_star_grid Flag to indicate single star or binary evolution + star1_formation_job_mesa_defaults (outdated) Path to the MESA job section defaults to form star 1 + star1_formation_job_posydon_defaults (outdated) Path to the MESA job section inlist of POSYDON to form star 1 + star1_formation_job_user (outdated) Path to the MESA job section inlist of the user to form star 1 + star2_formation_job_mesa_defaults (outdated) Path to the MESA job section defaults to form star 2 + star2_formation_job_posydon_defaults (outdated) Path to the MESA job section inlist of POSYDON to form star 2 + star2_formation_job_user (outdated) Path to the MESA job section inlist of the user to form star 2 + star1_formation_controls_mesa_defaults (outdated) Path to the MESA controls section defaults to form star 1 + star1_formation_controls_posydon_defaults (outdated) Path to the MESA controls section inlist of POSYDON to form star 1 + star1_formation_controls_user (outdated) Path to the MESA controls section inlist of the user to form star 1 + star2_formation_controls_mesa_defaults (outdated) Path to the MESA controls section defaults to form star 2 + star2_formation_controls_posydon_defaults (outdated) Path to the MESA controls section inlist of POSYDON to form star 2 + star2_formation_controls_user (outdated) Path to the MESA controls section inlist of the user to form star 2 + binary_controls_mesa_defaults (outdated) Path to the MESA controls section defaults to evolve the binary + binary_controls_posydon_defaults (outdated) Path to the MESA controls section inlist of POSYDON to evolve the binary + binary_controls_user (outdated) Path to the MESA controls section inlist of the user to evolve the binary + binary_job_mesa_defaults (outdated) Path to the MESA job section defaults to evolve the binary + binary_job_posydon_defaults (outdated) Path to the MESA job section inlist of POSYDON to evolve the binary + binary_job_user (outdated) Path to the MESA job section inlist of the user to evolve the binary + star1_job_mesa_defaults (outdated) Path to the MESA job section defaults to evolve star 1 + star1_job_posydon_defaults (outdated) Path to the MESA job section inlist of POSYDON to evolve star 1 + star1_job_user (outdated) Path to the MESA job section inlist of the user to evolve star 1 + star1_controls_mesa_defaults (outdated) Path to the MESA controls section defaults to evolve star 1 + star1_controls_posydon_defaults (outdated) Path to the MESA controls section inlist of POSYDON to evolve star 1 + star1_controls_user (outdated) Path to the MESA controls section inlist of the user to evolve star 1 + star2_job_mesa_defaults (outdated) Path to the MESA job section defaults to evolve star 2 + star2_job_posydon_defaults (outdated) Path to the MESA job section inlist of POSYDON to evolve star 2 + star2_job_user (outdated) Path to the MESA job section inlist of the user to evolve star 2 + star2_controls_mesa_defaults (outdated) Path to the MESA controls section defaults to evolve star 2 + star2_controls_posydon_defaults (outdated) Path to the MESA controls section inlist of POSYDON to evolve star 2 + star2_controls_user (outdated) Path to the MESA controls section inlist of the user to evolve star 2 + star_history_columns (outdated) Path to the history columns list of the stars + binary_history_columns (outdated) Path to the history columns list of the binary + profile_columns (outdated) Path to the profile columns list to write the final stellar profile + history_interval Interval how often MESA will add a model to the star's histories + binary_history Interval how often MESA will add a model to the binary history + history_star1 Flag, whether the history of star 1 should be saved + final_profile_star1 (outdated, done in :samp:`run_star_extras.f`) Flag, whether the final profil of star 1 should be saved + final_model_star1 Flag, whether the final model of star 1 should be saved + history_star2 Flag, whether the history of star 2 should be saved + final_profile_star2 (outdated, done in :samp:`run_star_extras.f`) Flag, whether the final profil of star 2 should be saved + final_model_star2 Flag, whether the final model of star 2 should be saved + ========================================= =========== + [mesa_extras] ------------- This section designates all the parameters for MESA makefiles and fortran files. -=========================== =================================================================================== - -=========================== =================================================================================== - .. code-block:: ini [mesa_extras] @@ -238,14 +308,31 @@ This section designates all the parameters for MESA makefiles and fortran files. ; star_run.f star_run = ${MESA_DIR}/star/work/src/run.f +.. table:: settings for the MESA extras + + ======================= =========== + Setting name Description + ======================= =========== + makefile_binary Path to the make file of MESA's binary module + makefile_star Path to the make file of MESA's star module + mesa_binary_extras Path to MESA's binary module default :samp:`run_binary_extras.f` + user_binary_extras Path to the users/POSYDON :samp:`run_binary_extras.f` + mesa_star_binary_extras Path to MESA's binary module default :samp:`run_star_extras.f` + user_star_binary_extras Path to the users/POSYDON :samp:`run_star_extras.f` + mesa_star1_extras Path to MESA's star module default :samp:`run_star_extras.f` + user_star1_extras Path to the users/POSYDON :samp:`run_star_extras.f` + mesa_star2_extras Path to MESA's star module default :samp:`run_star_extras.f` + user_star2_extras Path to the users/POSYDON :samp:`run_star_extras.f` + binary_run Path to MESA's binary module :samp:`binary_run.f` + star_run Path to MESA's star module :samp:`run.f` + ======================= =========== + + [run_parameters] ---------------- This section designates the run parameters for a grid. -==================== ======================================================== -==================== ======================================================== - .. code-block:: ini [run_parameters] @@ -257,3 +344,5 @@ This section designates the run parameters for a grid. ; run MESA on (i.e. generate grid points on the fly). grid = {PATH_TO_GRID} + +The :samp:`grid` specifies where to find the csv file to read the runs from. From 5159d792924720ec966d1a32563c73d824441055 Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Thu, 6 Jun 2024 16:14:26 +0200 Subject: [PATCH 204/319] Population Analysis Framework Refactor (#267) * add casting of scalar_names to float64 when writing to dataframe * separate BinaryPopulation creation from class initialisation * remove cast in singlestar and create standard scalar_names casting in io.py * add scalar_names type casting * add missed typo * add step_name to parser * create new BinaryPopulations in SyntheticPop.evolve() * remove MPI and RNG seeds using JOB_ID+metallicity * Fix for double entries in SyntheticPopulation.df_oneline * handle the binary history and oneline correctly with chunking * account for job array not starting at 0 * allow for job array runs without specifying mpi * add batch file selection from folder * clean up batch folders after merging runs * clean up typos * Altered the tutorial to reflect HPC changes and parse changes * adapt tutorials for new parallel run method * keep MPI capability, but do not import it on the cluster when using job arrays * add check if MPI and job arrays are ran together and raise an error * make sure communicator is not write to state. re-add combine_save_files to save function * additional check for MPI and job array runs. Exit code if parallel + job array requested * include local MPI explanation in tutorials * implemented matthias suggestions * shift entropy statement * typo fix in apply_logic function call * shift_index bug fix * restructure * change default pop synth to burst * restructure of Synthetic Class * add testing of the synthetic populations * allow for on-disk processing * implement mt, DTD, and merger efficiency for out-of-memory processing" * add basic tests for the popsynth classes. Not complete * fix for binary count not aligning with binary index * implement rate calculation + GRB selection * Adds chunksize bug fixes * Add population class" * add todo + remove older code" * remove leftover framework from initial concept * fix for synthetic pop; merge runs + reading from hdf for binary evolving" * read individual binary from hdf5 file * remove int cast again * read_fix * only open input file when loading in data * clean-up lines * Fix for nan values in formation channel creation * fix nan in formation channels * deep copy fix * implement multiprocessing to free memory on finishing a metallicity * account for numpy int64 * correct previous merge * Added additional loop checks for detached and CO-H(e)MS(_RLO) looping * when raising an error set state to 'ERR' and event to 'FAILED' * Add checks to make sure the SN type matches the appropriate the stellar state * implement PISN with massless_remnant * add star->massless_remnant to common_functions, and adapt flow_chart * move function to single_star to stop cyclical imports * remove leftover print * change order of CO * add comment on readability * add recalculation of the SN type and post-SN stellar state, if they do not match. If the recalculation does not match, force the SN_type to match the remnant. specifically (state; SN_Type) WD -> WD, NS/BH -> CCSN, PISN -> PISN. * adjust NS/BH check to make sure the logic works correctly * move check function to file scope level * change PISN logic to only calculate the SN_type if not a PISN * clean up code * separate BH and NS check BH can be CCSN and PPISN NS can be ECSN and CCSN * Change star.state to massless_remnant for PISN checking * set stellar state to massless)remnant * additional stellar state check * change in SN-CO match * np.max fix * remove TZO tagging if massless_remnant, since PISN can lead to a massless remnant too * add generic synthetic population creation * check if time is included in the input hist, only if not set to None * add plotting to SyntheticPopulation * remove old SyntheticPopulation code * remove old code that has been moved to Rates and Population in rate_calculation * rate calculation * redirect_CO_HeMS * implement a more flexible approach to populations, while storing the essential metadata information in the h5 file * add reindexing of binaries when writing to a new file * inheritance * add efficiency calculation, plotting for DTD and efficiency * add metallicity check when exporting data * add multi-metallicity warning * fix for formation_channel if in file * add chunking read for the formation_channels * fix create_formation_channels to filter out nans and add 'initial_MT' to the HMS-HMS-event-dic * add a history_lengths metadata to the file * transfer history_lengths to exported file and reindex them * remove reindexing from the appends when creating a new population * force history_lengths into a dataframe format instead of possibly pd.Series * Add correct selection of history_legnt * setu-popsyn for multimetallicity runs + rates calculation with the new population * fix mt_channel in rate calculation * fix metallicity fraction calculation * add MODEL_data loading when array being stored + fix empirical SFH calculation * add MODEL fix * add property histogram and properties in over_grid_slice * clean up of old classes * add example/standard transient selection functions * new module string; * add account option to setup-popsyn script * add extra comments * add _Zsun_ to set-popsyn? * add number of systems to mass_per_met * add PopulationRunner back for running multi-metallicity populations in a notebook * change tutorial 1 to new framework * change tutorial 2 to setup-popsyn scipt explanation * move formation_channel warning behind a verbose * add color option to hist_plotting and fix redirect issue with formation_channels * add custom label in plotting + removing leftover print statement * change df_synthetic to df_transient * clean up 10 binary notebook * add popsynth on HPC tutorial with no outputs * add BBH analysis in the new framework * remove created script for MPIruns * adding documentation * add docstrings for classes * move cosmology/redshift functions to rate_calculation and add Rates documnetation * cleanup * pre-commit code cleanup * rate_calculation cleanup * adding units tests for the new classes * add PopulationIO tests * add correct GRB_selection function * update tutorial up to GRBs * add overwrite=True as default for export_population * change mass_per_met to mass_per_metallicity " * add maximum string_length slicing on the formation_channels * change history chunking variable to history_chunksize * change mass_per_met to mass_per_metallicity * remove duplicate after merge * fix tilt track error * update tutorials * add sorting for parallel runs and add warning when trying to merge into already existing population. * increase merge walltime * add custom merge walltime" * only remove batch files once the target file has been close * notebook update * update mass_per_metallicity test * alllow for slice binary selection * add unit tests for Population class * formation channel read-in every time instead of storing * bug fix for metallicity fraction + add authorship * fix syntax error * reverse-MT formation channel fix * rename initialisation of spin_orbit_tilt" * update population_params_default; * Add both spin_orbit_tilt to dataframe length * carry chunksize initialisation to the base Population class * adjust export_selection to have append and overwrite parameters. Disallows either by default * Add m_disk_radiated and m_disk_accreted to the extra star columns with a dtype definition, since they're calculated during the evolution * turn on verbose + tqdm for the first tutorial * add verbose output for initial evolve + change to solar metallicity + warning for v1 reprocessed data * change history_lengths column name to length * add ZAMS mass selection from oneline * remove empty line * catch empty array input * add labels to plot * combine shift + fill * remove data_columns=True * add mem_per_cpu option + rename files to same standard * rename + moving of script generation * initial update tutorials * docstring cleanup * fix to not sampling from file * add option to overwrite batch directory. False by default * clean up of tutorial + changes for limited select function * fix efficiency calculation after data_columns change * tutorial change for efficiency * DTD plotting fix * change label for merger efficiency * fix for plotting selection * BBH tutorial fixes + cleaner plotting of hist_properties * clean notebook * Fix LGRB tutorial * update script name in tutorial * fix rate calculation if select_one_met is not given to take the full SFR * rewrite one metallicity tutorial with new framework * add fix for writing + appending to a population file with only 1 binary in it * bug fix for observable population calculation * remove autoreload calls * force metallicity onto the simulation properties when initiating a BinaryPopulation * remove reintroduced code from dev_merge * fix separation issue * rework debug tutorial * cleanup * step_SN mass check; force manual calculation if not matching * description of Population File * add documentation about the Population class * remove old lGRB MODEL parameters * remove Method section from the docstring * cleanup BBH notebook * cleanup synthetic_population * expand population_params * increase default chunksize * rename `mt_history` function `mt_history`was defined as a boolean and a function in the same scope. Renamed the function to `get_mt_history` to make sure they're different. --- bin/posydon-setup-popsyn | 159 + .../pop_syn/binary_star.rst | 31 +- .../pop_syn/population_params.rst | 115 +- .../pop_syn/single_star.rst | 21 +- .../pop_syn/synthetic_population.rst | 523 ++- .../10_binaries_pop_syn.ipynb | 1552 +++----- .../population-synthesis/bbh_analysis.ipynb | 2294 ++++------- .../population-synthesis/debug_pop.ipynb | 1980 +--------- .../population-synthesis/lgrb_pop_syn.ipynb | 1195 +----- .../one_met_pop_syn.ipynb | 1192 +----- .../population-synthesis/pop_syn.ipynb | 764 +--- .../population-synthesis/script.py | 5 - posydon/binary_evol/MESA/step_mesa.py | 15 +- posydon/binary_evol/SN/step_SN.py | 153 +- posydon/binary_evol/singlestar.py | 4 +- posydon/popsyn/binarypopulation.py | 83 +- posydon/popsyn/io.py | 62 +- posydon/popsyn/population_params_default.ini | 18 +- posydon/popsyn/rate_calculation.py | 1113 +----- posydon/popsyn/star_formation_history.py | 246 +- posydon/popsyn/synthetic_population.py | 3404 ++++++++++++----- posydon/popsyn/transient_select_funcs.py | 250 ++ .../tests/popsyn/test_synthetic_population.py | 644 ++++ posydon/utils/common_functions.py | 6 +- posydon/visualization/plot_pop.py | 285 +- 25 files changed, 6583 insertions(+), 9531 deletions(-) create mode 100644 bin/posydon-setup-popsyn delete mode 100644 docs/_source/tutorials-examples/population-synthesis/script.py create mode 100644 posydon/popsyn/transient_select_funcs.py create mode 100644 posydon/tests/popsyn/test_synthetic_population.py diff --git a/bin/posydon-setup-popsyn b/bin/posydon-setup-popsyn new file mode 100644 index 0000000000..a2c124cc7d --- /dev/null +++ b/bin/posydon-setup-popsyn @@ -0,0 +1,159 @@ +#!/usr/bin/env python +# script to setup a population run with one or multiple metallicities +# 1. Reads the args input file +# 2. Determine the number of metallicities +# 3. Create a submit file for each metallicity +# 4. Create script to merge job array runs into a single population file +# 5. Create a file to submit all the metallicities +# 6. Including the dependencies +# Author: Max Briel +import os +from posydon.popsyn.io import (binarypop_kwargs_from_ini, + create_run_script_text, + create_merge_script_text) +from posydon.utils.common_functions import convert_metallicity_to_string +from posydon.config import PATH_TO_POSYDON, PATH_TO_POSYDON_DATA +import argparse + +from posydon.popsyn.binarypopulation import BinaryPopulation +from posydon.popsyn.io import binarypop_kwargs_from_ini +from posydon.utils.common_functions import convert_metallicity_to_string +import argparse + + +def create_run_script(ini_file): + '''creates a run script for the population synthesis run''' + + filename =f'run_metallicity.py' + with open(filename, mode='w') as file: + file.write(create_run_script_text(ini_file)) + +def create_merge_script(ini_file): + '''creates a merge script for the population synthesis run''' + + filename='merge_metallicity.py' + with open(filename, mode='w') as file: + file.write(create_merge_script_text(ini_file)) + +def create_slurm_submit(metallicity, job_array_length, partition, email, walltime, account, mem_per_cpu, path_to_posydon, path_to_posydon_data): + '''creates the slurm submit script''' + + str_met = convert_metallicity_to_string(metallicity) + # 0 already included in job_array_length + job_array_length = job_array_length-1 + + text = f'''#!/bin/bash +#SBATCH --array=0-{job_array_length} +#SBATCH --job-name={str_met}_popsyn +#SBATCH --output=./{str_met}_logs/popsyn_%A_%a.out +#SBATCH --time={walltime} +#SBATCH --mem-per-cpu={mem_per_cpu} +''' + if account != None: + text += f'#SBATCH --account={account}\n' + if partition!=None: + text += f'#SBATCH --partition={partition}\n' + + if email != None: + text = text + \ + f'''#SBATCH --mail-type=FAIL +#SBATCH --mail-user={email} +''' + + text = text + \ + f'''export PATH_TO_POSYDON={path_to_posydon} +export PATH_TO_POSYDON_DATA={path_to_posydon_data} +srun python ./run_metallicity.py {metallicity} +''' + filename = f"{str_met}_Zsun_slurm_array.slurm" + with open(filename, mode='w') as file: + file.write(text) + +def create_slurm_merge(metallicity, partition, email, merge_walltime, account, mem_per_cpu, path_to_posydon, path_to_posydon_data): + + str_met = convert_metallicity_to_string(metallicity) + + # set walltime for merge script on debug-cpu + if partition=='debug-cpu': + merge_walltime='00:14:00' + + # add required parameters + text = f'''#!/bin/bash +#SBATCH --job-name={str_met}_Zsun_merge +#SBATCH --output=./{str_met}_logs/popsyn_merge.out +#SBATCH --mem-per-cpu={mem_per_cpu} +#SBATCH --time={merge_walltime} +''' + + # add account if provided + if account != None: + text += f'#SBATCH --account={account}\n' + + # add partition if provided + if partition != None: + text += f'#SBATCH --partition={partition}\n' + + # add email if provided + if email != None: + text = text + \ + f'''#SBATCH --mail-type=FAIL +#SBATCH --mail-user={email} +''' + text = text + \ + f'''export PATH_TO_POSYDON={path_to_posydon} +export PATH_TO_POSYDON_DATA={path_to_posydon_data} +srun python ./merge_metallicity.py {metallicity} +''' + filename = f'{str_met}_Zsun_merge_popsyn.slurm' + with open(filename, mode='w') as file: + file.write(text) + + +################# +# MAIN FUNCTION +################# + +if __name__ == '__main__': + + parser = argparse.ArgumentParser() + parser.add_argument('ini_file', help='a population ini file for which you want to setup a population run', type=str) + parser.add_argument('-j','--job_array', help='the number of job in the job array you want to run', type=int, default=100) + parser.add_argument('--email', help='an email for the slurm scripts', default=None) + parser.add_argument('--partition', help='what cluster partition you want to run the script on', default=None) + parser.add_argument('--walltime', help='the walltime you would like to use in SLURM format', default='23:00:00') + parser.add_argument('--merge_walltime', help='the walltime you would like to use for the merge script in SLURM format', default='12:00:00') + parser.add_argument('--mem_per_cpu', help='the memory per cpu you would like to use in SLURM format', default='4G') + parser.add_argument('--account', help='the account you would like to use', default=None) + args = parser.parse_args() + + synpop_params = binarypop_kwargs_from_ini(args.ini_file) + metallicities = synpop_params['metallicity'] + if synpop_params['number_of_binaries'] / args.job_array < 1: + print("The number of binaries is less than the job array length. Please increase the number of binaries or decrease the job array length") + exit() + + create_run_script(args.ini_file) + create_merge_script(args.ini_file) + + print("Created run script") + for MET in metallicities: + str_met = convert_metallicity_to_string(MET) + try: + os.mkdir(f'{str_met}_logs') + except: + pass + create_slurm_submit(MET, args.job_array, args.partition, args.email, args.walltime, args.account, args.mem_per_cpu, PATH_TO_POSYDON, PATH_TO_POSYDON_DATA) + print("SLURM script created") + create_slurm_merge(MET, args.partition, args.email, args.merge_walltime, args.account, args.mem_per_cpu, PATH_TO_POSYDON, PATH_TO_POSYDON_DATA) + + # create submission script for all SLURM + filename = 'slurm_submit.sh' + + with open(filename, mode='w') as file: + file.write('#!/bin/bash\n') + for MET in metallicities: + str_met = convert_metallicity_to_string(MET) + file.write(f'array=$(sbatch --parsable slurm_array_{str_met}_Zsun.slurm)\n') + file.write("echo '"+str_met+" job array submitted as '${array}\n") + file.write('merge=$(sbatch --parsable --dependency=afterok:${array} --kill-on-invalid-dep=yes '+f'slurm_merge_{str_met}_Zsun.slurm)\n') + file.write("echo '"+str_met+" merge job submitted as '${merge}\n") diff --git a/docs/_source/components-overview/pop_syn/binary_star.rst b/docs/_source/components-overview/pop_syn/binary_star.rst index fed7c9599b..0b4f54d5b4 100644 --- a/docs/_source/components-overview/pop_syn/binary_star.rst +++ b/docs/_source/components-overview/pop_syn/binary_star.rst @@ -34,8 +34,37 @@ The binary properties are defined as follows `eccentricity`, "Orbital eccentricity." `V_sys`, "Velocity of the centre of mass of the binary [Vx, Vy, Vz] in km/s." `mass_transfer_case`, "Mass transfer case, see MT case options." + `lg_mtransfer_rate`, "the logarithm of the mass transfer rate in Msun/yr." + `step_names`, "Names of the steps in the evolution." + `step_times`, 'Time spend in the steps in the evolution.' + `rl_relative_overflow_1`, "The relative overflow of the Roche Lobe of star 1." + `rl_relative_overflow_2`, "The relative overflow of the Roche Lobe of star 2." + `trap_radius`, "The trapping radius of the binary in R_sun." + `acc_radius`, "The accretion radius of the binary in R_sun." + `t_sync_rad_1`, "?" + `t_sync_conv_1`, "?" + `t_sync_rad_2`, "?" + `t_sync_conv_2`, "?" + `nearest_neighbour_distance`, "The distance to the nearest neighbour for NN interpolation." + + +Additional scalar properties can be added during the evolution. + +Since they do not change over time, they are not stored in the history. +These can requested and will be stored in the output oneline (See the :ref:`Synthetic Populatiohn` and :ref:`Population Parameter Guide` for more information). + +.. csv-table:: Additional columns + :header: "Properties", "Descriptions" + :widths: 50, 150 + 'interp_class_HMS_HMS', "Description of interp_class_HMS_HMS." + 'interp_class_CO_HMS_RLO', "Description of interp_class_CO_HMS_RLO." + 'interp_class_CO_HeMS', "Description of interp_class_CO_HeMS." + 'interp_class_CO_HeMS_RLO', "Description of interp_class_CO_HeMS_RLO." + 'mt_history_HMS_HMS', "Description of mt_history_HMS_HMS." + 'mt_history_CO_HMS_RLO', "Description of mt_history_CO_HMS_RLO." + 'mt_history_CO_HeMS', "Description of mt_history_CO_HeMS." + 'mt_history_CO_HeMS_RLO', "Description of mt_history_CO_HeMS_RLO." -TODO: add missing properties State options ~~~~~~~~~~~~~ diff --git a/docs/_source/components-overview/pop_syn/population_params.rst b/docs/_source/components-overview/pop_syn/population_params.rst index f2a5f9880f..ecb3b12df3 100644 --- a/docs/_source/components-overview/pop_syn/population_params.rst +++ b/docs/_source/components-overview/pop_syn/population_params.rst @@ -22,15 +22,40 @@ The environment variable `PATH_TO_POSYDON` will be read from your shell session. * - `PATH_TO_POSYDON` - `` + +-------------------- SimulationProperties -------------------- -TODO: add description +After the environment variables, the next part of the ``population_params.ini`` file +contains how systems will move through the simulation steps (Flow Chart) and the parameters for each step. + +These parameters are read in and used to create a :class:`~posydon.binary_evol.simulationproperties.SimulationProperties`` object. + +It allows for the addition of hooks before and after each step, and the evolution of a system (see :ref:`Custom Hooks `). +The ``SimulationProperties`` can be manually read and loaded in. +Note that the loading of the simulation steps is separate from creating the object. + +.. code-block:: python + + from posydon.binary_evol.simulationproperties import SimulationProperties + from posydon.popsyn.io import simprop_kwargs_from_ini + + # read from file + sim_props = simprop_kwargs_from_ini('population_params.ini', vebose=False) + + # create SimulationProperties object + sim = SimulationProperties(**sim_props) + + # load the steps + sim.load_steps() + Flow Chart ~~~~~~~~~~ -The flow chart is the core of POSYDON. It controls the mapping between a POSYDON binary object and its step evolution, see the :ref:`Flow Chart Object ` page for more details. +The flow chart is the core of POSYDON. +It controls the mapping between a POSYDON binary object and its step evolution, see the :ref:`Flow Chart Object ` page for more details. .. list-table:: :widths: 50 50 @@ -85,7 +110,8 @@ The MESA step is the most important step of POSYDON as it leverages the POSYDON Step Detached ~~~~~~~~~~~~~ -TODO: add description +The detached step uses analytical expressions and MESA single star grids to evolve the binary object when it is not interacting. + .. list-table:: :widths: 50 50 @@ -117,7 +143,8 @@ TODO: add description Step Disrupted ~~~~~~~~~~~~~~ -TODO: add description +The dirtupted step evolves a system when a supernova has unbound the binary components. +This class inherits from the detached step, but only evolves the remaining star in isolation. .. list-table:: :widths: 50 50 @@ -131,7 +158,9 @@ TODO: add description Step Merged ~~~~~~~~~~~ -TODO: add description +In this step, the system has undergone a merger and is now a single star. +This class inherits from the detached step, but only evolves the remaining star in isolation. + .. list-table:: :widths: 50 50 @@ -145,7 +174,8 @@ TODO: add description Step Initially Single ~~~~~~~~~~~~~~~~~~~~~ -TODO: add description +This step is used to evolve a single star system. +This class inherits from the detached step and evolves the star in isolation. .. list-table:: :widths: 50 50 @@ -159,7 +189,10 @@ TODO: add description Step Common Envelope ~~~~~~~~~~~~~~~~~~~~ -TODO: add description +The common envelope step is used to evolve a binary system when the primary or secondary star has initiated a common envelope phase. +It calculates the binding energy of the envelope and the energy available to eject it. +If the energy budget is greater than the binding energy, the envelope is ejected and the system is evolved to the next step. +If the energy budget is less than the binding energy, the system is merged. .. list-table:: :widths: 50 50 @@ -197,7 +230,10 @@ TODO: add description Step Supernova ~~~~~~~~~~~~~~ -TODO: add description +This steps performs the core-collapse supernova evolution of the star. +Multiple prescriptions are implemented and can be selected. +If a standard prescription is used, interpolators are available to speed up the calculation and predict additional properties of the collapse, such as the spin. + .. list-table:: :widths: 50 50 @@ -224,7 +260,7 @@ TODO: add description * - `max_NS_mass` - 2.5 (float in [0,inf]) * - `use_interp_values` - - True + - True (if True, use interpolation values for the SN properties, which are calculated during ``step_MESA``) * - `use_profiles` - True * - `use_core_masses` @@ -247,7 +283,8 @@ TODO: add description Step Double Compact Object ~~~~~~~~~~~~~~~~~~~~~~~~~~ -TODO: add description +In this step, the system has evolved to a double compact object system. +The merger time due to gravitational wave emission is calculated. .. list-table:: :widths: 50 50 @@ -265,7 +302,8 @@ TODO: add description Step End ~~~~~~~~ -TODO: add description +The final step of the simulation. +All succesfull systems should reach this step. .. list-table:: :widths: 50 50 @@ -281,7 +319,8 @@ TODO: add description Extra Hooks ~~~~~~~~~~~ -TODO: add description +It's possible to add custom hooks to the simulation steps. +A few example hooks are provides: ``TimingHooks`` and ``StepNamesHooks`` (See :ref:`Custom Hooks ` for more details) .. list-table:: :widths: 50 50 @@ -302,15 +341,40 @@ TODO: add description * - `kwargs_2` - {} + + BinaryPopulation ---------------- -TODO: add description +A :class:`~posydon.popsyn.binarypopulation.BinaryPopulation` is created to evolve the population using a given :class:`~posydon.binary_evol.simulationproperties.SimulationProperties` object. + +This class requires additional parameters, because it will require initial distributions +for to sample :class:`~posydon.binary_evol.binarystar.BinaryStar` objects from, +such as the masses and orbital parameters. +Moreover, it contains the parameters for metallicity and the practicality of running populations. +This includes, the number of binaries, the metallicity, how often to save the population to file. + +When reading the binary population arguments from a ``population_params.ini`` file, the + :class:`~posydon.binary_evol.simulationproperties.SimulationProperties` are read in automatically. + +.. code-block:: python + + from posydon.popsyn.binarypopulation import BinaryPopulation + from posydon.popsyn.io import binarypop_kwargs_from_ini + + # read from file + pop_params = binarypop_kwargs_from_ini('population_params.ini', vebose=False) + + # create BinaryPopulation object + pop = BinaryPopulation(**pop_params) + + BinaryPopulation Options ~~~~~~~~~~~~~~~~~~~~~~~~ -TODO: add description +These parameters contain options on how the population is evolved, in practical terms, ie. the number of binaries. +It also contains which sampling distributions to use for the initial conditions of the binaries. .. list-table:: :widths: 50 50 @@ -378,16 +442,21 @@ TODO: add description - 'zero' (options are 'zero', 'thermal', 'uniform') - Saving Output ------------- -TODO: add description +You can decide on your own output parameters for the population file. +The data is split in two different tables: the ``history`` table and the ``oneline`` table. + +The ``history`` table contains values that change throughout the evolution of the system, +while the ``oneline`` table contains values that are constant throughout the evolution of the system or only occur once. + BinaryStar Output ~~~~~~~~~~~~~~~~~ -TODO: add description +The :class:`~posydon.binary_evol.binarystar` class contains the binary systems and +the parameters here determine what output of that class will be outputted into the final population file. .. list-table:: :widths: 50 50 @@ -404,7 +473,9 @@ TODO: add description SingleStar 1 and 2 Output ~~~~~~~~~~~~~~~~~~~~~~~~~ -TODO: add description +This dictionary contains the parameters that will be saved in the output of the SingleStar objects in the system. +`only_select_columns` will be stored in the history table, and the initial and final step will be stored in the oneline table with the prefix :code:`S1` or :code:`S2` depending on the star, +`scalar_names` will only be stored in the oneline table. .. list-table:: :widths: 50 50 @@ -415,6 +486,10 @@ TODO: add description * - `include_S1` - True * - `only_select_columns` - - ['state', 'mass', 'log_R', 'log_L', 'lg_mdot', 'he_core_mass', 'he_core_radius', 'co_core_mass', 'co_core_radius', 'center_h1', 'center_he4', 'surface_h1', 'surface_he4', 'surf_avg_omega_div_omega_crit', 'spin',] (options are: 'state', 'metallicity', 'mass', 'log_R', 'log_L', 'lg_mdot', 'lg_system_mdot', 'lg_wind_mdot', 'he_core_mass', 'he_core_radius', 'c_core_mass', 'c_core_radius', 'o_core_mass', 'o_core_radius', 'co_core_mass', 'co_core_radius', 'center_h1', 'center_he4', 'center_c12', 'center_n14', 'center_o16', 'surface_h1', 'surface_he4', 'surface_c12', 'surface_n14', 'surface_o16', 'log_LH', 'log_LHe', 'log_LZ', 'log_Lnuc', 'c12_c12', 'center_gamma', 'avg_c_in_c_core', 'surf_avg_omega', 'surf_avg_omega_div_omega_crit', 'total_moment_of_inertia', 'log_total_angular_momentum', 'spin', 'conv_env_top_mass', 'conv_env_bot_mass', 'conv_env_top_radius', 'conv_env_bot_radius', 'conv_env_turnover_time_g', 'conv_env_turnover_time_l_b', 'conv_env_turnover_time_l_t', 'envelope_binding_energy', 'mass_conv_reg_fortides', 'thickness_conv_reg_fortides', 'radius_conv_reg_fortides', 'lambda_CE_1cent', 'lambda_CE_10cent', 'lambda_CE_30cent', 'lambda_CE_pure_He_star_10cent', 'profile') + - ['state', 'mass', 'log_R', 'log_L', 'lg_mdot', 'he_core_mass', 'he_core_radius', 'co_core_mass', 'co_core_radius', 'center_h1', 'center_he4', 'surface_h1', 'surface_he4', 'surf_avg_omega_div_omega_crit', 'spin',] (options are: 'state', 'metallicity', 'mass', 'log_R', 'log_L', 'lg_mdot', 'lg_system_mdot', 'lg_wind_mdot', 'he_core_mass', 'he_core_radius', 'c_core_mass', 'c_core_radius', 'o_core_mass', 'o_core_radius', 'co_core_mass', 'co_core_radius', 'center_h1', 'center_he4', 'center_c12', 'center_n14', 'center_o16', 'surface_h1', 'surface_he4', 'surface_c12', 'surface_n14', 'surface_o16', 'log_LH', 'log_LHe', 'log_LZ', 'log_Lnuc', 'c12_c12', 'center_gamma', 'avg_c_in_c_core', 'surf_avg_omega', 'surf_avg_omega_div_omega_crit', 'total_moment_of_inertia', 'log_total_angular_momentum', 'spin', 'conv_env_top_mass', 'conv_env_bot_mass', 'conv_env_top_radius', 'conv_env_bot_radius', 'conv_env_turnover_time_g', 'conv_env_turnover_time_l_b', 'conv_env_turnover_time_l_t', 'envelope_binding_energy', 'mass_conv_reg_fortides', 'thickness_conv_reg_fortides', 'radius_conv_reg_fortides', 'lambda_CE_1cent', 'lambda_CE_10cent', 'lambda_CE_30cent', 'lambda_CE_pure_He_star_10cent', 'profile [not currently supported]') * - `scalar_names` - - [ 'natal_kick_array', 'SN_type', 'f_fb', 'spin_orbit_tilt', 'm_disk_accreted', 'm_disk_radiated'] + - [ 'natal_kick_array', 'SN_type', 'f_fb', 'spin_orbit_tilt_first_SN','spin_orbit_tilt_second_SN', 'm_disk_accreted', 'm_disk_radiated'] + + + + diff --git a/docs/_source/components-overview/pop_syn/single_star.rst b/docs/_source/components-overview/pop_syn/single_star.rst index f882692e41..20ece309a4 100644 --- a/docs/_source/components-overview/pop_syn/single_star.rst +++ b/docs/_source/components-overview/pop_syn/single_star.rst @@ -57,7 +57,26 @@ The star properties are defined as follows `total_moment_of_inertia`, "Total moment of inertia in g*cm^2." `log_total_angular_momentum`, "log10 total angular momentum of the star g*cm^2*s^-1" `spin`, "Angular momentum of the star in g*cm^2*s^-1 or dimensionless BH spin." - `profile`, "Stellar profile from MESA." + `profile`, "Stellar profile from MESA. [not current supported for the initial-final interpolator]" + +Additional scalar properties are added during the evolution depending on which steps the star has undergone. These properties are not stored in the history. + +.. csv-table:: Additional output + :header: "Properties", "Descriptions" + :widths: 50, 150 + * - `natal_kick_array` + - The natal kick array for the star if it has undergone a SN. + [velocity, theta, phi, ?] + * - `SN_type` + - The supernova type of the star. + * - `f_fb` + - The fraction of fallback mass. + * - `spin_orbit_tilt_first_SN` + - The spin-orbit tilt after the first SN, if the star has undergone a SN. + * - `spin_orbit_tilt_second_SN` + - The spin-orbit tilt after the second SN, if a second SN has occurred. + * - `m_disk_radiated` + - The mass of the disk radiated in the collapse of the star. TODO: add missing properties diff --git a/docs/_source/components-overview/pop_syn/synthetic_population.rst b/docs/_source/components-overview/pop_syn/synthetic_population.rst index a722897520..bcf684ee38 100644 --- a/docs/_source/components-overview/pop_syn/synthetic_population.rst +++ b/docs/_source/components-overview/pop_syn/synthetic_population.rst @@ -1,4 +1,523 @@ .. _synthetic-population: -The Synthetic Population Object -=============================== \ No newline at end of file + + +The Population Object +=============================== + +The :class:`~posydon.popsyn.synthetic_population.Population` object is an interface for the population data, which is stored in a HDF5 file. +You can find more information about the file structure here: :ref:`population-file-structure`. + +The population file is created by a population synthesis run, and contains the history of each system in the population, and an oneline description of each system and its evolution. + +The :class:`~posydon.popsyn.synthetic_population.Population` object is created by passing the path to the population file to the constructor. +It will not load in the data immediately, but only when requested. + +.. code-block:: python + + from posydon.popsyn import Population + + pop = Population('path/to/population.h5') + + + +There are three main components of the :class:`~posydon.popsyn.synthetic_population.Population` object: + +- :class:`~posydon.popsyn.synthetic_population.Population.history`: the history of the population +- :class:`~posydon.popsyn.synthetic_population.Population.oneline`: the oneline data of the population +- :class:`~posydon.popsyn.synthetic_population.Population.formation_channels`: the formation channels of the population + +The :code:`formation_channels` is not immediately available, but can be created using the +:func:`~posydon.popsyn.synthetic_population.Population.calculate_formation_channels` function of the :class:`~posydon.popsyn.synthetic_population.Population` object. + + +Additionally, :meth:`Population.mass_per_metallicity` contains some essential metadata calculated from the populaiton synthesis run. +It is a pandas.DataFrame with the metallicity (in solar units) as the index and 3 columns: + +1. :code:`count`, the number of systems at that metallicity in the file. +2. :code:`simulated_mass`, the total ZAMS mass of the population run at that metallicity. +3. :code:`underlying_mass`, the total mass of the population run at that metallicity, assuming a binary fraction of 1. This is calculated in :func:`~posydon.popsyn.normalized_pop_mass.initial_total_underlying_mass`. + + + +history +-------- + +:class:`~posydon.popsyn.synthetic_population.Population.history`contains the full history of each system in the population. +The history is stored in a pandas DataFrame, where each row is a timestep in the evolution of a system. +The columns of the DataFrame are the parameters of the system at each timestep. + +The history contains the data from the binary and the individual stars. +See :ref:`Single Star` and :ref:`Binary Star` for more information about the columns in the history table. + +The single star data is stored with the prefix :code:`S1` or :code:`S2` based on which star. + +Accessing columns or binaries in the history table is easy. + +.. code-block:: python + + binary_0 = pop.history[0] + +This will return the complete history of the first binary in the population, including all its columns. + +If you want to access a specific column, you can do so by using the column name. + +.. code-block:: python + + mass_1 = pop.history['S1_mass'] + + +A more powerful feature is the :func:`posydon.popsyn.synthetic_population.History.select` function, which allows you to select specific rows or columns from the history table. Here's an example: + +.. note:: + The :func:`posydon.popsyn.synthetic_population.History.select` function only allows the use of :code:`where=` + for specific columns and the index. The columns are limited to those containing strings. + +.. code-block:: python + + # using where with the index + mass_10 = pop.history.select(columns=['S1_mass'], where='index==10') + + # using where with a string column + mass_ZAMS = pop.history.select(columns=['S1_mass'], where='event == "ZAMS"') + +If you want to have a peak, you can use the :meth:`~posydon.popsyn.synthetic_population.Population.head` or :meth:`~posydon.popsyn.synthetic_population.Population.tail` functions. + +.. code-block:: python + pop.history.head(10) + pop.history.tail(10) + + +Additional functions are made available for easy of use. + +If you want to check the length of the history of a system, you can use :attr:`Population.history.lengths` or :attr:`Population.history_lengths`. + +.. code-block:: python + + print(pop.history.lengths) + print(pop.history_lengths) + +The total number of systems in the population can be found with :attr:`~posydon.popsyn.synthetic_population.Population.History.number_of_systems`. + +.. code-block:: python + + print(pop.history.number_of_systems) + +Similarly, if you would like to check the indices in the file, you can use :attr:`Population.indices` or :attr:`Population.history.indices`. +The indices are useful in selecting systems from the population. + +It's also possible to check the columns in the history table with :attr:`Population.columns` or :attr:`Population.history.columns`. + +.. code-block:: python + + print(pop.indices) + print(pop.history.indices) + + print(pop.history.columns) + print(pop.columns['history']) + + +oneline +-------- + +:meth:`~posydon.popsyn.synthetic_population.Population.oneline` contains a single line description of each system in the population. +This is useful for a quick inspection of the population. +The oneline data is stored in a pandas DataFrame, where each row is a system in the population. + +Some properties over the evolution of the binary do not change, such as the natal kick properties or interpolation class. +Besides the initial and final properties of the system, this table also contains these data. + +The initial-final properties are those in the history table, but with the postfix :code:`_i` and :code:`_f` depending on the initial or final value. +The additional values are the scalar values from the individual stars and the binary properties (See :ref:`Single Star` and :ref:`Binary Star`). + +Additionally, WARNING, FAILED, and metallicity columns are available in the oneline table. + +.. csv-table:: Additional columns + :header: "Properties", "Descriptions" + :widths: 50, 150 + `FAILED`, Indicates if the system failed during the population synthesis run. + `WARNING`, Indicates if there were any warnings for the system during the population synthesis run. + `metallicity`, The metallicity of the system. + + +Like the :code:`history` access, you can access the oneline data by using the index of the system or the columns. + +.. code-block:: python + + binary_0 = pop.oneline[0] + mass = pop.oneline['S1_mass_i'] + selection = pop.oneline.select(columns=['S1_mass_i'], where='index==10') + +You can check the columns and indices of the oneline table with :attr:`Population.oneline.columns` and :attr:`Population.columns['oneline']`. +The length and indices of the oneline table can be found with :attr:`Population.oneline.lengths`, and :attr:`Population.oneline.indices`, respectively. + + +.. code-block:: python + + print(pop.oneline.number_of_systems) + print(pop.oneline.lengths) + print(pop.oneline.indices) + + +formation_channels +------------------ + +:class:`~posydon.popsyn.synthetic_population.Population.formation_channels` contains the formation channels of each system in the population. +The formation channels are stored in a pandas DataFrame, where each row is a system in the population. + +The formation channels are calculated by combining the `event` column in the history table into a single string using the :func:`~posydon.popsyn.synthetic_population.Population.calculate_formation_channels` function of the :class:`~posydon.popsyn.synthetic_population.Population` object. + +Two columns are available in the formation channels table: + +- `debug_channel` : A longer description of the formation channel, where additional events are included. + +- `channel` : A cleaned-up version of the history events, where events are separated by a `-`. + + +Additional Attributes +--------------------- + + +- :attr:`~posydon.popsyn.synthetic_population.Population.ini_parameters`: The parameters for the initial sampling conditions of the population synthesis run. + + +- :attr:`~posydon.popsyn.synthetic_population.Population.mass_per_metallicity`: The mass per metallicity bin for the population synthesis run. + The `underlying_mass` is calculated with the assumption that binary fraction == 1. + +- :attr:`~posydon.popsyn.synthetic_population.Population.history_lengths`: The length of the history of each system in the population. + This is created the first time the file is opened with the :class:`~posydon.popsyn.synthetic_population.Population` object. + + + +Exporting part of the population +-------------------------------- + +The class function :func:`Population.export_selection` allows you to export part of the population to a new HDF5 file. +It takes a list of indices of the systems you want to export, and the path to the new file. +This will copy the systems with the given indices to the new file, which includes their history, oneline data, and formation channels (if presen). + +.. code-block:: python + + pop.export_selection([0, 1, 2], 'path/to/new_population.h5') + + +If the file already exists, the function will raise an error. If you want to overwrite or append to the file, you can use the :code:`overwrite` argument or :code:`append` argument. + +.. code-block:: python + + pop.export_selection([0, 1, 2], 'path/to/new_population.h5', overwrite=True) + pop.export_selection([0, 1, 2], 'path/to/new_population.h5', append=True) + + +TransientPopulation +=================== + +The :class:`~posydon.popsyn.synthetic_population.TransientPopulation` object is an interface for the transient data of the population. +It inherits from the :class:`~posydon.popsyn.synthetic_population.Population` object, but also allows access to the transient populations in the population file. + +A transient population consists of instantaneous events in the population, such as supernovae, kilonovae, or gamma-ray bursts. +These have a single "moment" in time, and are not part of the evolution of the system. +The :class:`~posydon.popsyn.synthetic_population.TransientPopulation` class has been designed to handle these events, but future versions of the code may include more complex populations. + +.. code-block:: python + + from posydon.popsyn.synthetic_population import TransientPopulation + + # where transient_name is the name of the transient population in the file + trans_pop = TransientPopulation('path/to/population.h5', 'transient_name') + + +Creating a TransientPopulation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +The transient population is created using the :func:`posydon.popsyn.synthetic_population.Population.create_transient_population` function of the :class:`~posydon.popsyn.synthetic_population.Population` object. +This function creates a separate table with each transient in the population file. +It loops over all the systems in the population in chunks and applies the given function to them. + +The :func:`posydon.popsyn.synthetic_population.Population.create_transient_population` function takes a function as an argument: :code:`selection_function`. + +The :code:`selection_function` takes 3 arguments: :code:`history_chunk`, :code:`oneline_chunk`, and :code:`formation_channels_chunk` (optional). +These chunks are cut based on a given chunksize, which is set to 1000000 by default, and are cut on system. +This means that always a complete history of a system is passed to the function by :func:`posydon.popsyn.synthetic_population.Population.create_transient_population`. + +:code:`selection_function` is a function you can adapt to your own needs, and examples of building one are given in the :ref:`tutorial-examples.population-synthesis.bbh-analysis` or :ref:`tutorial-examples.population-synthesis.lgrb_pop_syn`. + +.. note:: + + The :code:`selection_function` should return a pandas DataFrame with at least a :code:`metallicity` and :code:`time` column of each event. + Moreover, every row should only contain a single event. + + +We provide a few standard selection functions for the most common transient populations, such as binary black holes and long gamma-ray bursts. +These functions are available in the :mod:`posydon.popsyn.transient_select_funcs` module. + + +Accessing TransientPopulation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +After loading a transient population, you keep access to the history and oneline data of the population. +Now, you can access the transient data of the population using :attr:`TrannsientPopulation.population`. + + +.. code-block:: python + + print(trans_pop.population) + + +Calculating Efficiencies +~~~~~~~~~~~~~~~~~~~~~~~~~ + +With this population, you can calculate additional information, such as the efficiency over metallicity. + +.. code-block:: python + + trans_pop.calculate_efficiency_over_metallicity(channels=True) + +:code:`channels=True` includes the formation channels in the efficiency calculation. + +Plotting +~~~~~~~~~~ + +The :class:`~posydon.popsyn.synthetic_population.TransientPopulation` contains a few plotting functions for ease. + +.. code-block:: python + # plots the efficiency over metallicity per channel + trans_pop.plot_efficiency_over_metallicity(channel=True) + + +.. code-block:: python + + log_bins = np.logspace(6.5, 10.5, 51) + trans_pop.plot_delay_time_distribution(metallicity=0.1, bins=log_bins) + + +The most useful function is :func:`plot_popsyn_over_grid_slice`. +It allows you to overplot properties of your TransienPopulation onto the grids. + +.. note:: + + Make sure that you've set your :code:`PATH_TO_POSYDON` and :code:`PATH_TO_POSYDON_DATA` environment variables correctly. + These are required to plot over the grid slices. + + +If you like to write to a folder, you can use :code:`plot_dir='path/to/dir'` and use :code:`save_fig=True`. + +.. code-block:: python + + # plot the HMS-HMS grid at 1e-4 with S1_spin and q=0.7 + plot_popsyn_over_grid_slice('HMS-HMS', 1e-4, slices=[0.7], prop='S1_spin', prop_range=[0,0.3], save_fig=False, channel='ZAMS_oRLO1_CC1_oRLO2_CC2_END') + + +Rates +===== + +The :class:`~posydon.popsyn.synthetic_population.Rates` object inherits from the :class:`~posydon.popsyn.synthetic_population.TransientPopulation` object +and is used to access the cosmic rate data of the transient population. + +It also allows the user to calculate the intrinsic rate density of the events in the population, and apply observational effects to the population. + +.. code-block:: python + + from posydon.popsyn.synthetic_population import Rates + + rates = Rates('path/to/population.h5', 'transient_name', 'SFH_identifier') + + +Creating a Rates object +~~~~~~~~~~~~~~~~~~~~~~~ + +Cosmic weights are added to the population file using the :func:`~posydon.popsyn.synthetic_population.TransientPopulation.calculate_cosmic_weights` function. +This function calculates the cosmic weights of the events in the population based on the birth redshifts and the population weight. +The function takes an ``SFH_identifier``, which is where the cosmic weights are stored in the population file. +The ``MODEL_in`` argument is used to specify the model parameters for the rate calculation. + +The table below shows the Default values and the supported values. + +.. csv-table:: MODEL_in + :header: "Parameter", "Value", "Description" + :widths: 30, 30, 150 + + "delta_t", 100, "The time interval to split the birth times into" + "SFR", "IllustrisTNG", 'The star formation history identifier [IllustrisTNG/Madau+Fragos17/Madau+Dickinson14/Neijssel+19]"" + "sigma_SFR", None, "The uncertainty in the SFR (float) or the identifier of the SFR uncertainty [Bavera+20/Neijssel+19](str)" + "Z_max", 1.0, "The maximum metallicity to consider [0.0-1.0]" + "select_one_met", False, "Select one metallicity. Requires only one metallicity in the population" + "dlogZ", None, "The metallicity bin width when selecting a single bin (float), the bin edges (tuple(float, float)), or if ``None`` with select_one_met=True, then all metallicities is used." + "Zsun", Zsun, "The solar metallicity" + + +Accessing rates data +~~~~~~~~~~~~~~~~~~~~~~ + +The cosmic rate data is stored in 3 different tables in the population file: + +1. :code:`birth` : A table containing the birth redshifts and lookback times used in the rate calculation. +2. :code:`z_events` : The redshifts of the events in the population and the birth redshifts of the events. +3. :code:`weights` : The weights of each event based on their birth redshifts and their population weight. + + +You can calculate the intrinsic rate density of the events in the population using :func:`posydon.popsyn.synthetic_population.Rates.calculate_intrinsic_rate_density`. +This populates the :code:`intrinsic_rate_density` table in the population file. + +.. code-block:: python + + # calculate the intrinsic rate density per formation channel + rates.calculate_intrinsic_rate_density(mt_channels=True) + + rates.plot_intrinsic_rate_density() + +The :class:`~posydon.popsyn.synthetic_population.Rates` object also contains information about the metallicity and redshift bins and edges. + +.. code-block:: python + + print(rates.centers_metallicity_bins) + print(rates.edges_metallicity_bins) + print(rates.centers_redshift_bins) + print(rates.edges_redshift_bins) + +Plotting Rates +~~~~~~~~~~~~~~~~~~~~~~ + +Besides plotting the intrinsic rate, you can plot the distribution of properties of the population. +You can use any property in the TransientPopulation table. + +.. code-block:: python + + rates.plot_hist_properties('S1_mass', intrinsice=True, label='S1', show=True) + + +Applying observational effects +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Although the intrinsic rate density is a useful quantity, it is not directly observable, especially for binary black holes. + +As such, we also include the possibility to apply observational effects to the population. +This is done using the :func:`posydon.popsyn.synthetic_population.Rates.calculate_observable_population` function. +It reweights the event weights based on the detection efficiency of the event. +The function takes a function as an argument: :code:`observable_func` and a ``observable_identifier``. + +The ``observable_func`` you give the function should take 3 arguments: +1. transient_chunk : The transient data of the population. +2. z_events_chunk : The redshifts of the events in the population. +3. weights_chunk : The weights of each event based on their birth redshifts and their population weight. + +The ``observable_func`` should take these arguments and use them to determine the detection efficiency of the event. +We have included an example in the :func:`posydon.popsyn.transient_select_funcs.DCO_detactability`. + +However, since that function requires a detection argument, it requires a wrapper to work with our function here. + +.. code-block:: python + + from posydon.popsyn.transient_select_funcs import DCO_detactability + def DCO_wrapper(transient_chunk, z_events_chunk, weights_chunk): + sensitivity = 'design_H1L1V1' + return DCO_detactability(sensitivity, transient_chunk, z_events_chunk, weights_chunk, verbose=False) + + # We also give it a name, which is used as an identifier in the file + rates.calculate_observable_population(DCO_wrapper, 'design_H1L1V1') + + +Accessing Observable Population data +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The observable population is accesed through the :code:`observable_population` attribute of the :class:`~posydon.popsyn.synthetic_population.Rates` object. +You require to know the observable_identifier to access the data, which can be accessed with :attr:`Rates.observable_population_names` + +.. code-block:: python + + print(rates.observable_population_names) + print(rates.observable_population('design_H1L1V1')) + +Plotting the observable population +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The observable population can be plotted in the same way as the intrinsic rate density. +However, you require to define which observable population you want to plot. + +.. code-block:: python + + rates.plot_hist_properties('S1_mass', intrinsic=False, observable='design_H1L1V1', label='S1', show=True) + +If you like to overplot multiple properties, you can set ``show=False`` and manually provide an axis. + +.. code-block:: python + + import matplotlib.pyplot as plt + import numpy as np + bins = np.linspace(0,100,101) + fig, ax = plt.subplots(1,1) + + rates.plot_hist_properties('S1_mass', intrinsice=True, observable='design_H1L1V1', bins=bins, ax = ax, label='S1', show=False) + rates.plot_hist_properties('S2_mass', intrinsice=True, observable='design_H1L1V1', bins=bins, ax = ax, label='S1', show=False) + + + +.. _population-file-structure: + +The Structure of Population Files +================================= + +The main output of a population synthesis run is a HDF5 population file. + +Each element in the file is stored as a pandas DataFrame. +While some elements are always present, because they're calculated as part of the population synthesis run, +other elements are optional and can be added by the user. + +The tables describe the location of the data inside the population file. +This is only necessary if you want to access the data directly from the file. +If you use the :meth:`~posydon.popsyn.synthetic_population.Population` object, you can access the data directly from the object. + +.. list-table:: Standard Components of a Population file + :widths: 50 150 + :header-rows: 1 + + * - Path + - Description + * - `history` + - The history of each system (single star or binary) in the population, where each system has a unique index. + * - `oneline` + - The oneline data of each system in the population. A description of the system in a single line, which is useful for quick inspection of the population. + * - `ini_parameters` + - The parameters for the initial sampling conditions of the population synthesis run. + * - `mass_per_metallicity` + - The mass per metallicity bin for the population synthesis run. + The `underlying_mass` is calculated with the assumption that binary fraction == 1. + +As you work with your population, you can add additional components to the population file. +Based on the components and the user given identifiers, the data is stored in the following locations in the population file. + +.. list-table:: Additional components + :widths: 50 150 + :header-rows: 1 + + * - Path + - Description + * - `history_lengths` + - The length of the history of each system in the population. This is created the first time the file is opened with the :class:`~posydon.popsyn.synthetic_population.Population` object. + * - `formation_channels` + - The formation channels of each system in the population. This combines the `event` column in the history table into a single string. :func:`~posydon.popsyn.synthetic_population.Population.calculate_formation_channels` is used to create this component. + * - `transiens/{transient_name}` + - The transient data of each system in the population. The transient data is stored in a separate table for each transient. This is created by :func:`~posydon.popsyn.synthetic_population.Population.create_transient_population`. + * - `transiens/{transient_name}/efficiencies` + - The transient efficiencies over metallicity. This is calculated with :func:`~posydon.popsyn.synthetic_population.TransientPopulation.get_efficiency_over_metallicity`. + * - `transiens/{transient_name}/rates/{SFH_identifier}/MODEL` + - The MODEL parameters for the specific transient rate calculations done with :func:`~posydon.popsyn.synthetic_population.TransientPopulation.calculate_cosmic_weights`. + * - `transiens/{transient_name}/rates/{SFH_identifier}/birth` + - A table containing the birth redshifts and lookback times used in the rate calculation. + * - `transiens/{transient_name}/rates/{SFH_identifier}/z_events` + - The redshifts of the events in the population and the birth redshifts of the events. + * - `transiens/{transient_name}/rates/{SFH_identifier}/weights` + - The weights of each event based on their birth redshifts and their population weight. + * - `transiens/{transient_name}/rates/{SFH_identifier}/intrinsic_rate_density` + - The intrinsic rate density of the events in the population, calculated with :func:`~posydon.popsyn.synthetic_population.Rates.calculate_intrinsic_rate_density`. + + + diff --git a/docs/_source/tutorials-examples/population-synthesis/10_binaries_pop_syn.ipynb b/docs/_source/tutorials-examples/population-synthesis/10_binaries_pop_syn.ipynb index 0014d34e35..b9b5992661 100644 --- a/docs/_source/tutorials-examples/population-synthesis/10_binaries_pop_syn.ipynb +++ b/docs/_source/tutorials-examples/population-synthesis/10_binaries_pop_syn.ipynb @@ -13,12 +13,11 @@ "source": [ "**Tutorial goal:**\n", "\n", - "- Create your first binary population\n", + "In this tutorial, we will run a small population of 10 binaries locally, and explore how to manipulate the output data from your population.\n", "\n", "**New concepts:**\n", "\n", - "- Default population synthesis model\n", - "- Synthetic Population class\n" + "- Population ini file" ] }, { @@ -31,18 +30,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "env: PATH_TO_POSYDON=/Users/simone/Google Drive/github/POSYDON-public/\n", - "env: PATH_TO_POSYDON_DATA=/Volumes/T7/\n" - ] - } - ], + "outputs": [], "source": [ "%env PATH_TO_POSYDON=/YOUR/POSYDON/PATH/\n", "%env PATH_TO_POSYDON_DATA=/YOUR/POSYDON_DATA/PATH/" @@ -61,31 +51,27 @@ "source": [ "----\n", "To run population synthesis with POSYDON, a `population_params.ini` file is required.\n", - "This file described how the stellar population is created and what prescriptions are implemented.\n", + "This file described how the stellar population is created and what prescriptions and parameters are implemented in specific steps.\n", "\n", - "POSYDON comes with a default `population_params_detault.ini` file found at `PATH_TO_POSYDON/posydon/popsyn` or have a look [here](population_params.ini).\n", + "POSYDON comes with a default `population_params_default.ini` file found at `PATH_TO_POSYDON/posydon/popsyn` or have a look [here](population_params.ini).\n", "\n", - "Here we will run the notebook with the default parameters, which runs 10 binaries at $10^{-4}Z_\\odot$.\n", + "The file is split in three main parts. You can find more details about their properties by clicking on their links.\n", + "1. **[SimulationProperties]():**\n", + " - these describe the properties and parameters of different steps in the evolution of a binary systems.\n", + "2. **[BinaryPopulation]():** \n", + " - parameters of the initial sampling of the binary population, such as initial mass function, period distribution, and metallicity.\n", + " - Also contains parameters on how the population is ran, such as how many binaries are kept in memory.\n", + "3. **[SavingOutput]()**\n", + " - Describes the data from the binary and each individual star to the output files.\n", "\n", - "First, let's copy the default population synthesis model to your working directory." + "We will copy the default population run parameter file to the current folder." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'./population_params.ini'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import os\n", "import shutil\n", @@ -99,1133 +85,478 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Running the Population Synthesis Model" + "\n", + "
        Reprocessed POSYDON v1 data \n", + "\n", + "If you're using the reprocessed POSYDON v1 dataset, you will only have solar metallicity available!\n", + "This means you will only be able to run populations with `metallicity = [1]`!\n", + "\n", + "You should be able to follow along with this tutorial and can follow along with the \"One metallicity notebook\", but the multi-metallicity component is not yet available.\n", + "
        " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "# Creating and running a binary population\n", + "\n", + "The copied `population_params.ini` contains the parameters to run 10 binaries at a metallicity of $Z=1 Z_\\odot$.\n", + "\n", + "If you open the file and scroll down to the **BinaryPopulation** section, you will see how they're defined:\n", + "\n", + "```\n", + "metallicity = [1] # [2., 1., 0.45, 0.2, 0.1, 0.01, 0.001, 0.0001]\n", + "# In units of solar metallicity\n", + "...\n", + "number_of_binaries = 10\n", + "# int\n", + "```\n", "\n", - "[SyntheticPopulation()](../../api_reference/posydon.popsyn.rst#posydon.popsyn.synthetic_population.SyntheticPopulation) samples the given initial distributions given in the `population_params.ini` file and generates the initial conditions for our 10 binaries." + "If you like to run a small population in a notebook, you can use the `PopulationRunner` to do this. If you want to run a specific binary instead, have a look at the [Binary Tutorial]().\n", + "\n", + "The `PopulationRunner` class takes the `.ini` file and sets-up a population run.\n", + "\n", + "This will create [BinaryPopulations]() for each metallicity defined in the `.ini` file.\n", + "In this case, we can check and see that a single BinaryPopulation is created and contains 10 binaries." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Z=1.00e-04 Z_sun\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/binarypopulation.py:571: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " return pd.concat(holder, axis=0, ignore_index=False)\n" - ] - } - ], + "outputs": [], "source": [ - "import pandas as pd\n", - "from posydon.popsyn.synthetic_population import SyntheticPopulation\n", + "from posydon.popsyn.synthetic_population import PopulationRunner\n", + "poprun = PopulationRunner('./population_params.ini', verbose=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('Number of binary populations:',len(poprun.binary_populations))\n", + "print('Metallicity:', poprun.binary_populations[0].metallicity)\n", + "print('Number of binaries:', poprun.binary_populations[0].number_of_binaries)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this tutorial, we set `verbose=True`, which shows you the progress of the population run.\n", + "This overwrites the population verbose set inside the `population_params.ini` file.\n", "\n", - "synth_pop = SyntheticPopulation('./population_params.ini')\n", - "synth_pop.evolve()" + "Now we are ready to evolve the binary population. This should take about 30 seconds, but depends on your machine." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "poprun.evolve()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "In the previous step, a synthetic binary population is created with the POSYDON default initial parameters and stored in `synth_pop`. With the last line of code the synthetic population will be evolved.\n", + "# Inspecting the population: Population class\n", + "\n", + "When you ran the population, you might have seen that a temporary folder with the name `1e+00_Zsun_batches` was created while the binaries were evolved.\n", + "This is a temporary folder in which populations are temporarly saved.\n", + "After the binary evolution has finished, the binaries in the folder are moved to a single file named `1e+00_Zsun_popululation.h5`. This is done automatically, when you run a population using the `PopulationRunner` class.\n", "\n", - "Congratulations! You run your first population synthesis model with POSYDON! 🎉" + "The created file contains 3 main components:\n", + "\n", + "1. **history:** the evolution of an individual binary in a pandas DataFrame\n", + "2. **oneline:** a single line to describe the initial and final conditions and some one-of parameters, such as the metallicity.\n", + "3. **mass_per_metallicity:** some metadata on the population, such as the total simulated mass, the actual underlying mass of the population, and the number of binaries in the file.\n", + "\n", + "The `Population` provides an interface with these components in the file, such that you're able to share the populations runs and can work with large population that do not fit in memory.\n", + "\n", + "\n", + "
        Older Population Files \n", + "\n", + "If you're using older population files, you can make them compatible with the `Population` class by calling `Population(pop_file, metallicity,ini_file)`, where the `metallicity` is in solar units. You will only need to do this once; afterwards you can initialise the class like normal.
        " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from posydon.popsyn.synthetic_population import Population" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pop = Population('1e+00_Zsun_population.h5', verbose=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Exploring the Simulation Output" + "The `pop.mas_per_met` shows some basic information about the population you've just created.\n", + "\n", + "\n", + "1. The index (**metallicity**) is the metallicity of your population in solar units.\n", + "2. **simulated mass** is the total ZAMS mass that has been evolved in the population.\n", + "3. **underlying mass** is the actual mass of the population if one integrates the IMF and period distribution fully.\n", + "4. **number_of_systems** shows the 10 systems in the file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pop.mass_per_metallicity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Here are the population synthesis files you just created, in this example we only visualize the first ten lines of the output table:" + "There are some additional metadata properties available, such as:\n", + "- `metallicities`: the metallicity in absolute metallicity\n", + "- `solar_metallicities`: the metallicities in the file in solar metallicity\n", + "- `number_of_systems`: the total number of systems in the Population file\n", + "- `indices`: the indices of the binaries in the file\n", + "- `columns`: the columns available in the `history` and `oneline` dataframes" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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        " - ], - "text/plain": [ - " state event time orbital_period \\\n", - "binary_index \n", - "0 detached ZAMS 1.511066e+09 479.288083 \n", - "0 RLO1 CC1 1.535382e+09 1220.670243 \n", - "0 disrupted NaN 1.535382e+09 NaN \n", - "0 disrupted CC2 1.548703e+09 NaN \n", - "0 disrupted NaN 1.548703e+09 NaN \n", - "0 disrupted END 1.548703e+09 NaN \n", - "1 detached ZAMS 3.316347e+09 0.911049 \n", - "1 contact oCE1 3.327664e+09 0.506718 \n", - "1 merged oMerging1 3.327664e+09 0.506718 \n", - "1 merged CC1 3.331178e+09 NaN \n", - "\n", - " eccentricity lg_mtransfer_rate step_names step_times \\\n", - "binary_index \n", - "0 0.0 NaN initial_cond 0.000000 \n", - "0 0.0 -4.442681 step_HMS_HMS 2.176263 \n", - "0 NaN NaN step_SN 0.001554 \n", - "0 NaN NaN step_disrupted 5.963153 \n", - "0 NaN NaN step_SN 0.001684 \n", - "0 NaN NaN step_end 0.000107 \n", - "1 0.0 NaN initial_cond 0.000000 \n", - "1 0.0 -1.876899 step_HMS_HMS 0.347768 \n", - "1 0.0 -1.876899 step_CE 0.000091 \n", - "1 NaN NaN step_merged 6.616714 \n", - "\n", - " S1_state S1_mass ... S2_he_core_mass \\\n", - "binary_index ... \n", - "0 H-rich_Core_H_burning 10.635159 ... NaN \n", - "0 H-rich_Central_C_depletion 4.387056 ... 1.411542e-14 \n", - "0 NS 1.346469 ... 1.411542e-14 \n", - "0 NS 1.346469 ... 2.970098e+00 \n", - "0 NS 1.346469 ... NaN \n", - "0 NS 1.346469 ... NaN \n", - "1 H-rich_Core_H_burning 15.575811 ... NaN \n", - "1 H-rich_Core_H_burning 14.908113 ... 1.411542e-14 \n", - "1 H-rich_Core_H_burning 14.908113 ... 1.411542e-14 \n", - "1 H-rich_Central_C_depletion 16.152142 ... NaN \n", - "\n", - " S2_he_core_radius S2_co_core_mass S2_co_core_radius \\\n", - "binary_index \n", - "0 NaN NaN NaN \n", - "0 2.053381e-16 1.365520e-14 1.239204e-16 \n", - "0 2.053381e-16 1.365520e-14 1.239204e-16 \n", - "0 NaN 2.062403e+00 5.067320e-02 \n", - "0 NaN NaN NaN \n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "1 2.053381e-16 1.365520e-14 1.239204e-16 \n", - "1 2.053381e-16 1.365520e-14 1.239204e-16 \n", - "1 NaN NaN NaN \n", - "\n", - " S2_center_h1 S2_center_he4 S2_surface_h1 S2_surface_he4 \\\n", - "binary_index \n", - "0 0.750999 2.490000e-01 NaN NaN \n", - "0 0.423320 5.766784e-01 0.388622 0.611377 \n", - "0 0.423320 5.766784e-01 0.388622 0.611377 \n", - "0 0.000000 7.232097e-14 0.719862 0.280118 \n", - "0 NaN NaN NaN NaN \n", - "0 NaN NaN NaN NaN \n", - "1 0.750999 2.490000e-01 NaN NaN \n", - "1 0.722955 2.770430e-01 0.750621 0.249361 \n", - "1 0.722955 2.770430e-01 0.750621 0.249361 \n", - "1 NaN NaN NaN NaN \n", - "\n", - " S2_surf_avg_omega_div_omega_crit S2_spin \n", - "binary_index \n", - "0 NaN NaN \n", - "0 0.907345 15.586620 \n", - "0 0.907345 15.586620 \n", - "0 0.024038 13.729559 \n", - "0 NaN 0.000000 \n", - "0 NaN 0.000000 \n", - "1 NaN NaN \n", - "1 0.988417 10.460393 \n", - "1 0.988417 10.460393 \n", - "1 NaN NaN \n", - "\n", - "[10 rows x 38 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "df = pd.read_hdf('./1.00e-04_Zsun_population.h5', key='history')\n", - "df.head(10)" + "# you can also access the total number of systems in the file with\n", + "print(pop.number_of_systems)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "

        \n", - "It is also possible to collapse the evolution output on a single row per binary system as shown in the next example." + "## Population.history" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
        \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
        state_ievent_itime_iorbital_period_ieccentricity_ilg_mtransfer_rate_istep_names_istep_times_istate_fevent_f...interp_class_HMS_HMSinterp_class_CO_HMS_RLOinterp_class_CO_HeMSinterp_class_CO_HeMS_RLOmt_history_HMS_HMSmt_history_CO_HMS_RLOmt_history_CO_HeMSmt_history_CO_HeMS_RLOFAILEDWARNING
        binary_index
        0detachedZAMS1.511066e+09479.2880830.0NaNinitial_cond0.0disruptedEND...stable_MTNaNNaNNaNStable RLOF during postMSNaNNaNNaN01
        1detachedZAMS3.316347e+090.9110490.0NaNinitial_cond0.0mergedEND...unstable_MTNaNNaNNaNUnstable contact phaseNaNNaNNaN01
        2detachedZAMS7.997196e+080.9643420.0NaNinitial_cond0.0mergedEND...unstable_MTNaNNaNNaNUnstable contact phaseNaNNaNNaN01
        3detachedZAMS6.368205e+081.8074440.0NaNinitial_cond0.0disruptedEND...stable_MTNaNNaNNaNStable RLOF during postMSNaNNaNNaN01
        4detachedZAMS5.028601e+091.6885410.0NaNinitial_cond0.0initial_RLOFEND...stable_MTNaNNaNNaNStable RLOF during postMSNaNNaNNaN00
        5detachedZAMS5.384771e+0959.6041850.0NaNinitial_cond0.0disruptedEND...stable_MTNaNNaNNaNStable RLOF during postMSNaNNaNNaN01
        6detachedZAMS1.242091e+103622.5998360.0NaNinitial_cond0.0disruptedEND...no_MTNaNNaNNaNno RLOFNaNNaNNaN01
        7detachedZAMS1.278701e+102.7851140.0NaNinitial_cond0.0mergedEND...unstable_MTNaNNaNNaNUnstable RLOF during postMSNaNNaNNaN01
        8detachedZAMS3.362532e+092.5229100.0NaNinitial_cond0.0detachedEND...stable_MTstable_MTNaNNaNStable contact phaseStable RLOF during postMSNaNNaN01
        9detachedZAMS7.402367e+094518.5482420.0NaNinitial_cond0.0disruptedEND...no_MTNaNNaNNaNno RLOFNaNNaNNaN01
        \n", - "

        10 rows × 100 columns

        \n", - "
        " - ], - "text/plain": [ - " state_i event_i time_i orbital_period_i \\\n", - "binary_index \n", - "0 detached ZAMS 1.511066e+09 479.288083 \n", - "1 detached ZAMS 3.316347e+09 0.911049 \n", - "2 detached ZAMS 7.997196e+08 0.964342 \n", - "3 detached ZAMS 6.368205e+08 1.807444 \n", - "4 detached ZAMS 5.028601e+09 1.688541 \n", - "5 detached ZAMS 5.384771e+09 59.604185 \n", - "6 detached ZAMS 1.242091e+10 3622.599836 \n", - "7 detached ZAMS 1.278701e+10 2.785114 \n", - "8 detached ZAMS 3.362532e+09 2.522910 \n", - "9 detached ZAMS 7.402367e+09 4518.548242 \n", - "\n", - " eccentricity_i lg_mtransfer_rate_i step_names_i step_times_i \\\n", - "binary_index \n", - "0 0.0 NaN initial_cond 0.0 \n", - "1 0.0 NaN initial_cond 0.0 \n", - "2 0.0 NaN initial_cond 0.0 \n", - "3 0.0 NaN initial_cond 0.0 \n", - "4 0.0 NaN initial_cond 0.0 \n", - "5 0.0 NaN initial_cond 0.0 \n", - "6 0.0 NaN initial_cond 0.0 \n", - "7 0.0 NaN initial_cond 0.0 \n", - "8 0.0 NaN initial_cond 0.0 \n", - "9 0.0 NaN initial_cond 0.0 \n", - "\n", - " state_f event_f ... interp_class_HMS_HMS \\\n", - "binary_index ... \n", - "0 disrupted END ... stable_MT \n", - "1 merged END ... unstable_MT \n", - "2 merged END ... unstable_MT \n", - "3 disrupted END ... stable_MT \n", - "4 initial_RLOF END ... stable_MT \n", - "5 disrupted END ... stable_MT \n", - "6 disrupted END ... no_MT \n", - "7 merged END ... unstable_MT \n", - "8 detached END ... stable_MT \n", - "9 disrupted END ... no_MT \n", - "\n", - " interp_class_CO_HMS_RLO interp_class_CO_HeMS \\\n", - "binary_index \n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "5 NaN NaN \n", - "6 NaN NaN \n", - "7 NaN NaN \n", - "8 stable_MT NaN \n", - "9 NaN NaN \n", - "\n", - " interp_class_CO_HeMS_RLO mt_history_HMS_HMS \\\n", - "binary_index \n", - "0 NaN Stable RLOF during postMS \n", - "1 NaN Unstable contact phase \n", - "2 NaN Unstable contact phase \n", - "3 NaN Stable RLOF during postMS \n", - "4 NaN Stable RLOF during postMS \n", - "5 NaN Stable RLOF during postMS \n", - "6 NaN no RLOF \n", - "7 NaN Unstable RLOF during postMS \n", - "8 NaN Stable contact phase \n", - "9 NaN no RLOF \n", - "\n", - " mt_history_CO_HMS_RLO mt_history_CO_HeMS \\\n", - "binary_index \n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "5 NaN NaN \n", - "6 NaN NaN \n", - "7 NaN NaN \n", - "8 Stable RLOF during postMS NaN \n", - "9 NaN NaN \n", - "\n", - " mt_history_CO_HeMS_RLO FAILED WARNING \n", - "binary_index \n", - "0 NaN 0 1 \n", - "1 NaN 0 1 \n", - "2 NaN 0 1 \n", - "3 NaN 0 1 \n", - "4 NaN 0 0 \n", - "5 NaN 0 1 \n", - "6 NaN 0 1 \n", - "7 NaN 0 1 \n", - "8 NaN 0 1 \n", - "9 NaN 0 1 \n", - "\n", - "[10 rows x 100 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "df_oneline = pd.read_hdf('./1.00e-04_Zsun_population.h5', key='oneline')\n", - "df_oneline.head(10)" + "pop.history.lengths" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "For the last example, only a selection of columns will be selected to visualize the evolution of the ninth binary (with a corresponding index value of eight) in the population:" + "`pop.history` loads in the full history of all the binaries into memory.\n", + "You can access individual or a selections of the population using several methods:\n", + "\n", + "\n", + "1. pop.history[5]\n", + "2. pop.history[[0,4]]\n", + "3. pop.history['time]\n", + "4. pop.history.select()\n", + "\n", + "The `select` function is the most powerfull way to access the binaries, because it allows you to perform selections based on the specific columns available in the history dataframe.\n", + "For example, below we can select on `state == 'RLO1'`, which gives us all the rows with RLO1 occuring.\n", + "\n", + "The available identifiers are limited to string columns (`state`, `event`, `step_names`, `S1_state`, `S2_state`), index, and columns names." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# select only binary_index 5\n", + "pop.history[5]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# select binary 0 and 4\n", + "pop.history[[0,4]]" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
        \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
        timestep_namesstateeventorbital_periodeccentricityS1_stateS2_stateS1_massS2_mass
        binary_index
        83.362532e+09initial_conddetachedZAMS2.5229100.000000H-rich_Core_H_burningH-rich_Core_H_burning93.54656793.023072
        83.366016e+09step_HMS_HMSdetachedCC13.9094720.000000H-rich_Central_C_depletionH-rich_Core_H_burning58.365944119.598219
        83.366016e+09step_SNdetachedNaN4.6481140.083735BHH-rich_Core_H_burning43.861220119.598219
        83.366233e+09step_detachedRLO2oRLO24.9022230.000000BHH-rich_Core_H_burning43.861220101.734326
        83.366846e+09step_CO_HMS_RLOdetachedCC22.8460540.000000BHH-rich_Central_C_depletion45.17553167.246779
        83.366846e+09step_SNdetachedNaN8.7124120.458613BHBH45.17553130.278034
        81.380000e+10step_dcodetachedmaxtime5.7818190.355638BHBH45.17553130.278034
        81.380000e+10step_enddetachedEND5.7818190.355638BHBH45.17553130.278034
        \n", - "
        " - ], - "text/plain": [ - " time step_names state event \\\n", - "binary_index \n", - "8 3.362532e+09 initial_cond detached ZAMS \n", - "8 3.366016e+09 step_HMS_HMS detached CC1 \n", - "8 3.366016e+09 step_SN detached NaN \n", - "8 3.366233e+09 step_detached RLO2 oRLO2 \n", - "8 3.366846e+09 step_CO_HMS_RLO detached CC2 \n", - "8 3.366846e+09 step_SN detached NaN \n", - "8 1.380000e+10 step_dco detached maxtime \n", - "8 1.380000e+10 step_end detached END \n", - "\n", - " orbital_period eccentricity S1_state \\\n", - "binary_index \n", - "8 2.522910 0.000000 H-rich_Core_H_burning \n", - "8 3.909472 0.000000 H-rich_Central_C_depletion \n", - "8 4.648114 0.083735 BH \n", - "8 4.902223 0.000000 BH \n", - "8 2.846054 0.000000 BH \n", - "8 8.712412 0.458613 BH \n", - "8 5.781819 0.355638 BH \n", - "8 5.781819 0.355638 BH \n", - "\n", - " S2_state S1_mass S2_mass \n", - "binary_index \n", - "8 H-rich_Core_H_burning 93.546567 93.023072 \n", - "8 H-rich_Core_H_burning 58.365944 119.598219 \n", - "8 H-rich_Core_H_burning 43.861220 119.598219 \n", - "8 H-rich_Core_H_burning 43.861220 101.734326 \n", - "8 H-rich_Central_C_depletion 45.175531 67.246779 \n", - "8 BH 45.175531 30.278034 \n", - "8 BH 45.175531 30.278034 \n", - "8 BH 45.175531 30.278034 " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "# let's check the BBH system at index 8\n", - "cols = ['time', 'step_names', 'state', 'event', 'orbital_period', 'eccentricity', 'S1_state', 'S2_state', 'S1_mass', 'S2_mass']\n", - "df.loc[8, cols]" + "pop.history['time'].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "

        \n", + "You can also check what columns are available in the history file:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pop.history.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# using the select function\n", + "pop.history.select(where='index == 9')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# selecting all RLO1 states and only time and state columns\n", + "pop.history.select(where='state == RLO2', columns=['time', 'state'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# selecting rows 10 to 16\n", + "pop.history.select(start=10, stop=16)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You might have notices while using the above functions that not all the binaries will have the same length in the history.\n", + "You can access these with `pop.history_lengths`. This information is also stored in the population file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pop.history_lengths" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Population.oneline\n", + "\n", + "`Population.oneline` provides a similar interface to accessing the DataFrame in the population file as `Population.history`, with similar functionality being available.\n", + "\n", + "The `select` function only has acces to:\n", + "- `index`\n", + "- column names \n", + "- string columns: `state_i`, `state_f`, `event_i`, `event_f`, `step_names_i`, `step_names_f`, `S1_state_i`, `S1_state_f`, `S2_state_i`, `S2_state_f`, `S1_SN_type`, `S2_SN_type`, `interp_class_HMS_HMS`, `interp_class_CO_HeMS`, `interp_class_CO_HMS_RLO`, `interp_class_CO_HeMS_RLO`, `mt_history_HMS_HMS`, `mt_history_CO_HeMS`, `mt_history_CO_HMS_RLO`, `mt_history_CO_HeMS_RLO`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pop.oneline[5]\n", + "pop.oneline[[0,4]]\n", + "pop.oneline.select(where='index == 9')\n", + "pop.oneline.select(where='index == [0,9]')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pop.oneline.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Population.formation_channels\n", + "\n", + "You might be interested in figuring out what sort of formation pathways/channels a binary has followed through its evolution.\n", + "\n", + "This is not a standard output of the population synthesis, but you can include it into the population file by calculating it. \n", + "If you would like more detail on the initial mass transfer, you can set `mt_history=True`.\n", + "\n", + "This will write the formation channels to the Population file, which can be accessed by `Population.formation_channels`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pop.calculate_formation_channels(mt_history=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# the formation channels are loaded in with pop.formation_channels\n", + "pop.formation_channels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next time you open this population file, the `formation_channels` will be available without having to be recalculated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pop = Population('1e+00_Zsun_population.h5')\n", + "pop.formation_channels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Selecting a sub-population\n", + "\n", + "You might just want a small sub-selection of the full population, especially if you're working with large population and multi-metallicity runs.\n", + "\n", + "The `Population.export_selection()` function will export just the indices of the binaries you're interested in into a new file.\n", + "The simulated and underlying mass will remain the same, since they are dependent on the population run.\n", + "\n", + "If we select just 2 binaries and export them, we create a new population of just the binaries you're interested in.\n", + "In the [BBH analysis]() and [GRB analysis]() tutorials, we show how to perform a selection with multiple criteria and metallicities.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "indices = [0,9]\n", + "pop.export_selection(indices, 'selected.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "selected = Population('selected.h5')\n", + "selected.mass_per_metallicity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you would like to know the simulated mass of just your population, you can calulate this using the online ZAMS values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "print('selected simulated mass: ', np.sum(selected.oneline[['S1_mass_i', 'S2_mass_i']].to_numpy()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default this export-selection will not overwrite nor append if the output file is already present.\n", + "You have to explicitly state what you would like to append to or overwrite the population file.\n", + "\n", + "\n", + "With the `append=True` you are able to combine multiple stellar populations into a single file. \n", + "This is especially useful when creating multi-metallicity populations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# this will overwrite the existing file\n", + "pop.export_selection(indices, 'selected.h5', overwrite=True)\n", "\n", + "selected = Population('selected.h5')\n", + "selected.mass_per_metallicity\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This will add to the file and add the extra simulated mass\n", + "pop.export_selection(indices, 'selected.h5', append=True)\n", + "selected = Population('selected.h5')\n", + "selected.mass_per_metallicity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

        \n", "\n", - "Feel free to explore the dataset! \n", + "Feel free to explore the small binary population you've just created!\n", "\n", "If you want to learn more about population synthesis and how to build more complex models it is advised to continue with the remaining tutorials and consult the POSYDON documentation." ] @@ -1238,7 +569,7 @@ "\n", "To speed up population synthesis runs, you can run on a computing cluster, as described in [HPC Facilities](pop_syn), or you can distribute the population synthesis across multiple cores on your local machine using MPI.\n", "\n", - "To enable local MPI runs, go into the `population_params_detault.ini` and change `use_MPI` to `True`.\n", + "To enable local MPI runs, go into the `population_params.ini` and change `use_MPI` to `True`.\n", "\n", "It's important to note that you cannot run have this option enabled for cluster runs!\n", "\n", @@ -1252,10 +583,10 @@ "outputs": [], "source": [ "%%writefile script.py\n", - "from posydon.popsyn.synthetic_population import SyntheticPopulation\n", + "from posydon.popsyn.synthetic_population import PopulationRunner\n", "\n", "if __name__ == \"__main__\":\n", - " synth_pop = SyntheticPopulation(\"./population_params.ini\")\n", + " synth_pop = PopulationRunner(\"./population_params.ini\")\n", " synth_pop.evolve()" ] }, @@ -1263,17 +594,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This script can be initiated using a local where NR_processors is the number of processors you would like to us." + "This script can be initiated using a local where `NR_processors` is the number of processors you would like to us." ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, + "metadata": {}, "outputs": [], "source": [ "mpiexec -n ${NR_processors} python script.py" @@ -1284,41 +611,22 @@ "metadata": {}, "source": [ "This will create a folder for each metallicity in the population and store output of the parallel runs in it.\n", - "You will have to concatenate these runs into a single population file per metallicity, which can be achieved using the following code:" + "\n", + "You will have to concatenate these runs manually into a single population file per metallicity, which can be achieved using the following code:" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, + "metadata": {}, "outputs": [], "source": [ - "from posydon.popsyn.synthetic_population import SyntheticPopulation\n", - "from posydon.config import PATH_TO_POSYDON_DATA\n", - "import os\n", - "\n", + "from posydon.popsyn.synthetic_population import PopulationRunner\n", "\n", - "synth_pop = SyntheticPopulation(\"./population_params.ini\")\n", - "# Get the path to the batches in the current folder\n", - "x = os.listdir('.')\n", - "path_to_batches = [i for i in x if i.endswith('_batches')]\n", - "synth_pop.merge_parallel_runs(path_to_batches)" + "synth_pop = PopulationRunner(\"./population_params.ini\")\n", + "for pop in synth_pop.binary_populations:\n", + " synth_pop.merge_parallel_runs(pop)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/docs/_source/tutorials-examples/population-synthesis/bbh_analysis.ipynb b/docs/_source/tutorials-examples/population-synthesis/bbh_analysis.ipynb index d6270c0b84..54d6fe0a7d 100644 --- a/docs/_source/tutorials-examples/population-synthesis/bbh_analysis.ipynb +++ b/docs/_source/tutorials-examples/population-synthesis/bbh_analysis.ipynb @@ -4,7 +4,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Analyzing Merging BBH Populations: Rates & Observations 🔍" + "# Stellar Transient Populations 🔍" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook, we will cover:\n", + "1. Selecting binaries and combining metallicities\n", + "2. Exploring the `TransientPopulation` class\n", + "\n", + "We will do this in the context of the merging binary black hole population." ] }, { @@ -20,18 +31,9 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "env: PATH_TO_POSYDON=/Users/simone/Google Drive/github/POSYDON-public/\n", - "env: PATH_TO_POSYDON_DATA=/Volumes/T7/\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "%env PATH_TO_POSYDON=/Users/simone/Google Drive/github/POSYDON-public/\n", "%env PATH_TO_POSYDON_DATA=/Volumes/T7/" @@ -41,7 +43,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Creating the Synthetic Poupulation DataFrame" + "## Creating multi-metallicity Populations\n", + "\n", + "\n", + "
        Reprocessed POSYDON v1 dataset \n", + "\n", + "Please note that with the reprocessed POSYDON v1 data, only solar metallicity is available.\n", + "You will not be able to follow along with the full tutorial!\n", + "If you would still like to explore a population at solar metallicity, you can follow the \"One metallicity\" tutorial.
        " ] }, { @@ -52,1681 +61,890 @@ } }, "source": [ - "We want to analyze the BBH population data we generated in the previous notebook. We will build a model that predicts the intrinsic and observable BBH population in the Universe. Please refer to the paper for more details about the procedure inolved in the convolution of the BBH synthetic popluation, i.e. the POSYDON population of merging BBH at the time of formation, with the star-formation history of the Universe and the selection effects of the gravitational-wave detector network.\n", + "In the previous tutorial, you generated 8 population files with 1000 binaries.\n", + "Since this is a small population of only 8.000 binaries, you could explore the complete population.\n", + "But the larger your populations get, the better it is to select specific binaries.\n", + "\n", + "For each population file, we can export the binaries we're interested in into a new file.\n", + "For this, we need to find the indices of the merging binaries. The relevant properties are that both S1_state and S2_state equal 'BH', while the binary has the `event == 'CO_contact`.\n", + "\n", + "We will load one of the population files to build our merging binaries selection.\n", + "\n", + "\n", + "\n", + "
        No indices? \n", "\n", - "In the first step, we load population parameters ini file and the parsed BBH population data from the previous notebook into the Synthetic Population class. " + "It might be that the population you generated does not contain any merging BBHs, they're rare after all.\n", + "\n", + "As such, we have provided an example population run at `$PATH_TO_POSYDON_DATA/population-synthesis/example/`, this simulation contains 10.000 binaries at the 8 metallicity.
        \n" ] }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Population successfully loaded!\n" - ] - }, - { - "data": { - "text/html": [ - "
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        3587detachedZAMS0.000000e+002.493602e+010.000000NaNinitial_cond0.000000H-rich_Core_H_burning70.069756...NaN7.155000e-012.703000e-01NaNNaNNaNNaN0.01422.913438e+071.447893e+08
        3587contactoDoubleCE13.631450e+066.304073e+010.000000-2.989131step_HMS_HMS0.037464H-rich_Core_He_burning44.118926...0.0000000.000000e+009.828315e-014.246537e-010.5614930.5774440.7608540.01422.913438e+071.447893e+08
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        3587detachedCC14.007490e+061.226986e+000.000000NaNstep_detached0.777758stripped_He_Central_C_depletion13.620462...0.4014831.917729e-341.605147e-029.893273e-1000.2476070.0068430.0779760.01422.913438e+071.447893e+08
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        10 rows × 41 columns

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        " - ], - "text/plain": [ - " state event time orbital_period \\\n", - "binary_index \n", - "3587 detached ZAMS 0.000000e+00 2.493602e+01 \n", - "3587 contact oDoubleCE1 3.631450e+06 6.304073e+01 \n", - "3587 detached NaN 3.631450e+06 2.371597e-01 \n", - "3587 detached CC1 4.007490e+06 1.226986e+00 \n", - "3587 detached NaN 4.007490e+06 1.358968e+00 \n", - "3587 detached redirect 4.007490e+06 1.358968e+00 \n", - "3587 detached CC2 4.027170e+06 1.377894e+00 \n", - "3587 detached NaN 4.027170e+06 1.611464e+00 \n", - "3587 contact CO_contact 2.917891e+09 2.638756e-08 \n", - "3587 contact END 2.917891e+09 2.638756e-08 \n", - "\n", - " eccentricity lg_mtransfer_rate step_names step_times \\\n", - "binary_index \n", - "3587 0.000000 NaN initial_cond 0.000000 \n", - "3587 0.000000 -2.989131 step_HMS_HMS 0.037464 \n", - "3587 0.000000 NaN step_CE 0.000137 \n", - "3587 0.000000 NaN step_detached 0.777758 \n", - "3587 0.101109 NaN step_SN 0.152370 \n", - "3587 0.101109 NaN step_CO_HeMS 0.000102 \n", - "3587 0.101108 NaN step_detached 0.452186 \n", - "3587 0.048133 NaN step_SN 0.149304 \n", - "3587 0.000000 NaN step_dco 1.252216 \n", - "3587 0.000000 NaN step_end 0.000048 \n", - "\n", - " S1_state S1_mass ... \\\n", - "binary_index ... \n", - "3587 H-rich_Core_H_burning 70.069756 ... \n", - "3587 H-rich_Core_He_burning 44.118926 ... \n", - "3587 stripped_He_Core_He_burning 35.566786 ... \n", - "3587 stripped_He_Central_C_depletion 13.620462 ... \n", - "3587 BH 13.120462 ... \n", - "3587 BH 13.120462 ... \n", - "3587 BH 13.120462 ... \n", - "3587 BH 13.120462 ... \n", - "3587 BH 13.120462 ... \n", - "3587 BH 13.120462 ... \n", - "\n", - " S2_co_core_radius S2_center_h1 S2_center_he4 S2_surface_h1 \\\n", - "binary_index \n", - "3587 NaN 7.155000e-01 2.703000e-01 NaN \n", - "3587 0.000000 0.000000e+00 9.828315e-01 4.246537e-01 \n", - "3587 0.000000 0.000000e+00 9.828315e-01 1.000000e-02 \n", - "3587 0.401483 1.917729e-34 1.605147e-02 9.893273e-100 \n", - "3587 0.401483 1.917729e-34 1.605147e-02 9.893273e-100 \n", - "3587 0.401483 1.917729e-34 1.605147e-02 9.893273e-100 \n", - "3587 0.130995 0.000000e+00 7.706932e-13 1.000000e-99 \n", - "3587 NaN NaN NaN NaN \n", - "3587 NaN NaN NaN NaN \n", - "3587 NaN NaN NaN NaN \n", - "\n", - " S2_surface_he4 S2_surf_avg_omega_div_omega_crit S2_spin \\\n", - "binary_index \n", - "3587 NaN NaN NaN \n", - "3587 0.561493 0.577444 0.760854 \n", - "3587 0.975800 NaN NaN \n", - "3587 0.247607 0.006843 0.077976 \n", - "3587 0.247607 0.006843 0.077976 \n", - "3587 0.247607 0.006843 0.077976 \n", - "3587 0.226684 0.015362 0.060340 \n", - "3587 NaN NaN 0.064849 \n", - "3587 NaN NaN 0.064849 \n", - "3587 NaN NaN 0.064849 \n", - "\n", - " metallicity simulated_mass_for_met underlying_mass_for_met \n", - "binary_index \n", - "3587 0.0142 2.913438e+07 1.447893e+08 \n", - "3587 0.0142 2.913438e+07 1.447893e+08 \n", - "3587 0.0142 2.913438e+07 1.447893e+08 \n", - "3587 0.0142 2.913438e+07 1.447893e+08 \n", - "3587 0.0142 2.913438e+07 1.447893e+08 \n", - "3587 0.0142 2.913438e+07 1.447893e+08 \n", - "3587 0.0142 2.913438e+07 1.447893e+08 \n", - "3587 0.0142 2.913438e+07 1.447893e+08 \n", - "3587 0.0142 2.913438e+07 1.447893e+08 \n", - "3587 0.0142 2.913438e+07 1.447893e+08 \n", - "\n", - "[10 rows x 41 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "import os\n", - "from posydon.config import PATH_TO_POSYDON_DATA\n", - "from posydon.popsyn.synthetic_population import SyntheticPopulation\n", + "from posydon.popsyn.synthetic_population import Population\n", "\n", - "# cosmological model parameters for the rate calculation\n", - "MODEL = {\n", - " 'delta_t' : 100, # Myr\n", - " 'SFR' : 'IllustrisTNG',\n", - " 'sigma_SFR' : None,\n", - " 'Z_max' : 1.,\n", - " 'Zsun' : 0.0142\n", - "}\n", + "pop = Population('1e+00_Zsun_population.h5') \n", + "tmp_data = pop.history.select(columns=['S1_state', 'S2_state', 'event'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", "\n", - "pop = SyntheticPopulation('./population_params.ini', verbose=True, MODEL=MODEL)\n", - "path = os.path.join(PATH_TO_POSYDON_DATA, \"POSYDON_data/tutorials/population-synthesis/example/\")\n", - "pop.load_pop(os.path.join(path,'BBH_population.h5'))\n", + "pop = Population('BBH_contact.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pop.oneline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`pop.history.select` is a read operation on the population file and can take quite a lot of time if the population is large.\n", "\n", - "pop.df.head(10)\n", - "# pop.df_oneline(10)" + "If you have sufficient memory, it is more efficient to select several columns at the same time, as done here, instead of selecting a single column each time." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Selection of S1 being a BH\n", + "S1_state = tmp_data['S1_state'] == 'BH'\n", + "# Selection of S2 being a BH\n", + "S2_state = tmp_data['S2_state'] == 'BH'\n", + "# Selection of the binary system being in contact during the double CO phase.\n", + "state = tmp_data['event'] == 'CO_contact'\n", + "\n", + "# get the indices of all systems\n", + "indices = tmp_data.index\n", + "\n", + "# delete the temporary data\n", + "del tmp_data\n", + "\n", + "# get a mask for the indices that satisfy all the conditions\n", + "mask = S1_state & S2_state & state\n", + "\n", + "# get the indices that satisfy all the conditions\n", + "selected_indices = indices[mask].to_list()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We now generate the synthetic BBH population buy using the `get_dco_at_formation` method. \n", - "- The `oneline_cols` allows you to extract values stored in the oneline dataframe.\n", - "- The `formation_channel` option allows to compute the formation channels of the BBH population.\n", + "The `selected_indices` has to be a list for the selection to work correctly.\n", "\n", - "The synthetic population cann be saved to a file using the `save_synthetic_pop` method and loaded back into memory using the `load_synthetic_pop` method." + "You can test you selection by doing the following:" ] }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Computing formation channels...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/synthetic_population.py:804: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'ZAMS_oDoubleCE1_CC1_redirect_CC2_CO_contact_END' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n", - " self.df_oneline.loc[index,'channel_debug'] = formation_channel\n", - "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/synthetic_population.py:808: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'ZAMS_oDoubleCE1_CC1_CC2_END' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n", - " self.df_oneline.loc[index,'channel'] = formation_channel\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Synthetic population successfully saved!\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/synthetic_population.py:451: PerformanceWarning: \n", - "your performance may suffer as PyTables will pickle object types that it cannot\n", - "map directly to c-types [inferred_type->mixed,key->block1_values] [items->Index(['state', 'event', 'step_names', 'S1_state', 'S2_state', 'channel'], dtype='object')]\n", - "\n", - " self.df_synthetic.to_hdf(path, key='history')\n" - ] - }, - { - "data": { - "text/html": [ - "
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        30756 rows × 12 columns

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        " - ], - "text/plain": [ - " metallicity time t_delay S1_state S2_state S1_mass \\\n", - "0 0.01420 4.027170 2913.863804 BH BH 13.120462 \n", - "1 0.01420 4.027170 1500.583774 BH BH 13.120462 \n", - "2 0.01420 4.849573 662.821800 BH BH 10.330899 \n", - "3 0.01420 4.544886 845.424610 BH BH 11.355246 \n", - "4 0.01420 4.530889 620.499837 BH BH 11.436823 \n", - "... ... ... ... ... ... ... \n", - "30751 0.00639 4.585168 128.572655 BH BH 13.380617 \n", - "30752 0.00639 5.751690 68.017543 BH BH 10.018834 \n", - "30753 0.00639 6.061904 88.863012 BH BH 20.971757 \n", - "30754 0.00639 5.290004 95.405272 BH BH 11.456069 \n", - "30755 0.00639 8.014044 12276.391282 BH BH 12.271066 \n", - "\n", - " S2_mass S1_spin S2_spin orbital_period eccentricity \\\n", - "0 12.911080 0.079649 0.064849 1.611464 0.048133 \n", - "1 12.911080 0.079649 0.063991 1.279098 0.123592 \n", - "2 9.888130 0.286911 0.249267 0.817643 0.165775 \n", - "3 10.251920 0.320931 0.298164 1.108194 0.379450 \n", - "4 10.900538 0.190982 0.167996 0.830194 0.107165 \n", - "... ... ... ... ... ... \n", - "30751 12.935158 0.467285 0.458164 0.507305 0.089335 \n", - "30752 9.020831 0.719064 0.694536 0.333537 0.155820 \n", - "30753 9.843552 0.068179 0.698055 0.468303 0.129531 \n", - "30754 11.171652 0.578478 0.595531 0.414186 0.102238 \n", - "30755 10.368996 0.112744 0.175554 2.613887 0.164393 \n", - "\n", - " channel \n", - "0 ZAMS_oDoubleCE1_CC1_CC2_END \n", - "1 ZAMS_oDoubleCE1_CC1_CC2_END \n", - "2 ZAMS_oDoubleCE1_CC1_CC2_END \n", - "3 ZAMS_oDoubleCE1_CC1_CC2_END \n", - "4 ZAMS_oDoubleCE1_CC1_CC2_END \n", - "... ... \n", - "30751 ZAMS_oDoubleCE1_CC1_CC2_END \n", - "30752 ZAMS_oDoubleCE1_CC1_CC2_END \n", - "30753 ZAMS_CC1_oRLO2_oCE2_CC2_END \n", - "30754 ZAMS_oDoubleCE1_CC1_CC2_END \n", - "30755 ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", - "\n", - "[30756 rows x 12 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(selected_indices)\n", + "pop.history[selected_indices]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ - "# generate the BBH synthetic population\n", - "pop.get_dco_at_formation(S1_state='BH', S2_state='BH', \n", - " oneline_cols=['S1_natal_kick_array_0', 'S2_natal_kick_array_0'],\n", - " formation_channels=True)\n", + "If your selected indices is empty, no BBH mergers were in your population.\n", "\n", - "# we can save the synthetic population\n", - "pop.save_synthetic_pop(os.path.join(path,'BBH_synthetic_population.h5'))\n", - "# pop.load_synthetic_pop(os.path.join(path,'BBH_synthetic_population.h5'))\n", + "Lets use an example set of populations to make sure there will be BBH in your population.\n", "\n", - "cols = ['metallicity','time','t_delay','S1_state','S2_state','S1_mass','S2_mass','S1_spin','S2_spin', 'orbital_period','eccentricity', 'channel']\n", - "pop.df_synthetic[cols]" + "**This can only be used with the multi-metallicity populations**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from posydon.popsyn.synthetic_population import Population\n", + "\n", + "PATH_TO_POSYDON_DATA = '/Users/max/Documents/POSYDON_data/240305/POSYDON_data/tutorials/population-synthesis/example/'\n", + "\n", + "files = ['1e-01_Zsun_population.h5',\n", + " '1e-02_Zsun_population.h5',\n", + " '1e-03_Zsun_population.h5',\n", + " '1e-04_Zsun_population.h5',\n", + " '1e+00_Zsun_population.h5',\n", + " '2e-01_Zsun_population.h5',\n", + " '2e+00_Zsun_population.h5',\n", + " '4.5e-01_Zsun_population.h5']\n", + "\n", + "\n", + "pop = Population(PATH_TO_POSYDON_DATA+files[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pop.history" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Compute the BBH Merger Efficiency, the Merger Rate Desity and the Detection Rate" + "We don't just want to write these binaries for a single metallicity. We want to export them for each metallicity file.\n", + "As such, we loop over each Population file and export the binaries to the same file. It's important to have the same columns in the populations, otherwise it's not possible to add them together.\n", + "\n", + "The indices of the binaries will be reset, when being exported, such that the new file contains unique indices for each binary." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from posydon.popsyn.synthetic_population import Population\n", + "# The names of the populations, if you followed along.\n", + "files = ['1e-01_Zsun_population.h5',\n", + " '1e-02_Zsun_population.h5',\n", + " '1e-03_Zsun_population.h5',\n", + " '1e-04_Zsun_population.h5',\n", + " '1e+00_Zsun_population.h5',\n", + " '2e-01_Zsun_population.h5',\n", + " '2e+00_Zsun_population.h5',\n", + " '4.5e-01_Zsun_population.h5']\n", + "\n", + "\n", + "for file in files:\n", + " pop = Population(PATH_TO_POSYDON_DATA+file)\n", + " # read the relevant data in one go\n", + " # (faster than reading it in chunks, but requires more memory)\n", + " tmp_data = pop.history.select(columns=['S1_state', 'S2_state', 'event'])\n", + " # Selection of S1 being a BH\n", + " S1_state = tmp_data['S1_state'] == 'BH'\n", + " # Selection of S2 being a BH\n", + " S2_state = tmp_data['S2_state'] == 'BH'\n", + " # Selection of the binary system being in contact during the double CO phase.\n", + " state = tmp_data['event'] == 'CO_contact'\n", + " indices = tmp_data.index\n", + " del tmp_data\n", + " mask = S1_state & S2_state & state\n", + " selected_indices = indices[mask].to_list()\n", + " print(f'File: {file}, Number of systems: {len(selected_indices)}')\n", + " \n", + " # set overwrite to False to add to the file\n", + " pop.export_selection(selected_indices, 'BBH_contact.h5', append=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can now compute the merger efficiency of the synthetic population at a given metallicity, i.e. the number of merging DCO per unit star formation and visualize the results." + "If you population did not contain any BBH mergers, all the \"Number of Systems\" will be 0 and you won't be able to open the file in the next cell.\n", + "\n", + "If you ran the population with the tutorial populations, you should have a total of 636 merging BBHs.\n", + "\n", + "We can confirm this by adding up the values above or by opening the new file. `Population.number_of_systems` gives us the total number of binaries in the file. \n", + "\n", + "You now see that the `mass_per_met` property contains information about all the metallicities in the file.\n", + "\n", + "You can even combined multiple runs at the same metallicity together, if you like. This will combine their simualted and underlying masses." ] }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "DCO merger efficiency at Z=2.84E-02: 8.34E-07 Msun^-1\n", - "DCO merger efficiency at Z=1.42E-02: 1.61E-06 Msun^-1\n", - "DCO merger efficiency at Z=6.39E-03: 5.25E-06 Msun^-1\n", - "DCO merger efficiency at Z=2.84E-03: 2.08E-05 Msun^-1\n", - "DCO merger efficiency at Z=1.42E-03: 1.83E-05 Msun^-1\n", - "DCO merger efficiency at Z=1.42E-04: 4.18E-05 Msun^-1\n", - "DCO merger efficiency at Z=1.42E-05: 5.75E-05 Msun^-1\n", - "DCO merger efficiency at Z=1.42E-06: 6.67E-05 Msun^-1\n" - ] - }, - { - "data": { - "image/png": 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biqJA0zTb3leIaOHasGEDjh492uxmtLwNGzY0uwkFhcNh82JC6/9HC9m5c6cZYqdSKVva4Ha74XK5oGlawZ6H3G43otEouxQnIiIiIqKmYohNREQLkt/vNwNbSZLMgCkUCtlWMWlUPgONDRQLMbpSV1UVIyMjWLlyJfx+PwRBgCzLCAQCZmVQdnV6vnm10rpVYz6sQ7aVK1ea9wt1q10Pc2Fb1nPbCIIAr9drdicejUbNcGJ4eJhjYRORLTo6OrBx48ZmN4NqYL3Iq5xeQYyLD7M/W2ohCALi8XjRaYyLCVv1M52IiIiIiBYGjolNREQLkjVUMoInTdOQTCaLViBXotxxqBslGo1CFEWkUilIkoR9+/Zh06ZN6O3tRSgUgtfrRSKRKBlgA623btWYD+uQLbvat1FB9lzYlvXeNtbu94eHh81lyLIMv99v67KIiGhuqrSy2TqcxpNPPmlza9Ki0SiCwSA8Hg/6+/vhdDrNCz2JiIiIiIiaiSE2EREtSC6XKyN4C4fDCIfDtlZMWudV6Y+W9ei+cd++ffB4PADSlejxeBypVAqpVAqxWAyhUKjssYJbbd2qMR/WIZvL5cr4wbvY+OfZavnBei5sy3pvG+t7iqIoUFUV4XAYO3fubMgY70REROVSVRU+nw8Oh8McDiMUCuHAgQNIJBK8+IqIiIiIiFoCQ2wiIlqwrMGbJEmQJAk7d+60bf6iKGZ0+xiNRst6naZp6O/vt71SVFVVxGIxW+bVautWjfmwDvmMjo6a98sdm1pV1Zou4Jgr27Le28Y6XSgUgiRJ7EqciGiBkmXZvHjQUO7Fggbr56PRtXgtywfSn9FOpxPRaBSDg4NIJBIIhUI5F3sRERERERE1G0NsIiKad8odv89aZaKqalk/3lUato2OjprzDAaDZb1+z5495ljVxVQ6TuHAwACi0ShGRkZsCQ1bad2q1cx1qNc6er1es+tsWZbLqjgOBoM1jwVfz21pl3pvG+u6hMNhCIJg2/AEREQ0t+T7HLRe8FVOTySJRMK8X+mFlvmWb1RgA+nPrEo++2VZbtkhQ4iIiIiIaH5iiE1ERPOO8YPcyMhIyWmtPyaWUzGZHTyWCuoEQUA8HocgCFBVtWTVaTAYhKIoBatErcuvNIg21i8YDKK3txcOhyPj1tvbi97eXjidTvh8vpLVtK20btVq5jpYny8n0K5k+lgsZoanPp+v6A/lxjoVGgu93H1h97Y8ffp0RW0ot512bpt8rBfHWMfJJiKi+cd6EVb251AymcypvLYO3TI8PFxy/mNjY+br8l3wVenyrf+3M8LsfMbHx3MekySpoqE4iIiIiIiIaqYTERHNcYlEQo9EInooFNJFUdQBmDe3261LkqRHIhE9lUrlvDYej+sAdFEUC847FovpsVhMlyQpZ/6iKOqSJJnTJBKJvPNJpVK61+vVAeiCIOihUMicNpFImPN2u9057YzH43osFtNDoVDGsl0uV8ayy5E9j1I3QRBKzrtV1q0W9VgHt9utRyIRPRaLma9JpVJ6LBbTI5GI7na7M6YPhUJ6LBbT4/G4OW9j/cuZdz6Dg4Pma/x+vzl9KpXSI5GI7nK5dJfLlTMP47iXJEkXBCHv8V5ouXZsy+zlCoJgLtd6jhnb0u/3Z2wfr9drbh+7t00piUTCbDMREc1/1s9xK+P/oNmMzwkAeiQSKThf47PU6/XatnxJkszp87VN19OfrdbPYIPL5cr5XE2lUmWtCxERERERUTUYYhMR0ZxnDfgEQci5Gc9Zw0GrQj8yWuedb775lpP9A2K2RCKhDw4O6i6Xy3yNIAi61+stGLhZg/NC6wagYIBuGBwc1AVB0F0ulx4KhXRJkjJuoVBIHxwc1L1eb05YX84Pk81cN7vUax2M7ReJREpOa72gwph3seO61L7Jt06iKJpBbz7GD+fF2lnofKplW5azXOPHfGsIUGz7FDt2qtk25XC5XPrg4GDVryciornD+tluXDRpXFhZSCKRMC9m8/v95meqcWGm8flf6v+V1SzfeuGX9f+/xutcLpeeSCTMz0Wv15t3fsb01s/nRCJR8cVfREREREREhTh0XddBRERE81p/f7/ZdbO1u+NiFEXBnj17oCgKBEFAKpWqcyuJ5ofe3l7E4/GcblyJiGh+kmUZoVDI7Ibb7XZjdHQ0bxfg2a8zuuk2ugMXRRFerxdDQ0MlX1/t8mVZRiQSMce5FgQBoihi165d5hAaqqoiGAxCVVUMDAxkdGkeCAQQDodz5q9pGkRRzBjLm4iIiIiIqFoMsYmIiOa5kZERBINBeL1eRCKRil6raRo2bdoETdMQiUQyxhAnolzhcBiRSASxWKzZTSEiIiIiIiIiIpqz2prdACIiIqqvffv2AQA8Hk/FrxUEAQMDAwDSFTlEVFwoFEIwGGx2M4iIiIiIiIiIiOY0hthERETzXF9fny3zKbdLS6L5LBAIoLe3F4FAIOe5cDgMIN2NKxEREREREREREVWP3YkTERHNc9FoFD6fD6IoIh6PVxRGy7JsVnCnUikG2bSgqaoKp9Np/h2Px+Fyucy/e3t7MTo6ym73iYiIiIiIiIiIasRKbCIionnO6/VicHAQqqqiv7+/7G7Bo9GoGWDHYjEG2LTgiaKY8bc1wA4GgxgYGGCATUREREREREREZAOG2ERERAtAKBQyg2in0wmPx4NoNJoRaGuaBkVRMDIyAqfTCZ/PB6/Xi0Qiwe6Rid7g9/sBpC/sANLV2cFgELIsIxKJNLNpRERERERERERE8wa7EyciIlpgVFWFJElQFAXJZBKqqkLTNAiCAFEUIYoiPB6PGdYRUaZwOIxQKIRkMglRFOF2uxEKhZrdLCIiIiIiIiIionmDITbNKdFoFHv27MHRo0fZrS0RERERERERERERERHRPMQQm+YMVVXhdDoBAKlUiiE2ERERERERERERERER0TzEMbFpzvB4PM1uAhERERERERERERERERHVGUNsmhMCgQDcbnezm0FEREREREREREREREREdcYQm1peOByGqqoIBoPNbgoRERERERERERERERER1VlHsxtAVIyqqgiFQojH40gmk81uDhERERERERERERERERHVGSuxW9zIyAgcDgdUVa1pPuFwGP39/ejt7TVvPp8Psizb1NL68Hg8iEQiEASh2U0hIiIiIiIiIiIiIiIiogZgiN2iVFWFx+OpuQttRVHQ29uLUCiEQCCAVCqFVCqFeDwOURTh8Xjg8/mgaZo9DbeRz+dDMBiEy+VqdlOIiIiIiIiIiIiIiIiIqEHYnXgL0DQN4+PjUFUViUQCsixDUZSa5yvLMjweD0RRRDwez6hmFkURoVAITqcTgUAAiqLkTNNMIyMjAAC/39/klhARERERERERERERERFRI7ESuwWMj4+bVdeKosDtdiORSNQUKGuaBp/PBwBFu+P2+/3w+/1QVRXbtm2renl2UhQFkiQhEok0uylERERERERERERERERE1GAOXdf1ZjeC8uvt7TW7+U4kEhBFsezX+nw+RKNRuFwuxOPxotOqqgqn0wkgHXh7vd6q21wrTdPQ39+PWCyWs77WdqZSqZapGiciIiIiIiIiIiIiIiIi+7ASex7SNA3RaBQA4Ha7S04viqIZGA8PDxeczufzweFw2HIrNNa3z+dDKBSqKLAnIiIiIiIiIiIiIiIiovmDY2LPQ+Fw2Ly/ZcuWsl4jiiJUVYWiKFBVNW+IPDQ0BI/HY0sb84XrIyMjEEWxqZXgRERERERERERERERERNRcDLHnoX379pn3y+1y2xpay7IMv9+fM43L5YLL5aq5fYVIkgRVVTNC+EJ6e3vN+83uAp2IiIiIiIiIiIiIiIiI7MMQex5SFMW839fXV9ZrrGF3qTG06yWRSBR9PhqNwufzmdPa2eW4pmk4ePCg+fc111yDrq4u2+ZPRERERETzy+TkJI4fP27+feedd5Z9ETERERERERERFccQe55RVTXj73KD3pUrV5r3x8fHbW1TPZQbzpfr4MGD+IM/+ANb50lERERERAvHAw88gPe///3NbgYtALIsQ1EUDA4ONrspREREREREddPW7AaQvTRNq+p11oqBaufRSMlkstlNICIiIiIiogVIVVU4HI6abuUMo1VIIBBAMBjM6IWtXNFotGCbZFmuuk2apqG3tzfvfI0e1fJRVRWBQABOp9Oc3ul0IhAIZFykHw6HEQwGq25fJTRNQzgchs/ng9PpNNfL6XTC5/Pl3U7BYLCifdqIZTQTt2FpIyMj6O3tte2W/VteMBjMez7mmzafUsuzrkc174HGfopGozZvWSIiIppPGGJTjlYMiDVNyxjrOxqNzomwnYiIiIiIiOaX7O+igiAgFAohEokgHo8jlUrl3CRJyniN2+2uatmKopjhbvY8y+H1epFKpZBIJMxKbuOi9lAoVFWbgHTIbO0xze12Ix6PI5FIYHR0NO9rgsEgnE4nACASiZjbKhKJQBAE9Pf3IxgMmkF3vX8D0DQNgUAAvb29CAQCZhsjkQgSiQQkScKWLVvM0N24iECWZYyMjJTVvkYsw5i+t7e34QHhXN+G0WgUHo/HDMT7+/vh8/nqEnz7/X4cOHAAkUgEAwMD0DTNvI2OjuLAgQN5b5FIBKFQCG63O+M12T0zhkIh81y3ntuapmHbtm0l2xePxxGJRDKWMzAwYL7PGQYHB5FIJJBIJOD3+3Pmkf1emEgkEI/HzQtVfD4fent7W+biAiIiImotDl3X9WY3gvKzXh1Z7hjQsizD4/GYf5e7e8PhsPmf/0peV28jIyPm1dbZ48sZ2yYWi1X9A4BBURT09/fXNI//8B/+Az71qU8BAHbv3o3x8XEMDAxg7969eacvNM2RI0cyujZ/4IEHcN1119XUtmYpZzvMhWXWOs9qXl/pa2o55sqZhsdl6y23Gcdlpa/jcVk+Hpe1zaOVjkuAx2YrLrPVP8vLnbbUdDwuq/flL38Z//iP/1jTPOLxOFwuly3todZm/d49ODhYMvzVNA2bNm0yv8OGQqGquwIPBAJm2CMIAlKpVFXzMTgcDgwODmJkZARA+b89ZDOqp43v76W+pxvrUey80TQNPp8P4+Pj0DQNfr+/quC+HNFo1KwY93q9GB0dLTrGvVEZPjQ0hOHhYWiaVnK/1nMZmqYhmUxClmVIkmQGt5Ik5QSL9TKXt6FxrCWTSQQCATO4lWXZfJ0oiohEInV5n7f+JiWKIhKJRFmvU1UVHo8HqqoiEonA6/UWnM7pdEIURTPsruR9yGibNbwutpxK1kOWZfh8PmiaBq/Xi0gkUlabiIiIaGHgmNjzzHyrTh4cHGzIOF9dXV01z2P16tXYvHkzAKCnp8f813gsWznTAMB1111X9PlWVu46tvoya51nNa+v9DV2HXM8LufOcptxXFb6Oh6X5eNxWds8Wvm4BHhstsIyW/2zvNxpS03H47J6q1evrnkednynoLnB+N7t9/vLql7es2eP+RqXy1XTd9yxsTEziNI0DdFotGBwVa5AIGCG2JIkVVyRLcsy3G530TAxe/pwOIxQKFQ0EBQEAbFYDE6ns66/dQSDwYz1Lyf09fv9cLvd6O/vL6tt9VxGf38/FEWBIAhwu93YtWtXVV3N12Kub8Nt27ZhYGAAsVgs43GXywW/349t27aZQXM9Llgq99zJJooiJEkyg+xSyzDOJyC9Pd1ud1nrYoT6pVh7YyiX2+3G0aNH0d/fj2g0am5jIiIiIoDdiRMRERERERERlc0YgqucsFeW5YwunWupMoxGoxBFMWO5dlQm9/X1mUF4NV36hkKhjJ7dSjGqtcsN32vp5ryUcDhsBqOhUKiiqmWjMrfZyzhw4AB0XTe7Yq+1p7pKzfVtaByPhc4lQRAyusQvNsZ7MxhBdDlVz9nvH62yLoIgmPtIUZSK3k+IiIhofmOIPc9Ue/UmZXrggQdw6NChvLeBgQEAwMDAQMbjn/zkJ5vcaiIiIiIiqsYnP/nJgv//L/Qd4IEHHmhuo6lpNE2Dy+Uq+f3b6KLYEAqFquqq2yBJEgKBQEb4K8uyLVXKQ0NDAGBWd5fL6Ma6ksrUSquEvV5vXX7rsIZloihWVSHvdruLhvGNWEYzfweaD9swHA5ndLOdj8vlMi8OUFW15cZv3rVrV8lKbMPg4GDGurRKYGxUvQPpfdLo3gSIiIioNTHEnmeyu+6p5sssg/Ar3Svmu2V3X2jc7OiCkIiIiIiIGs8YGqiS7wBzdUxxsodxYUMxdnYjbozPa4Q81kpUOwI1l8tlBuyVVHcPDw9XHYLJslz2tDt37qxqGcXs2bPHvF9LtbdxAUCzltFMc30bappmXrhhdLNdiPVCjWaO25wvrHa5XGWH2EC6/cZvf+FwuKJzsZ6Mqvjs+0RERLRwcUzsecaOALqaMWyIiIiIiIiojmamgbMvN7sVrW/51UB7fX/qEEWxZEW1nd2IA+mgyRpcBwIBM7yWJKmmgNwQDAYRCAQgyzJUVS2rajwajZbVjbGVUfUaDAaxc+fOsn7HKGfM30ooipJR6VnLuOKFqvIbsYxmmg/b0BgawFDsuN+yZUvB1zWKoijw+Xw559zAwEBF54fRRbrRU4TP58PRo0ebfowZ762qqlb0PkRERETzF0PseSY7gE4mk2X9J9Rasd3s/7S2gt27d5vVFtmP7969G3fddRc2btxY9PV2TDPXNWMd67HMWudZzesrfQ2Py/I1ax3tXm4zjstKX8fjsnw8LmubB4/L+uFnefWvt/u4LGc6Hpf1sXfvXpw8eRLXXnstTp48ibvuugsAMDEx0ZDlm86+DPyvtzR2mXPRnz4N9F5b10WUCtLs7kYcSAfV1iDcqJxWVRWqqkJRlIq69M5n586dZlW1JEklq16j0WhV4y8HAgEEg0FomoZNmzZhdHS05DatJbzMx1ptbscY0vnm0YhlNNN82IaiKMLv92NsbAw7d+4sep5aQ+JmFX8UCs8FQYCu6xXNy+v1wu/3IxwOm+9ZsVjMjmbWxFpVHo1GbblAh4iIiOYuh17p/3KoYXp7e81wOZFIlP2l1+FwmPfj8XhZX2StV3F7vd6mdo3UDIcPH8Ytt9xScrrPf/7zuO+++xralkOHDmHz5s11XSZRKTwuqRXxuKRWxWOTWhGPy+rdd999uP/++0tOV/dtmnqBIXY5GhBil+Lz+cwqbJfLhXg8XtP8ZFlGIBDIqb4Mh8Nm6Oz3+yvqBtzgcDiQSqXMi9mNtguCgFQqVfS1Ho8HkiSZv1VY2xOLxYqGjk6nM6dy1O12w+PxZIw/XC/W5Ve77VphGfkoioL+/n4A6RDYWsFvp/m8DfOxnteDg4M1dW2eTVVVsztzURQL9m5g9JRQae8Hqqqiv78/7zlt3caFjhfjopNS21/TNPT29pZcj2KCwSBGRkYApN8TWiFYJyIiouZhJfY85HK5zO6Wyu3iyDqdtYukhWpgYCBvJfZ8r2ohIiIiIqJMGzduxJ133pnz+MTEBMbHx5vQImpl0WjU1m7EgXSwlG/caWvldDgctiXgGxoaQjQaNccJLlQBraoqkslk1RXm8Xgc27Zty+gqWpbljLF5XS4XAoFAXUJYa4BeaizkVl5GMy2kbWiMSW+o9xjl1t4Sk8mkGSCHw2Hbu9eORCLmRQ+BQABut7upXXivXLnSvN+sbtuJiIiodTDEnofcbrf5RbDcMXGs09ndTddctHfvXlamEBERERGROaRQtnJ7c6KFQ9M07Nmzx/zbjm7EgXQwPjo6mvO4IAjwer1maJ49bnY1rN2UDw8PF/x9oFCwXi5BEBCPx83w3RpmGxRFMbsej8fjdQvWGjGk2nwftm2+b8Ph4WEzWA6FQnVti6qqZjVzI7hcLoRCIQSDQQDpHhaqqaC2i3XbVjLONxEREc1Pbc1uANnPekVoud2WGV8YRVFs6hWXRERERERERHPRnj17zKDL5XLZMpZrOByG1+stGJpZg2S7ulo2wixFUQqGSNFo1JYKab/fj3g8jlQqhUgkgsHBwZwh0TRNQ39/f90CLWvVa700YhnNNJ+3oaqqZvfWXq+37mM0i6IIXdczbolEwtbuy7NZzztVVc33gGaw7udmjT1ORERErYMh9jwkCII5ftTY2FjJ6a1dIjXzP6pEREREREREc1E9uhEHSlc8u91uM+AuFjpXwhpO5wvOotGo7WNWG1XloVAI8Xgcuq4jFouZwZqmaTVVfmezXrxfr6rTRiyjmRbCNtQ0DR6PB0A6wLbrvK6UKIoYHBysa4BuXbeRkZGM3wqbhUU2RERExO7E54hKrziVJAn9/f0lx7EypgXSV4rXY6wpIiIiIiIiqtHyq4E/fbrZrWh9y69u+CIr7UY8Go1CkiTEYrGi81VVFYqimCFaOSRJsqVi0+/3IxwOY2xsLKfCW5Ik26q+i3G73YjH4/D5fIhGo5BlGYqi5FRqVzvvcDgMwJ4ui6PRaEZBQaOW0UwLYRtu27YNqqrC7/c35JgvxePxZFwsk21kZKTqoFsURUQiEfh8PgCAz+fD0aNHG96Nu/ViBTvOdSIiIprbGGK3MGtwnUwmK3qtKIoYHR2Fz+eDz+dDIpHI+yU6HA6b/4lv1hWlrWj37t3o6enJ+3i+8fDstGrVKnz+85/P+Juo2XhcUivicUmtiscmtSIel9Xbu3cv9u7dm/P4xMREYxvS3gH0XtvYZVJZKu1GXFXVsoIhSZLg9/tLhtKqqqK/vx9A+ju+HSF2IBBAOBzOuTBeVVUkk8maKiQ9Hk/JAN8qEonA4XAAAMbHx20Jtoz1A2BLxem+fftyLjZoxDKaab5vQ4/HA0VREIlEihaGNFKxIQBlWca+fftqqtb2er3wer2IRqPmxTmN/q1wfHzcvN9KxzsRERE1B0PsFmH9z7iqqjlXeAYCAQQCAYiiaH7ZHRgYKPrF1+v1IhaLwefzob+/H6FQyKy01jQNwWAQ4XAYbrcbkUik4VdXtjLrf5qt7rrrrrove/Xq1bjvvvvqvhyiSvC4pFbE45JaFY9NakU8Lqt37NgxHDx4sNnNoBZVTTfiiUSirLFew+EwDhw4UPK7usvlgsvlgqIo0DQNsizXXK1rnefw8LAZ4kmShKGhoZrmLcsyNE2r6DcIt9sNWZZt61Laun4ASvZgV4qiKDnbpRHLaKb5vA09Hg/Gx8cRj8dbqhpYFMWCF4AoimJL99ujo6PmORqNRs2LCBpB0zRzX4ui2DK9DhAREVHzcEzsFuHxeODxeODz+RAMBs0rs41bMpnE8PAw9uzZA5/PB4/HU9ZVqG63G0ePHkUoFIIkSejt7UVvby82bdqEZDKJWCyGWCzGADvLwMAA7rzzzpzbxo0bm900IiIiIiJqoI0bN+b9bjAwMNDsplGTVdqNuGF8fLzkd3BZltHX11d2gGYdL9qubo+NeVrH2q41RDQUunC8EGM7rFy5suZlG0ZHR837wWCw6vkoioJkMpl3XzViGc00H7ehz+eDqqoFA2xFUcwut1tJLBbDli1bap5Pdk+NgUDADJbrzRqY29GjBBEREc19DLFbhK7r0HUdqVSqrJuu62V/cRQEAX6/H/F4PGMekUiEVzUWsHfvXvzoRz/KudW7K3EiIiIiImotu3fvzvvdIF8X47SwVNqNOJAOwBRFKRnGSpKUEUyXsnPnTvO+0RVwrYye3IB0oGRXgG3MrxJGiGbnbxgul8tsh6qqGBkZqWo+Pp+v4Po0YhnNNN+2oTXALtZtdzk9KTSSqqqQZdm2ixzcbnfG+5kdXbmXYvQYaSy/VbpwJyIiouZiiE1EREREREREVIFquhEHgOHhYQAoWoltdONrDZFLEQQhI/QZGxsr+7XFGG0Ih8MYHh6uKFgvRpblsqtqjYDO6/XaXok8ODhohnXGkGuV8Hg8EEWx6L5qxDKaab5sQ5/PB03TEI/Hi56fsVgMTqezouXXm3EuFeohRNO0ii9sCYVCDa3837ZtG4B0N+KNHoebiIiIWhdDbCIiIiIiIiKiMlXbjXgwGDSD72KVnEYgVemwX7t27cpoUylGdXOxrr2toXVfX1/R9aw0JBsZGUEwGCz6Ok3T4PP5IAhCRrfSdgqFQmZoFggEzDCzGEVR4PF4AKDgGMWNXkY+dlTkl2Oub8NAIGCem8Zwf9m3/v5+OJ1OWyueDbXsp3A4bLa90HuGcY4bwwKUq9IwOZlMVjQ9kN5HTqcTiqLA5XKVvIiAiIiIFhidiPRDhw7pAMzboUOHmt0kIiIiIiJqYfwOsXB5vV5zv4uiqMfjcT2RSGTc4vG4HovF9Egkovv9fl0QhIzjJRaLZcwzlUrpsVhMd7vd5jRut1uPx+Ml25NKpfR4PJ7RLgD64OCgnkgk9FQqlTF9IpHQJUky2yQIgi5Jkp5IJPLO3+Vy6QD0SCSSd9nG8o3pAOherzfvsnVd1wHofr9fT6VS5vr6/X49EomY2zIWi+mhUEgXBEF3uVwF22bMr9xbMYlEQvf7/RnrEIlEzPWIx+O6JEnmdg6FQkXn18hlWPeD9ThwuVx6LBYzn8+3Pwx2bMe5uA2t8yr3Vmg7VroNU6lUzvoA0OPxeMY+s96M95dQKJRxzrlcrrzbKhQKZbxfGduqXJIkmedoIUa7BgcHS66H0X5JkjLe76o5FoiIiGj+c+i6rpcTdhPNZ4cPH8Ytt9xi/n3o0CFs3ry5iS0iIiIiIqJWxu8QC1dvb2/NFa6JRCKjqrm/vx+KouRUIGqahlgsVnQsaKM9haoXNU0zlxeNRs3K5nzThUKhnLG9o9EogsEgEolEzmsKtds6T6/Xm1HR6XQ6M6otFUWBJEkYHx+HqqrmugwMDMDn85XsRtvhcBR93qqcn8A0TcPY2BhisRgURUEymcxp086dO2uqFrVzGYFAAOFwuOS0mqZBFMW8+xGwdzvOlW2oKAr6+/srXnah9a9kGw4ODlY9vnc+fr8fkiSZfweDQYyMjBQ810u9r1j5fD5s2bIl570BuNKjQqVEUYQoirYcC0RERDR/McQmQu4PUAMDA+jp6cmZbvfu3di9e3fd2rFr1y788pe/xPLly7Fs2TIsW7bMvJ/vsULPd3R01K2NREREREQLyd69e7F3796cxycmJjK6YWaITURERERERGQfJl1EeRQaE+yuu+6q63KPHj2K3/72tzXPZ8mSJWUH3qXuL1q0yIY1IyIiIiKam44dO4aDBw82uxlERERERERECwpDbKI8ClVib9y4sa7LPXfunC3zuXjxIi5evIjXXnut5nl1dXWVHXiXer6rq6ui7rWIiIiIiJpt48aNuPPOO3Mez67EJiIiIiIiIiL7sDtxIrTOeHYbNmzAyy+/3PDlNkpnZ6dtgfiSJUsYiBMRERFR07TKdwgiIiIiIiKi+YiV2EQtxK5K7FY1NTWFZDKJZDJZ87za29vLDsRLheM9PT0MxImIiIiIiIiIiIiIiFoEQ2yiFqHr+rwPse00MzMDTdOgaVrN83I4HLYF4kuXLkVbW1vtK0hERERERERERERERLRAMcQmahGzs7P4+te/jnPnzuHs2bM4d+5cxv18j507dw4zMzPNbvqcp+s6zp49i7Nnz9rSnfvSpUsr7h690LTt7e02rCEREREREREREREREdHcwRCbqEW0t7fjj/7ojyp6ja7ruHjxYtmBd7Fw/OzZs5ienq7T2i0s58+fx/nz53HixIma59Xd3W1bIN7Z2WnD2hEREREREREREREREdUXQ2yiOczhcKC7uxvd3d1Ys2ZNTfPSdR2Tk5NlBd7l3J+cnLRpLRe2Cxcu4MKFC3j11VdrntfixYsr7h49+/6KFSvQ19fHMcSJiIiIiIiIiIiIiKhuGGIT5bF792709PTkfXz37t2Nb1ADOBwOLF68GIsXL8aqVatqnt/ly5fLDrxLBeUXL160YQ3p0qVLuHTpEk6dOlXTfLq6unD11VfjmmuuwYYNG7Bhw4ac+1dddRXHBiciIqJ5Ye/evdi7d2/O4xMTE41vDBEREREREdECwRCbKI/x8fG8j991112NbcgctmjRIqxcuRIrV66seV7T09NmsF1rt+n8sbF2k5OTUFUVqqoWnGbRokVmqJ0v5N6wYQNWrVrFoJuIiIha3rFjx3Dw4MFmN4OIiIiIiIhoQWGITZTHwMBA3krsjRs3Nr4xhI6ODvT29qK3t7fmec3MzOD8+fO2dJt+/vx56LpuwxrOP5cvXy4r6L766qsLhtwbNmzA6tWrGXQTERFRU23cuBF33nlnzuMTExMFL34lIiIiIiIioto4dCYwRDh8+DBuueUW8+9Dhw5h8+bNTWwRzQWzs7OYmJioqat06/3Z2dlmr1LL6ezszAm6swNvBt1ERETUDPwOQURERERERFQ/rMQmIqpSW1sbli1bhmXLltU8L13XcfHixZoqw63/Tk9P27CGzTc1NYVjx47h2LFjBafp6OgoOEa38e+aNWsYdBMRERERERERERERzREMsYmIWoDD4UB3dze6u7uxdu3amual6zomJydrrgw37l++fNmmtayP6elpvPDCC3jhhRcKTmME3cXG6F6zZg3a29sb2HIiIiIiIiIiIiIiIsqHITYR0TzjcDiwePFiLF68GKtXr655fpOTk2ao/frrr+Oll14yb8ePH8/4e2pqyoY1sF+5Qff69euLjtG9du1aBt1ERERERERERERERHXGEJuIiIrq6upCV1cXrrrqKmzatAlbtmzJO93s7CxOnTqVEWznu9/KQfeLL76IF198seA07e3tWL9+fcGQ+5prrmHQTURERERERERERERUI4bYRERki7a2NqxZswZr1qzBwMBA3mmMoLtYyP3SSy+1bBfmMzMzOH78OI4fP15wmvb2dqxbty5nXG7r/bVr16Kjgx/BRERERERERERERET58Bd0IiJqGGvQ3d/fn3caXdfNoLtQyP3SSy9hcnKywa0vz8zMjNnGQoygu1jX5evWrWPQTUREREREREREREQLkkPXdb3ZjSBqtsOHD+OWW24x/x4YGEBPT0/OdLt378bu3bsb2DIiykfXdbz++usluy5v1aC7HG1tbTlBd3bgzaCbiIio/vbu3Yu9e/fmPD4xMYHx8XHz70OHDmHz5s0NbBkRERERERHR/MVfvonysP4YZXXXXXc1tiFElJfD4cCqVauwatUquFyuvNMYQXeprssvXbrU4NaXZ3Z2Fi+//DJefvll/PznP887TVtbG9auXVt0jO5169ahs7Ozwa0nIiKaP44dO4aDBw82uxlERERERERECwpDbKI8ClVib9y4sfGNIaKqWIPut771rXmn0XUdp0+fLhpyHz9+vKWD7ldeeQWvvPJKwaDb4XCYFd2Fui9fv349g24iIqICNm7ciDvvvDPn8exKbCIiIiIiIiKyD7sTJ0Jud+LsCpCIDLquI5lMZoTb+QLvixcvNrupVXM4HFi7dm3BkPuaa65h0E1ERJSF3yGIiIiIiIiI6oeV2EREREU4HA6sXLkSK1euxK233pp3Gl3XkUqliobcx48fb9mgW9d1nDhxAidOnMCTTz6ZdxqHw4E1a9YU7bp8/fr1WLRoUYNbT0RERLSwyLIMRVEwODjY7KYQERERERHVDUNsIiKiGjkcDvT19aGvr69k0J0v3LY+duHChQa3vjy6ruPkyZM4efJkwaAbQEbQXajr8q6urga2nIiIiMheqqrC6XTWNA9JkuD3+6t6bSAQgKqqcLvdcLlcFb02Go3C5/PlfS4Wi8HtdlfVJk3TsGnTJmialvOc1+tFJBLJ+zpVVREKhSDLMlRVBQCIogi3241gMAhRFAEA4XAYiUQCoVCoqvZVQtM0jI2NIRaLQVEUJJNJaJoGURThcrkQCARytlMwGITT6Sx7nzZiGXPByMgIgsFg2dOLomjeAoFA2cd/MBjEyMgIBEEoOI1x7NrZYaWiKNi3bx9kWYamaVBVFYIgoK+vD263Gz6fr+pzrpXwnCltZGQEw8PDts3v6NGjGcezcYxnEwQhZ9p8ent7iz6fSqUAVH7OGoz9tGvXLni93opfT0REC5hORPqhQ4d0AObt0KFDzW4SES1As7OzejKZ1J9++mn9wQcf1CVJ0v/7f//v+u7du3W3263fcMMNend3d8b71Vy8rVmzRu/v79f/4A/+QP+P//E/6l/4whf0b33rW/rBgwf1RCKhX7p0qdm7goiIqCR+h1i44vF4xr4XBEEPhUJ6JBLR4/G4nkqlcm6SJGW8JpFI1Lxsv99f1TxSqZSeSCT0wcFBs/0AdLfbXdX8dF3XQ6GQLoqi2Ta3263H43E9kUjoqVQq72uM5fv9/oztFo/H9cHBQV0QBH1wcFBPJBI1rW+5UqmU7vf7zXXwer26JEl6LBbTE4mEHovFzPUURVGPx+O6rut6LBbTAeihUKgllmFMLwiCHolEqt8gDZJIJPREIpGxXQDosVjMPCaMaWKxmD44OKi7XC4dgO5yucpex0Qiocfjcd3r9WYsJxQKmcdpoWO1UvF4XHe73WYbJUkyz/lUKqXHYjFzfQVB0CVJqngZkUhEd7vd5vnrcrnM46lR5vo508htaLy3xWIx89gwbsZnR75bLBbTJUnKOW6N9cxeRiKR0EOhUMa0LperZPuMbWldjtvtNrdz9rT5ztl8n3/GeRcKhczzttpjnoiIFiaG2EQ6f4AiorljdnZWT6VS+tNPP63v379flyRJ//M//3P9Yx/7mO52u/Ubb7xR7+npaXpQXett9erVusvl0t///vfrn/rUp/QvfOEL+je/+U39Rz/6EYNuIiJqCfwOsXAZ4QgAfXBwsOT0qVTKDEkqCSLzsYYGgiBUPR+DsQ61huuiKGYEJ7FYrOj0xnrkC2IMqVQqI2CqZ4gdiUQyQrJSYaYkSebFC0b7Su3Xei7DCIskSTKDIgBzKigyLlYAoIuiWHL67O1ZLuuFIHacQ9mM80AQhJLngXGMG0FjOSG68RprOG6EhMZxYg1z62UunzPN3obWY7CcY92QSCTMi4WKXbxhnEvWC4sq+dxxuVxlB9+VrodxgU2l5y0RES1cDLGJdP4ARUTzixF0P/PMM/pDDz2kh8Nh/S/+4i/0j33sY7rH49FvuukmfenSpU0Pqmu9rVq1Sne5XPr73vc+/b777tN//vOf6zMzM83e/EREtEDwO8TCZQQr5Yaq1sq2coKBYgRByAgmaq20NYLrSkL5bEZVqbXavFh4V2lVsbG+9QqxrSF+JaFvIpEo++KEei7DWt3o9XozLiaYSyF2KpWqOBCzbtdyj49qgrdyGee6IAgVXRBSyetcLlfBdU2lUhkXMdQrhJ0P50wzt2Etx2A575/GNrAup5J1GRwcLOt8quacNV5nvK/X+plIRETzXxuIiIhoXnE4HBAEAbfccgu2b9+OPXv24P7778c///M/45FHHsGvf/1rnD17Fpqm4dChQ3jooYcwOjqKv/iLv8DHP/5x3HPPPbjpppuwdOnSZq9KUadOnYKiKPjXf/1X3Hfffbj99tuxdu1afOQjH8F3vvMdJJPJZjeRiIiI5iHj/xjljM8syzKi0aj5d6GxocsRjUYhimLGciVJqnp+hr6+PnOM0nA4XPHrQ6EQAoFA2dMb46mWOy5qPcfBDofD5jiyoVCoorFzRVEsa3/WexkHDhyArutIpVKIRCLzYozlclmPu3A4bI6r3gzBYNA81w8cOGCO516OSCQCURShaRo8Hk/RZQCFz3tBEDA6Omr+7fP5ym5Dueb6OdMK27AWbrcbLpcLiUSi5LTZnxetsi6CIJj7SFGUij4/iIho4WGITUREtAA5HA6sWLECmzdvxvbt2/Enf/InuP/++/HVr34VP/jBD/DrX/8a586dw5kzZ3Do0CE8/PDD+MpXvoLPf/7z+MQnPoF77rkHN998M5YvX97sVclw6tQpfOMb38CHPvQhrFq1CnfccQeGh4fx1FNPQdf1ZjePiIiI5gFN0+ByuSAIQsnprKFBKBSqKNjKJkkSAoFARvgryzI0Tat6noahoSEA6TZbQ/dSNE1DMpmEy+Uq+zWKolTUNq/XW3JbV8ManoiiiMHBwYrn4Xa7i4bxjVhGPbbNXJF9Psmy3JR2yLJshq5er7ei88FghKqqqhYM9cLhMERRLBrWu1wu80IGVVWrujClkPlwzjR7G9ph165dZV+wMTg4mLEurRIYu1wu8+KEcDhc8ecCEREtHB3NbgARERG1ruXLl2Pz5s3YvHlzwWnOnj2Ll156CS+99BKOHz+e8a9x/+zZsw1sddrs7Cx+8pOf4Cc/+Qk+97nPYf369dixYwd27NgBt9uNZcuWNbxNRERE1ZqencarF15tdjNa3pruNehoq/9PHQMDAyWn2bNnjxkwu1yuqsIYg6ZpkGUZsVgMAOD3+81gJRwO1zRvo31GsCNJUtlV0sPDw1WHIrIsl11huXPnzqqWUcyePXvM+7VUew8NDRUM/huxDLrCjgs6qmFU9wJXLgiplNvtNs/BcDiMYDCYEdJrmmZeZBKNRoteoOtyucxAPxKJVFTJXMxcP2daYRtWSlXVnIs1XC5XRb1wRCIRbNq0CZqmIRwOw+fztUSPDcFg0PwcCwaD5ucbERGRFUNsIiIiqsny5ctx88034+abby44zdmzZ/Hyyy/nhNvWf8+cOVPXdr7yyiv4yle+gq985Svo7OzEHXfcYYbaN954IxwOR12XT0REVItXL7yK7d/d3uxmtLyHP/gwrl56dV2XIYpiyYpqO7sRB9JBtTVECQQC5o//kiTVHGID6RAhEAhAluW8wUk+0Wi0rG5trYygLhgMYufOnWVVEns8Hlu7ilYUJaPyr9zQPp9CVfmNWMZCl1292YxgTpZlsx2CIFRVhW3wer0Z3Whbg8rsoZKKnaNbtmwp+LpqzYdzptnbsFKKosDn8+W8xw4MDFT0fmh0kW70DOLz+XD06NGmv6cYn6Wqqlb0uUNERAsLQ2yiPHbv3o2enp68j+/evbvxDSIimuOWL1+O5cuX46abbio4zblz58yAu1BVt13VFVNTU/jhD3+IH/7wh/jMZz6DTZs2mYH2XXfdhe7ubluWQ0REc9/evXuxd+/enMcnJiYa3xhqCaWCFbu7EQfSQbU1CLdWTquqCkVRagrPgHS1s1FVLUlSySrIaDRaVWgYCAQQDAahaRo2bdqE0dHRktu0ljArH2s4aEfwmW8ejVjGQrdv3z7zvt/vr/kcqIb1vKx1H3k8HjPEHhsbyziGRFGE3+/H2NgYdu7cWfQ9xRpw9vX11dQmw3w4Z5q9DStVKDwXBKHiobK8Xq/Zg4fxGdUKlc8ul8vc1tFo1JYLsoiIaH5hiE2Ux/j4eN7H77rrrvou+NxZ4PIUsHw50NlZ32UREbWYZcuW4aabbioadJ8/fz4j5D5+/Dgef/xxPProo5icnKx62UePHsWXv/xlfPnLX8bixYtx9913m6E2rwYnIlrYjh07hoMHDza7GTSH2NmNOHBlnN/sgM6onAbS4U8l3cvmIwgCvF4votEowuFwyRC72mUODg5CkiSoqpoR+Lvdbng8nozxaOvFOnayHf/Xy1dp34hltApZliFJEhRFgaqqEAQBoijC7XZjaGioLhWf0Wg0YxzqWo//atm5n7O7D8++OKXcc+7JJ58079sV7M+Xc6aZ27BSdp/zkiSZFc+yLOf08NEM1v0ci8UYYhMRUQ6G2ER5DAwM5K3E3rhxY30XfPgw8NSv0ve7u9Nh9vIVwIoVb9x/4+/Fi+vbDiKiFrV06VLceOONuPHGGzMen5iYwKOPPor9+/fjwQcfxIsvvlj1Mi5duoSHHnoIDz30ED796U/jhhtuMAPtO+64A11dXbWuBhERzSEbN27EnXfemfP4xMREwYtfaeEyxlo12BFCSJKUd9xpa+V0OBy2JcQzxpE1xo0tVAGtqiqSyWTVQVM8Hse2bdsyug6WZTkjxHK5XAgEAnUJWaxVlk6n0/b5N2oZzaZpGvbs2WNWUA4NDZmBn6IoCAaDZVfbl8PodcAI40RRRCgUsr1SvxLWatmVK1fWNK/sit9qurHWNC3jPKp2jO5sC+mcqdc2LLY8QzKZhKZpkCQJ4XDY9guqI5EI+vv7AaR7xTDGYm8W6znTrG7biYiotTHEJspj79692Lx5c+MXfNYyHuyFC+nbyZO50y1alD/cXrEc6O4BOK4rES0wPT09eO9734v3vve90HUdv/nNb7B//37s378fjz32GKanp6ue93PPPYfnnnsOf/u3f4ulS5fC7XZjx44deM973oMNGzbYuBZERNSKCg0pdPjwYdxyyy2NbxC1LCPQM9jRjTiQDsZHR0dzHrdWTgO542ZXw9pN+fDwcMFwsFCwXi5BEBCPx83wPXtsYyAdghpdj8fj8boFLY0YF7bZY8/Wg9EdvKZpiMfjOdWqLpcLsVgMgUAAPp8Pfr+/7AstVFVFb29vzvKsJElqehUpkNkuu/dzNePADw8Pm20KhUJ1Ofbm+znTiG1oyHes15PL5UIoFEIwGASQ7sI+e8ztRrJu22qOdyIimv/amt0AIrI4e7a86S5fBl4/BSSOAL9UgIM/Av7v94FvfgP46igw9h3g4YeAn/0UOHwIOP4icOYMMDNT1+YTEbUCh8OBm2++GZ/5zGfwwx/+EKdPn0Y0GsXHP/5xrF27tqZ5nz9/Hg888AD8fj+uueYa3HrrrRgaGqo5KCciIqK5z+5uxIF0MO31eguGKNYg2a7ulI1ww+gaOp9oNGpLgOj3+xGPx5FKpRCJRDA4OJgThmqahv7+/roFHNnh6FxdRqP5fD5omoZQKFS0u2VJkiAIAsLhcEZ1azGiKCKVSmXcdF1HIpEwu7k3wvFWUmsYmF2JWulYzKqqZnSxXq+umefzOdOobWgQRRG6rmfcrMd5PVjfZ1VVNd/zm8G6n5s19jgREbU2VmITtQpdLz/ELmZmBkil0rdsDgewbNmVym1rBfcyjsNNRPPT8uXL8cEPfhAf/OAHoes6fvWrX5lV2k888QRmZ2ernvfTTz+Np59+Gl/4whcgCALuuece7NixA9u3b8eaNWtsXAsiIlro1nSvwcMffLjZzWh5a7qb8/lbj27EgXQAWCzMcLvdEATBHD9XVdWaK5b9fr8ZjodCoZxwPBqN2j5mtVFVbq38lmUZwWAQiqJA0zQEAgHEYjFblmdUmwO1B4/NXEazWAPpcrry3rlzJ8LhMHw+H1L5fqsokyiKGBwchNfrhdPpRDQahcfjse24qLZNxn6uNXjNfn0l57KmafB4PADS+8Tu8ZQXwjlT721YLuM4Ny7GrodIJGJ22T4yMgKPx2P7+3qlmtmtORERtS6G2ESt4uJFYGqqvsswgvKzZwG8lPt8d/eVcHvFisywm+NwE9E84HA48Na3vhVvfetb8Wd/9mc4ffo0HnnkEezfvx8PPfQQTp8+XfW8NU3D2NgYxsbGAABbtmwxx9IeGBhAWxs7wCEioup1tHXg6qVXN7sZlEel3YhHo1FIklQyeDPG/zVClXKUCr3L5ff7EQ6HMTY2lhNiS5JkW9V3MW63G/F4HD6fD9FoFLIsQ1GUolW/lcw7HA4DsKcL22g0CkEQMkKgRiyjWaz7v5zgyQjLSo21Xi5jLOxgMAhZlm2ZZzmM/WnthcC6n8fHx2uaf/brKznWt23bBlVVK+q2vRIL4Zyp9zaslMfjKRpij4yMVF0pLooiIpGI2ZuBz+fD0aNHG96Nu/ViBTve24mIaP7hr6lErWJiovljWV+4AJw8ATz/HPDkL4ADMvC97wL/52vA3n8G/iUKyDHgFz8Hnn0WOPFKut263tx2ExFVaeXKlfjQhz6Eb3zjG3j11VfxxBNP4M///M/R399f87yffPJJ3H///bj99tuxdu1afOQjH8F3vvOdnG4CiYiIaG6rtBtxVVXLCgqMMX+zu1XOvsXjcfM1RgBUK6MS2wgdrW1PJpM1VcxVEsoDmVXttYaEBms37OV2cV3Mvn37ckK3RiyjWfKNYV6M9Xh/8sknbWmDNfBqVOCY78ITa5fmlW6XYvOvJJT3eDxQFAWRSKRu22K+nzON2IaVEkWx4HutLMvYt29fTfO39n6RfTFWo1jf0yv9bCAiooWBITZRq1i1CvjEHuAPPwzseC/wzjuAt9wKbNwI9PUBHU3uOGFyEjhlHYf7UeBfvw988+vAP38FiOwDfvAQ8PjP3hiH+3h6HO4auuklImqk9vZ23H777fgf/+N/YHx8HCdOnMDXvvY1+Hw+rFixoqZ5nzp1Ct/4xjfwoQ99CKtWrcI73/lO/PVf/zWeeuop6LwQiIiIaM6qphvxRCJR1tif4XAYgUAAgiAUvblcLjPQ0zTNlvDHOs/h4WHzcUmSMDQ0VNO8ZVmuuNtlo5LSri6GresHoOYuexVFwcDAQMOXsZBZzyG7Lm4oRVGUnFDR7XZnPFbLfra+ttzzzOPxYHx8HPF4vK7V6PP5nGnUNqyUKIoFe+zIdyxWY3R01LzIJBqN2nYhVDmMYTCA9Lq2Qi8TRETUehhiE7WS9vZ0N97XXANsvgXY+g7g3e8BfLuAj/8J8EcfAd73fuDOu4G3ugDndenwu6urue2engaSSeDYMeDpp4CfPAbs/zfgO/8f8NVR4NvfAh78N+CxH6efP3Y0Pf30dHPbTURUxNq1a7F7926MjY3h1KlTOHjwIILBIN785jfXNN/Z2Vn89Kc/xZ/92Z/htttuw4YNG7Bnzx5873vfw7lz52xqPREREdVbpd2IG8bHx0tWYsuyjL6+vrK7V7VWMNpVRWjM0xhrG4Bt3TZXGjoa22HlypU1L9swOjpq3g8Gg1XPR1EUJJPJvPuqEctohkrDM+tFC0bX4nbSNK3m8ajLWUahMeet51y1+3lkZMS87/V6y9rXPp8PqqoiHo/nnV5RlIxK8VrNx3Om0dvQLrFYDFu2bKl5PoIgZFx8FQgEau5RoFzWwNyOYTCIiGh+YohNNFc4HEBPD7BuPXDjjcDbbgfcHuADXmD3x4GPfgz4dx8EtrmBLW8Drr8BWLsuPc51M83Opsfgfuk48OvD6UrtHzycrtz+6ijwja8D//oA8KNHASUOHDkCnHotXflNRNQiOjs78a53vQtf+MIX8PTTT+OFF17AP/3TP+F973sfumt8n33llVfwla98BR/4wAewcuVKbNu2DV/60pfwm9/8hlXaRERELazSbsSBdCCiKErJMFaSpIxgupSdO3ea96PRqC2BnnXc31AoZOu4w5UGFkaoYmelnsvlMtuhqmpGiFgJn89XcH0asYxmsB4H5QRe1gp667Fai+wLQerd1boRuOXrRcHtdpvnSzX7WVVVM7AVBCEjyC3EGr4W63K6nF4fyjXfzplmbEM7qKoKWZZtu6jF7XZnfH7Z0ZtHKZqmmce82+1uqQp4IiJqLQyxieaLxYuB1auB634HcPUDd/8e8P4/AP74o+kqbt9O4J7twNu3AjdvBjZsAJYvb4FxuCeAEyeA5559YxzuGPAv302Pwb33n9P35Vj6ueeeTU/LcbiJqMne9KY3IRAI4Pvf/z6SySQeeeQR/Of//J9x/fXX1zTfqakp/PCHP8RnPvMZ3HzzzRBFEZ/61Kfw4IMP4sKFCza1noiIiGpVTTfiwJWuuYtVYhvjUFtD5FIEQcgIAcbGxsp+bTFGG8LhMIaHhysK1ouRZbnsKksjsCm3OrUSg4ODZngTDAYr7krX4/FAFMWi+6oRy2g0a68D1u7mCzGOx1AoVNZ48OXIDhazgzdFUdDf32/LsjRNK3nuSpJkXmQRDAbLDgI1TTPHAhYEAfF4vOQ28vl80DSt5LSxWMz2yvf5cs40cxvWynjvLNRVejU9E4RCoYb29LBt2zYA6V4dyv38JCKihYkhNtFC0NkJ9K0ENm0Cbr0NuONdwL2/D3zo/7GMw31vehzuN78FuHYj0Nub7t68mSYn01XZiSPpKu0fPZqu2s4Yh/vhdHX3rw+nq73PnuU43ETUUF1dXfB4PPjbv/1bPPfcc/jtb3+Lv/u7v8P27dvRVeNwD8eOHcM//MM/4L3vfS9WrlyJHTt24O///u/rXmlCREREhVXbjXgwGDSD72KVfdaKzErs2rUro02lGBW0xbr2tobWfX19Rdez0tBkZGQEwWCw6Os0TYPP5yu7OrUaoVDIDFECgYAZbhWjKIoZPBYas7bRy8innl1sG23KvqAjm7GuXq+3aG8FyWSyouULgpBRmZ+9jWRZzhvyVbpNNE3Dtm3boGlayfM8FouZ6+jxeEpWERtBu6qqcLlcRSuCDYFAwNzeHo8n762/vx9Op9PWal2ruX7ONHsb1nJehsNhs+2FPiOM9/RKvzNWGiZXes4C6X3kdDqhKIp5zNt1YQsREc1PDp39VBLh8OHDuOWWW8y/Dx06hM2bNzexRS1C19OV0mfOAmfPpAPis2eBM2fSf1++3OwW5tfWBixdlq40X7Ei/a9xf9lyoKOj2S0kogViYmICjz76KPbv34/9+/fjhRdesG3eN9xwA3bs2IEdO3bgjjvuqDkwJyKiyvA7xMLl8/nMEMGoIsv+EV7TNCSTSWiahlgshrGxsYzgIhaLZQRwmqZhfHwcoVDIrOB0u91lVccZY/UODw9nhImDg4MIBALo6+vLaJ9R2WwEyIIgIBQKwe125w3Q+vv7oSgKIpFITpevxjqpqoo9e/aYwbjX60UoFMpZNgA4HA74/X6EQiH4fD7Isgy/329WTwqCAFVVoSgKhoeHzW1cKNxzVNC7WLGfwFRVRSgUMqs+vV4vdu3aBZfLhb6+PqiqivHxccRiMUSjUYRCobK6kG/EMqz7wXocGF0zW8PcQoFRNdtRVVUEAgFzHwYCAbhcLnP/BYNBc50LrYdxrkiSlBH6RiIRc7sUarOmaWYIbLzG6/VCURRs27YtIxQ2lmPd/gDyBsfJZBKqqiISiWScu263u6wA1ji/FEWBKIoIBALm+ZVMJqEoCiRJgizLEAQBQ0NDZe3nQCBQcVVyKpXKew6Wa76dM83chpUcgwbjc0SWZezbt898jzUCYCtVVRGNRs0LoURRND9Dyh3HPhwOIxAIwO/3Z4z1blXonC10Lhmfb5FIxPx8q+ZYICKihYkhNhH4A1TVLl26Em6fMULuN/5t5W5ve3qA5VnhtvE3QyAiqhNd1/Gb3/zGDLQfe+wxTE9P2zLvnp4euN1uM9TesGGDLfMlIqLC+B1i4ert7a25wjWRSGT82G8ExfnC8OzAu1B7igV9xvKi0ahZ2ZxvunzBghGKWMc1LtVu6zy9Xm9GhZ/T6cyovjMCvfHxcaiqaq7LwMAAfD5fyW607QrkrG0eGxtDLBaDoihmCGNt086dO2uqHrRzGUYoV2pao5I4334EatuOsiyboaxxboiiCK/Xi6GhoYJtM6rxgfzhujGv7PMlWzgchiRJ5rE4MDCQcQFIMBisemxlq+xjuRRFUbBv3z7IspxxbPf19cHlcmHXrl1ljwVcbffo+Y75hXrONHMb2nUMGrJDZmP+hc6jUp8jVj6fD1u2bMkbMlvP2UqIoghRFG05FoiIaGFhiE0E/gBVF1NTmaH22TNXKrrPn2/dMa0XL74SbptB94p00L1kSfPHECeieePs2bOQZdkMtU+cOGHbvN/ylreYgfbWrVvRwR4oiIhsx+8QRERERERERPXDEJsI/AGq4WZmgPPn3gi1jaDb0l35zEyzW5hfR0fhCu6lS9PdmBMRVUHXdfzqV78yA+0nnngCs7OztsxbEATcc8892LFjB7Zv3441a9bYMl8iooWO3yGIiIiIiIiI6ochNhFyf4AaGBhAT09PznS7d+/G7t27G9iyBUjXgYkJS/V2VjflrTwO97Jl+Su4ly3jONxEVJHTp0/jkUcewf79+/HQQw/h9OnTts17YGAAO3bswL333ouBgQG08QIcIqKi9u7di7179+Y8PjExgfHxcfNvhthERERERERE9mGITYTcELuQz3/+87jvvvvq3yDKT9eByck3gu2scHsujMO9whJuWyu6OQ43ERUxMzOD8fFxs0rbGpjUatWqVdi+fTt27NiBe+65B319fbbNm4hovrjvvvtw//33l5yOITYRERERERGRfRhiE4GV2POGdRzuM5Zwe86Mw73C0k35G39zHG4iynLy5Ek8/PDD2L9/Px555BGcOXPGlvm2tbVh69at5ljat956Kxx8/yEiYiU2ERERERERURMwxCYCx7NbEGZmgHPnrlRuW0Pucy08Dndn55WKbWsF94rlQA/H4SZa6KampvD444+bVdrPPPOMbfNev3493vOe92DHjh1wu91Yvny5bfMmIpoP+B2CiIiIiIiIqH4YYhOBP0AteOY43HkquFt4HG69rQ3oWQYsXQa9Zxn0JUsx29UDvasHs4u6oc+2QZ+aBaZnoRu3Kcv9N/7GtF7i+St/QwfaujvR1tOJtqWdaF/aibali9L/9nSifeki83HHkg5WcRI12PHjx/HQQw/hwQcfhCzLuGDTMAudnZ244447zCrtG2+8kec3ES14/A5BREREREREVD8MsYnAH6CoCF0HLl3KHHv7zBnob4TdjksXm93CvHQdmJnpxPTUIkxPL8LM1CLz/vTUIuh6e/0b0eZIB9pm4L0oHXQv60Rbz6LMELynE45OVpUT2WlychI//vGPzSrt559/3rZ5b9y40Qy07777bnR3d9s2byKiuYLfIYiIiIiIiIjqhyE2EfgD1Fyj6zowq1sqh9P3C1cc68BUqWrkK9PmmwbZf8+m2+JwzKCj8zLaOy6jo/MyOiz/tndMtexw1jMz7ZkB9/QiTE91YXpqEWZn2wE0vuGOrnYz1DYD7qzq7rY3gvC2JR1wtLXoxiVqUUeOHMFDDz2E/fv349FHH8Xk5KQt8+3q6sLdd9+NHTt24N5774UoirbMl4io1fE7BBEREREREVH9MMQmAn+AqlTRELlIWAzLtKWD4tww2Tp/zIl3rll0dEylQ+6sgLuj8zIcjtZcidnZtjwB9xt/T3eiGQF3jjbHGwF3ZpV3ThfnSzvR3rOIVd5EWS5cuIBHH30UDz74IPbv348XXnjBtnnfcMMNZpX2HXfcga6uLtvmTUTUSvgdgoiIiIiIiKh+GGITYe79AKXrOjCjI7uaWJ/Wc4PkgkFw/jA54/Uz+hwPkVuZjvb2qcwqbkvA3dY22+wG5qXrDkxNLcL05cWYurwYU5e7MDW1uHXC7QIyqryzw2+j4nsZq7xpYdJ1Hb/5zW/Mbscfe+wxTE9P2zLvnp4euN1u7NixA+95z3twzTXX2DJfIqJWMNe+QxARERERERHNJQyxidA6P0Cd/v9+A31yJjeEnr4SJhvdXjNEns90tLXNXOmWvPMyOjonzYC7vX2m2Q3MMTvblg60Ly/G1NRiTL9xf3a2o9lNq1wb0qF2zyK0LTPG9M4dw9v429HZgPHFiRro7NmzkGXZDLVPnDhh27zf/OY3m92Ob926FR0dc/A9gojoDa3yHYKIiIiIiIhoPmKITYTW+QHq5ft+Bv1S6wWU1FqMcbjNUNtSyd3e3lrjcM9Md2Bq6o1w+43K7empxdD1+dO9t6Or/Y1uy7O7Mc8Nv1nlTXONrut46qmnsH//fjz44IN44oknMDtrT08RK1aswLvf/W7s2LED27dvx5o1a2yZLxFRo7TKdwgiIiIiIiKi+YghNhFa5weoV/7yCcyen2r4cqkOHICjoy09FnNHW/r+G3+n7zvMx9DZBke79bk3ni/42raseTuuPOfQ4bg4AUychePsWeDsWeDsmTf+PQvYFD7VQteBmdkuTE0uwtRkunI7HW53oZW7JLeFtco7q0vzK9Xdi1jlTS3r9OnTeOSRR7B//348/PDDeP31122b98DAgDmW9sDAANrbefwTUWtrle8QRERERERERPMRQ2witM4PUCe+8AvMaJMNX+685EBG6JsbFGeFyAXDYkfx5zOC5ithMtoccLRSSTSQDrAnJq6E2mcs4fbZM8BUcy+g0B1tmO1aipn2pZhGN6amF2PqYhemzwMz56ehTy68Xgoci9ozuzE3xu9eyipvar6ZmRmMj4+b3Y6Pj4/bNu+rrroK27dvx7333ot77rkHfX19ts2biMgurfIdgoiIiIiIiGg+YohNhNb5Aerkl8Yxfepiw5dbF/lC5CIVyShQaZz5ekeBoDnP/NsZ5lVE14FLF4EzZ/OE3GeAS5ea17ZFi4C+PuhCH2a7l2N20TJMty3F7KQDM+cvY/b8FGYnpsz7M+enMDtxGWh+0XljOZAbcGd3cb50EdreqPhuW8QqV7LXq6++iocffhgPPvggHnnkEZw5c8aW+ba1tWHr1q1mlfatt97aehcJEdGC1CrfIYiIiIiIiIjmI4bYRGidH6Be/V8Kpk5M2DOztjcC305LxXGpoNjaLXXe542/84fJYIg8f12+nA6zUykgmbxyO3+ueW3q7gH6+iy3lUBvL9DRAX1Wh35pOh1on7/8RrA9lfm38di5y6zy7ulE+7IrAXf6sUVoX5Z+rq27k1XeVJGpqSk8/vjjZpX2M888Y9u8169fj/e85z3YsWMH3G43li9fbtu8iYgq0SrfIYiIiIiIiIjmI4bYRGidH6DO/fglzJyfyhzj2NptdXsZIbLRbTZDZGqEy5ctofZpIPXG/WZVbjscwPLl6UDbGnAvXwG0tRV8mT41i5mJ7ID7yn2zynsi/TdmF9hHZ3aVd09uF+fW8bxZ5U3Zjh8/joceegj79++HLMuYmLDngq2Ojg7ccccd2LFjB+69917ceOONrNImooZple8QRERERERERPMRQ2wi8AcoIlvpOnDxYjrUzgi4U8D0dHPa1N6ertI2K7b7gJV96WruCgMvXdehX5y+EnBPWLoxz+jSPB1+65cWYpV3Wzrgzg63ezrfqO6+0sU5q7wXnsnJSTz22GN48MEHsX//fjz//PO2zXvjxo1mt+N33303uru7bZs3EVE2focgIiIiIiIiqh+G2ETgD1BEDTE7C5w7lxtunzmTDr6boasrHWj39QErV16539Vl2yL06dkr3Zi/UcmdW919pep7oVZ551R3L+1Ee88b4TervOe1I0eOmFXajz76KCYnJ22Zb1dXF+6++24z1HY6nbbMl4jIwO8QRERERERERPXDEJsI/AGKqKmmpwFNywy3U0ng/PnmtWnp0iuBtnW87fb6BqhmlffEFGbPFajynrgSgi/IKu/ONrQtu1LlbYznveiaZVh8Qy8c7YW7jafWd+HCBTz66KPYv38/HnzwQbzwwgu2zfv666/Hvffeix07duCOO+5Al40Xq8xHMzMzOHbsGC5cuICNGzdi2bJlzW4SUcvhdwgiIiIiIiKi+mGITQT+AEXUkiYnrwTap09fqdy+fLk57XE4gBUrssbbXgksW1Z0vO160qdnM0Lt2WJdnE9MATPz+yO/bVknet62DktvX4v25Qwo5zpd1/Hss8+a3Y4/9thjmLZpSIKenh643W7s2LED73nPe3DNNdfYMt+5KJVK4bnnnsu4Pfvsszhy5Aguv/F+297eji996Uv4T//pP3HMcSILfocgIiIiIiIiqh+G2ETgD1BEc4auAxcmMrsjTybT423PNKkquaMjXaXd+0aX5H196fvd3RWPt11PGVXeloDbGL/b2qX5zPkp6JeaNH65HdocWLJ5JZZuXYdFm1YwdJsnzp49C1mWsX//fuzfvx8nTpywbd5vfvObzW7Ht27dis7OTtvm3Qqmp6dx9OhRM6C2BtavvfZa2fP5u7/7O3z605+uY0uJ5hZ+h6BmkWUZiqJgcHCw2U0hIiIiIiKqG4bYROAPUERz3uwscPZs7njbZ882b7ztxYuvBNrW6u1Fi5rTngpZq7ytYbc5lrcRfp+73NJV3h1rurF06zp0v3U12ro6mt0csomu63jqqafMQPvxxx/H7OysLfNesWIF3v3ud2PHjh3Yvn071qxZY8t8G+H06dN5q6oTiQSmpqZqnv+iRYvw85//HLfddlvtjSWaB/gdYuFSVRVOp7OmeUiSBL/fX9VrnU4nVFVFPB6Hy+Wq6LXRaBQ+ny/vc7FYDG63u6o2aZqGTZs2QdO0nOe8Xi8ikUje16mqilAoBFmWoaoqAEAURbjdbgSDQYiiCAAIh8NIJBIIhUJVta8SmqZhbGwMsVgMiqIgmUxC0zSIogiXy4VAIJCznYLBIJxOZ9n7tBHLmAtGRkYQDAbLnl4URfMWCATKPv6DwSBGRkYgCELBaYxj186fSRVFwb59+yDLMjRNg6qqEAQBfX19cLvd8Pl8VZ9z85Ed58XIyAiGh4dta9PRo0eLHjetgu9bpdX72DDeZ7IJglDWcdTb21v0+VQqBaDy902DsZ927doFr9db8euJiJqFITYR+AMU0bw1PZ2u0s4It5Ppau5mWbrM0h35GwG3INR9vO160nUd+qWZrID7ct6K75lzzanydnS1o9u1Gku3rkfn6u6GL5/q6/Tp03jkkUewf/9+PPzww3j99ddtm/fAwIBZpT0wMID2Jp+rU1NTUFU1b1W1netdyPXXX494PI6lS5fWfVlErY7fIRYuRVHQ399v/i0IAoaGhjICtmxjY2MIBALm34lEIu90lSzb7/dDkqSK56FpGpLJJCRJMoM9TdPgdrsRi8Uqnh+Q/lFdkiQziHa73QiFQmZgl+/He+MHf7/fj0AgYG4PVVWxb98+hMNh8zkjBKlmfculaRqCwSDC4TCAdPju8XjMfaqqKhRFMdsQiUTgcrkgyzI8Hg9CoVDJ6vhGLANIV+v7fD6Mjo62fFhhHDOhUMjcLkD6ooqBgQEAQDKZNKeNxWJmbwQulwtDQ0NlraOqqtA0DcPDw4hGo+bjoVAIXq8XfX19AGBLYKkoCoLBIGRZzgj3RFGEpmkYHx9HJBJBOByGIAgIhUIVh3zRaBSSJGF8fByapsHlckEURXg8npYIDCth53lhXCyQTCbNC2QMkUik4PtuMpk0jy/r8VHqYqFm74e5/r7VyO3XiGPD+HyNRqMZQbPL5UI8Hi/aPlVVoaoqJEkyl2O9oMvavkLvm/F4PGc9jIsNZFnGvn37oChK1e87RETNwBCbCLk/QA0MDKCnpydnut27d2P37t0NbBkR1cWlS1cC7ZQl4G7WeNttbYXH256HXWHr07NvVHUbwfblK3/n6eLc7irvLucKLN26HotvWglH+/zbvgvdzMwMxsfHzSrt8fFx2+Z91VVXYfv27dixYwfe/e53mz921sPrr7+eE1I/++yzUFXVtrHBq7V792587Wtfa2obiBpp79692Lt3b87jExMTGe8xDLEXDuOHeQAYHBwsWR2cXaVcbhCZTyAQMH+wFgTBrMyqlsPhwODgoFk9Vm247nQ6EQgEzB/tS1V1G+tRLCDSNA0+n88MN+oZYlsr1L1eL0ZHR4uGmeFwGMFgEENDQxgeHoamaSX3az2XYQQnsixDkiQoigKgtor/RrP2cCCKIhKJRNHps7dnoWr/bNYLQew4h7IZVZKCICASiRQ9D4xj3Ai7Dxw4UDJEN16TTCbNcNwIqIzjRBRFM0hsdfU8L6z7upxjyqCqKjweD1RVRSQSyXuRRCvsh7n8vtXs7VfPY8OYzul0miE/UNlnv9G2coLvSt43gSsXOWmaVtF7JxFRs7BfTaI8Cv3gfddddzW2IURUH4sXA+vXp28GXQcmJnK7JE+l0t2V19PsbHo5qRRg/c7R0XEl1Da6JV/ZByyZ25XEjo42tK/oQvuKrpLTZlR5m+N5X6nynjl7GZO/TUGfKn8fTSbOYDJxBu0rFqHnbevQ87a1aF82N7p5p9La29tx++234/bbb8f999+PV199FQ8//DD279+PH/zgBzhz5kzV83799dfxzW9+E9/85jfR1taGt7/97bj33nuxY8cO3HrrrRWPv3758mUkEom8VdVGxVEr2rt3LzweDz784Q83uylEDXHs2DEcPHiw2c2gFmKE0X6/v6zurffs2WO+xuVy1TSW9djYmPmjuKZpiEajNVfaBgIBM8SWJKniLrtlWYbb7S67glWWZYTDYYRCoaLhhCAIiMVicDqdebspt4u1C9hyQ1+/3w+3243+/v6y2lbPZfT395uVdW63G7t27TJD7Lmk0osDvV6veQFGNBpFIBAo6yIH63Fq9wWJPp8P0WgUgiDkrYjM15ZYLGa+btOmTSVft23bNgwMDOT0muByueD3+7Ft2zYzoKtmyIFGqve5V21VvSiKkCTJDCvzafZ+mOvvW83efvU8NqzLMD7DgPT2dLvdZa2LEeqXUs17mNvtxtGjR9Hf349oNGpuYyKiVsUQmyiPQpXYGzdubHxjiKgxHA5g6dL07U3XXnl8dhY4cya3S/Kz1QdhZZueBl57LX2zWrw4t2q7rw/o7Kx/mxrM4XDAsaQDbUs6gFX5p5m9MIWJ+GuYeOIVTJ++VPa8Z85cxtnYCzj7wxex5JarsHTrOiy6dnnFQSS1tjVr1uCjH/0oPvrRj2JqagqPP/64WaX9zDPPVD3f2dlZ/OxnP8PPfvYz/Nmf/RnWrVtndjvudruxfPlyAOkLMU6dOpW3qvro0aOYmZmxa1Vr0t3djRtuuMG83XjjjRgbG8P3vve9vNP/+3//7/G2t70N1113XYNbStR4GzduxJ133pnzeHYlNi0cxoVG5YS9sixndD9aS8VTNBqFKIoYGhoyK+MkSao5xO7r64PX60U0GjXD5UqEQiGEQqGyzwejWrvcdodCoYLjeNcqHA6bIU2lXasaVYJGVX6zlpFdvTsXA+xqWS/AMCpAq+lJwA7BYNA81w8cOFBROyKRiDnWvcfjKVhNaZw7hcJ6QRAwOjpqVnH6fL6yK0wbrRHnXi2MsDHf9mv2fpjr71vN3n61KnZsZBNFEaFQyFznVlkXo6cI4yKoci8CIiJqBobYRHns3buXXQESUVpbG9Dbm745LY9PTb0x3nbySsCdSgIXLtS/TZcuAa+8nL5ZLVuWFW73ASuEOT3edjnaujux7I6rsfR312PyiIbzj7+CS88mgXJ7IZ/RcfGpU7j41Cl0rutBz9Z16L5tNdoWze/tthB1dnbiXe96F971rnfhC1/4Ao4fP46HHnoI+/fvhyzLmJiYqHreJ06cwFe/+lV89atfRUdHB97xjndgcnISzz33XF2rxyp17bXXZoTVRmB99dVX51zAsX37diiKghdeeCFnPufOncOHPvQh/PSnP8WiRezJgOa3QkMKZQ9JRAuHMW5nud3+GkKhUE0BmyRJCAQCGeGvLMvQNK3mcXyHhoYQjUYrru42urF2uVxlh9iVhqxer9eWcYrztcMYp1wUxaoq5N1ut3kBQLOWUY9tM1dkn0+yLDel+3RZls3Az+v1VlU1aq3uLBQohcNhuN1uqKpa8L3E5XLB7XZDlmWoqmqOLd9KGnFe2GHXrl05lcJAc/fDfHjfmg/HcaFjI5/BwUHEYjFzXVolMDaq3sPhMMLhMAKBQEv33EBECxdDbCIiomp0dgKrV6dvVhcvpsPsZBI4ffrK/amp+rfp3Ln07YVjVx5rawMEIbNiu68PWDr/xtt2tDmw+PpeLL6+F9PJS5j4+QlMPHkSsxfKHz946sQEtH85gjP7j6Knfw16tq5H51VL6thqaqZrrrkGfr8ffr8fk5OTeOyxx8wq7eeee67q+U5PT+PHP/6xjS2tzNKlSzMCauP+7/zO76C7u/zhCARBwLe//W3ccccdeSvGx8fH8bnPfQ5f/OIX7Ww+ERWgT09j6uSrzW5Gy+tcuwaOjvr/1DEwMFByGju7ETfGCjV+NDd+eAbSgUAt8zbaZ3RTXkl19/DwsBl2VKqSwHHnzp1VLaOYPXv2mPcrrT63Mi4AaNYy6IpmXThoVFkC6X1VDbfbbZ6D+arKNU0zLzKJRqPQ9cJX7LpcLsiyDCBd5d1K4R/QmudFvkDV5XLlhI3N3g9z/X2r2duvGuUeG8VEIhFs2rQJmqYhHA7D5/PB7Xbb3dSKBYNB8/8SwWCw7GCeiKiRGGITERHZackSYMnVwPqrrzym68D5c5ndkSdPA5rWmPG2jWXiyJXHOzvfGGe7D1i58sr9JfMjsO3oW4wV79mE5e5rceHpUzj/+CuYeul82a/XL83g/E9fwfmfvoKu3xGwdOt6LL6xD462+RX80xVdXV1wu91wu934m7/5Gxw5csSs0n700UcxOTnZ7CZmcDgc2LhxY05F9Q033IB169bZ1i3+1q1b8Zd/+ZcFf5D90pe+hG3btuE973mPLcsjosKmTr6KRAv84NnqnLKMRRuuLj1hDURRLFlRbWc34gByqtACgYD5w7MkSTWH2ED6B+xAIGBWi5VTNR6NRivuGtUI6oLBIHbu3FlWJXE5449WQlGUjIrwWrpkL1SV34hlLHTZVf3NCIVkWTbbIQhCTZWMXq83owtna0hmDGNgKHaObtmypeDrmq0VzwtFUfJ28zwwMJDzvtPM/TAf3rfm2nFcybFRjNFFutE7i8/nw9GjR5v+vm78f0ZV1Yo++4mIGokhNhERUb05HMCy5enbtRuvPD4zk2e87dPpaup6m5oCXns1fbPq7r4SaBvV2729c3a8bUdnW7qiun8NLh8/h/NPnMCFp14DpsvtaxyY/K2Gyd9qaBe60PP2degZWIP2pew+eb677rrr8OlPfxqf/vSnceHCBTz66KPYv38/Hnzwwbzda9fL8uXLcyqqb7jhBlx33XVY0qCLTgYHB3HgwAGzEiLbRz/6UTz11FNYt25dQ9pDRNRspX7Ut7sbcSAdVFuDcGvltKqqUBSl5m5Ad+7caVZVS5JUsgIvGo1WFRoGAgEEg0FomoZNmzZhdHS05DatddzvbNZw0I7gM988GrGMhW7fvn3mfb/f35SucK3nZa37yOPxmCH22NhYxjEkiiL8fj/Gxsawc+fOou8p1nCtr6+vpjbZrRXPi0IBqSAIOZXCzdwP8+F9a64dx5UcG6V4vV6zFxXj/wmtUPnscrnMbR2NRm25KI6IyE4MsYmIiJqlvf1KWGw1NZU51rZx/9Kl+rfpwoX07eWXMh9fvjxrvO2VwIoV6e7K54hF1yxD3zXLsGLHJlwYP4nzT5zATKr86toZbRJnHz6Gs/IL6H7zKvRsXYdF1yyzreKVWld3dzfuvfde3Hvvvfj7v/97PPvss2ag/dhjj2F6uvwu6/Npa2vDpk2b8nYBvmbNmqYfY21tbfj617+OW2+9FadOncp5/tSpU/jjP/5jPPLII2ibQ+8JRET1Ymc34gDMi4iyAzqjchpIBw+1jrEpCII5hmk4HC4ZYle7zMHBQUiSBFVVMwJ/t9sNj8djjoVaT9YLs+yoOstXad+IZbQKWZYhSRIURYGqqhAEAaIowu12Y2hoqC7VhtFoNGMc6maNMWvnfs7uPjz74pRyz7knn3zSvF+PYL+W/d2K50Wl82jWfpgv71utchyXw+73XUmSzIpnWZZbYqxv636OxWIMsYmo5fBXJiIiolbT2QmsWQPcdDPwu+8Efv99wEc/Bnzko8C9vw+843eBG29Mj8fdgDEfAQBnzwLHjgJKHJBjwNh3gK+OAtEx4IcHgF/9EnjxhXQVeYVXJDdae08nlt15Ddb+/7Zg5UdvxuIbeiubwbSOC798Daf+4Sm89ve/wsT4SehTueMF0/zkcDhw00034b/9t/+GH/7whzh9+jS++93v4hOf+ETJSmRBEPD2t78dH/3oR/HXf/3X+O53v4vDhw/jwoULOHLkCB588EH8zd/8Dfx+P+68806sXbu26QG2Yd26dfj6179e8PkDBw7UNGYeEdF8YYzzabDjB3BJkvKOO20dJ9roWrxWxvARxpilhaiqimQyWXXIEY/Hc0IJWZYRDAbh8XjgcDjQ399v23pls1b4OZ3OObuMZjMuQvB4PBBFEZFIBLquI5VKYXR0FIqiYNOmTbaOWxyNRuHxeODz+cxlNjPgt1Zqrly5sqZ5ZVebVtOFsqZpGUFktWN0F5p3rfu7meeFMR6zpmlmDxbWoRnsXpbd+2EhvW/V8zgutLxGHRvW96tAIGDrUBnVsL5vtdrwA0REACuxiYiI5o4l3cCGbmDDhiuP6Xo6OM6u2j5zpjHjbZ8+nb5ZLVqUrtbu7cus3l68uL7tqZCjzYElN63EkptWYvr1izj/xAlMjL8K/VL5VbVTL59HKvpbnNl/FN0Da7D09nXoWDk/xhWn8ixfvhwf+MAH8IEPfAC6ruOpp57Cww8/jOPHj2PJkiUZVdWrVq1qmVC6Gtu3b8dnPvMZfPGLX8z7/J//+Z/jrrvuwtatWxvcMiKi1qBpGvbs2WP+bUc34kA6GB8dHc153Fo5DeSOm10Nazflw8PDBbvxLhSsl0sQBMTjcYTDYbOiM5sRIgSDQcTj8bqN09mIMUmbPe5pPRjdwWualveiBJfLhVgshkAgAJ/PB7/fX3a1tKqq6O3NvNDU6N3AIElS0ysYgcx22b2fqwm3hoeHzTaFQiHb2lSP/d3I8yLfMVVP9doPhvn+vlXv7WfV6GPD5XIhFAohGAwCSA8jkD3mdiNZt22zA3UionxYiU1ERDSXORzprr43bgJc/YDbA+z8Q+DjfwJ4dwK/tw247a3Am64Fli5rTJsuXwZOngR+82vgp48B//f7wP/5GvCNrwMP/hvw+M+A554FTp0CauyG2S4dVy2B8F4R6z73NvR+8HfQub6notfPXpjG+R+/jJNfHMfrXzuEi88moc+2dkU62c/hcOC2227DZz/7WXz5y1/GF7/4RfzJn/wJ7rjjDqxevXpOB9iGv/qrv8KWLVvyPjczM4MPfehDOT8yExEtFHZ3Iw6kg2mv11vwB3xrkGxXd8rGD+tGV8H5RKNRWwJEv9+PeDyOVCqFSCSCwcHBnHBM0zT09/fX7cf1RnxuzcfPRp/PB03TEAqFinb1K0kSBEFAOBzOqKwsRhRFpFKpjJuu60gkEmbPL0ZY2kpqDaKyqyArHQdYVdWMLtbt7Ba4Hvu7keeFKIrQdT3jZj2e7FTP/WCYz+9bjdh+Vo08NgzWzzpVVc3P3Waw7udmjT1ORFQMK7GJiIjmo/Z2YOXK9M3q8uXMiu3UG/cbMt72RPr20vErjxkhfF9W1fby5oy33baoHT1b1qJ7YA0uv3gOE4+/ggvPvA7MlBlI68Cl51K49FwK7X2LsfTt69AzsAZt3Z31bThRHUxfnsGlC5fRtaQTnV3prw2LFi3Ct7/9bbz1rW/FuXPncl7zwgsvYM+ePRgbG5sXoT1RK+lcuwbOMgOghaxz7ZqmLLce3YgD6UCo2A/pbrcbgiCY4+eqqlpzxbLf7zfD8VAolBOOR6NR28esNqrKrZXfRvfiiqJA0zQEAgHEYjFblmdUmwO1B4/NXEazWAPKQtX6Vjt37kQ4HIbP50Mqlap6uaIoYnBwEF6vF06n0+xa3K7joto2Gfu51tAv+/WVnMuapsHj8QBI7xM7u1i3c3+30nlhHE+nT5+2rcv7eu6HhfC+Vc/tV4l6HBvZIpGI2WX7yMgIPB6P7Z+tlapXjydERLVgJTYREdFCsmgRsHYtcPPNwDvvAH7//cBHdgN/9BHg3vcCW98B3HAjsGpVY8bb1vV01+dHjwLxcSD2CLDvO8A/fwX4bgR41Bhv+0Vg4nzDxtt2OBzounY5+v7wRqwbehuWv/tatK/oqmgeM8lLOLP/KF75618gGX0el18+X6fWEtlL13W89PzrGH/keTx98CjGH/ktjj93ynze6XTin/7pnwq+vlC3t0RUG0dHBxZtuJq3EjdHI/7/kqXSbsSN4K0UY1xOY4zoQjdr8GVXNbZRZT02NpbznCRJDakac7vdiMfjZmgmy3LebsernbfBjgrvaDSaU3XaiGU0i/U4Kyf0MIKaUmOtl0sURfPiDlmW6xYyZQuHwzlj5Fr38/j4eE3zz359sYrnbNu2bYOqqvD7/bYHf3bu71Y8L0q9HxtVweWo535YCO9b9dx+1bDz2MhmjCtvMHo7aDTrxQqVvOcQETUKQ2wiIqKFzuEAenqADdcAb7kVuOtu4ANe4GOfAP7ww8A97wYGtgCiExCE9PT1NjMDvP468PzzwM+fAB56EPjmN9Ldkn//AeCxHwOHDwEnTgCTk3VtSvvSRVh+95uwdnALVv7xTei6TqhsBtOzuDD+Kl7737/Ea//wK0z88jXo03Uer5yoSjPTs3h+/CW8+JvXMPtGDwT6rI7jz57CqeNnzOk+/OEP42Mf+1jB+fzpn/4pDh8+XPf2EhG1gkq7EVdVtazxPY0xf7O7Vc6+xeNx8zXZAVu1jErs7BBKVVUkk8maqrXKCfCtrD/y1xoSGqzdsNsRDO/bty8n8GnEMpql0osJrMf7k08+aUsbrGGLXRdvlJKv4tvapXmtF1lY519OxbPB4/FAURREIpG6bAs793crnheiKBZ8T5NlGfv27StrPvXeD/P9fave268adh0bhVh7IMm+IK5RrJ+rlX4+ExE1AkNsIiIiyq+tDVixAtgkAv0DgOceYNeH0uNtf9AH3P17wK23Ade8KR2CN8LkJHDyBPDrw8BPHgP+9QFg7z8DD/wL8OILdV20o92BJZuvwqo/eTPW/Ld+LH3Heji62iuax+UXzyG17zmcGP4Fzjx8DNNaA7pxJyrT5MUpHPrJMZx+JbebcAB44devYsZyAcb//t//GzfccEPeaS9duoRdu3bhwoULdWkrEVGrqKYb8UQiUda4k+FwGIFAAIIgFL25XC4z0NM0zZbgwTrP4eFh83FJkjA0NFTTvGVZrrjazKgOtKt7W+v6Aai5kldRFAwMDDR8GQuZ9Ryy6+KGUhRFyQm03G53xmO17Gfra8s9zzweD8bHxzN6LWhlrXheiKJYsEv6fPs8n0bsh/n8vtWqx7Edx0Ypo6Oj5oUf0WjUtovRymEMRQKk17XZ3ZkTEeXDEJuIiIgq09EBXHUVcP0NwNu3AjvuTXdHvvvjwPv+ALjjXcDNm4G169LdlzfCq68CD+0HDsSAixfrvrjOVd0Q3ufEus/dDuHfXYfOtd0VvX52YgrnfnQcJ0NP4vWv/xqXfpuCPtuYrtKJ8jmXvICnD6qYOFP4worLl6bx8pHXzb97enrwne98B4sKnOeHDx/Gf/2v/9X2thIRtYpKuxE3jI+Pl6zElmUZfX19ZXftaa2es6uCzZinMdY2kP6B3Y6AodLQ0dgOK1eurHnZBuvQF7V0j64oCpLJZN591YhlNEOlwY31ogWjq2k7aZpW9254NU0rOOa89Zyrdj9buyX2er1l7WufzwdVVRGPx/NOryhKRqV4teze33PpvIjFYtiyZUvRaRq1H4D5+b7VyO1np3KOjXIIgpBxAVwgELBt6IxSrIG5MUQDEVGrYYhNRERE9ujqAtatSwfYd7wLeP8fpIPtP/pj4D33pgPv669PB+DtlVUwl+3IEWDsO+luyBswfnZbVzuW3r4Oq//UhVWBt2DJW64C2irobl0HLv36NF7/6iG8+jdxnPvJy5i9OF2/BhPl8dqLGg799AVMTc6UnPaVI6cxeXHK/Pu2227Dl770pYLTS5KE7373u7a0k4io1VTajTiQ/jFeUZSSYawkSRnBdCk7d+4070ejUVsCPWNcbCD947ZdAbYxv0oYP+jbWSXmcrnMdqiqWvXYpj6fr+D6NGIZzWA9DsoJW6wV9NZjtRbZF4LUu6t1I+zJ14uC2+02z5dq9rOqqmZYKAhCRohYiDX4K9bdcTm9PpRi9/6eK+eFqqqQZbloCNvI/QDMv/etRm8/u5RzbFTC7XZn/B/Cjh5VStE0zXzfcbvdLVUBT0RkxRCbiIiI6sfhAHqWAm96U7rr8bu3pbsi//ifALv+MN1Fef8AsGlTuutyO8bbvnQJePRAehztc/m7Rbabw+FA16YVWPnhm7Dus2/Dcveb0La8sir06dcv4sy/qTjx1z9H6l9+i8snJurUWqI0Xddx7PCrOPLLV8ruCWB2RseLv3kt47FPfepTeP/731/wNZ/4xCdw7NixWppKRNRyqulGHLjSNXexSmxjHGpriFyKIAgZP0CPjY2V/dpijDaEw2EMDw9XFKwXI8ty2RV+RlhQbnVqJQYHB83gIBgMVtyNq8fjgSiKRfdVI5bRaNZeB6zdzRdiHI+hUKis8eDLkR1qZYc+iqKgv7/flmVpmlby3JUkybzIIhgMlh1CaZpmjkMrCALi8XjJbeTz+aBpWslpY7GYLZXv9djfc+G8MN6jCnWH3ej9YJgv71vN2n52KHVsVNM7RCgUamhvG9u2bQOQ7mmh3P/DEBE1A0NsIiIiary2NkDoBUQnMLAFuGc78IcfTofbH/ACd90NvOVWYMM1QHeV420fP56uyj70DDA7W3p6m7QvX4Tl7muxLrgFff/PjegSV1T0en1qFhO/OInX/peC1/7pKVx46jXo041rPy0M01MzePbnx/HKkdMVv/bU8TM4n7rSbb/D4cBXv/pVbNiwIe/0Z86cwYc//GFMTU3lfZ6IaK6pthvxYDBoBt/FqsqsFZmV2LVrV0abSjEqKot17W0Nrfv6+oquZ6U/2I+MjCAYDBZ9naZp8Pl8ZVenViMUCpk/4AcCATNYKUZRFDN4LDReaqOXkU89u9g22pR9QUc2Y129Xm/R3gqSyWRFyxcEIaMyP3sbybKcN2CqdJtomoZt27ZB07SS53ksFjPX0ePxlKxgNYJ2VVXhcrmKVqMaAoGAub09Hk/eW39/P5xOp62Vonbvb6D+50Utx384HDbXM997cbP2g2Guv281e/vV89gArnyuVtpDRKVhcqXvm0B6HzmdTiiKYr7v2HVxERFRXehEpB86dEgHYN4OHTrU7CYREZHVxYu6/vLLuv7MM7r+4x/p+gP/ouv//BVd/6d/KO/2ve/qevJ005p/+eR5Pfm93+ov/flP9ePBH1d8e/kvH9e1HxzVp7VLTVsHmj8unp/UlQNH9J8+cLjo7cmHnyv43DOPHdVnZ2cz5nvw4EG9ra0t4/9U1tvnPve5Jq0xUX3wO8TC5fV6zf0uiqIej8f1RCKRcYvH43osFtMjkYju9/t1QRAyjpdYLJYxz1QqpcdiMd3tdpvTuN1uPR6Pl2xPKpXS4/F4RrsA6IODg3oikdBTqVTG9IlEQpckyWyTIAi6JEl6IpHIO3+Xy6UD0CORSN5lG8s3pgOge73evMvWdV0HoPv9fj2VSpnr6/f79UgkYm7LWCymh0IhXRAE3eVyFWybMb9yb8UkEgnd7/dnrEMkEjHXIx6P65Ikmds5FAoVnV8jl2HdD9bjwOVy6bFYzHw+3/4wVLMdE4lExj40jtdEIqFHIhFdFMWS65FKpfREIqEPDg5mLMO6XYq91liG9RiNx+O6IAgZx42xHOv2B6DH4/GM7WNMF4vFcs5dt9td1v6IxWLm+SCKoh4KhczlGNvG2G6CIJS9n7PbXs6t0DlYzTljx/7Ox+7zopJ9bd3n8XhcD4VCGe9lLperLvthIb9v2XUcV7Md631sJBIJPRQKmdOIomhuq3JJkmSeY4UUet8s9H5m7CPr/zGqORaIiJrBoesNGDCSqMUdPnwYt9xyi/n3oUOHsHnz5ia2iIiIStJ14ORJ4McHAS1Vevq2NuCtrvStXmNylzB7aRoXfvkazj/+CqZfu1j6BdnagCU3r0TP1vXoElfAYUf367SgnHl9As89+RKmLxcf/3r1mwRsevNaPH1QxcXzl/NOc8OWDVi5fnnGY/fffz/uu+++vNM7HA7Isozf+73fq6rtRK2G3yEWrt7e3porXBOJREbFZX9/PxRFyamG0jQNsVis6FjQRnsKVVJpmmYuLxqNmpXN+aYLhUI51ZPRaBTBYDBjnNtS7bbO0+v1ZlSXOZ3OjMovRVEgSRLGx8ehqqq5LgMDA/D5fCW7C67k/0Pl/ASmaRrGxsYQi8WgKAqSyWROm3bu3FlT5ZqdywgEAgiHwyWnNSqJ8+1HoLbtKMsyJEmCLMvmuSGKIrxeL4aGhgq2zajGB/JXNBrzyj5fsoXDYUiSZB6LAwMDGV3zBoPBqsf1tco+lktRFAX79u2DLMsZx3ZfXx9cLhd27dpV9ji01XaPnu+Yr/WcqXZ/l2LHeWHXvjb4/X5IkmT+bdd+WKjvW3Yex0Bl23FwcLCux4Zx7BV6Lyv1WW7l8/mwZcuWvL0ZWN83KyGKIkRRtOVYICJqJIbYROAPUEREc9rMDKDEgV/9srxuw3t7gTvvBtasqX/bCtB1HZPqGUw8/gou/vo0UEVv4R2ru7F06zp0u1ajravD/kbSvHPyWApHnz6BUv/733jLGqwT++BwOJA8eQ7P/vx43ukW93TitrudaGu/MkLRzMwMfu/3fg8//vGP875m7dq1eOqpp7B69eqq14OoVfA7BBEREREREVH9cExsajkjIyPweDwIh8MZY4domgZFURAOh+HxeCDLchNbSURELaO9HdjyNuCDXqCcYCyVAh74F+CnPwGaNEavw+HAYqeAlX90M9YG34Zlv3cN2pZ2VjSP6dcuQPt+Aif+6hdIPXAEU69O1Km1NNfpszrUp09Afap4gN3e0Yab3n4N1jtXmlUNvWuWYsWq/OPSX5qYwsmjmb0gtLe341vf+lbBsV5PnjyJ3bt3Y7aB49QTEREREREREdHcwxCbWs7p06chyzICgQCcTiccDgccDgd6e3vR39+PQCAAURTL7oKFiIgWiL6VwPv/HbD1HUBHGZXJh54BxvYBx/NXmTZKx4ourLhnI9Z99m3o+9ANWLRxeekXWeiXZzDxxAm8+rcKToWfxoVnTkGfYUBIadOXZ/DrJ17MCZuzLe5ZhDe/axN61yzLeNzhcGDj5sK9Fhx/7hSmJqczHtuwYQO+9rWvFXzNQw89hP/5P/9n6cYTEREREREREdGCxRCb5hSXy4VYLJYx5ggREZGprQ14y62AbxewYUPp6c+fA/b/G/DoAeDSpfq3rwhHRxu6b12N1f/+Vqz+T29Fz9vWwtFZ2X/VJtUzSH7rWZwIPYmz8guYOZt/LGNaGC6cm8TTPz6KM6eKV+mvuKoHb37XJnQv68r7fM+KxVhzrZD3uZnpWRx/7vWcx9/3vvfh05/+dMFlfvazn0U8Hi/aLiIiIiIiIiIiWrg4Jja1nGAwCE3T4PF4zO7ERVGEy+WCKIp1WSbHsyMimod0Hfjt88DPfgpMTpaefskS4B3vBJxO4I2ulJtt9uI0JuKvYuKJE5h+/WLlM2hzYMktK7F063os2rjc7CKa5r/Ua+fx/JMvYWa6eFX+2k292HjLWrS1FT82Ll+ahiIfwWy+Kn8HcNvdzpwQ/NKlS3j729+Op556Ku88nU4nFEXB8uWV9T5A1Cr4HYKIiIiIiIiofsroa5Oo8ZxOJ7xeb7ObQUREc5nDAVx/A7DhmnSQnThSfPqLF4EDMeDI88A73wUsXdqYdhbRtqQDy955NZa+Yz0mExrOP34Cl35zGij3EsRZHReffh0Xn34dnWt70LN1HbpvW422rva6tpuaR9d1nFCTOHbo1aLTORzApjevxdpN+ceuzrZocQc2XL8SL/7mVJ6FAi8cfhU3vf1NGQ8vXrwY+/btg8vlwoULF3Jelkgk8MlPfhLf+MY3eIEFERERERERERFlYHfiRERENL91dwNuD/Du7UBPT+npX3gBGPsO8OvD6WruFuBoc2Dx7/Tiqo/cjLWDW7DsrmvQ1lPZtYhTJyegfe8ITvz1z6H9awJTp3JDRZrbZmd1JH51omSA3dHZjpu3Xlt2gG1Y51yJRUs68z6XevU8tNfO5zx+ww034Mtf/nLBeX7rW9/C17/+9YraQURERERERERE8x9DbCIiIloYNm5Kj5V9882lp52aAh77MfB/vw9oqfq3rQIdvYuxYvtGrBu6Hb27bsCiNy2r6PX65AzO/+wVvPqlOE595RlcPPw69JnWCOupelOT0/j1z17Aay9qRadbsnQR3nLnJqxYVcYFHVna29tw7c2rCz5/7PCryDdS0Uc/+lF8+MMfLvi6T33qU3j++ecrbg8REREREREREc1fDLFb3MjICBwOhzk2dLXC4TD6+/vR29tr3nw+H2RZtqml9tM0DSMjI/D5fOjv74fH48HIyAg0TWt204iIaK7q6gLuuBP4/fcDK1aUnv7ECSAaAZQ4MDNT//ZVwNHRhp63rsbqT96G1f/xNnQPrAE6Kvuv3eQRDae/8Ruc/H+fxNlHj2Pm/OU6tZbqaeLMJTx98CjOni5eXS+sWYo3v2sTFvcsqnpZV129HEt7l+R97sLZybwhusPhwD/+4z/C6XTmfd3ExAR27dqFyXLGriciIiIiIiIiogWBIXaLUlUVHo8HwWCwpvkoioLe3l6EQiEEAgGkUimkUinE43GIogiPxwOfz9dywfC+ffvQ398PQRAQCoUQj8cRCoWwb98+bNq0CYqiNLuJREQ0l61fD3h3Are9NT04cDEzM8CTvwC+913gVJ7xgFvAog3L0Oe9Hus/9zas2LEJ7X2LK3r9jDaJsz84hhPDv0By33OYfPFs3opaaj3JE+fwzGNHMXlxquh0669biZtuvwYdnbWNh+5wOLDxljUFn3/xN69hZir3go/ly5fj29/+Njo68neD/6tf/QqDg4M1tY2IiIiIiIiIiOaPygZTpLrQNA3j4+NQVRWJRAKyLNsS0sqyDI/HA1EUEY/HIQiC+ZwoigiFQnA6nQgEAlAUJWeaZstuj8vlQjweh9PpRH9/P+LxOFwuV/MaSEREc1tHB3D72wHndcDBR4HXXy8+/enT6SD7LbcC/QNAZ/6xgZuprbsTy961AUvfeTUuPZ/CxOOv4NLzKaDcPHpGx4VfvoYLv3wNnet7sHTreiy5dRXaFtUWfJL9dF3Hy789jRd/81rR6RxtDjhvXYfVbxJsW/byvm6svHo5Tr98Nue5qckZvHzkNN50U26341u2bMEXvvAFfOYzn8k737/7u7+D2+3G7//+79vWViIiIiIiIiIimptYid0CxsfHzaprRVHgdruRSCRqCpQ1TYPP5wMARCKRgvPy+/3w+/1QVRXbtm2renl2GhoaKhqoe71eAMCePXsa2CoiIpq3rroK+HcfTAfa7SXCWl0HnvoVEB0DXn6pIc2rhqPNgSU39uGqj92CtZ8ZwNJ3XY227squXZx6ZQKp7/4WJ4Z/Ae1BFdOnL9aptVSpmZlZ/FZ5uWSA3dnVjs2/e62tAbbh2ptXw9GWvxeDV46cxuSF/JXh/+W//Bds37694Hw/9rGP4aWXWvfcIiIiIiIiIiKixnDo7CuyZfX29prdfCcSCYiiWPZrfT4fotGoWb1cjKqq5hiFkUjEDIlblaIo6O/vBwDEYjG43e6a53n48GHccsst5t+HDh3C5s2ba54vERHNMWfOAD/+EfDKK+VNf+ONwNvfkR5ru8XpUzO48NQpnH/8BKZePl/5DBzA4ut70bN1PRZf31swwKT6unxxCs/+4jjOa5eKTte9vAs33f4mdHXXr8eAFw6/ipePnM773FUbluP6/g15n3vttddw66234uTJk3mfv/POO3HgwAG0l7qohKjJ+B2CiIiIiIiIqH5YiT0PaZqGaDQKAGUFvKIomgH58PBwwel8Ph8cDoctt1rG+rZWaHNsbCIistWKFcB73we8605g0aLS0z/7LDD2HUBV69+2Gjk629EzsBar/+NtWPXJW9H91tVAewVBtA5cei6F03sP4+QXx3Hu4EuYmSg+DjPZ63zqIp7+8dGSAXbfumV48x2b6hpgA8DV11+FjgJdzb/+0lmcS17I+9zq1avxjW98A44C49EfPHgQf/VXf2VbO4mIiIiIiIiIaO7hmNjzUDgcNu9v2bKlrNeIoghVVaEoClRVzVv1PTQ0BI/HY0sbC4XrgUAA4+PjCIVCZQXwiUTClvYQERGZHA7gppuBN10L/PQx4OjR4tNfuADEfgBs2gT87h1AT09j2lklh8OBrjctR9eblmPFvZswMf4qJp44gRltsux5zCQv4cxDR3Em9gK6b12FpVvXYdGGZXVsNb3+8hkcUV7B7GzxTpQ23HAVrrlhVcGA2E4dne14002roT51Iu/zxw6/ilveuTFvW9xuNz772c8WvIDy/vvvx91334077rjD1jYTEREREREREdHcwBB7Htq3b595v9xxta2htSzL8Pv9OdO4XC64XK6a21eIpmlmAC9JUlkhdi3jhhMRERXV0wPcsx1QE8BPf5IOq4s5ehR4+WVg6zuAG25Mh+Etrn3pIiy/6xose9cGXHo2ifOPv4LJ32rlz2B6Fhfir+JC/FV0XrMMS9++Dt1vWQVHJzv7sYuu6zj+7Cm89PzrRadra3PgOtd6XHX1iga1LG3NmwScUJO4eC73IohzyYs4/crZgm26//778eijj+KJJ57IeW52dhYf/vCH8dRTT6Gvr8/2dhMRERERERERUWvjL4zzkLWL7XJ/9LOGwaXG0K4XaxsCgUDB6VRLl612VYYTEREVJDqBnX+YHv+6lMuXgYM/Av7t/6bH154jHG0OLLl5JVZ94s1Y89/6sfR318OxuLLxiKeOn0Mq8jxOfOHnOPPQUUwni3d5TaXNTM/iuSdfKhlgL1rcgVvu2NjwABtIHzsbb1lT8PkXfv0aZmdm8z7X2dmJb3/721ixIn+7X3rpJXz84x+HrhevPiciIiIiIiIiovmHIfY8o2aNyZmvW/B8Vq5cad4fHx+3tU2VcLvdiEQiRauwI5EIgHRleDnV2kRERDXr6gLuvBt47+8Dy5eXnv6Vl4HoGPDUr4DZ/AFeq+pc1Q3h951Y97nbIXzgOnSuq6x79NmJaZw7+BJO/r9P4vX/cxiXnk9BL9EFNuW6dOEynnnsKJInzhWdbqmwGG+5cxOWCksa1LJcvauXQlid/ziZvDCFE2qy4Gs3btyI0dHRgs9///vfxz/8wz/U3EYiIiIiIiIiIppb2J34PKNpWlWvs1ZBVzsPO0iSBI/HA5fLlTeAl2UZ4XAYgiDgwIEDdWvHkSNHKn7NqlWrsHr16jq0hoiIWsbVGwDvTmD8yf8/e3ce30Z954//Jd9X7JHtnJDEHiUcSSiJ5CTcASzBAgVKkRK2LW32u40E7ba7v3axyC7dbXe3DfL22G13W6TQkpayEEvQlhTaIoX7TCwlQA5ySHbIfdgaO77iQ/P7w8wwkkf36LD9fj4e88BkRjMffeaQNO95vz/Ahx8AsTJER0eBd98BDh8GbrwRqKvPWjOVUFBSiKpVc1G5cg6Gj/Si752TGNxzDhhLMCDNA0P7uzG0vxtF9eWoXD0XlbpZKKgozmzDp4DergEc2HkUIxfGYi5Xf3ENNMvnorAw98+lNiydg91n/YDM4XHs4DnMXMCgpFT+p4fJZILZbBaHlYn07W9/G9dddx2uvPJKJZtMSJgzZ87g7NmzSb0mld8MhBBCCCGEEEIISQwFsckE3d3Rs2UyjWVZ2Gw2GAwGWCwWmM1mMAyDQCAAl8sFq9UKrVYLp9OZ0fGwP/e5zyX9mn/913/Fd7/7XcXbQgghJM8UF4+Pe71oEfDqq0B3V+zlz50FnnsWuHI5oNUBRZPr65dKpUJpQw1KG2owdn4Y/TtOoX/HSYz1DCe8jtFzg+h5IYDelzpRsXwWKq+ei5J5VRls9eR15mMO/t0nYj4fAQALLp+FixbXQZUnY69XVJdi9kI1TncGJ8wbGw3h6EdnoblybtTX/+QnP8Fbb72FvXv3Tph34cIF3HfffWhvb0dlZXKVAQhJ1M9//nN873vfy3UzCCGEEEIIIYQQ8oncp20QRUUGoFMJ9OYyExsAjEYjvF4vurq60NzcDLVaDYPBgJ07d8Jut8Pr9SZcJp0QQgjJmJmzgM/fC6xcBRTGGT86FAJ2+YBnncDJE9lpXwYUzihBdfMCzGlZhbovXY5STXJjMPMjIfTvPIUzP92FM794HwO7z4AfnVzl1jOF53l07jmFw7tiB7ALCgtw2ar5uPiS+rwJYAsWXDYThUXyPy9OdwYx0Bt9nPSKigo888wzKCsrk53/0Ucf4Zvf/KYi7SSEEEIIIYQQQggh+W9ypQKRuHIdgFYKwzCw2Wy5bgYhhBASW2HheHY1ywKvvQacOhl7eY4Dnv8DsGQJsPpqoKQkK81UmqpQhfJl9ShfVo+RMwPoe+cEBnxnwMcpfy01fKQX3Ud6UVAVQOXKOahcPRdFTGkGW52/RkfGcLD9OLgzfTGXK60oxmWr56OyWj7Qm2vFpUW4+JJ6HNl3RnZ+597TWHL1wqivX7ZsGf7rv/4LDzzwgOz8X/3qVzAYDLjvvvsUaS8hhBBCCCGEEEIIyV8UxCZExu9//3ssWrQoqdfMnDkzQ60hhBCS9xg1cNfdwP594+Ngj4zEXn7fPuDIEeD6G4CFDVlpYqYUz6qA+u5FqPmrBgzsOoO+d05i9PRAwq8P9Y3g/CtHcf61oyi7vA5VV89FqYbJuyzjTBnsG8ZH732Mwb7Y5dmr6ypw6cqLURxlXOl8MZetxanOIC4MTDwHuDP9CJ7ug3p29FLyZrMZbrcbzz77bNT5q1atoqo8RHFf+9rXYDKZknrN4cOHUxqGiJB0eTwe+Hw+tLS05LophBBCCCGEEJIxVE58isnkONHTyaJFi7B06dKkplmzZuW62YQQQnJJpQKWLAXW3gcsiJ5tKurvB/78J8DjBgYTD/rmq4LSIlRdNQ+z/0GLmeYrUH5FfXLfNEPA0N4unHt8D07/2Iu+t08gNDSasfbmg56z/fjw9UDcAPashQyWXLMw7wPYwHi584VLZked37n3NPhQ9HrpKpUKmzdvxoIFC2Tnnz9/Hn/913+N4eHEx2QnJBGzZs1K+vt/sg+9kqkjEAhApVKlNTkcjpS3b7FYYLVa4fP5kn6ty+WK2iaPx5NymziOg1qtll1vrAdEAoEALBYLNBqNuLxGo4HFYkEgEBCXczgcsFqtKbcvE1pbW1Pa9xqNBiaTCS6XK9dvIW1WqxUGgwE6nU7c/6lWCHQ4HOK6NBoN1Gp1TvqI4zg4HA6YTCaxHdL9JneeWK3WsHO6tbUVarVasWmyVF1Uou/yYRu5RH0YX6bPL6vVKnvtTvRcjLc96fuY7p8hhBCSCApiTzG1tbVh/5/KF10KhBNCCCFpqKoC/uo2oNkARBnfN4z/MLD1GeDgAcQcDHmSUKlUKGUZ1H3xcsx9eBVmNC9AwYzkyqaPnh0E97wfJ3+wA8HfH8bIqf4MtTZ3TnZ0Y+87RzA6EntM8MZls6G5ci4KCiZPZnrdvBmYUVsuO2/w/AWcPhKM+Xq1Wo2nn34ahVHGmt+xYwceeeSRtNtJCCGpivydLQyH5XQ64fV6EQwGJ0x2uz3sNXq9PqVt+3w+Mbgbuc5EGI1GBINB+P1+MZNbuAeQzpBeDocj7H6EXq+H1+uF3+/H5s2bZV9jtVqh0WgAAE6nU+wrp9MJhmGg0+lgtVrFQHe+BfJaWlrg9/vh9/thNpvD5skdB36/H16vVwzQm0wmqNXqvAkMpUKj0YBlWQQCgbT3D8uy0Gq14DhOkfUli+M4WCwWqNVqWCwWAOPHqNPphN/vh91ux8qVK8WHLoSHSDweD1pbW8PaazabsX37djidTjQ1NYHjOHHavHkztm/fLjs5nU7YbDbo9fqw10gf6IjH4/Fk/QEAJfsul9sQls/FAxSTvQ9dLhcMBoMYENfpdDCZTBm5vmX6/LLZbOI1W/q5yHEcmpub47bP6/XC6XSGbaepqUn8jiCgzxBCCEkQT/IWwzA8AB4A7/f7E3qN3+8XXwOADwaDCb3ObreLr2FZNo1WT0579uwJ67c9e/bkukmEEEKmgsFBnn/Zw/OP/Tyx6Y/beL63J9etVlxodIzvf/8Mf/qx3fxR6+spTacf2833v3+GD42O5frtpGVsLMQf3n2Cf+v3e2NO7/5xPx88fT7XzU1Zb/dA1Pf23osf8SPDo3HX8f3vfz/s+1nk9Oc//zkL74SQ6Og3xPTldrvF/d7S0hJ3+WAwGPb73mazpbxts9ksrodhmJTXIxDeQ7L3HiKxLMvbbDZxPW63O+bywvvwer1RlwkGg7xerxf7zmw2p9S2bJDei0n0norb7Rbfm9FozHALM0u67xO9DxWN1+sV1+V0OpVpYBxOp1PcptFojPse7HY7zzAMb7PZxH0Y7byWvp9k7rf5/X6eZdm4/RAMBnm/38/b7XZeq9WK27Lb7QlvKx2Z7LtsbCPX/cfzk7sPheu0Vqvl7XY77/f7ea/XG/Y6lmVjXuvTkenzS7i2C8sm+xmu1Wp5rVabUHum82cIIYTEQpnYU0xkJnZ3d3dCr5M+TUeZ2MD69etx4403Tpi2bNmS66YRQgiZTMrKgJuagdvvAKpmxF/+2FGgbSvw4QdAKHaG7mSiKixAxWdmYpblSsz+By0qV8+BqiS5r6HDHb3o/r+PcNK2E72eIxjrnXzlpEeGx7D/nSM43Rk7E7mssgSfuaERzKzoY0fnuxnqctRfXCM7b3R4DMcOnou7DqvViptvvjnq/C9/+cs4depUym0kJFFbtmyR/W2wfv36XDeN5Ijw+9lsNieUvbxhwwbxNVqtNq2xrNva2sCyrNgOJbIFhYw8ILXsbo/HA71en/C9BI/HA4fDAZvNBq1WG3U5hmHgdrsn3OfIR6m0Ua/Xo6OjAyzLwuVyQafTZaBl2SEck/m2rkRYrVax5L3dbhcrAcRiNpvh9XqxadOmuBmwqd5jY1lWPB+jZWILZdx1Oh3cbjfWrVuX0rZSlem+y/Q2ct1/wOTvw+bmZrAsC6/XC7PZLFZUaGlpQUdHB7RaLQKBAHQ6XUpDYMSTyfNLug232y3+fzLDeej1ejQ1NcVdbrp/hhBCSCwUxJ5iIj+8Ey2X4/f7xb+z/YMhH7W3t+O1116bMHV2dua6aYQQQiaj+QuAteuAZVfEX3Z0FHj7LeAPvwMSfBhtMimeUwn1PYsx959Wg7mTRdFM+bLT0YR6h9Hr+RgnH92Brv/bjwuBHvCToAz7wPkL+PD1AHrOxR7/vGZmJT5zQyPKZ5RmqWWZs/DyWVHLoJ8MdGOoP/aDCIWFhXjyySdRX18vO//MmTP48pe/jNAUeuCD5KfOzk7Z3wbt7e25bhrJEeFh8UQC2B6PJyzQ7HQ6U96uy+UCy7Jh200l6ByptrYWRqMRAFIqS2qz2cIC4fEIY1sL20xk/VMVwzDiMeHz+ZLqR5I+h8OB1tZWAOPHWWRJ31hYlk3rfE6EXq+HVqsNu2cntX37dvA8L5bhT3WYglRko+8yvY1c9h8w+ftQuJZH+xxiGCZsOAkhkJ4v4p1fUpGfvfnyXugzhBAyHVAQewqSPsmcaCa2dLmVK1cq3qbJpqmpCWvWrJkwNTQ05LpphBBCJqviYuDa64DP3QOo1fGXP3MGeNYJtO8ExsYy374sKygrQtW1F2H2t3So/+oylC2tA5IZ9jnEY/CDczjr+ACn/8uHvndPInQhP/spePo8Pny9A0P9IzGXm8vWYslVC1BUIj8W9GRTWlGMeYvqZOfxIR5H9p2Ju4558+bh17/+ddT5brcb//mf/5lyGwlJRENDg+xvg0Qya8jUxHEctFpt3AwwjuPCbnTbbLa0Hhq32+2wWCxhwV+Px6PI2MEbN24EkHx2N8dx6O7ujplRHSnZbDyj0TilK8ZptVoxsORwODKSrUgmkgZ8WJZNqUKCXq9P+GGMVK1bty5qpmiuzots9F02tpHL68pU6EOHwwGWZWNmMmu1WvHhgEAgkHfjN8c6vyK1tLSEvZd8CRjTZwghZKqjIPYUJH1yMNEPYulymf4CPhls2bIFr7766oSJSgYSQghJ2+w5wL0mQNcEFMT5KhYKAd728WD2FC2brFKpULZIjfr7l2COdRVm3DQfBVXFSa1j9PQAuN8fxskfvAfueT9GzsTOds4Wnudx/HAX9r97FGOj0bOFVSqAvXIuGq+YA1WUzOXJ6qLF9SguLZKd13WiF73d8ffV7bffjm9961tR5z/yyCN47733Um4jIfGsX79e9rcBDTU0vSXyEIOSZcQ5joPH4xFvVEuz6ZQICmi1WjHAnkx296ZNm1K+ke/xeBJedu3atSltY7IQMhoj/yaZs2HDBvHvdLL9hQdAlCB3D08ox5xPstF3+bh/lDTZ+5DjOPGhJ41GE/P10oecMl29IBYlzi9pKXaHw5HU51gm0WcIIWQqoyD2FCT9cuH1ehN6jfCUFsuyVE6cEEIIybTCQqBp5Xgwe9bs+MsHg+Plxd96ExiJnc07mRUxpai5tQFzH16F2vsuRcnC6qRez18YQ9/bJ3D6x16cffxDDO45B34sN6XGQ2MhHN51Akf2no65XFFJIZZcsxBzGhLIzp+ECosKsODymVHnd354KqFy8Js2bYo6ztvo6Cjuu+8+RTIRCclnobEQes8N0hRnCo1lfogBlmVhMBhiLqNkGXFg/Ga5NHCd7jjWcoQb3x6PJ+Gb+i6XK6nytMCnQ5hZrdaEr90GgyFuoGQyk96LSab/SWp8Pl9YtmI6yRyJVGVItE1y15Wmpqa8Oh6y0Xf5uH+UNBX6MLLyZ6xjVFrxM9GKoUpT6vySK5GeD79B6DOEEDKVyadFkEmNYRjo9Xp4PB60tbXF/UErfWqMntYihBBCsqi2Frj7c8DePcCO98bHw45lz4dAZydwww3j42xPUaqiAlQsn4WK5bMwfLwPfe+cwOD7Z8GPJB6YuHCYw4XDHAprSlG5eg4qV85B4YySDLb6U8NDoziw8yjOdw/GXK58RikuXz0fZZXZaVeuzFrA4GSgGwO9FybM6+OGcO54L2ZeXBNzHSUlJXjmmWewYsUK9PX1TZjf2dkJi8WCZ555BirV1MpmJ0TQF7yAJx95J9fNyHv3/8fVqK4vz+g24gUDlC4jDowHqqWBcCFzOhAIIBAIwOfzJVXSW87atWvF4Ljdbo+buedyuVIaQ9ZisYgB7MbGRmzevDlun06HinHSjECXy5VW5j6JTXqfTIlxkJVYR7TgHsMwCT3wly3Z6Lt83D9Kmgp9yLIszGYz2trasHbt2pifcdKAam1tbdptSYWS55fRaITZbIbD4RA/791utxLNTAt9hhBCpirKxJ4kkn2qy263g2GYhMazEr7YSMfQmO7Wr1+PG2+8ccJEJQMJIYQorqAAuOIzwNr7gIvnx1++7zzw4gvAy9uBwdhB0qmg5KIq1BovwdyNq1BzRyOK6sqSev1YzwX0vnQEJx/dga5nPsKFI70ZuREYCvHo7RrA0QNn8cHrgbgBbPWcKlxxfcOUD2AD4yXjG5ZFrzhwZN9pjCWQOblo0SI89thjUee3tbXhl7/8ZUptJCSWLVu2yP42oKGGSDRKlhEHPn3wPDJILX0IXYlsbIZhxGBxIiXK7XZ7Sg/Ct7S0iAEPIQCgUqlgMBjQ2tqa9fKsHo8HJpMJGo0GKpUKarUaOp0uqUxxJUiDQPECIqm22eFwwGAwQKfTQaPRQK1Wy+5ri8UiLqdWq6FSqZLqC47jYLVaxW2oVKqM9anwnoTtqNVqmEymmMeRdJ4S1QidTmfa53kuyywnIxt9l4/7R0lTpQ/tdjuCwWDcz5+dO3eKf6f7sFWqlD6/7HZ7WOZzPoz1ncxnCCGETCYUxM5j0i/2yZZbYVlWLG9iMpmilhFxOBxwuVxgGGbSfGHOhvb2drz22msTps7Ozlw3jRBCyFQ1YwZw+x3ATc1AWQKB2kMHgbZngMOHgDzKzsiUgopizLj+Ysz+dhPq/98ylF1WCySTcDvGY3D3WZz9xfs489Nd6N9xCqHhsZTbw4d4nO8ewLGD57D37SPY8eJH2PNmJ45+dBbDg7Ez6i9aXIfLVs1HUXFhytufbJiZVVDPqZKdNzw4ipP+roTW88UvfhFf+cpXos7/5je/iX379qXURkKi6ezslP1t0N7enuumkTzkcrkULSMOjN8slxt3WjpOtFI30IXhyeI9EB8IBNDd3Z1ycMTr9U4IZng8HlitVhgMBjHomcnAgBBANxgMYFkWTqcTPM8jGAxi8+bN8Pl8aGxsjJsYoJS6ujrx72j3gNJtM8uy0Gq14DgOgUAgakBZo9GImf7JBp0dDgcaGxtRV1cHp9OJYDAInuexceNGcZ4SfRoIBKDT6cSAe0dHB3ieF4fVMxgMUR+ykN4jy3aZemEsYWEf+Hw+WCyWvAiCJSIbfZfL/ZMN06kPOY4LC6hneozybJ5f0s93i8WS8xLeiXyGEELIZETlxPOE9AM9EAhMeIrNYrHAYrGAZVlxHJKmpqaY47oYjUa43W6YTCbodDrYbDYx01p4KtbhcECv18PpdObdGDG51NTUhMrKygn/3tDQkP3GEEIImT5UKuCSS4D5FwNvvwUcPhx7+aEhYLsHOHQIuP4GoEo+SDiVqApUKLtEjbJL1BjtHkLfeycxsPMUQgNxSrFLjJzsR/C5Q+Be7EBl02xUXTUXRXFKz/I8jz5uCL3n+tFzbgC9XQNJj7uqKlBh0fK5mDmfSep1U0XDktngTvfJPnNx7GAXZi1Qo6Qs/s+T//mf/8E777yDgwcPTpg3ODiI++67D++99x7KyzNbTphMHw0NDVizZs2Ef+/v76dANgnDcRw2bNgg/r8SZcSB8cC4dAxOgZA5LQQEI8fNToW0TPmmTZuilvGOFlhPFMMw8Hq9cDgcsNvtYWO3CoTgg9VqhdfrVaQvBUIpc47jZAPqWq0WbrcbFosFJpMJZrNZsbHHo5Hek5ELhijRZr1eD71ej3Xr1kGn00Vti5BxqdFoks62t9vtsvvLaDRCr9dDp9PBZDLBZrOlnB0bCATEwJzb7Q4rdSwE94XMfgAxS+Nn815YIBCAWq3O2vYyLRt9N9XvVU71Pty0aZP4IIzNZstoW7J9fmm1WthsNvEaaTAY4Pf7s7b9SPE+QwghZLKiTOw8YTAYYDAYYDKZYLVaEQgEwDCMOHV3d2PTpk3YsGGD+NRtIiW29Ho9Ojo6YLPZYLfboVaroVar0djYiO7ubrjdbrjd7in/pTBZW7ZswauvvjphopKBhBBCsqK8Amg2AH91GyDzUNUEHx8Zz8reu2daZGULimrLwNzWiLkbV0NtugTFFycXxOeHRtH35nGc+mE7zv5qDwb3dYEPjfffeNB6ECcOd2H/ux9jx4sH8OHrHTiy7wy4M31JB7CLSwux7NqF0zaADYyPAT67QX4cvNBYCB9/dCah9VRVVeGZZ55BSYl8KfYPP/wQ3/72t1NuJyGR1q9fL/vbgIYaIpGULiMOjAemjUZj1N/s0kCyUkFW4Ya8z+eLeiPc5XIpMhyZ2WyG1+tFMBgUy9VGBmc5joNOp1P0przJZALHcbDZbDHL2wpDtTkcjoyXOZdmPMuNG6tkmxO9B5TKvSJpmV259QnHqdVqlX14IREGgwHA+PETbbxfYTutra0xs8mzXTKe5/mwye/3xx1/Pl9lo++yuX9yYSr3YSAQEB8kMRqNGS/pnovzS/qZFQgEUhpiQynxPkMIIWSyoiB2nhA+XIPBYEITz/NRn4iOxDBM2A9DYXI6nVG/7BNCCCEkDyxsGB8re8nS+MuOjABvvgE8/3sgGMx0y/KKqrgAlbrZmP13KzDr68tRoZ0FFCVTaxwYOhjESdcBHHh8Nz584SB2vHgAH7zWgc69pxE83Yex0eSC1lKVNWX4zBoWM2orUl7HVDH/snoUFsv/BDlzhEN/z1BC61mxYgX+8z//M+r8X/ziF3juuedSaiMhhKQiE2XEgfgZz3q9Xgw0xgo6J0ManJa7+e9yuRS/lyBkldtsNni9XvA8D7fbLQYHOI5LK/NbShrcTeS+ilC23WQyKbL9REQGgSdDmwXxgid6vV58f6m0r7W1VTzOYx0TLMuK22lra5swT5DLzElgvC0tLS15NWZzLNnou3zaP5kwHfqQ4zjxYROj0ZizISyzcX5J31tra2vGH3hKhJKVSwghJNeonDghhBBCSD4rKRkvFb5oMfD6q0C8J+lPnQJcbYCuCbhyOVA4fcZdBoCS+TNQO/9S1NzBYqD9FPrePYmx4IUJy/EARssLMVRdhKGaYlyoLkZICK6OJl6aPJ6Z82vAfmYuCovo2VEAKC4pwvxLZ6Jzz2nZ+Z17TmPJNQugUsV/COEb3/gGPB4Ptm3bJjv/b//2b9HU1IQFCxak1WZC8kWVuhT3/8fVuW5G3qtSl2Z9m8mWEXe5XLDb7XC73THXK4znKQQCEmG32xXJOjObzXA4HGhra5uQ4W232zNeWhsYD3Z6vV6YTCa4XC54PB74fL6YWciJkLY9kRv9QtlqYZzwRBMKkiUNNEW+R6XbnOssPa1Wi0AgIE7JBFy2bt0atp5EthMZxNPr9eIYuUo8+OFyucAwTFoPdxgMhphjhbe2tuZFoDsbfZeP+0dJ06EPm5ubEQgEsjIUQyIyeX4JQxgID+WYTCZ0dHRkvepprM8QQgiZzOhuGiGEEELIZDB3LnCvCVihBQrifIULhYCdO4DnngXOJlaieaoprCzGjDXzMeehlaj7yhKUXKLGSFkBzs8uxbnFVTiuU+PkcgZBtgqDdaWfBrDTVFCoAjOrEguXzMLymzVYrL2IAtgR5jTWoqxSvhR4z7l+BE/3JbQelUqFX/3qV7joootk53Mchy984QsYVfChBEJyqaCwANX15TTFmQoKs3/NTbaMuDB8WDx2ux1mszlupTav1yu+RghapEvIcBWCoNK2d3d3p5XllUxQHgjPclNiHPpkS1hL99XOnTvT3n400vcW2Uf52uZUSYPoyb436fIajSbm5PP5wLIs6urqwtYhzeBWImty69ataQcKpZnjkTweT1jwPpey0Xf5uH+UNNX70GAwwOfzwel05kUAG8j8+WU0GsWHhSIfbMuWWJ8hhBAymdEdNUIIIYSQyaKoCFi1Gvj8vcDMmfGX7+4Cfvcc8M7b4+XGpxGe5zHYN4wzRzkcHRjEkXklOLlCjSBbhYH6UoRKlAta18ysxILLZ+GK6xuw6vbLsOTqhbhocT0qZmQ/G3AyKChQYeGSWVHnH9l7GqFQYmO719fX46mnnkJBlAc73nrrLXzve99LqZ2EEJKIVMqI+/3+hDJhHQ4HLBYLGIaJOWm12rCy20oELKTr3LRpk/jvdrsdGzduTGvdHo8n6TFahey/qVhaGBjfb0JwlmXZvMkYzXd+vz+hKfLBEunxDSBmhmYifD4fmpqa0loHy7JRqzMIwfh8kI2+y8f9o6Sp3IcGgwHt7e3wer0Zq1qRimycX5s3bxYfIHK5XIo9VJYI+gwhhExlFMQmRMb69etx4403Tpi2bNmS66YRQgghQF098LnPA1ddPR7YjoXngQ/eHy8xfuxYdtqXI0MDwzh9hMMh73F4XzqEXdsPw7/7JM4d68XIBYWycUM8SntGUDfI45IFtVj5V5di6TULcfEl9ZhRW4GCguTG4p6uaufOQHWd/Bjhg33DON2Z+Ljua9aswSOPPBJ1/ve//3288sorSbeREMGWLVtkfxusX78+100jOZZsGXFBe3t73Exsj8eD2trahEuCSrPulMp8E9YpHWtbqXLayWZUC/0QmVGbimSDFdKAu1CmW2nSYIdcOfh8bHM6uru7xb+TLXurVDB38+bN4t9WqzXl9fh8PnR3d2e0fK/b7cbKlSsztv5kZaPvJtP+ScVU7EOTyYRAIACv1yu7rM/nE0tu5xOlzi+GYcIeZLNYLElXmkhVvM8QQgiZzCiITYiM9vZ2vPbaaxOmzs7OXDeNEEIIGVdQMD7mtXEtEKWccpjeXuCFbcCrrwAXJo4RPRldGBzBmY85HPIdh9d9CD73Yfh3n8DZYz0YHlIwaN07guqjA5i1twfzd3Rj9r5eVO7uxtDWQzjzEy/Ov34MoYHplemeLpVKhYZls6POP/rRWYwOjyW8vu985zu4/vrrZefxPI8vfelLOHfuXNLtJAQAOjs7ZX8bKFHWmExuyZYRB8Zv4vt8vrjBWLvdHhaYjmft2rXi3y6XK+lMZzlms1n822azKToedLI32YVAgBLZZdL3kEiAQZr9Le1npXAcJwaY9Hq9bB/nqs2ZynwXqgXEKvEbTbJlkjmOky2DrNVqxeMwEAigtbU1qXYITCZTRoNGgUAAHo8nr4Kw2ei7ybJ/UjXV+lAawI5VtjuRKiTZpPT5pdfrw74LKFEZJZ5EPkMIIWQyoyA2ITKampqwZs2aCVNDQ0Oum0YIIYSEq6kB7rgTWHMTUCI/znCYAx8BW58GApOvHOfw4AjOHuvB4d0n4PMcgvelQzi86wTOHu3BBaWCyPwnQetj40Hri3d2Y/beXjDHBlHWOwpVRJXrsa4h9LzYgZObdqDbdRDDxxMbz5kAVUw5Zs6vkZ03OjKGowfPJryuoqIiPPXUU1Cr1bLzT5w4gfXr14PnEytTTohUQ0OD7G+DfCpNSrIvlTLiwKeluWNlYgvjUEuDyPEwDBN247qtrS3h18YitMHhcGDTpk1JBdZj8Xg8CWcGCkEGo9GoSKBBmjEvLZUejdCXNpstobHMk9Xc3AxgPKAb7ThSus2Jvg/pMS7Nno4l3nLScvKJnjdSLS0tYl8kUnXAZDJFLaXc0tIiBpysVmvS5X8NBgNYlk3qXE2WcJ7k22dONvpuMuyfdEyVPjSZTOA4Dl6vN+a1xe12511liHjnF8dxST8UZrPZsvrQSSKfIYQQMplREJsQGVu2bMGrr746YaKSgYQQQvKSSgVcdhmw7q+BRLJZBgcB90vAX/4M9Pdnvn0pGh4axbnjPfC/fxI+z2G0v3QIh7zHceYIh6F+hYLWKqBKXY6LFtdhydULsOq2y7BEexFml5airHcUBaHEVsOPhDDQfhpnfrYLZ36+GwO7zoAfTfDF09iCy2ehoFC+BPupQDcG+4YTXtf8+fPxxBNPRJ3/wgsv4L//+7+TbiMh69evl/1tQEMNTV+plhG3Wq1iMC1WNppwUz3ZgOm6devC2hSPkNEbq6qANGhdW1sb830me6O/tbUVVqs15us4joPJZALDMGGlcdMljI0a+TBCJCE4YzQaY2baJxrglfL5fNBoNPD5fNBqtQkFgJRsc7xxxh0OR0rvy2KxyGY+A+MPJAjlhO12e8qBHmlfxArKtba2IhAIxOwHm80mBn4sFovYf7H4fD4YDIawtkSTTlUEh8Mh7utkrwdKVGOIJ9N9l61tyMlG/wGTvw8tFot4jBoMBtlJp9NBo9FkpKJAps8v4fMx2jUtmmSDydn4DCGEkMmKgtiEEEIIIVNFRQVguBW45dbxv+Pp7ADangH27xsfOzvHRi6M4tyJXgTeP4ldL/vR/peDONh+HKc7gxjqTzyYGU8lU4Z5i+pw+VXzsfq2S/GZGxqxcMlsMLOqUFRaiPKl9Zj51Ssw+9s6VF0zD6rSwqTWP/zxeXRvPYCTth0YOpD8DYnppLS8GBctqpedx/PAkX2nk1rf3Xffja9//etR57e0tGRtbDpCyNQlLSPOsiz0ej0CgUDY5PP54PF44HK5YLFYoFarw0q5Rt5o5jgOHo8HBoNBDMoZDIaErlkcx8Hn82Hr1q3ivwUCAVitVgQCgQk3+QOBABwOh5i9ZTKZ4HA4opZcFoIOclnYQpZa5PbtdrvstgVmsxnBYBA+nw9qtVoMhAjjb3s8HrS2tqKxsREAYt6cV6lUCU8ClmXh9/uh1+thMpnCxi4NBAJwuVzQaDRwuVxhASC59x8IBMIygoX9L/SNMAn/7nA4xMBOIBCAzWZLKPigVJsFwsMSra2tYUFx4dix2+1hDw4Ix0ms8rh2ux02mw06nW7COh0OB3Q6HWpra+F2u2UzOzmOCwtKb926VfY4kvaFEJSTltEXzqWtW7cmFOQzGo3w+/0wm81wuVxQq9XiOoXtC/vOZDJBp9PBYDDEXHcyx0bkMdLa2gqdTieec7ECf9JzUJqlv3XrVjHrPVo2aSrnTjb6LpvbSKf/pnsfWiwW8Xz1eDxRJ+G6DkTPeE62HzN9fgnl14VlDAaD2FeJYFk2oUoR2fwMIYSQyUrFU009QrB3714sW7ZM/P89e/Zg6dKlOWwRIYQQkqYLF4B33wE+2p/Y8vPmATfcOF6ePEtGh8fQ09WP3nMD6DnXj4HezIzVXVlThur6CtTUV6K6rgJFxckFpUMXxjCw+wz63zmBkVMDSW+/7kuXo3yZfKCWAGOjIezafjjqOOZLr12ImvrKhNc3NDSE1atX44MPPpCdv3jxYni9XsyYMSOl9hIioN8Q05darU47S8/v94dlNet0Ovh8PtngttvtjjkWtNCeaDewOY4Tt+dyucTMZrnlbDbbhKxVl8sFq9Uqm7Ebrd3SdRqNxrCAqkajCbvh7vP5YLfb0d7eLgZTGIZBU1MTTCZT3FLAsYJDkeRugXk8Htjt9rAy1yzLwmg0YuPGjVHfm5BJnixhHGiTyYS1a9emFHhItc2RhACIx+NBIBAAwzBgWRbr1q1DS0uLGAxmGAa1tbVgGEacJyXsQyEQI12vkGEYb38KATG5c0AIWifSF8J7sFgsKZWR5jgObW1tcLvd8Pl86O7unnBMxttvVqs15fGH5ZjNZtmAWLQ+ixStD9M9d+S2k27fZXMb6fYfMH370OfzQafTJb3taH2QTD+2tLRk9PwSzt9on5PxPpOlTCYTVq5cKVsNIpefIYQQMplQEJsQ0A0oQgghU9jx48DrrwG9PfGXLSwEmlYCn7kSKFC+YM/oyBh6u8YD1r3nBtDfM6T4NgCgorp0PGBdX4HqukoUlyQXtI6G53kMd/ai750TGNzTBYQS+xqtKinArK8tR/GcxAOx082Zjzkc3nVCdl5lTRk+s6YxqZtb+/fvR1NTEwYG5B86+PKXv4xf//rXKbWVEAH9hiCEEEIIIYQQQjKnKNcNIIQQQgghGXTRRYBpLdC+E/jg/dhlw8fGgPfeBfyHgTU3AfXpZQ+PjYyht3sAPZ9kWvdzmQlal88oRY0k07q4NDNfcVUqFUoba1DaWIOx3mH07ziJvh2nEOqNXeqcHw7h3G/2YfbfLUdBRXFG2jbZzZxfg5OBbtkHG/p7hnD2aA9mLWASXt/ll1+On/3sZ/jbv/1b2fm/+c1voNfrcf/996faZEIIIYQQQgghhBCSQXmVif3ggw/muglx6XQ6fPWrX811M4jCKIuCEELItHD2LPDaq0DXufjLqlTAlcsBXRNQlFhQeGw0hPPd4wHrnnMD6OMGgQx80yyvKkF1fSVqPsm0LinL3XOZ/FgIg/u60P/OSVwIxM52L13EoP5vlkFVmHhG8XTSc64fe986IjuvpKwIK5oXobAo8QoBPM/jC1/4Ap555hnZ+ZWVldi1axcWL16cUnsJod8QhBBCCCGEEEJI5uRVELugoAAqlSqhMUJyQaVSwWg0YuvWrbluClFY5A2opqYmVFZOLPm5fv16rF+/PostI4QQQhQ2Njaeke1tH/87npqa8bGy582TWdV40FoY07ovOBgz0TtVZZXFnwStK1FTV4GS8vzMZh453Y/zrx7DwK4zUZepuu4iMJ9lo86f7j7acRTdJ8/Lzpt/6UzMv2xmUuvr6emBVqtFIBCQna/VavH222+jtLQ06baS6WPLli3YsmXLhH/v7+9He3u7+P8UxCaEEEIIIYQQQpSTd+XE9Xo92tract2MCXieR21tba6bQbJEejNK6sYbb8xuQwghhBClFRYCK7RAIwu8/ipw8mTs5Xt6gG1/AC5fglDTKpwfCIljWp8PDoJPcFzoZJRWFItjWtfUV6I0T4PWkYpnV0JtugT88BgG93bJLtP35nEUz61EpW52lls3OSxcMhvBU+dlH4Y4fvgcZi1kkjoeampq8PTTT+Paa6/F6OjohPk+nw8bN27Ej3/843SaTaa4zs5OvPbaa7luBiGEEEIIIYQQMq3kXRCbYRjU1NTkuhlkmouWid3Q0JD9xhBCCCGZwDDAnXcD+/cD770DDE8c1zkEFfqKZ6CnhEHPqSL0veRHSJV4OedElZQVfRK0rkTNzAqUVZQovo1sURWooF57KUZ+vhujpwdklwn+7hCKZ1WgZP6MLLcu/5VXlWAOW4uT/u4J80JjPD7efwaLtRcltc5Vq1bhBz/4AVpaWmTn/+QnP0FzczPuuOOOlNpMpr6GhgasWbNmwr9HZmITQgghhBBCCCFEOXlXTtxkMuVtue58bx9JHY1nRwghZFrr7wPeeAOhI0fQLwStS2pwvrgGoYJCxTdXXFqEmpnjWdY19ZUorSiGSjW1xoke7RrE6f/ZDX5wYvYvABRWl2DWN1agcMbkDdhnyujwGHyewxgdkS93/5k1jahiypNaZygUwu23346//OUvsvPr6+vx/vvvY55M2XxCoqHfEIQQQgghhBBCSObkXSY2IYQQ5fA8Dz7EIxTiERrjERoLffJ3aPzfx3iEQqFP/vvJ/E/+5iXLCq8HxjMmSyuKUVpejNKKEpSWF6GgUPnMTEJI5vEhHn09Q+g9N4SemiXonbcQoZDy2ykuLQwb07qsqmTKBa0jFdWVo+4Ll+Hcr/YAMo+MjvUOo+u3+zFzwxVQFdE1VKqopBDzL5uJjg9Pyc7v3HMaS69dmNQxVFBQgF//+te48sorcfr06Qnzz507hy996Utwu90oLFT+wQ1CCCGEEEIIIYQQkpy8CmIHg8FcNyGmfG8fISQ/hQeSIwLGIR68NJAsE1TmIwLJE+ZHrE9YRvj3bBAD2xUlKKso/uTvYpRVlKCkvBgFBVM7WEXIZMHzPPp7htBzbgC95/rR2zWAsVHlo9ZFJYWorvs007p8xtQPWsspW6xGze0sel4IyM4fPtIL7nk/mHsWTcv+iWV2gxqnOrox2DexzH1v1wC6T51H3dzq5NY5ezaefPJJ3HLLLbLzX3nlFWzatAmPPPJISm0mhBBCCCGEEEIIIcrJqyB2vo+Fne/tI4REx/N89IDxJ/8+JpudPP5fPjIAHbGOaIFk4bVT3fDQKIaHRnG+e1B2vhDkLqsoEQPcFOQmJPN4nsdA7wX0nOtH77kB9HT1Y2wkA0Hr0AiqqwpR0zgH1bOqUDGjlIKyn6i6bh5GTvRhYNcZ2fn9O06heF4lqq6iMtZSBQUqLFw6Gx+9d1R2/pG9Z6CePSPpzw+DwQCr1QqbzSY7/7vf/S5uvPFGXHfddUm3mRBCCCGEEEIIIYQoJ6+C2ISQqS1eIFkIEkcLJCcaMJaWzBZLY0+DQHI+SyXILfxNQW5CEsfzPAbPX0DPuYHxwHXXAEaH5ccVTkdhaBTVwxxqhntQPcyhcrQPqjMA+mYBN9wIqMoU3+ZkpVKpoP78IoycHcDIsT7ZZbjnAyieVYlSlh6YlFLPrkJNfSV6zvVPmDfUP4xTHd2Yp6lLer3//u//jldffRXvvffehHljY2MwmUzYsWMH5s+fn1K7CSGEEEIIIYQQQkj6KIhNyDQTFkiWLVEtFwxOMZAcUfqaAskkmrhB7vKi8aB2eTEFuQmR4Hkeg33D6D3XLwauMxG0LigAqkfPo6bvDGqEoLXcgmfOAM+5gOUrAK0OoLGFAQCq4kLU3b8EZ362C6G+kYkLhHh0PbUfs76xHEUMPQAgUKlUaFg2G++/Kl+O/eiBs5g5vwbFJcn9pCkuLsbTTz+N5cuXo7e3d8L8U6dO4a677sIbb7yBqqqqlNpOCCGEEEIIIYQQQtJDQWxC8kgfNzgx8CsNEieSefzJfAokk6lkeHAUw4OjUedPDHJ/OjY3BbnJVMLzPIb6Rz4JWo8HrkcuRD83UlVQqEJ1bQWq6ytRU1+BSqYcBXwI2L0L8J2I/eJQCPB5gUAAWHMjMGeO4u2bjIpqSlF3/xKcdXwAjE38LA71j6DrN/sw84ErUVBCwX9BZU0ZZi1kcOYIN2He2EgIxw6cQ+MVyR9jjY2N2Lx5M9atWyc7f/fu3bj//vvx7LPPoqCgIOn1E0IIIYQQQgghhJD0UBCbkDyy581OhGRubBOSSaoCFQoKVCgoLEBBoeTvAhVUhSoUFHzy74Uq8CEeFwZGcGFwBCMXlM/2TFX8IHexGNQurShGaTkFucnkMdQ//OmY1uf6MTyUgaB1gQozass/CVpXokpdLnNeFAK6JoDVAK+9Cpw+FXulXBD4w++ApcuAVauBkhLF2z3ZlC6sBnO3Btxzh2Xnj5zoR/DZQ6i971IaU1xiwWWzcO5Yj+x3pFMd3ZjToEb5jNKk17t27Vp4PB5s3rxZdv7vf/97/NM//RMeffTRpNdNCCGEEEIIIYQQQtKj4nl+UkfMbr31VvzlL3/JdTPIJLd3714sW7ZM/P+mpiZUVlZOWG79+vVYv359xtqx48UDGB3Jn8AgyZ4JAePIQPInfxfILKMqiDL/k79VEeuTLqMqUKUcKBkbDeHC4AguDAzjwsAIhgZGxgPcA8MYGhjJSEnjTIkMckvH5i4tK4aKgtwkiy4MjHySZT0euL4wKFN+Ok2qAhVmqMtRU1+J6voKzFCXo6AwiWxTngf27gF2vAeMJNC+qirg+huABQtTb/QUEvz9YfS/ezLq/JrbGjBjDY3HLHXswFl8/NFZ2XnqOVW4fPWClNZ74cIFNDc346233oq6zBNPPJHR738k/23ZsgVbtmyZ8O/9/f1ob28X/3/Pnj1YunRpFltGCCGEEEIIIYRMXZM+E9vj8eS6CWQKkt6Mkrrxxhszut2CQhWgfKyCJChqIPmTv1ViEFgaDJYGmyX/HiVgXCBdh7DeNALJuVRYVICKGaWoiJL9NjYa+iRre1gS4M7PIPfw4AiGB0eALpmZKqC0TJLFTUFuorDhwRFxPOuec/24MJCBoLUKqPokaF1TX4mq2nIUJhO0llvhsiuAhQ3AG68DRz+OvXxfH/CnF4FFi4FrrgXKy1Pf9hTA3Mli5HQ/hjsmjscMAD1/7kTxnEqUXVqb5Zblr7mL6nDqSFC26kbwVB+4s31gZiY/fnVpaSl+97vfYdWqVejs7JRdxmw2Q6PR4Prrr096/WRq6OzsxGuvvZbrZhBCCCGEEEIIIdPKpA5i9/T05LoJZIqKlond0NCQ0e1SIAySwG4SgWThb2F+nMzjT9c3+QPJ+aywqAAV1aWoqE4uyD30SWZ33gS5eYxnnFOQmyhkeGg0bEzrof5h5TeiAqqYctTUV6CmvhIzaitQWJSBcX1nzABuux04fAh4+y1gaCj28ocPAceOjgeyFy0eD4ZPQ6rCAtR98XKc+dlujPVcmLgAD3Q9/RFm/d0KFNdP74C/oLCwAAuXzMYh73HZ+Z17TuPKGytT+iyfOXMm/vjHP+Lqq6/G+fPnJ8wfGRnBPffcgx07doBl2aTXTya/hoYGrFmzZsK/R2ZiE0IIIYQQQgghRDmTupz4rl27oNfr0dUlF1UgJHGR5cRzVQpw18t+DJ6XuZmdZRMCyXGDwROXiVXC+tMS2BRIJtGNB7klAe7BkbD/z5sgdzwqoLRcGIs7IsBNQe5pYeTCKHq6BtB7djxwPdiXgaA1gCqmTBzTurq2HIXFhRnZTlSDg+OB7MOHElt+wQLguhvGA+HT1PDxPpx97H3wIyHZ+UWzyjHra8tRUDapnztVDM/z+PD1DvRx8g9LaJbPxeyF6pTX/6c//Qmf/exnEQrJ748lS5bg7bffRk1NTcrbIFNLvvyGIIQQQgghhBBCpqKc3RF78MEHEQgE0loHPfVOppoCSSArehnqZMc8ls9oDhtvmQLJJA+NZ3KXoaK6THb+pAly8xCzzGVFBrkrS8T/L6soQUl5EZ2Tk8zI8NgnmdYD6D3Xj4EMPZxUWVOGmvoKVNdXorquAkXZDlpHKi8HmvXA4sXjJcb7+mIv//HHgHMrsOoqYOnSaZmVXXJRFdT3Lkb3Mwdk54+eGUT31gOou38JPewCQKVSoWHZHOx5s1N2/sf7z6J+XnXKD3Dcdttt+PGPf4x/+Id/kJ2/b98+3Hfffdi2bRuKiujBAkIIIYQQQgghhJBMytndl8OHD2P79u1pr0etTj3bgpB8s+y6hk+CyKCgFSFxxA1yj4xhaHDiWNxCwHuyBLlVKqCkPKJMOQW588royBh6JWNaD/RmJmhdUV0qjmldXVeBopIcB62jWbAQMK0DdrwH7N0Te9mREeCtN8azt9fcCEzD73UVy2dh+GQ/+l47Jjt/aH83ej1HUHNLQ3Yblqeq6ypQN68aXScmjic+cmEUxw93YcHls1Je/ze/+U3s378fdrtddv6f//xnfPvb38Z///d/p7wNQgghhBBCCCGEEBJfzoLYTqcTLMuiqakppbHluru74fP5EAwGM9A6QnIjI+OVEjJNFRYXorK4EJWTPMjNS4LcvRiYMF82yF1RjLKKYpRWlKCkjILcShsdGcP57gH0nBtAz9l+9PfEGQc6ReUzSsUxravrKlBcOokyP0tKgOuuHx/3+rVXAI6LvfzpU4CrDdDqgOUrgMI8DdBnSM2tDRg52Y8LB+W/155/+SiK51ah4or6LLcsPy1cMgvdp86DD00cFenE4S7MblCjtLw4pXWrVCr87Gc/i/nA7U9/+lNcfvnleOCBB1LaBiGEEEIIIYQQQgiJL2d3QxmGwdq1a6FWq7Fp06aU1uHz+WAwGBRuGSGEkOkg2SD30MCwJOA9gtGRyRXkFoLaFORO3thoCL3dn45p3dczBEyMnaWtvKrkkzGtx0uEl0ymoHU0c+YAxrWAzwvs3gVEGWsYwPi89p1AwD+elT1rdtaamWuqAhXq7rsUZ/53N0a75B+KCDoPoHhmOYrnVGa5dfmnrLIEc9lanDjcNWFeKMTjyL4zuER3UcrrLy4uhtPpxFVXXYWDBw/KLvN3f/d3WLRoEfR6fcrbIYQQQgghhBBCCCHRqXiez8Bt2MRs374dDz/8MHbu3JnS63t6elBbW4uxsfwIJJDJa+/evVi2bJn4/3v27MHSpUtz2CJCSL4bHRn7ZBzu/A5yxyMX5Jb+PR2D3GOjITHTurerH33BQWTi21JZZYkYsK6pr0BJWWqZo5NGVxfw+qvAmTPxl1WpgGVXACtXAcVTvF8kRk7348zP3wd/Qf76UVhbhllfX47CyunTJ9GMjozB5zkctWrGFTc0Yoa6PK1tHDp0CKtXr45a+YlhGLz77ru49NJL09oOmbzoNwQhhBBCCCGEEJI5OQ1i9/T0gGVZdHVNzKJIVEFBAUKxsnoISQDdgCKEKE0Mcvd/Uqo8onT56Mjk+OxSqfDJONwTs7inSpA7NBbC+eAges71o/fcAM4HB2XLFKertKL4kzGtxwPXqZY7ntRCIWDPh8DOHcDoaPzlZ8wAbrgRuPjijDctXwzu60LXb/ZFnV+6iEH93yyDqnByn3dKONXRjcAHp2Tnzagtx7LrGtK+Pr3yyiu45ZZbMBrleF20aBHeffdd1NXVpbUdMjnRbwiSKx6PBz6fDy0tLbluCiGEEEIIIYRkTE4H4K2pqYFarU5rHUajUaHWEEIIIcop+qRUee3cGZirqUPjsjm4bNV8XHkji1W3X4ZVt1+KK29kcdmq+WhcNhtzNbWonTsDlTVlKCrO6cdzGJ4HhvpH0HOuH2c+5nD0o7M45DuBPW92wvvSIby7bT98nkPY+9YRHN59AscOnMXZoz3o7RrAhcER5PBZuahCIR69Xf04euAs9rzVifdePIC9bx3BsQPn0Ns1oFgAu6S8CDPn12DRinnQGhZBZ1iMRSvmYeZ8ZnoGsAGgoAD4zJWAaV1igenz54EXtgGvvAwMZWbs8XxTvqQO1YaFUedfOMyh508dWWxR/pq9UI3yGaWy8853D6Lr5Pm0t3HTTTfh5z//edT5hw8fhtFoxPDwcNrbIoRMHoFAACqVKq3J4XCkvH2LxQKr1Qqfz5f0a10uV9Q2eTyelNvEcRzUarXsek0mU9TXBQIBWCwWaDQacXmNRgOLxYJAICAu53A4YLVaU25fJrS2tia1zzUaDQwGAywWS1L7zmq1QqVSQa1WR52EbSjJ5/PBarVCp9OJ+0etVov7J53jhZBc4TgODocDJpMJGo1GPH80Gg1MJpPscW21WpO6ZmdjG7lEfRhfa2trzGt2shPHcWHrFz4XIie5ZeXE2570faTyHUfYTy6XS+GeJYRMRzm/S3748OG0Xt/W1qZQSwghhJDsKSouRGVNEkFutha1c8aD3IX5GuQ+wuHjj87ikO/4p0HuP34En+cw9r6duyB3KMSjt3sAxw6exd63j2DHix9hz5tHcPSjs+g9p2DQuqwI9RfXQLN8LrT68aD1Yu1FmLWAQVlFiSLbmDKqq4HbPwvceBNQKh+EDHPwAND2DOD3IyO13fPMjJvmo3xp9MzevjePo997Oostyk+qAhUalkYfO/3I3tMIjaVf9WLDhg34//6//y/q/FdffRVf//rX8/KhHUJIZkTeIGYYBjabDU6nE16vF8FgcMJkt9vDXqPX61Pats/nE4O7ketMhNFoRDAYhN/vFzO5GYYBANhstpTaBIwHmWtra8X/1+v18Hq98Pv92Lx5s+xrrFYrNBoNAMDpdIp95XQ6wTAMdDodrFarGOhO5MZ8NrW0tMDv98Pv98NsNofNc7vd4vsRlrHb7dBqtWhvb4dOp4NOp0voBr/NZoPf78f27duh1+vBcZw4bdy4MeyYU4LP54PBYIBOp4PH44HFYoHb7QbP8+jo6BCPO4PBALVanXbQyOPxQK1WU7CDZBTHcbBYLFCr1bBYLADGr0FOp1M8P1euXCk+VCM8aOLxeNDa2prQ9Scb2xCWz8U5M9n70OVyidctlUoFnU4Hk8mUkcC32WzG9u3b4XQ60dTUFHbd3rx5M7Zv3y47OZ1O2Gy2Cdd66UNdwPjngvD5Iv3s5jgOzc3Ncdvn9XrhdDrDttPU1CR+jxHE+pyT+77j9/vh9XrFB9FMJpMinxOEkOktp+XECckXVAqQEDLZjI6MyY/HPTj+/2OTpVx5gQql5dIy5ZKxucuLUZxCuXI+xKOvZ+iT8uD96O0aQGhM+a87xaWFqKmvFMe0LqssmfSl1XNiYAB4600g4E9s+YYG4LrrgcqqjDYr10IXxnDm57sxenpAfoEiFWZZrkTJ/BnZbVge2vfOEXBn+mXnLVwyCxctrk97G2NjY7jrrrvw4osvRl3mRz/6Eb71rW+lvS0yedBviOnL4/HAYDAAGL/BGy/4y3EcGhsbxZvrNpst5VLgFotFvBnMMEzagUuVSoWWlha0trYCAPx+P1iWTXo9QnaukC3tdrtjBuqF9+H1eqHVamWX4TgOJpMJ7e3t4DgOZrM5pcB9NgQCATEgz7Is/P7Y32tcLpeYoW40GuF0OhPajs/ng06nA6DM/o/U2toKq9UKhmHEAEc0wv7xeDzQarXYvn27+EBELBzHobu7Gx6PB3a7XQxC2e32CUESQpQQeb5t3rw55rEqVH7YuHEjNm3aBI7j4l63M7mNfDhnJnMfCteq7u5uWCwWMXDr8XjE17EsC6fTGfXzKB3S63Yinw+CQCAAg8GAQCAAp9MZtRqt8PnDsqwY7E7me4bQNmnwOtZ2knkfHo8HJpMJHMcl9VlHCCFSRbluACH5aP369aisrJT99/Xr12e/QYQQEqGouBBFNePZ3HKiBbmFv8dG8yPIzYd4DPUPY6h/GD0y8xMJcgNAvxi0HkBv10BG3l9RSaE4nnVNfSXKqyhorYiKCsBwC9DZAbzxBjAgH4wUdXYCJ04Aq68GLr98fOD2KaigtBD1X16CM/+7G6EBmfGYR3l0PbkPs76xAoUzpnemf8PS2dh9JiA779jBc5i1gEFxaXo/ewoLC/H000/j2muvxZ49e2SX+cd//Edccskl+OxnP5vWtkj+2bJlC7Zs2TLh3/v741yvyJQlBKPNZnNC2csbNmwQX6PVatMay7qtrU28Uc1xHFwuV9rDrFksFjGIbbfbk87I9ng80Ov1CQUwheUdDgdsNlvMgAHDMHC73dBoNHmXhR1JmoWeCKPRKD484HK5YLFYEgrQS/s42W3GI5R+ZRgGXq837sMMwv4RXtfY2Bj3dTqdDj6fDwzDQK/XY926dSmVxSfj5YAT2U/5Klvtt1qtYde3RIK+ZrMZer0eOp0uoWtPJreRD+fMZO/D5uZmNDU1we12h/27VquF2WxGc3OzGGiO9WBVqhL9bIzEsizsdrsYyI63DeHzEhjvT71en9B7EYL68aTymaPX69HR0SFWHhH6mBBCkpE/9UgJySPt7e147bXXJkydnZ25bhohhCREWq58nqYOjVfMwWWr52P5TRqsvuPTcuWXrroYDZJy5RXVpSgsyp+vB0KQu+dsP04f4fDx/rM45D2OPW90ov2TcuU7XjyAD17rwJG9ZxA83adYALuouBC1c2eg8Yo5uPImFiv/6hJcunI+5jbWomJGKQWwldbQCKxdB1y+JP6yw8PAG68B254HeriMNy1XiurKUfvXlwFRDrWx3mF0/XY/+Dx5KCVXKqrLMLtBLTtvbDSEox+dVWQ71dXV2LZtG2bOnCk7n+d5/PVf/zU++OADRbZH8kdnZ6fsb4P29vZcN43kSHd3N4DEym97PJ6wkq/pZCG5XC6wLBu2XSUyk2tra8VAeColP202m1j2NRFCtnaiwfd0ypznM2mfORyOuEGKTLJareJxun379qQCi06nEyzLguM4sUJBNNu3bwfP82LZ+FTL6pOJwxpMNtlov8PhEAOjNpstqaxlITM319vI9Tkz2ftQ+LyJ9lnJMEzYkBdCJni+EALRiWQ9R34/yJf3IlT2AMaz0pP5vkAIIQAFsQmR1dTUhDVr1kyYGhoact00QghRhBDkrptbPemD3EoFrQuLCqCeU4WGZbNx5Y0sVt52CS5bNR9z2VpUVpdR0DobSkuBG9YAd94N1NTEX/7kCcDZBuzeBYyNZb59OVC2WI2aO6LfSB4+0gvuD/5pPx7z/MtmRr02nToSxEDvBUW209DQgN/97ncoKZHPfu/r68Odd96J06dpzPKppKGhQfa3QVNTU66bRnKE4zhotdq42VVCCVOBzWZLK+vQbrfDYrGEBX89Ho8iwaCNGzcCgJjdnSihzG0ymWvJZhEajcaUM9nyWeSx4PF4ctIOYSxZYLyvU8lCFAJEwvjl0UzF/ZgLuTpWlJKN9kuDZSzLplQBQ6/Xx3zYJhvbyOU5MxX60OFwhJXZlqPVasWHAwKBQN6N37xu3bqEH3JqaWkJey/5EjAWst6B8X1CFTgIIcmgcuKEyNiyZQuNZ0cImdZilSvneR5jI6FPy5QPTixbni/lymMpLCrAjLoK1NRXoKa+EpU1FKjOG/PmAca1gLcdeH83ECtAOzYGvPcu4D8M3HAjECVLdjKrunYeRk70YcB3RnZ+/85TKJ5Xiaqr52W5ZfmjpLQIF11Sj4/3yfQRD3TuPY0lVy9QZFvXXnstfvnLX+L++++Xnf/xxx/jnnvuwcsvv4yyMvkhH8jkEm1IocgxsTMtNDaG813nsra9yWpGXT0KCgszvp1EHmJQsoy4MH6nUA7VbDaLN9odDkda6xbaJ9zot9vtCWdJb9q0KeWb5B6PJ+GMu7Vr16a0jckkV5m1QqYi8OnDDMnS6/Xi8SOMUztZy1xPBvk6NnyistH+DRs2iH+nU81h48aNUR/sycY2cmmy9yHHceKDWS6XK+ZDv1qtVny4wul0Zm2s8UiBQGDCtVOr1SZ1zjidTjQ2NoLjODgcDphMpryoemG1WsXvLVardUJ5d0IIiWZSBLE3btwIi8VCWbCEEEJIHlCpVCgqKURVSTmqmPIJ8/M1yF1QqEJ13adjWlfVlEFVQEHrvFVUBKy+CtBogNdeBc7FCdycOwf87lngyuWArmn89VOESqWC+p7FGDkzgJFjfbLLcNsCKJ5diVI2gQz2KWoeW4vTnUFcGBiZMI8704fgmT6oZ1Upsq0vfelL2L9/P37wgx/Izn/nnXfwt3/7t/jtb39LD8cQxZzvOofHv/G3uW5G3vvqz36JmlmzM7oNlmXjBuiULCMOjAeqpTfVLRaLeDPYbrenHcQGxm8qWywWeDwe2RvpclwuV0JlTqWEYKfVasXatWsTyjRMZEzQySYyEy0XQQaPxyO2g2GYtMaCNRqNYSWBJ3ugNV9FXlsmm2y03+fzhZ1fiT6UIyda1Y1sbCOXpkIfCkN/CGJ9rq1cuTLq67LF5/PBZDJN+ExtampK6vNPKJEuVIIxmUzo6OjI+TEmfHcKBAJJfc8ghJD8qQcag81mm/SlcgghhJDpQgxyM+Wom/dpufLLVy/A8ps0WHX7pVh526X4zJpGXLryYjQsnY05jbVQz6lCRXUpCgqV+XpSUKBCTX0lFlw2E8uub8Cq2y/DkqsX4uLF9ZihLqcA9mRRPxO4597xgHa8zDqeHy8t7mwDThzPTvuyRFVcgLr7l6Cgqlh+gRCPrqf2YzQ4lN2G5ZGCwgIsXDIr6vzOPafBh5Qru/7v//7vuPfee6PO/7//+z98//vfV2x7hJD8YTQaY95sV7qMOPBpKXGBkDkNjN+YV6I0pzTbOZEApMvlSinwKrwPjuPQ2NiYUEDLaDQqEqjPJ1u3bhX/NpvNaQWQUyV9uCLdILp0POy2tra01kXkuVyuuOOO57NstV96/VLi4RC5dWRjG7k0FfqQZVmYzWYwDAOz2Rzzc1gaJK6trU27LamIFjxnGCbpoaOMRqP44Fvkd5Jckn7OTeaHcQgh2TUpgtiEEEIImTpUKhWKpUHuRXVgP/NpkHv1HakFuVUFKlTXV2D+ZTOx7LqFWHX7pVh67UJcfOlMVNdWoICC1pNXQQGwfAVgWjdeajye3h5g2/PjGdwXlBkLOR8U1ZSi7v4lQKH8sRzqH0HXk/sQGp6a44Mnom5eNWaoJ1aIAIDB8xdw+mNOsW0VFBTg17/+dcygw3e+8520sy8JIZOPkmXEgU/Hj4283kjLQCuR9cowjBicT2RMULvdHtaGRLW0tIjBBOHmukqlgsFgQGtra9aTGDweD0wmEzQazXj1E7UaOp0OVqs1YyW+XS5X2DjUucpalvZ1ug9aSF/PcVxejnkqjBEr7GuVSgWNRgOTyZTQcZfqseJwOGAwGKDT6cTXCkEzIftS+HehPdKgmsfjgUqlmhCIkr4PYYrVDmn71Wo11Go1DAZDSmMAJ9OXSrU/UUoe18D4wx6R1/FsbCOXpkof2u12BIPBuNfYnTt3in/n4oEiIP2KLZHsdrvYrx6PJy/G+pbuZyonTghJFAWxP9Hb24vnnnsOGzduxIMPPogHH3wQP/zhD7F79+5cN40QQgiZVpINci9aMQ9Lr12I1bdfimXXNmD+pTNRXVepWEY3ySM1NcBn7wJuWAOUlMRf/qP9QNszQEdH5tuWJaULq6G+e1HU+SMn+hF89lDST+tPFSqVCg3LopcR/nj/GYyOKBfkr6ysxPPPP495MR6u+MpXvoL29nbFtkkIyW/C2JsCJW5KR2ZhC6SZ00rdnBbGRBbGEY0mEAigu7s75cCD1+udECjweDywWq0wGAxQqVTQ6XQZvekuBNANBgNYloXT6QTP8wgGg9i8eTN8Pl/CmeKJCAQCYiaqyWQSt5nLh52kmX91dXVprSsyezFXJXmjcTgc0Gg0aGtrg9Vqhd/vRzAYhNPpRG1trfgQhZx0jxWWZaHVasFxXFhw2mq1YsOGDbBYLPD7/eB5HjabDS6XCxqNRnwQQK/XIxgMTgjGud1u8d+FKVrJYIvFAovFgnXr1sHr9SIYDKKjowMGgwEWiwVqtTrhBw+S7Usl2p8MaR9rNJq015erbeTSdOpDjuPCAurC52AmtydMQiUV6RAhSpJ+vlgslpwPyyH9nMm3zwhCSP6a9nd3d+/ejVtuuQWNjY3YunUramtrodfrodVqce7cOXz1q19FXV0dfvnLX+a6qYQQQgjBxCD3rAUMauopaD1tqFTA5UuAtfcBDY3xlx8YAF76M+D+y/jfU0DlqjmovGpu1PmD759F3+vHstii/DKjtgL1F1fLzhsdHsPxQ3HGV0/SRRddhOeffx7l5VEywAcHcdddd+HYsem7TwiZLjiOw4YNG8T/V6KMODAeGJeOhy2QZk4DygSypWXKN23aFHW5aIH1RDEMA6/XC7vdHjXrTbixr1arFb/xLi1l7vV6YbPZwtqh1Wrhdruxdu1amEympN5rIBAQs1yFKTJL1W63w+/3pzUGrBKkWa9Kj5ea62CJlLAPtVotOjo6xNLCwjjgQgBNrrKAEseKXq+HzWYLCyjZbDb4fD54vd6wMsjS4Qqk1xOGYcAwTNjDAsJ7kE5yfD4fHA4HAoFA2HIMw6ClpQV2ux0cx6G5uTnufku1L9NpfzqyMQ5wrscazrSp3oebNm0Sr4U2my2jbYn8fNBoNBl9YEur1cJms4n/n+vhCKR9m0+fEYSQ/Dat7/Y+8MADaG5uxtq1a9HV1YWtW7fioYcewr333osNGzbg0UcfRXt7O3bu3ImtW7di5cqVOH/+fK6bTQghhBBCKiuBW/8KMNwCRAkehgkExrOyP/pofOzsSY65k0VJo3ygFgB6/tyJoQPT9+n2hZfPjjqEwAl/N4YGhhXdnk6nw29+85uo80+ePIm77roL/f39im6XEJJflC4jDowHpo1GY8zsSoFSJamF4JPP54t6kzlaYD1ZZrNZzAoVSsFGBrU5joNOp1P0hrfJZALHcRMCkpHsdjsYhoHD4Ui4zDnLshOyS3meh9/vF4MJFoslb8YoFfj9/rReH5lVl6txZSM5HA4xQ9rpdMqeS9LxySOPMyWPFem229raombhr1y5EgAUK8kufU9y2xTGDeY4LuYQAen2ZS5kakiAbG8jl6ZyHwYCgbChHTJd0p1lWfA8HzZJPxsyQfq5GggEUhoGRCnS/ZwvnxGEkPxXlOsG5Mott9wClUqFrq6uuMuyLIuXXnoJDocDWq0WPp8PM2bMyEIrCSGEEEJITKwGuOhi4J23gQMfxV72wgXgtVeAw4fGS5JXRw8C5ztVYQHqvng5zvzPboxxMuN+80DX0x9h1teXo3hmRfYbmGOlFcWYq6mTzbrmQzyO7DuDS5suVnSbRqMR//Ef/4FHHnlEdv6uXbvw5S9/GU6nEwUF0/pZYpKiGXX1+OrPqEJYPDPq6nOy3UyUEQfGA2Oxbm7r9Xox+CQEndPN/jabzWJw3GazTQiOu1yusMxRJQhZ5dLMZKG8uM/nA8dxsFgsioyhKQ0yJpIJvXbtWjgcDphMJgSDwZS3y7IsWlpaYDQaodFoxNLiuRwXlGVZMdCYbhAp8vVKVCFIl3DcAOP7OlqbNm7ciA0bNkCv14cto/SxIg3aCOeuHOm/cxyXdmaoUHFS2h9yy7hcrqiB83T7Mpukx3W6D2fkchu5NB36kOM4MTPZaDTmbGgH4bOhq6tLseErIjmdTrFKQmtrKwwGg+Kf48nKh88IQsjkMC3vnqxbtw4Mw+Avf/lLUq8zm8146KGHcPPNN2eoZYQQQgghJGmlpcCNNwF33JlYYPr4McC5FfjgfSAUynz7MqSwqgR19y+Bqlj+Kz0/NIauJ/chNDSa5Zblh4sX16G4tFB2XtfxXpzvVr68/D/90z/hS1/6UtT5zz33XNQgNyHxFBQWombWbJriTAWF8ud9JiVbRlwIXsYjjJUpjBEdbZIGD5XKxhayrNva2ibMs9vtWcnk0uv18Hq9YvDQ4/Eokpkq7aNEbqILN/7jjROeKJZlxQcTPB5PxoIWkRwOx4SStdIgRnt7e1rrj3x9rKzlVAjjx8aaIgPp0vcb65wzGo1iNQCpTB4rQrZ1Ngjl+/1+f9T9IgTYo41Tm25fZpP0uFYiG9zlck3Irs/GNnJpOvShUD7fbDbn9HgVxPteIB1nPlksy4a9R6HCRLZJH1ZQ+jOCEDJ1Tbsg9rPPPgu/3y/7QywRZrMZjY2N+NGPfqRwywghhBBCSFouvhgwrgU+c+X42NmxjI6OZ2///ndAApV58lXJRVVQ37s46vzRM4Po3noAfGjyl1BPVmFxIRZcPivq/I49p8ErXFpepVJh8+bNuOaaa6Ius2nTppilxwkhk0+yZcSFcWnjsdvtMJvNE0pTR05er1d8jVLjagoZl5HBuEAggO7u7rQyqJIdk1N64z3dQCuQfIlm6b7auXNn2tsHwm/eK/XgQTxyGd/SkubpPiAgXX8mxvq2Wq3QaDQxp8gS7dI2pXLMZvJYyeUYwC6XC1arFQaDATqdDhqNJu61I92+zCZptrkSgeGtW7dOCLJmYxu5NNX70GAwwOfzwel0Zu0aHA/LslHPLY/HE1aqPxXSaieRD99li/QzPNfjcxNCJo9pF8R++OGH8fjjj6e1DofDgR/84Afo7e1VqFWEEEIIIUQRxcXA1dcAn/s8UFsXf/mzZ4DnXMDOHcDYWObblwEVy2ehak300thD+7vR6zmSxRblj1kLGFRUl8rO6wsO4txx5b/Pl5WV4Xe/+x0WLlwYdZkNGzbgzTffVHzbhJDsS6WMuN/vT2gsSIfDAYvFAoZhYk5arVYMinIcp0gwQLrOTZs2if9ut9uxcePGtNbt8XiSzgATMvamStle6f5XIjCfCJ/PNyFAElnyOZ2scOlr0z1GlCINaOV74DXTAoEATCYTVCqVWEnBZrNh+/bt8Pv9cce4n0x9Kb1+Aekd18D4udPU1JT1beTSVO5Dg8GA9vb2sEof+YBl2ajDS8hdv1OxefNm8QEal8ul2INviRCGPQHG32uuy5kTQiaPaRXEfvbZZ9HY2Ijly5entR6GYdDc3JxyNjchhBBCCMmwWbOAz98LrFwFxBt/OBQCfF7A1QacOpmd9ims5tYGlF6ijjr//MtHMfDhxPGhpzqVSoWGpbOjzj+y7wzGxpQvKT9r1ixs27YNVVVVsvOHh4dxzz33oKOjQ/FtE0KyJ9ky4oL29va4WZgejwe1tbUJl9uUZrQplVUmrFMYaxsYv+mtxE3/ZAO3Qj/U1SXwgFocyQYCpAF3oVy0kjiOy3hZV6EUt9x7lx4vqZaJl5a5NRqNGSkTa7fbwfN8zCkyACR9v6n0cb4dK/F4PB7Z7EaXyyWOw97S0gK/3w+bzQatVptwRni6fZmIaO1PxebNm8W/0xn+wOfzobu7W/aYzsY2cmkq9qHJZEIgEIDX65Vd1ufzTajokA/cbrciQxAwDBP2sJ3FYlFkmI5ESAPmwpAahBCSiGkVxN66dSvWrl2ryLrWrVsX9ekoQgghhBCSBwoLAa1uvMT4nDnxl+c44A+/B954HRgeznTrFKUqUKHury9DUX151GWCzgMYOdWfxVblB2ZWFdSzowSTB0dw0i8/9mO6rrjiCjzzzDMoiPIQxblz53DnnXdSdSdCJrFky4gD4zfIfT5f3GCs3W4PC0zHI73X4XK5FAkySTMzbTabYgFsYX3JEG6yK5G5JX0Pidy8l2Z/K3VPKTJwmOkywkLwQK4CgF6vF/d1IBBIetzVQCAgBp8YhgkLSuWaNCCaSsZ7PhwryZA774UMbGD8nE7m3PN4POKxmW5fJkLJ4LhWqxXfayrHtcBkMkXts2xsI5emWh9KA9ixynYnUiklmwKBADwej2IPOej1+rDvK9kYi53jOPFzQq/X51UGPCEk/02rIPauXbvi/uDZuHEjCgsLUVRUhFdeeSXqclqtNmtPKpHsW79+PW688cYJ05YtW3LdNEIIIYQkS60G7voccN314+XG49m3F2h7BjgyuUpwF5QXoe7LS6AqLZSdzw+HcO43+zDWP5LlluXewqWzgSjDpB87eA7DQ6MZ2e4dd9yBH/7wh1Hn7927F/fddx9GRzOzfaKMLVu2yP42WL9+fa6bRnIolTLiwKeluWNlPwrjUMcr7yvFMEzYTWGlKscJbXA4HNi0aVNSgfVYPB5Pwll3wg18pTJ8pRnz0lLp0Qh9abPZFBvHODJIEhlE8Pl80Ol0imyL47i4x53dbhfvl1mt1oSDGhzHicFNhmHg9XpzOtZzpJaWFnFfJxIktFqtYcdlPhwrkaTrjQz6yo1XL71OxcowlQtM2+128VhIty9TbX86WlpaxGCd1WpNunSywWAAy7Ixr8XZ2EYuTZU+NJlM4Dgu7jXK7XbnpIpCLMJ5FK1UeirVPIRKDNnS3NwMYLyiQ6LflwghRDCtgtjd3d1oaGiIOn/Xrl2w2WzgeR6hUCjml7vGxsaMPylLcqe9vR2vvfbahKmzszPXTSOEEEJIKlQqYOkyYO19wILoYxWL+vuBP78IbPcAg4OZb59CimdVoHbdpVHnj3UPofv/9oMf47PYqtyrmFGKOQ3y5dZDYyEc/ehMxrb9D//wD2HlhiP96U9/wj/+4z9mbPskfZ2dnbK/DbI1ji3JP6mWEbdarWJAKVamlzSrNRnr1q0La1M8woP5sY5ladC6trY25vtM9iZ6a2srrFZrzNdxHAeTyaR4hq9QWS/yYYRIQuDDaDTGzLTv7k6uqgfDMGFJFpGV/jwej2zAItk+5jgOzc3N4Dgu7jHqdrvF92gwGOJmRAqB9kAgAK1WGzO7MdG2ZoLb7QbDMGEZyXI8Hg8cDseE8byVPFaSPU7kXiM9biIfNnA6nRMefpBeR6Ldx5RmXEv3Q2QJ+nT7MpX2p8tms4lBM4vFIu6nWHw+n/hwRiJVOLOxDTmZHoZAMNn70GKxiOeuwWCQnXQ6HTQajaIZz4J09pPD4RDbHu07gfAZnmycItlgcirXL5/PB41GA5/PJ35O5NODToSQSYKfBFQqFb958+a011NbWxtzvs/n41UqFV9QUMAXFBTEXV6lUqXdJpIf9uzZwwMQp6amJn7NmjUTpieeeCLXTSWEEEJIukIhnj90kOe3/IrnH/t5/GnLr3j+wIHx100SPZ4j/FHr61Gn4POHc93ErBseGuHf/eN+/q3f75Wd+rjBzG17eJi/6aabwr5vRk6PPfZYxrZP0vPEE0/I/jZoamoK24d79uzJdVNJlhiNRnG/syzLe71e3u/3h01er5d3u9280+nkzWYzzzBM2PHidrvD1hkMBnm3283r9XpxGb1ez3u93rjtCQaDvNfrDWsXAL6lpYX3+/18MBgMW97v9/N2u11sE8MwvN1u5/1+v+z6tVotD4B3Op2y2xa2LywHgDcajbLb5nmeB8CbzWY+GAyK79dsNvNOp1PsS7fbzdtsNp5hGF6r1UZtm7C+RKfIfpBuX+hrv9/PO51OnmVZHgBvs9li9r3f7+dbWlrCtuN0OqO+f+lrhW1I+9fr9fIMw4S9Z2E7ZrM5bDter1fcB8Ik9F/kcafX66O2Rcrtdov7kmVZ3mazidsR+kboN4ZhYvZPtPctd8xqtVre7XaHvRelSPc1y7K80+kMa4fQV9HOt3SPFWFbNpstbH9EHiNCH0uvAzabbUJ/OJ3OsONGWDfLsrLblx43drs9bHs2m008x4TjxWg0Rl1fun2ZbPtTPb/l2i3tB6PRGHaeer1e3m63i8dkssd1JreR7jkznfsw8pqZyKRUPyZz3ZZev71er3heSve1XF9JrynC+Rjr8zKS3W4Xr2vRRPuci/b5I+yjyOsYIYSkaloFsTUaDd/R0RFzGaPRyKtUqrjb5DiO12g0abeJ5IfIIDbdgCKEEEKmgYEBnt/uSSyQ/djPef6FP/J8b2+uW52QUCjEn3tyb8xAdl/7qVw3M+uOHzoXNYi9561OPpTBBxW6urr4xYsXR73ZVVhYyHs8noxtnyiPfkNMX5EB6VSmyJvMws1qhmHCJmBiwDtaeyJfK12HsD0heBRtObkbzUKQTk60dkvXaTQaw17DsmxYkEAIfGm12rD3otfrwwJu0aQboHG73bzRaAzbryzL8i0tLTEDqdLgQbT3LrevI9nt9rB+jHx4ITJwkOoUuR/i8Xq9fEtLy4T9wrKsGFhKlhDMiXa8SPsu2jGXjsh9LTwkEW9fR3t9oseKEHCTe58Mw4jLRTuX5faf8NCLsIzRaIzZBuHBBiHgLrx36Tnv9/t5o9HIa7Va8UGTRPsilb5MpP3pnt+RgsGgGARlWVb2mpPuAxRKbkOJc2a69qHX603pWhlNMutQ6rotTJFBZmH90Y6FeN8bpISHVuRIP+eSmViWVexYIIQQFc/zPPJcQUEBHA4HvvrVr6a1nrVr1+K+++7D5z//+ZjLdXR0oLa2FjU1NVGX2b59O2w2G1566aW02kTyw969e7Fs2TLx//fs2YOlS5fmsEWEEEIIyZqPPwbeeA3o64u/bFERsPoqYMlSoCC/R+YJXRjD2V/sxsipAfkFilSYZbkSJfNnZLdhORQaC2H3K34MRRkX/LLV81E7J3P9cfDgQaxevTpqWUGGYfDuu+/i0kujl4Qn+YN+QxBCCCGEEEIIIZmT33feFLZu3Tps3bo17nKNjY0xA9jA+Fg4a9euVapphBBCCCEkVxYsGB8re9kV8ZcdHQXeehN4/vdAMPlxwbKpoLQQdfcvQUFFkfwCozzOPbkPY73D2W1YDhUUFmDhktlR5x/ZexqhUOae8b3kkkvgcrlQWFgoO5/jONx5550pjTlHCCGEEEIIIYQQMpVMqyD2vffeC7fbjSNHjqS1np6eHrS1tVEQmxBCCCFkqiguBq69Drj7HkCtjr/86dOAywl424Gxscy3L0VFdeWo/cJlUb/1h3qH0fXbfeBHQ9ltWA7Vzp2B6roK2XmDfcM43RnM6Pabm5vxv//7v1HnHzp0CEajESMj8tnihBBCCCGEEEIIIdPBpAhiG41GNDU1KbKuRx99FGazOa11PPzww9iwYQOqq6sVaRMhhBBCCMkTc+YA95oAXVP8cuGhENC+E3jONR7UzlNli9SouZ2NOn/44/Pg/uDHJBhlSBEqlQoNy6JnYx89cBajI5l9MMFiseDv//7vo85/5ZVX8PWvf33a7BNCCCGEEEIIIYSQSJMiiN3W1obly5crsi6z2Qye5/FP//RPKb3+2WefhdvtxqOPPqpIewghhBBCSJ4pLASaVo4Hs2fNir98dzfw++eAt98C8jR7turaeajQRn8v/TtPof/dk1lsUW5VMeWYOV9++KDR4TEcO3A242340Y9+hNtuuy3q/M2bN+O//uu/Mt4OQgghhBBCCCGEkHw0KYLYSnvppZfQ1taGr33ta0m97tlnn4XZbIbH48lQywghhBBCSN6orR0vL37NtUBRlHGlpT78AHBuBY4ezXzbkqRSqaC+ZzGKL66Kugy3LYALAS57jcqxBZfPQkGhSnbeyUA3Lgxm9oGEwsJCPPPMM1i6dGnUZb797W/jhRdeyGg7CCGEEEIIIYQQQvLRtAxiA8Dhw4dx9uxZrFy5Eq+88krMZXt7e/HAAw/g4YcfRnt7OxoaGrLTSEIIIYQQklsFBcAVnwHWrgMunh9/+fPngRf/CLzyMjA0lPn2JUFVXID6+5egYEax/AIhHl1P7cdoML/anSml5cWYt6huwr+XVZbg0pXzUVKWwIMLaaqursa2bdtQX18vO5/nedx333348MMPM94WQgghhBBCCCGEkHySt0Hs3bt3Z3wbTqcTmzZtwoYNG7B48WJs3LgRzz33HF5++WW8/PLLePzxx7F27Vo0NjZCrVbj0KFDaGxszHi7CCGEEEJInplRDdx+B3DTzUBpafzlDx4A2p4BAoHMty0JhTWlqPvSEiBKBnKofxRdT+5DaDizY0Lni4sW1aO4dDxYXVRcgMZls7H8Zg1q586ASiXfR0prbGzE73//e5SUlMjO7+vrw5133okzZ85kpT2EEEIIIYQQQggh+UDF8zyf60bIKSwshNfrVWws7Hh27doFj8eDnTt3guM4AADLsjAYDLj33nuz0gaSO3v37sWyZcvE/9+zZ0/M0o6EEEIImcYGB4C33gL8hxNbfslS4OprEitJniX9O04h+NyhqPPLr5yJ2vsuzVogN5fOHuXQxw3h4ktnorikMGft+M1vfoOvfOUrUedfc8012L59O8rKyrLYKhIL/YYghBBCCCGEEEIyJ3/upEXgeR7d3d1Z296KFSuwYsWKrG2PEEIIIYRMUuUVgN4ALF4MvPE60N8fe/l9e4FTJwH9LYBanZ02xlG5ag6GT/ah/52TsvMH3z+L83MrUX1jAiXUJ7mZ8xnMzIO3+eUvfxn79+/Ho48+Kjv/7bffxoYNG/Cb3/xmWjxcQAghhBBCCCGEkOktb8uJAxAzogkhhBBCCMk7CxuAtfeNZ1rH090NPOcCPtoP5EkhJOazLEoaa6LO7/1LJwYPZO+hUgJ8//vfxz333BN1/m9/+1ts2rQpiy0ihBBCCCGEEEIIyY28DmJ7PJ5cN4EQQgghhJDoSkqA628A7robqIkeEAYAjI4Cr70KvOwBhoez0rxYVIUFqPviZShkoozxzQPdT3+EkbMD2W3YNFZQUIAnn3wyZoWof/7nf8azzz6bxVYRQgghhBBCCCGEZF9eB7Htdju+9rWv4eWXX0Zvb2+um0MIIYQQQoi8ufMA41pg+QogXqnnw4eBZ53A2TPZaVsMhVUlqLt/CVTF8j8L+KExdP1mH0JDo1lu2fRVWVmJ559/HnPnzo26zP333w+v15vFVhFCCCGEEEIIIYRkV14HsYHxQLbBYIBarcbKlSuxceNGvPzyy7luFiGEEEIIIeGKioDVVwGfvQuorIy9bG8v8PvfAR+8n/Py4iUXVUFtXBx1/ujZQXRvPQA+lB9l0KeDiy++GH/4wx9QVlYmO39wcBB33XUXjh8/nuWWEUIIIYQQQgghhGRH3gexeZ4XJ5/Ph9bWVhgMBhQWFioS1O7p6cHjjz+uYIsJIYQQQsi0Nu+TrOyFC2MvFwoB77wN/PlFYHAwO22LouLKWZix5uKo84f2d2NgV+4zx6eTlStX4je/+U3U+SdOnMDdd9+NgQEq904IIYQQQgghhJCpJ2+D2Pfeey9qampgMpnAMAyA8IC2UkHt9vZ2WCyWDL0LQgghhBAyLZWVAbfeBlxzLVAQ5yv3xx8DLidwIrdZtdW3NqDsUnXU+edfPUrZ2FlmMpnwb//2b1Hne71efPnLX0YoFMpiqwghhBBCCCGEEEIyL2+D2E6nE48++ij8fj9efvll+P1+PPbYYzAajYoGtTmOy84bIoQQQggh04tKBVzxGeBznweqa2IvO9APbHse2LljPEM7B1QFKtTedxmK6stl54+eHcTQR91ZbhV55JFH8IUvfCHq/GeffRb/8i//ksUWEUIIIYQQQgghhGSeiudzPAhfHD09PdiwYQPq6urwi1/8Qvz3jo4OeDweuN1ueDwe2WC0SqUK+3+tVgu9Xg+DwYCbb74ZALB582Y88MADGBsby+j7IPlt7969WLZsmfj/e/bswdKlS3PYIkIIIYRMKcPDwBuvA4cPxV92zlygWQ9UVWW+XTJGTvfj9H/5AJlfCSUN1Zj1wJXZb9Q0NzQ0hJtuugnvvvtu1GWefPJJfOlLX8piqwj9hiCEEEIIIYQQQjIn74PYApfLBZvNhtbWVtx0000T5qca1A4EAgCArq6ujLSbTA50A4oQQgghGcfzwMEDwJtvAKOjsZctLQVuvAloaMxO2yJ0Pf0RBt8/Kztv5oNXonRhdZZbRE6fPo1Vq1bh448/lp1fUlKCV155Bddcc02WWzZ90W8IQgghhBBCCCEkc/K2nHgko9EIt9uNrVu3Yt26dTh//nzY/MbGRmzYsAFtbW3o7u6G3++H3W6PW348GAzm4N0QQgghhJBpR6UCLr0M+LwRqKuLveyFC8Bf/gy89SaQg4pBM264OOq8868fy2JLiGD27NnYtm0bqqJk6A8PD+Nzn/scOjs7s9swQgghhBBCCCGEkAyYNJnYUh6PBw8//DD++Z//Gffcc09Cr4mVqa1WqykTe5qLzKJoampCZWXlhOXWr1+P9evXZ7FlhBBCCJmSRkeBd98B9u6Jv2x9PdBsAD55MDNbzm7+ABf8PRNnqIDZ39KheGZFVttDxm3btg133303ov2MW7ZsGd566y1UV1O2vFK2bNmCLVu2TPj3/v5+tLe3i/9PmdiEEEIIIYQQQohyJmUQW/DAAw8gGAzi8ccfx4wZM5J67a5du/DMM8/gP//zPymITSYEsaP513/9V3z3u9/NfIMIIYQQMj10BIBXXxkfMzuWoiLg+huASy7NTrsADB0M4tyv5IPslavmQP35xVlrCwn3ox/9CP/4j/8Ydf4dd9yBP/zhDygsLMxiq6au7373u/je974XdzkKYhNCCCGEEEIIIcopynUD0vHYY4/B5/PhpptuwgMPPICvfvWrCb92xYoVWLFiBQDg8ccfz1QTySQVLRO7oaEh+40hhBBCyNTVyAL1M4HtHuD0qejLjY4Cr7wMHD8OXHc9UFyc8aaVLmZQPKcSI6f6J8zr951GtWEhCmeUZLwdZKJvfetb2L9/P375y1/Kzn/hhRfw0EMP4cc//nGWWzY1NTQ0YM2aNRP+PTITm5Bs8Xg88Pl8aGlpyXVTCCGEEEIIISRjJs2Y2NFotVq0t7fj8OHDuPXWW3HkyJGkXr9q1aqw0uKEAOMlA1999dUJE5USJ4QQQojiZswA7robWKGNv+zBA8CzTuDcuYw3S6VSoeqGi+RnjvLoe/tExttA5KlUKvz85z+XDawKfvKTn2Dz5s1ZbNXUqQ3DKwABAABJREFUtX79etnfBnIlxsn0EAgEoFKp0pocDkfK27dYLLBarfD5fEm/1uVyRW2Tx+NJuU0cx0GtVsuu12QyRX1dIBCAxWKBRqMRl9doNLBYLAgEAuJyDocDVqs15fZlQmtra1L7XKPRwGAwwGKxJLXvrFYrVCoV1Gp11EnYhpJ8Ph+sVit0Op24f9Rqtbh/0jleCMkVjuPgcDhgMpmg0WjE80ej0cBkMske11arNalrdja2MRnQNXLyXyPpfElMa2trzOMv2SkyViUc45GT3LJy4m1P+j5S+U4r7CuXy6VwzxIybtIHsQWPPvoofvGLX+Dee+/FD3/4w4Rfx2R5bEFCCCGEEEImKCgAVq0G7rgTqIgz1nRPD/C7Z4E9HwIZHhmo4sqZKKwplZ3X9+5JhC6MZXT7JLqSkhI8++yzWLRoUdRlvva1r+GVV17JYqsImR4ibxgyDAObzQan0wmv14tgMDhhstvtYa/R6/Upbdvn84nB3ch1JsJoNCIYDMLv94uZ3MJ9EZvNllKbgPEgc21trfj/er0eXq8Xfr8/6gM1VqsVGo0GAOB0OsW+cjqdYBgGOp0OVqtVDHTnWwJCS0sL/H4//H4/zGZz2Dy32y2+H2EZu90uJmLodDrodLqEbvjabDb4/X5s374der0eHMeJ08aNG8OOOSX4fD4YDAbodDp4PB5YLBa43W7wPI+Ojg7xuDMYDFCr1WkHEjweD9RqNd38JhnFcRwsFgvUajUsFguA8WuQ0+kUz8+VK1eKD9UIQVSPx4PW1taErj/Z2Iaw/GQ4Z+gamfo10uVyia9XqVTQ6XQwmUxZC9xO9vMl2/1nNpuxfft2OJ1ONDU1hR2Dmzdvxvbt22Unp9MJm8024biVPsQHjB/jwrki/a7GcRyam5vjts/r9cLpdIZtp6mpSfzeKoh1zsp9v/X7/fB6veKDhyaTSZHvBYRMwE9Bra2tfFNTE7979+64y/p8Pl6lUmWhVSSf7dmzhwcgTnv27Ml1kwghhBAyXQ308/wft/H8Yz+PP/35RZ4fHMxoc3pfP8oftb4uO/W+eSyj2ybx7d+/n6+pqQn7Liud1Go1f+DAgVw3c0qi3xDTl9vtFvd7S0tL3OWDwSDPMIz4GpvNlvK2zWazuB6GYVJej0B4D8I6/X5/SuthWZa32Wzietxud8zlhffh9XqjLhMMBnm9Xi/2ndlsTqlt2eD3+8X3zrJs3OWdTqe4vNFoTHg7Xq9X0f0fSdiHDMPE3YfC/gHAa7VaPhgMJrSNYDDI+/1+3m6381qtVnw/drtdgXdAyESR51u8Y9Vut/MMw/A2m028/sS7bmdyG1PhnKFrZGLXSOE1Wq2Wt9vtvN/v571eb9hxwrJszM/OdE3m8yUf+k96DCZyrAv8fj/PsiwPgHc6nTGXE9adyvdKrVbLa7XahNqT7Ptwu91iPydz3hISz5TJxJZ66KGHsHXrVjz00EPYuHFjzGVXrFih2JNYhBBCCCGEpK28Arj9DmD1VeMZ2rF0do6XFz95MmPNqVw1B6qyQtl5fW8cBz+W2WxwEttll10Gl8uFwkL5fRQMBnHnnXfSbx5CFCRk/5jN5oSylzds2CC+RqvVpjWWdVtbG1iWFduhRCaekAEFpJbd7fF4oNfrE6505/F44HA4YLPZoNVGH0qDYRi43e6wDO98lWwbjUajeBy4XK6wfRCLtI+V7heTyQSr1QqGYeD1euNWCxD2j9FohM/nQ2Nj44TssUg6nQ5qtRo6nQ5utxvr1q1T8i1MK2q1Om5/57Nstd9qtYpDGtjtdrHSQyxmsxlerxebNm1KKKM0k9uYKucMXSMTu0Y2NzeDZVl4vV6YzWawLCt+b+jo6IBWq0UgEIBOp0tpSJF4Jvv5kuv+A1Kv+suyrPgdLN5xIhxbgmSGmNHr9Whqaoq7XCrnj16vR0dHB1iWhcvlgk6nS3odhMiZkkFsYPzEf+mll9DY2IiVK1fi/fffj7psTU1NFltGCCGEEEJIHCoVsHwFcNfnxsfMjqWvD9j2B8DnBUIhxZtSUFqEqqvmys4b4y5g8MOzim+TJEev1+NnP/tZ1PkHDx6E0WjEyMhIFltFyNTV3d0NILHy2x6PJyzQ7HQ6U96uy+UCy7Jh200l6ByptrYWRqMRAFIqAWmz2RIOMAAQx7YWtpnI+qciaZ85HI6cBiStVqt4nG7fvl18UCIRTqcTLMuC4zgYDIaYy27fvh08z4tl41Mtq08mDmsw2WSj/Q6HA62trQDGryOR5XFjYVk2oet1prcxnc+Z6XaNFD4bo32uMwwTNjyHEAhWymQ/X3Ldf0rQ6/XQarXw+/1xl438Ppgv74dhGHE/+Xy+pL4fEhLNlA1iC8xmM9xuNx566CE8+OCDuW4OIYQQQgghiZs9G7jXBLCa2MvxPLBzB/DCNqC/X/FmVF1zEVCokp13/vVj4DM8NjeJ78EHH8Q3vvGNqPNffvllfOMb36B9RYgCOI6DVquNm23DcVzYTUWbzZbUje9IdrsdFoslLPjr8XgUCQYJVeySze7mOA7d3d0xM6ojJZv9ZDQaU85symeRx4LH48lJO4TxRYHxvk5mXwqk2WOxblhPxf2YC7k6VpSSjfZLgycsy6ZUAUOv18d82CYb25jO58x0u0Y6HA6wLBszWK/VasUHGQKBgGJjD0+F8yWX/aekdevWJfzARktLS9j7yZeAsVarFR9QcDgcGct6J9PHlA9iA+Mf+C+99BKam5uxcuVKvPzyy7luEiGEEEIIIYkpLQX0BuCGNUCUktGiEycAVxvw8RFFm1BYXYKKFbNk542c6MeFw5yi2yOp+fGPf4xbb7016ny73Y6f/vSnWWwRIVNXIqUYlSwjznEcPB6PeFNQmr2kxE1YrVYrBgySye7etGlTyjdNkwlIrF27NqVtTCa5yqwVstcAxB2SLxq9Xi8eP7nOmJwOlKjAkEvZaP+GDRvEv9Op5hDrnMjGNsinpvI1kuM48SEyjSb2w8vSIHo61V2kJvv5kuv+S5XcZ6VQ8jxR0nLsDocjbx5ykp430r8JScW0CGILjEYjPB4PHnvsMdx3333o7e3NdZMIIYQQQgiJT6UCLl8CfP5eQK2OvezQEPCnF4F33gbGxhRrwowbLo467/zrxxTbDkldUVERtm7diiVLlkRd5lvf+hb+9Kc/ZbFVRCn8GI/R7iGa4kz8WOarDbAsG7dsspJlxIHxG5PSwHW641jLEW4yejyehG+gulyupMqBAp9m11mt1oSDEgaDIe6N6ckmMjMpF2WCPR6P2A6GYVLKMBRIs+Omagn4fBB5bZlsstF+n88Xdn4lOnSBnGhVN7KxjeluOl0jhWFKBLE+g1euXBn1damYCudLLvsvVT6fT/a7ZFNTU1JBbLky6fkw3ATLsuL3vWS+VxIipyjXDci0zs5OBAIBBAIBeL1e8W+/3w+3243HH38c99xzT66bSQghhBBCpoix0RB6zgyiqrYUJWUKf92urQPuuXc8QL1/X+xlP3gfOHlyPIu7ujrtTRfPqkDZ5bUY2j/xx/6FQxyGT/ShZF5V2tsh6ampqcG2bduwevVqnDt3bsL8UCgEk8mEJ554Im/GTiOJGeu5gFOtO3PdjLw3p2UlimrLMrqNeDdflS4jDowHqqWBcCFzWrjH4fP50rq5DoxnOwvBcbvdHjcQ6XK5UgoqWCwWMYDd2NiIzZs3x+3TdG5456utW7eKf5vN5rT3Xyqkx1S6ASKDwSCW3G1ra5v02cL5yOVyTerP7my1X3rsKRH4lFtHNrYx3U2nayTLsjCbzWhra8PatWtjfmeQBgNra2vTahMwNc6XXPZfqqIF0BmGSXr4J6PRCLPZDIfDIX4HdbvdSjQzLdKscpfLlVZFIjK9TYlM7M7OTrz88sv44Q9/iAcffBC33norFi9ejMLCQmg0GhgMBlgsFrGkQiAQgEqlQjAYhNFoxH333Zfrt0AIIYQQQia5of4RvP3sYfzyH9/A0//2Hh7/1hvY9tPdOPDeKQwPjSq3oeLi8dLizQagpCT2smfPAM86gcOHFdl0rGzsPsrGzhssy+K5555DcXGx7Pz+/n6sXbsWf//3f4/h4eEst46QqU/JMuLAp2W3I2/gS8szKhEwZBhGDBYnUqLcbrenVCKypaVFvMEs3GxVqVTiDf5sl8L0eDwwmUzQaDRQqVRQq9XQ6XRJZYony+VyhY2xmquAr7Sv033QQvp6juPycgxMYcxQYV+rVCpoNBqYTKaEjrtUjxWHwwGDwQCdTie+Vrix7/P5wtYptEcaaPF4PFCpVBMCwNL3IUyx2iFtv1qthlqthsFgSGlIgmT6Uqn2J0rJ4xoYD2RGXsezsY18QdfIcZm+RtrtdgSDwbjvdefOTx9oVCKwP1XOl1z1X6qULmVut9vDMp/zYbxv6b7Oh6A6mbwmTRB79+7deO6558RA9cqVK1FXVxcWqLZarWGBap7nY04Mw6C5uRkNDQ25fnuEEEIIIWSSGh0eg+8vR/Db77yDXe6PMTI0XsKbD/H4eF83PE/swxMtb+KlX+7FkT1dCI2FlNnwokXAvSZglvxY1aLhYWC7G3jtVWBkJK1NljRUo2TBDNl5Ax+cxSg3lNb6iXKuv/76sNJycn7605/ihhtuwMcff5ylVhEy9blcLkXLiAPjNyblxp2WjhOt1M1KYaxJYWzJaAKBALq7u1O+Ge31eifcPPZ4PLBarTAYDFCpVNDpdBm9CSsE0A0GA1iWhdPpBM/zCAaD2Lx5M3w+HxobGxUrfRwIBOByuWAwGGAymcRt5nJMTmkmWF1dXVrrisxoy2WZVjkOhwMajQZtbW2wWq3w+/0IBoNwOp2ora0Ny5KMlO6xwrIstFotOI4LC05brVZs2LABFosFfr8fPM/DZrOJ47oKQS69Xo9gMDghQON2u8V/F6ZoZaktFgssFgvWrVsHr9eLYDCIjo4OMfFHrVYn/OBBsn2pRPuTIe3jTA1DkI1t5BpdI/PvGslxXFhAWInx1KfT+ZKJ/ktkm8IkVM4Rki2VJj1XLBZLzkt4S8+ZfPtOQCaXvC0nvnHjRjEYHe2prkRKKwj191mWhUajEUtuNTY2KtxiQgghhBAynYTGQvjo3VPYsa0D/dyFmMuODodwaOdpHNp5GuUzirGoaTYuXTUHsxpmQKVSpd6I6mrgrs8BO3cA7++OvexH+4HTp8YzuFO8CaNSqTDjhovR9dv9E2eGgL43T4D5bPpP1xNlfOUrX8H+/ftjlgR+7733sGLFCvz2t7/FbbfdlsXWETL1cByHDRs2iP+vRBlxYDwwLvdQipA5LQQQIsfNToW0TPmmTZuilvGOFlhPFMMw8Hq9cDgcsNvtssEz4Uav1WqF1+tVpC8FQilzjuNkA+parRZutxsWiwUmkwlmsznhTMBAIAC1Wj1he1J2uz3tfaUEabuUHpM31zfPpUwmE1wuF7RaLbZv3x72XrVarRhUsVqtE7L7lDhW9Ho99Ho91q1bB51OB2D8+iAMfShlNBrF83rDhg3ifKHN0kAYy7IJ7TefzycGTBiGEV/DMAxaWlrAMAwsFguam5vjnmup9mU67U9HNsaanorjWdM1cly+XSM3bdoktslmsynepql+vmS6/yLJHeuZpNVqYbPZxCo5BoMBfr8/a9uPJO3ffPpOQCafvM3Ettls8Pl8CAaDUTOpBSzLQq/Xix+Ybrcbfr8foVAIhw8fxksvvYTHHnsMDz30EJqbmymATQghhBBCUsbzPAK7z+KZf9+BV578KG4AO9Lg+RF8+MoxuGzteOpf38WOP3aAOzOQeoMKC4GrrgZuuwMoizMObDAI/O5ZYN9eIMmxtgRlS+pQVCe/nf4dJxEaSC/bmyjrBz/4Qdzhk7q7u3H77bfjn//5nzE6qmDpe0KmGaXLiAPjgWmj0Rgzu1KgVLlV4eanz+eLetPR5XIpEmAwm81iVqhQHjQyWMJxHHQ6naI3QE0mEziOg81mi1lO1G63g2EYsepfIliWnZBdyvM8/H6/+FCREPjJJ+ne6I7MssrlWKNSDodDfNDD6XTKnkvSsXcjjzMljxXpttva2qJmmK5cuRIAFCvJLn1Pcts0m81gGAYcx8UcIiDdvsyFTJW7zvY2so2ukRPl+hoZCATCSqxnovz8VD5fstF/kViWnRDPkh7nmSD9HhUIBFIa9kUp0n2dL98JyOSUt0HsSEKguqWlJWagesOGDRSoJoQQQgghGXHiEIfn/tOLPz32IYKn0gg8f6LnzCB2/rEDT/3Lu3DZ2vHBK8cweD7FMYoXLACMa4F5F8VebmwMeON1wOMGLiQXgAcAVYEKVVHGxuaHQ+h771TS6ySZU1BQgKeeegr//u//joKC2D//fvCDH+CWW27BqVO0DwlJVibKiAPxM571er0YSIoVdE6GNDgtd6PV5XJBr9envR0pIavcZrPB6/WC53m43W7xRizHcWllfktJgy3RMs2lhLLt6QZUWJZFS0uLGAgRyubmUuQYremIfL2SmfOpkh43RqMxaps2btwoHoPSZZQ+VqQ38aXnbiTpvysR8NHr9WKVhWjnkXBORwucp9uX2STdbqayELOxjVyha2R4mwS5vEZyHCf2hdFoVLTE+nQ4XzLZf8kSjvNMBtGl76+1tTXhB0wyKR++E5DJK2/LiQuEJ69uvvnmXDeFEEIIIYRMU13H+/Du7/3o/LArY9s43dGL0x29eNN5CAuW1OKS1bPReOVMFJcUJr6Sykrgjs8Cu3cB7TtjZ1sH/MDZM0CzHpg9J6m2VmpnofelIwj1T8y67nv7OGZcdxFUxZPmedkpr6CgAI888giuvvpqfOELX8CZM2eiLvvKK69gxYoVeOaZZ7BmzZostpLEU1hTijktK3PdjLxXWFOa9W0mW0bc5XKJD+fHIoydmMxNfLvdrkiGj9lshsPhQFtb24QMb7vdrljWdyx6vR5er1csX+zxeODz+WJmBSZC2vZEbqoK5ZGFccITCerEwrKsWO7T4/Eoss5ECOWkpQ8p6PV68d/b29vTWn/k69PdT5E4jos7pmZtbW1YAFg65mis80go4R0pk8eKkG2dDUL5/liEAHu0Pk63L7NJelwr8WCPy+UCwzBhD+9kYxu5QtfI/LtGNjc3IxAIJFW2PVHT4XzJZP+lymAwxBxPvrW1NeVAtzCevPBgiclkQkdHR9ZLuUsfWFD6OwGZXvL6zlJjYyP8fj+MRiMKCwtx66234oc//CHef//9XDeNEEIIIYRMA+e7h7D91/vwzH/sSDiAXVxWiJV3NOAzN1+M8hnFSW+TD/E4sqcL7l/uwxMPvQnPE/vw8b4uhMZCia2goADQ6oC77gaqqmIve/488PwfxoPeSZQXVxUXouqaebLzQudHMLArepCU5E5zczN27dqF66+/PuZyp06dws0334xHH30UoVCCxx3JOFWhCkW1ZTTFmVSFqqzvm2TLiAcCgYRuJApjgkaWXY2cpMEpaaApHULGpRCUkLa9u7s7rYyaZDPrpBlF6QYRgORLNEv31c6dO9PePhB+MzdbN9TlHpqQZk6mW7pauv5MBJysVis0Gk3MKTITVNqmVI7ZTB4ruRwX1uVywWq1wmAwQKfTQaPRxL12pNuX2STNNlciA3Hr1q0TAm/Z2Eau0DXyU/lwjTQYDPD5fHA6nRnpi6l+vmS6/1LFsmzUa6nH4wkbmiEV0geKIh+2zBbpd7ZcV1Ugk1teB7EffvhhvPTSS+ju7sbOnTuh1+vx0ksvYcWKFWFB7d27d6e8jZ6eHjz33HPKNZoQQgghhEx6Q30jeMt1CE/9y7v46J1TQALx3YIiFa5sno/7/+NqrLqTxfVrL8H6R6/FZ79xJS5ZPRtFpUlkVH9i5MIYDrx3Ctt++j5+vfFtvNl2CGeO9IJPJOA8Zy5wrwloiDPMTigEvPcu8OILwEDiJdIrr5obNdv6/BvHwIdSG3ObZNa8efPw8ssvxw2yhUIhbNy4EXfffXfczDdCprNUyoj7/f6ExgZ0OBywWCxgGCbmpNVqw8puK3GDWLrOTZs2if9ut9uxcePGtNbt8XiSLssqZFtNlbK90v2vRGA+ET6fb8INc71eH/ZvsbLC4pG+Nt1jRCnSIEe+B14zLRAIwGQyQaVSiWOk2mw2bN++HX6/P+4Y95OpL6XXLyC94xoYP3eampqyvo3pjK6R4wwGA9rb2+H1ejOWjT6Vz5ds9F+qWJaNWpFH7lhMxebNm8WHTFwul2IPOiaC4zjxwQ9hmGBCUpXXQWzpB5ZWq8VDDz2El156CaFQKCyordVqUw5qt7e3pz1mByGEEEIImRpGhsfg/XMnnvzOO9jtOYqx0QSyUFXApavn4IvfvQrXmRajvKpEnFVQWICFS+tg+Jul+H+t18Hw/5ZgwdI6qAqSzxQc6B3G+y8fhXNTO57+3ntof7EDvecGY7+orAy45Vbg2uvHM7RjOXYUcLUBx44l1J7CymJUrpQvQz56dhBDH1HgM18VFRXBZrPhD3/4Q9xMsD/+8Y/QarWKZdYQMpUkW0Zc0N7eHvfc83g8qK2tTbj8ojTLSalMI2Gd0rG2lSrrmmxQQuiHurq6tLed7I1hacBdKJurJI7jFBn3ON42AoGA7HuXHi9CcDNZra2t4t9GozEjZUPtdjt4no85RQYE0h3PNt+OlXg8Ho9stpvL5YJGo4HL5RLHHLbZbNBqtQlnhCs5NnA00dqfis2bN4t/p3pcA+PXv+7ubtljOhvbyIV8O+6n6zXSZDIhEAjA6/XKLu/z+RSLaUzF8yWb/ac0t9utyJATDMOEPVxpsVjSriiQKGnAXIlhbsj0ltdB7FhfpJQKaudLqRZCCCGEEJI7obEQ9r5xHE995x28+/sAhgdHE3rdwmV1WPfPq6D/myWori+PuWxxaSEuWTUHd37jSqx/9Fpcv24xZjVUp9Te4KkBvPd8B5585B082+rFnteOYahv4vjUAACVCli2DLjnXiDejcrBQeCFbeOZ2WNjcdtRdd1FQJR4/PnXEguGk9y566674PP5oNPpYi535MgRXHfddfjf//3fxKoAEDJNJFtGHBi/aerz+eIGY+12e1hgOp61a9eKf7tcLkVu+EszM202m6LjkiZ7Q1O46apEJo/0PSRyM1ea/S3t53RE3u/K9L0p4WayXAUAvV4v7utAIBAWbElEIBAQAxIMw4QFKnJNGhBNJZszH46VZMid90IGNjB+Tidz7nk8HvHYTLcvE6FkoFKr1YrvNZXjWmAymaL2WTa2kQv5cNxP92ukNAAbq+R0IlVdEjHVzpds95+SAoEAPB6PYg+16PX6sO+nSlTriYfjOPGY1+v1eZcFTyafvA1iO53OpMqopBLU7u3tzasvCWQijuPQ2toqjtGj0Wig0+nSLjtCCCGEEAIAPM/Dv+sMnv63HXj1qQPo7xlO6HWzG6vxuW+twGf/7krUXxxn3GkZFdUl+MxN82F6uAlf/N5VWHlHA6pnxg6CR3Mq0IPXnj6IJ1rexAs//wCH2k9jdFgmAF1fD3zeCFxyafyV7t4FbPvD+JjZMRTVlqH8MzNl5w0f6cWFI72JvAWSQ42NjXjzzTfx4IMPxlxueHgYf/d3f4cvfOELOB/nuCBkOkiljDjwaWnuWA/tC+NQxyvvK8UwTNhNwra2toRfG4vQBofDgU2bNiUVWI/F4/EknIkl3NBVKsNXmjEvLZUejdCXNptNsXGMI2+cR95UTuQBo0RxHBf3uLPb7eIDAlarNeGb3BzHicFNhmHg9XpzOtZzpJaWFnFfJ3L/z2q1hh2X+XCsRJKuNzLoKzdevfQ6FSvrUC4wbbfbxWMh3b5Mtf3paGlpEYM3Vqs16VK6BoMBLMvGvBZnYxvZlg/H/XS+RppMJnAcF3dZt9utaOb7VDlfctV/ShGum9HiYqlUJhAqb2RLc3MzgPGqDol+PyYklrwNYt97772ork4tMwWQD2qvWLECzzzzjBjUVqvVCAQCefUFm3zK4XCgsbERbrcbTqcTfr8ffr8fTqcTVqs15Se2CCGEEEIA4PjBIJ5t9eLP9j3gTic2FjQzuwJ/ZVmGe1t0uOgStSLtYGZXYNWdLL70b1fh3hYdrrjxYpRVFSe9nlCIR+cH5/DS43vxq5Y3sf3X+3D0o26EpGNTFxcDN90M3NQMFBXFXuHp0+PlxeNkPsy44eKo886/TtnYk0FZWRl+/vOf46mnnkJlZWXMZZ955hmsXLkSe/bsyVLrCMk/qZYRt1qtYkApVvaPNGMrGevWrQtrUzxChl2srEpp0Lq2tjbm+0z2pmprayusVmvM13EcB5PJpHiGr1B2OvJhhEjCzXCj0Rgz0767O7khNBiGCcsqjyyD7fF4ZG9gJ9vHHMehubkZHMfFPUbdbrf4Hg0GQ9x7LkIQKRAIQKvVxsx4S7StmeB2u8EwTFhGshyPxwOHwzFhrFolj5VkjxO510iPm8hAmtPpnBDYk15HomWzSjOupfshsrxyun2ZSvvTZbPZxCCKxWIR91MsPp9PDDxGG7M229uQk8kS23SNnCgb10iLxSL2t8FgkJ2ERC8ls3UFk/18yXX/Aemdlw6HQ2x/tO+Awne2ZKsTJBtMTuXzyufzQaPRwOfzicc8xd2IIvhpyul08gzD8CqViq+trc11c0iElpYWHgCv1+snzPN6vTzDMLySh++ePXt4AOK0Z88exdZNCCEkc0aGh/lQKJTrZpBJ5uzR8/y2n+3m/8eyPeHpVy1v8HteP8aPjY5lpY2jo2N8xwdn+b88vod/7O9eSaqtkdMTLW/wbzoP8mc+7g0/X4JBnne18fxjP48/vf0Wz8c418443uePWl+fOD38Oj98pj/zHUYUs2/fPn7JkiVh343lpvLycv7Xv/51rpubU/QbYvoyGo3ifmdZlvd6vbzf7w+bvF4v73a7eafTyZvNZvE3rDC53e6wdQaDQd7tdvN6vV5cRq/X816vN257gsEg7/V6w9oFgG9paeH9fj8fDAbDlvf7/bzdbhfbxDAMb7fbeb/fL7t+rVbLA+CdTqfstoXtC8sB4I1Go+y2eZ7nAfBms5kPBoPi+zWbzbzT6RT70u128zabjWcYhtdqtVHbJqwv0SmyH6TbF/ra7/fzTqeTZ1mWB8DbbLaYfe/3+8V7GMLkdDqjvn/pa4VtSPtXuOchfc/Cdsxmc9h2vF6vuA+ESei/yONO7v6KHLfbLe5LlmV5m80mbkfoG6HfGIaJ2T/R3rfcMavVanm32x32XpQi3dcsy/JOpzOsHUJfRTvf0j1WhG3ZbLaw/RF5jAh9LL0O2Gy2Cf3hdDrDjhth3SzLym5fetzY7faw7dlsNvEcE44Xo9EYdX3p9mWy7U/1/JZrt7QfjEZj2Hnq9Xp5u90uHpPJHteZ3Ea65wxdIyfHNTKy7YlM0T5j0z1nJuP5olT/pdqHyRyD0mPR6/WK12HpuS3XX9LPEOH6G+v7USS73S6ez9FEO2ejnUvCfor83CJESSqen76DmnEch9raWqjVanR1deW6OeQTLpcLJpNJfGInkjQLW6nDd+/evVi2bJn4/3v27MHSpUsVWTchhJDM4Hkef/zvVqgAGMzfQGlFRa6bRPJcb9cgdmzrwIH3To3/vEpASVkhtH+1EJ+5eT6KSwoz28AohodG0bH7LA7sOI1j+7uRztcf9dxKXLp6NhavnI3quvLxca/ffQfY82H8F6++Cli+QnbW0MEgzv1KPjO3ctUcqD+/OPVGk6zr7+/HAw88gN/+9rdxl/3qV7+Kn/70pygvT60c/mRGvyGmL7VanXYGnN/vD8vI0ul08Pl8EzJWOI6D2+2OORa00J5o2S4cx4nbE35vyy3LcRxsNtuEbDqXywWr1Ro27mm8dkvXaTQawzKANBpNWHaOz+eD3W5He3s7AoGA+F6amppgMpniltlVqVQx50vJ3UPweDxi2WRhv7IsC6PRiI0bN0Z9b0ImOSCfMSWsK3JfR3I4HLDb7WI/NjU1hZX+VKoSXeR+iMfn82Hr1q1ilq6wX2pra6HVarFu3bqkx7m0WCxwOBxxM7OErEi5Yy4dkfuaYRiwLAu9Xh9zX0d7PZDYsWIymeByuWTPb4ZhEAwGAUQ/l+XOI4/HA5vNJmbl6fV6bN68OWobPB4PnE6nuD+F975u3TrxnBfG7g0EAuJxGGt96fZlIu1P9/yOxHEc2tra4Ha74fP50N3dPeGas3bt2rSyB5XchhLnDF0jE5PLa2Sq5dHl9peS58xkOV+U7D8g+T5UumKs2WyG3W4X/19Yf7TzKN73RCmTyYSVK1fKVk6QnrPJYFkWLMsqcjwQImdaB7GB8S8Djz/+OMbGZMYNJFnHcRzU6vHSnF6vV7ash/Cl2mAwKDZmDN2AIoSQyWf3X17A9l/9AgCgnjsPn/2HhzGrQbkx1MjUMdg3DO+fjuDD144hNJrYV9+CIhWuuPFiNP1VQ0qlvTOlv+cCDrefwYH3TuHsx+mNSzx3UQ0uXT0HGu0slJ09Brz6CnDhQuwX3XY7sGDhhH/meR5n/nsXRk71T3xNkQpzratQOKMkrfaS7OJ5Hps3b8Y3v/lNXIhzXCxfvhxOpxOLFi3KUuvyA/2GIIQQQgghhBBCMmfaB7E3b96MBx54gILYeUJ4sigTT9zGQjegCCFkcjkdOIynv/OPGBsdFf+tsLgYN/+NBVfcfGtST86SqWvkwhje334Uu146guGhBL/rqYDLVs/ByjsbxzOV81jwVD8O7jiNA++dwvmuoZTXU1CkwsKldViyohoLT+2C6vSp6AuXlACfvxeoYSbM6vedRrDtoOzLZtw0HzW3NqTcRpI7Pp8PJpMp7rhr1dXVeOKJJ/D5z38+Sy3LPfoNQQghhBBCCCGEZE5BrhuQa42NjYqVpM6E1tZWqFSquDeN4nE4HNDpdFCr1eJkMpng8XgUaqkyHA4HACRcAoMQQsj0M9Tfh20/2RQWwAaAsZERuB3/gzef/nWOWkbyxdhYCHteP47ffucdvPd8IOEAdsMVdbjvkVVoXr8k7wPYAKCeU4nVd7G4/z+uxucf0mHZDRehtLIo6fWERnl0vH8OL2wJ4Jev1aKjaGH0auvDw8Cf/zz+3wgVV85EYU2p7Mv63j2J0AV6aDRXBnqHceyjbhxqP42Th7mkfv8IQ/x87nOfi7lcb28v7r33XnzrW9/CyMhImi0mhBBCCCGEEELIdJf8Xa4pRq/XZzXjN1GBQAAWiyXtILPP50NzczNqa2thtVrF8tuBQAB2ux0GgwFGozHmuDnZIowfAnw6Vkprayu2bt0q/rtWq8XGjRtly4wTQgiZ+niex19+8d/oOXNadn5hcTEuveaGLLeK5Aue5xHYdRbv/iEA7vRAwq+b3ViNaz6vwbzF6gy2LnNUKhXmamowV1OD69Yuxsf7unHwvVPo+OAcxkZCSa3rwuAYXtxZhsXqubil4aT8QlwQeHk7cOtfAZKqB6rCAlRdNw89L3RMeAk/OIr+9lOYce1FSbWHJGfkwhi6T/aj63gfuo/3o+tEH7qO92HwfHhQef7latzyt8sSLpXPMAyee+45/OQnP0FLS0vMKlY/+clP8O6772Lr1q2YP39+Wu+HEEIIIYQQQggh09e0D2ID49nYucRxHNrb2xEIBOD3++HxeODz+dJer8fjgcFgAMuy8Hq9YUFqlmVhs9mg0WhgsVjg8/kmLJNt0mzzuro6GAwGGAwGeL1e8d+tVit0Oh3MZjPsdnsumkkIISSHdv3peRze+U7U+Tevt9C42NPU8QNBvP07P8509ib8GvWcClz1OQ0ar6yfMiXoC4sK0PiZejR+ph7Dg6Pw7zqLgztO4diBIKKnV090KFiN+vIL0M7ull/gSCe4F1+HatVKzKgrR0HBeP9VrpqD3u0fg5fJfu974ziqrpoHVeHU6OtcCo2F0HN2EF3HxwPWXcf70HWiH73nBhPaz0f3B/G7H/tw1zeXo5KRz56PpFKp8K1vfQurV6/GunXrcPz48ajLvvPOO1ixYgWeeuop3HrrrYm+LUIIIYQQQgghhBARBbHzQHt7OwwGAxiGQVNTE/R6PZxOJ3Q6nZiBnCyO42AymQAATqczanDabDbD6/XC4XCgubk5LGCcbdIgtt1uh81mg9FoDFvGZrMBGM/QFpYjhBAyPZw8dACv/fZXUedfdu0aXNFMwZLp5tyx83jndwF8vLcr4ddU1pRg1Z0sLrt6DgoKp+7oOiXlRbj8mrm4/Jq56AtewKH20zi44xTOHe1L6PXvnqhHXfkQFlbLZ7Uzx/bhxdc5HOmvBjOrAszs8Wn2/BkoOcRNWH6Mu4DBD8+iYvmsdN7WtMLzPPq5YTGjWsiuDp4cwNhocln2kbpP9OO5H3px19+vQM3MxMvnX3vttdi1axe++MUvwu12R12uq6sLt912G77zne/gX/7lX1BYWJhWewkhhBBCCCGEEDK9qPg8GhB648aN0Gg0+OpXv5rrpsjKdvvUarUYxPb7/WDZxDPLTCYTXC6XOIZdLIFAABqNBsB4wDsycJwtDocDFosFwHimeLQy79L2ut1uRcbP3rt3L5YtWyb+/549e7B06dK010sIIUQZg33n8aT1mzh/7qzsfPW8i/GlTT9BSVn+j2NMlNF7bhDvbQvg4I7TCWcYl5QXQfdXC3HFTRejuGT6BtS6TvTh4I7xgHZf94WYy5YWjsF4yREwZfJjHA+PqfDswYXoHvo0m7dUBRiqi1Aok93Oq0sx42+Worq+QszeJuMuDI6i+5OMaiG7uvtEPy4MjGZ0uxU1Jbjr75ejbl5VUq8bGxvDf/zHf+B73/te3DG29Xo9nnrqKcyaNbUeYKDfEIQQQgghhBBC/n/27ju8iTPbH/hX7tjGHtmml9gjUiEJSHZCKiSWEtKbBNly97J3L1KyLdndxAp7794ttxB5syVbI3kL+9uKpYT0guRsSCMLliAJpCKZDgFjjY1tcJ3fH96ZSPKMrDJq9vk8j54QT3mPpkmaM+95SepkVRI7Ly8PJpMJmzZtynQoktIdX6JJbI7joFaPjenY1NQk9l6ORqPRIBAIRE16C4lxJUjF5XK5xN7jE5ULF0p+6vX6qD1AYkU3oAghJHvxo6N48of/jYBvh+T0gqJifPZ/f4QZC2vTGxjJiNOnBtH+wj7sfvUwRodj+xqbX5CHi66ZD+2qs1BSFtsYwFMBP8rjqL8bH24/Br/3uGyytKpkAHedsx9F+dLbmztTCNdHZ2Fg5NMHA5ZOy8dZxdK93N/sHcZJIKz3tnr2P/87qxTFpZN7H40MjyJ4rP+fSeresZLgR3onfKAglYrLCnDLV5diVl1F3Mtu2bIFn/vc59DZ2Rl1vrlz52LTpk248sorEw0z69BvCEIIIYQQQgghJHWonPgk5HA4xH83NDTEtAzLsggEAvD5fAgEApIJ8/Xr18NgMCgSo1Tv6dA2hZ7WchiGAcdxYSXICSGETE7tz26WTWADQOO/3UMJ7ClgaGAEb7cdgG/LAQxJjLcsRaUCzr1sDi65uQ7Tq0pSHGHuUeWpMPdsBnPPZnD16nOwf89JfPSPY+h4tzPsAYGuM8Xw7J+DG9kjkuthSoZgqD2C5/zzwWPsQcO9AyOySexFxXk40TeCriN96DrSN276tIoiqGeVgpk9ltQWktyhY2/nAn6Ux6muM//sVT2WqO460gfuWD9GR7PmOWIAwEDfMJ766U7c+OWLMP9cdVzLXnfdddi5cyfWrFmDN998U3a+I0eOYOXKlXj44YfxrW99a9KMQ08IIYQQQgghhJDUoCT2JBTaU1xuLOxIoQlkj8cDs9k8bh6tVgutVpt0fHJC133yZOzjWhJCCJm8Dn/wHl776x9kpy9e0Ygl1yjzgBXJTiMjo3j/9SPY/tw+nO4ZjHm52otqsPx2Nu4SyVNVfmEe2KUzwC6dgYH+Ifh3nsDHOz7BsUA3hgdH0dE9HduPVuOSOdLf0c6q6MfyuZ3YdmQGAKB3FDg6NIo5heMT2TML81CRN4IemSGdT/cM4nTPII5EjKudV6DK2t7bp3sHxxLVh3vFkuBdR/owNBDbAxdKKyjKQ9WcMlTPK0fV3LH/lquL4f7dezhx4JTkMkMDI3j252/jevMS1F1UE1d78+fPxyuvvIL169fjRz/6kex8IyMjePDBB/H6669j48aNMf9WIYQQQgghhBBCyNSTdUlsn8+H9evXZzqMnObz+cR/V1VVxbRM6A2kicbQTiW9Xg+PxzNhD2uhzHoqk+qEEEIyq7+nG88+agM/Kp3pqp6/EI3/dm+aoyLpwvM89nqP4x9PBdB94nTMy81mK3HZnRrMXcSkLrhJrri0EBdcMRcXXDEX/CiPXm4A3Cf9CB7tQ2fHW6gZli4brZ3VhRP9xdjLjZWk3ntGOokNAItK8uHrjy/BOzrMZ7z39tDgCIJH+8Te1UI58P44HrBQkkoFMLNKUTW3HNXzylA9txxV88pQWTMNKon3fds3luG5X76No3u7Jdc3MjyKFx57F43/ej7OvXR2XLEUFhbikUcewRVXXIG1a9eip6dHdt6nnnoKWq0WTqcTOp0urnYIIYQQQgghhBAyNWRdEtvv98c0hjORFpn8jXUc7erqavHf7e3tisYUD6vVCo/HA4/HIztP6Hu0WCzpCIsQQkia8aOjeOGXP0Zvl3Svz4LiYtzyjfUoLKES0ZPRoQ+6sG2zH8f3S/cYlaKeXYrlt2tQd3ENlSlWkCpPhelVJZheVYIF51cBg7OAzU8AXFBy/sbaYzgVKMEnPUXoGuHRNTyKqoLxiex5hSq8rwJOK1RVO1rv7coZIb22Y+y9PTrKo/t4f1gZ8JOHe8ceqMhQJfAyphjV88rCEtbqOaUoKMyfeOF/Kp5WgFu+vhQvOXZj/27p6ys/ysOz8T0Mnh7GhSvnxx3nHXfcgYsuuggmkwk7d+6Una+jowOXX345Hn30UVgsFjpvCSGEEEIIIYQQEiarktjBoPTNMBI7oYdyvEJ7Yie6DiXo9XoYjUa4XC40Nzejqalp3DxWqxUA0NTUJDm2NiGEkNy3/SkX9u2SrwxiWPdVVM9fkMaISDqcOHAK25704+B7XTEvU8YU45Jb6nDe8tnIy5fu9UsUVFQErFoFPPE4MDi+93GBisddF59A37W3IMiNovftE8Cu4+Pmy1OpwBbnYc8ZmZriChkd5hE82ofg0Si9t/+Z3OZ5fFoK/GgfRoZSG5ucopL8TxPV88rFxHVJmTIl0wuL8nHDPRfCs/E97G0fv28AADzw6t8+wuCZYWivPyvuBLNGo8Gbb76J++67Dw6HQ3a+wcFB3HvvvXj99dfx2GOPobycyv8TQgghhBBCCCFkTFYlsSsrKzMdAgHQ1RX7jeNUcDqdMBgMYclqYKwHttVqhcvlQlNTU0p77O/duzfuZWbMmIGZM2emIBpCCJlaDr73Lt7Y9CfZ6Rdeex0uuOqaNEZEUq37xGn84+kAPt7xSczLFJcWQLvqLFy0cj4KimLviUoUUMkAjQbgheckJ6t6e1HevhXlN94M/lw1PjnYg+GTZ8bNx5YWYPj8CpzsPAPuk34MD6Y3aSzXeztd8vJVUM8ujUhYj41dnepeyfkFeTD822IUTSvAe68dkZ3vrScDGOgfxmV3aOKOqaSkBHa7HVdddRUsFgv6+/tl5/3zn/8Mn88Hl8uFCy64IK52lHL8+HGcOHEirmUS+c1ACCGEEEIIIYSQ2GRVEpskLzIBHdrDOlaZ7IktcLvdcLlc2LRpEzQaDbq6usCyLOrr6+H3+2Muk56o22+/Pe5lvvvd7+J73/ue4rEQQshU0scF8dzPfgiel05mzVhYi2u+SENJTBb9PYNof2Ef9rx6GKMjsdVozi/Mw0XXzIf2+rMU65lKErBwIXDpcuAfb0lPP3IEeGsbVFdcifKr54PbPD7ZlzfK45Jz1KiwLAgfe/tYP7hP+sF90ofgsX70BgdS/GZSr6KmZCxZPXcsWV01rwzMrFLkZ7B6QF6eCis/ey6KpxVg55YDsvPt3HIAA6eHseIz5yY0vvjnP/95LFu2DEajER988IHsfO+//z4aGhrgcDjwuc99Lu52kvWrX/0K3//+99PeLiGEEEIIIYQQQqRREnuSyYYEtFKMRiOMRmOmwyCEEJImo6MjeP7nj6AvKF0RpLBkGm7+xnoUFhWnOTKitMEzw9jlOYhd7gMYGhiJaRmVCjjv8jm45OY6lKtpLPSscPFSoLMT8Mv0Rt39LlBdgzLt2ejZsh+jfUPjZul98zCmXzkPqsK88LG3QwwNjPwzqd2P4Cf94I71jf03A723J1JSVjhW/nteSMJ6bhmKSrLzZ5dKpcLldy5CcWkB3noyIDvfe68dweDpYejXXoB8iTHOJ7J48WLs2LEDZrMZf/3rX2Xn6+/vx+c//3m8/vrr+MlPfoKSEjrXCSGEEEIIIYSQqSo776YQQgghZMp56/FNOLD7bdnp11m+hqq589IYEVHayPAo3nv9CHY814HTp8YnNOXUXVyD5bdpUDW3LIXRkbipVMCKlQAXBE6elJ7nta1QqdUov3wuetz7x00ePTWE/p3HUXbJbNlmCovzMWPhdMxYOD3s75nsvZ1fmIeqOWWflgGfO9a7urSiKOWlwFNBt6oWxdMKsPVvHwEyRRH2th/H4OkRrLIsQWECJfzLy8vx5z//GVdddRXuv/9+DEqMqS547LHHsH37djidzpRXYCKEEEIIIYQQQkh2oiT2JJNI+XAy3pNPPolFixbFtcyMGTNSFA0hhEx++9/dhW2Py/fOu/i6m3De5VenMSKiJH6Ux17vcbz1dAA9J07HvNycRZW47I5FmKOpTGF0JCmFhcD1q4AnHgfOjB/3GqOjwJaXULbqNpx6JQ/80Pie06deO4TS+llQxVmqWpWnSnnvbZUKqJxZiuq5/+xdPa8M1XPLUTFjWkKltbPZkhXzUVhSgLY/vA9+VDqTfWDPSTzzs1246SsXo3ha/D8lVSoV7r33XjQ0NMBkMmHfvn2y8/p8Pmi1WvzhD3/AbbfdFndb8fryl78Mk8kU1zJ79+5NaBgiQgghhBBCCCGETIyS2JNMVVX4DTyO4+JObFMiHFi0aBEWL16c6TAIIWRK6A124fmfPwLw0kmTmXUarPzCv6c5KqKUg+93YdtmP04cOBXzMlVzy7D8dg1qL6zOyV6tU870CkBvAJ57Vvo87u9D/httKKu/EL3bjo2bPHziNM580IVpF1QrFlLsvbf7cNy3F92f9I29leJBzNU3oGZBBarmlqFqThkKEuh1nKvOvXQ2iqYV4CXHbowMSyf7j+7txlM/2YlbvnYxpk0vSqid+vp6+Hw+/Ou//iueeeYZ2fm6u7tx++2344EHHsD//d//obCwMKH2YjFz5kzMnDkzZesnhBBCCCGEEEJIfCiJPckokYCOTIQTQgghqTI6MoLnftaM/m5OcnrRtFLccv9DKEhh4oKkxvH9Pdi22Y9DHwRjXqZcXYxLbmFx7vLZk66X66Q3bz5w2eXAm29IT//kE1TUVaI3rxAYHb9vT209pGgSW05o7+35i6bj2A/+GzOedobNw8xZg9mm707ZByjqLqrBzV+7GM//6h3ZMetPHDiFzT/y4db7liY8Rr1arcaTTz6JRx55BN/+9rcxMiLdFgA88sgjeOutt/C3v/0N8+bRsBKEEEIIIYQQQshUQEnsSSYyAd3V1RVTYpvjOPHf1BMbWLt2LcrKxo+7uXbtWqxduzb9ARFCyCT1pvMvOPTebtnpq+69H8zsOWmMiCSr+0Q//vFUAB+3H495meLSAuhuqMWFK+ehoHDq9HqddJZcCHR2Ah99KDk5r+MjMOeeA+798b13B/f3YGB/D4rPqkh1lACAkZ4eHL7/fvS9uW3cNG7TJhTV1qL6i2vTEks2mn+uGrd9Yxme+fkuDPQNS84TPNaPJ344lshmZpUm1E5eXh6ampqwfPly3H333Th69KjsvK+//jqWLVuGv/zlL9Dr9Qm1l6iNGzdi48aN4/7e19eX1jgIIYQQQgghhJCphJLYk0xkAjo0OR2N3+8X/82yrIIR5ab29nbJv69cuTK9gRBCyCTWscuLf2zeJDtde8OtOPvSy9MYEUlGf88g2p/rwJ7XjmBUZjzdSAWFebjo2gXQXr8QxaXU2z7nqVTAVVcDwSBwQvohhrKBj9FfXIvBgfEPC5569RCK/+WCVEeJwUOHcNByDwZDvv9GOt7cjFKdFtMuuijl8WSrWbUVuONbWjz96C70dw9KznOq6wye+JEPt359KWrmlyfc1tVXX42dO3fis5/9LF5++WXZ+U6cOIHrrrsO3/ve9/Cf//mfyMvLS7jNeOzbtw9bt25NS1uEEEIIIYQQQggZQ0nsSUir1cLn8wEY64kdi9D5GhoaUhJXLqmvr5fsiV1bW5v+YAghZBI6dbITz//iR7LTZy86B1d//otpjIgkavDMMHa6D2CX5yCGZUoPR1KpgPOvmIuGm+pQri5OcYQkrQoKgOuuB55wAadPj5us4nnUzD2ETw7UYWQkvEf2mfdOYuhEPwpnJNarNxb9vp049NWvYmSC78g1X/kKSi68MGVx5IrqueW48wEdnn50J3o6z0jOc7pnEE/+2Iebv3oxZrOVCbc1a9YsbNmyBd/73vfwP//zP7Lz8TyP7373u3jjjTfwpz/9CTNmzEi4zVjV1tZixYoV4/7e19cn+/ArIYQQQgghhBBCkqPieT62rjIk7dRqtdiT2u/3x9xD2mq1orm5GQBgt9thNpsnXEan04mJ73jamiz27NmDJUuWiP+/e/duLF68OIMREULI5DUyPIzWH3wbRz58T3J6cVkZ/uXhn6Fy5qw0R0biMTI8ij2vHUb78/tw+tRQzMuxS2dg+e0s1LPHPyxGJpFjR4FnngZGRyUnDw6U4PhRFuDDe9KWXTIb6jvPTklI3c89h6Prvw1+ULpXMQCoCgsx5//+F5W33JKSGHJVHzeAp3+2C11H5MtnFxTl4cZ7L8KC86tk54nViy++iM9//vM4efJk1PnmzZuH1tZWXH55Zqp20G8Ikikejwc+nw9NTU2ZDoUQQgghhBBCUiY99ddIWq1fv178t9frjWkZIYHNsuyUS2ATQghJrzc2/VE2gQ0Aq778TUpgZzF+lMdH24/hL997C69t+jjmBPacRZW4q0mHG+65kBLYU8HsOcAVV8lOLio+A3XNYQDhz9P2+T7ByCn5JHMieJ5H569/jSPfeiBqAjufYbBw4+8pgS2hjCnGHd/UYuZZ02XnGR4cxbO/fBuBnSeSbm/VqlXYuXMnli9fHnW+w4cPY8WKFfjJT34CejabpFMgEIBKpUrq5XA4Em7fYrHAarWKv+Pj4XK5ZGPyeDwJx8RxHNRqteR6TSaT7HKBQAAWiwUajUacX6PRwGKxIBAIiPM5HA5YrdaE40uF5ubmuPa5RqOBwWCAxWKJa99ZrVaoVCqo1WrZl9CGknw+H6xWK3Q6nbh/1Gq1uH+SOV4IyRSO4+BwOGAymaDRaMTzR6PRwGQySR7XVqs1rmt2OtrIBXSNpGukQIlzorm5Oeo+jvcV6xCs2YCuWxNL9fEhXGciX7EeSxO1F/o+EvldIewnl8ul8JbNDpTEnoQYhoFerwcAtLa2Tjh/6EUo234UEkIImVz83u3Y8fTjstPrb7kTi+ovTWNEJFY8z+PAnpNo3bAD7t+9J1taOFLV3DLc9JWLcMe3tEmVGiY56IILxl4yysq7UV4R0dN2mEfvm0cUC2F0cBBHH3oIJx79WdT5iurqUNu6CaU6nWJtTzYl5YW47RvLMO9cRnae0WEeLzrexQfbjibd3oIFC7B161bcf//9UecbHh7Grl27km6PkHhE3qxiGAY2mw1OpxNerxfBYHDcy263hy0j/GaPl8/nE5O7keuMhdFoRDAYhN/vF3tyMwwDALDZbAnFBIwlmauqPq3EoNfr4fV64ff70dLSIrmM1WqFRqMBADidTnFbOZ1OMAwDnU4Hq9UqJrqz7YZzU1MT/H4//H7/uAp4brdbfD/CPHa7HVqtFu3t7dDpdNDpdDHdbLTZbPD7/Whra4NerwfHceJr/fr1YcecEnw+HwwGA3Q6HTweDywWC9xuN3ieR0dHh3jcGQwGqNXqpG9gezweqNXqSXvjlWQHjuNgsVigVqthsVgAjF2DnE6neH42NDSID9UISVSPx4Pm5uaYrj/paEOYPxfOGbpGJn6NdLlc4vIqlQo6nQ4mkylrEoaxUvKcMJvNaGtrg9PpRH19fdh+bmlpQVtbm+TL6XTCZrONOzZCH5STkg37INevW+nchqk+Pmw2m3i9Cv2+zHEcGhsbJ4zP6/XC6XSGtVNfXy/+dhBEu25K/cbw+/3wer3iw58mk0mR72ZZhydZi2EYHmPdU3iv1xvXsn6/X1ze6XRGnddoNPIAeK1Wm0y4OW337t3itgbA7969O9MhEULIpNN9/BP+F19cwz+y+ibJ11/+8wF+eGgo02ESCcc6uvnNP/bxv7C0xfzauP51/v1tR/iRkdFMh08yaXiY5598gucf+5Xka/TXv+KPf/d5/qD1VfF16Htv8iNnhpNueqiri9/3uc/z7517XtTXvi/8Kz/McQq82alhaHCYf/aXb094DdjVdkCxNp1OJz99+vSw7+vCa/HixXxvb69ibcWDfkNMXW63W9zvTU1NE84fDAbDft/bbLaE2zabzeJ6GIZJeD0C4T0I6/T7/Qmth2VZ3mazietxu91R5xfeR7R7HcFgkNfr9eK2M5vNCcWWDn6/X3zvLMtOOL/T6RTnNxqNMbfj9XoV3f+RhH3IMMyE+1DYP8L9pGAwGFMbwWCQ9/v9vN1u57Varfh+7Ha7Au+AkPEiz7eJjlW73c4zDMPbbDbx+jPRdTuVbUyGc4aukbFdI4VltFotb7fbeb/fz3u93rDjhGXZuPMEmZDKcyJ0P8dyPAn8fj/PsmzUfEm27INcvm5lehum8vgQ5hPWnch3e61WG1MeLt7rJs+P/UYRtnE8185sRz2xs1jokyxdXV1xLcuyrPi0s8lkkn26yOFwwOVygWEYOJ3OhGOdbNauXYuVK1eOe23cuDHToRFCSE4aGR7Csz+14Uxfr+T0kukVuOm+JuQXFKQ5MhIN90k/XnTshuvhdhz+MLYn14vLCnCFcRE+9/3lOG/5HOTlKVu2jeSY/HzAcD1QJl1CXqUCqmYeRH7Bp2W++dPD6Gs/llSzAx0d2Hf33ehvb486X+Wdd2JhiwP5lVQlIFYFhflYZVmCcy6JPuzD660fY8dzHYqU+TYajfB6vbjooovC/l5eXg6Xy4UymeNLKRs3bpT8bbB27dqUtkuyl/Bb3Ww2x9R7ed26deIyWq02qbGsW1tbxSHAOI5TpCee0PMGSKx3t8fjgV6vF3t0xzK/w+GAzWaDVquVnY9hGLjd7rAe3tkq3hiNRqN4HLhcrrB9EE3oNlZ6u5hMJlitVjAMA6/XO2G1AGH/GI1G+Hw+1NXVTdizTafTQa1WQ6fTwe12Y82aNUq+hSlFrVZPuL2zWbrit1qt4pAGdrtdrPQQjdlshtfrxYYNG2LqyZjKNibLOUPXyNiukY2NjWBZFl6vF2azGSzLit8bOjo6oNVqEQgEoNPpEhpSJF1Sfd7F+n0jEsuy4vccuX2RDfsg169bmd6GqTw+Qttwu93i/8czzI9er0d9ff2E8yVyDdPr9ejo6ADLsnC5XNBNkkpzlMTOEh6PR3w5HI5xB5jFYkFzczNcLpc430QXJKPRCLfbLZbhCi0jIJSKsFgsYQc3GdPe3o6tW7eOe+3bty/ToRFCSE569c8bcXTvh7LTb/zKN1FRMyONEZFo+roHsPUvH+Kv3/8H/L7jMS1TUJgH3aqz8C//czmW6heioDA/xVGSnFFaCly/aiyhLSE/fwTVs/ZDpRoR/9b72mHwI4klP/u2b8f+uz+Dof0Hos4345vfxJz//R+oiooSamcqy8/Pg37tBViyYl7U+bY/04E3XHsVSWSfffbZeOutt/ClL31J/FtLSwvOO++8pNc9kX379kn+Nmif4CEJMnkJD5nHksD2eDxhieZkHh53uVxgWTas3USSzpGqqqpgNBoBIKHygzabLeYEA/DpMGZCm7GsfzIK3WYOhyOjCUmr1Soep21tbXHdH3I6nWBZFhzHwWAwRJ23ra0NPM+LZeMTLatPxg9rkGvSEb/D4UBzczOAsetIZGnWaFiWjel6neo2pvI5M9WukcJno9znOsMwYcNzCAnIbJOO8y4Zer0eWq0Wfr9/3LRs2Ae5ft3Khm2YjGjHR6TI7+TZ8l5CO6v6fL64vqNnK0piZwmDwQCDwSA+1RUIBMAwjPjq6urChg0bsG7dOphMJhgMhrCxrOUICWqbzQa73S4OFl9XV4euri643W4x0U0+VV9fjxUrVox71dbWZjo0QgjJOR9vfxO+55+SnX7J7SbULZv4KUSSeoOnh/GPpwP403e2YferhzE6OnHiSZWnwuKr5uLz/30Zlt+uQfE06k1PJMyYCVy9QnZyUdEA1DMOY6xaFjDCDeD0uyfibobb/CQOfOnfMdLdLTuPqrgY8376U9SY10GlokoBiVLlqXD13edAt+qsqPO93XYQL//xA4yOjCbd5rRp0/Cb3/wGv//97/Gtb30Ld999d9LrjEVtba3kb4NYnqAnkxPHcdBqtRP+juY4LuyGls1mS+rhcbvdDovFEpb8jeUB91isX78eQPy9uzmOQ1dXV9Qe1ZHi7XVjNBon5T2LyGMhlns8qSCMawmMbet49qUgtOdStJulk3E/ZkKmjhWlpCP+0Bv3LMsmVAFDr9dHfdgmHW1M5XNmql0jHQ4HWJaNmqzXarXigwyBQCDrxr1NxzmhhDVr1khu50zvg8lw3cr0NlSC3PEhpampKey9ZEvCWKvVig8nOByOrK7cEAu6y5gllOidIIdhGJjN5rieqpnqNm7ciMWLF2c6DEIIyXncJ8fw0q8flZ0+//wluGL159MYEZEyMjSK3a8eRvsL+3Cmdyjm5TTLZuDS21ioZ6e2lC+ZJM45F+jsBN59R3JyaVkPhipP4FT3TADAqVcPYdrFM2JKNPOjozjxs5/h5GPReyTm19Rgwa9+iWkRZalJYlQqFZbfrkFRaQG2PSH/tPoHbx7F0OlhGP5tMfILk3+OOt1lvNeuXSvZ5p49e7BkyZK0xTEyMoKenp60tZerKioqkC9T+UFJsTzEoGQZcY7j4PF4xNKFZrNZvOnncDiSWrcQn3DT0W63x3wDecOGDQnfsPN4PDHfp1i9enVCbeSSTPWsFXpNAZ8+zBAvvV4vHj8OhwNWq5Wq/aWQEhUYMikd8a9bt078dzLVHNavXy/7YE862iCfmszXSI7jxIfIXC5X1DyBVqsVE/pOpzOr7vdn4zkRCATGfR5ptdpx16Fs2Ae5ft3Khm0Yr1iPj2icTifq6urAcRwcDgdMJlNWVM2wWq3ibwWr1RpW/jzXUBKbEEIIISkxPDSEZ3/6MAb6+ySnT6uoxE1ffxB5abjRTKTxozw+2vEJ/vF0AKdOnol5ublnM7jsTg1m19E4wiROyy8Duk4Chw9LTq5QH8fQ4DScOT0dQ0f6MLCXQ8nZ6qirHD1zBke//W30PP9C1PmKz16EBY89hsJ50Utgk/hprzsLxdMK8MpfPhQ604/j33kCg79+BzdYLkRhMV33E9HT04NHH5V/MIyMue+++6BWR79uJItl2QkTdEqWEQfGEtWhN/gsFot4Y8putyedxAbGbnBZLBZ4PB7Jm3pSXC5XTCUXQwk38q1WK1avXh1TT0ODwZDT4/9KiewVk4kbnh6PR4yDYZiEehgKjEZjWHnSXE+0ZqvIa0uuSUf8Pp8v7PxKplenXNWNdLQx1U2la6QwTIkg2mdwQ0OD7HKZlI3nhM/ng8lkGvc9pb6+ftx3ikzvg8lw3cr0NoxXPMdHNEKJdKH6kslkQkdHR8av68LvlUAgENd3+2w0ZcqJ/+Y3v0FDQwOqq6tx/fXX49vf/namQyKEEEImta1//A0+CeyVnqhS4aavPYjyqur0BkUAjFWA2b/nJDb93w54fv9ezAns6nnluPmrF+P2by6jBDZJTF4eoL8OmD5dcrJKBVTNPIiCwgEAY72xoxk+eRIH/nXthAnssiuvxFl/+QslsFNo8VXzcN2XFiMvT77n/MH3uvD0o7sw0B97xQdCspHRaIx640/pMuLAp6XEBULPaWDsJqESZQJDezvHkoB0uVwJJRWE98FxHOrq6mJKaBmNRkUS9dlk06ZN4r/NZnNSyZFEhT5ckWyCKHSs19bW1qTWRaS5XK4Jxx3PZumKP/T6pUTiU2od6WhjqptK10iWZWE2m8VqqtG+M4Qm16qqqpKKSUnZeE7IJUcZhhnXSzjT+2AyXLcyvQ3jFc/xMRGj0Sg+bBr5OyCTQq+bufwAXM4nsevr61FdXR32WrNmTdg8DQ0NsFgs8Pl8CAaDcLvdePjhh3HOOefg7bffzlDkhBBCyOT14bbXsOul52SnX3bX3TjroqXpC4iIPunowVM/2Ylnf/42Th7qjWmZ6VUl0H/xAqz5jwactaSaxhEmySkpAa6/ASiQLgqVlzeK6pn7oVKNYOBjDoNHpI/Tgb17sW/1Gpye4Ps885m7seCxXyNfJnFOlHN2/SzccO+FUUuGHwt0Y/OPd6K/ZzCNkRGSXkqWEQc+HQc08gZ+aIlTJXq9MgwjJudjGZ/QbreHxRCrpqYm8camcKNPpVLBYDCgubk57eOeejwemEwmaDQaqFQqqNVq6HQ6WK3WlJWvdblcYWOsZqrXcui2TvZBi8jSuNk4/qIwXqWwr1UqFTQaDUwmU0zHXaLHisPhgMFggE6nE5cVbuALPcGEvwvxhN7g93g8UKlU426Kh74P4RUtjtD41Wo11Go1DAZDQuORxrMtlYo/Vkoe18BYIjPyOp6ONrIFXSPHpPoaabfbEQwGJ3yvO3bsEP+tdGI/mX2djedEvFVwMrkPJst1KxuO41glWyUpkt1uF7erx+PJirG+Q/dzLpcTz/kkttlsRjAYRDAYhMlkQmtra9gBsmbNGni9XvA8j7q6OrjdbgSDQWzZsgXTp0/PmqciCCGEkMkiePQwtth/Jjt94ZKLsfyuu9MYEQEA7pN+vOh4Fy5bOw5/xMW0TElZIa40nY3PfX85zr10NlRRelgSEpfqamDltbKTC4sGUTXzEAAevRK9sXvfeAP77v4MhmTKkgMAVCrMWv8QZv/Xf0ElkzAnyqu9sAa3fn0pikrkS4afPNSLJx7x4lRX7MMYEJIrhHEABUrcIIvshS0I7Tmt1I0yYbxPYUxDOYFAAF1dXQnfBPV6veNuWno8HlitVhgMBqhUKuh0upTeABQS6AaDASzLwul0gud5BINBtLS0wOfzxdxTPBaBQEDsiWoymcQ2lb6JGo/QXkjV1clVSIrsSZVNZW6BsXNEo9GgtbUVVqsVfr8fwWAQTqcTVVVV4kMUUpI9VliWhVarBcdxYclpq9WKdevWwWKxwO/3g+d52Gw2uFwuaDQaMcml1+vFe5+hiQHhHmfoS658qcVigcViEe+TBoNBdHR0wGAwwGKxQK1Wx/zgQbzbUon44xG6jTUaTdLry1QbmUbXyOy7RnIcF5aITHSMbqn1JruvM3lOCGMxC9dYn88XNuyK0m2lYh9MpetWqrZhtPbSdXyEXq8sFkvGh8IJvW5l2/eyeOT83Zw1a9aIT+rW1taGTdu5c6d44KjV6rB59Ho9vF4v6uvr8dvf/hZf+tKX0hw5yWZr165FWVmZ5N/Xrl2b/oAIISRHDA0O4JmfPIzB06clp5cxatz4tQeQl0fjoaZLX/cAdjzbgffeOAp+NLaSSAVFeViqX4ilhoUonpbzXxdJttJogM5lwK6dkpOnlZ5CBXMcPe+oULGqFgVMCQAg2NqKY9//ATAyIrtqVWkp5j3yQ0y/Vj5RTlJn7tkMbv+mFk//bBfO9EqXDu8+fhpP/NCLW+9bCvXs8d+7s8nGjRuxcePGcX/v6+tLfzAkq3Ech3Xr1on/r0QZcWAsMd7S0jLu70LPaeGmcuS42YkQypQHAgFs2LBBtmy6XGI9VgzDwOv1wuFwwG63SybPhJuMVqsVXq9X0XH8hFLmHMdJJtS1Wi3cbjcsFgtMJhPMZnPMPQEDgcC4cdkje7DZ7fak95USQuNSeuzGTN+4DWUymeByuaDVatHW1hb2XrVarXgz32q1jutVpsSxotfrodfrsWbNGuh0OgBj14dAIACv1xs2rzBcgcvlwrp168TpQsyhiTCWZWPabz6fT7xZzzCMuAzDMGhqagLDMLBYLGhsbJzwXEt0WyYTfzLSMSZppsc9TQW6Ro7Jtmvkhg0bxJhsNpsiMaViX6fznJA6nlIpFfsg0mS/bqVjGwrSfXxotVrYbDaxUpHBYBg35nY6hW7bbPpeFq+c74n90EMPwWazjUtgA5+W01KpVDCbzZLzPPzwwzRWDxmnvb0dW7duHffat29fpkMjhJCs9srGFpzY3yE5TaXKw01ffxBlTPq+QE5lA6eH8dZTfvzpO9uw57UjMSWwVXkqLLl6Hj7/35fh0ltZSmCT1Gu4BFi4UHZyhfoEppV0o/f1I+BHRvBJ8w9x7L++GzWBXTBzJmr/9EdKYGfYjIXTcecDWpQxxbLz9AYHsPlHPpw4cCqNkcVv3759kr8N2tvbMx0ayTJKlxEHxhLTRqMxau9KgVLlVoUbbz6fT/aGl8vlUiTBYDabxV6hQlnKyBvoHMdBp9MpevPNZDKB4zjYbLaoZSztdjsYhoHD4Yi5zDnLsuN6l/I8D7/fD5vNBgBiMiCbJHuTNbKHT7aM1epwOMQHPZxOp+S5FDr2buRxpuSxEtp2a2urbA/ThoYGAFCsJHvoe5JqUxjDlOO4qEMEJLstMyFV5a7T3Ua60TVyvExfIwOBQFiJdaXKz6diX6fznGBZFjzPh71CjyUlpWofRJrM1610bUNBOo8PQeh32UAgkNDQO0oJ3c/Z8r0sETl/Z7K9vR2//vWvZacJ5J4QbmhooJsPZJz6+nrJnthSD0IQQggZ8/5rf8c7bS/KTr989eewYPFFaYxoahoZGsW7Ww/B+8J+nOmT7gEpRaOdieW3sWBmlaYwOkIi5OUB1+qBzY8D3d2Ss6hnHMYJbwkOvfhz9HrkrzEAUHzB+Vjw61+jcNasVERL4qSeXYY7H9Di6Ud3ofuEdIWO06eG8OSPfbjpqxdj7iImvQHGqLa2FitWrBj3976+vrT+lqyoqMB9992XtvZyVUVFRUbaTUUZcWDspnG0G216vV5MPglJ52R7LJvNZvEeis1mG5ccd7lc0Ov1SbURSehVHtrzWygv7vP5wHEcLBaLIuP5hd6Al+tpHmr16tVwOBwwmUwIBoMJt8uyLJqammA0GqHRaMSyuZkco1DodQ8kf0M7cnkle84nSjhugLF9LRfT+vXrsW7dOuj1+rB5lD5WQm8gC+eulNC/cxyXdC81vV4vljOXuz+q1+vhcrlkE+fJbst0Cj2uU9UDLh1tZApdI8NjyoZrJMdxMBgMAMb2iVLfMZTc19l0TgjH0smTJxUrd5+qfSCYCtetVG/DWKXi+IjkdDrFyiTNzc0wGAyKf3eOVzZ8L0tUzvfEjvYB4vP5oFKpwDCMbPKxsrIyNYGRnLZx40a88sor415USpwQQqSdPHwQ7pZfyk6vvViLS2/PriepJ5vRUR4fvHUUf/7uW3jDtTfmBPa8cxkYrfVYZV5CCWySGcXFwPU3AIWFkpPz8kZRXb0Pw0ei/3Qpv+Ya1P7xj5TAzjIVNdNwxwNaVM8rl51n8MwInnl0F/bvOZnGyGK3du1ayd8GUiXGUyk/Px9qtZpeE7zy89M/ZEm8ZcSFG/MTEcbtE8aIlnuF3hdRqje20MtaqnKd3W5PS68SYRg44ca6x+NRpGdq6DaK5YaecBNyonHCY8WyrPhggsfjSdkN1EgOh2Pc+I+hN1STfSgncvloPfoSIYxlGe0VeY8w9P1GO+eMRqNYDSBUKo8Vobd1Ogjl+/1+v+x+ERLscmNmJrst0yn0uFaiN7jL5RrX8zQdbWQKXSM/lS3XyMbGRgQCAZjNZkXPLSX3dTaeExN91xJ6BMciVftAMBWuW6nehvFS8viIJIwtLxAqHqRb6MMKSn8vS6ecT2LLaWtrE/9dX18fdd7JWPaFEEIISZehgTN49icPY2jgjOT08qpq3PDVb0GVN2m/dmQUz/PY924nWv93O9o2vo9TXdL7IVL1/HLc8rWLcdv9yzCrLjO91ggRqdXAtY2ykwsKhzD7isVAvnSiu+pf/xXzf/Fz5ElU0iGZV1ZZjNu/uQyzWflrzfDQKJ7/1TvY6z2exsgIUUa8ZcQDgUBMPSuFMUEjy65GvkLH1I28AZ8oocdl5I3qQCCArq6upHpzxJLADxV6E1CJ6gfxJsJD99WOHTuSbh8Iv5Go1IMHE5HqzRharjfZBwRC1x9Lj754Wa1WaDSaqK/I8sOhMSVyzKbyWMnkeKQulwtWqxUGgwE6nQ4ajWbCa0ey2zKdQnubK5EY3rRp07iETzrayBS6Rn4qG66RBoMBPp8PTqdT8W2h5L7OxnOCZVnZ65XH4wkb/iCaVO4DwWS/bqVjG8ZLqeNDTmiFocgHXtMl9HtzvN+/s0nO303meenxHUM/GKI9ZbBz504sW7ZM8bgIIYSQqaLtd4+h8+B+yWmqvDzcdF8TSiuo8kkqHAt048kf78Rzv3wHJw/3xbTM9OoS6L94AdZ8uwELF1dDpVKlOEpCYlRbB9TL90qaVj6A2avuDP9jfj5mf/e/MGv9Q1BloPcliV1JWSFu+fpSzD9PLTvP6AiPLb/ZjffeOJLGyAhJTiJlxP1+f0zj0jkcDlgsFjAME/Wl1WrF+x4cxylyYzJ0nRs2bBD/brfbsX79+qTW7fF44u5MIPQeynSJUqWE7v90DUvg8/nG3ayNLPmcTI/H0GWTPUaUEnpzPdsTr6kWCARgMpmgUqnESgo2mw1tbW3w+/0TjnGfS9sy9PoFJHdcA2PnTmQHqXS0MZXRNXKMwWBAe3t7WFWSbJWN5wTLsrLl6KX2t5R07YPJfN3K1uNYieNjIi0tLeLDHy6XS7GHTWMhDDUEjL3XTJczT0bOJ7FZlsWuXbvG/T30JIz2lMHDDz+Mu+++OxWhEUIIIZPe7lc82POK/I3SK+/+AuaftziNEU0NwWN9eMH+Lh5v9uLIx1xMy5SUF+LK1Wfjc99bjnMvnQ1VHiWvSRbS6sAvOEt2svqcWag471wAQF5ZGRY89muoP/OZdEVHklRUUoCbv3Ix2KUzZOfheeDvf/wAO90H0hgZIYmJt4y4oL29fcJemB6PB1VVVTGX/gvtXaNUDxdhncJY28DYvRYlbkDGm5QQtkN1dXXSbcd7UzI04S6UUlUSx3EprxAolOKWeu+hx0uiZeJDS24ajcaUlKy02+3geT7qK/JmdOj7TWQbZ9uxMhGPxyN5D9TlcoljDDc1NcHv98Nms0Gr1cbcIzzZbRkLufgT0dLSIv47meEPfD4furq6JI/pdLSRCdl23E/Va6TJZEIgEIDX65Wc3+fzjas+ES+l93UunRNut3vCYR3SsQ9CTcbrVrq3oVJiOT5iwTBM2AOuFotFkaFxYhGaMBeGaMhVOZ/ENhqN4064xx9/HIFAACqVCizL4tprr5VctqWlBT6fDw888EA6QiWEEEImlc4D+9D221/LTme1DWi45U7Z6SR+vcEB/P1PH+CvP9iOwM4TMS1TUJyP+ptq8S//fRkuvnYB8gtz/usfmcT4kREc9/kw2DssO88cgx5li8/HWX/9C8qvuiqN0REl5Bfm4fp1i3He8tlR53vz8b34x9MB2cpbhGSDeMuIA2M363w+34TJWLvdHpaYnsjq1avFf7tcLkVu+If2zLTZbIolsIX1xUO44adEL5LQ9xDLjcTQ3t+h2zkZkYnDVJcRFm5kSlUA0Ov14r4OBAJxjwEZCATE+3IMw4TdIM+00IRoIr05s+FYiYfUeS/0wAbGzul4zj2PxyMem8luy1gomajUarXie03kuBaYTCbZbZaONjIhG477qX6NDE38RSt3HEtVl2iU3te5ck4EAgF4PJ6oCdh07YNQk+26lYltqIRYjo946PX6sN8ISlRMmgjHceJ1R6/XZ1UP+ETk/F1Ms9kMv9+PSy65BL/5zW9w7733YvXq1WJpTKmTqaenB2vWrME999yT8zuQEEIIyYTBM6fxzE8exvDggOT06TUzsOrL36BxsBUy0D+EbZv9+PN/bcN7rx8BPzpxUicvT4UlK+bh8z9YjktvYVE0rSANkRKSuJHeXhz88pfR9ac/4+Azz2F0RPr6kVeQjwW33YqSBfPTHCFRSl5+Hq79wvm46Jro+7D9+X14bdPHMV3zCEm3RMqIA5+W5o7W+1EYh3qi8r6hGIYJu7/R2toa87LRCDE4HA5s2LAhrsR6NB6PJ+YeQMLNRKV6+Ib2mA8tlS5H2JY2m02xcYwjb9hG3tD0+XzQ6XSKtMVx3ITHnd1uFx8QsFqtMd9g5ThOTG4yDAOv15vRsZ4jNTU1ifs6loSI1WoNOy6z4ViJFLreyKSv1Hj1odepaL3dpBLTdrtdPBaS3ZaJxp+MpqYmMXFgtVrjLuNqMBjAsmzUa3E62ki3bDjup/I10mQygeO4Ced1u91J93xPxb7OhXNCuDbJlcJO5z6INFmuW5nchsma6PhIpDqEUP0kXRobGwGMVVuI9TdKNpsUd5a3bNmCkydPwmw2w+FwiGWEHn74Ydx556c9wH74wx+ioaEBarUaLpcLPM+jubkZ3/72tzMYPclGa9euxcqVK8e9Nm7cmOnQCCEk43ieh6fll+g6ckhyel5+Pm6+z4pp0yvSHNnkMzw0gp3uA/jjf26D76X9GB4ajWm5RfUz8ZnvXYoVnzkXZZXFKY6SkOQNHTmC/Z/9HPpefQ0AMHhsP46/1w+5Triq06cB9xZgZCSNURIlqfJUuHL12Wi4qTbqfO++cghtf3gfoyOxXf9SYePGjZK/DdauXZuxmEhmJVpG3Gq1igmlaL1OQntsxWPNmjVhMU1E6HUVrVdlaNK6qqoq6vuM94Zec3MzrFZr1OU4joPJZFK8h69QdjryYYRIwk1Yo9EYtad9V1dXXO0zDBPWqzyyDLbH45G8eRrvNuY4Do2NjeA4bsJj1O12i+/RYDBM2DtLSCIFAgFotdqoPa1ijTUV3G43GIYJ65EsxePxwOFwjBurVsljJd7jRGqZ0OMmMpHmdDrHJfZCryNyvVlDe1yH7ofI8srJbstE4k+WzWYTb+BbLBZxP0Xj8/nExKPceKnpbkNKKkts0zVyvHRcIy0Wi7i9DQaD5Eun00Gj0SjWU1TpfQ2k/pxI5th3OBzi+5T6npWJfRAp169bmd6GqTw+gE+/N8dbISLeZHIi3xl8Ph80Gg18Pp943cmmhwsTxk8iTqeTt1gsfHNzM+/z+cZNt1gsvMlkGvd66KGHMhAtySa7d+/mAUz4+u53v5vpUAkhJOPe9rzAP7L6JtlX+7ObMx1izhsZGeXfe+MIv/Gh1/lfWNpifj35Ex//yb7uTIdPSFz633mX//DKK/n3zj0v7PXBJdfxwf99gucf+5X867VXMx0+UcAuz4EJr2/P/eptfmhwOCPxffe7343pt8Lu3bszEh9JP6PRKO53lmV5r9fL+/3+sJfX6+XdbjfvdDp5s9nMMwwTdry43e6wdQaDQd7tdvN6vV6cR6/X816vd8J4gsEg7/V6w+ICwDc1NfF+v58PBoNh8/v9ft5ut4sxMQzD2+123u/3S65fq9XyAHin0ynZttC+MB8A3mg0SrbN8zwPgDebzXwwGBTfr9ls5p1Op7gt3W43b7PZeIZheK1WKxubsL5YX5HbIbR9YVv7/X7e6XTyLMvyAHibzRZ12/v9fr6pqSmsHafTKfv+Q5cV2gjdvl6vl2cYJuw9C+2Yzeawdrxer7gPhJew/SKPO71eLxtLKLfbLe5LlmV5m80mtiNsG2G7MQwTdfvIvW+pY1ar1fJutzvsvSgldF+zLMs7nc6wOIRtJXe+JXusCG3ZbLaw/RF5jAjbOPQ6YLPZxm0Pp9MZdtwI62ZZVrL90OPGbreHtWez2cRzTDhejEaj7PqS3Zbxxp/o+S0Vd+h2MBqNYeep1+vl7Xa7eEzGe1ynso1kzxm6RubGNTIy9lhecp+x8e5vJfa1FKXPiXj2c+j+9nq94rUu9PzJpn2Qju2XjjaU2oaJbMdUHx9+vz/sc1z4DIz2HTWS3W4XzzM5ctdNueuZsI8ivztMJiqep0HGCNmzZw+WLFki/n99fT3KysrGzbd27VrqcUEImdKO7wvgL//5LYwMDUlOX9SwHLd+6z/EYT1IfHiex/53T2Lbk350HemLebmaBeW4/I5FWHBBdo0lRMhEetxuHHmwCfyZM5LTp11+P2Yuno7S8m75laxYCZx3fmoCJGnz/ptH8Pc/fiDb+x4A5p2rxo33XoiikvQOj7Bx40bJikx9fX1hPVh3796NxYsXpzEykilqtTrpHnB+vz+sR5ZOp4PP5xvXW4LjOLjd7qhjQQvxyPW04DhObM/lcok9m6Xms9ls43pYuVwuWK3WsLEwJ4o7dJ1GozGs94lGownrGeLz+WC329He3o5AICC+l/r6ephMpglLisbzvVPqFpjH4xHLJgv7lWVZGI1GrF+/Xva9CT3JAeneOsK6Ivd1JIfDAbvdLm7H+vr6sLKTVqs14TErQ0Xuh4n4fD5s2rRJ7KUr7JeqqipotVqsWbMm7iH6LBYLHA7HhL2ChF6RUsdcMiL3NcMwYFkWer0+6r6WWx6I7VgxmUxwuVyS5zfDMAgGgwDkz2Wp88jj8cBms4mfQ3q9Hi0tLbIxeDweOJ1OcX8K733NmjXiOS+M3RsIBMTjMNr6kt2WscSf7PkdieM4tLa2wu12w+fzoaura9w1Z/Xq1Un1XFOyDSXOGbpGxiaT18hEy6NL7a9k9nei+3oiSpwTSu1ngdlsht1uF/8/W/aBlFy5bim5DYH4tmNTU1NKjw/h+JO7lk30XT2UyWRCQ0ODZEWD0OtmPFiWBcuyihwL2YiS2IRgfBKbbkARQsh4A/39+NP6+8AdOyo5vXLmLHz+4UdRUlae5sgmh6P+bmzbvBdH90ZJ1kWoqCnB8ts0WKSbCVUePThAcgfP8+j63e9w/JEfIVrWsrC2HtOW/TtmzAmgqFg60Y28POCW24DZs1MULUkXv+84tvx2D0ZH5I+JWXUVuPmrF6OkrDCNkUmj3xCEEEIIIYQQQkjqTIoxsQkhhBCSWjzPY4vj57IJ7PyCAtx8/0OUwE5A19E+PP/rd/DED70xJ7CnTS/EVWvOwWe/txxnN8yiBDbJKfzQEI7913dx/IePRE9gL1yI+b/8HgpmTcfJ4wsxMpIvPePoKOB+CeiLvXoByU4a7Uzc9JWLUFAk/zP1k44ebP6RD33dA2mMjBBCCCGEEEIIIemW3jpsCuvp6YHH44Hb7UYgEBAHU2dZFgzDwGAwYPXq1aioqMhwpIQQQkhue3vL8/ho22uy01f8y5cwW3N2GiPKfb3BM9j+bAc+ePNo1PK5oQqK87HMsBBL9QvSXk6XECWM9PTg0H33oX/bW1Hnm6bVYv4vf4ECtRojK44juKkPJ48vxIzZHZCsKtbfD2x5Ebj1diBfJtlNcsLCC6px633L8Owv3sbg6WHJebqO9OGJR3y47b6lqKiZluYICSGEEEIIIYQQkg45WU58165dsFqt8Hg8YX8X3kpkvXydTgebzYZrrrkmbTGS3EKlAAkhRN4ngb3463cewMiwdDLhnOVX4ub7rTQOdozO9A3B99J+vPP3QxgZGo1pmbw8FRZfPQ/1N9aitKIoxRESkhqDBw/ioOUeDP7zwVM5Fbfcgjn/+z/IKxo71vmRURxrbsdI9wDKpp+Euka6IgQA4NzzxsbIputRzus8dApPP7oLp08Nyc5TVlmEW+9bhqq5ZWmM7FP0G4IQQgghhBBCCEmdnCon3tPTgzVr1kCn08Hj8YDn+bCB31UqVdgNdGF6e3s79Ho9Vq1ahZ6enkyETgghhOSkM329eOYnG2QT2MzsObjO8nVKYMdgeHAEvi378afvbMPOLQdiTmCf3TALn/3+pbj67nMogU1yVr9vJ/atuXvCBHbNV7+Kuc02MYENAKr8PJRfORcA0HeqCn2nGPkVfPgBsGePEiGTDKuZPx13PqBDubpYdp6+7kFs/pEPx/fTbzxCCCGEEEIIIWSyyZkk9r59+6DT6eByucb1uBaS1ZEvYR6VSjU2lueWLWBZFm+//XbG3gchhBCSK3iex0u/fhTdxz+RnJ5fWIhbvrEexaWlaY4st4yOjOK9N47gz999C9ue8GOgX/qBgEgLzldj9bcbcN2XFqNyBm1jkru6n3sOB9auxUhXl+w8qsJCzP1hM2Z89SuSD8WUXTIbqpJ8ACoEO+di4EyUEtLb3gCOHFEgcpJpzKxS3PmgDsws+Wvgmb4hvPzHD8CP5lyBMUIIIYQQQgghhESRE4MpdnR0YNGiRQA+LRnO8zxYloVerwfDMKiurgbDMOA4DidPngTHcQgEAmElx1UqFbq6uqDVauHz+XDxxRdn5P0QQgghucD3/NPYu2Ob7PRr11ows5ZNY0S5hed5dLzdibeeCiB4tC/m5WYsnI7L7tBgwflVKYyOkNTjeR4nH3sMJx79WdT58hkG83/5C5TqdLLz5BUXoHz5HJx65RCAPJw8vhCz5vqRXyDxUMjoKOB+CbjTCEyfnuS7IJk2vaoEd3xLi2d+vgudB3vHTS+tKMINliVQ5VFFEEIIIYQQQgghZDLJ+iR2d3c3dP+8oSUkrq1WK1avXo3KysqY1rFz50489thjaGlpEdfT2NiIjo4OTKcbW0TC2rVrUVY2fmy9tWvXYu3atekPiBBC0uzoxx/i1T//Tnb6eVeswIWN16cxotxydC+HN5/w41igO+ZlKmZMw/LbWCzSzqRkDMl5o4ODOPad76D7qaejzldUV4cF9sdQtHDhhOssv3weTr12GBjhMTpSiJPHF2LGnA6oVBI9cM+cAba8CNx6O1BYmOC7INmitKIIt39jGZ771Ts4uvfT62pxaQFuvW9pyqtVbNy4ERs3bhz3976+2B9QIoQQQgghhBBCSHyyPoltMpnAcRwAwOl04q677op7HcuWLYPdbkdzczOamprQ0tKCYDCIxsZGbN++XeGIyWTQ3t4u+feVK1emNxBCCMmA072n8MxPH8boyIjkdPXc+TCYv0rjYEs4eaQXbz0ZwL53OmNeZtr0QjTcVIcLrpyL/IKcGemFEFnDwSAOf+3r6Jf5PiUovfRSzP/Zo8iP8cHU/IoilC6bif72sSEOBgdKEeycg6oZMqXDOzuBV18BrtUDdL3KecWlhbjl60vxov1dHNjThcLifNzytaWonlee8rb37duHrVu3prwdQgghhBBCCCGEfCqrk9htbW3weDzQaDTYsmUL6urqklpfZWUl7HY7TCYTrrvuOni9XmzevBl33HGHQhGTyaK+vl6yJ3ZtbW36gyGEkDTiR0fx4i9/jFOdJySnFxQV45ZvPISikijj0U5Bp7rOYPuzHfhw21HwMQ7LWlicj2XXLcTFjQtQVJLVX8kIidlARwcO3nMPhvYfiDpf5V13Ys53vwtVUVFc659+9XwxiQ0A/b1VKCo+g/IKmfG29+4FamYAFy+Nqx2SnQqL8nHjvRfh73/8AOddNhuz6irS0m5tbS1WrFgx7u99fX2yD78SQgghhBBCCCEkOSqej/VWa/otWrQIwWAQXq9X8eShx+PBddddh0WLFuGjjz5SdN0k9+zZswdLliwR/3/37t1YvHhxBiMihJDM2PH043j1z7+XnX79PfdhyTWGNEaU3c70DcH34n688/dDGBkejWmZvHwVllw9D7obalFaEV8Cj5Bs1rd9Ow597esY7Y5eRn/Gt76J6n//94SrOXT+YQ/OvB+atOYxY3YHiqf1Sy+gUgE33AgsmLhkOSHxoN8QhBBCCCGEEEJI6mRtzcqOjg4EAgG0tLSkpPerXq/Hww8/DL/fj127dim+fkIIISTXHP7gPbz21z/ITl+8opES2P80PDgC30v78afvbMNO94GYE9jnXDILn/3ecly15hxKYJNJhdv8JA586d+jJrBVxcWY99OfombduqSGI5i+Yn7kmnHy+EIMD8uMfc3zgMcNTJBcJ4QQQgghhBBCCCHZI2trV7pcLmg0Gtx5550pa6OpqQkOhwObNm3C0qVLU9YOIYQQku36e7rx7KM28KPSydjq+QvR+G/3pjmq7DM6MooPth3D9mc70McNxLzcwguqsPx2DWYsnJ7C6AhJP350FCd+9jOcfMwedb78mhos+NUvMe2ii5Jus+isChQtnI7BA6fEv42OFuDkJwsxc04AqjyJQlODg8BLLwC33wnEWcKcEEIIIYQQQgghhKRf1vbEdrvdsFgsKW/nrrvugsfjSXk7hBBCSLbiR0fxwi9+hN6uk5LTC4tLcMs31qOwpCTNkWUPnucR2HUCf/vv7fj7nz6IOYE986zpuO3+pbjl60spgU0mndEzZ3DkgQcmTGAXn3026jb9TZEENgCoVCpMvzqyNzYwNDgNXZ3z5BcMBoG/v4yYB64nhBBCCCGEEEIIIRmTtT2xOzo6oNfrU97O3XffjSeeeCLl7RBCCCHZavtTLux72yc7Xb/uK6ievyCNEWWXIx9z2LZ5L44FemJepnLmNCy/TQONdkZSZZMJyVbDJ0/i0Je/gtNvvx11vrKrrsK8n/wY+eXlirZfckE1CmqmYbjzdNjfT/cxOHXqDKZP75RecF8H4PMCunpF4yGEEEIIIYQQQgghysraJHYgEADLsilvh2VZBAKBlLdDCCGEZKOD772LNzb9SXb6hddehwuuuiaNEWWPk4d78daTfux7V7qHupRpFUW45OY6nH/FHOTnZ23BG0KSMrB3Lw5a7sHQ4cNR52M+czdm/8d/QFWg/E8OVZ4K5VfNA7d577hp3Z2zMG0WUNAvk8hu3wFUVwO1dYrHRQghhBBCCCGEEEKUkbVJ7MrKyrS009XVlZZ2CCGEkGzTxwXx3KPN4HnpcbBnLKzFNV9M/dAe2eZU1xlsfzqAD/5xDIix6nBhST601y3ERdcuQFFJ1n69IiRpvW+8gcP33Y/R3l75mVQqzHrICvUXvpDSSgRl2pno2bIfo31DkQHgxIE5mF03CNUpmQoKL7cBd9wFqNUpi48QQgghhBBCCCGEJC5r77JWVVUhEAhg6dKlKW0nXT2+CSGEkGwyOjqC53/+CPq4oOT0wpJpuPkb61FYVJzmyDLnTO8QvC/uw7uvHMbIsHRiP1JevgoXrpgP3Q1nYdr0ohRHSEhmBTe14tgPfgCMjMjOoyotxbxHHsH0a1NfwUFVmI/yy+eix71/3LSRUzxOz2tA6d6twPDw+IWHhoCXXhhLZBdPnescIYQQQgghhBBCSK7I2iQ2y7LYtGlTypPYTqeTktiEEEKmnLce34QDu+XHsr3O8jVUzZ2XxogyZ2hwBO+8fBC+F/dj8Ix8ci6MCjjnklm49BYWFTXTUhsgIRnGj4zg+CM/Qtfvfx91voKZM7HgsV+j5IIL0hQZULZ8Dk69chD80PgHT3raezDt9muh8myRXri7G2jzAKtuAPKo/D8hhBBCCCGEEEJINsnauzV6vR4OhyOlbXR3d6O1tRUGgyGl7RBCCCHZZP87u7Dt8b/KTr/4uptw3uVXpzGizBgdGcWe1w7jT9/ZhreeDMScwF64uBpr/qMBhi8upgQ2mfRG+/tx6L77JkxgF19wPmqdrWlNYANAflkhyhpmS04bPnEaZ84wgFYnv4KDB4Ad21MTHCGEEEIIIYQQQghJWNb2xDaZTHjooYfwox/9CN/61rdS0obVakV3dzeMRmNK1k9y19q1a1FWVib597Vr16Y/IEIIUUhvsAvP/+IRgJce7HlmnQYrv/DvaY4qvXieR2DXCbz1ZADcJ/0xLzeztgKX36HBvHNpDF0yNQx9chyHvvxlnNmzJ+p85ddei3k/bEaexHendCi/ch563zoCSIwCcOrVQ5h2TwPQ2QkcGF92HACwaydQUwNoFqU2UJKzNm7ciI0bN477e19fX/qDIYQQQgghhBBCpoisTWLX1dWhsbERTU1N0Gq1uOYaZcfVa2lpgcPhgE6nQ21traLrJrmvvb1d8u8rV65MbyCEEKKg0ZERPPezZvR3c5LTi6aV4pb7H0JBYWF6A0ujwx8FsW2zH5909MS8DDOrFMtvY8EumwGVSpXC6AjJHmc+/BAHLfdg+NixqPNVrV2LmQ8+AFV+fpoiG6+gqgTTLpyB02+fGDdtcH8PBg6cQvG1jcCTTwAcJ72SV/4OMAxQXZPSWElu2rdvH7Zu3ZrpMAghhBBCCCGEkCkla5PYAGCz2VBfXw+9Xg+Px6NYInv9+vWw2WxQqVRoaWlRZJ1kcqmvr5fsiU0PPBBCctmbzj/j0Hu7Zaevuvd+MLPnpDGi9Ok81Iu3nvRj/+6TMS9TWlmES26uw3mXz0F+ftaOwEKI4kZ6enDw3nujJ7Dz8zH7P/8D6s98Jn2BRTH96vmSSWwAOLX1EIq/cAFw/Spg8xPA4OD4mYaHgZdeBO40AiUlKY6W5Jra2lqsWLFi3N/7+vpkH34lJJU8Hg98Ph+ampoyHQohhBBCCCGEpExW35HVarVYt24deJ6HXq/Hl7/8ZfT0xN5zKtKuXbvQ0NCA5uZmqFQqGI1GLF26VLmAyaSxceNGvPLKK+NeVEqcEJKrOnZ58Y/NrbLTtTfcirMvvTyNEaVHT+dpeH7/Hjb97/aYE9hFJfm49DYWn//BZVh81TxKYJMp5+Rvf4fhI0dlp+eVlWHBY49lTQIbAIrmlaN4ESM57cz7JzF0oh9g1MC1evmVnDoFuLcAoxJ1ycmUtnbtWsnfBlIlxsnUEAgEoFKpkno5HI6E27dYLLBarfD5fHEv63K5ZGPyeDwJx8RxHNRqteR6TSaT7HKBQAAWiwUajUacX6PRwGKxIBAIiPM5HA5YrdaE40sF4d5SrC+NRgODwQCLxRLXvrNarVCpVFCr1bIvoQ0l+Xw+WK1W6HQ6cf+o1Wpx/yRzvBCSKRzHweFwwGQyQaPRiOePRqOByWSSPK6tVmtc1+x0tJFJtA0n1tzcHPWaHe+Li6gmJXwuRL6k5pUyUXuh7yOR7zjCfnK5XApvWULIVJT1d2XtdjuWLVsGnudht9uhVqvx5S9/GS+//HJMy+/atQu/+c1v0NDQAJ1OB5/PB57nwbIsNm3alOLoCSGEkMw7dbITz//iR7LTZy86B1d//otpjCj1TvcO4vXWj/Hn772FD/9xDJAeAjxMXoEKF+sX4F/+53LU31CLwuLMlUcmJFP44WF0P/GE7PSCuXNw1l//gvKrrkxjVLGZfvV86Qk80Pva4bF/n3UW0HCJ/EqOHAb+/jIwMqJ8gISQSSPyBjHDMLDZbHA6nfB6vQgGg+Nedrs9bBm9PspDNVH4fD4xuRu5zlgYjUYEg0H4/X6xJzfDMADGquElyuFwoKqqSvx/vV4Pr9cLv98vWwHParVCo9EAAJxOp7itnE4nGIaBTqeD1WoVE92x3JhPp6amJvj9fvj9fpjN5rBpbrdbfD/CPHa7HVqtFu3t7dDpdNDpdDHd4LfZbPD7/Whra4NerwfHceJr/fr1YcecEnw+HwwGA3Q6HTweDywWC9xuN3ieR0dHh3jcGQwGqNXqpJNGHo8HarWakh0kpTiOg8VigVqthsViATB2DXI6neL52dDQID5UIzxo4vF40NzcHNP1J5VtuFwu8ZxTqVTQ6XQwmUxpTdrm+jYMlerrjtlsRltbG5xOJ+rr68Ou2y0tLWhra5N8OZ1O2Gy2cdf60Ie6gLHPBeHzJfSzm+M4NDY2Thif1+uF0+kMa6e+vl78HiOI9jkn9X3H7/fD6/WKD6KZTCZFPicIIVMcnwOCwSDPsiyvUql4lUrF5+Xlia9Fixbx1113Hb969Wr+nnvu4VevXs1fd911fH19fdh8wrIqlYpXq9V8R0dHpt8WySK7d+/mMZbi4AHwu3fvznRIhBCiiOGhIf4v33mQf2T1TZKvX3xxDc99cizTYSpm8Mwwv+O5Dt5x3yv8Lyxtsb3uaeM9v9/Dd3f2Zzp8QjKup+1l/r1zz5N8BUyr+aHjxzMdoqzR0VH+2E+8/EHrq+Nf//EaP9wzIMzI81te5PnHfiX/euZpnh8YyOwbIlmPfkNMXW63W9zvTU1NE84fDAZ5hmHEZWw2W8Jtm81mcT0MwyS8HoHwHoR1+v3+hNbDsixvs9nE9bjd7qjzC+/D6/XKzhMMBnm9Xi9uO7PZnFBs6eD3+8X3zrLshPM7nU5xfqPRGHM7Xq9X0f0fSdiHDMNMuA+F/QOA12q1fDAYjKmNYDDI+/1+3m6381qtVnw/drtdgXdAyHiR59tEx6rdbucZhuFtNpt4/Znoup2qNoTzTKvV8na7nff7/bzX6w1bjmXZqNdSJeTyNuT5zF53Qq/bsXw+CPx+P8+yLA+AdzqdUecT1p3I9wytVstrtdqY4on3fbjdbnHfxPNZRwghobK+JzYw9lSwz+cLe5KI53nwPA+/3w+PxwOXywWHwwGXywWPxwOv1yvOw/O8WFZJq9Wio6ODxjYmhBAyJbz+t/+HIx++Jzt91Ve+gcqZs9IYUWqMjIxi96uH8afvbMM/ng5g8ExsvSjPurAad//nJWhcewEqqqelOEpCsh8n0xtBVVKChS0OFMyYkeaIYqdSqVC+QqY39jCP3jePCDMCK68FQnoMjnP4EPDUk0Bfn+JxEkJyn9DTy2w2x9R7ed26deIyWq02qbGsW1tbwbKsGIcSvciE3m5AYr27PR4P9Hq92KM7lvkdDgdsNhu0Wq3sfAzDwO12h/Xwzlbxxmg0GsXjwOVyhe2DaEK3sdLbxWQywWq1gmEYeL3eCasFCPvHaDTC5/Ohrq5uXG/BSDqdDmq1GjqdDm63G2vWrFHyLUwparV6wu2dzdIVv9VqFYc0sNvtYqWHaMxmM7xeLzZs2BBTz95UttHY2AiWZeH1emE2m8GyrPg50tHRAa1Wi0AgIFYfTYVc34aZvu7E+tkYiWVZ8TN5onNFuB4L4hlyRK/Xo76+fsL5EvnM0ev16OjoAMuycLlc0Ol0ca+DEEJyIokNAJWVlXC73Xj44YdRWVkJAOPG/OH5T2uFho7DIEyzWq1ob28XlyeEEEImM793O9qfkS8LXH/LndDoLk1jRMrjeR57vcfx1+//A1v/8iH6ewZjWm5WXQXu+NYy3PyVi1E9rzzFURKSG4aOH0fv1q2S0yquvx75OfAduvSiGuRXFktO633rKEYH/vmAS2EhcP0NQLH0vACArpPAk08Awa4UREoIyWVdXWPXhVgS2MJD9wKn05lwuy6XCyzLhrWbSNI5UlVVFYxGIwAkVPLTZrPFnIQFII5tLbQZy/ono9Bt5nA4MpqQtFqt4nHa1tYmPigRC6fTCZZlwXEcDAZD1Hnb2trA87xYNj7Rsvpk/LAGuSYd8TscDjQ3NwMYu45ElkOOhmXZmK7XqWxDuFbKXecZhgkbrkFIAisp17chkNvXHb1eD61WC7/fP+G8kd8PUnE8JIJhGHEf+Xy+uL4vEEIIkENJbIHwpNmDDz6Iurq6sN7WAML+n+d5VFZWwmw2IxgMYsOGDRmOnhBCCEmPnhPH8eIvfyw7fe455+PKu7+QxoiUd+jDIFwPt+Ollt3oPn46pmWYWaW4wXIh7mrSYe7Z6hRHSEhu6X7yKdmxoBnjXWmOJjGq/DyUXzlXchp/ehh9O459+oeKCuC664GCAvkV9vYCT24GjhxROFJCSC7jOA5arXbC3lUcx4XdRLbZbHElByPZ7XZYLJaw5K/H41EkGbR+/XoA8ffu5jgOXV1dUXtUR4q3t6DRaEy4J1s2izwWPB5PRuIQxpIFxrZ1PPtSENpbMFqCYjLux0zI1LGilHTEH5osY1k2oQoYer0+6sM2qW7D4XCAZdmoD7hotVoxKRsIBBQde3gybEMg9687a9asifkhp6amprDjIVsSxlqtVnw4weFwpKxqACFkcsq5JDYw1ivbZrNh79698Pv9sNvtaGpqgtFoFEsy2Ww2eL1edHV14bHHHqPe14QQQqaMkeEhPPPTh3Gmr1dyesn0Ctx0XxPyoyVusljnoVN45ue78NRPduL4/lMxLVNWWYSVnzsXn/mvS8AumxFWyYUQMvYgKPe4dNKi6KyzMC2GEnPZouyS2VCV5EtO6339MPiRT6s3Ye484OZbgJIS+RUODgLPPQP49yocKSEkl8VSelPJMuIcx8Hj8Yg3gUN7qimRtNBqtWJSNZ7e3Rs2bEj4Jnk8iazVq1cn1EYuyVTPWqG3J/Dpwwzx0uv14vGT6V7lU4ESFRgyKR3xr1u3Tvx3MtUcop0TqWyD4zjxoSKNRhN1+dAHT5Kp9hEp17dhLpK6dgol42MVWord4XBkzUMvoZ81of8mhJCJ5Obd6xB1dXVhH3iEEELIVPfqnzfi2N6PZKff+JVvoqIme8e1nYh/5wkc2BNbed+iaQXQXr8QF127AIVF0kktQgjQv2MHhvYfkJzGmIw59eBHXnEBypfPwalXDo2bNsIN4PS7J1C6dOanf5w1G7j9DuD554CeHumVjo4CHvfYGNkXXZyiyAmJbnR0GAMDxyaecYorLp6NvLzU3upgWXbCHtVKlhEHxm5EhyauLRaLmLwWHuxPltVqhcVigcfjQSAQiKnXuMvliqnMaSihZ6HVasXq1atj6iVnMBgmXWI0sidaJkrcejweMQ6GYRLqhS0wGo1hJYFzPdGarSKvLbkmHfH7fL6w8yvWoQukyFXdSHUbwrAVgmjX5IaGBtnlEjUZtmGu8fl8MJlM4z5T6+vr4/r8E8rMC5VgTCYTOjo6Mr59hO9OgUAgru8ZhBCS80lsQgghhHzq4+1vwvf8U7LTL7ndhLpludOjUsoy/ULsefUwTp8akp0nvyAPF14zH7rrz0JJeWEaoyMkN3FyNxPz81F5223pDUYB5ZfPw6nXDgOhva7/6dTWQ5h2cURFhkpmLJH9wgvAiePyK9725liJ8csuB3IosU8mh4GBY3hz24pMh5H1Lr9sK6ZNm5/SNia60a50GXFgLFEdmggXek4HAgEEAgH4fL6kEpDAWG9noVe13W6fsFecy+VKKPFqsVhgtVrBcRzq6urQ0tIy4TZNJrmRrTZt2iT+22w2J73/EhF6TCWbRDcYDGISu7W1lZLYKeByubJmnNtEpCv+0GNPiYdDpNaR6jZYloXZbEZraytWr14d9TMkNMFZVVWVdCzA5NiGuUbuAQSGYcRhVGNlNBphNpvhcDjE7yRut1uJMJMS2qvc5XIp8gAeIWTyy8ly4oQQQggZj/vkGF769aOy0+efvwRXrP58GiNKjaJpBai/sVZ6ogo477LZ+NwPluOKuxZRApuQGIz09ODUS1skp5VfsxIFM3KvckN+RRFKl82UnDZ0tA8De7nxE6aVArfcCiw8K/rK330H8GwBhoeTD5QQMikpWUYc+LTsdmSSM7QcpxIJQ4ZhxGRxLCXK7XZ7QiVBm5qaxISMcHNdpVKJSdB0lz71eDwwmUzQaDRQqVRQq9XQ6XRioj0VXC5X2DjUmUr4hm7rZB+0CF2e47isHPNUGCNW2NcqlQoajQYmkymm4y7RY8XhcMBgMECn04nLCokcofel8HchntDEpMfjgUqlGpcADn0fwitaHKHxq9VqqNVqGAyGhIYkiGdbKhV/rJQ8roGxhz0ir+PpaMNutyMYDE54fdixY4f4b6Uehpks2zCXKFkKHhg7foTt6vF4FB0vPVGh+zkbkuqEkNxASWxCCCFkEhgeGsKzP30YA/19ktOnVVTipq8/iLz8yVFSe/FV81BREz6Gbe1FNbj7Py9B479egOlVUca3JYSE6X72WfADA5LTmBzu+Tb9avmemKdeHV9qHABQWAhcvwo4/4LoKw8ExsbJPnMmiQgJIZORy+VStIw4MHYjWmrc6dBxopW6OS2MKyqMxSonEAigq6sr4cSD1+sdl2zxeDywWq0wGAxQqVTQ6XQpvekuJNANBgNYloXT6QTP8wgGg2hpaYHP50NdXZ1ipY8DgQBcLhcMBgNMJpPYptKJi3iE9vyrrq5Oal2RPUCVKmusFIfDAY1Gg9bWVlitVvj9fgSDQTidTlRVVYX1JI+U7LHCsiy0Wi04jgtLTlutVqxbtw4WiwV+vx88z8Nms4njIAsPAuj1egSDwXEJTbfbLf5deMmVDLZYLLBYLFizZg28Xi+CwSA6OjpgMBhgsVigVqtjfvAg3m2pRPzxCN3GE40nnc1txILjuLBksFJjQ0+lbZhuwnjnwvXA5/OFDRGipNDPF4vFkvFhOUI/Z7LtM4IQkr2ytpz4I488EtO4ShqNBg888IDktJ07d6K1tRUajQZ6vR61tbUKR0kIIYRkh61//A0+CeyVnqhS4aavPYjyquRuTGWT/II8XHorC/fv3sNstgKX3bEIc89mMh0WITlJrpR4wcyZKL/yyjRHo5zCmaUoOb8KZ94ff4Nk4GMOg0d6UTS3fPyCeXnAVVcD5eXAju3yDRw7Bjy1GbjxJmB6hYKRE0JyFcdxWLdunfj/SpQRB8YS4y0tLeP+LvScFhJnkeNmJyK0TPmGDRtky3jLJdZjxTAMvF4vHA4H7Ha7ZPJMuLFvtVrh9XoVHTtTKGXOcZxkQl2r1cLtdsNiscBkMsFsNsfcWzoQCECtVo9rL5Tdbk96XykhNC6lx0vNdLIklMlkgsvlglarRVtbW9h71Wq1YgLNarWO68mpxLGi1+uh1+uxZs0a6HQ6AGPXh0AgAK/XGzav0WgUz+t169aJ04WYQx8WYFk2pv3m8/nEBBnDMOIyDMOgqakJDMPAYrGgsbFxwnMt0W2ZTPzJSMc4wJkca3jDhg3ieWyz2VISy2Tfhukk9fmQSlqtFjabTayaYjAYYsq3pErofs6mzwhCSHbL2p7YnZ2dsNvtcDgc4152ux12ux2bNm0KK5kSSRgzorW1FVqtFmeffTY2b96cxndBCCGEpN4Hb76KXS89Jzv9srvuxlkXLU1fQGlydv0s3PL1i3HngzpKYBOSoNN79mDgvfclp1XeeQdUBVn7zGtMpq+Q743dK9cbGxgb71qrA1ZeM5bUlsNxwJObgc4TiQdJCJk0lC4jDowlpo1GY9TelQKlSlILN7t9Pp/sTWaXy6VIEtZsNou9QoVSsJFJQo7joNPpFL3hbTKZwHEcbDZb1PK7drsdDMPA4XDEXOacZdlxvUt5noff7xfHGRcSntkk2cRGZK86pcbmTZbD4RAf9HA6nZLnUuj45JHHmZLHSmjbra2tsr3wGxoaAECxkuyh70mqTbPZDIZhwHFc1CECkt2WmZCqIQHS3YaUQCAQNixBqkppT+ZtmG4sy4Ln+bBX6GdDKoR+rgYCgYSGAVFK6H7Ols8IQkj2y9ok9sMPP4wtW7agoqICPM8DAHieR2VlJZqamuD3+9HV1RX25ShSXV2duJ6uri5s2LABDz74IFatWpWut0EIIYSkVPDoYbgdP5edvnDJxVh+191pjCh9VHkqLLygGiqVKtOhEJKzuh9/XHYac9ddaYwkNYrOqkDRwumS0/rfOYHh4ATlwM89D1h141iZcTn9/cDTTwEHDyYRKSEk16WijDgwcY9nvV4vJpKiJZ3jEZqclrqx7nK5oNfrk24nlNCr3Gazwev1gud5uN1u8cY7x3FJ9fwOFZpklOtpHkoo255s0pllWfF+FgCxtHgmRY5jnYzI5ZXsOZ+o0OPGaDTKxrR+/XrxGAydR+ljJTRpE3ruRgr9uxLJPb1eL1ZZkDuPhHNaLnGe7LZMp9B2U9XrNB1tRMNxnHj9MBqNig9LMBW2YbYQPhtSOZ536PHR3Nwc80NZqZQNnxGEkNyQ1V0r9Ho99u3bJz5xazKZoiatJyKU5LFYLDj77LPh9XpRUUGl/8h4a9euRVlZmeTf165dm/6ACCFEwtDgAJ75ycMYPH1acnoZo8aNX3sAeXmTYxxsQoiyRk+fRvczz0pOK12+HEULFqQ5IuWpVCpMv3o+Tv5Jorf5KND7+mEwt0wwBt+CBcCttwMvPDeWsJYyNAS8+Dxw9YqxxDeZVDZu3IiNGzeO+3tfX19a4yguno3LL9ua1jZzUXHx7LS3GW8ZcZfLBbvdDrfbHXW9wliZ8SQ67Xa7Ij26zGYzHA4HWltbx/XwFqrjpZper4fX6xXLF3s8Hvh8vqi9YWMRGnssN9GF8sjCOOGxJDOjYVlWLO/q8XgUWWcshHLSoQ8p6PV68e/t7e1JrT9y+WT3UySO4yYcQ7WqqiosARw6xmy080i4XxgplceK0Ns6HYTy/dEICXa5bZzstkyn0ONaiQd7XC4XGIYJe3gnHW1E09jYiEAgENdQB/GYCtsw2xgMhrCH4SI1NzcnnOhmWRZOp1N8wMZkMqGjoyPtZdxDH1ZQ+jOCEDJ5ZXUSGwDa2toQCATgcrlw5513KrJO4Unm1atX48UXX1RknWRykfvxtnLlyvQGQgghUbyysQUn9ndITlOp8nDT1x9EGZO+8ZYIIbnl1JYtGD11SnIak+Gbj0oquaAaBTXTMNw5/oGfvh3HUNG4EHmlUXpaA0BNDXD7ncDzzwFcUHqe0VHglb8DfX3AMu1YSXIyKezbtw9bt2Y+eZyXV4Bp0+RL5JPMibeMeCAQiOnGsTBu8kRJ6UAgII6z63A4FEliWywWOByOccm4QCCArq6upHpQGQyGCRP4oZxOp1h5p729Pekb3/GWaA7dVzt27FAkQRf6Hux2e1qSfm63e1zy0WQyiUmkZEtXh+7TVLwfq9UalkiVotfrw+II/Xcix2wqj5VMjgHscrmwY8cO+Hw+dHV1geO4CZOIyW7LdBKuXwAU6XG6adOmcedOOtqQYzAY4PP54HQ6U3btmOzbMBuxLCt7bnk8HmzatCmp3trCAyYul0t8+E7pHvwTCb3fnsv7ihCSXllbThwAOjo6YDKZ4PF4FEtgC+x2O/bu3Yvf/va3iq6XTA719fVYsWLFuFdtbW2mQyOEEADA+6/9He+0yT+Idfnqz2HB4ovSGBEhJNdwLulS4nmVlZhuyM0eDFJUeSqUXzVPcho/OIrefxyLbUXTpwO33Q7MnhN9vh3bgVe3jiW1yaRQW1sr+dugvr4+06GRLJBIGXG/3x/TWJAOhwMWiwUMw0R9abXasLLbSiQDQte5YcMG8e92ux3r169Pat0ejyfuEslCz7rJUnI2dP8n2wM6Vj6fb1yCRK/Xh/0tWi/AiYQum+wxopTQxGy2J15TTahwqVKpxDFxbTYb2tra4Pf7JxzjPpe2Zej1C0juuAbGzp3Iz/x0tCHFYDCgvb0dXq83pQ+/TOZtmK1YlpV9wEvq+p2IlpYW8QEal8s14YNBSuI4TnwwiGXZnO0xTwhJv6xOYq9evRoPPvggrr322pSs/7HHHkvpeBMkd23cuBGvvPLKuBeVEieEZIOThw/C3fJL2em1F2tx6e3JjdlHCJncBjo60L9jh+S0yltuQV5xcZojSq0y7UzklUn3tu594zD4oRgTziUlwE03AxPdRPrgfeClF8fKjJOct3btWsnfBlIlxsnUEm8ZcUF7e/uEvTA9Hg+qqqpi7nUcOs6tUqVlhXWGjrWtVOnreBO3wnaorq5Ouu14EwGhCXehXLSSOI5TZNzjidoIBAKS7z30eBGSm/Fqbm4W/200GlNSJtZut4Pn+aivyARQsmN+Z9uxMhGPxyPZu9HlckGj0cDlconjsttsNmi12ph7hCs5frocufgT0dLSIv470eMagNhbXeqYTkcboUwmEwKBALxer+S8Pp9Pdjz2REzGbZir3G63IkMQMAwT9rCdxWJJugpHrEIT5kpUjCGETB1Zm8TeuXMnfD4fHn744ZS1odfroVarsXnz5pS1QQghhChpaOAMnvnxBgwNnJGcXl5VjRu++i2o8rL2I54QkgW6n3hCdhpjmjylxAWqwnyUXz5Xctpo7xD6dx6PfWUFBYD+OuDCCapdHNgPPPMUcFpmHG1CSM6Lt4w4MHaj3efzTZiMFYZBi9Xq1avFfwulQpMV2jPTZrMpOnZzvDewhZvsSvTcCn0Psdy8D+39HbqdkxGZOFRiPNhohOSBVAUAvV4v7utAIBCWkI5FIBAQk08Mw4QlpTItNCGaSI/3bDhW4iF13gs9sAHENDxBKI/HIx6byW7LWCiZHNdqteJ7TeS4FphMJtltlo42QucREtjRSk7HUuUjVpNtG+aqQCAAj8ejWIJer9eHfV9RonrLRDiOEz8n9Hp9WobQIIRMHll7hztdYwLp9Xr87W9/S3k7hBBCiBLafvsYTh46IDlNlZeHm++zorSiMs1REUJyCT80BO7JJyWnlVx4IUrOPTe9AaVJ2fI5UBVK//w59doh8KN87CtTqYDLrwAuuzz6fCdOAE9uBrq52NdNCMkJiZQRBz4tzR2t96MwDvVE5X1DMQwTdg+ltbU15mWjEWJwOBzYsGFDXIn1aDweT8y97oQb+Er18A3tMR9aKl2OsC1tNpti4xhHJpoikwg+n08c5zxZHMdNeNzZ7XbxAQGr1RpzUoPjODG5yTAMvF5vRsd6jtTU1CTu61gSXFarNey4zIZjJVLoeiOTvlLj1Ydep6L10pVKTNvtdvFYSHZbJhp/MpqamsRkXSxjqkcyGAxgWTbqtTgdbZhMJnAcN+H55Xa7Fa8AMFm2YS4TziO5UumJVPMQKjGkS2NjI4Cxig7pHoebEJL7sjaJ3d7erlgJmWh0Ol3aymYQQgghydj9igd7tsrfULry7i9g3nkXpDEiQkgu6n31VYyc6JScxtx1V5qjSZ/8skKUNcyWnDZ84jTOvN8V/0ovunisV3a06hc9PWOJ7E9iHHubEJL1Ei0jbrVaxYRStN5yob1a47FmzZqwmCYi3AuJ1qsyNGldVVUV9X3GexO9ubkZVqs16nIcx8FkMinew1coOx35MEIkIXlkNBqj9rTv6orvM4RhmLBe5ZFlsD0ej2TCIt5tzHEcGhsbwXHchMeo2+0W36PBYJiwR6SQaA8EAtBqtVF7iMYaayq43W4wDBPWI1mKx+OBw+EYN563ksdKvMeJ1DKhx03kwwZOp3Pcww+h1xG5Hv+hPa5D90NkCfpkt2Ui8SfLZrOJSTOLxSLup2h8Pp94T1pujOJ0tWGxWMTjzmAwSL50Oh00Go2ivXVD5fo2jCbVQzkk24bD4RD3v9x3AuEzPN6KHvEmkxO5fvl8Pmg0Gvh8PvFzIpsedCKE5Ag+S6nVav7xxx9PeTsej4evqqpKeTsku+3evZsHIL52796d6ZAIISTMif0d/E8/fyf/yOqbJF9PPPw9fnRkJNNhEkJywAHLPfx755437vX+0mX88KlTmQ4vpYZOnuYPrn+VP2gd//rkV7sSX/Hhwzz/u9/w/GO/kn/9xsHzHQHl3gzJOPoNMXUZjUZxv7Msy3u9Xt7v94e9vF4v73a7eafTyZvNZp5hmLDjxe12h60zGAzybreb1+v14jx6vZ73er0TxhMMBnmv1xsWFwC+qamJ9/v9fDAYDJvf7/fzdrtdjIlhGN5ut/N+v19y/VqtlgfAO51OybaF9oX5APBGo1GybZ7neQC82Wzmg8Gg+H7NZjPvdDrFbel2u3mbzcYzDMNrtVrZ2IT1xfqK3A6h7Qvb2u/3806nk2dZlgfA22y2qNve7/fzTU1NYe04nU7Z9x+6rNBG6Pb1er08wzBh71lox2w2h7Xj9XrFfSC8hO0Xedzp9XrZWEK53W5xX7Isy9tsNrEdYdsI241hmKjbR+59Sx2zWq2Wd7vdYe9FKaH7mmVZ3ul0hsUhbCu58y3ZY0Voy2azhe2PyGNE2Mah1wGbzTZuezidzrDjRlg3y7KS7YceN3a7Paw9m80mnmPC8WI0GmXXl+y2jDf+RM9vqbhDt4PRaAw7T71eL2+328VjMt7jOhVtRJ7vsbzkrrlTdRsKlLjuxLsd47luh16/vV6veF6Gxim1rUKvKcL5GO3zMpLdbheva9G2ndTnnNznj7CPIq9jhBCSqKxNYqtUqrQksV0uF5+Xl5fydkh2oxtQhJBsNnC6n//d/RbZBLb9y2v5/p7uTIdJCMkBg8eO8e+df4FkEvvwQ+szHV5adP7lfckk9kHrq/yZfUlcS7tO8vyf/l/0RLb91zy/+13l3gzJKPoNMXVFJqQTeUXeZBZuVjMME/YCxie85eKJXDZ0HUJ7QvJIbj6pG81Ckk6KXNyh6zQajWHLsCwbligQEl9arTbsvej1+rCEm5xkEzRut5s3Go1h+5VlWb6pqSlqQiM0eSD33qX2dSS73R62HSMfXohMHCT6itwPE/F6vXxTU9O4/cKyrJhYipeQzJE7XkK3ndwxl4zIfS08JDHRvpZbPtZjRUiaSb1PhmHE+eTOZan9Jzz0IsxjNBqjxiA82CAk3IX3HnrO+/1+3mg08lqtVnzQJNZtkci2jCV+pRKwgmAwKCZBWZaVvOYk+wCFEm14vd6EznMpU3UbCpS67sSzHZW6bguvyCSzsH659zHR94ZQwkMrUkI/5+J5sSyr2LFACCEqnud5ZKFFixbhnnvuwQMPPJDSdn74wx/i4YcfxsmTJ1PaDslue/bswZIlS8T/3717NxYvXpzBiAghZAzP83jhFz/C+6+/Ijk9Lz8fa75nw9xzzktvYISQnNT5mB0nfvpTyWln/flPKFW4hGM2Gjzci+M/3yk5reSCatR8IYlhGfr6gBeeAyb6bbF0GXDJpWNja5OcRb8hCCGEEEIIIYSQ1MnaMbEZhkl4PIt47NixI6kxewghhJBUevfll2QT2ABw9ee+SAlsQkhM+NFRcI8/LjmtqK4O01Iwhl42KppXjuJFjOS0M++fxNCJ/sRXXlYG3Ho7MH9+9Pl27QT+/jIwMpJ4W4QQQgghhBBCCCGTWNYmsXU6HTweD3p6elLajsvlQn19fUrbIIQQQhJxfF8AL//eLjt9UcNyaG+8LY0REUJyWf/27Rg6eFByGmM0QjWFegVPv1omycwDva8dTm7lRUXAqhuBc86JPt/HH4312h4YSK49QgghhBBCCCGEkEkoa5PYJpMJPM+jpaUlZW388Ic/hEqlgslkSlkbhBBCSCIG+vvxzE82YGRoSHJ65cxZuP7e+6dU0knKyMhpnDjhwfvvr8eHH30v0+EQktU4l3QvbBQUoPL2qfVATPHZDApnl0lO6/N9gpFTg8k1kJ8PrLwWWDZB7/bDh4GnnwL6epNrjxBCCCGEEEIIIWSSydoktl6vBwA0NTXh1KlTiq+/u7sbVqsVAHDttdcqvn5CCCEkUTzPY4v9Z+COHZWcnl9QgJvvfwglZeVpjiw7DAycwOEjm/D2O2a8+poO77xrwZGjrTh6dDNGR5NMPBEySY1wHE5t2SI5bfo116CgujrNEWWWSqVC+QqZ3tjDPHrfPKJEI2PjXl91dfSxr7tOApufALq6km+TEEIIIYQQQgghZJLI2iQ2ADz44IPgeR6NjY2Kr7uxsREqlQpNTU2Kr5sQQghJxq4tz+Gjt16Xnb7iC/+O2Zqz0xhRZvE8j97eD7Fv36+wo/0uvP7GcnzwwbfR2dmG0dFPy/COjPSC43ZkMFJCslf3M8+CH5R+yIMxGdMcTXYovagG+ZXFktN63zqK0QGFxqu+YDFw3SqgoEB+nr4+4KnNwJEkS5kTQgghhBBCCCGETBJZncT+9re/DQDwer1YtWqVYuu9/vrr4fP5AADr169XbL2EEEJIsj4J7MXW//cb2ennLL8SS6+7KY0RZcbo6BC6ut7ARx/9N97cdg3+sf1G+AM/Qk/PrqjLnej0pCdAQnIIz/PgXC7JaQWzZ6PsiivSHFF2UOXnofzKueP+XlRbgarV50BVqOBPpdpa4JZbgZIS+XkGB4HnngX2fqxcu4QQQgghhBBCCCE5KquT2JWVlXjsscfA8zzcbjdqamqwefPmhNf3xBNPoLq6Gh6PByqVCo899hgqKioUjJgQQghJ3Jm+3rFxsIeHJaczs+fgOsvXJ+042ENDPTh27Gns3n0fXnu9ATt3fQEHD23EmTMHY15HZ2cbeJ5PYZSE5J4zu/dg4MMPJacxd94BVX5+miPKHmWXzIaqJB9QAdMWV2PGly/GzHsuxrTzq6HKU/haO3MWcPudQEWl/Dyjo0CbB3h7F0DXMkIIIYQQQgghhExhUWraZQez2Qy3243HH38cXV1dMBqN0Gq1WLNmDYxGI2pra6Muv2/fPrhcLmzatAk+nw88z0OlUsFoNGLdunXpeROEEELIBHiex0u/fhTdxz+RnJ5fWIhbvrEexaWlaY4stU6fPoATnW3o7GwDx+0Az0sn8GN15sxh9PZ9iOnl5ykUISG5T64XNlQqVN55V3qDyTJ5xQWoMp2LglmlKKyZlvoGKyuB2+8AXnweOH5cfr63tgG9vcBllwN5Wf3cMSGEEEIIIYQQQkhKZH0SGwCcTicMBgPa2toAjJUX9/l8sFqtYBgGVVVVYFkWDMMAADiOQyAQQFdXFziOE9cj9MzS6/XYtGlTut8GIYQQIsv3/NPYu2Ob7PRr11ows5ZNY0SpwfOj6Ol5G52dbTjR2Ya+vo8UWW9hoRo11degpkaP0mkLFVknIZPBaH8/ep57TnJa2WWXoWj+vDRHlH2mLa5Oc4PTgJtvBdrcwP798vPtfndsrOxrG6OPp00IIYQQQgghhBAyCeXM3RC32w2LxYKWlhaoVCoxIR0MBhEMBhEIBMLmjywlKpReNZlMlMAmhBCSVY5+/CFe/fPvZKefd8UKXNh4fRojUtbIyGl0db2Bzs42dJ58GYODnYqst7RUgxk1jaipaURl5TKoVFO3JDIhcnpe2oLR3l7JaYxxavfCzqjCQuC6VcAbrwHvvSc/X0cAeK4fuP6G6ONpE0IIIYQQQgghhEwyOZPEBgC73Q6dToeHHnoIHMdFHRM0dBrP86isrITNZqMS4oQQQrLK6d5TeOanD2N0ZERyunrufBjMX825cbAHBk6g8+TL6OxsQ1fX6xgdHVBgrXlgmHrU1DRiRk0jSkvrFFgnIZObXCnx/MpKlOv1aY6GhMnLA668GiifDmz/h/x8x44BT20GbrwJmF6RvvgIIYQQQgghhBBCMiinktjA2BjZZrMZzc3NcDgc43pgR2JZFhaLBQ8++GCaIiSEEEJiw4+O4sVf/hinOk9ITi8oKsYt33gIRSVpGKc1STzPo6/vI7FMeE/PLkXWm59fhuqqq1FT04iampUoLFQrsl5CpoKBQAdOe72S0ypvvw15RUVpjoiMo1IBy7RAWRmw9RVgdFR6Po4DntwMrLoRmDEjnRESQgghhBBCCCGEZETOJbEFTU1NaGpqQkdHBzweD/x+v5jQZlkWGo0Ger0edXXUS4sQQkh2an92MwK+HbLTG//tHsxYWJu+gOI0OjoEjtuOE51t6Oxsw5kzhxRZb3HxHMyo0aOmphFq9SXIyytWZL2ETDXc49K9sAGg8i4qJZ5VzjkXKC0DtrwIDA1Jz9PfDzzzFGC4DliwML3xEUIIIYQQQgghhKRZziaxBXV1dVQinChu7dq1KCsrk/z72rVr0x8QIWTSOfzBe3jtr3+Qnb54RSOWXGNIY0SxGRrqwcmTr6Czsw0nu7ZiePiUIuudPn0Jamr0mFHTiPLy83OufDoh2YYfGkL3k09JTiu5+CKUnHNOmiMiE5o/H7jtduD554H+Pul5hoaAF54Hrl4JnHdeOqOb0jZu3IiNGzeO+3tfn8x+IoQQQgghhBBCSNJyPolNSCq0t7dL/n3lypXpDYQQMin193Tj2Udt4GXKxlbPX4jGf7s3zVHJO336gNjbmuN2gOeHk15nXl4R1OrLUFOjR031NSgpmaNApIQQwalXXsHIyZOS0xijMc3RkJhV1wC33wG88BwQDErPw/PA1r8Dfb2AVjdWkpyk1L59+7B169ZMh0EIIYQQQgghhEwplMQmREJ9fb1kT+za2tr0B0MImVT40VG88IsfobdLOrlUWFyCW76xHoUlJWmO7FM8P4qenrf/mbj2oK/vY0XWW1hYhZrqa1AzoxFV6itRUDD+OksIUQbnki4lriotRcUNN6Y5GhKX6dOB2+4AXnoBOHpUfr72HUBvL3DV1UBeXvrim4Jqa2uxYsWKcX/v6+uTffiVEEIIIYQQQgghyaEkNiESNm7ciMWLF2c6DELIJLT9KRf2ve2Tna5f9xVUz1+QxojGjIycRlfXG+jsbEPnyZcxONipyHpLSzWYUdOImppGVFYug0qVr8h6CSHyho4dQ99rr0tOq7hhFfLL6QGSrFdcDNx0C/ByGxDwy8/3wftjpcf11wGFhemLb4qRG1Joz549WLJkSfoDIoQQQgghhBBCpoBJk8Tet28fOI5DV1cXqqqqwLIsKioqMh0WIYQQIjq45x28selPstMvbLweF1x1TdriGRg4gc6TL6Ozsw1dXa9jdHRAgbXmgWHqUVPTiBk1jSgtrVNgnYSQeHRv3gzIDFdApcRzSH4+oDcAb5UD77wtP9+BA8AzTwGrbgRKS9MXHyGEEEIIIYQQQkgK5WwSe9euXdi0aRM8Hg98PukebQzDQK/X4+6778Ydd9yR5ggJIYSQT/VxQTz3sx+C56UTSzPOqsM1a80pjYHnefT1fYQTnR50drahpydKUiQO+fllqK5egZqaRtRUr0BhoVqR9RJC4sePjoJzPS45rUijwbSlS9MbEEmOSgVcdjlQXg68+Yb8fCdOAE8+Adx4M8AwaQuPEEIIIYQQQgghJFVyLom9a9cuWK1WeDweAGM35OUEg0G4XC64XC5oNBrYbDZKZhNCCEm70dERPP/zR9DHBSWnF5ZMw833P4TCouIUtD0Ejtv+z/Gt23DmzCFF1ltcPAczavSoqWmEWn0J8vKUj50QEr/+t97C0OHDktMYoxEqlSrNERFFXHgRUFY2Vl58ZER6nlOngKc2A9ffAMyend74CCGEEEIIIYQQQhSWU0nse++9Fw6HA8BY8lqlUsV0I47neezduxdGoxEGgwGtra1UapwQQkjavPX433Bgt3yv5+ssX0PV3HmKtTc01I2TJ7eis7MNJ7u2Ynj4lCLrnT59CWpq9JhR04jy8vMpGUZIFpLrhY3CQlTedmt6gyHKYjXAtFLgpReAAZnhH86cAZ59Gmg0AHU0nAMhhBBCCCGEEEJyV04ksbu7u6HX6+Hz+cJ6Xof+m2EYVFVVAQC6urrAcZzkurZs2QKWZeH1enHWWWelNG5CCCFk/zu7sO3xv8lOv/i6m3De5Vcn3c7p0wfGeluf8IDrbgfPDye9zry8IqjVl4+VCa+5FiXF1LOPkGw2HAzilNstOW36tdei4J/flUkOmzMHuO0O4PnngF6ZB5RGRgD3S8DlVwJLlqQ3PkIIIYQQQgghhBCF5EQSu7GxETt37hST1lqtFmvWrIFerwfLsqisrJRddufOnWhvb4fT6RRLkHd1dcFgMKC9vZ16ZBNCCEmZ3mAXnv/FI4DM0Bcz6zRY+YV/T2jdPD+Knp63/1km3IO+vo+TCVVUWFiFmuprUDOjEVXqK1FQUKbIegkhqdfzzDPgh4YkpzFGY5qjISmjVgO33wG8+DzQ2Sk9D88Db7wG9PUCl1w6NrY2IWTS8Hg88Pl8aGpqynQohBBCCCGEEJIyeZkOYCLXX3+92ANbq9XC6/Wivb0dDz74IJYtWxY1gQ0Ay5Ytw7p167BlyxZ0dXXhrrvuAgD4/X7o9fp0vAVCCCFT0OjICJ77WTP6uznJ6UXTSnHL/Q+hoLAw5nWOjJzGiRMevP/+erz2+nK0e43Yv//XSSewS0s1OGuhGTrtJlx15Vu44IJmzJxxPSWwCckhPM+Dc7okpxXMnYOyyy9Lc0QkpcrKgFtuA+YviD7frp3Rx9EmhCQkEAiIw5sl+hKGSkuExWKB1WqFz+eLe1mXyyUbk/DgfyI4joNarZZcr8lkkl0uEAjAYrFAo9GI82s0GlgsFgQCAXE+h8MBq9WacHyp0NzcHNc+12g0MBgMsFgsce07q9UKlUoFtVot+4p1uL14+Hw+WK1W6HQ6cf+o1Wpx/yRzvBCSShzHweFwwGQyQaPRiOeIRqOByWSSPHatVmtc1+V0tJFJtA1j09zcHPXaHO8rsrKscP2PfEnNK2Wi9kLfRyLfZYR95XJJ/w4lhBAlZHUSu62tDW63GyqVClarFe3t7Vi2bFnC62MYBk6nE62treB5Hl6vF7/97W8VjJgQQggZ86bzzzj03m7Z6avuvR/M7DkTrmdg4DgOH/4b3n57HV59TYd33rXgyNFWDA2dTCK6PDDMpTh70bdx2XIPLlu+BYsWWcEw9VCp8pNYLyEkU868+y4GPpZ+oIW5406o8uncnnSKioBVNwDnnBt9vr0fA88/Kz+ONiEkbpE3jhmGgc1mg9PphNfrRTAYHPey2+1hyyT6UL3P5xOTu5HrjIXRaEQwGITf7xd7cjMMAwCw2WwJxQSMJZmrQoat0Ov18Hq98Pv9aGlpkVzGarVCo9EAAJxOp7itnE4nGIaBTqeD1WoVE92x3LBPp6amJvj9fvj9fpjN5rBpbrdbfD/CPHa7HVqtFu3t7dDpdNDpdDHd+LfZbPD7/Whra4NerwfHceJr/fr1YcecEnw+HwwGA3Q6HTweDywWC9xuN3ieR0dHh3jcGQwGqNXqpBNKHo8HarWakiAkaRzHwWKxQK1Ww2KxABi7zjidTvEcbGhoEB+cER4m8Xg8aG5ujukak442hPkzcV7k+jZ0uVzitUmlUkGn08FkMqUs8W02m9HW1gan04n6+vqw63NLSwva2tokX06nEzabbdw1PfThLWDs+i98joR+RnMch8bGxgnj83q9cDqdYe3U19eL31cE0T7PpL7X+P1+eL1e8YEzk8mkyOcBIYRI4rOYRqPh8/Ly+Iceekjxddvtdl6lUvHV1dWKr5vknt27d/MAxNfu3bszHRIhJIcFdrbzj6y+Sfb18u/tssuOjo7yp059wAc6fsFv33EH72ljFXn9/ZWL+Hfe/Sp/5OhmfnCwK41bgxCSDkf+8zv8e+eeN/513vn84KFDmQ6PpNLoKM9v/wfPP/ar6K/Wv/H8qVOZjnZSod8QU5fb7Rb3e1NT04TzB4NBnmEYcRmbzZZw22azWVwPwzAJr0cgvAdhnX6/P6H1sCzL22w2cT1utzvq/ML78Hq9svMEg0Fer9eL285sNicUWzr4/X7xvbMsO+H8TqdTnN9oNMbcjtfrVXT/RxL2IcMwE+5DYf8A4LVaLR8MBmNqIxgM8n6/n7fb7bxWqxXfj90u/xuJkIlEnlMTHY92u51nGIa32WziNWaia3Mq28iG8yKXt6FwPdJqtbzdbuf9fj/v9XrDlmNZNupnTrJCr8+xfA4I/H4/z7IsD4B3Op1R5xPWncj3Ca1Wy2u12pjiifd9uN1ucTvH85lGCCGxyNqe2Dt37kQgEIBWq8WGDRsUX7/ZbMZdd92FYDCIv//974qvnxBCyNTU03kCz//iR7LTZy86B1d//othfxsdHURX1xv48KMf4M1tK/GP7TciEPgxenreTiqWkuK5mD/vX7D04o24+qrtuHDJzzFn9u0oLFRPvDAhJGeM9vWh57nnJKeVXXEFCufNS3NEJK1UKqDhEuDqFdHHvu7qAp58AuhKppIHIQT4tCe22WyOqffyunXrxGW0Wm1SY1m3traCZVkxDiV66Qk94YDEend7PB7o9XqxR3cs8zscDthsNmi1Wtn5GIaB2+0O6+GdreKN0Wg0iseBy+UK2wfRhG5jpbeLyWSC1WoFwzDwer0TVgsQ9o/RaITP50NdXd24XoSRdDod1Go1dDod3G431qxZo+RbIFOU1WoVhy2w2+1iNYdozGYzvF4vNmzYEFPv4VS2kQ3nRa5vw8bGRrAsC6/XC7PZDJZlxc/bjo4OaLVaBAIB6HS6hIbiiEWsn4GRWJYVP3snuoYK111BPEOL6PV61NfXTzhfIp8ter0eHR0dYFkWLpcLOp0u7nUQQoicrE1i2+12qFQq2bJTSli/fj14nkdra2vK2iCEEDJ1jAwP47lHm3HmVI/k9JKyctx8nxX5BYUYGurGsWNP493dX8errzVg564v4NChP+DMmUNJxTB9+oVg6+7HJQ3P4vLLX8W5534P1dVXIS+vOKn1EkKyV8+LL2G0v19yGmO8K83RkIw5/wLg+huAggL5efr6gKeeBA4l91lDyFTX1dUFILby2x6PJyzR7HQ6E27X5XKBZdmwdhNJOkeqqqqC0WgEgIRKgdpstpiTsADEsa2FNmNZ/2QUus0cDseEyYtUslqt4nHa1tYmPigRC6fTCZZlwXEcDAZD1Hnb2trA87xYNj7RsvqECBwOB5qbmwGMXSsiSyFHw7JsTNfkVLeR6fMi17eh8Jki93nIMExYfkFIpGcTvV4PrVYLv98/4byR3wOy5f0Iw7gCY8NSxPO9gBBCosnaJHZ7eztYlsXSpUtT1oZWqxXHIyKEEEKS9frf/h+OfPS+7PTGez+L7oHn4fN9Dq+93oA9730Dx48/h5GR3oTbzMsrQnX1Spx77n/jiivewCUNT6Ku7muYPv18qKL1yCOETBqcTC+8fLUa5ddem+ZoSEaddRZwy21ASYn8PIODwHPPAFv/Dpw5k77YCJlEOI6DVqudsNcVx3FhN5dtNltcycFIdrsdFoslLPnr8XgUGSt6/fr1AOLv3c1xHLq6uqL2qI4Uby84o9GYcA+3bBZ5LHg8nozEIYwzC4xt63j2pSC0F2G0xMVk3I8kc0ITZSzLJlTlQq/XR32gJh1tZPK8mAzb0OFwgGXZqA8CabVa8eGAQCCQlWM3r1mzJuaHmZqamsLeT7YkjLVarfiAgsPhSFmvd0LI1JK1SexAIBDzU7nJ0Ov1GX3alRBCyOTg9/4D7c88EfFXHqWz+jHnkuNY+m+dOHzqG/j44/9BkHsLPD+ScFuFhVWYM/suXHjhr3DVle1YevFvMX/eZ1FSPDu5N0EIyTkDfj9O79wpOa3yttuQV1SU5ohIxs2cCdx+J1BZGX2+Dz4ANv0V+OB9gOfTExtJyvAojwOnB+g1wWt4ND3HcywlOZUsI85xHDwej3hzOLQXmxI347VarZhUjad394YNGxK+eR5P0nb16tUJtZFLlHgYIRFCL0bg04cZ4qXX68XjJ9O9ysnUsW7dOvHfyVRsiHbcp6ONTMr1bchxnPjwlUajibp86AM6yVRFUYLUNVIoeR6r0HLsDocjYw9CRQr9TAn9NyGEJCpKrbnM4jhuwg8fJWg0moz9UCCEEDI59Jw4jhd/+RMAQF7BKMrn9aGy9hQqFvaisDTxZHWo0tJFmFHTiJqaa1FZuQwqVb4i6yWE5DbO9bjsNColPoVVVgK33QG8+AJw/BP5+c6cAba+Anz4AXDl1UB1ddpCJPE7MjCIS96Sr/hCxmxffj4WTkvtMCosy07Yo1rJMuLA2A3q0MS1xWIRk9d2uz2pBLnAarXCYrHA4/EgEAjE1Gvc5XLFVP40lNBjzmq1YvXq1TH1QjQYDJMuMRrZQy0TpbU9Ho8YB8MwCfXCFhiNxrBywUqUuidEjs/nCzuHkukIJVdZIx1tZNJk2IbC8B6CaJ9dDQ0Nssulk8/ng8lkGvfZWV9fH9fnnFAmXaj4YjKZ0NHRkfHjTPiOFAgE4vo+QQghcrK2JzYwvrRSrrZBCCFk8hoZHsJzv/oeShccRN2qg1jyrx+BXXUI1ed1J5nAzgPDXIqzF30bly334LLlL2HRoiYwTD0lsAkhAAB+cBDdTz4pOW3a0qUoXrQovQGR7DJtGnDzLUBt7cTzHjsGPO4Etr0JDA2lPDRCcp3RaIx6I17pMuLAp6XEBaE9pwOBgCIlO0N7O8eSgHS5XAklXoX3wXEc6urqYipfbjQaFUnUZ5NNmzaJ/zabzUklkBMV+nBFskn00PGwW1tbk1oXIRMJvUYp8QCI1DrS0UYmTYZtyLIszGYzGIaB2WyO+lkbmiCuqqpKOpZEySXQGYYBH2d1JKPRKD7gFvndI5NCP8/iGaKEEEKkZHUSmxBCCMlGPM/jVO8H6Nj3S7zivgYzr3oJC1ccQ+VZvcgrSLyEZX5+OWbOvBEXXPAjXH3Vdui0f8HChV9CaWmdgtETQiaLUy//HSPBoOQ0xpT6YXlIDigsBAzXA0sunHhengfeeXusxHggQCXGCUmCkmXEgU/LbkcmOUPLdCrR65VhGDE5H0uJcrvdnlCp0KamJjHRINx0V6lUMBgMaG5uTntJVI/HA5PJBI1GA5VKBbVaDZ1OB6vVmrLKfS6XK2wc6kz1Wg7d1sk+aBG6PMdxWTEWqtVqhcFggE6ng1qtDqv4aLVaodFooFarYTKZou5rh8MBg8EAtVotHiMmk0nyWPV4POI8oS/hGI9ksVjGzRtZmTKe9pV436HtaTQaaDQa6HQ6OBwOcVo08cabCCWPXWDsgY7Ia3U62sikybIN7XY7gsHghNfRHTt2iP/OxENDAqVLmdvtdnHbejyerBjvO3Rfu93uDEZCCJkMKIlNCCGExGB0dBBdXW/gw4++jze3rcD27TchEPgxUHw0qfWWFM/F/Pn/gqUXb8TVV23HhUt+jjmzb0dhoVqhyAkhkxX3uHQp8bzSUlSsWpXmaEjWyssDKo8DvZuA4c6J5+/rA9wvAS8+D/T0pD4+QiYZl8ulaBlxYHwvbEFoz2mlbloLY44KY4zKCQQC6OrqSjgp4fV6xyURPB6PmHxTqVRi0ixVhAS6wWAAy7JwOp3geR7BYBAtLS3w+Xwx9xSPRSAQgMvlgsFggMlkEtvM5NisoT0Cq5McUiKyZ2Mmy/UKNBqNWNZWSNYKwxdWV1fD7XaLx7rUAxmBQAA6nQ4WiwUGgwEdHR3geR5erxfAWO/zyOX0ej38fj9aWlrE8Xo5joPb7Zbc1zabDXq9XozPZrOJSZ9E2k/2fet0OtjtdthsNgSDQfj9fvj9frS1tcHtdsNiscju20TjTURor9pUDUeZjjYyaSptQ47jwhLq6RijPPT8FyqmhA4FoqTQa4vFYsn48BuhnyfZ8FlACMltWTsmNpm6LBYLrFYrlXonhGTc0FA3Tp7cihOdHpw8uRUjI72KrHf69Av/Ob61HuXl50GlUimyXkLI1DF05Aj6Xn9dclrFTTcir6wszRGRrNX5MfDkl4Hh08Cpd4HylWMv1QQ/BQ8cAA7/DdDqgIuXAvk0lAUhE+E4DuvWrRP/X4ky4sBYYrylpWXc34We00KSNXLc7EQIZcoDgQA2bNggWzZdLrEeK4Zh4PV64XA4YLfbJXvtCjf8rVYrvF6vovcIhFLmHMdJJtS1Wq2YsDOZTDCbzTH3lg4EAlCrwx9Ijeztarfbk95XSgiNS+lxVDOdRAEgbmMhsQqMVUqwWCxoamqK+v4DgYCYdHO73WFljIUHEITqAcDY+R46nWVZNDU1idNZlpXcxgzDYP369XC5XGhraxOPxWTaT/R9Nzc3w+fzIRgMjouVYRixTamkVDLxJisdYwBnepzhVJvs23DDhg1hD4qkOhapz4FU0mq1sNls4kMiBoNh3Jjb6RS6fbPhs4AQktuoJzbJKhzHweFwiOWKLBYLmpubo75obA1CiJL6+/fjwMHfw+f7HF57vQF73vsGjh9/LqkEdl5eEaqrV+Lcc/8bV1zxBi5peBJ1dV/D9OnnUwKbEJIQ7onNsuWemSjjtJIp6M2fjyWwAQDDQK8HOPETYOCjiZcdGQF2bAdcrcDhQykNk5DJQOky4sBYYtpoNMrecA9NJCtVklq4Ce7z+WRvPrtcLkWSsGazGV6vF8FgUCwTG5lQ5jgOOp1O0RvhQhllm80Wtays3W4HwzBwOBwxl0JmWRbBYDDsxfM8/H6/mLgTkuPZJNmER2RiM5NjzkYSYhF6QwrnpvAwhdDzOJRQMttsNsuO5Succ83NzZJluUN7e0ZL2m7atGncuOhKtB/v+w4dp12O3MMrSsSbqFSV/U93G5k0mbdhIBAIG74hHSXdWZYFz/Nhr9DPgFQI/fwMBAKKVT1IROi+zqbPAkJIbsrqntjd3d0pb2OyfwnJNe3t7eK/A4FATCVWzGaz7NPhhBAyEZ4fQU/P2zjR2YbOzjb09X2syHoLC6tQU3MtamquRZX6ShQUUK9IQogy+JERcE9IlxIvPvtslFx0UZojIlmrvwt4R+KG9MhJoOt3QMmFQMUtQH5F9PVwHPDsM8Cis4HLLgdKS1MSLolubnERti8/P9NhZL25xUUZaTcVZcQBSCbXQun1ejAMI45BHAgEku6xbDabxUSVzWYblxx3uVyySapECb3KQ3/bC+XFfT4fOI6DxWJRZGzN0IR0LPcSVq9eDYfDAZPJhGAwmHC7Qs9co9EIjUYjlhbP5HihQq97IPn7Y5HLZ2t1vcgkrFarHfcgQ3Nzs7hdolUcEHpcBwIBtLa2jnuwg2EYmM1mcSxpuR6gDodDLLmtZPuhYnnfQrJJp9PBZrNJnh9Sf0tFvBMJPXZT1eM0HW1k0lTYhhzHiQ9YGI3GjA7fIHwGnDx5MmUdspxOp1gRobm5GQaDQfHP63hl62cBISR3ZHUS+//+7/9QWVmZ0jaUelKZKCPeJ6tZlk3pU2yEkMlpZKQfXV1v/DNx/TKGhk4qst4zwSKUl1yCi6/8OiorlkKlotKrhExZg73A8BmgtEbxVfdtewvDR45KTmOMd1GFB/Ip3x/GjkM5Z94d65E93QCUXg6oJijUtfdj4MB+oOFS4IILxsbbJmlTkKfCwmnFmQ6DSIi3jLjL5YLdbp8weSn0nhRuwMdioqR3rITEW2tr67j7Jna7PS33UvR6PbxeL0wmE1wuFzweD3w+X9Se07EIjT2Wm+tCQkAYQzjZh+iF+xhWqxUej0eRdcZC6CQQmjjU6/Xi30M7FSQicvlk91MkjuMmHFu1qqpqwjLBsSR0QnskT/Q+tFotAoGAbILOarWK29jhcIzrBepwOFBfXx92LCrZviCW9y0ck4FAACaTCQzDgGVZ1NfXQ6fTYfXq1eIDJ6FSEe9EQo9dJao0uFwuMAwTtp3S0UYmTYVt2NjYiEAgENeQEKlmMBiiJrGbm5sT7i0ulO4XKn2YTCZ0dHSkvZR76Pmt9GcBIWTqyeokdrw/1hLB8zzd6Msifr9fHGenvr5e9kNWGGvH6XRO+nFpCCHKGBg4js7Ol9HZ2Yau4BsYHR1Iep38KNB3rBTd+8rRvX86Zi9swA3/8QPk5VHympApjdsHvPtXoKAEaPgyUKBs0omTuemhKixExa23KtoWyWEjw8D230w8Hz8A9DwL9PuAytuBooXR5x8cBN54DXjvHeAaPTBjpiLhEpLL4i0jHggEYvodK4ybPFFSOhAIQKfTAYDY2zNZFosFDodjXOI2EAigq6srqZ5V8fY+djqd4n2b9vb2pG+IS42/HU3ovtqxY4ciCefQ92C329OSxHa73ePusZlMJjHBFO92kVq/IBXvJzQZLEev1094bMVy7oVuC+EhhmhYlkV1dbXsNL1eD4/Hgw0bNoy7PkhVO1CyfUEs71vYfhaLBYFAQKzwIMRjsVgkk4GpiHciwjUKQMyl/qPZtGnTuPMjHW1k0mTfhgaDAT6fD06nM6sqeAoVCaR4PB5s2rQpqZLnQlUTl8slPmSX7h7ooQ81ZdMxTwjJTVmdxAbGksxk6vD5fLDZbBM+UWcymSYcu4oQMrXxPI/evg/RecKDzs429Jx6R5H1jgzmoedgGXr2TUfPwXKMDIwlrMsYNW782gOUwCZkKuN54MDrwN4Xx55yGegBPngSWLwaUOihyeFgEKfa2iSnTTfoUaBWK9IOmQQ+eBbokRnHunIB0H0IQMhvreEjwMlfA6UNwPQbgLxp0dcf7AaecAFVPLDiBmAmlQokU1MiZcT9fn9MY0Q6HA60tbVNmHwSygILZbc9Hk/SPf1C17lhwwYxAWC328PG+U2Ex+MBx3FxPZAuJAEnS0nf0P2fbA/oWPl8vnGlnvV6fVi532R6hYeeB8keI6kU7/isShxzNpsNOp0OHMfB4XCIveFj6VWq1DEf6/vW6/Xw+/3weDxwu90IBALiOQuMXZfa29vDyp+nIt6JhF6jgOSOXWDs/Ig8btPRRiZN5m1oMBjE4zTb7h2zLCv7sI3P51Ok/HZLS4t43rpcrpiG61SK8PAL8OlDPIQQkgyq/0aySiAQmPDDzWq1oqqqKqmn0gghk9Po6CC6ut7Ahx99H29uW4Ht229CoOMnSSewS4rnYpS7GHufXYDdfzgH+z3zEdxbKSawVao83PT1B1HGUPKIkClr6DTwzp+Aj58fS2ALju0CDm9XrJnup54ChoYkp1XedZdi7ZBJ4B8yJRPziwHzK8A33/v/7J13eBNX1sZfueAGWC70Zkv0jmRKKiSWIIVULEhfUrBSN+XbWCFbstnmyLubstlNkCAJm2wKlkivSE4hgQSwFJqBgCXTO/bYxr3M94cyE8ke9Wr7/J5HD0b3ztwzM/deSfe95xzgimeAUXOdClmgcStw5h9Ao/DitCsioDoOMBoB/Z3A5peA2mMhMJ4gegb+hhHnKC8v9yrgms1mZGZm+rz47ixOhipkKndOLtc2ELwAweGvcMvdh2C9NwH/83M653r2xcvUXxiGCToftS9tuMuX7txfNBpNQOcvKSnh/y4oKAiLaKTT6cCyrMdXqPKLhzqHq3P+aedICcXFxYKCXLRyyDp7yioUCmi1WhgMBtTU1MBisfDrcFar1UUUi5a9q1ev5v8OtO8Cjuuprq4W7LeRaCOa9MZ7qFKpYLfb3QrYVquVD7cda5hMJsyePTvo83BRTjnUanXQ0TZ8xXluoBSgBEGEgpgWsbkJ12QyheVlMBii9kWLEMbbMzGbzdDr9REPg0IQROzS1laLkyc/xK7dv8bGb2fjx+134OjR19HcHNwi+oAB0yDJfQRzZn+MjI6nsXNdK84f6w+2s7s35YVLb8WoKdODao8giB5M3XFg67+BM3uEy3/6CKgLXthjWdZtKPHE4cORdsEFQbdB9BJO7AAObxYum1YApGUDA4cD8+4D7t4APFoBLPobMPLnRbPOBqDWAJzTAW2nvLcXPwBg5wDlNuBf84FXFgE/rALqhHO3E0Rvwd8w4gD40LzexFidTtfNa9YTS5cu5f/mQogGi3PuZK1WG9Lczf4ubHOL76Hw6HK+Bl8W9Z09S53vczB03cQQilyxnuBEBSFvXIVCwT9ru93uIkj7gt1u54UpsVjsIlj1VJzHni8hkLlNAp7gxGrOs5nbHCI0psLRvi84h37uikwmg1ar5ceu84aBaNnL2QQE1nc5uGiP0WojmvS2e+gsYHsK2e1vRIZIwM0NodrooFAoXL6XhCKcuzcYhuE/DxQKRUyFcScIoucS0yL2ypUrsWTJEuTn54fltWTJEpcfZbFISUkJRCJR0F/u9Ho95HI5MjIy+JdKpYrIB5g/ePqgZhgGKpXKp3BqBEH0bhobD+Hw4Vdhsd6Cb7+bjYo9j+L06U/Q0XE+4HPGxfVDVtZlmDjhL7j4os2YM/t95OY+hNba/jCvecntcTkzZJh7fWzu4iUIIsywLHB0K1D+MtBU7aFeB1D1VdDNNe/YgdZK4RCN6QVLIIqL6a/2RCRx54UNAHMFRLH0kcAFDwD3mIFHdgEL/wKMkAOtVcDZfwF1nwGdrd7bTRoLDHoYqBkIfP5b4NlJwKtXAlv0QL0PYjhB9CACCSMOODwvAc/5abnwn/6sV4jFYpfF4tLSUp+P9QRng16vR3FxsV/CuifMZrPPHnncwn6oPHydPea55+EJ7l5qtdqQrUV0FVC6rs1YrVY+z3mwMAzjtd/pdDp+g4BGo/F5rYhhGD7fqVgshsVi6RXrNUVFRXwf8SWygUqlcpkPhCgoKODPqdFo3Hphh6t9X/HWHjfPOAuE0bS3qKiIF+p8yZveFaVSCYlE4nG+jUQb0aS33EOVSgWGYbzOQyaTKSxRNYKF+0zMy8sTLA8kakekU3Hm5+cDcMwP5IBGEESoiOmVrkh4SYfqR0GosdvtUCqVQYVZARw/fDIyMqDVaqFWq1FTU8OHAZJIJFAqlfyHfKyjUqmwdOnSmAu9QxBE+GHZDtTWWlFp+zt+2HIFvv/hchyo/CsYZgtYtiPg8yYmZmLYsAJMn/YyLr3Egpkz1mDEiJuRlDQEANDW3IyPnitGW0uz4PH9M7Nw5YP/R8IRQfRFOlqBilJg33tAZ7vnuiPmAFOXBd1kjbvFPpEI4htuCPr8RC/h/Blgl5tFo9EXAsNmeD5ePBq48CFgxZfAwzsA5VNAeh1w9lmg2U20AWdECcCAy4FBjwJJ4x0e4Z89DvxzArB2MbBtDXD+tP/XRRAxRKBhxDUaDS/cePICc/Zq9Ydly375rPHF44/zQvYU2ttZtM7MzPR4nf6uK5SUlECj0Xg8jtvMHmoPX86LtOtmhK5w6yUFBQUePe2rqz1sZhOgax7krmGwzWazoJDh7z1mGAb5+flgGMZrHzWZTPw1KpVKr96SnNBut9shk8k8ej76amss4dxHPAluJSUlsNvtPkVi4Ma21WqF2Wz2eEw42vcFq9Xq8dlzGxyc5xsgevYC4MOeA445y5d1TqvVym/A8CUMfSTaECJS46Kn30O1Ws3P5UqlUvAll8shlUpD6u3sTDDPSq/X8/a7++znPqv9dXTzV0z29/MMcDwnqVQKq9XKfx70hg1NBEHEBjG96t5XJjuGYfgw2RqNxuVDNRjMZjPkcjkyMzNhsVhcdqtJJBJotVrodDoYjUbI5fKY+8HgjNFohNlsjsnQOwRBhIeOjkacOWPCnr1P4NvvLkC5RYVDh1ahoeFAUOdNTR2LMaPVkMtLccnFP2DyJC0GDVqI+PjUbnXLXl2Fc0cPC55HFBeHxQ9rkDowPSh7CILogTScBrb+x5Hv2hNxicAUFTDpBiA+MagmO843oO7TzwTL0i65GInDhgV1fqIXYVnr2GQhxLx7/TtXRg5w0cOOHNoPfA3IJQA2Au013o9NyAIy7wTEtwFx6QBY4OC3wCf/5xC0/3sNUP4q0HDWP5sIIgZwDiMukUigUChgt9tdXpxIZTQaoVarkZGR4SIMdV3v4NYFlEolLwAplUqfwl0zDAOr1Yp169bx73Ehnu12e7ff+na7HXq9nveYUqlU0Ov1ggvjzrl8hbywOc+wru3rdDrBtjkKCwtRU1PDb7znBAguxLLZbEZJSQlyc3MBwOOCuEgk8vnFIZFIYLPZoFAooFKpXPKF2u12GI1GSKVSGI1GF+FF6PrtdruL9yl3vKfrB1zTqXF9BXCIAcXFxS5ODe7asVqt/DPgXtz9U6vVyM3N5a/LF4FZq9XCZDJBJpNBo9FAKpWipKSEb4e7Nk4Qqq6uhlar9UvAdu4zzp7w69atg9lsdrmWYOFsdu6bxcXFXp8N4NpHOMHNOVQ/N17XrVvns0BZWFjI92NvXrnBtB/MdQO/pDNwnn8YhuE3ngh5dwZ7vwIZx84UFBTAZrOhsLAQRqORj0DpPBa5XN4qlQpyuRxKpdIvcTmcbYRiXPTVe+gcBt9sNrt9cZ8vgHtv50DuoT/zs/M8zW0Ykcvl/OerkLjOhWDn6iiVSv5++YJEIvEpQoK/nzPcc+I+D+x2O/950Fc0HYIgIoOIZVk22kYI8cQTT+Dee+9FTk5OWNupqqqCTqfDM888E9Z2PMF9kROLxcjLy4NMJoNarXYRlm02m187WhmGQW5uLpifw6h42mHGfdhzO6VikYyMDCgUirCFIqmoqMDUqVP5/+/evRtTpkwJS1sEQbinpeUUzp79EmfOlqGmZjM6O1uCPqdIFI/09DwMylYgO/typKbm+HTc7q9M+GLVC27LL7llOeZcR/l9CKLPcXI7sPc99yIhR+ogYPqtQP8hIWmWMRpx4ne/Fywb8a8XMHDhwpC0Q/RwOtqA56YC5092Lxs40uFZHZ8QfDunfgK+/Rw4mwCIfNgX3dkCnDcDDZsAdLqWieKB3EuAKTcAE68B0jznCY4l6DdE3yUjIyNoga3rb3y5XA6r1SoobptMJo+5oDl73C0aMwzDt2c0GnnPZqF6Wq22m4ek0WiERqNxyQ3tzW7ncxYUFLj8lpdKpS6L3FarFTqdDuXl5byIwa2PqFQqr2KfO0FGCKElMLPZDJ1OxwtFgGPRv6CgACtXrnR7bZygBwg7Yfi6nqPX66HT6fj7mJeX5yISajSagHPIOtP1OXiD25hgNptdnktmZiZkMhmWLVvmd75Tbv3Jm8DB/Ow5LtTn/EEqlcJut7t9Pt7Wyzi69hGxWAyJRAK1Wu13iGjOgaWqqspnocff9gO9bi4ipEKhQElJCXQ6He+VKZFIkJeXB41G43V9MpD7Few4doZhGJSWlsJkMsFqtaK6urrbvLJ06dKghLZQthGqcdEX72GgqRfcXb+/9zBU8zNHYWGhi4jMnd/dWPb2/cAZlUqF2bNnC0ZBcP488weJRAKJRBKS/kAQBOGOmBWxCdcfxv6K2NwuNl+EabvdzucCMRgMfv8ICTfcj2ydThe2/DG0AEUQ0YFlWZxv+Alnz5hx9mwZ6up3huS88fH9kZV1KQZlK5CVNR+JiWK/jj97+CDe/O3/ob1VWESXyGbj+sd/T2HECaIv0dkO7P8YOLrFe90hMxze1wlJIWv+4LKb0LRjR7f34zMzMe7rryDq1y9kbRE9mF1GYP3dwmWKPwIXPxra9mqqgS+/AM4yvtVvOwHUvg+0HRIuF8UDkvk/C9qLgVT34ZZjAfoNQRAEQRAEQRAEQRDhIwTb8IlYg2EYPgyVL7uxuF1TdrsdxcXFbkVsThgPBUVFRT6HBufC6Pi6s4wgiNims7MVDLMNZ846hOvm5mMhOW9y0nBkD8pHdrYCGeI5iIsLTNBpbW7CR88941bAHpA9CFc88BgJ2ATRl2iqBna+BdR7ma9E8cCExcCIuYAfu/i90XLggKCADQDp119PAjbxC1tWCb+fkALIfhX69jIygRtvAg7sB77fDDQ3e66fOAzIvg9o3AbUfQawja7lbAdg+9Lx+vhRQLLgZ0H7aiAlI/T2EwRBEARBEARBEAQRs5CI3Qvh8oAAwOzZs306hhOxufwgQl7fK1euhFKpDImNvgrSnE2cjQRB9Eza2hicO/cNzpw149y5jejoOB+S8w4cMB3Z2fnIHqRA/7QJfoV+EoJlWZj0/0b18aOC5XHx8Vj8sAYp/QcE1Q5BED2IM3uBilKg3Ys4l5wBTL/FEbI5xDDG9W7LxAVLQt4e0UM5agGObhMum740fF7NIhEwfgIwJgfY8gOwd4/3Y1JnA8mTHUJ2kwWAQHCwznag0ux4ffQIIL0MmHIjMOFKIEUc2msgCIIgCIIgCIIgCCLm6HUidl1dHQYOHOh3WW9i3bp1/N++5qJwFojNZrNg2G6ZTOZTrqBQwnl+U04Nguh5NDYewtmzZThz1oza2nKwbEfQ54yL64eMjIswKDsf2dmXIykpNLlmOXaVfYF9m75xW37prXdi+PiJIW2TIIgYpbMDsG0ADm30Xjd7EjBFBSSmhN6M1lbUfvCBYFmKTIYk2uRHcLjzwgaAuerwt5+UBFw6H5gwEfh2I3DurOf6cWmAuABIzXOEGG8XyOPN0dkGHNjgeMX3A6T5Dg/tCVcCyb3/9x1BEARBEARBEARB9EV6lYj9xBNPYM2aNTh7VnjBJDc3F8uWLcNLL70UYcsiC+e5DACZmb55XDiLxN5yaEeSbdsc3iTkhU0QsQ/LdqCubgfOnC3D2bNlaGg4EJLzJiZmIjv7cgzKzkdm5sWIj08NyXm7cvqgHV+u1bktHzt7HmRXXReWtgmCiDFa6oBdbwPMQc/1RHGAdCEw5hLH32HgfFkZOhhGsEzsJgUM0QepPwlUvCdclnspMCSCeZqHDAFuXAJU7Aa2bQXa2jzX75cDZD8ENGwGzpsAttVz/Y5WYP9njld8EjBWAVzwAJBzUcgugSAIgiAIgiAIgiCI6NOrRGy73Y6amhq35TU1NTh37lwELYo8drvd5f++ir9ZWVn83+Xl5SG1KRicBXmCIGKPjo5GVFd/97Nw/SXa2qpDct60tHHIzs7HoOx8DBw4AyJRfEjO646WxkZ89FwxOtwstKcPHoJF9z0SdLhygiB6ANWVwO51QKuXtAf9BgDTbgYycsNqDmMwCr4fl5aGgVcsCmvbRA+i/DWHt7IQc++NrC0AEBcHTJsOSKSOXNm2Ss/1RfFA/0uAlOlA3UdA827f2uloAX76BJhGYfUJgiAIgiAIgiAIorfRq0RsAmDceOp4w9kTO9BzhIOuojxBENGnpeUUzp79EmfOlqGmZhM6O714TPmASBSP9PQ8DMpWIDv7cqSm5gRvqI+wLIsNun+BOXlCsDw+IQGLH3kCyWn9I2YTQRBRgO0Eqr4G7GYI5ud1JkMKTF0GJA0Iq0mtR4+h4fvvBcsGXn014lLDE5mC6GG0twDlrwiXiccA46+IrD3OpKUBCiUwcSLw3bdAba3n+vHpQMZtQPM+oO5DoMOHzXEJKcA42tBBEARBEARBEARBEL0NErGJblRXh8aTMpT4GhY9VFRWevEWEWDQoEEYPHhwGKwhiOjCsizOn9+Hsz+HCa+r3xmS88bH90dW1qUYlK1AVtZ8JCaKQ3Jef9m+4RPs/+E7t+Xz77gHQ6XjImgRQRARp7UBqCgFzu33Xjf3MkCiCFv4cGdq330XYIUFdbGKQokTP1PxHtBwRrhsTiEQF95oJj4xchRQsBTYsR340Qp0dHiunzwRSJkAtGwBzn0EwEP98YuApOA3mp0+fRpnzri5j24I5DcDQRAEQRAEQRAEQRC+EbMi9po1awAACoUCOTk50TWmB9FVgHb2sPaVWPLELiwsRGlpKVQqVUTbvf766/0+5qmnnsIf//jHkNtCENHkzBkT9h/4M5qbj4XkfMnJI34OE66AWDwbcXH9QnLeQDllr8Q3r69xWz5+3sWYufDqCFpEEETEqT0M7HwLaPHiIZqYAkxZBmRPiIhZbEcHmHffFSxLmjAByVOnRsQOIsZhWeCHl4XLEtOAWbdF1h5PJCQA8jxg7DiHV/bRI57rsyKg3zxg7AIg7TBQtQ6oE/g+MuWGkJj30ksv4emnnw7JuQiCIAiCIAiCIAiCCJ6YFbGfeeYZ2O12iEQiiMViLFu2DAqFAgqFAgMHDoy2eTFLLAnQoUCn00Gn00XbDILosyQmZgQtYA8cMB3Z2fnIHqRA/7QJMZNXurnhvCMPdnu7YLl46DAsVP86ZuwlCCLEsCxwZDNw4FNHKHFPDBwFTL8FSBZHxDQAaNi8Ge0nTwqWiZcsobmJcHBkK3Biu3DZzJuBFHEkrfGN9HTgqqsBux3YvAlobPBcv6EZaBgMTH8BGJMCVH4M7HkfqD8BJKYC4xZGxGyCIAiCIAiCIAiCICJLzIrYlZWVMJvNMBgMKCsrw6pVq3gxUyaTQalUYunSpZg5c2Z0DSUIgujFpKfPQmJiJtrafE8zEBfXDxkZF2FQdj6ysy9HUtKQMFoYGCzL4ouXX0Dt6VOC5fGJibjm0ZVIonyzBNE7aW8G9qwHTu/2XnfUhcC4K4G4yH5tZgxGwfdF/foh/dprImoLEcNsWeW+bE5h5OzwF5EIkEqBUaOA8m3A7l1uQ+fz2CqBI/2A2bcCyr8Ax7YB1TagH31WEwRBEARBEARBEERvJGZFbAC85zUAVFVVwWQywWQyoaysDBaLBVqtFmKxGEqlEgqFotd5IQdCIOHDie68//77GDt2rF/HDBo0KEzWEET0EInikZ21ACdOCoe05UhMzPw5TPjlyMy8GPHxsb2gbP30Q1Ru+95t+eXL1RicI4mgRQRBRIz6E8DON4Gmc57rxScBk5cAQ6ZFxi4n2s+dQ/2XXwqWDVAqEU/f9wgAqD0G7PlAuEx6OTAoMqHvg6JfP+DCi4DxE4BvNwJuNpfxtLYCm74DfvoJuORSYNYFITPl/vvv9zuFUWVlZUBpiAiCIAiCIAiCIAiC8E5Mi9jO5ObmorCwEIWFDo+CH3/8ESaTCevWrUNpaSlKS0v5sIpPPvkkFAoFLr/88miaHBUyMzNd/s8wjN/CNgnhwNixYzFlypRom0EQMUF2tkJQxE5LG/ezcJ2PgQNnQCSKj4J1/nN8/z5sfPNVt+UTL5qPafmLImgRQRAR41g58NMHQKdwGgGe/kOBabcAadHZoFb7wYeAu1QHBUsibA0Rs5S/ArAdwmVz74usLcGSnQ1cfwOwdy+w9QegpcVz/bNngPfWA5OnAHPmAklJQZswePBgDB48OOjzEARBEARBEARBEAQRGnqMiN2VWbNmYdasWSgqKgIAmM1m3HvvvbDb7XjmmWeg1WoBAEqlEiqVCvn5+dE0N2KEQoDuKoQTBNG3ycy8GCJRPwAdSE/Pw6BsBbKzL0dqak60TfObpvP1+PgFLTo7hBf9M4ePhLLwQco1SxC9jY5WYN+HwAmL97rD84AJ1wLxieG3SwCWZcEYhUOJJ44cidS5cyNsERGTtDUB5a8Jl2VKgbGKyNoTCkQiYPJkIDcX+OF7YP9P3o/ZUwFU2YELLgTGjQ+/jQRBEARBEARBEARBRIweK2J3RaFQQCaToaqqCpWVlTAYDDCZTNiwYQM2bNjACxJWqxVfffUVLrvssihbHB66CtDV1dU+CdvOodjJExtYvnw50tLSBN9fvnx55A0iiCiSkJCGmTPWYMCAKUhMFEfbnIBhOzvx+X+eRf3ZM4LlCf2ScM2jT6BfckqELSMIIqw0nAF2vQWcP+m5XlwCMPE6h4gdRZp+/BGtdrtgmbhgCURxcRG2iIhJdhmBpmrhsrlqoCf3k5QU4LLLgYkTHSHGa2o8129qAk6cCKuIvXbtWqxdu7bb+w0NDWFrkyAIgiAIgiAIgiD6Or1GxHYmNzcXRUVFvJd2WVkZNmzYgL///e+w2Wx8nm0ul7ZCocDMmTOjaHHo6CpA+5on3Gaz8X9LJJQHtry8XPD9BQsWRNYQgogRMjMvirYJQVP+8XuwW7e5Lc+/615kj86JnEEEQYSfUzuBPesdntieSM0Cpt0KDBgWGbs8wBjXCxfExSH9hhsiawwRm7AssEUnXNZvADDj5sjaEy6GDQeWqIBdOwFLudsQ+0hOdoQUDyMHDx7EN998E9Y2CIIgCIIgCIIgCIJwpVeK2F3Jz89Hfn4+/v73v0OpVKKgoAClpaUuXtpisRhKpZIPPz5w4MBomx0wMpkMVqsVgMMT2xec682ePTssdvUk8vLyBD2xc3JyIm8MQRBBc3RfBb59+79uy6fMz8fUy5QRtIggiLDS2Q4c+BQ48r33uoOnAZNvBBKSw2+XFzrOn0fdZ58JlvW/5BIkDhkSYYuImOTQZuDULuGyWbcCyT33d0w34uOBmbMA6Vhg8ybgYFX3OvMucAjZYSQnJwfz58/v9n5DQ4Pbza8EQRAEQRAEQRAEQQRHnxCxnRGLxVixYgVWrFgBwBFefN26dSgrK0NpaSlKS0tRWFgIiUTCC9o9LfS4QqHgRWy7m3CUXXGuV1BQEBa7ehJr167FlClTom0GQRAhoLGuFp+8UAK2s1OwPGvkaOTfdV+ErSIIImw01TjCh9cd9VxPFA+MuwoYdYEjF28MUPfJp2CbmgTLxCr6fkb8zJaX3RSIgDmFETUlYgwYACy6Ajh4ENj0HXC+3vH+0GHA+Alhb95dSqGKigpMnTo17O0TBEEQBEEQBEEQRF8kZpOljR07FllZWbjpppvw3nvvoa6uLiztyGQyaLValJeXo6amBgaDAffccw/OnTuHVatWQaFQYNy4cWFpO1ysXLmS/9tisfh0DCd6SyQSCidOEESvge3sxGf//ifOV58TLE9MSsY1j65EYpg9uAiCiBBn9wFbXvQuYCeLgbxCYPSFMSNgAwCzXjiUeHx2NvoLeIESfRDmMLDvE+GycQuBLGlk7Yk0OTnA0mUO7+yEBOCSS2NqDBMEQRAEQRAEQRAEETpiVsQuKipCTk4OSktLUVBQgIyMDIwbNw5PPvkkvvzyy7C0mZ6ejiVLlkCn06G6uho2mw2rVq3ic2v3FMRiMZ/3u7S01Gt9s9nM/63RaMJmF0EQRKTZ8r4BB3dY3ZYrVjyArJGjImgRQRBhobMDqPwC2P5foF3Yk5knazww50EgfXRkbPOR5p9+QvPOnYJl4uuvgygxMcIWETHJtjUAKxxZBHPVkbUlWiQmAnPnAbfeDmRmRtsagiAIgiAIgiAIgiDCRMyK2IWFhbBYLKipqcG6detw44034ty5c3jmmWegVCoRHx+PK664Av/85z9x6NChsNiQm5vrEno8mjAM41d9nU4HsVgMhmFgNBq91gUcXumFhb00BCFBEH2OIxU7sbn0Tbfl0/IXYfIlPStdBEEQArTUAz++Chz82ktFESBdCMz8FdAvLRKW+QVjFPbCBoD0JUsiaAkRs7Q2AJb/CpdlTwCkl0fWnmhDUVQIgiAIgiAIgiAIolcT8zmx09PTUVBQwOdprqqqgsFggMlkwoYNG7BhwwYUFRVBLBZH19Aw4CxcV1dX+3WsRCLB6tWroVKpoFKpYLPZBMOE6/V6GI1GiMViGAyGYE3uNSxfvhxpad0XuN3lwyMIIrZoYGrwyb/+DtaNt9qgMbm4bDlt2iGIHk+1Hdj9DtBa77lev/7A1JuAzNgMtdzZ0oK6Dz8ULEvNy0NSbm6ELSJikp2lQDMjXDa30GNYbZZlUXa4DDvP7sTwtOG4MvdKpCelh8fOXsjatWuxdu3abu83NDRE3hiCIAiCIAiCIAiC6CPEvIjdldzcXBQVFfEhvs1mM0wmE8rKymC1WiESiRAfHw+5XI5ly5YhPz8fM2fOjK7RPuAc0ttut/Pe0RxqtRpqtRoSiYQX7PPy8jyK9wUFBTCZTFCpVJDL5dBqtbynNcMw0Gg00Ov1UCgUMBgMvXIjQKCUl5cLvr9gwYLIGkIQhN90dnbg0xf/gQamRrA8MTkFix95Aon9kiJsGUEQIYPtBA5uBGwbALCe64pzgWk3AUkDI2JaINSbzeiorRUsSy8gL2wCAMsCW3TCZUnpwPSb3B7a0tGCx795HF8d+Yp/7829b+KVRa9gcOrgUFvaKzl48CC++eabaJtBEDxmsxlWq7XHpT4jCIIgCIIgCILwh5gNJ+4rCoUCWq0W5eXlvLf2kiVLUFlZiccffxxyuRxZWVlYtmxZlC31jFKphFKphEqlgkajgd1uh1gs5l/V1dUoLi7GihUroFKpoFQqXYRvdygUClRVVUGr1UKn0yEjIwMZGRnIzc1FdXU1TCYTTCYTCdhdyMvLw/z587u9cnJyom0aQRBe+GH9Ozi8e4fb8oXqh5A5fEQELSIIIqS0NQI7XgdsX8CrgJ0zH5DdHdMCNgAwblK/xPXvj4GLFkXYGiImqfoGOLNXuEx2O5DUX7CotaMVj339mIuADQAH6w7id9/9DizrZQwRAICcnBzB3wZ5eXnRNo2IEna7HSKRKKiXXq8PuH21Wg2NRgOr1er3sUaj0a1NvqwxuINhGGRkZAieV6VSuT3ObrdDrVZDKpXy9aVSKdRqNex2O19Pr9dDo9EEbF84KCkp8euZS6VSKJVKqNVqv56dRqOBSCTi13OEXlwbocRqtUKj0UAul/PPJyMjg38+wfQXgggnDMNAr9dDpVJBKpXyY0QqlUKlUgn2Xc7RJ5ba6AnQPNg75kEaM94pKSnx2P/8fXVNHcv18a4vobpCeGvP+ToC+d7KPSdvaWsJIhz0eBFbiNLSUlRXV8NisaC4uBg5OTl8qGyj0YjZs2fjySefxPbt26NrqBMsy4JlWdTU1Pj0YlmWF+29IRaLXXKMcy+DwQCFQhHmK+uZrF27Fl9//XW3F4USJ4jY5tDO7fh+/Ttuy2csvBoTL7w0ghYRBBFSao8AW14Ezv7kuV5CCjDjDmDsFUBcfGRsC5DWo0fR+P0PgmUDr1mMuJSUCFtExCTuvLBFccCcFYJFbZ1t+M03v8HGoxsFy78/8T3eq3wvVBb2apYvXy7420AoxDjRN+i6mCgWi6HVamEwGLr97uZeXaOtBfpb3Gq18uJu13P6QkFBAWpqamCz2XhPbm5Tu1arDcgmwCEyZ2Zm8v9XKBSwWCyw2WxYvXq14DEajQZSqSPVh8FgcFmrEIvFkMvl/CZ/tVrt0yJuJCkqKoLNZoPNZuOj3nGYTCb+erg6Op0OMpkM5eXlkMvlkMvlPi0Ga7Va2Gw2lJWVQaFQgGEY/rVy5UqXPhcKrFYrlEol5HI5zGYz1Go1TCYTWJZFVVUV3++USiUyMjKCFhjMZjMyMjJoYZwIGoZhoFarkZGRAbVaDcAxzxgMBn4Mzp49m984w4moZrMZJSUlPs0xkWiDq98TxgXNg4HPg0ajkT9eJBJBLpdDpVJFVLTt6WMmkvewsLAQZWVlMBgMyMvLc+mDq1evRllZmeDLYDBAq9V267fOG/UARx/nxorz9zGGYZCfn+/VPovFwms9XBt5eXn8d1MOT2NW6DuszWaDxWLhNxeqVKqQfPYThF+wvQiVSsXGxcUJltXU1LAikYjNyMhgMzIyWJFIxMbFxbFxcXHsokWL2DVr1rBVVVWRNZiIGXbv3s3C4c7FAmB3794dbZMIgvCT+nNn2ZdW3Mr+Y+nVgq/XNb9m21pbo20mQRCB0NnJsoc3saz5tyxresLza8uLLNtYHW2LfebU88+zeyZMFHw17qLvIwTLsudsLPtUOss+NbD76+1bBA9p7WhlH/3qUXbq2qkeX/PenMeeOH8istfTi6DfEH0Xk8nEP/eioiKv9WtqalixWMwfo9VqA267sLCQP49YLA74PBzcNXDntNlsAZ1HIpGwWq2WP4/JZPJYn7sOi8Xitk5NTQ2rUCj4e1dYWBiQbZHAZrPx1y6RSLzWNxgMfP2CggKf27FYLCF9/l3hnqFYLPb6DLnnA4CVyWRsTU2NT23U1NSwNpuN1el0rEwm469Hp9OF4AqIvkrXMeWtP+p0OlYsFrNarZafY7zNzeFsozeMC5oHfZsHuWNkMhmr0+lYm83GWiwWl34ikUg8fj6Ggp48ZqJ9D537oC99ncNms7ESiYQFwBoMBo/1uHMH8t1RJpOxMpnMJ3v8vQ6TycTfY3/GLUEEQ6/0xBaC21msVCpRXV2N8vJyFBcX4/LLL8eGDRuwYsUKSKVSZGVl4f7778e7776Lurq66BpNEARB+ERnRwc++dff0VjLCJYnpabhmkdXIiExMbKGEQQRPO0twO63gZ8+AtgOz3VHzgPy7gVSMjzXixHYjg7UvivsCZs0aRKSp0yOsEVETLJ1DdyGzp+r7vZWe2c7nvz2SZgOmbye+nzbeTz9/dMUVpwg/ITzCiosLPTJe3nFihX8MTKZLKhc1qWlpZBIJLwdofDS4zyjgMC8u81mMxQKhc9pysxmM/R6PbRaLWQymdt6YrEYJpPJxcM7VvHXxoKCAr4fGI1Gl2fgCed7HOr7wqW3E4vFsFgsXqMFcM+noKAAVqsVubm53TzLuiKXy5GRkQG5XA6TyRTzqf+InoFGo+HTFuh0Oj6agye4iJXFxcU+eZOGs43eMi5oHvRtHszPz4dEIoHFYkFhYSEkEgn/3aCqqgoymQx2ux1yuTygtCG+0NPHTLTvYaBpWSUSCf89y1s/4foWhz9pZBQKhU9pjwIZP1zqWolEAqPRCLlc7vc5CMJf+oyI3RVuYjOZTOjs7MSGDRtwzz33ICMjA6tWreJDI8yZMwevvPJKtM0lCIIgPLDZ8CaO7t3ttnzRfQ9DPGRoBC0iCCIknD8JbP03cGqX53rx/YCpNwMTrwPiEiJjWwho+O47tJ86JVgmLlgS8pxuRA+k5Tzw4xvCZYMnAzmXuLzV0dmB3373W3x+8HOfm/ju2Hf4wPZBMFYSRJ+juroagG/ht81ms4vQzKU6CwSj0QiJROLSbiCic1cyMzP5dGWBhIfUarU+iw8A+NzWvqZICybMeSzjfM/0er3XBe1wotFo+H5aVlbGb5TwBYPBAIlEAoZhoFQqPdYtKyvjU+lRijsiFOj1epSUlABwzBVdw+N6QiKR+DQnh7uNvjwu+to8yH3+ufvsFovFLik4OBE4lPT0MRML9zAYFAoFZDIZbDab17pdv/PFyrWIxWL+GVmtVr++AxJEIPRZEbsrCoUCOp0OlZWVqKmpwcsvv4wbb7wRlZWVeOaZZ6JtHhFhli9fjgULFnR7Ud47gog9qn4sx5b3St2Wy668FuPmXBhBiwiCCAnHLcDWl4DGs57rpQ0B5jwIDJ0eGbtCCOPGe07Urx/SFy+OsDVETLLjbaDFTXSoufcCThsdOtlO/GHzH/Bp1ad+N1OytQSnGoQ3VBDA2rVrBX8bLF++PNqmEVGCYRjIZDKvnjgMw7gsOGq1Wr8Wxbui0+mgVqtdxF+z2RySXNErV64E4L93N8MwqK6u9uhR3RV/vaIKCgoC9nqKZbr2BbPZHBU7uLyjgONe+/MsOZw9yzwtZvfG50hED2fxRCKRBBTlQqFQeNxQE4k2+vK46GvzoF6vh0Qi8SjWy2QyfiOD3W4Pae7h3jBmon0PQ8GyZct83rBRVFTkci2xIhjLZDJ+c4Jerw9b1ACCAEjEFiQ9PR2FhYUwGAyorq7GgQMHom0SEWHKy8vxzTffdHsdPHgw2qYRBOFE3dkz+PQ/z7otHzp2PC697c4IWkQQRNB0tAF71gN7jEBnm+e6w2YBc+4H0gZFxrYQ0n72LOq/+lqwbMCiRYhPT4+sQUTs0dkJbHHjYZmSAUz7RRjrZDvx9PdP40Pbh25P1z+xP8aKxwqW1bfV408//InCirvh4MGDgr8NysvLo20aEUV8CdMYyjDiDMPAbDbzC4bOXk2hWJyVyWS8mOCPd3dxcXHAC6r+iBVLly4NqI2eRCg2IwQC59UG/LKZwV8UCgXff6LtTUn0HVasWMH/HUzEBk/9PhJtEL/Qm+dBhmH4jWJSqdTjuZxF9GAiuHSlp4+ZWLiH/iL0eciFO/cV51Dser0+aps9uuI8bpz/JohQQyI2QQiQl5eH+fPnd3vl5ORE2zSCIH6mo70dn7xQguZ6YQ+15LT+WPywBvEJlAebIHoMjWeBbS8Dx70IQ3EJwKQbgckqRyjxHkjtBx8A7e2CZWIfw6sSvRz7l8A5N5tpZb8C+qUCAFiWxV9/+CvePfCu21OlJqTiZcXL+Mf8fyAxTvhzcePRjfjY/nHQZvdGcnJyBH8b+CJihpJOlkVDcwe9vLw6I7AZQyKReA2bHMow4oBj0dJZuA42j7UQ3AKk2Wz2eXHVaDT6FSYU+MXzTqPR+CxYKJVKrwvWPY2uXkvRCCFsNpt5O8RicUDehxzOXnO9NQQ8ETtYrVaXMeRregIh3EXWiEQbfZ2+NA9yqUg4PH3Ozp492+1xgdIbxky076G/WK1Wwe+LeXl5fonYQiHSo7XhwxmJRMJ/p/PnuyNB+EvPSRpIEBFk7dq1mDJlSrTNIAjCA9+98zqO79/rtvyKBx5F+uAhEbSIIIigOLXb4X3d0eK5XkomMP1WYMDwyNgVBliWBWMQDtWaOHo0UufMFiwj+hg/rBJ+XxQPzL4HgKMvFW8tRul+92k1UhJS8LLiZcwcPBMAcP/M+/GC9QXBus9sfQbzhs3DoNSeF90gnCxfvlwwdHhFRQWmTp0aMTuaWjqxYbuXFAsEFs7MRlpyfFjb8LYoG+ow4oBDqHYWwjnPabvdDrvdDqvVGtTCO+DwdubEcZ1O51WINBqNAQkOarWaF7Bzc3OxevVqr/c0mIXwWGXdunX834WFhUE/v0Bw7lPBikdKpZIPx1taWhqyzRUEIYRz/wqF8Cl0jki00dfpS/OgRCJBYWEhSktLsXTpUo/fC5zFwMzMzKBs4ugNYyba99Bf3InnYrHY7whYBQUFKCwshF6v579nmkymUJgZFM5e5UajMaioQwThjl7liU3h7wiCIPoGNssWlH/k3uMs75obIZXPjaBFBEEETGc78NPHwK43vQvYg6YAcx/q0QI2ADRZLGh1k6JEvGQJRE55jok+ytlKoNLNosSkxYB4FFiWxd/L/463973t9jTJ8cn4T/5/IBvyy4Lg8inLMTlrsmD9utY6/PmHP9PvKoIIklCGEQd+CbvddXHfOXRjKARDsVjMi8W+hCjX6XQBhY8sKiriF565hViRSMQv/kc6TKbZbIZKpYJUKoVIJEJGRgbkcrlfnuL+YjQaXfKvRkvwdb7XwW60cD6eYZiYyI+p0WigVCohl8uRkZHh4s2v0WgglUqRkZHh1atNr9dDqVQiIyOD7yMqlUqwr5rNZr6O84vr411Rq9Xd6naNOuBP+6G4buf2pFIppFIp5HI59Ho9X+YJf+0NhFD2XcAhZHadqyPRRqxA86CDcM+DOp0ONTU1Xq9127Zt/N+hEvZ7y5iJ5j30l1CHMdfpdC6ez7GQ69v5OceCqE70TnqViH3vvffi8ccfd1teVFTkd5grgiAIIraoO3Man//nObflw8dPwsU33RFBiwiCCJhmBrDogSObPNcTxQHjrnZ4YCckR8S0cOLOCxvx8Ui//vqI2kLEKFs9LEjMvQ8sy+I563N4Y88bbqslxSfhxfwXMXuoq2d/QlwC/nLRX5AQJxyU66sjX+Gzqs8CMpsgCMeifCjDiAOORUuhvNPOeaJDtZDJ5aDkck66w263o7q6OuBFaovF0m1R2Ww28+KbSCTiRbNwwQnoSqUSEokEBoMBLMuipqYGq1evhtVqRW5ursf74A92ux1GoxFKpRIqlYpvM5q5Op29xLKysoI6V1dPt2iFb3VGKpXyEQs4IY5hGEilUmRlZcFkMvF9XWhDht1uh1wuh1qthlKpRFVVFViWhcViAeDwuux6nEKhgM1mw+rVq/n8rQzDwGQyCT5rrVYLhULB26fVankhIJD2g71uuVzOR2KoqamBzWaDzWZDWVkZTCYT1Gq122cbqL2B4OxlGa5UA5FoI9rQPBh78yDDMC5icKjyqfelMROue+ipPe7FRcdRq9Vh+Q7jPFbUanXUQ3g7j5lY+Nwneie9Kpx4fn4+8vPz3ZY/88wzEbSGIAiCCDUd7W346Pln0NxwXrA8ecBALH5Eg/iEXvXxRhC9k7M/ARWlQFuj53pJ6cC0mwHxmMjYFWY66utR98UXgmX9L70UiUMGR9giIuZorgW2vylcNnQ62FFz8eKPL+K13a+5PUViXCJeuOwFzBs2T7B8XMY43Dv9Xvx7+78Fy/+29W+YM2wOslOy/TafIPoyDMNgxYoV/P9DEUYccAjjzrkQOTjPaU5c6Jo3OxCcw5QXFxe7DePtTlj3FbFYDIvFAr1eD51OJ+i1yy0CazQaWCyWkNxLDi6UOcMwgoK6TCbjBTuVSoXCwkKfvQTtdjsyMjK6teeMTqeLCScLZ7tCna832gvrAPh7zAmrgCNSglqtRlFRkcfrt9vtvAhjMplcwtpywptz6GDn8PtcntCioiK+XCKRCN5jsViMlStXwmg0oqysjO+LwbQf6HWXlJTAarWipqamm61isZhvU0ioCMbeYIlErunemM+a5kEHsTYPFhcXu2xqCUff6+1jJhL3kEOor4cTmUwGrVbLbwhSKpWw2WwRa78rzvc2Fj73id5Jr/LEJgiCIHo3G//3Gk5W7ndbftUDj2FAFi24E0RMw3YCNhOw/b/eBezMccDcB3uNgA0AdZ98Ara5WbBMrOp9+T6JANj+FtAqvFkLc+/Fqp06rN7VXcziSIhLwPOXPY+LRlzksZm7pt2FSZmTBMtqW2rx1x/+SmHFCcJPQh1GHHAI0wUFBW4XYJ2F5FCFYuUWRq1Wq9sFSaPRGBLxobCwEBaLBTU1NXzY0K5CCsMwkMvlIV0c5cIoa7Vaj2FGdTodxGIx9Hq9z6GQJRIJampqXF4sy8Jms/HCHScKxRLBLoJ3FTajlYNUCM4WzkOOG5vcZgqhHPBcyOzCwkK3uV25MVdSUiIYctnZ+8+TaLtu3bpu+YBD0b6/1+2cn9gd7javhMLeQAlXuOtItxFpaB7sTrTnQbvd7hJiPVzh53vzmInUPeSQSCRgWdbl5dzPw4HzdyW73R6yCBeB4PycY+lzn+hdkIhNEARB9AgObNkM62cfui2fe8NS5M7Ki6BFBEH4Tet54MdXgaovAXgSx0SARAHMWg706x8h4yKDu1DiCYMGof+ll0bYGiLm6OwAtrgRoVKzsVpUh5d2vOT28ARRAp6d/ywuHem9LyXGJeLPF/0ZCSLh6CXmw2Z8cUg4agBBEN0JRxhxwLvHs0Kh4AVuT6KzPziL00KLsEaj0a1IFSicV7lWq4XFYgHLsjCZTPwiLcMwQXl+O+MsxLjzNHeGC9serNjCeeZyIgkXUjeadM3fGgxdjw+l53wo6dqPZDJZtw0ZJSUl/Fjy1O84j2sAKC0t7VYuFov5c+v1erf3WK/Xu4gQoWrfGV+umxMg5HK52/DRBQUF3ULzhsNebzj3r3B5IUaijWhB86CrTRzRnAcZhuHvRUFBQchDrPeFMRPue+grXD8Pp4DufG0lJSU+bzAJJ7H6uU/0fCjeKkEIsHz5cqSlpQm+v3z58sgbRBB9HObUSXyx6gW35SMnT8WFqlsjaBFBEH5TUwXsehtorfdcLzENmHoTkDU2MnZFkOa9e9FcUSFYln799RBRKgTigMkxVgR4bfwF+JcHATteFI+S+SW4bPRlPjc3IXMCCqcXuhXG//bD3zBn6BxkJvftXfVr167F2rVru73f0NAQUTtSkuKwcCZFnPFGSlLk9+r7G0bcaDRCp9PxeW/dwXlP+rPAL+RRGgiFhYXQ6/UoLS3t5uGt0+lC5vXtCYVCAYvFApVKBaPRCLPZDKvV6tFj0BecbfdlwZULkczlEPZF8PGERCLhQ4GazeaQnNMXuNyczsKlQqHg3y8vLw/q/F2PD/Y5dYVhGK/5NjMzM72GjfVlA4azR7K365DJZLDb7W4FG41Gw99jvV7fTdTQ6/XIy8tz6YuhbJ/Dl+vm+qTdbodKpYJYLIZEIkFeXh7kcjmWLl3KbzhxJhz2esO574Zi847RaIRYLHa5T5FoI1rQPBh782B+fj7sdrtfYdv9oS+MmXDfQ39RKpUe88mXlJQELHRzaRq4jSUqlQpVVVURD+PuPJeH+nOfIDjIE5sgBCgvL8c333zT7XXw4MFom0YQfY721lZ89FwxWhqFF4pTBqbj6oceR1x8fIQtIwjCJ1gWOPgNYF3jXcAW5wBzH+qVAjYAMMb1bsvES26MoCVEzLJlleDbr6en49naHW4PixPF4ZlLn4FyjP+eLPdMuwfjM8YLltW01OBvW/7m9zl7GwcPHhT8bRDsQqe/xIlESEuOp5eXV5xIFNHnAvgfRtxut/u0yMjlC+0akrXry2Kx8Mdwi8fBwnlTcoKFs+3V1dVBedv463Xn7G0UinEnlH/bE87Patu2bUG3D7gu9EZqoV1o04SzV6W/98XT+cMhRmk0GkilUo8vX7xEfRl7zvfCW5tWqxUSiQRZWVmC55JIJLwAU1xc3K3cObdpONr357oVCgVMJhM/vhmGgdVqhV6vh1qtRkZGhqCndTjs9YazHaHwQFy3bl030S0SbUQLmgd/IRbmQaVSCavVCoPBELZ70dvHTCTuob84R5/oitls9imFgycKCgr4ftZ1Q2WkcP5eFu2oCkTvhdw9CEKAvLw8QU/snJycyBtDEH2cr994Baer3OzSFolw9UOPo39mcD+AiZ5He2c7Pj/4OUp/KsWhukPISsnC2PSxkIqlGJsxFmPFYzGy/0jEx9HmhqjS1gRUGICze73XHXMJIF0E9NJn1tncjNqPPhIsS50zB/3oOwZxeh9g/6rb228N6I+/Z6a7PSxOFIe/Xfw3XJFzRUDNJsYn4i8X/QU3f3IzOtiObuVfHPwCC8csxMKchQGdvzeQk5OD+fPnd3u/oaEh4kI2EXsEEkbcZrP5lDdQr9ejrKzMq/gkk8kgk8lgtVrBMAzMZnPQnn7O5ywuLuYXSXU6XbdQwv5iNpvBMIxf3kIKhQJms7nXhPR1fv6RmkesVms3AVKhUEAikfAiQTDekM7jINg+Ek78zdkZij6n1Wohl8vBMAz0ej3vBeqLl2Go+ryv161QKGCz2WA2m2EymWC32/kxCzjmpfLycpfNM+Gw1xvOcxQQXN8FHOOja7+NRBt9GZoHHSiVSn5MhdOTtTePmUjdQ3+RSCRuo+5wG3qCZfXq1fwcbTQaQ7aZ0Re4jU6A64Ytggg15IlNEAKsXbsWX3/9dbcXhRIniMiyb/NG7NjwidvyC5bchDHTZ0bOICLqtHW24f3K93Hd+9dh5bcr8ePpH1HdXI0DNQfw2cHP8O/t/8YjXz2Cxe8txty35kL1kQpPfPsE1uxag6+PfI0j9UfQyXZG+zL6BnXHgC0vehewE5KB6bcD467qtQI2ANSbzOisqxMsE6vCHzqP6AFs7e4xUDqgP4qz3S86iyDCny/6M66WXB1U05OyJuGeafe4Lf/rlr+iprkmqDZ6MsuXLxf8bSAUYpzoW/gbRpyjvLzcq4BrNpuRmZnp82Ks86J8qDyQuHM659oOVchXfwUL7j4E670J+J+z0TnHKRdSN5QwDBN0HlZf2rDb7YLX7txfunoE+0pJSQn/d0FBQVhEBJ1OB5ZlPb68hej3lVDn9eSEHcA1z3xxcbGgQBOtvKLOnpMKhQJarRYGg4GP+MBFmeC8szmiZe/q1av5vwPtu4DjeqqrqwX7bSTaiAY0D7oSrXlQpVLBbre7FV+tVmvQecid6Y1jJtL3MFSYTCbMnj076POIxWKXDZRqtTroiAK+4vw5EIpUNgThDhKxCYIgiJik+vgxmPQvui0fPXUG5i25KYIWEdGktaMVhv0GXPPeNfj9pt/jcP1hr8e0dLRgX/U+fGL/BC9YX8BDXz6Eq969CvPemoebPr4Jv/3ut3ht92vYeHQjjp8/DpZlI3AlfQCWBY7+AGx7GfAmeg0YDsx5CBg8OTK2RRHGTS6suAEDMIDCbhFNNcCOd1zeWt8/DX/2IGADwNMXPo1rpdeGxAT1dDXGioVD+Vc3V6N4a/cQqATR1/E3jDjgWEy1Wq1exVidTicYttcdS5cu5f82GrgWHL0AAQAASURBVI0hEQOcc4ZqtdqQ5iz1d7GTW5ANhZeP8zX4stDr7FnqfJ+DoesmhnCHGOYWmoW8cRUKBf+s7Xa7ixDjC3a7nRcqxGKxi4DRU/E3JC4njnmCE6s5z2Zuc4jQmApH+76gVqvdevHJZDJotVp+7DpvGIiWvZxNQGB9l0OlUrmdkyLRRjSgedCVaMyDzuKrp5DT/kaP8ERvGzPRuIehgPscCNWmFoVC4fIdNBSh3L3BMAzf5xUKRURy2hN9FxKxCYIgiJijrbUFHz9XjNamJsHyNHEGrnroN4jrxV6bhIOWjha8tfctXPXuVfjT93/CsfPHgj5nU3sTKs5V4EPbh3jW8iweKHsAi9YvwgVvX4BbP7kVf9j0B/y34r/YfGwzTjWcInHbH9pbgIp1wL4PAIGwxC6MmAPk3QukxtYPynDQevgwGrdsESxLv+YaxCUnR9giIuawvgG0NfL//aB/Gp72ImD/ft7vccO4G0JmAhdWPF4k/Nn6WdVnKDtcFrL2CKKnE0gYceCXnLiePLG5kJDOIrI3xGKxywJiaWmpz8d6grNBr9ejuLjYL2HdE2az2WcPLW6xN1Qevs4e80I5irvC3UutVutXCHRPdF1Q77rgbLVaIZfLQ9IWwzBe+51Op+M3CGg0Gp8XwBmG4XNgisViWCyWkN2jaFJUVMT3EV8iG6hUKpf5QIiCggL+nBqNxq0Xdrja9xVv7XHzjLNgFE17i4qKePFGo9H4HUpXqVRCIpF4nG8j0UakoXmwO5GcB1UqFRiG8VrXZDKF3PO9t4yZaN7DYOG+/+Tl5QmWBxKZQKvVRjTSQ35+PgDHZ4Gv34EJIlBIxCYIgiBijq/W6nHm8EHBMpEoDlf/+nGkiTMiaxQRUZram/B6xeu4cv2VKN5ajFONp8LeZkNbA3ae3Yn3Kt/DP8r/AbVZDYVRgYvevgi3f3o7/rj5j3hz75v44cQPONt0lsTtrpw/BWx7CTi5w3O9uERgylJg0g1AfGJkbIsyzPp33ZZRKHECnR3A1l+8NT5OS8XvszPBikRuD3ly7pNYOiE0XjDOTMmegjun3um2/M/f/xm1LbUhb5cgehqBhhHXaDS8cOPJK8jZm8sfli1b5mKTNzjvO0+hvZ1F68zMTI/X6e+Ca0lJCTQajcfjGIaBSqUKuYcv50XadTNCV7hF8oKCAo+e9tXV1X613zUPctcw2GazWXBx2997zDAM8vPzwTCM1z5qMpn4a1QqlV695ziByW63QyaTefSE89XWWMK5j3gSYEpKSmC3232KxMCNbavVCrPZ7PGYcLTvC1ar1eOz54Q95/kGiJ69APiw54BjzuLGrSesVisvPPoShj4SbQgRznFB82B3IjEPqtVq/n4rlUrBl1wuh1QqDam3rjM9fcxE+x4GMy71ej1vu7vvedz3Mn+jE/grJvs7ZgHHM5JKpbBarXyf7w2b14gYhyUIgt29ezcLgH/t3r072iYRRJ+lYuOX7D+WXu329f36d6JtIhFGzreeZ9fsXMNe+s6l7NS1U2P6ddHbF7F3fHoH++fv/8y+tfctduuJrey5pnPRvoXR4biVZct+z7KmJzy/Nj/LsvUno21tROlsa2P3X3wJu2fCxG4v+w03Rts8IhbY8xHLPjWQZZ8ayH5WMpSd/toUj3PP6xWvh9WclvYW9rr3rnPb/hMbnwhr+z0J+g3RdykoKOCfu0QiYS0WC2uz2VxeFouFNZlMrMFgYAsLC1mxWOzSX0wmk8s5a2pqWJPJxCoUCr6OQqFgLRaLV3tqampYi8XiYhcAtqioiLXZbGxNTY1LfZvNxup0Ot4msVjM6nQ61mazCZ5fJpOxAFiDwSDYNtc+Vw8AW1BQINg2y7IsALawsJCtqanhr7ewsJA1GAz8vTSZTKxWq2XFYjErk8nc2sadz9dX1/vg3D53r202G2swGFiJRMICYLVarcd7b7PZ2KKiIpd2DAaD2+t3PpZrw/n+WiwWViwWu1wz105hYaFLOxaLhX8G3Iu7f137nUKhcGuLMyaTiX+WEomE1Wq1fDvcveHum1gs9nh/3F23UJ+VyWSsyWRyuZZg4Wx2bsfduBDCuY8UFBSwBoOBP44br976Z1e4Z1JUVBS29gO9bq4/SiQSlzHBnZMbk+6eeTD3K9Bx3LV95zHC2cBdt8ViYXU6HX9f/O274WwjFOMikHtI86Aw4ZoHu9ruy8vd52iw44Vle+aYCdU9DOQ++tMHnfuixWJhtVqty/ckmUwmeK+0Wi1fRyKR8PfKV3Q6HT+e3eFuzLobS9wzcv6OGkhfIIhAEbEsuRERREVFBaZOncr/f/fu3ZgyZUoULSKIvsm5o0fwvycfQXtLi2B5zgwZbnzijxDFUSCR3kZdax3e2vsW/rf3f355+cWL4nFl7pUYmjYUlUwlbIwNR+uPgkX0vt5kJmdirHgspGKpy7/pSelRsylsdLQB+z8Gjm31XnfoTGDi9UBCUritiinqv/wKR++/X7Bs6FN/QMbNN0fYIiLmWLsYOPgtTKkpeHxwNjo8eGA/Jn/Mo6d0qNh1Zhdu++w2dLKdguUvXv4iFoxaEHY7Yh36DdF3ycjICNo7zmazuXhryeVyWK3Wbt4sDMPAZDJ5zAXN2ePOE4ZhGL49o9HIezYL1dNqtd087YxGIzQajUtOVG92O5+zoKDAxTtIKpW6eO5YrVbodDqUl5fDbrfz15KXlweVSuU1BK/Iw7zZFaElMLPZDJ1OB7PZzD9XiUSCgoICrFy50u21cZ7kgLA3FXeurs+6K3q9Hjqdjr+PeXl5LmFBNRpNwDlFnen6HLxhtVqxbt06mM1ml+eSmZkJmUyGZcuW+Z0Dk8u57M1ri/OYFOpz/iCVSmG3290+H4vF4pOHXtc+IhaLIZFIoFar/Q4RrdfrodFoUFVV5bP3mr/tB3rdSqUSGo0GCoUCJSUl0Ol0vKeeRCJBXl4eNBqNV0/TQO5XsOPYGYZhUFpaCpPJBKvViurq6m7zytKlS4PyHgxlG6EaF8HcQ5oHhQnlPBhoeHSh/h7K8QL0nDETynsI+Hcfi4qKQtIHOQoLC13SL3B93N048vZd0BmVSoXZs2cLRk5wHrP+IJFIIJFIQtIXCMJfSMQmCNACFEHEAm3NzXjzt4/h3NHDguX9M7Nwu/ZfSB3YC4XAPkxtSy3e2PMG3tr7Furb6n0+LkGUgGvHXot7pt6DUQNHuZQ1tTfBXmuHjbHxwnZlTSWONxwPtfl+MShlUDdhWyqWYkC/AVG1K2AazwG73gLqvdxXUTww4RpHDmw/fiT2Fo488CDOl3XPIyxKTsa4jd8gfuDAKFhFxAwndwOrLsKXqSn4v8HZaPcwRn4969dYMX2F2/JQ86zlWby2+zXBskEpg/Dede/1zs05fkC/IQiCIAiCIAiCIAgifCRE2wCCIAiCAICyV1e5FbBFcXFY/LCGBOxexLmmc3h9z+t4Z987aGxv9Pm4xLhE3DjuRtw19S4M7z9csE5KQgqmZE3BlCxXIaGhrQF2xo5KpvIXcZupjEi+bQA403QGZ5rO4IcTP7i8PyR1iIuwzf2dmpgaEbsC4nQFsMcItDd7rpeSAUy7FRg4IjJ2xRhtp0/j/NdfC5YNXLSQBGwC2LIKG1OSvQrY98+4P6ICNgA8MPMBfH3ka1TVVnUrO9N0BiXbSvDXi/8aUZsIgiAIgiAIgiAIgug7kIhNEAIsX74caWlpgu8vX7488gYRRC9n91cmVHxjdlt+8U13YMTEyRG0iAgXZxrP4LWK12D4yYDmDi8CqBNJ8UkoGF+AO6fciSFpQwJqOy0xDdMGTcO0QdNc3q9vrXf12v753zNNZwJqx19ONZ7CqcZT2HR8k8v7w9OGY2yGq9e2JF2ClISUiNglSGcHUPkFcPhb73WzJwFTVEBiFO2NMrXvfwB0dAiWif0Mv0n0QhrOYdOBD/DIkEEeBewV01bg3hn3RtAwB0nxSfjThX/CHZ/dIZii4UPbh1iUswiXjrw04rZFmrVr12Lt2rXd3m9oaIi8MQRBEARBEARBEATRRyARmyAEKC8vF3x/wYIFkTWEIPoAZw8fRNmrq9yWS2SzMfuaGyNoEREOTjacxKu7X8X6/evR2tnq83EpCSlYNmEZfjXlV8hOyQ6LbQP6DcDMwTMxc/BMl/drW2q7CduVTCWqm6vDYkdXjjccx/GG49h4dCP/nggijBww0kXYHiceh5z0HCTFhznXdHMtsOttoPaQ53qiOEC6CBhzSZ8MH87BsiyY9UbBsn5jxiAlLy/CFhGxxg+bivFw1kC0eRgnd069Ew/NesivfG2hZObgmbhj8h34757/CpY//f3TeP+693tuWgQfOXjwIL755ptom0EQBEEQBEEQBEEQfQoSsQlCgLy8PEFP7JycnMgbQxC9mNbmJnz03DNob20RLB+QPQhXPPAYRHFxEbaMCBVH64/ild2v4P3K99He2e7zcWmJabhl4i24ffLtyEjOCKOF7klPSod8iBzyIXKX96ubq12E7QM1B2CrtaG2pTbsNrFgcaT+CI7UH8HXR77m348TxWH0gNGQiqW8sC0VS5EzMAeJ8YnBN3yuEtj9DtDmxeswaSAw9SYgIzf4Nns4jdu2oe2QcIqE9IIlURMlidhg27Hv8dDxz9Hi4fPt9sm341HZo1HvKw/OehBfH/0ah+q6b2A53Xga/yj/B56+8OkoWBY5cnJyMH/+/G7vNzQ0uN38ShAEQRAEQRAEQRBEcIhYlu0eG44g+hgVFRWYOnUq///du3djypQpHo4gCCJYWJbFpy/+A/s2CXs2xcXHY9kftRg+fmKELSNCwaG6Q1i9czU+tn+MDlY4nLIQA/oNwO2Tbsctk25BelLPyYHOsizONZ9zFbYZG2yMDfVt9VGzK0GUgNEDR7sI22PFYzFq4CgkxvkgbrOdQNWXgP1LQCCcsAuZUoeA3a9/SGzv6RwrKkLdhx91L4iPx7ivv0LCoEGRN4qICSynLLhvQyGaPESluGXiLXhizhNRF7A5rKesWP75csGw4gCwSrEKF424KMJWRR/6DUEQBEEQBEEQBEEQ4YM8sQmCIIiosKvsC7cCNgBceutdJGD3QOyMHfpdenxW9Rk62U6fjxMniXHH5Dtw88Sb0b8HiqAikQjZKdnITsnGvGHz+PdZlsXpxtOoZCp5gZvz4m5sbwy7Xe1sO+y1dthr7TAdMvHvJ8QlIDc9F2PTXXNujxowCvFx8Y5KreeB3aVA9QEvrYiA3MsASb4jlDiBjro61H+xQbCs/2ULSMDuw2w/vR33m+/3KGAvk1wXUwI2AMiGyHDrpFvxv73/Eyx/avNTeP+693vk/E0QBEEQBEEQBEEQRGxCIjZBEAQRcU4ftOPLtTq35WNnXwDZVddG0CIiWH6q/gn6nXqYDpnceuoJkZWcheVTlmPphKVITUwNo4XRQSQSYUjaEAxJG+LipciyLE40nHDJtV3JVKKqtgpN7U1ht6u9sx0Hag7gQI2rQJ0Un4Tc9FxcPlCKOzoSkNbpxYs+MRWYugzIGh9Ga3setR9/DLZFOE2CeMmSCFtDxAq7zuzCveZ7PW5gWdJvKJ68+E8xJWBzPDTrIXxz9BscqT/SrexU4yn80/JPPHXBU1GwjCAIgiAIgiAIgiCI3giJ2ARBEEREaWlsxEfPFaOjrU2wPH3wECy67+GYXMAnulNxrgK6HTp8deQrv44bnDIYd027C0vGLUFyQnKYrItdRCIRhvcfjuH9h+PSkZfy73eynTh2/piLsG1jbLAzdrR68NwMFS0dLZjT2oJ7WlkkijwL2K39hyJxxh0QpUQnZ3kswxiNgu8nDB6M/pdcEmFriFig4lwF1CY1Gjzklb+u/jz+sOQfiIvRiAapian404V/wp1f3ClYbtxvxMIxC3HB8AsibBlBEARBEARBEARBEL0RErEJgiCIiMGyLDbo/gXm5AnB8viEBCx+5Akkp1E40lhnx5kd0O3Q4dtj3/p13LC0Ybh76t24ftz1SIpPCt6Qxmqg5iAwaALQLy3480WZOFEcRg0YhVEDRmHBqAX8+x2dHTh6/qhD2K752Xu71uG53d7ZHpK2B4gS8KeMGVCkDPNa9/Xzdjx37BP0s73lEo6c+3dI6pA+uxGlqaICLXv2Cpal33gDRAn09buvsa96Hwo3FKK+rd5tncXnG/B02iTEDZsRQcv8J29oHm6eeDPe3ve2YPkfN/8R7173LtISwz8fn2ltgwgiZPejMUUQBEEQBEEQBEEQvRH6xU8QBEFEjO0bPsH+H75zWz7/jnswVDoughYR/lJ+shy6nTr8cOIHv44b2X8kVkxfgWsk1yAxPjE4I9qagX0fA9bXgaqf86onpgKz7wEWPNErxOyuxMfFY8zAMRgzcAzyR+fz77d1tuFI3RGXsOQ2xoZDdYfQzvoubk9MHIhnM+UYleD53tV3tuEPNTtgbj4JAGhvb8Sus7uw6+wul3oDEgdAKpZ2E7izU7J7vbhdu3692zIKJd732F+zHys2rEBda53bOleeb8Cfz5xD/NJ7I2hZ4DwiewQbj27EsfPHupUdbziO5yzP4Xfzfhe29g81tWDVkTN4+8Q53D48C38eNzJsbREEQRAEQRAEQRAEET1IxCYIgiAiwknbAXzz+hq35ePnXYyZC6+OoEWEr7Asiy0nt0C3Q4fyU+V+HZszMAcrpq/AVblXISEuyK8dJ3cB1jeAneuAZsa1rK0R2PwvYM/7wNXPAeMUwbXVQ0iMS4RELIFELHF5v62jDQfrDroI25VMJQ7XH0Yn2+lSd0nqaKwUT0GSKN5jW/taa/FYtQVHOtzn8+Wob6vH9jPbsf3Mdpf305PSIU3/RdgelzEOUrEUmcmZvl1wjMGyLNoOH0ZjuQWNFguaLBa0HjokWDd13jz0GzUqwhYS0cTG2LBiwwowLYzbOsqGRvztzDkkpI8CJlwVOeOCgAsrfveGuwXL1/20DsoxSswdNjek7e4534R/Hz6ND07XoIN1vPe/49V4NGcoMhPpZy1BEARBEARBEARB9Dbo1z5BEAQRdpobzuPj559BR7uwZ6h46DAsVP+613to9jRYlsV3x76DbqcOO87s8OvYseKxKJxeiIVjFiI+zrM46pHmWmCXEfjxDeD4j97rM4eBN5cAU5cAVzwD9B8ceNs9mMT4RIzLGIdxGa6RDVo6WnCw9iAqmUocrNmP2TXHMAfePeONDYfxDLMbLej0WtcTtS21sJ62wnra6vJ+ZnKmw3M7/Rdhe6x4LNKT0oNqL9SwHR1o+eknXrRutFrQceasT8eKCwrCbB0RS1TVVuHuL+5GdXO12zqXNTRCe/qs4wfZnBVAfM/5aTZn2Bwsm7AM635aJ1j+1Oan8O617yI1MTWodliWxQ+1Dfj3odMoq+7uzd7U2YlXj57Fb3KHBtUOQRAEQRAEQRAEQRCxR89ZKSEIgiB6JCzL4ouXX0Dt6VOC5fGJibjm0ZVISg1uoZsIHSzL4qsjX0G/U4+KcxV+HTsxcyLU09W4fPTliBPFBWoAcPh7R7jwiveB9ib/z7F7PVBpBpR/BmbdDsQFaEsvIyk+CRMyJ2BCUgZw6gDgRcBu6uzAX2p34cPGo2G1q7q5GtUnq7Ht5DaX97NTsjFWPLZbzu0B/QaE1R6OzpYWNO/c6RCsyy1o+vFHdDY0+H2euPR0DFD2jegABHC47jDu+eIenGs+57bOpY1N+Mfps44RmJDimKd6GI/KH8W3R7/F8Ybj3cqOnT+G563P48m5TwZ07k6WhelcHV48dArldZ6jP7xy9AzuGz0IafFBbJgiCIIgCIIgCIIgCCLmIBGbIAiCCCvWTz9E5bbv3ZZfvlyNwTkSt+VE5OhkO2E6ZIJ+px77a/b7dezUrKlQz1Bj/sj5gXvU158Cdrzt8Lo+VxnYOZxprgU++rUj/Pji54FB44M/Z2/g5HZg73tAR6vneqmDgEk34Kb288jjQpLXVqKyphKnGoU3pYSas01ncbbpbLcc7ENSh3QTtqViKdISg8uH3lFXh0arFU0WCxotVjTv2gW2rS2ocwJA+rXXIi4pKejzELHPkfojuOuLu3C66bTbOhc1NuHZ02fQj3tjxk1Aas8LqZ+WmIY/XvhHFJoKBcvf3vc2lGOUmD10ts/nbOtk8e6pGvzn8Gnsb2z26Zia9g68dbwaK0YN8rkdgiAIgiAIgiAIgiBiHxKxCUKA5cuXIy2t+0L48uXLsXz58sgbRBA9lOP792Hjm6+6LZ940XxMy18UQYsIITo6O/D5wc+xeudq2Gptfh07a/AsqKerceHwCwMTrzvaHR7T1teB/Z8DbIf/5/DGoU3AqouAix8DLnkMSOijYmJnO7D/E+DoD97rDpkOTLoRKQlJmAZg2qBpLsX1rfWwMTY+1zaXd/tM05nw2N6FU42ncKrxFDYd3+Ty/vC04bywPTbDIW5L0iVISUgRPE/bqVNoLC/nReuW/fsdkQBCSMKQIRj04AMhPScRmxw/fxx3f3G3x00e85qa8Pzps0hy7mZz1eE3LkxcMPwCFIwvgHG/UbD8qc1PYf21692OQY6GDocQverIaRxr8W/jyJB+CUhLCG+0jbVr12Lt2rXd3m8IIDIDQRAEQRAEQRAEQRC+QSI2QQhQXl4u+P6CBQsiawhB9GCaztfj4xe06OwQFiUzh4+EsvBByoMdRdo62/CJ/ROs2bUGh+oO+XXs7KGzce/0ezF76OzAnmG1Hfjxf8D2t4D6E/4fDwBJ6cB0FdDZAVjWAvAgPna0At88A1S86/DKzrkosDZ7Kk01wK43gbpjnuuJ4oHxVwMj5wEenuuAfgMwc/BMzBw80+X92pbabsJ2JVPpMS9wKDnecBzHG47j22Pf8u+JIMKI/iMwNl2K6U3ZGH+kA4MPnEXCrgNoP+rlfgRB4qhRGHD55ci6527Ep8dWbm8i9JxsOIm7vrgLJxrcz2ezm5rxr1Nnkey8USJ3PjB4UgQsDB//J/8/fHfsO5xsONmt7Ej9EfzL+i9o5mgEj61ua8erR8/ilaNnUNPu3yYmaUoSHhg9GEuGZiApzCkjDh48iG+++SasbRAEQRAEQRAEQRAE4QqJ2AQhQF5enqAndk5OTuSNIYgeCNvZic//8yzqzwp7ZSb0S8I1jz6BfsmePbOI8NDW0YYPbB9gza41OHbePxHvwuEXQj1dDdkQWQANNwF7P3J4XR/81nt9d+Rc4sgfO/laIPHnPjTjZuCjh4Ezez0fe3Y/sPYqx/HKP/XIEL5+c2YvUFEKtHsJzZssBqbdAqSPCrip9KR0yIbIuvWP6uZqXtB2FrlrW2oDbssbcZ0sck8CE492YuKRQ5h49CDSnVLrtoeyMZEISRMmIFUuR2qeHCkyORKHDA5lC0QMc6rhFO7+4m6P86msuRn/PnUGKV09/efdF2brwk//fv3xxwv+iHvN9wqWv7n3TSjHKF3mhaPNrdAdOY3/Ha9GU2enX+3NGJCCh0YPwZWD0hEfoY1wOTk5mD9/frf3Gxoa3G5+JQiCIAiCIAiCIAgiOEQsG+KYiQTRA6moqMDUqVP5/+/evRtTpkyJokUE0bPZ+oER37611m35onsfxtTLlJEziAAAtHS04N0D7+LV3a8Kesx5Yv7I+VBPV3cLK+0TJ3YA1jeAXaWOPNWB0H8oMPMWYNZtQJZUuE57K7D5X8A3JUBHi/dzpg0CrngGmLrEo9dxj6WzA7CZgEM+eA9mTwCmLAUSU8Nv18+wLItzzedche0ax9/1bfV+n69fG4txx1hMOgJMPMpi/DEWycGnsxZElJiI5OnTfxGtZ85E/MCB4WmMiGnONJ7BXV/chYN1B93WmdHWAd2x40jr+rMrIwd4yArExYfVxkjxh01/wHuV7wmWjRk4BoZrDDjcIsJ/Dp/Cu6dq0O7nr9D5GQPw4OjBuDijf8xEcaHfEARBEARBEARBEAQRPsgTmyAIgggpR/dV4Lt3XndbPmW+ggTsCNPU3gTjfiNe2/2a3zmLFaMVKJxeiElZfoa7bWKAXQbgxzccInYgiOKB8YsA2R3AWCUQ7+VrS0I/4NLfAFNuAD5+BKja6Ll+wxlg/d2OkOaLn3UISr2Fljpg1zsAU+WloggYuxAYcykgCm843m4ti0TITslGdko25g2bx7/PsixON54WDEve2P6LK3X/RhYTj7KYdITFhKMsJCeBBP8cOn2mMQnYPzIOp8ZloX3aOAyYPgtjB0+CVCxF1oBRiO8lIiThH2ebzuKeDfd4FLCnpQ7Hy3u2dBewAWBOYUQFbJZlwyr+/mb2b7Dp+CacbjzdrayyORGKzd/B1jHIr3OKACweJMaDYwZjxoDIbbIhCIIgCIIgCIIgCCL6kIhNEARBhIzGulp88kIJWDehQbNGjkb+3cLhRonQ09DWgHU/rcN/K/7rV05iEURYlLMIK6avwPiM8b43yLLAoU2OcOF7PvAevtodmRKHcD3jZmDAUP+Pz5ICd3wI7HgH+OJJoMnLtdvKgP/MAy5bCcx7wLtYHutU24Dd7wCt5z3X6zcAmHYTkCGJjF0+IhKJMCRtCIakDcGFIy7k3289ehQnNn+Jc1s3gd1egdSj58JmQ3V/YN8oEfaOFGHfKBEODwLYOBGAGgBbgT1bgT2Ouv3i+iE3PRdSsRTjMsZBmi7FWPFYjBgwAnER3hhARI6a5hqs2LAC9lq72zqTsyZj1bFjGCAkYCemOSJLRAiWZfGe9mmMmDAZ8sU3ICExMeRtDOw3EE9d8BQeKHvA0SaA1uQZaBq4GG3JE8H4kfK6n0iEZcMycd+owZCkJoXcVoIgCIIgCIIgCIIgYp8evkpLEARBxApsZyc++/c/cb5aWFhKTErGNY+uRGJScoQt63vUt9bjrb1v4Y29b/iVczheFI+rcq/CPdPvgSTdD2Gz/qTDm/nHN4Bq94KORxJSgMnXOcTrMRcGH95bJAJm3gyMWwhs+C2w423P9dubANMfHN7j17wAjJAH1340YDuBqq8BuxkO+cgDGRJg6k1A0oBIWOY3bGcnWior0WSxoLHcgkarFe0nTgAAUsLQ3vFMYO8oEfb9LFqfEsPnPtja2Yqfan7CTzU/AU6O78nxyZg3fB7um3EfJmdNDoPVRLSobanFig0rUMlUuq0zMXMi9JMKMbD8RuEKM28BktPDZGF39m3eiKofy1H1Yzl2f23C5cvVyJ2VF/J2Lh15KRZLroPx5Gk0DlyMjn6j/Tq+f3wcfjUiG4UjB2FIUuiFdoIgCIIgCIIgCIIgeg4kYhMEQRAhYcv7BhzcYXVbrljxALJGjoqgRX2P2pZa/G/v//Dmnjf9yimcIErAtWOvxT1T78GogT4+o4524MAGh9f1gQ0A64eLnTPDZgKy24GpBUCKOLBzeCItC7hhFTDjJuDjR72L7Cd3AWsUjjC/l/8uZkXebrQ2ABWlwLn93uvmLAAkipjKw8u2tqKpouIX0frHH9FZG2D+dC90ioCqIT97Wo8S4aeRItSmhT7EcnNHM74+8jUspyx486o3kZueG/I2iMjDCdg/1fzkts64jHHQK/VI/+hR9yeaqw6DdcK0NjVi4xuv8P9nTp7Au8/8EdK8uVhwxwqIhwQQ8UKApo5OvHOyGhvib0J9drtfx2YnJkA9ahDuGJ6F9ET6iUoQBEEQBEEQBEEQBInYBEEQRAg4UrETm0vfdFs+LX8RJl9yWQQt6ltUN1fj9YrX8fa+t11yBnsjMS4RN4y9AXdNuwsj+o/w7aBzNofH9fa3gPOnAjM4OR2YvgyYdTswbHpg5/AXyQLgvs3Axr8Dm14AOj0ILGwnsGUVsPcj4Kp/ABOvioyNgVJ7GNj5FuDN6z4xBZiyFMieGBm7PNBxvgFN27ej0VKOJosVTTt3gm0OMPy8F0RJSUiZMQOpeXKkyOXoN30qUjrPIYmxIa2mEuKfc24fqjuEdtY/4c0X6lvrod2mxSrFqpCfm4gs9a31uNd0L/ZW73VbR5ouxWrlamS0NAB7PhSuNFYBZI8Lk5Xd+eHddThf0z2tgq18Cw7usGL2tQWYc30BEvsFFrabaWvH2mNnsfroWZxr828MjUnuh/tHD8bSoZlIiafw+wThK2azGVarFUVFRdE2hSAIgiAIgiAIImyQiE0QBEEERQNTg0/+9XewrHAe7EFjcnHZ8sIIW9U3ONN4Bmsr1sKw34Cm9iafj0uKT0LB+AIsn7IcQ9N88MBrbQT2fghY3wAOfRe4wbmXArPuACYtdgiqkSYxBcj/g8Pr+6OHgaNbPdevOwa8czMw6VrgyhJg4LDI2OkrLAsc2Qwc+My7J/zAkcC0W4CUjMjY1oX2s2fRaLHyonXz3r1Ap/CcESxx6elIlckcorVMhpQpUyDq18+ljgTpkKRLoByj5N9r62jDobpDqGQqUfmzsF3JVOJw/WF0upnffGXTsU3YdnIbZg+dHdR5iOjR0NaA+8z3Yfe53W7r5AzMwZpFa5CVkgVsetr9uJx7b5is7M65Y0dg+eR9t+UdbW34Yf3b2LPxSyz41T0YmzcPIh9D6Z9oaYX+yBm8fvwcGjr8GyOpHSegnSLHDUMGISEu9JEQCCLc2O12SKXSoM6h0+lQWBjYd2S1Wg273Q6FQgGZTObXsUajESqVSrDMZDJBoVAEZBPDMMjNzQXDMN3KCgoKYDAYBI+z2+3QarUwm82w2x0RcyQSCRQKBTQaDSQSR4obvV4Pm80GrVYbkH3hoKSkBBqNxuf6EomEf6nVap+fnUajQUlJCcRisds63H1nWS8pZfzAarVi3bp1MJvNYBgGdrsdYrEYmZmZUCgUUKlUAfcXgggnDMOgtLQUJpMJVqsV1dXVYBgGEokEMpkMarW6W9/VaDSQSqU+z8uRaKMnQPMgzYPOhGJclJSUoLi4OGQ2VVVVeew3sQLNW74R7v7BzTVdEYvFPvWljAzPa241NTUA/J87ObhntWzZMhQUFPh9fE+FtrsTBEEQAdPZ2YFPX/w7GpgawfLE5BQsfuSJgL27CGFONpzE37b8DVesvwKv73ndZwE7JSEFy6csx+dLPscTc57wLmAf3w58/Bjwz4nAe+rABOwBw4BLfgP8+kfgVx8B01XREbCdGTIZuOsL4Op/AkkDvdff+yHwnznA1tVhE179pr0Z2PUWsP9j7wL2qAuAPHXEBGyWZdF6+DCYd9/D8d/+FrYrrsSBiy/BsYcfRs3rb6C5oiKk9zFh2DAMXLwYQ//4FHI//ADjv9+MUS+/hKy770bqrFndBGx3JMYnYmzGWFyRewUenPUgnrvsOXx0w0fYeutWGK8x4plLnsGKaStw2ajLMGrAKIjgn/D2nOW5kC6oEJGjsa0R95vvx44zO9zWGT1gNF5Z9AqyU7KBtibAsla4YtZYQJofHkMFYE6eQGJystd6dWdO4cN//BXvPvNHVB8/5rFuZWMzHtt3GHO+34uXj5zxS8BObN6D9NMlSD1WhOMn3iYBm+ixdBVqxWIxtFotDAYDLBYLampqur10Op3LMYEuelutVl7s7XpOXygoKEBNTQ1sNhvvyc0tyAUjEOv1emRmZvL/VygUsFgssNlsWL16teAx3MIoABgMBv5eGQwGiMViyOVyaDQa2O12qNVqQYE8mhQVFcFms8Fms3Vb3DWZTPz1cHV0Oh1kMhnKy8shl8shl8thNBq9tqPVamGz2VBWVgaFQgGGYfjXypUrXfpcKLBarVAqlZDL5TCbzVCr1TCZTGBZFlVVVXy/UyqVyMjIgF6vD6o9s9mMjIwMn+4FQXiCYRio1WpkZGRArXakbtFoNDAYDPwYnD17NtRqNaRSKaxWRzo0s9mMkpISn+aYSLTB1e8J44LmwcDnQaPRyB8vEokgl8uhUqmCnlOjQSjHRWFhIcrKymAwGJCXl+fyrFevXo2ysjLBl8FggFar7dY/uO9M7oj2c+jp81ak71+4+4dWq+XnLOfvxQzDID/f++94i8UCg8Hg0k5eXh7/G4HD09wp9FvCZrPBYrHwG1lVKlVIvoP1FEQsraYRBCoqKjB16lT+/7t378aUKVOiaBFB9Aw2G97E98a33ZZf/XARJl54aQQt6t0cO38Mr+x6Be9Xvo+2zjafj0tLTMPNE2/G7ZNvR2ZypufKTTXATgPw4+uO/NCBEJcAjL/CES58rAKIj+HAL3UngM+KHEK1L4ycDVzzAjAkip8R9SeAXW8Cjec814vvB0wuAIZMC6s5bEcHWvbvd+SytljQZLGg/cyZsLXXb6wUqfI8pMplSJXLkTjCx1D4IaapvQlVtVW8xzbnvX3svHsB8LkFz0Exhnbq9ySa2pvwQNkD2HZym9s6I/qPwNor1v6yMcj6OvDhQ8KVr/w7MDeyu9gb62rx3dv/xa6vTI4IDl6Ii09A3uLrMffGZeiX/Mumox/rGvHvw6fw6Zla+PsDsl/jNqTWfYLEVhv/XrwoHm9e9SamZPfc79z0G6LvYjaboVQ6onkUFRV5FX+7eilrtdqAQ4Gr1Wp+wUosFge9YC8SiVBUVMR7ndhsNt772R+kUinUajXvVeLNq5u7DovF4tYTj2EYqFQqlJeXg2EYFBYWBiTcRwJn73yJRAKbzeaxvrNHvCdP9a5YrVbI5XIAoXn+XeE8g8RiMb8I6w7u+ZjNZshkMpSVlfnk7cYwDKqrq2E2m6HT6fjF8mCiExBE1zG1evVqj/1Rr9dDo9Fg5cqVKC4uBsMwXufmcLbRG8YFzYO+zYPcMdXV1bznK8MwMJvNfD+RSCQwGAx+R1uJBuEcF87P2pc+xWG326FUKmG322EwGAQ9VmPhOfTkeSsW7l84+wdXTyqVQiKR8GK3P9/hOducxWtP7fhzHWazGSqVCgzD+DV/9lRieFWZIAiCiGUO7dyO79e/47Z8xsKrScAOEYfrDmP1rtX42PaxXzl7B/QbgNsm3YZbJ92K9KR09xU7Ox1e1tbXHTlcO1oCMzRrrEO4nnEzMGBIYOeINAOHAcveAPZ9Cnz6G0cIcU8c3QboLgUu/DUwvyjyXuXHyoGfPvCc0xsA+g91hA9PGxRyEzpbWtC8a5dDtLZa0GT9EZ3nz4e8HQBAQgKSp0zmResUmQwJXsIzRYqUhBRMzpqMyVmTXd4vP1mOO7+4U/CYF6wvYMGoBUiIo6/gPYHm9mY89OVDHgXs4WnD8eqiV38RsFkW2OJGYEkaCMy8OQyWeiZ1YDoWqn+NafmLUPbKKpyyH/BYv7OjHVs/MGLPd19j/m134+SEGfj34dP4jvFvnCeKRFgyJAONp17H92fXdSvvYDvwu02/w7rF69Av3reICQQRK3BidGFhoU/eyytWrOCPkclkQeWyLi0t5RfTGIaB0WgMOpygWq3mRWydTue3R7bZbIZCofA5XKfZbIZer4dWq/W4sCkWi2EymSCVSmPOC7srzl7ovlBQUMBvHjAajVCr1T4J9M732N82vaFSqWA0GiEWi2GxWLxuZuCeD3dcbm6u1+PkcjmsVivEYjEUCgWWLVvGi3UEESjO4V99FX0LCwuhUCggl8t9ml/C2UZvGRc0D/o2D+bn5yMvLw8mk8nlfZlMhsLCQuTn5/PinKeNXrFAuMdeoGHAJRIJdDodL1QKEe3n0NPnrWjfPyC8/cO5De67KOC4p76m8+GEfW8EMo8pFApUVVXx0Sy4+9xboXDiBEEQhN+crz6HT178u1uPrsG5Uiy4454IW9X7sNfasfLblbjm/WvwfuX7PgvY4iQxHpr1EL5Y8gXun3m/ewG77gSw8R/Ai7OA/14D7DL4L2AnpAAzbgHu/Ax4sBy4+JGeI2A7M/Eq4IEtwNz7AG9hojvbge+eBV66ALB/HQnrgI5WoMII7F3vXcAeJgdm3xcyAbujrg7nv/kGp//5LA7eehv2583Godtux5nnn0fDxm9DKmCLUlORduEFyH7oQYxe+xombN2C3HXrMKTocQzIz48ZAdsTeUPzcOlI4Q08B+sO4oPKDyJsEREILR0teOSrR7DlxBa3dYakDsGaRWswvP/wX948tAk45SZv9qzbgKQBIbbUd4aNnYBb//pPKAsfQvIAz6kUOkUiWNKHYNnROty00+6XgJ0aHwf1qEHYMm8Snp80Gtp5D7iNAlLJVEK3Mza9KgnCE9XV1QB8C79tNptdQqUG4ylhNBohkUhc2g2FZ3JmZiYvhAcSllCr1fLhKX2B89b2VXyPpTzYocT5nun1eq8LqeFEo9Hw/bSsrMwvb3yDwQCJRAKGYfgIBe4oKysDy7J82HjKJUsEi16v50UarVbrl9cy5yUY7Tb68rjoa/Mg9/nn7rNbLBa7pODgPGhjkUiMvWDghEYhr9ZoP4eePm9F+/6FAk/9oytdv3vHyvVw0SIAh1e6P9/FexokYhMEQRB+0dnRgU/+9Xc01dUKlielpuGaR1ciITExwpb1HvbX7MdvvvkNrn//enxs/xidrG/5RjOTM/GY/DF8seQLFE4vxIB+AmJJRxuw92PgrWXAc5OBL/8M1Bz038jhs4DFzwG/+Qm44WVgzIWAqIfnNk0aAFz5DLCizLcQ3DVVwOvXAe+qgYaz4bOr4Qyw7WXghJddlXEJwKQlwJQCRyjxAGk7dRp1n36Kk3/+C+zX34D9c+fhiPpenFu9Gk0WC9g230PZeyM+IwMDlAoM1miQYyjFhC0/YPSrr2LQAw8gbd48xKWmhqytSPKw7GG3ObNf2vESmtubI2wR4Q+tHa147OvHsOn4Jrd1BqcMxquLXsWoAaNcC3542c0RImB29Dd3ieLiMD1/Ee56XocZC6+GSOT6c7A9PgE7JuXhlZsexocLb8apQb6H689MjEdR7lBYLpiMp8eOwPBkxzyUkZyB3837ndvjXtn1Cvac2xPYBRFElGAYBjKZzOeQoRxarTagUN0cOp0OarXaRfw1m80h8VJeuXIlAPDe3b7ChcD1x8PGXw/DgoKCgL1tYpmufcFsNkfFDi7nJeC414F4S3EL2Vz+cnf0xudIRA/nRXuJRBJQlAuFQuFxQ00k2ujL46KvzYN6vd4lNLEQMpmM38hgt9tjMudtJMZFKFi2bJngvY7mc+gN81Zv6cfu+ocQRUVFLtcTK4Ix5/kOOJ5LT4zi4QskYhMEQRB+sdnwJo7udeNlBmDRfQ9DPGRoBC3qPew5twcPf/kwlny4BF8c/AKsj1lHB6cMhma2Bp8v+Rx3Tr0TqYkCwt/ZSsD0B+DZycC6W4H9nwM+iuM8yWJg7r3AvZuAwq+BvLuAZA9hynsqI+SO61P+2eFp7o2d7wD/ng1sf8unfLN+cWoXsPU/wPmTnuulZAGz7wdG5Pl1epZl0WKvQo3BgONPrESlciEq58/Hscf+DzVvvomWfftCek2JI0ci/brrMPRPT0Py6ScYt3kTRr74IrLuXI6UadMg6iWbX8ZnjMdiyWLBstONp/HWvrcibBHhK22dbfjNN7/BxqMb3dbJTsnGmkVrMHrgaNeCmkPAT58KHzR+EZAlDaGlwZHSfwAUd9+HW4ufw/Dxk9DSLwlbZl4M/S3/hw3zrweTnu3zuUYkJeIv40Zg2wWT8VjOUGQkdg+XrxyjxMIxCwWP72A78PtNv0dbR+g2yPRaOjuAphp6eXt1dkTkceTlef/MDWUYcS7PILdQ5exRE4qFQZlMxosJ/nh3FxcXB7yQ549YsXTp0oDa6ElEK2Q651EF/LKZwV8UCgXff6LtTUn0HVasWMH/HUzEBk/9PhJtEL/Qm+dBhmH4jWJcaGJ3OIvosZjrNhbHhdDnjkwmi7nn0NPnrWjfv0DxtX94wmAw8JuO9Hp91DbddMV5/nL+uzdBCfkIQoDly5cjLS1N8P3ly5dH3iCCiBGqfizHlvdK3ZbLrrwW4+ZcGEGLegc7z+yEbqfOo2gixNC0obhn6j24ftz1SIpP6l6htRHY84Ej1/XhzYEbmDsfkN0BTFwMJCYHfp6eRHwCcNGvgcnXAp/8H1Dp5ctpUzXw/n3AjreBxc8HL1Z1tgMHPgWOfO+97uCpwOQlQIL3Z8O2t6N57z40WsrRZLGi0WpFx7lzwdnqDpEISePHI1UuR4pchtS8PCQO6YGh5gPkgVkP4PODn6Ots7swt2bXGiwZt8Rzrnoi4rR1tkGzUYOvjnzltk5mcibWLFyD3PTc7oXbVrvfHDT33hBZGVpEw0fDtvxRvHb4JBr83N8sTYzDo2NH4rrBGUiM8x6J48m5T2LbyW2oaanpVra/Zj/W7FqD+2be55cNkWLt2rVYu3Ztt/cbGhoia0hLHbCpJLJt9kQuKgJSwpt+QiKRePWoDmUYccCxWOYsXKvVal681ul0QQnkHBqNBmq1GmazGXa73SevcaPR6FMoRmc47x2NRoOlS5f65IXoS97CnkZXb5lohBA2m828HWKxOKiclQUFBS6hS0MR6p4g3GG1Wl3GUDAene4ia0Sijb5OX5oHuVQkHJ4+Z2fPnu32uGgTi+PCarVCpVJ1+z6Sl5fX7btDNJ9Db5i3emI/9qd/eIILk85FWVKpVKiqqor63M79LrHb7X59h+9JkIhNEAKUl5cLvr9gwYLIGkIQMUTd2TP49D/Pui0fOnY8Lr3tzgha1POxnLJAt0OH70/4IFQ6MbL/SNwz7R5cK70WifFdPFdZFjj+o0O43r3eseAdCAOGA7NuBWbeCmQKiDV9hYwc4Faj415+/oQjtLcnqjY6cmXPLwIu/DWQEEBY72YG2PkWUHfEcz1RHDDuKmCU+1DunU1NaNqxkxetm7ZvR2djo/82+YAoMRHJ06b9IlrPmoX49L4r0o7oPwLLJizD//b+r1tZfWs9Xt39Kh6VPxoFywgh2jvbsfLblTAdMrmtI04SY83CNZCKBTaptDY45l0hBk0EJAtCY2iIONjUgpcOn8a6k9Vo6WThT4CuEScOYe6PGyE9UomMhVegY+ntSOzf3+txWSlZeHLuk3h84+OC5fqdelw++nJMyJzgsy2R4uDBg/jmm2+ibQYRQ3hbEAx1GHHAIVQ7C+Gc57TdbofdbofVag1q4R1weDtzXtU6nc6r947RaAxIcFCr1dBoNGAYBrm5uVi9erXXexrukKPRYN26dfzfhYWFQT+/QHDuU8GKR0qlkhdvSktLScQmwopz/wqF8Cl0jki00dfpS/OgRCJBYWEhSktLsXTpUo/fC5yFtczMzKBsCjWxOC7cCaRisRhsl8hy0XwOvWHe6on92J/+4Y2CggIUFhZCr9fz3/dNJvdrCJHC2avcaDSGZHNrLEEiNkEIkJeXJ+iJnZOTE9Z2z7eex/cnvodssAxZKVlhbYsg/KGjvR2fvFCC5nphQTQ5rT+ueeQJxCf0jlDA4YRlWWw9uRWrdqxC+SnhDTPuyBmYgxXTV+Cq3KuQENflI7yxGthlcIgop9yHe/dIXAIw4Upg1h3A2HwgLj6w8/Q2RCJgWgEgvRwwP+VeqOLoaHHkGt9lBK55ARg91/e2zv4EVKwD2po810tKB6bfAqS7hjPuYBg0Wq1otFjQVG5B0549QAhzWDsTl5aGFJkMqXIZUuVyJE+bhrjkPuKp7yMrpq/Ae5XvoaGtu7fmm3vfxC0Tb8GQtL7jnR6rdHR24Lff/RZfHPzCbZ30pHSsWbgG4zLGCVfYuQ5orhUum6t2u9Ek0uyqb8S/D5/GR6cZ+JlQAtKD+zBn+7cYefIQ/972Lz7BT5u/xSW3LMfUBQqI4jyL4YtyFuGLg1/AfLh7dIt2th2/3/R7vHn1m0iMi63vEzk5OZg/f3639xsaGtxufiX6NqEMIw78Ena76+I+5zkNOBYtgxUNxWIxCgoKYDQaodfrvYrYgbZZVFQEnU4Hu93uIvgrFAoolUqXPIqRwGw2Q6fTwWq1wm63QywWQyKRQKFQYOXKlWHxsDEajS75V6Ml+DqHwgx2o4Xz8QzDhGRjRbBoNBpYrVZUV1fDbrcjMzOT98TSaDQwGo2orq6GQqHA6tWr3T5rvV4Pg8GA8vJyMAwDsVgMhUIBtVrdra+azWYolcpu52IYBgqFottit1qtRmmpa7QzZzv9bT8U1+3cHic8iMVifr4xGAweF+39tTcQQtl3AeFoGZFoI1agedBBuOdBXz83t23bxv8djnk0mOcdi+PC33NE6zn0lnkrVvqxr4R67tXpdLzHs9ls7hYtKRo4P2uTyUQiNkH0BdauXYspU6ZEvF3raSse+/oxAEBuei5kg2WQD5Ejb0gehvUfFnF7CILju3dex/H9e92WX/HAoxg4aHAELep5sCyLTcc3QbdDh+1ntvt1rDRdCvUMNRaOWYh4Z2G5sxM4uBGwvgHs/cghngZC9nhg1u3AjJuA/vQc3ZKaCVz7IjD9JuCjh4FzBzzXP7MXeHWhI3d4/lNAith93c4OwG4GDn7t3Y6s8cCUpUC/NLQdP45Gi8UhWlssaDlQ6c8V+UX8oGykyvOQKpMhNU+OpAkTIIqnjQ6eyEzOxK+m/AovbX+pW1lLRwte3vEy/njhHyNvGAEAYNraodj2E5gWBo3tCmC4UrCeSBQHUXImbt7XDqCCf591/qMhF+w8o8txLEQO8bp5MLBpN7ru8e72f9ZLebd3/D0eqG33L1dwXGcnJh7YgTnbv8WgmtOCdZrq67BB9y/sLPsc+Xfei6Fjx7s9n0gkwm/n/RbbTm1DbUt30X9v9V68uutVqGcEll83XLhLKVRRUYGpU6dG3iAipjEajSENIw44FsuE8k47e07r9fqQCAArV66E0Wjk8x2684C22+2orq4OeIHUYrEgPz/fJeSl2Wx2WXyVyWRQq9VhWxhkGAYrVqzgPVZWrlzJL7JarVZoNBqfPcV9gfOY5xY/JRIJtFptVL3Mnb2TsrKC20jf1csqFkLgSqVSMAzDi6mZmZlgGAZyuRxqtRomkwlSqRRGoxGZmZndxpDdbodKpYLVaoVWq+XzYXLh8JVKJYqKilw2fCgUCthsNj58KYfJZEJeXl43G7VaLaqrq2E0GiEWi6HVanmhN5D2g71uuVwOAFi9erWL6OA8XtyJEYHaGwjOHn7e8rLGchvRhubB2JsHGYZx+SwMZd7oUDzvaI4L55zp1dXVYBgGOp0Oer0+5OGTw/Ec+tK8Fc5+7KlNjnD3D4PBwH9echu0ohnC23nuioXvX6GGRGyCiCEspyz831W1VaiqrcL6A+sBAMPThkM2xCFqy4fIkTMwB6IY8eohejc2yxaUf/Su2/K8a26EVO6Hp2kfg2VZfH3ka+h26lBxrsJrfWcmZEyAeoYa+aPzESdy8m6rPQZsfwv48Q2AOeT+BJ5ITAWm3AjIbgdGzY0ZL8EeQc5FwH2bgO+eA779J9DR6rl++avAvk+BK7XA5Ou63+uWemD3O0CN51w8LEToEM9C/U/taHzraTRaytF+/ESQF+OefmPGIEUuR6pcjtQ8ORJHj6bPnQD41eRf4Z1976C6ufsPifcr38evpvxKOL8yEXY6ARxtaQOQBiR0j8DjzOk2APAQ1SDRQ/7dNv+E41ggJU6EW4Zl4a7BA3Hs1E5Y6qq9em6frNyPN3/3f5h2+UJcfNMdSB0onE4gOyUbK+esxBPfPiFYvmrnKlw2+jKMz3AvhhNErMItDnOEIow44BDGV69e3e19Z89poHve7EBwDlNeXFzsdhHbnbDuK2KxGBaLhRffu+ZFBRwL6lzocYvFEtLFQS6UOcMwsFgs3UQ5mUwGk8kEtVoNlUqFwsJCnzcJ2O12ZGS4fi44L6wCjvsXba8dwNWuUHtaxkIOc+4ec+It4IiUoFarUVRU5PH67XY7LwCYTCYXD2KJRAKDweASOthZmOXyUxYVFfHlEolE8B6LxWJ+80hZWRnfF4NpP9DrLikpgdVqRU1NTTdbxWIx36bQAnkw9gZLJPKRRjvnaTigedBBrM2DxcXFvE1arTZkNoXjeUdyXAj1qXASrufA0dvnrXDfv65Eun/IZDJotVpoNBoAjlQCXXNuRxLn+xsL379Cje/JzwiCCDvOInZXjjccx8f2j/H090/j2vevxYLSBXjs68fw5t43sa96Hzo6e94CKRH71J05jc//85zb8uHjJ+Him+6IoEU9h062ExsOboDqIxV+/dWv/RKwp2ZNxYuXvwjDNQYoxygdAnZHG7DnQ+BNFfD8VOCrvwQmYI+QO0Jc/99PwPX/AUbPIwE7EBKSgAVPAPduAsZc5L3++ZOA4VfA2zcBjFOu6xo7sOVFrwJ2R3Mnjr5ixYHb/oCTf/wT6j76KLQCdlwckidPRsbtt2PE889j3LcbIf3icwz/218hXnIj+o0ZQwJ2gKQmpkI9XXiRv4PtwIs/vhhhiwiObSe2RtuEmEOcEI9HxwzBtgum4K/jR0IqHohLb1mOX/3j3xgzfZb3E7AsdpV9gdceUWP7hk/R6eb76VW5V+GyUZcJlrV3OsKKt3e2B3MpBBEVQh1GHHAI0wUFBW4X/5yF5FCFYuUW5Lgwo0IYjcaQiA+FhYWwWCyoqamBwWBAUVFRt4V1zoM0lItyKpUKDMNAq9V6DHGp0+kgFouh1+tdPIo8IZFIUFNT4/JiWRY2m40X7jiRIJYIdvG1q7AZS3lcOVs4L1BubHKbKYRywCuVjggthYWFbkNgc2OupKSkm0AHuHqeeRJt161b1y0fcCja9/e6nfMTu8Pd5pVQ2BsooTxXNNuINDQPdifa86DdbncJsR7KkMDheN6RHBcSiQQsy7q8nPtTKAnnc+DozfNWJO5fVyLZPzicv7NyEUeihfOzjqXvX6GCPLEJIkZoam9CxVnfRa7q5mqYDplgOuTIQzQgcQBmDZnFhyCfkjUFifGxlU+Q6FmwLItP//0PNDecFyxPHjAQix/RID6BPkqc6ejswBcHv8DqXatRyfgX2nnmoJlQz1DjouEX/SIYntkP/Pg6sOMdoOFMYEalZDhCYMtuB4ZEPlVCr2bQeOBXHwPb/wds+J37nLgc+z8Hqr4FLvstMHSSI4S4lxDBjZVncey1crTXNofMbFFSElKmT0eKXIZUeR5SZs1EfP/+ITs/4YpqvApv7HkDR88f7VZmOmTCrjO7MG3QtChY1nc523QWf/nhL8Cgv0XblJhgWFIi7h01CLcNy0JaQvc0AZnDR2LJk39C5bbv8dV/V6P+rOfPo+aG8yh75SVHiPG77sOICZNcykUiEX4/7/ewnLKgrrWu2/F7zu3B2oq1uGfaPcFdWG8jaSBwUe/KbxYWkgZGpdlwhBEHICiuOaNQKCAWi/ncm3a7PWiP5cLCQl6o0mq13cRxo9EY8pzVnFe5s+e32Wzmc/syDMOHQQ4W54V5X0LYLl26FHq9HiqVCjU1NQG3y3nmFhQU8KGclUplSK4pGJu4zQHBLnZ3PT6aYTU90VWElclk3QSdkpIS/r54ijjAeVzb7XaUlpZ229ghFotRWFgIvV7P55kX2pCi1+thsfzi1BCq9p3x5bq5hW+5XO42xLPQe+Gw1xvOfTdc3m+RaCNa0DzoalMszIMMw/CbQQoKCkKaxzeUzzuWxgXXn86dO+fyHSwYwvkc+sK8Fc775y/h6B9dMRgMfBSSkpISKJXKkH9H9pdY/f4VDOSJTRAxwq4zu9DOBu5xUt9Wj41HN+J56/O4/bPbceHbF+KeL+7BS9tfwpYTW9DU3hRCa4m+gEgkwqW33oUBWYMEy6964DEMyMqOsFWxS3tnOz6o/ADXf3A9NN9q/BKwZw+djTUL1+D1K1/HxSMuhqitEfjxTeCVRcB/ZgObXwxAwBYBksuAgtccXtdXPkMCdriIiwNkdwAPlgNTfcjlxbYDR78D7CZ4E7DPbtiPQ//aFLSAHTdwIPovWIBB//cYxrz1FsZv24oxb7yOwY88gv6XXEwCdphJjE/Eg7MedFv+vPV5sF0TGhNho5PtxO82/Q7VLYEvwPUWxqYm4bmJo7Bl3iSoRw0WFLA5RCIRxs25EHc++zLmLbkJ8YneN0ueOWjHO394HJ+/9BwaGNf7PSh1EJ6YIxxSHABe2v4SbEzvWjAOmrh4x8Y0enl+xbnvx+HC3zDi3KK9NzjvSaVSCZFI5PblvGgeKm9sTlwqLS3tVqbT6SLibaJQKGCxWPgFd7PZLBh23F+c75EvC33c4iSXJzxYuBywgOOawrWw2hVOSHXGeaG1vLw8qPN3Pd6Tp18gMAwDu93u8eWLAOXL4rKzR7K36+DK3YkFzmOl6/3n3svLy3Ppi6Fsn8OX6+Zs5XJbZ2Rk8CHJ9Xo9f3+7imDhsNcbztcTiigNRqOxm9dpJNqIFjQP/kKszIP5+fmw2+0oLCwMufAXyucdi+PC23cqzivYF8L5HPrCvBXO+xcooewfXeHSZXBwEQ8ijfNnaqi/f8UCJGITRIxQ31aPEf1HhOx8zR3N2HJyC17e8TLu2XAPLnz7Qtz26W14zvIcNh7dKOj1QhBdGT5+Im7XvgCJbLbL+3NvWIrcWXlRsiq2aOtow/r967H4vcX43abf4WDdQZ+PvXD4hfjvFf/Fq4texdyhcyA6ZgU+ehj4xwTgg/uBIz/4b9DAkcB8DfDwDuCO94GpNzpCXxPhp/9goOAV4Nb1gHi0SxHLAq3n41FfNwrtk5YBmTkeT9XR2Iojq37AmQ/3AJ3+i5sJQ4Zg4FVXYcgffo/cDz7A+B++x6hVLyN7xQqkymYhrl8/v89JBMeVuVdiYuZEwbKtJ7di8/HNEbao7/LGnjew6dimaJsRVWYNSMWrU3Owcc5E3DwsC/3ifP9ZmJiUjIuW3obl/3ip2/cDd1R8U4ZXH1HD8skH6Gj/ZdPmYsliXDryUsFj2jrbKKw40WPwN4y43W73KTcgly+0a0jWri9nD04hkSwQOG/KrgvYdrsd1dXVQXl5+CLgO+O8OBiswADAbyHc+Vlt27Yt6PYB1wXGUG088IaQp6NzKN9gNwg4n98XTz9/0Wg0kEqlHl++hCb2Zew53wtvbVqtVkgkEmRlZQmeSyKR8KJCcXFxt3LnnJrhaN+f61YoFDCZTPz45iI86PV6qNVqZGRkCHpah8NebzjbEQpheN26dd0En0i0ES1oHvyFWJgHlUolrFYrDAZDWO5FKJ93LI4LLsqDEGaz2adUCUD4n0Nvn7fCff8CJVT9wx3OkYS6bmyNFM7fj/39nt0ToBiwBBEj5I/OR/7ofJxsOAnrKSsspyywnLLAVhsaD5T2znbsOLMDO87swKu7X4UIIkzInAD5EDlkg2WQDZEhO4W8aonupAwYiOsf/z22ffQuvnvndYyYOBkXqm6NtllRp6WjBe8deA+v7n4VJxr8y0186chLoZ6uxvRB04HGauCHlwHr68DpPYEZE5cITLwKmHUHIL0sKh5IhBPjFGDVm9Dyzm/RaH4XTWcS0XimHwbMHovBN0xFXKLn59N0uAbHXtmGtnONPjfZTypFqkyG1Dw5UuR5SBwxnHJYxxhxojg8LHsY95nvEyx/3vo8Lhh+AeJEtMc0nFScrcDz1ucBAKLOZgw45yr2XDj8QlyVe5XLe12HEv9f21fAjncg6hJRQQQWkP0KyLn45+NFwse7+X9Xurfv3/mcj4+HCJP6J2N0cr+g5wjx0GG4QfMUbJat+Pq/q8Gc8vxZ2NrUiK9fX43dX23A5XeqMWrKdIhEIvxh3h9wwwc3oL6tvtsxu87uwut7XsddU+8KylaCCCeBhBG32Ww+5avT6/UoKyvzKj5xYYG5sNtmsznoUIbO5ywuLuYX53Q6nUue30Awm81gGMYnUY1DoVDAbDZHPXRpqHB+/qEQ5n3BarV2EyAVCoVL6FGj0RiwAO08DoLtI+HE31yRoehzWq0WcrkcDMNAr9fzkQ6MRiPEYrHH8RqqPu/rdSsUCthsNpjNZphMJtjtdn7MAo55qby83GXzTDjs9YbzHAUE13cBx/jo2m8j0UZfhuZBB0qlkh9TPcGDMhbHhUQicRuSnts4441IPIfePG/Fcj8ORf/wxurVq/nPSqPRGLJNpb7AbTgDXDfO9SZIxCaIGGNo2lBcJbkKV0kcC6g1zTWwnv5F1N5XvQ+dbGfQ7bBgsa96H/ZV78Obe98EAOQMzIF8iJx/De8/POh2iN6BKC4Oc64rwPAJkyAePBRx8X1XJG1qb4JxvxFrd6/F6abTfh2bPzofhdMLMTljIlD1NfD1ncC+j4GO1sCMGTQRmHU7MOMmII02oUSTztZWNO/ahcZyCxqtFjRZf0RnfT2AgYhLSsDQm2ciPW+k1/NUb7Tj9Lu7wbZ7mOfj45E8eTJS5XKHaC2TIcHPxTgiOlw0/CLMHjob2052917YV70Pn1d9zn/+E6Gnoa0BRRuLeM9eEdqR3PAtXz4pcxJWz12KfvE+RCro7AQMfwPOCaSOSMkE5P8DElNCZXpMI5XPwZhpM1H+0bvY8r4B7a0tHuufPXIIpX96EhMuvBTzb78LQzKH4PHZj+MPm/8gWP8/P/4HC0YtgCS99+X2Ino+/oYR5ygvL/e6wGQ2m5GZmenzQqBareYX5nU6XUgWsLhzOufaNhqNHnN0+4ov98AZmUwGs9kctPcm4Jov0hecw0JyIVZDCcMwfov6gbThLl+6TqfjvXY0Gk1Ai93OoTgLCgrCsoCt0+ki5tnlbx/xhrOooNVqeRG7uLhYUBwIdfu+4rwBRqFQuIxRq9WKdevWoaSkhPfO5q4jWvauXr0acrkcQOB9F3BcW3V1tWC/jUQb0YDmQVeiNQ+qVCrY7XZYLBZBu7iNZMGGZQ718+5J48JkMnn1TI3UcwB657wVyfsXanzpH74gFothMBj4c6nVan6DTLhxFsxD8R09FiFXD4KIcTKSM5A/Oh9Fs4uwbvE6bLppE15WvIwV01ZANliGxDjv+Qh95WDdQaw/sB5PfvckFq1fhIXGhVj57UoY9xthr7VTvk4CIydOQf/M4BeOeiKNbY14bfdruGL9FSjZVuKzgC2CCItyFmH9tevxvOw3mLzrI+BfM4A3bgAq3vVfwE5McwjXd5uA+38ALnyQBOwo0FFfj/MbN+L0c8/j4G23YX/ebBy69Tacee45NHyz8WcBG0gaNgA5j8/3KmB3trTj2GvbcKp0ZzcBW5SSgtQL5iH7gQcw+rVXMWHbVuQaSjHkCQ0GKBQkYPcgRCIRHpE94rb8xR9fRFtHW+QM6mP8bcvfcLj+sGBZSkIKSi4t8U3ABgBbmbCADQDy5X1GwOZI6NcP85bchDuffRnj5l7o0zE/bd6I1x65F1s/MOKanKtx0YiLBOu1drbi95t+j47OjlCaTBAhwd8w4oBjIc9qtXoVY3U6nWDYXncsXbqU/9toNIYkHx8nUAGORbFgvXmc8XeRjfMwCYU473wNvoRYdfYsdb7PwdBVqAm3AMgtcAp54yoUCv5Z2+12v3ND2u12Phy2WCzG6tWrg7Q2+vgbjpUTxzzBidWcZzO3OURoTIWjfV/gcl8LIZPJoNVq+bHr7NUWLXs5m4DA+i6HSqVyOydFoo1oQPOgK9GYB70Jf8AvG9qCJdTPu6eMC26+9STCRvI5AL1v3or0/QslvvQPf1AoFC6/BUIRzt0bDMPwc49CoQhLOpdYgERsguhh9O/XHxePuBi/lv0a/73yv9h882a8uuhVPDjzQVww7AKkJIRu0fREwwl8bP8YT3//NK57/zosKF2Ax75+DG/ufRN7z+2lxUSiT1DfWg/9Tj0WrV+EZy3Porq52qfj4kRxWCxZjPcXG/CP7Isx/pOVwHNTga//BjDCIopHRs4Grn0R+M1PwHX/BkbN6R5jlggbbadPo+6zz3DyL3+F/YYbsX/uPBwpVOOcToemcgvY1u6bEdLnjELO4/ORNHSAx3O3nKhDVcnXqLMcAwDE9+tA/xFNGLwgAzm6EkzYugVjXnsNgx56EGkXXIC41NSwXCMRGaYPmg7FaOEF+KPnj2L9gfURtqhv8JHtI3xo+9Bt+W/n/hY56Tm+n3DLKuH3RfHA7Lv9M64XMXDQYFz72JNY8uSfkDHce/SJtpZmfPvWWrxe9GuoB6jQP7G/YL2dZ3bif3v/F2pzCSIoAgkjDvySE9eTtxkXitBZRPaGWCx2WbgqLS31+VhPcDbo9XoUFxf7Jax7wmw2d8sD7A5ukTFUHr7OHvNCOYq7wt1LrVYbMi/Brou5XRc6rVYr70UVLAzDeO13zt77Go3G54VXhmF4ryOxWAyLxRJWT8pIUVRUxPcRX7y/VSqVy3wgREFBAX9OjUbj1gs7XO37irf2uHnGWayIpr1FRUW8aKDRaPwO4apUKiGRSDzOt5FoI9LQPNidSM6DKpUKDMN4rWsymULi+R6O590TxgX3PSMvL0+wPNLPgaO3zFvRun+hwlv/4CJE+INWq41oxI38/HwAjs/kWPR0DxUkYhNEDyc5IRmzh86GeoYa+oV6bLp5E9666i38Ju83WDBqAQb2Gxiytqqbq2E6ZMIzW5/B0o+X4uJ3Lsb95vvxyq5XsP30dvIeI3oVtS21+M/2/2DR+kV48ccXwbQwPh2XIErADWNvwEeXPI/ixjhIXrkKMPwKqDQD8DOaQWoWMO8Bh8f1PWZAdgeQ5FkQJYKHZVm0VFWBMRpxfOWTqFy4CJWXzsexRx9Dzf/+h5a9ex2hhN0gSozD0JtnYvgdcsT185y5hdlyGEf/XYbk5NMYmsdAcuVpjLvhFEZdUoOsoRVI+eZOiL77O9DWHOrLJKLIQ7KH3Oa+XrVjFRrbfM+HTnjncN1h/OWHv7gtvyr3Klwrvdb3E5498POcLsDka4F07+Jtbydnhgy/+vuLuOSW5UhMSvZav+b4UXz57HO444AMaU3CKUte/PFFHKw9GGJLCSIwAg0jrtFoeOHGk0eKszeXPyxbtszFJm9w3liecpE6i9aZmZker9Pfhb6SkhJoNBqPxzEMA5VKFXIPX86LtOtmhK5wC7QFBQUePe2rq33b6MrRNQ9y11yNZrNZcFHV33vMMAzy8/PBMIzXPmoymfhrVCqVXj23OIHJbrdDJpN59MLy1dZYwrmPeFr8Lykpgd1u9ykSAze2rVYrzGazx2PC0b4vWK1Wj8+eE/ac5xsgevYCjvmOW7xXq9X8uPWE1WrlhUd3uVIj3YYQ4RwXNA92JxLzoFqt5u+3UqkUfMnlckil0pB6iYb6eQPhHxfB9H+9Xs9fp9D3qWg9B46ePm9F+/4B4e0fwC/fj/2NEuGvmOzv3Ak4npNUKoXVauXnnt6widAdIpbiAxMEKioqMHXqVP7/u3fvxpQpU6JoUejoZDtRyVTCcsoC6ylHbu0zTWfC0lZyfDKmD5rO59SePmh6SD3DCSISVDdX4409b+DtfW+joa3B5+MS4xJxg2Qx7oofjBG7PwCObg3QAhEgvRyQ3Q5MuApISArwPISvsO3taN73E5os5Wi0WNFotaLj7NmAzpWYnYaRd89G8iix5zY7gaaqs0isXI/ENB+iWmSNA655Hsi5OCC7iNjjj5v/6Nbr+sGZD0I9IzSebn2dto423P7Z7ag4VyFYPrL/SBiuMaB/P2EPYEE++Q2wzY2QctcXwOh5AVjae6mvPouN/3sN+zZ941P9zngRtktqUCGpRUcXPXvW4Fl4bdFriI8TFrojTW/+DUF4xtmLkPN86LpwxDAMqqurwTAMTCYTSktLXRbbTCaTy+I9wzAoLy+HVqvlRSKFQuGTRwcXmre4uNhlYbqoqAhqtRqZmZku9nGezZyALBaLodVq3ebuk8vlsFqtMBgM3cIUctdkt9uxYsUKXhgvKCiAVqvt1jbgSK9RWFgIrVYLlUoFs9mMwsJC3utHLBbDbrfzORS5e+xOGBD5EZ3IeQnMbrdDrVbz7avVashkMr5tjUYDu90OrVbrdiGfe846nc5F7DAYDJDJZILX73wsJ35wxxQUFMBqtSI/P99FDOHa0Wq1LgKhkGBSXV0Nu90Og8Hg0u8UCoVPi95c37BarZBIJC55Haurq2G1WqHT6WA2myEWi7Fy5Uq/BEnnPuPcZ7nwp86iVbALstx9c95A4m5cCOHcRwoKCrBs2TIoFAqIxWKYzWZotVpUV1d77J9dycjIAMMwKCoq8rrZJND2A71uqVTK5wxWKBT8mODOyUVkcPfMg7lfgY7jru07jxHOBm4s2u12lJeXw2QywWg0ehzb7ghXG6EYF4HcQ5oHhQnXPOgpZL87ampqBD9HfSXUn3tChHpc+POsObjvXGazGevWreO/j3ACnzPRfg7O9MR5K1T3DwjsHoa7f9jtdhiNRn7jmUQi4b+P+/pZr9froVarUVhY6DZCibu5092cxv1WMBgM/G+FQPpDT4REbIJA31qAYlkWR+qPwHLKwr+Onj8alrYSRAmYnD0Z8iFy5A3Jw8zBM0PqGU4QoeRs01ms3b0WpftL0dTe5PNxSfFJWDLsYtxZ34Chez4FWs8HZkD6KGDWbcDMWwDx6MDOQfhEZ3MzmnbsRJPVgsZyC5p+/BGdjcF7vg6YMQzDbpMhPiXRc8WUTGDarcDA4cCRrcBHDwOn9/jWyKzbAOWfgdTYyydE+MephlO4+r2r0dLR0q0sLTENn934GTKSM6JgWe/in+X/xNqKtYJlCaIEvH7l65g2aJrvJ2yuBf45CRDa5DRsBlD4DaV6cMORPbvw5aurcPbIIZ/q16W2/T979x3mxHXuD/yr7YVlR0s1HS1udCRhG1zArOSKKxIkTmzj3CD5ptnJvVmFxLlxfolNtDc3vUnrBBI7jncl3HCXcAzuRhKmGbCRABts6mq2d83vDzHDaFddo7K77+d59HjNjOYczZyZkeY95z344FI/jk0IvSebFpvw1dlfTUcVEzaSfkOQUHwQKhVerzfkARUfKA4XDB8Y8I5Un2hBAr48u90ujGwOt164h2H8gzzxHJmx6i3epk6nCxmVUl1dHTJihA8GuFwu+Hw+4bOo1Wro9fqYqUZTfYjsdDqFYAR/XBUKBXQ6HdavXx/xs/EjyYHwQSV+WwOP9UBWqxUWi0XYj2q1OqTzgslkSno+S7GBxyEWj8eDhoYGOJ3OkONSVVUFpVKJNWvWJDz3Iv/gO1bwmB8xGa7NJYIPykY6Pm63O67RYQPbCMMwQmAr0VS4VqsVJpMJhw8fjjtIn2j5yX5urVYLk8kEjUaDuro6WCwWYYSYQqGAWq2GyWSK+RA/mf0lRTCIx7IsGhsb4XA44PF4hIf/4uvK6tWrU+okIWUZUp0XqexDug6GJ+V1MNn06OHae7bue7FIcV5Idax5A4OIuXQcxIbKdUvK/Qckvg/T3T747Ue6nsX6Ti6m1+uxePHisEFm8bUzEQqFAgqFQpL2MJRQEJsQ0AOoE+0n4DnpgedUcKT2IfZQWsqRQYaL5BcJI7WVE5QYWzo2LWUREq8T7Sewce9GbP5kc9hgUiSl+SVYPfpirD32McaeOphc4XmFwCU3B9OEK5YDOTKybLjpZ1l0eHaeD1rv2wf0Sjj9QZ4M42+fgzErZsVed9xsYLYOKBRlqejrAd79PfCGGYinDZaPA67fAMzTUbBsiPuV+1fYuHdj2GV3z74btYuHf4/adHr7+Nu433l/xOXfVX0XX5v7tcQ2+u6fgFfDz1+J2/8CLPxyYtsbYfr7+rDrtRfxduM/0dMZX+ehz8Z34INL/Wgt7wMQzPyz+dbNmDY6+x2+RvpvCEIIIYQQQgghJJ0oiE0I6AHUQP4uPzynPEL68f1N+xHgIs//mooZo2cIQW3VBBUmjZqUlnIIGejzts/x1z1/xTOHnkFvIP6AZlleMe7KY3D34d2o6os/6B1i3KXBwPX8NUD5mOS2QSLq/eILdLjc6PC40elyo/uTT9JWVvHMCzD5PjWKq2J0QJDlAbNuAKZdFTnwfNYLvPBd4HB8KXdRXQOs/BUgn5FQnUnuaO5uxo1P34jWntZBywrzCvHCHS/QfTFJZzrPYNXzq9DUFX5+qSsuuAIWrSXi3ORhBfqB3ysB/5HBy8rHAd/dR1NAxKmd9ePNJzdh37atca3fn8dhr6IZu6tb0J/PQTVBhb9d/7fEjl8a0G8IQgghhBBCCCEkfSiITQjoAVQsbT1t2HV6l5B+fM+ZPQkF/RIxsXxiSFB75uiZCaUWISSWT1s+xWN7HsMW7xb0cX1xv69CVoivdPTgq6eOozKQRKeOolHA3DsB5b3AZBWNoJUIx3Ho8XpDgta9n3+etvIKp01DmUqFMrUK5ZeMR8GZNyDrjTGasHg0MO/LADMjdgEcB+x6Cnj1h0Bn+OBbiIJSYPkPgCXfBPJjpDEnOemve/6K33h+E3bZrdW34pGrHslshYaBABfAN5zfwNufvx12eVVJFey32DGubFxiGz74MvCvL4Vfdk0tsOJHCdaUHD+4H6//7S84dSS+dLFtJX3YMccP/7Q8fHPBt3HXpXeluYbR0W8IQgghhBBCCCEkfSiITQjoAVSiuvu7sef0HrhPuuE55cHOUzsTmkM4EVUlVVCOVwpB7YvkFyGfUi6TJPiafajfXY+XDr+UUGaBSuThnqYmfLmlFRXJ3DKnXg4suhuYcwdQPCrx95Owug8fRvPTT6P52efQd/p0egqRyVB8ySVC0LpUqUTh+PEAFwB8W4HD/wYQo01UzQLmrgl2YkhE+1ngtYeAXU/Gt/6EucAtvwOmJD43Ecmuzr5OrHx6JU51nhq0TAYZNt+6GRfKL8xCzYauTXs34f/c/xdx+Z9q/oSrp1yd+Ib/cRvge2Pwv+cVBEdhV0xMfJsEgUA/djtfxdtP/QNd7W0x1y+6pRr5iyfi3Y924FHNbzG1YmoGahke/YYghBBCCCGEEELSh4LYhIAeQKWqL9CHA00HhJHanlMeNHc3p6WsUYWjsHD8QqgmqKCeoMacMXNQSKMPSRSf+D+BdbcVrx55FVysgKNIVX8Aa5ubsaalDWWJ3irLxgILvhRMGT7u4gRrTCIJdHai5dVX0WzfjA6XS/Lty4qKUDJ/HspU6mDQeuFC5FdUhK7U0wbsbQCaDsXaGqBYAcxcEUwlnizfG8EU402+OFaWAZcZgJofA8UVsVcnOcP+sR0/ffenYZctn7Icv6/5fYZrNHTtPbMXd790d8RMG/fMvgffX/z9xDd8aj/wpyvCL5unB1Y9lvg2SYiOlma8/dTj2P36q8GsFGEULByPki9fKvw/u/dzfPvan6KsfHSmqhmCfkMQQgghhBBCCCHpQ0FsQkAPoKQW4ALwsl4hqO0+6cbpzvSMlCzOL8b8cfOFkdrzx85HWWFZWsoiQ8v+s/th2W3B1k/jm2+TN66vD/c1t0LX2obShG6RMmCWBlDeDVx0I1BQlFiFSVgcx6Fr7z6wm+1oeeFFBNpij9KLV15FBUqVi4SgdcncucgrinLc2CPAnn8B3S3RN1xYHhx9PUai0bO9ncD2XwJv/wYIxJECf/Rk4Kb/BS65WZrySdr1Bfpwx3N34EjLkbDL/37D36GcoMxspYag9t526Lfo8VnrZ2GXX1p1KZ646QkU5Sdxfd7yIODeGH7Z11+nLAgSOnHoY2zd+BecOPRxyL/nTSxH6TcXQVYUmpGn39eMq0uXY94STSarCYB+QxBCCCGEEEIIIelEQWxCQA+g0o3jOBxrPQbXSZcwUjvSA+ZUFcgKMHvMbCGovXD8QlQWV6alLJKbdp/eDctuC7Yf257Q+yb29eE/2Bbc0daG4kTujJXTgoHrhXcBlVMSqyyJqJ9l0bzlBbCbN6P7wAFJtlkwfnxwhLVKhTK1GsWzZkGWH8f0BBwHfPomcOjVYCrxaCqnB+e/LknDdefkR8CWB4BjH8S3/iUrg8Hs0ZOkrwuRnOOoA99743thly0avwh/v+HvkMlkGa7V0PLDN3+ILb4tYZeVFpSicWUjZlTOSHzDHU3Ar2YD4aZOmawG1iXWWYrExgUC2PuGE28+uQmdrS1AaQHKvqVE3tjSsOsXsBy+tuD7GT9H6DcEIYQQQgghhBCSPgXZrgAhZPiTyWSYOnoqpo6eijsuvAMAcLL9JDynPMJI7UNsrNS88enj+rD7zG7sPrMbG/dthAwyXCi/UAhqqyaoMLZ0rCRlkdziOemBZbcF73z+TkLvm9zbh683N+O21nbEnZg+vygYIFTeA8xcBuSlkC6aCLhAAB0ffADWZkerwwGupyel7RXNnHk+aK1SoXDKlMQDHL2dwEd24PRHsdeddjUw63ogL47AeDImzAa+9mpwNKjz4dgjwg+8APi2AZqfAOqvpa9eRBKaaRrMGzsPe87sGbRs56md2HZsG5ZPXZ75ig0RW7xbIgawAeChKx5KLoANADsfDx/ABoDL709umyQqWV4e5q24DhdethRvNT6Og5M+jRjA5gIcDjQdo04ehBBCCCGEEELIMEMjsQnB4FEUarUa5eXlg9Zbu3Yt1q5dm8GajRxsFxsS1D7QdAD9XH9aypo+enpIUHtS+SR68DlEcRyHHSd24C+7/4IdJ3Yk9N7pvb1Yx7bgprYEgtfj5wQD1/NXA2VVCdeXhNd74gSan3kG7Oan0XvsWHIbyc9HyaWXokylQqlahTKlEgVjxqRWsZbjwJ5/Ap3+6OsVlACzdcH2kSktXwCvmICPnotv/SmLgVt+C0ygEYK57IMvPsB/vPYfYZfNYmbBfosd+dQZYZCjLUexestqdPR1hF1+s+JmbLhqQ3L3+v4+4HcLgeYwGWRGTQQe3EPTR6SZi30bbjZyB7Vju33QLbkfC8YtSFsdNm3ahE2bNg369/b2drhcLuH/aSQ2IYQQQgghhBAiHRqJTUgY4odRYsuXL89sRUYQpoTBimkrsGLaCgDBeS13ndolpCDfe2YvegKpjcrkHW05iqMtR/H0J08DACaWT4RyvBKqCSqoJ6gxs3ImBbVzHMdxeOfzd2DZbcHOUzsTem91Tw8MbAuub+9AXKGgogpg3qpg8HqSEqC2IQmutxetb7wB1m5H+5tvAYEYabojKJk3D8yqVRh9803Ir6iQqHIccPwD4OAWIFZnmopJwLy7gLIUA+aJGn0BsPofwMGXgRf/G2iJEfw/tgOwXAMs/Q6wrBYoDD+ikWTXZRdchisnXYm3P3970LJD7CG84HsBt826LQs1y129/b2o3V4bMYA9ZdQUPHT5Q8nf1w++FD6ADQCL/4MC2Gl2tMMbNYDde9CP9Tf8EmVFgzufSunIkSPYtm1bWssghBBCCCGEEEJIKApiExJGpJHYM2bMyHxlRqjywnIsnbwUSycvBQB093dj75m9wkjtD099GPGBdaJOtJ/AS4dfwkuHXwIAyIvlUE5QCiO1L5ZfTCPfcgTHcdh2bBssuyzYe3ZvQu+9uLsHBrYZmo5OxJX8e9oSYNHdwJzbgTQ/HB9Jun2HwW62o/nZ59B/9mxS28ivrMToW28Fo1uFkosvlraCfd3AgWeBEx/GXnfyZcBFK4H8uMfyS+/iG4EZVwGvPwJ8YIk+Z3egD3jrV8C+Z4CVvwaqr81cPUncHlA+EDaIDQB//PCPuGHmDSjOL85wrXLX73b+Dh+dDZ/uv0BWgLpr6jCqaFTyBbxvCf/v+UWAam3y2yUxNff68fqZFyMuD5zqwK0z70p7ABsI/gZYtmzZoH8fOBKbEEIIIYQQQggh0qF04oRgcDpxSgWY+/oCfTjYdFAYqe055UFzd3NayhpVOAoLxi+AeoIaqgkqzBkzB0X5NPIqkwJcAFs/3QrrbisONB1I6L1zurthZFuwvKMTMcfhlY8DFnw5GLwed1HS9SWhAh0daHnlVbCbN6PT7U56O+VLl6By1SpUaDTIK05DEK/tJLDnSaD9VPT18gqBS+8ALlgkfR1ScdwDbPkOcGLwnMphzV8DXP8oUD42vfUiCavdXouXD78cdtn31d/HPXPuyXCNctNbx9/Cfzr/M+Ly76m+h/vm3pd8ASf2AH+5KvyyBXcBd/w5+W2TqHoDvXj2i3+iqfd02OWFsiLcUH4LJo1VZLhmoeg3BCGEEEIIIYQQkj4UxCYE9ABqOAhwAfhYnzBS233SjVOdMQJRSSrOL8b8cfOFFOQLxi1AWWFZWsoa6foD/Xjt6Guw7rbiEHsoofcu7OqGkW3GlZ1d0YPXsjxglhZQ3g1cdEN2R9UOIxzHoWvPHrD2zWh58UUE2tuT2k7BxIlg7rwDlXfeiaIpUySupcgXO4H9zwCB3ujrlY0D5n8FGDUhfXVJRX8f8N6fgDc2AL1xZKsolQPXPQIsvItS5eeQz1o+w63P3oo+rm/QMqaYwUt3voSKIonS5w9RZzrPYNXzq9DU1RR2+ZILluAv2r8gTxZX7o3wnvsmsPOJ8MsM24BJC5PfNomI4zi8fuZFHGrfH3Gd68bdhpnl2e9sRr8hCCGEEEIIIYSQ9KF04oSQYSFPlodZ8lmYJZ+FNZesAcdxONZ2TAhoe0568Gnrp5KU1d3fjR0ndmDHiR0AgulKZ4+ZLaQgXzR+ESqLKyUpa6TqC/ThpcMvoX53PY60HEnoverOLtzPNuOyru7owWtmejBwveAuoHJyKtUlIn1+P1q2bAFr34zujz9ObiMFBahYsQKMbhXKr7wSsvw0pvPv7wU+fhE4/n7sdScsCI7ALsjhVM75BcCV3wFm3wa8+F/AIUf09Tv9wHPfAHb9C1j5G2DsrIxUk0Q3dfRU6C7S4amDTw1axnaz2LRvE7696NtZqFluCHAB/OitH0UMYFeVVOHRqx9NLYDdfhbYbQu/bNoSCmCn0b7WnVED2AsrL8+JADYhhBBCCCGEEELSi4LYhJBhSSaTYWrFVEytmIrbZ90OADjVcQqekx64TrrgOeXBJ/5PJCmrj+vD7jO7sfvMbmzatwkyyHCh/MLgSO2JKqjGqzCubJwkZQ13vf29eN77PB7b8xiOtR1L6L1LOjth9LdA1d0deaX8YuDSWwDlPcCMq4G8FAIcRMAFAuh47z2wdjtaHU5wvTFGM0dQpFCA0elQedutKBgzRuJahtHRBOz5J9D6efT1ZPnAxSuByZcPndHK8unAV2zAvqeBl01Ae/iUvIIjbwJ/Xgpc833gygeAApoyIduMC4x4zvscOvs6By17/KPH8eVLvoyxpSMzFfzf9/0d73z+TsTlj1z1SOr7xrMJ6I9wP7ncmNq2SURfdB3Du03/jrh8Ssl0LGYipHgnhBBCCCGEEELIsEJBbELIiDG+bDxumHkDbph5AwCA7WKx89ROYbT2/qb96Of6Uy6HA4eP/R/jY//Hwii66aOnQzVBJaQgnzxqMmRDJRiWAT39PXjmk2fw171/xRftXyT03ms6OmFgm7GguyfyShPmBUddz9MDZVUp1pbwer/4Auwzz6B589PoPX48qW3ISksx+qYbwazSoXTRwsydF6c+Aj6yAX1d0dcrkQPz7wJGpzGVebrIZMDcVUD1CsDxE8Dz9+jr93cD//45sNcO3PJbYNoVmaknCWts6VjcPftuWHdbBy3r7OvEX3b9BQ9d8VAWapZde8/sxe88v4u4/N7Z9+KqySkGOft7gQ8eC79s9GTgkpWpbZ+E1d7XBufp5xFAIOzyUfmjsWLcytRG2BNCCCGEEEIIIWTIoDmxCQHNZ0eCOno78OHpD4Wg9p7Te9ATiBIYTcGEsglQTVAJL0WlYkQGtTv7OrH5483YuHdjwnOYr2jvgIFtxpyeCKN+i0cD83TBUdcXLBw6I2hzHNfTg9Z/vwF2sx3tb74FJPk1omTBfDA6HUbfeBPyR5VLXMsoAv2A91Xg6Jux1x17KTBHDxSWpr9emXD0HWDLA8CZONO8q78G1PwEKGXSWi0SWVtPG258+kaw3eygZQWyAjx3+3OYNnpa5iuWJW09bVj9wmp81vpZ2OWzx8zGEzc+gcL8wtQK2vs0YL8v/LKanwBXfy+17ZNB+rl+vHCiASe6w3eIykc+brvgLowrnpjhmkVHvyEIIYQQQgghhJD0oZHYhBByTllhGZZOWoqlk5YCCI4O3ntmrxDU3nlqJzr6OiQp62THSbx0+CW8dPglAIC8WA7lBKWQgvxi+cUoyBu+l+iO3g40HmzExn0bI85pGo6M43BdewfWsS24OFLK6ulXAovuDs4JXFQmUY1Jt9cL1r4Zzc89h/6m+I+ZWD7DoPK2W1G5ahVKLsrCfKZdzcDepwD2SPT1ZHlA9XXA9KuDfw8X05cC978FvPVr4M3/A/pjdNJx/Q048CJwoxmYfTt1BMmCUUWjsG7eOvyv638HLevj+vCHnX9A3bK6LNQsOx55/5GIAezSglLUXVOXegAbAN7/S/h/LygBlPemvn0yyHtNb0QMYAPAVWO0ORfAJoQQQgghhBBCSHrRSGxCQKMoSHz6An046D8I94lgUNtzyhN2dJwUygvLsXD8QqgnqKEcr8TcsXNRlD/056ht62nDvw78C//46B8J7bs8jsNN7R1YxzZD0ds3eIXy8cDCLweD12MvlK7CI1ygvR0tr7wC1r4ZnTt3JrcRmQzlS5eC0a3CqJoa5BVlqR2fPRQMYPe2R1+vqAKY92VAPjMz9cqW0x8DLzwIHH07vvUvvB64+ZcAM3JG/eaK7v5u3PLMLRGnWmhY2YDZY2ZnuFaZ97z3efzorR9FXP7IVY/g1upbUy/ouAeovzb8skV3A7f9IfUySIiP2/bh32deirj80lELcM3Y6zJYo/jRbwhCCCGEEEIIISR9hu8wP0IIkVhBXgHmjJmDOWPm4J459yDABXC4+TDcJ91wnXTBfdKNUx2JpcSOpL23HW8ffxtvHw8GmIryijB/3PzgvNoTlFg4biHKCofOKOPm7mb8c/8/8cT+J9Da0xr3+wo4Divb2vF1tgXT+wYEr2V5wIXXBdOFX3gdIMXoOwKO49C1ezdYux0tL76EQEdy2QcKJl0A5o47wdx5BwonT5a4lgngAsDhfwO+rQBi9NuTVwNz1wDFFRmpWlaNuwi49wXgwyeA134MdLHR1//kVeCPbwErfgRcZgTy6StkphTnF+ObC7+Jh94OP//1bz2/hUVryXCtMutI8xH8/L2fR1y+UrFSmgA2AHwweA5yweX3S1MGEZztOYU3z74Wcfn4ogtw5ZgVGawRIYQQQgghhBBCcgWNxCYENIqCSIPjOBxvOy6kH3efdOPT1k/TUla+LB+zx8wOBrXHK6GcoERlcWVaykqFv8uPxz96HE8eeBLtsUbAihRwHO5obcN/NLdgcl9/6EL5TGDRV4GFdwGjJ0lc45Grz+9Hy/PPg7Xb0f3JoeQ2UliIipoaMKtWoXzpEsjy86WtZKJ62oF9DcDZT2KvO/NaQKEZXunD49V2CnhlPbDXHt/6FywAbvkdMGlhWqtFzusP9EO3RYdDbPhz87HrHsPlF1ye4VplRm9/L77y0lewv2l/2OVTK6aicWUjRhWNSr2wtlPAr+eET7U/42pg7Qupl0EEXf2dePqLx9Ha1xx2eWleGe6cdA9GFeRuxyL6DUEIIYQQQgghhKQPDaMhhBCJyGQyTKmYgikVU3DbrNsAAKc7TsN9yh1MQX7KjU/8cQTT4tDP9WPPmT3Yc2YPNu3bBAC4UH4hVONVUE0IvsaVjZOkrGSc6TyDv+/7OxoONqCzrzPu9xUFOKxqbcPXmlswsV8UvM4vDs5xrbwbmH4VkDcCA41pwAUCaH/nXbCb7WhzbgUXaZ7xGIpmVYPR6VB5660oqKqSuJZJYo8Ce/4FdIcPjggKy4A5q4GxF2emXrlo1HhA91dgwZeBF78LsDE633yxK5hu+YpvAMvXA8USBA9JVPl5+XhA+QC+/fq3wy7/jfs3ePLmJyEbhvOW/9bz24gB7AJZAequqZMmgA0Aro2R54q/3ChNGQRAsOPf62dejBjAlkEGzbhbcjqATQghhBBCCCGEkPSiIDYhhKTRuLJxuGHGDbhhxg0Agmm1d57aKYzU/ujsR+jn+mNsJT6f+D/BJ/5P8NTBpwAA0yqmCQFt5QQlpoyakvYAx8n2k9i4byPsH9vR3d8d9/tKAwHoW9uwtrkF4/oD5xdMnAco7wXm6YBSeRpqPDL1fv452KefQfPTT6P388+T2oasrAyjb7oRcp0OJQsW5E7wjOOAT98GDr0cTCUeTeVUYN5dQAmTkarlvAs1wDfeA974BfDuH4Fo1yYuALz7B+Cj54Gb/w+4KDfnqx1Olk1ZhkXjF2HnqcHz0+89uxeOow5cN2N4HYc3j72Jv3/094jLv6P8DuaOnRtxeUL6egDXX8Mvq5wGXHyTNOUQAICLfRufdR6OuPxy+TJMKp2WwRoRMrQ4nU54PB7U1tZmuyqEEEIIIYQQkjY0lI0QQjKosrgSy6cux3+p/wtP3vwk3vnyO7BqrTDON0I9QY2ivCLJyvq09VM8c+gZPPT2Q7jp6ZugsWtQu70WjQcbcch/CIFYAb4EfN72OX7+3s9x49M34J/7/xl3ALssEMB/sM145bPP8f0mNhjALq4EFn8dMGwD7n8LuGwdBbAlEOjpQcsrr+DTr6/DoRoNzvzhD0kFsEsXLsQFj/wcF725HZN+/nOULlyYOwHsvi5g9z+BT16MHcCeeiWgMlAAe6CicuC6nwGGN4BJytjrN38KPKkHbGuB1pPprt2IJpPJ8F3VdyMu//3O36Mv0JfBGqXXmc4zEecBB4Clk5bi3jn3SlfgR88CbRHa8GXrgLwsT48wjBzt8MLT/G7E5YqyizF/tDqDNSIkcT6fDzKZLKWX1WpNunyj0QiTyQSPx5Pwe+12e8Q6OZ3OpOvEsizkcnnY7er1+ojv8/l8MBqNqK6uFtavrq6G0WiEz+cT1rNarTCZTEnXLx3q6uoSOubV1dXQarUwGo0JHTuTyQSZTAa5XB7xxZchJY/HA5PJBJVKJRwfuVwuHJ9U2kuuYFkWVqsVer0e1dXVwr6srq6GXq8P+xlNJlNC528mysg22o+x1dXVRT2HE32xLBuyff46MfAVbt1wYpUn/hzJ3PP442S3xzmFFSGEEHIOBbEJISSLygrLsGTSEnxr0bew8YaNePeud/GPG/+BB5QP4MrJV6K8sFyysk51nMLLh1/Gz977Ge54/g4sa1iGB15/AP/Y9w/sO7MvqeDHZy2f4Sdv/Qg3b74RDQcb0BvnNir6A7jf34zXPvscD/qbURUIBNOE32EF/utAcGQnzbcrie5PPsHJDb/AoWXLcfzB76L9rbeCo5UTkC+Xo2rtWihe2IIZT/0LzKpVyCuXrm1KovVz4P3fA6f3RV8vvzg4+vrilUAeJaSJ6IL5wNedwA1mIJ5UzfueAf64OJiOOSBdBxkSatH4RVg+ZXnYZUdajuCZQ89ktkJpEuAC+OGbP0RTV1PY5VUlVXjkqkeQJ+Uc9u//Jfy/F5YFp7Igkmju9eP10y9GXC4vHIvlY2/Inc5RhEQwMCDAMAzMZjNsNhvcbjf8fv+gl8ViCXmPRqNJqmyPxyMEdwduMx46nQ5+vx9er1cYyc0wDADAbDYnVScgGGSuEk0ro9Fo4Ha74fV6UV9fH/Y9JpMJ1dXVAACbzSbsK5vNBoZhoFKpYDKZhEB3PIGYTKqtrYXX64XX64XBYAhZ5nA4hM/Dr2OxWKBUKuFyuaBSqaBSqeIK6JjNZni9XmzduhUajQYsywqv9evXh7Q5KXg8Hmi1WqhUKjidThiNRjgcDnAch8OHDwvtTqvVQi6XpxwkdDqdkMvlGQ1usSwLo9EIuVwOozE4ZYjJZILNZhOO1eLFi4UOFnynA6fTibq6urjaYibK4NfP9P7jDfX9aLfbhXYsk8mgUqmg1+vTEvg2GAzYunUrbDYb1Gp1yHlcX1+PrVu3hn3ZbDaYzeZB5764kw8QvE7w1xvxtZxlWdTU1MSsn9vths1mCylHrVYL9zVetOteuPuf1+uF2+0WOibp9XpJrhuEEEJGDhnHJfgkm5BhaN++fZg793w6yr1792LOnDlZrBEhQX2BPhz0H4TnpEdIQc52s2kpq6ygDIvGLxJSkM8dOxdF+eFHhh9uPoz69+vw0hdvox/x30Yq+/txd0sr7mpuRQXHAaMmAAvvAhbdDYypluqjjHj9be1ofeVlsDY7OnftSm4jMhnKr7oKzKpVqFhxLWRF0mUJkBTHAZ+7gIPPA7E6UYyaCMz/ClA2NjN1Gy6ajwEvfR84+FJ8609bAtzyW2DcCJ5nPI0+8X+CVc+vAhfm2juudBxevPNFlBaUZqFm0tm4dyN+5f5VxOV/1vwZV02+SroCj7mAxyI83FN/DVj5a+nKGsF6Az149ot/oqn3TNjlRbIi3DHpbjCFVWGX5yL6DTFyOZ1OaLVaAMEH+rGCvyzLYubMmUIwxWw2J50K3Gg0Cg//GYZJOXApk8lQW1uLuro6AIDX64VCoUh4O/zoXH60tMPhiBqo5z+H2+2GUhk++wvLstDr9XC5XGBZFgaDIanAfSb4fD4hIK9QKOD1eqOub7fbhRHqOp0ONpstrnI8Hg9UKhUAaY7/QHV1dTCZTGAYRghoRcIfH6fTCaVSia1btwodIqJhWRZNTU1wOp2wWCxC0NFisQwKiqXDwH1fX18ftd58FoD169djw4YNYFk25jmczjKyvf94Q3k/8m23qakJRqNRCNw6nU7hfQqFAjabLeL1KRXi8zie6wXP5/NBq9XC5/PBZrNBp9NFXK+6uhoKhUIIdidy3+HrJg5eRysnkc/hdDqh1+vBsmxC1z5CCCEjFw1BIiSHcD096DtzBoWTJmW7KiRHFOQVYM6YOZgzZg7unn03OI6Dr9knBLRdJ1041XFKkrI6+jrw9udv4+3P3wYAFOUVYd64eUJQe+G4hTh+9gDq330ErzR/DC6BQVJV/f24t7kFa1raUI484KIbAeU9wCwtkE+3IilwHIfODz8Ea7ej5eVXwHV0JLWdwkmTULnqTjB33JH716L+HuDAs8AXg+cIHmSSGrj4ViC/MO3VGnYqpwBfehLYvwV4uRZo/SL6+p++C/z5SuDq7wFXfQ8oLMlMPUeIC+UX4pbqW/C89/lBy053nsY/9/8TX5/39SzUTBp7Tu/B7zy/i7j83tn3ShvABoD3/hx52WVGacsaoTiOw/azr0UMYAPAteNuGlIBbDKy8cFog8EQ1+jldevWCe9RKpUpzWXd2NgoBCZYloXdbo8YyIiX0WgUgtgWiyXhEdlOpxMajSauACa/vtVqhdlsjhogYhgGDocD1dXVOTcKeyDxKPR46HQ6ofOA3W6H0WiMK0Av3seJlhkLn+qXYRi43e6YnRn448O/b+bMmTHfp1Kp4PF4wDAMNBoN1qxZk1Ra/GSZTKaQth5P0NdgMECj0UClUsXVDtNZRrb3H2+o78eamhqo1Wo4HI6Qf1cqlTAYDKipqRECzdE62iQr3mvlQAqFAhaLRQhkxyqDv34Cwf2p0Wji+ix8UD+WZK5BGo0Ghw8fFjJR8PuYEEIIiYQiB4TkkM5du3D07ntQOGkSStUqlKnVKFMvRtHMGZRWkQAIjpSoZqpRzVRj9cWrwXEcjrcdh+fU+ZHaR1uOSlJWT6BH2CYA5APoFyoS3zbG9fXhvuZW6FrbUCqfCaz4XnDkdcVESepIgL6mJjQ/9zxYux09cfbgHkhWWIgKrQaVq1ahfMkSyPKGwGwj7aeD81+3x5iHOa8QuOQ2YJIqM/UarmQyYPatgGIZsPX/ATv+CkTLwhDoBbaZgb2bgZW/AWZenamajgjfXPhNvHz4ZfQGegct+9uev0F/kR6VxZWpFdLTAbg3Asc9wQ4jGdDG9aO25yD6uPBZFWbLSvGAdyfgkzi9d6QsA4prgfGXSFvWCLW31YND7fsjLl9UeQVmlF2YwRoRkpqmpuB0B/EEe51OZ0ia31RGndntdigUCqxfv14YnWixWFIOYldVVUGn08FutwvB5USYzWaYzWa4XK641udHa8dbb7PZHHVe7aFK3HmAH0GazCh4KZhMJqGdbt26NaF62Gw2VFdXC6NEo43GHDhaO5MBWKvVKuxvs9mc0KhlflQun4EhW2Vkc//xhvp+5K8/kTqNMAyD+vp6YTSyXq+Pe6R0JvCB6HjqpFAoYDabhc+cK5+Fz/TAd8qItxMPIYSQkYmC2ITkkI5zP/p7P/8cvc9/jpbntwAA8seMCQa0VSqULVaj+KKLIMvPz2ZVSY6QyWSYUjEFUyqm4NbqWwEApztOw33KLaQg/8T/Sdi0s4nqj72KYGJfH77GtuDOzj4Uz749OJ/o9CuDgTCSMq6/H+3vvAPWvhmtr78O9A4OZMWj+MILweh1GH3LLSiQyyWuZRqd2A3s3xw7sFY2Npg+fBR1mpBMSWVwzvr5a4AtDwCnPoq+/tlDwN9XAgu/Clz3M6CMRllKYdKoSfjSJV/C4x89PmhZa28r/rrnr/ie+nvJF3DqAPDUXUBT5h5ycQB+Nm4Mjo0qD7u8LBBA3XEvCvsOZqxOuPz+zJU1jH3e9Rnebfp3xOVTSmZAzVyZwRoRkjqWZaFUKmOOpuNT1vLMZnNKQUqLxQKj0RgS/HU6nWBZNumRfbz169fDbrcnPLqbT23Mz/Ucj0QDbzqdLuXPl4sGtgWn05nRdNDicvmAoU6nS2rUqXh0aLSAVLaOIx8oA4L7PZlsCBqNRujska0ysn0eDIf9aLVaodFo4PP5Il6PlUolNBoNnE4nfD4frFZrVs7NSNasWTNoFHkktbW1cDgcwmfJlYAxP+rdarXCarXCaDSmJXU7IYSQoW8IDLUiZOTocIVPodN/9ixaX30VJx99FIfvuBMfX7EEnxnvx5n6enTs3AmuJzMjpMjQMK5sHG6YcQN+ePkPsfnWzXjzS2/i9yt+j/vm3If5Y+cjX5a+DhCTe/vwkzNn8VLfOHz5mp+i+L8OAndagBlXUQBbAj3HjuP0736PQxotPltnQOurryYcwM4rKwOj12NGYwNmPv8cqu65Z2gFsAGg5VjsAPaEecBl36QAdrpMvQwwbgdq/gcoiCNd+IdPAH9YDOxuDM5jTlK2bt46jCocFXbZkweexIn2E8lteP8LwfmhMxjABoDnR5XjpQgBbAB46GwTpvfFmPdeSvKZwIXXZa68Yaq9rw3OU89H7ExXUVCJmnErkSejn6Vk6FGr1THXkTKNOD9fKx9IEQdU+DmyU6FUKoWATiIBjg0bNggBp0Q5nc641129enVSZQwl2UqZzo/SBIKdGZKh0WiE9mO1WmOmOs60devWCX8nmmlALNr+yUQZ2TbU9yPLskJHHT7NdiTigGo2520Ody4plcqEzjGbzSZ0gLBarQlde9NJfO0R/00IIYSI0UhskrPsdjssFguamppCfvivX79+WPbO4/r60LEzjnldAQRaW9G2bRvatm0DAMhKSlC6cKEwUrt0wQLklZams7pkCKksrsTyqcuxfOpyoK8HHZ+9h12fPA/PCRfcXSewuzAf3Smmj57e24uvt/fi5lm3o/Dme4EL5ktTeYJATw/anE6w9s1of/fdpAOApUolmFWrMPqG65FXHjlQNCTMuh5o/hRoDpM6X5YPXHgTMHUJdZxIt/xC4Or/AmbfDrzwXeDwtujrd5wBnl4H7PoXcPOvgKqZGanmcCUvkWPtnLX4w4d/GLSsu78bf971Z/x06U/j32AgAGz7RTANfIYdKSjAI2Mid6ZZ2daOW9o6MlgjAJcbgaEwtUIO6+f64Tj9HDoD4Y9dvqwA2nG3oSSfvrPGK8AF0N7Xmu1q5Lzygoq0d4xQKBQxR1RLmUYcwKCRgEajUQheWyyWlALkPJPJBKPRKIzYi2fUuN1uTzg9LT+nt8lkwurVq+MaXRrPHLBDzcAR6RqNJuN1cDqdQj0YhknpWYtOpwtJAZ0Loz2B4H4W7+tU0u9HysCQiTKybTjsR34qCF6069zixYsjvi9TPB5P2BTgarU6oeshnyKdzwyi1+tx+PDhrLcz/l7q8/kSuu8QQggZWSiITXIOy7KoqamBRqMJ6S0IBH+419TUYPXq1Tnzg0gqXfv3g+tI7gEt19WFjvfeQ8d77wX/obAQpXPmBAPaKhXKlErkjx4tYW3JkNHbCRxzAUffDr4+24Gyvk4sAbDk3Co9APYVF8FdUgJ3STF2lhSjPc4H99U9PVhXOAk3LL4f+bNvBQrpQbRUug5+DHazHS3Pb0F/kqMy8quqUHn77WB0q1A8nH4M5uUD874MvP97oLf9/L+XMMF/r5yWtaqNSGOqgXueA3Y3AK/+EOg4G3197+vAn5YAy03Akm8Fg+EkKXfPvhv/OvAvnO0avM+fPfQs7p19LxRMHOd+VzPwtBH4+OU01DK6HgC148eiM8J9Z1pvLx46k+EHh8WVwMK7MlvmMPRu079xsvvziMuvHqPFuOIJGazR0Nfe14onj6c+4na4u2uyARWFlWktI1ZgReo04kAwUC0OhPMjp30+H3w+HzweT8qdvVevXi2MqrZYLDFHQdrt9qQCr0ajESaTCSzLYubMmaivr4+5T1Od9zsXNTQ0CH8bDIasdNYXt6lUg+harVYIYjc2NubMMxtxPaToKBBuG5koI9uGw35UKBQwGAxobGzE6tWro16XxUHiqqrsTIkUKXjOMAy4BDu363Q6IX03f4+KNyV5OolHldvtdkk6ZBFCCBleKIhNck5NTQ2MRmPY+WYMBgPUajVUKhUYhkkptVCu6T4o4RyPvb3o/PBDdH74IVD/GCCTofiSS87Pq61WoWDsWOnKI7mjqwX47INzQet3gONuIBA93XQRgEXdPVjU3YOvNwfnvj5YVAjPuaC2u6QY/gFzsF/Ux8E4fik0S2uRN2ZW+j7PCNPf1oaWl14Ca9+Mrt27k9tIXh7Kr74KzKpVqFi+HLKiImkrmStKKoG5a4CdGwFwwJiLgTl6oGiIjzIfqmQyYMGXgFlawPFj4MN/Rl+/rxNwPgzssQO3/A6YospINYebssIy3L/gfjzy/iODlgW4AH6383f4zbW/ib6R0x8H578++0l6KhnDb6sY7C8Of50q4DjUnTqL8kymoJflAyt/FbzGkKR93LYX+1ojZxiaXbEQF4+am8EaEZJZUqYRB86n3R4Y5ORHTgPB4E+qQUOGYYR5ZK1Wa8zf28mWWVtbC4vFAp/PFxLw12g00Gq1wny0meJ0OmGxWODxeODz+cAwDBQKBTQaDdavX5+W0Yp2uz1kHupsBXzFaYVT7Wghfj/LspJ0rJCClJ8RCJ9VIRNlZNtw2Y/xXrd27Ngh/J2tdix1O7BYLMKIZ6fTmRNzfYuPs8PhoCA2IYSQQSiITXIKn8oq2nxX/A/aeH5UDyWMTodRNTXodLvR4XKjw+VC10cfBdN7porj0L1/P7r374f/8ccBAEUzZwaD2moVytRqFE6enHo5JPM6moBP3w0GrI++DXyxC+BSazP5AGb39GJ2Ty++2tIKDsDhwkLsHKeAv2oq5k1fgcWL1iGvYJgGRzOM4zh07twJ1r4ZLS+/DK6zM6ntFE6eDEa3CpV33IHCiSNkHugxFwIKDSDLA2ZcE/wvya7yMcDtfwLmrwFeeBBoipHm7uTe4PzLlxmAFQ8BJZQ1JFGrLlqFf3z0D3zW+tmgZVs/3Ypdp3dhwbgF4d988GVg8zqgJ0Z64hlXA+MukaC2od7sOY1/tHgiLn+g/GLMWXCD5OVGVD4OuORmYCIFV1Nxpvsktp+NPLJnfPEFWFq1IoM1IiSz7Ha7pGnEgWDgIdy80+KR01arVZJA6Pr162G324V5YyONgPb5fGhqako60OR2u1FTUxOSOtjpdIYEsZRKZcQO7lJgWRbr1q0TRv+Jpy7zeDwwmUxxjxSPBz9ing8kKRQKmM3mrI4yF4/0HDNmTErbGjhaNVspmAcSj6iNNQ9yLpeRbSNpP7IsG3ItSvc85awo8xo/paLFYoHVapU8vbbNZoNKFexAbDQaQ+azzwbxdSdXrhmEEEJyCwWxSU7hf8A6nc6oP+QUCkXIF8rhokAuR4VGg4pzvc7729rQufNDdLhc6HC70LVrN7je6KNq49Vz+DB6Dh8Ge+6hSsGkC84FtYOvopkzIaP5ZHNP64nzo6yPvgOc+ig95cjygUkLgelLIZt+FRTTLoeiNPJ8pSRxfWfPovnZ58Bu3oyeJOf3kxUWokKrBaPXoezyyyEbifO3KigQkpMUy4D/fAfY/kvg7d8Agb4oK3PABxZg/xbgpv8FLl2ZqVoOC4V5hfj2om+jdnv4UQu/cf8Gf7v+b6H39EAA2P6/wBuPxi5g2Q+AZSbJ54c+3XEaD22J/F3vyklX4h7Nn6hzyhDT1d+J104/h34u/DlfmleG68bdhnxZftjlhAx1fFCUJ0UacSAYGK+vrx/07+KR08DgebOTIU5TvmHDhoi/yyMF1uPFMAzcbrcQfB84PzQQfD7Apx53u92SBlr4VOYsy8Ltdg8aaalUKuFwOGA0GqHX62EwGOLuJODz+SCXh/52YgdMD2SxWLI+AhIIrZfUI85zcQ7zTMwBnO15hjNhuO/HDRs2COeG2WxOa13CXS/SSalUwmw2w2QyAQhOAzBwzu1MEu/bXLxmEEIIyT4KYpOcwv8oXbduHRiGiZhCrLGxMSfnCJJa/qhRGHX1VRh19VUAgEB3N7p27w4GtV1udOzcmfQ82gP1ff4FWp7fgpbntwTLrqoSBbVVKL74Ysjy6YFjxvmPnh9lffQdoClNPy7yi4EpamD60uBrymVA8aj0lDWCcf39aH/rLbD2zWj997+BvmiBvciKL7oIjE6H0besREEGf/ASkpDCUqDmx8A8HbDlAeCz96Ov3/o50PAV4JKVwI11QCVlCInX9TOux8a9G7G/af+gZa6TLrx1/C1cPeXq4D90tQDP/idw4IXoGy0aBdxhSUunggAXwA/f+iGausKPtqgqqcLPr/o58iiAPaQEuAC2nn4BrX3NYZfLIINm/K0oL6jIcM0IyRyp04gDwcC0TqeLGEQxGo1CEFuqwCifppxPrx0ueGy32yUJfBgMBhgMBmHk444dO4QMbTyWZaFSqSQNZOv1erAsC7PZHDVVsMViQWNjI6xWK/R6fVzPIRQKRdh94/P5YLfbhf3rcDhyKm10qsdz4CjKbM0jHM3AzgRDtYxsG8770efzhaT6T3d663DXC/G1Ih1qa2vR0NAgXONNJlPWMl2Kj3MuXjMIIYRkHz0ZynF1dXWQyWQp90azWq1QqVSQy+XCS6/X59xoZv4HIcuy0Gq10Ov1gz47P19WtuaMyqa84mKULV6Msf/5n5j218dw8QfvY4atEeNrazGqpgb5ldLN39jf1ITW117DyUcfxeE7V+Hjy6/Ap0YjztTXo8OzE1xPj2RlkXM4DjjzCeDeBDxtAH49F/jtfODZ+4Gdj0sbwC4sBxTXAtc+BKx9CfjBp8B9LwXT+VavoAC2xHqOHcOp3/4Wh2o0+Mx4P1odjoQD2Hnl5WDWrMEMWyNmPvcsqu65mwLYZGgYfylw3yvAzb8CiuNIF37gBeCPlwPvW4FAf/rrNwzkyfLwoPLBiMt/6/ktAlwAOHMIeEwTO4BdVQ18fWvaRsVv2rcJ733xXsTlj171KMaWjk1L2SR93Ow7ONZ1JOLyK+TLMalkasbqQ0impSONOBB7xLNGoxEC3HxAIlXiQHi4wIbdbpe8Uzk/qtxsNsPtdoPjODgcDiHAzLJsSiO/xaxWq/AsJJ5U3vx0Z/yziGQpFArU1tYKASu73Q6tVpvSNlM1cB7rVAx8fzZTFIuJ65GuEaeZKCPbRsJ+5J9FAsFrQ7Y6mfDXinQG0MWfra6uLieeD+fKNYMQQkhuoZHYOcrn88FoNKb8JcLj8aCmpgZVVVUwmUzCj1GfzweLxQKtVgudTof6+vqcSHnEMAxsNpvw45B/EGAwGIQ0Yk1NTTh8+HBO1DfbZAUFKJ03D6Xz5mHM1+4DFwig+9AhdLhc6HS50LHDhb7TpyUpK9DWhvZt29G+bXuw7JISlC5YEBypvViN0gULkFdaKklZI0YgEEwHfvTt8yOt26U5XoMUVwLTlwDTrwy+LpgP5BempywCIJg5odXhBLvZjo53IwdrYilVq8Cs0mH09dchr6xMwhoSkkF5ecDi/wAuvgl4xQR89Fz09XtagZe/D+xuAG75Lc1RHIclk5bg8omX4/0Tg0e8H/QfxEvvmLFy+5+B7vCjZAUXXg/caQVKmbTUc/fp3fi95/cRl6+dsxZXTr4yLWWT9DnScQie5ncjLq8uuwTzRqsyWKPhqbygAndNzn764VyXjdH+iaYRt9vtsFgscDgizx8PnJ9DOZFAp8VikWREncFggNVqRWNj46AO5BaLJSOdyjUaDdxuN/R6Pex2uzBCO9rI6XiI6x5P0ISfmzfWPOHx4ufCNplMcDqdkmwzHlarFUBoJwWNRiP8u8vlSmn7A9+f6nGSivgzStHJw263D8ocmIkysm0k7Meamhr4fL6Epg9IJ61WG9I5aqC6urqkA90KhSLk+ater8/Ks1ZxZ4VcuWYQQgjJLRTEzgEsy8LlcsHn88Hr9Q5KnZUsp9MJrVYLhUIBt9sd8kWE/9FUXV0tpAkbuE626HQ6uN1urFu3TtgPVqsVVqtV+BFLwpPl5aHkootQctFFwF13geM49H72GTp2uM7Nq+1G76efSlIW19WFjvffR8f75x6WFxSgdM6cYEBbpUKZUinpyPBhob8X+GL3+YD1p+8AXTGCCckqH3cuNfiVwf+Onw3kUTr4TOg6eBCszY7mLVsQaE7u+OaPGQPmjttReecqFCtmSlxDQrJo9AXA6n8AB18GXvxvoOVY9PWPuwDrMmDJt4LzMhdRR45IZDIZHlQ9iC+/+OWwy/+w/x+4vrsZUbsvXfN9YPkPJZ//mtfa04ra7bXoizBf8pwxc/CdRd9JS9kkfZp7/fj36RcjLq8qHItlY68PnZedJCVPloeKQvp+nYsSTSPu8/ni+u3NpwePFZT2+XxQqYIdRaxWqyRBbKPRCKvVOihw6/P50NTUlNKIOa1WGzOAL2az2YRriMvlSjnQkejzFvGx2rFjhyQBZ/FnsFgsGQliOxyOQR0i9Hq9EDRM9TmU+Jhm4vPEi2/LACQZbdrQ0DBoP2aijGwb7vtRq9XC4/HAZrPlTPtVKBQRr7VOpxMNDQ0pjdbW6XTQ6XSw2+1CZ6xMjz4Xd37JtTZPCCEkN1A68Rzgcrmg1WphMpng8Xig0Wjg9XpTCiizLCv0prPZbBG3xc8/5fP5UFNTk3R5UmMYBmq1GgzDhNTd6XRCLpfnRJqboUAmk6Fo2jQwq+7EpA2PYtZrr2LWtjcw6f9+CebLX0LxhRdKV1hfHzp37cLZx/6KY//5DXx8xRL4br8DJ37+CFpeeUWyEeFDSm9XMFi9/X+Bx+8AfjEdeGwF4Pgx8PHL0gawR08G5q0GVv4G+JYL+O9PgoGiy43AxHkUwE6z/rY2+J9qwGGdHodvux3+J55IPICdl4dRy5Zhyh9+jwvf+DfG//d/UwCbDF8X3wh8833gim8AseY9DvQBb/8G+PMSwPt6Rqo3VM0dOxfa6eEf/hwvLEDj6AhTRRSNAlY/HpxSIk0BbI7j8LP3fobjbcfDLi8rKEPdNXUopCwhQ0pvoAevnnoWPVz4aWaKZEW4bvztKMwrynDNCMmcZNKIe73euOb+tFqtMBqNwu/iSC+lUhmSdluK38vibW7YsEH4d4vFgvXr16e0bafTmXDqan4k5XBJ1Sw+/qmOgI6Xx+MZFBDTaDQh/xZt1Gcs4vem2kakJG7LQGqfEQjuR7VanfEysm0470etVguXywW3250zAWwgGMSO1OEn3PmcDHFmTrvdLnQiyASWZYXOMwqFIqcyDxBCCMkdFMTOARqNBhzHwe/3w+FwxEw9Fg++J/jAL4DhmEwmAMEvQKl+QZSC1WpFdXU1GIaB3++H3+8P6UnOz1FTV1eXxVoOXYUTJqDy5ptxwU9+AsWW53Hhu+9gyh//gKq1a1Eydy6QL1Gwk+PQfeAA/E88geMPfhefXH0NvDfciC9+/GOwzz6LnmPHwXGcNGXlip72YIDl9Z8DG28CfjEN2Hhj8P+9rwO97dKVVaUAFt0N3P4X4IHdwHf3AavqAfV9wNgLARrtlHYcx6HD5cLnP1iPT66+Bicefhhde/cmvJ3CqVMx7sEHMOvfr2Oq5S+o0GggK6QgDhkBikcBN2wIzr08cV7s9f1Hgh2CnjYA7WfSXr2h6juLvoN8Wfh7uZWpRPvA+0OVAvi6E5h9a1rr9bz3ebx8+OWIyx+64iFMGz0trXUg0uI4DtvOvAp/b+Tz8dpxN6OyUJ7BWhGSWYmmEee5XK6YndadTieqqqriHnUsni9aqjS4/DbFc21Llfo60cAtvx/GjBmTctmJPm8RB9z51OJSYlk25fmo4ynD5/OF/ezi9sI/H0qU+PmMTqfLubTA9fX1wt/JfkYgeC40NTWF/XyZKCPbhuN+1Ov18Pl8cLvdYdf1eDzCIKFc4nA4sHjx4pS3w0/ryOOzdWaCOGAuRQYRQgghwxMFsYchPt0XgLh6sYnT04h7WA+k1+shk8kkeUX6Imq322E0GgfN41VbWwu/3x8ydxM/fxRJTYFcjoqaGkz4gQkz7TZc9P77mPrYYxhzvxGlahVkRdKNnOk5cgSszY4vfrAeXo0Gh1bU4Pj3a+FvaES31zv0gtqdLHDwFeC1HwP1NcGg9eN3BEdeH30b6O+Wrqzxs4HFXwd0fwO+dwD4zk7gtj8AC78MyKdT0DqD+s6cwdnHHoPvxptw9Kt3o/nZZ8F1dia0DVlREUavXIlpmzah+tVXMPb++1E4YUKaakxIjpusBNa9AVz3c6AwjnThuxuAP6iBnU8AQ+2+kQEzKmfgjvGXhV3WlJ+Pf1SK5oqdpQXWvQ6MvzStdTrSfASPvP9IxOW3KG7BLdW3pLUORHp7WtzwdhyIuFxZuQQzymZlsEaEZF6iacSBYEDE4/HEDMZaLJaQwHQsq1evFv7mU8OmSvz722w2Szp3c6IBCz6oIsVIPfFniCdYIx79Ld7PqRjYiUGK+X+j4YNF4TIAaDQa4Vj7fL6EBwz4fD7hGQ/DMCFByFyhVCqFNpfMZ+Tp9fqIbTcTZWTbcNuP4gB2tLTd8WTOyCSfzwen0ylZRweNRhNy/8rEs1aWZYXrhkajyakR8IQQQnILzYk9DIl7ssXbK0+hUMDn8wk9rMN9eVu/fr1k85NE+uGp1+tDfkCJMQwj/JCvqakBy7Iwm82UbkZi+aPKMeqqKzHqqisBAIHubnTt2ROcU3uHC507dyLQ0SFJWX1ffIGWLVvQsmVLsOyqKpSpVMK82iWXXAKZVCPDpdB2OjiP9dF3gCNvAyf3AkhDAEWWB0ycH5zPesaVwLQlQFlu/Wgaabi+PrS99RZYux1tb2wD+sLP5xpL8SWXgNHpUHnLSpoznhCx/AJg6beBS28FXvwv4FCMeTI7/cBz3wR2PRWcRmEsBcoABIP6b/0a/7nDjhemXICuMKnBN1WOxuqWNoxZ+uC59OHpvc/29PegdnstOvvCd/aZVjENP7riR2mtA5He512f4T3/GxGXTy2dCRWzNHMVIiQLkkkjDpzvOB5tJDbfMT2RYCDDMMLcpgDQ2NgY9nd1ogwGA6xWK6xWK1wul2TzpTqdTphMpriCdXzARqoRvnxA3ufzYcOGDTE/U2Njo/C+VKZ9ExsYFBsYkPJ4PFi3bh3cbnfKZbEsG7PdWSwWYT+bTCYolcq4nrXwmfL4bbvdbsn2kdRqa2tx9uxZ1NXVwWQygWGYhM4RrVYLhUIR9T2ZKCPbhst+1Ov1YFk25jkWbi75bOODv5FSpSeT3cFsNsPpdGZsFDY/paVCocj4PNyEEEKGFgpiD0MNDQ3C3/H+eBAHrZ1OZ9gvevGkJk8F39MvVpoepVKJrVu3QqVSpb23MgHyiotRplajTK0G7g8G87r2HwgGtV0udLpc6E907t8I+pua0OpwoPXcnD95o0ahVLkIZerFKFOrUDJ3LvIkHBkeU/OxYMD66NvB/575OD3l5BUGRyJOvzL4mnoZUDI6PWWRhPR89hnYzZvR/PQz6Dt1Kqlt5I0ahdG3rASzSoeSObMho1HzhEQmnw58xQbsexp4+QdAe4zz7sibwJ+XAtf8N3Dlg0DBCJ53t7stGNj/6FmMB/CVllb8lRncWaYjLw/1i3X4geYnGanWbzy/wf6m/WGXFeQVoG5ZHcoLyzNSFyKNtr5WOE89Dy5CR76KgkqsGHsz8mLNd0/IEJZsGnGTySQEmaON7BOPak3EmjVrhO2bzeaYQR8+WOFyuSIGLI1GY8go3mifM9GgCT/Ccv369RE/K8uy0Ov1ko/wdTgcqK6uFjojRBoFyAe6dDpd1JH2TU1NCZXPMAw0Go3wHMThcAwaCRkuQJXoPmZZVhgEEKuNOhwOmEwm1NXVQavVwmw2R/3MfJpln88HpVIJm82W0tR46U6pDgTPi8WLF0Ov18NoNMLhcITMCRyOx+MRzslI8xNnuoxwMrH/eEN9PxqNRtjtdmg0mogB6qamJiENfyppzcNJ5VhZrVbhOh9pX/DTNUQapBSJzWZLaMqERK97wODrxtatW3O24wshhJDcQEHsYUjcay7elDfiLwxS9PRNRiIBaaVSSV9yskRWUIDSeXNROm8uxty3FlwggB6vVxip3eFyJR3sGyjQ1ob27W+iffubwbKLi1G6YEEwqL5YjdIFC5BXFkfq2XhwHNDkOxe0Phe4Zo9Ks+2BCkqBqYvPBa2XApPVQJFEn4OkLNDdjdbXHGDtdnS8/37S2ylbvBiMbhUqrrsOeaWlEtaQkGFOJgPmrgKqVwCOnwCev0dfv78b+PcjwB47cMtvgelLMlPPXNJ0GHjqK8CpfcI/fa25BbaKUWgJk9Gk4YwLX209hikVU9Jare3HtuPxjx6PuPxB5YOYM2ZOWutApNXP9cFx+jl0BsJn5cmXFeC6cbehJJ/ue2R4E6cRVygU0Gg0g37PsiwrBEEcDgcaGxtDAhcDf8+yLAuXyyWMhgMgBBJjdSbnAy3iDu184MVoNKKqqiqkPPGIW+B82l2NRjMo4MF3Zvd4PGHTm/OfaWD5FosFCoViUNk8g8EAs9kMvV4PuVwOg8EgjJ5kGEbIFLdhwwYoFIqoI3wT6STKT2GlUCjg9XphNBqh1+thMBhgNBqhVCqFsk0mE3w+X9RgLn+cxfNK+3w+2O12KJXKiJ8fCAaM+M75TqdTCKbzn1v8bCZSOR6PZ9Axa2pqgs/ng81mC2l38QSzzGYztFotTCYTTCaTkA2PbxtNTU3weDywWCxwOp1gGCZmsDvcPuPrL57SrqGhAUqlMiR4H27fJXO8xXQ6HbxeL8xmsxAQ1Ol0WLNmjXDMfD4fXC4XHA4H7HZ7wp8xnWWkuv+A1Pdhuj9jOssQd8yJN212uA4lyezDRM5jHn8fcTqdaGhoEJ75hrsv8Nce/touvofEc/4rFIq4prJI9HrE399sNpuwzxNtC4QQQkYuGTfkJqEdOeRyufDl1Ov1xvWFw+fzhfSa8/v9cQV7+RQ9QPCLUDYC2XzddTpdzFQyLMtCLpejtrZWkrmC9u3bh7lz5wr/v3fvXsyZQw9Vk8FxHHqPHRMC2h1uF3qPfpqewgoKUDJntjBSvEypjD9FcyAAnDkYDFYfOTfSuu1EeupZVAFMuyIYsJ5xFXDBwpE9WjBHde3fD9a+Gc1btiDQ0pLUNvLHjQVz+x1gVt2JohkzpK0gISPV0XeALQ8Gr9nxUK0FND8FSpk0ViqHHNoK2L8GdLGDFm2srMCvquRh37ZSsRIbrt4QdpkUTnechm6LDk1d4UdoXDnpSvxJ8ycarTvEvHnWgY9aP4y4/NqxN+KiUXMjLh9u6DfEyCX+rZ6sgb/xVSoVPB5P2OC2w+GImtqZr0+00cx8eXa7XRjZHG69cIEFPiginhs6Vr3F2xz4G7+6ujokKM0HRV0uF3w+n/BZ1Gq1EGCOJtWAnNPpFIKy4oCvTqeLOkpc/Bwl0v4EYj/PsVqtsFgswn5Uq9UhnRf40dGpiudZi5jH40FDQwOcTmfIcamqqoJSqcSaNWsSnseWDyDGek7FjxwP1+akCMCKy2lsbITD4YDH4xECbuL2t3r16pQGUUhZhhT7D5B2H/LlDYX96PF4oFKpEi473D5IdB9KdR7zDAZDSBCZ336ka1Gs+4iYXq/H4sWLwwaZxde9RCgUCigUCknaAiGEkJGFgtg5LJkg9sAvZPEGsa1Wq9DTLtoX3XTjv5DbbLaoP4b41DNSBdvpAVR69Z48hU73uaC2y43uj9OUllsmQ/FFFwkjtctUKhSMGxdcFugHTuwWjbR+B+hMPPVRXEqrggFrfqT1xHlpn3OUJKe/pQUtL74I1r4ZXfv2xX5DOPn5GHXNNWD0Ooy6+mrICgulrSQhBOjrBt76DfDmL4H+ntjrj5oA3PALYM4dwZHdwxHHAe/8DnA+DHCBsKt0yWRYOeUCnCwYnHxJBhlst9hwcdXFklctwAVgcBjw/hfhs1mMKRkD+612jC0dK3nZJH0Otu7FG2dfjrh8TsUiXDUmvoejwwX9hiCEEEIIIYQQQtKH0omTQZKZ00QqfC9CvV4v9Hrmex/z6XM2bNiAqqoqbN26NW31OHToUMLvGTduHMaPH5+G2gx9hRPGo/CmmzD6ppsAAH1+Pzp37hRGa3d99BHQ3596QRyH7oMH0X3wIPz//CcAoGj8aJROykNZ+Rcok7egsLxf+njGqInAjCvPB67HXgzk0ciyXMVxHDp27EDz5s1oefU1cF1dSW2ncNo0MKtWofL221E4gc59QtKqoBhYbgLm3hkclX30rejrt50E7PcBu54Cbv4lwEzLSDUzpqcdeP7bwN7NUVcrKSjFN6rvxE+OPj9oGQcOv/X8Fn/S/Eny6m3cuzFiABsAHr3qUQpgDzGnu0/izbOvRVw+oXgSllRdm8EaSe/UqVM4ffp0Qu9J5jcDIYQQQgghhBBC4kNB7GFmYAA6mfQsqaZFS5XFYhHmXlq3bp0wtxifeqa+vj7mnGCpuv322xN+z09+8hM8/PDDktdlOCqQy1GxYgUqVqwAAATa29Hx4YfocLnQucOFzt27wfXEMdIuDj2nWtBzCmhGKYBSFJT2o2x8N8rG9aBsXA+KRvclHtRmpgeD1XzgWj5z+I70G0Z6T51C87PPoXnzZvQcTW6+c1lxMSquvw6MToeyxYsTSiFGCJHA2AuBtS8AO58AXnsobArtEJ+8CvzxTeDaHwGX3w/kD4Ovvv4jwFNfBU7uib4eMw340pO4dfyl2PT8HhxuPjxolTePvwnXCRfUEwfP85es3ad34w87/xBx+X1z7sPSyUslK4+kX1d/J1479Sz6Eb7DYWleGbTjbkO+bGhnnfnTn/6En/70p9muBiGEEEIIIYQQQs4ZBk/yiFi2A9BSUSgUksx1TYaGvPJyjLrySoy68koAQKCnB1179ggjtTs9HgQ6OiQpq68zHy1Hy9BytAwAkF/cLwS0S8f1oITpxaDpOcdeLEoPvgSonCJJXUj6cX19aNv+Jli7HW3btiU94r9k9mxU6lahcuVK5I8eLXEtCSEJkckA5d3ARTcAr64H9sSY27G3A3jtR8CeRuCW3wGTFmakmmnh/XdwhHmnP/p6iuWAbiNQVoUCAA8segAPvvFg2FV/7fk1nrjxCUk65bT2tKJ2ey36uL6wy+eOmYtvL/p2yuWQzAlwAWw9/QLa+lvCLpdBBs34W1FeMCrDNSOEEEIIIYQQQshwR0FsQkjOySsqQplKhTKVCoARXF8fuj58Hx1bnwsGtT85gf4uTpKy+rvz0XqsFK3HSoNlFwRQOrkYZXNnoWzpMpRcq0Ne1WRJyiKZ03P0KNjNT6P5mWfQl2BqUF5eRQUqb7kFjG4VSmbPlriGhJCUjRoHrHoMWPAl4IXvAWyMDAtf7ALqrwWu+AawfD1QPISCbhwHvPsHwPE/Eee/Fiz9NlDzcMio8xXTVmD+uPnYfXr3oNV3n96N1z97HTXTalKsIoefvfszHG87HnZ5eWE56q6pQ2F+YUrlkMxysW/jWNeRiMuXVF2LSSVTM1chQgghhBBCCCGEjBgUxB5mkkkfTgZ79tlnMWvWrITeM27cuDTVZoRq+QL49B3g6DuQHXkbpaf3oxTAmDkANxvoaSlAx+kidJwqQsfpYvR1SpPCMtCXh/ajvWg/uh94cT9kxRtROn8+yharUaZWo3ThQuSVlUlSFpFWoKsLra+9BtZmR8eOHUlvp+yyy8DodajQapFXUiJhDQkhaTFLA3zjPWDbL4B3/gBwUTIucIFgMPij54Cb/w+46PrM1TNZPR3Alu/EHnFeUArc+ntgvn7QIplMhgeVD+Jrr34t7Ft/5/kdlk1ZhoK85H8aPOd9Di8feTni8oeueAhTR1Owcyg50vEJdja/F3H5rPJLMbcivVP8ZNI3vvEN6PWDz59oDh06lNQ0RIQQQgghhBBCCImNgtjDTFVVVcj/syybcGCbAuHArFmzMGfOnGxXY+TguOAIuqPvAEffDv63yRdxdZkMKK7sQ3FlH+SzOsBxQG97fkhQu7dNmssb192Njh07zgdFCwpQMns2ytTBoHaZSon8ykpJyiLJ6dy3D82bN6N5ywsItLYmtY2CceNQeeedYO68A0XTp0tcQ0JI2hWVAdr/B8zTA1seAI67o6/f/Bnw5Gpg9u3AjWagYmJGqpkw9lPgqbuAEzHmv66cBnzpCeCCBRFXWTxxMa6afBXeOv7WoGW+Zh+2eLfgjgvvSKqah5sP49H3H424/NbqW7FSsTKpbZPsYHub8PrplyIuryoci2vGXCdJGvpcMX78eIwfPz7b1SCEEEIIIYQQQsg5FMQeZqQIQA8MhBMiOY4DznxyPmB99B2g5VjSm5PJgKJR/Sga1QlmZicAoLczD52ni9Bxthwd/tHoPtUtTd37+tC1eze6du9G09/+BshkKL7wwmBAe7EapSoVCukBaNr1Nzej+YUXwG7ejO6P9ie3kfx8jFq+HIxuFUZdfTVkBXRLJGTImzgP+A8HsOMxYOv/A3raoq//0bPBeaa1DwPKtUBeXgYqGafD2wHbWqDjbPT1ZlwN6P8OlI+JuckHlQ/i7eNvg8PgKTn++OEfcePMG1FSkFgGip7+HtRur0VnX2fY5dMqpuGHl/8woW2S7OoN9OC1U8+il+sJu7xIVozrxt+OwryiDNeMEEIIIYQQQgghIwk9sR9mBgagm5qa4gpssywr/E0jsYG1a9eivLw87L+vXbs28xUa6gIB4NS+YLD6yFvB/3acSU9ZJZXAtKUonHElCqcvxeiJC4D8AvSzLDo8O9HhcqHD5ULXvn1Af5SUs/HiOHR//DG6P/4Y/iefBAAUTp92bqT2YpQtVqNw8uRhNVIpWziOQ8cHO8Da7Wh97TVw3cl1TCiaPh2VulVgbr8dBTQNACHDT14+cLkRuORm4KVa4OCL0dfvbgZe+C6wqwG45TfA+EszUs2IOA5478/Aaw9FT40OBOf31v4sZP7raC6uuhg3KW7Ci77B++Rkx0k8deAprJ27NqHq/tr9axxoOhB2WUFeAeqW1aG8cPB3KpKbOI7DG2degb83cueJFeNuRmWhPIO1yr5NmzZh06ZNg/69vb0985UhhBBCCCGEEEJGCApiDzMDA9Di4HQ0Xq9X+FuhUEhYo6HJ5XKF/ffly5dntiJDVX8v8MWu8yOtP30X6GpOT1nl44HpS4HpVwb/O3522JF0+QyDihXXomLFtQCAQHs7OnftCga1d7jQuWsXuJ7wI44S1Xv0UzQf/RTNm58GABRMmCCM1C5Tq1FUXU1B7QT0njyF5meeAfv00+j99NOktiErKcHo668Ho1uFUrWa9j8hI0HlFODLTwL7twAvfR9o/SL6+p+9B/zlauCqB4Gr/xsoTGxEsiR6O4EtDwK7n4q+XkEJcMtvgQVfSriIby38Fl498ir6An2DltXvqcedF92J0UWj49rW9mPb8cT+JyIuf1D5IOaMoelZhpLdLS74Og5GXK6sXILpZdUZrFFuOHLkCLZt25btahBCCCGEEEIIISMKBbGHIaVSCY/HAyA4Ejse4vUWL16clnoNJWq1OuxI7BkzZmS+MkNBb1dw/lF+TuvPPgB60zQyZfQUYMa5gPX0q4Ax1cF84gnKKy9H+dKlKF+6FAAQ6OlB19696NgRHKnd6fEgINHomr6TJ9Hy4otoeTE48i2fYVCqVgmjtUsuuZhSWQ/A9faibft2sDY72rZvD47mT0LJ3LlgdKsw+uabkV9RIXEtCSFDwqW3ADOvAbb+LJhmPEwqbUGgF9j+v8Dep4Ojsmdek6laAuxnQMNXgS8+jL7e6CnB+a8nLUqqmCkVU7D6otV48sCTg5a19LRg496NeED5QMztnOo4hYfeeiji8isnX4m7Z9+dVB1Jdhzv/BTv+yMHaqeWzoSauTKDNcodM2bMwLJlywb9e3t7e8TOr4QQQgghhBBCCEmNjOO4KE/ySDbJ5XJhJLXX6417hLTJZEJdXR0AwGKxwGAwxHyPSqUSAt+JlDVc7Nu3D3PnzhX+f+/evZgzh0YORdTdBhz7ADhybqT1cRfQL80o5kHGzAodac1MS085A3B9feg6cBCdbpcwWrs/zswGicorK0OpUimM1i6ZNw95RSNznsnuw4fR/PTTYJ95Fv1nkks5n1dZicpbbgGjW4WSSy6RuIaEkCHtsx3Alu8Apz6Kb/2FXwWu+xlQVhV73VQceQtovDf2VBvTrwL0m4BRqU2FcLbzLG56+iZ09HUMWlaSX4IX73wR48vGR3x/f6AfRocR7594P+zyMSVjYL/VjrGlY1OqJ8mctr4WbP78cXQFBrcJABhdUIk7LrgbJfmlGa5ZbqPfEIQQQgghhBBCSPrQ0L9haP369UIQ2+12x/UePoCtUChGXACbxKHTD3z63vn04J9/GHuezmSNn3MuaH3uVTExPeXEICsoQOncOSidOwdV994LjuPQ4/Wiw+U+F9Tegb6TJyUpK9DRgfa33kL7W28Fyy4qQun8+Sg9l368bOFC5IXJDDBcBDo70fLqq2i2b0ZHCqOZyq64AoxOhwqtBnnFxRLWkBAybExdDBi3A+/8HthmBvq6oq//4RPAxy8D128A5q9OKvNHVBwHfFAPvLoeCJPeO8Tl9wPX/RzIL0y52DGlY3DvnHvx511/HrSsq78Lll0W/HjJjyO+f+O+jRED2ADw6NWPUgB7COnn+uA49XzEAHaBrADXjb+dAtiEEEIIIYQQQgjJKApiD0MMw0Cj0cDpdKKxsREWiyXq+k6nU/jbZDKlu3pkKGg7dS41+LnXyb2Imn41WbI84IIF50dZT1uS/tFuSZLJZCieNQvFs2ZB/qU14DgOvcePBwPaLhc6d7jQc/SoJGVxPT3Cds8CQH4+SmbPPj+vtlKJfIaRpKxs4TgOXXv3gd1sR8sLLyLQ1pbUdgomTEDlnXeAufNOFE2dKnEtCSHDUn4hcPX3gDm3Ay98F/C9EX39jrPAMwZg17+Alb8CqiTq7NfbBbz4PeDDf8aob3EwtfnCu6Qp95x759yLhoMNaOoaPPXM5k82454592D66OmDlu06vQt/2PmHiNu9b+59WDppqaR1Jen19tnXcaon8pzxV4+5DmOKIo/MJ4QQQgghhBBCCEkHCmIPEWyCaYwtFgtUKhVYloXdbodOp4u6LhCcSzue1ONkGGo+FgxWH3kr+N+zn6SnnPwiYLLq/CjrKZcBJaPTU1aayWQyFE2ZgqIpU8DcfjsAoPfUKXS63cJo7e6PPw6OsktVfz+69uxB1549aNq4EQBQfOGFwYC2Wo1SlRqFE4bGw+V+lkXzlhfAbt6M7gMHkttIQQEqrl0ORqdD+VVXQZafL2kdCSEjRJUCuPtZYHdjcCR0x9no6/v+DfxpCbDMBCz9dmojopuPB+e//twTfb3Rk4E1jwfvnRIrLyyHYb4Bv/jgF4OW9XP9+P3O3+OXy34Z8u+tPa0wbTehP0I2lrlj5uLbC78teV1J+hxo3YP9bbsiLp9TsQgXjaL02IQQQgghhBBCCMk8mhM7h8lEKSsdDgc0Gk1C77fb7dDr9QAiz3NttVphNBrBMAzcbveITSU+cD47tVqN8jDpm9euXYu1a9dmsGZpwHFAk+98avCjbwPsp+kpq6AUmHrZ+ZHWU9RA4chJRdnPsujw7ETHuXm1u/buA/rTk4a9cPo0lKnUwmjtwilTQq4h2cQFAuj44AOwNjtaHQ5wPcnNn140cyYY3SpU3nYbCsZSmlpCiIQ6moDXHoo9Kpo3fg5wy2+D6ckTdfQdoPEeoP109PWmLQVW/x0Ylb5OSj39Pbj12VtxvO142OVPrXwKc8YEA5gcx6F2ey1eOfJK2HXLC8thW2nD1NGUFWOoON19As998ST6Ef67ycTiyVg5cQ3yZdRZbNOmTdi0adOgf29vb4dLNBUKzYlNCCGEEEIIIYRIh4LYOUKc0tvn88FisQjzVAPBuaqNRiMUCgWYc2mE1Wq18He07fKBbLPZLIy0ZlkWJpMJVqsVGo0GNpst5raGs4FB7Eh+8pOf4OGHH05/haQUCACnD4iC1u8AbSfSU1bxaGDaFedGWl8VTBVeUJSesoagQHs7OnftEkZqd+7aBa67Oy1lFUyYgDKVShitXVRdDVleXlrKiqT35Ek0P/MMWPtm9B47ltQ2ZKWlGH3DDWB0q1CqVOZMYJ4QMkz5tgVTjDd541hZBiz+OlDzP/FlFeE4YMdjwCs/iD3/9eJ1wPWPZuQeusW7BT9864dhl11xwRWov64eAPDMJ8/gf975n4jb+cXVv8DNipvTUkcivc7+Djz9+eNo628Ju7wsvxx3XnAPygtGZbhmuenhhx/GT3/605jrURCbEEIIIYQQQgiRDgWxcwQfmIk3kMyyLGw2W9Q04eJ1+bmxfT6f8O8ajQZGozHhEd7D0bAaid3fB5zcAxw5F7T+9B2g05+essrGnAtYnxtpPWEukEejdeIV6OlB19595+a/3oFOtweB9va0lJXPMChVqYIjtdVqlFx6CWQF0s8owfX2ovWNN9Bs34y2N98MdqJIQsn8+WBWrcLom29C/ih6gE4IyaDeLuDNXwJv/QYI9MZev2IScFMdcOktkdfp6wZe/C9g5+PRt5VfBNz8K0B5d0JVTkWAC0C/RY+P/R+HXW7VWjGhfAK+9MKX0NnXGXadW6tvxSNXPZLOahIJBbgAXjppx/Guo2GX5yEPKyeuwQUlUzJcs9xFI7EJIYQQQgghhJDMoyA2IRgcxB5SD6D6uoHPd54faf3p+0BPa3rKqrjgfMB6+pXAuIsBGhkrGa6/H10HDgTn1d4RTEHe709PB4S8sjKUKpUoUwcD2yXz5iGvuDjp7XX7DoPdbEfzs8+h/2yMeWUjyK+sxOjbbgWzSoeSiy9Kui6EECKJU/uBLQ8An70f3/qXrARurAMqJ4f+e8vnQMPdwHFX+PfxKi4A1jwRnHojw7Yf245vbv1m2GWXVl0KDhwONB0Iu3z66OloWNmA8sLBnf9Ibnrfvx0fNkdu10urVmDeaOnnYR+OhvRvCEIIIYQQQgghJMdREJsQDLEHUD0dwLEd5+ezPrYD6OtKT1nyGcG04NOXBl/yGRS0ziCO49Dj8wnpxzt27EDfifSkgpcVFaF0/nyUqlUoUy9G6cKFyB8VPSAR6OhAyyuvgt28GZ1ud9Jlly9dAkanw6iampQC6YQQIrlAAPBsAhwPA93NsdcvqgBqfhxMM56XD3z6XnD+67aT0d839Qpg9T+AiglS1DphHMfhvlfvg/tkYtfygrwCPHHTE8K82ST3HW7/GK+dfi7i8lnll2LF2Jtp+o44DanfEIQQQgghhBBCyBBDQWxCkOMPoLqagc8+AI68FQxcf74zvvSmyRh3yflR1tOWDB5NRrKK4zj0Hv8cnW7XuaC2Cz1HjqSnsPx8lMyeLcyrXapUokAuB8dx6Nq7F6zNjpYXX0w6/XnBxIlg7rwDlXfeiaIplK6UEJLjWk8AL5uAj56Nb/3JKuDiG4E3zLHv2eqvATeYMzL/dTS7Tu/CV1/6akLv+b76+7hnzj1pqhGRmr/nLJ754gn0cj1hl1cVjsPtF3wFhXmFGa7Z0JXTvyEIIYQQQgghhJAhTvoJUQkhqWk/G5zHmh9pfWIPwCU3r3B0MmDiPFF68KVA+dg0lEOkIpPJUDRlMoqmTEblbbcBAPpOn0aH2y2M1u4+eBCQom9Sfz+69uxB1549aDo3B2TxhRcCALo/+SS5bRYWouLaa8HodShfuhSyfJo/nRAyRFRMBFb/HTj4SnBu65Zj0dc/7g6+oskrBG7+JaBaK1k1U7Fg3AKsmLoCr3/2elzrXzX5Knx1dmJBb5I9PYEevHb6uYgB7KK8Ylw3/jYKYBNCCCGEEEIIISRnUBCbkFxx8GXA+TBwOvyckynLKwAmLToXtL4SmHoZUMqkpyySMQXjxmH0DTdg9A03AAD6m5vR4fEI82p37tsH9PVJUlayweui6mowq1ah8rZbUTBmjCR1IYSQrLj4BmDGVcC/HwXe/3PyncxGTQTWPB68F+eQB5QP4I1jbyAQ43ONKRmDn1/5c+TJ8jJUM5IKjuPwxpmXwfaejbjOirE3o7JQnsFaEUIIIYQQQgghhERHQWxCwli7di3KywfPB7x27VqsXbs2PYUWFEsbwC4oAaYsPj/KespioCj6HMdk6MuvrETFtdei4tprAQTnre7ctUsYqd354YfgurvTXg9ZWRlG33gDmFU6lC5aSHNrEkKGj+JRwA2PAvP1wPPfAU7sTuz9Uy4Lzn89+oL01C8FCkaB26pvwzOHnom63qNXP4oxpdQpaajY1bIDhzs+jrhcxSzF9LLqDNZo6Nm0aRM2nctMI9ae5LQqhKTK6XTC4/GgtrY221UhhBBCCCGEkLSh4ROEhOFyubBt27ZBryPpmn8YAKZeHhwtnayiUUB1DbDix8B9rwA/+BRY+wJw7Q8BxXIKYI9QeWVlKF+yBOO+/S1M//smXLzjA0x/8kmM+973UL7sGuSNGiVpeaULFmDiz/4fLty+HZMeeQRlykUUwCaEDE+TFgHr/g1c9whQWBbfe5T3Bu/NORjA5n1j4TdQlBd5fu6vzf0alk5amsEakVQc7zyKD/zbIy6fVqqAqpKOZyxHjhwJ+9vA5XJlu2okS3w+H2QyWUovq9WadPlGoxEmkwkejyfh99rt9oh1cjqdSdeJZVnI5fKw29Xr9RHf5/P5YDQaUV1dLaxfXV0No9EIn88nrGe1WmEymZKuXzrU1dUldMyrq6uh1WphNBoTOnYmkwkymQxyuTziiy9DSh6PByaTCSqVSjg+crlcOD6ptJdcwbIsrFYr9Ho9qqurhX1ZXV0NvV4f9jOaTKaEzt9MlDFU0DlD50yulJFOdXV1Udteoi+WZUO2z7fvga9w64YTqzzx50jm+w1/nOx2u8R7lhAyUlEQm5Aw1Go1li1bNug1Y8aM9BVaVA5csDD+9UsY4OKbgw/P1/0bMB0F7n4auOa/gelLgiO7CRlAVlSEMuUijDWswzSLBRe9/x5mPr0ZE364HhXXXYf8qqqEt5nPMKi6914otjyPGQ1PQa7XI38UdZoghIwA+QXA0m8B33gPuPC6yOvlFQIrfw3c+rucvz9PLJ+Iuy69K+yyeWPn4VuLvpXhGpFU9HK9KJCF7yQ5uqASK8beTJ3N4jBjxoywvw3UanW2q0ayZOBDYoZhYDabYbPZ4Ha74ff7B70sFkvIezQaTVJlezweIbg7cJvx0Ol08Pv98Hq9wkhuhmEAAGazOak6AcEgc5Xot4RGo4Hb7YbX60V9fX3Y95hMJlRXBzNB2Gw2YV/ZbDYwDAOVSgWTySQEuuN5OJ9JtbW18Hq98Hq9MBgMIcscDofwefh1LBYLlEolXC4XVCoVVCpVXA/5zWYzvF4vtm7dCo1GA5Zlhdf69etD2pwUPB4PtFotVCoVnE4njEYjHA4HOI7D4cOHhXan1Wohl8tTDhw5nU7I5fKMBjxYloXRaIRcLofRaAQQbI82m004VosXLxY6WPABVKfTibq6urjaYibK4NfP9P5LFp0zyZ8zdrtdeL9MJoNKpYJer89Y4HaonzOZ3H8GgwFbt26FzWaDWq0OaX/19fXYunVr2JfNZoPZbB7UZsUduoBg++bPE/F9m2VZ1NTUxKyf2+2GzWYLKUetVgvfYXjRztdw33W8Xi/cbrfQCU2v10tyjyCEEHCEEG7v3r0cAOG1d+/e7FTk1Yc47iejw7/qZnFc470c976V407s5bj+/uzUkQxrgUCA6/J6uaanGrhj3/8+9/Hya7mPLr5k8OuSS7mjX/sPrvnll7n+7u5sV5sQQrIvEOC4PZuD9+uB9++j72a7dglp7W7ldM/ruLmb5gqvmsYa7tOWT7NdNZKEpu4z3FPHHuP+crhOeD125Ffcme6T2a7akJczvyFIxjkcDuG419bWxlzf7/dzDMMI7zGbzUmXbTAYhO0wDJP0dnj8Z+C36fV6k9qOQqHgzGazsB2HwxF1ff5zuN3uiOv4/X5Oo9EI+85gMCRVt0zwer3CZ1coFDHXt9lswvo6nS7uctxut6THfyD+GDIME/MY8scHAKdUKjm/3x9XGX6/n/N6vZzFYuGUSqXweSwWiwSfILaB+z5WvS0WC8cwDGc2m4W2GOscTmcZ2d5/UqFzJr5zhn+PUqnkLBYL5/V6ObfbHdJWFApF1GtpqobyOZPt/Sduf/G0c57X6+UUCgUHgLPZbFHX47edzHcMpVLJKZXKuOqT6OdwOBzCPk7knCWEkIEoiE0Il0MPoA68fP6h96/mctxmA8e5NnHcmUPBh+OEZEHPsWMc++yz3Of/8xPus299izv95z9zPceOZbtahBCSmzr8HPfOH4P38DfqOK79bLZrlJSznWe5P+38E2d8zcj94v1fcKc7Tme7SiQF3f3d3KsnnxWC2B+37st2lYaFnPkNQTKOf9geb1BVp9MJ7SSeh8XRMAwT8rA62sPtePCB60SC8gM5HA7OYDBwFoslriA23wkg3gft/OfN5SC23+9P+AG/uPNAvJ8tmUBCvPh2yjBMQp0ZEnkfH3RlGIbT6XQhHR8yEYQV7/NEyvN6vXF3RElnGdnef1Kicyb+cybSZ/X7/SEdGdIRiB0O50w2918q7S+eeyW/D8TlJPJZamtr4zqXkjlf+ffx9/BUv/8QQkYuCmITwuXQA6hOluM+fIrj/EezUz4hhBBCCBmWAoEAt5N9n3v77NZsV2XYyJnfECTj+GBtPKPoxKO2UxnpzHHB4LlSqQwZsabRaJLeHsdxwucQB1USpdFoOLfbHXcQmw8axLsvEu00kA3JPOAfGHSIZ3+kKyAnDiIlE8jhgxSx6jTwnBGPUkx3EFbcPpPJhiA+lyO9P91lZHP/SY3OmdjnTG1tbczAX7IjfeMx1M+ZbO8/jku9/UULwvPb5+/b4k4tiXQMSWcQm+NC93Eu38cJIbmL5sQmJJeUVAIL1gDMtGzXhBBCCCGEDCMymQwLKy/D0qoV2a4KIUMey7JQKpXCXNLR1tPr9cL/m81mKBSKpMu1WCwwGo3Q6XTCvzmdTknmil6/fj2AYJ0TmVuXZVk0NTVBqVTG/R5+HtN46XS6mPt6KBrYFpxOZ1bqwc8nCwT3dSLHksfP+cvPXx5Jto6jx+MR6qVQKIT54BOh0WhCzr1slDEcz4NEjLRzxmq1QqFQDJoTWUypVEKj0Qjbkmr+4eFwzmRz/0llzZo1UesvVltbG/JZol2LM0mpVApzalut1oS/AxBCCAWxCSGEEEIIIYSQXBcIAK0t9Ir1CgQycjjUanXMddatWycEmJVKZVIP6Hksy8LpdAoPgvn/ApDkobtSqRQCRHxwJR4bNmxI+kF5IgGo1atXJ1XGUCJFZ4RkmEwm4W++M0OiNBqN0H6sVmvcQZdMWbdunfC32WxOejvR9k8myiChhvM5w7Ks0Kmouro66rbEQXSbzZZUfQYa6udMtvdfMsJdN5VKZULXU5vNJnR2sVqtWevoMZD4nBH/TQgh8SjIdgUIIYQQQgghhBASQ3sb8OQ/s12L3HfXV4CK0WktQqFQxBxR7XQ6Q0Y0p/pg3Gq1hgSujUajELy2WCwpBch5JpMJRqMRTqcTPp8vrlHjdrsdXq83oXL4kXEmkwmrV6+Oa3SpVqvNucBoqgaORuNH0GWS0+kU6sEwTFIjSnk6nU4YnWo2mxPqDJFOHo8nZF9HGxkaS6QMDJkog4ysc6apqSlk3WjX5MWLF0d8XzKGwzmTzf2XDI/HA71eP+h+qlarE7r3MQyD+vp6IQuMXq/H4cOHs35N4b83+Xy+hL5jEEIIQCOxCQlr7dq1WL58+aDXpk2bsl01QgghhBBCSAZt2rQp7G+DtWvXZrtqJEt0Ol3UB+5SpxEHzqcS54lHTvt8PknSc4pHO8cTgLTb7UkFkfjPwbIsZs6cGVf6cp1OJ0mgPpc0NDQIfxsMhpSCYckSd65INSCo1WqFvxsbG1PalpTEbVmKoGe4bWSiDDKyzhmFQgGDwQCGYWAwGKLeQ8RBzqqqqpTqBAyPcyab+y8ZkYLnDMOA47iEtqXT6YRObwO/j2ST+HxNZNoSQgihIDYhYbhcLmzbtm3Q68iRI9muGiGEEEIIISSDjhw5Eva3gcvlynbVSI6SMo04cD7t9sCAjTglpxSjXhmGEYLz8aQot1gsSaUFra2tFQIK/AN2mUwGrVaLurq6jKc/dTqd0Ov1qK6uhkwmg1wuh0qlgslkSlu6YrvdHjKnbrZGLYv3daodLcTvZ1k2Z+Y9lfIzAsEg5sBzOhNl5BI6Z4LSfc5YLBb4/f6Yn3XHjh3C31IE9ofLOZOt/ZcMqdOYWywWYb86nc6cmOtbfJwdDkcWa0IIGWooiE1IGGq1GsuWLRv0mjFjRrarRgghhBBCCMmgGTNmhP1tEM+cyGTksdvtkqYRBwaPwuaJR05L9YCan1uUn0s0Ep/Ph6ampqSDD263e1CwwOl0wmQyQavVQiaTQaVSpfXBOx9A12q1UCgUsNls4DgOfr8f9fX18Hg8cY8Uj4fP54PdbodWq4VerxfKzOYcrOLRf2PGjElpWwNHMGYrLe9A4lGWsebGzeUycgGdM7l3zrAsGxIQlmJO9ZF0zqRj/8Uqj3/xWVTE04NISXyeGI3GrE/JIT5fcuX+QAgZGmhObELC2LRpE+bMmZPtahBCCCGEEEKybO3atWFTh+/btw9z587NfIVIzmJZFuvWrRP+X4o04kAwMF5fXz/o3/mR03zAaOC82cng05T7fD5s2LAhYtr0SIH1eDEMA7fbDavVCovFEnbULv9w32Qywe12Szp/Jp/KnGXZsAF1pVIJh8MBo9EIvV4Pg8EQ98hPn88HuVw+qDwxi8WS8rGSgrheUs+Zmu2ASTiZmBc223PPpgudM0G5ds5s2LBBqJPZbJa8TsP9nEn3/hML187TSalUwmw2CxlTtFrtoDm3M0m8b3Px/kAIyV00EpuQHHPq1Ck8/PDDwuvUqVPZrhIh1C5JTqJ2SXIVtU2Si6hdEpJ+UqcRB4KBaZ1OF/HBujiQLFV6Xf6Bt8fjifig2W63SxJQMhgMcLvd8Pv9QjrYgcExlmWhUqkkfeit1+vBsizMZnPU9LEWiwUMw8Bqtcad5lyhUMDv94e8OI6D1+uF2WwGACHQl0tSDW4MHFmXrbllo0lXqutMl5ENdM4Mlu1zxufzhaRYT0cK+uF8zmRi/4kpFApwHBfyErfxdBDfU30+X1JTgEhFfJxz8f5ACMldNBKbkBxz+vRp/PSnPxX+X6/XY/z48VmsESHULkluonZJchW1TZKLqF0OA+WjgLu+ku1a5L7yUVkpNh1pxIFgQCjaA26NRgOGYYT5VH0+X8ojlg0GgxAcN5vNg4LjdrsdGo0mpTIG4keVi0d+8+nFPR4PWJaF0WiUZB5NcXAt0khzsdWrV8NqtUKv18Pv9yddrkKhQG1tLXQ6Haqrq4U0ydmcG5QfdQ+kHkga+H4pR86nQvwZ0zUKMRNlZBOdM6F1yoVzhmVZaLVaAMFjImWK9ZFwzqRz/yWCb+Nnz56VLA3/QDabTUjZXldXB61WK/k9PFG5cn8ghAwNNBKbEEIIIYQQQgjJdXl5QMVoesV65WX+MUeiacT5QEws/HyZ/BzRkV7iQIhUo7H5UdaNjY2DllksloyM5tJoNHC73ULQzOl0hk07nijxPornQTr/8D/WPOHxUigUQscEp9OZtsDFQFarddC8q+JAhsvlSmn7A98fbbRuJok/oxSj+e12+6ARxpkoI5vonDkvV86Zmpoa+Hw+GAwGyQOwI+GcSef+S0as7wT8iPFk8HPJ8/isCpkm7qyQK/cHQsjQQEFsQgghhBBCCCGEkCQlmkbc5/PFNe8mPwfswDS7A19ut1t4z8CAS7L4kdgDg1A+nw9NTU0pjaKKJ4AvJn74nmrQCEDCgXDxsdqxY0fK5QOhD/Cl6ngQS7jRq+L0zKl2EBBvP57RupkiTrkvRWC4oaFhUNAtE2VkE50z5+XCOaPVauHxeGCz2dKyL4b7OZPu/ZcMhUIR8b7qdDrR0NCQ0vbFmU4GdrzLFPH9O9HvAYSQkY2C2IQQQgghhBBCCCFJSCaNuNfrjWs+SKvVCqPRCIZhor6USqUQ4GFZVpKAgHibGzZsEP7dYrFg/fr1KW3b6XQmPAqMH7U3XFI1i4+/FIH5eHg8nkFBEo1GE/JvqYxwFb831TYiJXFbBlL7jEBwP6rV6oyXMdLROROk1WrhcrlCslRIbTifM5nYf8lQKBQR0+SHa4fJqK+vFzqY2O12yTq9xYOf8gQIftZspzMnhAwtFMQmhBBCCCGEEEIISVCiacR5Lpcr5khsp9OJqqqquFNuike1STWyjN8mP9c2EHzwLcWD/0SDUPx+GDNmTMplJxoMEAfc+TTJUmJZNu2pXVmWjThfuri9JJsmXpzqVqfT5Vyq2Pr6euHvVFLhezweNDU1hf18mSgjW+icCZWtc0av18Pn88Htdodd3+PxhIwUT8VwPGcyuf+k5HA4sHjx4pS3wzBMSEc7o9EoyRQd8RAHzPmpAQghJF4UxCYkjLVr12L58uWDXps2bcp21QghhBBCCCEZtGnTprC/DdauXZvtqpEsSzSNOBB8SO7xeGIGYy0WS0hgOpbVq1cLf9vtdkkCPPy82EDwobNUAWx+e4ngH7RLMXpL/BnieYAvHv0t3s+pGNiJId2po/kAQrgMABqNRjjWPp8v4blXfT6fEIBiGCYkMJUrlEql0OaS+Yw8vV4fse1mooxsoXMmVDbOGXEANlra6XiyfMRjuJ0zmd5/UvH5fHA6nZJ1atFoNCHfVaTI3BILy7JCe9doNDk1Ap4QMjQUZLsChOSiSL3C+UD2kSNHMGPGjIgPrqRaZ6jLxmdMR5mpbjOZ9yf6HmqX8cvWZ5S63Gy0y0TfR+0yftQuU9sGtcv0oXt58u+Xul3Gsx61y/Q4cuQItm3blvZyyNCSTBpx4Hxq7mgjsfl5qBMJBjIMA51OJ9SpsbExJAidLIPBAKvVCqvVCpfLFffnjMXpdMJkMsUVrOMf4ks1wpcPyPt8PmzYsCHmZ2psbBTeF89c5vEYGCgZGKTweDxYt25dyHznyWJZNma7s1gswn42mUxQKpVxdRhgWVaY25RhGLjdbsn2kdRqa2tx9uxZ1NXVwWQygWGYhM4RrVYLhUIR9T2ZKCMb6JwZLJPnjF6vB8uyMT+bw+GQdK7h4XLOZGv/SYEP/kZKlZ5MVgKz2Qyn05mxUdg1NTUAghkdpPoOQQgZYThCCLd3714OgPBSq9XcsmXLBr02btzILVu2jAPALVu2LOL2UllnYF327t0rzYfMgnj2w1AoM9VtJvP+RN9D7TJ+2WiX6Sg3G+0y0fdRu4wftcvUtpFL7ZLjqG3mYpm5fi+Pd91Y61G7TI+NGzdylZWVHACusrKSW3but4FarR42+5Qkxu/3cwzDCMfebDbH9b7a2lrhPTabLeJ6BoOBS+Zxjc1mE7avUChiru92uzkAnMPhiLkOAE6j0UTdntlsFtaNtk3xeVNbW8v5/f6I6/r9fk6pVHIMw0RdL1FerzeuY6HT6TgAnE6ni3t78ex7juM4jUYTcd+azWbOYDAMeo/4eMRTDr//4l1f3EZjtWu3280pFAoOAKdUKjmv1xtz++G2keh5lCrxeaLT6WK2K7fbzWk0mpjtP9Nl8O/L1P6jcya8dJ8z/P2Abx/hXkqlUthutGtvsobyOZPt/Zdo+xOzWCzCe2Otk+j1V3z+hTtvoq0f7+cY2N6lvIcTQkYWSidOSBibNm3CG2+8Meg1nEe0EEIIIYQQQgZbu3YtFi5cCABYuHCh8NuAphoaucRpxBUKBTQaDXw+X8jL4/HA6XTCbrfDaDRCLpeHpEodOPKOZVk4nU5otVohja1Wq41rpBTLsvB4PGhoaBD+jU9X6/P5Bo3S8vl8sFqtwugovV4Pq9UaNj2vUqkURjuGS2/OjwIbWD4/SjHSCDGDwQC/3w+PxwO5XA6j0Qi73S7Mv+10OlFXV4eZM2cCQNTRijKZLO4XT6FQwOv1QqPRQK/Xh8wN6vP5YLfbUV1dDbvdDrPZHHH0GD9vrniOXP790T4/EBy9z6e15dsKEBxRumHDhpA5YiOV4/F4hGPAv/j9ZzQaMXPmTOFzxTOvsdlshsPhgFKphMlkQnV1Nerq6oRy+M+m1WqhUqnQ1NQEs9kcNUVvuH3Gtxl+tCsANDQ0wOl0hnyWcJI53mI6nQ5erxcGgwF2ux1yuRx6vT7kmHk8HlitVuj1eqhUKmi1Wjgcjrg+X7rLSHX/JbsP6ZwJL53njNFoFO4HTqcz4ou/bgKDR+ymer4AQ/eckWL/JbsfE2l/4nbo8XhQV1cHlUol3HPDZSDh06/z62i1WmFfxUOhUITULZJEzyP+GPHt3efzCe09V7N0EEJyH6UTJ4QQQgghhBBCCImTeA5Jn88HlUqV8DYGBi9qamrg8XjAMIzwoNflckGlUsHhcERNUztz5kywLBvyXgBCGnCWZeH1eqFQKGC326HX64X1+P+aTCawLAuz2Txobu/169fDZDKFncdSXG/x9pxOJ1QqFViWhU6nCwloKRQKIc2ww+GAx+OBxWLBhg0bhGAFwzBQq9Uwm81pS62sUCjgcDjgdDphsVhQU1MT0jlBp9Nh/fr1ER+88+lv+c8tXk/c0YHf9wMxDAOv1wur1QqLxSIcF7Vaja1btwrvMZlMEeeKTaTtxRtA0Gg0cLvdQseEhoYGbNiwQTguVVVVUCqVsNlsCc9tygeVxHXh//b5fNDr9QCCgRM+aJoOfADHbDajsbERDocDJpMJTU1NIe1Pr9ejvr4+qeBLOsrI9v6jcya8dJwzfDAwUekKFA61cyab+0+q9scbGFjnty8+h5qamoRzKNZ3Bp7BYIDD4UB1dXXY5eLzdaBon0OhUAjHcvXq1RS8JoSkjILYhBBCCCGEEEIIIXHy+/2SbzOVeVwTqY9OpwPHcQltX6fTRQy8JFPvgYE1pVIZ14iwSBL9PANpNJq4HvgPVFtbOyjgnwyDwRA1UG82m+OaO1xq4lH4UrFYLCkdayD14y3Gz7+bzjmopSxDiv0H0DmTLlKeM0qlUpK2LuX5Agydc0aq/cdLdFvpbH9Stu9oc1RLdb4SQkiqKJ04IYQQQgghhBBCCCGEEEIIIYSQnEEjsQkB0N3dHfL/hw4dirhue3u78N99+/ZJvs7AsqPVJdfFsx+GQpmpbjOZ9yf6HmqX8ctGu0xHudlol4m+j9pl/KhdpraNXGqXALXNXCwz1+/l8a4baz1ql5ktc+A+HPibghBCCCGEEEIIIcmTcVLnFSFkCHruuedw++23Z7sahBBCCCGEkCHq2WefxW233ZbtahBCCCGEEEIIIcMCpRMnhBBCCCGEEEIIIYQQQgghhBCSMyiITQghhBBCCCGEEEIIIYQQQgghJGdQOnFCALAsi23btgn/P3XqVBQXF2exRoQQQgghhJBc1t3djc8++0z4/2XLloFhmOxViBBCCCGEEEIIGUYoiE0IIYQQQgghhBBCCCGEEEIIISRnUDpxQgghhBBCCCGEEEIIIYQQQgghOYOC2IQQQgghhBBCCCGEEEIIIYQQQnIGBbEJIYQQQgghhBBCCCGEEEIIIYTkDApiE0IIIYQQQgghhBBCCCGEEEIIyRkUxCaEEEIIIYQQQgghhBBCCCGEEJIzKIhNCCGEEEIIIYQQQgghhBBCCCEkZ1AQmxBCCCGEEEIIIYQQQgghhBBCSM6gIDYhJCK73Q65XA6WZbNdFTICeTweGI1GqFQqVFdXCy+9Xg+n05nt6pERimVZmEwmaLVayOVyyOVyaLVamEwmulaSnMGyLLRaLYxGY7arQoYZq9UKrVYbcl82mUzw+XzZrhohhBBCCCGEEEKGGQpiE0LC8vl80Ov1FJQhWWEymbBu3Tro9Xq43W54vV54vV643W4hOKNSqah9koxyOp2YOXMmAMBiseDw4cOw2WxgGAZ1dXWQy+Woq6vLci3JSMWyLJxOJ4xGI2bOnAmn00mBRSIZlmWFgLXJZBp0X66urobVas12NQkhhBBCCCGEEDKMyDiO47JdCUJI7qmurhYefvv9fjAMk90KkRHD6XRCq9VCqVRi69atg9oey7KQy+UAAJ1OB5vNloVakpHG5/NBpVJh69atUCqVg5ZbrVZh1KvFYoHBYMh0FckIVl1djaamJigUCmg0Gng8HjidTmg0GjgcjmxXjwwDKpUKHo8HXq8XCoVi0HKj0Qir1QqHwwGNRpOFGhJCCCGEEEIIIWS4oZHYhJBBjEYjPYAkWePxeIT/hksbzjCM8ADdbrdntG5k5DIajVi/fn3YADYAGAwGYZnRaKQRsCSjvF4v/H4/3G43zGZz2CAjIcmyWq3weDzQ6XQR25bJZAIASmFPCCGEEEIIIYQQyVAQmxASwmq1wufzCQ8jCck0/iG5UqmM2JmCDxBSoIZkitPphMlkipouXBy8oQ4WhJDhwmKxAAAWL14ccR3+fuzz+cJ2QCOEEEIIIYQQQghJFAWxCSECn88Hs9lM6ZlJVikUCmGezXBp7MUjXGnEF8kE8dzrfDAnHLVaLfzt9XrTWSVCCMkIlmWFDCmRMlHw+EA2fY8khBBCCCGEEEKIFCiITYhIXV0dZDJZymlgrVYrVCoV5HK58NLr9Tk/MkWr1cJms9H81zlmpLfLgfjAtVKpRG1tbZZrM3KNpHbJMAx0Oh0YhomapaKpqSnkPSQ7RlLbJMNbLrRll8sl/F1VVRV1XfFobEIIIYQQQgghhJBUURCbEAQftmm12pRTaHs8HsjlcpjNZhiNRvj9fmGOSoVCAa1WC71eHzKqL1fo9XqYTKaYo2xI5lC7DMWyLIxGI5xOJ3Q6Hdxud7arNCKN1HZps9ng9/thMBgirsOPVgSip90l6TFS2yYZfnKpLYsD0vF2zhEHvgkh6eF0OqNOcUIIIYQQQgghwwJHyAjj9/s5h8PBWSwWrra2llMqlRyAkJfX6014uw6HgwPAKRQKzu/3h13HYrHEXCcbzGYzp9PpQv7N6/UK+yOX6jpcUbsMz+12cwqFImQ/mM3mbFdrxKB2mRi+rSoUimxXZdijthmdwWDgAHAajSbbVSEx5HpbNpvNcddDo9FwADiGYRKuLyFDjfi3UrIvi8WSdPn8dw63253we202W8Q6ORyOpOvk9/s5hmHCbnfgb00xr9fLGQyGkO/8CoWCMxgMIdcd/jqZS8TXyHheCoWC02g0nMFgSOjY1dbWCtfXSC++DCm53W7h3sQfH4ZhhOOTSnvJFX6/n7NYLJxOp+MUCoWwLxUKBafT6cJ+xtra2oTO30yUMVTQOTP0zxmpSHFemM3mqMc40Veu/rYbiK5b8Ul3++CvMwNf8balWOWJP0cy3zP5Y2Wz2STes4RkHgWxyYjDP9RjGIbTaDRcbW0t5/V6Q77EJvqwUPyDPdYXa/7BslKpTOVjSIYPEg5EQezMonYZm9/v52w2m/CFjr6IpR+1y/iJf1gk80CZJIbaZnQUxB46cr0tJxLE1ul0aXkgTEgucrvdgx5Yms1mzmazcW63m/P7/YNefMeRVDqoDCzbYDAktQ2/3895vd6QQE+q9w2z2RwSiNZoNJzb7ea8Xm/E35N8+XyAit9XfCCIYRjhupjK500nr9crBOIHdgjgPw+/jsPhCOmwpFQq4/5N4/V6ObfbHXKtBYIdfPl9LNXvdrfbLXRMUiqVnMViEdor3/mK/7wMw6QctHA4HBn/fef3+0OOmU6n4ywWC+dwOIRjxbdphUIh3E/5+3Y8HaszUQa//lD6fUznTHLnjM1m4zQajXC9ViqVQpsaSqQ8L/j7hcPhEPY//+Lvx+FefAfSgW0j1vfmbB+DoX7dyvT+y0T74K9XAwPN8Twn4PenuByNRiPs64HrhrtuhvvOyV/7zGazcO2U4l5NSDbREwZCzknlYSF/w4n3JiW+aWaT3+/nFApF2M9LQezcMBLbZSziuubaaIyRgtplKHEQigLY2UVtM4iC2ENfrrTlZEZiUxCbjAT8vT/e76MDRymnkllI/ABTiswH/GdINbiuUChCrhmxRhzynyPadye/3x/ywDsXg9g88fU0nqw84hHx0UaqDyTuxJCOzBf8MWQYJuYx5I8Pf8+J97kB/5DdYrGEZCDJ1AP2gfs+Vr0tFovQUYVvi7HO4XSWke39JxU6Z+I7Z/j3iIPjfIBKPDp2KPwOTed5IT7OiWRG83q9QgesSL/rcuEYDOXrVi7sv3S2D349ftvJfNdTKpUJ/3aL93PwHZ0SvXYSkkvoCQMh5yT7sNDv9yccUONvatFuUAN7fKXyilQvjUYT8SZMQezcMBLbZTzED+8oJVfmUbs8j/8xFKlDEMksaptBFMQe+nKlLYtHjlI6cULO4x/0xhtUFd8PUs3iwaem5beXamcq/vxO5Ts6P8pQfM2I9h090ZGm/OfN5SC2+Pob74NlceeBeD9bMg+w48W3U4ZhErr3JPI+8agwnU4X0vEhE0FY8T5PpLyBWVKitd10lpHt/SclOmfiP2cifVa/3x/SkSGXA9npPvdSOc6x7knZPgbD4bqV7TaczvbBb58/n8W/4eP9PLW1tXFd05K5bvLvi+f5BSG5qgCEkJRYrVbh78WLF8f1HoVCAZ/PB4/HA5/PB4VCMWid9evXQ6vVSlJHjUYz6N/q6uqgUCig0+kkKYPklqHaLuOl1WqFz2g2m1PaFsmc4dYunU4ntFotdDodbDabJOWT7BhubZOMXFK3ZfHfLMtG3U5TU9Og9xAyXPHt3Ww2x1zX6XTCbrcL/5/Kdwa73Q6FQoH169dDr9cDACwWS8q/6aqqqqDT6WC322G1WuP6XGJmsxlmsxkulyuu9U0mEwDEXW+z2Sx83uHEaDSirq4OQPD6bTKZsnYNNZlMQjvdunVrQvWw2Wyorq6Gz+eDVquF1+uNuO7WrVvBMIzw/x6PJ+k6J8pqtQr722w2w2AwxP1ehUIBm80W83tdusvI5v7LBSPtnOGvlRaLJexyhmFQX18PlUoFANDr9VHPv2zJxLmXCo1GA6VSGXbfZfsYDPXrVrb3nxSitY+BFAoFzGaz8Llz5fMwDAObzQaVSgWPxwOj0RjxmBCSk7IdRSckVyQ74kXcYyzeEaHiUaTZ6i0r7r2fyCsX06YOZyOtXfK9MBUKRdR6J5sKiEhjpLXLcPgRWJHqRBkssoPaZhCNxB76cqUti3v7x/oOOBRGShIiFX6ewVikTCPOccGMB/x5Kv6Nlsr3Dv794u/Xifzm47+/cxwX90jsZK5vDMPk9PUl2dFR4uMYz/eJdIwqFafHTzbVqHgbiRwncbtL5/cpqX4/8iNow53LmSgjWpm59n00FjpnYp8z/Ij7RKZ0ybV2kKnzItXjbDabw/52yuYxGA7XrVxpw+lqH+Lti7NRiT9PPPfEdI/E5ol/9+Vy5gZCBsoDISQl4p6vVVVVcb1H3HPW7XZLXaW4eL1ecMEpBcK+xCMExOvSyO2hYai2y8bGRmEkWLRegT6fT/ibRnwNHUO1XQ5kt9uxbt06uN3usL2U6+rqsGHDhizUjCRruLRNQqRuywzDCPdZ8b03HH75cBwtSUg4arU65jrr1q0TshgolUrU1tYmXR7LsnA6ncJ3D/F3EHEWhmQplUrhfE9kdM6GDRtgNBqTKtPpdMa97urVq5MqYyiJlfEiXfgRY0Awi0wyNBqN0H6sVmvMe0amrVu3Tvg70UwDYtH2TybKIKGG8znDsixYloXdbkd1dXXUbSmVSuHvXMsQlovnRbjrk1KpzLljMNSvW9nef8mKt31EY7PZhN9YVqs1oe876SS+don/JiTXURCbkBQMvIHFG0wbM2aM8He8KdeyKd6HoCQ3DOV2Ka5rtJRHO3bsEP6mh+VDw1Bul2J82jq32x3yQ0tsx44dcafxJdk3XNomIelqy/wDDofDEXEbfPBcoVBQ6vs04gIcujp66BXjxQW4tB8LhUIRMz2nlGnEgeB3EHHgWhw4liolJH++O53OuB/W2u32hFKPAuevTyaTKe4glFarjfkQfKgZmAo6G9dPp9Mp1INhmIjfb+Mh7vCeSjBEah6PJ2Rfp9IxX6lUhnT+ymQZZGSdM/y0Fbxo12Txb8+B78umXDwvPB5P2Pu3Wq0etI+zeQyGw3VrKLbhRNpHNHyadJ5er89apxsxhUIhfAdL5LseIdlGc2ITkoJkb0DiG3su3MRiaWpqoh9RQ8hQbpf8XDMajSbiwzCWZYXRJtHWI7llKLdLXl1dHUwmE2pra0MeSoudPXsWdrudRlAMIcOhbQ6USw+vSOakqy0bDAaYzWbhQUe44His+e6INLq7euFxHMp2NXKeUjsLJWVFaS0j1oNelmVDOlqazeaUswdZLJaQQDg/ctrn8wnz2qcSTAGCo5354LjFYokZiLTb7UkFkYxGoxDAnjlzJurr62Pu0+GYEayhoUH422AwpHz8kiFuU6kGBLVarTCvamNjY87cE8T1kCLoGW4bmSiDjKxzRqFQwGAwoLGxEatXr456DxEHonJpEEounheRficxDAOOC+0El81jMByuW0OxDSfSPmLR6XQwGAywWq3C98JonYIzRTyq3G63p5QliJBMoZHYhGRZLj5oZlk25MeB3W7PuQf0JL2y2S7dbjecTie0Wu2gtufxeKBSqcCyLHQ6XdbTDJHMyma75APY4r/DvfiHENl4oEKyJ1fu5SzLhvSod7lc8Hg8dA8ncYvUlt1uNxQKBVQqVUg6PP6BjNPphMVioYfuhJwjZRpx4Hza7YHfL8SpIKUIGDIMIwSL40lRbrFYkkpHWVtbKzzM5q8jMplMCOhkOu2m0+mEXq9HdXU1ZDIZ5HI5VCpVQiPFE2W324XvjTqdLmsBX/G+TrWjhfj9/HeSXCDlZwSCQcyB53QmysgldM4EpfucsVgs8Pv9MT+rOFOd1L9DUznWuXheJPoMKVvHYLhct3KhDSdC6meMFoslZOSzFNO/pEp8rHMhqE5IPCiITUgKBj7oS2a0ci49WK6rqxO+lDqdTjAMA4ZhsGHDBsjlcshkspyZx4NENhzapdvthtlsRkNDA2pqaiCXyyGXy7Fu3TpoNBq43e6QOWZI7hvK7dLn8yX0gJbmaR9ahnLb5PEP/+VyOWpqakIyqPDXUD44QIavdLZlhmHg9XphNpthMplQXV0NlUqFmpoaVFVVwev1UmYUQs6x2+2SphEHgg9Bw807LZ4nWqoHo3w2GX4ey0h8Ph+ampqS/t4TbmoWp9MJk8kErVYLmUwGlUqV1ge+fABdq9VCoVDAZrOB4zj4/X7U19fD4/Fg5syZUfdDInw+H+x2O7RaLfR6vVBmNjvmiu8d4uklkjFw9FyudPITj/BLV0r6TJSRC+icyb1zhmXZkOd0UmUEk+JYZ/O84OdjZllWyFZiNBrTck9JxzEYSdetdLXhWGVmqn2Ir1dGozHrKbzF161cuU8TEgulEyckBdl+aC212tranO7tS+IzXNqlUqmkkdbDyFBulwqFIuHUUWToGMptk0fXSgJkpi0bDAYKVhMSBcuyWLdunfD/UqQRB4KBcfHcijx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        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "pop.get_dco_merger_efficiency()\n", - "pop.plot_merger_efficiency(channels=True)" + "from posydon.popsyn.synthetic_population import Population\n", + "BBH_pop = Population('BBH_contact.h5', chunksize=10000)\n", + "print(BBH_pop.number_of_systems)\n", + "\n", + "BBH_pop.mass_per_metallicity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can also compute the merger rate density, i.e. the number of merging DCO per unit volume per unit time as a function of redshift. This calculation will generate a weight that can be used to reweight the synthetic population to obtain the intrinsic merging BBH population." + "# Transient population\n", + "\n", + "Although we now have selected all binaries with a BBH merger, we don't have the extact moment of the merger yet.\n", + "\n", + "For this we will create a `TransientPopulation`.\n", + "This class is used to hold information about a specific event/moment in time.\n", + "\n", + "In our case, this is the moment of \"CO_contact\". However, we might want to store \n", + "and calculate some additional values, such as the $M_\\mathrm{chirp}$ or $\\chi_\\mathrm{eff}$.\n", + "\n", + "The `Population` class has a function `create_transient_population` (see [here for more details]()).\n", + "\n", + "In short, it takes a `selection_function` and a `transient_name`.\n", + "\n", + "The `transient_name` is a string identifying the transient population in the file,\n", + "while `selection_function` extracts the `TransientPopulation` for us.\n", + "This can be any custom function you want it to be, as long it outputs a pandas DataFrame with a 'time' and 'metallicity' column.\n", + "\n", + "Several selection functions are provided in `posydon.popsyn.transient_select_funcs`:\n", + "1. BBH_selection_function\n", + "2. GRB_selection_function\n", + "\n", + "We have copied part of the BBH_selection_function below to explain how a `selections_function` works." ] }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 138/138 [01:25<00:00, 1.61it/s]\n", - "100%|██████████| 14/14 [00:38<00:00, 2.73s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "DCO merger rate density in the local Universe (z=0.00): 118.48 Gpc^-3 yr^-1\n", - "Intrinsic population successfully saved!\n" - ] - }, - { - "data": { - "text/html": [ - "
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        2888058 rows × 12 columns

        \n", - "
        " - ], - "text/plain": [ - " metallicity time t_delay S1_state S2_state S1_mass \\\n", - "0 0.014200 6.205715 4.287125e-08 BH BH 12.260188 \n", - "1 0.014200 4.705799 9.754266e-01 BH BH 12.850735 \n", - "2 0.014200 8.187239 2.075700e+00 BH BH 8.314851 \n", - "3 0.014200 6.647572 2.472628e+01 BH BH 9.826765 \n", - "4 0.014200 6.676527 1.680554e+01 BH BH 20.974131 \n", - "... ... ... ... ... ... ... \n", - "2888053 0.000001 9.558857 1.547540e+03 BH BH 12.404415 \n", - "2888054 0.000001 5.679476 5.173339e+02 BH BH 31.263586 \n", - "2888055 0.000001 6.972682 1.105689e+04 BH BH 22.020038 \n", - "2888056 0.000001 6.442639 1.310371e+04 BH BH 21.311099 \n", - "2888057 0.000001 6.862427 1.727560e+03 BH BH 26.153757 \n", - "\n", - " S2_mass S1_spin S2_spin orbital_period eccentricity \\\n", - "0 12.183039 0.050168 8.244612e-04 326.580311 0.999994 \n", - "1 20.143346 0.027546 5.791334e-17 2377.230212 0.999817 \n", - "2 8.315119 0.100591 6.554160e-03 38.651550 0.996045 \n", - "3 9.853595 0.032525 2.400078e-02 14.240625 0.980550 \n", - "4 12.500642 0.000531 4.772162e-04 696.487802 0.998932 \n", - "... ... ... ... ... ... \n", - "2888053 17.186149 0.353053 1.279050e-01 1.368867 0.072070 \n", - "2888054 34.303592 0.127590 1.671062e-01 1.495336 0.003466 \n", - "2888055 17.356224 0.156907 1.396343e-01 3.686468 0.234744 \n", - "2888056 36.473143 0.104700 1.194260e-01 4.546062 0.059527 \n", - "2888057 24.910536 0.116013 1.727154e-01 2.019769 0.054964 \n", - "\n", - " channel \n", - "0 ZAMS_CC1_oRLO2_CC2_END \n", - "1 ZAMS_CC1_CC2_END \n", - "2 ZAMS_oRLO1-reverse_CC1_CC2_END \n", - "3 ZAMS_oRLO1-contact_CC1_CC2_END \n", - "4 ZAMS_CC1_oRLO2_CC2_END \n", - "... ... \n", - "2888053 ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", - "2888054 ZAMS_oRLO1-contact_CC1_oRLO2_CC2_END \n", - "2888055 ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", - "2888056 ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", - "2888057 ZAMS_oRLO1-contact_CC1_oRLO2_CC2_END \n", - "\n", - "[2888058 rows x 12 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "pop.get_dco_merger_rate_density()\n", - "pop.save_intrinsic_pop(os.path.join(path,'BBH_intrinsic_population.h5'))\n", - "# pop.load_intrinsic_pop(os.path.join(path,'BBH_intrinsic_population.h5'))\n", - "pop.df_dco_intrinsic[cols]" + "import pandas as pd\n", + "def BBH_selection_function(history_chunk, oneline_chunk, formation_channels_chunk):\n", + " '''A BBH selection function to create a transient population of BBHs mergers.'''\n", + " \n", + " indices = oneline_chunk.index.to_numpy()\n", + " df_transients = pd.DataFrame(index = indices)\n", + " \n", + " df_transients['time'] = history_chunk[history_chunk['event'] == 'CO_contact']['time'] * 1e-6 #Myr\n", + " df_transients['metallicity'] = oneline_chunk['metallicity']\n", + "\n", + " return df_transients" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can now visualize the merger rate density as a function of redshift." + "A `selection_function` always has as an input a chunk of the history, oneline, and formation_pathways (optional).\n", + "For example, you set your `chunksize=10` when you initialise the `Population`, like we've done above for `BBH_pop`, each chunk will contain 10 binaries.\n", + "This means that the complete history, oneline and formation_pathways of those 10 binaries are passed to this function.\n", + "\n", + "\n", + "The `BBH_selection_function` selects the moment the binary reaches CO_contact as the moment of merger, and stores it in the `time` columns in Myr.\n", + "The metallicity is also outputted.\n", + "\n", + "We can test this function by inputting a single binary into it, as done below.\n", + "If you've used the example populations, you might not have calculated the formation_channels yet. We will set that input to None." ] }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "pop.plot_rate_density(DCO=True, channels=True, **{'ylim' : [0.05, 800]})" + "BBH_selection_function(BBH_pop.history[0], BBH_pop.oneline[0], None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can now compute the BBH detection rate. This calculation will generate a weight that can be used to reweight the synthetic population to obtain the observable merging BBH population. Here we consider a gravitational-wave detector composed of LIGO and Virgo at design sensitivity, refer to the v2 paper for further details. " + "This gives us a dataframe containing our 1 binary, its index (0), the moment of merger, and its metallicity.\n", + "Of course, we could like to know a bit more about our merger than just when it occurs.\n", + "\n", + "Lets expand our output with the BH masses, their spin, their tilt, and the orbital period at DCO formation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def BBH_selection_function(history_chunk, oneline_chunk, formation_channels_chunk):\n", + " '''A BBH selection function to create a transient population of BBHs mergers.'''\n", + " \n", + " indices = oneline_chunk.index.to_numpy()\n", + " df_transients = pd.DataFrame(index = indices)\n", + " \n", + " df_transients['time'] = history_chunk[history_chunk['event'] == 'CO_contact']['time'] * 1e-6 #Myr\n", + " df_transients['metallicity'] = oneline_chunk['metallicity']\n", + " \n", + " # Added properties\n", + " mask = (history_chunk['S1_state'] == 'BH') & (history_chunk['S2_state'] == 'BH') & (history_chunk['step_names'] == 'step_SN') & (history_chunk['state'] == 'detached')\n", + " df_transients['t_inspiral'] = df_transients['time'] - history_chunk[mask]['time']*1e-6\n", + " df_transients['S1_state'] = history_chunk[mask]['S1_state']\n", + " df_transients['S2_state'] = history_chunk[mask]['S2_state']\n", + " df_transients['S1_mass'] = history_chunk[mask]['S1_mass']\n", + " df_transients['S2_mass'] = history_chunk[mask]['S2_mass']\n", + " df_transients['S1_spin'] = history_chunk[mask]['S1_spin']\n", + " df_transients['S2_spin'] = history_chunk[mask]['S2_spin']\n", + " df_transients['S1_spin_orbit_tilt'] = oneline_chunk['S1_spin_orbit_tilt']\n", + " df_transients['S2_spin_orbit_tilt'] = oneline_chunk['S2_spin_orbit_tilt']\n", + " df_transients['orbital_period'] = history_chunk[mask]['orbital_period']\n", + " \n", + " return df_transients" ] }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 138/138 [13:07<00:00, 5.71s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "DCO detection rate at design_H1L1V1 sensitivity: 8047.26 yr^-1\n", - "observable population successfully saved!\n" - ] - }, - { - "data": { - "text/html": [ - "
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        25204880.0000016.4426391.310371e+04BHBH21.31109936.4731430.1047001.194260e-014.5460620.059527ZAMS_oRLO1_CC1_oRLO2_CC2_END
        \n", - "

        2520489 rows × 12 columns

        \n", - "
        " - ], - "text/plain": [ - " metallicity time t_delay S1_state S2_state S1_mass \\\n", - "0 0.014200 6.205715 4.287125e-08 BH BH 12.260188 \n", - "1 0.014200 4.705799 9.754266e-01 BH BH 12.850735 \n", - "2 0.014200 8.187239 2.075700e+00 BH BH 8.314851 \n", - "3 0.014200 6.647572 2.472628e+01 BH BH 9.826765 \n", - "4 0.014200 6.676527 1.680554e+01 BH BH 20.974131 \n", - "... ... ... ... ... ... ... \n", - "2520484 0.000001 4.893422 4.453281e+03 BH BH 44.218580 \n", - "2520485 0.000001 9.003064 9.227190e+03 BH BH 12.479949 \n", - "2520486 0.000001 4.633425 2.497501e+03 BH BH 36.989173 \n", - "2520487 0.000001 6.972682 1.105689e+04 BH BH 22.020038 \n", - "2520488 0.000001 6.442639 1.310371e+04 BH BH 21.311099 \n", - "\n", - " S2_mass S1_spin S2_spin orbital_period eccentricity \\\n", - "0 12.183039 0.050168 8.244612e-04 326.580311 0.999994 \n", - "1 20.143346 0.027546 5.791334e-17 2377.230212 0.999817 \n", - "2 8.315119 0.100591 6.554160e-03 38.651550 0.996045 \n", - "3 9.853595 0.032525 2.400078e-02 14.240625 0.980550 \n", - "4 12.500642 0.000531 4.772162e-04 696.487802 0.998932 \n", - "... ... ... ... ... ... \n", - "2520484 41.575484 0.012826 9.205180e-03 4.026325 0.103587 \n", - "2520485 17.623755 0.111116 1.095940e-01 2.702914 0.078041 \n", - "2520486 41.449699 0.013867 1.278169e-02 3.101543 0.141294 \n", - "2520487 17.356224 0.156907 1.396343e-01 3.686468 0.234744 \n", - "2520488 36.473143 0.104700 1.194260e-01 4.546062 0.059527 \n", - "\n", - " channel \n", - "0 ZAMS_CC1_oRLO2_CC2_END \n", - "1 ZAMS_CC1_CC2_END \n", - "2 ZAMS_oRLO1-reverse_CC1_CC2_END \n", - "3 ZAMS_oRLO1-contact_CC1_CC2_END \n", - "4 ZAMS_CC1_oRLO2_CC2_END \n", - "... ... \n", - "2520484 ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", - "2520485 ZAMS_oRLO1-contact_CC1_oRLO2_CC2_END \n", - "2520486 ZAMS_oRLO1-contact_CC1_oRLO2_CC2_END \n", - "2520487 ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", - "2520488 ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", - "\n", - "[2520489 rows x 12 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "pop.get_dco_detection_rate(sensitivity='design_H1L1V1') # if already computed use load_data=True\n", - "pop.save_observable_pop(os.path.join(path,'BBH_observable_population.h5'))\n", - "# pop.load_observable_pop(os.path.join(path,'BBH_observable_population.h5'))\n", - "pop.df_dco_observable[cols]" + "BBH_selection_function(BBH_pop.history[0], BBH_pop.oneline[0], None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Visualize the BBH Intrisic and Observable Property Distributions" + "With this new function, we have a lot more information available in the TransientPopulation.\n", + "\n", + "You can further customise this to your liking, if you want to store specific information.\n", + "It's also possible to calculate additional infomration based on any value in the history, oneline or formation_channels.\n", + "\n", + "We will import some functions to calculate $\\chi_\\mathrm{eff}$, $q$ and $\\mathcal{M}_\\mathrm{chirp}$ and also add them to the dataframe.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from posydon.popsyn.transient_select_funcs import chi_eff, mass_ratio, m_chirp\n", + "import pandas as pd\n", + "\n", + "def BBH_selection_function(history_chunk, oneline_chunk, formation_channels_chunk):\n", + " '''A BBH selection function to create a transient population of BBHs mergers.'''\n", + " \n", + " indices = oneline_chunk.index.to_numpy()\n", + " df_transients = pd.DataFrame(index = indices)\n", + " \n", + " df_transients['time'] = history_chunk[history_chunk['event'] == 'CO_contact']['time'] * 1e-6 #Myr\n", + " mask = (history_chunk['S1_state'] == 'BH') & (history_chunk['S2_state'] == 'BH') & (history_chunk['step_names'] == 'step_SN') & (history_chunk['state'] == 'detached')\n", + " df_transients['metallicity'] = oneline_chunk['metallicity']\n", + " df_transients['t_inspiral'] = df_transients['time'] - history_chunk[mask]['time']*1e-6\n", + " \n", + " df_transients['S1_state'] = history_chunk[mask]['S1_state']\n", + " df_transients['S2_state'] = history_chunk[mask]['S2_state']\n", + " df_transients['S1_mass'] = history_chunk[mask]['S1_mass']\n", + " df_transients['S2_mass'] = history_chunk[mask]['S2_mass']\n", + " df_transients['S1_spin'] = history_chunk[mask]['S1_spin']\n", + " df_transients['S2_spin'] = history_chunk[mask]['S2_spin']\n", + " df_transients['S1_spin_orbit_tilt'] = oneline_chunk['S1_spin_orbit_tilt']\n", + " df_transients['S2_spin_orbit_tilt'] = oneline_chunk['S2_spin_orbit_tilt']\n", + " df_transients['orbital_period'] = history_chunk[mask]['orbital_period']\n", + " df_transients['eccentricity'] = history_chunk[mask]['eccentricity']\n", + " \n", + " # Added\n", + " df_transients['chirp_mass'] = m_chirp(history_chunk[mask]['S1_mass'], history_chunk[mask]['S2_mass'])\n", + " df_transients['mass_ratio'] = mass_ratio(history_chunk[mask]['S1_mass'], history_chunk[mask]['S2_mass'])\n", + " df_transients['chi_eff'] = chi_eff(history_chunk[mask]['S1_mass'], history_chunk[mask]['S2_mass'], history_chunk[mask]['S1_spin'], history_chunk[mask]['S2_spin'], oneline_chunk['S1_spin_orbit_tilt'], oneline_chunk['S2_spin_orbit_tilt'])\n", + " \n", + " return df_transients" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "BBH_selection_function(BBH_pop.history[0], BBH_pop.oneline[0], None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can now generate distribution of the observable properties of merging BBH for the intrinsic and observable BBH population by reweighting the synthetic population with the corresponding weights and visualize the distribution as follows." + "Before running this selection on the whole population, we would like to include the formation_channels too.\n", + "First, we calculate them for the population:" ] }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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8oGAwqMuXL+u5557reNOHehqdx1Y4fd5a0c62FhYWKtNOf1d06tw7cU/rxHvv1LXQzXOy1zT63O7F74xe1Sv36VZ06/zv1c9kn9sBAHtFJx/Mui0SiSgSiSibzWpubk75fF7z8/NbfsWbnZ1VMBi01VmMHb3SJiUej1c6jEmlUpqcnKy8VigUKsfEDjeO43514sSJhr8it3usq1+PxWIdb/+6m/tAp67FeDy+pcOjZDLp2kNIs/Nol9PnrR29VMpR1ulz34v3tF68FlpVTuz2anKA3eul+zQ6hxJnHGj5fL7nSgidFAqFlEwmNTc3V2l/U91jZbmUoV292F7HMIxK+6hyclKWTCbr9l7ciNPHsZOqE73d/JjR6S/86lgaVdlq9Vj3YmcrZZ24FoPBYOX9zszMVDqhcYvd89hML523djqk6ca9z8lz30v3tE5dC25+H7XbwVy3VB+P7Z/bXv3OaBRzr3HrPt2JZ9pePf+9gsQZB9rS0pLjw0W4qbqEq5Zyj4+5XK4jPdTW6uG4F1RXf61+f1evXrVVNbbbx7GTwuFwZbqV87F92U53rFO9/ept7/ZYV7eF68XP9m6uxeo2p7FYrGlvw7V0+jNZ7zy2qpfOW/m6auVYOd3xVKfPfS/f0zp1Lbg5ukE54eiVJkvVth/T7cepF78zmsXcLieeUdy8T3fimbYXz38vIXEG9rGlpSU988wzTZczDKPyULSbm+72oUha6cDDydKMUChUiWt2drYy3Em5TVwz3T6OnVT9pT0/P297vepl7R4nu6qPzfZt7/ZYVw+D0WrHRt3o3K3da3H7ED3Nhvuobt9ZLZFIdOyz1ug8tqqXzlv5fbRS2hIIBLa8f7sx2dmHE+e+l+9pnboWnDwnzZSTiF5MnKuHH6v1ue3F74xmMZe12hN+p/XifbpVvXj+ewmJM/a16iontX45KxQK+/bDXZbP5239algeGmC3QwRUD2hvt4Si3IGGk6o74Uin00qlUi11zNHJ4zg7O9u1HrgNw6hUATZN0/aPGdXDz9g9j3YfOKsfgmptezfH2jCMLV/8zcaoLTNNU8FgsCu1JNq5Frc/5DXrebuVB57tOnUeW9FL56085Firn9HqXqrtJKWSvRIdp859t78b7OrkteDUOWmmfA6CweCut2WX3c9tdU/OtT63vfid0SzmsmbPfNWc+A52+j7djWfabp7/vYjEGR3ldtu07aq/tGrdoHK5nO1xWXvtvbXi0qVLTZcpH5/djqcciUQqVXSy2aytm24ikdjygOOE6vajiURChUKh5SpUuz2OpmlqZGSkMhRFo6qSnZRMJitfpnaqphcKhcqv3dXnsxm7nayVH4JisVjdbe/mWF+5cqVS8pBIJGw9CF+6dEmxWKxpiUUn7gPtXIvb42r0nkzT3PIAVx3z0tJS03aXnTyPrXDyvLWi+r208nBd/VmZnp62VaJV/Xmsd205ee67+d3Qik5dC06dk0ZM06zE283qqna+Q6uPZaPPbbe+MzoZs7T1eDfreTuRSHS8Ha7T9+lOPtM20q3zvydZQAclk0lLUuUvlUrZWm9ycrKyTjKZbLp89T4aWV5eriwXCAR2vO73+63l5eW660cikcr6uVyuaVzbVR+PmZmZpst3+jhUx9/sXAQCAcswjLqvt/peAoGAJcny+/0Nj93k5GTD/XZS9fGYnJxsa73dHMeZmZkt58zv97cU/24sLCxYhmFYkqxYLGZruUgk0nS7sVis8l4Mw2j6OSkvX2/bnTrWCwsLlt/vtyRZhmE0/Jw3uwZ3ex9otk2712IoFLJ1bEKh0JZrrXr7gUDAWlhY2LFOp89jtep7R7Nz2snzthvlGOx+h1WrvvfVOtZloVCosmz5/TZatpPnvpPfDU59f3fyWnDinNRTPv7duL+XP4flZ5xG95Lqz6Gdz63T3xlOxGxZ757rWs98ZZOTk1Yymdxyrufm5hpu1+59zMn79G6faVt5jnPi/Dt1r+im3okEe1Iul7Pm5uasmZkZK5lMVr7kqhODZDJpzczMWHNzc1sexsrrplKpLev5/X4rlUpZc3NzW24c5f1U33TLH9Ty9mupvjHFYjFreXnZWlhYsEKhUM0bdjmu7T8CBAKBSlyNbrALCws135dhGJX1q29sTh6H8sNR+aEiEolsOQfLy8vW3Nxc5cFo+8Nyq+9lu+qbZCwWqyy/vLxszczMWIFAwAoEAg230Um5XK4STyv73O1xLJubm9txTXXT8vJy5b2Uz+HCwoK1vLxs5XI5a3Jy0vL7/ZXPrR3l63BmZqay/Vqfj/Jr5eu6nk4d6+3vt/yeyp+lhYUFK5VKWYZhWKFQaMf1sNv7QDO7vRZrPZSV72vlc1f9+UulUtbk5KQVCoVqbrfT59HOvaNeArOb89Yp5f03emBspPqHiO0PqDMzM5ZhGFYymdxxTyhfY7UeQDt57nf7OevW93cnrwUnzkkt5WNf77PWSdXHc25uzopEIlYoFNpyvhYWFra891Z+DHLyO8OpmKt/cIlEIjuetyKRSOVzXZ04l99jddLa7n3Mqfu0ZbX+TLub57hOnP9u3Su6hcQZu1L+lan8wWn0tz1RqL6x1Fq2+terhYWFustWL1/vQax8E66Oo96HvPyeGsXVaF923lf1F4STx2FycnLLjbT6F9byOqFQqO6XUqvvpd6xn5yctAKBwJbSg/JNsNtCoVDLD8O7PY7bt1U+Bo1KPpxU65xUv4dWkpFYLLbjS76ccKZSKSuZTFqTk5NWJBKxfXw6daybvd96yaFl7f4+YEc716JlWZUHlnJSUy4lC4VCO95P9YNNo311+jzauXc0K2lv57x1Sjl52k2J9vb4y6X5sVisct2Uf0ApP7CHQiErEok0fAjtxLnvxndDp76/ax3Ldq8FJ87JduXj2I3vt+rkovxdXE5GDcNo+565Xae/M7oRc/marr7WyqW8ZeXXy+e6nFSX389u7mNO3KfLWnmmdeo5zu757/a9wmkey7IsAQAAoGJoaEimaWphYWHfdyKJzjBNU0NDQ/L7/VpeXnZ8f/F4vNK2NJfLdbUderv2YsxAGZ2DAQAAbFPu6dxur87A1atXJW3tABDA/kHiDAAAsE05+akeCgdopNxLdCtDHQLYO0icAQAAtvH7/UomkzJNs1K1FKhndnZWhUJByWRS/g4PcwSgN5A4AwAA1DA5OSnDMBwfZx5739TUlAzD0OTkpCv778QY8922F2PGwUbiDAAAUMfMzIwKhYISiYTboaBHTU9PK5/PK5VKdXW/hUKhMm2aZlf33a69GDNQ1ud2AAAAAL0qEAgolUopHo/r6aefphdgbFH+USWZTCoUCjm+v3w+r6WlJeXzeWWz2cr8RCKhpaUlGYZR+esVezFmoBaGowIAAGgiHo/r6tWrunnzJm1YIalUYhoMBhUIBDQzM9OVfY6MjFRKbbdfh+US3GQy6VqV8Vr2YsxALSTOAAAANsTjcc3PzyuXy7kdCnpAOByW3+/vWtIMwF0kzgAAADaV2zrTYdjBNj09rVu3bnEdAAcIiTMAAAAAAA3QqzYAAAAAAA2QOAMAAAAA0ACJMwAAAAAADZA4AwAAAADQAIkzAAAAAAANkDgDAAAAANAAiTMAAAAAAA2QOAMAAAAA0ACJMwAAAAAADZA4AwAAAADQQJ/bAWD/MU1T165dq/z/0Ucf1aFDh1yMCAAAAECve+edd/TKK69U/j8+Pi6/3+9eQFVInNFx165d02c+8xm3wwAAAACwhz377LP6B//gH7gdhiSqagMAAAAA0BCJMwAAAAAADVBVGx336KOPbvn/s88+q8cff9ylaKQbN25sqTrudjySNDExofn5eY2NjSmTyRBLj8bDtbN3Yum1eHrt2umlY0M8jXHtEE87eu26kXrn2BBPY7127WyPZ3te4SYSZ3Tc9o7AHn/8cY2OjroUzU69EM/g4GDlX2LZqtfiqca107uxlOMo/9sL8VRz+9rptWNDPPZx7WxFPPa4fd1IvXdsiMeeXrh2qvVSB8MkznDcxMRE5eawff7ExET3AwIAAADgmkwmU7OkfWVlpfvB2ETiDMfNz8/XnH/+/PnuBgIAAADAdYuLi1uGr90LSJzhuLGxsZolzsPDw90PBgAAAICrhoeHNT4+vmP+yspK3UI3t5E4w3GZTKan2koAAAAAcE+9JpvXr1/X2bNnux+QDSTOgAsmJiZ0/vz5nih176VYpN6Lp9f00vHppVik3ounl/TasSGevaPXjg3x7B29dmyIB7vlsSzLcjsI7C/bfyl68cUXXS1x7rV4sHdw7aBdXDtoF9cO2sF1g3b12rXTa/FU87odAAAAAAAAvYzEGQAAAACABkicAQAAAABogMQZAAAAAIAGSJwBAAAAAGiA4aiw7506dUpf/OIXt/wfsINrB+3i2kG7uHbQDq4btItrxz6Go0LH9XI38gAAAAB6Uy/nEVTVBgAAAACgARJnAAAAAAAaIHEGAAAAAKABEmcAAAAAABogcQYAAAAAoAESZwAAAAAAGiBxBgAAAACggT63A8D+NzExocHBwZrzJyYmuh8QAAAAANdkMhllMpkd81dWVrofjE0kznDc/Px8zfnnz5/vbiAAAAAAXLe4uKhr1665HUZLSJzhuLGxsZolzsPDw90PBgAAAICrhoeHNT4+vmP+yspK3UI3t3ksy7LcDgL7y/Xr13X27NnK/1988UWNjo66GBEAAACAXtfLeQQlzjgw/mDupbbW+3z4iQ5HAgAAAGAvoVdtAAAAAAAaoMQZAAAAQEW7tfT2I2oeoowSZwAAAADoMYVCQfF4XCMjI/J4PPJ4PBoZGVE8HlehUKgsl06nlUgkXIz0YCBxBgAAAIAekkgkNDIyIkmamZnR8vKylpeXNTMzI7/fr2AwqEQiUUmuTdNsafvZbFZDQ0OanZ11IPr9iaraAAAAANAj4vG40um0crmcAoHAltcCgYACgYAuX76saDSqYDBoa5umaWppaUnZbFapVEr5fF6StLS01PH49ytKnAEAAACgB2SzWaXTaSWTyR1JczW/36+5uTmdOHGi6TaDwaCGhoYUDAY1Nzenp59+upMhHxiUOAMAAABADyi3VY5EIraWTyaTikajDZd57rnn5Pf7K/8vlzajNZQ4AwAAAEAPaDWpjUQiW5LiWpq9DntInAEAAACgh2SzWdvLXrx40cFIUEbiDAAAAAA9wDAMSaUq23Z7yg6Hw5UeuOEcEmcAAAAA6AHxeFxSqRfsM2fO2BouKhKJaHJy0unQDjwSZwAAAADoAZOTk5VSZ9M0FY1G5fF4FA6HNT093VIVbnQWiTMAAAAA9Iha4zdns1klEgmFw2F5PB4Fg0Gl02mXIjyYSJwBAAAAoEf4/X7lcjmlUqm6Yznn83nF43ENDQ2pUCh0OcKDicQZAAAAAHpMLBZTLpfT8vKyZmZmNDk5uSORNk1TwWCQ5LkLSJwBAAAAoEf5/X5FIhElk0nlcjlZlqW5ublKEm2aZqVTMTinz+0AsP9NTExocHCw5vyJiYnuBwQAAADsYaFQSLlcTtFoVLOzs8pms8rn83WrdveaTCajTCazY/7Kykr3g7GJxBmOm5+frzn//Pnz3Q0EAAAA6GHhcFhzc3O2l5+ZmZHH45FUeubeK4nz4uKirl275nYYLSFxhuPGxsZqljgPDw93PxgAAACgR2WzWZmmKb/fb3udUCikbDarhYUF5wLrsOHhYY2Pj++Yv7KyUrfQzW0kznBcJpPR6Oio22EAAAAAPW9+fl6hUMj28oFAQNlsVidPnnQwqs6q12Tz+vXrOnv2bPcDsoHOwQAAAACgRySTyZaWz+fzktRSso3WkTgDAAAAQI/IZrNKJBK2li0UCspms4pEInumffNeReIMAAAAAD1kenpaiURCpmnWXcY0TUWjUfn9fl25cqWt/TTaPrYicQYAAACAHhGLxbS8vKx8Pq+hoSHF43HNzs4qn89XSpinp6d15swZSVIul2vamZhpmjJNU/l8XlNTU5X5zzzzTKVDsvIfaqNzsH2uUCjIMAzHlgcAAADQGYZhKJlMyu/3a25uTvl8XqlUSlNTUyoUCpUet8fGxpRMJhWLxZpuMx6PK51Ob0muy9OFQkHRaFRSKbk2DGNP9c7dTR7Lsiy3gzgI0um0UqmUCoVCZV4oFFI8Hne0IX8wGFQ+n1coFFI0GtXY2JgMw9jyYcnn85qbm9PVq1dlGIZyudyu9rm9N7wXX3yxJ3rV/oO5l9pa7/PhJzocCQAAAIDtejWPkChxdlw+n9eFCxd04sQJJRKJyq9ChUJBqVRK4XBYkUhEV65caWm8tlZls1lls9mGy/j9fs3MzDgWAwAAAADsRSTODspmswqHw5VS3OrEuFwNY2RkRPF4XPl83lb7BKeEQiHNzMy4tn8AAAAA6FV0DuaQci93khompLFYTLFYTIVCQRcuXOhihCWBQEBzc3Oam5sjaQYAAACAGihxdsilS5dkmqYCgUDTMdUSiYTS6bTy+bxmZ2cViUQ6GksymVQkElE+n9fS0pIk6cSJEwqFQiTLAAAAANAEibMDTNPU7OysJNnq+MswDBmGoUKhoKmpqY4nztX7AAAAAAC0hqraDkin05Xpc+fO2VqnnNSWx2cDAAAAAPQGEmcHPPPMM5Vpu1Whq0uDm/V+DQAAAADoHhJnB+Tz+cr0iRMnbK1TnWDvdhxlAAAAAEDnkDh32PZq1nbbFZ88ebIyPT8/39GYAAAAAADtI3HuMNM021qvusS53W00k81mFY1GNTQ0VPkLBoNb2mQDAAAAALaiV+0eVB4yqlNu3bqlcDgsqTT01ZUrV+T3+2WapqamphSPx5VIJDQzM2OrF/BW3bhxo+V1Tp06pdOnT3c8FgAAAADOe+ONN/Tmm2+2tE47eUO3kDh32Pakt51xkjtd4jw9Pa1UKqVYLLZlvt/vVzKZ1MjIiOLxuMLhsObm5jqePH/mM59peZ0vfvGL+tKXvtTROAAAAAB0x1e/+lV9+ctfdjuMjqGqdoc5Vc26XYZhaGZmZkfSXC0Wi1XaYkej0W6FBgAAAAB7AonzPjczM6NIJNJ0ufIypmkqkUg4HRYAAAAA7BlU1e6wdqpm94JwOKzp6WlJpardly9f7th7efbZZ/X444+3tM6pU6c6sm8AAAAA3fe5z32u5dqsN27caKuZZzeQOHfY9nGbTdNsOQF1I/nePmxWNpu1VVJtx+OPP67R0dGObAsAAABA7zt9+vS+6uyXqtod1omkd3vy3Q3b9/nCCy90PQYAAAAA6EUkzh22PQG1O7RUdadinUi+8/m8RkZGNDQ01NY4zb3WyRkAAAAAuIXEucO2J712E9CFhYXK9PZq0+24dOmSCoWCTNNUPB5vORHeq221AQAAAKDTSJwdEAgEKtN2S5yrlzt37tyuYygUCi0tvz3OkZGRXccAAAAAAPsBibMDQqFQZdpuAlu9XCc65aoutU4mk01LkLfHefHixV3HAAAAAAD7AYmzAy5fvlyZzuVyttbJ5/OSSglvJ6pqh0IhRSIRWZalycnJpsvPzc1tWZeq2gAAAABQwnBUDvD7/QqFQspms7p69apSqVTD5bPZbGU6kUg03f7s7KwMw9hSJXy7eDyukZER28Nhzc7OVqaTyWTT5QEAALBPfXPK7Qh6x6cuN18GBwIlzg5JpVLy+/0yTXNLUlpvWanUNjoWi9VdzjRNjYyMKBqNKhgManp6uu6yhmFocnJSFy5caBprOp2uVNWenJxsmJADAAAAcF6hUKgUhnk8Hnk8Ho2MjCgej29pZplOp20VvmF3SJwdYhiGrly5IkmKRqN12zqn02nNzs7K7/drZmam4Taz2eyW7UxNNf41sFxyHA6H6/aqPTs7q3g8LkmKxWKUNgMAAAAuSyQSlc56Z2ZmtLy8rOXlZc3MzMjv9ysYDCqRSFSSazsj6MzOziocDmtoaEgej0fBYFDRaLStoWsPIhJnB0UiEc3NzVUu7uqLsjxMVDweVygU0s2bN5u2bd5e5dpOW+hcLifDMDQ0NKREIqHZ2Vnl83lls1lFo1FFo1H5/X6lUqmmVcoBAAAAOCsej2t6elq5XE6pVEqBQEB+v19+v1+BQEDJZFI3b95UPp9XMBhsuj3TNBUOhzU1NaVoNKpcLqdcLqenn35a2Wy2Uqpd7nMJtZE4O6ycFCeTSaVSKQ0NDWloaEhnzpzR0tKS5ubmKsm1nW2VO/oyDKNpCXVZKpXS3NycCoWCLl26VPl1qVAoVD54jaqIAwAAAHBeNptVOp1WMpls2HzS7/drbm5OJ06caLrNCxcuyDAM5XI5xWKxSl9Jk5OTunnzpgKBgAqFgoLBIMlzA3QO1gV+v1+xWKwjyWkymWyrOnUoFNoyTBYAAACA3lJuq2x3eNpkMqloNNp0e/Vqlvr9fl25cqVSch2NRrWwsNBKyAcGJc4AAAAA0ANaLfGNRCINa66m02kZhlG3vyWp1EFxuYCtUCjQ5rkOEmcAAAAA6CHVw9U2c/HixZrzTdOsjPBT7misnupq4Xabgx40JM4AAAAA0APKnf8mEglbPWVLpRF0aiXGS0tLW/7fqNT53LlzdddDCYkzAAAAAPSA8jCxpmnqzJkzmp2dbbpOJBKpdCBczTAMxWKxSn9LjUbkqU6q7XQ4dhDRORgcNzExocHBwZrzJyYmuh8QAAAA0IMmJyeVSqVUKBRkmmal469QKKRwOLylPbIddoecfeGFFyrTjXrz7pRMJqNMJrNj/srKiuP7bheJMxw3Pz9fc/758+e7GwgAAADQ43K5nC5cuLClo7BsNrul3XMgEFA8Hu/IqD2maW7Z9uXLl3e9zWYWFxd17do1x/fTSSTOcNzY2FjNEufh4eHuBwMAAAD0ML/fr1wup3Q6rVQqVbOn7Xw+r3g8rkQioVwu17AadjNTU1OV9tTJZLJhL92dMjw8rPHx8R3zV1ZW6ha6uc1jWZbldhDYX65fv66zZ89W/v/iiy9qdHTUxYhK/mDupbbW+3z4iQ5HAgAA0MO+OeV2BL3jU86XvjZTLhF+4YUXlM1mdyTS5US7neS5UChUOhaLRCKu96jdq3mEROdgAAAAANCz/H6/IpGIksmkcrmcLMvS3NxcpS2yaZqVTsVaYZqmwuGwpN5ImnsdiTMAAAAA7CGhUEi5XE6RSESSapZEN3PhwgUVCgXFYjGSZhtInAEAAACgB5RLgO2qTnhbaRscDoeVz+c1MzNjq9dtkDgDAAAAQE/IZrOVjrrsKg9PtbCwYGv5cDis+fn5LSXWaI7EGQAAAAB6RKu9SpfbOp88ebLpstFoVIVCQblcruZ4zfl8vjJ2NLYicQYAAACAHpFMJltavty2uVzyXE910lyvB+5sNqsTJ060tP+DgsQZAAAAAHpENptVIpGwtWyhUFA2m1UkEqlZglwWjUZlmqZyuVzDcZrn5uYqw1Nhqz63AwAAAAAAvGt6elqSdPny5bqJrmmaikaj8vv9unLlSt1txeNxzc7OKhQK1e18bGlpSaZpqlAo2E7aDxoSZwAAAADoEbFYTMlkUtFoVENDQ4rFYgqHwzIMQ36/X4VCQfl8XlNTUzIMo2EpcjweVzqdllQqybZjbGysU29lXyFxBgAAAPCuT112O4IDyzAMJZNJ+f1+zc3NKZ/PK5VKaWpqSoVCQaZpyu/3a2xsTMlkUrFYrO628vl8JWluRaOq3AcZiTMAAAAA9IDtQ0oFAoG2x1kOBAKyLKsTYUF0DgYAAAAAQEMkzgAAAAAANEDiDAAAAABAAyTOAAAAAAA0QOIMAAAAAEAD9KoNx01MTGhwcLDm/ImJie4HBAAAAMA1mUxGmUxmx/yVlZXuB2MTiTMcNz8/X3P++fPnuxsIAAAAANctLi7q2rVrbofREhJnOG5sbKxmifPw8HD3gwEAAADgquHhYY2Pj++Yv7KyUrfQzW0kznBcJpPR6Oio22EAAAAA6AH1mmxev35dZ8+e7X5ANtA5GAAAAAAADZA4AwAAAADQAIkzAAAAAAANkDgDAAAAANAAiTMAAAAAAA2QOAMAAAAA0ACJMwAAAAAADZA4AwAAAADQAIkzAAAAAAAN9LkdAAAAAAB3WZal1fXNHfP7fV71+2qXta2ubcqS1db++rxeDfTV3u699U0Vrfa26/N6dKjP1/HtHun3yePxtLUu9gcSZwAAAOCAW13fVOpaYcf8T33otD76qL/mOv/u+UWtru1Mtu34hHFST42crPna1flXdOvOWlvbffLR4/qVDz1U87U//95renV5ta3txscNHR0gdTrIqKoNAAAAAEADJM4AAAAAADRA4gwAAAAAPaZQKCgej2tkZEQej0cej0cjIyOKx+MqFN6tVp9Op5VIJFyM9GCgoj4AAACAHX7rqcd07Eh/3df/6VPDu+ocrJ6LY4/uqnOwen79yYdtbXdto6i5H7wuSToyUOpozNvljsESiYSmp6cVi8U0MzMjwzAklZLpZ555RsFgULFYTPF4XPF4XLFYrKXtZ7NZRaNRXblyRZFIxIm3sO+QOAMAAADY4ciAr26P2uXXnXC4393tHh2QomOPOhKDHfF4XOl0WrlcToFAYMtrgUBAgUBAly9fVjQaVTAYtLVN0zS1tLSkbDarVCqlfD4vSVpaWup4/PsVVbUBAAAAoAdks1ml02klk8kdSXM1v9+vubk5nThxouk2g8GghoaGFAwGNTc3p6effrqTIR8YlDjDcRMTExocHKw5f2JiovsBAQAAAD2o3FbZbvXpZDKpaDTacJnnnntOfr+/8v9yabObMpmMMpnMjvkrKyvdD8YmEmc4bn5+vub88+fPdzcQAAAAoIe1mtRGIpEtSXEtzV53w+Lioq5du+Z2GC0hcYbjxsbGapY4Dw8Pdz8YAAAAoMdls1nbHX5dvHjR4Wg6b3h4WOPj4zvmr6ys1C10cxuJMxyXyWQ0OjrqdhgAAABATzMMQ4VCQYlEQhcvXrRVWhwOh7cMT7UX1Guyef36dZ09e7b7AdlA4gwAAAAA991b31T2b17fMi/0iw851tt3tXg8rkQiIdM0debMGVvDRTGcVHfQqzYAAAAA3Fe0LP3o9Ttb/todV7pVk5OTlTGbTdNUNBqVx+NROBzW9PS0stlsV+LATiTOAAAAANAjao3fnM1mlUgkFA6H5fF4FAwGlU6nXYrwYCJxBgAAAA64owN9+nz4iS1/Rwdo1ekGv9+vXC6nVCpVdyznfD6veDyuoaGhPde+ea8icQYAAACAHhOLxZTL5bS8vKyZmRlNTk7uSKRN01QwGCR57gISZwAAAADoUX6/X5FIRMlkUrlcTpZlaW5urpJEm6apeDzucpT7H4kzAAAAAOwhoVBIuVyu0qN2NptVPp93Oar9jcQZAAAAAHpAOBxuafmZmZnK9Pz8fKfDQRUSZwAAAADoAdlsVqZptrROKBSSJC0sLDgQEcpInAEAAIADbmOzqJdef3vL38Zm0e2wDqRWS47LbZ1PnjzpRDi4jz7mAQAAgANubbOov/j+T7fMi48b6vNRztZtyWSyUopsR7ltcyvroHV8EgAAAACgR2SzWSUSCVvLFgoFZbNZRSKRumM+ozMocQYAAACww+raZsvr9Pu86q9TSr26tilLVlux9Hm9Guirvd1765sqWu1t1+f16FCfr611nTQ9PS1Junz5svx+f81lTNNUNBqV3+/XlStX2tpPq+2pDzISZwAAAAA7fO35l1te51MfOq2PPuqv+dq/e36xrWRckj5hnNRTI7Xb8F6df0W37qy1td0nHz2uX/nQQ22t65RYLKZkMqloNKqhoSHFYjGFw2EZhiG/369CoaB8Pq+pqSkZhqFcLlc3uS4rJ8iFQkFTU1OV+c8884wCgYDGxsYq85pt66AicQYAAACAKp/60Gn96PW39eryalf3axiGksmk/H6/5ubmlM/nlUqlNDU1pUKhINM05ff7NTY2pmQyqVgs1nSb8Xhc6XR6S0Jcni4UCopGo5JKybVhGPTOXQeJMwAAAHDAHR3o0+fDT0iS/mDuJZejcdfRgT599FG/fvT6213f9/akNRAIKJVK7WqbqVRq19sAnYMBAAAAANAQJc5w3MTEhAYHB2vOn5iY6H5AAAAAqCs+brS9br2OwSTpnz41vKvOweq5OPborjoHq+fXn3y4st0j/b3XgdhelslklMlkdsxfWVnpfjA2kTjDcfUGcT9//nx3AwEAAEBTRwecSRGODDiTfB52KKl1aruQFhcXde3aNbfDaAmJMxw3NjZWs8R5eHi4+8EAAAAAcNXw8LDGx8d3zF9ZWalb6OY2Emc4LpPJaHR01O0wAAAAAPSAek02r1+/rrNnz3Y/IBvoHAwAAAAAgAZInAEAAAAAaIDEGQAAAACABkicAQAAAABogMQZAAAAAIAGSJy7JJ1OKxgMamhoqPIXjUaVzWbdDk2FQkFDQ0M9EQsAAAAA9BoSZ4fl83kNDQ0pmUwqHo9reXlZy8vLyuVyMgxD4XBY0WhUpmm6FmN5/27GAAAAAAC9inGcHZTNZhUOh2UYhnK5nPx+f+U1wzCUTCY1MjKieDyufD6/Y5luSKfTyufzXd0nAAAAAOwllDg7xDRNRaNRSdLMzEzdhDgWiykWi6lQKOjChQtdjLAUYzwe7+o+AQAAAGCvIXF2yKVLl2SapgKBgAKBQMNlE4mEpFK17tnZ2W6EJ6lURbvbJdwAAAAAsNeQODvANM1KAhwKhZoubxiGDMOQJE1NTTkaW1k6ndbS0pIuXrzYlf0BAAAAwF5F4uyAdDpdmT537pytdcqJcz6fV6FQcCSuMtM0lUgkGlYhBwAAAACUkDg74JlnnqlM201My4mzJMeHhbp06ZIuX768ZZ8AAAAAgNroVdsB1b1UnzhxwtY61Ql2LpfrdEgVs7OzKhQKmpmZcWwfAAAAALCfkDh32PZq1nZLdU+ePFmZnp+f72hMZaZp6tKlS3ruuecc2T4AAAD2KMuS1u9unecbkHz9tZdfuyvJam9f3n6pb6D2a+urklVsc7t9Ut+hOtu9J1mb7W1XkvqPSh5P++tjzyNx7jDTNNtar7rEud1tNHPp0iXFYrGmvXwDAADggFm/K33rD7fO++CvSo8Eay//nXQpyW3H8C9LZ/5O7df++t9LKz9vb7vvD0hPfLr2ay/+qWT+uL3tStInf1caGGx/fex5JM49aGlpqePbzGazyufzrlTRvnHjRsvrnDp1SqdPn3YgGgAAAABOe+ONN/Tmm2+2tE47eUO3kDh32Pakt51eq50ocY5Go65V0f7MZz7T8jpf/OIX9aUvfanjsQAAAMCm7VW3gRZ89atf1Ze//GW3w+gYetXuMKeqWe9GPB6nijYAAADQqlO/UPrz+Lq+60KhoHg8rpGREXk8Hnk8Ho2MjCgej2/pVymdTiuRSHQ9voOGEud9LpvNKpvNamFhwe1QAAAAsJe87yP1X/t4TLvqHKyej/2T3XUOVs/Zz7bXOZhL7ZoTiYSmp6cVi8U0MzNT6XC4UCjomWeeUTAYVCwWUzwerxSSNTM7O6tUKqX5+XmZpqlAICDDMBQOh22tf9CROHdYO1WznRSPx5VKpVyN4dlnn9Xjjz/e0jqnTp1yKBoAAADY0igRHTjqzD77jzi03cPObNcB8Xhc6XRauVxuR43RQCCgQCCgy5cvKxqNKhis03lbFdM0FY1GtbS0VMkNTNNUNpvV1NSUZmdnlUwmNTMz09Eaqp/73OcUjUZbWufGjRttNfPsBhLnDts+brNpmi0n051KvhOJhEKhkEKhUEe2167HH39co6OjrsYAAAAA9LpsNqt0Oq1kMtkwifX7/Zqbm9PIyEjTpqIXLlzQ2NiY5ubmtswPBAKKxWK6cOGC8vm8gsFgzWS9XadPn95Xnf3SxrnDOpH0bk++25HP55VOp10vbQYAAABgT7mtciQSsbV8Mpm0tb16OYHf79eVK1cq/2+1hPggIXHusO1Jr92hpap/KepE8h2NRl0ZegoAAABAe/L5fEvLRyKRhrlDOp2WYRhbOhPbLhAIVGqoFgoFpdPplmI4KEicO2z7hWu3l+3qzrvKjf/blUgktnwAAAAAAOwd2WzW9rIXL16sOd80TZmmqdnZWY2MjDTcRnX1bArfaiNxdkD1hWe3xLl6uXPnzu1q/7Ozs5qdna10W9/ob3p6urJeNBrd8hrd2gMAAODAWb8nvfhnW//W73Vl1+UCtEQiYbsALhwO10yMt+chjUqdq/MPu/nLQUPnYA4IhUKVahaNLtBq1cvZbdNQTyvtmqvHgZucnFQ4HK68ttuSbwAAAGDPsTalN/9267wnPt2VXcfj8UrSfObMGV25cqVpblDvdcMwFIvFdPXqVV28eLHhs311LtKJ/pb2IxJnB1y+fLlSkpvL5WytU060DcPYdcLaShXt6qrl586do3o3AAAA4JLJyUmlUikVCoXKMFJS6fk+HA633BwzlUrZKlR74YUXKtOdHJJqP6GqtgP8fn/lgr569WrT5avbMNipHj07O9tyxwEAAAAAel+tIaGy2awSiYTC4bA8Ho+CwWDHOvEqj+lcdvny5Y5sd78hcXZIKpWS3++vNMhvtqz07lhq9ZimqZGRkcpg59Xtk9tV3XaC9gwAAACAu/x+v3K5nFKpVN3S33w+r3g8rqGhIdtNQ+uZmpqq5ATJZLIjI/zsRyTODjEMozImWjQarXtBp9Npzc7Oyu/3N+3BLpvNbtnO1NTUruOsTpbtdkAAAACAfWZgUPrU5a1/A4NuR3WgxWIx5XI5LS8va2ZmRpOTkzsSadM0FQwG206eC4VCpTAuEolocnJy13HvVyTODopEIpqbm5Pf799RncI0TcXjccXjcYVCId28ebNp2+btv/600xY6n88rm81qdnZW0Wh0S7I8NTWl6elpZbNZZbNZEmkAAADAZX6/X5FIRMlkUrlcTpZlaW5urpJEl/OKVpmmWekYOBKJMAxVEyTODisnxclkUqlUSkNDQxoaGtKZM2e0tLSkubm5SnJtZ1vlX4EMw2jr4o5GowqHw4pGo8pms/L7/ZU/qZQ8l5ex0z4bAAAAQHeFQiHlcrlKj9rZbLblPpAuXLigQqGgWCxG0mwDvWp3gd/vVywWa9h+2a5kMqlkMtn2+gsLC7uOAQAAAEDnhcNhzc3N2V5+ZmZGHo9HkjQ/P2+7R+xwOKx8Pq+ZmZldD4V7UFDiDAAAAAA9oJ3mkuXRfOwWkIXDYc3Pz28psUZzJM4AAAAA0CPm5+dbWr5cynzy5Mmmy5Y7La415JVU6g+pPHY0tiJxBgAAAA66zQ3pjb/Z+re54XZUB1KrzTLLbZvLJc/1VCfN9ToZzmazOnHiREv7Pyho4wzHTUxMaHBw53AGExMTmpiY6H5AAAAA2GrzHen6s1vnffyS1H+kte34BiRff+3X1u5KstqJTvL2S30DtV9bX5WsYpvb7ZP6DrW3rkOy2awSiYStBLpQKCibzSoSiTRs31weTSeXyzXc3tzcXKWnbSdlMhllMpkd81dWVhzfd7tInOG4etVNzp8/391AatgsWipajW/gd9dKv7Ye6fdVOl8AAADY975zpfV1Pvir0iPBOttLl5Lcdgz/snTm79R+7a//vbTy8/a2+/6A9MSn21vXQeWxlS9fvlx39B3TNBWNRuX3+3XlSv1zFY/HNTs7q1AoVDcpXlpakmmaKhQKSiQSu46/mcXFRV27ds3x/XQSiTMcNzY2VrPEeXh4uPvBbPPT26t6dbnxDTx1rTSgfHzc0NEBPjIAAAD73gd/VXrzh5L5467vOhaLKZlMKhqNamhoSLFYTOFwWIZhyO/3q1AoKJ/Pa2pqSoZhKJfL1U2u4/G40um0pFJJth1jY2Odeit1DQ8Pa3x8fMf8lZWVltt4dwtZAByXyWQ0OjrqdhgAAACoZ2BQ+tTl0vQ3p9yNxW0Dg6VS8zd/2PVdG4ahZDIpv9+vubk55fN5pVIpTU1NqVAoyDRN+f1+jY2NKZlMNhzuNp/PV5LmVtRLwjupXpPN69ev6+zZs47vvx0kzgAAAADQA7YPKRUIBJRKpdraViAQkNWkSSLsI3HGvnZvfVNX51+RJH3vFVOSNPrwMfX56FAeAACgpk/+bvvr+up04CVJH49pV52D1fOxf7K7zsHqOftZydosTfcfbW/72DdInLGvFS1Lt+6sSZJW10s3vurb9fuOH9FDxw7vWG99s6jvv3q7GyECAAD0loGdfdN0ZrsOJZ+t9vxte7s7nxFxcJE440DzeT3yiZ6yAQAAANRHfVUAAAAAABogcQYAAAAAoAGqagN19PlKVbiPDPgkSR6qdAMAAAAHEokzUEO/z6uxx05Ikn57fMTlaAAAAAC4iaraAAAAAAA0QOIMAAAAAEADJM4AAAAAADRA4gwAAAAAQAMkzgAAAAAANECv2kANm0VLb779jiTpu6+YkqTRh4+p38dvTQAAAMBBQ+IM1FC0LC3eWpEkffOHb0iSnnjoARJnAAAA4AAiCwAAAAAAoAFKnLGv+bwePfnocUnSzZ+XSpA9HjcjAgAAALDXkDjDcRMTExocHKw5f2JiwtF9H+rz6Vc+9JAk6Xuv3HZ0XwAAAACay2QyymQyO+avrKx0PxibSJzhuPn5+Zrzz58/391AAAAAALhucXFR165dczuMlpA4w3FjY2M1S5yHh4e7HwwAAAAAVw0PD2t8fHzH/JWVlbqFbm4jcYbjMpmMRkdH3Q4DAAAAQA+o12Tz+vXrOnv2bPcDsoFetQEAAAAAaIDEGQAAAACABqiqjX3t3vqm/vx7r0mSfvDaW5KkJx56QH0+fjMCAAAAYA+JM/a1omXp1eVVSdJb99YlSZabAQEAAADYcyh2AwAAAACgARJnAAAAAAAaIHEGAAAAAKABEmcAAAAAABqgczCghn6fV58wTkqSPh9+wuVoAAAAALiJEmcAAAAAABogcQYAAAAAoAESZwAAAAAAGiBxBgAAAACgARJnAAAAAAAaoFdtoIZi0dLy3TVJ0kuvvy1JMh4cVJ+P35oAAACAg4bEGahh07L0ozfuSJL+4vs/lSTFxw0SZwAAAOAAIgsAAAAAAKABSpzhuImJCQ0ODtacPzEx4ei+vR6PPvjQA5LerXLtcXSPAAAAABrJZDLKZDI75q+srHQ/GJtInOG4+fn5mvPPnz/v+L4P9/v0ax95WJL0o9fvOL4/AAAAAI0tLi7q2rVrbofREhJnOG5sbKxmifPw8HD3gwEAAADgquHhYY2Pj++Yv7KyUrfQzW0kznBcJpPR6Oio22EAAAAA6AH1mmxev35dZ8+e7X5ANtA5GAAAAAAADZA4AwAAAADQAIkzAAAAAAAN0MYZ+9q99U1l/+Z1Se8OR2U8OKg+H78ZAQAAALCHxBn7WtGyKsNQLa2sSZLOPLizh28AAAAAqIdiNwAAAAAAGiBxBgAAAACgARJnAAAAAAAaIHEGAAAAAKABOgcDaujzehR8bEiSFB83JElH+n1uhgQAAADAJSTOQA0ej0f9Po8k6egAHxMAAADgIKOqNgAAAAAADZA4AwAAAADQAIkzAAAAAAANkDgDAAAAANAAvR4BNRQtS3fubUiSXlm6K0l62H9EPq/HzbAAAAAAuKCnE+fFxUVJ0vDwsKtx4ODZLFr6wU/fkiTN5l6VVBqWih62AQAAgIPH1SxgcXFRhUJBhUJBCwsLlelCoaDl5WUNDQ3p8uXL+r3f+z03w8QuTUxMaHBwsOb8iYmJ7gcEAAAAwDWZTEaZTGbH/JWVle4HY5OriXMoFNLNmzclSZZlye/3KxaL6Stf+YouXLjgZmjooPn5+Zrzz58/7/i+vR6PHhk6Ikk6drhfkkRlawAAAMA9i4uLunbtmtthtMT1eqeWZUmSJicn9ZWvfMXlaOCEsbGxmiXO3aiCf7jfp+jYo5KkV5dXHd8fAAAAgMaGh4c1Pj6+Y/7KykrdQje3uZ44ezyeSikz9qdMJqPR0VG3wwAAAADQA+o12bx+/brOnj3b/YBs6InhqJLJpNshAAAAAABQk+uJcyAQ0LFjx9wOAwAAAACAmlyvqj02Ntbw9T/7sz+ztR3DMPTRj360AxEBAAAAAPAu1xPnkZGRuq/dvHlTv//7vy/TNFUoFOTxbO0PudwTt2EY+o3f+A0SZwAAAABAx7meOPv9/rqvnTlzZkuvatPT0/oX/+JfVNabmZlh2Co09M7Gpr514+eSpJs/L40L9+iJI+rzut5KAQAAAMAesaeyh8nJSV24cEEej0fT09N7KmlOp9MKBoMaGhqq/EWjUWWz2a7sv1AoKJFIKBwOa2hoSB6PR8FgUNFoVLOzs12JwQ2bRUvfe+W2vvfKbb3+1j29/tY93R8BDQAAAABs2VOJsyRFo1FJ0sWLF22vc/PmTf3O7/yOUyE1lM/nNTQ0pGQyqXg8ruXlZS0vLyuXy8kwDIXDYUWjUZmm6VgM8XhcwWBQUqkH85s3b2p5eVmXL19WNptVNBrVyMhI15J4AAAAANhLXK+q3apym+hWeuIuFApKp9P6oz/6I6fCqimbzSocDsswDOVyuS3V0g3DUDKZ1MjIiOLxuPL5/I5lOiEcDksq/XiwfduRSEShUEhnzpxRoVBQOBzW3NycQqFQR2MAAAAAgL1sz5U4t6vTCWkzpmlWSsdnZmbq7j8WiykWi6lQKHS86vn09LQKhULD/fv9/i3jaIfDYRUKhY7GAQAAAAB72YFInJ2sBl3PpUuXZJqmAoGAAoFAw2UTiYSkUrXuTrY3npqaUqFQ0NDQkKanp+sut72EuTqRBgAAAICDzvXEuRtJ7QsvvOD4PqqZpllJgO1UezYMQ4ZhSColu52KofrYlpPzevuvVt2T+UHV5/XoI48c10ceOa7ffOox/eZTj+lwn8/tsAAAAAC4wPU2zqlUakfi1sh3vvMdSdJ/+k//SZaN7pGXlpaUTqd3jAHtpHQ6XZk+d+6crXUMw1ChUFA+n1ehUGjpmNSym6rpJ06c2NW+9wOPx6OjA6WPx4MPHHI5GgAAAABucj1xLhQKlbbAdlmWpUgk0tLyQ0NDrYbWtmeeeaYybTeBrU6Us9msYrHYruNIpVKVkuYrV67UXW57m+ZmVcsBAAAA4CBxPXGWZKvkuMzj8cjj8bS0Trfl8/nKtN3S2+oEO5fLdSSOcsdjzWwfhioej3dk/wAAAACwH/RE4tyKXk6YpZ2lt3arXJ88ebIy3e02xqlUqjKdTCZ3XU0cAAAAAPaTnkick8mk/H5/x9vWLi0tyTTNjnW4ZUe7nZ1Vlzh3sxfw6enpSgl5MpnU5ORkx/dx48aNltc5deqUTp8+3fFYAAAAADjvjTfe0JtvvtnSOu3kDd3ieuIci8X0hS98wdF9WJa1p4ZYWlpacnT7pmlqfn5eqVRKs7OzCgQCSiaTtnoAb8dnPvOZltf54he/qC996Usdj8Uuy7K0ur4pSfr5nXckSSeODsjr7V4ncwAAAMBe9dWvflVf/vKX3Q6jY1xPnEdGRvbFPsq2J73t9G7tVInzyMjIjqrksVisUuKPd20ULX3/1duSpD95/mVJUnzcqPS0DQAAAODgcH0c524kbH6/v2vVn7tZzbpVCwsLsixLlmVpeXlZMzMzunr1qoaGhhSNRns6dgAAAABwi6vFZ5FIpCsdURmGoc9+9rOO72cv8fv9ikQiCgQCCgaDmp2dVTab1XPPPdfx4aieffZZPf744y2tc+rUqY7s2+vx6OQDA5KkI/0+SRKVrQEAAABnfe5zn2t52OEbN2601cyzG1xNnL/yla90ZT9nzpzR1atXu7KvvVbl2TAMXblypVLiHAwGlcvlOpo8P/744xodHe3Y9lpxuN+n33pqWJJ0686aKzEAAAAAB83p06f3VWe/rlfV3m+29wzeTvXnbiffkUhkyz5b/WUIAAAAAPYzEucO60TS2+lhuey4ePFiZbpQKCidTnc9BgAAAADoRXsqcV5cXNR3v/tdfeMb39B3v/tdvfXWW26HtMP2pNfu0FLVJdNuVPfe3vP43Nxc12MAAAAAgF7U02PrfPe739UzzzyjbDarfD5fcxm/369QKKTf+I3f0D/8h/+wyxHWjqea3araCwsLlelOdJiWz+eVSCQkSYlEoukYzdvjrne8AQAAAOCg6cnE+bvf/a4SiYSy2awkybKsussuLy9rdnZWs7OzGhkZUTKZdD2BDgQClcTTbolz9XLnzp3bdQwXLlyoJO3ZbFYLCwsNE/J2S8oBAAAAYL/ruarav/M7v6NgMKhsNltJmD0eT8M/qZRc37hxQ5FIRH/37/5dV6txV5fuFgoFW+tULxeJRHa1/3w+v6Oku1kJ8vZEuRvDhHXD2kZRzy/c0vMLt/Tq8l29unxXm8X6P8QAAAAAwHY9kzjfvn1b586dUzqdlmVZlaS5PG1Zlo4fP64zZ87ozJkzOn78+JbXqn3961+XYRh6+eWX3Xgrunz5cmU6l8vZWqec2BqGseuktVYb6WbDS22P8+mnn95VDL1io1jUtwu39O3CLb26vKpXl1dVbFCDAQAAAAC265nE+cKFC8rn85UkOBAIKJlMKpfLaXl5WcViUUtLS7px44Zu3LihpaUlFYtFFYtF5XI5pVIphUKhyvpLS0sKh8OulDyX211LsjV+dLlKuqRKu+RGZmdnG5YgVyffoVBIc3NzTZPx6hgkKRaLNY0DAAAAAA6CnkicP/3pT1eS5kAgoFwup/n5eX3hC1/Qxz72MR0/frzh+h/72Md06dIlff3rX9fS0pI++9nPSip1uNWsUyynpFIp+f1+maap2dnZpstKpR8LGiWspmlqZGRE0WhUwWBQ09PTdZdNJpOS7HUMls1mt1QVL8cOAAAAAOiBxPm5557T3NycPB6PEomE5ufn9bGPfazt7fn9fs3MzOjq1auyLEu5XE7/9t/+2w5GbI9hGLpy5YokKRqN1m3rnE6nNTs7W4m7ke0J7tTUVN1lI5GIJicnFQ6HGybYhUJB8Xi88v/JyUlKmwEAAACgiuuJczwel8fj0eTkZMNEsFWRSER//Md/LMuybFV/dkIkEtHc3Jz8fr+CwaDS6XTlNdM0FY/HFY/HFQqFdPPmzabVqbeXAjdbPplMamZmRqlUSkNDQ5WeyvP5vPL5vKanpzUyMqJCoVAp6S+XVAMAAAAASlxNnP/6r/+6krR1Mmkui8Vi+uxnP6vl5WV985vf7Pj27SgnxclkspLADg0N6cyZM1paWtLc3FwlubazrcnJSUmlpLlZCbVUSt4XFhZ05coVmaapRCKhCxcuKBgMampqSpFIRDMzM8rlck07EDtIfF6PPvy+Y/rw+44pEnxEkeAjOtTnczssAAAAAC7wWI0GSXbYb//2b+vKlSvK5XL66Ec/6sg+8vm8xsbGFI/H9Ud/9EeO7ANbXb9+XWfPnq38/8UXX9To6Kgrsdxd21DqWql6+7cLtyRJwceG1O+z/5vR58NPOBIbAAAAgHf1Uh6xnaslzvPz8zIMw7GkWSp1uBUIBDQ/P+/YPgAAAAAA+5eriXOhUFAkEnF8P6FQqG7nXAAAAAAANOJq4lweXslpIyMjMk3T8f0AAAAAAPafPrcDaNYz9F7ZB+qbmJjQ4OBgzfkTExPdDwgAAACAazKZjDKZzI75Kysr3Q/GJtcTZ+x/9dqXnz9/vruBtMCyLG0US/3m3V3bkCQd6ffJ4/G4GRYAAACw5y0uLuratWtuh9ESEmc4bmxsrGaJ8/DwcPeDsWmjaCn38rIkVXrljo8bOjrARwYAAADYjeHhYY2Pj++Yv7Ky0rOdOpMFwHGZTKZnupEHAAAA4K56TTa3D0fVS0icsa955NGRAZ8kqc9HNWsAAAAArXM9cb59+7bj+6BH7YPryIBPvz1e6rl9dW3T5WgAAAAA7EWuJ86///u/r+PHjzu6j1Qq5ej2AQAAAAD7l+uJcz6fVzgcdnQflmXRGzIAAAAAoC2uJ85SKbEFAAAAAKAXed0OAAAAAACAXuZ6ibPf71c6nZbf73dk+6ZpKpFI6ObNm45sHwAAAACwv7meOF++fFmf/exnHd3HwsKCLl++7Og+0JvWN4u6/tpbkqSf3b4nSTr1nkPyeWnzDgAAAMAe1xNnwzAc30cwGHR8H+hN65tFffOHb0iSFm+tSJJOPjAgn0icAQAAANjjehtnp6poAwAAAADQCa4mzpOTk10pcTYMQ1/4whcc3w8AAAAAYP9xtar2V77yla7s58yZM13bFwAAAABgf3G9qjYAAAAAAL3M9c7BgF7k83j0wdMPSJL+/kfeJ0ka8PE7EwAAAHAQ9Vwm8G/+zb/Rpz/9aX3wgx/UuXPn9C//5b/UW2+95XZYOGC8Xo9OPnBIJx84pCceeo+eeOg96iNxBgAAAA6knilx/sY3vqFoNCrTNCVJlmXJ4/Eon88rmUwqnU7rn/2zf+ZukAAAAACAA6cnitD+9E//VOFwWMvLy7IsS5Lk8bw7zq5lWYrFYvpX/+pfuRUiAAAAAOCAcr3E+fbt24pGo/J4PPJ4PLIsq5I8l5XnT05OKhQK6cknn3QpWrRjYmJCg4ODNedPTEx0PyAAAAAArslkMspkMjvmr6ysdD8Ym1xPnKempiSVSpUNw1AoFNLIyIj8fr9M09StW7eUzWaVz+clSYlEQn/5l3/pZsho0fz8fM3558+f724gAAAAAFy3uLioa9euuR1GS1xNnG/evKnp6Wl5PB798R//sS5dulR32Xw+rwsXLmhubk7f+973KHXeQ8bGxmqWOA8PD3c/GAAAAACuGh4e1vj4+I75KysrdQvd3OZq4jw7OytJSiaTDZNmSQoEAnruuec0Njamqakp/cf/+B+7ESI6IJPJaHR01O0wWrK+WVTu5WVJ0h/MvSRJio8bOjrgeiUNAAAAYE+r12Tz+vXrOnv2bPcDssHVzsHm5uY0NDSk3/u937O1fCAQ0KVLlzQ3N+dwZAAAAAAAlLiaOM/Pz+vixYstrVM9ZBUAAAAAAE5ztd6paZoKBoMtrTMyMiJJeuutt3Ts2DEnwsI+cnSgT58PPyHp3SrXAAAAANAK18dxPnHiREvLnzlzZsdwVQAAAAAAOMX1xNnv97sdAgAAAAAAdbmeOHfDX//1X+vpp592OwwAAAAAwB50IBLnpaWlytBXAAAAAAC04kAkzqZpUiUcAAAAANAWV3vVlqTbt287vo9CoeD4PtCbNjaLKvx8RZJ06847kqShowPyej1uhgUAAABgD3E9cf793/99HT9+vOX1rly5Yms90zQ1NTUlj4dE6SBa2yzqL77/U0nSj964I0kKPjYkr7geAAAAANjjeuKcz+cVDodbXm9yctL2spZlaWhoqOV9AAAAAADgeuIsqeVxmSk9BgAAAAB0S08kzq1qNdEGAAAAAKBdrifOfr9f6XTasV6vTdNUIpHQ8vKyI9sHAAAAAOxvrifOly9f1mc/+1lH97GwsKBkMunoPrC/eD0eDZ8clCR96kOnJUn9vgMxehsAAACAbVxPnA3DcHwfwWDQ8X1gf/F5PXrv8cOSpI8+6nc3GAAAAACucr0Izakq2tuZptmV/QAAAAAA9hdXS5wnJye7UuJsGIa+8IUvOL4f1DYxMaHBwcGa8ycmJrofEAAAAADXZDIZZTKZHfNXVla6H4xNribOX/nKV7qynzNnznRtX9hpfn6+5vzz5893NxAAAAAArltcXNS1a9fcDqMlrrdxxv43NjZWs8R5eHi4+8EAAAAAcNXw8LDGx8d3zF9ZWalb6OY2Emc4LpPJaHR01O0wAAAAAPSAek02r1+/rrNnz3Y/IBtInIEa1jeL+t6rpiTpj68tSJL+6VPDOjLgczEqAAAAAG4gcQbq2Ni0JEmra5uSJEuWm+EAAAAAcInrw1EBAAAAANDLKHHGvnak36f4eGnIs/XNoiSpz+txMyQAAAAAewyJM/Y1j8ejowOly7zfRwULAAAAAK0jkwAAAAAAoAESZwAAAAAAGiBxBgAAAACgARJnAAAAAAAaoHMw7GubRUuvmauSpLdW1yVJDxzuk9dDz9oAAAAA7CFxxr72zsamZnOvSpJ+8NO3JEnBx4bk9ZE4AwAAALCHqtoAAAAAADRA4gwAAAAAQAMkzgAAAAAANEDiDAAAAABAA3QOBtTg9Xj0yNARSdInjJOSpD4vvzMBAAAABxGJM1CDz+vRI0NHJUlPjZx0ORoAAAAAbiJxhuMmJiY0ODhYc/7ExET3AwIAAADgmkwmo0wms2P+yspK94OxicQZjpufn685//z5890NBAAAAIDrFhcXde3aNbfDaAmJMxw3NjZWs8R5eHi4+8EAAAAAcNXw8LDGx8d3zF9ZWalb6OY2Emc4LpPJaHR01O0wAAAAAPSAek02r1+/rrNnz3Y/IBvoJhgAAAAAgAYocQZq2Ngs6vprb0mSvvb8oiTp4tijOtzvczEqAAAAAG4gcQZqsCStrm9Kkm7dWZMkFS3LxYgAAAAAuIWq2gAAAAAANEDi3CXpdFrBYFBDQ0OVv2g0qmw225X95/N5xeNxjYyMyOPxyOPxaGRkRPF4vGsxuOFwn0+/+dRj+s2nHtNHHjmujzxyXH1ej9thAQAAANhDSJwdls/nNTQ0pGQyqXg8ruXlZS0vLyuXy8kwDIXDYUWjUZmm6VgMiURCwWBQS0tLSiQSmpmZUTKZlFRK6MPhsILBoAqFgmMxuMXr9ejBBw7pwQcO6ehAn44O9MnjIXEGAAAAYB9tnB2UzWYVDodlGIZyuZz8fn/lNcMwlEwmK6W++Xx+xzKdEAwGZZqmFhYWZBjGltcmJycVj8eVTqeVz+c1MjKiubk5hUKhjsYAAAAAAHsZJc4OMU1T0WhUkjQzM1M3IY7FYorFYioUCrpw4UJHY5ienpZpmpXS7VpSqZRisVjl/+FweF+WPAMAAABAu0icHXLp0iWZpqlAIKBAINBw2UQiIalUrXt2drYj+y8UCkokEkqlUk1LscvVtsvi8XhHYgAAAACA/YDE2QGmaVYSYDvVng3DqJQIT01NdSSGZDJpu9q33+/fUuqczWYpdQYAAACA+0icHZBOpyvT586ds7VOOXHO5/MdSVrn5+dlmqbC4bBGRkaaLh8MBrf8f7/0tF0sWvr5nXf08zvv6O7ahu6ubchiPGYAAAAALSBxdsAzzzxTmbZb6lvdBrkTSWt18l0oFJpWAd/eBnphYWHXMfSCexub+pPnX9afPP+yvv/qbX3/1dvaKJI4AwAAALCPxNkB+Xy+Mn3ixAlb61Qn2LlcbtcxjI2N1d1+LdvjdHJ4LAAAAADYS0icO2x7Net6vVlvd/Lkycr0/Pz8ruNIJpOVfU9OTjZta709bjvVuwEAAADgIGAc5w5rt6S2ukS4E6W9gUCgperW2xPnZj2BAwAAAMBBQeLcg5aWlrq+z1QqVZn2+/22egO368aNGy2vc+rUKZ0+fbpjMQAAAADonjfeeENvvvlmS+u0kzd0C4lzh21Peu12Dlat2+2Lt/fkvX1c5936zGc+0/I6X/ziF/WlL32po3G0wuORHjp2WJL05KPHJUk+r8e1eAAAAIC95Ktf/aq+/OUvux1Gx5A4d9he7FQrkUhUpkOh0JYxnQ+qPq9XZx4clCT9yocecjkaAAAAAG6ic7ADbnZ2tjL8lWEYmpmZcTkiAAAAAOgtlDh3WDtVs91imqYuXbokqZQ053I5R+J/9tln9fjjj7e0zqlTpzoeBwAAAIDu+NznPqdoNNrSOjdu3GirmWc3kDh3WK3xkFtNRruVfEejUZmm6WjSLEmPP/64RkdHHdk2AAAAgN5z+vTpfdXZL1W1O6wTyef25NsJiURC2WzW8aQZAAAAAPY6EucO25702h1aqrpTMaeT2HQ6renpaQUCAZJmAAAAAGiCqtodtj0JtdvL9sLCQmXaMIwORrRVNptVPB5XKBTS3NycY/vZ6zY2i3rp9TuSpJn5VyRJv/7kwzrc73MzLAAAAAAuoMTZAYFAoDJtt8S5erlz5851PCapNF5zOBxWJBJpmDSbprllXOeDyJL01r11vXVvXa8ur+rV5VUVLcvtsAAAAAC4gMTZAaFQqDJtNwGtXi4SiXQ8pkKhoAsXLigWizUdcmpqakqzs7MdjwEAAAAA9iISZwdcvny5Mp3L5Wytk8/nJZWqaXe6qrZpmgqHw7p48aJSqZStWJysLt5Nh/p8igQfUST4iD78vmP68PuOyef1uB0WAAAAgD2ExNkBfr+/Uup89erVpstns9nKdCKRaLr87OxsJdG248KFCwqFQraS5nI81dXN9zKf16NHTxzVoyeO6tiRfh070i+vh8QZAAAAgH10DuaQVCqlYDAo0zQ1OzvbsPp1OaENBAKKxWJ1lzNNU8FgsFKtO5lManJysmEc4XBYJ06cUDwer1ttvNyB2dLSUqXt834pcQYAAACA3SJxdohhGLpy5Yqi0aii0agWFhZqJqPpdFqzs7Py+/1N2x5ns9ktye/U1FTDxDkej1dKs4PBYEuxAwAAAABKqKrtoHLv1X6/X8FgUOl0uvKaaZqKx+OVoaFu3rzZNGHdPtRVo+Wnp6e37K8VJM4AAAAA8C4SZ4eVk+JkMqlUKqWhoSENDQ3pzJkzlarR5eTazrbKJcyGYTQsoZ6ammo75v3SvhkAAAAAOoGq2l3g9/sVi8Uatl+2K5lMKplMNl1ueXl51/vaDyzL0ur6piRpfbMoSerzeuShgzAAAAAANpE4Y19bXd9U6lqpXXju5dKPCcHHhtTvI3EGAAAAYA9VtQEAAAAAaIDEGQAAAACABkicAQAAAABogDbOcNzExIQGBwdrzp+YmOh+QAAAAABck8lklMlkdsxfWVnpfjA2kTjDcfPz8zXnnz9/vruBtMAj6cTggCTpgw89IEny0hM3AECSvtnmkI+futzZOABgj1pcXNS1a9fcDqMlJM5w3NjYWM0S5+Hh4e4HY1Ofz6snHnqPJOnXPvKwy9EAAAAA+8fw8LDGx8d3zF9ZWalb6OY2Emc4LpPJaHR01O0wAAAAAPSAek02r1+/rrNnz3Y/IBvoHAwAAAAAgAZInAEAAAAAaIDEGQAAAACABkicAQAAAABogM7BgBo2Nosq/Lw0jtz/7/uvSZJCv/iQDvf73AwLAAD72hk2iyGzAKAmSpyBGixJSytrWlpZ049ev6MfvX5HRctyOywAAAAALiBxBgAAAACgAapqY18b8Hn19z/yPknSrTvvSJJ8Ho+bIQEAAADYY0icsa/1+bx64qH3SJJOPnDI5WgAAAAA7EVU1QYAAAAAoAESZwAAAAAAGiBxBgAAAACgARJnAAAAAAAaIHEGAAAAAKABetXGvnZ3bUOpawVJ0rcLtyRJwceG1O/jNyMAAAAA9pA9AAAAAADQAIkzAAAAAAANUFUbjpuYmNDg4GDN+RMTE90PCAAAAIBrMpmMMpnMjvkrKyvdD8YmEmc4bn5+vub88+fPdzcQAAAAAK5bXFzUtWvX3A6jJSTOcNzY2FjNEufh4eHuB2OTR9Kxw/2SpEeGjkiSvB6PixEBAAAA+8Pw8LDGx8d3zF9ZWalb6OY2Emc4LpPJaHR01O0wWtLn8+rDDx+TJEXHHnU5GgAAAGD/qNdk8/r16zp79mz3A7KBzsEAAAAAAGiAEmfsb5alvs1VSdKh4j1JUv/mqvqa/Ga04TvieGgAAAAA9gYSZ+xv63c19pM/kSS9d+WuJOnh147I623cXvnbH4g5HhoAAACAvYGq2gAAAAAANEDiDAAAAABAA1TVBmrYKBb1ylKpbfQ3fvi6JOmTjz+oQ30+N8MCAAAA4AISZxw4+fdGpP6jDZexLOn1t0qdiX3vlduSpE8YJx2PDQAAAEDvIXHGgbPhPSw16TXbs7GmR9YXJUkn75qlmcUPiI8MAAAAcPCQBWBf6x84rJMf+zVJ0vyLP5MkWd4BNe5TW+orrusT9/6rJOmRn98vnd74HyUddihSAAAAAL2KxBn7Wv/AIT0ROC9J+otbL0lS06QZAAAAAKrRqzYAAAAAAA2QOAMAAAAA0ACJMwAAAAAADZA4AwAAAADQAIkzAAAAAAAN0Ks29rXVlbf1vT+dliS999aKJOmnT/yWPANH3QwLAAAAwB5C4ox9zbKKstZXJUl9m/ckSR5ZboYEAAAAYI8hcYbjJiYmNDg4WHP+xMRE9wMCAAAA4JpMJqNMJrNj/srKSveDsYnEGY6bn5+vOf/8+fPdDQQAAACA6xYXF3Xt2jW3w2gJiTMcNzY2VrPEeXh4uPvBAAAAAHDV8PCwxsfHd8xfWVmpW+jmNhJnOC6TyWh0dNTtMFrikdTvLXU6f3TAJ0nyejwuRgQAAADsD/WabF6/fl1nz57tfkA2kDgDNfh8Xj10/LAk6clH/KWZ/T73AgIAAADgGsZxBgAAAACgAUqcgRo2vIc1//7flCQ99cnHSzP7GfsZAAAAOIhInIFaPB5t+I6Upgd2dmwGAAAA4OCgqjYAAAAAAA2QOAMAAAAA0ABVtYEaNouWfnp7VZL0/MItSVLwsSEN9PFbEwAAAHDQkDgDNRQtS68ulxLnbxdKifOTjx7XAJU0AAAAgAOHxBmowWNt6tTGzyRJx+69U5pZfEx8ZAAAAICDhywA+1pf/yEd+9CnJElvvvSGJMny9svTbL3imsZX5yRJj7xxfxiqjY9JOuRQpAAAAAB6FYkz9rWBQ4c1+sm/L0n6+t2XJKlp0gwAAAAA1WiwCQAAAABAAyTOAAAAAAA0QOIMAAAAAEADJM4AAAAAADRA4gwAAAAAQAP0qg3HTUxMaHBwsOb8iYkJR/d97+4dfe/P/9+SpAd/9rYk6ecfjEr9Rx3dLwAAAIDaMpmMMpnMjvkrKyvdD8YmEmc4bn5+vub88+fPO77vYnFTxTs/lyQdXr9bmmlZju8XAAAAQG2Li4u6du2a22G0hMQZjhsbG6tZ4jw8PNz9YAAAAAC4anh4WOPj4zvmr6ys1C10cxuJMxyXyWQ0OjrqdhgAAAAAekC9JpvXr1/X2bNnux+QDXQOBgAAAABAA5Q4A3V4PR5JUr+v9K9HHjfDAQAAAOASEmeghj6fVw/7j0iSxh47UZo54HMxIgAAAABuoao2AAAAAAANUOIM1LDhPaTvvTciSXrq3EhpZt8RFyMCAAAA4BZKnLsknU4rGAxqaGio8heNRpXNZl2JZ3p6Wh6PR4VCwZX99zyPV6sDJ7Q6cEJ64FTpz8vHBQAOLMuS1lZKf5trO/+KG/XXLW6Ulimvb1ndixsA0BGUODssn8/rwoULOnHihBKJhGKxmCSpUCgolUopHA4rEonoypUr8vv9jsdTKBQUj8ddS9gBANiT1u9K3/rD0vQrf7Xz9fe8Tzr5eO113/iBdO/2u+t/8nelgUFn4gQAOILE2UHZbFbhcFiGYSiXy21JjA3DUDKZ1MjIiOLxuPL5/I5ldss0Tc3Pz6tQKGhhYUHZbFb5fL5j2wcAAACAg4C6pw4xTVPRaFSSNDMzUzchjsViisViKhQKunDhQkdjmJ+fVzgcViKRUD6fVygU0sLCQldKtve6zaKln92+p5/dvqfvvmLqu6+YWt8suh0WAGCv2VwrlTYDAPY0SpwdcunSJZmmqUAgoEAg0HDZRCKhdDqtfD6v2dlZRSKRjsQQCoVk0Y6qLUXL0uKtFUnSN3/4hiTpiYceUL+P35oAAACAg4YswAGmaWp2dlZSKXltxjAMGYYhSZqamnI0NthkFXVs09SxTVNH1pZ0ZG1JsihxBgBUeTgoPfpLpb+hYbejAQA4iBJnB6TT6cr0uXPnbK1jGIYKhYLy+bwKhUIlkcbu+Pr6dfTMmCTpVnFJkmR5ffI0Wa+/+I5+9e6fS5Ie+dnR0sz1X5QODTgVKgBgr/H1ST6+FwDgICBxdsAzzzxTmbbbnrg6Uc5ms5Xet7E7hw4f1ZO/8huSpG9sviRJTZNmAAAAAKhGVW0HVPdcfeLECVvrVCfYuVyu0yEBAAAAANpE4txhhUJhy//tVrk+efJkZXp+fr6jMQEAAAAA2kfi3GGmaba1XnWJc7vbAAAAAAB0Hm2ce9DS0pLbIXTUjRs3Wl7n1KlTOn36tAPRAAAAAHDaG2+8oTfffLOlddrJG7qFxLnDtie9djsHq7bfSpw/85nPtLzOF7/4RX3pS1/qeCwAALTF45UGHyxN9x8tz3QtHADodV/96lf15S9/2e0wOobEucP2W9K7191bXdH3//L/I0ny/+S2JGnZ+HV5+o+4GRYAYK/pPyJ9/FJpeuXn7sYCAOg6Emfsa8XNDW0uvSxJeuDeXUmSaRXdDAkAAADAHkPi3GHtVM3e75599lk9/vjjLa1z6tQph6IBAAAA4LTPfe5zikajLa1z48aNtpp5dgOJc4dtH7fZNM2Wk+n9lnw//vjjGh0ddTsMAAC6z+OVjt5vG33qF+7P87kXDwB0yenTp/dVZ78kzh3WiaR3e/INAAD2KG+fdPoXS9Nn/5G7sQAA2sY4zh22Pem1O7RUdadi+63EGQAAAAD2MkqcO2x70mu3l+2FhYXKtGEYHYwI7ejzefXIUGm4kaeMk6WZA3xcAODA2liTXvmr0rRZ6nRSxx6RvFS7BoCDgBJnBwQCgcq03RLn6uXOnTvX8ZgAAMAuFNelxf9W+jN/XPqzNt2OCgDQJRShOSAUCimfz0uSCoWCrXWql4tEIo7EBfs2vQP6welfkyQ99dEzpZl9h12MCAAAAIBbKHF2wOXLlyvTuVzO1jrlRNswDKpq9wDL49Nbhx/WW4cfloYeK/1RHQ8AAAA4kEicHeD3+xUKhSRJV69ebbp8NputTCcSiabLz87OVhJt7D331jd1d23D1p9lWW6HCwDYjeKG9MbflP5e/LPS3/o9t6MCALSIqtoOSaVSCgaDMk1Ts7OzDatfp1IpSaW20bFYrO5ypmkqGAxWqnUnk0lNTk7uKk67nZehc67Ov6Jbd9ZsLRsfN3SUTskAYO+yitLdn5em3/zb0r9PfNq9eAAAbaHE2SGGYejKlSuSpGg0Wretczqd1uzsrPx+v2ZmZhpuM5vNbtnO1NRUW7FVJ8t2Oy87aIpFS7fuvKNbd97RS6+/rZdef1sbm0W3wwIAAADgAoqyHBSJRDQ3N6doNKpgMKhkMlkpUTZNU4lEQul0WqFQSDMzM03Hb97+up220NXVwAuFQqV0uywejysej8swjMr2x8bGDvxY0puWpR+9cUeS9Bff/6mkUulvn6/xb01rG0XlXl7eMi/42JAG+viNCgAAANirSJwdFgqFdPPmTV29elWpVGpLG+ZQKKS5ublKe2g725qcnNT09LQMw2haQi1J4XBY0taku3p6aWlpS8m1aZqamZmhZ2/L0qFiqQ1a3+ZqZV4zG8Wivl24tWXek48e1wCVOwAAAIA9i8S5C/x+v2KxWMP2y3Ylk0klk0nbyx/0zqW8vj4NvO8XJUnmmilJsjxeeZqs11+8p19fKf0w8chPjpZmrv+v0qHju47p4tijKtY4L6trm/ra8y/vevsAAAAAOovEGfva4SODCv69fyZJ+i9zL0lS06TZaYf7GdYKAAAA2EuoPwoAAAAAQAMkzgAAAAAANEBVbaBH+LwePfno8R3zAAAAALiLxBmOm5iY0ODgYM35ExMT3Q+oRx3q8+lXPvSQ22EAAAAAjspkMspkMjvmr6ysdD8Ym0ic4bj5+fma88+fP9/dQAAAaJtH6j9SmvT2vzsPANCyxcVFXbt2ze0wWkLiDMeNjY3VLHEeHh52fN/3Vld0/Zv/UZL0wMumJOntx0LylB9+AACwY+Co9Mv/vDS9vupqKACw1w0PD2t8fHzH/JWVlbqFbm4jcYbjMpmMRkdHXdl3cXNDaz/9G0mSf+WuJOmO9SuuxAIAAACgfpPN69ev6+zZs90PyAZ61QYAAAAAoAFKnAEAAJzi8UqH74+Y4P/A/Xk+9+IBALSFxBnoEffWN/Xn33tty7xff/JhHe7nAQsA9ixvn/Tej5SmP/Z/dzcWAEDbSJyBHlG0LL26vLpjHgAAAAB3kTgDNfi8Hj18vNTz9thjQ5KkPhslvx55dGTAt2MeAKAHfXPK/rLFTenO61vnPfCQ5KVWEAAcBCTOQA0ej0debynh7fd5yzObrndkwKffHh9xMjQAgBusTWlpYeu8wQclkTgDwEFA4gzUsOnt148evCBJemp0uDTTd8i9gAAAAAC4hsQZqMHy9OnW0fslx6efcDeYHmRZllbXN1te70i/Tx4bJfcAAABALyFxBtCy1fVNpa4VWl4vPm7o6AC3HQAHSHFDWl4sTb/0f5T+Nc5LfdRiAoC9hCdYAAAAp1hF6e2flqZ/ki/9O/zLkkicAWAvIXEGaihalu7c25AkvbJ0V5L0sP+IfF6qGXfKZtHSOxutV/cuo9o3AAAAuoXEGahhs2jpBz99S5I0m3tVkr1qxuubRV1/7a0t80YfPvZuz9yoeM1crRzbdlDtGwAAAN3CUyfQQeubRX3zh29smffEQw8ciMT5t556bMcY1tsdsTEWdoVlqa94r/7raytqeAvz9kt9A/b3BwAAANRB4ox9zevrk+/EY5KkO6u3JUmWx6tmFXz7N1cVeftPJEmP/Phoaeba/yoNHHcq1D3vyICvoyXAfcV7GvvJn9R9vf/bQ1KjHySGf1k683c6Fg8AAAAOLhJn7GuHjwzq4//w/ylJ+tbcS5LUNGlGcwM+r/7+R963Yx4AAACwH5E4AweRZUnrd3fO7z8q1epwq7gpbbxbbbpP0hND3vrL2/Cw/4ji40b9BdZWSqXKkixJq2uljsT6faX99dFRGwAAALqExBnoQd8u3JJUajPdSvvoz4efsLfg+l3pW3+4c/4nf1caGNw5//ar0nf/g/3lbfB5PU2qdvdLh+9ve31VA0co0QYAAIA7SJzhuImJCQ0O7kyuJiYmNDEx0f2AepTX49EHH3pAkvTS629LOuDVygeOSr/8z0vTayutr+/t72g4AAAA6IxMJqNMJrNj/spKG898XULiDMfNz8/XnH/+/PnuBtLjDvf79GsfeViS9KPX77gcjU3rq/Vf6zsieTtUStxmqTYAAAB6z+Lioq5du+Z2GC0hcYbjxsbGapY4Dw8Pdz+YPWZjsyirheXvrm1IKg375Gmz7XFLvnOl/mvn/hfpgVPOxwAAAIA9ZXh4WOPj4zvmr6ys1C10cxuJMxyXyWQ0Ojrqyr7fuXdXP/y//ndJ0pHFJUnS3Ud+WZ6+w67E06rCz1e0tLJme/nUtYIkKT5utD401McvlTr7quX4I6XXGyXKALCf+QakYYa4A4BOqNdk8/r16zp79mz3A7KBxBn72ubGuu7eLP1qdfJ2qRfp1YefcjOk3tV/pH4P2V5f6fX71jeLmn95ecsiY48NtdSRWSuKRUtLd7f+gHDi6IC8tXrWXl+V/vrfb533sX+yJX4AAACgFSTOAJxVLEp3b22dd/RkS+2f721s6k+ef3nLvLql6lZRWvn51nlrK6X5jXj7pL5DtmMCAADAwUHiDMCe/qOl4ackra9taH5jccvLT35iWP3ViWzf/RLejVXphX+zdVt2hrHaXJd++n1JkndjQw+9/ZreHPwFFb1t3LbsVDF/f0B64tOtbxsAGvK82wxm8MH7sxheDwD2GhJnoIcZDw7qzIONE8z1zaK+/+pt54PxeKqS3Q1t+LZVfR4YlFptV93I5pr0o69LknybRZ1ZXtato4aK3LYAbPfNKbcjqM/XL70/WJr++CV3YwEAtI0nUKCH9dloM+yRdOxwacziR4ZKyay3Gz1qAwAAAAcEiTNQg8/r0UPHSj1vP/nIcUnS4T6fmyHV1efz6sMPH5MkRccedTkaANinrDr9NVDtGgAOBBJnoAaPx1PpIbrSAVWtHpy3OTrQp8+Hn3AyNDRT1Ra7Je20nQZwcBQ3pDd/uHXeo79UGqYKALDv8aQI1FD09Ovm0CclSU998AOlmTwc7Q1b2mIDgA2WJRXXGy+zudGdWA6adtunf+pyZ+MAgCZInIEait4+vf6e0dJ/HmmhBNmypPXSeNHy+KT+w7WXW78nWZt1N9O3uaoN7+H64yqj89bvSS/+6dZ5Zz9b/xwC2D+K69Irf+V2FACAHkbiDHTS+l3pW39Ymj71C9LZf1R7ub/9z9Kbf1t3M2M/uaX59//mzp6rO8V3SBr9zM55B5m1KZk/3jkPAHajuCm99Wpp+uZ/Lf376C9JfdRiAoC9hMQZOIh8fbJOfUir61WJ4aa2VEX0yKMjA7U7RNsoWg4HCAD7RPWPcov/rfTv+wOSSJwBYC8hcQZqsCyrklT+/M47kqQTRwfktdFB2Bav5qTTH3K0ze1GsahXllYlSd/44euSpE8+/qAONekFfHV9U6lrhbqvHxnw6bfHR2q+dv0nb7UZLQDAUdVNhjbX6i/n7aNHcABoAYkzUMNG0dL3X70tSfqT51+WJMXHjXd72K633mZRt1dKibZWvi/p+zp+4QlHP2iWJb3+1j1J0vdeKcX8CeOkg3sEgH3G21+qPt3Oer2muslQo3bbpz4kDZ7qTkwAsA+QOAMdtLZZ1Euv39ky78nN4s4P2i/8PemJT7/7//VV6TtXHI+vU+pV4QaAnmcVpXe21Zo5dIyREwAADZE4Y1/zen3yPvCgJOnenbdLM230VN23eU//850ZSdIHXj1amrk+KQ0cs73vV5dLVeW+fa3QtKp23+aqxn5yy/a2AQBtKm5IP/vvW+cxHjMAoAkSZ+xrh48+oF96+l9Ikv6vuZdsr+eRpQGrVP25r3i/DZi1/zvE+q2nHquUJntU/weG0YeP6YmHHtgy70g/pdAAsCdtrr1brfub9+d98ncd7Z8DAPYaEmc4bmJiQoODO798JyYmNDEx0f2AIK2tqP+//Gt94sfLlVnz7/9NHRnwNW3HLUn9Pq/6fd3rVMbn9ejD7zumRz/2fln9pRoAzTo/AwDc93BQ8m27t3t5BATgnkwmo0wms2P+yspK94OxibsmHDc/P19z/vnz57sbCNzRd1j66D/eOa8FXo9Hx4/06/jQEWngaAeDA4ADwNe3N6qiFzdKbdDtWLv/cN1/1FYTLAC9ZXFxUdeuXXM7jJaQOMNxY2NjNUuch4eHux/MAbG69u74zF6PR4fdrEbt9UlDj7W+3sCg9KnLnY8HANCblhelt39qb9lyz+FUKQf2pOHhYY2Pj++Yv7KyUrfQzW0kznBcJpPR6Oio22H0NMvj1VuH3rdjXru+dn8ILUk6+cCAfuup4ba3BQAAAHRSvSab169f19mzZ7sfkA0kzkAP2PQe0g8e+vWu77ff59H65t7q9Ozu2saut3Gk3ycPVfsAAABgE4kz9rW1d+7pR/PPSZIOvfqGJOnee8/J09d6W6/VtU0dKlryeXcmXJZlaXV9c0sV6V7X7/PqoWOH9eryqtuhtOSl1+/omz98Y1fbiI8btjpBAwAAACQSZ+xzG+vv6K0flsbWOHV/XOVXT39UUuuJ83/4ziv6zMcP69ETOzunWl3fVOpaoTQe824Cvs9XXJPHspmEbxZ1qHh/6KzNVW14D/duRylr7fWUaPUf1ep66Xg88dADuru2ob8qLHUyMgDYH/qPltr9SqVhpiTJ2+9ePK0YGpb8H6j/+uaG9Fqua+EAQDUSZ6AHfcD8jh668wN7C1uWPn7/k/zxvhNa/8T/Y0tHKd4GSfTDx4/ovcdKPVw/+YnhtsZi/oNm42NblvruJ/bvffu6HnkrL0l6yjhpbwcf/FWtnn5SqWuFlmMre//QEX30UX/l/wO1htLy+KRTv7BzHgDsJR7Pu98BvdqT9vo96cU/LU3/7Pulf09/mCGyAPQ07lDAXufxqFx7vN/nVf9An2SzGrLP65FPpZX7B/ocKanuK97T2E/+pL2V/Y9KA0flefOHOnn3FS0deUyWp/Xb1k+WV/XEQ+/Zkjzv0H9YOvuP2osTAOryvFvi23/k3XkHmbUpmT8uTd+7fX+enWGoPKUSdUkafPD+rPY70gSAVpA4A028er+K9wtrS1pau6ljR3ZWeVvfLCr38rL6rTX5Vt9Tmukr/Vv0eNXrX+vPF25p/p0b2vAdab5wN5mvSOYrOixL/7dBS+uf+B8ZdgTYz7455XYEnefrlz7widL0L/9zV0PZ83z90vuDpemPX3I3Fqe1+1lgGEfAMSTOQA0+r0cPH9+aRH7sYb+K/bU/Mn1ej4KPDUmS7ujpHa9hdzzyqN/naak0HQAAAOgUnkCBGjwej7zbEt4+n1cbdaoyezylxK5Tfuz/uF45Hmy6XF/xHY2+/ueSpA3fIX3jlU29+I0FbfoON15vc1VjP7nVkVib2fAe1vz7f3PH/Kc++XjrG+vf2TEb0BGU7gDYD7iXAY4hcQZ60KbXXocuG74jyj2yMyntKR5P7SrgLlS53u0Y0Iz/DKDnFTel26+Wpu+ZpX8PHaMtMADsEokzUEPR06dXt5X4FtvolAq9ZbdjQG8f/3l1bVOWrKbrkXADDrAsqbje2jre/tLfw4Gd8/eLjXvSd/9Dafpn/73076O/1Ls9bAPAHkEmANRQ9PbvSJzxrk/8ON3Wet/+QKwy3XQYqzo+H36ipeXvrm3YGsrKV3xHI0v/Zcu8hRP/kza9h+qu8++eX9TqWvPxtrcn3AA6oLguvfJXra3zcKBU24VOBrfy+KQTI6XpD/5q6V8SbQDYgic54AAqevp1c+iTO+YdZB6rqBN3b26ZZ77/Uxo9c7ry//5a4z+3y7Kk9bstrOCRBmjjDew5xU3pzuul6VdzpX/f95FSD9G9wuuTjj1cmn6kh380Lm5Kb92vhn7zv5b+ffSXpD6SfADOI3EGbPBt3pPHRpVcn7WuD5jfkVTqFEsqdfRlt81ytxS9fXr9PaNuh2HblhLub560v+K2zk6+XajfIdqh4j29d2VrIvt/XH9d339jQ8nIR+zv0671u9K3/tD+8v1HGMoG2IusTWlpoTT9o6+X/j39od5KnPeK6vGfF/9b6d/3ByT11ncsgP2JxBmw4ewb/7uOrJttrWund2w450i/T/FxQ1JpvO16+jdX9fBrO4cgW6e6IgAAwIFH4owD49i9n0qSAq9+TQN9PtvrVbfL7TXe4roefvv7kkqJnyS9duxJbdrqyMyjjSbDVu0HHo+n0r64UVXrPnlrDkGmBuv806eGd3QOtrq2qa89//IuIgbQtoeDkq/B/W8/dQIGAOgqEmdgD/NaG3rkdm7LvIfu/MDWuhvew5p/5LecCOvAODJg/wcYAF3g66NTKwCAI0icsa95PF55+kvVb8vDSTEoEHrWxy+V2jLXdP/K3VyXfvr9rS/1WkdDe0V1B22ba42X9fhKHSgBAIADicQZjpuYmNDg4M6hPyYmJjQxMeHovo8Mvkef+K3/lyTp+X/7e21v58XT/7OtzsFqKXcSBjTVf6T5MDmba+92MFRGR0Ptqe6grdmwRv4PSP7HnI8J9nn7pPf+DzvnoXWb69JP7tde+m//uvTvx2P05A/AMZlMRplMZsf8lZWV7gdjE98wcNz8/HzN+efPn+9uILuweQDaAgPAnuLxSof9bkexT1ilcbElaX313XlO8fikU79Qmn7jb+7P6+BwfwB63uLioq5du+Z2GC0hcYbjxsbGapY4Dw8Pdz+YfWbDe1jz7//NNtem0roT+rxefcI4uWPeDt5+afiXd84DgP2u/7B09h+Vpt/8W3djAeCK4eFhjY+P75i/srJSt9DNbSTOcFwmk9Ho6N4ZM9htW8YsbkEv9/7dSc83GIt5h8LW6vmfaLBodQ/lr94fQqyv+I42fHXaHG+8IxU3dswekPTUo9tqKBRXpeomtB5vqVr2mb/T5A30JsuytLq+ef8/xaoSqq28Ho8O91e1C+4/KnlKP9isbRS1USwND3ak3yePZw/9kLO5Lv3sfjvz71wp/fuxf9KgfToAAKhWr8nm9evXdfbs2e4HZAOJM4CuafdHAbf8ws+/ru+972LtFwv/p/STfHsbHnyw1BHYXvPNKUnS6oZHqcXTkqQjm2/ryZVv1Vz8qK+oJ4/flYbv/0Dwyd+ttOHOvbysb9//ESQ+blSGDHNdveGMPNUdg1V1Krby8/uz6o8RDgAA9r4eeVIBABxUq2ubtpbzyOP8EGAMZwTALdU9/VvF2u2+rar26I14+ys1fAB0Bokz9rX1tXd088XnJUnLb9+RJB0bHJTP6/yXyV4rXcVO697dVb3dKBZl1ehfx9rY1Mbazmre2/VEFeZiUbp7v3r82v2eLj1Nev5u4O7ahl56/c6WeV97/mVb6x4Z8Om3x0d2zF/fLGp9036Jb08c126ofghvRbkpQa+rlUCQLGAvq+7p/523and+V1xvPgqAJD36S/wICHQYiTP2tfW1e/rZX/2ZfvLaa3rnzlJp3iNP6LT/AZcjw15w7+4d/eXX/jf95ddK///kr/9jvWeo1PHXmaWf6KE7jdtbHx3w6W6N0tTVfkvfWyk03X+tKsz31jfl25Ykrq9tSGqeiO9oc2zHxqr0wr8pTb92v2r6+xq1Fm/umz98Q0cGfJWq2nb1+TxaXdvU58NPbJl//bW39M0fvmF7O92oGv7GG2/oq1/9auX/n/vc53T69GlH97lD9UN4K/ZKU4JaCcQ+SBZ2fe30HZHO/S+l6fKPXXQ8uO+trK7phb99tfL/D7/nrE6f3NufBXRHT3xf7REkztj31tfX9fLLi5X/+46vtpQ4U3J8MBQ9fXrtPR+RpEpP5T/7cUFf//f/qLLMR/+nv1tJnH/s/7heud+JWD0nB/t1a2VnlTprFz2a/+f//lO97+XlLfPmNxbrd2JW5YMPPaBf+8jDbe+7llXvoOYf+JT+8aO3dMRbldAXNzTw0/vjwi7+19K/m2uSdUhaPK1VSY/crlEaalkaUOmY/eTYx7a81OfxqG9zVf/66z+Q5Xn36+tnt+9p8VYpQThUvFeZ/47n0JbSx+29nUued0tWK4lFZ0or33zzTX35y1+u/D8ajfIgAlt2fe14vdIDp0rTzcaFd8v6Pelv/3Npujwc1YMfZBzu7d56rfRDlo0fg1burev//N5i5f+n/4cVnT455GBw2C/4vrKPO1SXpNNppVIpFQrvljKFQiHF43GFQqEDEwN6Dz8M3OfxqOgpJU/lJHTDU/9hZdPb/EFmo29AG761pstt9+Sjx3Vi8FDL67nC49WG55CO9PfpaF9VvfRNS/K2Pg7sgNZ1fr2UaL+1cmPLa16PRw//5Ih+9OAF3Tq6s8q2JP36ykxl+s8Ho3rHs3MM9tW1zVKJ88BR6Zf/eWnm/Z7BN4rS2kbt5PmIz3K0FvCW3srvO9znk7dG05Ji0dK9jfptw4/0+xhwDr3L2nx3GKq75Q72an+mt/K8+yNXpTnBPr3S3/pp6dgMPuh2JADuI3F2WD6f14ULF3TixAklEgnFYqUhgwqFglKplMLhsCKRiK5cuSK/379vYwDQwOJ/2/r/5bv66Km3pRs1ln1lUDJ/XJr2f2BXu723vqns37y+ZV7oFx9qqTr3q8ulUuO/0pIOV5U4l4b32jpM1WsbS9r09OujKjWb+KjfktfaWsXcZ23o/Su3S8sfM+y/mRZ87fmXd1T3LivcPaS/+Jm/5mu/9YGfl0rVNz3qL95/WL9fbX59bUNH+q3abac9Xt1bL+puk3btq2ubO9p7/+ZTj+nBB3b+iLJ0d01/0qBteHzc0NGGe0PX+Abe7Vn+U5fdjWWv8/VLH7jfVKT8o9d+Vf5BYTtvf6lJQrXNDWn5vzgfE3DAkTg7KJvNKhwOyzAM5XK5LUmpYRhKJpMaGRlRPB5XPp/fscx+iQE4qC6OPapird7BdvgzrRa9+tqPu1SycH9YqeKGRz+6P6yUxyrKZ62r+Nqb0paS4w3ptdyudrfa79eRdbO0H4902FNKNr3FdT185/s7V7ife37g7drDfR1ZX9Jq/4nK/zcsj4ra0ODGsk56fqzX7lfxPvfew1q1BvTiT96SJPVtViXy5bafvoHSw7gN5fPTV3xHY3fKVWBL1ebnNxb1z35ltGbbad97HtSf/uC2Tr3ZvF274z5+qXnHX7V68nWTVaw5Zro2m7frB/Ydj2fPt+MH9ioSZ4eYpqloNCpJmpmZqZuMxmIx5XI5pdNpXbhwQbnc7h5Qey0G4CCzXXLbZ+mItan4cKmDK1+DvOXvPWTKt34/6Xus1H7tyU8M22rL6G1Qz/g9m6Y+fPc76v/JSltVrBu5M3Ba7/je09FtVuvzWPJ6i3ronZdLNTnvV20ee+NPVSxaOrNSSpgf+UlVGey37rd3/uCvSo80bqvuJrtDddXk7ZPeH9g679B7pL490gyg7O4t6c0fuh3FwVHdJ0ErCRql6QD2ORJnh1y6dEmmaSoQCCgQCDRcNpFIKJ1OK5/Pa3Z2VpFIZN/EgO6hrXLv+YO5l2wt94kfb+1d+qkdnVi967Cvqu3w/Qy7f6BP6lAv0euWR6ozspNHUp/Xks8rPXm8VEV7YPXtUihqnGzfOdR+RyM/e+DDKlZ1Blb0ODyWswO+98ptPfB263G/tbquY0d2lojfXdvQ91+9vWP+lg7Q+g5JT3y65X1qY+3d3qq/+/8t/XvsEcnbQvwkUe6pNYRc/1GG6ULpx5B6svc7h2p1SDc+6zhASJwdYJqmZmdnJclWp1uGYcgwDBUKBU1NTXUkae2FGFDfX3zrRf3052/pfQ8e09//5Fli6eF4es2/n/uuHrlXkP+BI/ro8M7X6ybrlqW++z1Of/yVn0mS7loDetUs9TD/4fsFwt+7Xb9l7LG+TY0eW9Uhr6VfOfW2Mn+ZU/5vXtP7HjymgIPnqujpU9HGcDo/euUNvX33Ha2fPKr3j/yiJMnrkR4+XqqaXCy+m9w/f38orJvLP9brf/Oeyo8Xm5ZHH9XPtFb06eu33y9JemTIvdbC9WotHOn3KXi/xsH6ZrFmEl0tk4ho8WfLGn7vkCb+bpMS9s21ncM8HT0l+Zo9MnhsV3vPZDJaXFzU8PCwJiYmtLq2KUuWVKNjNs+mR33FnfP7PJ3rrC3zl7kdx8eypNVN+zvweu7/sOWE+80rbKl1/nYxTFetY+OmSjwvv08TExNuh7PjWnbby7eLeux4nWpLXR7/udeODfFgt0icHZBOv1vyd+7cOVvrlJPWfD6vQqEgw9hdpzi9EAPq+8/f+v+3dzcxbqR3fsd/7Be9zEgz1a3xvNhjzwzbXi9sZ7UmJa93vYYCiAwMZAEfQs74FOQy5CnJKU0IuSYRyM0hWCCASeXgbHIZkQgUIBeHlDHejV9X5GI2K8deD6nxzOhtRmqW3rpb/cLKgV3VRTZZLHazm9Xd3w9AiOquKj7N+rNY/3qe51/X9bf/8LG+/nuvjj05DFJbgtieoPkvP3xPP37vhs6dfkPv/nv/V/qnWss6c/O/SZI++6g9r7jx7B86v7/9YEmvrm29dZbb+uSaPlpf0q21BbUmpvUXV37Rd1+5b+/Vr4fYvYwXd2+zl99++Ilu33+kV0490B+90ONWVz2cePqJ3mj+xPn/ZMjSZMjS0VBLfzbzYc91JrSql9Uu3HOr9XlJ0tp6S99/t+4UB7vzwQfO8uuP7un3Deml14a/NcxUj4rakhQKhTQ92T+ps4d4H52a1ORESD/4Yc2Jm3/xnah3Uugufmb7uHO++WTI0pamTbgKNw3wgx/8QD/+8Y819wff0KOXv67PXi+oZVl6bvm2JOnd6W9rZaOq/UutJzq9tnkib/fAnzGeaDo0mkS1+/2R2u9P/gP/IyW+dGJZf/ay9wWMHes339ttO3O/Q5Nbiw1ufG57vTc9bSO5/6i5WXfAPq4MYh93zv1ufW+TjT5/3w/+06XN9+e121sX2OMe2V/eXNf1T9b15p6+am/25/zcuXOBSAxpD3aKxHkXvPPOO85zv4W23ElqpVJxKl/v5zYA2B67N7SXh0urzr9273LIWtNkq/3zfgf1adf9jXsxQ8/r3elvey7zwvSypk/e9ZfIum7vtaNlfGqFpvRf/99Rvff+PZ3+4gv6w+8MTsglyeqT1LuLmHWbUEtTGwmbPZ96anJCa67uzylrRc84f1pLx7Ws43q6ZVtrE8d2ZQitXZ07EX1Vn5/d2mPulRR2Fj/r7fdOLOvUER8J2vpqz+GhxybW9cy0dHyyNTA2Pw290BGbnz3RHkFw+nOfanqqK3H2kXhth1093ov15Il+tthj2oXlGjpt37P41Jd89OD38PShdOf/Dr/eIBOTkvHa1p+vr+rYpKVnpjd6072G+jrb8h7qa1nS0lr74syatfH56XMBZN0KtaePbAhZ6/rKZ0KaXj6mL09+KOvKv1To5Eu7N43AsqTVjX2/vtL3b3NGG/R6f8YwXP5vbrVk7dJnATjMSJx3Qa22eWV+dnbWY8lN7uR2FMW5gtAG4LAZ1zzz2aXf6Uv3ru5oG1ZoQivyHp63Gmr56hEai1BIy+shLa5Ky+uhsbfz9OLP9G/+ZLMI17ce/y+duLl17vp7ryS1OrF5r2krNKH1id7FuyZbTxWyeiTz6y0d3Ug+OyqHSwqtLkoruzR82I/bfyf99n9v+fH3vvCJvvknRzX70n2dvl3ULY9NtEKTWtFmYrRmvz+TR6TdGho9Sq21zeJm16+0//Wobh5aW3Iuuix6DwLZfXf+Tt/70pK+efKoXn95yd9Q389Gpakj7eJ0PSytrusvPzB0+smzerjUPuZ85Rmz57K3Vo7rF483L/AcsVb0za9+Xl8Lv6jnTxzT0r0b0rHe0whGct/11UXpJ3/Rfv7RL/oOY/78cyF970t93h97/W/9K19FHHH4HJtY37zAMqzQhDR9XJZlaWl1azHJiVCo75SflbWW1lotLa+2FJre/N6xfN0N5HAicR6xRqPzdiN+hzufOrV5QnXt2rV93wYAwec1HLmXgN2kaGys0IQeH/mMJOnuia9s/Gx7BctO3y52/H/hmTf0Dy/Eey47t/BXml28seXnrZalV3tVDpd0svWc9Af/dFtt2xF7WOvDW9JCfevvl9tDmo+uPdKrD6p6svy81q3d6Y0bNFfZq4r9Ti2uhXre91s/y/ddZ+bep85Flz//6dZRCr1Ylpze224h9T/ZW2mFtNanGKAkTbVCskKTCk1OygpNarVrCP90rwr8t6rSyVekU1/suc3JT3+l0092fiux9XVLf31nWj+5f1Sr2nqh7DvGR8695e0LaV6FF/e1iWnde+5r+vOf/pWeer21ve7/3H27wfW19jD9fr3466uSuxgkBeh25Jnp9oVE5wLLsJ59QfrG21paXVf+x1tveXjqxBH98z9+veeq1d819fPGfX366QM9941/5vz86RqJcz8kziNmmua21nP39m53G0FqA4DdZ/dwH19d0KkeCdUgoZCcE0v4Z4UmZR5vzwe9MfunY25Np+4hxU9X1/V04V2de/mJXte0fv10RT9r3Ndya0IfN0/03ohl6Za85yqfWftUr060X+sffd5ORkJbintNrIc02aOwV/eFhi8ee9hRl/3YiY86Rg24i7X1s7weUqvH+d6ge6TbFeL9+CfP39SRid63CLPz75XWhKpP2u/Jzz/4TOfQd/O9ga8R6u55+sUl6Y2X9LQVUmutd6G0VSvUt7DfM5Mtne7zWlXzGf18oU8cSDr9+KSWZ97Q0ellLZ84pmvmZo/pdMjSmZmtvWTrlrS63m5rL60eCX6/pN+Pb63+vOfPv/D4gTME/OPn23OzF9dC0spGZtlak9YGXJhYXdL0xsWOKUsKPbwtPfdKR6+zpZBC00d6XliQJDX+z8amljp74TeGfR+dtOSUK3AVdrMvhqyuhaSB709ISzqqJR2VptoXS3peiNpo96K9b6ze9QzWZ+bUOvnZnq909GZVk5ZrKISd8H3jbS1ZR9pF/jYcmZrQs88c1ZGpCS0uLXb21v/4P24+X1/V1ISlI31uhdjx2f7Tf935y4mp/rfYW1vWpFo6OtU+3thTROwe3uXVdbX69a5OTEpTx3r/bm1ZE1arb0+u3+22JqYVmj4qKzSh1Y04C4WkqYneV/PWWi11b9ZaW9faytrObl0I30icA2hhYWHcTdhRG54+7fwi+tGPfqT3339/qG3MzMx09IBv19LiI31we0F3zc3hi9YnpjMvcVyWnq46/zZu3qMtLkFoz4s3/4MkaeWTZsfPZ98r6MXb/Qs8bb3WO3q93p9jaw90b3n4YV6fPrrvu/CW37aM0zjaY8dKt9W7v9InTzYvSjyqX1PoRO+huR2m/lYvHvub3r9b/khP1h5t/bklPafex7S1uxsn5evtOdfLj9rvjWVJX9X236PFZUvvbyQl/+OWOyntHFt8anVVn+vRBfbB2qwWtKL1p8f169tPtnRUffr4047YfGpN6N6D9snk1FL7pPNXWtBx11Dt/3nH0IPVfj3/N/v+Lc8/XNaT5fbc1CfLK7p+466kdi/1nVvtv+feg/ZQ+JtLd3W0z9x3d1vfe9BO+l9+fkWTrRU1Fv0XDQutLjmx07Kk92+250f/ovmsfvPouCYt79tLfvKo8zv42Yk1feNBo2dvYH35pOrLz220dWuS8PxiU7cWFvV48amerLT03LObf8dUyNLxxaUt69x7OqX31ld182jvcebhxVWdaLW383h5IzaOPdHC463Hozsrx3RvcTMhmNaqJhYWtfh0RY+W1jV95GHP17C3ObkRo3cfbxTzezypX37ww/bftvyRXmv2Trp7+bL1RMdvXZdenuyYS/9ofVLTc99Sw2rq8q89unt/3TmM+zfHv66nk88q/uIDvXLUTuZXpTvtz/hqK6Trj47r+oerWvdxzrLw4Kie/dp55/8/+eCJJkN3ey77lx+1z68mW6v66uLWNt88uqr7073333daS3px2rXfrY3XuPHv9Pe3H2htffMz+UcvLeqr5+d04uSivv/9/+yM0JEk3drc/pcXa/rC9AO9cqz3a/760XEt2wn+Lzu/ae8/M6ebz/f+TITvv6vXjzzQq0b7otK3T36k8Olpfe7kR7r+3/+t3v/0kR4/7Z1wPjj+qn5n/HHP371m/kyv6a5eP9V76P0H95/IXOr9tzw+8hk1Tv1jSdLdZ17X8d+fVGPF1OWf/laSdOLopL74mZM91/3YXNS9x53z6J9O3dFvrv+w42fvfWw6z49OTer631/vub07D5d19+GyFh890Pri5jr1+vs6Nj2aoTj3799Xs9kcvKDLhx92joTrzivGysJIlctlS+0xLNYwb28+n9/WekFsw5UrVzq2w4MHDx48ePDgwYMHDx7DPq5cubLtnGTUmLI2YkEY4hyENgAAAADAQUHiDAAAAACAB+Y4j5jfeyYf5DacO3dOV65ckWmaevjwoV5++WUdOeJ9m5tuo5rjDAAAAGDvbWeO88rKiu7cuaPnnntOhmHo3Llzu9S64ZE4j1j3PZNN0xw6kd1p4jvuNhiGoe9+97vbXh8AAAAAgoSh2iM2it7e7sR3P7YBAAAAAA4KEucR6044/d7WyV3Qa9Q9zuNoAwAAAAAcFCTOI9adcPqtcF2v153n4XB437cBAAAAAA4KEuddEIls3ojdb2+ve7mzZ88eiDYAAAAAwEFA4rwLYrGY87zRaPhax71cIpE4EG0AAAAAgIOAxHkXXLhwwXlerVZ9rVOr1SS1h0iPYph0ENoAAAAAAAcBifMuMAzD6fG9fPnywOUrlYrzPJPJDFy+VCo5Se642gAAAAAAhwWJ8y7J5/MyDEOmaapUKg1cVmrPS06lUn2XM01Tc3NzSiaTikajyuVye94GAAAAADhsQpZlWeNuxEFVKpWUTCYltStW9xr+XCgUlE6nZRiGqtWq5xBp9/akdq9ys9nc0zYAAAAAwGFDj/MuSiQSKpfLMgxD0WhUhULB+Z1pmkqn00qn04rFYrpx48bAhLX7NlN+EtxRtwEAAAAADht6nPeAaZq6fPmy8vl8R+XqWCzmJK1+ZTIZ5XI5hcNhlctl34nuKNsAAAAAAIcJiTMAAAAAAB4Yqo1AKhQKikajmpmZcR7JZLKj+vdhaAOGN+79VqvVlE6nNTc3p1AopFAopLm5OaXTaWIn4MYdO14ajYZmZmYC0RZsFaTYqVQqSiaTzjHILipK7ARTEGInl8s5bXDHjHt6H4Ipl8spFAp1jCbdC0GI27GwgACpVquWYRhWOBy28vm88/N6vW7Nz89bkqxEImE1m80D3QYMLwj7zf06+XzeKhaLVjabtcLhsCXJkmRFIhGrXq/vWhswvCDEziCRSMSSZBWLxbG1AVsFKXaq1apzrEmlUla1WrWazaZVrVatfD5vGYZhzc/P73o74E8QYqdcLlvhcNhKJBJWsVi06vW61Ww2rXK5bGWzWad9HHeCp16vW7FYzDm32KvziiDE7TiROCMwyuWyJckKh8N9P3D5fH7gMvu9DRheEPZbJBKxwuFw3y+vVCrlfMFJssrl8sjbgOEFIXYGsV+fxDlYghQ79usYhmFVq9WeyzSbTcswDI49ARCE2CkWi57xYlntmLGTM+JmfOyLGfl83pqfn3cupLofe5E4ByFux43EGYFgf6FL8jyIW9ZmAhKJRA5cGzC8IOw3u1d50JdEd/JMz/N4BSF2Bmk2mx0xQ+IcDEGKHfeFlX7HlGKx6PRGJxKJXWkH/AlC7NTrdV+vb7Njh++s8bATVsMwrFgsZs3Pz1v1et2Jo73YN0GI2yAgcUYgJBIJ3x8y+4A/6pPIILQBwxv3frO36edqfHcSFIvFRtIGbM+4Y8ePWCzWcXLE8SYYghI71WrV2bZ72GQ3dw9VOBweaRswnCDETiKRGCoOisWiMwUAwbGXiXMQ4jYIKA6GsTNNU6VSSZJ83RYrHA47t+G6ePHigWkDhheE/ZbNZrfcY70fwzCUSqWc/1cqlT0v6IG2IMTOIIVCQQsLC3rzzTf35PXgT5BiJ5lMOq/hPrYMag/GIyixUyqVhooDu63Xrl0bWRuwfwQlboOAxBlj567aePbsWV/r2B/IWq02ksQjCG3A8IKw365duybTNBWPxzU3Nzdw+Wg02vH/A1+BMqCCEDteTNNUJpNRsVj0fWEGeyMosVMoFJxtpdNpz2WLxaJSqZRSqZTy+fxIXh/DC0LsmKYpaXtJ8Ozs7I5fH/tPEOI2KEicMXbvvPOO89zvCaL7SukoEo8gtAHDC8J+c38hNBoN56qsn9eXpHq9vuM2YHhBiB0vb7/9ti5cuEDvYAAFJXay2azzPJFIDHz9fD6vfD5PTI1RUGJHaifQ9oiFQewkOxKJjOz1sX8EKW7HjcQZY1er1Zznfq9muj+41Wr1QLQBwwvCfjtz5kzf7ffS3U776j/2VhBip59SqaRGo6H5+fldew1sXxBixz3NwzAMkuF9Igix446XUqmkaDQ6sEcwk8lIGjyyAQdTEOI2KEicMVbdB2u/X/6nTp1ynu90zk0Q2oDhBWW/ZbNZ57Xn5+cHzv/pbref4d0YraDETi+maertt9/WpUuXdmX72JmgxE65XHaed1+8QzAFJXakzgS4Vqtpbm5OuVyu57K5XE61Wq3juw6HR5DiNghInDFW2+1tc1/J2mmPXRDagOEFZb9FIhHV63VZltUxdLKf7i8hhr7tvaDETi9vv/22UqkUcRFQQYkd99BH97ZzuZyi0ahCoZBmZmYUjUb7JkTYW0GJHal9kbf7GJPJZDQ3N9fRu5jL5ZTJZJTP5xkBc0gFKW6DgMQZ+97CwsK4mxCINmB449hv7sI8hmH4qlCJ4NmN2KlUKk7PDg6uUcROr6GT0WhU9+/fV7FYlGVZunHjhtLptDKZjGZmZgbWX0DwjfK4c/Xq1S3Jc6PRUDQaVTqdVjQaVT6fV7Va9V2xHejlIJ0jkzhjrLo/TNupHrvTK1lBaAOGtx/3W3d1SRKk8Qhq7CSTSRWLxZFvF6MThNjpXt8wDCWTSb311lsdw2nt299Vq1WnEJS7Oi72VhBip/v1q9Vqz57kQqHgXMRj9MvhFrS4HTcSZ4xVED5MQWgDhrcf95tdYEVq3wuRq/jjEcTYSafTDNHeB4IQO93TPeze535DaSORiHOsSafTHb3V2DtBiJ1estls35FPyWRSyWQysG3H7mPfdyJxBoA9UCqVnHmJ4XCYnkU4KpWKKpUKIxDgS3cPUKVS0YULFzzXcd92yO8tiHDwVSoVzczMaGFhQfV6vefFl1KppDfeeIMLLoBInDFm2xnycRDbgOHtp/1mV0qW2klztVrdV+0/aIL23qfT6Y657wiuIMRO9+1gDMMYOFLB3aPYaDQO1H1V94sgxI5bLpdTPB53hvOHw2Fls1nV6/Ut8WSapq/bVuHgCVrcjhuJM8ZqFPe03emHOghtwPD2036zh7qRNAdDkGInk8koFotRJG6fCELsdK/v93ZU7tvIMOJl7wUhdmx2texUKrVlpIv9PdXrYl48Hh/J62P/CFLcBgGJM8ZqFB8mvzdjD3IbMLz9st8ymYwqlQpJc4AEJXZqtZoKhQK9zftIEGKne/3t3FuXHue9F4TYkdrHnUwmo3A47HnsSaVSajabW0YrUJ39cAlK3AYFiTPGqvvD5LdkvfuK16h7nMfRBgxvP+y3QqGgXC6nSCRC0hwgQYkdqmjvP0GIne71/W7PvdxBuj3MfhGE2JHkTBvyU1PBMAyVy+WOQpZc6DtcghK3QUHijLHq/jD5HQJSr9ed59u52h60NmB4Qd9vlUpF6XRasViMpDlgghA7mUxGkUiEIdr7TBBiR1LHHFSq3u4PQYgd0zSdIl+JRML3evl83om5a9eu7agN2F+CELdBQuKMsXOfAPi9kuVe7uzZsweiDRheUPdbrVZTPB5XIpFQuVzuu5xpmhRbGZNxx06pVFKpVFIoFBr4yOVyznrJZLLjd+5bnGFvjDt2pM55zdvpATpIJ7L7ybhjx/6+2c7+H1S5HQfXuOM2SKbG3QAgFos5V0D9JhHu5Ya5ahrkNmB4QdxvjUZD58+fVyqVGjik7eLFizp16lTf+69i94w7doYZ7phOp53Xnp+f7yjQQwK098YdO1L7AkqhUBiqDW5+C4phtIIQO9tlJ08ccw6f/Ry3I2cBY9ZsNi1JliQrlUr5WsdePhwOH5g2YHhB22/NZtMKh8O+2xKLxaxisTjydmCwoMWOl0gk4rw28TJ+QYkdwzAsSZZhGAOXdbdZklUul0fWDvgXhNixt9dsNodar1qtWpKs+fn5kbQDO2cfAyRZ9Xp9114nCHEbFAzVxtgZhuHM87t8+fLA5d3VQP0MUyyVSs6VsnG1AbsjCLHjdv78ecViMd+9iZVKZeD9V7E7ghY72D+CEjv20Fn3vNV+3PNSmVs/PkGIHbv37+LFiwO352Z/r6XT6aHWQ7BxjjykcWfugGVZVr1ed66cDepRSSQSliQrEol4Lmf3/mnjqlc2m93zNmD3BSF2LKvdexyLxaxqtWrV6/Wej2q1alWrVatcLlvz8/MWh+DxCkrsDOLeXj6f3/H2sHNBiR17+UQi4asNkqxqtTpwu9g9446der0+9MiDYrE4suMZRsfd4zzs55pz5O3hrA2BYR+Y5THkJJ/PO0PTBg1LcW/PXmev24C9Me7YSaVSHcv7fRy0IUz70bhjxw/3yREnrsERhNhxn8z2i41sNstQ/4AZd+yUy2VnuUEX4+x2+B2ii73j3ufDTr/gHHl7SJwRKOVy2TIMY8vBvNlsOslJLBbzNTfH/mKwH36vfI2yDdg744od90npsI9YLDaKPx07FITjjps9KqFYLHb0FNonI9ls1iqXy1a5XOY4NGZBiJ16ve70HNl1E+r1ulUul534CYfD9DQHzLhjp16vd/QM2seVarVqFYtFK5vNWuFw2AqHw8yJDwD7mF8ul618Pt9R+8L+jGezWatYLPr6fuAceXtInBE4zWbTOSjYH07DMKxEIjH0wdseDhsOh4e68jXKNmDvjCN23L2Bwz4oshIcQTju2NzD59xt6X5IDN0OgqDETj6ft2KxWEfRMAoQBlsQYqder1vz8/NWLBZzjj127HB8CQ4/3wnd3w+DPvucIw8vZFmWJQAAAAAA0BNVtQEAAAAA8EDiDAAAAACABxJnAAAAAAA8kDgDAAAAAOCBxBkAAAAAAA8kzgAAAAAAeCBxBgAAAADAA4kzAAAAAAAeSJwBAAAAAPBA4gwAAAAAgAcSZwAAAAAAPJA4AwAAAADggcQZAAAAAAAPJM4AAAAAAHggcQYAAAAAwAOJMwAAAAAAHkicAQAAAADwQOIMAAAAAIAHEmcAAAAAADyQOAMAAAAA4IHEGQAAAAAADyTOAAAAAAB4IHEGAAAAAMADiTMAAAAAAB5InAEAAAAA8EDiDAAAAACABxJnAAAAAAA8kDgDAAAAAOCBxBkAAAAAAA8kzgAAAAAAeCBxBgAAAADAA4kzAAAAAAAeSJwBAAAAAPBA4gwAAAAAgAcSZwAAAAAAPJA4AwBwSJimqXQ6rbm5Oc3MzCgajSqTyexoe5VKZYQtBAAgmEicAQA4JKLRqObm5lSv13Xp0iXVajXlcjnNzMzINM2httVoNPTGG2/sTkMBAAgYEmcAAA6BQqGgSCSi+fl5SVIikVAqlZLU7jk+f/68722Zpql4PK5Lly4pFovtSnsBAAiSkGVZ1rgbAQAA9p5pmpqZmXH+X61WFYlEBq5jD/G2E28AAA46epwBADikDMPoSH4vXrw4cJ3z58939FYDAHAYkDgDAHCIuYuDlUolz7nO8XhcZ86cUTabHfp1TNNUoVBQMpl0ipOFQiHNzc0pmUxuKTJWKBSGKlzWaDSUy+UUjUadbc/MzGhubk7pdJoiZgCAHSFxBgDgEAuHwx3Dsy9fvtxzuWQyKcMwlM/nh9q+aZrKZDKamZlROp3W7Oysstmsrl69qmazqWKxqHg8rnQ6rXg8rkajoUajoXQ67atgWaPRcJLxcrmsCxcuqFqtyrIs3bhxQ/l8XoZhOMuUSqWh2g8AgMQcZwAADr1CoaB0Oi1JikQiqlarHb9Pp9NqNBoql8tDbbdWq+n8+fMyTVOpVErZbFaGYXi2I5PJaHZ2Vo1GQ6lUyjNRr1QqisfjMgxDxWLRs1CZaZpOz/ag7QIA0I3EGQCAQ667SFiz2XQS3Ewmo0qlsiWZHqRWqykajUqSstmsU817EHcS75XglkolJZNJSf6Kmtni8bgqlYpisdjQFwIAAIcXQ7UBADjkDMPo6K21h2vncjmVSiVdvXp1qO01Gg0naU4kEr6TZqmdLA8qPGYPz5akfD7vO2mWpGKxKKndWz3MHGoAwOFG4gwAAJxeXqmdXBYKBeXzeVWrVc/h1YO2denSpaHbMqj4mL397qrgfhiG4STyuVxOjUZj6PYBAA4fEmcAAKBEIuE8t3tjy+Xy0ElzrVZzKljHYrGh15e29oD32/6bb7459LalzsSeXmcAgB8kzgAAQJI6ktVsNqtwODz0Ntxzku3h1NthV8H22n48Ht/WtsPhsJPQU2UbAOAHiTMAAJDUmYhut3DWtWvXnOfbSbxtqVSq59zoUW3fvW6tVtv2dgAAhwOJMwAAUKPR0MWLF53/28Oht7Md204SWz/b384wcNvs7GzPbQIA0AuJMwAAh5xdBbtYLDrJrmma206e94OFhYVxNwEAsI+QOAMAcIiZpql4PK5Lly4pFot1FAmzb900DHcv82705I6qp9g0Tef5bvSMAwAOFhJnAAAOsfPnzyudTjsJ81tvveX8zr6f8zDOnDnjPN9JYttoNHr2eLsLmO1kbrK7bcPcBxoAcDiROAMAcEhFo1HFYrGOIlyRSGRHw7W77we9Xfl8vmeBMnel7u0WMHP/Tf1uewUAgBuJMwAAh1A8HteZM2eUzWa3/M49XNt9+yc/IpGIk4xWKpWOIdHDqNVqOnv27Jafx2Ixp4d4u9t3/029/n4AALqFLMuyxt0IAACwd+xe2349wo1Go+Meys1mc6gK1u71E4nE0D3PdrGyZrPZ8/e1Wk3RaFRS+7ZVwyT3O1kXAHB40eMMAMAhkk6nZZqmZzIbDoc7hjAXCoWhXiMcDjvDqEulknK5nO913cXK+olEIk7CWygUVCqVfG/fvmjg3gYAAIOQOAMAcEhkMhldu3bN19xg91xl9/2d/YrFYqpWqzIMQ5lMxknYvdg9ze5iZf2kUikn+U8mkwOTe7sXvNFoKJFIqFqtDvX3AAAONxJnAAAOAbtn9urVq76WTyQSHUXCunuN4/H4wJ7eSCSiGzduaH5+XoVCQTMzM0qn0yqVSmo0GjJNU41GQ6VSSfF4XPF4XMVisaNY2aA21ut1xWIxpdNpRaNRlUqljgS9VqspnU5rbm5OCwsLyufzOypaBgA4nJjjDADAAVcqlZTJZJweYL+65zqXy2XFYjFlMhlVKpWhem1N09Tly5dVLpdVq9Wc20EZhqEzZ84omUwqlUr53l6vtpZKJb3zzjtOUm4YhmZnZxWLxRSPxwf2YgMA0A+JMwAAB1ylUtGZM2eGSpptpmk6Q7xrtZoz/5n5wQCAw4TEGQAAAAAAD8xxBgAAAADAA4kzAAAAAAAeSJwBAAAAAPBA4gwAAAAAgAcSZwAAAAAAPJA4AwAAAADggcQZAAAAAAAPJM4AAAAAAHggcQYAAAAAwAOJMwAAAAAAHkicAQAAAADwQOIMAAAAAIAHEmcAAAAAADyQOAMAAAAA4IHEGQAAAAAADyTOAAAAAAB4IHEGAAAAAMADiTMAAAAAAB5InAEAAAAA8EDiDAAAAACABxJnAAAAAAA8kDgDAAAAAOCBxBkAAAAAAA8kzgAAAAAAeCBxBgAAAADAA4kzAAAAAAAeSJwBAAAAAPBA4gwAAAAAgIf/D1JkYPaczOn+AAAAAElFTkSuQmCC", - "text/plain": [ - "
        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "pop.plot_hist_properties('m_chirp', intrinsic=True, observable=True, pop='DCO', bins=50)\n", - "pop.plot_hist_properties('chi_eff', intrinsic=True, observable=True, pop='DCO', bins=50)\n", - "pop.plot_hist_properties('q', intrinsic=True, observable=True, pop='DCO', bins=50)\n", - "pop.plot_hist_properties(['S1_mass','S2_mass'], intrinsic=True, observable=True, pop='DCO', bins=50)\n", - "pop.plot_hist_properties(['S1_spin','S2_spin'], intrinsic=True, observable=True, pop='DCO', bins=50)" + "BBH_pop.calculate_formation_channels(mt_history=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "There exists multiple formation channels that leads to merging BBHs." + "Now we can include the formation_channels in `df_transients`." ] }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['ZAMS_oDoubleCE1_CC1_CC2_END', 'ZAMS_oRLO1_CC1_oRLO2_CC2_END',\n", - " 'ZAMS_CC1_oRLO2_CC2_END', 'ZAMS_oRLO1-contact_CC1_CC2_END',\n", - " 'ZAMS_CC1_CC2_END', 'ZAMS_oRLO1_CC1_CC2_END',\n", - " 'ZAMS_oRLO1-reverse_CC1_CC2_END',\n", - " 'ZAMS_oRLO1_CC1_oRLO2_oCE2_CC2_END',\n", - " 'ZAMS_oRLO1-contact_CC1_oRLO2_CC2_END',\n", - " 'ZAMS_oRLO1-reverse_CC1_oRLO2_oCE2_CC2_END',\n", - " 'ZAMS_CC1_oRLO2_oCE2_CC2_END', 'ZAMS_oRLO1-contact_CC2_CC1_END',\n", - " 'ZAMS_oRLO1-reverse_CC1_oRLO2_CC2_END',\n", - " 'ZAMS_oRLO1-contact_CC1_oRLO2_oCE2_CC2_END'], dtype=object)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def BBH_selection_function(history_chunk, oneline_chunk, formation_channels_chunk):\n", + " '''A BBH selection function to create a transient population of BBHs mergers.'''\n", + " \n", + " indices = oneline_chunk.index.to_numpy()\n", + " df_transients = pd.DataFrame(index = indices)\n", + " \n", + " df_transients['time'] = history_chunk[history_chunk['event'] == 'CO_contact']['time'] * 1e-6 #Myr\n", + " mask = (history_chunk['S1_state'] == 'BH') & (history_chunk['S2_state'] == 'BH') & (history_chunk['step_names'] == 'step_SN') & (history_chunk['state'] == 'detached')\n", + " df_transients['metallicity'] = oneline_chunk['metallicity']\n", + " df_transients['t_inspiral'] = df_transients['time'] - history_chunk[mask]['time']*1e-6\n", + " \n", + " df_transients['S1_state'] = history_chunk[mask]['S1_state']\n", + " df_transients['S2_state'] = history_chunk[mask]['S2_state']\n", + " df_transients['S1_mass'] = history_chunk[mask]['S1_mass']\n", + " df_transients['S2_mass'] = history_chunk[mask]['S2_mass']\n", + " df_transients['S1_spin'] = history_chunk[mask]['S1_spin']\n", + " df_transients['S2_spin'] = history_chunk[mask]['S2_spin']\n", + " df_transients['S1_spin_orbit_tilt'] = oneline_chunk['S1_spin_orbit_tilt']\n", + " df_transients['S2_spin_orbit_tilt'] = oneline_chunk['S2_spin_orbit_tilt']\n", + " df_transients['orbital_period'] = history_chunk[mask]['orbital_period']\n", + " df_transients['eccentricity'] = history_chunk[mask]['eccentricity']\n", + " \n", + " df_transients['chirp_mass'] = m_chirp(history_chunk[mask]['S1_mass'], history_chunk[mask]['S2_mass'])\n", + " df_transients['mass_ratio'] = mass_ratio(history_chunk[mask]['S1_mass'], history_chunk[mask]['S2_mass'])\n", + " df_transients['chi_eff'] = chi_eff(history_chunk[mask]['S1_mass'], history_chunk[mask]['S2_mass'], history_chunk[mask]['S1_spin'], history_chunk[mask]['S2_spin'], oneline_chunk['S1_spin_orbit_tilt'], oneline_chunk['S2_spin_orbit_tilt'])\n", + " \n", + " # added\n", + " df_transients = pd.concat([df_transients, formation_channels_chunk[['channel']]], axis=1) \n", + " \n", + " return df_transients\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "# formation channels\n", - "pop.df_synthetic['channel'].unique()" + "# NOTE: the formation channels have to be given as a dataframe, as such we call it with loc[[0]] for testing\n", + "BBH_selection_function(BBH_pop.history[0], BBH_pop.oneline[0], BBH_pop.formation_channels.loc[[0]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Each channel might imprint some obervational sigatures to the properties of merging BBHs. We can visualize the distribution of the intrinsic and observable BBH population as a function of the formation channel. For example let's consider the CE and SMT formation channels and see their spin distribution." + "The example binary gives us a dataframe with all the information we're interested in. But if somehting is missing feel free to customize the selection further!\n", + "\n", + "We now create the transient population with the `BBH_selection_function`, we've created and `create_transient_population`.\n", + "\n", + "This will create a `TransientPopulation` instance and write the transient population to the current file.\n", + "\n", + "`BBH_mergers.population` will load the complete dataframe we've just created. Each of the indices in this population refer back to the original binaries, which are still accessible through `BBH_mergers.history` or `BBH_mergers.oneline`." ] }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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- "text/plain": [ - "
        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "BBH_mergers = BBH_pop.create_transient_population(BBH_selection_function, 'BBH')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "pop.plot_hist_properties(['S1_spin','S2_spin'], intrinsic=True, observable=True, pop='DCO', bins=50, channel='ZAMS_oRLO1_CC1_oRLO2_oCE2_CC2_END') # CE channel\n", - "pop.plot_hist_properties(['S1_spin','S2_spin'], intrinsic=True, observable=True, pop='DCO', bins=50, channel='ZAMS_oRLO1_CC1_oRLO2_CC2_END') # SMT channel" + "BBH_mergers.population" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Visualize the BBH Population on the 2D MESA Grid Plot Slices" + "Since the TransienPopulation is stored in file, you can continue your analysis by opening the file using the `TransientPopulation` class.\n", + "You only need to do your selection once." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from posydon.popsyn.synthetic_population import TransientPopulation\n", + "\n", + "BBH_mergers = TransientPopulation(filename='BBH_contact.h5', transient_name='BBH')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "BBH_mergers.mass_per_metallicity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "You might want to visualize the portion of the parameter space is the MESA grids that leads to the formation of these BBHs. This can be done as follows.\n", - "- select the MESA grid type\n", - "- select the metallicity\n", - "- select the grid slice (if `None` all grid slices will be plotted entire)\n", - "- decide if you want to save the figures and where\n", - "- select the channels you want to plot" + "With `TransientPopulation` class you can compute the efficiency of the transient population at a given metallicity, i.e. the number of merging DCO per unit star formation and visualize the results." ] }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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JEtvb20ceM7VaLVqt1vD29vZ2lMvlqdbgnETWpeE4ck4CAACOY/lB4Nx2dnaiUqlEvV6Pbrcb5XI59vf3L3RgIUmS4ZIzJw1qrK2txdraWvR6vbh9+/aF1QenScNxNI5SqRTb29uxvb2dutog4tVynUmSnDhjo9lsDmdNRURUKpWpLu/pnETWpeE4GodzEgAA5JeZWsDULS0tDfc9mPUMk2q1Gu12O0qlUuzu7p742F6vF8vLyxHxarBxdXV1ZnXBpC7yOIp49an4u3fvxurqanS73Tg4OIiIiKtXr0a5XDZoSKoN/v8+zoyRJEliaWlpeLtcLsf29vZU6nBOIsvSchxFOCcBAADHuzLvAgDOK0mSaLfbERFjLXtTLBajWCxGr9eLBw8eGECEIwyOE8iSRqMx9iB3oVCItbW14fJpnU4ner3exH/3zklkXRqOo8OckwAAgMMsPwhk1uh+Dh988MFYzxkMjHS73QtfKgeA2djZ2YkkSaJSqQxnP51kZWXltdudTmfiGpyTyLo0HEcAAACn0dQCMuvx48fD78f9ZPHop30NvgBcDqMNoV6vN5wxdZzDMz/29/cnrsE5iaxLw3EEAABwGk0tILO63e7w+6tXr471nNGBxtP2OwEgG27duvXa7dOaSofPGYP96ybhnETWpeE4AgAAOI2mFpBJh5dpGne/hb/+678efr+zszPVmgCYj0ajMTwPrK+vn7qn1eFzyDhLrZ0lzzmJLJr3cQQAADAOTS0gk877aeDRTx37RDEcrdPpRLVajaWlpeHXysrKa3sGQZqUSqXY39+Pfr8fjUbj1McfHowvlUoTvb5zEpfBvI+j4zgnAQAAozS1gNw6ODiYdwmQKt9//31UKpVoNBpRq9Xi22+/jZcvX8a3334b5XI5arVaLC0t2fuHzGs2m8PvC4XCqTNSLoJzElkz6+PIOQkAADiKphaQSYcH/07b9+EoPhUPr9vY2IhqtRrb29tRLpeHx1WhUIhGoxHNZjOSJIlKpWIQkczqdruvzTAZZ0bKaZyTyJtZHEeHOScBAABH0dQCMsngH0xXsViMra2tWFtbO/Yxa2trw/1WqtXqRZUGU1Wv14ffl8vlE//mx+WcRN7M4jga5ZwEAAAcR1MLAIitra1YXV099XGDxyRJ8tqgJmRBu90ezugYDJoDZ3MRx5FzEgAAcBxNLSCTzrO0EzC5SqUy/H5jY8MMFTIjSZK4f/9+RLwaiN/d3Z3aucQ5ibyY5XF0Hs5JAACQP5paQCZdvXr1tdvnGcQwCAlnN1jqacA+JmRFtVqNJElmMhDvnERezPI4Og/nJAAAyB9NLSCTpjGIcngQEjjd4ePmyy+/nFMlML56vR6dTmdmA/HOSeTBrI+j83BOAgCA/NHUAjLp8CDGwcHBWM8b/fR8GgZjYN663W4sLy/H0tJStFqtMz/fUk+kXavVio2NjSiVSjMbiHdO4rK7iOMowjkJAAA4naYWkEmHB1PGHcTY398ffn94yRrIo/v370ev14skSaJWq515QNBAPGnW6XSiVqtFuVye6UC8cxKX2UUdRxHOSQAAwOk0tYDMKpVKw+/H/VT86OM++OCDqdcEWdPr9c70+MPH2vLy8jTLganpdrtRqVRidXU1tre3j31ckiRnPg6O4pzEZXTRx5FzEgAAcBpNLSCzyuXy8PtxB0FGH7e6ujr1miBrRmeHNBqNUz/lfvhYu3PnzizKgon0er24fft2rK2txdbW1omPffDgQbTb7Ylf0zmJy2Yex5FzEgAAcBpNLSCzPvnkk+H3u7u7Yz2n2+1GxKtBE0s9wauB+NXV1ej3+7G+vn7q40c/qV8uly31ROokSRKVSiXu3LkTzWbz1Md3u92pnA+ck7hM5nUcOScBAACn0dQCUqndbg8H+45TKBSGn4x/8uTJqZmdTmf4fb1en6xAyIBxjqNarRbtdnvsfUtGP4nfaDQmKQ9m4vbt21Eul8caiI94dW4YXTrwKM5J5M28jiPnJAAA4DRX5l0AcLmddYPvJEliZWVluJxMo9E48ZO6zWYzVlZWIkmSaLfbJy7fNBiYKZVKsba2dqa6YJ5meRwVi8VYX1+P27dvnzq7pNVqDTPX19dPHcCEi1apVOLq1atRq9WOXQJwcDwdHBwMZ3kcN8PEOYk8mudx5JwEAACcZqHf7/fnXQRwuSwsLAy/397efm2fkdO02+2oVqvD24VCIV6+fDn2c/b3948cVGm1WlGr1aJQKMTu7q5lnki9iz6OVlZW4urVq7G1tXXk8k2jmWtra2N/eh8uSq1Wi1ardebnFYvF2N/fP/I+5yTyJi3HkXMSAABwHDO1gImMLp/U6/XeGFSo1WpRq9WiWCwOByVu3bp17J4Hh38+zkDf6upqbG9vR7VajZWVlWg0GsNPvSdJEvV6PVqtVpTL5WMHR2Ce0nAc7e7uRq1Wi6WlpVhfX48PPvggisViHBwcRLPZjHa7HYVC4bXjC9JiY2PjXAPxEScfH85J5EmajiPnJAAA4DhmagETGcwmGXdQLkmS2NraOnFJpnq9HhsbG1EsFmN7e3vsT7AnSRJPnjyJZrP52nI55XI5arXamWa6wEVK03HU6XSi2WxGp9OJJEmiUChEsViMu3fvxtramgF4UmlpaenMy3QOrK+vn7gXj3MSeZHG48g5CQAAOExTCwAAAAAAgNR7a94FAAAAAAAAwGk0tQAAAAAAAEg9TS0AAAAAAABST1MLAAAAAACA1NPUAgAAAAAAIPU0tQAAAAAAAEg9TS0AAAAAAABST1MLAAAAAACA1NPUAgAAAAAAIPU0tQAAAAAAAEg9TS0AAAAAAABST1MLAAAAAACA1NPUAgAAAAAAIPU0tQAAAAAAAEg9TS0AAAAAAABST1MLAAAAAACA1NPUAgAAAAAAIPU0tQAAAAAAAEg9TS0AAAAAAABST1MLAAAAAACA1NPUAgAAAAAAIPU0tQAAAAAAAEg9TS0AAAAAAABST1MLAAAAAACA1NPUAgAAAAAAIPU0tQCAoY2NjajX6/MuI5N6vV4sLy9Ht9uddymp5e8LAAAAmISmFgAQERG1Wi0eP34cjUZj3qVkUrPZjF6vN+8yUm19fT263W5Uq9V5lwIAAABk0EK/3+/PuwgAYL5qtVo8efIkvv322ygUCvMuJ5OWlpbi6tWrsb+/P+9SUi1JknjnnXeiXC7H1tbWvMsBAAAAMsRMLQDmqt1uR61Wi0qlEisrK7G8vBxLS0vRbrfnXVputFqtaLVa8fnnn5+5odVut2NhYeHYr2kuNVetVk98rXku+9fpdCJJkqjVamM9Ps/vW6FQiM8//zza7XZsbGxc6GsDAAAA2XZl3gUAkG+9Xi92dnbsQzQn3W43arVarK+vR6lUOvPzy+VybG9vR5IkcXBwEI1G47Ul+DqdzlTqbLVab2Stra1FpVKJQqEQV69ePVf909JsNoc1jSPv71upVIpmsxm1Wi1KpVKUy+ULrwEAAADIHssPApAalUplOAC/tbUVq6urc67o8lteXo6ImNqSeZVKJYrFYrRarYh4NSvn5cuXE2UmSRK3b9+OYrE4nMG3trY2bCSlwcLCwrBRdR55fd+m/fcHAAAAXG6WHwQgNSqVyrxLyJVWqxW9Xm+qS93t7OxEtVod3k6SZOLM+/fvx6effvrajKPR15i3QSNqkpry+L5FRNTr9ej1epYhBAAAAMaiqQVAapx1PyfOL0mSqNfrUSwWx14y7zTdbjeSJIlyuRzFYvG1n59Xu92Oq1evRqFQeK3Rc+vWrUlKnaqzLj14WF7ft4hX71mhUBg2twAAAABOoqkFADnUarUiSZKo1WpTy+x0OsO9kUabM+dtViRJEg8ePIhms/nabKNSqZSaBmiSJNHtdidaKjOP79uoTz75JCIiVcsiAgAAAOmkqQUAOTRoIExz37Lt7e3hEpKlUmn48/M2Z+r1ejQajWH2wKABlAaDpQcnaQ7m8X0bNZjhNngvAQAAAI6jqQUAOdPpdKLX60WxWHxtZtA0cgeNk+Xl5eHP9/f3z5V1cHAwzBudcZSmvdeazWYUCoWJGkZ5fN9GFQqFKBaLkSRJtNvteZcDAAAApJimFgDkzNbWVkRMd+bOYP+nwUyj0WbZzs7OmfPq9Xp8+umnEfFqxtLovlBpmXHU7Xaj1+tNtCdZHt+3owxqswQhAAAAcBJNLQCYQLfbjVarlakZJoPZOysrK1PNHG2aTLI3VK1Wi08++WS4/9PhfaHSYtCAmWTpwTy+b0cZzCI7TyMPAAAAyA9NLQAyr9frxcbGRqysrMTS0tLwq1KpnHufno2NjahWq7G8vBwLCwuxvLwctVptOLOm2+3GyspK1Ov12N3djWq1mtrl3UYlSTJslkxz5s7ovlARrzdnRmcLnabb7cbBwcFre32ldV+oJ0+eRKlUmmgJxzy+b0cZ1Df69wkAAABwmKYWAJlWr9djeXk5tre3o9FoxMuXL+Ply5fx7bffRrVajXq9HktLS2PPpOp2u7G8vBz1ej0++OCD2N3djX6/H1tbW9HpdGJlZSUqlUrcv38/tra2Ynt7ezhTZ3RmTFqN1jir/bSOyh+3UXH//v3h8nmj2QNpaRy22+1IkmSiWVoR+XvfjlMoFI6cYQYAAAAwSlMLgExKkiRWVlZiY2MjGo1GbG9vv9YcKBQKsba2Ft9++20Ui8Vhg+skvV4vVlZWotfrRbPZjPX19eFAe6lUGs58GTQiBs2HUqkUq6ur0Wg0ZvOPnaIvv/wyIqa7HN3hfaEGztqcqdfrUavVhu/54Hlp3BdqsPTgnTt3zp2Rx/ftJIN/9+7u7pwrAQAAANJKUwuATLp9+3Z0u91YXV2N9fX1Yx9XKBTi888/j0KhEBsbG7GxsXHsYwezWQYNscOKxeLwtTY2NoZNiYiIra2tE+tIi0GT5OrVq1PLPGq2UcTrzZnR9+q4urrd7hvvexr3hUqSZPhvHm0knVXe3rfTDP4mLT8IAAAAHEdTC4DMGW0ojTM7arRJVa/Xjxw073Q6Y+019cEHHwy/H8zWyZLBv3GSZsxhh/eFGlhZWRl+v7+/f2JGtVo98v1M475QT548iYiYeOnBvL1vpxn8TWpqAQAAAMfR1AIgU5IkGS4jWCgUxt4X6u7du8Pvj2pGjM6IOWkW0+jrZXHvn8GSdBc9U+ukRsXGxkbcvXv3yN9lGveFajabUSgUYnV1daKcvL1vpxn8TR4cHMy5EgAAACCtrsy7AAA4i8EsmYiIW7dujf280SXYOp1OJEky1dlKWTOtf/tx+0JFjNec6fV68fjx4yP3UZp0X6gkSaJarUaxWJzarLrjlvs7qzS/b/OyvLwcEfFa7QAAAACjzNQCIFNGl1WbdD+jUaPNhZNmx4zOIsnKXkWjpj0L5rjZRhHjNWdqtVp8+umnx2YPjPteD/a7qtVq8c4777y2rOQ0DJpjky49mLb37Sjtdjuq1WosLy/H0tJSLC0txfLyclSr1Wi32+fOBQAAADgvTS0AMmV0kH+SJfQONwvK5fKwmXDSsoKjTbVPPvnk3K8/L9NcdjDi+H2hBk5q0LRarSiVSsc2Xs66L9Ty8nK88847Ua/Xo1AonGkm37harVYUi8WJG5ppet8Oa7VasbS0FI8fP467d+/G9vZ2vHz5Ml6+fDmsu9lsxvLysuYWAAAAcKE0tQDgf4w2Awb7do1KkiRarVZERDQajXM1NpIkiUqlcq6ZPq1WKyqVSiwvLw+/6vX6uWYiTWuJt5NmHEUc35xJkiSazWY0Go0TswfG2Rdqf38/Xr58Gbu7u9FoNMbeb21cg2UrJ52lNchKy/s2qlKpxNbWVuzu7sbW1lasrq6+VkuxWIy1tbXY3t6Ora2tePDgQVSr1TO9xnG+//77iJje0pgAAADA5aOpBUCmjM40mmQpvaMGzgczcAqFQmxsbMTGxsaw+dPpdGJlZSWSJIlGoxHr6+tjv9akS+IlSTJsYNXr9djf34/9/f3Y3d0d3jdotp1m8O+exjKEJ+0LNXBcc6ZarR67fN7gsWnbF2qw9OAs99MamMf7try8HKVSKba3t8dqCJZKpeGeXmdtnh1lUPe0ZxMCAAAAl4emFgCZMjp4fpbZRocfe9Rgf7vdjmKxGC9fvoxmsxmPHz+Od955J5aWlqLRaMTq6mrs7++fqaE1jSXxbt++Hb1eL3Z3d1+ru1AoRLPZjLW1tajVaicumzgwaFZMY6bW48ePT22aLC8vD78fNEAG7/NJTZ1p7Qs1Te12O8rl8sQzidL4vlWr1SiXyyfOADvO1tZWREy+z9ig0TrtGXYAAADA5XFl3gUAwFmsrq4Olwbc2dkZ+3mjjy0Wi0cOnD9+/HjYNFtbW5t4Rk7EqyXxRp114L/VakW3231jGbhR9Xo9Wq1W1Gq1N17vsEHGNGZqdTqduHv37omPGW2sDGYRPXjwYNioOc6k+0JN22Am3LSWHkzT+zaYOThoTp3H9vZ2LC0tRa1WO3cTctBo1dQCAAAAjmOmFgCZMtjTJ+J/l/Ubx+iA/WAZucN6vd5rTYE0GNT6wQcfHPuYQROg1+ud+n4McgZL4E2i2+2e2jg5vIze/fv3x5oNNMm+ULMw+D2srq5OnJW2961Wq51rhtZhjUbjyL3oxjVoPK+srExcCwAAAHA5aWoBkDmNRmM46D/OIHqv1xvOtFldXT22oXDr1q1ot9uv7aU1T0mSjLX/UsT/NkFOm20z+m+fpLHVbrfPVFfE/+4NdVpDp9vtpmo/rcHvYRoNrbS9b4O/gWm8x2tra7Gzs3OuYydJkuHz5v37BgAAANJLUwuA1BgdDD9pebxCoRDb29tRLBaj2+2euCRcr9cbzlhZXV09sekzyKnX67G0tBQLCwuvfS0tLcXS0lIsLy9HtVodNihmZXTJxKtXr5742NHZWicpFArDPaHOsnzjqCRJ4v79+8PvTzN4vUKhEJ9++umpj3/8+PHw+zTspzWtpQfT+L51Op2xmnXdbnesJmi5XB579uThOiJe/VstPwgAAAAcx55aAMxVr9cbfj148GD488FyaIP9rw4PdBeLxdjd3Y379+9Hq9WKTqcT9Xo9yuVyXL16NXq9Xjx+/HjYkGg0GrG+vn5iLaVS6cQl1AaNiCRJotfrRbvdjkKhEFtbWzOZXTLaoBo0OE4zTqOqXC5Hu90+dX+mUYN9l3Z3d4fvacSrpeLq9XoUi8W4devWkXXeunUrOp1OfPrpp0fe3+124+DgIJIkiS+//DI2NjaG9w1m2V29ejUKhcKxrzFLzWYzCoXCuX7HaX/fvvzyy1P39xpt4K6vr5+4VOEHH3wQX3755ZlntX355ZcR8erfDAAAAHAcTS0A5qperw8HzEdnER0cHAxnxhw3kD5oKPV6vWg2m9FsNqNer0eSJMOB/EajEXfu3BmrEVKv16PVakWpVIq7d+++8ZwkSeL777+PXq8X3W43er1eJEkSlUoltra2prI83eHXm4VarRbtdnvsGTWjs90iXm+w9Xq94e/puPegVCpFoVA48r4kSV7bQ2n0b2C03oFmszncU+0iDH7PpzVEj5KF921wrJz0bxidkbixsXFiU6tYLMb+/v6x9x9n8Lc46Ww4AAAA4HLT1AJgrk7bA2ocxWLxxIH2caysrES32z1T06Tb7cb9+/eH/512U+ssBo2JcRph5XI5CoXCcIbcacu9FYvF6Pf7567tpN9NoVCYKHvWms1mRJyv2XIZ3rfTlr087KRlQ48z2LPsuAYeAAAAwIA9tQDIvY2Njeh2u7G6unqmWUClUik+//zzKBQKkSTJzPfYOslZZ3V98sknERFzrTkLnjx5EqVS6dLu81QsFk/ch61QKAyXXxzsZXeS/f39WF5ePlMNT548iYi40Bl4AAAAQDZpagGQe48fP46IeG2puHENljmMiBObA+dxnr2jxn3OoIEwuo8Zr2u325EkyaVeEq9SqZzaqFpbW4uXL1/Gy5cvT91XrNPpnHnvscGMtEGjFQAAAOA4mloA5N5Zl1g7znmaUCcZnR102kyswbJv484oKhQK0Wg0IkmSaLVa567xMhssPXjnzp05VzI7q6ur0el0prJ/22A5y1KpNPZz2u129Hq9aDQaUz9+AAAAgMtHUwuA3BvMxBk0ec6i0+lEp9OJiOk3PwYzwCJOnwU2qHv0OadZX1+fyn5kaXWe/Z0GkiSJTqcTq6url77Zsra2FvV6feKcWq125r+lBw8eRLFYjPX19YlfHwAAALj8NLUAyL3V1dVYX1+PXq8XKysrYy8j2G63h0sWbm9vT735USgUhjOvTqtpcH+1Wj3Ta2xtbUWv15tKUyMtkiSJbrcb3W43IiJ2dnai2+2eqWE52OfpMi89ONBoNKLT6Uy0v1qr1YqDg4Mz7Ys12MtuMCMOAAAA4DSaWgAQrwb2B42p5eXlqFQqw6XRBgbNko2NjVheXo5qtRqrq6uxv79/5n2ExjVoNp2079GgeVMsFs9cR6lUimazOWwwZFm1Wo2FhYVYWlqK27dvx8HBwbDRePv27VhaWoqFhYWx9k5rNptRKBRm9ntNm+3t7bh///65GlutVisajUZ8/vnnYz9n0EhtNBq5eY8BAACAyS30+/3+vIsAgDTp9XrRbDaj2+3GwcFB9Hq9SJJkOHOqWCxGpVI506yUgWq1Gu12O8rl8omNqlHLy8vR6/Vif3//yD2zKpVKdDqd2N7ePneDoFarxZMnT+Lbb7+99MvtnabX68Xy8nKsra3lahZRr9eLSqUS5XJ5rD2ukiSJ+/fvR6/Xi88//3zsv5skSWJlZSVKpVJsbW1NXjgAAACQG5paAHABkiSJXq8X1Wo1er1eFAqF+Pzzz6NYLI7VPFhZWYmDg4PY2toaNq4GTYV2ux3NZvNcTbZRtVotdnZ2Ynd3d6KcrKvX67GxsRG7u7tRKpXmXc6Fq9fr0Wq14s6dO1GtVuPWrVvDv9FerxfdbjceP34cnU4nPvnkkzPvh1WpVKJQKGhoAQAAAGemqQUAMzSYmRURRzavBvs8jTNzq9VqRbPZHM4ai4i4detW1Ov1I2dwncdgucNGozGVvCyq1+vR7XbHnkl3GSVJEk+ePBnuuTZYhrNYLEapVIpKpRJ37tw586y+jY2N+P7773P99wUAAACcn6YWAAAAAAAAqffWvAsAAAAAAACA02hqAQAAAAAAkHqaWgAAAAAAAKSephYAAAAAAACpp6kFAAAAAABA6mlqAQAAAAAAkHqaWgAAAAAAAKSephYAAAAAAACpp6kFAAAAAABA6mlqAQAAAAAAkHqaWgAAAAAAAKSephYAAAAAAACpp6kFAAAAAABA6mlqAQAAAAAAkHqaWgAAAAAAAKSephYAAAAAAACpp6kFAAAAAABA6mlqAQAAAAAAkHqaWgAAAAAAAKSephYAAAAAAACpp6kFAAAAAABA6mlqAQAAAAAAkHqaWgAAAAAAAKSephYAAAAAAACpp6kFAAAAAABA6mlqAQAAAAAAkHqaWgAAAAAAAKSephYAAAAAAACp9/8A4En8PDPKAj4AAAAASUVORK5CYII=", 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- "text/plain": [ - "
        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "BBH_mergers.get_efficiency_over_metallicity()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "pop.plot_popsyn_over_grid_slice('HMS-HMS', 1e-4, slices=[0.7], save_fig=False) #plot_dir='./plots/'\n", - "pop.plot_popsyn_over_grid_slice('HMS-HMS', 1e-4, slices=[0.7], save_fig=False, channel='ZAMS_oRLO1_CC1_oRLO2_oCE2_CC2_END') # CE channel\n", - "pop.plot_popsyn_over_grid_slice('HMS-HMS', 1e-4, slices=[0.7], save_fig=False, channel='ZAMS_oRLO1_CC1_oRLO2_CC2_END') # SMT channel\n", - "pop.plot_popsyn_over_grid_slice('HMS-HMS', 1e-4, slices=[0.7], save_fig=False, channel='ZAMS_oRLO1-contact_CC1_oRLO2_CC2_END') # SMT-contact channel" + "# if you want to plot the channels, you can set channels=True\n", + "BBH_mergers.plot_efficiency_over_metallicity(channels=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "BBH_mergers.efficiency" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The `plot_popsyn_over_grid_slice` method allows you to display the properties of the merging DCO population as a colormap. Let's display the spin of the first born BH." + "You can also plot the delay time distribution based on the time columns in the TransientPopulation.\n" ] }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "pop.plot_popsyn_over_grid_slice('HMS-HMS', 1e-4, slices=[0.7], prop='S1_spin', prop_range=[0,0.3], save_fig=False, channel='ZAMS_oRLO1_CC1_oRLO2_CC2_END') # SMT channel" + "import matplotlib.pyplot as plt \n", + "import numpy as np\n", + "bins = np.logspace(6.5,10.5)\n", + "BBH_mergers.plot_delay_time_distribution(bins=bins)\n", + "# you can also set a specific metallicity. However, this normalises the rate with only the mass of the selected metallicity.\n", + "BBH_mergers.plot_delay_time_distribution(metallicity=0.1, bins=bins)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Cogratulations, you are now ready to analyze any DCO population data you generated with POSYDON. Feel free to further explore the BBH model or to use this tutorial to study BHNS and BNS populations." + "You might also be interested in seeing the evolution of the binary in more detail.\n", + "\n", + "Therefore, you can plot the TransientPopulation over a grid slice.\n", + "For example, below we plot binaries over the $q=0.7$ slice at $Z=10^{-4} Z_\\odot$ on the HMS-HMS grid.\n", + "\n", + "If no slice is give, all mass ratios are plotted.\n", + "\n", + "You can also plot a property from the TransientPopulation DataFrame as a colourmap.\n", + "And specific formation_channels if they're in the DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "BBH_mergers.plot_popsyn_over_grid_slice('HMS-HMS', 1e-4, slices=[0.7], save_fig=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "BBH_mergers.plot_popsyn_over_grid_slice('HMS-HMS', 1e-4, slices=[0.7], prop='S1_spin', prop_range=[0,0.3], save_fig=False, channel='ZAMS_oRLO1_CC1_oRLO2_CC2_END') # SMT channel" ] }, { "cell_type": "markdown", "metadata": {}, - "source": [] + "source": [ + "## Cosmic Rate\n", + "\n", + "So far these events do not consider the metallicity or star formation rate evolution of the Universe.\n", + "\n", + "POSYDON comes with several built-in star formation histories and metallicity evolutions, a few examples are:\n", + "- IllustrisTNG\n", + "- Neijssel2019\n", + "- Madau+Fragos2017\n", + "\n", + "We can apply these to our population with the `calculate_cosmic_weights` function.\n", + "Here, we will the metallicity and SFR evolution of the IllustrisTNG, which is the default model used, if no `MODEL_in` is given.\n", + "\n", + "The function returns an instance of the `Rates` class, which gives us access to some new variables:\n", + "- `z_birth`: the redshift and age of the universe at which we probe the star formation\n", + "- `z_events`: the redshift at which an event takes place\n", + "- `weights`: the weight of the event based on the SFR, its metallicity, and its weight in the population." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from posydon.popsyn.synthetic_population import TransientPopulation\n", + "BBH_mergers = TransientPopulation(filename='BBH_contact.h5', transient_name='BBH')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "MODEL = {\n", + " 'delta_t' : 100, # Myr\n", + " 'SFR' : 'IllustrisTNG', # Neijssel2019, Madau+Fragos2017\n", + " 'sigma_SFR' : None,\n", + " 'Z_max' : 2.,\n", + "}\n", + "\n", + "rates = BBH_mergers.calculate_cosmic_weights('IllustrisTNG', MODEL_in=MODEL)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rates.z_birth.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rates.z_events.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rates.weights.head(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The rates can also be accessed using the `Rates` class. You will need to provide your transient_name and SFH_identifier.\n", + "You can keep as many transients and SFHs in your file as you like." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from posydon.popsyn.synthetic_population import Rates\n", + "\n", + "rates = Rates(filename='BBH_contact.h5', transient_name='BBH', SFH_identifier='IllustrisTNG')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can directly work with the weights and the z_events to get events at specific redshifts.\n", + "\n", + "Or you can calculate the rate density over redshift using the `calculate_intrinsic_rate_density` function.\n", + "This will calculate the rate over redshift for you.\n", + "\n", + "You can use the output of the function immediately, or get them from `rates.intrinsic_rate_density`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "out = rates.calculate_intrinsic_rate_density(mt_channels=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rates.intrinsic_rate_density" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rates.plot_intrinsic_rate(channels=True, xlim=(0,10), ylim=(1e-2,1e3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Properties of the systems\n", + "\n", + "Sometime syou mgiht want some more details about the actual population, and its properties.\n", + "\n", + "`plot_hist_properties()` allows you to plot these.\n", + "\n", + "By default, the function will create its own figure and plot it. If you would like more control over the output, you can give it your own pyplot axis and set `show=False`.\n", + "This allows you to adapt and change the plot to your liking after finishing adding the properties." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "bins = np.linspace(0,100,101)\n", + "fig, ax = plt.subplots(1,1)\n", + "rates.plot_hist_properties('S1_mass', intrinsice=True, bins=bins, color='red', ax =ax, label='S1', show=False)\n", + "rates.plot_hist_properties('S2_mass', intrinsice=True, bins=bins, ax=ax, label='S2', show=False)\n", + "\n", + "ax.set_ylabel('Rate density [Gpc$^-3$ yr$^-1$]')\n", + "ax.set_xlabel('Mass [Msun]')\n", + "ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### observable population\n", + "\n", + "Sometimes, however, you're not just interested in the intrinsic population, and want an observable population.\n", + "This could be SNe detection fraction for a telescope survey, or the LVK detection efficiency for GW mergers.\n", + "\n", + "You can apply these using the `calculate_observable_population`. Similar to the `create_transient_population`, this function takes a `observable_func`, which described the observability of a transient.\n", + "\n", + "An `observable_func` takes chunks of\n", + "1. TransientPopulation\n", + "2. The z_events\n", + "3. The weights\n", + "\n", + "Using these, new weights are calculated.\n", + "The BBH analysis comes with a detection function for several different detector sensitivities and configurations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from posydon.popsyn.transient_select_funcs import DCO_detactability" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def DCO_wrapper(transient_chunk, z_events_chunk, weights_chunk):\n", + " sensitivity = 'design_H1L1V1'\n", + " return DCO_detactability(sensitivity, transient_chunk, z_events_chunk, weights_chunk, verbose=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We also give it a name, which is used as an identifier in the file\n", + "rates.calculate_observable_population(DCO_wrapper, 'design_H1L1V1')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We can now access this observable population\n", + "rates.observable_population('design_H1L1V1')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also plot the intrinsic and observable population together." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bins = np.linspace(0,100,101)\n", + "fig, ax = plt.subplots(1,1)\n", + "\n", + "rates.plot_hist_properties('S1_mass', intrinsice=True, observable='design_H1L1V1', bins=bins, ax = ax, label='S1', show=False)\n", + "ax.set_ylabel('Rate density [Gpc$^-3$ yr$^-1$]')\n", + "ax.set_xlabel('Mass [Msun]') \n", + "ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Cogratulations, you are now ready to analyze any DCO population data you generated with POSYDON. Feel free to further explore the BBH model or to use this tutorial to study other populations.\n", + "The next tutorials show you how to [select GRBs](), perform a [SFH calculation at a single metallicity](), and [how to run an individual binary]()." + ] } ], "metadata": { diff --git a/docs/_source/tutorials-examples/population-synthesis/debug_pop.ipynb b/docs/_source/tutorials-examples/population-synthesis/debug_pop.ipynb index 4f53720ddb..cd092c4f71 100644 --- a/docs/_source/tutorials-examples/population-synthesis/debug_pop.ipynb +++ b/docs/_source/tutorials-examples/population-synthesis/debug_pop.ipynb @@ -17,18 +17,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "env: PATH_TO_POSYDON=/Users/simone/Google Drive/github/POSYDON-public/\n", - "env: PATH_TO_POSYDON_DATA=/Volumes/T7/\n" - ] - } - ], + "outputs": [], "source": [ "%env PATH_TO_POSYDON=/Users/simone/Google Drive/github/POSYDON-public/\n", "%env PATH_TO_POSYDON_DATA=/Volumes/T7/" @@ -45,19 +36,9 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 10/10 [00:19<00:00, 1.92s/it]\n", - "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/binarypopulation.py:570: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " return pd.concat(holder, axis=0, ignore_index=False)\n" - ] - } - ], + "outputs": [], "source": [ "from posydon.popsyn.binarypopulation import BinaryPopulation\n", "from posydon.binary_evol.simulationproperties import SimulationProperties\n", @@ -371,6 +352,7 @@ " ]),\n", ")\n", "\n", + "\n", "def run_simulation(sim_prop, kwargs, file=None, indices=None):\n", "\n", "\n", @@ -410,7 +392,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -426,236 +408,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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        " - ], - "text/plain": [ - " state event time separation orbital_period eccentricity \\\n", - "binary_index \n", - "0 detached ZAMS 0.0 41.154712 3.385496 0.0 \n", - "\n", - " rl_relative_overflow_1 rl_relative_overflow_2 \\\n", - "binary_index \n", - "0 NaN NaN \n", - "\n", - " lg_mtransfer_rate mass_transfer_case ... \\\n", - "binary_index ... \n", - "0 NaN None ... \n", - "\n", - " S2_conv_env_turnover_time_l_b S2_conv_env_turnover_time_l_t \\\n", - "binary_index \n", - "0 NaN NaN \n", - "\n", - " S2_envelope_binding_energy S2_mass_conv_reg_fortides \\\n", - "binary_index \n", - "0 NaN NaN \n", - "\n", - " S2_thickness_conv_reg_fortides S2_radius_conv_reg_fortides \\\n", - "binary_index \n", - "0 NaN NaN \n", - "\n", - " S2_lambda_CE_1cent S2_lambda_CE_10cent S2_lambda_CE_30cent \\\n", - "binary_index \n", - "0 NaN NaN NaN \n", - "\n", - " S2_lambda_CE_pure_He_star_10cent \n", - "binary_index \n", - "0 NaN \n", - "\n", - "[1 rows x 126 columns]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "pop[i].restore()\n", "pop[i].to_df()" @@ -1248,18 +489,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1000.0, 0.858129274334538, 1.9157148786534735, 1.8675467897282945]\n", - "[None, None, None, None]\n" - ] - } - ], + "outputs": [], "source": [ "pop[i].star_1.natal_kick_array = [1000., 0.858129274334538, 1.9157148786534735, 1.8675467897282945]\n", "pop[i].star_2.natal_kick_array = [None, None, None, None] # default\n", @@ -1269,196 +501,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/simone/Google Drive/github/POSYDON-public/posydon/binary_evol/DT/step_detached.py:1677: RuntimeWarning: invalid value encountered in log10\n", - " current = np.log10(\n" - ] - }, - { - "data": { - "text/html": [ - "
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        " - ], - "text/plain": [ - " time step_names state event orbital_period \\\n", - "binary_index \n", - "0 0.000000e+00 initial_cond detached ZAMS 3.385496 \n", - "0 7.454614e+00 step_HMS_HMS detached 3.727594 \n", - "0 5.069171e+06 RLO1 CC1 5.833718 \n", - "0 5.069171e+06 step_SN disrupted NaN \n", - "0 6.117635e+06 step_disrupted disrupted CC2 NaN \n", - "0 6.117635e+06 step_SN disrupted NaN \n", - "0 6.117635e+06 step_end disrupted END NaN \n", - "\n", - " eccentricity S1_state \\\n", - "binary_index \n", - "0 0.0 H-rich_Core_H_burning \n", - "0 0.0 H-rich_Core_H_burning \n", - "0 0.0 H-rich_Central_C_depletion \n", - "0 NaN BH \n", - "0 NaN BH \n", - "0 NaN BH \n", - "0 NaN BH \n", - "\n", - " S2_state S1_mass S2_mass \n", - "binary_index \n", - "0 H-rich_Core_H_burning 43.384853 38.283497 \n", - "0 H-rich_Core_H_burning 44.774416 40.296974 \n", - "0 H-rich_Core_H_burning 26.196751 57.025587 \n", - "0 H-rich_Core_H_burning 25.675004 57.025587 \n", - "0 H-rich_Central_C_depletion 25.675004 37.995687 \n", - "0 BH 25.675004 36.254365 \n", - "0 BH 25.675004 36.254365 " - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "pop[i].evolve()\n", "pop[i].to_df(extra_columns={'step_names':'string'})[col]" @@ -1480,7 +525,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1489,17 +534,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1/1 [00:01<00:00, 1.45s/it]\n" - ] - } - ], + "outputs": [], "source": [ "# let's load up and rerun the binary index i\n", "pop = run_simulation(sim_prop, kwargs, file='./population.h5', indices=[i])" @@ -1507,442 +544,18 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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        " - ], - "text/plain": [ - " time step_names state event orbital_period \\\n", - "binary_index \n", - "0 0.000000e+00 initial_cond detached ZAMS 3.385496 \n", - "0 7.454614e+00 step_HMS_HMS detached 3.727594 \n", - "0 5.069171e+06 RLO1 CC1 5.833718 \n", - "0 5.069171e+06 step_SN disrupted NaN \n", - "0 6.117635e+06 step_disrupted disrupted CC2 NaN \n", - "0 6.117635e+06 step_SN disrupted NaN \n", - "0 6.117635e+06 step_end disrupted END NaN \n", - "\n", - " eccentricity S1_state \\\n", - "binary_index \n", - "0 0.0 H-rich_Core_H_burning \n", - "0 0.0 H-rich_Core_H_burning \n", - "0 0.0 H-rich_Central_C_depletion \n", - "0 NaN BH \n", - "0 NaN BH \n", - "0 NaN BH \n", - "0 NaN BH \n", - "\n", - " S2_state S1_mass S2_mass \n", - "binary_index \n", - "0 H-rich_Core_H_burning 43.384853 38.283497 \n", - "0 H-rich_Core_H_burning 44.774416 40.296974 \n", - "0 H-rich_Core_H_burning 26.196751 57.025587 \n", - "0 H-rich_Core_H_burning 25.675004 57.025587 \n", - "0 H-rich_Central_C_depletion 25.675004 37.995687 \n", - "0 BH 25.675004 36.254365 \n", - "0 BH 25.675004 36.254365 " - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# do the same thing as above\n", "pop[0].restore()\n", @@ -1967,202 +580,34 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "If you want to evolve a custom binary star, you can do so by crafting yourself the `BinaryStar` object and providing the `SimulationProperties` object. For example, let's evolve a neutron star with a low mass helium star in roche lobe overflow. " + "If you want to evolve a custom binary star, you can do so by crafting yourself the `BinaryStar` object and providing the `SimulationProperties` object. For example, let's evolve a neutron star with a low mass helium star in roche lobe overflow.\n", + "\n", + "Caution! \n", + "\n", + "While you can evolve a binary from an arbitrary state, you will need to provide data on the internal structure of the star, if you're starting in the detached step.\n", + "Otherwise the matching to a single star model will not work!\n", + "
        " ] }, { "cell_type": "code", - "execution_count": 106, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "STEP NAME STEP FUNCTION KWARGS\n", - "flow (, {})\n", - "step_HMS_HMS (, {'interpolation_path': None, 'interpolation_filename': None, 'interpolation_method': 'nearest_neighbour', 'save_initial_conditions': True, 'track_interpolation': False, 'stop_method': 'stop_at_max_time', 'stop_star': 'star_1', 'stop_var_name': None, 'stop_value': None, 'stop_interpolate': True, 'verbose': False, 'metallicity': 0.0001})\n", - "step_CO_HeMS (, {'interpolation_path': None, 'interpolation_filename': None, 'interpolation_method': 'nearest_neighbour', 'save_initial_conditions': True, 'track_interpolation': False, 'stop_method': 'stop_at_max_time', 'stop_star': 'star_1', 'stop_var_name': None, 'stop_value': None, 'stop_interpolate': True, 'verbose': False, 'metallicity': 0.0001})\n", - "step_CO_HMS_RLO (, {'interpolation_path': None, 'interpolation_filename': None, 'interpolation_method': 'nearest_neighbour', 'save_initial_conditions': True, 'track_interpolation': False, 'stop_method': 'stop_at_max_time', 'stop_star': 'star_1', 'stop_var_name': None, 'stop_value': None, 'stop_interpolate': True, 'verbose': False, 'metallicity': 0.0001})\n", - "step_CO_HeMS_RLO (, {'interpolation_path': None, 'interpolation_filename': None, 'interpolation_method': 'nearest_neighbour', 'save_initial_conditions': True, 'track_interpolation': False, 'stop_method': 'stop_at_max_time', 'stop_star': 'star_1', 'stop_var_name': None, 'stop_value': None, 'stop_interpolate': True, 'verbose': False, 'metallicity': 0.0001})\n", - "step_detached (, {'matching_method': 'minimize', 'do_wind_loss': True, 'do_tides': True, 'do_gravitational_radiation': True, 'do_magnetic_braking': True, 'do_stellar_evolution_and_spin_from_winds': True, 'RLO_orbit_at_orbit_with_same_am': False, 'verbose': False, 'metallicity': 0.0001})\n", - "step_disrupted (, {'grid_name_Hrich': None, 'grid_name_strippedHe': None, 'metallicity': 0.0001, 'dt': None, 'n_o_steps_history': None, 'matching_method': 'minimize', 'initial_mass': None, 'rootm': None, 'verbose': False, 'do_wind_loss': True, 'do_tides': True, 'do_gravitational_radiation': True, 'do_magnetic_braking': True, 'magnetic_braking_mode': 'RVJ83', 'do_stellar_evolution_and_spin_from_winds': True, 'RLO_orbit_at_orbit_with_same_am': False, 'list_for_matching_HMS': None, 'list_for_matching_postMS': None, 'list_for_matching_HeStar': None})\n", - "step_merged (, {'metallicity': 0.0001})\n", - "step_CE (, {'prescription': 'alpha-lambda', 'common_envelope_efficiency': 1.0, 'common_envelope_option_for_lambda': 'lambda_from_grid_final_values', 'common_envelope_lambda_default': 0.5, 'common_envelope_option_for_HG_star': 'optimistic', 'common_envelope_alpha_thermal': 1.0, 'core_definition_H_fraction': 0.1, 'core_definition_He_fraction': 0.1, 'CEE_tolerance_err': 0.001, 'common_envelope_option_after_succ_CEE': 'core_not_replaced_noMT', 'verbose': False})\n", - "step_SN (, {'mechanism': 'Patton&Sukhbold20-engine', 'engine': 'N20', 'PISN': 'Marchant+19', 'ECSN': 'Podsiadlowksi+04', 'conserve_hydrogen_envelope': True, 'max_neutrino_mass_loss': 0.5, 'kick': True, 'kick_normalisation': 'one_over_mass', 'sigma_kick_CCSN_NS': 265.0, 'sigma_kick_CCSN_BH': 265.0, 'sigma_kick_ECSN': 20.0, 'max_NS_mass': 2.5, 'use_interp_values': True, 'use_profiles': True, 'use_core_masses': True, 'approx_at_he_depletion': False, 'verbose': False})\n", - "step_dco (, {'n_o_steps_interval': None})\n", - "step_end (, {})\n" - ] - }, - { - "data": { - "text/html": [ - "
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        " - ], - "text/plain": [ - " time step_names state event orbital_period \\\n", - "binary_index \n", - "0 0.000000e+00 initial_cond detached ZAMS 168.143381 \n", - "0 5.772023e+02 step_HMS_HMS detached 193.069773 \n", - "0 4.004879e+07 RLO1 oCE1 144.350914 \n", - "0 4.004879e+07 step_CE detached 72.175457 \n", - "0 4.004881e+07 step_detached RLO1 oRLO1 234.884263 \n", - "0 4.004881e+07 RLO1 END 234.884263 \n", - "\n", - " eccentricity S1_state \\\n", - "binary_index \n", - "0 0.0 H-rich_Core_H_burning \n", - "0 0.0 H-rich_Core_H_burning \n", - "0 0.0 H-rich_Central_He_depleted \n", - "0 0.0 stripped_He_Central_He_depleted \n", - "0 0.0 stripped_He_Central_He_depleted \n", - "0 0.0 stripped_He_Central_He_depleted \n", - "\n", - " S2_state S1_mass S2_mass \n", - "binary_index \n", - "0 H-rich_Core_H_burning 7.974985 1.261677 \n", - "0 H-rich_Core_H_burning 7.860837 1.179126 \n", - "0 H-rich_Core_H_burning 7.719091 1.188623 \n", - "0 H-rich_Core_H_burning 2.734596 1.188623 \n", - "0 H-rich_Core_H_burning 1.921067 1.254592 \n", - "0 H-rich_Core_H_burning 1.921067 1.254592 " - ] - }, - "execution_count": 100, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "i = 0\n", "pop[i].to_df(extra_columns={'step_names':'string'})[col]" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/docs/_source/tutorials-examples/population-synthesis/lgrb_pop_syn.ipynb b/docs/_source/tutorials-examples/population-synthesis/lgrb_pop_syn.ipynb index 37d8e0524c..2659ce5966 100644 --- a/docs/_source/tutorials-examples/population-synthesis/lgrb_pop_syn.ipynb +++ b/docs/_source/tutorials-examples/population-synthesis/lgrb_pop_syn.ipynb @@ -16,18 +16,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "env: PATH_TO_POSYDON=/Users/simone/Google Drive/github/POSYDON-public/\n", - "env: PATH_TO_POSYDON_DATA=/Volumes/T7/\n" - ] - } - ], + "outputs": [], "source": [ "%env PATH_TO_POSYDON=/Users/simone/Google Drive/github/POSYDON-public/\n", "%env PATH_TO_POSYDON_DATA=/Volumes/T7/" @@ -37,1097 +28,247 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Creating the Synthetic Poupulation DataFrame" + "## Creating the GRB TransientPopulation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We will use the same BBH population as in the `Analyzing Merging BBH Populations: Rates & Observations` tutorial. To compute long-duration gamma-ray burst (LGRB) rates associated with the formation of merging BBHs, we will specity the following parameters to the `MODEL` parameters of the Synthetic Population class.\n", + "We will use the same BBH population as in the `Analyzing Merging BBH Populations: Rates & Observations` tutorial to compute the long gamma-ray burstss (LGRB) rate associated with the formation of merging BBHs.\n", "\n", - "(Note: the `population_params.ini` file needed for this tutorial was created in the **Large-Scale Population Synthesis on HPC Facilities** tutorial. Such file shuld be located in the same folder where the notebook of the current tutorial is located.)" + "Make sure the `BBH_contact.h5` file is present to continue with this tutorial." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Population successfully loaded!\n", - "Computing formation channels...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/synthetic_population.py:804: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'ZAMS_oDoubleCE1_CC1_redirect_CC2_CO_contact_END' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n", - " self.df_oneline.loc[index,'channel_debug'] = formation_channel\n", - "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/synthetic_population.py:808: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'ZAMS_oDoubleCE1_CC1_CC2_END' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n", - " self.df_oneline.loc[index,'channel'] = formation_channel\n", - "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/GRB.py:20: UserWarning: We only consider GRBs with isotropic equivalent energy larger than 1e+51 erg!\n", - " warnings.warn(\"We only consider GRBs with isotropic equivalent \"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Synthetic population successfully saved!\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/synthetic_population.py:451: PerformanceWarning: \n", - "your performance may suffer as PyTables will pickle object types that it cannot\n", - "map directly to c-types [inferred_type->mixed,key->block2_values] [items->Index(['state', 'event', 'step_names', 'S1_state', 'S2_state', 'channel'], dtype='object')]\n", - "\n", - " self.df_synthetic.to_hdf(path, key='history')\n" - ] - }, - { - "data": { - "text/html": [ - "
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        " - ], - "text/plain": [ - " metallicity time t_delay S1_state S2_state S1_mass \\\n", - "0 0.01420 4.027170 2913.863804 BH BH 13.120462 \n", - "1 0.01420 4.027170 1500.583774 BH BH 13.120462 \n", - "2 0.01420 4.849573 662.821800 BH BH 10.330899 \n", - "3 0.01420 4.544886 845.424610 BH BH 11.355246 \n", - "4 0.01420 4.530889 620.499837 BH BH 11.436823 \n", - "... ... ... ... ... ... ... \n", - "30751 0.00639 4.585168 128.572655 BH BH 13.380617 \n", - "30752 0.00639 5.751690 68.017543 BH BH 10.018834 \n", - "30753 0.00639 6.061904 88.863012 BH BH 20.971757 \n", - "30754 0.00639 5.290004 95.405272 BH BH 11.456069 \n", - "30755 0.00639 8.014044 12276.391282 BH BH 12.271066 \n", - "\n", - " S2_mass S1_spin S2_spin S1_E_GRB_iso S2_E_GRB_iso \\\n", - "0 12.911080 0.079649 0.064849 0.000000e+00 0.000000e+00 \n", - "1 12.911080 0.079649 0.063991 0.000000e+00 0.000000e+00 \n", - "2 9.888130 0.286911 0.249267 3.210409e+51 2.637513e+49 \n", - "3 10.251920 0.320931 0.298164 1.240025e+52 1.441788e+52 \n", - 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"30755 2.613887 0.164393 ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", - "\n", - "[30756 rows x 14 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "import os\n", - "from posydon.config import PATH_TO_POSYDON_DATA\n", - "from posydon.popsyn.synthetic_population import SyntheticPopulation\n", + "from posydon.popsyn.synthetic_population import Population\n", "\n", - "# cosmological model parameters for the rate calculation\n", - "MODEL = {\n", - " 'delta_t' : 100, # Myr\n", - " 'SFR' : 'IllustrisTNG',\n", - " 'sigma_SFR' : None,\n", - " 'Z_max' : 1.,\n", - " 'Zsun' : 0.0142,\n", - " # LGRB parameters\n", - " 'compute_GRB_properties' : True,\n", - " 'GRB_beaming' : 'Goldstein+15',\n", - " 'GRB_efficiency' : 0.01,\n", - " 'E_GRB_iso_min' : 1e51,\n", - "}\n", + "file = 'BBH_contact.h5'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "BBH_population = Population(file)\n", + "BBH_population.mass_per_metallicity" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "BBH_population.calculate_formation_channels(mt_history=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We had already selected only binaries that result in a merger in the `BBH_contact.h5` file, so we don't need to perform any selection on the binaries.\n", "\n", - "pop = SyntheticPopulation('./population_params.ini', verbose=True, MODEL=MODEL)\n", - "path = os.path.join(PATH_TO_POSYDON_DATA, \"POSYDON_data/tutorials/population-synthesis/example/\")\n", - "pop.load_pop(os.path.join(path,'BBH_population.h5'))\n", + "Thus, our next step is to create a selection function for LGRBs based on the properties in the history and onlines of the binaries.\n", "\n", - "# generate the BBH synthetic population\n", - "pop.get_dco_at_formation(S1_state='BH', S2_state='BH', \n", - " oneline_cols=['S1_natal_kick_array_0', 'S2_natal_kick_array_0'],\n", - " formation_channels=True)\n", + "We have already included a basic selection function in `posydon.popsyn.transient_select_func`, which select if a GRB occurs based on the presence of `m_disk_radiated` for either star.\n", + "It will also output some pre and post supernova properties.\n", "\n", - "# we can save the synthetic population\n", - "pop.save_synthetic_pop(os.path.join(path,'BBH_synthetic_population.h5'))\n", - "# pop.load_synthetic_pop(os.path.join(path,'BBH_synthetic_population.h5'))\n", + "As you can see below, the `GRB_selection` requires an additional input parameter of `S1_S2`. \n", + "The `Population.create_transient_population` function cannot pass these additional arguments to sub-function.\n", + "Thus, we will be required to wrap the `GRB_selection` functions.\n", "\n", - "cols = ['metallicity','time','t_delay','S1_state','S2_state','S1_mass','S2_mass',\n", - " 'S1_spin','S2_spin', 'S1_E_GRB_iso', 'S2_E_GRB_iso',\n", - " 'orbital_period','eccentricity', 'channel']\n", - "pop.df_synthetic[cols]" + "This is useful, if you would like to create similar population, but use different model parameters.\n" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "## Compute the BBH and LGRB rates" + "from posydon.popsyn.transient_select_funcs import GRB_selection\n", + "\n", + "GRB_selection(BBH_population.history[10], BBH_population.oneline[10], BBH_population.formation_channels.loc[[10]], S1_S2='S2')" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "Similar to the `Analyzing Merging BBH Populations: Rates & Observations` we compute the BBH merger rate density to obtain the BBH intrinsic population." + "import pandas as pd\n", + "\n", + "def GRB_wrapper(transient_chunk, oneline_chunk, formation_channels_chunk):\n", + " \"\"\"calculate the GRBs from star 1 and star 2 for a given chunk of the population\"\"\"\n", + " df_1 = GRB_selection(transient_chunk, oneline_chunk, formation_channels_chunk, S1_S2='S1')\n", + " df_2 = GRB_selection(transient_chunk, oneline_chunk, formation_channels_chunk, S1_S2='S2')\n", + " \n", + " # combine the two dataframes\n", + " if df_1 is not None and df_2 is not None:\n", + " GRB_df = pd.concat([df_1, df_2])\n", + " elif df_1 is not None:\n", + " GRB_df = df_1\n", + " elif df_2 is not None:\n", + " GRB_df = df_2\n", + " else:\n", + " return None\n", + " \n", + " return GRB_df" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 138/138 [00:55<00:00, 2.50it/s]\n", - "100%|██████████| 14/14 [00:26<00:00, 1.91s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "DCO merger rate density in the local Universe (z=0.00): 118.48 Gpc^-3 yr^-1\n", - "Intrinsic population successfully saved!\n" - ] - }, - { - "data": { - "text/html": [ - "
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        " - ], - "text/plain": [ - " metallicity time t_delay S1_state S2_state S1_mass \\\n", - "0 0.014200 6.205715 4.287125e-08 BH BH 12.260188 \n", - "1 0.014200 4.705799 9.754266e-01 BH BH 12.850735 \n", - "2 0.014200 8.187239 2.075700e+00 BH BH 8.314851 \n", - "3 0.014200 6.647572 2.472628e+01 BH BH 9.826765 \n", - "4 0.014200 6.676527 1.680554e+01 BH BH 20.974131 \n", - "... ... ... ... ... ... ... \n", - "2888053 0.000001 9.558857 1.547540e+03 BH BH 12.404415 \n", - "2888054 0.000001 5.679476 5.173339e+02 BH BH 31.263586 \n", - "2888055 0.000001 6.972682 1.105689e+04 BH BH 22.020038 \n", - "2888056 0.000001 6.442639 1.310371e+04 BH BH 21.311099 \n", - "2888057 0.000001 6.862427 1.727560e+03 BH BH 26.153757 \n", - "\n", - " S2_mass S1_spin S2_spin S1_E_GRB_iso S2_E_GRB_iso \\\n", - "0 12.183039 0.050168 8.244612e-04 0.0 0.000000e+00 \n", - "1 20.143346 0.027546 5.791334e-17 0.0 0.000000e+00 \n", - "2 8.315119 0.100591 6.554160e-03 0.0 0.000000e+00 \n", - "3 9.853595 0.032525 2.400078e-02 0.0 0.000000e+00 \n", - "4 12.500642 0.000531 4.772162e-04 0.0 0.000000e+00 \n", - "... ... ... ... ... ... \n", - "2888053 17.186149 0.353053 1.279050e-01 0.0 4.585059e+52 \n", - "2888054 34.303592 0.127590 1.671062e-01 0.0 6.742460e+52 \n", - "2888055 17.356224 0.156907 1.396343e-01 0.0 2.457570e+52 \n", - "2888056 36.473143 0.104700 1.194260e-01 0.0 9.105078e+53 \n", - "2888057 24.910536 0.116013 1.727154e-01 0.0 1.025357e+53 \n", - "\n", - " orbital_period eccentricity channel \n", - "0 326.580311 0.999994 ZAMS_CC1_oRLO2_CC2_END \n", - "1 2377.230212 0.999817 ZAMS_CC1_CC2_END \n", - "2 38.651550 0.996045 ZAMS_oRLO1-reverse_CC1_CC2_END \n", - "3 14.240625 0.980550 ZAMS_oRLO1-contact_CC1_CC2_END \n", - "4 696.487802 0.998932 ZAMS_CC1_oRLO2_CC2_END \n", - "... ... ... ... \n", - "2888053 1.368867 0.072070 ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", - "2888054 1.495336 0.003466 ZAMS_oRLO1-contact_CC1_oRLO2_CC2_END \n", - "2888055 3.686468 0.234744 ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", - "2888056 4.546062 0.059527 ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", - "2888057 2.019769 0.054964 ZAMS_oRLO1-contact_CC1_oRLO2_CC2_END \n", - "\n", - "[2888058 rows x 14 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "pop.get_dco_merger_rate_density()\n", - "pop.save_intrinsic_pop(os.path.join(path,'BBH_intrinsic_population.h5'))\n", - "# pop.load_intrinsic_pop(os.path.join(path,'BBH_intrinsic_population.h5'))\n", - "pop.df_dco_intrinsic[cols]" + "LGRB = BBH_population.create_transient_population(GRB_wrapper, 'LGRB')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's compute the LGRB rate density as a function of redshift." + "You've now created a LGRB population, where either the first and/or second star has some amount of radiative disk.\n", + "\n", + "This is, of course, just an example and the actual amount of disk required to power a LGRB will be higher than some of the values included in our \"LGRB\" rate.\n", + "\n", + "Moreover, the number of binaries in this population is insufficient to actually see the all unique events." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 138/138 [01:02<00:00, 2.20it/s]\n", - "100%|██████████| 14/14 [00:33<00:00, 2.39s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GRB (beamed) rate density in the local Universe (z=0.00): 0.35 Gpc^-3 yr^-1\n", - "Intrinsic population successfully saved!\n" - ] - }, - { - "data": { - "text/html": [ - "
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        .............................................
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        3137980 rows × 14 columns

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        " - ], - "text/plain": [ - " metallicity time t_delay S1_state S2_state S1_mass \\\n", - "0 0.014200 4.849573 662.821800 BH BH 10.330899 \n", - "1 0.014200 4.544886 845.424610 BH BH 11.355246 \n", - "2 0.014200 4.850661 336.796977 BH BH 10.415160 \n", - "3 0.014200 4.865192 671.702835 BH BH 10.427303 \n", - "4 0.014200 5.207981 247.878920 BH BH 9.514654 \n", - "... ... ... ... ... ... ... \n", - "3137975 0.000001 9.558857 1547.539864 BH BH 12.404415 \n", - "3137976 0.000001 5.679476 517.333924 BH BH 31.263586 \n", - "3137977 0.000001 6.972682 11056.893673 BH BH 22.020038 \n", - "3137978 0.000001 6.442639 13103.712599 BH BH 21.311099 \n", - "3137979 0.000001 6.862427 1727.560393 BH BH 26.153757 \n", - "\n", - " S2_mass S1_spin S2_spin S1_E_GRB_iso S2_E_GRB_iso \\\n", - "0 9.888130 0.286911 0.249267 3.210409e+51 NaN \n", - "1 10.251920 0.320931 0.298164 1.240025e+52 NaN \n", - "2 9.751447 0.340653 0.299044 9.060711e+52 NaN \n", - "3 9.478635 0.350639 0.312924 3.486785e+52 NaN 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ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", - "3137979 2.019769 0.054964 ZAMS_oRLO1-contact_CC1_oRLO2_CC2_END \n", - "\n", - "[3137980 rows x 14 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "pop.get_grb_rate_density() # if already computed use load_data=True\n", - "pop.save_intrinsic_pop(os.path.join(path,'LGRB_intrinsic_population.h5'), pop='GRB')\n", - "#pop.load_intrinsic_pop(os.path.join(path,'LGRB_intrinsic_population.h5'), pop='GRB')\n", - "pop.df_grb_intrinsic[cols]" + "LGRB.get_efficiency_over_metallicity()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "LGRB.plot_efficiency_over_metallicity(channels=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's visualise the BBH and LGRB rate densities as a function of redshift and compare it with GWTC-3 annd Perley+16 observational constrains.\n", - "- `channels` allows you to display the contribution of each formation channel to the total BBH and LGRB rate density.\n", - "- `grb_components` allows you to display the LGRB contribution from the primary star or the secondary star." + "After this selection, we follow similar steps to the BBH analysis for creating and plotting a cosmic star formation history weighted rate.\n", + "\n", + "If you've followed the previous tutorial on the BBH analaysis, you will still have access to those populations too." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "kwargs = dict(ylim=(0.05,800), zmax=10., show=True, path=None, channels=True,\n", - " GWTC3=True, Perley16=True, grb_components=False)\n", - "pop.plot_rate_density(DCO=True, GRB=True, **kwargs)" + "LGRB_rates = LGRB.calculate_cosmic_weights('IllustrisTNG')" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "## Visualize the LGRB Population Property Distributions" + "LGRB_rates.calculate_intrinsic_rate_density(mt_channels=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from posydon.popsyn.synthetic_population import Rates\n", + "BBH_rates = Rates('BBH_contact.h5', 'BBH', 'IllustrisTNG')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "BBH_rates.intrinsic_rate_density" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Similar to the DCO case, we can use the `plot_hist_properties` method to display the intrinsic and observable (beamed) distribution of the LGRB population." + "Below we plot the BBH rate and the un-normalised LGRB rate together, showing how you can access multiple event rates, and transient populations from the same file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.plot(BBH_rates.centers_redshift_bins, BBH_rates.intrinsic_rate_density['total'], label='BBH')\n", + "plt.plot(LGRB_rates.centers_redshift_bins, LGRB_rates.intrinsic_rate_density['total'], label='LGRB')\n", + "\n", + "plt.yscale(\"log\")\n", + "plt.xlim(0,10)\n", + "plt.legend()\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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M05RpmnX3USufy3adi9JH89MWk6af//znikajunLlil555ZW2d/XYS63j2Aynj1sz9rOu1dXV0t9Of1e069g7cU1rx3tv17ng5jHpNLU+t534neFXfrlON8Ot49+Jn8mQ1wEAvaydN3puGx8f1/j4uNLptJaWlpTJZLS8vGz7lXFxcVHRaLShwXsa4Zc+PYlEojSATyqV0vT0dOm5bDZb2ieN8GI/dqtjx47V/JV7v/u6/Pmpqam29x9u5TrQrnMxkUjYBqBKJpOe3dTUO46Ncvq47YdfamHKtfvY+/Ga5sdzoVnFRLETkw20h5+u02gPasSBNstkMr6rwXRSLBZTMpnU0tJSqf9S+YikxVqQ/fJjfyfTNEv9y4rJTlEymdxzdOpanN6P7VSeOLby40i7byDKY6nVRK3Zfe3HwW+K2nEuRqPR0vtdWFgoDQrklUaPYz1+Om77GSDIjWufk8feT9e0dp0LXn4f7XfAP7eU74/dn1u/fmfUitlvvLpOt+Oe1q/H3w9IxIE2W1tbc3x6ES+V18BVUxzRc2VlpS0jEFcbwdoPypv7lr+/a9euNdQU2O392E7xeLz0dzPHY/ey7R7oqHz95etudV+X9yX042e7lXOxvM/u1NRU3dGkq2n3Z3Kv49gsPx234nnVzL5yeiCwdh97P1/T2nUueDl7RTGB8UsXrXK79+nu/eTH74x6Me+XE/coXl6n23FP68fj7xck4gCasra2ppdeeqnucqZplm6yWrmI7566ppkBVZysbYnFYqW4FhcXS9PjFPsU1uP2fmyn8puA5eXlhl9Xvmyj+6lR5ftm97pb3dfl06Y0O9CUG4Pt7fdc3D2lU73pYcr7x5abmZlp22et1nFslp+OW/F9NFMbFIlEbO+/0Zga2YYTx97P17R2nQtOHpN6ikmJHxPx8unqqn1u/fidUS/momZnOmg3P16nm+XH4+8XJOJAk8qb2FT7ZS+bzXblxaJcJpNp6FfN4lQSrU4p8eKLL5b+brQGpTigiZPKB0WZm5tTKpVqaqCUdu7HxcVF10ZYN02z1OQ5l8s1/ONI+XRFjR7HRm9gy2+qqq27lX1tmqbtRqLeHMlFuVxO0WjUlVYc+zkXd9801htZvZkbqN3adRyb4afjVpyirtnPaPko5I0kuVJjNU5OHXu3vxsa1c5zwaljUk/xGESj0ZbX1ahGP7flI3VX+9z68TujXsxF9e75yjnxHez0ddqNe1o3j3+nIRGH73ndt2+38i/Bahe8lZWVhucF9tt7a8YLL7xQd5ni/ml1Pu/x8fFSk6R0Ot3QRXxmZsZ2w+SE8v63MzMzymazTTcZa3U/5nI5jYyMlKYuqdU0tJ2SyWTpy7mRpvjZbLb0a3z58ayn0UHvijdVU1NTe667lX394osvlmpGZmZmGrqxfuGFFzQ1NVW3RqUd14H9nIu746r1nnK5nO2GsDzmtbW1uv1W23kcm+HkcWtG+Xtp5ma9/LMyOzvbUI1b+edxr3PLyWPv5ndDM9p1Ljh1TGrJ5XKleN1sntvId2j5vqz1uXXrO6OdMUv2/V1vZPWZmZm292N2+jrdznvaWtw6/h3HAnwumUxakkqPVCrV0Oump6dLr0kmk3WXL99GLevr66XlIpFIxfPhcNhaX1/f8/Xj4+Ol16+srNSNa7fy/bGwsFB3+Xbvh/L46x2LSCRimaa55/PNvpdIJGJJssLhcM19Nz09XXO77VS+P6anp/f1ulb248LCgu2YhcPhpuJvxerqqmWapiXJmpqaami58fHxuuudmpoqvRfTNOt+TorL77Xudu3r1dVVKxwOW5Is0zRrfs7rnYOtXgfqrbPRczEWizW0b2KxmO1cK19/JBKxVldXK17T7uNYrvzaUe+YtvO4taIYQ6PfYeXKr33V9nVRLBYrLVt8v7WWbeexb+d3g1Pf3+08F5w4Jnsp7n83ru/Fz2HxHqfWtaT8c9jI59bp7wwnYrasj451tXu+ounpaSuZTNqO9dLSUs31Nnodc/I63eo9bTP3cU4cf6euFW7xRxRAmZWVFWtpaclaWFiwkslk6UuzPNFIJpPWwsKCtbS0ZLu5K742lUrZXhcOh61UKmUtLS3ZLkTF7ZRfxIsf/OL6qym/0E1NTVnr6+vW6uqqFYvFqn4BFOPa/aNCJBIpxVXrgr26ulr1fZmmWXp9+YXSyf1QvNkq3qSMj4/bjsH6+rq1tLRUutHaffPd7HvZrfyiOzU1VVp+fX3dWlhYsCKRiBWJRGquo51WVlZK8TSzzVb3Y9HS0lLFOeWm9fX10nspHsPV1VVrfX3dWllZsaanp61wOFz63DaieB4uLCyU1l/t81F8rnhe76Vd+3r3+y2+p+JnaXV11UqlUpZpmlYsFqs4H1q9DtTT6rlY7SaveF0rHrvyz18qlbKmp6etWCxWdb3tPo6NXDv2SohaOW7tUtx+rRvQWsp/2Nh9w7uwsGCZpmklk8mKa0LxHKt2Q9vOY9/q58yt7+92ngtOHJNqivt+r89aO5Xvz6WlJWt8fNyKxWK247W6ump77838uOTkd4ZTMZf/gDM+Pl5xvzU+Pl76XJcn4sX3WJ4E7/c65tR12rKav6dt5T6uHcffrWuFG0jE4TvFX8GKH8Raj92JR/mFqtqy5b+ura6u7rls+fJ73dgVL+rlcex10Si+p1px1dpWI++r/AvHyf0wPT1tuzCX/wJcfE0sFtvzS67Z97LXvp+enrYikYitdqN4UXVbLBZr+ua61f24e13FfVCrZsZJ1Y5J+XtoJrmZmpqquGkoJrCpVMpKJpPW9PS0NT4+3vD+ade+rvd+90o2Lav160Aj9nMuWpZVugEqJknFWrxYLFbxfspvlGptq93HsZFrR72WAPs5bu1STMZaqXHfHX+xtcHU1FTpvCn+IFNMAGKxmDU+Pl7zprYdx96N74Z2fX9X25f7PRecOCa7FfejG99v5clK8bu4mNyaprnva+Zu7f7OcCPm4jldfq4Va6GLis8Xj3UxSS++n1auY05cp4uauad16j6u0ePv9rXCSYZlWZYAAADgqKGhIeVyOa2urnb9oJ5oj1wup6GhIYXDYa2vrzu+vUQiUeqbu7Ky4mo//v3qxJgBicHaAAAAXFEcyb7RUbuBa9euSbIPyAigO5CIAwAAuKCYTJVPnQTUUhwFvJmpMQF0BhJxAAAAF4TDYSWTSeVyuVJTWmAvi4uLymazSiaTCrd5WiwA3iMRBwAAcMn09LRM02xovmP0tqtXr8o0TU1PT3uy/f3Mee61TowZvYtEHAAAwEULCwvKZrOamZnxOhT41OzsrDKZjFKplKvbzWazpb9zuZyr296vTowZkKSQ1wEAAAD0kkgkolQqpUQioeeff55RnmFT/JEmmUwqFos5vr1MJqO1tTVlMhml0+lS+czMjNbW1mSaZunhF50YM7Ab05cBAAB4IJFI6Nq1a7p16xZ9gCHpYY1uNBpVJBLRwsKCK9scGRkp1SrvPg+LNczJZNKzJvLVdGLMwG4k4gAAAB5JJBJaXl7WysqK16HAB+LxuMLhsGtJOADvkIgDAAB4qNhXnAHcetvs7Kzu3bvHeQD0CBJxAAAAAABcxKjpAAAAAAC4iEQcAAAAAAAXkYgDAAAAAOAiEnEAAAAAAFxEIg4AAAAAgItIxAEAAAAAcBGJOAAAAAAALiIRBwAAAADARSTiAAAAAAC4iEQcAAAAAAAXhbwOAGhELpfT9evXS/8/deqUDhw44GFEAAAAAPzuww8/1Jtvvln6/9jYmMLhsHcB/QGJODrC9evX9cUvftHrMAAAAAB0sJdffll/8id/4nUYNE0HAAAAAMBNJOIAAAAAALiIpunoCKdOnbL9/+WXX9YTTzzh2PYmJye1vLys0dFRzc/PO7YdSbp586at2b3T701y9/2xvfbjnGF7zeKcYXvN8OJ8kbp7n3b79nrhGuPFNrt5e26eM7u3tTuv8AqJODrC7oHZnnjiCZ07d86x7R06dKj0r5Pbqcbp9ya5//7YnrM4Z9heszhn2F4z3DhfpO7fp92+vXLdeI3xYpvdvr1ybl1npMq8wis0TQcAAAAAwEUk4gAAAAAAuIhEHAAAAAAAF5GIAwAAAADgIgZrA6qYnJzUpUuXNDw87HUojnD7/bG9ztft+7Tbt+eFbt+n3b49L3T7Pu327bnNi/fX7cew288ZvzEsy7K8DgKo58aNGzp//nzp/3//93/vyUjDTujm9wZncM6gWZwzaAbnC5rFOYNmuXnO+PX8pGk6AAAAAAAuIhEHAAAAAMBFJOIAAAAAALiIRBwAAAAAABeRiAMAAAAA4CKmLwM89uijj+ob3/iG7f9ALZwzaBbnDJrB+YJmcc6gWZwzTF+GDuHXaQcAAAAA+Jdf8whqxNGRJicndejQoarlk5OT7gcEAAAAwDPz8/Oan5+vKN/Y2HA/mAaQiKMjLS8vVy2/dOmSu4EAAAAA8Nzt27d1/fp1r8NoGIk4OtLo6GjVGvHh4WH3gwEAAADgqeHhYY2NjVWUb2xs7FmJ5yX6iKMj+LVvBwAAAAD/8msewfRlAAAAAAC4iEQcAAAAAAAXkYgDAAAAAOAiEnEAAAAAAFzEqOkAAAAAOla+YOnDnXzp/4N9QRmG4WFEQH0k4gAAAAAaYlmWNrfz9Rf8AzeS4ru5TS2uvFX6f2LM1MF+0hz4G2coAAAAgIZsbueVup5teHmSYqA6PhUAAAAAfKtaLXyxpv3+1o5eeyvnTWBAC0jEAQAAALTFHw0N6ulT4dL/+4Otjw1drRa+vKb9N29/0PI2ALeRiAMAAABoi39Y39TZEx+zJeMAKpGIAwAAANi3L188rcH+oNdhAB2FRBwAAADAvg32BxmQDWhS6502AAAAAABAw0jEAQAAAABwEYk4AAAAAAAuojMHAAAAgJbd39qpOc0YgI/wqQAAAADQkMG+oBJjZkUZgOaQiAMAAABoiGEY1HADbcCnCAAAAECJZVna3M7v+fyBUFDBgOFiRED3IREHAAAAekytZHtzK6/vvXpnz9eORx/XqWMHnQoN6Akk4gAAAECP2dzOVwysBsA9JOIAAAAAOtJAKKgvXTxdUQb4HYk4AAAAgI4UCBh65PABr8MAmkYiDgAAAKCmL188rcH+hzXNB1yuce4PBvS5pz5RUQZ0MhJxAAAAoMeUN+ne3Ho4aNvQoX7tNRj6YF9QhuHNSOmhYEBnT3zMk20DTiERBwAAAHoMTboBb9GmAwAAAAAAF1Ejjo40OTmpQ4cOVS2fnJx0PyAAAAC4rlCwtHZ/y1Z27GC/Anu1sUfXmp+f1/z8fEX5xsaG+8E0gEQcHWl5eblq+aVLl9wNBAAAAJ55sJPX91+9YytLjJk62E+a02tu376t69evex1GwzhD0ZFGR0er1ogPDw+7HwwAAAAATw0PD2tsbKyifGNjY89KPC+RiKMjzc/P69y5c16HAQAA0JGcaNLdFwzos08+VlHWqvtbO0pdz9rKqPXGbnt1Ub1x44bOnz/vfkB1cPYCAAAAPcaJJt19wYCePhVuMTKgNzBqOgAAAAAALiIRBwAAAADARSTiAAAAAAC4iEQcAAAAAAAXkYgDAAAAAOAiRk0HAAAA0LLNrby+++ptW9lXLg5rsD/oTUCAj5GIAwAAAGiZJUubW/mKMgCVaJoOAAAAAICLSMQBAAAAAHARiTgAAAAAAC4iEQcAAAAAwEUM1gYAAACgIx0IBTUefbyiDPA7EnEAAAAAHSkYMHTq2EGvwwCaRiIOAAAAwLf6ggF99snHKsqATkYiDgAAAPSYTmrS3RcM6OlTYa/DANqKRBwAAADoMTTpBrxFmw4AAAAAAFxEjTgAAACAjmRZlja387aywb6gDMPwKCKgMSTiAAAAADrS5nZeqetZW1lizNTBftIc+BtnKAAAANCFqtUWV0PSCriPTx0AAADQharVFu/2z8xjpRHJW23SHQoE9GnzeEVZqza38vruq7dtZV+5OKzBfn+O8g40gkQcAAAA6FH/Lbum/5Zdk9R6k+7+UEAXR47XX7BJlixtbuUryoBOxqjpAAAAAAC4iEQcAAAAAAAX0TQdAAAA6BFfvnh6z77Vg330uQbcQiIOAAAA9IjB/iCjpAM+QNN0AAAAAABcxM9h6EiTk5M6dOhQ1fLJyUn3AwIAAOhxD7bzurb8pq3sudFTGqDJO1wwPz+v+fn5ivKNjQ33g2kAiTg60vLyctXyS5cuuRsIAADoels7Be0UCns+3+r8292iYFm698FWRRnghtu3b+v69eteh9EwEnF0pNHR0ao14sPDw+4HAwAAutrKnXX9NHtvz+dbnX/bKf3BgD731CcqyoBuNDw8rLGxsYryjY2NPSvxvOS/KwbQgPn5eZ07d87rMAAAAHwrFAzo7ImPeR0G4Iq9uqjeuHFD58+fdz+gOvhJDAAAAAAAF5GIAwAAAADgIpqmAwAAADVETw/pwqmjkqTNrby+9+odjyNCEf3g0alIxAEAAIAa+kMB9dOQ1JfoB49ORSIOAAAAwLdCgYA+bR6vKAM6GYk4AAAAUObBdl7Xlt+0lT03ekoDfUGPItqf+1s7Sl3P2srcnmptcyuvYMDQgdD+911/KKCLI8frLwh0EBJxAAAAoEzBsnTvg62KMjTve6/e0YVTR/Xskye8DgXwFdp0AAAAAADgIhJxAAAAAABcRNN0AAAAoEGGDA32ByvK4A0/9IMH9oMzFAAAAGjQYH9QXx0baes6LcvS5na+uTj6gjIMf/0AMNgXVGLMrCgPBh7G+WA7rx+8dtf23BcunOy4QfCAdiARBwAAADy0uZ2vqNWtx4+1voZh1IypYFl6a32zoqyebhnFHijnr08vAAAAAJRhFHt0IwZrAwAAAADARSTiAAAAAAC4iKbpAAAAgIf6ggF99snHJEmbWw8HbTv3R0cU+sMgZ5tbeX3v1TuexQeg/agRd8nc3Jyi0aiGhoZKj4mJCaXT6Y6NIZvNamZmRvF4XENDQzIMQ9FoVBMTE1pcXGxz9AAAAN7bzhf0yzdztsd2vtDSOvuCAT19KqynT4V1ceS4Lo4c15GBPh3sD+lgf6hiujQAnY9E3GGZTEZDQ0NKJpNKJBJaX1/X+vq6VlZWZJqm4vG4JiYmlMvlOiqGRCKhaDQqSUomk7p165bW19d15coVpdNpTUxMaGRkxNUfGgAAAJy2nS/ox79+x/ZoNREH0Htomu6gdDqteDwu0zS1srKicDhces40TSWTSY2MjCiRSCiTyVQs49cY4vG4JOnWrVsVy46PjysWi+nMmTPKZrOKx+NaWlpSLBZr6/sCAADoFQf7Q/p6/KzXYQBoI2rEHZLL5TQxMSFJWlhY2DO5nZqa0tTUlLLZrC5fvuz7GGZnZ5XNZmuuLxwOK5lMlv4fj8eVzTY3NyYAAAAAdCsScYe88MILyuVyikQiikQiNZedmZmR9LAJeTv7VjsRw9WrV5XNZjU0NKTZ2dk9l9tdA16emAMAAPhZMGDowqmjtkfwDwOndZLiIHDlj74gt/+AH/BJdEAulysls400yTZNU6ZpSnqY6Po1hlwuZ+tHXkze91pfueXl5boxAAAA+MGBUFDPPnnC9jgQ6rwB08oHgSs+SMQBf6CPuAPm5uZKfz/zzDMNvcY0TWWzWWUyGWWz2YpE1g8xtNJ//dixY/t+LQAAQDfb3Mrru6/etpV95eIwo6UDXYyfxBzw0ksvlf5uNHktT3rbMdK4UzGkUimFw2GFw2EtLCzsua7dfcLrNY0HAADoVZYsbW7lbQ9LltdhAXAQNeIOyGQypb8brQkuT5ZXVlZ8G0NxYLd6difyiUSioRgAAABgt5MvKPvuhq3MfOSQQjQzL/WD310G+B2JeJvtrglutIn58ePHS3+32p/aDzGkUqnS38lksuWm9gAAAL1qK1/QD1//ra0sMWZ2XCIeMAx98sThirJWFPvBA52GRLzNygcza0Z5bfR+1+GXGGZnZ0s18slkUtPT0/te115u3rzZ9GseffRRPfbYY/UXBAAAQNsN9AX1+adONv264ij2u8vQe9555x39/ve/b+o1+8kb3EAi7kNra2teh9B0DLlcTsvLy0qlUlpcXFQkElEymWxoxPb9+OIXv9j0a77xjW/or/7qr9oeCwAA6C4PtvP6wWt3bWVfuHBSA32dNXhatwwCVxzFHvjOd76jb37zm16H0RYk4m22O4Hdz0jjrdaIuxnDyMhIRVP4qakpJZPJlkZZBwAA8ErBsvTW+mZFWacpDgK3uwyA9zqrY0kHaDWJ7rQYVldXZVmWLMvS+vq6FhYWdO3aNQ0NDWliYsIX+wMAAAAA/IQacbRNOBzW+Pi4IpGIotGoFhcXlU6n9corr7R9+rKXX35ZTzzxRFOvefTRR9saAwAAAAD3fO1rX9PExERTr7l58+a+urU6jUS8zfzQHNvrGEzT1IsvvliqEY9Go1pZWWlrMv7EE0/o3LlzbVsfAAAAOk+39INHYx577LGuGXyZRLzNds/Zncvlmk6MW02k/RDD+Pi4wuFwqWn6xMSEVldXW1onAACA1w72h/T1+Fmvw+hID7bzSv/qbVtZ7FMnWhoEj37w6FT0EW+zdtRG706kOzEGSXruuedKf2ezWc3NzbW8TgAAAHSmgmXpN29/YHs0Mgjeg+28FpbftD0ebOfrvg7wMxLxNtudwDY6DVj5oGbtrhH3Igbp4Yjq5ZaWllpeJwAAAHpLcRT78kcnjmIPlCMRb7PdCWyjo4aXN9s2TdOXMWQyGcXjccXjcaXT6abjyGQyDcUBAAAAAN2MRNwB5YOSNVobXb7cM88848sYLl++rHQ6rXQ6rXg8XjF/+G77rZkHAAAAgG5GIu6AWCxW+rteslptufHxcd/FkMlkKmrW69Vw7068W63pBwAA6EahQECfNo/bHqEAt+lAN+MT7oArV66U/l5ZWWnoNcWk1jTNtiSs7Y6hWp/xetOR7d7u888/31AcAAAAfrWTL+iNt//R9tjJF1paZ38ooIsjx22P/hC36UA34xPugHA4XKqRvnbtWt3ly/tbz8zM1F1+cXGxbm10u2MoT85jsZiWlpbq/mCwux/51NRU3TgAAAD8bCtf0A9f/63tsdViIg6g95CIOySVSpXm0V5cXKy7rPSwhrlWsprL5TQyMqKJiQlFo1HNzs66GkMymZT0MFEvb/peTTqdtjV1L8YCAACA5gz2BZUYM22PwRbm3gbgPRJxh5imqRdffFGSNDExsWc/7bm5OS0uLiocDmthYaHmOncnt1evXnU1hvHxcU1PTysej9f8ESCbzSqRSJT+Pz09TW04AADAPhmGoYP9IdvDMAyvwwLQAhJxB42Pj2tpaUnhcFjRaFRzc3Ol53K5nBKJhBKJhGKxmG7dulW3qffuGuVG+pK3O4ZkMqmFhQWlUikNDQ1pZmZG6XRamUxGmUxGs7OzGhkZUTabVSQS0crKSqkmHQAAoBMEDEOfPHHY9gh0YOLLIHCAf4W8DqDbFRPca9euKZVK2fpfF/ta12vmXb789PS0ZmdnZZpm3Rp0J2KQHib34+PjWlxc1NLSkmZmZpTNZpXL5RQOhzU+Pq7nn3++LaO/AwAAuG2gL6jPP3XS6zBaVhwEDoD/kIi7IBwOa2pqqi3Ns5PJ5L5qmNsZQ1ExIQcAAMD+PdjO69rym7ay50ZPaYB+4EDXIhEHAAAAPFSwLN37YKuiDP5kWZY2t/MNLz/YF6RPPyqQiAMAAAAOq5W8bW7VTuryBUt3c5u2spPhQQUDJHfFfvC7y5y0uZ1X6nr1QZCrSYyZOthP2gU7zggAAADAYc0mb+U+3MlrceUtW1knJncBw9DjQ4MVZa2gHzw6VWd9egEAAAB0pIG+oCZGTzX9uuIo9rvLgE5GIg4AAACUebCdV/pXb9vKYp860XGDp3XLIHB+G8V+sC+oxNjDKX9/+WZO/y275nFE6EQk4gAAAECZgmXpN29/YCt79snHPIpm/xgEzhmGYZS6BTx9KqynT4VLz21u5fW9V+94FBk6CYk4AAAA4IEvXzytwf7qtdODHVZr3as6rZ8+/IMzBwAAAHBYMGDowqmjtrLDAyEdCJFwA72IRBwAAABw2IFQUM8+ecLrMLpOt/SDR+8hEQcAAAAaVD5QV3kZ6vtwJ6+f3HzXVvaZJx5pqVUA/eDRqUjEAQAAgAaVD9SF5uQLll578z1b2afN+nOA+20U+3zB0t3cpq3sZHhQwYCh/mBAn3vqE7bn+oMBN8NDh+AqAgAAAMC3/DaK/Yc7eS2uvGUrS4yZOtgfUigY0NkTH/MoMnQSfp4BAAAAAMBFJOIAAADAHzzYzmtzK+91GAC6HE3T0ZEmJyd16NChquWTk5PuBwQAALrCD167q7fWN+sv2KQH23n94LW7trIvXDjJ6N5Am8zPz2t+fr6ifGNjw/1gGkAijo60vLxctfzSpUvuBgIAAHpKrYG6ailYVkWCz+jeQPvcvn1b169f9zqMhpGIoyONjo5WrREfHh52PxgAANAzag3UhfZ4sJ23/UhBVwE0Ynh4WGNjYxXlGxsbe1bieYkrBjrS/Py8zp0753UYAAAAjhsIBfWli6cryrqVU90D3HB/a0ep61lbGT/UuGOvLqo3btzQ+fPn3Q+oDs4IAAAA4A++cOFk1Sbjgx725Q4EDD1y+IBn2wfQfiTiAAAA6Dkf7uT1k5vv2so+88QjXTV4WjBg6MKpoxVlALxHIg4AAICeky9Yeu3N92xlnzaPexSNMw6Egnr2yRNehwGgChJxAAAAAL6xV/eAcl52FQDagUQcAAAAgG90U/cAYC8k4gAAAICPFQqW1u5v2cqOHexXgP7e9INHxyIRBwAAAHzswU5e33/1jq2sE6fEChiGjh/uryhrBf3g0ak669MLAAAAoCMN9AX15YvDVZ/baxT7A108Xzp6G4k4AAAA0IUebOf1g9fu2sq+cOGkL/tgd9Io9gOhoL508XRFGdAMEnEAAACgCxUsS2+tb1aUoTWBgKFHDh/wOgx0uIDXAQAAAAAA0EuoEQcAAAAcFjAMffLE4YoyAL2JRBwAAABw2EBfUJ9/6qTXYXSdTuoHD5QjEQcAAAAaxEBd+7e1U9DKnXVbWfT0kPpD++8tSz94dCoScQAAAKBBDNS1fzuFgn6avWcru3DqqPo7bNiqQsHS2v0tW9mxg/0KBAz1BQP67JOP2Z7rC3bW+4M7SMQBAAAAoEEPdvL6/qt3bGWJMVMH+0PqCwb09KmwN4Gho5CIAwAAoOcEDEPHD/dXlAGAG0jEAQAA0HMG+oL68sVhr8MA0KNIxAEAAOBblmVpczvf8PKDfUEZPqzZfrCdV/pXb9vKYp86wejeQI8iEQcAAIBvbW7nlbqebXj5Yl9dp9QaqKvm6yxLv3n7A1vZs7sG9QLQO0jEAQAAgAbVGqgLABrFFQMAAADwsQOhoMajj1eUwX82t/L67qu3bWVfuTiswX6OF+xIxAEAANDxiiOgd+PI58GAoVPHDnodhqO6ZRR7S5Y2t/IVZcBuJOIAAADoKF++eLqihrHZpuFbOwWt3Fm3lUVPD6k/FGg5Pr8IGIY+eeJwRZkfMYo9eg2JOAAAADrKYH+w5T7ZO4WCfpq9Zyu7cOqo+tU9ifhAX1Cff+qk12EAqKJ7rjQAAAAAAHQAasQBAAAc8O2lN/Z87uvxsy5GAgDwGxJxdKTJyUkdOnSoavnk5KT7AQEAAEcEA4YunDpaUdZLLMvS5rZ9ALDBvqAMn/b3dlMn9YOHs+bn5zU/P19RvrGx4X4wDSARR0daXl6uWn7p0iV3AwEAAI46EArq2SdPeB2Gpza380pdz9rKOnHuckNGxSB7hlpLmukHj6Lbt2/r+vXrXofRsM769AJ/MDo6WrVGfHh42P1gAACAJ3ph5PNuMtgf1FfHRqo+x7FEq4aHhzU2NlZRvrGxsWclnpdIxNGR5ufnde7cOa/DAAAAHuqFkc9b8WA7r/Sv3raVxT51QgN9wT1e4Z1OOpYHQkGNRx+vKIO39uqieuPGDZ0/f979gOogEQcAAAC6UMGy9Ju3P7CVPfvkYx5F0z2CAUOnjh30Ogx0OP/9xAQAAAAAQBejRhwAAABwWMAw9PjQYEUZgN5EIg4AAADferCd1w9eu2sr+8KFk77s51zLQF9QE6OnvA6j63RSP3igHIk4AAAAfKtgWXprfbOizCsM1LV/2/mCbtx931Z27uQR9QX331uWfvDoVCTiAAAAQIMYqGv/tvMF/fjX79jKzp443FIi7gXLsrS5nbeVDfYFZRiGQoGAPm0etz0XCnTW+4M7SMQBAAAAoEGb23mlrmdtZYkxUwf7Q+oPBXRx5PgerwQ+QiIOAACAnmPI0GB/sKIMANxAIg4AAICeM9gf1FfHRrwOA0CPIhEHAAAAHPbhTl4/ufmurewzTzzi6EBvH24XHFu3n21u2ftvBwyDUdThOyTiAAAALvv20ht7Pvf1+FkXI0Gzag3UVUu+YOm1N9+zle0e1KvdfvD63foLdaHvvXrH9v/HhwaZOg6+QyIOAAAANKjWQF0A0CiuGAAAoOtRA41O1h8M6HNPfaKirJ5BmmO77sF2XteW37SVPTd6iqbxqEAiDgAAgI7UKyOfh4IBnT3xMa/DcFS3HMuCZeneB1sVZcBuJOIAAADoSK2MfL6dL+jG3fdtZedOHlFfAzXNflJrELgvXDhZkQT6tZZ8v8dysC+oxJhZc5lAnf77gBdIxAEAANBztvMF/fjX79jKzp443HGJeK1B4HqhObRhGPTPR0fqrCsNAAAAAAAdjkQcAAAAAAAX0Y4DAAAAvhUwDH3yxOGKMqCeWv3nAa+RiAMAAOxDrSnR0D4DfUF9/qmTXofhqftbO8xdvg+1+s8DXuPTCwAAgI7ULSOf94qD/SF9PX626nMcS/QaEnEAANDTatVs75U0wB+6ZeRzdNax7A8G9LmnPlFRBjSDRBwAAAAAGhQKBnT2xMe8DgMdjp9uAAAAAABwETXiAAAAgMMChqHjh/srygD0JhJxAAAA+NaD7bzSv3rbVhb71AkN9HXWFFQDfUF9+eKw12EA8AkScQAAAPhWwbL0m7c/sJU9++RjHkXDQF2t2MkXlH13w1ZmPnJIIfYfehCJOAAAANCgvQbqur+1U3X5wb6gDJqgS5K28gX98PXf2soSY2ZXJeLBgKELp45WlAG7kYijI01OTurQoUNVyycnJ90PCADguVrTkAFOS13PVi1PjJk62M8tdze5v7VTcbyLx/lAKKhnnzzhUWS9bX5+XvPz8xXlGxsblQv7AFcFdKTl5eWq5ZcuXXI3EAAAAA8xCBzw0O3bt3X9+nWvw2gYiTg60ujoaNUa8eHhYfeDAQAAHedgf0hfj5/1OoyWMQgc8NDw8LDGxsYqyjc2NvasxPMSiTg60vz8vM6dO+d1GAAAAA3Z2ilo5c66rSx6ekj9oe7pHw14aa8uqjdu3ND58+fdD6gOEnEAAACgRYkxU5tbeX3v1TtVn98pFPTT7D1b2YVTR9UvEnGgF5GIAwAAAC1iQDb/of88/IwrBgAAaLtaI5h3Q79cwE19wYA+u2vu9L4umvLLKV70n3+wndcPXrtrK/vChZMa6Au6Ggf8j0QcAAAA8LG+YEBPnwp7HQYaULAsvbW+WVEG7EYiDgAAgI7UysjnO/mCsu/a5xc2HzmkUIfVNHfLIHDdMoo90CgScQAAAOxLJ3dB2MoX9MPXf2srS4yZHZeIMwgc0Jn4hAIAAAAA4CIScQAAAAAAXETTdAAAAPhWwDD0+NBgRVmvur+1Y/v/5lbeo0j8r1v6z6M7kYgDAABf6eR+x2i/gb6gJkZPeR1GQ2pNM9ZKwry5ldd3X73d8np6Df3n4Wck4gAAAOhIfhv5vNY0Y9979c6+12vJ6ooEfLAvqMSYWVEm+e9YAk4jEQcAAEBH6paRz3uFYRg62F89/eikY1mr5QPQKBJxAACADkGz/c412B/silpt1G75ADSKRBwAAADwsWpNuustD8DfSMQBAAAAh33l4rAsWbayRhPmWk26AXQmPtEAAAA9rFZzd8n7Ju8f7uT1k5vv2so+88QjOhDqrFrfwf7OiheAs0jEAQAA4Fv5gqXX3nzPVvZp87hH0aAV+YKlu7lNW9nJ8KCCgd6dFx69i0QcAAAAHaE4J/R2vqC+YEDb+YJW7qxLIjnvBB/u5LW48patLDFmdlWz+4Bh6JMnDleUAbt1z1kPAAAA32CEd3Srza28vvvqbVvZVy4Oa7A/qIG+oD7/1ElvAkNHIREHAADocvX6gQNonCWrYiq63QPxAfWQiAMAAKDnVJsSjGm/ALiFRBwAAMBHqL12B1OCAfASVx8AAAB0tXzB0oc7D5sSD/YFZTB4FgCPkYgDAAB0AWrS93Y3t1karbvbRunG3gwZFfO3G+JHGPgDVyEAAAB0hX9mHvM6BPjIYH9QXx0bcXWbD7bzSv/qbVtZ7FMnNMD4A9iFRBwAAABd4elTYVtt9/2tHb3x9gc6fqjfw6jQSwqWpd+8/YGt7NknH/MoGvgZiTgAAAC61o9//Y4+eeJwRXm+YOlubtNWdjI8qGCApsteYBR79BoScQAAAOzJz33PQwFD0dNDkh72/d4rcdtdQylJH+7kS/3Gi+g/7h1GsUev4WxHR5qcnNShQ4eqlk9OTrofEAAAcJ1hGOoLPqzBJokDetv8/Lzm5+cryjc2NtwPpgFcsdCRlpeXq5ZfunTJ3UAAAAAAeO727du6fv2612E0jEQcHWl0dLRqjfjw8LD7wQAAupafm2X3ioBh6Pjhh4OtFZue04sbjdjOF3Tj7vu2snMnj6gvGPAoIjhpeHhYY2NjFeUbGxt7VuJ5iUQcHWl+fl7nzp3zOgwAAOCwgb6gvnxxWJJ074Mtb4NBR9nOF/TjX79jKzt74jCJeJfaq4vqjRs3dP78efcDqoNEHAAAAIDjBkJBfeni6YoyiVHs0XtIxAEAAAA4LhAw9MjhA1Wf66RR7EOBgD5tHq8oA5rhvzMbAAAAAHyqPxTQxZHj9RcEauCnGwAAAAAAXEQiDgAAAACAi2iaDgAAAN/a2ilo5c66JOmt9fuSpE8cZRCvTnd/a8f2/82tvEeRAN4gEQcAAIBv7RQK+mn2niTprfWHo2qfODKgILOJd5xCwdLa/YdT0L32Zk6vv/WexxEB3iERBwAAAOC4Bzt5ff/VO16H4aiAYejxocGKMmA3EnEAAAB0pVrzVkvSU48fpVYWTXuwnde15TdtZc+NntJAX1ADfUFNjJ7yKDJ0EhJxAAAAdKVa81Yf7A/pwqkwiTiaVrAs3ftgq6IMaIavE/Hbt29LkoaHhz2NAwAAAEBrBvuCSoyZTS0PdCtPE/Hbt28rm80qm81qdXW19Hc2m9X6+rqGhoZ05coV/cVf/IWXYQIAAKALHTvYb0sMSfycZRiGDvb7uh4QcI2nn4RYLKZbt25JkizLUjgc1tTUlL71rW/p8uXLXoYGAAB86NtLb3gdArpIIEBiCMAbnl95rD/0p5ienta3vvUtj6MBAAAAAMBZnifihmGUasEBAAAAoB0O9of09fhZV7f54U5eP7n5rq3sM088ogMhuj3ALuB1AJKUTCa9DsFxc3NzikajGhoaKj0mJiaUTqc7NoZMJqNEIqGRkREZhiHDMDQyMqJEIuHq+wIAAAD8IF+w9Nqb79ke+QIjqqOS54l4JBLRkSNHvA7DMZlMRkNDQ0omk0okElpfX9f6+rpWVlZkmqbi8bgmJiaUy+U6KoaZmRlFo1Gtra1pZmZGCwsLpR9U5ubmFI/HFY1Glc1mHXpXAAAAANCZPG+aPjo6WvP5v/3bv21oPaZp6umnn25DRO2TTqcVj8dlmqZWVlYUDodLz5mmqWQyWapBzmQyFcv4NYZoNKpcLqfV1VWZpn0KiunpaSUSCc3NzSmTyWhkZERLS0uKxWJtfV8AAAAA0Kk8T8RHRkb2fO7WrVv667/+a+VyOWWzWRmGYXu+ONK6aZr6sz/7M18l4rlcThMTE5KkhYWFPZPbqakpraysaG5uTpcvX9bKyoqvY5idnVUul6uZsKdSKUkPa8YlKR6PV03aAQAAADfc39qpu8xgX7Ai3wCc4nnT9Fq1r2fOnNHy8rJu3rypQqGgq1evyrIsWZalo0ePamlpSWtra1peXvbdXOMvvPCCcrmcIpGIIpFIzWVnZmYkPWxCvri46NsYstmsZmZmlEql6taa7+73n0gkGg8cAAAAaKM33v5AqevZmo/N7bzXYaKHeJ6IN2N6elqXL1+WYRianZ317VzjuVyulMw20iTbNM1SbfHVq1d9G0MymWy46XxxTviidDpNf3EAANA0Q4YG+4Ma7A8qFDQUClJjicbs5At64+1/1Btv/6MO9gf1R0ODXocElHjeNL1ZExMT+tGPfqTnnnuu4dfcunVLs7Oz+g//4T84GNlHik2yJemZZ55p6DWmaSqbzSqTySibzbbcjNuJGJaXl5XL5Up9zldXV2uuLxqN2v6fTqdtyTkAAOhN3156Y8/ndk83Ndgf1FfHHnZl3NyixhKN28oX9MPXf+t1GEBVHVUjLn3Up7yZkdaz2awtMXXaSy+9VPq70Rrk8qS3HVN/ORFDeY12Nput24x+dyJfL3EHAAAAgF7QcTXi+9Xu0chryWQypb+PHTvW0GvK42vHgG1OxDA6OmpL0Ovt093bdXKKNgAAAKDcYF9QibHqrUw3t/L63qt39rXeYMDQhVNHK8qAZvREIu5mAri7H3SjTcyPHz9e+nt5edmXMSSTSU1MTCibzWp6erpu3/PdcdQaIR8AAABoJ8MwdLC//enOgVBQzz55ou3rRW/xPBF3I0n++c9/7vg2ivb7fsprl1vdJ07FEIlEmmpevjsRrzdyOwAAAAD0As8T8VQq1dTAZD/72c8kSf/lv/wXWZZVd/m1tTXNzc111JyAa2trXofQlhiK84lLD5P8RkZvb9TNmzebfs2jjz6qxx57rG0xAAAAAHDPO++8o9///vdNvWY/eYMbPE/Es9msJiYmmnqNZVkaHx9vavmhoaFmQ9uX3Qnsfvqmt1oj7ocYiiOvF+2eV7xVX/ziF5t+zTe+8Q391V/9VVvjAAAAztrOF3Tj7vuSpN+990CS9OjHDtAnF+hB3/nOd/TNb37T6zDawvNEXFJDNdtFhmHIMIymXuMmPwxI5ocYZmZmSn/HYjGmLQMAAPuynS/ox79+R5J0+96GJOn44X4FRSIOoHP5IhFvhl8TcHxkcXGxNLq6aZpaWFjwOCIAAADgI9VGVB/sC7a83oBh6Pjh/ooyYDdfJOLJZFLhcLjhabYatba2plwup6tXr7Z1vbW4OU2aH2PI5XJ64YUXJD1MwldWVhyJ5+WXX9YTTzzR1GseffTRtscBAACAztPKiOoPtvP6wWt3bWVfuHBSA31BDfQF9eWLw22IENV87Wtfa7pb882bN/fVrdVpnifiU1NT+su//EtHt2FZVtv7KO+l2tzZzSairSauXsYwMTGhXC7naBIuSU888YTOnTvnyLoBAACAvRQsS2+tb1aUwXmPPfZY1wy+HPA6ADfmlnZz/up2JJ6ttgzwKoaZmRml02nHk3AAAAAA6GSe14i7kayFw2HXBjDbncCura019B7L42t3jbgbMczNzWl2dlaRSESvvPIKSTgAYE/fXnrD6xAAAPCUpzXi4+PjTc0hvl+maepP//RPHd+OVJnANvoDwOrqaunvVveJ2zGk02klEgnFYjFqwgEAAACgDk8T8W9961t69tlnHd/OmTNndO3aNce3UxSJREp/757Tey/lyz3zzDMdE0Mmk1E8Htf4+LiWlpb2XC6Xy9nmFQcAAAC8ki9YenPtvu2RL9DPG+7xvGl6N4rFYspkMpLUcPJZvtz4+HhHxJDNZnX58mVNTU0plUrVXPbq1as6fvy4pqenG4oFAAAAcMqHO3ktrrxlK0uMmfseSb1oa6eglTvrtrLo6SH1hzwfmgs+wxnhgCtXrpT+XllZaeg1xaTZNM22NNd3OoZcLqd4PK7nnnuubhJeXLcb3RAAAAAAr+wUCvpp9p7tsVMoeB0WfKijEvHbt2/rl7/8pX70ox/pl7/8pd5//32vQ6oqHA4rFotJUkNN4tPpdOnvmZmZussvLi6WkmavYrh8+bJisVhDSXhx/eXN5QEAAACgV/m6afovf/lLvfTSS0qn03smnsWE88/+7M/0r//1v3Y5wr2lUilFo1HlcjktLi7WbOpdTGYjkYimpqb2XC6XyykajZaakCeTyZpNvZ2IQZLi8biOHTumRCKxZ7P34gBxa2trpb7j1IgDAAAAgE8T8V/+8pelOaklybL2HjhhfX1di4uLWlxc1MjIiJLJpC8SctM09eKLL2piYkITExNaXV2tmojOzc1pcXFR4XBYCwsLNdeZTqdtie/Vq1drJuJOxJBIJErHJRqN1lx2dywAAAAAAB82Tf83/+bfKBqNKp1OlxJwwzBqPqSHyfrNmzc1Pj6uf/Wv/pUvmq0XRxIPh8OKRqOam5srPZfL5ZRIJErTft26datusrp7WrBGktt2xjA7O2t7fTNIxAEAAADgId8k4u+9956eeeYZzc3NybKsUhJe/NuyLB09elRnzpzRmTNndPToUdtz5f7u7/5Opmnqzp07XrwVm2KCm0wmlUqlNDQ0pKGhIZ05c6bUbLuYKDeyrmINuGmadWuv2x3D1atXG9peNfQPBwAAAICHfNM0/fLly/rFL35RSqojkYief/55xWIxmaapo0eP7vnaX/ziF1peXtbCwkKp2fTa2pri8biWl5d15MgRV97DXsLhsKampur2vW5EMplUMpn0JIb19fX6CwEAALTRwf6Qvh4/K0n69tIbHkcDAO3hi0T8X/7Lf1kajC0SiejFF1/UH//xHzf8+j/+4z/WH//xH+uFF15QLpfTCy+8oL/5m7/R6uqqYrGYfvaznzkVOgAAAFpgWZZ2Ch+1bry/tSNJGuwLlrogAt1sJ1/QVr5yijM+A93N80T8lVde0dLSkgzD0PT0dEvNnyWVBhxbXFzUc889p5WVFf1v/9v/pj//8z9vU8QAAABol52CpZU7H7W6S11/ODBtYszUwX7Pb1WBCgHD0CdPHK4o26/suxv64eu/rSjnM9DdPO8jnkgk2paElxsfH9d//I//UZZlNTQvNgAAAFpjWZa284Waj0KN2XCATjDQF9Tnnzppewz0Bb0OCx3G059YfvGLXyibzSoajbY1CS+amprS0tKS/vZv/1Y//vGP9dnPfrbt2wAAAMBDu2u3q/knnziiI4N9LkUEAP7kaY14KpWSYRh68cUXHdvGlStXZFmWrl275tg2AAAA0F6bW3nd39qxPQA3FQpW6dzbPUsT0CpPa8SXl5dlmqaefvppx7YRiUQUiUS0vLzs2DYAAACwP6GAoaceP6rX33rPVv69V+3T0P7R0KDuffChJGnoYL8CAQaxQntUO/8kae3+lr7/h/PQyf7a5iOH9OWLpyvOeXQ3TxPxbDarRCLh+HZisZijte4AAADYH8Mw1Bes30jzH9Y39Zt3PpAkRU8PKSAScbTHhVNhHT98wPHt3N/aKQ1GWFRM8Af76WPeazxNxHO5nEZGRhzfzsjIiHK5nOPbAQCglzCnMxrx1ONHbYl2kJps+NDTp8Klv+9v7eiNtz/Q40ODTa/HkFGRVBv8aIQqPB8P3zTNrtgGAAAAKvUFAw3VeAN+8uNfv6OnHj9a9bkH23mlf/W2rSz2qRMa6AtqsD+or445X9GIzud5Ig4AAIDeFgoYip4ekvSwqe5etvOF0vJAKwb7gqVzbSBUvVl4tX7jklSwLP3m7Q9sZc8++Vh7A0TXIxEHAACApx72E3+YXNcaEIuadbSLYRiODb4GNIKrGQAAAAAALiIRBwAAAADARZ4n4u+9V73vRTsxYjoAAAAAwC887xjx13/91zp6tPqIhO2SSqUcXT8AAAAeDqK2e6RpBlZDL9nOF3Tj7vu2snMnjzC+ASp4nohnMhnF43FHt2FZlgyDL4FuMjk5qUOHDlUtn5ycdD8gAADAAFjoedv5gn7863dsZWdPHK6biJeP4l5ehsbNz89rfn6+onxjY8P9YBrgiyulZVleh4AOs7y8XLX80qVL7gYCAAAAtIgfsVp3+/ZtXb9+3eswGsbRRkcaHR2tWiM+PDzsfjAAAAAAPDU8PKyxsbGK8o2NjT0r8bzkeSIeDoc1NzencDjsyPpzuZxmZmZ069YtR9YPb8zPz+vcuXNehwEAANrs20tveB0CgA60VxfVGzdu6Pz58+4HVIfnifiVK1f0p3/6p45uY3V1VVeuXHF0GwAAAAAANMLzRNw0zfoLtSgajTq+DQAAgF5nWZY2t/O2ssG+IIPmAsAunifiTjVJBwAAgLt2CpZef+s9W1n09JD6giTiQF8woM8++VhFmSTlC5bu5jZtz50MDyrI9H9dy9NEfHp62pUacdM09Zd/+ZeObwcAgG5Df10AveZAKKjx6OMVZa3qCwb09Klw1ec+3MlrceUtW1lizGQk9S7m6ZH91re+5cp2zpw549q2AAAAAHSuYMDQqWMH93w+YBh6fGiwogxoBj+xAAAAAECDBvqCmhg95XUY6HAk4gAAAGiYZVnaKVhVn9vOF1yOBgA6k+8S8f/0n/6TFhYWlM1mFQ6HFY/H9e/+3b/TkSNHvA4NAACg5+0ULK3cWfc6DADoaL5JxH/0ox9pYmJCuVxO0sNfWw3DUCaTUTKZ1NzcnP78z//c2yABAAAAAGiRLxLxv/mbv9Fzzz0ny3rYzMkwDNt8k5ZlaWpqSrlcTv/23/5br8IEAAAA0OUsy9Lmdt5WNtgXtOUn+7G5ldd3X71tK/vKxWEN9rc+Ijs6j+eJ+HvvvaeJiYlS8m1ZVikhLyqWT09PKxaL6cKFCx5FCwAA0NuCAUP/5BMPuwz+7v0HWtvY8jgioL02t/NKXc/aytoxlZglS5tb+Yoy9CbPE/GrV69KevjLk2maisViGhkZUTgcVi6X071795ROp5XJZCRJMzMz+q//9b96GTIAAEDPChiGjgz2SZIG+4M688ihmsuHAp09rdO3l97Y87mvx8+6GAn84sOdvH5y811b2WeeeEQHQkEd7A9xXqAhnibit27d0uzsrAzD0H/8j/9RL7zwwp7LZjIZXb58WUtLS3rttdeoFQcAAPBYXzDgdQiA6/IFS6+9+Z6t7NPmcY+iQafyNBFfXFyUJCWTyZpJuCRFIhG98sorGh0d1dWrV/Wf//N/diNEAAB8o1bNnETtHAAAncLTRHxpaUlDQ0P6i7/4i4aWj0QieuGFF0oJPAAAACDxQxWAzuJpe6Ll5WU999xzTb2mfIozAAAAAAA6jac14rlcTtFotKnXjIyMSJLef/99HTlyxImwAAAAsAfLsrRTsI/0HAoYLU/t5KV6tekA0G6ej5p+7NixppY/c+ZMxfRmAAAAcMdOwdLKnXVbWfT0kPqCnZuIA+2yky8o++6Grcx85JBCDGyIXTxPxMPhsNchAADQ06gNBID22MoX9MPXf2srS4yZdRPxgVBQX7p4uqKsHsuytLn9cG7ygGFooK/+a+APPfHTzC9+8Qs9//zzXocBAAAAABUCAUOPHD5gewQC9VuZbG7nlbqeVep6Vj947a4LkaJdPK8Rd8Pa2hojrQMAul6tmm1GjAYAwD96IhHP5XI0gQcAAADQ1X75Zk5nTxzWwf6QPtzJ6yc337U9/5knHtGBBpq8w3meJ+Lvvfee49vIZrOObwMAAAAAvPLW+qbeWt/U2ROHJUn5gqXX3rTnWp82j3sRGqrwPBH/67/+ax09erTp17344osNvS6Xy+nq1asdPaUGAAAAAKB7eJ6IZzIZxePxpl83PT3d8LKWZWloaKjpbQAAAABAO4QCgYoa6VDg4djZhYKltftbtueOHexvaMA2dCbPE3FJTc8LTu02AAAAACf0BwP63FOfqChreb2hgC6OVG8a/mAnr++/esdWlhgzdbDfF+kaHNCRR7bZxB0AAAAAGhEKBnT2xMf2fD5gGDp+uL+iDGiG54l4OBzW3NycY6Oa53I5zczMaH193ZH1wxuTk5M6dOhQ1fLJyUn3AwIAAEBPGOgL6ssXh70OA7vMz89rfn6+onxjY8P9YBrgeSJ+5coV/emf/qmj21hdXVUymXR0G3DX8vJy1fJLly65GwgAAAAAz92+fVvXr1/3OoyGeZ6Im6bp+Dai0ajj24C7RkdHq9aIDw8Pux8MAAAAAE8NDw9rbGysonxjY2PPSjwveZ6IO9UkfbdcLufKduCO+fl5nTt3zuswAAAAAPjAXl1Ub9y4ofPnz7sfUB2eJuLT09Ou1Iibpqm//Mu/dHw7AAAAAFDNg+28ri2/aSt7bvSUBvqCHkUEL3maiH/rW99yZTtnzpxxbVsAAADdLGgY+uRjhyvKgG5xf2tHqetZW1k7phIrWJbufbBVUYbe5HnTdAAAAHSOQMDQ8cMHvA4D8MzWTkErd+wzMkVPD6k/FNBgX1CJMXuL30FqvFEFiTgAAAAANGinUNBPs/dsZRdOHVW/AjIMo+Wac/QGzhIAAAAA6EABw9AnTxyuKIP/kYgDAAAAQAca6Avq80+drPpcwDB0/HB/RRn8gUQcAAAAALrMQF9QX7447HUY2EPA6wAAAAAAAOgl1IgDAACgYdv56iNG9wWp3wHyBUt3c5u2spPhQQUDNAmHHYk4AAAAALTBhzt5La68ZStrZA7yA6GgxqOPV5She5GIAwAAAICHggFDp44d9DoMuIhEHAAAAAA60IPtvNK/ettWFvvUCQ30UZvudyTiAAAAANCBCpal37z9ga3s2ScfkyRt7VQfz6E/xHgOfkAiDgAAAABdZqdQ0E+z92xlF04dVT8TZ/kCRwEAAAAAABdRIw4AAAAADgsGDF04dbSiTJIsy9Lmdt723GBfUIbBtGfdikQcAAAAAP6gLxjQZ//Qz7q8rFUHQkE9++SJqs9tbueVup61lTUy7Rk6F0cWAAAAAP6gLxjQ06fCez5vyNBgf7CiDGgGiTgAAAAANGiwP6ivjo14HQY6HIO1AQAAAADgIhJxAAAAAABcRNN0AAAA2FiWpZ2CVfp/OwaqAgB8hEQcAAAANjsFSyt31iVJoaCh0dPHPI6odd9eesPrENDjHmzn9YPX7trKvnDhpAb6gnu8At2MRBwAgB5AEoJ2CRiGho8fqigDusXmVl7fffW2rewrF4crRkpvVsGy9Nb6ZkUZehOJOAAAAGr63XsPdPxwv/qCAQUDhj5+dMDrkADHWLK0uZWvKCvazhd04+77tufPnTyivmBAA6GgvnTxtO25gRA13qhEIg4AAIA97eQt3b63oeOH+70OBfCF7XxBP/71O7aysycOqy8YUCBg6JHDBzyKDJ2ERBwAAAAAOlDAMPT40GBFGfyPRBwAAAAAOtBAX1ATo6eqPmfIqOjXbogk3S9IxNGRJicndejQoarlk5OT7gcEAAAA+Mhgf1BfHRvxOgzXzM/Pa35+vqJ8Y2PD/WAaQCKOjrS8vFy1/NKlS+4GAgAAAMBzt2/f1vXr170Oo2Ek4uhIo6OjVWvEh4eH3Q8GAAAAkFQoWFq7v2UrO3awX4EATcKdNjw8rLGxsYryjY2NPSvxvEQijo40Pz+vc+fOeR0GAAAAUPJgJ6/vv3rHVpYYM3Wwv3ba1R8M6HNPfaKiDI3bq4vqjRs3dP78efcDqoNEHAAAAAA8FAoGdPbEx7wOAy4iEQcAAACADvThTl4/ufmurewzTzyiA6HgHq+AX5CIAwAAAEAHyhcsvfbme7ayT5vHJUnb+YJu3H3f9ty5k0fUR5N3XyARBwAAAIAus50v6Me/fsdWdvbEYRJxn+AoAAAAAADgImrEAQBw2beX3tjzua/Hz7oYCXqVZVnaKVi2MkMPB4wC4IyAYeiTJw5XlKE3kYgDAAD0mJ2CpZU767aywb6gLpwKS3qYHDw+NGh7noQBvSIUCJT6WZeXtWqgL6jPP3Wy6nP3t3aUup61lTUy7Rk6F0cWAAAANsGAoceHDnodBuCJ/lBAF0eO118QaAGJOAAAACqaqgOo7mB/iG5EaBkdgQAAAAAAcBGJOAAAAPRPPnHE6xAAoGeQiAMAAEChIIOxAYBb6CMOAAAAAA57sJ1X+ldv28pinzqhgb6gRxHBSyTiAAAAAPAHD7bzurb8pq3sudFTLSfMBcvSb97+wFb27JOP7bn85la+9HdfMKC+II2ZuwmJOAAAAAD8QcGydO+DrYqyop18Qdl3N2zPm48cUigY0IFQUOPRx23PHQjtL4H/3qt3Sn9/9snH9PSp8L7WA38iEQcAAACABm3lC/rh67+1lSXGTIWCAQUDhk4dO+hRZOgkJOIAAAAA0IEChqHjh/sryuB/JOIAAAAA0IEG+oL68sVhr8PAPpCIAwAAAICHBvuCSoyZez6/n4HaDvaH9PX42VbCgoNIxAEAAADAQ4Zh6GA/qVkv4WgDAOCAby+94XUIwJ4MQzpxZKCiDEBrLMvS5nbeVjbYF5TBBwy7kIgDAAD0mFAgoDOPHPI6DKDrbG7nlbqetZUlxsyWars3t/L67qu3bWVfuTiswf7W5jWHt0jEAQAAAMCnLFna3MpXlKGzkYgDAAAAQAfa2ilo5c66rSx6ekj9oeYHd4O7SMQBAAAAoAPtFAr6afaerezCqaPqV0A7+YKy727YnjMfOaTQPkZgR/uRiAMAAABAl9nKF/TD139rK0uMmSTiPkEiDgCAjzDaOgAA3Y9EHB1pcnJShw5VjvY6OTmpyclJ9wMCAKCD7OQLeuPtD2xlZ08cpqYMcFDAMPT40GBFGdpjfn5e8/PzFeUbGxuVC/sAiTg60vLyctXyS5cuuRsIAAAdyJL0/oPtijIAUjBg6MKpoxVlrRroC2pi9FTL60F1t2/f1vXr170Oo2Ek4uhIo6OjVWvEh4eH3Q8GAAAAXeNAKKhnnzzhdRho0vDwsMbGxirKNzY29qzE8xKJODrS/Py8zp0753UYAAD4lmVZ2ilUr+fezhdcjgboHoN9QSXGzIoyeGuvLqo3btzQ+fPn3Q+oDhJxAACALrRTsCrmFwbQOsMwdLCfNAqtYUQOAAAAAABcRCIOAAAAAICLaFMBAAAAAA77cCevn9x811b2mSce0YEQ/ct7EYk4AABAFzIkHTvUL0nayT8ctM189NCe0zCF2jA9E9ANHmzn9YPX7trKvnDhpAZaHJAtX7D02pvv2co+bR5vaZ3oXCTiAAAAXSgUDOjsiY95HQbQcQqWpbfWNyvKivIFS3dz9udPhgcVDBjqDwb0uac+YXuuP9hab+BQIFCRsIcC9DDudCTiAAAAwD58e+mNPZ/7evysi5HATR/u5LW48patLDFm6mB/yJEfwPpDAV0coea825CIAwAAAEAHMmRosD9YUQb/IxEHAAAAgA402B/UV8dGvA4D+0AiDgAAAABdZrAvqMSYWVEGfyARBwAAAIAuYxiGDvbvne5ZlqXN7XzD6xvsC8owaPbeLiTiAAAAXWgnX1D23Q1bmfnIIYVaHMEZQHfY3M4rdT3b8PLFAenQHuxJAACALmRJWtvYspWdeeSQN8EAPeL+1k5FcttqAvtgO69ry2/ayp4bPdXyvObwFok4AAAAAPhUwbJ074OtijKn/dHQoJ4+FS79v9X50GFHIg4AAAAAHWg7X9CNu+/bys6dPKK+NiTN/7C+qbMnPmZLxtE+JOIAAAAA0IG28wX9+Nfv2MrOnjisvmBA+YKlu7lN23Mnw4MKBvYecO3LF09XzEsOZ5CIu2Rubk6pVErZ7Ed9RmKxmBKJhGKxWFfEMDs7q5mZGa2urso0zfovAAAA6EHfXnpjz+e+Hj/rYiToZh/u5LW48patrF5/9cH+IAOyuYSG/g7LZDIaGhpSMplUIpHQ+vq61tfXtbKyItM0FY/HNTExoVwu17ExZLNZxeNxzczMtDdwAAAAAOhC/NzhoHQ6rXg8LtM0tbKyonA4XHrONE0lk0mNjIwokUgok8lULOPHGHK5nJaXl5XNZrW6uqp0Oq1MJtPWmAEAAIBuEzAMHT/cX1GG3kQi7pBcLqeJiQlJ0sLCwp7J7dTUlFZWVjQ3N6fLly9rZWXF1zEsLy8rHo8rHA5rdHRUsVhMCwsLikajjtbqAwAAAG4IGIY+eeJwRVmrBvqC+vLF4ZbXg+5AIu6QF154QblcTpFIRJFIpOayMzMzmpubUyaT0eLiosbHx30bQywWk+XCdAkAAACAFwb6gvr8Uye9DsNTTsyHDjv2pANyuZwWFxclqaFB0EzTlGmaymazunr1alsScT/EAAAAAHSbgVBQX7p4uqKs0wz2BZUYMyvK4A4ScQfMzc2V/n7mmWcaek0xCc5kMspmsy2POu6HGAAAANCcWiOqS4yq7geBgKFHDh/wOoyWGYZBDbeHGDXdAS+99FLp70YHXytPetPpdFfEAAAAAACoRCLugPJRxI8dO9bQa8qT5XYM2OaHGAAAAAAAlWiL0GbZrH1Qg0abdx8/frz09/LycsfHAAAAvNUXDOjIQJ/ef7DtdSgAJG3tFLRyZ91WFj09pP4QdaO9iES8zfY7hVd5bXSr04D5IQYAAOC8nUJBb65t2spOHRtUKMCNPbBfD7bzSv/qbVtZ7FMnNNDiQGY7hYJ+mr1nK7tw6qj6aaTck0jEfWhtbc3rEHwRQy03b95s+jWPPvqoHnvsMQeiAQDAG5Ylvf3+A1vZ40ODpb/Pnjis8klHQ4HW50IGul3BsvSbtz+wlT375Ef3kIWCpbX7W7bnjx3sVyBgqC8Y0GeftN9v9gVbS7SDAUMXTh2tKGtVvmDpbs7+Q97J8GBb1u2Ud955R7///e+bes1+8gY3kIi32e4EttGB0sq1Whvthxic9sUvfrHp13zjG9/QX/3VX7U9FgAA/CrUYgIAoNKDnby+/+odW1lxju2+YEBPnwq3dXsHQkE9++SJtq5Tkj7cyWtx5S1bmd/nCv/Od76jb37zm16H0Rb+3csdyg8JrB9iAIBeUG+aIQAAesGD7bwKllX1ucG+oAzDv7XsXiERBwAAAIAOdLA/5Iu55X/w2l29tb5Z9Tm/17J7hT3SZvtpBt6NMTjt5Zdf1hNPPNHUax599FGHogEAAADsNrfysspGadjcyru6/YFQUF+6eLqirJN97Wtf08TERFOvuXnz5r66tTqNRLzNds/Zncvlmk6MW02k/RCD05544gmdO3fO6zAAAACAqr776m3Xk+9ygYChRw4f8Gz7Tnjssce6ZvBlEvE2a0cCuzuR7sQYAAAAehljSACohUS8zXYnsGtraw0lxuUDrLW7RtyLGAAAAAC07sF2Xj947a6t7AsXTrY8r3k7feHCSRUsS5tbeX1v14jyqI5EvM12J7CNjmC+urpa+ts0zY6PAQAAOGsnX9B2vuB1GAAcVrCsioHQ9hqhvF2anQ/dTz8KdAoScQdEIhFlMhlJlXN676V8uWeeeaYrYgAAAM65cfd9bW571/8UQG1fuThsG6ytmsEWE9idfEHZdzdsZeYjhxSqkTQ3otZ86B/u5PWTm+/ayj7zxCM60OEDwbmNRNwBsVislARns9mGXlO+3Pj4eFfEAAAAAPSqwX7nE9OtfEE/fP23trLEmKlQMKBCwdLa/S3bc8cO9isQaG1O73zB0mtvvmcr+7R5vKV19qLWfipBVVeuXCn9vbKy0tBrikmzaZptaRbuhxgAAAAAeOPBTl7ff/WO7fFgh1Y0fkEi7oBwOKxYLCZJunbtWt3l0+l06e+ZmZm6yy8uLpaSZq9iAAAAAADsD4m4Q1KplMLhsHK5nBYXF+suKz3s1z01NbXncrlcTiMjI5qYmFA0GtXs7KzrMTSi0cHhAADA/p07eUTR00MVj1CLzU4BAM4jEXeIaZp68cUXJUkTExN79tOem5vT4uKiwuGwFhYWaq4znU7b1nP16lXXY9hLefLd6OBwAACgvnzB0lvr922PfMFSKBhQX5WHYZCIA60IGIYeHxq0PQJ8rtBmDNbmoPHxcS0tLZVqsJPJZKm2OZfLaWZmRnNzc4rFYlpYWKg7d/fu5xvpx93uGCR7M/ZsNluqTS9KJBJKJBIyTbO0vtHRUeYmBwBgH6pNXXTiyICCIjEAnDDQF9TE6Cmvw/DU5lZe3331tq3sKxeH6w5AFzAMffLE4YoyVCIRd1gsFtOtW7d07do1pVIpW//rWCympaWlUl/uRtY1PT2t2dlZmabZcO11O2OQpHg8Lsn+w0D532tra7ba+lwup4WFBUZiBwAAQMc7EApqPPp4RVmnGQgF9aWLpyvKJMmSpc0t+8Bu9aZikx7+iPH5p062L8guRiLugnA4rKmpqZb7XktSMplUMpn0NAbLqv8hBAAAALpRMGDo1LGDXofRskDA0COHD3gdRs+ijzgAAAAAAC4iEQcAAAAAwEUk4gAAAAAAuIg+4gCAnvftpTf2fO7r8bMuRgIA8NqHO3n95Oa7trLPPPFIRw7IBv8iEQcAAACAP8gXLL325nu2sk+bx0t/W5alzW37iOKDfUEZDk3T5dSUYIWCpbX7W7ayYwf7FQjsf90PtvNK/+ptW1nsUyc00MePGLuRiAMAAABAgza380pdz9rKEmOmDvY7k1o5NSXYg528vv/qHVtZq++jYFn6zdsf2MqeffKxfa+vm5GIAwAAAAAaEjAMHT/cX1GG5pCIAwAAAEAHGuwLKjFmVpQ5aaAvqC9fHHZ0G72ARBwAAAAAOpBhGHs2JT8QCmo8+nhFGfyBRBwAAMBjlmVpp2BJetjEM9jCYEkAIEnBgKFTxw56HQb2QCIOAADggvJke7ftfEGvv/VwlObh44f08aMDboYGAHAZiTgAAIALdgqWVu6s7+u1oSA15ECvYkqw7kQiDgAA4DO/e++Bjh/uV18woL5gQKOnj3kdEgCPeDElWCgQsM2dXixD+5CIAwAA+Mjb7z/Q5na+YnogANgtX7B0N7dpKzsZHmx5nIn+UEAXR45XfW5rp1DRuid6ekj9IRL1ZpCIAwAA+Mjmdt7rEAB0iA938lpcectWlhgzdbA/JMuyKq4ng31BGS3O+b1TKOin2Xu2sgunjqpfJOLNIBFHR5qcnNShQ4eqlk9OTrofEAAA+/DU40fVF6x+8xpi5HQALdjczit1PWsrKybp3Wh+fl7z8/MV5RsbG+4H04DuPAroesvLy1XLL1265G4gALret5fe8DoEdLFiH3AAQGtu376t69evex1Gw0jE0ZFGR0er1ogPDw+7HwwAAAAATw0PD2tsbKyifGNjY89KPC+RiKMjzc/P69y5c16HAQAAgC4TMIyKwRIDLfarhvP26qJ648YNnT9/3v2A6iARBwAAAIA/GOgL6ssXh70Ow1MPtvO6tvymrey50VN15y4PGIYeHxqsKEMlEnEAAIA2sSxLOwWrojxoGOoLVs7LC7il1ngXX4+fdTGSztcfDOhzT32ioqzTHAgFNR59vKJMejh3+b0PtmzPFazKa9tuA31BTYyeal+QXYxEHAAAoE12ClbF/LqS9MnHDuv44QMeRASg3ULBgM6e+JjXYbQsGDB06thBr8PoWZ330w0AAAAAAB2MRBwAAAAAABeRiAMAAAAA4CL6iAMAADjoqcePaiBUe6RhAP6xtVOoGOshenpI/SHqMNE+JOIAAAAO6gsGFAgwfQ/QKXYKBf00e89WduHUUfV71JjYqSnBLMvS5nbeVjbYF5TRwro/3MnrJzfftZV95olHSqOx4yMk4gAAAEAPY2qz5tzf2lHqetZWlhgzdbDfmdTKqSnBNrfzbX8f+YKl1958z1bGtI3VkYgDAAAAABpiyNBgf7CiDM0hEQcAAACADjQQCupLF09XlDlpsD+or46NOLqNXkAiDgAAAAAdKBAw9MjhA1Wf6w8G9LmnPlFRBn8gEQcAdIVafRwBAOg1oWBAZ098zOswsAcScQCArzBoEAAA6HYk4gAAAECH4MfK3sOUYN2JRBwAAAAAfMqLKcGCAUMXTh2tKEP7kIgDAAC0SShgKHp6qKIMAJxQKFhau79lKzt2sF+BFq87B0JBPfvkiarPbecLunH3fVvZuZNH1MdAcE0hEQcAAGgTwzDUFyTxhjcYtLL3PNjJ6/uv3rGVJcZMHex3Ls3bzhf041+/Yys7e+IwiXiTSMQBAAAAoMvc39pR6nrWVuZ0ko7G8bMFAAAAAAAu4ucQAACABlmWpZ2C1dCyNNNEN2CUdsAZJOIAAAAN2ilYWrmzXne54eOH9PGjAy5EBKDdDBka7A9WlJU7dqhfaxv2QdKAZpCIAwAAtNGxQ/062B/U+5vbkqTDAyEFDAZwAzrFYH9QXx0b2fP5g/0hXTgVrhiwrJs82M7rB6/dtZV94cJJDfTVnrs8YBg6fri/ogyVSMQBAADaaG1jy1ZTFj09pAAjqQPwmf5gQJ976hMVZZJUsCy9tb5pe65g1e+WM9AX1JcvDrctxm5GIg4AAAAATTh38ojOnjhc+v9gnZpiPwoFAzp74mNeh9GzSMQBAB2DOXLhR089frTmwGyhALXhQLfpCwYYkBEtIREHAABoATfkAIBmkYijI01OTurQoUNVyycnJ90PCAAAAIBn5ufnNT8/X1G+sbHhfjANIBFHR1peXq5afunSJXcDAQAAAOC527dv6/r1616H0TAScXSk0dHRqjXiw8PD7gcDAAAAOKSTpgTb2ilo5c66rSx6ekj9Iee77wwPD2tsbKyifGNjY89KPC+RiKMjzc/P69y5c16HAQDoMaGAoaceP1pRBvSiWgNofj1+1sVIuptTU4Ld39pR6nrWVpYYM3Wwf/8p4k6hoJ9m79nKLpw6qn45n4jv1UX1xo0bOn/+vOPbbxaJOAAAQIMMw2jpJhUAAIlEHAAAAAA60oFQUOPRxyvKnHSwP0SLhzYgEQcAAACADhQMGDp17GDV5/qCAX32yccqyuAPJOIAAAAA0GX6ggE9fSrsdRjYAz+JAAAAAADgImrEAQAAGmRZlja387aywb6gDJ9OJQSg83k5JRicQyIOAADQoJ2Cpdffes9WFj09pL4giTgAZ3gxJVjAMPTJE4crytA+JOIAAAAA0IGcaqUz0BfU5586WfW5nXxB2Xc3bGXmI4cUYiC4ppCIAwAAAEAH2tzOK3U9aytLjJk62O9cmreVL+iHr/+2Ypsk4s0hEQcAAACALrO5ldd3X71tK/vKxWEN9js7zzgaQyIOAACwS8GylC9YFeXb+YIH0QBA8yxZ2tzKV5TBH0jEAQAAdvngwY7++2/f9zoMAECXoiE/AAAAAAAuokYcAAD0HMuytLOr6XkoYDAfOABIerCdV/pXb9vKYp86oYG+2v3LDRkVfdANcV2thkQcAAD0nJ2CpZU767Yy5gMH0Ev6ggF99snHKsqkh+Nk/ObtD2zPPbtr2WoG+4P66thI+4LsYiTiAAAAuxweCCl6eqihZUMBkncAnacvGNDTp8Jeh9GzSMQBAAB2CRiGAtSOAwAcQiIOAAC6UrV+4EVMQwYA8BKJOAAA6ErV+oEDcMe3l96o+fzX42ddigTwJ6YvAwAAAADARdSIAwAAAIBPddKUYNv5gm7cfd9Wdu7kkdJo7PgIiTgAAAAA+JRTU4JtbuX13Vdv28q+cnG4Iulvxna+oB//+h1b2dkTh0nEqyARBwAAXSloGPrkY4clSdt/GLRt6GCfAkb1miSmIQPQSyxZ2tzKV5TBHSTiAACgKwUCho4fPuB1GADgmP5gQJ976hMVZU4a7AsqMWZWlKE5JOIAAAAA0IFCwYDOnvhY9ecCAX3aPF5R1irDMHSwnzSyVexBAAAAAOgy/aGALo4cr78gPEGveQAAAAAAXESNOADAdd9eesPrEAAA6AhMCdadSMTRkSYnJ3Xo0KGq5ZOTk+4HBADwhGVZ2inUH+WXG1YAncqLKcEChqHHhwYryvxsfn5e8/PzFeUbGxvuB9MAEnF0pOXl5arlly5dcjcQAICndgqWVu6sN7x89PQQSTkA1DHQF9TE6Kmqz+ULlu7mNm1lJ8ODCno8BeTt27d1/fp1T2NoBok4OtLo6GjVGvHh4WH3gwFQFc3PAQBwRnH+782tvL736h3bc4kx09FRzT/cyWtx5S1Xt9mI4eFhjY2NVZRvbGzsWYnnJRJxdKT5+XmdO3fO6zAAAAAA1+1Ovqt5sJ3XteU3bWXPjZ7SQJfO+b1XF9UbN27o/Pnz7gdUB4k4AAAAAHSZgmXp3gdbFWXwBxJxAADQVZ56/Oie/cBDHvdhBABAIhEHAABdpi8YYEA2oEvVGn/k6/GzLkYCtIZEHAAAAAB8arAvqMSY2dTyrfpwJ6+f3HzXVvaZJx7RgVB39i/3Aok4AAAAAPiUYRiOjEgeCgT0afN4RZn0cIqy1958z/bc7mWrOdgfomVCg0jEAQBAxwoYhoaPH6ooAwDU1h8K6OJI/eQaziARBwAAHSsYMPTxowNehwEAQFMYyQQAAAAAABdRIw4A2Ldao9cCALAffLegF5CIAwAAX7MsSzsFq+5yTFkGAOgUJOIAAMBztZLt7XxBr7/1XtXnikJBQ6OnjzkRGgCgQTv5grLvbtjKzEcOKcQPpRVIxAEAgOd2CpZW7qx7HQYA9IwH23ldW37TVvbc6CkNtDAP+Va+oB++/ltbWWLMJBGvgkQcAAAAAHpMwbJ074OtijK4g0QcAAB4LmAYenxosPT/373/QDt5bggBwG8GQkF96eLpijI0h0QcAHpcrdFpvx4/62Ik6GXBgKHHhw5Kkn73Xv0k/KnHjzI4GwDUEAwYunDqaEVZqwIBQ48cPtDyenodiTgAAPCV44f7dfxwf81lQgFDhtH6DSUAbzBFmfMOhIJ69skTXoeBPZCIAwAAX6GmGwDQ7fimAwAAAADARdSIAwAAAABKAoZR0UUoQHegtiIRBwDsiT58aIZlWdopPBxkzZCYNxYAOtRAX1Bfvjhc9blCwdLaffu0Z8cO9ivQhoHgegmJOAAAaIudgqWVO+uSpCMDffonJ480/tp8QTfuvm8rO3fyCMk8APjMg528vv/qHVtZYszUwX5Sy2awtwAAgOcsSZvb+YoyAMD+PNjO6wev3bWVfeHCSQ30Mee3H5CIAwAAR/zuvQc6frhffcGAdgoFvbm2Kelh7bcknTp2sDSn7fYfygAA7VGwLL21vllRBn8gEQcAAG33/oNtvf9guzTYj2VJb7//wLbMvY2tai8FAKDrkYgDQJeoNbDa1+NnXYwEAAD38T2ITkIiDgAAAAAo2doplAbfLIqeHlJ/iAE024VEHAB6ANOQAQCAcsGAoQunjlaUSdJOoaCfZu/Znrtw6qj6VTsRH+wLKjFmVpShEok4OtLk5KQOHTpUtXxyctL9gAAANYUChqKnh5p+DQDAGQdCQT375Im2rtMwDM+mMZufn9f8/HxF+cbGhvvBNIBEHB1peXm5avmlS5fcDQQA0BDDMNQXJLEGADjj9u3bun79utdhNIxEHB1pdHS0ao348PCw+8EAAAAA8NTw8LDGxsYqyjc2NvasxPMSiTg60vz8vM6dO+d1GAAAAAB8YK8uqjdu3ND58+fdD6gOEnEA8BkGVkOnyhcsr0MAAKAjkIgDAIC2yP7enwPiAADckS9YupvbtJWdDA+WRmPHR0jEAQAAAHS1Wq3Nvh4/67v1uuHBdl4/eO2urewLF05qoIXpxj7cyWtx5S1bWWLM9GwkdT9jjwAAgLYIMSo6AHSMgmXprfXNijK4g0QcAADYWJalnQb6e/cFAy5EAwDwkwOhoMajj1eUoTkk4gAAwGanYGnlznrd5Y4d6pckmY8cUigYkPnIIZ15xD61ZIh+gQDgiYBh6JMnDleUtSoYMHTq2MGW19PrSMQBwAOMjI5usLaxJUml5DtEDTkA+MZAX1Cff+qk12FgDyTiAACgZCdf0Ha+4HUYAOCaTh5wDZ2LRBwAAJS88fYHev/BttdhAADa7MF23jYY2+ZW3sNoQCIOAADqeurxo3sOzkY/cADwv/Sv3tZv3v6goWUNGRrsD1aUoX1IxAFgn2jKhl7SFwwwSjoA9IjB/qC+OjZS9TnLsrS5ba9NH+wLymjDQHC9hEQcAIAuspMvqNbEY6GAwc0SAGDfNrfzSl3P2soSY6YO9pNaNoO9BQBAF7lx9/2Kmopy1ZqYl///7InDVRN5mp8DANA+JOIAAPiYZVnaKVSv4w4YhoJNJsivv/VeRdmJIwOSpFPHBpmCDAC6VOxTJ/Tsk4/VXGawL1jzebQPiTjgsXfeeUff+c53Sv//2te+psceq32RhHv8ON/3P67f009+8P8u/f8zX/hf9bGh4x5GBCftFCyt3Fmv+tzjQ4N6fOhg3XV8uHlfb77x96X/nzp7XgcGP3rd2+8/KK0P4BqDZnHOdIYBHyXZ3P+SiAOe+/3vf69vfvObpf9PTEz03IWoGzmZwG+8t6a/+9//fen/T/9f/xU3PKhp+8MHWn39Z6X/f/z0E7ZEHCjHNQbN4pxpXq8P+Mr9L4m4a+bm5pRKpZTNfjSwQSwWUyKRUCwW69gY/PC+gKJe/1IDJOncySOlPt7b+ULVpugAANSynS/oxt33bWXnTh5h9ow2Yk86LJPJaGhoSMlkUolEQuvr61pfX9fKyopM01Q8HtfExIRyuVxHxeCH9wUAncSyLG3nC9rOF5Tfo8+3pNIy5Y9mhP4wzVhfMKDBvqCip4f0ZFh6/2d/U3o8GZaip4cqHgzIBgCQHn4X/fjX79gejXwfDYSC+tLF07bHQMg/TeL9hBpxB6XTacXjcZmmqZWVFYXD4dJzpmkqmUxqZGREiURCmUymYhm/xuCH9wW0kx/7gaP7lPf1Hj5+SB8/OlB1udfeymknX2sCso9GPg/UmYbMMAz1BQ2FApK1/WGpPBQQtRoAgLYLBAw9cviA12F0BBJxh+RyOU1MTEiSFhYW9kxEp6amtLKyorm5OV2+fFkrKyu+jsEP7wvYD5JtuKHWCOfN1mzXUqzxBgAAnYlvcYe88MILyuVyikQiikQiNZedmZmR9LC59+Lioq9j8MP7AgC/KtZ6V3vs7qv9u/cetDU5BwD0tm8vvbHnA/5DjbgDcrlcKfFsZMAy0zRlmqay2ayuXr2q8fFxX8bgh/fllvn5ed2+fVvDw8OanJz0Opy2c/v9ub29n/3d32rtd/+gYx//I/3Tf/G/dN32vNDt+7Qd22umP/d/+9l/0z9+cF8jhx7o4r/41/vant914jFke97q9n3a7dtzmxfvr9n7mf0mwMXXVXuPTg4+W/7+nvtf/2+ObQcPkYg7YG5urvT3M88809BriglrJpNRNpuVaZq+i8EP78st8/Pzun79usbGxro2EXfz/bm9vZ//3X/R6us/08hT/9SVL2e3t+eFbtyn5c3If/bK/0e3/n5ZZ86P6o8vf7Hm64KGoUCVQc32muu7mjurv9H62/+g3P07FYn4hcfDdV/fCYOqdeM500vb80K379Nu357b3Hp/5cn0/+vb/6G0zfU/+ueObbPI7WNYfr9GIu48EnEHvPTSS6W/Gx2krDxBTafTmpqa8l0MfnhfAM2r0A6WZWlzO19qLl4488915OBpFU78Ud2E+pOPHdZxBweioe83AKBTFQqW1u5v2cqOHeyv+gN2ryMRd0Amkyn9fezYsYZeU57YtmNgMydi8MP7AtB7ag2A1ohgwKgYXXynYLk2v3ZxhPNyy7f+D73/98s6/qkLrsQAAEAt3156Q2+tb0qS3lrf1L//0c3SD9OfNo83vJ4HO3l9/9U7trLEmKmD/aSdu7FH2iybzdr+32hT7OPHPzrBl5eXfReDH94XgPZpNrm1gn0y+g7ICvZpO19QKGDIqDJ1VsGyas6RXU+19ZbXXO/HP/nEER0Z7Nv361tVbYRzI78ta/tDUT8AAEBvIhFvs1wut6/Xldcc73cdTsbgh/flR/e3dvb92lrTD21u5WVpf8lMKBBQf6hyvd9eekM7+YIsSW++vyOj74DefH9Hs//11/q/P/tE3fUGA4YOhIJVn3uwnVfB2jveQuBhElcI9On+1o7+/Y9ulp4zJIXK9kP5ICTV1tvIYFhWsE8K7p147RQKqhFuTbvjtW3XCLQ0CvZe50O+YNn2w05BMvoO2P5fa7vV1ls+p3Ujdjfdjp4eUl+wMo384MGO/vtv3294vbs99fjRjv3VPHp6qGp5J/TnBgCgns2tvA72h9QfDOhzT31CkvS9/+O2JOk//HhVgYCh7XxhXzXpvagz73a63NramtchOBJDK+v88MMPbf//0Y9+pJs3b+6xdHVDQ0O2GvpaNjY2Sv/euHFjz+X+7bVf7vlcvQGX/umZY3ryE0cq3sf/c/G6fqfXmq5VLG7vqcfDevr/VLnt392+rV//7h/14U5eD46f1aHzR/Xg+An96NUV3Xtzte76/88fP6x/Zj5S9bn/79//Vm+/bz9Gr72VK/29GjylQ+cvazV4Qv/L/+Ml23JHB/s0fPxQ6f83bmyX/v7//Y939D/v3d9zvXt5cPysBs/saOvBhn53+zcVz7+V29S9Dz6s8sr6DoSCevLjH7OVbT14GOOH/Uf0o1f31wUjGDB0/uTRqs+9+8GH+ofcZun/9//xPR06f7n0/5//jzs6eDe357qrnYvbhYLeu9t4wrz1/rvK389p6/0Deu9uVr/TEfUFKhP8Dz7c0Xu//6Dh9e72dv5jGuwLlvbp1oP7evvOTb339j/ue53vbB/W/QP2r7vd77/4/nb+x2/0R//i/1JzfR++c0+/e7e15Lr8/VU7R9vt3bt3av7fCW6/R7bXPl6cL1J379Nu314vXGO82Ga17c28uL/tlt9fVfO727ft27vzm9L35O8CD+/hZ2//Rl/+58O2122/+z8lSe8U/1/2/Vp83X9/dFODffbv4d33v43e19+7d0/r641XJEjS//yf/9P2/915hWcstNXS0pIlqfRoVCqV2tfr3IrB6/f18ssv29bDgwcPHjx48ODBgwcPHs0+Xn755X3nJO3E0Kxt5ofm107E4If3BQAAAADdgEQcAAAAAAAX0Ue8zRqdX7vTYvD6fY2Njenll19WLpfT+++/r49//OPq7+9vah3N9BEHAAAA4C/76SO+tbWl3/3udzpy5IjC4bDGxsYciq45JOJttnt+7Vwu13QS22rS60QMXr+vcDisP/mTP9n36wEAAADAL2ia3mbtqDnenfT6IQY/vC8AAAAA6AYk4m22O9lsdMqu8sHQ2l0j3o4Y/PC+AAAAAKAbkIi32e5ks9HRxldXP5rH2TRN38Xgh/cFAAAAAN2ARNwBkUik9HejNcflyz3zzDO+jMEP7wsAAAAAOh2JuANisVjp72w229BrypcbHx/3ZQx+eF8AAAAA0OlIxB1w5cqV0t8rKysNvSaTyUh62Hy7HU24nYjBD+8LAAAAADodibgDwuFwqfb42rVrdZdPp9Olv2dmZuouv7i4WEpw3YzB6fcFAAAAAL2ARNwhqVRK4XBYuVxOi4uLdZeVHvbBnpqa2nO5XC6nkZERTUxMKBqNanZ21vUYnFgnAAAAAPQSw7Isy+sgutXi4qImJiYkPRw9vFrT7Lm5OSUSCYXDYa2srNRsvl2+PulhDfX6+rqrMTi1TgAAAADoFdSIO2h8fFxLS0sKh8OKRqOam5srPZfL5ZRIJJRIJBSLxXTr1q26yeruKcQaSW7bHYNT6wQAAACAXkGNuAtyuZyuXbumVCplG0U8FouVEtZGzczMaHZ2VqZpamlpqeEkt50xOLlOAAAAAOh2JOKAz2Wz2aZaFTS7PAAAAAB30TQd8LmJiQkZhqF4PK65uTllMhnlcrnS89lsVouLi0okEhoaGrKNIwDsJZvNamhoyDa7AZDNZjUzM6N4PK6hoSEZhqFoNKqJiYm6A3SiN2UyGSUSCY2MjMgwDBmGoZGRESUSCa4vaMjs7KwMw7C1rkT3mpubUzQa1dDQUOkxMTHRk9cLEnGgQ6TTaSUSidLFq/yGZ2JiotRXf2FhweNI0QkmJiaUy+VsP+qgtxWvL5KUTCZ169Ytra+v68qVK0qn05qYmNDIyEhP3iyhupmZGUWjUa2trWlmZkYLCwtKJpOSHt5sx+NxRaNREixUlc1mFY/HmeK2R2QyGQ0NDSmZTCqRSGh9fV3r6+ulQZ3j8Xjp3qRX0DQd8LloNFp33njpYd/8hYWFikH9gN2KsxpID3+4GR8f9zgieC0ej0vSnteQXC6nM2fOlG6QlpaWGAekx0WjUeVyuT3Hq0kkErbBXDlnelsul9Py8rKy2axWV1eVTqcr7m32mokHnS+dTisej8s0Ta2srFT9ninem9RapttQIw50uEgkoqWlpdJI9kAtxZkNgKLZ2Vlls9maP+SFw+FSTaf0MHGnlrN3zc7OKpfL1ZyeNJVKaWpqqvR/zpnetry8XKr9zmQyisViWl1d5b6lB+RyuVK3yVrfM1NTU5qamlI2m9Xly5ddjNA71IgDPheNRvX8889rfHxcmUxGa2trkqRjx44pFovxJYamxONxLS8vl2o2qRHH0NBQ6XxIJpOanp6uulw2m9XIyEjp/1NTU0qlUm6ECB8pngeN1HDncjkNDQ2V/h+LxbS0tOR0iOgg5dcfasS7U3GMkUgkopWVlZrLln/P9ML9CTXiQIcwTVPj4+OlXwzHx8dJwtGUubk5ra2t6bnnnvM6FPjE7nECavXV3H2DvLy87FRY8LFkMtnwd084HLbViqfTaWrFgR6Sy+VKA3020jXFNM3Sd83Vq1cdjc0PSMQBoAfkcrnSYEr8gIOiVs6FY8eOtS8QdIxii5p4PG5rIbGX4gCARQz2B/SO8nEinnnmmYZeU0zEM5lM1/9wRyIOAD3ghRde0JUrV2j2hwqpVErhcFjhcLjmrAu7b4gikYjTocGHys+D4vSZtey+5qyurjoSFwD/eemll0p/N/rDb/k1o9t/uCMRB4Aut7i4qGw2u2ffX/S2qamp0jQytfrj7b4hYtC/3jQ6Omr7f72b690tJ3ppaiKg15WPjN9oK6rya0q9PuWdjkQcALpYLpfTCy+8oBdffNHrUNDhygdmSyaTtK7oUeXHfnp6um6/z90tKRppzg6g8+3+7Df6nXH8+PHS390+FgmJONBB0um0JiYmNDQ0VHpEo1FbHxyg3AsvvKCpqSmaEaMls7OzpZqNWiOro/tFIhGtrq7KsizblHZ7oUsD0Jv22/qlvEa821vQkIgDHeDevXuKx+NKJpNKJBK6deuW1tfXdevWLcViMSUSCQ0NDXV9Xxo0J51OK5PJNHSzDOyWy+VKP/7NzMwoEoloaWmJJBxNKW9JEQ6HGxo5GQAklabs7VYhrwMAUN/s7KxSqZRtGhjp4U1NMpnUyMiIEomE4vF4Q3O7ojdMTEzolVde8ToMdJiRkZGKWsypqammpq0CpMpRj/lREOgdu5Po/Xx/UCMOwFOmaWphYaEiCS83NTVV6nszMTHhVmjwsUQiQZN07Eux2bFlWVpfX9fCwoKuXbumoaEhTUxMdP2NEdqnfF76WCxW83sMQHfhu6I+EnHA5xYWFmqOZFxUXKY4XzR6VzqdVjqdpvYJLQuHwxofH9fKyorC4bAWFxd15swZ20i4QDWLi4ul7lLFH5QBAB8hEQe6RDweL/09OzvLL5E9LJFI2PplAq0yTbM08n4ul1M0GiUZx56KszVID8+d4g85AHoHn/n6SMSBLrF7WggGbutNMzMzisVijBOAthsfH7fdWNENBnspdmEgCQd61+55w/dTQdTt1w4ScaCGubk5GYbh6CORSLQl1t0XvJ///OdtWS+a4+U5k8lkNDc3R214h+mk68xzzz1X+jubzTJ1okf8fM7MzMwonU6ThPuMn88ZdKd2fPZ339t2G0ZNB2oYHR1tqH92K8qblJfLZDKamJjQ2tqakslk04Pc0DTdG16eMxMTE/TD7EBenjPNGhkZsf1/aWmJAbg84NdzZm5uTrOzs4pEInrllVdIwn3Er+cMutfuJHptba2ha0L5/Wu3X0NIxIEaIpGIZ4nNCy+8UJr2JZFI6LnnnmvqgtTtFy+/8uqcKc7zTJP0zuPldSaTyZQGdyx2a6hl93WFfuLe8PKc2Us6nVYikVAsFtPS0pLX4WAXP54z6G67vy8arSBaXV0t/b2722W3IREHfGr3PL717J6vcXfNFbrb4uKistmsDMNo6nW7+/lOT08z2noPuXz5cunmKJ1Oa3V1teaNT7UaDiCTySgej2t8fLxmspfL5bS2ttb1N9cAHopEIqUfbBv9vihf7plnnnEkLr8gEQd8yjTN0sUrmUzWreHenbiX9+VE92umX3gikSidL9PT07bmhNwg945MJlNRQ5HJZGqeA7tvpDhfkM1mdfnyZU1NTdW9Dl29elXHjx/X9PS0S9EB8FIsFivdyzZawVS+nNPdKbxGIg74VCwWa2ru1fKmgLFYjKbpPaaZJunl58YzzzxDc/YeVe0aEYlEar5mZWXF9v/nn3++nSGhw+RyOcXjcT333HMN/RiYyWQY0AvoIVeuXNHs7Kykyu+PvRQTd9M0u/7HXkZNB3wqkUhocXGx4T41i4uLpb9pWgygnvKbnGK/3no3PbunRWSgtt52+fJlxWKxhlvkpNPpuj/2AOge4XC49GP/tWvX6i5f/h1THL+km1EjDviUaZqanp7W5cuX6/6KODc3Z2tqzI0Oain/cYc+vr0tmUxqYmKioYHa0um0rclgKpWi5U0Pi8fjOnbsmK2ry27Fa83a2lqp1Va313Bh/5jtpTulUilFo1HlcjktLi7WbG5e/FEvEon0xA+9JOKAjyWTSUWjUcXjcS0sLFS96V1cXCw19ZuamqI2HHWVJ9/c+PS28fHx0jgByWRyz7672WzW1qR4enq6J26SUF0ikSjVXEWj0YZfRxKO3fhhuPuZpqkXX3xRExMTmpiY2HNQ0Lm5OS0uLiocDvfMCP+GZVmW10EAqC2RSGhubk7T09N65plnZJqm1tbWlEqlShet/cw1jt6QyWS0tramXC6nl156ydaNIRwO68qVK6VWFKOjo9Ry9qDFxUXNzMxobW1NU1NTpdpO6WFNeLGJYCQS0Ysvvkirmx42Ozu77yajTG3W28qbHWezWaVSKdsUiKZpKpFIyDTN0vcQ30ndI51Ol2ZqKb9nzeVympmZ0dzcnGKx2J4VT92IRBzoEOl0WqlUSul0WrlcTuFwWKZp6vnnn9fU1FTPXLTQvJGRkVLT0VrnSS6XUyqV4gedHra4uKilpSUtLy8rm82WrjWxWEzPP/98149gi/qGhob23ZKG6RF7W3F6zUbvV3K5nBYWFrjudJFcLqdr164plUrZurTEYjElEomeGzyWRBwAAAAAABcxajoAAAAAAC4iEQcAAAAAwEUk4gAAAAAAuIhEHAAAAAAAF5GIAwAAAADgIhJxAAAAAABcRCIOAAAAAICLSMQBAAAAAHARiTgAAAAAAC4iEQcAAAAAwEUk4gAAAAAAuIhEHAAAAAAAF5GIAwAAAADgIhJxAAAAAABcRCIOAAAAAICLSMQBAAAAAHARiTgAAAAAAC4iEQcAAAAAwEUk4gAAAAAAuIhEHAAAAAAAF5GIAwAAAADgIhJxAAAAAABcRCIOAAAAAICLSMQBAEDLZmdnNTMz43UY6EKcWwC6kWFZluV1EAAAoHMlEgktLy9rZWXF61DQpeLxuMLhsBYWFrwOBQDagkQcAADsWyKR0LVr13Tr1i2Fw2Gvw0GXyuVyOnPmjGKxGMk4gK5A03QAQEdaXFxUIpFQPB5XNBrVyMiIhoaGtLi46HVoPWNubk5zc3N65ZVX2p6E53I5zczMaGhoSENDQ4pGoxzbHhYOh/XKK69ocXFRs7OzXocDAC0LeR0AAAD7kc1mtby8rEwm43UoPSmTySiRSGh6elqRSKSt685ms4pGo8rlclpZWVEkElE6nVY8HtfCwoLGx8fbuj10hkgkolQqpUQioUgkolgs5nVIALBvNE0HAHS8eDyudDotSSRqLhkZGZEkra6utn3d0WhUmUxGyWRS09PTymazSiQSSqfTisViWlpaavs20TmcPPcAwC00TQcAdLx4PO51CD1lbm5O2WzWkZGsFxcXS60cpqamJKmUhEsPa8vR22ZmZpTNZmmiDqCjkYgDADoeg4S5p9h32zTNUqLcTqlUSpJkmmbpuK6trZWep7UDpqamFA6HSwk5AHQiEnEAANCwubk55XI5JRKJtq87l8uVar7L+/+++OKLmpqaUjKZVDKZbPt20XmuXLki6aMfbgCg0/z/27v740TZLo7jv8zcBWA6CHQASQVqB7KpINqBTCrYgQ50K9hgB5AKNtCBbAUb6YDnj8zFg1lf8CVmjd/PjLO5bxEukMzkcM51LgJxAADQmgl8PiIz/fT0VP/cnG5gmnSNx+OjHxPnyVRjTKfTTx4JAOyHQBwAALSSpqmKopBt27Jt++j7bzZhoyM2NrEsS7ZtqyxLlrUDcJYIxAEAQCtxHEv6uCDZBFTN+eHAOuY+pDwdwDkiEAcA4BPkea7pdHpW2Twzf9vzvKPvu7kePNlwtGGmL7y8vHzySABgdwTiAICLZZZA8jxPnU6nfvX7/b3nnkZRJN/35TiOrq6u5DiORqNRHWjmeS7P8xQEgbIsk+/7Z7H8WlmWdYfqYwTKaZrq6uqqfjWD++l0uvRep9M5+Hj4esx92Lw3AeBcEIgDAC5SEARyHEdJkigMQy0WCy0WC/3+/Vu+7ysIAnU6ndYZ6zzP5TiOgiDQ3d2dsixTVVWK41hpmsrzPPX7fT08PCiOYyVJUnceN5nmf1lzjMeYH357e6skSepXM7ifTCZL72VZdvDx8PVYllVPYTiH3yEAaPrvswcAAMAplWWpbrerPM8VhuFfnbgty9JwONS3b9/U7Xbl+77G4/HGZbOKoqgzupPJZGl9bdd1lSSJHMdRmqYaj8d1IOu6rgaDge7u7j7gTI/r169fkt7GfAyWZS0F30EQ1D9/xPrk+Jps21ae5zysAXB2yIgDAC6KCcIHg8HG5bAsy9Lz87Msy1IURYqiaO22prTcBPHv2bZdHyuKoqX50HEcn8WyXKb09/r6+kP2b67JsQJ9XAZzP1KaDuDcEIgDAC5GMwjelOE2moF1EAQr/9g3S3pJm+dON7Pe59jl2ZzjR3Qzbz6YuL29Pfr+8XWZ+5FAHMC5IRAHAFyEsizr8mezBnEb9/f39c9mTndTM4jclC1uHu8c57OWZSnpYzLiza7XmzqyT6dTeZ4nx3Hqpnq4bOZ+fH19/eSRAMBuCMQBABfh6emp/nmXrGuzVDpN0zogvVQfkRFvzu/d9N3c3t7WD0bKsiT42oNpQtick3/OHMeRpIv/vQRwfgjEAQAXIUmS+udDgsn32exmoL6pPLYZNJ7jPOiPDHqbGfFN18Z1XY3H45WVCdiuLEtFUVT/CwD4PATiAICL0AySDymvfh9s93q9uux8U8l580HA4+Pj3sf/LB/VpE3avVHbR2TlL0Fzua9zfBgEAF8JgTgAAAdqBtmrSn7LstR0OpX01iRunyCoLEv1+/29ssHT6VT9fl+O49Svdc3n2ozjmJpjoFHbx1ssFsqy7Mss9/Xnzx9JPJwBcH4IxAEAF6GZ0T2kzHrVH/y2bct13aWlzkzAmqapPM9TWZYr1y3fpCxLpWmq0Wikm5ubpQ7tbT9vgu4gCDSfzzWfz5VlWf2eeUCwjTnvY5eoN5vdbWrUhuP5Stnwj2wiCAAfiUAcAHARmh22d8nqvt921RJls9lMtm1rsVhoMpno58+furm5UafTURiGGgwGms/nOwXhjuPo5uZGQRDIsqy9ssXdbldFUSjLsqVxW5alyWSi4XCo0WjUqou7Kb8/dka8WU1ARvwy5Xm+9/Jj5sFQ21UQAOBfQSAOALgIg8Gg/rnZHGyb5ra2ba/8g//nz591oD8cDpVlmRaLhRaLhZIkURiGOwcK8/m8LiPe5/PT6VR5nmswGKz9rCmjb1PubvZx7Ix420Ztm8xmM/m+Xy9t5jiORqPRzg9cRqPRUvm+53mtmppFUSTP8+rje54n3/eXsv2rpGlaTxnodDp1RYBZau/9Um3N/a0ar+/7awPaPM/V7/fleZ46nY46nc7e43Ec56/xrFMUhXzfr/dpKjRMwzjP8zSZTOQ4zl4N5Mx3TCAO4NwQiAMALoJt2xoOh5L+X/LdRhzH9c+TyWTlNkVRLGV2/wVmrHd3d2u3McFLURRbr4fZT5vgaxdmf/sEUmbefJIk+vHjh7Is03w+V5Ikenl50c3NTavS++l0qk6nU3+PpoT/x48f+v79ez21YBXHcfTz5089Pz/Xx39+fpb0Vmq/6SGHbdvyfb8+l7IsVRSFut2u7u7u6v1lWaaXlxd5nqc8z5Xnubrdrnzfr8cax7Fms5kcx1kZjK861r7jMdfXjGfTdXUcR9fX1/XDKTPOTqdTn9tkMpFlWfr169fafa1jHuQwrQHA2akAADhzk8mkklRJquI4XrvdYrGobNuuJFWu627d73w+r/c7GAzWbjccDitJVRiG1WKx2OcUtjLH6PV6W7ddLBb1uJMk2bituR7D4bD1PrMs22ns6zSv77bjNzW/7zAM127nuu7We8Lsa939kGXZ2uueJMnG62yOv2mMVVVVcRxXkirbtivXdVfeQ+PxuB7num3MPbLpWprz2fQn4C7jWXc/muNYltXqvX1+b5r35Hw+3/nzAPCZyIgDAM5eM7u3qXTasiwlSSLbtpXn+cZsZVEUdbn5YDBYyoy/Z/YTBIE6nY6urq6WXqYU2JQPz2azHc9wN81y721NrJpZ8U2aS1/tUtq/yaGN2izL2jjv/sePH5Ik3/dXZoCLoqi/O7Pte67rynVdpWn6V/a3Oad91fz2+/t7SW9TF7adhxnP/f39yoaAjuNIertmj4+PG7c59PvZZTzr7htTkfHt27e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- "text/plain": [ - "
        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "kwargs = dict(bins=100, xlog=False)\n", - "pop.plot_hist_properties(['S1_spin','S2_spin'], intrinsic=True, observable=True, pop='GRB', **kwargs)\n", - "kwargs = dict(bins=100, xlog=True)\n", - "pop.plot_hist_properties(['S1_E_GRB_iso','S2_E_GRB_iso'], intrinsic=True, observable=True, pop='GRB', **kwargs)\n", - "pop.plot_hist_properties('S2_f_beaming', intrinsic=True, observable=True, pop='GRB', **kwargs)" + "LGRB_rates.population" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1,1)\n", + "LGRB_rates.plot_hist_properties('S1_spin_postSN', intrinsic=True, label='S1', color='blue', ax=ax, show=False)\n", + "LGRB_rates.plot_hist_properties('S2_spin_postSN', intrinsic=True, label='S2', color='orange', ax=ax, show=False)\n", + "ax.legend()\n", + "ax.set_xlabel('BH spin post LGRB')\n", + "ax.set_ylabel('\\# Events in bin')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's possible to calculate more properties for the LGRBs using the `posydon.popsyn.GRB` module.\n", + "\n", + "It contains the `get_GRB_properties` that can provide a more detailed GRB properties based on empirical or simulation relations for the LGRB energy and beaming factor.\n", + "\n", + "Enjoy exploring this function and creating a more detailed GRB population!\n", + "Keep in mind that it's not possible to add additional columns to the transient population without overwritting the population.\n", + "\n", + "The next tutorial will focus on rerunning specific binaries in a population or setting up an unique binary yourself.\n", + "\n" ] } ], diff --git a/docs/_source/tutorials-examples/population-synthesis/one_met_pop_syn.ipynb b/docs/_source/tutorials-examples/population-synthesis/one_met_pop_syn.ipynb index b78aed4112..23593fd437 100644 --- a/docs/_source/tutorials-examples/population-synthesis/one_met_pop_syn.ipynb +++ b/docs/_source/tutorials-examples/population-synthesis/one_met_pop_syn.ipynb @@ -16,18 +16,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "env: PATH_TO_POSYDON=/Users/simone/Google Drive/github/POSYDON-public/\n", - "env: PATH_TO_POSYDON_DATA=/Volumes/T7/\n" - ] - } - ], + "outputs": [], "source": [ "%env PATH_TO_POSYDON=/Users/simone/Google Drive/github/POSYDON-public/\n", "%env PATH_TO_POSYDON_DATA=/Volumes/T7/" @@ -44,65 +35,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We will use the same BBH population as in the `Analyzing Merging BBH Populations: Rates & Observations` tutorial. Instead of computing the distributions and rates at all metallicities, we will just focus on one metallicity, $Z_\\odot$, and integrate the star formation history around solar metallicity, say $[0.5Z_\\odot,2Z_\\odot]$. Let's extract the merging BBH population from the $Z_\\odot$ population synthesis model. In this sample there are only ~200 systems, to increase the statistics we suggest you run a population containing x50 more binaries." + "We will use the same BBH population as in the `Analyzing Merging BBH Populations: Rates & Observations` tutorial. Instead of computing the distributions and rates at all metallicities, we will just focus on one metallicity, $Z_\\odot$, and integrate the star formation history around solar metallicity, say $[0.5Z_\\odot,2Z_\\odot]$. Let's extract the merging BBH population from the $Z_\\odot$ population synthesis model. In this sample there are only a few systems, to increase the statistics we suggest you run a population containing x50 more binaries." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Binary count with (S1_state, S2_state, binary_state, binary_event) equal\n", - "to (BH, BH, contact, None)\n", - "in /Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/1.00e+00_Zsun_population.h5 are 233\n", - "Total binaries found are 233\n", - "Population successfully saved!\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/synthetic_population.py:300: PerformanceWarning: \n", - "your performance may suffer as PyTables will pickle object types that it cannot\n", - "map directly to c-types [inferred_type->mixed,key->block1_values] [items->Index(['state', 'event', 'step_names', 'S1_state', 'S2_state'], dtype='object')]\n", - "\n", - " self.df.to_hdf(path, key='history')\n", - "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/synthetic_population.py:301: PerformanceWarning: \n", - "your performance may suffer as PyTables will pickle object types that it cannot\n", - "map directly to c-types [inferred_type->mixed,key->block2_values] [items->Index(['state_i', 'event_i', 'step_names_i', 'state_f', 'event_f',\n", - " 'step_names_f', 'S1_state_i', 'S1_state_f', 'S1_SN_type', 'S2_state_i',\n", - " 'S2_state_f', 'S2_SN_type', 'interp_class_HMS_HMS',\n", - " 'interp_class_CO_HMS_RLO', 'interp_class_CO_HeMS',\n", - " 'interp_class_CO_HeMS_RLO', 'mt_history_HMS_HMS',\n", - " 'mt_history_CO_HMS_RLO', 'mt_history_CO_HeMS',\n", - " 'mt_history_CO_HeMS_RLO'],\n", - " dtype='object')]\n", - "\n", - " self.df_oneline.to_hdf(path, key='oneline')\n" - ] - } - ], + "outputs": [], "source": [ "import os\n", - "from posydon.popsyn.synthetic_population import SyntheticPopulation\n", + "from posydon.popsyn.synthetic_population import Population\n", "from posydon.config import PATH_TO_POSYDON_DATA\n", "\n", - "path = os.path.join(PATH_TO_POSYDON_DATA, \"POSYDON_data/tutorials/population-synthesis/example/\")\n", - "files = [f for f in os.listdir(path) if '1.00e+00_Zsun_population' in f]\n", - "path_to_data = [os.path.join(path, file) for file in files] \n", + "PATH_TO_POSYDON_DATA = '/Users/max/Documents/POSYDON_data/240305/POSYDON_data/tutorials/population-synthesis/example/'\n", "\n", - "pop = SyntheticPopulation(path_to_ini='./population_params.ini', verbose=True)\n", + "pop = Population(PATH_TO_POSYDON_DATA+'1e+00_Zsun_population.h5')\n", "\n", - "pop.parse( path_to_data=path_to_data, S1_state='BH', S2_state='BH', binary_state='contact', invert_S1S2=False)\n", - "pop.save_pop(os.path.join(path,'BBH_population_1e+00_Zsun.h5'))\n", - "\n", - "pop.df.head(10)\n", - "del pop" + "pop.history.head(10)" ] }, { @@ -117,315 +67,117 @@ ] }, { - "cell_type": "code", - "execution_count": 3, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Population successfully loaded!\n", - "Computing formation channels...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/synthetic_population.py:804: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'ZAMS_oDoubleCE1_CC1_redirect_CC2_CO_contact_END' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n", - " self.df_oneline.loc[index,'channel_debug'] = formation_channel\n", - "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/synthetic_population.py:808: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'ZAMS_oDoubleCE1_CC1_CC2_END' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n", - " self.df_oneline.loc[index,'channel'] = formation_channel\n" - ] - }, - { - "data": { - "text/html": [ - "
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        " - ], - "text/plain": [ - " metallicity time t_delay S1_state S2_state S1_mass \\\n", - "0 0.0142 4.027170 2913.863804 BH BH 13.120462 \n", - "1 0.0142 4.027170 1500.583774 BH BH 13.120462 \n", - "2 0.0142 4.849573 662.821800 BH BH 10.330899 \n", - "3 0.0142 4.544886 845.424610 BH BH 11.355246 \n", - "4 0.0142 4.530889 620.499837 BH BH 11.436823 \n", - ".. ... ... ... ... ... ... \n", - "228 0.0142 7.928745 426.765027 BH BH 17.564588 \n", - "229 0.0142 4.530889 787.032676 BH BH 11.436823 \n", - "230 0.0142 4.848528 943.501358 BH BH 10.331316 \n", - "231 0.0142 4.256912 1266.769326 BH BH 12.239574 \n", - "232 0.0142 4.537286 658.997955 BH BH 11.338870 \n", - "\n", - " S2_mass S1_spin S2_spin orbital_period eccentricity \\\n", - "0 12.911080 0.079649 0.064849 1.611464 0.048133 \n", - "1 12.911080 0.079649 0.063991 1.279098 0.123592 \n", - "2 9.888130 0.286911 0.249267 0.817643 0.165775 \n", - "3 10.251920 0.320931 0.298164 1.108194 0.379450 \n", - "4 10.900538 0.190982 0.167996 0.830194 0.107165 \n", - ".. ... ... ... ... ... \n", - "228 8.155695 0.040933 0.015301 17.694336 0.957889 \n", - "229 10.900538 0.190982 0.168671 0.894027 0.022964 \n", - "230 9.972710 0.312185 0.277781 0.911809 0.093335 \n", - "231 11.551092 0.179405 0.165071 1.159215 0.175475 \n", - "232 10.753148 0.269757 0.242801 0.851837 0.137036 \n", - "\n", - " channel \n", - "0 ZAMS_oDoubleCE1_CC1_CC2_END \n", - "1 ZAMS_oDoubleCE1_CC1_CC2_END \n", - "2 ZAMS_oDoubleCE1_CC1_CC2_END \n", - "3 ZAMS_oDoubleCE1_CC1_CC2_END \n", - "4 ZAMS_oDoubleCE1_CC1_CC2_END \n", - ".. ... \n", - "228 ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", - "229 ZAMS_oDoubleCE1_CC1_CC2_END \n", - "230 ZAMS_oDoubleCE1_CC1_CC2_END \n", - "231 ZAMS_oDoubleCE1_CC1_CC2_END \n", - "232 ZAMS_oDoubleCE1_CC1_CC2_END \n", - "\n", - "[233 rows x 12 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "import os\n", - "import numpy as np\n", - "from posydon.config import PATH_TO_POSYDON_DATA\n", - "from posydon.popsyn.synthetic_population import SyntheticPopulation\n", - "\n", - "# cosmological model parameters for the rate calculation\n", - "MODEL = {\n", - " 'delta_t' : 100, # Myr\n", - " 'SFR' : 'Madau+Fragos17',\n", - " 'sigma_SFR' : 'Bavera+20',\n", - " 'Z_max' : 1.,\n", - " 'Zsun' : 0.0142,\n", - " 'select_one_met' : True,\n", - " 'dlogZ' : [np.log10(0.0142/2),np.log10(0.0142*2)],\n", - "}\n", - "\n", - "pop = SyntheticPopulation('./population_params.ini', verbose=True, MODEL=MODEL)\n", - "path = os.path.join(PATH_TO_POSYDON_DATA, \"POSYDON_data/tutorials/population-synthesis/example/\")\n", - "pop.load_pop(os.path.join(path,'BBH_population_1e+00_Zsun.h5'))\n", + "# Single metallicity population\n", "\n", - "# generate the BBH synthetic population\n", - "pop.get_dco_at_formation(S1_state='BH', S2_state='BH', \n", - " oneline_cols=['S1_natal_kick_array_0', 'S2_natal_kick_array_0'],\n", - " formation_channels=True)\n", + "Similar to in the multi-metallicity tutorial, we will setup a BBH selection from our $Z_\\odot$ population and then calculate the transient population." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# read the relevant data in one go\n", + "# (faster than reading it in chunks, but requires more memory)\n", + "tmp_data = pop.history.select(columns=['S1_state', 'S2_state', 'event'])\n", + "# Selection of S1 being a BH\n", + "S1_state = tmp_data['S1_state'] == 'BH'\n", + "# Selection of S2 being a BH\n", + "S2_state = tmp_data['S2_state'] == 'BH'\n", + "# Selection of the binary system being in contact during the double CO phase.\n", + "state = tmp_data['event'] == 'CO_contact'\n", + "indices = tmp_data.index\n", + "del tmp_data\n", + "mask = S1_state & S2_state & state\n", + "selected_indices = indices[mask].to_list()\n", + "print(f'Number of systems: {len(selected_indices)}')\n", "\n", - "# we can save the synthetic population\n", - "# pop.save_synthetic_pop(os.path.join(path,'BBH_synthetic_population.h5'))\n", - "# pop.load_synthetic_pop(os.path.join(path,'BBH_synthetic_population.h5'))\n", + "# set overwrite to False to add to the file\n", + "pop.export_selection(selected_indices, 'Zsun_BBH_contact.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from posydon.popsyn.synthetic_population import Population\n", + "pop = Population('Zsun_BBH_contact.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pop.calculate_formation_channels(mt_history=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pop.history" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pop.formation_channels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Transient Population" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from posydon.popsyn.transient_select_funcs import BBH_selection_function\n", + "BBH_mergers = pop.create_transient_population(BBH_selection_function, 'BBH')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Even though we have a single metallicity, we can still calculate the efficiency per $M_\\odot$ for it. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from posydon.popsyn.synthetic_population import TransientPopulation\n", "\n", - "cols = ['metallicity','time','t_delay','S1_state','S2_state','S1_mass','S2_mass',\n", - " 'S1_spin','S2_spin', 'orbital_period','eccentricity', 'channel']\n", - "pop.df_synthetic[cols]" + "BBH_mergers = TransientPopulation('Zsun_BBH_contact.h5', 'BBH')\n", + "BBH_mergers.get_efficiency_over_metallicity()" ] }, { @@ -439,664 +191,124 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Similar to the `Analyzing Merging BBH Populations: Rates & Observations` we compute the BBH merger rate density to obtain the BBH intrinsic population." + "Similar to the `Analyzing Merging BBH Populations: Rates & Observations` we compute the BBH merger rate density to obtain the BBH intrinsic population.\n", + "\n", + "However, because we are only working with a single metallicity, we have to set the metallicity limits we would like to work with!\n", + "\n", + "The `MODEL_in` parameter gives us the option to select only one metallicity and select only the star formation within a specific range. \n", + "This dictionary does not require a full model to be defined, and will use the default values if required.\n", + "\n", + "The `dlogZ` has to be given in log-space in units of solar metallicity.\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 138/138 [00:00<00:00, 162.70it/s]\n", - "100%|██████████| 7/7 [00:09<00:00, 1.30s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "DCO merger rate density in the local Universe (z=0.00): 10.74 Gpc^-3 yr^-1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/html": [ - "
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        \n", - "
        " - ], - "text/plain": [ - " metallicity time t_delay S1_state S2_state S1_mass \\\n", - "0 0.0142 6.205715 4.287125e-08 BH BH 12.260188 \n", - "1 0.0142 4.705799 9.754266e-01 BH BH 12.850735 \n", - "2 0.0142 8.187239 2.075700e+00 BH BH 8.314851 \n", - "3 0.0142 6.647572 2.472628e+01 BH BH 9.826765 \n", - "4 0.0142 6.676527 1.680554e+01 BH BH 20.974131 \n", - "... ... ... ... ... ... ... \n", - "26870 0.0142 7.928745 4.267650e+02 BH BH 17.564588 \n", - "26871 0.0142 4.530889 7.870327e+02 BH BH 11.436823 \n", - "26872 0.0142 4.848528 9.435014e+02 BH BH 10.331316 \n", - "26873 0.0142 4.256912 1.266769e+03 BH BH 12.239574 \n", - "26874 0.0142 4.537286 6.589980e+02 BH BH 11.338870 \n", - "\n", - " S2_mass S1_spin S2_spin orbital_period eccentricity \\\n", - "0 12.183039 0.050168 8.244612e-04 326.580311 0.999994 \n", - "1 20.143346 0.027546 5.791334e-17 2377.230212 0.999817 \n", - "2 8.315119 0.100591 6.554160e-03 38.651550 0.996045 \n", - "3 9.853595 0.032525 2.400078e-02 14.240625 0.980550 \n", - "4 12.500642 0.000531 4.772162e-04 696.487802 0.998932 \n", - "... ... ... ... ... ... \n", - "26870 8.155695 0.040933 1.530132e-02 17.694336 0.957889 \n", - "26871 10.900538 0.190982 1.686708e-01 0.894027 0.022964 \n", - "26872 9.972710 0.312185 2.777810e-01 0.911809 0.093335 \n", - "26873 11.551092 0.179405 1.650709e-01 1.159215 0.175475 \n", - "26874 10.753148 0.269757 2.428005e-01 0.851837 0.137036 \n", - "\n", - " channel \n", - "0 ZAMS_CC1_oRLO2_CC2_END \n", - "1 ZAMS_CC1_CC2_END \n", - "2 ZAMS_oRLO1-reverse_CC1_CC2_END \n", - "3 ZAMS_oRLO1-contact_CC1_CC2_END \n", - "4 ZAMS_CC1_oRLO2_CC2_END \n", - "... ... \n", - "26870 ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", - "26871 ZAMS_oDoubleCE1_CC1_CC2_END \n", - "26872 ZAMS_oDoubleCE1_CC1_CC2_END \n", - "26873 ZAMS_oDoubleCE1_CC1_CC2_END \n", - "26874 ZAMS_oDoubleCE1_CC1_CC2_END \n", - "\n", - "[26875 rows x 12 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "pop.get_dco_merger_rate_density()\n", - "# pop.save_intrinsic_pop(os.path.join(path,'BBH_intrinsic_population.h5'))\n", - "# pop.load_intrinsic_pop(os.path.join(path,'BBH_intrinsic_population.h5'))\n", - "pop.df_dco_intrinsic[cols]" + "import numpy as np\n", + "MODEL_in = {\n", + " 'select_one_met' : True,\n", + " 'dlogZ' : [np.log10(0.0142/2),np.log10(0.0142*2)],\n", + "}\n", + "\n", + "rates = BBH_mergers.calculate_cosmic_weights('IllustrisTNG', MODEL_in=MODEL_in)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "pop.plot_rate_density(DCO=True, channels=True, **{'ylim' : [0.01, 100]})" + "rates.calculate_intrinsic_rate_density(mt_channels=True)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 138/138 [00:13<00:00, 10.45it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "DCO detection rate at design_H1L1V1 sensitivity: 590.18 yr^-1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/html": [ - "
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        " - ], - "text/plain": [ - " metallicity time t_delay S1_state S2_state S1_mass \\\n", - "0 0.0142 6.205715 4.287125e-08 BH BH 12.260188 \n", - "1 0.0142 4.705799 9.754266e-01 BH BH 12.850735 \n", - "2 0.0142 8.187239 2.075700e+00 BH BH 8.314851 \n", - "3 0.0142 6.647572 2.472628e+01 BH BH 9.826765 \n", - "4 0.0142 6.676527 1.680554e+01 BH BH 20.974131 \n", - "... ... ... ... ... ... ... \n", - "19536 0.0142 7.214096 1.052488e+04 BH BH 12.340761 \n", - "19537 0.0142 3.443633 6.236191e+03 BH BH 12.585380 \n", - "19538 0.0142 4.900714 4.178555e+03 BH BH 17.470438 \n", - "19539 0.0142 7.154675 8.766378e+03 BH BH 10.272165 \n", - "19540 0.0142 3.781907 7.804838e+03 BH BH 10.729561 \n", - "\n", - " S2_mass S1_spin S2_spin orbital_period eccentricity \\\n", - "0 12.183039 0.050168 8.244612e-04 326.580311 0.999994 \n", - "1 20.143346 0.027546 5.791334e-17 2377.230212 0.999817 \n", - "2 8.315119 0.100591 6.554160e-03 38.651550 0.996045 \n", - "3 9.853595 0.032525 2.400078e-02 14.240625 0.980550 \n", - "4 12.500642 0.000531 4.772162e-04 696.487802 0.998932 \n", - "... ... ... ... ... ... \n", - "19536 8.201690 0.185531 1.411149e-01 2.783007 0.396922 \n", - "19537 12.508058 0.090103 2.004811e-03 250.806290 0.988294 \n", - "19538 16.106886 0.202329 4.508136e-04 1580.389855 0.997143 \n", - "19539 9.248458 0.090886 1.197166e-01 3.129920 0.527011 \n", - "19540 11.898371 0.179142 6.654702e-02 81.451235 0.971019 \n", - "\n", - " channel \n", - "0 ZAMS_CC1_oRLO2_CC2_END \n", - "1 ZAMS_CC1_CC2_END \n", - "2 ZAMS_oRLO1-reverse_CC1_CC2_END \n", - "3 ZAMS_oRLO1-contact_CC1_CC2_END \n", - "4 ZAMS_CC1_oRLO2_CC2_END \n", - "... ... \n", - "19536 ZAMS_CC1_oRLO2_CC2_END \n", - "19537 ZAMS_CC1_CC2_END \n", - "19538 ZAMS_oRLO1-reverse_CC1_CC2_END \n", - "19539 ZAMS_oRLO1_CC1_oRLO2_CC2_END \n", - "19540 ZAMS_CC1_CC2_END \n", - "\n", - "[19541 rows x 12 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "pop.get_dco_detection_rate(sensitivity='design_H1L1V1') # if already computed use load_data=True\n", - "# pop.save_observable_pop(os.path.join(path,'BBH_observable_population.h5'))\n", - "# pop.load_observable_pop(os.path.join(path,'BBH_observable_population.h5'))\n", - "pop.df_dco_observable[cols]" + "import matplotlib.pyplot as plt\n", + "\n", + "rates.plot_intrinsic_rate(channels=True, show=False)\n", + "plt.xlim(0,10)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from posydon.popsyn.transient_select_funcs import DCO_detactability\n", + "from posydon.popsyn.synthetic_population import Rates\n", + "\n", + "rates = Rates('Zsun_BBH_contact.h5', 'BBH', 'IllustrisTNG',)\n", + "\n", + "def DCO_wrapper(transient_chunk, z_events_chunk, weights_chunk):\n", + " sensitivity = 'design_H1L1V1'\n", + " return DCO_detactability(sensitivity, transient_chunk, z_events_chunk, weights_chunk, verbose=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We also give it a name, which is used as an identifier in the file\n", + "rates.calculate_observable_population(DCO_wrapper, 'design_H1L1V1')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We can now access this observable population\n", + "rates.observable_population('design_H1L1V1')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Visaulize the BBH Population Properties" + "## Visualize the BBH Population Properties\n", + "\n", + "Similar to the multi-metallicity tutorial, we are able to plot the properties of the intrinsic and observed populations.\n", + "In this case, we normalise both distributions using the `normalise=True` option." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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67dykxXMjb/Q3PT0t0zRlmuaWx2g7n8t6vRelj+anzSdNr7zyigKBgM6dO6eXX3657l09NrLZeayG0+etGrWsa25urvDY6e+Kep17J65p9dj3er0XGnlO2s1mn9t2/M5oVa1yna5Go85/O34mPc0OANjJ6nmj12jDw8MaHh5WIpHQzMyMUqmUZmdnS35lnJ6eViAQqGjwnkq0Sp+eaDRaGMAnHo9rZGSk8Fw6nS4ck0o04zh2qv7+/k1/5a71WBc/H4lE6t5/eDvXgXq9F6PRaMkAVLFYrGk3NVudx0o5fd5q0Sq1MMXqfe5b8ZrWiu+FauUTxXZMNlAfrXSdRn1QIw7UWSqVarkaTCcFg0HFYjHNzMwU+i8Vj0iarwWpVSv2dzJNs9C/LJ/s5MVisQ1Hp96M08exnooTx+38OFLvG4jiWDZrolbtsW7FwW/y6vFeDAQChf2dmpoqDArULJWex6200nmrZYCgRlz7nDz3rXRNq9d7oZnfR7UO+Ncoxcdj7ee2Vb8zNou51TTrOl2Pe9pWPf+tgEQcqLP5+XnHpxdppuIauHLyI3omk8m6jEBcbgTrVlDc3Ld4/y5cuFBRU+BGH8d6CoVChcfVnI+1y9Z7oKPi9Reve7vHurgvYSt+trfzXizusxuJRLYcTbqcen8mNzqP1Wql85Z/X1VzrJweCKze576Vr2n1ei80c/aKfALTKl20iq09pmuPUyt+Z2wVc62cuEdp5nW6Hve0rXj+WwWJOICqzM/P68UXX9xyOdM0CzdZ27mIr526ppoBVZysbQkGg4W4pqenC9Pj5PsUbqXRx7Geim8CZmdnK35d8bKVHqdKFR+bteve7rEunjal2oGmGjHYXq3vxbVTOm01PUxx/9hio6OjdfusbXYeq9VK5y2/H9XUBvn9/pL9rzSmSrbhxLlv5Wtavd4LTp6TreSTklZMxIunqyv3uW3F74ytYs6rdqaDemvF63S1WvH8twoScaBKxU1syv2yl06nO/JiUSyVSlX0q2Z+KontTilx/vz5wuNKa1DyA5o4qXhQlImJCcXj8aoGSqnncZyenm7YCOumaRaaPFuWVfGPI8XTFVV6Hiu9gS2+qSq37u0ca9M0S24ktpojOc+yLAUCgYa04qjlvbj2pnGrkdWruYFaq17nsRqtdN7yU9RV+xktHoW8kiRXqqzGyalz3+jvhkrV873g1DnZSv4cBAKBba+rUpV+botH6i73uW3F74ytYs7b6p6vmBPfwU5fpxtxT9vI899uSMTR8prdt2+t4i/Bche8ZDJZ8bzArbZv1Th79uyWy+SPz3bn8x4eHi40SUokEhVdxEdHR0tumJxQ3P92dHRU6XS66iZj2z2OlmVpcHCwMHXJZk1D6ykWixW+nCtpip9Opwu/xhefz61UOuhd/qYqEolsuO7tHOvz588XakZGR0crurE+e/asIpHIljUq9bgO1PJeXBvXZvtkWVbJDWFxzPPz81v2W63neayGk+etGsX7Us3NevFnZXx8vKIat+LP40bvLSfPfSO/G6pRr/eCU+dkM5ZlFeJtZPPcSr5Di4/lZp/bRn1n1DNmqfR4bzWy+ujoaN37MTt9na7nPe1mGnX+244NtLhYLGZLKvzF4/GKXjcyMlJ4TSwW23L54m1sZmFhobCc3+9f97zP57MXFhY2fP3w8HDh9clkcsu41io+HlNTU1suX+/jUBz/VufC7/fbpmlu+Hy1++L3+21Jts/n2/TYjYyMbLrdeio+HiMjIzW9bjvHcWpqquSc+Xy+quLfjrm5Ods0TVuSHYlEKlpueHh4y/VGIpHCvpimueXnJL/8Ruuu17Gem5uzfT6fLck2TXPTz/lW78HtXge2Wmel78VgMFjRsQkGgyXvteL1+/1+e25ubt1r6n0eixVfO7Y6p/U8b9uRj6HS77Bixde+csc6LxgMFpbN7+9my9bz3Nfzu8Gp7+96vhecOCcbyR//Rlzf85/D/D3OZteS4s9hJZ9bp78znIjZtj861+Xu+fJGRkbsWCxWcq5nZmY2XW+l1zEnr9Pbvaet5j7OifPv1LWiUVojCqBIMpm0Z2Zm7KmpKTsWixW+NIsTjVgsZk9NTdkzMzMlN3f518bj8ZLX+Xw+Ox6P2zMzMyUXovx2ii/i+Q9+fv3lFF/oIpGIvbCwYM/NzdnBYLDsF0A+rrU/Kvj9/kJcm12w5+bmyu6XaZqF1xdfKJ08DvmbrfxNyvDwcMk5WFhYsGdmZgo3Wmtvvqvdl7WKL7qRSKSw/MLCgj01NWX7/X7b7/dvuo56SiaThXiq2eZ2j2PezMzMuvdUIy0sLBT2JX8O5+bm7IWFBTuZTNojIyO2z+crfG4rkX8fTk1NFdZf7vORfy7/vt5IvY712v3N71P+szQ3N2fH43HbNE07GAyuez9s9zqwle2+F8vd5OWva/lzV/z5i8fj9sjIiB0MBsuut97nsZJrx0YJ0XbOW73kt7/ZDehmin/YWHvDOzU1ZZumacdisXXXhPx7rNwNbT3P/XY/Z436/q7ne8GJc1JO/thv9Fmrp+LjOTMzYw8PD9vBYLDkfM3NzZXsezU/Ljn5neFUzMU/4AwPD6+73xoeHi58rosT8fw+FifBtV7HnLpO23b197TbuY+rx/lv1LWiEUjE0XLyv4LlP4ib/a1NPIovVOWWLf51bW5ubsNli5ff6MYuf1EvjmOji0Z+nzaLa7NtVbJfxV84Th6HkZGRkgtz8S/A+dcEg8ENv+Sq3ZeNjv3IyIjt9/tLajfyF9VGCwaDVd9cb/c4rl1X/hhsVjPjpHLnpHgfqkluIpHIupuGfAIbj8ftWCxmj4yM2MPDwxUfn3od6632d6Nk07a3fx2oRC3vRdu2CzdA+SQpX4sXDAbX7U/xjdJm26r3eazk2rFVS4Bazlu95JOx7dS4r40/39ogEokU3jf5H2TyCUAwGLSHh4c3vamtx7lvxHdDvb6/yx3LWt8LTpyTtfLHsRHfb8XJSv67OJ/cmqZZ8zVzrXp/ZzQi5vx7uvi9lq+Fzss/nz/X+SQ9vz/buY45cZ3Oq+ae1qn7uErPf6OvFU4ybNu2BQAAAEf19fXJsizNzc11/KCeqA/LstTX1yefz6eFhQXHtxeNRgt9c5PJZEP78deqHWMGJAZrAwAAaIj8SPaVjtoNXLhwQVLpgIwAOgOJOAAAQAPkk6niqZOAzeRHAa9makwA7YFEHAAAoAF8Pp9isZgsyyo0pQU2Mj09rXQ6rVgsJl+dp8UC0Hwk4gAAAA0yMjIi0zQrmu8YO9vY2JhM09TIyEhTtl/LnOfN1o4xY+ciEQcAAGigqakppdNpjY6ONjsUtKjx8XGlUinF4/GGbjedThceW5bV0G3Xqh1jBiTJ0+wAAAAAdhK/3694PK5oNKoXXniBUZ5RIv8jTSwWUzAYdHx7qVRK8/PzSqVSSiQShfLR0VHNz8/LNM3CX6tox5iBtZi+DAAAoAmi0aguXLigy5cv0wcYknI1uoFAQH6/X1NTUw3Z5uDgYKFWee37MF/DHIvFmtZEvpx2jBlYi0QcAACgSaLRqGZnZ5VMJpsdClpAKBSSz+drWBIOoHlIxAEAAJoo31ecAdx2tvHxcd28eZP3AbBDkIgDAAAAANBAjJoOAAAAAEADkYgDAAAAANBAJOIAAAAAADQQiTgAAAAAAA1EIg4AAAAAQAORiAMAAAAA0EAk4gAAAAAANBCJOAAAAAAADUQiDgAAAABAA5GIAwAAAADQQCTiAAAAAAA0kKfZAQCVsCxLFy9eLPz/yJEj2rVrVxMjAgAAANDq7t+/r6tXrxb+f/LkSfl8vuYF9CEScbSFixcv6otf/GKzwwAAAADQxl566SX983/+z5sdBk3TAQAAAABoJBJxAAAAAAAaiKbpaAtHjhwp+f9LL72kRx991PHtnjlzRrOzsxoaGtLk5KTj26tVq8f55ptvlnQtaNT5q0WrH0upPWKU2iPOdoiRz099tUOMUnvE2Q4xtsvnpx2OpdQecbZDjFLrx9kunx1p62O5dl/W5hXNQiKOtrB2YLZHH31Ux48fd3y7vb29hX8bsb1atUuceY06f7Voh2PZDjFK7RFnO8S4Fp+f7WmHGKX2iLMdYlyrVT8/7XIs2yHOdohRap8481r1syNVfyxbZcBnmqYDAAAAANBAJOIAAAAAADQQiTgAAAAAAA1EIg4AAAAAQAMxWBuwiTNnzui5557TwMBAs0PZVLvE2Q7a4Vi2Q4xSe8TZDjG2k3Y4nu0Qo9QecbZDjO2iXY5lO8TZDjFK7RNnO2jXY2nYtm03OwhgK5cuXdKJEycK///7v//7lh25Eetx/oDa8fkBasfnB6hNJ312WnVfaJoOAAAAAEADkYgDAAAAANBAJOIAAAAAADQQiTgAAAAAAA1EIg4AAAAAQAMxfRkAxx04cEBf+9rXSv4PoDJ8foDa8fkBasNnx3lMX4a20KrTDgAAAABoXa2aR9A0HQAAAACABiIRBwAAAACggegjjrZ05swZ9fb2li0/c+ZM4wMCAAAA0DSTk5OanJxcV764uNj4YCpAIo62NDs7W7b8ueeea2wgAAAAAJruypUrunjxYrPDqBiJONrS0NBQ2RrxgYGBxgcDAAAAoKkGBgZ08uTJdeWLi4sbVuI1E6Omoy206miHAAAAAFpXq+YRDNYGAAAAAEADkYgDAAAAANBAJOIAAAAAADQQiTgAAAAAAA1EIg4AAAAAQAMxfRmA+vreWGXLPX/O2TgAAACAFkWNOAAAAAAADUSNOAC0Idu2tbSS2fD5LrdLHnf531rvLq+W/L/H65ZhGHWNDwCAYn8y83qzQ2gZXw0da9q20+m0YrGYEomE0um0JMk0TQWDQY2Ojso0TUnSxMSE5ubmFIvFmhZrp6NGHADa0NJKRvGL6Q3/0u8vbvjatctultADAIDOMDo6qsHBQUnS1NSUFhYWtLCwoKmpKfl8PgUCAY2OjiqdTisajcqyrKrWn0gk1NfXp+npaQei7zzUiAMAAABAB4tGo5qYmFAymZTf7y95zu/3y+/369y5cwqHwwoEAhWt07Iszc/PK5FIKB6PK5VKSZLm5+frHn8nokYcAHaQtc3SAQBAZ0skEpqYmFAsFluXhBfz+XyamZlRf3//lusMBALq6+tTIBDQzMyMXnjhhXqGvCOQiDfIxMRE4Q2b/wuHw0okEg3ZfiqVUjQa1eDgoAzDkGEYGhwcVDQa3VYMzd4vAAAAABsbHR2VJA0PD1e0fCX9wl9++WXZtl1o2h4MBrcV405EIu6wVCqlvr4+xWIxRaPRQl+MZDIp0zQVCoUUDoer7oNRjdHRUQUCAc3Pz2t0dFRTU1OFD9jExIRCoZACgUBhwIZKtMJ+ASj1288+ouhJU9GTpswHe5sdDgAAaAH5JuOVGh4els/n23SZrZ7H1ugj7qBEIqFQKCTTNJVMJkvesKZpKhaLFWqlU6nUumXqIRAIyLIszc3NFUZBzBsZGSn0F0mlUhocHNTMzMyWv2i1wn4BWK+ny63dXVzWAQDAeolEQpFIpKJlT58+7XA0oEbcIZZlKRwOS1JhJMJyIpGIIpGI0um0Tp06VdcYxsfHZVlWoZa6nHg8XvKBDIVCm9aMt8J+AQAAAKhMPg8YHR2tuLVqKBQqjLAOZ5CIO+Ts2bOyLKswCuFm8v02UqlU3Yb7T6fTGh0dVTwe37I2em0/kGg0uuGyzd4vAAAAAJXL39tblqWjR49WdF8+PDyskZERp0Pb0UjEHWBZVuENXsnABaZpFn6pGhsbq0sMsVis4ubgPp+vpFY8kUiUrRVvhf0CAAAAULmRkZHCPXm+dathGAqFQhofH2eQ5SYhEXfAxMRE4fHTTz9d0WvyH45UKlXVoGkbmZ2dlWVZFTcrWTtfYLkPZCvsFwAAAIDqlJs/PJFIaHR0VKFQSIZhKBAIlNzvw1kk4g548cUXC48rrZUu7sNdj1+lipPedDq9ZROUtX3I5+bm1i3TCvsFAAAAoDo+n0/JZFLxeHzD7qX56Y77+vqoQGsAEnEHFE8R0N/fX9FrihPbZDK57RiGhoY2XH85a+MsN5BDK+wXAAAAgNpEIhElk8nC/N8jIyPrEnPLsqqe2hjVY56bOlv7ht1otPK19u/fX3g8Ozu77ThisZjC4bDS6bRGRka27NO9Nu61zdlbZb8A5PR43YqeNNeVAQAAbMXn82l4eFjDw8OFsnxT9VQqJcuyFI1GNTMz08QoOxuJeJ1VOiXAWsU1x7Wuo5jf7y/bvHwjaxPtcr+M1aLe+wUgxzAM5gwHAAB1EwwGlUwmFQ6HNT09rUQioVQqteVMSagNd3EtaH5+vuHbjMfjhcc+n6+iUdGrVc/9evPNN6t+zYEDB3Tw4MG6xQAAAAC0ulAoVFXN9tTUlAzDkJRr0dpKifh7772nGzduVPWaWvKGRiARr7O1yWalg5oVa3TN8doRzdfOKy613n598YtfrPo1X/va1/T1r3+9bjEA7cjrdun5xw+uKwMAAJ0pkUjIsqyq7t+DwaASiURVLWwb4Vvf+pa+8Y1vNDuMuuDuq87asfn16Oho4XEwGCyZUzyvHfcLwHpet0tPHfGV/JGIAwDQ2aodqylfC1483hPqi7uvHS7f/0PKDcA2NTXV5IgAAAAA1FO5Fq+byc+W5ER3VeTQNL3Oammy3SyWZens2bOSckl4MpncMP5W26+XXnpJjz76aFWvOXDggEPRoBH+ZOb1ml731dCxOkfSGjJZW29bSyVlh309cruMJkUEAABaVX5E9EoS8nQ6rUQioeHh4ZbqHy5JX/nKVxQOh6t6zZtvvllTt1ankYjXWbn5uKtNYhuV9IbDYVmWtWUSLrXefj366KM6fvx43dYHtJv7qxlNJ6+VlEVPmoykDgAAyhofH5cknTt3bsP7csuyFA6H5fP5dP78+Zq242SX1oMHD3bM4Ms0Ta+zeiSba5NeJ4yOjiqRSFSUhEvts18AAAAASkUiES0sLCiVSqmvr0/RaFTT09OFQZsTiYTGx8d19OhRSaooP7AsS5ZlKZVKaWxsrFD+4osvFgaIy/9hPapO6mxtsjk/P19RElv8BnW6RnxiYkLj4+Py+/16+eWXK9peO+wXAAAAgFKmaSoWi8nn82lmZkapVErxeFxjY2NKp9OFlq5DQ0OKxWJlB25eKxqNamJiouT+Pv84nU4Xmo/nW9+22ujrrYBEvM7WJpuV/gJU/OY0TbOOEZVKJBKKRqMKBoNVzSfY6vsFoDJLyxn92Q+ulJT9zrMD6ulyNycgAMCO0KljtrSDtUmw3+9XPB7f1jrj8fi217HT0TTdAcWDGqydf3sjxcs9/fTTdY9Jyo1+GAqFNDw8vGkSbllWybziea26XwAqZ8vW0nKm5M+W3eywAAAAdhQScQcUD/NfLqEtp3i54eHhuseUTqd16tQpRSKRLacoGxsb0/T09LryVtwvAAAAAGg3JOIOOHfuXOFxMpms6DX5ufpM06x7E27LshQKhXT69OmKmpCkUqmyMbTafgEAAABAOyIRd4DP5yvUHl+4cGHL5ROJROHx6OjolsvnRzis1KlTpxQMBivux5FIJMrOGej0fgEAAADATsBgbQ6Jx+MKBAKyLEvT09ObNsvOJ8h+v3/TUQoty1IgECg0947FYhoZGdk0jlAopP7+fkWj0Q2bk+cHXpufny/0Hd+o9tqJ/QLQuWzb1tJKpuLlu9wuedz8RgwAADobibhDTNPU+fPnFQ6HFQ6HNTc3Vza5nZiY0PT0tHw+35Z9txOJREkyPTY2tmkiHo1GC7XSgUCgqtg3e67e+wWgcy2tZBS/WNmYEpL06088pGOHHnAwIgAAgOaj2sFB+dHJfT6fAoGAJiYmCs9ZlqVoNFqYSuzy5ctb9qFeO4XYZsuPj4+XbK8aW8VR7/0CAAAAgJ2ERNxh+WQ0FospHo+rr69PfX19Onr0aKEpeD6prWRd+Rpw0zQ3rWkeGxurOeZy/cPLxVKv/QIAAACAnYSm6Q3g8/kUiUTq0k86FospFottudzCwsK2t7WVeu4XAKx1d3l1XbP26ElTu7v46gIAAO2NuxkAQEP99rOPqKfLXfa5LgZqAwAAOwCJOACgoXq63NRqAwCAHY07IQBoQ90et7787CPrygAAAND6SMQBoA25XIYe3LOr2WEAAACgBiTiAADH9Hjdip4015UBAADsZCTiALCDeFwuPWPuX1fmFMMw6A8OAACwBndHALCDdHlcenZw/9YLAgAAwDHMEwMAAAAAQANRIw4AbSibtTV/d7mkrH93l1wuo0kRAQAAoFIk4gDQhu6tZvTnP3irpCx60qQ/NgAAQBvgjg1t6cyZM+rt7S1bfubMmcYHBKCsTNbW29ZSSdlhX4/c1NwDAIA6mpyc1OTk5LryxcXFxgdTARJxtKXZ2dmy5c8991xjAwGwqfurGU0nr5WUUXMPAADq7cqVK7p48WKzw6gYd0JoS0NDQ2VrxAcGBhofDNBG7q1kdGH2aknZ6aEj6mZubwAA0MYGBgZ08uTJdeWLi4sbVuI1E4k42tLk5KSOHz/e7DCAtpO1bd28s7yuDAAAR31vrNkRtI7nzzU7go60URfVS5cu6cSJE40PaAtMXwYAaElet0vPP36w5M/r5msLAIBapdNpRaNRDQ4OyjAMGYahwcFBRaNRpdPpwnITExMaHR1tYqSdjzsaAEBL8rpdeuqIr+SPRBwAgNqMjo5qcHBQkjQ1NaWFhQUtLCxoampKPp9PgUBAo6OjhWTdsqwt1zk9Pa1QKKS+vj4ZhqFAIKBwOKyJiQmH96b9cUcDAAAAAB0sGo1qfHxcyWRS8Xhcfr9fPp9PPp9Pfr9fsVhMly9fViqVUiAQ2HJ9lmUpFAppbGxM4XBYyWRSyWRSL7zwghKJRKHWPZVKNWDv2hN9xAEAAACgQyUSCU1MTCgWi8nv92+4nM/n08zMjAYHB7esDT916pSGhoY0MzNTUu73+xWJRHTq1KlCUp9MJjfd7k5FjTgAAAAAdKh8X+/h4eGKlo/FYhWtLx6Pl33e5/Pp/Pnzhf+Hw+GKtrvTkIgDAAAAQIeqtnn48PCwfD7fhs9PTEzINM2Swd3W8vv9CgaDknIDxNFnfD0ScQAAAADocIlEouJlT58+XbbcsixZlqXp6enCwG8bKW6OPjU1VfG2dwoScQBAS1pazuhPL86V/C0tZ5odFgAAbcU0TUm5JuWVjIQuSaFQqGyiPT8/X/L/zWrFn3766Q1fBxJxAECLsmVraTlT8mfLbnZYAAC0lWg0KilXm3306FFNT09v+Zrh4WGNjIysKzdNU5FIRD6fT5FIpJDkl1OcpPf399cQeWcjEQcAAACADjUyMlJImC3LUjgclmEYCoVCGh8fr6rJupQbpG1hYWHDwdryXnnllcJjRk1fj+nLAKAN7fK4NRx4eF0ZAADAWslksjClWF4ikShJwv1+v6LRqCKRyLa3Z1lWybrPnTu37XV2GmrEAaANuV2GjvTvLvlzu4xmhwUAAFqQz+dTMplUPB7fsHY6lUopGo2qr69v077flRgbGyv0R4/FYpuOwr5TUSMOAHBMt8etLz/7yLoyAADQeJFIRJFIpFBj/corryiRSJTUlFuWpUAgoGQyuWkf8I2k02mNj49L2rivOUjEAWBHcbsMPXlk37oyp7hchh7cs8ux9QMAgOr5fD4NDw9reHi4UJZIJDQ6OqpUKiXLshSNRjUzM1PVei3LUigUkpRLwpm2bGM0TQeAHWSXx60vPH6o5I++5QAAIBgMKplMFpLztTXllTh16pTS6bQikQhJ+BZIxAEAAACgQ+VrqCtVnEDPzs5WtZ1UKqWpqaktR1QHiTgAtCXbtnV3ebXkz7aZYxsAAJRKJBKFgdMqFQwGJUlzc3MVLR8KhTQ7O1tSo47NkYgDQBtaWskofjFd8re0kml2WAAAoAVVU7MtfTTv9/79+7dcNhwOK51OK5lMlh2RPZVKKRwOV7X9nYBEHADgmGzW1vt37pf8ZbPU3AMA0EixWKyq5fN9w/M14xspTsI3GmE9kUiov7+/qu3vBCTiAADH3FvN6M9/8FbJ371Vau4BAGik/IjolUin00okEhoeHt5wznEpl4RblqVkMrnpPOEzMzMaHBysNuSOx/RlALCD3FvJ6C9ffbuk7DefPKxuLyOnAwDQyfJze587d27DxNmyLIXDYfl8Pp0/f37DdUWjUU1PTysYDG44GNz8/Lwsy1I6na74R4CdhEQcAHaQrG3r2sLSujIAANC5IpGIYrGYwuGw+vr6FIlEFAqFZJqmfD6f0um0UqmUxsbGZJrmprXc0WhUExMTknI17ZUYGhqq1650DBJxAEBL8rhcesbcv64MAABUzjRNxWIx+Xw+zczMKJVKKR6Pa2xsTOl0WpZlyefzaWhoSLFYTJFIZMN1pVKpQhJejc2aru9UJOJoS2fOnFFvb2/Z8jNnzjQ+IAB11+Vx6dnBrUdrBQC0gefPNTuCHWvtFGR+v7/meb79fn/LTpc6OTmpycnJdeWLi4uND6YCJOJoSxtNwfDcc881NhAAAAAATXflyhVdvHix2WFUjEQcbWloaKhsjfjAwEDjgwEAAADQVAMDAzp58uS68sXFxarnUW8EEnG0pcnJSR0/frzZYQAAAABoARt1Ub106ZJOnDjR+IC2wKg3AAAAAAA0EIk4AAAAAAANRNN0AEBLureS0YXZqyVlp4eOqNvrblJEAAAA9UEiDgBoSVnb1s07y+vKAAAA2h1N0wEAAAAAaCBqxAGgDXW5Xfr1Jx5aVwYAAIDWRyIOAG3I43bp2KEHmh0GAAAAakAiDgBwzC6PW8OBh9eVAQAA7GQk4gCwg7gMQ48d2rOuzClul6Ej/bsdWz8AAEA7IhEHgB2k2+vWbzxxuNlhAAAA7GiM7AMAAAAAQAORiAMAAAAA0EA0TQeANnR3eVXxi+mSsuhJU7u7uKwDAAC0Ou7YAACOsW1bSyuZkrIer1uGgwPEAQAAtDoScQCAY5ZWMtTcAwAArMGdEADsIPdWMkr8w/WSsuCnDqnby9zeAAAAjUIiDgA7SNa29cb1OyVlX3j8YJOiAQB0snLdk7xul7zu8uNFLy1nZMuuaVsel0tdnvLrvbeSUdaubb1ul6FdnvI/Vm9nvRJdtXY6EnEAQEtyuww9eWTfujIAQHso1z3p+ccP6qkjvrLL/9kPrmhpOVP2ua08Y+7Xs4P7yz53Yfaqbt5Zrmm9Tx7Zpy88fqjsc3/56tu6trBU03olumrtdJx5AEBL2uVxb3jzAwAA0M6YRxwAAABAQ9Ra4436SKfTikajGhwclGEYMgxDg4ODikajSqc/ar0wMTGh0dHRJkba+UjEAQAAAKBBHju0R48d2iNXg/uHj46OanBwUJI0NTWlhYUFLSwsaGpqSj6fT4FAQKOjo4Vk3bKsqtafSCTU19en6elpB6LvPDRNBwAAANAQxz++d8PnfufZgW0N1raR00NHtjVY20Z+88nDNa23Gf3Co9GoJiYmlEwm5ff7S57z+/3y+/06d+6cwuGwAoFAReu0LEvz8/NKJBKKx+NKpVKSpPn5+brH34lIxAEAAAA0hGeTxLany5mpNJ2aorNdpv5MJBKamJhQLBZbl4QX8/l8mpmZ0eDg4Ja14YFAQKlUSj6fT8FgUC+88EIhEUdlSMTRls6cOaPe3t6y5WfOnGl8QAAAAEALyvf1Hh4ermj5WCymcDi86TIvv/yyfD5f4f+tkIRPTk5qcnJyXfni4mLjg6kAiTja0uzsbNny5557rrGBAHDMvZWM/vLVt0vKfvPJw21TAwEAQCuoNkkeHh4uSbLL2er5Zrhy5YouXrzY7DAqRiKOtjQ0NFS2RnxgYKDxwQBwRNa2183PWmsfPwAAdrpEIqFIJFLRsqdPn3Y4mvobGBjQyZMn15UvLi5uWInXTCTiaEuTk5M6fvx4s8MAAAAAWpppmkqn0xodHdXp06crqs0OhUIl05m1g426qF66dEknTpxofEBbIBEHgDbkdbv0/OMH15UBAIDWc28lo8Q/XC8pC37qUEO6W0WjUY2OjsqyLB09elTnz5/fsr94pf3JUTsScQBoQ163S08d8TU7DAAAUIGsbeuN63dKyr6w5gd1p4yMjCgejyudTsuyrMJAbMFgUKFQSH6/X8FgsCGx4CMk4gAAx3S5Xfr1Jx5aVwYAABonmUzq1KlTJQO3JRIJJRKJwv/9fr+i0WjF/cixPdwNAcAO4jIMPdzXU/LnMjae03W7PG6Xjh16oOTPQyIOAEBD+Xw+JZNJxePxDecST6VSikaj6uvra7v+4e2IGnEA2EG6vW6Fh440OwwAANAEkUhEkUhElmUpkUjolVdeUSKRKKkptyxLgUBAyWRSpmk2MdrORiIOAAAAoO52d3n01dCxZoeBMnw+n4aHh0sGZUskEhodHVUqlZJlWYpGo5qZmWlilJ2N9oEAAAAAsMMFg0Elk8lCcr62phz1RY04ALShpeWM/uwHV0rKfufZAfV0OT8NCgAAaB+hUKiqmu2pqSkZH44fMzs7u2GfcmwPNeIA0IZs2VpazpT82bKbHRYAAGgxiURClmVV9Zr8dGZzc3MORASJGnEAgIPuLq8qfrF05NXoSVO7u/j6AQCgUWZnZ6uaK9zv9yuRSGj//v0ORrWzcScEADvI/dWMvv/m+yVln3v0Qe3y0KQdAIBOFYvFqkrE833Dq3kNqkPTdADYQTJZW69evVXyl8nSpB0AUH+rmaxev/5Byd9qJtvssHak/IjolUin00okEhoeHqZ/uIOoEQcAtCSXYeixQ3vWlQEA2sNyJqu/eu2dkrLffvaRqgcW9bpd8rrL1x9uZ4wUj8ulLk/59d5byShr17Zet8toyZZm4+PjkqRz587J5/OVXcayLIXDYfl8Pp0/f76m7VTbH32nIhEHALSkbq9bv/HE4WaHAQCoo//0g7eqfs3zjx/UU0d8ZZ/7sx9c0dJypqZYnjH369nB8n2gL8xe1c07yzWt98kj+/SFxw/V9FqnRCIRxWIxhcNh9fX1KRKJKBQKyTRN+Xw+pdNppVIpjY2NyTRNJZPJDZP1vHzCnU6nNTY2Vih/8cUX5ff7NTQ0VCjbal07EYk4AAAAADjs+ccP6o3rH+jawlJDt2uapmKxmHw+n2ZmZpRKpRSPxzU2NqZ0Oi3LsuTz+TQ0NKRYLKZIJLLlOqPRqCYmJkoS7PzjdDqtcDgsKZesm6bJ6OtlkIgDAAAAqLvdXR59NXRMkvQnM683OZrm2t3l0VNHfHrj+gcN3/baJNjv9ysej29rnfF4fNvr2OkYrA0AAAAAgAaiRhwAAACAo6InzZpfu9FAbZL0O88ObGuwto2cHjqyrcHaNvKbTx4urLfH23oDuqFxSMQBAAAAOGp3lzNpR7UjsFeq26Ek2an1ov2QiAMAWtK9lYwS/3C9pCz4qUPcxAAAgLZHIg4AaElZ29Yb1++UlH3h8YNNigYAAKB+GKwNAAAAAIAGokYcANqQx+XSM+b+dWUAAABofSTiaEtnzpxRb29v2fIzZ840PiCgwbo8Lj07uH/rBQEAAHaAyclJTU5OritfXFxsfDAVIBFHW5qdnS1b/txzzzU2EACb8rpden5Nv+7NpqEBAACoxZUrV3Tx4sVmh1ExEnG0paGhobI14gMDA40PBmgjLsPQ/j1d68qc4nW79NQRn2PrBwAAkHJ5wMmTJ9eVLy4ubliJ10wk4mhLk5OTOn78eLPDANpOt9et3352oNlhAAAA1NVGXVQvXbqkEydOND6gLdA+EAAAAACABiIRBwAAAACggWiaDgBt6N5KRhdmr5aUnR46om6vu0kRAQAAoFIk4gDQhrK2rZt3lteVAQAAoPWRiAMAHLO0nNGf/eBKSdnvPDugni5q7gEAwM5FIg4AO8jyalbJtxZKygKP9KnL48yQIbZsLS1n1pUBAADsZCTiALCDrGaz+mH6ZknZk0f2qYuxOwEAABqGRBwA0JJchqGH+3rWlQEAALQ7EnEAQEvq9roVHjrS7DAAALWybWnlbmmZu0tye8svv3xXqrX7kssrebrKP7eyJNnZGtfrkTy7NljvPcnOlH+uEt7dEj8w71gk4gAAAADqb+Wu9P1/X1r22K9KDwfKL//jiVzSXIuBz0tHf7n8cz/5z9Li+7Wt9+N+6divlX/u778jWT+vbb2S9Lnfl7p6a3892hqdAlG1dDrt6PIAAAAA0MlIxBtkYmJCgUBAfX19hb9wOKxEItGUeMbHx2UYRk1JcjgclmEYCoVCmpiYUCqVkmVZhefT6bSmp6cVjUYL+wkAAACsa6oO7FAk4g5LpVLq6+tTLBZTNBrVwsKCFhYWlEwmZZqmQqGQwuFwSSLrpHQ6rVAopNHR0W2vK5FIKBqNFn5gMAxDhmFocHBQ4XBYExMTkqSpqaltbwsAAADoCAc+mfsz3A3fdDqdVjQa1eDgYMm9ezQaLamgm5iYqEu+gI3RR9xBiURCoVBIpmkqmUzK5/MVnjNNU7FYrPDGT6VS65bZLsuyNDs7q3Q6rbm5OSUSCaVSqbqtfyvBYFBTU1N13ScAAAC0sYee2Pi5z0a0rcHaNvKZf729wdo2cuJLtQ3W1qR+4aOjoxofH1ckEtHU1JRM05SUS85ffPFFBQIBRSIRRaNRRaNRRSKRLdc5PT2teDyu2dlZWZYlv99fqGys5PU7GYm4QyzLKjTJ3iwZjUQiSiaTmpiY0KlTp5RMJusWw+zsrEKhkHw+n4aGhgqJcSAQcLQG3u/3KxaLKRgMOrYNAJ3v/mpG33+zdHCdzz36oHZ5Gl+DAACok80S267dzmzT27P1MjWtt9uZ9TogGo1qYmJCyWRSfr+/5Dm/3y+/369z584pHA4rENhgML0i+Vxnfn5e0WhU8XhclmUpkUhobGxM09PTisVimpqaWrc95JCIO+Ts2bOFX4W2evONjo4W+lpPT09reHi4LjEEg0HZdo2/Km4hFotpeHhYqVRK8/PzkqT+/n4Fg0FqwAHURSZr69Wrt0rKnjH3NykaAADaUyKR0MTEhGKx2KZ5ic/n08zMjAYHB7estDt16pSGhoY0MzNTUu73+xWJRHTq1CmlUikFAoGyyT9IxB1hWZamp6clqaJaYdM0ZZqm0um0xsbG6paIOy0fNwAAAIDWlO/rXWmOEYvFNh1sOb++eDxe9nmfz6fz588XatbD4bDm5uaqCXlHYLA2B+QHKZOkp59+uqLX5BPaVCrFdF8AtuR2GXryyL6SP7fLaHZYAACgxVQ7RtTw8PCmLVwnJiYKlYgb8fv9hQrJdDpdkh8hh0TcAS+++GLhcaXNtItrlps1pRmA9rHL49YXHj9U8kffaQAAsJFqcozTp0+XLbcsq9D6d3BwcNN1FDdHZxal9Wia7oDiX536+/srek1xwl7PAdsAoJk8Lte6ft0eF78BAwB2mJV70j/+99KyT/6zhgz4lq+9Hh0d1enTpyuqKAyFQmVrvPNjQ+Wl0+kNu6oWtwxe+zqQiNfd2jdspX2o9+//6EZ1dna2rjEBQJ4hQz1d7nVlTunyuPTsIAOsAQB2ODsj3fjH0rJjv9aQTUejUY2OjsqyLB09elTnz5/fsr/4Rs+bpqlIJKILFy7o9OnTm+Y6xXlRpZWTOwmJeJ3VOi1Y8S9TTk4tVm+JRELxeLykqYtpmhXPPQigsXq63Pq9k5s3JQMAAJ1jZGRE8Xhc6XS6ZIrlYDCoUChU0p+7EvF4fMOB2oq98sorhceMmr4eiXgLaoemGzdv3lQoFJKUGznx/Pnz8vl8sixLY2NjhV/epqamHJlP/M0336z6NQcOHNDBgwfrHgsAAADQypLJZGFKsbxEIlFSmeb3++tWmZafUzzv3Llz216nJL333nu6ceNGVa+pJW9oBBLxOlubRNcyp3Y71IiPj48rHo+v+6D6fD7FYjENDg4qGo0qFAppZmam7sn4F7/4xapf87WvfU1f//rX6xoHAAAA0Op8Pp+SyaQmJiYUj8fLjqSeSqUKlWnJZHJb0xSPjY0VcppYLFZTTlTOt771LX3jG9+oy7qajUS8ztohid4u0zR17ty5TfuWRCIRxWIxpdNphcNhLSwsNDBCoPPdW8noL199u6TsN588rG4vI6cDAFpEV6/0fH1qQlEfkUhEkUikUGP9yiuvKJFIlCTmlmUpEAjUnIyn02mNj49LyvU1HxkZqVv8nYSha1G1qampLQd4kD4a5MGyLI2OjjodFrCjZG1b1xaWSv6ytt3ssAAAQBvw+XwaHh5WLBZTMpmUbduamZkp9OW2LEvRaLTq9VqWVei+Ojw8zLRlm6BGvM7q1eyiE4RCocKvYePj4zp37lzdjs9LL72kRx99tKrXHDhwoC7bBlC5eysZXZi9WlJ2eugINfcAALSYYDCoZDKpcDis6enpQk15NQOtnTp1Sul0WpFIpKIB3ar1la98pTDYXKXefPPNmrq1Oo1EvM7WDs1vWVbVyWenJPNrm7IkEomKatIr8eijj+r48eN1WRewk6xksrr09u2SsuOH98rrdqaBVNa2dfPO8royAADQGPkxmyo1NTUlw8hNbTo7O1txIh4KhZRKpSpuPVuLgwcPdszgyzRNr7N6JNGdMs/e2v0onsIAQHOsZLL63s/eK/lbyWSbHRYAAHBIIpGoehyr/EDLc3NzFS0fCoU0OzurZDLpWBLeaUjE62xt8lnpVGTFH45WrRFPpVIaHBxUX1+fJiYmqn79ThjIDkD9uAxD+/d0lfy5PvyFHgAAVG52draq5fO14Pv3799y2XA4rHQ6rWQyWbb2PJVKVd2cfCegaXqdrU2iK00+i39t2s5UAU46e/as0um0JCkajer06dNV/WjQqj8woLF+kL4pSfrh6utNjgStrtvr1m8/O9DsMAAAtcqsSjffKC3b/5jkJgVptFgsVtV0wvlR1Ld6TXESvtG9fiKR6JgWv/XEp8ABfr+/8OattEa8eLmnn37akbi2K5+EV2rtvg8ODtYzHAAAALSyzH3p0kulZZ89K3l7qluPu0tye8s/t3xXUo1jj7i8kqer/HMrS5JdY9ctl0fy7KrttQ5JJBIaHR1VLBbbctl0Ol0Y22mz/uHhcFiWZSmZTG66vpmZmcJI6vgIibgDgsFgIRGvNHktXq5V+1WYplnYr1gstmUN99p9P336tFOhAQAAoB38+Hz1r3nsV6WHAxusbyKXNNdi4PPS0V8u/9xP/rO0+H5t6/24Xzr2a7W91kH52Yw2m8nIsiyFw2H5fD6dP7/xuYpGo5qenlYwGNwwyZ6fn5dlWUqn00xlXAaJuAPOnTtXeKNv9QtRXj7BNU2zZZumB4NBmaZZ8XyAxaMzBoNBmqYDAABg53rsV6UbP5Osnzd805FIRLFYTOFwWH19fYpEIgqFQjJNUz6fT+l0WqlUSmNjYzJNc9Om5tFotDBeVCKRqGj7Q0ND9dqVjsFgbQ7w+XyF/hQXLlzYcvniN3AlvxZNT08XEvdGyv/yVWm/9+np6cLjSprBAAAAoIN09UrPn8v97XRdvRvX6jvMNM1Ca9aZmZlCReHY2JhOnTqlwcFBhcNhzczMKBaLKZlMblgxmEqlahq0mQq59agRd0g8HlcgEJBlWZqent60uXl+snu/369IJLLhcpZlKRAIFJp8x2IxjYyMbCvOakYyN01TIyMjOnXq1JY1/RMTE4U4R0ZGKp5/EAAAAED9rJ2CzO/3F/KPavn9ftl2jX3yUYJE3CGmaer8+fMKh8MKh8Oam5sr+8vSxMSEpqen5fP5tmzynUgkSvpdj42N1ZSIFyfflQ4mlxeLxRQIBBQKhTQ1NVX2163p6WlFo1FJHzWDAeCcH344Ev1KJiuve/OGTiuZrJJvLUiSnjG3npKkmZZXP4o1L/BIn7o8NOYCgLbzud+v/bXuDQZUk6TPRrStwdo28pl/vb3B2jZy4kuSnck99u6ubf3oCCTiDhoeHtbMzIzC4bACgYBisVihxtuyLI2OjmpiYkLBYHDDpLbY2ucr6Ute3Ow9nU6v+/UrGo0qGo0W+odIuT4cm8WSTCYVjUbV19enkZERPf300zJNU/Pz84rH44UfFor3FwCqtZrNFn5kyHvyyD510asKANpPV69D63Uoma12ZPeK19vtzHrRdkjEHRYMBnX58mVduHBB8Xi8pA94MBjUzMxMxXP6BYNBjYyMaHx8vOJB0/KjGBYn1sWP5+fnNTY2Vvi/ZVmampracuT2eDyucDiseDyuiYkJWZYln89X6IMSiUToCwIAAAAAZZCIN4DP51MkEqlL7XAsFquqqbeTfTiCwWDFPyIAqC+XYeixQ3skSa9f/0CSZDQzIAAAAFSMRBwA2lC3163feOKwJOmN63eaHA0AAACqQSIOAHCM22XoySP71pUBAADsZCTiAADH7PK49YXHDzU7DAAAgJZCIg4AO4jX7SpMW/bV0LEmRwMAALAzMQcMAAAAAAANRCIOAAAAAEAD0TQdANrQvZWMEv9wXdJH05eZD/bK4+b3VQAAgFZHIg4AbShr24Vpy+YXlyVJRx/sbWZIAAAAqBCJOADAMfdWMvrLV98uKfvNJw+r2+tuUkQAAADNRyIOADtINmtr4W6uBr0RTdqztq1rC0vrygAAAHYyEnG0pTNnzqi3d30z3DNnzujMmTONDwhoExnb1hvv5Zq0/9Vr70iSoidN+pYDAIC2Njk5qcnJyXXli4uLjQ+mAiTiaEuzs7Nly5977rnGBgLAMYYM9XS515UBAACsdeXKFV28eLHZYVSMRBxtaWhoqGyN+MDAQOODQcf6k5nXa3rdV0PH6hzJztTT5dbvnRxsdhgAAKANDAwM6OTJk+vKFxcXN6zEayYScbSlyclJHT9+vNlhAAAAAGgBG3VRvXTpkk6cONH4gLZAp0AAAAAAABqIRBwAAAAAgAYiEQcAAAAAoIFIxAEAAAAAaCAGawMAtKSVTFaX3r5dUnb88F55mfMcAAC0ORJxAEBLWslk9b2fvVdSduzQHhJxAADQ9ribAQAAAACggagRB4A25DIMPdzXI0na2+2VJBnNDAgAAAAVIxEHgDbU7XUrPHREknRtYanJ0QAAAKAaJOIAAMe4DEOPHdqzrgwAAGAnIxEHADim2+vWbzxxuNlhAAAAtBQScQDYQTwuQ4FH+iRJ0ZOmJKnH625mSAAAADsOiTgA7CCGYcjrzjUN393FVwAAAEAzMH0ZAAAAAAANRHUIgJZl27ZWs3bZ5wxJHnf53xIzWVtZO/e6u8urknLNr40OGiTs/mpG33/zfUnS5fcXJUlH+nvkcfH7KgAAQKsjEQfQslaztpJvLZR9rsfr1pNHfGWfe+fWUmFKr/jFtKRcf+hOaoqdydp69eotSdL12/ckqTCvOAAAAFpb59yVAgBazr2VjBL/cL2kLPipQ+pmgDgAALCDkYgD6DgrmfLN2SFlbVt37uWa61+dvytJOuzrkdvlTLP9rG3rjet3Ssq+8PhBR7YFAADQLkjEAWAHyWRt/fSd25Kk6eQ1SZ3XbB8AAKDVcecFoK088fA+ed0ubVZ/e3hfd6HfNAAAANBqSMQBtBWv2yXvBqOl57kcamaNxtrd5dFXQ8eaHQYAAEDdMc8NAAAAAAANRCIOAAAAAEAD0TQdbenMmTPq7e0tW37mzJnGBwQAAACgaSYnJzU5ObmufHFxsfHBVIBEHG1pdna2bPlzzz3X2EAAAAAANN2VK1d08eLFZodRMRJxtKWhoaGyNeIDAwONDwYAAABAUw0MDOjkyZPryhcXFzesxGsmEnG0pcnJSR0/frzZYQBw0Gomq/T7pc3JzAd75dli1HwAALDzbNRF9dKlSzpx4kTjA9oCiTiAlmUY0qG93evKsDMsZ7L6q9feKSmLnjRJxAEAQNsjEQfQsjwul44+uL4LAgAAANDOSMQBbJ9tSyt3c48zy+ufN9ySy93QkDzuXNV5T1duu4Y6qyrdZRjav6dLktTjze8jAAAA2gGJOIDtW7krff/f5x5f/dH6532fkHyPNCwcr9uloUf6JUm/d3KwYdttpG6vW7/97IAk6eadMj9+AAAAoGWRiAMAHOMyDD3c17OuDAAAYCcjEQcAOKbb61Z46EizwwAAAGgpJOIAsIN4XIaeeHifJOnLz+a6C3R7Gtt/HwAAYKcjEQfgjMMByf3hJcb4MNHLrOjQB5ckSU++c0GS9PcH/x/KuLvLrUGrmaxev36npOzYoT1MX7UNhmFod1fuvDy4Z1eTowEAANiZSMQBOMPtkdxdawptebP3JEk9K5YkyZC94SpsSbfvrawrAwAAANoZiTiAjpPJ2rrxwX1J0t9etSRJxw/vlbeDatKXV7NKvrUgSbq2kJs67qF9PXK7GAgNAACg1ZGIA+g4WdvWlZuLkqTv/ew9Sbkm7Z2UiK9ms/ph+qYk6drCkiTp0N5uuZlNHAAAoOWRiAMAHHN/NaPvv/l+SdnnHn1QuxggDgAA7GAtnYhfuXJFkjQwMNDUOACgU9i2raWVjCTp/Tu55vv9u7vkcqhJeyZr69Wrt0rKnjH3O7ItAACAdtHURPzKlStKp9NKp9Oam5srPE6n01pYWFBfX5/OnTunP/iDP2hmmADQMVaztl67lkuM//z/viJP9p5+9/MDhZHUy+rqbVB0AAAAO0NTE/FgMKjLly9LytXS+Hw+RSIRffOb39SpU6eaGRqAqhiStyf30OX9qAwtzZO9p6Ff/Lm8P+yTNus///y5xgUFAACwAzS9abpt5yYjGhkZ0Te/+c0mRwOgJl27pc//H7nHK0tNDQWdo8frVvSkua4MAACg3TU9ETcMo1ALDgBAnmEYmzeZBwAAaFMtMZdPLBZrdggAAAAAADRE06sa/H6/9u7d2+wwAACS9NmzH/X3BwAAgCOanogPDQ1t+vxf/MVfVLQe0zT11FNP1SEiANjBvD3rR0nPrEo33ygt2/+Y5G76VwgAAEBbavpd1ODg4IbPXb58WX/8x38sy7KUTqdlGKWjMOdHWjdNU7/1W79FIg4ATsjcly69VFr2ud8nEQcAAKhR0++ifD7fhs8dPXpUs7Ozhf+Pj4/rj/7ojwqvm5qaYpozoBVkVqR3Xss9vv127t89hyQXI1yjdpmsrbet0lH4D/t65HYxNR4AAGhvTU/EqzEyMqKZmRl997vf1fj4OEk40Coyy9Ibf517PD+X+7f3QUlrEnHDpTtdByRJ1/d8WpJkGxsn64ak/t6udWXYGe6vZjSdvFZSFj1pMpI6AABoe213NxMOh/Xd735Xp0+frvg1ly9f1vj4uP7Df/gPDkaGRjpz5ox6e3vLlp85c6bxAaEyLo+snk9Iki73f37LxT1ul44desDpqAAAANDmJicnNTk5ua58cXGx8cFUoO0S8Xyf8mpGWk+n05qYmCAR7yDFXRaKPffcc40NBGgSQ4Z6unKtCTzu2toJZA2vLvd9Tp959LC8ng+/Dtxdm78IAACgBV25ckUXL15sdhgVa7tEvFab9UVH+xkaGipbIz4wMND4YNByvG6XnjH3S5K+GjrW5Gic0dPl1u+dzP0wubScqWkdWZdH1x84ruxhU6K5NwAAaGMDAwM6efLkuvLFxcUNK/GaaUfceVmW1ewQUGeTk5M6fvx4s8MAsAWXYWj/nq51ZQAAAPW0URfVS5cu6cSJE40PaAtNT8QbkSS/8sorjm8DALBet9et3352oNlhAAAAtJSmJ+LxeFymaVa8/I9//GNJ0n/9r/9Vtm1vufz8/LwmJibWzUEOADuR22Xo0w/lxtgYDjwsSdrlYZo5AACARmp6Ip5OpxUOh6t6jW3bGh4ermr5vr6+akMDUG/ZVR1YfF2S9OnrfylJ+scDv6qMa1fZxVczWaXfLx3p0nywVx63y9k4O5jLMLS3xytJOtK/u8nRAAAA7ExNT8QlVVSznWcYhgzDqOo1AFqEndWu1Q8kSXvvvyNJMuzsxotLml9cLik7+uD6QfoAAACAdtISiXg1SMABbCWbtbVwN5fAv349l/h3Wk36SiarS2/fliS9e+ueJOnAA7vkdlXRDWf5rvTjidKyz0akLmrKAQAAnNQSiXgsFpPP51N/f39d1zs/Py/LsjQ2NlbX9QJobRnb1hvv3ZEk/dVruZr36Emz4xLx7/3sPUnSlZu55vv793TJrWrGw7CllaX1ZQAAAHBU0xPxSCSiP/zDP3R0G7ZtKxaLOboNAMB6y6tZJd9aKCkLPNKnLk/n/CgCAABQraYn4oODgx2xDQBoB7ZtazWbq/W+u7wqbyYrj9uQUVVNeuVWs1n9MH2zpOzJI/vUJRJxAACwczU9Eff5fA3ZRiPmKweAVreatQs11P9R9zX0iwUNPdInr3uTRNy7W/rc768vAwAAQE2amogPDw9XNYd4rUzT1Je+9CXHtwMAHckwpC5GqwcAAKiXpibi3/zmNxuynaNHj+rChQsN2RawI3X1Ss+fyz3+XnNDQefo9rj15WcfWVcGAADQ7preNB0AgHJcLkMP7tnV7DAAAADqjtFyAAAAAABooLaqEb9y5Yosy9L8/Lz6+/tlmqb27t3b7LAAAAAAAKhYSyfif/u3f6sXX3xRiURCqVSq7DI+n0/BYFC/9Vu/pX/xL/5FgyMEgB0gm5FuXSst2/ew5KK/NgAAQC1aMhH/27/9W42OjiqRSEjKzXu7kYWFBU1PT2t6elqDg4OKxWIk5ABQT6v3pL/9L6Vln/t9RlIHAACoUcv1Ef+3//bfKhAIKJFIFBJwwzA2/ZNyyfqbb76p4eFh/dN/+k91+/btZu4GsLNkVqX3/iH3t3gj92dnmx0V2lw2a+v9O/dL/rLZjX+YBQAAaBctUyN+69YtBYNBpVKpkhrw4sc+n0/9/f2SpPn5eVmWVXZdf/3Xfy3TNJVMJvXII4+UXQZAHWXuS5deyj2+8bPcv0d+SXJ3lS5nuLTk9UmS5ncflSTZxsa/BxqS9nZ715VhZ7i3mtGf/+CtkrLoSVO7u1rmqwsAAKAmLXM3c+rUKf3kJz8pJN5+v18vvPCCgsGgTNPUvn37NnztT37yE83OzmpqaqrQnH1+fl6hUEizs7MM6Aa0CpdHN3cPSpJefzC05eIet0ufPsznFwAAAJ2lJRLxX/u1XysMxub3+3X+/Hl95jOfqfj1n/nMZ/SZz3xGZ8+elWVZOnv2rL7zne9obm5OwWBQP/7xj50KHQDaVtbw6Nq+gD7zyEPyej4ceM3l3fxFAAAA2LamJ+Ivv/yyZmZmZBiGRkZGNDY2tq31+Xw+TU1NaXp6WqdPn1YymdR//I//Ub/7u79bp4gBtDqPy1DgkT5JuabMktTj7awRvnd3efTV0DFJ0p/MvF7TOrIur67tCyj7iCnR3BsAAKBhmj5YWzQarVsSXmx4eFh/+qd/Ktu2NTo6Wrf1Amh9hmHI63bJ63Zpd5dHu7s8hYEd0ViGDPV0uUv+DHr6AwCAHa6pVSA/+clPlE6nFQgE6pqE50UiEc3MzOgv/uIv9L3vfU/PP/983beB5jhz5ox6e9dPnXTmzBmdOXOm8QEBKKuny63fOznY7DAAAECHm5yc1OTk5LryxcXFxgdTgaYm4vF4XIZh6Pz5845t49y5c/rOd76jCxcukIh3kNnZ2bLlzz33XGMDAdqM2zD02ME9kqRff+IhSVKXu+mNowAAALblypUrunjxYrPDqFhTE/HZ2VmZpqmnnnrKsW34/X75/f4NEze0p6GhobI14gMDA40PBpXLrmr/3TlJ0rH3ZyRJc/2/ooxrV9nFV7NZXZ1fKik70t8jj4vEsVYul6H9e3LH+9ihB5ocDQAAQH0MDAzo5MmT68oXFxdbMhdsaiKeTqcVjUYd304wGHS01h2NNzk5qePHjzc7DFTLzqpnxZIk9d+9LElK931+48Vt6frteyVlD/f1OBYeAAAA2tNGXVQvXbqkEydOND6gLTQ1EbcsS4ODzvcdHBwclGVZjm8HQGvI2rbu3FuVJF2dvytJOuzrkdvV4EHCbFtauVv+OZdX8nTVvOrVTFbp93N9nm7euS9J6tvdJVc1+7iyJP3kP5eWfeZfS15+7AAAAHBS0+erMU2zI7YBoHVksrZ++s5tSdJ08pqk3DRmuxs9RdfKXen7/778cwOfl47+cs2rXs5k9VevvSNJeuO9O5KkwCN9clUzIrmdlRbfX18GAAAARzU9EQcAdK6VTFaX3r5dUnb88F55GSAOAADsYCTiAADHrGSy+t7P3ispO3ZoD4k4AADY0UjEAaDZGthXeyWTVfKtBUnS/zuzpKFf3NTQI32bJ8aeHunpf7O+DAAAADUhEQeARvrs2VyC7fJ+VNbqfbVdLmnPgdKybFZaXtz6tXbtA9IBAAB0qqYn4rdu3XJ8G4yYDjjMu1v63O/nHmeWc/8WJ5r4iLdH6uptdhTbd/em9Mq3t17u6a/UvIldHreGAw+vKwMAAGh3TU/E//iP/1j79u1zdBvxeNzR9QM7nmF8lFy6qQFFfbhdho707252GAAAAHXX9EQ8lUopFAo5ug3btmUYDZ4/GECBbUtLGUPKGFq1c5/FbNb+6N8NKjlXM3ajQgQAAAAapumJuJRLlAF0rqWMofiVg/Jk7+vYUq4FzG17SZL07gNLOri/fK1nfi5wAAAAoJO0RCIOAGgzu/d/NC5A3sqS9OPzzYkHAACgjTQ9Eff5fJqYmJDP53Nk/ZZlaXR0VJcvX3Zk/ZWamJhQPB5XOp0ulAWDQUWjUQWDwYbHMz4+rtHRUc3Nzck0zZrX02r7hc7icRlayTQ7CpTlcnXGoHMAAABN0PRE/Ny5c/rSl77k6Dbm5uZ07tw5R7exkVQqpVOnTqm/v1+jo6OKRCKSpHQ6rXg8rlAopOHhYZ0/f96xHyOKpdNpRaNRJRKJba2n1fYLTZbNSLeu5R7fs3L/7torGZvMTQ1swbZtLa35JabH62bMDwAA0Paanohvpza2UoFAwPFtlJNIJBQKhWSappLJZElCapqmYrGYBgcHFY1GlUql1i2zXZZlaXZ2Vul0WnNzc0okEkqlUtteb7P3Cy1o9Z70t/8l9/jdv8v9e+SX1o2gbhsuLbj6JEm9B47KbRg6uG/jWtXjh/dq7QgSHlc7JWFGbrqytWWoyNJKRvGL6ZKy6ElTu7ua/tUFAACwLU2/m+nUBM2yLIXDYUnS1NTUhvsZiUSUTCY1MTGhU6dOKZlM1i2G2dlZhUIh+Xw+DQ0NKRgMampqSoFAoOa51Vthv9C+MoZXr3j8kqRDh4LyujevMfds8XzL69otff7/aHYUAAAAaDFNvcsdGRlpSI24aZr6wz/8Q8e3U+zs2bOyLEt+v19+v3/TZUdHRyXlmntPT0/XLYZgMCjbtrWwsKCZmRnFYrFtH+9W2C8A9WEbbl3f82llDn9G+rg/9+dq+u+zAAAAHa+pd1zf/OY3G7Kdo0ePNmxbUq7WOJ94VjJgmWmaMk1T6XRaY2NjGh4edjrEmnTqfqHzeFyGnng4N03al599RJLU7dlgsvI21eN1K3oy98PaSiYrqfpm+xlXly73f17ZR02pHs29Pd3SU//P9WUAAAAo0ebtPlvTxMRE4fHTTz9d0WvyNdWpVKpkBPJW0qn7hc5jGIZ2d3m0u8ujB/fs0oN7dsnVVn3Lt1a8j163S163q/mDmLncUt8jpX+uzvoBBAAAoB5og+iAF198sfC40j7wxU3GE4lEYRTyVtKp+wXAObu7PPpq6FizwwAAAGgpLZeIf/vb39bU1JTS6bR8Pp9CoZD+6I/+SHv37m12aBUrHpm8v7+/otcUJ7atOrBZp+4X0HQuT65/9toyJzZlGBrYnxup/vnHD0rSloPmAQAAoL5aJhH/7ne/q3A4XBjN27ZtGYahVCqlWCymiYkJ/e7v/m5zg6zA2ubXlQ6Otn///sLj2dnZusZUD526X2gst72qT2V+JknyLXTL43Lp577PKuPq2uKVbSqzIr3zWmnZQ09Ibm9pmWeXdOzXGhKS22XoY/ty/bafOuJryDYBAABQqiUS8e985zs6ffq0bDs3Y7BhGCV9HW3bViQSkWVZ+nf/7t81K8yK1DotWHHNca3rcFKn7hcawzCkHndWnuyKBrLXJEk9d3L9tq/uCzQ5OgdllqU3/rq07ODj6xNxAAAA7ChNT8Rv3bqlcDhcSL5t2y4k5Hn58pGREQWDQT355JNNirYx5ufnmx2CI+q5X2+++WbVrzlw4IAOHjxYtxhQuR63rd87ekPKLOvq7VuSpGsODp5m27aWVjKSpPfv3Jck9e/u6qgB2zJZW29bS5Kk20srkqQ93R65qhmwbeWe9PffKS078SXJW+NI57YtrdwtLfPuzv0SAwAAsE3vvfeebty4UdVraskbGqHpifjY2Jik3I2zaZoKBoMaHByUz+eTZVm6efOmEolEoX/y6Oio/sf/+B/NDHlTa5PNSgc1K9aKNcettl9f/OIXq37N1772NX3961+vWwxoXatZW69dyyX8f/6DtyRJ0ZOmdtdjiq4WcX81o+lkrnXBT9+5LUkKPNInl7uKpNfOSNbP15fVauWu9P1/X1r2ud+XunprXycAAMCHvvWtb+kb3/hGs8Ooi6belV6+fFnj4+MyDEN/+qd/qrNnz264bCqV0qlTpzQzM6NXX321ZWvFWzGJrodO3S9IfzLzek2vYyRsVGI1k1X6+gclZeaDvfIwQBwAANjBmnonND09LUmKxWKbJuGS5Pf79fLLL8u27UItOgCgtS1nsvqr194p+VvOZJsdFgAAQFM1tUZ8ZmZGfX19+oM/+IOKlvf7/Tp79mwhgW9FtTTZbgettl8vvfSSHn300apec+DAAYeiAbap3n21N9tUJqtXr1mSpG//r2U99fN5PfmwjynMAABAy/vKV76icDhc1WvefPPNmrq1Oq2pifjs7KxeeOGFql4TDof17W9/26GItm/t/NqWZVWdxLZa0iu13n49+uijOn78eN3WBzRVvftqb2E1kxsQc2k5o5WMvcXSAAAAreHgwYMdM/hyUxNxy7IUCFQ3ddHg4KAk6fbt29q7d68TYW1LPZLNtUlvK+jU/UKdeHqkp/9N7vHyYu5f10dTdK1kpUu3e+TKuGWv5uYMt227ZJpCYK0ut0u//sRD68oAAADaXdOHEK42OTt69Oi66c1aydr9mZ+fryiJLR4MrR1qxDtlv1AnLpe058Om/2VGyF7JGvre+3vlyd7XseXdkqQ9NrNaYXMet0vHDj3Q7DAAAADqrulVC52WnK3dn0pHG5+bmys8Nk2zjhHVR6fuFwAAAAA0WtMT8Ub4yU9+UnVf9O3w+/2Fx2vn395I8XJPP/103WOqh07dLwAAAABopB2RiM/Pzzd0pPVgMFh4nE6nK3pN8XLDw8N1j6keOnW/gK1kbVsrmWzFf3eXV3V3ebWlu9EAAACgeZreR7wRahnhezvOnTun8fFxSVIymazoNalUSlKu+XarNuHu1P0CtnLn3qp++s7tipePX8z9ABV99pB2OxUUAAAA2lbTa8Rv3brl+DYqrb2tF5/PV6g9vnDhwpbLJxKJwuPR0dEtl5+eni4kuI3k9H6hjWWz0p0bub/lxdwftcEAAABAWU2vEf/jP/5j7du3r+rXnT9/vqLXWZalsbGxhk+TFI/HFQgEZFmWpqenN22WHY/HJeX6YEcikQ2Xy0/3lv9hIRaLaWRkZFtxVjroWp4T+4UOsLokvfLt3OO3P/yR6MgvSe6uksVsw9CikRtV3eX1ye0yZIuh01He3eXVQuuCvOhJU7u7mv7VBQAAsC1Nv5tJpVIKhUJVv66aBNS2bfX19VW9je0wTVPnz59XOBxWOBzW3Nxc2abZExMTmp6els/n09TU1KbrTCQSJbX7Y2NjNSXixcl3pYOu5TmxX9g5MkaXvu99RpJ06GNBeTt9TuiuXun5c82OAgAAAC2m6Ym4pKoHNGp07XathoeHNTMzo3A4rEAgoFgsVqgZtixLo6OjmpiYUDAY1NTU1Jb92Nc+X0mf6+Lm4el0ulBLnReNRhWNRmWaZmH9Q0NDm8ZS7/0CWt2ebo8Cj2z+Y95KJqvXrjnf1aaebMOl+d1HlX3wkOT58EcRw93coAAAAHaAlkjEq9VOIxEHg0FdvnxZFy5cUDweL+krHQwGNTMzUzIa+VbrGhkZ0fj4uEzTrKimOd/aoDgZLn48Pz+vsbGxwv8ty9LU1NSWI5zXc7+AenO7DH36ob2SpOHAw5KkXZ7KEkzbtrW0kpGUS64lyeMyWq72vtvj1peffURSrgm3lIuzGhnXLr3+YEjPf9qU6tHc271LOv7F0jLPru2vFwAAoMM0PRH3+XyamJhwrNY0X0O7sLDgyPor4fP5FIlE6tJPOhaLKRaLVby8kz9a1HO/gHpyGYb29nglSUf6qxu3fGklU+iXnHwrd90IPNInr3vzJNfjMvTEw7lxK/IJcneFyX8tXC5DD+7JJbkt02fa7ZEOfqq07MMfCQAAAPCRpt+9nTt3Tl/60pcc3cbc3FxVySsAVMswjEJCnE+QIfV43YqeNNeVAQAA7GRNT8QbMbd0IBBwfBsAUDPDLR345PoyB7gMQw/39UiSnjH3S5I8Luea3Rf/QAEAAICcpt8dNWogr2qn6QJQfy57VYOZXLPvvltJedwuvf3AE8q6vE2OzCGZVenmG6Vl+x/LNeEu5u2WTvzLhoTkdhl6uC/XXP/Zwf0N2SYAAABKNTURHxkZaUiNuGma+sM//EPHtwNgcy47o8HMZUnSntuGXC5D7+75tLLq1ET8vnTppdKyz/3++kQcAAAAO0pT7wa/+c1vNmQ7R48ebdi2AKy322Prq49elzLLurpoSZKuVTnCdzVs29ZqNjdQYX5E8R6vu22mPqxENmtr/u6ypG3s48o96R//e2nZJ/9ZrobeAX8y83pVy69ksoUB8/LN6AEAADoB1TIAKvLMzydKC75XlBhllqWrP2psQJtYzdqFBC4/Anr0pOloX+Xiac/ev3NfktTvseVU7+t7qxn9+Q/ekqTC/OWVjO5ews5IN/6xtOzYr9Ue1PKi9P1/X1r2ud+XunprXycAAEAHIhEHgDpYzdqFhDifIEefPaTqJk/rPJmsrbfn70qSbi+tSJL2dHvk6qDWCQAAANUiEQcAOOb+akbTybclST9957akXM29q5qaewAAgA5DIg4AzdbAvtqrmawuvZ1LiP/Lj1Z1/Jql44f3OjqFGQAAAEqRiANAs9W7r/Zmm5IKfdlvLq7o7nJGtu3IpgAAALABEnG0pTNnzqi3d/0AUGfOnNGZM2caH9BO5/JIH/sn68s+tJqV0nd3ycgYWl7NTVVm23ZHjWKO+nMZhgb25z7nzz9+UJLkdVNzDwAA1pucnNTk5OS68sXFxcYHUwEScbSl2dnZsuXPPfdcYwNBjuGSun0bPr2cNfRX7/rkyd7XseVcYrXHlsjDsRm3y9DH9uWa5z91xNfcYAAAQEu7cuWKLl682OwwKkYijrY0NDRUtkZ8YGCg8cEAa/wwfTP3YHlRz7x7s+S52ftvatXdU1LmySxp6Bc39SxzZQMAANRkYGBAJ0+eXFe+uLi4YSVeM5GIoy1NTk7q+PHjzQ4DAAAAQAvYqIvqpUuXdOLEicYHtAUScQAo0uV26defeEiSdPPOfUmSmzb0AAAAqCMScQAo4nG7dOzQA5Kk/Xt2NTkaAAAAdCIScQDbZ9tSdqW0zOVlNDYAAACgDBJxANuXXZGu/qi07MgvSe6uNQsaWjFy05etuLs/bPJNso7yVjJZvXrNkiT96cU5SdLvPDugni53E6MCAADYPhJxAA2z6urS97y/Ikk6dDjY8XNCr7i6NfvxL0uSPnv1/5QkDV37T+taCriyKzr8wWvSlR5p4JcbHmcrW83YkqSl5YwkyZbdzHAAAADqgkQcAJxiGIWpyrIub5ODWc82XLq96yFlfQck94e1zAa1zQAAAE4jEQfQcdyGoccO7pGkwgjoXR1W+77L49Zw4GFJ0u2lXP98t6u6Zv4Z1y799NBv6pefMKWuOnwduLukx351fRkAAABKkIgD6Dgul1EY8Tw/Anql7i6vKn4xLUn6YfqmJCnwSN+WzejdLkOffmjvujKnuF2GjvTvliTt7WmR2na3V3o4UFq2vNqcWAAAAFoYiTgA1IHLMFonIW4h3R63vvzsI5JyP3JIksfBHygAAADaAYk4ADSZbRi673lA6u6VfJ/IFTrUV9swpEN7uyVJTx7ZJ8nZmnuXy9CDH7ZO2F2P5u8AAAAdgLsiAA3jsjM6krkqSeq/c0kel0s3ej+prKszL0WGndED969LknatfiBJuu/ulYzSZu624dGN3mN69GP7pc/8K0dj8rhcOvpgryTpC48fcnRbAAAAKK8z734BtCSXvapjmdclSXsWVuVyGbq521S2Qy9F7uyyPv3ef5MkHVjM7ffbDzyhrNFZA8cBAACgOp159wugpfS4bUUH3pMyy7p+55Yk6V26CW+LbdtaWsnNrb2SyUrK9b02jCoO7Op9Kf03pWXmc5JnV32CBAAAQFkk4gAcZxjSbo8tGbY8hv1RoUNWMlkl31qQJP3JTK4mOnrSdLSPsm3bWs3aJWVu295g6e1bWskURnfP72tudPcqjmt2VfpFqrRs4POSakzEl+9KP54oLftsROraXdv6AAAAOhSJOADUwWrWLiTEec883N2kaJrFllaWSkqy2azm79yX9NGo6T1ed3U19wAAAB2GRBwA4Jh7qxn9+Q/eliS9di3XLaHqmnsAAIAOQyIOAE1m2Bntu/cL6eaC9Pr/lSt0qK/2aiar16/fkST9RcrWJ9++pU9+7AF5XAwgBwAA0Cgk4gDQZIad1Z7lG9IHdz7qs72dvtqbsCXdvrciSfqFdU8P3VuVg13ZAQAAUAaJOIDtc3mkA4+vL/tQxpbevueVkbF1N5Mrt22bfsLYlMsw9HBfjyTpGXO/JFFzDwAAOgKJOIDtM1xS74ENn76fMTT9i355svd17P4eSdIeu74Dpz/z849G676Xden6rU/k/nMlP2L3X0geW3r+XP02Cke5XYYe7sudv2cH9zc5GgAAgPqhagEAAAAAgAYiEQcAAAAAoIFomg4ARbxul55//KAk6d1b9yTl+ioDAAAA9UIijrZ05swZ9fb2li0/c+ZM4wNCx/C6XXrqiE+S9L2fvdfcYAAAAFCRyclJTU5OritfXFxsfDAVIBFHW5qdnS1b/txzzzU2ELSf741VthyDugEAALSNK1eu6OLFi80Oo2Ik4mhLQ0NDZWvEBwYGGh8MpMyydPVHpWVHfklydzUnHnSE1UxWl96+LUn6Tz+4Ikk6PXRE3V53E6MCAACtaGBgQCdPnlxXvri4uGElXjORiKMtTU5O6vjx480OA1Vade3SX3edkiQdOhKU193Z40WuuHbp1Y8NS5I8mSVJUtbgslspW9LSSkaSdPPOsiQpa9tNjAgAALSqjbqoXrp0SSdOnGh8QFvgjhAAnGK4tNTVL0ladfc0OZj1bBla8vpk794veT6sZTY6+8cRAACAVkAiDqDjeAxbT/XelCT9kwdXJUleV2fVpHa5Xfr1Jx6SJN28c1+S5K5ydPeMu1uvPnRazwyZ6uqqw9eByysNfL60zO3d/noBAAA6DIk4gI7jMWw92v2BJOkpX3X91Jcyhv7s5w9KrjnNvjUvSXryYd+WzejdhqHHDu5ZV+YUj9ulY4cekCTt37PLse1UxdMlHf3l0rLl1ebEAgAA0MJIxAGgiG1LSxmXtJzRaqbyWnSXy2idhLiF7PK4NRx4WJJ0e2lFkuR2MS87AADY2UjEAaDpDK24uiXvbqn3wQ+LnOmrbUjq7821EnjsUK4G3+Vgzb3bZehI/25J0t4emqkDAABIJOIAGsiwMzqUvS5J2n83La/b0HzPI7I7dSRxO6ueFUvSR6Omr7q6pTWJb9bl0fUHjsv8+H7ps2cdDam4SftvPHHY0W0BAACgvA69+wXQitz2qp5c/XtJ0p6bd+VyGZr9+Je16u6sS9FKJitJ8maW9E/enpIkPfjBP0iSfr7nSWVd5WuG764a0vKqerxuGQ7WUgMAAKC5OuvuF0BL6nbb+vKR96XMsqwPbkuSbnRwnpl8a0GStCt7Tw8v5mrC79zbJ0n6m9UjWjbKDyD3t9otKa3oSVO76zGK+VZWl6WrPyotO/JLuUHXAAAA4BgScQCOcxnSg7syUiajJVeutnht8+x6up916f+ycgOEvWrk+if/zifeV4+7c6Ywu7u8qvjFtCTph+ncVG2BR/q2HN29RHZFuvK/Sss+7pdUYyK+siT95D+Xln3mX0ve1ptDHQAAoJlIxAF0HFvSsp1LSJcyuX/tzsnBW5edlRbfX1OU0dKHU5jlm+x7XAZN7wEAwI5GIg4ALeAB94qef/C29PjB6mq1W9zSSkbxH+Rq7vNN9nM19yTiAABg5yIRB4A6CzzSJyk3WNvht3PNsg8btyRJ3XuurhuszbAz2rd8XU9pj7T6d9LP5Vhf7dVMVun3FyVJ/7+/f0eD1z+QeaBXHlfnJP8AAACtjkQcAOosX6PtkUsuV67m12Pk2sZ3u7LK5vvJf8iVXdWDy7+QrJ6P+mxvp6/2JmxJ84vLkqQ331uUb3FZRx/srft2AAAAsDEScQDbZ7il/sH1ZR/K2tL8slvKuHU/+2HNq207OmAb2p9hSIf2dkuSnjySG3Xe7eI9AwAA2h+JOIDtc7mlvYc3fPpextCfX31Qnux9Hbu3V5K0x86Npg5sxONyFWrrv/D4oSZHAwAAUD90CgQAAAAAoIFIxAEAAAAAaCCapgNAEY9Leqb/jmTu17WFu5Ikl4N92e9nXfruwhH9yspbui6vZMzryYd98m79UgAAALQpEnEAKNLlsvVs/6I0uF8/TN90fHu2pBXbpYxtaMU2pIzt+DYBAADQXDRNBwAAAACggagRB7B9mRXpF8nSso8HJDcNrFG71UxWr1+/I0mamr0qSfrNJw+r2+ve7GUAAAAtj0QcQB3YUnZlfdkaq0aX/sb7y5Kkg4efl8ft0qqruwHxNUfG1aWfHvwNSdKu1Q8kSVmDJLJStqTb93Lvq2sLS5KkrE3TfQAA0P5IxAE0jmFo2eiSJK24eyR3Z/eOsQ23bnfn5le/73mgydGUY2jV1S3bW3wumNwdAADAaSTiaEtnzpxRb29v2fIzZ840PiC0FI9h69M9liTp0/3ZXFmH5fxet0vPP35QkvTurXuSqh/dfdXdrdmHf1uBZ011ddXh68DlkT7uX18GAADgsMnJSU1OTq4rX1xcbHwwFeAOCW1pdna2bPlzzz3X2EDQkjyGrU/vtiRJz/ZX1xT8XsbQhV/0Sz+4olev5tZx/PBeeVqs9t7rdumpIz5J0vd+9l5zg8nz7JKO/Vpp2fJqc2IBAAA7ypUrV3Tx4sVmh1ExEnG0paGhobI14gMDA40PppV9b6yChb7keBjtJGtLN5c90p1lLa1kJJXr7Y5Kdbld+vUnHpIk3bxzX5LkdnBedgAAsDMNDAzo5MmT68oXFxc3rMRrJhJxtKXJyUkdP3682WEAdWFLWjG8WjW8Ur7PtkN9tQ1Je7tzo9k/3Ncjqfom7dXwuF06dijXP37/nl2ObQcAAOxsG3VRvXTpkk6cONH4gLZAIg6gYQw7q77sgiRp77235XW79MGuQ7I7dSRx25Yn+2H/7Q9Hlc8aHmlN4rtidOl73l/Rww/slh7+fP36bJfhcbv06cN7JUnhoSOObAMAAACbIxEH0DBue0VPr6YkSXtuWHK5DM1+/Mtadfc0OTJneLL3NPSLP5ckHf7gNUnS2w88oazB/OoAAAA7GYk4AMftctsa/vi8jMyyFm/fkSTdbIVuwtlVyc6WlmUMebL3pcySdmXv6b7Rwc2pMyvSO6+Vlj30hOTmhwIAAAAnkYgDcJzbkI70rEiZFV1150bRnnewX/Jy1qW/uZ0bIOyNn+cG9Tv98Xl1u9cMu/beT6V7t0qKvFlDQ3d6pV/M6WOLd/WXvWHH4tyOpeWM/uwHVyRJs2/NS5KefNgnbzWju2eWpTf+urTs4OO1J+Ir96S//05p2YkvSd7u2tYHAADQoUjEAXScrKTbmVwyeXM5d5nLdtjQ57ZsLS3nRnVfzbTIztkZyfr5+jIAAACUIBEHgCbyGLY+3WNJkj7dn5XM/fK4WmvO8u24u7yq+MXXJUk/TN+UJAUe6auu5h4AAKDDkIgDQBN5DFvHe+bVu/y+/olnn7TrivTuFcf6aq9ms7o6vyRJ+pvX39Mj79/Rkf7dHZX8AwAAtDoScQA718FPlw7WllmVrqUaHoZhZ+S7d1Waf/+jPtvb6au9CduWrt/OTan2d9dua9ft+3q4b7d05X/mFsgsS+6ujVfw/Lm6xwQAALDTkIgD2D7DLfk+sb7sQ7YtLWUMKWNo1TY+KnRwwLZibntVrvk3peLB2voGJBeXwFZmSOrvzf0o8NihPZIkV4PeMwAAAE7iLhTA9rncku+RDZ9eyhiKXzkoT/a+ji3tkyTt2Su5GpRTGXZG7jvvSK6iRHztDwdoOR63S8cOPSBJ+o0nDjc5GgAAgPqhUyAAAAAAAA1EIg4AAAAAQAORiANAEcOQPrZrRR/bu0t7dnl0cO8uR7uyL2ddevnWYc3de0Cv3tqtV69ZWs1mt34hAAAA2hZ9xAEgz3DJ07tfR3slfSqgn66+o0/0P6CMg1N7ZSV9kPHqftatuxmXtJyRbW/5MgAAALQxEnEAyHN5pIOfyj0+8S/1+juvNzceAAAAdCQScQDbl1mR3n2ttOxjTzgyDzZ2jtVMVun3FyVJ/+21tyVJwU8dUrfXvdnLAAAAWh6JOIA6sKWVu+vL1lg1vPq/vb8kSTrwsV+Rx+XSqmtXA+JrjozLqzcePCVJ6lmZlyRlDZLIStmS5heXJUlvXL8jSfrC4webGBEAAEB9kIgDaBzDpTvGHklSr7dfXndnjxdpGx7d3D0oSVry9jc5GgAAALQKEnEAHcctW2b3B5KkT+2TXNllGWsr7Nucx+XSM+Z+SdK1hdzOuaoc3n3V3aMffiKiJ3/FlPf7/6/tB2W4pQOfLC1z0QIAAABgLRJxAB3H67Ll770pSXr2gKTMsnS1sqHI72UM/eW7Pmn2qn769m1J0rFDe+Rpsdr7Lo9Lzw7mEvEfpm82OZoPebulE/+ytGx5tTmxAAAAtDAScQDIy67KuPGmdt/cK72R0acX7ijZ/Yxs7Wl2ZG3L63bp+Q/7db97656k6mvuAQAAOg2JOADk2Vm57r6v/tUl6e4uPbx6Vz+xP9vsqOrKkNTz4ajj+/d0SXI2Mfa6XXrqiE+S9L2fvefYdgAAANoJiTiAxrGz2mPnRr/evTIvT8alJa9PMlqr2XejLRtd+uuuU3p4727pE5/P9dnucuby7HG79OSHifFvPzvgyDYAAACwORJxtKUzZ86ot7e3bPmZM2caHxAq4rFX9L+t/EiStOfdG3K5DM1+/MtadffUtkLblif7YXPn7Mr6pwsJviF5d6951vnm0Z7MkoZ+8eeSpIdvJSVJbz/whLIu5lcHAACop8nJSU1OTq4rX1xcbHwwFSARR1uanZ0tW/7cc881NhBUpMtl69c/ZsnILGv5Vu5ieKsOebAne6+Q6B7+4LV1z9/e9ZCkg5LbK308sP0NdprMqnTzjdzjxRu5f3fv3/EtFAAAQPu5cuWKLl682OwwKkYijrY0NDRUtkZ8YGCg8cF0sqIaZ8NelW1scMnIZjddjcclHdtzX8os66onV3N928F+yRnb0LXlXv0806+f/qJPkvSbH7PU7a5s5PR2cG8lowuzVyVJr161JEnHD++tbnT3zH3p0ku5xzd+lvv3yC9J7q7aglq5J/3jfy8t++Q/y42mDgAA4KCBgQGdPHlyXfni4uKGlXjNRCKOtjQ5Oanjx483O4yOV1zj/ODdtG70Hiu/oHWlcUFVwJa0mPHIUpfuL+WSymzn5OCSpKxt6+adZUnS0kpGUm6/m8rOSDf+sbTs2K81JxYAALCjbNRF9dKlSzpx4kTjA9oCiTiApsj3mXZlVzbtM/3DT0QaFVJTuGXL7P5AkvSpfZKO7JPb1TnTey0tZ/RnP5iTJM2+NS9JevJhn7wtNi87AABAI5GIA6jYnvvv6a63b33i7GqdS8m7ez6tu3aX/mb1iDJy6aFmB7QFr8uWf/cN9axaemq3T+qfl+bnpf2PSe76H9dM1tY7t5YkST9Kz+vwwl0d3tcjd923lGPL1tJyrsZ+NdP0OnsAAICWQJUEgIrsWv1AvntXmx3GlrKGR1mXV8tGlzIb9WlvMS47o/13L+f6aV96KfeXue/ItrK2rWsLS7q2sKQfX5nXtYUlZW0SZAAAgEZqj7tUAK2tb0DyfaK0jCm6sE2GpL3duffRw325Ke5cDg7yBwAA0Cgk4gC2b4um6XdXDcWvHJQne1/H7vokScd6bsljUBOLjXncLn368F5JUnjoSJOjAQAAqB+apgMAAAAA0EDUiAM73DM/n9jwOVd2RYc/eK2B0QAAAACdjxpxAChiSNrftar9vV3q8brV1+uVk72Sl7Mu/a8PDumt+3t06XaPLr19S6vZrINbBAAAQLNRIw6gjRladXVLyo2Wni+rfXUueXbv1bHdkj55XJdX39XggX3KuJz7zTIr6f2Vbi1mPHKvuqV7q2IQcwAAgM5GIg6gba26uzX78G9LkjzZex89UWuFsssjfeyJ3OPP/Cv99P3XtxfgdqwsffTYu1titHAAAICOQSKOqqXTaZmm6djyaADblrIrkiTDXpW9wXzbLjvTyKhQ7MfnP3r8ud+XunqbF0uTrGazujqf+0Hiuz+7Lkn63KMPapfH3cywAAAAto0+4g0yMTGhQCCgvr6+wl84HFYikWi7GMLhsAzDUCgU0sTEhFKplCzLKjyfTqc1PT2taDRa2AZaTHZFuvoj6eqP1L/01oaL9d37eV03mzG8esXj1ysev97d/Und6D2mrNG5SVXW8Opy3+d0ue9zsrqPyOo+IruD97febFu6fvuert++p1ev3tKrV28pk6XdPgAAaH8k4g5LpVLq6+tTLBZTNBrVwsKCFhYWlEwmZZqmQqGQwuFwSSLbLjEkEglFo9FCcm8YhgzD0ODgoMLhsCYmcqNxT01NObBXaEe24dKCq08Lrj7d8zyg+54HJKNzL0NZl0fXHziu6w8c151dB3Vn10HZHby/AAAAqAxN0x2USCQUCoVkmqaSyaR8Pl/hOdM0FYvFNDg4qGg0qlQqtW6Zdo8hGAxqamqq7vuE+ttz/z3d9fYp6/KWlLdrbbVL0se7FiVJj+3JJb6uTuhinc1It65JktyZrH6p7wPd2/2QLr+f29dqu5Gvuro1+/Ev68lnBuTVaq5wzXugKoZb8n2itMzVnu8hAAAAJ5GIO8SyrEKT7M2S0UgkomQyqYmJCZ06dUrJZLLtY/D7/YrFYgoGg9taDxqjZ8VSz4qlu96+ZodSN12urJ594IYk6dmPVTdy272MocSNvdJrb+v16x9IkswHe+VxO1eTvSyv/sb7yzq8p0f6+P+WS4y7ii7P3t25f1fvSX/7XyRJuyT9b5L0xO/r1V/cqW3DhqFVd0+u/7m7azu78GGc3dJn/lVp2fLq9tcLAADQYUjEHXL27FlZliW/3y+/37/psqOjo4W+1tPT0xoeHm75GGKxmIaHh5VKpTQ/Py9J6u/vVzAYpAa8g8z3PCKju7SGM7vBwG7N4Mqu6sDiP0rK1epL0mLXg7U3/86uSjff0ur7+6R/+B965OZt/d2uz8h+0OGB0gxDy+rSqmuXlE+Mu1rnOG+Hx+XSM+Z+SdK1hbuSJBcjwAMAgB2uM+70WoxlWZqenpakimqFTdOUaZpKp9MaGxurSyLeiBjyr0Hnsg2P7BbOmVz2io4ufF+S5Lt3VZJ019tXeyJuZ+W+844OLd+W7hhaWbmrn3Y9Wa9wW4bHnTupPV25ZuPGduZe30KXx6VnB3OJ+A/TNx3bDgAAQDth1CAH5Acpk6Snn366otfkE9pUKqV0Ot0RMQDr2La67GV12ctyZVfkyq7khsZGw3jdLg090q+hR/r1eycH9XsnBwsJOQAAABqDRNwBL774YuFxpc20i2uW6zGlWSvEAKzlsZf13Mr/1HMr/1OfuPOqDn/wmlx25/Yh9mTuaejaf9LQtf+kw7df1eHbr8qV7dz9BQAAQGVomu6AVCpVeNzf31/Ra4qT5XoM2NYKMQB5Xpet5x+8LVdmWfatD/sJayfUhNvyZO9JUtEPDi2030WjsOuelft3196OnlIOAACgFZCI19naJt2V9qHev39/4fHs7GzbxwAU87qkp3xLUmZZVz3Ljm9vOetScjH3fr757gOSpOCB2+p2t1ASvE2r2az+8d0P9I+pa/rp27nR3Y8d2lPd6O5Fo7Dr3b/L/Xvkl2ofQX31vpT+m9Iy8znJs6u29QEAAHQoqj3qzLKsml5XXBtd6zqaEUMikVA4HFZfX1/hLxAIlPRRBxotK+kXy736xXKv3rjTrTfudCvbOTm4pFzX+tv3VvUL655u31vR7Xsrza9rz65Kv0iV/tEUHwAAYB1qxFtQfjqwVo7h5s2bCoVCknJTn50/f14+n0+WZWlsbEzRaFSjo6OamppyZD7xN998s+rXHDhwQAcPHqx7LMB2uCR9vGtRkvTYHpd0aE9HTe91byWjC8krkqRXr1qSpOOH9zo6LzsAAOhM7733nm7cuFHVa2rJGxqBRLzO1iawtcypvd0a8UbEMD4+rng8rkgksm5bsVhMg4ODikajCoVCmpmZqXsy/sUvfrHq13zta1/T17/+9brGAWxXlyurZx/IfaE8+7Gs9MRhR7eXydq68cF9SdJr127p4O17OvDALjk1bnrWtnXzTq47wtJKRlJL9ZIHAABt5Fvf+pa+8Y1vNDuMuqBKos62m0S3QwymaWpqampdEl4sEokU+qaHw2FH4wFQuaxt68rNRV25uaiLr9/Q5fcXle20dvsAAAAtjkQcVZuamtLw8PCWy+WXsSxLo6OjTocFoMMYknq8bvV43dq/p0v793R1VLN9AACwc9E0vc5qaQbeiTFIUigU0vj4uKRcU/Zz587VLbaXXnpJjz76aFWvOXDgQF22jeotZQz92c8flCd7XwNL+yRJj3bfltugJhYb87hdevKIT5L0288ONDUWAADQfF/5yleqbm375ptv1tSt1Wkk4nW2ds5uy7KqTj63m6y2QgzS+mnTEolERTXplXj00Ud1/PjxuqxrRzJcUncuIb7vyV0GbAdrGm1bWsq45Mm6lLFz2yEFBwAAQDUOHjzYMYMv0zS9zuqRwK5NpNsxhnLreOWVV7a9TtSJyyN97AnpY0/oRu8x3eg9JtvgdzkAAACgEUjE62xt8lnpVGTFA6zVu0a8XjGkUikNDg6qr6+vpnnCW2EgO2ArhqS9noz2dnu0y+PWA90eOdkreTnr0g8+OKAffHBA/+3dffpvr72tex+OLr6Op0d6+t9IT/8brQT+d736sWGtunY5GB0AAACcQBVYna1NYCtNPufm5gqP1zbpbpUYzp49q3Q6LUmKRqM6ffp0VT8atErfdWBjhjy7enR8l6THBnU9c12fPLhPGQfnvM5K+sVyb27rd7ql63f0hcc3aHLlckl7PhzrYHlVS10fOBbXhr43tvFzmWXp6o9yjwd+uTHxAAAAtCEScQf4/X6lUilJlddGFy/39NNPt2QM+SS8Umu3Ozg4WNXrga2sunv0w0/kptF75udFrTSyNa7Q7ZU+Hsg9/uxZvXrr9e0FCAAAAJRB03QHBIPBwuNKk9fi5eoxoJkTMRTXksdisS1ruNdu9/Tp0xXFAQCSlMnaurZwV9cW7uoHczf1g7mbWl6t9VcWAACA1kEi7oBz584VHieTyYpek6+9Nk1z203TnYohGAxqeHhYtm1rZGRky/XNzMyUvJam6cgYHr3qOaFXPSf0Xo+pm7uPKmu4mx2WY7KGR9f2BXRtX0C3dz2k27sekm1w2a1U1rZ1bWFJ1xaW9MP0Tf0wfVOrWRJxAADQ/rgjdIDP5yvUSF+4cGHL5ROJROHx6OjolstPT08XkuZGxhCNRjU9PV1xn/Pp6enC41gsVtFr0CDZVenmm9LNN+Vb+rl8Sz+XYW8wQFgd2YZb112HdN11SHe9/Vry9uemUutQWZf3o0S8+7Budx+W3cE/PAAAAKAynXsH3GTxeFw+n0+WZZUkpBstK+X6dUcikQ2XsyxLg4ODCofDCgQCGh8fb2gMpmlqZGREp06d2nRdkjQxMVFomj4yMiK/37/la9BAdlb64B3pg3e0Z/mG9izfkGF3Tk2jS9IB7z0d8N7Twz3LerhnWS4nhz5vlGxWunNDunNDrrvv6/i++3rs4G7193apv7er6tHdV1279OrHhrUS+N+lw/7cn8u7jQANybs799f7YO6vg39oAQAAqBV3SA4xTVPnz5+XJIXD4Q37aU9MTGh6elo+n09TU1ObrjORSJSsZ2xsk9GLHYohX7MdCoU2rBmfnp5WNBqVJEUiEWrD0XBdrqxO7n1XJ/e+q/DHFxT++IK63XZFr72fNfTdGw/ouz+7rsvvL+ry+4ut0xx6dUl65dvSK99W90/+P/rVxf+m3/hUn44dekDHDj0gT7WjuxsuLXX1S70HpK7e3J+xjV8s8oPdfTwgffZs7s/bU/v6AAAAOhSJuIOGh4c1MzMjn8+nQCBQMve2ZVmKRqOKRqMKBoO6fPnyln3D1/axrqQveb1jkHJ9zk3TVF9fn0ZHRwtN5ROJhMLhsMLhsHw+n+LxeKGmHXCCYa9q/9057b87p56VefWszOdq+2uVzcheeEs333tbN//+u9p/40d6/9YHsivL4VGG22XoySP79OSRfTq0t1uH9nZvK9cHAADoBCTiDssnuLFYTPF4XH19ferr69PRo0c1Pz+vmZmZQqJcybryg6SZprll7bUTMeTF43HNzMwonU7r7NmzCgQChVr3WCymy5cvb9rMHqgHd3ZFj73/sh57/2Xtv3tZ++9elms7fd3tjNy3fq6H77+ph28l9enl1+RpQN/5TrbL49YXHj+kLzx+SEcf7NXRB3vlcfHVAwAAdjbmEW8An8+nSCRSl8Q0FovV1NS7njHkBYPBkmnS0D5sW1rN5qolV+3cv/eyLmW3+G1ul5GlNrPNed0uPWPulyR9NXSsydEAAADsTCTiwA60lDH0qtUrSbq91CVJ+pvVI1o2ujZ93W/0/VzdRu1Nvz3Z+/rV5ZclSQO3b8lj2Hr7gSeU3dYAYa3LnbmnE+/9fyVJhz64JEm60ftJZV1cegEAAHYy7gaBnSKzIunDzs7Zxja39rikZ/rvyJVdlte6J0lyqfM7Xhuy1bNiSZK82XsflrbQfmez0t2bucfLi7l/vbu3N2AbAAAAtkQiDuwU774mrdyVJHmzhqTehm26y2Xr2f5FKbOsq957W79gm1ayhv5uqV+StHTjAUnS5/bf0S5XCyXB27SazSp9Y1Fzf/+OXr/+gSTJfLC3upHT86OwS9Lbqdy/R35J+v+3d/fBbdz5nec/3Xjgkx4ASrJsWbIocOyZWB7bQ1Jje+1ZOSNwsnXrSa5ypH2VvZmo6lZELlc1t/vHieu6v1J1VSrq7vau8kduyWxtKTN7ubLI3Z2ck63dJZ2Jk3HsGZOM7Yw8HtuEZD1Zj2SL4jOA7vujCQggQRIg8cz3q4rFZrPR+IFEo/vbv+/v+/OsnxmxJjshTV91ly/+zYP9eTe5PwAAgBpFIA6g5iRkKLrgBuBL9xolSc8HZ2qqPKXjSHdnl/T5rVlNzi5Jko7sLd3NleyNSkjWZXf50k/d74+2SSIQBwAASEcgDkCSdGLXdXm8GxdrQ2GZkvYtZwkcbPBKwQaZNZQavhBL6M2PrkiSPr4+LUl6Yv+O/Oc8BwAAqCEE4gAkST4zIf8GsZHtSHdi9ZKkK/NugbUD9TF5aiduLDm/aev4rhuSpBce3SN1HCrq89m2o6k5twf9s1szap5dVLDRX7RkAdtxdHVqXpI0vRCTVFGj5AEAAMqCQBzYLh5+WskQyLClvXvqJEnXr92TJDWZsQ13seSYenv6YUnSxDU35TvSckuNXkKrapFwHH12a0aS9J9+Yavj5ow6DgdrKWsfAACg4hGIA7UmviRd+Zm7bH3hft91UPI8mCLM65Ee3+0Gz3fubhyAA+XiXU63aPB7JEmGSL8AAADVj0AcqDV27EGhrGThrJ2PSPKUrUkLCUPnrzXLay/pwHIRtZa6GXkMetKxNp/HVMdht/r97x1vLXNrAAAACodAHEDR2Y50d8krr53QHtu9IUAIDgAAgO2KQBxV6eTJk2pqWj1V08mTJ3Xy5MnSN6jUfnJm7d8l0lLTAQAAgG3g3LlzOnfu3Kr1s7OzpW9MDgjEUZVGR0ezrn/55ZdL2xDUHENSo8eW/B75TFP1PrOoo5JjtqG/n3fTr+dv75Q+uakXv7JXdd4sQwm89dKzvyNJiscS+nj+shKmX9JcEVsIAABQ+S5duqS333673M3IGYE4qlJHR0fWHvGWlpbSNwY1xJDX69UzwZh05BFNx+/o648EFS/inNcJGYouj5tfutcoXbmn50N7sm9seqTgYUmSsxTXdH28aO0CAACoJi0tLTp+/Piq9bOzs2t24pUTgTiq0rlz53T06NFyN6NqzcUN9V96SJJ0dcoNAl8JXla9aZezWXmLm/UaffR7kiTTdqu/24Z38wPQPT7psefd5Zf+mUbnPy1AKwEAAFBsaw1RvXDhgp566qnSN2gDBOIAqpdhKO5pkCTZ5oPp2agEVxsStqPb9xclSR9csSRJRw/skq+IGQoAAAClQCAOoGRsw6tfep6QJO2pv6k6w5ZjlG9atWJzDI9u7nhSkhSYv7y8jiAyV7bj6NJdt8DKTz65JUl6Yv8OAnEAAFD1CMQBlIxteHTVc0iSdN/vKFZlqfD5Sph+XWx+SZK0f+bjMrcGAAAAlYJAHEDNMSXt8rhjxvf43YJmZjFLn5eK40gxt0K6GUvo8E5HCU+DdtW7afn5vsSE6dfHD72iZ59+TL7YtLvS3MppwZCSQwR8DcurauEPDwAAUFgE4gBqjt+09Z3ANUnSC4+tUYF8DUu2oTGrUZq4q6tTbtD7yO4GeSohko/NSe/8oSSpXtJvS9I3T+nG3eT4+EUpkf2hcbN+VVDsGB5N1x+QE3hMqg9svX0rit1Jkpao7A4AALASgTiAsjDtmExtnJruTcxLyh5IGk5COxdvSpLq4vclSYueJmmz47DthOx7t3TxRlD68K9Vd/OeLvlatX9XvTxFnU08zaWfSvr3kjdLxbnEknTlZ+5yy7fc7z//Y3Vcu7vhbkcf/V6qsF0pmYahx/fvkCR9etP9H1XALQ0AAICyIhAHUBYHZy/Ir9iG23Vc+5Gk7IGkx17Sk7f+XJK0b9adauz6zqdlbzYQdxLyTE3oyEKTNDUr3+KcrnoPb25fkCTV+zx65ekDkqTPbs6UuTUAAACVgUAcQM78hq3O3W7Kd8ehoCSp3sNcYdXEaxpqP+z+7yLHQ5KkBl/tVq4HAACoRATiQM1JK5iVvq4ATEPa7XV7sffWrTEYGSkee1Ffvf1fJD3osb/TGJJjlO+j1zAM+Tzu+6HRzykAAACgHLgKA2pNesGsCuExpWd2u4XP/PPL44Tjtd+Tbji2di1+KenBGHbDceRs9r6I6ZMOPecuv/iD1OrRxc8zN3MSCs5/IUmKe+olSYlVN2eUUYVdiaUHz0GlcwAAgKIiEAdQdHWmo2/vcwPRhvtuYbHb9lFJ2YNx04nr4S3Mux13DH06v9v9YbJJktQemJPfrPLg3zAkj1+StGjW653P70iSPptyi94dam6Q13THx9/ceXTj/aVVYU8VgTv0XOo58mYnpBm3eJ6ujrnfH3navTkEAACAFAJxAGVhrzdf9cbF1NcVdwx9PB+QJE1PNkqSntk1J/8ma7hVooTt6MMr9yRJN6cXJEkHg6Wvip7BSUiTE+7yZ25Kvh76GoE4AADACgTiAFBGpqRdHnfc/R6/O+d2JUxZXigLsYRGPrku6cH0ZaG9TfJ6auiuCAAAQJ4IxAHkzHak+wm3d/POoltpu9mfKHjg6BimpusekSRd3d3uPncZC5wVk9+09Z2AW4n+hcf2FP35bMfRzIIb8F+dmtfO+Zh21ntVrLDYdpzUtGWTs+449CN7m4r0bAAAANWhNq9sAazLZzr69b3TkqS/X3LTm73GxuOnlxxTw/celST98oqb8h1puaVGb2HHXjuGR9P17tzTyUAchZGwHX38pfu//w9/F1fHl9PqOBwsWiAOAACA1QjEgVqTXjAracd+yXwwV7TPlJ4NzEuS5ifvl7J1AAAAwLZHIA7UmvSCWUlNeyV5sm5eCgsJQ2/eCEiSJqbd1PYXdtyS39xiVTZk5U3Mq+PajzLWjT76PcU9ZS7mliefx9TzITdd/593PlHm1gAAABQOgTiAorMd6eq8OyXW7Zg7rzUhOAAAALYrhgUCAAAAAFBC9IgDwAo+w5E8hkzDkNdT3LnE4o6hT+d3uz9MutXE2wNz8puFLYAHAACAykEgDqDimHZc+2Z/JUl65svzkqRfPPSbSnjqi/7cPtNRR3BWOtysWMLRNx4NKl7EOa/jjqGP5wOSpOlJtxL9M7vm5CdfCQAAoGYRiAPb0HzC0J9c3itJ+mK5F/Y3AldVVzHF0xz57AVJUkPMkiQZWt1DHDfr9OHDXZLcAmXS8nzjm+1M9villm+5y7/+ut6Lf7rJHQEAAABrIxAHtiHHkeYTbpfrkuN+r8pEaMPUvL9ZkjIrglfli8FKtu1oam5JkvTpTXeavdDeJnmLmKEAAABQCgTiqEonT55UU1NT1vUnT54sfYOALBzD1GTjEUnSnrmJ5XXFHXNeSxKOo89uzUiS/uKjLyVJkeMhAnEAALDKuXPndO7cuVXrZ2dnS9+YHBCIoyqNjo5mXf/yyy+XtiHAOhJmnT7d2ylJap67WObWAAAA1K5Lly7p7bffLnczckYgjqrU0dGRtUe8paWl+E/uOFJsbu3fGx7JV/yiYlibIclvuOPdGzzu91rriDYNQ3t2uHOzN/g8ktzXnY+E6dNne0/o2V87JN/C3eUdc1oAAADVp6WlRcePH1+1fnZ2ds1OvHLiigtV6dy5czp69Gh5njw2J73zh2v/ft9Xpad+u3TtwSp1pq3fbL4sSXrhyJ68HhuzpQvTDdIVSzfuuQXj9u2sk8esrEi+3ufR919okSTdnVna1D4cw6u7ja1y9oWkpn1bb9SKYneSpKX41vcLAACwgbWGqF64cEFPPfVU6Ru0AQJxANXLsVNV1ZNV0+Nm/ea7vx1b8ZlJffRlszT+ruI3LF33HtKeHXvlybu/GZLbc38w6BbS21Xvk5R/zz0AAECtIRAHULW89qKeuTEkSXp45mNJ0vWdT8s2fJvboR2X984neny+SbozpYaFOb3Z1F2o5m5L9T6PujsOSZKuTs2XuTUAAACVgUAcqBbpY9MTK1KRTa9kFL+StN+wdXzXDUnSNx4NSJLqPMwVVk28pqGnD+6WJH3vhcOSpHqvp5xNAgAA2HYIxIFC+OYpybc8j7WxHNTEFqRf/cfM7b76X22+kFv62PQrP8v83b6vFWaM7wZMQ9rnc8dNH2qIFf35qp3HXlTr5F9LejB92WTDYTlG+T56DcNQo999/r076srWDgAAgO2MQBwoBF+D5F9Rxd1JSLd/lbnuid8oflvSC2ZVCNOQHt/hBvDO8lyO22EmaMOxU9OWJceyG/WPySnyIGnb8Oli8MVV6wAAAFAZCMQBFF29x9ErD9+TJL07d7fozxd3DF1a3CFJarDcTIWju+blq6Hofylua+yLKUnS1Sl3yMIjuxvkMQ3Zplc3d+Ywq8DS7IMsi0t/434/9Jx7M2czHFtK/n9v/dL9vudxycOpBgAAIB1XRwBqTtwx9MGsO23ZnTuNkqQndizIZ9bOePa4beu9qBv0Joug7d9VX97q7nZcuv2Ju3zhx+73F39AIA4AALACV0dANTvQ7gY5JodytTLkFsGTpAaP+z2n2dfe/t+lSw9Jkg7ec3vEj125rHrTztjsvcd6CtbWzViMJ/RO9KYk6eIdd1jCoeYGec0aSk8AAADIE1fvQDXzeDefRrwJjiMtOm4ANRd3o8UGj7PpabvXfB7D1IzfLT53c8eTy+tqs7J3nWnrN5svS5JeOLKn6M/nOI7mYwlJ0p2ZRTUuxdXgK14/esJ29OEVd1jCzWm3TkByXnEAAFCZ0q8XNuIxDdUxA0veCMSBbchrSs83z0iSPl6YdtcZG6dtLzqm/nzqMUnSB3JTviMtt9ToLWzKt2N4ZDW4z3Ox+aWC7nu7i9uOPrrqBsb/789j6rh2Tx2Hg6KU2+blerFiGobqfVyoAAAq33wsof63ozlt+8yh3fr21/YXuUW1h0AcyJfhkQKPrV5XRfymoxea3TRhWVZZ2wJUu1wvVvbs8Ov7L7QUv0EAAKDiEYgD+fLVS9/4J+VuRVVZSBgaub1LkvTpfTe1vb3prvwrxjOjMEw7rodnLkiSOq7+UJL0wSOvKu7Z5Bz2ZeI1DbUfDkqSIsdDkqQGepQBAEANIBAHUHS2I3024waB15bc4PsbTcWfxmz7cmQ6cUmS115Iras2hmHI53FHrzf6a+90lT4FXVL74aD8XgrZAQCq00IsoTc/vJ6x7rvPHGBoVha1d2UDAEAVSJ+CLumZQ7vlF4E4AKDyfP+Fw2rwrw6oPeaDkq+246SmVU1fh9UIxAGgjOKOoUuLOyRJDZZbTfzornn5iMWqWraLFbPQ0wsAAFBCDX5PTWaolQt/SQAVx3Di2jvnFr968uabkqRf7fuOEmZd0Z/bZzp6oXlGatmjd6PSsUebFfcULyqOO4Y+mHWnLbtzx61E/8SOBflM7h5XMy5WAADAerhKAKqFr1F68QfucmLJ/W5ubtKphYSh89eaJUkXLXfs9su7vqyY4mmG46gufl+StGvxy+V1q9uWMP36+KFXJCm1vW14Nj8c2vRJh55zl1/8gUYXP1fcrK4CZwAAAKh8BOJAvuKLUvSvMteFXpa8Re6tNQzJ3+Que/xb2pXtSHeX3MN/OuEG85URgufHMTyarj8gSVr07kz7xSZ3aBgP/rb+JsU9DVtrILbEdhzNLLhF565MzkmSDgQaMsaiAQCAwjMNQ3t2+FetQ+EQiAP5suPStfHMdS0vSVoRiNfAfOPYGscwNV33iKQHNwocTmI5S9iOPv5yWpI0NHZVkjuNGSnfAAAUV73Po++/0FLuZtQ0rmaAYmG+8W0vYdbp4/3flfQgxb4SrVXN1HGk+cTaNw4aPI64r+BWi33m0O5V6wAAANZCIA4AVcgwpAaPO6DAb7jf8w39bMOni8EX9ej+PXps7ubyjh9kbcwnDPVfemjNx0dabqnRS1G5Oq9H3/7a/nI3AwAAVBECcQA1p9601bXnkiTphdCevB4bt6XoXJ10877uzixKkoKNfpkV1sPZ4HH0e0duS5Lede5usHV2tunVzZ1HtbD/EenuO1tv1Ipid5IkZ2v1DAAAAGoRgThQLeyEdM8dJ6sFy/1et0sytvGE044jr70gSTLtmCTJNrzadL60Yys2d19/c3WvpDFZ16d0x7NPbS17Zebd31w9GnwFql2wotidJJmxRKrYS/J5avcvCQAAkBsCcVSlkydPqqmpKev6kydPlr5BpRBfkD74U3f5xt+73w89t+UK6tXMay+o49qPJEkH7n8kSbq+82nZxuamdZMdl/fW3+vJuSbp1g3tmp/Tm03dhWrutpRe7OXuzFJ5GwMAAGrWuXPndO7cuVXrZ2dnS9+YHBCIoyqNjo5mXf/yyy+XtiHbjM9w9NyOW5KkZx8OSJL8JmOEq4nXNNR+OCjJrUAuuWOcs2nwOIq0uP/vedvUDy/vLU0jAQBAWS3FbY19MZWxrv1wUH5v5WZiXrp0SW+//Xa5m5EzAnFUpY6Ojqw94i0tLaVvzFrKNd94EXkMR4fq3Pmcn9jBHNsb8dhLesz6uSQpMH9ZknSv/lE5ZZzGzjAM+TxucvhG04AZhh4UY4tX40zzAABgM+K2rfeimTVonjm0W35VbiDe0tKi48ePr1o/Ozu7ZideORGIoyqdO3dOR48eLXcz1pfrfOPbgGlIBxvctOTFmeUx3eVsUIkYTkL7Zz6WJO1YcgurTdc9UvRAPH3+8qu72yUtj51HUSzEEnrzw+sZ6777zAHVF2rsPQAA2NBaQ1QvXLigp556qvQN2gBXZgCKrt7jqPtRN73p3cXNVfjOR8IxdH3J7bHfM+Pe+Ag1LqqCs6nyFrOlC9Pua/x8YackqaVuRl7DkWN4NF1/QNKDQDyrpTnp5wPu8uX33O+PtkueTY6xd2xpcdpdnvrC/b77oGTWdkBqO46uTs2vWrcRQ4Ya/J5V6wAAqFamYejx/TtWrcNqBOIAak7MMfSzGXf+62s3GiW5c157a2g8e8w29JM7uyRJV2fdj/KD/ll5jXxeoyPFlgPI5arz0hb+Rnb8QSHBZGHBF3+QqqCOTA1+j37veGu5mwEAQMHU+zx65ekD5W5GVSAQBwAUzVLc1tgVNwvi6pRbX+CR3Q3yVNi87AAAAKVEIA6g4jiGoXlfQJI02XhkeV0N5ZWnqTdtde25JEl6IbSnvI0pgvRiL8n07f276uUhBRsAAGxjBOLANuQxpWd2u72T/vn77rocUpIXbFN/PvWYJOk9+0HKd6qydoE4hld3G92U3U/3dhZ03wBQMI4jxeYe/Gx6154ZI7YgOYkHP/sa3akJAADbEoE4kC/DlJr2rl5XRepMR9/e5wbgDfeLXzwN1c9vOvrHD1ur1gHbWmxOeucPH/z8aJv0xG9k3/YX/06yLj/4mfoJALCtEYgD+fI1SN885S7/5Iz7/af/1+rtEkvSlZ+5yy3fKknTKtWibeidu24FzV/Ouuu+3jApH4FcUZh2XPtmfyVJeubL85KkXzz0m0p46je9T68pPbFjsSDty/05DT19cLck6XsvHJYk1XtruwI7AADYHgjEARRdwpY+vOemsl91pxHXkw1T8m2lQjfW4chnu3/ohpglSTKq8G9tGIYa/e5pau+ONdJ9q1gsYevC9emMdUcP7JLPU10ZNhlWpmrno4yp2o7jaD6W2HjDNKZhaNWtrWvjUstLq3u6l2Yze8MBoEYtxBIa+eXNjHXhX9uveh830lciEAcAoAxiCVs/+eRWxron9u+o7kB8Zap2PsqYqj0fS6j/7Whej3l8/w698rXdRWoRtqOFWEK2k99N0wafRwa1BlBBbMfRZzdnMtZ9+2sPlak1lY1AHADKKOEYur7UIEnaM+P2+oYaF+Wt4lgMAJC/86NXdHdmKa/HRI6HUplDAKoLRy6AimM4cTXPfyFJeuLOsCRpovkfKmEWPz3ZazjqCMxKh4OybUffOBCQU8Q5r2OOoZ/NuHeKr914UIney/h5AACAmkUgDlQLb4N07J+6y0vLFc9M36Z2tZAw9OaNgCRpYtrdxws7bslv2lttZUEYjpMa29w8d1GSFA2+tGq7hOnTZ3tPSJIaYpOSJNvwaNPDoU2fjEfb5JOk53v094sTkq+BtD/kLDlneixhb5hiHkvYGvtiSpL0fA3OIQ8AANZGIA7kK55WDd1ye22166BkFrkIhWlKO/a5y1scR2k70tV5vyTpdswtN1QZIXh+0ucbn/c1p/1ikzs0jAd/2x37NO+f2loDa8hc3FD/pcwxXsWYQz5degGtOzNuxfbmRr/MImYooAi+ecqdbSKd40jx5cqNyd/5Gkvbrg18/4XDavCv/bluGobkLJSwRQBQOoaMVZ+Bhjj/FhKBOJAvOyZd+qm7nKyCu/MRSSsv2IwHF5bJecerbL5xbI0jQ/O+gCQpZibrK3MSy1XcdvTR1XuSpB+96970YjxkhfPUSUf/68x19QHJk+V/VrejFC3atAa/Z8P32sJsQpdv3s9Y91gsoXp/MVuGWvVqx6F1i7XNLyX0w+XPQqDYGvwe/d7x1nI3o6ZxNQMUi8cnPdruLifnHce2kvDU68NHXpX0YBox1B7TMPT4fjeo/HQ5KNu2t1s8XumhXyt3K/JmGoYOBhtWrduI7Ti6O5tZXOtgnlWvsf0sxR8MS0lqPxxkeidgmyEQB0opvuT2qG+kjPPpYhsxfe6cx9KDYRYGF4L5qvd59MrTByRp1ZQtqA71Po+6Ow6VuxnYJuK2naonkfTMod3ya/2sOY9p6JlDu1etA1CdCMSBUrryswdp7esp43y6taDOsPVK0B028FyLO3a8wZNbL1XCka4v+KTJOU3PuzdNdtR7c+odK6VGr6N//pWbkqR3V1zQ5czrl458y13O5X25wrxtSvG06gKOX3qoXT7TkS9ZWNDXIDm2vPai6mx3PK0vMS/vigvOuFnPzSfUhPmlhLQUz1y5FJcvYcswJK/JECVsTp3Xo29/bX+5mwGgQAjEgWph29LccsCVrJpOz3lWhiHVG26AmFcxMcfR4vy8/uLKXsn5SFevTum+uUttLXtkevg7r/TDy3uzrv/1vdN6drmwoLGUUNC4r6dunNeBuTlJ0sPX61cVXBt99HuKexpW7QuoNn/68yur3svexLw6rk1pV71XRw/sXuORAIDthEAcqBbxeen9f+0uXx93vx96TvJs36pA7sXtjyRJB++NSZKu73xa9iandZMdk+/GuJ6ZbZJuXNGeuTm92dRdqOZuSw1+j06+0CK936xYgrGzWGYnpHtXM9ftLsHsEwAAVAgCcQA58xqOnm1ye+W/vtdNvfSZ2zC4chx57eqftuj/HP40tfz85fXT2xdsU1en3IJkB4OVNc0UqlB8QfrgTzPXMSQHACpGLGHrwvXpjHVHD+ySz8PwmkIhEAdK6dBz0qNtmeti89LP/7g87cmT13D0lXq3KvSzgW3cE28vpHricxE367Vr4bok6X7dfjkUREOtWpqV3vnDzHW1HGAbZmqKwiQnS818xzA12XhEavJL+/ZIkhZt6Z1PbmZs9+JX9qrOy+cDgPKLJWz95JNbkpQqLth+OLhhIB5LPJgV4PnQnuI2ssoRiAOl5PVL2n4BrGlIe/xuD/q0xy2Atp3up3rtBe1a/FKSNOPfV/RA3DFMzfjdMdo3dzy5vG5rz5mtAN5aVmVJNO6RXvyBRhc/d39vL+iZLwe31J5KshBLaOSXbkCVnL4stLdJXnoNqsZiPKF3Pr+TsS6XoLihcYee/2//Rca659fc+tfcoo/LU1QlluL68Mq1zMdy0QoA2waBOKrSyZMn1dS0uofl5MmTOnnyZOkbhHXVexx9/zH3buq78U1W+M6D7Uh34/WSpCvz7njxA/Ux1VK9tbgtRefqJElXFt1U8QP+eXkMR47hkdXwmCTpYvNLa+7Dk1jQU7f+P0nS/vsXJEm3m74q21x9ath0ATxJMk3J31Szxdhsx0lNWza5PKf0kb012gNcoxK2ow+v3MtYl0tQbBiGGv1cSqE0FmIJvfnh9Yx1333mAPOPo6IYknbVu9deB4Pueb9UM8+cO3dO586dW7V+dna2JM+fL84eqEqjo6NZ17/88sulbQgq0pJj6u3phyVJE9fcIDXSciv/ALKCLdmG/uJGQJJ0dcbNsngleFkeI/fXaMhRQ8yS5PZSu2rnb1TpfB4zFez9884nytwaAJXOdhxdnZpftQ6oJF6PqScP7JIkdXccKulzX7p0SW+//XZJn3MrCMRRlTo6OrL2iLe0tJS+MYCkDx/pVsysX/P3pu2m5NtG9X/s5jJv+XvxTzfcBgAAoFBaWlp0/PjxVetnZ2fX7MQrp+q/IsS2dO7cOR09erTczSgQQ/KtTNmt/BzquXj+bfTmOGTWMQwtendKkqbrHlleV0njbQ3FVwTdMbNBcc/agfhaU6plG3vd4KGHAwAAIB9rDVG9cOGCnnrqqdI3aAME4kC5+Rull/5ZSZ/SNKTHd7ipyM7yuJlcwtxF29R/tg5Kkt6zd+T9vM83z+S0nWN4dbvJTdX9eP93836eYot76jV68PsF2deWxl5XsOcvD6z5O9OO6cD9jyRJV3e3l6pJAAAAFYNAHMhbWg92qpez8nuw09V7HL3ysFuY6N253IunOXLHXwMAJMWXpCs/y1x36LnlGTLSxBakX/3HzHWh7xS3bagKyWmhYgmbaaGAbYZAHMhXeg92bH7dTVE55m3TLTWept7jyKyueyg5MZy49s5FJUlP3nxTkvSrfd9RwqwrZ7NSbMPzIOPhoVckSQlz9bR+HtPQk4+4BV+62t1MDOZYRkWxY9Kln2aue7RNq6apdBLS7V9lrms5UdSmAQAqG4E4gE25OjWX92M+XpjWk41FaEwOfnh576p13zt0R3vrEmVoTXEZjqO6uDufdXL+csOx13tIaRnmgxoA9QfW3Mw0DO1qcLNODjWX6Y1TRLbtaGrOne6M+ceB7cOQoQa/e1PRW0vzagKS4ratK5NuR9VffnJTkvTiV/ZyIz0LAnEAOUsvLLYZ3jym1iqn/XUx3Sx3I1DzEo6jz265dRP+4iP3hknkeIhAHKhxDX6Pfu94qyRpfqn2bgZje3Mc6ea0W4fowyvuMEiGUmRHIA4gZ+mFxWqVz3T0cAkDcduR7sbdautX5t3e3wP1MdFJAgAAULsIxIEiWDnP8npzKpt2XPtm3bGDv/PNx9yVjzwtebJPd7UdGE5CuxeuSZKOTLrjLy8Hvpl1HHE5pP/Pkm43fVW2mf9H6pJj6u3phyVJE9fc9OtIy62aqqAOACX3kzO5bffrrxe3HQCwBgJxoMxMJ6YjU++4P3z2ifv9oa+tDsS99dKzv+MuL1jLD97cIbyQMDRy2y2C9el9Nw22vemu/GZl9HYbjq0dS7clSftnPpYkXckyzZVt+HQx+KIkacfiLUmSY3hUp9Vzc2dTv3K+btMrPfx11TvSsx0Rffi3l/T1uofkWVHRLeN/tuxuY0g2H6kAAADIAVeNQL4SMelLdw5kTV93v+/YL5lFLkJheqTgYXe5PrClXdmO9NmMmw59bckNvr/RlPs0ZpXCNr26ufOoJGUExoY2OTe3YUr1AZmSGh4Kab4pzodkMTiOTCcuSfIm3IIucbPeHfuQsZmjuO3+/+aW3O0bfB4ZBnn7Vc/bIB37p6vXVbFkJtTo4ueKezJfizcxr45rKzKl5qJ676o7jpLxkwCw/XCNCeQrsSR99l/c5ckJ93vTXklUg1zL5PSc/v1PPkj9/Nu//qyad9VeFWzkxnTiOnDfvZnVce1HkqTRR7+3KniJ205qztz+t93p2CLHQ2r0b69T161bt/RHf/RHqZ9///d/Xw899FAZW7QOj196/Dur161kmtKOfaVpU4Uy5N5YkqQ9O9y/kclNpoK7NTWjP/qz91I///5vPa+HgjvK2CKgOsxYk7r3zp9KkqyJu3rxu7+z4Vz3yM/2upoBishxpPmEexG1YGd+UHV88cdrVgyPxRN6eOZDdznhzivrdRzV0uWYdX9O/+bNd1M/f7vjCQJx1AzTMHQw6N5E2FXvDikp5PF7+/Zt/cEf/EHq5+7u7goOxH3SwdXDSCqdaRipYDh9XT4O3htzH2fHZJuZQ4tMO5a6+XR1eZiNx2PqmUMBSdL3X2jZRKuRi9vWrP7gh3+Z+rn7+NfLHojHErYuXJ+WJN2452ZF7NtZt2oYFFBOK889z/7Df6SdQbJ3ColAvEQGBgbU39+vaDSaWhcOhxWJRBQOh6u2DZXwuirFfMJQ/yX34vjqVOZJ/tmmu/pK/f2sjxu596heXLokSbqx3Pv3zPMJNdYVr62A5I6nv7njSUlSYP7y8jruduer3udRd8chSdLVqfkytwabUe/zEAyjZGIJWz/5xK1rcunurCQ3K8KzwS08Q1Jzk3vD6PH97nUGWRRA9eKKq8jGx8cVDAbV19enSCSiqakpTU1NaWxsTKFQSJ2dneru7pZlWVXVhkp4XcB21uBxFGm5pUjLLb0SvKxXgpdVl+fUcgnTr4vNL+li80uyGh6T1fCYHGN7DLGYX0pobim+4ZfjVGn1eseRlmYzv2ILa28fX3ywXbW+ZqDGeT2mnti/U0/s36lXnj6gV54+oHrf9vjMBmoRPeJFNDIyos7OToVCIY2NjSkQCKR+FwqF1NfXp9bWVkUiEY2Pj6/aplLbUAmvq5bEnBzvhzmOFJtzlxNL7nfTt6rAFbbAcSQ75i4vzcqbmM9aRCzdeumoxWQYDwrR1VdItftq8ifvXtL8UmLD7ap2THpsTnrnDzPXBR6TvvFPsm8f/Svp2ri7/OIPJH9TUZsHAMB2V4VXF9XBsix1d3dLkgYHB9cMRHt6ejQ2NqaBgQGdOHFCY2NjFd2GSnhd21b6hfWVn0mSnIPPaV4Pctg9plRnZu/NWkgYWi5ArXm7NpJhvIkFPfvleUnSgWl3nP2NHUcz5vNeOaf7ejLGcX5xXx3KXkQMwBbZtjS34ths3OMWcQMAYBvgjFckp06dkmVZamtrU1tb27rb9vb2SnLTvYeGhiq6DZXwuqrJd3Zf0yvBy2qpm1lzmxO7rm96/8lx6cmvd+6uXYDmzRuB1HY/vLx3089ZWRx57QV57QWZTnx5SizSare7WMLWB1esjK9YgqyBVEZNuqVZ6WoZbpTG56X3/3XmV5zx9dii9CEZiSUpsSTDjqnRp9SXYccYfgHkIGE78u19LPV1b0mybY6dQqJHvAgsy0oFnrkULAuFQgqFQopGozpz5oy6uroqsg2V8LpK7dy5c7p06ZJaWlp08uTJvB/vNxMbpg37zIQ2TpBd3wefX9flO/f1oe+eGo5lD8Ynpn26Havf4jPVvs+u3NL9uUXF9jTq0dZfK3dzsjr3n8Z06caUWh4O6uQ/qtwK1X/xzi/05Z1pPbJ3l/7xi0/lvwPHUZ3tjmtOzjeupVmtOnV5/G617mXphZCSnti/I+u0Kx988IEsy1IgENCzzz6bfxtrQXI6xgL40b8Z0MOX/0zBYLM62pffm9/skfyVM0vCVj/XMxmSz82YsQ3vg3UrOIahRe9OSdJ03SOSpLhj6OqUe3Pk3Qk3O6D9cFB+r1mEdhZHxbUxS+bY3ulZ/c//4EHm2N7pX0ixYMUNvxh9689078ZVNT/8qL75nd8ud3PWVHH/8yyqoY1S5bczlnDU+NWXUj9fmZFCjiOzAuf1qfS/5VoIxItgYGAgtXzs2LGcHpMMWMfHxxWNRhUKhSquDZXwukrt3Llzevvtt3X8+PGKPrA/mPhSX9yw9OWeXfpd22TM8BZ8dvmWvrx7X8377coNxP/zuN7+8KKOP3MkayDuGIbmfQFJ0mTjkeV1pU+A+o/vXNDffXpV33ji4KYCcZ+9oO/ODkqSDl5zAznfe0F3DEa6x7+T95RZv/tCixw5+kdn/kf99Kd/o5de+pb+5f/w5/rhu1/k3c7FeELvfH5HknTxjlsB+VBzg7yVlmb91H9T9Kf4f/7tj/S8/b5aWg6r4+knl9dWVg9Kts/1pbidmrM+KT0oXpO/UXrpn0mSrv/q6pqbOYZXt5uekCR9vP+7ktwbRlen3Od8b3kIzTOHdsuvB4F4pZ9/qqGN1WLsrTcV/bt31Pr0Nys+EK/0/3k1tFGqnnZWg2r9W1bYVUJteOONN1LLuRYpSw9QR0ZGKrINlfC6ACwzTMUamnVl3s10uB2rT9UAcAyv7ja26m5jqz7d26lP93YqYa6eD89jL+rJm2/qyZtvat/sp9o3+6kMJ17iF1IeDX6PGv1emXZMTmxRph1Tg39z1YcTtqMPr9zTh1fu6eb0gm5OL1Rm5quXORHXErdtvRe9m/EVt7mhicoST9j69OZ9fXrzvv78o+v684+uayG21Zw6oLAMSQ0+jxp8Hu3Z4deeHX6m2VsDPeJFMD4+nlpubm7O6THpgW0hCpsVow2V8LqweS/suKX1LivznfoK5WV4fZp59DkNXWvW1Wl3yMErwcuqz+P/aDi2di1+KUmqi99fXufI4XxZEl7TUPvhoCS3OrvkXrwAQDaOpMlZd9aUz266tWe+/bWHytgiYDWvx9QzhwKSpO+/0FLWtlQ6AvECi0ajGT/nmoq9Z8+e1PLo6GjFtaESXhe2xk+6OlBRDMOQz+Pe9ajKKdKAKjC589f0v/3tX6d+fvW/e0oP+SqnZgGA7Yszf4FZlrWpx6X3HG92H8VsQyW8rm0pEZcSi+5ybF5x23ZTXm334r1WpiFbzVDMdHt5k2OdneXiIKYdW66O7o4hLrb050iYPjlGYT82/Yatzt3XJEkdh9ze0XqPI9txp5zLxvb4ZeSYZuyxF2U42W/ClOLvtxXeLO2Ltf/38jXtzFg3lzClpeWU+qVZzS8lHhR3S8pW5E1SvZlQo0/yrpdJEJuXHFsLsYTsLDnn6c+XLC7nS8zLK1OO4VHC9Gfdbcb/ZmlWCdvRYnx1munS/KyaGusyfp6bm5G8mcUX670emaYhxRbcNi9zHEdx21FsKS5pxdCDpbh8KyrKx2bvP/h7GqbqGnbIY2YpQBZf1PxC5v/I5/PIiHvkGKZi0Z+6+1uMuQX10iWWVH/9PZmGpJZvLT/xgzbPLcXd12dmyRBwnOwV4JNt8JhZC/NJ7v/KML0yfHWyTZ/7PMvrJbco4PsXJyVJ/Uuz8q6xn2w6ctwu9d5M2KuKEc7PTEvLQyT8XlNNjXXyew33/WuYqcJwSbGE7c4IkFiSErGc25rk3gAyVhXTiydsLSVsd5+JpTUf/6CNpuZm7qXWN/g8MgxjVUG01Hvcjkvxxbzbm3qP+xqltFRX23a0EE9Ii4sP3s/J86Tjy5jmc151mosbD95DU1+qzuuRp2nvqunzHMfR/BZSv/0eM6/30GYk37vZmIah+jWybBbjCSU2UQHbNn0yvNk/0yS33sJmh3UYMtYcIpR6r+faxhXH+Fo3O1Pv9U1KvddXWOvzfKN2JqXe6ysfk3yvb1Kd15P98zzLe30xUYljrGoLgXgFmpycLHcTitKGrexzcTHzhP2Xf/mX+vzzz/PaRzAYzOihz8Xs7Gzq+4ULF9yVsTnp4k13+bqbzqvELc07ft247l4I3bnnXlxdnL+zYcr3omPq7XtuUaOH9/8DSZLv80tq8HmluxPS53+Z2vaTm/fd8WD33Q/sC5cXlTCvaWF6Uom5aS364rp4fbHi0syv3ppa9+dsPtd+SdJHTd9wV1y5IknaP/Ox9t6/kNoumasxO+P+r27fv5tWvTg/phPX9em4bs/aWry3pOi1O9p57f9O/f6L4PO6V3/IfQ36dUnS0zNukS6lXYRu1k2fezF+U9JUzNSbN4LZt9vzDTV+rVUL05O6cf3auu837+Qb2j2/dhGpQv79VppfjKW+R6/dSa03nbjuLx9bE0vSjqU7+jL+uRJmZmD51PXzunLfPfb9iUXNLCbcY6MxMxD/4d9eSi3vmf1cj07/nVYOnvn06i75shRP+9bOKwo949N9444+/eUv9VB8OvNxv1yU75f/XpqbenD8ZZF8vmen3f/F3lt1Mg1DdxtbdWN39qkeQ3f/SjuWbkuSLlwP6P5iXBO3V091uDA/r1MnWlM/j/+7/0Pv/uRxfRF4IWO77z57QMFGv/TZsDR5KbV+PpbQr27e14WJkVV/Y4+9oKM3P8t8wr/9F6nFRe9OPfby7+qR3ZkBoCQtfvFz/XL07Yx1z+5dkHW/RfGlnTr/SfJzKqbEigs/jx3TP9a8Ar645Cx/nl78X1O//+CqpYk9xzXrX51267b5zVXrkw4GG7S3KfuNql98eU+e3UE1PXVCNxsP6A/+5D9n/P7pLwdX/Q9zcW3XNxSdvrPxhpLm7/+VHp3+O9mOo2eX39/Nt93/y3/6+YPtnts/p6MnWrWzaU4X/u3/IjUGpa9nzj7yyZfT+vnFSe2f+Vj70z4Xc/XswYDkq5Pavp+x/tKdWf31p7dTx9Nakm3csXNO/+Ff/k+p9UcPLB9vz53K2P7Le/MavnBTuxeu6PDUe3m396v7d7rDN9r+iRuML5uaW9KbH1xf3u/y+3n5PPnTRJ2anjqR2vZffVynfVfmdHRu+Y89dkWt+3Zo5wu/m7FPSZqPxTX4/tqfnxv5h0/sU8ve1dXZ52Nx3bjk7vfedUuStDTv3hhYWpjTjUufrXqMJMVsW/euu59RN0z3mursGttK0v5ddfqNpx7J+rufRe/oVzfWnlp1LTcbW1T/laXM66I0H1y29NFVK+/9SlKdz9Rrxx7L+rvkez3XNjY95c84xr//D1qybpt8r29W97GD7vXaCsn3er7tTEp9nq+QfK9vVufR/Vk/z7O91yenJpWYs1I/37/5hW58YWc9l67lwoX8bxBu5O7du5qayryOvHPnTur7n/3Zn616zOXLlzN+XhlXlI2DghoeHnbkDuNx8vnz9vf3b+pxpWpDuV/Xj3/844z98MUXX3zxxRdffPHFF1985fv14x//eNMxSSHVal5r2VRC+nUx2lAJrwsAAAAAagGBOAAAAAAAJcQY8QLLdX7tamtDuV/X8ePH9eMf/1iWZWl6eloPP/yw/P61i4Vks5kx4gAAAAAqQ7Yx4htZWlrSjRs3tGvXLgUCAR0/frxIrcsPgXiBrZxf27KsvIPYrQa9xWhDuV9XIBDQb/3Wb2368QAAAABQKUhNL7BC9ByvDHoroQ2V8LoAAAAAoBYQiBfYymAz1ym70ouhFbpHvBBtqITXBQAAAAC1gEC8wFYGm7lWG5+YmEgth0KhimtDJbwuAAAAAKgFBOJF0NbWllrOtec4fbtjx45VZBsq4XUBAAAAQLUjEC+CcDicWo5Gozk9Jn27rq6uimxDJbwuAAAAAKh2BOJF8Prrr6eWx8bGcnrM+Pi4JDd9uxAp3MVoQyW8LgAAAACodgTiRRAIBFK9x+fPn99w+5GRkdRyb2/vhtsPDQ2lAtxStqHYrwsAAAAAtgMC8SLp7+9XIBCQZVkaGhracFvJHYPd09Oz5naWZam1tVXd3d1qb2/X2bNnS96GYuwTAAAAALYTw3Ecp9yNqFVDQ0Pq7u6W5FYPz5aaPTAwoEgkokAgoLGxsXXTt9P3J7k91FNTUyVtQ7H2CQAAAADbBT3iRdTV1aXh4WEFAgG1t7drYGAg9TvLshSJRBSJRBQOh3Xx4sUNg9WVU4jlEtwWug3F2icAAAAAbBf0iJeAZVk6f/68+vv7M6qIh8PhVMCaq97eXp09e1ahUEjDw8M5B7mFbEMx9wkAAAAAtY5AHACAKhGNRvPKMsp3ewAA1sI5qLBITQewKWfPnpVhGDnPKb+WgYEBtbe3KxgMpr66u7szqu4DtWQrx053d7cMw1BnZ6cGBgY0Pj4uy7JSv49GoxoaGlIkEkkdS0C1Gx8fVyQSUWtrqwzDkGEYam1tVSQS2dK5gvMPtoNCHj+cgwrMAYA8TExMOOFw2JHkSHImJiY2tZ+xsTEnEAg4oVDI6e/vz9j/6dOnHUlOV1eXMzU1VaCWA+VViGOnra0t9fiNvgKBwKaPT6BSpJ8P+vv7ncHBQaevr88JhUKp93pbW1te73XOP9guCn38cA4qLFLTAazJsiyNjo4qGo1qYmJCIyMjq+awX6ty/npGRkbU2dmpUCiksbGxVYUIpQeV99fbBqhUxTp22tvbV+0nm3A4rMHBQY4bVLX29nZZlrVmTZxIJJJRMHZ4eHjD+jScf7BdFOP44RxUWKSmA1jT6OioOjs71dvbq/HxcYXDYU1MTGzpg9WyrFSq0nof0j09Perp6VE0GtWJEyc2/XxAORTj2MlFW1ubhoeHUzNbANXq7Nmzsixr3SlQ+/v71dPTk/q5s7Nz3SEfnH+wXRTj+MkF56D80CMOIG/BYDA1JijfXr3u7m4NDQ2pra1NY2Nj624bjUbV2toqyb1o6urq2nSbgUqwlWNHcnsjXnvtNXV1dWl8fFyTk5OSpObmZoXDYS58UBOSn/259NBZlqVgMJj6ORwOa3h4OOu2nH+wHRTr+JE4BxWat9wNALB9WJaloaEhScppertQKKRQKKRoNKozZ85wIQQsSx4bQC3q6+vL+YI+EAiop6cnlWI7MjKStVIz5x9sF8U4flbiHFQYpKYDKJn0sUjHjh3L6THJD/rx8fEtp0wBACrf6OioLMtSZ2dnqld6Pe3t7Rk/Z6sEzfkH20Uxjh8UB4E4gJJ54403Usu53q1Nv+PKyQEAal960JucDmk9K3vmJiYmVm3D+QfbRTGOHxQHgTiAkkmvtNnc3JzTY9IvmDYa0wcAqH4dHR0ZP28UOK88n6TPa5zE+QfbRTGOHxQHgTiAkliZ1pfr2KI9e/aklkdHRwvaJgBA5enr60udI06fPr3hmO6V55eV6bicf7CdFPr4QfEQiAMoic3eYU2/k8tdWuCBkZERdXd3KxgMpr7a29szxsIC1aitrU0TExNyHEd9fX0bbr8ykGhra8v4mfMPtpNCHz9r4Ry0dQTiAKpGcpoMYDu7e/euOjs71dfXp0gkoosXL2pqakoXL15UOBxWJBJRMBhkTCu2jf7+/tRyIBDIqSp6vjj/oFble/xwDiocAnEAJbHyImYzc03SIwFIZ8+eVXd3d2qO2OSxFAgE1NfXp/7+/lTFXC6EUOtWVjTP1gPI+QfILpfjZyXOQYVDIA6gJLiIAbYuFAppcHBQPT09a27T09OTGh/Y3d1dqqYBZdHb25taDofDWY8Nzj9AdrkcP+k4BxUWgTgAAFVicHBQXV1dG26X3MayrIwLLaCWDA0NpXrckgECgNxs5vjhHFRYBOIASmIzqYAANqezszO1fPbsWXoEUXMsy9KpU6ckuUHE2NjYmucZzj9ApnyOn83gHJQbAnEAJVGIeSq5mAJys3J6JsbpodZ0d3fLsqycggjOP0CmfI6fzeAclBsCcQAlUYgP+ZUXUwCyW3msvP/++2VqCVB4vb29GhkZyTmI4PwDPJDv8bMZnINyQyAOoCRWfijnOhVMes8FPRLYrsbHx9Xa2qpgMLipOVpJC0StGBgY0NmzZ9XW1pZzEMH5B3Bt5viROAcVC4E4gJJY+WGf64fyxMREanllqhOwXZw6dUrRaFSWZSkSieR9UUMQgVowMjKiSCSicDicVxDB+QfY/PEjcQ4qFgJxACXT1taWWs61RyJ9u2PHjhW8TUA1SJ/nNRcrj6/W1tZCNgcoufHxcXV2dqqrq0vDw8NrbmdZVtbjhfMPtrOtHj+cg4qDQBxAyYTD4dRyrh/q6dvlMmUGUIvSe+P6+vo27F1YeXy9+uqrxWgWUBLRaFQnTpxQT0/PhlMsnTlzRkNDQ6vWc/7BdlWI44dzUHEQiAMomddffz21PDY2ltNjxsfHJbknAVIDsV2Fw2F1dXXJcRydPn16w+3TezzC4TBpgahalmWps7NTr776qvr7+zfcfnx8POu5gvMPtqNCHT+cg4qDQBxAyQQCgVSvxPnz5zfcPn26i97e3qK1C6h0kUhEQ0NDOY/LS+/R6OvrK1KrgOI7ceKEwuFwTkGE5J430tPQkzj/YDsq1PHDOag4vOVuAIDqlm/Bjv7+frW3t8uyLA0NDa2b7pc8cbS1tamnp2crzQQqTj7HTigU0unTp3XixIkNe/MGBgZSaYGnT5/OelEFVIPOzk41NzcrEomsmU6ePI4mJydTvXBr9V5z/sF2Usjjh3NQkTgAkCdJqa/h4eG8Hz84OJh6/MTERNZt+vv7HUlOIBBYcxug2mz12Glra3PC4bAzNTWV9ffpx1ZPT88WWwuUT09PT8bxkutXKBRad7+cf7AdFOv44RxUWIbjOE6xgnwA1S89PS8ajaq/vz81bk5y75JGIhGFQqHUGKCOjo4NxwONjIyou7tbkpu2lOxxsCxLvb29GhgYUDgc1uDgIGOLUJWKdexEIhENDAzo9OnTOnbsmEKhkCYnJ9Xf36+hoSEFAoGMYwqoNmfPnt10Ong4HF63KrTE+Qe1rdjHD+egwiEQB7AuwzAk5T4HpGVZGhwczKnCrGVZOn/+vPr7+zPSpsLhcGquS6BaFfPYGRkZUX9/v0ZGRmRZlgKBgEKhkF577TX19PQQPKCqBYPBvIc9JZ0+fTqnMamcf1CrSnH8cA4qDAJxAAAAAABKiKrpAAAAAACUEIE4AAAAAAAlRCAOAAAAAEAJEYgDAAAAAFBCBOIAAAAAAJQQgTgAAAAAACVEIA4AAAAAQAkRiAMAAAAAUEIE4gAAAAAAlBCBOAAAAAAAJUQgDgAAAABACRGIAwAAAABQQgTiAAAAAACUEIE4AAAAAAAlRCAOAAAAAEAJEYgDAAAAAFBCBOIAAAAAAJQQgTgAAAAAACVEIA4AAAAAQAkRiAMAAAAAUEIE4gAAAAAAlBCBOAAAAAAAJUQgDgAAAABACRGIAwCAmmVZlgzDWPXV29tb7qZVpLX+XpFIpNxNA4Ca4i13AwAAAIotFAqpra0t9XNra2sZW1PZurq6UsuWZWlkZKSMrQGA2kQgDgAAal5XV5f6+vrK3YyKFwgENDg4mPrZsiwFg8EytggAahOp6QAAAGsYGhrKmqpdjBT37u7udZ9rfHy8YM8FACgvesQBAADWEA6HNTw8LMuyNDk5qb6+PkWj0dTvC5W2PTAwsGpfPT096uzsVCAQUHNzc0ZqPQCguhGIAwAArCEQCCgcDqd+HhwcVDgc1sDAgCRlBOWbZVmW+vv7FQ6HNTQ0JMkNwvv7+7e8bwBAZSI1HQAAIEejo6Pq7u5O/WxZ1pb3eerUKf3xH/9xRo94+nMAAGoPgTgAAEAOxsfHZVmWwuGwQqFQxvrNGhoaUnNzswKBQEZQ39HRsZWmAgAqHIE4AABADkZGRlJp6umB+GbT0y3L0pkzZ9Tf35/RG97W1qZAILCltgIAKhuBOAAAQA6Gh4fV2dkpSRmF0zYbiPf29qamVBseHk6tTx+TDgCoTQTiAAAAOUjvEW9tbU2tn5iY2NS+JicnU/tL7xFPBvsAgNpF1XQAAIANJMeBJ3vC01PTR0dH895fb2+v3nrrLUluj3r6+HB6xAGg9tEjDgAAsIH03nBpa2PEI5GIXn/99dQ48JXjwwEAtY9AHACAIopGo2pvb1cwGFRra2tq/umV20QikdR2wWBQnZ2dGQFauqGhIXV2dqq1tTW130gkUpCptJBd+vhwKTMQz+fvPj4+rsnJSXV1dWXsO4necADYHgjEAQAoEsuy1N7err6+Pk1NTSkcDisSiai3tze1TSQSUWdnpzo7O/XWW29pampKFy9e1OTkpDo7OxWJRFLbRqNRtba2anh4WH19fZqYmNDU1JT6+vo0MDCgYDC46cJhWN/KHnFpc73iyTnDV+47ifHhALA9EIgDAFAkvb296unpSQVwySDr7Nmzsiwr9fPExIS6urpSqcqBQCAVrA0MDGh8fFwjIyNqb29Xf3+/+vv7M1KYu7q6Us+RHrijMFaOD0/KNxDv7e1VJBLJmJqM8eEAsD1RrA0AgCI5f/68Ll68mPp5cnIytdze3q6urq7U9FUrpQd5Z86c0fj4uMbGxjLWZ9t+rXR2bF623nAp8380Pj6+bhAdjUY1Pj6+6v/N+HAA2J4IxAEAKIKhoSF1dHRk9H6mT3MVCoXWDMKlzKB9aGhIw8PDawbhK7dHYa0cH57U3t6eWt5oCrPu7m4NDg5m3XcSveEAsH2Qmg4AQBG88cYb6u7uzliX3vvZ39+/7uPTU53TU8/XkkyfTg/8c2VZlgYGBtTd3Z0qAGcYhlpbW9Xd3b2ql31gYCBjnPtGotGozp49mypGZxhGRpG5Su/Fz6VHfL3U9LNnz+q1117LeiOF8eEAsD0RiAMAUASvvfaaXn311dTPlmWlguVwOLxu77aU2VO60bhvy7JSgWBHR0fObbQsS729vQoGg4pEImpublZfX1+qaNzg4GCqYFxnZ6ei0WiqwnsulcKj0WgquB8eHtbrr7+usbExOY6jixcvqr+/X4FAILXN0NBQzm0vlbXGh0u5BeLRaFRvvPGGTp8+nfV3jA8HgO2J1HQAAIogfXoqKf+ez/TtNwrQNtOrOj4+rhMnTsiyLPX09Kivr29Vb3pbW5va2trU09OjgYEBtbe3q7m5Oaf9j4yMqLOzU4FAQMPDw6teQyAQUDgcVjgc1uuvv67u7m51d3erp6dnw2yBUlqrN1zKLRCPRCKrqqSn7ztpK+PDh4aG9MYbb6SmRpOk5uZmtbW16bXXXlv1XgQAlB894gAAlEA+Y4HTe89zCdDeeOON1HIuQdf4+Lja29tlWZb6+vpSPdPrSQbruVQHT85zLklvvfXWhq83PVgfGBioqBTttcaHJ60XjA8MDKRuZqy176TN9IYnp6x744039Nprr2l4eFhTU1OamppKtbu/v79isw0AYDsjEAcAoASSvZ+BQGDD4Hp0dDS1/Nprr22472SQFQqFNkx5j0ajqSJjXV1dWVOm19LT06Oenp4N958cG79ymrWNJIuZjYyM5DUGvZjW6xGX1g7ELctSf3//ugX5tjI+vLOzU4ODgxobG9Pg4KC6uroy2hIKhdTT06Ph4WENDg7qzJkzq2oWAADKh0AcAIAiSx/DnUvPZz49pek9nbnMIZ6+zVop0+tZL7BM338gENgwaF8pEAikbgycPXs2p973YsolK2GtQLy7u3vdv+9Wxoe3traqra1tw0r6SW1tbRobG5NEQTgAqBQE4gAAFNn58+dTy7kEQunB9UY9yunjqVempa9MRx4fH0/1wobD4U1VWE+O7c4mff/pherykX6joNy94m+88caGAXJra2tqORnsDg0NKRQKrfu/2+z48O7uboXD4Q1viGSTzDjI5YYNAKC4KNYGAECR5Ts+PNfe82g0mhFYp/eOWpal7u5uOY6TWpcetG8lTbm7uztr1fT0/W+25zUUCikQCMiyrLKPax4ZGdlwaEB6EJ3s5T5z5kwqKF/LZsaHj4yMKBqNZp2PPFfDw8OpKvlbKRAHANgaesQBACiy9PHhG6US5zNueL209IGBgVWp4eljz3NJaV5LT09P1rHlhdp/+mOT6eHlMD4+vmGQvDI1/dSpUzn1Vm9mfHgkEtlUT/hKfX19Zc82AIDtjkAcAIAiSh8LnEvPZ3oF9I22f//991PLK9PSz5w5syrYSh/DvJVAeS3p+99M2ntS+hRp5RonnrzJsVGvcbYx4hv938bHx/MeH54+B/1W9fT0aHR0NKe54AEAxUEgDgBAEW12/vBcqqsng8CVQXUkElFPT09Rgu1SSM6FXS6WZenUqVOp5Y0kbzoEAoGcCuCl32zJNT18ZGQk56npcskiCIfDGe9NAEBpEYgDAFBE6WOBNypgZllWKvDLpdhZMh092etuWZYikYii0WjWFOb15rwuhEL1ZKcHv6W6mTAyMqKBgQFFIhEFg8FUG9rb2zUwMKCRkZE1g/KOjg5JbhX6bJkAySJ2Q0ND6u3t1dmzZ1O/i0ajGhgY0NDQ0LrP8f777+vYsWPrvobu7m61t7ervb19w9TzY8eOZWRUAABKi2JtAAAUUWdnp0ZGRvT6669vmK6d7AW3LCunMbyhUEhjY2Pq7e3VkSNH1NzcrK6urozgP11HR0eqt3QrgXI0GlU0Gl2VJh0OhzUwMCApt/HV6+0/qRQFxaLRaEa2Qvr/KRqNpm54JOfrXqmtrU2BQCDr7yzLSs3bntz3yvdB+vj+/v7+rNO+WZa17vsnGo1m1Aw4e/bsuuPJQ6GQJiYm1vw9AKC4CMQBACiinp6evObT3qja9krJ+aRzEYlEUoHy4OBg3vN8JyWro68MtLu7u1P7Hx4ezlrQbSPp6dKFGA+di1AolFFdPl/rBbyBQGBL+85VejZCLsqd/g8A2x2p6QAAbBNtbW2p4Ha9NOiNjI+PZ02TDofDqR7sze4/fQq0QlQIrxWhUGjdLIZAIKD+/v5Uj/tGN2cmJiYy5kAHAJQWgTgAANtIeqCbLEiWj2g0qtHR0TULh6UXK8t3iqzx8fFUenVPTw/zXKfp7OzcMLju6enR1NSUpqamNswmGBkZKVnGAQBgNQJxAAC2kVAolArohoaGMgqHbcSyLHV2dq5bGbytrS0V7CeLkOWqu7t71T7g6urq2lIWQ7rkGH9udABA+RCIAwCwzYTDYY2NjSkQCKi3t1eRSGTDAC8ajaq9vV2RSGTDabR6eno0ODgoKXPc+Hr7bm1tVTQaVVdXV97j5LeLnp6evLMMsolEIqT9A0CZEYgDALANtbW16eLFizp9+rQGBgYUDAYViUQ0NDSUmg4tWYm7s7NTnZ2dGhwczLkAW1dXlyYmJhQOhxWJRNTe3q6hoaGMgH98fFyRSEStra2anJxUf39/KoDHan19falp0DZrYGBAk5OTmy7UBwAoDAJxAAC2qUAgoL6+Pk1NTam/v1+Tk5Pq7e1Va2urgsGg2tvb1d/fr+7ubk1MTOSdypxMg5+YmNBrr72mM2fO6MiRIzIMQ8FgMJWKPjg4qKmpKYLDHAwPD+vUqVObCsYHBgbU19ent956qwgtAwDkw3BKMacGAABAGViWpWAwqNOnT9dMOnZy3vNwOKy+vr4N56e3LEunTp1SNBrVW2+9teH2Kx8bDAbV09PDuH0AKCB6xAEAAKpIKBTSxMSEAoGAjhw5okgksqqQW3JYQXd3t44cOaJjx46l6gIAAMqPHnEAAFCzkj26K9VKD7llWTp//rwGBwdT1dAlN1hva2tTZ2enXn311ZwD8LX+XvSIA0BhecvdAAAAgGLKVuW9tbW1DC0pvEAgoJ6enoKOr8/292pvby/Y/gEA9IgDAAAAAFBSjBEHAAAAAKCECMQBAAAAACghAnEAAAAAAEqIQBwAAAAAgBIiEAcAAAAAoIQIxAEAAAAAKCECcQAAAAAASohAHAAAAACAEiIQBwAAAACghAjEAQAAAAAoIQJxAAAAAABKiEAcAAAAAIASIhAHAAAAAKCECMQBAAAAACghAnEAAAAAAEqIQBwAAAAAgBIiEAcAAAAAoIQIxAEAAAAAKCECcQAAAAAASohAHAAAAACAEiIQBwAAAACghAjEAQAAAAAoIQJxAAAAAABKiEAcAAAAAIASIhAHAAAAAKCECMQBAAAAACghAnEAAAAAAEqIQBwAAAAAgBIiEAcAAAAAoIT+f/2KNVA/22E4AAAAAElFTkSuQmCC", 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", - "text/plain": [ - "
        " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "kwargs = dict(bins=50, xlog=False)\n", - "pop.plot_hist_properties('m_chirp', intrinsic=True, observable=True, pop='DCO', **kwargs)\n", - "pop.plot_hist_properties('chi_eff', intrinsic=True, observable=True, pop='DCO', **kwargs)\n", - "pop.plot_hist_properties(['S1_mass','S2_mass'], intrinsic=True, observable=True, pop='DCO', **kwargs)\n", - "pop.plot_hist_properties(['S1_spin','S2_spin'], intrinsic=True, observable=True, pop='DCO', **kwargs)" + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "bins = np.linspace(0,100,101)\n", + "fig, ax = plt.subplots(1,1)\n", + "\n", + "rates.plot_hist_properties('S1_mass', intrinsice=True, observable='design_H1L1V1', normalise=True, bins=bins, ax = ax, label='S1', show=False)\n", + "ax.set_ylabel('PDF')\n", + "ax.set_xlabel('Mass [Msun]') \n", + "ax.legend()\n", + "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/docs/_source/tutorials-examples/population-synthesis/pop_syn.ipynb b/docs/_source/tutorials-examples/population-synthesis/pop_syn.ipynb index b74b2e7ea9..dccb8960c9 100644 --- a/docs/_source/tutorials-examples/population-synthesis/pop_syn.ipynb +++ b/docs/_source/tutorials-examples/population-synthesis/pop_syn.ipynb @@ -4,7 +4,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Large-Scale Population Synthesis on HPC Facilities 🚀" + "# Large-Scale Population Synthesis on HPC Facilities 🚀\n", + "\n", + "**Tutorial goal**\n", + "\n", + "This tutorial will cover how to setup and run a large, multi-metallicity population run on a HPC with slurm.\n", + "\n", + "We will dive deeper into the `population_params.ini` file and the `Population` class for multi-metallicity populations." ] }, { @@ -16,18 +22,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "env: PATH_TO_POSYDON=/Users/simone/Google Drive/github/POSYDON-public/\n", - "env: PATH_TO_POSYDON_DATA=/Volumes/T7/\n" - ] - } - ], + "outputs": [], "source": [ "%env PATH_TO_POSYDON=/Users/simone/Google Drive/github/POSYDON-public/\n", "%env PATH_TO_POSYDON_DATA=/Volumes/T7/" @@ -37,14 +34,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Creating the Initialization File to Rub the Binary Population Synthesis Model" + "## Creating the Initialization File for multi-metallicity runs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's copy the default populatio synthesis modle to your working directory." + "Let's copy the default population synthesis ini file to your working directory.\n", + "Make sure you're on a cluster to be able to run and submit the jobs.\n" ] }, { @@ -68,190 +66,124 @@ "Open the `population_params.ini` file and do the following edits to run a large model at 8 differet metallicities:\n", "\n", "- set `metallicity = [2., 1., 0.45, 0.2, 0.1, 0.01, 0.001, 0.0001]`\n", - "- set `number_of_binaries = 1000000`" + "- set `number_of_binaries = 100`\n", + "\n", + "You might also want to make sure the `dump_rate` is set to 10 binaries. \n", + "`dump_rate = 10`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Running the Population Synthesis Model " + "## Setting-up the Population Synthesis Model " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Write the binary population simulation script to a file to run the population synthesis model with slurm on the HPC facility." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Writing script.py\n" - ] - } - ], - "source": [ - "%%writefile script.py\n", - "from posydon.popsyn.synthetic_population import SyntheticPopulation\n", + "POSYDON provides `setup-popsyn` that you can use to setup a (multi-)metallicity population run on a HPC facility.\n", "\n", - "if __name__ == \"__main__\":\n", - " synth_pop = SyntheticPopulation(\"./population_params.ini\")\n", - " synth_pop.evolve()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Run the simulation on the HPC facility using the slurm magic command. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Note: if you do not have the slurm magic commads installed, you can install it with the following line\n", - "# !pip install git+https://github.com/NERSC/slurm-magic.git" + "This will split each metallicity into a separate slurm job-array and a dependent job which will automatically merge the output of the separate jobs.\n", + "\n", + "Below are two examples, but you might require to adjust the inputs for your email, cluster and available partitions.\n", + "\n", + "\n", + "
        Older POSYDON installations \n", + "\n", + "If the `setup-popsyn` command is not available, you might have to install POSYDON.\n", + "1. Go to the directory: `cd $PATH_TO_POSYDON`\n", + "2. Uninstall using pip `pip uninstall posydon`\n", + "3. Reinstall: if you're using the development version: `pip install .`\n", + " \n", + "See the [Installation instructions]() if any issues occur.\n", + "
        \n" ] }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "shellscript" + } + }, "outputs": [], "source": [ - "%load_ext slurm_magic" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following slurm file works on UNIGE HPC facility." + "# example for yggdrasil\n", + "posydon-setup-popsyn population_params.ini --job_array=10 --walltime=00:14:00 --partition=debug-cpu --email=max.briel@unige.ch --account=fragkos" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "shellscript" + } + }, "outputs": [], "source": [ - "%%sbatch\n", - "#!/bin/bash\n", - "#SBATCH --array=0-34\n", - "#SBATCH --partition=private-astro-cpu\n", - "#SBATCH --job-name=pop_syn\n", - "#SBATCH --output=./pop_synth_%A_%a.out\n", - "#SBATCH --mail-type=FAIL\n", - "#SBATCH --mail-user=user@email.ch\n", - "#SBATCH --time=24:00:00\n", - "#SBATCH --mem-per-cpu=4G\n", - "\n", - "export PATH_TO_POSYDON=/srv/beegfs/scratch/shares/astro/posydon/simone/POSYDON-public/\n", - "export PATH_TO_POSYDON_DATA=/srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/POSYDON_data/230914/\n", - "python ./script.py" + "# example for quest cluster\n", + "posydon-setup-popsyn population_params.ini --job-array=10 --walltime=00:14:00" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The following slurm file works on Northwestern HPC facility." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%sbatch\n", - "#!/bin/bash\n", - "#SBATCH --account=b1119\n", - "#SBATCH --partition=posydon-priority\n", - "#SBATCH --array=0-34\n", - "#SBATCH --job-name=pop_syn\n", - "#SBATCH --output=./pop_syn.out\n", - "#SBATCH --mail-type=FAIL\n", - "#SBATCH --mail-user=user@email.ch\n", - "#SBATCH --time=24:00:00\n", - "#SBATCH --mem-per-cpu=4G\n", + "`setup-popsyn --help` should provide a complete list of possible input parameters.\n", + "\n", + "\n", + "### walltime and job_array number fine-tuning\n", + "\n", + "The above examples will setup the population run with an array of 10 jobs for each metallicity. As such, each job will run 10 binaries of the 100 binaries per metallicity.\n", + "\n", + "Fine-tuning the `dump_rate` compared to the number of binaries each job runs can be helpful. However, the larger the `dump_rate` the higher the memory footprint of each job.\n", + "As a default, 4Gb of RAM is requested per job.\n", "\n", - "export PATH_TO_POSYDON=/projects/b1119/ssb7065/POSYDON-public/\n", - "export PATH_TO_POSYDON_DATA=/projects/b1119/POSYDON_GRIDS/POSYDON_popsynth_data/v2/230816/\n", - "python ./script.py" + "Similarly, the `walltime` and `job_array` can be fine-tuned. A single binary takes about 1-2 seconds to run. Depending on the number of binaries each job does, you might want to raise or lower the walltime for optimal perfomance.\n", + "\n", + "For example, with 100.000 binaries split over 100 jobs (per metallicity), means that every job runs 1.000 binaries. `dump_rate=1000` is a good amount of binaries to keep in memory, when you're doing an initial-final run (see here for more details on different run types). This will take around 33 minutes per job. So a walltime of `00:45:00` is reasonable.\n", + "\n", + "Instead of setting a `dump_rate`, it's also possible to set a `ram_per_cpu`. The code will try to stay below 90\\% of this limit, but this is not always guaranteed due to additional python overhead." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Combining runs into single metallicity files\n", + "After you've ran the above setup, you should now have several files in your work directory.\n", "\n", - "The above process creates a temporary batch folder per metallicity in which the sub-processes deposit their output.\n", - "After the processes are done, the files have to be combined into a population file per metallicity.\n", - "The follow code allows you to perform this concatenation, which is similar to the code shown in [the first tutorial](10_binaries_pop_syn)." + "You can submit all job arrays and merged jobs using `slurm_submit.sh`\n" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "shellscript" + } + }, "outputs": [], "source": [ - "%%writefile concat_runs.py\n", - "from posydon.popsyn.synthetic_population import SyntheticPopulation\n", - "from posydon.config import PATH_TO_POSYDON_DATA\n", - "import os\n", - "\n", - "if __name__ == \"__main__\":\n", - " synth_pop = SyntheticPopulation(\"./population_params.ini\")\n", - " # Get the path to the batches in the current folder\n", - " x = os.listdir('.')\n", - " path_to_batches = [i for i in x if i.endswith('_batches')]\n", - " synth_pop.merge_parallel_runs(path_to_batches)" + "sh slurm_submit.sh" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "This script can be manually ran after the job array has finished or we can submit another SLURM job, which only starts once the job array has finished.\n", + "If one of your runs fails, you can manually submit them again after fixing the issue.\n", + "The `*_logs` folder will contain the logs of each job in the job_array.\n", "\n", - "For this job to start once the job array has finished, the `job-name` has to be the same as the job array and `dependency=singleton` has to be set.\n", - "If the job array does not finish correctly, this job will never run!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%sbatch\n", - "#!/bin/bash\n", - "#SBATCH --job-name=pop_syn\n", - "#SBATCH --partition=private-astro-cpu\n", - "#SBATCH --output=population_concat.out\n", - "#SBATCH --mail-type=FAIL\n", - "#SBATCH --mail-user=user@email.ch\n", - "#SBATCH --time=01:00:00\n", - "#SBATCH --mem=4GB\n", - "#SBATCH --dependency=singleton\n", + "## Populations inspection\n", "\n", - "export PATH_TO_POSYDON=/srv/beegfs/scratch/shares/astro/posydon/simone/POSYDON-public/\n", - "export PATH_TO_POSYDON_DATA=/srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/POSYDON_data/230914/\n", - "python concat_runs.py" + "Once the runs and the mergers have finished. You should have 8 files named `MET_Zsun_population.h5`, where `MET` are the metallicities.\n", + "\n", + "We will inspect two to check the number of binaries in the population." ] }, { @@ -260,554 +192,24 @@ "metadata": {}, "outputs": [], "source": [ - "%%sbatch\n", - "#!/bin/bash\n", - "#SBATCH --job-name=pop_syn\n", - "#SBATCH --account=b1119\n", - "#SBATCH --partition=posydon-priority\n", - "#SBATCH --output=population_concat.out\n", - "#SBATCH --mail-type=FAIL\n", - "#SBATCH --mail-user=user@email.ch\n", - "#SBATCH --time=01:00:00\n", - "#SBATCH --mem=4G\n", - "#SBATCH --dependency=singleton\n", + "from posydon.popsyn.synthetic_population import Population\n", "\n", - "export PATH_TO_POSYDON=/projects/b1119/ssb7065/POSYDON-public/\n", - "export PATH_TO_POSYDON_DATA=/projects/b1119/POSYDON_GRIDS/POSYDON_popsynth_data/v2/230816/\n", - "python concat_runs.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Parsing the Population Synthesis Model Output" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If everything is set up correctly, the simulation will generate 8 different population synthesis models, one for each metallicity containig 1 million binaries each. The simulation will take a few hours to complete. For convinience, we have already run the simulation and the results are available in the `.../POSYDON_data/tutorials/population-synthesis/example/` folder." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['/Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/1.00e+00_Zsun_population.h5',\n", - " '/Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/1.00e-01_Zsun_population.h5',\n", - " '/Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/1.00e-02_Zsun_population.h5',\n", - " '/Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/1.00e-03_Zsun_population.h5',\n", - " '/Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/1.00e-04_Zsun_population.h5',\n", - " '/Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/2.00e+00_Zsun_population.h5',\n", - " '/Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/2.00e-01_Zsun_population.h5',\n", - " '/Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/4.50e-01_Zsun_population.h5']" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import os\n", - "from posydon.config import PATH_TO_POSYDON_DATA\n", - "\n", - "path = os.path.join(PATH_TO_POSYDON_DATA, \"POSYDON_data/tutorials/population-synthesis/example/\")\n", - "files = sorted([f for f in os.listdir(path) if f.endswith('Zsun_population.h5')])\n", - "path_to_data = [os.path.join(path, file) for file in files] \n", - "path_to_data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we show how you can parse the simulation results and save the subpopulation of merging binary black holes (BBH)." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Binary count with (S1_state, S2_state, binary_state, binary_event) equal\n", - "to (BH, BH, contact, None)\n", - "in /Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/1.00e+00_Zsun_population.h5 are 233\n", - "in /Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/1.00e-01_Zsun_population.h5 are 2643\n", - "in /Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/1.00e-02_Zsun_population.h5 are 5974\n", - "in /Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/1.00e-03_Zsun_population.h5 are 8320\n", - "in /Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/1.00e-04_Zsun_population.h5 are 9683\n", - "in /Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/2.00e+00_Zsun_population.h5 are 121\n", - "in /Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/2.00e-01_Zsun_population.h5 are 3021\n", - "in /Volumes/T7/POSYDON_data/tutorials/population-synthesis/example/4.50e-01_Zsun_population.h5 are 761\n", - "Total binaries found are 30756\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/synthetic_population.py:300: PerformanceWarning: \n", - "your performance may suffer as PyTables will pickle object types that it cannot\n", - "map directly to c-types [inferred_type->mixed,key->block1_values] [items->Index(['state', 'event', 'step_names', 'S1_state', 'S2_state'], dtype='object')]\n", - "\n", - " self.df.to_hdf(path, key='history')\n", - "/Users/simone/Google Drive/github/POSYDON-public/posydon/popsyn/synthetic_population.py:301: PerformanceWarning: \n", - "your performance may suffer as PyTables will pickle object types that it cannot\n", - "map directly to c-types [inferred_type->mixed,key->block2_values] [items->Index(['state_i', 'event_i', 'step_names_i', 'state_f', 'event_f',\n", - " 'step_names_f', 'S1_state_i', 'S1_state_f', 'S1_SN_type', 'S2_state_i',\n", - " 'S2_state_f', 'S2_SN_type', 'interp_class_HMS_HMS',\n", - " 'interp_class_CO_HMS_RLO', 'interp_class_CO_HeMS',\n", - " 'interp_class_CO_HeMS_RLO', 'mt_history_HMS_HMS',\n", - " 'mt_history_CO_HMS_RLO', 'mt_history_CO_HeMS',\n", - " 'mt_history_CO_HeMS_RLO'],\n", - " dtype='object')]\n", - "\n", - " self.df_oneline.to_hdf(path, key='oneline')\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Population successfully saved!\n" - ] - }, - { - "data": { - "text/html": [ - "
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        \n", - "
        " - ], - "text/plain": [ - " state event time orbital_period \\\n", - "binary_index \n", - "3587 detached ZAMS 0.000000e+00 2.493602e+01 \n", - "3587 contact oDoubleCE1 3.631450e+06 6.304073e+01 \n", - "3587 detached NaN 3.631450e+06 2.371597e-01 \n", - "3587 detached CC1 4.007490e+06 1.226986e+00 \n", - "3587 detached NaN 4.007490e+06 1.358968e+00 \n", - "3587 detached redirect 4.007490e+06 1.358968e+00 \n", - "3587 detached CC2 4.027170e+06 1.377894e+00 \n", - "3587 detached NaN 4.027170e+06 1.611464e+00 \n", - "3587 contact CO_contact 2.917891e+09 2.638756e-08 \n", - "3587 contact END 2.917891e+09 2.638756e-08 \n", - "\n", - " eccentricity lg_mtransfer_rate step_names step_times \\\n", - "binary_index \n", - "3587 0.000000 NaN initial_cond 0.000000 \n", - "3587 0.000000 -2.989131 step_HMS_HMS 0.037464 \n", - "3587 0.000000 NaN step_CE 0.000137 \n", - "3587 0.000000 NaN step_detached 0.777758 \n", - "3587 0.101109 NaN step_SN 0.152370 \n", - "3587 0.101109 NaN step_CO_HeMS 0.000102 \n", - "3587 0.101108 NaN step_detached 0.452186 \n", - "3587 0.048133 NaN step_SN 0.149304 \n", - "3587 0.000000 NaN step_dco 1.252216 \n", - "3587 0.000000 NaN step_end 0.000048 \n", - "\n", - " S1_state S1_mass ... \\\n", - "binary_index ... \n", - "3587 H-rich_Core_H_burning 70.069756 ... \n", - "3587 H-rich_Core_He_burning 44.118926 ... \n", - "3587 stripped_He_Core_He_burning 35.566786 ... \n", - "3587 stripped_He_Central_C_depletion 13.620462 ... \n", - "3587 BH 13.120462 ... \n", - "3587 BH 13.120462 ... \n", - "3587 BH 13.120462 ... \n", - "3587 BH 13.120462 ... \n", - "3587 BH 13.120462 ... \n", - "3587 BH 13.120462 ... \n", - "\n", - " S2_co_core_radius S2_center_h1 S2_center_he4 S2_surface_h1 \\\n", - "binary_index \n", - "3587 NaN 7.155000e-01 2.703000e-01 NaN \n", - "3587 0.000000 0.000000e+00 9.828315e-01 4.246537e-01 \n", - "3587 0.000000 0.000000e+00 9.828315e-01 1.000000e-02 \n", - "3587 0.401483 1.917729e-34 1.605147e-02 9.893273e-100 \n", - "3587 0.401483 1.917729e-34 1.605147e-02 9.893273e-100 \n", - "3587 0.401483 1.917729e-34 1.605147e-02 9.893273e-100 \n", - "3587 0.130995 0.000000e+00 7.706932e-13 1.000000e-99 \n", - "3587 NaN NaN NaN NaN \n", - "3587 NaN NaN NaN NaN \n", - "3587 NaN NaN NaN NaN \n", - "\n", - " S2_surface_he4 S2_surf_avg_omega_div_omega_crit S2_spin \\\n", - "binary_index \n", - "3587 NaN NaN NaN \n", - "3587 0.561493 0.577444 0.760854 \n", - "3587 0.975800 NaN NaN \n", - "3587 0.247607 0.006843 0.077976 \n", - "3587 0.247607 0.006843 0.077976 \n", - "3587 0.247607 0.006843 0.077976 \n", - "3587 0.226684 0.015362 0.060340 \n", - "3587 NaN NaN 0.064849 \n", - "3587 NaN NaN 0.064849 \n", - "3587 NaN NaN 0.064849 \n", - "\n", - " metallicity simulated_mass_for_met underlying_mass_for_met \n", - "binary_index \n", - "3587 0.0142 2.913438e+07 1.447893e+08 \n", - "3587 0.0142 2.913438e+07 1.447893e+08 \n", - "3587 0.0142 2.913438e+07 1.447893e+08 \n", - "3587 0.0142 2.913438e+07 1.447893e+08 \n", - "3587 0.0142 2.913438e+07 1.447893e+08 \n", - "3587 0.0142 2.913438e+07 1.447893e+08 \n", - "3587 0.0142 2.913438e+07 1.447893e+08 \n", - "3587 0.0142 2.913438e+07 1.447893e+08 \n", - "3587 0.0142 2.913438e+07 1.447893e+08 \n", - "3587 0.0142 2.913438e+07 1.447893e+08 \n", - "\n", - "[10 rows x 41 columns]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from posydon.popsyn.synthetic_population import SyntheticPopulation\n", + "file1 = '1e-01_Zsun_population.h5'\n", + "file2 = '1e-02_Zsun_population.h5'\n", "\n", - "pop = SyntheticPopulation(path_to_ini='./population_params.ini', verbose=True)\n", + "pop1 = Population(file1)\n", + "print(pop1.mass_per_metallicity)\n", "\n", - "pop.parse(path_to_data=path_to_data, S1_state='BH', S2_state='BH', binary_state='contact', invert_S1S2=False)\n", - "pop.save_pop(os.path.join(path,'BBH_population.h5'))\n", - "\n", - "pop.df.head(10)" + "pop2 = Population(file2)\n", + "print(pop2.mass_per_metallicity)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "You can now do the same for the other subpopulations of interest. Try sorting black hole-neutron star systems (BHNS; remember to set `invert_S1S2 = True` to find BHNS systems where the NS is formed first) and binary neutron star systems (BNS)." + "In the next tutorial, we will look into selecting specific events and combining the different metallicity runs into a single population!" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/docs/_source/tutorials-examples/population-synthesis/script.py b/docs/_source/tutorials-examples/population-synthesis/script.py deleted file mode 100644 index f122552dc2..0000000000 --- a/docs/_source/tutorials-examples/population-synthesis/script.py +++ /dev/null @@ -1,5 +0,0 @@ -from posydon.popsyn.synthetic_population import SyntheticPopulation - -if __name__ == "__main__": - synth_pop = SyntheticPopulation(f"./population_params.ini") - synth_pop.evolve() diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index 668aca0384..3ca0f5a421 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -1414,10 +1414,6 @@ def __call__(self, binary): 'while the period is inside the grid.') else: - if len(self.binary.state_history) > 2: - if self.binary.state_history[-2] == 'detached': - set_binary_to_failed(self.binary) - raise GridError('CO_HMS_RLO binary outside grid and coming from detached') self.binary.state = "detached" self.binary.event = "redirect_from_CO_HMS_RLO" @@ -1519,7 +1515,6 @@ def __call__(self, binary): set_binary_to_failed(self.binary) raise GridError(f'The mass of m1 ({m1}) is outside the grid,' 'while the period is inside the grid.') - # period inside the grid, but m2 outside the grid elif ((not self.flip_stars_before_step and self.p_min <= p <= self.p_max and @@ -1530,11 +1525,6 @@ def __call__(self, binary): 'while the period is inside the grid.') else: - if len(self.binary.state_history) > 2: - if self.binary.state_history[-2] == 'detached': - set_binary_to_failed(self.binary) - raise GridError('CO_HeMS_RLO binary outside grid and coming from detached') - self.binary.state = "detached" self.binary.event = "redirect_from_CO_HeMS_RLO" return @@ -1632,10 +1622,7 @@ def __call__(self, binary): elif (self.p_min <= p <= self.p_max) and (m2 < self.m2_min or m2 > self.m2_max): set_binary_to_failed(self.binary) raise GridError(f'The mass of m2 ({m2}) is outside the grid,' - 'while the period is inside the grid.') - #elif p > self.p_max: - # binary.event = 'redirect_from_CO_HeMS' - # return + ' while the period is inside the grid.') else: self.binary.state = 'detached' self.binary.event = 'redirect_from_CO_HeMS' diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index d28b312405..e48c037936 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -19,6 +19,7 @@ "Jeffrey Andrews ", "Tassos Fragos ", "Matthias Kruckow ", + "Max Briel ", ] __credits__ = [ @@ -529,92 +530,64 @@ def collapse_star(self, star): warnings.warn(f'{MODEL_NAME_SEL}: The collapsed star ' 'was not interpolated! If use_profiles ' 'or use_core_masses is set to True, ' - 'continue with the collapse.') + 'continue with the collapse.') + else: # store some properties of the star object # to be used for collapse verification pre_SN_star = copy.deepcopy(star) - + MODEL_properties = getattr(star, MODEL_NAME_SEL) - for key, value in MODEL_properties.items(): - setattr(star, key, value) - if star.state == 'WD': - for key in STARPROPERTIES: - if key in ["he_core_mass"]: - setattr(star, key, star.mass) - elif key in ["co_core_mass"]: - if star.center_he4 < THRESHOLD_CENTRAL_ABUNDANCE: + # Check if SN_type matches the CO_type in used MODEL + if check_SN_CO_match(MODEL_properties['SN_type'], + MODEL_properties['state']): + for key, value in MODEL_properties.items(): + setattr(star, key, value) + + if star.state == 'WD': + for key in STARPROPERTIES: + if key in ["he_core_mass"]: setattr(star, key, star.mass) - else: - setattr(star, key, 0.) - elif key not in ["state", "mass", "spin", - "m_disk_accreted", - "m_disk_radiated", "center_h1", - "center_he4", "center_c12", - "center_n14", "center_o16"]: - setattr(star, key, None) + elif key in ["co_core_mass"]: + if star.center_he4 < THRESHOLD_CENTRAL_ABUNDANCE: + setattr(star, key, star.mass) + else: + setattr(star, key, 0.) + elif key not in ["state", "mass", "spin", + "m_disk_accreted", + "m_disk_radiated", "center_h1", + "center_he4", "center_c12", + "center_n14", "center_o16"]: + setattr(star, key, None) - else: - for key in STARPROPERTIES: - if key not in ["state", "mass", "spin", - "m_disk_accreted", - "m_disk_radiated"]: - setattr(star, key, None) + else: + for key in STARPROPERTIES: + if key not in ["state", "mass", "spin", + "m_disk_accreted", + "m_disk_radiated"]: + setattr(star, key, None) + + # No remnant if a PISN happens + if star.SN_type == 'PISN': + convert_star_to_massless_remnant(star=star) + # the mass is set to None + # but an orbital kick is still applied. + # Since the mass is set to None, this will lead to a disruption + # TODO: make it skip the kick caluclation + + if getattr(star, 'SN_type') != 'PISN': + star.log_R = np.log10(CO_radius(star.mass, star.state)) + return - # check if SN_type matches the predicted CO - # and force the SN_type to match the predicted CO. - # ie WD is no SN - # 1. Check if SN_type and star state match - # Non-matching SN_type and star state - if not check_SN_CO_match(star): - # raise a warning + else: + # raise a warning warnings.warn(f'{MODEL_NAME_SEL}: The SN_type ' 'does not match the predicted CO! ' - 'Recalculating the SN_type and CO') - # recalculate the SN_type and CO - # change some star properties back - m_PISN = self.PISN_prescription(pre_SN_star) - # no remnant if a PISN happens - if pd.isna(m_PISN): - star.SN_type = 'PISN' - star.state = 'massless_remnant' - else: - _, _, star.state = self.compute_m_rembar(pre_SN_star, m_PISN) - star.SN_type = self.check_SN_type(pre_SN_star.c_core_mass, - pre_SN_star.he_core_mass, - pre_SN_star.mass)[-1] - - # check if the new SN_type matches new SN_type - if not check_SN_CO_match(star): - # still doesn't match - # raise a warning - warnings.warn('The SN_type still does not match. ' - 'Forced the SN type to match the ' - 'predicted CO.') - if star.state == 'WD': - star.SN_type = 'WD' - elif star.state == 'NS' or star.state == 'BH': - star.SN_type = 'CCSN' - elif star.state == 'massless_remnant': - star.SN_type = 'PISN' - else: - raise ValueError('Star state not recognized.') - - del pre_SN_star + 'If use_profiles ' + 'or use_core_masses is set to True, ' + 'continue with the collapse.') - # No remnant if a PISN happens - if star.SN_type == 'PISN': - convert_star_to_massless_remnant(star=star) - # the mass is set to None - # but an orbital kick is still applied. - # Since the mass is set to None, this will lead to a disruption - # TODO: make it skip the kick caluclation - - if getattr(star, 'SN_type') != 'PISN': - star.log_R = np.log10(CO_radius(star.mass, star.state)) - return - # Verifies the selection of core-collapse mechnism to perform # the collapse if self.mechanism in [ @@ -1763,6 +1736,7 @@ def SNCheck( for key in BINARYPROPERTIES: if key not in ['nearest_neighbour_distance','event']: setattr(binary, key, None) + if flag_binary: # update the tilt if not binary.first_SN_already_occurred: @@ -1885,7 +1859,8 @@ def generate_kick(self, star, sigma): return Vkick - def get_combined_tilt(self,tilt_1, tilt_2, true_anomaly_1, true_anomaly_2): + + def get_combined_tilt(self, tilt_1, tilt_2, true_anomaly_1, true_anomaly_2): """Get the combined spin-orbit-tilt after two supernovae, assuming the spin as not realigned with the orbital angular momentum after SN1 @@ -2522,13 +2497,15 @@ def __call__(self, star, conserve_hydrogen_envelope=False): -def check_SN_CO_match(star): +def check_SN_CO_match(SN_type, state): '''Check if the SN type matches the stellar state of the given star. Parameters ---------- - star : SingleStar object - Star object containing the star properties. + SN_type : str + SN type of the star. + state : str + Stellar state of the star. Returns ------- @@ -2536,19 +2513,19 @@ def check_SN_CO_match(star): True if the SN type matches the stellar state of the star. ''' # TODO: remove star.state == PISN, because PISN shouldn't be a stellar state - if star.state == 'PISN': - star.state = 'massless_remnant' + if state == 'PISN': + state = 'massless_remnant' correct_SN_type = True - if star.state == 'WD' and star.SN_type != "WD": + if state == 'WD' and SN_type != "WD": correct_SN_type = False - elif (star.state == "NS") and \ - (star.SN_type != 'ECSN' and - star.SN_type != "CCSN"): + elif (state == "NS") and \ + (SN_type != 'ECSN' and + SN_type != "CCSN"): correct_SN_type = False - elif (star.state =="BH") and \ - (star.SN_type != "CCSN" and - star.SN_type != 'PPISN'): + elif (state =="BH") and \ + (SN_type != "CCSN" and + SN_type != 'PPISN'): correct_SN_type = False - elif (star.state == "massless_remnant" and star.SN_type != 'PISN'): + elif (state == "massless_remnant" and SN_type != 'PISN'): correct_SN_type = False return correct_SN_type diff --git a/posydon/binary_evol/singlestar.py b/posydon/binary_evol/singlestar.py index 812a919a82..51d8039508 100644 --- a/posydon/binary_evol/singlestar.py +++ b/posydon/binary_evol/singlestar.py @@ -142,9 +142,9 @@ def __init__(self, **kwargs): if not hasattr(self, 'natal_kick_array'): self.natal_kick_array = [None] * 4 if not hasattr(self, 'spin_orbit_tilt_first_SN'): - self.spin_orbit_tilt_SN1 = None + self.spin_orbit_tilt_first_SN = None if not hasattr(self, 'spin_orbit_tilt_second_SN'): - self.spin_orbit_tilt_SN2 = None + self.spin_orbit_tilt_second_SN = None if not hasattr(self, 'f_fb'): self.f_fb = None if not hasattr(self, 'SN_type'): diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index 36c72e856c..38b2901969 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -50,12 +50,33 @@ get_kick_samples_from_file) from posydon.utils.common_functions import (orbital_period_from_separation, orbital_separation_from_period) +from posydon.popsyn.normalized_pop_mass import initial_total_underlying_mass + from posydon.popsyn.defaults import default_kwargs from posydon.popsyn.io import binarypop_kwargs_from_ini from posydon.utils.constants import Zsun from posydon.utils.posydonerror import POSYDONError,initial_condition_message from posydon.utils.common_functions import set_binary_to_failed +saved_ini_parameters = ['metallicity', + "number_of_binaries", + 'binary_fraction_scheme', + 'binary_fraction_const', + 'star_formation', + 'max_simulation_time', + 'primary_mass_scheme', + 'primary_mass_min', + 'primary_mass_max', + 'secondary_mass_scheme', + 'secondary_mass_min', + 'secondary_mass_max', + 'orbital_scheme', + 'orbital_period_scheme', + 'orbital_period_min', + 'orbital_period_max', + 'eccentricity_scheme'] + + # 'event' usually 10 but 'detached (Integration failure)' can occur HISTORY_MIN_ITEMSIZE = {'state': 30, 'event': 25, 'step_names': 21, 'S1_state': 31, 'S2_state': 31, @@ -101,7 +122,6 @@ def __init__(self, **kwargs): for key, arg in kwargs.items(): self.kwargs[key] = arg self.number_of_binaries = self.kwargs.get('number_of_binaries') - self.population_properties = self.kwargs.get('population_properties', SimulationProperties()) atexit.register(lambda: BinaryPopulation.close(self)) @@ -110,6 +130,12 @@ def __init__(self, **kwargs): # grab all metallicities in population or use single metallicity self.metallicities = self.kwargs.get('metallicities', [self.metallicity]) + # force the metallicity on to the simulation properties + for key in STEP_NAMES_LOADING_GRIDS: + if key in self.population_properties.kwargs: + self.population_properties.kwargs[key][1].update({'metallicity': self.metallicity}) + + self.population_properties.max_simulation_time = self.kwargs.get( 'max_simulation_time') # years @@ -447,7 +473,7 @@ def combine_saved_files(self, absolute_filepath, file_names, **kwargs): history_cols = pd.read_hdf(file_names[0], key='history').columns oneline_cols = pd.read_hdf(file_names[0], key='oneline').columns - history_tmp = pd.read_hdf(file_names[0], key='history') + #history_tmp = pd.read_hdf(file_names[0], key='history') history_min_itemsize = {key: val for key, val in HISTORY_MIN_ITEMSIZE.items() @@ -458,19 +484,49 @@ def combine_saved_files(self, absolute_filepath, file_names, **kwargs): mode = kwargs.get('mode', 'a') complib = kwargs.get('complib', 'zlib') complevel = kwargs.get('complevel', 9) - + + with pd.HDFStore(absolute_filepath, mode=mode, complevel=complevel, complib=complib) as store: + simulated_mass = 0 + number_of_systems = 0 for f in file_names: # strings itemsize set by first append max value, # which may not be largest string try: store.append('history', pd.read_hdf(f, key='history'), min_itemsize=history_min_itemsize) - store.append('oneline', pd.read_hdf(f, key='oneline'), + + oneline = pd.read_hdf(f, key='oneline') + simulated_mass += oneline['S1_mass_i'].sum() + oneline['S2_mass_i'].sum() + if 'metallicity' not in oneline.columns: + met_df = pd.DataFrame(data={'metallicity': [self.metallicity] * len(oneline)}, index=oneline.index) + oneline = pd.concat([oneline, met_df], axis=1) + + number_of_systems += len(oneline) + + store.append('oneline', oneline, min_itemsize=oneline_min_itemsize) - os.remove(f) + except Exception: print(traceback.format_exc(), flush=True) + + # store population metadata + tmp_df = pd.DataFrame() + for c in saved_ini_parameters: + tmp_df[c] = [self.kwargs[c]] + store.append('ini_parameters', tmp_df) + + tmp_df = pd.DataFrame( + index=[self.metallicity], + data={'simulated_mass': simulated_mass, + 'underlying_mass': initial_total_underlying_mass(df=simulated_mass, **self.kwargs)[0], + 'number_of_systems': number_of_systems}) + tmp_df.index.name = 'metallicity' + store.append('mass_per_metallicity', tmp_df) + + # only remove the files once they've been written to the new file + for f in file_names: + os.remove(f) def close(self): """Close loaded h5 files from SimulationProperties.""" @@ -521,7 +577,7 @@ def __init__(self, file_name=None, **kwargs): self.oneline_dfs = [] if (('read_samples_from_file' in self.kwargs) and - (self.kwargs['read_samples_from_file']!='')): + (self.kwargs['read_samples_from_file'] != '')): self.binary_generator = BinaryGenerator(\ sampler=get_samples_from_file, **kwargs) else: @@ -532,6 +588,7 @@ def __init__(self, file_name=None, **kwargs): if file_name: self.store_file = file_name + self.store_file = file_name def append(self, binary): """Add a binary instance internaly.""" @@ -647,6 +704,10 @@ def from_hdf(self, indices=None, where=None, restore=False): If true, restore binaries back to initial conditions. """ + # check if numpy64, since where does not support this + if isinstance(indices, np.int64): + indices = int(indices) + if where is None: if indices is None: raise ValueError("You must specify either the binary indices or a query string " @@ -659,7 +720,7 @@ def from_hdf(self, indices=None, where=None, restore=False): with pd.HDFStore(self.store_file, mode='r') as store: hist = store.select(key='history', where=query_str) oneline = store.select(key='oneline', where=query_str) - + binary_holder = [] for i in np.unique(hist.index): binary = BinaryStar.from_df( @@ -729,11 +790,11 @@ def save(self, fname, **kwargs): with pd.HDFStore(fname, mode=mode, complevel=complevel, complib=complib) as store: history_df = self.to_df(**kwargs) - store.append('history', history_df, data_columns=True) + store.append('history', history_df) online_df = self.to_oneline_df(**kwargs) - store.append('oneline', online_df, data_columns=True) - + store.append('oneline', online_df) + return @@ -822,7 +883,7 @@ def draw_initial_samples(self, orbital_scheme='separation', **kwargs): indices = np.arange(self._num_gen, self._num_gen+N_binaries, 1) # kicks - if 'read_samples_from_file' in kwargs: + if (('read_samples_from_file' in kwargs) and (kwargs['read_samples_from_file'] != '')): kick1, kick2 = get_kick_samples_from_file(**kwargs) else: if 'number_of_binaries' in kwargs: diff --git a/posydon/popsyn/io.py b/posydon/popsyn/io.py index ccd52d2b6e..17088dbd15 100644 --- a/posydon/popsyn/io.py +++ b/posydon/popsyn/io.py @@ -128,11 +128,14 @@ EXTRA_BINARY_COLUMNS_DTYPES = { 'step_names' : 'string', - 'step_times' : 'float64', + 'step_times' : 'float64', } # no default extras for history attributes -EXTRA_STAR_COLUMNS_DTYPES = {} +EXTRA_STAR_COLUMNS_DTYPES = { + 'm_disk_radiated': 'float64', + 'm_disk_accreted': 'float64', +} SCALAR_NAMES_DTYPES = { 'natal_kick_array_0': 'float64', @@ -141,7 +144,8 @@ 'natal_kick_array_3': 'float64', 'SN_type':'string', 'f_fb': 'float64', - 'spin_orbit_tilt': 'float64', + 'spin_orbit_tilt_first_SN': 'float64', + 'spin_orbit_tilt_second_SN': 'float64', } @@ -543,3 +547,55 @@ def binarypop_kwargs_from_ini(path, verbose=False): **sim_prop_kwargs) return pop_kwargs + + +def create_run_script_text(ini_file): + + + text=["from posydon.popsyn.binarypopulation import BinaryPopulation", + "from posydon.popsyn.io import binarypop_kwargs_from_ini", + "from posydon.utils.common_functions import convert_metallicity_to_string", + "import argparse", + + 'if __name__ == "__main__":', + " parser = argparse.ArgumentParser()", + " parser.add_argument('metallicity', type=float)", + " args = parser.parse_args()", + f" ini_kw = binarypop_kwargs_from_ini('{ini_file}')", + " ini_kw['metallicity'] = args.metallicity", + " str_met = convert_metallicity_to_string(args.metallicity)", + " ini_kw['temp_directory'] = str_met+'_Zsun_' + ini_kw['temp_directory']", + " synpop = BinaryPopulation(**ini_kw)", + " synpop.evolve()"] + + text = '\n'.join(text) + return text + + +def create_merge_script_text(ini_file): + + + text = ['from posydon.popsyn.binarypopulation import BinaryPopulation', + 'from posydon.popsyn.io import binarypop_kwargs_from_ini', + 'from posydon.utils.common_functions import convert_metallicity_to_string', + 'import argparse', + 'import os', + 'if __name__ == "__main__":', + ' parser = argparse.ArgumentParser()', + ' parser.add_argument("metallicity", type=float)', + ' args = parser.parse_args()', + f' ini_kw = binarypop_kwargs_from_ini("{ini_file}")', + ' ini_kw["metallicity"] = args.metallicity', + ' str_met = convert_metallicity_to_string(args.metallicity)', + ' ini_kw["temp_directory"] = str_met+"_Zsun_" + ini_kw["temp_directory"]', + ' synpop = BinaryPopulation(**ini_kw)', + ' path_to_batch = ini_kw["temp_directory"]', + ' tmp_files = [os.path.join(path_to_batch, f) for f in os.listdir(path_to_batch) if os.path.isfile(os.path.join(path_to_batch, f))]', + ' tmp_files = sorted(tmp_files, key=lambda x: int(x.split(".")[-1]))', + ' synpop.combine_saved_files(str_met+ "_Zsun_population.h5", tmp_files)', + ' print("done")', + ' if len(os.listdir(path_to_batch)) == 0:', + ' os.rmdir(path_to_batch)'] + + text = '\n'.join(text) + return text diff --git a/posydon/popsyn/population_params_default.ini b/posydon/popsyn/population_params_default.ini index c9756c9ffc..9a7950f15a 100644 --- a/posydon/popsyn/population_params_default.ini +++ b/posydon/popsyn/population_params_default.ini @@ -266,7 +266,7 @@ # convert BinaryStars into DataFrames after evolution use_MPI = False # use only for local MPI runs - metallicity = [0.0001] # [2., 1., 0.45, 0.2, 0.1, 0.01, 0.001, 0.0001] + metallicity = [1] # [2., 1., 0.45, 0.2, 0.1, 0.01, 0.001, 0.0001] # In units of solar metallicity # Random Number Generation @@ -278,7 +278,7 @@ #'const' 'Moe_17' binary_fraction_const = 1 # float 0< fraction <=1 - star_formation = 'constant' + star_formation = 'burst' # 'constant' 'burst' 'custom_linear' 'custom_log10' 'custom_linear_histogram' 'custom_log10_histogram' max_simulation_time = 13.8e9 # float (0,inf) @@ -420,10 +420,11 @@ #'profile', ] scalar_names=[ - 'natal_kick_array', - 'SN_type', + 'natal_kick_array', + 'SN_type', 'f_fb', - 'spin_orbit_tilt', + 'spin_orbit_tilt_first_SN', + 'spin_orbit_tilt_second_SN', ] [SingleStar_2_output] @@ -487,8 +488,9 @@ #'profile', ] scalar_names=[ - 'natal_kick_array', - 'SN_type', + 'natal_kick_array', + 'SN_type', 'f_fb', - 'spin_orbit_tilt', + 'spin_orbit_tilt_first_SN', + 'spin_orbit_tilt_second_SN', ] diff --git a/posydon/popsyn/rate_calculation.py b/posydon/popsyn/rate_calculation.py index 1afb90dc2c..54c12f9e3c 100644 --- a/posydon/popsyn/rate_calculation.py +++ b/posydon/popsyn/rate_calculation.py @@ -1,933 +1,208 @@ +__author__ = [ + "Simone Bavera ", + "Max Briel ", +] -__author__ = ['Simone Bavera '] +from posydon.utils.constants import Zsun -import os -import warnings +from astropy.cosmology import Planck15 as cosmology +from astropy import constants as const import numpy as np -import pandas as pd -from tqdm import tqdm import scipy as sp +from astropy.cosmology import z_at_value from scipy.interpolate import interp1d from astropy import units as u -from astropy import constants as const # TODO: replace this in favour of POSYDON constats module -from astropy.cosmology import Planck15 as cosmology # TODO: update to Planck18 once we update astropy module -from astropy.cosmology import z_at_value -import posydon.popsyn.selection_effects as selection_effects -from posydon.utils.constants import Zsun -from posydon.popsyn.star_formation_history import (star_formation_rate, - mean_metallicity, - std_log_metallicity_dist, - get_illustrisTNG_data, - fractional_SFR_at_given_redshift) -from posydon.utils.data_download import PATH_TO_POSYDON_DATA -from posydon.popsyn.GRB import GRB_PROPERTIES, get_GRB_properties - -PATH_TO_PDET_GRID = os.path.join(PATH_TO_POSYDON_DATA, 'selection_effects/pdet_grid.hdf5') DEFAULT_MODEL = { - 'delta_t' : 100, # Myr - 'SFR' : 'IllustrisTNG', - 'sigma_SFR' : None, - 'Z_max' : 1., - 'select_one_met' : False, - 'dlogZ' : None, # e.g, [np.log10(0.0142/2),np.log10(0.0142*2)] - 'Zsun' : Zsun, - 'compute_GRB_properties' : False, - 'GRB_beaming' : 1., # e.g., 0.5, 'Goldstein+15' - 'GRB_efficiency' : 0., # e.g., 0.01 - 'E_GRB_iso_min' : 0., # e.g., 1e51 erg + "delta_t": 100, # Myr + "SFR": "IllustrisTNG", + "sigma_SFR": None, + "Z_max": 1.0, + "select_one_met": False, + "dlogZ": None, # e.g, [np.log10(0.0142/2),np.log10(0.0142*2)] + "Zsun": Zsun, } -class Rates(object): - """Compute DCO rates.""" - def __init__(self, df, verbose=False, **kwargs): - """Compute DCO rates. - - Parameters - ---------- - df : object - Pandas dataframe containing the synthetic binary population. - delta_t : float - Cosmic time bin size used to bin distribute the synthetc binary - population. - SFR : string - Star-formation-rate history you want to use: - 'Madau+Dickinson14', 'Madau+Fragos17', 'Neijssel+19', 'IllustrisTNG' - sigma_SFR : string - Standard deviation of the trucated log-normal metallicity distribution - assumed for the star-formation rate history in the case you select - 'Madau+Dickinson14', 'Madau+Fragos17', 'Neijssel+19', - Z_max : float - The absolute maximum metallicity of the star formation rate history. - NOTE: This should be used when assuming a truncated log-normal - distribution of metallcities to not overpredict 1, also it can be - used with the IllustrisTNG SFR to prevent unphysically high - metallicities. - select_one_met : bool - If `True` your synthetic binary population should contain only one - descrete metallicity. - dlogZ : float or range - Used when `select_one_met=True` to select the metallicity range - around which you want to integrate the SFR history. If float value - value is provided we assume a symmetric range dlogZ/2 around the - provided metallcity else we consider the range provided - [Z_min, Z_max] which could also be asymmetric. - verbose : bool - `True` if you want the print statements. - - - TODO: add the definitios of the variables used by this class. - - """ - - self.df = df - self.verbose = verbose - - if kwargs: - for key in kwargs: - if key not in DEFAULT_MODEL: - raise ValueError(key + " is not a valid parameter name!") - for varname in DEFAULT_MODEL: - default_value = DEFAULT_MODEL[varname] - setattr(self, varname, kwargs.get(varname, default_value)) - else: - for varname in DEFAULT_MODEL: - default_value = DEFAULT_MODEL[varname] - setattr(self, varname, default_value) - - if self.compute_GRB_properties: - for key in GRB_PROPERTIES: - if key not in self.df: - warnings.warn("GRB properties not in the dataset, " - "computing them right now!") - self.df = get_GRB_properties(self.df, - self.GRB_efficiency, - self.GRB_beaming, - self.E_GRB_iso_min) - - ###################################################### - ### DCO detection rate and merger rate density ### - ### see arXiv:1906.12257, arXiv:2010.16333, ### - ### arXiv:2106.15841, arXiv:221210924, ### - ### arXiv:2204.02619, arXiv:2011.10057 ### - ### arXiv:2012.02274 ### - ###################################################### - - def chi_eff(self, m_1, m_2, a_1, a_2, tilt_1, tilt_2): - return (m_1*a_1*np.cos(tilt_1)+m_2*a_2*np.cos(tilt_2))/(m_1+m_2) - - def m_chirp(self, m_1, m_2): - return (m_1*m_2)**(3./5)/(m_1+m_2)**(1./5) - - def mass_ratio(self, m_1, m_2): - q = m_2/m_1 - q[q>1.] = 1./q[q>1.] - return q - - def get_data(self, var, index=None): - """Resample class elements. - - Parameters - ---------- - var : string - Key of self.df. This method support a list of custom definitios for - DCO studies like q, m_tot, m_chirp, chi_eff. - index : list of int - List of indicies of the binaries of interest. - - Returns - ------- - array - Array containing the self.df[var][index]. - - """ - if var not in self.df: - # compute some useful quantities that are not defined in df_synthetic - if var == 'q': - values = self.mass_ratio(self.df["S1_mass"], self.df["S2_mass"]) - elif var == 'm_tot': - values = self.df["S1_mass"] + self.df["S2_mass"] - elif var == 'm_chirp': - values = self.m_chirp(self.df["S1_mass"], self.df["S2_mass"]) - elif var == 'chi_eff': - # check if tilts are provided - if ("S1_spin_orbit_tilt_second_SN" not in self.df or - "S2_spin_orbit_tilt_second_SN" not in self.df): - warnings.warn('Spin tilts not in dataset! Assuming spins ' - 'are aligned to orbital angular momentum ' - 'to compute chi_eff!') - tilt_BH1 = np.zeros(len(self.df["S1_spin"])) - tilt_BH2 = tilt_BH1 - else: - tilt_BH1 = self.df["S1_spin_orbit_tilt_second_SN"] - tilt_BH2 = self.df["S2_spin_orbit_tilt_second_SN"] - values = self.chi_eff(self.df["S1_mass"], - self.df["S2_mass"], - self.df["S1_spin"], - self.df["S2_spin"], - tilt_BH1, - tilt_BH2) - else: - raise ValueError(f'Definition for {var} not available!') +def get_shell_comoving_volume(z_hor_i, z_hor_f, sensitivity="infinite"): + """Compute comoving volume corresponding to a redshift shell. + + Parameters + ---------- + z_hor_i : double + Cosmological redshift. Lower bound of the integration. + z_hor_f : double + Cosmological redshift. Upper bound of the integration. + sensitivity : string + hoose which GW detector sensitivity you want to use. At the moment + only 'infinite' is available, i.e. p_det = 1. + + Returns + ------- + double + Retruns the comoving volume between the two shells z_hor_i + and z_hor_f in Gpc^3. + + """ + c = const.c.to("Gpc/yr").value # Gpc/yr + H_0 = cosmology.H(0).to("1/yr").value # km/Gpc*s + + def E(z): + Omega_m = cosmology.Om0 + Omega_L = 1 - cosmology.Om0 + return np.sqrt(Omega_m * (1.0 + z) ** 3 + Omega_L) + + def f(z, sensitivity): + if sensitivity == "infinite": + return ( + 1.0 + / (1.0 + z) + * 4 + * np.pi + * c + / H_0 + * (get_comoving_distance_from_redshift(z) * 10 ** (-3.0)) ** 2.0 + / E(z) + ) else: - values = self.df[var] + # TODO: peanut-shaped antenna patter comoving volume calculation + raise ValueError("Sensitivity not supported!") - if index is not None: - return values[index].values - else: - return values.values - - def get_redshift_from_cosmic_time_interpolator(self): - """Interpolator to compute the cosmological redshift given the cosmic time. - - Returns - ------- - object - Returns the trained interpolator object. - - """ - # astropy z_at_value method is too slow to compute z_mergers efficinty - # we must implment interpolation - t = np.linspace(1e-2, cosmology.age(1e-08).value*0.9999999, 1000) - z = np.zeros(1000) - for i in range(1000): - z[i] = z_at_value(cosmology.age, t[i] * u.Gyr) - f_z_m = interp1d(t, z, kind='cubic') - return f_z_m - - def get_redshift_from_cosmic_time(self, t_cosm): - """Compute the cosmological redshift given the cosmic time.. - - Parameters - ---------- - t_cosm : array doubles - Cosmic time to which you want to know the redhisft. - - Returns - ------- - array doubles - Cosmolgocial redshift corresponding to the cosmic time. - - """ - interpolator = self.get_redshift_from_cosmic_time_interpolator() - return interpolator(t_cosm) - - def get_cosmic_time_from_redshift(self, z): - """Compute the cosmic time from redshift. - - Parameters - ---------- - z : double - Cosmological redshift. - - Returns - ------- - double - Return age of the cosmic time in Gyr given the redshift z. - - """ - return cosmology.age(z).value # Gyr - - def get_comoving_disntance_from_redshift(self, z): - """Compute the comoving distance from redshift. - - Parameters - ---------- - z : double - Cosmological redshift. - - Returns - ------- - double - Comoving distance in Mpc corresponding to the redhisft z. - - """ - return cosmology.comoving_distance(z).value # Mpc - - def get_cosmic_time_at_dco_merger(self, z_birth): - """Get cosmic time at DCO merger. - - Parameters - ---------- - z_birth : double - Cosmological redshift of formation of the DCO system - (must be the same for every binary). - - Returns - ------- - double - Cosmic time in Gyr at DCO merger for all binaries born at z_birth. - - """ - n = self.df.shape[0] - t_birth = self.get_cosmic_time_from_redshift(z_birth) * np.ones(n) # Gyr - return t_birth + (self.df["time"] + self.df["t_delay"]) * 10 ** (-3) #Gyr - - def get_redshift_at_dco_merger(self, z_birth): - """Get redshift of merger of DCOs. - - Parameters - ---------- - z_birth : double - Redshift of formation of the DCO system (must be the same for every - binary). - - Returns - ------- - double - Redshift of merger of DCOs born at z_birth. - - """ - n = self.df.shape[0] - t_merger = self.get_cosmic_time_at_dco_merger(z_birth) - z_merger = np.ones(n) * np.nan - bool_merger = t_merger < self.get_cosmic_time_from_redshift(0.) * np.ones(n) # check if the binary merges - z_merger[bool_merger] = z_at_value(cosmology.age, t_merger[bool_merger] * u.Gyr) - return z_merger - - def get_centers_metallicity_bins(self): - """Return the centers of the metallicity bins. - - Returns - ------- - array double - Returns sampled metallicities of the populattion. This correponds - to the center of each metallicity bin. - - """ - return np.unique(self.df['metallicity'].values) - - def get_edges_metallicity_bins(self): - """Return the edges of the metallicity bins. - - Returns - ------- - array double - Returns the edges of all metallicity bins. We assume metallicities - were binned in log-space. - - """ - met_val = np.log10(self.get_centers_metallicity_bins()) - bin_met = np.zeros(len(met_val)+1) - # if more than one metallicty bin - if len(met_val) > 1 : - bin_met[0] = met_val[0] - (met_val[1] - met_val[0]) / 2. - bin_met[-1] = met_val[-1] + (met_val[-1] - met_val[-2]) / 2. - bin_met[1:-1] = met_val[:-1] + (met_val[1:] - met_val[:-1]) / 2. - # one metallicty bin - elif len(met_val) == 1 : - if isinstance(self.dlogZ, float): - bin_met[0] = met_val[0] - self.dlogZ / 2. - bin_met[-1] = met_val[0] + self.dlogZ / 2. - elif isinstance(self.dlogZ, list) or isinstance(self.dlogZ, np.array): - bin_met[0] = self.dlogZ[0] - bin_met[-1] = self.dlogZ[1] - - return 10**bin_met - - - def get_centers_redshift_bins(self): - """Compute redshift bin centers. - - Returns - ------- - array doubles - We devide the cosmic time history of the Universe in equally spaced - bins of cosmic time of self.delta_t (100 Myr default) an compute the - redshift corresponding to center of these bins. - - """ - # generate t_birth at the middle of each self.delta_t bin - t_birth_bin = [cosmology.age(0.).value] - t_birth = [] - # devide the - self.n_redshift_bin_centers = int(cosmology.age(0).to('Myr').value/self.delta_t) - for i in range(self.n_redshift_bin_centers+1): - t_birth.append(t_birth_bin[i] - self.delta_t*1e-3/2.) # Gyr - t_birth_bin.append(t_birth_bin[i] - self.delta_t*1e-3) # Gyr - t_birth = np.array(t_birth) - # compute the redshift - z_birth = [] - for i in range(self.n_redshift_bin_centers+1): - z_birth.append(z_at_value(cosmology.age, t_birth[i] * u.Gyr)) - z_birth = np.array(z_birth) - - return z_birth - - def get_edges_redshift_bins(self): - """Compute redshift bin edges. - - Returns - ------- - array doubles - We devide the cosmic time history of the Universe in equally spaced - bins of cosmic time of self.delta_t (100 Myr default) an compute the - redshift corresponding to edges of these bins. - - """ - # generate t_birth at the middle of each self.delta_t bin - t_birth_bin = [cosmology.age(0.).value] - for i in range(self.n_redshift_bin_centers+1): - t_birth_bin.append(t_birth_bin[i] - self.delta_t*1e-3) # Gyr - # compute the redshift - z_birth_bin = [] - for i in range(self.n_redshift_bin_centers): - # do not count first edge, we add z=0. later - z_birth_bin.append(z_at_value(cosmology.age, t_birth_bin[i+1] * u.Gyr)) - # add the first and last bin edge at z=0. and z=inf.=100 - z_birth_bin = np.array([0.]+z_birth_bin+[100.]) - - return z_birth_bin - - def merger_rate_weight(self, z_birth, z_merger, p_det, i): - """Compute the merger rate weight (w_ijk) in yr^-1 units. - - Parameters - ---------- - z_birth : array doubles - Cosmological redshift of formation of the binary systems. This MUST - be the same for all binaries. - z_merger : array doubles - Cosmological redshift of merger of the binary systems. - p_det : array doubles - Detection probability of the binary. - i : array integers - Indicies correponding to the binaries you want to select out of the - population. - - Returns - ------- - array doubles - Return the cosmological weights w_ijk (detection rate contibution) - of the binary k in the metallicity bin j born at redshift birth i - as in Eq. (B.8) of Bavera et al. (2020). - - """ - # get SFR at a given population birth redshift - SFR_at_z_birth = star_formation_rate(self.SFR, z_birth) - - # get metallicity bin edges - met_bins = self.get_edges_metallicity_bins() - - # distribute the DCO to the corresponding bin - binplace = np.digitize(self.get_data("metallicity", i), met_bins) - - # compute fSFR assuming log-normal distributed metallicities - fSFR = fractional_SFR_at_given_redshift(z_birth, SFR_at_z_birth, - self.SFR, self.sigma_SFR, - met_bins, binplace, - self.Z_max, self.select_one_met) - - # simulated mass per given metallicity corrected for the unmodeled - # single and binary stellar mass - M_model = self.get_data("underlying_mass_for_met",i) - - # speed of light - c = const.c.to('Mpc/yr').value # Mpc/yr - - # delta cosmic time bin - deltaT = self.delta_t * 10 ** 6 # yr - - # comoving distance corresponding to the DCO merger redshift - D_c = self.get_comoving_disntance_from_redshift(z_merger) - - # DCO cosmological weights B.8 in Bavera et al. (2020) - return 4.*np.pi * c * D_c**2 * p_det * deltaT * fSFR / M_model # yr^-1 - - - def compute_merger_rate_weights(self, sensitivity, flag_pdet=True, path_to_dir='./', extention='npz'): - """Compute the cosmological weights of the DCO population. - - This function will create a directory path_to_dir/DCOs/sensitivity where - it will save the weigths, binary indicies k, z_formation, z_merger. - This is needed for scalability as a ~100k DCO population generates ~10M - non-zero weights assuming a delta_t=100Myr. - - Parameters - ---------- - sensitivity : string - GW detector sensitivity and network configuration you want to use, see arXiv:1304.0670v3 - detector sensitivities are taken from: https://dcc.ligo.org/LIGO-T2000012-v2/public - available sensitivity keys (for Hanford, Livingston, Virgo network): - 'O3actual_H1L1V1' : aligo_O3actual_H1.txt, aligo_O3actual_L1.txt, avirgo_O3actual.txt - 'O4low_H1L1V1' : aligo_O4low.txt, aligo_O4low.txt, avirgo_O4high_NEW.txt - 'O4high_H1L1V1' : aligo_O4high.txt, aligo_O4high.txt, avirgo_O4high_NEW.txt - 'design_H1L1V1' : AplusDesign.txt, AplusDesign.txt, avirgo_O5high_NEW.txt - 'infinite': intrinsic merging DCO population, i.e. p_det = 1 - flag_pdet : bool - This is a control variable. In order to be sure you want to run - infinite sensitivity set `p_det=False`. - path_to_dir : string - Path to the workingn directory where you want to store the - cosmological weights. - - """ - # check if the folder three exists, otherwise create it - DCO_dir = os.path.join(path_to_dir,'DCOs') - if 'DCOs' not in os.listdir(path_to_dir): - os.makedirs(DCO_dir) - - sensitivity_dir = os.path.join(DCO_dir, f'{sensitivity}_sensitivity') - if f'{sensitivity}_sensitivity' not in os.listdir(DCO_dir): - os.makedirs(sensitivity_dir) - - # index of all DCOs - n = self.df.shape[0] - index = np.arange(0, n) - - # redshif of each time bin - z_birth = self.get_centers_redshift_bins() - - # define interpolator - get_redshift_from_time = self.get_redshift_from_cosmic_time_interpolator() - - # hashmap to store everythig - data = {'index': {}, 'z_merger': {}, 'weights': {}} - - # load and store detector selection effects interpolator - if sensitivity != 'infinite': - self.sel_eff = selection_effects.KNNmodel(grid_path=PATH_TO_PDET_GRID, - sensitivity_key=sensitivity) - - # loop over all redshift bins - for i in tqdm(range(len(z_birth))): - - # compute the merger time of each DCOs - t_merger = self.get_cosmic_time_at_dco_merger(z_birth[i]) - - # detectable population - if flag_pdet == True and sensitivity != 'infinite': - - # sort out systems not merging within the Hubble time - bool_merger = t_merger < cosmology.age(1e-08).value*0.9999999 * np.ones(n) - - # if there are no merging DCO, continue - if len(index[bool_merger]) == 0: - data['index'][str(z_birth[i])] = np.array([]) - data['z_merger'][str(z_birth[i])] = np.array([]) - data['weights'][str(z_birth[i])] = np.array([]) - continue - - # get some quantities to compute detection probabilities - data_slice = pd.DataFrame() - data_slice['m1'] = self.get_data('S1_mass', index[bool_merger]) - data_slice['q'] = self.get_data('q', index[bool_merger]) - z_m = get_redshift_from_time(t_merger[bool_merger]) - data_slice['z'] = z_m - data_slice['chieff'] = self.get_data('chi_eff', index[bool_merger]) - - # compute detection probabilities - p_det = self.sel_eff.predict_pdet(data_slice) - - # sort out undetectable sources - bool_detect = p_det > 0. - - # if there are no detectable DCO, continue - if len(index[bool_merger][bool_detect]) == 0: - data['index'][str(z_birth[i])] = np.array([]) - data['z_merger'][str(z_birth[i])] = np.array([]) - data['weights'][str(z_birth[i])] = np.array([]) - continue - - # store index and redshift of merger - data['index'][str(z_birth[i])] = index[bool_merger][bool_detect] - data['z_merger'][str(z_birth[i])] = z_m[bool_detect] - - # compute and store marger rate weights as in eq. B.8 in Bavera et al. (2020) - z_b = np.ones(len(index[bool_merger][bool_detect]))*z_birth[i] - w_ijk = self.merger_rate_weight(z_b, z_m[bool_detect], - p_det[bool_detect], - index[bool_merger][bool_detect]) - data['weights'][str(z_birth[i])] = w_ijk - - # intrinsic population - elif flag_pdet == False and sensitivity == 'infinite': - - # sort out systems not merging within the Hubble time - bool_merger = t_merger < cosmology.age(1e-08).value*0.9999999 * np.ones(n) - - # if there are no merging DCO, continue - if len(index[bool_merger]) == 0: - data['index'][str(z_birth[i])] = np.array([]) - data['z_merger'][str(z_birth[i])] = np.array([]) - data['weights'][str(z_birth[i])] = np.array([]) - continue - - # get merger redshifts - z_m = get_redshift_from_time(t_merger[bool_merger]) - - # set detection probabilities to 1 - p_det = np.ones(len(index[bool_merger])) - - # store index and redshift of merger - data['index'][str(z_birth[i])] = index[bool_merger] - data['z_merger'][str(z_birth[i])] = z_m - - # compute and store marger rate weights as in eq. B.8 in Bavera et al. (2020) - z_b = np.ones(len(index[bool_merger]))*z_birth[i] - w_ijk = self.merger_rate_weight(z_b, z_m, p_det, index[bool_merger]) - data['weights'][str(z_birth[i])] = w_ijk - - else: - raise ValueError('Missmatch between sensitivity and flag_pdet!') - - # TODO: implemet the saving to h5 files - if extention == 'npz': - if self.verbose: - print('Formatting the data ....') - data_to_save = [[],[],[],[]] - for i, dict in enumerate([data[key] for key in data.keys()]): - for key in dict.keys(): - data_to_save[i].extend(dict[key].tolist()) - if i == 0: - data_to_save[3].extend(np.ones(len(dict[key]))*float(key)) - if self.verbose: - print('Saving the data ....') - for i, key in enumerate(data.keys()): - if key == 'index': - fmt_str = '%i' - else: - fmt_str = '%.8E' - np.savez(os.path.join(sensitivity_dir, f"{key}.npz"), - key=data_to_save[i], fmt=fmt_str) - - np.savez(os.path.join(sensitivity_dir,"z_formation.npz"), - key=data_to_save[3], fmt='%.8E') - else: - raise ValueError('Extension not supported!') - - def load_merger_rate_weights(self, sensitivity, path_to_dir='./', extention='npz'): - """Load the cosmological weights of the DCO populatio. - - Parameters - ---------- - sensitivity : string - GW detector sensitivity and network configuration you want to use, see arXiv:1304.0670v3 - detector sensitivities are taken from: https://dcc.ligo.org/LIGO-T2000012-v2/public - available sensitivity keys (for Hanford, Livingston, Virgo network): - 'O3actual_H1L1V1' : aligo_O3actual_H1.txt, aligo_O3actual_L1.txt, avirgo_O3actual.txt - 'O4low_H1L1V1' : aligo_O4low.txt, aligo_O4low.txt, avirgo_O4high_NEW.txt - 'O4high_H1L1V1' : aligo_O4high.txt, aligo_O4high.txt, avirgo_O4high_NEW.txt - 'design_H1L1V1' : AplusDesign.txt, AplusDesign.txt, avirgo_O5high_NEW.txt - 'infinite': intrinsic merging DCO population, i.e. p_det = 1 - path_to_dir : string - Path to the directory where you the cosmological weights are stored. - - Returns - ------- - array doubles - Return the cosmological weights, z_formation, z_merger and binary - index k associated to each weighted binary. - - """ - dir_ = os.path.join(path_to_dir, f'DCOs/{sensitivity}_sensitivity') - - if extention == 'npz': - if self.verbose: - print('Loading the data ...') - index = np.load(os.path.join(dir_, 'index.npz'), allow_pickle=True)['key'] - z_formation = np.load(os.path.join(dir_, 'z_formation.npz'), allow_pickle=True)['key'] - z_merger = np.load(os.path.join(dir_, 'z_merger.npz'), allow_pickle=True)['key'] - weights = np.load(os.path.join(dir_, 'weights.npz'), allow_pickle=True)['key'] - else: - raise ValueError('Extension not supported!') - return index, z_formation, z_merger, weights - - - def get_shell_comovig_volume(self, z_hor_i, z_hor_f, sensitivity='infinite'): - """Compute comoving volume corresponding to a redshift shell. - - Parameters - ---------- - z_hor_i : double - Cosmological redshift. Lower bound of the integration. - z_hor_f : double - Cosmological redshift. Upper bound of the integration. - sensitivity : string - hoose which GW detector sensitivity you want to use. At the moment - only 'infinite' is available, i.e. p_det = 1. - - Returns - ------- - double - Retruns the comoving volume between the two shells z_hor_i - and z_hor_f in Gpc^3. - - """ - c = const.c.to('Gpc/yr').value # Gpc/yr - H_0 = cosmology.H(0).to('1/yr').value # km/Gpc*s - def E(z): - Omega_m = cosmology.Om0 - Omega_L = 1-cosmology.Om0 - return np.sqrt(Omega_m*(1.+z)**3+Omega_L) - def f(z,sensitivity): - if sensitivity=='infinite': - return (1./(1.+z) * 4*np.pi*c / H_0 - * (self.get_comoving_disntance_from_redshift(z) - * 10**(-3.))**2. / E(z)) - else: - # TODO: peanut-shaped antenna patter comoving volume calculation - raise ValueError('Sensitivity not supported!') - return sp.integrate.quad(f, z_hor_i, z_hor_f, args=(sensitivity))[0] # Gpc^3 - - - def compute_rate_density(self, w_ijk, z_event, observable='DCO', sensitivity='infinite', index=None): - """Compute the GRB/DCO rate density. - - Parameters - ---------- - w_ijk : array doubles - Cosmological weights computed with Eq. B.8 of Bavera et at. (2020). - z_event : array doubles - Cosmolgocial redshift of the event you are tracking. - observable : string - Event you are tracking, available: - 'DCOs': merger event of a DCO system - 'GRB1': gamma ray bursts of star 1 - 'GRB2': gamma ray bursts of star 2 - sensitivity : string - This takes into account the detector sensitivity, available: - 'infinite': p_det = 1 - 'beamed': TODO for GRBs - - Returns - ------- - array doubles - Return the DCOs merger rate density (Gpc^-3 yr^-1) as a function of - cosmolgocial redshift as in Eq. (D.1) in Bavera et al. (2022) - arXiv:2106.15841 - - """ - - z_hor = self.get_edges_redshift_bins() - n = len(z_hor) - - if observable=='DCO': - z_merger_DCO = z_event - Rate_DCO = np.zeros(n-1) - if sensitivity=='infinite': - for i in range(1,n): - # compute Eq. (D.1) in Bavera et al. (2022) arXiv:2106.15841 - cond_DCO = np.logical_and(z_merger_DCO>z_hor[i-1], - z_merger_DCO<=z_hor[i]) - Rate_DCO[i-1] = (sum(w_ijk[cond_DCO]) - /self.get_shell_comovig_volume(z_hor[i-1], - z_hor[i], - sensitivity)) - return Rate_DCO # Gpc^-3 yr^-1 - else: - raise ValueError('Unsupported sensitivity!') - - elif observable in ['GRB1','GRB2']: - s = observable[-1] - z_GRB = z_event - if sensitivity=='beamed': - if index is not None: - f_beaming = self.get_data(f"S{s}_f_beaming",index) - flag_GRB = self.get_data(f"GRB{s}",index) - else: - raise ValueError('Missing f_beaming parameter!') - Rate_GRB = np.zeros(n-1) - for i in range(1,n): - cond_GRB = np.logical_and(np.logical_and(z_GRB>z_hor[i-1], z_GRB<=z_hor[i]), - flag_GRB) - f_fb = f_beaming[cond_GRB] - Rate_GRB[i-1] = (sum(w_ijk[cond_GRB]*f_fb) - /self.get_shell_comovig_volume(z_hor[i-1], - z_hor[i], - sensitivity='infinite')) - return Rate_GRB - - elif sensitivity=='infinite': - if index is not None: - flag_GRB = self.get_data(f"GRB{s}",index) - else: - raise ValueError('Missing f_beaming parameter!') - Rate_GRB = np.zeros(n-1) - for i in range(1,n): - if index is None: - raise ValueError('Provide index comlumn to identify GRB systems.') - cond_GRB = np.logical_and(np.logical_and(z_GRB>z_hor[i-1], z_GRB<=z_hor[i]), - flag_GRB) - Rate_GRB[i-1] = (sum(w_ijk[cond_GRB]) - /self.get_shell_comovig_volume(z_hor[i-1], - z_hor[i], - sensitivity)) - return Rate_GRB - else: - raise ValueError('Unknown sensitivity!') - else: - raise ValueError('Unknown observable!') - - ############################## - ##### GRB class methods ##### - ############################## - - def get_time_GRB(self, z_birth, event=None): - """Get the time of the GRB. - - Parameters - ---------- - z_brith : double - Redshif of birth. - event : string - Event you are tracking, either first or second core collpase: - 'CC1', 'CC2'. - - Returns - ------- - t_BRB : double - Cosmic time of the GRB event in Gyr. - - """ - if event not in ['CC1', 'CC2']: - raise ValueError(f'Unknown event {event}!') - n = self.df.shape[0] - t_birth = self.get_cosmic_time_from_redshift(z_birth) * np.ones(n) # Gyr - t_GRB = t_birth + self.df[f"time_{event}"] * 10 ** (-3) # Gyr - return t_GRB - - - def compute_GRB_rate_weights(self, sensitivity='infinity', path_to_dir='./', extention='npz'): - """Compute the cosmological weights of the transient events associated to the population. - - This function will create a directory path_to_dir/GRBs/sensitivity where - it will save the weigths, binary indicies k, z_formation, z_grb. - This is needed for scalability as a ~100k DCO population generates ~10M - non-zero weights assuming a delta_t=100Myr. - - Parameters - ---------- - sensitivity : string - Assume there are no selection effects. Available: - 'infinite': whole GRB population, i.e. p_det = 1 - path_to_dir : string - Path to the workingn directory where you want to store the - cosmological weights. - - """ - - # check if the folder three exists, otherwise create it - GRB_dir = os.path.join(path_to_dir,'GRBs') - if 'GRBs' not in os.listdir(path_to_dir): - os.makedirs(GRB_dir) - - sensitivity_dir = os.path.join(GRB_dir, f'{sensitivity}_sensitivity') - if f'{sensitivity}_sensitivity' not in os.listdir(GRB_dir): - os.makedirs(sensitivity_dir) - - # index of all DCOs - n = self.df.shape[0] - index = np.arange(0, n) - - # redshif of each time bin - z_birth = self.get_centers_redshift_bins() - - # define interpolator - get_redshift_from_time = self.get_redshift_from_cosmic_time_interpolator() - - # hashmap to store everythig - data = {'index_1': {}, 'z_grb_1': {}, 'weights_1': {}, - 'index_2': {}, 'z_grb_2': {}, 'weights_2': {}} - - # loop over all redshift bins - for i in tqdm(range(len(z_birth))): - - for s, event in enumerate(['CC1', 'CC2']): - # compute the CC time of each compact object - t_CC = self.get_time_GRB(z_birth[i], event) - - # intrinsic population - if sensitivity == 'infinite': - - # sort out system not emitting GRBs - bool_GRB = np.logical_and(t_CC < cosmology.age(1e-08).value*0.9999999 , self.df[f"GRB{s+1}"]) - - # if there are no system emitting any GRB, continue - if len(index[bool_GRB]) == 0: - data[f'index_{s+1}'][str(z_birth[i])] = np.array([]) - data[f'z_grb_{s+1}'][str(z_birth[i])] = np.array([]) - data[f'weights_{s+1}'][str(z_birth[i])] = np.array([]) - continue - - # get GRB redshifts - z_GRB = get_redshift_from_time(t_CC[bool_GRB]) - - # set detection probabilities to 1 - p_det = np.ones(len(index[bool_GRB])) - - # store index and redshift of merger - data[f'index_{s+1}'][str(z_birth[i])] = index[bool_GRB] - data[f'z_grb_{s+1}'][str(z_birth[i])] = z_GRB - - # compute and store cosmological rate weights as in eq. B.8 in Bavera et al. (2020) - z_b = np.ones(len(index[bool_GRB]))*z_birth[i] - w_ijk = self.merger_rate_weight(z_b, z_GRB, p_det, index[bool_GRB]) - data[f'weights_{s+1}'][str(z_birth[i])] = w_ijk - - else: - raise ValueError('Unknown sensitivity!') - - # TODO: implemet the saving to h5 files - if extention == 'npz': - if self.verbose: - print('Formatting the data ....') - for s in [1,2]: - data_to_save = [[],[],[],[]] - for i, dict in enumerate([data[key] for key in data.keys() if f'{s}' in key]): - for key in dict.keys(): - data_to_save[i].extend(dict[key].tolist()) - if i == 0: - data_to_save[-1].extend(np.ones(len(dict[key]))*float(key)) - if self.verbose: - print('Saving the data ....') - for i, key in enumerate([key for key in data.keys() if f'{s}' in key]): - if key == 'index': - fmt_str = '%i' - else: - fmt_str = '%.8E' - np.savez(os.path.join(sensitivity_dir, f"{key}.npz"), - key=data_to_save[i], fmt=fmt_str) - - np.savez(os.path.join(sensitivity_dir,f"z_formation_{s}.npz"), - key=data_to_save[-1], fmt='%.8E') - else: - raise ValueError('Extension not supported!') - - - def load_grb_rate_weights(self, sensitivity, path_to_dir='./', extention='npz'): - """Load the cosmological weights of the transient events associated to the population. - - Parameters - ---------- - sensitivity : string - Assume there are no selection effects. Available: - 'infinite': whole GRB population, i.e. p_det = 1 - path_to_dir : string - Path to the directory where you the cosmological weights are stored. - - Returns - ------- - array doubles - Return the cosmological weights, z_formation, z_GRB and binary - index k associated to each weighted binary. - - """ - dir_ = os.path.join(path_to_dir, f'GRBs/{sensitivity}_sensitivity') - - if extention == 'npz': - if self.verbose: - print('Loading the data ...') - index_1 = np.load(os.path.join(dir_, 'index_1.npz'), allow_pickle=True)['key'] - z_formation_1 = np.load(os.path.join(dir_, 'z_formation_1.npz'), allow_pickle=True)['key'] - z_grb_1 = np.load(os.path.join(dir_, 'z_grb_1.npz'), allow_pickle=True)['key'] - weights_1 = np.load(os.path.join(dir_, 'weights_1.npz'), allow_pickle=True)['key'] - index_2 = np.load(os.path.join(dir_, 'index_2.npz'), allow_pickle=True)['key'] - z_formation_2 = np.load(os.path.join(dir_, 'z_formation_2.npz'), allow_pickle=True)['key'] - z_grb_2 = np.load(os.path.join(dir_, 'z_grb_2.npz'), allow_pickle=True)['key'] - weights_2 = np.load(os.path.join(dir_, 'weights_2.npz'), allow_pickle=True)['key'] - else: - raise ValueError('Extension not supported!') - return index_1, z_formation_1, z_grb_1, weights_1, index_2, z_formation_2, z_grb_2, weights_2 + return sp.integrate.quad(f, z_hor_i, z_hor_f, args=(sensitivity))[0] # Gpc^3 + + +def get_comoving_distance_from_redshift(z): + """Compute the comoving distance from redshift. + + Parameters + ---------- + z : double + Cosmological redshift. + + Returns + ------- + double + Comoving distance in Mpc corresponding to the redhisft z. + + """ + return cosmology.comoving_distance(z).value # Mpc + + +def get_cosmic_time_from_redshift(z): + """Compute the cosmic time from redshift. + + Parameters + ---------- + z : double + Cosmological redshift. + + Returns + ------- + double + Return age of the cosmic time in Gyr given the redshift z. + + """ + return cosmology.age(z).value # Gyr + + +def redshift_from_cosmic_time_interpolator(): + """Interpolator to compute the cosmological redshift given the cosmic time. + + Returns + ------- + object + Returns the trained interpolator object. + + """ + # astropy z_at_value method is too slow to compute z_mergers efficinty + # we must implement interpolation + t = np.linspace(1e-2, cosmology.age(1e-08).value * 0.9999999, 1000) + z = np.zeros(1000) + for i in range(1000): + z[i] = z_at_value(cosmology.age, t[i] * u.Gyr) + f_z_m = interp1d(t, z, kind="cubic") + return f_z_m + + +def get_redshift_from_cosmic_time(t_cosm): + """Compute the cosmological redshift given the cosmic time.. + + Parameters + ---------- + t_cosm : array doubles + Cosmic time to which you want to know the redhisft. + + Returns + ------- + array doubles + Cosmolgocial redshift corresponding to the cosmic time. + + Note + ---- + - The function uses the interpolator redshift_from_cosmic_time_interpolator, + which is created each time the function is called. `z_at_value` from astropy + can be used for single values, but it is too slow for arrays. + """ + return redshift_from_cosmic_time_interpolator(t_cosm) + + +def get_redshift_bin_edges(delta_t): + """Compute the redshift bin edges. + + Parameters + ---------- + delta_t : double + Time interval in Myr. + + Returns + ------- + array doubles + Redshift bin edges. + + """ + n_redshift_bin_centers = int(cosmology.age(0).to("Myr").value / delta_t) + # generate t_birth at the middle of each self.delta_t bin + t_birth_bin = [cosmology.age(0.0).value] + for i in range(n_redshift_bin_centers + 1): + t_birth_bin.append(t_birth_bin[i] - delta_t * 1e-3) # Gyr + # compute the redshift + z_birth_bin = [] + for i in range(n_redshift_bin_centers): + # do not count first edge, we add z=0. later + z_birth_bin.append(z_at_value(cosmology.age, t_birth_bin[i + 1] * u.Gyr)) + # add the first and last bin edge at z=0. and z=inf.=100 + z_birth_bin = np.array([0.0] + z_birth_bin + [100.0]) + + return z_birth_bin + + +def get_redshift_bin_centers(delta_t): + """Compute the redshift bin centers. + + Parameters + ---------- + delta_t : double + Time interval in Myr. + + Returns + ------- + array doubles + Redshift bin centers. + """ + # generate t_birth at the middle of each self.delta_t bin + t_birth_bin = [cosmology.age(0.0).value] + t_birth = [] + n_redshift_bin_centers = int(cosmology.age(0).to("Myr").value / delta_t) + for i in range(n_redshift_bin_centers + 1): + t_birth.append(t_birth_bin[i] - delta_t * 1e-3 / 2.0) # Gyr + t_birth_bin.append(t_birth_bin[i] - delta_t * 1e-3) # Gyr + t_birth = np.array(t_birth) + # compute the redshift + z_birth = [] + for i in range(n_redshift_bin_centers + 1): + # z_at_value is from astopy.cosmology + z_birth.append(z_at_value(cosmology.age, t_birth[i] * u.Gyr)) + z_birth = np.array(z_birth) + + return z_birth diff --git a/posydon/popsyn/star_formation_history.py b/posydon/popsyn/star_formation_history.py index 62600d76ab..cb78aed66e 100644 --- a/posydon/popsyn/star_formation_history.py +++ b/posydon/popsyn/star_formation_history.py @@ -2,10 +2,11 @@ __authors__ = [ - 'Simone Bavera ', + "Simone Bavera ", "Kyle Akira Rocha ", "Devina Misra ", "Konstantinos Kovlakas ", + "Max Briel ", ] import os @@ -15,17 +16,26 @@ from posydon.utils.data_download import PATH_TO_POSYDON_DATA from posydon.utils.constants import age_of_universe from posydon.utils.common_functions import ( - rejection_sampler, histogram_sampler, read_histogram_from_file) + rejection_sampler, + histogram_sampler, + read_histogram_from_file, +) from posydon.utils.constants import Zsun from scipy.interpolate import interp1d from astropy.cosmology import Planck15 as cosmology -SFH_SCENARIOS = ["burst", "constant", "custom_linear", "custom_log10", - "custom_linear_histogram", "custom_log10_histogram"] +SFH_SCENARIOS = [ + "burst", + "constant", + "custom_linear", + "custom_log10", + "custom_linear_histogram", + "custom_log10_histogram", +] -def get_formation_times(N_binaries, star_formation='constant', **kwargs): +def get_formation_times(N_binaries, star_formation="constant", **kwargs): """Get formation times of binaries in a population based on a SFH scenario. Parameters @@ -49,46 +59,50 @@ def get_formation_times(N_binaries, star_formation='constant', **kwargs): array The formation times array. """ - RNG = kwargs.get('RNG', np.random.default_rng()) + RNG = kwargs.get("RNG", np.random.default_rng()) scenario = star_formation.lower() - if scenario == 'burst': - burst_time = kwargs.get('burst_time', 0.0) - return np.ones(N_binaries)*burst_time + if scenario == "burst": + burst_time = kwargs.get("burst_time", 0.0) + return np.ones(N_binaries) * burst_time - max_time_default = kwargs.get('max_simulation_time', age_of_universe) - max_time = kwargs.get('max_time', max_time_default) + max_time_default = kwargs.get("max_simulation_time", age_of_universe) + max_time = kwargs.get("max_time", max_time_default) - if scenario == 'constant': - min_time = kwargs.get('min_time', 0.0) + if scenario == "constant": + min_time = kwargs.get("min_time", 0.0) return RNG.uniform(size=N_binaries, low=min_time, high=max_time) if scenario in ["custom_linear", "custom_log10"]: - custom_ages_file = kwargs.get('custom_ages_file') + custom_ages_file = kwargs.get("custom_ages_file") x, y = np.loadtxt(custom_ages_file, unpack=True) current_binary_ages = rejection_sampler(x, y, N_binaries) if "log10" in scenario: - current_binary_ages = 10.0 ** current_binary_ages + current_binary_ages = 10.0**current_binary_ages return max_time - current_binary_ages if scenario in ["custom_linear_histogram", "custom_log10_histogram"]: - custom_ages_file = kwargs.get('custom_ages_file') + custom_ages_file = kwargs.get("custom_ages_file") x, y = read_histogram_from_file(custom_ages_file) current_binary_ages = histogram_sampler(x, y) if "log10" in scenario: - current_binary_ages = 10.0 ** current_binary_ages + current_binary_ages = 10.0**current_binary_ages return max_time - current_binary_ages raise ValueError( - "Unknown star formation scenario '{}' given. Valid options: {}". - format(star_formation, ",".join(SFH_SCENARIOS))) + "Unknown star formation scenario '{}' given. Valid options: {}".format( + star_formation, ",".join(SFH_SCENARIOS) + ) + ) + def get_illustrisTNG_data(verbose=False): """Load IllustrisTNG SFR dataset.""" if verbose: - print('Loading IllustrisTNG data...') - return np.load(os.path.join(PATH_TO_POSYDON_DATA, 'SFR/IllustrisTNG.npz')) + print("Loading IllustrisTNG data...") + return np.load(os.path.join(PATH_TO_POSYDON_DATA, "SFR/IllustrisTNG.npz")) + def star_formation_rate(SFR, z): """Star formation rate in M_sun yr^-1 Mpc^-3. @@ -111,19 +125,26 @@ def star_formation_rate(SFR, z): per year. """ if SFR == "Madau+Fragos17": - return 0.01 * (1. + z) ** 2.6 / (1. + ((1. + z) / 3.2) ** 6.2) # M_sun yr^-1 Mpc^-3 + return ( + 0.01 * (1.0 + z) ** 2.6 / (1.0 + ((1.0 + z) / 3.2) ** 6.2) + ) # M_sun yr^-1 Mpc^-3 elif SFR == "Madau+Dickinson14": - return 0.015 * (1. + z) ** 2.7 / (1. + ((1. + z) / 2.9) ** 5.6) # M_sun yr^-1 Mpc^-3 + return ( + 0.015 * (1.0 + z) ** 2.7 / (1.0 + ((1.0 + z) / 2.9) ** 5.6) + ) # M_sun yr^-1 Mpc^-3 elif SFR == "Neijssel+19": - return 0.01 * (1. + z) ** 2.77 / (1. + ((1. + z) / 2.9) ** 4.7) # M_sun yr^-1 Mpc^-3 - elif SFR=='IllustrisTNG': + return ( + 0.01 * (1.0 + z) ** 2.77 / (1.0 + ((1.0 + z) / 2.9) ** 4.7) + ) # M_sun yr^-1 Mpc^-3 + elif SFR == "IllustrisTNG": illustris_data = get_illustrisTNG_data() - SFR = illustris_data['SFR'] # M_sun yr^-1 Mpc^-3 - redshifts = illustris_data['redshifts'] + SFR = illustris_data["SFR"] # M_sun yr^-1 Mpc^-3 + redshifts = illustris_data["redshifts"] SFR_interp = interp1d(redshifts, SFR) return SFR_interp(z) else: - raise ValueError('Invalid SFR!') + raise ValueError("Invalid SFR!") + def mean_metallicity(SFR, z): """Empiric mean metallicity function. @@ -146,11 +167,12 @@ def mean_metallicity(SFR, z): """ if SFR == "Madau+Fragos17" or SFR == "Madau+Dickinson14": - return 10 ** (0.153 - 0.074 * z ** 1.34) * Zsun + return 10 ** (0.153 - 0.074 * z**1.34) * Zsun elif SFR == "Neijssel+19": - return 0.035*10**(-0.23*z) + return 0.035 * 10 ** (-0.23 * z) else: - raise ValueError('Invalid SFR!') + raise ValueError("Invalid SFR!") + def std_log_metallicity_dist(sigma): """Standard deviation of the log-metallicity distribution. @@ -162,87 +184,122 @@ def std_log_metallicity_dist(sigma): """ if isinstance(sigma, str): - if sigma == 'Bavera+20': + if sigma == "Bavera+20": return 0.5 elif sigma == "Neijssel+19": return 0.39 else: - raise ValueError('Uknown sigma choice!') + raise ValueError("Uknown sigma choice!") elif isinstance(sigma, float): return sigma else: - raise ValueError(f'Invalid sigma value {sigma}!') + raise ValueError(f"Invalid sigma value {sigma}!") -def fractional_SFR_at_given_redshift(z, SFR_at_z, SFR, sigma, bins, binplace, Z_max, select_one_met=False): - """Integrated SFR over deltaZ at a given z as in Eq. (B.9) of Bavera et al. (2020). + +def SFR_Z_fraction_at_given_redshift( + z, SFR, sigma, metallicity_bins, Z_max, select_one_met +): + """'Fraction of the SFR at a given redshift z in a given metallicity bin as in Eq. (B.8) of Bavera et al. (2020). Parameters ---------- + z : np.array + Cosmological redshift. SFR : string Star formation rate assumption: - Madau+Fragos17 see arXiv:1606.07887 - Madau+Dickinson14 see arXiv:1403.0007 - IllustrisTNG see see arXiv:1707.03395 - Neijssel+19 see arXiv:1906.08136 - Z : double - Metallicity. + sigma : double / string + Standard deviation of the log-metallicity distribution. + If string, it can be 'Bavera+20' or 'Neijssel+19'. + metallicity_bins : array + Metallicity bins edges in absolute metallicity. + Z_max : double + Maximum metallicity in absolute metallicity. + select_one_met : bool + If True, the function returns the fraction of the SFR in the given metallicity bin. + If False, the function returns the fraction of the SFR in the given metallicity bin and the fraction of the SFR in the metallicity bin Returns ------- - double - The total mass of stars formed per comoving volume at a given redshift z - for a given metallicity range deltaZ. - + array + Fraction of the SFR in the given metallicity bin at the given redshift. + In absolute metallicity. """ - # Z_left_edge == bins[binplace-1] - # Z_right_edge == bins[binplace] - if SFR == "Madau+Fragos17" or SFR=="Madau+Dickinson14": - # assume a truncated log10-normal distribution of metallicities - # see Eq. (B.2) in Bavera et al. (2020) + + if SFR == "Madau+Fragos17" or SFR == "Madau+Dickinson14": sigma = std_log_metallicity_dist(sigma) - mu = np.log10(mean_metallicity(SFR, z)) - sigma**2*np.log(10)/2. - # renormalisation constant + mu = np.log10(mean_metallicity(SFR, z)) - sigma**2 * np.log(10) / 2.0 + # renormalisation constant. We can use mu[0], since we integrate over the whole metallicity range norm = stats.norm.cdf(np.log10(Z_max), mu[0], sigma) - fSFR = SFR_at_z*(stats.norm.cdf(np.log10(bins[binplace]), mu, sigma)/norm - - stats.norm.cdf(np.log10(bins[binplace-1]), mu, sigma)/norm) + fSFR = np.empty((len(z), len(metallicity_bins) - 1)) + fSFR[:, :] = np.array( + [(stats.norm.cdf(np.log10(metallicity_bins[1:]), m, sigma) / norm + - stats.norm.cdf(np.log10(metallicity_bins[:-1]), m, sigma) / norm + ) for m in mu ] + ) if not select_one_met: - #left edge - fSFR[binplace == 1] = SFR_at_z[0]*stats.norm.cdf(np.log10(bins[1]), mu[0], sigma)/norm + fSFR[:, 0] = stats.norm.cdf(np.log10(metallicity_bins[1]), mu, sigma) / norm + fSFR[:,-1] = norm - stats.norm.cdf(np.log(metallicity_bins[-1]), mu, sigma)/norm + elif SFR == "Neijssel+19": # assume a truncated ln-normal distribution of metallicities sigma = std_log_metallicity_dist(sigma) - mu = np.log(mean_metallicity(SFR, z))-sigma**2/2. + mu = np.log(mean_metallicity(SFR, z)) - sigma**2 / 2.0 # renormalisation constant norm = stats.norm.cdf(np.log(Z_max), mu[0], sigma) - fSFR = SFR_at_z*(stats.norm.cdf(np.log(bins[binplace]), mu, sigma)/norm - - stats.norm.cdf(np.log(bins[binplace-1]), mu, sigma)/norm) + fSFR = np.empty((len(z), len(metallicity_bins) - 1)) + fSFR[:, :] = np.array( + [ + ( + stats.norm.cdf(np.log(metallicity_bins[1:]), m, sigma) / norm + - stats.norm.cdf(np.log(metallicity_bins[:-1]), m, sigma) / norm + ) + for m in mu + ] + ) if not select_one_met: - #left edge - fSFR[binplace == 1] = SFR_at_z[0]*stats.norm.cdf(np.log(bins[1]), mu[0], sigma)/norm - elif SFR == 'IllustrisTNG': + fSFR[:, 0] = stats.norm.cdf(np.log(metallicity_bins[1]), mu, sigma) / norm + fSFR[:,-1] = norm - stats.norm.cdf(np.log(metallicity_bins[-1]), mu, sigma)/norm + + elif SFR == "IllustrisTNG": # numerically itegrate the IlluystrisTNG SFR(z,Z) illustris_data = get_illustrisTNG_data() - redshifts = illustris_data['redshifts'] - Z = illustris_data['mets'] - M = illustris_data['M'] # Msun + redshifts = illustris_data["redshifts"] + Z = illustris_data["mets"] + M = illustris_data["M"] # Msun # only use data within the metallicity bounds (no lower bound) - valid_met_idxs = np.where(Z <= Z_max)[0] - # get the index of the correct redshift in the data - redz_idx = np.where(redshifts <= z[0])[0][0] - # take values of the data at this redshift between our metallicity bounds - Z_dist = M[redz_idx, valid_met_idxs] - if Z_dist.sum() == 0.: - fSFR = np.zeros(len(z)) - else: - Z_dist_cdf = np.cumsum(Z_dist)/Z_dist.sum() - Z_dist_cdf_interp = interp1d(np.log10(Z[valid_met_idxs]), Z_dist_cdf, fill_value="extrapolate") - fSFR = SFR_at_z*(Z_dist_cdf_interp(np.log10(bins[binplace])) - - Z_dist_cdf_interp(np.log10(bins[binplace-1]))) - if not select_one_met: - #left edge - fSFR[binplace == 1] = SFR_at_z[0]*Z_dist_cdf_interp(np.log10(bins[1])) + Z_max_mask = Z <= Z_max + redshift_indices = np.array([np.where(redshifts <= i)[0][0] for i in z]) + Z_dist = M[:, Z_max_mask][redshift_indices] + fSFR = np.zeros((len(z), len(metallicity_bins) - 1)) + + for i in range(len(z)): + if Z_dist[i].sum() == 0.0: + continue + else: + # We add a final point to the CDF and metallicities to ensure normalisation to 1 + Z_dist_cdf = np.cumsum(Z_dist[i]) / Z_dist[i].sum() + Z_dist_cdf = np.append(Z_dist_cdf, 1) + Z_x_values = np.append(np.log10(Z[Z_max_mask]), 0) + Z_dist_cdf_interp = interp1d(Z_x_values, Z_dist_cdf) + + fSFR[i, :] = (Z_dist_cdf_interp(np.log10(metallicity_bins[1:])) + - Z_dist_cdf_interp(np.log10(metallicity_bins[:-1]))) + + if not select_one_met: + # add the fraction of the SFR in the first and last bin + # or the only bin without selecting one metallicity + if len(metallicity_bins) == 2: + fSFR[i, 0] = 1 + else: + + fSFR[i, 0] = Z_dist_cdf_interp(np.log10(metallicity_bins[1])) + fSFR[i, -1] = 1 - Z_dist_cdf_interp(np.log10(metallicity_bins[-1])) else: - raise ValueError('Invalid SFR!') + raise ValueError("Invalid SFR!") return fSFR @@ -267,23 +324,34 @@ def integrated_SFRH_over_redshift(SFR, sigma, Z, Z_max): metallicity Z. """ + def E(z, Omega_m=cosmology.Om0): - Omega_L = 1.- Omega_m - return (Omega_m*(1.+z)**3 + Omega_L)**(1./2.) - def f(z,Z): + Omega_L = 1.0 - Omega_m + return (Omega_m * (1.0 + z) ** 3 + Omega_L) ** (1.0 / 2.0) + + def f(z, Z): if SFR == "Madau+Fragos17" or SFR == "Madau+Dickinson14": sigma = std_log_metallicity_dist(sigma) - mu = np.log10(mean_metallicity(SFR, z))-sigma**2*np.log(10)/2. - H_0 = cosmology.H0.to('1/yr').value # yr + mu = np.log10(mean_metallicity(SFR, z)) - sigma**2 * np.log(10) / 2.0 + H_0 = cosmology.H0.to("1/yr").value # yr # put a cutoff on metallicity at Z_max norm = stats.norm.cdf(np.log10(Z_max), mu, sigma) - return star_formation_rate(SFR, z)*stats.norm.pdf(np.log10(Z), mu, sigma)/norm*(H_0*(1.+z)*E(z))**(-1) + return ( + star_formation_rate(SFR, z) + * stats.norm.pdf(np.log10(Z), mu, sigma) + / norm + * (H_0 * (1.0 + z) * E(z)) ** (-1) + ) elif SFR == "Neijssel+19": sigma = std_log_metallicity_dist(sigma) - mu = np.log10(mean_metallicity(SFR, z))-sigma**2/2. - H_0 = cosmology.H0.to('1/yr').value # yr - return star_formation_rate(SFR, z)*stats.norm.pdf(np.log(Z), mu, sigma)*(H_0*(1.+z)*E(z))**(-1) + mu = np.log10(mean_metallicity(SFR, z)) - sigma**2 / 2.0 + H_0 = cosmology.H0.to("1/yr").value # yr + return ( + star_formation_rate(SFR, z) + * stats.norm.pdf(np.log(Z), mu, sigma) + * (H_0 * (1.0 + z) * E(z)) ** (-1) + ) else: - raise ValueError('Invalid SFR!') + raise ValueError("Invalid SFR!") - return sp.integrate.quad(f, 1e-10, np.inf, args=(Z,))[0] # M_sun yr^-1 Mpc^-3 + return sp.integrate.quad(f, 1e-10, np.inf, args=(Z,))[0] # M_sun yr^-1 Mpc^-3 diff --git a/posydon/popsyn/synthetic_population.py b/posydon/popsyn/synthetic_population.py index 18fe832fd2..cf9238a422 100644 --- a/posydon/popsyn/synthetic_population.py +++ b/posydon/popsyn/synthetic_population.py @@ -1,13 +1,35 @@ -"""Evolve multiple BinaryPopulations together. - -e.g. with multiple metallicities +"""Processing of population files. + +This module contains classes and functions to process population files. +Population files are HDF5 files containing the history and oneline dataframes +of a population of binary systems. The history dataframe contains the detailed +evolution of each binary system, while the oneline dataframe contains the +final state of each binary system. + +Classes +------- +PopulationRunner + A class to handle the evolution of binary populations. + +Population + A class to handle population files. +History + A class to handle the history dataframe of a population file. +Oneline + A class to handle the oneline dataframe of a population file. + +TransientPopulation + A class to handle transient populations. + +Rates + A class to handle the cosmic rates of in a population file. """ __authors__ = [ "Simone Bavera ", "Kyle Akira Rocha ", "Monica Gallegos-Garcia ", - "Max Merlijn Briel < max.briel@gmail.com", + "Max Briel ", ] import warnings @@ -15,1080 +37,2624 @@ import pandas as pd from tqdm import tqdm import os -import copy -import multiprocessing as mp +from matplotlib import pyplot as plt + from posydon.utils.constants import Zsun from posydon.popsyn.io import binarypop_kwargs_from_ini -from posydon.popsyn.binarypopulation import BinaryPopulation -from posydon.utils.common_functions import convert_metallicity_to_string from posydon.popsyn.normalized_pop_mass import initial_total_underlying_mass -from posydon.popsyn.rate_calculation import Rates import posydon.visualization.plot_pop as plot_pop -from posydon.popsyn.GRB import get_GRB_properties, GRB_PROPERTIES - -# TODO: temp import, remove after TF2 classification is implemented in pop synth -from posydon.interpolation.IF_interpolation import IFInterpolator -from posydon.binary_evol.binarystar import BinaryStar -from posydon.binary_evol.singlestar import SingleStar from posydon.utils.common_functions import convert_metallicity_to_string +from astropy.cosmology import Planck15 as cosmology +from astropy import constants as const + +from posydon.popsyn.rate_calculation import ( + get_shell_comoving_volume, + get_comoving_distance_from_redshift, + get_cosmic_time_from_redshift, + redshift_from_cosmic_time_interpolator, + DEFAULT_MODEL, + get_redshift_bin_edges, + get_redshift_bin_centers, +) + +from posydon.popsyn.star_formation_history import ( + star_formation_rate, + SFR_Z_fraction_at_given_redshift, +) + +from posydon.popsyn.binarypopulation import ( + BinaryPopulation, + HISTORY_MIN_ITEMSIZE, + ONELINE_MIN_ITEMSIZE, +) + + +############################################################################### + +parameter_array = [ + "number_of_binaries", + "binary_fraction_scheme", + "binary_fraction_const", + "star_formation", + "max_simulation_time", + "primary_mass_scheme", + "primary_mass_min", + "primary_mass_max", + "secondary_mass_scheme", + "secondary_mass_min", + "secondary_mass_max", + "orbital_scheme", + "orbital_period_scheme", + "orbital_period_min", + "orbital_period_max", + "eccentricity_scheme", +] + + +class PopulationRunner: + """A class to handle the evolution of binary populations. + + Attributes + ---------- + pop_params : dict + The parameters of the population population. + solar_metallicities : list of float + The metallicities of the populations in solar units. + binary_populations : list of BinaryPopulation + The binary populations. + verbose : bool + If `True`, print additional information. + """ -class SyntheticPopulation: + def __init__(self, path_to_ini, verbose=False): + """Initialize the binary populations from an ini file. + + Parameters + ---------- + path_to_ini : str + The path to the ini file. + verbose : bool, optional + If True, print additional information. Default is False. + + Raises + ------ + ValueError + If the provided `path_to_ini` does not have a '.ini' extension. + + Notes + ----- + This method initializes the `PopulationRunner` object by reading the binary population parameters from an ini file specified by `path_to_ini`. + It also allows for optional verbosity, which, when set to True, enables additional information to be printed in operation of the class. + + The `path_to_ini` should be a valid path to an ini file. If the provided `path_to_ini` does not have a '.ini' extension, a `ValueError` is raised. + + Examples + -------- + >>> sp = PopulationRunner('/path/to/ini/file.ini') + >>> sp = PopulationRunner('/path/to/ini/file.ini', verbose=True) - def __init__(self, path_to_ini, verbose=False, MODEL={}): """ + if ".ini" not in path_to_ini: + raise ValueError("You did not provide a valid path_to_ini!") + else: + self.pop_params = binarypop_kwargs_from_ini(path_to_ini) + self.solar_metallicities = self.pop_params["metallicity"] + self.verbose = verbose + if not isinstance(self.solar_metallicities, list): + self.solar_metallicities = [self.solar_metallicities] + + self.binary_populations = [] + for MET in self.solar_metallicities: + ini_kw = binarypop_kwargs_from_ini(path_to_ini) + # overwrite the ini_kw verbose parameter + ini_kw["verbose"] = self.verbose + ini_kw['tqdm'] = self.verbose + ini_kw["metallicity"] = MET + ini_kw["temp_directory"] = ( + convert_metallicity_to_string(MET) + + "_Zsun_" + + ini_kw["temp_directory"] + ) + self.binary_populations.append(BinaryPopulation(**ini_kw)) + + def evolve(self, overwrite=False): + """Evolve the binary populations. + + This method is responsible for evolving the binary populations. It iterates over each population + in the `binary_populations` list and calls the `evolve` method on each population. After evolving + each population, it merges them using the `merge_parallel_runs` method. + + Note: + The `merge_parallel_runs` method is called only if the `comm` attribute of the population is `None`. + + """ + for pop in self.binary_populations: + # check if the temp directory exists + if os.path.exists(pop.kwargs["temp_directory"]) and not overwrite: + raise FileExistsError(f"The {pop.kwargs['temp_directory']} directory already exists! Please remove it or rename it before running the population.") + elif os.path.exists(pop.kwargs["temp_directory"]) and overwrite: + if self.verbose: + print(f"Removing {pop.kwargs['temp_directory']} directory...") + os.removedirs(pop.kwargs["temp_directory"]) + + pop.evolve() + if pop.comm is None: + self.merge_parallel_runs(pop) + + def merge_parallel_runs(self, pop): + """Merge the parallel runs of the population. + Parameters ---------- + pop : BinaryPopulation + The binary population whose files have to be merged. - path : str - Path to the inifile to parse. You can supply a list in the - metallicity parameter to evolve more than one population. """ - self.synthetic_pop_params = None - self.metallicities = None - self.binary_populations = None + if os.path.exists(convert_metallicity_to_string(pop.metallicity) + "_Zsun_population.h5"): + raise FileExistsError( + f"{convert_metallicity_to_string(pop.metallicity)}_Zsun_population.h5 already exists!\n" + +"Files were not merged. You can use PopulationRunner.merge_parallel_runs() to merge the files manually." + ) + + path_to_batch = pop.kwargs["temp_directory"] + + tmp_files = [ + os.path.join(path_to_batch, f) + for f in os.listdir(path_to_batch) + if os.path.isfile(os.path.join(path_to_batch, f)) + ] + if self.verbose: + print(f"Merging {len(tmp_files)} files...") + + pop.combine_saved_files( + convert_metallicity_to_string(pop.metallicity) + "_Zsun_population.h5", + tmp_files, + ) + if self.verbose: + print("Files merged!") + print(f"Removing files in {path_to_batch}...") + # remove files + if len(os.listdir(path_to_batch)) == 0: + os.rmdir(path_to_batch) + + + +################## +# Helper classes # +################## + + +class History: + """A class to handle the history dataframe of a population file. + + This class provides methods to handle the history dataframe of a population file. + It allows accessing and manipulating the history table based on various keys and conditions. + + Attributes + ---------- + filename : str + The path to the population file. + verbose : bool + If `True`, print additional information. + chunksize : int + The chunksize to use when reading the history file. + lengths : pd.DataFrame + The number of rows of each binary in the history dataframe. + number_of_systems : int + The number of systems in the history dataframe. + columns : list of str + The columns of the history dataframe. + indices : np.ndarray + The binary indices of the history dataframe. + """ + + def __init__(self, filename, verbose=False, chunksize=100000): + """Initialise the history dataframe. + + This class is used to handle the history dataframe of a population file. + On initialisation, the history_lengths are calculated and stored in the population file, + if not present in the file. The history dataframe is not loaded into memory. + Parameters + ---------- + filename : str + The path to the population file. + verbose : bool + If `True`, print additional information. + chunksize : int (default=100000) + The chunksize to use when reading the history file. + """ + self.filename = filename self.verbose = verbose - self.MODEL = MODEL - # DCOs - self.df = None - self.df_oneline = None - self.df_synthetic = None - self.df_dco_intrinsic = None - self.df_dco_observable = None - self.met_merger_efficiency = None - self.merger_efficiency = None - self.dco_z_rate_density = None - self.dco_rate_density = None - # GRBs - self.df_grb_intrinsic = None - self.df_grb_observable = None - self.grb_z_rate_density = None - self.grb_rate_density = None - - if '.ini' not in path_to_ini: - raise ValueError('You did not provide a valid path_to_ini!') - else: - self.synthetic_pop_params = binarypop_kwargs_from_ini(path_to_ini) - self.metallicities = self.synthetic_pop_params['metallicity'] - if not isinstance( self.metallicities, list): - self.metallicities = [self.metallicities] - self.binary_populations = None + self.chunksize = chunksize + self.lengths = None + self.number_of_systems = None + self.columns = None + if not os.path.exists(filename): + raise FileNotFoundError(f"{filename} does not exist!") + + # add history_lengths + with pd.HDFStore(filename, mode="a") as store: + # get the history lengths from the file + if "/history_lengths" in store.keys(): + self.lengths = store["history_lengths"] + else: + if self.verbose: + print( + "history_lengths not found in population file. Calculating history lengths..." + ) + history_events = store.select_column("history", "index") + tmp_df = pd.DataFrame( + history_events.groupby(history_events).count(), + ) + tmp_df.rename(columns={"index": "length"}, inplace=True) + self.lengths = tmp_df + del tmp_df + if self.verbose: + print("Storing history lengths in population file!") + store.put("history_lengths", pd.DataFrame(self.lengths), format="table") + del history_events + + self.columns = store.select("history", start=0, stop=0).columns.to_list() + + self.indices = self.lengths.index.to_numpy() + self.number_of_systems = len(self.lengths) + + def __getitem__(self, key): + """Return the history table based on the provided key. + + + Parameters + ---------- + key : int, list of int, np.ndarray, or str + The key to use for indexing the history dataframe. + + Returns + ------- + pd.DataFrame + The history table based on the provided key. + + Raises + ------ + ValueError + If the key type is invalid or if the column name(s) are not valid. + + Examples + -------- + # Get a single row by index + >>> population[0] + Returns the history table for the row with index 0. + + # Get multiple rows by index + >>> population[[0, 1, 2]] + Returns the history table for the rows with indices 0, 1, and 2. + + # Get rows based on a boolean mask + >>> mask = population['age'] > 30 + >>> population[mask] + Returns the history table for the rows where the 'age' column is greater than 30. + + # Get a specific column + >>> population['time'] + Returns the 'age' column from the history table. + + # Get multiple columns + >>> population[['S1_mass', 'S2_mass']] + Returns the 'S1_mass' and 'S2_mass' columns from the history table. + """ + if isinstance(key, slice): + if key.start is None: + pre = 0 + else: + pre = key.start + if key.stop is None: + chunk = self.number_of_systems + else: + chunk = key.stop - pre + indices = list(range(pre, pre + chunk)) + return pd.read_hdf(self.filename, key="history", where='index in indices') + + elif isinstance(key, int): + return pd.read_hdf(self.filename, where="index == key", key="history") - def create_binary_populations(self): - """Create a list of BinaryPopulation objects.""" - self.binary_populations = [] - for met in self.metallicities[::-1]: - ini_kw = copy.deepcopy(self.synthetic_pop_params) - ini_kw['metallicity'] = met - ini_kw['temp_directory'] = self.create_met_prefix(met) + self.synthetic_pop_params['temp_directory'] - self.binary_populations.append(BinaryPopulation(**ini_kw)) - - def get_ini_kw(self): - return self.synthetic_pop_params.copy() - - def evolve(self): - """Evolve population(s) at given Z(s).""" - if self.binary_populations is None: - self.create_binary_populations() - while self.binary_populations: - pop = self.binary_populations.pop() + # list of indices + elif isinstance(key, list) and all(isinstance(x, int) for x in key): + if len(key) == 0: + return pd.DataFrame() + else: + with pd.HDFStore(self.filename, mode="r") as store: + return store.select("history", where="index in key") + # numpy array + elif isinstance(key, np.ndarray) and (key.dtype == int): + if len(key) == 0: + return pd.DataFrame() + else: + indices = key.tolist() + with pd.HDFStore(self.filename, mode="r") as store: + return store.select("history", where="index in indices") + + # boolean mask + elif (isinstance(key, np.ndarray) and key.dtype == bool) or (isinstance(key, pd.DataFrame) and all(key.dtypes == bool)): + # return empty if no values + if len(key) == 0: + return pd.DataFrame() - if self.verbose: - print(f'Z={pop.kwargs["metallicity"]:.2e} Z_sun') - - process = mp.Process(target=pop.evolve) - process.start() - process.join() + out_df = pd.DataFrame() + for i in range(0, len(key), self.chunksize): + tmp_df = pd.read_hdf( + self.filename, key="history", start=i, stop=i + self.chunksize + )[key[i : i + self.chunksize]] + out_df = pd.concat([out_df, tmp_df]) + return out_df + + # single column + elif isinstance(key, str): + if key in self.columns: + return pd.read_hdf(self.filename, key="history", columns=[key]) + else: + raise ValueError(f"{key} is not a valid column name!") - pop.close() - del pop + # multiple columns + elif isinstance(key, list) and all(isinstance(x, str) for x in key): + if all(x in self.columns for x in key): + return pd.read_hdf(self.filename, key="history", columns=key) + else: + raise ValueError(f"Not all columns in {key} are valid column names!") + else: + raise ValueError("Invalid key type!") + def __len__(self): + """Return the number of rows in the history table - def merge_parallel_runs(self, path_to_batches): + Returns + ------- + int + The number of rows in the history table. """ - Merge the folder or list of folders into a single file per metallicity. + return np.sum(self.lengths.values) + + def head(self, n=10): + """Return the first n rows of the history table Parameters ---------- - path_to_batches : str or list of str - Path to the folder(s) containing the batch folders. + n : int, optional + The number of rows to return. Default is 10. + + Returns + ------- + pandas.DataFrame + The first n rows of the history table. + """ - - if isinstance(path_to_batches, str): - path_to_batches = [path_to_batches] - # check if path_to_batches is the same length as the number of metallicities - if len(path_to_batches) != len(self.metallicities): - raise ValueError('The number of metallicity and batch directories do not match!') - - for met, path_to_batch in zip(self.metallicities, path_to_batches): - met_prefix = self.create_met_prefix(met) - tmp_files = [os.path.join(path_to_batch, f) \ - for f in os.listdir(path_to_batch) \ - if os.path.isfile(os.path.join(path_to_batch, f))] - - BinaryPopulation(**self.get_ini_kw()).combine_saved_files(met_prefix+ 'population.h5', tmp_files) - print(f'Population at Z={met:.2e} Z_sun successfully merged!') - if len(os.listdir(path_to_batch)) == 0: - os.rmdir(path_to_batch) - elif self.verbose: - print(f'{path_to_batch} is not empty, it was not removed!') - - @staticmethod - def create_met_prefix(met): - """Append a prefix to the name of directories for batch saving.""" - return convert_metallicity_to_string(met) + '_Zsun_' - - def apply_logic(self, df, S1_state=None, S2_state=None, binary_state=None, - binary_event=None, step_name=None, invert_S1S2=False, - warn=True): - """Select binaries in a dataframe given some properties. + return pd.read_hdf(self.filename, key="history", start=0, stop=n) + + def tail(self, n=10): + """Return the last n rows of the history table. + + Parameters: + ----------- + n : int, optional + Number of rows to return. Default is 10. + + Returns: + -------- + pandas.DataFrame + The last n rows of the history table. + + """ + return pd.read_hdf(self.filename, key="history", start=-n) + + def __repr__(self): + """Return a string representation of the object. + + Returns: + str: A string representation of the object. + """ + return pd.read_hdf(self.filename, key="history").__repr__() + + def _repr_html_(self): + """Return the HTML representation of the history dataframe. + + This method reads the history data from an HDF file and returns + the HTML representation of the data using the `_repr_html_` method of the + pandas DataFrame. + + Returns + ------- + str + The HTML representation of the history dataframe. + """ + return pd.read_hdf(self.filename, key="history")._repr_html_() + + + def select(self, where=None, start=None, stop=None, columns=None): + """Select a subset of the history table based on the given conditions. + + This method allows you to query and retrieve a subset of data from the history table + stored in an HDFStore file. You can specify conditions using the `where` parameter, + which is a string representing the query condition to apply to the data. You can also + specify the starting and ending indices of the data to select using the `start` and + `stop` parameters. Additionally, you can choose to select specific columns by providing + a list of column names using the `columns` parameter. Parameters ---------- - df : pd.DataFrame - POSYDON binary population synthesis dataframe. - S1_state : str - Star1 stellar state. - S2_state : str - Star2 stellar state. - binary_state : str - Binary state. - binary_event : str - Binary event. - step_name : str - Name of posydon step. - invert_S1S2 : bool - If `True` isolated also sort S1_state=S2_state and S2_state=S1_state - systems. + where : str, optional + A string representing the query condition to apply to the data. + start : int, optional + The starting index of the data to select. + stop : int, optional + The ending index of the data to select. + columns : list, optional + A list of column names to select. Returns ------- - pd.DataFrame of bools - List of binaries to select given the search parameters. + pandas.DataFrame + The selected data as a DataFrame. + """ + with pd.HDFStore(self.filename, mode="r") as store: + return store.select( + "history", where=where, start=start, stop=stop, columns=columns + ) + +class Oneline: + """A class to handle the oneline dataframe of a population file. + + The `Oneline` class provides methods to manipulate and retrieve data from the oneline dataframe of a population file. + + Attributes + ---------- + filename : str + The path to the population file. + verbose : bool + If `True`, print additional information. + chunksize : int + The chunksize to use when reading the oneline file. + number_of_systems : int + The number of systems in the oneline dataframe. + indices : np.ndarray + The binary indices of the oneline dataframe. + columns : list of str + + """ + + def __init__(self, filename, verbose=False, chunksize=100000): + """Initialize a Oneline class instance. + + Parameters + ---------- + filename : str + The path to the HDFStore file containing the Oneline population data. + verbose : bool, optional + If True, print additional information during initialization. Default is False. + chunksize : int, optional + The number of rows to read from the HDFStore file at a time. Default is 10000. """ - if not invert_S1S2 and S1_state != S2_state and warn: - warnings.warn('Note that invert_S1S2=False, hence you are not parsing ' - f'the dataset for {S1_state}-{S2_state} binaries and ' - f'and not for for {S1_state}-{S2_state}. If this is ' - 'done on purpose, ignore this message!') + self.filename = filename + self.verbose = verbose + self.chunksize = chunksize + self.number_of_systems = None + self.indices = None + if not os.path.exists(filename): + raise FileNotFoundError(f"{filename} does not exist!") - sel_all = df['S1_state'].astype(bool) + with pd.HDFStore(filename, mode="r") as store: + self.indices = store.select_column("oneline", "index").to_numpy() + self.columns = store.select("oneline", start=0, stop=0).columns.to_list() - if S1_state is not None: - S1_logic = (df['S1_state'] == S1_state) - else: - S1_logic = sel_all - if S2_state is not None: - S2_logic = (df['S2_state'] == S2_state) - else: - S2_logic = sel_all - if binary_state is not None: - binary_state_logic = (df['state'] == binary_state) - else: - binary_state_logic = sel_all - if binary_event is not None: - binary_event_logic = (df['event'] == binary_event) - else: - binary_event_logic = sel_all - if step_name is not None: - step_name_logic = (df['step_names'] == step_name) - else: - step_name_logic = sel_all - - if invert_S1S2: - S1_logic_inverted = (df['S1_state'] == S2_state) - S2_logic_inverted = (df['S2_state'] == S1_state) - # find systems - logic = ((S1_logic & S2_logic & binary_state_logic & - binary_event_logic & step_name_logic) | - (S1_logic_inverted & S2_logic_inverted & - binary_state_logic & binary_event_logic & - step_name_logic)) + self.number_of_systems = len(self.indices) + + def __getitem__(self, key): + """Get a subset of the oneline table based on the given key. + + Parameters + ---------- + key : slice, int, list, np.ndarray, str + The key to select the subset of the oneline table. + + Returns + ------- + pd.DataFrame + The subset of the oneline table. + + Raises + ------ + ValueError + If the key is of invalid type or contains invalid values. + + Examples + -------- + # Get a slice of the oneline table + >>> subset = population[10:20] + + # Get a single row from the oneline table + >>> row = population[5] + + # Get multiple rows from the oneline table using a list of indices + >>> rows = population[[1, 3, 5]] + + # Get rows from the oneline table using a boolean array + >>> mask = population['age'] > 30 + >>> filtered_rows = population[mask] + + # Get a specific column from the oneline table + >>> column = population['age'] + + # Get multiple columns from the oneline table using a list of column names + >>> columns = population[['age', 'gender']] + """ + if isinstance(key, slice): + if key.start is None: + pre = 0 + else: + pre = key.start + if key.stop is None: + chunk = self.number_of_systems + else: + chunk = key.stop - pre + return pd.read_hdf( + self.filename, key="oneline", start=pre, stop=pre + chunk + ) + elif isinstance(key, int): + return pd.read_hdf(self.filename, where="index == key", key="oneline") + elif isinstance(key, list) and all(isinstance(x, int) for x in key): + return pd.read_hdf(self.filename, where="index in key", key="oneline") + elif isinstance(key, np.ndarray) and (key.dtype == int): + indices = key.tolist() + return pd.read_hdf(self.filename, where="index in indices", key="oneline") + elif isinstance(key, list) and all(isinstance(x, float) for x in key): + raise ValueError("elements in list are not integers! Try casting to int.") + elif isinstance(key, pd.DataFrame) and all(key.dtypes == bool): + indices = self.indices[key.to_numpy().flatten()].tolist() + return pd.read_hdf(self.filename, where="index in indices", key="oneline") + elif isinstance(key, np.ndarray) and key.dtype == bool: + indices = self.indices[key].tolist() + return pd.read_hdf(self.filename, where="index in indices", key="oneline") + elif isinstance(key, str): + if key in self.columns: + return pd.read_hdf(self.filename, key="oneline", columns=[key]) + else: + raise ValueError(f"{key} is not a valid column!") + elif isinstance(key, list) and all(isinstance(x, str) for x in key): + if all(x in self.columns for x in key): + return pd.read_hdf(self.filename, key="oneline", columns=key) + else: + raise ValueError(f"Not all columns in {key} are valid column names!") else: - # find systems - logic = (S1_logic & S2_logic & binary_state_logic & - binary_event_logic & step_name_logic) + raise ValueError("Invalid key type!") - return logic + def __len__(self): + """ + Get the number of systems in the oneline table. + Returns + ------- + int + The number of systems in the oneline table. + """ + return self.number_of_systems - def parse(self, path_to_data, S1_state=None, S2_state=None, binary_state=None, - binary_event=None, step_name=None, invert_S1S2=False, chunksize=500000): - """Sort binaries of interests given some properties. - - It also stores the underlying stellar mass and - the initial simulated stellar mass for each metallicity. + def head(self, n=10): + """Get the first n rows of the oneline table. Parameters ---------- - S1_state : str - Star1 stellar state. - S2_state : str - Star2 stellar state. - binary_state : str - Binary state. - binary_event : str - Binary event. - step_name : str - Name of posydon step. - invert_S1S2 : bool - If `True` isolated also sort S1_state=S2_state and S2_state=S1_state - systems. - chunksize : int - Read the POSYDON binary population in chuncks to prevent OFM error. + n : int, optional + The number of rows to return. Default is 10. + Returns + ------- + pd.DataFrame + The first n rows of the oneline table. """ - - # if the user provided a single string instead of a list of strings - if type(path_to_data) is str and ('.h5' in path_to_data): - path_to_data = [path_to_data] - - # catch the case where the user did not provide a path to data - if (isinstance(path_to_data, list)): - for path in path_to_data: - if os.path.splitext(path)[-1] != '.h5': - raise ValueError('You did not provide a valid path_to_data!') - else: - raise ValueError('You did not provide a valid path_to_data!') - - df_sel = pd.DataFrame() - df_sel_oneline = pd.DataFrame() - count = 0 - tmp = 0 - shift_index = 0 - if self.verbose: - print('Binary count with (S1_state, S2_state, binary_state, binary_event, step_name) equal') - print(f'to ({S1_state}, {S2_state}, {binary_state}, {binary_event}, {step_name})') - if invert_S1S2: - print(f'and ({S2_state}, {S1_state}, {binary_state}, {binary_event}, {step_name})') - for k, file in enumerate(path_to_data): - df_sel_met = pd.DataFrame() - sel_met = [] - last_binary_df = None - - # read metallicity from path - met = float(file.split('/')[-1].split('_Zsun')[0])*Zsun - simulated_mass_for_met = 0. - - for i, df in enumerate(pd.read_hdf(file, key='history', chunksize=chunksize)): - - df = pd.concat([last_binary_df, df]) - - last_binary_df = df.loc[[df.index[-1]]] - df.drop(df.index[-1], inplace=True) - - logic = self.apply_logic(df, - S1_state = S1_state, - S2_state = S2_state, - binary_state = binary_state, - binary_event = binary_event, - step_name = step_name, - invert_S1S2 = invert_S1S2) - - # select systems - # remove duplicate indicies, e.g. if selecting 'contact' state it appears twice - # if no specific event is selected (the second time is from the copied END event) - sel = df.loc[logic].index.drop_duplicates() - - # count systems - count += len(np.unique(sel)) - - # read the simulated ZAMS mass - sel_ZAMS = df['event'] == 'ZAMS' - mass = sum(df['S1_mass'][sel_ZAMS]+df['S2_mass'][sel_ZAMS]) - simulated_mass_for_met += mass - - # sort systems - if any(sel): - df_tmp = pd.DataFrame() - df_tmp = df.loc[sel] - # store metallicity - df_tmp['metallicity'] = met - # concatenate results - df_sel_met = pd.concat([df_sel_met, df_tmp]) - del df_tmp - - # check last binary if it should be included - if last_binary_df is not None: - logic = self.apply_logic(last_binary_df, - S1_state = S1_state, - S2_state = S2_state, - binary_state = binary_state, - binary_event = binary_event, - step_name = step_name, - invert_S1S2 = invert_S1S2) - - # The last binary is selected - if any(logic) == True: - df_tmp = last_binary_df.loc[logic] - df_sel_met = pd.concat([df_sel_met, df_tmp]) + return pd.read_hdf(self.filename, key="oneline", start=0, stop=n) - # get unique indicies - sel_met = df_sel_met.index.drop_duplicates() - - # store simulated and underlying stellar mass - df_sel_met['simulated_mass_for_met'] = simulated_mass_for_met - df_sel_met['underlying_mass_for_met'] = initial_total_underlying_mass(df=simulated_mass_for_met, **self.synthetic_pop_params)[0] # This used to be init_kw + def tail(self, n=10): + """ + Get the last n rows of the oneline table. - # concatenate results with shifted indices for each metallicity - df_sel_met.index += shift_index - df_sel = pd.concat([df_sel, df_sel_met]) - del df_sel_met + Parameters + ---------- + n : int, optional + The number of rows to return. Default is 10. - # load, parse and store oneline dataframe - # this dataframe is smaller, we can load it all at once - df_sel_met_oneline = pd.read_hdf(file, key='oneline') - df_sel_met_oneline = df_sel_met_oneline.loc[sel_met] - df_sel_met_oneline['metallicity'] = met + Returns + ------- + pd.DataFrame + The last n rows of the oneline table. + """ + return pd.read_hdf(self.filename, key="oneline", start=-n) - df_sel_met_oneline.index += shift_index - df_sel_oneline = pd.concat([df_sel_oneline, df_sel_met_oneline]) + def __repr__(self): + """ + Get a string representation of the oneline table. - if self.verbose: - print(f'in {file} are {count-tmp}') - tmp = count - - # shift the index for the next metallicity - shift_index += max(np.unique(df.index)) + 1 + Returns + ------- + str + The string representation of the oneline table. + """ + return pd.read_hdf(self.filename, key="oneline").__repr__() + + def _repr_html_(self): + """ + Get an HTML representation of the oneline table. + + Returns + ------- + str + The HTML representation of the oneline table. + """ + return pd.read_hdf(self.filename, key="oneline")._repr_html_() - if self.verbose: - print('Total binaries found are', count) - # save parsed population as synthetic population - if self.df is not None: - warnings.warn('Overwriting the df population!') - self.df = df_sel - self.df_oneline = df_sel_oneline + def select(self, where=None, start=None, stop=None, columns=None): + """Select a subset of the oneline table based on the given conditions. - def save_pop(self, path='./parsed_population.h5'): - """Save parsed population. + This method allows you to filter and extract a subset of rows from the oneline table stored in an HDF file. + You can specify conditions to filter the rows, define the range of rows to select, and choose specific columns to include in the subset. Parameters ---------- - path : str - Path to file where you want to export the dataset. + where : str, optional + A condition to filter the rows of the oneline table. Default is None. + start : int, optional + The starting index of the subset. Default is None. + stop : int, optional + The ending index of the subset. Default is None. + columns : list, optional + The column names to include in the subset. Default is None. + + Returns + ------- + pd.DataFrame + The selected subset of the oneline table. + + Examples + -------- + # Select rows based on a condition + >>> df = Oneline.select(where="S2_mass_i > 30") + + # Select rows from index 10 to 20 + >>> df = Oneline.select(start=10, stop=20) + # Select specific columns + >>> df = Oneline.select(columns=['S1_mass_i', 'S1_mass_f']) """ - if self.df is None: - raise ValueError('Nothing to save! The population was not parsed.') - elif self.df_oneline is None: - raise ValueError('Missing oneline dataframe!') - else: - self.df.to_hdf(path, key='history') - self.df_oneline.to_hdf(path, key='oneline') + with pd.HDFStore(self.filename, mode="r") as store: + return store.select( + "oneline", where=where, start=start, stop=stop, columns=columns + ) + + +class PopulationIO: + """A class to handle the input/output of population files. + + This class provides methods to load and save population files in HDF5 format. + It also includes methods to load and save metadata and ini parameters. + + Attributes + ---------- + mass_per_metallicity : pandas.DataFrame + A DataFrame containing mass per metallicity data. + ini_params : dict + A dictionary containing some ini parameters, described in parameter_array. + + """ + + def __init__(self): + self.verbose = False + + + def _load_metadata(self, filename): + """Load the metadata from the file. + + Parameters + ---------- + filename : str + The name of the file to load the metadata from. + + Raises + ------ + ValueError + If the filename does not contain '.h5' extension. + + """ + if ".h5" not in filename: + raise ValueError( + f"{filename} does not contain .h5 in the se.\n Is this a valid population file?" + ) + + self._load_ini_params(filename) + self._load_mass_per_metallicity(filename) + + def _save_mass_per_metallicity(self, filename): + """Save the mass per metallicity data to the file. + + Parameters + ---------- + filename : str + The name of the file to save the mass per metallicity data to. + + """ + with pd.HDFStore(filename, mode="a") as store: + store.put("mass_per_metallicity", self.mass_per_metallicity) if self.verbose: - print('Population successfully saved!') + print("mass_per_metallicity table written to population file!") - def load_pop(self, path): - """Load parsed population. + def _load_mass_per_metallicity(self, filename): + """Load the mass per metallicity data from the file. Parameters ---------- - path : str - Path to dataset. + filename : str + The name of the file to load the mass per metallicity data from. """ - if self.df is None and self.df_oneline is None : - self.df = pd.read_hdf(path, key='history') - self.df_oneline = pd.read_hdf(path, key='oneline') + with pd.HDFStore(filename, mode="r") as store: + self.mass_per_metallicity = store["mass_per_metallicity"] if self.verbose: - print('Population successfully loaded!') - else: - raise ValueError('You already have a population stored in memory!') + print("mass_per_metallicity table read from population file!") - def get_dco_at_formation(self, S1_state, S2_state, oneline_cols=None, formation_channels=False, mt_history=False): - """Populates `df_synthetic` with DCOs at their formation. - - If `formation_channels` is `True` the `channel` column is added to the - `df_synthetic` dataframe. + def _save_ini_params(self, filename): + """Save the ini parameters to the file. - if MODEL on class initialization is not None and - "compute_GRB_properties" in MODEL. - If MODEL["compute_GRB_properties"] is `True` the following columns are - in the MODEL: + Parameters + ---------- + filename : str + The name of the file to save the ini parameters to. - - 'GRB_efficiency', - - 'GRB_beaming', - - 'E_GRB_iso_min' + """ + with pd.HDFStore(filename, mode="a") as store: + # write ini parameters to file + tmp_df = pd.DataFrame() + for c in parameter_array: + tmp_df[c] = [self.ini_params[c]] + store.put("ini_parameters", tmp_df) - The following columns are added to the `df_synthetic` dataframe: - - S1_m_disk_radiated - - S2_m_disk_radiated + def _load_ini_params(self, filename): + """Load the ini parameters from the file. - Note: by default this function looks for the symmetric state - S1_state = S2_sate and S2_state = S1_sate. + The values loaded from file are stored in the parameter_array. Parameters ---------- - S1_state : str - Star1 stellar state. - S2_state : str - Star2 stellar state. - oneline_cols : list str - List of columns preset in the oneline dataframe you want to export - into the synthetic population. - formation_channels : bool - Compute the formation channel, a string containing the binary - event evolution. - mt_history : bool - If `True`, split the event oRLO1/oRLO2 into oRLO1-contact/oRLO2-contact, - oRLO1-reverse/oRLO2-reverse and oRLO1/oRLO2. This is useful to - identify binaries undergoing contact stable mass-transfer phases and - reverse mass-transfer phase . - - """ - # compute GRB properties boolean - compute_GRB_properties = (self.MODEL is not None and - "compute_GRB_properties" in self.MODEL and - self.MODEL["compute_GRB_properties"]) - - # add channel column to oneline dataframe - if formation_channels: + filename : str + The name of the file to load the ini parameters from. + + """ + # load ini parameters + with pd.HDFStore(filename,mode="r",) as store: + tmp_df = store["ini_parameters"] + self.ini_params = {} + for c in parameter_array: + self.ini_params[c] = tmp_df[c][0] + + +########################## +# Main interface classes # +########################## + + +class Population(PopulationIO): + """A class to handle population files. + + This class provides methods to handle population files. It includes methods to read and write population files, + as well as methods to access and manipulate the history and oneline dataframes. + + Attributes + ---------- + history : History + The history dataframe of the population. + oneline : Oneline + The oneline dataframe of the population. + formation_channels : pd.DataFrame + The formation channels dataframe of the population. + ini_params : dict + The parameters from the ini file used to create the population. + mass_per_metallicity : pd.DataFrame + The mass per metallicity dataframe of the population. + solar_metallicities : np.ndarray + The solar metallicities of the population. + metallicities : np.ndarray + The metallicities of the population. + indices : np.ndarray + The indices of the binaries in the population. + verbose : bool + If `True`, print additional information. + chunksize : int + The chunksize to use when reading the population file. + filename : str + The path to the population file. + number_of_systems : int + The number of systems in the population. + history_lengths : pd.DataFrame + The number of rows of each binary in the history dataframe. The index is the binary index. + + """ + + def __init__( + self, filename, metallicity=None, ini_file=None, verbose=False, chunksize=1000000 + ): + """Initialize the Population object. + + The Population object is initialised by creating History and Oneline objects, + which refer back to the population file (filename). The formation channels are also + linked to the population file, if present. The mass per metallicity data is loaded + from the file, if present, or calculated and saved to the file if not present. + + If the mass per metallicity data is not present in the file, you can provide a metallicity + and the ini file (used to create the population) to calculate and save the mass per metallicity data. + You only need to do this once for a given population file. However, all systems in the population + will be given the same metallicity. + + Parameters + ----------- + filename : str + The path to the population file + metallicity : float, optional + The metallicity of the population in solar units. + ini_file : str, optional + The path to the ini file used to create the population. + verbose : bool, optional + If `True`, print additional information. + chunksize : int, optional + The chunksize to use when reading the population file. + + Raises + ------ + ValueError + If the provided filename does not contain '.h5' extension. + If the population file does not contain a history table. + If the population file does not contain an oneline table. + If the population file does not contain an ini_parameters table. + If the population file does not contain a mass_per_metallicity table and no metallicity for the file was given. + + Examples + -------- + # When the population file contains a mass_per_metallicity table + >>> pop = Population('/path/to/population_file.h5') + + # When the population file does not contain a mass_per_metallicity table + >>> pop = Population('/path/to/population_file.h5', metallicity=0.02, ini_file='/path/to/ini_file.ini') + """ + + self.filename = filename + self.verbose = verbose + self.chunksize = chunksize + + self.mass_per_metallicity = None + self.number_of_systems = None + self.history_lengths = None + + # if the user provided a single string instead of a list of strings + if not (".h5" in filename): + raise ValueError( + f"{filename} does not contain .h5 in the se.\n Is this a valid population file?" + ) + + # read the population file + with pd.HDFStore(filename, mode="r") as store: + keys = store.keys() + + # check if pop contains history + if "/history" not in keys: + raise ValueError(f"{filename} does not contain a history table!") + else: + self.history = History(filename, self.verbose, self.chunksize) + + # check if pop contains oneline + if "/oneline" not in keys: + raise ValueError(f"{filename} does not contain an oneline table!") + else: + self.oneline = Oneline(filename, self.verbose, self.chunksize) + + # check if formation channels are present + if "/formation_channels" not in keys: if self.verbose: - print('Computing formation channels...') - self.get_formation_channels(mt_history=mt_history) - - # to avoid the user making mistake automatically check the inverse of - # the stellar states, since the df is already parsed this will not - # take too much extra time - logic = self.apply_logic(self.df, S1_state=S1_state, - S2_state=S2_state, - binary_state='detached', - step_name = 'step_SN', - invert_S1S2=True) - self.df_synthetic = self.df.loc[logic].copy() - - # compute the inspiral timescale from the integrated orbit - # this estimate is better than evaluating Peters approxiamtion - time_contact = self.df.loc[self.df['event'] == 'END',['time']] - # NOTE/TODO: we change the units of time in the dataframe to Myr - # this might be confusion to the user? Note that Myr are convinient - # when inspecting the data frame. - self.df_synthetic['t_delay'] = (time_contact - self.df_synthetic[['time']])*1e-6 # Myr - self.df_synthetic['time'] *= 1e-6 # Myr - - # add properties of the oneline dataframe - if self.df_oneline is not None: - # TODO: add kicks as well by default? - save_cols = ['S1_spin_orbit_tilt_second_SN', 'S2_spin_orbit_tilt_second_SN'] - if compute_GRB_properties: - save_cols += ['S1_m_disk_radiated', 'S2_m_disk_radiated'] - if formation_channels: - save_cols.append('channel') - if oneline_cols is not None: - for c in oneline_cols: - if c not in save_cols: - save_cols.append(c) - for c in save_cols: - if c in self.df_oneline: - self.df_synthetic[c] = self.df_oneline[c] - else: - warnings.warn(f'The column {c} is not present in the ' - 'oneline dataframe.') - - # compute GRB properties - if compute_GRB_properties: - if ('GRB_efficiency' not in self.MODEL or - self.MODEL['GRB_efficiency'] is None): - raise ValueError('Missing GRB_efficiency variable in the MODEL!') - if ('GRB_beaming' not in self.MODEL or - self.MODEL['GRB_beaming'] is None): - raise ValueError('Missing GRB_beaming variable in the MODEL!') - if ('E_GRB_iso_min' not in self.MODEL or - self.MODEL['E_GRB_iso_min'] is None): - raise ValueError('Missing GRB_beaming variable in the MODEL!') - self.df_synthetic = get_GRB_properties(self.df_synthetic, - self.MODEL['GRB_efficiency'], - self.MODEL['GRB_beaming'], - self.MODEL['E_GRB_iso_min'] - ) - # get time_CC1 and time_CC2 note that for GRB calculations we - # do not use the "time" column indicating the time of formation - # of the DCO systems as there might be cases where - # CC2 might happen before CC1 due to mass ratio reversal - DCO = [[S1_state, None], [None, S2_state]] - events = ['CC1', 'CC2'] - for i, event in enumerate(events): - logic = self.apply_logic(self.df, S1_state=DCO[i][0], - S2_state=DCO[i][1], - binary_state='detached', - step_name = 'step_SN', - invert_S1S2=False, - warn=False) - last_index = -1 - time = [] - for index, value in self.df.loc[logic,'time'].items(): - if index != last_index: - time.append(value) - last_index = index - if len(time) != self.df_synthetic.shape[0]: - raise ValueError('Missing values in time_{event}!') - self.df_synthetic[f'time_{event}'] = np.array(time)*1e-6 # Myr - - # for convinience reindex the DataFrame - n_rows = len(self.df_synthetic.index) - self.df_synthetic = self.df_synthetic.set_index(np.linspace(0,n_rows-1,n_rows,dtype=int)) - - def save_synthetic_pop(self, path='./synthetic_population.h5'): - """Save synthetc population. + warnings.warn(f"{filename} does not contain formation channels!") + self._formation_channels = None + else: + self._formation_channels = pd.read_hdf( + self.filename, key="formation_channels" + ) + + # if an ini file is given, read the parameters from the ini file + if ini_file is not None: + self.ini_params = binarypop_kwargs_from_ini(ini_file) + self._save_ini_params(filename) + self._load_ini_params(filename) + else: + if "/ini_parameters" not in keys: + raise ValueError( + f"{filename} does not contain an ini_parameters table!" + ) + else: + self._load_ini_params(filename) + + # check if pop contains mass_per_metallicity table + if "/mass_per_metallicity" in keys and metallicity is None: + self._load_mass_per_metallicity(filename) + self.solar_metallicities = self.mass_per_metallicity.index.to_numpy() + self.metallicities = self.solar_metallicities * Zsun + elif metallicity is None: + raise ValueError( + f"{filename} does not contain a mass_per_metallicity table and no metallicity for the file was given!" + ) + + # calculate the metallicity information. This assumes the metallicity is for the whole file! + if metallicity is not None and ini_file is not None: + if "/mass_per_metallicity" in keys: + warnings.warn( + f"{filename} already contains a mass_per_metallicity table. Overwriting the table!" + ) + + simulated_mass = np.sum(self.oneline[["S1_mass_i", "S2_mass_i"]].to_numpy()) + underlying_mass = initial_total_underlying_mass( + df=simulated_mass, **self.ini_params + )[0] + self.mass_per_metallicity = pd.DataFrame( + index=[metallicity], + data={ + "simulated_mass": simulated_mass, + "underlying_mass": underlying_mass, + "number_of_systems": len(self.oneline), + }, + ) + + self._save_mass_per_metallicity(filename) + self.solar_metallicities = self.mass_per_metallicity.index.to_numpy() + self.metallicities = self.solar_metallicities * Zsun + + elif metallicity is not None and ini_file is None: + raise ValueError( + f"{filename} does not contain a mass_per_metallicity table and no ini file was given!" + ) + + # add number of systems + self.history_lengths = self.history.lengths + self.number_of_systems = self.oneline.number_of_systems + self.indices = self.history.indices + + def export_selection(self, selection, filename, overwrite=False, append=False, history_chunksize=1000000): + """Export a selection of the population to a new file + + This method exports a selection of systems from the population to a new file. + The selected systems are specified by their indices in the population. + + By default the selected systems will overwrite the file if it already exists. + If overwrite is set to False, the selected systems will be appended to + the existing file if it already exists and the indices will be shifted + based on the current length of data in the file that is being appended to. + Parameters ---------- - path : str - Path to dataset. - + selection : list of int + The indices of the systems to export. + filename : str + The name of the export file to create or append to. + chunksize : int, optional + The number of systems to export at a time. Default is 1000000. + + Raises + ------ + ValueError + If the filename does not contain ".h5" extension. + + Warnings + -------- + UserWarning + If there is no "metallicity" column in the oneline dataframe and the population file contains multiple metallicities. + + Notes + ----- + - The exported systems will be appended to the existing file if it already exists. + - The indices of the exported systems will be shifted based on the current length of data in the file. + - The "oneline" and "history" dataframes of the selected systems will be written to the file. + - If available, the "formation_channels" dataframe and "history_lengths" dataframe of the selected systems will also be written to the file. + - The "metallicity" column of the oneline dataframe will be added if it is not present, using the metallicity of the population file. + - The "mass_per_metallicity" dataframe will be updated with the number of selected systems. + + Examples + -------- + # Export systems with indices [0, 1, 2] to a new file named "selected.h5" + >>> population.export_selection([0, 1, 2], "selected.h5") + + # Export systems with indices [3, 4, 5] to an existing file named "existing.h5" + >>> population.export_selection([3, 4, 5], "existing.h5") + + # Export systems with indices [6, 7, 8] to a new file named "selected.h5" in chunks of 2 + >>> population.export_selection([6, 7, 8], "selected.h5", history_chunksize=2) """ - if self.df_synthetic is None: - raise ValueError('Nothing to save!') + + if not (".h5" in filename): + raise ValueError( + f"{filename} does not contain .h5 in the se.\n Is this a valid population file?" + ) + + # overwrite and append cannot both be True + if append and overwrite: + raise ValueError("Both overwrite and append cannot be True!") + + # check for file existence + if os.path.exists(filename) and not overwrite and not append: + raise FileExistsError(f"{filename} already exists! Set overwrite or append to True to continue!") + + if overwrite: + mode = 'w' + elif append: + mode = 'a' else: - self.df_synthetic.to_hdf(path, key='history') + mode = 'w' + + history_cols = self.history.columns + oneline_cols = self.oneline.columns + + history_min_itemsize = { + key: val for key, val in HISTORY_MIN_ITEMSIZE.items() if key in history_cols + } + oneline_min_itemsize = { + key: val for key, val in ONELINE_MIN_ITEMSIZE.items() if key in oneline_cols + } + + with pd.HDFStore(filename, mode=mode) as store: + # shift all new indices by the current length of data in the file + last_index_in_file = -1 + + if "/oneline" in store.keys(): + last_index_in_file = np.sort(store["oneline"].index)[-1] + elif "/history" in store.keys(): + last_index_in_file = np.sort(store["history"].index)[-1] + + if "/history" in store.keys() and self.verbose: + print("history in file. Appending to file") + + if "/oneline" in store.keys() and self.verbose: + print("oneline in file. Appending to file") + + if "/formation_channels" in store.keys() and self.verbose: + print("formation_channels in file. Appending to file") + + if "/history_lengths" in store.keys() and self.verbose: + print("history_lengths in file. Appending to file") + + # TODO: I need to shift the indices of the binaries or should I reindex them? + # since I'm storing the information, reindexing them should be fine. + + if last_index_in_file == -1: + last_index_in_file = 0 + reindex = { + i: j + for i, j in zip(selection, + np.arange(last_index_in_file, last_index_in_file + len(selection), 1), + ) + } + else: + reindex = { + i: j + for i, j in zip(selection, + np.arange(last_index_in_file + 1, last_index_in_file + len(selection) + 1,1,), + ) + } + + if "metallicity" not in self.oneline.columns: + warnings.warn( + "No metallicity column in oneline dataframe! Using the metallicity of the population file and adding it to the oneline." + ) + if len(self.metallicities) > 1: + raise ValueError( + "The population file contains multiple metallicities. Please add a metallicity column to the oneline dataframe!" + ) + + if self.verbose: + print("Writing selected systems to population file...") + + # write oneline of selected systems + for i in tqdm( + range(0, len(selection), self.chunksize), + total=len(selection) // self.chunksize, + disable=not self.verbose, + ): + + tmp_df = self.oneline[selection[i : i + self.chunksize]] + if "metallicity" in tmp_df.columns: + tmp_df["metallicity"] = tmp_df["metallicity"].astype("float") + else: + tmp_df["metallicity"] = self.metallicities[0] + tmp_df.rename(index=reindex, inplace=True) + store.append( + "oneline", + tmp_df, + format="table", + min_itemsize=oneline_min_itemsize, + index=False, + ) + if self.verbose: - print('Synthetic population successfully saved!') + print("Oneline: Done") + + # write history of selected systems + for i in tqdm( + range(0, len(selection), history_chunksize), + total=len(selection) // history_chunksize, + disable=not self.verbose, + ): + tmp_df = self.history[selection[i : i + history_chunksize]] + tmp_df.rename(index=reindex, inplace=True) + store.append( + "history", + tmp_df, + format="table", + min_itemsize=history_min_itemsize, + index=False, + ) + + if self.verbose: + print("History: Done") + + # write formation channels of selected systems + if self.formation_channels is not None: + for i in tqdm( + range(0, len(selection), self.chunksize), + total=len(selection) // self.chunksize, + disable=not self.verbose, + ): + tmp_df = self.formation_channels.loc[selection[i : i + self.chunksize]] + tmp_df.rename(index=reindex, inplace=True) + store.append( + "formation_channels", + tmp_df, + format="table", + min_itemsize={"channel_debug": 100, "channel": 100}, + index=False, + ) + + ## METADATA + + # write the history lengths + for i in tqdm( + range(0, len(selection), self.chunksize), + total=len(selection) // self.chunksize, + disable=not self.verbose, + ): + tmp_df = self.history.lengths.loc[selection[i : i + self.chunksize]] + tmp_df.rename(index=reindex, inplace=True) + store.append( + "history_lengths", pd.DataFrame(tmp_df), format="table", index=False + ) + + # write mass_per_metallicity + if "/mass_per_metallicity" in store.keys(): + self_mass = self.mass_per_metallicity + self_mass["number_of_systems"] = len(selection) + tmp_df = pd.concat([store["mass_per_metallicity"], self_mass]) + mass_per_metallicity = tmp_df.groupby(tmp_df.index).sum() + store.put("mass_per_metallicity", mass_per_metallicity) + + else: + self_mass = self.mass_per_metallicity + self_mass["number_of_systems"] = len(selection) + store.put("mass_per_metallicity", self_mass) + + # write ini parameters + self._save_ini_params(filename) + + @property + def formation_channels(self): + """ + Retrieves the formation channels from the population file. + + Returns: + pandas.DataFrame or None: The formation channels if available, otherwise None. + """ + with pd.HDFStore(self.filename, mode="r") as store: + if "/formation_channels" in store.keys(): + self._formation_channels = pd.read_hdf( + self.filename, key="formation_channels" + ) + else: + if self.verbose: + warnings.warn("No formation channels in the population file!") + self._formation_channels = None + + return self._formation_channels + + def calculate_formation_channels(self, mt_history=False): + """Calculate the formation channels of the population. + + mt_history is a boolean that determines if the detailed mass-transfer history + from the HMS-HMS grid is included in the formation channels. - def load_synthetic_pop(self, path): - """Load synthetc population. Parameters ---------- - path : str - Path to dataset. + mt_history : bool, optional + If `True`, include the mass-transfer history in the formation channels. Default is False. + Raises + ------ + ValueError + If the mt_history_HMS_HMS column is not present in the oneline dataframe. """ - if self.df_synthetic is None: - self.df_synthetic = pd.read_hdf(path, key='history') - if self.verbose: - print('Synthetic population successfully loaded!') - else: - raise ValueError('You already have an synthetic population stored in memory!') - - def get_dco_merger_efficiency(self): - """Compute the DCO merger efficinty per Msun for each metallicities.""" - metallicities = np.unique(self.df_synthetic['metallicity']) - efficiencies = [] - self.met_merger_efficiency = sorted(metallicities)[::-1] - for met in self.met_merger_efficiency: - sel = (self.df_synthetic['metallicity'] == met) - count = self.df_synthetic[sel].shape[0] - underlying_stellar_mass = self.df_synthetic.loc[sel,'underlying_mass_for_met'].values[0] - eff = count/underlying_stellar_mass - efficiencies.append(eff) - print(f'DCO merger efficiency at Z={met:1.2E}: {eff:1.2E} Msun^-1') - self.met_merger_efficiency = np.array(self.met_merger_efficiency) - self.merger_efficiency = {'total' : np.array(efficiencies)} - # if the channel column is present compute the merger efficiency per channel - if "channel" in self.df_synthetic: - channels = np.unique(self.df_synthetic['channel']) - for ch in channels: - efficiencies = [] - for met in self.met_merger_efficiency: - sel = (self.df_synthetic['metallicity'] == met) & (self.df_synthetic['channel'] == ch) - count = self.df_synthetic[sel].shape[0] - if count > 0: - underlying_stellar_mass = self.df_synthetic.loc[sel,'underlying_mass_for_met'].values[0] - eff = count/underlying_stellar_mass - else: - eff = np.nan - efficiencies.append(eff) - # print(f'Z={met:1.2E} {ch}: {eff:1.2E} Msun^-1') - self.merger_efficiency[ch] = np.array(efficiencies) - - - def compute_cosmological_weights(self, sensitivity, flag_pdet, working_dir, load_data, pop='DCO'): - """Compute the GRB/DCO merger rate weights. + + if self.verbose: + print("Calculating formation channels...") + + # load the HMS-HMS interp class + HMS_HMS_event_dict = { + "initial_MT": "initial_MT", + "stable_MT": "oRLO1", + "stable_reverse_MT": "oRLO1-reverse", + "no_MT": "None", + "unstable_MT": "oCE1/oDoubleCE1", + } + + unique_binary_indices = self.indices + + # check if formation channels already exist + with pd.HDFStore(self.filename, mode="a") as store: + if "/formation_channels" in store.keys(): + print("Formation channels already exist in the parsed population file!") + print("Channels will be overwriten") + del store["formation_channels"] + + def get_events(group): + # for now, only append information for RLO1; unstable_MT information already exists + if "oRLO1" in group["interp_class_HMS_HMS"].tolist() or "oRLO1-reverse" in group["interp_class_HMS_HMS"].tolist() : + combined_events = ( + group["event"].iloc[0] + "_" + group["interp_class_HMS_HMS"].iloc[0] + ) + tmp = [combined_events] + tmp.extend(group["event"].iloc[1:]) + combined_events = "_".join(tmp) + else: + combined_events = "_".join(group["event"]) + return pd.Series({"channel_debug": combined_events}) + + def get_mt_history(row): + if ( + pd.notna(row["mt_history_HMS_HMS"]) + and row["mt_history_HMS_HMS"] == "Stable contact phase" + ): + return row["channel"].replace("oRLO1", "oRLO1-contact") + # reverse is already included in the interpolation class now! + # elif ( + # pd.notna(row["mt_history_HMS_HMS"]) + # and row["mt_history_HMS_HMS"] == "Stable reverse mass-transfer phase" + # ): + # return row["channel"].replace("oRLO1", "oRLO1-reverse") + else: + return row["channel"] + + previous = 0 + + for i in tqdm( + range(0, len(unique_binary_indices), self.chunksize), + disable=not self.verbose, + ): + selection = unique_binary_indices[i : i + self.chunksize] + + # create the dataframe for the chunk + df = pd.DataFrame(index=selection, columns=["channel_debug", "channel"]) + end = ( + previous + + self.history_lengths.iloc[i : i + self.chunksize].sum().iloc[0] + ) + + # get the history of chunk events and transform the interp_class_HMS_HMS + interp_class_HMS_HMS = self.oneline.select( + start=i, stop=i + self.chunksize, columns=["interp_class_HMS_HMS"] + ) + events = self.history.select(start=previous, stop=end, columns=["event"]) + + mask = ~pd.isna(interp_class_HMS_HMS["interp_class_HMS_HMS"].values) + interp_class_HMS_HMS.loc[mask, "interp_class_HMS_HMS"] = ( + interp_class_HMS_HMS[mask] + .apply(lambda x: HMS_HMS_event_dict[x["interp_class_HMS_HMS"]], axis=1) + .values + ) + del mask + + previous = end + # combine based on the index, this allows for an easier apply later + merged = pd.merge( + events.dropna(), interp_class_HMS_HMS, left_index=True, right_index=True + ) + del events, interp_class_HMS_HMS + + merged.index.name = "binary_index" + df["channel_debug"] = merged.groupby("binary_index").apply(get_events) + del merged + df["channel"] = ( + df["channel_debug"] + .str.replace("_redirect_from_ZAMS", "") + .str.replace("_redirect_from_CO_HMS_RLO", "") + .str.replace("_redirect_from_CO_HeMS_RLO", "") + .str.replace("_redirect_from_CO_HeMS", "") + .str.replace("_CO_contact", "") + ) + + if mt_history: + columns = self.oneline.columns + if "mt_history_HMS_HMS" not in columns: + raise ValueError( + "mt_history_HMS_HMS not saved in the oneline dataframe!" + ) + else: + tmp_df = pd.DataFrame( + index=selection, columns=["channel", "mt_history_HMS_HMS"] + ) + tmp_df["channel"] = df["channel"] + x = self.oneline.select( + start=i, stop=i + self.chunksize, columns=["mt_history_HMS_HMS"] + ) + tmp_df["mt_history_HMS_HMS"] = x + df["channel"] = tmp_df.apply(get_mt_history, axis=1) + del tmp_df + del x + + self._write_formation_channels(self.filename, df) + del df + + if self.verbose: + print("formation_channels written to population file!") + + def _write_formation_channels(self, filename, df): + """Write the formation channels to the population file + + This will append the formation channels to the population file, while restricting the maximum + length of the channel and channel_debug columns to 100 characters. Parameters ---------- - sensitivity : str - GW detector sensitivity and network configuration you want to use, see arXiv:1304.0670v3 - detector sensitivities are taken from: https://dcc.ligo.org/LIGO-T2000012-v2/public - available sensitivity keys (for Hanford, Livingston, Virgo network): - 'O3actual_H1L1V1' : aligo_O3actual_H1.txt, aligo_O3actual_L1.txt, avirgo_O3actual.txt - 'O4low_H1L1V1' : aligo_O4low.txt, aligo_O4low.txt, avirgo_O4high_NEW.txt - 'O4high_H1L1V1' : aligo_O4high.txt, aligo_O4high.txt, avirgo_O4high_NEW.txt - 'design_H1L1V1' : AplusDesign.txt, AplusDesign.txt, avirgo_O5high_NEW.txt - 'infinite': intrinsic merging DCO population, i.e. p_det = 1 - flag_pdet : bool - `True` if you use sensitivity != 'infinite'. - working_dir : str - Working directory where the weights will be saved. - load_data : bool - `True` if you want to load the weights computed by this function - in your working directory. - **kwargs : dict - Kwargs containing the model parameters of your rate calculation. - See posydon/popsyn/rate_calculation.py + filename : str + The name of the file to write the formation channels to. + df : pd.DataFrame + The dataframe containing the formation channels. + """ + str_length = 100 + + with pd.HDFStore(filename, mode="a") as store: + df['channel'] = df['channel'].str.slice(0, str_length) + df['channel_debug'] = df['channel_debug'].str.slice(0, str_length) + store.append( + "formation_channels", + df, + format="table", + min_itemsize={"channel_debug": str_length, "channel": str_length}, + ) + + def __len__(self): + """Get the number of systems in the population. Returns ------- - array doubles - Return the cosmological weights, z_formation, z_merger and binary - index k associated to each weighted binary. + int + The number of systems in the population. """ + return self.number_of_systems - # TODO: make the class inputs kwargs - self.rates = Rates(self.df_synthetic, **self.MODEL) + @property + def columns(self): + """ + Returns a dictionary containing the column names of the history and oneline dataframes. + + Returns + ------- + dict + A dictionary with keys 'history' and 'oneline', where the values are the column names of the respective dataframes. + """ + return {"history": self.history.columns, "oneline": self.oneline.columns} - # compute DCO merger rate density - if pop == 'DCO': - if not load_data: - self.rates.compute_merger_rate_weights(sensitivity=sensitivity, flag_pdet=flag_pdet, path_to_dir=working_dir) - index, z_formation, z_merger, w_ijk = self.rates.load_merger_rate_weights(sensitivity, path_to_dir=working_dir) + def create_transient_population( + self, func, transient_name, oneline_cols=None, hist_cols=None + ): + """Given a function, create a TransientPopulation - return index, z_formation, z_merger, w_ijk - elif pop == 'GRB': - if not load_data: - self.rates.compute_GRB_rate_weights(sensitivity=sensitivity, path_to_dir=working_dir) - index_1, z_formation_1, z_grb_1, w_ijk_1, \ - index_2, z_formation_2, z_grb_2, w_ijk_2 = self.rates.load_grb_rate_weights(sensitivity, path_to_dir=working_dir) + This method creates a transient population using the provided function. + `func` is given the history, oneline, and formation channels dataframes as arguments. + The function should return a dataframe containing the transient population, which needs to contain the columns 'time' and 'metallicity'. - return index_1, z_formation_1, z_grb_1, w_ijk_1, index_2, z_formation_2, z_grb_2, w_ijk_2 - else: - raise ValueError('Population not recognized!') + Processing is done in chunks to avoid memory issues and a pandas DataFrame is stored at `'/transients/transient_name'` in the population file. - def resample_synthetic_population(self, index, z_formation, z_event, w_ijk, export_cols=None, - pop='DCO', reset_grb_properties=None): - """Resample synthetc population to obtain intrinsic/observable population. + The creation of the transient population can be sped up by limiting the oneline_cols and hist_cols to only the columns needed for the function. + If you do not provide these, all columns will be used, which can be slow for large populations. Parameters ---------- - index : array int - Index k of each binary corresponding to the synthetc dataframe - proeprties. - z_formation : array float - Redshift of formation of each binary. - z_merger : array float - Redshift of merger of each binary. - w_ijk : array float - Cosmological weights computed with Eq. B.8 of Bavera et at. (2020). - export_cols : list str - List of additional columns to save in the intrinsic/observable - population. + func : function + Function to apply to the parsed population to create a transient population. + The function needs to take 3 arguments: + - history_chunk : pd.DataFrame + - oneline_chunk : pd.DataFrame + - formation_channels_chunk : pd.DataFrame + and return a pd.DataFrame containing the transient population, which needs to contain the columns 'time' and 'metallicity'. + + oneline_cols : list of str, optional + Columns to extract from the oneline dataframe. Default is all columns. + + hist_cols : list of str, optional + Columns to extract from the history dataframe. Default is all columns. Returns ------- - pd.DataFrame - Resampled synthetc population to intrinsic or detecatable - population. - - """ - # compute GRB properties boolean - compute_GRB_properties = (self.MODEL is not None and - "compute_GRB_properties" in self.MODEL and - self.MODEL["compute_GRB_properties"]) - - # drop all zero weights to save memory - sel = w_ijk > 0. - index = index[sel] - z_formation = z_formation[sel] - z_event = z_event[sel] - w_ijk = w_ijk[sel] - - # export results, we do a selections of columns else the dataframe - # risks to go out of memory - df = pd.DataFrame() - df['weight'] = w_ijk - df['z_formation'] = z_formation - if pop == 'DCO': - df['z_merger'] = z_event - elif pop == 'GRB': - df['z_grb'] = z_event + TransientPopulation + A TransientPopulation object for interfacing with the transient population + + Raises + ------ + ValueError + If the transient population requires a time column or if the transient population contains duplicate columns. + + Examples + -------- + See the tutorials for examples of how to use this method. + + """ + + with pd.HDFStore(self.filename, mode="a") as store: + if f"/transients/{transient_name}" in store.keys(): + print("overwriting transient population") + del store["transients/" + transient_name] + + min_itemsize = { + "channel": 100, + } + if hist_cols is not None: + if "time" not in hist_cols: + raise ValueError("The transient population requires a time column!") + + min_itemsize.update( + { + key: val + for key, val in HISTORY_MIN_ITEMSIZE.items() + if key in hist_cols + } + ) else: - raise ValueError('Population not recognized!') - save_cols = ['metallicity','time','t_delay','S1_state','S2_state', - 'S1_mass','S2_mass','S1_spin','S2_spin', - 'orbital_period','eccentricity', 'q', 'm_tot', - 'm_chirp', 'chi_eff'] - if compute_GRB_properties: - save_cols += GRB_PROPERTIES - if "channel" in self.rates.df: - save_cols.append('channel') - if export_cols is not None: - for c in export_cols: - if c not in save_cols: - save_cols.append(c) - for c in save_cols: - df[c] = self.rates.get_data(c, index) - - # the same binary system can emit two GRBs, we remove the - # GRB properties of the other GRB to prevent mistakes - if reset_grb_properties is not None: - if reset_grb_properties == 'GRB1': - for key in GRB_PROPERTIES: - if "S1" in key: - df[key] = np.nan - elif reset_grb_properties == 'GRB2': - for key in GRB_PROPERTIES: - if "S2" in key: - df[key] = np.nan + hist_cols = self.history.columns + min_itemsize.update(HISTORY_MIN_ITEMSIZE) + + if oneline_cols is not None: + min_itemsize.update( + { + key: val + for key, val in ONELINE_MIN_ITEMSIZE.items() + if key in oneline_cols + } + ) + else: + oneline_cols = self.oneline.columns + min_itemsize.update(ONELINE_MIN_ITEMSIZE) + + # setup a mapping to the size of each history colummn + history_lengths = self.history_lengths + unique_binary_indices = self.indices + + previous = 0 + for i in tqdm( + range(0, len(unique_binary_indices), self.chunksize), + disable=not self.verbose, + ): + end = previous + history_lengths[i : i + self.chunksize].sum().iloc[0] + + oneline_chunk = self.oneline.select( + start=i, stop=i + self.chunksize, columns=oneline_cols + ) + + history_chunk = self.history.select( + start=previous, stop=end, columns=hist_cols + ) + + if self.formation_channels is not None: + formation_channels_chunk = self.formation_channels[ + i : i + self.chunksize + ] else: - raise ValueError(f'reset_grb_properties=={reset_grb_properties} key not recognized!') + formation_channels_chunk = None + + syn_df = func(history_chunk, oneline_chunk, formation_channels_chunk) + + if len(syn_df.columns) != len(syn_df.columns.unique()): + raise ValueError("Transient population contains duplicate columns!") + + # filter out the columns in min_itemsize that are not in the dataframe + min_itemsize = { + key: val for key, val in min_itemsize.items() if key in syn_df.columns + } + + with pd.HDFStore(self.filename, mode="a") as store: + store.append( + "transients/" + transient_name, + syn_df, + format="table", + min_itemsize=min_itemsize, + ) + + previous = end + + synth_pop = TransientPopulation( + self.filename, transient_name, verbose=self.verbose + ) + return synth_pop + + def plot_binary_evolution(self, index): + """Plot the binary evolution of a system + + This method is not currently implemented. + """ + pass + + +class TransientPopulation(Population): + """A class representing a population of transient events. + + This class allows you to calculate additional properties of the population, + such as the efficiency of events per Msun for each solar metallicity, and to + calculate the cosmic weights of the transient population. - return df + Attributes + ---------- + population : pandas.DataFrame + DataFrame containing the whole transient population. + transient_name : str + Name of the transient population. + efficiency : pandas.DataFrame + DataFrame containing the efficiency of events per Msun for each solar metallicity. + columns : list + List of columns in the transient population. + + """ + + def __init__(self, filename, transient_name, verbose=False, chunksize=100000): + """Initialise the TransientPopulation object. + + This method initializes the TransientPopulation object by linking it to the population file. + The transient population linked is located at '/transients/{transient_name}' in the population file. - def get_dco_merger_rate_density(self, export_cols=None, working_dir='./', load_data=False): - """Compute the merger rate density as a function of redshift. Parameters ---------- - export_cols : list str - List of additional columns to save in the intrinsic/observable - population. - working_dir : str - Working directory where the weights will be saved. - load_data : bool - `True` if you want to load the weights computed by this function - in your working directory. - - """ - if self.df_synthetic is None: - raise ValueError('You first need to isolated the DCO synthetic population!') - - # compute cosmological weights (detection rate weights with infinite sensitivity) - sensitivity='infinite' - flag_pdet = False - index, z_formation, z_merger, w_ijk = self.compute_cosmological_weights(sensitivity, flag_pdet, working_dir=working_dir, load_data=load_data, pop='DCO') - # compute rate density weights - self.dco_z_rate_density = self.rates.get_centers_redshift_bins() - total_rate = self.rates.compute_rate_density(w_ijk, z_merger, observable='DCO', sensitivity=sensitivity) - self.dco_rate_density = {'total' : total_rate} - if "channel" in self.df_synthetic: - channels = np.unique(self.df_synthetic['channel']) - for ch in tqdm(channels): - sel = (self.rates.get_data('channel', index) == ch) - rate = self.rates.compute_rate_density(w_ijk[sel], z_merger[sel], observable='DCO', sensitivity=sensitivity) - self.dco_rate_density[ch] = rate - - print(f'DCO merger rate density in the local Universe (z={self.dco_z_rate_density[0]:1.2f}): {round(total_rate[0],2)} Gpc^-3 yr^-1') - - # export the intrinsic DCO population - self.df_dco_intrinsic = self.resample_synthetic_population(index, z_formation, z_merger, w_ijk, export_cols=export_cols, pop='DCO') - - - def get_grb_rate_density(self, export_cols=None, working_dir='./', load_data=False): - """Compute the GRB density as a function of redshift. + filename : str + The name of the file containing the population. The file should be in HDF5 format. + transient_name : str + The name of the transient population within the file. + verbose : bool, optional + If `True`, additional information will be printed during the initialization process. + chunksize : int, optional + The chunksize to use when reading the population file (default is 100000). + + Raises + ------ + ValueError + If the specified transient population name is not found in the file. + + Notes + ----- + The population data is stored in an HDF5 file format. The file should contain a group named '/transients' which + holds all the transient populations. The specified transient population name should be a valid group name within + '/transients'. If the transient population has associated efficiencies, they will be loaded as well. + + Examples + -------- + >>> filename = 'population_data.h5' + >>> transient_name = 'BBH' + >>> population = TransientPopulation(filename, transient_name, verbose=True) + """ + super().__init__(filename, verbose=verbose, chunksize=chunksize) + + with pd.HDFStore(self.filename, mode="r") as store: + if "/transients/" + transient_name not in store.keys(): + raise ValueError( + f"{transient_name} is not a valid transient population in {filename}!" + ) + + self.transient_name = transient_name + if "/transients/" + transient_name + "/efficiencies" in store.keys(): + self._load_efficiency(filename) + + @property + def population(self): + """Returns the entire transient population as a pandas DataFrame. + + This method retrieves the transient population data from a file and returns it as a pandas DataFrame. + Please note that if the transient population is too large, it may consume a significant amount of memory. + + Returns + ------- + pd.DataFrame + A DataFrame containing the transient population data. + """ + return pd.read_hdf(self.filename, key="transients/" + self.transient_name) + + def _load_efficiency(self, filename): + """Load the efficiency from the file + + Parameters: + filename (str): The path to the file containing the efficiency data. + + Returns: + None + + Raises: + None + + """ + with pd.HDFStore(filename, mode="r") as store: + self.efficiency = store[ + "transients/" + self.transient_name + "/efficiencies" + ] + if self.verbose: + print("Efficiency table read from population file!") + + def _save_efficiency(self, filename): + """Save the efficiency to the file. + + Args: + filename (str): The name of the file to save the efficiency to. + + Returns: + None + """ + with pd.HDFStore(filename, mode="a") as store: + store.put( + "transients/" + self.transient_name + "/efficiencies", self.efficiency + ) + + @property + def columns(self): + """Return the columns of the transient population. + + Returns: + list: A list of column names in the transient population. + """ + if not hasattr(self, "_columns"): + with pd.HDFStore(self.filename, mode="r") as store: + self._columns = store.select( + "transients/" + self.transient_name, start=0, stop=0 + ).columns + return self._columns + + def select(self, where=None, start=None, stop=None, columns=None): + """ + Select a subset of the transient population. + + This method allows you to filter and extract a subset of rows from the oneline table stored in an HDF file. + You can specify conditions to filter the rows, define the range of rows to select, and choose specific columns to include in the subset. Parameters ---------- - export_cols : list str - List of additional columns to save in the intrinsic/observable - population. - working_dir : str - Working directory where the weights will be saved. - load_data : bool - `True` if you want to load the weights computed by this function - in your working directory. - - """ - if self.df_synthetic is None: - raise ValueError('You first need to isolated the DCO synthetic population!') - - # compute cosmological weights (detection rate weights with infinite sensitivity) - sensitivity='infinite' - flag_pdet = False - index_1, z_formation_1, z_grb_1, w_ijk_1, \ - index_2, z_formation_2, z_grb_2, w_ijk_2 = self.compute_cosmological_weights(sensitivity, flag_pdet, working_dir=working_dir, load_data=load_data, pop='GRB') - - # compute beamed rate density weights - # note we compute the beamed rate density for GRBs as this is what is often reported in the literature - # as the beaming factor is not known a priori - self.grb_z_rate_density = self.rates.get_centers_redshift_bins() - total_GRB1 = self.rates.compute_rate_density(w_ijk_1, z_grb_1, observable='GRB1', sensitivity='beamed', index=index_1) - total_GRB2 = self.rates.compute_rate_density(w_ijk_2, z_grb_2, observable='GRB2', sensitivity='beamed', index=index_2) - total_rate = total_GRB1 + total_GRB2 - self.grb_rate_density = {'total' : total_rate, 'total_GRB1' : total_GRB1, 'total_GRB2': total_GRB2} - if "channel" in self.df_synthetic: - channels = np.unique(self.df_synthetic['channel']) - for ch in tqdm(channels): - sel1 = (self.rates.get_data('channel', index_1) == ch) - if any(sel1): - self.grb_rate_density[ch+'_GRB1'] = self.rates.compute_rate_density(w_ijk_1[sel1], z_grb_1[sel1], observable='GRB1', sensitivity='beamed', index=index_1[sel1]) - else: - self.grb_rate_density[ch+'_GRB1'] = np.zeros(len(self.grb_z_rate_density)) - sel2 = (self.rates.get_data('channel', index_2) == ch) - if any(sel2): - self.grb_rate_density[ch+'_GRB2'] = self.rates.compute_rate_density(w_ijk_2[sel2], z_grb_2[sel2], observable='GRB2', sensitivity='beamed', index=index_2[sel2]) - else: - self.grb_rate_density[ch+'_GRB2'] = np.zeros(len(self.grb_z_rate_density)) - self.grb_rate_density[ch] = self.grb_rate_density[ch+'_GRB1'] + self.grb_rate_density[ch+'_GRB2'] - - print(f'GRB (beamed) rate density in the local Universe (z={self.grb_z_rate_density[0]:1.2f}): {round(total_rate[0],2)} Gpc^-3 yr^-1') - - # export the intrinsic grb intrisic population - # TODO: instead of concatenating two dataframe and duplicating the information of the same - # binary system we should combine the two datraframes where we have two columns for GRB1 and GRB2 - # such dataframe will have z_grb_1, z_grb_2, weight_1, weight_2 - # when this is addressed, remove reset_grb_properties feature - self.df_grb_intrinsic = pd.DataFrame() - df_grb_1 = self.resample_synthetic_population(index_1, z_formation_1, z_grb_1, w_ijk_1, export_cols=export_cols, pop='GRB', reset_grb_properties='GRB2') - df_grb_2 = self.resample_synthetic_population(index_2, z_formation_2, z_grb_2, w_ijk_2, export_cols=export_cols, pop='GRB', reset_grb_properties='GRB1') - self.df_grb_intrinsic = pd.concat([df_grb_1, df_grb_2], ignore_index=True, sort=False) - # the observable population accounts for beaming - self.df_grb_observable = self.df_grb_intrinsic.copy() - for i in [1,2]: - sel = self.df_grb_observable[f'S{i}_f_beaming'] > 0 - self.df_grb_observable.loc[sel,'weight'] *= self.df_grb_observable.loc[sel,f'S{i}_f_beaming'] - - def get_dco_detection_rate(self, sensitivity='design_H1L1V1', export_cols=None, working_dir='./', load_data=False): - """Compute the detection rate per yr. + where : str, optional + A condition to filter the rows of the oneline table. Default is None. + start : int, optional + The starting index of the subset. Default is None. + stop : int, optional + The ending index of the subset. Default is None. + columns : list, optional + The column names to include in the subset. Default is None. + + Returns + ------- + pd.DataFrame + The selected subset of the oneline table. + + Examples + -------- + # Select rows based on a condition + >>> df = transpop.select(where="S2_mass_i > 30") + + # Select rows from index 10 to 20 + >>> df = transpop.select(start=10, stop=20) + + # Select specific columns + >>> df = transpop.select(columns=['time', 'metallicity']) + """ + return pd.read_hdf( + self.filename, + key="transients/" + self.transient_name, + where=where, + start=start, + stop=stop, + columns=columns, + ) + + def get_efficiency_over_metallicity(self): + """ + Compute the efficiency of events per Msun for each solar_metallicities. + + This method calculates the efficiency of events per solar mass for each solar metallicity value in the transient population. + It first checks if the efficiencies have already been computed and if so, overwrites them. + Then, it iterates over each metallicity value and calculates the efficiency by dividing the count of events with the underlying stellar mass. + The efficiencies are stored in a DataFrame with the metallicity values as the index and the 'total' column representing the efficiency for all channels. + If the 'channel' column is present, it also computes the merger efficiency per channel and adds the results to the DataFrame. + """ + if hasattr(self, "efficiency"): + print("Efficiencies already computed! Overwriting them!") + with pd.HDFStore(self.filename, mode="a") as store: + if ( + "/transients/" + self.transient_name + "/efficiencies" + in store.keys() + ): + del store["transients/" + self.transient_name + "/efficiencies"] + + metallicities = self.mass_per_metallicity.index.to_numpy() + + met_columns = self.select(columns=["metallicity"]).value_counts() + + met_columns.index = [i[0] for i in met_columns.index.to_numpy().flatten()] + + combined_df = pd.concat([met_columns, self.mass_per_metallicity], axis=1) + combined_df.fillna(0, inplace=True) + combined_df.sort_index(inplace=True) + + efficiencies = combined_df['count']/combined_df['underlying_mass'] + for MET, value in efficiencies.sort_index().items(): + print(f"Efficiency at Z={MET:1.2E}: {value:1.2E} Msun^-1") + + self.efficiency = pd.DataFrame( + efficiencies, columns=["total"] + ) + self.efficiency.index.name = "metallicity" + # if the channel column is present compute the merger efficiency per channel + if "channel" in self.columns: + + data = self.select(columns=["channel", "metallicity"]).value_counts() + channels = np.unique(data.index.get_level_values(0)) + + for ch in channels: + self.efficiency[ch] = (data[ch] / self.mass_per_metallicity["underlying_mass"]).fillna(0) + # save the efficiency + self._save_efficiency(self.filename) + + def calculate_cosmic_weights(self, SFH_identifier, MODEL_in=None): + """ + Calculate the cosmic weights of the transient population. + + This method calculates the cosmic weights of the transient population based on the provided star formation history identifier and model parameters. + It performs various calculations and stores the results in an HDF5 file at the location '/transients/{transient_name}/rates/{SFH_identifier}'. + This allows for multiple star formation histories to be used with the same transient population. + + The default MODEL parameters are used if none are provided, which come from the IllustrisTNG model. Parameters ---------- - sensitivity : str - GW detector sensitivity and network configuration you want to use, see arXiv:1304.0670v3 - detector sensitivities are taken from: https://dcc.ligo.org/LIGO-T2000012-v2/public - available sensitivity keys (for Hanford, Livingston, Virgo network): - 'O3actual_H1L1V1' : aligo_O3actual_H1.txt, aligo_O3actual_L1.txt, avirgo_O3actual.txt - 'O4low_H1L1V1' : aligo_O4low.txt, aligo_O4low.txt, avirgo_O4high_NEW.txt - 'O4high_H1L1V1' : aligo_O4high.txt, aligo_O4high.txt, avirgo_O4high_NEW.txt - 'design_H1L1V1' : AplusDesign.txt, AplusDesign.txt, avirgo_O5high_NEW.txt - export_cols : list str - List of additional columns to save in the intrinsic/observable - population. - working_dir : str - Working directory where the weights will be saved. - load_data : bool - `True` if you want to load the weights computed by this function - in your working directory. - - """ - if self.df_synthetic is None: - raise ValueError('You first need to isolated the DCO synthetic population!') - - # compute detection rate weights - flag_pdet = True - index, z_formation, z_merger, w_ijk = self.compute_cosmological_weights(sensitivity, flag_pdet, working_dir=working_dir, load_data=load_data) - print(f'DCO detection rate at {sensitivity} sensitivity: {sum(w_ijk):1.2f} yr^-1') - - # export the observable DCO population - # TODO: store p_det - self.df_dco_observable = self.resample_synthetic_population(index, z_formation, z_merger, w_ijk, export_cols=export_cols) - - def get_formation_channels(self, mt_history): - """Get formation channel and add to df and df_oneline.""" - - # loop through each binary - unique_binary_index = np.unique(self.df.index) - for index in unique_binary_index: - - # get event column and information from interpolated classes - df_binary = self.df.loc[index,['event']].dropna() - intep_cls = [key for key in self.df_oneline.keys() if 'interp_class' in key] - df_binary_online = self.df_oneline.loc[index, intep_cls].dropna() - event_array = df_binary['event'].values.tolist() - - # make interpolated class information consistent with event column - HMS_HMS_event_dict = {'stable_MT':'oRLO1', 'stable_reverse_MT':'oRLO1', 'no_MT':'None', 'unstable_MT':'oCE1/oDoubleCE1'} - event_HMS_HMS = HMS_HMS_event_dict[df_binary_online['interp_class_HMS_HMS']] + SFH_identifier : str + Identifier for the star formation history. + MODEL_in : dict, optional + Dictionary containing the model parameters. If not provided, the default model parameters will be used. - # for now, only append information for RLO1; unstable_MT information already exists - if event_HMS_HMS == 'oRLO1': - event_array.insert(1, event_HMS_HMS) - formation_channel = "_".join(event_array) - else: - formation_channel = "_".join(event_array) + Returns + ------- + Rates + An instance of the Rates class. + + Raises + ------ + ValueError + If a parameter name in MODEL_in is not valid. + + Notes + ----- + This function calculates the cosmic weights of the transient population based on the provided star formation history + identifier and model parameters. It performs various calculations and stores the results in an HDF5 file. + + The cosmic weights are computed for each event in the population, taking into account the metallicity, redshift, + and birth time of the events. The weights are calculated using the provided model parameters and the underlying mass + distribution. + + The calculated weights, along with the corresponding redshifts of the events, are stored in the HDF5 file for further analysis. + These can be accessed using the Rates class. + + Examples + -------- + >>> transient_population = TransientPopulation('filename.h5', 'transient_name') + >>> transient_population.calculate_cosmic_weights('IllustrisTNG', MODEL_in=DEFAULT_MODEL) + """ - # TODO: drop the envent CO_contact - # TODO: once we trust the redirection to the detached step - # drop also the redirect event + # Set model to DEFAULT or provided MODEL parameters + # Allows for partial model specification + if MODEL_in is None: + MODEL = DEFAULT_MODEL + else: + for key in MODEL_in: + if key not in DEFAULT_MODEL: + raise ValueError(key + " is not a valid parameter name!") - # TODO: for debugging purposes we keep the full channel - self.df_oneline.loc[index,'channel_debug'] = formation_channel - # clean the redirect and CO_contact events - formation_channel = formation_channel.replace('_redirect', '') - formation_channel = formation_channel.replace('_CO_contact', '') - self.df_oneline.loc[index,'channel'] = formation_channel + # write the DEFAULT_MODEL with updates parameters to self.MODEL. + MODEL = DEFAULT_MODEL + MODEL.update(MODEL_in) - if mt_history and 'mt_history_HMS_HMS' not in self.df_oneline: - raise ValueError('mt_history_HMS_HMS not saved in the oneline dataframe!') - else: - # split oRLO1 into oRLO1, oRLO1-contact and oRLO1-reverse - sel = ((self.df_oneline['mt_history_HMS_HMS'] == 'Stable contact phase') & - self.df_oneline['channel'].str.contains('oRLO1')) - self.df_oneline.loc[sel, 'channel'] = self.df_oneline.loc[sel, 'channel'].apply(lambda x: x.replace('oRLO1', 'oRLO1-contact')) - sel = ((self.df_oneline['mt_history_HMS_HMS'] == 'Stable reverse mass-transfer phase') & - self.df_oneline['channel'].str.contains('oRLO1')) - self.df_oneline.loc[sel, 'channel'] = self.df_oneline.loc[sel, 'channel'].apply(lambda x: x.replace('oRLO1', 'oRLO1-reverse')) + path_in_file = ( + "/transients/" + self.transient_name + "/rates/" + SFH_identifier + "/" + ) - # TODO: do the above split for unstable MT as well + with pd.HDFStore(self.filename, mode="a") as store: + if path_in_file + "MODEL" in store.keys(): + store.remove(path_in_file + "MODEL") + if self.verbose: + print("Cosmic weights already computed! Overwriting them!") + if path_in_file + "weights" in store.keys(): + store.remove(path_in_file + "weights") + if path_in_file + "z_events" in store.keys(): + store.remove(path_in_file + "z_events") + if path_in_file + "birth" in store.keys(): + store.remove(path_in_file + "birth") + + self._write_MODEL_data(self.filename, path_in_file, MODEL) + + rates = Rates( + self.filename, self.transient_name, SFH_identifier, verbose=self.verbose + ) + + z_birth = rates.centers_redshift_bins + t_birth = get_cosmic_time_from_redshift(z_birth) + nr_of_birth_bins = len(z_birth) + # write birth to the population file + with pd.HDFStore(self.filename, mode="a") as store: + store.put( + path_in_file + "birth", pd.DataFrame(data={"z": z_birth, "t": t_birth}) + ) + + get_redshift_from_cosmic_time = redshift_from_cosmic_time_interpolator() + indices = self.indices + + # sample the SFH for only the events that are within the Hubble time + # only need to sample the SFH at each metallicity and z_birth + # Not for every event! + SFR_at_z_birth = star_formation_rate(rates.MODEL["SFR"], z_birth) + # get metallicity bin edges + met_edges = rates.edges_metallicity_bins + + # get the fractional SFR at each metallicity and z_birth + fSFR = SFR_Z_fraction_at_given_redshift( + z_birth, + rates.MODEL["SFR"], + rates.MODEL["sigma_SFR"], + met_edges, + rates.MODEL["Z_max"], + rates.MODEL["select_one_met"], + ) + + # simulated mass per given metallicity corrected for the unmodeled + # single and binary stellar mass + M_model = rates.mass_per_metallicity.loc[rates.centers_metallicity_bins / Zsun][ + "underlying_mass" + ].values + + # speed of light + c = const.c.to("Mpc/yr").value # Mpc/yr + + # delta cosmic time bin + deltaT = rates.MODEL["delta_t"] * 10**6 # yr + + for i in tqdm( + range(0, len(indices), self.chunksize), + desc="event loop", + disable=not self.verbose, + ): + selected_indices = ( + self.select(start=i, stop=i + self.chunksize, columns=["index"]) + .index.to_numpy() + .flatten() + ) + if len(selected_indices) == 0: + continue + + # selected_indices = indices[i:i+self.chunksize] + delay_time = ( + self.select( + start=i, stop=i + self.chunksize, columns=["time"] + ).to_numpy() + * 1e-3 + ) # Gyr + + t_events = t_birth + delay_time + hubble_time_mask = t_events <= cosmology.age(1e-08).value * 0.9999999 + + # get the redshift of the events + z_events = np.full(t_events.shape, np.nan) + z_events[hubble_time_mask] = get_redshift_from_cosmic_time( + t_events[hubble_time_mask] + ) + + D_c = get_comoving_distance_from_redshift(z_events) # Mpc + + # the events have to be in solar metallicity + met_events = ( + self.select(start=i, stop=i + self.chunksize, columns=["metallicity"]) + .to_numpy() + .flatten() + * Zsun + ) + + weights = np.zeros((len(met_events), nr_of_birth_bins)) + for i, met in enumerate(rates.centers_metallicity_bins): + mask = met_events == met + weights[mask, :] = ( + 4.0 + * np.pi + * c + * D_c[mask] ** 2 + * deltaT + * (fSFR[:, i] * SFR_at_z_birth) + / M_model[i] + ) # yr^-1 + + with pd.HDFStore(self.filename, mode="a") as store: + store.append( + path_in_file + "weights", + pd.DataFrame(data=weights, index=selected_indices), + format="table", + ) + store.append( + path_in_file + "z_events", + pd.DataFrame(data=z_events, index=selected_indices), + format="table", + ) + return rates + + def plot_efficiency_over_metallicity(self, **kwargs): + """ + Plot the efficiency over metallicity. + Parameters + ---------- + channel : bool, optional + If True, plot the subchannels. Default is False. + """ + if not hasattr(self, "efficiency"): + raise ValueError( + "First you need to compute the efficiency over metallicity!" + ) + plot_pop.plot_merger_efficiency( + self.efficiency.index.to_numpy() * Zsun, self.efficiency, **kwargs + ) + + def plot_delay_time_distribution( + self, metallicity=None, ax=None, bins=100, color="black" + ): + """ + Plot the delay time distribution of the transient population. - def save_intrinsic_pop(self, path='./intrinsic_population_type.h5', pop='DCO'): - """Save intrinsic population. + This method plots the delay time distribution of the transient population. If a specific metallicity is provided, + the delay time distribution of the population at that metallicity will be plotted. Otherwise, the delay time distribution + of the entire population will be plotted. Parameters ---------- - path : str - Path to dataset. + metallicity : float or None + The metallicity value to select a specific population. If None, the delay time distribution of the entire population will be plotted. + ax : matplotlib.axes.Axes or None + The axes object to plot the distribution on. If None, a new figure and axes will be created. + bins : int + The number of bins to use for the histogram. + color : str + The color of the histogram. + + Raises + ------ + ValueError + If the specified metallicity is not present in the population. + + Notes + ----- + - The delay time distribution is normalized by the total mass of the population if no metallicity is specified. + Otherwise, it is normalized by the mass of the population at the specified metallicity. """ - if pop == 'DCO': - if self.df_dco_intrinsic is None: - raise ValueError('Nothing to save!') - else: - self.df_dco_intrinsic.to_hdf(path.replace('type', pop), key='history') - if self.verbose: - print('Intrinsic population successfully saved!') - elif pop == 'GRB': - if self.df_grb_intrinsic is None: - raise ValueError('Nothing to save!') - else: - self.df_grb_intrinsic.to_hdf(path.replace('type', pop), key='history') - if self.verbose: - print('Intrinsic population successfully saved!') + if ax is None: + fig, ax = plt.subplots() + + if metallicity is None: + time = self.select(columns=["time"]).values + time = time * 1e6 # yr + h, bin_edges = np.histogram(time, bins=bins) + h = h / np.diff(bin_edges) / self.mass_per_metallicity["underlying_mass"].sum() + else: - raise ValueError('Population not recognized!') + if not any(np.isclose(metallicity, self.solar_metallicities)): + raise ValueError("The metallicity is not present in the population!") + + time = self.select(columns=['metallicity', 'time']) + time = time[time['metallicity'] == metallicity].drop(columns=['metallicity']).values + time = time * 1e6 # yr + h, bin_edges = np.histogram(time, bins=bins) + h = ( + h + / np.diff(bin_edges) + / self.mass_per_metallicity["underlying_mass"][metallicity] + ) + + ax.step(bin_edges[:-1], h, where="post", color=color) + ax.set_xscale("log") + ax.set_yscale("log") + ax.set_xlabel("Time [yr]") + ax.set_ylabel("Number of events/Msun/yr") - def load_intrinsic_pop(self, path, pop='DCO'): - """Load intrinsic population. + def plot_popsyn_over_grid_slice(self, grid_type, met_Zsun, **kwargs): + """ + Plot the transients over the grid slice. Parameters ---------- - path : str - Path to dataset. + grid_type : str + The type of grid to plot. + met_Zsun : float + The metallicity of the Sun. + **kwargs + Additional keyword arguments to pass to the plot_pop.plot_popsyn_over_grid_slice function. """ - if pop == 'DCO': - if self.df_dco_intrinsic is None: - self.df_dco_intrinsic = pd.read_hdf(path, key='history') - if self.verbose: - print('Intrinsic population successfully loaded!') - else: - raise ValueError('You already have an intrinsic population stored in memory!') - elif pop == 'GRB': - if self.df_grb_intrinsic is None: - self.df_grb_intrinsic = pd.read_hdf(path, key='history') - if self.verbose: - print('Intrinsic population successfully loaded!') - else: - raise ValueError('You already have an intrinsic population stored in memory!') - def save_observable_pop(self, path='./observable_population_type.h5', pop='DCO'): - """Save observable population. + plot_pop.plot_popsyn_over_grid_slice( + pop=self, grid_type=grid_type, met_Zsun=met_Zsun, **kwargs + ) + + def _write_MODEL_data(self, filename, path_in_file, MODEL): + """ + Write the MODEL data to the HDFStore file. Parameters ---------- - path : str - Path to dataset. + filename : str + The path to the HDFStore file. + path_in_file : str + The path within the HDFStore file to store the MODEL data. + MODEL : dict + The MODEL data to be stored. """ - if pop == 'DCO': - if self.df_dco_observable is None: - raise ValueError('Nothing to save!') + with pd.HDFStore(filename, mode="a") as store: + if (MODEL["dlogZ"] is not None) and (not isinstance(MODEL["dlogZ"], float)): + store.put(path_in_file + "MODEL", pd.DataFrame(MODEL)) else: - self.df_dco_observable.to_hdf(path.replace('type', pop), key='history') - if self.verbose: - print('observable population successfully saved!') - elif pop == 'GRB': - if self.df_grb_observable is None: - raise ValueError('Nothing to save!') + store.put(path_in_file + "MODEL", pd.DataFrame(MODEL, index=[0])) + if self.verbose: + print("MODEL written to population file!") + + +class Rates(TransientPopulation): + """Class representing rates of a transient population. + + Attributes + ---------- + SFH_identifier : str + The identifier for the star formation history. + base_path : str + The base path for accessing the rates data. + MODEL : dict + The model data for the star formation history. + weights : pandas.DataFrame + The weights of the transient population. + z_birth : pandas.DataFrame + The redshift of the birth bins. + z_events : pandas.DataFrame + The redshift of the events. + intrinsic_rate_density : pandas.DataFrame + The intrinsic rate density of the transient population. + observable_population_names : list + The names of the observable populations. + edges_metallicity_bins : np.ndarray + The edges of the metallicity bins of the star formation history. + centers_metallicity_bins : np.ndarray + The centers of the metallicity bins of the star formation history. + edges_redshift_bins : np.ndarray + The edges of the redshift bins of the star formation history. + centers_redshift_bins : np.ndarray + The centers of the redshift bins of the star formation history. + + """ + + def __init__(self, filename, transient_name, SFH_identifier, verbose=False, chunksize=100000): + """ + Initialize the Rates object. + + This method initializes a Rates object by linking it to the population file + with the specified transient name and star formation history identifier. + The path in the file is '/transients/{transient_name}/rates/{SFH_identifier}'. + + Parameters: + ----------- + filename : str + The path to the file containing the transient population data. + transient_name : str + The name of the transient. + SFH_identifier : str + The identifier for the star formation history. + verbose : bool, optional + Whether to print verbose output. Default is False. + chunksize : int, optional + The chunksize to use when reading the population file (Default is 100000). + """ + + super().__init__(filename, transient_name, verbose=verbose, chunksize=chunksize) + self.SFH_identifier = SFH_identifier + + self.base_path = ( + "/transients/" + self.transient_name + "/rates/" + self.SFH_identifier + "/" + ) + + with pd.HDFStore(self.filename, mode="r") as store: + if ( + "/transients/" + + self.transient_name + + "/rates/" + + self.SFH_identifier + + "/MODEL" + not in store.keys() + ): + raise ValueError( + f"{self.SFH_identifier} is not a valid SFH_identifier in {filename}!" + ) + + # load in the SFH_model + self._read_MODEL_data(self.filename) + + def _read_MODEL_data(self, filename): + """ + Reads the MODEL data from the specified file. + + Parameters + ---------- + filename : str + The path to the file containing the MODEL data. + """ + with pd.HDFStore(filename, mode="r") as store: + tmp_df = store[self.base_path + "MODEL"] + if len(tmp_df) > 1: + self.MODEL = tmp_df.iloc[0].to_dict() + self.MODEL["dlogZ"] = [tmp_df["dlogZ"].min(), tmp_df["dlogZ"].max()] else: - self.df_grb_observable.to_hdf(path.replace('type', pop), key='history') - if self.verbose: - print('observable population successfully saved!') - else: - raise ValueError('Population not recognized!') + self.MODEL = tmp_df.iloc[0].to_dict() + + if self.verbose: + print("MODEL read from population file!") + + @property + def weights(self): + """ + Retrieves the weights from the HDFStore. + + The rows are indexed by the binary index of the events, while to columns are indexed by the redshift of the birth bins. + + Returns + ------- + pandas.DataFrame + The weights DataFrame. + """ + with pd.HDFStore(self.filename, mode="r") as store: + return store[self.base_path + "weights"] + + @property + def z_birth(self): + """ + Retrieves the 'birth' data from the HDFStore. + + The 'birth' DataFrame contains the redshift and age of the Universe of the birth bins with columns 'z' and 't'. + + Returns + ------- + pandas.DataFrame + The 'birth' DataFrame, which contains the redshift and age of the Universe of the birth bins. + """ + with pd.HDFStore(self.filename, mode="r") as store: + return store[self.base_path + "birth"] + + @property + def z_events(self): + """ + Returns the 'z_events' data from the HDFStore. + + The 'z_events' data contains the redshifts at which the events occur. + The rows of the returned DataFrame are indexed by the binary index of the events. + The columns of the returned DataFrame are indexed by the redshift of the birth bins. + + Returns + ------- + pandas.DataFrame + The 'z_events' data from the HDFStore. + + """ + with pd.HDFStore(self.filename, mode="r") as store: + return store[self.base_path + "z_events"] + + def select_rate_slice(self, key, start=None, stop=None): + """Selects a slice of a rates dataframe at key. - def load_observable_pop(self, path, pop='DCO'): - """Load observable population. + This method allows you to select a slice in rows from the diffferent rates dataframes. + The slice is selected based on the start and stop indices. + The key specifies which rates dataframe to select, and must be one of ['weights', 'z_events', 'birth']. Parameters ---------- - path : str - Path to dataset. + key : str + The key to select the slice from. Must be one of ['weights', 'z_events', 'birth']. + start : int, optional + The starting index of the slice. Defaults to None. + stop : int, optional + The ending index of the slice. Defaults to None. + Returns + ------- + pandas.DataFrame + The selected slice of rates. + + Raises + ------ + ValueError + If the key is not one of ['weights', 'z_events', 'birth']. """ - if pop == 'DCO': - if self.df_dco_observable is None: - self.df_dco_observable = pd.read_hdf(path, key='history') - if self.verbose: - print('observable population successfully loaded!') - else: - raise ValueError('You already have an observable population stored in memory!') - elif pop == 'GRB': - if self.df_grb_observable is None: - self.df_grb_observable = pd.read_hdf(path, key='history') - if self.verbose: - print('observable population successfully loaded!') - else: - raise ValueError('You already have an observable population stored in memory!') - else: - raise ValueError('Population not recognized!') + if key not in ["weights", "z_events", "birth"]: + raise ValueError("key not in [weights, z_events, birth]") - def plot_merger_efficiency(self, **kwargs): - """Plot merger rate efficinty.""" - if self.met_merger_efficiency is None or self.merger_efficiency is None: - raise ValueError('First you need to compute the merger efficinty!') - plot_pop.plot_merger_efficiency(self.met_merger_efficiency, self.merger_efficiency, **kwargs) + with pd.HDFStore(self.filename, mode="r") as store: + return store.select(self.base_path + key, start=start, stop=stop) - def plot_hist_properties(self, var, intrinsic=False, observable=False, pop=None, **kwargs): - """Plot histogram of intrinsic/observable properites. + def calculate_intrinsic_rate_density(self, mt_channels=False): + """ + Compute the intrinsic rate density over redshift of the transient population. + + Besides returning the intrinsic rate density, this method also stores the results in the HDF5 file for further analysis. + This can be accessed using the intrinsic_rate_density attribute of the Rates class. Parameters ---------- - var : str - Property to plot stored in intrinsic/observable dataframe. - intrinsic : bool - `True` if you want to deplay the intrisc population. - observable : bool - `True` if you want to deplay the observable population. - **kwargs : dict - ploting arguments - - """ - if pop == 'DCO': - if self.df_dco_intrinsic is None and self.df_dco_observable is None: - raise ValueError('First you need to compute the merger rate density!') - if intrinsic: - df_intrinsic = self.df_dco_intrinsic - else: - df_intrinsic = None - if observable: - df_observable = self.df_dco_observable - else: - df_observable = None - elif pop == 'GRB': - if self.df_grb_intrinsic is None and self.df_grb_observable is None: - raise ValueError('First you need to compute the merger rate density!') - if intrinsic: - df_intrinsic = self.df_grb_intrinsic - else: - df_intrinsic = None - if observable: - df_observable = self.df_grb_observable - else: - df_observable = None + mt_channels : bool, optional + Flag indicating whether to calculate the intrinsic rate density for each channel separately. Default is False. + + Returns + ------- + pandas.DataFrame + DataFrame containing the intrinsic rate density values. + """ + z_events = self.z_events.to_numpy() + weights = self.weights.to_numpy() + z_horizon = self.edges_redshift_bins + n = len(z_horizon) + + if mt_channels: + channels = self.select(columns=["channel"]) + unique_channels = np.unique(channels) else: - raise ValueError('Population not recognized!') - plot_pop.plot_hist_properties(var, df_intrinsic=df_intrinsic, df_observable=df_observable, pop=pop, **kwargs) - - def plot_rate_density(self, DCO=False, GRB=False, **kwargs): - """Plot DCO and GRB rate densities.""" - if not DCO and not GRB: - raise ValueError('You need to choose at least one population to plot!') - if DCO: - if self.dco_z_rate_density is None or self.dco_rate_density is None: - raise ValueError('First you need to compute the merger rate density!') + unique_channels = [] + + intrinsic_rate_density = pd.DataFrame(index=z_horizon[:-1], columns=["total"]) + + normalisation = np.zeros(n - 1) + + for i in tqdm(range(1, n), total=n - 1, disable=not self.verbose): + normalisation[i - 1] = get_shell_comoving_volume( + z_horizon[i - 1], z_horizon[i], "infinite" + ) + + for i in tqdm(range(1, n), total=n - 1, disable=not self.verbose): + mask = (z_events > z_horizon[i - 1]) & (z_events <= z_horizon[i]) + for ch in unique_channels: + mask_ch = channels.to_numpy() == ch + intrinsic_rate_density.loc[z_horizon[i - 1], ch] = ( + np.nansum(weights[mask & mask_ch]) / normalisation[i - 1] + ) + + intrinsic_rate_density.loc[z_horizon[i - 1], "total"] = ( + np.nansum(weights[mask]) / normalisation[i - 1] + ) + + with pd.HDFStore(self.filename, mode="a") as store: + store.put(self.base_path + "intrinsic_rate_density", intrinsic_rate_density) + + return intrinsic_rate_density + + def calculate_observable_population(self, observable_func, observable_name): + """ + Calculate an observable population. + + The observable population is calculated based on the provided observable function. + It should recalculate your weights based on an observability probability given certain transinet parameters. + + This function requires the following input chunks: + 1. transient_pop_chunk + 2. z_events_chunk + 3. weights_chunk + The observable function should output a DataFrame with the same shape as the weights_chunk. + + The observable population is stored in the HDF5 file at + the location '/transients/{transient_name}/rates/observable/{observable_name}'. + + Parameters + ---------- + observable_func : function + The observability function that takes the TransientPopulation as input. + observable_name : str + The name of the observable. + + Note + ---- + - If the observable population already exists in the file, it will be overwritten. + """ + + with pd.HDFStore(self.filename, mode="a") as store: + # remove the observable population if it already exists + if ( + "/transients/" + + self.transient_name + + "/rates/observable/" + + observable_name + in store.keys() + ): + if self.verbose: + print("Overwriting observable population!") + del store[ + "transients/" + + self.transient_name + + "/rates/observable/" + + observable_name + ] + + # loop over the transient population and calculate the new weights, while writing to the file + for i in tqdm( + range(0, len(self), self.chunksize), + total=len(self) // self.chunksize, + disable=not self.verbose, + ): + transient_pop_chunk = self.select(start=i, stop=i + self.chunksize) + weights_chunk = self.select_rate_slice( + "weights", start=i, stop=i + self.chunksize + ) + z_events_chunk = self.select_rate_slice( + "z_events", start=i, stop=i + self.chunksize + ) + new_weights = observable_func( + transient_pop_chunk, z_events_chunk, weights_chunk + ) + + with pd.HDFStore(self.filename, mode="a") as store: + store.append( + "transients/" + + self.transient_name + + "/rates/observable/" + + observable_name, + new_weights, + format="table", + ) + + def observable_population(self, observable_name): + """Return the observable population based on the provided name. + + This method returns the observable population based on the provided name, + which can take a while to load if the population is large. + It loads the observable population from '/transients/{transient_name}/rates/observable/{observable_name}' in the HDF5 file. + + Parameters + ---------- + observable_name : str + The name of the observable population to return. + + Returns + ------- + pandas.DataFrame + The observable population based on the provided name. + """ + + with pd.HDFStore(self.filename, mode="r") as store: + if ( + "/transients/" + + self.transient_name + + "/rates/observable/" + + observable_name + not in store.keys() + ): + raise ValueError( + f"{observable_name} is not a valid observable population!" + ) else: - z_dco = self.dco_z_rate_density - rate_dco = self.dco_rate_density - else: - z_dco = None - rate_dco = None - if GRB: - if self.grb_z_rate_density is None or self.grb_rate_density is None: - raise ValueError('First you need to compute the GRB rate density!') + return store[ + "transients/" + + self.transient_name + + "/rates/observable/" + + observable_name + ] + + @property + def observable_population_names(self): + """Return the names of the observable populations in the associated file. + + Returns + ------- + list + The names of the observable populations. + """ + + with pd.HDFStore(self.filename, mode="r") as store: + return [ + key.split("/")[-1] + for key in store.keys() + if "/transients/" + self.transient_name + "/rates/observable/" in key + ] + + @property + def intrinsic_rate_density(self): + """Return the intrinsic rate density of the transient population at the specified SFH_identifier and transient_name. + + The data is read from the HDF5 file at '/transients/{transient_name}/rates/{SFH_identifier}/intrinsic_rate_density'. + + Returns + ------- + pandas.DataFrame + The intrinsic rate density of the transient population. + + """ + with pd.HDFStore(self.filename, mode="r") as store: + if self.base_path + "intrinsic_rate_density" not in store.keys(): + raise ValueError( + "First you need to compute the intrinsic rate density!" + ) else: - z_grb = self.grb_z_rate_density - rate_grb = self.grb_rate_density + return store[self.base_path + "intrinsic_rate_density"] + + def plot_hist_properties( + self, prop, intrinsic=True, observable=None, bins=50, channel=None, **kwargs + ): + """Plot a histogram of a given property available in the transient population. + + This method plots a histogram of a given property available in the transient population. + The property can be intrinsic or observable, and the histogram can be plotted for a specific channel if provided. + + Parameters + ---------- + prop : str + The property to plot the histogram for. + intrinsic : bool, optional + If True, plot the intrinsic property. Default is True. + observable : str, optional + The observable population name to plot the histogram for. Default is None. + bins : int, optional + The number of bins to use for the histogram. Default is 50. + channel : str, optional + The channel to plot the histogram for. Default is None. + A channel column must be present in the transient population. + **kwargs + Additional keyword arguments to pass to the plot + + Raises + ------ + ValueError + If the specified property is not a valid property in the transient population. + If the specified observable is not a valid observable population. + If the specified channel is not present in the transient population. + + """ + + if prop not in self.columns: + raise ValueError( + f"{prop} is not a valid property in the transient population!" + ) + + # get the property and its associated weights in the population. + + df = self.select(columns=[prop]) + kwargs['xlabel'] = prop + df["property"] = df[prop] + del df[prop] + if intrinsic: + df["intrinsic"] = np.sum(self.weights, axis=1) + + if observable is not None: + with pd.HDFStore(self.filename, mode="r") as store: + if ( + "/transients/" + + self.transient_name + + "/rates/observable/" + + observable + not in store.keys() + ): + raise ValueError( + f"{observable} is not a valid observable population!" + ) + else: + df["observable"] = np.sum( + store[ + "transients/" + + self.transient_name + + "/rates/observable/" + + observable + ], + axis=1, + ) + if channel is not None: + df["channel"] = self.select(columns=["channel"]) + df = df[df["channel"] == channel] + if len(df) == 0: + raise ValueError( + f"{channel} is not present in the transient population!" + ) + plot_pop.plot_hist_properties(df, bins=bins, **kwargs) + else: - z_grb = None - rate_grb = None - plot_pop.plot_rate_density(z_dco, rate_dco, z_grb, rate_grb, **kwargs) + # plot the histogram using plot_pop.plot_hist_properties + plot_pop.plot_hist_properties(df, bins=bins, **kwargs) - def plot_popsyn_over_grid_slice(self, grid_type, met_Zsun, **kwargs): - """Plot popsyn over grid slice.""" - plot_pop.plot_popsyn_over_grid_slice(self, grid_type, met_Zsun, **kwargs) + def plot_intrinsic_rate(self, channels=False, **kwargs): + """Plot the intrinsic rate density of the transient population.""" + + plot_pop.plot_rate_density(self.intrinsic_rate_density, channels=channels, **kwargs) + + @property + def edges_metallicity_bins(self): + """Return the edges of the metallicity bins. + + Returns + ------- + array float + Returns the edges of all metallicity bins. We assume metallicities + were binned in log-space. + + """ + met_val = np.log10(self.centers_metallicity_bins) + bin_met = np.zeros(len(met_val) + 1) + # if more than one metallicty bin + if len(met_val) > 1: + bin_met[0] = met_val[0] - (met_val[1] - met_val[0]) / 2.0 + bin_met[-1] = met_val[-1] + (met_val[-1] - met_val[-2]) / 2.0 + bin_met[1:-1] = met_val[:-1] + (met_val[1:] - met_val[:-1]) / 2.0 + # one metallicty bin + elif len(met_val) == 1: + if self.MODEL["dlogZ"] is None: + bin_met[0] = -9 + bin_met[-1] = 0 + elif isinstance(self.MODEL["dlogZ"], float): + bin_met[0] = met_val[0] - self.MODEL["dlogZ"] / 2.0 + bin_met[-1] = met_val[0] + self.MODEL["dlogZ"] / 2.0 + elif isinstance(self.MODEL["dlogZ"], list) or isinstance( + self.MODEL["dlogZ"], np.array + ): + bin_met[0] = self.MODEL["dlogZ"][0] + bin_met[-1] = self.MODEL["dlogZ"][1] + + return 10**bin_met + + @property + def centers_metallicity_bins(self): + """Return the centers of the metallicity bins. + + Returns + ------- + array float + Returns sampled metallicities of the population. This corresponds + to the center of each metallicity bin. + """ + return np.sort(self.metallicities) + + @property + def edges_redshift_bins(self): + """Compute redshift bin edges. + + Returns + ------- + array floats + We devide the cosmic time history of the Universe in equally spaced + bins of cosmic time of self.MODEL['delta_t'] (100 Myr default) an compute the + redshift corresponding to edges of these bins. + + """ + return get_redshift_bin_edges(self.MODEL["delta_t"]) + + @property + def centers_redshift_bins(self): + """Compute redshift bin centers. + + Returns + ------- + array floats + We devide the cosmic time history of the Universe in equally spaced + bins of cosmic time of self.MODEL['delta_t'] (100 Myr default) an compute the + redshift corresponding to center of these bins. + + """ + return get_redshift_bin_centers(self.MODEL["delta_t"]) diff --git a/posydon/popsyn/transient_select_funcs.py b/posydon/popsyn/transient_select_funcs.py new file mode 100644 index 0000000000..d02363496f --- /dev/null +++ b/posydon/popsyn/transient_select_funcs.py @@ -0,0 +1,250 @@ +import numpy as np +import pandas as pd +import posydon.popsyn.selection_effects as selection_effects +from posydon.config import PATH_TO_POSYDON_DATA +import os +from tqdm import tqdm + + +import warnings +# This is to suppress the performance warnings from pandas +# These warnings are not important for the user +# We should alter the code to remove these warnings, but for now we suppress them +warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning) + +PATH_TO_PDET_GRID = os.path.join(PATH_TO_POSYDON_DATA, 'POSYDON_data/selection_effects/pdet_grid.hdf5') + +def GRB_selection(history_chunk, oneline_chunk, formation_channels_chunk=None, S1_S2='S1'): + """A GRB selection function to create a transient population of LGRBs. + + This function requires a wrapper function to be used to create a transient population, because + of the extra parameters that are needed to be passed to the function (S1_S2). + + >>> def GRB_selection_wrapper(history_chunk, oneline_chunk, formation_channels_chunk=None): + ... return GRB_selection(history_chunk, oneline_chunk, formation_channels_chunk, S1_S2='S1') + + This is an example function for selecting LGRBs and some of their properties. + Additional properties from the history or oneline can be added to the transient population. + The output dataframe contains the following columns: + - time : the time of the event + - metallicity : the metallicity of the event + - S1_m_disk_radiated : the mass of the disk radiated by the first star + - S2_m_disk_radiated : the mass of the disk radiated by the second star + + Columns with pre and post SN data: + - S1_mass : the mass of the first star + - S2_mass : the mass of the second star + - S1_spin : the spin of the first star + - S2_spin : the spin of the second star + - orbital_period : the orbital period of the binary + - eccentricity : the eccentricity of the binary + + Parameters + ---------- + history_chunk : pd.DataFrame + The history chunk of the population. + oneline_chunk : pd.DataFrame + The oneline chunk of the population. + formation_channels_chunk : pd.DataFrame (can be made optional) + The formation pathway chunk of the population. + S1_S2 : str + The star that will be the progenitor of the GRB. Can be either 'S1' or 'S2'. + + Returns + ------- + pd.DataFrame + A DataFrame containing the transient population of LGRBs with the columns mentioned above. + + Example + ------- + + >>> GRB_selection_wrapper(pop.history[0], pop.oneline[0], pop.formation_channels.loc[0]) + >>> pop.create_transient_population(GRB_selection_wrapper, name='GRB_S1') + """ + columns_pre_post = ['orbital_period', 'eccentricity', 'S1_spin', 'S2_spin'] + columns = [] + oneline_columns = ['metallicity', 'S1_m_disk_radiated', 'S2_m_disk_radiated'] + + if S1_S2 == 'S1': + indices_selection = oneline_chunk.index[oneline_chunk['S1_m_disk_radiated'] > 0.0].to_numpy() + oneline_chunk = oneline_chunk.drop(columns=['S2_m_disk_radiated']) + elif S1_S2 == 'S2': + indices_selection = oneline_chunk.index[oneline_chunk['S2_m_disk_radiated'] > 0.0].to_numpy() + oneline_chunk = oneline_chunk.drop(columns=['S1_m_disk_radiated']) + else: + raise ValueError('S1_S2 must be either S1 or S2') + # no events in this chunk + if len(indices_selection) == 0: + return pd.DataFrame() + # filter out the events that are not relevant for the LGRB formation + selection = history_chunk.loc[indices_selection] + if S1_S2 == 'S1': + S_mask = (selection['S1_state'] == 'BH') & (selection['S1_state'] != 'BH').shift(1) & (selection['step_names'] == 'step_SN') + elif S1_S2 == 'S2': + S_mask = (selection['S2_state'] == 'BH') & (selection['S2_state'] != 'BH').shift(1) & (selection['step_names'] == 'step_SN') + + GRB_df_synthetic = pd.DataFrame(index=indices_selection) + # Pre and post SN data + post_SN_hist = selection[S_mask] + pre_mask = S_mask.shift(-1, fill_value=False) + pre_SN_hist = selection[pre_mask] + + if S1_S2 == 'S1': + columns_pre_post.append('S1_mass') + columns.append('S2_mass') + elif S1_S2 == 'S2': + columns_pre_post.append('S2_mass') + columns.append('S1_mass') + + + for col in columns_pre_post: + GRB_df_synthetic[col+'_preSN'] = pre_SN_hist[col].values + GRB_df_synthetic[col+'_postSN'] = post_SN_hist[col].values + + # unchanged columns + for col in columns: + GRB_df_synthetic[col] = post_SN_hist[col].values + + # oneline data + for col in oneline_chunk.columns: + GRB_df_synthetic[col] = oneline_chunk.loc[indices_selection][col].values + + if any(formation_channels_chunk != None): + formation_channels_chunk = formation_channels_chunk.loc[indices_selection] + if S1_S2 == 'S1': + GRB_df_synthetic['channel'] = formation_channels_chunk['channel'].str.split('_CC1').str[0].apply(lambda x: x+'_CC1') + elif S1_S2 == 'S2': + GRB_df_synthetic['channel'] = formation_channels_chunk['channel'].str.split('_CC2').str[0].apply(lambda x: x+'_CC2') + + # calculate the time! + GRB_df_synthetic['time'] = post_SN_hist['time'].values * 1e-6 # convert to Myr + + return GRB_df_synthetic + + +def chi_eff(m_1, m_2, a_1, a_2, tilt_1, tilt_2): + '''Calculate the effective spin of two masses.''' + return (m_1*a_1*np.cos(tilt_1)+m_2*a_2*np.cos(tilt_2))/(m_1+m_2) + +def m_chirp(m_1, m_2): + '''Calculate the chirp mass of two masses.''' + return (m_1*m_2)**(3./5)/(m_1+m_2)**(1./5) + +def mass_ratio(m_1, m_2): + '''Calculate the mass ratio of two masses.''' + q = m_2/m_1 + q[q>1.] = 1./q[q>1.] + return q + +def BBH_selection_function(history_chunk, oneline_chunk, formation_channels_chunk=None): + '''A BBH selection function to create a transient population of BBHs mergers. + + This is an example function for selecting BBH mergers and some of their properties. + Additional properties from the history or oneline can be added to the transient population. + The output dataframe contains the following columns: + - time : the time of the event + - metallicity : the metallicity of the event + All other columns are optional. + + Parameters + ---------- + history_chunk : pd.DataFrame + The history chunk of the DCO population. + oneline_chunk : pd.DataFrame + The oneline chunk of the DCO population. + formation_channels_chunk : pd.DataFrame (can be made optional) + The formation pathway chunk of the DCO population. + + Returns + ------- + df_transients : pd.DataFrame + A DataFrame containing the transient population of BBHs. + This DataFrame contains the following columns: + - time : the time of the event + - metallicity : the metallicity of the event + + ''' + + indices = oneline_chunk.index.to_numpy() + df_transients = pd.DataFrame(index = indices) + + df_transients['time'] = history_chunk[history_chunk['event'] == 'END']['time'] * 1e-6 #Myr + mask = (history_chunk['S1_state'] == 'BH') & (history_chunk['S2_state'] == 'BH') & (history_chunk['step_names'] == 'step_SN') & (history_chunk['state'] == 'detached') + + df_transients['t_inspiral'] = df_transients['time'] - history_chunk[mask]['time']*1e-6 + df_transients['metallicity'] = oneline_chunk['metallicity'] + df_transients['S1_state'] = history_chunk[mask]['S1_state'] + df_transients['S2_state'] = history_chunk[mask]['S2_state'] + df_transients['S1_mass'] = history_chunk[mask]['S1_mass'] + df_transients['S2_mass'] = history_chunk[mask]['S2_mass'] + df_transients['S1_spin'] = history_chunk[mask]['S1_spin'] + df_transients['S2_spin'] = history_chunk[mask]['S2_spin'] + df_transients['S1_spin_orbit_tilt'] = oneline_chunk['S1_spin_orbit_tilt'] + df_transients['S2_spin_orbit_tilt'] = oneline_chunk['S2_spin_orbit_tilt'] + df_transients['orbital_period'] = history_chunk[mask]['orbital_period'] + df_transients['chirp_mass'] = m_chirp(history_chunk[mask]['S1_mass'], history_chunk[mask]['S2_mass']) + df_transients['mass_ratio'] = mass_ratio(history_chunk[mask]['S1_mass'], history_chunk[mask]['S2_mass']) + df_transients['chi_eff'] = chi_eff(history_chunk[mask]['S1_mass'], history_chunk[mask]['S2_mass'], history_chunk[mask]['S1_spin'], history_chunk[mask]['S2_spin'], oneline_chunk['S1_spin_orbit_tilt'], oneline_chunk['S2_spin_orbit_tilt']) + df_transients['eccentricity'] = history_chunk[mask]['eccentricity'] + + if formation_channels_chunk is not None: + df_transients = pd.concat([df_transients, formation_channels_chunk[['channel']]], axis=1) + + return df_transients + + +def DCO_detactability(sensitivity, transient_pop_chunk, z_events_chunk, z_weights_chunk, verbose=False): + '''Calculate the observability of a DCO population. + + Parameters + ---------- + sensitivity : float + The sensitivity of the detector. + Available sensitivities are (for Hanford, Livingston, Virgo network): + - 'O3actual_H1L1V1' : aligo_O3actual_H1.txt, aligo_O3actual_L1.txt, avirgo_O3actual.txt + - 'O4low_H1L1V1' : aligo_O4low.txt, aligo_O4low.txt, avirgo_O4high_NEW.txt + - 'O4high_H1L1V1' : aligo_O4high.txt, aligo_O4high.txt, avirgo_O4high_NEW.txt + - 'design_H1L1V1' : AplusDesign.txt, AplusDesign.txt, avirgo_O5high_NEW.txt + + GW detector sensitivity and network configuration you want to use, see arXiv:1304.0670v3 + detector sensitivities are taken from: https://dcc.ligo.org/LIGO-T2000012-v2/public + + ''' + available_sensitiveies = ['O3actual_H1L1V1', 'O4low_H1L1V1', 'O4high_H1L1V1', 'design_H1L1V1'] + if sensitivity not in available_sensitiveies: + raise ValueError(f'Unknown sensitivity {sensitivity}. Available sensitivities are {available_sensitiveies}') + else: + sel_eff = selection_effects.KNNmodel(grid_path=PATH_TO_PDET_GRID, + sensitivity_key=sensitivity) + + data_slice = pd.DataFrame() + data_slice['m1'] = transient_pop_chunk['S1_mass'] + + if 'q' in transient_pop_chunk.columns: + data_slice['q'] = transient_pop_chunk['q'] + else: + data_slice['q'] = mass_ratio(transient_pop_chunk['S1_mass'].to_numpy(), transient_pop_chunk['S2_mass'].to_numpy()) + + if 'chi_eff' in transient_pop_chunk.columns: + data_slice['chieff'] = transient_pop_chunk['chi_eff'] + + else: + data_slice['chieff'] = chi_eff(transient_pop_chunk['S1_mass'], + transient_pop_chunk['S2_mass'], + transient_pop_chunk['S1_spin'], + transient_pop_chunk['S2_spin'], + transient_pop_chunk['S1_spin_orbit_tilt'], + transient_pop_chunk['S2_spin_orbit_tilt']) + + detectable_weights = z_weights_chunk.to_numpy() + for i in tqdm(range(z_events_chunk.shape[1]), total=z_events_chunk.shape[1], disable= not verbose): + data_slice['z'] = z_events_chunk.iloc[:,i] + mask = ~np.isnan(data_slice['z']).to_numpy() + if np.sum(mask) == 0: + detectable_weights[mask, i] = 0.0 + else: + detectable_weights[mask, i] = detectable_weights[mask, i] * sel_eff.predict_pdet(data_slice[mask]) + + return pd.DataFrame(detectable_weights, index=z_events_chunk.index, columns=z_events_chunk.columns) + + \ No newline at end of file diff --git a/posydon/tests/popsyn/test_synthetic_population.py b/posydon/tests/popsyn/test_synthetic_population.py new file mode 100644 index 0000000000..60e1a13acf --- /dev/null +++ b/posydon/tests/popsyn/test_synthetic_population.py @@ -0,0 +1,644 @@ +import os +import numpy as np +import pandas as pd +import pytest +from posydon.config import PATH_TO_POSYDON +import tempfile +from posydon.utils.constants import Zsun + +from posydon.popsyn.synthetic_population import ( + PopulationRunner, + History, + Oneline, + PopulationIO, + parameter_array, + Population +) + + +# Test the PopulationRunner class +class TestPopulationRunner: + # Test the initialisation of the PopulationRunner class + def test_init(self): + # Test the initialisation of the PopulationRunner class + poprun = PopulationRunner(PATH_TO_POSYDON+'posydon/popsyn/population_params_default.ini', verbose=True) + + # Check if the verbose attribute is set correctly + assert poprun.verbose == True, 'Verbose attribute is not set correctly' + + # Check if the solar_metallicities attribute is a list + assert isinstance(poprun.solar_metallicities, list), 'solar_metallicities attribute is not a list' + + # Check if the binary_populations attribute is a list + assert isinstance(poprun.binary_populations, list), 'binary_populations attribute is not a list' + + def test_init_invalid_ini_file(self): + with pytest.raises(ValueError): + PopulationRunner('invalid_file') + + def test_single_metallicity(self): + # copy the default ini file to a new file + new_ini_file = 'test_population_params.ini' + with open(PATH_TO_POSYDON+'posydon/popsyn/population_params_default.ini', 'r') as file: + data = file.read() + start = data.find('metallicity') + replace_str = 'metallicity = 0.0001' + data_new = data[:start] + replace_str + data[start+22:] + with open(new_ini_file, 'w') as file: + file.write(data_new) + + poprun = PopulationRunner(new_ini_file) + assert poprun.binary_populations[0].metallicity == 0.0001 + + def test_evolve(self, mocker): + mocker.patch('posydon.popsyn.binarypopulation.BinaryPopulation.evolve', return_value=None) + mocker.patch('posydon.popsyn.binarypopulation.BinaryPopulation.combine_saved_files', return_value=None) + + poprun = PopulationRunner(PATH_TO_POSYDON+'/posydon/popsyn/population_params_default.ini') + # set population to 1 binary + for pop in poprun.binary_populations: + pop.number_of_systems = 1 + + # create a temporary directory with 1e-04_Zsun_batches + os.makedirs('1e-04_Zsun_batches', exist_ok=True) + + poprun.evolve() + assert poprun.binary_populations, 'binary_populations attribute is empty after calling the evolve method' + + + def test_evolve_file_exists(self, mocker): + mocker.patch('posydon.popsyn.binarypopulation.BinaryPopulation.evolve', return_value=None) + mocker.patch('posydon.popsyn.binarypopulation.BinaryPopulation.combine_saved_files', return_value=None) + + # Create a temporary file with the 1e-04_ZSun_population.h5 name + open('1e-04_Zsun_population.h5', 'w').close() + + poprun = PopulationRunner(PATH_TO_POSYDON+'/posydon/popsyn/population_params_default.ini') + # set population to 1 binary + for pop in poprun.binary_populations: + pop.number_of_systems = 1 + + with pytest.raises(FileExistsError): + poprun.evolve() + + + # Test the evolve method + def test_changed_binarypop(self): + poprun = PopulationRunner(PATH_TO_POSYDON+'/posydon/popsyn/population_params_default.ini') + # test + assert poprun.binary_populations[0].metallicity == 0.0001 + # Check if the temp_directory attribute is set correctly + assert poprun.binary_populations[0].kwargs['temp_directory'] == '1e-04_Zsun_batches', 'temp_directory attribute is not set correctly' + + # Check if the binary_populations attribute is not empty after calling the evolve method + assert poprun.binary_populations, 'binary_populations attribute is empty after calling the evolve method' + + @classmethod + def teardown_class(cls): + if os.path.exists('1e-04_Zsun_batches'): + os.rmdir('1e-04_Zsun_batches') + if os.path.exists('test_population_params.ini'): + os.remove('test_population_params.ini') + if os.path.exists('1e-04_Zsun_population.h5'): + os.remove('1e-04_Zsun_population.h5') + + + +# Test the History class +class TestHistory: + + @classmethod + def setup_class(cls): + # Set up a test HDF5 file using pandas HDFStore + cls.filename = 'test_population.h5' + with pd.HDFStore(cls.filename, 'w') as store: + # Create a history dataframe + history_data = pd.DataFrame({'time': [1, 2, 3], 'event': ['ZAMS','oRLO1', 'CEE']}) + store.append('history',history_data, data_columns=True) + + cls.filename2 = 'test_population2.h5' + with pd.HDFStore(cls.filename2, 'w') as store: + # Create a history dataframe + history_data = pd.DataFrame({'time': [1, 2, 3], 'event': ['ZAMS','oRLO1', 'CEE']}) + store.append('history',history_data, data_columns=True) + + @classmethod + def teardown_class(cls): + os.remove(cls.filename) + + def setup_method(self): + self.history = History(self.filename, verbose=False, chunksize=10000) + + def test_init(self): + history = History(self.filename2, verbose=True, chunksize=10000) + assert history.filename == self.filename2, 'Filename is not set correctly' + assert history.verbose == True, 'Verbose attribute is not set correctly' + assert history.chunksize == 10000, 'Chunksize attribute is not set correctly' + + expected_lengths = pd.DataFrame(index=[0, 1, 2],data={'index': [1, 1, 1]}) + expected_lengths.index.name = 'index' + pd.testing.assert_frame_equal(history.lengths, expected_lengths, 'Lengths attribute is not equal to the expected dataframe') + + assert history.number_of_systems == 3, 'Number of systems attribute is not None' + assert history.columns.to_list() == ['time', 'event'], 'Columns attribute is not None' + + assert isinstance(history.indices, np.ndarray), 'Indices attribute is not an ndarray' + np.testing.assert_array_equal(history.indices, np.array([0, 1, 2]), 'Indices attribute is not equal to the expected list') + + with pytest.raises(FileNotFoundError): + History('invalid_filename.h5', verbose=False, chunksize=10000) + + def test_init_verbose_true(self): + history = History(self.filename, chunksize=10000) + assert history.verbose == False, 'Verbose attribute is not set correctly' + + + def test_getitem_single_index(self): + df = self.history[0] + assert isinstance(df, pd.DataFrame), 'Returned object is not a DataFrame' + assert len(df) == 1, 'Returned DataFrame does not have the correct length' + + + def test_getitem_multiple_indices(self): + df = self.history[[0, 1, 2]] + assert isinstance(df, pd.DataFrame), 'Returned object is not a DataFrame' + assert len(df) == 3, 'Returned DataFrame does not have the correct length' + + def test_getitem_index_array(self): + indices = np.array([0, 1, 2]) + df = self.history[indices] + assert isinstance(df, pd.DataFrame), 'Returned object is not a DataFrame' + assert len(df) == 3, 'Returned DataFrame does not have the correct length' + + def test_getitem_single_column(self): + column = 'time' + df = self.history[column] + assert isinstance(df, pd.DataFrame), 'Returned object is not a DataFrame' + assert len(df.columns) == 1, 'Returned DataFrame does not have the correct number of columns' + + def test_getitem_invalid_column(self): + column = 'invalid_column' + with pytest.raises(ValueError): + self.history[column] + + def test_getitem_invalid_keys(self): + columns = ['time', 'invalid_column'] + with pytest.raises(ValueError): + self.history[columns] + + def test_getitem_boolean_mask_numpy(self): + mask = (self.history['time'] > 1).to_numpy() + df = self.history[mask] + assert isinstance(df, pd.DataFrame), 'Returned object is not a DataFrame' + + def test_getitem_boolean_mask_pandas(self): + mask = self.history['time'] > 1 + df = self.history[mask] + assert isinstance(df, pd.DataFrame), 'Returned object is not a DataFrame' + + def test_getitem_multiple_columns(self): + columns = ['time', 'event'] + df = self.history[columns] + assert isinstance(df, pd.DataFrame), 'Returned object is not a DataFrame' + assert len(df.columns) == 2, 'Returned DataFrame does not have the correct number of columns' + + def test_getitem_invalid_key(self): + with pytest.raises(ValueError): + self.history[{1: 2}] + + def test_len(self): + length = len(self.history) + assert isinstance(length, int), 'Returned object is not an integer' + assert length == 3, 'Returned length is not correct' + + def test_head(self): + n = 2 + df = self.history.head(n) + assert isinstance(df, pd.DataFrame), 'Returned object is not a DataFrame' + assert len(df) == n, 'Returned DataFrame does not have the correct length' + + def test_tail(self): + n = 2 + df = self.history.tail(n) + assert isinstance(df, pd.DataFrame), 'Returned object is not a DataFrame' + assert len(df) == n, 'Returned DataFrame does not have the correct length' + + def test_repr(self): + representation = self.history.__repr__() + assert isinstance(representation, str), 'Returned object is not a string' + + def test_repr_html(self): + html_representation = self.history._repr_html_() + assert isinstance(html_representation, str), 'Returned object is not a string' + + + def test_select(self): + df = self.history.select(where="time > 1", start=0, stop=10, columns=['event', 'time']) + assert isinstance(df, pd.DataFrame), 'Returned object is not a DataFrame' + assert len(df.columns) == 2, 'Returned DataFrame does not have the correct number of columns' + assert len(df) == 2, 'Returned DataFrame does not have the correct length' + + + +# Test the Oneline class +class TestOneline: + + @classmethod + def setup_class(cls): + # Set up a test HDF5 file using pandas HDFStore + cls.filename = 'test_oneline.h5' + with pd.HDFStore(cls.filename, 'w') as store: + # Create a oneline dataframe + oneline_data = pd.DataFrame({'time': [1, 2, 3], 'S1_mass_i': ['30','30', '70']}) + store.append('oneline', oneline_data, data_columns=True) + + def setup_method(self): + self.oneline = Oneline(self.filename, verbose=False, chunksize=10000) + + def test_init(self): + oneline = Oneline(self.filename, verbose=True, chunksize=5000) + + assert oneline.filename == self.filename, 'Filename is not set correctly' + assert oneline.verbose == True, 'Verbose attribute is not set correctly' + assert oneline.chunksize == 5000, 'Chunksize attribute is not set correctly' + assert oneline.number_of_systems == 3, 'Number of systems attribute is not set correctly' + assert oneline.columns.to_list() == ['time', 'S1_mass_i'], 'Columns attribute is not set correctly' + assert oneline.number_of_systems == 3, 'Number of systems attribute is not set correctly' + + assert isinstance(oneline.indices, np.ndarray), 'Indices attribute is not an ndarray' + np.testing.assert_array_equal(oneline.indices, np.array([0, 1, 2]), 'Indices attribute is not equal to the expected list') + + with pytest.raises(FileNotFoundError): + Oneline('invalid_filename.h5', verbose=False, chunksize=10000) + + def test_getitem_single_index(self): + df = self.oneline[0] + assert isinstance(df, pd.DataFrame), 'Returned object is not a DataFrame' + assert len(df) == 1, 'Returned DataFrame does not have the correct length' + + def test_getitem_multiple_indices(self): + df = self.oneline[[0, 1, 2]] + assert isinstance(df, pd.DataFrame), 'Returned object is not a DataFrame' + assert len(df) == 3, 'Returned DataFrame does not have the correct length' + + def test_getitem_index_array(self): + indices = np.array([0, 1, 2]) + df = self.oneline[indices] + assert isinstance(df, pd.DataFrame), 'Returned object is not a DataFrame' + assert len(df) == 3, 'Returned DataFrame does not have the correct length' + pd.testing.assert_frame_equal(df, + pd.DataFrame({'time': [1, 2, 3], + 'S1_mass_i': ['30','30', '70']}), + 'Returned DataFrame is not equal to the expected DataFrame') + + def test_getitem_slice(self): + df = self.oneline[0:2] + assert isinstance(df, pd.DataFrame), 'Returned object is not a DataFrame' + assert len(df) == 2, 'Returned DataFrame does not have the correct length' + pd.testing.assert_frame_equal(df, + pd.DataFrame({'time': [1, 2], + 'S1_mass_i': ['30','30']}),) + def test_getitem_endslice(self): + df = self.oneline[:2] + assert isinstance(df, pd.DataFrame), 'Returned object is not a DataFrame' + assert len(df) == 2, 'Returned DataFrame does not have the correct length' + pd.testing.assert_frame_equal(df, + pd.DataFrame({'time': [1, 2], + 'S1_mass_i': ['30','30']}),) + def test_getitem_beginslice(self): + df = self.oneline[1:] + assert isinstance(df, pd.DataFrame), 'Returned object is not a DataFrame' + assert len(df) == 2, 'Returned DataFrame does not have the correct length' + pd.testing.assert_frame_equal(df, + pd.DataFrame(index=[1, 2], + data={'time': [2, 3], + 'S1_mass_i': ['30', '70']}),) + def test_getitem_float_indices(self): + with pytest.raises(ValueError): + self.oneline[[0.5, 1.2]] + + + + def test_getitem_single_column(self): + column = 'time' + df = self.oneline[column] + assert isinstance(df, pd.DataFrame), 'Returned object is not a DataFrame' + assert len(df.columns) == 1, 'Returned DataFrame does not have the correct number of columns' + + def test_getitem_boolean_mask_numpy(self): + mask = (self.oneline['time'] > 1).to_numpy().flatten() + df = self.oneline[mask] + assert isinstance(df, pd.DataFrame), 'Returned object is not a DataFrame' + + def test_getitem_boolean_mask_pandas(self): + mask = self.oneline['time'] > 1 + print(mask) + df = self.oneline[mask] + assert isinstance(df, pd.DataFrame), 'Returned object is not a DataFrame' + + def test_getitem_multiple_columns(self): + columns = ['time', 'S1_mass_i'] + df = self.oneline[columns] + assert isinstance(df, pd.DataFrame), 'Returned object is not a DataFrame' + assert len(df.columns) == 2, 'Returned DataFrame does not have the correct number of columns' + + def test_getitems_multiple_columns_invalid(self): + columns = ['time', 'invalid_column'] + with pytest.raises(ValueError): + self.oneline[columns] + + def test_getitem_invalid_key_type(self): + with pytest.raises(ValueError): + self.oneline[{1: 2}] + + def test_getitem_invalid_key(self): + with pytest.raises(ValueError): + self.oneline['invalid_key'] + + def test_len(self): + length = len(self.oneline) + assert isinstance(length, int), 'Returned object is not an integer' + assert length == 3, 'Returned length is not correct' + + def test_head(self): + n = 2 + df = self.oneline.head(n) + assert isinstance(df, pd.DataFrame), 'Returned object is not a DataFrame' + assert len(df) == n, 'Returned DataFrame does not have the correct length' + + def test_tail(self): + n = 2 + df = self.oneline.tail(n) + assert isinstance(df, pd.DataFrame), 'Returned object is not a DataFrame' + assert len(df) == n, 'Returned DataFrame does not have the correct length' + + def test_repr(self): + representation = self.oneline.__repr__() + assert isinstance(representation, str), 'Returned object is not a string' + + def test_repr_html(self): + html_representation = self.oneline._repr_html_() + assert isinstance(html_representation, str), 'Returned object is not a string' + + def test_select(self): + df = self.oneline.select(where="time > 1", start=0, stop=10, columns=['S1_mass_i', 'time']) + assert isinstance(df, pd.DataFrame), 'Returned object is not a DataFrame' + assert len(df.columns) == 2, 'Returned DataFrame does not have the correct number of columns' + assert len(df) == 2, 'Returned DataFrame does not have the correct length' + + + @classmethod + def teardown_class(cls): + os.remove(cls.filename) + +# Test the PopulationIO class +class TestPopulationIO: + + def setup_method(self): + self.filename = "test_population.h5" + + def teardown_method(self): + if os.path.exists(self.filename): + os.remove(self.filename) + + def test_init(self): + pop_io = PopulationIO() + assert pop_io.verbose == False, "Verbose attribute is not set correctly" + + + def test_invalid_filename(self): + pop_io = PopulationIO() + with pytest.raises(ValueError): + pop_io._load_metadata("invalid_filename") + + def test_save_and_load_mass_per_met(self): + population_io = PopulationIO() + population_io.verbose = True + population_io.mass_per_metallicity = pd.DataFrame({"metallicity": [0.02, 0.04], "mass": [1.0, 2.0]}) + population_io._save_mass_per_metallicity(self.filename) + + loaded_io = PopulationIO() + loaded_io.verbose = True + loaded_io._load_mass_per_metallicity(self.filename) + pd.testing.assert_frame_equal(population_io.mass_per_metallicity, loaded_io.mass_per_metallicity) + + def test_save_and_load_ini_params(self): + population_io = PopulationIO() + population_io.ini_params = {i:10 for i in parameter_array} + population_io._save_ini_params(self.filename) + + loaded_io = PopulationIO() + loaded_io._load_ini_params(self.filename) + + assert population_io.ini_params == loaded_io.ini_params, "Loaded ini_params are not equal to the saved ini_params" + + def test_save_and_load_metadata(self): + pop_io = PopulationIO() + pop_io.verbose = True + pop_io.ini_params = {i:10 for i in parameter_array} + pop_io._save_ini_params(self.filename) + pop_io.mass_per_metallicity = pd.DataFrame({"metallicity": [0.02, 0.04], "mass": [1.0, 2.0]}) + pop_io._save_mass_per_metallicity(self.filename) + + load_io = PopulationIO() + load_io._load_metadata(self.filename) + + assert pop_io.ini_params == load_io.ini_params, "Loaded ini_params are not equal to the saved ini_params" + assert pop_io.mass_per_metallicity.equals(load_io.mass_per_metallicity), "Loaded mass_per_metallicity is not equal to the saved mass_per_metallicity" + + + +class TestPopulation: + def setup_method(self): + pass + + def teardown_method(self): + # Clean up any resources used by the test + pass + + def setup_class(self): + self.filename1 = "no_mass_per_met_population.h5" + self.filename2 = "history_population.h5" + self.filename3 = "oneline_population.h5" + self.history_data = pd.DataFrame({'time': [1, 2, 3], 'event': ['ZAMS','oRLO1', 'CEE']}) + self.oneline_data = pd.DataFrame({'time': [1, 2, 3], 'S1_mass_i': [30, 30, 70], 'S2_mass_i': [30, 30, 70.]}) + self.formation_channels = pd.DataFrame({'channel': ['channel1', 'channel2', 'channel3'], 'channel_debug':['debug1', 'debug2', 'debug3']}) + + # create a file with only history and oneline data + with pd.HDFStore(self.filename1, 'w') as store: + store.append('history',self.history_data, data_columns=True) + store.append('oneline', self.oneline_data, data_columns=True) + + with pd.HDFStore(self.filename2, 'w') as store: + store.append('history', self.history_data, data_columns=True) + + with pd.HDFStore(self.filename3, 'w') as store: + store.append('oneline', self.oneline_data, data_columns=True) + + def teardown_class(self): + if os.path.exists(self.filename1): + os.remove(self.filename1) + if os.path.exists(self.filename2): + os.remove(self.filename2) + if os.path.exists(self.filename3): + os.remove(self.filename3) + + @pytest.fixture + def mass_per_met_pop(self): + self.filename = "mass_per_met_population.h5" + with pd.HDFStore(self.filename, 'w') as store: + store.append('history', self.history_data, data_columns=True) + store.append('oneline', self.oneline_data, data_columns=True) + + pop = Population(self.filename, verbose=True, metallicity=0.02, ini_file=PATH_TO_POSYDON+'/posydon/popsyn/population_params_default.ini') + yield + if os.path.exists(self.filename): + os.remove(self.filename) + + @pytest.fixture + def no_mass_per_met_pop(self): + self.filename = "no_mass_per_met_population.h5" + with pd.HDFStore(self.filename, 'w') as store: + store.append('history', self.history_data, data_columns=True) + store.append('oneline', self.oneline_data, data_columns=True) + yield + if os.path.exists(self.filename): + os.remove(self.filename) + + @pytest.fixture + def mass_per_met_pop_channels(self): + self.filename = "mass_per_met_population.h5" + with pd.HDFStore(self.filename, 'w') as store: + store.append('history', self.history_data, data_columns=True) + store.append('oneline', self.oneline_data, data_columns=True) + store.append('formation_channels', self.formation_channels, data_columns=True) + pop = Population(self.filename, verbose=True, metallicity=0.02, ini_file=PATH_TO_POSYDON+'/posydon/popsyn/population_params_default.ini') + yield + if os.path.exists(self.filename): + os.remove(self.filename) + + @pytest.fixture + def clean_up_selection_file(self): + self.outfile = "test_selection.h5" + yield + if os.path.exists(self.outfile): + os.remove(self.outfile) + + def test_init_invalid_file(self): + with pytest.raises(ValueError): + pop = Population('invalid_filename') + + def test_init_no_history(self): + with pytest.raises(ValueError): + pop = Population(self.filename3) + + def test_init_no_oneline(self): + with pytest.raises(ValueError): + pop = Population(self.filename2) + + def test_init_no_mass_per_met(self): + with pytest.raises(ValueError): + pop = Population(self.filename1, verbose=True) + + + def test_init_mass_per_met_calc(self, no_mass_per_met_pop: None): + pop = Population(self.filename, verbose=True, metallicity=1., ini_file=PATH_TO_POSYDON+'/posydon/popsyn/population_params_default.ini') + # check that the history and oneline data are read correctly + pd.testing.assert_frame_equal(pop.history[:], self.history_data) + pd.testing.assert_frame_equal(pop.oneline[:], self.oneline_data) + assert pop.solar_metallicities == [1.] + assert pop.metallicities == [1*Zsun] + pd.testing.assert_frame_equal(pop.mass_per_metallicity, pd.DataFrame(index=[1.], data={'simulated_mass': [260.], 'underlying_mass': [1462.194834], 'number_of_systems': [3]})) + + pop = Population(self.filename, verbose=True, metallicity=1., ini_file=PATH_TO_POSYDON+'/posydon/popsyn/population_params_default.ini') + # check that the history and oneline data are the same + pd.testing.assert_frame_equal(pop.history[:], self.history_data) + pd.testing.assert_frame_equal(pop.oneline[:], self.oneline_data) + assert pop.solar_metallicities == [1.] + assert pop.metallicities == [1*Zsun] + pd.testing.assert_frame_equal(pop.mass_per_metallicity, pd.DataFrame(index=[1.], data={'simulated_mass': [260.], 'underlying_mass': [1462.194834], 'number_of_systems': [3]})) + + + def test_init(self,mass_per_met_pop: None): + pop = Population(self.filename) + # check that the history and oneline data are read correctly + pd.testing.assert_frame_equal(pop.history[:], self.history_data) + pd.testing.assert_frame_equal(pop.oneline[:], self.oneline_data) + assert pop.metallicities == [0.02*Zsun] + assert pop.solar_metallicities == [0.02] + tmp_df = pd.DataFrame(index=[0, 1, 2], data={'index': [1, 1, 1]}) + tmp_df.index.name = 'index' + pd.testing.assert_frame_equal(pop.history_lengths, tmp_df) + pd.testing.assert_frame_equal(pop.mass_per_metallicity, pd.DataFrame(index=[0.02], data={'simulated_mass': [260.], 'underlying_mass': [1462.194834], 'number_of_systems': [3]})) + + + def test_read_formation_channels(self, mass_per_met_pop_channels: None): + pop = Population(self.filename) + # check that the formation channels are read correctly + assert pop.formation_channels.equals(self.formation_channels) + + def test_export_selection(self, mass_per_met_pop: None, clean_up_selection_file: None): + selection = [1, 2] + chunksize = 1000 + pop = Population(self.filename) + pop.export_selection(selection, self.outfile, chunksize) + assert os.path.exists(self.outfile) + assert pd.read_hdf(self.outfile, 'history').shape[0] == 2 + assert pd.read_hdf(self.outfile, 'oneline').shape[0] == 2 + + def test_bad_name_export_selection(self, mass_per_met_pop: None): + selection = [1, 2] + chunksize = 1000 + pop = Population(self.filename) + with pytest.raises(ValueError): + pop.export_selection(selection, 'test_selection.csv', history_chunksize=chunksize) + + def test_append_selection(self, mass_per_met_pop: None, clean_up_selection_file: None): + selection = [1, 2] + chunksize = 1000 + pop = Population(self.filename) + pop.export_selection(selection, self.outfile, overwrite=True, history_chunksize=chunksize) + pop.export_selection(selection, self.outfile, overwrite=False, history_chunksize=chunksize) + + assert pd.read_hdf(self.outfile, 'history').shape[0] == 4 + assert pd.read_hdf(self.outfile, 'oneline').shape[0] == 4 + + def test_no_formation_channels(self, mass_per_met_pop: None): + pop = Population(self.filename, verbose=True) + assert pop.formation_channels is None + + def test_len(self, mass_per_met_pop: None): + pop = Population(self.filename) + assert len(pop) == 3 + + def test_columns(self, mass_per_met_pop: None): + pop = Population(self.filename) + columns = pop.columns + + assert columns['history'].tolist() == self.history_data.columns.tolist() + assert columns['oneline'].tolist() == self.oneline_data.columns.tolist() + + + + + # Test formation channel calculation, I need a specific test file for this, + # since it requires specific columns to be present in the oneline and history dataframes + + # Test create_transient_population method requires a specific test file for this, + # since it requires specific columns to be present in the oneline and history dataframes + + +class TestTransientPopulation: + pass + # to implement + + + +class TestRates: + pass + # to implement + +# Run the tests + +if __name__ == '__main__': + pytest.main() diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index 39993ce2c7..4a119dcb9a 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -253,9 +253,9 @@ def orbital_separation_from_period(period_days, m1_solar, m2_solar): """ # cast to float64 to avoid overflow - m1_solar = np.float64(m1_solar) - m2_solar = np.float64(m2_solar) - period_days = np.float64(period_days) + m1_solar = np.asarray(m1_solar, dtype="float64") + m2_solar = np.asarray(m2_solar, dtype="float64") + period_days = np.asarray(period_days, dtype="float64") separation_cm = (const.standard_cgrav * (m1_solar * const.Msun + m2_solar * const.Msun) diff --git a/posydon/visualization/plot_pop.py b/posydon/visualization/plot_pop.py index 33e4eca5dc..d49dfeece4 100644 --- a/posydon/visualization/plot_pop.py +++ b/posydon/visualization/plot_pop.py @@ -1,4 +1,7 @@ -__authors__ = ["Simone Bavera "] +__authors__ = [ + "Simone Bavera ", + "Max Briel ", + ] import os import numpy as np @@ -71,31 +74,25 @@ def plot_grb_rate_density(z, rate_density, zmax=10., channels=False, plt.errorbar(z_P16,rate_P16,xerr=rate_P16_error_x,yerr=rate_P16_error_y, fmt='.', color='black', label=r'$\mathcal{R}_\mathrm{LGRB}(E_\mathrm{iso}^{45-450\,\mathrm{keV}} > 10^{51} \, \mathrm{erg})$ SHOALS survey') -def plot_rate_density(z_dco=None, R_dco=None, z_grb=None, R_grb=None, **kwargs): +def plot_rate_density(intrinsic_rates, channels=False, **kwargs): plt.figure() - title1 = '' - title2 = '' - if z_dco is not None and R_dco is not None: - title1 += 'DCO merger' - plot_merger_rate_density(z_dco, R_dco, **kwargs) - # do not display twice the channel labels - kwargs['label_channels'] = False - if z_grb is not None and R_grb is not None: - title2 += 'GRB beamed' - plot_grb_rate_density(z_grb, R_grb, **kwargs) - if title1 and title2: - title = title1 + ' \& ' + title2 + ' rate densities' - else: - title = title1 + title2 + ' rate density' - plt.title(title) + + plt.plot(intrinsic_rates.index, intrinsic_rates['total'], label='total', color='black') + + if channels: + for i, ch in enumerate([key for key in intrinsic_rates.keys() if key != 'total']): + if ch != 'total': + plt.plot(intrinsic_rates.index, intrinsic_rates[ch], label=ch, color=COLORS[i]) plt.yscale('log') plt.ylabel(r'$\mathcal{R} \,[\mathrm{Gpc}^{-3}\,\mathrm{yr}^{-1}]$') plt.xlabel(r'$z$') plt.legend(loc='center left', bbox_to_anchor=(1, 0.5)) if 'ylim' in kwargs: plt.ylim(kwargs['ylim']) + if 'xlim' in kwargs: + plt.xlim(kwargs['xlim']) if 'path' in kwargs and isinstance(kwargs['path'],str): - plt.savefig(path) + plt.savefig(kwargs['path']) if 'show' in kwargs and kwargs['show']: plt.show() @@ -105,13 +102,16 @@ def plot_merger_efficiency(met, merger_efficiency, show=True, path=None, channel plt.title(title) plt.plot(met/Zsun, merger_efficiency['total'], label='total', color='black') if channels: - for i, ch in enumerate(merger_efficiency.keys()): + unique_channels = [key for key in merger_efficiency.keys() if key != 'total'] + if len(unique_channels) > len(cm.colors): + raise ValueError('Too many channels to plot!') + for i, ch in enumerate(unique_channels): if ch != 'total': plt.plot(met/Zsun, merger_efficiency[ch], label=ch, color=COLORS[i]) plt.yscale('log') plt.xscale('log') plt.xlabel(r'$Z/Z_\odot$') - plt.ylabel(r'\#DCOs [$M_\odot^{-1}$]') + plt.ylabel('\#events [$M_\odot^{-1}$]') plt.legend(loc='center left', bbox_to_anchor=(1, 0.5)) if path: plt.savefig(path) @@ -119,152 +119,83 @@ def plot_merger_efficiency(met, merger_efficiency, show=True, path=None, channel plt.show() -def plot_hist_properties(x, df_intrinsic=None, df_observable=None, +def plot_hist_properties(df, ax=None, df_intrinsic=None, df_observable=None, channel=None, show=True, path=None, alpha=0.5, - range=None, bins=20, xlog=False, ylog=False, - pop=None, **kwargs): - if pop is None: - raise ValueError('Population type not specified.') - - if df_intrinsic is not None and df_observable is not None: + range=None, bins=20, normalise=False,color=COLORS[0], label='', **kwargs): + if ax is None: + fig, ax = plt.subplots(1,1, figsize=(5,5)) + + df_columns = df.columns + if 'intrinsic' in df_columns and 'observable' in df_columns: title = r'Intrinsic vs. observable (dashed) population' - elif df_intrinsic is not None: + elif 'intrinsic' in df_columns: title = r'Intrinsic population' - elif df_observable is not None: + elif 'observable' in df_columns: title = r'Observable population' else: raise ValueError('You should provide either an intrinsic or a ' 'detectable population.') - plt.figure() - if df_intrinsic is not None: - # normalise weight, for GRB this will conserve GRB1/GRB2 ratio - df_intrinsic['weight'] /= sum(df_intrinsic['weight']) - if channel is not None: - sel = (df_intrinsic['weight'] > 0) & (df_intrinsic['channel'] == channel) - title += f'\n{channel}' - else: - sel = df_intrinsic['weight'] > 0 - if isinstance(x, str): - if pop == 'GRB': - sel_tmp = sel & ~df_intrinsic[x].isna() - else: - sel_tmp = sel - values, weights = df_intrinsic.loc[sel_tmp,[x,'weight']].values.T - if xlog: - mask = values > 0 - values = np.log10(values[mask]) - weights = weights[mask] - plt.hist(values, weights=weights, color=COLORS[0], - alpha=alpha, range=range, bins=bins) - elif isinstance(x, list): - for i, x_i in enumerate(x): - if "S1" in x_i: - label = 'S1' - s = 1 - elif "S2" in x_i: - label = 'S2' - s = 2 - else: - label = x_i - if pop == 'GRB': - sel_tmp = sel & ~df_intrinsic[x_i].isna() & df_intrinsic[f'GRB{s}'] - else: - sel_tmp = sel - values, weights = df_intrinsic.loc[sel_tmp,[x_i,'weight']].values.T - if xlog: - mask = values > 0 - values = np.log10(values[mask]) - weights = weights[mask] - plt.hist(values, weights=weights, - color=COLORS[i], label=label, - alpha=alpha, range=range, bins=bins) - if df_observable is not None: - # normalise weight, for GRB this will conserve GRB1/GRB2 ratio - df_observable['weight'] /= sum(df_observable['weight']) - if channel is not None: - sel = (df_observable['weight'] > 0) & (df_observable['channel'] == channel) - if channel not in title: - title += f'\n{channel}' - else: - sel = df_observable['weight'] > 0 - if isinstance(x, str): - if pop == 'GRB': - sel_tmp = sel & ~df_observable[x].isna() - else: - sel_tmp = sel - values, weights = df_observable.loc[sel_tmp,[x,'weight']].values.T - if xlog: - mask = values > 0 - values = np.log10(values[mask]) - weights = weights[mask] - plt.hist(values, weights=weights, - color=COLORS[0], - histtype=u'step', linestyle='--', - alpha=alpha, range=range, bins=bins) - elif isinstance(x, list): - for i, x_i in enumerate(x): - if "S1" in x_i: - label = 'S1' - s = 1 - elif "S2" in x_i: - label = 'S2' - s = 2 - else: - label = x_i - if pop == 'GRB': - sel_tmp = sel & ~df_observable[x_i].isna() & df_observable[f'GRB{s}'] - else: - sel_tmp = sel - values, weights = df_observable.loc[sel_tmp,[x_i,'weight']].values.T - if xlog: - mask = values > 0 - values = np.log10(values[mask]) - weights = weights[mask] - plt.hist(values, weights=weights, - color=COLORS[i], label=label, - histtype=u'step', linestyle='--', - alpha=alpha, range=range, bins=bins) - plt.title(title) - if ylog: - plt.yscale('log') - plt.ylabel(r'PDF') - try: - if isinstance(x, str): - if not xlog: - plt.xlabel(DEFAULT_LABELS[x][0]) - else: - plt.xlabel(DEFAULT_LABELS[x][1]) - else: - if not xlog: - plt.xlabel(DEFAULT_LABELS[x[0]][0]) - else: - plt.xlabel(DEFAULT_LABELS[x[0]][1]) - except: - if isinstance(x, str): - if not xlog: - plt.xlabel(x) - else: - plt.xlabel('log10_'+x) - else: - if not xlog: - plt.xlabel(x[0]) - else: - plt.xlabel('log10_'+x[0]) - if isinstance(x, list): - plt.legend(loc=1) + if 'intrinsic' in df_columns: + values = df['intrinsic'] + if normalise: + values /= sum(values) + + ax.hist(df['property'], + weights=values, + color=color, + alpha=alpha, + range=range, + bins=bins, + label=label+' intrinsic') + + if 'observable' in df_columns: + values = df['observable'] + if normalise: + values /= sum(values) + + ax.hist(df['property'], + weights=values, + color=color, + alpha=alpha, + range=range, + histtype=u'step', + linestyle='--', + bins=bins, + label=label+' observable') + + ax.set_title(title) + + if 'xlabel' in kwargs: + ax.set_xlabel(kwargs['xlabel']) + if normalise: + ax.set_ylabel(r'PDF') + else: + ax.set_ylabel(r'\#events in bin') + + if 'yscale' in kwargs: + ax.set_yscale(kwargs['yscale']) + if 'xscale' in kwargs: + ax.set_xscale(kwargs['xscale']) + if path: plt.savefig(path) if show: plt.show() + + def plot_popsyn_over_grid_slice(pop, grid_type, met_Zsun, slices=None, channel=None, plot_dir='./', prop=None, prop_range=None, log_prop=False, alpha=0.3, s=5., show_fig=True, save_fig=True, close_fig=True, plot_extension='png', verbose=False): + + # Check if step_names in pop.history data + if 'step_names' not in pop.history.columns: + raise ValueError('Formation channel information not available in popsynth data.') # load grid met = convert_metallicity_to_string(met_Zsun) @@ -273,13 +204,12 @@ def plot_popsyn_over_grid_slice(pop, grid_type, met_Zsun, slices=None, channel=N grid.load(grid_path) # check if formation channel information is avaialbe - if channel is not None and 'channel' not in pop.df_oneline.keys(): - if verbose: - print('Computing formation channels...') - pop.get_formation_channels() + if channel is not None and 'channel' not in pop.columns: + raise ValueError('Formation channel information not available in popsynth data.') if 'CO' in grid_path: # compact object mass slices + # TODO: THIS SELECTION DOES NOT WORK! m_COs = np.unique(np.around(grid.initial_values['star_2_mass'],1)) m_COs_edges = 10**((np.log10(np.array(m_COs)[1:])+np.log10(np.array(m_COs)[:-1]))*0.5) m2 = [0.]+m_COs_edges.tolist()+[2*m_COs_edges[-1]] @@ -300,19 +230,28 @@ def plot_popsyn_over_grid_slice(pop, grid_type, met_Zsun, slices=None, channel=N else: raise ValueError('Grid type not supported!') - + + if channel is not None: + channel_sel = 'channel == '+str(channel) + else: + channel_sel = '' + for i, var in enumerate(vars): if slices is not None and round(var,2) not in np.around(slices,2): if verbose: print(f'Skipping {round(var,2)}') continue + + met_indices = np.where(pop.select(where=channel_sel, columns=['metallicity']) == met_Zsun)[0].tolist() if 'HMS-HMS' in grid_path: slice_3D_var_range = (dq_edges[i],dq_edges[i+1]) - # select popsynth binaries in the given mass ratio range - sel = (pop.df['metallicity'] == met_Zsun*Zsun) & (pop.df['event'] == 'ZAMS') - q = pop.df['S2_mass'].values/pop.df['S1_mass'].values + + sel = 'index in '+str(met_indices)+' & event == "ZAMS"' + data = pop.history.select(where=sel, columns=['S1_mass','S2_mass' ,'orbital_period']) + + q = data['S2_mass'].values/data['S1_mass'].values q[q>1] = 1./q[q>1] - mask = sel & (q>=slice_3D_var_range[0]) & (q<=slice_3D_var_range[1]) + mask = (q>=slice_3D_var_range[0]) & (q<=slice_3D_var_range[1]) elif 'CO' in grid_path: if i == len(m2): continue @@ -320,13 +259,17 @@ def plot_popsyn_over_grid_slice(pop, grid_type, met_Zsun, slices=None, channel=N # select popsynth binaries in the given compact object mass # TODO: implement the case of reversal mass ratio if 'CO-HMS_RLO' in grid_path: - sel = (pop.df['metallicity'] == met_Zsun*Zsun) & (pop.df['event'] == 'oRLO2') - m_CO = pop.df['S1_mass'].values - mask = sel & (m_CO>=slice_3D_var_range[0]) & (m_CO<=slice_3D_var_range[1]) + + sel = 'index in '+str(met_indices)+' & event == "oRLO2"' + data = pop.history.select(where=sel, columns=['S1_mass','S2_mass' ,'orbital_period']) + m_CO = data['S1_mass'].values + mask = (m_CO>=slice_3D_var_range[0]) & (m_CO<=slice_3D_var_range[1]) + elif 'CO-HeMS' in grid_path: - sel = (pop.df['metallicity'] == met_Zsun*Zsun) & (pop.df['step_names'] == 'step_CE') - m_CO = pop.df['S1_mass'].values - mask = sel & (m_CO>=slice_3D_var_range[0]) & (m_CO<=slice_3D_var_range[1]) + sel = 'index in '+str(met_indices)+' & step_names == "step_CE"' + data = pop.history.select(where=sel, columns=['S1_mass','S2_mass' ,'orbital_period']) + m_CO = data['S1_mass'].values + mask = (m_CO>=slice_3D_var_range[0]) & (m_CO<=slice_3D_var_range[1]) else: raise ValueError('Grid type not supported!') # TODO: skip plotting slice if there are no data @@ -348,17 +291,19 @@ def plot_popsyn_over_grid_slice(pop, grid_type, met_Zsun, slices=None, channel=N grid_3D=True, slice_3D_var_str=slice_3D_var_str, slice_3D_var_range=slice_3D_var_range, verbose=False, **PLOT_PROPERTIES) - # pop synth - # only plot selected channel - if channel is not None: - sel_binary_index = pop.df_oneline.loc[pop.df_oneline['channel'] == channel].index.values.tolist() - mask = mask & (pop.df.index.isin(sel_binary_index)) - log10_m1 = np.log10(pop.df.loc[mask,'S1_mass'].values) - log10_p = np.log10(pop.df.loc[mask,'orbital_period'].values) + + log10_m1 = np.log10(data.loc[mask,'S1_mass'].values) + log10_p = np.log10(data.loc[mask,'orbital_period'].values) # plot color map of a given DCO variable if prop is not None: - sel = pop.df.index.isin(pop.df.loc[mask].index.values.tolist()) & (pop.df['event'] == 'CO_contact') - prop_values = pop.df.loc[sel,prop].values + if prop not in pop.columns: + raise ValueError(f'Property {prop} not available in popsynth data.') + + # basically only for DCO systems + met_indices = np.array(met_indices)[mask].tolist() + prop_sel = 'index in '+str(met_indices) + ' & event == "CO_contact"' + prop_data = pop.history.select(where=prop_sel, columns=[prop]) + prop_values = prop_data[prop].values vmin = prop_range[0] vmax = prop_range[1] if log_prop: From 15d70ce5ff153049049b8e578f306be37db0c576 Mon Sep 17 00:00:00 2001 From: Kyle Rocha <48293898+ka-rocha@users.noreply.github.com> Date: Thu, 6 Jun 2024 09:30:04 -0500 Subject: [PATCH 205/319] update absolute imports and default ini with some docs (#310) --- posydon/popsyn/io.py | 26 ++++++++++--- posydon/popsyn/population_params_default.ini | 41 +++++++++++++++----- 2 files changed, 52 insertions(+), 15 deletions(-) diff --git a/posydon/popsyn/io.py b/posydon/popsyn/io.py index 17088dbd15..f7fcfe7ace 100644 --- a/posydon/popsyn/io.py +++ b/posydon/popsyn/io.py @@ -418,13 +418,27 @@ def simprop_kwargs_from_ini(path, verbose=False): # from posydon.... import .... # ^ import as ^ name import_and_name = sect_dict.pop('import') - package = sect_dict.pop('absolute_import', None) - - # import module - module = importlib.import_module(import_and_name[0], - package=package) + absolute_import_and_name = sect_dict.pop('absolute_import', None) + + # Use absolute import if provided + if absolute_import_and_name is not None: + # unpack user specified py file, and class name to import + import_location, class_name = absolute_import_and_name + absolute_import_location = os.path.abspath(import_location) + # Format: remove leading / in abs path, replace / with dots, remove '.py' + location_as_module_name = absolute_import_location[1:].replace('/', '.').replace('.py', '') + + # create spec and load module + spec = importlib.util.spec_from_file_location( location_as_module_name, location=absolute_import_location) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + else: + # Use builtin posydon classes + import_location, class_name = import_and_name + # import module + module = importlib.import_module(import_location) # extract class or function - cls = getattr(module, import_and_name[1]) + cls = getattr(module, class_name) # match the form SimulationProperties expects parser_dict[section] = (cls, sect_dict) diff --git a/posydon/popsyn/population_params_default.ini b/posydon/popsyn/population_params_default.ini index 9a7950f15a..b5278a5ae9 100644 --- a/posydon/popsyn/population_params_default.ini +++ b/posydon/popsyn/population_params_default.ini @@ -10,13 +10,15 @@ [flow] import = ['posydon.binary_evol.flow_chart', 'flow_chart'] + # builtin posydon flow absolute_import = None - # 'package' kwarg for importlib.import_module + # If given, use an absolute filepath to user defined flow: ['', ''] [step_HMS_HMS] import = ['posydon.binary_evol.MESA.step_mesa', 'MS_MS_step'] + # builtin posydon step absolute_import = None - # 'package' kwarg for importlib.import_module + # If given, use an absolute filepath to user defined step: ['', ''] interpolation_path = None # found by default interpolation_filename = None @@ -43,8 +45,9 @@ [step_CO_HeMS] import = ['posydon.binary_evol.MESA.step_mesa', 'CO_HeMS_step'] + # builtin posydon step absolute_import = None - # 'package' kwarg for importlib.import_module + # If given, use an absolute filepath to user defined step: ['', ''] interpolation_path = None # found by default interpolation_filename = None @@ -70,8 +73,9 @@ [step_CO_HMS_RLO] import = ['posydon.binary_evol.MESA.step_mesa', 'CO_HMS_RLO_step'] + # builtin posydon step absolute_import = None - # 'package' kwarg for importlib.import_module + # If given, use an absolute filepath to user defined step: ['', ''] interpolation_path = None # found by default interpolation_filename = None @@ -97,8 +101,9 @@ [step_CO_HeMS_RLO] import = ['posydon.binary_evol.MESA.step_mesa', 'CO_HeMS_RLO_step'] + # builtin posydon step absolute_import = None - # 'package' kwarg for importlib.import_module + # If given, use an absolute filepath to user defined step: ['', ''] interpolation_path = None # found by default interpolation_filename = None @@ -125,8 +130,9 @@ [step_detached] import = ['posydon.binary_evol.DT.step_detached', 'detached_step'] + # builtin posydon step absolute_import = None - # 'package' kwarg for importlib.import_module + # If given, use an absolute filepath to user defined step: ['', ''] matching_method = 'minimize' #'minimize' 'root' do_wind_loss = True @@ -146,17 +152,27 @@ [step_disrupted] import = ['posydon.binary_evol.DT.step_disrupted','DisruptedStep'] + # builtin posydon step + absolute_import = None + # If given, use an absolute filepath to user defined step: ['', ''] [step_merged] import = ['posydon.binary_evol.DT.step_merged','MergedStep'] + # builtin posydon step + absolute_import = None + # If given, use an absolute filepath to user defined step: ['', ''] [step_initially_single] import = ['posydon.binary_evol.DT.step_initially_single','InitiallySingleStep'] + # builtin posydon step + absolute_import = None + # If given, use an absolute filepath to user defined step: ['', ''] [step_CE] import = ['posydon.binary_evol.CE.step_CEE', 'StepCEE'] + # builtin posydon step absolute_import = None - # 'package' kwarg for importlib.import_module + # If given, use an absolute filepath to user defined step: ['', ''] prescription='alpha-lambda' # 'alpha-lambda' common_envelope_efficiency=1.0 @@ -186,6 +202,7 @@ [step_SN] import = ['posydon.binary_evol.SN.step_SN', 'StepSN'] + # builtin posydon step absolute_import = None # 'package' kwarg for importlib.import_module mechanism = 'Patton&Sukhbold20-engine' @@ -228,22 +245,28 @@ [step_dco] import = ['posydon.binary_evol.DT.double_CO', 'DoubleCO'] + # builtin posydon step absolute_import = None - # 'package' kwarg for importlib.import_module + # If given, use an absolute filepath to user defined step: ['', ''] n_o_steps_interval = None [step_end] import = ['posydon.binary_evol.step_end', 'step_end'] + # builtin posydon step absolute_import = None - # 'package' kwarg for importlib.import_module + # If given, use an absolute filepath to user defined step: ['', ''] [extra_hooks] import_1 = ['posydon.binary_evol.simulationproperties', 'TimingHooks'] + # builtin posydon hook absolute_import_1 = None + # If given, use an absolute filepath to user defined step: ['', ''] kwargs_1 = {} import_2 = ['posydon.binary_evol.simulationproperties', 'StepNamesHooks'] + # builtin posydon hook absolute_import_2 = None + # If given, use an absolute filepath to user defined step: ['', ''] kwargs_2 = {} From 07ebd74279221fd89cbfd0259619f90051f8fb7e Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 6 Jun 2024 16:50:10 +0200 Subject: [PATCH 206/319] Update termination_flags.py (#317) Change `and` to `or` --- posydon/grids/termination_flags.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/posydon/grids/termination_flags.py b/posydon/grids/termination_flags.py index 7ee1a93d31..3fddb7a375 100644 --- a/posydon/grids/termination_flags.py +++ b/posydon/grids/termination_flags.py @@ -131,8 +131,8 @@ def get_mass_transfer_flag(binary_history, history1, history2, return "contact_during_MS" where_transfer = rate > LG_MTRANSFER_RATE_THRESHOLD - where_rlof_1 = where_rl_rel_1 & where_transfer - where_rlof_2 = where_rl_rel_2 & where_transfer + where_rlof_1 = where_rl_rel_1 | where_transfer + where_rlof_2 = where_rl_rel_2 | where_transfer if not np.any(where_rlof_1) and not np.any(where_rlof_2): return "no_RLOF" From ee337136d12145b5cd99b8031a5d382a287f3c3d Mon Sep 17 00:00:00 2001 From: Camille Liotine Date: Thu, 6 Jun 2024 10:14:10 -0500 Subject: [PATCH 207/319] Fixes to error checking (#320) * add back code in step_mesa from PR#255 * add traceback as binary attribute --- posydon/binary_evol/DT/double_CO.py | 2 +- posydon/binary_evol/DT/step_detached.py | 2 +- posydon/binary_evol/MESA/step_mesa.py | 59 +++++++++++++++++++------ posydon/popsyn/binarypopulation.py | 7 +++ 4 files changed, 54 insertions(+), 16 deletions(-) diff --git a/posydon/binary_evol/DT/double_CO.py b/posydon/binary_evol/DT/double_CO.py index 8395631e83..a85db9ebd5 100644 --- a/posydon/binary_evol/DT/double_CO.py +++ b/posydon/binary_evol/DT/double_CO.py @@ -105,7 +105,7 @@ def ev_contact(t, y): if s.status == -1: binary.state += ' (Integration failure)' - raise NumericalError("Integrations failed", s.message) + raise NumericalError("Integrations failed: " + s.message) elif s.status == 1: diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index a87f4df7d4..c41f9bb75b 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -842,7 +842,7 @@ def sq_diff_function(x): sol = minimize(sq_diff_function, x0, method="TNC", bounds=bnds) except: - raise NumericalError("SciPy numerical differentiation occured outside boundary ", + raise NumericalError("SciPy numerical differentiation occured outside boundary " "while matching to single star track") # if still not acceptable matching, we fail the system: diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index 3ca0f5a421..c21e3a1956 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -1398,23 +1398,36 @@ def __call__(self, binary): # period inside the grid, but m1 outside the grid elif ((not self.flip_stars_before_step and self.p_min <= p <= self.p_max and - (m1 < self.m1_min or m1 > self.m1_max) - )): + (m1 < self.m1_min or m1 > self.m1_max))): set_binary_to_failed(self.binary) raise GridError(f'The mass of m1 ({m1}) is outside the grid,' - 'while the period is inside the grid.') + ' while the period is inside the grid.') # period inside the grid, but m2 outside the grid elif ((not self.flip_stars_before_step and self.p_min <= p <= self.p_max and - (m2 < self.m2_min or m2 > self.m2_max) - )): + (m2 < self.m2_min or m2 > self.m2_max))): set_binary_to_failed(self.binary) raise GridError(f'The mass of m2 ({m2}) is outside the grid,' - 'while the period is inside the grid.') + ' while the period is inside the grid.') + # period inside the grid, but m1 outside the grid (flipped stars) + elif ((self.flip_stars_before_step and + self.p_min <= p <= self.p_max and + (m2 < self.m1_min or m2 > self.m1_max))): + set_binary_to_failed(self.binary) + raise GridError(f'The mass of m1 ({m2}) is outside the grid,' + ' while the period is inside the grid.') + + # period inside the grid, but m2 outside the grid (flipped stars) + elif ((self.flip_stars_before_step and + self.p_min <= p <= self.p_max and + (m1 < self.m2_min or m1 > self.m2_max))): + set_binary_to_failed(self.binary) + raise GridError(f'The mass of m2 ({m1}) is outside the grid,' + ' while the period is inside the grid.') + else: - self.binary.state = "detached" self.binary.event = "redirect_from_CO_HMS_RLO" return @@ -1510,19 +1523,34 @@ def __call__(self, binary): # period inside the grid, but m1 outside the grid elif ((not self.flip_stars_before_step and self.p_min <= p <= self.p_max and - (m1 < self.m1_min or m1 > self.m1_max) - )): + (m1 < self.m1_min or m1 > self.m1_max))): set_binary_to_failed(self.binary) raise GridError(f'The mass of m1 ({m1}) is outside the grid,' - 'while the period is inside the grid.') + ' while the period is inside the grid.') + # period inside the grid, but m2 outside the grid elif ((not self.flip_stars_before_step and self.p_min <= p <= self.p_max and - (m2 < self.m2_min or m2 > self.m2_max) - )): + (m2 < self.m2_min or m2 > self.m2_max))): set_binary_to_failed(self.binary) raise GridError(f'The mass of m2 ({m2}) is outside the grid,' - 'while the period is inside the grid.') + ' while the period is inside the grid.') + + # period inside the grid, but m1 outside the grid with flipped stars + elif ((self.flip_stars_before_step and + self.p_min <= p <= self.p_max and + m1 < self.m2_min or m1 > self.m2_max)): + set_binary_to_failed(self.binary) + raise GridError(f'The mass of m2 ({m1}) is outside the grid,' + ' while the period is inside the grid.') + + # period inside the grid, but m2 outside the grid with flipped stars + elif ((self.flip_stars_before_step and + self.p_min <= p <= self.p_max and + m2 < self.m1_min or m2 > self.m1_max)): + set_binary_to_failed(self.binary) + raise GridError(f'The mass of m1 ({m2}) is outside the grid,' + ' while the period is inside the grid.') else: self.binary.state = "detached" @@ -1613,16 +1641,19 @@ def __call__(self, binary): self.p_min <= p <= self.p_max and ecc == 0.)): super().__call__(binary) + # period inside the grid, but m1 outside the grid elif (self.p_min <= p <= self.p_max) and (m1 < self.m1_min or m1 > self.m1_max): set_binary_to_failed(self.binary) raise GridError(f'The mass of m1 ({m1}) is outside the grid,' - 'while the period is inside the grid.') + ' while the period is inside the grid.') + # period inside the grid, but m2 outside the grid elif (self.p_min <= p <= self.p_max) and (m2 < self.m2_min or m2 > self.m2_max): set_binary_to_failed(self.binary) raise GridError(f'The mass of m2 ({m2}) is outside the grid,' ' while the period is inside the grid.') + else: self.binary.state = 'detached' self.binary.event = 'redirect_from_CO_HeMS' diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index 38b2901969..f13bf0329e 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -320,15 +320,22 @@ def _safe_evolve(self, **kwargs): with warnings.catch_warnings(record=True) as w: try: binary.evolve() + except POSYDONError as posydon_error: set_binary_to_failed(binary) + binary.traceback = traceback.format_exc() + if self.kwargs.get("error_checking_verbose", False): posydon_error.add_note(initial_condition_message(binary)) traceback.print_exception(posydon_error) + except Exception as e: set_binary_to_failed(binary) + binary.traceback = traceback.format_exc() + e.add_note(initial_condition_message(binary)) traceback.print_exception(e) + if len(w) > 0: warnings.simplefilter("always") binary.warning_message = [x.message for x in w] From 2ee526f6f925064cf57f4c224333b695b0da6b2e Mon Sep 17 00:00:00 2001 From: sgossage Date: Fri, 7 Jun 2024 02:35:58 -0500 Subject: [PATCH 208/319] Rerun for He stars and rapid mass transfer (CO-HeMS and moderate mass ratio HMS-HMS) (#313) * added new rerun type called dedt_force_mltpp and updated docs for it * fixed typo in rerun type name * updated docs * updated docs, SHA and name of new rerun to dedt_hepulse * updated SHA and docs * removed PR#275s dedt_energy_eqn rerun b/c the new dedt_hepulse contains it * added PR275 rerun back in -- may want to run prior to dedt_hepulse * Updated SHA * updated docs * updated SHA * updated docs and SHAs * updated SHA * updated SHA --------- Co-authored-by: Seth Shearer Gossage Co-authored-by: Seth Shearer Gossage Co-authored-by: Seth Shearer Gossage Co-authored-by: Seth Shearer Gossage --- bin/posydon-run-pipeline | 6 +++++- .../post_processing/pipeline/pipeline_steps.rst | 3 ++- 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/bin/posydon-run-pipeline b/bin/posydon-run-pipeline index 4771034d35..964f8df672 100644 --- a/bin/posydon-run-pipeline +++ b/bin/posydon-run-pipeline @@ -666,7 +666,9 @@ def copy_ini_file(grid_type, rerun_metallicity, rerun_type, destination, elif rerun_type == 'initial_He': replace_text = "fix_iniHe_pre_rerun5-0e736a853c3c2e522b76299a54aea8f5eea15d08" elif rerun_type == "dedt_energy_eqn": - replace_text = "seth_dedt_energy_eqn-deac5c26392b36cb7a8ff337e0d53c26568a3971" + replace_text = "seth_dedt_energy_eqn-f1803afe400ed16a4fad47b0bad6abbf0965cae1" + elif rerun_type == "dedt_hepulse": + replace_text = "seth_dedt_hepulse-0274be9895480cae3aa2a4d14b056a2447d5921b" elif rerun_type == 'other': # e.g. 'envelope_mass_limit', 'fe_core_infall_limit' # this rerun uses the default inlist commit return @@ -835,6 +837,8 @@ def logic_rerun(grid, rerun_type): runs_to_rerun += [i] runs_to_rerun = np.array(runs_to_rerun) elif rerun_type == 'dedt_energy_eqn': + termination_flags = TF1_POOL_ERROR + elif rerun_type == 'dedt_hepulse': termination_flags = TF1_POOL_ERROR elif rerun_type == 'envelope_mass_limit': # maybe 'fe_core_infall_limit' as well? runs_to_rerun = None # implement logic diff --git a/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst b/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst index 85631b7abc..fd97fdfd5f 100644 --- a/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst +++ b/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst @@ -204,6 +204,7 @@ type of the rerun specifying the logic and changes, and the cluster name. HeMB_MLTp_mesh workaround it turns off magnetic braking for He stars; it uses less extreme parameters of the MLT++; it changes some more input values to change the resulation close to the surface more_mesh workaround it modifies the remeshing and allows for more cells in MESA conv_bdy_weight caution it disabled the convective_bdy_weight where this caused segmentation faults (this avoids a bug in the old MESA version r11701) - dedt_energy_eqn caution it enables MESA's dedt-form of the energy equation for numerical stability during superthermal mass transfer + dedt_energy_eqn caution it enables MESA's dedt-form of the energy equation for numerical stability during rapid (superthermal) mass transfer + dedt_hepulse caution it enables MESA's dedt-form of the energy equation for rapid mass transfer. At stripped HeZAMS, several MLT++ changes, v_flag and lnPgas_flag set to .true., and convective_bdy_weight disabled to help with stripped He star superadiabatic envelopes, pulsations, and WD cooling. ===================== ============== =========== From 2fbe8cea476225233de40be55716e52e9f843780 Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Tue, 11 Jun 2024 17:25:13 +0200 Subject: [PATCH 209/319] fix filenamaes in generated `slurm_submit.sh` for popsynth (#330) * fix name in slurm_submit.sh * Update posydon-setup-popsyn --- bin/posydon-setup-popsyn | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/bin/posydon-setup-popsyn b/bin/posydon-setup-popsyn index a2c124cc7d..8734d1f290 100644 --- a/bin/posydon-setup-popsyn +++ b/bin/posydon-setup-popsyn @@ -153,7 +153,7 @@ if __name__ == '__main__': file.write('#!/bin/bash\n') for MET in metallicities: str_met = convert_metallicity_to_string(MET) - file.write(f'array=$(sbatch --parsable slurm_array_{str_met}_Zsun.slurm)\n') + file.write(f'array=$(sbatch --parsable {str_met}_Zsun_slurm_array.slurm)\n') file.write("echo '"+str_met+" job array submitted as '${array}\n") - file.write('merge=$(sbatch --parsable --dependency=afterok:${array} --kill-on-invalid-dep=yes '+f'slurm_merge_{str_met}_Zsun.slurm)\n') + file.write('merge=$(sbatch --parsable --dependency=afterok:${array} --kill-on-invalid-dep=yes '+f'{str_met}_Zsun_merge_popsyn.slurm)\n') file.write("echo '"+str_met+" merge job submitted as '${merge}\n") From 2deae1b797f7a10c35ca11dbb321af6387cd979a Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 13 Jun 2024 15:52:25 +0200 Subject: [PATCH 210/319] Update posydon-run-pipeline (#329) Use slurm ID to let jobs wait instead of random time. This scales better with a larger number of slurm jobs. --- bin/posydon-run-pipeline | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bin/posydon-run-pipeline b/bin/posydon-run-pipeline index 964f8df672..dfdc298db5 100644 --- a/bin/posydon-run-pipeline +++ b/bin/posydon-run-pipeline @@ -1359,7 +1359,7 @@ if __name__ == '__main__': # NOTE: this prevents job arrays starting at the same time to read # the same files at the same time (e.g. when creatign LITE/ORIGINAL grid, # or when creating a directory that does not exist yet) - time.sleep(random.uniform(0.,30.)) + time.sleep(SLURM_I) STEP = os.path.splitext(os.path.basename(PATH_TO_CSV_FILE))[0] if STEP=='step_1': # grid slices creation From cd1052ce43cf5eececcc5b2d079dc847cb22d3ff Mon Sep 17 00:00:00 2001 From: ZepeiX <77278187+ZepeiX@users.noreply.github.com> Date: Fri, 14 Jun 2024 10:23:57 -0500 Subject: [PATCH 211/319] Zepei fix interpolator (#325) * fix NS mass above 2.5 * fix NaN mass issue * Update IF_interpolation.py * Update IF_interpolation.py * Update IF_interpolation.py --- bin/posydon-run-pipeline | 4 +++- posydon/interpolation/IF_interpolation.py | 12 +++++++++--- 2 files changed, 12 insertions(+), 4 deletions(-) diff --git a/bin/posydon-run-pipeline b/bin/posydon-run-pipeline index dfdc298db5..cef1ee0219 100644 --- a/bin/posydon-run-pipeline +++ b/bin/posydon-run-pipeline @@ -421,7 +421,9 @@ def train_interpolators(i, path_to_csv_file, verbose=False): "interp_classes": interp_classes, "out_keys": out_keys, "class_method": "kNN", - "c_keys": [f'S{i}_{MODEL_NAME}_CO_interpolation_class'], + "c_keys": [f'S{i}_{MODEL_NAME}_CO_interpolation_class', + f'S{i}_{MODEL_NAME}_CO_type', + f'S{i}_{MODEL_NAME}_SN_type'], "c_key": f'S{i}_{MODEL_NAME}_CO_interpolation_class' }, ) diff --git a/posydon/interpolation/IF_interpolation.py b/posydon/interpolation/IF_interpolation.py index cef0b7bb0f..7e0e250c21 100644 --- a/posydon/interpolation/IF_interpolation.py +++ b/posydon/interpolation/IF_interpolation.py @@ -418,6 +418,13 @@ def __init__(self, grid=None, in_keys=None, out_keys=None, in_scaling=None, self.interp_method = interp_method self.interpolator = None + ignored_classes = ['not_converged', 'ignored_no_BH', 'ignored_no_RLO', 'initial_MT'] + self.valid_classes = np.unique(grid.final_values["interpolation_class"]).tolist() + # A list of mass-transfer classes that are kept for interpolations (exclude e.g. 'not_converged' class). + for i in ignored_classes: + while(i in self.valid_classes): + self.valid_classes.remove(i) + if interp_classes is not None: if not isinstance(interp_classes, list): raise ValueError("interp_classes must be a list of " @@ -467,7 +474,7 @@ def __init__(self, grid=None, in_keys=None, out_keys=None, in_scaling=None, self.interp_in_q = self._interpIn_q(grid) self.XT, self.YT = self._grid2array(grid) self.valid = self._setValid( - grid.final_values[self.c_key], self.XT) + grid.final_values["interpolation_class"], self.XT) self.N = np.sum(self.valid >= 0) analize_nans(self.out_keys, self.YT, self.valid) @@ -598,8 +605,7 @@ def _setValid(self, ic, X): which = np.isnan(np.sum(X, axis=1)) valid[which] = -1 print(f"Discarded {np.sum(which)} binaries with nans in input values.") - - for i, flag in enumerate(self.interp_classes): + for i, flag in enumerate(self.valid_classes): valid[ic == flag] = i + 1 if(self.interp_in_q): # if HMS-HMS grid, take out q = 1 From 6cb6accfb960e5d34ccec2dd3e3dc2d9d5cbef66 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Sat, 15 Jun 2024 15:27:58 +0200 Subject: [PATCH 212/319] Add correction for eccentric pre SN orbits for SNflag2 (#316) * add correction for eccentric pre SN orbits for SNflag2 * Update step_SN.py --------- Co-authored-by: Jeff Andrews --- posydon/binary_evol/SN/step_SN.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index e48c037936..b181cfd454 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -1696,8 +1696,13 @@ def SNCheck( # SNflag2: Equations 22-23, Willems, B., Henninger, M., Levin, T., et al. 2005, ApJ, 625, 324 # (see, e.g., Kalogera, V. & Lorimer, D.R. 2000, ApJ, 530, 890) - tmp1 = 2 - Mtot_pre / Mtot_post * (Vkick / Vr - 1) ** 2 - tmp2 = 2 - Mtot_pre / Mtot_post * (Vkick / Vr + 1) ** 2 + # The derivation in the papers above assume a circular pre SN + # orbit. Hence, need a correction for eccentric pre SN orbits: + eccentric_orbit_correction = Vr**2 * rpre / (G * Mtot_pre) + tmp1 = 2 - Mtot_pre / Mtot_post * (Vkick / Vr - 1) ** 2\ + * eccentric_orbit_correction + tmp2 = 2 - Mtot_pre / Mtot_post * (Vkick / Vr + 1) ** 2\ + * eccentric_orbit_correction SNflag2 = ((rpre / Apost - tmp1 < err) and (err > tmp2 - rpre / Apost)) From d2392c0b93a98587acfc2f526f2e454e708d8c2a Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Sat, 15 Jun 2024 18:30:35 +0200 Subject: [PATCH 213/319] step_SN mass check; force manual calculation if not matching (#324) * step_SN mass check; force manual calculation if not matching * remove preSN values, since we perform check before overwriting star" * check explicitly if interp values are NaN * Update step_SN.py --------- Co-authored-by: Camille --- posydon/binary_evol/SN/step_SN.py | 24 +++++++++++------------- 1 file changed, 11 insertions(+), 13 deletions(-) diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index b181cfd454..f9e6eaec92 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -532,16 +532,15 @@ def collapse_star(self, star): 'or use_core_masses is set to True, ' 'continue with the collapse.') - else: - # store some properties of the star object - # to be used for collapse verification - pre_SN_star = copy.deepcopy(star) - + else: MODEL_properties = getattr(star, MODEL_NAME_SEL) - # Check if SN_type matches the CO_type in used MODEL - if check_SN_CO_match(MODEL_properties['SN_type'], - MODEL_properties['state']): + ## Check if SN_type mismatches the CO_type in MODEL or if interpolated MODEL properties are NaN + ## If either are true, interpolated values cannot be used for this SN + if (check_SN_CO_match(MODEL_properties['SN_type'], MODEL_properties['state']) and + ~np.isnan(MODEL_properties['mass'])): + + for key, value in MODEL_properties.items(): setattr(star, key, value) @@ -581,12 +580,11 @@ def collapse_star(self, star): return else: - # raise a warning warnings.warn(f'{MODEL_NAME_SEL}: The SN_type ' - 'does not match the predicted CO! ' - 'If use_profiles ' - 'or use_core_masses is set to True, ' - 'continue with the collapse.') + 'does not match the predicted CO, or the interpolated ' + 'values for the SN remnant are NaN. ' + 'If use_profiles or use_core_masses is set to True, ' + 'continue with the collapse.') # Verifies the selection of core-collapse mechnism to perform # the collapse From 07f51346e485b78499fe9c0e174f889e72afc4f8 Mon Sep 17 00:00:00 2001 From: ZepeiX <77278187+ZepeiX@users.noreply.github.com> Date: Sat, 15 Jun 2024 12:43:22 -0500 Subject: [PATCH 214/319] Update termination_flags.py The change of #317 breaks the termination flag 2. --- posydon/grids/termination_flags.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/posydon/grids/termination_flags.py b/posydon/grids/termination_flags.py index 3fddb7a375..7ee1a93d31 100644 --- a/posydon/grids/termination_flags.py +++ b/posydon/grids/termination_flags.py @@ -131,8 +131,8 @@ def get_mass_transfer_flag(binary_history, history1, history2, return "contact_during_MS" where_transfer = rate > LG_MTRANSFER_RATE_THRESHOLD - where_rlof_1 = where_rl_rel_1 | where_transfer - where_rlof_2 = where_rl_rel_2 | where_transfer + where_rlof_1 = where_rl_rel_1 & where_transfer + where_rlof_2 = where_rl_rel_2 & where_transfer if not np.any(where_rlof_1) and not np.any(where_rlof_2): return "no_RLOF" From f1a686cd775edcf748b8f53d8300aa88c42294af Mon Sep 17 00:00:00 2001 From: ZepeiX <77278187+ZepeiX@users.noreply.github.com> Date: Mon, 17 Jun 2024 20:31:57 -0500 Subject: [PATCH 215/319] Update common_functions.py setting lambda=np.nan raises the error ValueError("lambda_CE has a negative value") --- posydon/utils/common_functions.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index 4a119dcb9a..c4aa0e3226 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -2233,7 +2233,7 @@ def calculate_lambda_from_profile( m1_i, radius1, len(donor_mass), " vs ", ind_core, mc1_i, rc1_i) print("Ebind_i from profile ", Ebind_i) print("lambda_CE ", lambda_CE) - if not (lambda_CE > -tolerance): + if not (lambda_CE > -tolerance) and not np.isnan(lambda_CE): raise ValueError("lambda_CE has a negative value") return lambda_CE, mc1_i, rc1_i From 7e9ab418a743e74c296519fc7de146ff116c7bb6 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Tue, 18 Jun 2024 16:11:47 +0200 Subject: [PATCH 216/319] put MESA condition back; additional check which star dominates RLO (#334) --- posydon/grids/termination_flags.py | 9 ++++++--- posydon/utils/common_functions.py | 6 +++--- 2 files changed, 9 insertions(+), 6 deletions(-) diff --git a/posydon/grids/termination_flags.py b/posydon/grids/termination_flags.py index 7ee1a93d31..cafacb50f0 100644 --- a/posydon/grids/termination_flags.py +++ b/posydon/grids/termination_flags.py @@ -125,14 +125,17 @@ def get_mass_transfer_flag(binary_history, history1, history2, rel2 = binary_history["rl_relative_overflow_2"] rate = binary_history["lg_mtransfer_rate"] + # warning: all changes in the following lines need to be aligned with the + # function posydon.utils.common_functions.infer_mass_transfer_case where_rl_rel_1 = rel1 > RL_RELATIVE_OVERFLOW_THRESHOLD where_rl_rel_2 = rel2 > RL_RELATIVE_OVERFLOW_THRESHOLD + where_rl_rel_1_dominates = rel1 >= rel2 + where_rl_rel_2_dominates = rel1 < rel2 if np.any(where_rl_rel_1 & where_rl_rel_2): return "contact_during_MS" - where_transfer = rate > LG_MTRANSFER_RATE_THRESHOLD - where_rlof_1 = where_rl_rel_1 & where_transfer - where_rlof_2 = where_rl_rel_2 & where_transfer + where_rlof_1 = (where_rl_rel_1 | where_transfer) & where_rl_rel_1_dominates + where_rlof_2 = (where_rl_rel_2 | where_transfer) & where_rl_rel_2_dominates if not np.any(where_rlof_1) and not np.any(where_rlof_2): return "no_RLOF" diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index c4aa0e3226..8b08943fa4 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -1366,9 +1366,9 @@ def infer_mass_transfer_case(rl_relative_overflow, if rl_relative_overflow is None or lg_mtransfer_rate is None: return MT_CASE_NO_RLO - if (rl_relative_overflow <= RL_RELATIVE_OVERFLOW_THRESHOLD - or (lg_mtransfer_rate <= LG_MTRANSFER_RATE_THRESHOLD - and rl_relative_overflow < 0.0)): + if ((rl_relative_overflow <= RL_RELATIVE_OVERFLOW_THRESHOLD) and + ((lg_mtransfer_rate <= LG_MTRANSFER_RATE_THRESHOLD) and + (rl_relative_overflow < 0.0))): if verbose: print("checking rl_relative_overflow / lg_mtransfer_rate,", rl_relative_overflow, lg_mtransfer_rate) From 016a9b1357d242a87738d1cd8606f0d042a729dd Mon Sep 17 00:00:00 2001 From: tassos25 Date: Tue, 18 Jun 2024 16:32:08 +0200 Subject: [PATCH 217/319] Update setup.py (#336) --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 8997887bd3..8a96692aaa 100644 --- a/setup.py +++ b/setup.py @@ -71,7 +71,7 @@ 'astropy >= 5.2.2', 'pandas >= 2.0.0', 'scikit-learn < 1.3.0', # 1.2.2 - 'matplotlib >= 3.7.1', + 'matplotlib >= 3.7.1, <= 3.8.0', 'matplotlib-label-lines >= 0.5.2', 'h5py >= 3.8.0', 'psutil >= 5.9.4', From ceacdb21f03b257ba927fd8381263de1292d6458 Mon Sep 17 00:00:00 2001 From: ZepeiX <77278187+ZepeiX@users.noreply.github.com> Date: Sat, 22 Jun 2024 09:19:59 +0200 Subject: [PATCH 218/319] missing lambda in final values for CO-HeMS grids (#338) * Update IF_interpolation.py Solve the missing lambda for CO-HeMS grids * Update IF_interpolation.py --- posydon/interpolation/IF_interpolation.py | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/posydon/interpolation/IF_interpolation.py b/posydon/interpolation/IF_interpolation.py index 7e0e250c21..fdcfe2b3fc 100644 --- a/posydon/interpolation/IF_interpolation.py +++ b/posydon/interpolation/IF_interpolation.py @@ -1009,12 +1009,16 @@ def _fillNans(self, ic): if any(wnan[self.valid > 0]): k1r = KNeighborsRegressor(n_neighbors=1) wT = (~wnan) & (self.valid > 0) - xs = MatrixScaler(self._bestInScaling(ic)[1], - self.XT[self.valid >= 0, :]) - k1r.fit(xs.normalize(self.XT[wT, :]), self.YT[wT, i]) - wt = wnan & (self.valid > 0) - self.YT[wt, i] = k1r.predict(xs.normalize(self.XT[wt, :])) - + if wT.any(): + xs = MatrixScaler(self._bestInScaling(ic)[1], + self.XT[self.valid >= 0, :]) + k1r.fit(xs.normalize(self.XT[wT, :]), self.YT[wT, i]) + wt = wnan & (self.valid > 0) + self.YT[wt, i] = k1r.predict(xs.normalize(self.XT[wt, :])) + else: + wt = wnan & (self.valid > 0) + self.YT[wt, i] = 0.0 + self.out_nan_keys.append(self.out_keys[i]) # BASE INTERPOLATORS From 2862f8070832aacfc6ac9b0f23ff5db6c8ee9c4f Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Fri, 5 Jul 2024 17:36:28 +0200 Subject: [PATCH 219/319] Podsiadlowski spelling + docstring (#349) * Update MODELS.py Podsiadlowksi -> Podsiadlowski * Update population_params_default.ini Podsiadlowksi -> Podsiadlowski * Update step_SN.py Podsiadlowksi -> Podsiadlowski add option in docstring (incl. reference) --- posydon/binary_evol/SN/step_SN.py | 24 ++++++++++++-------- posydon/grids/MODELS.py | 20 ++++++++-------- posydon/popsyn/population_params_default.ini | 4 ++-- 3 files changed, 27 insertions(+), 21 deletions(-) diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index f9e6eaec92..7060668047 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -76,7 +76,7 @@ "mechanism": 'Patton&Sukhbold20-engine', "engine": 'N20', "PISN": "Marchant+19", - "ECSN": "Podsiadlowksi+04", + "ECSN": "Podsiadlowski+04", "conserve_hydrogen_envelope" : False, "max_neutrino_mass_loss": NEUTRINO_MASS_LOSS_UPPER_LIMIT, "max_NS_mass": STATE_NS_STARMASS_UPPER_LIMIT, @@ -162,6 +162,8 @@ class StepSN(object): supernova. Avialable options: + - 'Podsiadlowski+04': Determines the electron capture supernova in + terms of the He core mass at pre-supernova, taking limits from [7]_. - 'Tauris+15': Determines the electron capture supernova in terms of the CO core mass at pre-supernova, taking the limits from [4]_. @@ -240,6 +242,10 @@ class StepSN(object): Simulating Turbulence-aided Neutrino-driven Core-collapse Supernova Explosions in One Dimension + .. [7] Podsiadlowski, P., Langer, N., Poelarends, A. J. T., Rappaport, S., + Heger, A., and Pfahl, E. 2004, ApJ, 612, 1044. The Effects of Binary + Evolution on the Dynamics of Core Collapse and Neutron Star Kicks + """ def __init__(self, **kwargs): @@ -975,10 +981,10 @@ def check_SN_type(self, m_core, m_He_core, m_star): "domain of electron-capture SN and Fe core-collapse SN." ) - elif self.ECSN == 'Podsiadlowksi+04': + elif self.ECSN == 'Podsiadlowski+04': # Limits on He core mass progenitors of ECSN, default on cosmic - min_M_He_ECSN = 1.4 # Msun from Podsiadlowksi+2004 - max_M_He_ECSN = 2.5 # Msun from Podsiadlowksi+2004 + min_M_He_ECSN = 1.4 # Msun from Podsiadlowski+2004 + max_M_He_ECSN = 2.5 # Msun from Podsiadlowski+2004 if m_He_core < min_M_He_ECSN: # The birth of a white dwarf is assumed @@ -1119,7 +1125,7 @@ def compute_m_rembar(self, star, m_PISN): m_proto = 1.1 if star.SN_type == "ECSN": - if self.ECSN == 'Podsiadlowksi+04': + if self.ECSN == 'Podsiadlowski+04': m_proto = 1.38 else: m_proto = m_core @@ -1158,7 +1164,7 @@ def compute_m_rembar(self, star, m_PISN): m_proto = 1.6 if star.SN_type == "ECSN": - if self.ECSN == 'Podsiadlowksi+04': + if self.ECSN == 'Podsiadlowski+04': m_proto = 1.38 else: m_proto = m_core @@ -1192,7 +1198,7 @@ def compute_m_rembar(self, star, m_PISN): elif self.mechanism == self.Sukhbold16_engines: if star.SN_type == "ECSN": - if self.ECSN == 'Podsiadlowksi+04': + if self.ECSN == 'Podsiadlowski+04': m_proto = 1.38 else: m_proto = m_core @@ -1209,7 +1215,7 @@ def compute_m_rembar(self, star, m_PISN): elif self.mechanism == self.Couch20_engines: if star.SN_type == "ECSN": - if self.ECSN == 'Podsiadlowksi+04': + if self.ECSN == 'Podsiadlowski+04': m_proto = 1.38 else: m_proto = m_core @@ -1223,7 +1229,7 @@ def compute_m_rembar(self, star, m_PISN): elif self.mechanism == self.Patton20_engines: if star.SN_type == "ECSN": - if self.ECSN == 'Podsiadlowksi+04': + if self.ECSN == 'Podsiadlowski+04': m_proto = 1.38 else: m_proto = m_core diff --git a/posydon/grids/MODELS.py b/posydon/grids/MODELS.py index dc45c37d19..aaaf668081 100644 --- a/posydon/grids/MODELS.py +++ b/posydon/grids/MODELS.py @@ -19,7 +19,7 @@ "mechanism": "direct", "engine": "", "PISN": "Marchant+19", - "ECSN": "Podsiadlowksi+04", + "ECSN": "Podsiadlowski+04", "conserve_hydrogen_envelope" : False, "max_neutrino_mass_loss": NEUTRINO_MASS_LOSS_UPPER_LIMIT, "max_NS_mass": STATE_NS_STARMASS_UPPER_LIMIT, @@ -32,7 +32,7 @@ "mechanism": "Fryer+12-rapid", "engine": "", "PISN": "Marchant+19", - "ECSN": "Podsiadlowksi+04", + "ECSN": "Podsiadlowski+04", "conserve_hydrogen_envelope" : False, "max_neutrino_mass_loss": NEUTRINO_MASS_LOSS_UPPER_LIMIT, "max_NS_mass": STATE_NS_STARMASS_UPPER_LIMIT, @@ -45,7 +45,7 @@ "mechanism": "Fryer+12-delayed", "engine": "", "PISN": "Marchant+19", - "ECSN": "Podsiadlowksi+04", + "ECSN": "Podsiadlowski+04", "conserve_hydrogen_envelope" : False, "max_neutrino_mass_loss": NEUTRINO_MASS_LOSS_UPPER_LIMIT, "max_NS_mass": STATE_NS_STARMASS_UPPER_LIMIT, @@ -58,7 +58,7 @@ "mechanism": "Sukhbold+16-engine", "engine": "N20", "PISN": "Marchant+19", - "ECSN": "Podsiadlowksi+04", + "ECSN": "Podsiadlowski+04", "conserve_hydrogen_envelope" : False, "max_neutrino_mass_loss": NEUTRINO_MASS_LOSS_UPPER_LIMIT, "max_NS_mass": STATE_NS_STARMASS_UPPER_LIMIT, @@ -71,7 +71,7 @@ "mechanism": "Patton&Sukhbold20-engine", "engine": "N20", "PISN": "Marchant+19", - "ECSN": "Podsiadlowksi+04", + "ECSN": "Podsiadlowski+04", "conserve_hydrogen_envelope" : False, "max_neutrino_mass_loss": NEUTRINO_MASS_LOSS_UPPER_LIMIT, "max_NS_mass": STATE_NS_STARMASS_UPPER_LIMIT, @@ -84,7 +84,7 @@ "mechanism": "direct", "engine": "", "PISN": "Marchant+19", - "ECSN": "Podsiadlowksi+04", + "ECSN": "Podsiadlowski+04", "conserve_hydrogen_envelope" : True, "max_neutrino_mass_loss": NEUTRINO_MASS_LOSS_UPPER_LIMIT, "max_NS_mass": STATE_NS_STARMASS_UPPER_LIMIT, @@ -97,7 +97,7 @@ "mechanism": "Fryer+12-rapid", "engine": "", "PISN": "Marchant+19", - "ECSN": "Podsiadlowksi+04", + "ECSN": "Podsiadlowski+04", "conserve_hydrogen_envelope" : True, "max_neutrino_mass_loss": NEUTRINO_MASS_LOSS_UPPER_LIMIT, "max_NS_mass": STATE_NS_STARMASS_UPPER_LIMIT, @@ -110,7 +110,7 @@ "mechanism": "Fryer+12-delayed", "engine": "", "PISN": "Marchant+19", - "ECSN": "Podsiadlowksi+04", + "ECSN": "Podsiadlowski+04", "conserve_hydrogen_envelope" : True, "max_neutrino_mass_loss": NEUTRINO_MASS_LOSS_UPPER_LIMIT, "max_NS_mass": STATE_NS_STARMASS_UPPER_LIMIT, @@ -123,7 +123,7 @@ "mechanism": "Sukhbold+16-engine", "engine": "N20", "PISN": "Marchant+19", - "ECSN": "Podsiadlowksi+04", + "ECSN": "Podsiadlowski+04", "conserve_hydrogen_envelope" : True, "max_neutrino_mass_loss": NEUTRINO_MASS_LOSS_UPPER_LIMIT, "max_NS_mass": STATE_NS_STARMASS_UPPER_LIMIT, @@ -136,7 +136,7 @@ "mechanism": "Patton&Sukhbold20-engine", "engine": "N20", "PISN": "Marchant+19", - "ECSN": "Podsiadlowksi+04", + "ECSN": "Podsiadlowski+04", "conserve_hydrogen_envelope" : True, "max_neutrino_mass_loss": NEUTRINO_MASS_LOSS_UPPER_LIMIT, "max_NS_mass": STATE_NS_STARMASS_UPPER_LIMIT, diff --git a/posydon/popsyn/population_params_default.ini b/posydon/popsyn/population_params_default.ini index b5278a5ae9..d084ab897f 100644 --- a/posydon/popsyn/population_params_default.ini +++ b/posydon/popsyn/population_params_default.ini @@ -213,8 +213,8 @@ # 'Patton&Sukhbold20-engine' or None for the others PISN = "Marchant+19" # None, "Marchant+19" - ECSN = "Podsiadlowksi+04" - # "Tauris+15", "Podsiadlowksi+04" + ECSN = "Podsiadlowski+04" + # "Tauris+15", "Podsiadlowski+04" conserve_hydrogen_envelope = False # True, False max_neutrino_mass_loss = 0.5 From 88c81f234621574ab40f14f8069111075b8a676d Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Fri, 5 Jul 2024 17:40:28 +0200 Subject: [PATCH 220/319] replace scipy.integrate.simps call with scipy.integrate.simpson call (#344) --- posydon/binary_evol/SN/profile_collapse.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/posydon/binary_evol/SN/profile_collapse.py b/posydon/binary_evol/SN/profile_collapse.py index 4ae0814e9e..583c2fa001 100644 --- a/posydon/binary_evol/SN/profile_collapse.py +++ b/posydon/binary_evol/SN/profile_collapse.py @@ -175,7 +175,7 @@ def f_temp1(t): f_temp2 = (f_nu_AM*density[:index_initial_BH+1] * angular_frequency[:index_initial_BH+1] * radius[:index_initial_BH+1]**4) - temp2 = integrate.simps(f_temp2, x=radius[:index_initial_BH + 1]) + temp2 = integrate.simpson(f_temp2, x=radius[:index_initial_BH + 1]) J_initial_BH = 2 * np.pi * temp1 * temp2 From a2fe944ef3745053f30b5f3ff5790944c83437c5 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 11 Jul 2024 17:05:39 +0200 Subject: [PATCH 221/319] Add user modules (#296) * add casting of scalar_names to float64 when writing to dataframe * separate BinaryPopulation creation from class initialisation * remove cast in singlestar and create standard scalar_names casting in io.py * add scalar_names type casting * add missed typo * add step_name to parser * create new BinaryPopulations in SyntheticPop.evolve() * remove MPI and RNG seeds using JOB_ID+metallicity * Fix for double entries in SyntheticPopulation.df_oneline * handle the binary history and oneline correctly with chunking * account for job array not starting at 0 * allow for job array runs without specifying mpi * add batch file selection from folder * clean up batch folders after merging runs * clean up typos * Altered the tutorial to reflect HPC changes and parse changes * adapt tutorials for new parallel run method * keep MPI capability, but do not import it on the cluster when using job arrays * add check if MPI and job arrays are ran together and raise an error * make sure communicator is not write to state. re-add combine_save_files to save function * additional check for MPI and job array runs. Exit code if parallel + job array requested * include local MPI explanation in tutorials * implemented matthias suggestions * shift entropy statement * typo fix in apply_logic function call * shift_index bug fix * restructure * change default pop synth to burst * restructure of Synthetic Class * add testing of the synthetic populations * allow for on-disk processing * implement mt, DTD, and merger efficiency for out-of-memory processing" * add basic tests for the popsynth classes. Not complete * fix for binary count not aligning with binary index * implement rate calculation + GRB selection * Adds chunksize bug fixes * Add population class" * add todo + remove older code" * remove leftover framework from initial concept * fix for synthetic pop; merge runs + reading from hdf for binary evolving" * read individual binary from hdf5 file * remove int cast again * read_fix * only open input file when loading in data * clean-up lines * Fix for nan values in formation channel creation * fix nan in formation channels * deep copy fix * implement multiprocessing to free memory on finishing a metallicity * account for numpy int64 * correct previous merge * Added additional loop checks for detached and CO-H(e)MS(_RLO) looping * when raising an error set state to 'ERR' and event to 'FAILED' * Add checks to make sure the SN type matches the appropriate the stellar state * implement PISN with massless_remnant * add star->massless_remnant to common_functions, and adapt flow_chart * move function to single_star to stop cyclical imports * remove leftover print * change order of CO * add comment on readability * add recalculation of the SN type and post-SN stellar state, if they do not match. If the recalculation does not match, force the SN_type to match the remnant. specifically (state; SN_Type) WD -> WD, NS/BH -> CCSN, PISN -> PISN. * adjust NS/BH check to make sure the logic works correctly * move check function to file scope level * change PISN logic to only calculate the SN_type if not a PISN * clean up code * separate BH and NS check BH can be CCSN and PPISN NS can be ECSN and CCSN * Change star.state to massless_remnant for PISN checking * set stellar state to massless)remnant * additional stellar state check * change in SN-CO match * np.max fix * remove TZO tagging if massless_remnant, since PISN can lead to a massless remnant too * add generic synthetic population creation * check if time is included in the input hist, only if not set to None * add plotting to SyntheticPopulation * remove old SyntheticPopulation code * remove old code that has been moved to Rates and Population in rate_calculation * rate calculation * redirect_CO_HeMS * implement a more flexible approach to populations, while storing the essential metadata information in the h5 file * add reindexing of binaries when writing to a new file * inheritance * add efficiency calculation, plotting for DTD and efficiency * add metallicity check when exporting data * add multi-metallicity warning * fix for formation_channel if in file * add chunking read for the formation_channels * fix create_formation_channels to filter out nans and add 'initial_MT' to the HMS-HMS-event-dic * add a history_lengths metadata to the file * transfer history_lengths to exported file and reindex them * remove reindexing from the appends when creating a new population * force history_lengths into a dataframe format instead of possibly pd.Series * Add correct selection of history_legnt * setu-popsyn for multimetallicity runs + rates calculation with the new population * fix mt_channel in rate calculation * fix metallicity fraction calculation * add MODEL_data loading when array being stored + fix empirical SFH calculation * add MODEL fix * add property histogram and properties in over_grid_slice * clean up of old classes * add example/standard transient selection functions * new module string; * add account option to setup-popsyn script * add extra comments * add _Zsun_ to set-popsyn? * add number of systems to mass_per_met * add PopulationRunner back for running multi-metallicity populations in a notebook * change tutorial 1 to new framework * change tutorial 2 to setup-popsyn scipt explanation * move formation_channel warning behind a verbose * add color option to hist_plotting and fix redirect issue with formation_channels * add custom label in plotting + removing leftover print statement * change df_synthetic to df_transient * clean up 10 binary notebook * add popsynth on HPC tutorial with no outputs * add BBH analysis in the new framework * remove created script for MPIruns * adding documentation * add docstrings for classes * move cosmology/redshift functions to rate_calculation and add Rates documnetation * cleanup * pre-commit code cleanup * rate_calculation cleanup * adding units tests for the new classes * add PopulationIO tests * add correct GRB_selection function * update tutorial up to GRBs * add overwrite=True as default for export_population * change mass_per_met to mass_per_metallicity " * add maximum string_length slicing on the formation_channels * change history chunking variable to history_chunksize * change mass_per_met to mass_per_metallicity * remove duplicate after merge * fix tilt track error * update tutorials * add sorting for parallel runs and add warning when trying to merge into already existing population. * increase merge walltime * add custom merge walltime" * only remove batch files once the target file has been close * notebook update * update mass_per_metallicity test * alllow for slice binary selection * add unit tests for Population class * formation channel read-in every time instead of storing * bug fix for metallicity fraction + add authorship * fix syntax error * reverse-MT formation channel fix * rename initialisation of spin_orbit_tilt" * update population_params_default; * Add both spin_orbit_tilt to dataframe length * carry chunksize initialisation to the base Population class * Add user_modules and example to modify the flow chart * allow mass ratio smaller than 0.05 * the underlying mass shouldn't cause errors * add missing values * remove doublicates * remove 'flat_mass_ratio_all' option * add documentation --------- Co-authored-by: Max Briel Co-authored-by: Max <14039563+maxbriel@users.noreply.github.com> --- .../pop_syn/user_modules.rst | 8 +++ .../stellar-binary-simulation.rst | 11 ++++ posydon/popsyn/binarypopulation.py | 2 +- posydon/popsyn/independent_sample.py | 3 +- posydon/popsyn/normalized_pop_mass.py | 9 ++- posydon/popsyn/transient_select_funcs.py | 2 +- posydon/user_modules/my_flow_chart_example.py | 55 +++++++++++++++++++ 7 files changed, 86 insertions(+), 4 deletions(-) create mode 100644 docs/_source/components-overview/pop_syn/user_modules.rst create mode 100644 posydon/user_modules/my_flow_chart_example.py diff --git a/docs/_source/components-overview/pop_syn/user_modules.rst b/docs/_source/components-overview/pop_syn/user_modules.rst new file mode 100644 index 0000000000..67f1443ba8 --- /dev/null +++ b/docs/_source/components-overview/pop_syn/user_modules.rst @@ -0,0 +1,8 @@ +.. _user_modules: + +User Modules +------------ + +To allow users to add code easily within the POSYDON namespace without modifying the code core we added a directory called :samp:`user_modules`. There users can put any own code and simply import it as any other function within the posydon name space. + +Additionally, we have there an example file of a case which modifies the :ref:`flow chart `. diff --git a/docs/_source/components-overview/stellar-binary-simulation.rst b/docs/_source/components-overview/stellar-binary-simulation.rst index ebe4fe7db9..81ee6b9f61 100644 --- a/docs/_source/components-overview/stellar-binary-simulation.rst +++ b/docs/_source/components-overview/stellar-binary-simulation.rst @@ -89,6 +89,17 @@ The evolutionary hooks allows the use to execute code between POSYDON steps. The pop_syn/custom_hooks + +User Functions and Modules +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +To allow users to easier add functions for their own needs without changing the main code, we have a dedicated directory for :samp:`user_modules`. + +.. toctree:: + :maxdepth: 1 + + pop_syn/user_modules + ---- diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index f13bf0329e..dc4d89ed61 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -20,7 +20,7 @@ "Konstantinos Kovlakas ", "Devina Misra ", "Simone Bavera ", - "Max Briel ", + "Max Briel ", "Matthias Kruckow ", ] diff --git a/posydon/popsyn/independent_sample.py b/posydon/popsyn/independent_sample.py index 382c09a0ae..14dde9c616 100644 --- a/posydon/popsyn/independent_sample.py +++ b/posydon/popsyn/independent_sample.py @@ -347,7 +347,8 @@ def generate_secondary_masses(primary_masses, # Generate secondary masses if secondary_mass_scheme == 'flat_mass_ratio': - mass_ratio_min = np.max([secondary_mass_min / primary_masses,np.ones(len(primary_masses))*0.05], axis=0) + mass_ratio_min = np.max([secondary_mass_min / primary_masses, + np.ones(len(primary_masses))*0.05], axis=0) mass_ratio_max = np.min([secondary_mass_max / primary_masses, np.ones(len(primary_masses))], axis=0) secondary_masses = ( diff --git a/posydon/popsyn/normalized_pop_mass.py b/posydon/popsyn/normalized_pop_mass.py index 720abcb322..71492d86d5 100644 --- a/posydon/popsyn/normalized_pop_mass.py +++ b/posydon/popsyn/normalized_pop_mass.py @@ -5,9 +5,12 @@ "Devina Misra ", "Simone Bavera ", "Konstantinos Kovlakas ", + "Matthias Kruckow ", ] +import numpy as np +import warnings from posydon.popsyn import independent_sample from scipy.integrate import quad @@ -104,7 +107,11 @@ def fraction_simulated(f_bin, m_1, m_2, m_min, m_max, alpha2 = 2.35 alpha3 = 2.35 else: - raise ValueError("Primary/Secondary mass scheme not included") + warnings.warn("Scheme not included yet: primary_mass_scheme=" + f"{kwargs['primary_mass_scheme']}, secondary_mass_scheme" + f"={kwargs['secondary_mass_scheme']}") + return np.nan, np.nan, np.nan +# raise ValueError("Scheme not included yet") f_bin = 0.7 m_min = 0.01 diff --git a/posydon/popsyn/transient_select_funcs.py b/posydon/popsyn/transient_select_funcs.py index d02363496f..dbc0854573 100644 --- a/posydon/popsyn/transient_select_funcs.py +++ b/posydon/popsyn/transient_select_funcs.py @@ -247,4 +247,4 @@ def DCO_detactability(sensitivity, transient_pop_chunk, z_events_chunk, z_weight return pd.DataFrame(detectable_weights, index=z_events_chunk.index, columns=z_events_chunk.columns) - \ No newline at end of file + diff --git a/posydon/user_modules/my_flow_chart_example.py b/posydon/user_modules/my_flow_chart_example.py new file mode 100644 index 0000000000..ef38a5592e --- /dev/null +++ b/posydon/user_modules/my_flow_chart_example.py @@ -0,0 +1,55 @@ +"""An example of a modified function of the flow chart.""" + + +__authors__ = [ + "Matthias Kruckow ", +] + +import posydon.binary_evol.flow_chart as fc +from posydon.binary_evol.flow_chart import (flow_chart, STAR_STATES_NORMALSTAR) + +def modified_flow_chart(FLOW_CHART=None, CHANGE_FLOW_CHART=None): + """Derive a flow chart from POSYDON's standard one and modify it. + + Parameters + ---------- + FLOW_CHART : dict or None + CHANGE_FLOW_CHART : dict or None + + Returns + ------- + dict + Modified flow chart. + """ + + # get POSYDON's default flow chart + if FLOW_CHART is None: + if CHANGE_FLOW_CHART is None: + MY_FLOW_CHART = fc.flow_chart() + else: + MY_FLOW_CHART = fc.flow_chart(CHANGE_FLOW_CHART=CHANGE_FLOW_CHART) + else: + if CHANGE_FLOW_CHART is None: + MY_FLOW_CHART = fc.flow_chart(FLOW_CHART=FLOW_CHART) + else: + MY_FLOW_CHART = fc.flow_chart(FLOW_CHART=FLOW_CHART, + CHANGE_FLOW_CHART=CHANGE_FLOW_CHART) + # modify the flow chart + for key in MY_FLOW_CHART.keys(): + # here changing to go always from ZAMS to step_detached + if ((len(key)>3) and (key[3]=='ZAMS') and + (key[2] in fc.BINARY_STATES_ZAMS)): # check for event + MY_FLOW_CHART[key] = 'step_detached' + # here changing all starting RLO to end instead + if ((len(key)>2) and ('RLO' in key[2])): # check for state + MY_FLOW_CHART[key] = 'step_end' + # adding new entries to the flow to send RLO to end which would otherwise + # be jumped over in the HMS-HMS step + for s1 in STAR_STATES_NORMALSTAR: + for s2 in STAR_STATES_NORMALSTAR: + MY_FLOW_CHART[(s1, s2, 'RLO1', 'oRLO1')] = 'step_end' + MY_FLOW_CHART[(s2, s1, 'RLO1', 'oRLO1')] = 'step_end' + MY_FLOW_CHART[(s1, s2, 'RLO2', 'oRLO2')] = 'step_end' + MY_FLOW_CHART[(s2, s1, 'RLO2', 'oRLO2')] = 'step_end' + + return MY_FLOW_CHART From 4d627f1e4aaa5c2a59c290d453651c9c58c70474 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 11 Jul 2024 17:15:28 +0200 Subject: [PATCH 222/319] move creation of export dirs, to make it dynamical (#339) --- bin/posydon-setup-pipeline | 51 +++++++++----------------------------- 1 file changed, 12 insertions(+), 39 deletions(-) diff --git a/bin/posydon-setup-pipeline b/bin/posydon-setup-pipeline index 9edf5b7ff2..18b990f9e2 100644 --- a/bin/posydon-setup-pipeline +++ b/bin/posydon-setup-pipeline @@ -543,39 +543,6 @@ class PostProcessingPipeline: os.makedirs(step_log_path) - def create_export_dirs(self): - """Create directories for the finally exported data.""" - - if 'pipeline setup' not in self.pipeline_kwargs.keys(): - raise KeyError('pipeline setup not in pipeline_kwargs!') - setup_kwargs = self.pipeline_kwargs['pipeline setup'] - - # create data dir tree to export the datasets - if (('EXPORT_DATASET' in setup_kwargs.keys()) and - setup_kwargs['EXPORT_DATASET']): - if 'PATH' in setup_kwargs.keys(): - data_path = os.path.join(setup_kwargs['PATH'], 'POSYDON_data') - else: - data_path = os.path.join('.', 'POSYDON_data') - # create main directory for the POSYDON data - if not os.path.isdir(data_path): - os.makedirs(data_path) - # determine sub directories - dirs = [] - grid_dirs = ['HMS-HMS', 'CO-HMS_RLO', 'CO-HeMS', 'CO-HeMS_RLO', - 'single_HMS', 'single_HeMS'] - interp_dirs = ['interpolators', 'interpolators/1NN_1NN', - 'interpolators/linear3c_kNN'] - for name1 in grid_dirs: - dirs.append(os.path.join(data_path, name1)) - for name2 in interp_dirs: - dirs.append(os.path.join(data_path, name1, name2)) - # create sub directories - for dir_ in dirs: - if not os.path.isdir(dir_): - os.makedirs(dir_) - - def slurm_job(job_name, step_name, PATH_TO_GRIDS=None, @@ -1000,17 +967,24 @@ def create_csv(GRID_TYPES=[], grids.append(os.path.join(PATH_TO_GRIDS, grid_type, VERSION, metallicity, compression, grid_slice+'.h5')) - export_path.append(os.path.join(PATH, 'POSYDON_data', - export_dir, metallicity+'.h5')) + full_export_dir = os.path.join(PATH, 'POSYDON_data', + export_dir) + if not os.path.isdir(full_export_dir): + os.makedirs(full_export_dir) + export_path.append(os.path.join(full_export_dir, + metallicity+'.h5')) for method in [['linear','linear3c_kNN'], ['1NN','1NN_1NN']]: grids.append(os.path.join(PATH_TO_GRIDS, grid_type, VERSION, metallicity, 'interpolation_objects', 'IF_'+method[0]+RLO+'.pkl')) - export_path.append(os.path.join(PATH, - 'POSYDON_data', export_dir, - 'interpolators', method[1], + full_export_dir = os.path.join(PATH, + 'POSYDON_data', export_dir, + 'interpolators', method[1]) + if not os.path.isdir(full_export_dir): + os.makedirs(full_export_dir) + export_path.append(os.path.join(full_export_dir, metallicity+'.pkl')) elif step_name == 'rerun': # RERUN # This step needs the grid to get the runs from, the @@ -1450,6 +1424,5 @@ if __name__ == '__main__': pipeline = PostProcessingPipeline(ini_file_path) pipeline.create_csv_and_slurm_job_files() pipeline.create_log_dirs() - pipeline.create_export_dirs() From beb75a99a6d3bef862e26a12f6e7ffcb2d285613 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 11 Jul 2024 17:25:59 +0200 Subject: [PATCH 223/319] Update scrubbing.py (#348) Another place where we need to adjust the RLO condition --- posydon/grids/scrubbing.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/posydon/grids/scrubbing.py b/posydon/grids/scrubbing.py index 19d47b7b00..33b5f9efd2 100644 --- a/posydon/grids/scrubbing.py +++ b/posydon/grids/scrubbing.py @@ -122,9 +122,8 @@ def keep_after_RLO(bh, h1, h2): if rlo_1_or_2 is None: raise ValueError("No `rl_relative_overflow` in any star history.") - # This needs to be aligned with run_binary_extras.f, there it is less - # restrictive, which is fine - conditions_met = rlo_1_or_2 & rate + # This needs to be aligned with run_binary_extras.f + conditions_met = rlo_1_or_2 | rate where_conditions_met = np.where(conditions_met)[0] if len(where_conditions_met) == 0: From 6f72972d835b2fc119f7ff1f4d668386d56a0811 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 11 Jul 2024 17:28:14 +0200 Subject: [PATCH 224/319] Update PISN range in step_SN.py (#352) Use He-core mass range for PISN. (previously the CO-core mass value was compared to He-core mass) --- posydon/binary_evol/SN/step_SN.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index 7060668047..b52360dea5 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -887,7 +887,7 @@ def PISN_prescription(self, star): # this is the 8th-order polynomial fit of table 1 # value, see COSMIC paper (Breivik et al. 2020) polyfit = ( - -6.29429263e5 + - 6.29429263e5 + 1.15957797e5 * m_He_core - 9.28332577e3 * m_He_core ** 2.0 + 4.21856189e2 * m_He_core ** 3.0 @@ -899,7 +899,12 @@ def PISN_prescription(self, star): ) m_PISN = polyfit - elif m_He_core > 61.10 and m_He_core < 113.29: + elif m_He_core > 61.10 and m_He_core < 124.12: + # in Breivik et al. (2020) they qoute the CO core mass + # range as 54.48 Date: Thu, 11 Jul 2024 17:28:49 +0200 Subject: [PATCH 225/319] IF interpolator `out_nan_keys` as an input (#342) * add out_nan_keys * add better selection of out_nan_keys * Update posydon-run-pipeline * Update posydon-run-pipeline --------- Co-authored-by: Jeff Andrews --- bin/posydon-run-pipeline | 16 ++++++++++++++++ posydon/interpolation/IF_interpolation.py | 8 ++++++-- 2 files changed, 22 insertions(+), 2 deletions(-) diff --git a/bin/posydon-run-pipeline b/bin/posydon-run-pipeline index cef1ee0219..6a47d1871b 100644 --- a/bin/posydon-run-pipeline +++ b/bin/posydon-run-pipeline @@ -376,6 +376,13 @@ def train_interpolators(i, path_to_csv_file, verbose=False): and any(~np.isnan(grid.final_values[key])) and "MODEL" not in key)] + + out_nan_keys = [key for key in grid.final_values.dtype.names if ( + key != "model_number" and + (type(grid.final_values[key][0]) != np.str_) + and all(np.isnan(grid.final_values[key])) + and "MODEL" not in key)] + # define all string keys in final values c_keys = ['interpolation_class', 'S1_state', 'S2_state', 'mt_history'] for MODEL_NAME in MODELS.keys(): @@ -389,6 +396,7 @@ def train_interpolators(i, path_to_csv_file, verbose=False): "interp_method": interp_method, "interp_classes": interp_classes, "out_keys": out_keys, + "out_nan_keys": out_nan_keys, "class_method": "kNN", "c_keys": c_keys, "c_key": "interpolation_class" @@ -407,6 +415,13 @@ def train_interpolators(i, path_to_csv_file, verbose=False): (type(grid.final_values[key][0]) != np.str_) and any(~np.isnan(grid.final_values[key])) and f"S{i}_{MODEL_NAME}" in key)] + + out_nan_keys = [key for key in grid.final_values.dtype.names if ( + key != "model_number" + and (type(grid.final_values[key][0]) != np.str_) + and all(np.isnan(grid.final_values[key])) + and f"S{i}_{MODEL_NAME}" in key)] + # get interpolations classes dynamically interp_method = [] interp_classes = [] @@ -420,6 +435,7 @@ def train_interpolators(i, path_to_csv_file, verbose=False): "interp_method": interp_method, "interp_classes": interp_classes, "out_keys": out_keys, + "out_nan_keys": out_nan_keys, "class_method": "kNN", "c_keys": [f'S{i}_{MODEL_NAME}_CO_interpolation_class', f'S{i}_{MODEL_NAME}_CO_type', diff --git a/posydon/interpolation/IF_interpolation.py b/posydon/interpolation/IF_interpolation.py index fdcfe2b3fc..2726db001b 100644 --- a/posydon/interpolation/IF_interpolation.py +++ b/posydon/interpolation/IF_interpolation.py @@ -359,7 +359,7 @@ def save(self, filename): class BaseIFInterpolator: """Class handling the initial-final interpolation for POSYDON.""" - def __init__(self, grid=None, in_keys=None, out_keys=None, in_scaling=None, + def __init__(self, grid=None, in_keys=None, out_keys=None, out_nan_keys=None, in_scaling=None, out_scaling=None, filename=None, interp_method="linear", interp_classes=None, class_method="kNN", c_keys=None, c_key=None): @@ -379,6 +379,9 @@ def __init__(self, grid=None, in_keys=None, out_keys=None, in_scaling=None, out_keys : list of strings The keys for which the interpolator is supposed to provide values, by default all keys are used. + out_nan_keys : list of strings + The keys for which the interpolator is supposed to provide values, + but are all nan in the grid. in_scaling : list of strings The scalings for the input keys, by default these scalings are optimized through Monte Carlo Cross Validation. @@ -407,7 +410,7 @@ def __init__(self, grid=None, in_keys=None, out_keys=None, in_scaling=None, """ self.in_keys, self.out_keys = in_keys, out_keys - self.out_nan_keys = [] + self.out_nan_keys = out_nan_keys self.in_scaling, self.out_scaling = in_scaling, out_scaling self.n_in, self.n_out = 0, 0 self.in_scalers, self.out_scalers = [], [] @@ -457,6 +460,7 @@ def __init__(self, grid=None, in_keys=None, out_keys=None, in_scaling=None, and (type(grid.final_values[key][0]) != np.str_) and any(~np.isnan(grid.final_values[key])) ] + if self.out_nan_keys is None: self.out_nan_keys = [ key for key in grid.final_values.dtype.names if type(grid.final_values[key][0]) != np.str_ From 693c1cdc07207e5ee3e06819f5b8ebcabb2d31aa Mon Sep 17 00:00:00 2001 From: ezapartas Date: Thu, 18 Jul 2024 17:29:27 +0300 Subject: [PATCH 226/319] Keep abundaces in envelope at collapse & M4,mu4 values (#340) * added the M4 mu4 qunatities as stored in step_SN, to be kept in the singlestar object as scalars * added the variables also in CC_quantities in grids/post_processing.py * allowing for ejecta h1,he4,o16 at final values * updated post_processing * implemented Matthias comments * added the h1_mass_ej etc labels in io, interpolation, singlestar, and step_mesa files * added the *_He_depletion * added M4, mu4 correctly in the stings of post-processing, in the interpolation.py, and in step_mesa * checking if M4,mu4 are None * added in plot_defaults * added in plot_defaults * scalar *He_depletion values are not in SCALAR_NAMES_DTYPES * allow for None in the ccquantities M4, mu4, which after become np.nan * allow also for Mej to be None * typo * enclosed_mass typo * testing with prints the profile * replace profile['h1'] with 'x_mass_fraction_H', same for he4 and took away the o16_mass_ej from everywhere. Also change the default thershold values for ploting M4, mu4 * caclulating correctly the ejecta masses in the profile * taking away the +1 in the profile index of the boundary * making the lengths of all arrays equal and implementing sanity checks * checking values with various prints * taking away the prints --- posydon/binary_evol/MESA/step_mesa.py | 46 ++++---- posydon/binary_evol/SN/profile_collapse.py | 67 ++++++++++- posydon/binary_evol/SN/step_SN.py | 131 ++++++++++++--------- posydon/binary_evol/singlestar.py | 30 +++-- posydon/grids/post_processing.py | 60 +++++----- posydon/interpolation/interpolation.py | 5 +- posydon/popsyn/io.py | 82 +++++++------ posydon/visualization/plot_defaults.py | 33 +++++- 8 files changed, 292 insertions(+), 162 deletions(-) diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index c21e3a1956..fc27e3f9d2 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -252,7 +252,7 @@ def load_Interp(self, filename): """Load the interpolator that has been trained on the grid.""" if self.verbose: print("loading Interp: {}".format(filename)) - + # Check if interpolation files exist if not os.path.exists(filename): data_download() @@ -335,7 +335,7 @@ def __call__(self, binary): self.step(binary, interp_method=self.interpolation_method) if (self.stop_method == 'stop_at_max_time' and binary.time >= binary.properties.max_simulation_time): - + # self.flush_history = True # needed??? # stop_at_condition looks through the MESA output appended to the @@ -441,10 +441,10 @@ def update_properties_NN(self, star_1_CO=False, star_2_CO=False, setattr(binary, 'state', binary_state) setattr(binary, 'event', binary_event) setattr(binary, 'mass_transfer_case', MT_case) - + if binary.state == 'initial_RLOF': return - + if track_interpolation or self.save_initial_conditions: len_binary_hist = len(getattr(binary, "time_history")) length_binary_hist = len(cb_bh['age']) - 1 @@ -672,7 +672,7 @@ def update_properties_NN(self, star_1_CO=False, star_2_CO=False, setattr(binary, 'state', binary_state) setattr(binary, 'event', binary_event) setattr(binary, 'mass_transfer_case', MT_case) - + culmulative_mt_case = self.termination_flags[1] setattr(self.binary, f'culmulative_mt_case_{self.grid_type}', culmulative_mt_case) setattr(self.binary, f'interp_class_{self.grid_type}', interpolation_class) @@ -760,7 +760,7 @@ def update_properties_NN(self, star_1_CO=False, star_2_CO=False, history_of_attribute = (np.log10( 10**cb_bh[key_bh][0] + cf.bondi_hoyle( binary, accretor, donor, idx=len_binary_hist, - wind_disk_criteria=True, scheme='Kudritzki+2000'))) + wind_disk_criteria=True, scheme='Kudritzki+2000'))) if 10**history_of_attribute > edd: history_of_attribute = np.log10(edd) accretor.lg_mdot_history.append(history_of_attribute) @@ -805,7 +805,8 @@ def update_properties_NN(self, star_1_CO=False, star_2_CO=False, cb.final_values[f'S{i+1}_{MODEL_NAME}_CO_type'] != 'None'): values = {} for key in ['state', 'SN_type', 'f_fb', 'mass', 'spin', - 'm_disk_accreted', 'm_disk_radiated']: + 'm_disk_accreted', 'm_disk_radiated', 'M4', 'mu4', + 'h1_mass_ej', 'he4_mass_ej']: if key == "state": state = cb.final_values[f'S{i+1}_{MODEL_NAME}_CO_type'] values[key] = state @@ -896,7 +897,7 @@ def initial_final_interpolation(self, star_1_CO=False, star_2_CO=False): setattr(self.binary, f'interp_class_{self.grid_type}', interpolation_class) mt_history = self.classes['mt_history'] # mass transfer history (TF12 plot label) setattr(self.binary, f'mt_history_{self.grid_type}', mt_history) - + #TODO: add classifier for tf2 #setattr(self.binary, f'culmulative_mt_case', self.classes['termination_flags_2']) S1_state_inferred = cf.check_state_of_star(self.binary.star_1, @@ -933,7 +934,7 @@ def initial_final_interpolation(self, star_1_CO=False, star_2_CO=False): 10**fv[key_bh] + cf.bondi_hoyle( binary, accretor, donor, idx=-1, wind_disk_criteria=True, scheme='Kudritzki+2000')) - + mdot_edd = cf.eddington_limit(binary, idx=-1)[0] if 10**tmp_lg_mdot > mdot_edd: tmp_lg_mdot = np.log10(mdot_edd) @@ -969,7 +970,8 @@ def initial_final_interpolation(self, star_1_CO=False, star_2_CO=False): self.classes[f'S{i+1}_{MODEL_NAME}_CO_type'] != 'None'): values = {} for key in ['state', 'SN_type', 'f_fb', 'mass', 'spin', - 'm_disk_accreted', 'm_disk_radiated']: + 'm_disk_accreted', 'm_disk_radiated', 'M4', 'mu4', + 'h1_mass_ej', 'he4_mass_ej']: if key == "state": state = self.classes[f'S{i+1}_{MODEL_NAME}_CO_type'] values[key] = state @@ -1019,7 +1021,7 @@ def stop_at_condition(self, raise ValueError( 'Star can only be "star_1" or "star_2", you passed {0}'. format(star)) - + i = np.where(np.array(property_history) <= value)[0][-1] elif property in BINARYPROPERTIES: @@ -1067,7 +1069,7 @@ def stop_at_condition(self, continue interpolated_quanties[star][key] = self.interpolate_at_t( t, t_before, t_after, v_before, v_after) - + for key in BINARYPROPERTIES: if key in ['state', 'event', 'mass_transfer_case']: interpolated_quanties['binary'][key] = getattr( @@ -1258,7 +1260,7 @@ def __call__(self, binary): self.p_min <= p <= self.p_max): self.flip_stars_before_step = True super().__call__(self.binary) - + # redirect if outside grid for period elif (state_1 == 'H-rich_Core_H_burning' and state_2 == 'H-rich_Core_H_burning' and @@ -1394,7 +1396,7 @@ def __call__(self, binary): ecc == 0.)): super().__call__(self.binary) - + # period inside the grid, but m1 outside the grid elif ((not self.flip_stars_before_step and self.p_min <= p <= self.p_max and @@ -1418,7 +1420,7 @@ def __call__(self, binary): set_binary_to_failed(self.binary) raise GridError(f'The mass of m1 ({m2}) is outside the grid,' ' while the period is inside the grid.') - + # period inside the grid, but m2 outside the grid (flipped stars) elif ((self.flip_stars_before_step and self.p_min <= p <= self.p_max and @@ -1426,7 +1428,7 @@ def __call__(self, binary): set_binary_to_failed(self.binary) raise GridError(f'The mass of m2 ({m1}) is outside the grid,' ' while the period is inside the grid.') - + else: self.binary.state = "detached" self.binary.event = "redirect_from_CO_HMS_RLO" @@ -1507,7 +1509,7 @@ def __call__(self, binary): 'The star_1.state = %s, star_2.state = %s, binary.state = %s, ' 'binary.event = %s and not CO - HeMS - oRLO1/oRLO2!' % (state_1, state_2, state, event)) - + # redirect if outside grids if ((not self.flip_stars_before_step and self.m1_min <= m1 <= self.m1_max and @@ -1519,7 +1521,7 @@ def __call__(self, binary): self.p_min <= p <= self.p_max and ecc == 0.)): super().__call__(self.binary) - + # period inside the grid, but m1 outside the grid elif ((not self.flip_stars_before_step and self.p_min <= p <= self.p_max and @@ -1527,7 +1529,7 @@ def __call__(self, binary): set_binary_to_failed(self.binary) raise GridError(f'The mass of m1 ({m1}) is outside the grid,' ' while the period is inside the grid.') - + # period inside the grid, but m2 outside the grid elif ((not self.flip_stars_before_step and self.p_min <= p <= self.p_max and @@ -1535,9 +1537,9 @@ def __call__(self, binary): set_binary_to_failed(self.binary) raise GridError(f'The mass of m2 ({m2}) is outside the grid,' ' while the period is inside the grid.') - + # period inside the grid, but m1 outside the grid with flipped stars - elif ((self.flip_stars_before_step and + elif ((self.flip_stars_before_step and self.p_min <= p <= self.p_max and m1 < self.m2_min or m1 > self.m2_max)): set_binary_to_failed(self.binary) @@ -1647,7 +1649,7 @@ def __call__(self, binary): set_binary_to_failed(self.binary) raise GridError(f'The mass of m1 ({m1}) is outside the grid,' ' while the period is inside the grid.') - + # period inside the grid, but m2 outside the grid elif (self.p_min <= p <= self.p_max) and (m2 < self.m2_min or m2 > self.m2_max): set_binary_to_failed(self.binary) diff --git a/posydon/binary_evol/SN/profile_collapse.py b/posydon/binary_evol/SN/profile_collapse.py index 583c2fa001..ffa4451c05 100644 --- a/posydon/binary_evol/SN/profile_collapse.py +++ b/posydon/binary_evol/SN/profile_collapse.py @@ -24,6 +24,71 @@ 'Aldo Batta ', ] +def get_ejecta_element_mass_at_collapse(star, compact_object_mass, verbose): + """Calculate the masses of H1, He4, and O16 in the ejecta. + Parameters + ---------- + star : object + Star object of a collapsing star containing the MESA profile. + + compact_object_mass : float + The mass of the compact object (in Msun), hence all above this mass will be ejected. + + verbose : bool + If `True`, it prints some informations. + + Returns + ------- + h1_mass_ej : float + Hydrogen mass in the ejecta. (in Msun) + he4_mass_ej : float + Helium mass in the ejecta. (in Msun) + """ + + + # read star quantities + profile_mass_all = star.profile['mass'][::-1] # cell outer total mass in Msun + # shell's mass + dm_all = profile_mass_all[1:] - profile_mass_all[:-1] + + if 'x_mass_fraction_H' in star.profile.dtype.names: + XH_all = star.profile['x_mass_fraction_H'][::-1] # h1 mass fraction + if 'y_mass_fraction_He' in star.profile.dtype.names: + YHe_all = star.profile['y_mass_fraction_He'][::-1] # he4 mass fraction + + #print("profile_mass_all[-1], compact_object_mass", profile_mass_all[-1], compact_object_mass) + if profile_mass_all[-1] <= compact_object_mass: + # This catches the case that all the star's profile is collapsed. + h1_mass_ej = 0.0 + he4_mass_ej = 0.0 + else: + # Find the index where the profile mass exceeds the compact object mass + i_rem = np.argmax(profile_mass_all > compact_object_mass) + #print("i_rem, len(dm_all)", i_rem, len(dm_all)) + #print("mass coordinate above which it is ejected", profile_mass_all[i_rem]) + + # Ensure the index is within bounds + if i_rem < len(dm_all): + # Calculate the ejected mass of H1 and He4 + dm_ejected = dm_all[i_rem:] + XH_ejected = XH_all[i_rem + 1:] # +1 because dm_all has one less element than profile_mass_all + YHe_ejected = YHe_all[i_rem + 1:] + + # Calculate the ejected masses only if the lengths match + if len(dm_ejected) == len(XH_ejected) == len(YHe_ejected): + h1_mass_ej = np.sum(dm_ejected * XH_ejected) + he4_mass_ej = np.sum(dm_ejected * YHe_ejected) + else: + h1_mass_ej = 0.0 + he4_mass_ej = 0.0 + print("Warning: Mismatch in array lengths, cannot calculate ejected masses accurately.") + else: + h1_mass_ej = 0.0 + he4_mass_ej = 0.0 + print("Warning: Index out of bounds, cannot calculate ejected masses.") + #print(h1_mass_ej, he4_mass_ej, np.sum(dm_ejected)) + return h1_mass_ej, he4_mass_ej + def get_initial_BH_properties(star, mass_collapsing, mass_central_BH, neutrino_mass_loss, max_neutrino_mass_loss, @@ -555,7 +620,7 @@ def f_temp2(x): return [ M_BH_total, a_BH_total, - m_disk_accreted, + m_disk_accreted, m_disk_radiated, # np.array(M_BH_array), # np.array(a_BH_array), diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index b52360dea5..258943833c 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -48,7 +48,8 @@ from posydon.binary_evol.binarystar import BINARYPROPERTIES from posydon.binary_evol.singlestar import STARPROPERTIES, convert_star_to_massless_remnant -from posydon.binary_evol.SN.profile_collapse import do_core_collapse_BH +from posydon.binary_evol.SN.profile_collapse import (do_core_collapse_BH, + get_ejecta_element_mass_at_collapse) from posydon.binary_evol.flow_chart import (STAR_STATES_CO, STAR_STATES_CC, STAR_STATES_C_DEPLETION) @@ -201,7 +202,7 @@ class StepSN(object): This option uses the core masses at carbon depletion to determine the core collapse outcoume (classical population sythesis threatment). - + allow_spin_None : bool This option does not determine the spin during core collapse while setting other values like in use_core_masses. (used to avoid jumps @@ -417,7 +418,7 @@ def __call__(self, binary): # collapse star self.collapse_star(star=binary.star_1) self._reset_other_star_properties(star=binary.star_2) - + elif binary.event == "CC2": # collapse star self.collapse_star(star=binary.star_2) @@ -498,7 +499,7 @@ def collapse_star(self, star): for key in ['m_disk_accreted', 'm_disk_radiated']: if getattr(star, key) is None: setattr(star, key, np.nan) - + # Verifies if the star is in state state where it can # explode if state in STAR_STATES_CC: @@ -522,25 +523,25 @@ def collapse_star(self, star): if self.verbose: print('matched to model:', tmp) MODEL_NAME_SEL = tmp - + # check if selected MODEL is supported if MODEL_NAME_SEL is None: raise ValueError('Your model assumptions are not' - 'supported!') + 'supported!') elif getattr(star, MODEL_NAME_SEL) is None: # NOTE: this option is needed to do the collapse - # for stars evolved with the step_detached or + # for stars evolved with the step_detached or # step_disrupted. # allow to continue with the collapse with profile # or core masses warnings.warn(f'{MODEL_NAME_SEL}: The collapsed star ' 'was not interpolated! If use_profiles ' 'or use_core_masses is set to True, ' - 'continue with the collapse.') - - else: + 'continue with the collapse.') + + else: MODEL_properties = getattr(star, MODEL_NAME_SEL) - + ## Check if SN_type mismatches the CO_type in MODEL or if interpolated MODEL properties are NaN ## If either are true, interpolated values cannot be used for this SN if (check_SN_CO_match(MODEL_properties['SN_type'], MODEL_properties['state']) and @@ -549,30 +550,30 @@ def collapse_star(self, star): for key, value in MODEL_properties.items(): setattr(star, key, value) - + if star.state == 'WD': for key in STARPROPERTIES: if key in ["he_core_mass"]: setattr(star, key, star.mass) elif key in ["co_core_mass"]: - if star.center_he4 < THRESHOLD_CENTRAL_ABUNDANCE: + if star.center_he4 < THRESHOLD_CENTRAL_ABUNDANCE: setattr(star, key, star.mass) - else: + else: setattr(star, key, 0.) elif key not in ["state", "mass", "spin", "m_disk_accreted", "m_disk_radiated", "center_h1", "center_he4", "center_c12", "center_n14", "center_o16"]: - setattr(star, key, None) - - else: + setattr(star, key, None) + + else: for key in STARPROPERTIES: if key not in ["state", "mass", "spin", "m_disk_accreted", "m_disk_radiated"]: setattr(star, key, None) - + # No remnant if a PISN happens if star.SN_type == 'PISN': convert_star_to_massless_remnant(star=star) @@ -580,18 +581,18 @@ def collapse_star(self, star): # but an orbital kick is still applied. # Since the mass is set to None, this will lead to a disruption # TODO: make it skip the kick caluclation - + if getattr(star, 'SN_type') != 'PISN': star.log_R = np.log10(CO_radius(star.mass, star.state)) return - + else: warnings.warn(f'{MODEL_NAME_SEL}: The SN_type ' 'does not match the predicted CO, or the interpolated ' 'values for the SN remnant are NaN. ' 'If use_profiles or use_core_masses is set to True, ' 'continue with the collapse.') - + # Verifies the selection of core-collapse mechnism to perform # the collapse if self.mechanism in [ @@ -624,16 +625,16 @@ def collapse_star(self, star): if key in ["he_core_mass"]: setattr(star, key, star.mass) elif key in ["co_core_mass"]: - if star.center_he4 < THRESHOLD_CENTRAL_ABUNDANCE: + if star.center_he4 < THRESHOLD_CENTRAL_ABUNDANCE: setattr(star, key, star.mass) - else: + else: setattr(star, key, 0.) elif key not in ["state", "mass", "spin", "m_disk_accreted", "m_disk_radiated", "center_h1", "center_he4", "center_c12", "center_n14", "center_o16"]: - setattr(star, key, None) + setattr(star, key, None) return # check if the star was disrupted by the PISN @@ -674,6 +675,8 @@ def collapse_star(self, star): star.m_disk_accreted = 0. star.m_disk_radiated = 0. star.state = 'NS' + star.h1_mass_ej, star.he4_mass_ej = \ + get_ejecta_element_mass_at_collapse(star,star.mass,verbose=self.verbose) elif self.use_core_masses: # If the profile is not available the star spin @@ -702,7 +705,9 @@ def collapse_star(self, star): star.m_disk_accreted = 0.0 star.m_disk_radiated = 0.0 star.state = "NS" - + star.h1_mass_ej, star.he4_mass_ej = \ + np.nan, np.nan + elif self.allow_spin_None: # If the profile is not available and spin can stay # undetermined @@ -714,6 +719,8 @@ def collapse_star(self, star): star.state = "BH" else: star.state = "NS" + star.h1_mass_ej, star.he4_mass_ej = \ + np.nan, np.nan else: for key in STARPROPERTIES: @@ -744,16 +751,16 @@ def collapse_star(self, star): if key in ["he_core_mass"]: setattr(star, key, star.mass) elif key in ["co_core_mass"]: - if star.center_he4 < THRESHOLD_CENTRAL_ABUNDANCE: + if star.center_he4 < THRESHOLD_CENTRAL_ABUNDANCE: setattr(star, key, star.mass) - else: + else: setattr(star, key, 0.) elif key not in ["state", "mass", "spin", "m_disk_accreted", "m_disk_radiated", "center_h1", "center_he4", "center_c12", "center_n14", "center_o16"]: - setattr(star, key, None) + setattr(star, key, None) return # check if the star was disrupted by the PISN @@ -801,6 +808,8 @@ def collapse_star(self, star): setattr(star, key, None) set_binary_to_failed(self.binary) raise ModelError("Invalid core state: " + str(state)) + star.h1_mass_ej, star.he4_mass_ej = \ + get_ejecta_element_mass_at_collapse(star,star.mass,verbose=self.verbose) elif self.use_core_masses: star.mass = m_grav @@ -827,7 +836,9 @@ def collapse_star(self, star): star.m_disk_accreted = 0.0 star.m_disk_radiated = 0.0 star.state = "NS" - + star.h1_mass_ej, star.he4_mass_ej = \ + np.nan, np.nan + elif self.allow_spin_None: # If the profile is not available and spin can stay # undetermined @@ -839,6 +850,8 @@ def collapse_star(self, star): star.state = "BH" else: star.state = "NS" + star.h1_mass_ej, star.he4_mass_ej = \ + np.nan, np.nan else: for key in STARPROPERTIES: @@ -901,7 +914,7 @@ def PISN_prescription(self, star): elif m_He_core > 61.10 and m_He_core < 124.12: # in Breivik et al. (2020) they qoute the CO core mass - # range as 54.480. The orbital angular momentum vector is in Z direction, which completes the @@ -1279,17 +1292,17 @@ def orbital_kick(self, binary): psi: The angle in the orbital plane between the X axis and the pre-core collapse relative velocity. (psi = pi/2 points in Y direction) - + theta : The polar angle between the kick velocity and the pre-core collapse relative velocity of the M_he_star with respect to M_companion. - + phi : The corresponding azimuthal angle such that phi=0 is on the Z axis. tilt : The angle between pre- and post- supernova orbital angular momentum vectors - + Parameters ---------- @@ -1325,7 +1338,7 @@ def orbital_kick(self, binary): binary.separation = new_separation if binary.state != "disrupted" and binary.state != "initially_single_star" and binary.state != "merged": binary.state = "detached" - + binary.event = None binary.time = binary.time_history[-1] binary.eccentricity = binary.eccentricity_history[-1] @@ -1519,9 +1532,9 @@ def orbital_kick(self, binary): binary.orbital_period = np.nan binary.mass_transfer_case = 'None' binary.first_SN_already_occurred = True - + else: - + # The binary exist: flag_binary is True if the binary is not disrupted flag_binary = True @@ -1537,7 +1550,7 @@ def orbital_kick(self, binary): # Eq 15, Wong, T.-W., Valsecchi, F., Fragos, T., & Kalogera, V. 2012, ApJ, 747, 111 # orbital separation at the time of the exlosion rpre = Apre * (1.0 - epre * np.cos(E_ma)) - + true_anomaly = 2 * np.arctan( np.sqrt((1 + epre) / (1 - epre)) * np.tan(E_ma / 2) ) @@ -1608,9 +1621,9 @@ def orbital_kick(self, binary): epost = np.sqrt(1 - x) # Compute COM velocity, VS, post SN - # VS_pre in COM frame is 0. So VS_post in COM frame is + # VS_pre in COM frame is 0. So VS_post in COM frame is # VS_post - VS_pre in our working frame - + VC0x = M_he_star * Vr * cos_psi / Mtot_pre VC0y = M_he_star * Vr * sin_psi / Mtot_pre VC0z = 0 @@ -1636,7 +1649,7 @@ def orbital_kick(self, binary): tilt = np.arccos((Vky + Vr * sin_psi) / np.sqrt( Vkz ** 2 + (Vky + Vr * sin_psi) ** 2 )) # Track direction of tilt - if Vkz < 0: tilt *= -1 + if Vkz < 0: tilt *= -1 def SNCheck( M_he_star, @@ -1750,7 +1763,7 @@ def SNCheck( for key in BINARYPROPERTIES: if key not in ['nearest_neighbour_distance','event']: setattr(binary, key, None) - + if flag_binary: # update the tilt if not binary.first_SN_already_occurred: @@ -1764,9 +1777,9 @@ def SNCheck( # Assume progenitor has aligned with the preSN orbital angular momentum binary.star_2.spin_orbit_tilt_second_SN = tilt binary.star_1.spin_orbit_tilt_second_SN = self.get_combined_tilt( - tilt_1 = binary.star_1.spin_orbit_tilt_first_SN, - tilt_2 = tilt, - true_anomaly_1 = binary.true_anomaly_first_SN, + tilt_1 = binary.star_1.spin_orbit_tilt_first_SN, + tilt_2 = tilt, + true_anomaly_1 = binary.true_anomaly_first_SN, true_anomaly_2 = true_anomaly ) binary.true_anomaly_second_SN = true_anomaly @@ -1774,16 +1787,16 @@ def SNCheck( # Assume progenitor has aligned with the preSN orbital angular momentum binary.star_1.spin_orbit_tilt_second_SN = tilt binary.star_2.spin_orbit_tilt_second_SN = self.get_combined_tilt( - tilt_1 = binary.star_1.spin_orbit_tilt_first_SN, - tilt_2 = tilt, - true_anomaly_1 = binary.true_anomaly_first_SN, + tilt_1 = binary.star_1.spin_orbit_tilt_first_SN, + tilt_2 = tilt, + true_anomaly_1 = binary.true_anomaly_first_SN, true_anomaly_2 = true_anomaly ) binary.true_anomaly_second_SN = true_anomaly else: raise ValueError(f"Binary is in SN step but binary state is not CC1 or CC2: {binary.state}") - # compute new orbital period before reseting the binary properties + # compute new orbital period before reseting the binary properties binary.state = "detached" binary.event = None binary.separation = Apost / const.Rsun @@ -1807,7 +1820,7 @@ def SNCheck( binary.star_1.spin_orbit_tilt_second_SN = np.nan binary.star_2.spin_orbit_tilt_second_SN = np.nan - + binary.state = "disrupted" binary.event = None binary.separation = np.nan @@ -1872,7 +1885,7 @@ def generate_kick(self, star, sigma): Vkick = 0.0 return Vkick - + def get_combined_tilt(self, tilt_1, tilt_2, true_anomaly_1, true_anomaly_2): """Get the combined spin-orbit-tilt after two supernovae, assuming @@ -1882,10 +1895,10 @@ def get_combined_tilt(self, tilt_1, tilt_2, true_anomaly_1, true_anomaly_2): Parameters ---------- tilt_1: float - Angle, in radians, through which the orbital plane was tilted + Angle, in radians, through which the orbital plane was tilted by SN1 tilt_2: float - Angle, in radians, through which the orbital plane was tilted + Angle, in radians, through which the orbital plane was tilted by SN2 true_anomaly_1: float Angle, in radians, of the true anomaly at the moment of SN1 @@ -1905,7 +1918,7 @@ def get_combined_tilt(self, tilt_1, tilt_2, true_anomaly_1, true_anomaly_2): cos_tilt = np.dot((0,0,1),rotate(x_prime, tilt_2).dot(z_prime)) combined_tilt = np.arccos(cos_tilt) return combined_tilt - + def C_abundance_for_H_stars(self, CO_core_mass): """Get the C abundance for a H-star given it's CO core mass.""" return 0.20/CO_core_mass + 0.15 @@ -2021,6 +2034,8 @@ def Patton20_corecollapse(self, star, engine, conserve_hydrogen_envelope=False): M4, mu4 = self.get_M4_mu4_Patton20(CO_core_mass, C_core_abundance) M4 = M4[0] mu4 = mu4[0] + star.M4 = M4 + star.mu4 = mu4 k1 = Ertl16_k_parameters[engine][0] k2 = Ertl16_k_parameters[engine][1] @@ -2513,14 +2528,14 @@ def __call__(self, star, conserve_hydrogen_envelope=False): def check_SN_CO_match(SN_type, state): '''Check if the SN type matches the stellar state of the given star. - + Parameters ---------- SN_type : str SN type of the star. state : str Stellar state of the star. - + Returns ------- correct_SN_type : bool @@ -2541,5 +2556,5 @@ def check_SN_CO_match(SN_type, state): SN_type != 'PPISN'): correct_SN_type = False elif (state == "massless_remnant" and SN_type != 'PISN'): - correct_SN_type = False + correct_SN_type = False return correct_SN_type diff --git a/posydon/binary_evol/singlestar.py b/posydon/binary_evol/singlestar.py index 51d8039508..e9db3cea72 100644 --- a/posydon/binary_evol/singlestar.py +++ b/posydon/binary_evol/singlestar.py @@ -153,6 +153,15 @@ def __init__(self, **kwargs): self.m_disk_accreted = None if not hasattr(self, 'm_disk_radiated'): self.m_disk_radiated = None + if not hasattr(self, 'h1_mass_ej'): + self.h1_mass_ej = None + if not hasattr(self, 'he4_mass_ej'): + self.he4_mass_ej = None + if not hasattr(self, 'M4'): + self.M4 = None + if not hasattr(self, 'mu4'): + self.mu4 = None + # the following quantities are updated in mesa_step.py @@ -165,7 +174,8 @@ def __init__(self, **kwargs): # core masses at He depletion for quantity in ['avg_c_in_c_core_at_He_depletion', 'co_core_mass_at_He_depletion']: - setattr(self, quantity, None) + if not hasattr(self, quantity): + setattr(self, quantity, None) # core collapse quantities for MODEL_NAME in MODELS.keys(): @@ -187,28 +197,28 @@ def restore(self, i=0, hooks=None): i == 0, i.e. the star will be restored to its initial state. hooks : list List of extra hooks associated with the SimulationProperties() of the BinaryStar() - object containing this SingleStar(), if applicable. This parameter is - automatically set when restoring a BinaryStar() object. + object containing this SingleStar(), if applicable. This parameter is + automatically set when restoring a BinaryStar() object. """ if hooks is None: hooks = [] - + # Move current star properties to the ith step, using its history for p in STARPROPERTIES: setattr(self, p, getattr(self, '{}_history'.format(p))[i]) ## delete the star history after the i-th index setattr(self, p + '_history', getattr(self, p + '_history')[0:i+1]) - + ## if running with extra hooks, restore any extra hook columns for hook in hooks: if hasattr(hook, 'extra_star_col_names'): extra_columns = getattr(hook, 'extra_star_col_names') - + for col in extra_columns: - setattr(self, col, getattr(self, col)[0:i+1]) - + setattr(self, col, getattr(self, col)[0:i+1]) + def to_df(self, **kwargs): """Return history parameters from the star in a DataFrame. @@ -269,7 +279,7 @@ def to_df(self, **kwargs): # shape of data_to_save (history columns , time steps) data_to_save = [getattr(self, key) for key in keys_to_save] - + col_lengths = [len(x) for x in data_to_save] max_col_length = np.max(col_lengths) @@ -406,7 +416,7 @@ def from_run(run, history=False, profile=False, which_star=None): attr = colname[3:] final_value = run.final_values[colname] setattr(star, attr, final_value) - + star.state_history = [check_state_of_star(star, i=i, star_CO=False) for i in range(n_steps)] star.state = star.state_history[-1] diff --git a/posydon/grids/post_processing.py b/posydon/grids/post_processing.py index 9ea7c97a8c..94006069b7 100644 --- a/posydon/grids/post_processing.py +++ b/posydon/grids/post_processing.py @@ -30,7 +30,9 @@ CC_quantities = ['state', 'SN_type', 'f_fb', 'mass', 'spin', - 'm_disk_accreted', 'm_disk_radiated', 'CO_interpolation_class'] + 'm_disk_accreted', 'm_disk_radiated', 'CO_interpolation_class', + 'M4', 'mu4', + 'h1_mass_ej', 'he4_mass_ej'] def assign_core_collapse_quantities_none(EXTRA_COLUMNS, star_i, MODEL_NAME=None): """"Assign None values to all core collapse properties.""" @@ -43,40 +45,44 @@ def assign_core_collapse_quantities_none(EXTRA_COLUMNS, star_i, MODEL_NAME=None) EXTRA_COLUMNS[f'S{star_i}_{MODEL_NAME}_{quantity}'].append(None) def print_CC_quantities(EXTRA_COLUMNS, star, MODEL_NAME=None): - format_string = "{:<50} {:<33} {:12} {:10} {:15} {:10} {:25} {:25}" - format_val_preSN = "{:<50} {:<33} {:12} {:10} {:7.2f} {:12.2f} {:25} {:25}" - format_val = "{:<50} {:<33} {:12} {:1.2f} {:13.2f} {:12.2f} {:20.2f} {:20.2f}" + format_string = "{:<50} {:<33} {:12} {:10} {:15} {:10} {:25} {:25} {:25} {:25} {:25} {:25}" + format_val_preSN = "{:<50} {:<33} {:12} {:10} {:7.2f} {:12.2f} {:25} {:25} {:25} {:25} {:25} {:25}" + format_val = "{:<50} {:<33} {:12} {:1.2f} {:13.2f} {:12.2f} {:20.2f} {:20.2f} {:20.2f} {:20.2f} {:20.2f} {:20.2f}" if MODEL_NAME is None: print('') print(format_string.format( "mechanism", "state", "SN type", "f_fb", "mass [Msun]", "spin", "m_disk_accreted [Msun]", - "m_disk_radiated [Msun]")) + "m_disk_radiated [Msun]", "M4 [m/Msun]", "mu4 [(dm/Msun)/(dr/1000km)]", + "h1_mass_ej [Msun]", "he4_mass_ej [Msun]")) print('') try: print(format_val_preSN.format( 'PRE SN STAR', star.state, '', - '', star.mass, star.spin, '', '')) + '', star.mass, star.spin, '', '', '', '', '', '')) except Exception as e: warnings.warn('Failed to print star values!') print('Warning in preSN: ', e) print('') else: try: - if star.spin==None: - spin = np.nan - else: - spin = star.spin + checked_quantities_for_None = {} + for quantity in ["spin", "M4", "mu4", "h1_mass_ej", "he4_mass_ej"]: + if getattr(star, quantity)==None: + checked_quantities_for_None[quantity] = np.nan + else: + checked_quantities_for_None[quantity] = getattr(star, quantity) print(format_val.format(MODEL_NAME, star.state, star.SN_type, star.f_fb, - star.mass, spin, star.m_disk_accreted, - star.m_disk_radiated)) + star.mass, checked_quantities_for_None["spin"], star.m_disk_accreted, + star.m_disk_radiated, checked_quantities_for_None["M4"], checked_quantities_for_None["mu4"], + checked_quantities_for_None["h1_mass_ej"], checked_quantities_for_None["he4_mass_ej"])) except Exception as e: warnings.warn('Failed to print star values!') print('Warning in', MODEL_NAME, ': ', e) - - - + + + def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, single_star=False, verbose=False): """Compute post processed quantity of any grid. @@ -122,7 +128,7 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, """ EXTRA_COLUMNS = {} - + for star in [1, 2]: # core masses at He depletion. stellar states and composition for quantity in ['avg_c_in_c_core_at_He_depletion', @@ -137,7 +143,7 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, for MODEL_NAME, MODEL in MODELS.items(): for quantity in CC_quantities: EXTRA_COLUMNS[f'S{star}_{MODEL_NAME}_{quantity}'] = [] - + # remove star 2 columns in case of single star grid if single_star: for key in list(EXTRA_COLUMNS.keys()): @@ -213,9 +219,9 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, getattr(star, f'{quantity}_{val}cent')) # aboundances try: - s_o = (1. - star.surface_h1 - star.surface_he4 - star.surface_c12 + s_o = (1. - star.surface_h1 - star.surface_he4 - star.surface_c12 - star.surface_n14 - star.surface_o16) - c_o = (1. - star.center_h1 - star.center_he4 - star.center_c12 + c_o = (1. - star.center_h1 - star.center_he4 - star.center_c12 - star.center_n14 - star.center_o16) except TypeError as ex: s_o = 0. @@ -243,19 +249,19 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, if 'CE' in quantity: for val in [1, 10, 30, 'pure_He_star_10']: EXTRA_COLUMNS[f'S{j+1}_{quantity}_{val}cent'].append(None) - else: + else: EXTRA_COLUMNS[f'S{j+1}_{quantity}'].append(None) - + # core collpase quantities if not single_star: if interpolation_class in ['no_MT', 'stable_MT', 'stable_reverse_MT']: - if (star_2_CO or (TF1 in TF1_POOL_STABLE and + if (star_2_CO or (TF1 in TF1_POOL_STABLE and ('primary' in TF1 or 'Primary' in TF1))): star = binary.star_1 star_i = 1 assign_core_collapse_quantities_none(EXTRA_COLUMNS, 2) - elif (TF1 in TF1_POOL_STABLE and + elif (TF1 in TF1_POOL_STABLE and ('secondary' in TF1 or 'Secondary' in TF1)): star = binary.star_2 star_i = 2 @@ -297,7 +303,7 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, for MODEL_NAME, MODEL in MODELS.items(): mechanism = MODEL['mechanism']+MODEL['engine'] - SN = StepSN(**MODEL, allow_spin_None=True) + SN = StepSN(**MODEL, allow_spin_None=True) star_copy = copy.copy(star) try: flush = False @@ -308,7 +314,7 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, flush = True warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} is not a string!') elif quantity != 'CO_interpolation_class': - if quantity=='spin': + if quantity in ['spin', 'M4', 'mu4', "h1_mass_ej", "he4_mass_ej"]: if ((not isinstance(getattr(star_copy, quantity), float)) and (getattr(star_copy, quantity) != None)): flush = True @@ -346,7 +352,7 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, # star not explodable assign_core_collapse_quantities_none(EXTRA_COLUMNS, star_i) - else: + else: # inital_RLOF, unstable_MT not_converged assign_core_collapse_quantities_none(EXTRA_COLUMNS, 1) assign_core_collapse_quantities_none(EXTRA_COLUMNS, 2) @@ -369,7 +375,7 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, flush = True warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} is not a string!') elif quantity != 'CO_interpolation_class': - if quantity == 'spin': + if quantity in ['spin', 'M4', 'mu4', "h1_mass_ej", "he4_mass_ej"]: if ((not isinstance(getattr(star_copy, quantity), float)) and (getattr(star_copy, quantity) != None)): flush = True diff --git a/posydon/interpolation/interpolation.py b/posydon/interpolation/interpolation.py index 5b0fec3b1e..2f8b767cb7 100644 --- a/posydon/interpolation/interpolation.py +++ b/posydon/interpolation/interpolation.py @@ -465,12 +465,13 @@ def __init__(self, path, verbose=False): 'S1_r_core_CE_30cent', 'S1_r_core_CE_pure_He_star_10cent' ) - + # core collapse keys keys = [] for MODEL_NAME in MODELS.keys(): for key in ['CO_type', 'SN_type', 'f_fb', 'mass', 'spin', - 'm_disk_accreted', 'm_disk_radiated']: + 'm_disk_accreted', 'm_disk_radiated','M4', 'mu4', + 'h1_mass_ej', 'he4_mass_ej']: keys.append('S1_' + MODEL_NAME + '_' + key ) self.final_keys += tuple(keys) diff --git a/posydon/popsyn/io.py b/posydon/popsyn/io.py index f7fcfe7ace..430ce8221a 100644 --- a/posydon/popsyn/io.py +++ b/posydon/popsyn/io.py @@ -146,15 +146,21 @@ 'f_fb': 'float64', 'spin_orbit_tilt_first_SN': 'float64', 'spin_orbit_tilt_second_SN': 'float64', + 'M4': 'float64', + 'mu4': 'float64', + 'avg_c_in_c_core_at_He_depletion' : 'float64', + 'co_core_mass_at_He_depletion' : 'float64', + 'h1_mass_ej': 'float64', + 'he4_mass_ej': 'float64', } -def clean_binary_history_df(binary_df, extra_binary_dtypes_user=None, +def clean_binary_history_df(binary_df, extra_binary_dtypes_user=None, extra_S1_dtypes_user=None, extra_S2_dtypes_user=None): """Take a posydon binary history DataFrame from the BinaryStar.to_df method and clean the data for saving by setting Data Types of the columns explicitly. - + Parameters --------- binary_df : DataFrame @@ -166,15 +172,15 @@ def clean_binary_history_df(binary_df, extra_binary_dtypes_user=None, Same as above, but only for star 1. extra_S2_dtypes_user : dict, optional Same as above, but only for star 2. - + Returns ------- binary_df : DataFrame A cleaned binary history ready for saving to HDF. """ - + assert isinstance( binary_df, pd.DataFrame ) - + # User specified extra binary and star columns if extra_binary_dtypes_user is None: extra_binary_dtypes_user = {} @@ -188,21 +194,21 @@ def clean_binary_history_df(binary_df, extra_binary_dtypes_user=None, # Set data types for all columns explicitly hist_columns = set(binary_df.columns) - + # combine columns for binary history with extras - BP_comb_extras_dict = {**BINARYPROPERTIES_DTYPES, **EXTRA_BINARY_COLUMNS_DTYPES, + BP_comb_extras_dict = {**BINARYPROPERTIES_DTYPES, **EXTRA_BINARY_COLUMNS_DTYPES, **extra_binary_dtypes_user} # combine columns for star 1 and star 2 history with extras - SP_comb_S1_dict = {**STARPROPERTIES_DTYPES, **EXTRA_STAR_COLUMNS_DTYPES, + SP_comb_S1_dict = {**STARPROPERTIES_DTYPES, **EXTRA_STAR_COLUMNS_DTYPES, **extra_S1_dtypes_user} - SP_comb_S2_dict = {**STARPROPERTIES_DTYPES, **EXTRA_STAR_COLUMNS_DTYPES, + SP_comb_S2_dict = {**STARPROPERTIES_DTYPES, **EXTRA_STAR_COLUMNS_DTYPES, **extra_S2_dtypes_user} - + # All default & user passed keys which we have mappings for binary_keys = set( BP_comb_extras_dict.keys() ) S1_keys = set( ['S1_' + key for key in SP_comb_S1_dict.keys()] ) S2_keys = set( ['S2_' + key for key in SP_comb_S2_dict.keys()] ) - + # Find common keys between the binary_df and our column-dtype mapping common_keys = hist_columns & ( binary_keys | S1_keys | S2_keys ) @@ -228,17 +234,17 @@ def clean_binary_history_df(binary_df, extra_binary_dtypes_user=None, return binary_df -def clean_binary_oneline_df(oneline_df, extra_binary_dtypes_user=None, +def clean_binary_oneline_df(oneline_df, extra_binary_dtypes_user=None, extra_S1_dtypes_user=None, extra_S2_dtypes_user=None): """Take a posydon binary oneline DataFrame from the BinaryStar.to_oneline_df method and clean the data for saving by setting Data Types of the columns explicitly. - + This method is similar to clean_binary_history_df since they have many overalapping columns, with a few extras and different naming. - + Note: there may be edge cases not handed if new scalar_names are added. - + Parameters --------- binary_df : DataFrame @@ -250,14 +256,14 @@ def clean_binary_oneline_df(oneline_df, extra_binary_dtypes_user=None, Same as above, but only for star 1. extra_S2_dtypes_user : dict, optional Same as above, but only for star 2. - + Returns ------- binary_df : DataFrame A cleaned binary history ready for saving to HDF. """ assert isinstance( oneline_df, pd.DataFrame ) - + # User specified extra binary and star columns if extra_binary_dtypes_user is None: extra_binary_dtypes_user = {} @@ -271,37 +277,37 @@ def clean_binary_oneline_df(oneline_df, extra_binary_dtypes_user=None, # Set data types for all columns explicitly oneline_columns = set(oneline_df.columns) - + # combine columns for binary history with extras - BP_comb_extras_dict = {**BINARYPROPERTIES_DTYPES, **EXTRA_BINARY_COLUMNS_DTYPES, + BP_comb_extras_dict = {**BINARYPROPERTIES_DTYPES, **EXTRA_BINARY_COLUMNS_DTYPES, **extra_binary_dtypes_user} # combine columns for star 1 and star 2 history with extras - SP_comb_S1_dict = {**STARPROPERTIES_DTYPES, **EXTRA_STAR_COLUMNS_DTYPES, + SP_comb_S1_dict = {**STARPROPERTIES_DTYPES, **EXTRA_STAR_COLUMNS_DTYPES, **SCALAR_NAMES_DTYPES, **extra_S1_dtypes_user} - SP_comb_S2_dict = {**STARPROPERTIES_DTYPES, **EXTRA_STAR_COLUMNS_DTYPES, + SP_comb_S2_dict = {**STARPROPERTIES_DTYPES, **EXTRA_STAR_COLUMNS_DTYPES, **SCALAR_NAMES_DTYPES, **extra_S2_dtypes_user} - + # All default & user passed keys which we have mappings for - binary_keys = set( [key + '_i' for key in BP_comb_extras_dict.keys()] + binary_keys = set( [key + '_i' for key in BP_comb_extras_dict.keys()] + [key + '_f' for key in BP_comb_extras_dict.keys()] ) - S1_keys = set( ['S1_' + key + '_i' for key in SP_comb_S1_dict.keys()] + S1_keys = set( ['S1_' + key + '_i' for key in SP_comb_S1_dict.keys()] + ['S1_' + key + '_f' for key in SP_comb_S1_dict.keys()] ) S2_keys = set( ['S2_' + key + '_i' for key in SP_comb_S2_dict.keys()] + ['S2_' + key + '_f' for key in SP_comb_S2_dict.keys()] ) scalar_keys = set( ['S1_' + key for key in SCALAR_NAMES_DTYPES.keys()] + ['S2_' + key for key in SCALAR_NAMES_DTYPES.keys()] ) - + # Find common keys between the binary_df and our column-dtype mapping - common_keys = oneline_columns & ( binary_keys + common_keys = oneline_columns & ( binary_keys | S1_keys - | S2_keys + | S2_keys | scalar_keys) - + # Create a dict with column-dtype mapping only for columns in binary_df common_dtype_dict = {} - # helper function to remove the '_i' and '_f' + # helper function to remove the '_i' and '_f' strip_prefix_and_suffix = lambda key : key.replace('_i', '').replace('_f', '') \ .replace('S1_', '').replace('S2_', '') for key in common_keys: @@ -324,7 +330,7 @@ def clean_binary_oneline_df(oneline_df, extra_binary_dtypes_user=None, if val == 'string': convert_to_obj[key] = 'object' oneline_df = oneline_df.astype( convert_to_obj ) - + return oneline_df @@ -427,7 +433,7 @@ def simprop_kwargs_from_ini(path, verbose=False): absolute_import_location = os.path.abspath(import_location) # Format: remove leading / in abs path, replace / with dots, remove '.py' location_as_module_name = absolute_import_location[1:].replace('/', '.').replace('.py', '') - + # create spec and load module spec = importlib.util.spec_from_file_location( location_as_module_name, location=absolute_import_location) module = importlib.util.module_from_spec(spec) @@ -517,7 +523,7 @@ def binarypop_kwargs_from_ini(path, verbose=False): # MPI needs to be turned off for job arrays else: pop_kwargs['comm'] = None - + # Check if we are running as a job array if JOB_ID is not None and pop_kwargs['use_MPI'] is True: raise ValueError('MPI must be turned off for job arrays.') @@ -530,7 +536,7 @@ def binarypop_kwargs_from_ini(path, verbose=False): else: pop_kwargs['RANK'] = None pop_kwargs['size'] = None - + # right now binary, S1, and S2 output kwargs are all passed # into the BinaryPopulation during init elif section == 'BinaryStar_output': @@ -564,8 +570,8 @@ def binarypop_kwargs_from_ini(path, verbose=False): def create_run_script_text(ini_file): - - + + text=["from posydon.popsyn.binarypopulation import BinaryPopulation", "from posydon.popsyn.io import binarypop_kwargs_from_ini", "from posydon.utils.common_functions import convert_metallicity_to_string", @@ -581,13 +587,13 @@ def create_run_script_text(ini_file): " ini_kw['temp_directory'] = str_met+'_Zsun_' + ini_kw['temp_directory']", " synpop = BinaryPopulation(**ini_kw)", " synpop.evolve()"] - + text = '\n'.join(text) return text def create_merge_script_text(ini_file): - + text = ['from posydon.popsyn.binarypopulation import BinaryPopulation', 'from posydon.popsyn.io import binarypop_kwargs_from_ini', @@ -610,6 +616,6 @@ def create_merge_script_text(ini_file): ' print("done")', ' if len(os.listdir(path_to_batch)) == 0:', ' os.rmdir(path_to_batch)'] - + text = '\n'.join(text) return text diff --git a/posydon/visualization/plot_defaults.py b/posydon/visualization/plot_defaults.py index 41888167b2..362cc1b0d2 100644 --- a/posydon/visualization/plot_defaults.py +++ b/posydon/visualization/plot_defaults.py @@ -541,7 +541,7 @@ 'Unstable case Bn': ['D', 1, list_of_colors[1], 'Unstable RLOF during postMS'], 'Unstable case BBA': - ['D', 2, list_of_colors[0], + ['D', 2, list_of_colors[0], 'Unstable RLOF during stripped He star'], 'Unstable case BBB': ['D', 2, list_of_colors[0], @@ -549,7 +549,7 @@ 'Unstable case Cn': ['D', 1, list_of_colors[1], 'Unstable RLOF during postMS'], 'Unstable case CBA': - ['D', 2, list_of_colors[0], + ['D', 2, list_of_colors[0], 'Unstable RLOF during stripped He star'], 'Unstable case CBB': ['D', 2, list_of_colors[0], @@ -742,7 +742,7 @@ def add_flag_to_MARKERS_COLORS_LEGENDS(MARKERS_COLORS_LEGENDS, flag): """Add not pre defined stuff to DEFAULT_MARKERS_COLORS_LEGENDS. - + Parameters ---------- MARKERS_COLORS_LEGENDS : dict of lists @@ -750,7 +750,7 @@ def add_flag_to_MARKERS_COLORS_LEGENDS(MARKERS_COLORS_LEGENDS, flag): and legend text for each flag. flag : str The flag itself. - + """ if flag not in MARKERS_COLORS_LEGENDS.keys(): if ('case_' in flag): # unknown MT flag @@ -1048,6 +1048,15 @@ def add_flag_to_MARKERS_COLORS_LEGENDS(MARKERS_COLORS_LEGENDS, flag): r'$\log_{10}(M_\mathrm{disk, acc} / M_\odot)$'] DEFAULT_LABELS[f'MODEL{i:02d}_m_disk_radiated'] = [r'$M_\mathrm{disk, rad} \, [M_\odot]$', r'$\log_{10}(M_\mathrm{disk, rad} / M_\odot)$'] + DEFAULT_LABELS[f'MODEL{i:02d}_M4'] = [r'$M_4 [= m/M_\odot]_{s=4}$', + r'$\log_{10}(M_4)$'] + DEFAULT_LABELS[f'MODEL{i:02d}_mu4'] = [r'$\mu_4 \, [(dm/M_\odot)/(dr/1000\mathrm{km/s})]_{s=4}$', + r'$\log_{10}(\mu_4)$'] + DEFAULT_LABELS[f'MODEL{i:02d}_h1_mass_ej'] = [r'$M_\mathrm{H,ej} \, [M_\odot]$', + r'$\log_{10}(M_\mathrm{H,ej} / M_\odot)$'] + DEFAULT_LABELS[f'MODEL{i:02d}_he4_mass_ej'] = [r'$M_\mathrm{He,ej} \, [M_\odot]$', + r'$\log_{10}(M_\mathrm{He,ej} / M_\odot)$'] + # pre defined plottings @@ -1117,6 +1126,22 @@ def add_flag_to_MARKERS_COLORS_LEGENDS(MARKERS_COLORS_LEGENDS, flag): 'zmin' : 0., 'zmax' : 3. }, + 'S1_MODEL_DEFAULT_M4' : { + 'zmin' : 1., + 'zmax' : 4. + }, + 'S1_MODEL_DEFAULT_mu4' : { + 'zmin' : 0.0, + 'zmax' : .5 + }, + 'S1_MODEL_DEFAULT_h1_mass_ej' : { + 'zmin' : 0., + 'zmax' : 20 + }, + 'S1_MODEL_DEFAULT_he4_mass_ej' : { + 'zmin' : 0., + 'zmax' : 20 + }, # interpolator stuff 'INTERP_ERROR_DEFAULT' : { 'term_flag' : None, From 0752c69735786f5e555ac28bde0fa3b4ee4bd692 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 18 Jul 2024 16:55:07 +0200 Subject: [PATCH 227/319] replace np.NaN and np.NAN by np.nan (#357) * replace np.NaN by np.nan * replace np.NAN by np.nan --- bin/posydon-setup-pipeline | 4 ++-- posydon/binary_evol/binarystar.py | 10 +++++----- posydon/binary_evol/singlestar.py | 6 +++--- 3 files changed, 10 insertions(+), 10 deletions(-) diff --git a/bin/posydon-setup-pipeline b/bin/posydon-setup-pipeline index 18b990f9e2..a152215fa1 100644 --- a/bin/posydon-setup-pipeline +++ b/bin/posydon-setup-pipeline @@ -1231,12 +1231,12 @@ def create_csv(GRID_TYPES=[], print(f'In {column} the following grids are skipped!') for row in drop_rows: print(df.at[row,column]) - df.at[row,column] = np.NaN + df.at[row,column] = np.nan print('') newcol = df[column].dropna().to_list() if len(newcol)==0: drop_columns.append(column) - newcol.extend((df.shape[0]-len(newcol))*[np.NaN]) + newcol.extend((df.shape[0]-len(newcol))*[np.nan]) df[column] = newcol # report about empty tasks and remove them if len(drop_columns)>0: diff --git a/posydon/binary_evol/binarystar.py b/posydon/binary_evol/binarystar.py index e6b42196f2..ca3a7c0f36 100644 --- a/posydon/binary_evol/binarystar.py +++ b/posydon/binary_evol/binarystar.py @@ -334,7 +334,7 @@ def to_df(self, **kwargs): Can be used in combination with `extra_columns`. null_value : float Replace all None values with something else (for saving). - Default is np.NAN. + Default is np.nan. include_S1, include_S2 : bool Choose to include star 1 or 2 data to the DataFrame. The default is to include both. @@ -389,9 +389,9 @@ def to_df(self, **kwargs): str(err) + "\n\nAvailable attributes in BinaryStar: \n{}". format(self.__dict__.keys())) - # Convert None to np.NAN by default + # Convert None to np.nan by default bin_data = np.array(data_to_save, dtype=object) - bin_data[where_none] = kwargs.get('null_value', np.NAN) + bin_data[where_none] = kwargs.get('null_value', np.nan) bin_data = np.transpose(bin_data) @@ -420,11 +420,11 @@ def to_df(self, **kwargs): if kwargs.get('include_S1', True): # we are hard coding the prefix frames.append(self.star_1.to_df( - prefix='S1_', null_value=kwargs.get('null_value', np.NAN), + prefix='S1_', null_value=kwargs.get('null_value', np.nan), **kwargs.get('S1_kwargs', {}))) if kwargs.get('include_S2', True): frames.append(self.star_2.to_df( - prefix='S2_', null_value=kwargs.get('null_value', np.NAN), + prefix='S2_', null_value=kwargs.get('null_value', np.nan), **kwargs.get('S2_kwargs', {}))) binary_df = pd.concat(frames, axis=1) diff --git a/posydon/binary_evol/singlestar.py b/posydon/binary_evol/singlestar.py index e9db3cea72..481aea78f4 100644 --- a/posydon/binary_evol/singlestar.py +++ b/posydon/binary_evol/singlestar.py @@ -244,7 +244,7 @@ def to_df(self, **kwargs): Include the star's profile in the dataframe (NOT RECOMMENDED) null_value : float, optional Replace all None values with something else (for saving). - Default is np.NAN. + Default is np.nan. prefix : str, optional Prefix to all column names. (e.g. 'star_1', 'S1') Default has no prefix. @@ -303,8 +303,8 @@ def to_df(self, **kwargs): # casting into object array keeps things general star_data = np.array(data_to_save, dtype=object) - # Convert None to np.NAN by default - star_data[where_none] = kwargs.get('null_value', np.NAN) + # Convert None to np.nan by default + star_data[where_none] = kwargs.get('null_value', np.nan) # sets rows as time steps, columns as history output star_data = np.transpose(star_data) From 78540d6f6e11ee31de4574dd0c8bb74ee80cb813 Mon Sep 17 00:00:00 2001 From: Konstantinos Kovlakas <22239544+kkovlakas@users.noreply.github.com> Date: Thu, 18 Jul 2024 17:56:12 +0300 Subject: [PATCH 228/319] Fixing a small typo in grids IO (#358) --- posydon/grids/io.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/posydon/grids/io.py b/posydon/grids/io.py index e52d96dd66..67722e95e7 100644 --- a/posydon/grids/io.py +++ b/posydon/grids/io.py @@ -144,7 +144,7 @@ def joined_exists(folder, filename, allow_gzip=True): elif file in ["final_star1.mod", "final_star1.mod.gz"]: self.final_star1_path = fullpath elif file in ["final_star2.mod", "final_star2.mod.gz"]: - self.final_star1_path = fullpath + self.final_star2_path = fullpath elif file in ["out.txt", "out.txt.gz"] and self.binary: self.out_txt_path = fullpath elif ((file in ["out_star1_formation_step0.txt", From 1fb20c1a876c19a68eaa3c7d43637e589d4db182 Mon Sep 17 00:00:00 2001 From: ezapartas Date: Thu, 8 Aug 2024 18:00:09 +0300 Subject: [PATCH 229/319] matplotlib changes (#322) * jsut fixing the get_cmap line that crashes in matplotib 3.9 * typo * changed get_cmap instances in synth_data_3D too and updated the matlotlib setup requirement to >=3.9.0 --- .../psy_cris/synthetic_data/synth_data_3D.py | 2 +- posydon/visualization/plot_pop.py | 82 +++++++++---------- setup.py | 4 +- 3 files changed, 44 insertions(+), 44 deletions(-) diff --git a/posydon/active_learning/psy_cris/synthetic_data/synth_data_3D.py b/posydon/active_learning/psy_cris/synthetic_data/synth_data_3D.py index e77f2a0476..0dd6ee8abf 100644 --- a/posydon/active_learning/psy_cris/synthetic_data/synth_data_3D.py +++ b/posydon/active_learning/psy_cris/synthetic_data/synth_data_3D.py @@ -177,7 +177,7 @@ def get_output_3D(X,Y,Z): # 3D Plots - from matplotlib.cm import get_cmap + from matplotlib.colormaps import get_cmap fig = plt.figure() ax = fig.add_subplot(111, projection='3d') diff --git a/posydon/visualization/plot_pop.py b/posydon/visualization/plot_pop.py index d49dfeece4..051d8ecb77 100644 --- a/posydon/visualization/plot_pop.py +++ b/posydon/visualization/plot_pop.py @@ -2,10 +2,10 @@ "Simone Bavera ", "Max Briel ", ] - + import os import numpy as np -import matplotlib.pyplot as plt +import matplotlib as mpl from posydon.utils.common_functions import PATH_TO_POSYDON from posydon.visualization.plot_defaults import DEFAULT_LABELS from posydon.utils.constants import Zsun @@ -19,10 +19,10 @@ plt.style.use(os.path.join(PATH_TO_POSYDON, "posydon/visualization/posydon.mplstyle")) -cm = plt.cm.get_cmap('tab20') +cm = mpl.colormaps.get_cmap('tab20') COLORS = [cm.colors[i] for i in range(len(cm.colors)) if i%2==0] + [cm.colors[i] for i in range(len(cm.colors)) if i%2==1] -def plot_merger_rate_density(z, rate_density, zmax=10., channels=False, +def plot_merger_rate_density(z, rate_density, zmax=10., channels=False, GWTC3=False, label='DCO', **kwargs): plt.plot(z[z 10^{51} \, \mathrm{erg})$ SHOALS survey') - + plt.errorbar(z_P16,rate_P16,xerr=rate_P16_error_x,yerr=rate_P16_error_y, fmt='.', color='black', + label=r'$\mathcal{R}_\mathrm{LGRB}(E_\mathrm{iso}^{45-450\,\mathrm{keV}} > 10^{51} \, \mathrm{erg})$ SHOALS survey') + def plot_rate_density(intrinsic_rates, channels=False, **kwargs): - plt.figure() - + plt.figure() + plt.plot(intrinsic_rates.index, intrinsic_rates['total'], label='total', color='black') - + if channels: for i, ch in enumerate([key for key in intrinsic_rates.keys() if key != 'total']): if ch != 'total': @@ -118,14 +118,14 @@ def plot_merger_efficiency(met, merger_efficiency, show=True, path=None, channel if show: plt.show() - + def plot_hist_properties(df, ax=None, df_intrinsic=None, df_observable=None, channel=None, show=True, path=None, alpha=0.5, range=None, bins=20, normalise=False,color=COLORS[0], label='', **kwargs): if ax is None: fig, ax = plt.subplots(1,1, figsize=(5,5)) - + df_columns = df.columns if 'intrinsic' in df_columns and 'observable' in df_columns: title = r'Intrinsic vs. observable (dashed) population' @@ -141,7 +141,7 @@ def plot_hist_properties(df, ax=None, df_intrinsic=None, df_observable=None, values = df['intrinsic'] if normalise: values /= sum(values) - + ax.hist(df['property'], weights=values, color=color, @@ -149,12 +149,12 @@ def plot_hist_properties(df, ax=None, df_intrinsic=None, df_observable=None, range=range, bins=bins, label=label+' intrinsic') - + if 'observable' in df_columns: values = df['observable'] if normalise: values /= sum(values) - + ax.hist(df['property'], weights=values, color=color, @@ -164,35 +164,35 @@ def plot_hist_properties(df, ax=None, df_intrinsic=None, df_observable=None, linestyle='--', bins=bins, label=label+' observable') - + ax.set_title(title) - + if 'xlabel' in kwargs: ax.set_xlabel(kwargs['xlabel']) if normalise: ax.set_ylabel(r'PDF') else: ax.set_ylabel(r'\#events in bin') - + if 'yscale' in kwargs: ax.set_yscale(kwargs['yscale']) if 'xscale' in kwargs: ax.set_xscale(kwargs['xscale']) - + if path: plt.savefig(path) if show: plt.show() - - - - -def plot_popsyn_over_grid_slice(pop, grid_type, met_Zsun, slices=None, channel=None, - plot_dir='./', prop=None, prop_range=None, - log_prop=False, alpha=0.3, s=5., + + + + +def plot_popsyn_over_grid_slice(pop, grid_type, met_Zsun, slices=None, channel=None, + plot_dir='./', prop=None, prop_range=None, + log_prop=False, alpha=0.3, s=5., show_fig=True, save_fig=True, close_fig=True, plot_extension='png', verbose=False): - + # Check if step_names in pop.history data if 'step_names' not in pop.history.columns: raise ValueError('Formation channel information not available in popsynth data.') @@ -206,7 +206,7 @@ def plot_popsyn_over_grid_slice(pop, grid_type, met_Zsun, slices=None, channel=N # check if formation channel information is avaialbe if channel is not None and 'channel' not in pop.columns: raise ValueError('Formation channel information not available in popsynth data.') - + if 'CO' in grid_path: # compact object mass slices # TODO: THIS SELECTION DOES NOT WORK! @@ -230,25 +230,25 @@ def plot_popsyn_over_grid_slice(pop, grid_type, met_Zsun, slices=None, channel=N else: raise ValueError('Grid type not supported!') - + if channel is not None: channel_sel = 'channel == '+str(channel) else: channel_sel = '' - + for i, var in enumerate(vars): if slices is not None and round(var,2) not in np.around(slices,2): if verbose: print(f'Skipping {round(var,2)}') continue - + met_indices = np.where(pop.select(where=channel_sel, columns=['metallicity']) == met_Zsun)[0].tolist() if 'HMS-HMS' in grid_path: slice_3D_var_range = (dq_edges[i],dq_edges[i+1]) - + sel = 'index in '+str(met_indices)+' & event == "ZAMS"' data = pop.history.select(where=sel, columns=['S1_mass','S2_mass' ,'orbital_period']) - + q = data['S2_mass'].values/data['S1_mass'].values q[q>1] = 1./q[q>1] mask = (q>=slice_3D_var_range[0]) & (q<=slice_3D_var_range[1]) @@ -259,12 +259,12 @@ def plot_popsyn_over_grid_slice(pop, grid_type, met_Zsun, slices=None, channel=N # select popsynth binaries in the given compact object mass # TODO: implement the case of reversal mass ratio if 'CO-HMS_RLO' in grid_path: - + sel = 'index in '+str(met_indices)+' & event == "oRLO2"' data = pop.history.select(where=sel, columns=['S1_mass','S2_mass' ,'orbital_period']) m_CO = data['S1_mass'].values mask = (m_CO>=slice_3D_var_range[0]) & (m_CO<=slice_3D_var_range[1]) - + elif 'CO-HeMS' in grid_path: sel = 'index in '+str(met_indices)+' & step_names == "step_CE"' data = pop.history.select(where=sel, columns=['S1_mass','S2_mass' ,'orbital_period']) @@ -291,14 +291,14 @@ def plot_popsyn_over_grid_slice(pop, grid_type, met_Zsun, slices=None, channel=N grid_3D=True, slice_3D_var_str=slice_3D_var_str, slice_3D_var_range=slice_3D_var_range, verbose=False, **PLOT_PROPERTIES) - + log10_m1 = np.log10(data.loc[mask,'S1_mass'].values) log10_p = np.log10(data.loc[mask,'orbital_period'].values) # plot color map of a given DCO variable if prop is not None: if prop not in pop.columns: raise ValueError(f'Property {prop} not available in popsynth data.') - + # basically only for DCO systems met_indices = np.array(met_indices)[mask].tolist() prop_sel = 'index in '+str(met_indices) + ' & event == "CO_contact"' @@ -323,7 +323,7 @@ def plot_popsyn_over_grid_slice(pop, grid_type, met_Zsun, slices=None, channel=N plt.colorbar(label=label, orientation='horizontal') else: plt.scatter(log10_m1, log10_p, s=s, marker='v', color='black', alpha=alpha, zorder=0.5) - + if save_fig: plt.savefig(os.path.join(plot_dir, fname%var), bbox_inches='tight') if show_fig: diff --git a/setup.py b/setup.py index 8a96692aaa..6eb2256bb7 100644 --- a/setup.py +++ b/setup.py @@ -71,7 +71,7 @@ 'astropy >= 5.2.2', 'pandas >= 2.0.0', 'scikit-learn < 1.3.0', # 1.2.2 - 'matplotlib >= 3.7.1, <= 3.8.0', + 'matplotlib >= 3.9.0', 'matplotlib-label-lines >= 0.5.2', 'h5py >= 3.8.0', 'psutil >= 5.9.4', @@ -103,7 +103,7 @@ # for experimental visualization features, e.g. VDH diagrams "vis": ["PyQt5 >= 5.15.9"], # for profile macjhine learning features, e.g. profile interpolation - "ml": ["tensorflow >= 2.13.0"], + "ml": ["tensorflow >= 2.13.0"], # for running population synthesis on HPC facilities "hpc": ["mpi4py >= 3.0.3"], } From 488b858a93e1020552b46da4fc0d065d0d998b1a Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 22 Aug 2024 16:14:40 +0200 Subject: [PATCH 230/319] Add POSYDON Warnings (#300) * Create posydonwarning.py Setup * add in-build warnings list * added InterpolationWarning to step_SN * fix typo in posydonwarnings * Merge remote-tracking branch 'origin/POSYDON_warnings' into POSYDON_warnings * delete repeat InterpolationWarning * adding POSYDON warnings to step_SN, step_MESA * first UnsupportedModelWarning in step_detached * fixing unresolved merge * adding POSYDON warnings to many more binary evolution files * fixing error message format * imported warning * adding EvolutionWarning to step_merge * adding warnings to synthetic_population * small changes in warning class and synthetic_population * add messages to all POSYDON warnings * Fix author list in posydonwarning.py * add warning filters to binarypopulation, catch div by 0 warnings * adding threshold * add warning to compress-mesa * typos * typos * add warnings to posydon-run-grid * add warnings to posydon-run-pipeline * add warnings to posydon-setup-grid * add warnings to posydon-setup-pipeline * add warnings to posydon-setup-popsyn * add missed OverwriteWarnings * change RLO warnings to POSYDON warnings * revert setting of END state in binarystar * fix negative omega warning in detached step and functions in SN step * add warning to downsampling.py * add warnings to grids/io.py * add Pwarn * add POSYDON warnings to post_processing.py * add POSYDON warnings to psygrid.py * typo * add POSYDON warnings to scrubbing.py * turn warning into error in termination_flags.py * use Pwarn * add POSYDON warning to plot2D.py * add POSYDON warnings to gridutils.py * add POSYDON warning to sample_from_file.py * better way to get subclasses * add simple filter functions * Use new filter functions in post_processing.py * revert changes to ev_rlo functions in step_detached * add use of Pwarn to binary evolution files * add Pwarn to step_CEE * add Pwarn to step_merged * remove outdated code from step_SN * revert changes in step_detached * print warnings and save as binary attribute in binarypopulation * add comment and fix indent in binarypopulation * allow None * fix setting WARNINGS column in oneline df * remove warnings import and fix warning message * catch warnings, and apply own default filter and statistics * add filter_first; rename no_record->record * use Catch_POSYDON_Warnings; add got_called, has_records * add printing of warning stats to the pipeline * add POSYDON to be more specific * use POSYDON warnings in profile_interpolation * add POSYDON warnings to popsyn, where missing * remove unnecessary import of warnings * add more comments, to tell differences to python warnings * add options for private registry and using python catching on top * Update posydon-run-pipeline * Update posydon-run-pipeline * Update posydon-run-pipeline * add comments to binarypopulation * corrext and expand comments --------- Co-authored-by: Monica Gallegos-Garcia Co-authored-by: Camille Co-authored-by: Jeff Andrews --- bin/compress-mesa | 7 +- bin/posydon-run-grid | 55 +- bin/posydon-run-pipeline | 42 +- bin/posydon-setup-grid | 41 +- bin/posydon-setup-pipeline | 17 +- bin/posydon-setup-popsyn | 21 +- posydon/binary_evol/CE/step_CEE.py | 36 +- posydon/binary_evol/DT/double_CO.py | 2 +- posydon/binary_evol/DT/step_detached.py | 113 ++-- posydon/binary_evol/DT/step_merged.py | 16 +- posydon/binary_evol/MESA/step_mesa.py | 15 +- posydon/binary_evol/SN/step_SN.py | 51 +- posydon/binary_evol/binarystar.py | 12 +- posydon/grids/downsampling.py | 7 +- posydon/grids/io.py | 46 +- posydon/grids/post_processing.py | 62 +- posydon/grids/psygrid.py | 121 ++-- posydon/grids/scrubbing.py | 8 +- posydon/grids/termination_flags.py | 7 +- posydon/interpolation/IF_interpolation.py | 16 +- .../interpolation/profile_interpolation.py | 17 +- posydon/popsyn/GRB.py | 8 +- posydon/popsyn/binarypopulation.py | 30 +- posydon/popsyn/normalized_pop_mass.py | 8 +- posydon/popsyn/sample_from_file.py | 54 +- posydon/popsyn/synthetic_population.py | 20 +- posydon/utils/common_functions.py | 44 +- posydon/utils/gridutils.py | 23 +- posydon/utils/posydonwarning.py | 615 ++++++++++++++++++ posydon/visualization/plot2D.py | 7 +- 30 files changed, 1149 insertions(+), 372 deletions(-) create mode 100644 posydon/utils/posydonwarning.py diff --git a/bin/compress-mesa b/bin/compress-mesa index 7452522ec8..628865a1a0 100644 --- a/bin/compress-mesa +++ b/bin/compress-mesa @@ -5,6 +5,7 @@ import shutil import random import argparse from tqdm import tqdm +from posydon.utils.posydonwarning import Pwarn def textsize(filesize, floatfmt=".3g", base=1024, threshold=1000): @@ -192,9 +193,11 @@ def compress_dir(args): print(f"Original size {textsize(og_size)} | "\ f"Compressed size {textsize(new_size)}") if len(to_remove)>0: - print("Warning: still files to remove:", to_remove) + Pwarn("Still files to remove: {}".format(to_remove), + "IncompletenessWarning") if len(to_compress)>0: - print("Warning: still files to compress:", to_compress) + Pwarn("Still files to compress: {}".format(to_compress), + "IncompletenessWarning") if __name__ == "__main__": diff --git a/bin/posydon-run-grid b/bin/posydon-run-grid index 7912a8f3b5..db272d48bd 100755 --- a/bin/posydon-run-grid +++ b/bin/posydon-run-grid @@ -2,7 +2,6 @@ ############################################################################## # IMPORT ALL NECESSARY PYTHON PACKAGES ############################################################################## -import warnings import argparse import os import shutil @@ -17,6 +16,7 @@ import numpy as np import pandas from posydon.utils import gridutils as utils +from posydon.utils.posydonwarning import Pwarn from posydon.grids.psygrid import PSyGrid from posydon.active_learning.psy_cris.utils import parse_inifile from collections import ChainMap @@ -205,8 +205,8 @@ def create_working_directory(grid_param_dict, args): ############# check for existance of precomputed model if os.path.isdir(work_dir): - print ("folder "+ work_dir +" exists, removing and rerunning") - sys.stdout.flush() + Pwarn("Directory {} exists, removing and rerunning".format(work_dir), + "OverwriteWarning") shutil.rmtree(work_dir) ############# create work directory and change into it @@ -244,12 +244,18 @@ def create_binary_inlists(binary_controls, binary_job, / ! end of job namelist """.format(binary_inlist_project) - with open(os.path.join(work_dir, 'inlist'), 'w') as f: + filename = os.path.join(work_dir, 'inlist') + if os.path.exists(filename): + Pwarn('Replace '+filename, "OverwriteWarning") + with open(filename, 'w') as f: f.write(binary_controls_str) f.write('\n') f.write(binary_job_str) - with open(os.path.join(work_dir, 'inlist_grid_points'), 'w') as inlist_grid_points: + filename = os.path.join(work_dir, 'inlist_grid_points') + if os.path.exists(filename): + Pwarn('Replace '+filename, "OverwriteWarning") + with open(filename, 'w') as inlist_grid_points: if binary_controls: # Write the binary controls for this grid point inlist_grid_points.write("&binary_controls\n") @@ -285,7 +291,10 @@ def create_binary_inlists(binary_controls, binary_job, inlist_grid_points.write("/ ! end of binary_job namelist\n") if star1_binary_controls: - with open(os.path.join(work_dir, 'inlist_grid_star1_binary_controls'), 'w') as inlist_grid_points: + filename = os.path.join(work_dir, 'inlist_grid_star1_binary_controls') + if os.path.exists(filename): + Pwarn('Replace '+filename, "OverwriteWarning") + with open(filename, 'w') as inlist_grid_points: # Write the star1 controls for this grid point inlist_grid_points.write("&controls\n") for k, v in star1_binary_controls.items(): @@ -303,7 +312,10 @@ def create_binary_inlists(binary_controls, binary_job, inlist_grid_points.write("/ ! end of controls namelist\n") if star2_binary_controls: - with open(os.path.join(work_dir, 'inlist_grid_star2_binary_controls'), 'w') as inlist_grid_points: + filename = os.path.join(work_dir, 'inlist_grid_star2_binary_controls') + if os.path.exists(filename): + Pwarn('Replace '+filename, "OverwriteWarning") + with open(filename, 'w') as inlist_grid_points: # Write the star2 controls for this grid point inlist_grid_points.write("&controls\n") for k, v in star2_binary_controls.items(): @@ -321,7 +333,10 @@ def create_binary_inlists(binary_controls, binary_job, inlist_grid_points.write("/ ! end of controls namelist\n") if star1_binary_job: - with open(os.path.join(work_dir, 'inlist_grid_star1_binary_job'), 'w') as inlist_grid_points: + filename = os.path.join(work_dir, 'inlist_grid_star1_binary_job') + if os.path.exists(filename): + Pwarn('Replace '+filename, "OverwriteWarning") + with open(filename, 'w') as inlist_grid_points: # Write the star1 job for this grid point inlist_grid_points.write("&star_job\n") for k, v in star1_binary_job.items(): @@ -339,7 +354,10 @@ def create_binary_inlists(binary_controls, binary_job, inlist_grid_points.write("/ ! end of star_job namelist\n") if star2_binary_job: - with open(os.path.join(work_dir, 'inlist_grid_star2_binary_job'), 'w') as inlist_grid_points: + filename = os.path.join(work_dir, 'inlist_grid_star2_binary_job') + if os.path.exists(filename): + Pwarn('Replace '+filename, "OverwriteWarning") + with open(filename, 'w') as inlist_grid_points: # Write the star2 job for this grid point inlist_grid_points.write("&star_job\n") for k, v in star2_binary_job.items(): @@ -384,12 +402,18 @@ def create_star_formation(mass, work_dir, star_inlist_project, initial_z=None, n / ! end of job namelist """.format(star_inlist_project) - with open(os.path.join(work_dir, 'inlist'), 'w') as f: + filename = os.path.join(work_dir, 'inlist') + if os.path.exists(filename): + Pwarn('Replace '+filename, "OverwriteWarning") + with open(filename, 'w') as f: f.write(binary_controls_str) f.write('\n') f.write(binary_job_str) - with open(os.path.join(work_dir, 'inlist_grid_points'), 'w') as inlist_grid_points: + filename = os.path.join(work_dir, 'inlist_grid_points') + if os.path.exists(filename): + Pwarn('Replace '+filename, "OverwriteWarning") + with open(filename, 'w') as inlist_grid_points: inlist_grid_points.write("&controls\n") inlist_grid_points.write("initial_mass = {0:.10f}d0\n".format(mass)) if initial_z is not None: @@ -416,8 +440,8 @@ def move_mesa_output(work_dir, final_dir, copyinstead=False): """ if not work_dir == final_dir: if os.path.isdir(final_dir): - print ("folder "+ final_dir +" exists, replacing with new data") - sys.stdout.flush() + Pwarn("Directory {} exists,".format(work_dir) +\ + " replacing with new data", "OverwriteWarning") shutil.rmtree(final_dir) if copyinstead: print ("copy data from " + work_dir+ " to "+ final_dir) @@ -629,7 +653,10 @@ def do_root_process_logic(comm, grid, args, grid_params_binary_controls, grid_pa print('Process number {0} finished their run'.format(child)) print('Saving grid data and sampling a new grid point...') sys.stdout.flush() - with open(os.path.join(args.output_directory, 'grid_results.csv'), 'w') as f: + filename = os.path.join(args.output_directory, 'grid_results.csv') + if os.path.exists(filename): + Pwarn('Replace '+filename, "OverwriteWarning") + with open(filename, 'w') as f: grid.to_csv(f, index=False,) print('Sampling next grid point for process {0}'.format(child)) diff --git a/bin/posydon-run-pipeline b/bin/posydon-run-pipeline index 6a47d1871b..2e587d7972 100644 --- a/bin/posydon-run-pipeline +++ b/bin/posydon-run-pipeline @@ -82,6 +82,7 @@ from posydon.grids.post_processing import (post_process_grid, from posydon.grids.MODELS import MODELS from posydon.utils.common_functions import PATH_TO_POSYDON from posydon.utils.gridutils import get_new_grid_name +from posydon.utils.posydonwarning import (Pwarn,print_stats) from posydon.interpolation.IF_interpolation import IFInterpolator from posydon.visualization.plot_defaults import PRE_SET_PLOTS from posydon.visualization.interpolation import EvaluateIFInterpolator @@ -148,9 +149,8 @@ def create_grid_slice(i, path_to_csv_file, verbose=False, overwrite_psygrid=True stop_before_carbon_depletion = df.loc[i,'stop_before_carbon_depletion'] if 'HMS' not in grid_type: stop_before_carbon_depletion = False - if verbose: - print('stop_before_carbon_depletion set to False for non HMS'\ - f' gird: {grid_type} in {grid_path}') + Pwarn('stop_before_carbon_depletion set to False for non HMS'+\ + f' grid: {grid_type} in {grid_path}', "ReplaceValueWarning") else: stop_before_carbon_depletion = False @@ -178,9 +178,9 @@ def create_grid_slice(i, path_to_csv_file, verbose=False, overwrite_psygrid=True print(f'processing: {grid_path} with compression: {compression}') if os.path.isfile(grid_output): if overwrite_psygrid: - print(f'replacing file: {grid_output}') + Pwarn(f'replacing file: {grid_output}', "OverwriteWarning") else: - print(f'file {grid_output} already exists!') + raise FileExistsError(f'file {grid_output} already exists!') else: print(f'saving to file: {grid_output}') @@ -289,8 +289,8 @@ def calculate_extra_values(i, path_to_csv_file, verbose=False): # post processed quantities are appended to the grid object, hence # we create a copy of it and append back to it if os.path.exists(processed_grid_path): - if verbose: - print('Post processed grid file alredy exist, removing it.') + Pwarn('Post processed grid file already exists, removing it.', + "OverwriteWarning") os.remove(processed_grid_path) shutil.copyfile(grid_path, processed_grid_path) @@ -569,6 +569,8 @@ def export_dataset(i, path_to_csv_file, verbose=False): else: raise KeyError(f'No export_path in {path_to_csv_file}') + if os.path.exists(export_path): + Pwarn(f'Replace {export_path}', "OverwriteWarning") if verbose: print(f'copying {grid_path} to {export_path}') # copy the file @@ -592,7 +594,7 @@ def zams_file_name(metallicity): if '2e+00_Zsun' in metallicity: zams_filename = 'zams_z2.84m2_y0.2915.data' elif (('1e+00_Zsun' in metallicity)or(pd.isna(metallicity))): - # in v1 there is no metallicity, hence am empty string + # in v1 there is no metallicity, hence an empty string zams_filename = 'zams_z1.42m2_y0.2703.data' elif '4.5e-01_Zsun' in metallicity: zams_filename = 'zams_z6.39m3_y0.2586.data' @@ -629,6 +631,9 @@ def copy_ini_file(grid_type, rerun_metallicity, rerun_type, destination, Cluster name (e.g. yggdrasil or quest). """ + # check that the 'destination' exists + if not os.path.exists(destination): + os.makedirs(destination) # copy the default ini file to directory if cluster in ['quest','yggdrasil']: if os.path.isdir(PATH_TO_POSYDON): @@ -915,6 +920,8 @@ def rerun(i, path_to_csv_file, verbose=False): if 'cluster' in df.keys(): cluster = df.loc[i,'cluster'] else: + Pwarn('Cluster not specified, use quest as cluster', + "ReplaceValueWarning") cluster = 'quest' # load grid @@ -1033,6 +1040,7 @@ def plot_grid(i, path_to_csv_file, verbose=False): multipage = True plot_extension = 'pdf' else: + Pwarn('No plot extension, use pdf', "ReplaceValueWarning") plot_extension = 'pdf' if 'path_to_interpolator' in df.keys(): path_to_interpolator = df.loc[i,'path_to_interpolator'] @@ -1088,7 +1096,8 @@ def plot_grid(i, path_to_csv_file, verbose=False): for attr in PRE_SET_PLOTS[set_name].keys(): plot_attributes[attr] = PRE_SET_PLOTS[set_name][attr] else: - print(f'Warning: {set_name} not in PRE_SET_PLOTS.') + Pwarn(f'{set_name} not in PRE_SET_PLOTS', + "UnsupportedModelWarning") elif '_MODEL' in quantity_to_plot: short_quantity = quantity_to_plot.split('_MODEL')[-1] short_quantity = short_quantity[short_quantity.find('_')+1:] @@ -1102,7 +1111,8 @@ def plot_grid(i, path_to_csv_file, verbose=False): for attr in PRE_SET_PLOTS[set_name].keys(): plot_attributes[attr] = PRE_SET_PLOTS[set_name][attr] else: - print(f'Warning: {set_name} not in PRE_SET_PLOTS.') + Pwarn(f'{set_name} not in PRE_SET_PLOTS', + "UnsupportedModelWarning") elif quantity_to_plot[:6]=='LOG10_': short_quantity = quantity_to_plot[6:] plot_attributes['plot_dir_name'] = short_quantity @@ -1242,8 +1252,9 @@ def plot_grid(i, path_to_csv_file, verbose=False): if multipage: # close and reopen handler # (WARNING: this will overwrite the pdf file) - print(f'delete and reopen: {os.path.join(plot_dir, name)}' - f'_all.{plot_extension}') + Pwarn('delete and reopen: '+\ + f'{os.path.join(plot_dir, name)}'+\ + f'_all.{plot_extension}', "OverwriteWarning") del pdf_handler pdf_handler = PdfPages(os.path.join(plot_dir, name) +\ '_all_slices.' +plot_extension, @@ -1321,7 +1332,8 @@ def do_check(i, path_to_csv_file, verbose=False): print(f'{quantity}: {count} in {grid_path}') #TODO: add more checks else: - print(f'Do not know how to do {check_to_do} for {grid_path}!') + Pwarn(f'Do not know how to do {check_to_do} for'+\ + f' {grid_path}!', "UnsupportedModelWarning") if __name__ == '__main__': @@ -1364,11 +1376,13 @@ if __name__ == '__main__': if SLURM_I<0: # check value raise ValueError(f'The slurm index cannot be negative: {SLURM_I}') else: + Pwarn('No slurm index given, use 0', "ReplaceValueWarning") SLURM_I = 0 if n_args > 4: # verbose flag VERBOSE = bool(int(sys.argv[4])) else: + Pwarn('No verbose flag given, use True', "ReplaceValueWarning") VERBOSE = True # chose grid slice given the slurm jobarray index @@ -1407,4 +1421,4 @@ if __name__ == '__main__': if 'check' in STEP: # do checks, e.g. failure rate do_check(SLURM_I, PATH_TO_CSV_FILE, verbose=VERBOSE) - + print_stats() diff --git a/bin/posydon-setup-grid b/bin/posydon-setup-grid index d733743f6c..34d86bcfc8 100755 --- a/bin/posydon-setup-grid +++ b/bin/posydon-setup-grid @@ -11,6 +11,7 @@ import glob from posydon.utils import configfile from posydon.utils import gridutils as utils +from posydon.utils.posydonwarning import Pwarn from posydon.grids.psygrid import PSyGrid from posydon.active_learning.psy_cris.utils import parse_inifile @@ -78,8 +79,8 @@ def find_inlist_from_scenario(source, gitcommit, system_type): ) (clone, err) = proc.communicate() else: - print("git repository is already there, using that") - + Pwarn("git repository is already there, using that", + "OverwriteWarning") inlists_dir = '{0}/.posydon_mesa_inlists'.format(os.environ['HOME']) branch = gitcommit.split('-')[0] @@ -107,7 +108,8 @@ def find_inlist_from_scenario(source, gitcommit, system_type): ) (clone, err) = proc.communicate() else: - print("git repository is already there, using that") + Pwarn("git repository is already there, using that", + "OverwriteWarning") inlists_dir = '{0}/.user_mesa_inlists'.format(os.environ['HOME']) branch = gitcommit.split('-')[0] @@ -263,6 +265,9 @@ def find_inlist_from_scenario(source, gitcommit, system_type): "x_logical_ctrl(1)=.true.") # write star1 formation step to file special_single_star_user_inlist = os.path.join(os.getcwd(), "special_single_star_user_inlist") + if os.path.exists(special_single_star_user_inlist): + Pwarn('Replace '+special_single_star_user_inlist, + "OverwriteWarning") with open(special_single_star_user_inlist, 'wb') as f: f.write(b'&controls\n\n') f.write('\t{0} = {1}\n'.format("x_logical_ctrl(1)", ".true.").encode('utf-8')) @@ -295,6 +300,9 @@ def find_inlist_from_scenario(source, gitcommit, system_type): "x_logical_ctrl(1)=.true.") # write star1 formation step to file special_single_star_user_inlist = os.path.join(os.getcwd(), "special_single_star_user_inlist") + if os.path.exists(special_single_star_user_inlist): + Pwarn('Replace '+special_single_star_user_inlist, + "OverwriteWarning") with open(special_single_star_user_inlist, 'wb') as f: f.write(b'&controls\n\n') f.write('\t{0} = {1}\n'.format("x_logical_ctrl(1)", ".true.").encode('utf-8')) @@ -541,6 +549,9 @@ def construct_static_inlist(mesa_inlists, grid_parameters, working_directory=os. final_star1_formation_controls.pop("zams_filename", None) # write star1 formation step to file + if os.path.exists(step_inlists['inlist_file']): + Pwarn('Replace '+step_inlists['inlist_file'], + "OverwriteWarning") with open(step_inlists['inlist_file'], 'wb') as f: f.write(b'&controls\n\n') for k,v in final_star1_formation_controls.items(): @@ -643,6 +654,9 @@ def construct_static_inlist(mesa_inlists, grid_parameters, working_directory=os. final_star2_formation_job['save_model_when_terminate'] = '.true.' final_star2_formation_job['save_model_filename'] = "'initial_star2_step{0}.mod'".format(step) + if os.path.exists(step_inlists['inlist_file']): + Pwarn('Replace '+step_inlists['inlist_file'], + "OverwriteWarning") with open(step_inlists['inlist_file'], 'wb') as f: f.write(b'&controls\n\n') for k,v in final_star2_formation_controls.items(): @@ -722,6 +736,8 @@ def construct_static_inlist(mesa_inlists, grid_parameters, working_directory=os. ########################################## # now that we have all the parameters and their correct values # we now write our own inlist_project, inlist1 and inlist2 for the binary + if os.path.exists(inlist_binary_project): + Pwarn('Replace '+inlist_binary_project, "OverwriteWarning") with open(inlist_binary_project, 'wb') as f: f.write(b'&binary_controls\n\n') for k,v in final_binary_controls.items(): @@ -739,6 +755,8 @@ def construct_static_inlist(mesa_inlists, grid_parameters, working_directory=os. / ! end of binary_job namelist """) + if os.path.exists(inlist_star1_binary): + Pwarn('Replace '+inlist_star1_binary, "OverwriteWarning") with open(inlist_star1_binary, 'wb') as f: f.write(b'&controls\n\n') for k,v in final_star1_binary_controls.items(): @@ -760,6 +778,8 @@ def construct_static_inlist(mesa_inlists, grid_parameters, working_directory=os. """) + if os.path.exists(inlist_star2_binary): + Pwarn('Replace '+inlist_star2_binary, "OverwriteWarning") with open(inlist_star2_binary, 'wb') as f: f.write(b'&controls\n\n') for k,v in final_star2_binary_controls.items(): @@ -813,6 +833,8 @@ def make_executables(mesa_extras, working_directory=os.getcwd()): if os.path.exists(binary_make_folder): shutil.rmtree(binary_make_folder) os.makedirs(binary_make_folder) + if os.path.exists('mk'): + Pwarn('Replace mk', "OverwriteWarning") with open('mk', "w") as f: # first we need to cd into the make folder for k, v in mesa_extras.items(): @@ -967,7 +989,8 @@ if __name__ == '__main__': print("WE ARE USING THE EXTRA FILE FROM TYPE {0}".format(extras_files_types[-1])) for k in mesa_extras.keys(): if ('binary_extras' in k) and (extras_files_types[-1] not in k): - print("Section mesa_extras value {0} is being set to None".format(k)) + Pwarn("Section mesa_extras value {0} is being set to".format(k)+\ + " None", "ReplaceValueWarning") mesa_extras[k] = None binary_exe, star1_exe, star2_exe = make_executables(mesa_extras=mesa_extras, @@ -1049,6 +1072,8 @@ if __name__ == '__main__': # now we need to know how this person plans to run the above created # command. As a shell script? As a SLURM submission? In some other way? if args.submission_type == 'shell': + if os.path.exists('grid_command.sh'): + Pwarn('Replace grid_command.sh', "OverwriteWarning") with open('grid_command.sh', 'w') as f: f.write('#!/bin/bash\n\n') f.write('export OMP_NUM_THREADS={0}\n\n'.format(slurm['number_of_cpus_per_task'])) @@ -1066,6 +1091,8 @@ if __name__ == '__main__': else: raise ValueError('Grid format not recognized, please feed in an acceptable format: csv') grid_script = 'job_array_grid_submit.slurm' + if os.path.exists(grid_script): + Pwarn('Replace '+grid_script, "OverwriteWarning") with open(grid_script, 'w') as f: f.write('#!/bin/bash\n') @@ -1090,6 +1117,8 @@ if __name__ == '__main__': f.write(command_line) else: grid_script = 'mpi_grid_submit.slurm' + if os.path.exists(grid_script): + Pwarn('Replace '+grid_script, "OverwriteWarning") with open(grid_script, 'w') as f: f.write('#!/bin/bash\n') @@ -1111,6 +1140,8 @@ if __name__ == '__main__': f.write('export MESA_DIR={0}\n\n\n'.format(os.environ['MESA_DIR'])) f.write(command_line) # create a cleanup script + if os.path.exists('cleanup.slurm'): + Pwarn('Replace cleanup.slurm', "OverwriteWarning") with open('cleanup.slurm', 'w') as f: f.write('#!/bin/bash\n') @@ -1134,6 +1165,8 @@ if __name__ == '__main__': f.write('chmod -fR g+rwX .\n') f.write('\necho \"Done.\"') # create a runfile script + if os.path.exists('run_grid.sh'): + Pwarn('Replace run_grid.sh', "OverwriteWarning") with open('run_grid.sh', 'w') as f: f.write('#!/bin/bash\n') f.write('ID_GRID=$(sbatch --parsable {0})\n'.format(grid_script)) diff --git a/bin/posydon-setup-pipeline b/bin/posydon-setup-pipeline index a152215fa1..b99c9b1077 100644 --- a/bin/posydon-setup-pipeline +++ b/bin/posydon-setup-pipeline @@ -18,6 +18,7 @@ from pprint import pformat from posydon.popsyn.io import parse_inifile from posydon.utils.common_functions import PATH_TO_POSYDON from posydon.utils.gridutils import get_new_grid_name +from posydon.utils.posydonwarning import Pwarn # this data processing pipeline was designed assuming POSYDON v2 data structure ''' @@ -286,8 +287,8 @@ class PostProcessingPipeline: and attribute in\ self.pipeline_kwargs[last_step].keys()): # infer missing attribute from last step - print(f"Copy {attribute} from {last_step} to " - f"{step_number}") + Pwarn(f"Copy {attribute} from {last_step} to " + f"{step_number}", "ReplaceValueWarning") self.pipeline_kwargs[step_number][attribute] = \ self.pipeline_kwargs[last_step][attribute] else: @@ -372,6 +373,7 @@ class PostProcessingPipeline: if 'VERBOSE' in setup_kwargs.keys(): VERBOSE = setup_kwargs['VERBOSE'] else: + Pwarn("VERBOSE not given, use False", "ReplaceValueWarning") VERBOSE = False if VERBOSE: @@ -384,6 +386,8 @@ class PostProcessingPipeline: plot_dirs = [] pipeline_steps = [] + if os.path.exists('run_pipeline.sh'): + Pwarn('Replace run_pipeline.sh', "OverwriteWarning") # create a runfile script with open('run_pipeline.sh', 'w') as runfile: if runfile.writable(): @@ -488,6 +492,8 @@ class PostProcessingPipeline: if 'PATH' in setup_kwargs.keys(): PATH = setup_kwargs['PATH'] else: + Pwarn("PATH not given, use current directory", + "ReplaceValueWarning") PATH = '.' df['pipeline_files'] = previously_created_files +\ list(set(plot_dirs)) @@ -527,6 +533,8 @@ class PostProcessingPipeline: if 'PATH' in setup_kwargs.keys(): logs_path = os.path.join(setup_kwargs['PATH'], 'logs') else: + Pwarn("PATH not given, use current directory", + "ReplaceValueWarning") logs_path = os.path.join('.', 'logs') # create logs in working dir to store all slurm outputs if not os.path.isdir(logs_path): @@ -594,6 +602,8 @@ def slurm_job(job_name, if not os.path.isfile(path_to_csv_file): raise FileNotFoundError(f"Missing csv file: {path_to_csv_file}") + if os.path.exists(f'{job_name}.slurm'): + Pwarn(f'Replace {job_name}.slurm', "OverwriteWarning") # create slurm file with open(f'{job_name}.slurm', 'w') as f: # get STEPID @@ -604,6 +614,7 @@ def slurm_job(job_name, elif job_name == 'rerun': STEPID = 'R' else: + Pwarn("No step ID detected, leave it empty", "ReplaceValueWarning") STEPID = '' # write slurm file content f.write("#!/bin/bash\n") @@ -1307,6 +1318,8 @@ def create_cleanup_slurm_job(job_name, ((job_name == 'pipeline') and (GROUP is not None))): if not os.path.isdir(PATH): raise NotADirectoryError(f"PATH={PATH} not found.") + if os.path.exists(f'{job_name}_cleanup.slurm'): + Pwarn(f'Replace {job_name}_cleanup.slurm', "OverwriteWarning") with open(f'{job_name}_cleanup.slurm', 'w') as f: # write slurm file content f.write("#!/bin/bash\n") diff --git a/bin/posydon-setup-popsyn b/bin/posydon-setup-popsyn index 8734d1f290..88dc5561b6 100644 --- a/bin/posydon-setup-popsyn +++ b/bin/posydon-setup-popsyn @@ -8,23 +8,23 @@ # 6. Including the dependencies # Author: Max Briel import os +import argparse from posydon.popsyn.io import (binarypop_kwargs_from_ini, create_run_script_text, create_merge_script_text) -from posydon.utils.common_functions import convert_metallicity_to_string from posydon.config import PATH_TO_POSYDON, PATH_TO_POSYDON_DATA -import argparse - from posydon.popsyn.binarypopulation import BinaryPopulation from posydon.popsyn.io import binarypop_kwargs_from_ini from posydon.utils.common_functions import convert_metallicity_to_string -import argparse +from posydon.utils.posydonwarning import Pwarn def create_run_script(ini_file): '''creates a run script for the population synthesis run''' filename =f'run_metallicity.py' + if os.path.exists(filename): + Pwarn('Replace '+filename, "OverwriteWarning") with open(filename, mode='w') as file: file.write(create_run_script_text(ini_file)) @@ -32,6 +32,8 @@ def create_merge_script(ini_file): '''creates a merge script for the population synthesis run''' filename='merge_metallicity.py' + if os.path.exists(filename): + Pwarn('Replace '+filename, "OverwriteWarning") with open(filename, mode='w') as file: file.write(create_merge_script_text(ini_file)) @@ -66,6 +68,8 @@ export PATH_TO_POSYDON_DATA={path_to_posydon_data} srun python ./run_metallicity.py {metallicity} ''' filename = f"{str_met}_Zsun_slurm_array.slurm" + if os.path.exists(filename): + Pwarn('Replace '+filename, "OverwriteWarning") with open(filename, mode='w') as file: file.write(text) @@ -105,6 +109,8 @@ export PATH_TO_POSYDON_DATA={path_to_posydon_data} srun python ./merge_metallicity.py {metallicity} ''' filename = f'{str_met}_Zsun_merge_popsyn.slurm' + if os.path.exists(filename): + Pwarn('Replace '+filename, "OverwriteWarning") with open(filename, mode='w') as file: file.write(text) @@ -129,8 +135,9 @@ if __name__ == '__main__': synpop_params = binarypop_kwargs_from_ini(args.ini_file) metallicities = synpop_params['metallicity'] if synpop_params['number_of_binaries'] / args.job_array < 1: - print("The number of binaries is less than the job array length. Please increase the number of binaries or decrease the job array length") - exit() + raise ValueError("The number of binaries is less than the job array" + " length. Please increase the number of binaries or" + " decrease the job array length") create_run_script(args.ini_file) create_merge_script(args.ini_file) @@ -149,6 +156,8 @@ if __name__ == '__main__': # create submission script for all SLURM filename = 'slurm_submit.sh' + if os.path.exists(filename): + Pwarn('Replace '+filename, "OverwriteWarning") with open(filename, mode='w') as file: file.write('#!/bin/bash\n') for MET in metallicities: diff --git a/posydon/binary_evol/CE/step_CEE.py b/posydon/binary_evol/CE/step_CEE.py index dd6aab135b..2db944ea51 100644 --- a/posydon/binary_evol/CE/step_CEE.py +++ b/posydon/binary_evol/CE/step_CEE.py @@ -38,16 +38,12 @@ import pandas as pd from posydon.utils import common_functions as cf from posydon.utils import constants as const -import warnings from posydon.binary_evol.binarystar import BINARYPROPERTIES from posydon.binary_evol.singlestar import STARPROPERTIES from posydon.utils.common_functions import PATH_TO_POSYDON from posydon.utils.common_functions import check_state_of_star from posydon.utils.common_functions import calculate_lambda_from_profile, calculate_Mejected_for_integrated_binding_energy -from posydon.utils.posydonerror import FlowError - - -warnings.simplefilter('always', UserWarning) +from posydon.utils.posydonwarning import Pwarn MODEL = {"prescription": 'alpha-lambda', @@ -357,8 +353,8 @@ def calculate_lambda_CE(self, donor, verbose=False): donor.he_core_mass, donor.co_core_mass) elif donor.profile is None: - warnings.warn("Profile does not exist -- Proceeding with " - "default_lambda alpha-CE prescription") + Pwarn("Donor profile does not exist -- proceeding with " + "default_lambda alpha-CE prescription", "ApproximationWarning") # like in the "default_lambda" option lambda_CE = self.common_envelope_lambda_default if donor_type == 'He_core': @@ -589,10 +585,10 @@ def CEE_simple_alpha_prescription( rc1_f = donor.co_core_radius if mc1_f > mc1_i: mc1_f = mc1_i - warnings.warn( - "donor core mass final (after even stable, postCEE MT)" - " assumed higher than postCEE core mass. Equalized to " - "postCEE mass") + Pwarn( + "The final donor core mass (even after stable, postCEE MT)" + " is higher than the postCEE core mass. Now equalizing to " + "postCEE mass", "ApproximationWarning") if not double_CE: mc2_f = mc2_i rc2_f = rc2_i @@ -608,10 +604,10 @@ def CEE_simple_alpha_prescription( rc2_f = 10.**(comp_star.log_R) if mc2_f > mc2_i: mc2_f = mc2_i - warnings.warn("accretor's core mass final (after even " - "non-conservative stable, postCEE MT) " - "assumed higher that postCEE core mass. " - "Equialized to postCEE mass") + Pwarn("The accretor's final core mass (even after " + "non-conservative stable, postCEE MT) is " + "higher that postCEE core mass. " + "Now equalizing to postCEE mass", "ApproximationWarning") if verbose: print("difference between m1 core mass defined by CEE step" " / to the final one as pre CEE : ", mc1_f, mc1_i) @@ -829,11 +825,14 @@ def CEE_simple_alpha_prescription( if not double_CE and donor.profile is None: Mejected_donor = 0.0 Mejected_comp = 0.0 - warnings.warn("mass_loss_during_CEE_merged == True, but no profile found for the donor star. Proceeding with no partial mass ejection.") + Pwarn("mass_loss_during_CEE_merged == True, but no profile found " + "for the donor star. Proceeding with no partial mass ejection.", "ApproximationWarning") elif double_CE and (donor.profile is None or comp_star.profile is None): Mejected_comp = 0.0 Mejected_comp = 0.0 - warnings.warn("mass_loss_during_CEE_merged == True, but not profile found the donor or companion star in double_CE. Proceeding with no partial mass ejection.") + Pwarn("mass_loss_during_CEE_merged == True, but not profile found " + "for the donor or companion star in double_CE. Proceeding with no partial mass ejection.", + "ApproximationWarning") else: @@ -879,7 +878,8 @@ def CEE_simple_alpha_prescription( if (Mejected_donor > m1_i - mc1_i) or (Mejected_comp > m2_i - mc2_i): Mejected_donor = (m1_i - mc1_i) -0.01 # at least this value of envelope is left. Mejected_comp = (m2_i - mc2_i) -0.01 - warnings.warn("Mejected of at least one star in double CEE is found to be more that the initial envelope. Reduced both to their initial_envelope - 0.01 Msun") + Pwarn("M_ejected of at least one star in double CEE is found to be more than the initial envelope. " + "Reducing both to their initial_envelope - 0.01 Msun", "ApproximationWarning") donor.mass = m1_i - Mejected_donor donor.log_R = np.nan diff --git a/posydon/binary_evol/DT/double_CO.py b/posydon/binary_evol/DT/double_CO.py index a85db9ebd5..263c8fe0dd 100644 --- a/posydon/binary_evol/DT/double_CO.py +++ b/posydon/binary_evol/DT/double_CO.py @@ -45,7 +45,7 @@ def __call__(self, binary): max_time = binary.properties.max_simulation_time assert ( max_time - binary.time > 0.0 - ), "max_time is lower than the current time." + ), "max_time is loXer than the current time." r1 = CO_radius(self.m1, self.state1) * constants.Rsun / 100000 # in km r2 = CO_radius(self.m2, self.state2) * constants.Rsun / 100000 # in km diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index c41f9bb75b..d4976e02d4 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -37,8 +37,8 @@ ) from posydon.binary_evol.flow_chart import (STAR_STATES_CC, STAR_STATES_CO) import posydon.utils.constants as const -from posydon.utils.posydonerror import NumericalError -from posydon.utils.posydonerror import MatchingError,POSYDONError +from posydon.utils.posydonerror import NumericalError, MatchingError, POSYDONError +from posydon.utils.posydonwarning import Pwarn LIST_ACCEPTABLE_STATES_FOR_HMS = ["H-rich_Core_H_burning"] @@ -1169,7 +1169,8 @@ def get_star_data(binary, star1, star2, htrack, interp1d_pri = get_star_data( binary, primary, secondary, primary.htrack, False)[0] else: - raise MatchingError("During matching primary is either should be either normal or not normal. `non_existent_companion` should be zero.") + raise MatchingError("During matching, the primary should either be normal or not normal." + "`non_existent_companion` should be zero.") if interp1d_sec is None or interp1d_pri is None: @@ -1212,9 +1213,11 @@ def ev_rlo1(t, y): """ sep = y[0] ecc = y[1] + RL = roche_lobe_radius(interp1d_sec["mass"](t - t_offset_sec) - / interp1d_pri["mass"](t - t_offset_pri), - (1 - ecc) * sep) + / interp1d_pri["mass"](t - t_offset_pri), + (1 - ecc) * sep) + # 95% filling of the RL is enough to assume beginning of RLO, # as we do in CO-HMS_RLO grid return interp1d_sec["R"](t - t_offset_sec) - 0.95*RL @@ -1243,9 +1246,11 @@ def ev_rlo2(t, y): """ sep = y[0] ecc = y[1] + RL = roche_lobe_radius(interp1d_pri["mass"](t - t_offset_pri) - / interp1d_sec["mass"](t - t_offset_sec), - (1 - ecc) * sep) + / interp1d_sec["mass"](t - t_offset_sec), + (1 - ecc) * sep) + return interp1d_pri["R"](t - t_offset_pri) - 0.95*RL @event(True, 1) @@ -1272,9 +1277,11 @@ def ev_rel_rlo1(t, y): """ sep = y[0] ecc = y[1] + RL = roche_lobe_radius(interp1d_sec["mass"](t - t_offset_sec) - / interp1d_pri["mass"](t - t_offset_pri), - (1 - ecc) * sep) + / interp1d_pri["mass"](t - t_offset_pri), + (1 - ecc) * sep) + return (interp1d_sec["R"](t - t_offset_sec) - RL) / RL @event(True, 1) @@ -1301,9 +1308,11 @@ def ev_rel_rlo2(t, y): """ sep = y[0] ecc = y[1] + RL = roche_lobe_radius(interp1d_pri["mass"](t - t_offset_pri) - / interp1d_sec["mass"](t - t_offset_sec), - (1 - ecc) * sep) + / interp1d_sec["mass"](t - t_offset_sec), + (1 - ecc) * sep) + return (interp1d_pri["R"](t - t_offset_pri) - RL) / RL @event(True, -1) @@ -1327,7 +1336,6 @@ def get_omega(star, is_secondary = True): if self.verbose and self.verbose != 1: print("calculating initial omega from angular momentum and" " moment of inertia", omega_in_rad_per_year) - # except TypeError: else: # we equate secondary's initial omega to surf_avg_omega # (although the critical rotation should be improved to @@ -1378,13 +1386,10 @@ def get_omega(star, is_secondary = True): return omega_in_rad_per_year if (ev_rlo1(binary.time, [binary.separation, binary.eccentricity]) >= 0 - or ev_rlo2(binary.time, - [binary.separation, binary.eccentricity]) - >= 0): + or ev_rlo2(binary.time, [binary.separation, binary.eccentricity]) >= 0): binary.state = "initial_RLOF" return - # binary.event = "END" - # TODO: put it out of its misery here! + else: if not (max_time - binary.time > 0.0): raise ValueError("max_time is lower than the current time. " @@ -1512,23 +1517,14 @@ def get_omega(star, is_secondary = True): else: # self.dt is None and self.n_o_steps_history is None t = np.array([s.t[-1]]) - # TODO: this variable is not used. What is happening? - orb_params = s.sol(t) - - sep_interp, ecc_interp, omega_interp_sec, omega_interp_pri = s.sol( - t) + sep_interp, ecc_interp, omega_interp_sec, omega_interp_pri = s.sol(t) mass_interp_sec = interp1d_sec[self.translate["mass"]] mass_interp_pri = interp1d_pri[self.translate["mass"]] - # s.sol(t)[0] - # interp1d = dict() - interp1d_sec["sep"] = s.sol(t)[0] - # lambda x: orb_params[0][np.argmax(x == t[:, None], 0)] - interp1d_sec["ecc"] = s.sol(t)[1] - # lambda x: orb_params[1][np.argmax(x == t[:, None], 0)] - interp1d_sec["omega"] = s.sol(t)[2] - # lambda x: orb_params[2][np.argmax(x == t[:, None], 0)] - interp1d_pri["omega"] = s.sol(t)[3] + interp1d_sec["sep"] = sep_interp + interp1d_sec["ecc"] = ecc_interp + interp1d_sec["omega"] = omega_interp_sec + interp1d_pri["omega"] = omega_interp_pri interp1d_sec["porb"] = orbital_period_from_separation( sep_interp, mass_interp_sec(t - t_offset_sec), @@ -1537,7 +1533,7 @@ def get_omega(star, is_secondary = True): sep_interp, mass_interp_pri(t - t_offset_pri), mass_interp_sec(t - t_offset_sec)) - interp1d_sec["time"] = t # binary.time + x - t0 + interp1d_sec["time"] = t # time_interp = binary.time + t - t0 for obj, prop in zip( @@ -1553,13 +1549,11 @@ def get_omega(star, is_secondary = True): # For star objects, the state is calculated # further below history = [current] * len(t[:-1]) - # elif key in ("state") and obj == secondary: - # current = check_state_of_star(obj, star_CO=False) - # history = [current] * len(t[:-1]) + elif (key in ["surf_avg_omega_div_omega_crit"] and obj == secondary): - # In fact I replace the actual surf_avg_w with the ef- - # fective omega which takes into account the whole star + # replace the actual surf_avg_w with the effective + # omega, which takes into account the whole star # key = 'effective_omega' # in rad/sec # current = s.y[2][-1] / 3.1558149984e7 # history_of_attribute = s.y[2][:-1] / 3.1558149984e7 @@ -1582,6 +1576,7 @@ def get_omega(star, is_secondary = True): / omega_crit_current_sec) history = (interp1d_sec["omega"][:-1] / const.secyer / omega_crit_hist_sec) + elif (key in ["surf_avg_omega_div_omega_crit"] and obj == primary): if primary.co: @@ -1608,10 +1603,12 @@ def get_omega(star, is_secondary = True): / const.secyer / omega_crit_current_pri) history = (interp1d_pri["omega"][:-1] / const.secyer / omega_crit_hist_pri) + elif key in ["surf_avg_omega"] and obj == secondary: # current = interp1d["omega"](t[-1]) / const.secyer current = interp1d_sec["omega"][-1] / const.secyer history = interp1d_sec["omega"][:-1] / const.secyer + elif key in ["surf_avg_omega"] and obj == primary: if primary.co: current = None @@ -1619,6 +1616,7 @@ def get_omega(star, is_secondary = True): else: current = interp1d_pri["omega"][-1] / const.secyer history = interp1d_pri["omega"][:-1] / const.secyer + elif key in ["rl_relative_overflow_1"] and obj == binary: if binary.star_1.state in ("BH", "NS", "WD","massless_remnant"): current = None @@ -1637,6 +1635,7 @@ def get_omega(star, is_secondary = True): history = ev_rel_rlo2(t[:-1], [interp1d_sec["sep"][:-1], interp1d_sec["ecc"][:-1]]) + elif key in ["rl_relative_overflow_2"] and obj == binary: if binary.star_2.state in ("BH", "NS", "WD","massless_remnant"): current = None @@ -1655,10 +1654,12 @@ def get_omega(star, is_secondary = True): history = ev_rel_rlo2(t[:-1], [interp1d_sec["sep"][:-1], interp1d_sec["ecc"][:-1]]) + elif key in ["separation", "orbital_period", "eccentricity", "time"]: current = interp1d_sec[self.translate[key]][-1].item() history = interp1d_sec[self.translate[key]][:-1] + elif (key in ["total_moment_of_inertia"] and obj == secondary): current = interp1d_sec[self.translate[key]]( @@ -1667,6 +1668,7 @@ def get_omega(star, is_secondary = True): history = interp1d_sec[self.translate[key]]( t[:-1] - t_offset_sec) * ( const.msol * const.rsol ** 2) + elif key in ["total_moment_of_inertia"] and obj == primary: if primary.co: current = getattr(obj, key) @@ -1678,20 +1680,22 @@ def get_omega(star, is_secondary = True): history = interp1d_pri[self.translate[key]]( t[:-1] - t_offset_pri) * ( const.msol * const.rsol ** 2) - elif (key in ["log_total_angular_momentum"] - and obj == secondary): + + elif (key in ["log_total_angular_momentum"] and obj == secondary): + current = np.log10( (interp1d_sec["omega"][-1] / const.secyer) - * (interp1d_sec[ - self.translate["total_moment_of_inertia"]]( - t[-1] - t_offset_sec).item() * ( - const.msol * const.rsol ** 2))) + * (interp1d_sec[ + self.translate["total_moment_of_inertia"]](t[-1] - t_offset_sec).item() * + (const.msol * const.rsol ** 2))) + history = np.log10( (interp1d_sec["omega"][:-1] / const.secyer) * (interp1d_sec[ self.translate["total_moment_of_inertia"]]( t[:-1] - t_offset_sec) * ( const.msol * const.rsol ** 2))) + elif (key in ["log_total_angular_momentum"] and obj == primary): if primary.co: @@ -1710,6 +1714,7 @@ def get_omega(star, is_secondary = True): self.translate["total_moment_of_inertia"]]( t[:-1] - t_offset_pri) * ( const.msol * const.rsol ** 2))) + elif key in ["spin"] and obj == secondary: current = ( const.clight @@ -1732,6 +1737,7 @@ def get_omega(star, is_secondary = True): / (const.standard_cgrav * ( interp1d_sec[self.translate["mass"]]( t[:-1] - t_offset_sec) * const.msol)**2)) + elif key in ["spin"] and obj == primary: if primary.co: current = getattr(obj, key) @@ -1759,6 +1765,7 @@ def get_omega(star, is_secondary = True): self.translate["mass"]]( t[:-1] - t_offset_pri) * const.msol)**2)) + elif (key in ["lg_mdot", "lg_wind_mdot"] and obj == secondary): # in detached step, lg_mdot = lg_wind_mdot @@ -1800,12 +1807,14 @@ def get_omega(star, is_secondary = True): history[i] = np.log10(np.abs( interp1d_sec[self.translate[key]]( t[i] - t_offset_sec))) + elif (self.translate[key] in interp1d_sec and obj == secondary): current = interp1d_sec[self.translate[key]]( t[-1] - t_offset_sec).item() history = interp1d_sec[self.translate[key]]( t[:-1] - t_offset_sec) + elif (self.translate[key] in interp1d_pri and obj == primary): if primary.co: @@ -1816,9 +1825,11 @@ def get_omega(star, is_secondary = True): t[-1] - t_offset_pri).item() history = interp1d_pri[self.translate[key]]( t[:-1] - t_offset_pri) + elif key in ["profile"]: current = None history = [current] * len(t[:-1]) + else: current = np.nan history = np.ones_like(t[:-1]) * current @@ -1853,8 +1864,7 @@ def get_omega(star, is_secondary = True): def get_star_final_values(star, htrack, m0): grid = self.grid_Hrich if htrack else self.grid_strippedHe get_final_values = grid.get_final_values - # TODO: this variable is never used! - get_final_state = grid.get_final_state + for key in self.final_keys: setattr(star, key, get_final_values('S1_%s' % (key), m0)) @@ -1862,6 +1872,7 @@ def get_star_profile(star, htrack, m0): grid = self.grid_Hrich if htrack else self.grid_strippedHe get_profile = grid.get_profile profile_new = np.array(get_profile('mass', m0)[1]) + for i in self.profile_keys: profile_new[i] = get_profile(i, m0)[0] profile_new['omega'] = star.surf_avg_omega @@ -2638,15 +2649,15 @@ def diffeq( ) else: - print("WARNING: Magnetic braking is not being calculated in the " + Pwarn("WARNING: Magnetic braking is not being calculated in the " "detached step. The given magnetic_braking_mode string \"", magnetic_braking_mode, "\" does not match the available " "built-in cases. To enable magnetic braking, please set " - "magnetc_braking_mode to one of the following strings:") - print("\"RVJ83\" for Rappaport, Verbunt, & Joss 1983") - print("\"G18\" for Garraffo et al. 2018") - print("\"M15\" for Matt et al. 2015") - print("\"CARB\" for Van & Ivanova 2019") + "magnetc_braking_mode to one of the following strings: " + "\"RVJ83\" for Rappaport, Verbunt, & Joss 1983" + "\"G18\" for Garraffo et al. 2018" + "\"M15\" for Matt et al. 2015" + "\"CARB\" for Van & Ivanova 2019", "UnsupportedModelWarning") if verbose and verbose != 1: print("magnetic_braking_mode = ", magnetic_braking_mode) diff --git a/posydon/binary_evol/DT/step_merged.py b/posydon/binary_evol/DT/step_merged.py index 14c5b8478e..70ca96e425 100644 --- a/posydon/binary_evol/DT/step_merged.py +++ b/posydon/binary_evol/DT/step_merged.py @@ -16,7 +16,7 @@ from posydon.binary_evol.DT.step_isolated import IsolatedStep from posydon.utils.posydonerror import FlowError -import warnings +from posydon.utils.posydonwarning import Pwarn from posydon.binary_evol.flow_chart import ( STAR_STATES_H_RICH, @@ -263,7 +263,7 @@ def mass_weighted_avg(star1=star_base,star2=comp, abundance_name="center_h1", ma merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight1="co_core_mass") merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight1="co_core_mass") else: - warnings.warn("weird compbination of CO core masses during merging") + Pwarn("weird compbination of CO core masses during merging", "EvolutionWarning") # weigheted mixing on the surface abundances based on the envelopes of the two stars merged_star.surface_h1 = mass_weighted_avg(abundance_name = "surface_h1", mass_weight1="H-rich_envelope_mass", mass_weight2="H-rich_envelope_mass") @@ -303,7 +303,7 @@ def mass_weighted_avg(star1=star_base,star2=comp, abundance_name="center_h1", ma elif (star_base.co_core_mass > 0 and comp.co_core_mass == 0): # star_base with CO core and the comp has just a He core (is a HeMS star) pass # the central abundances are kept as the ones of star_base else: - warnings.warn("weird compbination of CO core masses during merging") + Pwarn("weird compbination of CO core masses during merging", "EvolutionWarning") for key in STARPROPERTIES: # these stellar attributes become np.nan @@ -337,7 +337,7 @@ def mass_weighted_avg(star1=star_base,star2=comp, abundance_name="center_h1", ma elif (star_base.co_core_mass == 0 and comp.co_core_mass > 0): # star_base is the HeMS Star and comp has a CO core pass # the central abundances are kept as the ones of star_base else: - warnings.warn("weird compbination of CO core masses during merging") + Pwarn("weird compbination of CO core masses during merging", "EvolutionWarning") for key in STARPROPERTIES: # these stellar attributes become np.nan @@ -381,7 +381,7 @@ def mass_weighted_avg(star1=star_base,star2=comp, abundance_name="center_h1", ma merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight1="co_core_mass") merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight1="co_core_mass") else: - warnings.warn("weird compbination of CO core masses during merging") + Pwarn("weird compbination of CO core masses during merging", "EvolutionWarning") for key in STARPROPERTIES: # these stellar attributes become np.nan @@ -425,7 +425,7 @@ def mass_weighted_avg(star1=star_base,star2=comp, abundance_name="center_h1", ma merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight1="co_core_mass") merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight1="co_core_mass") else: - warnings.warn("weird compbination of CO core masses during merging") + Pwarn("weird compbination of CO core masses during merging", "EvolutionWarning") for key in STARPROPERTIES: # these stellar attributes become np.nan @@ -469,7 +469,7 @@ def mass_weighted_avg(star1=star_base,star2=comp, abundance_name="center_h1", ma merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight1="co_core_mass") merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight1="co_core_mass") else: - warnings.warn("weird compbination of CO core masses during merging") + Pwarn("weird compbination of CO core masses during merging", "EvolutionWarning") # weigheted mixing on the surface abundances based on the He-rich envelopes of the two stars merged_star.surface_h1 = mass_weighted_avg(abundance_name = "surface_h1", mass_weight1="He-rich_envelope_mass", mass_weight2="He-rich_envelope_mass") @@ -512,7 +512,7 @@ def mass_weighted_avg(star1=star_base,star2=comp, abundance_name="center_h1", ma merged_star.center_n14 = mass_weighted_avg(abundance_name = "center_n14", mass_weight1="co_core_mass") merged_star.center_o16 = mass_weighted_avg(abundance_name = "center_o16", mass_weight1="co_core_mass") else: - warnings.warn("weird compbination of CO core masses during merging") + Pwarn("weird compbination of CO core masses during merging", "EvolutionWarning") for key in STARPROPERTIES: # these stellar attributes become np.nan diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index fc27e3f9d2..6284274d60 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -15,7 +15,6 @@ import os -import warnings import numpy as np from posydon.interpolation.interpolation import psyTrackInterp @@ -31,7 +30,7 @@ from posydon.utils.data_download import data_download, PATH_TO_POSYDON_DATA from posydon.grids.MODELS import MODELS from posydon.utils.posydonerror import FlowError, GridError - +from posydon.utils.posydonwarning import Pwarn # left POSYDON, right MESA POSYDON_TO_MESA = { @@ -454,14 +453,14 @@ def update_properties_NN(self, star_1_CO=False, star_2_CO=False, MESA_history_bug_fix = False if length_binary_hist != length_star_hist: MESA_history_bug_fix = True - warnings.warn( - 'The MESA star_history and binary_history do not match ' + Pwarn( + 'The MESA star_history and binary_history do not match: ' 'lenght %i != %i. This will cause errors, e.g. ' 'get_binary_state_and_event_and_mt_case take ' - 'star.mdot_history - star.lg_wind_mdot, to ' - 'avoid the code to break we happened np.nan ' - 'to the missing values!' % - (length_binary_hist, length_star_hist)) + 'star.mdot_history - star.lg_wind_mdot. To ' + 'avoid the code breaking, we append np.nan ' + 'to the missing values.' % + (length_binary_hist, length_star_hist), "ReplaceValueWarning") # update properties for key in BINARYPROPERTIES: diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index 258943833c..d33d3072ad 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -30,7 +30,6 @@ import os -import warnings import numpy as np import scipy as sp import copy @@ -55,6 +54,7 @@ from posydon.grids.MODELS import MODELS from posydon.utils.posydonerror import ModelError +from posydon.utils.posydonwarning import Pwarn from posydon.utils.common_functions import set_binary_to_failed from pandas import read_csv @@ -534,11 +534,10 @@ def collapse_star(self, star): # step_disrupted. # allow to continue with the collapse with profile # or core masses - warnings.warn(f'{MODEL_NAME_SEL}: The collapsed star ' + Pwarn(f'{MODEL_NAME_SEL}: The collapsed star ' 'was not interpolated! If use_profiles ' 'or use_core_masses is set to True, ' - 'continue with the collapse.') - + 'continue with the collapse.', "InterpolationWarning") else: MODEL_properties = getattr(star, MODEL_NAME_SEL) @@ -587,12 +586,12 @@ def collapse_star(self, star): return else: - warnings.warn(f'{MODEL_NAME_SEL}: The SN_type ' + Pwarn(f'{MODEL_NAME_SEL}: The SN_type ' 'does not match the predicted CO, or the interpolated ' 'values for the SN remnant are NaN. ' 'If use_profiles or use_core_masses is set to True, ' - 'continue with the collapse.') - + 'continue with the collapse.', "ApproximationWarning") + # Verifies the selection of core-collapse mechnism to perform # the collapse if self.mechanism in [ @@ -977,13 +976,13 @@ def check_SN_type(self, m_core, m_He_core, m_star): if m_star < 0.5: m_rembar = m_star if ((m_core < 0.)or(m_He_core < 0.)): - warnings.warn('Invalid co/He core masses! ' - 'Setting m_WD=m_star!') + Pwarn('Invalid co/He core masses! ' + 'Setting m_WD=m_star!', "ApproximationWarning") else: - warnings.warn('co/He core masses are zero! ' - 'Setting m_WD=m_star!') + Pwarn('co/He core masses are zero! ' + 'Setting m_WD=m_star!', "ApproximationWarning") else: - raise ModelError('Invalid co/He core masses!') + raise ModelError('Invalid co/He core masses! Cannot complete SN.') f_fb = 1.0 # no SN the no kick is assumed state = "WD" @@ -1018,13 +1017,13 @@ def check_SN_type(self, m_core, m_He_core, m_star): if m_star < 0.5: m_rembar = m_star if ((m_core < 0.)or(m_He_core < 0.)): - warnings.warn('Invalid co/He core masses! ' - 'Setting m_WD=m_star!') + Pwarn('Invalid co/He core masses! ' + 'Setting m_WD=m_star!', "ApproximationWarning") else: - warnings.warn('co/He core masses are zero! ' - 'Setting m_WD=m_star!') + Pwarn('co/He core masses are zero! ' + 'Setting m_WD=m_star!', "ApproximationWarning") else: - raise ModelError('Invalid co/He core masses!') + raise ModelError('Invalid co/He core masses! Cannot complete SN.') f_fb = 1.0 # no SN the no kick is assumed state = "WD" @@ -1058,13 +1057,13 @@ def check_SN_type(self, m_core, m_He_core, m_star): if m_star < 0.5: m_rembar = m_star if ((m_core < 0.)or(m_He_core < 0.)): - warnings.warn('Invalid co/He core masses! ' - 'Setting m_WD=m_star!') + Pwarn('Invalid co/He core masses! ' + 'Setting m_WD=m_star!', "ApproximationWarning") else: - warnings.warn('co/He core masses are zero! ' - 'Setting m_WD=m_star!') + Pwarn('co/He core masses are zero! ' + 'Setting m_WD=m_star!', "ApproximationWarning") else: - raise ModelError('Invalid co/He core masses!') + raise ModelError('Invalid co/He core masses! Cannot complete SN.') f_fb = 1.0 # no SN the no kick is assumed state = "WD" @@ -2075,10 +2074,10 @@ def Patton20_corecollapse(self, star, engine, conserve_hydrogen_envelope=False): class Sukhbold16_corecollapse(object): """Compute supernova final remnant mass, fallback fraction and CO type. - This consider the nearest neighboor of the He core mass of the star, - previous to the collapse. Considering a set of data for which the He core - mass of the compact object projenitos previous the collapse, the final - remnant mass and final stellar state of the compact object is known. + This considers the He core mass of the nearest neighbor of the star + prior to the collapse. Using a set of data for the He core + mass of the compact object progenitors prior the collapse, the final + remnant mass and stellar state of the compact object are known. Parameters ---------- diff --git a/posydon/binary_evol/binarystar.py b/posydon/binary_evol/binarystar.py index ca3a7c0f36..4b7877f0bf 100644 --- a/posydon/binary_evol/binarystar.py +++ b/posydon/binary_evol/binarystar.py @@ -27,7 +27,6 @@ import signal -import warnings import copy import numpy as np import pandas as pd @@ -191,6 +190,7 @@ def __init__(self, star_1=None, star_2=None, index=None, properties=None, def evolve(self): """Evolve a binary from start to finish.""" + self.properties.pre_evolve(self) # Code to make sure start time is less than max_simulation_time @@ -204,8 +204,8 @@ def evolve(self): n_steps = 0 try: while (self.event != 'END' and self.event != 'FAILED' - and self.event not in self.properties.end_events - and self.state not in self.properties.end_states): + and self.event not in self.properties.end_events + and self.state not in self.properties.end_states): signal.alarm(MAXIMUM_STEP_TIME) self.run_step() @@ -226,10 +226,8 @@ def run_step(self): next_step_name = self.properties.flow.get(total_state) if next_step_name is None: - warnings.warn("Undefined next step given stars/binary states " - "{}.".format(total_state)) + raise ValueError("Undefined next step given stars/binary states {}.".format(total_state)) self.event = 'END' - return next_step = getattr(self.properties, next_step_name, None) if next_step is None: @@ -603,7 +601,7 @@ def to_oneline_df(self, scalar_names=[], history=True, **kwargs): oneline_df['FAILED'] = [1] else: oneline_df['FAILED'] = [0] - if hasattr(self, 'warning_message'): + if hasattr(self, 'warnings'): oneline_df['WARNING'] = [1] else: oneline_df['WARNING'] = [0] diff --git a/posydon/grids/downsampling.py b/posydon/grids/downsampling.py index ef2623c94a..5c2a37fc4b 100644 --- a/posydon/grids/downsampling.py +++ b/posydon/grids/downsampling.py @@ -42,8 +42,8 @@ import numpy as np -import warnings import sys +from posydon.utils.posydonwarning import Pwarn sys.setrecursionlimit(100000) @@ -101,7 +101,7 @@ def __init__(self, independent, dependent, verbose=False): self.rescale() def say(self, message): - """Speak to the standard output, only if `.verbose` is False.""" + """Speak to the standard output, only if `.verbose` is True.""" if self.verbose: print(message) @@ -133,7 +133,8 @@ def find_downsample(self, max_err=None, max_interval=None): if max_err < 0.0 or not np.isfinite(max_err): raise ValueError("`max_err` must be a non-negative finite number.") if max_err >= 1.0: - warnings.warn("`max_err` >= 1.0 is like disabling downsampling.") + Pwarn("`max_err` >= 1.0 is like disabling downsampling.", + "InappropriateValueWarning") keep = np.zeros_like(t, dtype=bool) # initially keep no row diff --git a/posydon/grids/io.py b/posydon/grids/io.py index 67722e95e7..fefffc75cc 100644 --- a/posydon/grids/io.py +++ b/posydon/grids/io.py @@ -47,18 +47,19 @@ import os import glob import gzip -import warnings from posydon.utils.gridutils import read_MESA_data_file +from posydon.utils.posydonwarning import Pwarn POSYDON_FORMAT_OPTIONS = { # subfolders in the grid parent folder that are unnecessary "ignored folders": ["make", "star1", "star2", "binary", "data", - "new_data", "template", ".ipynb_checkpoints"], + "column_lists", "new_data", "template", + ".ipynb_checkpoints"], # which files contain useful metadata concerning the grid - "grid metadata": ["grid_test.csv", - "grid_test.csv.gz"], + "grid metadata": ["grid_test.csv", "grid_test.csv.gz", + "grid.csv", "grid.csv.gz"], # which files contain useful metadata concerning individual grids "run metadata": ["inlist_grid_points", "summary.txt", "out.txt", "inlist_grid_point.gz", "summary.txt.gz", "out.txt.gz"] @@ -145,11 +146,11 @@ def joined_exists(folder, filename, allow_gzip=True): self.final_star1_path = fullpath elif file in ["final_star2.mod", "final_star2.mod.gz"]: self.final_star2_path = fullpath - elif file in ["out.txt", "out.txt.gz"] and self.binary: + elif ((file in [BINARY_OUTPUT_FILE, BINARY_OUTPUT_FILE+".gz"]) and + (self.binary)): self.out_txt_path = fullpath - elif ((file in ["out_star1_formation_step0.txt", - "out_star1_formation_step0.txt.gz"]) - and not self.binary): + elif ((file in [SINGLE_OUTPUT_FILE, SINGLE_OUTPUT_FILE+".gz"]) and + not self.binary): self.out_txt_path = fullpath elif file in POSYDON_FORMAT_OPTIONS["run metadata"]: self.metadata_files.append(fullpath) @@ -172,6 +173,17 @@ def joined_exists(folder, filename, allow_gzip=True): self.initial_profile2_path = joined_exists( self.path, 'LOGS2/initial_profile.data', allow_gzip=True) + if ((self.history1_path is None) and (self.history2_path is None) and + (self.binary_history_path is None) and + (self.final_profile1_path is None) and + (self.final_profile2_path is None) and + (self.initial_profile1_path is None) and + (self.initial_profile2_path is None) and + (self.final_star1_path is None) and (self.final_star2_path is None) + and (self.out_txt_path is None) and (len(self.metadata_files)==0)): + Pwarn("No relevant files found in {}".format(self.path), + "MissingFilesWarning") + if self.verbose: self.report() @@ -183,11 +195,16 @@ def isfound(var): print("-" * 80) print("DATA FOUND") print("-" * 80) + print("MESA screen output :", isfound(self.out_txt_path)) print("History of Star 1 :", isfound(self.history1_path)) print("History of Star 2 :", isfound(self.history2_path)) print("Binary history :", isfound(self.binary_history_path)) print("Final profile of Star 1 :", isfound(self.final_profile1_path)) print("Final profile of Star 2 :", isfound(self.final_profile2_path)) + print("Initial profile of Star 1:", + isfound(self.initial_profile1_path)) + print("Initial profile of Star 2:", + isfound(self.initial_profile2_path)) print("Final model of Star 1 :", isfound(self.final_star1_path)) print("Final model of Star 2 :", isfound(self.final_star2_path)) print("-" * 80) @@ -260,8 +277,8 @@ def _get_input_folders(self): fullpath = os.path.dirname(os.path.abspath(out_file)) params_part = initial_values_from_dirname(fullpath) if params_part in folders: - warnings.warn("Run in {} substitutes run in {}". - format(fullpath, folders[params_part])) + Pwarn("Run in {} substitutes run in {}".format(fullpath, + folders[params_part]), "ReplaceValueWarning") folders[params_part] = fullpath # discover metadata files @@ -291,7 +308,8 @@ def read_posydon_format(self): new_run = RunReader(fullpath, fmt=self.fmt, binary=self.binary, verbose=False) if new_run.out_txt_path is None: - warnings.warn("Folder "+fullpath+" has no stdout file.") + Pwarn("Folder "+fullpath+" has no stdout file.", + "MissingFilesWarning") self.runs.append(new_run) def infer_history_columns(self, BH_cols, H1_cols, H2_cols): @@ -304,7 +322,7 @@ def infer_history_columns(self, BH_cols, H1_cols, H2_cols): H1_cols : array-like Which columns to consider from `history1`. H2_cols : array-like - Which columns to consider from `history1`. + Which columns to consider from `history2`. Returns ------- @@ -390,7 +408,9 @@ def read_initial_values(mesa_dir): continue fields = line.strip().split("=") if len(fields) != 2: - return None + Pwarn("Multiple `=`, skipping line in {}.".format(path), + "InappropriateValueWarning") + continue varname = fields[0].strip() valueparts = fields[1].split("d") value = float(valueparts[0].strip()) diff --git a/posydon/grids/post_processing.py b/posydon/grids/post_processing.py index 94006069b7..77091e2035 100644 --- a/posydon/grids/post_processing.py +++ b/posydon/grids/post_processing.py @@ -14,7 +14,7 @@ import numpy as np from tqdm import tqdm import copy -import warnings +from posydon.utils.posydonwarning import (Pwarn, Catch_POSYDON_Warnings) __authors__ = [ @@ -61,8 +61,8 @@ def print_CC_quantities(EXTRA_COLUMNS, star, MODEL_NAME=None): 'PRE SN STAR', star.state, '', '', star.mass, star.spin, '', '', '', '', '', '')) except Exception as e: - warnings.warn('Failed to print star values!') - print('Warning in preSN: ', e) + Pwarn("Failed to print star values!\nWarning in preSN: "+\ + "{}".format(e), "InappropriateValueWarning") print('') else: try: @@ -78,11 +78,11 @@ def print_CC_quantities(EXTRA_COLUMNS, star, MODEL_NAME=None): star.m_disk_radiated, checked_quantities_for_None["M4"], checked_quantities_for_None["mu4"], checked_quantities_for_None["h1_mass_ej"], checked_quantities_for_None["he4_mass_ej"])) except Exception as e: - warnings.warn('Failed to print star values!') - print('Warning in', MODEL_NAME, ': ', e) - - - + Pwarn("Failed to print star values!\nWarning in "+\ + "{}: {}".format(MODEL_NAME, e), "InappropriateValueWarning") + + + def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, single_star=False, verbose=False): """Compute post processed quantity of any grid. @@ -189,30 +189,22 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, EXTRA_COLUMNS['S%s_state' % (j+1)].append(check_state_of_star( star, star_CO=False)) # core masses at he depletion - with warnings.catch_warnings(record=True) as w: + with Catch_POSYDON_Warnings(record=True) as cpw: if ((star.avg_c_in_c_core_at_He_depletion is None) or (star.co_core_mass_at_He_depletion is None)): calculate_Patton20_values_at_He_depl(star) - if len(w) > 0: - print(w[0].message) - print(f'The warning was raised by {grid.MESA_dirs[i]} ' - f'in calculate_Patton20_values_at_He_depl(star_{j+1}).') EXTRA_COLUMNS[f'S{j+1}_avg_c_in_c_core_at_He_depletion'].append( star.avg_c_in_c_core_at_He_depletion) EXTRA_COLUMNS[f'S{j+1}_co_core_mass_at_He_depletion'].append( star.co_core_mass_at_He_depletion) # CE quantities - with warnings.catch_warnings(record=True) as w: + with Catch_POSYDON_Warnings(record=True) as cpw: try: CEE_parameters_from_core_abundance_thresholds(star) except Exception as ex: print(ex) print(f'The exception was raised by {grid.MESA_dirs[i]} ' - f'in CEE_parameters_from_core_abundance_thresholds(star_{j+1}).') - if len(w) > 0: - print(w[0].message) - print(f'The warning was raised by {grid.MESA_dirs[i]} ' - f'in CEE_parameters_from_core_abundance_thresholds(star_{j+1}).') + f'in CEE_parameters_from_core_abundance_thresholds(star_{j+1}).') for quantity in ['lambda_CE', 'm_core_CE', 'r_core_CE']: for val in [1, 10, 30, 'pure_He_star_10']: EXTRA_COLUMNS[f'S{j+1}_{quantity}_{val}cent'].append( @@ -280,21 +272,19 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, else: assign_core_collapse_quantities_none(EXTRA_COLUMNS, 1) assign_core_collapse_quantities_none(EXTRA_COLUMNS, 2) - warnings.warn(f'{grid.MESA_dirs[i]} ended with ' - 'TF1=gamma_center_limit however ' - 'the star has center_gamma < 10. ' - 'This star cannot go through step_SN ' - 'appending NONE compact object ' - 'properties!') + Pwarn(f'{grid.MESA_dirs[i]} ended with ' + 'TF1=gamma_center_limit however the star has ' + 'center_gamma < 10. This star cannot go through ' + 'step_SN appending NONE compact object ' + 'properties!', "InappropriateValueWarning") continue else: assign_core_collapse_quantities_none(EXTRA_COLUMNS, 1) assign_core_collapse_quantities_none(EXTRA_COLUMNS, 2) - warnings.warn(f'{grid.MESA_dirs[i]} ended with ' - f'TF={TF1} and IC={interpolation_class}. ' - 'This star cannot go through step_SN ' - 'appending NONE compact object ' - 'properties!') + Pwarn(f'{grid.MESA_dirs[i]} ended with TF={TF1} and ' + f'IC={interpolation_class}. This star cannot go ' + 'through step_SN appending NONE compact object ' + 'properties!', "InappropriateValueWarning") continue if star.state in STAR_STATES_CC: @@ -312,16 +302,16 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, if quantity in ['state', 'SN_type']: if not isinstance(getattr(star_copy, quantity), str): flush = True - warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} is not a string!') + Pwarn(f'{MODEL_NAME} {mechanism} {quantity} is not a string!', "InappropriateValueWarning") elif quantity != 'CO_interpolation_class': if quantity in ['spin', 'M4', 'mu4', "h1_mass_ej", "he4_mass_ej"]: if ((not isinstance(getattr(star_copy, quantity), float)) and (getattr(star_copy, quantity) != None)): flush = True - warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} is not a float nor None!') + Pwarn(f'{MODEL_NAME} {mechanism} {quantity} is not a float nor None!', "InappropriateValueWarning") elif not isinstance(getattr(star_copy, quantity), float): flush = True - warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} is not a float!') + Pwarn(f'{MODEL_NAME} {mechanism} {quantity} is not a float!', "InappropriateValueWarning") except Exception as e: flush = True if verbose: @@ -373,16 +363,16 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, if quantity in ['state', 'SN_type']: if not isinstance(getattr(star_copy, quantity), str): flush = True - warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} is not a string!') + Pwarn(f'{MODEL_NAME} {mechanism} {quantity} is not a string!', "InappropriateValueWarning") elif quantity != 'CO_interpolation_class': if quantity in ['spin', 'M4', 'mu4', "h1_mass_ej", "he4_mass_ej"]: if ((not isinstance(getattr(star_copy, quantity), float)) and (getattr(star_copy, quantity) != None)): flush = True - warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} is not a float nor None!') + Pwarn(f'{MODEL_NAME} {mechanism} {quantity} is not a float nor None!', "InappropriateValueWarning") elif not isinstance(getattr(star_copy, quantity), float): flush = True - warnings.warn(f'{MODEL_NAME} {mechanism} {quantity} is not a float!') + Pwarn(f'{MODEL_NAME} {mechanism} {quantity} is not a float!', "InappropriateValueWarning") except Exception as e: flush = True if verbose: diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index e22d8ee477..e6085e9a40 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -183,7 +183,6 @@ import glob import json import ast -import warnings import h5py import numpy as np import pandas as pd @@ -213,6 +212,7 @@ from posydon.grids.downsampling import TrackDownsampler from posydon.grids.scrubbing import scrub, keep_after_RLO, keep_till_central_abundance_He_C from posydon.utils.ignorereason import IgnoreReason +from posydon.utils.posydonwarning import (Pwarn, Catch_POSYDON_Warnings) HDF5_MEMBER_SIZE = 2**31 - 1 # maximum HDF5 file size when splitting @@ -510,22 +510,15 @@ def create(self, MESA_grid_path, psygrid_path=None, overwrite=False, self._create_psygrid(MESA_grid_path, hdf5=hdf5, slim=slim, fmt=fmt) else: - collected_warnings = [] - with warnings.catch_warnings(record=True) as caught_warnings: + with Catch_POSYDON_Warnings(record=True) as caught_warnings: self._create_psygrid(MESA_grid_path, hdf5=hdf5, slim=slim, fmt=fmt) - collected_warnings = caught_warnings - - if warn == "end": - for warning_message in collected_warnings: - warnings.showwarning(warning_message.message, - warning_message.category, - warning_message.filename, - warning_message.lineno, - line="") - else: - # for consistency - assert warn == "suppress" + if warn == "suppress": + caught_warnings.reset_cache() + else: + # for consistency + assert warn == "end" + self.load() @@ -553,7 +546,8 @@ def _create_psygrid(self, MESA_path, hdf5, slim=False, fmt="posydon"): if eep is not None: if binary_grid: - warnings.warn("Selected EEPs, switching to single-star grid.") + Pwarn("Selected EEPs, switching to single-star grid.", + "ReplaceValueWarning") self.config["binary"] = True binary_grid = True self._discover_eeps(eep) @@ -668,8 +662,8 @@ def decide_columns(key_in_config, defaults): # if no binary history, ignore this run if binary_grid and binary_history is None: ignore.reason = "ignored_no_binary_history" - warnings.warn("Ignored MESA run because of missing binary " - "history in: {}\n".format(run.path)) + Pwarn("Ignored MESA run because of missing binary history in:" + " {}\n".format(run.path), "MissingFilesWarning") if not initial_RLO_fix: continue @@ -681,16 +675,16 @@ def decide_columns(key_in_config, defaults): try: eep_path = self.eeps[os.path.basename(run.path)] except KeyError: - warnings.warn("No matching EEP file for `" + run.path - + "`. Ignoring run.") + Pwarn("No matching EEP file for `" + run.path +\ + "`. Ignoring run.", "MissingFilesWarning") continue history1 = read_EEP_data_file(eep_path, H1_columns) if self.config["He_core_fix"]: history1 = fix_He_core(history1) if not binary_grid and history1 is None: - warnings.warn("Ignored MESA run because of missing " - "history in: {}\n".format(run.path)) + Pwarn("Ignored MESA run because of missing history in:" + " {}\n".format(run.path), "MissingFilesWarning") ignore.reason = "ignored_no_history1" continue @@ -737,11 +731,13 @@ def decide_columns(key_in_config, defaults): len_diff = len(history1)-len(history1_mod) if len_diff<0: #shorten history1_mod history1_mod = history1_mod[:len_diff] - warnings.warn("Reduce mod in {}\n".format(run.history1_path)) + Pwarn("Reduce mod in {}\n".format(run.history1_path), + "ReplaceValueWarning") elif len_diff>0: #entend history1_mod add_mod = np.full(len_diff,history1_mod[-1]) history1_mod = np.concatenate((history1_mod, add_mod)) - warnings.warn("Expand mod in {}\n".format(run.history1_path)) + Pwarn("Expand mod in {}\n".format(run.history1_path), + "ReplaceValueWarning") else: ignore.reason = "corrupted_history1" if "star_age" in H1_columns: @@ -755,11 +751,13 @@ def decide_columns(key_in_config, defaults): len_diff = len(history1)-len(history1_age) if len_diff<0: #shorten history1_age history1_age = history1_age[:len_diff] - warnings.warn("Reduce age in {}\n".format(run.history1_path)) + Pwarn("Reduce age in {}\n".format(run.history1_path), + "ReplaceValueWarning") elif len_diff>0: #entend history1_age add_age = np.full(len_diff,history1_age[-1]) history1_age = np.concatenate((history1_age, add_age)) - warnings.warn("Expand age in {}\n".format(run.history1_path)) + Pwarn("Expand age in {}\n".format(run.history1_path), + "ReplaceValueWarning") else: ignore.reason = "corrupted_history1" else: @@ -778,11 +776,13 @@ def decide_columns(key_in_config, defaults): len_diff = len(history2)-len(history2_mod) if len_diff<0: #shorten history2_mod history2_mod = history2_mod[:len_diff] - warnings.warn("Reduce mod in {}\n".format(run.history2_path)) + Pwarn("Reduce mod in {}\n".format(run.history2_path), + "ReplaceValueWarning") elif len_diff>0: #entend history2_mod add_mod = np.full(len_diff,history2_mod[-1]) history2_mod = np.concatenate((history2_mod, add_mod)) - warnings.warn("Expand mod in {}\n".format(run.history2_path)) + Pwarn("Expand mod in {}\n".format(run.history2_path), + "ReplaceValueWarning") else: ignore.reason = "corrupted_history2" if "star_age" in H2_columns: @@ -796,11 +796,13 @@ def decide_columns(key_in_config, defaults): len_diff = len(history2)-len(history2_age) if len_diff<0: #shorten history2_age history2_age = history2_age[:len_diff] - warnings.warn("Reduce age in {}\n".format(run.history2_path)) + Pwarn("Reduce age in {}\n".format(run.history2_path), + "ReplaceValueWarning") elif len_diff>0: #entend history2_age add_age = np.full(len_diff,history2_age[-1]) history2_age = np.concatenate((history2_age, add_age)) - warnings.warn("Expand age in {}\n".format(run.history2_path)) + Pwarn("Expand age in {}\n".format(run.history2_path), + "ReplaceValueWarning") else: ignore.reason = "corrupted_history2" else: @@ -819,11 +821,13 @@ def decide_columns(key_in_config, defaults): len_diff = len(binary_history)-len(binary_history_mod) if len_diff<0: #shorten binary_history_mod binary_history_mod = binary_history_mod[:len_diff] - warnings.warn("Reduce mod in {}\n".format(run.binary_history_path)) + Pwarn("Reduce mod in {}\n".format(run.binary_history_path), + "ReplaceValueWarning") elif len_diff>0: #entend binary_history_mod add_mod = np.full(len_diff,binary_history_mod[-1]) binary_history_mod = np.concatenate((binary_history_mod, add_mod)) - warnings.warn("Expand mod in {}\n".format(run.binary_history_path)) + Pwarn("Expand mod in {}\n".format(run.binary_history_path), + "ReplaceValueWarning") else: ignore.reason = "corrupted_binary_history" if "age" in BH_columns: @@ -837,11 +841,13 @@ def decide_columns(key_in_config, defaults): len_diff = len(binary_history)-len(binary_history_age) if len_diff<0: #shorten binary_history_age binary_history_age = binary_history_age[:len_diff] - warnings.warn("Reduce age in {}\n".format(run.binary_history_path)) + Pwarn("Reduce age in {}\n".format(run.binary_history_path), + "ReplaceValueWarning") elif len_diff>0: #entend binary_history_age add_age = np.full(len_diff,binary_history_age[-1]) binary_history_age = np.concatenate((binary_history_age, add_age)) - warnings.warn("Expand age in {}\n".format(run.binary_history_path)) + Pwarn("Expand age in {}\n".format(run.binary_history_path), + "ReplaceValueWarning") else: ignore.reason = "corrupted_binary_history" else: @@ -870,8 +876,9 @@ def decide_columns(key_in_config, defaults): binary_history_len = 0 if binary_grid and binary_history_len == 0: ignore.reason = "ignored_scrubbed_history" - warnings.warn("Ignored MESA run because of scrubbed binary" - " history in: {}\n".format(run.path)) + Pwarn("Ignored MESA run because of scrubbed binary" + " history in: {}\n".format(run.path), + "InappropriateValueWarning") if not initial_RLO_fix: continue if history1 is not None: @@ -880,8 +887,9 @@ def decide_columns(key_in_config, defaults): history1_len = 0 if not binary_grid and history1_len == 0: ignore.reason = "ignored_scrubbed_history" - warnings.warn("Ignored MESA run because of scrubbed" - " history in: {}\n".format(run.path)) + Pwarn("Ignored MESA run because of scrubbed" + " history in: {}\n".format(run.path), + "InappropriateValueWarning") continue try: #get mass from binary history @@ -923,11 +931,12 @@ def decide_columns(key_in_config, defaults): run.final_profile2_path, P2_columns) if not binary_grid and final_profile1 is None: if self.config["accept_missing_profile"]: - warnings.warn("Including MESA run despite the missing " - "profile in {}\n".format(run.path)) + Pwarn("Including MESA run despite the missing profile" + " in: {}\n".format(run.path), + "MissingFilesWarning") else: - warnings.warn("Ignored MESA run because of missing " - "profile in: {}\n".format(run.path)) + Pwarn("Ignored MESA run because of missing profile in:" + " {}\n".format(run.path), "MissingFilesWarning") ignore.reason = "ignored_no_final_profile" continue @@ -1181,9 +1190,10 @@ def decide_columns(key_in_config, defaults): # check that new MESA path is added at run_index lenMESA_dirs = len(self.MESA_dirs) if lenMESA_dirs!=run_index: - warnings.warn("Non synchronous indexing: " + - "run_index={} != ".format(run_index) + - "length(MESA_dirs)={}".format(lenMESA_dirs)) + Pwarn("Non synchronous indexing: " +\ + "run_index={} != ".format(run_index) +\ + "length(MESA_dirs)={}".format(lenMESA_dirs), + "InappropriateValueWarning") #general fix for termination_flag in case of initial RLO in binaries if binary_grid and initial_RLO_fix: @@ -1202,17 +1212,20 @@ def decide_columns(key_in_config, defaults): mass1 = grid_point["star_1_mass"] else: mass1 = self.initial_values[i]["star_1_mass"] - warnings.warn("No star_1_mass in "+grid.runs[i].path) + Pwarn("No star_1_mass in "+grid.runs[i].path, + "ReplaceValueWarning") if "star_2_mass" in grid_point: mass2 = grid_point["star_2_mass"] else: mass2 = self.initial_values[i]["star_2_mass"] - warnings.warn("No star_2_mass in "+grid.runs[i].path) + Pwarn("No star_2_mass in "+grid.runs[i].path, + "ReplaceValueWarning") if "period_days" in grid_point: period = grid_point["period_days"] else: period = self.initial_values[i]["period_days"] - warnings.warn("No period_days in "+grid.runs[i].path) + Pwarn("No period_days in "+grid.runs[i].path, + "ReplaceValueWarning") nearest = get_nearest_known_initial_RLO(mass1, mass2, detected_initial_RLO) if period=0: #check for index range if run_included_at[i]>=run_index: - warnings.warn("run {} has a run_index out of ".format(i) + - "range: {}>={}".format(run_included_at[i], run_index)) + Pwarn("run {} has a run_index out of ".format(i) + + "range: {}>={}".format(run_included_at[i], run_index), + "InappropriateValueWarning") continue #copy initial values or fill with nan if not existing in original for colname in dtype_initial_values.names: diff --git a/posydon/grids/scrubbing.py b/posydon/grids/scrubbing.py index 33b5f9efd2..0de82a6b36 100644 --- a/posydon/grids/scrubbing.py +++ b/posydon/grids/scrubbing.py @@ -8,11 +8,11 @@ import numpy as np -import warnings from posydon.utils.limits_thresholds import ( RL_RELATIVE_OVERFLOW_THRESHOLD, LG_MTRANSFER_RATE_THRESHOLD, THRESHOLD_CENTRAL_ABUNDANCE, THRESHOLD_CENTRAL_ABUNDANCE_LOOSE_C ) +from posydon.utils.posydonwarning import Pwarn def scrub(tables, models, ages): @@ -223,7 +223,8 @@ def keep_till_central_abundance_He_C(bh, h1, h2, if (h1["center_he4"][i]0: + Pwarn(f"{len(testing_failed)} binaries failed", + "IncompletenessWarning") # extract training data print("extracting training data") @@ -76,7 +79,9 @@ def __init__(self, train_path, test_path, hms_s2=False, if 'omega' in self.names: self.names.append('norm_omega') - warnings.warn(f"{len(training_failed)} training binaries failed") + if len(training_failed)>0: + Pwarn(f"{len(training_failed)} training binaries failed", + "IncompletenessWarning") def scrape(self,grid,ind,hms_s2): """Extracts profile data from one MESA run. @@ -124,7 +129,8 @@ def scrape(self,grid,ind,hms_s2): profile_new = f(np.linspace(0,1,200)) profiles[i] = profile_new else: - warnings.warn(f"{prof} profile not saved in grid, will not be included in file") + Pwarn(f"{prof} profile not saved in grid, will not be " + "included in file", "InappropriateValueWarning") if 'omega' in self.names: profiles[-1]= profiles[self.names.index('omega')]/ \ @@ -604,7 +610,8 @@ def learn_bounds(self,training_epochs,training_patience): callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=training_patience) if len(indices)==0: - warnings.warn(f"no training data available for {state}") + Pwarn(f"no training data available for {state}", + "InappropriateValueWarning") loss_history[state]=np.nan self.empty.append(state) diff --git a/posydon/popsyn/GRB.py b/posydon/popsyn/GRB.py index f15963a327..042b10e9d5 100644 --- a/posydon/popsyn/GRB.py +++ b/posydon/popsyn/GRB.py @@ -1,7 +1,7 @@ __author__ = ['Simone Bavera '] -import warnings import numpy as np +from posydon.utils.posydonwarning import Pwarn from posydon.utils.constants import Msun, clight GRB_PROPERTIES = ['GRB1', 'S1_eta', 'S1_f_beaming', 'S1_E_GRB', 'S1_E_GRB_iso', 'S1_L_GRB_iso', @@ -17,8 +17,8 @@ def get_GRB_properties(df, GRB_efficiency, GRB_beaming, E_GRB_iso_min=0.): # reminder the user about the threshold cut if E_GRB_iso_min != 0.: - warnings.warn("We only consider GRBs with isotropic equivalent " - f"energy larger than {E_GRB_iso_min} erg!") + Pwarn("We only consider GRBs with isotropic equivalent energy larger " + f"than {E_GRB_iso_min} erg!", "ApproximationWarning") # define some methods @@ -125,4 +125,4 @@ def flag_GRB_systems(i, sel): compute_L_GRB_iso(i, sel) flag_GRB_systems(i, sel) - return df \ No newline at end of file + return df diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index dc4d89ed61..0a0c6e7e2f 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -30,7 +30,6 @@ import pandas as pd import numpy as np -import warnings import traceback import atexit import os @@ -56,6 +55,7 @@ from posydon.popsyn.io import binarypop_kwargs_from_ini from posydon.utils.constants import Zsun from posydon.utils.posydonerror import POSYDONError,initial_condition_message +from posydon.utils.posydonwarning import (Pwarn, Catch_POSYDON_Warnings) from posydon.utils.common_functions import set_binary_to_failed saved_ini_parameters = ['metallicity', @@ -317,7 +317,11 @@ def _safe_evolve(self, **kwargs): binary = self.manager.generate(index=index, **self.kwargs) binary.properties = self.population_properties - with warnings.catch_warnings(record=True) as w: + # catch POSYDON warnings: record them to be possibly printed at + # then and of the "with" context; use a new registry for this + # context instead of the global one + with Catch_POSYDON_Warnings(record=True, own_registry=True) as cpw: + try: binary.evolve() @@ -336,10 +340,22 @@ def _safe_evolve(self, **kwargs): e.add_note(initial_condition_message(binary)) traceback.print_exception(e) - if len(w) > 0: - warnings.simplefilter("always") - binary.warning_message = [x.message for x in w] + # record if there were warnings caught during the binary + # evolution, this is needed to update the WARNINGS column in + # the oneline dataframe; this will only be updated if POSYDON + # warnings occur, NOT general python warnings + if cpw.got_called(): + binary.warnings = True + + # if the user wants to print all POSYDON warnings to stderr + # (warnings_verbose=True), no action is needed, because it will + # be printed at the end of the "with" context; to avoid the + # printing clear the warnings cache before the end of the + # context + if not self.kwargs.get("warnings_verbose", False): + cpw.reset_cache() + if breakdown_to_df: self.manager.breakdown_to_df(binary, **self.kwargs) @@ -453,8 +469,8 @@ def save(self, save_path, **kwargs): if os.path.isdir(absolute_filepath): file_name = 'backup_save_pop_data.h5' file_path = os.path.join(dir_name, file_name) - warnings.warn('The provided path is a directory - saving ' - 'to {0} instead.'.format(file_path), Warning) + Pwarn('The provided path is a directory - saving ' + 'to {0} instead.'.format(file_path), "ReplaceValueWarning") self.combine_saved_files(absolute_filepath, tmp_files, **kwargs) diff --git a/posydon/popsyn/normalized_pop_mass.py b/posydon/popsyn/normalized_pop_mass.py index 71492d86d5..bcc91e92f0 100644 --- a/posydon/popsyn/normalized_pop_mass.py +++ b/posydon/popsyn/normalized_pop_mass.py @@ -10,7 +10,7 @@ import numpy as np -import warnings +from posydon.utils.posydonwarning import Pwarn from posydon.popsyn import independent_sample from scipy.integrate import quad @@ -107,9 +107,9 @@ def fraction_simulated(f_bin, m_1, m_2, m_min, m_max, alpha2 = 2.35 alpha3 = 2.35 else: - warnings.warn("Scheme not included yet: primary_mass_scheme=" - f"{kwargs['primary_mass_scheme']}, secondary_mass_scheme" - f"={kwargs['secondary_mass_scheme']}") + Pwarn("Scheme not included yet: primary_mass_scheme=" + f"{kwargs['primary_mass_scheme']}, secondary_mass_scheme" + f"={kwargs['secondary_mass_scheme']}", "UnsupportedModelWarning") return np.nan, np.nan, np.nan # raise ValueError("Scheme not included yet") diff --git a/posydon/popsyn/sample_from_file.py b/posydon/popsyn/sample_from_file.py index 3440b6b0d5..98486836ff 100644 --- a/posydon/popsyn/sample_from_file.py +++ b/posydon/popsyn/sample_from_file.py @@ -9,12 +9,12 @@ import os import numpy as np import pandas as pd -import warnings from posydon.popsyn.independent_sample import (generate_orbital_periods, generate_orbital_separations, generate_eccentricities, generate_primary_masses, generate_secondary_masses) +from posydon.utils.posydonwarning import Pwarn PRIMARY_MASS_NAMES = ['s1_mass', 'primary_mass', 'mass_1', 'm_1', 'm1'] SECONDARY_MASS_NAMES = ['s2_mass', 'secondary_mass', 'mass_2', 'm_2', 'm2'] @@ -104,8 +104,8 @@ def get_samples_from_file(orbital_scheme='', **kwargs): # Get eccentricities key = infer_key(available_keys=df.keys(), allowed_keys=ECCENTRICITY_NAMES) if key=='': - warnings.warn(f'No eccentricity column found in {filename}, hence get' - ' independent random ones.') + Pwarn(f'No eccentricity column found in {filename}, hence get' + ' independent random ones.', "ReplaceValueWarning") kwargs['number_of_binaries'] = set_n eccentricity_set = generate_eccentricities(**kwargs) else: @@ -114,8 +114,8 @@ def get_samples_from_file(orbital_scheme='', **kwargs): # Get primary masses key = infer_key(available_keys=df.keys(), allowed_keys=PRIMARY_MASS_NAMES) if key=='': - warnings.warn(f'No primary mass column found in {filename}, hence get' - ' independent random ones.') + Pwarn(f'No primary mass column found in {filename}, hence get' + ' independent random ones.', "ReplaceValueWarning") kwargs['number_of_binaries'] = set_n m1_set = generate_primary_masses(**kwargs) else: @@ -125,8 +125,8 @@ def get_samples_from_file(orbital_scheme='', **kwargs): key = infer_key(available_keys=df.keys(), allowed_keys=SECONDARY_MASS_NAMES) if key=='': - warnings.warn(f'No secondary mass column found in {filename}, hence' - ' get independent random ones.') + Pwarn(f'No secondary mass column found in {filename}, hence get' + ' independent random ones.', "ReplaceValueWarning") kwargs['number_of_binaries'] = set_n m2_set = generate_secondary_masses(m1_set, **kwargs) else: @@ -137,8 +137,8 @@ def get_samples_from_file(orbital_scheme='', **kwargs): key = infer_key(available_keys=df.keys(), allowed_keys=SEPARATION_NAMES) if key=='': - warnings.warn(f'No separation column found in {filename}, hence' - ' get independent random ones.') + Pwarn(f'No separation column found in {filename}, hence get' + ' independent random ones.', "ReplaceValueWarning") kwargs['number_of_binaries'] = set_n orbital_scheme_set = generate_orbital_separations(**kwargs) else: @@ -147,8 +147,8 @@ def get_samples_from_file(orbital_scheme='', **kwargs): # Get orbital periods key = infer_key(available_keys=df.keys(), allowed_keys=PERIOD_NAMES) if key=='': - warnings.warn(f'No period column found in {filename}, hence get' - ' independent random ones.') + Pwarn(f'No period column found in {filename}, hence get' + ' independent random ones.', "ReplaceValueWarning") kwargs['number_of_binaries'] = set_n orbital_scheme_set = generate_orbital_periods(m1_set, **kwargs) else: @@ -216,8 +216,8 @@ def get_kick_samples_from_file(**kwargs): key = infer_key(available_keys=df.keys(),\ allowed_keys=PRIMARY_KICK_VELOCITY_NAMES) if key=='': - warnings.warn('No kick velocity column of primary found in ' - f'{filename}, hence do not set them.') + Pwarn(f'No kick velocity column of primary found in {filename},' + ' hence do not set them.', "ReplaceValueWarning") w_set = np.array(set_n*[None]) else: w_set = np.array(df[key]) @@ -225,8 +225,8 @@ def get_kick_samples_from_file(**kwargs): key = infer_key(available_keys=df.keys(),\ allowed_keys=PRIMARY_KICK_AZIMUTHAL_ANGLE_NAMES) if key=='': - warnings.warn('No azimuthal angle column of primary found in ' - f'{filename}, hence do not set them.') + Pwarn(f'No azimuthal angle column of primary found in {filename},' + ' hence do not set them.', "ReplaceValueWarning") phi_set = np.array(set_n*[None]) else: phi_set = np.array(df[key]) @@ -234,8 +234,8 @@ def get_kick_samples_from_file(**kwargs): key = infer_key(available_keys=df.keys(),\ allowed_keys=PRIMARY_KICK_POLAR_ANGLE_NAMES) if key=='': - warnings.warn('No polar angle column of primary found in ' - f'{filename}, hence do not set them.') + Pwarn(f'No polar angle column of primary found in {filename},' + ' hence do not set them.', "ReplaceValueWarning") theta_set = np.array(set_n*[None]) else: theta_set = np.array(df[key]) @@ -243,8 +243,8 @@ def get_kick_samples_from_file(**kwargs): key = infer_key(available_keys=df.keys(),\ allowed_keys=PRIMARY_KICK_MEAN_ANOMALY_NAMES) if key=='': - warnings.warn('No mean anomaly column of primary found in ' - f'{filename}, hence do not set them.') + Pwarn(f'No mean anomaly column of primary found in {filename},' + ' hence do not set them.', "ReplaceValueWarning") mean_anomaly_set = np.array(set_n*[None]) else: mean_anomaly_set = np.array(df[key]) @@ -256,8 +256,8 @@ def get_kick_samples_from_file(**kwargs): key = infer_key(available_keys=df.keys(),\ allowed_keys=SECONDARY_KICK_VELOCITY_NAMES) if key=='': - warnings.warn('No kick velocity column of secondary found in ' - f'{filename}, hence do not set them.') + Pwarn(f'No kick velocity column of secondary found in {filename},' + ' hence do not set them.', "ReplaceValueWarning") w_set = np.array(set_n*[None]) else: w_set = np.array(df[key]) @@ -265,8 +265,8 @@ def get_kick_samples_from_file(**kwargs): key = infer_key(available_keys=df.keys(),\ allowed_keys=SECONDARY_KICK_AZIMUTHAL_ANGLE_NAMES) if key=='': - warnings.warn('No azimuthal angle column of secondary found in ' - f'{filename}, hence do not set them.') + Pwarn(f'No azimuthal angle column of secondary found in {filename},' + ' hence do not set them.', "ReplaceValueWarning") phi_set = np.array(set_n*[None]) else: phi_set = np.array(df[key]) @@ -274,8 +274,8 @@ def get_kick_samples_from_file(**kwargs): key = infer_key(available_keys=df.keys(),\ allowed_keys=SECONDARY_KICK_POLAR_ANGLE_NAMES) if key=='': - warnings.warn('No polar angle column of secondary found in ' - f'{filename}, hence do not set them.') + Pwarn(f'No polar angle column of secondary found in {filename},' + ' hence do not set them.', "ReplaceValueWarning") theta_set = np.array(set_n*[None]) else: theta_set = np.array(df[key]) @@ -283,8 +283,8 @@ def get_kick_samples_from_file(**kwargs): key = infer_key(available_keys=df.keys(),\ allowed_keys=SECONDARY_KICK_MEAN_ANOMALY_NAMES) if key=='': - warnings.warn('No mean anomaly column of secondary found in ' - f'{filename}, hence do not set them.') + Pwarn(f'No mean anomaly column of secondary found in {filename},' + ' hence do not set them.', "ReplaceValueWarning") mean_anomaly_set = np.array(set_n*[None]) else: mean_anomaly_set = np.array(df[key]) diff --git a/posydon/popsyn/synthetic_population.py b/posydon/popsyn/synthetic_population.py index cf9238a422..0264109afa 100644 --- a/posydon/popsyn/synthetic_population.py +++ b/posydon/popsyn/synthetic_population.py @@ -32,7 +32,6 @@ "Max Briel ", ] -import warnings import numpy as np import pandas as pd from tqdm import tqdm @@ -44,7 +43,7 @@ from posydon.popsyn.normalized_pop_mass import initial_total_underlying_mass import posydon.visualization.plot_pop as plot_pop from posydon.utils.common_functions import convert_metallicity_to_string - +from posydon.utils.posydonwarning import Pwarn from astropy.cosmology import Planck15 as cosmology from astropy import constants as const @@ -957,7 +956,7 @@ def __init__( # check if formation channels are present if "/formation_channels" not in keys: if self.verbose: - warnings.warn(f"{filename} does not contain formation channels!") + print(f"{filename} does not contain formation channels!") self._formation_channels = None else: self._formation_channels = pd.read_hdf( @@ -990,9 +989,8 @@ def __init__( # calculate the metallicity information. This assumes the metallicity is for the whole file! if metallicity is not None and ini_file is not None: if "/mass_per_metallicity" in keys: - warnings.warn( - f"{filename} already contains a mass_per_metallicity table. Overwriting the table!" - ) + Pwarn(f"{filename} already contains a mass_per_metallicity " + "table. Overwriting the table!", "OverwriteWarning") simulated_mass = np.sum(self.oneline[["S1_mass_i", "S2_mass_i"]].to_numpy()) underlying_mass = initial_total_underlying_mass( @@ -1049,7 +1047,7 @@ def export_selection(self, selection, filename, overwrite=False, append=False, h Warnings -------- - UserWarning + ReplaceValueWarning If there is no "metallicity" column in the oneline dataframe and the population file contains multiple metallicities. Notes @@ -1144,9 +1142,9 @@ def export_selection(self, selection, filename, overwrite=False, append=False, h } if "metallicity" not in self.oneline.columns: - warnings.warn( - "No metallicity column in oneline dataframe! Using the metallicity of the population file and adding it to the oneline." - ) + Pwarn("No metallicity column in oneline dataframe! Using the " + "metallicity of the population file and adding it to the" + " oneline.", "ReplaceValueWarning") if len(self.metallicities) > 1: raise ValueError( "The population file contains multiple metallicities. Please add a metallicity column to the oneline dataframe!" @@ -1260,7 +1258,7 @@ def formation_channels(self): ) else: if self.verbose: - warnings.warn("No formation channels in the population file!") + print("No formation channels in the population file!") self._formation_channels = None return self._formation_channels diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index 8b08943fa4..25864e607c 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -22,8 +22,8 @@ from scipy.optimize import newton from scipy.integrate import quad from posydon.utils import constants as const +from posydon.utils.posydonwarning import Pwarn import copy -import warnings from scipy.interpolate import PchipInterpolator from posydon.utils.limits_thresholds import (THRESHOLD_CENTRAL_ABUNDANCE, THRESHOLD_HE_NAKED_ABUNDANCE, REL_LOG10_BURNING_THRESHOLD, @@ -1541,7 +1541,7 @@ def cumulative_mass_transfer_flag(MT_cases, shift_cases=False): case_2_min = MT else: # unknown donor - warnings.warn("MT case with unknown donor: {}".format(MT)) + Pwarn("MT case with unknown donor: {}".format(MT), "EvolutionWarning") corrected_MT_cases.append(MT) else: corrected_MT_cases = MT_cases.copy() @@ -1908,8 +1908,8 @@ def calculate_core_boundary(donor_mass, # ind_core=np.argmax(element[::-1]>=core_element_fraction_definition) else: ind_core = -1 - warnings.warn("Profile columns not enough to calculate the core " - "boundaries for CE, all star considered an envelope") + Pwarn("Stellar profile columns were not enough to calculate the core-envelope " + "boundaries for CE, entire star is now considered an envelope", "ApproximationWarning") return ind_core # starting from the surface, both conditions become True when element @@ -2067,7 +2067,7 @@ def linear_interpolation_between_two_cells(array_y, array_x, x_target, if top == bot: y_target = array_y[top] - warnings.warn("linear interpolation between the same point") + Pwarn("linear interpolation occured between the same point", "InterpolationWarning") if verbose: print("linear interpolation, but at the edge") print("x_target,top, bot, len(array_x), y_target", @@ -2281,14 +2281,14 @@ def get_mass_radius_dm_from_profile(profile, m1_i=0.0, # checking if mass of profile agrees with the mass of the binary object if np.abs(donor_mass[0] - m1_i) > tolerance: - warnings.warn("Donor mass from the binary class object " - "and the profile do not agree") - print("mass profile/object:", (donor_mass[0]), (m1_i)) + Pwarn("Donor mass from the binary class object " + "and the profile do not agree", "ClassificationWarning") + #print("mass profile/object:", (donor_mass[0]), (m1_i)) # checking if radius of profile agrees with the radius of the binary if np.abs(donor_radius[0] - radius1) > tolerance: - warnings.warn("Donor radius from the binary class object " - "and the profile do not agree") - print("radius profile/object:", (donor_radius[0]), (radius1)) + Pwarn("Donor radius from the binary class object " + "and the profile do not agree", "ClassificationWarning") + #print("radius profile/object:", (donor_radius[0]), (radius1)) # MANOS: if dm exists as a column, else calculate it from mass column if "dm" in profile.dtype.names: @@ -2346,8 +2346,8 @@ def get_internal_energy_from_profile(common_envelope_option_for_lambda, elif ((common_envelope_option_for_lambda != "lambda_from_profile_gravitational") and (not("energy" in profile.dtype.names))): - warnings.warn("Profile does not include internal energy -- " - "Proceeding with 'lambda_from_profile_gravitational'") + Pwarn("Profile does not include internal energy -- " + "proceeding with 'lambda_from_profile_gravitational'", "ApproximationWarning") # initiate specific internal energy as 0 specific_donor_internal_energy = profile["radius"] * 0.0 elif ((common_envelope_option_for_lambda @@ -2418,9 +2418,9 @@ def calculate_H2recombination_energy(profile, tolerance=0.001): """ if "x_mass_fraction_H" not in profile.dtype.names: - warnings.warn("Profile does not include Hydrogen mass fraction " + Pwarn("Profile does not include Hydrogen mass fraction " "calculate H2 recombination energy -- " - "H2 recombination energy is assumed 0") + "H2 recombination energy is assumed 0", "ApproximationWarning") specific_donor_H2recomb_energy = profile["radius"] * 0.0 else: # Dissociation energy [cm^1] from Cheng+2018: @@ -2458,9 +2458,9 @@ def calculate_recombination_energy(profile, tolerance=0.001): and ("neutral_fraction_H" in profile.dtype.names) and ("neutral_fraction_He" in profile.dtype.names) and ("avg_charge_He" in profile.dtype.names))): - warnings.warn("Profile does not include mass fractions and ionizations" + Pwarn("Profile does not include mass fractions and ionizations" " of elements to calculate recombination energy " - "-- recombination energy is assumed 0") + "-- recombination energy is assumed 0", "ApproximationWarning") specific_donor_recomb_energy = profile["radius"] * 0.0 else: # from MESA/binary/private/binary_ce.f90 @@ -2574,8 +2574,8 @@ def calculate_binding_energy(donor_mass, donor_radius, donor_dm, Grav_energy = Grav_energy + Grav_energy_of_cell U_i = U_i + specific_internal_energy[i]*donor_dm[i]*const.Msun if Grav_energy > 0.0: - print("Grav_energy, donor_mass, donor_dm, donor_radius", - Grav_energy, donor_mass, donor_dm, donor_radius) + #print("Grav_energy, donor_mass, donor_dm, donor_radius", + # Grav_energy, donor_mass, donor_dm, donor_radius) if not (Grav_energy < tolerance): raise ValueError("CEE problem calculating gravitational energy, " "giving positive values.") @@ -2583,7 +2583,7 @@ def calculate_binding_energy(donor_mass, donor_radius, donor_dm, # an a_th fraction of its internal energy Ebind_i = Grav_energy + factor_internal_energy * U_i if not (Ebind_i < tolerance): - warnings.warn("Ebind_i of the envelope is found positive") + Pwarn("Ebind_i of the envelope is positive", "EvolutionWarning") if verbose: print("integration of gravitational energy surface to core " "[Grav_energy], integration of internal energy surface to " @@ -2645,8 +2645,8 @@ def calculate_Mejected_for_integrated_binding_energy(profile, Ebind_threshold, ind_threshold = i-1 if donor_mass[ind_threshold]< mc1_i or donor_radius[ind_threshold]", + "Camille Liotine ", + "Matthias Kruckow ", +] + + +import copy +import sys +import warnings + + +class POSYDONWarning(Warning): + """General POSYDON warning class.""" + def __init__(self, message=''): + self.message = message + + def __str__(self): + return repr(self.message) + +class ApproximationWarning(POSYDONWarning): + """Warning that a physical approximation was used during binary evolution.""" + def __init__(self, message=''): + super().__init__(message) + +class BinaryParsingWarning(POSYDONWarning): + """Warnings related to parsing of binaries.""" + def __init__(self, message=''): + super().__init__(message) + +class ClassificationWarning(POSYDONWarning): + """Warnings related to classification during binary evolution.""" + def __init__(self, message=''): + super().__init__(message) + +class EvolutionWarning(POSYDONWarning): + """Warning that something unexpeted occurred during the binary evolution, but + the evolution is able to continue (binary did not fail).""" + def __init__(self, message=''): + super().__init__(message) + +class InappropriateValueWarning(POSYDONWarning): + """Warnings that a strange value is used.""" + def __init__(self, message=''): + super().__init__(message) + +class IncompletenessWarning(POSYDONWarning): + """Warnings when not all tasks could be done.""" + def __init__(self, message=''): + super().__init__(message) + +class InterpolationWarning(POSYDONWarning): + """Warnings related to interpolation during binary evolution.""" + def __init__(self, message=''): + super().__init__(message) + +class MissingFilesWarning(POSYDONWarning): + """Warnings related to missing files.""" + def __init__(self, message=''): + super().__init__(message) + +class OverwriteWarning(POSYDONWarning): + """Warning that a data will get overwritten.""" + def __init__(self, message=''): + super().__init__(message) + +class ReplaceValueWarning(POSYDONWarning): + """Warning that a value got replaced.""" + def __init__(self, message=''): + super().__init__(message) + +class UnsupportedModelWarning(POSYDONWarning): + """Warnings related to selecting a model that is not supported.""" + def __init__(self, message=''): + super().__init__(message) + + +# All POSYDON warnings subclasses should be defined beforehand +_POSYDONWarning_subclasses = {cls.__name__: cls for cls in\ + POSYDONWarning.__subclasses__()} + +def _get_POSYDONWarning_class(category): + """Inferring the POSYDONWarning class. + + Parameters + ---------- + category : str or a POSYDON warnings category class + A string, which will be converted into a POSYDONWarning class. + + Returns + ------- + POSYDONWarning or None + A POSYDONWarning class or None if the input is neither a + POSYDONWarning class nor convertable to one (all unknown strings + are converted to the base POSYDONWarning). + """ + global _POSYDONWarning_subclasses + if isinstance(category, str): + if category in _POSYDONWarning_subclasses.keys(): + category = _POSYDONWarning_subclasses[category] + else: + category = POSYDONWarning + if isinstance(category, type) and issubclass(category, POSYDONWarning): + return category + else: + return None + + +# Defining an own resistry for the warnings, which will additionally save how +# often a warning occured. This is simply a dictionary with the warning +# characteristics as key pointing to an integer given the count. +_POSYDON_WARNINGS_REGISTRY = {} + +def get_stats(): + """Return the statistics of the POSYDON warnings.""" + global _POSYDON_WARNINGS_REGISTRY + return _POSYDON_WARNINGS_REGISTRY + +def print_stats(): + """Prints the statistics of the POSYDON warnings.""" + global _POSYDON_WARNINGS_REGISTRY + if len(_POSYDON_WARNINGS_REGISTRY)==0: + print("No POSYDON warnings occured.") + else: + print("There have been POSYDON warnings in the global registry:\n", + _POSYDON_WARNINGS_REGISTRY) + +def _apply_POSYDON_filter(warning=dict(message="No warning"), registry=None): + # In python warnings, this functionality is spead over two functions. It + # compares the characteristics of a warning with the ones in the registry. + # We use the warnings class and the code position (filename + line number) + # as characteristics, while python's standard omits the code filename but + # therefore adds the full warnings text, which causes different keys for + # "i is negative, i=-1" and "i is negative, i=-3" to be different warnings + # in python, but considered to have the same warning characteristics for + # POSYDON. + """Filter a warning. + + Parameters + ---------- + warning : dict (default: {message="No warning"}) + Dictionary containing all options passed to warnings.warn. + registry : dict or None (default: None) + Warnings registry. If None, use the global one. + + Returns + ------- + warning or None in case it got filtered out. + """ + if registry is None: + global _POSYDON_WARNINGS_REGISTRY + registry = _POSYDON_WARNINGS_REGISTRY + if not isinstance(warning, dict): + raise TypeError("warning must be a dictionary.") + # Get stack level + stacklevel = warning.get('stacklevel', 0) + if not isinstance(stacklevel, int): + raise TypeError("stacklevel must be an integer.") + # Get category + category = warning.get('category', None) + if not(isinstance(category, type) and issubclass(category, Warning)): + category = _get_POSYDONWarning_class(category) + if not isinstance(category, type): + # A non valid category will default to a Userwarning + category = UserWarning + # Get filename and lineno from frame at stacklevel + try: + frame = sys._getframe(stacklevel) + except: + g = sys.__dict__ + filename = "sys" + lineno = 1 + else: + g = frame.f_globals + filename = frame.f_code.co_filename + lineno = frame.f_lineno + # Get module + if isinstance(g, dict): + module = g.get('__name__', "posydonwarnings") + else: + module = "posydonwarnings" + # Check registry + if not isinstance(registry, dict): + print("Reset registry, old was:", registry) + registry = {} + # Set key for registry: + # We do not use the warnings text, to allow it to contain detailed + # information, while still identifying warnings with same origin + key = (category, filename, lineno) + # Get message text + text = warning.get('message', "") + if not isinstance(text, str): + raise TypeError("message must be a string.") + # Search the filters: + # Here we still uses the python filters + for item in warnings.filters: + action, msg, cat, mod, ln = item + if ((msg is None or msg.match(text)) and + issubclass(category, cat) and + (mod is None or mod.match(module)) and + (ln == 0 or lineno == ln)): + break + else: + action = "default" + # Apply action + if action == "ignore": + return None + elif action in ["always", "default", "module", "once"]: + # Compared to standard python not only save a occurence but count them + if key in registry: + registry[key] += 1 + if action in ["default", "module", "once"]: + return None + else: + registry[key] = 1 + return warning + +def _issue_warn(warning=dict(message="No warning"), registry=None): + """Issue a warning. + + Parameters + ---------- + warning : dict (default: {message="No warning"}) + Dictionary containing all options passed to warnings.warn. + registry : dict or None (default: None) + Warnings registry. If None, use the global one. + """ + filtered_warning = _apply_POSYDON_filter(warning, registry) + if filtered_warning is None: + return + else: + warnings.warn(**filtered_warning) + + +class _Caught_POSYDON_Warnings: + """Class which stores caught warnings.""" + def __init__(self, catch_warnings=False, record=True, filter_first=True, + registry=None): + """Constructor of the object. + + Parameters + ---------- + catch_warnings : bool (default: False) + Determines, whether warnings are caught. + record : bool (default: True) + Determines, whether warnings are recorded. + filter_first : bool (default: True) + Determines, whether warnings are filtered before recorded or + discarded. + registry : dict or None (default: None) + Warnings registry. If None, use the global one. + """ + self.catch_warnings = catch_warnings + self.caught_warnings = [] + self.record = record + self.filter_first = filter_first + self._got_called = False + if registry is None: + global _POSYDON_WARNINGS_REGISTRY + self.registry = _POSYDON_WARNINGS_REGISTRY + else: + self.registry = registry + if not isinstance(catch_warnings, bool): + raise TypeError("catch_warnings must be a boolean.") + if not isinstance(record, bool): + raise TypeError("record must be a boolean.") + if not isinstance(filter_first, bool): + raise TypeError("filter_first must be a boolean.") + if not isinstance(self.registry, dict): + raise TypeError("registry must be a dictionary.") + + def __str__(self): + """Return the status of the object as a string.""" + if self.catch_warnings: + if self.record: + ret = "POSYDON warnings will be caught and recorded." + if self.filter_first: + ret += " Filters are applied before recording." + else: + ret = "POSYDON warnings will be caught and discarded." + else: + ret = "POSYDON warnings are shown." + ncaught = len(self.caught_warnings) + if ncaught>0: + if ncaught==1: + ret += "\nThere is 1 warning recorded." + else: + ret += "\nThere are {} warnings recorded.".format(ncaught) + global _POSYDON_WARNINGS_REGISTRY + if self.registry!=_POSYDON_WARNINGS_REGISTRY: + ret += " Currently a private registry is used, it contains:\n" + ret += "{}".format(self.registry) + return ret + + def __call__(self, new_warning=None, empty_cache=False, + change_settings=None): + """Deal with changes and new warnings + + Parameters + ---------- + new_warning : dict or None (default: None) + Dictionary containing all options passed to warnings.warn. + empty_cache : bool (default: False) + If True, the caught warnings will be reset first. + change_settings : dict or None (default: None) + The dictionary can contain any attribute this class has. The + attributes get the new values. (Changing the filter_first, while + there are recorded warnings, the statistics may count warnings + twice or not at all.) + """ + self._got_called = True + # Check if there is anything to do + if not empty_cache and new_warning is None and change_settings is None: + raise ValueError("Nothing to do: either empty_cache has to be True" + " or new_warning/change_settings needs to be set.") + if empty_cache: + # Clear the cache + self.reset_cache() + if change_settings is not None: + if not isinstance(change_settings, dict): + raise TypeError("change_settings has to be a dict or None.") + # Change attributes + for attr,val in change_settings.items(): + if attr=="caught_warnings": + # Protect the list of warnings from changes + continue + if attr=="registry": + # special treatment of registry + if val is None: + global _POSYDON_WARNINGS_REGISTRY + self.registry = _POSYDON_WARNINGS_REGISTRY + else: + self.registry = change_settings[attr] + continue + if hasattr(self, attr): + if isinstance(val,type(getattr(self, attr))): + setattr(self, attr, val) + else: + raise TypeError(f"{attr} has to be a " + f"{type(getattr(self, attr))}.") + else: + raise AttributeError(f"{attr} unknown to " + "_Caught_POSYDON_Warnings.") + if ((self.catch_warnings==False) and + (len(self.caught_warnings)>0)): + # If there are recorded warnings issue them and empty the list + for w in self.caught_warnings: + w["stacklevel"] += 2 + if self.filter_first: + warnings.warn(**w) + else: + _issue_warn(w, self.registry) + self.caught_warnings = [] + if new_warning is not None: + # Process new warning + if self.catch_warnings: + # Catch warning + if self.filter_first: + # Apply POSYDON filtering/stats + new_warning = _apply_POSYDON_filter(new_warning, + self.registry) + if self.record and new_warning is not None: + # Add warning to caught ones + self.caught_warnings.append(new_warning) + else: + # Issue warning + new_warning["stacklevel"] += 2 + _issue_warn(new_warning, self.registry) + + def __del__(self): + """Destructor of the object. It will issue still recorded warnings.""" + if len(self.caught_warnings)>0: + # If there are recorded warnings issue them. + self.catch_warnings = False + print("There are still recorded warnings:") + for w in self.caught_warnings: + w["stacklevel"] = 2 + if self.filter_first: + warnings.warn(**w) + else: + _issue_warn(w, self.registry) + + def got_called(self): + """Returns, whether the object got called.""" + return self._got_called + + def has_records(self): + """Checks whether there are records of caught warnings. + + Returns + ------- + True if there are recorded warnings otherwise False. + """ + return len(self.caught_warnings)>0 + + def get_cache(self, empty_cache=False): + """Get caught warnings. + + Parameters + ---------- + empty_cache : bool (default: False) + If True, the caught warnings will be reset. + + Returns + ------- + List of recorded warnings. + """ + cache = copy.copy(self.caught_warnings) + if empty_cache: + self.reset_cache() + return cache + + def reset_cache(self): + """Resets the caught warnings.""" + self.caught_warnings = [] + +# Here we store all our caught POSYDON warnings +_CAUGHT_POSYDON_WARNINGS = _Caught_POSYDON_Warnings() + + +class Catch_POSYDON_Warnings: + # We use our own context manager, which does not overwrite functions. + # Instead it can only catch POSYDON warnings issued via the Pwarn function. + """Context manager class to catch POSYDON warnings.""" + def __init__(self, catch_warnings=True, record=True, filter_first=True, + own_registry=False, use_python_catch=False): + """Constructor of the object. + + Parameters + ---------- + catch_warnings : bool (default: False) + Determines, whether warnings are caught. + record : bool (default: True) + Determines, whether warnings are recorded. + filter_first : bool (default: True) + Determines, whether warnings are filtered before recorded or + discarded. + own_registry : bool (default: False) + Determines, whether the global POSYDON warnings registry should be + used or an own one only valid within the context. + use_python_catch : bool (default: False) + If enabled, it put the python catch_warnings on top of the POSYDON + catches. This brings all the drawbacks of the standard catching, + hence it is strongly recommended to be not used (properly written + code will not contain any use case for this option, hence it is + only for backward compatibility.). + """ + self.catch_warnings = catch_warnings + self.record = record + self.filter_first = filter_first + if own_registry: + self.context_registry = {} + else: + # no own registry will use the global _POSYDON_WARNINGS_REGISTRY + self.context_registry = None + if use_python_catch: + self.python_catch = warnings.catch_warnings(record=self.record) + else: + self.python_catch = None + + def __enter__(self): + """Enable catching.""" + global _CAUGHT_POSYDON_WARNINGS + _CAUGHT_POSYDON_WARNINGS(change_settings={ + 'catch_warnings': self.catch_warnings, + 'record': self.record, + 'filter_first': self.filter_first, + '_got_called': False, + 'registry': self.context_registry}) + if isinstance(self.python_catch, warnings.catch_warnings): + # enter catch of python as well (should be done last) + self.python_catch.__enter__() + return _CAUGHT_POSYDON_WARNINGS + + def __exit__(self, exc_type, exc_value, exc_traceback): + """Disable catching.""" + if isinstance(self.python_catch, warnings.catch_warnings): + # exit catch of python as well (should be done first) + self.python_catch.__exit__() + # reset it (needed because it cannot enter the context for the same + # object twice) + self.python_catch = None + global _CAUGHT_POSYDON_WARNINGS + # If the cache is not cleared before, it will issue all recorded + # warnings. + _CAUGHT_POSYDON_WARNINGS(change_settings={'catch_warnings': False, + 'registry': None}) + return False + + +def Pwarn(message, category=None, stacklevel=2, **kwargs): + """Issueing a warning via warnings.warn. + + Parameters + ---------- + message : str + The message printed in the warning. + category : str or a warnings category class (default: None) + A string, which will be converted into a POSYDONWarning class. + stacklevel : int (default: 2) + The stack level passed to warnings.warn, defaults to 2. + **kwargs : dict (optional) + Dictionary containing extra options passed to warnings.warn. + """ + global _CAUGHT_POSYDON_WARNINGS + if not isinstance(message, str): + raise TypeError("message must be a string.") + if not isinstance(stacklevel, int): + raise TypeError("stacklevel must be an integer.") + if not(isinstance(category, type) and issubclass(category, Warning)): + category = _get_POSYDONWarning_class(category) + if (isinstance(category, type) and issubclass(category, POSYDONWarning)): + # deal with POSYDON warnings + _CAUGHT_POSYDON_WARNINGS(new_warning=dict({"message": message, + "category": category, + "stacklevel": stacklevel}, + **kwargs)) + else: + # deal with python warnings + warnings.warn(message=message, category=category, + stacklevel=stacklevel, **kwargs) + +def SetPOSYDONWarnings(action="default", category=POSYDONWarning, **kwargs): + """Add the warnings filter for POSYDON warnings. + + Parameters + ---------- + action : str (default: "default") + The behaviour for those warnings. Should be one out of + "default", "error", "ignore", "always", "module", or "once" + category : str or a warnings category class (default: POSYDONWarning) + A string, which will be converted into a POSYDONWarning class. + **kwargs : dict (optional) + Dictionary containing extra options passed to + warnings.filterwarnings. + """ + category = _get_POSYDONWarning_class(category) + if isinstance(category, type) and issubclass(category, POSYDONWarning): + warnings.filterwarnings(action=action, category=category, **kwargs) + +def NoPOSYDONWarnings(category=POSYDONWarning): + """Switch the warnings filter to ignore for POSYDON warnings. + + Parameters + ---------- + category : str or a warnings category class (default: POSYDONWarning) + A string, which will be converted into a POSYDONWarning class. + """ + SetPOSYDONWarnings(action="ignore", category=category) + +def AllPOSYDONWarnings(category=POSYDONWarning): + """Switch the warnings filter to always for POSYDON warnings. + + Parameters + ---------- + category : str or a warnings category class (default: POSYDONWarning) + A string, which will be converted into a POSYDONWarning class. + """ + SetPOSYDONWarnings(action="always", category=category) + + +# The base class of all warnings is "Warning", which is derived from Exception +# +# List of Python's default warnings (sub)classes in alphabetic order +# BytesWarning: Base category for warnings related to bytes and bytearray. +# DeprecationWarning: Base category for warnings about deprecated features when +# those warnings are intended for other Python developers (ignored by +# default, unless triggered by code in __main__). +# EncodingWarning: Base class for warnings related to encodings. +# FutureWarning: Base category for warnings about deprecated features when +# those warnings are intended for end users of applications that are +# written in Python. +# ImportWarning: Base category for warnings triggered during the process of +# importing a module (ignored by default). +# PendingDeprecationWarning: Base category for warnings about features that +# will be deprecated in the future (ignored by default). +# ResourceWarning: Base category for warnings related to resource usage +# (ignored by default). +# RuntimeWarning: Base category for warnings about dubious runtime features. +# SyntaxWarning: Base category for warnings about dubious syntactic features. +# UnicodeWarning: Base category for warnings related to Unicode. +# UserWarning: The default category for warn(). +# +# for more details see https://docs.python.org/3/library/exceptions.html#warnings +# and https://docs.python.org/3/library/warnings.html +# https://github.com/python/cpython/blob/3.11/Lib/warnings.py diff --git a/posydon/visualization/plot2D.py b/posydon/visualization/plot2D.py index 40e751cde5..43587945a4 100644 --- a/posydon/visualization/plot2D.py +++ b/posydon/visualization/plot2D.py @@ -13,7 +13,6 @@ "Matthias Kruckow ", ] -import warnings import numpy as np import matplotlib.pyplot as plt from posydon.utils.gridutils import add_field @@ -23,6 +22,7 @@ PLOT_PROPERTIES, DEFAULT_LABELS) from posydon.visualization.combine_TF import combine_TF12 import copy +from posydon.utils.posydonwarning import Pwarn class plot2D(object): @@ -608,8 +608,9 @@ def plot_panel(self, ax, extra_grid_call=False): vmax=self.zmax, ) except: - warnings.warn(f'Failed to plot values for flag {flag}, ' - 'likely all values are NaN.') + Pwarn(f'Failed to plot values for flag {flag}, ' + 'likely all values are NaN.', + "InappropriateValueWarning") sc_last = sc # collect scatters for legend if self.MARKERS_COLORS_LEGENDS[flag][3] not in scatters_legend: From 2093203874d5a542b5232284fded35b1f79f0f97 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 22 Aug 2024 20:39:18 +0200 Subject: [PATCH 231/319] new rerun types LBV_wind and LBV_wind+* (#363) * new rerun types LBV_wind and LBV_wind+thermohaline_mixing; prepare more rerun combinations with LBV_wind * add luminosity limit, change quotation marks * first update of git hashes * Update posydon-run-pipeline exclude TPAGB stars from LBV wind reruns * Update posydon-run-pipeline exclude (stripped) He stars update INLIST hashes * Update posydon-run-pipeline lower surface X limit to 0.1 * LBV_wind+dedt_energy_eqn Add hash for LBV_wind+dedt_energy_eqn. Uncomment LBV_wind+HeMB_MLTp_mesh and LBV_wind+dedt_hepulse. --- bin/posydon-run-pipeline | 73 ++++++++++++++++++- .../pipeline/pipeline_steps.rst | 5 +- 2 files changed, 72 insertions(+), 6 deletions(-) diff --git a/bin/posydon-run-pipeline b/bin/posydon-run-pipeline index 2e587d7972..8788ab5b29 100644 --- a/bin/posydon-run-pipeline +++ b/bin/posydon-run-pipeline @@ -692,6 +692,18 @@ def copy_ini_file(grid_type, rerun_metallicity, rerun_type, destination, replace_text = "seth_dedt_energy_eqn-f1803afe400ed16a4fad47b0bad6abbf0965cae1" elif rerun_type == "dedt_hepulse": replace_text = "seth_dedt_hepulse-0274be9895480cae3aa2a4d14b056a2447d5921b" + elif rerun_type == 'LBV_wind': + replace_text = "matthias_LBV_wind-051343856f50dc5518abdf28904d62df4caacaff" + elif rerun_type == 'LBV_wind+thermohaline_mixing': + replace_text = "matthias_LBV_wind-051343856f50dc5518abdf28904d62df4caacaff" +# elif rerun_type == 'LBV_wind+HeMB_MLTp_mesh': +# raise ValueError(f'Not yet supported rerun type {rerun_type}!') +# replace_text = "matthias_LBV_wind-051343856f50dc5518abdf28904d62df4caacaff" #TODO: update to new MESA-INLIST branch/commit + elif rerun_type == 'LBV_wind+dedt_energy_eqn': + replace_text = "seth_lbv_thermo_dedt-936f76f82d44b16139ff89e41e5b0f2232fd9b30" +# elif rerun_type == 'LBV_wind+dedt_hepulse': +# raise ValueError(f'Not yet supported rerun type {rerun_type}!') +# replace_text = "matthias_LBV_wind-051343856f50dc5518abdf28904d62df4caacaff" #TODO: update to new MESA-INLIST branch/commit elif rerun_type == 'other': # e.g. 'envelope_mass_limit', 'fe_core_infall_limit' # this rerun uses the default inlist commit return @@ -811,10 +823,13 @@ def logic_rerun(grid, rerun_type): if np.max(np.logical_or(tpagb_1, tpagb_2)) == True: runs_to_rerun += [i] runs_to_rerun = np.array(runs_to_rerun) - elif rerun_type == 'thermohaline_mixing': + elif ((rerun_type == 'thermohaline_mixing') or + (rerun_type == 'LBV_wind+thermohaline_mixing')): termination_flags = TF1_POOL_ERROR new_mesa_flag = {'thermohaline_coeff' : 17.5} - elif rerun_type == 'HeMB_MLTp_mesh': + elif ((rerun_type == 'HeMB_MLTp_mesh') +# or (rerun_type == 'LBV_wind+HeMB_MLTp_mesh') + ): termination_flags = TF1_POOL_ERROR elif rerun_type == 'more_mesh': termination_flags = TF1_POOL_ERROR @@ -859,10 +874,60 @@ def logic_rerun(grid, rerun_type): if np.isnan(iniY) or iniY<0.96: runs_to_rerun += [i] runs_to_rerun = np.array(runs_to_rerun) - elif rerun_type == 'dedt_energy_eqn': + elif ((rerun_type == 'dedt_energy_eqn') or + (rerun_type == 'LBV_wind+dedt_energy_eqn')): termination_flags = TF1_POOL_ERROR - elif rerun_type == 'dedt_hepulse': + elif ((rerun_type == 'dedt_hepulse') +# or (rerun_type == 'LBV_wind+dedt_hepulse') + ): termination_flags = TF1_POOL_ERROR + elif rerun_type == 'LBV_wind': + N_runs = len(grid) + runs_to_rerun = [] + hot_wind_full_on_T = 1.2e4 # inlist value + for i in range(N_runs): + if grid[i].history1 is not None: + s1_L = 10**grid[i].history1['log_L'] + s1_R = 10**grid[i].history1['log_R'] + s1_HD_limit = (1.0e-5 * s1_R * np.sqrt(s1_L)) + lbv_1 = np.logical_and(s1_L > 6.0e5, s1_HD_limit > 1.0) + # exclude (stripped) He stars + s1_X_surf = grid[i].history1['surface_h1'] + lbv_1 = np.logical_and(lbv_1, s1_X_surf >= 0.1) + # exclude stars going through TPAGB + s1_Teff = 10**grid[i].history1['log_Teff'] + s1_Yc = grid[i].history1['center_he4'] + s1_he_shell_mass = grid[i].history1['he_core_mass'] -\ + grid[i].history1['co_core_mass'] + tpagb_1 = np.logical_and(s1_Teff <= hot_wind_full_on_T,\ + s1_Yc <= 1e-6) + tpagb_1 = np.logical_and(tpagb_1, s1_he_shell_mass <= 1e-1) + else: + lbv_1 = np.array([False]) + tpagb_1 = np.array([False]) + if grid[i].history2 is not None: + s2_L = 10**grid[i].history2['log_L'] + s2_R = 10**grid[i].history2['log_R'] + s2_HD_limit = (1.0e-5 * s2_R * np.sqrt(s2_L)) + lbv_2 = np.logical_and(s2_L > 6.0e5, s2_HD_limit > 1.0) + # exclude (stripped) He stars + s2_X_surf = grid[i].history2['surface_h1'] + lbv_2 = np.logical_and(lbv_2, s2_X_surf >= 0.1) + # exclude stars going through TPAGB + s2_Teff = 10**grid[i].history2['log_Teff'] + s2_Yc = grid[i].history2['center_he4'] + s2_he_shell_mass = grid[i].history2['he_core_mass'] -\ + grid[i].history2['co_core_mass'] + tpagb_2 = np.logical_and(s2_Teff <= hot_wind_full_on_T,\ + s2_Yc <= 1e-6) + tpagb_2 = np.logical_and(tpagb_2, s2_he_shell_mass <= 1e-1) + else: + lbv_2 = np.array([False]) + tpagb_2 = np.array([False]) + if np.max(np.logical_or(lbv_1, lbv_2)) == True: + if np.max(np.logical_or(tpagb_1, tpagb_2)) == False: + runs_to_rerun += [i] + runs_to_rerun = np.array(runs_to_rerun) elif rerun_type == 'envelope_mass_limit': # maybe 'fe_core_infall_limit' as well? runs_to_rerun = None # implement logic else: diff --git a/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst b/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst index fd97fdfd5f..9ca99b1ebd 100644 --- a/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst +++ b/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst @@ -201,10 +201,11 @@ type of the rerun specifying the logic and changes, and the cluster name. opacity_max caution it uses a fixed maximum opacity of 0.5 (this is only a last option change to get more stability) TPAGBwind default in v3+ it enables the MESA inlist commit, which changes the wind during the TPAGB phase thermohaline_mixing default in v3+ it uses thermohaline mixing in the inlist - HeMB_MLTp_mesh workaround it turns off magnetic braking for He stars; it uses less extreme parameters of the MLT++; it changes some more input values to change the resulation close to the surface + HeMB_MLTp_mesh caution it turns off magnetic braking for He stars; it uses less extreme parameters of the MLT++ (this can cause significant changes in the radius evolution of stars); it changes some more input values to change the resulation close to the surface more_mesh workaround it modifies the remeshing and allows for more cells in MESA conv_bdy_weight caution it disabled the convective_bdy_weight where this caused segmentation faults (this avoids a bug in the old MESA version r11701) dedt_energy_eqn caution it enables MESA's dedt-form of the energy equation for numerical stability during rapid (superthermal) mass transfer - dedt_hepulse caution it enables MESA's dedt-form of the energy equation for rapid mass transfer. At stripped HeZAMS, several MLT++ changes, v_flag and lnPgas_flag set to .true., and convective_bdy_weight disabled to help with stripped He star superadiabatic envelopes, pulsations, and WD cooling. + dedt_hepulse caution it enables MESA's dedt-form of the energy equation for rapid mass transfer; at stripped HeZAMS, several MLT++ changes, v_flag and lnPgas_flag set to .true., and convective_bdy_weight disabled to help with stripped He star superadiabatic envelopes, pulsations, and WD cooling + LBV_wind default in v3+ it turns on LBV winds when crossing the Humphreys-Davidson limit as intended (due to a bug this was only applied after a retry); additionally, there are reruns `LBV_wind+thermohaline_mixing`, `LBV_wind+HeMB_MLTp_mesh`, `LBV_wind+dedt_energy_eqn`, `LBV_wind+dedt_hepulse`, which combine the two rerun types ===================== ============== =========== From 5657878510f8dc6c4b731ba6f701c0376786aae8 Mon Sep 17 00:00:00 2001 From: Kyle Rocha <48293898+ka-rocha@users.noreply.github.com> Date: Thu, 29 Aug 2024 09:24:12 -0500 Subject: [PATCH 232/319] Suport ZAMS binaries with initial eccentricity (#337) * Adding Matthias's code with custom flow chart * add step HMS-HMS RLO to step_mesa * Start adding code for custom flow * Getting initial_eccentricity_flow_chart working with HMS-HMS_RLO grid * Remove some spaces * Update flow_chart.py Fixing H-He mixed states for flow in initial eccentricity * Update step_mesa.py Fix interp in q for hms-hms rlo. * Update step_mesa.py Fixed out of grid error catching in hms-hms-rlo --- posydon/binary_evol/MESA/step_mesa.py | 149 ++++++++++++++++++++++++++ posydon/binary_evol/flow_chart.py | 51 ++++++++- 2 files changed, 199 insertions(+), 1 deletion(-) diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index 6284274d60..c8ec84fc29 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -1659,3 +1659,152 @@ def __call__(self, binary): self.binary.state = 'detached' self.binary.event = 'redirect_from_CO_HeMS' return + + +class HMS_HMS_RLO_step(MesaGridStep): + """Class for performing the MESA step for a HMS-HMS RLO binary. + For binaries with an initial eccentricity at ZAMS, + we evolve them first with step detached and map to the HMS-HMS RLO grid + using `initial_eccentricity_flow_chart`.""" + + def __init__(self, metallicity=1., grid_name=None, *args, **kwargs): + """Initialize a HMS_HMS_RLO_step instance.""" + self.grid_type = 'HMS_HMS_RLO' + self.interp_in_q = True + if grid_name is None: + metallicity = convert_metallicity_to_string(metallicity) + grid_name = 'HMS-HMS_RLO/' + metallicity + '_Zsun.h5' + super().__init__(metallicity=metallicity, + grid_name=grid_name, + *args, **kwargs) + # special stuff for my step goes here + # If nothing to do, no init necessary + + # load grid boundaries + m1_initial_values = self._psyTrackInterp.grid.initial_values['star_1_mass'] + m2_initial_values = self._psyTrackInterp.grid.initial_values['star_2_mass'] + self.m1_min = np.min(m1_initial_values) + self.m1_max = np.max(m1_initial_values) + self.m2_min = np.min(m2_initial_values) + self.m2_max = np.max(m2_initial_values) + self.p_min = np.min(self._psyTrackInterp.grid.initial_values['period_days']) + self.p_max = np.max(self._psyTrackInterp.grid.initial_values['period_days']) + self.q_min = np.min(m2_initial_values/m1_initial_values) + self.q_max = np.max(m2_initial_values/m1_initial_values) + self.minimum_star_mass = self.m2_min # 0.5 (M_sun) + + def __call__(self, binary): + """Evolve a binary using the MESA step.""" + # grid set up assume no star is CO + self.star_1_CO = False + self.star_2_CO = False + # check binary is ready before calling the step + self.binary = binary + event = binary.event + state = binary.state + state_1 = binary.star_1.state + state_2 = binary.star_2.state + m1 = binary.star_1.mass + m2 = binary.star_2.mass + p = binary.orbital_period + ecc = binary.eccentricity + mass_ratio = m2/m1 + + # TODO: import states from flow_chart.py + FOR_RLO_STATES = ["H-rich_Core_H_burning", + "H-rich_Shell_H_burning", + "H-rich_Core_He_burning", + "H-rich_Central_He_depleted", + "H-rich_Core_C_burning", + "stripped_He_Core_H_burning", + "H-rich_Central_C_depletion", # filtered out below + "H-rich_non_burning"] + + # check the star states + # TODO: import states from flow_chart.py + if (state_2 in FOR_RLO_STATES and (state_1 in FOR_RLO_STATES) + and event == "oRLO1"): + self.flip_stars_before_step = False + # catch and redirect double core collapse, this happens if q=1: + if state_1 == 'H-rich_Central_C_depletion': + self.binary.event = 'CC1' + return + # TODO: import states from flow_chart.py + elif (state_1 in FOR_RLO_STATES and (state_2 in FOR_RLO_STATES) + and event == "oRLO2"): + self.flip_stars_before_step = False + # catch and redirect double core collapse, this happens if q=1: + if state_2 == 'H-rich_Central_C_depletion': + self.binary.event = 'CC2' + return + else: + set_binary_to_failed(self.binary) + raise FlowError( + 'The star_1.state = %s, star_2.state = %s, binary.state = %s, ' + 'binary.event = %s and not HMS - HMS - oRLO1/oRLO2!' + % (state_1, state_2, state, event)) + # redirect if outside grids + # HMS-HMS grid is sampled in q so check explicity vs m1 and m2 + if ((not self.flip_stars_before_step and + self.m1_min <= m1 <= self.m1_max and + np.max([self.q_min, self.minimum_star_mass/m1]) <= mass_ratio <= self.q_max and + self.p_min <= p <= self.p_max and + ecc == 0.) or (self.flip_stars_before_step and + self.m1_min <= m2 <= self.m1_max and + np.max([self.q_min, self.minimum_star_mass/m2]) <= 1/mass_ratio <= self.q_max and + self.p_min <= p <= self.p_max and + ecc == 0.)): + super().__call__(self.binary) + + # period inside the grid, but m1 outside the grid + elif ((not self.flip_stars_before_step and + self.p_min <= p <= self.p_max and + (m1 < self.m1_min or m1 > self.m1_max))): + set_binary_to_failed(self.binary) + raise GridError(f'The mass of m1 ({m1}) is outside the grid,' + ' while the period is inside the grid.') + + # period inside the grid, but m2 outside the grid + elif ((not self.flip_stars_before_step and + self.p_min <= p <= self.p_max and + (m2 < self.m2_min or m2 > self.m2_max))): + set_binary_to_failed(self.binary) + raise GridError(f'The mass of m2 ({m2}) is outside the grid,' + ' while the period is inside the grid.') + + # period inside the grid, but q outside the grid + elif (not self.flip_stars_before_step and + self.p_min <= p <= self.p_max and + (np.max([self.q_min, self.minimum_star_mass/m1]) > mass_ratio or mass_ratio > self.q_max) ): + set_binary_to_failed(self.binary) + raise GridError(f'The mass ratio ({mass_ratio}) is outside the grid,' + ' while the period is inside the grid.') + + + # period inside the grid, but m1 outside the grid (flipped stars) + elif ((self.flip_stars_before_step and + self.p_min <= p <= self.p_max and + (m2 < self.m1_min or m2 > self.m1_max))): + set_binary_to_failed(self.binary) + raise GridError(f'The mass of m1 ({m2}) is outside the grid,' + ' while the period is inside the grid.') + + # period inside the grid, but m2 outside the grid (flipped stars) + elif ((self.flip_stars_before_step and + self.p_min <= p <= self.p_max and + (m1 < self.m2_min or m1 > self.m2_max))): + set_binary_to_failed(self.binary) + raise GridError(f'The mass of m2 ({m1}) is outside the grid,' + ' while the period is inside the grid.') + + # period inside the grid, but q outside the grid (flipped stars) + elif (self.flip_stars_before_step and + self.p_min <= p <= self.p_max and + (np.max([self.q_min, self.minimum_star_mass/m2]) > 1/mass_ratio or 1/mass_ratio > self.q_max) ): + set_binary_to_failed(self.binary) + raise GridError(f'The mass ratio ({1/mass_ratio}) is outside the grid,' + ' while the period is inside the grid.') + else: + self.binary.state = "detached" + self.binary.event = "redirect_from_HMS_HMS_RLO" + return diff --git a/posydon/binary_evol/flow_chart.py b/posydon/binary_evol/flow_chart.py index 18881a64ba..c45d53376a 100644 --- a/posydon/binary_evol/flow_chart.py +++ b/posydon/binary_evol/flow_chart.py @@ -302,7 +302,7 @@ -def flow_chart(FLOW_CHART=POSYDON_FLOW_CHART, CHANGE_FLOW_CHART=None): +def flow_chart(FLOW_CHART=None, CHANGE_FLOW_CHART=None): """Generate the flow chart. Default step nomenclature: @@ -332,6 +332,9 @@ def flow_chart(FLOW_CHART=POSYDON_FLOW_CHART, CHANGE_FLOW_CHART=None): Flow chart. """ + if FLOW_CHART is None: + FLOW_CHART = POSYDON_FLOW_CHART.copy() + if CHANGE_FLOW_CHART is not None: for key in CHANGE_FLOW_CHART.keys(): if key in FLOW_CHART.keys(): @@ -339,3 +342,49 @@ def flow_chart(FLOW_CHART=POSYDON_FLOW_CHART, CHANGE_FLOW_CHART=None): FLOW_CHART[key] = CHANGE_FLOW_CHART[key] return FLOW_CHART + + +def initial_eccentricity_flow_chart(FLOW_CHART=None, CHANGE_FLOW_CHART=None): + """Modify POSYDON's default flow to: + ZAMS binaries -> detached + oRLO1/oRLO2 -> HMS-HMS RLO grid + + Parameters + ---------- + FLOW_CHART : dict or None + CHANGE_FLOW_CHART : dict or None + + Returns + ------- + dict + Modified flow chart. + """ + + # get POSYDON's default flow chart + if FLOW_CHART is None: + if CHANGE_FLOW_CHART is None: + MY_FLOW_CHART = flow_chart() + else: + MY_FLOW_CHART = flow_chart(CHANGE_FLOW_CHART=CHANGE_FLOW_CHART) + else: + if CHANGE_FLOW_CHART is None: + MY_FLOW_CHART = flow_chart(FLOW_CHART=FLOW_CHART) + else: + MY_FLOW_CHART = flow_chart(FLOW_CHART=FLOW_CHART, + CHANGE_FLOW_CHART=CHANGE_FLOW_CHART) + + # modify the default flow chart + for key in MY_FLOW_CHART.keys(): + s1_state, s2_state, state, event = key + # always take ZAMS binaries to step_detached + if event == 'ZAMS' and state in BINARY_STATES_ZAMS: # check for event + MY_FLOW_CHART[key] = 'step_detached' + + # Add two stars initating RLO (now coming from detached) into flow chart + # Add stripped stars (from winds) initating RLO + for s1 in STAR_STATES_H_RICH_EVOLVABLE + STAR_STATES_HE_RICH_EVOLVABLE: + for s2 in STAR_STATES_H_RICH_EVOLVABLE + STAR_STATES_HE_RICH_EVOLVABLE: + MY_FLOW_CHART[(s1, s2, 'RLO1', 'oRLO1')] = 'step_HMS_HMS_RLO' + MY_FLOW_CHART[(s1, s2, 'RLO2', 'oRLO2')] = 'step_HMS_HMS_RLO' + + return MY_FLOW_CHART From 120c96be7dae19c668ea2c4c2d4e7e4cbe5431cd Mon Sep 17 00:00:00 2001 From: Camille Liotine Date: Thu, 12 Sep 2024 09:20:17 -0500 Subject: [PATCH 233/319] Fix RLO warnings in detached step (#371) * temporarily import warnings for testing * fix warning messages * change inputs to base RLO function, move warning catching * allow for arrays of floats as inputs to RLO function * allow for zero values in a_orb and only replace invalid array values with nan * Update common_functions.py minor fixes in comment and equality * fix array shape * fix to fill empty arrays --- posydon/binary_evol/CE/step_CEE.py | 8 +-- posydon/binary_evol/DT/step_detached.py | 34 ++++++------ posydon/binary_evol/DT/step_isolated.py | 20 +------ posydon/tests/binary_evol/CE/test_CEE.py | 34 +++++------- posydon/utils/common_functions.py | 67 +++++++++++++++++------- 5 files changed, 84 insertions(+), 79 deletions(-) diff --git a/posydon/binary_evol/CE/step_CEE.py b/posydon/binary_evol/CE/step_CEE.py index 2db944ea51..ebb2c7b505 100644 --- a/posydon/binary_evol/CE/step_CEE.py +++ b/posydon/binary_evol/CE/step_CEE.py @@ -540,8 +540,8 @@ def CEE_simple_alpha_prescription( # now we check if the roche Lobe of any of the cores that spiralled-in # will be filled if reached this final separation - RL1 = cf.roche_lobe_radius(mc1_i/mc2_i, separation_postCEE/const.Rsun) - RL2 = cf.roche_lobe_radius(mc2_i/mc1_i, separation_postCEE/const.Rsun) + RL1 = cf.roche_lobe_radius(mc1_i, mc2_i, separation_postCEE/const.Rsun) + RL2 = cf.roche_lobe_radius(mc2_i, mc1_i, separation_postCEE/const.Rsun) if verbose: print("donor radius / core radius / RL1:", radius1, rc1_i, RL1) @@ -836,8 +836,8 @@ def CEE_simple_alpha_prescription( else: - separation_for_inner_RLO1 = rc1_i / cf.roche_lobe_radius(mc1_i/mc2_i, a_orb=1) - separation_for_inner_RLO2 = rc2_i / cf.roche_lobe_radius(mc2_i/mc1_i, a_orb=1) + separation_for_inner_RLO1 = rc1_i / cf.roche_lobe_radius(mc1_i, mc2_i, a_orb=1) + separation_for_inner_RLO2 = rc2_i / cf.roche_lobe_radius(mc2_i, mc1_i, a_orb=1) separation_before_merger = max( separation_for_inner_RLO1, separation_for_inner_RLO2 ) * const.Rsun diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index d4976e02d4..6d08cf0ea0 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -1211,12 +1211,13 @@ def ev_rlo1(t, y): solar radii. """ + pri_mass = interp1d_pri["mass"](t - t_offset_pri) + sec_mass = interp1d_sec["mass"](t - t_offset_sec) + sep = y[0] ecc = y[1] - RL = roche_lobe_radius(interp1d_sec["mass"](t - t_offset_sec) - / interp1d_pri["mass"](t - t_offset_pri), - (1 - ecc) * sep) + RL = roche_lobe_radius(sec_mass, pri_mass, (1 - ecc) * sep) # 95% filling of the RL is enough to assume beginning of RLO, # as we do in CO-HMS_RLO grid @@ -1244,13 +1245,14 @@ def ev_rlo2(t, y): solar radii. """ + pri_mass = interp1d_pri["mass"](t - t_offset_pri) + sec_mass = interp1d_sec["mass"](t - t_offset_sec) + sep = y[0] ecc = y[1] + + RL = roche_lobe_radius(pri_mass, sec_mass, (1 - ecc) * sep) - RL = roche_lobe_radius(interp1d_pri["mass"](t - t_offset_pri) - / interp1d_sec["mass"](t - t_offset_sec), - (1 - ecc) * sep) - return interp1d_pri["R"](t - t_offset_pri) - 0.95*RL @event(True, 1) @@ -1275,12 +1277,13 @@ def ev_rel_rlo1(t, y): radius. """ + pri_mass = interp1d_pri["mass"](t - t_offset_pri) + sec_mass = interp1d_sec["mass"](t - t_offset_sec) + sep = y[0] ecc = y[1] - - RL = roche_lobe_radius(interp1d_sec["mass"](t - t_offset_sec) - / interp1d_pri["mass"](t - t_offset_pri), - (1 - ecc) * sep) + + RL = roche_lobe_radius(sec_mass, pri_mass, (1 - ecc) * sep) return (interp1d_sec["R"](t - t_offset_sec) - RL) / RL @@ -1306,12 +1309,13 @@ def ev_rel_rlo2(t, y): radius. """ + pri_mass = interp1d_pri["mass"](t - t_offset_pri) + sec_mass = interp1d_sec["mass"](t - t_offset_sec) + sep = y[0] ecc = y[1] - - RL = roche_lobe_radius(interp1d_pri["mass"](t - t_offset_pri) - / interp1d_sec["mass"](t - t_offset_sec), - (1 - ecc) * sep) + + RL = roche_lobe_radius(pri_mass, sec_mass, (1 - ecc) * sep) return (interp1d_pri["R"](t - t_offset_pri) - RL) / RL diff --git a/posydon/binary_evol/DT/step_isolated.py b/posydon/binary_evol/DT/step_isolated.py index 8080d7c3dd..b8767b4fff 100644 --- a/posydon/binary_evol/DT/step_isolated.py +++ b/posydon/binary_evol/DT/step_isolated.py @@ -7,28 +7,10 @@ "Konstantinos Kovlakas " ] - -import os import numpy as np -from scipy.integrate import solve_ivp -from scipy.interpolate import PchipInterpolator -from scipy.optimize import minimize -from scipy.optimize import root from posydon.utils.data_download import PATH_TO_POSYDON_DATA -from posydon.binary_evol.binarystar import BINARYPROPERTIES -from posydon.binary_evol.singlestar import STARPROPERTIES -from posydon.interpolation.data_scaling import DataScaler -from posydon.utils.common_functions import ( - bondi_hoyle, - orbital_period_from_separation, - orbital_separation_from_period, - roche_lobe_radius, - check_state_of_star, - PchipInterpolator2 -) -from posydon.binary_evol.flow_chart import (STAR_STATES_CC) -import posydon.utils.constants as const +from posydon.utils.common_functions import orbital_separation_from_period from posydon.binary_evol.DT.step_detached import detached_step from posydon.utils.posydonerror import FlowError diff --git a/posydon/tests/binary_evol/CE/test_CEE.py b/posydon/tests/binary_evol/CE/test_CEE.py index 7e07503829..0b4190ff31 100644 --- a/posydon/tests/binary_evol/CE/test_CEE.py +++ b/posydon/tests/binary_evol/CE/test_CEE.py @@ -51,9 +51,8 @@ def test_common_envelope_1(self): giantstar = SingleStar(**PROPERTIES_STAR1) compstar = SingleStar(**PROPERTIES_STAR2) - q = giantstar.mass / compstar.mass orbital_separation_for_RLOF = 10**giantstar.log_R / cf.roche_lobe_radius( - q, a_orb=1) + giantstar.mass, compstar.mass, a_orb=1) orbital_period_for_RLOF = cf.orbital_period_from_separation( orbital_separation_for_RLOF, giantstar.mass, compstar.mass) PROPERTIES_BINARY = { @@ -113,9 +112,8 @@ def test_common_envelope_2(self): } compstar = SingleStar(**PROPERTIES_STAR2) - q = giantstar_withprofile.mass / compstar.mass orbital_separation_for_RLOF = 10**giantstar_withprofile.log_R / cf.roche_lobe_radius( - q, a_orb=1) + giantstar_withprofile.mass, compstar.mass, a_orb=1) orbital_period_for_RLOF = cf.orbital_period_from_separation( orbital_separation_for_RLOF, giantstar_withprofile.mass, compstar.mass) @@ -180,9 +178,8 @@ def test_common_envelope_3(self): } compstar = SingleStar(**PROPERTIES_STAR2) - q = giantstar_withprofile.mass / compstar.mass orbital_separation_for_RLOF = 10**giantstar_withprofile.log_R / cf.roche_lobe_radius( - q, a_orb=1) + giantstar_withprofile.mass, compstar.mass, a_orb=1) orbital_period_for_RLOF = cf.orbital_period_from_separation( orbital_separation_for_RLOF, giantstar_withprofile.mass, compstar.mass) @@ -243,9 +240,8 @@ def test_common_envelope_4(self): } compstar = SingleStar(**PROPERTIES_STAR2) - q = giantstar_withprofile.mass / compstar.mass orbital_separation_for_RLOF = 10**giantstar_withprofile.log_R / cf.roche_lobe_radius( - q, a_orb=1) + giantstar_withprofile.mass, compstar.mass, a_orb=1) orbital_period_for_RLOF = cf.orbital_period_from_separation( orbital_separation_for_RLOF, giantstar_withprofile.mass, compstar.mass) @@ -309,9 +305,8 @@ def test_common_envelope_5(self): } compstar = SingleStar(**PROPERTIES_STAR2) - q = giantstar_withprofile.mass / compstar.mass orbital_separation_for_RLOF = 10**giantstar_withprofile.log_R / cf.roche_lobe_radius( - q, a_orb=1) + giantstar_withprofile.mass, compstar.mass, a_orb=1) orbital_period_for_RLOF = cf.orbital_period_from_separation( orbital_separation_for_RLOF, giantstar_withprofile.mass, compstar.mass) @@ -367,9 +362,8 @@ def test_common_envelope_6(self): } compstar = SingleStar(**PROPERTIES_STAR2) - q = giantstar_withprofile.mass / compstar.mass orbital_separation_for_RLOF = 10**giantstar_withprofile.log_R / cf.roche_lobe_radius( - q, a_orb=1) + giantstar_withprofile.mass, compstar.mass, a_orb=1) orbital_period_for_RLOF = cf.orbital_period_from_separation( orbital_separation_for_RLOF, giantstar_withprofile.mass, compstar.mass) @@ -390,7 +384,7 @@ def test_common_envelope_6(self): #self.assertTrue(binary_withprofile.event == 'redirect', # "CEE test 6 failed") self.assertTrue((10**giantstar_withprofile.log_R - cf.roche_lobe_radius( - giantstar_withprofile.mass / compstar.mass, + giantstar_withprofile.mass, compstar.mass, a_orb=cf.orbital_separation_from_period( binary_withprofile.orbital_period, giantstar_withprofile.mass, compstar.mass))), "CEE test 6 failed") @@ -437,9 +431,8 @@ def test_common_envelope_7(self): } compstar = SingleStar(**PROPERTIES_STAR2) - q = giantstar_withprofile.mass / compstar.mass orbital_separation_for_RLOF = 10**giantstar_withprofile.log_R / cf.roche_lobe_radius( - q, a_orb=1) + giantstar_withprofile.mass, compstar.mass, a_orb=1) orbital_period_for_RLOF = cf.orbital_period_from_separation( orbital_separation_for_RLOF, giantstar_withprofile.mass, compstar.mass) @@ -461,7 +454,7 @@ def test_common_envelope_7(self): #self.assertTrue(binary_withprofile.event == 'redirect', # "CEE test 7 failed") self.assertTrue((10**giantstar_withprofile.log_R - cf.roche_lobe_radius( - giantstar_withprofile.mass / compstar.mass, + giantstar_withprofile.mass, compstar.mass, a_orb=cf.orbital_separation_from_period( binary_withprofile.orbital_period, giantstar_withprofile.mass, compstar.mass))), "CEE test 7 failed") @@ -504,9 +497,8 @@ def test_common_envelope_8(self): } #radius = 10km compstar = SingleStar(**PROPERTIES_STAR2) - q = giantstar_withprofile.mass / compstar.mass orbital_separation_for_RLOF = 10**giantstar_withprofile.log_R / cf.roche_lobe_radius( - q, a_orb=1) + giantstar_withprofile.mass, compstar.mass, a_orb=1) orbital_period_for_RLOF = cf.orbital_period_from_separation( orbital_separation_for_RLOF, giantstar_withprofile.mass, compstar.mass) @@ -569,9 +561,8 @@ def test_common_envelope_9(self): } #radius = 10km compstar = SingleStar(**PROPERTIES_STAR2) - q = giantstar_withprofile.mass / compstar.mass orbital_separation_for_RLOF = 10**giantstar_withprofile.log_R / cf.roche_lobe_radius( - q, a_orb=1) + giantstar_withprofile.mass, compstar.mass, a_orb=1) orbital_period_for_RLOF = cf.orbital_period_from_separation( orbital_separation_for_RLOF, giantstar_withprofile.mass, compstar.mass) @@ -638,9 +629,8 @@ def test_common_envelope_10(self): } #radius = 10km compstar = SingleStar(**PROPERTIES_STAR2) - q = giantstar_withprofile.mass / compstar.mass orbital_separation_for_RLOF = 10**giantstar_withprofile.log_R / cf.roche_lobe_radius( - q, a_orb=1) + giantstar_withprofile.mass, compstar.mass, a_orb=1) orbital_period_for_RLOF = cf.orbital_period_from_separation( orbital_separation_for_RLOF, giantstar_withprofile.mass, compstar.mass) diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index 25864e607c..50535736e2 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -13,6 +13,7 @@ "Scott Coughlin ", "Kyle Akira Rocha ", "Matthias Kruckow ", + "Camille Liotine ", ] @@ -195,39 +196,67 @@ def rzams(m, z=0.02, Zsun=0.02): return r -''' - - -Receives: -q -> -a_orb -> - -Returns: -RL -> Roche lobe radius in similar units as a_orb -''' - - -def roche_lobe_radius(q, a_orb=1): +def roche_lobe_radius(m1, m2, a_orb=1): """Approximate the Roche lobe radius from [1]_. Parameters ---------- - q : float - Dimensionless mass ratio = MRL/Mcomp, where - MRL is the mass of the star we calculate the RL and - Mcomp is the mass of its companion star. - a_orb : float + m1 : float, ndarray of floats + the mass of the star for which we calculate the Roche lobe + m2 : float, ndarray of floats + the mass of the companion star + a_orb : float, ndarray of floats Orbital separation. The return value will have the same unit. Returns ------- - float + float, ndarray of floats Roche lobe radius in similar units as a_orb References ---------- .. [1] Eggleton, P. P. 1983, ApJ, 268, 368 """ + ## catching if a_orb is an empty array or is an array with invalid separation values + if isinstance(a_orb, np.ndarray): + ## if array is empty, fill with NaN values + if a_orb.size == 0: + Pwarn("Trying to compute RL radius for binary with invalid separation", "EvolutionWarning") + a_orb = np.full([1 if s==0 else s for s in a_orb.shape], np.nan, dtype=np.float64) + ## if array contains invalid values, replace with NaN + elif np.any(a_orb < 0): + Pwarn("Trying to compute RL radius for binary with invalid separation", "EvolutionWarning") + a_orb[a_orb < 0] = np.nan + ## catching if a_orb is a float with invalid separation value + elif a_orb < 0: + Pwarn("Trying to compute RL radius for binary with invalid separation", "EvolutionWarning") + a_orb = np.nan + + + if isinstance(m1, np.ndarray): + if m1.size == 0: + Pwarn("Trying to compute RL radius for nonexistent object", "EvolutionWarning") + m1 = np.full([1 if s==0 else s for s in m1.shape], np.nan, dtype=np.float64) + elif np.any(m1 <= 0): + Pwarn("Trying to compute RL radius for nonexistent object", "EvolutionWarning") + m1[m1 <= 0] = np.nan + elif m1 <=0: + Pwarn("Trying to compute RL radius for nonexistent object", "EvolutionWarning") + m1 = np.nan + + + if isinstance(m2, np.ndarray): + if m2.size == 0: + Pwarn("Trying to compute RL radius for nonexistent companion", "EvolutionWarning") + m2 = np.full([1 if s==0 else s for s in m2.shape], np.nan, dtype=np.float64) + elif np.any(m2 <= 0): + Pwarn("Trying to compute RL radius for nonexistent companion", "EvolutionWarning") + m2[m2 <= 0] = np.nan + elif m2 <=0: + Pwarn("Trying to compute RL radius for nonexistent companion", "EvolutionWarning") + m2 = np.nan + + q = m1/m2 RL = a_orb * (0.49 * q**(2. / 3.)) / ( 0.6 * q**(2. / 3.) + np.log(1 + q**(1. / 3)) ) From 0645493da6b99bde74ce1a99975a022e929a31e2 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 12 Sep 2024 16:21:05 +0200 Subject: [PATCH 234/319] Reruns with no age limit for low mass stars (#372) * Update posydon-run-pipeline add `no_age_limit` rerun * documentation * Add no_age_limit+thermohaline_mixing Add 'no_age_limit+thermohaline_mixing' to use the same git hash as 'no_age_limit' and the same selection logic as 'thermohaline_mixing' * Update documentation Update pipeline_steps.rst * add no_age_limit+dedt_energy_eqn add no_age_limit+dedt_energy_eqn * update docs Update pipeline_steps.rst * Update posydon-run-pipeline use same quotation marks --- bin/posydon-run-pipeline | 14 ++++++++++++-- .../post_processing/pipeline/pipeline_steps.rst | 3 ++- 2 files changed, 14 insertions(+), 3 deletions(-) diff --git a/bin/posydon-run-pipeline b/bin/posydon-run-pipeline index 8788ab5b29..5000fb4646 100644 --- a/bin/posydon-run-pipeline +++ b/bin/posydon-run-pipeline @@ -704,6 +704,12 @@ def copy_ini_file(grid_type, rerun_metallicity, rerun_type, destination, # elif rerun_type == 'LBV_wind+dedt_hepulse': # raise ValueError(f'Not yet supported rerun type {rerun_type}!') # replace_text = "matthias_LBV_wind-051343856f50dc5518abdf28904d62df4caacaff" #TODO: update to new MESA-INLIST branch/commit + elif rerun_type == 'no_age_limit': + replace_text = "matthias_no_age_limit-a119ea9548cca9b6b7ddfe31309ced1ae40c271e" + elif rerun_type == 'no_age_limit+thermohaline_mixing': + replace_text = "matthias_no_age_limit-a119ea9548cca9b6b7ddfe31309ced1ae40c271e" + elif rerun_type == 'no_age_limit+dedt_energy_eqn': + replace_text = "matthias_lbv_thermo_dedt_noAgeLimit-dcc42e1d6d89fc267826970643e7f4f1bf463171" elif rerun_type == 'other': # e.g. 'envelope_mass_limit', 'fe_core_infall_limit' # this rerun uses the default inlist commit return @@ -824,7 +830,8 @@ def logic_rerun(grid, rerun_type): runs_to_rerun += [i] runs_to_rerun = np.array(runs_to_rerun) elif ((rerun_type == 'thermohaline_mixing') or - (rerun_type == 'LBV_wind+thermohaline_mixing')): + (rerun_type == 'LBV_wind+thermohaline_mixing') or + (rerun_type == 'no_age_limit+thermohaline_mixing')): termination_flags = TF1_POOL_ERROR new_mesa_flag = {'thermohaline_coeff' : 17.5} elif ((rerun_type == 'HeMB_MLTp_mesh') @@ -875,7 +882,8 @@ def logic_rerun(grid, rerun_type): runs_to_rerun += [i] runs_to_rerun = np.array(runs_to_rerun) elif ((rerun_type == 'dedt_energy_eqn') or - (rerun_type == 'LBV_wind+dedt_energy_eqn')): + (rerun_type == 'LBV_wind+dedt_energy_eqn') or + (rerun_type == 'no_age_limit+dedt_energy_eqn')): termination_flags = TF1_POOL_ERROR elif ((rerun_type == 'dedt_hepulse') # or (rerun_type == 'LBV_wind+dedt_hepulse') @@ -928,6 +936,8 @@ def logic_rerun(grid, rerun_type): if np.max(np.logical_or(tpagb_1, tpagb_2)) == False: runs_to_rerun += [i] runs_to_rerun = np.array(runs_to_rerun) + elif rerun_type == 'no_age_limit': + termination_flags = ['max_age'] elif rerun_type == 'envelope_mass_limit': # maybe 'fe_core_infall_limit' as well? runs_to_rerun = None # implement logic else: diff --git a/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst b/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst index 9ca99b1ebd..242d806581 100644 --- a/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst +++ b/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst @@ -206,6 +206,7 @@ type of the rerun specifying the logic and changes, and the cluster name. conv_bdy_weight caution it disabled the convective_bdy_weight where this caused segmentation faults (this avoids a bug in the old MESA version r11701) dedt_energy_eqn caution it enables MESA's dedt-form of the energy equation for numerical stability during rapid (superthermal) mass transfer dedt_hepulse caution it enables MESA's dedt-form of the energy equation for rapid mass transfer; at stripped HeZAMS, several MLT++ changes, v_flag and lnPgas_flag set to .true., and convective_bdy_weight disabled to help with stripped He star superadiabatic envelopes, pulsations, and WD cooling - LBV_wind default in v3+ it turns on LBV winds when crossing the Humphreys-Davidson limit as intended (due to a bug this was only applied after a retry); additionally, there are reruns `LBV_wind+thermohaline_mixing`, `LBV_wind+HeMB_MLTp_mesh`, `LBV_wind+dedt_energy_eqn`, `LBV_wind+dedt_hepulse`, which combine the two rerun types + LBV_wind default in v3+ it turns on LBV winds when crossing the Humphreys-Davidson limit as intended (due to a bug this was only applied after a retry); additionally, there are reruns `LBV_wind+thermohaline_mixing`, `LBV_wind+dedt_energy_eqn`, which combine the two rerun types + no_age_limit default in v3+ it allows low mass stars to evolve beyond the age of the universe, which is needed for grids where we jump on past ZAMS; additionally, there are reruns `no_age_limit+thermohaline_mixing` and `no_age_limit+dedt_energy_eqn`, which combine the two rerun types ===================== ============== =========== From 82ecd3cbe2c9627e1c140873459ef6fe0b48d4c9 Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Thu, 12 Sep 2024 16:24:49 +0200 Subject: [PATCH 235/319] TransientPopulation overplot crash fix (#374) * check if no binaries are selected for the slice to mitigate crashing * Update plot_pop.py space change --- posydon/visualization/plot_pop.py | 26 ++++++++++++++++++-------- 1 file changed, 18 insertions(+), 8 deletions(-) diff --git a/posydon/visualization/plot_pop.py b/posydon/visualization/plot_pop.py index 051d8ecb77..19559820de 100644 --- a/posydon/visualization/plot_pop.py +++ b/posydon/visualization/plot_pop.py @@ -6,6 +6,7 @@ import os import numpy as np import matplotlib as mpl +import pandas as pd from posydon.utils.common_functions import PATH_TO_POSYDON from posydon.visualization.plot_defaults import DEFAULT_LABELS from posydon.utils.constants import Zsun @@ -245,9 +246,11 @@ def plot_popsyn_over_grid_slice(pop, grid_type, met_Zsun, slices=None, channel=N met_indices = np.where(pop.select(where=channel_sel, columns=['metallicity']) == met_Zsun)[0].tolist() if 'HMS-HMS' in grid_path: slice_3D_var_range = (dq_edges[i],dq_edges[i+1]) - - sel = 'index in '+str(met_indices)+' & event == "ZAMS"' - data = pop.history.select(where=sel, columns=['S1_mass','S2_mass' ,'orbital_period']) + if len(met_indices) == 0: + data = pd.DataFrame(columns=['S1_mass','S2_mass', 'orbital_period']) + else: + sel = 'index in '+str(met_indices)+' & event == "ZAMS"' + data = pop.history.select(where=sel, columns=['S1_mass','S2_mass', 'orbital_period']) q = data['S2_mass'].values/data['S1_mass'].values q[q>1] = 1./q[q>1] @@ -259,15 +262,22 @@ def plot_popsyn_over_grid_slice(pop, grid_type, met_Zsun, slices=None, channel=N # select popsynth binaries in the given compact object mass # TODO: implement the case of reversal mass ratio if 'CO-HMS_RLO' in grid_path: - - sel = 'index in '+str(met_indices)+' & event == "oRLO2"' - data = pop.history.select(where=sel, columns=['S1_mass','S2_mass' ,'orbital_period']) + if len(met_indices) == 0: + data = pd.DataFrame(columns=['S1_mass','S2_mass', 'orbital_period']) + else: + sel = 'index in '+str(met_indices)+' & event == "oRLO2"' + data = pop.history.select(where=sel, columns=['S1_mass','S2_mass', 'orbital_period']) + m_CO = data['S1_mass'].values mask = (m_CO>=slice_3D_var_range[0]) & (m_CO<=slice_3D_var_range[1]) elif 'CO-HeMS' in grid_path: - sel = 'index in '+str(met_indices)+' & step_names == "step_CE"' - data = pop.history.select(where=sel, columns=['S1_mass','S2_mass' ,'orbital_period']) + if len(met_indices) == 0: + data = pd.DataFrame(columns=['S1_mass','S2_mass', 'orbital_period']) + else: + sel = 'index in '+str(met_indices)+' & step_names == "step_CE"' + data = pop.history.select(where=sel, columns=['S1_mass','S2_mass', 'orbital_period']) + m_CO = data['S1_mass'].values mask = (m_CO>=slice_3D_var_range[0]) & (m_CO<=slice_3D_var_range[1]) else: From f8f5d019f0a6ed97bde53a3a0efb25ba68d28246 Mon Sep 17 00:00:00 2001 From: Camille Liotine Date: Thu, 12 Sep 2024 09:42:10 -0500 Subject: [PATCH 236/319] fix get_redshift_from_cosmic_time() function (#381) --- posydon/popsyn/rate_calculation.py | 28 ++++++++++++++++------------ 1 file changed, 16 insertions(+), 12 deletions(-) diff --git a/posydon/popsyn/rate_calculation.py b/posydon/popsyn/rate_calculation.py index 54c12f9e3c..3657403ffb 100644 --- a/posydon/popsyn/rate_calculation.py +++ b/posydon/popsyn/rate_calculation.py @@ -10,7 +10,7 @@ import numpy as np import scipy as sp from astropy.cosmology import z_at_value -from scipy.interpolate import interp1d +from scipy.interpolate import CubicSpline from astropy import units as u @@ -111,32 +111,34 @@ def redshift_from_cosmic_time_interpolator(): Returns ------- - object - Returns the trained interpolator object. + CubicSpline object + Returns the trained SciPy CubicSpline interpolator object. """ - # astropy z_at_value method is too slow to compute z_mergers efficinty - # we must implement interpolation + # astropy z_at_value method is too slow to compute z_mergers efficiently + # for arrays of values, so we must implement interpolation + ## the interpolation object covers all possible values of t, z + t = np.linspace(1e-2, cosmology.age(1e-08).value * 0.9999999, 1000) z = np.zeros(1000) for i in range(1000): z[i] = z_at_value(cosmology.age, t[i] * u.Gyr) - f_z_m = interp1d(t, z, kind="cubic") + f_z_m = CubicSpline(t, z) return f_z_m def get_redshift_from_cosmic_time(t_cosm): - """Compute the cosmological redshift given the cosmic time.. + """Compute the cosmological redshift given the cosmic time. Parameters ---------- - t_cosm : array doubles - Cosmic time to which you want to know the redhisft. + t_cosm : float, ndarray of floats + Cosmic time(s) for which you want to know the redhisft. Returns ------- - array doubles - Cosmolgocial redshift corresponding to the cosmic time. + ndarray of floats + Cosmolgocial redshift(s) corresponding to t_cosm. Note ---- @@ -144,7 +146,9 @@ def get_redshift_from_cosmic_time(t_cosm): which is created each time the function is called. `z_at_value` from astropy can be used for single values, but it is too slow for arrays. """ - return redshift_from_cosmic_time_interpolator(t_cosm) + trained_tz_interp = redshift_from_cosmic_time_interpolator() + return trained_tz_interp(t_cosm) + def get_redshift_bin_edges(delta_t): From 251b3c6d895e0ef19169f2f34af968937c53b308 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 12 Sep 2024 16:44:36 +0200 Subject: [PATCH 237/319] Replace job_start for running grids (#380) * turn start+end to end+before_end+now * Reduce number of CPUs for cleanup jobs Usually, cleanup is limited by the file system, hence request less CPUs. --- bin/posydon-run-grid | 22 ++++++++++++---------- bin/posydon-setup-grid | 4 ++-- 2 files changed, 14 insertions(+), 12 deletions(-) diff --git a/bin/posydon-run-grid b/bin/posydon-run-grid index db272d48bd..0d4a1d6396 100755 --- a/bin/posydon-run-grid +++ b/bin/posydon-run-grid @@ -124,12 +124,13 @@ def parse_commandline(): parser.add_argument("--keep_photos", action="store_true", default=False, help="Do not delete photos at run completion") - parser.add_argument("--job_start", type=int, default=0, - help="Start time of the job (in seconds)") - - parser.add_argument("--job_end", type=int, default=600, + now = int(time.time()) + parser.add_argument("--job_end", type=int, default=now+60*60*24*365, help="End time of the job (in seconds)") + parser.add_argument("--job_before_end", type=int, default=300, + help="Time span till end of job (in seconds)") + args = parser.parse_args() if args.grid_type not in ['fixed', 'dynamic']: @@ -468,7 +469,6 @@ def run_mesa(mesa_executable, work_dir, final_dir, **kwargs): keep_profiles = kwargs.pop('keep_profiles', False) keep_photos = kwargs.pop('keep_photos', False) keep_work_dir = kwargs.pop('keep_work_dir', False) - job_time = kwargs.pop('job_end', 600) - kwargs.pop('job_start', 0) os.chdir(work_dir) if not work_dir == final_dir: @@ -499,10 +499,12 @@ def run_mesa(mesa_executable, work_dir, final_dir, **kwargs): os.kill(child_ID, 9) move_mesa_output(work_dir, final_dir, keep_work_dir) elif child_ID==0: + now = int(time.time()) + child_end = kwargs.pop('job_end', now+60*60*24*365) - kwargs.pop('job_before_end', 300) #child process: waits till short before the job will get killed by slurm to copy the data beforehand and exit this process after the copy finished - if job_time>300: - #if job last longer than 5 minutes (300 seconds) get remaining time till 5 minutes before its end - wait_time = job_time-300 + if child_end>now: + #if the child job still needs to wait, get time to wait + wait_time = child_end - now else: #otherwise wait 1 minute wait_time = 60 @@ -764,7 +766,7 @@ def run_grid_point(grid, star1_formation, star2_formation, # run the binary exectuable run_mesa(args.mesa_binary_executable, work_dir, final_dir, keep_profiles=args.keep_profiles, keep_photos=args.keep_photos, - job_start=args.job_start, job_end=args.job_end) + job_end=args.job_end, job_before_end=args.job_before_end) # find out what happened mesa_result = extract_mesa_results(final_dir) @@ -901,7 +903,7 @@ if __name__ == '__main__': run_mesa(args.mesa_star1_executable, work_dir, final_dir, outfile_name='out_star1_formation_step{0}.txt'.format(index), keep_profiles=args.keep_profiles, keep_photos=args.keep_photos, - job_start=args.job_start, job_end=args.job_end, + job_end=args.job_end, job_before_end=args.job_before_end, keep_work_dir=index Date: Tue, 17 Sep 2024 11:56:59 -0500 Subject: [PATCH 238/319] add missing binary states to flow charts and error catching fo future missing states --- posydon/binary_evol/DT/double_CO.py | 2 +- posydon/binary_evol/DT/step_detached.py | 24 ++++++++++++++++-------- posydon/binary_evol/flow_chart.py | 4 ++-- 3 files changed, 19 insertions(+), 11 deletions(-) diff --git a/posydon/binary_evol/DT/double_CO.py b/posydon/binary_evol/DT/double_CO.py index 263c8fe0dd..852c001861 100644 --- a/posydon/binary_evol/DT/double_CO.py +++ b/posydon/binary_evol/DT/double_CO.py @@ -105,7 +105,7 @@ def ev_contact(t, y): if s.status == -1: binary.state += ' (Integration failure)' - raise NumericalError("Integrations failed: " + s.message) + raise NumericalError("Integration failed: " + s.message) elif s.status == 1: diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index 6d08cf0ea0..fd41ece67d 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -1174,11 +1174,16 @@ def get_star_data(binary, star1, star2, htrack, if interp1d_sec is None or interp1d_pri is None: - # binary.event = "END" - binary.state += " (GridMatchingFailed)" - if self.verbose or self.verbose == 1: - print("Failed matching") - return + + if binary.state in ['detached', 'disrupted', 'merged']: + binary.state += " (GridMatchingFailed)" + if self.verbose or self.verbose == 1: + print("Failed matching") + return + else: + raise ValueError("need to add new binary state to flow chart for evolution to end correctly ", + "when grid matching fails: ", binary.state," (GridMatchingFailed)") + t0_sec = interp1d_sec["t0"] t0_pri = interp1d_pri["t0"] m01 = interp1d_sec["m0"] @@ -1503,10 +1508,13 @@ def get_omega(star, is_secondary = True): print("solution of ODE", s) if s.status == -1: print("Integration failed", s.message) - binary.state += ' (Integration failure)' - # binary.event = "END" + + if binary.state in ['detached']: + binary.state += ' (Integration failure)' + else: + raise ValueError("need to add new binary state to flow chart for evolution to end correctly ", + "when detached integration fails: ", binary.state, " (Integration failure)") return - # raise RuntimeError("Integration failed", s.message) if self.dt is not None and self.dt > 0: t = np.arange(binary.time, s.t[-1] + self.dt/2.0, self.dt)[1:] diff --git a/posydon/binary_evol/flow_chart.py b/posydon/binary_evol/flow_chart.py index c45d53376a..f949874cc9 100644 --- a/posydon/binary_evol/flow_chart.py +++ b/posydon/binary_evol/flow_chart.py @@ -229,9 +229,9 @@ # catch states to be ended -for b in ['initial_RLOF', +for b in ['initial_RLOF', 'RLO2 (OutsideGrid)' 'detached (Integration failure)', - 'detached (GridMatchingFailed)', 'RLO2 (OutsideGrid)']: + 'detached (GridMatchingFailed)', 'disrupted (GridMatchingFailed)', 'merged (GridMatchingFailed)']: for s1 in STAR_STATES_ALL: for s2 in STAR_STATES_ALL: for e in BINARY_EVENTS_ALL: From 6534f3497d215792807eaf0903192238c0a47d9b Mon Sep 17 00:00:00 2001 From: Camille Date: Wed, 18 Sep 2024 10:19:07 -0500 Subject: [PATCH 239/319] add verbose block, move return statement --- posydon/binary_evol/DT/step_detached.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index fd41ece67d..87d424c11e 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -1506,15 +1506,17 @@ def get_omega(star, is_secondary = True): print("ODE solver duration: " f"{t_after_ODEsolution-t_before_ODEsolution:.6g}") print("solution of ODE", s) - if s.status == -1: - print("Integration failed", s.message) + if s.status == -1: + if self.verbose: + print("Integration failed", s.message) + if binary.state in ['detached']: - binary.state += ' (Integration failure)' + binary.state += ' (Integration failure)' + return else: raise ValueError("need to add new binary state to flow chart for evolution to end correctly ", - "when detached integration fails: ", binary.state, " (Integration failure)") - return + "when detached integration fails: ", binary.state, " (Integration failure)") if self.dt is not None and self.dt > 0: t = np.arange(binary.time, s.t[-1] + self.dt/2.0, self.dt)[1:] From dee2183a31aca4099b9c22aed1ce6d435246912a Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Thu, 19 Sep 2024 16:20:50 +0200 Subject: [PATCH 240/319] add an escape option if value cannot be found in the grids (#385) --- posydon/interpolation/interpolation.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/posydon/interpolation/interpolation.py b/posydon/interpolation/interpolation.py index 2f8b767cb7..b08841fb7a 100644 --- a/posydon/interpolation/interpolation.py +++ b/posydon/interpolation/interpolation.py @@ -603,6 +603,9 @@ def get_final_values(self, key, M_new): kvalue_low = self.grid_final_values[mass_low][key] while (kvalue_low is None or np.isnan(kvalue_low)): + # escape if no lower mass is available + if np.sum(mass_low > self.grid_mass) == 0: + break mass_low = np.max(self.grid_mass[mass_low > self.grid_mass]) try: kvalue_low = self.grid_final_values[mass_low][key] @@ -617,6 +620,9 @@ def get_final_values(self, key, M_new): kvalue_high = self.grid_final_values[mass_high][key] while (kvalue_high is None or np.isnan(kvalue_high)): + # escape if no higher mass is available + if np.sum(mass_high < self.grid_mass) == 0: + break mass_high = np.min(self.grid_mass[mass_high < self.grid_mass]) try: kvalue_high = self.grid_final_values[mass_high][key] From 6303bd2a1925bb104c913d6d3d6bd497daea56a3 Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Thu, 19 Sep 2024 16:27:53 +0200 Subject: [PATCH 241/319] Population() fix for large memory usage when reading files (#386) * add an escape option if value cannot be found in the grids * Fix pd.read_hdf() memory usage + restructure code move select, tail and head into a DFInterface class from which History and Oneline inherit * add init parameter. fix copy bug + generalise (html) repr. * add missing return in Oneline.__get_item__() * replace Oneline slice selection with self.select() * Change docstrings - Add that you can only query where on the index or string columns. - Changed some wrongly named tables in the docstring - Change the example `where` selection. --- posydon/popsyn/synthetic_population.py | 223 +++++++++++++++++++------ 1 file changed, 168 insertions(+), 55 deletions(-) diff --git a/posydon/popsyn/synthetic_population.py b/posydon/popsyn/synthetic_population.py index 0264109afa..590abf61c3 100644 --- a/posydon/popsyn/synthetic_population.py +++ b/posydon/popsyn/synthetic_population.py @@ -224,8 +224,118 @@ def merge_parallel_runs(self, pop): # Helper classes # ################## +class DFInterface: + """A class to handle the interface between the population file and the History and Oneline classes.""" + + def __init__(self): + self.filename = None + self.chunksize = None + + def head(self, key, n=10): + """Return the first n rows of the key table + + Parameters + ---------- + key: str + The key of the table + n : int, optional + The number of rows to return. Default is 10. + + Returns + ------- + pandas.DataFrame + The first n rows of the key table. + + """ + with pd.HDFStore(self.filename, mode="r") as store: + return store.select(key, start=0, stop=n) + + def tail(self, key, n=10): + """ + Get the last n rows of the key table. + + Parameters + ---------- + key : str + The key of the table. + n : int, optional + The number of rows to return. Default is 10. + + Returns + ------- + pd.DataFrame + The last n rows of the key table. + """ + with pd.HDFStore(self.filename, mode="r") as store: + return store.select(key, start=-n) + + + def select(self, key, where=None, start=None, stop=None, columns=None): + '''Select a subset of the key table based on the given conditions. + + Parameters + ---------- + key : str + The key of the table to select from. + where : str, optional + A string representing the query condition to apply to the data. + start : int, optional + The starting index of the data to select. + stop : int, optional + The ending index of the data to select. + columns : list, optional + A list of column names to select. + + Returns + ------- + pandas.DataFrame + The selected data as a DataFrame. + ''' + # we have to chunk the read because of memory issues + with pd.HDFStore(self.filename, mode="r") as store: + iterator = store.select(key, where=where, start=start, stop=stop, columns=columns, chunksize=self.chunksize) + # read the data in chunks and concatenate once (faster than concat every chunk!) + out = [] + for chunk in iterator: + out.append(chunk) + out = pd.concat(out, axis=0) + return out + + def get_repr(self, key): + '''Return a string representation of the key table. + + Parameters + ---------- + key : str + The key of the table to return the string representation of. + + Returns + ------- + str + The string representation of the key table. + + ''' + with pd.HDFStore(self.filename, mode="r") as store: + return store.select(key, start=0, stop=10).__repr__() + + def get_html_repr(self, key): + """Return the HTML representation of the key table. + + Parameters + ---------- + key : str + The key of the table to return the HTML representation of. + + Returns + ------- + str + The HTML representation of the key table. + """ + with pd.HDFStore(self.filename, mode="r") as store: + return store.select(key, start=0, stop=10)._repr_html_() + -class History: +class History(DFInterface): """A class to handle the history dataframe of a population file. This class provides methods to handle the history dataframe of a population file. @@ -353,52 +463,52 @@ def __getitem__(self, key): else: chunk = key.stop - pre indices = list(range(pre, pre + chunk)) - return pd.read_hdf(self.filename, key="history", where='index in indices') - + return self.select(where=f'index in {indices}') + + # single index elif isinstance(key, int): - return pd.read_hdf(self.filename, where="index == key", key="history") - + return self.select(where=f"index == {key}") + # list of indices elif isinstance(key, list) and all(isinstance(x, int) for x in key): if len(key) == 0: return pd.DataFrame() else: - with pd.HDFStore(self.filename, mode="r") as store: - return store.select("history", where="index in key") + return self.select(where=f"index in {key}") + # numpy array elif isinstance(key, np.ndarray) and (key.dtype == int): if len(key) == 0: return pd.DataFrame() else: indices = key.tolist() - with pd.HDFStore(self.filename, mode="r") as store: - return store.select("history", where="index in indices") + return self.select(where=f"index in {indices}") # boolean mask elif (isinstance(key, np.ndarray) and key.dtype == bool) or (isinstance(key, pd.DataFrame) and all(key.dtypes == bool)): # return empty if no values if len(key) == 0: return pd.DataFrame() - - out_df = pd.DataFrame() - for i in range(0, len(key), self.chunksize): - tmp_df = pd.read_hdf( - self.filename, key="history", start=i, stop=i + self.chunksize - )[key[i : i + self.chunksize]] - out_df = pd.concat([out_df, tmp_df]) - return out_df + # We cannot use self.select because we're using a boolean mask across the entire table + # This can be optimized by only selecting indices that are True instead of the entire table + with pd.HDFStore(self.filename, mode="r") as store: + iterator = store.select("history", chunksize=self.chunksize) + out = [] + for i, chunk in enumerate(iterator): + out.append(chunk[key[i : i + self.chunksize]]) + return pd.concat(out, axis=0) # single column elif isinstance(key, str): if key in self.columns: - return pd.read_hdf(self.filename, key="history", columns=[key]) + return self.select(columns=[key]) else: raise ValueError(f"{key} is not a valid column name!") # multiple columns elif isinstance(key, list) and all(isinstance(x, str) for x in key): if all(x in self.columns for x in key): - return pd.read_hdf(self.filename, key="history", columns=key) + return self.select(columns=key) else: raise ValueError(f"Not all columns in {key} are valid column names!") else: @@ -428,7 +538,7 @@ def head(self, n=10): The first n rows of the history table. """ - return pd.read_hdf(self.filename, key="history", start=0, stop=n) + return super().head("history", n) def tail(self, n=10): """Return the last n rows of the history table. @@ -444,7 +554,7 @@ def tail(self, n=10): The last n rows of the history table. """ - return pd.read_hdf(self.filename, key="history", start=-n) + return super().tail("history", n) def __repr__(self): """Return a string representation of the object. @@ -452,8 +562,8 @@ def __repr__(self): Returns: str: A string representation of the object. """ - return pd.read_hdf(self.filename, key="history").__repr__() - + return super().get_repr("history") + def _repr_html_(self): """Return the HTML representation of the history dataframe. @@ -466,8 +576,7 @@ def _repr_html_(self): str The HTML representation of the history dataframe. """ - return pd.read_hdf(self.filename, key="history")._repr_html_() - + return super().get_html_repr("history") def select(self, where=None, start=None, stop=None, columns=None): """Select a subset of the history table based on the given conditions. @@ -483,6 +592,7 @@ def select(self, where=None, start=None, stop=None, columns=None): ---------- where : str, optional A string representing the query condition to apply to the data. + It is only possible to query on the index or string columns. start : int, optional The starting index of the data to select. stop : int, optional @@ -495,13 +605,14 @@ def select(self, where=None, start=None, stop=None, columns=None): pandas.DataFrame The selected data as a DataFrame. """ - with pd.HDFStore(self.filename, mode="r") as store: - return store.select( - "history", where=where, start=start, stop=stop, columns=columns - ) + return super().select(key='history', + where=where, + start=start, + stop=stop, + columns=columns) -class Oneline: +class Oneline(DFInterface): """A class to handle the oneline dataframe of a population file. The `Oneline` class provides methods to manipulate and retrieve data from the oneline dataframe of a population file. @@ -596,32 +707,32 @@ def __getitem__(self, key): chunk = self.number_of_systems else: chunk = key.stop - pre - return pd.read_hdf( - self.filename, key="oneline", start=pre, stop=pre + chunk - ) + indices = list(range(pre, pre + chunk)) + return self.select(where=f'index in {indices}') + elif isinstance(key, int): - return pd.read_hdf(self.filename, where="index == key", key="oneline") + return self.select(where=f"index == {key}") elif isinstance(key, list) and all(isinstance(x, int) for x in key): - return pd.read_hdf(self.filename, where="index in key", key="oneline") + return self.select(where=f"index in {key}") elif isinstance(key, np.ndarray) and (key.dtype == int): indices = key.tolist() - return pd.read_hdf(self.filename, where="index in indices", key="oneline") + return self.select(where=f"index in {indices}") elif isinstance(key, list) and all(isinstance(x, float) for x in key): raise ValueError("elements in list are not integers! Try casting to int.") elif isinstance(key, pd.DataFrame) and all(key.dtypes == bool): indices = self.indices[key.to_numpy().flatten()].tolist() - return pd.read_hdf(self.filename, where="index in indices", key="oneline") + return self.select(where=f"index in {indices}") elif isinstance(key, np.ndarray) and key.dtype == bool: indices = self.indices[key].tolist() - return pd.read_hdf(self.filename, where="index in indices", key="oneline") + return self.select(where=f"index in {indices}") elif isinstance(key, str): if key in self.columns: - return pd.read_hdf(self.filename, key="oneline", columns=[key]) + return self.select(columns=[key]) else: raise ValueError(f"{key} is not a valid column!") elif isinstance(key, list) and all(isinstance(x, str) for x in key): if all(x in self.columns for x in key): - return pd.read_hdf(self.filename, key="oneline", columns=key) + return self.select(columns=key) else: raise ValueError(f"Not all columns in {key} are valid column names!") else: @@ -651,7 +762,7 @@ def head(self, n=10): pd.DataFrame The first n rows of the oneline table. """ - return pd.read_hdf(self.filename, key="oneline", start=0, stop=n) + return super().head("oneline", n) def tail(self, n=10): """ @@ -667,7 +778,7 @@ def tail(self, n=10): pd.DataFrame The last n rows of the oneline table. """ - return pd.read_hdf(self.filename, key="oneline", start=-n) + return super().tail("oneline", n) def __repr__(self): """ @@ -678,7 +789,7 @@ def __repr__(self): str The string representation of the oneline table. """ - return pd.read_hdf(self.filename, key="oneline").__repr__() + return super().get_repr("oneline") def _repr_html_(self): """ @@ -689,8 +800,7 @@ def _repr_html_(self): str The HTML representation of the oneline table. """ - return pd.read_hdf(self.filename, key="oneline")._repr_html_() - + return super().get_html_repr("oneline") def select(self, where=None, start=None, stop=None, columns=None): """Select a subset of the oneline table based on the given conditions. @@ -701,7 +811,8 @@ def select(self, where=None, start=None, stop=None, columns=None): Parameters ---------- where : str, optional - A condition to filter the rows of the oneline table. Default is None. + A condition to filter the rows of the oneline table. Default is None. + It is only possible to query on the index or string columns. start : int, optional The starting index of the subset. Default is None. stop : int, optional @@ -717,7 +828,7 @@ def select(self, where=None, start=None, stop=None, columns=None): Examples -------- # Select rows based on a condition - >>> df = Oneline.select(where="S2_mass_i > 30") + >>> df = Oneline.select(where="event == 'ZAMS'") # Select rows from index 10 to 20 >>> df = Oneline.select(start=10, stop=20) @@ -725,10 +836,11 @@ def select(self, where=None, start=None, stop=None, columns=None): # Select specific columns >>> df = Oneline.select(columns=['S1_mass_i', 'S1_mass_f']) """ - with pd.HDFStore(self.filename, mode="r") as store: - return store.select( - "oneline", where=where, start=start, stop=stop, columns=columns - ) + return super().select(key='oneline', + where=where, + start=start, + stop=stop, + columns=columns) class PopulationIO: @@ -1724,13 +1836,14 @@ def select(self, where=None, start=None, stop=None, columns=None): """ Select a subset of the transient population. - This method allows you to filter and extract a subset of rows from the oneline table stored in an HDF file. + This method allows you to filter and extract a subset of rows from the transient table stored in an HDF file. You can specify conditions to filter the rows, define the range of rows to select, and choose specific columns to include in the subset. Parameters ---------- where : str, optional - A condition to filter the rows of the oneline table. Default is None. + A condition to filter the rows of the transient table. Default is None. + It is only possible to search on the index or string columns. start : int, optional The starting index of the subset. Default is None. stop : int, optional @@ -1741,12 +1854,12 @@ def select(self, where=None, start=None, stop=None, columns=None): Returns ------- pd.DataFrame - The selected subset of the oneline table. + The selected subset of the transient table. Examples -------- # Select rows based on a condition - >>> df = transpop.select(where="S2_mass_i > 30") + >>> df = transpop.select(where="S1_state == 'BH'") # Select rows from index 10 to 20 >>> df = transpop.select(start=10, stop=20) From 15ef83d80b17650ca43ceb1506f5e61e72d4b876 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 19 Sep 2024 16:41:01 +0200 Subject: [PATCH 242/319] Initial RLO boundary limit (#389) * Update limits_thresholds.py Add new threshold: MIN_COUNT_INITIAL_RLO_BOUNDARY * Update termination_flags.py Return the default if the initial RLO boundary isn't well enough populated. * Add warning * Update limits_thresholds.py Try larger value. --- posydon/grids/termination_flags.py | 25 ++++++++++++++++++++++++- posydon/utils/limits_thresholds.py | 4 ++++ 2 files changed, 28 insertions(+), 1 deletion(-) diff --git a/posydon/grids/termination_flags.py b/posydon/grids/termination_flags.py index 1459511f45..603cfadd4f 100644 --- a/posydon/grids/termination_flags.py +++ b/posydon/grids/termination_flags.py @@ -29,12 +29,14 @@ infer_star_state, cumulative_mass_transfer_flag, infer_mass_transfer_case ) from posydon.utils.limits_thresholds import ( - RL_RELATIVE_OVERFLOW_THRESHOLD, LG_MTRANSFER_RATE_THRESHOLD + RL_RELATIVE_OVERFLOW_THRESHOLD, LG_MTRANSFER_RATE_THRESHOLD, + MIN_COUNT_INITIAL_RLO_BOUNDARY ) from posydon.visualization.combine_TF import ( TF1_POOL_STABLE, TF1_POOL_UNSTABLE, TF1_POOL_INITIAL_RLO, TF1_POOL_ERROR, TF2_POOL_NO_RLO, TF2_POOL_INITIAL_RLO ) +from posydon.utils.posydonwarning import Pwarn # variables needed for inferring star states @@ -324,12 +326,33 @@ def get_detected_initial_RLO(grid): def get_nearest_known_initial_RLO(mass1, mass2, known_initial_RLO): + """Find the nearest system of initial RLO in the known ones + + Parameters + ---------- + mass1 : float + star_1_mass of the run to check. + mass2 : float + star_2_mass of the run to check. + known_initial_RLO : list of dict + Boundary to apply. + + Retruns + ------- + dict + Containing the key parameters (e.g. initial masses, period) of the + nearest initial RLO run. + """ #default values d2min = 1.0e+99 nearest = {"star_1_mass": 0.0, "star_2_mass": 0.0, "period_days": 0.0, } + if len(known_initial_RLO) Date: Thu, 19 Sep 2024 16:44:21 +0200 Subject: [PATCH 243/319] Small Population fixes (#390) * remove printing for intrinsic rate calculation + do not fail TransientPopulation calculation if empty population is created * fix channel grid_overplotting * Add docstring + change warning --- posydon/popsyn/synthetic_population.py | 15 +++++++++++---- posydon/visualization/plot_pop.py | 2 +- 2 files changed, 12 insertions(+), 5 deletions(-) diff --git a/posydon/popsyn/synthetic_population.py b/posydon/popsyn/synthetic_population.py index 590abf61c3..8790f4a8f9 100644 --- a/posydon/popsyn/synthetic_population.py +++ b/posydon/popsyn/synthetic_population.py @@ -1595,8 +1595,8 @@ def create_transient_population( Returns ------- - TransientPopulation - A TransientPopulation object for interfacing with the transient population + TransientPopulation or None + A TransientPopulation object for interfacing with the transient population or None if no systems are present in the TransientPopulation. Raises ------ @@ -1689,6 +1689,12 @@ def create_transient_population( ) previous = end + + # it can happen that no systems are selected, in which case nothing has been appended to the file in the loop + with pd.HDFStore(self.filename, mode="r") as store: + if '/transients/'+transient_name not in store.keys(): + Pwarn("No systems selected for the transient population!", "POSYDONWarning") + return None synth_pop = TransientPopulation( self.filename, transient_name, verbose=self.verbose @@ -1906,8 +1912,9 @@ def get_efficiency_over_metallicity(self): combined_df.sort_index(inplace=True) efficiencies = combined_df['count']/combined_df['underlying_mass'] - for MET, value in efficiencies.sort_index().items(): - print(f"Efficiency at Z={MET:1.2E}: {value:1.2E} Msun^-1") + if self.verbose: + for MET, value in efficiencies.sort_index().items(): + print(f"Efficiency at Z={MET:1.2E}: {value:1.2E} Msun^-1") self.efficiency = pd.DataFrame( efficiencies, columns=["total"] diff --git a/posydon/visualization/plot_pop.py b/posydon/visualization/plot_pop.py index 19559820de..49afe97577 100644 --- a/posydon/visualization/plot_pop.py +++ b/posydon/visualization/plot_pop.py @@ -233,7 +233,7 @@ def plot_popsyn_over_grid_slice(pop, grid_type, met_Zsun, slices=None, channel=N if channel is not None: - channel_sel = 'channel == '+str(channel) + channel_sel = 'channel == "'+str(channel)+'"' else: channel_sel = '' From d332c99b45d5cf5eb60ef81af21597b4a850df5e Mon Sep 17 00:00:00 2001 From: Camille Liotine Date: Thu, 19 Sep 2024 10:00:54 -0500 Subject: [PATCH 244/319] add catch to suppress python warning in detached step (#392) * add warning catch to detached step * update comment * change POSYDON warning type --- posydon/binary_evol/DT/step_detached.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index 6d08cf0ea0..f76728913c 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -1686,9 +1686,17 @@ def get_omega(star, is_secondary = True): const.msol * const.rsol ** 2) elif (key in ["log_total_angular_momentum"] and obj == secondary): + + current_omega = interp1d_sec["omega"][-1] + + ## add a warning catch if the current omega has an invalid value + ## (otherwise python will throw an insuppressible warning when taking the log) + if interp1d_sec["omega"][-1] <=0: + Pwarn("Trying to compute log angular momentum for object with no spin", "InappropriateValueWarning") + current_omega = np.nan current = np.log10( - (interp1d_sec["omega"][-1] / const.secyer) + (current_omega / const.secyer) * (interp1d_sec[ self.translate["total_moment_of_inertia"]](t[-1] - t_offset_sec).item() * (const.msol * const.rsol ** 2))) From 5acad138a071a538297f0b8e27698a6c51d1a14b Mon Sep 17 00:00:00 2001 From: sgossage Date: Thu, 19 Sep 2024 11:43:20 -0500 Subject: [PATCH 245/319] Add LBV_wind+dedt+hepulse rerun (#393) * Update posydon-run-pipeline enabled LBV_wind+dedt_hepulse rerun and updated associated SHA * Update pipeline_steps.rst added/updated descriptions for new rerun types * Update posydon-run-pipeline updated SHA for rerun after removing age limit stopping conditions * Update pipeline_steps.rst updated description for new hepulse rerun that removes stopping conditions for low mass stars based at TAMS/Hubble time * Update posydon-run-pipeline updated SHA * Update posydon-run-pipeline update SHA * Update posydon-run-pipeline update SHA * Update posydon-run-pipeline update SHA * Update posydon-run-pipeline update SHA --- bin/posydon-run-pipeline | 8 +++----- .../post_processing/pipeline/pipeline_steps.rst | 5 ++++- 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/bin/posydon-run-pipeline b/bin/posydon-run-pipeline index 5000fb4646..f7b7845ea1 100644 --- a/bin/posydon-run-pipeline +++ b/bin/posydon-run-pipeline @@ -701,9 +701,8 @@ def copy_ini_file(grid_type, rerun_metallicity, rerun_type, destination, # replace_text = "matthias_LBV_wind-051343856f50dc5518abdf28904d62df4caacaff" #TODO: update to new MESA-INLIST branch/commit elif rerun_type == 'LBV_wind+dedt_energy_eqn': replace_text = "seth_lbv_thermo_dedt-936f76f82d44b16139ff89e41e5b0f2232fd9b30" -# elif rerun_type == 'LBV_wind+dedt_hepulse': -# raise ValueError(f'Not yet supported rerun type {rerun_type}!') -# replace_text = "matthias_LBV_wind-051343856f50dc5518abdf28904d62df4caacaff" #TODO: update to new MESA-INLIST branch/commit + elif rerun_type == 'LBV_wind+dedt_hepulse': + replace_text = "seth_lbv_thermo_dedt_hepulse-77640b5f773825129d6e3bf29267e52c90f328a2" elif rerun_type == 'no_age_limit': replace_text = "matthias_no_age_limit-a119ea9548cca9b6b7ddfe31309ced1ae40c271e" elif rerun_type == 'no_age_limit+thermohaline_mixing': @@ -886,8 +885,7 @@ def logic_rerun(grid, rerun_type): (rerun_type == 'no_age_limit+dedt_energy_eqn')): termination_flags = TF1_POOL_ERROR elif ((rerun_type == 'dedt_hepulse') -# or (rerun_type == 'LBV_wind+dedt_hepulse') - ): + or (rerun_type == 'LBV_wind+dedt_hepulse')): termination_flags = TF1_POOL_ERROR elif rerun_type == 'LBV_wind': N_runs = len(grid) diff --git a/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst b/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst index 242d806581..d9783572fb 100644 --- a/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst +++ b/docs/_source/components-overview/post_processing/pipeline/pipeline_steps.rst @@ -206,7 +206,10 @@ type of the rerun specifying the logic and changes, and the cluster name. conv_bdy_weight caution it disabled the convective_bdy_weight where this caused segmentation faults (this avoids a bug in the old MESA version r11701) dedt_energy_eqn caution it enables MESA's dedt-form of the energy equation for numerical stability during rapid (superthermal) mass transfer dedt_hepulse caution it enables MESA's dedt-form of the energy equation for rapid mass transfer; at stripped HeZAMS, several MLT++ changes, v_flag and lnPgas_flag set to .true., and convective_bdy_weight disabled to help with stripped He star superadiabatic envelopes, pulsations, and WD cooling - LBV_wind default in v3+ it turns on LBV winds when crossing the Humphreys-Davidson limit as intended (due to a bug this was only applied after a retry); additionally, there are reruns `LBV_wind+thermohaline_mixing`, `LBV_wind+dedt_energy_eqn`, which combine the two rerun types + LBV_wind default in v3+ it turns on LBV winds when crossing the Humphreys-Davidson limit as intended (due to a bug this was only applied after a retry); additionally, there are reruns `LBV_wind+thermohaline_mixing`, `LBV_wind+dedt_energy_eqn`, which combine the two rerun types. Any additional changes to these reruns are described here as LBV_wind+rerun_type no_age_limit default in v3+ it allows low mass stars to evolve beyond the age of the universe, which is needed for grids where we jump on past ZAMS; additionally, there are reruns `no_age_limit+thermohaline_mixing` and `no_age_limit+dedt_energy_eqn`, which combine the two rerun types + LBV_wind+dedt caution it enables MESA's dedt-form of the energy equation for numerical stability during rapid (superthermal) mass transfer and sets lnPgas_flag to .true. for numerical stability. Also disabled convective_bdy_weight as a degenerate core is forming (as probed by the central Coulomb coupling parameter) to avoid segmentation faults. + LBV_wind+hepulse caution it contains the LBV_wind+dedt_energy_rerun; additionally, at stripped HeZAMS, the thresholds to trigger MLT++ are relaxed, and several timestep controls limiting the allowed variation of lgTeff and (cell-wise) T, as well as controls limiting the allowed variation of donor envelope mass are relaxed during mass transfer to improve convergence during envelope stripping. Also removes stopping conditions for Hubble time and TAMS that would be enforced for models less massive than roughly G-type stars, relevant to single_* and CO_* grids. + ===================== ============== =========== From 6c3ea7ee387affcd1a69a1bee4abbc7d9db0e668 Mon Sep 17 00:00:00 2001 From: Camille Date: Mon, 23 Sep 2024 10:23:41 -0500 Subject: [PATCH 246/319] remove failed states from flow and raise POSYDON errors instead --- posydon/binary_evol/DT/double_CO.py | 5 +++-- posydon/binary_evol/DT/step_detached.py | 29 ++++++++----------------- posydon/binary_evol/flow_chart.py | 4 +--- posydon/popsyn/binarypopulation.py | 1 - 4 files changed, 13 insertions(+), 26 deletions(-) diff --git a/posydon/binary_evol/DT/double_CO.py b/posydon/binary_evol/DT/double_CO.py index 852c001861..98a8744249 100644 --- a/posydon/binary_evol/DT/double_CO.py +++ b/posydon/binary_evol/DT/double_CO.py @@ -104,8 +104,9 @@ def ev_contact(t, y): t_final = [t_inspiral] if s.status == -1: - binary.state += ' (Integration failure)' - raise NumericalError("Integration failed: " + s.message) + failed_state = binary.state + set_binary_to_failed(binary) + raise NumericalError(f"Integration failed for {failed_state} DCO!") elif s.status == 1: diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index 87d424c11e..cd2e708522 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -1169,20 +1169,15 @@ def get_star_data(binary, star1, star2, htrack, interp1d_pri = get_star_data( binary, primary, secondary, primary.htrack, False)[0] else: - raise MatchingError("During matching, the primary should either be normal or not normal." - "`non_existent_companion` should be zero.") + raise ValueError("During matching, the primary should either be normal (stellar object) or ", + "not normal (CO, nonexistent companion).") if interp1d_sec is None or interp1d_pri is None: - - if binary.state in ['detached', 'disrupted', 'merged']: - binary.state += " (GridMatchingFailed)" - if self.verbose or self.verbose == 1: - print("Failed matching") - return - else: - raise ValueError("need to add new binary state to flow chart for evolution to end correctly ", - "when grid matching fails: ", binary.state," (GridMatchingFailed)") + failed_state = binary.state + set_binary_to_failed(binary) + raise MatchingError(f"Grid matching failed for {failed_state} binary.") + t0_sec = interp1d_sec["t0"] t0_pri = interp1d_pri["t0"] @@ -1508,15 +1503,9 @@ def get_omega(star, is_secondary = True): print("solution of ODE", s) if s.status == -1: - if self.verbose: - print("Integration failed", s.message) - - if binary.state in ['detached']: - binary.state += ' (Integration failure)' - return - else: - raise ValueError("need to add new binary state to flow chart for evolution to end correctly ", - "when detached integration fails: ", binary.state, " (Integration failure)") + failed_state = binary.state + set_binary_to_failed(binary) + raise NumericalError(f"Integration failed for {failed_state} binary.") if self.dt is not None and self.dt > 0: t = np.arange(binary.time, s.t[-1] + self.dt/2.0, self.dt)[1:] diff --git a/posydon/binary_evol/flow_chart.py b/posydon/binary_evol/flow_chart.py index f949874cc9..54e54d1874 100644 --- a/posydon/binary_evol/flow_chart.py +++ b/posydon/binary_evol/flow_chart.py @@ -229,9 +229,7 @@ # catch states to be ended -for b in ['initial_RLOF', 'RLO2 (OutsideGrid)' - 'detached (Integration failure)', - 'detached (GridMatchingFailed)', 'disrupted (GridMatchingFailed)', 'merged (GridMatchingFailed)']: +for b in ['initial_RLOF']: for s1 in STAR_STATES_ALL: for s2 in STAR_STATES_ALL: for e in BINARY_EVENTS_ALL: diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index 0a0c6e7e2f..b1c8125357 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -77,7 +77,6 @@ 'eccentricity_scheme'] -# 'event' usually 10 but 'detached (Integration failure)' can occur HISTORY_MIN_ITEMSIZE = {'state': 30, 'event': 25, 'step_names': 21, 'S1_state': 31, 'S2_state': 31, 'mass_transfer_case': 16, From 88c28def1b9477c8a0c86d2bf79972550897627c Mon Sep 17 00:00:00 2001 From: Camille Date: Wed, 25 Sep 2024 10:51:15 -0500 Subject: [PATCH 247/319] add more info to DCO error message --- posydon/binary_evol/DT/double_CO.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/posydon/binary_evol/DT/double_CO.py b/posydon/binary_evol/DT/double_CO.py index 98a8744249..147457ff85 100644 --- a/posydon/binary_evol/DT/double_CO.py +++ b/posydon/binary_evol/DT/double_CO.py @@ -106,7 +106,7 @@ def ev_contact(t, y): if s.status == -1: failed_state = binary.state set_binary_to_failed(binary) - raise NumericalError(f"Integration failed for {failed_state} DCO!") + raise NumericalError(f"Integration failed for {failed_state} DCO ({self.state1}, {self.state2}): ", s.message) elif s.status == 1: From f3cc8043aa87111baea496b8160e94d97346c70d Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 26 Sep 2024 17:09:10 +0200 Subject: [PATCH 248/319] Update independent_sample.py (#394) Add default value for orbital_scheme and update doc-string --- posydon/popsyn/independent_sample.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/posydon/popsyn/independent_sample.py b/posydon/popsyn/independent_sample.py index 14dde9c616..e5b004106e 100644 --- a/posydon/popsyn/independent_sample.py +++ b/posydon/popsyn/independent_sample.py @@ -17,13 +17,13 @@ from posydon.utils.common_functions import rejection_sampler -def generate_independent_samples(orbital_scheme, **kwargs): +def generate_independent_samples(orbital_scheme='period', **kwargs): """Randomly generate a population of binaries at ZAMS. Parameters ---------- - number_of_binaries : int - Number of binaries that require randomly sampled orbital separations + orbital_scheme : str (default: 'period') + The scheme to use to get either orbital periods or separations **kwargs : dictionary kwargs from BinaryPopulation class From 215128efb2d4572dc4814254de5ac03f44bede2b Mon Sep 17 00:00:00 2001 From: sgossage Date: Thu, 26 Sep 2024 17:09:27 -0500 Subject: [PATCH 249/319] Update posydon-run-pipeline (#396) update SHA for hepulse rerun -- fixing seg faults that could occur when storing variables for point masses --- bin/posydon-run-pipeline | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bin/posydon-run-pipeline b/bin/posydon-run-pipeline index f7b7845ea1..a462d8e823 100644 --- a/bin/posydon-run-pipeline +++ b/bin/posydon-run-pipeline @@ -702,7 +702,7 @@ def copy_ini_file(grid_type, rerun_metallicity, rerun_type, destination, elif rerun_type == 'LBV_wind+dedt_energy_eqn': replace_text = "seth_lbv_thermo_dedt-936f76f82d44b16139ff89e41e5b0f2232fd9b30" elif rerun_type == 'LBV_wind+dedt_hepulse': - replace_text = "seth_lbv_thermo_dedt_hepulse-77640b5f773825129d6e3bf29267e52c90f328a2" + replace_text = "seth_lbv_thermo_dedt_hepulse-2737b3bce69066751477c802a6e515183e13d303" elif rerun_type == 'no_age_limit': replace_text = "matthias_no_age_limit-a119ea9548cca9b6b7ddfe31309ced1ae40c271e" elif rerun_type == 'no_age_limit+thermohaline_mixing': From 91b8bcd7113b2773e6085e6e27bd67a6a40f5577 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Tue, 1 Oct 2024 10:46:54 +0200 Subject: [PATCH 250/319] Relax number formating for mesa inputs (#401) * Update posydon-run-grid Always use exponential writing for the `inlist_grid_points` and replace `e` by `d`, which is needed for FORTRAN. * Update posydon-run-grid Ensure, that only the float is in the replacement and adopt other cases where inputs for MESA are written. --- bin/posydon-run-grid | 30 ++++++++++++++++++++---------- 1 file changed, 20 insertions(+), 10 deletions(-) diff --git a/bin/posydon-run-grid b/bin/posydon-run-grid index 0d4a1d6396..84c138101b 100755 --- a/bin/posydon-run-grid +++ b/bin/posydon-run-grid @@ -262,7 +262,8 @@ def create_binary_inlists(binary_controls, binary_job, inlist_grid_points.write("&binary_controls\n") for k, v in binary_controls.items(): if (type(v) == float) and (not v.is_integer()): - inlist_grid_points.write("\t{0} = {1:.10f}d0\n".format(k, v)) + inlist_grid_points.write("\t{0} = ".format(k)+"{0:.10e}\n"\ + .format(v).replace('e','d')) elif (type(v) == float) and (v.is_integer()): inlist_grid_points.write("\t{0} = {1}\n".format(k, int(v))) elif type(v) == bool: @@ -279,7 +280,8 @@ def create_binary_inlists(binary_controls, binary_job, inlist_grid_points.write("&binary_job\n") for k, v in binary_job.items(): if (type(v) == float) and (not v.is_integer()): - inlist_grid_points.write("\t{0} = {1:.10f}d0\n".format(k, v)) + inlist_grid_points.write("\t{0} = ".format(k)+"{0:.10e}\n"\ + .format(v).replace('e','d')) elif (type(v) == float) and (v.is_integer()): inlist_grid_points.write("\t{0} = {1}\n".format(k, int(v))) elif type(v) == bool: @@ -300,7 +302,8 @@ def create_binary_inlists(binary_controls, binary_job, inlist_grid_points.write("&controls\n") for k, v in star1_binary_controls.items(): if (type(v) == float) and (not v.is_integer()): - inlist_grid_points.write("\t{0} = {1:.10f}d0\n".format(k, v)) + inlist_grid_points.write("\t{0} = ".format(k)+"{0:.10e}\n"\ + .format(v).replace('e','d')) elif (type(v) == float) and (v.is_integer()): inlist_grid_points.write("\t{0} = {1}\n".format(k, int(v))) elif type(v) == bool: @@ -321,7 +324,8 @@ def create_binary_inlists(binary_controls, binary_job, inlist_grid_points.write("&controls\n") for k, v in star2_binary_controls.items(): if (type(v) == float) and (not v.is_integer()): - inlist_grid_points.write("\t{0} = {1:.10f}d0\n".format(k, v)) + inlist_grid_points.write("\t{0} = ".format(k)+"{0:.10e}\n"\ + .format(v).replace('e','d')) elif (type(v) == float) and (v.is_integer()): inlist_grid_points.write("\t{0} = {1}\n".format(k, int(v))) elif type(v) == bool: @@ -342,7 +346,8 @@ def create_binary_inlists(binary_controls, binary_job, inlist_grid_points.write("&star_job\n") for k, v in star1_binary_job.items(): if (type(v) == float) and (not v.is_integer()): - inlist_grid_points.write("\t{0} = {1:.10f}d0\n".format(k, v)) + inlist_grid_points.write("\t{0} = ".format(k)+"{0:.10e}\n"\ + .format(v).replace('e','d')) elif (type(v) == float) and (v.is_integer()): inlist_grid_points.write("\t{0} = {1}\n".format(k, int(v))) elif type(v) == bool: @@ -363,7 +368,8 @@ def create_binary_inlists(binary_controls, binary_job, inlist_grid_points.write("&star_job\n") for k, v in star2_binary_job.items(): if (type(v) == float) and (not v.is_integer()): - inlist_grid_points.write("\t{0} = {1:.10f}d0\n".format(k, v)) + inlist_grid_points.write("\t{0} = ".format(k)+"{0:.10e}\n"\ + .format(v).replace('e','d')) elif (type(v) == float) and (v.is_integer()): inlist_grid_points.write("\t{0} = {1}\n".format(k, int(v))) elif type(v) == bool: @@ -416,14 +422,18 @@ def create_star_formation(mass, work_dir, star_inlist_project, initial_z=None, n Pwarn('Replace '+filename, "OverwriteWarning") with open(filename, 'w') as inlist_grid_points: inlist_grid_points.write("&controls\n") - inlist_grid_points.write("initial_mass = {0:.10f}d0\n".format(mass)) + inlist_grid_points.write("initial_mass = "+"{0:.10e}\n"\ + .format(mass).replace('e','d')) if initial_z is not None: - inlist_grid_points.write("initial_z = {0:.10f}d0\n".format(initial_z)) - inlist_grid_points.write("Zbase = {0:.10f}d0\n".format(initial_z)) + inlist_grid_points.write("initial_z = "+"{0:.10e}\n"\ + .format(initial_z).replace('e','d')) + inlist_grid_points.write("Zbase = "+"{0:.10e}\n"\ + .format(initial_z).replace('e','d')) inlist_grid_points.write("/ ! end of star_controls namelist\n") inlist_grid_points.write("&star_job\n") if new_Z: - inlist_grid_points.write("new_Z = {0:.10f}d0\n".format(initial_z)) + inlist_grid_points.write("new_Z = "+"{0:.10e}\n"\ + .format(initial_z).replace('e','d')) inlist_grid_points.write("/ ! end of star_job namelist\n") inlist_grid_points.close() From 7072e2e54b88b2ca119eb82ef268764fa6c08a4e Mon Sep 17 00:00:00 2001 From: Philipp Moura Srivastava Date: Thu, 3 Oct 2024 10:14:09 -0500 Subject: [PATCH 251/319] Update IF_interpolation.py (#399) * Update IF_interpolation.py Updated k to be a minimum of 3 to prioritize smoothness * Update IF_interpolation.py so k is at least 3 * Update IF_interpolation.py refinement to make k = 3 * Update IF_interpolation.py --- posydon/interpolation/IF_interpolation.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/posydon/interpolation/IF_interpolation.py b/posydon/interpolation/IF_interpolation.py index a223749ac0..686ca72d44 100644 --- a/posydon/interpolation/IF_interpolation.py +++ b/posydon/interpolation/IF_interpolation.py @@ -1432,6 +1432,9 @@ def xtrain(self, XT, yT, **opts): n_opt = np.argmax(acc) + 1 + if n_opt < 3: # k must be at least 3 + n_opt = 3 + self.train(XT, yT, K=n_opt) From 04d8816f4de8559fe18bfd1756898ac444cead3c Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Tue, 8 Oct 2024 14:52:44 +0200 Subject: [PATCH 252/319] Update setup.py Add additional numpy requirement --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 6eb2256bb7..33d6cb99f5 100644 --- a/setup.py +++ b/setup.py @@ -64,7 +64,7 @@ # the correct way to do this is to make sure that they are available on # conda and pip for all platforms we support (see prerequisites doc page). install_requires = [ - 'numpy >= 1.24.2', + 'numpy >= 1.24.2,<2.0.0', 'scipy >= 1.10.1', 'iminuit >= 2.21.3', 'configparser >= 5.3.0', From 9b203c09e6f22d1bbca2995d83926a9b534f2b60 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Mon, 14 Oct 2024 13:28:45 +0200 Subject: [PATCH 253/319] Add "last" to TF12 for stable MT (#405) --- posydon/visualization/plot_defaults.py | 44 +++++++++++++------------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/posydon/visualization/plot_defaults.py b/posydon/visualization/plot_defaults.py index 362cc1b0d2..2eddd0774b 100644 --- a/posydon/visualization/plot_defaults.py +++ b/posydon/visualization/plot_defaults.py @@ -466,50 +466,50 @@ 'Stable contact': ['s', 2, list_of_colors[3], 'Stable contact phase'], 'Stable case A': - ['s', 2, list_of_colors[2], 'Stable RLOF during MS'], + ['s', 2, list_of_colors[2], 'Last stable RLOF during MS'], 'Stable case B': - ['s', 2, list_of_colors[1], 'Stable RLOF during postMS'], + ['s', 2, list_of_colors[1], 'Last stable RLOF during postMS'], 'Stable case C': - ['s', 2, list_of_colors[1], 'Stable RLOF during postMS'], + ['s', 2, list_of_colors[1], 'Last stable RLOF during postMS'], 'Stable case BA': - ['s', 2, list_of_colors[0], 'Stable RLOF during stripped He star'], + ['s', 2, list_of_colors[0], 'Last stable RLOF during stripped He star'], 'Stable case BB': - ['s', 2, list_of_colors[0], 'Stable RLOF during stripped He star'], + ['s', 2, list_of_colors[0], 'Last stable RLOF during stripped He star'], # hot fix for case AA should be removed later: 'Stable case AA': - ['s', 2, list_of_colors[2], 'Stable RLOF during MS'], + ['s', 2, list_of_colors[2], 'Last stable RLOF during MS'], 'Stable case AB': - ['s', 2, list_of_colors[1], 'Stable RLOF during postMS'], + ['s', 2, list_of_colors[1], 'Last stable RLOF during postMS'], 'Stable case AC': - ['s', 2, list_of_colors[1], 'Stable RLOF during postMS'], + ['s', 2, list_of_colors[1], 'Last stable RLOF during postMS'], 'Stable case An': - ['s', 2, list_of_colors[1], 'Stable RLOF during postMS'], + ['s', 2, list_of_colors[1], 'Last stable RLOF during postMS'], 'Stable case ABA': - ['s', 2, list_of_colors[0], 'Stable RLOF during stripped He star'], + ['s', 2, list_of_colors[0], 'Last stable RLOF during stripped He star'], 'Stable case ABB': - ['s', 2, list_of_colors[0], 'Stable RLOF during stripped He star'], + ['s', 2, list_of_colors[0], 'Last stable RLOF during stripped He star'], 'Stable case BC': - ['s', 2, list_of_colors[1], 'Stable RLOF during postMS'], + ['s', 2, list_of_colors[1], 'Last stable RLOF during postMS'], 'Stable case Bn': - ['s', 2, list_of_colors[1], 'Stable RLOF during postMS'], + ['s', 2, list_of_colors[1], 'Last stable RLOF during postMS'], 'Stable case BBA': - ['s', 2, list_of_colors[0], 'Stable RLOF during stripped He star'], + ['s', 2, list_of_colors[0], 'Last stable RLOF during stripped He star'], 'Stable case BBB': - ['s', 2, list_of_colors[0], 'Stable RLOF during stripped He star'], + ['s', 2, list_of_colors[0], 'Last stable RLOF during stripped He star'], 'Stable case Cn': - ['s', 2, list_of_colors[1], 'Stable RLOF during postMS'], + ['s', 2, list_of_colors[1], 'Last stable RLOF during postMS'], 'Stable case CBA': - ['s', 2, list_of_colors[0], 'Stable RLOF during stripped He star'], + ['s', 2, list_of_colors[0], 'Last stable RLOF during stripped He star'], 'Stable case CBB': - ['s', 2, list_of_colors[0], 'Stable RLOF during stripped He star'], + ['s', 2, list_of_colors[0], 'Last stable RLOF during stripped He star'], 'Stable case BABB': - ['s', 2, list_of_colors[0], 'Stable RLOF during stripped He star'], + ['s', 2, list_of_colors[0], 'Last stable RLOF during stripped He star'], 'Stable case BAn': - ['s', 2, list_of_colors[0], 'Stable RLOF during stripped He star'], + ['s', 2, list_of_colors[0], 'Last stable RLOF during stripped He star'], 'Stable case BBn': - ['s', 2, list_of_colors[0], 'Stable RLOF during stripped He star'], + ['s', 2, list_of_colors[0], 'Last stable RLOF during stripped He star'], 'Stable case n': - ['s', 2, list_of_colors[1], 'Stable RLOF while non burning'], + ['s', 2, list_of_colors[1], 'Last stable RLOF while non burning'], 'Unstable contact': ['D', 1, list_of_colors[3], 'Unstable contact phase'], 'Unstable case A': From 13448f093ec79a2499bf5a72e5134e5b72a58424 Mon Sep 17 00:00:00 2001 From: Elizabeth Teng <41969755+elizabethteng@users.noreply.github.com> Date: Tue, 15 Oct 2024 14:15:21 -0500 Subject: [PATCH 254/319] update profile interpolation for consistency with paper (#397) * update for consistency with updated methods for paper * make nan warning more specific * make prediction inputs in linear space to match IF interpolation * add customizable nn shape to composition profiles * streamline Composition code for learn_bounds(), separate H-rich H-burning profiles from H-rich advanced burning profiles, add code to customize neural network shape for composition profiles * set up prediction framework for Composition that creates the 'H+He' profile first and subtracts H profile to get He profile * separate density profiles and composition profiles training * replace np.isnan with pd.isna * change InappropriateValueWarning to IncompletenessWarning in the case of nans in final values * typo * set random seed value to None by default to make it optional, update docstring * change 'lr' variable name to 'learning_rate' * added warning for if stars that should have a flat profile are not flat * eliminated redundant line * fixed docstring for density class * clarified the order of returned objects, added a condition for case where no models are selected for training * added returns so as to not exit functions after posydon warning * tie up loose ends * corrected issue with an error check specific to one parameter * small change to definition of scalars to comply with new pd command * fixed double equal sign that should be single equal sign * fix ref to predicted mass transfer classes * add empty array for predictions * fix issue with predictions * remove unnecessary print statement --- .../interpolation/profile_interpolation.py | 622 ++++++++++-------- 1 file changed, 364 insertions(+), 258 deletions(-) diff --git a/posydon/interpolation/profile_interpolation.py b/posydon/interpolation/profile_interpolation.py index 819058d4e6..05c2f43990 100644 --- a/posydon/interpolation/profile_interpolation.py +++ b/posydon/interpolation/profile_interpolation.py @@ -13,6 +13,8 @@ from posydon.utils.posydonwarning import Pwarn # Math and ML +import os +import random import numpy as np import pandas as pd try: @@ -20,11 +22,10 @@ except ImportError: raise ImportError('tensorflow is not installed. Please run `pip install .[ml]` in the POSYDON base directory') tf.get_logger().setLevel('ERROR') -from tensorflow.keras import layers, losses, models, optimizers +from tensorflow.keras import layers, losses, models, optimizers, backend, utils from sklearn.decomposition import PCA from scipy.interpolate import interp1d - class CompileData: def __init__(self, train_path, test_path, hms_s2=False, @@ -50,7 +51,8 @@ def __init__(self, train_path, test_path, hms_s2=False, for i in range(len(test)): try: scalars,profiles = self.scrape(test,i,hms_s2) - self.test_scalars = self.test_scalars.append(scalars,ignore_index=True) + self.test_scalars = pd.concat([self.test_scalars, + pd.DataFrame(scalars)],ignore_index=True) self.test_profiles.append(profiles) except: testing_failed.append(i) @@ -70,7 +72,8 @@ def __init__(self, train_path, test_path, hms_s2=False, for i in range(len(train)): try: scalars,profiles = self.scrape(train,i,hms_s2) - self.scalars = self.scalars.append(scalars,ignore_index=True) + self.scalars = pd.concat([self.scalars, + pd.DataFrame(scalars)],ignore_index=True) self.profiles.append(profiles) except: training_failed.append(i) @@ -110,14 +113,14 @@ def scrape(self,grid,ind,hms_s2): df = df.reset_index(drop=True) # grab input values, final star 1 state, final star 1 mass - total_mass = grid.final_values[mass_key][ind] - - scalars = {"m1":grid.initial_values["star_1_mass"][ind], - "m2":grid.initial_values["star_2_mass"][ind], - "p":grid.initial_values["period_days"][ind], - "MT_class":grid.final_values["interpolation_class"][ind], - "star_state":grid.final_values[state_key][ind], - "total_mass":total_mass} + total_mass = df["mass"].iloc[-1] + + scalars = {"m1":[grid.initial_values["star_1_mass"][ind]], + "m2":[grid.initial_values["star_2_mass"][ind]], + "p":[grid.initial_values["period_days"][ind]], + "MT_class":[grid.final_values["interpolation_class"][ind]], + "star_state":[grid.final_values[state_key][ind]], + "total_mass":[total_mass]} # grab output vectors, interpolate to normalize profiles=np.zeros([1+len(self.names),200]) @@ -134,9 +137,16 @@ def scrape(self,grid,ind,hms_s2): if 'omega' in self.names: profiles[-1]= profiles[self.names.index('omega')]/ \ - grid.final_values['S1_surf_avg_omega_div_omega_crit'][ind] + (grid.final_values['S1_surf_avg_omega'][ind]/ \ + grid.final_values['S1_surf_avg_omega_div_omega_crit'][ind]) - return scalars, profiles + if not pd.isna(grid.final_values['S1_surf_avg_omega_div_omega_crit'][ind]): + return scalars, profiles + else: + Pwarn("nan in final values, binary will not be" + "included in file","IncompletenessWarning") + else: + return scalars, profiles def save(self, filename): """Save extracted profile data. @@ -149,13 +159,22 @@ def save(self, filename): for key in SAVE_ATTRS if key not in DONT_SAVE} with open(filename, 'wb') as f: pickle.dump(myattrs, f) - + class ProfileInterpolator: - def __init__(self): + def __init__(self,seed_value=None): """Interfaces with other classes, trains models and predicts profiles. + Args: + seed_value (None or int) : random seed to ensure consistent results + default value for Teng+24 is 1234 """ + if seed_value is not None: + os.environ['PYTHONHASHSEED']=str(seed_value) + random.seed(seed_value) + np.random.seed(seed_value) + tf.random.set_seed(seed_value) + utils.set_random_seed(seed_value) def load_profiles(self,filename,valid_split=0.2): """Load and process extracted profile data. @@ -192,61 +211,104 @@ def load_profiles(self,filename,valid_split=0.2): self.initial = np.log10(linear_initial)[binaries[split:]] self.scalars = self.scalars.iloc[binaries[split:]] - def train(self,IF_interpolator,density_epochs=3000,density_patience=200, - comp_bounds_epochs=500,comp_bounds_patience=50,loss_history=False,hms_s2=False): + def train(self,IF_interpolator,train_density=True,train_comp=True,density_epochs=1000, + density_patience=200,comp_bounds_epochs=500,comp_bounds_patience=50,loss_history=False, + hms_s2=False,depth=12,width=256,depthn=12,widthn=256,learning_rate=0.0001): """Trains models for density, H mass fraction, and He mass fraction profile models. Args: IF_interpolator (str) : path to '.pkl' file for IF interpolator. + train_density (Boolean) : option to train Density model + train_comp (Boolean) : option to train Composition model density_epochs (int) : number of epochs used to train density profile model density_patience (int) : patience parameter for NN callback in density profile model comp_bounds_epochs (int) : number of epochs used to train composition profiles model comp_bounds_patience (int) : patience parameter for NN callback in composition profiles model loss_history (Boolean) : option to return training and validation loss histories hms_s2 (Boolean) : option to do profiles of star 2 in HMS-HMS grid + depth (int) : depth of neural network for principal component weights + width (int) : width of neural network for principal component weights + depthn (int) : depth of neural network for normalizing value + widthn (int) : width of neural network for normalizing value + learning_rate (float) : learning rate for neural network training Returns: + self.dens.loss_history (array-like) : training and validation loss history for density profiles. + Returned first if both models are trained. self.comp.loss_history (array-like) : training and validation loss history for composition profiles - self.dens.loss_history (array-like) : training and validation loss history for density profiles + Returned second if both models are trained. - """ - # instantiate and train composition (H and He mass fraction) profiles model - self.comp = Composition(self.initial, - self.profiles[:,self.names.index("x_mass_fraction_H")], - self.profiles[:,self.names.index("y_mass_fraction_He")], - self.scalars["star_state"], - self.valid_initial, - self.valid_profiles[:,self.names.index("x_mass_fraction_H")], - self.valid_profiles[:,self.names.index("y_mass_fraction_He")], - self.valid_scalars["star_state"], - IF_interpolator, - comp_bounds_epochs,comp_bounds_patience,hms_s2) - - # instantiate and train density profile model - self.dens = Density(self.initial, - self.profiles[:,self.names.index("logRho")], - self.valid_initial, - self.valid_profiles[:,self.names.index("logRho")], - IF_interpolator,hms_s2=hms_s2) - self.dens.train(prof_epochs=density_epochs,prof_patience=density_patience) + """ + self.train_comp=train_comp + self.train_density=train_density + + if train_comp==True: + # instantiate and train composition (H and He mass fraction) profiles model + self.comp = Composition(self.initial, + self.profiles[:,self.names.index("x_mass_fraction_H")], + self.profiles[:,self.names.index("y_mass_fraction_He")], + self.scalars["star_state"], + self.valid_initial, + self.valid_profiles[:,self.names.index("x_mass_fraction_H")], + self.valid_profiles[:,self.names.index("y_mass_fraction_He")], + self.valid_scalars["star_state"], + IF_interpolator, + comp_bounds_epochs,comp_bounds_patience,hms_s2) + if train_density==True: + # instantiate and train density profile model + self.dens = Density(self.initial, + self.profiles[:,self.names.index("logRho")], + self.scalars["MT_class"], + self.valid_initial, + self.valid_profiles[:,self.names.index("logRho")], + self.valid_scalars["MT_class"], + IF_interpolator,hms_s2=hms_s2, + depth=depth, width=width, + depthn=depthn, widthn=widthn) + self.dens.train(prof_epochs=density_epochs, + prof_patience=density_patience, + learning_rate=learning_rate) if loss_history==True: - return self.comp.loss_history, self.dens.loss_history - + if train_comp==True and train_density==True: + return self.dens.loss_history, self.comp.loss_history + elif train_comp!=True and train_density==True: + return self.dens.loss_history + elif train_comp==True and train_density!=True: + return self.comp.loss_history + else: + PWarn("No models selected for training","IncompletenessWarning") + return + def predict(self,inputs): """Predict density, H mass fraction, and He mass fraction profiles from inputs. Args: - inputs (array-like) : log-space initial conditions of N binaries to predict, shape (N,3). + inputs (array-like) : positive linear-space initial conditions of N binaries to predict, shape (N,3). + density (Boolean) : option to train Density model + comp (Boolean) : option to train Composition model Returns: - mass_coords (array-like) : linear-scale mass enclosed profile coordinates. - density_profiles (array-like) : log-scale density profile coordinates. - h_profiles (array-like) : H mass fraction profile coordinates - he_profiles (array-like) : He mass fraction profile coordinates + mass_coords (array-like) : linear-scale mass enclosed profile coordinates. + Returned first. + density_profiles (array-like) : log-scale density profile coordinates. + Returned second if density model is trained. + h_profiles (array-like) : H mass fraction profile coordinates. + Returned after mass_coords (and density_profiles) if composition model is trained. + he_profiles (array-like) : He mass fraction profile coordinates. + Returned after h_profiles if composition model is trained. """ - mass_coords, density_profiles = self.dens.predict(inputs) - mass_coords, h_profiles, he_profiles = self.comp.predict(inputs) - - return mass_coords, density_profiles, h_profiles, he_profiles - - + if self.train_density==True: + mass_coords, density_profiles = self.dens.predict(inputs) + if self.train_comp==True: + mass_coords, h_profiles, he_profiles = self.comp.predict(inputs) + + if self.train_comp==True and self.train_density==True: + return mass_coords, density_profiles, h_profiles, he_profiles + elif self.train_comp!=True and self.train_density==True: + return mass_coords, density_profiles + elif self.train_comp==True and self.train_density!=True: + return mass_coords, h_profiles, he_profiles + else: + PWarn("No models were trained","IncompletenessWarning") + return + def save(self, filename): """Save complete profiles interpolation model. Args: @@ -296,124 +358,144 @@ def mono_renorm(arr): return profiles_mono - - class Density: - def __init__(self,initial,profiles,valid_initial, - valid_profiles,IF_interpolator,n_comp=8, hms_s2=False): + def __init__(self,initial,profiles,mt,valid_initial, + valid_profiles,valid_mt,IF_interpolator,n_comp=8, hms_s2=False, + depth=12,width=256,depthn=12,widthn=256): """Creates and trains density profile model. Args: initial (array-like) : log-space initial conditions for training data. profiles (array-like) : final density profiles for training data. + mt (array-like) : mass transfer classes corresponding to training set binaries. valid_initial (array-like) : log-space initial conditions for validation data. valid_profiles (array-like) : final density profiles for validation data. + valid_mt (array-like): mass transfer classes corresponding to validation set binaries. IF_interpolator (string) : path to .pkl file for IF interpolator for central density, final mass values n_comp (int) : number of PCA components. hms_s2 (Boolean) : option to do profiles of star 2 in HMS-HMS grid + depth (int) : depth of neural network for principal component weights + width (int) : width of neural network for principal component weights + depthn (int) : depth of neural network for normalizing value + widthn (int) : width of neural network for normalizing value """ self.n_comp = n_comp self.hms_s2 = hms_s2 # process training data self.initial = initial # initial conditions in log space - self.rho_min = np.min(profiles,axis=1) - rho_max = np.max(profiles,axis=1) - profiles_norm = (profiles-self.rho_min[:,np.newaxis])\ - /(rho_max-self.rho_min)[:,np.newaxis] # minmax normalized profiles - self.pca = PCA(n_components=self.n_comp).fit(profiles_norm) # perform principal component analysis - pca_weights_unscaled = self.pca.transform(profiles_norm) - self.scaling = np.std(pca_weights_unscaled,axis=0) - self.pca_weights = pca_weights_unscaled/self.scaling # scaled PCA weights + self.mt = mt + self.surf_val = profiles[:,-1] + self.center_val = profiles[:,0] # process validation data self.valid_initial = valid_initial - self.valid_rho_min = np.min(valid_profiles,axis=1) - valid_rho_max = np.max(valid_profiles,axis=1) - valid_profiles_norm = (valid_profiles-self.valid_rho_min[:,np.newaxis])\ - /(valid_rho_max-self.valid_rho_min)[:,np.newaxis] - valid_pca_weights_unscaled = self.pca.transform(valid_profiles_norm) - self.valid_pca_weights = valid_pca_weights_unscaled/self.scaling - - # instantiate models - self.model_prof = models.Sequential([ - layers.Dense(15,input_dim=3,activation=None), - layers.Dense(15,input_dim=15,activation="relu"), - layers.Dense(15,input_dim=15,activation="relu"), - layers.Dense(15,input_dim=15,activation="tanh"), - layers.Dense(15,input_dim=15,activation="tanh"), - layers.Dense(10,input_dim=10,activation="tanh"), - layers.Dense(10,input_dim=10,activation="tanh"), - layers.Dense(n_comp,input_dim=10,activation=None)]) + self.valid_mt = valid_mt + self.valid_surf_val = valid_profiles[:,-1] + self.valid_center_val = valid_profiles[:,0] + + # process profiles for modeling + profiles_norm = (profiles-self.surf_val[:,np.newaxis])\ + /(self.center_val-self.surf_val)[:,np.newaxis] # minmax normalized profiles + valid_profiles_norm = (valid_profiles-self.valid_surf_val[:,np.newaxis])\ + /(self.valid_center_val-self.valid_surf_val)[:,np.newaxis] + + self.pca = PCA(n_components=self.n_comp).fit(profiles_norm) # perform PCA + self.pca_weights = self.pca.transform(profiles_norm) + self.valid_pca_weights = self.pca.transform(valid_profiles_norm) + + # instantiate model + self.prof_models = {} + for mt in self.mt.unique(): + model = models.Sequential() + model.add(layers.Dense(width,input_dim=3,activation='relu')) + for i in range(depth-1): + model.add(layers.Dense(width,input_dim=width,activation='relu')) + model.add(layers.Dense(n_comp,input_dim=width,activation=None)) + self.prof_models[mt] = model - self.model_rho = models.Sequential([ - layers.Dense(10,input_dim=3,activation='relu'), - layers.Dense(10,input_dim=10,activation='relu'), - layers.Dense(10,input_dim=10,activation='tanh'), - layers.Dense(10,input_dim=10,activation='tanh'), - layers.Dense(10,input_dim=10,activation='tanh'), - layers.Dense(10,input_dim=10,activation='tanh'), - layers.Dense(1,activation=None)]) + self.model_norm = models.Sequential() + self.model_norm.add(layers.Dense(widthn,input_dim=3,activation="relu")) + for i in range(depthn-1): + self.model_norm.add(layers.Dense(widthn,input_dim=widthn,activation='relu')) + self.model_norm.add(layers.Dense(1,input_dim=widthn,activation=None)) self.model_IF = IFInterpolator() # instantiate POSYDON initial-final interpolator object self.model_IF.load(filename=IF_interpolator) - - def train(self,loss=losses.MeanSquaredError(),prof_epochs=3000,prof_patience=200): + self.interp = self.model_IF.interpolators[0] + + def train(self,loss=losses.MeanSquaredError(),prof_epochs=1000,prof_patience=200,learning_rate=0.0001): """Trains NN models. Args: loss (object) : loss function for training. prof_epochs (int) : number of epochs used to train neural network prof_patience (int) : patience parameter for callback in neural network + learning_rate (float) : learning rate for neural network training """ print("training on PCA weights...") - self.model_prof.compile(optimizers.Adam(clipnorm=1),loss=loss) - callback = tf.keras.callbacks.EarlyStopping(monitor="loss",patience=prof_patience) - history = self.model_prof.fit(self.initial,self.pca_weights, - epochs=prof_epochs,callbacks=[callback],verbose=0, - validation_data=(self.valid_initial, - self.valid_pca_weights)) - - self.model_rho.compile(optimizers.Adam(clipnorm=1),loss=loss) + self.loss_history = {} + for mt in self.mt.unique(): + if mt=="not_converged" or mt=="initial_MT": + pass + else: + print(mt) + inds = np.where(self.mt==mt)[0] + valid_inds = np.where(self.valid_mt==mt)[0] + self.prof_models[mt].compile(optimizers.Adam(clipnorm=1,learning_rate=learning_rate),loss=loss) + callback = tf.keras.callbacks.EarlyStopping(monitor="loss",patience=prof_patience) + history = self.prof_models[mt].fit(self.initial[inds],self.pca_weights[inds], + epochs=prof_epochs,callbacks=[callback],verbose=0, + validation_data=(self.valid_initial[valid_inds], + self.valid_pca_weights[valid_inds])) + self.loss_history[mt] = np.array([history.history['loss'],history.history['val_loss']]) + + self.model_norm.compile(optimizers.Adam(clipnorm=1,learning_rate=learning_rate),loss=loss) callback = tf.keras.callbacks.EarlyStopping(monitor="loss",patience=40) - history = self.model_rho.fit(self.initial,self.rho_min, - epochs=500, callbacks=[callback], verbose=0, - validation_data=(self.valid_initial, - self.valid_rho_min)) - - self.loss_history = np.array([history.history['loss'],history.history['val_loss']]) + self.model_norm.fit(self.initial,self.surf_val,epochs=300,callbacks=[callback], + validation_data = (self.valid_initial,self.valid_surf_val)) print("done training") def predict(self,inputs): """Predicts profile for n sets of given inputs, in array of shape (n,3). Args: - inputs (array-like) : log-space initial conditions of N binaries to predict, shape (N,3). + inputs (array-like) : positive linear-space initial conditions of N binaries to predict, shape (N,3). Returns: mass_coords (array-like) : linear-scale mass enclosed profile coordinates. density_profiles (array_like) : log-scale density profile coordinates. """ - # predict PCA weights - regress_prof = lambda x: self.model_prof(x) - pca_weights_pred = regress_prof(inputs).numpy() + pred_profiles = np.zeros([len(inputs),200]) + + # predict MT class + pred_mt = self.interp.test_classifiers(inputs)['interpolation_class'] + for mt in self.mt.unique(): + inds = np.where(pred_mt==mt)[0] + if mt=="not_converged" or mt=="initial_MT": + pred_profiles[inds] = np.ones([len(inds),200])*np.nan + else: + # predict PCA weights + regress_prof = lambda x: self.prof_models[mt](x) + pca_weights_pred = regress_prof(np.log10(inputs[inds])).numpy() + pca_pred = self.pca.inverse_transform(pca_weights_pred) + pred_profiles[inds] = pca_pred # predict surface density - regress_rho = lambda x: self.model_rho(x) - min_rho = regress_rho(inputs).numpy()[:,0] + regress_norm = lambda x: self.model_norm(x) + min_rho = regress_norm(np.log10(inputs)).numpy()[:,0] # IF interpolate final mass, center density if self.hms_s2==False: - m_ind = self.model_IF.interpolators[0].out_keys.index("star_1_mass") - center_ind = self.model_IF.interpolators[0].out_keys.index('S1_log_center_Rho') + m_ind = self.interp.out_keys.index("star_1_mass") + center_ind = self.interp.out_keys.index('S1_log_center_Rho') else: - m_ind = self.model_IF.interpolators[0].out_keys.index("star_2_mass") - center_ind = self.model_IF.interpolators[0].out_keys.index('S2_log_center_Rho') - max_rho = self.model_IF.interpolators[0].test_interpolator(10**inputs)[:,center_ind] - pred_mass = self.model_IF.interpolators[0].test_interpolator(10**inputs)[:,m_ind] + m_ind = self.interp.out_keys.index("star_2_mass") + center_ind = self.interp.out_keys.index('S2_log_center_Rho') + max_rho = self.interp.test_interpolator(inputs)[:,center_ind] + pred_mass = self.interp.test_interpolator(inputs)[:,m_ind] # reconstruct profile - norm_prof = self.pca.inverse_transform(pca_weights_pred*self.scaling) - density_profiles = norm_prof*(max_rho[:,np.newaxis]-min_rho[:,np.newaxis]) \ + density_profiles = pred_profiles*(max_rho[:,np.newaxis]-min_rho[:,np.newaxis]) \ + min_rho[:,np.newaxis] # construct mass enclosed profile coordinates @@ -426,7 +508,8 @@ class Composition: def __init__(self,initial,h_profiles,he_profiles,star_state, valid_initial,valid_h_profiles,valid_he_profiles,valid_star_state, - IF_interpolator,training_epochs=500, training_patience=50,hms_s2=False): + IF_interpolator, training_epochs=500, training_patience=50,hms_s2=False, + depth=12, width=256, learning_rate=0.0001): """Creates and trains H mass fraction and He mass fraction profiles model. Args: initial (array-like) : log-space initial conditions for training data. @@ -441,6 +524,9 @@ def __init__(self,initial,h_profiles,he_profiles,star_state, training_epochs (int) : number of epochs used to train neural networks training_patience (int) : patience parameter for callback in neural networks hms_s2 (Boolean) : option to do profiles of star 2 in HMS-HMS grid + depth (int) : depth of neural network + width (int) : width of neural network + learning_rate (float) : learning rate for neural network training """ self.hms_s2 = hms_s2 @@ -492,20 +578,41 @@ def __init__(self,initial,h_profiles,he_profiles,star_state, self.valid_sort_ind[name] = np.where(self.valid_star_state==name)[0] # create and train models for profile boundaries - self.bounds_models, self.loss_history = self.learn_bounds(training_epochs,training_patience) + self.bounds_models, self.loss_history = self.learn_bounds(training_epochs, + training_patience, + depth=depth, + width=width, + learning_rate=learning_rate) - def learn_bounds(self,training_epochs,training_patience): + def learn_bounds(self,training_epochs,training_patience,depth,width,learning_rate): """Creates and trains NNs to predict boundary points for each star 1 state. Args: training_epochs (int) : number of epochs used to train neural networks training_patience (int) : patience parameter for callback in neural networks + depth (int) : depth of neural network + width (int) : width of neural network + learning_rate (float) : learning rate for neural network training Returns: b_models (array-like) : dictionary containing boundary models. loss_history (array-like) : training and validation loss histories """ b_models = {} loss_history = {} - self.empty = [] # keep track of which star states have no data + + def calc_bevel_bounds(profs): + # calculate first and last points in each profile with large increases + # i.e. boundaries of 'bevel' shape + nonflat=[] # ensures that training data only has the correct "non-flat" shape + bounds = [] # collect information on parameters for given H and He profile shapes + for i in range(len(profs)): + diff = np.where(profs[i][1:]-profs[i][:-1]>0.002)[0] + if len(diff>=2): + bounds.append(np.array([diff[0]+1,diff[-1]+1])/200) + nonflat.append(i) + else: + bounds.append(np.array([np.nan,np.nan])) + return np.array(bounds), nonflat + for state in ['stripped_He_Core_He_burning', 'stripped_He_Core_C_burning', 'stripped_He_Central_He_depleted', @@ -522,116 +629,87 @@ def learn_bounds(self,training_epochs,training_patience): inputs = self.initial[indices] h_prof = self.h_profiles[indices] he_prof = self.he_profiles[indices] + plus_prof = h_prof + he_prof # identify input/output testing data for class valid_indices = self.valid_sort_ind[state] valid_inputs = self.valid_initial[valid_indices] valid_h_prof = self.valid_h_profiles[valid_indices] valid_he_prof = self.valid_he_profiles[valid_indices] - - # calculate boundary points for training and testing data - if state in ['H-rich_Shell_H_burning', - 'H-rich_Core_H_burning', - 'H-rich_Core_He_burning', - 'H-rich_Core_C_burning']: # profiles with 2 boundary points - outs = 2 - bounds = [] # collect information on parameters for given H and He profile shapes - nonflat = [] # ensures that training data only has the correct "non-flat" shape - # to avoid issues with calculating the boundary points - valid_bounds = [] - valid_nonflat = [] - # calculate first and last points in each H profile with large increases - # (He profiles have the same boundary points) - for i in range(len(indices)): - try: - diff = np.where(h_prof[i][1:]-h_prof[i][:-1]>0.002)[0] - bounds.append(np.array([diff[0]+1,diff[-1]+1])/200) - nonflat.append(i) - except: - bounds.append([np.nan,np.nan]) - for i in range(len(valid_indices)): - try: - diff = np.where(valid_h_prof[i][1:]-valid_h_prof[i][:-1]>0.002)[0] - valid_bounds.append(np.array([diff[0]+1,diff[-1]+1])/200) - valid_nonflat.append(i) - except: - valid_bounds.append([np.nan,np.nan]) - - elif state in ['H-rich_Central_He_depleted', - 'H-rich_Central_C_depletion']: # H profiles with 1 boundary point - outs = 3 - # calculate the point in each H profile with the largest increase - hbound = (np.argmax(h_prof[:,1:]-h_prof[:,:-1],axis=1)+1)/200 - valid_hbound = (np.argmax(valid_h_prof[:,1:]-valid_h_prof[:,:-1],axis=1)+1)/200 - max_He = np.max(he_prof,axis=1) # maximum He mass fraction - valid_max_He = np.max(valid_he_prof,axis=1) - dep_He = (np.argmax(he_prof[:,1:]-he_prof[:,:-1],axis=1)+1)/200 # location of Helium depletion zone - valid_dep_He = (np.argmax(valid_he_prof[:,1:]-valid_he_prof[:,:-1],axis=1)+1)/200 - bounds = np.transpose([hbound,dep_He,max_He]) - valid_bounds = np.transpose([valid_hbound,valid_dep_He,valid_max_He]) - - elif state in ['stripped_He_Core_He_burning', - 'stripped_He_Core_C_burning', - 'stripped_He_Central_He_depleted', - 'stripped_He_Central_C_depletion']: - outs = 2 - bounds = [] - nonflat = [] # ensures that training data only has the correct "non-flat" shape - # to avoid issues with calculating the boundary points - valid_bounds = [] - valid_nonflat = [] - # calculate first and points in each profile with large increases - for i in range(len(indices)): - try: - diff = np.where(he_prof[i][1:]-he_prof[i][:-1]>0.002)[0] - bounds.append(np.array([diff[0]+1,diff[-1]+1])/200) - nonflat.append(i) - except: - bounds.append([np.nan,np.nan]) - for i in range(len(valid_indices)): - try: - diff = np.where(valid_he_prof[i][1:]-valid_he_prof[i][:-1]>0.002)[0] - valid_bounds.append(np.array([diff[0]+1,diff[-1]+1])/200) - valid_nonflat.append(i) - except: - valid_bounds.append([np.nan,np.nan]) - - # instantiate and train model on bounds - model = models.Sequential([ - layers.Dense(10,input_dim=3,activation=None), - layers.Dense(10,input_dim=10,activation='relu'), - layers.Dense(10,input_dim=10,activation='relu'), - layers.Dense(10,input_dim=10,activation='tanh'), - layers.Dense(10,input_dim=10,activation='tanh'), - layers.Dense(10,input_dim=10,activation='tanh'), - layers.Dense(outs,input_dim=10,activation="sigmoid")]) - - model.compile(optimizers.Adam(clipnorm=1),loss=losses.MeanSquaredError()) - callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=training_patience) + valid_plus_prof = valid_h_prof + valid_he_prof if len(indices)==0: Pwarn(f"no training data available for {state}", "InappropriateValueWarning") loss_history[state]=np.nan - self.empty.append(state) + + else: + + # calculate boundary points for training and testing data + if state in ['H-rich_Shell_H_burning', + 'H-rich_Core_H_burning']: # profiles with 2 boundary points + outs=2 + # calculate first and last points in each H profile with large increases + # (He profiles have the same boundary points) + bounds,nonflat = calc_bevel_bounds(h_prof) + valid_bounds, valid_nonflat = calc_bevel_bounds(valid_h_prof) + + if state in ['H-rich_Core_He_burning', + 'H-rich_Core_C_burning']: # profiles with 2 boundary points + outs=4 + # calculate first and last points in each H profile with large increases + # (He profiles have the same boundary points) + hbounds,nonflat_a = calc_bevel_bounds(h_prof) + valid_hbounds,valid_nonflat_a = calc_bevel_bounds(valid_h_prof) + pbounds,nonflat_p = calc_bevel_bounds(plus_prof) + valid_pbounds,valid_nonflat_p = calc_bevel_bounds(valid_plus_prof) + nonflat = np.intersect1d(nonflat_a,nonflat_p) + valid_nonflat = np.intersect1d(valid_nonflat_a,valid_nonflat_p) + bounds = np.transpose([hbounds[:,0],hbounds[:,1],pbounds[:,0],pbounds[:,1]]) + valid_bounds = np.transpose([valid_hbounds[:,0],valid_hbounds[:,1], + valid_pbounds[:,0],valid_pbounds[:,1]]) + + + elif state in ['H-rich_Central_He_depleted', + 'H-rich_Central_C_depletion']: # H profiles with 1 boundary point + outs=3 + # calculate the point in each H profile with the largest increase + hbound = (np.argmax(h_prof[:,1:]-h_prof[:,:-1],axis=1)+1)/200 + valid_hbound = (np.argmax(valid_h_prof[:,1:]-valid_h_prof[:,:-1],axis=1)+1)/200 + # calculate first and points in each "H + He" profile with large increases + bounds,nonflat = calc_bevel_bounds(plus_prof) + valid_bounds, valid_nonflat = calc_bevel_bounds(valid_plus_prof) + bounds = np.insert(bounds,0,hbound,axis=1) + valid_bounds = np.insert(valid_bounds,0,valid_hbound,axis=1) - elif state in ['H-rich_Central_He_depleted', - 'H-rich_Central_C_depletion']: - history = model.fit(inputs,np.array(bounds), - epochs=training_epochs,verbose=0,callbacks=[callback], - validation_data=(valid_inputs, - np.array(valid_bounds))) - loss_history[state] = np.array([history.history['loss'],history.history['val_loss']]) + elif state in ['stripped_He_Core_He_burning', + 'stripped_He_Core_C_burning', + 'stripped_He_Central_He_depleted', + 'stripped_He_Central_C_depletion']: + outs=2 + # calculate first and points in each profile with large increases + bounds,nonflat = calc_bevel_bounds(he_prof) + valid_bounds, valid_nonflat = calc_bevel_bounds(valid_he_prof) + + # instantiate and train model on bounds + model = models.Sequential() + model.add(layers.Dense(width,input_dim=3,activation="relu")) + for i in range(depth-1): + model.add(layers.Dense(width,input_dim=width,activation="relu")) + model.add(layers.Dense(outs,input_dim=10,activation="sigmoid")) - else: - history = model.fit(inputs[nonflat],np.array(bounds)[nonflat], + model.compile(optimizers.Adam(clipnorm=1,learning_rate=learning_rate), + loss=losses.MeanSquaredError()) + callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=training_patience) + + history = model.fit(inputs[nonflat],bounds[nonflat], epochs=training_epochs,verbose=0,callbacks=[callback], validation_data=(valid_inputs[valid_nonflat], - np.array(valid_bounds)[valid_nonflat])) + valid_bounds[valid_nonflat])) loss_history[state] = np.array([history.history['loss'],history.history['val_loss']]) - b_models[state] = model - print(f"finished {state}") + b_models[state] = model + print(f"finished {state}") return b_models, loss_history def predict_single(self,initial,center_H,surface_H,center_He,surface_He,star_state): @@ -650,93 +728,121 @@ def predict_single(self,initial,center_H,surface_H,center_He,surface_He,star_sta if "stripped_He" in star_state: H = np.zeros(200) # stripped Helium stars have no Hydrogen - if star_state == "stripped_He_non_burning": - He = np.ones(200)*surface_He # non-burning stars have a flat He profile - - if star_state in ['stripped_He_Core_He_burning', - 'stripped_He_Core_C_burning', - 'stripped_He_Central_He_depleted', - 'stripped_He_Central_C_depletion']: + # if there is no training data in the state, return nans + if len(self.sort_ind[star_state])==0: + H = np.ones(200)*np.nan + He = np.ones(200)*np.nan + + elif star_state == "stripped_He_non_burning": + if surface_He != center_He: + Pwarn("Non-burning helium star unexpectedly does not have a flat profile. " + "The difference in value between the surface and center helium " + f"fraction values is {center_He-surface_He}. Their average " + "will be used to create a flat profile.", + "InappropriateValueWarning") + He = np.ones(200)*np.mean([surface_He,center_He]) # non-burning stars have a flat He profile + + elif 'stripped_He_C' in star_state: # predicting profile shape parameters - b = self.bounds_models[star_state](tf.convert_to_tensor([initial])).numpy()[0] + b = self.bounds_models[star_state](tf.convert_to_tensor([initial])).numpy()[0] He = np.ones(200) * center_He He[int(b[1]*200):] = surface_He - f_He = interp1d(b,[center_He,surface_He],fill_value="extrapolate") + f_He = interp1d(b,[center_He,surface_He], + fill_value=(center_He,surface_He), + bounds_error=False) # use f to construct the profile points in the shell burning region He[int(b[0]*200):int(b[1]*200)] = f_He(np.linspace(0,1,200)[int(b[0]*200):int(b[1]*200)]) - if star_state in ["H-rich_non_burning","None"]: # these states have flat H and He profiles + elif star_state in ["H-rich_non_burning","None"]: # these states have flat H and He profiles H = np.ones(200)*surface_H He = np.ones(200)*surface_He - if star_state in ["WD","NS","BH"]: # profiles for compact objects are arbitrary -- return nans + elif star_state in ["WD","NS","BH"]: # profiles for compact objects are arbitrary -- return nans H = np.ones(200)*np.nan He = np.ones(200)*np.nan # construct step-shaped profile - H and He profiles have symmetrical shapes - if star_state in ["H-rich_Central_He_depleted", + elif star_state in ["H-rich_Central_He_depleted", "H-rich_Central_C_depletion"]: # predicting profile shape parameters b = self.bounds_models[star_state](tf.convert_to_tensor([initial])).numpy()[0] H = np.ones(200)*center_H H[int(b[0]*200):] = surface_H - He = np.ones(200) * center_He - He[int(b[1]*200):] = b[2] - He[int(b[0]*200):] = surface_He + # construct "H+He" profile + plus = np.ones(200) * (center_H + center_He) + plus[int(b[2]*200):] = (surface_H + surface_He) + f_plus = interp1d(b[1:],[center_H + center_He, surface_H + surface_He], + fill_value=(center_H + center_He, surface_H + surface_He), + bounds_error=False) + plus[int(b[1]*200):int(b[2]*200)] = f_plus(np.linspace(0,1,200)[int(b[1]*200):int(b[2]*200)]) + He = plus - H # construct shell-shaped profile - H and He profiles have symmetrical shapes - if star_state in ["H-rich_Shell_H_burning", - "H-rich_Core_H_burning", - "H-rich_Core_He_burning", + elif star_state in ["H-rich_Shell_H_burning", + "H-rich_Core_H_burning"]: + # predicting profile shape parameters + b = self.bounds_models[star_state](tf.convert_to_tensor([initial])).numpy()[0] + H = np.ones(200) * center_H + H[int(b[1]*200):] = surface_H + f_H = interp1d(b,[center_H,surface_H],fill_value=(center_H,surface_H),bounds_error=False) + # use f to construct the profile points in the shell burning region + H[int(b[0]*200):int(b[1]*200)] = f_H(np.linspace(0,1,200)[int(b[0]*200):int(b[1]*200)]) + # "H+He" profile is flat: + plus = np.ones(200) * (surface_H + surface_He) + He = plus - H + + elif star_state in ["H-rich_Core_He_burning", "H-rich_Core_C_burning"]: # predicting profile shape parameters - b = self.bounds_models[star_state](tf.convert_to_tensor([initial])).numpy()[0] - new = np.ones([2,200]) * np.array([center_H,center_He])[:,np.newaxis] - new[:,int(b[1]*200):] = np.array([surface_H,surface_He])[:,np.newaxis] - f_H = interp1d(b,[center_H,surface_H],fill_value="extrapolate") - f_He = interp1d(b,[center_He,surface_He],fill_value="extrapolate") + b = self.bounds_models[star_state](tf.convert_to_tensor([initial])).numpy()[0] + H = np.ones(200) * center_H + H[int(b[1]*200):] = surface_H + f_H = interp1d(b[:2],[center_H,surface_H],fill_value=(center_H,surface_H),bounds_error=False) # use f to construct the profile points in the shell burning region - new[0][int(b[0]*200):int(b[1]*200)] = f_H(np.linspace(0,1,200)[int(b[0]*200):int(b[1]*200)]) - new[1][int(b[0]*200):int(b[1]*200)] = f_He(np.linspace(0,1,200)[int(b[0]*200):int(b[1]*200)]) - H = new[0] - He = new[1] + H[int(b[0]*200):int(b[1]*200)] = f_H(np.linspace(0,1,200)[int(b[0]*200):int(b[1]*200)]) + # "H+He" profile is beveled as well: + plus = np.ones(200) * (center_H+center_He) + plus[int(b[3]*200):] = surface_H+surface_He + f_plus = interp1d(b[2:],[center_H+center_He,surface_H+surface_He], + fill_value=(center_H+center_He,surface_H+surface_He),bounds_error=False) + plus[int(b[2]*200):int(b[3]*200)] = f_plus(np.linspace(0,1,200)[int(b[2]*200):int(b[3]*200)]) + He = plus - H - # if there is no training data in the state, return nans - if star_state in self.empty: - H = np.ones(200)*np.nan - He = np.ones(200)*np.nan - return H, He def predict(self,inputs): """Predict H mass fraction profiles from inputs. Args: - inputs (array-like) : log-space initial conditions of N binaries to predict, shape (N,3). + inputs (array-like) : positive linear-space initial conditions of N binaries to predict, shape (N,3). Returns: mass_coords (array-like) : linear-scale mass enclosed profile coordinates. h_profiles (array_like) : H mass fraction profile coordinates. he_profiles (array_like) : He mass fraction profile coordinates. """ # IF interpolate H mass fraction values at center, surface; final star state - center_h_vals = self.interp.test_interpolator(10**inputs)[:,self.c_h_ind] - surface_h_vals = self.interp.test_interpolator(10**inputs)[:,self.s_h_ind] - center_he_vals = self.interp.test_interpolator(10**inputs)[:,self.c_he_ind] - surface_he_vals = self.interp.test_interpolator(10**inputs)[:,self.s_he_ind] + center_h_vals = self.interp.test_interpolator(inputs)[:,self.c_h_ind] + surface_h_vals = self.interp.test_interpolator(inputs)[:,self.s_h_ind] + center_he_vals = self.interp.test_interpolator(inputs)[:,self.c_he_ind] + surface_he_vals = self.interp.test_interpolator(inputs)[:,self.s_he_ind] # IF interpolate final masses, final star states if self.hms_s2==False: m_ind = self.model_IF.interpolators[0].out_keys.index("star_1_mass") - star_state_vals = self.interp.test_classifiers(10**inputs)['S1_state'] + star_state_vals = self.interp.test_classifiers(inputs)['S1_state'] else: m_ind = self.model_IF.interpolators[0].out_keys.index("star_2_mass") - star_state_vals = self.interp.test_classifiers(10**inputs)['S2_state'] - pred_mass = self.interp.test_interpolator(10**inputs)[:,m_ind] + star_state_vals = self.interp.test_classifiers(inputs)['S2_state'] + pred_mass = self.interp.test_interpolator(inputs)[:,m_ind] # generate predicted profiles pred_profiles = [] for i in range(len(inputs)): - pred_H,pred_He = self.predict_single(inputs[i],center_h_vals[i],surface_h_vals[i], - center_he_vals[i],surface_he_vals[i],star_state_vals[i]) + pred_H,pred_He = self.predict_single(np.log10(inputs[i]), + center_h_vals[i], + surface_h_vals[i], + center_he_vals[i], + surface_he_vals[i], + star_state_vals[i]) pred_profiles.append([pred_H,pred_He]) # generate mass enclosed profile coordinates From 709dcd5dd1d142164770402acb53f3205eda3110 Mon Sep 17 00:00:00 2001 From: Camille Liotine Date: Thu, 17 Oct 2024 10:05:01 -0500 Subject: [PATCH 255/319] rename "stripped_He_Core_H_burning" state and add to flow chart (#404) * stripped_He_Core_H * add "accreted_He_Core_H_burning" state * add rounding for surface_h1, center_h1 values when determining star state * move accreted_He state to list of HMS star states * fix plot defaults * add accreted_He_non_burning state * add states back to plot defaults * add "accreted_He_Core_He_burning" state * remove testing print statement --- posydon/binary_evol/CE/step_CEE.py | 6 +++-- posydon/binary_evol/DT/step_detached.py | 12 ++++++---- posydon/binary_evol/MESA/step_mesa.py | 31 +++++++++++++------------ posydon/binary_evol/flow_chart.py | 19 +++++++++------ posydon/utils/common_functions.py | 16 +++++-------- posydon/visualization/plot_defaults.py | 20 +++++++++------- 6 files changed, 58 insertions(+), 46 deletions(-) diff --git a/posydon/binary_evol/CE/step_CEE.py b/posydon/binary_evol/CE/step_CEE.py index ebb2c7b505..fa580723cc 100644 --- a/posydon/binary_evol/CE/step_CEE.py +++ b/posydon/binary_evol/CE/step_CEE.py @@ -90,11 +90,13 @@ "H-rich_Core_He_burning", "H-rich_Central_He_depleted", "H-rich_Central_C_depletion", - "H-rich_non_burning" + "H-rich_non_burning", + "accreted_He_non_burning" ] STAR_STATE_POST_HeMS = [ + 'accreted_He_Core_He_burning', 'stripped_He_Core_He_burning', 'stripped_He_Central_He_depleted', 'stripped_He_Central_C_depletion', @@ -212,7 +214,7 @@ def __call__(self, binary): # Check to make sure binary can go through a CE mergeable_donor = (donor_star.state in [ - 'H-rich_Core_H_burning', 'stripped_He_Core_He_burning']) + 'H-rich_Core_H_burning', 'stripped_He_Core_He_burning', 'accreted_He_Core_He_burning']) mergeable_HG_donor = ( self.common_envelope_option_for_HG_star == "pessimistic" and donor_star.state in ['H-rich_Shell_H_burning']) diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index 5d40f6852a..8010dfd0d7 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -40,7 +40,7 @@ from posydon.utils.posydonerror import NumericalError, MatchingError, POSYDONError from posydon.utils.posydonwarning import Pwarn -LIST_ACCEPTABLE_STATES_FOR_HMS = ["H-rich_Core_H_burning"] +LIST_ACCEPTABLE_STATES_FOR_HMS = ["H-rich_Core_H_burning", "accreted_He_Core_H_burning"] LIST_ACCEPTABLE_STATES_FOR_postMS = [ "H-rich_Shell_H_burning", @@ -48,14 +48,16 @@ "H-rich_Central_He_depleted", "H-rich_Core_C_burning", "H-rich_Central_C_depletion", - "H-rich_non_burning"] + "H-rich_non_burning", + "accreted_He_non_burning"] LIST_ACCEPTABLE_STATES_FOR_HeStar = [ + 'accreted_He_Core_He_burning', 'stripped_He_Core_He_burning', 'stripped_He_Shell_He_burning', # includes stars burning C in core 'stripped_He_Central_He_depleted', # includes stars burning C in core 'stripped_He_Central_C_depletion', - 'stripped_He_non_burning' # includes stars burning C in core + 'stripped_He_non_burning' ] STAR_STATES_H_RICH = [ @@ -66,7 +68,9 @@ 'H-rich_Shell_He_burning', 'H-rich_Core_C_burning', 'H-rich_Central_C_depletion', - 'H-rich_non_burning' + 'H-rich_non_burning', + 'accreted_He_Core_H_burning', + 'accreted_He_non_burning' ] ''' diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index c8ec84fc29..30b4339d50 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -309,7 +309,6 @@ def __call__(self, binary): if self.flip_stars_before_step: flip_stars(binary) binary.state = 'initial_RLOF' - # binary.event = 'END' return binary_start_time = binary.time @@ -422,21 +421,16 @@ def update_properties_NN(self, star_1_CO=False, star_2_CO=False, cb_bh = cb.binary_history cb_hs = [cb.history1, cb.history2] cb_fps = [cb.final_profile1, cb.final_profile2] - - # TOOD: I removed this which is now done in get_final_MESA_step_time - # find the nearest_neighbour and the distance - # self.closest_binary, self.nearest_neighbour_distance, \ - # self.termination_flags = self._psyTrackInterp.evaluate(self.binary) + if (cb_bh['age'].size <= 1 or cb_bh['star_1_mass'].size <= 1): setattr(binary, "state", "initial_RLOF") - # setattr(binary, "event", "END") return # check if the first interpolation gives 'initial_RLOF' interpolation_class = self.termination_flags[0] binary_state, binary_event, MT_case = ( cf.get_binary_state_and_event_and_mt_case( - binary, interpolation_class, verbose=self.verbose)) + binary, interpolation_class, verbose=self.verbose)) setattr(binary, 'state', binary_state) setattr(binary, 'event', binary_event) setattr(binary, 'mass_transfer_case', MT_case) @@ -706,7 +700,7 @@ def update_properties_NN(self, star_1_CO=False, star_2_CO=False, getattr(stars[1], "state_history").extend(state2_hist) binary_state, binary_event, MT_case = ( cf.get_binary_state_and_event_and_mt_case_array( - binary, N=length_hist, verbose=self.verbose)) + binary, N=length_hist, verbose=self.verbose)) getattr(binary, "state_history").extend(binary_state) getattr(binary, "event_history").extend(binary_event) getattr(binary, "mass_transfer_case_history").extend(MT_case) @@ -916,6 +910,7 @@ def initial_final_interpolation(self, star_1_CO=False, star_2_CO=False): setattr(self.binary, 'event', binary_event) setattr(self.binary, 'mass_transfer_case', MT_case) + if binary.state == 'initial_RLOF': return @@ -1356,9 +1351,10 @@ def __call__(self, binary): "H-rich_Core_He_burning", "H-rich_Central_He_depleted", "H-rich_Core_C_burning", - "stripped_He_Core_H_burning", + "accreted_He_Core_H_burning", "H-rich_Central_C_depletion", # filtered out below - "H-rich_non_burning"] + "H-rich_non_burning", + "accreted_He_non_burning"] # check the star states # TODO: import states from flow_chart.py @@ -1475,6 +1471,7 @@ def __call__(self, binary): # TODO: import states from flow_chart.py CO_He_STATES = [ + 'accreted_He_Core_He_burning', 'stripped_He_Core_He_burning', 'stripped_He_Shell_He_burning', 'stripped_He_Central_He_depleted', @@ -1482,7 +1479,8 @@ def __call__(self, binary): # include systems that are on the brink of He exhaustion 'stripped_He_non_burning', # include systems post CE with core_definition_H_fraction=0.1 - 'H-rich_non_burning' + 'H-rich_non_burning', + 'accreted_He_non_burning' ] # check the star states @@ -1599,6 +1597,7 @@ def __call__(self, binary): # TODO: import states from flow_chart.py CO_He_STATES = [ + 'accreted_He_Core_He_burning', 'stripped_He_Core_He_burning', 'stripped_He_Shell_He_burning', 'stripped_He_Central_He_depleted', @@ -1606,7 +1605,8 @@ def __call__(self, binary): # include systems that are on the brink of He exhaustion 'stripped_He_non_burning', # include systems post CE with core_definition_H_fraction=0.1 - 'H-rich_non_burning' + 'H-rich_non_burning', + 'accreted_He_non_burning' ] # TODO: import states from flow_chart.py if (state_2 in ['WD', 'NS', 'BH'] @@ -1716,9 +1716,10 @@ def __call__(self, binary): "H-rich_Core_He_burning", "H-rich_Central_He_depleted", "H-rich_Core_C_burning", - "stripped_He_Core_H_burning", + "accreted_He_Core_H_burning", "H-rich_Central_C_depletion", # filtered out below - "H-rich_non_burning"] + "H-rich_non_burning", + "accreted_He_non_burning"] # check the star states # TODO: import states from flow_chart.py diff --git a/posydon/binary_evol/flow_chart.py b/posydon/binary_evol/flow_chart.py index 54e54d1874..89f7521f2b 100644 --- a/posydon/binary_evol/flow_chart.py +++ b/posydon/binary_evol/flow_chart.py @@ -29,6 +29,9 @@ 'H-rich_Core_C_burning', 'H-rich_Central_C_depletion', 'H-rich_non_burning', + 'accreted_He_Core_H_burning', + 'accreted_He_non_burning', + 'accreted_He_Core_He_burning', 'stripped_He_Core_He_burning', 'stripped_He_Central_He_depleted', 'stripped_He_Central_C_depletion', @@ -49,7 +52,8 @@ [STAR_STATES_H_RICH.remove(x) for x in ['stripped_He_Core_He_burning', 'stripped_He_Central_He_depleted', 'stripped_He_Central_C_depletion', - 'stripped_He_non_burning']] + 'stripped_He_non_burning', + 'accreted_He_Core_He_burning']] STAR_STATES_HE_RICH = STAR_STATES_NORMALSTAR.copy() [STAR_STATES_HE_RICH.remove(x) for x in ['H-rich_Core_H_burning', @@ -58,7 +62,8 @@ 'H-rich_Central_He_depleted', 'H-rich_Shell_He_burning', 'H-rich_Core_C_burning', - 'H-rich_Central_C_depletion']] + 'H-rich_Central_C_depletion', + 'accreted_He_Core_H_burning']] STAR_STATES_C_DEPLETION = [st for st in STAR_STATES_ALL if "C_depletion" in st] @@ -68,11 +73,12 @@ STAR_STATES_HE_RICH_EVOLVABLE = list(set(STAR_STATES_HE_RICH) - set(STAR_STATES_C_DEPLETION)) -# CE ejcetion happens istantanously, the star does not readjust before -# we infer the state, if core_definition_H_fraction=0.1 then surface_h1=0.1 + +# CE ejection happens instantanously, so the star does not readjust before +# we infer the state. If core_definition_H_fraction=0.1, then surface_h1=0.1, # and the state is H-rich_non_burning which we stil want to evolve thorugh # the step_CO_HeMS -STAR_STATES_HE_RICH_EVOLVABLE.append('H-rich_non_burning') +STAR_STATES_HE_RICH_EVOLVABLE.extend(['H-rich_non_burning']) BINARY_STATES_ALL = [ 'initially_single_star', @@ -207,12 +213,11 @@ 'stripped_He_non_burning', 'H-rich_non_burning', 'H-rich_Shell_H_burning', + 'accreted_He_non_burning' ] BINARY_STATES_CC = BINARY_STATES_ALL.copy() -#BINARY_STATES_CC = BINARY_STATES_ALL.copy() -#BINARY_STATES_CC.remove('disrupted') for b in BINARY_STATES_CC: for s1 in STAR_STATES_CC: diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index 50535736e2..1ae860cbe9 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -42,7 +42,7 @@ BURNING_STATES = ["Core_H_burning", "Core_He_burning", "Shell_H_burning", "Central_He_depleted", "Central_C_depletion"] -RICHNESS_STATES = ["H-rich", "stripped_He"] +RICHNESS_STATES = ["H-rich", "stripped_He", "accreted_He"] COMPACT_OBJECTS = ["WD", "NS", "BH","massless_remnant"] ALL_STAR_STATES = COMPACT_OBJECTS + [STATE_UNDETERMINED] @@ -50,13 +50,6 @@ for rich_in in RICHNESS_STATES for burning in BURNING_STATES]) -# `ALL_STAR_STATES` includes the following strings: -# 'WD', 'NS', 'BH', 'undetermined_evolutionary_state', -# 'H-rich_Core_H_burning', 'H-rich_Core_He_burning', -# 'H-rich_Shell_H_burning', 'H-rich_Central_He_depleted', -# 'stripped_He_Core_He_burning', 'stripped_Central_He_depleted', -# 'H-rich_Central_C_depletion', 'stripped_He_Central_C_depletion' - # Mass-transfer cases in form of integer flags MT_CASE_NO_RLO = 0 MT_CASE_A = 1 @@ -1341,7 +1334,7 @@ def infer_star_state(star_mass=None, surface_h1=None, return STATE_UNDETERMINED rich_in = ("H-rich" if surface_h1 > THRESHOLD_HE_NAKED_ABUNDANCE - else "stripped_He") + else ("accreted_He" if round(surface_h1, 10) LOG10_BURNING_THRESHOLD and log_LH - log_Lnuc > REL_LOG10_BURNING_THRESHOLD) burning_He = (log_LHe > LOG10_BURNING_THRESHOLD @@ -1369,7 +1362,7 @@ def infer_star_state(star_mass=None, surface_h1=None, burning = "Shell_H_burning" else: burning = "non_burning" - + return "{}_{}".format(rich_in, burning) @@ -1902,9 +1895,12 @@ def calculate_core_boundary(donor_mass, "H-rich_Core_C_burning", "H-rich_Central_C_depletion", "H-rich_non_burning", + "accreted_He_Core_H_burning", + "accreted_He_non_burning" ] # ENHANCEMENT: this list needs to be imported from e.g. flow_chart.py STAR_STATE_He = [ + 'accreted_He_Core_He_burning', 'stripped_He_Core_He_burning', 'stripped_He_Central_He_depleted', 'stripped_He_Central_C_depletion', diff --git a/posydon/visualization/plot_defaults.py b/posydon/visualization/plot_defaults.py index 2eddd0774b..756e064c2a 100644 --- a/posydon/visualization/plot_defaults.py +++ b/posydon/visualization/plot_defaults.py @@ -381,11 +381,13 @@ 'termination_flag_3': { 'H-rich_non_burning': - ['v', 2, 'tab:orange', 'H-rich H non-buring'], + ['v', 2, 'tab:orange', 'H-rich H non-burning'], + 'accreted_He_Core_H_burning': + ['v', 2, 'black', 'accreted He core H burning'], 'H-rich_Core_H_burning': - ['s', 2, 'tab:olive', 'H-rich core H buring'], + ['s', 2, 'tab:olive', 'H-rich core H burning'], 'H-rich_Shell_H_burning': - ['s', 2, 'tab:red', 'H-rich shell H buring'], + ['s', 2, 'tab:red', 'H-rich shell H burning'], 'H-rich_Core_C_burning': ['s', 2, 'tab:pink', 'H-rich core C burning'], 'H-rich_Central_C_depletion': @@ -415,17 +417,19 @@ 'NS': ['*', 1, 'tab:gray', 'NS'], 'ignored_no_binary_history': - ['s', 2, 'tab:olive', 'H-rich core H buring'], + ['s', 2, 'tab:olive', 'H-rich core H burning'], 'ignored_no_RLO': - ['s', 2, 'tab:olive', 'H-rich core H buring'], + ['s', 2, 'tab:olive', 'H-rich core H burning'], }, 'termination_flag_4': { 'H-rich_non_burning': - ['v', 2, 'tab:orange', 'H-rich H non-buring'], + ['v', 2, 'tab:orange', 'H-rich H non-burning'], + 'accreted_He_Core_H_burning': + ['v', 2, 'black', 'accreted He core H burning'], 'H-rich_Core_H_burning': - ['s', 2, 'tab:olive', 'H-rich core H buring'], + ['s', 2, 'tab:olive', 'H-rich core H burning'], 'H-rich_Shell_H_burning': - ['s', 2, 'tab:red', 'H-rich shell H buring'], + ['s', 2, 'tab:red', 'H-rich shell H burning'], 'H-rich_Core_C_burning': ['s', 2, 'tab:pink', 'H-rich core C burning'], 'H-rich_Central_C_depletion': From ee54c4083f0a9aa58722f933b909e88b77b60716 Mon Sep 17 00:00:00 2001 From: Camille Liotine Date: Thu, 17 Oct 2024 10:12:22 -0500 Subject: [PATCH 256/319] allow double He-rich star binaries in RLO to merge (#414) --- posydon/binary_evol/DT/step_merged.py | 22 ++++++++++++++++++---- posydon/binary_evol/flow_chart.py | 6 ++++++ 2 files changed, 24 insertions(+), 4 deletions(-) diff --git a/posydon/binary_evol/DT/step_merged.py b/posydon/binary_evol/DT/step_merged.py index 70ca96e425..4913dd0e6c 100644 --- a/posydon/binary_evol/DT/step_merged.py +++ b/posydon/binary_evol/DT/step_merged.py @@ -85,22 +85,36 @@ def __init__( def __call__(self,binary): + merged_star_properties = self.merged_star_properties + if self.verbose: print("Before Merger", binary.star_1.state,binary.star_2.state,binary.state, binary.event) print("M1 , M2, he_core_mass1, he_core_mass2: ", binary.star_1.mass,binary.star_2.mass, binary.star_1.he_core_mass, binary.star_2.he_core_mass) print("star_1.center_he4, star_2.center_he4, star_1.surface_he4, star_2.surface_he4: ", binary.star_1.center_he4,binary.star_2.center_he4, binary.star_1.surface_he4,binary.star_2.surface_he4) + if binary.state == "merged": if binary.event == 'oMerging1': - binary.star_1,binary.star_2 = merged_star_properties(binary.star_1,binary.star_2) + binary.star_1, binary.star_2 = merged_star_properties(binary.star_1, binary.star_2) elif binary.event == 'oMerging2': - binary.star_2,binary.star_1 = merged_star_properties(binary.star_2,binary.star_1) + binary.star_2, binary.star_1 = merged_star_properties(binary.star_2, binary.star_1) else: - raise FlowError("binary.state='merged' but binary.event != 'oMerging1/2'") + raise ValueError("binary.state='merged' but binary.event != 'oMerging1/2'") + + ## assume that binaries in RLO with two He-rich stars always merge + elif binary.star_1.state in STAR_STATES_HE_RICH and binary.star_2.state in STAR_STATES_HE_RICH: + binary.state = "merged" + if binary.event == 'oRLO1': + binary.star_1, binary.star_2 = merged_star_properties(binary.star_1, binary.star_2) + elif binary.event == 'oRLO2': + binary.star_2, binary.star_1 = merged_star_properties(binary.star_2, binary.star_1) + else: + raise ValueError("step_merged initiated for He stars but RLO not initiated") else: - raise FlowError("step_merging initiated but binary.state != 'merged'") + raise ValueError("step_merged initiated but binary is not in valid merging state!") binary.event = None + if self.verbose: print("After Merger", binary.star_1.state,binary.star_2.state,binary.state, binary.event) print("M_merged , he_core_mass merged: ", binary.star_1.mass, binary.star_1.he_core_mass) diff --git a/posydon/binary_evol/flow_chart.py b/posydon/binary_evol/flow_chart.py index 89f7521f2b..e4d116da23 100644 --- a/posydon/binary_evol/flow_chart.py +++ b/posydon/binary_evol/flow_chart.py @@ -188,6 +188,12 @@ POSYDON_FLOW_CHART[(s1, s2, 'detached', "redirect_from_CO_HeMS_RLO")] = 'step_detached' POSYDON_FLOW_CHART[(s2, s1, 'detached', "redirect_from_CO_HeMS_RLO")] = 'step_detached' +## He-rich star roche-lobe overflow onto another He-rich star +## assume these systems always merge +for s1 in STAR_STATES_HE_RICH: + for s2 in STAR_STATES_HE_RICH: + POSYDON_FLOW_CHART[(s1, s2, 'RLO1', "oRLO1")] = 'step_merged' + POSYDON_FLOW_CHART[(s2, s1, 'RLO2', "oRLO2")] = 'step_merged' # Binaries that go to common envelope From 815b484510e90c6207027dbcf926b403a63bf6f5 Mon Sep 17 00:00:00 2001 From: Camille Liotine Date: Thu, 17 Oct 2024 10:14:12 -0500 Subject: [PATCH 257/319] update star states in step_SN (#416) --- posydon/binary_evol/SN/step_SN.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index d33d3072ad..27e1926997 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -418,11 +418,13 @@ def __call__(self, binary): # collapse star self.collapse_star(star=binary.star_1) self._reset_other_star_properties(star=binary.star_2) + binary.update_star_states() elif binary.event == "CC2": # collapse star self.collapse_star(star=binary.star_2) self._reset_other_star_properties(star=binary.star_1) + binary.update_star_states() else: raise ValueError("Something went wrong: " "invalid call of supernova step!") From b334fbcb6d0daef2665a3146051095802be909cd Mon Sep 17 00:00:00 2001 From: Elizabeth Teng <41969755+elizabethteng@users.noreply.github.com> Date: Fri, 1 Nov 2024 09:20:10 -0500 Subject: [PATCH 258/319] Automatic testing of installation (#425) * Create continuous_integration.yml Testing installation for mac, ubuntu, windows with python 3.11 (can add other versions). Automatically triggered for all PRs to development and main. * Create install_extras.yml scheduled weekly cronjob to test installation with all extras from setup.py * QA for installation test I am inserting a requirement for a nonexistent package just to make sure my automatic installation test workflow catches it. Will revert this change afterwards. * reverting change for QA it worked! reverting now * Update continuous_integration.yml removing windows * Update continuous_integration.yml remove windows condition * Update install_extras.yml remove windows --- .github/workflows/continuous_integration.yml | 32 ++++++++++++++++++++ .github/workflows/install_extras.yml | 28 +++++++++++++++++ 2 files changed, 60 insertions(+) create mode 100644 .github/workflows/continuous_integration.yml create mode 100644 .github/workflows/install_extras.yml diff --git a/.github/workflows/continuous_integration.yml b/.github/workflows/continuous_integration.yml new file mode 100644 index 0000000000..8c86047253 --- /dev/null +++ b/.github/workflows/continuous_integration.yml @@ -0,0 +1,32 @@ +name: POSYDON Continuous Integration + +on: + pull_request: + branches: [main, development] + +jobs: + test: + runs-on: ${{ matrix.os }} + + strategy: + matrix: + os: [macos-latest, ubuntu-latest] + python-version: ['3.11'] # can add more if we want to support + + steps: + - name: Checkout repository + uses: actions/checkout@v2 + + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + + - name: Install POSYDON without extras + run: | + python -m pip install --upgrade pip + pip install . + + # - name: Run all tests + # run: | + # pytest posydon/unit_tests/ diff --git a/.github/workflows/install_extras.yml b/.github/workflows/install_extras.yml new file mode 100644 index 0000000000..961840c841 --- /dev/null +++ b/.github/workflows/install_extras.yml @@ -0,0 +1,28 @@ +name: Testing installation of extras in setup.py + +on: + schedule: + - cron: "0 0 * * 0" # Runs at 00:00 (UTC) every Sunday + +jobs: + test: + runs-on: ${{ matrix.os }} + + strategy: + matrix: + os: [macos-latest, ubuntu-latest] + python-version: ['3.11'] # can add more if we want to support + + steps: + - name: Checkout repository + uses: actions/checkout@v2 + + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + + - name: Install POSYDON with extras + run: | + python -m pip install --upgrade pip + pip install ".[doc,vis,ml,hpc]" From 20f182cab806ac3890aa8850ef9114d70db75dc7 Mon Sep 17 00:00:00 2001 From: Camille Liotine Date: Thu, 7 Nov 2024 09:10:45 -0600 Subject: [PATCH 259/319] always initialize values for separation and orbital period in BinaryStar (#426) * add explicit separation and orbital period initialization in binarystar constructor * add catch if star masses are None --- posydon/binary_evol/binarystar.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/posydon/binary_evol/binarystar.py b/posydon/binary_evol/binarystar.py index 4b7877f0bf..80ea4e97d4 100644 --- a/posydon/binary_evol/binarystar.py +++ b/posydon/binary_evol/binarystar.py @@ -158,6 +158,15 @@ def __init__(self, star_1=None, star_2=None, index=None, properties=None, for key, val in binary_kwargs.items(): setattr(self, key, val) + + if getattr(self.star_1, "mass") is not None and getattr(self.star_2, "mass") is not None: + if getattr(self, "separation") is None and getattr(self, "orbital_period") is not None: + setattr(self, "separation", + orbital_separation_from_period(self.orbital_period, self.star_1.mass, self.star_2.mass)) + elif getattr(self, "orbital_period") is None and getattr(self, "separation") is not None: + setattr(self, "orbital_period", + orbital_period_from_separation(self.separation, self.star_1.mass, self.star_2.mass)) + if not hasattr(self, 'inspiral_time'): self.inspiral_time = None if not hasattr(self, 'mass_transfer_case'): From 52b6913c6dd139b56b0865b6aba9f8225655027d Mon Sep 17 00:00:00 2001 From: Camille Liotine Date: Thu, 7 Nov 2024 09:11:00 -0600 Subject: [PATCH 260/319] error catch for He-rich stars in RLO onto H-rich stars (#423) * add error catch for He-rich stars in RLO onto H-rich stars in detached step * minor changes --- posydon/binary_evol/DT/step_detached.py | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index 8010dfd0d7..c17e7e421f 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -37,7 +37,7 @@ ) from posydon.binary_evol.flow_chart import (STAR_STATES_CC, STAR_STATES_CO) import posydon.utils.constants as const -from posydon.utils.posydonerror import NumericalError, MatchingError, POSYDONError +from posydon.utils.posydonerror import NumericalError, MatchingError, POSYDONError, FlowError from posydon.utils.posydonwarning import Pwarn LIST_ACCEPTABLE_STATES_FOR_HMS = ["H-rich_Core_H_burning", "accreted_He_Core_H_burning"] @@ -1926,6 +1926,19 @@ def get_star_profile(star, htrack, m0): else: binary.state = "RLO1" binary.event = "oRLO1" + + if (binary.state == "RLO1" + and binary.star_1.state in LIST_ACCEPTABLE_STATES_FOR_HeStar + and binary.star_2.state in STAR_STATES_H_RICH): + set_binary_to_failed(binary) + raise FlowError("Evolution of He-rich stars in RLO onto H-rich stars after HMS-HMS not yet supported.") + + elif (binary.state == "RLO2" + and binary.star_2.state in LIST_ACCEPTABLE_STATES_FOR_HeStar + and binary.star_1.state in STAR_STATES_H_RICH): + set_binary_to_failed(binary) + raise FlowError("Evolution of He-rich stars in RLO onto H-rich stars after HMS-HMS not yet supported.") + elif s.t_events[2]: # reached t_max of track. End of life (possible collapse) of From c6c01533c1cc547d85d571007fb9739c1d12770e Mon Sep 17 00:00:00 2001 From: dimsour94 <60255936+dimsour94@users.noreply.github.com> Date: Wed, 13 Nov 2024 12:44:48 +0200 Subject: [PATCH 261/319] New normalized_pop_mass.py file (#376) * normalized_pop_mass * synthetic * authors added * space added * # added * if statement added * Replacing error with a Warning * df1,df2 added in binarypopulation.py * dataframe handling removed * changed from None to 0.0 * underlying mass calculation separate function * change sum to nansum * add checks for underlying_mass existence * simulated_mass not summed again for each loop * rename df and inclusion of f_bin_nature * inclusion of f_bin nature * getting single and binary mass from the independent sample option * modified warnings * binary fraction correction * store the calculated underlying mass to the file * updated doc-strings and making it consistent at case boundaries * correcting some typos and respecting the 80 characters line length --------- Co-authored-by: Dimitris Souropanis Co-authored-by: Dimitris Souropanis Co-authored-by: Max Briel Co-authored-by: Eirini Co-authored-by: Dimitris Souropanis Co-authored-by: Dimitris Souropanis --- posydon/popsyn/binarypopulation.py | 21 ++- posydon/popsyn/normalized_pop_mass.py | 178 ++++++++++++++++++------- posydon/popsyn/synthetic_population.py | 81 +++++++++-- 3 files changed, 217 insertions(+), 63 deletions(-) diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index b1c8125357..8331380b8a 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -509,8 +509,11 @@ def combine_saved_files(self, absolute_filepath, file_names, **kwargs): with pd.HDFStore(absolute_filepath, mode=mode, complevel=complevel, complib=complib) as store: - simulated_mass = 0 - number_of_systems = 0 + simulated_mass = 0.0 + simulated_mass_single = 0.0 + simulated_mass_binaries = 0.0 + number_of_systems=0 + for f in file_names: # strings itemsize set by first append max value, # which may not be largest string @@ -519,7 +522,16 @@ def combine_saved_files(self, absolute_filepath, file_names, **kwargs): min_itemsize=history_min_itemsize) oneline = pd.read_hdf(f, key='oneline') - simulated_mass += oneline['S1_mass_i'].sum() + oneline['S2_mass_i'].sum() + + # split weight between single and binary stars + mask = oneline["state_i"] == "initially_single_star" + filtered_data_single = oneline[mask] + filtered_data_binaries = oneline[~mask] + + simulated_mass_binaries += np.nansum(filtered_data_binaries[["S1_mass_i", "S2_mass_i"]].to_numpy()) + simulated_mass_single += np.nansum(filtered_data_single[["S1_mass_i"]].to_numpy()) + simulated_mass = simulated_mass_single + simulated_mass_binaries + if 'metallicity' not in oneline.columns: met_df = pd.DataFrame(data={'metallicity': [self.metallicity] * len(oneline)}, index=oneline.index) oneline = pd.concat([oneline, met_df], axis=1) @@ -541,7 +553,8 @@ def combine_saved_files(self, absolute_filepath, file_names, **kwargs): tmp_df = pd.DataFrame( index=[self.metallicity], data={'simulated_mass': simulated_mass, - 'underlying_mass': initial_total_underlying_mass(df=simulated_mass, **self.kwargs)[0], + 'simulated_mass_single': simulated_mass_single, + 'simulated_mass_binaries': simulated_mass_binaries, 'number_of_systems': number_of_systems}) tmp_df.index.name = 'metallicity' store.append('mass_per_metallicity', tmp_df) diff --git a/posydon/popsyn/normalized_pop_mass.py b/posydon/popsyn/normalized_pop_mass.py index bcc91e92f0..8d0cafd67f 100644 --- a/posydon/popsyn/normalized_pop_mass.py +++ b/posydon/popsyn/normalized_pop_mass.py @@ -6,55 +6,89 @@ "Simone Bavera ", "Konstantinos Kovlakas ", "Matthias Kruckow ", + "Dimitris Souropanis ", ] - import numpy as np -from posydon.utils.posydonwarning import Pwarn from posydon.popsyn import independent_sample from scipy.integrate import quad +from posydon.utils.posydonwarning import Pwarn + +def initial_total_underlying_mass( + simulated_mass=None, simulated_mass_single=None, + simulated_mass_binaries=None, f_bin=0.7, **kwargs +): -def initial_total_underlying_mass(df=None, **kwargs): """Compute the initial total mass of the population. - Parameters + + Parameters + ---------- + simulated_mass, simulated_mass_single, simulated_mass_binaries : float + Total simulated mass, simulated mass of binary systems and simulated + mass of single stars, respectively. + f_bin: float + The binary fraction of your population in "nature". + If not provided, the default value is set to 0.7 + + Parameters are expected to be provided as keyword arguments (kwargs): + -------- + primary_mass_min: float + minimum initial mass of the primary star + primary_mass_max: float + maximum initial mass of the primary star + binary_fraction_const: float + Binary fraction used in the simulations + primary_mass_scheme: string + Kroupa2001 or Salpeter options + secondary_mass_scheme: string + mass ratio distribution + ---------- - df : DataFrame - Data frame of a population class. If nothing is provided the code will - sample initial conditions. - primary_mass_min : type - Description of parameter `primary_mass_min`. - primary_mass_max : type - Description of parameter `primary_mass_max`. - primary_mass_scheme : type - Description of parameter `primary_mass_scheme`. - secondary_mass_scheme : type - Description of parameter `secondary_mass_scheme`. - **kwargs : type - Description of parameter `**kwargs`. + Returns ------- - type - Description of returned object. + underlying_total_mass: float + The underlying total mass of the population: float + f_corr_single_stars, f_corr_binaries; float + Correction factors for singles and binaries, respectively. + + """ # ENHANCEMENT: the code should assume default values of the POSYDON sampler # if not provided in kwargs. + + + f_bin_nature = f_bin #This parameter represents the preferred fraction + # of binary systems within your population, + # allowing users to input any value ranging from 0.1 to 1. + f_bin_simulated = kwargs['binary_fraction_const'] - if df is None: + m_min = 0.01 + m_max = 200.0 + m_a = kwargs['primary_mass_min'] + m_b = kwargs['primary_mass_max'] + + if simulated_mass is None: initial_ZAMS_mass = independent_sample.generate_independent_samples( **kwargs) initial_ZAMS_mass_1 = initial_ZAMS_mass[2] initial_ZAMS_mass_2 = initial_ZAMS_mass[3] - initial_ZAMS_TOTAL_mass = (sum(initial_ZAMS_mass_1) - + sum(initial_ZAMS_mass_2)) - elif isinstance(df, float): - initial_ZAMS_TOTAL_mass = df + n_binaries = int(f_bin_simulated * len(initial_ZAMS_mass_1)) + initial_ZAMS_TOTAL_binaries = ( + sum(initial_ZAMS_mass_1[:n_binaries]) + + sum(initial_ZAMS_mass_2[:n_binaries])) + initial_ZAMS_TOTAL_single = sum(initial_ZAMS_mass_1[n_binaries:len(initial_ZAMS_mass_1)]) + initial_ZAMS_TOTAL_mass = initial_ZAMS_TOTAL_binaries + initial_ZAMS_TOTAL_single + else: - sel = df['event'] == 'ZAMS' - initial_ZAMS_TOTAL_mass = sum(df['S1_mass'][sel]+df['S2_mass'][sel]) + initial_ZAMS_TOTAL_mass = simulated_mass + initial_ZAMS_TOTAL_single= simulated_mass_single + initial_ZAMS_TOTAL_binaries= simulated_mass_binaries + def imf_part_1(m, m_min, alpha1): return (m/m_min)**-alpha1 @@ -65,10 +99,10 @@ def imf_part_2(m, m_1, m_min, alpha1, alpha2): def imf_part_3(m, m_1, m_2, m_min, alpha1, alpha2, alpha3): return ((m_1/m_min)**-alpha1)*((m_2/m_1)**-alpha2)*((m/m_2)**-alpha3) - def mean_mass_of_binary(f0, f_bin, m_1, m_2, m_min, m_max, + def mean_mass_of_binary(f0, f_bin_nature, m_1, m_2, m_min, m_max, alpha1, alpha2, alpha3): a1, a2, a3 = alpha1, alpha2, alpha3 - mean_mass = f0 * (1 + (f_bin/2)) * ( + mean_mass = f0 * (1 + (f_bin_nature/2)) * ( (1/(2-a1)) * ((m_1**(2-a1) - m_min**(2-a1))/(m_min**-a1)) + ((1/(2-a2)) * (m_1/m_min)**-a1 * ((m_2**(2-a2) - m_1**(2-a2))/(m_1**-a2))) @@ -76,17 +110,17 @@ def mean_mass_of_binary(f0, f_bin, m_1, m_2, m_min, m_max, * ((m_max**(2-a3) - m_2**(2-a3))/(m_2**-a3)))) return mean_mass - def mean_mass_simulated(alpha3, m_a, m_b): + def mean_mass_simulated(alpha3, m_a, m_b, f_bin_simulated): a = alpha3 - mean_mass_sim = (3/2) * ((1-a) / (2-a)) * ( + mean_mass_sim = (1+(f_bin_simulated/2)) * ((1-a) / (2-a)) * ( (m_b**(2-a)-m_a**(2-a))/(m_b**(1-a)-m_a**(1-a))) return mean_mass_sim - def fraction_simulated(f_bin, m_1, m_2, m_min, m_max, + def fraction_simulated( m_1, m_2, m_min, m_max, alpha1, alpha2, alpha3): a1, a2, a3 = alpha1, alpha2, alpha3 - f_model = f_bin * f0 * (1/(1-a3)) * (m_1/m_min)**(-a1) * (m_2/m_1)**( - -a2) * ((m_b**(1-a3)-m_a**(1-a3)) / (m_2**-a3)) + f_model = f0 * (1/(1-a3)) * (m_1/m_min)**(-a1) * (m_2/m_1)**( + -a2) * ((m_b**(1-a3)-m_a**(1-a3)) / (m_2**-a3)) return f_model # Kroupa P., 2001, MNRAS, 322, 231 @@ -111,26 +145,70 @@ def fraction_simulated(f_bin, m_1, m_2, m_min, m_max, f"{kwargs['primary_mass_scheme']}, secondary_mass_scheme" f"={kwargs['secondary_mass_scheme']}", "UnsupportedModelWarning") return np.nan, np.nan, np.nan -# raise ValueError("Scheme not included yet") - f_bin = 0.7 - m_min = 0.01 - m_max = 200.0 - m_a = kwargs['primary_mass_min'] - m_b = kwargs['primary_mass_max'] + f0 = 1/(quad(imf_part_1, m_min, m_1, args=(m_min, alpha1))[0] - + quad(imf_part_2, m_1, m_2, args=(m_1, m_min, alpha1, alpha2))[0] - + quad(imf_part_3, m_2, m_max, args=(m_1, m_2, m_min, alpha1, - alpha2, alpha3))[0]) + + quad(imf_part_2, m_1, m_2, args=(m_1, m_min, alpha1, alpha2))[0] + + quad(imf_part_3, m_2, m_max, args=(m_1, m_2, m_min, alpha1, alpha2, alpha3))[0]) + + + f_corr_single_stars = (fraction_simulated(m_1, m_2, m_min, m_max, alpha1, alpha2, alpha3) + * mean_mass_simulated(alpha3, m_a, m_b, f_bin_simulated) + / mean_mass_of_binary(f0, f_bin_nature, m_1, m_2, m_min, m_max, alpha1, alpha2, alpha3)) + + + f_corr_binaries = (fraction_simulated(m_1, m_2, m_min, m_max, alpha1, alpha2, alpha3) + * mean_mass_simulated(alpha3, m_a, m_b, f_bin_simulated) + / mean_mass_of_binary(f0, f_bin_nature, m_1, m_2, m_min, m_max, alpha1, alpha2, alpha3)) + + + if (f_bin_simulated == 1): #when you have simulated only binary stars + if (f_bin_nature != 0): + + f_corr_binaries = f_corr_binaries*f_bin_nature + f_corr_single_stars=0 + + + underlying_total_mass=initial_ZAMS_TOTAL_binaries/f_corr_binaries + + + else: + Pwarn("We apologize for not having simulated single stars. Please " + "provide a value greater than 0 and less than or equal to 1.", + "UnsupportedModelWarning") + return np.nan, np.nan, np.nan + + if (f_bin_simulated != 1): #when you have modeled both binary and single stars + if (f_bin_nature == 0): #but you want the underlying mass for a population + # consisting of only single stars + + + + f_corr_single_stars= (f_corr_single_stars*(1-f_bin_nature))/(1-f_bin_simulated) + f_corr_binaries=0 + underlying_total_mass=initial_ZAMS_TOTAL_single/f_corr_single_stars + + + if (f_bin_nature == 1): #you want the underlying mass for a population + # consisting of only binary stars + + f_corr_binaries = f_corr_binaries*(f_bin_nature/f_bin_simulated) + f_corr_single_stars=0 + + underlying_total_mass=initial_ZAMS_TOTAL_binaries/f_corr_binaries - f_corr = (fraction_simulated(f_bin, m_1, m_2, m_min, - m_max, alpha1, alpha2, alpha3) - * mean_mass_simulated(alpha3, m_a, m_b) - / mean_mass_of_binary(f0, f_bin, m_1, m_2, m_min, m_max, - alpha1, alpha2, alpha3)) + + else: #you want the underlying mass for a population + #consisting of both single and binary stars + + f_corr_binaries = f_corr_binaries*(f_bin_nature/f_bin_simulated) + f_corr_single_stars= (f_corr_single_stars*(1-f_bin_nature))/(1-f_bin_simulated) + underlying_total_mass = ((initial_ZAMS_TOTAL_single / f_corr_single_stars) + + (initial_ZAMS_TOTAL_binaries / f_corr_binaries)) - f_model = fraction_simulated(f_bin, m_1, m_2, m_min, m_max, - alpha1, alpha2, alpha3) + + - return initial_ZAMS_TOTAL_mass / f_corr, f_corr, f_model + return underlying_total_mass, f_corr_single_stars, f_corr_binaries + diff --git a/posydon/popsyn/synthetic_population.py b/posydon/popsyn/synthetic_population.py index 8790f4a8f9..d346ad456d 100644 --- a/posydon/popsyn/synthetic_population.py +++ b/posydon/popsyn/synthetic_population.py @@ -1104,16 +1104,23 @@ def __init__( Pwarn(f"{filename} already contains a mass_per_metallicity " "table. Overwriting the table!", "OverwriteWarning") - simulated_mass = np.sum(self.oneline[["S1_mass_i", "S2_mass_i"]].to_numpy()) - underlying_mass = initial_total_underlying_mass( - df=simulated_mass, **self.ini_params - )[0] + # only load in required columns + tmp_data = self.oneline[['state_i', 'S1_mass_i', 'S2_mass_i']] + mask = tmp_data["state_i"] == "initially_single_star" + filtered_data_single = tmp_data[mask] + filtered_data_binaries = tmp_data[~mask] + + simulated_mass_single = np.nansum(filtered_data_single[["S1_mass_i"]].to_numpy()) + simulated_mass_binaries = np.nansum(filtered_data_binaries[["S1_mass_i", "S2_mass_i"]].to_numpy()) + simulated_mass = simulated_mass_single + simulated_mass_binaries + del tmp_data, filtered_data_single, filtered_data_binaries + self.mass_per_metallicity = pd.DataFrame( - index=[metallicity], - data={ - "simulated_mass": simulated_mass, - "underlying_mass": underlying_mass, - "number_of_systems": len(self.oneline), + index=[metallicity], + data={"simulated_mass": simulated_mass, + "simulated_mass_single": simulated_mass_single, + "simulated_mass_binaries": simulated_mass_binaries, + "number_of_systems": len(self.oneline), }, ) @@ -1130,6 +1137,53 @@ def __init__( self.history_lengths = self.history.lengths self.number_of_systems = self.oneline.number_of_systems self.indices = self.history.indices + + def calculate_underlying_mass(self, f_bin=0.7, overwrite=False): + """Calculate the underlying mass of the population. + + Adds the underlying mass of the population to the mass_per_metallicity table. + + This method calculates the underlying mass of the population based on the simulated mass + of the population and the boundaries of the sampled mass distribution. + You can specify the fraction of binaries in the population using the `f_bin` parameter. + + Parameters + ---------- + f_bin : float, optional + The fraction of binaries in the population. Default is 0.7. + overwrite : bool, optional + If `True`, overwrite the underlying mass values if they already exist. Default is `False`. + + Returns + ------- + np.ndarray + The underlying mass of the population. + """ + + if 'underlying_mass' in self.mass_per_metallicity.columns: + warn_text="underlying_mass already exists in the mass_per_metallicity table." + if overwrite: + Pwarn(warn_text+" Overwriting the underlying_mass values.", "OverwriteWarning") + else: + Pwarn(warn_text+" Not overwriting the underlying_mass values, skipping it.", "IncompletenessWarning") + return + + + underlying_mass = np.zeros(len(self.mass_per_metallicity)) + for i in range(len(self.mass_per_metallicity)): + underlying_mass[i] = initial_total_underlying_mass( + simulated_mass=self.mass_per_metallicity['simulated_mass'].iloc[i], + simulated_mass_single=self.mass_per_metallicity['simulated_mass_single'].iloc[i], + simulated_mass_binaries=self.mass_per_metallicity['simulated_mass_binaries'].iloc[i], + f_bin=f_bin, + **self.ini_params)[0] + + self.mass_per_metallicity['underlying_mass'] = underlying_mass + + # save it to the file + self._save_mass_per_metallicity(self.filename) + + return underlying_mass def export_selection(self, selection, filename, overwrite=False, append=False, history_chunksize=1000000): """Export a selection of the population to a new file @@ -1903,6 +1957,9 @@ def get_efficiency_over_metallicity(self): metallicities = self.mass_per_metallicity.index.to_numpy() + if 'underlying_mass' not in self.mass_per_metallicity.columns: + raise ValueError("Underlying mass not calculated! Please calculate the underlying mass first!") + met_columns = self.select(columns=["metallicity"]).value_counts() met_columns.index = [i[0] for i in met_columns.index.to_numpy().flatten()] @@ -1992,6 +2049,9 @@ def calculate_cosmic_weights(self, SFH_identifier, MODEL_in=None): path_in_file = ( "/transients/" + self.transient_name + "/rates/" + SFH_identifier + "/" ) + + if 'underlying_mass' not in self.mass_per_metallicity.columns: + raise ValueError("Underlying mass not calculated! Please calculate the underlying mass first!") with pd.HDFStore(self.filename, mode="a") as store: if path_in_file + "MODEL" in store.keys(): @@ -2167,6 +2227,9 @@ def plot_delay_time_distribution( Otherwise, it is normalized by the mass of the population at the specified metallicity. """ + if 'underlying_mass' not in self.mass_per_metallicity.columns: + raise ValueError("Underlying mass not calculated! Please calculate the underlying mass first!") + if ax is None: fig, ax = plt.subplots() From cb6226121b2d2c7a196cfd2e0d9ece6dea9e9db8 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Wed, 13 Nov 2024 14:53:36 +0100 Subject: [PATCH 262/319] Update README.md (#434) Add v2 paper --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 38a9258d4c..57f8200f05 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ POSYDON is a next-generation single- and binary-star population synthesis framew POSYDON is being developed by a collaborative team of astrophysicists and computer scientists led by Principal Investigators Tassos Fragos (Université de Genève) and Vicky Kalogera (Northwestern University). -In [Fragos et al. (2023)](https://ui.adsabs.harvard.edu/abs/2023ApJS..264...45F/abstract), we describe the detailed methodology and implementation of POSYDON, including the assumed physics of stellar and binary evolution, the extensive grids of detailed single- and binary-star models, the postprocessing, classification, and interpolation methods we developed for use with the grids, and the treatment of evolutionary phases that are not based on precalculated grids. +In [Fragos et al. (2023)](https://ui.adsabs.harvard.edu/abs/2023ApJS..264...45F/abstract) and [Andrews et al. (2024)](https://ui.adsabs.harvard.edu/abs/2024arXiv241102376A/abstract), we describe the detailed methodology and implementation of POSYDON, including the assumed physics of stellar and binary evolution, the extensive grids of detailed single- and binary-star models, the postprocessing, classification, and interpolation methods we developed for use with the grids, and the treatment of evolutionary phases that are not based on precalculated grids. ### For instruction on how to install and use POSYDON [see here](https://posydon.org/docs/index.html). From d64b01159ca536ac44274d83379e8be8284a23d5 Mon Sep 17 00:00:00 2001 From: Elizabeth Teng <41969755+elizabethteng@users.noreply.github.com> Date: Wed, 13 Nov 2024 07:55:39 -0600 Subject: [PATCH 263/319] update installation test to include M1 and intel chips for macos (#428) * Update continuous_integration.yml add macos-14 github runner which uses the apple silicon M1 chip: https://github.blog/changelog/2024-01-30-github-actions-introducing-the-new-m1-macos-runner-available-to-open-source/ * Update continuous_integration.yml macos-13 runs on intel chip, macos-14 runs on apple silicon M1 chip * Update continuous_integration.yml changed the M chip specification from macos-14 to macos-latest --- .github/workflows/continuous_integration.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/continuous_integration.yml b/.github/workflows/continuous_integration.yml index 8c86047253..8fe2ee40f5 100644 --- a/.github/workflows/continuous_integration.yml +++ b/.github/workflows/continuous_integration.yml @@ -10,7 +10,7 @@ jobs: strategy: matrix: - os: [macos-latest, ubuntu-latest] + os: [macos-13, macos-latest, ubuntu-latest] python-version: ['3.11'] # can add more if we want to support steps: From 713ca6192d8d4978e948c2232c13ddfccd513d86 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Fri, 22 Nov 2024 08:20:46 +0100 Subject: [PATCH 264/319] Add default legend titles based on the termination flag (#432) * add default legend titles based on the termination flag * typo * special case of SN_type, CO_type, state --- posydon/visualization/plot2D.py | 15 +++++++++++++++ posydon/visualization/plot_defaults.py | 13 +++++++++++++ 2 files changed, 28 insertions(+) diff --git a/posydon/visualization/plot2D.py b/posydon/visualization/plot2D.py index 43587945a4..99fa166b74 100644 --- a/posydon/visualization/plot2D.py +++ b/posydon/visualization/plot2D.py @@ -199,6 +199,21 @@ def __init__( sub_varname, default_value ) setattr(self, varname, temp_var) + # for the default 2D legend infer it from the termination_flag + if (hasattr(self, "legend2D") and + (("title" not in self.legend2D.keys()) or + (self.legend2D["title"]==PLOT_PROPERTIES["legend2D"]["title"]))): + if (termination_flag in DEFAULT_LABELS.keys()): + self.legend2D["title"] = DEFAULT_LABELS[termination_flag][0] + elif (('SN_type' in termination_flag) and + ('SN_type' in DEFAULT_LABELS.keys())): + self.legend2D["title"] = DEFAULT_LABELS['SN_type'][0] + elif (('CO_type' in termination_flag) and + ('CO_type' in DEFAULT_LABELS.keys())): + self.legend2D["title"] = DEFAULT_LABELS['CO_type'][0] + elif (('state' in termination_flag) and + ('state' in DEFAULT_LABELS.keys())): + self.legend2D["title"] = DEFAULT_LABELS['state'][0] # plotting fonts plt.rcParams.update(self.rcParams) diff --git a/posydon/visualization/plot_defaults.py b/posydon/visualization/plot_defaults.py index 756e064c2a..0f3e6c99b2 100644 --- a/posydon/visualization/plot_defaults.py +++ b/posydon/visualization/plot_defaults.py @@ -1041,6 +1041,19 @@ def add_flag_to_MARKERS_COLORS_LEGENDS(MARKERS_COLORS_LEGENDS, flag): 'S2_f_beaming': [r'$f_\mathrm{beaming}$', r'$\log_{10}(f_\mathrm{beaming})$'], 'S1_eta' : [r'$\eta$', r'$\log_{10}(\eta)$'], 'S2_eta' : [r'$\eta$', r'$\log_{10}(\eta)$'], + + # Termination flags (one entry for each key in DEFAULT_MARKERS_COLORS_LEGENDS) + 'termination_flag_1' : [r'MESA termination\_code', r'MESA termination\_code'], + 'termination_flag_2' : [r'Mass transfer history', r'Mass transfer history'], + 'termination_flag_3' : [r'Final stellar state of star 1', r'Final stellar state of star 1'], + 'termination_flag_4' : [r'Final stellar state of star 2', r'Final stellar state of star 2'], + 'combined_TF12' : [r'Final mass transfer state', r'Final mass transfer state'], + 'debug' : [r'Final binary state', r'Final binary state'], + 'interpolation_class' : [r'Interpolation class', r'Interpolation class'], + 'interpolation_class_errors' : [r'Interpolation class', r'Interpolation class'], + 'SN_type' : [r'Supernova type', r'Supernova type'], + 'CO_type' : [r'Compact object type', r'Compact object type'], + 'state' : [r'Remnant state', r'Remnant state'], } # add core collapse MODEL variables From 7e19735e54f93bc85e71d32807711a1e6d5a5f2f Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Tue, 26 Nov 2024 17:27:04 +0100 Subject: [PATCH 265/319] change to release on publish (#452) --- .github/workflows/publish-to-anaconda.yml | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/.github/workflows/publish-to-anaconda.yml b/.github/workflows/publish-to-anaconda.yml index 14181931d6..5d545352df 100644 --- a/.github/workflows/publish-to-anaconda.yml +++ b/.github/workflows/publish-to-anaconda.yml @@ -1,9 +1,8 @@ name: publish_conda on: - push: - branches: - - main + release: + types: [published] jobs: publish: From 6321eb5290ad160a7ae332da913b46b51fea607a Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Wed, 27 Nov 2024 16:35:25 +0100 Subject: [PATCH 266/319] Update publish-to-anaconda.yml (#455) --- .github/workflows/publish-to-anaconda.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/publish-to-anaconda.yml b/.github/workflows/publish-to-anaconda.yml index 5d545352df..bfda43019d 100644 --- a/.github/workflows/publish-to-anaconda.yml +++ b/.github/workflows/publish-to-anaconda.yml @@ -13,7 +13,7 @@ jobs: uses: actions/checkout@v2 - name: publish-to-conda - uses: scottcoughlin2014/publish_conda_package_action@v1.0.0-beta + uses: POSYDON-code/publish_to_anaconda@v1.0.1 with: CondaDir: 'conda' Platforms: 'noarch' From 9eb1d4be12bc013c3d37471b47a82c4201d7cb07 Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Wed, 27 Nov 2024 17:46:10 +0100 Subject: [PATCH 267/319] small fix to BBH selection function (#403) * small fix to BBH selection function * rename spin_orbit_tilt to spin_orbit_tilt_at_merger * add docstring info + change function name (not called anywhere) --- posydon/popsyn/transient_select_funcs.py | 24 ++++++++++++++++++------ 1 file changed, 18 insertions(+), 6 deletions(-) diff --git a/posydon/popsyn/transient_select_funcs.py b/posydon/popsyn/transient_select_funcs.py index dbc0854573..56f7cc8031 100644 --- a/posydon/popsyn/transient_select_funcs.py +++ b/posydon/popsyn/transient_select_funcs.py @@ -179,12 +179,12 @@ def BBH_selection_function(history_chunk, oneline_chunk, formation_channels_chun df_transients['S2_mass'] = history_chunk[mask]['S2_mass'] df_transients['S1_spin'] = history_chunk[mask]['S1_spin'] df_transients['S2_spin'] = history_chunk[mask]['S2_spin'] - df_transients['S1_spin_orbit_tilt'] = oneline_chunk['S1_spin_orbit_tilt'] - df_transients['S2_spin_orbit_tilt'] = oneline_chunk['S2_spin_orbit_tilt'] + df_transients['S1_spin_orbit_tilt_at_merger'] = oneline_chunk['S1_spin_orbit_tilt_second_SN'] + df_transients['S2_spin_orbit_tilt_at_merger'] = oneline_chunk['S2_spin_orbit_tilt_second_SN'] df_transients['orbital_period'] = history_chunk[mask]['orbital_period'] df_transients['chirp_mass'] = m_chirp(history_chunk[mask]['S1_mass'], history_chunk[mask]['S2_mass']) df_transients['mass_ratio'] = mass_ratio(history_chunk[mask]['S1_mass'], history_chunk[mask]['S2_mass']) - df_transients['chi_eff'] = chi_eff(history_chunk[mask]['S1_mass'], history_chunk[mask]['S2_mass'], history_chunk[mask]['S1_spin'], history_chunk[mask]['S2_spin'], oneline_chunk['S1_spin_orbit_tilt'], oneline_chunk['S2_spin_orbit_tilt']) + df_transients['chi_eff'] = chi_eff(history_chunk[mask]['S1_mass'], history_chunk[mask]['S2_mass'], history_chunk[mask]['S1_spin'], history_chunk[mask]['S2_spin'], oneline_chunk['S1_spin_orbit_tilt_second_SN'], oneline_chunk['S2_spin_orbit_tilt_second_SN']) df_transients['eccentricity'] = history_chunk[mask]['eccentricity'] if formation_channels_chunk is not None: @@ -193,7 +193,7 @@ def BBH_selection_function(history_chunk, oneline_chunk, formation_channels_chun return df_transients -def DCO_detactability(sensitivity, transient_pop_chunk, z_events_chunk, z_weights_chunk, verbose=False): +def DCO_detectability(sensitivity, transient_pop_chunk, z_events_chunk, z_weights_chunk, verbose=False): '''Calculate the observability of a DCO population. Parameters @@ -208,6 +208,18 @@ def DCO_detactability(sensitivity, transient_pop_chunk, z_events_chunk, z_weight GW detector sensitivity and network configuration you want to use, see arXiv:1304.0670v3 detector sensitivities are taken from: https://dcc.ligo.org/LIGO-T2000012-v2/public + + Note: The population must have the following columns: + - S1_mass : the mass of the first BH + + For the following columns, the function will try to calculate them if they are not present: + - q : the mass ratio + - chi_eff : the effective spin of the BHs + + For q, S2_mass must be present. + For chi_eff, S1_Mass, S2_mass, S1_spin, S2_spin, S1_spin_orbit_tilt_at_merger, S2_spin_orbit_tilt_at_merger must be present. + + These have to be present and a valid value. If not, the function will raise an error! ''' available_sensitiveies = ['O3actual_H1L1V1', 'O4low_H1L1V1', 'O4high_H1L1V1', 'design_H1L1V1'] @@ -233,8 +245,8 @@ def DCO_detactability(sensitivity, transient_pop_chunk, z_events_chunk, z_weight transient_pop_chunk['S2_mass'], transient_pop_chunk['S1_spin'], transient_pop_chunk['S2_spin'], - transient_pop_chunk['S1_spin_orbit_tilt'], - transient_pop_chunk['S2_spin_orbit_tilt']) + transient_pop_chunk['S1_spin_orbit_tilt_at_merger'], + transient_pop_chunk['S2_spin_orbit_tilt_at_merger']) detectable_weights = z_weights_chunk.to_numpy() for i in tqdm(range(z_events_chunk.shape[1]), total=z_events_chunk.shape[1], disable= not verbose): From d21c9b4cf3b1d356c0f9b1f40d4ae8084c5db5ab Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Wed, 27 Nov 2024 17:47:42 +0100 Subject: [PATCH 268/319] Tutorial updates with recent PRs + feedback from collaboration meeting (#406) * normalized_pop_mass * synthetic * authors added * space added * # added * if statement added * Replacing error with a Warning * df1,df2 added in binarypopulation.py * dataframe handling removed * changed from None to 0.0 * underlying mass calculation separate function * change sum to nansum * add checks for underlying_mass existence * simulated_mass not summed again for each loop * rename df and inclusion of f_bin_nature * inclusion of f_bin nature * getting single and binary mass from the independent sample option * modified warnings * binary fraction correction * store the calculated underlying mass to the file * remove note on mpi4py installation on tutorial" * update 10 binaries tutorial * remove mpi4py todo. Installtion only required for multi-core MESA runs * add FAQ answer for select columns error: * consistent naming of channels * update default parameters" * Update popsyn script to 5G default * add custom flow example * add memory usage FAQ * add custom step and flow in notebook * change focus of debug population on evolving single binaries * add references to new notebook * remove output of cells; should be added back to make nicer notebooks * clean tutorial output * add additional FAQ and installation issue * clean up documnation for population params file, SingleStar, and BinaryStar * additional changes * change tutorial to 10 binaries + 1 metallicity, as per discussion * remove reference to personal datapath * cleanup bbh notebook * add warning about default values * Update normalized_pop_mass.py make it the same as development. * Update normalized_pop_mass.py * implement comments Matthias + Seth * fixed additional comment * additional missed comments * Update custom_step_and_flow.py * Update step_CEE.py * Update population_params.rst add warning_verbose and error_checking_verbose * Update population_params_default.ini Add error_checking_verbose and warnings_verbose * Update code-questions.rst Add papers + remove example approximation. --------- Co-authored-by: Dimitris Souropanis Co-authored-by: Dimitris Souropanis Co-authored-by: Eirini Co-authored-by: Dimitris Souropanis Co-authored-by: Dimitris Souropanis --- bin/posydon-setup-popsyn | 2 +- docs/_source/_static/custom.css | 10 +- .../pop_syn/binary_star.rst | 227 +++- .../pop_syn/population_params.rst | 1082 ++++++++++++----- .../pop_syn/single_star.rst | 219 +++- .../pop_syn/synthetic_population.rst | 109 +- .../getting-started/installation-guide.rst | 11 +- .../troubleshooting-faqs/code-questions.rst | 84 +- .../installation-issues.rst | 13 +- .../large_pop_runs_memory.png | Bin 0 -> 39811 bytes .../10_binaries_pop_syn.ipynb | 313 +++-- .../population-synthesis/bbh_analysis.ipynb | 270 ++-- .../population-synthesis/binary-pop-syn.rst | 23 +- ...g_pop.ipynb => custom_step_and_flow.ipynb} | 395 ++---- .../custom_step_and_flow.py | 45 + .../evolve_single_binaries.ipynb | 496 ++++++++ .../population-synthesis/lgrb_pop_syn.ipynb | 35 +- .../one_met_pop_syn.ipynb | 34 +- .../population-synthesis/pop_syn.ipynb | 97 +- .../population_params_end.ini | 519 ++++++++ posydon/binary_evol/CE/step_CEE.py | 2 +- posydon/popsyn/population_params_default.ini | 13 +- posydon/popsyn/synthetic_population.py | 8 +- 23 files changed, 2902 insertions(+), 1105 deletions(-) create mode 100644 docs/_source/troubleshooting-faqs/large_pop_runs_memory.png rename docs/_source/tutorials-examples/population-synthesis/{debug_pop.ipynb => custom_step_and_flow.ipynb} (74%) create mode 100644 docs/_source/tutorials-examples/population-synthesis/custom_step_and_flow.py create mode 100644 docs/_source/tutorials-examples/population-synthesis/evolve_single_binaries.ipynb create mode 100644 docs/_source/tutorials-examples/population-synthesis/population_params_end.ini diff --git a/bin/posydon-setup-popsyn b/bin/posydon-setup-popsyn index 88dc5561b6..c1d860009a 100644 --- a/bin/posydon-setup-popsyn +++ b/bin/posydon-setup-popsyn @@ -128,7 +128,7 @@ if __name__ == '__main__': parser.add_argument('--partition', help='what cluster partition you want to run the script on', default=None) parser.add_argument('--walltime', help='the walltime you would like to use in SLURM format', default='23:00:00') parser.add_argument('--merge_walltime', help='the walltime you would like to use for the merge script in SLURM format', default='12:00:00') - parser.add_argument('--mem_per_cpu', help='the memory per cpu you would like to use in SLURM format', default='4G') + parser.add_argument('--mem_per_cpu', help='the memory per cpu you would like to use in SLURM format', default='5G') parser.add_argument('--account', help='the account you would like to use', default=None) args = parser.parse_args() diff --git a/docs/_source/_static/custom.css b/docs/_source/_static/custom.css index e296b41b6f..9d9fa810c7 100644 --- a/docs/_source/_static/custom.css +++ b/docs/_source/_static/custom.css @@ -12,10 +12,16 @@ background-color: black; } -p { +/* p { text-align: justify; -} +} */ + .wy-table-responsive table td { white-space: normal; } + + +.population-params-table td { + white-space: nowrap !important; +} \ No newline at end of file diff --git a/docs/_source/components-overview/pop_syn/binary_star.rst b/docs/_source/components-overview/pop_syn/binary_star.rst index 0b4f54d5b4..2065235da5 100644 --- a/docs/_source/components-overview/pop_syn/binary_star.rst +++ b/docs/_source/components-overview/pop_syn/binary_star.rst @@ -4,7 +4,10 @@ The BinaryStar object ====================== -The BinaryStar object is composed of two SingleStar objects and contains the current and past states of the binary. Only parameters in the BINARYPROPERTIES list are stored in the history. The current parameter value of the star object is accessed as, e.g. `binary.orbital_period` while his past history with `binary.orbital_period_history`. The two stars are accesses as, e.g. `binary.star_1.mass` while his past history with `binary.star_1.mass_history`. +The ``BinaryStar`` object is composed of two ``SingleStar`` objects (see :ref:`single-star`) and contains the current and past states of the binary. +Only parameters in the ``BINARYPROPERTIES`` list are stored in the history. +The current parameter value of the star object is accessed with, e.g. ``binary.orbital_period`` and the past history via ``binary.orbital_period_history``. +The two stars are accessed with, e.g. (for star 1), ``binary.star_1.mass`` and the past history via ``binary.star_1.mass_history``. To use BinaryStar object import it using: @@ -22,48 +25,86 @@ BINARYPROPERTIES The binary properties are defined as follows -.. csv-table:: BINARYPROPERTIES - :header: "Properties", "Descriptions" - :widths: 50, 150 - - `state`, "The state of the binary, see state options." - `event`, "The event of the binary, see event options." - `time`, "Age of the binary system in yr." - `separation`, "Orbital separation in R_sun." - `orbital_period`, "Orbital period in days." - `eccentricity`, "Orbital eccentricity." - `V_sys`, "Velocity of the centre of mass of the binary [Vx, Vy, Vz] in km/s." - `mass_transfer_case`, "Mass transfer case, see MT case options." - `lg_mtransfer_rate`, "the logarithm of the mass transfer rate in Msun/yr." - `step_names`, "Names of the steps in the evolution." - `step_times`, 'Time spend in the steps in the evolution.' - `rl_relative_overflow_1`, "The relative overflow of the Roche Lobe of star 1." - `rl_relative_overflow_2`, "The relative overflow of the Roche Lobe of star 2." - `trap_radius`, "The trapping radius of the binary in R_sun." - `acc_radius`, "The accretion radius of the binary in R_sun." - `t_sync_rad_1`, "?" - `t_sync_conv_1`, "?" - `t_sync_rad_2`, "?" - `t_sync_conv_2`, "?" - `nearest_neighbour_distance`, "The distance to the nearest neighbour for NN interpolation." +.. list-table:: BINARYPROPERTIES + :header-rows: 1 + :widths: 50 150 + + * - Properties + - Descriptions + * - ``state`` + - The state of the binary, see state options. + * - ``event`` + - The event of the binary, see event options. + * - ``time`` + - Age of the binary system in yr. + * - ``separation`` + - Orbital separation in R_sun. + * - ``orbital_period`` + - Orbital period in days. + * - ``eccentricity`` + - Orbital eccentricity. + * - ``V_sys`` + - Velocity of the centre of mass of the binary [Vx, Vy, Vz] in km/s. + * - ``mass_transfer_case`` + - Mass transfer case, see MT case options. + * - ``lg_mtransfer_rate`` + - The logarithm of the mass transfer rate in Msun/yr. + * - ``step_names`` + - Names of the steps in the evolution. + * - ``step_times`` + - Time spent in the steps in the evolution. + * - ``rl_relative_overflow_1`` + - The relative overflow of the Roche Lobe of star 1. + * - ``rl_relative_overflow_2`` + - The relative overflow of the Roche Lobe of star 2. + * - ``trap_radius`` + - The trapping radius of the binary in R_sun. + * - ``acc_radius`` + - The accretion radius of the binary in R_sun. + * - ``t_sync_rad_1`` + - The synchronization time of the radiative zone of star 1 in yr. + * - ``t_sync_conv_1`` + - The synchronization time of the convective zones of star 1 in yr. + * - ``t_sync_rad_2`` + - The synchronization time of the radiative zone of star 2 in yr. + * - ``t_sync_conv_2`` + - The synchronization time of the convective zones of star 2 in yr. + * - ``nearest_neighbour_distance`` + - The distance to the nearest neighbour for NN interpolation Additional scalar properties can be added during the evolution. Since they do not change over time, they are not stored in the history. -These can requested and will be stored in the output oneline (See the :ref:`Synthetic Populatiohn` and :ref:`Population Parameter Guide` for more information). +These can be requested and will be stored in the output oneline (See the :ref:`Synthetic Population` and :ref:`Population Parameter Guide` for more information). -.. csv-table:: Additional columns - :header: "Properties", "Descriptions" - :widths: 50, 150 - 'interp_class_HMS_HMS', "Description of interp_class_HMS_HMS." - 'interp_class_CO_HMS_RLO', "Description of interp_class_CO_HMS_RLO." - 'interp_class_CO_HeMS', "Description of interp_class_CO_HeMS." - 'interp_class_CO_HeMS_RLO', "Description of interp_class_CO_HeMS_RLO." - 'mt_history_HMS_HMS', "Description of mt_history_HMS_HMS." - 'mt_history_CO_HMS_RLO', "Description of mt_history_CO_HMS_RLO." - 'mt_history_CO_HeMS', "Description of mt_history_CO_HeMS." - 'mt_history_CO_HeMS_RLO', "Description of mt_history_CO_HeMS_RLO." +Additional columns +~~~~~~~~~~~~~~~~~~ + +The additional columns are defined as follows: + +.. list-table:: Additional columns + :header-rows: 1 + :widths: 50 150 + + * - Properties + - Descriptions + * - ``interp_class_HMS_HMS`` + - The interpolation class for the HMS_HMS phase. + * - ``interp_class_CO_HMS_RLO`` + - The interpolation class for the CO_HMS_RLO phase. + * - ``interp_class_CO_HeMS`` + - The interpolation class for the CO_HeMS phase. + * - ``interp_class_CO_HeMS_RLO`` + - The interpolation class for the CO_HeMS_RLO phase. + * - ``mt_history_HMS_HMS`` + - The mass transfer history for the HMS_HMS phase. + * - ``mt_history_CO_HMS_RLO`` + - The mass transfer history for the CO_HMS_RLO phase. + * - ``mt_history_CO_HeMS`` + - The mass transfer history for the CO_HeMS phase. + * - ``mt_history_CO_HeMS_RLO`` + - The mass transfer history for the CO_HeMS_RLO phase. State options @@ -71,49 +112,104 @@ State options Binary states are defined according to the following table: -.. csv-table:: States - :header: "State", "Description" - :widths: 10, 30 +.. list-table:: States + :header-rows: 1 + :widths: 10 30 + + * - State + - Description + * - ``initially_single_star`` + - The binary was initially a single star. + * - ``detached`` + - The stars in the binary are in a detached state. + * - ``RLO1`` + - The binary is Roche Lobe overflowing, star 1 is overfilling the RL. + * - ``RLO2`` + - The binary is Roche Lobe overflowing, star 2 is overfilling the RL. + * - ``contact`` + - The stars in the binary are in contact. + * - ``disrupted`` + - The binary was disrupted. + * - ``merged`` + - The stars in the binary merged. + * - ``initial_RLOF`` + - The binary is in the initial Roche Lobe overflow. + * - ``maxtime`` + - Max time of the evolution was reached. + * - ``FAILED`` + - The evolution failed. - `detached`, "The stars in the binary are in a detached state." - `contact`, "The stars in the binary are in contact." - `RLO1`, "The binary is Roche Lobe overflowing, star 1 is overfilling the RL." - `RLO2`, "The binary is Roche Lobe overflowing, star 2 is overfilling the RL." - `CE`, "The binary is in a Common Envelope phase." - `disrupted`, "The binary was disrupted." - `MaxTimeChanged`, "Max time of the evolution was reached." - -TODO: update properties Event options ~~~~~~~~~~~~~ Binary events are defined according to the following table: -.. csv-table:: Events - :header: "State", "Description" - :widths: 10, 30 +.. list-table:: Events + :header-rows: 1 + :widths: 10 30 + + * - Event + - Description + * - ``ZAMS`` + - Zero Age Main Sequence + * - ``CC1`` + - Core collapse of star 1. + * - ``CC2`` + - Core collapse of star 2. + * - ``oRLO1`` + - The binary is at onset of Roche Lobe overflow, star 1 is overfilling the RL. + * - ``oRLO2`` + - The binary is at onset of Roche Lobe overflow, star 2 is overfilling the RL. + * - ``oCE1`` + - The binary is at the onset of Common Envelope initiated by star 1. + * - ``oCE2`` + - The binary is at the onset of Common Envelope initiated by star 2. + * - ``oDoubleCE1`` + - The binary is at the onset of Double Common Envelope initiated by star 1. + Both stars are post main-sequence. + * - ``oDoubleCE2`` + - The binary is at the onset of Double Common Envelope initiated by star 2. + Both stars are post main-sequence. + * - ``CO_contact`` + - The binary reached contact in the compact object phase. + * - ``redirect_from_ZAMS`` + - The binary was redirected from ZAMS for a variety of reasons. + * - ``redirect_from_CO_HMS_RLO`` + - The binary was redirected from CO_HMS_RLO for a variety of reasons. + * - ``redirect_from_CO_HeMS`` + - The binary was redirected from CO_HeMS for a variety of reasons. + * - ``redirect_from_CO_HeMS_RLO`` + - The binary was redirected from CO_HeMS_RLO for a variety of reasons. + * - ``MaxTime_exceeded`` + - The maximum time of the evolution was exceeded. + * - ``maxtime`` + - The maximum time of the evolution was reached. + * - ``oMerging1`` + - The binary is at the onset of merging, star 1 is overfilling the RL. + * - ``oMerging2`` + - The binary is at the onset of merging, star 2 is overfilling the RL. + * - ``None`` + - No event occurred. + * - ``ERR`` + - An error occurred in the evolution. + * - ``END`` + - The binary evolution was stopped. - `CC1`, "Core collapse of star 1." - `CC2`, "Core collapse of star 2." - `oRLO1`, "The binary is at onset of Roche Lobe overflow, star 1 is overfilling the RL." - `oRLO2`, "The binary is at onset of Roche Lobe overflow, star 2 is overfilling the RL." - `oCE`, "The binary is at the onset of Common Envelope." - `None`, "No event occurred." - `END`, "The binary evolution was stopped." - -TODO: update properties Mass Transfer case ~~~~~~~~~~~~~~~~~~ The mass transfer cases are stored in `mt_history_GRIDTYPE` and are defined according to the following table: TODO: add the table below -.. csv-table:: Mass transfer cases - :header: "State", "Description" - :widths: 10, 30 +.. list-table:: Mass transfer cases + :header-rows: 1 + :widths: 10 30 - `None`, "The binary is not Roche Lobe overflowing." + * - Case + - Description + * - ``None`` + - The binary is not Roche Lobe overflowing. TODO: update properties @@ -146,3 +242,4 @@ The simplest method is to provide the two star objects and `kwargs` of the initi binary = BinaryStar(star_1, star_2, **kwargs3) + diff --git a/docs/_source/components-overview/pop_syn/population_params.rst b/docs/_source/components-overview/pop_syn/population_params.rst index ecb3b12df3..40f17c6d46 100644 --- a/docs/_source/components-overview/pop_syn/population_params.rst +++ b/docs/_source/components-overview/pop_syn/population_params.rst @@ -6,7 +6,13 @@ POSDYON Population Synthesis Configuration Guide This documentation provides a detailed overview of the configuration options available in the Posydon software package. -TODO: fill each table cell with a description of the parameter and the options + +.. warning:: + The default values in the population_params.ini file included in POSYDON + have not been calibrated, validated, or are used by the POSYDON team. They are + often an ad-hoc choice, and the user should carefully consider the values + of each parameter for their science case. + Environment Variables --------------------- @@ -23,12 +29,11 @@ The environment variable `PATH_TO_POSYDON` will be read from your shell session. - `` --------------------- SimulationProperties -------------------- After the environment variables, the next part of the ``population_params.ini`` file -contains how systems will move through the simulation steps (Flow Chart) and the parameters for each step. +contains how systems will move through the simulation steps [Flow Chart](flow_chart.rst) and the parameters for each step. These parameters are read in and used to create a :class:`~posydon.binary_evol.simulationproperties.SimulationProperties`` object. @@ -58,133 +63,238 @@ The flow chart is the core of POSYDON. It controls the mapping between a POSYDON binary object and its step evolution, see the :ref:`Flow Chart Object ` page for more details. .. list-table:: - :widths: 50 50 - :header-rows: 1 - - * - Parameter - - Description/Value - * - `import` - - ['posydon.binary_evol.flow_chart', 'flow_chart'] - * - absolute_import = None - - 'package' (kwarg for importlib.import_module) + :widths: 10 80 10 + :class: population-params-table + :header-rows: 1 + + + * - Parameter + - Description + - Default Value + + * - ``import`` + - | The import path for the flow chart and the name of the flow chart function. + | The flow chart function is used to determine the next step of the binary object. + - ``['posydon.binary_evol.flow_chart', 'flow_chart']`` + + * - ``absolute_import`` + - | An absolute import of a custom step. It follows the same structure as ``import``. + | You can find an example in the tutorials. + - ``None`` Step MESA (HMS-HMS, CO-HMS_RLO, CO-HeMS, CO-HeMS_RLO) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The MESA step is the most important step of POSYDON as it leverages the POSYDON MESA grids to evolve the binary object according to one of the supported MESA binary-star grids. +Below we give the default values for the HMS-HMS step, as an example. + .. list-table:: - :widths: 50 50 - :header-rows: 1 + :widths: 10 80 10 + :class: population-params-table + :header-rows: 1 + + * - Parameter + - Description + - Default Value + + * - ``import`` + - | The import path for the step and the name of the step class. + - ``['posydon.binary_evol.DT.step_MESA', 'step_HMS_HMS']`` + + * - ``absolute_import`` + - | An absolute import of a custom step. It follows the same structure as ``import``. + | ``['import.path', 'name_of_step']`` + - ``None`` + + * - ``interpolation_path`` + - | The path to the interpolation file for the step. + | If None, the path is found by default with + | ``PATH_TO_POSYDON_DATA`` and ``step_name``. + - ``None`` + + * - ``interpolation_filename`` + - | The name of the interpolation file for the step. + | If None, the name is found by default with + | ``PATH_TO_POSYDON_DATA``, ``step_name``, and ``metallicity``. + - ``None`` + + * - ``interpolation_method`` + - | The interpolation method used to interpolate the MESA grid. + + * ``'nearest_neighbour'`` : nearest neighbour interpolation + * ``'linear3c_kNN'`` : linear interpolation with closest neighbours + * ``'1NN_1NN'`` : 1NN interpolation with 1NN interpolation? + - ``'linear3c_kNN'`` + + * - ``save_initial_conditions`` + - | Store the initial conditions when using nearest neighbour interpolation. + - ``True`` + + * - ``track_interpolation`` + - | Track the interpolation when using nearest neighbour interpolation. + - ``False`` + + * - ``stop_method`` + - | The method to stop the evolution of a binary + + * ``'stop_at_end'`` : stop at the end of the simulation + * ``'stop_at_max_time'`` : stop at the maximum time + * ``'stop_at_condition'`` : stop at a condition + + - ``'stop_at_max_time'`` + + * - ``stop_star`` + - | Specifies the star to stop for the condition. + | Relevant when ``stop_method`` is ``'stop_at_condition'``. + + * ``'star_1'`` : stop condition applied to star 1 + * ``'star_2'`` : stop condition applied to star 2 + + - ``'star_1'`` + + * - ``stop_var_name`` + - | The variable name for the stop condition. + | Only applicable when ``stop_method`` is ``'stop_at_condition'``. + - ``None`` + + * - ``stop_value`` + - | The value at which to stop. + | Relevant when ``stop_method`` is ``'stop_at_condition'``. + - ``None`` + + * - ``stop_interpolate`` + - | Specifies whether to interpolate when stopping. + - ``True`` + + * - ``verbose`` + - | Enables verbose mode. + - ``False`` - * - Parameter - - Description/Value - * - `import` - - ['posydon.binary_evol.MESA.step_mesa', 'MS_MS_step'] - * - absolute_import = None - - 'package' (kwarg for importlib.import_module) - * - `interpolation_path` - - None (found by default) - * - `interpolation_filename` - - None (found by default) - * - `interpolation_method` - - '1NN_1NN' ('nearest_neighbour', 'linear3c_kNN', '1NN_1NN' are options) - * - `save_initial_conditions` - - True (only for interpolation_method='nearest_neighbour') - * - `track_interpolation` - - False - * - `stop_method` - - 'stop_at_max_time' ('stop_at_end', 'stop_at_max_time', 'stop_at_condition' are options) - * - `stop_star` - - 'star_1' (only for stop_method='stop_at_condition', 'star_1' and 'star_2' are options) - * - `stop_var_name` - - None (only for stop_method='stop_at_condition', string) - * - `stop_value` - - None (only for stop_method='stop_at_condition', float) - * - `stop_interpolate` - - True - * - `verbose` - - False Step Detached ~~~~~~~~~~~~~ The detached step uses analytical expressions and MESA single star grids to evolve the binary object when it is not interacting. - +It evolves the binary object in isolation until Roche lobe overflow occurs. .. list-table:: - :widths: 50 50 - :header-rows: 1 - - * - Parameter - - Description/Value - * - `import` - - ['posydon.binary_evol.DT.step_detached', 'detached_step'] - * - `absolute_import` - - None ('package' kwarg for importlib.import_module) - * - `matching_method` - - 'minimize' (options 'minimize' 'root') - * - `do_wind_loss` - - True - * - `do_tides` - - True - * - `do_gravitational_radiation` - - True - * - `do_magnetic_braking` - - True - * - `do_stellar_evolution_and_spin_from_winds` - - True - * - `RLO_orbit_at_orbit_with_same_am` - - False - * - `verbose` - - False + :widths: 10 80 10 + :class: population-params-table + :header-rows: 1 + + * - Parameter + - Description + - Default Value + + * - ``import`` + - | The import path for the step and the name of the step class. + - ``['posydon.binary_evol.DT.step_detached', 'detached_step']`` + + * - ``absolute_import`` + - | An absolute import of a custom step. It follows the same structure as ``import``. + | ``['import.path', 'name_of_step']`` + - ``None`` + + * - ``matching_method`` + - | The method to match the MESA single star grid to the binary object. + + * ``'minimize'`` : minimize the difference between the MESA single star grid and the binary object + * ``'root'`` : find the root of the difference between the MESA single star grid and the binary object + + - ``'minimize'`` + + * - ``do_wind_loss`` + - | Enable wind loss. + - ``True`` + + * - ``do_tides`` + - | Enable tides. + - ``True`` + + * - ``do_gravitational_radiation`` + - | Enable gravitational radiation. + - ``True`` + + * - ``do_magnetic_braking`` + - | Enable magnetic braking. + - ``True`` + + * - ``do_stellar_evolution_and_spin_from_winds`` + - | Enable stellar evolution and spin from winds. + - ``True`` + + * - ``RLO_orbit_at_orbit_with_same_am`` + - | Orbit at Roche lobe overflow with the same angular momentum. + | Relevant in eccentric systems. + - ``False`` + + * - ``verbose`` + - | Enables verbose mode. + - ``False`` Step Disrupted ~~~~~~~~~~~~~~ The dirtupted step evolves a system when a supernova has unbound the binary components. This class inherits from the detached step, but only evolves the remaining star in isolation. +This means that this step uses the single stars loaded by the detached step. .. list-table:: - :widths: 50 50 - :header-rows: 1 + :widths: 10 80 10 + :class: population-params-table + :header-rows: 1 - * - Parameter - - Description/Value - * - `import` - - ['posydon.binary_evol.DT.step_disrupted','DisruptedStep'] + * - Parameter + - Description + - Default Value + + * - ``import`` + - | The import path for the step and the name of the step class. + - ``['posydon.binary_evol.DT.step_disrupted', 'DisruptedStep']`` Step Merged ~~~~~~~~~~~ In this step, the system has undergone a merger and is now a single star. This class inherits from the detached step, but only evolves the remaining star in isolation. - +This means that this step uses the single stars loaded by the detached step. .. list-table:: - :widths: 50 50 - :header-rows: 1 + :widths: 10 80 10 + :class: population-params-table + :header-rows: 1 - * - Parameter - - Description/Value - * - `import` - - ['posydon.binary_evol.DT.step_merged','MergedStep'] + * - Parameter + - Description + - Default Value + + * - ``import`` + - | The import path for the step and the name of the step class. + - ``['posydon.binary_evol.DT.step_merged', 'MergedStep']`` Step Initially Single ~~~~~~~~~~~~~~~~~~~~~ This step is used to evolve a single star system. This class inherits from the detached step and evolves the star in isolation. +This means that this step uses the single stars loaded by the detached step. + .. list-table:: - :widths: 50 50 - :header-rows: 1 + :widths: 10 80 10 + :class: population-params-table + :header-rows: 1 + + * - Parameter + - Description + - Default Value + + * - ``import`` + - | The import path for the step and the name of the step class. + - ``['posydon.binary_evol.DT.step_initially_single','InitiallySingleStep']`` - * - Parameter - - Description/Value - * - `import` - - ['posydon.binary_evol.DT.step_initially_single','InitiallySingleStep'] Step Common Envelope ~~~~~~~~~~~~~~~~~~~~ @@ -195,90 +305,207 @@ If the energy budget is greater than the binding energy, the envelope is ejected If the energy budget is less than the binding energy, the system is merged. .. list-table:: - :widths: 50 50 - :header-rows: 1 + :widths: 10 80 10 + :class: population-params-table + :header-rows: 1 + + * - Parameter + - Description + - Default Value + + * - ``import`` + - | The import path for the step and the name of the step class. + - ``['posydon.binary_evol.CE.step_CEE', 'StepCEE']`` + + * - ``absolute_import`` + - | An absolute import of a custom step. It follows the same structure as ``import``. + | ``['import.path', 'name_of_step']`` + - ``None`` + + * - ``prescription`` + - | The prescription used for the common envelope evolution. + - ``'alpha-lambda'`` + + * - ``common_envelope_efficiency`` + - | The efficiency of the common envelope ejection. + - ``1.0`` + + * - ``common_envelope_option_for_lambda`` + - | The option for calculating the lambda parameter. + + * ``'default_lambda'`` : default lambda value + * ``'lambda_from_grid_final_values'`` : lambda from grid final values + * ``'lambda_from_profile_gravitational'`` : lambda from profile gravitational + * ``'lambda_from_profile_gravitational_plus_internal'`` : lambda from profile gravitational plus internal + * ``'lambda_from_profile_gravitational_plus_internal_minus_recombination'`` : lambda from profile gravitational plus internal minus recombination + - ``'lambda_from_grid_final_values'`` + + * - ``common_envelope_lambda_default`` + - | The default lambda value, used only for the ``'default_lambda'`` option. + - ``0.5`` + + * - ``common_envelope_option_for_HG_star`` + - | The option for handling Hertzsprung gap stars in common envelope evolution. + + * ``'optimistic'`` : optimistic scenario + * ``'pessimistic'`` : pessimistic scenario + - ``'optimistic'`` + + * - ``common_envelope_alpha_thermal`` + - | The alpha thermal parameter, used only for the ``'lambda_from_profile_gravitational_plus_internal'`` and ``'lambda_from_profile_gravitational_plus_internal_minus_recombination'`` options. + - ``1.0`` + + * - ``core_definition_H_fraction`` + - | The hydrogen fraction for defining the core. + + * ``0.01`` + * ``0.1`` + * ``0.3`` + - ``0.1`` + + * - ``core_definition_He_fraction`` + - | The helium fraction for defining the core. + - ``0.1`` + + * - ``CEE_tolerance_err`` + - | The tolerance error for the common envelope evolution. + - ``0.001`` + + * - ``common_envelope_option_after_succ_CEE`` + - | The option for handling the system after a successful common envelope ejection. + + * ``'core_not_replaced_noMT'`` : core not replaced, no mass transfer + * ``'core_replaced_noMT'`` : core replaced, no mass transfer + * ``'core_not_replaced_stableMT'`` : core not replaced, stable mass transfer + * ``'core_not_replaced_windloss'`` : core not replaced, wind loss + - ``'core_not_replaced_noMT'`` + + * - ``verbose`` + - | Enables verbose mode. + - ``False`` - * - Parameter - - Description/Value - * - `import` - - ['posydon.binary_evol.CE.step_CEE', 'StepCEE'] - * - `absolute_import` - - None('package' kwarg for importlib.import_module) - * - `prescription` - - 'alpha-lambda' - * - `common_envelope_efficiency` - - 1.0 (float in [0, inf]) - * - `common_envelope_option_for_lambda` - - 'lambda_from_grid_final_values' (options are: (1) 'default_lambda', (2) 'lambda_from_grid_final_values', (3) 'lambda_from_profile_gravitational', (4) 'lambda_from_profile_gravitational_plus_internal', (5) 'lambda_from_profile_gravitational_plus_internal_minus_recombination') - * - `common_envelope_lambda_default` - - 0.5 (float in [0, inf] used only for option (1)) - * - `common_envelope_option_for_HG_star` - - 'optimistic' (options are 'optimistic', 'pessimistic') - * - `common_envelope_alpha_thermal` - - 1.0 (float in [0, inf] used only for option for (4), (5)) - * - `core_definition_H_fraction` - - 0.1 (options are 0.01, 0.1, 0.3) - * - `core_definition_He_fraction` - - 0.1 - * - `CEE_tolerance_err` - - 0.001 (float in [0, inf]) - * - `common_envelope_option_after_succ_CEE` - - 'core_not_replaced_noMT' (options are 'core_not_replaced_noMT' 'core_replaced_noMT', 'core_not_replaced_stableMT' 'core_not_replaced_windloss') - * - `verbose` - - False Step Supernova ~~~~~~~~~~~~~~ -This steps performs the core-collapse supernova evolution of the star. +This step performs the core-collapse supernova evolution of the star. Multiple prescriptions are implemented and can be selected. -If a standard prescription is used, interpolators are available to speed up the calculation and predict additional properties of the collapse, such as the spin. +If a pretrained prescription is used (``use_interp_values=True``), interpolators are available to speed up the calculation and predict additional properties of the collapse, such as the spin. +The collection of trained prescriptions can be found in the ``MODELS.py`` file at ``posydon/grids/MODELS.py``. +.. warning:: + If you want to use a combination that does not have a trained model, please turn off ``use_interp_values``, + otherwise your population runs will fail! Depending on ``use_core_masses``, + the core masses or stellar profiles are used to determine the supernova properties. -.. list-table:: - :widths: 50 50 - :header-rows: 1 - * - Parameter - - Description/Value - * - `import` - - ['posydon.binary_evol.SN.step_SN', 'StepSN'] - * - `absolute_import` - - None ('package' kwarg for importlib.import_module) - * - `mechanism` - - 'Patton&Sukhbold20-engine' (options are: 'direct', Fryer+12-rapid', 'Fryer+12-delayed', 'Sukhbold+16-engine', 'Patton&Sukhbold20-engine') - * - `engine` - - 'N20' (options are 'N20' for 'Sukhbold+16-engine', 'Patton&Sukhbold20-engine' or None for the others) - * - `PISN` - - 'Marchant+19' (options are None, "Marchant+19") - * - `ECSN` - - "Podsiadlowksi+04" (options are "Tauris+15", "Podsiadlowksi+04") - * - `conserve_hydrogen_envelope` - - True - * - `max_neutrino_mass_loss` - - 0.5 (float in [0,inf]) - * - `max_NS_mass` - - 2.5 (float in [0,inf]) - * - `use_interp_values` - - True (if True, use interpolation values for the SN properties, which are calculated during ``step_MESA``) - * - `use_profiles` - - True - * - `use_core_masses` - - True - * - `approx_at_he_depletion` - - False - * - `kick` - - True - * - `kick_normalisation` - - 'one_over_mass' (options are "one_minus_fallback", "one_over_mass", "NS_one_minus_fallback_BH_one", "one", "zero") - * - `sigma_kick_CCSN_NS` - - 265.0 (float in [0,inf]) - * - `sigma_kick_CCSN_BH` - - 265.0 (float in [0,inf]) - * - `sigma_kick_ECSN` - - 20.0 (float in [0,inf]) - * - `verbose` - - False + + + +.. list-table:: + :widths: 10 80 10 + :class: population-params-table + :header-rows: 1 + + * - Parameter + - Description + - Default Value + + * - ``import`` + - | The import path for the step and the name of the step class. + - ``['posydon.binary_evol.SN.step_SN', 'StepSN']`` + + * - ``absolute_import`` + - | An absolute import of a custom step. It follows the same structure as ``import``. + | ``['import.path', 'name_of_step']`` + - ``None`` + + * - ``mechanism`` + - | The mechanism used for the supernova. + + * ``'direct'`` + * ``'Fryer+12-rapid'`` + * ``'Fryer+12-delayed'`` + * ``'Sukhbold+16-engine'`` + * ``'Patton&Sukhbold20-engine'`` + - ``'Patton&Sukhbold20-engine'`` + + * - ``engine`` + - | The engine used for the supernova. + | Relevant for ``'Sukhbold+16-engine'`` and ``'Patton&Sukhbold20-engine'`` mechanisms. + - ``'N20'`` + + * - ``PISN`` + - | The prescription used for pair-instability supernova. + + * ``None`` + * ``'Marchant+19'`` + - ``'Marchant+19'`` + + * - ``ECSN`` + - | The prescription used for electron-capture supernova. + + * ``'Tauris+15'`` + * ``'Podsiadlowksi+04'`` + - ``'Podsiadlowksi+04'`` + + * - ``conserve_hydrogen_envelope`` + - | Conserve the hydrogen envelope during the supernova. + - ``True`` + + * - ``max_neutrino_mass_loss`` + - | The maximum mass loss due to neutrinos. + - ``0.5`` + + * - ``max_NS_mass`` + - | The maximum mass of a neutron star. + - ``2.5`` + + * - ``use_interp_values`` + - | Use interpolation values for the supernova properties, which are calculated during ``step_MESA``. + - ``True`` + + * - ``use_profiles`` + - | Use profiles for the supernova. + - ``True`` + + * - ``use_core_masses`` + - | Use core masses for the supernova. + - ``True`` + + * - ``approx_at_he_depletion`` + - | Approximate at helium depletion. + - ``False`` + + * - ``kick`` + - | Apply a kick to the remnant. + - ``True`` + + * - ``kick_normalisation`` + - | The normalisation method for the kick. + + * ``'one_minus_fallback'`` + * ``'one_over_mass'`` + * ``'NS_one_minus_fallback_BH_one'`` + * ``'one'`` + * ``'zero'`` + - ``'one_over_mass'`` + + * - ``sigma_kick_CCSN_NS`` + - | The standard deviation of the kick velocity for core-collapse supernova neutron stars. + - ``265.0`` + + * - ``sigma_kick_CCSN_BH`` + - | The standard deviation of the kick velocity for core-collapse supernova black holes. + - ``265.0`` + + * - ``sigma_kick_ECSN`` + - | The standard deviation of the kick velocity for electron-capture supernova. + - ``20.0`` + + * - ``verbose`` + - | Enables verbose mode. + - ``False`` Step Double Compact Object ~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -287,17 +514,26 @@ In this step, the system has evolved to a double compact object system. The merger time due to gravitational wave emission is calculated. .. list-table:: - :widths: 50 50 - :header-rows: 1 + :widths: 10 80 10 + :class: population-params-table + :header-rows: 1 - * - Parameter - - Description/Value - * - `import` - - ['posydon.binary_evol.DT.double_CO', 'DoubleCO'] - * - `absolute_import` - - None ('package' kwarg for importlib.import_module) - * - `n_o_steps_interval` - - None + * - Parameter + - Description + - Default Value + + * - ``import`` + - | The import path for the step and the name of the step class. + - ``['posydon.binary_evol.DT.double_CO', 'DoubleCO']`` + + * - ``absolute_import`` + - | An absolute import of a custom step. It follows the same structure as ``import``. + | ``['import.path', 'name_of_step']`` + - ``None`` + + * - ``n_o_steps_interval`` + - | The number of steps interval for the double compact object evolution. + - ``None`` Step End ~~~~~~~~ @@ -306,40 +542,61 @@ The final step of the simulation. All succesfull systems should reach this step. .. list-table:: - :widths: 50 50 - :header-rows: 1 + :widths: 10 80 10 + :class: population-params-table + :header-rows: 1 - * - Parameter - - Description/Value - * - `import` - - ['posydon.binary_evol.step_end', 'step_end'] - * - `absolute_import` - - None ('package' kwarg for importlib.import_module) + * - Parameter + - Description + - Default Value + + * - ``import`` + - | The import path for the step and the name of the step class. + - ``['posydon.binary_evol.step_end', 'step_end']`` + + * - ``absolute_import`` + - | An absolute import of a custom step. It follows the same structure as ``import``. + - ``None`` Extra Hooks ~~~~~~~~~~~ It's possible to add custom hooks to the simulation steps. A few example hooks are provides: ``TimingHooks`` and ``StepNamesHooks`` (See :ref:`Custom Hooks ` for more details) +Each new hook has a unique import number, starting from 1. .. list-table:: - :widths: 50 50 - :header-rows: 1 + :widths: 10 80 10 + :class: population-params-table + :header-rows: 1 - * - Parameter - - Description/Value - * - `import` - - ['posydon.binary_evol.simulationproperties', 'TimingHooks'] - * - `absolute_import_1` - - None - * - `kwargs_1` - - {} - * - `import` - - ['posydon.binary_evol.simulationproperties', 'StepNamesHooks'] - * - `absolute_import_2` - - None - * - `kwargs_2` - - {} + * - Parameter + - Description + - Default Value + + * - ``import_1`` + - | The import path for the hook and the name of the hook class. + - ``['posydon.binary_evol.simulationproperties', 'TimingHooks']`` + + * - ``absolute_import_1`` + - | An absolute import of a custom hook. It follows the same structure as ``import``. + - ``None`` + + * - ``kwargs_1`` + - | Additional keyword arguments for the hook. + - ``{}`` + + * - ``import_2`` + - | The import path for the hook and the name of the hook class. + - ``['posydon.binary_evol.simulationproperties', 'StepNamesHooks']`` + + * - ``absolute_import_2`` + - | An absolute import of a custom hook. It follows the same structure as ``import``. + - ``None`` + + * - ``kwargs_2`` + - | Additional keyword arguments for the hook. + - ``{}`` @@ -377,69 +634,175 @@ These parameters contain options on how the population is evolved, in practical It also contains which sampling distributions to use for the initial conditions of the binaries. .. list-table:: - :widths: 50 50 - :header-rows: 1 + :widths: 10 80 10 + :class: population-params-table + :header-rows: 1 - * - Parameter - - Description/Value - * - `optimize_ram` - - True (save population in batches) - * - `ram_per_cpu` - - None (set maximum ram per cpu before batch saving in GB) - * - `dump_rate` - - 1000 (batch save after evolving N binaries) - * - `temp_directory` - - 'batches' (folder for keeping batch files) - * - `tqdm` - - False (progress bar) - * - `breakdown_to_df` - - True (convert BinaryStars into DataFrames after evolution) - * - `use_MPI` - - True ( if True evolve with MPI, equivalent to the following: from mpi4py import MPI, comm = MPI.COMM_WORLD) - * - `metallicity` - - [2., 1., 0.45, 0.2, 0.1, 0.01, 0.001, 0.0001] (In units of solar metallicity) - * - `entropy` - - `None` (Random Number Generation: uses system entropy (recommended)) - * - `number_of_binaries` - - 1000000 (int) - * - `star_formation` - - 'burst' (options are 'constant' 'burst' 'custom_linear' 'custom_log10' 'custom_linear_histogram' 'custom_log10_histogram') - * - `max_simulation_time` - - 13.8e9 (float in [0,inf]) - * - `binary_fraction` - - 1 (float 0< fraction <=1) - * - `primary_mass_scheme` - - 'Kroupa2001' (options are 'Salpeter', 'Kroupa1993', 'Kroupa2001') - * - `primary_mass_min` - - 6.5 (float in [0,300]) - * - `primary_mass_max` - - 250.0 (float in [0,300]) - * - `secondary_mass_scheme` - - 'flat_mass_ratio' (options are 'flat_mass_ratio', 'q=1') - * - `secondary_mass_min` - - 0.35 (float in [0,300]) - * - `secondary_mass_max` - - 250.0 (float in [0,300]) - * - `orbital_scheme`` - - 'period' (options are 'separation', 'period') - * - `orbital_period_scheme` - - 'Sana+12_period_extended' (used only for orbital_scheme = 'period') - * - `orbital_period_min` - - 0.75 (float i [0,inf]) - * - `orbital_period_max` - - 6000.0 (float i [0,inf]) - * - `#orbital_separation_scheme` - - 'log_uniform' (used only for orbital_scheme = 'separation', 'log_uniform', 'log_normal') - * - `#orbital_separation_min` - - 5.0 (float i [0,inf]) - * - `#orbital_separation_max` - - 1e5 (float i [0,inf]) - * - `#log_orbital_separation_mean` - - None (float i [0,inf] used only for orbital_separation_scheme ='log_normal') - * - `#log_orbital_separation_sigma` - - None (float i [0,inf] used only for orbital_separation_scheme ='log_normal') - * - `eccentricity_scheme` - - 'zero' (options are 'zero', 'thermal', 'uniform') + * - Parameter + - Description + - Default Value + + * - ``optimize_ram`` + - | Save population in batches. + - ``True`` + + * - ``ram_per_cpu`` + - | Set maximum RAM per CPU before batch saving (in GB). + - ``None`` + + * - ``dump_rate`` + - | Batch save after evolving N binaries. + - ``2000`` + + * - ``temp_directory`` + - | Folder for keeping batch files. + - ``'batches'`` + + * - ``tqdm`` + - | Enable progress bar. + - ``False`` + + * - ``breakdown_to_df`` + - | Convert BinaryStars into DataFrames after evolution. + - ``True`` + + * - ``use_MPI`` + - | If True, evolve with MPI (equivalent to: ``from mpi4py import MPI, comm = MPI.COMM_WORLD``). + - ``True`` + + * - ``metallicity`` + - | In units of solar metallicity. Supported values ``[2., 1., 0.45, 0.2, 0.1, 0.01, 0.001, 0.0001]`` + - ``[1.]`` + + * - ``error_checking_verbose`` + - | If True, write all POSYDON errors to stderr at runtime + - ``False`` + + * - ``warnings_verbose`` + - | If True, write all POSYDON warnings to stderr at runtime + - ``False`` + + * - ``entropy`` + - | Random Number Generation: uses system entropy. + - ``None`` + + * - ``number_of_binaries`` + - | Number of binaries to evolve. + - ``10`` + + * - ``star_formation`` + - | What star formation is used to sample the binaries. + | Options: + + * ``'constant'``: sample with a constant rate over time + * ``'burst'``: sample from a burst of star formation + * ``'custom_linear'``: sample with a custom linear rate over time + * ``'custom_log10'``: sample with a custom log10 rate over time + * ``'custom_linear_histogram'``: sample from a custom linear histogram rate over time + * ``'custom_log10_histogram'``: sample from a custom log10 histogram rate over time + - ``'burst'`` + + * - ``max_simulation_time`` + - | Maximum simulation time (in years). + - ``13.8e9`` + + * - ``binary_fraction`` + - | Fraction of binaries (0 < fraction <= 1). + - ``1.0`` + + * - ``primary_mass_scheme`` + - | Options: + + * ``Salpeter``: `Salpeter E. E., 1955, ApJ, 121, 161 `_ + * ``'Kroupa1993'``: `Kroupa P., Tout C. A., Gilmore G., 1993, MNRAS, 262, 545 `_ + * ``'Kroupa2001'``: `Kroupa P., 2001, MNRAS, 322, 231 `_ + - ``'Kroupa2001'`` + + * - ``primary_mass_min`` + - | Minimum primary mass (in solar masses) + | limits: 0-300 + - ``7.0`` + + * - ``primary_mass_max`` + - | Maximum primary mass (in solar masses). + | Needs to be larger than ``secondary_mass_min``. + | limits: 0-300 + - ``150.0`` + + * - ``secondary_mass_scheme`` + - | Options: + + * ``'flat_mass_ratio'``: flat mass ratio distribution + * ``'q=1'``: mass ratio of 1. Ignores ``secondary_mass_min/max`` and sets the secondary mass to the primary mass. + - ``'flat_mass_ratio'`` + + * - ``secondary_mass_min`` + - | Minimum secondary mass (in solar masses). + | Is required to be smaller than the minimum primary mass. + | limits: 0-270 + - ``0.35`` + + * - ``secondary_mass_max`` + - | Maximum secondary mass (in solar masses). + | limits: 0-270 + - ``150.0`` + + * - ``orbital_scheme`` + - | How to the orbital parameter is sampled. + | Options: + + * ``'separation'``: use orbital separation + * ``'period'``: use orbital period + - ``'period'`` + + * - ``orbital_period_scheme`` + - | Used only for ``orbital_scheme = 'period'``. + | Options: + + * ``Sana+12_period_extended``: `Sana et al. 2012 `_ + - ``'Sana+12_period_extended'`` + + * - ``orbital_period_min`` + - | Minimum orbital period (in days). + - ``0.75`` + + * - ``orbital_period_max`` + - | Maximum orbital period (in days). + - ``6000.0`` + + * - ``#orbital_separation_scheme`` + - | Used only for ``orbital_scheme = 'separation'``. + | Options: + + * ``'log_uniform'``: log-uniform distribution + * ``'log_normal'``: log-normal distribution + - ``'log_uniform'`` + + * - ``#orbital_separation_min`` + - | Minimum orbital separation (in solar radii). + - ``5.0`` + + * - ``#orbital_separation_max`` + - | Maximum orbital separation (in solar radii). + - ``1e5`` + + * - ``#log_orbital_separation_mean`` + - | Used only for ``orbital_separation_scheme = 'log_normal'``. + - ``None`` + + * - ``#log_orbital_separation_sigma`` + - | Used only for ``orbital_separation_scheme = 'log_normal'``. + - ``None`` + + * - ``eccentricity_scheme`` + - | How the initial binary eccentricity is sampled. + | Options: + + * ``'zero'`` : zero eccentricity + * ``'thermal'``: thermal distribution + * ``'uniform'``: uniform distribution + + - ``'zero'`` Saving Output @@ -457,18 +820,80 @@ BinaryStar Output The :class:`~posydon.binary_evol.binarystar` class contains the binary systems and the parameters here determine what output of that class will be outputted into the final population file. +`scalar_names` will only be stored in the oneline table. .. list-table:: - :widths: 50 50 - :header-rows: 1 - - * - Parameter - - Description/Value - * - `extra_columns` - - {'step_names':'string', 'step_times':'float64'} ('step_times' with from posydon.binary_evol.simulationproperties import TimingHooks) - * - `only_select_columns` - - ['state', 'event', 'time', 'orbital_period', 'eccentricity', 'lg_mtransfer_rate'] (all options: 'state', 'event', 'time', 'separation', 'orbital_period', 'eccentricity', 'V_sys', 'rl_relative_overflow_1', 'rl_relative_overflow_2', 'lg_mtransfer_rate', 'mass_transfer_case', 'trap_radius', 'acc_radius', 't_sync_rad_1', 't_sync_conv_1', 't_sync_rad_2', 't_sync_conv_2', 'nearest_neighbour_distance') - + :widths: 10 80 10 + :class: population-params-table + :header-rows: 1 + + * - Parameter + - Description + - Default Value + + * - ``extra_columns`` + - | Additional columns to include in the output. + | Note: ``'step_times'`` requires ``TimingHooks`` from ``posydon.binary_evol.simulationproperties``. + - .. code:: python + + {'step_names':'string', 'step_times':'float64'} + + * - ``only_select_columns`` + - | Columns to include in the output. + | Example: ``['state', 'event', 'time', 'orbital_period', 'eccentricity', 'lg_mtransfer_rate']`` + | Options: + + * ``'state'``: The state of the binary system. + * ``'event'``: The event occurring in the binary system. + * ``'time'``: The time of the event in years. + * ``'separation'``: The separation between the binary stars in solar radii. + * ``'orbital_period'``: The orbital period of the binary system in days. + * ``'eccentricity'``: The eccentricity of the binary orbit. + * ``'V_sys'``: The systemic velocity of the binary system. + * ``'rl_relative_overflow_1'``: The Roche lobe relative overflow for star 1. + * ``'rl_relative_overflow_2'``: The Roche lobe relative overflow for star 2. + * ``'lg_mtransfer_rate'``: The logarithm of the mass transfer rate. + * ``'mass_transfer_case'``: The case of mass transfer. + * ``'trap_radius'``: The trapping radius. + * ``'acc_radius'``: The accretion radius. + * ``'t_sync_rad_1'``: The synchronization timescale for radiative zones star 1. + * ``'t_sync_conv_1'``: The synchronization timescale for convective zones star 1. + * ``'t_sync_rad_2'``: The synchronization timescale for radiative zones star 2. + * ``'t_sync_conv_2'``: The synchronization timescale for convective zones star 2. + * ``'nearest_neighbour_distance'``: The distance to the nearest neighbour. + - .. code:: python + + ['state', + 'event', + 'time', + 'orbital_period', + 'eccentricity', + 'lg_mtransfer_rate'] + + * - ``scalar_names`` + - | Scalars of the binary to include in the output. + | Options: + + * ``'interp_class_HMS_HMS'``: interpolation class for HMS-HMS + * ``'interp_class_CO_HMS_RLO'``: interpolation class for CO-HMS-RLO + * ``'interp_class_CO_HeMS'``: interpolation class for CO-HeMS + * ``'interp_class_CO_HeMS_RLO'``: interpolation class for CO-HeMS-RLO + * ``'mt_history_HMS_HMS'``: mass transfer history for HMS-HMS + * ``'mt_history_CO_HMS_RLO'``: mass transfer history for CO-HMS-RLO + * ``'mt_history_CO_HeMS'``: mass transfer history for CO-HeMS + * ``'mt_history_CO_HeMS_RLO'``: mass transfer history for CO-HeMS-RLO + + - .. code:: python + + ['interp_class_HMS_HMS', + 'interp_class_CO_HMS_RLO', + 'interp_class_CO_HeMS', + 'interp_class_CO_HeMS_RLO', + 'mt_history_HMS_HMS', + 'mt_history_CO_HMS_RLO', + 'mt_history_CO_HeMS', + 'mt_history_CO_HeMS_RLO'] + SingleStar 1 and 2 Output ~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -478,18 +903,115 @@ This dictionary contains the parameters that will be saved in the output of the `scalar_names` will only be stored in the oneline table. .. list-table:: - :widths: 50 50 - :header-rows: 1 - - * - Parameter - - Description/Value - * - `include_S1` - - True - * - `only_select_columns` - - ['state', 'mass', 'log_R', 'log_L', 'lg_mdot', 'he_core_mass', 'he_core_radius', 'co_core_mass', 'co_core_radius', 'center_h1', 'center_he4', 'surface_h1', 'surface_he4', 'surf_avg_omega_div_omega_crit', 'spin',] (options are: 'state', 'metallicity', 'mass', 'log_R', 'log_L', 'lg_mdot', 'lg_system_mdot', 'lg_wind_mdot', 'he_core_mass', 'he_core_radius', 'c_core_mass', 'c_core_radius', 'o_core_mass', 'o_core_radius', 'co_core_mass', 'co_core_radius', 'center_h1', 'center_he4', 'center_c12', 'center_n14', 'center_o16', 'surface_h1', 'surface_he4', 'surface_c12', 'surface_n14', 'surface_o16', 'log_LH', 'log_LHe', 'log_LZ', 'log_Lnuc', 'c12_c12', 'center_gamma', 'avg_c_in_c_core', 'surf_avg_omega', 'surf_avg_omega_div_omega_crit', 'total_moment_of_inertia', 'log_total_angular_momentum', 'spin', 'conv_env_top_mass', 'conv_env_bot_mass', 'conv_env_top_radius', 'conv_env_bot_radius', 'conv_env_turnover_time_g', 'conv_env_turnover_time_l_b', 'conv_env_turnover_time_l_t', 'envelope_binding_energy', 'mass_conv_reg_fortides', 'thickness_conv_reg_fortides', 'radius_conv_reg_fortides', 'lambda_CE_1cent', 'lambda_CE_10cent', 'lambda_CE_30cent', 'lambda_CE_pure_He_star_10cent', 'profile [not currently supported]') - * - `scalar_names` - - [ 'natal_kick_array', 'SN_type', 'f_fb', 'spin_orbit_tilt_first_SN','spin_orbit_tilt_second_SN', 'm_disk_accreted', 'm_disk_radiated'] - + :widths: 10 80 10 + :class: population-params-table + :header-rows: 1 + + * - Parameter + - Description + - Default Value + + * - ``include_S1`` + - | Include SingleStar 1 in the output. + - ``True`` + + * - ``only_select_columns`` + - | Columns to include in the output for SingleStar 1. + | Example: ``['state', 'mass', 'log_R', 'log_L', 'lg_mdot', 'he_core_mass', 'he_core_radius', 'co_core_mass', 'co_core_radius', 'center_h1', 'center_he4', 'surface_h1', 'surface_he4', 'surf_avg_omega_div_omega_crit', 'spin']`` + | Options: + + * ``'state'``: The state of the star. + * ``'metallicity'``: The metallicity of the star. + * ``'mass'``: The mass of the star. + * ``'log_R'``: The logarithm of the radius of the star. + * ``'log_L'``: The logarithm of the luminosity of the star. + * ``'lg_mdot'``: The logarithm of the mass loss rate. + * ``'lg_system_mdot'``: The logarithm of the system mass loss rate. + * ``'lg_wind_mdot'``: The logarithm of the wind mass loss rate. + * ``'he_core_mass'``: The mass of the helium core. + * ``'he_core_radius'``: The radius of the helium core. + * ``'c_core_mass'``: The mass of the carbon core. + * ``'c_core_radius'``: The radius of the carbon core. + * ``'o_core_mass'``: The mass of the oxygen core. + * ``'o_core_radius'``: The radius of the oxygen core. + * ``'co_core_mass'``: The mass of the carbon-oxygen core. + * ``'co_core_radius'``: The radius of the carbon-oxygen core. + * ``'center_h1'``: The hydrogen fraction at the center. + * ``'center_he4'``: The helium fraction at the center. + * ``'center_c12'``: The carbon-12 fraction at the center. + * ``'center_n14'``: The nitrogen-14 fraction at the center. + * ``'center_o16'``: The oxygen-16 fraction at the center. + * ``'surface_h1'``: The hydrogen fraction at the surface. + * ``'surface_he4'``: The helium fraction at the surface. + * ``'surface_c12'``: The carbon-12 fraction at the surface. + * ``'surface_n14'``: The nitrogen-14 fraction at the surface. + * ``'surface_o16'``: The oxygen-16 fraction at the surface. + * ``'log_LH'``: The logarithm of the hydrogen burning luminosity. + * ``'log_LHe'``: The logarithm of the helium burning luminosity. + * ``'log_LZ'``: The logarithm of the metallicity burning luminosity. + * ``'log_Lnuc'``: The logarithm of the nuclear burning luminosity. + * ``'c12_c12'``: The carbon-12 to carbon-12 reaction rate. + * ``'center_gamma'``: The central gamma value. + * ``'avg_c_in_c_core'``: The average carbon fraction in the carbon core. + * ``'surf_avg_omega'``: The average surface angular velocity. + * ``'surf_avg_omega_div_omega_crit'``: The ratio of the average surface angular velocity to the critical angular velocity. + * ``'total_moment_of_inertia'``: The total moment of inertia. + * ``'log_total_angular_momentum'``: The logarithm of the total angular momentum. + * ``'spin'``: The spin of the star. + * ``'conv_env_top_mass'``: The mass at the top of the convective envelope. + * ``'conv_env_bot_mass'``: The mass at the bottom of the convective envelope. + * ``'conv_env_top_radius'``: The radius at the top of the convective envelope. + * ``'conv_env_bot_radius'``: The radius at the bottom of the convective envelope. + * ``'conv_env_turnover_time_g'``: The turnover time of the convective envelope (gravity). + * ``'conv_env_turnover_time_l_b'``: The turnover time of the convective envelope (local buoyancy). + * ``'conv_env_turnover_time_l_t'``: The turnover time of the convective envelope (local turbulence). + * ``'envelope_binding_energy'``: The binding energy of the envelope. + * ``'mass_conv_reg_fortides'``: The mass of the convective region for tides. + * ``'thickness_conv_reg_fortides'``: The thickness of the convective region for tides. + * ``'radius_conv_reg_fortides'``: The radius of the convective region for tides. + * ``'lambda_CE_1cent'``: The lambda parameter for common envelope evolution (1%). + * ``'lambda_CE_10cent'``: The lambda parameter for common envelope evolution (10%). + * ``'lambda_CE_30cent'``: The lambda parameter for common envelope evolution (30%). + * ``'lambda_CE_pure_He_star_10cent'``: The lambda parameter for common envelope evolution for pure helium stars (10%). + * ``'profile [not currently supported]'``: The profile (not currently supported). + - .. code:: python + + ['state', + 'mass', + 'log_R', + 'log_L', + 'lg_mdot', + 'he_core_mass', + 'he_core_radius', + 'co_core_mass', + 'co_core_radius', + 'center_h1', + 'center_he4', + 'surface_h1', + 'surface_he4', + 'surf_avg_omega_div_omega_crit', + 'spin',] + + + * - ``scalar_names`` + - | Scalars to include in the output for SingleStar 1. + | Example: ``['natal_kick_array', 'SN_type', 'f_fb', 'spin_orbit_tilt_first_SN', 'spin_orbit_tilt_second_SN', 'm_disk_accreted', 'm_disk_radiated']`` + | Options: + + * ``'natal_kick_array'``: The array of natal kicks. + * ``'SN_type'``: The type of supernova. + * ``'f_fb'``: The fallback fraction. + * ``'spin_orbit_tilt_first_SN'``: The spin-orbit tilt after the first supernova. + * ``'spin_orbit_tilt_second_SN'``: The spin-orbit tilt after the second supernova. + * ``'m_disk_accreted'``: The mass accreted onto the disk. + * ``'m_disk_radiated'``: The mass radiated from the disk. + - .. code:: python + + ['natal_kick_array', + 'SN_type', + 'f_fb', + 'spin_orbit_tilt_first_SN', + 'spin_orbit_tilt_second_SN',] diff --git a/docs/_source/components-overview/pop_syn/single_star.rst b/docs/_source/components-overview/pop_syn/single_star.rst index 20ece309a4..bf48bde6f9 100644 --- a/docs/_source/components-overview/pop_syn/single_star.rst +++ b/docs/_source/components-overview/pop_syn/single_star.rst @@ -4,7 +4,9 @@ The SingleStar object ===================== -The Single Star object contains the current and past states of the star. Only parameters in the STARPROPERTIES list are stored in the history. The current parameter value of the star object is accessed as, e.g. `star.mass` while its past history with `star.mass_history`. +The Single Star object contains the current and past states of the star. +Only parameters in the ``STARPROPERTIES`` list are stored in the history. +The current parameter value of the star object is accessed as, e.g. ``star.mass`` while its past history with ``star.mass_history``. To use SingleStar object import it using: @@ -21,85 +23,167 @@ STARPROPERTIES The star properties are defined as follows -.. csv-table:: STARPROPERTIES - :header: "Properties", "Descriptions" - :widths: 50, 150 - - `state`, "The state of the star, see state options." - `metallicity`, "Fractional metal content (Z) of the star." - `mass`, "Stellar mass in M_sun." - `log_R`, "log10 stellar radius in R_sun." - `log_L`, "log10 surface stellar luminosity in L_sun." - `mdot`, "Stellar mass-loss rate in Msun/yr." - `lg_wind_mdot`, "log10 stellar wind mass-loss rate in Msun/yr." - `he_core_mass`, "Helium core mass in M_sun." - `he_core_radius`, "Helium core radius in R_sun." - `c_core_mass`, "Carbon core mass in M_sun." - `c_core_radius`, "Carbon core mass in M_sun." - `o_core_mass`, "Oxygen core mass in M_sun." - `o_core_radius`, "Oxygen core radius in R_sun." - `center_h1`, "Hydrogen central mass fraction abundance." - `center_he4`, "Helium central mass fraction abundance." - `center_c12`, "Carbon central mass fraction abundance." - `center_n14`, "Nitrogen central mass fraction abundance." - `center_o16`, "Oxygen central mass fraction abundance." - `surface_h1`, "Hydrogen surface mass fraction abundance." - `surface_he4`, "Helium surface mass fraction abundance." - `surface_c12`, "Carbon surface mass fraction abundance." - `surface_n14`, "Nitrogen surface mass fraction abundance." - `surface_o16`, "Oxygen surface mass fraction abundance." - `log_LH`, "log10 total thermal power from PP and CNO, excluding neutrinos devided by L_sun." - `log_LHe`, "log10 total thermal power from triple-alpha, excluding neutrinos devided by L_sun." - `log_LZ`, "log10 total burning power excluding LH and LHe and photodisintegrations devided by L_sun." - `log_Lnuc`, "log10 total nuclear reaction luminosity (LH + LHe LZ) in L_sun." - `c12_c12`, "log10 total luminosity for c12_c12 reaction in L_sun." - `surf_avg_omega_div_omega_crit`, "Average surface omega divided by critical omega." - `total_moment_of_inertia`, "Total moment of inertia in g*cm^2." - `log_total_angular_momentum`, "log10 total angular momentum of the star g*cm^2*s^-1" - `spin`, "Angular momentum of the star in g*cm^2*s^-1 or dimensionless BH spin." - `profile`, "Stellar profile from MESA. [not current supported for the initial-final interpolator]" +.. list-table:: STARPROPERTIES + :header-rows: 1 + :widths: 50 150 + + * - Properties + - Descriptions + * - ``state`` + - The state of the star, see state options. + * - ``metallicity`` + - Fractional metal content (Z) of the star. + * - ``mass`` + - Stellar mass in M_sun. + * - ``log_R`` + - log10 stellar radius in R_sun. + * - ``log_L`` + - log10 surface stellar luminosity in L_sun. + * - ``mdot`` + - Stellar mass-loss rate in Msun/yr. + * - ``lg_wind_mdot`` + - log10 stellar wind mass-loss rate in Msun/yr. + * - ``he_core_mass`` + - Helium core mass in M_sun. + * - ``he_core_radius`` + - Helium core radius in R_sun. + * - ``c_core_mass`` + - Carbon core mass in M_sun. + * - ``c_core_radius`` + - Carbon core radius in R_sun. + * - ``o_core_mass`` + - Oxygen core mass in M_sun. + * - ``o_core_radius`` + - Oxygen core radius in R_sun. + * - ``center_h1`` + - Hydrogen central mass fraction abundance. + * - ``center_he4`` + - Helium central mass fraction abundance. + * - ``center_c12`` + - Carbon central mass fraction abundance. + * - ``center_n14`` + - Nitrogen central mass fraction abundance. + * - ``center_o16`` + - Oxygen central mass fraction abundance. + * - ``surface_h1`` + - Hydrogen surface mass fraction abundance. + * - ``surface_he4`` + - Helium surface mass fraction abundance. + * - ``surface_c12`` + - Carbon surface mass fraction abundance. + * - ``surface_n14`` + - Nitrogen surface mass fraction abundance. + * - ``surface_o16`` + - Oxygen surface mass fraction abundance. + * - ``log_LH`` + - log10 total thermal power from PP and CNO, excluding neutrinos devided by L_sun. + * - ``log_LHe`` + - log10 total thermal power from triple-alpha, excluding neutrinos devided by L_sun. + * - ``log_LZ`` + - log10 total burning power excluding LH and LHe and photodisintegrations devided by L_sun. + * - ``log_Lnuc`` + - log10 total nuclear reaction luminosity (LH + LHe LZ) in L_sun. + * - ``c12_c12`` + - log10 total luminosity for c12_c12 reaction in L_sun. + * - ``surf_avg_omega_div_omega_crit`` + - Average surface omega divided by critical omega. + * - ``total_moment_of_inertia`` + - Total moment of inertia in g*cm^2. + * - ``log_total_angular_momentum`` + - log10 total angular momentum of the star g*cm^2*s^-1 + * - ``spin`` + - Angular momentum of the star in g*cm^2*s^-1 or dimensionless BH spin. + * - ``profile`` + - Stellar profile from MESA. [not currently supported for the initial-final interpolator] Additional scalar properties are added during the evolution depending on which steps the star has undergone. These properties are not stored in the history. -.. csv-table:: Additional output - :header: "Properties", "Descriptions" - :widths: 50, 150 - * - `natal_kick_array` - - The natal kick array for the star if it has undergone a SN. - [velocity, theta, phi, ?] - * - `SN_type` +.. list-table:: Additional output + :header-rows: 1 + :widths: 50 150 + + * - Properties + - Descriptions + * - ``natal_kick_array`` + - | The natal kick array for the star if it has undergone a SN. + | contains: + + * velocity + * theta + * phi + * mean anomaly + + * - ``SN_type`` - The supernova type of the star. - * - `f_fb` + * - ``f_fb`` - The fraction of fallback mass. - * - `spin_orbit_tilt_first_SN` + * - ``spin_orbit_tilt_first_SN`` - The spin-orbit tilt after the first SN, if the star has undergone a SN. - * - `spin_orbit_tilt_second_SN` + * - ``spin_orbit_tilt_second_SN`` - The spin-orbit tilt after the second SN, if a second SN has occurred. - * - `m_disk_radiated` + * - ``m_disk_radiated`` - The mass of the disk radiated in the collapse of the star. + * = ``m_disk_accreted`` + - The mass of the disk accreted in the collapse of the star. -TODO: add missing properties - State options ~~~~~~~~~~~~~ -Star states are defined on their relative position on the HR diagram as follows: - -.. csv-table:: State - :header: "State", "Description" - :widths: 10, 30 - - `PreMS`, "The star is on the pre-main-sequence phase." - `MS`, "The star is in the Main Sequence phase." - `PostMS`, "The star is in the post Main Sequence phase." - `HeMS`, "The star is at the beginning of the He burning phase." - `PostHeMS`, "The star is evolving through the beginning of the He burning phase." - `WD`, "The star is a White Dwarf." - `NS`, "The star is a Neutron Star." - `BH`, "The star is a Black Hole." - -TODO: update the states to POSYDON v2.0.0 +Star states are defined by their burning and surface properties. +These states are combined to describe the stellar state. +We also have additional extra states for objects that are not stars. + +.. list-table:: Surface state + :header-rows: 1 + :widths: 10 30 + + * - State + - Description + * - ``H-rich`` + - The star has a hydrogen-rich surface. + * - ``stripped_He`` + - The star has a stripped helium surface. + * - ``accreted_He`` + - The star has accreted a helium rich layer on its surface. + +.. list-table:: Burning state + :header-rows: 1 + :widths: 10 30 + + * - State + - Description + * - ``non_burning`` + - The star is not burning. + * - ``Core_H_burning`` + - The star is burning hydrogen in its core. + * - ``Shell_H_burning`` + - The star is burning hydrogen in a shell. + * - ``Core_He_burning`` + - The star is burning helium in its core. + * - ``Central_He_depleted`` + - The star has a helium depleted core. + * - ``Shell_He_burning`` + - The star is burning helium in a shell. + * - ``Core_C_burning`` + - The star is burning carbon in its core. + * - ``Central_C_depletion`` + - The star has a carbon depleted core. + +.. list-table:: Additional States + :header-rows: 1 + :widths: 10 30 + + * - State + - Description + * - ``WD`` + - The star is a White Dwarf. + * - ``NS`` + - The star is a Neutron Star. + * - ``BH`` + - The star is a Black Hole. + * - ``massless_remnant`` + - The star exploded or merged. Only its companion is left as a single star. Basic example ~~~~~~~~~~~~~ @@ -114,3 +198,4 @@ The simplest method is to provide `kwargs` of the initial stellar parameters. SingleStar(**kwargs) Now, the SingleStar object is ready to be used. + diff --git a/docs/_source/components-overview/pop_syn/synthetic_population.rst b/docs/_source/components-overview/pop_syn/synthetic_population.rst index bcf684ee38..e95430de28 100644 --- a/docs/_source/components-overview/pop_syn/synthetic_population.rst +++ b/docs/_source/components-overview/pop_syn/synthetic_population.rst @@ -1,16 +1,17 @@ .. _synthetic-population: +.. py:currentmodule:: posydon.popsyn.synthetic_population The Population Object =============================== -The :class:`~posydon.popsyn.synthetic_population.Population` object is an interface for the population data, which is stored in a HDF5 file. +The :class:`~Population` object is an interface for the population data, which is stored in a HDF5 file. You can find more information about the file structure here: :ref:`population-file-structure`. The population file is created by a population synthesis run, and contains the history of each system in the population, and an oneline description of each system and its evolution. -The :class:`~posydon.popsyn.synthetic_population.Population` object is created by passing the path to the population file to the constructor. +The :class:`~Population` object is created by passing the path to the population file to the constructor. It will not load in the data immediately, but only when requested. .. code-block:: python @@ -21,17 +22,17 @@ It will not load in the data immediately, but only when requested. -There are three main components of the :class:`~posydon.popsyn.synthetic_population.Population` object: +There are three main components of the :class:`~Population` object: -- :class:`~posydon.popsyn.synthetic_population.Population.history`: the history of the population -- :class:`~posydon.popsyn.synthetic_population.Population.oneline`: the oneline data of the population -- :class:`~posydon.popsyn.synthetic_population.Population.formation_channels`: the formation channels of the population +- :class:`~Population.history`: the history of the population +- :class:`~Population.oneline`: the oneline data of the population +- :class:`~Population.formation_channels`: the formation channels of the population The :code:`formation_channels` is not immediately available, but can be created using the -:func:`~posydon.popsyn.synthetic_population.Population.calculate_formation_channels` function of the :class:`~posydon.popsyn.synthetic_population.Population` object. +:func:`~Population.calculate_formation_channels` function of the :class:`~Population` object. -Additionally, :meth:`Population.mass_per_metallicity` contains some essential metadata calculated from the populaiton synthesis run. +Additionally, :meth:`Population.mass_per_metallicity` contains some essential metadata calculated from the populaiton synthesis run. It is a pandas.DataFrame with the metallicity (in solar units) as the index and 3 columns: 1. :code:`count`, the number of systems at that metallicity in the file. @@ -43,7 +44,7 @@ It is a pandas.DataFrame with the metallicity (in solar units) as the index and history -------- -:class:`~posydon.popsyn.synthetic_population.Population.history`contains the full history of each system in the population. +:class:`~Population.history` contains the full history of each system in the population. The history is stored in a pandas DataFrame, where each row is a timestep in the evolution of a system. The columns of the DataFrame are the parameters of the system at each timestep. @@ -67,10 +68,10 @@ If you want to access a specific column, you can do so by using the column name. mass_1 = pop.history['S1_mass'] -A more powerful feature is the :func:`posydon.popsyn.synthetic_population.History.select` function, which allows you to select specific rows or columns from the history table. Here's an example: +A more powerful feature is the :func:`History.select` function, which allows you to select specific rows or columns from the history table. Here's an example: .. note:: - The :func:`posydon.popsyn.synthetic_population.History.select` function only allows the use of :code:`where=` + The :func:`History.select` function only allows the use of :code:`where=` for specific columns and the index. The columns are limited to those containing strings. .. code-block:: python @@ -81,7 +82,7 @@ A more powerful feature is the :func:`posydon.popsyn.synthetic_population.Histor # using where with a string column mass_ZAMS = pop.history.select(columns=['S1_mass'], where='event == "ZAMS"') -If you want to have a peak, you can use the :meth:`~posydon.popsyn.synthetic_population.Population.head` or :meth:`~posydon.popsyn.synthetic_population.Population.tail` functions. +If you want to have a peak, you can use the :meth:`~Population.head` or :meth:`~Population.tail` functions. .. code-block:: python pop.history.head(10) @@ -90,23 +91,23 @@ If you want to have a peak, you can use the :meth:`~posydon.popsyn.synthetic_pop Additional functions are made available for easy of use. -If you want to check the length of the history of a system, you can use :attr:`Population.history.lengths` or :attr:`Population.history_lengths`. +If you want to check the length of the history of a system, you can use :attr:`Population.history.lengths` or :attr:`Population.history_lengths`. .. code-block:: python print(pop.history.lengths) print(pop.history_lengths) -The total number of systems in the population can be found with :attr:`~posydon.popsyn.synthetic_population.Population.History.number_of_systems`. +The total number of systems in the population can be found with :attr:`~Population.History.number_of_systems`. .. code-block:: python print(pop.history.number_of_systems) -Similarly, if you would like to check the indices in the file, you can use :attr:`Population.indices` or :attr:`Population.history.indices`. +Similarly, if you would like to check the indices in the file, you can use :attr:`Population.indices` or :attr:`Population.history.indices`. The indices are useful in selecting systems from the population. -It's also possible to check the columns in the history table with :attr:`Population.columns` or :attr:`Population.history.columns`. +It's also possible to check the columns in the history table with :attr:`Population.columns` or :attr:`Population.history.columns`. .. code-block:: python @@ -120,7 +121,7 @@ It's also possible to check the columns in the history table with :attr:`Populat oneline -------- -:meth:`~posydon.popsyn.synthetic_population.Population.oneline` contains a single line description of each system in the population. +:meth:`~Population.oneline` contains a single line description of each system in the population. This is useful for a quick inspection of the population. The oneline data is stored in a pandas DataFrame, where each row is a system in the population. @@ -130,14 +131,14 @@ Besides the initial and final properties of the system, this table also contains The initial-final properties are those in the history table, but with the postfix :code:`_i` and :code:`_f` depending on the initial or final value. The additional values are the scalar values from the individual stars and the binary properties (See :ref:`Single Star` and :ref:`Binary Star`). -Additionally, WARNING, FAILED, and metallicity columns are available in the oneline table. +Additionally, ``WARNING``, ``FAILED``, and metallicity columns are available in the oneline table. .. csv-table:: Additional columns :header: "Properties", "Descriptions" :widths: 50, 150 - `FAILED`, Indicates if the system failed during the population synthesis run. - `WARNING`, Indicates if there were any warnings for the system during the population synthesis run. - `metallicity`, The metallicity of the system. + ``FAILED``, Indicates if the system failed during the population synthesis run. + ``WARNING``, Indicates if there were any warnings for the system during the population synthesis run. + ``metallicity``, The metallicity of the system. Like the :code:`history` access, you can access the oneline data by using the index of the system or the columns. @@ -148,15 +149,15 @@ Like the :code:`history` access, you can access the oneline data by using the in mass = pop.oneline['S1_mass_i'] selection = pop.oneline.select(columns=['S1_mass_i'], where='index==10') -You can check the columns and indices of the oneline table with :attr:`Population.oneline.columns` and :attr:`Population.columns['oneline']` and :attr:`Population.columns['oneline']`. .. code-block:: python print(pop.oneline.columns) print(pop.columns['oneline']) -The number of systems in the population can be found with :attr:`Population.oneline.number_of_systems`. -The length and indices of the oneline table can be found with :attr:`Population.oneline.lengths`, and :attr:`Population.oneline.indices`, respectively. +The number of systems in the population can be found with :attr:`Population.oneline.number_of_systems`. +The length and indices of the oneline table can be found with :attr:`Population.oneline.lengths`, and :attr:`Population.oneline.indices`, respectively. .. code-block:: python @@ -169,10 +170,10 @@ The length and indices of the oneline table can be found with :attr:`Population. formation_channels ------------------ -:class:`~posydon.popsyn.synthetic_population.Population.formation_channels` contains the formation channels of each system in the population. +:class:`~Population.formation_channels` contains the formation channels of each system in the population. The formation channels are stored in a pandas DataFrame, where each row is a system in the population. -The formation channels are calculated by combining the `event` column in the history table into a single string using the :func:`~posydon.popsyn.synthetic_population.Population.calculate_formation_channels` function of the :class:`~posydon.popsyn.synthetic_population.Population` object. +The formation channels are calculated by combining the `event` column in the history table into a single string using the :func:`~Population.calculate_formation_channels` function of the :class:`~Population` object. Two columns are available in the formation channels table: @@ -185,21 +186,21 @@ Additional Attributes --------------------- -- :attr:`~posydon.popsyn.synthetic_population.Population.ini_parameters`: The parameters for the initial sampling conditions of the population synthesis run. +- :attr:`~Population.ini_parameters`: The parameters for the initial sampling conditions of the population synthesis run. -- :attr:`~posydon.popsyn.synthetic_population.Population.mass_per_metallicity`: The mass per metallicity bin for the population synthesis run. +- :attr:`~Population.mass_per_metallicity`: The mass per metallicity bin for the population synthesis run. The `underlying_mass` is calculated with the assumption that binary fraction == 1. -- :attr:`~posydon.popsyn.synthetic_population.Population.history_lengths`: The length of the history of each system in the population. - This is created the first time the file is opened with the :class:`~posydon.popsyn.synthetic_population.Population` object. +- :attr:`~Population.history_lengths`: The length of the history of each system in the population. + This is created the first time the file is opened with the :class:`~Population` object. Exporting part of the population -------------------------------- -The class function :func:`Population.export_selection` allows you to export part of the population to a new HDF5 file. +The class function :func:`Population.export_selection` allows you to export part of the population to a new HDF5 file. It takes a list of indices of the systems you want to export, and the path to the new file. This will copy the systems with the given indices to the new file, which includes their history, oneline data, and formation channels (if presen). @@ -219,12 +220,12 @@ If the file already exists, the function will raise an error. If you want to ove TransientPopulation =================== -The :class:`~posydon.popsyn.synthetic_population.TransientPopulation` object is an interface for the transient data of the population. -It inherits from the :class:`~posydon.popsyn.synthetic_population.Population` object, but also allows access to the transient populations in the population file. +The :class:`~TransientPopulation` object is an interface for the transient data of the population. +It inherits from the :class:`~Population` object, but also allows access to the transient populations in the population file. A transient population consists of instantaneous events in the population, such as supernovae, kilonovae, or gamma-ray bursts. These have a single "moment" in time, and are not part of the evolution of the system. -The :class:`~posydon.popsyn.synthetic_population.TransientPopulation` class has been designed to handle these events, but future versions of the code may include more complex populations. +The :class:`~TransientPopulation` class has been designed to handle these events, but future versions of the code may include more complex populations. .. code-block:: python @@ -238,15 +239,15 @@ Creating a TransientPopulation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -The transient population is created using the :func:`posydon.popsyn.synthetic_population.Population.create_transient_population` function of the :class:`~posydon.popsyn.synthetic_population.Population` object. +The transient population is created using the :func:`Population.create_transient_population` function of the :class:`~Population` object. This function creates a separate table with each transient in the population file. It loops over all the systems in the population in chunks and applies the given function to them. -The :func:`posydon.popsyn.synthetic_population.Population.create_transient_population` function takes a function as an argument: :code:`selection_function`. +The :func:`Population.create_transient_population` function takes a function as an argument: :code:`selection_function`. The :code:`selection_function` takes 3 arguments: :code:`history_chunk`, :code:`oneline_chunk`, and :code:`formation_channels_chunk` (optional). These chunks are cut based on a given chunksize, which is set to 1000000 by default, and are cut on system. -This means that always a complete history of a system is passed to the function by :func:`posydon.popsyn.synthetic_population.Population.create_transient_population`. +This means that always a complete history of a system is passed to the function by :func:`Population.create_transient_population`. :code:`selection_function` is a function you can adapt to your own needs, and examples of building one are given in the :ref:`tutorial-examples.population-synthesis.bbh-analysis` or :ref:`tutorial-examples.population-synthesis.lgrb_pop_syn`. @@ -264,7 +265,7 @@ Accessing TransientPopulation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ After loading a transient population, you keep access to the history and oneline data of the population. -Now, you can access the transient data of the population using :attr:`TrannsientPopulation.population`. +Now, you can access the transient data of the population using :attr:`TrannsientPopulation.population`. .. code-block:: python @@ -286,7 +287,7 @@ With this population, you can calculate additional information, such as the effi Plotting ~~~~~~~~~~ -The :class:`~posydon.popsyn.synthetic_population.TransientPopulation` contains a few plotting functions for ease. +The :class:`~TransientPopulation` contains a few plotting functions for ease. .. code-block:: python # plots the efficiency over metallicity per channel @@ -299,7 +300,7 @@ The :class:`~posydon.popsyn.synthetic_population.TransientPopulation` contains a trans_pop.plot_delay_time_distribution(metallicity=0.1, bins=log_bins) -The most useful function is :func:`plot_popsyn_over_grid_slice`. +The most useful function is :func:`plot_popsyn_over_grid_slice`. It allows you to overplot properties of your TransienPopulation onto the grids. .. note:: @@ -319,7 +320,7 @@ If you like to write to a folder, you can use :code:`plot_dir='path/to/dir'` and Rates ===== -The :class:`~posydon.popsyn.synthetic_population.Rates` object inherits from the :class:`~posydon.popsyn.synthetic_population.TransientPopulation` object +The :class:`~Rates` object inherits from the :class:`~TransientPopulation` object and is used to access the cosmic rate data of the transient population. It also allows the user to calculate the intrinsic rate density of the events in the population, and apply observational effects to the population. @@ -334,7 +335,7 @@ It also allows the user to calculate the intrinsic rate density of the events in Creating a Rates object ~~~~~~~~~~~~~~~~~~~~~~~ -Cosmic weights are added to the population file using the :func:`~posydon.popsyn.synthetic_population.TransientPopulation.calculate_cosmic_weights` function. +Cosmic weights are added to the population file using the :func:`~TransientPopulation.calculate_cosmic_weights` function. This function calculates the cosmic weights of the events in the population based on the birth redshifts and the population weight. The function takes an ``SFH_identifier``, which is where the cosmic weights are stored in the population file. The ``MODEL_in`` argument is used to specify the model parameters for the rate calculation. @@ -364,7 +365,7 @@ The cosmic rate data is stored in 3 different tables in the population file: 3. :code:`weights` : The weights of each event based on their birth redshifts and their population weight. -You can calculate the intrinsic rate density of the events in the population using :func:`posydon.popsyn.synthetic_population.Rates.calculate_intrinsic_rate_density`. +You can calculate the intrinsic rate density of the events in the population using :func:`Rates.calculate_intrinsic_rate_density`. This populates the :code:`intrinsic_rate_density` table in the population file. .. code-block:: python @@ -374,7 +375,7 @@ This populates the :code:`intrinsic_rate_density` table in the population file. rates.plot_intrinsic_rate_density() -The :class:`~posydon.popsyn.synthetic_population.Rates` object also contains information about the metallicity and redshift bins and edges. +The :class:`~Rates` object also contains information about the metallicity and redshift bins and edges. .. code-block:: python @@ -400,7 +401,7 @@ Applying observational effects Although the intrinsic rate density is a useful quantity, it is not directly observable, especially for binary black holes. As such, we also include the possibility to apply observational effects to the population. -This is done using the :func:`posydon.popsyn.synthetic_population.Rates.calculate_observable_population` function. +This is done using the :func:`Rates.calculate_observable_population` function. It reweights the event weights based on the detection efficiency of the event. The function takes a function as an argument: :code:`observable_func` and a ``observable_identifier``. @@ -428,8 +429,8 @@ However, since that function requires a detection argument, it requires a wrappe Accessing Observable Population data ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -The observable population is accesed through the :code:`observable_population` attribute of the :class:`~posydon.popsyn.synthetic_population.Rates` object. -You require to know the observable_identifier to access the data, which can be accessed with :attr:`Rates.observable_population_names` +The observable population is accesed through the :code:`observable_population` attribute of the :class:`~Rates` object. +You require to know the observable_identifier to access the data, which can be accessed with :attr:`Rates.observable_population_names` .. code-block:: python @@ -473,7 +474,7 @@ other elements are optional and can be added by the user. The tables describe the location of the data inside the population file. This is only necessary if you want to access the data directly from the file. -If you use the :meth:`~posydon.popsyn.synthetic_population.Population` object, you can access the data directly from the object. +If you use the :meth:`~Population` object, you can access the data directly from the object. .. list-table:: Standard Components of a Population file :widths: 50 150 @@ -501,15 +502,15 @@ Based on the components and the user given identifiers, the data is stored in th * - Path - Description * - `history_lengths` - - The length of the history of each system in the population. This is created the first time the file is opened with the :class:`~posydon.popsyn.synthetic_population.Population` object. + - The length of the history of each system in the population. This is created the first time the file is opened with the :class:`~Population` object. * - `formation_channels` - - The formation channels of each system in the population. This combines the `event` column in the history table into a single string. :func:`~posydon.popsyn.synthetic_population.Population.calculate_formation_channels` is used to create this component. + - The formation channels of each system in the population. This combines the `event` column in the history table into a single string. :func:`~Population.calculate_formation_channels` is used to create this component. * - `transiens/{transient_name}` - - The transient data of each system in the population. The transient data is stored in a separate table for each transient. This is created by :func:`~posydon.popsyn.synthetic_population.Population.create_transient_population`. + - The transient data of each system in the population. The transient data is stored in a separate table for each transient. This is created by :func:`~Population.create_transient_population`. * - `transiens/{transient_name}/efficiencies` - - The transient efficiencies over metallicity. This is calculated with :func:`~posydon.popsyn.synthetic_population.TransientPopulation.get_efficiency_over_metallicity`. + - The transient efficiencies over metallicity. This is calculated with :func:`~TransientPopulation.get_efficiency_over_metallicity`. * - `transiens/{transient_name}/rates/{SFH_identifier}/MODEL` - - The MODEL parameters for the specific transient rate calculations done with :func:`~posydon.popsyn.synthetic_population.TransientPopulation.calculate_cosmic_weights`. + - The MODEL parameters for the specific transient rate calculations done with :func:`~TransientPopulation.calculate_cosmic_weights`. * - `transiens/{transient_name}/rates/{SFH_identifier}/birth` - A table containing the birth redshifts and lookback times used in the rate calculation. * - `transiens/{transient_name}/rates/{SFH_identifier}/z_events` @@ -517,7 +518,7 @@ Based on the components and the user given identifiers, the data is stored in th * - `transiens/{transient_name}/rates/{SFH_identifier}/weights` - The weights of each event based on their birth redshifts and their population weight. * - `transiens/{transient_name}/rates/{SFH_identifier}/intrinsic_rate_density` - - The intrinsic rate density of the events in the population, calculated with :func:`~posydon.popsyn.synthetic_population.Rates.calculate_intrinsic_rate_density`. + - The intrinsic rate density of the events in the population, calculated with :func:`~Rates.calculate_intrinsic_rate_density`. diff --git a/docs/_source/getting-started/installation-guide.rst b/docs/_source/getting-started/installation-guide.rst index 3df0269af9..aa1d7bebef 100644 --- a/docs/_source/getting-started/installation-guide.rst +++ b/docs/_source/getting-started/installation-guide.rst @@ -104,11 +104,11 @@ For users interested in the latest features and developments, you can install PO Refer back to the recommended installation steps, starting from :ref:`point 4 `, to download the required dataset and set the necessary environment variables. -Using POSYDON on HPC Facilities +Running grids using POSYDON on HPC Facilities ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -(TODO: check whether it is still needed) -If you are planning to run POSYDON's population synthesis on a High-Performance Computing (HPC) facility, it's essential to have `mpi4py` installed to enable parallel computations. +If you are planning to create MESA grids using POSYDON on HPC facilities, it's essential to have ``mpi4py`` installed to take advantage of parallel computations. +You do not need to have ``mpi4py`` installed if you are only running population synthesis simulations. 1. **Install mpi4py via Anaconda (Recommended)**: @@ -122,8 +122,9 @@ If you are planning to run POSYDON's population synthesis on a High-Performance pip install ".[hpc]" - .. warning:: - Users have reported issues when trying to install `mpi4py` via pip. If you encounter any issues, try installing `mpi4py` through Anaconda. If you cannot solve the issue, please refer to the :ref:`Troubleshooting Guide ` or seek support from the community or developers, see the :ref:`contact us ` page. + +.. warning:: + Users have reported issues when trying to install `mpi4py` via pip. If you encounter any issues, try installing `mpi4py` through Anaconda. If you cannot solve the issue, please refer to the :ref:`Troubleshooting Guide ` or seek support from the community or developers, see the :ref:`contact us ` page. Machine Learning Modules Installation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/_source/troubleshooting-faqs/code-questions.rst b/docs/_source/troubleshooting-faqs/code-questions.rst index 22a7781874..aea8ee2382 100644 --- a/docs/_source/troubleshooting-faqs/code-questions.rst +++ b/docs/_source/troubleshooting-faqs/code-questions.rst @@ -11,26 +11,88 @@ Frequently Asked Questions 1. **How do I start a basic population synthesis simulation with POSYDON?** - Answer: Start with the :ref:`binary population synthesis guide ` to learn how to run POSYDON. -2. **Which parameters can I customize for my simulations?** - - Answer: POSYDON allows customization of ... (TODO list or briefly describe customizable parameters). +2. **I'm getting an error when using the** ``where`` **parameter in the** ``select`` **function** -3. **I'm getting an error message when trying to run a simulation. What does it mean?** - - Answer: Error messages typically indicate ... (TODO provide guidance on interpreting common error messages or point to a troubleshooting guide). + If you're trying to select based on values in an unsupported column, you'll receive an error message like below. + Only specific columns are supported for selection. These are limited to string columns, the indices, and the column names. + Please perform the selection in-memory instead. -4. **How can I visualize the results from my simulation?** - - Answer: You can utilize the experimental visualization libraries by ... (TODO provide a brief overview or link to visualization documentation). + .. code-block:: python -5. **How do I use the new machine learning features in POSYDON?** - - Answer: To leverage the ML features, ensure you've installed the necessary dependencies. Then, you can ... (TODO brief overview or link to ML features documentation). + population.history.select('S1_mass > 10') + + The above code will produce the following error: + .. code-block:: python + + ValueError: The passed where expression: S1_mass > 10 + contains an invalid variable reference + all of the variable references must be a reference to + an axis (e.g. 'index' or 'columns'), or a data_column + The currently defined references are: index,columns,state,event,step_names,S1_state,S2_state + + +3. **How should I tune my memory usage for a population synthesis run?** + + A population run requires at a bare minimum of 4GB of memory per CPU. + However, this restricts the number of binaries you can keep in memory and requires a :code:`dump_rate < 1000` to keep the memory usage low, which slows down the simulation. + + As such, 5GB per CPU is a better starting point. This allows you to keep more binaries in memory and increases the speed of the population synthesis run. + The figure below can be used to fine-tune your memory usage. + It shows the memory usage of two large population synthesis runs with a 7GB and a 8GB limit with a :code:`dump_rate` of 8000 and 10.000 binaries respectively. + + .. image:: ./large_pop_runs_memory.png + :alt: Memory usage of two large population synthesis runs. + :width: 700px + + The memory usage of the 8000 :code:`dump_rate` run is stable at around 6GB, while the 10.000 :code:`dump_rate` run is stable at around 6.8GB. + + +4. **What should the walltime and job array size be for my population synthesis run?** + + The :code:`walltime`` and job array size are dependent on the number of binaries you want to simulate and the memory usage of the simulation. + The job array size should be set such that the number of binaries per job is at least 1000, since there's a minimum overhead per job due to loading the grids. + + The :code:`walltime` depends on the number of binaries per job, where each binary takes about 1-2 seconds to run. + For example, with 100.000 binaries split over 100 jobs (per metallicity), which means that every job runs 1.000 binaries. This will take around 33 minutes per job. So a :code:`walltime` of `00:45:00` is reasonable. + + The balance between :code:`walltime` and the size of the job array is important. + If the :code:`walltime` is too long, it might be worth increasing the job array size to decrease the time per job and allow the population synthesis to finish faster. + But if the :code:`walltime` is too short, the job array size should be decreased, since each job has an initial overhead that is not dependent on the number of binaries in the job. + + .. note:: + The processing time increases if you make the `dump_rate` too low due to many I/O operations. + +5. **I am unable to open HDF5 files created by POSYDON. What should I do?** + + If you're on a Mac, there might be an issue with the HDF5 installation. + Make sure you have the `hdf5` and `pytables` packages installed through conda in your environment with `conda install hdf5 pytables` before running POSYDON! + Although they are dependencies of POSYDON, sometimes they're not installed correctly on Mac. 6. **Are there any examples or tutorials available?** - Answer: Yes, you can check our :ref:`roadmap ` for tutorials related to different POSYDON components, including population synthesis, creating core datasets, and running your own MESA grids with POSYDON. 7. **Can I run POSYDON on an HPC facility?** - - Answer: Absolutely! Ensure you've installed ``mpi4py`` as outlined in :ref:`our installation guide `. Refer to `our HPC guide <../tutorials-examples/population-synthesis/pop_syn.ipynb>`_ for detailed instructions on running POSYDON in an HPC environment. + - Answer: Absolutely! Refer to `our HPC guide <../tutorials-examples/population-synthesis/pop_syn.ipynb>`_ for detailed instructions on running POSYDON in an HPC environment. + +8. **Help, I'm stuck! Where can I get support?** + - Please check `our email group `_ if your question hasn't been answered yet. + Otherwise, please email us at posydon-users [at] googlegroups.com + +9. **How can I stay updated with the latest features and updates?** + - Answer: You can regularly visit our `official website `_ for news and updates. + +10. **I've come across a FAILED binary. What does this mean?** + - Answer: A FAILED binary is one that has encountered an error during the simulation due to the default flow and steps of POSYDON being unable to evolve them. + This can be due to a variety of reasons, for example: + + - The evolutionary state of the binary is not represented in the currently supported stellar evolution grids. + For example, we do not have a grid for Roche lobe overflow between two helium stars. + - The binary has masses outside the grid range. For example, the HMS-HMS grid does not contain binaries with a secondary mass below 0.5. + - The binary could not be matched to single star or a binary due to a too large matching error. + +10. **What approximations does POSYDON make?** + This is a complex question and the best location to look at would be the POSYDON papers: `Fragos et al. (2022) `_ and `Andrews et al. (submitted) `_. -8. **How can I stay updated with the latest features and updates?** - - Answer: You can regularly visit our `official website `_ for news and updates. Also, consider subscribing to our mailing list. Additional Resources ~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/_source/troubleshooting-faqs/installation-issues.rst b/docs/_source/troubleshooting-faqs/installation-issues.rst index bae0d1af30..1160afc8a4 100644 --- a/docs/_source/troubleshooting-faqs/installation-issues.rst +++ b/docs/_source/troubleshooting-faqs/installation-issues.rst @@ -12,17 +12,20 @@ Common Installation Issues - **Description**: Sometimes, certain dependencies might fail to install or conflict with pre-existing ones. - **Solution**: Try installing the failed dependencies separately using ``pip`` or ``conda`` before installing POSYDON. If using ``conda``, consider creating a fresh environment specifically for POSYDON. -2. **Insufficient Storage Space**: + We try our best to keep the dependencies updated and compatible. However, if you encounter issues, please let us know! + +2. **Failed to Set Environment Variables**: + - **Description**: After installation, you might forget to set the ``PATH_TO_POSYDON`` and ``PATH_TO_POSYDON_DATA`` environment variables. + - **Solution**: Refer back to the [installation guide](installation-guide) to ensure all post-installation steps are followed. + +3. **Insufficient Storage Space**: - **Description**: POSYDON requires around 40GB of free storage space for the lite MESA simulation library and related files. - **Solution**: Ensure you have enough space on your installation drive. Delete unnecessary files or consider using a larger storage solution. -3. **Proxy or Network Issues**: +4. **Proxy or Network Issues**: - **Description**: Installation might fail if you're behind a strict network proxy or firewall. - **Solution**: If you're using ``conda``, you can set proxy settings using the ``--proxy`` flag. For pip, you can use the ``--proxy`` flag as well. -4. **Failed to Set Environment Variables**: - - **Description**: After installation, you might forget to set the ``PATH_TO_POSYDON`` and ``PATH_TO_POSYDON_DATA`` environment variables. - - **Solution**: Refer back to the [installation guide](link-to-installation-guide) to ensure all post-installation steps are followed. 5. **Error with Experimental Visualization Libraries**: - **Description**: Issues after installing the experimental visualization libraries with ``pip install ".[vis]"``. diff --git a/docs/_source/troubleshooting-faqs/large_pop_runs_memory.png b/docs/_source/troubleshooting-faqs/large_pop_runs_memory.png new file mode 100644 index 0000000000000000000000000000000000000000..369da0b9446685e6a82ef1e0ab783ddb9aae74b1 GIT binary patch literal 39811 zcmb@uWmFt(*Dlz&yGziZ!QI`0Lx2FmEx5ZAJVArIJHcHW3j`;)ySqC~@jUN$&RJ*H z`DWId`N1NA?&_+$_PzIIMYyt}GzuaiA_xRR`6wfy3Iah&fItup@UXy7GF4Nifp5Id 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z9(p?9qI8aZx6e$4j6?s;jSO~-uLz>IZQ>ty_4j{p;lcu;l*qiE{)ta1N|DgmQ0KBa zVXPnSdM_}`7RZv9Zo0`PZD!%yupc8Bzoj||fk5M$ddfCMn{m`lrOk__s88T7@s4y+ zQA(Qjg*ErtYpd_ImK6qWR3S&^eYkSl1bXqXq@<7j7Cir-$O+9kcJ>1VunO8(f`B0U z_4*xnISzg~IpiCnTnE(7vJFN^#xg?*}x&;2(%e#fH# literal 0 HcmV?d00001 diff --git a/docs/_source/tutorials-examples/population-synthesis/10_binaries_pop_syn.ipynb b/docs/_source/tutorials-examples/population-synthesis/10_binaries_pop_syn.ipynb index b9b5992661..1b917dc959 100644 --- a/docs/_source/tutorials-examples/population-synthesis/10_binaries_pop_syn.ipynb +++ b/docs/_source/tutorials-examples/population-synthesis/10_binaries_pop_syn.ipynb @@ -12,12 +12,7 @@ "metadata": {}, "source": [ "**Tutorial goal:**\n", - "\n", - "In this tutorial, we will run a small population of 10 binaries locally, and explore how to manipulate the output data from your population.\n", - "\n", - "**New concepts:**\n", - "\n", - "- Population ini file" + "In this tutorial, we will run a small population of 10 binaries locally, and explore how to manipulate the output data from your population." ] }, { @@ -42,27 +37,36 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Creating the Initialisation File" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ + "\n", "----\n", + "\n", + "## Initialisation File\n", + "\n", "To run population synthesis with POSYDON, a `population_params.ini` file is required.\n", "This file described how the stellar population is created and what prescriptions and parameters are implemented in specific steps.\n", "\n", - "POSYDON comes with a default `population_params_default.ini` file found at `PATH_TO_POSYDON/posydon/popsyn` or have a look [here](population_params.ini).\n", + "POSYDON comes with a default `population_params_default.ini` file found at `PATH_TO_POSYDON/posydon/popsyn` or have a look [here](../../../../../posydon/popsyn/population_params_default.ini).\n", + "\n", + "
        \n", + " \n", + " **Default values**\n", + " \n", + " The default values in the population_params.ini file included in POSYDON \n", + " have not been calibrated, validated, or are used by the POSYDON team. They are\n", + " often an ad-hoc choice, and the user should carefully consider the values\n", + " of each parameter for their science case.\n", + "\n", + "
        \n", "\n", - "The file is split in three main parts. You can find more details about their properties by clicking on their links.\n", - "1. **[SimulationProperties]():**\n", - " - these describe the properties and parameters of different steps in the evolution of a binary systems.\n", - "2. **[BinaryPopulation]():** \n", - " - parameters of the initial sampling of the binary population, such as initial mass function, period distribution, and metallicity.\n", - " - Also contains parameters on how the population is ran, such as how many binaries are kept in memory.\n", - "3. **[SavingOutput]()**\n", - " - Describes the data from the binary and each individual star to the output files.\n", + "The parameters in this file can be found [here](../../components-overview/pop_syn/population_params.html), and is split in three parts. You can find more details about their properties by clicking on their links.\n", + "\n", + "1. **[SimulationProperties](../../components-overview/pop_syn/population_params.rst#simulationproperties):**\n", + " - describe the properties and parameters of different steps in the evolution of a binary system.\n", + "2. **[BinaryPopulation](../../components-overview/pop_syn/population_params.rst#binarypopulation):** \n", + " - Parameters of the initial sampling of the binary population, such as initial mass function, period distribution, and metallicity.\n", + " - contains parameters on how the population is run, such as how many binaries are kept in memory.\n", + "3. **[SavingOutput](../../components-overview/pop_syn/population_params.rst#saving-output)**\n", + " - Describes what data from the binary and each individual star is written to the output file.\n", "\n", "We will copy the default population run parameter file to the current folder." ] @@ -86,44 +90,44 @@ "metadata": {}, "source": [ "\n", - "
        Reprocessed POSYDON v1 data \n", + "----\n", + "\n", + "## Running a BinaryPopulation in a notebook\n", + "\n", "\n", - "If you're using the reprocessed POSYDON v1 dataset, you will only have solar metallicity available!\n", + "
        \n", + "\n", + "**Reprocessed POSYDON v1 data**\n", + "\n", + "If you're using the reprocessed POSYDON v1 dataset, you will only have solar ($Z_\\odot$) metallicity available!\n", "This means you will only be able to run populations with `metallicity = [1]`!\n", + "You should be able to follow along with this tutorial and can follow along with the [One metallicity notebook](one_met_pop_syn.ipynb), but the multi-metallicity component is not yet available.\n", "\n", - "You should be able to follow along with this tutorial and can follow along with the \"One metallicity notebook\", but the multi-metallicity component is not yet available.\n", - "
        " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Creating and running a binary population\n", + "
        \n", "\n", - "The copied `population_params.ini` contains the parameters to run 10 binaries at a metallicity of $Z=1 Z_\\odot$.\n", "\n", - "If you open the file and scroll down to the **BinaryPopulation** section, you will see how they're defined:\n", + "The copied `population_params.ini` contains the parameters to run 10 binaries at the one of the metallicities currently supported by POSYDON.\n", + "Open the `population_params.ini` file, and go down to the **BinaryPopulation** section. This is where the properties of the simulation are set.\n", + "There you should find:\n", "\n", "```\n", - "metallicity = [1] # [2., 1., 0.45, 0.2, 0.1, 0.01, 0.001, 0.0001]\n", + "metallicity = [1.] #[2., 1., 0.45, 0.2, 0.1, 0.01, 0.001, 0.0001]\n", "# In units of solar metallicity\n", "...\n", "number_of_binaries = 10\n", "# int\n", "```\n", + "Let's leave the settings for now, but you can always change these if you would like a different metallicity or larger population. For the latter, also see the [HPC tutorial](pop_syn.ipynb).\n", "\n", - "If you like to run a small population in a notebook, you can use the `PopulationRunner` to do this. If you want to run a specific binary instead, have a look at the [Binary Tutorial]().\n", - "\n", - "The `PopulationRunner` class takes the `.ini` file and sets-up a population run.\n", + "If you like to run a small population in a notebook, you can use the `PopulationRunner` ([documentation](../../api_reference/posydon.popsyn.rst#posydon.popsyn.synthetic_population.PopulationRunner)) to do this. If you want to run a specific binary instead, have a look at the [Binary Tutorial](evolve_single_binaries.ipynb) instead. The `PopulationRunner` class takes the `population_params.ini` file and sets-up a multi-metallicity population run for inside a notebook.\n", "\n", - "This will create [BinaryPopulations]() for each metallicity defined in the `.ini` file.\n", - "In this case, we can check and see that a single BinaryPopulation is created and contains 10 binaries." + "It will create `BinaryPopulation`s ([documentation](../../api_reference/posydon.popsyn.rst#posydon.popsyn.binarypopulation.BinaryPopulation)) for each metallicity defined in the `population_params.ini` file. In our case, we can check and see that a single `BinaryPopulation` is created.\n", + "We can check if the metallicity and number of binaries are correctly set before starting our simulation. " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -146,10 +150,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "If you changed the parameters in the `population_params.ini` file, you should have the following output:\n", + "\n", + "```\n", + "> Number of binary populations: 1\n", + "> Metallicity: 1\n", + "> Number of binaries: 10\n", + "```\n", + "\n", + "The `BinaryPopulation` class does the actual simulation setup and evolution of the binaries at a specific metallicity, but does not take of file cleanup after a succesfull population run. This is why `PopulationRunner` is used in a local environment. In a HPC facility, a setup script is available `posydon-setup-popsyn` that will create the required folders and scripts to run a large population. See the [HPC tutorial](pop_syn.ipynb) for more information.\n", + "\n", "For this tutorial, we set `verbose=True`, which shows you the progress of the population run.\n", "This overwrites the population verbose set inside the `population_params.ini` file.\n", - "\n", - "Now we are ready to evolve the binary population. This should take about 30 seconds, but depends on your machine." + "Now we are ready to evolve the binary population. This should take about 30 seconds depending on your machine." ] }, { @@ -165,11 +179,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Inspecting the population: Population class\n", "\n", - "When you ran the population, you might have seen that a temporary folder with the name `1e+00_Zsun_batches` was created while the binaries were evolved.\n", - "This is a temporary folder in which populations are temporarly saved.\n", - "After the binary evolution has finished, the binaries in the folder are moved to a single file named `1e+00_Zsun_popululation.h5`. This is done automatically, when you run a population using the `PopulationRunner` class.\n", + "-----\n", + "\n", + "## Inspecting the population: Population class\n", + "\n", + "When you ran the population, you might have seen that a temporary folder with the name `1e+00_Zsun_batches` was created while the binaries were being evolved. This is a temporary folder in which populations are temporarly saved.\n", + "After the binary evolution has finished, the binaries in the folder are moved to a single file named `1e+00_Zsun_popululation.h5`. This is done automatically when you run a population using the `PopulationRunner` class.\n", + "When you run multiple metallicity a file will be created for each metallicity.\n", "\n", "The created file contains 3 main components:\n", "\n", @@ -177,43 +194,41 @@ "2. **oneline:** a single line to describe the initial and final conditions and some one-of parameters, such as the metallicity.\n", "3. **mass_per_metallicity:** some metadata on the population, such as the total simulated mass, the actual underlying mass of the population, and the number of binaries in the file.\n", "\n", - "The `Population` provides an interface with these components in the file, such that you're able to share the populations runs and can work with large population that do not fit in memory.\n", + "The `Population` class provides an interface to these components in the file, such that you're able to share the populations runs and can work with large populations that do not fit in memory. We will now explore the population file using the `Population` class. You can find a more extensive description [here](../../components-overview/pop_syn/synthetic_population.rst) or look at the class [documentation](../../api_reference/posydon.popsyn.rst#posydon.popsyn.synthetic_population.Population).\n", "\n", "\n", - "
        Older Population Files \n", + "
        \n", "\n", - "If you're using older population files, you can make them compatible with the `Population` class by calling `Population(pop_file, metallicity,ini_file)`, where the `metallicity` is in solar units. You will only need to do this once; afterwards you can initialise the class like normal.
        " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from posydon.popsyn.synthetic_population import Population" + "**Older Population Files**\n", + "\n", + "If you're using older population files from before the Population class rework, you can make them compatible with the `Population` class by calling `Population(pop_file, metallicity,ini_file)`, where the `metallicity` is in solar units. You will only need to do this once; afterwards you can initialise the class like normal.\n", + "\n", + "
        " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "pop = Population('1e+00_Zsun_population.h5', verbose=True)" + "from posydon.popsyn.synthetic_population import Population\n", + "pop = Population('1e+00_Zsun_population.h5')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The `pop.mas_per_met` shows some basic information about the population you've just created.\n", "\n", + "Let's start with the `pop.mass_per_metallicity`. \n", + "It contains some basic information about the population you've just created.\n", "\n", - "1. The index (**metallicity**) is the metallicity of your population in solar units.\n", - "2. **simulated mass** is the total ZAMS mass that has been evolved in the population.\n", - "3. **underlying mass** is the actual mass of the population if one integrates the IMF and period distribution fully.\n", - "4. **number_of_systems** shows the 10 systems in the file." + "1. The index (`metallicity`) is the metallicity of your population in solar units.\n", + "2. `simulated mass` the total ZAMS mass that has been evolved in the population.\n", + "3. `simulated_mass_single` the total ZAMS mass from initially single stars.\n", + "4. `simulated_mass_binaries` the total ZAMS mass from initially binaries.\n", + "5. `number_of_systems` shows the number of systems in the file.\n" ] }, { @@ -229,12 +244,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", "There are some additional metadata properties available, such as:\n", - "- `metallicities`: the metallicity in absolute metallicity\n", - "- `solar_metallicities`: the metallicities in the file in solar metallicity\n", - "- `number_of_systems`: the total number of systems in the Population file\n", - "- `indices`: the indices of the binaries in the file\n", - "- `columns`: the columns available in the `history` and `oneline` dataframes" + "\n", + "- `metallicities` the metallicity in absolute metallicity\n", + "- `solar_metallicities` the metallicities in the file in solar metallicity\n", + "- `number_of_systems` the total number of systems in the Population file\n", + "- `indices` the indices of the binaries in the file\n", + "- `columns` the columns available in the `history` and `oneline` dataframes\n", + "- `ini_params` the parameters from the `ini` file that describe the initial sampling of your population.\n" ] }, { @@ -251,35 +269,29 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Population.history" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pop.history.lengths" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`pop.history` loads in the full history of all the binaries into memory.\n", - "You can access individual or a selections of the population using several methods:\n", + "## Population.history\n", + "\n", + "`pop.history` contains the evolutionary histories of each binary, as it was evolved by POSYDON. You can find more information about this [here](../../components-overview/pop_syn/synthetic_population.rst#history) (or look at the [documentation](../../api_reference/posydon.popsyn.rst#posydon.popsyn.synthetic_population.History) of the class).\n", + "\n", + "\n", + "It allows you to load specific information or binaries into memory without having to load them all at once.\n", + "\n", + "
        \n", + "\n", + "**Note**\n", + "\n", + "Calling `pop.history` will load all binaries and all their columns into memory. This can take a while and can even cause the notebook to crash.\n", + "\n", + "
        \n", "\n", + "You can access individual or selections of the population using several methods:\n", "\n", + "```\n", "1. pop.history[5]\n", "2. pop.history[[0,4]]\n", "3. pop.history['time]\n", "4. pop.history.select()\n", - "\n", - "The `select` function is the most powerfull way to access the binaries, because it allows you to perform selections based on the specific columns available in the history dataframe.\n", - "For example, below we can select on `state == 'RLO1'`, which gives us all the rows with RLO1 occuring.\n", - "\n", - "The available identifiers are limited to string columns (`state`, `event`, `step_names`, `S1_state`, `S2_state`), index, and columns names." + "```\n" ] }, { @@ -315,7 +327,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "You can also check what columns are available in the history file:" + "\n", + "You can also check what columns are available in the history file.\n", + "This is possible in two ways:" ] }, { @@ -327,6 +341,33 @@ "pop.history.columns" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pop.columns['history']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `select` function is the most powerful way to access the binaries, because it allows you to perform selections based on the specific columns available in the history dataframe.\n", + "For example, below we can select on `state == 'RLO1'`, which gives us all the rows with RLO1 occuring.\n", + "\n", + "The available identifiers are limited to string columns (`state`, `event`, `step_names`, `S1_state`, `S2_state`), index, and columns names.\n", + "\n", + "
        \n", + "\n", + "**Not all columns are available**\n", + "\n", + "It's not currently possible to select on all columns in the population file. **Only string columns, the indices and columns names are available!** \n", + "\n", + "
        \n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -362,7 +403,7 @@ "metadata": {}, "source": [ "You might have notices while using the above functions that not all the binaries will have the same length in the history.\n", - "You can access these with `pop.history_lengths`. This information is also stored in the population file." + "You can access these with `pop.history_lengths` or `pop.history.lengths`. They provide the same information." ] }, { @@ -374,15 +415,32 @@ "pop.history_lengths" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pop.history.lengths" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "-----\n", + "\n", "## Population.oneline\n", "\n", "`Population.oneline` provides a similar interface to accessing the DataFrame in the population file as `Population.history`, with similar functionality being available.\n", "\n", - "The `select` function only has acces to:\n", + "The `oneline` DataFrame contains, as the name suggests, a single line per binary. It contains initial and final conditions and some additional varibales, such as the `SN_type`.\n", + "You can find more information about this [here](../../components-overview/pop_syn/synthetic_population.rst#oneline) or look at the [api reference](../../api_reference/posydon.popsyn.rst#posydon.popsyn.synthetic_population.Oneline) of the class.\n", + "\n", + "\n", + "The `select` function only has access to:\n", + "\n", "- `index`\n", "- column names \n", "- string columns: `state_i`, `state_f`, `event_i`, `event_f`, `step_names_i`, `step_names_f`, `S1_state_i`, `S1_state_f`, `S2_state_i`, `S2_state_f`, `S1_SN_type`, `S2_SN_type`, `interp_class_HMS_HMS`, `interp_class_CO_HeMS`, `interp_class_CO_HMS_RLO`, `interp_class_CO_HeMS_RLO`, `mt_history_HMS_HMS`, `mt_history_CO_HeMS`, `mt_history_CO_HMS_RLO`, `mt_history_CO_HeMS_RLO`" @@ -394,9 +452,33 @@ "metadata": {}, "outputs": [], "source": [ - "pop.oneline[5]\n", - "pop.oneline[[0,4]]\n", - "pop.oneline.select(where='index == 9')\n", + "pop.oneline[5]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pop.oneline[[0,4]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pop.oneline.select(where='index == 9')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "pop.oneline.select(where='index == [0,9]')" ] }, @@ -406,15 +488,22 @@ "metadata": {}, "outputs": [], "source": [ - "pop.oneline.columns" + "pop.oneline.columns\n", + "# or \n", + "# pop.columns['oneline']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "-----\n", + "\n", "## Population.formation_channels\n", "\n", + "While you can see the all the main evolutionary steps in the evolution of a binary, it is useful to have a summary overview of a binary's evolutionary pathway, also known as formation channel\n", + "\n", "You might be interested in figuring out what sort of formation pathways/channels a binary has followed through its evolution.\n", "\n", "This is not a standard output of the population synthesis, but you can include it into the population file by calculating it. \n", @@ -425,7 +514,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -468,16 +557,16 @@ "You might just want a small sub-selection of the full population, especially if you're working with large population and multi-metallicity runs.\n", "\n", "The `Population.export_selection()` function will export just the indices of the binaries you're interested in into a new file.\n", - "The simulated and underlying mass will remain the same, since they are dependent on the population run.\n", + "The simulated mass will remain the same, since they are dependent on the population run.\n", "\n", "If we select just 2 binaries and export them, we create a new population of just the binaries you're interested in.\n", - "In the [BBH analysis]() and [GRB analysis]() tutorials, we show how to perform a selection with multiple criteria and metallicities.\n", + "In the [BBH analysis](bbh_analysis.ipynb) and [GRB analysis](lgrb_pop_syn.ipynb) tutorials, we show how to perform a selection with multiple criteria and across metallicities.\n", "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -558,16 +647,19 @@ "\n", "Feel free to explore the small binary population you've just created!\n", "\n", - "If you want to learn more about population synthesis and how to build more complex models it is advised to continue with the remaining tutorials and consult the POSYDON documentation." + "If you want to learn more about population synthesis and how to perform more complex selection and population, continue with [Large scale population on a HPC setup](pop_syn.ipynb) or with [BBH analysis](bbh_analysis.ipynb)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "----\n", + "\n", "### Local MPI runs\n", "\n", - "To speed up population synthesis runs, you can run on a computing cluster, as described in [HPC Facilities](pop_syn), or you can distribute the population synthesis across multiple cores on your local machine using MPI.\n", + "To speed up population synthesis runs, you can run on a computing cluster, as described in [HPC Facilities](pop_syn.ipynb), or you can distribute the population synthesis across multiple cores on your local machine using MPI.\n", "\n", "To enable local MPI runs, go into the `population_params.ini` and change `use_MPI` to `True`.\n", "\n", @@ -594,7 +686,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This script can be initiated using a local where `NR_processors` is the number of processors you would like to us." + "This script can be initiated using a local where `NR_processors` is the number of processors you would like to us.\n", + "`mpi4py` needs to be installed for this. Please see the [Installation guide](../../getting-started/installation-guide.rst) for more info." ] }, { @@ -631,7 +724,7 @@ ], "metadata": { "kernelspec": { - "display_name": "development311", + "display_name": "posydon_env", "language": "python", "name": "python3" }, diff --git a/docs/_source/tutorials-examples/population-synthesis/bbh_analysis.ipynb b/docs/_source/tutorials-examples/population-synthesis/bbh_analysis.ipynb index 54d6fe0a7d..7c9a564950 100644 --- a/docs/_source/tutorials-examples/population-synthesis/bbh_analysis.ipynb +++ b/docs/_source/tutorials-examples/population-synthesis/bbh_analysis.ipynb @@ -12,45 +12,31 @@ "metadata": {}, "source": [ "In this notebook, we will cover:\n", + "\n", "1. Selecting binaries and combining metallicities\n", "2. Exploring the `TransientPopulation` class\n", + "3. Calculating cosmic rates\n", "\n", "We will do this in the context of the merging binary black hole population." ] }, - { - "cell_type": "markdown", - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, - "source": [ - "If you haven't done so yet, export the path POSYDON environment variables. For example:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%env PATH_TO_POSYDON=/Users/simone/Google Drive/github/POSYDON-public/\n", - "%env PATH_TO_POSYDON_DATA=/Volumes/T7/" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "----\n", + "\n", "## Creating multi-metallicity Populations\n", "\n", "\n", - "
        Reprocessed POSYDON v1 dataset \n", + "
        Reprocessed POSYDON v1 dataset \n", "\n", "Please note that with the reprocessed POSYDON v1 data, only solar metallicity is available.\n", "You will not be able to follow along with the full tutorial!\n", - "If you would still like to explore a population at solar metallicity, you can follow the \"One metallicity\" tutorial.
        " + "If you would still like to explore a population at solar metallicity, you can follow the [One metallicity tutorial](one_met_pop_syn.ipynb).\n", + "\n", + "
        " ] }, { @@ -72,11 +58,12 @@ "\n", "\n", "\n", - "
        No indices? \n", + "
        No indices? \n", "\n", "It might be that the population you generated does not contain any merging BBHs, they're rare after all.\n", "\n", - "As such, we have provided an example population run at `$PATH_TO_POSYDON_DATA/population-synthesis/example/`, this simulation contains 10.000 binaries at the 8 metallicity.
        \n" + "As such, we have provided an example population run at `$PATH_TO_POSYDON_DATA/population-synthesis/example/`, this simulation contains 10.000 binaries at the 8 metallicity. \n", + "
        \n" ] }, { @@ -91,26 +78,6 @@ "tmp_data = pop.history.select(columns=['S1_state', 'S2_state', 'event'])\n" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "pop = Population('BBH_contact.h5')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pop.oneline" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -169,11 +136,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "If your selected indices is empty, no BBH mergers were in your population.\n", + "If your selected indices are empty, no BBH mergers occurred in your population. They're quite rare after all!\n", "\n", - "Lets use an example set of populations to make sure there will be BBH in your population.\n", + "Let's use an example set of populations to make sure there will be BBH in your population.\n", "\n", - "**This can only be used with the multi-metallicity populations**" + "**This can only be used with the multi-metallicity populations!**" ] }, { @@ -184,19 +151,10 @@ "source": [ "from posydon.popsyn.synthetic_population import Population\n", "\n", - "PATH_TO_POSYDON_DATA = '/Users/max/Documents/POSYDON_data/240305/POSYDON_data/tutorials/population-synthesis/example/'\n", - "\n", - "files = ['1e-01_Zsun_population.h5',\n", - " '1e-02_Zsun_population.h5',\n", - " '1e-03_Zsun_population.h5',\n", - " '1e-04_Zsun_population.h5',\n", - " '1e+00_Zsun_population.h5',\n", - " '2e-01_Zsun_population.h5',\n", - " '2e+00_Zsun_population.h5',\n", - " '4.5e-01_Zsun_population.h5']\n", - "\n", + "data_path = f'PATH/TO/TUTORIAL/DATA/'\n", "\n", - "pop = Population(PATH_TO_POSYDON_DATA+files[0])" + "# Let's load one of the populations\n", + "pop = Population(data_path+'1e-01_Zsun_population.h5')" ] }, { @@ -205,7 +163,7 @@ "metadata": {}, "outputs": [], "source": [ - "pop.history" + "pop.oneline.columns" ] }, { @@ -225,6 +183,7 @@ "outputs": [], "source": [ "from posydon.popsyn.synthetic_population import Population\n", + "\n", "# The names of the populations, if you followed along.\n", "files = ['1e-01_Zsun_population.h5',\n", " '1e-02_Zsun_population.h5',\n", @@ -237,7 +196,7 @@ "\n", "\n", "for file in files:\n", - " pop = Population(PATH_TO_POSYDON_DATA+file)\n", + " pop = Population(data_path+file)\n", " # read the relevant data in one go\n", " # (faster than reading it in chunks, but requires more memory)\n", " tmp_data = pop.history.select(columns=['S1_state', 'S2_state', 'event'])\n", @@ -254,22 +213,22 @@ " print(f'File: {file}, Number of systems: {len(selected_indices)}')\n", " \n", " # set overwrite to False to add to the file\n", - " pop.export_selection(selected_indices, 'BBH_contact.h5', append=True)" + " pop.export_selection(selected_indices, data_path+'BBH_contact.h5', append=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "If you population did not contain any BBH mergers, all the \"Number of Systems\" will be 0 and you won't be able to open the file in the next cell.\n", + "If your population did not contain any BBH mergers, all the \"Number of Systems\" will be 0, and you won't be able to open the file in the next cell.\n", "\n", "If you ran the population with the tutorial populations, you should have a total of 636 merging BBHs.\n", "\n", "We can confirm this by adding up the values above or by opening the new file. `Population.number_of_systems` gives us the total number of binaries in the file. \n", "\n", - "You now see that the `mass_per_met` property contains information about all the metallicities in the file.\n", + "You now see that the `mass_per_metallicity` property contains information about all the metallicities in the file.\n", "\n", - "You can even combined multiple runs at the same metallicity together, if you like. This will combine their simualted and underlying masses." + "You can even combine multiple runs at the same metallicity together, if you like. This will combine their simulated mass." ] }, { @@ -279,7 +238,7 @@ "outputs": [], "source": [ "from posydon.popsyn.synthetic_population import Population\n", - "BBH_pop = Population('BBH_contact.h5', chunksize=10000)\n", + "BBH_pop = Population(data_path+'BBH_contact.h5', chunksize=10000)\n", "print(BBH_pop.number_of_systems)\n", "\n", "BBH_pop.mass_per_metallicity" @@ -289,29 +248,82 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Transient population\n", + "### Underlying mass\n", "\n", - "Although we now have selected all binaries with a BBH merger, we don't have the extact moment of the merger yet.\n", + "Before we move on, we have to discuss the occurrence rate of each binary in the population.\n", "\n", - "For this we will create a `TransientPopulation`.\n", - "This class is used to hold information about a specific event/moment in time.\n", + "We have sampled several distributions at the start of our simulations.\n", + "These include:\n", "\n", - "In our case, this is the moment of \"CO_contact\". However, we might want to store \n", - "and calculate some additional values, such as the $M_\\mathrm{chirp}$ or $\\chi_\\mathrm{eff}$.\n", + "- Initial mass function\n", + "- Period\n", + "- mass ratio\n", + "- binary fraction\n", "\n", - "The `Population` class has a function `create_transient_population` (see [here for more details]()).\n", + "The default parameters do not sample the complete distributions.\n", + "For example, we sample the binary between 7 and 150 $M_\\odot$, and a binary fraction of 1.\n", + "While you can use the population as is, you should consider the weight of the distributions not sampled.\n", "\n", - "In short, it takes a `selection_function` and a `transient_name`.\n", + "You can calculate the underlying mass of these distributions, which is the actual weight of the binary in our population.\n", + "Since we've stored essential information about the population run, we're able to calculate this even after our selection.\n", + "This will return the `underlying_mass` of the sampled population and add it to the population file.\n", + "\n", + "We will need these weights to be able to continue with the rest of the calculations.\n", + "\n", + "
        \n", + "\n", + "**Caveats**\n", + "\n", + "Currently, only changing the binary fraction (`f_bin`) is supported in the calculation of the underlying mass!\n", + "The initially sampled population can have any binary fraction.\n", + "\n", + "Additionally, only parts of the period, IMF, and mass ratio distribution are included.\n", + "We allow for different parameter limits to be set for sampling, but these are not included in this normalisation!\n", + "\n", + "
        " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This will calculate the underlying mass for the population \n", + "# with a binary fraction of 0.7 (default value)\n", + "BBH_pop.calculate_underlying_mass(f_bin=0.7, overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "-----\n", "\n", + "## Transient population\n", + "\n", + "Although we now have selected all binaries with a BBH merger, we don't have the exact moment of the merger yet.\n", + "This will be required to calculate merger rates across cosmic time.\n", + "\n", + "\n", + "For this we will create a `TransientPopulation`.\n", + "This class is used to hold information about a specific event/moment in time.\n", + "In our case, this is the moment of \"CO_contact\", the moment of the BBH merger.\n", + "However, we might want to store and calculate some additional values, such as the $M_\\mathrm{chirp}$ or $\\chi_\\mathrm{eff}$.\n", + "\n", + "The `Population` class has a function `create_transient_population` (see [here for more details](../../api_reference/posydon.popsyn.rst#posydon.popsyn.synthetic_population.Population.create_transient_population)).\n", + "In short, it takes a `selection_function` and a `transient_name`.\n", "The `transient_name` is a string identifying the transient population in the file,\n", "while `selection_function` extracts the `TransientPopulation` for us.\n", "This can be any custom function you want it to be, as long it outputs a pandas DataFrame with a 'time' and 'metallicity' column.\n", "\n", "Several selection functions are provided in `posydon.popsyn.transient_select_funcs`:\n", - "1. BBH_selection_function\n", - "2. GRB_selection_function\n", "\n", - "We have copied part of the BBH_selection_function below to explain how a `selections_function` works." + "1. [BBH_selection_function](../../api_reference/posydon.popsyn.rst#posydon.popsyn.transient_select_funcs.BBH_selection_function)\n", + "2. [GRB_selection_function](../../api_reference/posydon.popsyn.rst#posydon.popsyn.transient_select_funcs.GRB_selection)\n", + "\n", + "We have copied part of the `BBH_selection_function` below to explain how a `selections_function` works." ] }, { @@ -321,7 +333,7 @@ "outputs": [], "source": [ "import pandas as pd\n", - "def BBH_selection_function(history_chunk, oneline_chunk, formation_channels_chunk):\n", + "def BBH_selection_function(history_chunk, oneline_chunk, formation_channels_chunk=None):\n", " '''A BBH selection function to create a transient population of BBHs mergers.'''\n", " \n", " indices = oneline_chunk.index.to_numpy()\n", @@ -337,13 +349,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "A `selection_function` always has as an input a chunk of the history, oneline, and formation_pathways (optional).\n", - "For example, you set your `chunksize=10` when you initialise the `Population`, like we've done above for `BBH_pop`, each chunk will contain 10 binaries.\n", + "A `selection_function` always has as an input a chunk of the history, oneline, and formation_pathways (optional). For example, you set your `chunksize=10000` when you initialise the `Population`, like we've done above for `BBH_pop`, each chunk will contain 10000 binaries.\n", "This means that the complete history, oneline and formation_pathways of those 10 binaries are passed to this function.\n", "\n", - "\n", "The `BBH_selection_function` selects the moment the binary reaches CO_contact as the moment of merger, and stores it in the `time` columns in Myr.\n", - "The metallicity is also outputted.\n", + "The `metallicity` is also outputted, since this will be essential when combining it with the star formation history.\n", "\n", "We can test this function by inputting a single binary into it, as done below.\n", "If you've used the example populations, you might not have calculated the formation_channels yet. We will set that input to None." @@ -363,9 +373,9 @@ "metadata": {}, "source": [ "This gives us a dataframe containing our 1 binary, its index (0), the moment of merger, and its metallicity.\n", - "Of course, we could like to know a bit more about our merger than just when it occurs.\n", "\n", - "Lets expand our output with the BH masses, their spin, their tilt, and the orbital period at DCO formation." + "Of course, we could like to know a bit more about our merger than just when it occurs.\n", + "Let's expand our output with the BH masses, their spin, their tilt, and the orbital period at DCO formation." ] }, { @@ -392,8 +402,9 @@ " df_transients['S2_mass'] = history_chunk[mask]['S2_mass']\n", " df_transients['S1_spin'] = history_chunk[mask]['S1_spin']\n", " df_transients['S2_spin'] = history_chunk[mask]['S2_spin']\n", - " df_transients['S1_spin_orbit_tilt'] = oneline_chunk['S1_spin_orbit_tilt']\n", - " df_transients['S2_spin_orbit_tilt'] = oneline_chunk['S2_spin_orbit_tilt']\n", + " # we distinguish the tilt of the spin to the orbit after the first and second SN.\n", + " df_transients['S1_spin_orbit_tilt_merger'] = oneline_chunk['S1_spin_orbit_tilt_second_SN']\n", + " df_transients['S2_spin_orbit_tilt_merger'] = oneline_chunk['S2_spin_orbit_tilt_second_SN']\n", " df_transients['orbital_period'] = history_chunk[mask]['orbital_period']\n", " \n", " return df_transients" @@ -415,7 +426,7 @@ "With this new function, we have a lot more information available in the TransientPopulation.\n", "\n", "You can further customise this to your liking, if you want to store specific information.\n", - "It's also possible to calculate additional infomration based on any value in the history, oneline or formation_channels.\n", + "It's also possible to calculate additional information based on any value in the history, oneline or formation_channels.\n", "\n", "We will import some functions to calculate $\\chi_\\mathrm{eff}$, $q$ and $\\mathcal{M}_\\mathrm{chirp}$ and also add them to the dataframe.\n" ] @@ -427,7 +438,6 @@ "outputs": [], "source": [ "from posydon.popsyn.transient_select_funcs import chi_eff, mass_ratio, m_chirp\n", - "import pandas as pd\n", "\n", "def BBH_selection_function(history_chunk, oneline_chunk, formation_channels_chunk):\n", " '''A BBH selection function to create a transient population of BBHs mergers.'''\n", @@ -446,15 +456,21 @@ " df_transients['S2_mass'] = history_chunk[mask]['S2_mass']\n", " df_transients['S1_spin'] = history_chunk[mask]['S1_spin']\n", " df_transients['S2_spin'] = history_chunk[mask]['S2_spin']\n", - " df_transients['S1_spin_orbit_tilt'] = oneline_chunk['S1_spin_orbit_tilt']\n", - " df_transients['S2_spin_orbit_tilt'] = oneline_chunk['S2_spin_orbit_tilt']\n", + " # we distinguish the tilt of the spin to the orbit after the first and second SN.\n", + " df_transients['S1_spin_orbit_tilt_merger'] = oneline_chunk['S1_spin_orbit_tilt_second_SN']\n", + " df_transients['S2_spin_orbit_tilt_merger'] = oneline_chunk['S2_spin_orbit_tilt_second_SN']\n", " df_transients['orbital_period'] = history_chunk[mask]['orbital_period']\n", " df_transients['eccentricity'] = history_chunk[mask]['eccentricity']\n", " \n", " # Added\n", " df_transients['chirp_mass'] = m_chirp(history_chunk[mask]['S1_mass'], history_chunk[mask]['S2_mass'])\n", " df_transients['mass_ratio'] = mass_ratio(history_chunk[mask]['S1_mass'], history_chunk[mask]['S2_mass'])\n", - " df_transients['chi_eff'] = chi_eff(history_chunk[mask]['S1_mass'], history_chunk[mask]['S2_mass'], history_chunk[mask]['S1_spin'], history_chunk[mask]['S2_spin'], oneline_chunk['S1_spin_orbit_tilt'], oneline_chunk['S2_spin_orbit_tilt'])\n", + " df_transients['chi_eff'] = chi_eff(history_chunk[mask]['S1_mass'],\n", + " history_chunk[mask]['S2_mass'],\n", + " history_chunk[mask]['S1_spin'],\n", + " history_chunk[mask]['S2_spin'],\n", + " oneline_chunk['S1_spin_orbit_tilt_second_SN'],\n", + " oneline_chunk['S2_spin_orbit_tilt_second_SN'])\n", " \n", " return df_transients" ] @@ -472,8 +488,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Before running this selection on the whole population, we would like to include the formation_channels too.\n", - "First, we calculate them for the population:" + "Before running this selection on the whole population, we would like to include the `formation_channels` too.\n", + "These are calculated for the systems in the population file and add a good overview of the evolution of binaries." ] }, { @@ -482,6 +498,7 @@ "metadata": {}, "outputs": [], "source": [ + "# mt_history=True adds detailed information about the mass transfer in the HMS-HMS grid\n", "BBH_pop.calculate_formation_channels(mt_history=True)" ] }, @@ -515,14 +532,20 @@ " df_transients['S2_mass'] = history_chunk[mask]['S2_mass']\n", " df_transients['S1_spin'] = history_chunk[mask]['S1_spin']\n", " df_transients['S2_spin'] = history_chunk[mask]['S2_spin']\n", - " df_transients['S1_spin_orbit_tilt'] = oneline_chunk['S1_spin_orbit_tilt']\n", - " df_transients['S2_spin_orbit_tilt'] = oneline_chunk['S2_spin_orbit_tilt']\n", + " # we distinguish the tilt of the spin to the orbit after the first and second SN.\n", + " df_transients['S1_spin_orbit_tilt_merger'] = oneline_chunk['S1_spin_orbit_tilt_second_SN']\n", + " df_transients['S2_spin_orbit_tilt_merger'] = oneline_chunk['S2_spin_orbit_tilt_second_SN']\n", " df_transients['orbital_period'] = history_chunk[mask]['orbital_period']\n", " df_transients['eccentricity'] = history_chunk[mask]['eccentricity']\n", " \n", " df_transients['chirp_mass'] = m_chirp(history_chunk[mask]['S1_mass'], history_chunk[mask]['S2_mass'])\n", " df_transients['mass_ratio'] = mass_ratio(history_chunk[mask]['S1_mass'], history_chunk[mask]['S2_mass'])\n", - " df_transients['chi_eff'] = chi_eff(history_chunk[mask]['S1_mass'], history_chunk[mask]['S2_mass'], history_chunk[mask]['S1_spin'], history_chunk[mask]['S2_spin'], oneline_chunk['S1_spin_orbit_tilt'], oneline_chunk['S2_spin_orbit_tilt'])\n", + " df_transients['chi_eff'] = chi_eff(history_chunk[mask]['S1_mass'],\n", + " history_chunk[mask]['S2_mass'],\n", + " history_chunk[mask]['S1_spin'],\n", + " history_chunk[mask]['S2_spin'],\n", + " oneline_chunk['S1_spin_orbit_tilt_second_SN'],\n", + " oneline_chunk['S2_spin_orbit_tilt_second_SN'])\n", " \n", " # added\n", " df_transients = pd.concat([df_transients, formation_channels_chunk[['channel']]], axis=1) \n", @@ -537,14 +560,17 @@ "outputs": [], "source": [ "# NOTE: the formation channels have to be given as a dataframe, as such we call it with loc[[0]] for testing\n", - "BBH_selection_function(BBH_pop.history[0], BBH_pop.oneline[0], BBH_pop.formation_channels.loc[[0]])" + "# Make sure you use double brackets for loc to return a dataframe\n", + "BBH_selection_function(BBH_pop.history[0],\n", + " BBH_pop.oneline[0],\n", + " BBH_pop.formation_channels.loc[[0]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The example binary gives us a dataframe with all the information we're interested in. But if somehting is missing feel free to customize the selection further!\n", + "The example binary gives us a dataframe with all the information we're interested in. But if something is missing feel free to customize the selection further!\n", "\n", "We now create the transient population with the `BBH_selection_function`, we've created and `create_transient_population`.\n", "\n", @@ -575,8 +601,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Since the TransienPopulation is stored in file, you can continue your analysis by opening the file using the `TransientPopulation` class.\n", - "You only need to do your selection once." + "Since the Transient Population is stored in the same file, you can continue your analysis by opening the file using the `TransientPopulation` class.\n", + "You only need to do your selection once and you can then continue from this stage." ] }, { @@ -603,7 +629,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "With `TransientPopulation` class you can compute the efficiency of the transient population at a given metallicity, i.e. the number of merging DCO per unit star formation and visualize the results." + "With `TransientPopulation` class you can compute the efficiency of the transient population at a given metallicity, i.e. the number of merging DCO per unit star formation and visualize the results.\n", + "\n", + "
        \n", + "\n", + "**Underlying mass**\n", + "\n", + "Make sure you've calculated the underlying mass for the population, as done earlier in this tutorial.\n", + "\n", + "
        \n", + "\n", + "Applying the underlying mass normalisation assumes that no events occur outside the sampled parameter space. \n" ] }, { @@ -647,7 +683,6 @@ "metadata": {}, "outputs": [], "source": [ - "import matplotlib.pyplot as plt \n", "import numpy as np\n", "bins = np.logspace(6.5,10.5)\n", "BBH_mergers.plot_delay_time_distribution(bins=bins)\n", @@ -666,7 +701,7 @@ "\n", "If no slice is give, all mass ratios are plotted.\n", "\n", - "You can also plot a property from the TransientPopulation DataFrame as a colourmap.\n", + "You can also plot a property from the `TransientPopulation.population` DataFrame as a colourmap.\n", "And specific formation_channels if they're in the DataFrame." ] }, @@ -692,11 +727,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", + "-----\n", + "\n", "## Cosmic Rate\n", "\n", "So far these events do not consider the metallicity or star formation rate evolution of the Universe.\n", "\n", "POSYDON comes with several built-in star formation histories and metallicity evolutions, a few examples are:\n", + "\n", "- IllustrisTNG\n", "- Neijssel2019\n", "- Madau+Fragos2017\n", @@ -705,6 +744,8 @@ "Here, we will the metallicity and SFR evolution of the IllustrisTNG, which is the default model used, if no `MODEL_in` is given.\n", "\n", "The function returns an instance of the `Rates` class, which gives us access to some new variables:\n", + "\n", + "\n", "- `z_birth`: the redshift and age of the universe at which we probe the star formation\n", "- `z_events`: the redshift at which an event takes place\n", "- `weights`: the weight of the event based on the SFR, its metallicity, and its weight in the population." @@ -767,8 +808,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The rates can also be accessed using the `Rates` class. You will need to provide your transient_name and SFH_identifier.\n", - "You can keep as many transients and SFHs in your file as you like." + "Similar to the `TransientPopulation`, the rates can also be accessed using the `Rates` class. You will need to provide your `transient_name` and `SFH_identifier`.\n", + "These rates are also stored in the file. You can have as many transients and star formation histories in your population file as you want." ] }, { @@ -859,20 +900,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### observable population\n", + "### Observable population\n", "\n", "Sometimes, however, you're not just interested in the intrinsic population, and want an observable population.\n", - "This could be SNe detection fraction for a telescope survey, or the LVK detection efficiency for GW mergers.\n", + "This could be a supernova detection fraction for a telescope survey, or the LVK detection efficiency for GW mergers.\n", "\n", "You can apply these using the `calculate_observable_population`. Similar to the `create_transient_population`, this function takes a `observable_func`, which described the observability of a transient.\n", "\n", "An `observable_func` takes chunks of\n", - "1. TransientPopulation\n", - "2. The z_events\n", - "3. The weights\n", "\n", - "Using these, new weights are calculated.\n", - "The BBH analysis comes with a detection function for several different detector sensitivities and configurations." + "1. `TransientPopulation.population`\n", + "2. The `TransientPopulation.z_events`\n", + "3. The `TransientPopulation.weights`\n", + "\n", + "Using these, new weights are calculated and stored as a separate observable population of your TransientPopulation.\n", + "The BBH analysis framework comes with a detection function for several detector sensitivities and configurations." ] }, { @@ -943,7 +985,7 @@ "metadata": {}, "source": [ "Cogratulations, you are now ready to analyze any DCO population data you generated with POSYDON. Feel free to further explore the BBH model or to use this tutorial to study other populations.\n", - "The next tutorials show you how to [select GRBs](), perform a [SFH calculation at a single metallicity](), and [how to run an individual binary]()." + "The next tutorials show you how to [select GRBs](lgrb_pop_syn.ipynb), perform a [SFH calculation at a single metallicity](one_met_pop_syn.ipynb), and [how to run an individual binary](debug_pop.ipynb)." ] } ], diff --git a/docs/_source/tutorials-examples/population-synthesis/binary-pop-syn.rst b/docs/_source/tutorials-examples/population-synthesis/binary-pop-syn.rst index 13910c30ab..47ca299a87 100644 --- a/docs/_source/tutorials-examples/population-synthesis/binary-pop-syn.rst +++ b/docs/_source/tutorials-examples/population-synthesis/binary-pop-syn.rst @@ -32,10 +32,6 @@ II. Large-Scale Population Synthesis on HPC Facilities 🚀 In this tutorial, you'll learn to run massive simulations of 1 million binaries across 8 different metallicities. -.. note:: - - Ensure you've installed `mpi4py` to use this tutorial. If not, follow our :ref:`installation guide `. - .. toctree:: pop_syn @@ -79,14 +75,27 @@ X-ray binaries: computing the X-ray luminosity function 🩻 TODO: bring v1 tutorial to v2 leveraging the SyntheticPopulation class -Debugging the Evolution of a Single Binary saved in a POSYDON Population model 🐞 +Evolving Single Binaries 🐞 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -This notebook shows you how to run the BinaryPopulation class in debug mode, which allows you to follow the evolution of a single binary through the population synthesis code. This is useful for debugging purposes, or for understanding the evolution of a single binary in detail, e.g. by changing the natal kicks. +This notebook shows you how to evolve a single binary using POSYDON. +It allows you to test and to quickly understand the evolution of a single binary in detail. +Moreover, it shows you how re-evolve binaries and change their properties, such as the natal kicks. .. toctree:: - debug_pop + evolve_single_binaries + + +Creating custom steps and flow chart for the evolution of a binary 📊 +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +In this tutorial, we show you how to import your own custom steps and flow chart for a population and in a Jupyter notebook. + +.. toctree:: + + custom_step_and_flow + Advanced Visualization with Van den Heuvel Diagrams 🎨 diff --git a/docs/_source/tutorials-examples/population-synthesis/debug_pop.ipynb b/docs/_source/tutorials-examples/population-synthesis/custom_step_and_flow.ipynb similarity index 74% rename from docs/_source/tutorials-examples/population-synthesis/debug_pop.ipynb rename to docs/_source/tutorials-examples/population-synthesis/custom_step_and_flow.ipynb index cd092c4f71..5814b9d864 100644 --- a/docs/_source/tutorials-examples/population-synthesis/debug_pop.ipynb +++ b/docs/_source/tutorials-examples/population-synthesis/custom_step_and_flow.ipynb @@ -4,15 +4,104 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Debugging POSYDON Binary Population Synthesis 🐞" + "# Custom POSYDON steps and flow chart\n", + "\n", + "The evolution of binaries in POSYDON is determined by the flow chart.\n", + "It uses the stellar states, `S1_state` and `S2_state`, the binary `state` and `event` to determine to which step the binary goes next.\n", + "\n", + "For example, in the default flow chart, the state `('H-rich_Core_C_depleted','H-rich_Core_H_burning', 'detached', 'CC1')` will be sent to `step_SN`.\n", + "\n", + "If your specific need requires, it is possible to change the flow and introduce your own steps.\n", + "This is possible through\n", + "\n", + "1. Importing an additional flow chart and step from the `population_params.ini` file\n", + "2. Changing a step inside a notebook\n", + "\n", + "\n", + "## Population_params.ini changes\n", + "\n", + "If you want to run large populations with an adapted flowchart or custom step, this is the location to change the loaded steps.\n", + "\n", + "### Importing an adapted flowchart\n", + "\n", + "One of the first things the `population_params.ini` file defines is the flow chart.\n", + "By default, the POSYDON default `flow_chart` is imported:\n", + "```\n", + "[flow]\n", + " import = ['posydon.binary_evol.flow_chart', 'flow_chart']\n", + " # builtin posydon flow\n", + " absolute_import = None\n", + " # If given, use an absolute filepath to user defined flow: ['', '']\n", + "```\n", + "\n", + "We can change this to import our custom flow. In this example, the binary is immediately send to `step_end` without any evolution. Make sure to change the path to your absolute path of the example file, if you want to run a population with it.\n", + "\n", + "Additionally, you can put the python file into the `user_modules` folder to make it part of the POSYDON namespace.\n", + "\n", + "```\n", + "[flow]\n", + " import = None\n", + " # builtin posydon flow\n", + " absolute_import = ['custom_step_and_flow.py', 'end_flow_chart']\n", + " # If given, use an absolute filepath to user defined flow: ['', '']\n", + "```\n", + "\n", + "We can check this change by loading the Simulation Property arguments.\n", + "Below you can see the example, where we send all the states straight to `step_end`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from posydon.popsyn.io import simprop_kwargs_from_ini\n", + "from posydon.binary_evol.simulationproperties import SimulationProperties\n", + "\n", + "sim_kwargs = simprop_kwargs_from_ini('population_params.ini')\n", + "sim_prop = SimulationProperties(**sim_kwargs)\n", + "sim_prop.flow[0]()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sim_kwargs = simprop_kwargs_from_ini('population_params_end.ini')\n", + "sim_prop = SimulationProperties(**sim_kwargs)\n", + "sim_prop.flow[0]()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "If you haven't done so yet, export the path POSYDON environment variables.\n", - "Set these parameters in your `.bash_profile` or `.zshrc` if you use POSYDON regularly." + "\n", + "## Changing an existing step to a custom step\n", + "\n", + "Similarly, you can change one of standard POSYDON steps to a custom one.\n", + "We will replace the common envelope step by one that halves the orbital period.\n", + "\n", + "```\n", + "[step_CE]\n", + " import = ['posydon.binary_evol.CE.step_CEE', 'StepCEE']\n", + " # builtin posydon step\n", + " absolute_import = None\n", + " # If given, use an absolute filepath to user defined step: ['', '']\n", + "```\n", + "\n", + "```\n", + "[step_CE]\n", + " import = None \n", + " # builtin posydon step\n", + " absolute_import = ['custom_step_and_flow.py', 'my_CE_step']\n", + " # If given, use an absolute filepath to user defined step: ['', '']\n", + "```\n", + "\n", + "You can see the step class in the example file [custom_step_and_flow.py](custom_step_and_flow.py)." ] }, { @@ -21,17 +110,19 @@ "metadata": {}, "outputs": [], "source": [ - "%env PATH_TO_POSYDON=/Users/simone/Google Drive/github/POSYDON-public/\n", - "%env PATH_TO_POSYDON_DATA=/Volumes/T7/" + "sim_kwargs = simprop_kwargs_from_ini('population_params_end.ini')\n", + "sim_kwargs['step_CE']\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "To debug the evolution of a binary systems you should load the POSYDON BinaryPopulation object in memory and then call the `restore` and `evolve`. This will run the evolution of the binary system and display any error traceback. You can debug the code by setting breakpoints in the POSYDON code until you devug the specific binary evolution.\n", + "## Adapting the flow and steps inside your notebook\n", "\n", - "The trick is to do this is not dump the binary population to disk but to keep them in memory. This is done by setting the `optimize_ram=False`." + "Although you can always load in the population parameters file into your notebook, maybe you want to create a unique flow inside a notebook. For this you need to set up all the steps manually inside the notebook.\n", + "\n", + "This is definitely not the easiest way to change option within POSYDON." ] }, { @@ -379,262 +470,17 @@ "\n", " return pop\n", "\n", - "if __name__ == '__main__' :\n", - " pop = run_simulation(sim_prop, kwargs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's say we want to debug binary_index that follows." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i = 0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Re-evolving a Binary Star" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "col = ['time', 'step_names', 'state', 'event', 'orbital_period', 'eccentricity', 'S1_state', 'S2_state', 'S1_mass', 'S2_mass']\n", - "pop[i].to_df(extra_columns={'step_names':'string'})[col]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pop[i].restore()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pop[i].to_df()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Even thought we reset the binary, the natal kick properties stored in the natal kick array are not reset. This is because we want to be able to re-evolve the binary to the same final state. In case you want to reset the natal kick array values, then set the list to `None` values." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# kick magnitude km/s, azimuthal angle rad, polar angle rad and mean anomaly rad (TODO check documentation)\n", - "print(pop[i].star_1.natal_kick_array)\n", - "print(pop[i].star_2.natal_kick_array)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pop[i].evolve()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pop[i].to_df(extra_columns={'step_names':'string'})[col]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Re-evolving a Binary Star with a different kick" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pop[i].restore()\n", - "pop[i].to_df()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pop[i].star_1.natal_kick_array = [1000., 0.858129274334538, 1.9157148786534735, 1.8675467897282945]\n", - "pop[i].star_2.natal_kick_array = [None, None, None, None] # default\n", - "print(pop[i].star_1.natal_kick_array)\n", - "print(pop[i].star_2.natal_kick_array)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pop[i].evolve()\n", - "pop[i].to_df(extra_columns={'step_names':'string'})[col]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Debugging a Binary Star from a population h5 file" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Assume we run the population somewhere else and you have the h5 file associated with it and you want to load the population in memory and re-evolve a binary. Here is how you do it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "del pop # delete the above population" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# let's load up and rerun the binary index i\n", - "pop = run_simulation(sim_prop, kwargs, file='./population.h5', indices=[i])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pop[0].to_df(extra_columns={'step_names':'string'})[col]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# do the same thing as above\n", - "pop[0].restore()\n", - "print(pop[i].star_1.natal_kick_array)\n", - "print(pop[i].star_2.natal_kick_array)\n", - "pop[i].star_1.natal_kick_array = [1000., 0.858129274334538, 1.9157148786534735, 1.8675467897282945]\n", - "pop[i].star_2.natal_kick_array = [None, None, None, None] # default\n", - "print(pop[i].star_1.natal_kick_array)\n", - "print(pop[i].star_2.natal_kick_array)\n", - "pop[i].evolve()\n", - "pop[i].to_df(extra_columns={'step_names':'string'})[col]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Evolving a Binary Star starting from an arbitrary state" + " \n", + "col = ['time', 'step_names', 'state', 'event', 'orbital_period', 'eccentricity', 'S1_state', 'S2_state', 'S1_mass', 'S2_mass']\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "If you want to evolve a custom binary star, you can do so by crafting yourself the `BinaryStar` object and providing the `SimulationProperties` object. For example, let's evolve a neutron star with a low mass helium star in roche lobe overflow.\n", + "### Adapted flow chart\n", "\n", - "Caution! \n", - "\n", - "While you can evolve a binary from an arbitrary state, you will need to provide data on the internal structure of the star, if you're starting in the detached step.\n", - "Otherwise the matching to a single star model will not work!\n", - "
        " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from posydon.binary_evol.singlestar import SingleStar\n", - "from posydon.binary_evol.binarystar import BinaryStar\n", - "sim_prop.load_steps(verbose=True) # load steps" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "binary = BinaryStar(star_1 = SingleStar(**{'state' : 'NS',\n", - " 'mass' : 1.1,\n", - " 'spin' : 0.,}),\n", - " star_2 = SingleStar(**{'state' : 'stripped_He_Core_He_burning',\n", - " 'mass' : 2.5,\n", - " 'natal_kick_array' : [10., 0., 0., 0.]}),\n", - " **{'time' : 0.,\n", - " 'state' : 'RLO2',\n", - " 'event' : 'oRLO2',\n", - " 'orbital_period' : 1.,\n", - " 'eccentricity' : 0.},\n", - " properties = sim_prop,\n", - " )\n", - "binary.evolve()\n", - "binary.to_df(extra_columns={'step_names':'string'})[col]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Mapping a MESA step to the `detached_step`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You might want to use the `detached_step` instetad of a MESA step. Here is how you do it. As an example we replace the HMS-HMS MESA step with the detached step. Notice that the `detached_step` does not evolve the binary system post onset of RLO1. Hence, unless the `flow_chart` support such binary event, binary state and stellar states, else these systems will not be furthter evovlved." + "You might want to use the `detached_step` instead of a MESA step. Here is how you do it. As an example we replace the HMS-HMS MESA step with the detached step. Notice that the `detached_step` does not evolve the binary system post onset of RLO1. Hence, unless the `flow_chart` support such binary event, binary state and stellar states, else these systems will not be further evolved." ] }, { @@ -664,16 +510,8 @@ "\n", "kwargs['from_hdf'] = False\n", "\n", - "if __name__ == '__main__' :\n", - " pop = run_simulation(sim_prop, kwargs)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ + "pop = run_simulation(sim_prop, kwargs)\n", + "# output the first binary\n", "i = 0\n", "pop[i].to_df(extra_columns={'step_names':'string'})[col]" ] @@ -682,14 +520,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Crating your own step" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here, you learn how to create your own step. As an example we create a step that does a fake common envelope by taking half the orbital period." + "### Adapted step\n", + "\n", + "We implement a custom common envelope step, which is the same as the on in the `custom_step_and_flow.py` custom step." ] }, { @@ -746,19 +579,15 @@ "\n", "sim_prop = SimulationProperties(**sim_kwargs)\n", "\n", - "\n", - "pop = run_simulation(sim_prop, kwargs)" + "pop = run_simulation(sim_prop, kwargs)\n", + "i = 0\n", + "pop[i].to_df(extra_columns={'step_names':'string'})[col]" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [ - "i = 0\n", - "pop[i].to_df(extra_columns={'step_names':'string'})[col]" - ] + "source": [] } ], "metadata": { diff --git a/docs/_source/tutorials-examples/population-synthesis/custom_step_and_flow.py b/docs/_source/tutorials-examples/population-synthesis/custom_step_and_flow.py new file mode 100644 index 0000000000..646eeaee63 --- /dev/null +++ b/docs/_source/tutorials-examples/population-synthesis/custom_step_and_flow.py @@ -0,0 +1,45 @@ +from posydon.binary_evol.flow_chart import (flow_chart, STAR_STATES_NORMALSTAR) + +def end_flow_chart(FLOW_CHART=None): + FLOW_CHART = {} + for s1 in STAR_STATES_NORMALSTAR: + for s2 in STAR_STATES_NORMALSTAR: + FLOW_CHART[(s1, s2, 'detached', 'ZAMS')] = 'step_end' + return FLOW_CHART + + + +class my_CE_step(object): + """Compute a fake CE event. + Requires the input star to be an H-rich and evolved star. + Otherwise, it will fail.""" + + def __init__(self, verbose=False): + self.verbose = verbose + + def __call__(self, binary): + + if self.verbose: + print('The orbital separation post CE is half the pre CE orbital separation!') + + # Determine which star is the donor and which is the companion + if binary.event in ["oCE1", "oDoubleCE1"]: + donor_star = binary.star_1 + comp_star = binary.star_2 + elif binary.event in ["oCE2", "oDoubleCE2"]: + donor_star = binary.star_2 + comp_star = binary.star_1 + else: + raise ValueError("CEE does not apply if `event` is not " + "`oCE1`, `oDoubleCE1`, `oCE2`, or `oDoubleCE1`") + + binary.orbital_period /= 2. + if donor_star.he_core_mass is None: + raise ValueError("The donor star must be an evolved star with a He core mass") + if donor_star.state != 'H-rich': + raise ValueError("The donor star must be an H-rich star") + + donor_star.mass = donor_star.he_core_mass # lose envelope + donor_star.state = donor_star.state.replace('H-rich', 'stripped_He') + binary.state = 'detached' + binary.event = None diff --git a/docs/_source/tutorials-examples/population-synthesis/evolve_single_binaries.ipynb b/docs/_source/tutorials-examples/population-synthesis/evolve_single_binaries.ipynb new file mode 100644 index 0000000000..4130c85adc --- /dev/null +++ b/docs/_source/tutorials-examples/population-synthesis/evolve_single_binaries.ipynb @@ -0,0 +1,496 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Evolve individual binaries 🐞" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It can be extremely useful to evolve a single binary from specific initial conditions or to start at a specific evolutionary state.\n", + "This is useful for debugging binaries and checking if your custom steps or flow are correctly working.\n", + "\n", + "This tutorial will cover:\n", + "\n", + "- How to initialise a zero-age main sequence binary (ZAMS).\n", + "- How to re-evolve a binary in an existing population.\n", + "- How to evolve a binary from specific initial states.\n", + "\n", + "\n", + "## Evolve a ZAMS binary\n", + "\n", + "To evolve a binary from ZAMS, we will:\n", + "\n", + "\n", + "1. Load the standard simulation properties.\n", + "2. Load the steps. \n", + "3. Initialise the binary.\n", + "4. Evolve it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import shutil\n", + "from posydon.config import PATH_TO_POSYDON\n", + "\n", + "path_to_params = os.path.join(PATH_TO_POSYDON, \"posydon/popsyn/population_params_default.ini\")\n", + "shutil.copyfile(path_to_params, './population_params.ini')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# load the function to load the simulation properties from the ini file\n", + "from posydon.popsyn.io import simprop_kwargs_from_ini\n", + "from posydon.binary_evol.simulationproperties import SimulationProperties\n", + "\n", + "# Load the simulation properties from the default ini file. \n", + "sim_kwargs = simprop_kwargs_from_ini('population_params.ini')\n", + "# manually add the metallicity to each step that requires it\n", + "metallicity = {'metallicity':1}\n", + "\n", + "sim_kwargs['step_HMS_HMS'][1].update(metallicity)\n", + "sim_kwargs['step_CO_HeMS'][1].update(metallicity)\n", + "sim_kwargs['step_CO_HMS_RLO'][1].update(metallicity)\n", + "sim_kwargs['step_CO_HeMS_RLO'][1].update(metallicity)\n", + "sim_kwargs['step_detached'][1].update(metallicity)\n", + "sim_kwargs['step_disrupted'][1].update(metallicity)\n", + "sim_kwargs['step_merged'][1].update(metallicity)\n", + "sim_kwargs['step_initially_single'][1].update(metallicity)\n", + "\n", + "sim_prop = SimulationProperties(**sim_kwargs)\n", + "# Load the steps and required data\n", + "sim_prop.load_steps(verbose=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# load the binary and single star classes\n", + "from posydon.binary_evol.singlestar import SingleStar\n", + "from posydon.binary_evol.binarystar import BinaryStar" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "STAR1 = SingleStar(**{'mass': 30.782576, \n", + " 'state': 'H-rich_Core_H_burning'})\n", + "STAR2 = SingleStar(**{'mass':20.273864,\n", + " 'state': 'H-rich_Core_H_burning'})\n", + "\n", + "BINARY = BinaryStar(STAR1, STAR2, \n", + " **{'time': 0.0, 'state': 'detached', 'event': 'ZAMS', 'orbital_period':3513.150157, 'eccentricity': 0.0},\n", + " properties = sim_prop)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "# Note: depending on the evolution of the binary, you might get some warnings about the Roche lobe calculation.\n", + "BINARY.evolve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You're now able to access the binary and evolutionary information. Using the function, `BinaryStar.to_df()` we can inspect the evolution of the systm.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# we will add the step names to the dataframe\n", + "col = ['time', 'step_names', 'state', 'event', 'orbital_period', 'eccentricity', 'S1_state', 'S2_state', 'S1_mass', 'S2_mass']\n", + "\n", + "BINARY.to_df(extra_columns={'step_names':'string'})[col]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Re-evolving the Binary\n", + "\n", + "The above binary might have been disrupted in the first or second SN due to the strength of the supernova kick.\n", + "You can restore the binary completely or to a specific state and re-evolve it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# restore to the original state\n", + "BINARY.restore()\n", + "BINARY.to_df()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "BINARY.evolve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Even thought we reset the binary, the natal kick properties stored in the natal kick array are not reset. This is because we want to be able to re-evolve the binary to the same final state. In case you want to reset the natal kick array values, then set the list to `None` values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# kick magnitude km/s, azimuthal angle rad, polar angle rad and mean anomaly rad (TODO check documentation)\n", + "print(BINARY.star_1.natal_kick_array)\n", + "print(BINARY.star_2.natal_kick_array)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This will be exactly the same evolution as the previous one\n", + "BINARY.to_df(extra_columns={'step_names':'string'})[col]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Changing the kick\n", + "\n", + "We will restore the `BINARY` to its initial state and increase the first kick velocity to disrupt the binary after the first supernova." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "BINARY.restore()\n", + "BINARY.to_df()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# [velocity, azimuthal angle, polar angle, phase]\n", + "BINARY.star_1.natal_kick_array = [1000., 0.858129274334538, 1.9157148786534735, 1.8675467897282945]\n", + "BINARY.star_2.natal_kick_array = [None, None, None, None] # default\n", + "print(BINARY.star_1.natal_kick_array)\n", + "print(BINARY.star_2.natal_kick_array)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "BINARY.evolve()\n", + "BINARY.to_df(extra_columns={'step_names':'string'})[col]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Evolve from specific step\n", + "\n", + "You can provide the row number (starting at 0), to reset the binary to that specific evolutionary phase.\n", + "This can be useful for debugging a specific evolutionary state." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "BINARY.restore(3)\n", + "BINARY.to_df()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading a binary from an existing population" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Assume we run the population somewhere else and you have the h5 file associated with it, and you want to load the population in memory and re-evolve a binary. \n", + "We will have to load the `population_params.ini` file associated with the Population file to re-evolve the binary in the same way.\n", + "\n", + "We will use the `BBH_contact.h5` file in the example dataset and use the standard `population_params.ini` SimulationProperties from earlier in this notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from posydon.popsyn.synthetic_population import Population\n", + "\n", + "\n", + "data_path = f'PATH/TO/TUTORIAL/POPULATIONS/'\n", + "\n", + "pop = Population(f'{data_path}/BBH_contact.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [], + "source": [ + "# Because the original population contains two extre columns. These have to be defined manually here.\n", + "# Otherwise, the reseting of the binaries fails.\n", + "BINARY = BinaryStar.from_df(pop.history[0],\n", + " extra_columns={'step_names':'string','step_times':'float'})\n", + "\n", + "# you have to set the simulation properties for the binary\n", + "BINARY.properties = sim_prop\n", + "\n", + "# Set the natal kick arrays from the oneline to get the exact same evolution as the original binary\n", + "BINARY.star_1.natal_kick_array = pop.oneline[0][['S1_natal_kick_array_0', 'S1_natal_kick_array_1', 'S1_natal_kick_array_2', 'S1_natal_kick_array_3']].values.flatten()\n", + "BINARY.star_2.natal_kick_array = pop.oneline[0][['S2_natal_kick_array_0', 'S2_natal_kick_array_1', 'S2_natal_kick_array_2', 'S2_natal_kick_array_3']].values.flatten()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "BINARY.to_df(extra_columns={'step_names':'string'})[col]" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [], + "source": [ + "BINARY.restore()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "BINARY.evolve()\n", + "BINARY.to_df(extra_columns={'step_names':'string'})[col]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evolving a Binary Star starting from an arbitrary state" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you want to evolve a custom binary star, you can do so by crafting yourself the `BinaryStar` object and providing the `SimulationProperties` object. For example, let's evolve a neutron star with a low mass helium star in Roche lobe overflow.\n", + "And start a binary in the detached step, which requires a bit of additional input data.\n", + "\n", + "We will use the standard SimulationProperties, loaded earlier in this notebook.\n", + "\n", + "\n", + "
        Caution! \n", + "\n", + "While you can evolve a binary from an arbitrary state, you will need to provide data on the internal structure of the star, if you're starting in the detached step.\n", + "Otherwise, the matching to a single star model will not work!\n", + "
        " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# we initialise the binary to be tight and Roche lobe overflowing in a circular orbit.\n", + "binary = BinaryStar(star_1 = SingleStar(**{'state' : 'NS',\n", + " 'mass' : 1.1,\n", + " 'spin' : 0.,}),\n", + " star_2 = SingleStar(**{'state' : 'H-rich_Core_H_burning',\n", + " 'mass' : 2.5,\n", + " 'natal_kick_array' : [10., 0., 0., 0.]}),\n", + " **{'time' : 0.,\n", + " 'state' : 'RLO2',\n", + " 'event' : 'oRLO2',\n", + " 'orbital_period' : 1.,\n", + " 'eccentricity' : 0.},\n", + " properties = sim_prop,\n", + " )\n", + "binary.evolve()\n", + "binary.to_df(extra_columns={'step_names':'string'})[col]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# we perform the same evolution, but with a stripped helium star to go to the CO_HeMS_RLO grid\n", + "binary = BinaryStar(star_1 = SingleStar(**{'state' : 'NS',\n", + " 'mass' : 1.1,\n", + " 'spin' : 0.,}),\n", + " star_2 = SingleStar(**{'state' : 'stripped_He_Core_He_burning',\n", + " 'mass' : 2.5,\n", + " 'natal_kick_array' : [10., 0., 0., 0.]}),\n", + " **{'time' : 0.,\n", + " 'state' : 'RLO2',\n", + " 'event' : 'oRLO2',\n", + " 'orbital_period' : 1.,\n", + " 'eccentricity' : 0.},\n", + " properties = sim_prop,\n", + " )\n", + "binary.evolve()\n", + "binary.to_df(extra_columns={'step_names':'string'})[col]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will change the binary to a much wider system and have it start in the detached state.\n", + "This requires additional starting information to allow the star to be matched to the single star grids." + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": {}, + "outputs": [], + "source": [ + "from posydon.utils.constants import Zsun\n", + "from posydon.utils.common_functions import orbital_separation_from_period\n", + "\n", + "import numpy as np\n", + "Z = 1.0\n", + "\n", + "# Setup the central abundances\n", + "zams_table = {2.: 2.915e-01,\n", + " 1.: 2.703e-01,\n", + " 0.45: 2.586e-01,\n", + " 0.2: 2.533e-01,\n", + " 0.1: 2.511e-01,\n", + " 0.01: 2.492e-01,\n", + " 0.001: 2.49e-01,\n", + " 0.0001: 2.49e-01}\n", + "\n", + "Y = zams_table[Z]\n", + "Z = Z*Zsun\n", + "X = 1 - Y - Z" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "STAR1 = SingleStar(**{'mass':1.2,\n", + " 'state': 'NS'})\n", + "\n", + "STAR2 = SingleStar(**{'mass': 17.782576, \n", + " 'state': 'H-rich_Core_H_burning',\n", + " # add the metallicity and central abundances\n", + " 'metallicity':Z, \n", + " 'center_h1':X,\n", + " 'center_he4':Y, \n", + " # add a numerical value for the radius\n", + " 'log_R': np.nan,\n", + " # add the helium core mass\n", + " 'he_core_mass': 0.0,\n", + " })\n", + "\n", + "binary = BinaryStar(STAR1, STAR2,\n", + " **{'time' : 0.,\n", + " 'state' : 'detached',\n", + " 'event' : None,\n", + " 'orbital_period' : 5000.,\n", + " # calculate the separation; current bug that the separation is not automatically calculated if orbital period is given\n", + " 'separation': orbital_separation_from_period(5000., 17.782576, 1.2),\n", + " 'eccentricity' : 0.},\n", + " properties = sim_prop,\n", + " )\n", + "binary.evolve()\n", + "binary.to_df(extra_columns={'step_names':'string'})[col]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "posydon_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/_source/tutorials-examples/population-synthesis/lgrb_pop_syn.ipynb b/docs/_source/tutorials-examples/population-synthesis/lgrb_pop_syn.ipynb index 2659ce5966..332466684a 100644 --- a/docs/_source/tutorials-examples/population-synthesis/lgrb_pop_syn.ipynb +++ b/docs/_source/tutorials-examples/population-synthesis/lgrb_pop_syn.ipynb @@ -7,23 +7,6 @@ "# Computing Long-Duration Gamma-Ray Bursts from Double Compact Object Populations 🌌" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you haven't done so yet, export the path POSYDON environment variables. For example:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%env PATH_TO_POSYDON=/Users/simone/Google Drive/github/POSYDON-public/\n", - "%env PATH_TO_POSYDON_DATA=/Volumes/T7/" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -35,9 +18,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We will use the same BBH population as in the `Analyzing Merging BBH Populations: Rates & Observations` tutorial to compute the long gamma-ray burstss (LGRB) rate associated with the formation of merging BBHs.\n", + "We will use the same BBH population as in the `Analyzing Merging BBH Populations: Rates & Observations` tutorial to compute the long gamma-ray bursts (LGRB) rate associated with the formation of merging BBHs.\n", "\n", - "Make sure the `BBH_contact.h5` file is present to continue with this tutorial." + "Make sure the `BBH_contact.h5` file is present to continue with this tutorial.\n", + "You can find a copy of it in the example dataset." ] }, { @@ -96,12 +80,15 @@ "source": [ "from posydon.popsyn.transient_select_funcs import GRB_selection\n", "\n", - "GRB_selection(BBH_population.history[10], BBH_population.oneline[10], BBH_population.formation_channels.loc[[10]], S1_S2='S2')" + "GRB_selection(BBH_population.history[10],\n", + " BBH_population.oneline[10],\n", + " BBH_population.formation_channels.loc[[10]],\n", + " S1_S2='S2')" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -109,7 +96,9 @@ "\n", "def GRB_wrapper(transient_chunk, oneline_chunk, formation_channels_chunk):\n", " \"\"\"calculate the GRBs from star 1 and star 2 for a given chunk of the population\"\"\"\n", + " # select potential GRBs from star 1\n", " df_1 = GRB_selection(transient_chunk, oneline_chunk, formation_channels_chunk, S1_S2='S1')\n", + " # select potential GRBs from star 2\n", " df_2 = GRB_selection(transient_chunk, oneline_chunk, formation_channels_chunk, S1_S2='S2')\n", " \n", " # combine the two dataframes\n", @@ -141,8 +130,10 @@ "You've now created a LGRB population, where either the first and/or second star has some amount of radiative disk.\n", "\n", "This is, of course, just an example and the actual amount of disk required to power a LGRB will be higher than some of the values included in our \"LGRB\" rate.\n", + "Moreover, the number of binaries in this population is insufficient to actually see the all unique events.\n", "\n", - "Moreover, the number of binaries in this population is insufficient to actually see the all unique events." + "For now, let's continue and calculate the metallicity bias function for the LGRBs.\n", + "We don't need to calculate the `underlying_mass` of the population, because we already did this in the [BBH tutorial](bbh_analysis.ipynb)." ] }, { diff --git a/docs/_source/tutorials-examples/population-synthesis/one_met_pop_syn.ipynb b/docs/_source/tutorials-examples/population-synthesis/one_met_pop_syn.ipynb index 23593fd437..df134a1b5a 100644 --- a/docs/_source/tutorials-examples/population-synthesis/one_met_pop_syn.ipynb +++ b/docs/_source/tutorials-examples/population-synthesis/one_met_pop_syn.ipynb @@ -35,7 +35,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We will use the same BBH population as in the `Analyzing Merging BBH Populations: Rates & Observations` tutorial. Instead of computing the distributions and rates at all metallicities, we will just focus on one metallicity, $Z_\\odot$, and integrate the star formation history around solar metallicity, say $[0.5Z_\\odot,2Z_\\odot]$. Let's extract the merging BBH population from the $Z_\\odot$ population synthesis model. In this sample there are only a few systems, to increase the statistics we suggest you run a population containing x50 more binaries." + "We will use the same BBH population as in the `Analyzing Merging BBH Populations: Rates & Observations` tutorial. Instead of computing the distributions and rates at all metallicities, we will just focus on one metallicity, $Z_\\odot$, and integrate the star formation history around solar metallicity, say $[0.5Z_\\odot,2Z_\\odot]$. Let's extract the merging BBH population from the $Z_\\odot$ population synthesis model. In this sample there are only a few systems, to increase the statistics we suggest you run a population containing x50 more binaries, if you want to do a more thorough analysis." ] }, { @@ -48,9 +48,9 @@ "from posydon.popsyn.synthetic_population import Population\n", "from posydon.config import PATH_TO_POSYDON_DATA\n", "\n", - "PATH_TO_POSYDON_DATA = '/Users/max/Documents/POSYDON_data/240305/POSYDON_data/tutorials/population-synthesis/example/'\n", + "data_path = f'{PATH_TO_POSYDON_DATA}/POSYDON_data/tutorial/population-synthesis/examples/'\n", "\n", - "pop = Population(PATH_TO_POSYDON_DATA+'1e+00_Zsun_population.h5')\n", + "pop = Population(data_path+'1e+00_Zsun_population.h5')\n", "\n", "pop.history.head(10)" ] @@ -70,7 +70,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Single metallicity population\n", + "## Single metallicity population\n", "\n", "Similar to in the multi-metallicity tutorial, we will setup a BBH selection from our $Z_\\odot$ population and then calculate the transient population." ] @@ -100,13 +100,6 @@ "pop.export_selection(selected_indices, 'Zsun_BBH_contact.h5')" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -123,7 +116,7 @@ "metadata": {}, "outputs": [], "source": [ - "pop.calculate_formation_channels(mt_history=True)" + "pop.history" ] }, { @@ -132,7 +125,7 @@ "metadata": {}, "outputs": [], "source": [ - "pop.history" + "pop.calculate_formation_channels(mt_history=True)" ] }, { @@ -165,7 +158,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Even though we have a single metallicity, we can still calculate the efficiency per $M_\\odot$ for it. " + "Even though we have a single metallicity, we can still calculate the efficiency per $M_\\odot$ for it. \n", + "\n", + "We still have to calculate the `underlying_mass` for the population before calculating the efficiency." ] }, { @@ -177,6 +172,10 @@ "from posydon.popsyn.synthetic_population import TransientPopulation\n", "\n", "BBH_mergers = TransientPopulation('Zsun_BBH_contact.h5', 'BBH')\n", + "\n", + "# returns the underlying mass and stores it in the mass_per_metallicity attribute\n", + "BBH_mergers.calculate_underlying_mass(f_bin=0.7)\n", + "\n", "BBH_mergers.get_efficiency_over_metallicity()" ] }, @@ -302,13 +301,6 @@ "ax.legend()\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/docs/_source/tutorials-examples/population-synthesis/pop_syn.ipynb b/docs/_source/tutorials-examples/population-synthesis/pop_syn.ipynb index dccb8960c9..a6dda68b76 100644 --- a/docs/_source/tutorials-examples/population-synthesis/pop_syn.ipynb +++ b/docs/_source/tutorials-examples/population-synthesis/pop_syn.ipynb @@ -6,43 +6,24 @@ "source": [ "# Large-Scale Population Synthesis on HPC Facilities 🚀\n", "\n", - "**Tutorial goal**\n", "\n", "This tutorial will cover how to setup and run a large, multi-metallicity population run on a HPC with slurm.\n", - "\n", - "We will dive deeper into the `population_params.ini` file and the `Population` class for multi-metallicity populations." + "We will dive deeper into the `population_params.ini` file and how the `Population` class works for multi-metallicity populations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "If you haven't done so yet, export the path POSYDON environment variables. For example:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%env PATH_TO_POSYDON=/Users/simone/Google Drive/github/POSYDON-public/\n", - "%env PATH_TO_POSYDON_DATA=/Volumes/T7/" + "## Initialization File for multi-metallicity runs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Creating the Initialization File for multi-metallicity runs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's copy the default population synthesis ini file to your working directory.\n", - "Make sure you're on a cluster to be able to run and submit the jobs.\n" + "Let's copy the default population synthesis `ini` file to your working directory.\n", + "Make sure you're on a cluster to be able to run and submit the jobs!\n" ] }, { @@ -63,42 +44,50 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Open the `population_params.ini` file and do the following edits to run a large model at 8 differet metallicities:\n", + "Open the `population_params.ini` file and you should see all 8 currently supported metallicities present. \n", + "\n", + "```\n", + "metallicity = [2., 1., 0.45, 0.2, 0.1, 0.01, 0.001, 0.0001]\n", + "```\n", "\n", - "- set `metallicity = [2., 1., 0.45, 0.2, 0.1, 0.01, 0.001, 0.0001]`\n", - "- set `number_of_binaries = 100`\n", + "For our test run, we do not require a large population, so set the number of binaries to 1000.\n", + "You might also want to make sure the `dump_rate` is set to 100 binaries for this example.\n", "\n", - "You might also want to make sure the `dump_rate` is set to 10 binaries. \n", - "`dump_rate = 10`" + "\n", + "```\n", + "dump_rate = 100\n", + "...\n", + "number_of_binaries = 1000\n", + "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Setting-up the Population Synthesis Model " + "## Setup the Population Synthesis Run" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "POSYDON provides `setup-popsyn` that you can use to setup a (multi-)metallicity population run on a HPC facility.\n", - "\n", + "POSYDON provides `posydon-setup-popsyn` as a command line script that you can use to setup a (multi-metallicity) population run on a HPC facility.\n", "This will split each metallicity into a separate slurm job-array and a dependent job which will automatically merge the output of the separate jobs.\n", - "\n", + "This removes the need to manually setup each slurm job.\n", "Below are two examples, but you might require to adjust the inputs for your email, cluster and available partitions.\n", "\n", + "
        \n", "\n", - "
        Older POSYDON installations \n", + "**Older POSYDON installations**\n", "\n", - "If the `setup-popsyn` command is not available, you might have to install POSYDON.\n", + "If the `posydon-setup-popsyn` command is not available, you might have to install POSYDON.\n", "1. Go to the directory: `cd $PATH_TO_POSYDON`\n", "2. Uninstall using pip `pip uninstall posydon`\n", "3. Reinstall: if you're using the development version: `pip install .`\n", " \n", - "See the [Installation instructions]() if any issues occur.\n", - "
        \n" + "See the [Installation instructions](../../getting-started/installation-guide.rst) if any issues occur.\n", + "
        \n" ] }, { @@ -112,7 +101,12 @@ "outputs": [], "source": [ "# example for yggdrasil\n", - "posydon-setup-popsyn population_params.ini --job_array=10 --walltime=00:14:00 --partition=debug-cpu --email=max.briel@unige.ch --account=fragkos" + "posydon-setup-popsyn population_params.ini --job_array=10 \\\n", + " --walltime=00:14:00 \\ \n", + " --mem_per_cpu=5G \\\n", + " --partition=debug-cpu \\ \n", + " --email=max.briel@unige.ch \\ \n", + " --account=fragkos" ] }, { @@ -126,7 +120,11 @@ "outputs": [], "source": [ "# example for quest cluster\n", - "posydon-setup-popsyn population_params.ini --job-array=10 --walltime=00:14:00" + "posydon-setup-popsyn population_params.ini --job-array=10 \\\n", + " --walltime=00:14:00 \\\n", + " --partition=posydon-std \\ \n", + " --email=max.briel@unige.ch \\\n", + " --mem_per_cpu=5G \n" ] }, { @@ -136,18 +134,14 @@ "`setup-popsyn --help` should provide a complete list of possible input parameters.\n", "\n", "\n", - "### walltime and job_array number fine-tuning\n", + "### Walltime and job_array number fine-tuning\n", "\n", - "The above examples will setup the population run with an array of 10 jobs for each metallicity. As such, each job will run 10 binaries of the 100 binaries per metallicity.\n", + "The above examples will setup the population run with an array of 10 jobs for each metallicity. As such, each job will run 100 binaries of the 1000 binaries per metallicity.\n", "\n", - "Fine-tuning the `dump_rate` compared to the number of binaries each job runs can be helpful. However, the larger the `dump_rate` the higher the memory footprint of each job.\n", - "As a default, 4Gb of RAM is requested per job.\n", + "Fine-tuning the `dump_rate`, `mem_per_cpu`, number of `job-array`'s, and `walltime` compared to the total number of binaries can be helpful in optimizing your cluster usage. See [the FAQ](../../troubleshooting-faqs/code-questions.rst) on more detailed guidance on the memory footprint of a population synthesis run in POSYDON.\n", + "By default, 5GB of RAM is requested per job, which fits well with the default `dump_rate` of 2000.\n", "\n", - "Similarly, the `walltime` and `job_array` can be fine-tuned. A single binary takes about 1-2 seconds to run. Depending on the number of binaries each job does, you might want to raise or lower the walltime for optimal perfomance.\n", - "\n", - "For example, with 100.000 binaries split over 100 jobs (per metallicity), means that every job runs 1.000 binaries. `dump_rate=1000` is a good amount of binaries to keep in memory, when you're doing an initial-final run (see here for more details on different run types). This will take around 33 minutes per job. So a walltime of `00:45:00` is reasonable.\n", - "\n", - "Instead of setting a `dump_rate`, it's also possible to set a `ram_per_cpu`. The code will try to stay below 90\\% of this limit, but this is not always guaranteed due to additional python overhead." + "As a rule of thumb for the `walltime`, a single binary takes about 1-2 seconds to run. In the example run, each job of 100 binaries should take around 2 to 3 minutes. However, some additional setup time for loading the grids is required! See [the FAQ](../../troubleshooting-faqs/code-questions.rst) for another example of ```walltime``` estimation." ] }, { @@ -208,13 +202,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In the next tutorial, we will look into selecting specific events and combining the different metallicity runs into a single population!" + "In the next tutorial, we will look into selecting specific events and combining the different metallicity runs into a single population and selecting the binary black hole mergers in them! [BBH analysis](bbh_analysis.ipynb)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "posydon_env", "language": "python", "name": "python3" }, diff --git a/docs/_source/tutorials-examples/population-synthesis/population_params_end.ini b/docs/_source/tutorials-examples/population-synthesis/population_params_end.ini new file mode 100644 index 0000000000..5acd8a667b --- /dev/null +++ b/docs/_source/tutorials-examples/population-synthesis/population_params_end.ini @@ -0,0 +1,519 @@ +# POSYDON default BinaryPopulation inifile, use ConfigParser syntax + +[environment_variables] + PATH_TO_POSYDON = '' + + +;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; +;;;;;;;; SimulationProperties ;;;;;;;;;; +;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; + +[flow] + import = None + # builtin posydon flow + absolute_import = ['custom_step_and_flow.py', 'end_flow_chart'] + # If given, use an absolute filepath to user defined flow: ['', ''] + +[step_HMS_HMS] + import = ['posydon.binary_evol.MESA.step_mesa', 'MS_MS_step'] + # builtin posydon step + absolute_import = None + # If given, use an absolute filepath to user defined step: ['', ''] + interpolation_path = None + # found by default + interpolation_filename = None + # found by default + interpolation_method = 'linear3c_kNN' + # 'nearest_neighbour' 'linear3c_kNN' '1NN_1NN' + save_initial_conditions = True + # only for interpolation_method='nearest_neighbour' + track_interpolation = False + # True False + stop_method = 'stop_at_max_time' + # 'stop_at_end' 'stop_at_max_time' 'stop_at_condition' + stop_star = 'star_1' + # only for stop_method='stop_at_condition' 'star_1' 'star_2' + stop_var_name = None + # only for stop_method='stop_at_condition' str + stop_value = None + # only for stop_method='stop_at_condition' float + stop_interpolate = True + # True False + verbose = False + # True False + + +[step_CO_HeMS] + import = ['posydon.binary_evol.MESA.step_mesa', 'CO_HeMS_step'] + # builtin posydon step + absolute_import = None + # If given, use an absolute filepath to user defined step: ['', ''] + interpolation_path = None + # found by default + interpolation_filename = None + # found by default + interpolation_method = 'linear3c_kNN' + # 'nearest_neighbour' 'linear3c_kNN' '1NN_1NN' + save_initial_conditions = True + # only for interpolation_method='nearest_neighbour' + track_interpolation = False + # True False + stop_method = 'stop_at_max_time' + # 'stop_at_end' 'stop_at_max_time' 'stop_at_condition' + stop_star = 'star_1' + # only for stop_method='stop_at_condition' 'star_1' 'star_2' + stop_var_name = None + # only for stop_method='stop_at_condition' str + stop_value = None + # only for stop_method='stop_at_condition' float + stop_interpolate = True + # True False + verbose = False + # True False + +[step_CO_HMS_RLO] + import = ['posydon.binary_evol.MESA.step_mesa', 'CO_HMS_RLO_step'] + # builtin posydon step + absolute_import = None + # If given, use an absolute filepath to user defined step: ['', ''] + interpolation_path = None + # found by default + interpolation_filename = None + # found by default + interpolation_method = 'linear3c_kNN' + # 'nearest_neighbour' 'linear3c_kNN' '1NN_1NN' + save_initial_conditions = True + # only for interpolation_method='nearest_neighbour' + track_interpolation = False + # True False + stop_method = 'stop_at_max_time' + # 'stop_at_end' 'stop_at_max_time' 'stop_at_condition' + stop_star = 'star_1' + # only for stop_method='stop_at_condition' 'star_1' 'star_2' + stop_var_name = None + # only for stop_method='stop_at_condition' str + stop_value = None + # only for stop_method='stop_at_condition' float + stop_interpolate = True + # True False + verbose = False + # True False + +[step_CO_HeMS_RLO] + import = ['posydon.binary_evol.MESA.step_mesa', 'CO_HeMS_RLO_step'] + # builtin posydon step + absolute_import = None + # If given, use an absolute filepath to user defined step: ['', ''] + interpolation_path = None + # found by default + interpolation_filename = None + # found by default + interpolation_method = 'linear3c_kNN' + # 'nearest_neighbour' 'linear3c_kNN' '1NN_1NN' + save_initial_conditions = True + # only for interpolation_method='nearest_neighbour' + track_interpolation = False + # True False + stop_method = 'stop_at_max_time' + # 'stop_at_end' 'stop_at_max_time' 'stop_at_condition' + stop_star = 'star_1' + # only for stop_method='stop_at_condition' 'star_1' 'star_2' + stop_var_name = None + # only for stop_method='stop_at_condition' str + stop_value = None + # only for stop_method='stop_at_condition' float + stop_interpolate = True + # True False + verbose = False + # True False + + +[step_detached] + import = ['posydon.binary_evol.DT.step_detached', 'detached_step'] + # builtin posydon step + absolute_import = None + # If given, use an absolute filepath to user defined step: ['', ''] + matching_method = 'minimize' + #'minimize' 'root' + do_wind_loss = True + # True False + do_tides = True + # True False + do_gravitational_radiation = True + # True False + do_magnetic_braking = True + # True False + do_stellar_evolution_and_spin_from_winds = True + # True False + RLO_orbit_at_orbit_with_same_am = False + # True False + verbose = False + # True False + +[step_disrupted] + import = ['posydon.binary_evol.DT.step_disrupted','DisruptedStep'] + # builtin posydon step + absolute_import = None + # If given, use an absolute filepath to user defined step: ['', ''] + +[step_merged] + import = ['posydon.binary_evol.DT.step_merged','MergedStep'] + # builtin posydon step + absolute_import = None + # If given, use an absolute filepath to user defined step: ['', ''] + +[step_initially_single] + import = ['posydon.binary_evol.DT.step_initially_single','InitiallySingleStep'] + # builtin posydon step + absolute_import = None + # If given, use an absolute filepath to user defined step: ['', ''] + +[step_CE] + import = None + # builtin posydon step + absolute_import = ['custom_step_and_flow.py', 'my_CE_step'] + # If given, use an absolute filepath to user defined step: ['', ''] + prescription='alpha-lambda' + # 'alpha-lambda' + common_envelope_efficiency=1.0 + # float in (0, inf) + common_envelope_option_for_lambda='lambda_from_grid_final_values' + # (1) 'default_lambda', (2) 'lambda_from_grid_final_values', + # (3) 'lambda_from_profile_gravitational', + # (4) 'lambda_from_profile_gravitational_plus_internal', + # (5) 'lambda_from_profile_gravitational_plus_internal_minus_recombination' + common_envelope_lambda_default=0.5 + # float in (0, inf) used only for option (1) + common_envelope_option_for_HG_star="optimistic" + # 'optimistic', 'pessimistic' + common_envelope_alpha_thermal=1.0 + # float in (0, inf) used only for option for (4), (5) + core_definition_H_fraction=0.1 + # 0.01, 0.1, 0.3 + core_definition_He_fraction=0.1 + # 0.1 + CEE_tolerance_err = 0.001 + # float (0, inf) + common_envelope_option_after_succ_CEE = 'core_not_replaced_noMT' + # 'core_not_replaced_noMT' 'core_replaced_noMT' + # 'core_not_replaced_stableMT' 'core_not_replaced_windloss' + verbose = False + # True False + +[step_SN] + import = ['posydon.binary_evol.SN.step_SN', 'StepSN'] + # builtin posydon step + absolute_import = None + # 'package' kwarg for importlib.import_module + mechanism = 'Patton&Sukhbold20-engine' + # 'direct', Fryer+12-rapid', 'Fryer+12-delayed', + # 'Sukhbold+16-engine', 'Patton&Sukhbold20-engine' + engine = 'N20' + # 'N20' for 'Sukhbold+16-engine', + # 'Patton&Sukhbold20-engine' or None for the others + PISN = "Marchant+19" + # None, "Marchant+19" + ECSN = "Podsiadlowski+04" + # "Tauris+15", "Podsiadlowski+04" + conserve_hydrogen_envelope = False + # True, False + max_neutrino_mass_loss = 0.5 + # float (0,inf) + max_NS_mass = 2.5 + # float (0,inf) + use_interp_values = True + # True, False + use_profiles = True + # True, False + use_core_masses = True + # True, False + approx_at_he_depletion = False + # True, False + kick = True + # True, False + kick_normalisation = 'one_over_mass' + # "one_minus_fallback", "one_over_mass", + # "NS_one_minus_fallback_BH_one", "one", "zero" + sigma_kick_CCSN_NS = 265.0 + # float (0,inf) + sigma_kick_CCSN_BH = 265.0 + # float (0,inf) + sigma_kick_ECSN = 20.0 + # float (0,inf) + verbose = False + # True False + +[step_dco] + import = ['posydon.binary_evol.DT.double_CO', 'DoubleCO'] + # builtin posydon step + absolute_import = None + # If given, use an absolute filepath to user defined step: ['', ''] + n_o_steps_interval = None + +[step_end] + import = ['posydon.binary_evol.step_end', 'step_end'] + # builtin posydon step + absolute_import = None + # If given, use an absolute filepath to user defined step: ['', ''] + +[extra_hooks] + import_1 = ['posydon.binary_evol.simulationproperties', 'TimingHooks'] + # builtin posydon hook + absolute_import_1 = None + # If given, use an absolute filepath to user defined step: ['', ''] + kwargs_1 = {} + + import_2 = ['posydon.binary_evol.simulationproperties', 'StepNamesHooks'] + # builtin posydon hook + absolute_import_2 = None + # If given, use an absolute filepath to user defined step: ['', ''] + kwargs_2 = {} + + +;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; +;;;;;;;; BinaryPopulation ;;;;;;;;;; +;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; + +[BinaryPopulation_options] + optimize_ram = True + # save population in batches + ram_per_cpu = None + # set maximum ram per cpu before batch saving (GB) + dump_rate = 2000 + # batch save after evolving N binaries + temp_directory = 'batches' + # folder for keeping batch files + tqdm = False + # progress bar + breakdown_to_df = True + # convert BinaryStars into DataFrames after evolution + use_MPI = False + # use only for local MPI runs + metallicity = [1.] #[2., 1., 0.45, 0.2, 0.1, 0.01, 0.001, 0.0001] + # In units of solar metallicity + + # Random Number Generation + entropy = None + # `None` uses system entropy (recommended) + number_of_binaries = 10 + # int + binary_fraction_scheme = 'const' + #'const' 'Moe_17' + binary_fraction_const = 1 + # float 0< fraction <=1 + star_formation = 'burst' + # 'constant' 'burst' 'custom_linear' 'custom_log10' 'custom_linear_histogram' 'custom_log10_histogram' + max_simulation_time = 13.8e9 + # float (0,inf) + + read_samples_from_file = '' + # path to file to read initial parameters from (if empty string get random samples) + primary_mass_scheme = 'Kroupa2001' + # 'Salpeter', 'Kroupa1993', 'Kroupa2001' + primary_mass_min = 7 + # float (0,130) + primary_mass_max = 150.0 + # float (0,130) + secondary_mass_scheme = 'flat_mass_ratio' + # 'flat_mass_ratio', 'q=1' + secondary_mass_min = 0.35 + # float (0,130) + secondary_mass_max = 150.0 + # float (0,130) + orbital_scheme = 'period' + # 'separation', 'period' + orbital_period_scheme = 'Sana+12_period_extended' + # used only for orbital_scheme = 'period' + orbital_period_min = 0.75 + # float (0,inf) + orbital_period_max = 6000.0 + # float (0,inf) + #orbital_separation_scheme = 'log_uniform' + # used only for orbital_scheme = 'separation', 'log_uniform', 'log_normal' + #orbital_separation_min = 5.0 + # float (0,inf) + #orbital_separation_max = 1e5 + # float (0,inf) + #log_orbital_separation_mean = None + # float (0,inf) used only for orbital_separation_scheme ='log_normal' + #log_orbital_separation_sigma = None + # float (0,inf) used only for orbital_separation_scheme ='log_normal' + eccentricity_scheme = 'zero' + # 'zero' 'thermal' 'uniform' + + +;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; +;;;;;;;; Saving Output ;;;;;;;;;; +;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; + +[BinaryStar_output] + extra_columns = {'step_names':'string', 'step_times':'float64'} + # 'step_times' with from posydon.binary_evol.simulationproperties import TimingHooks + + # LIST BINARY PROPERTIES + only_select_columns=[ + 'state', + 'event', + 'time', + #'separation', + 'orbital_period', + 'eccentricity', + #'V_sys', + #'rl_relative_overflow_1', + #'rl_relative_overflow_2', + 'lg_mtransfer_rate', + #'mass_transfer_case', + #'trap_radius', + #'acc_radius', + #'t_sync_rad_1', + #'t_sync_conv_1', + #'t_sync_rad_2', + #'t_sync_conv_2', + #'nearest_neighbour_distance', + ] + scalar_names=[ + 'interp_class_HMS_HMS', + 'interp_class_CO_HMS_RLO', + 'interp_class_CO_HeMS', + 'interp_class_CO_HeMS_RLO', + 'mt_history_HMS_HMS', + 'mt_history_CO_HMS_RLO', + 'mt_history_CO_HeMS', + 'mt_history_CO_HeMS_RLO', + ] + +[SingleStar_1_output] + # LIST STAR PROPERTIES TO SAVE + include_S1=True + # True, False + only_select_columns=[ + 'state', + #'metallicity', + 'mass', + 'log_R', + 'log_L', + 'lg_mdot', + #'lg_system_mdot', + #'lg_wind_mdot', + 'he_core_mass', + 'he_core_radius', + #'c_core_mass', + #'c_core_radius', + #'o_core_mass', + #'o_core_radius', + 'co_core_mass', + 'co_core_radius', + 'center_h1', + 'center_he4', + #'center_c12', + #'center_n14', + #'center_o16', + 'surface_h1', + 'surface_he4', + #'surface_c12', + #'surface_n14', + #'surface_o16', + #'log_LH', + #'log_LHe', + #'log_LZ', + #'log_Lnuc', + #'c12_c12', + #'center_gamma', + #'avg_c_in_c_core', + #'surf_avg_omega', + 'surf_avg_omega_div_omega_crit', + #'total_moment_of_inertia', + #'log_total_angular_momentum', + 'spin', + #'conv_env_top_mass', + #'conv_env_bot_mass', + #'conv_env_top_radius', + #'conv_env_bot_radius', + #'conv_env_turnover_time_g', + #'conv_env_turnover_time_l_b', + #'conv_env_turnover_time_l_t', + #'envelope_binding_energy', + #'mass_conv_reg_fortides', + #'thickness_conv_reg_fortides', + #'radius_conv_reg_fortides', + #'lambda_CE_1cent', + #'lambda_CE_10cent', + #'lambda_CE_30cent', + #'lambda_CE_pure_He_star_10cent', + #'profile', + ] + scalar_names=[ + 'natal_kick_array', + 'SN_type', + 'f_fb', + 'spin_orbit_tilt_first_SN', + 'spin_orbit_tilt_second_SN', + ] + +[SingleStar_2_output] + # LIST STAR PROPERTIES TO SAVE + include_S2 = True + # True, False + only_select_columns = [ + 'state', + #'metallicity', + 'mass', + 'log_R', + 'log_L', + 'lg_mdot', + #'lg_system_mdot', + #'lg_wind_mdot', + 'he_core_mass', + 'he_core_radius', + #'c_core_mass', + #'c_core_radius', + #'o_core_mass', + #'o_core_radius', + 'co_core_mass', + 'co_core_radius', + 'center_h1', + 'center_he4', + #'center_c12', + #'center_n14', + #'center_o16', + 'surface_h1', + 'surface_he4', + #'surface_c12', + #'surface_n14', + #'surface_o16', + #'log_LH', + #'log_LHe', + #'log_LZ', + #'log_Lnuc', + #'c12_c12', + #'center_gamma', + #'avg_c_in_c_core', + #'surf_avg_omega', + 'surf_avg_omega_div_omega_crit', + #'total_moment_of_inertia', + #'log_total_angular_momentum', + 'spin', + #'conv_env_top_mass', + #'conv_env_bot_mass', + #'conv_env_top_radius', + #'conv_env_bot_radius', + #'conv_env_turnover_time_g', + #'conv_env_turnover_time_l_b', + #'conv_env_turnover_time_l_t', + #'envelope_binding_energy', + #'mass_conv_reg_fortides', + #'thickness_conv_reg_fortides', + #'radius_conv_reg_fortides', + #'lambda_CE_1cent', + #'lambda_CE_10cent', + #'lambda_CE_30cent', + #'lambda_CE_pure_He_star_10cent', + #'profile', + ] + scalar_names=[ + 'natal_kick_array', + 'SN_type', + 'f_fb', + 'spin_orbit_tilt_first_SN', + 'spin_orbit_tilt_second_SN', + ] diff --git a/posydon/binary_evol/CE/step_CEE.py b/posydon/binary_evol/CE/step_CEE.py index fa580723cc..fe398c32f5 100644 --- a/posydon/binary_evol/CE/step_CEE.py +++ b/posydon/binary_evol/CE/step_CEE.py @@ -201,7 +201,7 @@ def __call__(self, binary): star_to_merge = "2" else: raise ValueError("CEE does not apply if `event` is not " - "`oCE1`, 'oDoubleCE1' or `oCE2`, 'oDoubleCE1'") + "`oCE1`, `oDoubleCE1`, `oCE2`,or `oDoubleCE1`") # Check for double CE double_CE = binary.event in ["oDoubleCE1", "oDoubleCE2"] diff --git a/posydon/popsyn/population_params_default.ini b/posydon/popsyn/population_params_default.ini index d084ab897f..19cc6f6340 100644 --- a/posydon/popsyn/population_params_default.ini +++ b/posydon/popsyn/population_params_default.ini @@ -279,7 +279,7 @@ # save population in batches ram_per_cpu = None # set maximum ram per cpu before batch saving (GB) - dump_rate = 10 + dump_rate = 2000 # batch save after evolving N binaries temp_directory = 'batches' # folder for keeping batch files @@ -289,9 +289,14 @@ # convert BinaryStars into DataFrames after evolution use_MPI = False # use only for local MPI runs - metallicity = [1] # [2., 1., 0.45, 0.2, 0.1, 0.01, 0.001, 0.0001] + metallicity = [1.] #[2., 1., 0.45, 0.2, 0.1, 0.01, 0.001, 0.0001] # In units of solar metallicity + error_checking_verbose = False + # if True, write all POSYDON errors to stderr at runtime, default=False + warnings_verbose = False + # if True, write all POSYDON warnings to stderr at runtime, default=False + # Random Number Generation entropy = None # `None` uses system entropy (recommended) @@ -299,7 +304,7 @@ # int binary_fraction_scheme = 'const' #'const' 'Moe_17' - binary_fraction_const = 1 + binary_fraction_const = 1.0 # float 0< fraction <=1 star_formation = 'burst' # 'constant' 'burst' 'custom_linear' 'custom_log10' 'custom_linear_histogram' 'custom_log10_histogram' @@ -310,7 +315,7 @@ # path to file to read initial parameters from (if empty string get random samples) primary_mass_scheme = 'Kroupa2001' # 'Salpeter', 'Kroupa1993', 'Kroupa2001' - primary_mass_min = 7 + primary_mass_min = 7.0 # float (0,130) primary_mass_max = 150.0 # float (0,130) diff --git a/posydon/popsyn/synthetic_population.py b/posydon/popsyn/synthetic_population.py index d346ad456d..a6cadd52a1 100644 --- a/posydon/popsyn/synthetic_population.py +++ b/posydon/popsyn/synthetic_population.py @@ -1429,7 +1429,7 @@ def formation_channels(self): return self._formation_channels - def calculate_formation_channels(self, mt_history=False): + def calculate_formation_channels(self, mt_history=True): """Calculate the formation channels of the population. mt_history is a boolean that determines if the detailed mass-transfer history @@ -2478,7 +2478,7 @@ def select_rate_slice(self, key, start=None, stop=None): with pd.HDFStore(self.filename, mode="r") as store: return store.select(self.base_path + key, start=start, stop=stop) - def calculate_intrinsic_rate_density(self, mt_channels=False): + def calculate_intrinsic_rate_density(self, channels=False): """ Compute the intrinsic rate density over redshift of the transient population. @@ -2487,7 +2487,7 @@ def calculate_intrinsic_rate_density(self, mt_channels=False): Parameters ---------- - mt_channels : bool, optional + channels : bool, optional Flag indicating whether to calculate the intrinsic rate density for each channel separately. Default is False. Returns @@ -2500,7 +2500,7 @@ def calculate_intrinsic_rate_density(self, mt_channels=False): z_horizon = self.edges_redshift_bins n = len(z_horizon) - if mt_channels: + if channels: channels = self.select(columns=["channel"]) unique_channels = np.unique(channels) else: From bd3ede23a87f8086a064c803b289d52d388ca2e6 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Mon, 2 Dec 2024 17:03:07 +0100 Subject: [PATCH 269/319] Update step_merged.py (#457) Turn print into ModelError, because variables are otherwise undefined. --- posydon/binary_evol/DT/step_merged.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/posydon/binary_evol/DT/step_merged.py b/posydon/binary_evol/DT/step_merged.py index 4913dd0e6c..6e9f24bbd4 100644 --- a/posydon/binary_evol/DT/step_merged.py +++ b/posydon/binary_evol/DT/step_merged.py @@ -549,7 +549,7 @@ def mass_weighted_avg(star1=star_base,star2=comp, abundance_name="center_h1", ma ## in this case, want CO companion object to stay the same, and base star to be assigned massless remnant return massless_remnant, merged_star else: - print("Combination of merging star states not expected: ", s1, s2) + raise ModelError(f"Combination of merging star states not expected: {s1} {s2}") # ad hoc spin of merged star to be used in the detached step merged_star.surf_avg_omega_div_omega_crit = self.merger_critical_rot From 206f5b5f7878a7ab68bffee709ef5a7019b073d4 Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Tue, 3 Dec 2024 15:01:25 +0100 Subject: [PATCH 270/319] fix log vs log10 typo (#460) In the MD/MF metallicity fraction calculation, there was a typo for the outer bin in transforming the absolute metallicity into a log10 value. It was log, but should be log10! --- posydon/popsyn/star_formation_history.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/posydon/popsyn/star_formation_history.py b/posydon/popsyn/star_formation_history.py index cb78aed66e..f7c219adcf 100644 --- a/posydon/popsyn/star_formation_history.py +++ b/posydon/popsyn/star_formation_history.py @@ -242,7 +242,7 @@ def SFR_Z_fraction_at_given_redshift( ) if not select_one_met: fSFR[:, 0] = stats.norm.cdf(np.log10(metallicity_bins[1]), mu, sigma) / norm - fSFR[:,-1] = norm - stats.norm.cdf(np.log(metallicity_bins[-1]), mu, sigma)/norm + fSFR[:,-1] = norm - stats.norm.cdf(np.log10(metallicity_bins[-1]), mu, sigma)/norm elif SFR == "Neijssel+19": # assume a truncated ln-normal distribution of metallicities From e93bbf40e5baaac63128d75a63b1e5027ac85b64 Mon Sep 17 00:00:00 2001 From: Camille Liotine Date: Wed, 4 Dec 2024 04:34:28 -0600 Subject: [PATCH 271/319] remove "redirect" steps from history dataframe (#421) * skip redirect steps when appending to binary history and step names * skip appending step_times for redirect steps * add "history_verbose" option * add missing verbose params to default .ini file * handle history_verbose setting for all step hooks * remove history_verbose check from pre-step hooks * update docs and .ini file with history_verbose parameter * Merge branch 'development' into camille_remove_redirect_from_df * update binary_star docs --- .../components-overview/pop_syn/binary_star.rst | 4 ++++ .../pop_syn/population_params.rst | 5 +++++ posydon/binary_evol/binarystar.py | 12 +++++++++--- posydon/binary_evol/simulationproperties.py | 17 +++++++++++++---- posydon/popsyn/binarypopulation.py | 4 ++++ posydon/popsyn/population_params_default.ini | 5 +++-- 6 files changed, 38 insertions(+), 9 deletions(-) diff --git a/docs/_source/components-overview/pop_syn/binary_star.rst b/docs/_source/components-overview/pop_syn/binary_star.rst index 2065235da5..cccab0b220 100644 --- a/docs/_source/components-overview/pop_syn/binary_star.rst +++ b/docs/_source/components-overview/pop_syn/binary_star.rst @@ -175,12 +175,16 @@ Binary events are defined according to the following table: - The binary reached contact in the compact object phase. * - ``redirect_from_ZAMS`` - The binary was redirected from ZAMS for a variety of reasons. + - Only recorded if history_verbose = True * - ``redirect_from_CO_HMS_RLO`` - The binary was redirected from CO_HMS_RLO for a variety of reasons. + - Only recorded if history_verbose = True * - ``redirect_from_CO_HeMS`` - The binary was redirected from CO_HeMS for a variety of reasons. + - Only recorded if history_verbose = True * - ``redirect_from_CO_HeMS_RLO`` - The binary was redirected from CO_HeMS_RLO for a variety of reasons. + - Only recorded if history_verbose = True * - ``MaxTime_exceeded`` - The maximum time of the evolution was exceeded. * - ``maxtime`` diff --git a/docs/_source/components-overview/pop_syn/population_params.rst b/docs/_source/components-overview/pop_syn/population_params.rst index 40f17c6d46..4ffd36841a 100644 --- a/docs/_source/components-overview/pop_syn/population_params.rst +++ b/docs/_source/components-overview/pop_syn/population_params.rst @@ -681,6 +681,11 @@ It also contains which sampling distributions to use for the initial conditions * - ``warnings_verbose`` - | If True, write all POSYDON warnings to stderr at runtime - ``False`` + + * - ``history_verbose`` + - | If True, record extra functional steps in the output DataFrames + - | (These extra steps represent internal workings of POSYDON rather than physical phases of evolution) + - ``False`` * - ``entropy`` - | Random Number Generation: uses system entropy. diff --git a/posydon/binary_evol/binarystar.py b/posydon/binary_evol/binarystar.py index 80ea4e97d4..a51634d40f 100644 --- a/posydon/binary_evol/binarystar.py +++ b/posydon/binary_evol/binarystar.py @@ -178,8 +178,9 @@ def __init__(self, star_1=None, star_2=None, index=None, properties=None, self.true_anomaly_SN2 = None if not hasattr(self, 'first_SN_already_occurred'): self.first_SN_already_occurred = False - # if not hasattr(self, 'V_sys'): - # self.V_sys = [0, 0, 0] + + if not hasattr(self, 'history_verbose'): + self.history_verbose = False # store interpolation_class and mt_history for each step_MESA for grid_type in ['HMS_HMS','CO_HMS_RLO','CO_HeMS','CO_HeMS_RLO']: @@ -252,6 +253,12 @@ def run_step(self): def append_state(self): """Update the history of the binaries' properties.""" + + ## do not append redirect steps to the binary history if history_verbose=False + if not self.history_verbose and getattr(self, "event") is not None: + if "redirect" in getattr(self, "event"): + return + # Append to the binary history lists for item in BINARYPROPERTIES: getattr(self, item + '_history').append(getattr(self, item)) @@ -355,7 +362,6 @@ def to_df(self, **kwargs): """ extra_binary_cols_dict = kwargs.get('extra_columns', {}) extra_columns = list(extra_binary_cols_dict.keys()) - extra_columns_dtypes_user = list(extra_binary_cols_dict.values()) all_keys = (["binary_index"] + [key+'_history' for key in BINARYPROPERTIES] diff --git a/posydon/binary_evol/simulationproperties.py b/posydon/binary_evol/simulationproperties.py index eef1bba0dc..130a14b59d 100644 --- a/posydon/binary_evol/simulationproperties.py +++ b/posydon/binary_evol/simulationproperties.py @@ -67,9 +67,7 @@ def __init__(self, properties=None, **kwargs): # for debugging purposes if not hasattr(self, 'max_n_steps_per_binary'): self.max_n_steps_per_binary = 100 - #if not hasattr(self, "verbose_binary_errors"): - # self.verbose_binary_errors = False - + # Set functions for evolution for key, val in kwargs.items(): if "step" not in key: # skip loading steps @@ -148,7 +146,7 @@ def pre_step(self, binary, step_name): ------- binary : instance of - """ + """ for hooks in self.all_hooks_classes: hooks.pre_step(binary, step_name) if hasattr(self, 'extra_pre_step'): @@ -173,6 +171,11 @@ def post_step(self, binary, step_name): binary : instance of """ + ## do not call extra step hooks if history_verbose=False + if not binary.history_verbose and binary.event is not None: + if "redirect" in binary.event: + return binary + for hooks in self.all_hooks_classes: hooks.post_step(binary, step_name) if hasattr(self, 'extra_post_step'): @@ -255,13 +258,16 @@ def pre_step(self, binary, step_name): def post_step(self, binary, step_name): """Record the duration of the step.""" + binary.step_times.append(time.time() - self.step_start_time) + if len(binary.event_history) > len(binary.step_times): diff = len(binary.event_history) - len(binary.step_times) binary.step_times += [None] * (diff) elif len(binary.event_history) < len(binary.step_times): last_items = len(binary.event_history) binary.step_times = binary.step_times[-(last_items - 1):] + return binary def post_evolve(self, binary): @@ -294,14 +300,17 @@ def pre_step(self, binary, step_name): def post_step(self, binary, step_name): """Record the step name.""" + binary.step_names.append(step_name) len_binary_hist = len(binary.event_history) len_step_names = len(binary.step_names) diff = len_binary_hist - len_step_names + if len_binary_hist > len_step_names: binary.step_names += [None] * (diff) elif len_binary_hist < len_step_names: binary.step_names = binary.step_names[-(len_binary_hist - 1):] + return binary def post_evolve(self, binary): diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index 8331380b8a..aadfde33ee 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -137,6 +137,8 @@ def __init__(self, **kwargs): self.population_properties.max_simulation_time = self.kwargs.get( 'max_simulation_time') # years + + self.history_verbose = self.kwargs.get("history_verbose", False) self.entropy = self.kwargs.get('entropy', None) seq = np.random.SeedSequence(entropy=self.entropy) @@ -1000,6 +1002,7 @@ def draw_initial_binary(self, **kwargs): separation=separation, orbital_period=orbital_period, eccentricity=eccentricity, + history_verbose=self.kwargs.get("history_verbose", False) ) star1_params = dict( mass=m1, @@ -1031,6 +1034,7 @@ def draw_initial_binary(self, **kwargs): separation=separation, orbital_period=orbital_period, eccentricity=eccentricity, + history_verbose=self.kwargs.get("history_verbose", False) ) star1_params = dict( mass=m1, diff --git a/posydon/popsyn/population_params_default.ini b/posydon/popsyn/population_params_default.ini index 19cc6f6340..3532e8b430 100644 --- a/posydon/popsyn/population_params_default.ini +++ b/posydon/popsyn/population_params_default.ini @@ -291,12 +291,13 @@ # use only for local MPI runs metallicity = [1.] #[2., 1., 0.45, 0.2, 0.1, 0.01, 0.001, 0.0001] # In units of solar metallicity - error_checking_verbose = False # if True, write all POSYDON errors to stderr at runtime, default=False warnings_verbose = False # if True, write all POSYDON warnings to stderr at runtime, default=False - + history_verbose = False + # if True, record extra functional steps in the output DataFrames + # (These steps represent internal workings of POSYDON rather than physical phases of evolution) # Random Number Generation entropy = None # `None` uses system entropy (recommended) From 14b90a5ce5e88809b95aa6bed80bdd487402d595 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Fri, 6 Dec 2024 15:59:12 +0100 Subject: [PATCH 272/319] Start to get unit tests back: utils (#382) * rename rae2a to rad2a * add unit_tests: readme, test_constants.py, test_limits_thresholds.py * add template * add authors to ignorereason.py * add test_ignorereason.py * add test_posydonerror.py * edit docstring * add test_posydonwarning.py * update readme * add test_data_download.py * transfer to pytest (outstanding: test_posydonwarning.py, test_data_download.py) * account for the comments of Max * transfer test_posydonwarning.py to pytest * transfer test_data_download.py to pytest * add configfile.py * cleanup * Create test_installation.py using this as a simple example to try out automated testing framework * Create continuous_integration.yml Automatic trigger for tests (currently just installation but can include any combination) for PRs created to main or development. Tests on ubuntu, mac, and windows environments. Currently just testing python 3.11 but can add other versions if desired. * Update continuous_integration.yml fixed path to installation test file * Update continuous_integration.yml making mac the first test out of the three OSes * Delete posydon/unit_tests/test_installation.py realized this could just be included in the github workflow * Update continuous_integration.yml changed to include full posydon installation step to remove redundancy. Currently including all extras in the setup.py but can remove any if needed. * Update continuous_integration.yml removing extras from the every-PR test. I'll move them to a weekly test instead. * Create install_extras.yml scheduled cron job testing installation of setup.py extras, every sunday * Update continuous_integration.yml * Delete .github/workflows/install_extras.yml * Delete .github/workflows/continuous_integration.yml * add test_gridutils.py * add test_common_functions.py * update doc * merge development and update tests; include test in continuous_integration.yml * add pytest/cov install to continuous_integration.yml * add posydon install to unit test run * Change to environment python version For some reason, the environment `pytest` command doesn't get overwritten correctly with the local runner environment one. Probably because the PATH order is weirdly set. Running `pytest` through `python -m` works. * add environment varable exports * Update continuous_integration.yml clarify step name for CI * remove old code * Update continuous_integration.yml adding a piece to enforce minimum coverage of 100% of specified code when running the tests. if the coverage is below 100%, pytest will exit and the github action will fail. 100% coverage means that every line of code of posydon.utils (as specified in --cov=posydon.utils) is run at least once in the test suite execution. * Update README.md typo * Update README.md clarify language about QA for tests * Update README.md typo * Update README.md added info about enforcing 100% coverage in the pytest command * add more about predefined fixtures to the README * comments till test_read_histogram_from_file * comments on test_read_histogram_from_file * comments till test_CO_radius * deal with comments till test_calculate_core_boundary * remaining comments in test_common_functions.py * finish addressing first round of comments by Max * use np as alias for totest.np * fix typo in data_download.py * fix typo in test_common_functions.py * fix typo in docstring of common_functions.py * fix typo in test_common_functions.py * fix typo in common_functions.py * Update test_common_functions.py change naming of data examples to be more descriptive * Update test_common_functions.py fix bug caused by extra spaces * add comments and more names * remove unused bad_profile2 * more cleanup * follow PEP8 strictly, reset attributes calculated for CEE --------- Co-authored-by: Elizabeth Teng <41969755+elizabethteng@users.noreply.github.com> Co-authored-by: Max <14039563+maxbriel@users.noreply.github.com> --- .github/workflows/continuous_integration.yml | 15 +- posydon/tests/utils/test_beaming.py | 63 - ..._evolutionary_state_in_commun_functions.py | 34 - posydon/tests/utils/test_configfile.py | 97 - posydon/tests/utils/test_eddington_limit.py | 61 - posydon/tests/utils/test_keplers.py | 66 - .../tests/utils/test_merger_from_period.py | 35 - posydon/tests/utils/test_merger_from_sep.py | 44 - posydon/tests/utils/test_rzams.py | 51 - posydon/unit_tests/README.md | 159 ++ posydon/unit_tests/test_template.py | 22 + .../unit_tests/utils/test_common_functions.py | 2394 +++++++++++++++++ posydon/unit_tests/utils/test_configfile.py | 302 +++ posydon/unit_tests/utils/test_constants.py | 493 ++++ .../unit_tests/utils/test_data_download.py | 238 ++ posydon/unit_tests/utils/test_gridutils.py | 781 ++++++ posydon/unit_tests/utils/test_ignorereason.py | 103 + .../utils/test_limits_thresholds.py | 163 ++ posydon/unit_tests/utils/test_posydonerror.py | 186 ++ .../unit_tests/utils/test_posydonwarning.py | 756 ++++++ posydon/utils/common_functions.py | 238 +- posydon/utils/configfile.py | 15 +- posydon/utils/constants.py | 2 +- posydon/utils/data_download.py | 4 +- posydon/utils/gridutils.py | 9 +- posydon/utils/ignorereason.py | 6 + posydon/utils/posydonerror.py | 36 +- posydon/utils/posydonwarning.py | 24 +- 28 files changed, 5831 insertions(+), 566 deletions(-) delete mode 100644 posydon/tests/utils/test_beaming.py delete mode 100644 posydon/tests/utils/test_check_evolutionary_state_in_commun_functions.py delete mode 100644 posydon/tests/utils/test_configfile.py delete mode 100644 posydon/tests/utils/test_eddington_limit.py delete mode 100644 posydon/tests/utils/test_keplers.py delete mode 100644 posydon/tests/utils/test_merger_from_period.py delete mode 100644 posydon/tests/utils/test_merger_from_sep.py delete mode 100644 posydon/tests/utils/test_rzams.py create mode 100644 posydon/unit_tests/README.md create mode 100644 posydon/unit_tests/test_template.py create mode 100644 posydon/unit_tests/utils/test_common_functions.py create mode 100644 posydon/unit_tests/utils/test_configfile.py create mode 100644 posydon/unit_tests/utils/test_constants.py create mode 100644 posydon/unit_tests/utils/test_data_download.py create mode 100644 posydon/unit_tests/utils/test_gridutils.py create mode 100644 posydon/unit_tests/utils/test_ignorereason.py create mode 100644 posydon/unit_tests/utils/test_limits_thresholds.py create mode 100644 posydon/unit_tests/utils/test_posydonerror.py create mode 100644 posydon/unit_tests/utils/test_posydonwarning.py diff --git a/.github/workflows/continuous_integration.yml b/.github/workflows/continuous_integration.yml index 8fe2ee40f5..62447a1070 100644 --- a/.github/workflows/continuous_integration.yml +++ b/.github/workflows/continuous_integration.yml @@ -25,8 +25,15 @@ jobs: - name: Install POSYDON without extras run: | python -m pip install --upgrade pip - pip install . + pip install . - # - name: Run all tests - # run: | - # pytest posydon/unit_tests/ + - name: Run all tests in posydon/unit_tests + run: | + python -m pip install --upgrade pip + pip install . + pip install pytest + pip install pytest-cov + export PATH_TO_POSYDON=./ + export PATH_TO_POSYDON_DATA=./ + export MESA_DIR=./ + python -m pytest posydon/unit_tests/ --cov=posydon.utils --cov-branch --cov-report term-missing --cov-fail-under=100 diff --git a/posydon/tests/utils/test_beaming.py b/posydon/tests/utils/test_beaming.py deleted file mode 100644 index 6b52537d3a..0000000000 --- a/posydon/tests/utils/test_beaming.py +++ /dev/null @@ -1,63 +0,0 @@ -import unittest -from posydon.utils.common_functions import beaming -from posydon.binary_evol.singlestar import SingleStar -from posydon.binary_evol.binarystar import BinaryStar - -class TestEddingtonBeaming(unittest.TestCase): - def test_beaming_1(self): - # One of the stars should be a compact object (NS or BH) and it should go in - STAR1_PROP = {"mass": 1.4, "state": "NS"} - STAR2_PROP = {"surface_h1": 0.7} - BINARY_PROP = { - "lg_mtransfer_rate": -2 - } - star1 = SingleStar(**STAR1_PROP) - star2 = SingleStar(**STAR2_PROP) - binary = BinaryStar(star1, star2, **BINARY_PROP) - - self.assertAlmostEqual( - beaming(binary)[0], - 874.55367846, - places=5, - msg="Should be 874.5537." - ) - - def test_beaming_2(self): - # One of the stars should be a compact object (NS or BH) and it should go in - # the star1 argument position - STAR1_PROP = {"mass": 1.4, "state": "NS"} - STAR2_PROP = {"surface_h1": 0.7} - BINARY_PROP = { - "lg_mtransfer_rate": -2 - } - star1 = SingleStar(**STAR1_PROP) - star2 = SingleStar(**STAR2_PROP) - binary = BinaryStar(star1, star2, **BINARY_PROP) - - self.assertAlmostEqual( - beaming(binary)[1], - 3.524620449e-10, - places=5, - msg="Should be 3.524620e-10." - ) - - def test_beaming_3(self): - # One of the stars should be a compact object (NS or BH) and it should go in - STAR1_PROP = {"mass": 10.0, "state": "BH"} - STAR2_PROP = {"surface_h1": 0.9} - BINARY_PROP = { - "lg_mtransfer_rate": -2 - } - star1 = SingleStar(**STAR1_PROP) - star2 = SingleStar(**STAR2_PROP) - binary = BinaryStar(star1, star2, **BINARY_PROP) - - self.assertAlmostEqual( - beaming(binary)[0], - 37.466319, - places=5, - msg="Should be 37.4663193." - ) - -if __name__ == "__main__": - unittest.main() diff --git a/posydon/tests/utils/test_check_evolutionary_state_in_commun_functions.py b/posydon/tests/utils/test_check_evolutionary_state_in_commun_functions.py deleted file mode 100644 index 0fcbb87813..0000000000 --- a/posydon/tests/utils/test_check_evolutionary_state_in_commun_functions.py +++ /dev/null @@ -1,34 +0,0 @@ -from posydon.binary_evol.singlestar import SingleStar -from posydon.utils.common_functions import check_state_of_star -import unittest - -# TODO: this is outdated -Target_state =[ - 'H-ZAMS', 'He-ZAMS' - 'Near_H-ZAMS', - 'H-MS', - 'Shell_H_burning', - 'H-rich Core_He_burning', 'stripped_He Core_He_burning', - 'H-rich Shell_He_burning', 'stripped_He Core_He_burning', - 'H-rich Core_C_burning', 'stripped_He Core_C_burning', - 'H-rich Central_C_depletion', 'stripped_He Central_C_depletion' -] - -# class Test_check_state_of_star(unittest.TestCase): -# def test_evolutionary_state(self): -# attr = { -# "center_h1": 0.5, -# "center_he4": 0.48, -# "center_c12": 0.0, -# "surface_h1": 0.7, -# "log_Lnuc": 0.7, -# "log_LH": 0.7, -# "c12_c12": 0.0} -# -# #out = check_state_of_star(star) -# star = SingleStar(**attr) -# state = check_state_of_star(star) -# self.assertIn(state, Target_state) - -if __name__ == "__main__": - unittest.main() diff --git a/posydon/tests/utils/test_configfile.py b/posydon/tests/utils/test_configfile.py deleted file mode 100644 index 308ee9cf68..0000000000 --- a/posydon/tests/utils/test_configfile.py +++ /dev/null @@ -1,97 +0,0 @@ -"""Unit-testing module for the `ConfigFile` class.""" - -import os -import unittest -import numpy as np -from numpy.testing import assert_array_equal -from posydon.utils.common_functions import PATH_TO_POSYDON -from posydon.utils.configfile import ConfigFile - - -configuration = { - "name": "Yoda", - "age": 900, - "dimensions": np.array([26.0, 11.1, 5.2]), - "apprentices": ["Dooku", "Windu"], -} - - -class TestConfigFile(unittest.TestCase): - """Unit-test class for the `ConfigFile` class.""" - - @classmethod - def setUpClass(cls): - """Get path to temporary file.""" - cls.path = os.path.join(PATH_TO_POSYDON, "posydon/tests/data/tmp.cfg") - - @classmethod - def tearDownClass(cls): - """Ensure temporary file was deleted after tests.""" - if os.path.exists(cls.path): - os.remove(cls.path) - - def setUp(self): - """Create a ConfigFile object before each test.""" - self.config = ConfigFile() - self.config.update(configuration) - - def test_IO(self): - """Test writing/reading from disk, and serialization.""" - # save to disk and destroy the object - self.config.save(self.path, overwrite=False) - del self.config - - # read it and test data integrity - self.config = ConfigFile(self.path) - self.assertEqual(len(self.config), len(configuration)) - for key in configuration: - original = configuration[key] - fromdisk = self.config[key] - if np.iterable(original): - assert_array_equal(original, fromdisk) - else: - self.assertEqual(original, fromdisk) - - # delete temporary file - os.remove(self.path) - - # check `print` and `str` methods - print(str(self)) - print(len(str(self).split("\m"))) # TODO: Should this be r"\m"? - - def test_getters(self): - """Test the two ways to get entries.""" - self.assertEqual(self.config.name, self.config["name"]) - - def test_setters(self): - """Test the setting of entries one-by-one.""" - self.config["name"] = "Yodah" - self.assertEqual(self.config["name"], "Yodah") - - self.config["side"] = "light" - self.assertTrue("side" in self.config.keys()) - del self.config["side"] - self.assertTrue("side" not in self.config.keys()) - - def test_dict_methods(self): - """Test the `items`, `keys`, `values` methods, and iteration.""" - for key, value in self.config.items(): # check items() - self.assertIn(key, configuration) # check key - self.assertIn(key, self.config) # check __iter__ - self.assertIn(key, self.config.keys()) # check keys() - if not np.iterable(value): - self.assertIn(value, self.config.values()) # check values() - - def test_update(self): - """Test the `update` method for altering or adding entries.""" - self.config.update({"age": 901, "starring": "Star Wars"}) - self.assertEqual(self.config.age, 901) - self.assertEqual(self.config.starring, "Star Wars") - - def test_len(self): - """The the `len` method applied on ConfigFile.""" - self.assertEqual(len(self.config), len(configuration)) - - -if __name__ == "__main__": - unittest.main() diff --git a/posydon/tests/utils/test_eddington_limit.py b/posydon/tests/utils/test_eddington_limit.py deleted file mode 100644 index 3e4bd19ea8..0000000000 --- a/posydon/tests/utils/test_eddington_limit.py +++ /dev/null @@ -1,61 +0,0 @@ -import unittest -from posydon.utils.common_functions import eddington_limit -from posydon.binary_evol.singlestar import SingleStar -from posydon.binary_evol.binarystar import BinaryStar - - -class TestEddingtonLimit(unittest.TestCase): - def test_eddington_1(self): - # One of the stars should be a compact object (NS or BH) and it should go in - # the star1 argument position - STAR1_PROP = {"mass": 2.831, "state": "NS"} - STAR2_PROP = {"surface_h1": 0.715} - - star1 = SingleStar(**STAR1_PROP) - star2 = SingleStar(**STAR2_PROP) - binary = BinaryStar(star1, star2) - - self.assertAlmostEqual( - eddington_limit(binary, idx=-1)[0], - 1.274160439238623e-07, - places=5, - msg="Should be 1.27416e-07.", - ) - - def test_eddington_2(self): - # One of the stars should be a compact object (NS or BH) and it should go in - # the star1 argument position - STAR1_PROP = {"mass": 2.831, "state": "BH"} - STAR2_PROP = {"surface_h1": 0.715} - - star1 = SingleStar(**STAR1_PROP) - star2 = SingleStar(**STAR2_PROP) - binary = BinaryStar(star1, star2) - - self.assertAlmostEqual( - eddington_limit(binary, idx=-1)[1], - 0.0571909584179, - places=5, - msg="Should be 0.05719.", - ) - - def test_eddington_3(self): - # One of the stars should be a compact object (NS or BH) and it should go in - # the star1 argument position - STAR1_PROP = {"mass": 1.5, "state": "NS"} - STAR2_PROP = {"surface_h1": 0.85} - - star1 = SingleStar(**STAR1_PROP) - star2 = SingleStar(**STAR2_PROP) - binary = BinaryStar(star1, star2) - - self.assertAlmostEqual( - eddington_limit(binary, idx=-1)[0], - 1.7768657384e-08, - places=5, - msg="Should be 1.77687e-08.", - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/posydon/tests/utils/test_keplers.py b/posydon/tests/utils/test_keplers.py deleted file mode 100644 index ec5d8c768c..0000000000 --- a/posydon/tests/utils/test_keplers.py +++ /dev/null @@ -1,66 +0,0 @@ -import unittest -import posydon.utils.constants as const -from posydon.utils.common_functions import ( - orbital_separation_from_period, - orbital_period_from_separation, -) - - -class TestKeplerThirdLaw(unittest.TestCase): - def test_sep_from_per_1(self): - - self.assertAlmostEqual( - orbital_separation_from_period(10.0, 1.0, 1.0), - 24.60349357746796, - places=5, - msg="Should be 24.603493 Rsun.", - ) - - def test_sep_from_per_2(self): - - self.assertAlmostEqual( - orbital_separation_from_period(5.0, 3.0, 1.0), - 19.527805793482745, - places=5, - msg="Should be 19.52780579 Rsun.", - ) - - def test_sep_from_per_3(self): - - self.assertAlmostEqual( - orbital_separation_from_period(10.0, 0.0001, 1.0), - 19.52845669864617, - places=5, - msg="Should be 19.5284567 Rsun.", - ) - - def test_per_from_sep_1(self): - - self.assertAlmostEqual( - orbital_period_from_separation(10.0, 1.0, 1.0), - 2.591606452663911, - places=5, - msg="Should be 2.5916 days.", - ) - - def test_per_from_sep_2(self): - - self.assertAlmostEqual( - orbital_period_from_separation(5.0, 3.0, 1.0), - 0.6479016131659777, - places=5, - msg="Should be 0.6479 days.", - ) - - def test_per_from_sep_3(self): - - self.assertAlmostEqual( - orbital_period_from_separation(10.0, 0.001, 1.0), - 3.6632538244566186, - places=5, - msg="Should be 3.6633 days.", - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/posydon/tests/utils/test_merger_from_period.py b/posydon/tests/utils/test_merger_from_period.py deleted file mode 100644 index 4ad397c332..0000000000 --- a/posydon/tests/utils/test_merger_from_period.py +++ /dev/null @@ -1,35 +0,0 @@ -import unittest -from posydon.utils.common_functions import inspiral_timescale_from_orbital_period - - -class TestMerger_porb(unittest.TestCase): - def test_merger_porb_1(self): - - self.assertAlmostEqual( - inspiral_timescale_from_orbital_period(15.0, 30.0, 1.0, 0.0), - 372.10532488, - places=5, - msg="Should be 372.1053Myr.", - ) - - def test_merger_porb_2(self): - - self.assertAlmostEqual( - inspiral_timescale_from_orbital_period(15.0, 30.0, 1.0, 0.2), - 320.75044687, - places=5, - msg="Should be 320.7504Myr.", - ) - - def test_merger_porb_3(self): - - self.assertAlmostEqual( - inspiral_timescale_from_orbital_period(15.0, 30.0, 1.0, 0.5), - 133.138635978858, - places=5, - msg="Should be 133.1386Myr.", - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/posydon/tests/utils/test_merger_from_sep.py b/posydon/tests/utils/test_merger_from_sep.py deleted file mode 100644 index 88ff07ed12..0000000000 --- a/posydon/tests/utils/test_merger_from_sep.py +++ /dev/null @@ -1,44 +0,0 @@ -import unittest -from posydon.utils.common_functions import inspiral_timescale_from_separation - - -class TestMerger_sep(unittest.TestCase): - def test_merger_sep_1(self): - - self.assertAlmostEqual( - inspiral_timescale_from_separation(15.0, 30.0, 14.97, 0.0), - 372.668437, - places=5, - msg="Should be 372.6684Myr.", - ) - - def test_merger_sep_2(self): - - self.assertAlmostEqual( - inspiral_timescale_from_separation(15.0, 30.0, 14.97, 0.2), - 321.23584297, - places=5, - msg="Should be 321.2358Myr.", - ) - - def test_merger_sep_3(self): - - self.assertAlmostEqual( - inspiral_timescale_from_separation(15.0, 30.0, 14.97, 0.5), - 133.3401165, - places=5, - msg="Should be 133.3401Myr.", - ) - - def test_merger_sep_4(self): - # Tests the continuity of the function approaching 0 ecc. - self.assertAlmostEqual( - inspiral_timescale_from_separation(15.0, 30.0, 14.97, 0.00001), - 372.668437, - places=5, - msg="Should be 372.668Myr.", - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/posydon/tests/utils/test_rzams.py b/posydon/tests/utils/test_rzams.py deleted file mode 100644 index 2c6fb576b6..0000000000 --- a/posydon/tests/utils/test_rzams.py +++ /dev/null @@ -1,51 +0,0 @@ -import unittest -from posydon.utils.common_functions import is_number, rzams - - -class TestIsNumber(unittest.TestCase): - def test_is_number_1(self): - - self.assertTrue( - is_number(17.5), - msg="Should be True.", - ) - - def test_is_number_2(self): - - self.assertFalse( - is_number("not a float"), - msg="Should be False.", - ) - - -class TestRZAMS(unittest.TestCase): - def test_rzams_1(self): - - self.assertAlmostEqual( - rzams(1.0), - 0.88824945, - places=5, - msg="Should be 0.8882 Rsun.", - ) - - def test_rzams_2(self): - - self.assertAlmostEqual( - rzams(10.0), - 3.941905620, - places=5, - msg="Should be 3.9419 Rsun.", - ) - - def test_rzams_3(self): - - self.assertAlmostEqual( - rzams(100.0), - 17.22611585, - places=5, - msg="Should be 17.2261 Rsun.", - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/posydon/unit_tests/README.md b/posydon/unit_tests/README.md new file mode 100644 index 0000000000..67ecaee7f7 --- /dev/null +++ b/posydon/unit_tests/README.md @@ -0,0 +1,159 @@ +# POSYDON: unit tests + +Here we collect our unit tests for the POSYDON code. We are using the [pytest package](https://docs.pytest.org/en/stable/index.html). + +Before using it you need to install pytest or consider to upgrade it + + pip install -U pytest + +The tests in each `TESTFILE` can be run via + + pytest TESTFILE + +If you like to run all unit tests you can use + + pytest $PATH_TO_POSYDON/posydon/unit_tests/ + +## File structure + +As already visible from the commands how to run all unit tests we have a the `unit_tests` directory. In there are individual directories to combine several tests into one suite. It is a natural choice to reuse the topics used in the code itself. For example all the unit tests for files inside `$PATH_TO_POSYDON/posydon/utils` are put in `$PATH_TO_POSYDON/posydon/unit_tests/utils`. In those suites, there should be a file for each code file, which is simply extended by the prefix `test_`, e.g. `test_posydonerror.py` contains the unit tests of `posydonerror.py`. If the code file is in a subdirectory, may add this to the prefix. *I'd discourage to create deep directory structures inside the `unit_tests`.* + +## How to add a new unit test + +There is a template file `$PATH_TO_POSYDON/posydon/unit_tests/test_template.py`, which can be copied. Please rename/replace the copy to fulfill the [File structure](#file-structure). Please update the doc-string, the author list, the imported modules, and the test classes. + +### Conventions + +To keep things in a common style, here are some conventions to follow. You can never make too many tests as long as they are unique! + +#### Doc string + +Each test file should have a doc-string. This should at least contain the information which part of the code will be tested. + +#### Author list + +Like in code files we add an author list to those files to let others know whom to ask when doing changes in such a file. + +#### Imports + +We always need to import the module we'd like to test. *I recommend to import it with the local name `totest`.* + +There might be additional modules/functions needed to do the testing. *I recommend to not import stuff, which is already imported in the module, you like to test, instead use the reference in the tested module, e.g. if the module to test imports X, access it via `totest.X` in the unit test, to ensure using the same code, which is not subject to this test.* + +#### Test functions + +For a small single test you can simply define a single test function. The name should start with the prefix `test`. *I recommend to use the prefix `test_`.* The actual test is usually done by an assert statement. Usually, there will be several tests collected in a [class](#test-classes), thus single functions will be rare. + +#### Test classes + +To group tests it is useful to create classes which contain several test functions. All test classes should have the prefix `Test`. *I recommend to continue with a capital letter after the prefix.* In each class there should be test functions, again having a prefix `test`. *I recommend to use the prefix `test_`.* It should be noted, that the test functions inside a class require a parameter containing the class object. *I recommend to use the standard `self`.* It should be noted, that there is no guarantee for the order the tests are run in. Thus, each test should be considered a stand alone. + +##### Check the elements of a module + +The first thing to check is the completeness and types of the module elements. *I suggest to use the build-in function `dir`.* All variables should be tested for their type via `isinstance`. All classes and functions can be tested for their type by making us of `isclass` and `isroutine` form the `inspect` module, which should return `True`. *I suggest to put all those checks into one test class with functions for each element.* + +##### Check values + +Beside the existence and the type of a variable, we should verify the integrity of it's default value. *I suggest to put all those value checks into one test class with a function for each variable.* Depending on the kind of variable different tests can be done, e.g. a fixed value for a constant, a range of a changeable variable, or needed items of a list ... + +##### Check functions + +Functions in the module need checks according to their functionality. This should coincide with the doc-string of each function. *I suggest to have one class with all the function tests and a test function for each function in the module, which gets tested.* If the functions need variables, you may [use fixtures](#using-fixtures). + +Functions may include prints statements. To capture outputs to `stdout` and `stderr` pytest has global defined fixtures `capsys` (and `capsysbinary` for bytes data instead of usual text; addtionally, there are `capfd` and `capfdbinary` to capture the file descriptors `1` and `2`). + +To access the captured content you simply call the `readouterr` function of the fixture. It will return a `(out, err)` namedtuple. Here an example how to get the stdout content, captured so far: + + capsys.readouterr().out + +It should be noted, that the call of `readouterr` will clear the buffer, hence if you like to get both `out` and `err` at the same time, you need to store the namedtuple and access the components from the stored version, e.g. + + captured_output = capsys.readouterr() + captured_output.out + captured_output.err + +In case you like to access what is captured to far but keep it in the buffer, you'd need to reprint the part you read, e.g. + + captured_output = capsys.readouterr() + captured_output.out + print(captured_output.out) + +Using those fixtures as arguments to a function will capture all outputs of that function. To exclude some parts from getting its output captured you need to put that into the `disabled` context of the used fixture. It should be noted, that only the capturing is disabled, but the fixture object is still available, e.g. + + with capsys.disabled(): + print(capsys.readouterr().out) + +will print the collected prints to stdout, at this moment all together (and clears the buffer). + +##### Check classes + +Each class inside a module should be get its components checked like a module itself. *I suggest to have a test class for each class in the tested module and the test of each class function should get an own test function.* Again, [fixtures](#using-fixtures) can be used to ensure that all tests run under the same conditions. A commonly useful fixture for a class test is an object of this class initialized with the defaults. + +#### Using fixtures + +You can define [fixtures](https://docs.pytest.org/en/stable/how-to/fixtures.html) at any level. You need to decorate them with the [`pytest.fixture` function](https://docs.pytest.org/en/stable/reference/reference.html#pytest-fixture), which needs to be imported, via + + @pytest.fixture + +*I suggest to import the function via `from pytest import fixture` and than call it via `@fixture`.* + +Fixtures replace the `setUp` and `tearDown`. To use a fixture to prepare something before a test, you can simply write it as a function and the variable will contain the returned value. + +For cleaning things up after a test, instead of having a final return, you separate setUp and tearDown with a `yield` statement, which ensure that all before is executed when the fixture is requested and the stuff after when it get deleted (usually at the end of the test function). For chains of fixtures it should be noted, that the clean up happens in the reverse order to the creation, because the innermost fixture will get deleted first. + +There are some useful [predefined fixtures](https://docs.pytest.org/en/stable/reference/fixtures.html). One was already introduced earlier, `capsys` can be used to interact with captured output. Another very useful one is `tmp_path`, which is a path object to a directory, which is created for each test function and will be subject to removal afterwards. Hence, whenever you need to read/write files for the test, it should be done in there. In most cases, `tmp_path` will not be used in your test function, but you use it in a self-defined fixture, which e.g. creates a file for the function. A third predefined fixture of interest is `monkeypatch`. This can be used to replace the objects inside the tested code just for the test. More [predefined fixtures can be found online](https://docs.pytest.org/en/stable/reference/fixtures.html). + +#### Catching raised errors + +Pytest has the [context manager `pytest.raises`](https://docs.pytest.org/en/stable/reference/reference.html#pytest-raises) to catch raised errors. You use it like other context managers via a `with` statement. Beside the expected exception, you can specify a `match`, which will be checked against the error message as a regular expression, e.g.: + + with pytest.raises(TypeError, match="Error message"): + raise TypeError("Error message") + +The context object can be used to check for more details of the raised error (this is useful to overcome some limitations of escape characters in regular expressions). Here an example how to check the error message from the context object: + + with pytest.raises(TypeError) as error_info: + raise TypeError("Error message") + assert error_info.value.args[0] == "Error message" + +It should be noted that the error type is will match subclasses successfully. + +#### Catching warnings + +Usually, pytest will catch all warnings and print them at the end of all tests. If your test will cause a warning which you don't like to have displayed, you can filter the warnings caught by pytest. To filter all warnings in a function or class you can decorate it with a filter, e.g. `@pytest.mark.filterwarnings("ignore:WARNINGTEXT")`. There are more things you can do on [warnings in pytest](https://docs.pytest.org/en/stable/how-to/capture-warnings.html), but you should use that only were needed. But you should be careful with the pytest warning catching, because it overwrites some parts of the python warnings, which even interferes badly with our POSYDON warnings (especially the filter changes). By using the `pytest.warns` context you can capture and check for warnings the same way as for [errors](#catching-raised-errors). + +### Check that it can fail + +Whenever you write a test, it should succeed on the existing code. On the other hand, you should also try out introducing a temporary change that you expect to lead to a failure to ensure the test is doing its job. + +## How to update a unit test + +First, check what is already there. Second, edit or add new stuff following the [conventions](#conventions) and test that it works by let it fail once. + +## Check code coverage + +There is a plug-in for `pytest` to measure the coverage of executed code, called [pytest-cov](https://pypi.org/project/pytest-cov). It can be installed via + + pip install pytest-cov + +and run by including the `cov` option in the `pytest` call + + pytest TESTFILE --cov=MODULE + +Strictly speaking it runs the `pytest` inside of [coverage](https://coverage.readthedocs.io). Thus you can run it although via + + coverage run -m pytest TESTFILE + +*I suggest to use the `--branch` option, which is `--cov-branch` in pytest.* The latter version to run the test has the advantage that you can make use of all the options provided by `coverage`, while running it through the plug-in will give you only access to the options implemented in there. On the other hand, running it with the plug-in will exclude the test code from coverage and give you the coverage report together with the report of the tests, while running it via `coverage` the report in the file `.coverage` needs to readout via + + coverage report + +*I suggest to use the `-m` option to show uncovered lines, which is `--cov-report term-missing` in pytest.* + +We should aim for 100% coverage. If there is code which should be excluded from the coverage, please mark it with `# pragma: no cover`. The main usage is for code, which under normal conditions should never run, e.g. in POSYDONwarnings is a part to act, when the python `sys` module fails. + +You can also enforce 100% coverage in the Github action performing the tests by adding `--cov-fail-under=100` to the pytest command, for example: + + python -m pytest posydon/unit_tests/ --cov=posydon.utils --cov-branch --cov-report term-missing --cov-fail-under=100 + +This particular line ensures that the tests in `posydon/unit_tests/` run 100% of the code in `posydon.utils`, and that the check will otherwise fail. diff --git a/posydon/unit_tests/test_template.py b/posydon/unit_tests/test_template.py new file mode 100644 index 0000000000..093f1fd307 --- /dev/null +++ b/posydon/unit_tests/test_template.py @@ -0,0 +1,22 @@ +"""Unit tests of posydon/user_modules/my_flow_chart_example.py +""" + +__authors__ = [ + "John Doe " +] + +# import the module which will be tested +import posydon.user_modules.my_flow_chart_example as totest + +# import other needed code for the tests, which is not already imported in the +# module you like to test + + +# define single test functions +def test_name(): + pass + +# define test classes collecting several test functions +class TestClass: + def test_name(self): + assert True diff --git a/posydon/unit_tests/utils/test_common_functions.py b/posydon/unit_tests/utils/test_common_functions.py new file mode 100644 index 0000000000..ebfb7ec814 --- /dev/null +++ b/posydon/unit_tests/utils/test_common_functions.py @@ -0,0 +1,2394 @@ +"""Unit tests of posydon/utils/common_functions.py +""" + +__authors__ = [ + "Matthias Kruckow " +] + +# import the module which will be tested +import posydon.utils.common_functions as totest +# aliases +np = totest.np +os = totest.os + +# import other needed code for the tests, which is not already imported in the +# module you like to test +from pytest import fixture, raises, warns, approx +from inspect import isroutine, isclass +from scipy.interpolate import interp1d +from posydon.binary_evol.binarystar import BinaryStar +from posydon.binary_evol.singlestar import SingleStar +from posydon.utils.posydonwarning import (EvolutionWarning,\ + InappropriateValueWarning, ApproximationWarning, InterpolationWarning,\ + ReplaceValueWarning, ClassificationWarning) + +@fixture +def binary(): + # initialize a BinaryStar instance, which is a required argument + return BinaryStar() + +@fixture +def star(): + # initialize a SingleStar instance, which is a required argument + return SingleStar() + +@fixture +def star_profile(): + # generate a profile of a test star + cols = ['mass', 'dm', 'radius', 'log_R', 'energy', 'x_mass_fraction_H',\ + 'y_mass_fraction_He', 'z_mass_fraction_metals',\ + 'neutral_fraction_H', 'neutral_fraction_He', 'avg_charge_He'] + profile = np.empty((4,), dtype=[(f, 'f8') for f in cols]) + profile['mass'] = np.array([1.0, 0.5, 0.1, 0.001]) + # get dm as differences from mass and the last mass entry (like next would + # be 0) + profile['dm'][:-1] = profile['mass'][:-1]-profile['mass'][1:] + profile['dm'][-1] = profile['mass'][-1] + profile['radius'] = np.array([1.0, 0.5, 0.1, 0.001]) + # get log10 of radius + profile['log_R'] = np.log10(profile['radius']) + profile['energy'] = np.array([1.0, 0.5, 0.1, 0.001]) * 1.0e+16 + profile['x_mass_fraction_H'] = np.array([0.7, 0.5, 0.1, 0.001]) + profile['y_mass_fraction_He'] = np.array([0.2, 0.5, 0.8, 0.2]) + # get Z=1-X-Y + profile['z_mass_fraction_metals'] = 1.0 - profile['x_mass_fraction_H']\ + - profile['y_mass_fraction_He'] + profile['neutral_fraction_H'] = np.array([1.0, 0.5, 0.1, 0.0]) + profile['neutral_fraction_He'] = np.array([1.0, 0.5, 0.1, 0.0]) + profile['avg_charge_He'] = np.array([0.0, 0.5, 1.1, 1.8]) + return profile + +# define test classes collecting several test functions +class TestElements: + # check for objects, which should be an element of the tested module + def test_dir(self): + elements = ['ALL_RLO_CASES', 'ALL_STAR_STATES', 'BURNING_STATES',\ + 'CEE_parameters_from_core_abundance_thresholds',\ + 'COMPACT_OBJECTS', 'CO_radius',\ + 'DEFAULT_CE_OPTION_FOR_LAMBDA', 'He_MS_lifetime',\ + 'LG_MTRANSFER_RATE_THRESHOLD', 'LOG10_BURNING_THRESHOLD',\ + 'MT_CASE_A', 'MT_CASE_B', 'MT_CASE_BA', 'MT_CASE_BB',\ + 'MT_CASE_BC', 'MT_CASE_C', 'MT_CASE_NONBURNING',\ + 'MT_CASE_NO_RLO', 'MT_CASE_TO_STR',\ + 'MT_CASE_UNDETERMINED', 'MT_STR_TO_CASE',\ + 'PATH_TO_POSYDON', 'PchipInterpolator',\ + 'PchipInterpolator2', 'Pwarn',\ + 'REL_LOG10_BURNING_THRESHOLD', 'RICHNESS_STATES',\ + 'RL_RELATIVE_OVERFLOW_THRESHOLD',\ + 'STATE_NS_STARMASS_UPPER_LIMIT', 'STATE_UNDETERMINED',\ + 'Schwarzschild_Radius', 'THRESHOLD_CENTRAL_ABUNDANCE',\ + 'THRESHOLD_HE_NAKED_ABUNDANCE', '__authors__',\ + '__builtins__', '__cached__', '__doc__', '__file__',\ + '__loader__', '__name__', '__package__', '__spec__',\ + 'beaming', 'bondi_hoyle',\ + 'calculate_H2recombination_energy',\ + 'calculate_Mejected_for_integrated_binding_energy',\ + 'calculate_Patton20_values_at_He_depl',\ + 'calculate_binding_energy', 'calculate_core_boundary',\ + 'calculate_lambda_from_profile',\ + 'calculate_recombination_energy', 'check_state_of_star',\ + 'check_state_of_star_history_array', 'const',\ + 'convert_metallicity_to_string', 'copy',\ + 'cumulative_mass_transfer_flag',\ + 'cumulative_mass_transfer_numeric',\ + 'cumulative_mass_transfer_string', 'eddington_limit',\ + 'flip_stars', 'get_binary_state_and_event_and_mt_case',\ + 'get_binary_state_and_event_and_mt_case_array',\ + 'get_i_He_depl', 'get_internal_energy_from_profile',\ + 'get_mass_radius_dm_from_profile', 'histogram_sampler',\ + 'infer_mass_transfer_case', 'infer_star_state',\ + 'initialize_empty_array',\ + 'inspiral_timescale_from_orbital_period',\ + 'inspiral_timescale_from_separation', 'interp1d',\ + 'inverse_sampler', 'is_number',\ + 'linear_interpolation_between_two_cells', 'newton', 'np',\ + 'orbital_period_from_separation',\ + 'orbital_separation_from_period', 'os', 'pd',\ + 'period_change_stabe_MT', 'period_evol_wind_loss',\ + 'profile_recomb_energy', 'quad',\ + 'read_histogram_from_file', 'rejection_sampler',\ + 'roche_lobe_radius', 'rotate', 'rzams',\ + 'separation_evol_wind_loss', 'set_binary_to_failed',\ + 'spin_stable_mass_transfer', 'stefan_boltzmann_law'] + assert dir(totest) == elements, "There might be added or removed "\ + + "objects without an update on the "\ + + "unit test." + + def test_instance_PATH_TO_POSYDON(self): + assert isinstance(totest.PATH_TO_POSYDON, str) + + def test_instance_STATE_UNDETERMINED(self): + assert isinstance(totest.STATE_UNDETERMINED, str) + + def test_instance_BURNING_STATES(self): + assert isinstance(totest.BURNING_STATES, list) + + def test_instance_RICHNESS_STATES(self): + assert isinstance(totest.RICHNESS_STATES, list) + + def test_instance_COMPACT_OBJECTS(self): + assert isinstance(totest.COMPACT_OBJECTS, list) + + def test_instance_ALL_STAR_STATES(self): + assert isinstance(totest.ALL_STAR_STATES, list) + + def test_instance_MT_CASE_NO_RLO(self): + assert isinstance(totest.MT_CASE_NO_RLO, int) + + def test_instance_MT_CASE_A(self): + assert isinstance(totest.MT_CASE_A, int) + + def test_instance_MT_CASE_B(self): + assert isinstance(totest.MT_CASE_B, int) + + def test_instance_MT_CASE_C(self): + assert isinstance(totest.MT_CASE_C, int) + + def test_instance_MT_CASE_BA(self): + assert isinstance(totest.MT_CASE_BA, int) + + def test_instance_MT_CASE_BB(self): + assert isinstance(totest.MT_CASE_BB, int) + + def test_instance_MT_CASE_BC(self): + assert isinstance(totest.MT_CASE_BC, int) + + def test_instance_MT_CASE_NONBURNING(self): + assert isinstance(totest.MT_CASE_NONBURNING, int) + + def test_instance_MT_CASE_UNDETERMINED(self): + assert isinstance(totest.MT_CASE_UNDETERMINED, int) + + def test_instance_ALL_RLO_CASES(self): + assert isinstance(totest.ALL_RLO_CASES, set) + + def test_instance_MT_CASE_TO_STR(self): + assert isinstance(totest.MT_CASE_TO_STR, dict) + + def test_instance_MT_STR_TO_CASE(self): + assert isinstance(totest.MT_STR_TO_CASE, dict) + + def test_instance_DEFAULT_CE_OPTION_FOR_LAMBDA(self): + assert isinstance(totest.DEFAULT_CE_OPTION_FOR_LAMBDA, str) + + def test_instance_is_number(self): + assert isroutine(totest.is_number) + + def test_instance_stefan_boltzmann_law(self): + assert isroutine(totest.stefan_boltzmann_law) + + def test_instance_rzams(self): + assert isroutine(totest.rzams) + + def test_instance_roche_lobe_radius(self): + assert isroutine(totest.roche_lobe_radius) + + def test_instance_orbital_separation_from_period(self): + assert isroutine(totest.orbital_separation_from_period) + + def test_instance_orbital_period_from_separation(self): + assert isroutine(totest.orbital_period_from_separation) + + def test_instance_eddington_limit(self): + assert isroutine(totest.eddington_limit) + + def test_instance_beaming(self): + assert isroutine(totest.beaming) + + def test_instance_bondi_hoyle(self): + assert isroutine(totest.bondi_hoyle) + + def test_instance_rejection_sampler(self): + assert isroutine(totest.rejection_sampler) + + def test_instance_inverse_sampler(self): + assert isroutine(totest.inverse_sampler) + + def test_instance_histogram_sampler(self): + assert isroutine(totest.histogram_sampler) + + def test_instance_read_histogram_from_file(self): + assert isroutine(totest.read_histogram_from_file) + + def test_instance_inspiral_timescale_from_separation(self): + assert isroutine(totest.inspiral_timescale_from_separation) + + def test_instance_inspiral_timescale_from_orbital_period(self): + assert isroutine(totest.inspiral_timescale_from_orbital_period) + + def test_instance_spin_stable_mass_transfer(self): + assert isroutine(totest.spin_stable_mass_transfer) + + def test_instance_check_state_of_star(self): + assert isroutine(totest.check_state_of_star) + + def test_instance_check_state_of_star_history_array(self): + assert isroutine(totest.check_state_of_star_history_array) + + def test_instance_get_binary_state_and_event_and_mt_case(self): + assert isroutine(totest.get_binary_state_and_event_and_mt_case) + + def test_instance_get_binary_state_and_event_and_mt_case_array(self): + assert isroutine(totest.get_binary_state_and_event_and_mt_case_array) + + def test_instance_CO_radius(self): + assert isroutine(totest.CO_radius) + + def test_instance_He_MS_lifetime(self): + assert isroutine(totest.He_MS_lifetime) + + def test_instance_Schwarzschild_Radius(self): + assert isroutine(totest.Schwarzschild_Radius) + + def test_instance_flip_stars(self): + assert isroutine(totest.flip_stars) + + def test_instance_set_binary_to_failed(self): + assert isroutine(totest.set_binary_to_failed) + + def test_instance_infer_star_state(self): + assert isroutine(totest.infer_star_state) + + def test_instance_infer_mass_transfer_case(self): + assert isroutine(totest.infer_mass_transfer_case) + + def test_instance_cumulative_mass_transfer_numeric(self): + assert isroutine(totest.cumulative_mass_transfer_numeric) + + def test_instance_cumulative_mass_transfer_string(self): + assert isroutine(totest.cumulative_mass_transfer_string) + + def test_instance_cumulative_mass_transfer_flag(self): + assert isroutine(totest.cumulative_mass_transfer_flag) + + def test_instance_get_i_He_depl(self): + assert isroutine(totest.get_i_He_depl) + + def test_instance_calculate_Patton20_values_at_He_depl(self): + assert isroutine(totest.calculate_Patton20_values_at_He_depl) + + def test_instance_CEE_parameters_from_core_abundance_thresholds(self): + assert isroutine(totest.CEE_parameters_from_core_abundance_thresholds) + + def test_instance_initialize_empty_array(self): + assert isroutine(totest.initialize_empty_array) + + def test_instance_calculate_core_boundary(self): + assert isroutine(totest.calculate_core_boundary) + + def test_instance_period_evol_wind_loss(self): + assert isroutine(totest.period_evol_wind_loss) + + def test_instance_separation_evol_wind_loss(self): + assert isroutine(totest.separation_evol_wind_loss) + + def test_instance_period_change_stabe_MT(self): + assert isroutine(totest.period_change_stabe_MT) + + def test_instance_linear_interpolation_between_two_cells(self): + assert isroutine(totest.linear_interpolation_between_two_cells) + + def test_instance_calculate_lambda_from_profile(self): + assert isroutine(totest.calculate_lambda_from_profile) + + def test_instance_get_mass_radius_dm_from_profile(self): + assert isroutine(totest.get_mass_radius_dm_from_profile) + + def test_instance_get_internal_energy_from_profile(self): + assert isroutine(totest.get_internal_energy_from_profile) + + def test_instance_calculate_H2recombination_energy(self): + assert isroutine(totest.calculate_H2recombination_energy) + + def test_instance_calculate_recombination_energy(self): + assert isroutine(totest.calculate_recombination_energy) + + def test_instance_profile_recomb_energy(self): + assert isroutine(totest.profile_recomb_energy) + + def test_instance_calculate_binding_energy(self): + assert isroutine(totest.calculate_binding_energy) + + def test_instance_calculate_Mejected_for_integrated_binding_energy(self): + assert isroutine(totest.\ + calculate_Mejected_for_integrated_binding_energy) + + def test_instance_PchipInterpolator2(self): + assert isclass(totest.PchipInterpolator2) + + def test_instance_convert_metallicity_to_string(self): + assert isroutine(totest.convert_metallicity_to_string) + + def test_instance_rotate(self): + assert isroutine(totest.rotate) + + +class TestValues: + # check that the values fit + def test_value_PATH_TO_POSYDON(self): + assert '/' in totest.PATH_TO_POSYDON + + def test_value_STATE_UNDETERMINED(self): + assert totest.STATE_UNDETERMINED == "undetermined_evolutionary_state" + + def test_value_BURNING_STATES(self): + for v in ["Core_H_burning", "Core_He_burning", "Shell_H_burning",\ + "Central_He_depleted", "Central_C_depletion"]: + # check required values + assert v in totest.BURNING_STATES, "missing entry" + + def test_value_RICHNESS_STATES(self): + for v in ["H-rich", "stripped_He", "accreted_He"]: + # check required values + assert v in totest.RICHNESS_STATES, "missing entry" + + def test_value_COMPACT_OBJECTS(self): + for v in ["WD", "NS", "BH", "massless_remnant"]: + # check required values + assert v in totest.COMPACT_OBJECTS, "missing entry" + + def test_value_ALL_STAR_STATES(self): + for v in totest.COMPACT_OBJECTS: + # check required values of COMPACT_OBJECTS + assert v in totest.ALL_STAR_STATES, "missing entry" + # check required STATE_UNDETERMINED + assert totest.STATE_UNDETERMINED in totest.ALL_STAR_STATES,\ + "missing entry" + for v1 in totest.RICHNESS_STATES: + for v2 in totest.BURNING_STATES: + # check required values of combinatios of RICHNESS_STATES and + # BURNING_STATES + v = v1+"_"+v2 + assert v in totest.ALL_STAR_STATES, "missing entry" + + def test_value_MT_CASE_NO_RLO(self): + assert totest.MT_CASE_NO_RLO == 0 + + def test_value_MT_CASE_A(self): + assert totest.MT_CASE_A == 1 + + def test_value_MT_CASE_B(self): + assert totest.MT_CASE_B == 2 + + def test_value_MT_CASE_C(self): + assert totest.MT_CASE_C == 3 + + def test_value_MT_CASE_BA(self): + assert totest.MT_CASE_BA == 4 + + def test_value_MT_CASE_BB(self): + assert totest.MT_CASE_BB == 5 + + def test_value_MT_CASE_BC(self): + assert totest.MT_CASE_BC == 6 + + def test_value_MT_CASE_NONBURNING(self): + assert totest.MT_CASE_NONBURNING == 8 + + def test_value_MT_CASE_UNDETERMINED(self): + assert totest.MT_CASE_UNDETERMINED == 9 + + def test_value_ALL_RLO_CASES(self): + for v in [totest.MT_CASE_A, totest.MT_CASE_B, totest.MT_CASE_C,\ + totest.MT_CASE_BA, totest.MT_CASE_BB, totest.MT_CASE_BC,\ + totest.MT_CASE_NONBURNING]: + # check required values + assert v in totest.ALL_RLO_CASES, "missing entry" + + def test_value_MT_CASE_TO_STR(self): + for v in [totest.MT_CASE_NO_RLO, totest.MT_CASE_A, totest.MT_CASE_B,\ + totest.MT_CASE_C, totest.MT_CASE_BA, totest.MT_CASE_BB,\ + totest.MT_CASE_BC, totest.MT_CASE_NONBURNING,\ + totest.MT_CASE_UNDETERMINED]: + # check required values + assert v in totest.MT_CASE_TO_STR.keys(), "missing entry" + for k,v in totest.MT_CASE_TO_STR.items(): + assert isinstance(v, str) + + def test_value_MT_STR_TO_CASE(self): + for k,v in totest.MT_STR_TO_CASE.items(): + # check required items + assert v in totest.MT_CASE_TO_STR.keys() + + def test_value_DEFAULT_CE_OPTION_FOR_LAMBDA(self): + assert totest.DEFAULT_CE_OPTION_FOR_LAMBDA == "lambda_from_profile_"\ + + "gravitational_plus_internal_minus_recombination" + + +class TestFunctions: + @fixture + def csv_path_failing_3_data_lines(self, tmp_path): + # a temporary path to csv file for testing + # it contains 4 lines, where three having data in there + path = os.path.join(tmp_path, "hist_fail.csv") + with open(path, "w") as test_file: + test_file.write("0.0,1.0\n") + test_file.write("1.0,1.0\n\n") + test_file.write("2.0,1.0\n") + return path + + @fixture + def csv_path_failing_empty_line(self, tmp_path): + # a temporary path to csv file for testing + # it only contains an empty line + path = os.path.join(tmp_path, "hist_fail2.csv") + with open(path, "w") as test_file: + test_file.write("") + return path + + @fixture + def csv_path_failing_element_counts(self, tmp_path): + # a temporary path to csv file for testing + # it contains an commented line and two data lines with the same number + # of elements + path = os.path.join(tmp_path, "hist_fail3.csv") + with open(path, "w") as test_file: + test_file.write("#0.0,1.0\n") + test_file.write("0.0,1.0,2.0\n") + test_file.write("1.0,1.0,1.0\n") + return path + + @fixture + def csv_path_failing_element_types(self, tmp_path): + # a temporary path to csv file for testing + # it contains 2 data lines, where the elements of the second one are + # not interpretable as floats + path = os.path.join(tmp_path, "hist_fail4.csv") + with open(path, "w") as test_file: + test_file.write("0.0,1.0,2.0\n") + test_file.write("Unit,Test\n") + return path + + @fixture + def csv_path_ex1(self, tmp_path): + # a temporary path to csv file for testing + # correct data with a comment line + path = os.path.join(tmp_path, "hist.csv") + with open(path, "w") as test_file: + test_file.write("#0.1,1.1\n") + test_file.write("0.1,1.1,2.1\n") + test_file.write("1.0,1.0\n") + return path + + @fixture + def csv_path_ex2(self, tmp_path): + # a temporary path to csv file for testing + # correct data with an empty line + path = os.path.join(tmp_path, "hist2.csv") + with open(path, "w") as test_file: + test_file.write("0.2,1.2,2.2\n\n") + test_file.write("2.0,2.0\n") + return path + + # test functions + def test_is_number(self): + # missing argument + with raises(TypeError, match="missing 1 required positional argument"): + totest.is_number() + # try a string of a float + assert totest.is_number("1.2e-3") + # try a non-float string + assert totest.is_number("test") == False + + def test_stefan_boltzmann_law(self): + # missing argument + with raises(TypeError, match="missing 2 required positional "\ + +"arguments: 'L' and 'R'"): + totest.stefan_boltzmann_law() + # examples + tests = [(1.0, 1.0, approx(5.77603658298e+3, abs=6e-9)),\ + (2.0, 1.0, approx(6.86890380099e+3, abs=6e-9)),\ + (1.0, 2.0, approx(4.08427463621e+3, abs=6e-9))] + for (L, R, T) in tests: + assert totest.stefan_boltzmann_law(L, R) == T + + def test_rzams(self): + # missing argument + with raises(TypeError, match="missing 1 required positional "\ + +"argument: 'm'"): + totest.rzams() + # examples + tests = [(1.0, approx(0.88824945030, abs=6e-12)),\ + (2.0, approx(1.61021038543, abs=6e-12))] + for (m, r) in tests: + assert totest.rzams(m) == r + assert totest.rzams(1.0, z=1.0) == approx(0.85822941705, abs=6e-12) + assert totest.rzams(1.0, Zsun=1.0) == approx(0.84963691291, abs=6e-12) + + def test_roche_lobe_radius(self, capsys): + # missing argument + with raises(TypeError, match="missing 2 required positional "\ + +"arguments: 'm1' and 'm2'"): + totest.roche_lobe_radius() + # bad input + for a in [-1.0, np.array([]), np.array([-1.0])]: + with warns(EvolutionWarning, match="Trying to compute RL radius "\ + +"for binary with invalid "\ + +"separation"): + assert np.isnan(totest.roche_lobe_radius(1.0, 1.0, a_orb=a)) + for m in [0.0, np.array([]), np.array([0.0])]: + with warns(EvolutionWarning, match="Trying to compute RL radius "\ + +"for nonexistent object"): + assert np.isnan(totest.roche_lobe_radius(m, 1.0)) + for m in [0.0, np.array([]), np.array([0.0])]: + with warns(EvolutionWarning, match="Trying to compute RL radius "\ + +"for nonexistent companion"): + assert np.isnan(totest.roche_lobe_radius(1.0, m)) + # examples + tests = [(1.0, 1.0, 1.0, approx(0.37892051838, abs=6e-12)),\ + (2.0, 1.0, 1.0, approx(0.44000423753, abs=6e-12)),\ + (1.0, 2.0, 1.0, approx(0.32078812033, abs=6e-12)),\ + (1.0, 1.0, 2.0, approx(0.75784103676, abs=6e-12))] + for (m1, m2, a, r) in tests: + if a == 1.0: + assert totest.roche_lobe_radius(m1, m2) == r + else: + assert totest.roche_lobe_radius(m1, m2, a_orb=a) == r + m1s = np.array([m1 for (m1, m2, a, r) in tests]) + m2s = np.array([m2 for (m1, m2, a, r) in tests]) + a_orbs = np.array([a for (m1, m2, a, r) in tests]) + assert np.allclose(totest.roche_lobe_radius(m1s, m2s, a_orb=a_orbs),\ + np.array([r.expected for (m1, m2, a, r) in tests])) + # check that roche lobe sum never exceeds orbital separation + for m in [1.0e+1, 1.0e+2, 1.0e+3, 1.0e+4, 1.0e+5, 1.0e+6, 1.0e+7]: + assert totest.roche_lobe_radius(m, 1.0)\ + + totest.roche_lobe_radius(1.0, m) < 1.0 + + def test_orbital_separation_from_period(self): + # missing argument + with raises(TypeError, match="missing 3 required positional "\ + +"arguments: 'period_days', 'm1_solar', "\ + +"and 'm2_solar'"): + totest.orbital_separation_from_period() + # examples + tests = [(1.0, 15.0, 30.0, approx(14.9643417735, abs=6e-11)),\ + (2.0, 15.0, 30.0, approx(23.7544118733, abs=6e-11)),\ + (1.0, 30.0, 30.0, approx(16.4703892879, abs=6e-11)),\ + (1.0, 15.0, 60.0, approx(17.7421890201, abs=6e-11))] + for (P, m1, m2, r) in tests: + assert totest.orbital_separation_from_period(P, m1, m2) == r + + def test_orbital_period_from_separation(self): + # missing argument + with raises(TypeError, match="missing 3 required positional "\ + +"arguments: 'separation', 'm1', and "\ + +"'m2'"): + totest.orbital_period_from_separation() + # examples + tests = [(1.0, 15.0, 30.0, approx(1.72773763511e-2, abs=6e-14)),\ + (2.0, 15.0, 30.0, approx(4.88677999159e-2, abs=6e-14)),\ + (1.0, 30.0, 30.0, approx(1.49626468308e-2, abs=6e-14)),\ + (1.0, 15.0, 60.0, approx(1.33829981748e-2, abs=6e-14))] + for (a, m1, m2, r) in tests: + assert totest.orbital_period_from_separation(a, m1, m2) == r + + def test_eddington_limit(self, binary): + # missing argument + with raises(TypeError, match="missing 1 required positional "\ + +"argument: 'binary'"): + totest.eddington_limit() + # bad input + with raises(ValueError, match="Eddington limit is being calculated "\ + +"for a non-CO"): + totest.eddington_limit(binary) + with raises(IndexError, match="index 2 is out of bounds"): + binary.star_1.state = 'WD' + totest.eddington_limit(binary, idx=2) + # examples: 1Msun accretor is star1 + binary.star_1.mass = 1.0 + tests = [('WD', (approx(4.01681147088e-17, abs=6e-29),\ + approx(6.40623627946e+7, abs=6e-5))),\ + ('NS', (approx(2.17747384546e-8, abs=6e-20),\ + approx(0.11817658993, abs=6e-12))),\ + ('BH', (approx(4.49942509871e-8, abs=6e-20),\ + approx(5.71909584179e-2, abs=6e-14)))] + for (CO, r) in tests: + binary.star_1.state = CO + assert totest.eddington_limit(binary) == r + binary.star_1.state = None + # examples: 1.2Msun accretor is star2, while donor has X_surf=0.1 + binary.star_2.mass = 1.2 + tests = [('WD', (approx(5.89502616630e-17, abs=6e-29),\ + approx(8.16917025429e+7, abs=6e-5))),\ + ('NS', (approx(3.39586943808e-8, abs=6e-20),\ + approx(0.14181190792, abs=6e-12))),\ + ('BH', (approx(8.42046955292e-8, abs=6e-20),\ + approx(5.71909584179e-2, abs=6e-14)))] + for (CO, r) in tests: + binary.star_2.state = CO + binary.star_1.surface_h1 = 0.1 + assert totest.eddington_limit(binary) == r + # examples: 1.3Msun accretor is star1 with history + binary.star_1.state = 'BH' + binary.star_1.mass_history = np.array([1.3, 1.3]) + binary.star_1.state_history = np.array([None, None]) + with raises(ValueError, match='COtype must be "BH", "NS", or "WD"'): + totest.eddington_limit(binary, idx=0) + tests = [('WD', (approx(3.68044499210e-17, abs=6e-29),\ + approx(9.08923688740e+7, abs=6e-5))),\ + ('NS', (approx(2.17747384546e-8, abs=6e-20),\ + approx(0.15362956691, abs=6e-12))),\ + ('BH', (approx(5.84925262833e-8, abs=6e-20),\ + approx(5.71909584179e-2, abs=6e-14)))] + for (CO, r) in tests: + binary.star_1.state_history = np.array([None, CO]) + assert totest.eddington_limit(binary, idx=0) == r + + def test_beaming(self, binary): + # missing argument + with raises(TypeError, match="missing 1 required positional "\ + +"argument: 'binary'"): + totest.beaming() + # bad input + with raises(ValueError, match="Eddington limit is being calculated "\ + +"for a non-CO"): + totest.beaming(binary) + # examples: 1Msun accretor is star1 with no mass-transfer rate + binary.star_1.mass = 1.0 + tests = [('WD', (np.nan, 1)), ('NS', (np.nan, 1)), ('BH', (np.nan, 1))] + for (CO, r) in tests: + binary.star_1.state = CO + assert totest.beaming(binary) == r + # examples: 1.2Msun accretor is star1 + binary.star_1.mass = 1.2 + binary.lg_mtransfer_rate = -7.5 + tests = [('WD', (approx(7.80787388850, abs=6e-12),\ + approx(1.04303350643e-16, abs=6e-28))),\ + ('NS', (approx(2.98994840792e-8, abs=6e-20), 1)),\ + ('BH', (approx(3.16227766017e-8, abs=6e-20), 1))] + for (CO, r) in tests: + binary.star_1.state = CO + assert totest.beaming(binary) == r + + def test_bondi_hoyle(self, binary, monkeypatch): + def mock_rand(shape): + return np.zeros(shape) + def mock_rand2(shape): + return np.full(shape, 0.1) + # missing argument + with raises(TypeError, match="missing 3 required positional "\ + +"arguments: 'binary', 'accretor', and "\ + +"'donor'"): + totest.bondi_hoyle() + # bad input + with raises(RuntimeError, match="Failed to converge after 100 "\ + +"iterations"): + totest.bondi_hoyle(binary, binary.star_1, binary.star_2) + # examples: + binary.separation = 1.0 #a semi-major axis of 1Rsun + binary.eccentricity = 0.1 #a small eccentricity + binary.star_1.mass = 1.1 #accretor's mass is 1.1Msun + binary.star_1.state = 'WD' #accretor is WD + binary.star_2.mass = 1.2 #donor's mass is 1.2Msun + binary.star_2.lg_wind_mdot = -10.0 #donor's wind is 10^{-10}Msun/yr + binary.star_2.he_core_radius = 0.2 #donor's he-core rad. is 0.2Rsun + binary.star_2.log_R = -0.5 #donor's radius is 10^{-0.5}Rsun + binary.star_2.surface_h1 = 0.7 #donor's X_surf=0.7 + binary.star_2.log_L = 0.3 #donor's lum. is 10^{0.3}Lsun + with raises(UnboundLocalError, match="cannot access local variable "\ + +"'f_m' where it is not "\ + +"associated with a value"): + # undefined scheme + totest.bondi_hoyle(binary, binary.star_1, binary.star_2, scheme='') + monkeypatch.setattr(np.random, "rand", mock_rand) + assert totest.bondi_hoyle(binary, binary.star_1, binary.star_2) ==\ + approx(3.92668160462e-17, abs=6e-29) + assert totest.bondi_hoyle(binary, binary.star_1, binary.star_2,\ + scheme='Kudritzki+2000') ==\ + approx(3.92668160462e-17, abs=6e-29) + binary.star_2.log_R = 1.5 #donor's radius is 10^{1.5}Rsun + assert totest.bondi_hoyle(binary, binary.star_1, binary.star_2,\ + scheme='Kudritzki+2000') ==\ + approx(3.92668160462e-17, abs=6e-29) + binary.star_2.log_R = -1.5 #donor's radius is 10^{-1.5}Rsun + assert totest.bondi_hoyle(binary, binary.star_1, binary.star_2,\ + scheme='Kudritzki+2000') == 1e-99 + binary.star_2.surface_h1 = 0.25 #donor's X_surf=0.25 + assert totest.bondi_hoyle(binary, binary.star_1, binary.star_2) ==\ + 1e-99 + binary.star_2.lg_wind_mdot = -4.0 #donor's wind is 10^{-4}Msun/yr + assert totest.bondi_hoyle(binary, binary.star_1, binary.star_2) ==\ + 1e-99 + assert totest.bondi_hoyle(binary, binary.star_1, binary.star_2,\ + wind_disk_criteria=False) ==\ + approx(5.34028698228e-17, abs=6e-29) # form always a disk + monkeypatch.setattr(np.random, "rand", mock_rand2) # other angle + binary.star_1.state = 'BH' #accretor is BH + assert totest.bondi_hoyle(binary, binary.star_1, binary.star_2,\ + wind_disk_criteria=False) ==\ + approx(5.13970075150e-8, abs=6e-20) + + def test_rejection_sampler(self, monkeypatch): + def mock_uniform(low=0.0, high=1.0, size=1): + return np.linspace(low, high, num=size) + def mock_interp1d(x, y): + if x[0] > x[-1]: + raise ValueError + return interp1d(x, y) + def mock_pdf(x): + return 1.0 - np.sqrt(x) + # bad input + with raises(TypeError, match="'>=' not supported between instances "\ + +"of 'NoneType' and 'float'"): + totest.rejection_sampler() + with raises(TypeError, match="'>=' not supported between instances "\ + +"of 'NoneType' and 'float'"): + totest.rejection_sampler(x=np.array([0.0, 1.0])) + with raises(IndexError, match="too many indices for array: array is "\ + +"0-dimensional, but 1 were indexed"): + totest.rejection_sampler(y=np.array([0.0, 1.0])) + with raises(AssertionError): + totest.rejection_sampler(x=np.array([0.0, 1.0]),\ + y=np.array([-0.4, 0.6])) + with raises(TypeError, match="'>=' not supported between instances "\ + +"of 'NoneType' and 'float'"): + totest.rejection_sampler(x_lim=np.array([0.0, 1.0])) + with raises(TypeError, match="'NoneType' object is not subscriptable"): + totest.rejection_sampler(pdf=mock_pdf) + # examples: + monkeypatch.setattr(np.random, "uniform", mock_uniform) + monkeypatch.setattr(totest, "interp1d", mock_interp1d) + tests = [(np.array([0.0, 1.0]), np.array([0.4, 0.6]), 5,\ + np.array([0.0, 0.25, 0.5, 0.75, 1.0])),\ + (np.array([1.0, 0.0]), np.array([0.2, 0.8]), 5,\ + np.array([0.0, 0.25, 0.5, 0.0, 0.0])),\ + (np.array([1.0, 0.0]), np.array([0.2, 0.8]), 6,\ + np.array([0.0, 0.2, 0.4, 0.0, 0.5, 0.0]))] + for (x, y, s, r) in tests: + assert np.array_equal(totest.rejection_sampler(x=x, y=y, size=s),\ + r) + assert np.array_equal(totest.rejection_sampler(x_lim=np.array([0.0,\ + 1.0]),\ + pdf=mock_pdf, size=5),\ + np.array([0.0, 0.25, 0.0, 0.0, 0.0])) + assert np.array_equal(totest.rejection_sampler(x=np.array([1.0, 0.0]),\ + y=np.array([0.2, 0.8]),\ + x_lim=np.array([0.0,\ + 1.0]),\ + pdf=mock_pdf, size=5),\ + np.array([0.0, 0.25, 0.0, 0.0, 0.0])) + + def test_inverse_sampler(self, monkeypatch): + def mock_uniform(low=0.0, high=1.0, size=1): + return np.linspace(low, high, num=size) + # missing argument + with raises(TypeError, match="missing 2 required positional "\ + +"arguments: 'x' and 'y'"): + totest.inverse_sampler() + # bad input + with raises(ValueError, match="diff requires input that is at least "\ + +"one dimensional"): + totest.inverse_sampler(x=None, y=None) + with raises(AssertionError): + totest.inverse_sampler(x=np.array([1.0, 0.0]),\ + y=np.array([0.4, 0.6])) + with raises(AssertionError): + totest.inverse_sampler(x=np.array([0.0, 1.0]),\ + y=np.array([-0.4, 0.6])) + # examples: + monkeypatch.setattr(np.random, "uniform", mock_uniform) + assert np.allclose(totest.inverse_sampler(x=np.array([0.0, 1.0]),\ + y=np.array([0.4, 0.6]),\ + size=5),\ + np.array([0.0, 0.29128785, 0.54950976, 0.78388218,\ + 1.0])) + assert np.allclose(totest.inverse_sampler(x=np.array([0.0, 1.0]),\ + y=np.array([0.6, 0.4]),\ + size=4),\ + np.array([0.0, 0.2919872, 0.61952386, 1.0])) + with warns(RuntimeWarning, match="invalid value encountered in "\ + +"divide"): + assert np.allclose(totest.inverse_sampler(x=np.array([0.0, 1.0]),\ + y=np.array([0.5, 0.5]),\ + size=5),\ + np.array([0.0, 0.25, 0.5, 0.75, 1.0])) + + def test_histogram_sampler(self, monkeypatch): + def mock_uniform(low=0.0, high=1.0, size=1): + return np.linspace(low, high, num=size) + def mock_choice(a, size=None, replace=True, p=None): + if isinstance(a, int): + a=np.arange(a) + sample = [] + for (v, q) in zip(a, p): + sample += round(size*q) * [v] + if len(sample) < size: + sample += (size-len(sample)) * [a[-1]] + return np.array(sample[:size]) + # missing argument + with raises(TypeError, match="missing 2 required positional "\ + +"arguments: 'x_edges' and 'y'"): + totest.histogram_sampler() + # bad input + with raises(TypeError, match="'>=' not supported between instances "\ + +"of 'NoneType' and 'float'"): + totest.histogram_sampler(x_edges=None, y=None) + with raises(AssertionError): + totest.histogram_sampler(x_edges=np.array([0.0, 0.5, 1.0]),\ + y=np.array([-0.4, 0.6])) + with raises(AssertionError): + totest.histogram_sampler(x_edges=np.array([0.0, 1.0]),\ + y=np.array([0.4, 0.6])) + # examples: + monkeypatch.setattr(np.random, "uniform", mock_uniform) + monkeypatch.setattr(np.random, "choice", mock_choice) + assert np.allclose(totest.histogram_sampler(x_edges=np.array([0.0,\ + 0.5,\ + 1.0]),\ + y=np.array([0.2, 0.8]),\ + size=5),\ + np.array([0.0, 0.5, 0.66666667, 0.83333333, 1.0])) + assert np.array_equal(totest.histogram_sampler(x_edges=np.array([0.0,\ + 0.5,\ + 1.0]\ + ), y=np.array([0.2,\ + 0.8]),\ + size=4),\ + np.array([0.0, 0.5, 0.75, 1.0])) + + def test_read_histogram_from_file(self, csv_path_failing_3_data_lines,\ + csv_path_failing_empty_line,\ + csv_path_failing_element_counts,\ + csv_path_failing_element_types,\ + csv_path_ex1, csv_path_ex2): + # missing argument + with raises(TypeError, match="missing 1 required positional "\ + +"argument: 'path'"): + totest.read_histogram_from_file() + # bad input + with raises(TypeError, match="expected str, bytes or os.PathLike "\ + +"object, not NoneType"): + totest.read_histogram_from_file(path=None) + with raises(IndexError, match="More than two lines found in the "\ + +"histogram document."): + totest.read_histogram_from_file(path=csv_path_failing_3_data_lines) + with raises(IndexError, match="Less than two lines found in the "\ + +"histogram document."): + totest.read_histogram_from_file(path=csv_path_failing_empty_line) + with raises(IndexError, match="The number of elements in the second "\ + +"data line is not one less than the "\ + +"number in the first data line."): + totest.read_histogram_from_file(path=\ + csv_path_failing_element_counts) + with raises(IndexError, match="The number of elements in the second "\ + +"data line is not one less than the "\ + +"number in the first data line."): + totest.read_histogram_from_file(path=csv_path_failing_element_types) + # examples: + arrays = totest.read_histogram_from_file(path=csv_path_ex1) + assert np.array_equal(arrays[0], np.array([0.1, 1.1, 2.1])) + assert np.array_equal(arrays[1], np.array([1.0, 1.0])) + arrays = totest.read_histogram_from_file(path=csv_path_ex2) + assert np.array_equal(arrays[0], np.array([0.2, 1.2, 2.2])) + assert np.array_equal(arrays[1], np.array([2.0, 2.0])) + + def test_inspiral_timescale_from_separation(self): + # missing argument + with raises(TypeError, match="missing 4 required positional "\ + +"arguments: 'star1_mass', 'star2_mass',"\ + +" 'separation', and 'eccentricity'"): + totest.inspiral_timescale_from_separation() + # bad input + with raises(TypeError) as error_info: + totest.inspiral_timescale_from_separation(None, None, None, None) + assert error_info.value.args[0] == "unsupported operand type(s) for "\ + + "*: 'NoneType' and 'float'" + with raises(ValueError, match="Mass of star 1 is <= 0, which is not "\ + +"a physical value."): + totest.inspiral_timescale_from_separation(0.0, 0.5, 0.5, 0.5) + with raises(ValueError, match="Mass of star 2 is <= 0, which is not "\ + +"a physical value."): + totest.inspiral_timescale_from_separation(0.5, 0.0, 0.5, 0.5) + with raises(ValueError, match="Separation is <= 0, which is not a "\ + +"physical value."): + totest.inspiral_timescale_from_separation(0.5, 0.5, 0.0, 0.5) + with raises(ValueError, match="Eccentricity is < 0, which is not a "\ + +"physical value."): + totest.inspiral_timescale_from_separation(0.5, 0.5, 0.5, -0.5) + with raises(ValueError, match="Eccentricity is >= 1, which is not a "\ + +"physical value."): + totest.inspiral_timescale_from_separation(0.5, 0.5, 0.5, 1.5) + # examples: + tests = [(0.5, 0.5, 0.5, 0.5, approx(13.44121924697, abs=6e-12)),\ + (1.5, 0.5, 0.5, 0.5, approx( 2.24020320783, abs=6e-12)),\ + (0.5, 1.5, 0.5, 0.5, approx( 2.24020320783, abs=6e-12)),\ + (0.5, 0.5, 1.5, 0.5, approx( 1.08873875900e+3, abs=6e-9)),\ + (0.5, 0.5, 0.5, 0.0, approx(37.56647511040, abs=6e-12)),\ + (0.5, 0.5, 0.5, 1.0e-5, approx(37.56647487803, abs=6e-12)),\ + (0.5, 0.5, 0.5, 1.0-1.0e-5, approx(2.41297178064e-15,\ + abs=6e-27))] + for (m1, m2, a, e, r) in tests: + assert totest.inspiral_timescale_from_separation(m1, m2, a, e) == r + + def test_inspiral_timescale_from_orbital_period(self): + # missing argument + with raises(TypeError, match="missing 4 required positional "\ + +"arguments: 'star1_mass', 'star2_mass',"\ + +" 'orbital_period', and 'eccentricity'"): + totest.inspiral_timescale_from_orbital_period() + # bad input + with raises(TypeError) as error_info: + totest.inspiral_timescale_from_orbital_period(None, None, None, None) + assert error_info.value.args[0] == "unsupported operand type(s) for "\ + + "*: 'NoneType' and 'float'" +# with raises(ValueError, match="Mass of star 1 is <= 0, which is not "\ +# +"a physical value."): +# totest.inspiral_timescale_from_orbital_period(0.0, 0.5, 0.5, 0.5) +# with raises(ValueError, match="Mass of star 2 is <= 0, which is not "\ +# +"a physical value."): +# totest.inspiral_timescale_from_orbital_period(0.5, 0.0, 0.5, 0.5) +# with raises(ValueError, match="Separation is <= 0, which is not a "\ +# +"physical value."): +# totest.inspiral_timescale_from_orbital_period(0.5, 0.5, 0.0, 0.5) +# with raises(ValueError, match="Eccentricity is < 0, which is not a "\ +# +"physical value."): +# totest.inspiral_timescale_from_orbital_period(0.5, 0.5, 0.5, -0.5) +# with raises(ValueError, match="Eccentricity is >= 1, which is not a "\ +# +"physical value."): +# totest.inspiral_timescale_from_orbital_period(0.5, 0.5, 0.5, 1.5) + # examples: + tests = [(0.5, 0.5, 0.5, 0.5, approx(1.06110684309e+4, abs=6e-8)),\ + (1.5, 0.5, 0.5, 0.5, approx(4.45636949266e+3, abs=6e-9)),\ + (0.5, 1.5, 0.5, 0.5, approx(4.45636949266e+3, abs=6e-9)),\ + (0.5, 0.5, 1.5, 0.5, approx(1.98647206096e+5, abs=6e-7)),\ + (0.5, 0.5, 0.5, 0.0, approx(2.96565684095e+4, abs=6e-8)),\ + (0.5, 0.5, 0.5, 1.0e-5, approx(2.96565682261e+4, abs=6e-8)),\ + (0.5, 0.5, 0.5, 1.0-1.0e-5, approx(1.90490224255e-12,\ + abs=6e-24))] + for (m1, m2, P, e, r) in tests: + assert totest.inspiral_timescale_from_orbital_period(m1, m2, P, e)\ + == r + + def test_spin_stable_mass_transfer(self): + # missing argument + with raises(TypeError, match="missing 3 required positional "\ + +"arguments: 'spin_i', "\ + +"'star_mass_preMT', and "\ + +"'star_mass_postMT'"): + totest.spin_stable_mass_transfer() + # examples: + tests = [(None, 1.0, 1.0), (-1.0, 1.0, 1.0), (1.0, None, 1.0),\ + (1.0, -1.0, 1.0), (1.0, 1.0, None), (1.0, 1.0, -1.0)] + for (si, mi, mf) in tests: + assert totest.spin_stable_mass_transfer(si, mi, mf) is None + assert totest.spin_stable_mass_transfer(0.5, 0.9, 1.0) ==\ + approx(0.69217452285, abs=6e-12) + assert np.isnan(totest.spin_stable_mass_transfer(1.0, 1.0, 0.1)) + assert totest.spin_stable_mass_transfer(1.0, 0.1, 1.0) == 1.0 + + def test_check_state_of_star(self, star): + # missing argument + with raises(TypeError, match="missing 1 required positional "\ + +"argument: 'star'"): + totest.check_state_of_star() + # bad input + with raises(AttributeError, match="'NoneType' object has no "\ + +"attribute 'mass'"): + totest.check_state_of_star(None, None, None) + # examples: + assert totest.check_state_of_star(star) ==\ + "undetermined_evolutionary_state" + assert totest.check_state_of_star(star, i=0) ==\ + "undetermined_evolutionary_state" + tests = [('WD', 1.0), ('NS', 1.5), ('BH', 9.0)] + for (CO, m) in tests: + star.mass = m + star.state = CO + assert totest.check_state_of_star(star, star_CO=True) == star.state + n = len(tests) + star.mass_history = n * star.mass_history + star.surface_h1_history = n * star.surface_h1_history + star.center_h1_history = n * star.center_h1_history + star.center_he4_history = n * star.center_he4_history + star.center_c12_history = n * star.center_c12_history + star.log_LH_history = n * star.log_LH_history + star.log_LHe_history = n * star.log_LHe_history + star.log_Lnuc_history = n * star.log_Lnuc_history + for i in range(n): + assert totest.check_state_of_star(star, i=i) ==\ + "undetermined_evolutionary_state" + for i in range(n): + star.mass_history[i] = tests[i][1] + star.state = 'BH' + assert totest.check_state_of_star(star, i=i, star_CO=True) ==\ + totest.infer_star_state(star_mass=tests[i][1], star_CO=True) + star.state = 'WD' + assert totest.check_state_of_star(star, i=i, star_CO=True) == 'WD' + + def test_check_state_of_star_history_array(self, star): + # missing argument + with raises(TypeError, match="missing 1 required positional "\ + +"argument: 'star'"): + totest.check_state_of_star_history_array() + # bad input + with raises(TypeError, match="bad operand type for unary -: "\ + +"'NoneType'"): + totest.check_state_of_star_history_array(None, None, None) + with raises(IndexError, match="list index out of range"): + totest.check_state_of_star_history_array(star, N=10) + # examples: + assert totest.check_state_of_star_history_array(star) ==\ + ["undetermined_evolutionary_state"] + masses = [1.0, 1.5, 9.0] + n = len(masses) + star.mass_history = n * star.mass_history + star.surface_h1_history = n * star.surface_h1_history + star.center_h1_history = n * star.center_h1_history + star.center_he4_history = n * star.center_he4_history + star.center_c12_history = n * star.center_c12_history + star.log_LH_history = n * star.log_LH_history + star.log_LHe_history = n * star.log_LHe_history + star.log_Lnuc_history = n * star.log_Lnuc_history + for i in range(n): + assert totest.check_state_of_star_history_array(star, N=i+1) ==\ + (i+1) * ["undetermined_evolutionary_state"] + COs = [] + for i, m in enumerate(masses): + star.mass_history[i] = m + COs.append(totest.infer_star_state(star_mass=m, star_CO=True)) + for i in range(n): + star.state = 'BH' + assert totest.check_state_of_star_history_array(star, N=i+1,\ + star_CO=True) == COs[-i-1:] + star.state = 'WD' + assert totest.check_state_of_star_history_array(star, N=i+1,\ + star_CO=True) == (i+1) * ['WD'] + + def test_get_binary_state_and_event_and_mt_case(self, binary, monkeypatch): + def mock_infer_mass_transfer_case(rl_relative_overflow,\ + lg_mtransfer_rate, donor_state,\ + verbose=False): + if rl_relative_overflow is not None: + if rl_relative_overflow > 0: + return totest.MT_CASE_A + else: + return totest.MT_CASE_UNDETERMINED + else: + return totest.MT_CASE_NO_RLO + # missing argument + with raises(TypeError, match="missing 1 required positional "\ + +"argument: 'binary'"): + totest.get_binary_state_and_event_and_mt_case() + # bad input + with raises(TypeError, match="argument of type 'NoneType' is not "\ + +"iterable"): + totest.get_binary_state_and_event_and_mt_case(binary) + # examples: no binary + assert totest.get_binary_state_and_event_and_mt_case(None) ==\ + [None, None, 'None'] + # examples: not converged + assert totest.get_binary_state_and_event_and_mt_case(binary,\ + interpolation_class='not_converged') == [None, None, 'None'] + # examples: initial MT + assert totest.get_binary_state_and_event_and_mt_case(binary,\ + interpolation_class='initial_MT') ==\ + ['initial_RLOF', None, 'None'] + # examples: detached + binary.star_1.state = "test_state" + binary.star_2.state = "test_state" + assert totest.get_binary_state_and_event_and_mt_case(binary) ==\ + ['detached', None, 'None'] + binary.star_1.state_history[0] = "test_state" + binary.star_2.state_history[0] = "test_state" + assert totest.get_binary_state_and_event_and_mt_case(binary, i=0) ==\ + ['detached', None, 'None'] + # bad input + with raises(IndexError, match="list index out of range"): + totest.get_binary_state_and_event_and_mt_case(binary, i=1) + # examples: mass transfer + monkeypatch.setattr(totest, "infer_mass_transfer_case",\ + mock_infer_mass_transfer_case) + ## donor is star 1 + binary.rl_relative_overflow_1 = 1.0 + tests = [(None, None), ('unstable_MT', 'oCE1')] + for (IC, e) in tests: + assert totest.get_binary_state_and_event_and_mt_case(binary,\ + interpolation_class=IC) == ['RLO1', e, 'case_A1'] + ## both stars overfill RL leading to double CE initiated by star 1 + binary.rl_relative_overflow_2 = 1.0 + tests = [(None, None), ('unstable_MT', 'oDoubleCE1')] + for (IC, e) in tests: + assert totest.get_binary_state_and_event_and_mt_case(binary,\ + interpolation_class=IC) == ['contact', e, 'None'] + ## classical CE initiated by star 1 + binary.star_2.state = "BH" + assert totest.get_binary_state_and_event_and_mt_case(binary,\ + interpolation_class='unstable_MT') == ['contact', 'oCE1',\ + 'None'] + binary.star_2.state = "test_state" + ## both stars overfill RL leading to double CE initiated by star 2 + binary.rl_relative_overflow_2 = 2.0 + tests = [(None, None), ('unstable_MT', 'oDoubleCE2')] + for (IC, e) in tests: + assert totest.get_binary_state_and_event_and_mt_case(binary,\ + interpolation_class=IC) == ['contact', e, 'None'] + ## classical CE initiated by star 2 + binary.star_1.state = "BH" + assert totest.get_binary_state_and_event_and_mt_case(binary,\ + interpolation_class='unstable_MT') == ['contact', 'oCE2',\ + 'None'] + binary.star_1.state = "test_state" + ## donor is star 2 + binary.rl_relative_overflow_1 = None + tests = [(None, None), ('unstable_MT', 'oCE2')] + for (IC, e) in tests: + assert totest.get_binary_state_and_event_and_mt_case(binary,\ + interpolation_class=IC) == ['RLO2', e, 'case_A2'] + # examples: undefined + binary.rl_relative_overflow_2 = -1.0 + assert totest.get_binary_state_and_event_and_mt_case(binary) ==\ + ['undefined', None, 'None'] + # examples: star 2 becomes WD + binary.star_2.center_gamma = 10.0 + assert totest.get_binary_state_and_event_and_mt_case(binary) ==\ + ['undefined', 'CC2', 'None'] + # examples: star 1 becomes WD + binary.star_1.center_gamma = 10.0 + assert totest.get_binary_state_and_event_and_mt_case(binary) ==\ + ['undefined', 'CC1', 'None'] + # examples: no center_gamma_history + binary.star_1.center_gamma_history=[] + binary.star_2.center_gamma_history=[] + assert totest.get_binary_state_and_event_and_mt_case(binary, i=0) ==\ + ['detached', None, 'None'] + + def test_get_binary_state_and_event_and_mt_case_array(self, binary,\ + monkeypatch): + def mock_get_binary_state_and_event_and_mt_case(binary,\ + interpolation_class=None, i=None, verbose=False): + return ['Unit', 'Test', 'None'] + # missing argument + with raises(TypeError, match="missing 1 required positional "\ + +"argument: 'binary'"): + totest.get_binary_state_and_event_and_mt_case_array() + # bad input + with raises(TypeError, match="argument of type 'NoneType' is not "\ + +"iterable"): + totest.get_binary_state_and_event_and_mt_case_array(binary) + with raises(IndexError, match="list index out of range"): + totest.get_binary_state_and_event_and_mt_case_array(binary, N=10) + # examples: no binary + assert totest.get_binary_state_and_event_and_mt_case_array(None) ==\ + (None, None, 'None') + # examples: detached + binary.star_1.state = "test_state" + binary.star_2.state = "test_state" + assert totest.get_binary_state_and_event_and_mt_case_array(binary) ==\ + ('detached', None, 'None') + # examples: several mocked entries + monkeypatch.setattr(totest, "get_binary_state_and_event_and_mt_case",\ + mock_get_binary_state_and_event_and_mt_case) + mock_return = mock_get_binary_state_and_event_and_mt_case(binary) + for i in range(4): + assert totest.get_binary_state_and_event_and_mt_case_array(binary,\ + N=i) ==\ + (i*[mock_return[0]], i*[mock_return[1]], i*[mock_return[2]]) + assert totest.get_binary_state_and_event_and_mt_case_array(binary,\ + N=-1) ==\ + ([], [], []) + + def test_CO_radius(self): + # missing argument + with raises(TypeError, match="missing 2 required positional "\ + +"arguments: 'M' and 'COtype'"): + totest.CO_radius() + # bad input + with raises(ValueError, match="Compact object mass must be a "\ + +"positive value"): + totest.CO_radius(0.0, 'BH') + with raises(ValueError, match="COtype not in the list of valid "\ + +"options"): + totest.CO_radius(1.0, 'TEST') + # examples: + tests = [('WD', 0.5, approx(5.24982189818e-3, abs=6e-15)),\ + ('WD', 1.0, approx(4.16678640191e-3, abs=6e-15)),\ + ('NS', 1.5, approx(1.79602862151e-5, abs=6e-17)),\ + ('NS', 2.0, approx(1.79602862151e-5, abs=6e-17)),\ + ('BH', 7.0, totest.Schwarzschild_Radius(7.0)),\ + ('BH', 9.0, totest.Schwarzschild_Radius(9.0))] + for (CO, m, r) in tests: + assert totest.CO_radius(m, CO) == r + + def test_He_MS_lifetime(self): + # missing argument + with raises(TypeError, match="missing 1 required positional "\ + +"argument: 'mass'"): + totest.He_MS_lifetime() + # bad input + with raises(TypeError, match="'<=' not supported between instances "\ + +"of 'NoneType' and 'float'"): + totest.He_MS_lifetime(None) + with raises(ValueError, match="Too low mass: 0.0"): + totest.He_MS_lifetime(0.0) + # examples: + tests = [(1.0, 1.0e+8), (3.0, approx(3.47136114375e+7, abs=6e-5)),\ + (30.0, approx(6.95883527992e+5, abs=6e-7)), (300.0, 3.0e+5)] + for (m, t) in tests: + assert totest.He_MS_lifetime(m) == t + + def test_Schwarzschild_Radius(self): + # missing argument + with raises(TypeError, match="missing 1 required positional "\ + +"argument: 'M'"): + totest.Schwarzschild_Radius() + # bad input + with raises(TypeError) as error_info: + totest.Schwarzschild_Radius(None) + assert error_info.value.args[0] == "unsupported operand type(s) for "\ + + "*: 'float' and 'NoneType'" + # examples: + tests = [(7.0, approx(2.97147953078e-5, abs=6e-17)),\ + (70.0, approx(2.97147953078e-4, abs=6e-16))] + for (m, r) in tests: + assert totest.Schwarzschild_Radius(m) == r + + def test_flip_stars(self, binary): + # missing argument + with raises(TypeError, match="missing 1 required positional "\ + +"argument: 'binary'"): + totest.flip_stars() + # bad input + with raises(AttributeError, match="'NoneType' object has no "\ + +"attribute 'star_1'"): + totest.flip_stars(None) + # examples: set stars and check swap + for attr in binary.star_1.__dict__: + setattr(binary.star_1, attr, 1) + for attr in binary.star_2.__dict__: + setattr(binary.star_2, attr, 2) + totest.flip_stars(binary) + for attr in binary.star_1.__dict__: + assert getattr(binary.star_1, attr) == 2 + for attr in binary.star_2.__dict__: + assert getattr(binary.star_2, attr) == 1 + # examples: binary states + tests = [('RLO1', 'RLO2'), ('RLO2', 'RLO1')] + for (s, n) in tests: + binary.state = s + binary.state_history[0] = s + totest.flip_stars(binary) + assert binary.state == n + assert binary.state_history[0] == n + # examples: binary events + tests = [('oRLO1', 'oRLO2'), ('oRLO2', 'oRLO1'), ('oCE1', 'oCE2'),\ + ('oCE2', 'oCE1'), ('CC1', 'CC2'), ('CC2', 'CC1')] + for (e, n) in tests: + binary.event = e + binary.event_history[0] = e + totest.flip_stars(binary) + assert binary.event == n + assert binary.event_history[0] == n + # examples: set binary values and check swap + for v in binary.__dict__: + if v[-1] == 1: + setattr(binary, v, 1) + elif v[-1] == 2: + setattr(binary, v, 2) + totest.flip_stars(binary) + for v in binary.__dict__: + if v[-1] == 1: + assert getattr(binary, v) == 2 + elif v[-1] == 2: + assert getattr(binary, v) == 1 + + def test_set_binary_to_failed(self, binary): + # missing argument + with raises(TypeError, match="missing 1 required positional "\ + +"argument: 'binary'"): + totest.set_binary_to_failed() + # bad input + with raises(AttributeError, match="'NoneType' object has no "\ + +"attribute 'state'"): + totest.set_binary_to_failed(None) + # examples: + totest.set_binary_to_failed(binary) + assert binary.state == "ERR" + assert binary.event == "FAILED" + + def test_infer_star_state(self): + # bad input + with raises(TypeError, match="'<=' not supported between instances "\ + +"of 'NoneType' and 'float'"): + totest.infer_star_state(star_CO=True) + # examples: undetermined + assert totest.infer_star_state() == totest.STATE_UNDETERMINED + # examples: compact objects + tests = [(0.5*totest.STATE_NS_STARMASS_UPPER_LIMIT, "NS"),\ + (totest.STATE_NS_STARMASS_UPPER_LIMIT, "NS"),\ + (2.0*totest.STATE_NS_STARMASS_UPPER_LIMIT, "BH")] + for (m, CO) in tests: + assert totest.infer_star_state(star_mass=m, star_CO=True) == CO + # examples: loop over all cases + THNA = totest.THRESHOLD_HE_NAKED_ABUNDANCE + TCA = totest.THRESHOLD_CENTRAL_ABUNDANCE + LBT = totest.LOG10_BURNING_THRESHOLD + for sH1 in [2.0*THNA, THNA, 0.5*THNA]: + for cH1 in [2.0*TCA, TCA, 0.5*TCA]: + for cHe4 in [2.0*TCA, TCA, 0.5*TCA]: + for cC12 in [2.0*TCA, TCA, 0.5*TCA]: + for lgLH in [2.0*LBT, LBT, 0.5*LBT]: + for lgLHe in [2.0*LBT, LBT, 0.5*LBT]: + if sH1 > THNA: + rich = "H-rich" + elif sH1 < cH1: + rich = "accreted_He" + else: + rich = "stripped_He" + if ((cH1<=TCA) and (cHe4<=TCA) and\ + (cC12<=TCA)): + burn = "Central_C_depletion" + elif ((cH1<=TCA) and (cHe4<=TCA) and\ + (cC12>TCA)): + burn = "Central_He_depleted" + elif ((cH1>TCA) and (lgLH>LBT)): + burn = "Core_H_burning" + elif ((cH1>TCA) and (lgLH<=LBT)): + burn = "non_burning" + elif lgLHe > LBT: + burn = "Core_He_burning" + elif lgLH > LBT: + burn = "Shell_H_burning" + else: + burn = "non_burning" + assert totest.infer_star_state(surface_h1=sH1,\ + center_h1=cH1,\ + center_he4=\ + cHe4,\ + center_c12=\ + cC12,\ + log_LH=lgLH,\ + log_LHe=lgLHe,\ + log_Lnuc=lgLH\ + +lgLHe\ + ) == rich + "_"\ + + burn + + def test_infer_mass_transfer_case(self, capsys): + # missing argument + with raises(TypeError, match="missing 3 required positional "\ + +"arguments: 'rl_relative_overflow', "\ + +"'lg_mtransfer_rate', and "\ + +"'donor_state'"): + totest.infer_mass_transfer_case() + # examples: no RLO + assert totest.infer_mass_transfer_case(None, None, None) ==\ + totest.MT_CASE_NO_RLO + assert totest.infer_mass_transfer_case(None, 1.0, "test") ==\ + totest.MT_CASE_NO_RLO + assert totest.infer_mass_transfer_case(1.0, None, "test") ==\ + totest.MT_CASE_NO_RLO + RROT = totest.RL_RELATIVE_OVERFLOW_THRESHOLD + LMRT = totest.LG_MTRANSFER_RATE_THRESHOLD + assert totest.infer_mass_transfer_case(min(-1.0, RROT), LMRT, "test")\ + == totest.MT_CASE_NO_RLO + assert totest.infer_mass_transfer_case(min(-1.0, RROT), LMRT, "test",\ + verbose=True) == totest.MT_CASE_NO_RLO + assert "checking rl_relative_overflow / lg_mtransfer_rate" in\ + capsys.readouterr().out + # examples: MT cases + tests = [("test_non_burning", totest.MT_CASE_NONBURNING),\ + ("H-rich_Core_H_burning", totest.MT_CASE_A),\ + ("H-rich_Core_He_burning", totest.MT_CASE_B),\ + ("H-rich_Shell_H_burning", totest.MT_CASE_B),\ + ("H-rich_Central_He_depleted", totest.MT_CASE_C),\ + ("H-rich_Central_C_depletion", totest.MT_CASE_C),\ + ("H-rich_undetermined", totest.MT_CASE_UNDETERMINED),\ + ("stripped_He_Core_He_burning", totest.MT_CASE_BA),\ + ("stripped_He_Central_He_depleted", totest.MT_CASE_BB),\ + ("stripped_He_Central_C_depletion", totest.MT_CASE_BB),\ + ("stripped_He_undetermined", totest.MT_CASE_UNDETERMINED),\ + ("test_undetermined", totest.MT_CASE_UNDETERMINED)] + for (ds, c) in tests: + assert totest.infer_mass_transfer_case(2*RROT, 2*LMRT, ds) == c + + def test_cumulative_mass_transfer_numeric(self): + # missing argument + with raises(TypeError, match="missing 1 required positional "\ + +"argument: 'MT_cases'"): + totest.cumulative_mass_transfer_numeric() + # bad input + with raises(TypeError, match="object of type 'NoneType' has no len()"): + totest.cumulative_mass_transfer_numeric(None) + # examples: no cases, undetermined, and no RLO + tests = [([], [totest.MT_CASE_UNDETERMINED]),\ + ([totest.MT_CASE_UNDETERMINED],\ + [totest.MT_CASE_UNDETERMINED]),\ + ([totest.MT_CASE_NO_RLO], [totest.MT_CASE_NO_RLO])] + for (MT, r) in tests: + assert totest.cumulative_mass_transfer_numeric(MT) == r + # examples: cut out undetermined + tests = [[], [totest.MT_CASE_UNDETERMINED],\ + [totest.MT_CASE_UNDETERMINED, totest.MT_CASE_UNDETERMINED]] + for preA in tests: + for preB in tests: + for postB in tests: + if preA + preB + postB == []: # at least one undetermined + continue + mt = preA + [totest.MT_CASE_A] + preB + [totest.MT_CASE_B]\ + + postB + assert totest.cumulative_mass_transfer_numeric(mt) ==\ + [totest.MT_CASE_UNDETERMINED, totest.MT_CASE_A,\ + totest.MT_CASE_B] + # examples: cut out no RLO + tests = [[], [totest.MT_CASE_NO_RLO], [totest.MT_CASE_NO_RLO,\ + totest.MT_CASE_NO_RLO]] + for preA in tests: + for preB in tests: + for postB in tests: + mt = preA + [totest.MT_CASE_A] + preB + [totest.MT_CASE_B]\ + + postB + assert totest.cumulative_mass_transfer_numeric(mt) ==\ + [totest.MT_CASE_A, totest.MT_CASE_B] + # examples: undetermined and no RLO + assert totest.cumulative_mass_transfer_numeric(\ + [totest.MT_CASE_UNDETERMINED, totest.MT_CASE_NO_RLO]) ==\ + [totest.MT_CASE_UNDETERMINED, totest.MT_CASE_NO_RLO] + assert totest.cumulative_mass_transfer_numeric(\ + [totest.MT_CASE_NO_RLO, totest.MT_CASE_UNDETERMINED]) ==\ + [totest.MT_CASE_UNDETERMINED, totest.MT_CASE_NO_RLO] + # examples: cut out duplicates + for i in range(1,4): + mt = i*[totest.MT_CASE_A] + 2*i*[totest.MT_CASE_B]\ + + 3*i*[totest.MT_CASE_A] + assert totest.cumulative_mass_transfer_numeric(mt) ==\ + [totest.MT_CASE_A, totest.MT_CASE_B, totest.MT_CASE_A] + # examples: from strings + assert totest.cumulative_mass_transfer_numeric(["A", "no_RLO", "A",\ + "B", "A", "A"]) ==\ + [totest.MT_CASE_A, totest.MT_CASE_B, totest.MT_CASE_A] + + def test_cumulative_mass_transfer_string(self): + # missing argument + with raises(TypeError, match="missing 1 required positional "\ + +"argument: 'cumulative_integers'"): + totest.cumulative_mass_transfer_string() + # bad input + with raises(TypeError, match="object of type 'NoneType' has no len()"): + totest.cumulative_mass_transfer_string(None) + with warns(InappropriateValueWarning, match="Unknown MT case:"): + # unknown case + totest.cumulative_mass_transfer_string([-1]) + # examples: no cases + assert totest.cumulative_mass_transfer_string([]) == "?" + # examples: undetermined + assert totest.cumulative_mass_transfer_string(\ + [totest.MT_CASE_UNDETERMINED]) == "?" + # examples: no RLO + assert totest.cumulative_mass_transfer_string(\ + [totest.MT_CASE_NO_RLO]) == "no_RLO" + # examples: cases for both stars + for c in totest.ALL_RLO_CASES: + assert totest.cumulative_mass_transfer_string([c, 10+c]) ==\ + "case_" + totest.MT_CASE_TO_STR[c] + "1/"\ + + totest.MT_CASE_TO_STR[c] + "2" + + def test_cumulative_mass_transfer_flag(self): + # missing argument + with raises(TypeError, match="missing 1 required positional "\ + +"argument: 'MT_cases'"): + totest.cumulative_mass_transfer_flag() + # bad input + with raises(AttributeError, match="'NoneType' object has no "\ + +"attribute 'copy'"): + totest.cumulative_mass_transfer_flag(None) + with warns(EvolutionWarning, match="MT case with unknown donor:"): + with warns(InappropriateValueWarning): + totest.cumulative_mass_transfer_flag([30], shift_cases=True) + # examples: + assert totest.cumulative_mass_transfer_flag([totest.MT_CASE_A,\ + totest.MT_CASE_B,\ + 10+totest.MT_CASE_A,\ + totest.MT_CASE_A,\ + 10+totest.MT_CASE_B,\ + 10+totest.MT_CASE_A]) ==\ + 'case_A1/B1/A2/A1/B2/A2' + assert totest.cumulative_mass_transfer_flag([totest.MT_CASE_A,\ + totest.MT_CASE_B,\ + 10+totest.MT_CASE_A,\ + totest.MT_CASE_A,\ + 10+totest.MT_CASE_B,\ + 10+totest.MT_CASE_A],\ + shift_cases=True) ==\ + 'case_A1/B1/A2/B1/B2' + + def test_get_i_He_depl(self): + # missing argument + with raises(TypeError, match="missing 1 required positional "\ + +"argument: 'history'"): + totest.get_i_He_depl() + # bad input + with raises(AttributeError, match="'NoneType' object has no "\ + +"attribute 'dtype'"): + totest.get_i_He_depl(None) + with raises(TypeError, match="argument of type 'NoneType' is not "\ + +"iterable"): + totest.get_i_He_depl(np.array([])) + # examples: incomplete history to search through + assert totest.get_i_He_depl(np.array([[]], dtype=([('surface_h1',\ + 'f8')]))) == -1 + # examples: no he depletion + test_history = np.array([(1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0),\ + (1.0, 0.0, 0.5, 0.5, 0.0, 1.0, 1.0),\ + (1.0, 0.0, 0.2, 0.2, 0.0, 1.0, 1.0)],\ + dtype=([('surface_h1', 'f8'),\ + ('center_h1', 'f8'),\ + ('center_he4', 'f8'),\ + ('center_c12', 'f8'),\ + ('log_LH', 'f8'), ('log_LHe', 'f8'),\ + ('log_Lnuc', 'f8')])) + assert totest.get_i_He_depl(test_history) == -1 + # examples: he depletion at 1 + test_history = np.array([(1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0),\ + (1.0, 0.0, 0.0, 0.5, 0.0, 1.0, 1.0),\ + (1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0)],\ + dtype=([('surface_h1', 'f8'),\ + ('center_h1', 'f8'),\ + ('center_he4', 'f8'),\ + ('center_c12', 'f8'),\ + ('log_LH', 'f8'), ('log_LHe', 'f8'),\ + ('log_Lnuc', 'f8')])) + assert totest.get_i_He_depl(test_history) == 1 + + def test_calculate_Patton20_values_at_He_depl(self, star): + # missing argument + with raises(TypeError, match="missing 1 required positional "\ + +"argument: 'star'"): + totest.calculate_Patton20_values_at_He_depl() + # bad input + with raises(AttributeError, match="'NoneType' object has no "\ + +"attribute 'state_history'"): + totest.calculate_Patton20_values_at_He_depl(None) + # examples: no history + star.co_core_mass_at_He_depletion = 0.5 + star.avg_c_in_c_core_at_He_depletion = 0.5 + star.state_history = None + totest.calculate_Patton20_values_at_He_depl(star) + assert star.co_core_mass_at_He_depletion is None + assert star.avg_c_in_c_core_at_He_depletion is None + # examples: no He_depleted in history + star.co_core_mass_at_He_depletion = 0.5 + star.avg_c_in_c_core_at_He_depletion = 0.5 + for s in ["test", "H-rich_Core_H_burning", "H-rich_Core_He_burning",\ + "H-rich_Shell_H_burning", "H-rich_Central_C_depletion",\ + "H-rich_undetermined", "stripped_He_Core_He_burning",\ + "stripped_He_Central_C_depletion",\ + "stripped_He_undetermined"]: + star.state_history = [s] + totest.calculate_Patton20_values_at_He_depl(star) + assert star.co_core_mass_at_He_depletion is None + assert star.avg_c_in_c_core_at_He_depletion is None + # examples: loop through star types with He depletion + tests = [("H-rich_Central_He_depleted", 0.1),\ + ("stripped_He_Central_He_depleted", 0.2)] + for (s, v) in tests: + star.state_history = ["test", s, s] + star.co_core_mass_history = [0.0, v, 1.0] + star.avg_c_in_c_core_history = [0.0, v, 1.0] + totest.calculate_Patton20_values_at_He_depl(star) + assert star.co_core_mass_at_He_depletion == v + assert star.avg_c_in_c_core_at_He_depletion == v + + def test_CEE_parameters_from_core_abundance_thresholds(self, monkeypatch,\ + capsys, star): + def mock_calculate_core_boundary(donor_mass, donor_star_state,\ + profile, mc1_i=None,\ + core_element_fraction_definition=\ + None, CO_core_in_Hrich_star=False): + return core_element_fraction_definition + def mock_calculate_lambda_from_profile(profile, donor_star_state,\ + m1_i=np.nan, radius1=np.nan,\ + common_envelope_option_for_lambda\ + =totest.\ + DEFAULT_CE_OPTION_FOR_LAMBDA,\ + core_element_fraction_definition\ + =0.1, ind_core=None,\ + common_envelope_alpha_thermal=\ + 1.0, tolerance=0.001,\ + CO_core_in_Hrich_star=False,\ + verbose=False): + return core_element_fraction_definition,\ + core_element_fraction_definition,\ + core_element_fraction_definition + # missing argument + with raises(TypeError, match="missing 1 required positional "\ + +"argument: 'star'"): + totest.CEE_parameters_from_core_abundance_thresholds() + # bad input + with raises(AttributeError, match="'NoneType' object has no "\ + +"attribute 'mass'"): + totest.CEE_parameters_from_core_abundance_thresholds(None) + with raises(TypeError) as error_info: + totest.CEE_parameters_from_core_abundance_thresholds(star) + assert error_info.value.args[0] == "unsupported operand type(s) for "\ + + "** or pow(): 'float' and "\ + + "'NoneType'" + star.log_R = 0.0 + star.profile = np.array([(1.0), (1.0), (1.0)],\ + dtype=([('mass', 'f8')])) + with raises(TypeError, match="argument of type 'NoneType' is not "\ + +"iterable"): + totest.CEE_parameters_from_core_abundance_thresholds(star) + # examples: missing state with profile and verbose + star.state = "test_state" + totest.CEE_parameters_from_core_abundance_thresholds(star,\ + verbose=True) + captured_out = capsys.readouterr().out + for out in ["is not what expected during the "\ + +"CEE_parameters_from_core_abundance_thresholds.",\ + "star_state", "m_core_CE_1cent,m_core_CE_10cent,"\ + +"m_core_CE_30cent,m_core_CE_pure_He_star_10cent",\ + "r_core_CE_1cent,r_core_CE_10cent,r_core_CE_30cent,"\ + +"r_core_CE_pure_He_star_10cent",\ + "lambda_CE_1cent,lambda_CE_10cent,lambda_CE_30cent,"\ + +"lambda_CE_pure_He_star_10cent"]: + assert out in captured_out + for attr in ['m_core_CE_1cent', 'm_core_CE_10cent',\ + 'm_core_CE_30cent', 'm_core_CE_pure_He_star_10cent',\ + 'r_core_CE_1cent', 'r_core_CE_10cent',\ + 'r_core_CE_30cent', 'r_core_CE_pure_He_star_10cent',\ + 'lambda_CE_1cent', 'lambda_CE_10cent',\ + 'lambda_CE_30cent', 'lambda_CE_pure_He_star_10cent']: + assert np.isnan(getattr(star, attr)) + monkeypatch.setattr(totest, "calculate_core_boundary",\ + mock_calculate_core_boundary) + monkeypatch.setattr(totest, "calculate_lambda_from_profile",\ + mock_calculate_lambda_from_profile) + for s in ["test_state", "H-rich_test", "stripped_He_test"]: + star.state = s + totest.CEE_parameters_from_core_abundance_thresholds(star) + assert capsys.readouterr().out == "" + for attr in ['m_core_CE_1cent', 'm_core_CE_10cent',\ + 'm_core_CE_30cent', 'm_core_CE_pure_He_star_10cent',\ + 'r_core_CE_1cent', 'r_core_CE_10cent',\ + 'r_core_CE_30cent', 'r_core_CE_pure_He_star_10cent',\ + 'lambda_CE_1cent', 'lambda_CE_10cent',\ + 'lambda_CE_30cent', 'lambda_CE_pure_He_star_10cent']: + if s == "test_state": + assert np.isnan(getattr(star, attr)) + elif (("stripped_He" in s) and ("pure_He" not in attr) and\ + ("m_core" in attr)): + assert getattr(star, attr) == star.mass + elif (("stripped_He" in s) and ("pure_He" not in attr) and\ + ("r_core" in attr)): + assert getattr(star, attr) == 10 ** star.log_R + elif (("stripped_He" in s) and ("pure_He" not in attr) and\ + ("lambda" in attr)): + assert np.isnan(getattr(star, attr)) + elif "1cent" in attr: + assert getattr(star, attr) == 0.01 + elif "10cent" in attr: + assert getattr(star, attr) == 0.1 + elif "30cent" in attr: + assert getattr(star, attr) == 0.3 + else: + assert np.isnan(getattr(star, attr)) + # reset attributes set by the tested function + setattr(star, attr, -1.0) + # examples: no profile + star.profile = None + totest.CEE_parameters_from_core_abundance_thresholds(star) + for attr in ['m_core_CE_1cent', 'm_core_CE_10cent',\ + 'm_core_CE_30cent', 'm_core_CE_pure_He_star_10cent',\ + 'r_core_CE_1cent', 'r_core_CE_10cent',\ + 'r_core_CE_30cent', 'r_core_CE_pure_He_star_10cent',\ + 'lambda_CE_1cent', 'lambda_CE_10cent',\ + 'lambda_CE_30cent', 'lambda_CE_pure_He_star_10cent']: + assert np.isnan(getattr(star, attr)) + + def test_initialize_empty_array(self): + # missing argument + with raises(TypeError, match="missing 1 required positional "\ + +"argument: 'arr'"): + totest.initialize_empty_array() + # bad input + with raises(AttributeError, match="'NoneType' object has no "\ + +"attribute 'copy'"): + totest.initialize_empty_array(None) + # examples: + test_array = np.array([(1.0, "test"), (1.0, "test")],\ + dtype=([('mass', 'f8'), ('state', 'U8')])) + empty_array = totest.initialize_empty_array(test_array) + for i in range(len(empty_array)): + assert np.isnan(empty_array['mass'][i]) # nan float + assert empty_array['state'][i] == 'nan' # nan str + + def test_calculate_core_boundary(self, star_profile): + # missing argument + with raises(TypeError, match="missing 3 required positional "\ + +"arguments: 'donor_mass', "\ + +"'donor_star_state', and 'profile'"): + totest.calculate_core_boundary() + # bad input + with raises(ValueError, match="Not possible to calculate the core "\ + +"boundary of the donor in CE"): + totest.calculate_core_boundary(None, None, None) + with warns(ApproximationWarning, match="Stellar profile columns were "\ + +"not enough to calculate the "\ + +"core-envelope boundaries for"\ + +" CE, entire star is now "\ + +"considered an envelope"): + assert totest.calculate_core_boundary(None, None, None,\ + core_element_fraction_definition\ + =0.1) == -1 + with raises(AttributeError, match="'NoneType' object has no "\ + +"attribute 'dtype'"): + totest.calculate_core_boundary(None, "H-rich_Core_H_burning",\ + None,\ + core_element_fraction_definition=\ + 0.1) + # examples: get He core in H rich star + assert totest.calculate_core_boundary(None, "H-rich_Core_H_burning",\ + star_profile,\ + core_element_fraction_definition\ + =0.1) == 2 + # examples: get CO core in H rich star + assert totest.calculate_core_boundary(None, "H-rich_Core_H_burning",\ + star_profile,\ + core_element_fraction_definition\ + =0.3, CO_core_in_Hrich_star=True\ + ) == 3 + # examples: get CO core in H rich star without CO core + assert totest.calculate_core_boundary(None, "H-rich_Core_H_burning",\ + star_profile,\ + core_element_fraction_definition\ + =0.1, CO_core_in_Hrich_star=True\ + ) == -1 + # examples: get CO core in He star without a match + assert totest.calculate_core_boundary(None,\ + "stripped_He_Core_He_burning",\ + star_profile,\ + core_element_fraction_definition\ + =0.1) == -1 + # examples: given core mass + test_mass = np.array([1.0, 0.7, 0.4, 0.1, 0.0]) + assert totest.calculate_core_boundary(test_mass, None, None,\ + mc1_i=0.1) == 4 + # examples: both given: ignores core mass + assert totest.calculate_core_boundary(test_mass,\ + "stripped_He_Core_He_burning",\ + star_profile,\ + core_element_fraction_definition\ + =0.1, mc1_i=0.1) == -1 + + def test_period_evol_wind_loss(self): + # missing argument + with raises(TypeError, match="missing 4 required positional "\ + +"arguments: 'M_current', 'M_init', "\ + +"'Mcomp', and 'P_init'"): + totest.period_evol_wind_loss() + # bad input + with raises(TypeError) as error_info: + totest.period_evol_wind_loss(None, None, None, None) + assert error_info.value.args[0] == "unsupported operand type(s) for "\ + + "+: 'NoneType' and 'NoneType'" + # examples: + tests = [(0.5, 0.5, 0.5, 0.5, 0.5), (1.5, 0.5, 0.5, 0.5, 0.5/4),\ + (0.5, 1.5, 0.5, 0.5, 0.5*4), (0.5, 0.5, 1.5, 0.5, 0.5),\ + (0.5, 0.5, 0.5, 1.5, 1.5)] + for (M1, Mi, M2, Pi, r) in tests: + assert totest.period_evol_wind_loss(M1, Mi, M2, Pi) == r + + def test_separation_evol_wind_loss(self): + # missing argument + with raises(TypeError, match="missing 4 required positional "\ + +"arguments: 'M_current', 'M_init', "\ + +"'Mcomp', and 'A_init'"): + totest.separation_evol_wind_loss() + # bad input + with raises(TypeError) as error_info: + totest.separation_evol_wind_loss(None, None, None, None) + assert error_info.value.args[0] == "unsupported operand type(s) for "\ + + "+: 'NoneType' and 'NoneType'" + # examples: + tests = [(0.5, 0.5, 0.5, 0.5, 0.5), (1.5, 0.5, 0.5, 0.5, 0.5/2),\ + (0.5, 1.5, 0.5, 0.5, 0.5*2), (0.5, 0.5, 1.5, 0.5, 0.5),\ + (0.5, 0.5, 0.5, 1.5, 1.5)] + for (M1, Mi, M2, ai, r) in tests: + assert totest.separation_evol_wind_loss(M1, Mi, M2, ai) == r + + def test_period_change_stabe_MT(self): + # missing argument + with raises(TypeError, match="missing 4 required positional "\ + +"arguments: 'period_i', 'Mdon_i', "\ + +"'Mdon_f', and 'Macc_i'"): + totest.period_change_stabe_MT() + # bad input + with raises(TypeError) as error_info: + totest.period_change_stabe_MT(None, None, None, None) + assert error_info.value.args[0] == "unsupported operand type(s) for "\ + + "-: 'NoneType' and 'NoneType'" + tests = [(-1.0, 0.0), (2.0, 0.0), (0.0, -1.0), (0.0, 2.0)] + for (a, b) in tests: + with raises(ValueError) as error_info: + totest.period_change_stabe_MT(0.5, 0.5, 0.5, 0.5, alpha=a,\ + beta=b) + assert error_info.value.args[0] == "In period_change_stabe_MT, "\ + + "mass transfer efficiencies,"\ + + f" alpha, beta: {a}, {b} "\ + + "are not in the [0-1] range." + with raises(ValueError, match="Donor gains mass from 0.5 to 1.5"): + totest.period_change_stabe_MT(0.5, 0.5, 1.5, 0.5) + # examples: + tests = [(0.5, 0.5, 0.5, 0.5, 0.0, 0.0, 0.5),\ + (1.5, 0.5, 0.5, 0.5, 0.0, 0.0, 1.5),\ + (0.5, 1.5, 0.5, 0.5, 0.0, 0.0, 0.5),\ + (0.5, 0.5, 0.5, 1.5, 0.0, 0.0, 0.5),\ + (0.5, 1.0, 0.5, 1.0, 0.0, 0.0,\ + approx(1.18518518519, abs=6e-12)),\ + (0.5, 0.5, 0.5, 0.5, 0.5, 0.0, 0.5),\ + (1.5, 0.5, 0.5, 0.5, 0.5, 0.0, 1.5),\ + (0.5, 1.5, 0.5, 0.5, 0.5, 0.0,\ + approx(0.57735026919, abs=6e-12)),\ + (0.5, 0.5, 0.5, 1.5, 0.5, 0.0, 0.5),\ + (0.5, 1.0, 0.5, 1.0, 0.5, 0.0,\ + approx(0.94573367371, abs=6e-12)),\ + (0.5, 0.5, 0.5, 0.5, 0.0, 0.5, 0.5),\ + (1.5, 0.5, 0.5, 0.5, 0.0, 0.5, 1.5),\ + (0.5, 1.5, 0.5, 0.5, 0.0, 0.5, approx(0.375, abs=6e-12)),\ + (0.5, 0.5, 0.5, 1.5, 0.0, 0.5, 0.5),\ + (0.5, 1.0, 0.5, 1.0, 0.0, 0.5,\ + approx(1.36956865306, abs=6e-12)),\ + (0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5),\ + (1.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1.5),\ + (0.5, 1.5, 0.5, 0.5, 0.5, 0.5,\ + approx(0.58390782780, abs=6e-12)),\ + (0.5, 0.5, 0.5, 1.5, 0.5, 0.5, 0.5),\ + (0.5, 1.0, 0.5, 1.0, 0.5, 0.5,\ + approx(1.05670344636, abs=6e-12)),\ + (0.5, 0.5, 0.5, 0.5, 0.0, 1.0, 0.5),\ + (1.5, 0.5, 0.5, 0.5, 0.0, 1.0, 1.5),\ + (0.5, 1.5, 0.5, 0.5, 0.0, 1.0,\ + approx(0.13385261754, abs=6e-12)),\ + (0.5, 0.5, 0.5, 1.5, 0.0, 1.0, 0.5),\ + (0.5, 1.0, 0.5, 1.0, 0.0, 1.0,\ + approx(1.58670336106, abs=6e-12))] + for (Pi, Mdi, Mdf, Mai, a, b, r) in tests: + assert totest.period_change_stabe_MT(Pi, Mdi, Mdf, Mai, alpha=a,\ + beta=b) == r + + def test_linear_interpolation_between_two_cells(self, capsys): + # missing argument + with raises(TypeError, match="missing 3 required positional "\ + +"arguments: 'array_y', 'array_x', and "\ + +"'x_target'"): + totest.linear_interpolation_between_two_cells() + # bad input + with raises(TypeError, match="'>=' not supported between instances "\ + +"of 'NoneType' and 'NoneType'"): + totest.linear_interpolation_between_two_cells(None, None, None) + # examples: automatic interpolation + test_array_x = np.array([0.0, 0.2, 1.0]) + test_array_y = np.array([1.0, 0.4, 0.0]) + assert totest.linear_interpolation_between_two_cells(test_array_y,\ + test_array_x,\ + 0.1) == 0.7 + # examples: interpolation over multiple cells with verbose + assert totest.linear_interpolation_between_two_cells(test_array_y,\ + test_array_x,\ + 0.1, top=2,\ + bot=0,\ + verbose=True) ==\ + 0.9 + captured_out = capsys.readouterr().out + for out in ["linear interpolation",\ + "x_target, top, bot, len(array_x)",\ + "x_top, x_bot, y_top, y_bot, y_target"]: + assert out in captured_out + # examples: extrapolation + assert totest.linear_interpolation_between_two_cells(test_array_y,\ + test_array_x,\ + 0.1, top=2) ==\ + 0.45 + assert totest.linear_interpolation_between_two_cells(test_array_y,\ + test_array_x,\ + 0.1, bot=1) ==\ + 0.45 + with warns(ReplaceValueWarning, match="top=3 is too large, use last "\ + +"element in array_y"): + with warns(InterpolationWarning): + assert totest.linear_interpolation_between_two_cells(\ + test_array_y, test_array_x, 0.1, top=3) == 0.0 + with warns(ReplaceValueWarning, match="bot=-1 is too small, use "\ + +"first element"): + with warns(InterpolationWarning): + assert totest.linear_interpolation_between_two_cells(\ + test_array_y, test_array_x, 0.1, top=0) == 1.0 + with warns(InterpolationWarning, match="bot=2 is too large: use y at "\ + +"top=1"): + assert totest.linear_interpolation_between_two_cells(test_array_y,\ + test_array_x,\ + 0.1, top=1,\ + bot=2) == 0.4 + test_array_y = np.array([1.0, 0.4, 0.0, -1.0]) + with warns(InterpolationWarning, match="array_x too short, use y at "\ + +"top=3"): + assert totest.linear_interpolation_between_two_cells(test_array_y,\ + test_array_x,\ + 0.1, top=3)\ + == -1.0 + + def test_calculate_lambda_from_profile(self, star_profile, capsys): + # missing argument + with raises(TypeError, match="missing 2 required positional "\ + +"arguments: 'profile' and "\ + +"'donor_star_state'"): + totest.calculate_lambda_from_profile() + # bad input + with raises(AttributeError, match="'NoneType' object has no "\ + +"attribute 'dtype'"): + totest.calculate_lambda_from_profile(None, None) + with raises(ValueError, match="state test not supported in CEE"): + totest.calculate_lambda_from_profile(star_profile, "test",\ + ind_core=1) + with raises(ValueError, match="lambda_CE has a negative value"): + with warns(EvolutionWarning, match="Ebind_i of the envelope is "\ + +"positive"): + totest.calculate_lambda_from_profile(star_profile,\ + "H-rich_test", ind_core=1) + # examples: get He core in H rich star + lambda_CE, Mc, Rc =\ + totest.calculate_lambda_from_profile(star_profile, "test",\ + common_envelope_alpha_thermal\ + =0.1) + assert lambda_CE == approx(3.16722966582e+33, abs=6e+21) + assert Mc == approx(0.0, abs=6e-12) + assert Rc == approx(0.0, abs=6e-12) + lambda_CE, Mc, Rc =\ + totest.calculate_lambda_from_profile(star_profile, "test",\ + ind_core=0,\ + common_envelope_alpha_thermal\ + =0.1) + assert np.isnan(lambda_CE) + assert Mc == star_profile['mass'][0] + assert Rc == star_profile['radius'][0] + # examples: He core in H rich star + lambda_CE, Mc, Rc =\ + totest.calculate_lambda_from_profile(star_profile, "H-rich_test",\ + ind_core=2,\ + common_envelope_alpha_thermal\ + =0.1, verbose=True) + assert lambda_CE == approx(3.35757244514e+33, abs=6e+21) + assert Mc == 0.1 + assert Rc == 0.1 + captured_out = capsys.readouterr().out + for out in ["lambda_CE_top, lambda_CE_bot",\ + "m1_i, radius1, len(profile) vs ind_core, mc1_i, rc1_i",\ + "Ebind_i from profile ", "lambda_CE "]: + assert out in captured_out + # examples: CO core in H rich star + lambda_CE, Mc, Rc =\ + totest.calculate_lambda_from_profile(star_profile, "H-rich_test",\ + ind_core=2,\ + common_envelope_alpha_thermal\ + =0.1,\ + CO_core_in_Hrich_star=True) + assert lambda_CE == approx(4.25658010995e+32, abs=6e+20) + assert Mc == approx(1.03333333333, abs=6e-12) + assert Rc == approx(1.03333333333, abs=6e-12) + # examples: CO core in He rich star should be the same + assert totest.calculate_lambda_from_profile(star_profile,\ + "H-rich_test", ind_core=2,\ + common_envelope_alpha_thermal\ + =0.1,\ + CO_core_in_Hrich_star=True\ + ) == totest.calculate_lambda_from_profile(star_profile,\ + "stripped_He_test",\ + ind_core=2,\ + common_envelope_alpha_thermal\ + =0.1) + + def test_get_mass_radius_dm_from_profile(self, star_profile): + # missing argument + with raises(TypeError, match="missing 1 required positional "\ + +"argument: 'profile'"): + totest.get_mass_radius_dm_from_profile() + # bad input + with raises(AttributeError, match="'NoneType' object has no "\ + +"attribute 'dtype'"): + totest.get_mass_radius_dm_from_profile(None) + for f in star_profile.dtype.names: + with raises(ValueError, match="One or many of the mass and/or "\ + +"radius needed columns in the "\ + +"profile is not provided for the "\ + +"CEE"): + totest.get_mass_radius_dm_from_profile(star_profile[[f]]) + # examples: profile with radius + with warns(ClassificationWarning, match="Donor mass from the binary "\ + +"class object and the "\ + +"profile do not agree"): + with warns(ClassificationWarning, match="Donor radius from the "\ + +"binary class object "\ + +"and the profile do not "\ + +"agree"): + test_profile = star_profile[['mass', 'radius']] + d_mass, d_radius, d_dm =\ + totest.get_mass_radius_dm_from_profile(test_profile) + assert np.array_equal(d_mass, star_profile['mass']) + assert np.array_equal(d_radius, star_profile['radius']) + assert np.array_equal(d_dm, star_profile['dm']) + # get total mass and radius + M = star_profile['mass'].max() + R = star_profile['radius'].max() + # examples: profile with log_R + d_mass, d_radius, d_dm =\ + totest.get_mass_radius_dm_from_profile(star_profile[['mass',\ + 'log_R']],\ + m1_i = M, radius1 = R) + assert np.array_equal(d_mass, star_profile['mass']) + assert np.array_equal(d_radius, star_profile['radius']) + assert np.array_equal(d_dm, star_profile['dm']) + # examples: profile with radius and dm (in grams) + d_mass, d_radius, d_dm =\ + totest.get_mass_radius_dm_from_profile(star_profile[['mass', 'dm',\ + 'radius']],\ + m1_i = M, radius1 = R) + assert np.array_equal(d_mass, star_profile['mass']) + assert np.array_equal(d_radius, star_profile['radius']) + # convert Msun to grams + d_dm = d_dm * totest.const.Msun + assert np.array_equal(d_dm, star_profile['dm']) + + def test_get_internal_energy_from_profile(self, star_profile, monkeypatch): + def mock_calculate_H2recombination_energy(profile, tolerance=0.001): + return np.array(len(profile)*[1.0e+99]) + def mock_calculate_recombination_energy(profile, tolerance=0.001): + return np.array(len(profile)*[1.0e+99]) + # missing argument + with raises(TypeError, match="missing 2 required positional "\ + +"arguments: 'common_envelope_option_for"\ + +"_lambda' and 'profile'"): + totest.get_internal_energy_from_profile() + # bad input + with raises(AttributeError, match="'NoneType' object has no "\ + +"attribute 'dtype'"): + totest.get_internal_energy_from_profile(None, None) + with raises(ValueError, match="unsupported: common_envelope_option"\ + +"_for_lambda = test"): + totest.get_internal_energy_from_profile("test",\ + star_profile[['energy']]) + bad_profile = np.array([-1.0, -1.0, -1.0, -1.0],\ + dtype=([('energy', 'f8')])) + for CEOFL in ["lambda_from_profile_gravitational_plus_internal",\ + "lambda_from_profile_gravitational_plus_internal_minus"\ + +"_recombination"]: + with raises(ValueError, match="CEE problem calculating internal "\ + +"energy, giving negative values."): + totest.get_internal_energy_from_profile(CEOFL, bad_profile) + with monkeypatch.context() as m: + m.setattr(totest, "calculate_H2recombination_energy",\ + mock_calculate_H2recombination_energy) + m.setattr(totest, "calculate_recombination_energy",\ + mock_calculate_recombination_energy) + with raises(ValueError) as error_info: + totest.get_internal_energy_from_profile(CEOFL,\ + star_profile[[\ + 'energy']]) + assert error_info.value.args[0] == "CEE problem calculating "\ + + "recombination (and H2 "\ + + "recombination) energy, "\ + + "remaining internal "\ + + "energy giving negative "\ + + "values." + # examples: no internal energy + assert np.array_equal(np.array([0, 0, 0, 0]),\ + totest.get_internal_energy_from_profile(\ + "lambda_from_profile_gravitational",\ + star_profile[['radius']])) + with warns(ApproximationWarning, match="Profile does not include "\ + +"internal energy -- "\ + +"proceeding with 'lambda_from"\ + +"_profile_gravitational'"): + assert np.array_equal(np.array([0, 0, 0, 0]),\ + totest.get_internal_energy_from_profile("test",\ + star_profile[[\ + 'radius']])) + # examples: with internal energy + assert np.allclose(totest.get_internal_energy_from_profile(\ + "lambda_from_profile_gravitational_plus_internal",\ + star_profile), np.array([9.99850443e+15,\ + 4.99893173e+15,\ + 9.99786347e+14,\ + 9.99786347e+12])) + + def test_calculate_H2recombination_energy(self, star_profile): + # missing argument + with raises(TypeError, match="missing 1 required positional "\ + +"argument: 'profile'"): + totest.calculate_H2recombination_energy() + # bad input + with raises(AttributeError, match="'NoneType' object has no "\ + +"attribute 'dtype'"): + totest.calculate_H2recombination_energy(None) + with raises(ValueError, match="CEE problem calculating H2 "\ + +"recombination energy, giving "\ + +"negative values"): + bad_profile = np.array([-1.0, -1.0, -1.0, -1.0],\ + dtype=([('x_mass_fraction_H', 'f8')])) + totest.calculate_H2recombination_energy(bad_profile) + # examples: profile with Hydrogen mass fraction + assert np.allclose(np.array([1.49557421e+12, 1.06826729e+12,\ + 2.13653458e+11, 2.13653458e+09]),\ + totest.calculate_H2recombination_energy(\ + star_profile[['x_mass_fraction_H']])) + # examples: profile without Hydrogen mass fraction + with warns(ApproximationWarning, match="Profile does not include "\ + +"Hydrogen mass fraction "\ + +"calculate H2 recombination "\ + +"energy -- H2 recombination "\ + +"energy is assumed 0"): + assert np.array_equal(np.array([0, 0, 0, 0]),\ + totest.calculate_H2recombination_energy(\ + star_profile[['radius']])) + + def test_calculate_recombination_energy(self, star_profile): + # missing argument + with raises(TypeError, match="missing 1 required positional "\ + +"argument: 'profile'"): + totest.calculate_recombination_energy() + # bad input + with raises(AttributeError, match="'NoneType' object has no "\ + +"attribute 'dtype'"): + totest.calculate_recombination_energy(None) + with raises(ValueError, match="CEE problem calculating "\ + +"recombination energy, giving "\ + +"negative values"): + cols = ['x_mass_fraction_H', 'y_mass_fraction_He',\ + 'neutral_fraction_H', 'neutral_fraction_He',\ + 'avg_charge_He'] + bad_profile = np.empty((2,), dtype=[(f, 'f8') for f in cols]) + bad_profile['x_mass_fraction_H'] = np.array([-1.0, -1.0]) + bad_profile['y_mass_fraction_He'] = np.array([-1.0, -1.0]) + bad_profile['neutral_fraction_H'] = np.array([0.5, 0.5]) + bad_profile['neutral_fraction_He'] = np.array([0.5, 0.5]) + bad_profile['avg_charge_He'] = np.array([0.5, 0.5]) + totest.calculate_recombination_energy(bad_profile) + # examples: profile with mass fraction and ionization information + assert np.allclose(np.array([0.00000000e+00, 4.73639308e+12,\ + 7.53791976e+12, 3.29724895e+12]),\ + totest.calculate_recombination_energy(\ + star_profile[cols])) + # examples: profile without mass fraction and ionization information + with warns(ApproximationWarning, match="Profile does not include "\ + +"mass fractions and "\ + +"ionizations of elements to "\ + +"calculate recombination "\ + +"energy -- recombination "\ + +"energy is assumed 0"): + assert np.array_equal(np.array([0, 0, 0, 0]),\ + totest.calculate_recombination_energy(\ + star_profile[['radius']])) + + def test_profile_recomb_energy(self): + # missing argument + with raises(TypeError, match="missing 5 required positional "\ + +"arguments: 'x_mass_fraction_H', "\ + +"'y_mass_fraction_He', 'frac_HII', "\ + +"'frac_HeII', and 'frac_HeIII'"): + totest.profile_recomb_energy() + # bad input + with raises(TypeError) as error_info: + totest.profile_recomb_energy(None, None, None, None, None) + assert error_info.value.args[0] == "unsupported operand type(s) for "\ + + "*: 'float' and 'NoneType'" + # examples: + tests = [(0.5, 0.5, 0.5, 0.5, 0.5,\ + approx(9.49756867371e+12, abs=6e+0)),\ + (0.1, 0.5, 0.5, 0.5, 0.5,\ + approx(6.89384394360e+12, abs=6e+0)),\ + (0.5, 0.1, 0.5, 0.5, 0.5,\ + approx(4.50323846485e+12, abs=6e+0)),\ + (0.5, 0.5, 0.1, 0.5, 0.5,\ + approx(6.89384394360e+12, abs=6e+0)),\ + (0.5, 0.5, 0.5, 0.1, 0.5,\ + approx(8.31217893947e+12, abs=6e+0)),\ + (0.5, 0.5, 0.5, 0.5, 0.1,\ + approx(5.68862819909e+12, abs=6e+0))] + for (X, Y, fHII, fHeII, fHeIII, r) in tests: + assert totest.profile_recomb_energy(X, Y, fHII, fHeII, fHeIII) == r + + def test_calculate_binding_energy(self, star_profile, capsys): + # missing argument + with raises(TypeError, match="missing 7 required positional "\ + +"arguments: 'donor_mass', "\ + +"'donor_radius', 'donor_dm', "\ + +"'specific_internal_energy', "\ + +"'ind_core', "\ + +"'factor_internal_energy', and "\ + +"'verbose'"): + totest.calculate_binding_energy() + # bad input + with raises(TypeError, match="'NoneType' object cannot be "\ + +"interpreted as an integer"): + totest.calculate_binding_energy(None, None, None, None, None,\ + None, None) + with raises(ValueError, match="CEE problem calculating "\ + +"gravitational energy, giving "\ + +"positive values"): + totest.calculate_binding_energy(-1.0*star_profile['mass'],\ + star_profile['radius'],\ + star_profile['dm'],\ + star_profile['energy'], 1, 0.1,\ + False) + # examples: + with warns(EvolutionWarning, match="Ebind_i of the envelope is "\ + +"positive"): + assert totest.calculate_binding_energy(star_profile['mass'],\ + -1.0e+60\ + *star_profile['radius'],\ + star_profile['dm'],\ + star_profile['energy'], 1,\ + 0.5, False) == 4.973e+48 + assert totest.calculate_binding_energy(star_profile['mass'],\ + star_profile['radius'],\ + star_profile['dm'],\ + star_profile['energy'], 2,\ + 0.5, False) ==\ + approx(3.547071313426e+48, abs=6e+36) + assert totest.calculate_binding_energy(star_profile['mass'],\ + star_profile['radius'],\ + star_profile['dm'],\ + star_profile['energy'], 1, 0.1,\ + True) ==\ + approx(-9.02693714763e+47, abs=6e+35) + captured_out = capsys.readouterr().out + for out in ["integration of gravitational energy surface to core "\ + +"[Grav_energy], integration of internal energy surface "\ + +"to core [U_i] (0 if not taken into account)",\ + "Ebind = Grav_energy + factor_internal_energy*U_i : "]: + assert out in captured_out + + def test_calculate_Mejected_for_integrated_binding_energy(self,\ + star_profile): + # missing argument + with raises(TypeError, match="missing 4 required positional "\ + +"arguments: 'profile', "\ + +"'Ebind_threshold', 'mc1_i', and "\ + +"'rc1_i'"): + totest.calculate_Mejected_for_integrated_binding_energy() + # bad input + with raises(AttributeError, match="'NoneType' object has no "\ + +"attribute 'dtype'"): + totest.calculate_Mejected_for_integrated_binding_energy(None,\ + None,\ + None,\ + None) + # examples: + # get total mass and radius + M = star_profile['mass'].max() + R = star_profile['radius'].max() + assert totest.calculate_Mejected_for_integrated_binding_energy(\ + star_profile, 1.0, 1.0, 1.0, m1_i=M, radius1=R) == 0.0 + with warns(EvolutionWarning, match="partial mass ejected is greater "\ + +"than the envelope mass"): + assert totest.calculate_Mejected_for_integrated_binding_energy(\ + star_profile, 1.0e+50, 1.0, 1.0, m1_i=M, radius1=R) == 0.0 + + def test_convert_metallicity_to_string(self): + # missing argument + with raises(TypeError, match="missing 1 required positional "\ + +"argument: 'Z'"): + totest.convert_metallicity_to_string() + # bad input + with raises(ValueError, match="Metallicity None not supported! "\ + +"Available metallicities in POSYDON "\ + +"v2 are"): + totest.convert_metallicity_to_string(None) + # examples: + tests = [(2e+00, '2e+00'), (1e+00, '1e+00'), (4.5e-01, '4.5e-01'),\ + (2e-01, '2e-01'), (1e-01, '1e-01'), (1e-02, '1e-02'),\ + (1e-03, '1e-03'), (1e-04, '1e-04')] + for (Z, s) in tests: + assert totest.convert_metallicity_to_string(Z) == s + + def test_rotate(self): + # missing argument + with raises(TypeError, match="missing 2 required positional "\ + +"arguments: 'axis' and 'angle'"): + totest.rotate() + # bad input + with raises(TypeError, match="object of type 'NoneType' has no len()"): + totest.rotate(None, None) + with raises(ValueError, match="axis should be of dimension 3"): + totest.rotate(np.array([0]), 0.0) + with raises(ValueError, match="axis is a point"): + totest.rotate(np.array([0, 0, 0]), 0.0) + # examples: + for x in range(3): + for y in range(3): + for z in range(3): + if ((x!=0) or (y!=0) or (z!=0)): + # angle=0 -> no rotation + assert np.array_equal(totest.rotate(\ + np.array([x, y, z]), 0.0),\ + np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])) + # inverse rotation around inverted axis + assert np.array_equal(totest.rotate(\ + np.array([-x, -y, -z]), -1.0),\ + totest.rotate(np.array([x, y, z]), 1.0)) + # examples: angle=1.0 + s = np.sin(1.0) + c = np.cos(1.0) + assert np.array_equal(totest.rotate(np.array([1, 0, 0]), 1.0),\ + np.array([[1, 0, 0], [0, c, -s], [0, s, c]])) + assert np.array_equal(totest.rotate(np.array([0, 1, 0]), 1.0),\ + np.array([[c, 0, s], [0, 1, 0], [-s, 0, c]])) + assert np.array_equal(totest.rotate(np.array([0, 0, 1]), 1.0),\ + np.array([[c, -s, 0], [s, c, 0], [0, 0, 1]])) + assert np.allclose(totest.rotate(np.array([1, 1, 0]), 1.0),\ + np.array([[0.77015115, 0.22984885, 0.59500984],\ + [0.22984885, 0.77015115, -0.59500984],\ + [-0.59500984, 0.59500984, 0.54030231]])) + assert np.allclose(totest.rotate(np.array([1, 0, 1]), 1.0),\ + np.array([[0.77015115, -0.59500984, 0.22984885],\ + [0.59500984, 0.54030231, -0.59500984],\ + [0.22984885, 0.59500984, 0.77015115]])) + assert np.allclose(totest.rotate(np.array([0, 1, 1]), 1.0),\ + np.array([[0.54030231,-0.59500984, 0.59500984],\ + [0.59500984, 0.77015115, 0.22984885],\ + [-0.59500984, 0.22984885, 0.77015115]])) + + +class TestPchipInterpolator2: + @fixture + def PchipInterpolator2(self): + # initialize an instance of the class with defaults + return totest.PchipInterpolator2([0.0, 1.0], [1.0, 0.0]) + + @fixture + def PchipInterpolator2_True(self): + # initialize an instance of the class with defaults + return totest.PchipInterpolator2([0.0, 1.0], [-0.5, 0.5],\ + positive=True) + + @fixture + def PchipInterpolator2_False(self): + # initialize an instance of the class with defaults + return totest.PchipInterpolator2([0.0, 1.0], [-0.5, 0.5],\ + positive=False) + + # test the PchipInterpolator2 class + def test_init(self, PchipInterpolator2, PchipInterpolator2_True,\ + PchipInterpolator2_False): + assert isroutine(PchipInterpolator2.__init__) + # check that the instance is of correct type and all code in the + # __init__ got executed: the elements are created and initialized + assert isinstance(PchipInterpolator2, totest.PchipInterpolator2) + assert isinstance(PchipInterpolator2.interpolator,\ + totest.PchipInterpolator) + assert PchipInterpolator2.positive == False + assert PchipInterpolator2_True.positive == True + assert PchipInterpolator2_False.positive == False + + def test_call(self, PchipInterpolator2, PchipInterpolator2_True,\ + PchipInterpolator2_False): + assert isroutine(PchipInterpolator2.__call__) + assert PchipInterpolator2(0.1) == 0.9 + assert PchipInterpolator2_True(0.1) == 0.0 + assert PchipInterpolator2_False(0.1) == -0.4 + assert np.allclose(PchipInterpolator2([0.1, 0.8]),\ + np.array([0.9, 0.2])) + assert np.allclose(PchipInterpolator2_True([0.1, 0.8]),\ + np.array([0.0, 0.3])) + assert np.allclose(PchipInterpolator2_False([0.1, 0.8]),\ + np.array([-0.4, 0.3])) + diff --git a/posydon/unit_tests/utils/test_configfile.py b/posydon/unit_tests/utils/test_configfile.py new file mode 100644 index 0000000000..bf0541ac87 --- /dev/null +++ b/posydon/unit_tests/utils/test_configfile.py @@ -0,0 +1,302 @@ +"""Unit tests of posydon/utils/configfile.py +""" + +__authors__ = [ + "Matthias Kruckow " +] + +# import the module which will be tested +import posydon.utils.configfile as totest +# aliases +np = totest.np +os = totest.os + +# import other needed code for the tests, which is not already imported in the +# module you like to test +from pytest import fixture, raises +from inspect import isclass, isroutine +from ast import AST, parse + +# define test classes collecting several test functions +class TestElements: + # check for objects, which should be an element of the tested module + def test_dir(self): + elements = ['ConfigFile', 'VariableKey', '__authors__',\ + '__builtins__', '__cached__', '__doc__', '__file__',\ + '__loader__', '__name__', '__package__', '__spec__',\ + 'ast', 'configparser', 'copy', 'json', 'np', 'operator',\ + 'os', 'parse_inifile'] + assert dir(totest) == elements, "There might be added or removed "\ + + "objects without an update on the "\ + + "unit test." + + def test_instance_ConfigFile(self): + assert isclass(totest.ConfigFile) + + def test_instance_parse_inifile(self): + assert isroutine(totest.parse_inifile) + + def test_instance_VariableKey(self): + assert isclass(totest.VariableKey) + + +class TestFunctions: + @fixture + def ini_path(self, tmp_path): + # a temporary path to ini file for testing + path = os.path.join(tmp_path, "test.ini") + with open(path, "w") as test_file: + test_file.write("[run_parameters]\n") + test_file.write("test_bool1 = True\n") + test_file.write("test_bool2 = False\n") + test_file.write("test_None = None\n") + test_file.write("\n[mesa_inlists]\n") + test_file.write("test_float = 0.1\n") + test_file.write("test_int = 10\n") + test_file.write("test_list = ['Unit Test1', 'Unit Test2']\n") + test_file.write("test_str1 = 'Unit Test'\n") + test_file.write("test_str2 = 'Unit,Test'\n") + test_file.write("test_BinOp = ${test_int}+1\n") + test_file.write("\n[mesa_extras]\n") + test_file.write("test_else = print('1')\n") + test_file.write("\n[slurm]\n") + test_file.write("test_exception = ast.parse('X=1', mode='eval')\n") + return path + + # test functions + def test_parse_inifile(self, ini_path, monkeypatch): + def mock_parse(source, filename='', mode='exec', *,\ + type_comments=False, feature_version=None, optimize=-1): + mock_node = AST() + mock_node.body = parse(source, mode='eval') + return mock_node + # missing argument + with raises(TypeError, match="missing 1 required positional "\ + +"argument: 'inifile'"): + totest.parse_inifile() + # bad input + with raises(TypeError, match="'int' object is not iterable"): + totest.parse_inifile(1) + # read test ini + rp, s, mi, me = totest.parse_inifile(ini_path) + assert rp == {'MESA_DIR'.lower(): os.environ['MESA_DIR'],\ + 'test_bool1'.lower(): True, 'test_bool2'.lower(): False,\ + 'test_None'.lower(): None} + assert s == {'MESA_DIR'.lower(): os.environ['MESA_DIR'],\ + 'test_exception'.lower(): ["ast.parse('X=1'",\ + "mode='eval')"]} + assert mi == {'MESA_DIR'.lower(): os.environ['MESA_DIR'],\ + 'test_float'.lower(): 0.1, 'test_int'.lower(): 10,\ + 'test_list'.lower(): ['Unit Test1', 'Unit Test2'],\ + 'test_str1'.lower(): 'Unit Test',\ + 'test_str2'.lower(): ['Unit', 'Test'],\ + 'test_BinOp'.lower(): 11} + assert me == {'MESA_DIR'.lower(): os.environ['MESA_DIR'],\ + 'test_else'.lower(): "print('1')"} + with monkeypatch.context() as mp: + # replace parse to check recursion of _eval + mp.setattr(totest.ast, "parse", mock_parse) + # replace content of test.ini to check Error + with open(ini_path, "w") as test_file: + test_file.write("[Unit Test]\n") + test_file.write("test_value = 'Test'\n") + with raises(KeyError, match="'run_parameters'"): + totest.parse_inifile(ini_path) + + +class TestConfigFile: + @fixture + def ConfigFile(self): + # initialize an instance of the class with defaults + return totest.ConfigFile() + + @fixture + def json_path(self, tmp_path): + # a temporary path to json file for testing + path = os.path.join(tmp_path, "test.json") + with open(path, "w") as test_file: + test_file.write('{\n "Unit": "Test"\n}\n') + return path + + @fixture + def test_entries(self): + # entries for testing + return {'Unit1': "Test", 'Unit2': "Again"} + + @fixture + def ConfigFile_filled(self, test_entries): + # initialize an instance of the class with defaults + ConfigFile = totest.ConfigFile() + ConfigFile.update(test_entries) + return ConfigFile + + # test the ConfigFile class + def test_init(self, ConfigFile, tmp_path, json_path): + assert isroutine(ConfigFile.__init__) + # check that the instance is of correct type and all code in the + # __init__ got executed: the elements are created and initialized + assert isinstance(ConfigFile, totest.ConfigFile) + assert ConfigFile.entries == {} + assert ConfigFile.path is None + # error on directory + with raises(IsADirectoryError, match="Is a directory: '"\ + +str(tmp_path)+"'"): + totest.ConfigFile(path=tmp_path) + # non-existing path + test_path = os.path.join(tmp_path, "does_not_exist.test") + test_ConfigFile = totest.ConfigFile(test_path) + assert test_ConfigFile.path == test_path + test_ConfigFile = totest.ConfigFile(json_path) + assert test_ConfigFile.path == json_path + + def test_deepcopy(self, ConfigFile): + assert isroutine(ConfigFile.deepcopy) + ConfigFile.entries['Unit'] = "Test" + ConfigFile.entries['second'] = "test" + ConfigFile.path = "Test_path" + test_ConfigFile = ConfigFile.deepcopy() + # not same object + assert test_ConfigFile is not ConfigFile + # but same content + assert test_ConfigFile.entries == ConfigFile.entries + assert test_ConfigFile.path == ConfigFile.path + + def test_serialize(self, ConfigFile): + assert isroutine(ConfigFile._serialize) + # serialize any numpy array + np_array = np.array([2, 1, 0]) + assert ConfigFile._serialize(data=np_array) == [2, 1, 0] + np_array = np.array(["2", "1", "0"]) + assert ConfigFile._serialize(data=np_array) == ["2", "1", "0"] + # other data don't give a return + assert ConfigFile._serialize(data="Test") is None + + def test_save(self, ConfigFile, tmp_path): + assert isroutine(ConfigFile.save) + # bad input + with raises(ValueError, match="No path passed."): + ConfigFile.save() + with raises(PermissionError, match="JSON file not saved: overwrite "\ + +"not permitted."): + ConfigFile.save(path=tmp_path, overwrite=False) + test_path = os.path.join(tmp_path, "save.test") + ConfigFile.path = test_path + ConfigFile.entries['Unit'] = "Test" + ConfigFile.save() + assert os.path.isfile(test_path) + # overwrite file + ConfigFile.entries['second'] = "test" + ConfigFile.save(overwrite=True) + assert os.path.isfile(test_path) + with open(test_path, "r") as test_file: + assert test_file.read() == '{\n "Unit": "Test",\n'\ + + ' "second": "test"\n}' + # ignore ConfigFile.path + test_path2 = os.path.join(tmp_path, "save2.test") + ConfigFile.path = test_path2 + ConfigFile.save(path=test_path) + assert os.path.isfile(test_path2) == False + + def test_load(self, ConfigFile, json_path): + assert isroutine(ConfigFile.load) + # bad input + with raises(ValueError, match="No path passed."): + ConfigFile.load() + # load mock file with path as keyword + ConfigFile.load(path=json_path) + assert ConfigFile.entries == {'Unit': "Test"} + # set path and entries + ConfigFile.path = json_path + ConfigFile.entries['Unit'] = "ToReplace" + # try to load mock file without permission to overwrite + with raises(PermissionError,\ + match="Not allowed to update the entries"): + ConfigFile.load() + assert ConfigFile.entries == {'Unit': "ToReplace"} + # load mock file and overwrite entry + ConfigFile.load(can_update=True) + assert ConfigFile.entries == {'Unit': "Test"} + + def test_getattr(self, ConfigFile): + assert isroutine(ConfigFile.__getattr__) + # bad input + with raises(KeyError, match='Test'): + ConfigFile.Test + # add a value to entries and get it + ConfigFile.entries['Unit'] = "Test" + assert ConfigFile.Unit == "Test" + + def test_getitem(self, ConfigFile): + assert isroutine(ConfigFile.__getitem__) + # bad input + with raises(KeyError, match='Test'): + ConfigFile['Test'] + # add a value to entries and get it + ConfigFile.entries['Unit'] = "Test" + assert ConfigFile['Unit'] == "Test" + + def test_setitem(self, ConfigFile): + assert isroutine(ConfigFile.__setitem__) + # add a value to entries and get it + ConfigFile['Unit'] = "Test" + assert ConfigFile.entries['Unit'] == "Test" + + def test_delitem(self, ConfigFile_filled): + assert isroutine(ConfigFile_filled.__delitem__) + with raises(KeyError, match="'Unit'"): + del ConfigFile_filled['Unit'] + # delete entries + del ConfigFile_filled['Unit1'] + del ConfigFile_filled['Unit2'] + assert ConfigFile_filled.entries == {} + + def test_iter(self, ConfigFile_filled, test_entries): + assert isroutine(ConfigFile_filled.__iter__) + # loop over entries + iteration = 0 + for key in ConfigFile_filled: + iteration += 1 + assert ConfigFile_filled.entries[key] == test_entries[key] + # check that it was iterated over all keys in test_entries + assert iteration == len(test_entries) + + def test_update(self, ConfigFile, test_entries): + assert isroutine(ConfigFile.update) + ConfigFile.update(test_entries) + assert ConfigFile.entries == test_entries + + def test_keys(self, ConfigFile_filled, test_entries): + assert isroutine(ConfigFile_filled.keys) + assert ConfigFile_filled.keys() == test_entries.keys() + + def test_values(self, ConfigFile_filled, test_entries): + assert isroutine(ConfigFile_filled.values) + assert isinstance(ConfigFile_filled.values(),\ + type(test_entries.values())) + iteration = 0 + for v in ConfigFile_filled.values(): + iteration += 1 + assert v in test_entries.values() + # check that it was iterated over all values in test_entries + assert iteration == len(test_entries.values()) + + def test_items(self, ConfigFile_filled, test_entries): + assert isroutine(ConfigFile_filled.items) + assert ConfigFile_filled.items() == test_entries.items() + + def test_repr(self, ConfigFile_filled): + assert isroutine(ConfigFile_filled.__repr__) + assert repr(ConfigFile_filled) == "Unit1: Test\nUnit2: Again\n" + + def test_contains(self, ConfigFile_filled, test_entries): + assert isroutine(ConfigFile_filled.__contains__) + for key in test_entries: + assert key in ConfigFile_filled + + def test_len(self, ConfigFile_filled, test_entries): + assert isroutine(ConfigFile_filled.__len__) + assert len(ConfigFile_filled) == len(test_entries) + + +#class TestVariableKey: +# # test the VariableKey class: looks to be not used as long as using python3 diff --git a/posydon/unit_tests/utils/test_constants.py b/posydon/unit_tests/utils/test_constants.py new file mode 100644 index 0000000000..084c60453c --- /dev/null +++ b/posydon/unit_tests/utils/test_constants.py @@ -0,0 +1,493 @@ +"""Unit tests of posydon/utils/constants.py +""" + +__authors__ = [ + "Matthias Kruckow " +] + +# import the module which will be tested +import posydon.utils.constants as totest + +# import other needed code for the tests, which is not already imported in the +# module you like to test +from pytest import approx + +# define test classes collecting several test functions +class TestElements: + # check for objects, which should be an element of the tested module + def test_dir(self): + elements = ['H_weight', 'He_weight', 'Lsun', 'Lsun33', 'Msun',\ + 'Msun33', 'Qconv', 'Rsun', 'Rsun11', 'SNcheck_ERR',\ + 'Teffsol', 'Zsun', '__authors__', '__builtins__',\ + '__cached__', '__doc__', '__file__', '__loader__',\ + '__name__', '__package__', '__spec__', 'a2rad',\ + 'age_of_universe', 'agesol', 'amu', 'asol', 'au',\ + 'aursun', 'avo', 'boltz_sigma', 'boltzm', 'cgas',\ + 'clight', 'crad', 'day2sec', 'dayyer', 'ev2erg', 'fine',\ + 'hbar', 'hion', 'inversecm2erg', 'kerg', 'kev', 'km2cm',\ + 'loggsol', 'lsol', 'ly', 'm_earth', 'm_jupiter',\ + 'mbolsol', 'mbolsun', 'me', 'mev_amu', 'mev_to_ergs',\ + 'mn', 'mp', 'msol', 'pc', 'pi', 'planck_h', 'qe',\ + 'r_earth', 'r_jupiter', 'rad2a', 'rbohr', 'rhonuc',\ + 'rsol', 'secyer', 'semimajor_axis_jupiter', 'ssol',\ + 'standard_cgrav', 'weinfre', 'weinlam'] + assert dir(totest) == elements, "There might be added or removed "\ + + "objects without an update on the "\ + + "unit test." + + def test_instance_pi(self): + assert isinstance(totest.pi, (float,int)),\ + "pi is of type: " + str(type(totest.pi)) + + def test_instance_a2rad(self): + assert isinstance(totest.a2rad, (float,int)),\ + "a2rad is of type: " + str(type(totest.a2rad)) + + def test_instance_rad2a(self): + assert isinstance(totest.rad2a, (float,int)),\ + "rad2a is of type: " + str(type(totest.rad2a)) + + def test_instance_standard_cgrav(self): + assert isinstance(totest.standard_cgrav, (float,int)),\ + "standard_cgrav is of type: "\ + + str(type(totest.standard_cgrav)) + + def test_instance_planck_h(self): + assert isinstance(totest.planck_h, (float,int)),\ + "planck_h is of type: " + str(type(totest.planck_h)) + + def test_instance_hbar(self): + assert isinstance(totest.hbar, (float,int)),\ + "hbar is of type: " + str(type(totest.hbar)) + + def test_instance_qe(self): + assert isinstance(totest.qe, (float,int)),\ + "qe is of type: " + str(type(totest.qe)) + + def test_instance_avo(self): + assert isinstance(totest.avo, (float,int)),\ + "avo is of type: " + str(type(totest.avo)) + + def test_instance_clight(self): + assert isinstance(totest.clight, (float,int)),\ + "clight is of type: " + str(type(totest.clight)) + + def test_instance_kerg(self): + assert isinstance(totest.kerg, (float,int)),\ + "kerg is of type: " + str(type(totest.kerg)) + + def test_instance_boltzm(self): + assert isinstance(totest.boltzm, (float,int)),\ + "boltzm is of type: " + str(type(totest.boltzm)) + + def test_instance_cgas(self): + assert isinstance(totest.cgas, (float,int)),\ + "cgas is of type: " + str(type(totest.cgas)) + + def test_instance_kev(self): + assert isinstance(totest.kev, (float,int)),\ + "kev is of type: " + str(type(totest.kev)) + + def test_instance_amu(self): + assert isinstance(totest.amu, (float,int)),\ + "amu is of type: " + str(type(totest.amu)) + + def test_instance_mn(self): + assert isinstance(totest.mn, (float,int)),\ + "mn is of type: " + str(type(totest.mn)) + + def test_instance_mp(self): + assert isinstance(totest.mp, (float,int)),\ + "mp is of type: " + str(type(totest.mp)) + + def test_instance_me(self): + assert isinstance(totest.me, (float,int)),\ + "me is of type: " + str(type(totest.me)) + + def test_instance_rbohr(self): + assert isinstance(totest.rbohr, (float,int)),\ + "rbohr is of type: " + str(type(totest.rbohr)) + + def test_instance_fine(self): + assert isinstance(totest.fine, (float,int)),\ + "fine is of type: " + str(type(totest.fine)) + + def test_instance_hion(self): + assert isinstance(totest.hion, (float,int)),\ + "hion is of type: " + str(type(totest.hion)) + + def test_instance_ev2erg(self): + assert isinstance(totest.ev2erg, (float,int)),\ + "ev2erg is of type: " + str(type(totest.ev2erg)) + + def test_instance_inversecm2erg(self): + assert isinstance(totest.inversecm2erg, (float,int)),\ + "inversecm2erg is of type: " + str(type(totest.inversecm2erg)) + + def test_instance_mev_to_ergs(self): + assert isinstance(totest.mev_to_ergs, (float,int)),\ + "mev_to_ergs is of type: " + str(type(totest.mev_to_ergs)) + + def test_instance_mev_amu(self): + assert isinstance(totest.mev_amu, (float,int)),\ + "mev_amu is of type: " + str(type(totest.mev_amu)) + + def test_instance_Qconv(self): + assert isinstance(totest.Qconv, (float,int)),\ + "Qconv is of type: " + str(type(totest.Qconv)) + + def test_instance_boltz_sigma(self): + assert isinstance(totest.boltz_sigma, (float,int)),\ + "boltz_sigma is of type: " + str(type(totest.boltz_sigma)) + + def test_instance_crad(self): + assert isinstance(totest.crad, (float,int)),\ + "crad is of type: " + str(type(totest.crad)) + + def test_instance_ssol(self): + assert isinstance(totest.ssol, (float,int)),\ + "ssol is of type: " + str(type(totest.ssol)) + + def test_instance_asol(self): + assert isinstance(totest.asol, (float,int)),\ + "asol is of type: " + str(type(totest.asol)) + + def test_instance_weinlam(self): + assert isinstance(totest.weinlam, (float,int)),\ + "weinlam is of type: " + str(type(totest.weinlam)) + + def test_instance_weinfre(self): + assert isinstance(totest.weinfre, (float,int)),\ + "weinfre is of type: " + str(type(totest.weinfre)) + + def test_instance_rhonuc(self): + assert isinstance(totest.rhonuc, (float,int)),\ + "rhonuc is of type: " + str(type(totest.rhonuc)) + + def test_instance_Zsun(self): + assert isinstance(totest.Zsun, (float,int)),\ + "Zsun is of type: " + str(type(totest.Zsun)) + + def test_instance_msol(self): + assert isinstance(totest.msol, (float,int)),\ + "msol is of type: " + str(type(totest.msol)) + + def test_instance_rsol(self): + assert isinstance(totest.rsol, (float,int)),\ + "rsol is of type: " + str(type(totest.rsol)) + + def test_instance_lsol(self): + assert isinstance(totest.lsol, (float,int)),\ + "lsol is of type: " + str(type(totest.lsol)) + + def test_instance_agesol(self): + assert isinstance(totest.agesol, (float,int)),\ + "agesol is of type: " + str(type(totest.agesol)) + + def test_instance_Msun(self): + assert isinstance(totest.Msun, (float,int)),\ + "Msun is of type: " + str(type(totest.Msun)) + + def test_instance_Rsun(self): + assert isinstance(totest.Rsun, (float,int)),\ + "Rsun is of type: " + str(type(totest.Rsun)) + + def test_instance_Lsun(self): + assert isinstance(totest.Lsun, (float,int)),\ + "Lsun is of type: " + str(type(totest.Lsun)) + + def test_instance_Msun33(self): + assert isinstance(totest.Msun33, (float,int)),\ + "Msun33 is of type: " + str(type(totest.Msun33)) + + def test_instance_Rsun11(self): + assert isinstance(totest.Rsun11, (float,int)),\ + "Rsun11 is of type: " + str(type(totest.Rsun11)) + + def test_instance_Lsun33(self): + assert isinstance(totest.Lsun33, (float,int)),\ + "Lsun33 is of type: " + str(type(totest.Lsun33)) + + def test_instance_ly(self): + assert isinstance(totest.ly, (float,int)),\ + "ly is of type: " + str(type(totest.ly)) + + def test_instance_pc(self): + assert isinstance(totest.pc, (float,int)),\ + "pc is of type: " + str(type(totest.pc)) + + def test_instance_secyer(self): + assert isinstance(totest.secyer, (float,int)),\ + "secyer is of type: " + str(type(totest.secyer)) + + def test_instance_dayyer(self): + assert isinstance(totest.dayyer, (float,int)),\ + "dayyer is of type: " + str(type(totest.dayyer)) + + def test_instance_age_of_universe(self): + assert isinstance(totest.age_of_universe, (float,int)),\ + "age_of_universe is of type: "\ + + str(type(totest.age_of_universe)) + + def test_instance_Teffsol(self): + assert isinstance(totest.Teffsol, (float,int)),\ + "Teffsol is of type: " + str(type(totest.Teffsol)) + + def test_instance_loggsol(self): + assert isinstance(totest.loggsol, (float,int)),\ + "loggsol is of type: " + str(type(totest.loggsol)) + + def test_instance_mbolsun(self): + assert isinstance(totest.mbolsun, (float,int)),\ + "mbolsun is of type: " + str(type(totest.mbolsun)) + + def test_instance_mbolsol(self): + assert isinstance(totest.mbolsol, (float,int)),\ + "mbolsol is of type: " + str(type(totest.mbolsol)) + + def test_instance_m_earth(self): + assert isinstance(totest.m_earth, (float,int)),\ + "m_earth is of type: " + str(type(totest.m_earth)) + + def test_instance_r_earth(self): + assert isinstance(totest.r_earth, (float,int)),\ + "r_earth is of type: " + str(type(totest.r_earth)) + + def test_instance_au(self): + assert isinstance(totest.au, (float,int)),\ + "au is of type: " + str(type(totest.au)) + + def test_instance_aursun(self): + assert isinstance(totest.aursun, (float,int)),\ + "aursun is of type: " + str(type(totest.aursun)) + + def test_instance_m_jupiter(self): + assert isinstance(totest.m_jupiter, (float,int)),\ + "m_jupiter is of type: " + str(type(totest.m_jupiter)) + + def test_instance_r_jupiter(self): + assert isinstance(totest.r_jupiter, (float,int)),\ + "r_jupiter is of type: " + str(type(totest.r_jupiter)) + + def test_instance_semimajor_axis_jupiter(self): + assert isinstance(totest.semimajor_axis_jupiter, (float,int)),\ + "semimajor_axis_jupiter is of type: "\ + + str(type(totest.semimajor_axis_jupiter)) + + def test_instance_km2cm(self): + assert isinstance(totest.km2cm, (float,int)),\ + "km2cm is of type: " + str(type(totest.km2cm)) + + def test_instance_day2sec(self): + assert isinstance(totest.day2sec, (float,int)),\ + "day2sec is of type: " + str(type(totest.day2sec)) + + def test_instance_H_weight(self): + assert isinstance(totest.H_weight, (float,int)),\ + "H_weight is of type: " + str(type(totest.H_weight)) + + def test_instance_He_weight(self): + assert isinstance(totest.He_weight, (float,int)),\ + "He_weight is of type: " + str(type(totest.He_weight)) + + def test_instance_SNcheck_ERR(self): + assert isinstance(totest.SNcheck_ERR, (float,int)),\ + "SNcheck_ERR is of type: " + str(type(totest.SNcheck_ERR)) + + +class TestValues: + # check that the values fit + # use delta of last digit times 0.6 + def test_value_pi(self): + assert 3.1415926535897932384626433832795028841971693993751 ==\ + approx(totest.pi, abs=6e-50) + + def test_value_a2rad(self): + assert 1.7453292519943295e-2 == approx(totest.a2rad, abs=6e-19) + + def test_value_rad2a(self): + assert 5.729577951308232e+1 == approx(totest.rad2a, abs=6e-15) + + def test_value_standard_cgrav(self): + assert 6.67428e-8 == approx(totest.standard_cgrav, abs=6e-14) + + def test_value_planck_h(self): + assert 6.62606896e-27 == approx(totest.planck_h, abs=6e-36) + + def test_value_hbar(self): + assert 1.05457163e-27 == approx(totest.hbar, abs=6e-36) + + def test_value_qe(self): + assert 4.80320440e-10 == approx(totest.qe, abs=6e-19) + + def test_value_avo(self): + assert 6.02214129e+23 == approx(totest.avo, abs=6e+14) + + def test_value_clight(self): + assert 2.99792458e+10 == approx(totest.clight, abs=6e+1) + + def test_value_kerg(self): + assert 1.3806504e-16 == approx(totest.kerg, abs=6e-24) + + def test_value_boltzm(self): + assert 1.3806504e-16 == approx(totest.kerg, abs=6e-24) + + def test_value_cgas(self): + assert 8.314471780895016e+7 == approx(totest.cgas, abs=6e-1) + + def test_value_kev(self): + assert 8.617385e-5 == approx(totest.kev, abs=6e-12) + + def test_value_amu(self): + assert 1.6605389210321898e-24 == approx(totest.amu, abs=6e-33) + + def test_value_mn(self): + assert 1.6749286e-24 == approx(totest.mn, abs=6e-32) + + def test_value_mp(self): + assert 1.6726231e-24 == approx(totest.mp, abs=6e-32) + + def test_value_me(self): + assert 9.1093826e-28 == approx(totest.me, abs=6e-36) + + def test_value_rbohr(self): + assert 5.291771539809704e-9 == approx(totest.rbohr, abs=6e-18) + + def test_value_fine(self): + assert 7.297352926107705e-3 == approx(totest.fine, abs=6e-11) + + def test_value_hion(self): + assert 1.3605698140e+1 == approx(totest.hion, abs=6e-10) + + def test_value_ev2erg(self): + assert 1.602176565e-12 == approx(totest.ev2erg, abs=6e-22) + + def test_value_inversecm2erg(self): + assert 1.9864455003959037e-16 ==\ + approx(totest.inversecm2erg, abs=6e-25) + + def test_value_mev_to_ergs(self): + assert 1.6021765649999999e-6 == approx(totest.mev_to_ergs, abs=6e-16) + + def test_value_mev_amu(self): + assert 9.648533645956869e+17 == approx(totest.mev_amu, abs=6e+8) + + def test_value_Qconv(self): + assert 9.648533645956868e+17 == approx(totest.Qconv, abs=6e+8) + + def test_value_boltz_sigma(self): + assert 5.670373e-5 == approx(totest.boltz_sigma, abs=6e-12) + + def test_value_crad(self): + assert 7.565731356724124e-15 == approx(totest.crad, abs=6e-22) + + def test_value_ssol(self): + assert 5.670373e-5 == approx(totest.ssol, abs=6e-12) + + def test_value_asol(self): + assert 7.565731356724124e-15 == approx(totest.asol, abs=6e-22) + + def test_value_weinlam(self): + assert 2.897768496231288e-1 == approx(totest.weinlam, abs=6e-9) + + def test_value_weinfre(self): + assert 5.878932774535368e+10 == approx(totest.weinfre, abs=6e+2) + + def test_value_rhonuc(self): + assert 2.342e+14 == approx(totest.rhonuc, abs=6e+10) + + def test_value_Zsun(self): + assert 1.42e-2 == approx(totest.Zsun, abs=6e-5) + + def test_value_msol(self): + assert 1.9892e+33 == approx(totest.msol, abs=6e+28) + + def test_value_rsol(self): + assert 6.9598e+10 == approx(totest.rsol, abs=6e+5) + + def test_value_lsol(self): + assert 3.8418e+33 == approx(totest.lsol, abs=6e+28) + + def test_value_agesol(self): + assert 4.57e+9 == approx(totest.agesol, abs=6e+6) + + def test_value_Msun(self): + assert 1.9892e+33 == approx(totest.Msun, abs=6e+28) + + def test_value_Rsun(self): + assert 6.9598e+10 == approx(totest.Rsun, abs=6e+5) + + def test_value_Lsun(self): + assert 3.8418e+33 == approx(totest.Lsun, abs=6e+28) + + def test_value_Msun33(self): + assert 1.9892 == approx(totest.Msun33, abs=6e-5) + + def test_value_Rsun11(self): + assert 6.9598e-1 == approx(totest.Rsun11, abs=6e-6) + + def test_value_Lsun33(self): + assert 3.8418 == approx(totest.Lsun33, abs=6e-5) + + def test_value_ly(self): + assert 9.460528e+17 == approx(totest.ly, abs=6e+10) + + def test_value_pc(self): + assert 3.0856770322224e+18 == approx(totest.pc, abs=6e+11) + + def test_value_secyer(self): + assert 3.1558149984e+7 == approx(totest.secyer, abs=6e-4) + + def test_value_dayyer(self): + assert 3.6525e+2 == approx(totest.dayyer, abs=6e-3) + + def test_value_age_of_universe(self): + assert 1.38e+10 == approx(totest.age_of_universe, abs=6e+7) + + def test_value_Teffsol(self): + assert 5.7770e+3 == approx(totest.Teffsol, abs=6e-2) + + def test_value_loggsol(self): + assert 4.4378893534131256 == approx(totest.loggsol, abs=6e-17) + + def test_value_mbolsun(self): + assert 4.746 == approx(totest.mbolsun, abs=6e-4) + + def test_value_mbolsol(self): + assert 4.746 == approx(totest.mbolsol, abs=6e-4) + + def test_value_m_earth(self): + assert 5.9764e+27 == approx(totest.m_earth, abs=6e+22) + + def test_value_r_earth(self): + assert 6.37e+8 == approx(totest.r_earth, abs=6e+5) + + def test_value_au(self): + assert 1.495978921e+13 == approx(totest.au, abs=6e+3) + + def test_value_aursun(self): + assert 2.1495e+2 == approx(totest.aursun, abs=6e-3) + + def test_value_m_jupiter(self): + assert 1.8986e+30 == approx(totest.m_jupiter, abs=6e+25) + + def test_value_r_jupiter(self): + assert 6.9911e+9 == approx(totest.r_jupiter, abs=6e+4) + + def test_value_semimajor_axis_jupiter(self): + assert 7.7857e+13 == approx(totest.semimajor_axis_jupiter, abs=6e+8) + + def test_value_km2cm(self): + assert 1.0e5 == approx(totest.km2cm, abs=6e-16) + + def test_value_day2sec(self): + assert 8.64000e+4 == approx(totest.day2sec, abs=6e-2) + + def test_value_H_weight(self): + assert 1.00782503207 == approx(totest.H_weight, abs=6e-12) + + def test_value_He_weight(self): + assert 4.002603254131 == approx(totest.He_weight, abs=6e-13) + + def test_value_SNcheck_ERR(self): + assert 1e-10 == approx(totest.SNcheck_ERR, abs=6e-31) diff --git a/posydon/unit_tests/utils/test_data_download.py b/posydon/unit_tests/utils/test_data_download.py new file mode 100644 index 0000000000..ba6c1d99d6 --- /dev/null +++ b/posydon/unit_tests/utils/test_data_download.py @@ -0,0 +1,238 @@ +"""Unit tests of posydon/utils/data_download.py +""" + +__authors__ = [ + "Matthias Kruckow " +] + +# import the module which will be tested +import posydon.utils.data_download as totest +os = totest.os + +# import other needed code for the tests, which is not already imported in the +# module you like to test +from pytest import fixture, raises, approx +from inspect import isclass, isroutine +from shutil import rmtree +from contextlib import chdir +from unittest.mock import patch + +# define test classes collecting several test functions +class TestElements: + # check for objects, which should be an element of the tested module + def test_dir(self): + elements = ['PATH_TO_POSYDON_DATA', 'ProgressBar', '__authors__',\ + '__builtins__', '__cached__', '__doc__', '__file__',\ + '__loader__', '__name__', '__package__', '__spec__',\ + 'data_download', 'data_url', 'file', 'hashlib',\ + 'original_md5', 'os', 'progressbar', 'tarfile', 'tqdm',\ + 'urllib'] + assert dir(totest) == elements, "There might be added or removed "\ + + "objects without an update on the "\ + + "unit test." + + def test_instance_PATH_TO_POSYDON_DATA(self): + assert isinstance(totest.PATH_TO_POSYDON_DATA, (str, bytes,\ + os.PathLike)) + + def test_instance_file(self): + assert isinstance(totest.file, (str, bytes, os.PathLike)) + + def test_instance_data_url(self): + assert isinstance(totest.data_url, (str, bytes, os.PathLike)) + + def test_instance_original_md5(self): + assert isinstance(totest.original_md5, (str, bytes, os.PathLike)) + + def test_instance_ProgressBar(self): + assert isclass(totest.ProgressBar) + + def test_instance_data_download(self): + assert isroutine(totest.data_download) + + +class TestValues: + # check that the values fit + def test_value_PATH_TO_POSYDON_DATA(self): + assert "POSYDON_data" in totest.PATH_TO_POSYDON_DATA + + def test_value_file(self): + assert "POSYDON_data.tar.gz" in totest.file + + def test_value_data_url(self): + assert totest.data_url == "https://zenodo.org/record/6655751/files/"\ + + "POSYDON_data.tar.gz" + + def test_value_original_md5(self): + assert totest.original_md5 == "8873544d9a568ebb85bccffbf1bdcd99" + + +class TestFunctions: + @fixture + def test_path(self, tmp_path): + # a temporary path to POSYDON_data for testing + return os.path.join(tmp_path, "POSYDON_data") + + @fixture + def download_statement(self): + # statement that the download started + return "Downloading POSYDON data from Zenodo to PATH_TO_POSYDON_DATA=" + + @fixture + def failed_MD5_statement(self): + # statement that MD5 verfication failed + return "Failed to read the tar.gz file for MD5 verificaton" + + @fixture + def extraction_statement(self): + # statement that the tar extraction started + return "Extracting POSYDON data from tar file..." + + @fixture + def removal_statement(self): + # statement that the tar file gets removed + return "Removed downloaded tar file." + + # test functions + def test_data_download(self, capsys, monkeypatch, test_path,\ + download_statement, failed_MD5_statement,\ + extraction_statement, removal_statement): + def mock_urlretrieve(url, filename=None, reporthook=None, data=None): + return None + class mock_TarFile: + def getmembers(self): + return [] + def extract(self, member, path='', set_attrs=True, *,\ + numeric_owner=False, filter=None): + return + class mock_open: + def __init__(self, name=None, mode='r', fileobj=None,\ + bufsize=10240, **kwargs): + pass + def __enter__(self): + return mock_TarFile() + def __exit__(self, exc_type, exc_value, exc_traceback): + return False + def mock_urlretrieve2(url, filename=None, reporthook=None, data=None): + os.mkdir(test_path) + if (isinstance(filename, str) and (len(filename)>=8)): + test_ID = filename[-8] + else: + test_ID = "" + with open(os.path.join(test_path, "test"+test_ID+".txt"), "w")\ + as test_file: + test_file.write("Unit Test\n") + with chdir(os.path.join(test_path,"..")): + os.system("tar -czf POSYDON_data"+test_ID\ + +".tar.gz POSYDON_data") + rmtree(test_path) + return None + + # bad input + with raises(TypeError, match="path should be string, bytes, "\ + +"os.PathLike or integer"): + totest.data_download(file={}) + totest.data_download(file="./") + assert capsys.readouterr().out == "" + totest.data_download(file="./", verbose=True) + assert capsys.readouterr().out == "POSYDON data alraedy exists at ./\n" + # skip real download: do nothing instead + with monkeypatch.context() as mp: + mp.setattr(totest.urllib.request, "urlretrieve", mock_urlretrieve) + mp.setattr(totest.tarfile, "open", mock_open) + # mocked download: fails MD5check, no tar file to remove + totest.data_download(file=test_path+".tar.gz", verbose=True) + captured_output = capsys.readouterr() + assert download_statement in captured_output.out + assert failed_MD5_statement in captured_output.out + assert extraction_statement in captured_output.out + assert removal_statement not in captured_output.out + # without MD5 check + totest.data_download(file=test_path+".tar.gz", MD5_check=False) + captured_output = capsys.readouterr() + assert download_statement in captured_output.out + assert failed_MD5_statement not in captured_output.out + assert extraction_statement in captured_output.out + assert removal_statement not in captured_output.out + # skip real download: create mock file instead + with monkeypatch.context() as mp: + mp.setattr(totest.urllib.request, "urlretrieve", mock_urlretrieve2) + # mocked download: fails MD5check, which removes tar file and + # causes a FileNotFoundError + with raises(FileNotFoundError, match="No such file or directory:"): + totest.data_download(file=test_path+"0.tar.gz", verbose=True) + captured_output = capsys.readouterr() + assert download_statement in captured_output.out + assert failed_MD5_statement in captured_output.out + assert extraction_statement in captured_output.out + assert removal_statement not in captured_output.out + # return expected hash for testfile + with patch("hashlib.md5") as p_md5: + p_md5.return_value.hexdigest.return_value = totest.original_md5 + totest.data_download(file=test_path+"1.tar.gz") + captured_output = capsys.readouterr() + assert download_statement in captured_output.out + assert failed_MD5_statement not in captured_output.out + assert extraction_statement in captured_output.out + assert removal_statement not in captured_output.out + assert os.path.exists(test_path) + assert os.path.exists(os.path.join(test_path, "test1.txt")) + rmtree(test_path) # removed extracted data for next test + # with verification output + totest.data_download(file=test_path+"2.tar.gz", verbose=True) + captured_output = capsys.readouterr() + assert download_statement in captured_output.out + assert "MD5 verified" in captured_output.out + assert extraction_statement in captured_output.out + assert removal_statement in captured_output.out + assert os.path.exists(test_path) + assert os.path.exists(os.path.join(test_path, "test2.txt")) +# rmtree(test_path) # removed extracted data for next test + + +class TestProgressBar: + @fixture + def ProgressBar(self): + # initialize an instance of the class with defaults + return totest.ProgressBar() + + # test the ProgressBar class + def test_init(self, ProgressBar): + assert isroutine(ProgressBar.__init__) + # check that the instance is of correct type and all code in the + # __init__ got executed: the elements are created and initialized + assert isinstance(ProgressBar, totest.ProgressBar) + assert ProgressBar.pbar is None + assert isinstance(ProgressBar.widgets, list) + + def test_call(self, ProgressBar): + assert isroutine(ProgressBar.__call__) + # missing argument + with raises(TypeError, match="missing 3 required positional "\ + +"arguments: 'block_num', 'block_size', "\ + +"and 'total_size'"): + ProgressBar() + # bad input + with raises(TypeError, match="'<' not supported between instances of "\ + +"'str' and 'int'"): + ProgressBar("Test", 1, 1) + # the progressbar starts before the error + # hence, tearDown for pbar needed + ProgressBar.pbar = None + with raises(TypeError, match="'<' not supported between instances of "\ + +"'str' and 'int'"): + ProgressBar(1, "Test", 1) + # the progressbar starts before the error + # hence, tearDown for pbar needed + ProgressBar.pbar = None + with raises(TypeError, match="'>' not supported between instances of "\ + +"'int' and 'str'"): + ProgressBar(1, 1, "Test") + # the progressbar starts before the error + # hence, tearDown for pbar needed + ProgressBar.pbar = None + for i in range(9): + ProgressBar(i, 1, 8) + assert ProgressBar.pbar.percentage == approx(i*12.5) + ProgressBar(9, 1, 8) + assert ProgressBar.pbar.percentage == approx(100.0) diff --git a/posydon/unit_tests/utils/test_gridutils.py b/posydon/unit_tests/utils/test_gridutils.py new file mode 100644 index 0000000000..7dd0107250 --- /dev/null +++ b/posydon/unit_tests/utils/test_gridutils.py @@ -0,0 +1,781 @@ +"""Unit tests of posydon/utils/gridutils.py +""" + +__authors__ = [ + "Matthias Kruckow " +] + +# import the module which will be tested +import posydon.utils.gridutils as totest +# aliases +np = totest.np +os = totest.os +pd = totest.pd + +# import other needed code for the tests, which is not already imported in the +# module you like to test +from pytest import fixture, raises, warns, approx +from inspect import isclass, isroutine +from posydon.utils.posydonwarning import MissingFilesWarning + +# define test classes collecting several test functions +class TestElements: + # check for objects, which should be an element of the tested module + def test_dir(self): + elements = ['LG_MTRANSFER_RATE_THRESHOLD', 'Msun', 'Pwarn', 'Rsun',\ + 'T_merger_P', 'T_merger_a', '__authors__', '__builtins__',\ + '__cached__', '__doc__', '__file__', '__loader__',\ + '__name__', '__package__', '__spec__', 'add_field',\ + 'beta_gw', 'cgrav', 'clean_inlist_file', 'clight',\ + 'convert_output_to_table', 'find_index_nearest_neighbour',\ + 'find_nearest', 'fix_He_core', 'get_cell_edges',\ + 'get_final_proposed_points', 'get_new_grid_name', 'gzip',\ + 'join_lists', 'kepler3_a', 'np', 'os', 'pd',\ + 'read_EEP_data_file', 'read_MESA_data_file', 'secyear'] + assert dir(totest) == elements, "There might be added or removed "\ + + "objects without an update on the "\ + + "unit test." + + def test_instance_join_lists(self): + assert isroutine(totest.join_lists) + + def test_instance_read_MESA_data_file(self): + assert isroutine(totest.read_MESA_data_file) + + def test_instance_read_EEP_data_file(self): + assert isroutine(totest.read_EEP_data_file) + + def test_instance_fix_He_core(self): + assert isroutine(totest.fix_He_core) + + def test_instance_add_field(self): + assert isroutine(totest.add_field) + + def test_instance_get_cell_edges(self): + assert isroutine(totest.get_cell_edges) + + def test_instance_find_nearest(self): + assert isroutine(totest.find_nearest) + + def test_instance_find_index_nearest_neighbour(self): + assert isroutine(totest.find_index_nearest_neighbour) + + def test_instance_get_final_proposed_points(self): + assert isroutine(totest.get_final_proposed_points) + + def test_instance_T_merger_P(self): + assert isroutine(totest.T_merger_P) + + def test_instance_beta_gw(self): + assert isroutine(totest.beta_gw) + + def test_instance_kepler3_a(self): + assert isroutine(totest.kepler3_a) + + def test_instance_T_merger_a(self): + assert isroutine(totest.T_merger_a) + + def test_instance_convert_output_to_table(self): + assert isroutine(totest.convert_output_to_table) + + def test_instance_clean_inlist_file(self): + assert isroutine(totest.clean_inlist_file) + + def test_instance_get_new_grid_name(self): + assert isroutine(totest.get_new_grid_name) + + +class TestFunctions: + @fixture + def data_path(self, tmp_path): + # a temporary data path for testing + return os.path.join(tmp_path, "history.data") + + @fixture + def no_path(self, tmp_path): + # a path which does not exist for testing + return os.path.join(tmp_path, "does_not_exist.test") + + @fixture + def MESA_data(self): + # mock data: 3 columns and 2 rows; it contains different + # types(int, float) and different signs(positive, negative) + return np.array([(1, 2, 3.3), (1, -2, -3.3)],\ + dtype=[('COL1', '4}".format(v)) + else: + test_file.write(" {:>4}".format(v)) + return data_path + + @fixture + def EEP_data_path(self, data_path, MESA_data): + # a temporary data file for testing + # it contains 11 header lines, a line with the column headers and the + # table data given in MESA_data + with open(data_path, "w") as test_file: + for i in range(11): + test_file.write("Test HEADER{}\n".format(i+1)) + for k in MESA_data.dtype.names: + test_file.write("{:<5}".format(k)) + for j in range(len(MESA_data)): + for (i, v) in enumerate(MESA_data[j]): + if v == int(v): + v = int(v) + if i == 0: + test_file.write("\n{:>4}".format(v)) + else: + test_file.write(" {:>4}".format(v)) + return data_path + + @fixture + def MESA_BH_data_tight_orbit(self): + # mock data: 4 columns(physical quanties the code looks for) and 2 rows + return np.array([(1.0, 15.0, 30.0, -5.0), (1.0, 15.0, 30.0, -4.0)],\ + dtype=[('period_days', '17}".format(v)) + else: + test_file.write(" {:>17}".format(v)) + return path + + @fixture + def MESA_BH_data_wide_orbit(self): + # mock data: 4 columns(physical quanties the code looks for) and 2 rows + return np.array([(100.0, 15.0, 30.0, -5.0),\ + (100.0, 15.0, 30.0, -4.0)],\ + dtype=[('period_days', '17}".format(v)) + else: + test_file.write(" {:>17}".format(v)) + return path + + @fixture + def MESA_SH_data(self): + # mock data + return np.array([(1.0, 4.0, 0.0, 0.0), (2.0, 4.0, 5.0, 4.0)],\ + dtype=[('log_L', '12}".format(v)) + else: + test_file.write(" {:>12}".format(v)) + return path + + @fixture + def H2_path(self, tmp_path, MESA_SH_data): + # a temporary path of star2 history file for testing + # it contains 5 header lines, a line with the column headers and the + # table data given in MESA_SH_data + path = os.path.join(tmp_path, "history2.data") + with open(path, "w") as test_file: + for i in range(5): + test_file.write("Test HEADER{}\n".format(i+1)) + for k in MESA_SH_data.dtype.names: + test_file.write("{:<13}".format(k)) + for j in range(len(MESA_SH_data)): + for (i, v) in enumerate(MESA_SH_data[j]): + if v == int(v): + v = int(v) + if i == 0: + test_file.write("\n{:>12}".format(v)) + else: + test_file.write(" {:>12}".format(v)) + return path + + @fixture + def out_path(self, tmp_path): + # a temporary path of out file for testing + # it contains 5 header lines + path = os.path.join(tmp_path, "out.txt") + with open(path, "w") as test_file: + for i in range(5): + test_file.write("Test HEADER{}\n".format(i+1)) + return path + + @fixture + def out_gz_path(self, tmp_path): + # a temporary path of zipped out file for testing + # it contains 5 header lines and the statement of initial RLO + path = os.path.join(tmp_path, "out_gz.txt") + with open(path, "w") as test_file: + for i in range(5): + test_file.write("Test HEADER{}\n".format(i+1)) + test_file.write("model is overflowing at ZAMS\n") + os.system(f"gzip -1 {path}") + return path + + @fixture + def out_path_CE(self, tmp_path): + # a temporary path of out file for testing + # it contains 5 header lines and the statement of entering CE + path = os.path.join(tmp_path, "out2.txt") + with open(path, "w") as test_file: + for i in range(5): + test_file.write("Test HEADER{}\n".format(i+1)) + test_file.write("TURNING ON CE\n") + return path + + @fixture + def out_path_strong_RLO(self, tmp_path): + # a temporary path of out file for testing + # it contains 5 header lines and the statement of too strong RLO + path = os.path.join(tmp_path, "out3.txt") + with open(path, "w") as test_file: + for i in range(5): + test_file.write("Test HEADER{}\n".format(i+1)) + test_file.write("Terminate because accretor (r-rl)/rl > "\ + +"accretor_overflow_terminate\n") + return path + + @fixture + def out_path_CE_strong_RLO(self, tmp_path): + # a temporary path of out file for testing + # it contains 5 header lines and the statement of entering CE and too + # strong RLO + path = os.path.join(tmp_path, "out4.txt") + with open(path, "w") as test_file: + for i in range(5): + test_file.write("Test HEADER{}\n".format(i+1)) + test_file.write("TURNING ON CE\n") + test_file.write("Terminate because accretor (r-rl)/rl > "\ + +"accretor_overflow_terminate\n") + return path + + @fixture + def out_path_C_depletion(self, tmp_path): + # a temporary path of out file for testing + # it contains 5 header lines and the statement of carbon depletion + path = os.path.join(tmp_path, "out5.txt") + with open(path, "w") as test_file: + for i in range(5): + test_file.write("Test HEADER{}\n".format(i+1)) + test_file.write("Terminate due to primary depleting carbon\n") + return path + + @fixture + def out_path_CE_C_depletion(self, tmp_path): + # a temporary path of out file for testing + # it contains 5 header lines and the statement of entering CE and + # carbon depletion + path = os.path.join(tmp_path, "out6.txt") + with open(path, "w") as test_file: + for i in range(5): + test_file.write("Test HEADER{}\n".format(i+1)) + test_file.write("TURNING ON CE\n") + test_file.write("Terminate due to primary depleting carbon\n") + return path + + @fixture + def ini_path(self, tmp_path): + # a temporary path of ini file for testing + # it contains a section tag, 2 varibales (float, str), and a comment + path = os.path.join(tmp_path, "test.ini") + with open(path, "w") as test_file: + test_file.write("[TEST INI SECTION]\n") + test_file.write("test_float = 1.0\n") + test_file.write("test_string = 'unit'\n") + test_file.write("!test_comment = 0\n") + return path + + @fixture + def ini_path2(self, tmp_path): + # a temporary path of ini file for testing + # it contains a 2 lines with &, 2 variables (int, empty str) + path = os.path.join(tmp_path, "test2.ini") + with open(path, "w") as test_file: + test_file.write("TEST INI SECTION 1&\n") + test_file.write("test_int = 1\n") + test_file.write("TEST INI SECTION 2&\n") + test_file.write("test_empty = ''\n") + return path + + # test functions + def test_join_lists(self): + # missing argument + with raises(TypeError, match="missing 2 required positional "\ + +"arguments: 'A' and 'B'"): + totest.join_lists() + # bad input + with raises(TypeError, match="'int' object is not iterable"): + totest.join_lists(1, []) + with raises(TypeError, match="'int' object is not iterable"): + totest.join_lists([], 1) + # test cases + tests = [([1, 2, 3], [4, 5], [1, 2, 3, 4, 5]),\ + ([1, 2, 3], [4, 1], [1, 2, 3, 4]),\ + ([1, 2, 1], [4, 1], [1, 2, 1, 4])] + for (A, B, r) in tests: + assert totest.join_lists(A, B) == r + + def test_read_MESA_data_file(self, no_path, MESA_data_path, MESA_data): + # missing argument + with raises(TypeError, match="missing 2 required positional "\ + +"arguments: 'path' and 'columns'"): + totest.read_MESA_data_file() + # path is None or non-existing + assert totest.read_MESA_data_file(None, ['Test']) is None + assert totest.read_MESA_data_file(no_path, ['Test']) is None + # read full test file + assert np.array_equal(MESA_data,\ + totest.read_MESA_data_file(MESA_data_path,\ + MESA_data.dtype.names\ + )) + # read columns individually from test file + for k in MESA_data.dtype.names: + assert np.array_equal(MESA_data[[k]],\ + totest.read_MESA_data_file(MESA_data_path,\ + [k])) + # read column pairs from test file + k0 = MESA_data.dtype.names[0] + for k in MESA_data.dtype.names: + if k != k0: + assert np.array_equal(MESA_data[[k0, k]],\ + totest.read_MESA_data_file(\ + MESA_data_path, [k0, k])) + # warning for non readable data + with open(MESA_data_path, "w") as test_file: + test_file.write(10*"Test\n") + with warns(MissingFilesWarning, match="Problems with reading file "\ + +MESA_data_path): + assert totest.read_MESA_data_file(MESA_data_path,\ + MESA_data.dtype.names) is None + + def test_read_EEP_data_file(self, no_path, EEP_data_path, MESA_data): + # missing argument + with raises(TypeError, match="missing 2 required positional "\ + +"arguments: 'path' and 'columns'"): + totest.read_EEP_data_file() + # path is None or non-existing + assert totest.read_EEP_data_file(None, ['Test']) is None + with warns(MissingFilesWarning, match="Problems with reading file "\ + +no_path): + assert totest.read_EEP_data_file(no_path, ['Test']) is None + # read full test file + assert np.array_equal(MESA_data,\ + totest.read_EEP_data_file(EEP_data_path,\ + MESA_data.dtype.names)) + # read columns individually from test file + for k in MESA_data.dtype.names: + assert np.array_equal(MESA_data[[k]],\ + totest.read_EEP_data_file(EEP_data_path,\ + [k])) + # read column pairs from test file + k0 = MESA_data.dtype.names[0] + for k in MESA_data.dtype.names: + if k != k0: + assert np.array_equal(MESA_data[[k0, k]],\ + totest.read_EEP_data_file(EEP_data_path,\ + [k0, k])) + # warning for non readable data + with open(EEP_data_path, "w") as test_file: + test_file.write(15*"Test\n") + with warns(match="Problems with reading file "+EEP_data_path): + assert totest.read_EEP_data_file(EEP_data_path,\ + MESA_data.dtype.names) is None + + def test_fix_He_core(self, MESA_data): + # missing argument + with raises(TypeError, match="missing 1 required positional "\ + +"argument: 'history'"): + totest.fix_He_core() + # history is None + assert totest.fix_He_core(None) is None + # history is ndarray without the required columns + assert np.array_equal(MESA_data, totest.fix_He_core(MESA_data)) + # history is ndarray with the required columns and corrects them + test_history = np.array([(1, 2, 3, 4), (1, 2, 4, 3), (2, 1, 3, 4),\ + (2, 1, 4, 3)],\ + dtype=[('he_core_mass', '" +] + +# import the module which will be tested +import posydon.utils.ignorereason as totest + +# import other needed code for the tests, which is not already imported in the +# module you like to test +from pytest import fixture, raises +from inspect import isclass, isroutine + +# define test classes collecting several test functions +class TestElements: + # check for objects, which should be an element of the tested module + def test_dir(self): + elements = ['IGNORE_REASONS_PRIORITY', 'IgnoreReason', '__authors__',\ + '__builtins__', '__cached__', '__doc__', '__file__',\ + '__loader__', '__name__', '__package__', '__spec__'] + assert dir(totest) == elements, "There might be added or removed "\ + + "objects without an update on the "\ + + "unit test." + + def test_instance_IGNORE_REASONS_PRIORITY(self): + assert isinstance(totest.IGNORE_REASONS_PRIORITY, (list)),\ + "IGNORE_REASONS_PRIORITY is of type: "\ + + str(type(totest.IGNORE_REASONS_PRIORITY)) + + def test_instance_IgnoreReason(self): + assert isclass(totest.IgnoreReason) + + +class TestValues: + # check that the values fit + def test_value_IGNORE_REASONS_PRIORITY(self): + for v in totest.IGNORE_REASONS_PRIORITY: + assert isinstance(v, str) + v_last = None + for v in ['ignored_no_history1', 'ignored_no_binary_history',\ + 'corrupted_history1', 'corrupted_binary_history',\ + 'corrupted_history2', 'ignored_scrubbed_history',\ + 'ignored_no_final_profile', 'ignored_no_RLO']: + # check required values + assert v in totest.IGNORE_REASONS_PRIORITY, "missing entry" + # check required order + if v_last is not None: + assert totest.IGNORE_REASONS_PRIORITY.index(v_last) <\ + totest.IGNORE_REASONS_PRIORITY.index(v),\ + f"the priority order has changed for: {v_last} or {v}" + v_last = v + + +class TestIgnoreReason: + @fixture + def NewInstance(self): + # initialize an instance of the class for each test + return totest.IgnoreReason() + + # test the IgnoreReason class + def test_init(self, NewInstance): + assert isroutine(NewInstance.__init__) + # check that the instance is of correct type and all code in the + # __init__ got executed: the elements are created and initialized + assert isinstance(NewInstance, totest.IgnoreReason) + # strictly speaking, the following test does not test the variable + # asignments in the __init__, because they call the __setattr__ + assert NewInstance.reason is None + assert NewInstance.order is None + + def test_bool(self, NewInstance): + assert isroutine(NewInstance.__bool__) + assert bool(NewInstance) == False + NewInstance.reason = totest.IGNORE_REASONS_PRIORITY[0] + assert bool(NewInstance) + + def test_setattr(self, NewInstance): + assert isroutine(NewInstance.__setattr__) + # try to set order: shouldn't change anything + NewInstance.order = 0 + assert NewInstance.reason is None + assert NewInstance.order is None + # set all reasons in decreasing order and compare it + for r in reversed(totest.IGNORE_REASONS_PRIORITY): + o = totest.IGNORE_REASONS_PRIORITY.index(r) + NewInstance.reason = r + assert NewInstance.order == o + assert NewInstance.reason == r + # try to set reason of lowest priority: reason of higher pririty will + # be kept + NewInstance.reason = totest.IGNORE_REASONS_PRIORITY[-1] + assert NewInstance.order == o + assert NewInstance.reason == r + # unset the reason: set back to None + NewInstance.reason = None + assert NewInstance.reason is None + assert NewInstance.order is None + # try error on non existing reason + assert '' not in totest.IGNORE_REASONS_PRIORITY + with raises(ValueError, match="Ignore reason `` not recognized"): + NewInstance.reason = '' diff --git a/posydon/unit_tests/utils/test_limits_thresholds.py b/posydon/unit_tests/utils/test_limits_thresholds.py new file mode 100644 index 0000000000..7cafec49d0 --- /dev/null +++ b/posydon/unit_tests/utils/test_limits_thresholds.py @@ -0,0 +1,163 @@ +"""Unit tests of posydon/utils/limits_thresholds.py +""" + +__authors__ = [ + "Matthias Kruckow " +] + +# import the module which will be tested +import posydon.utils.limits_thresholds as totest + +# import other needed code for the tests, which is not already imported in the +# module you like to test + + +# define test classes collecting several test functions +class TestElements: + # check for objects, which should be an element of the tested module + def test_dir(self): + elements = ['LG_MTRANSFER_RATE_THRESHOLD', 'LOG10_BURNING_THRESHOLD',\ + 'MIN_COUNT_INITIAL_RLO_BOUNDARY',\ + 'NEUTRINO_MASS_LOSS_UPPER_LIMIT',\ + 'REL_LOG10_BURNING_THRESHOLD',\ + 'RL_RELATIVE_OVERFLOW_THRESHOLD',\ + 'STATE_NS_STARMASS_UPPER_LIMIT',\ + 'THRESHOLD_CENTRAL_ABUNDANCE',\ + 'THRESHOLD_CENTRAL_ABUNDANCE_LOOSE_C',\ + 'THRESHOLD_HE_NAKED_ABUNDANCE',\ + 'THRESHOLD_NUCLEAR_LUMINOSITY', '__authors__',\ + '__builtins__', '__cached__', '__doc__', '__file__',\ + '__loader__', '__name__', '__package__', '__spec__', 'np'] + assert dir(totest) == elements, "There might be added or removed "\ + + "objects without an update on the "\ + + "unit test." + + def test_instance_RL_RELATIVE_OVERFLOW_THRESHOLD(self): + assert isinstance(totest.RL_RELATIVE_OVERFLOW_THRESHOLD, (float,\ + int)),\ + "RL_RELATIVE_OVERFLOW_THRESHOLD is of type: "\ + + str(type(totest.RL_RELATIVE_OVERFLOW_THRESHOLD)) + + def test_instance_LG_MTRANSFER_RATE_THRESHOLD(self): + assert isinstance(totest.LG_MTRANSFER_RATE_THRESHOLD, (float, int)),\ + "LG_MTRANSFER_RATE_THRESHOLD is of type: "\ + + str(type(totest.LG_MTRANSFER_RATE_THRESHOLD)) + + def test_instance_MIN_COUNT_INITIAL_RLO_BOUNDARY(self): + assert isinstance(totest.MIN_COUNT_INITIAL_RLO_BOUNDARY, (int)),\ + "MIN_COUNT_INITIAL_RLO_BOUNDARY is of type: "\ + + str(type(totest.MIN_COUNT_INITIAL_RLO_BOUNDARY)) + + def test_instance_THRESHOLD_CENTRAL_ABUNDANCE(self): + assert isinstance(totest.THRESHOLD_CENTRAL_ABUNDANCE, (float, int)),\ + "THRESHOLD_CENTRAL_ABUNDANCE is of type: "\ + + str(type(totest.THRESHOLD_CENTRAL_ABUNDANCE)) + + def test_instance_THRESHOLD_CENTRAL_ABUNDANCE_LOOSE_C(self): + assert isinstance(totest.THRESHOLD_CENTRAL_ABUNDANCE_LOOSE_C, (float,\ + int)),\ + "THRESHOLD_CENTRAL_ABUNDANCE_LOOSE_C is of type: "\ + + str(type(totest.THRESHOLD_CENTRAL_ABUNDANCE_LOOSE_C)) + + def test_instance_THRESHOLD_HE_NAKED_ABUNDANCE(self): + assert isinstance(totest.THRESHOLD_HE_NAKED_ABUNDANCE, (float, int)),\ + "THRESHOLD_HE_NAKED_ABUNDANCE is of type: "\ + + str(type(totest.THRESHOLD_HE_NAKED_ABUNDANCE)) + + def test_instance_THRESHOLD_NUCLEAR_LUMINOSITY(self): + assert isinstance(totest.THRESHOLD_NUCLEAR_LUMINOSITY, (float, int)),\ + "THRESHOLD_NUCLEAR_LUMINOSITY is of type: "\ + + str(type(totest.THRESHOLD_NUCLEAR_LUMINOSITY)) + + def test_instance_REL_LOG10_BURNING_THRESHOLD(self): + assert isinstance(totest.REL_LOG10_BURNING_THRESHOLD, (float, int)),\ + "REL_LOG10_BURNING_THRESHOLD is of type: "\ + + str(type(totest.REL_LOG10_BURNING_THRESHOLD)) + + def test_instance_LOG10_BURNING_THRESHOLD(self): + assert isinstance(totest.LOG10_BURNING_THRESHOLD, (float, int)),\ + "LOG10_BURNING_THRESHOLD is of type: "\ + + str(type(totest.LOG10_BURNING_THRESHOLD)) + + def test_instance_STATE_NS_STARMASS_UPPER_LIMIT(self): + assert isinstance(totest.STATE_NS_STARMASS_UPPER_LIMIT, (float, int)),\ + "STATE_NS_STARMASS_UPPER_LIMIT is of type: "\ + + str(type(totest.STATE_NS_STARMASS_UPPER_LIMIT)) + + def test_instance_NEUTRINO_MASS_LOSS_UPPER_LIMIT(self): + assert isinstance(totest.NEUTRINO_MASS_LOSS_UPPER_LIMIT, (float,\ + int)),\ + "NEUTRINO_MASS_LOSS_UPPER_LIMIT is of type: "\ + + str(type(totest.NEUTRINO_MASS_LOSS_UPPER_LIMIT)) + + +class TestLimits: + # check for validity ranges +# def test_limits_RL_RELATIVE_OVERFLOW_THRESHOLD(self): + # has no limits + +# def test_limits_LG_MTRANSFER_RATE_THRESHOLD(self): + # has no limits + + def test_limits_MIN_COUNT_INITIAL_RLO_BOUNDARY(self): + # an count shouldn't be negative + assert totest.MIN_COUNT_INITIAL_RLO_BOUNDARY >= 0,\ + "MIN_COUNT_INITIAL_RLO_BOUNDARY should be 0 or larger" + + def test_limits_THRESHOLD_CENTRAL_ABUNDANCE(self): + # an abundance should be in [0,1] + assert totest.THRESHOLD_CENTRAL_ABUNDANCE >= 0.0,\ + "THRESHOLD_CENTRAL_ABUNDANCE should be in the range [0,1], "\ + + "but it is below" + assert totest.THRESHOLD_CENTRAL_ABUNDANCE <= 1.0,\ + "THRESHOLD_CENTRAL_ABUNDANCE should be in the range [0,1], "\ + + "but it is above" + + def test_limits_THRESHOLD_CENTRAL_ABUNDANCE_LOOSE_C(self): + # an abundance should be in [0,1] + assert totest.THRESHOLD_CENTRAL_ABUNDANCE_LOOSE_C >= 0.0,\ + "THRESHOLD_CENTRAL_ABUNDANCE_LOOSE_C should be in the range "\ + + "[0,1], but it is below" + assert totest.THRESHOLD_CENTRAL_ABUNDANCE_LOOSE_C <= 1.0,\ + "THRESHOLD_CENTRAL_ABUNDANCE_LOOSE_C should be in the range "\ + + "[0,1], but it is above" + # it should be limited by THRESHOLD_CENTRAL_ABUNDANCE + assert totest.THRESHOLD_CENTRAL_ABUNDANCE_LOOSE_C >=\ + totest.THRESHOLD_CENTRAL_ABUNDANCE,\ + "the loose condition should be less strict" + + def test_limits_THRESHOLD_HE_NAKED_ABUNDANCE(self): + # an abundance should be in [0,1] + assert totest.THRESHOLD_HE_NAKED_ABUNDANCE >= 0.0,\ + "THRESHOLD_HE_NAKED_ABUNDANCE should be in the range [0,1], "\ + + "but it is below" + assert totest.THRESHOLD_HE_NAKED_ABUNDANCE <= 1.0,\ + "THRESHOLD_HE_NAKED_ABUNDANCE should be in the range [0,1], "\ + + "but it is above" + + def test_limits_THRESHOLD_NUCLEAR_LUMINOSITY(self): + # an fraction should be in [0,1] + assert totest.THRESHOLD_NUCLEAR_LUMINOSITY >= 0.0,\ + "THRESHOLD_NUCLEAR_LUMINOSITY should be in the range [0,1], "\ + + "but it is below" + assert totest.THRESHOLD_NUCLEAR_LUMINOSITY <= 1.0,\ + "THRESHOLD_NUCLEAR_LUMINOSITY should be in the range [0,1], "\ + + "but it is above" + + def test_limits_REL_LOG10_BURNING_THRESHOLD(self): + # the log of a fraction should be <=0 + assert totest.REL_LOG10_BURNING_THRESHOLD <= 0.0, "a fraction should "\ + + "be in the range [0,1], hence the log of it can't be positive" + +# def test_limits_LOG10_BURNING_THRESHOLD(self): + # has no limits + + def test_limits_STATE_NS_STARMASS_UPPER_LIMIT(self): + # a mass should be >0 + assert totest.STATE_NS_STARMASS_UPPER_LIMIT > 0.0,\ + "a mass has to be positve" + + def test_limits_NEUTRINO_MASS_LOSS_UPPER_LIMIT(self): + # a mass limit should be >=0 + assert totest.NEUTRINO_MASS_LOSS_UPPER_LIMIT >= 0.0,\ + "there shouldn't be a mass gain" diff --git a/posydon/unit_tests/utils/test_posydonerror.py b/posydon/unit_tests/utils/test_posydonerror.py new file mode 100644 index 0000000000..87eb975866 --- /dev/null +++ b/posydon/unit_tests/utils/test_posydonerror.py @@ -0,0 +1,186 @@ +"""Unit tests of posydon/utils/posydonerror.py +""" + +__authors__ = [ + "Matthias Kruckow " +] + +# import the module which will be tested +import posydon.utils.posydonerror as totest + +# import other needed code for the tests, which is not already imported in the +# module you like to test +from pytest import fixture, raises +from inspect import isclass, isroutine + +@fixture +def artificial_object(): + # create a dict as test object + return {'Test': 'object'} + +@fixture +def BinaryStar(): + # initialize a BinaryStar instance, which is a required argument + return totest.BinaryStar() + +@fixture +def SingleStar(): + # initialize a SingleStar instance, which is a required argument + return totest.SingleStar() + +# define test classes collecting several test functions +class TestElements: + # check for objects, which should be an element of the tested module + def test_dir(self): + elements = ['BinaryStar', 'FlowError', 'GridError', 'MatchingError',\ + 'ModelError', 'NumericalError', 'POSYDONError',\ + 'SingleStar', '__authors__', '__builtins__', '__cached__',\ + '__doc__', '__file__', '__loader__', '__name__',\ + '__package__', '__spec__', 'copy',\ + 'initial_condition_message'] + assert dir(totest) == elements, "There might be added or removed "\ + + "objects without an update on the "\ + + "unit test." + + def test_instance_POSYDONError(self): + assert isclass(totest.POSYDONError) + assert issubclass(totest.POSYDONError, Exception) + with raises(totest.POSYDONError, match="Test"): + raise totest.POSYDONError("Test") + + def test_instance_FlowError(self): + assert isclass(totest.FlowError) + assert issubclass(totest.FlowError, totest.POSYDONError) + with raises(totest.FlowError, match="Test"): + raise totest.FlowError("Test") + + def test_instance_GridError(self): + assert isclass(totest.GridError) + assert issubclass(totest.GridError, totest.POSYDONError) + with raises(totest.GridError, match="Test"): + raise totest.GridError("Test") + + def test_instance_MatchingError(self): + assert isclass(totest.MatchingError) + assert issubclass(totest.MatchingError, totest.POSYDONError) + with raises(totest.MatchingError, match="Test"): + raise totest.MatchingError("Test") + + def test_instance_ModelError(self): + assert isclass(totest.ModelError) + assert issubclass(totest.ModelError, totest.POSYDONError) + with raises(totest.ModelError, match="Test"): + raise totest.ModelError("Test") + + def test_instance_NumericalError(self): + assert isclass(totest.NumericalError) + assert issubclass(totest.NumericalError, totest.POSYDONError) + with raises(totest.NumericalError, match="Test"): + raise totest.NumericalError("Test") + + def test_instance_initial_condition_message(self): + assert isroutine(totest.initial_condition_message) + + +class TestFunctions: + # test functions + def test_initial_condition_message(self, BinaryStar, artificial_object): + with raises(TypeError, match="The binary must be a BinaryStar object"): + message = totest.initial_condition_message(binary=\ + artificial_object) + assert "Failed Binary Initial Conditions" in\ + totest.initial_condition_message(binary=BinaryStar) + with raises(TypeError, match="is not iterable"): + message = totest.initial_condition_message(binary=BinaryStar,\ + ini_params=1) + with raises(TypeError, match="can only concatenate str"): + message = totest.initial_condition_message(binary=BinaryStar,\ + ini_params=[1,2]) + assert totest.initial_condition_message(binary=BinaryStar, ini_params=\ + ["a: 1\n", "b: 2\n"]) ==\ + "a: 1\nb: 2\n" + + +class TestPOSYDONError: + @fixture + def POSYDONError(self): + # initialize an instance of the class with defaults + return totest.POSYDONError() + + @fixture + def POSYDONError_position(self): + # initialize an instance of the class with a positional argument + return totest.POSYDONError("test message on position") + + @fixture + def POSYDONError_key(self): + # initialize an instance of the class with a message via key + return totest.POSYDONError(message="test message with key") + + @fixture + def POSYDONError_object(self, artificial_object): + # initialize an instance of the class with an artifical object in a + # list via key + return totest.POSYDONError(objects=[artificial_object]) + + @fixture + def POSYDONError_SingleStar(self, SingleStar): + # initialize an instance of the class with a SingleStar object + return totest.POSYDONError(objects=SingleStar) + + @fixture + def POSYDONError_BinaryStar(self, BinaryStar): + # initialize an instance of the class with a BinaryStar object + return totest.POSYDONError(objects=BinaryStar) + + @fixture + def POSYDONError_List(self, artificial_object, SingleStar, BinaryStar): + # initialize an instance of the class with a list of objects + return totest.POSYDONError(objects=[artificial_object, SingleStar,\ + BinaryStar]) + + # test the POSYDONError class + def test_init(self, POSYDONError, POSYDONError_position, POSYDONError_key,\ + POSYDONError_object, artificial_object): + assert isroutine(POSYDONError.__init__) + # check that the instance is of correct type and all code in the + # __init__ got executed: the elements are created and initialized + assert isinstance(POSYDONError, totest.POSYDONError) + # check defaults + assert POSYDONError.message == "" + assert POSYDONError.objects is None + # test a passed message via positional argument + assert POSYDONError_position.message == "test message on position" + assert POSYDONError_position.objects is None + # test a passed message via key + error_object = totest.POSYDONError(message="test message with key") + assert POSYDONError_key.message == "test message with key" + assert POSYDONError_key.objects is None + # test a passed object + assert POSYDONError_object.message == "" + assert POSYDONError_object.objects == [artificial_object] + # test requests on input parameters + with raises(TypeError, match="message must be a string"): + error_object = totest.POSYDONError(message=artificial_object) + with raises(TypeError, match="objects must be None, a list, a "\ + +"SingleStar object, or a BinaryStar "\ + +"object"): + error_object = totest.POSYDONError(objects=artificial_object) + + + def test_str(self, POSYDONError_position, POSYDONError_object,\ + POSYDONError_SingleStar, POSYDONError_BinaryStar,\ + POSYDONError_List): + assert isroutine(POSYDONError_position.__str__) + assert str(POSYDONError_position) == "\ntest message on position" + # test passed objects + assert str(POSYDONError_object) == "\n" + assert "OBJECT #()"\ + in str(POSYDONError_SingleStar) + assert "OBJECT #()"\ + in str(POSYDONError_BinaryStar) + assert "OBJECT #1" not in str(POSYDONError_List) + assert "OBJECT #2 ()" in str(POSYDONError_List) + assert "OBJECT #3 ()" in str(POSYDONError_List) diff --git a/posydon/unit_tests/utils/test_posydonwarning.py b/posydon/unit_tests/utils/test_posydonwarning.py new file mode 100644 index 0000000000..4ef24eb4a6 --- /dev/null +++ b/posydon/unit_tests/utils/test_posydonwarning.py @@ -0,0 +1,756 @@ +"""Unit tests of posydon/utils/posydonwarning.py +""" + +__authors__ = [ + "Matthias Kruckow " +] + +# ensure that python forgets about previous imports of posydonwarning, e.g. in +# other tests, before importing it here +from sys import modules as sys_modules +sys_modules.pop('posydon.utils.posydonwarning') + +# import the module which will be tested +import posydon.utils.posydonwarning as totest + +# import other needed code for the tests, which is not already imported in the +# module you like to test +from pytest import fixture, raises, warns +from inspect import isclass, isroutine + +# define test classes collecting several test functions +class TestElements: + # check for objects, which should be an element of the tested module + def test_dir(self): + elements = ['AllPOSYDONWarnings', 'ApproximationWarning',\ + 'BinaryParsingWarning', 'Catch_POSYDON_Warnings',\ + 'ClassificationWarning', 'EvolutionWarning',\ + 'InappropriateValueWarning', 'IncompletenessWarning',\ + 'InterpolationWarning', 'MissingFilesWarning',\ + 'NoPOSYDONWarnings', 'OverwriteWarning', 'POSYDONWarning',\ + 'Pwarn', 'ReplaceValueWarning', 'SetPOSYDONWarnings',\ + 'UnsupportedModelWarning', '_CAUGHT_POSYDON_WARNINGS',\ + '_Caught_POSYDON_Warnings', '_POSYDONWarning_subclasses',\ + '_POSYDON_WARNINGS_REGISTRY', '__authors__',\ + '__builtins__', '__cached__', '__doc__', '__file__',\ + '__loader__', '__name__', '__package__', '__spec__',\ + '_apply_POSYDON_filter', '_get_POSYDONWarning_class',\ + '_issue_warn', 'copy', 'get_stats', 'print_stats', 'sys',\ + 'warnings'] + assert dir(totest) == elements, "There might be added or removed "\ + + "objects without an update on the "\ + + "unit test." + + def test_instance_POSYDONWarning(self): + assert isclass(totest.POSYDONWarning) + assert issubclass(totest.POSYDONWarning, Warning) + + def test_instance_ApproximationWarning(self): + assert isclass(totest.ApproximationWarning) + assert issubclass(totest.ApproximationWarning, totest.POSYDONWarning) + + def test_instance_BinaryParsingWarning(self): + assert isclass(totest.BinaryParsingWarning) + assert issubclass(totest.BinaryParsingWarning, totest.POSYDONWarning) + + def test_instance_ClassificationWarning(self): + assert isclass(totest.ClassificationWarning) + assert issubclass(totest.ClassificationWarning, totest.POSYDONWarning) + + def test_instance_EvolutionWarning(self): + assert isclass(totest.EvolutionWarning) + assert issubclass(totest.EvolutionWarning, totest.POSYDONWarning) + + def test_instance_InappropriateValueWarning(self): + assert isclass(totest.InappropriateValueWarning) + assert issubclass(totest.InappropriateValueWarning,\ + totest.POSYDONWarning) + + def test_instance_IncompletenessWarning(self): + assert isclass(totest.IncompletenessWarning) + assert issubclass(totest.IncompletenessWarning, totest.POSYDONWarning) + + def test_instance_InterpolationWarning(self): + assert isclass(totest.InterpolationWarning) + assert issubclass(totest.InterpolationWarning, totest.POSYDONWarning) + + def test_instance_MissingFilesWarning(self): + assert isclass(totest.MissingFilesWarning) + assert issubclass(totest.MissingFilesWarning, totest.POSYDONWarning) + + def test_instance_OverwriteWarning(self): + assert isclass(totest.OverwriteWarning) + assert issubclass(totest.OverwriteWarning, totest.POSYDONWarning) + + def test_instance_ReplaceValueWarning(self): + assert isclass(totest.ReplaceValueWarning) + assert issubclass(totest.ReplaceValueWarning, totest.POSYDONWarning) + + def test_instance_UnsupportedModelWarning(self): + assert isclass(totest.UnsupportedModelWarning) + assert issubclass(totest.UnsupportedModelWarning,\ + totest.POSYDONWarning) + + def test_instance_POSYDONWarning_subclasses(self): + assert isinstance(totest._POSYDONWarning_subclasses, (dict)) + + def test_instance_get_POSYDONWarning_class(self): + assert isroutine(totest._get_POSYDONWarning_class) + + def test_instance_POSYDON_WARNINGS_REGISTRY(self): + assert isinstance(totest._POSYDON_WARNINGS_REGISTRY, (dict)) + + def test_instance_get_stats(self): + assert isroutine(totest.get_stats) + + def test_instance_print_stats(self): + assert isroutine(totest.print_stats) + + def test_instance_apply_POSYDON_filter(self): + assert isroutine(totest._apply_POSYDON_filter) + + def test_instance_issue_warn(self): + assert isroutine(totest._issue_warn) + + def test_instance_Caught_POSYDON_Warnings(self): + assert isclass(totest._Caught_POSYDON_Warnings) + + def test_instance_CAUGHT_POSYDON_WARNINGS(self): + assert isinstance(totest._CAUGHT_POSYDON_WARNINGS,\ + totest._Caught_POSYDON_Warnings) + + def test_instance_Catch_POSYDON_Warnings(self): + assert isclass(totest.Catch_POSYDON_Warnings) + + def test_instance_Pwarn(self): + assert isroutine(totest.Pwarn) + + def test_instance_SetPOSYDONWarnings(self): + assert isroutine(totest.SetPOSYDONWarnings) + + def test_instance_NoPOSYDONWarnings(self): + assert isroutine(totest.NoPOSYDONWarnings) + + def test_instance_AllPOSYDONWarnings(self): + assert isroutine(totest.AllPOSYDONWarnings) + + +class TestValues: + # check that the values fit + def test_value_POSYDONWarning_subclasses(self): + assert 'ApproximationWarning' in totest._POSYDONWarning_subclasses + + def test_value_POSYDON_WARNINGS_REGISTRY(self): + assert totest._POSYDON_WARNINGS_REGISTRY == {} + + def test_value_CAUGHT_POSYDON_WARNINGS(self): + assert totest._CAUGHT_POSYDON_WARNINGS.catch_warnings == False + assert totest._CAUGHT_POSYDON_WARNINGS.record + assert totest._CAUGHT_POSYDON_WARNINGS.filter_first + assert totest._CAUGHT_POSYDON_WARNINGS.caught_warnings == [] + assert totest._CAUGHT_POSYDON_WARNINGS.registry is\ + totest._POSYDON_WARNINGS_REGISTRY + + +class TestFunctions: + @fixture + def clear_registry(self): + yield + # empty the global POSYDON warnings registry after each test + keys = [] + for k in totest._POSYDON_WARNINGS_REGISTRY: + keys.append(k) + for k in keys: + del totest._POSYDON_WARNINGS_REGISTRY[k] + + @fixture + def reset_filter(self): + yield + # set POSYDON warnings back to default + totest.warnings.filterwarnings(action='ignore',\ + category=ResourceWarning) + totest.warnings.filterwarnings(action='default',\ + category=totest.POSYDONWarning) + + # test functions + def test_get_POSYDONWarning_class(self): + # missing argument + with raises(TypeError, match="missing 1 required positional argument"): + totest._get_POSYDONWarning_class() + # default to POSYDONWarning + assert totest._get_POSYDONWarning_class("") == totest.POSYDONWarning + assert totest._get_POSYDONWarning_class(totest.POSYDONWarning) ==\ + totest.POSYDONWarning + # check subclasses of POSYDONWarning + for (k, v) in totest._POSYDONWarning_subclasses.items(): + assert totest._get_POSYDONWarning_class(k) == v + assert totest._get_POSYDONWarning_class(v) == v + # bad input + assert totest._get_POSYDONWarning_class(1) is None + + def test_get_stats(self): + assert totest.get_stats() == totest._POSYDON_WARNINGS_REGISTRY + + def test_print_stats(self, capsys, clear_registry): + # empty the global POSYDON warnings registry before this test + keys = [] + for k in totest._POSYDON_WARNINGS_REGISTRY: + keys.append(k) + for k in keys: + del totest._POSYDON_WARNINGS_REGISTRY[k] + # no warnings to print + totest.print_stats() + assert "No POSYDON warnings occured.\n" == capsys.readouterr().out + # add an artifical entry in the warnings registry and get the printout + totest._POSYDON_WARNINGS_REGISTRY = {'Unit': 'Test'} + totest.print_stats() + assert "There have been POSYDON warnings in the global registry:\n "\ + + str(totest._POSYDON_WARNINGS_REGISTRY) + "\n" ==\ + capsys.readouterr().out + + def test_apply_POSYDON_filter(self, capsys, clear_registry, reset_filter): + # wrong arguments + with raises(TypeError, match="warning must be a dictionary"): + totest._apply_POSYDON_filter(warning="Test") + with raises(TypeError, match="message must be a string"): + totest._apply_POSYDON_filter(warning={'message': 1}) + with raises(TypeError, match="stacklevel must be an integer"): + totest._apply_POSYDON_filter(warning={'stacklevel': "Test"}) + with raises(TypeError, match="registry must be a dictionary or None"): + totest._apply_POSYDON_filter(registry="Test") + # check default warning + assert totest._apply_POSYDON_filter() == dict(message="No warning") + # check that further default warnings are filtered out but added to the + # registry + for i in range(10): + for (k, v) in totest._POSYDON_WARNINGS_REGISTRY.items(): + assert v == i + 1 + assert totest._apply_POSYDON_filter() is None + # check usage of python filter with a python warning + totest.warnings.filterwarnings(action='ignore',\ + category=ResourceWarning) + assert totest._apply_POSYDON_filter(warning={'message': "Test",\ + 'category':\ + ResourceWarning}) is None + # check the route of an always filter + totest.warnings.filterwarnings(action='always',\ + category=ResourceWarning) + for i in range(10): + assert totest._apply_POSYDON_filter(warning={'message': "Test"\ + +str(i),\ + 'category':\ + ResourceWarning}) ==\ + {'message': "Test"+str(i), 'category': ResourceWarning} + for (k, v) in totest._POSYDON_WARNINGS_REGISTRY.items(): + if "ResourceWarning" in k: + assert v == i + # check the route of an error filter + totest.warnings.filterwarnings(action='error',\ + category=ResourceWarning) + assert totest._apply_POSYDON_filter(warning={'message': "Test"+str(i),\ + 'category':\ + ResourceWarning}) ==\ + {'message': "Test"+str(i), 'category': ResourceWarning} + + def test_issue_warn(self, capsys, monkeypatch, recwarn, clear_registry): + def mock_apply_POSYDON_filter(warning=dict(message="No warning"),\ + registry=None): + return None + + # wrong arguments + with raises(TypeError, match="warning must be a dictionary"): + totest._issue_warn(warning="Test") + with raises(TypeError, match="message must be a string"): + totest._issue_warn(warning={'message': 1}) + with raises(TypeError, match="stacklevel must be an integer"): + totest._issue_warn(warning={'stacklevel': "Test"}) + with raises(TypeError, match="registry must be a dictionary or None"): + totest._issue_warn(registry="Test") + # check default warning + with warns(UserWarning, match="No warning") as warn1: + totest._issue_warn() + assert len(warn1) == 1 + # check filtered warning + monkeypatch.setattr(totest, "_apply_POSYDON_filter", mock_apply_POSYDON_filter) + totest._issue_warn() + assert len(recwarn) == 0 + + def test_Pwarn(self): + # missing argument + with raises(TypeError, match="missing 1 required positional argument"): + totest.Pwarn() + # wrong arguments + with raises(TypeError, match="message must be a string"): + totest.Pwarn(1) + with raises(TypeError, match="stacklevel must be an integer"): + totest.Pwarn("Unit", stacklevel="Test") + # check output of POSYDONwarning + with warns(totest.POSYDONWarning, match="Unit test") as warn1: + totest.Pwarn("Unit test", "POSYDONWarning") + assert len(warn1) == 1 + # check output of FutureWarning + with warns(FutureWarning, match="Unit test") as warn2: + totest.Pwarn("Unit test", FutureWarning) + assert len(warn2) == 1 + # check output of UserWarning (default if unspecified) + with warns(UserWarning, match="Unit test") as warn3: + totest.Pwarn("Unit test") + assert len(warn3) == 1 + + def test_SetPOSYDONWarnings(self): + totest.SetPOSYDONWarnings(action="once") + assert str(totest.warnings.filters[0]) == "('once', None, , None, 0)" + totest.SetPOSYDONWarnings() + assert str(totest.warnings.filters[0]) == "('default', None, , None, 0)" + # non POSYDON warnings have no effect + totest.SetPOSYDONWarnings(category=UserWarning) + assert str(totest.warnings.filters[0]) == "('default', None, , None, 0)" + + def test_NoPOSYDONWarnings(self, reset_filter): + totest.NoPOSYDONWarnings() + assert str(totest.warnings.filters[0]) == "('ignore', None, , None, 0)" + + def test_AllPOSYDONWarnings(self, reset_filter): + totest.AllPOSYDONWarnings() + assert str(totest.warnings.filters[0]) == "('always', None, , None, 0)" + + +class TestPOSYDONWarning: + @fixture + def POSYDONWarning(self): + # initialize an instance of the class with defaults + return totest.POSYDONWarning() + + # test the POSYDONWarning class + def test_init(self, POSYDONWarning): + assert isroutine(POSYDONWarning.__init__) + # check that the instance is of correct type and all code in the + # __init__ got executed: the elements are created and initialized + assert isinstance(POSYDONWarning, totest.POSYDONWarning) + assert POSYDONWarning.message == '' + + def test_str(self, POSYDONWarning): + assert isroutine(POSYDONWarning.__str__) + assert str(POSYDONWarning) == "''" + + +class TestApproximationWarning: + @fixture + def ApproximationWarning(self): + # initialize an instance of the class with defaults + return totest.ApproximationWarning() + + # test the ApproximationWarning class + def test_init(self, ApproximationWarning): + assert isroutine(ApproximationWarning.__init__) + # check that the instance is of correct type and all code in the + # __init__ got executed: the elements are created and initialized + assert isinstance(ApproximationWarning, totest.ApproximationWarning) + assert ApproximationWarning.message == '' + + +class TestBinaryParsingWarning: + @fixture + def BinaryParsingWarning(self): + # initialize an instance of the class with defaults + return totest.BinaryParsingWarning() + + # test the BinaryParsingWarning class + def test_init(self, BinaryParsingWarning): + assert isroutine(BinaryParsingWarning.__init__) + # check that the instance is of correct type and all code in the + # __init__ got executed: the elements are created and initialized + assert isinstance(BinaryParsingWarning, totest.BinaryParsingWarning) + assert BinaryParsingWarning.message == '' + + +class TestClassificationWarning: + @fixture + def ClassificationWarning(self): + # initialize an instance of the class with defaults + return totest.ClassificationWarning() + + # test the ClassificationWarning class + def test_init(self, ClassificationWarning): + assert isroutine(ClassificationWarning.__init__) + # check that the instance is of correct type and all code in the + # __init__ got executed: the elements are created and initialized + assert isinstance(ClassificationWarning, totest.ClassificationWarning) + assert ClassificationWarning.message == '' + + +class TestEvolutionWarning: + @fixture + def EvolutionWarning(self): + # initialize an instance of the class with defaults + return totest.EvolutionWarning() + + # test the EvolutionWarning class + def test_init(self, EvolutionWarning): + assert isroutine(EvolutionWarning.__init__) + # check that the instance is of correct type and all code in the + # __init__ got executed: the elements are created and initialized + assert isinstance(EvolutionWarning, totest.EvolutionWarning) + assert EvolutionWarning.message == '' + + +class TestInappropriateValueWarning: + @fixture + def InappropriateValueWarning(self): + # initialize an instance of the class with defaults + return totest.InappropriateValueWarning() + + # test the InappropriateValueWarning class + def test_init(self, InappropriateValueWarning): + assert isroutine(InappropriateValueWarning.__init__) + # check that the instance is of correct type and all code in the + # __init__ got executed: the elements are created and initialized + assert isinstance(InappropriateValueWarning, totest.InappropriateValueWarning) + assert InappropriateValueWarning.message == '' + + +class TestIncompletenessWarning: + @fixture + def IncompletenessWarning(self): + # initialize an instance of the class with defaults + return totest.IncompletenessWarning() + + # test the IncompletenessWarning class + def test_init(self, IncompletenessWarning): + assert isroutine(IncompletenessWarning.__init__) + # check that the instance is of correct type and all code in the + # __init__ got executed: the elements are created and initialized + assert isinstance(IncompletenessWarning, totest.IncompletenessWarning) + assert IncompletenessWarning.message == '' + + +class TestInterpolationWarning: + @fixture + def InterpolationWarning(self): + # initialize an instance of the class with defaults + return totest.InterpolationWarning() + + # test the InterpolationWarning class + def test_init(self, InterpolationWarning): + assert isroutine(InterpolationWarning.__init__) + # check that the instance is of correct type and all code in the + # __init__ got executed: the elements are created and initialized + assert isinstance(InterpolationWarning, totest.InterpolationWarning) + assert InterpolationWarning.message == '' + + +class TestMissingFilesWarning: + @fixture + def MissingFilesWarning(self): + # initialize an instance of the class with defaults + return totest.MissingFilesWarning() + + # test the MissingFilesWarning class + def test_init(self, MissingFilesWarning): + assert isroutine(MissingFilesWarning.__init__) + # check that the instance is of correct type and all code in the + # __init__ got executed: the elements are created and initialized + assert isinstance(MissingFilesWarning, totest.MissingFilesWarning) + assert MissingFilesWarning.message == '' + + +class TestOverwriteWarning: + @fixture + def OverwriteWarning(self): + # initialize an instance of the class with defaults + return totest.OverwriteWarning() + + # test the OverwriteWarning class + def test_init(self, OverwriteWarning): + assert isroutine(OverwriteWarning.__init__) + # check that the instance is of correct type and all code in the + # __init__ got executed: the elements are created and initialized + assert isinstance(OverwriteWarning, totest.OverwriteWarning) + assert OverwriteWarning.message == '' + + +class TestReplaceValueWarning: + @fixture + def ReplaceValueWarning(self): + # initialize an instance of the class with defaults + return totest.ReplaceValueWarning() + + # test the ReplaceValueWarning class + def test_init(self, ReplaceValueWarning): + assert isroutine(ReplaceValueWarning.__init__) + # check that the instance is of correct type and all code in the + # __init__ got executed: the elements are created and initialized + assert isinstance(ReplaceValueWarning, totest.ReplaceValueWarning) + assert ReplaceValueWarning.message == '' + + +class TestUnsupportedModelWarning: + @fixture + def UnsupportedModelWarning(self): + # initialize an instance of the class with defaults + return totest.UnsupportedModelWarning() + + # test the UnsupportedModelWarning class + def test_init(self, UnsupportedModelWarning): + assert isroutine(UnsupportedModelWarning.__init__) + # check that the instance is of correct type and all code in the + # __init__ got executed: the elements are created and initialized + assert isinstance(UnsupportedModelWarning,\ + totest.UnsupportedModelWarning) + assert UnsupportedModelWarning.message == '' + + +class Test_Caught_POSYDON_Warnings: + @fixture + def clear_registry(self): + yield + # empty the global POSYDON warnings registry after each test + keys = [] + for k in totest._POSYDON_WARNINGS_REGISTRY: + keys.append(k) + for k in keys: + del totest._POSYDON_WARNINGS_REGISTRY[k] + + @fixture + def _Caught_POSYDON_Warnings(self, clear_registry): + # initialize an instance of the class with defaults + _Caught_POSYDON_Warnings = totest._Caught_POSYDON_Warnings() + yield _Caught_POSYDON_Warnings + # empty the cache to not print remaining records + _Caught_POSYDON_Warnings.caught_warnings = [] + + @fixture + def test_dict(self): + # a dictionary as a registry for testing + return {'Unit': "Test"} + + # test the _Caught_POSYDON_Warnings class + def test_init(self, _Caught_POSYDON_Warnings): + assert isroutine(_Caught_POSYDON_Warnings.__init__) + # check that the instance is of correct type and all code in the + # __init__ got executed: the elements are created and initialized + assert isinstance(_Caught_POSYDON_Warnings,\ + totest._Caught_POSYDON_Warnings) + assert _Caught_POSYDON_Warnings.catch_warnings == False + assert _Caught_POSYDON_Warnings.caught_warnings == [] + assert _Caught_POSYDON_Warnings.record + assert _Caught_POSYDON_Warnings.filter_first + assert _Caught_POSYDON_Warnings._got_called == False + assert _Caught_POSYDON_Warnings.registry is\ + totest._POSYDON_WARNINGS_REGISTRY + # bad input + with raises(TypeError, match="catch_warnings must be a boolean"): + totest._Caught_POSYDON_Warnings(catch_warnings="Test") + with raises(TypeError, match="record must be a boolean"): + totest._Caught_POSYDON_Warnings(record="Test") + with raises(TypeError, match="filter_first must be a boolean"): + totest._Caught_POSYDON_Warnings(filter_first="Test") + with raises(TypeError, match="registry must be a dictionary"): + totest._Caught_POSYDON_Warnings(registry="Test") + + def test_str(self, _Caught_POSYDON_Warnings, test_dict): + assert isroutine(_Caught_POSYDON_Warnings.__str__) + assert str(_Caught_POSYDON_Warnings) == "POSYDON warnings are shown." + # check with different setups + test_cases = [{'catch_warnings': True, 'record': True,\ + 'filter_first': True, 'registry': None,\ + 'str': "POSYDON warnings will be caught and recorded. "\ + +"Filters are applied before recording."},\ + {'catch_warnings': True, 'record': True,\ + 'filter_first': False, 'registry': None,\ + 'str': "POSYDON warnings will be caught and recorded."\ + },\ + {'catch_warnings': True, 'record': False,\ + 'filter_first': True, 'registry': None,\ + 'str': "POSYDON warnings will be caught and discarded."\ + },\ + {'catch_warnings': True, 'record': False,\ + 'filter_first': False, 'registry': None,\ + 'str': "POSYDON warnings will be caught and discarded."\ + }, + {'catch_warnings': False, 'record': True,\ + 'filter_first': True, 'registry': test_dict,\ + 'str': "Currently a private registry is used, it "\ + +"contains:\n{'Unit': 'Test'}"}] + for tc in test_cases: + caught = totest._Caught_POSYDON_Warnings(catch_warnings=\ + tc['catch_warnings'],\ + record=tc['record'],\ + filter_first=\ + tc['filter_first'],\ + registry=tc['registry']) + assert tc['str'] in str(caught) + # check artifical caught_warnings + for i in range(4): + _Caught_POSYDON_Warnings.caught_warnings = i * [test_dict] + if i==1: + assert "There is 1 warning recorded." in\ + str(_Caught_POSYDON_Warnings) + elif i>1: + assert "There are "+str(i)+" warnings recorded.".format(i) in\ + str(_Caught_POSYDON_Warnings) + else: + assert "recorded" not in str(_Caught_POSYDON_Warnings) + + def test_call(self, _Caught_POSYDON_Warnings, test_dict): + assert isroutine(_Caught_POSYDON_Warnings.__call__) + assert _Caught_POSYDON_Warnings._got_called == False + # bad input + with raises(ValueError, match="Nothing to do: either empty_cache has "\ + +"to be True or new_warning/"\ + +"change_settings needs to be set."): + _Caught_POSYDON_Warnings() + assert _Caught_POSYDON_Warnings._got_called + _Caught_POSYDON_Warnings.caught_warnings = [test_dict] + _Caught_POSYDON_Warnings(empty_cache=True) + assert _Caught_POSYDON_Warnings.caught_warnings == [] + # change_settings + with raises(TypeError, match="change_settings has to be a dict"): + _Caught_POSYDON_Warnings(change_settings="Test") + for s in _Caught_POSYDON_Warnings.__dict__: + if s == 'caught_warnings': + _Caught_POSYDON_Warnings(change_settings={s: "Test"}) + elif s == 'registry': + _Caught_POSYDON_Warnings(change_settings={s: "Test"}) + _Caught_POSYDON_Warnings(change_settings={s: None}) + else: + with raises(TypeError, match="has to be a"): + _Caught_POSYDON_Warnings(change_settings={s: "Test"}) + with raises(AttributeError, match="unknown to "\ + +"_Caught_POSYDON_Warnings"): + _Caught_POSYDON_Warnings(change_settings=test_dict) + # change setting to catch warnings and add a new one to the record list + _Caught_POSYDON_Warnings(change_settings={'catch_warnings': True},\ + new_warning={'message': "Test"}) + assert _Caught_POSYDON_Warnings.catch_warnings + assert _Caught_POSYDON_Warnings.caught_warnings ==\ + [{'message': "Test"}] + # unset catching: recorded warnings are issued and list is emptied + with warns(UserWarning, match="Test"): + _Caught_POSYDON_Warnings(change_settings={'catch_warnings': False}) + assert _Caught_POSYDON_Warnings.caught_warnings == [] + # change setting to catch warnings and add a new one to the record list + _Caught_POSYDON_Warnings(change_settings={'catch_warnings': True,\ + 'filter_first': False},\ + new_warning={'message': "Test",\ + 'stacklevel': -1}) + assert _Caught_POSYDON_Warnings.catch_warnings + assert _Caught_POSYDON_Warnings.filter_first == False + assert _Caught_POSYDON_Warnings.caught_warnings ==\ + [{'message': "Test", 'stacklevel': -1}] + # change setting to record warnings and try to add a new one + _Caught_POSYDON_Warnings(change_settings={'record': False},\ + new_warning={'message': "no record"}) + assert _Caught_POSYDON_Warnings.record == False + assert _Caught_POSYDON_Warnings.caught_warnings ==\ + [{'message': "Test", 'stacklevel': -1}] # still has old record + # unset catching: recorded warnings are issued and list is emptied + with warns(UserWarning, match="Test") as winfo: + _Caught_POSYDON_Warnings(change_settings={'catch_warnings': False}) + assert "posydonwarning.py" in winfo._list[0].filename + assert _Caught_POSYDON_Warnings.caught_warnings == [] + + def test_del(self, capsys, _Caught_POSYDON_Warnings): + assert isroutine(_Caught_POSYDON_Warnings.__del__) + # create an object with a catched warning, call the destructor and + # empty it while recording the stdout and stderr + cpw = totest._Caught_POSYDON_Warnings(catch_warnings=True) + cpw(new_warning={'message': "Unit Test"}) + with warns(UserWarning, match="Unit Test"): + cpw.__del__() + assert capsys.readouterr().err ==\ + "There are still recorded warnings:\n" + assert cpw.catch_warnings == False + cpw.caught_warnings[0]['message'] += "Test" + cpw.filter_first = False + with warns(UserWarning, match="Unit TestTest"): + cpw.__del__() + assert capsys.readouterr().err ==\ + "There are still recorded warnings:\n" + cpw.caught_warnings = [] + + def test_got_called(self, _Caught_POSYDON_Warnings): + assert isroutine(_Caught_POSYDON_Warnings.got_called) + assert _Caught_POSYDON_Warnings.got_called() == False + _Caught_POSYDON_Warnings._got_called = True + assert _Caught_POSYDON_Warnings.got_called() + + def test_has_records(self, _Caught_POSYDON_Warnings, test_dict): + assert isroutine(_Caught_POSYDON_Warnings.has_records) + assert _Caught_POSYDON_Warnings.has_records() == False + _Caught_POSYDON_Warnings.caught_warnings = [test_dict] + assert _Caught_POSYDON_Warnings.has_records() + + def test_get_cache(self, _Caught_POSYDON_Warnings, test_dict): + assert isroutine(_Caught_POSYDON_Warnings.get_cache) + assert _Caught_POSYDON_Warnings.get_cache() == [] + _Caught_POSYDON_Warnings.caught_warnings = [test_dict] + assert _Caught_POSYDON_Warnings.get_cache() == [test_dict] + # clear the cache + assert _Caught_POSYDON_Warnings.get_cache(empty_cache=True) ==\ + [test_dict] + assert _Caught_POSYDON_Warnings.get_cache() == [] + + def test_reset_cache(self, _Caught_POSYDON_Warnings, test_dict): + assert isroutine(_Caught_POSYDON_Warnings.reset_cache) + _Caught_POSYDON_Warnings.caught_warnings = [test_dict] + _Caught_POSYDON_Warnings.reset_cache() + assert _Caught_POSYDON_Warnings.caught_warnings == [] + + +class TestCatch_POSYDON_Warnings: + @fixture + def clear_registry(self): + yield + # empty the global POSYDON warnings registry after each test + keys = [] + for k in totest._POSYDON_WARNINGS_REGISTRY: + keys.append(k) + for k in keys: + del totest._POSYDON_WARNINGS_REGISTRY[k] + + @fixture + def Catch_POSYDON_Warnings(self, clear_registry): + # initialize an instance of the class with defaults + return totest.Catch_POSYDON_Warnings() + + # test the Catch_POSYDON_Warnings class + def test_init(self, Catch_POSYDON_Warnings): + assert isroutine(Catch_POSYDON_Warnings.__init__) + # check that the instance is of correct type and all code in the + # __init__ got executed: the elements are created and initialized + assert Catch_POSYDON_Warnings.catch_warnings + assert Catch_POSYDON_Warnings.record + assert Catch_POSYDON_Warnings.filter_first + assert Catch_POSYDON_Warnings.context_registry is None + assert Catch_POSYDON_Warnings.python_catch is None + # check other inputs + cpw = totest.Catch_POSYDON_Warnings(own_registry=True,\ + use_python_catch=True) + assert cpw.context_registry == {} + assert isinstance(cpw.python_catch, totest.warnings.catch_warnings) + + def test_enter_exit(self, Catch_POSYDON_Warnings): + assert isroutine(Catch_POSYDON_Warnings.__enter__) + + def test_exit(self, Catch_POSYDON_Warnings): + assert isroutine(Catch_POSYDON_Warnings.__exit__) + + def test_context(self, capsys): + with totest.Catch_POSYDON_Warnings() as cpw: + assert cpw is totest._CAUGHT_POSYDON_WARNINGS + assert cpw.catch_warnings + assert cpw.record + assert cpw.filter_first + assert cpw.registry is totest._POSYDON_WARNINGS_REGISTRY + with totest.Catch_POSYDON_Warnings(use_python_catch=True) as cpw: + assert cpw is totest._CAUGHT_POSYDON_WARNINGS + assert cpw.catch_warnings + assert cpw.record + assert cpw.filter_first + assert cpw.registry is totest._POSYDON_WARNINGS_REGISTRY diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index 1ae860cbe9..7ee8703350 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -19,6 +19,7 @@ import os import numpy as np +import pandas as pd from scipy.interpolate import interp1d from scipy.optimize import newton from scipy.integrate import quad @@ -98,6 +99,7 @@ def is_number(s): def stefan_boltzmann_law(L, R): """Compute the effective temperature give the luminosity and radius.""" + #TODO: check for invalid or negative inputs return (L * const.Lsun / (4.0 * np.pi * (R*const.Rsun) ** 2.0) / const.boltz_sigma) ** (1.0 / 4.0) @@ -124,6 +126,7 @@ def rzams(m, z=0.02, Zsun=0.02): """ + #TODO: check for invalid or negative inputs m = np.asanyarray(m) xz = [ 0.0, 3.970417e-01, -3.2913574e-01, 3.4776688e-01, 3.7470851e-01, @@ -210,43 +213,54 @@ def roche_lobe_radius(m1, m2, a_orb=1): .. [1] Eggleton, P. P. 1983, ApJ, 268, 368 """ - ## catching if a_orb is an empty array or is an array with invalid separation values + ## catching if a_orb is an empty array or is an array with invalid + ## separation values if isinstance(a_orb, np.ndarray): ## if array is empty, fill with NaN values if a_orb.size == 0: - Pwarn("Trying to compute RL radius for binary with invalid separation", "EvolutionWarning") - a_orb = np.full([1 if s==0 else s for s in a_orb.shape], np.nan, dtype=np.float64) + Pwarn("Trying to compute RL radius for binary with invalid" + " separation", "EvolutionWarning") + a_orb = np.full([1 if s==0 else s for s in a_orb.shape], np.nan, + dtype=np.float64) ## if array contains invalid values, replace with NaN elif np.any(a_orb < 0): - Pwarn("Trying to compute RL radius for binary with invalid separation", "EvolutionWarning") + Pwarn("Trying to compute RL radius for binary with invalid" + " separation", "EvolutionWarning") a_orb[a_orb < 0] = np.nan ## catching if a_orb is a float with invalid separation value elif a_orb < 0: - Pwarn("Trying to compute RL radius for binary with invalid separation", "EvolutionWarning") + Pwarn("Trying to compute RL radius for binary with invalid separation", + "EvolutionWarning") a_orb = np.nan - if isinstance(m1, np.ndarray): if m1.size == 0: - Pwarn("Trying to compute RL radius for nonexistent object", "EvolutionWarning") - m1 = np.full([1 if s==0 else s for s in m1.shape], np.nan, dtype=np.float64) + Pwarn("Trying to compute RL radius for nonexistent object", + "EvolutionWarning") + m1 = np.full([1 if s==0 else s for s in m1.shape], np.nan, + dtype=np.float64) elif np.any(m1 <= 0): - Pwarn("Trying to compute RL radius for nonexistent object", "EvolutionWarning") + Pwarn("Trying to compute RL radius for nonexistent object", + "EvolutionWarning") m1[m1 <= 0] = np.nan - elif m1 <=0: - Pwarn("Trying to compute RL radius for nonexistent object", "EvolutionWarning") + elif m1 <= 0: + Pwarn("Trying to compute RL radius for nonexistent object", + "EvolutionWarning") m1 = np.nan - if isinstance(m2, np.ndarray): if m2.size == 0: - Pwarn("Trying to compute RL radius for nonexistent companion", "EvolutionWarning") - m2 = np.full([1 if s==0 else s for s in m2.shape], np.nan, dtype=np.float64) + Pwarn("Trying to compute RL radius for nonexistent companion", + "EvolutionWarning") + m2 = np.full([1 if s==0 else s for s in m2.shape], np.nan, + dtype=np.float64) elif np.any(m2 <= 0): - Pwarn("Trying to compute RL radius for nonexistent companion", "EvolutionWarning") + Pwarn("Trying to compute RL radius for nonexistent companion", + "EvolutionWarning") m2[m2 <= 0] = np.nan - elif m2 <=0: - Pwarn("Trying to compute RL radius for nonexistent companion", "EvolutionWarning") + elif m2 <= 0: + Pwarn("Trying to compute RL radius for nonexistent companion", + "EvolutionWarning") m2 = np.nan q = m1/m2 @@ -274,6 +288,7 @@ def orbital_separation_from_period(period_days, m1_solar, m2_solar): The separation of the binary in solar radii. """ + #TODO: check for invalid or negative inputs # cast to float64 to avoid overflow m1_solar = np.asarray(m1_solar, dtype="float64") m2_solar = np.asarray(m2_solar, dtype="float64") @@ -305,6 +320,7 @@ def orbital_period_from_separation(separation, m1, m2): The orbital period in days. """ + #TODO: check for invalid or negative inputs return const.dayyer * ((separation / const.aursun)**3.0 / (m1 + m2)) ** 0.5 @@ -321,7 +337,7 @@ def eddington_limit(binary, idx=-1): Returns ------- - list + tuple The Eddington accretion limit and radiative efficiency in solar units. """ @@ -354,10 +370,10 @@ def eddington_limit(binary, idx=-1): else: raise ValueError('COtype must be "BH", "NS", or "WD"') - if surface_h1[i] is None: + if pd.isna(surface_h1[i]): surface_h1[i] = 0.7155 if state_acc[i] == "BH": - r_isco = 6 + r_isco = 6 #TODO: get r_isco as input/calculate from spin # m_ini is the accretor mass at zero spin m_ini = m_acc[i] * np.sqrt(r_isco / 6) eta = 1 - np.sqrt(1 - (min(m_acc[i], @@ -393,7 +409,7 @@ def beaming(binary): Returns ------- - list + tuple The super-Eddington isotropic-equivalent accretion rate and beaming factor respcetively in solar units. @@ -405,8 +421,12 @@ def beaming(binary): """ mdot_edd = eddington_limit(binary, idx=-1)[0] - rlo_mdot = 10**binary.lg_mtransfer_rate + if binary.lg_mtransfer_rate is None: + rlo_mdot = np.nan + else: + rlo_mdot = 10**binary.lg_mtransfer_rate + print(rlo_mdot, mdot_edd) if rlo_mdot >= mdot_edd: if rlo_mdot > 8.5 * mdot_edd: # eq. 8 in King A. R., 2009, MNRAS, 393, L41-L44 @@ -417,7 +437,7 @@ def beaming(binary): mdot_beam = mdot_edd * (1 + np.log(rlo_mdot / mdot_edd)) / b else: b = 1 - mdot_beam = 10**binary.lg_mtransfer_rate + mdot_beam = rlo_mdot return mdot_beam, b @@ -478,6 +498,7 @@ def bondi_hoyle(binary, accretor, donor, idx=-1, wind_disk_criteria=True, ecc = np.atleast_1d( np.asanyarray([*binary.eccentricity_history, binary.eccentricity], dtype=float)[idx]) + #TODO: use stars in the binary as donor and accretor m_acc = np.atleast_1d( np.asanyarray([*accretor.mass_history, accretor.mass], dtype=float)[idx]) @@ -579,6 +600,7 @@ def bondi_hoyle(binary, accretor, donor, idx=-1, wind_disk_criteria=True, # make it Eddington-limited mdot_edd = eddington_limit(binary, idx=idx)[0] + print(mdot_acc, mdot_edd) mdot_acc = np.minimum(mdot_acc, mdot_edd) return np.squeeze(mdot_acc) @@ -683,7 +705,7 @@ def inverse_sampler(x, y, size=1): # if nan values found, then flat CDF for which the inverse is undefined... where_nan = np.where(~np.isfinite(sample)) - n_where_nan = len(where_nan) + n_where_nan = len(where_nan[0]) # ... in that case, simply sample randomly from each flat bin! if n_where_nan: assert np.all(dy_bins[where_nan] == 0) @@ -768,7 +790,15 @@ def read_histogram_from_file(path): continue arrays.append(np.fromstring(line.strip(), dtype=float, sep=",")) if len(arrays) > 2: - raise RuntimeError("More than two lines found in the histogram document.") + raise IndexError("More than two lines found in the histogram" + " document.") + if len(arrays) < 2: + raise IndexError("Less than two lines found in the histogram" + " document.") + if len(arrays[0]) - 1 != len(arrays[1]): + raise IndexError("The number of elements in the second data line is" + " not one less than the number in the first data" + " line.") return arrays @@ -894,7 +924,9 @@ def spin_stable_mass_transfer(spin_i, star_mass_preMT, star_mass_postMT): Based on Thorne 1974 eq. 2a. """ - if star_mass_preMT is None or star_mass_postMT is None: + if ((star_mass_preMT is None) or (star_mass_preMT<=0.0) or + (star_mass_postMT is None) or (star_mass_postMT<=0.0) or + (spin_i is None) or (spin_i<0.0)): return None z1 = 1+(1-spin_i**2)**(1/3)*((1+spin_i)**(1/3)+(1-spin_i)**(1/3)) z2 = (3*spin_i**2+z1**2)**0.5 @@ -1061,7 +1093,8 @@ def get_binary_state_and_event_and_mt_case(binary, interpolation_class=None, verbose=verbose) # convert to strings mt_flag_1_str = cumulative_mass_transfer_string([mt_flag_1]) - mt_flag_2_str = cumulative_mass_transfer_string([mt_flag_2]) + mt_flag_2_str = cumulative_mass_transfer_string([mt_flag_2+10 if mt_flag_2\ + in ALL_RLO_CASES else mt_flag_2]) rlof1 = mt_flag_1 in ALL_RLO_CASES rlof2 = mt_flag_2 in ALL_RLO_CASES @@ -1070,7 +1103,10 @@ def get_binary_state_and_event_and_mt_case(binary, interpolation_class=None, if rlof1 and rlof2: # contact condition result = ['contact', None, 'None'] if interpolation_class == 'unstable_MT': - result = ['contact', 'oCE1', 'None'] + if rl_overflow1>=rl_overflow2: # star 1 initiated CE + result = ['contact', 'oCE1', 'None'] + else: # star 2 initiated CE + result = ['contact', 'oCE2', 'None'] elif no_rlof: # no MT in any star result = ['detached', None, 'None'] elif rlof1 and not rlof2: # only in star 1 @@ -1203,13 +1239,15 @@ def He_MS_lifetime(mass): He MS time duration in yr. """ - if mass < 2.0: + if mass <=0.0: + raise ValueError(f"Too low mass: {mass}") + elif mass < 2.0: he_t_ms = 10 ** 8 elif mass >= 2.0 and mass < 10.0: he_t_ms = 10**(-2.6094 * np.log10(mass) + 8.7855) elif mass >= 10.0 and mass < 100.0: he_t_ms = 10**(-0.69897 * np.log10(mass) + 6.875) - elif mass >= 100.0: + else: # mass >= 100.0 he_t_ms = 3 * 10 ** 5 return he_t_ms @@ -1334,7 +1372,8 @@ def infer_star_state(star_mass=None, surface_h1=None, return STATE_UNDETERMINED rich_in = ("H-rich" if surface_h1 > THRESHOLD_HE_NAKED_ABUNDANCE - else ("accreted_He" if round(surface_h1, 10) LOG10_BURNING_THRESHOLD and log_LH - log_Lnuc > REL_LOG10_BURNING_THRESHOLD) burning_He = (log_LHe > LOG10_BURNING_THRESHOLD @@ -1422,7 +1461,8 @@ def cumulative_mass_transfer_numeric(MT_cases): Parameters ---------- MT_cases : array-like - A list of the integer MT flags at sequential history steps. + A list of the integer MT flags at sequential history steps. If the + cases are instead given in string format they are converted first. Returns ------- @@ -1501,7 +1541,8 @@ def cumulative_mass_transfer_string(cumulative_integers): caseA/B/A : case A, then B, and A again (although unphysical). """ - assert len(cumulative_integers) != 0 + if len(cumulative_integers) == 0: + return "?" result = "" added_case_word = False for integer in cumulative_integers: @@ -1509,7 +1550,7 @@ def cumulative_mass_transfer_string(cumulative_integers): result += "?" elif integer == MT_CASE_NO_RLO: result += "no_RLO" - else: + elif ((integer in MT_CASE_TO_STR) or (integer-10 in MT_CASE_TO_STR)): if not added_case_word: result += "case_" added_case_word = True @@ -1519,6 +1560,9 @@ def cumulative_mass_transfer_string(cumulative_integers): result += MT_CASE_TO_STR[integer] + '1' # from star 1 else: result += MT_CASE_TO_STR[integer-10] + '2' # from star 2 + else: + Pwarn("Unknown MT case: {}".format(integer),\ + "InappropriateValueWarning") return result @@ -1563,12 +1607,13 @@ def cumulative_mass_transfer_flag(MT_cases, shift_cases=False): case_2_min = MT else: # unknown donor - Pwarn("MT case with unknown donor: {}".format(MT), "EvolutionWarning") + Pwarn("MT case with unknown donor: {}".format(MT),\ + "EvolutionWarning") corrected_MT_cases.append(MT) else: corrected_MT_cases = MT_cases.copy() return cumulative_mass_transfer_string( - cumulative_mass_transfer_numeric(MT_cases) + cumulative_mass_transfer_numeric(corrected_MT_cases) ) @@ -1696,6 +1741,7 @@ def CEE_parameters_from_core_abundance_thresholds(star, verbose=False): """ mass = star.mass radius = 10.**star.log_R + star_state = star.state m_core_CE_1cent = 0.0 m_core_CE_10cent = 0.0 m_core_CE_30cent = 0.0 @@ -1708,7 +1754,6 @@ def CEE_parameters_from_core_abundance_thresholds(star, verbose=False): if profile is not None and isinstance(profile, np.ndarray): mass_prof = profile["mass"] - star_state = star.state m_core = 0.0 r_core = 0.0 @@ -1844,6 +1889,7 @@ def initialize_empty_array(arr): res[colname] = np.nan if np.issubsctype(res[colname], str): res[colname] = np.nan + #TODO: handle h5py.string_dtype() return res @@ -1933,8 +1979,9 @@ def calculate_core_boundary(donor_mass, # ind_core=np.argmax(element[::-1]>=core_element_fraction_definition) else: ind_core = -1 - Pwarn("Stellar profile columns were not enough to calculate the core-envelope " - "boundaries for CE, entire star is now considered an envelope", "ApproximationWarning") + Pwarn("Stellar profile columns were not enough to calculate the"+\ + " core-envelope boundaries for CE, entire star is now"+\ + " considered an envelope", "ApproximationWarning") return ind_core # starting from the surface, both conditions become True when element @@ -2024,7 +2071,7 @@ def separation_evol_wind_loss(M_current, M_init, Mcomp, A_init): def period_change_stabe_MT(period_i, Mdon_i, Mdon_f, Macc_i, alpha=0.0, beta=0.0): - """Change the binary period after a semi-detahed stable MT phase. + """Change the binary period after a semi-detached stable MT phase. Calculated in Sorensen, Fragos et al. 2017A&A...597A..12S. Note that MT efficiencies are assumed constant (i.e., not time-dependent) @@ -2055,11 +2102,14 @@ def period_change_stabe_MT(period_i, Mdon_i, Mdon_f, Macc_i, """ DM_don = Mdon_i - Mdon_f # mass lost from donor (>0) + if DM_don < 0: + raise ValueError("Donor gains mass from {} to {}".format(Mdon_i, + Mdon_f)) Macc_f = Macc_i + (1.-beta)*(1.-alpha)*DM_don if alpha < 0.0 or beta < 0.0 or alpha > 1.0 or beta > 1.0: raise ValueError("In period_change_stabe_MT, mass transfer " - "efficiencies, alpha, beta {}{} are not in the [0-1] " - "range.".format(alpha, beta)) + "efficiencies, alpha, beta: {}, {} are not in the " + "[0-1] range.".format(alpha, beta)) if beta != 1.0: # Eq. 7 of Sorensen+Fragos et al. 2017 period_f = (period_i * (Mdon_f/Mdon_i)**(3.*(alpha-1.)) * (Macc_f/Macc_i)**(3./(beta-1.)) @@ -2077,26 +2127,37 @@ def period_change_stabe_MT(period_i, Mdon_i, Mdon_f, Macc_i, def linear_interpolation_between_two_cells(array_y, array_x, x_target, top=None, bot=None, verbose=False): """Interpolate quantities between two star profile shells.""" - if ((np.isnan(top) or top is None) and (np.isnan(bot) or bot is None)): + if ((top is None or np.isnan(top)) and (bot is None or np.isnan(bot))): top = np.argmax(array_x >= x_target) bot = top - 1 - elif np.isnan(bot) or bot is None: + elif bot is None or np.isnan(bot): bot = top - 1 - elif np.isnan(top) or top is None: + elif top is None or np.isnan(top): top = bot + 1 - if top > len(array_x): - y_target = array_y[top] + if top >= len(array_y): + Pwarn("top={} is too large, use last element in array_y".format(top), + "ReplaceValueWarning") + top = len(array_y)-1 + if top >= len(array_x): + Pwarn("array_x too short, use y at top={}".format(top), + "InterpolationWarning") + return array_y[top] if bot < 0: + Pwarn("bot={} is too small, use first element".format(bot), + "ReplaceValueWarning") bot = 0 if top == bot: y_target = array_y[top] - Pwarn("linear interpolation occured between the same point", "InterpolationWarning") - if verbose: - print("linear interpolation, but at the edge") - print("x_target,top, bot, len(array_x), y_target", - x_target, top, bot, len(array_x), y_target) + Pwarn("linear interpolation occured between the same point: x_target," + " top, bot, len(array_x), y_target = {}, {}, {}, {}, {}".format(\ + x_target, top, bot, len(array_x), y_target), + "InterpolationWarning") + elif bot > top: + y_target = array_y[top] + Pwarn("bot={} is too large: use y at top={}".format(bot, top), + "InterpolationWarning") else: x_top = array_x[top] x_bot = array_x[bot] @@ -2110,7 +2171,7 @@ def linear_interpolation_between_two_cells(array_y, array_x, x_target, if verbose: print("linear interpolation") - print("x_target,top, bot, len(array_x)", + print("x_target, top, bot, len(array_x)", x_target, top, bot, len(array_x)) print("x_top, x_bot, y_top, y_bot, y_target", x_top, x_bot, y_top, y_bot, y_target) @@ -2224,6 +2285,9 @@ def calculate_lambda_from_profile( elem_prof = profile["y_mass_fraction_He"] elif "stripped_He" in donor_star_state: elem_prof = profile["y_mass_fraction_He"] + else: + raise ValueError("state {} not supported in CEE"\ + .format(donor_star_state)) mc1_i = linear_interpolation_between_two_cells( donor_mass, elem_prof, core_element_fraction_definition, ind_core, ind_core-1, verbose) @@ -2301,7 +2365,7 @@ def get_mass_radius_dm_from_profile(profile, m1_i=0.0, if ("radius" in profile.dtype.names): donor_radius = profile["radius"] - elif ("log_R" in profile.dtype.names): + else: #if ("log_R" in profile.dtype.names): donor_radius = 10**profile["log_R"] # checking if mass of profile agrees with the mass of the binary object @@ -2422,6 +2486,9 @@ def get_internal_energy_from_profile(common_envelope_option_for_lambda, raise ValueError( "CEE problem calculating recombination (and H2 recombination) " "energy, remaining internal energy giving negative values.") + else: + raise ValueError("unsupported: common_envelope_option_for_lambda = {}"\ + .format(common_envelope_option_for_lambda)) return specific_donor_internal_energy @@ -2498,7 +2565,7 @@ def calculate_recombination_energy(profile, tolerance=0.001): frac_HeI = profile["neutral_fraction_He"] avg_charge_He = profile["avg_charge_He"] - for i in range(len(frac_HI)): + for i in range(len(frac_HeI)): frac_HeI[i] = min(1., frac_HeI[i]) # knowing the frac_HeI and the avg_charge_He, # we can solve for frac_HeII and frac_HeIII @@ -2672,9 +2739,11 @@ def calculate_Mejected_for_integrated_binding_energy(profile, Ebind_threshold, if donor_mass[ind_threshold]< mc1_i or donor_radius[ind_threshold]", + "Matthias Kruckow ", +] + + IGNORE_REASONS_PRIORITY = [ # missing data files 'ignored_no_history1', # history1 is always needed diff --git a/posydon/utils/posydonerror.py b/posydon/utils/posydonerror.py index f107d7864f..31a373b5af 100644 --- a/posydon/utils/posydonerror.py +++ b/posydon/utils/posydonerror.py @@ -24,12 +24,18 @@ def __init__(self, message="", objects=None): ---------- message : str POSYDONError message. - objects : None or list of objects. + objects : None or list of objects A list of accompanied objects (or None if not set), that will be handled differently when `str` method is used. This error can only accept BinaryStar, SingleStar, or a list of each. """ + if not isinstance(message, str): + raise TypeError("The error message must be a string.") + if ((objects is not None) and + (not isinstance(objects, (list, SingleStar, BinaryStar)))): + raise TypeError("The error objects must be None, a list, a " + "SingleStar object, or a BinaryStar object.") self.message = message # copy the objects: we must know their state at the moment of the error self.objects = copy.copy(objects) @@ -39,13 +45,13 @@ def __str__(self): """Create the text that accompanies this exception.""" result = "" if self.objects is not None: - if isinstance(self.objects,list): + if isinstance(self.objects, list): for i, obj in enumerate(self.objects): if isinstance(obj, (BinaryStar, SingleStar)): result += f"\n\nOBJECT #{i+1} ({type(obj)}):\n{str(obj)}" - elif isinstance(obj, (BinaryStar, SingleStar)): + elif isinstance(self.objects, (BinaryStar, SingleStar)): result += f"\n\nOBJECT #({type(self.objects)}):\n{str(self.objects)}" - else: + else: # pragma: no cover pass return result + '\n'+ super().__str__() @@ -68,7 +74,25 @@ class ModelError(POSYDONError): class NumericalError(POSYDONError): """POSYDON error specific for when a binary FAILS due to limitations of numerical methods.""" -def initial_condition_message(binary,ini_params = None ): +def initial_condition_message(binary, ini_params=None): + """Generate a message with the initial conditions. + + Parameters + ---------- + binary : BinaryStar + BinaryStar object to take the initial conditions from. + ini_params : None or iterable of str + If None take the initial conditions from the binary, otherwise add + each item of it to the message. + + Returns + ------- + string + The message with the initial conditions. + + """ + if not isinstance(binary, BinaryStar): + raise TypeError("The binary must be a BinaryStar object.") if ini_params is None: ini_params = ["\nFailed Binary Initial Conditions:\n", f"S1 mass: {binary.star_1.mass_history[0]} \n", @@ -83,7 +107,7 @@ def initial_condition_message(binary,ini_params = None ): f"S2 natal kick array: { binary.star_2.natal_kick_array}\n"] message = "" for i in ini_params: - message += i + message += i return message diff --git a/posydon/utils/posydonwarning.py b/posydon/utils/posydonwarning.py index 66bb4d70a8..738b4c86a2 100644 --- a/posydon/utils/posydonwarning.py +++ b/posydon/utils/posydonwarning.py @@ -176,9 +176,16 @@ def _apply_POSYDON_filter(warning=dict(message="No warning"), registry=None): ------- warning or None in case it got filtered out. """ + # Check registry and warning if registry is None: global _POSYDON_WARNINGS_REGISTRY + if not isinstance(_POSYDON_WARNINGS_REGISTRY, dict): # pragma: no cover + raise TypeError("_POSYDON_WARNINGS_REGISTRY is corrupt and can't " + "be used for the registry, hence registry can't be" + " None.") registry = _POSYDON_WARNINGS_REGISTRY + elif not isinstance(registry, dict): + raise TypeError("registry must be a dictionary or None.") if not isinstance(warning, dict): raise TypeError("warning must be a dictionary.") # Get stack level @@ -195,7 +202,7 @@ def _apply_POSYDON_filter(warning=dict(message="No warning"), registry=None): # Get filename and lineno from frame at stacklevel try: frame = sys._getframe(stacklevel) - except: + except: # pragma: no cover g = sys.__dict__ filename = "sys" lineno = 1 @@ -206,12 +213,8 @@ def _apply_POSYDON_filter(warning=dict(message="No warning"), registry=None): # Get module if isinstance(g, dict): module = g.get('__name__', "posydonwarnings") - else: + else: # pragma: no cover module = "posydonwarnings" - # Check registry - if not isinstance(registry, dict): - print("Reset registry, old was:", registry) - registry = {} # Set key for registry: # We do not use the warnings text, to allow it to contain detailed # information, while still identifying warnings with same origin @@ -221,7 +224,7 @@ def _apply_POSYDON_filter(warning=dict(message="No warning"), registry=None): if not isinstance(text, str): raise TypeError("message must be a string.") # Search the filters: - # Here we still uses the python filters + # Here we still use the python filters for item in warnings.filters: action, msg, cat, mod, ln = item if ((msg is None or msg.match(text)) and @@ -374,7 +377,10 @@ def __call__(self, new_warning=None, empty_cache=False, (len(self.caught_warnings)>0)): # If there are recorded warnings issue them and empty the list for w in self.caught_warnings: - w["stacklevel"] += 2 + if "stacklevel" in w: + w["stacklevel"] += 2 + else: + w["stacklevel"] = 2 if self.filter_first: warnings.warn(**w) else: @@ -401,7 +407,7 @@ def __del__(self): if len(self.caught_warnings)>0: # If there are recorded warnings issue them. self.catch_warnings = False - print("There are still recorded warnings:") + print("There are still recorded warnings:", file=sys.stderr) for w in self.caught_warnings: w["stacklevel"] = 2 if self.filter_first: From f432718b964790143dd84d4e87bb2a3bbf0042a5 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Tue, 10 Dec 2024 04:28:22 +0100 Subject: [PATCH 273/319] period_change_stabe_MT -> period_change_stable_MT (#467) --- posydon/binary_evol/CE/step_CEE.py | 8 ++++---- .../unit_tests/utils/test_common_functions.py | 20 +++++++++---------- posydon/utils/common_functions.py | 6 +++--- 3 files changed, 17 insertions(+), 17 deletions(-) diff --git a/posydon/binary_evol/CE/step_CEE.py b/posydon/binary_evol/CE/step_CEE.py index fe398c32f5..dfccf7105e 100644 --- a/posydon/binary_evol/CE/step_CEE.py +++ b/posydon/binary_evol/CE/step_CEE.py @@ -636,13 +636,13 @@ def CEE_simple_alpha_prescription( # An assumed stable mass transfer case after postCEE with # fully non-conservative MT and mass lost from the vicinity # of the accretor: - orbital_period_f = cf.period_change_stabe_MT( + orbital_period_f = cf.period_change_stable_MT( orbital_period_postCEE, Mdon_i=mc1_i, Mdon_f=mc1_f, Macc_i=mc2_i, alpha=0.0, beta=1.0) if double_CE: # a reverse stable MT assumed to be happening # at the same time - orbital_period_f = cf.period_change_stabe_MT( + orbital_period_f = cf.period_change_stable_MT( orbital_period_f, Mdon_i=mc2_i, Mdon_f=mc2_f, Macc_i=mc1_i, alpha=0.0, beta=1.0) if verbose: @@ -656,13 +656,13 @@ def CEE_simple_alpha_prescription( "core_not_replaced_windloss"]: # An assumed wind loss after postCEE with mass lost # from the vicinity of the donor - orbital_period_f = cf.period_change_stabe_MT( + orbital_period_f = cf.period_change_stable_MT( orbital_period_postCEE, Mdon_i=mc1_i, Mdon_f=mc1_f, Macc_i=mc2_i, alpha=1.0, beta=0.0) if double_CE: # a wind mass loss from the 2nd star assumed to be # happening at the same time - orbital_period_f = cf.period_change_stabe_MT( + orbital_period_f = cf.period_change_stable_MT( orbital_period_f, Mdon_i=mc2_i, Mdon_f=mc2_f, Macc_i=mc1_i, alpha=1.0, beta=0.0) if verbose: diff --git a/posydon/unit_tests/utils/test_common_functions.py b/posydon/unit_tests/utils/test_common_functions.py index ebfb7ec814..e50c04e96d 100644 --- a/posydon/unit_tests/utils/test_common_functions.py +++ b/posydon/unit_tests/utils/test_common_functions.py @@ -104,7 +104,7 @@ def test_dir(self): 'linear_interpolation_between_two_cells', 'newton', 'np',\ 'orbital_period_from_separation',\ 'orbital_separation_from_period', 'os', 'pd',\ - 'period_change_stabe_MT', 'period_evol_wind_loss',\ + 'period_change_stable_MT', 'period_evol_wind_loss',\ 'profile_recomb_energy', 'quad',\ 'read_histogram_from_file', 'rejection_sampler',\ 'roche_lobe_radius', 'rotate', 'rzams',\ @@ -282,8 +282,8 @@ def test_instance_period_evol_wind_loss(self): def test_instance_separation_evol_wind_loss(self): assert isroutine(totest.separation_evol_wind_loss) - def test_instance_period_change_stabe_MT(self): - assert isroutine(totest.period_change_stabe_MT) + def test_instance_period_change_stable_MT(self): + assert isroutine(totest.period_change_stable_MT) def test_instance_linear_interpolation_between_two_cells(self): assert isroutine(totest.linear_interpolation_between_two_cells) @@ -1806,28 +1806,28 @@ def test_separation_evol_wind_loss(self): for (M1, Mi, M2, ai, r) in tests: assert totest.separation_evol_wind_loss(M1, Mi, M2, ai) == r - def test_period_change_stabe_MT(self): + def test_period_change_stable_MT(self): # missing argument with raises(TypeError, match="missing 4 required positional "\ +"arguments: 'period_i', 'Mdon_i', "\ +"'Mdon_f', and 'Macc_i'"): - totest.period_change_stabe_MT() + totest.period_change_stable_MT() # bad input with raises(TypeError) as error_info: - totest.period_change_stabe_MT(None, None, None, None) + totest.period_change_stable_MT(None, None, None, None) assert error_info.value.args[0] == "unsupported operand type(s) for "\ + "-: 'NoneType' and 'NoneType'" tests = [(-1.0, 0.0), (2.0, 0.0), (0.0, -1.0), (0.0, 2.0)] for (a, b) in tests: with raises(ValueError) as error_info: - totest.period_change_stabe_MT(0.5, 0.5, 0.5, 0.5, alpha=a,\ + totest.period_change_stable_MT(0.5, 0.5, 0.5, 0.5, alpha=a,\ beta=b) - assert error_info.value.args[0] == "In period_change_stabe_MT, "\ + assert error_info.value.args[0] == "In period_change_stable_MT, "\ + "mass transfer efficiencies,"\ + f" alpha, beta: {a}, {b} "\ + "are not in the [0-1] range." with raises(ValueError, match="Donor gains mass from 0.5 to 1.5"): - totest.period_change_stabe_MT(0.5, 0.5, 1.5, 0.5) + totest.period_change_stable_MT(0.5, 0.5, 1.5, 0.5) # examples: tests = [(0.5, 0.5, 0.5, 0.5, 0.0, 0.0, 0.5),\ (1.5, 0.5, 0.5, 0.5, 0.0, 0.0, 1.5),\ @@ -1863,7 +1863,7 @@ def test_period_change_stabe_MT(self): (0.5, 1.0, 0.5, 1.0, 0.0, 1.0,\ approx(1.58670336106, abs=6e-12))] for (Pi, Mdi, Mdf, Mai, a, b, r) in tests: - assert totest.period_change_stabe_MT(Pi, Mdi, Mdf, Mai, alpha=a,\ + assert totest.period_change_stable_MT(Pi, Mdi, Mdf, Mai, alpha=a,\ beta=b) == r def test_linear_interpolation_between_two_cells(self, capsys): diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index 7ee8703350..6b2eef726b 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -2069,8 +2069,8 @@ def separation_evol_wind_loss(M_current, M_init, Mcomp, A_init): return 10.0**log10A -def period_change_stabe_MT(period_i, Mdon_i, Mdon_f, Macc_i, - alpha=0.0, beta=0.0): +def period_change_stable_MT(period_i, Mdon_i, Mdon_f, Macc_i, + alpha=0.0, beta=0.0): """Change the binary period after a semi-detached stable MT phase. Calculated in Sorensen, Fragos et al. 2017A&A...597A..12S. @@ -2107,7 +2107,7 @@ def period_change_stabe_MT(period_i, Mdon_i, Mdon_f, Macc_i, Mdon_f)) Macc_f = Macc_i + (1.-beta)*(1.-alpha)*DM_don if alpha < 0.0 or beta < 0.0 or alpha > 1.0 or beta > 1.0: - raise ValueError("In period_change_stabe_MT, mass transfer " + raise ValueError("In period_change_stable_MT, mass transfer " "efficiencies, alpha, beta: {}, {} are not in the " "[0-1] range.".format(alpha, beta)) if beta != 1.0: # Eq. 7 of Sorensen+Fragos et al. 2017 From e8e93ff35baf4dfff432332cf88d068f329b48de Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Tue, 10 Dec 2024 16:30:54 +1300 Subject: [PATCH 274/319] PSygrid force int input (#440) * PSygrid force int input force the user to input an integer. This includes numpy versions of int. If this is not forces, the `initial_values` and `final_values` can be loaded with a 1 value array/list, but the `history1`, etc are not loaded with that input. As a result, `None` is returned for those and it looks like the history is empty. * add extra check for `contains` raises an error in index in `__contains__` and `__getitem__` is not an integer * Change error type of IndexError Preferred way to raise error: """if key is a value outside the set of indexes for the sequence (after any special interpretation of negative values), IndexError should be raised. For mapping types, if key is missing (not in the container), KeyError should be raised.""" https://docs.python.org/3/reference/datamodel.html#object.__getitem__ * change to 'index' * edited docstring to match arg name --------- Co-authored-by: Elizabeth Teng <41969755+elizabethteng@users.noreply.github.com> --- posydon/grids/psygrid.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index e6085e9a40..27832ef8d6 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -1540,11 +1540,13 @@ def one_if_ok(data): return ret - def __getitem__(self, idx): - """Return a PSyRunView instance for the run with index `idx`.""" - if idx not in self: - raise KeyError("Index {} out of bounds.".format(idx)) - return PSyRunView(self, idx) + def __getitem__(self, index): + """Return a PSyRunView instance for the run with index `index`.""" + if not np.issubdtype(type(index), int): + raise TypeError("Index {} is not of type int".format(index)) + if index not in self: + raise IndexError("Index {} out of bounds.".format(index)) + return PSyRunView(self, index) def get_pandas_initial_final(self): """Convert the initial/final values into a single Pandas dataframe.""" @@ -1564,6 +1566,8 @@ def __len__(self): def __contains__(self, index): """Return True if run with index `index` is in the grid.""" + if not np.issubdtype(type(index), int): + raise TypeError("Index {} is not of type int".format(index)) return 0 <= index < self.n_runs def __iter__(self): From a3761e1686d3d692d53b6118b18ece4a51c1fe51 Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Fri, 13 Dec 2024 04:17:45 +1300 Subject: [PATCH 275/319] Backpropagate changes on main/max_requirements into development (#456) * Update README.md * Update install.rst * Update install.rst * Adding an option to create a VH diagram w/ BinaryStar objects (#158) * Adding an option to create a VH diagram w/ BinaryStar objects * Update VHdiagram.py * Update VHdiagram.py --------- Co-authored-by: Jeff Andrews * Update README.md - main (#188) * Update README.md Update the readme to remove the installation instructions so that we do not have to maintain the same instructions in two places. Currently, the instructions in the readme are not the same as the ones in the documentation. * Update README.md I also edited the text to match the deve version * V1 python_utils fix (#191) * add python_utils==3.5.2 requirement * Update setup.py Limit `more-itertools` to its latest python3.7 version. * Update V1 conda setup (#199) * update conda dependencies for latest python3.7 packages dependencies + remove pyqt5 dependency * update astropy + pyqt5 versions * change pyqt to 5.15.4 * Updates in installation instructions in the V1 documentation (#201) * Updated installation instructions in the v1 documentation * Simplifying sections and removing git-lfs mention * Update install.rst * No change? * Adding OSX-ARM installtion instructions. * Update install.rst Add the conda channels to the OSX-ARM architecture --------- Co-authored-by: Jeff Andrews Co-authored-by: Max <14039563+maxbriel@users.noreply.github.com> * conda_v1 fix (#266) * change dependencies to have a maximum and release the numpy dependency * add maximum to setup.py too * point to updated GH action * Change numpy dependency * fix * change numpy version in setup.py too * solving for an old sk-learn numpy issue * Update publish-to-anaconda.yml (#270) changes publish-to-anaconda to development version. * update v1 for scikit-learn version (#292) * update v1 for scikit-learn version * Update setup.py * Update meta.yaml numpy 1.20 deprecates "np.float", which exists in v1 code * Update setup.py * Update conda version number (#304) This is required to publish Elizabeth's changes to conda * fix maximum requirements * fix scikit-learn version and remove email from AUTHOR_EMAIL. * v1 in mesa_dir the search for "v1/" should be in the full mesa_dir not the dirname * update zenodo link and md5 hash * change installation instructions to specifically request v1.0.4 for installation (this version) * update index.rst year * add licence date change * add change in conf.py of year * year change in LICENSE.md * Update meta.yaml * Update meta.yaml * change publish-to-anaconda to publish on a github release publish * typo fixed in documenation * Change installation instructions to specifically request v1.0.4 (#450) * change installation instructions to specifically request v1.0.4 for installation (this version) * update index.rst year * add licence date change * add change in conf.py of year * year change in LICENSE.md * publish only to anaconda on releases * fix found typos in v1 documentation * Update publish-to-anaconda.yml (#454) Point towards POSYDON-code/ version of the publish-to-anaconda action. * remove redundant lines in post_processing.py * add e-mail to setup.py * remove updated VH Diagram plotting code for a single binary. Surrounding code has change, see PR#456. * change default mem_per_cpu to 7G" * update version + installation instriction + link to releases + zenodo --------- Co-authored-by: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Co-authored-by: sgossage Co-authored-by: Jeff Andrews Co-authored-by: tassos25 Co-authored-by: Konstantinos Kovlakas <22239544+kkovlakas@users.noreply.github.com> Co-authored-by: Elizabeth Teng <41969755+elizabethteng@users.noreply.github.com> Co-authored-by: mkruckow <122798003+mkruckow@users.noreply.github.com> Co-authored-by: mkruckow --- LICENSE.md | 2 +- bin/posydon-setup-popsyn | 2 +- conda/meta.yaml | 40 +++++++++--------- docs/_source/conf.py | 14 +++++++ .../getting-started/installation-guide.rst | 2 +- docs/_source/index.rst | 12 +++--- .../user-guides/database-query-system.rst | 2 +- docs/_source/user-guides/web-application.rst | 4 -- posydon/grids/io.py | 4 +- posydon/grids/post_processing.py | 22 +++++----- posydon/grids/psygrid.py | 12 +++--- posydon/interpolation/IF_interpolation.py | 2 +- posydon/utils/data_download.py | 4 +- setup.py | 42 +++++++++---------- 14 files changed, 87 insertions(+), 77 deletions(-) diff --git a/LICENSE.md b/LICENSE.md index accf365dc2..9f086551ce 100644 --- a/LICENSE.md +++ b/LICENSE.md @@ -1,6 +1,6 @@ BSD 3-Clause License -Copyright (c) 2022, Tassos Fragos +Copyright (c) 2024, Tassos Fragos All rights reserved. Redistribution and use in source and binary forms, with or without diff --git a/bin/posydon-setup-popsyn b/bin/posydon-setup-popsyn index c1d860009a..10d8e01e20 100644 --- a/bin/posydon-setup-popsyn +++ b/bin/posydon-setup-popsyn @@ -128,7 +128,7 @@ if __name__ == '__main__': parser.add_argument('--partition', help='what cluster partition you want to run the script on', default=None) parser.add_argument('--walltime', help='the walltime you would like to use in SLURM format', default='23:00:00') parser.add_argument('--merge_walltime', help='the walltime you would like to use for the merge script in SLURM format', default='12:00:00') - parser.add_argument('--mem_per_cpu', help='the memory per cpu you would like to use in SLURM format', default='5G') + parser.add_argument('--mem_per_cpu', help='the memory per cpu you would like to use in SLURM format', default='7G') parser.add_argument('--account', help='the account you would like to use', default=None) args = parser.parse_args() diff --git a/conda/meta.yaml b/conda/meta.yaml index 1b38004fab..0b7c18ef0c 100644 --- a/conda/meta.yaml +++ b/conda/meta.yaml @@ -1,5 +1,5 @@ {% set name = "posydon" %} -{% set version = "1.0" %} +{% set version = "2.0.0-pre1" %} package: name: "{{ name|lower }}" @@ -16,27 +16,29 @@ build: requirements: host: - pip - - python==3.7 + - python==3.11 - setuptools>=38.2.5 run: - - python==3.7 - - numpy==1.19.1 - - scipy==1.5.2 - - iminuit==1.4.9 - - configparser==5.0.0 - - pandas==1.3.0 - - scikit-learn==0.21.3 - - matplotlib - - h5py==3.7.0 - - psutil==5.6.7 - - tqdm==4.48.2 - - pytables==3.6.1 - - astropy==4.0.0 - - progressbar2 - - mpi4py - - matplotlib-label-lines - - pyqt + - python==3.11 + - numpy>=1.24.2,<2.0.0 + - scipy>=1.10.1,<=1.14.1 + - iminuit>=2.21.3,<=2.30.1 + - configparser>=5.3.0,<=7.1.0 + - pandas>=2.0.0,<=2.2.3 + - scikit-learn=1.2.2 + - matplotlib>=3.9.0,<=3.9.2 + - h5py>=3.8.0,<=3.12.1 + - psutil>=5.9.4,<=6.1.0 + - tqdm>=4.65.0,<=4.67.0 + - pytables>=3.8.0,<=3.10.1 + - astropy>=5.2.2,<=6.1.6 + - progressbar2>=4.2.0,<=4.5.0 + - mpi4py>=3.0.3 + - matplotlib-label-lines>=0.5.2,<=0.7.0 + - pyqt>=5.15.9,<=5.15.11 + - hurry.filesize>=0.9,<=0.9 + - python-dotenv>=1.0.0,<=1.0.1 test: imports: diff --git a/docs/_source/conf.py b/docs/_source/conf.py index c92e0888a2..6dab9a759b 100644 --- a/docs/_source/conf.py +++ b/docs/_source/conf.py @@ -120,6 +120,20 @@ # The master toctree document. master_doc = 'index' +# General information about the project. +project = u'posydon' +copyright = u'2024, Tassos Fragos' +author = u'POSYDON Collaboration' + +# The version info for the project you're documenting, acts as replacement for +# |version| and |release|, also used in various other places throughout the +# built documents. +# +# The short X.Y version. +version = posydon_version +# The full version, including alpha/beta/rc tags. +release = posydon_version + # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # diff --git a/docs/_source/getting-started/installation-guide.rst b/docs/_source/getting-started/installation-guide.rst index aa1d7bebef..f64a2d8627 100644 --- a/docs/_source/getting-started/installation-guide.rst +++ b/docs/_source/getting-started/installation-guide.rst @@ -53,7 +53,7 @@ Using Anaconda (Recommended) get-posydon-data - Alternatively, you can manually download the dataset from Zenodo using the provided `link `_. (TODO: update link to v2) + Alternatively, you can manually download the dataset from Zenodo using the provided `link `_. (TODO: update link to v2) 5. **Set Environment Variables** diff --git a/docs/_source/index.rst b/docs/_source/index.rst index d3b63b232a..b6f6d048c3 100644 --- a/docs/_source/index.rst +++ b/docs/_source/index.rst @@ -58,9 +58,6 @@ How is this Documentation Structured? :caption: User Guides user-guides/user-roadmap - user-guides/web-application - user-guides/database-query-system - .. toctree:: :maxdepth: 1 @@ -118,10 +115,10 @@ How is this Documentation Structured? .. toctree:: :maxdepth: 1 - :caption: Release Notes and Changelog + :caption: Releases and Datasets - release-notes/version-history - release-notes/major-updates + Releases + Datasets .. toctree:: @@ -131,6 +128,9 @@ How is this Documentation Structured? contact-support/contact-information + + + Acknowledgments --------------- diff --git a/docs/_source/user-guides/database-query-system.rst b/docs/_source/user-guides/database-query-system.rst index 9b0f1f8393..02428fd933 100644 --- a/docs/_source/user-guides/database-query-system.rst +++ b/docs/_source/user-guides/database-query-system.rst @@ -55,7 +55,7 @@ Best Practices - Always double-check your query parameters to ensure accuracy. - For large data retrievals, consider off-peak hours to minimize server load. -- Regularly update yourself with database schema changes from the [version-history.rst](version-history.rst) page. + Support & Feedback ------------------ diff --git a/docs/_source/user-guides/web-application.rst b/docs/_source/user-guides/web-application.rst index ff26358007..1226ad1b06 100644 --- a/docs/_source/user-guides/web-application.rst +++ b/docs/_source/user-guides/web-application.rst @@ -45,7 +45,3 @@ Feedback & Support We're always looking to improve the web application. If you encounter any issues or have suggestions for new features, please refer to the [contact-information.rst](contact-information.rst) page to get in touch with us. -Updates & Changelog -------------------- - -For information about updates, improvements, and fixes in the web application, refer to the [version-history.rst](version-history.rst) page. diff --git a/posydon/grids/io.py b/posydon/grids/io.py index fefffc75cc..722a1a36e3 100644 --- a/posydon/grids/io.py +++ b/posydon/grids/io.py @@ -431,12 +431,12 @@ def initial_values_from_dirname(mesa_dir): """Use the name of the directory for inferring the main initial values.""" dirname = str(os.path.basename(os.path.normpath(mesa_dir))) if "initial_mass" in dirname: # single-star grid - if "v1/" in dirname: # version 1 dirnames don't contain initial_z + if "v1/" in mesa_dir: # version 1 dirnames don't contain initial_z variable_names = ["initial_mass"] else: variable_names = ["initial_mass", "initial_z"] else: # binary-star grid - if "v1/" in dirname: # version 1 dirnames don't contain initial_z + if "v1/" in mesa_dir: # version 1 dirnames don't contain initial_z variable_names = ["m1", "m2", "initial_period_in_days"] else: variable_names = ["m1", "m2", "initial_period_in_days", "initial_z"] diff --git a/posydon/grids/post_processing.py b/posydon/grids/post_processing.py index 77091e2035..8599adca3f 100644 --- a/posydon/grids/post_processing.py +++ b/posydon/grids/post_processing.py @@ -87,8 +87,8 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, single_star=False, verbose=False): """Compute post processed quantity of any grid. - This function post process any supported grid and computes: - - Core collpase quantities for 5 prescritions given the fiducial POSYDON + This function post processes any supported grid and computes: + - Core collapse quantities for 5 prescriptions given the fiducial POSYDON assumption given in MODEL plus: A: direct collapse B: Fryer+12-rapid @@ -98,13 +98,13 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, for each prescrition we store the compact object state (WD/NS/BH), SN type (WD, ECSN, CCSN, PISN, PPISN), fallback mass fraction f_gb, compact object mass and spin. - - Core masses at he-depletion (used in Patton core collpase) - - Mass envelopes for common ennvelope step. + - Core masses at he-depletion (used in Patton core collapse) + - Mass envelopes for common envelope step. Parameters ---------- grid : PSyGrid - MESA gri in PSyGrid format. + MESA grid in PSyGrid format. index : None, touple or int If None, loop over all indicies otherwise provide a range, e.g. [10,20] or a index, e.g. 42. @@ -115,7 +115,7 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, single_star : bool If `True` the PSyGrid contains single stars. verbose : bool - If `True` print on screen the results of each core collpase. + If `True` print the results of each core collapse on screen. Returns ------- @@ -124,7 +124,7 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, processed values. This is used to ensure one to one mapping when appending the extra columns back to a grid. EXTRA_COLUMNS: dict - Dictionary containing all post processe quantities. + Dictionary containing all post processed quantities. """ EXTRA_COLUMNS = {} @@ -244,7 +244,7 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, else: EXTRA_COLUMNS[f'S{j+1}_{quantity}'].append(None) - # core collpase quantities + # core collapse quantities if not single_star: if interpolation_class in ['no_MT', 'stable_MT', 'stable_reverse_MT']: @@ -435,7 +435,7 @@ def add_post_processed_quantities(grid, MESA_dirs_EXTRA_COLUMNS, EXTRA_COLUMNS, """Append post processed quantity to a grid. This function appends the quantities computed in post_process_grid to any - grid. Note that this function ensure you can append the quantities only if + grid. Note that this function ensures you can append the quantities only if the grid follows the order of MESA_dirs_EXTRA_COLUMNS. Parameters @@ -447,9 +447,9 @@ def add_post_processed_quantities(grid, MESA_dirs_EXTRA_COLUMNS, EXTRA_COLUMNS, processed values. This is used to ensure one to one mapping when appending the extra columns back to a grid. EXTRA_COLUMNS: dict - Dictionary containing all post processe quantities. + Dictionary containing all post processed quantities. verbose : bool - If `True` print on screen the results of each core collpase. + If `True` print the results of each core collapse on screen. """ # check correspondance of EXTRA_COLUMNS with grid diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index 27832ef8d6..67f8ebe202 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -26,7 +26,7 @@ NOTE: you can use a family of HDF5 files in case of file size restrictions. For example, using `mygrid.h5.%d` for the path when creating/loading grids, - implied that split files will be `mygrid.h5.0`, `mygrid.h5.1`, and so on. + implies that split files will be `mygrid.h5.0`, `mygrid.h5.1`, and so on. When overwriting the file, all the family is overwritten (effectively, the files are emptied to avoid remainder split files from previous operations.) @@ -90,8 +90,8 @@ to select the exact set of columns, provide them in a tuple instead of list. -II. READING DATA FROM GRID --------------------------- +II. READING DATA FROM A GRID +---------------------------- mygrid = PSyGrid("thegrid.h5") # opens a stored grid # Indexing... @@ -125,7 +125,7 @@ for run in mygrid: print(run) -c) Getting number of runs: +c) Getting the number of runs: n_runs = len(psygrid) @@ -159,8 +159,8 @@ myrun.history1.star_mass + myrun.history1.star_mass -loads the table two times! To reduce reading times, store in local variables -all the tables that are going to be needed more than once: +loads the table two times! To reduce reading times, store all the tables +that are going to be needed more than once in local variables: myhistory1 = myrun.history1 myhistory1.star_mass + myhistory1.star_mass diff --git a/posydon/interpolation/IF_interpolation.py b/posydon/interpolation/IF_interpolation.py index 686ca72d44..4dcf46f5f9 100644 --- a/posydon/interpolation/IF_interpolation.py +++ b/posydon/interpolation/IF_interpolation.py @@ -1,4 +1,4 @@ -"""Module for performing initial-final inteprolation. +"""Module for performing initial-final interpolation. We showcase the initial-final interpolator which plays a critical role in the evolving binary populations. To use the initial-final interpolator we first diff --git a/posydon/utils/data_download.py b/posydon/utils/data_download.py index f61a3f51e8..b928080921 100644 --- a/posydon/utils/data_download.py +++ b/posydon/utils/data_download.py @@ -17,8 +17,8 @@ file = os.path.join(PATH_TO_POSYDON_DATA, "POSYDON_data.tar.gz") PATH_TO_POSYDON_DATA = os.path.join(PATH_TO_POSYDON_DATA, 'POSYDON_data/') -data_url = "https://zenodo.org/record/6655751/files/POSYDON_data.tar.gz" -original_md5 = "8873544d9a568ebb85bccffbf1bdcd99" +data_url = "https://zenodo.org/record/14205146/files/POSYDON_data.tar.gz" +original_md5 = "cf645a45b9b92c2ad01e759eb1950beb" class ProgressBar(): def __init__(self): diff --git a/setup.py b/setup.py index 33d6cb99f5..8e453fe60b 100644 --- a/setup.py +++ b/setup.py @@ -64,22 +64,22 @@ # the correct way to do this is to make sure that they are available on # conda and pip for all platforms we support (see prerequisites doc page). install_requires = [ - 'numpy >= 1.24.2,<2.0.0', - 'scipy >= 1.10.1', - 'iminuit >= 2.21.3', - 'configparser >= 5.3.0', - 'astropy >= 5.2.2', - 'pandas >= 2.0.0', - 'scikit-learn < 1.3.0', # 1.2.2 - 'matplotlib >= 3.9.0', - 'matplotlib-label-lines >= 0.5.2', - 'h5py >= 3.8.0', - 'psutil >= 5.9.4', - 'tqdm >= 4.65.0', - 'tables >= 3.8.0', - 'progressbar2 >= 4.2.0', # for downloading data - 'hurry.filesize >= 0.9', - 'python-dotenv >= 1.0.0', + 'numpy >= 1.24.2, < 2.0.0', + 'scipy >= 1.10.1, <= 1.14.1', + 'iminuit >= 2.21.3, <= 2.30.1', + 'configparser >= 5.3.0, <= 7.1.0', + 'astropy >= 5.2.2, <= 6.1.6', + 'pandas >= 2.0.0, <= 2.2.3', + 'scikit-learn == 1.2.2', + 'matplotlib >= 3.9.0, <= 3.9.2', + 'matplotlib-label-lines >= 0.5.2, <= 0.7.0', + 'h5py >= 3.8.0, <= 3.12.1', + 'psutil >= 5.9.4, <= 6.1.0', + 'tqdm >= 4.65.0, <= 4.67.0', + 'tables >= 3.8.0, <= 3.10.1', + 'progressbar2 >= 4.2.0, <= 4.5.0', # for downloading data + 'hurry.filesize >= 0.9, <= 0.9', + 'python-dotenv >= 1.0.0, <= 1.0.1', ] tests_require = [ @@ -101,7 +101,7 @@ "pandoc", ], # for experimental visualization features, e.g. VDH diagrams - "vis": ["PyQt5 >= 5.15.9"], + "vis": ["PyQt5 >= 5.15.9, <= 5.15.11"], # for profile macjhine learning features, e.g. profile interpolation "ml": ["tensorflow >= 2.13.0"], # for running population synthesis on HPC facilities @@ -118,7 +118,7 @@ PACKAGENAME = "posydon" DISTNAME = "posydon" AUTHOR = "POSYDON Collaboration" -AUTHOR_EMAIL = "scottcoughlin2014@u.northwestern.edu" +AUTHOR_EMAIL = "posydon.team@gmail.com" LICENSE = "GPLv3+" DESCRIPTION = "POSYDON the Next Generation of Population Synthesis" GITHUBURL = "https://github.com/POSYDON-code/POSYDON" @@ -143,14 +143,12 @@ install_requires=install_requires, tests_require=tests_require, extras_require=extras_require, - python_requires=">3.5, <4", + python_requires=">3.10, <3.12", use_2to3=False, classifiers=[ "Development Status :: 4 - Beta", "Programming Language :: Python", - "Programming Language :: Python :: 3.6", - "Programming Language :: Python :: 3.7", - "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.11", "Intended Audience :: Science/Research", "Intended Audience :: End Users/Desktop", "Intended Audience :: Science/Research", From 29062643f922dacb295946df48ba8a54490ffb9c Mon Sep 17 00:00:00 2001 From: Jeff Andrews Date: Thu, 12 Dec 2024 10:23:06 -0500 Subject: [PATCH 276/319] update zenodo url and md5 (#470) Co-authored-by: mkruckow --- posydon/unit_tests/utils/test_data_download.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/posydon/unit_tests/utils/test_data_download.py b/posydon/unit_tests/utils/test_data_download.py index ba6c1d99d6..cafd3dd632 100644 --- a/posydon/unit_tests/utils/test_data_download.py +++ b/posydon/unit_tests/utils/test_data_download.py @@ -60,11 +60,11 @@ def test_value_file(self): assert "POSYDON_data.tar.gz" in totest.file def test_value_data_url(self): - assert totest.data_url == "https://zenodo.org/record/6655751/files/"\ + assert totest.data_url == "https://zenodo.org/record/14205146/files/"\ + "POSYDON_data.tar.gz" def test_value_original_md5(self): - assert totest.original_md5 == "8873544d9a568ebb85bccffbf1bdcd99" + assert totest.original_md5 == "cf645a45b9b92c2ad01e759eb1950beb" class TestFunctions: From 13ec9547bd13f75d8e723287b64edf0f07614e4b Mon Sep 17 00:00:00 2001 From: Camille Liotine Date: Thu, 12 Dec 2024 10:06:58 -0600 Subject: [PATCH 277/319] error check for undefined states in BinaryStar (#468) * add error check for undefined states in BinaryStar, add undefined states to flow chart * update posydonerror unit test * update unit test again * remove BinaryStar and SingleStar from posydonerror, move initcond message to BinaryStar * fix unit tests again --- .../binary_evol/DT/step_initially_single.py | 2 - posydon/binary_evol/DT/step_merged.py | 1 - posydon/binary_evol/binarystar.py | 104 ++++++++++++------ posydon/binary_evol/flow_chart.py | 6 + posydon/popsyn/binarypopulation.py | 6 +- posydon/unit_tests/utils/test_posydonerror.py | 94 ++-------------- posydon/utils/common_functions.py | 12 +- posydon/utils/posydonerror.py | 68 +----------- 8 files changed, 94 insertions(+), 199 deletions(-) diff --git a/posydon/binary_evol/DT/step_initially_single.py b/posydon/binary_evol/DT/step_initially_single.py index 8b17bf1bd7..9bd43e2498 100644 --- a/posydon/binary_evol/DT/step_initially_single.py +++ b/posydon/binary_evol/DT/step_initially_single.py @@ -13,8 +13,6 @@ from posydon.binary_evol.flow_chart import ( STAR_STATES_H_RICH, STAR_STATES_HE_RICH) -from posydon.utils.posydonerror import FlowError -from posydon.utils.posydonerror import POSYDONError LIST_ACCEPTABLE_STATES_FOR_HMS = ["H-rich_Core_H_burning"] LIST_ACCEPTABLE_STATES_FOR_HeMS = ["stripped_He_Core_He_burning"] diff --git a/posydon/binary_evol/DT/step_merged.py b/posydon/binary_evol/DT/step_merged.py index 6e9f24bbd4..dedb9d989e 100644 --- a/posydon/binary_evol/DT/step_merged.py +++ b/posydon/binary_evol/DT/step_merged.py @@ -14,7 +14,6 @@ from posydon.binary_evol.singlestar import STARPROPERTIES, convert_star_to_massless_remnant from posydon.utils.common_functions import check_state_of_star from posydon.binary_evol.DT.step_isolated import IsolatedStep -from posydon.utils.posydonerror import FlowError from posydon.utils.posydonwarning import Pwarn diff --git a/posydon/binary_evol/binarystar.py b/posydon/binary_evol/binarystar.py index a51634d40f..631231852e 100644 --- a/posydon/binary_evol/binarystar.py +++ b/posydon/binary_evol/binarystar.py @@ -36,6 +36,8 @@ check_state_of_star, orbital_period_from_separation, orbital_separation_from_period, get_binary_state_and_event_and_mt_case) from posydon.popsyn.io import (clean_binary_history_df, clean_binary_oneline_df) +from posydon.binary_evol.flow_chart import UNDEFINED_STATES +from posydon.utils.posydonerror import FlowError # star property: column names in binary history for star 1 and star 2 @@ -196,7 +198,6 @@ def __init__(self, star_1=None, star_2=None, index=None, properties=None, self.properties = properties else: self.properties = SimulationProperties() - def evolve(self): """Evolve a binary from start to finish.""" @@ -212,44 +213,43 @@ def evolve(self): max_n_steps = self.properties.max_n_steps_per_binary n_steps = 0 - try: - while (self.event != 'END' and self.event != 'FAILED' + + while (self.event != 'END' and self.event != 'FAILED' and self.event not in self.properties.end_events and self.state not in self.properties.end_states): - signal.alarm(MAXIMUM_STEP_TIME) - self.run_step() - - n_steps += 1 - if max_n_steps is not None: - if n_steps > max_n_steps: - raise RuntimeError("Exceeded maximum number of steps ({})" - .format(max_n_steps)) - finally: - signal.alarm(0) # turning off alarm - self.properties.post_evolve(self) + + signal.alarm(MAXIMUM_STEP_TIME) + self.run_step() + + n_steps += 1 + if max_n_steps is not None: + if n_steps > max_n_steps: + raise RuntimeError("Exceeded maximum number of steps ({})".format(max_n_steps)) + + signal.alarm(0) # turning off alarm + self.properties.post_evolve(self) def run_step(self): """Evolve the binary through one evolutionary step.""" - try: - total_state = (self.star_1.state, self.star_2.state, self.state, - self.event) - next_step_name = self.properties.flow.get(total_state) - - if next_step_name is None: - raise ValueError("Undefined next step given stars/binary states {}.".format(total_state)) - self.event = 'END' - - next_step = getattr(self.properties, next_step_name, None) - if next_step is None: - raise ValueError( - "Next step name '{}' does not correspond to a function in " - "SimulationProperties.".format(next_step_name)) - - self.properties.pre_step(self, next_step_name) - next_step(self) - finally: - self.append_state() - self.properties.post_step(self, next_step_name) + + total_state = (self.star_1.state, self.star_2.state, self.state, self.event) + if total_state in UNDEFINED_STATES: + raise FlowError(f"Binary failed with a known undefined state in the flow:\n{total_state}") + + next_step_name = self.properties.flow.get(total_state) + if next_step_name is None: + raise ValueError("Undefined next step given stars/binary states {}.".format(total_state)) + + next_step = getattr(self.properties, next_step_name, None) + if next_step is None: + raise ValueError("Next step name '{}' does not correspond to a function in " + "SimulationProperties.".format(next_step_name)) + + self.properties.pre_step(self, next_step_name) + next_step(self) + + self.append_state() + self.properties.post_step(self, next_step_name) def append_state(self): """Update the history of the binaries' properties.""" @@ -929,3 +929,39 @@ def from_run(run, history=False, profiles=False): binary.star_2.profile = run.final_profile2 return binary + + def initial_condition_message(self, ini_params=None): + """Generate a message with the initial conditions. + + Parameters + ---------- + + ini_params : None or iterable of str + If None take the initial conditions from the binary, otherwise add + each item of it to the message. + + Returns + ------- + string + The message with the binary initial conditions. + """ + + if ini_params is None: + ini_params = ["\nFailed Binary Initial Conditions:\n", + f"S1 mass: {self.star_1.mass_history[0]} \n", + f"S2 mass: {self.star_2.mass_history[0]} \n", + f"S1 state: {self.star_1.state_history[0]} \n", + f"S2 state: {self.star_2.state_history[0]}\n", + f"orbital period: {self.orbital_period_history[0] } \n", + f"eccentricity: {self.eccentricity_history[0]} \n", + f"binary state: {self.state_history[0] }\n", + f"binary event: {self.state_history[0] }\n", + f"S1 natal kick array: {self.star_1.natal_kick_array }\n", + f"S2 natal kick array: {self.star_2.natal_kick_array}\n"] + + message = "" + for i in ini_params: + message += i + + return message + diff --git a/posydon/binary_evol/flow_chart.py b/posydon/binary_evol/flow_chart.py index e4d116da23..cac4dd12c5 100644 --- a/posydon/binary_evol/flow_chart.py +++ b/posydon/binary_evol/flow_chart.py @@ -113,6 +113,12 @@ 'oMerging2' ] +## a list of known total binary states that can occur, +## but are not in the flow chart and will not be added to POSYDON +UNDEFINED_STATES = [ + ('NS', 'H-rich_Core_H_burning', 'disrupted', 'CC2'), + ('NS', 'H-rich_Core_H_burning', 'detached', 'CC2') +] # dynamically construct the flow chart POSYDON_FLOW_CHART = {} diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index aadfde33ee..a2cd1daa07 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -54,7 +54,7 @@ from posydon.popsyn.defaults import default_kwargs from posydon.popsyn.io import binarypop_kwargs_from_ini from posydon.utils.constants import Zsun -from posydon.utils.posydonerror import POSYDONError,initial_condition_message +from posydon.utils.posydonerror import POSYDONError from posydon.utils.posydonwarning import (Pwarn, Catch_POSYDON_Warnings) from posydon.utils.common_functions import set_binary_to_failed @@ -331,14 +331,14 @@ def _safe_evolve(self, **kwargs): binary.traceback = traceback.format_exc() if self.kwargs.get("error_checking_verbose", False): - posydon_error.add_note(initial_condition_message(binary)) + posydon_error.add_note(binary.initial_condition_message()) traceback.print_exception(posydon_error) except Exception as e: set_binary_to_failed(binary) binary.traceback = traceback.format_exc() - e.add_note(initial_condition_message(binary)) + e.add_note(binary.initial_condition_message()) traceback.print_exception(e) # record if there were warnings caught during the binary diff --git a/posydon/unit_tests/utils/test_posydonerror.py b/posydon/unit_tests/utils/test_posydonerror.py index 87eb975866..0437dd6c52 100644 --- a/posydon/unit_tests/utils/test_posydonerror.py +++ b/posydon/unit_tests/utils/test_posydonerror.py @@ -18,26 +18,15 @@ def artificial_object(): # create a dict as test object return {'Test': 'object'} -@fixture -def BinaryStar(): - # initialize a BinaryStar instance, which is a required argument - return totest.BinaryStar() - -@fixture -def SingleStar(): - # initialize a SingleStar instance, which is a required argument - return totest.SingleStar() - # define test classes collecting several test functions class TestElements: # check for objects, which should be an element of the tested module def test_dir(self): - elements = ['BinaryStar', 'FlowError', 'GridError', 'MatchingError',\ + elements = ['FlowError', 'GridError', 'MatchingError',\ 'ModelError', 'NumericalError', 'POSYDONError',\ - 'SingleStar', '__authors__', '__builtins__', '__cached__',\ + '__authors__', '__builtins__', '__cached__',\ '__doc__', '__file__', '__loader__', '__name__',\ - '__package__', '__spec__', 'copy',\ - 'initial_condition_message'] + '__package__', '__spec__'] assert dir(totest) == elements, "There might be added or removed "\ + "objects without an update on the "\ + "unit test." @@ -78,28 +67,6 @@ def test_instance_NumericalError(self): with raises(totest.NumericalError, match="Test"): raise totest.NumericalError("Test") - def test_instance_initial_condition_message(self): - assert isroutine(totest.initial_condition_message) - - -class TestFunctions: - # test functions - def test_initial_condition_message(self, BinaryStar, artificial_object): - with raises(TypeError, match="The binary must be a BinaryStar object"): - message = totest.initial_condition_message(binary=\ - artificial_object) - assert "Failed Binary Initial Conditions" in\ - totest.initial_condition_message(binary=BinaryStar) - with raises(TypeError, match="is not iterable"): - message = totest.initial_condition_message(binary=BinaryStar,\ - ini_params=1) - with raises(TypeError, match="can only concatenate str"): - message = totest.initial_condition_message(binary=BinaryStar,\ - ini_params=[1,2]) - assert totest.initial_condition_message(binary=BinaryStar, ini_params=\ - ["a: 1\n", "b: 2\n"]) ==\ - "a: 1\nb: 2\n" - class TestPOSYDONError: @fixture @@ -117,70 +84,23 @@ def POSYDONError_key(self): # initialize an instance of the class with a message via key return totest.POSYDONError(message="test message with key") - @fixture - def POSYDONError_object(self, artificial_object): - # initialize an instance of the class with an artifical object in a - # list via key - return totest.POSYDONError(objects=[artificial_object]) - - @fixture - def POSYDONError_SingleStar(self, SingleStar): - # initialize an instance of the class with a SingleStar object - return totest.POSYDONError(objects=SingleStar) - - @fixture - def POSYDONError_BinaryStar(self, BinaryStar): - # initialize an instance of the class with a BinaryStar object - return totest.POSYDONError(objects=BinaryStar) - - @fixture - def POSYDONError_List(self, artificial_object, SingleStar, BinaryStar): - # initialize an instance of the class with a list of objects - return totest.POSYDONError(objects=[artificial_object, SingleStar,\ - BinaryStar]) - # test the POSYDONError class - def test_init(self, POSYDONError, POSYDONError_position, POSYDONError_key,\ - POSYDONError_object, artificial_object): + def test_init(self, POSYDONError, POSYDONError_position, POSYDONError_key): assert isroutine(POSYDONError.__init__) # check that the instance is of correct type and all code in the # __init__ got executed: the elements are created and initialized assert isinstance(POSYDONError, totest.POSYDONError) # check defaults assert POSYDONError.message == "" - assert POSYDONError.objects is None # test a passed message via positional argument assert POSYDONError_position.message == "test message on position" - assert POSYDONError_position.objects is None # test a passed message via key - error_object = totest.POSYDONError(message="test message with key") assert POSYDONError_key.message == "test message with key" - assert POSYDONError_key.objects is None - # test a passed object - assert POSYDONError_object.message == "" - assert POSYDONError_object.objects == [artificial_object] # test requests on input parameters with raises(TypeError, match="message must be a string"): error_object = totest.POSYDONError(message=artificial_object) - with raises(TypeError, match="objects must be None, a list, a "\ - +"SingleStar object, or a BinaryStar "\ - +"object"): - error_object = totest.POSYDONError(objects=artificial_object) - - def test_str(self, POSYDONError_position, POSYDONError_object,\ - POSYDONError_SingleStar, POSYDONError_BinaryStar,\ - POSYDONError_List): + def test_str(self, POSYDONError_position): assert isroutine(POSYDONError_position.__str__) - assert str(POSYDONError_position) == "\ntest message on position" - # test passed objects - assert str(POSYDONError_object) == "\n" - assert "OBJECT #()"\ - in str(POSYDONError_SingleStar) - assert "OBJECT #()"\ - in str(POSYDONError_BinaryStar) - assert "OBJECT #1" not in str(POSYDONError_List) - assert "OBJECT #2 ()" in str(POSYDONError_List) - assert "OBJECT #3 ()" in str(POSYDONError_List) + assert str(POSYDONError_position) == "test message on position" + \ No newline at end of file diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index 6b2eef726b..64a79e6942 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -426,7 +426,6 @@ def beaming(binary): else: rlo_mdot = 10**binary.lg_mtransfer_rate - print(rlo_mdot, mdot_edd) if rlo_mdot >= mdot_edd: if rlo_mdot > 8.5 * mdot_edd: # eq. 8 in King A. R., 2009, MNRAS, 393, L41-L44 @@ -600,7 +599,6 @@ def bondi_hoyle(binary, accretor, donor, idx=-1, wind_disk_criteria=True, # make it Eddington-limited mdot_edd = eddington_limit(binary, idx=idx)[0] - print(mdot_acc, mdot_edd) mdot_acc = np.minimum(mdot_acc, mdot_edd) return np.squeeze(mdot_acc) @@ -2372,12 +2370,11 @@ def get_mass_radius_dm_from_profile(profile, m1_i=0.0, if np.abs(donor_mass[0] - m1_i) > tolerance: Pwarn("Donor mass from the binary class object " "and the profile do not agree", "ClassificationWarning") - #print("mass profile/object:", (donor_mass[0]), (m1_i)) + # checking if radius of profile agrees with the radius of the binary if np.abs(donor_radius[0] - radius1) > tolerance: Pwarn("Donor radius from the binary class object " "and the profile do not agree", "ClassificationWarning") - #print("radius profile/object:", (donor_radius[0]), (radius1)) # MANOS: if dm exists as a column, else calculate it from mass column if "dm" in profile.dtype.names: @@ -2650,12 +2647,14 @@ def calculate_binding_energy(donor_mass, donor_radius, donor_dm, """ # Sum of gravitational energy from surface to core boundary Grav_energy = 0.0 + # Sum of internal energy from surface to core boundary. This is 0 if # 'lambda_from_profile_gravitational' or (thermal+radiation+recombination) # for "lambda_from_profile_gravitational_plus_internal" or # (thermal+radiation) for # "lambda_from_profile_gravitational_plus_internal_minus_recombination" U_i = 0.0 + # sum from surface to the core. Your core boundary is in element [ind_core] # in a normal MESA (and POSYDON) profile for i in range(ind_core): @@ -2665,12 +2664,12 @@ def calculate_binding_energy(donor_mass, donor_radius, donor_dm, # integral of gravitational energy as we go deeper into the star Grav_energy = Grav_energy + Grav_energy_of_cell U_i = U_i + specific_internal_energy[i]*donor_dm[i]*const.Msun + if Grav_energy > 0.0: - #print("Grav_energy, donor_mass, donor_dm, donor_radius", - # Grav_energy, donor_mass, donor_dm, donor_radius) if not (Grav_energy < tolerance): raise ValueError("CEE problem calculating gravitational energy, " "giving positive values.") + # binding energy of the enevelope equals its gravitational energy + # an a_th fraction of its internal energy Ebind_i = Grav_energy + factor_internal_energy * U_i @@ -2738,7 +2737,6 @@ def calculate_Mejected_for_integrated_binding_energy(profile, Ebind_threshold, if donor_mass[ind_threshold]< mc1_i or donor_radius[ind_threshold]", ] - -import copy -from posydon.binary_evol.binarystar import BinaryStar -from posydon.binary_evol.singlestar import SingleStar - - class POSYDONError(Exception): """General POSYDON exception class.""" - def __init__(self, message="", objects=None): + def __init__(self, message=""): """General POSYDON exception. Parameters ---------- message : str POSYDONError message. - objects : None or list of objects - A list of accompanied objects (or None if not set), that will be - handled differently when `str` method is used. This error can - only accept BinaryStar, SingleStar, or a list of each. - """ if not isinstance(message, str): raise TypeError("The error message must be a string.") - if ((objects is not None) and - (not isinstance(objects, (list, SingleStar, BinaryStar)))): - raise TypeError("The error objects must be None, a list, a " - "SingleStar object, or a BinaryStar object.") + self.message = message - # copy the objects: we must know their state at the moment of the error - self.objects = copy.copy(objects) super().__init__(self.message) def __str__(self): """Create the text that accompanies this exception.""" - result = "" - if self.objects is not None: - if isinstance(self.objects, list): - for i, obj in enumerate(self.objects): - if isinstance(obj, (BinaryStar, SingleStar)): - result += f"\n\nOBJECT #{i+1} ({type(obj)}):\n{str(obj)}" - elif isinstance(self.objects, (BinaryStar, SingleStar)): - result += f"\n\nOBJECT #({type(self.objects)}):\n{str(self.objects)}" - else: # pragma: no cover - pass - return result + '\n'+ super().__str__() + return super().__str__() # Subclasses of POSYODONError in alphabetic order # Before you add a new subclass check the list of python error classes at the @@ -74,42 +48,6 @@ class ModelError(POSYDONError): class NumericalError(POSYDONError): """POSYDON error specific for when a binary FAILS due to limitations of numerical methods.""" -def initial_condition_message(binary, ini_params=None): - """Generate a message with the initial conditions. - - Parameters - ---------- - binary : BinaryStar - BinaryStar object to take the initial conditions from. - ini_params : None or iterable of str - If None take the initial conditions from the binary, otherwise add - each item of it to the message. - - Returns - ------- - string - The message with the initial conditions. - - """ - if not isinstance(binary, BinaryStar): - raise TypeError("The binary must be a BinaryStar object.") - if ini_params is None: - ini_params = ["\nFailed Binary Initial Conditions:\n", - f"S1 mass: {binary.star_1.mass_history[0]} \n", - f"S2 mass: {binary.star_2.mass_history[0]} \n", - f"S1 state: {binary.star_1.state_history[0]} \n", - f"S2 state: { binary.star_2.state_history[0]}\n", - f"orbital period: { binary.orbital_period_history[0] } \n", - f"eccentricity: { binary.eccentricity_history[0]} \n", - f"binary state: { binary.state_history[0] }\n", - f"binary event: { binary.state_history[0] }\n", - f"S1 natal kick array: { binary.star_1.natal_kick_array }\n", - f"S2 natal kick array: { binary.star_2.natal_kick_array}\n"] - message = "" - for i in ini_params: - message += i - return message - # There are 5 base exception classes in python: BaseException, Exception, # ArithmeticError, BufferError, LookupError. Those are not aimed to be raised From 1473e735eac631fe435f1c5018ab61fc0b907844 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 16 Jan 2025 13:38:46 +0100 Subject: [PATCH 278/319] Update step_merged.py (#480) Import ModelError in step merged. --- posydon/binary_evol/DT/step_merged.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/posydon/binary_evol/DT/step_merged.py b/posydon/binary_evol/DT/step_merged.py index dedb9d989e..8599e04c83 100644 --- a/posydon/binary_evol/DT/step_merged.py +++ b/posydon/binary_evol/DT/step_merged.py @@ -14,7 +14,7 @@ from posydon.binary_evol.singlestar import STARPROPERTIES, convert_star_to_massless_remnant from posydon.utils.common_functions import check_state_of_star from posydon.binary_evol.DT.step_isolated import IsolatedStep - +from posydon.utils.posydonerror import ModelError from posydon.utils.posydonwarning import Pwarn from posydon.binary_evol.flow_chart import ( From 2ca491f217455069c5c02ad2bc60a3ea4935b893 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 16 Jan 2025 17:05:21 +0100 Subject: [PATCH 279/319] No check for WD formation in double CE case (#484) * Update common_functions.py Move double CE into contact detection and return afterwards to skip checks on WD formation. * add tests which would fail on the old code --- .../unit_tests/utils/test_common_functions.py | 11 ++++++++ posydon/utils/common_functions.py | 28 +++++++++---------- 2 files changed, 24 insertions(+), 15 deletions(-) diff --git a/posydon/unit_tests/utils/test_common_functions.py b/posydon/unit_tests/utils/test_common_functions.py index e50c04e96d..1addd9857d 100644 --- a/posydon/unit_tests/utils/test_common_functions.py +++ b/posydon/unit_tests/utils/test_common_functions.py @@ -1151,6 +1151,17 @@ def mock_infer_mass_transfer_case(rl_relative_overflow,\ binary.star_2.center_gamma_history=[] assert totest.get_binary_state_and_event_and_mt_case(binary, i=0) ==\ ['detached', None, 'None'] + # both stars overfill RL leading to double CE while WD condition is + # fulfilled as well + tests = [(2.0, 1.0, 'oDoubleCE1'), (1.0, 2.0, 'oDoubleCE2')] + for (ro1, ro2, e) in tests: + binary.rl_relative_overflow_1 = ro1 + binary.rl_relative_overflow_2 = ro2 + assert totest.get_binary_state_and_event_and_mt_case(binary,\ + interpolation_class='unstable_MT') == ['contact', e, 'None'] + # stable contact leading to WD formation + assert totest.get_binary_state_and_event_and_mt_case(binary,\ + interpolation_class=None) == ['contact', 'CC1', 'None'] def test_get_binary_state_and_event_and_mt_case_array(self, binary,\ monkeypatch): diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index 64a79e6942..182a650678 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -1103,8 +1103,21 @@ def get_binary_state_and_event_and_mt_case(binary, interpolation_class=None, if interpolation_class == 'unstable_MT': if rl_overflow1>=rl_overflow2: # star 1 initiated CE result = ['contact', 'oCE1', 'None'] + # Check for double CE + comp_star = binary.star_2 + if comp_star.state not in ["H-rich_Core_H_burning", + "stripped_He_Core_He_burning", "WD", + "NS", "BH"]: + result[1] = "oDoubleCE1" else: # star 2 initiated CE result = ['contact', 'oCE2', 'None'] + # Check for double CE + comp_star = binary.star_1 + if comp_star.state not in ["H-rich_Core_H_burning", + "stripped_He_Core_He_burning", "WD", + "NS", "BH"]: + result[1] = "oDoubleCE2" + return result elif no_rlof: # no MT in any star result = ['detached', None, 'None'] elif rlof1 and not rlof2: # only in star 1 @@ -1120,21 +1133,6 @@ def get_binary_state_and_event_and_mt_case(binary, interpolation_class=None, else: # undetermined in any star result = ["undefined", None, 'None'] - if result[1] == "oCE1": - # Check for double CE - comp_star = binary.star_2 - if comp_star.state not in [ - "H-rich_Core_H_burning", - "stripped_He_Core_He_burning", "WD", "NS", "BH"]: - result[1] = "oDoubleCE1" - elif result[1] == "oCE2": - # Check for double CE - comp_star = binary.star_1 - if comp_star.state not in [ - "H-rich_Core_H_burning", - "stripped_He_Core_He_burning", "WD", "NS", "BH"]: - result[1] = "oDoubleCE2" - if ("Central_C_depletion" in state1 or "Central_He_depleted" in state1 or (gamma1 is not None and gamma1 >= 10.0)): # WD formation From 14683115246471cd5d0b29bd6d0baa1502d84cba Mon Sep 17 00:00:00 2001 From: Camille Liotine Date: Thu, 16 Jan 2025 10:44:35 -0600 Subject: [PATCH 280/319] handle H-rich RLO binaries and clarify verbosity of detached step (#430) * clean-up code and clarify verbosity of matching in detached step * reorganizing some code in detached step, clarifying verbose output * fix bug in matching, add criteria to check for HMS-HMS-RLO binaries in detached step * add list of step names to SimulationProperties * add separate error catch for HMS-HMS binaries in RLO in detached step * Merge remote-tracking branch 'origin/development' into camille_matching_strippedHe_to_Hrich * add ClassificationError to unit tests * remove space at end of line in unit tests * fix unit test * remove classification error from unit tests * fix classification error * fix line length in test_posydonerror.py --- posydon/binary_evol/DT/step_detached.py | 1184 ++++++++--------- posydon/binary_evol/MESA/step_mesa.py | 29 +- posydon/binary_evol/flow_chart.py | 37 +- posydon/binary_evol/simulationproperties.py | 3 + posydon/popsyn/binarypopulation.py | 13 +- posydon/unit_tests/utils/test_posydonerror.py | 18 +- posydon/utils/posydonerror.py | 3 + 7 files changed, 593 insertions(+), 694 deletions(-) diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index c17e7e421f..7ee639e20d 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -10,6 +10,7 @@ "Konstantinos Kovlakas ", "Kyle Akira Rocha ", "Jeffrey Andrews ", + "Camille Liotine ", ] import os @@ -35,9 +36,14 @@ convert_metallicity_to_string, set_binary_to_failed, ) -from posydon.binary_evol.flow_chart import (STAR_STATES_CC, STAR_STATES_CO) +from posydon.binary_evol.flow_chart import ( + STAR_STATES_CC, + STAR_STATES_CO, + STAR_STATES_H_RICH_EVOLVABLE, + STAR_STATES_HE_RICH_EVOLVABLE, + ) import posydon.utils.constants as const -from posydon.utils.posydonerror import NumericalError, MatchingError, POSYDONError, FlowError +from posydon.utils.posydonerror import NumericalError, MatchingError, POSYDONError, FlowError, ClassificationError from posydon.utils.posydonwarning import Pwarn LIST_ACCEPTABLE_STATES_FOR_HMS = ["H-rich_Core_H_burning", "accreted_He_Core_H_burning"] @@ -73,12 +79,6 @@ 'accreted_He_non_burning' ] -''' -STAR_STATES_CO = ['BH', - 'NS', - 'WD', - ] -''' DEFAULT_TRANSLATION = { "time": "time", @@ -358,7 +358,7 @@ def __init__( # DataScaler instance, created the first time it is requested self.stored_scalers = {} - if verbose: + if self.verbose: print( dt, n_o_steps_history, @@ -413,15 +413,12 @@ def __init__( self.profile_keys = DEFAULT_PROFILE_KEYS if grid_name_Hrich is None: - grid_name_Hrich = os.path.join( - 'single_HMS', self.metallicity+'_Zsun.h5') + grid_name_Hrich = os.path.join('single_HMS', self.metallicity+'_Zsun.h5') self.grid_Hrich = GRIDInterpolator(os.path.join(path, grid_name_Hrich)) if grid_name_strippedHe is None: - grid_name_strippedHe = os.path.join( - 'single_HeMS', self.metallicity+'_Zsun.h5') - self.grid_strippedHe = GRIDInterpolator( - os.path.join(path, grid_name_strippedHe)) + grid_name_strippedHe = os.path.join('single_HeMS', self.metallicity+'_Zsun.h5') + self.grid_strippedHe = GRIDInterpolator(os.path.join(path, grid_name_strippedHe)) # Initialize the matching lists: m_min_H = np.min(self.grid_Hrich.grid_mass) @@ -474,8 +471,7 @@ def __init__( [m_min_He, m_max_He], [0, None] ] - def square_difference(self, x, htrack, - mesa_labels, posydon_attributes, colscalers, scales): + def square_difference(self, x, htrack, mesa_labels, posydon_attributes, colscalers, scales): """Compute the square distance used for scaling.""" result = 0.0 for mesa_label, posy_attr, colscaler, scale_of_mesa_label in zip( @@ -642,6 +638,39 @@ def match_to_single_star(self, star, htrack): the properties of the secondary. """ + def get_posydon_attributes(list_for_matching, star): + list_of_attributes = [] + for attr in list_for_matching: + list_of_attributes.append(getattr(star, attr)) + return list_of_attributes + + def sq_diff_function(x): + return self.square_difference( + x, htrack=htrack, mesa_labels=MESA_labels, + posydon_attributes=posydon_attributes, + colscalers=colscalers, scales=scales) + + def get_MESA_labels(list_for_matching): + + MESA_labels = list_for_matching[0] + + rs = list_for_matching[1] + colscalers = list_for_matching[2] + bnds = [] + for i in range(3, len(list_for_matching)): + bnds.append(list_for_matching[i]) + + if self.verbose: + print("Matching parameters and their normalizations:\n", MESA_labels, rs) + + scales = [] + for MESA_label, colscaler in zip(MESA_labels, colscalers): + scale_of_attribute = scale(MESA_label, htrack, colscaler) + scales.append(scale_of_attribute) + + return MESA_labels, rs, colscalers, bnds, scales + + if htrack: self.grid = self.grid_Hrich else: @@ -653,157 +682,116 @@ def match_to_single_star(self, star, htrack): scale = self.scale initials = None - # tolerance 1e-8 + tolerance_matching_integration = 1e-2 tolerance_matching_integration_hard = 1e-1 + if self.verbose: - print(matching_method) + print(f"\nMatching process started in detached step for {star.state} star " + f"with matching method = {matching_method}") + if matching_method == "root": + if star.state in LIST_ACCEPTABLE_STATES_FOR_HMS: x0 = get_root0(["center_h1", "mass"], [star.center_h1, star.mass], htrack, rs=[0.7, 300]) sol = root( lambda x: [ - get_track_val("center_h1", htrack, *x) - - star.center_h1, - get_track_val("mass", htrack, *x) - star.mass, - ], - x0, - method="hybr", - ) + get_track_val("center_h1", htrack, *x) - star.center_h1, + get_track_val("mass", htrack, *x) - star.mass], + x0, method="hybr") else: x0 = get_root0( ["he_core_mass", "mass"], [star.he_core_mass, star.mass], htrack, - rs=[11, 300], - ) + rs=[11, 300]) + sol = root( lambda x: [ - get_track_val("he_core_mass", htrack, *x) - - star.he_core_mass, - get_track_val("mass", htrack, *x) - star.mass, - ], - x0, - method="hybr", - ) + get_track_val("he_core_mass", htrack, *x) - star.he_core_mass, + get_track_val("mass", htrack, *x) - star.mass], + x0, method="hybr") + if not sol.success or sol.x[1] < 0: initials = (np.nan, np.nan) else: initials = sol.x - elif matching_method == "minimize": - - def posydon_attribute(list_for_matching, star): - list_of_attributes = [] - for attr in list_for_matching: - list_of_attributes.append(getattr(star, attr)) - return list_of_attributes + elif matching_method == "minimize": + if star.state in LIST_ACCEPTABLE_STATES_FOR_HMS: list_for_matching = self.list_for_matching_HMS elif star.state in LIST_ACCEPTABLE_STATES_FOR_postMS: list_for_matching = self.list_for_matching_postMS - elif star.state in LIST_ACCEPTABLE_STATES_FOR_HeStar: list_for_matching = self.list_for_matching_HeStar - - MESA_labels = list_for_matching[0] - posydon_attributes = posydon_attribute(MESA_labels, star) - rs = list_for_matching[1] - colscalers = list_for_matching[2] - bnds = [] - for i in range(3, len(list_for_matching)): - bnds.append(list_for_matching[i]) - - if self.verbose or self.verbose == 1: - print("Matching attributes and their normalizations :", - MESA_labels, rs) + + MESA_labels, rs, colscalers, bnds, scales = get_MESA_labels(list_for_matching) + for i in MESA_labels: if i not in self.root_keys: - raise AttributeError("Expected matching parameter not " - "added in the single star grid options.") - - scales = [] - for MESA_label, colscaler in zip(MESA_labels, colscalers): - scale_of_attribute = scale(MESA_label, htrack, colscaler) - scales.append(scale_of_attribute) + raise AttributeError(f"Expected matching parameter {i} not added in the single star grid options.") + + posydon_attributes = get_posydon_attributes(MESA_labels, star) x0 = get_root0(MESA_labels, posydon_attributes, htrack, rs=rs) - - def sq_diff_function(x): - return self.square_difference( - x, htrack=htrack, mesa_labels=MESA_labels, - posydon_attributes=posydon_attributes, - colscalers=colscalers, scales=scales) - sol = minimize(sq_diff_function, x0, method="TNC", bounds=bnds) - # alternative matching - # 1st, different minimization method - if (np.abs(sol.fun) > tolerance_matching_integration - or not sol.success): - if self.verbose or self.verbose == 1: - print("Alternative matching in detached step, 1st step " - "because either", np.abs(sol.fun), ">", - tolerance_matching_integration, - "or sol.success = ", sol.success) + + ## Alternative matching attempts if default matching fails! + # 1st attempt: use a different minimization method + if (np.abs(sol.fun) > tolerance_matching_integration or not sol.success): + + if self.verbose: + print("\nAlternative matching started (1st attempt) " + "because previous attempt was unsuccessful:\n", + f"tolerance {np.abs(sol.fun)} > {tolerance_matching_integration}", + f"or sol.success = {sol.success}") + print("(Now trying an alternative minimization method)") + sol = minimize(sq_diff_function, x0, method="Powell") - # 2nd, alternative matching parameters - if (np.abs(sol.fun) > tolerance_matching_integration - or not sol.success): + # 2nd attempt: use alternative matching parameters + if (np.abs(sol.fun) > tolerance_matching_integration or not sol.success): + + if self.verbose: + print("\nAlternative matching started (2nd attempt) " + "because previous attempt was unsuccessful:\n", + f"tolerance {np.abs(sol.fun)} > {tolerance_matching_integration}", + f"or sol.success = {sol.success}") + print("(Now trying to match with alternative parameters)") + if star.state in LIST_ACCEPTABLE_STATES_FOR_HMS: list_for_matching = self.list_for_matching_HMS_alternative elif star.state in LIST_ACCEPTABLE_STATES_FOR_postMS: - list_for_matching = ( - self.list_for_matching_postMS_alternative) + list_for_matching = (self.list_for_matching_postMS_alternative) elif star.state in LIST_ACCEPTABLE_STATES_FOR_HeStar: - list_for_matching = ( - self.list_for_matching_HeStar_alternative) - - MESA_labels = list_for_matching[0] - posydon_attributes = posydon_attribute(MESA_labels, star) - rs = list_for_matching[1] - colscalers = list_for_matching[2] - bnds = [] - for i in range(3, len(list_for_matching)): - bnds.append(list_for_matching[i]) - - if self.verbose or self.verbose == 1: - print("Alternative matching in detached step, 2nd step " - "because", np.abs(sol.fun), ">", - tolerance_matching_integration, - "or sol.success = ", sol.success) - print("Matching alternative attributes and their " - "normalizations :", MESA_labels, rs) - - scales = [] - for MESA_label, colscaler in zip(MESA_labels, colscalers): - scale_of_attribute = scale(MESA_label, htrack, colscaler) - scales.append(scale_of_attribute) - - def sq_diff_function(x): - return self.square_difference( - x, htrack=htrack, mesa_labels=MESA_labels, - posydon_attributes=posydon_attributes, - colscalers=colscalers, scales=scales) + list_for_matching = (self.list_for_matching_HeStar_alternative) + + MESA_labels, rs, colscalers, bnds, scales = get_MESA_labels(list_for_matching) + posydon_attributes = get_posydon_attributes(MESA_labels, star) x0 = get_root0(MESA_labels, posydon_attributes, htrack, rs=rs) - sol = minimize(sq_diff_function, x0, method="TNC", bounds=bnds) - # 3rd Alternative matching with a H-rich grid for He-star and vice verse (not for HMS stars) - if (np.abs(sol.fun) > tolerance_matching_integration - or not sol.success): + # 3rd attempt: match an He-star with an H-rich grid, or vice versa (not applicable for HMS stars) + if (np.abs(sol.fun) > tolerance_matching_integration or not sol.success): + if (star.state in LIST_ACCEPTABLE_STATES_FOR_HeStar or star.state in LIST_ACCEPTABLE_STATES_FOR_postMS): - if self.verbose: - print("Alternative matching in detached step, 3rd step because ", - np.abs(sol.fun), ">", tolerance_matching_integration , - " or sol.success = ", sol.success) + Pwarn("Attempting to match an He-star with an H-rich grid or post-MS star with a" + " stripped-He grid", "EvolutionWarning") + if self.verbose: + print("\nAlternative matching started (3rd attempt) " + "because previous attempt was unsuccessful:\n", + f"tolerance {np.abs(sol.fun)} > {tolerance_matching_integration}", + f"or sol.success = {sol.success}") + print("(Now trying to match star to a different grid)") + if star.state in LIST_ACCEPTABLE_STATES_FOR_HeStar: htrack = True list_for_matching = self.list_for_matching_HeStar @@ -811,116 +799,72 @@ def sq_diff_function(x): htrack = False list_for_matching = self.list_for_matching_postMS - MESA_labels = list_for_matching[0] - posydon_attributes = posydon_attribute(MESA_labels, star) - rs = list_for_matching[1] - colscalers = list_for_matching[2] - bnds = [] - for i in range(3, len(list_for_matching)): - bnds.append(list_for_matching[i]) - - if self.verbose or self.verbose == 1: - print("Matching attributes and their normalizations :", - MESA_labels, rs) + MESA_labels, rs, colscalers, bnds, scales = get_MESA_labels(list_for_matching) + for i in MESA_labels: if i not in self.root_keys: - raise AttributeError("Expected matching parameter not " - "added in the single star grid options.") - - scales = [] - for MESA_label, colscaler in zip(MESA_labels, colscalers): - scale_of_attribute = scale(MESA_label, htrack, colscaler) - scales.append(scale_of_attribute) - - def sq_diff_function(x): - return self.square_difference( - x, htrack=htrack, mesa_labels=MESA_labels, - posydon_attributes=posydon_attributes, - colscalers=colscalers, scales=scales) - - x0 = get_root0( - MESA_labels, posydon_attributes, htrack, rs=rs) + raise AttributeError(f"Expected matching parameter {i} not added " + "in the single star grid options.") + + posydon_attributes = get_posydon_attributes(MESA_labels, star) + x0 = get_root0(MESA_labels, posydon_attributes, htrack, rs=rs) - # bnds = ([m_min_H, m_max_H], [0, None]) try: - sol = minimize(sq_diff_function, x0, - method="TNC", bounds=bnds) + sol = minimize(sq_diff_function, x0, method="TNC", bounds=bnds) except: raise NumericalError("SciPy numerical differentiation occured outside boundary " "while matching to single star track") - # if still not acceptable matching, we fail the system: - if (np.abs(sol.fun) > tolerance_matching_integration_hard - or not sol.success): - ''' - if ((self.get_track_val("mass", star.htrack, *sol.x) - - self.get_track_val( - "he_core_mass", star.htrack, *sol.x)) - / self.get_track_val( - "mass", star.htrack, *sol.x) >= 0.05): - ''' - if self.verbose or self.verbose == 1: - print("minimization in matching not successful, with", - np.abs(sol.fun), ">", tolerance_matching_integration, - "tolerance") + # if matching is still not successful, set result to NaN: + if (np.abs(sol.fun) > tolerance_matching_integration_hard or not sol.success): + if self.verbose: + print("\nMatching result is NOT successful, with tolerance ", + np.abs(sol.fun), ">", tolerance_matching_integration_hard) initials = (np.nan, np.nan) - ''' - star.fun = np.nan - star.stiching_rel_mass_difference = np.nan - star.stiching_rel_radius_difference = np.nan - star.stiching_rel_inertia_difference = np.nan - ''' + elif np.abs(sol.fun) < tolerance_matching_integration_hard: - if self.verbose or self.verbose == 1: - print("minimization in matching considered acceptable," - " with", f'{np.abs(sol.fun):.8f}', "<", - tolerance_matching_integration, "tolerance") + if self.verbose: + print("\nMatching result is considered successful, with tolerance " + f'{np.abs(sol.fun):.8f}', "<", tolerance_matching_integration_hard) initials = sol.x - ''' - star.fun = sol.fun - star.stiching_rel_mass_difference = ( - self.get_track_val("mass", htrack, *sol.x) - star.mass - ) / star.mass - if star.log_R in MESA_label: - star.stiching_rel_logRadius_difference = ( - self.get_track_val("log_R", htrack, *sol.x) - - star.log_R) / star.log_R - else: - star.stiching_rel_logRadius_difference = np.nan - if (star.total_moment_of_inertia is not None - and not np.isnan(star.total_moment_of_inertia)): - star.stiching_rel_inertia_difference = ( - self.get_track_val("inertia", htrack, *sol.x) - - star.total_moment_of_inertia - ) / star.total_moment_of_inertia - ''' + if self.verbose: + if not np.isnan(initials[0]): + print( + "Matching completed for", star.state, "star!\n" + f"Matched to track with intial mass m0 = {initials[0]:.3f} [Msun]" + f" at time t0 = {initials[1]/1e6:.3f} [Myrs] \n", + "and m(t0), log10(R(t0), center_he(t0), surface_he4(t0), " + "surface_h1(t0), he_core_mass(t0), center_c12(t0) = \n", + f'{self.get_track_val("mass", htrack, *sol.x):.3f}', + f'{self.get_track_val("log_R", htrack, *sol.x):.3f}', + f'{self.get_track_val("center_he4", htrack, *sol.x):.4f}', + f'{self.get_track_val("surface_he4", htrack, *sol.x):.4f}', + f'{self.get_track_val("surface_h1", htrack, *sol.x):.4f}', + f'{self.get_track_val("he_core_mass", htrack, *sol.x):.3f}', + f'{self.get_track_val("center_c12", htrack, *sol.x):.4f}\n', + "The same values of the original star at the end of the previous " + "step were: \n", + f'{star.mass:.3f}', + f'{star.log_R:.3f}', + f'{star.center_he4:.4f}', + f'{star.surface_he4:.4f}', + f'{star.surface_h1:.4f}', + f'{star.he_core_mass:.3f}', + f'{star.center_c12:.4f}' + ) + else: + print( + "Matching completed unsuccessfully for star with properties: \n" + f'mass = {star.mass:.3f}, ', + f'log_R = {star.log_R:.3f}, ', + f'center_he4 = {star.center_he4:.4f}, ', + f'surface_he4 = {star.surface_he4:.4f}, ', + f'surface_h1 = {star.surface_h1:.4f}, ', + f'he_core_mass = {star.he_core_mass:.3f}, ', + f'center_c12 = {star.center_c12:.4f}' + ) - if self.verbose or self.verbose == 1: - print( - "matching ", star.state, - " star with track of intial mass m0, at time t0:", - f'{initials[0]:.3f} [Msun],', - f'{initials[1]/1e6:.3f} [Myrs]', "\n", - "with m(t0), log10(R(t0), center_he(t0), surface_he4(t0), " - "surface_h1(t0), he_core_mass(t0), center_c12(t0) = \n", - f'{self.get_track_val("mass", htrack, *sol.x):.3f}', - f'{self.get_track_val("log_R", htrack, *sol.x):.3f}', - f'{self.get_track_val("center_he4", htrack, *sol.x):.4f}', - f'{self.get_track_val("surface_he4", htrack, *sol.x):.4f}', - f'{self.get_track_val("surface_h1", htrack, *sol.x):.4f}', - f'{self.get_track_val("he_core_mass", htrack, *sol.x):.3f}', - f'{self.get_track_val("center_c12", htrack, *sol.x):.4f}\n', - "The same values of the secondary at the end of the previous " - "step was = \n", - f'{star.mass:.3f}', - f'{star.log_R:.3f}', - f'{star.center_he4:.4f}', - f'{star.surface_he4:.4f}', - f'{star.surface_h1:.4f}', - f'{star.he_core_mass:.3f}', - f'{star.center_c12:.4f}' - ) return initials[0], initials[1], htrack def __repr__(self): @@ -929,127 +873,8 @@ def __repr__(self): def __call__(self, binary): """Evolve the binary until RLO or compact object formation.""" - KEYS = self.KEYS - KEYS_POSITIVE = self.KEYS_POSITIVE - - companion_1_exists = (binary.star_1 is not None - and binary.star_1.state != "massless_remnant") - companion_2_exists = (binary.star_2 is not None - and binary.star_2.state != "massless_remnant") - - if companion_1_exists: - if companion_2_exists: # to evolve a binary star - self.non_existent_companion = 0 - else: # star1 is a single star - self.non_existent_companion = 2 - else: - if companion_2_exists: # star2 is a single star - self.non_existent_companion = 1 - else: # no star in the system - raise POSYDONError("There is no star to evolve. Who summoned me?") - - if self.non_existent_companion == 0: #no isolated evolution, detached step of an actual binary - # the primary in a real binary is potential compact object, or the more evolved star - if (binary.star_1.state in STAR_STATES_CO - and binary.star_2.state in STAR_STATES_H_RICH): - primary = binary.star_1 - secondary = binary.star_2 - secondary.htrack = True - primary.htrack = secondary.htrack - primary.co = True - - elif (binary.star_1.state in STAR_STATES_CO - and binary.star_2.state in LIST_ACCEPTABLE_STATES_FOR_HeStar): - primary = binary.star_1 - secondary = binary.star_2 - secondary.htrack = False - primary.htrack = secondary.htrack - primary.co = True - - elif (binary.star_2.state in STAR_STATES_CO - and binary.star_1.state in STAR_STATES_H_RICH): - primary = binary.star_2 - secondary = binary.star_1 - secondary.htrack = True - primary.htrack = secondary.htrack - primary.co = True - - elif (binary.star_2.state in STAR_STATES_CO - and binary.star_1.state in LIST_ACCEPTABLE_STATES_FOR_HeStar): - primary = binary.star_2 - secondary = binary.star_1 - secondary.htrack = False - primary.htrack = secondary.htrack - primary.co = True - elif (binary.star_1.state in STAR_STATES_H_RICH - and binary.star_2.state in STAR_STATES_H_RICH): - primary = binary.star_1 - secondary = binary.star_2 - secondary.htrack = True - primary.htrack = True - primary.co = False - elif (binary.star_1.state in LIST_ACCEPTABLE_STATES_FOR_HeStar - and binary.star_2.state in STAR_STATES_H_RICH): - primary = binary.star_1 - secondary = binary.star_2 - secondary.htrack = True - primary.htrack = False - primary.co = False - elif (binary.star_2.state in LIST_ACCEPTABLE_STATES_FOR_HeStar - and binary.star_1.state in STAR_STATES_H_RICH): - primary = binary.star_2 - secondary = binary.star_1 - secondary.htrack = True - primary.htrack = False - primary.co = False - elif (binary.star_1.state in LIST_ACCEPTABLE_STATES_FOR_HeStar - and binary.star_2.state - in LIST_ACCEPTABLE_STATES_FOR_HeStar): - primary = binary.star_1 - secondary = binary.star_2 - secondary.htrack = False - primary.htrack = False - primary.co = False - else: - raise ValueError("States not recognized!") - - # star 1 is a massless remnant, only star 2 exists - elif self.non_existent_companion == 1: - # we force primary.co=True for all isolated evolution, - # where the secondary is the one evolving one - primary = binary.star_1 - primary.co = True - primary.htrack = False - secondary = binary.star_2 - if (binary.star_2.state in STAR_STATES_H_RICH): - secondary.htrack = True - elif (binary.star_2.state in LIST_ACCEPTABLE_STATES_FOR_HeStar): - secondary.htrack = False - elif (binary.star_2.state in STAR_STATES_CO): - # only a compact object left - return - else: - raise ValueError("State not recognized!") - # star 2 is a massless remnant, only star 1 exists - elif self.non_existent_companion == 2: - primary = binary.star_2 - primary.co = True - primary.htrack = False - secondary = binary.star_1 - if (binary.star_1.state in STAR_STATES_H_RICH): - secondary.htrack = True - elif (binary.star_1.state in LIST_ACCEPTABLE_STATES_FOR_HeStar): - secondary.htrack = False - elif (binary.star_1.state in STAR_STATES_CO): - return - else: - raise ValueError("State not recognized!") - else: - raise POSYDONError("Non existent companion has not a recognized value!") - - def get_star_data(binary, star1, star2, htrack, - co, copy_prev_m0=None, copy_prev_t0=None): + def get_star_data(binary, star1, star2, htrack, co, copy_prev_m0=None, copy_prev_t0=None): """Get and interpolate the properties of stars. The data of a compact object can be stored as a copy of its @@ -1083,9 +908,9 @@ def get_star_data(binary, star1, star2, htrack, t_before_matching = time.time() m0, t0, htrack = self.match_to_single_star(star1, htrack) t_after_matching = time.time() - if self.verbose or self.verbose == 1: - print("Matching duration: " - f"{t_after_matching-t_before_matching:.6g}") + + if self.verbose: + print(f"Matching duration: {t_after_matching-t_before_matching:.6g} sec\n") if pd.isna(m0) or pd.isna(t0): return None, None, None @@ -1098,7 +923,8 @@ def get_star_data(binary, star1, star2, htrack, # check if m0 is in the grid if m0 < self.grid.grid_mass.min() or m0 > self.grid.grid_mass.max(): set_binary_to_failed(binary) - raise MatchingError(f"The mass {m0} is out of the single star grid range and cannot be matched to a track.") + raise MatchingError(f"The mass {m0} is out of the single star grid range and " + "cannot be matched to a track.") get_track = self.grid.get @@ -1115,8 +941,7 @@ def get_star_data(binary, star1, star2, htrack, for key in KEYS[1:]: if key in KEYS_POSITIVE: positive = True - interp1d[key] = PchipInterpolator2(age, kvalue[key], - positive=positive) + interp1d[key] = PchipInterpolator2(age, kvalue[key], positive=positive) else: interp1d[key] = PchipInterpolator2(age, kvalue[key]) except ValueError: @@ -1126,13 +951,14 @@ def get_star_data(binary, star1, star2, htrack, age = np.delete(age, i_bad) for key in KEYS[1:]: kvalue[key] = np.delete(kvalue[key], i_bad) + for key in KEYS[1:]: if key in KEYS_POSITIVE: positive = True - interp1d[key] = PchipInterpolator2(age, kvalue[key], - positive=positive) + interp1d[key] = PchipInterpolator2(age, kvalue[key], positive=positive) else: interp1d[key] = PchipInterpolator2(age, kvalue[key]) + interp1d["inertia"] = PchipInterpolator( age, kvalue["inertia"] / (const.msol * const.rsol**2)) interp1d["Idot"] = interp1d["inertia"].derivative() @@ -1146,6 +972,7 @@ def get_star_data(binary, star1, star2, htrack, interp1d["max_time"] = max_time interp1d["t0"] = t0 interp1d["m0"] = m0 + if co: kvalue["mass"] = np.zeros_like(kvalue["mass"]) + star2.mass kvalue["R"] = np.zeros_like(kvalue["log_R"]) @@ -1154,45 +981,9 @@ def get_star_data(binary, star1, star2, htrack, interp1d["R"] = PchipInterpolator(age, kvalue["R"]) interp1d["mdot"] = PchipInterpolator(age, kvalue["mdot"]) interp1d["Idot"] = PchipInterpolator(age, kvalue["mdot"]) - return interp1d, m0, t0 - - # get the matched data of two stars, respectively - interp1d_sec, m0, t0 = get_star_data( - binary, secondary, primary, secondary.htrack, co=False) - - primary_not_normal = (primary.co) or (self.non_existent_companion in [1,2]) - primary_normal = (not primary.co) and self.non_existent_companion == 0 - - if primary_not_normal: - # copy the secondary star except mass which is of the primary, - # and radius, mdot, Idot = 0 - interp1d_pri = get_star_data( - binary, secondary, primary, secondary.htrack, co=True, - copy_prev_m0=m0, copy_prev_t0=t0)[0] - elif primary_normal: - interp1d_pri = get_star_data( - binary, primary, secondary, primary.htrack, False)[0] - else: - raise ValueError("During matching, the primary should either be normal (stellar object) or ", - "not normal (CO, nonexistent companion).") - - - if interp1d_sec is None or interp1d_pri is None: - failed_state = binary.state - set_binary_to_failed(binary) - raise MatchingError(f"Grid matching failed for {failed_state} binary.") - - - t0_sec = interp1d_sec["t0"] - t0_pri = interp1d_pri["t0"] - m01 = interp1d_sec["m0"] - m02 = interp1d_pri["m0"] - t_max_sec = interp1d_sec["t_max"] - t_max_pri = interp1d_pri["t_max"] - t_offset_sec = binary.time - t0_sec - t_offset_pri = binary.time - t0_pri - max_time = interp1d_sec["max_time"] + return interp1d, m0, t0 + @event(True, 1) def ev_rlo1(t, y): """Difference between radius and Roche lobe at a given time. @@ -1330,43 +1121,53 @@ def ev_max_time1(t, y): @event(True, -1) def ev_max_time2(t, y): return t_max_pri + t_offset_pri - t + + def get_omega(star, is_secondary=True): + """Calculate the spin of a star. + + Parameters + ---------- + star : SingleStar object + + Returns + ------- + float + spin of the star in radians per year + """ - # make a function to get the spin of two stars - def get_omega(star, is_secondary = True): if (star.log_total_angular_momentum is not None and star.total_moment_of_inertia is not None and not np.isnan(star.log_total_angular_momentum) and not np.isnan(star.total_moment_of_inertia)): + + # the last factor converts rad/s to rad/yr omega_in_rad_per_year = ( 10.0 ** star.log_total_angular_momentum - / star.total_moment_of_inertia * const.secyer - ) # the last factor transforms it from rad/s to rad/yr - if self.verbose and self.verbose != 1: - print("calculating initial omega from angular momentum and" - " moment of inertia", omega_in_rad_per_year) + / star.total_moment_of_inertia * const.secyer) + + if self.verbose: + print("calculating initial omega using angular momentum and moment of inertia") else: - # we equate secondary's initial omega to surf_avg_omega + # we equate the secondary's initial omega to surf_avg_omega # (although the critical rotation should be improved to - # take into accoun radiation pressure) - if (star.surf_avg_omega is not None - and not np.isnan(star.surf_avg_omega)): - omega_in_rad_per_year = star.surf_avg_omega * const.secyer - # the last factor transforms it from rad/s to rad/yr. - if self.verbose and self.verbose != 1: - print("calculating initial omega from surf_avg_omega", - omega_in_rad_per_year) + # take into account radiation pressure) + + if (star.surf_avg_omega is not None and not np.isnan(star.surf_avg_omega)): + + if self.verbose: + print("calculating initial omega using surf_avg_omega") + + omega_in_rad_per_year = star.surf_avg_omega * const.secyer + elif (star.surf_avg_omega_div_omega_crit is not None and not np.isnan(star.surf_avg_omega_div_omega_crit)): - if (star.log_R is not None - and not np.isnan(star.log_R)): + + if (star.log_R is not None and not np.isnan(star.log_R)): omega_in_rad_per_year = ( star.surf_avg_omega_div_omega_crit * np.sqrt( const.standard_cgrav * star.mass * const.msol - / ((10.0 ** (star.log_R) * const.rsol) ** 3)) - * const.secyer) - # the last factor transforms it from rad/s to rad/yr - # EDIT: We assume POSYDON surf_avg_omega is provided in - # rad/yr already. + / ((10.0 ** (star.log_R) * const.rsol) ** 3)) * const.secyer) + else: if is_secondary: radius_to_be_used = interp1d_sec["R"](interp1d_sec["t0"]) @@ -1374,24 +1175,204 @@ def get_omega(star, is_secondary = True): else: radius_to_be_used = interp1d_pri["R"](interp1d_pri["t0"]) mass_to_be_used = interp1d_pri["mass"](interp1d_pri["t0"]) + + if self.verbose: + print("calculating initial omega using surf_avg_omega_div_omega_crit") + omega_in_rad_per_year = ( star.surf_avg_omega_div_omega_crit * np.sqrt( const.standard_cgrav * mass_to_be_used * const.msol - / ((radius_to_be_used * const.rsol) ** 3)) - * const.secyer) - - if self.verbose and self.verbose != 1: - print("calculating initial omega from " - "surf_avg_omega_div_omega_crit", - omega_in_rad_per_year) + / ((radius_to_be_used * const.rsol) ** 3)) * const.secyer) + else: omega_in_rad_per_year = 0.0 - if self.verbose and self.verbose != 1: - print("could calculate initial omega", - omega_in_rad_per_year) - if self.verbose and self.verbose != 1: - print("initial omega_in_rad_per_year", omega_in_rad_per_year) + if self.verbose: + print("could not calculate initial omega, setting to zero") + + if self.verbose: + print("calculated omega_in_rad_per_year: ", omega_in_rad_per_year) + return omega_in_rad_per_year + + def get_star_final_values(star, htrack, m0): + + grid = self.grid_Hrich if htrack else self.grid_strippedHe + get_final_values = grid.get_final_values + + for key in self.final_keys: + setattr(star, key, get_final_values('S1_%s' % (key), m0)) + + def get_star_profile(star, htrack, m0): + + grid = self.grid_Hrich if htrack else self.grid_strippedHe + get_profile = grid.get_profile + profile_new = np.array(get_profile('mass', m0)[1]) + + for i in self.profile_keys: + profile_new[i] = get_profile(i, m0)[0] + profile_new['omega'] = star.surf_avg_omega + + star.profile = profile_new + + + KEYS = self.KEYS + KEYS_POSITIVE = self.KEYS_POSITIVE + + binary_sim_prop = getattr(binary, "properties") ## simulation properties of the binary + all_step_names = getattr(binary_sim_prop, "all_step_names") + + companion_1_exists = (binary.star_1 is not None + and binary.star_1.state != "massless_remnant") + companion_2_exists = (binary.star_2 is not None + and binary.star_2.state != "massless_remnant") + + if companion_1_exists: + if companion_2_exists: # to evolve a binary star + self.non_existent_companion = 0 + else: # star1 is a single star + self.non_existent_companion = 2 + else: + if companion_2_exists: # star2 is a single star + self.non_existent_companion = 1 + else: # no star in the system + raise POSYDONError("There is no star to evolve. Who summoned me?") + + if self.non_existent_companion == 0: #no isolated evolution, detached step of an actual binary + # the primary in a real binary is potential compact object, or the more evolved star + if (binary.star_1.state in STAR_STATES_CO + and binary.star_2.state in STAR_STATES_H_RICH): + primary = binary.star_1 + secondary = binary.star_2 + secondary.htrack = True + primary.htrack = secondary.htrack + primary.co = True + + elif (binary.star_1.state in STAR_STATES_CO + and binary.star_2.state in LIST_ACCEPTABLE_STATES_FOR_HeStar): + primary = binary.star_1 + secondary = binary.star_2 + secondary.htrack = False + primary.htrack = secondary.htrack + primary.co = True + + elif (binary.star_2.state in STAR_STATES_CO + and binary.star_1.state in STAR_STATES_H_RICH): + primary = binary.star_2 + secondary = binary.star_1 + secondary.htrack = True + primary.htrack = secondary.htrack + primary.co = True + + elif (binary.star_2.state in STAR_STATES_CO + and binary.star_1.state in LIST_ACCEPTABLE_STATES_FOR_HeStar): + primary = binary.star_2 + secondary = binary.star_1 + secondary.htrack = False + primary.htrack = secondary.htrack + primary.co = True + elif (binary.star_1.state in STAR_STATES_H_RICH + and binary.star_2.state in STAR_STATES_H_RICH): + primary = binary.star_1 + secondary = binary.star_2 + secondary.htrack = True + primary.htrack = True + primary.co = False + elif (binary.star_1.state in LIST_ACCEPTABLE_STATES_FOR_HeStar + and binary.star_2.state in STAR_STATES_H_RICH): + primary = binary.star_1 + secondary = binary.star_2 + secondary.htrack = True + primary.htrack = False + primary.co = False + elif (binary.star_2.state in LIST_ACCEPTABLE_STATES_FOR_HeStar + and binary.star_1.state in STAR_STATES_H_RICH): + primary = binary.star_2 + secondary = binary.star_1 + secondary.htrack = True + primary.htrack = False + primary.co = False + elif (binary.star_1.state in LIST_ACCEPTABLE_STATES_FOR_HeStar + and binary.star_2.state + in LIST_ACCEPTABLE_STATES_FOR_HeStar): + primary = binary.star_1 + secondary = binary.star_2 + secondary.htrack = False + primary.htrack = False + primary.co = False + else: + raise ValueError("States not recognized!") + + # star 1 is a massless remnant, only star 2 exists + elif self.non_existent_companion == 1: + # we force primary.co=True for all isolated evolution, + # where the secondary is the one evolving one + primary = binary.star_1 + primary.co = True + primary.htrack = False + secondary = binary.star_2 + if (binary.star_2.state in STAR_STATES_H_RICH): + secondary.htrack = True + elif (binary.star_2.state in LIST_ACCEPTABLE_STATES_FOR_HeStar): + secondary.htrack = False + elif (binary.star_2.state in STAR_STATES_CO): + # only a compact object left + return + else: + raise ValueError("State not recognized!") + + # star 2 is a massless remnant, only star 1 exists + elif self.non_existent_companion == 2: + primary = binary.star_2 + primary.co = True + primary.htrack = False + secondary = binary.star_1 + if (binary.star_1.state in STAR_STATES_H_RICH): + secondary.htrack = True + elif (binary.star_1.state in LIST_ACCEPTABLE_STATES_FOR_HeStar): + secondary.htrack = False + elif (binary.star_1.state in STAR_STATES_CO): + return + else: + raise ValueError("State not recognized!") + else: + raise POSYDONError("Non existent companion has not a recognized value!") + + # get the matched data of two stars, respectively + interp1d_sec, m0, t0 = get_star_data(binary, secondary, primary, secondary.htrack, co=False) + + primary_not_normal = (primary.co) or (self.non_existent_companion in [1,2]) + primary_normal = (not primary.co) and self.non_existent_companion == 0 + + if primary_not_normal: + # copy the secondary star except mass which is of the primary, + # and radius, mdot, Idot = 0 + interp1d_pri = get_star_data( + binary, secondary, primary, secondary.htrack, co=True, + copy_prev_m0=m0, copy_prev_t0=t0)[0] + elif primary_normal: + interp1d_pri = get_star_data( + binary, primary, secondary, primary.htrack, False)[0] + else: + raise ValueError("During matching, the primary should either be normal (stellar object) or ", + "not normal (CO, nonexistent companion).") + + + if interp1d_sec is None or interp1d_pri is None: + failed_state = binary.state + set_binary_to_failed(binary) + raise MatchingError(f"Grid matching failed for {failed_state} binary.") + + + t0_sec = interp1d_sec["t0"] + t0_pri = interp1d_pri["t0"] + m01 = interp1d_sec["m0"] + m02 = interp1d_pri["m0"] + t_max_sec = interp1d_sec["t_max"] + t_max_pri = interp1d_pri["t_max"] + t_offset_sec = binary.time - t0_sec + t_offset_pri = binary.time - t0_pri + max_time = interp1d_sec["max_time"] + if (ev_rlo1(binary.time, [binary.separation, binary.eccentricity]) >= 0 or ev_rlo2(binary.time, [binary.separation, binary.eccentricity]) >= 0): @@ -1501,9 +1482,8 @@ def get_omega(star, is_secondary = True): t_after_ODEsolution = time.time() - if self.verbose and self.verbose != 1: - print("ODE solver duration: " - f"{t_after_ODEsolution-t_before_ODEsolution:.6g}") + if self.verbose: + print(f"\nODE solver duration: {t_after_ODEsolution-t_before_ODEsolution:.6g}") print("solution of ODE", s) if s.status == -1: @@ -1541,82 +1521,59 @@ def get_omega(star, is_secondary = True): mass_interp_sec(t - t_offset_sec)) interp1d_sec["time"] = t - # time_interp = binary.time + t - t0 - for obj, prop in zip( - [secondary, primary, binary], - [STARPROPERTIES, STARPROPERTIES, BINARYPROPERTIES], - ): + ## UPDATE STAR AND BINARY PROPERTIES WITH INTERPOLATED VALUES + for obj, prop in zip([secondary, primary, binary], + [STARPROPERTIES, STARPROPERTIES, BINARYPROPERTIES]): + for key in prop: if key in ["event", "mass_transfer_case", "nearest_neighbour_distance", "state", "metallicity", "V_sys"]: current = getattr(obj, key) - # For star objects, the state is calculated - # further below + # For star objects, the state is calculated further below history = [current] * len(t[:-1]) - elif (key in ["surf_avg_omega_div_omega_crit"] - and obj == secondary): - # replace the actual surf_avg_w with the effective - # omega, which takes into account the whole star + elif (key in ["surf_avg_omega_div_omega_crit"] and obj == secondary): + # replace the actual surf_avg_w with the effective omega, + # which takes into account the whole star # key = 'effective_omega' # in rad/sec # current = s.y[2][-1] / 3.1558149984e7 # history_of_attribute = s.y[2][:-1] / 3.1558149984e7 - omega_crit_current_sec = np.sqrt( - const.standard_cgrav - * interp1d_sec[self.translate["mass"]]( - t[-1] - t_offset_sec).item() * const.msol - / (interp1d_sec[self.translate["R"]]( - t[-1] - t_offset_sec).item() * const.rsol) ** 3 - ) - omega_crit_hist_sec = np.sqrt( - const.standard_cgrav - * interp1d_sec[self.translate["mass"]]( - t[:-1] - t_offset_sec) * const.msol - / (interp1d_sec[self.translate["R"]]( - t[:-1] - t_offset_sec) * const.rsol) ** 3 - ) - - current = (interp1d_sec["omega"][-1] / const.secyer - / omega_crit_current_sec) - history = (interp1d_sec["omega"][:-1] / const.secyer - / omega_crit_hist_sec) + omega_crit_current_sec = np.sqrt(const.standard_cgrav + * interp1d_sec[self.translate["mass"]](t[-1] - t_offset_sec).item() * const.msol + / (interp1d_sec[self.translate["R"]](t[-1] - t_offset_sec).item() * const.rsol)**3) - elif (key in ["surf_avg_omega_div_omega_crit"] - and obj == primary): + omega_crit_hist_sec = np.sqrt(const.standard_cgrav + * interp1d_sec[self.translate["mass"]](t[:-1] - t_offset_sec) * const.msol + / (interp1d_sec[self.translate["R"]](t[:-1] - t_offset_sec) * const.rsol)**3) + + current = (interp1d_sec["omega"][-1] / const.secyer / omega_crit_current_sec) + history = (interp1d_sec["omega"][:-1] / const.secyer / omega_crit_hist_sec) + + elif (key in ["surf_avg_omega_div_omega_crit"] and obj == primary): if primary.co: current = None history = [current] * len(t[:-1]) elif not primary.co: # TODO: change `item()` to 0 - omega_crit_current_pri = np.sqrt( - const.standard_cgrav - * interp1d_pri[self.translate["mass"]]( - t[-1] - t_offset_pri).item() * const.msol - / (interp1d_pri[self.translate["R"]]( - t[-1] - t_offset_pri).item() * const.rsol) - ** 3) - - omega_crit_hist_pri = np.sqrt( - const.standard_cgrav - * interp1d_pri[self.translate["mass"]]( - t[:-1] - t_offset_pri) * const.msol - / (interp1d_pri[self.translate["R"]]( - t[:-1] - t_offset_pri) * const.rsol) ** 3) - - current = (interp1d_pri["omega"][-1] - / const.secyer / omega_crit_current_pri) - history = (interp1d_pri["omega"][:-1] - / const.secyer / omega_crit_hist_pri) + omega_crit_current_pri = np.sqrt(const.standard_cgrav + * interp1d_pri[self.translate["mass"]](t[-1] - t_offset_pri).item() * const.msol + / (interp1d_pri[self.translate["R"]](t[-1] - t_offset_pri).item() * const.rsol)**3) + + omega_crit_hist_pri = np.sqrt(const.standard_cgrav + * interp1d_pri[self.translate["mass"]](t[:-1] - t_offset_pri) * const.msol + / (interp1d_pri[self.translate["R"]](t[:-1] - t_offset_pri) * const.rsol)**3) + + current = (interp1d_pri["omega"][-1] / const.secyer / omega_crit_current_pri) + history = (interp1d_pri["omega"][:-1] / const.secyer / omega_crit_hist_pri) - elif key in ["surf_avg_omega"] and obj == secondary: - # current = interp1d["omega"](t[-1]) / const.secyer + elif (key in ["surf_avg_omega"] and obj == secondary): current = interp1d_sec["omega"][-1] / const.secyer history = interp1d_sec["omega"][:-1] / const.secyer - elif key in ["surf_avg_omega"] and obj == primary: + elif (key in ["surf_avg_omega"] and obj == primary): if primary.co: current = None history = [current] * len(t[:-1]) @@ -1624,69 +1581,51 @@ def get_omega(star, is_secondary = True): current = interp1d_pri["omega"][-1] / const.secyer history = interp1d_pri["omega"][:-1] / const.secyer - elif key in ["rl_relative_overflow_1"] and obj == binary: + elif (key in ["rl_relative_overflow_1"] and obj == binary): if binary.star_1.state in ("BH", "NS", "WD","massless_remnant"): current = None history = [current] * len(t[:-1]) + elif secondary == binary.star_1: - current = ev_rel_rlo1(t[-1], - [interp1d_sec["sep"][-1], - interp1d_sec["ecc"][-1]]) - history = ev_rel_rlo1(t[:-1], - [interp1d_sec["sep"][:-1], - interp1d_sec["ecc"][:-1]]) + current = ev_rel_rlo1(t[-1], [interp1d_sec["sep"][-1], interp1d_sec["ecc"][-1]]) + history = ev_rel_rlo1(t[:-1], [interp1d_sec["sep"][:-1], interp1d_sec["ecc"][:-1]]) + elif secondary == binary.star_2: - current = ev_rel_rlo2(t[-1], - [interp1d_sec["sep"][-1], - interp1d_sec["ecc"][-1]]) - history = ev_rel_rlo2(t[:-1], - [interp1d_sec["sep"][:-1], - interp1d_sec["ecc"][:-1]]) + current = ev_rel_rlo2(t[-1], [interp1d_sec["sep"][-1], interp1d_sec["ecc"][-1]]) + history = ev_rel_rlo2(t[:-1], [interp1d_sec["sep"][:-1], interp1d_sec["ecc"][:-1]]) - elif key in ["rl_relative_overflow_2"] and obj == binary: + elif (key in ["rl_relative_overflow_2"] and obj == binary): if binary.star_2.state in ("BH", "NS", "WD","massless_remnant"): current = None history = [current] * len(t[:-1]) + elif secondary == binary.star_2: - current = ev_rel_rlo1(t[-1], - [interp1d_sec["sep"][-1], - interp1d_sec["ecc"][-1]]) - history = ev_rel_rlo1(t[:-1], - [interp1d_sec["sep"][:-1], - interp1d_sec["ecc"][:-1]]) + current = ev_rel_rlo1(t[-1], [interp1d_sec["sep"][-1], interp1d_sec["ecc"][-1]]) + history = ev_rel_rlo1(t[:-1], [interp1d_sec["sep"][:-1], interp1d_sec["ecc"][:-1]]) + elif secondary == binary.star_1: - current = ev_rel_rlo2(t[-1], - [interp1d_sec["sep"][-1], - interp1d_sec["ecc"][-1]]) - history = ev_rel_rlo2(t[:-1], - [interp1d_sec["sep"][:-1], - interp1d_sec["ecc"][:-1]]) + current = ev_rel_rlo2(t[-1], [interp1d_sec["sep"][-1], interp1d_sec["ecc"][-1]]) + history = ev_rel_rlo2(t[:-1], [interp1d_sec["sep"][:-1], interp1d_sec["ecc"][:-1]]) - elif key in ["separation", "orbital_period", - "eccentricity", "time"]: + elif key in ["separation", "orbital_period", "eccentricity", "time"]: current = interp1d_sec[self.translate[key]][-1].item() history = interp1d_sec[self.translate[key]][:-1] - elif (key in ["total_moment_of_inertia"] - and obj == secondary): + elif (key in ["total_moment_of_inertia"] and obj == secondary): current = interp1d_sec[self.translate[key]]( - t[-1] - t_offset_sec).item() * ( - const.msol * const.rsol ** 2) + t[-1] - t_offset_sec).item() * (const.msol * const.rsol**2) history = interp1d_sec[self.translate[key]]( - t[:-1] - t_offset_sec) * ( - const.msol * const.rsol ** 2) + t[:-1] - t_offset_sec) * (const.msol * const.rsol**2) - elif key in ["total_moment_of_inertia"] and obj == primary: + elif (key in ["total_moment_of_inertia"] and obj == primary): if primary.co: current = getattr(obj, key) history = [current] * len(t[:-1]) else: current = interp1d_pri[self.translate[key]]( - t[-1] - t_offset_pri).item() * ( - const.msol * const.rsol ** 2) + t[-1] - t_offset_pri).item() * (const.msol * const.rsol**2) history = interp1d_pri[self.translate[key]]( - t[:-1] - t_offset_pri) * ( - const.msol * const.rsol ** 2) + t[:-1] - t_offset_pri) * (const.msol * const.rsol**2) elif (key in ["log_total_angular_momentum"] and obj == secondary): @@ -1695,151 +1634,112 @@ def get_omega(star, is_secondary = True): ## add a warning catch if the current omega has an invalid value ## (otherwise python will throw an insuppressible warning when taking the log) if interp1d_sec["omega"][-1] <=0: - Pwarn("Trying to compute log angular momentum for object with no spin", "InappropriateValueWarning") + Pwarn("Trying to compute log angular momentum for object with no spin", + "InappropriateValueWarning") current_omega = np.nan current = np.log10( (current_omega / const.secyer) * (interp1d_sec[ self.translate["total_moment_of_inertia"]](t[-1] - t_offset_sec).item() * - (const.msol * const.rsol ** 2))) - + (const.msol * const.rsol ** 2))) history = np.log10( (interp1d_sec["omega"][:-1] / const.secyer) - * (interp1d_sec[ - self.translate["total_moment_of_inertia"]]( - t[:-1] - t_offset_sec) * ( - const.msol * const.rsol ** 2))) + * (interp1d_sec[self.translate["total_moment_of_inertia"]]( + t[:-1] - t_offset_sec) * (const.msol * const.rsol**2))) - elif (key in ["log_total_angular_momentum"] - and obj == primary): + elif (key in ["log_total_angular_momentum"] and obj == primary): if primary.co: current = getattr(obj, key) history = [current] * len(t[:-1]) else: current = np.log10( (interp1d_pri["omega"][-1] / const.secyer) - * (interp1d_pri[ - self.translate["total_moment_of_inertia"]]( - t[-1] - t_offset_pri).item() * ( - const.msol * const.rsol ** 2))) + * (interp1d_pri[self.translate["total_moment_of_inertia"]]( + t[-1] - t_offset_pri).item() * (const.msol * const.rsol**2))) history = np.log10( (interp1d_pri["omega"][:-1] / const.secyer) - * (interp1d_pri[ - self.translate["total_moment_of_inertia"]]( - t[:-1] - t_offset_pri) * ( - const.msol * const.rsol ** 2))) + * (interp1d_pri[self.translate["total_moment_of_inertia"]]( + t[:-1] - t_offset_pri) * (const.msol * const.rsol**2))) - elif key in ["spin"] and obj == secondary: - current = ( - const.clight + elif (key in ["spin"] and obj == secondary): + current = (const.clight * (interp1d_sec["omega"][-1] / const.secyer) - * interp1d_sec[ - self.translate["total_moment_of_inertia"]]( - t[-1] - t_offset_sec).item() - * (const.msol * const.rsol ** 2) - / (const.standard_cgrav * ( - interp1d_sec[self.translate["mass"]]( - t[-1] - t_offset_sec).item() - * const.msol) ** 2)) - history = ( - const.clight + * interp1d_sec[self.translate["total_moment_of_inertia"]]( + t[-1] - t_offset_sec).item() * (const.msol * const.rsol**2) + / (const.standard_cgrav * (interp1d_sec[self.translate["mass"]]( + t[-1] - t_offset_sec).item() * const.msol)**2)) + history = (const.clight * (interp1d_sec["omega"][:-1] / const.secyer) - * interp1d_sec[ - self.translate["total_moment_of_inertia"]]( - t[:-1] - t_offset_sec) - * (const.msol * const.rsol ** 2) - / (const.standard_cgrav * ( - interp1d_sec[self.translate["mass"]]( + * interp1d_sec[self.translate["total_moment_of_inertia"]]( + t[:-1] - t_offset_sec)* (const.msol * const.rsol**2) + / (const.standard_cgrav * (interp1d_sec[self.translate["mass"]]( t[:-1] - t_offset_sec) * const.msol)**2)) - elif key in ["spin"] and obj == primary: + elif (key in ["spin"] and obj == primary): if primary.co: current = getattr(obj, key) history = [current] * len(t[:-1]) else: - current = ( - const.clight + current = (const.clight * (interp1d_pri["omega"][-1] / const.secyer) - * interp1d_pri[ - self.translate["total_moment_of_inertia"]]( - t[-1] - t_offset_pri).item() - * (const.msol * const.rsol ** 2) - / (const.standard_cgrav * ( - interp1d_pri[self.translate["mass"]]( - t[-1] - t_offset_pri).item() - * const.msol)**2)) - history = ( - const.clight * (interp1d_pri["omega"][:-1] - / const.secyer) - * interp1d_pri[ - self.translate["total_moment_of_inertia"]]( - t[:-1] - t_offset_pri) - * (const.msol * const.rsol ** 2) - / (const.standard_cgrav * (interp1d_pri[ - self.translate["mass"]]( - t[:-1] - t_offset_pri) - * const.msol)**2)) + * interp1d_pri[self.translate["total_moment_of_inertia"]]( + t[-1] - t_offset_pri).item() * (const.msol * const.rsol**2) + / (const.standard_cgrav * (interp1d_pri[self.translate["mass"]]( + t[-1] - t_offset_pri).item() * const.msol)**2)) + history = (const.clight + * (interp1d_pri["omega"][:-1] / const.secyer) + * interp1d_pri[self.translate["total_moment_of_inertia"]]( + t[:-1] - t_offset_pri) * (const.msol * const.rsol**2) + / (const.standard_cgrav * (interp1d_pri[self.translate["mass"]]( + t[:-1] - t_offset_pri) * const.msol)**2)) - elif (key in ["lg_mdot", "lg_wind_mdot"] - and obj == secondary): + elif (key in ["lg_mdot", "lg_wind_mdot"] and obj == secondary): # in detached step, lg_mdot = lg_wind_mdot - if interp1d_sec[self.translate[key]]( - t[-1] - t_offset_sec) == 0: + if interp1d_sec[self.translate[key]](t[-1] - t_offset_sec) == 0: current = -98.99 else: - current = np.log10( - np.abs(interp1d_sec[self.translate[key]]( - t[-1] - t_offset_sec))).item() + current = np.log10(np.abs(interp1d_sec[self.translate[key]]( + t[-1] - t_offset_sec))).item() + history = np.ones_like(t[:-1]) + for i in range(len(t)-1): - if interp1d_sec[self.translate[key]]( - t[i] - t_offset_sec) == 0: + if interp1d_sec[self.translate[key]](t[i] - t_offset_sec) == 0: history[i] = -98.99 else: - history[i] = np.log10( - np.abs(interp1d_sec[self.translate[key]]( + history[i] = np.log10(np.abs(interp1d_sec[self.translate[key]]( t[i] - t_offset_sec))) - elif key in ["lg_mdot", "lg_wind_mdot"] and obj == primary: + elif (key in ["lg_mdot", "lg_wind_mdot"] and obj == primary): if primary.co: current = None history = [current] * len(t[:-1]) else: - if interp1d_sec[self.translate[key]]( - t[-1] - t_offset_sec) == 0: + if interp1d_sec[self.translate[key]](t[-1] - t_offset_sec) == 0: current = -98.99 else: - current = np.log10(np.abs( - interp1d_sec[self.translate[key]]( + current = np.log10(np.abs(interp1d_sec[self.translate[key]]( t[-1] - t_offset_sec))).item() history = np.ones_like(t[:-1]) for i in range(len(t)-1): - if (interp1d_sec[self.translate[key]]( - t[i] - t_offset_sec) == 0): + if (interp1d_sec[self.translate[key]](t[i] - t_offset_sec) == 0): history[i] = -98.99 else: - history[i] = np.log10(np.abs( - interp1d_sec[self.translate[key]]( + history[i] = np.log10(np.abs(interp1d_sec[self.translate[key]]( t[i] - t_offset_sec))) - elif (self.translate[key] in interp1d_sec - and obj == secondary): - current = interp1d_sec[self.translate[key]]( - t[-1] - t_offset_sec).item() - history = interp1d_sec[self.translate[key]]( - t[:-1] - t_offset_sec) + elif (self.translate[key] in interp1d_sec and obj == secondary): + current = interp1d_sec[self.translate[key]](t[-1] - t_offset_sec).item() + history = interp1d_sec[self.translate[key]](t[:-1] - t_offset_sec) - elif (self.translate[key] in interp1d_pri - and obj == primary): + elif (self.translate[key] in interp1d_pri and obj == primary): if primary.co: current = getattr(obj, key) history = [current] * len(t[:-1]) else: - current = interp1d_pri[self.translate[key]]( - t[-1] - t_offset_pri).item() - history = interp1d_pri[self.translate[key]]( - t[:-1] - t_offset_pri) + current = interp1d_pri[self.translate[key]](t[-1] - t_offset_pri).item() + history = interp1d_pri[self.translate[key]](t[:-1] - t_offset_pri) elif key in ["profile"]: current = None @@ -1855,8 +1755,7 @@ def get_omega(star, is_secondary = True): secondary.state = check_state_of_star(secondary, star_CO=False) for timestep in range(-len(t[:-1]), 0): - secondary.state_history[timestep] = check_state_of_star( - secondary, i=timestep, star_CO=False) + secondary.state_history[timestep] = check_state_of_star(secondary, i=timestep, star_CO=False) if primary.state == "massless_remnant": @@ -1867,34 +1766,16 @@ def get_omega(star, is_secondary = True): binary, primary, secondary, slice(-len(t), None), wind_disk_criteria=True, scheme='Kudritzki+2000')) primary.lg_mdot = np.log10(mdot_acc.item(-1)) - primary.lg_mdot_history[len(primary.lg_mdot_history) - len(t) - + 1:] = np.log10(mdot_acc[:-1]) + primary.lg_mdot_history[len(primary.lg_mdot_history) - len(t) + 1:] = np.log10(mdot_acc[:-1]) else: primary.state = check_state_of_star(primary, star_CO=False) for timestep in range(-len(t[:-1]), 0): - primary.state_history[timestep] = check_state_of_star( - primary, i=timestep, star_CO=False) - - def get_star_final_values(star, htrack, m0): - grid = self.grid_Hrich if htrack else self.grid_strippedHe - get_final_values = grid.get_final_values - - for key in self.final_keys: - setattr(star, key, get_final_values('S1_%s' % (key), m0)) - - def get_star_profile(star, htrack, m0): - grid = self.grid_Hrich if htrack else self.grid_strippedHe - get_profile = grid.get_profile - profile_new = np.array(get_profile('mass', m0)[1]) + primary.state_history[timestep] = check_state_of_star(primary, i=timestep, star_CO=False) - for i in self.profile_keys: - profile_new[i] = get_profile(i, m0)[0] - profile_new['omega'] = star.surf_avg_omega + ## CHECK IF THE BINARY IS IN RLO + if s.t_events[0] or s.t_events[1]: - star.profile = profile_new - - if s.t_events[0] or s.t_events[1]: # reached RLOF if self.RLO_orbit_at_orbit_with_same_am: # final circular orbit conserves angular momentum # compared to the eccentric orbit @@ -1906,12 +1787,11 @@ def get_star_profile(star, htrack, m0): binary.orbital_period *= (1 - s.y[1][-1]) ** 1.5 assert np.abs( - binary.orbital_period - - orbital_period_from_separation( - binary.separation, secondary.mass, primary.mass - ) + binary.orbital_period - orbital_period_from_separation( + binary.separation, secondary.mass, primary.mass) ) / binary.orbital_period < 10 ** (-2) binary.eccentricity = 0 + if s.t_events[0]: if secondary == binary.star_1: binary.state = "RLO1" @@ -1919,6 +1799,7 @@ def get_star_profile(star, htrack, m0): else: binary.state = "RLO2" binary.event = "oRLO2" + elif s.t_events[1]: if secondary == binary.star_1: binary.state = "RLO2" @@ -1927,57 +1808,64 @@ def get_star_profile(star, htrack, m0): binary.state = "RLO1" binary.event = "oRLO1" - if (binary.state == "RLO1" - and binary.star_1.state in LIST_ACCEPTABLE_STATES_FOR_HeStar - and binary.star_2.state in STAR_STATES_H_RICH): + if ('step_HMS_HMS_RLO' not in all_step_names): + if ((binary.star_1.state in STAR_STATES_HE_RICH_EVOLVABLE + and binary.star_2.state in STAR_STATES_H_RICH_EVOLVABLE) + or (binary.star_1.state in STAR_STATES_H_RICH_EVOLVABLE + and binary.star_2.state in STAR_STATES_HE_RICH_EVOLVABLE)): set_binary_to_failed(binary) - raise FlowError("Evolution of He-rich stars in RLO onto H-rich stars after HMS-HMS not yet supported.") + raise FlowError("Evolution of H-rich/He-rich stars in RLO onto H-rich/He-rich stars after " + "HMS-HMS not yet supported.") - elif (binary.state == "RLO2" - and binary.star_2.state in LIST_ACCEPTABLE_STATES_FOR_HeStar - and binary.star_1.state in STAR_STATES_H_RICH): + elif (binary.star_1.state in STAR_STATES_H_RICH_EVOLVABLE + and binary.star_2.state in STAR_STATES_H_RICH_EVOLVABLE): set_binary_to_failed(binary) - raise FlowError("Evolution of He-rich stars in RLO onto H-rich stars after HMS-HMS not yet supported.") - + raise ClassificationError("Binary is in the detached step but has stable RLO with two HMS stars - " + "should it have undergone CE (was its HMS-HMS interpolation class unstable MT?)") + + ## CHECK IF STARS WILL UNDERGO CC elif s.t_events[2]: - # reached t_max of track. End of life (possible collapse) of - # secondary + # reached t_max of track. End of life (possible collapse) of secondary if secondary == binary.star_1: binary.event = "CC1" else: binary.event = "CC2" + get_star_final_values(secondary, secondary.htrack, m01) get_star_profile(secondary, secondary.htrack, m01) + if not primary.co and primary.state in STAR_STATES_CC: # simultaneous core-collapse of the other star as well primary_time = t_max_pri + t_offset_pri - t[-1] secondary_time = t_max_sec + t_offset_sec - t[-1] + if primary_time == secondary_time: - # we manually check if s.t_events[3] should also - # be happening simultaneously + # we manually check if s.t_events[3] should also be happening simultaneously get_star_final_values(primary, primary.htrack, m02) get_star_profile(primary, primary.htrack, m02) + if primary.mass != secondary.mass: raise POSYDONError( "Both stars are found to be ready for collapse " "(i.e. end of their life) during the detached " "step, but do not have the same mass") + elif s.t_events[3]: - # reached t_max of track. End of life (possible collapse) of - # primary + # reached t_max of track. End of life (possible collapse) of primary if secondary == binary.star_1: binary.event = "CC2" else: binary.event = "CC1" + get_star_final_values(primary, primary.htrack, m02) get_star_profile(primary, primary.htrack, m02) + else: # Reached max_time asked. if binary.properties.max_simulation_time - binary.time < 0.0: binary.event = "MaxTime_exceeded" else: binary.event = "maxtime" - # binary.event = "MaxTime_exceeded" def event(terminal, direction=0): diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index 30b4339d50..2d58946719 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -276,31 +276,29 @@ def get_final_MESA_step_time(self): """ if self.interpolation_method == 'nearest_neighbour': self.closest_binary, self.nearest_neighbour_distance, \ - self.termination_flags = self._psyTrackInterp.evaluate( - self.binary) + self.termination_flags = self._psyTrackInterp.evaluate(self.binary) if self.closest_binary.binary_history is None: return key = POSYDON_TO_MESA['binary']['time'] max_MESA_sim_time = self.closest_binary.binary_history[key][-1] elif self.interpolation_method in self.supported_interp_methods: - self.final_values, self.classes = self._Interp.evaluate( - self.binary) + self.final_values, self.classes = self._Interp.evaluate(self.binary) - max_MESA_sim_time = self.final_values[ - POSYDON_TO_MESA['binary']['time']] + max_MESA_sim_time = self.final_values[POSYDON_TO_MESA['binary']['time']] else: - raise ValueError("unknown interpolation method: {}". - format(self.interpolation_method)) + raise ValueError("unknown interpolation method: {}".format(self.interpolation_method)) + return max_MESA_sim_time def __call__(self, binary): """Evolve a binary using the MESA step.""" + if not isinstance(binary, BinaryStar): raise ValueError("Must be an instance of BinaryStar") if not hasattr(self, 'step'): - raise ValueError("No step defined for {}".format( - self.__name__)) + raise ValueError("No step defined for {}".format(self.__name__)) + if self.flip_stars_before_step: flip_stars(binary) max_MESA_sim_time = self.get_final_MESA_step_time() @@ -311,16 +309,14 @@ def __call__(self, binary): binary.state = 'initial_RLOF' return - binary_start_time = binary.time step_will_exceed_max_time = (binary.time+max_MESA_sim_time - > binary.properties.max_simulation_time) + > binary.properties.max_simulation_time) if (step_will_exceed_max_time and self.stop_method == 'stop_at_max_time'): - # self.step(binary, interp_method='nearest_neighbour') + if self.interpolation_method != 'nearest_neighbour': self.closest_binary, self.nearest_neighbour_distance, \ - self.termination_flags = self._psyTrackInterp.evaluate( - self.binary) + self.termination_flags = self._psyTrackInterp.evaluate(self.binary) if self.track_interpolation: self.flush_history = False @@ -331,6 +327,7 @@ def __call__(self, binary): track_interpolation=True) else: self.step(binary, interp_method=self.interpolation_method) + if (self.stop_method == 'stop_at_max_time' and binary.time >= binary.properties.max_simulation_time): @@ -362,12 +359,14 @@ def __call__(self, binary): interpolate=self.stop_interpolate, star_1_CO=self.star_1_CO, star_2_CO=self.star_2_CO) + if self.flip_stars_before_step: flip_stars(binary) if binary.time > binary.properties.max_simulation_time: binary.event = 'MaxTime_exceeded' elif binary.time == binary.properties.max_simulation_time: binary.event = 'maxtime' + return def step(self, binary, interp_method=None): diff --git a/posydon/binary_evol/flow_chart.py b/posydon/binary_evol/flow_chart.py index cac4dd12c5..6cf0552851 100644 --- a/posydon/binary_evol/flow_chart.py +++ b/posydon/binary_evol/flow_chart.py @@ -80,6 +80,19 @@ # the step_CO_HeMS STAR_STATES_HE_RICH_EVOLVABLE.extend(['H-rich_non_burning']) +# core collapse +STAR_STATES_CC = [ + 'H-rich_Central_C_depletion', + 'H-rich_Central_He_depleted', + 'stripped_He_Central_He_depleted', + 'stripped_He_Central_C_depletion', + # catch runs with gamma center limit which map to WD + 'stripped_He_non_burning', + 'H-rich_non_burning', + 'H-rich_Shell_H_burning', + 'accreted_He_non_burning' + ] + BINARY_STATES_ALL = [ 'initially_single_star', 'detached', @@ -91,6 +104,8 @@ 'initial_RLOF' ] +BINARY_STATES_CC = BINARY_STATES_ALL.copy() + BINARY_EVENTS_ALL = [ None, 'CC1', @@ -113,6 +128,9 @@ 'oMerging2' ] +BINARY_EVENTS_OF_SN_OR_AFTER_DETACHED = BINARY_EVENTS_ALL.copy() +[BINARY_EVENTS_OF_SN_OR_AFTER_DETACHED.remove(x) for x in ['CC1','CC2','MaxTime_exceeded','maxtime']] + ## a list of known total binary states that can occur, ## but are not in the flow chart and will not be added to POSYDON UNDEFINED_STATES = [ @@ -215,22 +233,6 @@ POSYDON_FLOW_CHART[(s2, s1, 'contact', 'oDoubleCE2')] = 'step_CE' -# core collapse -STAR_STATES_CC = [ - 'H-rich_Central_C_depletion', - 'H-rich_Central_He_depleted', - 'stripped_He_Central_He_depleted', - 'stripped_He_Central_C_depletion', - # catch runs with gamma center limit which map to WD - 'stripped_He_non_burning', - 'H-rich_non_burning', - 'H-rich_Shell_H_burning', - 'accreted_He_non_burning' - ] - - -BINARY_STATES_CC = BINARY_STATES_ALL.copy() - for b in BINARY_STATES_CC: for s1 in STAR_STATES_CC: for s2 in STAR_STATES_ALL: @@ -262,9 +264,6 @@ POSYDON_FLOW_CHART[(s2, s1, b, e)] = 'step_initially_single' -BINARY_EVENTS_OF_SN_OR_AFTER_DETACHED = BINARY_EVENTS_ALL.copy() -[BINARY_EVENTS_OF_SN_OR_AFTER_DETACHED.remove(x) for x in ['CC1','CC2','MaxTime_exceeded','maxtime']] - for b in ['disrupted']: for s1 in STAR_STATES_ALL: for s2 in STAR_STATES_ALL: diff --git a/posydon/binary_evol/simulationproperties.py b/posydon/binary_evol/simulationproperties.py index 130a14b59d..dd995314fd 100644 --- a/posydon/binary_evol/simulationproperties.py +++ b/posydon/binary_evol/simulationproperties.py @@ -69,9 +69,12 @@ def __init__(self, properties=None, **kwargs): self.max_n_steps_per_binary = 100 # Set functions for evolution + self.all_step_names = [] ## list of strings of all evolutionary steps for key, val in kwargs.items(): if "step" not in key: # skip loading steps setattr(self, key, val) + elif "step" in key: + self.all_step_names.append(key) self.steps_loaded = False def load_steps(self, verbose=False): diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index a2cd1daa07..a40feb02b5 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -41,22 +41,22 @@ from posydon.binary_evol.binarystar import BinaryStar from posydon.binary_evol.singlestar import (SingleStar,properties_massless_remnant) from posydon.binary_evol.simulationproperties import SimulationProperties + from posydon.popsyn.star_formation_history import get_formation_times from posydon.popsyn.independent_sample import (generate_independent_samples, binary_fraction_value) from posydon.popsyn.sample_from_file import (get_samples_from_file, get_kick_samples_from_file) -from posydon.utils.common_functions import (orbital_period_from_separation, - orbital_separation_from_period) from posydon.popsyn.normalized_pop_mass import initial_total_underlying_mass - from posydon.popsyn.defaults import default_kwargs + from posydon.popsyn.io import binarypop_kwargs_from_ini from posydon.utils.constants import Zsun from posydon.utils.posydonerror import POSYDONError from posydon.utils.posydonwarning import (Pwarn, Catch_POSYDON_Warnings) -from posydon.utils.common_functions import set_binary_to_failed +from posydon.utils.common_functions import (orbital_period_from_separation, orbital_separation_from_period, + set_binary_to_failed) saved_ini_parameters = ['metallicity', "number_of_binaries", @@ -99,7 +99,8 @@ # BinaryPopulation will enforce a constant metallicity accross all steps that # load stellar or binary models by checked this list of steps. STEP_NAMES_LOADING_GRIDS = [ - 'step_HMS_HMS', 'step_CO_HeMS', 'step_CO_HMS_RLO', 'step_CO_HeMS_RLO', 'step_detached','step_isolated','step_disrupted','step_initially_single', 'step_merged' + 'step_HMS_HMS', 'step_CO_HeMS', 'step_CO_HMS_RLO', 'step_CO_HeMS_RLO', 'step_HMS_HMS_RLO', + 'step_detached','step_isolated','step_disrupted','step_initially_single', 'step_merged' ] @@ -133,7 +134,7 @@ def __init__(self, **kwargs): for key in STEP_NAMES_LOADING_GRIDS: if key in self.population_properties.kwargs: self.population_properties.kwargs[key][1].update({'metallicity': self.metallicity}) - + self.population_properties.max_simulation_time = self.kwargs.get( 'max_simulation_time') # years diff --git a/posydon/unit_tests/utils/test_posydonerror.py b/posydon/unit_tests/utils/test_posydonerror.py index 0437dd6c52..3695fe60d1 100644 --- a/posydon/unit_tests/utils/test_posydonerror.py +++ b/posydon/unit_tests/utils/test_posydonerror.py @@ -22,11 +22,11 @@ def artificial_object(): class TestElements: # check for objects, which should be an element of the tested module def test_dir(self): - elements = ['FlowError', 'GridError', 'MatchingError',\ - 'ModelError', 'NumericalError', 'POSYDONError',\ - '__authors__', '__builtins__', '__cached__',\ - '__doc__', '__file__', '__loader__', '__name__',\ - '__package__', '__spec__'] + elements = ['ClassificationError', 'FlowError', 'GridError',\ + 'MatchingError', 'ModelError', 'NumericalError',\ + 'POSYDONError', '__authors__', '__builtins__',\ + '__cached__', '__doc__', '__file__', '__loader__',\ + '__name__', '__package__', '__spec__'] assert dir(totest) == elements, "There might be added or removed "\ + "objects without an update on the "\ + "unit test." @@ -36,6 +36,12 @@ def test_instance_POSYDONError(self): assert issubclass(totest.POSYDONError, Exception) with raises(totest.POSYDONError, match="Test"): raise totest.POSYDONError("Test") + + def test_instance_ClassificationError(self): + assert isclass(totest.ClassificationError) + assert issubclass(totest.ClassificationError, totest.POSYDONError) + with raises(totest.ClassificationError, match="Test"): + raise totest.ClassificationError("Test") def test_instance_FlowError(self): assert isclass(totest.FlowError) @@ -103,4 +109,4 @@ def test_init(self, POSYDONError, POSYDONError_position, POSYDONError_key): def test_str(self, POSYDONError_position): assert isroutine(POSYDONError_position.__str__) assert str(POSYDONError_position) == "test message on position" - \ No newline at end of file + diff --git a/posydon/utils/posydonerror.py b/posydon/utils/posydonerror.py index 1d2e482faf..4d967f6e55 100644 --- a/posydon/utils/posydonerror.py +++ b/posydon/utils/posydonerror.py @@ -32,6 +32,9 @@ def __str__(self): # Subclasses of POSYODONError in alphabetic order # Before you add a new subclass check the list of python error classes at the # end of this file + +class ClassificationError(POSYDONError): + """POSYDON error specific for binary classification errors.""" class FlowError(POSYDONError): """POSYDON error specific for binary evolution flow errors.""" From 5b7785c49bb4e0ab78f0609f0aa0668357b71356 Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Fri, 17 Jan 2025 08:46:25 +0100 Subject: [PATCH 281/319] Grid slice plotting system selection fix (#475) * replace selection + add docstring * Update plot_pop.py changes: - plot_extension doc string - prop description if None --- posydon/visualization/plot_pop.py | 70 ++++++++++++++++++++++++++----- 1 file changed, 59 insertions(+), 11 deletions(-) diff --git a/posydon/visualization/plot_pop.py b/posydon/visualization/plot_pop.py index 49afe97577..1265328afc 100644 --- a/posydon/visualization/plot_pop.py +++ b/posydon/visualization/plot_pop.py @@ -187,16 +187,66 @@ def plot_hist_properties(df, ax=None, df_intrinsic=None, df_observable=None, - -def plot_popsyn_over_grid_slice(pop, grid_type, met_Zsun, slices=None, channel=None, - plot_dir='./', prop=None, prop_range=None, - log_prop=False, alpha=0.3, s=5., - show_fig=True, save_fig=True, close_fig=True, - plot_extension='png', verbose=False): +def plot_popsyn_over_grid_slice(pop, + grid_type, + met_Zsun, + slices=None, + channel=None, + plot_dir='./', + prop=None, + prop_range=None, + log_prop=False, + alpha=0.3, + s=5., + show_fig=True, + save_fig=True, + close_fig=True, + plot_extension='png', + verbose=False): + '''Plot the population across a specific slice of the grid. + + Parameters + ---------- + pop : Population + Population object. + grid_type : str + Type of grid to be plotted. Options: 'HMS-HMS', 'CO-HMS_RLO', 'CO-HeMS', 'CO_HeMS_RLO'. + met_Zsun : float + Metallicity of the grid. + slices : list of floats + List of slices to plot. If None, all slices are plotted. + channel : str + Formation channel to plot. If None, all channels are plotted. + plot_dir : str + Directory where to save the plots. + prop : str + Property to plot on the grid. If None, no property colour is plotted. + prop_range : list of floats + Range of the property to plot. + log_prop : bool + If True, the property is plotted in log scale. + alpha : float + Transparency of the points. + s : float + Size of the points. + show_fig : bool + If True, the plot is shown. + save_fig : bool + If True, the plot is saved. + close_fig : bool + If True, the plot is closed. + plot_extension : str + File extension of the plot. + verbose : bool + If True, print information about the slices being plotted. + ''' # Check if step_names in pop.history data if 'step_names' not in pop.history.columns: raise ValueError('Formation channel information not available in popsynth data.') + # check if formation channel information is available + if channel is not None and 'channel' not in pop.columns: + raise ValueError('Formation channel information not available in popsynth data.') # load grid met = convert_metallicity_to_string(met_Zsun) @@ -204,10 +254,6 @@ def plot_popsyn_over_grid_slice(pop, grid_type, met_Zsun, slices=None, channel=N grid = PSyGrid(verbose=False) grid.load(grid_path) - # check if formation channel information is avaialbe - if channel is not None and 'channel' not in pop.columns: - raise ValueError('Formation channel information not available in popsynth data.') - if 'CO' in grid_path: # compact object mass slices # TODO: THIS SELECTION DOES NOT WORK! @@ -243,7 +289,9 @@ def plot_popsyn_over_grid_slice(pop, grid_type, met_Zsun, slices=None, channel=N print(f'Skipping {round(var,2)}') continue - met_indices = np.where(pop.select(where=channel_sel, columns=['metallicity']) == met_Zsun)[0].tolist() + met_sel = pop.select(where=channel_sel, columns=['metallicity']) + met_indices = met_sel[met_sel['metallicity'] == met_Zsun].index.tolist() + if 'HMS-HMS' in grid_path: slice_3D_var_range = (dq_edges[i],dq_edges[i+1]) if len(met_indices) == 0: From 4d05dec7ab1e3a96453d5c52af4c89205c63c823 Mon Sep 17 00:00:00 2001 From: Camille Liotine Date: Thu, 23 Jan 2025 10:03:59 -0600 Subject: [PATCH 282/319] add missing binaries to UNDEFINED_STATES in flow chart (#489) * add missing binaries to UNDEFINED_STATES in flow chart * add WD systems to undefined states * add for loop for undefined states in flow chart --- posydon/binary_evol/flow_chart.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/posydon/binary_evol/flow_chart.py b/posydon/binary_evol/flow_chart.py index 6cf0552851..eaef99a2f1 100644 --- a/posydon/binary_evol/flow_chart.py +++ b/posydon/binary_evol/flow_chart.py @@ -133,10 +133,10 @@ ## a list of known total binary states that can occur, ## but are not in the flow chart and will not be added to POSYDON -UNDEFINED_STATES = [ - ('NS', 'H-rich_Core_H_burning', 'disrupted', 'CC2'), - ('NS', 'H-rich_Core_H_burning', 'detached', 'CC2') -] +UNDEFINED_STATES = [] +for s1 in STAR_STATES_CO: + for b in ['disrupted', 'detached']: + UNDEFINED_STATES.append((s1, 'H-rich_Core_H_burning', b, 'CC2')) # dynamically construct the flow chart POSYDON_FLOW_CHART = {} From bcb3367d871e78d4d9ef8cc5dba62ee6ed6b500d Mon Sep 17 00:00:00 2001 From: Jeff Andrews Date: Mon, 27 Jan 2025 13:36:51 -0500 Subject: [PATCH 283/319] Update issue templates (#491) * Update issue templates * Update bug_report.md --- .github/ISSUE_TEMPLATE/bug_report.md | 30 ++++++++++++++++++++++++++++ 1 file changed, 30 insertions(+) create mode 100644 .github/ISSUE_TEMPLATE/bug_report.md diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 0000000000..69ce49cea6 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,30 @@ +--- +name: Bug report +about: Create a report to help us improve +title: "[BUG]: " +labels: '["bug"]' +assignees: '' + +--- + +**Describe the bug** +A clear and concise description of what the bug is. + +**To Reproduce** +Steps to reproduce the behavior. Specifically, please provide a minimal reproducible example (see e.g., https://stackoverflow.com/help/minimal-reproducible-example). If you are running a population or a model grid, please include an .ini file. If a specific binary is under question, please include that binary's initial conditions (i.e., initial stars' masses, orbital period, etc.). + +**Expected behavior** +A clear and concise description of what you expected to happen. + +**Screenshots** +If applicable, add screenshots to help explain your problem. For populations this could include a binary history highlighting columns of interest. + +**POSYDON Version:** +Describe which version of POSYDON you are using. If you are using specific POSYDON grids, please include those grids as well as their version numbers. + +**System Configuration (please complete the following information):** +- Operating System [e.g., OSX, Ubuntu, etc.] +- HPC scheduler (if applicable) [e.g., SLURM] + +**Additional context** +Add any other context about the problem here. From a0ec0e2edd8fb13f3c54e24ea4128aabc95ead1b Mon Sep 17 00:00:00 2001 From: Jeff Andrews Date: Tue, 28 Jan 2025 10:03:44 -0500 Subject: [PATCH 284/319] Update installation-guide.rst to include note about adding conda-forge (#495) * Update installation-guide.rst to include note about adding conda-forge I came across an issue related to installation. It turns out that many of the required packages specify versions only available on conda-forge which can cause a huge headache for unaware users. I have updated the installation documentation to include this note. * Adding conda-forge documentation to troubleshooting page --------- Co-authored-by: astroJeff --- docs/_source/getting-started/installation-guide.rst | 6 ++++++ docs/_source/troubleshooting-faqs/installation-issues.rst | 2 +- 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/docs/_source/getting-started/installation-guide.rst b/docs/_source/getting-started/installation-guide.rst index f64a2d8627..313520d0fa 100644 --- a/docs/_source/getting-started/installation-guide.rst +++ b/docs/_source/getting-started/installation-guide.rst @@ -29,6 +29,12 @@ Using Anaconda (Recommended) conda activate posydon_env + As many of the required versions of packages are available on ``conda-forge`` you may need to add this channel to conda if you have not already: + + .. code-block:: bash + + conda config --add channels conda-forge + 3. **Install POSYDON** .. warning:: diff --git a/docs/_source/troubleshooting-faqs/installation-issues.rst b/docs/_source/troubleshooting-faqs/installation-issues.rst index 1160afc8a4..9ef3123232 100644 --- a/docs/_source/troubleshooting-faqs/installation-issues.rst +++ b/docs/_source/troubleshooting-faqs/installation-issues.rst @@ -10,7 +10,7 @@ Common Installation Issues 1. **Failed Dependencies**: - **Description**: Sometimes, certain dependencies might fail to install or conflict with pre-existing ones. - - **Solution**: Try installing the failed dependencies separately using ``pip`` or ``conda`` before installing POSYDON. If using ``conda``, consider creating a fresh environment specifically for POSYDON. + - **Solution**: Try installing the failed dependencies separately using ``pip`` or ``conda`` before installing POSYDON. If using ``conda``, consider creating a fresh environment specifically for POSYDON. Additionally, some of the package versions are only available on ``conda-forge``. Please ensure that ``conda-forge`` has been added to conda as a channel using the command ``conda config --add channels conda-forge``. We try our best to keep the dependencies updated and compatible. However, if you encounter issues, please let us know! From 79aab3b94862e0a7ae0a0cefd10c47d6043a458a Mon Sep 17 00:00:00 2001 From: Kyle Rocha <48293898+ka-rocha@users.noreply.github.com> Date: Tue, 28 Jan 2025 10:50:56 -0800 Subject: [PATCH 285/319] Protect run_psycris_sequence, from misc imports (#436) * add dunder main to run_psycris_sequence, add type checks to cmd args * Add some docs * Delete random file --- .../run_params/run_psycris_sequence.py | 237 ++++++++++-------- 1 file changed, 126 insertions(+), 111 deletions(-) diff --git a/posydon/active_learning/psy_cris/run_params/run_psycris_sequence.py b/posydon/active_learning/psy_cris/run_params/run_psycris_sequence.py index 0999313bff..55da7a8729 100644 --- a/posydon/active_learning/psy_cris/run_params/run_psycris_sequence.py +++ b/posydon/active_learning/psy_cris/run_params/run_psycris_sequence.py @@ -12,115 +12,130 @@ from posydon.active_learning.psy_cris.utils import do_dynamic_sampling from posydon.active_learning.psy_cris.utils import calc_performance -parser = argparse.ArgumentParser() -parser.add_argument( "-ini", dest='inifile_path', type=str, help="Path to psy-cris inifile.") -parser.add_argument( "-dir", dest='save_data_dir', type=str, help="Path to directory to save data.") - -parser.add_argument( "-id", dest='run_id', type=str, help="Integer or str used to identify runs.", - default = np.random.randint(low=10000, high=99999) ) -parser.add_argument( "-n", dest='n_sequences', type=int, help="N sequences to run.", - default=1) -parser.add_argument( "-num_start", dest='n_starting_points', type=int, help="N points to start with in even grid.", - default=3**3) -parser.add_argument( "-num_fin", dest='n_final_points', type=int, help="Total num points to converge to.", - default=6**3) -parser.add_argument( "-ppi", dest='points_per_iter', type=int, help="Num points between each iteration.", - default=70) -parser.add_argument( "-cls", dest='performance_cls_name', type=str, help="Classification algorithm used in performance calc.", - default="linear") -parser.add_argument( "-regr", dest='performance_regr_name', type=str, help="Regression algorithm used in performance calc.", - default="rbf") -parser.add_argument( "-res", dest='performance_res', type=int, help="Resolution to compute performance.", - default=40) -parser.add_argument( "-length_scale_mult", dest='length_scale_multiplier', type=float, help="Small class proposal length scale mult of normal.", - default=0.3333) -parser.add_argument( "-percent_increase", dest='percent_increase', type=float, help="Icrease points per iter by a percent of the training set.", - default=-1) - - -args = parser.parse_args() - -inifile_path = args.inifile_path -save_data_dir = args.save_data_dir -if not os.path.isfile(inifile_path): - raise ValueError("Inifile not found. Please check that the path is correct.\nGiven: {}".format(inifile_path)) -if not os.path.isdir(save_data_dir): - raise ValueError("Directory not found. Please check that save_data_dir exists.\nGiven: {}".format(save_data_dir)) - -run_ID = args.run_id -n_psycris_sequences = args.n_sequences - -n_starting_points = args.n_starting_points -n_final_points = args.n_final_points -new_points_per_iter = args.points_per_iter -length_scale_mult = args.length_scale_multiplier -if args.percent_increase < 0: - percent_increase = None -else: - percent_increase = args.percent_increase - -psy_cris_kwargs_dict = parse_inifile(inifile_path, verbose=False) -dimensionality = len(psy_cris_kwargs_dict["TableData_kwargs"]["input_cols"]) - -print("\n\n"+">"*18 + " RUNNING PSYCRIS SEQUENCE ID: {} ".format(run_ID) + "<"*18) -print("FILE: {}".format(inifile_path)) -print("SAVE DIR: {}\n".format(save_data_dir)) -print("n_starting_points: {}".format(n_starting_points)) -print("n_final_points: {}".format(n_final_points)) -print("new_points_per_iter: {}".format(new_points_per_iter)) -print("dimensionality: {}".format(dimensionality)) -print("length_scale_multiplier: {}".format(length_scale_mult)) -print("percent_increase: {}\n".format(percent_increase)) - - -start_iters_time = time.time() -for i in range(n_psycris_sequences): - print("START - do_dynamic_sampling - {0}i{1}".format(run_ID,i)) - kwargs_per_iter = copy.deepcopy(psy_cris_kwargs_dict) - dfs_per_iter, preds_per_iter = do_dynamic_sampling(N_starting_points=n_starting_points, - N_final_points=n_final_points, - new_points_per_iter=new_points_per_iter, - verbose=True, - threshold= 1e-6, - jitter=True, - dim=dimensionality, - length_scale_mult=length_scale_mult, - percent_increase=percent_increase, - **kwargs_per_iter ) - try: - print("\n\tSaving dfs...") - f_name_backup = save_data_dir + "/{0}i{1}_dfs_per_iter".format(run_ID,i) - with open(f_name_backup, "wb") as f: - pickle.dump( dfs_per_iter, f) - - print("\tSTART - calc_performance - {0}i{1}".format(run_ID,i)) - print("\tcls: {}, regr: {}, res: {}".format(args.performance_cls_name, args.performance_regr_name, args.performance_res) ) - acc_per_iter, conf_matrix_per_iter, abs_regr_frac_diffs_per_iter = \ - calc_performance(dfs_per_iter, - cls_name=args.performance_cls_name, - regr_name=args.performance_regr_name, - resolution=args.performance_res, verbose=False) - - f1_path = save_data_dir + "/{0}i{1}_acc_per_iter".format(run_ID,i) - f2_path = save_data_dir + "/{0}i{1}_conf_matrix_per_iter".format(run_ID,i) - f3_path = save_data_dir + "/{0}i{1}_abs_regr_frac_diffs_per_iter".format(run_ID,i) - print("\tSaving files:") - try: - np.save( f1_path, acc_per_iter, allow_pickle=True ) - print("\t\t{}".format(f1_path)) - except Exception as err: - print("\t\tFAILED: {0}, err: {1}".format(f1_path, err)) - try: - np.save( f2_path, conf_matrix_per_iter, allow_pickle=True ) - print("\t\t{}".format(f2_path)) - except Exception as err: - print("\t\tFAILED: {0}, err: {1}".format(f2_path, err)) +def main(): + """Run a psy-cris sequence on synthetic data from the command line. + + Parameters + ---------- + N/A + + Notes + ----- + This code is used in the work of: + Rocha et al. 2022, ApJ, 938, 64. doi:10.3847/1538-4357/ac8b05 + """ + parser = argparse.ArgumentParser() + parser.add_argument( "-ini", dest='inifile_path', type=str, help="Path to psy-cris inifile.") + parser.add_argument( "-dir", dest='save_data_dir', type=str, help="Path to directory to save data.") + + parser.add_argument( "-id", dest='run_id', type=str, help="Integer or str used to identify runs.", + default = np.random.randint(low=10000, high=99999) ) + parser.add_argument( "-n", dest='n_sequences', type=int, help="N sequences to run.", + default=1) + parser.add_argument( "-num_start", dest='n_starting_points', type=int, help="N points to start with in even grid.", + default=3**3) + parser.add_argument( "-num_fin", dest='n_final_points', type=int, help="Total num points to converge to.", + default=6**3) + parser.add_argument( "-ppi", dest='points_per_iter', type=int, help="Num points between each iteration.", + default=70) + parser.add_argument( "-cls", dest='performance_cls_name', type=str, help="Classification algorithm used in performance calc.", + default="linear") + parser.add_argument( "-regr", dest='performance_regr_name', type=str, help="Regression algorithm used in performance calc.", + default="rbf") + parser.add_argument( "-res", dest='performance_res', type=int, help="Resolution to compute performance.", + default=40) + parser.add_argument( "-length_scale_mult", dest='length_scale_multiplier', type=float, help="Small class proposal length scale mult of normal.", + default=0.3333) + parser.add_argument( "-percent_increase", dest='percent_increase', type=float, help="Icrease points per iter by a percent of the training set.", + default=-1) + + + args = parser.parse_args() + + inifile_path = args.inifile_path + save_data_dir = args.save_data_dir + if not isinstance(inifile_path, str) or not os.path.isfile(inifile_path): + raise ValueError("Inifile not found. Please check that the path is correct.\nGiven: {}".format(inifile_path)) + if not isinstance(save_data_dir, str) or not os.path.isdir(save_data_dir): + raise ValueError("Directory not found. Please check that save_data_dir exists.\nGiven: {}".format(save_data_dir)) + + run_ID = args.run_id + n_psycris_sequences = args.n_sequences + + n_starting_points = args.n_starting_points + n_final_points = args.n_final_points + new_points_per_iter = args.points_per_iter + length_scale_mult = args.length_scale_multiplier + if args.percent_increase < 0: + percent_increase = None + else: + percent_increase = args.percent_increase + + psy_cris_kwargs_dict = parse_inifile(inifile_path, verbose=False) + dimensionality = len(psy_cris_kwargs_dict["TableData_kwargs"]["input_cols"]) + + print("\n\n"+">"*18 + " RUNNING PSYCRIS SEQUENCE ID: {} ".format(run_ID) + "<"*18) + print("FILE: {}".format(inifile_path)) + print("SAVE DIR: {}\n".format(save_data_dir)) + print("n_starting_points: {}".format(n_starting_points)) + print("n_final_points: {}".format(n_final_points)) + print("new_points_per_iter: {}".format(new_points_per_iter)) + print("dimensionality: {}".format(dimensionality)) + print("length_scale_multiplier: {}".format(length_scale_mult)) + print("percent_increase: {}\n".format(percent_increase)) + + + start_iters_time = time.time() + for i in range(n_psycris_sequences): + print("START - do_dynamic_sampling - {0}i{1}".format(run_ID,i)) + kwargs_per_iter = copy.deepcopy(psy_cris_kwargs_dict) + dfs_per_iter, preds_per_iter = do_dynamic_sampling(N_starting_points=n_starting_points, + N_final_points=n_final_points, + new_points_per_iter=new_points_per_iter, + verbose=True, + threshold= 1e-6, + jitter=True, + dim=dimensionality, + length_scale_mult=length_scale_mult, + percent_increase=percent_increase, + **kwargs_per_iter ) try: - np.save( f3_path, np.array(abs_regr_frac_diffs_per_iter, dtype=object), allow_pickle=True ) - print("\t\t{}".format(f3_path)) - except Exception as err: - print("\t\tFAILED: {0}, err: {1}".format(f3_path, err)) - - except Exception as exc: - print("\tPerformance calculation Failed!\n\tErr:{}\n\n".format(exc)) -print("END {0}\nTotal Time : {1:.3f} min".format(run_ID,(time.time()-start_iters_time)/60) ) + print("\n\tSaving dfs...") + f_name_backup = save_data_dir + "/{0}i{1}_dfs_per_iter".format(run_ID,i) + with open(f_name_backup, "wb") as f: + pickle.dump( dfs_per_iter, f) + + print("\tSTART - calc_performance - {0}i{1}".format(run_ID,i)) + print("\tcls: {}, regr: {}, res: {}".format(args.performance_cls_name, args.performance_regr_name, args.performance_res) ) + acc_per_iter, conf_matrix_per_iter, abs_regr_frac_diffs_per_iter = \ + calc_performance(dfs_per_iter, + cls_name=args.performance_cls_name, + regr_name=args.performance_regr_name, + resolution=args.performance_res, verbose=False) + + f1_path = save_data_dir + "/{0}i{1}_acc_per_iter".format(run_ID,i) + f2_path = save_data_dir + "/{0}i{1}_conf_matrix_per_iter".format(run_ID,i) + f3_path = save_data_dir + "/{0}i{1}_abs_regr_frac_diffs_per_iter".format(run_ID,i) + print("\tSaving files:") + try: + np.save( f1_path, acc_per_iter, allow_pickle=True ) + print("\t\t{}".format(f1_path)) + except Exception as err: + print("\t\tFAILED: {0}, err: {1}".format(f1_path, err)) + try: + np.save( f2_path, conf_matrix_per_iter, allow_pickle=True ) + print("\t\t{}".format(f2_path)) + except Exception as err: + print("\t\tFAILED: {0}, err: {1}".format(f2_path, err)) + try: + np.save( f3_path, np.array(abs_regr_frac_diffs_per_iter, dtype=object), allow_pickle=True ) + print("\t\t{}".format(f3_path)) + except Exception as err: + print("\t\tFAILED: {0}, err: {1}".format(f3_path, err)) + + except Exception as exc: + print("\tPerformance calculation Failed!\n\tErr:{}\n\n".format(exc)) + print("END {0}\nTotal Time : {1:.3f} min".format(run_ID,(time.time()-start_iters_time)/60) ) + +if __name__ == "__main__": + main() \ No newline at end of file From a7c72ca03841aa03f27dd3bdf71e08a0d575d641 Mon Sep 17 00:00:00 2001 From: Camille Liotine Date: Tue, 4 Feb 2025 09:45:46 -0600 Subject: [PATCH 286/319] add more dump_rate info to docs (#500) * add dump_rate info to docs and ini file * Update docs/_source/components-overview/pop_syn/population_params.rst Co-authored-by: Max <14039563+maxbriel@users.noreply.github.com> * Update population_params.rst * Update code-questions.rst * Update population_params_default.ini --------- Co-authored-by: Jeff Andrews Co-authored-by: Max <14039563+maxbriel@users.noreply.github.com> --- docs/_source/components-overview/pop_syn/population_params.rst | 1 + docs/_source/troubleshooting-faqs/code-questions.rst | 3 +++ posydon/popsyn/population_params_default.ini | 2 +- 3 files changed, 5 insertions(+), 1 deletion(-) diff --git a/docs/_source/components-overview/pop_syn/population_params.rst b/docs/_source/components-overview/pop_syn/population_params.rst index 4ffd36841a..6f7a77bfda 100644 --- a/docs/_source/components-overview/pop_syn/population_params.rst +++ b/docs/_source/components-overview/pop_syn/population_params.rst @@ -652,6 +652,7 @@ It also contains which sampling distributions to use for the initial conditions * - ``dump_rate`` - | Batch save after evolving N binaries. + - | To facilitate I/O performance, this should be at least 500 for populations of 100.000 binaries or more. - ``2000`` * - ``temp_directory`` diff --git a/docs/_source/troubleshooting-faqs/code-questions.rst b/docs/_source/troubleshooting-faqs/code-questions.rst index aea8ee2382..89d67acdfd 100644 --- a/docs/_source/troubleshooting-faqs/code-questions.rst +++ b/docs/_source/troubleshooting-faqs/code-questions.rst @@ -46,6 +46,9 @@ Frequently Asked Questions The memory usage of the 8000 :code:`dump_rate` run is stable at around 6GB, while the 10.000 :code:`dump_rate` run is stable at around 6.8GB. + In general, the :code:`dump_rate` should be at least 500 for populations of 100.000 binaries or more. + Setting a very low :code:`dump_rate` for larger populations can potentially introduce I/O issues during the reading, writing, and merging of output files. + 4. **What should the walltime and job array size be for my population synthesis run?** diff --git a/posydon/popsyn/population_params_default.ini b/posydon/popsyn/population_params_default.ini index 3532e8b430..2dbe5dfe75 100644 --- a/posydon/popsyn/population_params_default.ini +++ b/posydon/popsyn/population_params_default.ini @@ -281,6 +281,7 @@ # set maximum ram per cpu before batch saving (GB) dump_rate = 2000 # batch save after evolving N binaries + # this should be at least 500 for populations of 100,000 binaries or more temp_directory = 'batches' # folder for keeping batch files tqdm = False @@ -298,7 +299,6 @@ history_verbose = False # if True, record extra functional steps in the output DataFrames # (These steps represent internal workings of POSYDON rather than physical phases of evolution) - # Random Number Generation entropy = None # `None` uses system entropy (recommended) number_of_binaries = 10 From 5969e13456a85f4cf1d48389461e3cdcee71d95f Mon Sep 17 00:00:00 2001 From: Camille Liotine Date: Tue, 4 Feb 2025 10:04:06 -0600 Subject: [PATCH 287/319] replace scipy interp1d with alternatives (#433) * replace all instances of interp1d except for in profile interpolation * remove interp1d from common functions unit test * add parameter type checks to rejection sampler * changes to common functions unit testing * fix unit test * fix unit tests again * define own interp1d and revert replacements * clean out white spaces * replace interp1d in profile_interpolation * readd ClassificationError * add fill_value, left, and right * cleanup * remove import * Update interp1d to include option for fill_value="extrapolate" * edit error message for fill_value * Update test_interpolators.py to include fill_value="extrapolate" * update all to ndarray; add missing unit tests * fix docstring * correct description --------- Co-authored-by: mkruckow Co-authored-by: mkruckow <122798003+mkruckow@users.noreply.github.com> Co-authored-by: Elizabeth Teng <41969755+elizabethteng@users.noreply.github.com> --- posydon/binary_evol/DT/step_detached.py | 33 ++-- posydon/binary_evol/SN/step_SN.py | 130 +------------ posydon/interpolation/interpolation.py | 6 +- .../interpolation/profile_interpolation.py | 14 +- posydon/popsyn/star_formation_history.py | 7 +- .../unit_tests/utils/test_common_functions.py | 79 ++------ .../unit_tests/utils/test_interpolators.py | 174 ++++++++++++++++++ posydon/utils/common_functions.py | 25 +-- posydon/utils/interpolators.py | 142 ++++++++++++++ 9 files changed, 378 insertions(+), 232 deletions(-) create mode 100644 posydon/unit_tests/utils/test_interpolators.py create mode 100644 posydon/utils/interpolators.py diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index 7ee639e20d..4714bd8e43 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -27,26 +27,25 @@ from posydon.binary_evol.singlestar import STARPROPERTIES from posydon.interpolation.interpolation import GRIDInterpolator from posydon.interpolation.data_scaling import DataScaler -from posydon.utils.common_functions import ( - bondi_hoyle, - orbital_period_from_separation, - roche_lobe_radius, - check_state_of_star, - PchipInterpolator2, - convert_metallicity_to_string, - set_binary_to_failed, -) -from posydon.binary_evol.flow_chart import ( - STAR_STATES_CC, - STAR_STATES_CO, - STAR_STATES_H_RICH_EVOLVABLE, - STAR_STATES_HE_RICH_EVOLVABLE, - ) +from posydon.utils.common_functions import (bondi_hoyle, + orbital_period_from_separation, + roche_lobe_radius, + check_state_of_star, + convert_metallicity_to_string, + set_binary_to_failed) +from posydon.utils.interpolators import PchipInterpolator2 +from posydon.binary_evol.flow_chart import (STAR_STATES_CC, + STAR_STATES_CO, + STAR_STATES_H_RICH_EVOLVABLE, + STAR_STATES_HE_RICH_EVOLVABLE) import posydon.utils.constants as const -from posydon.utils.posydonerror import NumericalError, MatchingError, POSYDONError, FlowError, ClassificationError +from posydon.utils.posydonerror import (NumericalError, MatchingError, + POSYDONError, FlowError, + ClassificationError) from posydon.utils.posydonwarning import Pwarn -LIST_ACCEPTABLE_STATES_FOR_HMS = ["H-rich_Core_H_burning", "accreted_He_Core_H_burning"] +LIST_ACCEPTABLE_STATES_FOR_HMS = ["H-rich_Core_H_burning", + "accreted_He_Core_H_burning"] LIST_ACCEPTABLE_STATES_FOR_postMS = [ "H-rich_Shell_H_burning", diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index 27e1926997..a63e5b475e 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -59,7 +59,7 @@ from pandas import read_csv from sklearn import neighbors -from scipy.interpolate import interp1d +from posydon.utils.interpolators import interp1d import json @@ -2315,34 +2315,20 @@ def __init__(self, turbulence_strength, path_engine_dataset, verbose): + " ..." ) - # Check if interpolation files exist - filename = os.path.join(path_to_Couch_datasets, - 'explDatsSTIR2.json') + filename = os.path.join(path_to_Couch_datasets, 'explDatsSTIR2.json') if not os.path.exists(filename): data_download() Couch_data_file = open(filename) - # Couch_data = json.loads(Couch_data_file) Couch_data = json.load(Couch_data_file) Couch_data_file.close() Couch_data = Couch_data[turbulence_strength] - # breakpoint() - # #names = ['MZAMS',['rmax','texp','Eexp']] - # my_names = ['MZAMS','Eexp'] - # my_formats = ['f8','f8'] - # dtype = dict(names = my_names, formats= my_formats) - # #dt = np.dtype() - # #Couch_data_ar = np.array(list(Couch_data.items()), dtype=dtype) - # - # #Couch_data_ar = np.array(Couch_data[turbulence_strength]) - # #Couch_data_ar = [np.array(Couch_data[MZAMS]) - # for MZAMS in Couch_data] - # #Couch_data_ar = np.array([(MZAMS,rest["Eexp"]) for (MZAMS,rest) - # in Couch_data.items()], dtype=dtype) + Couch_MZAMS = [] Couch_Eexp = [] Couch_state = [] + for MZAMS, rest in Couch_data.items(): Couch_MZAMS.append(float(MZAMS)) Couch_Eexp.append(rest["Eexp"]) @@ -2350,49 +2336,14 @@ def __init__(self, turbulence_strength, path_engine_dataset, verbose): Couch_state.append(int(14)) # BH else: Couch_state.append(int(13)) # NS - # Couch_MZAMS = np.array(Couch_MZAMS, dtype=dict( - # names=my_names[0], formats=my_formats[0])) - # Couch_Eexp = np.array(Couch_Eexp,dtype=dict( - # names=my_names[1], formats= my_formats[1])) - # Couch_data_ar = np.array() - # breakpoint() - # print(Couch_data) - - # we need Sukhbold data for their cores - Sukhbold_data = read_csv( - # path_to_Sukhbold_datasets + "results_N20_table.csv" - path_to_Couch_datasets + "Sukhbold_Mzams_He_c_core.csv", - usecols=[0, 1]) + + # we need Sukhbold data for their cores + Sukhbold_data = read_csv(path_to_Couch_datasets + "Sukhbold_Mzams_He_c_core.csv", + usecols=[0, 1]) MZAMS = Sukhbold_data["Mzams"] He_core_mass = Sukhbold_data["He_c_mass"] - self.MZAMS_He_core_mass_Sukhbold_interpolator = interp1d( - MZAMS, He_core_mass) - # def MZAMS_He_core_mass_Sukhbold_interpolator(MZams): - # return Sukhbold_data[Sukhbold_data["Mzams"]==MZams][ - # "He_c_mass"] - - # # Classifier to assign the He core mass of Sukhbold - # # as a function of the MZAMS - # # taking the first nearest neighbor - # n_neighbors = 1 - # self.stellar_ZAMS_classifier = neighbors.KNeighborsClassifier( - # n_neighbors, weights="distance" - # ) - # self.stellar_ZAMS_classifier.fit( - # np.array(Sukhbold_data["Mzams"]).reshape( - # (len(Sukhbold_data["Mzams"]), 1) - # ), - # Sukhbold_data["He_c_mass"], - # ) - # MZAMS = np.array( - # Sukhbold_data["Mzams"] - # ) - # He_c_mass = np.array( - # Sukhbold_data["He_c_mass"] - # ) - # self.MZAMS_He_core_mass_Sukhbold_interpolator = interp1d(MZAMS, - # He_c_mass) + self.MZAMS_He_core_mass_Sukhbold_interpolator = interp1d(MZAMS, He_core_mass) Couch_He_c_mass = self.MZAMS_He_core_mass_Sukhbold_interpolator( Couch_MZAMS) @@ -2408,71 +2359,10 @@ def __init__(self, turbulence_strength, path_engine_dataset, verbose): n_neighbors, weights="distance" ) self.stellar_type_classifier.fit( - np.array(Couch_He_c_mass).reshape( - (len(Couch_He_c_mass), 1) - ), + np.array(Couch_He_c_mass).reshape((len(Couch_He_c_mass), 1)), np.array(Couch_state), ) - ''' - breakpoint() - - print("Training the remnant mass interpolator ...") - - # Interpolator to compute the remnant mass - # as a function of the He core mass pre-supernova - # and the stellar type of the remnant. - NS_rem_mass = np.array( - Engine_data[Engine_data["stellar_state"] == 13]["Rem_mass"] - ) - NS_He_prog = np.array( - Engine_data[Engine_data["stellar_state"] == 13]["He_c_mass"] - ) - self.mass_NS_interpolator = interp1d(NS_He_prog, NS_rem_mass) - - BH_rem_mass = np.array( - Engine_data[Engine_data["stellar_state"] == 14]["Rem_mass"] - ) - BH_He_prog = np.array( - Engine_data[Engine_data["stellar_state"] == 14]["He_c_mass"] - ) - self.mass_BH_interpolator = interp1d(BH_He_prog, BH_rem_mass) - - if verbose: - print("Done ...\n") - - # Gets the neutron-star mass in terms of the He core mass - # if a succesful explotion is predicted - def extrapolate1d_NS(value, interpolator): - x = interpolator.x - - if (value >= np.min(x)) and (value <= np.max(x)): - result = interpolator(value) - elif value < np.min(x): - result = interpolator(np.min(x)) - elif value > np.max(x): - result = interpolator(np.max(x)) - - return result - - # Gets the black-hole mass in terms of the He core mass - # if a unsuccesful explotion is predicted - def extrapolate1d_BH(value, interpolator): - x = interpolator.x - - if (value >= np.min(x)) and (value <= np.max(x)): - result = interpolator(value) - elif value < np.min(x): - result = interpolator(np.min(x)) - elif value > np.max(x): - result = value - - return result - - self.extrapolate_NS = extrapolate1d_NS - self.extrapolate_BH = extrapolate1d_BH - ''' - else: raise ValueError( "Turbulence strength " + self.turbulence_strength + " is not " diff --git a/posydon/interpolation/interpolation.py b/posydon/interpolation/interpolation.py index b08841fb7a..1aa3c3931f 100644 --- a/posydon/interpolation/interpolation.py +++ b/posydon/interpolation/interpolation.py @@ -8,11 +8,11 @@ import pickle import numpy as np -from scipy import interpolate from sklearn.neighbors import NearestNeighbors from .data_scaling import DataScaler from posydon.grids.psygrid import PSyGrid from posydon.grids.MODELS import MODELS +from posydon.utils.interpolators import interp1d class psyTrackInterp: @@ -707,13 +707,13 @@ def get_profile(self, key, M_new): m_cor = m_cor_high idx = np.argmax(mass_high == self.grid_mass) profile_old = grid[idx].final_profile1 - f = interpolate.interp1d(m_cor_low, kvalue_low) + f = interp1d(m_cor_low, kvalue_low) kvalue_low = f(m_cor) else: m_cor = m_cor_low idx = np.argmax(mass_low == self.grid_mass) profile_old = grid[idx].final_profile1 - f = interpolate.interp1d(m_cor_high, kvalue_high) + f = interp1d(m_cor_high, kvalue_high) kvalue_high = f(m_cor) weight = (M_new - mass_low) / (mass_high - mass_low) diff --git a/posydon/interpolation/profile_interpolation.py b/posydon/interpolation/profile_interpolation.py index 05c2f43990..73990d1936 100644 --- a/posydon/interpolation/profile_interpolation.py +++ b/posydon/interpolation/profile_interpolation.py @@ -11,6 +11,7 @@ from posydon.grids.psygrid import PSyGrid from posydon.interpolation.IF_interpolation import IFInterpolator from posydon.utils.posydonwarning import Pwarn +from posydon.utils.interpolators import interp1d # Math and ML import os @@ -24,7 +25,6 @@ tf.get_logger().setLevel('ERROR') from tensorflow.keras import layers, losses, models, optimizers, backend, utils from sklearn.decomposition import PCA -from scipy.interpolate import interp1d class CompileData: @@ -748,8 +748,7 @@ def predict_single(self,initial,center_H,surface_H,center_He,surface_He,star_sta He = np.ones(200) * center_He He[int(b[1]*200):] = surface_He f_He = interp1d(b,[center_He,surface_He], - fill_value=(center_He,surface_He), - bounds_error=False) + fill_value=(center_He,surface_He)) # use f to construct the profile points in the shell burning region He[int(b[0]*200):int(b[1]*200)] = f_He(np.linspace(0,1,200)[int(b[0]*200):int(b[1]*200)]) @@ -772,8 +771,7 @@ def predict_single(self,initial,center_H,surface_H,center_He,surface_He,star_sta plus = np.ones(200) * (center_H + center_He) plus[int(b[2]*200):] = (surface_H + surface_He) f_plus = interp1d(b[1:],[center_H + center_He, surface_H + surface_He], - fill_value=(center_H + center_He, surface_H + surface_He), - bounds_error=False) + fill_value=(center_H + center_He, surface_H + surface_He)) plus[int(b[1]*200):int(b[2]*200)] = f_plus(np.linspace(0,1,200)[int(b[1]*200):int(b[2]*200)]) He = plus - H @@ -784,7 +782,7 @@ def predict_single(self,initial,center_H,surface_H,center_He,surface_He,star_sta b = self.bounds_models[star_state](tf.convert_to_tensor([initial])).numpy()[0] H = np.ones(200) * center_H H[int(b[1]*200):] = surface_H - f_H = interp1d(b,[center_H,surface_H],fill_value=(center_H,surface_H),bounds_error=False) + f_H = interp1d(b,[center_H,surface_H],fill_value=(center_H,surface_H)) # use f to construct the profile points in the shell burning region H[int(b[0]*200):int(b[1]*200)] = f_H(np.linspace(0,1,200)[int(b[0]*200):int(b[1]*200)]) # "H+He" profile is flat: @@ -797,14 +795,14 @@ def predict_single(self,initial,center_H,surface_H,center_He,surface_He,star_sta b = self.bounds_models[star_state](tf.convert_to_tensor([initial])).numpy()[0] H = np.ones(200) * center_H H[int(b[1]*200):] = surface_H - f_H = interp1d(b[:2],[center_H,surface_H],fill_value=(center_H,surface_H),bounds_error=False) + f_H = interp1d(b[:2],[center_H,surface_H],fill_value=(center_H,surface_H)) # use f to construct the profile points in the shell burning region H[int(b[0]*200):int(b[1]*200)] = f_H(np.linspace(0,1,200)[int(b[0]*200):int(b[1]*200)]) # "H+He" profile is beveled as well: plus = np.ones(200) * (center_H+center_He) plus[int(b[3]*200):] = surface_H+surface_He f_plus = interp1d(b[2:],[center_H+center_He,surface_H+surface_He], - fill_value=(center_H+center_He,surface_H+surface_He),bounds_error=False) + fill_value=(center_H+center_He,surface_H+surface_He)) plus[int(b[2]*200):int(b[3]*200)] = f_plus(np.linspace(0,1,200)[int(b[2]*200):int(b[3]*200)]) He = plus - H diff --git a/posydon/popsyn/star_formation_history.py b/posydon/popsyn/star_formation_history.py index f7c219adcf..e2a4c6c343 100644 --- a/posydon/popsyn/star_formation_history.py +++ b/posydon/popsyn/star_formation_history.py @@ -21,7 +21,7 @@ read_histogram_from_file, ) from posydon.utils.constants import Zsun -from scipy.interpolate import interp1d +from posydon.utils.interpolators import interp1d from astropy.cosmology import Planck15 as cosmology @@ -286,8 +286,8 @@ def SFR_Z_fraction_at_given_redshift( Z_x_values = np.append(np.log10(Z[Z_max_mask]), 0) Z_dist_cdf_interp = interp1d(Z_x_values, Z_dist_cdf) - fSFR[i, :] = (Z_dist_cdf_interp(np.log10(metallicity_bins[1:])) - - Z_dist_cdf_interp(np.log10(metallicity_bins[:-1]))) + fSFR[i, :] = (Z_dist_cdf_interp(np.log10(metallicity_bins[1:])) + - Z_dist_cdf_interp(np.log10(metallicity_bins[:-1]))) if not select_one_met: # add the fraction of the SFR in the first and last bin @@ -295,7 +295,6 @@ def SFR_Z_fraction_at_given_redshift( if len(metallicity_bins) == 2: fSFR[i, 0] = 1 else: - fSFR[i, 0] = Z_dist_cdf_interp(np.log10(metallicity_bins[1])) fSFR[i, -1] = 1 - Z_dist_cdf_interp(np.log10(metallicity_bins[-1])) else: diff --git a/posydon/unit_tests/utils/test_common_functions.py b/posydon/unit_tests/utils/test_common_functions.py index 1addd9857d..13ecd4ce55 100644 --- a/posydon/unit_tests/utils/test_common_functions.py +++ b/posydon/unit_tests/utils/test_common_functions.py @@ -15,12 +15,12 @@ # module you like to test from pytest import fixture, raises, warns, approx from inspect import isroutine, isclass -from scipy.interpolate import interp1d from posydon.binary_evol.binarystar import BinaryStar from posydon.binary_evol.singlestar import SingleStar from posydon.utils.posydonwarning import (EvolutionWarning,\ InappropriateValueWarning, ApproximationWarning, InterpolationWarning,\ ReplaceValueWarning, ClassificationWarning) +from posydon.utils.interpolators import interp1d @fixture def binary(): @@ -71,10 +71,8 @@ def test_dir(self): 'MT_CASE_BC', 'MT_CASE_C', 'MT_CASE_NONBURNING',\ 'MT_CASE_NO_RLO', 'MT_CASE_TO_STR',\ 'MT_CASE_UNDETERMINED', 'MT_STR_TO_CASE',\ - 'PATH_TO_POSYDON', 'PchipInterpolator',\ - 'PchipInterpolator2', 'Pwarn',\ - 'REL_LOG10_BURNING_THRESHOLD', 'RICHNESS_STATES',\ - 'RL_RELATIVE_OVERFLOW_THRESHOLD',\ + 'PATH_TO_POSYDON', 'Pwarn', 'REL_LOG10_BURNING_THRESHOLD',\ + 'RICHNESS_STATES', 'RL_RELATIVE_OVERFLOW_THRESHOLD',\ 'STATE_NS_STARMASS_UPPER_LIMIT', 'STATE_UNDETERMINED',\ 'Schwarzschild_Radius', 'THRESHOLD_CENTRAL_ABUNDANCE',\ 'THRESHOLD_HE_NAKED_ABUNDANCE', '__authors__',\ @@ -313,9 +311,6 @@ def test_instance_calculate_Mejected_for_integrated_binding_energy(self): assert isroutine(totest.\ calculate_Mejected_for_integrated_binding_energy) - def test_instance_PchipInterpolator2(self): - assert isclass(totest.PchipInterpolator2) - def test_instance_convert_metallicity_to_string(self): assert isroutine(totest.convert_metallicity_to_string) @@ -728,22 +723,27 @@ def mock_interp1d(x, y): def mock_pdf(x): return 1.0 - np.sqrt(x) # bad input - with raises(TypeError, match="'>=' not supported between instances "\ - +"of 'NoneType' and 'float'"): + with raises(ValueError, match="x and y PDF values must be specified"\ + +" if no PDF function is provided for"\ + +" rejection sampling"): totest.rejection_sampler() - with raises(TypeError, match="'>=' not supported between instances "\ - +"of 'NoneType' and 'float'"): + with raises(ValueError, match="x and y PDF values must be specified"\ + +" if no PDF function is provided for"\ + +" rejection sampling"): totest.rejection_sampler(x=np.array([0.0, 1.0])) - with raises(IndexError, match="too many indices for array: array is "\ - +"0-dimensional, but 1 were indexed"): + with raises(ValueError, match="x and y PDF values must be specified"\ + +" if no PDF function is provided for"\ + +" rejection sampling"): totest.rejection_sampler(y=np.array([0.0, 1.0])) with raises(AssertionError): totest.rejection_sampler(x=np.array([0.0, 1.0]),\ y=np.array([-0.4, 0.6])) - with raises(TypeError, match="'>=' not supported between instances "\ - +"of 'NoneType' and 'float'"): + with raises(ValueError, match="x and y PDF values must be specified"\ + +" if no PDF function is provided for"\ + +" rejection sampling"): totest.rejection_sampler(x_lim=np.array([0.0, 1.0])) - with raises(TypeError, match="'NoneType' object is not subscriptable"): + with raises(ValueError, match="x_lim must be specified for passed PDF"\ + +" function in rejection sampling"): totest.rejection_sampler(pdf=mock_pdf) # examples: monkeypatch.setattr(np.random, "uniform", mock_uniform) @@ -2358,48 +2358,3 @@ def test_rotate(self): [0.59500984, 0.77015115, 0.22984885],\ [-0.59500984, 0.22984885, 0.77015115]])) - -class TestPchipInterpolator2: - @fixture - def PchipInterpolator2(self): - # initialize an instance of the class with defaults - return totest.PchipInterpolator2([0.0, 1.0], [1.0, 0.0]) - - @fixture - def PchipInterpolator2_True(self): - # initialize an instance of the class with defaults - return totest.PchipInterpolator2([0.0, 1.0], [-0.5, 0.5],\ - positive=True) - - @fixture - def PchipInterpolator2_False(self): - # initialize an instance of the class with defaults - return totest.PchipInterpolator2([0.0, 1.0], [-0.5, 0.5],\ - positive=False) - - # test the PchipInterpolator2 class - def test_init(self, PchipInterpolator2, PchipInterpolator2_True,\ - PchipInterpolator2_False): - assert isroutine(PchipInterpolator2.__init__) - # check that the instance is of correct type and all code in the - # __init__ got executed: the elements are created and initialized - assert isinstance(PchipInterpolator2, totest.PchipInterpolator2) - assert isinstance(PchipInterpolator2.interpolator,\ - totest.PchipInterpolator) - assert PchipInterpolator2.positive == False - assert PchipInterpolator2_True.positive == True - assert PchipInterpolator2_False.positive == False - - def test_call(self, PchipInterpolator2, PchipInterpolator2_True,\ - PchipInterpolator2_False): - assert isroutine(PchipInterpolator2.__call__) - assert PchipInterpolator2(0.1) == 0.9 - assert PchipInterpolator2_True(0.1) == 0.0 - assert PchipInterpolator2_False(0.1) == -0.4 - assert np.allclose(PchipInterpolator2([0.1, 0.8]),\ - np.array([0.9, 0.2])) - assert np.allclose(PchipInterpolator2_True([0.1, 0.8]),\ - np.array([0.0, 0.3])) - assert np.allclose(PchipInterpolator2_False([0.1, 0.8]),\ - np.array([-0.4, 0.3])) - diff --git a/posydon/unit_tests/utils/test_interpolators.py b/posydon/unit_tests/utils/test_interpolators.py new file mode 100644 index 0000000000..c668eddad4 --- /dev/null +++ b/posydon/unit_tests/utils/test_interpolators.py @@ -0,0 +1,174 @@ +"""Unit tests of posydon/utils/interpolators.py +""" + +__authors__ = [ + "Matthias Kruckow " +] + +# import the module which will be tested +import posydon.utils.interpolators as totest +# aliases +np = totest.np + +# import other needed code for the tests, which is not already imported in the +# module you like to test +from pytest import fixture, raises, warns, approx +from inspect import isroutine, isclass + +# define test classes collecting several test functions +class TestElements: + # check for objects, which should be an element of the tested module + def test_dir(self): + elements = ['PchipInterpolator', 'PchipInterpolator2', '__authors__',\ + '__builtins__', '__cached__', '__doc__', '__file__',\ + '__loader__', '__name__', '__package__', '__spec__',\ + 'interp1d', 'np'] + assert dir(totest) == elements, "There might be added or removed "\ + + "objects without an update on the "\ + + "unit test." + + def test_instance_interp1d(self): + assert isclass(totest.interp1d) + + def test_instance_PchipInterpolator2(self): + assert isclass(totest.PchipInterpolator2) + + +class Testinterp1d: + @fixture + def data(self): + # test data + return ([0.0, 1.0], [1.0, 0.0]) + + @fixture + def interp1d(self, data): + # initialize an instance of the class with defaults + return totest.interp1d(data[0], data[1]) + + # test the interp1d class + def test_init(self, data, interp1d): + assert isroutine(interp1d.__init__) + # check that the instance is of correct type and all code in the + # __init__ got executed: the elements are created and initialized + assert isinstance(interp1d, totest.interp1d) + assert np.array_equal(interp1d.x, np.array(data[0])) + assert np.array_equal(interp1d.y, np.array(data[1])) + assert interp1d.kind == 'linear' + assert interp1d.below is None + assert interp1d.above is None + assert interp1d.extrapolate == False + # bad input + with raises(TypeError, match="missing 2 required positional "\ + +"arguments: 'x' and 'y'"): + totest.interp1d() + with raises(NotImplementedError, match="kind = test is not supported"): + totest.interp1d(x=data[0], y=data[1], kind='test') + with raises(TypeError, match="'NoneType' object is not subscriptable"): + totest.interp1d(None, None) + with raises(ValueError): + totest.interp1d('test', 'test') + with raises(NotImplementedError, match="fill_value has to be "\ + +"'extrapolate' or a tuple "\ + +"with 1 or 2 elements"): + totest.interp1d(x=data[0], y=data[1], fill_value='test') + with raises(TypeError): + totest.interp1d(x=data[0], y=data[1], fill_value=('test', 'test')) + with raises(TypeError): + totest.interp1d(x=data[0], y=data[1], left='test') + with raises(TypeError): + totest.interp1d(x=data[0], y=data[1], right='test') + # examples + kinds = ['linear'] + for k in kinds: + test_interp1d = totest.interp1d(x=data[0], y=data[1], kind=k) + assert test_interp1d.kind == k + # examples + test_interp1d = totest.interp1d(x=data[0], y=data[1],\ + fill_value='extrapolate') + assert test_interp1d.extrapolate == True + # examples + tests = [({'fill_value': (2.0,)}, 2.0, 2.0),\ + ({'fill_value': (2.0, 3.0)}, 2.0, 3.0),\ + ({'left': 2.0}, 2.0, None),\ + ({'right': 3.0}, None, 3.0),\ + ({'left': 2.0, 'right': 3.0}, 2.0, 3.0),\ + ({'fill_value': (1.2, 1.3), 'left': 2.0, 'right': 3.0},\ + 2.0, 3.0)] + for (kwargs, b, a) in tests: + test_interp1d = totest.interp1d(x=data[0], y=data[1], **kwargs) + if b is None: + assert test_interp1d.below is None + else: + assert test_interp1d.below == b + if a is None: + assert test_interp1d.above is None + else: + assert test_interp1d.above == a + + def test_call(self, data, interp1d): + assert isroutine(interp1d.__call__) + # examples + for x,y in zip(data[0], data[1]): + assert interp1d(x) == y + tests = [(0.0, 1.0), (0.2, approx(0.8, abs=6e-12)),\ + (0.5, approx(0.5, abs=6e-12)), (0.9, approx(0.1, abs=6e-12)),\ + (1.4, 0.0), (-0.1, 1.0)] + for (x,y) in tests: + assert interp1d(x) == y + # examples: extrapolate + test_interp1d = totest.interp1d(x=data[0], y=data[1],\ + fill_value='extrapolate') + tests[-1] = (-0.1, approx(1.1, abs=6e-12)) + tests[-2] = (1.4, approx(-0.4, abs=6e-12)) + for (x,y) in tests: + assert test_interp1d(x) == y + # bad value + interp1d.kind = 'test' + with raises(NotImplementedError, match="kind = test is not supported"): + interp1d(0.2) + + +class TestPchipInterpolator2: + @fixture + def PchipInterpolator2(self): + # initialize an instance of the class with defaults + return totest.PchipInterpolator2([0.0, 1.0], [1.0, 0.0]) + + @fixture + def PchipInterpolator2_True(self): + # initialize an instance of the class with defaults + return totest.PchipInterpolator2([0.0, 1.0], [-0.5, 0.5],\ + positive=True) + + @fixture + def PchipInterpolator2_False(self): + # initialize an instance of the class with defaults + return totest.PchipInterpolator2([0.0, 1.0], [-0.5, 0.5],\ + positive=False) + + # test the PchipInterpolator2 class + def test_init(self, PchipInterpolator2, PchipInterpolator2_True,\ + PchipInterpolator2_False): + assert isroutine(PchipInterpolator2.__init__) + # check that the instance is of correct type and all code in the + # __init__ got executed: the elements are created and initialized + assert isinstance(PchipInterpolator2, totest.PchipInterpolator2) + assert isinstance(PchipInterpolator2.interpolator,\ + totest.PchipInterpolator) + assert PchipInterpolator2.positive == False + assert PchipInterpolator2_True.positive == True + assert PchipInterpolator2_False.positive == False + + def test_call(self, PchipInterpolator2, PchipInterpolator2_True,\ + PchipInterpolator2_False): + assert isroutine(PchipInterpolator2.__call__) + assert PchipInterpolator2(0.1) == 0.9 + assert PchipInterpolator2_True(0.1) == 0.0 + assert PchipInterpolator2_False(0.1) == -0.4 + assert np.allclose(PchipInterpolator2([0.1, 0.8]),\ + np.array([0.9, 0.2])) + assert np.allclose(PchipInterpolator2_True([0.1, 0.8]),\ + np.array([0.0, 0.3])) + assert np.allclose(PchipInterpolator2_False([0.1, 0.8]),\ + np.array([-0.4, 0.3])) + diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index 182a650678..95ee5582e9 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -20,18 +20,17 @@ import os import numpy as np import pandas as pd -from scipy.interpolate import interp1d from scipy.optimize import newton from scipy.integrate import quad from posydon.utils import constants as const from posydon.utils.posydonwarning import Pwarn import copy -from scipy.interpolate import PchipInterpolator from posydon.utils.limits_thresholds import (THRESHOLD_CENTRAL_ABUNDANCE, THRESHOLD_HE_NAKED_ABUNDANCE, REL_LOG10_BURNING_THRESHOLD, LOG10_BURNING_THRESHOLD, STATE_NS_STARMASS_UPPER_LIMIT, RL_RELATIVE_OVERFLOW_THRESHOLD, LG_MTRANSFER_RATE_THRESHOLD ) +from posydon.utils.interpolators import interp1d PATH_TO_POSYDON = os.environ.get("PATH_TO_POSYDON") @@ -627,6 +626,9 @@ def rejection_sampler(x=None, y=None, size=1, x_lim=None, pdf=None): """ if pdf is None: + if ((x is None) or (y is None)): + raise ValueError("x and y PDF values must be specified if no PDF" + " function is provided for rejection sampling") assert np.all(y >= 0.0) try: pdf = interp1d(x, y) @@ -643,6 +645,9 @@ def rejection_sampler(x=None, y=None, size=1, x_lim=None, pdf=None): y_rand = np.random.uniform(0, y.max(), n) values = np.hstack([values, x_rand[y_rand <= pdf(x_rand)]]) else: + if x_lim is None: + raise ValueError("x_lim must be specified for passed PDF function" + " in rejection sampling") x_rand = np.random.uniform(x_lim[0], x_lim[1], size) pdf_max = max(pdf(np.random.uniform(x_lim[0], x_lim[1], 50000))) y_rand = np.random.uniform(0, pdf_max, size) @@ -2743,22 +2748,6 @@ def calculate_Mejected_for_integrated_binding_energy(profile, Ebind_threshold, return M_ejected - -class PchipInterpolator2: - """Interpolation class.""" - - def __init__(self, *args, positive=False, **kwargs): - """Initialize the interpolator.""" - self.interpolator = PchipInterpolator(*args, **kwargs) - self.positive = positive - - def __call__(self, *args, **kwargs): - """Use the interpolator.""" - result = self.interpolator(*args, **kwargs) - if self.positive: - result = np.maximum(result, 0.0) - return result - def convert_metallicity_to_string(Z): """Check if metallicity is supported by POSYDON v2.""" # check supported metallicity diff --git a/posydon/utils/interpolators.py b/posydon/utils/interpolators.py new file mode 100644 index 0000000000..a6159ddc1b --- /dev/null +++ b/posydon/utils/interpolators.py @@ -0,0 +1,142 @@ +"""Own interpolator classes.""" + + +__authors__ = [ + "Matthias Kruckow ", +] + + +import numpy as np +from scipy.interpolate import PchipInterpolator + + +class interp1d: + """Interpolation class for one dimensional interpolation.""" + def __init__(self, x, y, kind='linear', **kwargs): + """Initialize the interpolator. + + Parameters + ---------- + x : ndarray-like + X-coordinates of the data to interpolate. + y : ndarray-like + Y-coordinates of the data to interpolate. Needs to have the same + length as `x`. + kind : str (default: 'linear') + Currently only 'linear' is allowed. + - 'linear' : uses np.interp + **kwargs : dict (optional) + Dictionary containing extra parameters. + - 'left' : float + Y-value to return for evaluations below the data `x`. + - 'right' : float + Y-value to return for evaluations above the data `x`. + - 'fill_value' : 'extrapolate' or tuple of size 1 or 2 + Superseded by 'left' or 'right' + - 'extrapolate' : use the first and last elements in the + data to determine the extrapolation below and above, + respectively; ignores fixed values for below and above + - size 1 : use y-value for below and above + - size 2 : use first y-value for below and second for above + + """ + self.x = np.array(x) + self.y = np.array(y) + if kind not in ['linear']: + raise NotImplementedError(f"kind = {kind} is not supported") + self.kind = kind + self.below = None + self.above = None + self.extrapolate = False + if 'fill_value' in kwargs: + if kwargs['fill_value'] == 'extrapolate': + self.extrapolate = True + elif (isinstance(kwargs['fill_value'], tuple) and + (len(kwargs['fill_value']) == 1)): + self.below = kwargs['fill_value'][0] + self.above = kwargs['fill_value'][0] + elif (isinstance(kwargs['fill_value'], tuple) and + (len(kwargs['fill_value']) == 2)): + self.below = kwargs['fill_value'][0] + self.above = kwargs['fill_value'][1] + else: + raise NotImplementedError("fill_value has to be 'extrapolate' " + "or a tuple with 1 or 2 elements") + if 'left' in kwargs: + self.below = kwargs['left'] + if 'right' in kwargs: + self.above = kwargs['right'] + # check that the interpolator can be called + assert self.__call__(x[0]) == y[0] + + def __call__(self, x_new): + """Use the interpolator. + + Parameters + ---------- + x_new : float or ndarray + X-coordinate(s) to get interpolated y-value(s) for. + + Returns + ------- + ndarray + Interpolated y-values. + + """ + x_n = np.array(x_new) + if self.kind == 'linear': + y_interp = np.array(np.interp(x=x_n, xp=self.x, fp=self.y, + left=self.below, right=self.above)) + if (self.extrapolate and (len(self.x)>1)): + below_mask = x_n < self.x[0] + above_mask = x_n > self.x[-1] + if np.any(below_mask): + slope_below = ((self.y[1]-self.y[0]) + / (self.x[1]-self.x[0])) + y_interp[below_mask] = self.y[0] + slope_below * ( + x_n[below_mask]-self.x[0]) + if np.any(above_mask): + slope_above = ((self.y[-1]-self.y[-2]) + / (self.x[-1]-self.x[-2])) + y_interp[above_mask] = self.y[-1] + slope_above * ( + x_n[above_mask]-self.x[-1]) + return y_interp + else: + raise NotImplementedError(f"kind = {self.kind} is not supported") + + +class PchipInterpolator2: + """Interpolation class.""" + def __init__(self, *args, positive=False, **kwargs): + """Initialize the interpolator. + + Parameters + ---------- + positive : bool (default: False) + If `True` request all y-values to be positive. + *args, **kwargs : dict (optional) + Arguments passed to original PchipInterpolator init. + + """ + self.interpolator = PchipInterpolator(*args, **kwargs) + self.positive = positive + + def __call__(self, *args, **kwargs): + """Use the interpolator. + + Parameters + ---------- + *args, **kwargs : dict (optional) + Arguments passed to original PchipInterpolator call. + + Returns + ------- + ndarray-like + Interpolated values. + + """ + result = self.interpolator(*args, **kwargs) + if self.positive: + result = np.maximum(result, 0.0) + return result + From 83d7ac8bf2c9f39744fc074f7e645c655cd59074 Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Fri, 7 Feb 2025 21:55:42 +1300 Subject: [PATCH 288/319] Add support for unsorted input in new interpolators (#502) * add check for increasing or decreasing arrays. Flip accordingly * add unit test for flipping + add check for x,y being None * remove None check * shuffle stuff around * move tests to init * use np.flip to create a copy of the original data instead of a flipped view. Just in case the data gets changed along the way * implement suggestions * add sorting for non-monotonic values. Required for some step_SN parameters * Update test_interpolators.py * fix typo --------- Co-authored-by: Elizabeth Teng <41969755+elizabethteng@users.noreply.github.com> --- posydon/unit_tests/utils/test_interpolators.py | 12 +++++++++++- posydon/utils/interpolators.py | 18 +++++++++++++++--- 2 files changed, 26 insertions(+), 4 deletions(-) diff --git a/posydon/unit_tests/utils/test_interpolators.py b/posydon/unit_tests/utils/test_interpolators.py index c668eddad4..b3eb2686e9 100644 --- a/posydon/unit_tests/utils/test_interpolators.py +++ b/posydon/unit_tests/utils/test_interpolators.py @@ -63,7 +63,7 @@ def test_init(self, data, interp1d): totest.interp1d() with raises(NotImplementedError, match="kind = test is not supported"): totest.interp1d(x=data[0], y=data[1], kind='test') - with raises(TypeError, match="'NoneType' object is not subscriptable"): + with raises(ValueError): totest.interp1d(None, None) with raises(ValueError): totest.interp1d('test', 'test') @@ -77,6 +77,16 @@ def test_init(self, data, interp1d): totest.interp1d(x=data[0], y=data[1], left='test') with raises(TypeError): totest.interp1d(x=data[0], y=data[1], right='test') + # x not strictly monotonic + example_data = ([0.0, 1.0, 0.5], [0.0, 0.5, 1.0]) + test_interp1d = totest.interp1d(example_data[0], example_data[1]) + assert np.array_equal(test_interp1d.x, [0.0, 0.5, 1.0]) + assert np.array_equal(test_interp1d.y, [0.0, 1.0, 0.5]) + + # examples: reversible data + test_interp1d = totest.interp1d(data[0][::-1], data[1][::-1]) + assert np.array_equal(test_interp1d.x, np.array(data[0])) + assert np.array_equal(test_interp1d.y, np.array(data[1])) # examples kinds = ['linear'] for k in kinds: diff --git a/posydon/utils/interpolators.py b/posydon/utils/interpolators.py index a6159ddc1b..81be872df6 100644 --- a/posydon/utils/interpolators.py +++ b/posydon/utils/interpolators.py @@ -39,12 +39,24 @@ def __init__(self, x, y, kind='linear', **kwargs): - size 1 : use y-value for below and above - size 2 : use first y-value for below and second for above - """ - self.x = np.array(x) - self.y = np.array(y) + """ if kind not in ['linear']: raise NotImplementedError(f"kind = {kind} is not supported") self.kind = kind + self.x = np.array(x) + self.y = np.array(y) + # check that x is increasing + if not np.all(np.diff(self.x) > 0): + # if instead being strictly decreasing, flip data + if np.all(np.diff(self.x) < 0): + self.x = np.flip(self.x) + self.y = np.flip(self.y) + else: + # make them strictly increasing if neither + indices = np.argsort(self.x) + self.x = self.x[indices] + self.y = self.y[indices] + self.below = None self.above = None self.extrapolate = False From 593efb783d935646467b0d7f798114bef9653ef8 Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Fri, 7 Feb 2025 22:01:01 +1300 Subject: [PATCH 289/319] replace NaN/None in input chi_eff calculation with 0 (#501) * add nan checks for spins + tilts * add docstring * remove np.ndarray wraping" * implement comments on using pd.na: * Update transient_select_funcs.py remove empty line generalize warning text * Update transient_select_funcs.py --------- Co-authored-by: mkruckow <122798003+mkruckow@users.noreply.github.com> Co-authored-by: Jeff Andrews --- posydon/popsyn/transient_select_funcs.py | 44 ++++++++++++++++++++++-- 1 file changed, 41 insertions(+), 3 deletions(-) diff --git a/posydon/popsyn/transient_select_funcs.py b/posydon/popsyn/transient_select_funcs.py index 56f7cc8031..d52fb57a46 100644 --- a/posydon/popsyn/transient_select_funcs.py +++ b/posydon/popsyn/transient_select_funcs.py @@ -4,9 +4,9 @@ from posydon.config import PATH_TO_POSYDON_DATA import os from tqdm import tqdm - - import warnings +from posydon.utils.posydonwarning import Pwarn + # This is to suppress the performance warnings from pandas # These warnings are not important for the user # We should alter the code to remove these warnings, but for now we suppress them @@ -123,7 +123,45 @@ def GRB_selection(history_chunk, oneline_chunk, formation_channels_chunk=None, S def chi_eff(m_1, m_2, a_1, a_2, tilt_1, tilt_2): - '''Calculate the effective spin of two masses.''' + '''Calculate the effective spin of two masses. + + Parameters + ---------- + m_1 : np.ndarray + The mass of the first BH. + m_2 : np.ndarray + The mass of the second BH. + a_1 : np.ndarray + The spin of the first BH. + a_2 : np.ndarray + The spin of the second BH. + tilt_1 : np.ndarray + The tilt of the first BH. + tilt_2 : np.ndarray + The tilt of the second BH. + + Returns + ------- + np.ndarray + The effective spin of the BHs. + + ''' + if pd.isna(a_1).any(): + Pwarn("a_1 contains undefined values, replacing them with 0.0", + 'ReplaceValueWarning') + a_1[pd.isna(a_1)] = 0.0 + if pd.isna(a_2).any(): + Pwarn("a_2 contains undefined values, replacing them with 0.0", + 'ReplaceValueWarning') + a_2[pd.isna(a_2)] = 0.0 + if pd.isna(tilt_1).any(): + Pwarn("tilt_1 contains undefined values, replacing them with 0.0", + 'ReplaceValueWarning') + tilt_1[pd.isna(tilt_1)] = 0.0 + if pd.isna(tilt_2).any(): + Pwarn("tilt_2 contains undefined values, replacing them with 0.0", + 'ReplaceValueWarning') + tilt_2[pd.isna(tilt_2)] = 0. return (m_1*a_1*np.cos(tilt_1)+m_2*a_2*np.cos(tilt_2))/(m_1+m_2) def m_chirp(m_1, m_2): From d7a8bb54b149d23870b0b0aa412e5bed13e3d1a3 Mon Sep 17 00:00:00 2001 From: Jeff Andrews Date: Fri, 7 Feb 2025 04:53:06 -0500 Subject: [PATCH 290/319] updating the data_download routines slightly (#496) * updating the data_download routines slightly * Fixing unit tests accordingly * Fixing one more unit test * Adding comment to force unit tests to re-run * Added DataError as a POSYDONError and included raise DataError for missing PATH_TO_POSYDON_DATA environmental variable * revert DataError and use NameError; add unittest * Use FileExistsError --------- Co-authored-by: astroJeff Co-authored-by: mkruckow --- .../unit_tests/utils/test_data_download.py | 20 ++++++++++++++----- posydon/utils/data_download.py | 12 +++++++---- 2 files changed, 23 insertions(+), 9 deletions(-) diff --git a/posydon/unit_tests/utils/test_data_download.py b/posydon/unit_tests/utils/test_data_download.py index cafd3dd632..f4dae12fd4 100644 --- a/posydon/unit_tests/utils/test_data_download.py +++ b/posydon/unit_tests/utils/test_data_download.py @@ -81,7 +81,7 @@ def download_statement(self): @fixture def failed_MD5_statement(self): # statement that MD5 verfication failed - return "Failed to read the tar.gz file for MD5 verificaton" + return "Failed to read the tar.gz file for MD5 verification" @fixture def extraction_statement(self): @@ -132,10 +132,20 @@ def mock_urlretrieve2(url, filename=None, reporthook=None, data=None): with raises(TypeError, match="path should be string, bytes, "\ +"os.PathLike or integer"): totest.data_download(file={}) - totest.data_download(file="./") - assert capsys.readouterr().out == "" - totest.data_download(file="./", verbose=True) - assert capsys.readouterr().out == "POSYDON data alraedy exists at ./\n" + # bad input + with monkeypatch.context() as mp: + mock_environ = totest.os.environ.copy() + mock_environ.pop("PATH_TO_POSYDON_DATA") + mp.setattr(totest.os, "environ", mock_environ) + with raises(NameError, match="You must define the "\ + +"PATH_TO_POSYDON_DATA environment "\ + +"variable before downloading "\ + +"POSYDON datasets"): + totest.data_download(file=test_path+".tar.gz") + # bad input + with raises(FileExistsError, match="POSYDON data already exists at "\ + +"./"): + totest.data_download(file="./") # skip real download: do nothing instead with monkeypatch.context() as mp: mp.setattr(totest.urllib.request, "urlretrieve", mock_urlretrieve) diff --git a/posydon/utils/data_download.py b/posydon/utils/data_download.py index b928080921..ea9b9940e0 100644 --- a/posydon/utils/data_download.py +++ b/posydon/utils/data_download.py @@ -57,9 +57,12 @@ def data_download(file=file, MD5_check=True, verbose=False): """ # First, make sure the path does not exist if os.path.exists(file): - if verbose: - print('POSYDON data alraedy exists at', file) - return + raise FileExistsError(f'POSYDON data already exists at {file}') + + # Second, check to make sure PATH_TO_POSYDON_DATA is defined + if "PATH_TO_POSYDON_DATA" not in os.environ: + raise NameError('You must define the PATH_TO_POSYDON_DATA environment ' + 'variable before downloading POSYDON datasets') # Split the file into its directory and filename directory, filename = os.path.split(file) @@ -89,7 +92,7 @@ def data_download(file=file, MD5_check=True, verbose=False): # Raise value error raise ValueError("MD5 verification failed!.") except: - print('Failed to read the tar.gz file for MD5 verificaton, ' + print('Failed to read the tar.gz file for MD5 verification, ' 'cannot guarantee file integrity (this error seems to ' 'happen only on macOS).') @@ -99,6 +102,7 @@ def data_download(file=file, MD5_check=True, verbose=False): for member in tqdm(iterable=tar.getmembers(), total=len(tar.getmembers())): tar.extract(member=member, path=directory) + # remove tar files after extracted if os.path.exists(file): if verbose: print('Removed downloaded tar file.') From f4d68edc29ee3621c722e14df2e3a5681ed10042 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 13 Feb 2025 07:30:23 +0100 Subject: [PATCH 291/319] Use of `PATH_*` variables (#505) * update and test config * always import PATHs from config * put the downloaded data on the path without POSYDON_data * use dirname, where POSYDON_data should be skipped * debugging * debugging * use os.path.join * cleanup * Update config.py docstring --- .github/workflows/continuous_integration.yml | 6 +- bin/posydon-run-pipeline | 2 +- bin/posydon-setup-pipeline | 2 +- bin/posydon-setup-popsyn | 4 +- posydon/binary_evol/CE/step_CEE.py | 2 +- posydon/binary_evol/DT/step_detached.py | 2 +- posydon/binary_evol/DT/step_disrupted.py | 2 +- .../binary_evol/DT/step_initially_single.py | 2 +- posydon/binary_evol/DT/step_isolated.py | 2 +- posydon/binary_evol/DT/step_merged.py | 2 +- posydon/binary_evol/MESA/step_mesa.py | 18 ++--- posydon/binary_evol/SN/step_SN.py | 3 +- posydon/config.py | 30 +++++++- posydon/popsyn/star_formation_history.py | 2 +- posydon/popsyn/transient_select_funcs.py | 2 +- .../active_learning/psy_cris/test_utils.py | 2 +- posydon/tests/binary_evol/CE/test_CEE.py | 2 +- .../binary_evol/DT/test_step_detached.py | 2 +- .../binary_evol/SN/test_profile_collapse.py | 2 +- posydon/tests/binary_evol/SN/test_step_SN.py | 2 +- posydon/tests/binary_evol/test_BinaryStar.py | 2 +- posydon/tests/binary_evol/test_SingleStar.py | 2 +- posydon/tests/grids/test_psygrid.py | 2 +- posydon/tests/visualization/test_VHdiagram.py | 2 +- posydon/tests/visualization/test_plot1D.py | 2 +- posydon/tests/visualization/test_plot2D.py | 2 +- posydon/unit_tests/test_config.py | 76 +++++++++++++++++++ .../unit_tests/utils/test_common_functions.py | 8 +- .../unit_tests/utils/test_data_download.py | 1 + posydon/utils/common_functions.py | 2 - posydon/utils/data_download.py | 11 ++- posydon/visualization/VH_diagram/Presenter.py | 2 +- posydon/visualization/plot_pop.py | 6 +- 33 files changed, 151 insertions(+), 58 deletions(-) create mode 100644 posydon/unit_tests/test_config.py diff --git a/.github/workflows/continuous_integration.yml b/.github/workflows/continuous_integration.yml index 62447a1070..b653032fb8 100644 --- a/.github/workflows/continuous_integration.yml +++ b/.github/workflows/continuous_integration.yml @@ -29,11 +29,11 @@ jobs: - name: Run all tests in posydon/unit_tests run: | - python -m pip install --upgrade pip - pip install . + # python -m pip install --upgrade pip + # pip install . pip install pytest pip install pytest-cov export PATH_TO_POSYDON=./ export PATH_TO_POSYDON_DATA=./ export MESA_DIR=./ - python -m pytest posydon/unit_tests/ --cov=posydon.utils --cov-branch --cov-report term-missing --cov-fail-under=100 + python -m pytest posydon/unit_tests/ --cov=posydon.utils --cov=posydon.config --cov-branch --cov-report term-missing --cov-fail-under=100 diff --git a/bin/posydon-run-pipeline b/bin/posydon-run-pipeline index a462d8e823..c4e0da934c 100644 --- a/bin/posydon-run-pipeline +++ b/bin/posydon-run-pipeline @@ -80,7 +80,7 @@ from posydon.grids.psygrid import (PSyGrid, from posydon.grids.post_processing import (post_process_grid, add_post_processed_quantities) from posydon.grids.MODELS import MODELS -from posydon.utils.common_functions import PATH_TO_POSYDON +from posydon.config import PATH_TO_POSYDON from posydon.utils.gridutils import get_new_grid_name from posydon.utils.posydonwarning import (Pwarn,print_stats) from posydon.interpolation.IF_interpolation import IFInterpolator diff --git a/bin/posydon-setup-pipeline b/bin/posydon-setup-pipeline index b99c9b1077..7b36b5c688 100644 --- a/bin/posydon-setup-pipeline +++ b/bin/posydon-setup-pipeline @@ -16,7 +16,7 @@ import pandas as pd import numpy as np from pprint import pformat from posydon.popsyn.io import parse_inifile -from posydon.utils.common_functions import PATH_TO_POSYDON +from posydon.config import PATH_TO_POSYDON from posydon.utils.gridutils import get_new_grid_name from posydon.utils.posydonwarning import Pwarn diff --git a/bin/posydon-setup-popsyn b/bin/posydon-setup-popsyn index 10d8e01e20..e0dd2f246d 100644 --- a/bin/posydon-setup-popsyn +++ b/bin/posydon-setup-popsyn @@ -149,9 +149,9 @@ if __name__ == '__main__': os.mkdir(f'{str_met}_logs') except: pass - create_slurm_submit(MET, args.job_array, args.partition, args.email, args.walltime, args.account, args.mem_per_cpu, PATH_TO_POSYDON, PATH_TO_POSYDON_DATA) + create_slurm_submit(MET, args.job_array, args.partition, args.email, args.walltime, args.account, args.mem_per_cpu, PATH_TO_POSYDON, os.path.dirname(PATH_TO_POSYDON_DATA)) print("SLURM script created") - create_slurm_merge(MET, args.partition, args.email, args.merge_walltime, args.account, args.mem_per_cpu, PATH_TO_POSYDON, PATH_TO_POSYDON_DATA) + create_slurm_merge(MET, args.partition, args.email, args.merge_walltime, args.account, args.mem_per_cpu, PATH_TO_POSYDON, os.path.dirname(PATH_TO_POSYDON_DATA)) # create submission script for all SLURM filename = 'slurm_submit.sh' diff --git a/posydon/binary_evol/CE/step_CEE.py b/posydon/binary_evol/CE/step_CEE.py index dfccf7105e..6a29912d48 100644 --- a/posydon/binary_evol/CE/step_CEE.py +++ b/posydon/binary_evol/CE/step_CEE.py @@ -40,7 +40,7 @@ from posydon.utils import constants as const from posydon.binary_evol.binarystar import BINARYPROPERTIES from posydon.binary_evol.singlestar import STARPROPERTIES -from posydon.utils.common_functions import PATH_TO_POSYDON +from posydon.config import PATH_TO_POSYDON from posydon.utils.common_functions import check_state_of_star from posydon.utils.common_functions import calculate_lambda_from_profile, calculate_Mejected_for_integrated_binding_energy from posydon.utils.posydonwarning import Pwarn diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index 4714bd8e43..1898f9a463 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -22,7 +22,7 @@ from scipy.optimize import minimize from scipy.optimize import root -from posydon.utils.data_download import PATH_TO_POSYDON_DATA +from posydon.config import PATH_TO_POSYDON_DATA from posydon.binary_evol.binarystar import BINARYPROPERTIES from posydon.binary_evol.singlestar import STARPROPERTIES from posydon.interpolation.interpolation import GRIDInterpolator diff --git a/posydon/binary_evol/DT/step_disrupted.py b/posydon/binary_evol/DT/step_disrupted.py index 2afcba46d8..a46134d257 100644 --- a/posydon/binary_evol/DT/step_disrupted.py +++ b/posydon/binary_evol/DT/step_disrupted.py @@ -8,7 +8,7 @@ ] -from posydon.utils.data_download import PATH_TO_POSYDON_DATA +from posydon.config import PATH_TO_POSYDON_DATA from posydon.binary_evol.DT.step_isolated import IsolatedStep from posydon.binary_evol.flow_chart import ( diff --git a/posydon/binary_evol/DT/step_initially_single.py b/posydon/binary_evol/DT/step_initially_single.py index 9bd43e2498..35fd2a426e 100644 --- a/posydon/binary_evol/DT/step_initially_single.py +++ b/posydon/binary_evol/DT/step_initially_single.py @@ -8,7 +8,7 @@ ] -from posydon.utils.data_download import PATH_TO_POSYDON_DATA +from posydon.config import PATH_TO_POSYDON_DATA from posydon.binary_evol.DT.step_isolated import IsolatedStep from posydon.binary_evol.flow_chart import ( diff --git a/posydon/binary_evol/DT/step_isolated.py b/posydon/binary_evol/DT/step_isolated.py index b8767b4fff..2e02034ee2 100644 --- a/posydon/binary_evol/DT/step_isolated.py +++ b/posydon/binary_evol/DT/step_isolated.py @@ -9,7 +9,7 @@ import numpy as np -from posydon.utils.data_download import PATH_TO_POSYDON_DATA +from posydon.config import PATH_TO_POSYDON_DATA from posydon.utils.common_functions import orbital_separation_from_period from posydon.binary_evol.DT.step_detached import detached_step from posydon.utils.posydonerror import FlowError diff --git a/posydon/binary_evol/DT/step_merged.py b/posydon/binary_evol/DT/step_merged.py index 8599e04c83..f10cf12f60 100644 --- a/posydon/binary_evol/DT/step_merged.py +++ b/posydon/binary_evol/DT/step_merged.py @@ -10,7 +10,7 @@ import numpy as np -from posydon.utils.data_download import PATH_TO_POSYDON_DATA +from posydon.config import PATH_TO_POSYDON_DATA from posydon.binary_evol.singlestar import STARPROPERTIES, convert_star_to_massless_remnant from posydon.utils.common_functions import check_state_of_star from posydon.binary_evol.DT.step_isolated import IsolatedStep diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index 2d58946719..351113bc47 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -27,7 +27,8 @@ convert_metallicity_to_string, CO_radius, infer_star_state, set_binary_to_failed,) -from posydon.utils.data_download import data_download, PATH_TO_POSYDON_DATA +from posydon.config import PATH_TO_POSYDON_DATA +from posydon.utils.data_download import data_download from posydon.grids.MODELS import MODELS from posydon.utils.posydonerror import FlowError, GridError from posydon.utils.posydonwarning import Pwarn @@ -200,18 +201,17 @@ def __init__( # Set the interpolation path if interpolation_path is None: - interpolation_path = ( - self.path + os.path.split(grid_name)[0] - + '/interpolators/%s/' % self.interpolation_method) + interpolation_path = os.path.join(self.path, + os.path.split(grid_name)[0], + 'interpolators/%s' % self.interpolation_method) # Set the interpolation filename if interpolation_filename is None: - interpolation_filename = ( - interpolation_path - + os.path.split(grid_name)[1].replace('h5', 'pkl')) + interpolation_filename = os.path.join(interpolation_path, + os.path.split(grid_name)[1].replace('h5', 'pkl')) else: - interpolation_filename = (interpolation_path - + interpolation_filename) + interpolation_filename = os.path.join(interpolation_path, + interpolation_filename) self.load_Interp(interpolation_filename) diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index a63e5b475e..0779168c0f 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -35,7 +35,8 @@ import copy import pandas as pd -from posydon.utils.data_download import PATH_TO_POSYDON_DATA, data_download +from posydon.config import PATH_TO_POSYDON_DATA +from posydon.utils.data_download import data_download import posydon.utils.constants as const from posydon.utils.common_functions import (is_number, CO_radius, orbital_period_from_separation, inspiral_timescale_from_separation, diff --git a/posydon/config.py b/posydon/config.py index ff7f0d8cb9..ce3535167f 100644 --- a/posydon/config.py +++ b/posydon/config.py @@ -3,6 +3,32 @@ load_dotenv() +def ensure_path(name): + """Get the 'PATH_*' variable set in the environment + + Parameters + ---------- + name : str + Name of the environment variable. + + Returns + ------- + str + Defined path or raises an error if the variable is not defined and + pointing to a valid path. + + """ + if not isinstance(name, str): + raise TypeError("'name' has to be a string.") + value = os.getenv(name) + if value is None: + raise NameError(f"{name} is not defined in the environment.") + if not os.path.isdir(value): + raise NotADirectoryError(f"{value} given in {name} is an invalid " + "path.") + return value + # POSYDON environment variables -PATH_TO_POSYDON = os.getenv("PATH_TO_POSYDON") -PATH_TO_POSYDON_DATA = os.getenv("PATH_TO_POSYDON_DATA") \ No newline at end of file +PATH_TO_POSYDON = ensure_path("PATH_TO_POSYDON") +PATH_TO_POSYDON_DATA = os.path.join(ensure_path("PATH_TO_POSYDON_DATA"), + "POSYDON_data") diff --git a/posydon/popsyn/star_formation_history.py b/posydon/popsyn/star_formation_history.py index e2a4c6c343..c314a66663 100644 --- a/posydon/popsyn/star_formation_history.py +++ b/posydon/popsyn/star_formation_history.py @@ -13,7 +13,7 @@ import numpy as np import scipy as sp from scipy import stats -from posydon.utils.data_download import PATH_TO_POSYDON_DATA +from posydon.config import PATH_TO_POSYDON_DATA from posydon.utils.constants import age_of_universe from posydon.utils.common_functions import ( rejection_sampler, diff --git a/posydon/popsyn/transient_select_funcs.py b/posydon/popsyn/transient_select_funcs.py index d52fb57a46..bab3175268 100644 --- a/posydon/popsyn/transient_select_funcs.py +++ b/posydon/popsyn/transient_select_funcs.py @@ -12,7 +12,7 @@ # We should alter the code to remove these warnings, but for now we suppress them warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning) -PATH_TO_PDET_GRID = os.path.join(PATH_TO_POSYDON_DATA, 'POSYDON_data/selection_effects/pdet_grid.hdf5') +PATH_TO_PDET_GRID = os.path.join(PATH_TO_POSYDON_DATA, 'selection_effects/pdet_grid.hdf5') def GRB_selection(history_chunk, oneline_chunk, formation_channels_chunk=None, S1_S2='S1'): """A GRB selection function to create a transient population of LGRBs. diff --git a/posydon/tests/active_learning/psy_cris/test_utils.py b/posydon/tests/active_learning/psy_cris/test_utils.py index d6258cc018..ffb84535a1 100644 --- a/posydon/tests/active_learning/psy_cris/test_utils.py +++ b/posydon/tests/active_learning/psy_cris/test_utils.py @@ -4,7 +4,7 @@ import numpy as np import pandas as pd import os -from posydon.utils.common_functions import PATH_TO_POSYDON +from posydon.config import PATH_TO_POSYDON from posydon.active_learning.psy_cris.utils import ( parse_inifile, diff --git a/posydon/tests/binary_evol/CE/test_CEE.py b/posydon/tests/binary_evol/CE/test_CEE.py index 0b4190ff31..09c2c4a144 100644 --- a/posydon/tests/binary_evol/CE/test_CEE.py +++ b/posydon/tests/binary_evol/CE/test_CEE.py @@ -1,7 +1,7 @@ import unittest import os import numpy as np -from posydon.utils.common_functions import PATH_TO_POSYDON +from posydon.config import PATH_TO_POSYDON from posydon.utils import common_functions as cf from posydon.binary_evol.binarystar import BinaryStar from posydon.binary_evol.singlestar import SingleStar diff --git a/posydon/tests/binary_evol/DT/test_step_detached.py b/posydon/tests/binary_evol/DT/test_step_detached.py index 0786ca287b..8474c3c258 100644 --- a/posydon/tests/binary_evol/DT/test_step_detached.py +++ b/posydon/tests/binary_evol/DT/test_step_detached.py @@ -1,7 +1,7 @@ import os import unittest -from posydon.utils.common_functions import PATH_TO_POSYDON +from posydon.config import PATH_TO_POSYDON from posydon.binary_evol.DT.step_detached import detached_step from posydon.binary_evol.DT.step_detached import diffeq diff --git a/posydon/tests/binary_evol/SN/test_profile_collapse.py b/posydon/tests/binary_evol/SN/test_profile_collapse.py index 3bca69138a..47b8e82bd4 100644 --- a/posydon/tests/binary_evol/SN/test_profile_collapse.py +++ b/posydon/tests/binary_evol/SN/test_profile_collapse.py @@ -1,6 +1,6 @@ import unittest import os -from posydon.utils.common_functions import PATH_TO_POSYDON +from posydon.config import PATH_TO_POSYDON from posydon.grids.psygrid import PSyGrid import posydon.utils.constants as const from posydon.binary_evol.singlestar import SingleStar diff --git a/posydon/tests/binary_evol/SN/test_step_SN.py b/posydon/tests/binary_evol/SN/test_step_SN.py index 1c501adef5..274f3276d5 100644 --- a/posydon/tests/binary_evol/SN/test_step_SN.py +++ b/posydon/tests/binary_evol/SN/test_step_SN.py @@ -1,6 +1,6 @@ import unittest -from posydon.utils.common_functions import PATH_TO_POSYDON +from posydon.config import PATH_TO_POSYDON from posydon.binary_evol.SN.step_SN import StepSN from posydon.binary_evol.binarystar import BinaryStar from posydon.binary_evol.singlestar import SingleStar diff --git a/posydon/tests/binary_evol/test_BinaryStar.py b/posydon/tests/binary_evol/test_BinaryStar.py index 8e22d34321..8df2b329b4 100644 --- a/posydon/tests/binary_evol/test_BinaryStar.py +++ b/posydon/tests/binary_evol/test_BinaryStar.py @@ -1,7 +1,7 @@ import unittest import os import numpy as np -from posydon.utils.common_functions import PATH_TO_POSYDON +from posydon.config import PATH_TO_POSYDON from posydon.binary_evol.singlestar import SingleStar from posydon.binary_evol.binarystar import BinaryStar from posydon.grids.psygrid import PSyGrid diff --git a/posydon/tests/binary_evol/test_SingleStar.py b/posydon/tests/binary_evol/test_SingleStar.py index f101b315c6..eea390c232 100644 --- a/posydon/tests/binary_evol/test_SingleStar.py +++ b/posydon/tests/binary_evol/test_SingleStar.py @@ -1,7 +1,7 @@ import unittest import os import numpy as np -from posydon.utils.common_functions import PATH_TO_POSYDON +from posydon.config import PATH_TO_POSYDON from posydon.binary_evol.singlestar import SingleStar from posydon.grids.psygrid import PSyGrid diff --git a/posydon/tests/grids/test_psygrid.py b/posydon/tests/grids/test_psygrid.py index c67f14815d..ffa9619688 100644 --- a/posydon/tests/grids/test_psygrid.py +++ b/posydon/tests/grids/test_psygrid.py @@ -4,7 +4,7 @@ # import os # import shutil # import zipfile -# from posydon.utils.common_functions import PATH_TO_POSYDON +# from posydon.config import PATH_TO_POSYDON # from posydon.grids.psygrid import PSyGrid # # diff --git a/posydon/tests/visualization/test_VHdiagram.py b/posydon/tests/visualization/test_VHdiagram.py index f618096a6f..cb61c2e46b 100644 --- a/posydon/tests/visualization/test_VHdiagram.py +++ b/posydon/tests/visualization/test_VHdiagram.py @@ -1,6 +1,6 @@ import unittest from unittest.mock import patch -from posydon.utils.common_functions import PATH_TO_POSYDON +from posydon.config import PATH_TO_POSYDON import os from posydon.visualization.VH_diagram.Presenter import Presenter, PresenterMode diff --git a/posydon/tests/visualization/test_plot1D.py b/posydon/tests/visualization/test_plot1D.py index dc4d64778e..7386e319cb 100644 --- a/posydon/tests/visualization/test_plot1D.py +++ b/posydon/tests/visualization/test_plot1D.py @@ -1,7 +1,7 @@ import unittest from unittest.mock import patch import os -from posydon.utils.common_functions import PATH_TO_POSYDON +from posydon.config import PATH_TO_POSYDON from posydon.grids.psygrid import PSyGrid PATH_TO_GRID = os.path.join( diff --git a/posydon/tests/visualization/test_plot2D.py b/posydon/tests/visualization/test_plot2D.py index a843c93642..c128079ba7 100644 --- a/posydon/tests/visualization/test_plot2D.py +++ b/posydon/tests/visualization/test_plot2D.py @@ -1,6 +1,6 @@ import unittest from unittest.mock import patch -from posydon.utils.common_functions import PATH_TO_POSYDON +from posydon.config import PATH_TO_POSYDON import os from posydon.grids.psygrid import PSyGrid diff --git a/posydon/unit_tests/test_config.py b/posydon/unit_tests/test_config.py new file mode 100644 index 0000000000..703138fa22 --- /dev/null +++ b/posydon/unit_tests/test_config.py @@ -0,0 +1,76 @@ +"""Unit tests of posydon/config.py +""" + +__authors__ = [ + "Matthias Kruckow " +] + +# import the module which will be tested +import posydon.config as totest +# aliases +os = totest.os + +# import other needed code for the tests, which is not already imported in the +# module you like to test +from pytest import raises +from inspect import isroutine + + +# define test classes collecting several test functions +class TestElements: + # check for objects, which should be an element of the tested module + def test_dir(self): + elements = ['PATH_TO_POSYDON', 'PATH_TO_POSYDON_DATA', '__builtins__',\ + '__cached__', '__doc__', '__file__', '__loader__',\ + '__name__', '__package__', '__spec__', 'ensure_path',\ + 'load_dotenv', 'os'] + assert dir(totest) == elements, "There might be added or removed "\ + + "objects without an update on the "\ + + "unit test." + + def test_instance_ensure_path(self): + assert isroutine(totest.ensure_path) + + def test_instance_PATH_TO_POSYDON_DATA(self): + assert isinstance(totest.PATH_TO_POSYDON_DATA, (str, bytes,\ + os.PathLike)) + + def test_instance_PATH_TO_POSYDON(self): + assert isinstance(totest.PATH_TO_POSYDON, (str, bytes, os.PathLike)) + + +class TestValues: + # check that the values fit + def test_value_PATH_TO_POSYDON(self): + assert totest.PATH_TO_POSYDON is not None + + def test_value_PATH_TO_POSYDON_DATA(self): + assert totest.PATH_TO_POSYDON_DATA is not None + assert "POSYDON_data" in totest.PATH_TO_POSYDON_DATA + + +class TestFunctions: + # test functions + def test_ensure_path(self): + # missing argument + with raises(TypeError, match="missing 1 required positional "\ + +"argument: 'name'"): + totest.ensure_path() + # bad input + with raises(TypeError, match="'name' has to be a string."): + totest.ensure_path(None) + # bad input + with raises(NameError, match="DOES_NOT_EXIST is not defined in the "\ + +"environment."): + totest.ensure_path("DOES_NOT_EXIST") + # bad input + for k,i in os.environ.items(): + # search for first item, which is no path to check the error + if not os.path.isdir(i): + with raises(NotADirectoryError, match=f"{i} given in {k} is "\ + +"an invalid path."): + totest.ensure_path(k) + break + # example + for n in ["PATH_TO_POSYDON", "PATH_TO_POSYDON_DATA"]: + assert totest.ensure_path(n) == os.getenv(n) diff --git a/posydon/unit_tests/utils/test_common_functions.py b/posydon/unit_tests/utils/test_common_functions.py index 13ecd4ce55..76ab1eab4a 100644 --- a/posydon/unit_tests/utils/test_common_functions.py +++ b/posydon/unit_tests/utils/test_common_functions.py @@ -71,7 +71,7 @@ def test_dir(self): 'MT_CASE_BC', 'MT_CASE_C', 'MT_CASE_NONBURNING',\ 'MT_CASE_NO_RLO', 'MT_CASE_TO_STR',\ 'MT_CASE_UNDETERMINED', 'MT_STR_TO_CASE',\ - 'PATH_TO_POSYDON', 'Pwarn', 'REL_LOG10_BURNING_THRESHOLD',\ + 'Pwarn', 'REL_LOG10_BURNING_THRESHOLD',\ 'RICHNESS_STATES', 'RL_RELATIVE_OVERFLOW_THRESHOLD',\ 'STATE_NS_STARMASS_UPPER_LIMIT', 'STATE_UNDETERMINED',\ 'Schwarzschild_Radius', 'THRESHOLD_CENTRAL_ABUNDANCE',\ @@ -112,9 +112,6 @@ def test_dir(self): + "objects without an update on the "\ + "unit test." - def test_instance_PATH_TO_POSYDON(self): - assert isinstance(totest.PATH_TO_POSYDON, str) - def test_instance_STATE_UNDETERMINED(self): assert isinstance(totest.STATE_UNDETERMINED, str) @@ -320,9 +317,6 @@ def test_instance_rotate(self): class TestValues: # check that the values fit - def test_value_PATH_TO_POSYDON(self): - assert '/' in totest.PATH_TO_POSYDON - def test_value_STATE_UNDETERMINED(self): assert totest.STATE_UNDETERMINED == "undetermined_evolutionary_state" diff --git a/posydon/unit_tests/utils/test_data_download.py b/posydon/unit_tests/utils/test_data_download.py index f4dae12fd4..4c22919377 100644 --- a/posydon/unit_tests/utils/test_data_download.py +++ b/posydon/unit_tests/utils/test_data_download.py @@ -7,6 +7,7 @@ # import the module which will be tested import posydon.utils.data_download as totest +# aliases os = totest.os # import other needed code for the tests, which is not already imported in the diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index 95ee5582e9..5a899568e9 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -32,8 +32,6 @@ ) from posydon.utils.interpolators import interp1d -PATH_TO_POSYDON = os.environ.get("PATH_TO_POSYDON") - # Constants related to inferring star states STATE_UNDETERMINED = "undetermined_evolutionary_state" diff --git a/posydon/utils/data_download.py b/posydon/utils/data_download.py index ea9b9940e0..dbae0d5610 100644 --- a/posydon/utils/data_download.py +++ b/posydon/utils/data_download.py @@ -11,12 +11,10 @@ import progressbar import tarfile from tqdm import tqdm +from posydon.config import PATH_TO_POSYDON_DATA -# get path to data, if not provided use the working directory -PATH_TO_POSYDON_DATA = os.environ.get("PATH_TO_POSYDON_DATA",'./') -file = os.path.join(PATH_TO_POSYDON_DATA, "POSYDON_data.tar.gz") -PATH_TO_POSYDON_DATA = os.path.join(PATH_TO_POSYDON_DATA, 'POSYDON_data/') - +file = os.path.join(os.path.dirname(PATH_TO_POSYDON_DATA), + "POSYDON_data.tar.gz") data_url = "https://zenodo.org/record/14205146/files/POSYDON_data.tar.gz" original_md5 = "cf645a45b9b92c2ad01e759eb1950beb" @@ -99,7 +97,8 @@ def data_download(file=file, MD5_check=True, verbose=False): # extract each file print('Extracting POSYDON data from tar file...') with tarfile.open(file) as tar: - for member in tqdm(iterable=tar.getmembers(), total=len(tar.getmembers())): + for member in tqdm(iterable=tar.getmembers(), + total=len(tar.getmembers())): tar.extract(member=member, path=directory) # remove tar files after extracted diff --git a/posydon/visualization/VH_diagram/Presenter.py b/posydon/visualization/VH_diagram/Presenter.py index 625f430bad..d6b4cf358b 100644 --- a/posydon/visualization/VH_diagram/Presenter.py +++ b/posydon/visualization/VH_diagram/Presenter.py @@ -13,7 +13,7 @@ from .PresenterMode import PresenterMode from posydon.utils.common_functions import orbital_separation_from_period -from posydon.utils.common_functions import PATH_TO_POSYDON +from posydon.config import PATH_TO_POSYDON from datetime import datetime import os diff --git a/posydon/visualization/plot_pop.py b/posydon/visualization/plot_pop.py index 1265328afc..63ded6f311 100644 --- a/posydon/visualization/plot_pop.py +++ b/posydon/visualization/plot_pop.py @@ -7,7 +7,7 @@ import numpy as np import matplotlib as mpl import pandas as pd -from posydon.utils.common_functions import PATH_TO_POSYDON +from posydon.config import PATH_TO_POSYDON, PATH_TO_POSYDON_DATA from posydon.visualization.plot_defaults import DEFAULT_LABELS from posydon.utils.constants import Zsun from posydon.grids.psygrid import PSyGrid @@ -16,8 +16,6 @@ import matplotlib.pyplot as plt from posydon.visualization.plot_defaults import DEFAULT_LABELS -PATH_TO_POSYDON_DATA = os.environ.get("PATH_TO_POSYDON_DATA",'./') - plt.style.use(os.path.join(PATH_TO_POSYDON, "posydon/visualization/posydon.mplstyle")) cm = mpl.colormaps.get_cmap('tab20') @@ -250,7 +248,7 @@ def plot_popsyn_over_grid_slice(pop, # load grid met = convert_metallicity_to_string(met_Zsun) - grid_path = os.path.join(PATH_TO_POSYDON_DATA, f'POSYDON_data/{grid_type}/{met}_Zsun.h5') + grid_path = os.path.join(PATH_TO_POSYDON_DATA, f'{grid_type}/{met}_Zsun.h5') grid = PSyGrid(verbose=False) grid.load(grid_path) From 313315a643fde9beb4aee9b9d0fafe4e0b203451 Mon Sep 17 00:00:00 2001 From: Camille Liotine Date: Thu, 27 Feb 2025 13:31:03 -0600 Subject: [PATCH 292/319] Fixes to Troubleshooting & FAQ Docs (#517) * initial read-through fixes * more changes to code-questions docs * Update docs/_source/troubleshooting-faqs/code-questions.rst Co-authored-by: mkruckow <122798003+mkruckow@users.noreply.github.com> * Update docs/_source/troubleshooting-faqs/code-questions.rst Co-authored-by: mkruckow <122798003+mkruckow@users.noreply.github.com> * replace @ symbol --------- Co-authored-by: mkruckow <122798003+mkruckow@users.noreply.github.com> --- .../troubleshooting-faqs/code-questions.rst | 57 +++++++++---------- .../installation-issues.rst | 2 +- 2 files changed, 28 insertions(+), 31 deletions(-) diff --git a/docs/_source/troubleshooting-faqs/code-questions.rst b/docs/_source/troubleshooting-faqs/code-questions.rst index 89d67acdfd..a58291c52c 100644 --- a/docs/_source/troubleshooting-faqs/code-questions.rst +++ b/docs/_source/troubleshooting-faqs/code-questions.rst @@ -9,7 +9,7 @@ Frequently Asked Questions ~~~~~~~~~~~~~~~~~~~~~~~~~~ 1. **How do I start a basic population synthesis simulation with POSYDON?** - - Answer: Start with the :ref:`binary population synthesis guide ` to learn how to run POSYDON. + Start with the :ref:`binary population synthesis guide ` to learn how to run POSYDON. 2. **I'm getting an error when using the** ``where`` **parameter in the** ``select`` **function** @@ -22,79 +22,76 @@ Frequently Asked Questions population.history.select('S1_mass > 10') The above code will produce the following error: + .. code-block:: python - ValueError: The passed where expression: S1_mass > 10 - contains an invalid variable reference - all of the variable references must be a reference to - an axis (e.g. 'index' or 'columns'), or a data_column - The currently defined references are: index,columns,state,event,step_names,S1_state,S2_state + ValueError: The passed where expression S1_mass > 10 + contains an invalid variable reference. All of the variable references + must be a reference to an axis (e.g. ‘index’ or ‘columns’), or a data_column. + The currently defined references are: index, columns, state, event, step_names, S1_state, S2_state 3. **How should I tune my memory usage for a population synthesis run?** - A population run requires at a bare minimum of 4GB of memory per CPU. + A population run requires a bare minimum of 4GB of memory per CPU. However, this restricts the number of binaries you can keep in memory and requires a :code:`dump_rate < 1000` to keep the memory usage low, which slows down the simulation. As such, 5GB per CPU is a better starting point. This allows you to keep more binaries in memory and increases the speed of the population synthesis run. The figure below can be used to fine-tune your memory usage. - It shows the memory usage of two large population synthesis runs with a 7GB and a 8GB limit with a :code:`dump_rate` of 8000 and 10.000 binaries respectively. + It shows the memory usage of two large population synthesis runs with a 7GB and a 8GB limit with a :code:`dump_rate` of 8000 and 10,000 binaries respectively. .. image:: ./large_pop_runs_memory.png :alt: Memory usage of two large population synthesis runs. :width: 700px - The memory usage of the 8000 :code:`dump_rate` run is stable at around 6GB, while the 10.000 :code:`dump_rate` run is stable at around 6.8GB. + The memory usage of the 8,000 :code:`dump_rate` run is stable at around 6GB, while the 10,000 :code:`dump_rate` run is stable at around 6.8GB. - In general, the :code:`dump_rate` should be at least 500 for populations of 100.000 binaries or more. + In general, the :code:`dump_rate` should be at least 500 for populations of 100,000 binaries or more. Setting a very low :code:`dump_rate` for larger populations can potentially introduce I/O issues during the reading, writing, and merging of output files. 4. **What should the walltime and job array size be for my population synthesis run?** - The :code:`walltime`` and job array size are dependent on the number of binaries you want to simulate and the memory usage of the simulation. + The :code:`walltime` and job array size are dependent on the number of binaries you want to simulate and the memory usage of the simulation. The job array size should be set such that the number of binaries per job is at least 1000, since there's a minimum overhead per job due to loading the grids. The :code:`walltime` depends on the number of binaries per job, where each binary takes about 1-2 seconds to run. - For example, with 100.000 binaries split over 100 jobs (per metallicity), which means that every job runs 1.000 binaries. This will take around 33 minutes per job. So a :code:`walltime` of `00:45:00` is reasonable. + For example, 100,000 binaries split over 100 jobs (per metallicity) means that every job runs 1,000 binaries. This will take around 33 minutes per job. So a :code:`walltime` of :code:`00:45:00` is reasonable. The balance between :code:`walltime` and the size of the job array is important. If the :code:`walltime` is too long, it might be worth increasing the job array size to decrease the time per job and allow the population synthesis to finish faster. But if the :code:`walltime` is too short, the job array size should be decreased, since each job has an initial overhead that is not dependent on the number of binaries in the job. .. note:: - The processing time increases if you make the `dump_rate` too low due to many I/O operations. + The processing time increases if you make the :code:`dump_rate` too low due to many I/O operations. -5. **I am unable to open HDF5 files created by POSYDON. What should I do?** - +5. **I am unable to open HDF5 files created by POSYDON. What should I do?** If you're on a Mac, there might be an issue with the HDF5 installation. - Make sure you have the `hdf5` and `pytables` packages installed through conda in your environment with `conda install hdf5 pytables` before running POSYDON! + Make sure you have the :code:`hdf5` and :code:`pytables` packages installed through conda in your environment with :code:`conda install hdf5 pytables` before running POSYDON! Although they are dependencies of POSYDON, sometimes they're not installed correctly on Mac. 6. **Are there any examples or tutorials available?** - - Answer: Yes, you can check our :ref:`roadmap ` for tutorials related to different POSYDON components, including population synthesis, creating core datasets, and running your own MESA grids with POSYDON. + Yes, you can check our :ref:`roadmap ` for tutorials related to different POSYDON components, including population synthesis, creating core datasets, and running your own MESA grids with POSYDON. 7. **Can I run POSYDON on an HPC facility?** - - Answer: Absolutely! Refer to `our HPC guide <../tutorials-examples/population-synthesis/pop_syn.ipynb>`_ for detailed instructions on running POSYDON in an HPC environment. + Absolutely! Refer to `our HPC guide <../tutorials-examples/population-synthesis/pop_syn.ipynb>`_ for detailed instructions on running POSYDON in an HPC environment. 8. **Help, I'm stuck! Where can I get support?** - - Please check `our email group `_ if your question hasn't been answered yet. + Please check `our email group `_ if your question hasn't been answered yet. Otherwise, please email us at posydon-users [at] googlegroups.com 9. **How can I stay updated with the latest features and updates?** - - Answer: You can regularly visit our `official website `_ for news and updates. + You can regularly visit our `official website `_ for news and updates. 10. **I've come across a FAILED binary. What does this mean?** - - Answer: A FAILED binary is one that has encountered an error during the simulation due to the default flow and steps of POSYDON being unable to evolve them. - This can be due to a variety of reasons, for example: + A :code:`FAILED` binary has encountered an error during the simulation due to POSYDON being unable to evolve it. This can be due to a variety of reasons: - - The evolutionary state of the binary is not represented in the currently supported stellar evolution grids. - For example, we do not have a grid for Roche lobe overflow between two helium stars. - - The binary has masses outside the grid range. For example, the HMS-HMS grid does not contain binaries with a secondary mass below 0.5. - - The binary could not be matched to single star or a binary due to a too large matching error. + - The evolutionary state of the binary is not represented in the currently-supported stellar evolution grids. For example, we do not have a grid for Roche lobe overflow between two helium stars. + - The binary has masses outside the grid range. For example, the HMS-HMS grid does not contain binaries with a secondary mass below 0.5. + - The binary could not be matched to single star or a binary due to a too large matching error. -10. **What approximations does POSYDON make?** - This is a complex question and the best location to look at would be the POSYDON papers: `Fragos et al. (2022) `_ and `Andrews et al. (submitted) `_. +11. **What approximations does POSYDON make?** + This is a complex question and the best answer is provided in the POSYDON papers: `Fragos et al. (2023) `_ and `Andrews et al. (submitted) `_. Additional Resources @@ -109,6 +106,6 @@ Additional Resources Still Have Questions? ~~~~~~~~~~~~~~~~~~~~~ -If your query remains unanswered, we're here to help! Reach out to our community through the :ref:`support channels ` or consider checking our :ref:`general installation FAQ ` for non-usage related questions. +If your question remains unanswered, we're here to help! Reach out to our community through the :ref:`support channels ` or consider checking our :ref:`general installation FAQ ` for non-usage related questions. -Your feedback helps us improve. If you think a common question should be added here, don't hesitate to suggest it! +Your feedback helps us improve the code and documentation. If you think a common question should be added here, don't hesitate to suggest it! diff --git a/docs/_source/troubleshooting-faqs/installation-issues.rst b/docs/_source/troubleshooting-faqs/installation-issues.rst index 9ef3123232..1eab0fd896 100644 --- a/docs/_source/troubleshooting-faqs/installation-issues.rst +++ b/docs/_source/troubleshooting-faqs/installation-issues.rst @@ -16,7 +16,7 @@ Common Installation Issues 2. **Failed to Set Environment Variables**: - **Description**: After installation, you might forget to set the ``PATH_TO_POSYDON`` and ``PATH_TO_POSYDON_DATA`` environment variables. - - **Solution**: Refer back to the [installation guide](installation-guide) to ensure all post-installation steps are followed. + - **Solution**: Refer back to the :ref:`installation guide ` to ensure all post-installation steps are followed. 3. **Insufficient Storage Space**: - **Description**: POSYDON requires around 40GB of free storage space for the lite MESA simulation library and related files. From bcd7301c2c8e79b2baef1c8e438e81361bd62851 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Tue, 4 Mar 2025 07:54:12 +0100 Subject: [PATCH 293/319] Add files to installation (#526) * add init files to detect packages; include files in MANIFEST.in * add comment --- MANIFEST.in | 15 +++++++++++++++ .../psy_cris/run_params/__init__.py | 0 posydon/unit_tests/__init__.py | 0 posydon/unit_tests/grids/__init__.py | 0 posydon/unit_tests/popsyn/__init__.py | 0 posydon/unit_tests/utils/__init__.py | 0 posydon/user_modules/__init__.py | 0 setup.py | 2 ++ 8 files changed, 17 insertions(+) create mode 100644 posydon/active_learning/psy_cris/run_params/__init__.py create mode 100644 posydon/unit_tests/__init__.py create mode 100644 posydon/unit_tests/grids/__init__.py create mode 100644 posydon/unit_tests/popsyn/__init__.py create mode 100644 posydon/unit_tests/utils/__init__.py create mode 100644 posydon/user_modules/__init__.py diff --git a/MANIFEST.in b/MANIFEST.in index 8f6ed16a6c..80a1c93cee 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,3 +1,18 @@ +# add the main README and this file include README.md MANIFEST.in + +# add the versioneer include versioneer.py include posydon/_version.py + +# add additional README files +recursive-include posydon README.md + +# add default ini files +recursive-include posydon *.ini + +# add matplotlib style file +include posydon/visualization/posydon.mplstyle + +# add images for Van den Heuvel diagrams +include posydon/visualization/VH_diagram/draws/*.png diff --git a/posydon/active_learning/psy_cris/run_params/__init__.py b/posydon/active_learning/psy_cris/run_params/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/posydon/unit_tests/__init__.py b/posydon/unit_tests/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/posydon/unit_tests/grids/__init__.py b/posydon/unit_tests/grids/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/posydon/unit_tests/popsyn/__init__.py b/posydon/unit_tests/popsyn/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/posydon/unit_tests/utils/__init__.py b/posydon/unit_tests/utils/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/posydon/user_modules/__init__.py b/posydon/user_modules/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/setup.py b/setup.py index 8e453fe60b..9dc0a7f263 100644 --- a/setup.py +++ b/setup.py @@ -123,6 +123,8 @@ DESCRIPTION = "POSYDON the Next Generation of Population Synthesis" GITHUBURL = "https://github.com/POSYDON-code/POSYDON" +# Additional included files via include_package_data are defined in MANIFEST.in + setup( name=DISTNAME, provides=[PACKAGENAME], From f84636bc25b902865b1c364f76a0f9e2f34f78ec Mon Sep 17 00:00:00 2001 From: Camille Liotine Date: Wed, 5 Mar 2025 01:55:05 -0600 Subject: [PATCH 294/319] Fix logic flow in matching (#521) * fix logic flow in matching, clarify matching verbose output * add "center_h1" to matching verbose output * make if/else checks and flow more consistent --- posydon/binary_evol/DT/step_detached.py | 121 +++++++++++++++--------- 1 file changed, 74 insertions(+), 47 deletions(-) diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index 1898f9a463..e70c2ddf64 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -738,28 +738,34 @@ def get_MESA_labels(list_for_matching): x0 = get_root0(MESA_labels, posydon_attributes, htrack, rs=rs) sol = minimize(sq_diff_function, x0, method="TNC", bounds=bnds) + ## save initial matching solution as best solution so far + best_sol = sol ## Alternative matching attempts if default matching fails! # 1st attempt: use a different minimization method - if (np.abs(sol.fun) > tolerance_matching_integration or not sol.success): - + if (np.abs(best_sol.fun) > tolerance_matching_integration or not best_sol.success): + if self.verbose: - print("\nAlternative matching started (1st attempt) " - "because previous attempt was unsuccessful:\n", - f"tolerance {np.abs(sol.fun)} > {tolerance_matching_integration}", - f"or sol.success = {sol.success}") + print (f"Initial matching attempt was unsuccessful:" + f"\n tolerance {np.abs(sol.fun)} > {tolerance_matching_integration}, sol.success = {sol.success}") + + print("\nAlternative matching started (1st attempt)") print("(Now trying an alternative minimization method)") sol = minimize(sq_diff_function, x0, method="Powell") + ## if alternative matching has a better solution, make it the new best solution + if (np.abs(sol.fun) < np.abs(best_sol.fun) and sol.success): + best_sol = sol + # 2nd attempt: use alternative matching parameters - if (np.abs(sol.fun) > tolerance_matching_integration or not sol.success): + if (np.abs(best_sol.fun) > tolerance_matching_integration or not best_sol.success): if self.verbose: - print("\nAlternative matching started (2nd attempt) " - "because previous attempt was unsuccessful:\n", - f"tolerance {np.abs(sol.fun)} > {tolerance_matching_integration}", - f"or sol.success = {sol.success}") + print (f"Alternative matching (1st attempt) was unsuccessful:" + f"\n tolerance {np.abs(sol.fun)} > {tolerance_matching_integration}, sol.success = {sol.success}") + + print("\nAlternative matching started (2nd attempt)") print("(Now trying to match with alternative parameters)") if star.state in LIST_ACCEPTABLE_STATES_FOR_HMS: @@ -775,22 +781,26 @@ def get_MESA_labels(list_for_matching): x0 = get_root0(MESA_labels, posydon_attributes, htrack, rs=rs) sol = minimize(sq_diff_function, x0, method="TNC", bounds=bnds) + if (np.abs(sol.fun) < np.abs(best_sol.fun) and sol.success): + best_sol = sol + # 3rd attempt: match an He-star with an H-rich grid, or vice versa (not applicable for HMS stars) - if (np.abs(sol.fun) > tolerance_matching_integration or not sol.success): + if (np.abs(best_sol.fun) > tolerance_matching_integration or not best_sol.success): + + if self.verbose: + print (f"Alternative matching (2nd attempt) was unsuccessful:" + f"\n tolerance {np.abs(sol.fun)} > {tolerance_matching_integration}, sol.success = {sol.success}") if (star.state in LIST_ACCEPTABLE_STATES_FOR_HeStar or star.state in LIST_ACCEPTABLE_STATES_FOR_postMS): + if self.verbose: + print("\nAlternative matching started (3rd attempt)") + print("(Now trying to match He-star or post-MS star to a different grid)") + Pwarn("Attempting to match an He-star with an H-rich grid or post-MS star with a" " stripped-He grid", "EvolutionWarning") - - if self.verbose: - print("\nAlternative matching started (3rd attempt) " - "because previous attempt was unsuccessful:\n", - f"tolerance {np.abs(sol.fun)} > {tolerance_matching_integration}", - f"or sol.success = {sol.success}") - print("(Now trying to match star to a different grid)") - + if star.state in LIST_ACCEPTABLE_STATES_FOR_HeStar: htrack = True list_for_matching = self.list_for_matching_HeStar @@ -810,48 +820,65 @@ def get_MESA_labels(list_for_matching): try: sol = minimize(sq_diff_function, x0, method="TNC", bounds=bnds) + if (np.abs(sol.fun) < np.abs(best_sol.fun) and sol.success): + best_sol = sol + + if self.verbose: + print (f"Alternative matching (3rd attempt) completed:" + f"\n tolerance {np.abs(sol.fun)} > {tolerance_matching_integration}, " + f"sol.success = {sol.success}") except: raise NumericalError("SciPy numerical differentiation occured outside boundary " "while matching to single star track") # if matching is still not successful, set result to NaN: - if (np.abs(sol.fun) > tolerance_matching_integration_hard or not sol.success): + if (np.abs(best_sol.fun) > tolerance_matching_integration_hard or not best_sol.success): if self.verbose: - print("\nMatching result is NOT successful, with tolerance ", - np.abs(sol.fun), ">", tolerance_matching_integration_hard) + print("\nFinal matching result is NOT successful with best tolerance ", + np.abs(best_sol.fun), ">", tolerance_matching_integration_hard) initials = (np.nan, np.nan) - elif np.abs(sol.fun) < tolerance_matching_integration_hard: + else: if self.verbose: - print("\nMatching result is considered successful, with tolerance " - f'{np.abs(sol.fun):.8f}', "<", tolerance_matching_integration_hard) - initials = sol.x + print("\nFinal matching result is considered successful with best tolerance " + f'{np.abs(best_sol.fun):.8f}', "<", tolerance_matching_integration_hard) + initials = best_sol.x if self.verbose: if not np.isnan(initials[0]): - print( - "Matching completed for", star.state, "star!\n" - f"Matched to track with intial mass m0 = {initials[0]:.3f} [Msun]" - f" at time t0 = {initials[1]/1e6:.3f} [Myrs] \n", - "and m(t0), log10(R(t0), center_he(t0), surface_he4(t0), " - "surface_h1(t0), he_core_mass(t0), center_c12(t0) = \n", - f'{self.get_track_val("mass", htrack, *sol.x):.3f}', - f'{self.get_track_val("log_R", htrack, *sol.x):.3f}', - f'{self.get_track_val("center_he4", htrack, *sol.x):.4f}', - f'{self.get_track_val("surface_he4", htrack, *sol.x):.4f}', - f'{self.get_track_val("surface_h1", htrack, *sol.x):.4f}', - f'{self.get_track_val("he_core_mass", htrack, *sol.x):.3f}', - f'{self.get_track_val("center_c12", htrack, *sol.x):.4f}\n', - "The same values of the original star at the end of the previous " - "step were: \n", - f'{star.mass:.3f}', - f'{star.log_R:.3f}', - f'{star.center_he4:.4f}', - f'{star.surface_he4:.4f}', + val_names = [" ", "mass", "log_R", "center_h1", "surface_h1", "he_core_mass", "center_he4", "surface_he4", + "center_c12"] + + initial_vals = [ + "initial values", + f'{star.mass:.3f}', + f'{star.log_R:.3f}', + f'{star.center_h1:.3f}', f'{star.surface_h1:.4f}', f'{star.he_core_mass:.3f}', + f'{star.center_he4:.4f}', + f'{star.surface_he4:.4f}', f'{star.center_c12:.4f}' - ) + ] + + matched_vals = [ + "matched values", + f'{self.get_track_val("mass", htrack, *best_sol.x):.3f}', + f'{self.get_track_val("log_R", htrack, *best_sol.x):.3f}', + f'{self.get_track_val("center_h1", htrack, *best_sol.x):.3f}', + f'{self.get_track_val("surface_h1", htrack, *best_sol.x):.4f}', + f'{self.get_track_val("he_core_mass", htrack, *best_sol.x):.3f}', + f'{self.get_track_val("center_he4", htrack, *best_sol.x):.4f}', + f'{self.get_track_val("surface_he4", htrack, *best_sol.x):.4f}', + f'{self.get_track_val("center_c12", htrack, *best_sol.x):.4f}' + ] + + output_table = [val_names, initial_vals, matched_vals] + + print("\nMatching completed for", star.state, "star!\n") + for row in output_table: + print("{:>14} {:>5} {:>5} {:>9} {:>10} {:>12} {:>10} {:>11} {:>10}".format(*row)) + else: print( "Matching completed unsuccessfully for star with properties: \n" From a61cac4af79c2ff6a5d2e2a5be799c7b2d58af60 Mon Sep 17 00:00:00 2001 From: Camille Liotine Date: Wed, 5 Mar 2025 01:59:18 -0600 Subject: [PATCH 295/319] add accreted_He_Shell_H_burning stars to flow, detached step (#522) --- posydon/binary_evol/DT/step_detached.py | 2 ++ posydon/binary_evol/flow_chart.py | 5 ++++- 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index e70c2ddf64..6825b0bf9d 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -54,6 +54,7 @@ "H-rich_Core_C_burning", "H-rich_Central_C_depletion", "H-rich_non_burning", + "accreted_He_Shell_H_burning", "accreted_He_non_burning"] LIST_ACCEPTABLE_STATES_FOR_HeStar = [ @@ -75,6 +76,7 @@ 'H-rich_Central_C_depletion', 'H-rich_non_burning', 'accreted_He_Core_H_burning', + 'accreted_He_Shell_H_burning', 'accreted_He_non_burning' ] diff --git a/posydon/binary_evol/flow_chart.py b/posydon/binary_evol/flow_chart.py index eaef99a2f1..3a28881017 100644 --- a/posydon/binary_evol/flow_chart.py +++ b/posydon/binary_evol/flow_chart.py @@ -30,6 +30,7 @@ 'H-rich_Central_C_depletion', 'H-rich_non_burning', 'accreted_He_Core_H_burning', + 'accreted_He_Shell_H_burning', 'accreted_He_non_burning', 'accreted_He_Core_He_burning', 'stripped_He_Core_He_burning', @@ -63,7 +64,8 @@ 'H-rich_Shell_He_burning', 'H-rich_Core_C_burning', 'H-rich_Central_C_depletion', - 'accreted_He_Core_H_burning']] + 'accreted_He_Core_H_burning', + 'accreted_He_Shell_H_burning']] STAR_STATES_C_DEPLETION = [st for st in STAR_STATES_ALL if "C_depletion" in st] @@ -90,6 +92,7 @@ 'stripped_He_non_burning', 'H-rich_non_burning', 'H-rich_Shell_H_burning', + 'accreted_He_Shell_H_burning', 'accreted_He_non_burning' ] From d952aaa2fe09eb0b8044655b3a562a7327e1a52f Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Wed, 5 Mar 2025 09:01:01 +0100 Subject: [PATCH 296/319] Issues with undefined values of SN_MODELS (#482) * Update step_mesa.py replace undefined variable key and check fv for CO_type * Update step_mesa.py Check for key and otherwise show warning and set value to None. * Update step_SN.py np.isnan -> pd.isna * Update step_mesa.py change None to np.nan * Update step_mesa.py Set model to None. * Update step_mesa.py ensure None is not overwritten * add end of step prints for verbose * typo * use pa.notna --- posydon/binary_evol/MESA/step_mesa.py | 18 +++++++++++++----- posydon/binary_evol/SN/step_SN.py | 15 +++++++++------ 2 files changed, 22 insertions(+), 11 deletions(-) diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index 351113bc47..5fba787124 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -367,6 +367,9 @@ def __call__(self, binary): elif binary.time == binary.properties.max_simulation_time: binary.event = 'maxtime' + if self.verbose: + print(f"End of step MESA (grid={self.grid_type}):\n", binary) + return def step(self, binary, interp_method=None): @@ -808,7 +811,7 @@ def update_properties_NN(self, star_1_CO=False, star_2_CO=False, values[key] = cb.final_values[f'S{i+1}_{MODEL_NAME}_{key}'] setattr(star, MODEL_NAME, values) else: - setattr(star, key, None) + setattr(star, MODEL_NAME, None) def initial_final_interpolation(self, star_1_CO=False, star_2_CO=False): """Update the binary through initial-final interpolation.""" @@ -963,18 +966,23 @@ def initial_final_interpolation(self, star_1_CO=False, star_2_CO=False): self.classes[f'S{i+1}_{MODEL_NAME}_CO_type'] != 'None'): values = {} for key in ['state', 'SN_type', 'f_fb', 'mass', 'spin', - 'm_disk_accreted', 'm_disk_radiated', 'M4', 'mu4', - 'h1_mass_ej', 'he4_mass_ej']: + 'm_disk_accreted', 'm_disk_radiated', 'M4', + 'mu4', 'h1_mass_ej', 'he4_mass_ej']: if key == "state": state = self.classes[f'S{i+1}_{MODEL_NAME}_CO_type'] values[key] = state elif key == "SN_type": values[key] = self.classes[f'S{i+1}_{MODEL_NAME}_{key}'] - else: + elif f'S{i+1}_{MODEL_NAME}_{key}' in fv: values[key] = fv[f'S{i+1}_{MODEL_NAME}_{key}'] + else: + Pwarn(f"S{i+1}_{MODEL_NAME}_{key} not found in fv", + "UnsupportedModelWarning") + values = None + break setattr(star, MODEL_NAME, values) else: - setattr(star, key, None) + setattr(star, MODEL_NAME, None) # STOPPING METHODS diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index 0779168c0f..89d34e64cd 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -452,6 +452,9 @@ def __call__(self, binary): elif state1 in STAR_STATES_C_DEPLETION and state2 in STAR_STATES_CO: binary.event = "CC1" + if self.verbose: + print(f"End of step SN:\n", binary) + def check(self): """Check the internal integrity and the values of the parameters.""" if self.kick_distribution is None: @@ -547,7 +550,7 @@ def collapse_star(self, star): ## Check if SN_type mismatches the CO_type in MODEL or if interpolated MODEL properties are NaN ## If either are true, interpolated values cannot be used for this SN if (check_SN_CO_match(MODEL_properties['SN_type'], MODEL_properties['state']) and - ~np.isnan(MODEL_properties['mass'])): + pd.notna(MODEL_properties['mass'])): for key, value in MODEL_properties.items(): @@ -640,7 +643,7 @@ def collapse_star(self, star): return # check if the star was disrupted by the PISN - if np.isnan(m_rembar): + if pd.isna(m_rembar): convert_star_to_massless_remnant(star=star) return @@ -766,7 +769,7 @@ def collapse_star(self, star): return # check if the star was disrupted by the PISN - if np.isnan(m_rembar): + if pd.isna(m_rembar): convert_star_to_massless_remnant(star=star) return @@ -943,7 +946,7 @@ def PISN_prescription(self, star): print("") print("The star did NOT lose any mass because of " "PPIN or PISN.") - elif not np.isnan(m_PISN): + elif not pd.isna(m_PISN): print("") print( "The star with initial mass {:2.2f}".format(m_He_core), @@ -1121,7 +1124,7 @@ def compute_m_rembar(self, star, m_PISN): "There is no information in the evolutionary history" "about STAR_STATES_CC." ) - if m_core is None or np.isnan(m_core): + if m_core is None or pd.isna(m_core): # This should not happen raise ValueError("The CO core mass is not correct! CO core = {}". format(m_core)) @@ -1731,7 +1734,7 @@ def SNCheck( and (err > tmp2 - rpre / Apost)) # SNflag3: check that epost does not exeed 1 or is nan - if epost >= 1.0 or np.isnan(epost): + if epost >= 1.0 or pd.isna(epost): SNflag3 = False else: SNflag3 = True From 64a7d2bb364edd46dd964ef388f2800811d87a49 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 6 Mar 2025 08:50:09 +0100 Subject: [PATCH 297/319] replace isnan (#527) --- posydon/active_learning/psy_cris/classify.py | 2 +- posydon/active_learning/psy_cris/data.py | 2 +- posydon/active_learning/psy_cris/regress.py | 10 ++++---- posydon/binary_evol/CE/step_CEE.py | 2 +- posydon/binary_evol/DT/step_detached.py | 25 +++++++++---------- posydon/grids/psygrid.py | 6 ++--- posydon/interpolation/IF_interpolation.py | 21 ++++++++-------- posydon/interpolation/interpolation.py | 9 ++++--- posydon/popsyn/synthetic_population.py | 2 +- posydon/popsyn/transient_select_funcs.py | 2 +- posydon/utils/common_functions.py | 10 ++++---- posydon/visualization/VH_diagram/Presenter.py | 13 +++++----- posydon/visualization/interpolation.py | 3 ++- posydon/visualization/plot2D.py | 21 ++++++++-------- 14 files changed, 65 insertions(+), 63 deletions(-) diff --git a/posydon/active_learning/psy_cris/classify.py b/posydon/active_learning/psy_cris/classify.py index 8d13842351..b000a781ea 100644 --- a/posydon/active_learning/psy_cris/classify.py +++ b/posydon/active_learning/psy_cris/classify.py @@ -376,7 +376,7 @@ def __remove_nans(self, trans_probs, verbose=False): which is useful for finding good or bad values. """ - bool_row_index_where_nan = np.isnan(trans_probs.T[0]) + bool_row_index_where_nan = pd.isna(trans_probs.T[0]) # where_nan = np.where(bool_row_index_where_nan)[0] where_not_nan = np.where(~bool_row_index_where_nan)[0] # how_many_rows = len(trans_probs.T[0]) diff --git a/posydon/active_learning/psy_cris/data.py b/posydon/active_learning/psy_cris/data.py index e6ab1aa2da..067cd161d1 100644 --- a/posydon/active_learning/psy_cris/data.py +++ b/posydon/active_learning/psy_cris/data.py @@ -393,7 +393,7 @@ def __init__( converted_val = float( val ) # if fails -> straight to except (skips next line) - if math.isnan(converted_val): + if pd.isna(converted_val): bad_cols.append(col_num) else: cols_with_float.append(col_num) diff --git a/posydon/active_learning/psy_cris/regress.py b/posydon/active_learning/psy_cris/regress.py index ce48dc3b5e..bffaf3933d 100644 --- a/posydon/active_learning/psy_cris/regress.py +++ b/posydon/active_learning/psy_cris/regress.py @@ -243,9 +243,9 @@ def _get_cleaned_regression_data_(self, training_x, training_y, Cleaned output data free of undefined values. """ - if np.sum(np.isnan(training_y)) > 0: - where_undef = np.where(np.isnan(training_y))[0] - where_def = np.where(~np.isnan(training_y))[0] + if np.sum(pd.isna(training_y)) > 0: + where_undef = np.where(pd.isna(training_y))[0] + where_def = np.where(pd.notna(training_y))[0] need_to_clean = True elif self._undefined_p_change_val_ in training_y: where_undef = np.where(self._undefined_p_change_val_ @@ -770,7 +770,7 @@ def cross_validate(self, regressor_name, class_key, col_key, alpha, predicted_values_rbf = self._predict( "RBF", class_key, col_key, cross_val_test_input ) - where_nan = np.where(np.isnan(predicted_values_linear))[0] + where_nan = np.where(pd.isna(predicted_values_linear))[0] if len(where_nan) > 0: print("{0}: {1} nan points out of {2}. Used rbf instead.". format(regressor_key, len(where_nan), @@ -886,7 +886,7 @@ def mult_diffs(self, regressor_name, class_key, col_keys, alpha, cutoff, p_diffs, diffs = self.cross_validate( regressor_name, class_key, col_key, alpha, verbose=verbose ) - where_not_nan = np.where(np.invert(np.isnan(p_diffs)))[0] + where_not_nan = np.where(pd.notna(p_diffs))[0] p_diffs_holder.append(p_diffs[where_not_nan]) diff --git a/posydon/binary_evol/CE/step_CEE.py b/posydon/binary_evol/CE/step_CEE.py index 6a29912d48..00f5850b44 100644 --- a/posydon/binary_evol/CE/step_CEE.py +++ b/posydon/binary_evol/CE/step_CEE.py @@ -511,7 +511,7 @@ def CEE_simple_alpha_prescription( ebind_i = (-const.standard_cgrav / lambda1_CE * (m1_i * const.Msun * (m1_i - mc1_i) * const.Msun) / (radius1 * const.Rsun)) - if (double_CE and (not pd.isna(lambda2_CE))): + if (double_CE and pd.notna(lambda2_CE)): ebind_i += (-const.standard_cgrav / lambda2_CE * (m2_i * const.Msun * (m2_i - mc2_i) * const.Msun) / (radius2 * const.Rsun)) diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index 6825b0bf9d..59a0d01e11 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -847,7 +847,7 @@ def get_MESA_labels(list_for_matching): initials = best_sol.x if self.verbose: - if not np.isnan(initials[0]): + if pd.notna(initials[0]): val_names = [" ", "mass", "log_R", "center_h1", "surface_h1", "he_core_mass", "center_he4", "surface_he4", "center_c12"] @@ -1165,8 +1165,8 @@ def get_omega(star, is_secondary=True): if (star.log_total_angular_momentum is not None and star.total_moment_of_inertia is not None - and not np.isnan(star.log_total_angular_momentum) - and not np.isnan(star.total_moment_of_inertia)): + and pd.notna(star.log_total_angular_momentum) + and pd.notna(star.total_moment_of_inertia)): # the last factor converts rad/s to rad/yr omega_in_rad_per_year = ( @@ -1180,17 +1180,16 @@ def get_omega(star, is_secondary=True): # (although the critical rotation should be improved to # take into account radiation pressure) - if (star.surf_avg_omega is not None and not np.isnan(star.surf_avg_omega)): + if pd.notna(star.surf_avg_omega): if self.verbose: print("calculating initial omega using surf_avg_omega") omega_in_rad_per_year = star.surf_avg_omega * const.secyer - elif (star.surf_avg_omega_div_omega_crit is not None - and not np.isnan(star.surf_avg_omega_div_omega_crit)): + elif pd.notna(star.surf_avg_omega_div_omega_crit): - if (star.log_R is not None and not np.isnan(star.log_R)): + if pd.notna(star.log_R): omega_in_rad_per_year = ( star.surf_avg_omega_div_omega_crit * np.sqrt( const.standard_cgrav * star.mass * const.msol @@ -2120,9 +2119,9 @@ def diffeq( f5 = 1 + 3 * e ** 2 + (3 / 8) * e ** 4 # equilibrium timecale - if ((M_env_sec != 0.0 and not np.isnan(M_env_sec)) - and (DR_env_sec != 0.0 and not np.isnan(DR_env_sec)) and ( - Renv_middle_sec != 0.0 and not np.isnan(Renv_middle_sec))): + if ((pd.notna(M_env_sec) and M_env_sec != 0.0) + and (pd.notna(DR_env_sec) and DR_env_sec != 0.0) + and (pd.notna(Renv_middle_sec) and Renv_middle_sec != 0.0)): # eq. (31) of Hurley et al. 2002, generalized for convective layers # not on surface too tau_conv_sec = 0.431 * ((M_env_sec * DR_env_sec * Renv_middle_sec @@ -2132,9 +2131,9 @@ def diffeq( print("something wrong with M_env/DR_env/Renv_middle", M_env_sec, DR_env_sec, Renv_middle_sec) tau_conv_sec = 1.0e99 - if ((M_env_pri != 0.0 and not np.isnan(M_env_pri)) - and (DR_env_pri != 0.0 and not np.isnan(DR_env_pri)) and ( - Renv_middle_pri != 0.0 and not np.isnan(Renv_middle_pri))): + if ((pd.notna(M_env_pri) and M_env_pri != 0.0) + and (pd.notna(DR_env_pri) and DR_env_pri != 0.0) + and (pd.notna(Renv_middle_pri) and Renv_middle_pri != 0.0)): # eq. (31) of Hurley et al. 2002, generalized for convective layers # not on surface too tau_conv_pri = 0.431 * ((M_env_pri * DR_env_pri * Renv_middle_pri diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index 67f8ebe202..285eda69d6 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -1123,7 +1123,7 @@ def decide_columns(key_in_config, defaults): for colname, value in grid_point.items(): if colname in self.initial_values.dtype.names: self.initial_values[i][colname] = value - if np.isnan(self.initial_values[i]["Z"]): + if pd.isna(self.initial_values[i]["Z"]): # try to get metallicity from directory name params_from_path = initial_values_from_dirname(run.path) if (len(params_from_path)==4) or\ @@ -1940,8 +1940,8 @@ def say(msg): format(colname, tablename)) if np.any(data1 != data2): for val1, val2 in zip(data1, data2): - if ((val1 == val2) or (val1 is None and val2 is None) - or (np.isnan(val1) and np.isnan(val2))): + if ((val1 == val2) or (pd.isna(val1) + and pd.isna(val2))): continue say("Column `{}` in `{}` is not the same ({} != {}).". format(colname, tablename, val1, val2)) diff --git a/posydon/interpolation/IF_interpolation.py b/posydon/interpolation/IF_interpolation.py index 4dcf46f5f9..29936402d8 100644 --- a/posydon/interpolation/IF_interpolation.py +++ b/posydon/interpolation/IF_interpolation.py @@ -180,6 +180,7 @@ class relies on the BaseIFInterpolator class to perform the interpolation from posydon.utils.posydonwarning import Pwarn # Maths import numpy as np +import pandas as pd # Machine Learning from scipy.interpolate import LinearNDInterpolator from sklearn.neighbors import KNeighborsRegressor @@ -458,13 +459,13 @@ def __init__(self, grid=None, in_keys=None, out_keys=None, out_nan_keys=None, in key for key in grid.final_values.dtype.names if key != "model_number" and (type(grid.final_values[key][0]) != np.str_) - and any(~np.isnan(grid.final_values[key])) + and any(pd.notna(grid.final_values[key])) ] if self.out_nan_keys is None: self.out_nan_keys = [ key for key in grid.final_values.dtype.names if type(grid.final_values[key][0]) != np.str_ - and all(np.isnan(grid.final_values[key])) + and all(pd.isna(grid.final_values[key])) ] self.constraints = find_constraints_to_apply(self.out_keys) @@ -489,7 +490,7 @@ def __init__(self, grid=None, in_keys=None, out_keys=None, out_nan_keys=None, in print("\nFilling missing values (nans) with 1NN") self._fillNans(grid.final_values[self.c_key]) elif self.interp_method == '1NN': - self.YT[np.isnan(self.YT)] = -100 + self.YT[pd.isna(self.YT)] = -100 if (self.in_scaling is None) or (self.out_scaling is None): if self.interp_method == '1NN': @@ -606,7 +607,7 @@ def _setValid(self, ic, X): print(f"Discarded {np.sum(which)} binaries with " f"interpolation_class = [{flag}]") - which = np.isnan(np.sum(X, axis=1)) + which = pd.isna(np.sum(X, axis=1)) valid[which] = -1 print(f"Discarded {np.sum(which)} binaries with nans in input values.") for i, flag in enumerate(self.valid_classes): @@ -861,7 +862,7 @@ def _interpIn_q(self, grid): m1 = grid.initial_values['star_1_mass'].copy() m2 = grid.initial_values['star_2_mass'].copy() # Make sure nans do not affect - wnan = np.isnan(m1) | np.isnan(m2) + wnan = pd.isna(m1) | pd.isna(m2) m1, m2 = m1[~wnan], m2[~wnan] tol = 1 @@ -982,7 +983,7 @@ def scale_one(in_scaling, klass = None): out_scaling = [] for i in range(self.n_out): - if where_min[i] < r or np.isnan(where_min[i]): + if where_min[i] < r or pd.isna(where_min[i]): out_scaling.append(out_scalings[0][i]) else: out_scaling.append(out_scalings[1][i]) @@ -1006,7 +1007,7 @@ def scale_one(in_scaling, klass = None): def _fillNans(self, ic): """Fill nan values i numerical magnitudes with 1NN.""" for i in range(self.n_out): - wnan = np.isnan(self.YT[:, i]) | np.isinf(self.YT[:, i]) + wnan = pd.isna(self.YT[:, i]) | np.isinf(self.YT[:, i]) if any(np.isinf(self.YT[:, i])): print(f"inftys: {np.sum(np.isinf(self.YT[:, i]))}, " f"{self.out_keys[i]}") @@ -1155,7 +1156,7 @@ def predict(self, Xt, scaler = None, klass = None): super().predict(Xt) Ypred = self.interpolator[0](Xt) - wnan = np.isnan(Ypred[:, 0]) + wnan = pd.isna(Ypred[:, 0]) # 1NN interpolation for binaries out of hull if np.any(wnan): Ypred[wnan, :] = self.interpolator[1].predict(Xt[wnan, :]) @@ -1516,7 +1517,7 @@ def __init__(self, norms, XT): self.scalers = [] for i in range(self.N): self.scalers.append(DataScaler()) - which = ~np.isnan(XT[:, i]) + which = pd.notna(XT[:, i]) self.scalers[i].fit(XT[which, i], method=norms[i]) def normalize(self, X): @@ -1802,7 +1803,7 @@ def analize_nans(out_keys, YT, valid): for i, key in enumerate(out_keys): n = [] for v in range(4): - n.append(np.sum(np.isnan(YT[valid == v, i]))) + n.append(np.sum(pd.isna(YT[valid == v, i]))) if np.sum(np.array(n)) > 0: print(f"[i_MT] = {n[0]:5d}, [no_MT] = {n[1]:5d}, " f"[st_MT] = {n[2]:5d}, [u_MT] = {n[3]:5d} : {i:3d} {key}") diff --git a/posydon/interpolation/interpolation.py b/posydon/interpolation/interpolation.py index 1aa3c3931f..7f6742cdc6 100644 --- a/posydon/interpolation/interpolation.py +++ b/posydon/interpolation/interpolation.py @@ -8,6 +8,7 @@ import pickle import numpy as np +import pandas as pd from sklearn.neighbors import NearestNeighbors from .data_scaling import DataScaler from posydon.grids.psygrid import PSyGrid @@ -100,9 +101,9 @@ def train(self, method='NearestNeighbor'): # mask out binaries with any nan value # mask = np.logical_and(self.grid.final_values['interpolation_class'] # != 'not_converged', - # np.invert([np.isnan(XTn)[x,:].any() for x in range(XTn.shape[0])])) + # np.invert([pd.isna(XTn)[x,:].any() for x in range(XTn.shape[0])])) mask = (self.grid.final_values['interpolation_class'] - != 'not_converged') & ~np.isnan(np.sum(XT, axis=1)) + != 'not_converged') & pd.notna(np.sum(XT, axis=1)) self.valid_ind = np.arange(XT.shape[0])[mask] if self.method == 'NearestNeighbor': @@ -602,7 +603,7 @@ def get_final_values(self, key, M_new): self.load_grid(mass_low) kvalue_low = self.grid_final_values[mass_low][key] - while (kvalue_low is None or np.isnan(kvalue_low)): + while pd.isna(kvalue_low): # escape if no lower mass is available if np.sum(mass_low > self.grid_mass) == 0: break @@ -619,7 +620,7 @@ def get_final_values(self, key, M_new): self.load_grid(mass_high) kvalue_high = self.grid_final_values[mass_high][key] - while (kvalue_high is None or np.isnan(kvalue_high)): + while pd.isna(kvalue_high): # escape if no higher mass is available if np.sum(mass_high < self.grid_mass) == 0: break diff --git a/posydon/popsyn/synthetic_population.py b/posydon/popsyn/synthetic_population.py index a6cadd52a1..bcc6fb1425 100644 --- a/posydon/popsyn/synthetic_population.py +++ b/posydon/popsyn/synthetic_population.py @@ -1517,7 +1517,7 @@ def get_mt_history(row): ) events = self.history.select(start=previous, stop=end, columns=["event"]) - mask = ~pd.isna(interp_class_HMS_HMS["interp_class_HMS_HMS"].values) + mask = pd.notna(interp_class_HMS_HMS["interp_class_HMS_HMS"].values) interp_class_HMS_HMS.loc[mask, "interp_class_HMS_HMS"] = ( interp_class_HMS_HMS[mask] .apply(lambda x: HMS_HMS_event_dict[x["interp_class_HMS_HMS"]], axis=1) diff --git a/posydon/popsyn/transient_select_funcs.py b/posydon/popsyn/transient_select_funcs.py index bab3175268..57bab4caf9 100644 --- a/posydon/popsyn/transient_select_funcs.py +++ b/posydon/popsyn/transient_select_funcs.py @@ -289,7 +289,7 @@ def DCO_detectability(sensitivity, transient_pop_chunk, z_events_chunk, z_weight detectable_weights = z_weights_chunk.to_numpy() for i in tqdm(range(z_events_chunk.shape[1]), total=z_events_chunk.shape[1], disable= not verbose): data_slice['z'] = z_events_chunk.iloc[:,i] - mask = ~np.isnan(data_slice['z']).to_numpy() + mask = pd.notna(data_slice['z']).to_numpy() if np.sum(mask) == 0: detectable_weights[mask, i] = 0.0 else: diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index 5a899568e9..cdc4bc287d 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -2126,12 +2126,12 @@ def period_change_stable_MT(period_i, Mdon_i, Mdon_f, Macc_i, def linear_interpolation_between_two_cells(array_y, array_x, x_target, top=None, bot=None, verbose=False): """Interpolate quantities between two star profile shells.""" - if ((top is None or np.isnan(top)) and (bot is None or np.isnan(bot))): + if (pd.isna(top) and pd.isna(bot)): top = np.argmax(array_x >= x_target) bot = top - 1 - elif bot is None or np.isnan(bot): + elif pd.isna(bot): bot = top - 1 - elif top is None or np.isnan(top): + elif pd.isna(top): top = bot + 1 if top >= len(array_y): @@ -2252,7 +2252,7 @@ def calculate_lambda_from_profile( # get mass and radius and dm from profile donor_mass, donor_radius, donor_dm = get_mass_radius_dm_from_profile( profile, m1_i, radius1, tolerance) - # if np.isnan(m1_i) or m1_i is None or np.isnan(radius1) or radius1 is None + # if pd.isna(m1_i) or pd.isna(radius1) m1_i = donor_mass[0] radius1 = donor_radius[0] specific_internal_energy = get_internal_energy_from_profile( @@ -2321,7 +2321,7 @@ def calculate_lambda_from_profile( m1_i, radius1, len(donor_mass), " vs ", ind_core, mc1_i, rc1_i) print("Ebind_i from profile ", Ebind_i) print("lambda_CE ", lambda_CE) - if not (lambda_CE > -tolerance) and not np.isnan(lambda_CE): + if not (lambda_CE > -tolerance) and pd.notna(lambda_CE): raise ValueError("lambda_CE has a negative value") return lambda_CE, mc1_i, rc1_i diff --git a/posydon/visualization/VH_diagram/Presenter.py b/posydon/visualization/VH_diagram/Presenter.py index d6b4cf358b..a050ab8c9c 100644 --- a/posydon/visualization/VH_diagram/Presenter.py +++ b/posydon/visualization/VH_diagram/Presenter.py @@ -18,6 +18,7 @@ from datetime import datetime import os import numpy as np +import pandas as pd def to_megayears(nb): @@ -165,18 +166,18 @@ def get_max_distance(data): """ if data["separation"].state_after is not None: - if np.isnan(data["separation"].state_after) or ( + if pd.isna(data["separation"].state_after) or ( data["separation"].state_before > data["separation"].state_after ): return ( data["separation"].state_before - if (not np.isnan(data["separation"].state_before)) + if pd.notna(data["separation"].state_before) else 0 ) else: return data["separation"].state_after else: - if np.isnan(data["separation"].state_before): + if pd.isna(data["separation"].state_before): return 0 else: return data["separation"].state_before @@ -638,9 +639,7 @@ def _get_distance_representation(self, simplified_distance, max_distance): if max_distance == 0: return 0 - if simplified_distance.state_after is not None and not np.isnan( - simplified_distance.state_after - ): + if pd.notna(simplified_distance.state_after): if simplified_distance.state_after < 1: simplified_distance.state_after = 1 if max_distance < 1: @@ -650,7 +649,7 @@ def _get_distance_representation(self, simplified_distance, max_distance): / np.log(max_distance)) else: - if np.isnan(simplified_distance.state_before): + if pd.isna(simplified_distance.state_before): return 0 elif simplified_distance.state_before < 1: simplified_distance.state_before = 1 diff --git a/posydon/visualization/interpolation.py b/posydon/visualization/interpolation.py index 9e986fd248..b402877e51 100644 --- a/posydon/visualization/interpolation.py +++ b/posydon/visualization/interpolation.py @@ -7,6 +7,7 @@ import matplotlib.pyplot as plt import numpy as np +import pandas as pd class EvaluateIFInterpolator: """ Class that is helpful for evaluating interpolation performance @@ -146,7 +147,7 @@ def __find_labels(self, key): def __clean_errs(self, errs): - errs = errs[~np.isnan(errs).any(axis = 1)] # dropping nans + errs = errs[pd.notna(errs).any(axis = 1)] # dropping nans errs = errs[~np.isinf(errs).any(axis = 1)] # dropping infs return errs diff --git a/posydon/visualization/plot2D.py b/posydon/visualization/plot2D.py index 99fa166b74..aeada3c2a4 100644 --- a/posydon/visualization/plot2D.py +++ b/posydon/visualization/plot2D.py @@ -14,6 +14,7 @@ ] import numpy as np +import pandas as pd import matplotlib.pyplot as plt from posydon.utils.gridutils import add_field from posydon.utils.constants import Zsun @@ -506,9 +507,9 @@ def plot_panel(self, ax, extra_grid_call=False): for i in range(len(self.x_var[selection])): if not isinstance(self.x_var_oRLO[selection][i], float): - if (not any(np.isnan( + if (not any(pd.isna( self.x_var_oRLO[selection][i])) - and not any(np.isnan( + and not any(pd.isna( self.y_var_oRLO[selection][i]))): plt.plot( self.x_var[selection][i], @@ -562,8 +563,8 @@ def plot_panel(self, ax, extra_grid_call=False): if not isinstance(self.x_var_oRLO[selection][i], float): if not any( - np.isnan(self.x_var_oRLO[selection][i]) - ) and not any(np.isnan( + pd.isna(self.x_var_oRLO[selection][i]) + ) and not any(pd.isna( self.y_var_oRLO[selection][i])): plt.plot( self.x_var[selection][i], @@ -793,11 +794,11 @@ def update_values_to_plot(self, termination_flag): # fix max min color bar if self.z_var is not None: if self.zmin is None: - not_nan = np.invert(np.isnan(self.z_var)) + not_nan = pd.notna(self.z_var) self.zmin = min(self.z_var[not_nan]) if self.zmax is None: - not_nan = np.invert(np.isnan(self.z_var)) + not_nan = pd.notna(self.z_var) self.zmax = max(self.z_var[not_nan]) def get_x_var(self): @@ -1243,9 +1244,9 @@ def plot_panels(self, axs, l_ax=None, legend_idxs=[2]): for i in range(len(self.x_var[selection])): if not isinstance(self.x_var_oRLO[selection] [i], float): - if (not any(np.isnan( + if (not any(pd.isna( self.x_var_oRLO[selection][i])) - and not any(np.isnan( + and not any(pd.isna( self.y_var_oRLO[selection][i]))): ax.plot( self.x_var[selection][i], @@ -1301,9 +1302,9 @@ def plot_panels(self, axs, l_ax=None, legend_idxs=[2]): for i in range(len(self.x_var[selection])): if not isinstance(self.x_var_oRLO [selection][i], float): - if not any(np.isnan( + if not any(pd.isna( self.x_var_oRLO[selection][i]))\ - and not any(np.isnan( + and not any(pd.isna( self.y_var_oRLO[selection][i])): ax.plot( self.x_var[selection][i], From 7053f243304f94f8e2b7158be0fb83c5c2e5bffc Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Mon, 10 Mar 2025 22:02:39 +1300 Subject: [PATCH 298/319] Fix logic for single star fraction population normalisation (#509) This fixes a bug in the logic. For `f_bin_simulated != 1`, the logic would result in the second if/else statement always being run, despite wanting to run the `f_bin_nature == 0`. --- posydon/popsyn/normalized_pop_mass.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/posydon/popsyn/normalized_pop_mass.py b/posydon/popsyn/normalized_pop_mass.py index 8d0cafd67f..a37175f361 100644 --- a/posydon/popsyn/normalized_pop_mass.py +++ b/posydon/popsyn/normalized_pop_mass.py @@ -190,7 +190,7 @@ def fraction_simulated( m_1, m_2, m_min, m_max, underlying_total_mass=initial_ZAMS_TOTAL_single/f_corr_single_stars - if (f_bin_nature == 1): #you want the underlying mass for a population + elif (f_bin_nature == 1): #you want the underlying mass for a population # consisting of only binary stars f_corr_binaries = f_corr_binaries*(f_bin_nature/f_bin_simulated) From eed90df3b0ea94c15b75d5d814318adfb4481afa Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Mon, 10 Mar 2025 22:05:23 +1300 Subject: [PATCH 299/319] Pulsational Pair-Instability top-down mass loss approach (#417) * implement Hendriks+23 SN prescription * Add commentedMODELS to train for Hendriks+23 with and without hydrogen envelope conservation * add additioanl PISN parameters in default population.ini file. Not necessary to include when running pops * add paper equation comment + limit metallicity to Z >= 1e-4 for equation. Lower values use 1e-4 PPI prescription * add new parameter for conserve_hydrogen_PPI * Add to default model * Allow CCSN to use conserve_hydrogen * define m_CO_core_PISN_min/max for easier understanding * Update the documentation with the new parameters --- .../pop_syn/population_params.rst | 14 ++++ posydon/binary_evol/SN/step_SN.py | 69 +++++++++++++++++++ posydon/grids/MODELS.py | 31 ++++++++- posydon/popsyn/population_params_default.ini | 11 ++- 4 files changed, 123 insertions(+), 2 deletions(-) diff --git a/docs/_source/components-overview/pop_syn/population_params.rst b/docs/_source/components-overview/pop_syn/population_params.rst index 6f7a77bfda..ad7a286fc4 100644 --- a/docs/_source/components-overview/pop_syn/population_params.rst +++ b/docs/_source/components-overview/pop_syn/population_params.rst @@ -440,8 +440,17 @@ The collection of trained prescriptions can be found in the ``MODELS.py`` file a * ``None`` * ``'Marchant+19'`` + * ``'Hendriks+23'`` - ``'Marchant+19'`` + * - ``PISN_CO_shift`` + - | The shift in CO core mass for the start of the Hendriks+23 PPI prescription + - ``0.0`` + + * - ``PPI_extra_mass_loss`` + - | Additional PPI mass loss for the Hendriks+23 prescription + - ``0.0`` + * - ``ECSN`` - | The prescription used for electron-capture supernova. @@ -453,6 +462,11 @@ The collection of trained prescriptions can be found in the ``MODELS.py`` file a - | Conserve the hydrogen envelope during the supernova. - ``True`` + * - ``conserve_hydrogen_PPI`` + - | Include the hydrogen envelope during the calculation of the + | PPI mass loss with the Hendriks+23 prescription + - ``False`` + * - ``max_neutrino_mass_loss`` - | The maximum mass loss due to neutrinos. - ``0.5`` diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index 89d34e64cd..e883a9ac8f 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -78,8 +78,11 @@ "mechanism": 'Patton&Sukhbold20-engine', "engine": 'N20', "PISN": "Marchant+19", + "PISN_CO_shift": 0.0, + "PPI_extra_mass_loss": 0.0, "ECSN": "Podsiadlowski+04", "conserve_hydrogen_envelope" : False, + "conserve_hydrogen_PPI" : False, "max_neutrino_mass_loss": NEUTRINO_MASS_LOSS_UPPER_LIMIT, "max_NS_mass": STATE_NS_STARMASS_UPPER_LIMIT, "use_interp_values": True, @@ -899,6 +902,7 @@ def PISN_prescription(self, star): # perform the PISN prescription in terms of the # He core mass at pre-supernova m_He_core = star.he_core_mass + m_CO_core = star.co_core_mass m_star = star.mass if self.PISN == "Marchant+19": if m_He_core >= 31.99 and m_He_core <= 61.10: @@ -931,6 +935,71 @@ def PISN_prescription(self, star): m_PISN = m_star else: m_PISN = m_He_core + + elif self.PISN == 'Hendriks+23': + # Hendriks et al. 2023 PISN prescription + # 10.1093/mnras/stad2857 + # Shifting PPI and PISN gap + # works by removing delta_M_PPI from the star + # and then applying any remnant mass prescription + + delta_M_CO_shift = self.PISN_CO_shift if self.PISN_CO_shift is not None else 0.0 + delta_M_PPI_extra_ML = self.PPI_extra_mass_loss if self.PPI_extra_mass_loss is not None else 0.0 + + m_CO_core_PISN_min = 38 + delta_M_CO_shift + m_CO_core_PISN_max = 114 + delta_M_CO_shift + + if ((m_CO_core >= m_CO_core_PISN_min) + and m_CO_core <= m_CO_core_PISN_max): + + # delta_PPI -> -inf if Z -> 0 + # limit mass loss to Z = 1e-4 for Z below it. + # 1e-4 is the lowest metallicity in the Hendriks et al. 2023 + if star.metallicity < 1e-4: + Z = 1e-4 + else: + Z = star.metallicity + # Hendriks et al. 2023 Equation 6 + # 10.1093/mnras/stad2857 + delta_M_PPI = ( + (0.0006 * np.log10(Z * const.Zsun) + 0.0054) + * (m_CO_core - delta_M_CO_shift - 34.8)**3 + - 0.0013 * (m_CO_core - delta_M_CO_shift - 34.8)**2 + + delta_M_PPI_extra_ML + ) + if self.verbose: + print(f"delta_M_PPI: {delta_M_PPI} Msun") + else: + delta_M_PPI = 0.0 + + if delta_M_PPI <= 0.0: + # no PPI -> use CCSN prescription + if self.conserve_hydrogen_envelope: + m_PISN = m_star + else: + m_PISN = m_He_core + else: + # PPI occurs + if self.conserve_hydrogen_PPI: + m_PISN = m_star - delta_M_PPI + else: + m_PISN = m_He_core - delta_M_PPI + + if m_PISN < 0.0: + m_PISN = np.nan + else: + PISN_star = copy.deepcopy(star) + PISN_star.mass = m_PISN + if PISN_star.he_core_mass > m_PISN: + PISN_star.he_core_mass = m_PISN + if PISN_star.co_core_mass > m_PISN: + PISN_star.co_core_mass = m_PISN + m_rembar, _, _ = self.compute_m_rembar(PISN_star, m_PISN) + + if m_rembar < 10: + m_PISN = np.nan + else: + m_PISN = m_rembar elif is_number(self.PISN) and m_He_core > self.PISN: m_PISN = self.PISN diff --git a/posydon/grids/MODELS.py b/posydon/grids/MODELS.py index aaaf668081..2e910abb43 100644 --- a/posydon/grids/MODELS.py +++ b/posydon/grids/MODELS.py @@ -145,5 +145,34 @@ "use_core_masses": False, "approx_at_he_depletion": False, }, - + # "MODEL11": { + # "mechanism": "Patton&Sukhbold20-engine", + # "engine": "N20", + # "PISN": "Hendriks+23", + # "PISN_CO_shift": 0.0, + # "PPI_extra_mass_loss": 0.0, + # "ECSN": "Podsiadlowski+04", + # "conserve_hydrogen_envelope" : True, + # "max_neutrino_mass_loss": NEUTRINO_MASS_LOSS_UPPER_LIMIT, + # "max_NS_mass": STATE_NS_STARMASS_UPPER_LIMIT, + # "use_interp_values": False, + # "use_profiles": True, + # "use_core_masses": False, + # "approx_at_he_depletion": False, + # }, + # "MODEL12": { + # "mechanism": "Patton&Sukhbold20-engine", + # "engine": "N20", + # "PISN": "Hendriks+23", + # "PISN_CO_shift": 0.0, + # "PPI_extra_mass_loss": 0.0, + # "ECSN": "Podsiadlowski+04", + # "conserve_hydrogen_envelope" : False, + # "max_neutrino_mass_loss": NEUTRINO_MASS_LOSS_UPPER_LIMIT, + # "max_NS_mass": STATE_NS_STARMASS_UPPER_LIMIT, + # "use_interp_values": False, + # "use_profiles": True, + # "use_core_masses": False, + # "approx_at_he_depletion": False, + # }, } diff --git a/posydon/popsyn/population_params_default.ini b/posydon/popsyn/population_params_default.ini index 2dbe5dfe75..81843d9a2c 100644 --- a/posydon/popsyn/population_params_default.ini +++ b/posydon/popsyn/population_params_default.ini @@ -212,11 +212,20 @@ # 'N20' for 'Sukhbold+16-engine', # 'Patton&Sukhbold20-engine' or None for the others PISN = "Marchant+19" - # None, "Marchant+19" + # None, "Marchant+19", "Hendriks+23" + PISN_CO_shift = 0.0 + # float (-inf,inf) + # Only when using Hendriks+23 + PPI_extra_mass_loss = 0.0 + # float (0,inf) + # Only when using Hendriks+23 ECSN = "Podsiadlowski+04" # "Tauris+15", "Podsiadlowski+04" conserve_hydrogen_envelope = False # True, False + conserve_hydrogen_PPI = False + # True, False + # Only when using Hendriks+23 max_neutrino_mass_loss = 0.5 # float (0,inf) max_NS_mass = 2.5 From 61ddf322b3696c17e7ea63c777febf3bdd3634bb Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Mon, 10 Mar 2025 22:07:18 +1300 Subject: [PATCH 300/319] Blandford-Znajek jet energy calculation (#429) * Add BZ jet calculation option + restructure do_core_collapse_BH output * Update profile_collapse.py * fix verbose print statement * extend doc string + fix return on l 404 * remove ==BH statement * shift star.mass definition upwards before the check * remove double set of m_disk_x * Update posydon/binary_evol/SN/profile_collapse.py Co-authored-by: mkruckow <122798003+mkruckow@users.noreply.github.com> * typo fix --------- Co-authored-by: mkruckow <122798003+mkruckow@users.noreply.github.com> --- posydon/binary_evol/SN/profile_collapse.py | 212 ++++++++++++++------- posydon/binary_evol/SN/step_SN.py | 27 ++- 2 files changed, 161 insertions(+), 78 deletions(-) diff --git a/posydon/binary_evol/SN/profile_collapse.py b/posydon/binary_evol/SN/profile_collapse.py index ffa4451c05..36729f9c90 100644 --- a/posydon/binary_evol/SN/profile_collapse.py +++ b/posydon/binary_evol/SN/profile_collapse.py @@ -341,46 +341,56 @@ def do_core_collapse_BH(star, Returns ------- - M_BH_total : float - Mass of the final BH in M_sun. - a_BH_total : float - Dimensionless spin of the final BH. - M_BH_array : array floats - BH mass evelution in g. - a_BH_array : array floats - Dimensionless spin evolution. - - J_accreted_array : array floats - Angular momentum accreted from a given shell by the BH in CGS units. - J_total_array : array floats - Total angular momentum in accreted shells plus BH's initial angular - momentum in CGS units. - J_disk_shell_array : array floats - Angular momentum accreted from the shell's part collapsing to form a - disk in CGS units. - radiation_eff_array : array floats - Fraction of accretion disk radiated away, this is one minus accretion - efficiency. - r_isco_array : array floats - Radius of the innermost stable circular orbit in cm. - j_isco_array : array floats - Specific angular momentum at the innermost stable circular orbit in - CGS. - M_direct_collapse_array : array floats - Cumulative mass accreted through direct collapse in g. - M_disk_array : array floats - Cumulative mass accreted thorugh the disk in g. - dm_direct_array : array floats - Shell's mass accreted through direct collapse in g. - dm_disk_array : array floats - Shell's mass accreted thorugh the disk in g. - j_shell_array : array floats - Shell's specific angular momentum in CGS. - M_total_array : array floats - Cumulative mass of shells and initial BH in g. - a_star_array : array floats - Dimensionless spin parameter of the star. - + core_collapse_results : dict + A dictionary containing the following keys: + 'M_BH_total' : float + The mass of the final BH in M_sun. + 'a_BH_total' : float + The dimensionless spin of the final BH. + 'm_disk_accreted' : float + The mass of the disk accreted by the BH in M_sun. + 'm_disk_radiated' : float + The mass of the disk radiated away in M_sun. + 'BZ_jet_power_total' : float + The total Blandford-Znajek jet power in erg/s. + + # Additional keys that are not used in the current implementation: + # 'BZ_jet_power_array' : np.array(BZ_jet_power_array), + # Blandford-Znajek jet power at each shell collapse in erg/s + # 'M_BH_array' : np.array(M_BH_array) + # BH mass evolution in g + # 'a_BH_array': np.array(a_BH_array) + # Dimensionless spin evolution + # 'J_accreted_array': np.array(J_accreted_array) + # Angular momentum accreted from a given shell by the BH in CGS units. + # 'J_total_array': np.array(J_total_array) + # Total angular momentum in accreted shells + BH's initial J + # 'J_disk_shell_array': np.array(J_disk_shell_array) + # Angular momentum accreted from the shell's part collapsing to form a + # disk in CGS units. + # 'radiation_eff_array': np.array(radiation_eff_array) + # Fraction of accretion disk radiated away, this is one minus accretion + # efficiency. + # 'r_isco_array': np.array(r_isco_array) + # Radius of the innermost stable circular orbit in cm. + # 'j_isco_array': np.array(j_isco_array) + # Specific angular momentum at ISCO (prograde orbits) in CGS. + # 'M_direct_collapse_array': np.array(M_direct_collapse_array) + # Cumulative mass accreted through direct collapse in g. + # 'M_disk_array': np.array(M_disk_array) + # Cumulative mass accreted through the disk in g. + # 'dm_direct_array': np.array(dm_direct_array) + # Mass in shell with j < j_isco (direct collapse) in g + # 'dm_disk_array': np.array(dm_disk_array) + # Mass in shell with j > j_isco (forms a disk) in g + # 'j_shell_array': np.array(j_shell_array) + # Shell's specific angular momentum in CGS + # 'M_total_array': np.array(M_total_array) + # Integrated mass (shells + initial BH) in g + # 'a_star_array': np.array(a_star_array) + # Star's spin parameter + # 'max_he_mass_ejected': max_he_mass_ejected + # Max He mass that can be ejected during the disk formation """ # convert to CGS units G = const.standard_cgrav @@ -401,12 +411,30 @@ def do_core_collapse_BH(star, # check that there is matter falling onto the BH if len(enclosed_mass) == 0: arr = np.array([np.nan]) - return [ - M_BH / Mo, a_BH, np.nan, np.nan - #M_BH / Mo, a_BH, arr, arr, arr, arr, arr, arr, - #arr, arr, arr, np.array([0.]), arr, arr, arr, arr, - #arr, arr - ] + return { + 'M_BH_total' : M_BH / Mo, + 'a_BH_total' : a_BH, + 'm_disk_accreted' : np.nan, + 'm_disk_radiated' : np.nan, + `BZ_jet_power_total` : np.nan, + # 'BZ_jet_power_array' : arr, + # 'M_BH_array' : arr, + # 'a_BH_array' : arr, + # 'J_accreted_array' : arr, + # 'J_total_array' : arr, + # 'J_disk_shell_array' : arr, + # 'radiation_eff_array' : arr, + # 'r_isco_array' : arr, + # 'j_isco_array' : arr, + # 'M_direct_collapse_array' : arr, + # 'M_disk_array' : arr, + # 'dm_direct_array' : arr, + # 'dm_disk_array' : arr, + # 'j_shell_array' : arr, + # 'M_total_array' : arr, + # 'a_star_array' : arr + # 'max_he_mass_ejected' : arr, + } # shell's specific angular momentum at equator j_shells = Omega_shells * radius_shells**2 @@ -452,7 +480,8 @@ def do_core_collapse_BH(star, j_shell_array = [j_shells[0]] # shell's specific angular momentum a_star_array = [a_BH] # star's spin parameter J_disk_shell_array = [0.] # angular momentum of the shell's disk - + BZ_jet_power_array = [0.] + BZ_jet_power_total = 0. # compute BH properties as each shell collapses for i, value in enumerate(dm): @@ -573,6 +602,16 @@ def f_temp2(x): raise ValueError( "We got a={:.5g} from shell {} containing {:.5g} M_sun".format( a_BH, i, dm_shell / Mo)) + + # calculate the potential BZ jet power at this moment of the collapse + # We assume full efficiency for the magnetic flux and a BH spin + # dependence of a^2 for the BH spin efficiency. + # just an energy total per collapse step. + BZ_power = BZ_jet_power(M_dot=dm_disk, + eta_phi=1, + eta_a=a_BH**2) + BZ_jet_power_array.append(BZ_power) + BZ_jet_power_total += BZ_power # Append all quantities to the arrays J_accreted_array.append(J_BH) @@ -617,25 +656,60 @@ def f_temp2(x): 'was formed around the BH.') print('') - return [ - M_BH_total, - a_BH_total, - m_disk_accreted, - m_disk_radiated, - # np.array(M_BH_array), - # np.array(a_BH_array), - # np.array(J_accreted_array), - # np.array(J_total_array), - # np.array(J_disk_shell_array), - # np.array(radiation_eff_array), - # np.array(r_isco_array), - # np.array(j_isco_array), - # np.array(M_direct_collapse_array), - # np.array(M_disk_array), - # np.array(dm_direct_array), - # np.array(dm_disk_array), - # np.array(j_shell_array), - # np.array(M_total_array), - # np.array(a_star_array), - # max_he_mass_ejected, - ] + return { + 'M_BH_total' : M_BH_total, + 'a_BH_total' : a_BH_total, + 'm_disk_accreted' : m_disk_accreted, + 'm_disk_radiated' : m_disk_radiated, + 'BZ_jet_power_total' : BZ_jet_power_total, + # 'BZ_jet_power_array' : np.array(BZ_jet_power_array), + # 'M_BH_array' : np.array(M_BH_array), + # 'a_BH_array': np.array(a_BH_array), # Dimensionless spin evolution + # 'J_accreted_array': np.array(J_accreted_array), # Angular momentum accreted by BH + # 'J_total_array': np.array(J_total_array), # Total angular momentum in accreted shells + BH's initial J + # 'J_disk_shell_array': np.array(J_disk_shell_array), # Angular momentum of the shell's disk + # 'radiation_eff_array': np.array(radiation_eff_array), # Fraction of accretion disk radiated away + # 'r_isco_array': np.array(r_isco_array), # Radius of ISCO + # 'j_isco_array': np.array(j_isco_array), # Specific angular momentum at ISCO (prograde orbits) + # 'M_direct_collapse_array': np.array(M_direct_collapse_array), # Integrated mass accreted through direct collapse + # 'M_disk_array': np.array(M_disk_array), # Integrated mass accreted through disk + # 'dm_direct_array': np.array(dm_direct_array), # Mass in shell with j < j_isco (direct collapse) + # 'dm_disk_array': np.array(dm_disk_array), # Mass in shell with j > j_isco (forms a disk) + # 'j_shell_array': np.array(j_shell_array), # Shell's specific angular momentum + # 'M_total_array': np.array(M_total_array), # Integrated mass (shells + initial BH) + # 'a_star_array': np.array(a_star_array), # Star's spin parameter + # 'max_he_mass_ejected': max_he_mass_ejected, # Max He mass that can be ejected during the disk formation + } + + +def BZ_jet_power(M_dot, eta_phi, eta_a): + """Compute the Blandford-Znajek jet power. + + This function computes the Blandford-Znajek jet power given the mass + accretion, the efficiency factor for the magnetic flux, and the + efficiency factor for the BH spin. + This is based on the decomposition of the jet power in terms of the + magnetic flux and the BH spin, see Gottlieb et al. (2023, 2024). + We do not assume any disk state in this calculation, i.e. + magnetically arrested disk (MAD), Neutrino dominated accretion flow (NDAF), + or advection dominated accretion flow (ADAF). + However, the functions for eta_phi and eta_a can be dependent on the disk + type. Moreover, the efficiency factors are not constant and + can change with the magnetic field and BH spin. + + Parameters + ---------- + M_dot : float + Mass accretion rate in g. + eta_phi : float + Efficiency factor for the magnetic flux. + eta_a : float + Efficiency factor for the BH spin. + + Returns + ------- + P_jet : float + Blandford-Znajek jet power in erg. + """ + P_jet = M_dot * const.clight**2 * eta_phi * eta_a # erg + return P_jet diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index e883a9ac8f..43b33249e5 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -672,10 +672,15 @@ def collapse_star(self, star): max_neutrino_mass_loss=self.max_neutrino_mass_loss, verbose=self.verbose ) - star.mass = final_BH[0] - star.spin = final_BH[1] - star.m_disk_accreted = final_BH[2] - star.m_disk_radiated = final_BH[3] + # set post-collapse properties/information to store + for i in final_BH.keys(): + setattr(star, i, final_BH[i]) + + # set specific properties manually + star.mass = final_BH['M_BH_total'] + star.spin = final_BH['a_BH_total'] + star.m_disk_accreted = final_BH['m_disk_accreted'] + star.m_disk_radiated = final_BH['m_disk_radiated'] star.state = "BH" else: star.mass = m_grav @@ -798,14 +803,18 @@ def collapse_star(self, star): max_neutrino_mass_loss=self.max_neutrino_mass_loss, verbose=self.verbose ) - star.mass = final_BH[0] + # set post-collapse properties/information to store + for i in final_BH.keys(): + setattr(star, i, final_BH[i]) + # set specific properties manually + star.mass = final_BH['M_BH_total'] + star.spin = final_BH['a_BH_total'] + if m_grav != star.mass and self.verbose: print("The star formed a disk during the collapse " - "and lost", round(final_BH[0] - m_rembar, 2), + "and lost", round(final_BH['M_BH_total'] - m_rembar, 2), "M_sun.") - star.spin = final_BH[1] - star.m_disk_accreted = final_BH[2] - star.m_disk_radiated = final_BH[3] + elif star.state == "NS": star.mass = m_grav star.m_disk_accreted = 0.0 From 67a4fbd81f3e8f39a524dae961403e490bfaca75 Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Mon, 10 Mar 2025 22:13:13 +1300 Subject: [PATCH 301/319] Split popsyn plotting into separate components for future modularity (#476) * split popsyn plotting into separate components for future modularity * swap m1 and m2 for CO grid plotting * additional comment * add small changes to doc strings * add value error + doc string * save file to add else --- posydon/visualization/plot_pop.py | 561 ++++++++++++++++++------------ 1 file changed, 337 insertions(+), 224 deletions(-) diff --git a/posydon/visualization/plot_pop.py b/posydon/visualization/plot_pop.py index 63ded6f311..82256ee1f8 100644 --- a/posydon/visualization/plot_pop.py +++ b/posydon/visualization/plot_pop.py @@ -21,57 +21,34 @@ cm = mpl.colormaps.get_cmap('tab20') COLORS = [cm.colors[i] for i in range(len(cm.colors)) if i%2==0] + [cm.colors[i] for i in range(len(cm.colors)) if i%2==1] -def plot_merger_rate_density(z, rate_density, zmax=10., channels=False, - GWTC3=False, label='DCO', **kwargs): - - plt.plot(z[z 10^{51} \, \mathrm{erg})$ SHOALS survey') + else: + ax.errorbar(z_P16,rate_P16,xerr=rate_P16_error_x,yerr=rate_P16_error_y, fmt='.', color='black', + label=r'$\mathcal{R}_\mathrm{LGRB}(E_\mathrm{iso}^{45-450\,\mathrm{keV}} > 10^{51} \, \mathrm{erg})$ SHOALS survey') def plot_rate_density(intrinsic_rates, channels=False, **kwargs): plt.figure() @@ -184,207 +161,343 @@ def plot_hist_properties(df, ax=None, df_intrinsic=None, df_observable=None, plt.show() - -def plot_popsyn_over_grid_slice(pop, - grid_type, - met_Zsun, - slices=None, - channel=None, - plot_dir='./', - prop=None, - prop_range=None, - log_prop=False, - alpha=0.3, - s=5., - show_fig=True, - save_fig=True, - close_fig=True, - plot_extension='png', - verbose=False): - '''Plot the population across a specific slice of the grid. +def plot_popsyn_over_grid_slice(pop, grid_type, met_Zsun, + termination_flag='combined_TF12', + slices=None, channel=None, + plot_dir='./', prop=None, prop_range=None, + log_prop=False, alpha=0.3, s=5., + show_fig=True, save_fig=True, close_fig=True, + plot_extension='png', verbose=False): + '''Plot the population synthesis data over a grid slice + + Outputs one or multiple plots of the population synthesis data over a grid slice. + Either stores the plots in a directory or shows them. Parameters ---------- pop : Population - Population object. + Population object grid_type : str - Type of grid to be plotted. Options: 'HMS-HMS', 'CO-HMS_RLO', 'CO-HeMS', 'CO_HeMS_RLO'. + Grid type to get data for. + Options are 'HMS-HMS', 'CO-HMS_RLO', 'CO-HeMS', 'CO-HeMS_RLO'. met_Zsun : float - Metallicity of the grid. - slices : list of floats - List of slices to plot. If None, all slices are plotted. - channel : str - Formation channel to plot. If None, all channels are plotted. - plot_dir : str - Directory where to save the plots. - prop : str - Property to plot on the grid. If None, no property colour is plotted. - prop_range : list of floats - Range of the property to plot. - log_prop : bool - If True, the property is plotted in log scale. - alpha : float - Transparency of the points. - s : float - Size of the points. - show_fig : bool - If True, the plot is shown. - save_fig : bool - If True, the plot is saved. - close_fig : bool - If True, the plot is closed. - plot_extension : str - File extension of the plot. - verbose : bool - If True, print information about the slices being plotted. + Metallicity of the population in solar units + termination_flag : str + Termination flag for the grid + slices : list (optional) + List of slices to plot + channel : str (optional) + Formation channel to be plotted + plot_dir : str (optional) + Directory to save the plots + prop : str (optional) + Property to plot on top of the grid slice + prop_range : list (optional) + Range of the property to plot + log_prop : bool (optional) + Logarithmic scale for the property + alpha : float (optional) + Transparency of the data points + s : float (optional) + Size of the data points + show_fig : bool (optional) + Show the figure + save_fig : bool (optional) + Save the figure + close_fig : bool (optional) + Close the figure + plot_extension : str (optional) + File extension for saving the plot + verbose : bool (optional) + Print information ''' # Check if step_names in pop.history data if 'step_names' not in pop.history.columns: raise ValueError('Formation channel information not available in popsynth data.') - # check if formation channel information is available - if channel is not None and 'channel' not in pop.columns: + # check if formation channel information is avaialbe + + if channel is not None and 'channel' not in pop.population.columns: raise ValueError('Formation channel information not available in popsynth data.') - # load grid + # get population data from the popsyn for the given metallicity + data = get_population_data(pop=pop, metallicity=met_Zsun, + grid_type=grid_type, channel=channel, prop=prop) + # Setup the grid met = convert_metallicity_to_string(met_Zsun) grid_path = os.path.join(PATH_TO_POSYDON_DATA, f'{grid_type}/{met}_Zsun.h5') grid = PSyGrid(verbose=False) grid.load(grid_path) + bin_centers,bin_edges, PLOT_PROPERTIES, tmp_fname, slice_3D_var_str = \ + setup_grid_slice_plotting(grid, grid_type, plot_extension) - if 'CO' in grid_path: - # compact object mass slices - # TODO: THIS SELECTION DOES NOT WORK! + for i, bin_center in enumerate(bin_centers): + # skip slices not in the list + if slices is not None and round(bin_center,2) not in np.around(slices,2): + if verbose: + print(f'Skipping {round(bin_center,2)}') + continue + + slice_3D_var_range = (bin_edges[i], bin_edges[i+1]) + + # add additional information about the plot_slice to the title + if slice_3D_var_str == 'mass_ratio': + PLOT_PROPERTIES['title'] = f'q = {bin_center:.2f}' + elif slice_3D_var_str == 'star_2_mass': + PLOT_PROPERTIES['title'] = '$M_\mathrm{CO}'+f' = {bin_center:.2f} M_{{\odot}}$' + # Check for channel + if channel is not None: + PLOT_PROPERTIES['title'] += f'\n {channel}' + + # change the file name for the plot + PLOT_PROPERTIES['fname'] = tmp_fname % bin_center + + # change size of the figure for properties + if prop is not None: + PLOT_PROPERTIES['figsize'] = (4,5.5) + + # plot the grid slice + plot_grid_slice(grid, + slice_3D_var_str, + slice_3D_var_range, + termination_flag=termination_flag, + PLOT_PROPERTIES=PLOT_PROPERTIES) + + # plot data on top of the grid slice + plot_population_data(data, slice_3D_var_str, slice_3D_var_range, + prop, prop_range, log_prop, alpha, s) + + if save_fig: + plt.savefig(os.path.join(plot_dir, PLOT_PROPERTIES['fname']), bbox_inches='tight') + if show_fig: + plt.show() + if close_fig: + plt.close() + +def get_population_data(pop, metallicity, grid_type, channel=None, prop=None): + '''get the population data of a given metallicity for plotting + + Parameters + ---------- + pop : Population + Population object + metallicity : float + Metallicity of the population in solar units + grid_type : str + Grid type to get data for. + Options are 'HMS-HMS', 'CO-HMS_RLO', 'CO-HeMS', 'CO-HeMS_RLO'. + channel : str + Formation channel to be plotted + + Returns + ------- + data : DataFrame + Population data of the given metallicity and channel and grid type + ''' + + # 1. mask channel + if channel is not None: + channel_mask = pop.population['channel'] == channel + else: + channel_mask = True + + # 2. mask metallicity + metallicity_mask = pop.population['metallicity'] == metallicity + + # 3. Get the history data for the selected population based on the grid/step names + selected_indices = pop.population.index[channel_mask & metallicity_mask].tolist() + + where_string = "(index in "+str(selected_indices)+")" + + data = pop.history.select(where=where_string, + columns=['S1_mass', + 'S2_mass', + 'orbital_period', + 'event', + 'step_names']).copy() + + # select the right data based on the grid type + if grid_type == 'HMS-HMS': + data = data[data['event'] == 'ZAMS'] + elif grid_type == 'CO-HMS_RLO': + data = data[data['event'] == 'oRLO2'] + # swap the masses since data has CO as the primary + tmp_S1 = data['S2_mass'].copy() + data['S2_mass'] = data['S1_mass'].copy() + data['S1_mass'] = tmp_S1 + + elif grid_type == 'CO-HeMS': + data = data[data['event'] == 'step_CE'] + # swap the masses since data has CO as the primary + tmp_S1 = data['S2_mass'].copy() + data['S2_mass'] = data['S1_mass'].copy() + data['S1_mass'] = tmp_S1 + else: + print('grid type not recognized') + + data['q'] = data['S2_mass']/data['S1_mass'] + + # only relevant for Compact Object (CO) grids + data.loc[data['q']>1, 'q'] = 1./data['q'][data['q']>1] + + # add prop of DCO merger to the data + if prop is not None: + if prop not in pop.population.columns: + raise ValueError(f'Property {prop} not available in pop.population.') + data[prop] = pop.population[prop][channel_mask & metallicity_mask].values + + # remove unnecessary columns + data.drop(columns=['event', 'step_names'], inplace=True) + return data + +def setup_grid_slice_plotting(grid, grid_type, plot_extension): + '''Setup the values for plotting the grid slice + + Parameters + ---------- + grid : PSyGrid + PSyGrid object + grid_type : str + Grid type to get data for. + Options are 'HMS-HMS', 'CO-HMS_RLO', 'CO-HeMS', 'CO-HeMS_RLO'. + plot_extension : str + File extension for saving the plot + + Returns + ------- + bin_centers : list + List of bin centers + bin_edges : list + List of bin edges + PLOT_PROPERTIES : dict + Dictionary of plot properties + slice_3D_var_str : str + String of the 3D variable to slice the grid + tmp_fname : str + Template file name for saving the plot + ''' + + if grid_type == "HMS-HMS": + grid_q_unique = np.unique(np.around(grid.initial_values['star_2_mass']/grid.initial_values['star_1_mass'],2)) + delta_q = (grid_q_unique[1:]-grid_q_unique[:-1])*0.5 + q_edges = (grid_q_unique[:-1]+delta_q).tolist() + + # set output variables + bin_edges = [0.]+q_edges+[1.] + bin_centers = grid_q_unique.tolist() + tmp_fname = 'grid_q_%1.2f.' + plot_extension + slice_3D_var_str='mass_ratio' + + elif 'CO' in grid_type: m_COs = np.unique(np.around(grid.initial_values['star_2_mass'],1)) m_COs_edges = 10**((np.log10(np.array(m_COs)[1:])+np.log10(np.array(m_COs)[:-1]))*0.5) m2 = [0.]+m_COs_edges.tolist()+[2*m_COs_edges[-1]] - vars = m_COs - fname = 'grid_m_%1.2f.' + plot_extension - title = '$m_\mathrm{CO}=%1.2f\,M_\odot$' + + bin_edges = m2 + bin_centers = m_COs + tmp_fname = 'grid_m_%1.2f.' + plot_extension slice_3D_var_str='star_2_mass' - elif 'HMS-HMS' in grid_path: - # mass ratio slices - qs = np.unique(np.around(grid.initial_values['star_2_mass']/grid.initial_values['star_1_mass'],2)) - dq = (qs[1:]-qs[:-1])*0.5 - dq_edges = (qs[:-1]+dq).tolist() - dq_edges = [0.]+dq_edges+[1.] - vars = qs.tolist() - fname = 'grid_q_%1.2f.' + plot_extension - title = '$q=%1.2f$' - slice_3D_var_str='mass_ratio' else: - raise ValueError('Grid type not supported!') - + print('grid type not recognized') + + PLOT_PROPERTIES = { + 'show_fig' : False, + 'close_fig' : False, + 'log10_x' : True, + 'log10_y' : True, + 'legend2D' : {'bbox_to_anchor' : (1.03, 0.5)}, + } + + return bin_centers, bin_edges, PLOT_PROPERTIES, tmp_fname, slice_3D_var_str + +def plot_population_data(data, + slice_3D_var_str, + slice_3D_var_range, + prop=None, + prop_range=None, + log_prop=False, + alpha=0.3, + s=5): + '''Plot the population data based on the grid slice parameters + + Parameters + ---------- + data : DataFrame + Population data + slice_3D_var_str : str + String of the 3D variable to slice the grid + slice_3D_var_range : list + Range of the 3D variable to slice the grid + prop : str (optional) + Property to plot on top of the grid slice + prop_range : list (optional) + Range of the property to plot + log_prop : bool (optional) + Logarithmic scale for the property + alpha : float (optional) + Transparency of the data points + s : float (optional) + Size of the data points + ''' - if channel is not None: - channel_sel = 'channel == "'+str(channel)+'"' + # get only slice data + if slice_3D_var_str == 'mass_ratio': + var = 'q' + elif slice_3D_var_str == 'star_2_mass': + var = 'S2_mass' else: - channel_sel = '' - - for i, var in enumerate(vars): - if slices is not None and round(var,2) not in np.around(slices,2): - if verbose: - print(f'Skipping {round(var,2)}') - continue - - met_sel = pop.select(where=channel_sel, columns=['metallicity']) - met_indices = met_sel[met_sel['metallicity'] == met_Zsun].index.tolist() + raise ValueError(f"Unknown slice_3D_var_str: {slice_3D_var_str}") + + # make it exclusive for the upper limit + mask = (data[var] >= slice_3D_var_range[0]) & (data[var] < slice_3D_var_range[1]) + + if prop is not None: + if prop not in data.columns: + raise ValueError(f'Property {prop} not available in popsynth data.') + if prop_range is None: + prop_range = [data[prop].min(), data[prop].max()] - if 'HMS-HMS' in grid_path: - slice_3D_var_range = (dq_edges[i],dq_edges[i+1]) - if len(met_indices) == 0: - data = pd.DataFrame(columns=['S1_mass','S2_mass', 'orbital_period']) - else: - sel = 'index in '+str(met_indices)+' & event == "ZAMS"' - data = pop.history.select(where=sel, columns=['S1_mass','S2_mass', 'orbital_period']) - - q = data['S2_mass'].values/data['S1_mass'].values - q[q>1] = 1./q[q>1] - mask = (q>=slice_3D_var_range[0]) & (q<=slice_3D_var_range[1]) - elif 'CO' in grid_path: - if i == len(m2): - continue - slice_3D_var_range = (m2[i],m2[i+1]) - # select popsynth binaries in the given compact object mass - # TODO: implement the case of reversal mass ratio - if 'CO-HMS_RLO' in grid_path: - if len(met_indices) == 0: - data = pd.DataFrame(columns=['S1_mass','S2_mass', 'orbital_period']) - else: - sel = 'index in '+str(met_indices)+' & event == "oRLO2"' - data = pop.history.select(where=sel, columns=['S1_mass','S2_mass', 'orbital_period']) - - m_CO = data['S1_mass'].values - mask = (m_CO>=slice_3D_var_range[0]) & (m_CO<=slice_3D_var_range[1]) - - elif 'CO-HeMS' in grid_path: - if len(met_indices) == 0: - data = pd.DataFrame(columns=['S1_mass','S2_mass', 'orbital_period']) - else: - sel = 'index in '+str(met_indices)+' & step_names == "step_CE"' - data = pop.history.select(where=sel, columns=['S1_mass','S2_mass', 'orbital_period']) - - m_CO = data['S1_mass'].values - mask = (m_CO>=slice_3D_var_range[0]) & (m_CO<=slice_3D_var_range[1]) - else: - raise ValueError('Grid type not supported!') - # TODO: skip plotting slice if there are no data - try: - PLOT_PROPERTIES = { - 'figsize' : (4,3.5) if prop is None else (4,5.5), - 'path_to_file' : plot_dir, - 'show_fig' : False, - 'close_fig' : False, - #'fname' : fname%var, - 'title' : title%var if channel is None else title%var + '\n' + channel, - 'log10_x' : True, - 'log10_y' : True, - 'legend2D' : {'bbox_to_anchor' : (1.03, 0.5)}, - } - # grid - grid.plot2D('star_1_mass', 'period_days', None, - termination_flag='combined_TF12', - grid_3D=True, slice_3D_var_str=slice_3D_var_str, - slice_3D_var_range=slice_3D_var_range, - verbose=False, **PLOT_PROPERTIES) - - log10_m1 = np.log10(data.loc[mask,'S1_mass'].values) - log10_p = np.log10(data.loc[mask,'orbital_period'].values) - # plot color map of a given DCO variable - if prop is not None: - if prop not in pop.columns: - raise ValueError(f'Property {prop} not available in popsynth data.') - - # basically only for DCO systems - met_indices = np.array(met_indices)[mask].tolist() - prop_sel = 'index in '+str(met_indices) + ' & event == "CO_contact"' - prop_data = pop.history.select(where=prop_sel, columns=[prop]) - prop_values = prop_data[prop].values - vmin = prop_range[0] - vmax = prop_range[1] - if log_prop: - prop_values = np.log10(prop_values) - vmin = np.log10(vmin) - vmax = np.log10(vmax) - plt.scatter(log10_m1, log10_p, c=prop_values, s=s, vmin=vmin, vmax=vmax, marker='v', cmap='viridis', alpha=alpha, zorder=0.5) - if prop in DEFAULT_LABELS: - if log_prop: - label = DEFAULT_LABELS[prop][1] - else: - label = DEFAULT_LABELS[prop][0] - else: - label = prop - if log_prop: - label = 'log10_'+label - plt.colorbar(label=label, orientation='horizontal') + vmin = prop_range[0] + vmax = prop_range[1] + prop_values = data[prop][mask].values + + if log_prop: + prop_values = np.log10(prop_values) + vmin = np.log10(vmin) + vmax = np.log10(vmax) + + plt.scatter(np.log10(data['S1_mass'][mask]), + np.log10(data['orbital_period'][mask]), + c=prop_values, + s=s, + vmin=vmin, + vmax=vmax, + marker='v', + cmap='viridis', + alpha=alpha, + zorder=0.5) + if prop in DEFAULT_LABELS: + if log_prop: + label = DEFAULT_LABELS[prop][1] else: - plt.scatter(log10_m1, log10_p, s=s, marker='v', color='black', alpha=alpha, zorder=0.5) - - if save_fig: - plt.savefig(os.path.join(plot_dir, fname%var), bbox_inches='tight') - if show_fig: - plt.show() - if close_fig: - plt.close() - except: - raise ValueError(f'Failed to plot slice %1.2f'%var) + label = DEFAULT_LABELS[prop][0] + else: + label = prop + if log_prop: + label = 'log10_'+label + plt.colorbar(label=label, orientation='horizontal') + else: + plt.scatter(np.log10(data['S1_mass'][mask]), + np.log10(data['orbital_period'][mask]), + s=s, + marker='v', + color='black', + alpha=alpha, + zorder=0.5) + +def plot_grid_slice(grid, slice_3D_var_str, slice_3D_var_range, termination_flag='combined_TF12', PLOT_PROPERTIES=None): + grid.plot2D('star_1_mass', 'period_days', None, + termination_flag=termination_flag, + grid_3D=True, slice_3D_var_str=slice_3D_var_str, + slice_3D_var_range=slice_3D_var_range, + verbose=False, **PLOT_PROPERTIES) \ No newline at end of file From 7f990013cf8788e4feb943b709a5445976609479 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Tue, 11 Mar 2025 14:16:42 +0100 Subject: [PATCH 302/319] Use information about dominating star in infer_mass_transfer_case (#532) * use information about dominating star in infer_mass_transfer_case * Update profile_collapse.py correct typo * separate best_fit and new htrack * Update step_detached.py cleanup * cleanup blanks --------- Co-authored-by: Max Briel --- posydon/binary_evol/DT/step_detached.py | 7 ++++--- posydon/binary_evol/SN/profile_collapse.py | 2 +- .../unit_tests/utils/test_common_functions.py | 18 ++++++++++++++++-- posydon/utils/common_functions.py | 19 +++++++++++++++++-- 4 files changed, 38 insertions(+), 8 deletions(-) diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index 59a0d01e11..9cf30fb30e 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -804,10 +804,10 @@ def get_MESA_labels(list_for_matching): " stripped-He grid", "EvolutionWarning") if star.state in LIST_ACCEPTABLE_STATES_FOR_HeStar: - htrack = True + new_htrack = True list_for_matching = self.list_for_matching_HeStar elif star.state in LIST_ACCEPTABLE_STATES_FOR_postMS: - htrack = False + new_htrack = False list_for_matching = self.list_for_matching_postMS MESA_labels, rs, colscalers, bnds, scales = get_MESA_labels(list_for_matching) @@ -818,12 +818,13 @@ def get_MESA_labels(list_for_matching): "in the single star grid options.") posydon_attributes = get_posydon_attributes(MESA_labels, star) - x0 = get_root0(MESA_labels, posydon_attributes, htrack, rs=rs) + x0 = get_root0(MESA_labels, posydon_attributes, new_htrack, rs=rs) try: sol = minimize(sq_diff_function, x0, method="TNC", bounds=bnds) if (np.abs(sol.fun) < np.abs(best_sol.fun) and sol.success): best_sol = sol + htrack = new_htrack if self.verbose: print (f"Alternative matching (3rd attempt) completed:" diff --git a/posydon/binary_evol/SN/profile_collapse.py b/posydon/binary_evol/SN/profile_collapse.py index 36729f9c90..f95f0fdfa3 100644 --- a/posydon/binary_evol/SN/profile_collapse.py +++ b/posydon/binary_evol/SN/profile_collapse.py @@ -416,7 +416,7 @@ def do_core_collapse_BH(star, 'a_BH_total' : a_BH, 'm_disk_accreted' : np.nan, 'm_disk_radiated' : np.nan, - `BZ_jet_power_total` : np.nan, + 'BZ_jet_power_total' : np.nan, # 'BZ_jet_power_array' : arr, # 'M_BH_array' : arr, # 'a_BH_array' : arr, diff --git a/posydon/unit_tests/utils/test_common_functions.py b/posydon/unit_tests/utils/test_common_functions.py index 76ab1eab4a..e929d71b3b 100644 --- a/posydon/unit_tests/utils/test_common_functions.py +++ b/posydon/unit_tests/utils/test_common_functions.py @@ -1051,7 +1051,7 @@ def test_check_state_of_star_history_array(self, star): def test_get_binary_state_and_event_and_mt_case(self, binary, monkeypatch): def mock_infer_mass_transfer_case(rl_relative_overflow,\ lg_mtransfer_rate, donor_state,\ - verbose=False): + dominating_star=True, verbose=False): if rl_relative_overflow is not None: if rl_relative_overflow > 0: return totest.MT_CASE_A @@ -1404,7 +1404,21 @@ def test_infer_mass_transfer_case(self, capsys): ("stripped_He_undetermined", totest.MT_CASE_UNDETERMINED),\ ("test_undetermined", totest.MT_CASE_UNDETERMINED)] for (ds, c) in tests: - assert totest.infer_mass_transfer_case(2*RROT, 2*LMRT, ds) == c + assert totest.infer_mass_transfer_case(RROT+1.0, LMRT+1.0, ds) == c + assert totest.infer_mass_transfer_case(RROT+1.0, LMRT-1.0, ds) == c + assert totest.infer_mass_transfer_case(RROT+1.0, LMRT+1.0, ds,\ + dominating_star=False) == c + assert totest.infer_mass_transfer_case(RROT+1.0, LMRT-1.0, ds,\ + dominating_star=False) == c + assert totest.infer_mass_transfer_case(RROT-1.0, LMRT+1.0, ds) == c + assert totest.infer_mass_transfer_case(RROT-1.0, LMRT-1.0, ds)\ + == totest.MT_CASE_NO_RLO + assert totest.infer_mass_transfer_case(RROT-1.0, LMRT+1.0, ds,\ + dominating_star=False)\ + == totest.MT_CASE_NO_RLO + assert totest.infer_mass_transfer_case(RROT-1.0, LMRT-1.0, ds,\ + dominating_star=False)\ + == totest.MT_CASE_NO_RLO def test_cumulative_mass_transfer_numeric(self): # missing argument diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index cdc4bc287d..1e9f2c72c7 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -1088,9 +1088,17 @@ def get_binary_state_and_event_and_mt_case(binary, interpolation_class=None, gamma2 = None # get numerical MT cases + if ((rl_overflow1 is not None) and (rl_overflow2 is not None)): + dominating_star1 = (rl_overflow1 >= rl_overflow2) + elif rl_overflow2 is not None: + dominating_star1 = False + else: + dominating_star1 = True mt_flag_1 = infer_mass_transfer_case(rl_overflow1, lg_mtransfer, state1, + dominating_star=dominating_star1, verbose=verbose) mt_flag_2 = infer_mass_transfer_case(rl_overflow2, lg_mtransfer, state2, + dominating_star=not dominating_star1, verbose=verbose) # convert to strings mt_flag_1_str = cumulative_mass_transfer_string([mt_flag_1]) @@ -1407,15 +1415,22 @@ def infer_star_state(star_mass=None, surface_h1=None, def infer_mass_transfer_case(rl_relative_overflow, lg_mtransfer_rate, donor_state, + dominating_star=True, verbose=False): """Infer the mass-transfer case of a given star. Parameters ---------- rl_relative_overflow : float + Relative Roche lobe overflowing parameter. lg_mtransfer_rate : float + The mass transfer rate in log_10. donor_state : str Values of star parameters at a specific step. + dominating_star : bool (default: True) + Whether this star is the orgin of the mass transfer rate. + verbose : bool (default: False) + In case we want additional information printed to standard output. Returns ------- @@ -1427,8 +1442,8 @@ def infer_mass_transfer_case(rl_relative_overflow, return MT_CASE_NO_RLO if ((rl_relative_overflow <= RL_RELATIVE_OVERFLOW_THRESHOLD) and - ((lg_mtransfer_rate <= LG_MTRANSFER_RATE_THRESHOLD) and - (rl_relative_overflow < 0.0))): + ((lg_mtransfer_rate <= LG_MTRANSFER_RATE_THRESHOLD) or + (not dominating_star))): if verbose: print("checking rl_relative_overflow / lg_mtransfer_rate,", rl_relative_overflow, lg_mtransfer_rate) From ae5f086ae3d34d6e82e7e2b40a67a32ab37a3494 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 13 Mar 2025 15:12:35 +0100 Subject: [PATCH 303/319] Update setup.py (#536) sphinx at least 8.2.2 --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 9dc0a7f263..4111d55980 100644 --- a/setup.py +++ b/setup.py @@ -92,7 +92,7 @@ # to build documentation "doc": [ "ipython", - "sphinx >= 6.1.3", + "sphinx >= 8.2.2", "numpydoc", "sphinx_rtd_theme", "sphinxcontrib_programoutput", From 15b8215c1665bc35889f31091432741131aafcd6 Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Thu, 13 Mar 2025 15:18:29 +0100 Subject: [PATCH 304/319] GitHub action fix (#537) * change to v4 * add to CI for testing checkout and setup python versions * change other versions to 'latest' --- .github/workflows/continuous_integration.yml | 4 ++-- .github/workflows/deploy-github-pages.yml | 6 +++--- .github/workflows/install_extras.yml | 4 ++-- .github/workflows/publish-to-anaconda.yml | 2 +- 4 files changed, 8 insertions(+), 8 deletions(-) diff --git a/.github/workflows/continuous_integration.yml b/.github/workflows/continuous_integration.yml index b653032fb8..2543859ba0 100644 --- a/.github/workflows/continuous_integration.yml +++ b/.github/workflows/continuous_integration.yml @@ -15,10 +15,10 @@ jobs: steps: - name: Checkout repository - uses: actions/checkout@v2 + uses: actions/checkout@v4 - name: Set up Python - uses: actions/setup-python@v2 + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} diff --git a/.github/workflows/deploy-github-pages.yml b/.github/workflows/deploy-github-pages.yml index 567172e0aa..b962ff1af3 100644 --- a/.github/workflows/deploy-github-pages.yml +++ b/.github/workflows/deploy-github-pages.yml @@ -15,9 +15,9 @@ jobs: python-version: [3.11] steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v2 + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Get pip cache dir @@ -26,7 +26,7 @@ jobs: python -m pip install --upgrade pip echo "::set-output name=dir::$(pip cache dir)" - name: pip cache - uses: actions/cache@v2 + uses: actions/cache@v4 with: path: ${{ steps.pip-cache.outputs.dir }} key: ${{ runner.os }}-pip-${{ hashFiles('**/setup.py', '**/requirements.txt') }} diff --git a/.github/workflows/install_extras.yml b/.github/workflows/install_extras.yml index 961840c841..03817b7cef 100644 --- a/.github/workflows/install_extras.yml +++ b/.github/workflows/install_extras.yml @@ -15,10 +15,10 @@ jobs: steps: - name: Checkout repository - uses: actions/checkout@v2 + uses: actions/checkout@v4 - name: Set up Python - uses: actions/setup-python@v2 + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} diff --git a/.github/workflows/publish-to-anaconda.yml b/.github/workflows/publish-to-anaconda.yml index bfda43019d..e900afebfd 100644 --- a/.github/workflows/publish-to-anaconda.yml +++ b/.github/workflows/publish-to-anaconda.yml @@ -10,7 +10,7 @@ jobs: steps: - name: Checkout - uses: actions/checkout@v2 + uses: actions/checkout@v4 - name: publish-to-conda uses: POSYDON-code/publish_to_anaconda@v1.0.1 From 96cb7034487654a623dba20869d827152ac56830 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 13 Mar 2025 15:19:00 +0100 Subject: [PATCH 305/319] Update MESA-grid tutorials (#535) * update first tutorial * add shell loop; make shell script executeable * add cleanup to shell run * reuse already loaded grid * tutorial2 * tutorial 4 * tutorial 3: changes before submission only * complete tutorial 3 --- bin/posydon-setup-grid | 34 +- .../MESA-grids/1_hms_hms.ipynb | 305 +++++++++---- .../MESA-grids/laptop.ipynb | 248 +++++++--- .../MESA-grids/running_a_grid.ipynb | 430 +++++++++--------- .../MESA-grids/running_single_hms.ipynb | 66 +-- grid_params/grid_params.ini | 12 +- 6 files changed, 664 insertions(+), 431 deletions(-) diff --git a/bin/posydon-setup-grid b/bin/posydon-setup-grid index 710a9e9168..571d44c211 100755 --- a/bin/posydon-setup-grid +++ b/bin/posydon-setup-grid @@ -452,12 +452,12 @@ def construct_static_inlist(mesa_inlists, grid_parameters, working_directory=os. final_star2_binary_controls['extra_controls_inlist1_name'] = "'inlist_grid_star2_binary_controls'" if grid_params_star1_binary_job: - final_star1_binary_job['read_extra_star_job_inlist1'] = '.true.' - final_star1_binary_job['extra_star_job_inlist1_name'] = "'inlist_grid_star1_binary_job'" + final_star1_binary_job['read_extra_star_job_inlist1'] = '.true.' + final_star1_binary_job['extra_star_job_inlist1_name'] = "'inlist_grid_star1_binary_job'" if grid_params_star2_binary_job: - final_star2_binary_job['read_extra_star_job_inlist1'] = '.true.' - final_star2_binary_job['extra_star_job_inlist1_name'] = "'inlist_grid_star2_binary_job'" + final_star2_binary_job['read_extra_star_job_inlist1'] = '.true.' + final_star2_binary_job['extra_star_job_inlist1_name'] = "'inlist_grid_star2_binary_job'" # We want to point the binary_job section to the star1 and star2 inlist we just made final_binary_job['inlist_names(1)'] = "'{0}'".format(inlist_star1_binary) @@ -1077,19 +1077,31 @@ if __name__ == '__main__': with open('grid_command.sh', 'w') as f: f.write('#!/bin/bash\n\n') f.write('export OMP_NUM_THREADS={0}\n\n'.format(slurm['number_of_cpus_per_task'])) - f.write('export MESASDK_ROOT={0}\n'.format(os.environ['MESASDK_ROOT'])) f.write('source $MESASDK_ROOT/bin/mesasdk_init.sh\n') - f.write('export MESA_DIR={0}\n\n\n'.format(os.environ['MESA_DIR'])) + f.write('export MESA_DIR={0}\n\n'.format(os.environ['MESA_DIR'])) + if slurm['job_array']: + f.write('for SLURM_ARRAY_TASK_ID in ') + for i in range(len(grid_df)): + f.write('{0} '.format(i)) + f.write('; do ') f.write(command_line) + if slurm['job_array']: + f.write(' ; done\n\n') + # do cleanup + f.write('compress-mesa .\n') + if 'newgroup' in slurm.keys(): + f.write('echo \"Change group to {0}\"\n'.format(slurm['newgroup'])) + f.write('chgrp -fR {0} .\n'.format(slurm['newgroup'])) + f.write('echo \"Change group permission to rwX at least\"\n') + f.write('chmod -fR g+rwX .\n') + f.write('\necho \"Done.\"') + # make the script executable + os.system("chmod 755 grid_command.sh") elif args.submission_type == 'slurm': # if slurm will we submit as job array or MPI if slurm['job_array']: - if '.csv' in run_parameters['grid']: - grid = pandas.read_csv(run_parameters['grid']) - else: - raise ValueError('Grid format not recognized, please feed in an acceptable format: csv') grid_script = 'job_array_grid_submit.slurm' if os.path.exists(grid_script): Pwarn('Replace '+grid_script, "OverwriteWarning") @@ -1099,7 +1111,7 @@ if __name__ == '__main__': f.write('#SBATCH --account={0}\n'.format(slurm['account'])) f.write('#SBATCH --partition={0}\n'.format(slurm['partition'])) f.write('#SBATCH -N 1\n') - f.write('#SBATCH --array=0-{0}\n'.format(len(grid)-1)) + f.write('#SBATCH --array=0-{0}\n'.format(len(grid_df)-1)) f.write('#SBATCH --cpus-per-task {0}\n'.format(slurm['number_of_cpus_per_task'])) f.write('#SBATCH --ntasks-per-node 1\n') f.write('#SBATCH --time={0}\n'.format(slurm['walltime'])) diff --git a/docs/_source/tutorials-examples/MESA-grids/1_hms_hms.ipynb b/docs/_source/tutorials-examples/MESA-grids/1_hms_hms.ipynb index ec68bb2568..188075f0ac 100644 --- a/docs/_source/tutorials-examples/MESA-grids/1_hms_hms.ipynb +++ b/docs/_source/tutorials-examples/MESA-grids/1_hms_hms.ipynb @@ -13,19 +13,19 @@ "source": [ "If you haven't done it already, export all the relevan POSYDON and MESA enviroment variables.\n", "\n", - "Tip: create a file called `.bash_posydon` with the following content:\n", + "Tip: create a file called `.bash_POSYDON_MESA` with the following content (please adopt the PATHs to your system):\n", "```bash\n", - "export PATH_TO_POSYDON=/srv/beegfs/scratch/shares/astro/posydon/simone/documentation/POSYDON/\n", - "export PATH_TO_POSYDON_DATA=/srv/beegfs/scratch/shares/astro/posydon/POSYDON_GRIDS_v2/POSYDON_data/230914/\n", + "export PATH_TO_POSYDON=~/software/POSYDON/\n", + "export PATH_TO_POSYDON_DATA=~/software/POSYDON/data/\n", "\n", - "export MESA_DIR=/srv/beegfs/scratch/shares/astro/posydon/software/mesa-r11701-posydon-v2-fiximplicit\n", + "export MESA_DIR=~/software/mesa-r11701_witheoschange_andwithreverseMTchange_fiximplicitmdot\n", "export OMP_NUM_THREADS=4\n", - "export MESASDK_ROOT=/srv/beegfs/scratch/shares/astro/posydon/software/mesasdk-r11701\n", + "export MESASDK_ROOT=~/software/mesasdk\n", "source $MESASDK_ROOT/bin/mesasdk_init.sh\n", "```\n", "so that every time you open a new terminal you can just run:\n", "```bash\n", - "source .bash_posydon\n", + "source .bash_POSYDON_MESA\n", "```" ] }, @@ -33,7 +33,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "If you haven't done it already, please download the `development` branch of the `POSYDON-MESA-INLISTS` submodule. This is the branch that contains the simulation properties of all POSYDON MESA grids." + "If you haven't done it already, please download the `development` branch of the `POSYDON-MESA-INLISTS` submodule, see [Architecting MESA Simulation Grids with POSYDON](https://posydon.org/POSYDON/tutorials-examples/MESA-grids/running-grids.html). This is the branch that contains the simulation properties of all POSYDON MESA grids." ] }, { @@ -47,23 +47,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "From the submodule, you can now copy in your working directory the API ini file configuration of HMS-HMS binaries: `$PATH_TO_POSYDON/grid_params/POSYDON-MESA-INLISTS/r11701/running_scripts/HMS-HMS_yggdrasil.ini`. \n", + "You can now copy in your working directory the API ini file configuration of HMS-HMS binaries: `$PATH_TO_POSYDON/grid_params/grid_params.ini`. \n", "\n", - "For each grid we support two ini file configured to the Northwestern and UNIGE HPC clusters." + "Note: in the `POSYDON-MESA-INLISTS` submodule we have more example ini files at `$PATH_TO_POSYDON/grid_params/POSYDON-MESA-INLISTS/r11701/running_scripts/`. For each grid we support two ini file configured to the Northwestern and UNIGE HPC clusters." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'./HMS-HMS_yggdrasil.ini'" + "'./HMS-HMS.ini'" ] }, - "execution_count": 5, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -73,8 +73,8 @@ "import shutil\n", "from posydon.config import PATH_TO_POSYDON\n", "\n", - "path_to_ini = os.path.join(PATH_TO_POSYDON, \"grid_params/POSYDON-MESA-INLISTS/r11701/running_scripts/HMS-HMS_yggdrasil.ini\")\n", - "shutil.copyfile(path_to_ini, './HMS-HMS_yggdrasil.ini')" + "path_to_ini = os.path.join(PATH_TO_POSYDON, \"grid_params/grid_params.ini\")\n", + "shutil.copyfile(path_to_ini, './HMS-HMS.ini')" ] }, { @@ -83,20 +83,22 @@ "source": [ "Open the file and edit the following parameters:\n", "- `user=your_username`\n", - "- `account=your_account`\n", + "- `partition=the_slurm_partition_to_run_on` (can be skipped if not run via SLURM)\n", + "- `account=your_slurm_account` (can be skipped if not run via SLURM)\n", "- `email=your_email`\n", "- `posydon_github_root=your_path_to_POSYDON`\n", + "- `grid=your_grid_csv_file` (will get created in the next section)\n", "\n", "The `scanrio` parameter of the ini file is where the magic happens. This parameter let's us decide which simulation we want to run. The syntax is the following:\n", "- the first argument is the name of the submodule `posydon` which points to the POSYDON-MESA-INLISTS submodule (other options are avaialble, e.g. `user` which points to a private submodule)\n", - "- the second argument is the name of the branch and commit of the submodule we want to use connected by a dash `-`. In this case we want to use the `development` branch of the submodule and it's latest commit, so we write `development-c4f90ce2d93595f66751011d2002fc3ab3d090ec`\n", + "- the second argument is the name of the branch and commit of the submodule we want to use connected by a dash `-`. In this case we want to use the `development` branch of the submodule and it's latest commit, so we write `development-c1f75acacc3bd645197c5d3e920a121e94d95a80`\n", "- the third argument is the name of the simulation grid we want to run, in this case we want to run a HMS-HMS grid. The name of the grid is `HMS-HMS`.\n", "\n", - "To summarize, you should have `scenario = ['posydon', 'development-c4f90ce2d93595f66751011d2002fc3ab3d090ec', 'HMS-HMS']`\n", + "To summarize, you should have `scenario = ['posydon', 'development-c1f75acacc3bd645197c5d3e920a121e94d95a80', 'HMS-HMS']`\n", "\n", - "The `zams_file` points to the ZAMS model used to generate the HMS stars in the binary system. POSYDON v2.0.0 supports 8 different metallicities, here we use the default value that points to the 0.1Zsun POSYDON MESA ZAMS model `$PATH_TO_POSYDON/grid_params/POSYDON-MESA-INLISTS/r11701/ZAMS_models/zams_z1.42m3_y0.2511.data`\n", + "The `zams_file` points to the ZAMS model used to generate the HMS stars in the binary system. POSYDON v2.0.0 supports 8 different metallicities, here we would like to use the 0.1Zsun POSYDON MESA ZAMS model `${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS/r11701/ZAMS_models/zams_z1.42m3_y0.2511.data`\n", "\n", - "The last parameter of this file is `grid` and points to the `csv` file containing the initial coditions of the grid. In this case we want to run a `HMS-HMS` grid consisting of one system, so we keep `grid = grid_test.csv` and generate such file specifying the metallicity, the two star masses and the initial orbital period." + "The last parameter of this file is `grid` and points to the `csv` file containing the initial coditions of the grid. In this case we want to run a `HMS-HMS` grid consisting of one system, so we set `grid = grid_test.csv` and generate such file specifying the metallicity, the two star masses and the initial orbital period." ] }, { @@ -115,13 +117,11 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import csv\n", - "import numpy as np\n", - "import pandas as pd\n", "\n", "with open('./grid_test.csv', 'w', newline='') as file:\n", " writer = csv.writer(file)\n", @@ -138,14 +138,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "1_hms_hms.ipynb grid_test.csv\tHMS-HMS_yggdrasil.ini\n" + "1_hms_hms.ipynb grid_test.csv\tHMS-HMS.ini\n" ] } ], @@ -160,7 +160,7 @@ "Let's now use the magic of POSYDON to set up the MESA simulation. In your termianal run\n", "\n", "```bash\n", - "posydon-setup-grid --grid-type fixed --inifile HMS-HMS_yggdrasil.ini --submission-type slurm\n", + "posydon-setup-grid --grid-type fixed --inifile HMS-HMS.ini --submission-type slurm\n", "```\n", "\n", "The following files will be created:" @@ -168,15 +168,16 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "1_hms_hms.ipynb grid_test.csv\t\tmk\t\t\t\tstar1\n", - "binary\t\t HMS-HMS_yggdrasil.ini\tslurm_job_array_grid_submit.sh\tstar2\n" + "1_hms_hms.ipynb column_lists\tjob_array_grid_submit.slurm star1\n", + "binary\t\t grid_test.csv\tmk\t\t\t star2\n", + "cleanup.slurm\t HMS-HMS.ini\trun_grid.sh\n" ] } ], @@ -200,24 +201,25 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Submitted batch job 28453658\n" + "job_array_grid_submit.slurm submitted as 38720106\n", + "cleanup.slurm submitted as 38720107\n" ] } ], "source": [ - "!sbatch slurm_job_array_grid_submit.sh" + "!./run_grid.sh" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -225,12 +227,13 @@ "output_type": "stream", "text": [ " JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)\n", - " 28453658_[0] private-a mesa_gri bavera PD 0:00 1 (Priority)\n" + " 38720107 private-a mesa_gri kruckow PD 0:00 1 (Dependency)\n", + " 38720106_0 private-a mesa_gri kruckow R 0:22 1 cpu146\n" ] } ], "source": [ - "!squeue -u bavera" + "!squeue -u kruckow" ] }, { @@ -251,11 +254,15 @@ "text": [ "1_hms_hms.ipynb\n", "binary\n", + "cleanup.slurm\n", + "column_lists\n", "grid_test.csv\n", - "HMS-HMS_yggdrasil.ini\n", - "mesa_grid.28453658_0.out\n", + "HMS-HMS.ini\n", + "job_array_grid_submit.slurm\n", + "mesa_cleanup.out\n", + "mesa_grid.38720106_0.out\n", "mk\n", - "slurm_job_array_grid_submit.sh\n", + "run_grid.sh\n", "star1\n", "star2\n", "Zbase_0.0014_m1_30.0000_m2_21.0000_initial_z_1.4200e-03_initial_period_in_days_1.0000e+01_grid_index_0\n" @@ -275,14 +282,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "binary_history_columns.list inlist_grid_points\n", - "binary_history.data\t inlist_grid_star1_binary_controls\n", - "final_star1.mod\t\t inlist_grid_star2_binary_controls\n", - "final_star2.mod\t\t LOGS1\n", + "binary_history_columns.list inlist_grid_star1_binary_controls\n", + "binary_history.data.gz\t inlist_grid_star2_binary_controls\n", + "final_star1.mod.gz\t LOGS1\n", "history_columns.list\t LOGS2\n", - "initial_star1.mod\t out.txt\n", - "initial_star2.mod\t profile_columns.list\n", - "inlist\t\t\t tmp.hdf5\n" + "initial_star1.mod.gz\t out.txt.gz\n", + "initial_star2.mod.gz\t profile_columns.list\n", + "inlist\t\t\t tmp.hdf5\n", + "inlist_grid_points\n" ] } ], @@ -292,7 +299,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -301,7 +308,7 @@ "text": [ "\n", "\n", - " read /srv/beegfs/scratch/shares/astro/posydon/simone/documentation/running_mesa/running_1_hms_hms/binary/inlist_project\n", + " read /home/users/k/kruckow/tutorial_run_MESA/binary/inlist_project\n", " read inlist_grid_points\n", " version_number 11701\n", " read inlist_grid_star1_binary_controls\n", @@ -457,53 +464,175 @@ " All this and more can be saved in binary_history.data during the run.\n", " num_steps_to_relax_rotation 50\n", " relax to omega: wanted-current, current, wanted 1 7.2722052166430393D-06 0.0000000000000000D+00 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 2 7.1239371671217664D-06 1.4826804952127298D-07 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 3 6.9775031537422630D-06 2.9470206290077610D-07 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 4 6.8325228134160023D-06 4.3968240322703724D-07 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 5 6.6880335934326623D-06 5.8417162321037739D-07 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 6 6.5429720944820478D-06 7.2923312216099155D-07 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 7 6.3960414376057951D-06 8.7616377903724423D-07 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 8 6.2481185101766360D-06 1.0240867064664032D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 9 6.0988144371784416D-06 1.1733907794645978D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 2 7.1239370299804958D-06 1.4826818666254330D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 3 6.9775031634253124D-06 2.9470205321772722D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 4 6.8325227781707742D-06 4.3968243847226474D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 5 6.6880336129568242D-06 5.8417160368621492D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 6 6.5429721100078805D-06 7.2923310663515874D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 7 6.3960414375365654D-06 8.7616377910647371D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 8 6.2481184946104557D-06 1.0240867220325836D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 9 6.0988143280937228D-06 1.1733908885493166D-06 7.2722052166430393D-06\n", " 10 7.649681 4.541E+04 5.083637 5.083787 30.000000 30.000000 0.746765 0.000508 0.251100 0.747141 -6.159624 2598 0\n", - " 3.021836 0.768039 0.749893 -23.011309 3.935137 -99.000000 0.000000 0.251795 0.000441 0.001420 0.251427 0.009963 3 0\n", - " 2.3543E+03 16.667399 5.081908 1.622938 4.361599 -8.707435 0.000000 0.000004 0.000133 0.000071 1.432E-03 0.000E+00 max increase\n", - "\n", - " relax to omega: wanted-current, current, wanted 10 5.9494923842667907D-06 1.3227128323762491D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 11 5.8047902383525235D-06 1.4674149782905158D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 12 5.6656472219130140D-06 1.6065579947300258D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 13 5.5253850379006039D-06 1.7468201787424357D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 14 5.3813998139082310D-06 1.8908054027348084D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 15 5.2360332812183832D-06 2.0361719354246558D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 16 5.0905569484589031D-06 2.1816482681841367D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 17 4.9451036922867509D-06 2.3271015243562889D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 18 4.7996593421142631D-06 2.4725458745287766D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 19 4.6542152662843934D-06 2.6179899503586464D-06 7.2722052166430393D-06\n", + " 3.021833 0.768039 0.749892 -23.011310 3.935137 -99.000000 0.000000 0.251795 0.000441 0.001420 0.251427 0.009963 3 0\n", + " 2.3543E+03 16.667399 5.081908 1.622938 4.361600 -8.707435 0.000000 0.000004 0.000133 0.000071 1.432E-03 0.000E+00 max increase\n", + "\n", + " relax to omega: wanted-current, current, wanted 10 5.9494920708119283D-06 1.3227131458311106D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 11 5.8047898071657684D-06 1.4674154094772709D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 12 5.6656471713199783D-06 1.6065580453230609D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 13 5.5253851216154814D-06 1.7468200950275575D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 14 5.3814004626389407D-06 1.8908047540040986D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 15 5.2360336852655910D-06 2.0361715313774479D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 16 5.0905570727440271D-06 2.1816481438990118D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 17 4.9451037265468788D-06 2.3271014900961605D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 18 4.7996593714595307D-06 2.4725458451835086D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 19 4.6542152930004046D-06 2.6179899236426347D-06 7.2722052166430393D-06\n", " 20 7.648797 4.525E+04 5.069089 5.069239 30.000000 30.000000 0.746765 0.000508 0.251100 0.747141 -6.164975 2613 0\n", - " 6.032136 0.764405 0.746596 -23.057888 3.920548 -99.000000 0.000000 0.251795 0.000441 0.001420 0.251427 0.009955 2 0\n", - " 2.1539E+06 16.663052 5.069239 1.607921 4.359365 -8.704639 0.000000 0.000004 0.000133 0.000071 1.432E-03 0.000E+00 max increase\n", - "\n", - " relax to omega: wanted-current, current, wanted 20 4.5087711728839026D-06 2.7634340437591372D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 21 4.3633267819036798D-06 2.9088784347393599D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 22 4.2178823225008446D-06 3.0543228941421948D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 23 4.0724377789053992D-06 3.1997674377376405D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 24 3.9269931252400189D-06 3.3452120914030209D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 25 3.7815483288686108D-06 3.4906568877744285D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 26 3.6361034654341649D-06 3.6361017512088744D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 27 3.4906587737615971D-06 3.7815464428814422D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 28 3.3452144110082463D-06 3.9269908056347930D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 29 3.1997702456217982D-06 4.0724349710212411D-06 7.2722052166430393D-06\n", - " 30 7.648797 4.525E+04 5.069078 5.069228 30.000000 30.000000 0.746765 0.000508 0.251100 0.747141 -6.164960 2610 0\n", - " 9.042435 0.764409 0.746606 -23.057923 3.920536 -99.000000 0.000000 0.251795 0.000441 0.001420 0.251427 0.009955 2 0\n", + " 6.032133 0.764405 0.746596 -23.057889 3.920548 -99.000000 0.000000 0.251795 0.000441 0.001420 0.251427 0.009955 2 0\n", + " 2.1538E+06 16.663052 5.069239 1.607921 4.359365 -8.704639 0.000000 0.000004 0.000133 0.000071 1.432E-03 0.000E+00 max increase\n", + "\n", + " relax to omega: wanted-current, current, wanted 20 4.5087711966205017D-06 2.7634340200225373D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 21 4.3633268036162395D-06 2.9088784130267999D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 22 4.2178823416292592D-06 3.0543228750137802D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 23 4.0724377954490792D-06 3.1997674211939601D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 24 3.9269931389606084D-06 3.3452120776824310D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 25 3.7815483392570430D-06 3.4906568773859964D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 26 3.6361034724003977D-06 3.6361017442426417D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 27 3.4906587782149381D-06 3.7815464384281013D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 28 3.3452144143481404D-06 3.9269908022948989D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 29 3.1997702486009012D-06 4.0724349680421382D-06 7.2722052166430393D-06\n", + " 30 7.648796 4.525E+04 5.069078 5.069228 30.000000 30.000000 0.746765 0.000508 0.251100 0.747141 -6.164960 2610 0\n", + " 9.042433 0.764409 0.746606 -23.057923 3.920536 -99.000000 0.000000 0.251795 0.000441 0.001420 0.251427 0.009955 2 0\n", " 2.2053E+09 16.663054 5.069228 1.607910 4.359335 -8.704658 0.000000 0.000004 0.000133 0.000071 1.432E-03 0.000E+00 max increase\n", "\n", - " relax to omega: wanted-current, current, wanted 30 3.0543261335993711D-06 4.2178790830436683D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 31 2.9088820289801618D-06 4.3633231876628776D-06 7.2722052166430393D-06\n" + " relax to omega: wanted-current, current, wanted 30 3.0543261358872070D-06 4.2178790807558323D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 31 2.9088820315467191D-06 4.3633231850963203D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 32 2.7634379268261852D-06 4.5087672898168542D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 33 2.6179938218649762D-06 4.6542113947780632D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 34 2.4725497163505801D-06 4.7996555002924592D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 35 2.3271056116451454D-06 4.9450996049978939D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 36 2.1816615088007590D-06 5.0905437078422803D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 37 2.0362174007229542D-06 5.2359878159200852D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 38 1.8907732940142874D-06 5.3814319226287519D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 39 1.7453291891721467D-06 5.5268760274708927D-06 7.2722052166430393D-06\n", + " 40 7.648795 4.525E+04 5.069062 5.069212 30.000000 30.000000 0.746765 0.000508 0.251100 0.747141 -6.164940 2607 0\n", + " 12.052733 0.764417 0.746620 -23.057971 3.920520 -99.000000 0.000000 0.251795 0.000441 0.001420 0.251427 0.009955 2 0\n", + " 2.2582E+12 16.663057 5.069212 1.607894 4.359294 -8.704686 0.000000 0.000004 0.000133 0.000071 1.432E-03 0.000E+00 max increase\n", + "\n", + " relax to omega: wanted-current, current, wanted 40 1.5998850831994643D-06 5.6723201334435750D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 41 1.4544409770744422D-06 5.8177642395685971D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 42 1.3089968705342554D-06 5.9632083461087839D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 43 1.1635527639419701D-06 6.1086524527010692D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 44 1.0181086591042797D-06 6.2540965575387596D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 45 8.7266455123154744D-07 6.3995406654114919D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 46 7.2722044498914098D-07 6.5449847716538984D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 47 5.8177633970541403D-07 6.6904288769376253D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 48 4.3633223233433363D-07 6.8358729843087057D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 49 2.9088812460997866D-07 6.9813170920330607D-06 7.2722052166430393D-06\n", + " 50 7.648794 4.525E+04 5.069042 5.069192 30.000000 30.000000 0.746765 0.000508 0.251100 0.747141 -6.164913 2607 0\n", + " 15.063033 0.764426 0.746639 -23.058033 3.920500 -99.000000 0.000000 0.251795 0.000441 0.001420 0.251427 0.009955 2 0\n", + " 2.3124E+15 16.663062 5.069192 1.607873 4.359240 -8.704722 0.000000 0.000004 0.000133 0.000071 1.432E-03 0.000E+00 max increase\n", + "\n", + " relax to omega: wanted-current, current, wanted 50 1.4544401957845492D-07 7.1267611970645844D-06 7.2722052166430393D-06\n", + " final step: wanted-current, current, wanted 51 -8.8872453572253797D-14 7.2722053055154929D-06 7.2722052166430393D-06\n", + " num_steps_to_relax_rotation 50\n", + " relax to omega: wanted-current, current, wanted 1 7.2722052166430393D-06 0.0000000000000000D+00 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 2 7.1233476044913972D-06 1.4885761215164192D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 3 6.9765158364664393D-06 2.9568938017660038D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 4 6.8315785028308242D-06 4.4062671381221501D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 5 6.6872664451027811D-06 5.8493877154025819D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 6 6.5422553499448946D-06 7.2994986669814492D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 7 6.3946688756986521D-06 8.7753634094438770D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 8 6.2457320323138947D-06 1.0264731843291449D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 9 6.0961527996288285D-06 1.1760524170142110D-06 7.2722052166430393D-06\n", + " 2 10 7.626746 4.039E+04 4.702123 4.702274 21.000000 21.000000 0.746746 0.000504 0.251100 0.747186 -5.802804 2094 0\n", + " 3.397968 0.888883 0.660278 -24.009890 3.553286 -99.000000 0.000000 0.251813 0.000447 0.001420 0.251381 0.011524 3 0\n", + " 5.6097E+03 16.729885 4.698975 1.243311 4.233606 -8.722599 0.000000 0.000004 0.000133 0.000071 1.433E-03 0.000E+00 max increase\n", + "\n", + " relax to omega: wanted-current, current, wanted 10 5.9497664059555575D-06 1.3224388106874818D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 11 5.8096280128646104D-06 1.4625772037784294D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 12 5.6702121385033817D-06 1.6019930781396575D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 13 5.5268762195040200D-06 1.7453289971390194D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 14 5.3814893418133806D-06 1.8907158748296590D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 15 5.2359962060341824D-06 2.0362090106088570D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 16 5.0905469533439396D-06 2.1816582632991002D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 17 4.9451025348753598D-06 2.3271026817676795D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 18 4.7996593159162278D-06 2.4725459007268115D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 19 4.6542137048352694D-06 2.6179915118077704D-06 7.2722052166430393D-06\n", + " 2 20 7.626057 4.027E+04 4.690568 4.690719 21.000000 21.000000 0.746746 0.000504 0.251100 0.747186 -5.806944 2142 0\n", + " 6.408268 0.886095 0.658647 -24.047363 3.541695 -99.000000 0.000000 0.251813 0.000447 0.001420 0.251381 0.011517 2 0\n", + " 5.1209E+06 16.726495 4.690719 1.231378 4.231839 -8.720788 0.000000 0.000004 0.000133 0.000071 1.433E-03 0.000E+00 max increase\n", + "\n", + " relax to omega: wanted-current, current, wanted 20 4.5087702091765951D-06 2.7634350074664442D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 21 4.3633258925594751D-06 2.9088793240835646D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 22 4.2178814610967289D-06 3.0543237555463109D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 23 4.0724368772888120D-06 3.1997683393542269D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 24 3.9269922854427176D-06 3.3452129312003218D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 25 3.7815476480289336D-06 3.4906575686141058D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 26 3.6361029746011353D-06 3.6361022420419041D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 27 3.4906584885813882D-06 3.7815467280616511D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 28 3.3452142083025420D-06 3.9269910083404973D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 29 3.1997700535865694D-06 4.0724351630564699D-06 7.2722052166430393D-06\n", + " 2 30 7.626056 4.027E+04 4.690559 4.690709 21.000000 21.000000 0.746746 0.000504 0.251100 0.747186 -5.806932 2161 0\n", + " 9.418568 0.886099 0.658656 -24.047394 3.541686 -99.000000 0.000000 0.251813 0.000447 0.001420 0.251381 0.011517 2 0\n", + " 5.2432E+09 16.726497 4.690709 1.231368 4.231817 -8.720799 0.000000 0.000004 0.000133 0.000071 1.433E-03 0.000E+00 max increase\n", + "\n", + " relax to omega: wanted-current, current, wanted 30 3.0543259269715730D-06 4.2178792896714663D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 31 2.9088818039901709D-06 4.3633234126528685D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 32 2.7634376805293879D-06 4.5087675361136514D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 33 2.6179935564114802D-06 4.6542116602315591D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 34 2.4725494315840545D-06 4.7996557850589848D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 35 2.3271053084760396D-06 4.9450999081669997D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 36 2.1816611779797231D-06 5.0905440386633162D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 37 2.0362170531092207D-06 5.2359881635338187D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 38 1.8907729258279075D-06 5.3814322908151319D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 39 1.7453287980464179D-06 5.5268764185966214D-06 7.2722052166430393D-06\n", + " 2 40 7.626055 4.027E+04 4.690545 4.690696 21.000000 21.000000 0.746746 0.000504 0.251100 0.747186 -5.806914 2153 0\n", + " 12.428868 0.886105 0.658667 -24.047437 3.541672 -99.000000 0.000000 0.251813 0.000447 0.001420 0.251381 0.011517 2 0\n", + " 5.3691E+12 16.726501 4.690696 1.231354 4.231787 -8.720815 0.000000 0.000004 0.000133 0.000071 1.433E-03 0.000E+00 max increase\n", + "\n", + " relax to omega: wanted-current, current, wanted 40 1.5998846692471412D-06 5.6723205473958981D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 41 1.4544405402861448D-06 5.8177646763568946D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 42 1.3089964117734896D-06 5.9632088048695497D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 43 1.1635522807878633D-06 6.1086529358551760D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 44 1.0181081448464963D-06 6.2540970717965430D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 45 8.7266401721243792D-07 6.3995411994306014D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 46 7.2721988481735897D-07 6.5449853318256804D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 47 5.8177575181154132D-07 6.6904294648314980D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 48 4.3633161713273364D-07 6.8358735995103057D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 49 2.9088748859537223D-07 6.9813177280476671D-06 7.2722052166430393D-06\n", + " 2 50 7.626054 4.027E+04 4.690527 4.690678 21.000000 21.000000 0.746746 0.000504 0.251100 0.747186 -5.806891 2153 0\n", + " 15.439168 0.886113 0.658682 -24.047492 3.541654 -99.000000 0.000000 0.251813 0.000447 0.001420 0.251381 0.011517 2 0\n", + " 5.4979E+15 16.726505 4.690678 1.231337 4.231748 -8.720836 0.000000 0.000004 0.000133 0.000071 1.433E-03 0.000E+00 max increase\n", + "\n", + " relax to omega: wanted-current, current, wanted 50 1.4544334521563968D-07 7.1267618714273997D-06 7.2722052166430393D-06\n", + " final step: wanted-current, current, wanted 51 -7.8765733440910391D-13 7.2722060043003737D-06 7.2722052166430393D-06\n", + " 1 7.648422 4.523E+04 5.343064 5.343138 29.999999 29.999999 0.746869 0.000476 0.251100 0.747139 -6.166319 2606 0\n", + " 1.307197 0.763236 0.746854 -23.077820 4.293714 -7.350350 0.000000 0.251689 0.000467 0.001420 0.251428 0.009954 4 0\n", + " 2.0286E+01 16.661525 5.068843 1.573581 4.358537 -8.705162 0.000000 0.000013 0.000133 0.000071 1.433E-03 0.000E+00 max increase\n", + "\n", + " 2 1 7.625915 4.026E+04 4.849915 4.850013 21.000000 21.000000 0.746868 0.000475 0.251100 0.747186 -5.807440 2125 0\n", + " 1.307197 0.885645 0.658746 -24.055524 3.785636 -7.994602 0.000000 0.251688 0.000475 0.001420 0.251381 0.011516 5 0\n", + " 2.0286E+01 16.725936 4.690535 1.203320 4.231541 -8.720928 0.000000 0.000008 0.000133 0.000071 1.433E-03 0.000E+00 b_companion\n", + "\n", + " saving initial models\n", + " 10 7.647466 4.517E+04 5.017842 5.017992 29.999977 29.999977 0.746916 0.000476 0.251100 0.747118 -6.169780 2531 0\n", + " 2.019828 0.760299 0.747480 -23.127544 3.892503 -7.353486 0.000000 0.251639 0.000479 0.001420 0.251446 0.009954 4 0\n", + " 5.2660E+02 16.657613 5.067885 1.556907 4.356530 -8.706430 0.000000 0.000004 0.000133 0.000071 1.436E-03 0.000E+00 max increase\n", + "\n", + " 2 10 7.625061 4.021E+04 4.645284 4.645433 20.999995 20.999995 0.746937 0.000465 0.251100 0.747172 -5.811002 2175 0\n", + " 2.019828 0.882850 0.659232 -24.101626 3.538730 -7.998093 0.000000 0.251616 0.000491 0.001420 0.251393 0.011514 3 0\n", + " 5.2660E+02 16.722315 4.689495 1.180081 4.229992 -8.721628 0.000000 0.000004 0.000133 0.000071 1.435E-03 0.000E+00 max increase\n", + "\n", + "bin 10 50.999971 72.417537 10.000011 0.000E+00 0.700000 0 1 0.000E+00 -2.267E-01 4.539E+54 -1.281E+38 0.000E+00\n", + " 2.019828 29.999977 5.590882 10.055301 0.000E+00 150.922990 29.701262 -8.118E-01 -4.431E-08 1.000E+99 6.342E+51 -1.398E+35 -7.998E+33\n", + " 5.2660E+02 20.999995 4.562810 10.036142 -1.651E+49 215.604158 25.237109 -8.192E-01 -1.004E-08 0.000E+00 2.880E+51 -1.280E+38 1\n", + "\n", + "gzip: stdout: Broken pipe\n" ] } ], "source": [ - "!head -n 200 Zbase_0.0014_m1_30.0000_m2_21.0000_initial_z_1.4200e-03_initial_period_in_days_1.0000e+01_grid_index_0/out.txt" + "!gzip -cd Zbase_0.0014_m1_30.0000_m2_21.0000_initial_z_1.4200e-03_initial_period_in_days_1.0000e+01_grid_index_0/out.txt.gz | head -n 320" ] }, { @@ -516,7 +645,7 @@ ], "metadata": { "kernelspec": { - "display_name": "posydon_env", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -530,9 +659,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.7" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/_source/tutorials-examples/MESA-grids/laptop.ipynb b/docs/_source/tutorials-examples/MESA-grids/laptop.ipynb index 9180a1c851..80e8b259bd 100644 --- a/docs/_source/tutorials-examples/MESA-grids/laptop.ipynb +++ b/docs/_source/tutorials-examples/MESA-grids/laptop.ipynb @@ -14,7 +14,7 @@ "For debugging purposes you might want to run a single binary on your laptop to test a quick modification of the inlists, run_star_extras.f or run_binary_extras.f. Assume we want to run the same binary as in the previous tutorial. To use the POSYDON API to run a single binary, you can use the following command:\n", "\n", "```bash\n", - "posydon-setup-grid --grid-type fixed --inifile HMS-HMS_yggdrasil.ini --submission-type shell\n", + "posydon-setup-grid --grid-type fixed --inifile HMS-HMS.ini --submission-type shell\n", "```\n", "\n", "Similar to before, in our directorty we have:" @@ -42,25 +42,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We now export the `SLURM_ARRAY_TASK_ID` enviroment variable corresponding to the csv index of the `grid_test.csv` we want to run, `0` in this case in the temrinal:\n", + "The `grid_command.sh` will run all the binaries in `gird_test.csv`. In case you like to run only one specific simulation you can either remove all other initial conditions from the `gird_test.csv` before running `posydon-setup-grid` (You can rerun it whenever you made changes). Or you can modify the loop in `grid_command.sh` by removing all the indexes you do not like to run.\n", "\n", - "```bash\n", - "export $SLURM_ARRAY_TASK_ID=0\n", - "```\n", - "\n", - "We can now run the MESA binary simulation in the terminal with the following command:\n", + "We can now run the MESA binary simulation in the terminal with the following command:\n", "\n", "```bash\n", - "chmod +x grid_command.sh\n", "./grid_command.sh\n", "``````\n", "\n", - "The terminal will not display an output, the screen output is saved to an `out.txt` file in the simulation directory." + "The terminal will not display the MESA output, the screen output of MESA is saved to an `out.txt` file in the simulation directory." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -68,9 +63,10 @@ "output_type": "stream", "text": [ "binary\n", + "column_lists\n", "grid_command.sh\n", "grid_test.csv\n", - "HMS-HMS_yggdrasil.ini\n", + "HMS-HMS.ini\n", "laptop.ipynb\n", "mk\n", "star1\n", @@ -85,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -93,12 +89,13 @@ "output_type": "stream", "text": [ "binary_history_columns.list inlist_grid_star1_binary_controls\n", - "binary_history.data\t inlist_grid_star2_binary_controls\n", - "history_columns.list\t LOGS1\n", - "initial_star1.mod\t LOGS2\n", - "initial_star2.mod\t out.txt\n", - "inlist\t\t\t profile_columns.list\n", - "inlist_grid_points\t tmp.hdf5\n" + "binary_history.data.gz\t inlist_grid_star2_binary_controls\n", + "final_star1.mod.gz\t LOGS1\n", + "history_columns.list\t LOGS2\n", + "initial_star1.mod.gz\t out.txt.gz\n", + "initial_star2.mod.gz\t profile_columns.list\n", + "inlist\t\t\t tmp.hdf5\n", + "inlist_grid_points\n" ] } ], @@ -108,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -117,7 +114,7 @@ "text": [ "\n", "\n", - " read /srv/beegfs/scratch/shares/astro/posydon/simone/documentation/running_mesa/running_on_laptop/binary/inlist_project\n", + " read /home/users/k/kruckow/tutorial_run_MESA_2/binary/inlist_project\n", " read inlist_grid_points\n", " version_number 11701\n", " read inlist_grid_star1_binary_controls\n", @@ -273,53 +270,175 @@ " All this and more can be saved in binary_history.data during the run.\n", " num_steps_to_relax_rotation 50\n", " relax to omega: wanted-current, current, wanted 1 7.2722052166430393D-06 0.0000000000000000D+00 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 2 7.1239371671217664D-06 1.4826804952127298D-07 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 3 6.9775031537422630D-06 2.9470206290077610D-07 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 4 6.8325228134160023D-06 4.3968240322703724D-07 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 5 6.6880335934326623D-06 5.8417162321037739D-07 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 6 6.5429720944820478D-06 7.2923312216099155D-07 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 7 6.3960414376057951D-06 8.7616377903724423D-07 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 8 6.2481185101766360D-06 1.0240867064664032D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 9 6.0988144371784416D-06 1.1733907794645978D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 2 7.1239370299804958D-06 1.4826818666254330D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 3 6.9775031634253124D-06 2.9470205321772722D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 4 6.8325227781707742D-06 4.3968243847226474D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 5 6.6880336129568242D-06 5.8417160368621492D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 6 6.5429721100078805D-06 7.2923310663515874D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 7 6.3960414375365654D-06 8.7616377910647371D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 8 6.2481184946104557D-06 1.0240867220325836D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 9 6.0988143280937228D-06 1.1733908885493166D-06 7.2722052166430393D-06\n", " 10 7.649681 4.541E+04 5.083637 5.083787 30.000000 30.000000 0.746765 0.000508 0.251100 0.747141 -6.159624 2598 0\n", - " 3.021836 0.768039 0.749893 -23.011309 3.935137 -99.000000 0.000000 0.251795 0.000441 0.001420 0.251427 0.009963 3 0\n", - " 2.3543E+03 16.667399 5.081908 1.622938 4.361599 -8.707435 0.000000 0.000004 0.000133 0.000071 1.432E-03 0.000E+00 max increase\n", - "\n", - " relax to omega: wanted-current, current, wanted 10 5.9494923842667907D-06 1.3227128323762491D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 11 5.8047902383525235D-06 1.4674149782905158D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 12 5.6656472219130140D-06 1.6065579947300258D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 13 5.5253850379006039D-06 1.7468201787424357D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 14 5.3813998139082310D-06 1.8908054027348084D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 15 5.2360332812183832D-06 2.0361719354246558D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 16 5.0905569484589031D-06 2.1816482681841367D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 17 4.9451036922867509D-06 2.3271015243562889D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 18 4.7996593421142631D-06 2.4725458745287766D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 19 4.6542152662843934D-06 2.6179899503586464D-06 7.2722052166430393D-06\n", + " 3.021833 0.768039 0.749892 -23.011310 3.935137 -99.000000 0.000000 0.251795 0.000441 0.001420 0.251427 0.009963 3 0\n", + " 2.3543E+03 16.667399 5.081908 1.622938 4.361600 -8.707435 0.000000 0.000004 0.000133 0.000071 1.432E-03 0.000E+00 max increase\n", + "\n", + " relax to omega: wanted-current, current, wanted 10 5.9494920708119283D-06 1.3227131458311106D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 11 5.8047898071657684D-06 1.4674154094772709D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 12 5.6656471713199783D-06 1.6065580453230609D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 13 5.5253851216154814D-06 1.7468200950275575D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 14 5.3814004626389407D-06 1.8908047540040986D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 15 5.2360336852655910D-06 2.0361715313774479D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 16 5.0905570727440271D-06 2.1816481438990118D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 17 4.9451037265468788D-06 2.3271014900961605D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 18 4.7996593714595307D-06 2.4725458451835086D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 19 4.6542152930004046D-06 2.6179899236426347D-06 7.2722052166430393D-06\n", " 20 7.648797 4.525E+04 5.069089 5.069239 30.000000 30.000000 0.746765 0.000508 0.251100 0.747141 -6.164975 2613 0\n", - " 6.032136 0.764405 0.746596 -23.057888 3.920548 -99.000000 0.000000 0.251795 0.000441 0.001420 0.251427 0.009955 2 0\n", - " 2.1539E+06 16.663052 5.069239 1.607921 4.359365 -8.704639 0.000000 0.000004 0.000133 0.000071 1.432E-03 0.000E+00 max increase\n", - "\n", - " relax to omega: wanted-current, current, wanted 20 4.5087711728839026D-06 2.7634340437591372D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 21 4.3633267819036798D-06 2.9088784347393599D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 22 4.2178823225008446D-06 3.0543228941421948D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 23 4.0724377789053992D-06 3.1997674377376405D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 24 3.9269931252400189D-06 3.3452120914030209D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 25 3.7815483288686108D-06 3.4906568877744285D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 26 3.6361034654341649D-06 3.6361017512088744D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 27 3.4906587737615971D-06 3.7815464428814422D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 28 3.3452144110082463D-06 3.9269908056347930D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 29 3.1997702456217982D-06 4.0724349710212411D-06 7.2722052166430393D-06\n", - " 30 7.648797 4.525E+04 5.069078 5.069228 30.000000 30.000000 0.746765 0.000508 0.251100 0.747141 -6.164960 2610 0\n", - " 9.042435 0.764409 0.746606 -23.057923 3.920536 -99.000000 0.000000 0.251795 0.000441 0.001420 0.251427 0.009955 2 0\n", + " 6.032133 0.764405 0.746596 -23.057889 3.920548 -99.000000 0.000000 0.251795 0.000441 0.001420 0.251427 0.009955 2 0\n", + " 2.1538E+06 16.663052 5.069239 1.607921 4.359365 -8.704639 0.000000 0.000004 0.000133 0.000071 1.432E-03 0.000E+00 max increase\n", + "\n", + " relax to omega: wanted-current, current, wanted 20 4.5087711966205017D-06 2.7634340200225373D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 21 4.3633268036162395D-06 2.9088784130267999D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 22 4.2178823416292592D-06 3.0543228750137802D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 23 4.0724377954490792D-06 3.1997674211939601D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 24 3.9269931389606084D-06 3.3452120776824310D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 25 3.7815483392570430D-06 3.4906568773859964D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 26 3.6361034724003977D-06 3.6361017442426417D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 27 3.4906587782149381D-06 3.7815464384281013D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 28 3.3452144143481404D-06 3.9269908022948989D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 29 3.1997702486009012D-06 4.0724349680421382D-06 7.2722052166430393D-06\n", + " 30 7.648796 4.525E+04 5.069078 5.069228 30.000000 30.000000 0.746765 0.000508 0.251100 0.747141 -6.164960 2610 0\n", + " 9.042433 0.764409 0.746606 -23.057923 3.920536 -99.000000 0.000000 0.251795 0.000441 0.001420 0.251427 0.009955 2 0\n", " 2.2053E+09 16.663054 5.069228 1.607910 4.359335 -8.704658 0.000000 0.000004 0.000133 0.000071 1.432E-03 0.000E+00 max increase\n", "\n", - " relax to omega: wanted-current, current, wanted 30 3.0543261335993711D-06 4.2178790830436683D-06 7.2722052166430393D-06\n", - " relax to omega: wanted-current, current, wanted 31 2.9088820289801618D-06 4.3633231876628776D-06 7.2722052166430393D-06\n" + " relax to omega: wanted-current, current, wanted 30 3.0543261358872070D-06 4.2178790807558323D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 31 2.9088820315467191D-06 4.3633231850963203D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 32 2.7634379268261852D-06 4.5087672898168542D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 33 2.6179938218649762D-06 4.6542113947780632D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 34 2.4725497163505801D-06 4.7996555002924592D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 35 2.3271056116451454D-06 4.9450996049978939D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 36 2.1816615088007590D-06 5.0905437078422803D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 37 2.0362174007229542D-06 5.2359878159200852D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 38 1.8907732940142874D-06 5.3814319226287519D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 39 1.7453291891721467D-06 5.5268760274708927D-06 7.2722052166430393D-06\n", + " 40 7.648795 4.525E+04 5.069062 5.069212 30.000000 30.000000 0.746765 0.000508 0.251100 0.747141 -6.164940 2607 0\n", + " 12.052733 0.764417 0.746620 -23.057971 3.920520 -99.000000 0.000000 0.251795 0.000441 0.001420 0.251427 0.009955 2 0\n", + " 2.2582E+12 16.663057 5.069212 1.607894 4.359294 -8.704686 0.000000 0.000004 0.000133 0.000071 1.432E-03 0.000E+00 max increase\n", + "\n", + " relax to omega: wanted-current, current, wanted 40 1.5998850831994643D-06 5.6723201334435750D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 41 1.4544409770744422D-06 5.8177642395685971D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 42 1.3089968705342554D-06 5.9632083461087839D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 43 1.1635527639419701D-06 6.1086524527010692D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 44 1.0181086591042797D-06 6.2540965575387596D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 45 8.7266455123154744D-07 6.3995406654114919D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 46 7.2722044498914098D-07 6.5449847716538984D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 47 5.8177633970541403D-07 6.6904288769376253D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 48 4.3633223233433363D-07 6.8358729843087057D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 49 2.9088812460997866D-07 6.9813170920330607D-06 7.2722052166430393D-06\n", + " 50 7.648794 4.525E+04 5.069042 5.069192 30.000000 30.000000 0.746765 0.000508 0.251100 0.747141 -6.164913 2607 0\n", + " 15.063033 0.764426 0.746639 -23.058033 3.920500 -99.000000 0.000000 0.251795 0.000441 0.001420 0.251427 0.009955 2 0\n", + " 2.3124E+15 16.663062 5.069192 1.607873 4.359240 -8.704722 0.000000 0.000004 0.000133 0.000071 1.432E-03 0.000E+00 max increase\n", + "\n", + " relax to omega: wanted-current, current, wanted 50 1.4544401957845492D-07 7.1267611970645844D-06 7.2722052166430393D-06\n", + " final step: wanted-current, current, wanted 51 -8.8872453572253797D-14 7.2722053055154929D-06 7.2722052166430393D-06\n", + " num_steps_to_relax_rotation 50\n", + " relax to omega: wanted-current, current, wanted 1 7.2722052166430393D-06 0.0000000000000000D+00 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 2 7.1233476044913972D-06 1.4885761215164192D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 3 6.9765158364664393D-06 2.9568938017660038D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 4 6.8315785028308242D-06 4.4062671381221501D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 5 6.6872664451027811D-06 5.8493877154025819D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 6 6.5422553499448946D-06 7.2994986669814492D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 7 6.3946688756986521D-06 8.7753634094438770D-07 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 8 6.2457320323138947D-06 1.0264731843291449D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 9 6.0961527996288285D-06 1.1760524170142110D-06 7.2722052166430393D-06\n", + " 2 10 7.626746 4.039E+04 4.702123 4.702274 21.000000 21.000000 0.746746 0.000504 0.251100 0.747186 -5.802804 2094 0\n", + " 3.397968 0.888883 0.660278 -24.009890 3.553286 -99.000000 0.000000 0.251813 0.000447 0.001420 0.251381 0.011524 3 0\n", + " 5.6097E+03 16.729885 4.698975 1.243311 4.233606 -8.722599 0.000000 0.000004 0.000133 0.000071 1.433E-03 0.000E+00 max increase\n", + "\n", + " relax to omega: wanted-current, current, wanted 10 5.9497664059555575D-06 1.3224388106874818D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 11 5.8096280128646104D-06 1.4625772037784294D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 12 5.6702121385033817D-06 1.6019930781396575D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 13 5.5268762195040200D-06 1.7453289971390194D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 14 5.3814893418133806D-06 1.8907158748296590D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 15 5.2359962060341824D-06 2.0362090106088570D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 16 5.0905469533439396D-06 2.1816582632991002D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 17 4.9451025348753598D-06 2.3271026817676795D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 18 4.7996593159162278D-06 2.4725459007268115D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 19 4.6542137048352694D-06 2.6179915118077704D-06 7.2722052166430393D-06\n", + " 2 20 7.626057 4.027E+04 4.690568 4.690719 21.000000 21.000000 0.746746 0.000504 0.251100 0.747186 -5.806944 2142 0\n", + " 6.408268 0.886095 0.658647 -24.047363 3.541695 -99.000000 0.000000 0.251813 0.000447 0.001420 0.251381 0.011517 2 0\n", + " 5.1209E+06 16.726495 4.690719 1.231378 4.231839 -8.720788 0.000000 0.000004 0.000133 0.000071 1.433E-03 0.000E+00 max increase\n", + "\n", + " relax to omega: wanted-current, current, wanted 20 4.5087702091765951D-06 2.7634350074664442D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 21 4.3633258925594751D-06 2.9088793240835646D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 22 4.2178814610967289D-06 3.0543237555463109D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 23 4.0724368772888120D-06 3.1997683393542269D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 24 3.9269922854427176D-06 3.3452129312003218D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 25 3.7815476480289336D-06 3.4906575686141058D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 26 3.6361029746011353D-06 3.6361022420419041D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 27 3.4906584885813882D-06 3.7815467280616511D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 28 3.3452142083025420D-06 3.9269910083404973D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 29 3.1997700535865694D-06 4.0724351630564699D-06 7.2722052166430393D-06\n", + " 2 30 7.626056 4.027E+04 4.690559 4.690709 21.000000 21.000000 0.746746 0.000504 0.251100 0.747186 -5.806932 2161 0\n", + " 9.418568 0.886099 0.658656 -24.047394 3.541686 -99.000000 0.000000 0.251813 0.000447 0.001420 0.251381 0.011517 2 0\n", + " 5.2432E+09 16.726497 4.690709 1.231368 4.231817 -8.720799 0.000000 0.000004 0.000133 0.000071 1.433E-03 0.000E+00 max increase\n", + "\n", + " relax to omega: wanted-current, current, wanted 30 3.0543259269715730D-06 4.2178792896714663D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 31 2.9088818039901709D-06 4.3633234126528685D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 32 2.7634376805293879D-06 4.5087675361136514D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 33 2.6179935564114802D-06 4.6542116602315591D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 34 2.4725494315840545D-06 4.7996557850589848D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 35 2.3271053084760396D-06 4.9450999081669997D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 36 2.1816611779797231D-06 5.0905440386633162D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 37 2.0362170531092207D-06 5.2359881635338187D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 38 1.8907729258279075D-06 5.3814322908151319D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 39 1.7453287980464179D-06 5.5268764185966214D-06 7.2722052166430393D-06\n", + " 2 40 7.626055 4.027E+04 4.690545 4.690696 21.000000 21.000000 0.746746 0.000504 0.251100 0.747186 -5.806914 2153 0\n", + " 12.428868 0.886105 0.658667 -24.047437 3.541672 -99.000000 0.000000 0.251813 0.000447 0.001420 0.251381 0.011517 2 0\n", + " 5.3691E+12 16.726501 4.690696 1.231354 4.231787 -8.720815 0.000000 0.000004 0.000133 0.000071 1.433E-03 0.000E+00 max increase\n", + "\n", + " relax to omega: wanted-current, current, wanted 40 1.5998846692471412D-06 5.6723205473958981D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 41 1.4544405402861448D-06 5.8177646763568946D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 42 1.3089964117734896D-06 5.9632088048695497D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 43 1.1635522807878633D-06 6.1086529358551760D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 44 1.0181081448464963D-06 6.2540970717965430D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 45 8.7266401721243792D-07 6.3995411994306014D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 46 7.2721988481735897D-07 6.5449853318256804D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 47 5.8177575181154132D-07 6.6904294648314980D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 48 4.3633161713273364D-07 6.8358735995103057D-06 7.2722052166430393D-06\n", + " relax to omega: wanted-current, current, wanted 49 2.9088748859537223D-07 6.9813177280476671D-06 7.2722052166430393D-06\n", + " 2 50 7.626054 4.027E+04 4.690527 4.690678 21.000000 21.000000 0.746746 0.000504 0.251100 0.747186 -5.806891 2153 0\n", + " 15.439168 0.886113 0.658682 -24.047492 3.541654 -99.000000 0.000000 0.251813 0.000447 0.001420 0.251381 0.011517 2 0\n", + " 5.4979E+15 16.726505 4.690678 1.231337 4.231748 -8.720836 0.000000 0.000004 0.000133 0.000071 1.433E-03 0.000E+00 max increase\n", + "\n", + " relax to omega: wanted-current, current, wanted 50 1.4544334521563968D-07 7.1267618714273997D-06 7.2722052166430393D-06\n", + " final step: wanted-current, current, wanted 51 -7.8765733440910391D-13 7.2722060043003737D-06 7.2722052166430393D-06\n", + " 1 7.648422 4.523E+04 5.343064 5.343138 29.999999 29.999999 0.746869 0.000476 0.251100 0.747139 -6.166319 2606 0\n", + " 1.307197 0.763236 0.746854 -23.077820 4.293714 -7.350350 0.000000 0.251689 0.000467 0.001420 0.251428 0.009954 4 0\n", + " 2.0286E+01 16.661525 5.068843 1.573581 4.358537 -8.705162 0.000000 0.000013 0.000133 0.000071 1.433E-03 0.000E+00 max increase\n", + "\n", + " 2 1 7.625915 4.026E+04 4.849915 4.850013 21.000000 21.000000 0.746868 0.000475 0.251100 0.747186 -5.807440 2125 0\n", + " 1.307197 0.885645 0.658746 -24.055524 3.785636 -7.994602 0.000000 0.251688 0.000475 0.001420 0.251381 0.011516 5 0\n", + " 2.0286E+01 16.725936 4.690535 1.203320 4.231541 -8.720928 0.000000 0.000008 0.000133 0.000071 1.433E-03 0.000E+00 b_companion\n", + "\n", + " saving initial models\n", + " 10 7.647466 4.517E+04 5.017842 5.017992 29.999977 29.999977 0.746916 0.000476 0.251100 0.747118 -6.169780 2531 0\n", + " 2.019828 0.760299 0.747480 -23.127544 3.892503 -7.353486 0.000000 0.251639 0.000479 0.001420 0.251446 0.009954 4 0\n", + " 5.2660E+02 16.657613 5.067885 1.556907 4.356530 -8.706430 0.000000 0.000004 0.000133 0.000071 1.436E-03 0.000E+00 max increase\n", + "\n", + " 2 10 7.625061 4.021E+04 4.645284 4.645433 20.999995 20.999995 0.746937 0.000465 0.251100 0.747172 -5.811002 2175 0\n", + " 2.019828 0.882850 0.659232 -24.101626 3.538730 -7.998093 0.000000 0.251616 0.000491 0.001420 0.251393 0.011514 3 0\n", + " 5.2660E+02 16.722315 4.689495 1.180081 4.229992 -8.721628 0.000000 0.000004 0.000133 0.000071 1.435E-03 0.000E+00 max increase\n", + "\n", + "bin 10 50.999971 72.417537 10.000011 0.000E+00 0.700000 0 1 0.000E+00 -2.267E-01 4.539E+54 -1.281E+38 0.000E+00\n", + " 2.019828 29.999977 5.590882 10.055301 0.000E+00 150.922990 29.701262 -8.118E-01 -4.431E-08 1.000E+99 6.342E+51 -1.398E+35 -7.998E+33\n", + " 5.2660E+02 20.999995 4.562810 10.036142 -1.651E+49 215.604158 25.237109 -8.192E-01 -1.004E-08 0.000E+00 2.880E+51 -1.280E+38 1\n", + "\n", + "gzip: stdout: Broken pipe\n" ] } ], "source": [ - "!head -n 200 Zbase_0.0014_m1_30.0000_m2_21.0000_initial_z_1.4200e-03_initial_period_in_days_1.0000e+01_grid_index_0/out.txt" + "!gzip -cd Zbase_0.0014_m1_30.0000_m2_21.0000_initial_z_1.4200e-03_initial_period_in_days_1.0000e+01_grid_index_0/out.txt.gz | head -n 320" ] }, { @@ -328,16 +447,11 @@ "source": [ "Congratulation, you now know how to harvast the power of POSYDON to run a grid of simulations on a supercomputer!" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "posydon_env", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -351,9 +465,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.7" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/_source/tutorials-examples/MESA-grids/running_a_grid.ipynb b/docs/_source/tutorials-examples/MESA-grids/running_a_grid.ipynb index 56d66abd94..0181545089 100644 --- a/docs/_source/tutorials-examples/MESA-grids/running_a_grid.ipynb +++ b/docs/_source/tutorials-examples/MESA-grids/running_a_grid.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "It is now time to become a POSYDON MESA architect. In this notebook, we will run a real HMS-HMS grid. We will run a downsampled version (100 models) of the HMS-HMS, 0.1Zsun, q=0.7 grid slice, using the same ini file as in the previous notebook." + "It is now time to become a POSYDON MESA architect. In this notebook, we will run a real HMS-HMS grid. We will run a downsampled version (100 models) of the HMS-HMS, 0.1Zsun, q=0.7 grid slice, using the same `HMS-HMS.ini` file as in the [first notebook](https://posydon.org/POSYDON/tutorials-examples/MESA-grids/1_hms_hms.html), where we run one binary." ] }, { @@ -25,12 +25,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's copy the code use in `$PATH_TO_POSYDON/grid_params/POSYDON-MESA-INLISTS/r11701/running_scripts/parameter_space_v2/create_csv.ipynb` to create the csv file and edit it to run a downsampled version of the grid." + "Let's copy some code used in `$PATH_TO_POSYDON/grid_params/POSYDON-MESA-INLISTS/r11701/running_scripts/parameter_space_v2/create_csv.ipynb` to create the `grid_test.csv` file and edit it to run a downsampled version of the grid." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -45,12 +45,9 @@ "import os\n", "import csv\n", "import numpy as np\n", - "import pandas as pd\n", "\n", "def log_range(x_min,x_max,x_n):\n", " return 10**np.linspace(np.log10(x_min),np.log10(x_max), x_n)\n", - "def lin_range(x_min,x_max,x_n):\n", - " return np.linspace(x_min,x_max, x_n)\n", "\n", "# digit rounding pick 10 to be sure we resolve 10^-4Zsun\n", "NDIG = 10\n", @@ -62,24 +59,23 @@ "m1 = log_range(m1_min,m1_max,m1_n)\n", "q_n = 1\n", "q = [0.7]\n", - "p_min = 10**(-1.)\n", - "p_max = 6105\n", + "p_min = 1.0e-1\n", + "p_max = 1.0e+4\n", "p_n = 10\n", "p = log_range(p_min,p_max,p_n)\n", "Z_n = 1\n", "met = [0.1*Zsun]\n", "print('total resolution Z_n * m1_n * q_n * p_n=', Z_n * m1_n * q_n * p_n)\n", "\n", - "for Z in met:\n", - " # save entire grid in a single file\n", - " with open('./grid_test.csv', 'w', newline='') as file:\n", - " writer = csv.writer(file)\n", - " writer.writerow(['initial_z','Zbase','m1','m2','initial_period_in_days'])\n", + "# save entire grid in a single file\n", + "with open('./grid_test.csv', 'w', newline='') as file:\n", + " writer = csv.writer(file)\n", + " writer.writerow(['initial_z','Zbase','m1','m2','initial_period_in_days'])\n", + " for Z in met:\n", " for i in range(m1_n):\n", " for j in range(q_n):\n", " for k in range(p_n):\n", - " if m1[i]*q[j] >= 0.5:\n", - " writer.writerow([round(Z,NDIG),round(Z,NDIG),round(m1[i],NDIG),round(m1[i]*q[j],NDIG),round(p[k],NDIG)])" + " writer.writerow([round(Z,NDIG),round(Z,NDIG),round(m1[i],NDIG),round(m1[i]*q[j],NDIG),round(p[k],NDIG)])" ] }, { @@ -93,11 +89,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We are now ready to run the simulation, with the following commands:\n", + "We are now ready to run the simulation, with the following commands in your terminal (Please remember to have the POSYDON and MESA variables being set before):\n", "\n", "```bash\n", - "posydon-setup-grid --grid-type fixed --inifile HMS-HMS_yggdrasil.ini --submission-type slurm\n", - "sbatch slurm_job_array_grid_submit.sh\n", + "posydon-setup-grid --grid-type fixed --inifile HMS-HMS.ini --submission-type slurm\n", + "./run_grid.sh\n", "```\n", "\n", "Sit back relax, and wait for the grid to finish." @@ -105,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -113,213 +109,217 @@ "output_type": "stream", "text": [ "binary\n", - "grid.csv\n", - "HMS-HMS_yggdrasil.ini\n", - "mesa_grid.28454093_0.out\n", - "mesa_grid.28454093_10.out\n", - "mesa_grid.28454093_11.out\n", - "mesa_grid.28454093_12.out\n", - "mesa_grid.28454093_13.out\n", - "mesa_grid.28454093_14.out\n", - "mesa_grid.28454093_15.out\n", - "mesa_grid.28454093_16.out\n", - "mesa_grid.28454093_17.out\n", - "mesa_grid.28454093_18.out\n", - "mesa_grid.28454093_19.out\n", - "mesa_grid.28454093_1.out\n", - "mesa_grid.28454093_20.out\n", - "mesa_grid.28454093_21.out\n", - "mesa_grid.28454093_22.out\n", - "mesa_grid.28454093_23.out\n", - "mesa_grid.28454093_24.out\n", - "mesa_grid.28454093_25.out\n", - "mesa_grid.28454093_26.out\n", - "mesa_grid.28454093_27.out\n", - "mesa_grid.28454093_28.out\n", - "mesa_grid.28454093_29.out\n", - "mesa_grid.28454093_2.out\n", - "mesa_grid.28454093_30.out\n", - "mesa_grid.28454093_31.out\n", - "mesa_grid.28454093_32.out\n", - "mesa_grid.28454093_33.out\n", - "mesa_grid.28454093_34.out\n", - "mesa_grid.28454093_35.out\n", - "mesa_grid.28454093_36.out\n", - 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"display_name": "posydon_env", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -351,9 +351,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.7" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/_source/tutorials-examples/MESA-grids/running_single_hms.ipynb b/docs/_source/tutorials-examples/MESA-grids/running_single_hms.ipynb index 989df8d55a..959d9eae92 100644 --- a/docs/_source/tutorials-examples/MESA-grids/running_single_hms.ipynb +++ b/docs/_source/tutorials-examples/MESA-grids/running_single_hms.ipynb @@ -25,37 +25,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "You can get the template to run a 0.1Zsun HMS star by running the following code cell. Do not forget to fill up the header of the `ini` file with the appropriate information corresponding to your HPC account. In this example we will run the following MESA inlist branch and commit\n", + "You can reuse your `HMS-HMS.ini` file from the previous tutorials. Simply make a copy of it and call it for example `single_HMS.ini`. Now change the line\n", "```ini\n", - "scenario = ['posydon', 'development-c4f90ce2d93595f66751011d2002fc3ab3d090ec', 'HMS-HMS']\n", + "single_star_grid = False\n", + "```\n", + "to\n", + "```ini\n", + "single_star_grid = True\n", "```" ] }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'./single_HMS_yggdrasil.ini'" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import os\n", - "import shutil\n", - "from posydon.config import PATH_TO_POSYDON\n", - "\n", - "path_to_ini = os.path.join(PATH_TO_POSYDON, \"grid_params/POSYDON-MESA-INLISTS/r11701/running_scripts/single_HMS_yggdrasil.ini\")\n", - "shutil.copyfile(path_to_ini, './single_HMS_yggdrasil.ini')" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -67,19 +46,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We now need to generate the `grid.csv` containing 10 initial values for 10 stars, in this case the initial values are just the mass and metallicity paramters. You can do this by running the following code cell. The code that follows was copied from `$PATH_TO_POSYDON/grid_params/POSYDON-MESA-INLISTS/r11701/running_scripts/parameter_space_v2/create_csv.ipynb`." + "We now need to generate the `grid.csv` containing 10 initial values for 10 stars, in this case the initial values are just the mass and metallicity paramters. You can do this by running the following code cell. The code that follows was copied from `$PATH_TO_POSYDON/grid_params/POSYDON-MESA-INLISTS/r11701/running_scripts/parameter_space_v2/create_csv.ipynb` an adopted to the needs here." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import csv\n", "import numpy as np\n", - "import pandas as pd\n", "\n", "def log_range(x_min,x_max,x_n):\n", " return 10**np.linspace(np.log10(x_min),np.log10(x_max), x_n)\n", @@ -88,23 +66,23 @@ "NDIG = 10\n", "\n", "Zsun = 0.0142\n", - "m1_min = 5.\n", - "m1_max = 100.\n", + "m1_min = 5.0\n", + "m1_max = 100.0\n", "m1_n = 10\n", "m1 = log_range(m1_min,m1_max,m1_n)\n", "met = [0.1*Zsun]\n", "\n", - "for Z in met:\n", - " with open('./grid.csv', 'w', newline='') as file:\n", - " writer = csv.writer(file)\n", - " writer.writerow(['initial_z','Zbase','initial_mass'])\n", + "with open('./grid_test.csv', 'w', newline='') as file:\n", + " writer = csv.writer(file)\n", + " writer.writerow(['initial_z','Zbase','initial_mass'])\n", + " for Z in met:\n", " for i in range(m1_n):\n", " writer.writerow([round(Z,NDIG),round(Z,NDIG),round(m1[i],NDIG)])" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -126,7 +104,7 @@ } ], "source": [ - "!head -n 12 ./grid.csv" + "!cat ./grid_test.csv" ] }, { @@ -140,11 +118,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We are now ready to run the simulation, with the following commands:\n", + "We are now ready to run the simulation, with the following commands (Please remember to have the POSYDON and MESA variables being set before):\n", "\n", "```bash\n", - "posydon-setup-grid --grid-type fixed --inifile single_HMS_yggdrasil.ini --submission-type slurm\n", - "sbatch slurm_job_array_grid_submit.sh\n", + "posydon-setup-grid --grid-type fixed --inifile single_HMS.ini --submission-type slurm\n", + "./run_grid.sh\n", "```\n", "\n", "Sit back relax, and wait for the grid to finish. Check out the advanced tutorial on how to generate the PSyGrid object for single stars." @@ -153,7 +131,7 @@ ], "metadata": { "kernelspec": { - "display_name": "posydon_env", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -167,9 +145,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.7" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/grid_params/grid_params.ini b/grid_params/grid_params.ini index d116ebe300..c06cd8edfe 100644 --- a/grid_params/grid_params.ini +++ b/grid_params/grid_params.ini @@ -28,7 +28,7 @@ account={ACCOUNT} walltime='2-00:00:00' ; work-directory: place, where MESA writes during runtime -work_dir={WORK_DIRECTORY} +; work_dir={WORK_DIRECTORY} ;email email={YOUR_EMAIL_ADDRESS} @@ -37,7 +37,7 @@ email={YOUR_EMAIL_ADDRESS} ; if empty string no changes on group and permission ; group on yggdrasil: GL_S_Astro_POSYDON ; group on Quest: b1119 -newgroup='' +; newgroup={GROUP} [mesa_inlists] @@ -58,7 +58,7 @@ posydon_github_root={PATH_TO_POSYDON_DIRECTORY} ; NOTE: You can use the scenario logic below and *still* supply your own local ; user mesa inlists that will overwrite or tweak some of the physics associated ; with the scenario. -scenario = ['posydon', 'main-9fca4599683fd214a7ac9d21e79f5ce2c0d74396', 'HMS-HMS'] +scenario = ['posydon', 'main-18710e943edd926a5653c4cdb7d6e18e5bdb35a2', 'HMS-HMS'] ; zams_filename if a zams_filename is supplied this supercedes any star1 or star2 formation inlists ; and skips to running the binary with this pre-computed zams model. @@ -101,7 +101,7 @@ binary_job_mesa_defaults = ${posydon_github_root}/grid_params/defaults/r11701/bi ; star1_job star1_job_mesa_defaults = ${posydon_github_root}/grid_params/defaults/r11701/star/star_job.defaults ; star1_job_posydon_defaults = ${posydon_github_root}/grid_params/POSYDON-MESA-INLISTS/r11701/default_common_inlists/binary/inlist1 -; star1_job_user = ${user_template_root}/binary/inlist1 +; star1_job_user = ${user_template_root}/binary/inlist1 ; star1_control star1_controls_mesa_defaults = ${posydon_github_root}/grid_params/defaults/r11701/star/controls.defaults @@ -145,11 +145,11 @@ final_profile_star1 = False final_model_star1 = True ; save history of star2 -history_star2 = False +history_star2 = True ; save profile of star2 final_profile_star2 = False ; save final model of star2 -final_model_star2 = False +final_model_star2 = True [mesa_extras] From e8ed3425c7430163294cd5e05ca8c4ed8334f42e Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 13 Mar 2025 15:57:04 +0100 Subject: [PATCH 306/319] Modular data download (#507) * create data sets * add new functions * Update datasets.py * add command line interaction * add todos to not download full default always as soon as we have more datasets * add more error handling * use dataset name more in outputs * adpot documantation * Update data_download.py filename -> filepath in error * Update installation-guide.rst More info for the PATH* variables * correct typos and add more information * adjust line length --------- Co-authored-by: Jeff Andrews --- bin/get-posydon-data | 4 +- .../getting-started/installation-guide.rst | 33 ++- posydon/binary_evol/MESA/step_mesa.py | 4 +- posydon/binary_evol/SN/step_SN.py | 3 + .../unit_tests/utils/test_data_download.py | 262 +++++++++++++----- posydon/unit_tests/utils/test_datasets.py | 68 +++++ posydon/utils/data_download.py | 208 +++++++++++--- posydon/utils/datasets.py | 167 +++++++++++ 8 files changed, 626 insertions(+), 123 deletions(-) create mode 100644 posydon/unit_tests/utils/test_datasets.py create mode 100644 posydon/utils/datasets.py diff --git a/bin/get-posydon-data b/bin/get-posydon-data index ccb0ac0f39..b37e94cecc 100644 --- a/bin/get-posydon-data +++ b/bin/get-posydon-data @@ -1,5 +1,5 @@ #!/usr/bin/env python -from posydon.utils.data_download import data_download +from posydon.utils.data_download import _get_posydon_data if __name__ == '__main__': - data_download(verbose=True) + _get_posydon_data() diff --git a/docs/_source/getting-started/installation-guide.rst b/docs/_source/getting-started/installation-guide.rst index 313520d0fa..1410badccb 100644 --- a/docs/_source/getting-started/installation-guide.rst +++ b/docs/_source/getting-started/installation-guide.rst @@ -46,32 +46,39 @@ Using Anaconda (Recommended) conda install -c posydon posydon -.. _posydon-env: + .. note:: + Please remember the current path (get it via :code:`pwd`) or the one you specified for the next step. -4. **Download the Dataset** +.. _posydon-env: - .. warning:: - The POSYDON v2.0.0 dataset is not yet available on Zenodo. The above instructions currently point to the POSYDON v1.0.0 dataset release. Please refer to the development version of the dataset available on Northwestern and UNIGE HPC facilities for now. To have access to latest pre-release dataset (230914) you must be a POSYDON core developer, please refer to the #developers Slack channel. +4. **Set Environment Variables** - You can use POSYDON's built-in API command (by default the downloaded data will be saved in the current directory, hence please navigate to your desired location first): + Export the required paths (please change the location names accordingly to your installation from the previous step): .. code-block:: bash - get-posydon-data + export PATH_TO_POSYDON=/path/to/your/posydon/installation + export PATH_TO_POSYDON_DATA=/path/where/you/want/to/store/data + + The path for the data location is up to your choice but we recommend :code:`PATH_TO_POSYDON_DATA=$PATH_TO_POSYDON/data`. - Alternatively, you can manually download the dataset from Zenodo using the provided `link `_. (TODO: update link to v2) + .. note:: + You can add these lines to your :code:`~/.bashrc` or :code:`~/.bash_profile` or your shell equivalent to ensure the environment variables are set every time you open a new terminal. + +5. **Download the Dataset** -5. **Set Environment Variables** + .. warning:: + The POSYDON v2.0.0 dataset is not yet available on Zenodo. The instructions currently point to the POSYDON v1.0.0 dataset release. Please refer to the development version of the dataset available on Northwestern and UNIGE HPC facilities for now. To have access to latest pre-release dataset (241028) you must be a POSYDON core developer, please refer to the #developers Slack channel. - Export the required paths (please change the location names accordingly to your installation): + You can use POSYDON's built-in API command (the downloaded data will be saved in the directory specified by :code:`PATH_TO_POSYDON_DATA`): .. code-block:: bash - export PATH_TO_POSYDON=/path/to/your/posydon/installation - export PATH_TO_POSYDON_DATA=/path/where/you/want/to/store/data + get-posydon-data - .. note:: - You can add these lines to your ``~/.bashrc`` or ``~/.bash_profile`` or your shell equivalent to ensure the environment variables are set every time you open a new terminal. + May use :code:`get-posydon-data -h` to see all the options for this command, which allows to list all the datasets and download the one of your choice. + + Alternatively, you can manually download the datasets from Zenodo. You can find the POSYDON datasets on the `POSYDON community `_ on Zenodo. .. _dev-version: diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index 5fba787124..03b9db483f 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -238,7 +238,7 @@ def load_psyTrackInterp(self, grid_name): filename = os.path.join(self.path,grid_name) if not (os.path.exists(filename.replace('%d','0')) or os.path.exists(filename.replace('_%d',''))): - data_download() + data_download() #TODO: specify dataset if self.verbose: print("loading psyTrackInterp: {}".format(filename)) @@ -254,7 +254,7 @@ def load_Interp(self, filename): # Check if interpolation files exist if not os.path.exists(filename): - data_download() + data_download() #TODO: specify dataset # Load interpolator self._Interp = IFInterpolator() diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index 43b33249e5..5a689bc6cb 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -344,6 +344,7 @@ def format_data_Patton20(file_name): filename = os.path.join(self.path_to_Patton_datasets, file_name) if not os.path.exists(filename): + #TODO: specify dataset, e.g. 'auxiliary' when it exists data_download() # Reading the dataset @@ -2207,6 +2208,7 @@ def __init__(self, engine, path_engine_dataset, verbose): filename = os.path.join(path_engine_dataset, "results_" + self.engine + "_table.csv") if not os.path.exists(filename): + #TODO: specify dataset, e.g. 'auxiliary' when it exists data_download() Engine_data = read_csv(filename) @@ -2400,6 +2402,7 @@ def __init__(self, turbulence_strength, path_engine_dataset, verbose): # Check if interpolation files exist filename = os.path.join(path_to_Couch_datasets, 'explDatsSTIR2.json') if not os.path.exists(filename): + #TODO: specify dataset, e.g. 'auxiliary' when it exists data_download() Couch_data_file = open(filename) diff --git a/posydon/unit_tests/utils/test_data_download.py b/posydon/unit_tests/utils/test_data_download.py index 4c22919377..65d18d18d6 100644 --- a/posydon/unit_tests/utils/test_data_download.py +++ b/posydon/unit_tests/utils/test_data_download.py @@ -1,4 +1,5 @@ """Unit tests of posydon/utils/data_download.py + """ __authors__ = [ @@ -12,60 +13,46 @@ # import other needed code for the tests, which is not already imported in the # module you like to test -from pytest import fixture, raises, approx +from pytest import fixture, raises, warns, approx from inspect import isclass, isroutine from shutil import rmtree from contextlib import chdir from unittest.mock import patch +from posydon.utils.posydonwarning import ReplaceValueWarning # define test classes collecting several test functions class TestElements: # check for objects, which should be an element of the tested module def test_dir(self): - elements = ['PATH_TO_POSYDON_DATA', 'ProgressBar', '__authors__',\ + elements = ['COMPLETE_SETS', 'PATH_TO_POSYDON_DATA', 'ProgressBar',\ + 'Pwarn', 'ZENODO_COLLECTION', '__authors__',\ '__builtins__', '__cached__', '__doc__', '__file__',\ '__loader__', '__name__', '__package__', '__spec__',\ - 'data_download', 'data_url', 'file', 'hashlib',\ - 'original_md5', 'os', 'progressbar', 'tarfile', 'tqdm',\ - 'urllib'] + '_get_posydon_data', '_parse_commandline', 'argparse',\ + 'data_download', 'download_one_dataset', 'hashlib',\ + 'list_datasets', 'os', 'progressbar', 'tarfile',\ + 'textwrap', 'tqdm', 'urllib'] assert dir(totest) == elements, "There might be added or removed "\ + "objects without an update on the "\ + "unit test." - def test_instance_PATH_TO_POSYDON_DATA(self): - assert isinstance(totest.PATH_TO_POSYDON_DATA, (str, bytes,\ - os.PathLike)) - - def test_instance_file(self): - assert isinstance(totest.file, (str, bytes, os.PathLike)) - - def test_instance_data_url(self): - assert isinstance(totest.data_url, (str, bytes, os.PathLike)) - - def test_instance_original_md5(self): - assert isinstance(totest.original_md5, (str, bytes, os.PathLike)) + def test_instance_parse_commandline(self): + assert isroutine(totest._parse_commandline) def test_instance_ProgressBar(self): assert isclass(totest.ProgressBar) - def test_instance_data_download(self): - assert isroutine(totest.data_download) - - -class TestValues: - # check that the values fit - def test_value_PATH_TO_POSYDON_DATA(self): - assert "POSYDON_data" in totest.PATH_TO_POSYDON_DATA + def test_instance_list_datasets(self): + assert isroutine(totest.list_datasets) - def test_value_file(self): - assert "POSYDON_data.tar.gz" in totest.file + def test_instance_download_one_dataset(self): + assert isroutine(totest.download_one_dataset) - def test_value_data_url(self): - assert totest.data_url == "https://zenodo.org/record/14205146/files/"\ - + "POSYDON_data.tar.gz" + def test_instance_data_download(self): + assert isroutine(totest.data_download) - def test_value_original_md5(self): - assert totest.original_md5 == "cf645a45b9b92c2ad01e759eb1950beb" + def test_instance_get_posydon_data(self): + assert isroutine(totest._get_posydon_data) class TestFunctions: @@ -77,7 +64,7 @@ def test_path(self, tmp_path): @fixture def download_statement(self): # statement that the download started - return "Downloading POSYDON data from Zenodo to PATH_TO_POSYDON_DATA=" + return "Downloading POSYDON data '{}' from Zenodo to " @fixture def failed_MD5_statement(self): @@ -87,7 +74,7 @@ def failed_MD5_statement(self): @fixture def extraction_statement(self): # statement that the tar extraction started - return "Extracting POSYDON data from tar file..." + return "Extracting POSYDON data '{}' from tar file..." @fixture def removal_statement(self): @@ -95,9 +82,44 @@ def removal_statement(self): return "Removed downloaded tar file." # test functions - def test_data_download(self, capsys, monkeypatch, test_path,\ - download_statement, failed_MD5_statement,\ - extraction_statement, removal_statement): + def test_parse_commandline(self, monkeypatch): + def mock_parse_args(parser): + return self.commandline_args + def mock_error(parser, message): + raise totest.argparse.ArgumentError(None, message) + with monkeypatch.context() as mp: + mp.setattr(totest.argparse.ArgumentParser, "parse_args", + mock_parse_args) + mp.setattr(totest.argparse.ArgumentParser, "error", mock_error) + # bad input + self.commandline_args = totest.argparse.Namespace(dataset='Test',\ + listedsets=None, nomd5check=False, verbose=False) + with raises(totest.argparse.ArgumentError,\ + match="unknown dataset, use -l to show defined sets"): + totest._parse_commandline() + # example + self.commandline_args = totest.argparse.Namespace(dataset='v1',\ + listedsets=None, nomd5check=False, verbose=False) + assert totest._parse_commandline() == self.commandline_args + + def test_list_datasets(self, capsys): + for v in [True, False]: + # individual sets + totest.list_datasets(individual_sets=True, verbose=v) + captured_output = capsys.readouterr() + assert "Defined individual sets are:" in captured_output.out + if v: + assert "more information at" in captured_output.out + # complete sets + totest.list_datasets(individual_sets=False, verbose=v) + captured_output = capsys.readouterr() + assert "Defined complete sets are:" in captured_output.out + if v: + assert "more information at" in captured_output.out + + def test_download_one_dataset(self, capsys, monkeypatch, test_path,\ + download_statement, failed_MD5_statement,\ + extraction_statement, removal_statement): def mock_urlretrieve(url, filename=None, reporthook=None, data=None): return None class mock_TarFile: @@ -130,76 +152,182 @@ def mock_urlretrieve2(url, filename=None, reporthook=None, data=None): return None # bad input - with raises(TypeError, match="path should be string, bytes, "\ - +"os.PathLike or integer"): - totest.data_download(file={}) + with raises(TypeError, match="'dataset' should be a string."): + totest.download_one_dataset(dataset=None) + # bad input + with raises(KeyError, match="The dataset 'Test' is not defined."): + totest.download_one_dataset(dataset='Test') + # bad input + with monkeypatch.context() as mp: + mock_ZENODO_COLLECTION = {'Test': {'data' : None, 'md5': None}} + mp.setattr(totest, "ZENODO_COLLECTION", mock_ZENODO_COLLECTION) + with raises(ValueError, match="The dataset 'Test' has no "\ + +"publication yet."): + totest.download_one_dataset(dataset='Test') # bad input with monkeypatch.context() as mp: - mock_environ = totest.os.environ.copy() - mock_environ.pop("PATH_TO_POSYDON_DATA") - mp.setattr(totest.os, "environ", mock_environ) - with raises(NameError, match="You must define the "\ - +"PATH_TO_POSYDON_DATA environment "\ - +"variable before downloading "\ - +"POSYDON datasets"): - totest.data_download(file=test_path+".tar.gz") + mock_PATH_TO_POSYDON_DATA = "./DOES_NOT_EXIST/POSYDON_data" + mp.setattr(totest, "PATH_TO_POSYDON_DATA",\ + mock_PATH_TO_POSYDON_DATA) + with raises(NotADirectoryError, match="PATH_TO_POSYDON_DATA does "\ + +"not refer to a valid "\ + +"directory."): + totest.download_one_dataset(dataset='v1_for_v2.0.0-pre1') # bad input - with raises(FileExistsError, match="POSYDON data already exists at "\ - +"./"): - totest.data_download(file="./") + with monkeypatch.context() as mp: + mock_ZENODO_COLLECTION = {'Test': {'data' : "./", 'md5': "Unit"}} + mp.setattr(totest, "ZENODO_COLLECTION", mock_ZENODO_COLLECTION) + with raises(FileExistsError, match="POSYDON data already exists "\ + +"at"): + totest.download_one_dataset(dataset='Test') # skip real download: do nothing instead with monkeypatch.context() as mp: + mp.setattr(totest, "PATH_TO_POSYDON_DATA", test_path) + mock_ZENODO_COLLECTION = {'Test': {'data': "POSYDON_data.tar.gz", + 'md5': "Unit"}} + mp.setattr(totest, "ZENODO_COLLECTION", mock_ZENODO_COLLECTION) mp.setattr(totest.urllib.request, "urlretrieve", mock_urlretrieve) mp.setattr(totest.tarfile, "open", mock_open) # mocked download: fails MD5check, no tar file to remove - totest.data_download(file=test_path+".tar.gz", verbose=True) + totest.download_one_dataset(dataset='Test', verbose=True) captured_output = capsys.readouterr() - assert download_statement in captured_output.out + assert download_statement.format('Test') in captured_output.out assert failed_MD5_statement in captured_output.out - assert extraction_statement in captured_output.out + assert extraction_statement.format('Test') in captured_output.out + assert removal_statement not in captured_output.out + # without MD5 check + totest.download_one_dataset(dataset='Test', MD5_check=False) + captured_output = capsys.readouterr() + assert download_statement.format('Test') in captured_output.out + assert failed_MD5_statement not in captured_output.out + assert extraction_statement.format('Test') in captured_output.out assert removal_statement not in captured_output.out # without MD5 check - totest.data_download(file=test_path+".tar.gz", MD5_check=False) + mock_ZENODO_COLLECTION['Test']['md5'] = None + with warns(ReplaceValueWarning, match="MD5 undefined, skip MD5 "\ + +"check."): + totest.download_one_dataset(dataset='Test') captured_output = capsys.readouterr() - assert download_statement in captured_output.out + assert download_statement.format('Test') in captured_output.out assert failed_MD5_statement not in captured_output.out - assert extraction_statement in captured_output.out + assert extraction_statement.format('Test') in captured_output.out assert removal_statement not in captured_output.out # skip real download: create mock file instead with monkeypatch.context() as mp: + mp.setattr(totest, "PATH_TO_POSYDON_DATA", test_path) + mock_ZENODO_COLLECTION = {'Test0': {'data': "POSYDON_data0.tar.gz", + 'md5': "Unit"}, + 'Test1': {'data': "POSYDON_data1.tar.gz", + 'md5': "Unit"}, + 'Test2': {'data': "POSYDON_data2.tar.gz", + 'md5': "Unit"}} + mp.setattr(totest, "ZENODO_COLLECTION", mock_ZENODO_COLLECTION) mp.setattr(totest.urllib.request, "urlretrieve", mock_urlretrieve2) # mocked download: fails MD5check, which removes tar file and # causes a FileNotFoundError with raises(FileNotFoundError, match="No such file or directory:"): - totest.data_download(file=test_path+"0.tar.gz", verbose=True) + totest.download_one_dataset(dataset='Test0', verbose=True) captured_output = capsys.readouterr() - assert download_statement in captured_output.out + assert download_statement.format('Test0') in captured_output.out assert failed_MD5_statement in captured_output.out - assert extraction_statement in captured_output.out + assert extraction_statement.format('Test0') in captured_output.out assert removal_statement not in captured_output.out # return expected hash for testfile with patch("hashlib.md5") as p_md5: - p_md5.return_value.hexdigest.return_value = totest.original_md5 - totest.data_download(file=test_path+"1.tar.gz") + p_md5.return_value.hexdigest.return_value\ + = mock_ZENODO_COLLECTION['Test1']['md5'] + totest.download_one_dataset(dataset='Test1') captured_output = capsys.readouterr() - assert download_statement in captured_output.out + assert download_statement.format('Test1')\ + in captured_output.out assert failed_MD5_statement not in captured_output.out - assert extraction_statement in captured_output.out + assert extraction_statement.format('Test1')\ + in captured_output.out assert removal_statement not in captured_output.out assert os.path.exists(test_path) assert os.path.exists(os.path.join(test_path, "test1.txt")) rmtree(test_path) # removed extracted data for next test # with verification output - totest.data_download(file=test_path+"2.tar.gz", verbose=True) + totest.download_one_dataset(dataset='Test2', verbose=True) captured_output = capsys.readouterr() - assert download_statement in captured_output.out + assert download_statement.format('Test2')\ + in captured_output.out assert "MD5 verified" in captured_output.out - assert extraction_statement in captured_output.out + assert extraction_statement.format('Test2')\ + in captured_output.out assert removal_statement in captured_output.out assert os.path.exists(test_path) assert os.path.exists(os.path.join(test_path, "test2.txt")) # rmtree(test_path) # removed extracted data for next test + def test_data_download(self, capsys, monkeypatch): + def mock_download_one_dataset(**kwargs): + self.kwargs = kwargs + return + # bad input + with raises(TypeError, match="'set_name' should be a string."): + totest.data_download(set_name=None) + # bad input + with raises(KeyError, match="The dataset 'Test' is not defined."): + totest.data_download(set_name='Test') + # skip real download: do nothing instead + with monkeypatch.context() as mp: + # test single dataset + mock_ZENODO_COLLECTION = {'Test': {}, 'Test2': {}} + mp.setattr(totest, "ZENODO_COLLECTION", mock_ZENODO_COLLECTION) + mp.setattr(totest, "download_one_dataset", mock_download_one_dataset) + for v in [True, False]: + self.kwargs = None + totest.data_download(set_name='Test', verbose=v) + assert self.kwargs['dataset'] == 'Test' + assert self.kwargs['MD5_check'] == True + assert self.kwargs['verbose'] == v + if v: + assert "You are downloading a single data set, which "\ + + "might not contain all the data needed.\n"\ + == capsys.readouterr().out + # test complete set + mock_COMPLETE_SETS = {'Unit': ['Test', 'Test2']} + mp.setattr(totest, "COMPLETE_SETS", mock_COMPLETE_SETS) + self.kwargs = None + totest.data_download(set_name='Unit') + assert self.kwargs['dataset'] == 'Test2' + assert self.kwargs['MD5_check'] == True + assert self.kwargs['verbose'] == False + + def test_get_posydon_data(self, monkeypatch): + def mock_parse_commandline(): + return self.commandline_args + def mock_list_datasets(**kwargs): + self.list_printed = kwargs + def mock_data_download(**kwargs): + self.downloaded = kwargs + with monkeypatch.context() as mp: + mp.setattr(totest, "_parse_commandline", mock_parse_commandline) + mp.setattr(totest, "list_datasets", mock_list_datasets) + mp.setattr(totest, "data_download", mock_data_download) + # examples + for l in ['complete', 'individual']: + for v in [True, False]: + self.list_printed = None + self.commandline_args = totest.argparse.Namespace(\ + dataset='v1', listedsets=l, nomd5check=False, verbose=v) + totest._get_posydon_data() + assert self.list_printed['individual_sets']\ + == (l == 'individual') + assert self.list_printed['verbose'] == v + # examples + for n in [True, False]: + for v in [True, False]: + for d in ['v1', 'v2']: + self.downloaded = None + self.commandline_args = totest.argparse.Namespace(\ + dataset=d, listedsets=None, nomd5check=n, verbose=v) + totest._get_posydon_data() + assert self.downloaded['set_name'] == d + assert self.downloaded['MD5_check'] == (not n) + assert self.downloaded['verbose'] == v + class TestProgressBar: @fixture diff --git a/posydon/unit_tests/utils/test_datasets.py b/posydon/unit_tests/utils/test_datasets.py new file mode 100644 index 0000000000..b78fdda3f6 --- /dev/null +++ b/posydon/unit_tests/utils/test_datasets.py @@ -0,0 +1,68 @@ +"""Unit tests of posydon/utils/datasets.py + +""" + +__authors__ = [ + "Matthias Kruckow " +] + +# import the module which will be tested +import posydon.utils.datasets as totest + +# import other needed code for the tests, which is not already imported in the +# module you like to test + + +# define test classes collecting several test functions +class TestElements: + # check for objects, which should be an element of the tested module + def test_dir(self): + elements = ['COMPLETE_SETS', 'ZENODO_COLLECTION', '__authors__',\ + '__builtins__', '__cached__', '__doc__', '__file__',\ + '__loader__', '__name__', '__package__', '__spec__'] + assert dir(totest) == elements, "There might be added or removed "\ + + "objects without an update on the "\ + + "unit test." + + def test_instance_ZENODO_COLLECTION(self): + assert isinstance(totest.ZENODO_COLLECTION, dict) + + def test_instance_COMPLETE_SETS(self): + assert isinstance(totest.COMPLETE_SETS, dict) + + +class TestValues: + # check that the values fit + def test_value_ZENODO_COLLECTION(self): + # check datasets + for k,s in totest.ZENODO_COLLECTION.items(): + assert isinstance(s, dict), f"ZENODO_COLLECTION['{k}'] should be "\ + + f"a dictionary, but is {type(s)}" + # check required entries for each dataset + for e in ['data', 'md5']: + # entry is string or None (for not yet published datasets) + assert e in s, f"The '{e}' entry is missing for "\ + + f"ZENODO_COLLECTION['{k}']." + assert isinstance(s[e], (str, type(None))),\ + f"ZENODO_COLLECTION['{k}']['{e}'] should be a string "\ + + f"or None, but is {type(s[e])}" + for e in ['description', 'title', 'url']: + # entry is string + assert e in s, f"The '{e}' entry is missing for "\ + + f"ZENODO_COLLECTION[{k}]." + assert isinstance(s[e], str),\ + f"ZENODO_COLLECTION['{k}']['{e}'] should be a string, "\ + + f"but is {type(s[e])}" + + def test_value_COMPLETE_SETS(self): + # check base versions + for k in ['v1', 'v2']: + assert k in totest.COMPLETE_SETS + # check complete datasets + for k,s in totest.COMPLETE_SETS.items(): + assert isinstance(s, list), f"COMPLETE_SETS['{k}'] should be a "\ + + f"list, but is {type(s)}" + # check that individual datasets are defined + for v in s: + assert v in totest.ZENODO_COLLECTION.keys(),\ + f"'{v}' is an unknown dataset." diff --git a/posydon/utils/data_download.py b/posydon/utils/data_download.py index dbae0d5610..240296ea28 100644 --- a/posydon/utils/data_download.py +++ b/posydon/utils/data_download.py @@ -1,22 +1,61 @@ -"""Functions handling the download of data from Zenodo.""" +"""Functions for bin/get-posydon-data to handle the download from Zenodo + +""" __authors__ = [ "Jeff J Andrews ", "Simone Bavera ", + "Matthias Kruckow ", ] -import os -import urllib.request +import argparse import hashlib +import os import progressbar import tarfile +import textwrap +import urllib.request from tqdm import tqdm from posydon.config import PATH_TO_POSYDON_DATA +from posydon.utils.datasets import COMPLETE_SETS, ZENODO_COLLECTION +from posydon.utils.posydonwarning import Pwarn -file = os.path.join(os.path.dirname(PATH_TO_POSYDON_DATA), - "POSYDON_data.tar.gz") -data_url = "https://zenodo.org/record/14205146/files/POSYDON_data.tar.gz" -original_md5 = "cf645a45b9b92c2ad01e759eb1950beb" +def _parse_commandline(): + """Parse the arguments given on the command-line + + Returns + ------- + Namespace + All the passed arguments from the commoand line or their defaults. + + """ + defined_sets = list(COMPLETE_SETS.keys()) + list(ZENODO_COLLECTION.keys()) + parser = argparse.ArgumentParser(description="Downloading POSYDON data " + "from Zenodo") + parser.add_argument('dataset', + help="Name of the dataset to download (default: v1)", + nargs='?', + default='v1') + parser.add_argument('-l', '--listedsets', + help="list the datasets: 'complete' shows the full " + "dataset able to run POSYDON, 'individual' lists " + "the datasets on zenodo, which might need others " + "to run population synthesis (default: complete)", + nargs='?', + const='complete', + choices=['complete', 'individual']) + parser.add_argument('-n', '--nomd5check', + help="do not confirm md5 checksum (default: False)", + default=False, + action='store_true') + parser.add_argument('-v', '--verbose', + help="run in Verbose Mode (default: False)", + default=False, + action='store_true') + args = parser.parse_args() + if args.dataset not in defined_sets: + raise parser.error("unknown dataset, use -l to show defined sets") + return args class ProgressBar(): def __init__(self): @@ -40,40 +79,92 @@ def __call__(self, block_num, block_size, total_size): else: self.pbar.finish() -def data_download(file=file, MD5_check=True, verbose=False): - """Download data files from Zenodo if they do not exist. - - Parameters - ---------- - file : string - - Filename of the data file to be downloaded - MD5_check : boolean - - Use the MD5 check to make sure data is not corrupted - verbose : boolean - - verbose output +def list_datasets(individual_sets=False, verbose=False): + """Print a list of available datasets + + Parameters + ---------- + individual_sets : boolean (default: False) + Show the individual sets or only the complete sets. + verbose : boolean (default: False) + Enables verbose output. """ - # First, make sure the path does not exist - if os.path.exists(file): - raise FileExistsError(f'POSYDON data already exists at {file}') - - # Second, check to make sure PATH_TO_POSYDON_DATA is defined - if "PATH_TO_POSYDON_DATA" not in os.environ: - raise NameError('You must define the PATH_TO_POSYDON_DATA environment ' - 'variable before downloading POSYDON datasets') + if individual_sets: + print("Defined individual sets are:") + for dataset in ZENODO_COLLECTION: + prefix = f" - '{dataset}': " + indent = " "*len(prefix) + wrapper = textwrap.TextWrapper(initial_indent=prefix, width=80, + subsequent_indent=indent) + print(wrapper.fill(ZENODO_COLLECTION[dataset]['title'])) + if verbose: + wrapper = textwrap.TextWrapper(initial_indent=indent, width=80, + subsequent_indent=indent) + print(wrapper.fill(ZENODO_COLLECTION[dataset]['description'])) + print(wrapper.fill("more information at " + +ZENODO_COLLECTION[dataset]['url'])) + else: + print("Defined complete sets are:") + for set_name,complete_set in COMPLETE_SETS.items(): + print(f" - '{set_name}' consisting of:") + for dataset in complete_set: + prefix = f" - '{dataset}': " + indent = " "*len(prefix) + wrapper = textwrap.TextWrapper(initial_indent=prefix, width=80, + subsequent_indent=indent) + print(wrapper.fill(ZENODO_COLLECTION[dataset]['title'])) + if verbose: + wrapper = textwrap.TextWrapper(initial_indent=indent, width=80, + subsequent_indent=indent) + print(wrapper.fill(ZENODO_COLLECTION[dataset]['description'])) + print(wrapper.fill("more information at " + +ZENODO_COLLECTION[dataset]['url'])) + +def download_one_dataset(dataset='v1_for_v2.0.0-pre1', MD5_check=True, + verbose=False): + """Download a data set from Zenodo if they do not exist. + + Parameters + ---------- + dataset : string (default: 'v1_for_v2.0.0-pre1') + Name of the data set to be in COMPLETE_SETS or ZENODO_COLLECTION. + MD5_check : boolean (default: True) + Use the MD5 check to make sure data is not corrupted. + verbose : boolean (default: False) + Enables verbose output. - # Split the file into its directory and filename - directory, filename = os.path.split(file) + """ + if not isinstance(dataset, str): + raise TypeError("'dataset' should be a string.") + if dataset not in ZENODO_COLLECTION: + raise KeyError(f"The dataset '{dataset}' is not defined.") + + # First, generate filename and make sure the path does not exist + data_url = ZENODO_COLLECTION[dataset]['data'] + if data_url is None: + raise ValueError(f"The dataset '{dataset}' has no publication yet.") + original_md5 = ZENODO_COLLECTION[dataset]['md5'] + if original_md5 is None: + MD5_check = False + Pwarn("MD5 undefined, skip MD5 check.", "ReplaceValueWarning") + filename = os.path.basename(data_url) + directory = os.path.dirname(PATH_TO_POSYDON_DATA) + filepath = os.path.join(directory, filename) + if not os.path.isdir(os.path.dirname(filepath)): + raise NotADirectoryError("PATH_TO_POSYDON_DATA does not refer to a " + "valid directory.") + if os.path.exists(filepath): + raise FileExistsError(f"POSYDON data already exists at {filepath}.") # Download the data - print('Downloading POSYDON data from Zenodo ' - f'to PATH_TO_POSYDON_DATA={PATH_TO_POSYDON_DATA}') - urllib.request.urlretrieve(data_url, file, ProgressBar()) + print(f"Downloading POSYDON data '{dataset}' from Zenodo to {directory}") + urllib.request.urlretrieve(data_url, filepath, ProgressBar()) # Compare original MD5 with freshly calculated if MD5_check: try: - with open(file, "rb") as file_to_check: + with open(filepath, "rb") as file_to_check: # read contents of the file data = file_to_check.read() @@ -85,7 +176,7 @@ def data_download(file=file, MD5_check=True, verbose=False): print("MD5 verified.") else: # Delete file - we cannot rely upon that data - os.remove(file) + os.remove(filepath) # Raise value error raise ValueError("MD5 verification failed!.") @@ -95,17 +186,56 @@ def data_download(file=file, MD5_check=True, verbose=False): 'happen only on macOS).') # extract each file - print('Extracting POSYDON data from tar file...') - with tarfile.open(file) as tar: + print(f"Extracting POSYDON data '{dataset}' from tar file...") + with tarfile.open(filepath) as tar: for member in tqdm(iterable=tar.getmembers(), total=len(tar.getmembers())): tar.extract(member=member, path=directory) # remove tar files after extracted - if os.path.exists(file): + if os.path.exists(filepath): if verbose: print('Removed downloaded tar file.') - os.remove(file) - return + os.remove(filepath) + +def data_download(set_name='v1', MD5_check=True, verbose=False): + """Download data files from Zenodo if they do not exist. - return + Parameters + ---------- + set_name : string (default: 'v1') + Name of the data set to be in COMPLETE_SETS or ZENODO_COLLECTION. + MD5_check : boolean (default: True) + Use the MD5 check to make sure data is not corrupted. + verbose : boolean (default: False) + Enables verbose output. + + """ + if not isinstance(set_name, str): + raise TypeError("'set_name' should be a string.") + # Check whether the set is in the complete sets or just a single dataset. + if set_name in COMPLETE_SETS: + for dataset in COMPLETE_SETS[set_name]: + download_one_dataset(dataset=dataset, MD5_check=MD5_check, + verbose=verbose) + elif set_name in ZENODO_COLLECTION: + if verbose: + print("You are downloading a single data set, which might not " + "contain all the data needed.") + download_one_dataset(dataset=set_name, MD5_check=MD5_check, + verbose=verbose) + else: + raise KeyError(f"The dataset '{set_name}' is not defined.") + +def _get_posydon_data(): + """Run the data download or list the datasets + + """ + args = _parse_commandline() + if args.listedsets == 'complete': + list_datasets(individual_sets=False, verbose=args.verbose) + elif args.listedsets == 'individual': + list_datasets(individual_sets=True, verbose=args.verbose) + else: + data_download(set_name=args.dataset, MD5_check=not args.nomd5check, + verbose=args.verbose) diff --git a/posydon/utils/datasets.py b/posydon/utils/datasets.py new file mode 100644 index 0000000000..a03c3d8f4f --- /dev/null +++ b/posydon/utils/datasets.py @@ -0,0 +1,167 @@ +"""Collection of all POSYDON datasets on Zenodo + +Each publication on Zenodo should have and entry in 'ZENODO_COLLECTION' with + - 'data' : str or None + The url to the data (archive) to download. + - 'description' : str + A informative help text to tell about the dataset. + - 'md5' : str or None + The md5 checksum corresponding to the file given in 'data'. + - 'title' : str + The title of the dataset. + - 'url' : str + The url to the Zenodo page of the dataset. + +Individual datasets can be combined to a complete set as input for the POSYDON +code. The complete sets are collected as lists of individual sets, which get +layered in definition order. + +""" + +__authors__ = [ + "Matthias Kruckow ", +] + +ZENODO_COLLECTION = {'POSYDON': + {'data': None, + 'description': "The collection of all POSYDON data "\ + + "sets.", + 'md5': None, + 'title': "POSYDON community", + 'url': "https://zenodo.org/communities/posydon" + }} +COMPLETE_SETS = {} + +# auxiliary data +ZENODO_COLLECTION['auxiliary'] = { + 'data': None, #TODO + 'description': "Auxiliary data for POSYDON. It contains data on "\ + + "supernova prescriptions (Sukhbold+2016, Couch+2020, "\ + + "Patton+Sukhbold2020), star formation history "\ + + "(IllustrisTNG), and detector sensitivity (of O3, O4low, "\ + + "O4high, design of three gravitational wave detectors "\ + + "H1, L1, V1).", #TODO + 'md5': None, #TODO + 'title': "Auxiliary POSYDON data", #TODO + 'url': "https://zenodo.org/communities/posydon" #TODO +} + +# v1 +ZENODO_COLLECTION['v1_for_v2.0.0-pre1'] = { + 'data': "https://zenodo.org/record/14205146/files/POSYDON_data.tar.gz", + 'description': "The POSYDON v1 dataset post-processed to work with the "\ + + "v2.0.0-pre1 version of the code. It contains "\ + + "single-HMS, single-HeMS, HMS-HMS, CO-HMS_RLO, CO-HeMS, "\ + + "CO-HeMS_RLO all at solar metallicity. Additionally, it "\ + + "includes data on supernova prescriptions "\ + + "(Sukhbold+2016, Couch+2020, Patton+Sukhbold2020), star "\ + + "formation history (IllustrisTNG), and detector "\ + + "sensitivity (of O3, O4low, O4high, design of three "\ + + "gravitational wave detectors H1, L1, V1).", + 'md5': "cf645a45b9b92c2ad01e759eb1950beb", + 'title': "Re-postprocessed POSYDON v1.0 dataset compatible with code "\ + + "release v2.0.0-pre1", + 'url': "https://zenodo.org/records/14205146" +} +ZENODO_COLLECTION['super-Eddington_v1'] = { + 'data': "https://zenodo.org/records/14216817/files/"\ + + "POSYDON_data_super_Eddington.tar.gz", + 'description': "Data for super-Eddington accretion compatible with "\ + + "'v1_for_v2.0.0-pre1'. It contains CO-HMS_RLO, CO-HeMS, "\ + + "CO-HeMS_RLO all at solar metallicity to replace the "\ + + "corresponding data in 'v1_for_v2.0.0-pre1'", + 'md5': "64b46fbd65cb54819371a1cbc714019c", + 'title': "Re-postprocessed POSYDON v1.0 dataset, assuming "\ + + "super-Eddington accretion, compatible with code release "\ + + "v2.0.0-pre1", + 'url': "https://zenodo.org/records/14216817" +} +COMPLETE_SETS['v1'] = ['v1_for_v2.0.0-pre1'] +COMPLETE_SETS['super-Eddington_v1'] = ['v1_for_v2.0.0-pre1', + 'super-Eddington_v1'] + +# v2 +ZENODO_COLLECTION['v2_grids_2Zsun'] = { + 'data': None, #TODO + 'description': "The POSYDON v2 dataset at twice solar metallicity. It "\ + + "contains single-HMS, single-HeMS, HMS-HMS, CO-HMS_RLO, "\ + + "CO-HeMS, CO-HeMS_RLO all at twice solar metallicity.", #TODO + 'md5': None, #TODO + 'title': "POSYDON v2.0 dataset at 2 Zsun", #TODO + 'url': "https://zenodo.org/communities/posydon" #TODO +} +ZENODO_COLLECTION['v2_grids_1Zsun'] = { + 'data': None, #TODO + 'description': "The POSYDON v2 dataset at solar metallicity. It "\ + + "contains single-HMS, single-HeMS, HMS-HMS, CO-HMS_RLO, "\ + + "CO-HeMS, CO-HeMS_RLO all at solar metallicity.", #TODO + 'md5': None, #TODO + 'title': "POSYDON v2.0 dataset at Zsun", #TODO + 'url': "https://zenodo.org/communities/posydon" #TODO +} +ZENODO_COLLECTION['v2_grids_0.45Zsun'] = { + 'data': None, #TODO + 'description': "The POSYDON v2 dataset at 0.45 solar metallicity. It "\ + + "contains single-HMS, single-HeMS, HMS-HMS, CO-HMS_RLO, "\ + + "CO-HeMS, CO-HeMS_RLO all at 0.45 solar metallicity.", #TODO + 'md5': None, #TODO + 'title': "POSYDON v2.0 dataset at 0.45 Zsun", #TODO + 'url': "https://zenodo.org/communities/posydon" #TODO +} +ZENODO_COLLECTION['v2_grids_0.2Zsun'] = { + 'data': None, #TODO + 'description': "The POSYDON v2 dataset at 0.2 solar metallicity. It "\ + + "contains single-HMS, single-HeMS, HMS-HMS, CO-HMS_RLO, "\ + + "CO-HeMS, CO-HeMS_RLO all at 0.2 solar metallicity.", #TODO + 'md5': None, #TODO + 'title': "POSYDON v2.0 dataset at 0.2 Zsun", #TODO + 'url': "https://zenodo.org/communities/posydon" #TODO +} +ZENODO_COLLECTION['v2_grids_0.1Zsun'] = { + 'data': None, #TODO + 'description': "The POSYDON v2 dataset at 0.1 solar metallicity. It "\ + + "contains single-HMS, single-HeMS, HMS-HMS, CO-HMS_RLO, "\ + + "CO-HeMS, CO-HeMS_RLO all at 0.1 solar metallicity.", #TODO + 'md5': None, #TODO + 'title': "POSYDON v2.0 dataset at 0.1 Zsun", #TODO + 'url': "https://zenodo.org/communities/posydon" #TODO +} +ZENODO_COLLECTION['v2_grids_0.01Zsun'] = { + 'data': None, #TODO + 'description': "The POSYDON v2 dataset at 0.01 solar metallicity. It "\ + + "contains single-HMS, single-HeMS, HMS-HMS, CO-HMS_RLO, "\ + + "CO-HeMS, CO-HeMS_RLO all at 0.01 solar metallicity.", #TODO + 'md5': None, #TODO + 'title': "POSYDON v2.0 dataset at 0.01 Zsun", #TODO + 'url': "https://zenodo.org/communities/posydon" #TODO +} +ZENODO_COLLECTION['v2_grids_1e-3Zsun'] = { + 'data': None, #TODO + 'description': "The POSYDON v2 dataset at 10^{-3} solar metallicity. It "\ + + "contains single-HMS, single-HeMS, HMS-HMS, CO-HMS_RLO, "\ + + "CO-HeMS, CO-HeMS_RLO all at 10^{-3} solar metallicity.", #TODO + 'md5': None, #TODO + 'title': "POSYDON v2.0 dataset at 10^{-3} Zsun", #TODO + 'url': "https://zenodo.org/communities/posydon" #TODO +} +ZENODO_COLLECTION['v2_grids_1e-4Zsun'] = { + 'data': None, #TODO + 'description': "The POSYDON v2 dataset at 10^{-4} solar metallicity. It "\ + + "contains single-HMS, single-HeMS, HMS-HMS, CO-HMS_RLO, "\ + + "CO-HeMS, CO-HeMS_RLO all at 10^{-4} solar metallicity.", #TODO + 'md5': None, #TODO + 'title': "POSYDON v2.0 dataset at 10^{-4} Zsun", #TODO + 'url': "https://zenodo.org/communities/posydon" #TODO +} +COMPLETE_SETS['v2'] = ['auxiliary', 'v2_grids_2Zsun', 'v2_grids_1Zsun', + 'v2_grids_0.45Zsun', 'v2_grids_0.2Zsun', + 'v2_grids_0.1Zsun', 'v2_grids_0.01Zsun', + 'v2_grids_1e-3Zsun', 'v2_grids_1e-4Zsun'] +COMPLETE_SETS['v2_2Zsun'] = ['auxiliary', 'v2_grids_2Zsun'] +COMPLETE_SETS['v2_1Zsun'] = ['auxiliary', 'v2_grids_1Zsun'] +COMPLETE_SETS['v2_0.45Zsun'] = ['auxiliary', 'v2_grids_0.45Zsun'] +COMPLETE_SETS['v2_0.2Zsun'] = ['auxiliary', 'v2_grids_0.2Zsun'] +COMPLETE_SETS['v2_0.1Zsun'] = ['auxiliary', 'v2_grids_0.1Zsun'] +COMPLETE_SETS['v2_0.01Zsun'] = ['auxiliary', 'v2_grids_0.01Zsun'] +COMPLETE_SETS['v2_1e-3Zsun'] = ['auxiliary', 'v2_grids_1e-3Zsun'] +COMPLETE_SETS['v2_1e-4Zsun'] = ['auxiliary', 'v2_grids_1e-4Zsun'] From 2b4dd8165cc3fca2d6884e52995995beafcbb919 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 13 Mar 2025 16:00:27 +0100 Subject: [PATCH 307/319] Decouple ECSN treatment from SN_MODELS (#504) * Update step_SN.py escape for ECSN in SN_MODELS * add check for ECSN to not use interpolated values * add warning for mismatching SN_type and reorder other warnings * replace np.isnan by pd.isna * attemped to overwrite ECSN in SN_MODEL * add ReplaceValueWarning * add warning if ECSN leads to BH * Update step_SN.py typo * Update step_SN.py typo * Update step_SN.py another typo * Update step_SN.py bug fix --- posydon/binary_evol/SN/step_SN.py | 82 ++++++++++++++++++++++--------- 1 file changed, 60 insertions(+), 22 deletions(-) diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index 5a689bc6cb..0d3722ca93 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -514,6 +514,7 @@ def collapse_star(self, star): # explode if state in STAR_STATES_CC: + SN_type = "" # if no profile is avaiable but interpolation quantities are, # use those, else continue with or without profile. if self.use_interp_values: @@ -522,11 +523,13 @@ def collapse_star(self, star): for MODEL_NAME, MODEL in MODELS.items(): tmp = MODEL_NAME for key, val in MODEL.items(): - if "use_" in key: + if "use_" in key or key=="ECSN": + # escape values, which are allowed to differ continue if getattr(self, key) != val: if self.verbose: - print(tmp, 'mismatch:', key, getattr(self, key), val) + print(tmp, 'mismatch:', key, + getattr(self, key), val) tmp = None break if tmp is not None: @@ -544,19 +547,52 @@ def collapse_star(self, star): # step_disrupted. # allow to continue with the collapse with profile # or core masses - Pwarn(f'{MODEL_NAME_SEL}: The collapsed star ' - 'was not interpolated! If use_profiles ' - 'or use_core_masses is set to True, ' - 'continue with the collapse.', "InterpolationWarning") + Pwarn(f'{MODEL_NAME_SEL}: The collapsed star was not ' + 'interpolated! If use_profiles or use_core_masses ' + 'is set to True, continue with the collapse.', + "InterpolationWarning") else: MODEL_properties = getattr(star, MODEL_NAME_SEL) - ## Check if SN_type mismatches the CO_type in MODEL or if interpolated MODEL properties are NaN - ## If either are true, interpolated values cannot be used for this SN - if (check_SN_CO_match(MODEL_properties['SN_type'], MODEL_properties['state']) and - pd.notna(MODEL_properties['mass'])): - - + SN_type = self.check_SN_type(m_core=star.co_core_mass, + m_He_core=star.he_core_mass, + m_star=star.mass)[3] + if self.use_profiles and star.profile is not None: + alternative = "Instead use profiles." + elif self.use_core_masses: + alternative = "Instead use core masses." + elif self.allow_spin_None: + alternative = "Instead use core mass without spin." + else: + alternative = "" + + if MODEL_properties['SN_type'] == "ECSN": + # overwrite ECSN in SN MODEL + MODEL_properties['SN_type'] = SN_type + Pwarn(f"ECSN in SN_MODEL replaced by {SN_type}", + "ReplaceValueWarning") + + if SN_type == "ECSN": + # do not use interpolated values for ECSN range instead + # behave like use_core_masses=True + pass + ## star's SN_type mismatches one from the MODEL + elif SN_type != MODEL_properties['SN_type']: + Pwarn(f"The SN_type does not match the star: {SN_type}" + f"!={MODEL_properties['SN_type']}."+alternative, + "ApproximationWarning") + ## Check if SN_type mismatches the CO_type in MODEL + elif not check_SN_CO_match(MODEL_properties['SN_type'], + MODEL_properties['state']): + Pwarn(f"{MODEL_NAME_SEL}: The SN_type does not match " + "the predicted CO."+alternative, + "ApproximationWarning") + ## Check if there is no interpolated remnant mass + elif pd.isna(MODEL_properties['mass']): + Pwarn(f"There is no interpolated remnant mass." + +alternative, "ApproximationWarning") + ## Otherwise interpolated values can be used for this SN + else: for key, value in MODEL_properties.items(): setattr(star, key, value) @@ -588,19 +624,13 @@ def collapse_star(self, star): convert_star_to_massless_remnant(star=star) # the mass is set to None # but an orbital kick is still applied. - # Since the mass is set to None, this will lead to a disruption + # Since the mass is set to None, this will lead to + # a disruption # TODO: make it skip the kick caluclation if getattr(star, 'SN_type') != 'PISN': star.log_R = np.log10(CO_radius(star.mass, star.state)) return - - else: - Pwarn(f'{MODEL_NAME_SEL}: The SN_type ' - 'does not match the predicted CO, or the interpolated ' - 'values for the SN remnant are NaN. ' - 'If use_profiles or use_core_masses is set to True, ' - 'continue with the collapse.', "ApproximationWarning") # Verifies the selection of core-collapse mechnism to perform # the collapse @@ -692,11 +722,15 @@ def collapse_star(self, star): star.h1_mass_ej, star.he4_mass_ej = \ get_ejecta_element_mass_at_collapse(star,star.mass,verbose=self.verbose) - elif self.use_core_masses: + elif self.use_core_masses or SN_type == "ECSN": # If the profile is not available the star spin # is used to get the compact object spin star.mass = m_grav if m_grav >= self.max_NS_mass: + if SN_type == "ECSN": + Pwarn("An ECSN should not form a black hole: " + f"m_grav={m_grav}.", + "InappropriateValueWarning") # see Eq. 14, Fryer, C. L., Belczynski, K., Wiktorowicz, # G., Dominik, M., Kalogera, V., & Holz, D. E. (2012), ApJ, 749(1), 91. @@ -829,9 +863,13 @@ def collapse_star(self, star): star.h1_mass_ej, star.he4_mass_ej = \ get_ejecta_element_mass_at_collapse(star,star.mass,verbose=self.verbose) - elif self.use_core_masses: + elif self.use_core_masses or SN_type == "ECSN": star.mass = m_grav if m_grav >= self.max_NS_mass: + if SN_type == "ECSN": + Pwarn("An ECSN should not form a black hole: " + f"m_grav={m_grav}.", + "InappropriateValueWarning") # see Eq. 14, Fryer, C. L., Belczynski, K., Wiktorowicz, # G., Dominik, M., Kalogera, V., & Holz, D. E. (2012), ApJ, 749(1), 91. From aad8cf3da72f45a0002f974525e1886b28901bf6 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 13 Mar 2025 16:04:14 +0100 Subject: [PATCH 308/319] Add WDs to infer_star_state (#512) * add WDs to infer_star_state, incl. testing and new limits * add references --- .../unit_tests/utils/test_common_functions.py | 33 +++++++++++-- .../utils/test_limits_thresholds.py | 22 +++++++++ posydon/utils/common_functions.py | 17 ++++++- posydon/utils/limits_thresholds.py | 48 ++++++++++++++++++- 4 files changed, 111 insertions(+), 9 deletions(-) diff --git a/posydon/unit_tests/utils/test_common_functions.py b/posydon/unit_tests/utils/test_common_functions.py index e929d71b3b..62e255a03d 100644 --- a/posydon/unit_tests/utils/test_common_functions.py +++ b/posydon/unit_tests/utils/test_common_functions.py @@ -73,7 +73,9 @@ def test_dir(self): 'MT_CASE_UNDETERMINED', 'MT_STR_TO_CASE',\ 'Pwarn', 'REL_LOG10_BURNING_THRESHOLD',\ 'RICHNESS_STATES', 'RL_RELATIVE_OVERFLOW_THRESHOLD',\ + 'STATE_NS_STARMASS_LOWER_LIMIT',\ 'STATE_NS_STARMASS_UPPER_LIMIT', 'STATE_UNDETERMINED',\ + 'STATE_WD_STARMASS_UPPER_LIMIT',\ 'Schwarzschild_Radius', 'THRESHOLD_CENTRAL_ABUNDANCE',\ 'THRESHOLD_HE_NAKED_ABUNDANCE', '__authors__',\ '__builtins__', '__cached__', '__doc__', '__file__',\ @@ -1311,18 +1313,39 @@ def test_set_binary_to_failed(self, binary): assert binary.event == "FAILED" def test_infer_star_state(self): - # bad input - with raises(TypeError, match="'<=' not supported between instances "\ - +"of 'NoneType' and 'float'"): - totest.infer_star_state(star_CO=True) # examples: undetermined assert totest.infer_star_state() == totest.STATE_UNDETERMINED # examples: compact objects - tests = [(0.5*totest.STATE_NS_STARMASS_UPPER_LIMIT, "NS"),\ + tests = [(None, "massless_remnant"), (-1.0, "massless_remnant"),\ + (0.0, "massless_remnant"),\ + (0.5*min(totest.STATE_NS_STARMASS_LOWER_LIMIT,\ + totest.STATE_WD_STARMASS_UPPER_LIMIT), "WD"),\ + (totest.STATE_NS_STARMASS_LOWER_LIMIT, "NS"),\ + (0.5*(totest.STATE_NS_STARMASS_LOWER_LIMIT + +totest.STATE_NS_STARMASS_UPPER_LIMIT), "NS"),\ (totest.STATE_NS_STARMASS_UPPER_LIMIT, "NS"),\ (2.0*totest.STATE_NS_STARMASS_UPPER_LIMIT, "BH")] for (m, CO) in tests: assert totest.infer_star_state(star_mass=m, star_CO=True) == CO + # examples: WDs + m = totest.STATE_WD_STARMASS_UPPER_LIMIT + for sH1 in [None, 0.0, 0.1]: + for cH1 in [None, 0.0, 0.1]: + for cHe4 in [None, 0.0, 0.1]: + for cC12 in [None, 0.0, 0.1]: + if (((sH1 is None) or (sH1<=0)) and\ + ((cH1 is None) or (cH1<=0)) and + ((cHe4 is None) or (cHe4<=0)) and + ((cC12 is None) or (cC12<=0))): + CO = "NS" + else: + CO = "WD" + assert totest.infer_star_state(star_mass=m,\ + surface_h1=sH1,\ + center_h1=cH1,\ + center_he4=cHe4,\ + center_c12=cC12,\ + star_CO=True) == CO # examples: loop over all cases THNA = totest.THRESHOLD_HE_NAKED_ABUNDANCE TCA = totest.THRESHOLD_CENTRAL_ABUNDANCE diff --git a/posydon/unit_tests/utils/test_limits_thresholds.py b/posydon/unit_tests/utils/test_limits_thresholds.py index 7cafec49d0..ed05f8f532 100644 --- a/posydon/unit_tests/utils/test_limits_thresholds.py +++ b/posydon/unit_tests/utils/test_limits_thresholds.py @@ -21,7 +21,9 @@ def test_dir(self): 'NEUTRINO_MASS_LOSS_UPPER_LIMIT',\ 'REL_LOG10_BURNING_THRESHOLD',\ 'RL_RELATIVE_OVERFLOW_THRESHOLD',\ + 'STATE_NS_STARMASS_LOWER_LIMIT',\ 'STATE_NS_STARMASS_UPPER_LIMIT',\ + 'STATE_WD_STARMASS_UPPER_LIMIT',\ 'THRESHOLD_CENTRAL_ABUNDANCE',\ 'THRESHOLD_CENTRAL_ABUNDANCE_LOOSE_C',\ 'THRESHOLD_HE_NAKED_ABUNDANCE',\ @@ -79,6 +81,16 @@ def test_instance_LOG10_BURNING_THRESHOLD(self): "LOG10_BURNING_THRESHOLD is of type: "\ + str(type(totest.LOG10_BURNING_THRESHOLD)) + def test_instance_STATE_WD_STARMASS_UPPER_LIMIT(self): + assert isinstance(totest.STATE_WD_STARMASS_UPPER_LIMIT, (float, int)),\ + "STATE_WD_STARMASS_UPPER_LIMIT is of type: "\ + + str(type(totest.STATE_WD_STARMASS_UPPER_LIMIT)) + + def test_instance_STATE_NS_STARMASS_LOWER_LIMIT(self): + assert isinstance(totest.STATE_NS_STARMASS_LOWER_LIMIT, (float, int)),\ + "STATE_NS_STARMASS_LOWER_LIMIT is of type: "\ + + str(type(totest.STATE_NS_STARMASS_LOWER_LIMIT)) + def test_instance_STATE_NS_STARMASS_UPPER_LIMIT(self): assert isinstance(totest.STATE_NS_STARMASS_UPPER_LIMIT, (float, int)),\ "STATE_NS_STARMASS_UPPER_LIMIT is of type: "\ @@ -152,6 +164,16 @@ def test_limits_REL_LOG10_BURNING_THRESHOLD(self): # def test_limits_LOG10_BURNING_THRESHOLD(self): # has no limits + def test_limits_STATE_WD_STARMASS_UPPER_LIMIT(self): + # a mass should be >0 + assert totest.STATE_WD_STARMASS_UPPER_LIMIT > 0.0,\ + "a mass has to be positve" + + def test_limits_STATE_NS_STARMASS_LOWER_LIMIT(self): + # a mass should be >0 + assert totest.STATE_NS_STARMASS_LOWER_LIMIT > 0.0,\ + "a mass has to be positve" + def test_limits_STATE_NS_STARMASS_UPPER_LIMIT(self): # a mass should be >0 assert totest.STATE_NS_STARMASS_UPPER_LIMIT > 0.0,\ diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index 1e9f2c72c7..92e2503916 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -27,7 +27,8 @@ import copy from posydon.utils.limits_thresholds import (THRESHOLD_CENTRAL_ABUNDANCE, THRESHOLD_HE_NAKED_ABUNDANCE, REL_LOG10_BURNING_THRESHOLD, - LOG10_BURNING_THRESHOLD, STATE_NS_STARMASS_UPPER_LIMIT, + LOG10_BURNING_THRESHOLD, STATE_WD_STARMASS_UPPER_LIMIT, + STATE_NS_STARMASS_LOWER_LIMIT, STATE_NS_STARMASS_UPPER_LIMIT, RL_RELATIVE_OVERFLOW_THRESHOLD, LG_MTRANSFER_RATE_THRESHOLD ) from posydon.utils.interpolators import interp1d @@ -1373,7 +1374,19 @@ def infer_star_state(star_mass=None, surface_h1=None, log_LH=None, log_LHe=None, log_Lnuc=None, star_CO=False): """Infer the star state (corresponding to termination flags 2 and 3).""" if star_CO: - return "NS" if star_mass <= STATE_NS_STARMASS_UPPER_LIMIT else "BH" + if ((star_mass is None) or (star_mass<=0)): + return "massless_remnant" + elif ((((surface_h1 is not None) and (surface_h1>0)) or + ((center_h1 is not None) and (center_h1>0)) or + ((center_he4 is not None) and (center_he4>0)) or + ((center_c12 is not None) and (center_c12>0)) or + (star_mass < STATE_NS_STARMASS_LOWER_LIMIT)) and + (star_mass <= STATE_WD_STARMASS_UPPER_LIMIT)): + return "WD" + elif (star_mass <= STATE_NS_STARMASS_UPPER_LIMIT): + return "NS" + else: + return "BH" if surface_h1 is None: return STATE_UNDETERMINED diff --git a/posydon/utils/limits_thresholds.py b/posydon/utils/limits_thresholds.py index fecf143fa4..9ff0b7b1cd 100644 --- a/posydon/utils/limits_thresholds.py +++ b/posydon/utils/limits_thresholds.py @@ -36,7 +36,51 @@ # COMPACT OBJECT LIMITS -STATE_NS_STARMASS_UPPER_LIMIT = 2.5 # maximum mass of a neutron star (in Msol) -NEUTRINO_MASS_LOSS_UPPER_LIMIT = 0.5 # maximum loss in neutrinos (in Msol) +# Theoretical WD mass limits of spherical WDs depend on composition +## classical Chandrasekhar limit, see Eq. (63) in Chandrasekhar (1935): +## M3 = 5.728 / µ^2 Msol = 1.432 Msol for µ = 2 +## from Eq. (10.3) in Pols (2009) or Eq. (43) in Bressan & Shepherd (2024) +## following Kippenhahn & Weigert (1990): MCh = 1.459 * (2/µe)^2 Msol +## from Eq. (57) in Rueda & Ruffini (2013): 1.44 Msol +## from Table 1 in Carvalho, Marinho & Malheiro (2017): M(Newt.) = 1.4546 Msol, +## M(SR) = 1.4358 Msol, M(GR) = 1.4154 Msol, M(non-rel. Newt.) = 1.4564 Msol +## review from Saumon et al. (2022): canonical Chandrasekhar mass of 1.4 Msol +# Roting and/or magnetic WDs can have a mass above the Chandrasekhar limit +## from Das & Mukhopadhyay (2012) about magnetic WDs: up to 2.3-2.6 Msol +# There are direct and indirect observational constraints +## most massive WD from observations in Vennes et al. (1997): 1.41+-0.04 Msol +## massive WDs in solar neighborhood in Kilic et al. (2021): 1.351+-0.006 Msol +## overluminous Type Ia supernova require WDs up to 2.8 Msol, see Das & +## Mukhopadhyay (2013) +# We use a conservative value for non-rotating WDs of 1.46 Msol +STATE_WD_STARMASS_UPPER_LIMIT = 1.46 # maximum mass of a white dwarf (in Msol) +# The limits for neutron stars depend on the underlying equation of state +## secure upper bound on the neutron star mass in Kalogera & Baym (1996): +## mmax = 2.9 Msol +## maximum non-rotating neutron star masses in Fryer et al. (2015): +## mmax = 2.3-2.4 Msol +## from Table 4 in Alsing, Silva & Berti (2017): mmax = 1.96-2.79 Msol +# Most extreme masses of neutron stars observed in NS+WD binaries: +## largest mass from Antoniadis et al. (2013): MNS = 2.01+-0.04 Msol +## lowest mass from Fonseca et al. (2016): MNS = 1.18+0.10-0.09 Msol +# Most extreme masses of neutron stars observed from black widows: +## largest millisecond pulsar from Freire et al. (2008): MNS = 2.74+-0.21 Msol +## from van Kerkwijk, Breton & Kulkarni (2011): MNS = 2.40+-0.12 Msol +## from Romani et al. (2012): MNS = 2.68+-0.14 Msol +## from Romani et al. (2015): MNS = 2.63+0.3-0.2 Msol +# Most massive NS in X-ray binaries: +## from Falanga et al. (2015): MNS = 2.12+-0.16 Msol +# Combining different EM observations from Alsing, Silva & Berti (2017): +# MNS < 2.6 Msol +# Most massive NS in GW mergers: +## GW190814 from Abbott et al. (2021): m2 = 2.59+0.08-0.09 Msol +## GW200210_092254 from Abbott et al. (2023): m2 = 2.83+0.47-0.42 Msol +# We use a conservative value of >1.1 Msol and the canonical value of <2.5 Msol +STATE_NS_STARMASS_LOWER_LIMIT = 1.1 # minimum mass of a neutron star (in Msol) +STATE_NS_STARMASS_UPPER_LIMIT = 2.5 # maximum mass of a neutron star (in Msol) + +# During a collapse escaping neutrinos reduce the mass ending up in the compact +# object +NEUTRINO_MASS_LOSS_UPPER_LIMIT = 0.5 # maximum loss in neutrinos (in Msol) From 1cb434aadd2f6de7d988473e6626b8bd8b4a3a2c Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Tue, 18 Mar 2025 09:16:56 +0100 Subject: [PATCH 309/319] Update setup.py (#541) change >3.10 to >=3.11 --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 4111d55980..aae38c6883 100644 --- a/setup.py +++ b/setup.py @@ -145,7 +145,7 @@ install_requires=install_requires, tests_require=tests_require, extras_require=extras_require, - python_requires=">3.10, <3.12", + python_requires=">=3.11, <3.12", use_2to3=False, classifiers=[ "Development Status :: 4 - Beta", From e0b7e8568b301e8e04ec045e9fc75b122d162212 Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Tue, 18 Mar 2025 09:18:14 +0100 Subject: [PATCH 310/319] Upgrade setuptools (#542) * create new pyproject.toml; try to reuse setup.py as much as possible * add refs --- pyproject.toml | 98 ++++++++++++++++++++++++++++++++++++++++++++++++++ setup.py | 15 ++++---- 2 files changed, 105 insertions(+), 8 deletions(-) create mode 100644 pyproject.toml diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000000..b472809245 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,98 @@ +# following PEP 621: https://peps.python.org/pep-0621 +# details: https://packaging.python.org/en/latest/guides/writing-pyproject-toml + +[build-system] +requires = ["setuptools >= 76.0.0", "versioneer"] +build-backend = "setuptools.build_meta" + +[project] +dynamic = [ + "description", + "license", + "version", + "requires-python", + "classifiers", + "dependencies", + "optional-dependencies", +] +name = "posydon" +#description = "POSYDON the Next Generation of Population Synthesis" +authors = [ + {name = "POSYDON Collaboration", email = "posydon.team@gmail.com"}, +] +maintainers = [ + {name = "POSYDON Collaboration", email = "posydon.team@gmail.com"}, +] +#license = 'GPLv3+' +#version = "2.0.0.dev" +#requires-python = ">=3.11, <3.12" +readme = "README.md" +keywords = [ + "POSYDON", + "Astronomy", + "Binary Stars", + "Population Synthesis", + "MESA", +] +#classifiers = [ +# 'Development Status :: 4 - Beta', +# 'Intended Audience :: Science/Research', +# 'Intended Audience :: End Users/Desktop', +# 'Topic :: Scientific/Engineering', +# 'Topic :: Scientific/Engineering :: Astronomy', +# 'Topic :: Scientific/Engineering :: Physics', +# 'Programming Language :: Python', +# 'Programming Language :: Python :: 3.11', +# 'Operating System :: POSIX', +# 'Operating System :: Unix', +# 'Operating System :: MacOS', +# 'Natural Language :: English', +# 'License :: OSI Approved :: GNU General Public License v3 (GPLv3+)', +#] +#dependencies = [ +# 'numpy < 2.0.0, >= 1.24.2', +# 'scipy <= 1.14.1, >= 1.10.1', +# 'iminuit <= 2.30.1, >= 2.21.3', +# 'configparser <= 7.1.0, >= 5.3.0', +# 'astropy <= 6.1.6, >= 5.2.2', +# 'pandas <= 2.2.3, >= 2.0.0', +# 'scikit-learn == 1.2.2', +# 'matplotlib <= 3.9.2, >= 3.9.0', +# 'matplotlib-label-lines <= 0.7.0, >= 0.5.2', +# 'h5py <= 3.12.1, >= 3.8.0', +# 'psutil <= 6.1.0, >= 5.9.4', +# 'tqdm <= 4.67.0, >= 4.65.0', +# 'tables <= 3.10.1, >= 3.8.0', +# 'progressbar2 <= 4.5.0, >= 4.2.0', +# 'hurry.filesize <= 0.9, >= 0.9', +# 'python-dotenv <= 1.0.1, >= 1.0.0', +#] + +#[project.optional-dependencies] +#doc = [ +# 'ipython', +# 'sphinx >= 8.2.2', +# 'numpydoc', +# 'sphinx_rtd_theme', +# 'sphinxcontrib_programoutput', +# 'PSphinxTheme', +# 'nbsphinx', +# 'pandoc' +#] +#vis = [ +# 'PyQt5 <= 5.15.11, >= 5.15.9' +#] +#ml = [ +# 'tensorflow >= 2.13.0' +#] +#hpc = [ +# 'mpi4py >= 3.0.3' +#] + +[project.urls] +Homepage = "https://posydon.org" +Documentation = "https://posydon.org/POSYDON" +Repository = "https://github.com/POSYDON-code/POSYDON.git" +Issues = "https://github.com/POSYDON-code/POSYDON/issues" +Changelog = "https://github.com/POSYDON-code/POSYDON/releases" + diff --git a/setup.py b/setup.py index aae38c6883..26b571645d 100644 --- a/setup.py +++ b/setup.py @@ -48,13 +48,13 @@ # DEPENDENCIES +setup_requires = [ + 'setuptools >= 76.0.0', +] if 'test' in sys.argv: - setup_requires = [ - 'setuptools', + setup_requires += [ 'pytest-runner', ] -else: - setup_requires = [] # These pretty common requirement are commented out. Various syntax types @@ -149,18 +149,17 @@ use_2to3=False, classifiers=[ "Development Status :: 4 - Beta", - "Programming Language :: Python", - "Programming Language :: Python :: 3.11", "Intended Audience :: Science/Research", "Intended Audience :: End Users/Desktop", - "Intended Audience :: Science/Research", - "Natural Language :: English", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Astronomy", "Topic :: Scientific/Engineering :: Physics", + "Programming Language :: Python", + "Programming Language :: Python :: 3.11", "Operating System :: POSIX", "Operating System :: Unix", "Operating System :: MacOS", + "Natural Language :: English", "License :: OSI Approved :: GNU General Public License v3 (GPLv3+)", ], ) From 8a3f45aac7fd690443637ce1b13852987d094f7e Mon Sep 17 00:00:00 2001 From: mkruckow <122798003+mkruckow@users.noreply.github.com> Date: Thu, 20 Mar 2025 09:48:08 +0100 Subject: [PATCH 311/319] set post SN binary to disrupted in case there is a massless_remnant (#548) --- posydon/binary_evol/SN/step_SN.py | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/posydon/binary_evol/SN/step_SN.py b/posydon/binary_evol/SN/step_SN.py index 0d3722ca93..eea02d6bd4 100644 --- a/posydon/binary_evol/SN/step_SN.py +++ b/posydon/binary_evol/SN/step_SN.py @@ -1655,6 +1655,27 @@ def orbital_kick(self, binary): binary.mass_transfer_case = 'None' binary.first_SN_already_occurred = True + elif ((binary.star_1.state == 'massless_remnant') or + (binary.star_2.state == 'massless_remnant')): + # the binary should be disrupted when a massless_remnant formed + # update the tilt + if not binary.first_SN_already_occurred: + binary.star_1.spin_orbit_tilt_first_SN = np.nan + binary.star_2.spin_orbit_tilt_first_SN = np.nan + binary.first_SN_already_occurred = True + else: + binary.star_1.spin_orbit_tilt_second_SN = np.nan + binary.star_2.spin_orbit_tilt_second_SN = np.nan + + binary.state = "disrupted" + binary.event = None + binary.separation = np.nan + binary.eccentricity = np.nan + binary.V_sys = np.array([0, 0, 0]) + binary.time = binary.time_history[-1] + binary.orbital_period = np.nan + binary.mass_transfer_case = 'None' + else: # The binary exist: flag_binary is True if the binary is not disrupted From 9155935e916cf77e196245445c296158ef64f790 Mon Sep 17 00:00:00 2001 From: Max <14039563+maxbriel@users.noreply.github.com> Date: Thu, 20 Mar 2025 14:38:24 +0100 Subject: [PATCH 312/319] Update population_params_default.ini (#544) Update to give the correct usage for the engine if 'other' mechanisms are used --- posydon/popsyn/population_params_default.ini | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/posydon/popsyn/population_params_default.ini b/posydon/popsyn/population_params_default.ini index 81843d9a2c..0f65288bd7 100644 --- a/posydon/popsyn/population_params_default.ini +++ b/posydon/popsyn/population_params_default.ini @@ -210,7 +210,7 @@ # 'Sukhbold+16-engine', 'Patton&Sukhbold20-engine' engine = 'N20' # 'N20' for 'Sukhbold+16-engine', - # 'Patton&Sukhbold20-engine' or None for the others + # 'Patton&Sukhbold20-engine' or '' for the others PISN = "Marchant+19" # None, "Marchant+19", "Hendriks+23" PISN_CO_shift = 0.0 From 5e0646e8bb9433369be4084ae1c197b81217024f Mon Sep 17 00:00:00 2001 From: prinse1545 Date: Thu, 26 Jun 2025 09:27:20 -0500 Subject: [PATCH 313/319] new interpolator --- posydon/interpolation/data_scaling.py | 43 +- posydon/interpolation/new_interpolator.py | 462 ++++++++++++++++++ posydon/unit_tests/interpolation/__init__.py | 0 .../interpolation/test_IF_interpolation.py | 22 + 4 files changed, 512 insertions(+), 15 deletions(-) create mode 100644 posydon/interpolation/new_interpolator.py create mode 100644 posydon/unit_tests/interpolation/__init__.py create mode 100644 posydon/unit_tests/interpolation/test_IF_interpolation.py diff --git a/posydon/interpolation/data_scaling.py b/posydon/interpolation/data_scaling.py index 901e66f61c..7fbbd9ee7f 100644 --- a/posydon/interpolation/data_scaling.py +++ b/posydon/interpolation/data_scaling.py @@ -8,6 +8,18 @@ import numpy as np +eps = 1.0e-16 + +SCALING_OPTIONS = [ + "min_max", + "max_abs", + "standardize", + "log_min_max", + "neg_log_min_max", + "log_max_abs", + "log_standardize", + "neg_log_standardize" +] class DataScaler: """Data Normalization class. @@ -68,27 +80,28 @@ def fit(self, x, method='none', lower=-1.0, upper=1.0): if method == 'min_max': assert upper > lower, "upper must be greater than lower" self.lower, self.upper = lower, upper - self.params = [x.min(axis=0), x.max(axis=0)] + self.params = [np.nanmin(x, axis=0), np.nanmax(x, axis=0)] elif method == 'log_min_max': assert upper > lower, "upper must be greater than lower" self.lower, self.upper = lower, upper - self.params = [np.log10(x.min(axis=0)), np.log10(x.max(axis=0))] + self.params = [np.log10(np.nanmin(x, axis=0)), np.log10(np.nanmax(x, axis=0))] + elif method == 'neg_log_min_max': assert upper > lower, "upper must be greater than lower" self.lower, self.upper = lower, upper - self.params = [np.log10((-x).min(axis=0)), - np.log10((-x).max(axis=0))] + self.params = [np.log10(np.nanmin(-x, axis=0)), + np.log10(np.nanmax(-x, axis=0))] elif method == 'max_abs': - self.params = [np.abs(x).max(axis=0)] + self.params = [np.nanmax(np.abs(x), axis=0)] elif method == 'log_max_abs': - self.params = [np.abs(np.log10(x)).max(axis=0)] - elif method == 'standarize': - self.params = [x.mean(axis=0), x.std(axis=0)] - elif method == 'log_standarize': + self.params = [np.nanmax(np.abs(np.log10(x)), axis=0)] + elif method == 'standardize': + self.params = [np.nanmean(x, axis=0), np.nanstd(x, axis=0)] + elif method == 'log_standardize': # log will be computed in transform again - self.params = [np.log10(x).mean(axis=0), np.log10(x).std(axis=0)] - elif method == 'neg_log_standarize': # log(-x) - self.params = [np.log10(-x).mean(axis=0), np.log10(-x).std(axis=0)] + self.params = [np.nanmean(np.log10(x), axis=0), np.nanstd(np.log10(x), axis=0)] + elif method == 'neg_log_standardize': # log(-x) + self.params = [np.nanmean(np.log10(-x), axis=0), np.nanstd(np.log10(-x), axis=0)] elif method == 'log': self.params = [] elif method == 'none': # no transformation @@ -135,12 +148,12 @@ def transform(self, x): x_t = x / self.params[0] elif self.method == 'log_max_abs': x_t = np.log10(x) / self.params[0] - elif self.method == 'standarize': + elif self.method == 'standardize': x_t = (x - self.params[0]) / self.params[1] - elif self.method == 'log_standarize': + elif self.method == 'log_standardize': # log will be computed in transform again x_t = (np.log10(x) - self.params[0]) / self.params[1] - elif self.method == 'neg_log_standarize': + elif self.method == 'neg_log_standardize': x_t = (np.log10(-x) - self.params[0]) / self.params[1] elif self.method == 'log': x_t = np.log10(x) diff --git a/posydon/interpolation/new_interpolator.py b/posydon/interpolation/new_interpolator.py new file mode 100644 index 0000000000..e7b0b34fe6 --- /dev/null +++ b/posydon/interpolation/new_interpolator.py @@ -0,0 +1,462 @@ +""" +Module implementing initial-final (IF) interpolation. + +""" + +import numpy as np +import os +import pickle +from datetime import date + +from scipy.spatial import Delaunay +# POSYDON +from posydon.grids.psygrid import PSyGrid +from posydon.interpolation.data_scaling import DataScaler, SCALING_OPTIONS +from posydon.utils.posydonwarning import Pwarn +from posydon.interpolation.constraints import ( + find_constraints_to_apply, sanitize_interpolated_quantities) + +# ML Imports +from sklearn.neighbors import KNeighborsClassifier +from sklearn.model_selection import train_test_split +from sklearn.metrics import balanced_accuracy_score + +import sys + +eps = 1.0e-16 + +# INITIAL-FINAL INTERPOLATOR +class IFInterpolator: + """Class handling initial-final interpolation. + + Class handling initial-final interpolation with support to interpolate + certain keys by differing classes. + """ + + def __init__(self, grids = None, interpolators = None): + """Initialize the IFInterpolator class. + + Parameters + ---------- + grid : PSyGrid + The training grid or tuple of PSyGrid (training, validation) + interpolators : list of dictionaries + Contain parameters for the BaseIFIneterpolators to be constructed + + """ + self.interpolators = [] + self.interpolator_parameters = interpolators + self.grids = grids + + if self.interpolator_parameters is not None: + out_keys = [] + + for params in self.interpolator_parameters: + + if ("out_keys" not in params + and len(self.interpolator_parameters) > 1): + raise ValueError("Overlapping out keys between different " + "interpolators are not permited!") + elif "out_keys" not in params: + continue + + for key in params["out_keys"]: + if(key in out_keys): + raise ValueError( + f"Overlapping out keys between different " + f"interpolators are not permited! ({key} in more " + f"than one set of out_keys)") + else: + out_keys.append(key) + + + def train(self): + """Train the interpolator(s) on the PSyGrid used for construction.""" + for interpolator in self.interpolator_parameters: + interp = BaseIFInterpolator(grids=self.grids, + **interpolator) + interp.train() + self.interpolators.append(interp) + + self.interp_in_q = self.interpolators[0].interp_in_q + + def evaluate(self, binary, sanitization_verbose=False): + """Get the output vector approximation. + + Get the output vector approximation as well as all of the + classifications given a Binary Star + + Parameters + ---------- + binary : BinaryStar + A class which is an input vector which are to be classified and for + which an output vector will be approximated. + + Returns + ------- + Output space approximation as a tuple containing two dictionary + + """ + ynums = [] + ycats = [] + n = [] + + for interpolator in self.interpolators: + ynum, ycat, ns = interpolator.evaluate(binary) + + ynums.extend(ynum) + ycats.extend(ycat) + n.extend(ns) + + return ynums, ycats, n + + + def test_interpolator(self, initial_values, sanitization_verbose = False): + """ Method that can take in a 2-D numpy array for more efficient use of the interpolator + + Parameters + ---------- + initial_values : numpy array + A numpy array containing the in-key values of the binaries to be evolved + + Return + ------ + Interpolated values + """ + new_values = np.array(initial_values, copy = True) + + if self.interp_in_q: + new_values.T[1] = new_values.T[1] / new_values.T[0] + + final_values = [] + + for interpolator in self.interpolators: + final_values.append(interpolator.test_interpolator(new_values).T) + + return np.concatenate(final_values).T + + def test_classifiers(self, initial_values): + """ Method that can take in a 2-D numpy array for more efficient use of classifiers + + Parameters + ---------- + initial_values : numpy array + A numpy array containing the in-key values of the binaries to be classified + + Return + ------ + Classified values + """ + new_values = np.array(initial_values, copy = True) + + if self.interp_in_q: + new_values.T[1] = new_values.T[1] / new_values.T[0] + + classes = {} + + for interpolator in self.interpolators: + + classes = {**classes, **interpolator.test_classifiers(new_values)} + + return classes + + + + def load(self, filename): + """Load a saved IFInterpolator from a .pkl file. + + Parameters + ---------- + filename : string + Path to the .pkl file + + """ + with open(filename, 'rb') as f: + self.interpolators = pickle.load(f) + + self.interp_in_q = self.interpolators[0].interp_in_q + + def save(self, filename): + """Save the interpolator to a .pkl file. + + Parameters + ---------- + filename : string + Path where .pkl file will be saved + + """ + with open(filename, "wb") as f: + pickle.dump(self.interpolators, f) + +class BaseIFInterpolator: + + def __init__(self, grids, in_keys, out_keys, interpolation_class, max_k): + + if type(grids) != list: + sys.exit("Please provide a list of PSyGrids containing both a training and validation grid to train the interpolator") + else: + + self.in_keys = in_keys + + self.continous_out_keys = out_keys # keys to be interpolated which correspond to numerical quantities + self.discrete_out_keys = out_keys # keys to be interpolated which correspond to discrete quantities + + self.interpolation_class = interpolation_class + self.max_k = max_k + + self.training_grid = self.preprocess_grid(grids[0], training_grid = True) + self.validation_grid = self.preprocess_grid(grids[1]) + + self.triangulate(self.training_grid) + + def train(self): + self.find_hyperparameters() + # self.optimize_normalization() + + def evaluate(self, initial_values): + + if self.classifier is None: + sys.exit("Please find classifier hyperparameters before using interpolator") + + if not self.training: + initial_values = ( np.log10(initial_values + eps) - self.iv_min ) / (self.iv_max - self.iv_min + eps) + + classes = self.classifier.predict(initial_values) + + interpolated_values = [] + n = [] + + for iv, klass in zip(initial_values, classes): + if klass == "initial_MT": + continue + + triangulation = self.training_grid["triangulations"][klass] + + simplex = triangulation.find_simplex(iv) + + if simplex == -1: + interpolated_values.append( + self.get_nearest_neighbor(iv) + ) + continue + + vertices = triangulation.simplices[simplex] + + class_inds = self.training_grid["class_inds"][klass] + + final_values = self.training_grid["final_values"][class_inds][vertices] + + if self.training: + final_values = self.normalize_output(final_values, klass) + + barycentric_weights = self.compute_barycentric_coordinates(iv, triangulation.points[vertices])[..., np.newaxis] + + interpolated = np.sum(final_values * barycentric_weights, axis = 0) + + n.append({ + "weights": barycentric_weights, + "ics": triangulation.points[vertices], + "ic": iv, + "final_values": final_values, + "interpolated": interpolated + }) + + # interpolated = self.denormalize_output(interpolated, klass) + interpolated_values.append(interpolated) + + interpolated_values = np.array(interpolated_values) + classes = np.array(classes) + + return interpolated_values, classes, n + + def find_hyperparameters(self): + + k_accuracies = [] + + for k in range(1, self.max_k): + + validation_classifier = KNeighborsClassifier(n_neighbors = k, weights = "distance") + validation_classifier.fit(self.training_grid["initial_values"], self.training_grid["classes"]) + + predicted_classes = validation_classifier.predict(self.validation_grid["initial_values"]) + + k_accuracies.append( + balanced_accuracy_score(predicted_classes, self.validation_grid["classes"]) + ) + + self.k_accuracies = np.array(k_accuracies) + + self.k_star = self.k_accuracies.argmax() + + self.classifier = KNeighborsClassifier(n_neighbors = self.k_star, weights = "distance") + self.classifier.fit(self.training_grid["initial_values"], self.training_grid["classes"]) + + def optimize_normalization(self): + + output_dim = len(self.continous_out_keys) + + all_scalers = [] + scaler_accuracies = np.zeros(shape = (len(SCALING_OPTIONS), len(self.classes), output_dim)) + + self.training = True + + for i, option in enumerate(SCALING_OPTIONS): + scalers = [[DataScaler()] * output_dim] * len(self.classes) + + for j, klass in enumerate(self.classes): + for parameter in range(output_dim): + param_min = np.nanmin(self.training_grid["final_values"][:, parameter]) + if param_min < 0 and "log" in option: + scalers[j][parameter].fit(self.training_grid["final_values"][:, parameter], "none") + else: + scalers[j][parameter].fit(self.training_grid["final_values"][:, parameter], option) + + self.out_scalers = scalers + + class_mask = self.validation_grid["class_inds"][klass] + class_ivs = self.validation_grid["initial_values"][class_mask] + + interpolated_values, predicted_classes, _ = self.evaluate(class_ivs) + predicted_class_mask = np.where(predicted_classes != "initial_MT") # taking out mispredictions of initial MT + + relative_errors = np.abs( + (interpolated_values - self.validation_grid["final_values"][class_mask][predicted_class_mask]) + / (self.validation_grid["final_values"][class_mask][predicted_class_mask] + eps) + ).mean(axis = 0) + + scaler_accuracies[i][j] = relative_errors + + all_scalers.append(scalers) + + all_scalers = np.array(all_scalers, dtype = object) + + star_scalers = [] + star_scaler_inds = scaler_accuracies.argmax(axis = 0) + + for c in range(len(self.classes)): + star_scalers.append( + all_scalers[:, c][star_scaler_inds[c], np.arange(len(self.continous_out_keys))] + ) + + self.out_scalers = np.array(star_scalers, dtype = object) + self.normalize_triangulations() + + self.training = False + + # =================== helper methods below =========================== + + def preprocess_grid(self, grid, training_grid = False): + + final_values = np.array(grid.final_values[self.continous_out_keys].tolist()) + + valid_inds = np.where( + (grid.final_values["interpolation_class"] != "not_converged") & + (grid.final_values["interpolation_class"] != "ignored_no_RLO") & + (grid.final_values["interpolation_class"] != "ignored_no_binary_history") + )[0] + + initial_values = np.array(grid.initial_values[self.in_keys][valid_inds].tolist()) + # determining if should interp in q + if training_grid: + m1, m2 = 10**initial_values[:, 0], 10**initial_values[:, 1] + self.interp_in_q = (m2[m1 > 0.95 * m1.max()].min() / m2[m1 < 1.05 * m1.min()].min() > 2) + self.interp_in_q = False + + initial_values = np.log10(initial_values + eps) + + if self.interp_in_q: + initial_values[:, 1] = (10**initial_values[:, 1] - eps) / (10**initial_values[:, 0] - eps) + + if training_grid: + self.iv_min = initial_values.min(axis = 0, keepdims = True) + self.iv_max = initial_values.max(axis = 0, keepdims = True) + + class_labels = np.unique(grid.final_values[valid_inds][self.interpolation_class]) + class_inds = dict(zip( + class_labels, + [np.where(grid.final_values[valid_inds][self.interpolation_class] == label)[0] for label in class_labels] + )) + + return { + "initial_values": (initial_values - self.iv_min) / (self.iv_max - self.iv_min + eps), + "final_values": np.array(grid.final_values[self.continous_out_keys][valid_inds].tolist()), + "final_classes": grid.final_values[valid_inds][self.discrete_out_keys], + "classes": grid.final_values[valid_inds][self.interpolation_class], + "class_inds": class_inds, + } + + def triangulate(self, grid_dict): + + self.classes = np.unique(grid_dict["classes"]).tolist() + self.classes.remove("initial_MT") + + triangulations = {} + + for klass in self.classes: + + class_inds = grid_dict["class_inds"][klass] + triangulations[klass] = Delaunay(grid_dict["initial_values"][class_inds]) + + grid_dict["triangulations"] = triangulations + + def compute_barycentric_coordinates(self, point, coords): + + T = np.array([ + coords[0] - coords[3], + coords[1] - coords[3], + coords[2] - coords[3] + ]) # our matrix + T = T.T + T_I = np.linalg.inv(T) + + r_a = point - coords[3] + + weights = (T_I @ r_a).tolist() + + weights.append(1 - weights[0] - weights[1] - weights[2]) + + weights = np.array(weights) / sum(weights) + + return weights + + def get_nearest_neighbor(self, iv): + + dists = np.sqrt(np.square(self.training_grid["initial_values"] - iv).sum(axis = 1)) + sorted_inds = dists.argsort() + + return self.training_grid["final_values"][sorted_inds[0]] + + def normalize_output(self, input, klass): + class_ind = self.classes.index(klass) + scalers = self.out_scalers[class_ind] + + ret_value = np.zeros_like(input) + + for dim, scaler in enumerate(scalers): + ret_value[:, dim] = scaler.transform(input[:, dim]) + + return ret_value + + + def denormalize_output(self, input, klass): + class_ind = self.classes.index(klass) + scalers = self.out_scalers[class_ind] + + ret_value = np.zeros_like(input) + + for dim, scaler in enumerate(scalers): + ret_value[dim] = scaler.inv_transform(input[dim]) + + return ret_value + + def normalize_triangulations(self): + + new_final_values = np.zeros_like(self.training_grid["final_values"]) + + for i, (klass, fv) in enumerate(zip(self.training_grid["classes"], self.training_grid["final_values"])): + if klass == "initial_MT": # should be taken out in preprocessing + continue + new_final_values[i] = self.normalize_output(np.array([fv]), klass) + + self.training_grid["final_values"] = new_final_values + \ No newline at end of file diff --git a/posydon/unit_tests/interpolation/__init__.py b/posydon/unit_tests/interpolation/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/posydon/unit_tests/interpolation/test_IF_interpolation.py b/posydon/unit_tests/interpolation/test_IF_interpolation.py new file mode 100644 index 0000000000..72ed259132 --- /dev/null +++ b/posydon/unit_tests/interpolation/test_IF_interpolation.py @@ -0,0 +1,22 @@ +"""Unit tests of posydon/interpolation/IF_interpolation.py +""" + +__authors__ = [ + "Philipp Rajah de Moura Srivastava " +] + +# import the module which will be tested +from posydon.interpolation.new_interpolator import IFInterpolator + +# import other needed code for the tests, which is not already imported in the +# module you like to test + + +# define single test functions +def test_name(): + pass + +# define test classes collecting several test functions +class TestClass: + def test_name(self): + assert True From 8af666d84cadb805447cf2d58a1de1bca81a7789 Mon Sep 17 00:00:00 2001 From: prinse1545 Date: Thu, 31 Jul 2025 09:39:49 -0500 Subject: [PATCH 314/319] sync --- posydon/interpolation/constraints.py | 68 ++++ posydon/interpolation/data_scaling.py | 68 ++-- posydon/interpolation/new_interpolator.py | 403 +++++++++------------- 3 files changed, 277 insertions(+), 262 deletions(-) diff --git a/posydon/interpolation/constraints.py b/posydon/interpolation/constraints.py index 70d875a222..d7fe62b4d9 100644 --- a/posydon/interpolation/constraints.py +++ b/posydon/interpolation/constraints.py @@ -50,6 +50,14 @@ from posydon.utils.common_functions import (stefan_boltzmann_law, orbital_separation_from_period) +CLASSIFICATION_KEYS = [ + "S<*>_state", + "mt_hist", + "S<*>_MOD_SN_type", + "S<*>_MOD_CO_type" +] + +N_MODELS = 11 # how many super nova models are there? # toggle this flag to enable/disable constraints (used for debugging) INTERPOLATION_CONSTRAINTS_ON = True @@ -511,3 +519,63 @@ def sanitize_interpolated_quantities(fvalues, constraints, verbose=False): constraint["constraint"]) return sanitized + + +def mt_constraint(classes): + + interpolation_class = classes["interpolation_class"] + + if interpolation_class == "initial_MT": + classes["mt_hist"] == "ini_RLO" + elif interpolation_class == "no_MT": + classes["mt_hist"] = "no_RLO" + elif interpolation_class == "stable_MT": + pass + elif interpolation_class == "unstable_MT": + pass + elif interpolation_class == "stable_reverse_MT": + pass + + +CLASS_CONSTRAINTS = { + "S<*>_state": None, + "mt_hist": mt_constraint, + "S<*>_MOD_SN_type": None, + "S<*>_MOD_CO_type": None +} + +def apply_class_constraint(key_name, classes): + + if key_name not in classes.keys(): + return + else: + CLASS_CONSTRAINTS[key_name](classes) + +def sanitize_classes(classes, ): + + assert(type(classes) == dict) + + if "interpolation_class" not in classes.keys(): + raise ValueError( + "Interpolation class must be present as a classified quantity to enforce classification constraints!" + ) + + for key in CLASSIFICATION_KEYS: + if "<*>" in key: + + for star in range(2): + key_name = key.replace("<*>", f"{star}") + + if "MOD" in key_name: + + for model in range(N_MODELS): + key_name = key_name.replace("", f"{model}") + + apply_class_constraint(key_name, classes) + + else: + apply_class_constraint(key_name, classes) + else: + + apply_class_constraint(key, classes) + \ No newline at end of file diff --git a/posydon/interpolation/data_scaling.py b/posydon/interpolation/data_scaling.py index 7fbbd9ee7f..84f27d92f1 100644 --- a/posydon/interpolation/data_scaling.py +++ b/posydon/interpolation/data_scaling.py @@ -7,18 +7,24 @@ import numpy as np +import warnings +import sys + +# Convert UserWarning to an error +warnings.simplefilter("error", RuntimeWarning) eps = 1.0e-16 SCALING_OPTIONS = [ + "none", "min_max", "max_abs", - "standardize", - "log_min_max", - "neg_log_min_max", - "log_max_abs", - "log_standardize", - "neg_log_standardize" + # "standardize", + "log_min_max", # has + # "neg_log_min_max", # has + "log_max_abs", # has + # "log_standardize", # has + # "neg_log_standardize" # has ] class DataScaler: @@ -84,24 +90,24 @@ def fit(self, x, method='none', lower=-1.0, upper=1.0): elif method == 'log_min_max': assert upper > lower, "upper must be greater than lower" self.lower, self.upper = lower, upper - self.params = [np.log10(np.nanmin(x, axis=0)), np.log10(np.nanmax(x, axis=0))] + self.params = [self.log(np.nanmin(x, axis=0)), self.log(np.nanmax(x, axis=0))] elif method == 'neg_log_min_max': assert upper > lower, "upper must be greater than lower" self.lower, self.upper = lower, upper - self.params = [np.log10(np.nanmin(-x, axis=0)), - np.log10(np.nanmax(-x, axis=0))] + self.params = [self.log(np.nanmin(-x, axis=0)), + self.log(np.nanmax(-x, axis=0))] elif method == 'max_abs': self.params = [np.nanmax(np.abs(x), axis=0)] elif method == 'log_max_abs': - self.params = [np.nanmax(np.abs(np.log10(x)), axis=0)] + self.params = [np.nanmax(np.abs(self.log(x)), axis=0)] elif method == 'standardize': self.params = [np.nanmean(x, axis=0), np.nanstd(x, axis=0)] elif method == 'log_standardize': # log will be computed in transform again - self.params = [np.nanmean(np.log10(x), axis=0), np.nanstd(np.log10(x), axis=0)] + self.params = [np.nanmean(self.log(x), axis=0), np.nanstd(self.log(x), axis=0)] elif method == 'neg_log_standardize': # log(-x) - self.params = [np.nanmean(np.log10(-x), axis=0), np.nanstd(np.log10(-x), axis=0)] + self.params = [np.nanmean(self.log(-x), axis=0), np.nanstd(self.log(-x), axis=0)] elif method == 'log': self.params = [] elif method == 'none': # no transformation @@ -137,26 +143,26 @@ def transform(self, x): x_t = ((x - self.params[0]) / (self.params[1] - self.params[0]) * (self.upper - self.lower) + self.lower) elif self.method == 'log_min_max': - x_t = ((np.log10(x) - self.params[0]) + x_t = ((self.log(x) - self.params[0]) / (self.params[1] - self.params[0]) * (self.upper - self.lower) + self.lower) elif self.method == 'neg_log_min_max': - x_t = ((np.log10(-x) - self.params[0]) + x_t = ((self.log(-x) - self.params[0]) / (self.params[1] - self.params[0]) * (self.upper - self.lower) + self.lower) elif self.method == 'max_abs': x_t = x / self.params[0] elif self.method == 'log_max_abs': - x_t = np.log10(x) / self.params[0] + x_t = self.log(x) / self.params[0] elif self.method == 'standardize': x_t = (x - self.params[0]) / self.params[1] elif self.method == 'log_standardize': # log will be computed in transform again - x_t = (np.log10(x) - self.params[0]) / self.params[1] + x_t = (self.log(x) - self.params[0]) / self.params[1] elif self.method == 'neg_log_standardize': - x_t = (np.log10(-x) - self.params[0]) / self.params[1] + x_t = (self.log(-x) - self.params[0]) / self.params[1] elif self.method == 'log': - x_t = np.log10(x) + x_t = self.log(x) else: # no transformation x_t = x @@ -214,24 +220,38 @@ def inv_transform(self, x_t): / (self.upper - self.lower) * (self.params[1] - self.params[0]) + self.params[0]) elif self.method == 'log_min_max': - x = 10 ** ((x_t - self.lower) / (self.upper - self.lower) + x = self.unlog((x_t - self.lower) / (self.upper - self.lower) * (self.params[1] - self.params[0]) + self.params[0]) elif self.method == 'neg_log_min_max': - x = -10 ** ((x_t - self.lower) / (self.upper - self.lower) + x = -self.unlog((x_t - self.lower) / (self.upper - self.lower) * (self.params[1] - self.params[0]) + self.params[0]) elif self.method == 'max_abs': x = x_t * self.params[0] elif self.method == 'log_max_abs': - x = 10 ** (x_t * self.params[0]) + x = self.unlog(x_t * self.params[0]) elif self.method == 'standarize': x = x_t * self.params[1] + self.params[0] elif self.method == 'log_standarize': - x = 10 ** (x_t * self.params[1] + self.params[0]) + x = self.unlog(x_t * self.params[1] + self.params[0]) elif self.method == 'neg_log_standarize': - x = -10 ** (x_t * self.params[1] + self.params[0]) + x = -self.unlog(x_t * self.params[1] + self.params[0]) elif self.method == 'log': - x = 10 ** x_t + x = self.unlog(x_t) else: # no transformation x = x_t return x + + def log(self, x): + logged = None + try: + logged = np.log10(x + eps) + except RuntimeWarning: + print(self.method) + print(x, np.isinf(x).any(), np.isnan(x).any(), (x < 0).any(), np.nanmin(x)) + # sys.exit() + + return logged + + def unlog(self, x): + return (10 ** x) - eps diff --git a/posydon/interpolation/new_interpolator.py b/posydon/interpolation/new_interpolator.py index e7b0b34fe6..1e5db82275 100644 --- a/posydon/interpolation/new_interpolator.py +++ b/posydon/interpolation/new_interpolator.py @@ -25,303 +25,212 @@ eps = 1.0e-16 -# INITIAL-FINAL INTERPOLATOR -class IFInterpolator: - """Class handling initial-final interpolation. - - Class handling initial-final interpolation with support to interpolate - certain keys by differing classes. - """ - - def __init__(self, grids = None, interpolators = None): - """Initialize the IFInterpolator class. - - Parameters - ---------- - grid : PSyGrid - The training grid or tuple of PSyGrid (training, validation) - interpolators : list of dictionaries - Contain parameters for the BaseIFIneterpolators to be constructed - - """ - self.interpolators = [] - self.interpolator_parameters = interpolators - self.grids = grids - - if self.interpolator_parameters is not None: - out_keys = [] - - for params in self.interpolator_parameters: - - if ("out_keys" not in params - and len(self.interpolator_parameters) > 1): - raise ValueError("Overlapping out keys between different " - "interpolators are not permited!") - elif "out_keys" not in params: - continue - - for key in params["out_keys"]: - if(key in out_keys): - raise ValueError( - f"Overlapping out keys between different " - f"interpolators are not permited! ({key} in more " - f"than one set of out_keys)") - else: - out_keys.append(key) - - - def train(self): - """Train the interpolator(s) on the PSyGrid used for construction.""" - for interpolator in self.interpolator_parameters: - interp = BaseIFInterpolator(grids=self.grids, - **interpolator) - interp.train() - self.interpolators.append(interp) - - self.interp_in_q = self.interpolators[0].interp_in_q - - def evaluate(self, binary, sanitization_verbose=False): - """Get the output vector approximation. - - Get the output vector approximation as well as all of the - classifications given a Binary Star - - Parameters - ---------- - binary : BinaryStar - A class which is an input vector which are to be classified and for - which an output vector will be approximated. - - Returns - ------- - Output space approximation as a tuple containing two dictionary - - """ - ynums = [] - ycats = [] - n = [] - - for interpolator in self.interpolators: - ynum, ycat, ns = interpolator.evaluate(binary) - - ynums.extend(ynum) - ycats.extend(ycat) - n.extend(ns) - - return ynums, ycats, n - - - def test_interpolator(self, initial_values, sanitization_verbose = False): - """ Method that can take in a 2-D numpy array for more efficient use of the interpolator - Parameters - ---------- - initial_values : numpy array - A numpy array containing the in-key values of the binaries to be evolved - - Return - ------ - Interpolated values - """ - new_values = np.array(initial_values, copy = True) - - if self.interp_in_q: - new_values.T[1] = new_values.T[1] / new_values.T[0] - - final_values = [] - - for interpolator in self.interpolators: - final_values.append(interpolator.test_interpolator(new_values).T) +class IFInterpolator: - return np.concatenate(final_values).T + def __init__(self, grids, in_keys, out_keys, max_k): - def test_classifiers(self, initial_values): - """ Method that can take in a 2-D numpy array for more efficient use of classifiers + if type(grids) != list: + sys.exit("Please provide a list of PSyGrids containing both a training and validation grid to train the interpolator") + else: - Parameters - ---------- - initial_values : numpy array - A numpy array containing the in-key values of the binaries to be classified + self.in_keys = in_keys - Return - ------ - Classified values - """ - new_values = np.array(initial_values, copy = True) + self.out_key_dict = out_keys + self.continuous_out_keys = sum(list(out_keys.values()), []) # keys to be interpolated which correspond to numerical quantities + self.discrete_out_keys = list(out_keys.keys()) # keys to be interpolated which correspond to discrete quantities + self.constraints = find_constraints_to_apply(self.continuous_out_keys) - if self.interp_in_q: - new_values.T[1] = new_values.T[1] / new_values.T[0] + # ============= checks ============= + if "interpolation_class" not in self.discrete_out_keys: + sys.exit("The key \"interpolation_class\" needs to be provided as one of the interpolation keys") - classes = {} - - for interpolator in self.interpolators: + self.max_k = max_k - classes = {**classes, **interpolator.test_classifiers(new_values)} + self.training_grid = self.preprocess_grid(grids[0], training_grid = True) + self.validation_grid = self.preprocess_grid(grids[1]) - return classes + self.triangulate(self.training_grid) + # =============== usage statistics variables ============ + self.outside_convex_hull = dict(zip(self.discrete_out_keys, [0] * len(self.discrete_out_keys))) + self.inside_convex_hull = dict(zip(self.discrete_out_keys, [0] * len(self.discrete_out_keys))) + def stats(self, _print = False): + percentages = [] + + for key in self.discrete_out_keys: + percentages.append( + self.outside_convex_hull[key] / (self.outside_convex_hull[key] + self.inside_convex_hull[key]) + ) + if _print: + print(f"Total of {sum(pecentages) / len(percentages):.2f} outside of hul") + return dict(zip(self.discrete_out_keys, percentages)) - def load(self, filename): - """Load a saved IFInterpolator from a .pkl file. + def train(self): + self.classifiers = dict( + zip( + self.discrete_out_keys, + [self.find_hyperparameters(key) for key in self.discrete_out_keys] + ) + ) + # self.out_scalers = dict( + # zip( + # self.discrete_out_keys, + # [self.optimize_normalization(key) for key in self.discrete_out_keys] + # ) + # ) + self.training = False + + def interpolate(self, iv, klass): - Parameters - ---------- - filename : string - Path to the .pkl file + interpolated = [] + ics = {} + weights = {} - """ - with open(filename, 'rb') as f: - self.interpolators = pickle.load(f) + for key, c in zip(self.discrete_out_keys, klass): - self.interp_in_q = self.interpolators[0].interp_in_q + triangulation = self.training_grid["triangulations"][key][c] - def save(self, filename): - """Save the interpolator to a .pkl file. + simplex = triangulation.find_simplex(iv) - Parameters - ---------- - filename : string - Path where .pkl file will be saved + if simplex == -1: + interpolated.extend( + self.get_nearest_neighbor(iv, key) + ) + self.outside_convex_hull[key] += 1 + continue + else: + self.inside_convex_hull[key] += 1 - """ - with open(filename, "wb") as f: - pickle.dump(self.interpolators, f) + vertices = triangulation.simplices[simplex] + ics[key] = triangulation.points[vertices] -class BaseIFInterpolator: + class_inds = self.training_grid["class_inds"][key][c] - def __init__(self, grids, in_keys, out_keys, interpolation_class, max_k): + final_values = np.array(self.training_grid["final_values"][key][class_inds][vertices].tolist()) - if type(grids) != list: - sys.exit("Please provide a list of PSyGrids containing both a training and validation grid to train the interpolator") - else: + # if self.training: + # final_values = self.normalize_output(final_values, c) - self.in_keys = in_keys + barycentric_weights = self.compute_barycentric_coordinates(iv, triangulation.points[vertices])[..., np.newaxis] + weights[key] = barycentric_weights - self.continous_out_keys = out_keys # keys to be interpolated which correspond to numerical quantities - self.discrete_out_keys = out_keys # keys to be interpolated which correspond to discrete quantities + # denormalized = self.denormalize_output( + # np.sum(final_values * barycentric_weights, axis = 0), + # klass + # ) - self.interpolation_class = interpolation_class - self.max_k = max_k + interpolated.extend(np.sum(final_values * barycentric_weights, axis = 0)) - self.training_grid = self.preprocess_grid(grids[0], training_grid = True) - self.validation_grid = self.preprocess_grid(grids[1]) + meta_data = { + "weights": weights, + "ics": ics, + "ic": iv, + "interpolated": interpolated + } - self.triangulate(self.training_grid) - - def train(self): - self.find_hyperparameters() - # self.optimize_normalization() + return interpolated, meta_data def evaluate(self, initial_values): - if self.classifier is None: + if self.classifiers is None: sys.exit("Please find classifier hyperparameters before using interpolator") + + interpolation_class_ind = self.discrete_out_keys.index("interpolation_class") if not self.training: initial_values = ( np.log10(initial_values + eps) - self.iv_min ) / (self.iv_max - self.iv_min + eps) - classes = self.classifier.predict(initial_values) + classes = np.array([clasifier["classifier"].predict(initial_values) for clasifier in self.classifiers.values()]).T interpolated_values = [] n = [] for iv, klass in zip(initial_values, classes): - if klass == "initial_MT": + if klass[interpolation_class_ind] == "initial_MT": continue - triangulation = self.training_grid["triangulations"][klass] - - simplex = triangulation.find_simplex(iv) - - if simplex == -1: - interpolated_values.append( - self.get_nearest_neighbor(iv) - ) - continue - - vertices = triangulation.simplices[simplex] - - class_inds = self.training_grid["class_inds"][klass] - - final_values = self.training_grid["final_values"][class_inds][vertices] - - if self.training: - final_values = self.normalize_output(final_values, klass) - - barycentric_weights = self.compute_barycentric_coordinates(iv, triangulation.points[vertices])[..., np.newaxis] - - interpolated = np.sum(final_values * barycentric_weights, axis = 0) - - n.append({ - "weights": barycentric_weights, - "ics": triangulation.points[vertices], - "ic": iv, - "final_values": final_values, - "interpolated": interpolated - }) + interpolated, meta_data = self.interpolate(iv, klass) + interpolated = self.apply_continuous_constraints(interpolated) - # interpolated = self.denormalize_output(interpolated, klass) interpolated_values.append(interpolated) + n.append(meta_data) interpolated_values = np.array(interpolated_values) classes = np.array(classes) - + print("interp_values", interpolated_values.shape) return interpolated_values, classes, n - def find_hyperparameters(self): + def find_hyperparameters(self, klass): k_accuracies = [] for k in range(1, self.max_k): validation_classifier = KNeighborsClassifier(n_neighbors = k, weights = "distance") - validation_classifier.fit(self.training_grid["initial_values"], self.training_grid["classes"]) + validation_classifier.fit( + self.training_grid["initial_values"], + self.training_grid["final_classes"][klass] + ) predicted_classes = validation_classifier.predict(self.validation_grid["initial_values"]) k_accuracies.append( - balanced_accuracy_score(predicted_classes, self.validation_grid["classes"]) + balanced_accuracy_score( + predicted_classes, + self.validation_grid["final_classes"][klass] + ) ) self.k_accuracies = np.array(k_accuracies) - self.k_star = self.k_accuracies.argmax() + k_star = self.k_accuracies.argmax() + + classifier = KNeighborsClassifier(n_neighbors = k_star, weights = "distance") + classifier.fit( + self.training_grid["initial_values"], + self.training_grid["final_classes"][klass] + ) - self.classifier = KNeighborsClassifier(n_neighbors = self.k_star, weights = "distance") - self.classifier.fit(self.training_grid["initial_values"], self.training_grid["classes"]) + return {"classifier": classifier, "k_star": k_star, "k_accuracies": k_accuracies} - def optimize_normalization(self): + def optimize_normalization(self, key): - output_dim = len(self.continous_out_keys) + output_dim = len(self.out_key_dict[key]) + classes = np.unique(self.training_grid["final_classes"][key]) all_scalers = [] - scaler_accuracies = np.zeros(shape = (len(SCALING_OPTIONS), len(self.classes), output_dim)) + scaler_accuracies = np.zeros(shape = (len(SCALING_OPTIONS), len(classes), output_dim)) self.training = True + self.scaler_types = [] for i, option in enumerate(SCALING_OPTIONS): - scalers = [[DataScaler()] * output_dim] * len(self.classes) + scalers = [[DataScaler() for _ in range(output_dim)] for _ in range(len(classes))] - for j, klass in enumerate(self.classes): + for j, klass in enumerate(classes): for parameter in range(output_dim): - param_min = np.nanmin(self.training_grid["final_values"][:, parameter]) + param_min = np.nanmin(self.training_grid["final_values"][keys][:, parameter]) + if param_min < 0 and "log" in option: - scalers[j][parameter].fit(self.training_grid["final_values"][:, parameter], "none") + scalers[j][parameter].fit(self.training_grid["final_values"][keys][:, parameter], "none") else: - scalers[j][parameter].fit(self.training_grid["final_values"][:, parameter], option) - - self.out_scalers = scalers + scalers[j][parameter].fit(self.training_grid["final_values"][keys][:, parameter], option) + + self.out_scalers = scalers + + for j, klass in enumerate(self.classes): class_mask = self.validation_grid["class_inds"][klass] class_ivs = self.validation_grid["initial_values"][class_mask] - + interpolated_values, predicted_classes, _ = self.evaluate(class_ivs) predicted_class_mask = np.where(predicted_classes != "initial_MT") # taking out mispredictions of initial MT relative_errors = np.abs( - (interpolated_values - self.validation_grid["final_values"][class_mask][predicted_class_mask]) - / (self.validation_grid["final_values"][class_mask][predicted_class_mask] + eps) + (interpolated_values - self.validation_grid["final_values"][keys][class_mask][predicted_class_mask]) + / (self.validation_grid["final_values"][keys][class_mask][predicted_class_mask] + eps) ).mean(axis = 0) scaler_accuracies[i][j] = relative_errors @@ -332,22 +241,24 @@ def optimize_normalization(self): star_scalers = [] star_scaler_inds = scaler_accuracies.argmax(axis = 0) + self.scaler_accuracies = scaler_accuracies - for c in range(len(self.classes)): + for c in range(len(classes)): star_scalers.append( - all_scalers[:, c][star_scaler_inds[c], np.arange(len(self.continous_out_keys))] + all_scalers[:, c][star_scaler_inds[c], np.arange(len(keys))] ) self.out_scalers = np.array(star_scalers, dtype = object) self.normalize_triangulations() self.training = False + return np.array(star_scalers, dtype = object) # =================== helper methods below =========================== def preprocess_grid(self, grid, training_grid = False): - final_values = np.array(grid.final_values[self.continous_out_keys].tolist()) + final_values = np.array(grid.final_values[self.continuous_out_keys].tolist()) valid_inds = np.where( (grid.final_values["interpolation_class"] != "not_converged") & @@ -371,31 +282,39 @@ def preprocess_grid(self, grid, training_grid = False): self.iv_min = initial_values.min(axis = 0, keepdims = True) self.iv_max = initial_values.max(axis = 0, keepdims = True) - class_labels = np.unique(grid.final_values[valid_inds][self.interpolation_class]) - class_inds = dict(zip( - class_labels, - [np.where(grid.final_values[valid_inds][self.interpolation_class] == label)[0] for label in class_labels] - )) + class_inds = {} + + for key in self.discrete_out_keys: + class_labels = np.unique(grid.final_values[valid_inds][key]) + class_inds[key] = dict(zip( + class_labels, + [np.where(grid.final_values[valid_inds][key] == label)[0] for label in class_labels] + )) return { "initial_values": (initial_values - self.iv_min) / (self.iv_max - self.iv_min + eps), - "final_values": np.array(grid.final_values[self.continous_out_keys][valid_inds].tolist()), - "final_classes": grid.final_values[valid_inds][self.discrete_out_keys], - "classes": grid.final_values[valid_inds][self.interpolation_class], + "final_values": dict(zip(self.out_key_dict.keys(), [grid.final_values[valid_inds][keys] for keys in self.out_key_dict.values()])), # np.array(grid.final_values[self.continuous_out_keys][valid_inds].tolist()), + "final_classes": dict(zip(self.discrete_out_keys, np.array(grid.final_values[self.discrete_out_keys][valid_inds].tolist()).T)), "class_inds": class_inds, } def triangulate(self, grid_dict): - self.classes = np.unique(grid_dict["classes"]).tolist() - self.classes.remove("initial_MT") - triangulations = {} - for klass in self.classes: + for label_name in self.discrete_out_keys: + classes = np.unique(grid_dict["final_classes"][label_name]).tolist() + if "initial_MT" in classes: + classes.remove("initial_MT") - class_inds = grid_dict["class_inds"][klass] - triangulations[klass] = Delaunay(grid_dict["initial_values"][class_inds]) + class_triangulations = {} + + for klass in classes: + + class_inds = grid_dict["class_inds"][label_name][klass] + class_triangulations[klass] = Delaunay(grid_dict["initial_values"][class_inds]) + + triangulations[label_name] = class_triangulations grid_dict["triangulations"] = triangulations @@ -419,12 +338,20 @@ def compute_barycentric_coordinates(self, point, coords): return weights - def get_nearest_neighbor(self, iv): + def get_nearest_neighbor(self, iv, key): dists = np.sqrt(np.square(self.training_grid["initial_values"] - iv).sum(axis = 1)) sorted_inds = dists.argsort() - return self.training_grid["final_values"][sorted_inds[0]] + return np.array(self.training_grid["final_values"][key][sorted_inds[0]].tolist()) + + def apply_continuous_constraints(self, interpolated): + sanitized = sanitize_interpolated_quantities( + dict(zip(self.continuous_out_keys, interpolated)), + self.constraints, verbose=False + ) + + return np.array([sanitized[key] for key in self.continuous_out_keys]) def normalize_output(self, input, klass): class_ind = self.classes.index(klass) @@ -449,14 +376,14 @@ def denormalize_output(self, input, klass): return ret_value - def normalize_triangulations(self): + def normalize_triangulations(self, out_keys): - new_final_values = np.zeros_like(self.training_grid["final_values"]) + new_final_values = np.zeros_like(self.training_grid["final_values"][out_keys]) - for i, (klass, fv) in enumerate(zip(self.training_grid["classes"], self.training_grid["final_values"])): + for i, (klass, fv) in enumerate(zip(self.training_grid["classes"], self.training_grid["final_values"][out_keys])): if klass == "initial_MT": # should be taken out in preprocessing continue new_final_values[i] = self.normalize_output(np.array([fv]), klass) - self.training_grid["final_values"] = new_final_values + self.training_grid["final_values"][out_keys] = new_final_values \ No newline at end of file From e8082e6ca0af8cc020efa25de68a81332f12c70d Mon Sep 17 00:00:00 2001 From: prinse1545 Date: Thu, 15 Jan 2026 09:26:35 -0600 Subject: [PATCH 315/319] mainly syncing / backing up, but rewrote much of the normalization logic --- posydon/interpolation/IF_interpolation.py | 10 +- posydon/interpolation/new_interpolator.py | 274 +++++++++++++++------- posydon/interpolation/preprocessing.py | 83 +++++++ 3 files changed, 278 insertions(+), 89 deletions(-) create mode 100644 posydon/interpolation/preprocessing.py diff --git a/posydon/interpolation/IF_interpolation.py b/posydon/interpolation/IF_interpolation.py index 29936402d8..786364fd74 100644 --- a/posydon/interpolation/IF_interpolation.py +++ b/posydon/interpolation/IF_interpolation.py @@ -196,6 +196,7 @@ class relies on the BaseIFInterpolator class to perform the interpolation from posydon.interpolation.constraints import ( find_constraints_to_apply, sanitize_interpolated_quantities) +import time # INITIAL-FINAL INTERPOLATOR class IFInterpolator: @@ -269,13 +270,16 @@ def evaluate(self, binary, sanitization_verbose=False): """ ynums = {} ycats = {} - + # s = time.time() for interpolator in self.interpolators: ynum, ycat = interpolator.evaluate(binary, sanitization_verbose) ynums = {**ynums, **ynum} ycats = {**ycats, **ycat} + # e = time.time() + # print(f"Iterated over {len(self.interpolators)} interpolators in {e - s}") + return ynums, ycats @@ -666,7 +670,11 @@ def test_interpolator(self, Xt): if isinstance(self.interp_method, list): Xtn = self.X_scaler.normalize(Xt, classes) + # s = time.time() Ypredn = self.interpolator.predict(Xtn, classes, self.X_scaler) + # e = time.time() + + # print(f"Predicted one interpolator value in {e - s} seconds") else: Xtn = self.X_scaler.normalize(Xt) Ypredn = self.interpolator.predict(Xtn) diff --git a/posydon/interpolation/new_interpolator.py b/posydon/interpolation/new_interpolator.py index 1e5db82275..6c8afbe9af 100644 --- a/posydon/interpolation/new_interpolator.py +++ b/posydon/interpolation/new_interpolator.py @@ -3,6 +3,11 @@ """ +__authors__ = [ + "Philipp Moura Srivastava ", +] + + import numpy as np import os import pickle @@ -12,6 +17,14 @@ # POSYDON from posydon.grids.psygrid import PSyGrid from posydon.interpolation.data_scaling import DataScaler, SCALING_OPTIONS +from posydon.interpolation.preprocessing import ( + normalize, + unnormalize, + find_normalization_evaluation_matrix, + compute_statistics, + IN_SCALING_OPTIONS, + OUT_SCALING_OPTIONS) + from posydon.utils.posydonwarning import Pwarn from posydon.interpolation.constraints import ( find_constraints_to_apply, sanitize_interpolated_quantities) @@ -22,6 +35,7 @@ from sklearn.metrics import balanced_accuracy_score import sys +import time eps = 1.0e-16 @@ -63,7 +77,7 @@ def stats(self, _print = False): self.outside_convex_hull[key] / (self.outside_convex_hull[key] + self.inside_convex_hull[key]) ) if _print: - print(f"Total of {sum(pecentages) / len(percentages):.2f} outside of hul") + print(f"Total of {sum(percentages) / len(percentages):.2f} outside of hull") return dict(zip(self.discrete_out_keys, percentages)) @@ -74,25 +88,30 @@ def train(self): [self.find_hyperparameters(key) for key in self.discrete_out_keys] ) ) - # self.out_scalers = dict( - # zip( - # self.discrete_out_keys, - # [self.optimize_normalization(key) for key in self.discrete_out_keys] - # ) - # ) - self.training = False + self.out_scalers = dict( + zip( + self.discrete_out_keys, + [self.optimize_normalization(key) for key in self.discrete_out_keys] + ) + ) - def interpolate(self, iv, klass): + + def interpolate(self, iv, klass, sn_model): interpolated = [] ics = {} weights = {} - for key, c in zip(self.discrete_out_keys, klass): + interpolation_class_ind = self.discrete_out_keys.index("interpolation_class") + sn_class_ind = self.discrete_out_keys.index(sn_model) + klass = [klass[interpolation_class_ind], klass[sn_class_ind]] + classification_schemes = ["interpolation_class", sn_model] + + for key, c in zip(classification_schemes, klass): triangulation = self.training_grid["triangulations"][key][c] - simplex = triangulation.find_simplex(iv) + simplex = triangulation.find_simplex(iv) if simplex == -1: interpolated.extend( @@ -109,19 +128,36 @@ def interpolate(self, iv, klass): class_inds = self.training_grid["class_inds"][key][c] final_values = np.array(self.training_grid["final_values"][key][class_inds][vertices].tolist()) - - # if self.training: - # final_values = self.normalize_output(final_values, c) - + print("Before", final_values) + if np.isnan(final_values).any(): + print("Do final values have NaNs?", np.isnan(final_values).any()) + # if False or self.training: + # if not np.isnan(final_values).any(): + # final_values = normalize(final_values, self._stats[0], self._stats[1], True) + print("After", final_values) barycentric_weights = self.compute_barycentric_coordinates(iv, triangulation.points[vertices])[..., np.newaxis] - weights[key] = barycentric_weights + if np.isnan(barycentric_weights).any(): + print("Do barycentric weights have NaNs?", np.isnan(barycentric_weights).any()) + weights[key] = barycentric_weights + print(final_values, barycentric_weights) + # if self.training: + # if not np.isnan(final_values).any(): + # final_values = unnormalize( + # np.sum(final_values * barycentric_weights, axis = 0), + # self._stats[0], self._stats[1], True + # ) + # else: + # final_values = np.sum(final_values * barycentric_weights, axis = 0) + + if np.isnan(final_values).any(): + print("Output has nans") # denormalized = self.denormalize_output( # np.sum(final_values * barycentric_weights, axis = 0), # klass # ) - - interpolated.extend(np.sum(final_values * barycentric_weights, axis = 0)) + interpolated.extend(final_values) + meta_data = { "weights": weights, @@ -132,127 +168,187 @@ def interpolate(self, iv, klass): return interpolated, meta_data - def evaluate(self, initial_values): + def evaluate(self, initial_values, sn_model = "S1_SN_MODEL_v2_01_SN_type"): if self.classifiers is None: sys.exit("Please find classifier hyperparameters before using interpolator") interpolation_class_ind = self.discrete_out_keys.index("interpolation_class") - if not self.training: - initial_values = ( np.log10(initial_values + eps) - self.iv_min ) / (self.iv_max - self.iv_min + eps) - - classes = np.array([clasifier["classifier"].predict(initial_values) for clasifier in self.classifiers.values()]).T + classes = np.array([ + cl["classifier"].predict(normalize(initial_values, cl["stats"][0], cl["stats"][1], cl["log"])) + for cl in self.classifiers.values()]).T interpolated_values = [] n = [] - + for iv, klass in zip(initial_values, classes): if klass[interpolation_class_ind] == "initial_MT": continue - interpolated, meta_data = self.interpolate(iv, klass) - interpolated = self.apply_continuous_constraints(interpolated) + interpolated, meta_data = self.interpolate(iv, klass, sn_model) + interpolated = self.apply_continuous_constraints(interpolated, sn_model) interpolated_values.append(interpolated) n.append(meta_data) interpolated_values = np.array(interpolated_values) classes = np.array(classes) - print("interp_values", interpolated_values.shape) + return interpolated_values, classes, n def find_hyperparameters(self, klass): - - k_accuracies = [] + input_matrix = [] + for k in range(1, self.max_k): + row = [] + for opt in IN_SCALING_OPTIONS: + row.append( + [k, opt] + ) + input_matrix.append(row) + + kwargs = { + "input_matrix": input_matrix, + "self": self, + "klass": klass + } + + def kwargs_fnc(**kwargs): + + kwargs = { + "self": kwargs["kwargs"]["self"], + "k": kwargs["item"][0], + "scaling": kwargs["item"][1] + } + + return kwargs + + def eval_fnc(self, k, scaling): validation_classifier = KNeighborsClassifier(n_neighbors = k, weights = "distance") + + training_initial_values = self.training_grid["initial_values"] + + stats = compute_statistics(training_initial_values, scaling) + training_initial_values = normalize( + training_initial_values, stats[0], stats[1], "log" in scaling) + validation_classifier.fit( - self.training_grid["initial_values"], + training_initial_values, self.training_grid["final_classes"][klass] ) - predicted_classes = validation_classifier.predict(self.validation_grid["initial_values"]) - - k_accuracies.append( - balanced_accuracy_score( - predicted_classes, - self.validation_grid["final_classes"][klass] - ) + validation_initial_values = self.validation_grid["initial_values"] + validation_initial_values = normalize( + validation_initial_values, stats[0], stats[1], "log" in scaling) + predicted_classes = validation_classifier.predict(validation_initial_values) + + bacc = balanced_accuracy_score( + self.validation_grid["final_classes"][klass], + predicted_classes ) - self.k_accuracies = np.array(k_accuracies) + return bacc, stats + + eval_matrix, stat_matrix = find_normalization_evaluation_matrix(eval_fnc, kwargs_fnc, kwargs) + + k_star = list(np.unravel_index(eval_matrix.argmax(), eval_matrix.shape)) + + classifier = KNeighborsClassifier(n_neighbors = k_star[0] + 1, weights = "distance") + + training_initial_values = self.training_grid["initial_values"] + + scaling = IN_SCALING_OPTIONS[k_star[1]] - k_star = self.k_accuracies.argmax() + stats = compute_statistics(training_initial_values, scaling) + training_initial_values = normalize( + training_initial_values, stats[0], stats[1], "log" in scaling) - classifier = KNeighborsClassifier(n_neighbors = k_star, weights = "distance") classifier.fit( - self.training_grid["initial_values"], + training_initial_values, self.training_grid["final_classes"][klass] ) - return {"classifier": classifier, "k_star": k_star, "k_accuracies": k_accuracies} + return { + "classifier": classifier, + "stats": stat_matrix[*k_star], + "log": "log" in IN_SCALING_OPTIONS[k_star[1]], + "k_star": k_star, + "eval_matrix": eval_matrix + } def optimize_normalization(self, key): - output_dim = len(self.out_key_dict[key]) - classes = np.unique(self.training_grid["final_classes"][key]) + input_matrix = [] - all_scalers = [] - scaler_accuracies = np.zeros(shape = (len(SCALING_OPTIONS), len(classes), output_dim)) + labels = np.unique(self.training_grid["final_classes"][key]) + labels = np.delete(labels, np.where(labels == "initial_MT")[0]) - self.training = True - self.scaler_types = [] + for label in labels: + row = [] + for opt in OUT_SCALING_OPTIONS: + row.append( + [label, opt] + ) + input_matrix.append(row) + + kwargs = { + "input_matrix": input_matrix, + "self": self, + "key": key + } - for i, option in enumerate(SCALING_OPTIONS): - scalers = [[DataScaler() for _ in range(output_dim)] for _ in range(len(classes))] + def kwargs_fnc(**kwargs): - for j, klass in enumerate(classes): - for parameter in range(output_dim): - param_min = np.nanmin(self.training_grid["final_values"][keys][:, parameter]) + kwargs = { + "self": kwargs["kwargs"]["self"], + "key": kwargs["kwargs"]["key"], + "klass": kwargs["item"][0], + "scaling": kwargs["item"][1] + } - if param_min < 0 and "log" in option: - scalers[j][parameter].fit(self.training_grid["final_values"][keys][:, parameter], "none") - else: - scalers[j][parameter].fit(self.training_grid["final_values"][keys][:, parameter], option) + return kwargs - self.out_scalers = scalers + def eval_fnc(self, key, klass, scaling): + self.training = True + self.scaling = scaling - for j, klass in enumerate(self.classes): + klass_inds = np.where(self.validation_grid["final_classes"][key] == klass)[0] - class_mask = self.validation_grid["class_inds"][klass] - class_ivs = self.validation_grid["initial_values"][class_mask] - - interpolated_values, predicted_classes, _ = self.evaluate(class_ivs) - predicted_class_mask = np.where(predicted_classes != "initial_MT") # taking out mispredictions of initial MT + training_final_values = self.training_grid["final_values"][key][klass_inds] + self._stats = compute_statistics(training_final_values, scaling) + print("Does input have NaNs?", np.isnan(self.validation_grid["initial_values"][klass_inds]).any()) + interpolated, classes, _ = self.evaluate(self.validation_grid["initial_values"][klass_inds]) - relative_errors = np.abs( - (interpolated_values - self.validation_grid["final_values"][keys][class_mask][predicted_class_mask]) - / (self.validation_grid["final_values"][keys][class_mask][predicted_class_mask] + eps) - ).mean(axis = 0) + classes = classes[np.where(classes[:, 0] != "initial_MT")[0]] + predicted_klass_inds = np.where((classes[:, 0] == klass) | (classes[:, 1] == klass))[0] - scaler_accuracies[i][j] = relative_errors + # needs to be fixed to include any arbitrary SN model but this will do for now + ground_truth = np.concatenate( + [self.validation_grid["final_values"]["interpolation_class"], self.validation_grid["final_values"]["S1_SN_MODEL_v2_01_SN_type"]], axis = 1 + ) - all_scalers.append(scalers) + errors = np.abs( + (interpolated[predicted_klass_inds] - ground_truth[klass_inds][predicted_klass_inds]) / + (ground_truth[klass_inds][predicted_klass_inds] + eps) + ) - all_scalers = np.array(all_scalers, dtype = object) + self.training = False - star_scalers = [] - star_scaler_inds = scaler_accuracies.argmax(axis = 0) - self.scaler_accuracies = scaler_accuracies + return errors.mean(), self._stats - for c in range(len(classes)): - star_scalers.append( - all_scalers[:, c][star_scaler_inds[c], np.arange(len(keys))] - ) + eval_matrix, stat_matrix = find_normalization_evaluation_matrix(eval_fnc, kwargs_fnc, kwargs) - self.out_scalers = np.array(star_scalers, dtype = object) - self.normalize_triangulations() + opt = list(np.unravel_index(eval_matrix.argmax(), eval_matrix.shape)) - self.training = False - return np.array(star_scalers, dtype = object) + return { + "stats": stat_matrix[opt], + "log": "log" in OUT_SCALING_OPTIONS[opt[1]], + "scaling": OUT_SCALING_OPTIONS[opt[1]], + "eval_matrix": eval_matrix + } # =================== helper methods below =========================== @@ -292,8 +388,8 @@ def preprocess_grid(self, grid, training_grid = False): )) return { - "initial_values": (initial_values - self.iv_min) / (self.iv_max - self.iv_min + eps), - "final_values": dict(zip(self.out_key_dict.keys(), [grid.final_values[valid_inds][keys] for keys in self.out_key_dict.values()])), # np.array(grid.final_values[self.continuous_out_keys][valid_inds].tolist()), + "initial_values": 10**initial_values, + "final_values": dict(zip(self.out_key_dict.keys(), [np.array(grid.final_values[valid_inds][keys].tolist()) for keys in self.out_key_dict.values()])), # np.array(grid.final_values[self.continuous_out_keys][valid_inds].tolist()), "final_classes": dict(zip(self.discrete_out_keys, np.array(grid.final_values[self.discrete_out_keys][valid_inds].tolist()).T)), "class_inds": class_inds, } @@ -345,13 +441,14 @@ def get_nearest_neighbor(self, iv, key): return np.array(self.training_grid["final_values"][key][sorted_inds[0]].tolist()) - def apply_continuous_constraints(self, interpolated): + def apply_continuous_constraints(self, interpolated, sn_model): + keys = self.out_key_dict["interpolation_class"] + self.out_key_dict[sn_model] + sanitized = sanitize_interpolated_quantities( - dict(zip(self.continuous_out_keys, interpolated)), + dict(zip(keys, interpolated)), self.constraints, verbose=False ) - - return np.array([sanitized[key] for key in self.continuous_out_keys]) + return np.array([sanitized[key] for key in keys]) def normalize_output(self, input, klass): class_ind = self.classes.index(klass) @@ -386,4 +483,5 @@ def normalize_triangulations(self, out_keys): new_final_values[i] = self.normalize_output(np.array([fv]), klass) self.training_grid["final_values"][out_keys] = new_final_values + \ No newline at end of file diff --git a/posydon/interpolation/preprocessing.py b/posydon/interpolation/preprocessing.py new file mode 100644 index 0000000000..8db1482718 --- /dev/null +++ b/posydon/interpolation/preprocessing.py @@ -0,0 +1,83 @@ +""" +Module implementing preprocessing for IF Interpolation +""" + +__authors__ = [ + "Philipp Moura Srivastava ", +] + +import numpy as np + +eps = 1.0e-16 + +IN_SCALING_OPTIONS = [ + "min-max", + "standard", + "log_min-max", + "log_standard" +] + +OUT_SCALING_OPTIONS = [ + # "log_min-max", + # "log_standard" + "min-max" +] + + +def normalize(data, shift, scale, log = False): + if log: + if np.isnan(data).any() or np.isinf(data).any(): + raise ValueError("nans or infs detected in crucial grid parameters") + + if not (data < 0).any(): + data = np.log10(data + eps) + + return (data - shift) / (scale + eps) + +def unnormalize(data, shift, scale, log = False): + data = (data * (scale + eps)) + shift + + if log: + data = 10**data - eps + + return data + +def compute_statistics(data, scaling): + + computations = [ + lambda data: [data.min(axis = 0), data.max(axis = 0) - data.min(axis = 0)], + lambda data: [data.mean(axis = 0), data.std(axis = 0)], + lambda data: [np.log10(data + eps).min(axis = 0), np.log10(data + eps).max(axis = 0) - np.log10(data + eps).min(axis = 0)], + lambda data: [np.log10(data + eps).mean(axis = 0), np.log10(data + eps).std(axis = 0)], + ] + compute = dict(zip(IN_SCALING_OPTIONS, computations)) # this line assumes that all other options are a subset of IN_SCALING_OPTION + return compute[scaling](data) + + +def find_normalization_evaluation_matrix(eval_fnc, kwarg_fnc, kwargs): + # eval_fnc - test_classifier?, what's changing + # kwarg_fnc - input to eval_fnc + # kwargs - inputs we iterate over + + normalization_eval_matrix = [] + normalization_stat_matrix = [] + + for row in kwargs["input_matrix"]: + eval_row = [] + stat_row = [] + + for col in row: + acc, stat = eval_fnc(**kwarg_fnc(**{"item": col, "kwargs": kwargs})) + + eval_row.append( + acc + ) + stat_row.append(stat) + + normalization_eval_matrix.append(eval_row) + normalization_stat_matrix.append(stat_row) + + normalization_eval_matrix = np.array(normalization_eval_matrix) + normalization_stat_matrix = np.array(normalization_stat_matrix) + + return normalization_eval_matrix, normalization_stat_matrix From 70c467bd9d3637c1d58136107c70a9a8edf25ee4 Mon Sep 17 00:00:00 2001 From: prinse1545 Date: Wed, 15 Apr 2026 10:16:14 -0500 Subject: [PATCH 316/319] backing up --- posydon/interpolation/new_interpolator.py | 400 ++++++++++++++++------ posydon/interpolation/preprocessing.py | 86 +++-- posydon/visualization/interpolation.py | 6 +- 3 files changed, 362 insertions(+), 130 deletions(-) diff --git a/posydon/interpolation/new_interpolator.py b/posydon/interpolation/new_interpolator.py index 6c8afbe9af..eae69feb56 100644 --- a/posydon/interpolation/new_interpolator.py +++ b/posydon/interpolation/new_interpolator.py @@ -7,7 +7,6 @@ "Philipp Moura Srivastava ", ] - import numpy as np import os import pickle @@ -18,10 +17,8 @@ from posydon.grids.psygrid import PSyGrid from posydon.interpolation.data_scaling import DataScaler, SCALING_OPTIONS from posydon.interpolation.preprocessing import ( - normalize, - unnormalize, + Transformer, find_normalization_evaluation_matrix, - compute_statistics, IN_SCALING_OPTIONS, OUT_SCALING_OPTIONS) @@ -41,8 +38,25 @@ class IFInterpolator: + """ Class used to train interpolator and carry out interpolation. Familiarity with the over all system, which can + be gained by referencing section 3 of 2411.02376, is required to understand the documentation + """ def __init__(self, grids, in_keys, out_keys, max_k): + """ Class constructor + + Parameters + ---------- + grids : list of PSyGrid + Contains both the training grid and validation grid in the first and second postions, respectively + in_keys: list of strings + Contains the names of the parameters that define the input space + out_keys: dict + Keys correspond to classifiers to be trained, and values are a list of strings specifying which parameters + are to be interpolated using the respective classifier + max_k: int + The maximum number of k that is considered when optimizing k for each classifier + """ if type(grids) != list: sys.exit("Please provide a list of PSyGrids containing both a training and validation grid to train the interpolator") @@ -69,7 +83,23 @@ def __init__(self, grids, in_keys, out_keys, max_k): self.outside_convex_hull = dict(zip(self.discrete_out_keys, [0] * len(self.discrete_out_keys))) self.inside_convex_hull = dict(zip(self.discrete_out_keys, [0] * len(self.discrete_out_keys))) + # variable to control whether or not we are debugging + self.debug_mode = False + def stats(self, _print = False): + """ Returns statistics regarding the number of samples that were interpolated with their initial condition outside + of the convex hull for its respective predicted class + + Parameters + ---------- + _print: boolean + Controls whether statistics are printed as well as returned as a dictionary + + Return Values + ------------- + dict: a dictionary that has the percentage of points outside the convex hull for + each classification scheme + """ percentages = [] for key in self.discrete_out_keys: @@ -82,21 +112,38 @@ def stats(self, _print = False): return dict(zip(self.discrete_out_keys, percentages)) def train(self): - self.classifiers = dict( + """ method used to find optimal hyperparamters for classification (k) and which normalization + schemes work best for interpolation + """ + self.is_training = True + self.classifiers = dict(# Finding classification hyperparameters zip( self.discrete_out_keys, [self.find_hyperparameters(key) for key in self.discrete_out_keys] ) ) - self.out_scalers = dict( + self.out_scalers = dict(# Fincing interpolation normalization schemes zip( self.discrete_out_keys, [self.optimize_normalization(key) for key in self.discrete_out_keys] ) ) + self.is_training = False def interpolate(self, iv, klass, sn_model): + """ a method which performs interpolation for a respective initial value and its + predicted class (convex hull) + + Parameters + ---------- + iv: np.ndarray (3,) + an initial value containing the primary and secondary mass as well as the orbital period + klass: string + a predicted class label + sn_model: string + the specified super nova model being used + """ interpolated = [] ics = {} @@ -107,11 +154,11 @@ def interpolate(self, iv, klass, sn_model): klass = [klass[interpolation_class_ind], klass[sn_class_ind]] classification_schemes = ["interpolation_class", sn_model] - for key, c in zip(classification_schemes, klass): + for key, c in zip(classification_schemes, klass): # interpolating based in mass transfer type and supernova outcome separately triangulation = self.training_grid["triangulations"][key][c] - - simplex = triangulation.find_simplex(iv) + + simplex = -1 if triangulation == "1NN" else triangulation.find_simplex(iv) if simplex == -1: interpolated.extend( @@ -128,35 +175,46 @@ def interpolate(self, iv, klass, sn_model): class_inds = self.training_grid["class_inds"][key][c] final_values = np.array(self.training_grid["final_values"][key][class_inds][vertices].tolist()) - print("Before", final_values) - if np.isnan(final_values).any(): - print("Do final values have NaNs?", np.isnan(final_values).any()) - # if False or self.training: - # if not np.isnan(final_values).any(): - # final_values = normalize(final_values, self._stats[0], self._stats[1], True) - print("After", final_values) + + # ============= handling cases where nans exist in final values ========== + nans = np.isnan(final_values) + num_nans = nans.sum(axis = 0) + not_nans = np.where((num_nans < 2) & (num_nans > 0))[0] # indices of dimensions that should not be nan despite containing nans in neighbors + nans_tie = np.where(num_nans == 2)[0] # if there is a tie in the number of nans, we flip a coin to decide if output will be nan or not + + if self.debug_mode: + print(final_values) + + for tie in nans_tie: + coin = np.random.rand() # draws from uniform distribution 0-1 + if coin > 0.5: + np.append(not_nans, tie) + + # =============== performing general interpolation ================== + if not self.is_training: + label_dict = self.out_scalers[key]["label_dict"] + + # final_values = self._transform.normalize(final_values) if self.is_training else self.out_scalers[key]["transform"][label_dict[c]].normalize(final_values) + final_values = final_values barycentric_weights = self.compute_barycentric_coordinates(iv, triangulation.points[vertices])[..., np.newaxis] - if np.isnan(barycentric_weights).any(): - print("Do barycentric weights have NaNs?", np.isnan(barycentric_weights).any()) weights[key] = barycentric_weights - print(final_values, barycentric_weights) - # if self.training: - # if not np.isnan(final_values).any(): - # final_values = unnormalize( - # np.sum(final_values * barycentric_weights, axis = 0), - # self._stats[0], self._stats[1], True - # ) - # else: - # final_values = np.sum(final_values * barycentric_weights, axis = 0) - - if np.isnan(final_values).any(): - print("Output has nans") - # denormalized = self.denormalize_output( - # np.sum(final_values * barycentric_weights, axis = 0), - # klass - # ) - interpolated.extend(final_values) + + i_values = np.sum(final_values * barycentric_weights, axis = 0) + + # =========== fixing those values that were interpolated to nan because they contained an acceptable amount of nans in neighbors ========= + for dim in not_nans: + where_not_nan = np.where(nans[:, dim] == 0) + nan_weights = barycentric_weights[where_not_nan] + nan_weights /= nan_weights.sum() + + i_values[dim] = (final_values[:, dim][where_not_nan] * nan_weights[:, 0]).sum() + + + # i_values = self._transform.unnormalize(i_values[np.newaxis, ...]) if self.is_training else self.out_scalers[key]["transform"][label_dict[c]].unnormalize(i_values[np.newaxis, ...]) + i_values = [i_values] + + interpolated.extend(i_values[0]) meta_data = { @@ -169,6 +227,27 @@ def interpolate(self, iv, klass, sn_model): return interpolated, meta_data def evaluate(self, initial_values, sn_model = "S1_SN_MODEL_v2_01_SN_type"): + """ The main method of the class used to classify and interpolate + + Parameters + ---------- + initial_values: np.ndaarray + starting stellar masses and orbital period in numpy array + sn_model: string + specifies supernova model to be used during interpolation + + Return Values + ------------- + interpolated_values: np.ndarray + contains all interpolated values, all classification schemes are concatenated into one array + classes: np.ndarray + a list of lists, each list has two classes. The first is the mass transfer type and the second + is the compact object type which is used for the supernova + n: list of dicts + each dict containing meta data information about the interpolation such as + neighbors used and distances found + + """ if self.classifiers is None: sys.exit("Please find classifier hyperparameters before using interpolator") @@ -176,7 +255,7 @@ def evaluate(self, initial_values, sn_model = "S1_SN_MODEL_v2_01_SN_type"): interpolation_class_ind = self.discrete_out_keys.index("interpolation_class") classes = np.array([ - cl["classifier"].predict(normalize(initial_values, cl["stats"][0], cl["stats"][1], cl["log"])) + cl["classifier"].predict(cl["transform"].normalize(initial_values)) for cl in self.classifiers.values()]).T interpolated_values = [] @@ -187,19 +266,36 @@ def evaluate(self, initial_values, sn_model = "S1_SN_MODEL_v2_01_SN_type"): continue interpolated, meta_data = self.interpolate(iv, klass, sn_model) - interpolated = self.apply_continuous_constraints(interpolated, sn_model) + interpolated = self.apply_continuous_constraints(interpolated, sn_model) interpolated_values.append(interpolated) n.append(meta_data) - + interpolated_values = np.array(interpolated_values) + classes = np.array(classes) return interpolated_values, classes, n def find_hyperparameters(self, klass): + """ finds optimal k for a specified classifier + + Parameters + ---------- + klass: string + classifier to consider + + Return Values + ------------- + dict: dict + contains classifier information and more + """ input_matrix = [] + """ + matrix that considers different number of neighbors with different + in_scaling options + """ for k in range(1, self.max_k): row = [] @@ -216,6 +312,9 @@ def find_hyperparameters(self, klass): } def kwargs_fnc(**kwargs): + """ helper function handling passing of arguments for functions given + to the preprocessing modules + """ kwargs = { "self": kwargs["kwargs"]["self"], @@ -226,14 +325,30 @@ def kwargs_fnc(**kwargs): return kwargs def eval_fnc(self, k, scaling): + """ the preprocessing module evaluates every point in input_matrix (specified above) + which considers different input_scalings and numbers of neighbors. This function gives + a score for each value of k paired with a normalization + + Parameters + ---------- + k: int + number of neighbors used + scaling: string + specifies normalization + + Return Values + ------------- + bacc: float + an accuracy score + stats: statistics used + """ validation_classifier = KNeighborsClassifier(n_neighbors = k, weights = "distance") training_initial_values = self.training_grid["initial_values"] - stats = compute_statistics(training_initial_values, scaling) - training_initial_values = normalize( - training_initial_values, stats[0], stats[1], "log" in scaling) + transform = Transformer(training_initial_values, scaling) + training_initial_values = transform.normalize(training_initial_values) validation_classifier.fit( training_initial_values, @@ -241,8 +356,7 @@ def eval_fnc(self, k, scaling): ) validation_initial_values = self.validation_grid["initial_values"] - validation_initial_values = normalize( - validation_initial_values, stats[0], stats[1], "log" in scaling) + validation_initial_values = transform.normalize(validation_initial_values) predicted_classes = validation_classifier.predict(validation_initial_values) bacc = balanced_accuracy_score( @@ -250,36 +364,47 @@ def eval_fnc(self, k, scaling): predicted_classes ) - return bacc, stats + return bacc, transform - eval_matrix, stat_matrix = find_normalization_evaluation_matrix(eval_fnc, kwargs_fnc, kwargs) + eval_matrix, stat_matrix = find_normalization_evaluation_matrix(eval_fnc, kwargs_fnc, kwargs) # getting matrix - k_star = list(np.unravel_index(eval_matrix.argmax(), eval_matrix.shape)) + k_star = list(np.unravel_index(eval_matrix.argmax(), eval_matrix.shape)) # optimal number of neighbors - classifier = KNeighborsClassifier(n_neighbors = k_star[0] + 1, weights = "distance") + classifier = KNeighborsClassifier(n_neighbors = k_star[0] + 1, weights = "distance") # defining classifier training_initial_values = self.training_grid["initial_values"] scaling = IN_SCALING_OPTIONS[k_star[1]] - stats = compute_statistics(training_initial_values, scaling) - training_initial_values = normalize( - training_initial_values, stats[0], stats[1], "log" in scaling) + transform = Transformer(training_initial_values, scaling) + training_initial_values = transform.normalize(training_initial_values) # taking care of normalization classifier.fit( training_initial_values, self.training_grid["final_classes"][klass] - ) + ) # training classifier return { "classifier": classifier, - "stats": stat_matrix[*k_star], + "transform": stat_matrix[*k_star], "log": "log" in IN_SCALING_OPTIONS[k_star[1]], "k_star": k_star, "eval_matrix": eval_matrix } def optimize_normalization(self, key): + """ method to find optimal normalization per class + + Parameters + ---------- + key: string + specifies parameter + + Return Values + ------------- + dict: dict + interpolator information and more + """ input_matrix = [] @@ -301,6 +426,9 @@ def optimize_normalization(self, key): } def kwargs_fnc(**kwargs): + """ helper function handling passing of arguments for functions given + to the preprocessing modules + """ kwargs = { "self": kwargs["kwargs"]["self"], @@ -312,14 +440,34 @@ def kwargs_fnc(**kwargs): return kwargs def eval_fnc(self, key, klass, scaling): + """ the preprocessing module evaluates every point in input_matrix (specified above) + which considers different classes and output scalings. This function gives + a score for each value of class label paired with a normalization + + Parameters + ---------- + key: string + parameter considered + klass: string + class label + scaling: string + specifies normalization + + Return Values + ------------- + errors: float + an accuracy score + stats: statistics used + """ self.training = True self.scaling = scaling klass_inds = np.where(self.validation_grid["final_classes"][key] == klass)[0] - training_final_values = self.training_grid["final_values"][key][klass_inds] - self._stats = compute_statistics(training_final_values, scaling) - print("Does input have NaNs?", np.isnan(self.validation_grid["initial_values"][klass_inds]).any()) + training_final_values = self.training_grid["final_values"][key][self.training_grid["class_inds"][key][klass]] + + self._transform = Transformer(training_final_values, scaling) + interpolated, classes, _ = self.evaluate(self.validation_grid["initial_values"][klass_inds]) classes = classes[np.where(classes[:, 0] != "initial_MT")[0]] @@ -330,29 +478,49 @@ def eval_fnc(self, key, klass, scaling): [self.validation_grid["final_values"]["interpolation_class"], self.validation_grid["final_values"]["S1_SN_MODEL_v2_01_SN_type"]], axis = 1 ) + # some interpolated and ground truth values will be NaN, e.g., for keys describing CE phenomnea are NaN if no CE is present, need to filter these NaNs out + nans_mask = ~(np.isnan(interpolated[predicted_klass_inds]) + np.isnan(ground_truth[klass_inds][predicted_klass_inds])) + errors = np.abs( - (interpolated[predicted_klass_inds] - ground_truth[klass_inds][predicted_klass_inds]) / - (ground_truth[klass_inds][predicted_klass_inds] + eps) + (interpolated[predicted_klass_inds][nans_mask] - ground_truth[klass_inds][predicted_klass_inds][nans_mask]) / + (ground_truth[klass_inds][predicted_klass_inds][nans_mask] + eps) ) self.training = False - return errors.mean(), self._stats - - eval_matrix, stat_matrix = find_normalization_evaluation_matrix(eval_fnc, kwargs_fnc, kwargs) - - opt = list(np.unravel_index(eval_matrix.argmax(), eval_matrix.shape)) + error_mean = errors.mean() if len(errors) > 0 else np.inf + return error_mean, Transformer(training_final_values, scaling) + eval_matrix, stat_matrix = find_normalization_evaluation_matrix(eval_fnc, kwargs_fnc, kwargs) # finding normalization + + # opt = tuple(np.unravel_index(eval_matrix.argmin(axis = 0), eval_matrix.shape)) + opt = [eval_matrix.argmin(axis = 0), np.arange(eval_matrix.shape[1])] + print(eval_matrix) return { - "stats": stat_matrix[opt], - "log": "log" in OUT_SCALING_OPTIONS[opt[1]], - "scaling": OUT_SCALING_OPTIONS[opt[1]], - "eval_matrix": eval_matrix + "transform": stat_matrix[opt[0], opt[1]], + "eval_matrix": eval_matrix, + "label_dict": dict(zip(labels, np.arange(len(labels)))) } # =================== helper methods below =========================== def preprocess_grid(self, grid, training_grid = False): + """ method that takes PSyGrid object and processes it into nice + numpy arrays and dictionaries + + Parameters + --------- + grid: PSyGrid + grid + training_grid: bool + specifies whether this is a training grid (regularly sampled) + + Return Values + ------------- + dict + PSyGrid processed into dict such that it contains only information + important to IF interpolation + """ final_values = np.array(grid.final_values[self.continuous_out_keys].tolist()) @@ -365,8 +533,6 @@ def preprocess_grid(self, grid, training_grid = False): initial_values = np.array(grid.initial_values[self.in_keys][valid_inds].tolist()) # determining if should interp in q if training_grid: - m1, m2 = 10**initial_values[:, 0], 10**initial_values[:, 1] - self.interp_in_q = (m2[m1 > 0.95 * m1.max()].min() / m2[m1 < 1.05 * m1.min()].min() > 2) self.interp_in_q = False initial_values = np.log10(initial_values + eps) @@ -395,6 +561,16 @@ def preprocess_grid(self, grid, training_grid = False): } def triangulate(self, grid_dict): + """ method that constructs Delaunay triangulations stored in class memory + when given a grid + + Parameters + ---------- + grid_dict: dict + a dictionary containing grid information created with preprocess grid + method + + """ triangulations = {} @@ -404,17 +580,41 @@ def triangulate(self, grid_dict): classes.remove("initial_MT") class_triangulations = {} - + for klass in classes: class_inds = grid_dict["class_inds"][label_name][klass] - class_triangulations[klass] = Delaunay(grid_dict["initial_values"][class_inds]) + + if class_inds.shape[0] < 5: + print(f"too few training samples for {klass}") + class_triangulations[klass] = "1NN" + else: + + try: + class_triangulations[klass] = Delaunay(grid_dict["initial_values"][class_inds]) + except: + print(f"Geometry wrong for {klass}, using 1NN") + class_triangulations[klass] = "1NN" triangulations[label_name] = class_triangulations grid_dict["triangulations"] = triangulations def compute_barycentric_coordinates(self, point, coords): + """ helper method that computes barycentric coordinates which are the weights to use + for the nearest neighbors + + Parameters + ---------- + point: np.ndarray + initial values inside tetrahedral (we are trying to predict) + coords: np.ndarray + coordinates of neighbors which are vertices of tetrahedra + + Return Values + ------------- + np.ndarray: weight for each coord + """ T = np.array([ coords[0] - coords[3], @@ -435,6 +635,19 @@ def compute_barycentric_coordinates(self, point, coords): return weights def get_nearest_neighbor(self, iv, key): + """ finds the nearest neighbor in the training grid + + Parameters + ---------- + iv: np.ndarray + contains initial stellar masses and orbital period + key: string + specifies parameter + + Return Values + ------------- + np.ndarray: output parameters of nearest neighbors + """ dists = np.sqrt(np.square(self.training_grid["initial_values"] - iv).sum(axis = 1)) sorted_inds = dists.argsort() @@ -442,6 +655,20 @@ def get_nearest_neighbor(self, iv, key): return np.array(self.training_grid["final_values"][key][sorted_inds[0]].tolist()) def apply_continuous_constraints(self, interpolated, sn_model): + """ method that applies constraints to our outputs + + Parameters + ---------- + interpolated: np.ndarray + interpolated values + sn_model: string + specifies super nova model used + + Return Values + ------------- + np.ndarray: contains interpolated values with constraints applied + + """ keys = self.out_key_dict["interpolation_class"] + self.out_key_dict[sn_model] sanitized = sanitize_interpolated_quantities( @@ -450,38 +677,5 @@ def apply_continuous_constraints(self, interpolated, sn_model): ) return np.array([sanitized[key] for key in keys]) - def normalize_output(self, input, klass): - class_ind = self.classes.index(klass) - scalers = self.out_scalers[class_ind] - - ret_value = np.zeros_like(input) - - for dim, scaler in enumerate(scalers): - ret_value[:, dim] = scaler.transform(input[:, dim]) - - return ret_value - - - def denormalize_output(self, input, klass): - class_ind = self.classes.index(klass) - scalers = self.out_scalers[class_ind] - - ret_value = np.zeros_like(input) - - for dim, scaler in enumerate(scalers): - ret_value[dim] = scaler.inv_transform(input[dim]) - - return ret_value - - def normalize_triangulations(self, out_keys): - - new_final_values = np.zeros_like(self.training_grid["final_values"][out_keys]) - - for i, (klass, fv) in enumerate(zip(self.training_grid["classes"], self.training_grid["final_values"][out_keys])): - if klass == "initial_MT": # should be taken out in preprocessing - continue - new_final_values[i] = self.normalize_output(np.array([fv]), klass) - - self.training_grid["final_values"][out_keys] = new_final_values \ No newline at end of file diff --git a/posydon/interpolation/preprocessing.py b/posydon/interpolation/preprocessing.py index 8db1482718..963dcd9b3c 100644 --- a/posydon/interpolation/preprocessing.py +++ b/posydon/interpolation/preprocessing.py @@ -8,9 +8,10 @@ import numpy as np -eps = 1.0e-16 +eps = 1.0e-32 IN_SCALING_OPTIONS = [ + "none", "min-max", "standard", "log_min-max", @@ -18,40 +19,77 @@ ] OUT_SCALING_OPTIONS = [ - # "log_min-max", - # "log_standard" - "min-max" + "none", + "log_min-max", + "log_standard", + "min-max", + "standard" ] +class Transformer: + + def __init__(self, data, scaling): + """ + If a dimension contains negative values we assume that it is in log space and unlog it. + This is an assumption that we know doesn't hold since things like rates can be negative, but it + simplifies the preprocessing code for now. + """ + data = data.copy() + self.logged = (data < 0.0).any(axis = 0) + data[:, self.logged] = 10**data[:, self.logged] + + computations = [ + lambda data: [0, 1], + lambda data: [data.min(axis = 0), data.max(axis = 0) - data.min(axis = 0)], + lambda data: [data.mean(axis = 0), data.std(axis = 0)], + lambda data: [np.log10(data + eps).min(axis = 0), np.log10(data + eps).max(axis = 0) - np.log10(data + eps).min(axis = 0)], + lambda data: [np.log10(data + eps).mean(axis = 0), np.log10(data + eps).std(axis = 0)], + ] + compute = dict(zip(IN_SCALING_OPTIONS, computations)) # this line assumes that all other options are a subset of IN_SCALING_OPTION -def normalize(data, shift, scale, log = False): - if log: - if np.isnan(data).any() or np.isinf(data).any(): - raise ValueError("nans or infs detected in crucial grid parameters") + self.log = "log" in scaling + self.shift, self.scale = compute[scaling](data) - if not (data < 0).any(): + def normalize(self, data): + + data = data.copy() + + if self.logged.any() and data.shape[1] == self.logged.shape[0]: + data[:, self.logged] = 10**data[:, self.logged] + + if self.log: data = np.log10(data + eps) - return (data - shift) / (scale + eps) + return (data - self.shift) / (self.scale + eps) + + # def unnormalize(self, data): -def unnormalize(data, shift, scale, log = False): - data = (data * (scale + eps)) + shift + # data = data.copy() - if log: - data = 10**data - eps + # data = (data * (self.scale + eps)) + self.shift - return data + # if self.log: + # data = 10**data - eps + # if self.logged.any() and data.shape[1] == self.logged.shape[0]: + # data[:, self.logged] = np.log10(data[:, self.logged]) + # else: + # if self.logged.any() and data.shape[1] == self.logged.shape[0]: + # data[:, self.logged] = np.log10(data[:, self.logged]) -def compute_statistics(data, scaling): + # return data + def unnormalize(self, data): + data = data.copy() + data = (data * (self.scale + eps)) + self.shift + if self.log: + non_logged = ~self.logged + if non_logged.any() and data.shape[1] == self.logged.shape[0]: + data[:, non_logged] = 10**data[:, non_logged] - eps + elif data.shape[1] != self.logged.shape[0]: + data = 10**data - eps # fallback: no column info available + if self.logged.any() and data.shape[1] == self.logged.shape[0]: + data[:, self.logged] = np.log10(np.maximum(data[:, self.logged], eps)) + return data - computations = [ - lambda data: [data.min(axis = 0), data.max(axis = 0) - data.min(axis = 0)], - lambda data: [data.mean(axis = 0), data.std(axis = 0)], - lambda data: [np.log10(data + eps).min(axis = 0), np.log10(data + eps).max(axis = 0) - np.log10(data + eps).min(axis = 0)], - lambda data: [np.log10(data + eps).mean(axis = 0), np.log10(data + eps).std(axis = 0)], - ] - compute = dict(zip(IN_SCALING_OPTIONS, computations)) # this line assumes that all other options are a subset of IN_SCALING_OPTION - return compute[scaling](data) def find_normalization_evaluation_matrix(eval_fnc, kwarg_fnc, kwargs): diff --git a/posydon/visualization/interpolation.py b/posydon/visualization/interpolation.py index b402877e51..263f11e628 100644 --- a/posydon/visualization/interpolation.py +++ b/posydon/visualization/interpolation.py @@ -50,12 +50,12 @@ def __compute_errs(self): (self.test_grid.final_values["interpolation_class"] != "not_converged") & (self.test_grid.final_values["interpolation_class"] != "initial_MT") ) - + print("initial value", iv.shape) i = self.interpolator.test_interpolator(iv) # interpolated # ifv = fv[ivalid_inds] - + print("interpolated", i.shape) self.errs = {} - + print("fv", fv.shape) with np.errstate(divide='ignore', invalid='ignore'): self.errs["relative"] = np.abs((fv - i) / fv) self.errs["absolute"] = np.abs(fv - i) From 97d81fc56e0f5866af3a454dbd960aa42b77b24b Mon Sep 17 00:00:00 2001 From: prinse1545 Date: Wed, 22 Apr 2026 15:59:53 -0500 Subject: [PATCH 317/319] sync --- posydon/binary_evol/MESA/step_mesa.py | 5 ++-- posydon/interpolation/new_interpolator.py | 31 ++++++++++++++++++++--- 2 files changed, 31 insertions(+), 5 deletions(-) diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index 03b9db483f..2537ac7d2f 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -22,7 +22,7 @@ from posydon.binary_evol.singlestar import STARPROPERTIES from posydon.utils import common_functions as cf from posydon.binary_evol.binarystar import BinaryStar -from posydon.interpolation.IF_interpolation import IFInterpolator +from posydon.interpolation.new_interpolator import IFInterpolator from posydon.utils.common_functions import (flip_stars, convert_metallicity_to_string, CO_radius, infer_star_state, @@ -212,6 +212,7 @@ def __init__( else: interpolation_filename = os.path.join(interpolation_path, interpolation_filename) + print(interpolation_filename) self.load_Interp(interpolation_filename) @@ -257,7 +258,7 @@ def load_Interp(self, filename): data_download() #TODO: specify dataset # Load interpolator - self._Interp = IFInterpolator() + self._Interp = IFInterpolator(load = True) self._Interp.load(filename=filename) def close(self): diff --git a/posydon/interpolation/new_interpolator.py b/posydon/interpolation/new_interpolator.py index eae69feb56..8617ff8407 100644 --- a/posydon/interpolation/new_interpolator.py +++ b/posydon/interpolation/new_interpolator.py @@ -42,7 +42,7 @@ class IFInterpolator: be gained by referencing section 3 of 2411.02376, is required to understand the documentation """ - def __init__(self, grids, in_keys, out_keys, max_k): + def __init__(self, grids = None, in_keys = None, out_keys = None, max_k = None, load = False): """ Class constructor Parameters @@ -58,8 +58,10 @@ def __init__(self, grids, in_keys, out_keys, max_k): The maximum number of k that is considered when optimizing k for each classifier """ - if type(grids) != list: + if type(grids) != list and not load: sys.exit("Please provide a list of PSyGrids containing both a training and validation grid to train the interpolator") + elif load: + print("Constructed in Loading Mode") else: self.in_keys = in_keys @@ -495,7 +497,7 @@ class label # opt = tuple(np.unravel_index(eval_matrix.argmin(axis = 0), eval_matrix.shape)) opt = [eval_matrix.argmin(axis = 0), np.arange(eval_matrix.shape[1])] - print(eval_matrix) + return { "transform": stat_matrix[opt[0], opt[1]], "eval_matrix": eval_matrix, @@ -677,5 +679,28 @@ def apply_continuous_constraints(self, interpolated, sn_model): ) return np.array([sanitized[key] for key in keys]) + def save(self, filename): + """ + Saves the IFInterpolator instance to a pickle file. + + Parameters + ---------- + filename : str + Path or filename where the object should be saved (e.g., 'interpolator.pkl'). + """ + try: + with open(filename, 'wb') as f: + pickle.dump(self, f, protocol=pickle.HIGHEST_PROTOCOL) + print(f"Successfully saved interpolator to {filename}") + except Exception as e: + print(f"Error saving interpolator: {e}") + + def load(self, filename): + """ + Loads an IFInterpolator instance from a pickle file. + """ + with open(filename, 'rb') as f: + return pickle.load(f) + \ No newline at end of file From 401dae9870db9529a9b3a246bef4161fc709971d Mon Sep 17 00:00:00 2001 From: prinse1545 Date: Thu, 23 Apr 2026 08:28:42 -0500 Subject: [PATCH 318/319] visualization for new interpolation --- posydon/visualization/new_interpolation.py | 393 +++++++++++++++++++++ 1 file changed, 393 insertions(+) create mode 100644 posydon/visualization/new_interpolation.py diff --git a/posydon/visualization/new_interpolation.py b/posydon/visualization/new_interpolation.py new file mode 100644 index 0000000000..897571b491 --- /dev/null +++ b/posydon/visualization/new_interpolation.py @@ -0,0 +1,393 @@ +""" Module to evaluate IFInterpolator class """ + +__authors__ = [ + "Philipp Moura Srivastava " + "Simone Bavera " +] + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns + +eps = 1.0e-16 + +class EvaluateIFInterpolator: + """ Class that is helpful for evaluating interpolation performance + """ + def __init__(self, interpolator, test_grid, sn_method = "S1_SN_MODEL_v2_01_SN_type"): + """ Initialize the EvaluateIFInterpolator class + + Parameters + ---------- + interpolator : IFInterpolator + Interpolator that user wants to test + test_grid : PSyGrid + Grid object containing testing tracks + + """ + + self.interpolator = interpolator + + self.test_grid = test_grid + + # assuming that in_keys are the same for all interpolators + self.in_keys = interpolator.in_keys + self.out_keys = interpolator.continuous_out_keys + self.sn_method = sn_method + + self.__compute_errs() + + def __compute_errs(self): + """ Method that computes both interpolation and classification errors """ + + iv = np.array(self.test_grid.initial_values[self.in_keys].tolist(), + dtype=float) # initial values + fv = np.array(self.test_grid.final_values[self.out_keys].tolist(), + dtype=float) # final values + ic = self.test_grid.final_values["interpolation_class"] # final values + + + + i, c, _ = self.interpolator.evaluate(iv) # interpolated + ivalid_inds = np.where(c[:, 0] != "initial_MT")[0] + fv = fv[ivalid_inds] + + self.errs = {} + nans_mask = np.isnan(fv) + np.isnan(i) + + with np.errstate(divide='ignore', invalid='ignore'): + self.errs["relative"] = np.abs((fv - i) / (fv + eps)) + + self.errs["absolute"] = np.abs(fv - i) + self.errs["valid_inds"] = ivalid_inds + + + cvalid_inds = np.where( + self.test_grid.final_values["interpolation_class"] != "not_converged" + )[0] + + self.cfv = self.test_grid.final_values[cvalid_inds] + + _, c, _ = self.interpolator.evaluate(iv[cvalid_inds]) # classifying + + # computing confusion matrices + self.matrices = {} + + for i, value in enumerate(c.T): + + key = "interpolation_class" if i == 0 else self.sn_method + + labels = self.cfv[key] + + classes = self.__find_labels(key) + + matrix = {} + + # catch cases where nothing is cl,assified, e.g. S2_MODELXX_SN_type + # when S2 is a compact object + if len(classes) == 1 and classes[0] == 'None': + matrix['None'] = 1. + else: + for _class in classes: + class_inds = np.where(labels == _class)[0] + pred_classes, counts = np.unique(value[class_inds], return_counts = True) + + row = {c: 0 for c in classes} + + for pred_class, count in zip(pred_classes, counts): + row[pred_class] = count / len(class_inds) + + matrix[_class] = row + + + self.matrices[key] = matrix + + # saving classes + self.c = c + self.ic = ic + + + + def __format(self, s, title = False): + """ Method that formats keys for plots + + Parameters + ---------- + s : str + string to be formatted + + """ + + return s.replace("_", " ").title() + + def __find_labels(self, key): + """ Method that finds labels in classifier + + Parameters + ---------- + key : str + name of the classifier + + Returns + ------- + list of class labels + + """ + + labels = np.unique(self.interpolator.training_grid["final_classes"][key]) + + return labels + + def __clean_errs(self, errs): + nans = np.isnan(errs).any(axis = 1) + infs = np.isinf(errs).any(axis = 1) + + # errs += 1.0e-16 + # errs = np.array(np.log10(errs[(~np.isnan(errs)) & (~np.isinf(errs))]), dtype = object) # dropping nans and infs + errs = errs[(~np.isnan(errs).any(axis = 1)) & (~np.isinf(errs).any(axis = 1))] + + return errs + + + def violin_plots(self, err_type = "relative", keys = None, + save_path = None, close_fig = False): + """ Method that plots distribution of specified error for given keys and + optionally saves it. + + Parameters + ---------- + + err_type : str + Either relative or absolute, default is relative + keys : list + A list of keys for which the errors will be shown, by default is all of them + save_path: str + The path where the figure should be saved to + close_fig: bool + Flag whether figure should be closed + + """ + + if keys is None: + keys = self.out_keys + + k_inds = [self.out_keys.index(key) for key in keys] + dirty_errs = self.errs[err_type][:, k_inds] + errs = np.log10(self.__clean_errs(self.errs[err_type][:, k_inds]) + 1.0e-16) + + n_tracks = self.test_grid.final_values["star_1_mass"].shape[0] + + + stable_inds = np.where(self.ic[self.errs["valid_inds"]] == "stable_MT") + no_inds = np.where(self.ic[self.errs["valid_inds"]] == "no_MT") + unstable_inds = np.where(self.ic[self.errs["valid_inds"]] == "unstable_MT") + #TODO: add interpolation class "stable_reverse_MT" + + stable_errs = np.log10(self.__clean_errs(self.errs[err_type].T[k_inds].T[stable_inds]) + 1.0e-16) + no_errs = np.log10(self.__clean_errs(self.errs[err_type].T[k_inds].T[no_inds]) + 1.0e-16) + unstable_errs = np.log10(self.__clean_errs(self.errs[err_type].T[k_inds].T[unstable_inds]) + 1.0e-16) + + + plt.rcParams.update({"font.size": 32, "font.family": "stixgeneral"}) + + fig, axs = plt.subplots(1, 1, + figsize = (24, 10), + tight_layout = True) + + rel_plot = axs.violinplot(errs, showmedians = True, points = 1000) + stable_plot = axs.violinplot(stable_errs, showmedians = True, points = 1000) + no_plot = axs.violinplot(no_errs, showmedians = True, points = 1000) + unstable_plot = axs.violinplot(unstable_errs, showmedians = True, points = 1000) + + + axs.set_title(f"Distribution of {err_type.capitalize()} Errors") + axs.set_xticks(np.arange(1, len(keys) + 1), + labels = [ + f"{self.__format(ec)} ({(med * 100):.2f}%)" for ec, med in zip(keys, np.nanmedian(dirty_errs, axis = 0)) + ], rotation = 20) + axs.set_ylim(-4, 2) + axs.set_ylabel("Errors in Log 10 Scale") + axs.grid(axis = "y") + + def halve_paths(field, color, right = True): + + for i, path in enumerate(field.get_paths()): + + # getting mean + m = np.mean(path.vertices[:, 0]) + + first = m if right == True else -np.inf + second = np.inf if right == True else m + + # modify the paths to not go further left than the center + field.get_paths()[i].vertices[:, 0] = np.clip(path.vertices[:, 0], first, second) + field.set_edgecolor(color) + + def customize_violinplot(plot, color, outlined = False, right = True): + + halve_paths(plot["cmins"], color, right = right) + halve_paths(plot["cmaxes"], color, right = right) + halve_paths(plot["cmedians"], color, right = right) + + for pc in plot["bodies"]: + + halve_paths(pc, color, right = right) + + + pc.set_facecolor(color if not outlined else "None") + pc.set_edgecolor(color) + pc.set_linewidth(4) + pc.set_alpha(0.75) + + customize_violinplot(rel_plot, "coral") + customize_violinplot(stable_plot, "#1e90ff", True, False) + customize_violinplot(no_plot, "crimson", True, False) + customize_violinplot(unstable_plot, "olive", True, False) + + axs.legend( + [rel_plot["bodies"][0], stable_plot["bodies"][0], no_plot["bodies"][0], unstable_plot["bodies"][0]], + ["Relative Error", "Stable MT Error", "No MT Error", "Unstable MT Error"], + bbox_to_anchor = (0, 1.02, 1, 0.2), + loc = "lower left", + mode = "expand", + ncol = 4 + ) + + plt.show() + + if save_path is not None: + fig.save(save_path) + + # close figure + if close_fig: + plt.close(fig) + + def confusion_matrix(self, key, params = {}, save_path = None, + close_fig = False): + """ Method that plots confusion matrices to evaluate classification + + Parameters + ---------- + key : str + The key for the classifier of interest + params : dict + Extra params to pass to matplolib, x_labels (list), y_labels (list), title (str) + save_path : str + The path where the figure should be saved to + close_fig: bool + Flag whether figure should be closed + + """ + + if key not in self.matrices.keys(): + raise Exception("Key not in List of Matrices") + + arr_mat = [] + + for k, value in self.matrices[key].items(): + arr_mat.append(list(value.values())) + + figsize = params["figsize"] if "figsize" in params.keys() else (4, 8) + + fig, ax = plt.subplots(1, 1, figsize = figsize, constrained_layout = True) + + im = ax.imshow(arr_mat) + + x_axis = [self.__format(x) for x in self.matrices[key].keys()] if "x_axis" not in params.keys() else params["x_axis"] + y_axis = [self.__format(y) for y in self.matrices[key][list(self.matrices[key].keys())[0]].keys()] if "y_axis" not in params.keys() else params["y_axis"] + title = f"Confusion Matrix for {self.__format(key)}" if "title" not in params.keys() else params["title"] + + # Show all ticks and label them with the respective list entries + ax.set_xticks(np.arange(len(x_axis)), labels = x_axis) + ax.set_yticks(np.arange(len(y_axis)), labels = y_axis) + + # Rotate the tick labels and set their alignment. + plt.setp(ax.get_xticklabels(), rotation = 45, ha = "right", + rotation_mode = "anchor") + + # Loop over data dimensions and create text annotations. + for i in range(len(arr_mat)): + for j in range(len(arr_mat[i])): + text = ax.text(j, i, f"{100 * arr_mat[i][j]:.2f}", + ha = "center", va = "center", color = "w" if arr_mat[i][j] < 0.9 else "black") + + ax.set_xlabel("Predicted") + ax.set_ylabel("Actual") + ax.set_title(title) + + cax = ax.inset_axes([1.1, 0.1, 0.05, 0.8]) + + fig.colorbar(im, ax = ax, cax = cax, pad = 1) + + fig.tight_layout() + + if save_path is not None: # saving + fig.save(save_path) + + # close figure + if close_fig: + plt.close(fig) + + def classifiers(self): + """ Method that lists classifiers available """ + _, c, _ = self.interpolator.evaluate( + np.array([ + self.test_grid.initial_values[self.in_keys].tolist() + ])[0] + ) + + return list(classes.keys()) + + def keys(self): + """ Method that lists out keys available """ + + return self.interpolator.continuous_out_keys + + def decision_boundaries(self): + + pass + + def plot2D(self, key, slice_3D_var_str, slice_3D_var_range, PLOT_PROPERTIES): + + k_ind = self.out_keys.index(key) + + if slice_3D_var_str == 'mass_ratio': + var = self.test_grid.initial_values["star_2_mass"] / self.test_grid.initial_values["star_1_mass"] + elif slice_3D_var_str == 'star_2_mass': + var = self.test_grid.initial_values["star_2_mass"] + else: + raise ValueError("slice_3D_var_str must be either 'mass_ratio' or 'star_2_mass'") + + slice = (var >= slice_3D_var_range[0]) & (var <= slice_3D_var_range[1]) + + slice_errs = self.errs["relative"].T[k_ind] + slice_errs = np.array([slice_errs[i] if in_slice and i in self.errs["valid_inds"][0] else np.nan for i, in_slice in enumerate(slice)]) + + # find inf and assign large value else they are not plotted + slice_errs[np.isinf(slice_errs)] = 1e99 + + fig = self.test_grid.plot2D('star_1_mass', 'period_days', slice_errs, + termination_flag='interpolation_class_errors', + grid_3D=True, slice_3D_var_str=slice_3D_var_str, + slice_3D_var_range=slice_3D_var_range, + verbose=False, **PLOT_PROPERTIES) + + + def violinplot_with_nans(data, axs, **kwargs): + """ + Wrapper around plt.violinplot that handles NaNs by dropping them per column. + + data: 2D array-like of shape (n, d), or 1D array + """ + data = np.asarray(data, dtype=float) + + if data.ndim == 1: + clean = [data[~np.isnan(data)]] + else: + # Extract each column, dropping NaNs independently + clean = [data[:, i][~np.isnan(data[:, i])] for i in range(data.shape[1])] + + return axs.violinplot(clean, **kwargs) + + From 27a51e3c6b8e485fe517c184a62f1745d69d9d22 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 23 Apr 2026 15:08:31 +0000 Subject: [PATCH 319/319] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- bin/posydon-setup-grid | 3 +- .../run_params/run_psycris_sequence.py | 2 +- posydon/interpolation/IF_interpolation.py | 4 +- posydon/interpolation/constraints.py | 2 +- posydon/interpolation/data_scaling.py | 7 +- posydon/interpolation/new_interpolator.py | 100 +++++++++--------- posydon/interpolation/preprocessing.py | 6 +- posydon/visualization/new_interpolation.py | 38 ++++--- 8 files changed, 82 insertions(+), 80 deletions(-) diff --git a/bin/posydon-setup-grid b/bin/posydon-setup-grid index 4fc7effac7..31c049baaf 100755 --- a/bin/posydon-setup-grid +++ b/bin/posydon-setup-grid @@ -15,8 +15,7 @@ from posydon.grids.psygrid import PSyGrid from posydon.utils import configfile from posydon.utils import gridutils as utils from posydon.utils.posydonwarning import Pwarn -from posydon.grids.psygrid import PSyGrid -from posydon.active_learning.psy_cris.utils import parse_inifile + ############################################################################### # DEFINE COMMANDLINE ARGUMENTS diff --git a/posydon/active_learning/psy_cris/run_params/run_psycris_sequence.py b/posydon/active_learning/psy_cris/run_params/run_psycris_sequence.py index 151828fca4..80cb05b5e2 100644 --- a/posydon/active_learning/psy_cris/run_params/run_psycris_sequence.py +++ b/posydon/active_learning/psy_cris/run_params/run_psycris_sequence.py @@ -146,4 +146,4 @@ def main(): main() if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/posydon/interpolation/IF_interpolation.py b/posydon/interpolation/IF_interpolation.py index 67c03013e5..c4976d8145 100644 --- a/posydon/interpolation/IF_interpolation.py +++ b/posydon/interpolation/IF_interpolation.py @@ -193,7 +193,9 @@ class relies on the BaseIFInterpolator class to perform the interpolation # POSYDON from posydon.grids.psygrid import PSyGrid from posydon.interpolation.constraints import ( - find_constraints_to_apply, sanitize_interpolated_quantities) + find_constraints_to_apply, + sanitize_interpolated_quantities, +) # INITIAL-FINAL INTERPOLATOR diff --git a/posydon/interpolation/constraints.py b/posydon/interpolation/constraints.py index c19ac4129e..0ee3a662b0 100644 --- a/posydon/interpolation/constraints.py +++ b/posydon/interpolation/constraints.py @@ -586,4 +586,4 @@ def sanitize_classes(classes, ): else: apply_class_constraint(key, classes) - \ No newline at end of file + diff --git a/posydon/interpolation/data_scaling.py b/posydon/interpolation/data_scaling.py index 84f27d92f1..0f5d9a6fc6 100644 --- a/posydon/interpolation/data_scaling.py +++ b/posydon/interpolation/data_scaling.py @@ -6,9 +6,10 @@ ] -import numpy as np -import warnings import sys +import warnings + +import numpy as np # Convert UserWarning to an error warnings.simplefilter("error", RuntimeWarning) @@ -252,6 +253,6 @@ def log(self, x): # sys.exit() return logged - + def unlog(self, x): return (10 ** x) - eps diff --git a/posydon/interpolation/new_interpolator.py b/posydon/interpolation/new_interpolator.py index 8617ff8407..47064db36d 100644 --- a/posydon/interpolation/new_interpolator.py +++ b/posydon/interpolation/new_interpolator.py @@ -7,38 +7,40 @@ "Philipp Moura Srivastava ", ] -import numpy as np import os import pickle +import sys +import time from datetime import date +import numpy as np from scipy.spatial import Delaunay +from sklearn.metrics import balanced_accuracy_score +from sklearn.model_selection import train_test_split + +# ML Imports +from sklearn.neighbors import KNeighborsClassifier + # POSYDON from posydon.grids.psygrid import PSyGrid -from posydon.interpolation.data_scaling import DataScaler, SCALING_OPTIONS +from posydon.interpolation.constraints import ( + find_constraints_to_apply, + sanitize_interpolated_quantities, +) +from posydon.interpolation.data_scaling import SCALING_OPTIONS, DataScaler from posydon.interpolation.preprocessing import ( - Transformer, - find_normalization_evaluation_matrix, IN_SCALING_OPTIONS, - OUT_SCALING_OPTIONS) - + OUT_SCALING_OPTIONS, + Transformer, + find_normalization_evaluation_matrix, +) from posydon.utils.posydonwarning import Pwarn -from posydon.interpolation.constraints import ( - find_constraints_to_apply, sanitize_interpolated_quantities) - -# ML Imports -from sklearn.neighbors import KNeighborsClassifier -from sklearn.model_selection import train_test_split -from sklearn.metrics import balanced_accuracy_score - -import sys -import time eps = 1.0e-16 class IFInterpolator: - """ Class used to train interpolator and carry out interpolation. Familiarity with the over all system, which can + """ Class used to train interpolator and carry out interpolation. Familiarity with the over all system, which can be gained by referencing section 3 of 2411.02376, is required to understand the documentation """ @@ -103,7 +105,7 @@ def stats(self, _print = False): each classification scheme """ percentages = [] - + for key in self.discrete_out_keys: percentages.append( self.outside_convex_hull[key] / (self.outside_convex_hull[key] + self.inside_convex_hull[key]) @@ -131,10 +133,10 @@ def train(self): ) ) self.is_training = False - + def interpolate(self, iv, klass, sn_model): - """ a method which performs interpolation for a respective initial value and its + """ a method which performs interpolation for a respective initial value and its predicted class (convex hull) Parameters @@ -159,8 +161,8 @@ def interpolate(self, iv, klass, sn_model): for key, c in zip(classification_schemes, klass): # interpolating based in mass transfer type and supernova outcome separately triangulation = self.training_grid["triangulations"][key][c] - - simplex = -1 if triangulation == "1NN" else triangulation.find_simplex(iv) + + simplex = -1 if triangulation == "1NN" else triangulation.find_simplex(iv) if simplex == -1: interpolated.extend( @@ -220,7 +222,7 @@ def interpolate(self, iv, klass, sn_model): meta_data = { - "weights": weights, + "weights": weights, "ics": ics, "ic": iv, "interpolated": interpolated @@ -243,26 +245,26 @@ def evaluate(self, initial_values, sn_model = "S1_SN_MODEL_v2_01_SN_type"): interpolated_values: np.ndarray contains all interpolated values, all classification schemes are concatenated into one array classes: np.ndarray - a list of lists, each list has two classes. The first is the mass transfer type and the second + a list of lists, each list has two classes. The first is the mass transfer type and the second is the compact object type which is used for the supernova n: list of dicts each dict containing meta data information about the interpolation such as neighbors used and distances found - + """ if self.classifiers is None: sys.exit("Please find classifier hyperparameters before using interpolator") - + interpolation_class_ind = self.discrete_out_keys.index("interpolation_class") classes = np.array([ - cl["classifier"].predict(cl["transform"].normalize(initial_values)) + cl["classifier"].predict(cl["transform"].normalize(initial_values)) for cl in self.classifiers.values()]).T interpolated_values = [] n = [] - + for iv, klass in zip(initial_values, classes): if klass[interpolation_class_ind] == "initial_MT": continue @@ -272,7 +274,7 @@ def evaluate(self, initial_values, sn_model = "S1_SN_MODEL_v2_01_SN_type"): interpolated = self.apply_continuous_constraints(interpolated, sn_model) interpolated_values.append(interpolated) n.append(meta_data) - + interpolated_values = np.array(interpolated_values) classes = np.array(classes) @@ -280,7 +282,7 @@ def evaluate(self, initial_values, sn_model = "S1_SN_MODEL_v2_01_SN_type"): return interpolated_values, classes, n def find_hyperparameters(self, klass): - """ finds optimal k for a specified classifier + """ finds optimal k for a specified classifier Parameters ---------- @@ -292,7 +294,7 @@ def find_hyperparameters(self, klass): dict: dict contains classifier information and more """ - + input_matrix = [] """ matrix that considers different number of neighbors with different @@ -328,7 +330,7 @@ def kwargs_fnc(**kwargs): def eval_fnc(self, k, scaling): """ the preprocessing module evaluates every point in input_matrix (specified above) - which considers different input_scalings and numbers of neighbors. This function gives + which considers different input_scalings and numbers of neighbors. This function gives a score for each value of k paired with a normalization Parameters @@ -342,7 +344,7 @@ def eval_fnc(self, k, scaling): ------------- bacc: float an accuracy score - stats: statistics used + stats: statistics used """ validation_classifier = KNeighborsClassifier(n_neighbors = k, weights = "distance") @@ -353,7 +355,7 @@ def eval_fnc(self, k, scaling): training_initial_values = transform.normalize(training_initial_values) validation_classifier.fit( - training_initial_values, + training_initial_values, self.training_grid["final_classes"][klass] ) @@ -363,7 +365,7 @@ def eval_fnc(self, k, scaling): bacc = balanced_accuracy_score( self.validation_grid["final_classes"][klass], - predicted_classes + predicted_classes ) return bacc, transform @@ -382,7 +384,7 @@ def eval_fnc(self, k, scaling): training_initial_values = transform.normalize(training_initial_values) # taking care of normalization classifier.fit( - training_initial_values, + training_initial_values, self.training_grid["final_classes"][klass] ) # training classifier @@ -390,7 +392,7 @@ def eval_fnc(self, k, scaling): "classifier": classifier, "transform": stat_matrix[*k_star], "log": "log" in IN_SCALING_OPTIONS[k_star[1]], - "k_star": k_star, + "k_star": k_star, "eval_matrix": eval_matrix } @@ -420,7 +422,7 @@ def optimize_normalization(self, key): [label, opt] ) input_matrix.append(row) - + kwargs = { "input_matrix": input_matrix, "self": self, @@ -443,7 +445,7 @@ def kwargs_fnc(**kwargs): def eval_fnc(self, key, klass, scaling): """ the preprocessing module evaluates every point in input_matrix (specified above) - which considers different classes and output scalings. This function gives + which considers different classes and output scalings. This function gives a score for each value of class label paired with a normalization Parameters @@ -459,7 +461,7 @@ class label ------------- errors: float an accuracy score - stats: statistics used + stats: statistics used """ self.training = True self.scaling = scaling @@ -494,7 +496,7 @@ class label return error_mean, Transformer(training_final_values, scaling) eval_matrix, stat_matrix = find_normalization_evaluation_matrix(eval_fnc, kwargs_fnc, kwargs) # finding normalization - + # opt = tuple(np.unravel_index(eval_matrix.argmin(axis = 0), eval_matrix.shape)) opt = [eval_matrix.argmin(axis = 0), np.arange(eval_matrix.shape[1])] @@ -507,7 +509,7 @@ class label # =================== helper methods below =========================== def preprocess_grid(self, grid, training_grid = False): - """ method that takes PSyGrid object and processes it into nice + """ method that takes PSyGrid object and processes it into nice numpy arrays and dictionaries Parameters @@ -536,7 +538,7 @@ def preprocess_grid(self, grid, training_grid = False): # determining if should interp in q if training_grid: self.interp_in_q = False - + initial_values = np.log10(initial_values + eps) if self.interp_in_q: @@ -551,7 +553,7 @@ def preprocess_grid(self, grid, training_grid = False): for key in self.discrete_out_keys: class_labels = np.unique(grid.final_values[valid_inds][key]) class_inds[key] = dict(zip( - class_labels, + class_labels, [np.where(grid.final_values[valid_inds][key] == label)[0] for label in class_labels] )) @@ -561,7 +563,7 @@ def preprocess_grid(self, grid, training_grid = False): "final_classes": dict(zip(self.discrete_out_keys, np.array(grid.final_values[self.discrete_out_keys][valid_inds].tolist()).T)), "class_inds": class_inds, } - + def triangulate(self, grid_dict): """ method that constructs Delaunay triangulations stored in class memory when given a grid @@ -582,11 +584,11 @@ def triangulate(self, grid_dict): classes.remove("initial_MT") class_triangulations = {} - + for klass in classes: class_inds = grid_dict["class_inds"][label_name][klass] - + if class_inds.shape[0] < 5: print(f"too few training samples for {klass}") class_triangulations[klass] = "1NN" @@ -682,7 +684,7 @@ def apply_continuous_constraints(self, interpolated, sn_model): def save(self, filename): """ Saves the IFInterpolator instance to a pickle file. - + Parameters ---------- filename : str @@ -703,4 +705,4 @@ def load(self, filename): return pickle.load(f) - \ No newline at end of file + diff --git a/posydon/interpolation/preprocessing.py b/posydon/interpolation/preprocessing.py index 963dcd9b3c..ceb2a43df4 100644 --- a/posydon/interpolation/preprocessing.py +++ b/posydon/interpolation/preprocessing.py @@ -30,7 +30,7 @@ class Transformer: def __init__(self, data, scaling): """ - If a dimension contains negative values we assume that it is in log space and unlog it. + If a dimension contains negative values we assume that it is in log space and unlog it. This is an assumption that we know doesn't hold since things like rates can be negative, but it simplifies the preprocessing code for now. """ @@ -99,7 +99,7 @@ def find_normalization_evaluation_matrix(eval_fnc, kwarg_fnc, kwargs): normalization_eval_matrix = [] normalization_stat_matrix = [] - + for row in kwargs["input_matrix"]: eval_row = [] stat_row = [] @@ -111,7 +111,7 @@ def find_normalization_evaluation_matrix(eval_fnc, kwarg_fnc, kwargs): acc ) stat_row.append(stat) - + normalization_eval_matrix.append(eval_row) normalization_stat_matrix.append(stat_row) diff --git a/posydon/visualization/new_interpolation.py b/posydon/visualization/new_interpolation.py index 897571b491..798ce06fe9 100644 --- a/posydon/visualization/new_interpolation.py +++ b/posydon/visualization/new_interpolation.py @@ -61,7 +61,7 @@ def __compute_errs(self): self.errs["absolute"] = np.abs(fv - i) self.errs["valid_inds"] = ivalid_inds - + cvalid_inds = np.where( self.test_grid.final_values["interpolation_class"] != "not_converged" @@ -79,11 +79,11 @@ def __compute_errs(self): key = "interpolation_class" if i == 0 else self.sn_method labels = self.cfv[key] - + classes = self.__find_labels(key) matrix = {} - + # catch cases where nothing is cl,assified, e.g. S2_MODELXX_SN_type # when S2 is a compact object if len(classes) == 1 and classes[0] == 'None': @@ -100,17 +100,17 @@ def __compute_errs(self): matrix[_class] = row - + self.matrices[key] = matrix # saving classes self.c = c self.ic = ic - + def __format(self, s, title = False): - """ Method that formats keys for plots + """ Method that formats keys for plots Parameters ---------- @@ -128,7 +128,7 @@ def __find_labels(self, key): ---------- key : str name of the classifier - + Returns ------- list of class labels @@ -194,7 +194,7 @@ def violin_plots(self, err_type = "relative", keys = None, fig, axs = plt.subplots(1, 1, figsize = (24, 10), tight_layout = True) - + rel_plot = axs.violinplot(errs, showmedians = True, points = 1000) stable_plot = axs.violinplot(stable_errs, showmedians = True, points = 1000) no_plot = axs.violinplot(no_errs, showmedians = True, points = 1000) @@ -202,7 +202,7 @@ def violin_plots(self, err_type = "relative", keys = None, axs.set_title(f"Distribution of {err_type.capitalize()} Errors") - axs.set_xticks(np.arange(1, len(keys) + 1), + axs.set_xticks(np.arange(1, len(keys) + 1), labels = [ f"{self.__format(ec)} ({(med * 100):.2f}%)" for ec, med in zip(keys, np.nanmedian(dirty_errs, axis = 0)) ], rotation = 20) @@ -211,7 +211,7 @@ def violin_plots(self, err_type = "relative", keys = None, axs.grid(axis = "y") def halve_paths(field, color, right = True): - + for i, path in enumerate(field.get_paths()): # getting mean @@ -239,14 +239,14 @@ def customize_violinplot(plot, color, outlined = False, right = True): pc.set_edgecolor(color) pc.set_linewidth(4) pc.set_alpha(0.75) - + customize_violinplot(rel_plot, "coral") customize_violinplot(stable_plot, "#1e90ff", True, False) customize_violinplot(no_plot, "crimson", True, False) customize_violinplot(unstable_plot, "olive", True, False) axs.legend( - [rel_plot["bodies"][0], stable_plot["bodies"][0], no_plot["bodies"][0], unstable_plot["bodies"][0]], + [rel_plot["bodies"][0], stable_plot["bodies"][0], no_plot["bodies"][0], unstable_plot["bodies"][0]], ["Relative Error", "Stable MT Error", "No MT Error", "Unstable MT Error"], bbox_to_anchor = (0, 1.02, 1, 0.2), loc = "lower left", @@ -351,14 +351,14 @@ def decision_boundaries(self): def plot2D(self, key, slice_3D_var_str, slice_3D_var_range, PLOT_PROPERTIES): k_ind = self.out_keys.index(key) - + if slice_3D_var_str == 'mass_ratio': var = self.test_grid.initial_values["star_2_mass"] / self.test_grid.initial_values["star_1_mass"] elif slice_3D_var_str == 'star_2_mass': var = self.test_grid.initial_values["star_2_mass"] else: raise ValueError("slice_3D_var_str must be either 'mass_ratio' or 'star_2_mass'") - + slice = (var >= slice_3D_var_range[0]) & (var <= slice_3D_var_range[1]) slice_errs = self.errs["relative"].T[k_ind] @@ -366,7 +366,7 @@ def plot2D(self, key, slice_3D_var_str, slice_3D_var_range, PLOT_PROPERTIES): # find inf and assign large value else they are not plotted slice_errs[np.isinf(slice_errs)] = 1e99 - + fig = self.test_grid.plot2D('star_1_mass', 'period_days', slice_errs, termination_flag='interpolation_class_errors', grid_3D=True, slice_3D_var_str=slice_3D_var_str, @@ -377,17 +377,15 @@ def plot2D(self, key, slice_3D_var_str, slice_3D_var_range, PLOT_PROPERTIES): def violinplot_with_nans(data, axs, **kwargs): """ Wrapper around plt.violinplot that handles NaNs by dropping them per column. - + data: 2D array-like of shape (n, d), or 1D array """ data = np.asarray(data, dtype=float) - + if data.ndim == 1: clean = [data[~np.isnan(data)]] else: # Extract each column, dropping NaNs independently clean = [data[:, i][~np.isnan(data[:, i])] for i in range(data.shape[1])] - - return axs.violinplot(clean, **kwargs) - + return axs.violinplot(clean, **kwargs)

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z<+@q3l?2KzyG-4VyDR*@Dq2kCAOGG!{UXK9(mwz0|M_<2XaBJU_ph%tNQX6#cml5J)UJQ6bQ&yH@M`Pp9vW5ytVF$Z>kGam9+i~K(_$o+ZuI0CN^3QBWn1Kacs KH|6L#2K@`+MCM@t literal 0 HcmV?d00001 diff --git a/posydon/visualization/VH_diagram/GraphVisualizer.py b/posydon/visualization/VH_diagram/GraphVisualizer.py index 83be6bd40c..4145c11e28 100644 --- a/posydon/visualization/VH_diagram/GraphVisualizer.py +++ b/posydon/visualization/VH_diagram/GraphVisualizer.py @@ -9,8 +9,7 @@ from PyQt5.QtWidgets import (QWidget, QFrame, QHBoxLayout, QVBoxLayout, QLabel, QGridLayout) from PyQt5.QtCore import Qt, QPoint, QSize -from PyQt5.QtGui import QPainter, QPixmap - +from PyQt5.QtGui import QPainter, QPixmap, QFont from .MathTextLabel import MathTextLabel from dataclasses import dataclass @@ -194,13 +193,13 @@ def get_attach_point_top(self): """Get coordinate to connect this widget with the previous one.""" return self._center_widget.mapToGlobal( self._center_widget.rect().topLeft() - ) + QPoint(self._center_widget.width() / 2, 0) + ) + QPoint(int(self._center_widget.width() / 2), 0) def get_attach_point_bot(self): """Get coordinate to connect this widget with the next one.""" return self._center_widget.mapToGlobal( self._center_widget.rect().bottomLeft() - ) + QPoint(self._center_widget.width() / 2, 0) + ) + QPoint(int(self._center_widget.width() / 2), 0) def prepare_case(infos: CaseInfos): @@ -308,6 +307,7 @@ class StateInfos(Infos): S1_filename: str S2_filename: str + event_filename : str distance: int top_texts: list bot_texts: list @@ -348,7 +348,7 @@ def __init__(self): self.done = False layout = QGridLayout() - layout.setContentsMargins(0, 0, 0, 0) + layout.setContentsMargins(0, 10, 0, 10) self.setLayout(layout) self._bg_container = QLabel() @@ -461,20 +461,20 @@ def set_bot_text(self, text, index): if index >= len(self._bot_labels): print(f"Can't set text to bot label {index}") return - + self._bot_labels[index].setText(text) def get_attach_point_top(self): """Get coordinate to connect this widget with the previous one.""" return self._bg_container.mapToGlobal( self._bg_container.rect().topLeft() - ) + QPoint(self._bg_container.width() / 2, 0) + ) + QPoint(int(self._bg_container.width() / 2), 0) def get_attach_point_bot(self): """Get coordinate to connect this widget with the next one.""" return self._bg_container.mapToGlobal( self._bg_container.rect().bottomLeft() - ) + QPoint(self._bg_container.width() / 2, 0) + ) + QPoint(int(self._bg_container.width() / 2), 0) def resizeEvent(self, event): """Resize the event.""" @@ -495,7 +495,7 @@ def paintEvent(self, event): # override paintEvent of QWidget * (1 - self._distance)) y_S1 = self._bg.height() / 2 - self._S1_pixmap.height() / 2 - painter.drawPixmap(x_S1, y_S1, self._S1_pixmap) + painter.drawPixmap(int(x_S1), int(y_S1), self._S1_pixmap) x_S2 = ( self._bg.width() / 2 @@ -503,12 +503,12 @@ def paintEvent(self, event): # override paintEvent of QWidget ) y_S2 = self._bg.height() / 2 - self._S2_pixmap.height() / 2 - painter.drawPixmap(x_S2, y_S2, self._S2_pixmap) + painter.drawPixmap(int(x_S2), int(y_S2), self._S2_pixmap) if not self._event_pixmap.isNull(): x_event = self._bg.width() / 2 - self._event_pixmap.width() / 2 - painter.drawPixmap(x_event, 0, self._event_pixmap) + painter.drawPixmap(int(x_event), 0, self._event_pixmap) self._bg_container.setPixmap(self._bg) @@ -609,6 +609,9 @@ def __init__(self, grid, column_id, column_span): def set_title(self, title): """Set the title.""" + font = QFont() + font.setPointSize(25) + self._title_label.setFont(font) self._title_label.setText(title) def add_item(self, item): @@ -708,12 +711,14 @@ def draw(self, surface): end = surface.mapFromGlobal( connection.to_item.get_attach_point_top()) - painter.drawLine(start, end) + painter.drawLine(start + QPoint(0, 36) + ,end + QPoint(0, -8)) left = end + QPoint(-4, -8) rigth = end + QPoint(4, -8) painter.setBrush(Qt.black) + painter.drawEllipse(start + QPoint(0, 36), 2, 2) painter.drawPolygon(end, left, rigth) @@ -748,6 +753,7 @@ def __init__(self): super(GraphVisualizer, self).__init__() self._layout = QGridLayout() + self._layout.setContentsMargins(0, 0, 0, 0) self.setLayout(self._layout) self._next_column = 0 diff --git a/posydon/visualization/VH_diagram/MainWindow.py b/posydon/visualization/VH_diagram/MainWindow.py index ff1137cc3e..b57af2201f 100644 --- a/posydon/visualization/VH_diagram/MainWindow.py +++ b/posydon/visualization/VH_diagram/MainWindow.py @@ -73,8 +73,8 @@ def __init__(self): self.setWindowTitle("Simulation visualisation") screen_geometry = QApplication.desktop().screenGeometry() - self.resize(screen_geometry.width() * 0.66, - screen_geometry.height() * 0.66) + self.resize(int(screen_geometry.width() * 0.66), + int(screen_geometry.height() * 0.66)) self._visualizer = GraphVisualizer() self._option_window = OptionsWindow() diff --git a/posydon/visualization/VH_diagram/OptionsWindow.py b/posydon/visualization/VH_diagram/OptionsWindow.py index b019ec14bb..7247da95f7 100644 --- a/posydon/visualization/VH_diagram/OptionsWindow.py +++ b/posydon/visualization/VH_diagram/OptionsWindow.py @@ -37,8 +37,8 @@ def __init__(self): self.setWindowTitle("Visualizer Options") screen_geometry = QApplication.desktop().screenGeometry() - self.resize(screen_geometry.width() * 0.20, - screen_geometry.height() * 0.20) + self.resize(int(screen_geometry.width() * 0.20), + int(screen_geometry.height() * 0.20)) self._dropdow_options_signals = { self._mode_to_text[PresenterMode.DETAILED]: diff --git a/posydon/visualization/VH_diagram/ParseDataFrame.py b/posydon/visualization/VH_diagram/ParseDataFrame.py new file mode 100644 index 0000000000..e4da14e468 --- /dev/null +++ b/posydon/visualization/VH_diagram/ParseDataFrame.py @@ -0,0 +1,98 @@ +"""Counting distincts occurence of binary simulation from a file""" + +__authors__ = [ + "Wène Kouarfate " +] + +import numpy as np +import pandas as pd +import os +from collections import Counter + +class ParseDataFrame: + """Handle the binary parsing""" + def __init__(self, + filename, + path="./", + key = 'history', + column_list = ['state','event','S1_state','S2_state'], + index_name = 'binary_index', + start=None, + stop=None, + chunk_size = 500000): + + self.path = path + self.filename = filename + self.key = key + self.column_list = column_list + self.index_name = index_name + self.chunk_size = chunk_size + self.start = start + self.stop = stop + + self.index_list = dict() + self.counts = Counter() + + self._parse_dataf_groupby_col3_chunk() + self.count_dict = Counter({self.index_list[k]: self.counts[k] + for k in self.counts.keys()}) + + + def _f_lambda(self, df_gb): + """function to be given as key argument to DataFrameGroupBy.apply()""" + h = hash(tuple(df_gb.to_numpy().ravel()))#.to_numpy() recommanded by pandas doc instead of .values + + self.counts[h] += 1 + self.index_list.setdefault(h, df_gb.index[0]) + + return None + + + def _parse_dataf_groupby_col3_chunk(self): + file_path = os.path.join(self.path, self.filename) + rdf = pd.DataFrame(columns = self.column_list, index=pd.Index([], name=self.index_name)) + + for dataf in pd.read_hdf(file_path, self.key, + columns=self.column_list, + start = self.start, + stop = self.stop, + chunksize=self.chunk_size): + + dataf = pd.concat([rdf, dataf]) + rdf = dataf.loc[[dataf.index[-1]]] + dataf = dataf.drop(dataf.index[-1]) + + gb_df_col = dataf.groupby(by=dataf.index.name) + gb_df_col.apply(self._f_lambda) + + self._f_lambda(rdf) + + def get_frequencies(self): + total = sum(self.counts.values()) + return Counter({self.index_list[k]: 100 * self.counts[k] / total + for k in self.counts.keys()}) + + def get_most_numpy(self, k): + #one can then acess columns for VHDiagramm_m + return np.array(self.count_dict.most_common(k)) + + def parse_dataf_gb_iter_chunk(dataf, index_list, cnt): + """a more relevant parser imo but turns out to take more time than than groupby/apply""" + file_path = os.path.join(self.path, self.filename) + rdf = pd.DataFrame(columns = self.column_list, index=pd.Index([], name=self.index_name)) + + for dataf in pd.read_hdf(file_path, self.key, + columns=self.column_list, + start = self.start, + stop = self.stop, + chunksize=self.chunk_size): + + dataf = pd.concat([rdf, dataf]) + rdf = dataf.loc[[dataf.index[-1]]] + dataf = dataf.drop(dataf.index[-1]) + + gb_df_col = dataf.groupby(dataf.index.name) + for i,s in gb_df_col.__iter__(): + h = hash(tuple(df_gb.to_numpy().ravel())) + self.counts[h] += 1 + self.index_list.setdefault(h, df_gb.index[0]) \ No newline at end of file diff --git a/posydon/visualization/VH_diagram/Presenter.py b/posydon/visualization/VH_diagram/Presenter.py index 26c86a7af1..a737072981 100644 --- a/posydon/visualization/VH_diagram/Presenter.py +++ b/posydon/visualization/VH_diagram/Presenter.py @@ -349,6 +349,7 @@ def __init__(self, filename, path="./"): self._set_visualisation_diagram) self._visualizer.distance_representation_required().connect( self._set_distance_representation) + breakpoint() self._visualizer.save_required().connect(self.screen) self._present_mode = PresenterMode.DETAILED @@ -958,7 +959,8 @@ def _digram_presentation(self, data): or simplified_data[-1]["state"].state_after == "merged" ): aditional_info = CaseInfos(self._state_id) + aditional_info.border_width = 2 aditional_info.centered_text = simplified_data[-1][ "state"].state_after aditional_info.connected = False - self._visualizer().add_line([aditional_info]) + self._visualizer().add_line([aditional_info]) \ No newline at end of file diff --git a/posydon/visualization/VH_diagram/PresenterMultiple.py b/posydon/visualization/VH_diagram/PresenterMultiple.py new file mode 100644 index 0000000000..19a0c50f90 --- /dev/null +++ b/posydon/visualization/VH_diagram/PresenterMultiple.py @@ -0,0 +1,462 @@ +from posydon.visualization.VHdiagram import DisplayMode +from posydon.visualization.VH_diagram.Presenter import Presenter, PresenterMode, get_max_distance,CaseInfos,equal_with_epsilon,get_event_state_filename,file_exist,get_star_state_filename +from posydon.visualization.VH_diagram.GraphVisualizer import columnTYPE,StateInfos,ConnectedItem, GraphVisualizerCase + +from PyQt5.QtWidgets import QApplication +from PyQt5.QtCore import QTimer +import matplotlib.pyplot as plt +from IPython.display import Image, display +from enum import Enum, auto +import os + +class VHdiagramm_m: + """Handle a multiple or hierarchical VH diagram plot.""" + + def __init__( + self, + filename, + path="./", + index=[0], + *, + frequency=None, + hierarchy=False, + presentMode=PresenterMode.DIAGRAM, + displayMode=DisplayMode.WINDOW, + figsize=(10, 8) + ): + """Initialize a VHdiagram_m instance.""" + self._app = ( + QApplication.instance() + ) + if not self._app: + self._app = QApplication([]) + + #PRESENTING HIERARCHICALLY + if hierarchy : + self._presenter = Presenter_h(filename=filename, path=path, frequency=frequency) + self._presenter.present_h(index, presentMode) + #PRESENTING MULTIPLES SIDE BY SIDE + else: + self._presenter = Presenter_m(filename=filename, path=path, frequency=frequency) + self._presenter.present_m(index, presentMode) + + if displayMode == DisplayMode.INLINE_B: + #void singleShot(int msec, Functor functor) + #This static function calls functor after a given time interval. + QTimer.singleShot(0, lambda: self._display_inline_b()) + elif displayMode == DisplayMode.INLINE_S: + QTimer.singleShot(0, lambda: self._display_inline_s(figsize)) + + self._app.exec_() + + #Return indexes witch have been sorted hierarchycally for tree view + def get_sorted_index(self): + if type(self._presenter) == Presenter_h : + return [dataf.index[0] for dataf in self._presenter._columns_data] + else :#return empty if Presenter_h has no been called + return [] + + def _display_inline_b(self): + filepath = self._presenter.screen() + self._presenter.close() + display(Image(filename=filepath)) + + def _display_inline_s(self, figsize): + filepath = self._presenter.screen() + self._presenter.close() + image = plt.imread(filepath) + + plt.figure(figsize=figsize) + #Display data as an image, i.e., on a 2D regular raster. + plt.imshow(image, aspect="auto") + plt.axis("off") + +class Presenter_m(Presenter): + """Handle multiple Diagram view""" + def __init__(self,filename, path="./", frequency=None): + super(Presenter_m, self).__init__(filename, path) + self._frequency = frequency + + def present_m(self, indexes, mode=PresenterMode.DETAILED): + """Preset the binary.""" + self._present_mode = mode + self._visualizer.options().set_showed_mode(self._present_mode) + self._visualizer().reset() + + if self._frequency== None : + self._frequency = {i: 1/len(indexes) for i in indexes} + + for i in indexes : + self._prepare_corresponding_data(i) + self._update_visualisation_m()#redefined + + self._main_window.show() + + def _update_visualisation_m(self): + """Update the display according to current_index and present_mode.""" + if self._current_index is None: + return + + if self._present_mode == PresenterMode.DETAILED: + self._prepare_basic_columns_m() + self._detailed_presentation(self._current_data) + elif self._present_mode == PresenterMode.REDUCED: + self._prepare_basic_columns_m() + self._reduced_presentation(self._current_data) + elif self._present_mode == PresenterMode.SIMPLIFIED: + self._prepare_basic_columns_m() + self._simplified_presentation(self._current_data) + elif self._present_mode == PresenterMode.DIAGRAM: + self._prepare_diagram_columns_m() + self._digram_presentation(self._current_data) + + def _prepare_basic_columns_m(self):#THE REST USES 3 COLUMNS COL_SPAN=1 + """Add columns for Detailled/Reduced/Simplified View.""" + self._time_id = self._visualizer().add_column(columnTYPE.TIMELINE) + self._visualizer().get_column(self._time_id).set_title(f"{self._current_index} : TIME") + + self._S1_id = self._visualizer().add_column(columnTYPE.CONNECTED) + self._visualizer().get_column(self._S1_id).set_title("S1") + + self._event_id = self._visualizer().add_column(columnTYPE.CONNECTED) + self._visualizer().get_column(self._event_id).set_title("EVENT/STATE") + + self._S2_id = self._visualizer().add_column(columnTYPE.CONNECTED) + self._visualizer().get_column(self._S2_id).set_title("S2") + + def _prepare_diagram_columns_m(self): + """Add columns for multiple Diagram View.""" + self._time_id = self._visualizer().add_column(columnTYPE.TIMELINE) + self._state_id = self._visualizer().add_column(columnTYPE.CONNECTED) + self._visualizer().get_column(self._state_id).set_title(f"{self._current_index} : {self._frequency[self._current_index] : .2f} %") + +class Presenter_h(Presenter): + """Handles drawing binaries as hierarchical trees""" + def __init__(self,filename, path="./", frequency=None): + super(Presenter_h, self).__init__(filename, path) + self._frequency = frequency + self._column_width = 400 #fixed but could be dynamic + + def present_h(self, indexes, mode=None): + """Preset the binary.""" + self._present_mode = PresenterMode.DIAGRAM #only available mode + self._visualizer.options().set_showed_mode(self._present_mode) #keep + self._visualizer().reset() + + if self._frequency == None :#default uniform percentage + self._frequency = {i: 100/len(indexes) for i in indexes} + + self._columns_indexes = []#list of corresponding binaries indexes + self._columns_data = []#list of corresponding dataframes + self._infos = []#list of corresponding CaseInfos/StateInfos (with pictures filenames to compare) + self._edges = []#List of edges in final tree view + + for i in indexes : + #For every index prepare dataframe, add column and record it + self._prepare_corresponding_data(i) + self._columns_data.append(self._current_data.copy()) + self._update_visualisation_h() + + self._diagram_presentation_h()#sorting removing duplicate setting edges etc + self._main_window.show()#Graphical présentation + + def _sort_h(self): + """Hierarchical sorting of binaries the way python natureally sort tuples. + Tuples of représentation filenames here""" + self._infos.sort(key=lambda info_col : self._tuple_h(info_col)) + #Reajusting column_id witch may be different from when created in _prepare_diagram_line() + new_col_data = [] + + for i in range(len(self._infos)): + new_col_data.append( + self._columns_data[self._infos[i][0].column_id] + ) + for info in self._infos[i]: + info.column_id = i#don't forget the column_id + + self._columns_data = new_col_data + + def _tuple_h(self, info_col): + """Return a list of filenames of the picture (or centered_text) from the list of + StateInfos/CaseInfos for a given column""" + return [(os.path.split(info.S1_filename)[-1] + + os.path.split(info.S2_filename)[-1] if ( + info.event_filename == None + )else os.path.split(info.event_filename)[-1]) if ( + type(info)==StateInfos + )else info.centered_text + for info in info_col] + + def _compareInfos(self, info0, info1): + """Conpare two Infos objects (can be CaseInfos or StateInfos)""" + if type(info0) == type(info1): + if type(info0) == StateInfos : + if info0.event_filename == None == info1.event_filename : + return ( + os.path.split(info0.S1_filename)[-1]==os.path.split(info1.S1_filename)[-1] + ) and ( + os.path.split(info0.S2_filename)[-1]==os.path.split(info1.S2_filename)[-1] + ) + else :#One of event_filenames may still be None + return info0.event_filename == info1.event_filename + else : + return info0.centered_text == info1.centered_text + else : return False + + def _remove_duplicate(self,start_col, + end_col, + line, + parent_col=None): + """Remove consecutive horizontally duplicated state to get the tree view + (recursively)""" + if start_col > end_col : + return + if start_col == end_col : + for info in self._infos[start_col][line : ] : + #edges are now vertical except maybe the state at line + info.connected = True + j = info.column_id + #binary's relative percentage + rel = self._frequency[self._columns_data[j].index[0]] + if type(info)==StateInfos : + info.bot_texts = [f"{self._columns_data[start_col].index[0]} : {rel: .30f} %"] + else : + info.bot_left_text = f"{self._columns_data[start_col].index[0]} : {rel: .30f} %" + + if (0 < line < len(self._infos[start_col]) ) and ( + type(self._infos[start_col][line-1]) == CaseInfos + ) : + self._infos[start_col][line].connected = False + self._edges.extend([[line-1, + parent_col, + line, + start_col] ]) + + return + + shortest_col = min(self._infos[start_col : end_col+1] , + key=lambda info_col: len(info_col)) + + #if no column is shorter that the length of the line + if len(shortest_col) > line: + info_col0 = self._infos[start_col] + #Range of columns with same state at current line + rg_column = [[start_col]] + + for j in range(start_col+1, end_col+1): + info_col1 = self._infos[j] + if self._compareInfos(info_col0[line], info_col1[line]): + #replacing by empty state + info_col0[line] = CaseInfos( + info_col0[line].column_id, + centered_txt = " " + ) + info_col0[line].connected = False + else: + #recording a new range + rg_column[-1].append(j-1) + rg_column.append([j]) + #righ state become comparaison basis for the loop + info_col0 = info_col1 + rg_column[-1].append(end_col) + #an edge (A --> B) is represented by [x1, y1, x2, y2] where A:(x1,y1) and B:(x2,y2) + self._edges.extend([[line-1, + parent_col, + line, + c[0] + (c[1]-c[0])//2] for c in rg_column]) + + self._center_column(rg_column, line) + + #recursive call with line+1 + for start,end in rg_column : + self._remove_duplicate(start, end, line+1, + parent_col = start + (end - start)//2) + + #if one column is shorter than the line's length separate range in two + else :#(there may be more than one shorter column and/or in sequence) + no_col = self._infos.index(shortest_col, start_col, end_col+1) + + self._remove_duplicate(start_col, no_col-1, line, parent_col = parent_col) + self._remove_duplicate(no_col+1, end_col, line, parent_col = parent_col) + + + def _center_column(self, range_column, line): + """move states in a range of columns from far right to center + also setting the sum of same states's frequencies appearance to be printed""" + for k,j in range_column: + if j - k >= 1 : + info = self._infos[j][line] + self._infos[j][line] = CaseInfos( + info.column_id, + centered_txt = " " + ) + self._infos[j][line].connected = False + + info.column_id = self._infos[k + (j-k)//2][line].column_id + relative_freq = sum( + [self._frequency[self._columns_data[i].index[0]] + for i in range(k,j+1)] + ) + if type(info)==StateInfos : + info.bot_texts = [f"{self._columns_data[k].index[0]} to {self._columns_data[j].index[0]} : {relative_freq: .25f} %"] + else : + info.bot_left_text = f"{self._columns_data[k].index[0]} to {self._columns_data[j].index[0]} : {relative_freq: .25f} %" + #swapping + self._infos[k + (j-k)//2][line] = info + elif j==k : + info = self._infos[j][line] + relative_freq = self._frequency[self._columns_data[j].index[0]] + if type(info)==StateInfos : + info.bot_texts = [f"{self._columns_data[k].index[0]} to {self._columns_data[j].index[0]} : {relative_freq: .25f} %"] + else : + info.bot_left_text = f"{self._columns_data[k].index[0]} to {self._columns_data[j].index[0]} : {relative_freq: .25f} %" + + + def _update_visualisation_h(self): + """Hierarchy PresenterMode for the moment is only PresenterMode.DIAGRAM""" + if self._current_index is None: + return + + if self._present_mode == PresenterMode.DETAILED: + self._prepare_basic_columns()#irrelevant + elif self._present_mode == PresenterMode.REDUCED: + self._prepare_basic_columns()#irrelevant + self._reduced_presentation(self._current_data) + elif self._present_mode == PresenterMode.SIMPLIFIED: + self._prepare_basic_columns()#irrelevant + self._simplified_presentation(self._current_data) + elif self._present_mode == PresenterMode.DIAGRAM: + self._prepare_diagram_columns_h() + + + def _prepare_diagram_columns_h(self): + """Add columns for Diagram view set column minimum width and add index to _columns_indexes""" + self._state_id = self._visualizer().add_column(columnTYPE.CONNECTED) + self._visualizer()._layout.setColumnMinimumWidth(self._state_id, self._column_width) + self._columns_indexes.append(self._state_id) + + def _diagram_presentation_h(self): + + simplified_datas = []#list of columns's simplified datas + for i in self._columns_indexes: + simplified_datas.append( + self._simplify_data(self._columns_data[i]) + ) + + for i in self._columns_indexes: + simplified_data = simplified_datas[i] + self._state_id = i + max_distance_data = max(simplified_data, key=get_max_distance) + max_distance = get_max_distance(max_distance_data) + + self._visualizer()._columns[i]._row_index = 16#forget why + + #Filling the list of StateInfos of each lines of the column + self._infos.append([]) + for line_data in simplified_data: + dline = self._prepare_diagram_line(line_data, max_distance) + self._infos[-1].append(dline[0]) + + #We add a CaseInfos according to state_after of last simplified_datas + if ( + simplified_data[-1]["state"].state_after == "disrupted" + or simplified_data[-1]["state"].state_after == "merged" + ): + aditional_info = CaseInfos(self._state_id) + aditional_info.border_width = 2 + aditional_info.centered_text = simplified_data[-1][ + "state"].state_after + self._infos[-1].append(aditional_info) + aditional_info.connected = False + + self._sort_h()#Sorting columns hierarchically according to pictures filenames + self._remove_duplicate(0,len(self._infos)-1,0)#Removing horizontal duplicate + + #Setting percentage of each column to be printed (above the column) + for i in self._columns_indexes: + self._state_id = i + binary_index = self._columns_data[i].index[0] + self._visualizer()._columns[i]._row_index = 16 #forget why + + self._visualizer().get_column(i).set_title(f"{binary_index} : {self._frequency[binary_index]: .4f} %") + for line_info in self._infos[i]: + self._visualizer().add_line([line_info]) + + self._paint_obl_edges() + + def _prepare_diagram_line(self, data, max_distance): + + state_info = StateInfos(self._state_id) + if self._distance_representation and "separation" in data: + state_info.distance = self._get_distance_representation( + data["separation"], max_distance + ) + else: + state_info.distance = 1 + + state_before_filename = os.path.join( + self.PATH_TO_DRAWS, + get_event_state_filename( + data["S1_state"].state_before, + data["state"].state_before, + data["S2_state"].state_before, + suffix=".png", + ), + ) + state_after_filename = os.path.join( + self.PATH_TO_DRAWS, + get_event_state_filename( + data["S1_state"].state_before, + data["state"].state_before, + data["S2_state"].state_before, + suffix=".png", + ), + ) + if (data["state"].state_after != "detached" + and file_exist(state_after_filename)): + state_info.event_filename = state_after_filename + elif data["state"].state_before != "detached" and file_exist( + state_before_filename + ): + state_info.event_filename = state_before_filename + else: + if data["S1_state"].state_after is not None: + state_info.S1_filename = os.path.join( + self.PATH_TO_DRAWS, + get_star_state_filename( + data["S1_state"].state_after, suffix=".png" + ), + ) + else: + state_info.S1_filename = os.path.join( + self.PATH_TO_DRAWS, + get_star_state_filename( + data["S1_state"].state_before, suffix=".png" + ), + ) + + if data["S2_state"].state_after is not None: + state_info.S2_filename = os.path.join( + self.PATH_TO_DRAWS, + get_star_state_filename( + data["S2_state"].state_after, suffix=".png" + ), + ) + else: + state_info.S2_filename = os.path.join( + self.PATH_TO_DRAWS, + get_star_state_filename( + data["S2_state"].state_before, suffix=".png" + ), + ) + state_info.connected = False + #Traditionnaly returning a list with StateInfos/CaseInfos/PointInfos so we return a list + return [state_info] + + def _paint_obl_edges(self): + """Adding oblic edges to be painted as ConnectedItem from edges added in self._edges""" + for i1,j1,i2,j2 in self._edges[1:] : + self._visualizer()._columns[j1]._connected_items.append( + ConnectedItem( + self._visualizer()._columns[j1]._items[i1], + self._visualizer()._columns[j2]._items[i2])) \ No newline at end of file diff --git a/posydon/visualization/VH_diagram/SimulationModel.py b/posydon/visualization/VH_diagram/SimulationModel.py index fc3ef8cc51..3a70c59a22 100644 --- a/posydon/visualization/VH_diagram/SimulationModel.py +++ b/posydon/visualization/VH_diagram/SimulationModel.py @@ -22,10 +22,11 @@ def __init__(self, filename, path="./"): def load_csv(self): """Load dataframe as CSV with .gz compression.""" - self._df = pd.read_hdf( + """self._df = pd.read_hdf( os.path.join(self.path, self.filename), compression="gzip", key='history', low_memory=False - ) + )""" + return None def get_by_binary_index(self, index): """Return a copy of dataframe's slice with binary_index == index. @@ -41,4 +42,10 @@ def get_by_binary_index(self, index): Copy of a sub-dataframe with binary_index == index """ - return self._df.loc[index].copy() + #return self._df.loc[index].copy() + return pd.read_hdf( + os.path.join(self.path, self.filename), + where=f"index == {index}", + compression="gzip", + key='history', low_memory=False + ) From 3898614c05877ac17c8fe51dd06a977631d96dac Mon Sep 17 00:00:00 2001 From: Konstantinos Kovlakas <22239544+kkovlakas@users.noreply.github.com> Date: Thu, 28 Sep 2023 16:36:01 +0200 Subject: [PATCH 122/319] Create deploy-github-pages-unstable.yml (#145) Adding GitHub Actions workflow for deploying the unstable version documentation. --- .../deploy-github-pages-unstable.yml | 55 +++++++++++++++++++ 1 file changed, 55 insertions(+) create mode 100644 .github/workflows/deploy-github-pages-unstable.yml diff --git a/.github/workflows/deploy-github-pages-unstable.yml b/.github/workflows/deploy-github-pages-unstable.yml new file mode 100644 index 0000000000..d51cdb7891 --- /dev/null +++ b/.github/workflows/deploy-github-pages-unstable.yml @@ -0,0 +1,55 @@ +name: Website Deploy (Unstable) + +on: + push: + branches: + - development + +jobs: + build: + + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + python-version: [3.7] + + steps: + - uses: actions/checkout@v2 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + - name: Get pip cache dir + id: pip-cache + run: | + python -m pip install --upgrade pip + echo "::set-output name=dir::$(pip cache dir)" + - name: pip cache + uses: actions/cache@v2 + with: + path: ${{ steps.pip-cache.outputs.dir }} + key: ${{ runner.os }}-pip-${{ hashFiles('**/setup.py', '**/requirements.txt') }} + restore-keys: | + ${{ runner.os }}-pip- + - name: Install dependencies + run: | + sudo apt-get update + sudo apt-get install gfortran swig libhdf5-serial-dev libmpich-dev + python -m pip install coverage cpp-coveralls flake8 pytest + python -m pip install .[doc] + python -m pip install .[ml] + - name: Make dependencies + env: + PATH_TO_POSYDON: /home/runner/work/POSYDON/POSYDON/ + run: | + cd docs && make html; cd ../ + touch docs/_build/html/.nojekyll + - name: Deploy to GitHub Pages + if: success() + uses: crazy-max/ghaction-github-pages@v2 + with: + target_branch: gh-pages-dev + build_dir: docs/_build/html/unstable + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} From 77041e4e7276211a4712b2f29345192dcf3da168 Mon Sep 17 00:00:00 2001 From: Konstantinos Kovlakas <22239544+kkovlakas@users.noreply.github.com> Date: Thu, 28 Sep 2023 16:37:13 +0200 Subject: [PATCH 123/319] Correcting the boolean logic in deciding single/binary evolution in the detached step. (#140) --- posydon/binary_evol/DT/step_detached.py | 20 ++++++++++++++------ 1 file changed, 14 insertions(+), 6 deletions(-) diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index c3919f314d..e225a710fe 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -923,13 +923,21 @@ def __call__(self, binary): KEYS = self.KEYS KEYS_POSITIVE = self.KEYS_POSITIVE - if binary.star_1 is None or binary.star_1.state == "massless_remnant": - self.non_existent_companion = 1 - if binary.star_2 is None or binary.star_2.state == "massless_remnant": - self.non_existent_companion = 2 + companion_1_exists = (binary.star_1 is not None + and binary.star_1.state != "massless_remnant") + companion_2_exists = (binary.star_2 is not None + and binary.star_2.state != "massless_remnant") + + if companion_1_exists: + if companion_2_exists: # to evolve a binary star + self.non_existent_companion = 0 + else: # star1 is a single star + self.non_existent_companion = 2 else: - # detached step of an actual binary - self.non_existent_companion = 0 + if companion_2_exists: # star2 is a single star + self.non_existent_companion = 1 + else: # no star in the system + raise Exception("There is no star to evolve. Who summoned me?") if self.non_existent_companion == 0: #no isolated evolution, detached step of an actual binary # the primary in a real binary is potential compact object, or the more evolved star From 536976331a792557641f9108acf9cd575e493a47 Mon Sep 17 00:00:00 2001 From: Konstantinos Kovlakas <22239544+kkovlakas@users.noreply.github.com> Date: Thu, 28 Sep 2023 16:39:11 +0200 Subject: [PATCH 124/319] Update deploy-github-pages-unstable.yml Correcting the PYTHON version in the unstable documentation github action workflow --- .github/workflows/deploy-github-pages-unstable.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/deploy-github-pages-unstable.yml b/.github/workflows/deploy-github-pages-unstable.yml index d51cdb7891..566190f5e6 100644 --- a/.github/workflows/deploy-github-pages-unstable.yml +++ b/.github/workflows/deploy-github-pages-unstable.yml @@ -12,7 +12,7 @@ jobs: strategy: fail-fast: false matrix: - python-version: [3.7] + python-version: [3.11] steps: - uses: actions/checkout@v2 From cb05dd59dee44e8edfe8aae629182cb001ccc899 Mon Sep 17 00:00:00 2001 From: ezapartas Date: Thu, 28 Sep 2023 17:55:05 +0300 Subject: [PATCH 125/319] redirect keeps info from before (#141) * manoscommit * added a change of a redirect if statement in visualization/VH_diagram * adding redirecting from ZAMS in the elif statement in detached * event length to 20 --------- Co-authored-by: kasdaglie --- posydon/binary_evol/DT/step_detached.py | 2 +- posydon/binary_evol/MESA/step_mesa.py | 74 +++++++++---------- posydon/binary_evol/flow_chart.py | 35 +++++---- posydon/popsyn/binarypopulation.py | 2 +- posydon/visualization/VH_diagram/Presenter.py | 4 +- 5 files changed, 63 insertions(+), 54 deletions(-) diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index e225a710fe..e9b3265253 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -1072,7 +1072,7 @@ def get_star_data(binary, star1, star2, htrack, with np.errstate(all="ignore"): # get the initial m0, t0 track - if binary.event == 'ZAMS': + if binary.event == 'ZAMS' or binary.event == 'redirect_from_ZAMS': # ZAMS stars in wide (non-mass exchaging binaries) that are # directed to detached step at birth m0, t0 = star1.mass, 0 diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index 71a82845c5..ae5e150d47 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -787,16 +787,16 @@ def update_properties_NN(self, star_1_CO=False, star_2_CO=False, for key in key_post_processed: setattr(star, key, cb.final_values['S%d_%s' % (k+1, key)]) - # update nearest neighbor core collapse quantites + # update nearest neighbor core collapse quantites if interpolation_class != 'unstable_MT': for MODEL_NAME in MODELS.keys(): for i, star in enumerate(stars): - if (not stars_CO[i] and + if (not stars_CO[i] and cb.final_values[f'S{i+1}_{MODEL_NAME}_CO_type'] != 'None'): values = {} for key in ['state', 'SN_type', 'f_fb', 'mass', 'spin', 'm_disk_accreted', 'm_disk_radiated']: - if key == "state": + if key == "state": state = cb.final_values[f'S{i+1}_{MODEL_NAME}_CO_type'] values[key] = state elif key == "SN_type": @@ -963,12 +963,12 @@ def initial_final_interpolation(self, star_1_CO=False, star_2_CO=False): if interpolation_class != 'unstable_MT': for MODEL_NAME in MODELS.keys(): for i, star in enumerate(stars): - if (not stars_CO[i] and + if (not stars_CO[i] and self.classes[f'S{i+1}_{MODEL_NAME}_CO_type'] != 'None'): values = {} for key in ['state', 'SN_type', 'f_fb', 'mass', 'spin', 'm_disk_accreted', 'm_disk_radiated']: - if key == "state": + if key == "state": state = self.classes[f'S{i+1}_{MODEL_NAME}_CO_type'] values[key] = state elif key == "SN_type": @@ -1241,7 +1241,7 @@ def __init__(self, metallicity=1., grid_name=None, *args, **kwargs): *args, **kwargs) # special stuff for my step goes here # If nothing to do, no init necessary - + # load grid boundaries self.m1_min = min(self._psyTrackInterp.grid.initial_values['star_1_mass']) self.m1_max = max(self._psyTrackInterp.grid.initial_values['star_1_mass']) @@ -1264,35 +1264,35 @@ def __call__(self, binary): m2 = self.binary.star_2.mass mass_ratio = m2/m1 p = self.binary.orbital_period - if (state_1 == 'H-rich_Core_H_burning' and - state_2 == 'H-rich_Core_H_burning' and + if (state_1 == 'H-rich_Core_H_burning' and + state_2 == 'H-rich_Core_H_burning' and event == 'ZAMS' and - self.m1_min <= m1 <= self.m1_max and - self.q_min <= mass_ratio <= self.q_max and + self.m1_min <= m1 <= self.m1_max and + self.q_min <= mass_ratio <= self.q_max and self.p_min <= p <= self.p_max): self.flip_stars_before_step = False super().__call__(self.binary) - elif (state_1 == 'H-rich_Core_H_burning' and - state_2 == 'H-rich_Core_H_burning' and + elif (state_1 == 'H-rich_Core_H_burning' and + state_2 == 'H-rich_Core_H_burning' and event == 'ZAMS' and self.m1_min <= m2 <= self.m1_max and - self.q_min <= 1./mass_ratio <= self.q_max and + self.q_min <= 1./mass_ratio <= self.q_max and self.p_min <= p <= self.p_max): self.flip_stars_before_step = True super().__call__(self.binary) # redirect if outside grid - elif (state_1 == 'H-rich_Core_H_burning' and - state_2 == 'H-rich_Core_H_burning' and - event == 'ZAMS' and + elif (state_1 == 'H-rich_Core_H_burning' and + state_2 == 'H-rich_Core_H_burning' and + event == 'ZAMS' and p > self.p_max): - self.binary.event = 'redirect' + self.binary.event = 'redirect_from_ZAMS' return # redirect if CC1 - elif (state_1 == 'H-rich_Central_C_depletion'): + elif (state_1 == 'H-rich_Central_C_depletion'): self.binary.event = 'CC1' return # redirect if CC2 - elif (state_2 == 'H-rich_Central_C_depletion'): + elif (state_2 == 'H-rich_Central_C_depletion'): self.binary.event = 'CC2' return else: @@ -1317,7 +1317,7 @@ def __init__(self, metallicity=1., grid_name=None, *args, **kwargs): *args, **kwargs) # special stuff for my step goes here # If nothing to do, no init necessary - + # load grid boundaries self.m1_min = min(self._psyTrackInterp.grid.initial_values['star_1_mass']) self.m1_max = max(self._psyTrackInterp.grid.initial_values['star_1_mass']) @@ -1376,18 +1376,18 @@ def __call__(self, binary): % (state_1, state_2, state, event)) # redirect if outside grids if ((not self.flip_stars_before_step and - self.m1_min <= m1 <= self.m1_max and - self.m2_min <= m2 <= self.m2_max and + self.m1_min <= m1 <= self.m1_max and + self.m2_min <= m2 <= self.m2_max and self.p_min <= p <= self.p_max and ecc == 0.) or (self.flip_stars_before_step and - self.m1_min <= m2 <= self.m1_max and - self.m2_min <= m1 <= self.m2_max and + self.m1_min <= m2 <= self.m1_max and + self.m2_min <= m1 <= self.m2_max and self.p_min <= p <= self.p_max and ecc == 0.)): super().__call__(self.binary) else: self.binary.state = "detached" - self.binary.event = "redirect" + self.binary.event = "redirect_from_CO_HMS_RLO" return class CO_HeMS_RLO_step(MesaGridStep): @@ -1405,7 +1405,7 @@ def __init__(self, metallicity=1., grid_name=None, *args, **kwargs): *args, **kwargs) # special stuff for my step goes here # If nothing to do, no init necessary - + # load grid boundaries self.m1_min = min(self._psyTrackInterp.grid.initial_values['star_1_mass']) self.m1_max = max(self._psyTrackInterp.grid.initial_values['star_1_mass']) @@ -1466,18 +1466,18 @@ def __call__(self, binary): % (state_1, state_2, state, event)) # redirect if outside grids if ((not self.flip_stars_before_step and - self.m1_min <= m1 <= self.m1_max and - self.m2_min <= m2 <= self.m2_max and + self.m1_min <= m1 <= self.m1_max and + self.m2_min <= m2 <= self.m2_max and self.p_min <= p <= self.p_max and ecc == 0.) or (self.flip_stars_before_step and - self.m1_min <= m2 <= self.m1_max and - self.m2_min <= m1 <= self.m2_max and + self.m1_min <= m2 <= self.m1_max and + self.m2_min <= m1 <= self.m2_max and self.p_min <= p <= self.p_max and ecc == 0.)): super().__call__(self.binary) else: self.binary.state = "detached" - self.binary.event = "redirect" + self.binary.event = "redirect_from_CO_HeMS_RLO" return class CO_HeMS_step(MesaGridStep): @@ -1495,7 +1495,7 @@ def __init__(self, metallicity=1., grid_name=None, *args, **kwargs): *args, **kwargs) # special stuff for my step goes here # If nothing to do, no init necessary - + # load grid boundaries self.m1_min = min(self._psyTrackInterp.grid.initial_values['star_1_mass']) self.m1_max = max(self._psyTrackInterp.grid.initial_values['star_1_mass']) @@ -1561,15 +1561,15 @@ def __call__(self, binary): # redirect if outside grids # remember that in MESA the CO object is star_2 if ((not self.flip_stars_before_step and - self.m1_min <= m1 <= self.m1_max and - self.m2_min <= m2 <= self.m2_max and + self.m1_min <= m1 <= self.m1_max and + self.m2_min <= m2 <= self.m2_max and self.p_min <= p <= self.p_max and ecc == 0.) or (self.flip_stars_before_step and - self.m1_min <= m2 <= self.m1_max and - self.m2_min <= m1 <= self.m2_max and + self.m1_min <= m2 <= self.m1_max and + self.m2_min <= m1 <= self.m2_max and self.p_min <= p <= self.p_max and ecc == 0.)): super().__call__(binary) else: - self.binary.event = 'redirect' + self.binary.event = 'redirect_from_CO_HeMS' return diff --git a/posydon/binary_evol/flow_chart.py b/posydon/binary_evol/flow_chart.py index 62d149768d..19e17780ab 100644 --- a/posydon/binary_evol/flow_chart.py +++ b/posydon/binary_evol/flow_chart.py @@ -97,7 +97,10 @@ 'oDoubleCE1', 'oDoubleCE2', 'CO_contact', - 'redirect', + 'redirect_from_ZAMS', + 'redirect_from_CO_HMS_RLO', + 'redirect_from_CO_HeMS', + 'redirect_from_CO_HeMS_RLO', 'MaxTime_exceeded', 'maxtime', 'oMerging1', @@ -122,7 +125,7 @@ for b in BINARY_STATES_ZAMS: for s1 in STAR_STATES_ZAMS: for s2 in STAR_STATES_ZAMS: - POSYDON_FLOW_CHART[(s1, s2, b, 'redirect')] = 'step_detached' + POSYDON_FLOW_CHART[(s1, s2, b, 'redirect_from_ZAMS')] = 'step_detached' # stripped_He star on a detached binary another H- or stripped_He star @@ -145,19 +148,13 @@ for s2 in STAR_STATES_CO: POSYDON_FLOW_CHART[(s1, s2, 'RLO1', 'oRLO1')] = 'step_CO_HMS_RLO' POSYDON_FLOW_CHART[(s2, s1, 'RLO2', 'oRLO2')] = 'step_CO_HMS_RLO' - -# He-rich star roche-lobe overflow onto a compact object -for s1 in STAR_STATES_HE_RICH_EVOLVABLE: - for s2 in STAR_STATES_CO: - POSYDON_FLOW_CHART[(s1, s2, 'RLO1', 'oRLO1')] = 'step_CO_HeMS_RLO' - POSYDON_FLOW_CHART[(s2, s1, 'RLO2', 'oRLO2')] = 'step_CO_HeMS_RLO' # H-rich star on a detached binary with a compact object # that fall outside the grid and has been returned by step_CO_HMS_RLO for s1 in STAR_STATES_H_RICH: for s2 in STAR_STATES_CO: - POSYDON_FLOW_CHART[(s1, s2, 'detached', "redirect")] = 'step_detached' - POSYDON_FLOW_CHART[(s2, s1, 'detached', "redirect")] = 'step_detached' + POSYDON_FLOW_CHART[(s1, s2, 'detached', "redirect_from_CO_HMS_RLO")] = 'step_detached' + POSYDON_FLOW_CHART[(s2, s1, 'detached', "redirect_from_CO_HMS_RLO")] = 'step_detached' # stripped_He star on a detached binary with a compact object for s1 in STAR_STATES_HE_RICH_EVOLVABLE: @@ -165,13 +162,25 @@ POSYDON_FLOW_CHART[(s1, s2, 'detached', None)] = 'step_CO_HeMS' POSYDON_FLOW_CHART[(s2, s1, 'detached', None)] = 'step_CO_HeMS' - # stripped_He star on a detached binary with a compact object # that fall outside the grid and has been returned by step_CO_HeMS for s1 in STAR_STATES_HE_RICH: for s2 in STAR_STATES_CO: - POSYDON_FLOW_CHART[(s1, s2, 'detached', "redirect")] = 'step_detached' - POSYDON_FLOW_CHART[(s2, s1, 'detached', "redirect")] = 'step_detached' + POSYDON_FLOW_CHART[(s1, s2, 'detached', "redirect_from_CO_HeMS")] = 'step_detached' + POSYDON_FLOW_CHART[(s2, s1, 'detached', "redirect_from_CO_HeMS")] = 'step_detached' + +# He-rich star roche-lobe overflow onto a compact object +for s1 in STAR_STATES_HE_RICH_EVOLVABLE: + for s2 in STAR_STATES_CO: + POSYDON_FLOW_CHART[(s1, s2, 'RLO1', 'oRLO1')] = 'step_CO_HeMS_RLO' + POSYDON_FLOW_CHART[(s2, s1, 'RLO2', 'oRLO2')] = 'step_CO_HeMS_RLO' + +# He-rich star roche-lobe overflow onto a compact object +# that fall outside the grid and has been returned by step_CO_HeMS_RLO +for s1 in STAR_STATES_HE_RICH: + for s2 in STAR_STATES_CO: + POSYDON_FLOW_CHART[(s1, s2, 'detached', "redirect_from_CO_HeMS_RLO")] = 'step_detached' + POSYDON_FLOW_CHART[(s2, s1, 'detached', "redirect_from_CO_HeMS_RLO")] = 'step_detached' # Binaries that go to common envelope diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index 2e54e6e029..2f6d7f006c 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -52,7 +52,7 @@ # 'event' usually 10 but 'detached (Integration failure)' can occur -HISTORY_MIN_ITEMSIZE = {'state': 30, 'event': 10, 'step_names': 20, +HISTORY_MIN_ITEMSIZE = {'state': 30, 'event': 25, 'step_names': 20, 'S1_state': 31, 'S2_state': 31, 'mass_transfer_case': 7, 'S1_SN_type': 5, 'S2_SN_type': 5} diff --git a/posydon/visualization/VH_diagram/Presenter.py b/posydon/visualization/VH_diagram/Presenter.py index a737072981..625f430bad 100644 --- a/posydon/visualization/VH_diagram/Presenter.py +++ b/posydon/visualization/VH_diagram/Presenter.py @@ -829,7 +829,7 @@ def _reduce_data(self, data): for index, row in data.iterrows(): - if row["event"] == "END" or row["event"] == "redirect": + if row["event"] == "END" or "redirect" in row["event"]: continue new_data = to_simplified_data(row) @@ -963,4 +963,4 @@ def _digram_presentation(self, data): aditional_info.centered_text = simplified_data[-1][ "state"].state_after aditional_info.connected = False - self._visualizer().add_line([aditional_info]) \ No newline at end of file + self._visualizer().add_line([aditional_info]) From 350d3ea519d6cc0d9f0f566032854f23b5bcdbd2 Mon Sep 17 00:00:00 2001 From: ezapartas Date: Thu, 28 Sep 2023 17:55:33 +0300 Subject: [PATCH 126/319] =?UTF-8?q?changed=20the=20Thorne-Zytkow=20objects?= =?UTF-8?q?=20correctly=20to=20non=5Fexistent=5Fcompanion=E2=80=A6=20(#142?= =?UTF-8?q?)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * changed the Thorne-Zytkow objects correctly to non_existent_companion=1/2 * Update step_detached.py --------- Co-authored-by: Jeff Andrews --- posydon/binary_evol/DT/step_detached.py | 27 +++++++++++-------------- 1 file changed, 12 insertions(+), 15 deletions(-) diff --git a/posydon/binary_evol/DT/step_detached.py b/posydon/binary_evol/DT/step_detached.py index e9b3265253..e8c13244d6 100644 --- a/posydon/binary_evol/DT/step_detached.py +++ b/posydon/binary_evol/DT/step_detached.py @@ -1001,24 +1001,10 @@ def __call__(self, binary): secondary.htrack = False primary.htrack = False primary.co = False - elif (binary.star_1.state in STAR_STATES_CO - and binary.star_2.state - in 'massless_remnant'): - binary.state += " Thorne–Żytkow object" - if self.verbose or self.verbose == 1: - print("Formation of Thorne–Żytkow object, nothing to do further") - return - elif (binary.star_2.state in STAR_STATES_CO - and binary.star_1.state - in 'massless_remnant'): - binary.state += " Thorne–Żytkow object" - if self.verbose or self.verbose == 1: - print("Formation of Thorne–Żytkow object, nothing to do further") - return else: raise Exception("States not recognized!") - # non-existent, far away, star + # star 1 is a massless remnant, only star 2 exists elif self.non_existent_companion == 1: # we force primary.co=True for all isolated evolution, # where the secondary is the one evolving one @@ -1030,9 +1016,15 @@ def __call__(self, binary): secondary.htrack = True elif (binary.star_2.state in LIST_ACCEPTABLE_STATES_FOR_HeStar): secondary.htrack = False + elif (binary.star_2.state in STAR_STATES_CO): + binary.state += " Thorne-Zytkow object" + if self.verbose or self.verbose == 1: + print("Formation of Thorne-Zytkow object, nothing to do further") + return else: raise Exception("State not recognized!") + # star 2 is a massless remnant, only star 1 exists elif self.non_existent_companion == 2: primary = binary.star_2 primary.co = True @@ -1042,6 +1034,11 @@ def __call__(self, binary): secondary.htrack = True elif (binary.star_1.state in LIST_ACCEPTABLE_STATES_FOR_HeStar): secondary.htrack = False + elif (binary.star_1.state in STAR_STATES_CO): + binary.state += " Thorne-Zytkow object" + if self.verbose or self.verbose == 1: + print("Formation of Thorne-Zytkow object, nothing to do further") + return else: raise Exception("State not recognized!") else: From f32eff415529a5d24496ad929871bc8cabd5a4e8 Mon Sep 17 00:00:00 2001 From: Konstantinos Kovlakas <22239544+kkovlakas@users.noreply.github.com> Date: Thu, 28 Sep 2023 17:04:28 +0200 Subject: [PATCH 127/319] Update deploy-github-pages-unstable.yml (#147) --- .github/workflows/deploy-github-pages-unstable.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/deploy-github-pages-unstable.yml b/.github/workflows/deploy-github-pages-unstable.yml index 566190f5e6..e28e76aebb 100644 --- a/.github/workflows/deploy-github-pages-unstable.yml +++ b/.github/workflows/deploy-github-pages-unstable.yml @@ -50,6 +50,6 @@ jobs: uses: crazy-max/ghaction-github-pages@v2 with: target_branch: gh-pages-dev - build_dir: docs/_build/html/unstable + build_dir: docs/_build/html/ env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} From d8119b871de1a71a21af37c043b609181b44b098 Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Thu, 12 Oct 2023 16:11:40 +0200 Subject: [PATCH 128/319] TF2 remake and support reverse MT flag in TF12 (#137) * remove wrong code for kick in eccentric orbit * introducing step_CO-HeMS_RLO * spelling * minor change to catch collapsing stars states in step * add interp_class_CO_HeMS_RLO to binarystar.py * fix bug on characters lenght of step_names * PR88 defaul params ini change * minor changes * fix two small bugs * fix SN MODEL properties for nearest_neighbour interpolation * update TF2 and comibined TF12 * TMP debug edit to plot2D * add TF2 labels * debug * add new TF2 * debug code * remove debug option plot2d * add mt_history in post_processing.py * add mt_history column to be classified * move mt_history code in post_processing.py * debug post processing * debug * debug * add support for mt_history in pop synth * increase lenght mt_history oneline dataframe * synthetic population now identifies formation channels with contact and reverse MT * save ram in synthetic population pop synth evolution * update color palette * debug --- bin/run-pipeline | 2 +- posydon/binary_evol/MESA/step_mesa.py | 8 +- posydon/binary_evol/binarystar.py | 4 +- posydon/grids/post_processing.py | 15 +- posydon/grids/psygrid.py | 2 + posydon/grids/termination_flags.py | 76 ++++---- posydon/interpolation/interpolation.py | 4 +- posydon/popsyn/binarypopulation.py | 5 +- posydon/popsyn/synthetic_population.py | 54 ++++-- posydon/utils/common_functions.py | 7 +- posydon/visualization/combine_TF.py | 240 +++++++++++++------------ posydon/visualization/plot_defaults.py | 207 ++++++++++++++------- posydon/visualization/plot_pop.py | 19 +- 13 files changed, 401 insertions(+), 242 deletions(-) diff --git a/bin/run-pipeline b/bin/run-pipeline index 2530fba5f2..f7dea50a5b 100644 --- a/bin/run-pipeline +++ b/bin/run-pipeline @@ -302,7 +302,7 @@ def train_interpolators(i, path_to_csv_file, verbose=False): and "MODEL" not in key)] # define all string keys in final values - c_keys = ['interpolation_class', 'S1_state', 'S2_state'] + c_keys = ['interpolation_class', 'S1_state', 'S2_state', 'mt_history'] for MODEL_NAME in MODELS.keys(): for i in range(1,3): c_keys.append(f'S{i}_{MODEL_NAME}_SN_type') diff --git a/posydon/binary_evol/MESA/step_mesa.py b/posydon/binary_evol/MESA/step_mesa.py index ae5e150d47..24672e4395 100644 --- a/posydon/binary_evol/MESA/step_mesa.py +++ b/posydon/binary_evol/MESA/step_mesa.py @@ -656,7 +656,7 @@ def update_properties_NN(self, star_1_CO=False, star_2_CO=False, # DEBUG # print(key, missing_values_star_1) # print('fixed', len(getattr(self.binary.star_1, - # key + "_history"))) + # key + "_history"))) # convert these flags to default POSYDON star states setattr(stars[0], 'state', cf.check_state_of_star(stars[0], star_CO=stars_CO[0])) @@ -670,6 +670,10 @@ def update_properties_NN(self, star_1_CO=False, star_2_CO=False, setattr(binary, 'event', binary_event) setattr(binary, 'mass_transfer_case', MT_case) + setattr(self.binary, f'interp_class_{self.grid_type}', interpolation_class) + mt_history = self.termination_flags[2] # mass transfer history (TF12 plot label) + setattr(self.binary, f'mt_history_{self.grid_type}', mt_history) + if self.save_initial_conditions: # history N is how much to look back in the history # here N=1 as we only appended back the first entry @@ -884,6 +888,8 @@ def initial_final_interpolation(self, star_1_CO=False, star_2_CO=False): # infer stellar states interpolation_class = self.classes['interpolation_class'] setattr(self.binary, f'interp_class_{self.grid_type}', interpolation_class) + mt_history = self.classes['mt_history'] # mass transfer history (TF12 plot label) + setattr(self.binary, f'mt_history_{self.grid_type}', mt_history) S1_state_inferred = cf.check_state_of_star(self.binary.star_1, star_CO=star_1_CO) diff --git a/posydon/binary_evol/binarystar.py b/posydon/binary_evol/binarystar.py index 4f8d5a391f..3e19465f98 100644 --- a/posydon/binary_evol/binarystar.py +++ b/posydon/binary_evol/binarystar.py @@ -165,10 +165,12 @@ def __init__(self, star_1=None, star_2=None, index=None, properties=None, # if not hasattr(self, 'V_sys'): # self.V_sys = [0, 0, 0] - # store innterpolation_class for each step_MESA + # store interpolation_class and mt_history for each step_MESA for grid_type in ['HMS_HMS','CO_HMS_RLO','CO_HeMS','CO_HeMS_RLO']: if not hasattr(self, f'interp_class_{grid_type}'): setattr(self, f'interp_class_{grid_type}', None) + if not hasattr(self, f'mt_history_{grid_type}'): + setattr(self, f'mt_history_{grid_type}', None) # SimulationProperties object - parameters & parameterizations if isinstance(properties, SimulationProperties): diff --git a/posydon/grids/post_processing.py b/posydon/grids/post_processing.py index 9c5cf91985..3292df06ed 100644 --- a/posydon/grids/post_processing.py +++ b/posydon/grids/post_processing.py @@ -9,7 +9,8 @@ CEE_parameters_from_core_abundance_thresholds, check_state_of_star) from posydon.grids.MODELS import MODELS -from posydon.visualization.combine_TF import TF1_POOL_STABLE +from posydon.visualization.combine_TF import combine_TF12, TF1_POOL_STABLE +from posydon.visualization.plot_defaults import DEFAULT_MARKERS_COLORS_LEGENDS import numpy as np from tqdm import tqdm import copy @@ -114,7 +115,7 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, """ EXTRA_COLUMNS = {} - + for star in [1, 2]: # core masses at He depletion. stellar states and composition for quantity in ['avg_c_in_c_core_at_He_depletion', @@ -368,6 +369,14 @@ def post_process_grid(grid, index=None, star_2_CO=True, MODELS=MODELS, '%s has not the correct dimension! Error occoured after ' 'collapsing binary index=%s' % (key, i)) + # add MT history column by combining TF1 and TF2 + if not single_star: + interp_class = grid.final_values['interpolation_class'] + TF2 = grid.final_values['termination_flag_2'] + combined_TF12 = combine_TF12(interp_class, TF2) + mt_history = [DEFAULT_MARKERS_COLORS_LEGENDS['combined_TF12'][TF12][3] for TF12 in combined_TF12] + EXTRA_COLUMNS['mt_history'] = mt_history + # to avoid confusion rename core-collaspe compact object state "MODEL_NAME_state" # to "MODEL_NAME_CO_type" for MODEL_NAME in MODELS.keys(): @@ -409,7 +418,7 @@ def add_post_processed_quantities(grid, MESA_dirs_EXTRA_COLUMNS, EXTRA_COLUMNS, 'EXTRA_COLUMNS do not follow the correct order of grid!') for column in EXTRA_COLUMNS.keys(): - if "state" in column or "type" in column: + if "state" in column or "type" in column or column == 'mt_history': values = np.asarray(EXTRA_COLUMNS[column], str) else: values = np.asarray(EXTRA_COLUMNS[column], float) diff --git a/posydon/grids/psygrid.py b/posydon/grids/psygrid.py index 91ea97eafa..59963e6bde 100644 --- a/posydon/grids/psygrid.py +++ b/posydon/grids/psygrid.py @@ -1289,6 +1289,7 @@ def update_final_values(self): for dtype in self.final_values.dtype.descr: if (dtype[0].startswith("termination_flag") or dtype[0] == "interpolation_class" + or dtype[0] == "mt_history" or "_type" in dtype[0] or "_state" in dtype[0]): dtype = (dtype[0], H5_REC_STR_DTYPE.replace("U", "S")) new_dtype.append(dtype) @@ -1341,6 +1342,7 @@ def load(self, filepath=None): for dtype in self.final_values.dtype.descr: if (dtype[0].startswith("termination_flag") or dtype[0] == "interpolation_class" + or dtype[0] == "mt_history" or "_type" in dtype[0] or "_state" in dtype[0]): dtype = (dtype[0], H5_REC_STR_DTYPE.replace("S", "U")) new_dtype.append(dtype) diff --git a/posydon/grids/termination_flags.py b/posydon/grids/termination_flags.py index 9641555091..91a20a07a2 100644 --- a/posydon/grids/termination_flags.py +++ b/posydon/grids/termination_flags.py @@ -99,20 +99,16 @@ def get_mass_transfer_flag(binary_history, history1, history2, Returns ------- flag_system_evolution_history : string - Possible flags are: "contact during MS", "no_RLOF", a cumulative MT - flag + "_from_star1/2", or in case of missing binary history data, - "undetermined_flag_mass_transfer_with_no_binary_history". + Possible flags are: "None", "initial_RLOF", "contact_during_MS", + "no_RLOF", a cumulative MT flag, e.g, "case_A1/B1/A2" where the + index indicates the donor star. """ - if mesa_flag is not None: - if "overflow from L2" in mesa_flag: - if "at ZAMS" in mesa_flag: - return "initial_RLOF" - else: - return "L2_RLOF" - - if binary_history is None: - return "undetermined_flag_mass_transfer_with_no_binary_history" + if mesa_flag in TF1_POOL_ERROR: + return "None" + + if mesa_flag in TF1_POOL_INITIAL_RLO: + return "initial_RLOF" rel1 = binary_history["rl_relative_overflow_1"] rel2 = binary_history["rl_relative_overflow_2"] @@ -127,37 +123,49 @@ def get_mass_transfer_flag(binary_history, history1, history2, where_rlof_1 = where_rl_rel_1 & where_transfer where_rlof_2 = where_rl_rel_2 & where_transfer + if not np.any(where_rlof_1) and not np.any(where_rlof_2): + return "no_RLOF" + + MT = np.array([None]*len(where_rlof_1)) + if np.any(where_rlof_1): - star = 1 star_history = history1 star_mass = binary_history["star_1_mass"] where_rlof = where_rlof_1 rel_overflow = rel1 - elif np.any(where_rlof_2): - star = 2 + indices_with_rlo = np.arange(len(where_rlof))[where_rlof] + if not start_at_RLO and indices_with_rlo[0] == 0: + return "initial_RLOF" + mass_transfer_cases = [] + for index in indices_with_rlo: + star_state = check_state_from_history( + history=star_history, mass=star_mass, model_index=index) + mt_case = infer_mass_transfer_case( + rl_relative_overflow=rel_overflow[index], + lg_mtransfer_rate=rate[index], donor_state=star_state) + mass_transfer_cases.append(mt_case) + MT[where_rlof] = mass_transfer_cases + + if np.any(where_rlof_2): star_history = history2 star_mass = binary_history["star_2_mass"] where_rlof = where_rlof_2 rel_overflow = rel2 - else: - return "no_RLOF" - - indices_with_rlo = np.arange(len(where_rlof))[where_rlof] - - if not start_at_RLO and indices_with_rlo[0] == 0: - return "initial_RLOF" - - mass_transfer_cases = [] - for index in indices_with_rlo: - star_state = check_state_from_history( - history=star_history, mass=star_mass, model_index=index) - mt_case = infer_mass_transfer_case( - rl_relative_overflow=rel_overflow[index], - lg_mtransfer_rate=rate[index], donor_state=star_state) - mass_transfer_cases.append(mt_case) - - flag = cumulative_mass_transfer_flag(mass_transfer_cases) - return flag + "_from_star{}".format(star) + indices_with_rlo = np.arange(len(where_rlof))[where_rlof] + if not start_at_RLO and indices_with_rlo[0] == 0: + return "initial_RLOF" + mass_transfer_cases = [] + for index in indices_with_rlo: + star_state = check_state_from_history( + history=star_history, mass=star_mass, model_index=index) + mt_case = infer_mass_transfer_case( + rl_relative_overflow=rel_overflow[index], + lg_mtransfer_rate=rate[index], donor_state=star_state) + mass_transfer_cases.append(mt_case) + MT[where_rlof] = [t+10 for t in mass_transfer_cases] # shift by 10 + + flag = cumulative_mass_transfer_flag([t for t in MT if t is not None]) + return flag def check_state_from_history(history, mass, model_index=-1): diff --git a/posydon/interpolation/interpolation.py b/posydon/interpolation/interpolation.py index 5cadc9861c..950044ff88 100644 --- a/posydon/interpolation/interpolation.py +++ b/posydon/interpolation/interpolation.py @@ -122,7 +122,9 @@ def evaluate(self, binary, print_dist=False): distance, index = self.model.kneighbors(Xtn) closest_run = self.grid[self.valid_ind[index[0, 0]]] tflags = [closest_run.final_values['interpolation_class'], - closest_run.final_values['termination_flag_2']] + closest_run.final_values['termination_flag_2'], + closest_run.final_values['mt_history'], + ] dist = np.array([binary.star_1.mass - closest_run.initial_values['star_1_mass'], binary.star_2.mass diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index 2f6d7f006c..b0e8323bd2 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -64,7 +64,10 @@ 'mass_transfer_case_i': 7, 'mass_transfer_case_f': 7, 'S1_SN_type': 5, 'S2_SN_type': 5, 'interp_class_HMS_HMS' : 15, 'interp_class_CO_HeMS' : 15, - 'interp_class_CO_HMS_RLO' : 15, 'interp_class_CO_HeMS_RLO' : 15} + 'interp_class_CO_HMS_RLO' : 15, 'interp_class_CO_HeMS_RLO' : 15, + 'mt_history_HMS_HMS' : 40, 'mt_history_CO_HeMS' : 40, + 'mt_history_CO_HMS_RLO' : 40, 'mt_history_CO_HeMS_RLO' : 40, + } # BinaryPopulation will enforce a constant metallicity accross all steps that # load stellar or binary models by checked this list of steps. diff --git a/posydon/popsyn/synthetic_population.py b/posydon/popsyn/synthetic_population.py index 1d61f4a8d4..fd4f423423 100644 --- a/posydon/popsyn/synthetic_population.py +++ b/posydon/popsyn/synthetic_population.py @@ -22,6 +22,11 @@ import posydon.visualization.plot_pop as plot_pop from posydon.popsyn.GRB import get_GRB_properties, GRB_PROPERTIES +# TODO: temp import, remove after TF2 classification is implemented in pop synth +from posydon.interpolation.IF_interpolation import IFInterpolator +from posydon.binary_evol.binarystar import BinaryStar +from posydon.binary_evol.singlestar import SingleStar +from posydon.utils.common_functions import convert_metallicity_to_string class SyntheticPopulation: @@ -65,7 +70,7 @@ def __init__(self, path_to_ini, path_to_data=None, verbose=False, MODEL={}): self.metallicity = [self.metallicity] self.binary_populations = [] - for met in self.metallicity: + for met in self.metallicity[::-1]: self.ini_kw = binarypop_kwargs_from_ini(path_to_ini) self.ini_kw['metallicity'] = met self.ini_kw['temp_directory'] = self.create_met_prefix(met) + self.ini_kw['temp_directory'] @@ -81,11 +86,13 @@ def get_ini_kw(self): def evolve(self): """Evolve population(s) at given Z(s).""" - for ind, pop in enumerate( self.binary_populations ): + while self.binary_populations: + pop = self.binary_populations.pop() print( f'Z={pop.kwargs["metallicity"]:.2e} Z_sun' ) pop.evolve() met_prefix = f'{pop.kwargs["metallicity"]:.2e}_Zsun_' pop.save( met_prefix + 'population.h5' ) + del pop @staticmethod def create_met_prefix(met): @@ -239,7 +246,7 @@ def parse(self, S1_state=None, S2_state=None, binary_state=None, df_sel_met = pd.concat([df_sel_met, df_tmp]) del df_tmp - if k > 0: + if k > 0 and df_sel.shape[0] > 0: shift_index = max(np.unique(df_sel.index)) + 1 df_sel_met.index += shift_index @@ -257,7 +264,7 @@ def parse(self, S1_state=None, S2_state=None, binary_state=None, df_sel_met_oneline = df_sel_met_oneline.loc[sel_met] df_sel_met_oneline['metallicity'] = met - if k > 0: + if k > 0 and df_sel_oneline.shape[0] > 0: shift_index = max(np.unique(df_sel_oneline.index)) + 1 df_sel_met_oneline.index += shift_index @@ -312,7 +319,7 @@ def load_pop(self, path): else: raise ValueError('You already have a population stored in memory!') - def get_dco_at_formation(self, S1_state, S2_state, oneline_cols=None, formation_channels=False): + def get_dco_at_formation(self, S1_state, S2_state, oneline_cols=None, formation_channels=False, mt_history=False): """Sort synthetic population, i.e. DCO at formation. Note: by default this function looks for the symmetric state @@ -327,6 +334,14 @@ def get_dco_at_formation(self, S1_state, S2_state, oneline_cols=None, formation_ oneline_cols : list str List of columns preset in the oneline dataframe you want to export into the synthetic population. + formation_channels : bool + Compute the formation channel, a string containing the binary + event evolution. + mt_history : bool + If `True`, split the event oRLO1/oRLO2 into oRLO1-contact/oRLO2-contact, + oRLO1-reverse/oRLO2-reverse and oRLO1/oRLO2. This is useful to + identify binaries undergoing contact stable mass-transfer phases and + reverse mass-transfer phase . """ # compute GRB properties boolean @@ -338,7 +353,7 @@ def get_dco_at_formation(self, S1_state, S2_state, oneline_cols=None, formation_ if formation_channels: if self.verbose: print('Computing formation channels...') - self.get_formation_channels() + self.get_formation_channels(mt_history=mt_history) # to avoid the user making mistake automatically check the inverse of # the stellar states, since the df is already parsed this will not @@ -757,7 +772,7 @@ def get_dco_detection_rate(self, sensitivity='design_H1L1V1', export_cols=None, # TODO: store p_det self.df_dco_observable = self.resample_synthetic_population(index, z_formation, z_merger, w_ijk, export_cols=export_cols) - def get_formation_channels(self): + def get_formation_channels(self, mt_history): """Get formation channel and add to df and df_oneline.""" # loop through each binary @@ -780,13 +795,32 @@ def get_formation_channels(self): formation_channel = "_".join(event_array) else: formation_channel = "_".join(event_array) - + # TODO: drop the envent CO_contact # TODO: once we trust the redirection to the detached step # drop also the redirect event - + + # TODO: for debugging purposes we keep the full channel + self.df_oneline.loc[index,'channel_debug'] = formation_channel + # clean the redirect and CO_contact events + formation_channel = formation_channel.replace('_redirect', '') + formation_channel = formation_channel.replace('_CO_contact', '') self.df_oneline.loc[index,'channel'] = formation_channel - + + if mt_history and 'mt_history_HMS_HMS' not in self.df_oneline: + raise ValueError('mt_history_HMS_HMS not saved in the oneline dataframe!') + else: + # split oRLO1 into oRLO1, oRLO1-contact and oRLO1-reverse + sel = ((self.df_oneline['mt_history_HMS_HMS'] == 'Stable contact phase') & + self.df_oneline['channel'].str.contains('oRLO1')) + self.df_oneline.loc[sel, 'channel'] = self.df_oneline.loc[sel, 'channel'].apply(lambda x: x.replace('oRLO1', 'oRLO1-contact')) + sel = ((self.df_oneline['mt_history_HMS_HMS'] == 'Stable reverse mass-transfer phase') & + self.df_oneline['channel'].str.contains('oRLO1')) + self.df_oneline.loc[sel, 'channel'] = self.df_oneline.loc[sel, 'channel'].apply(lambda x: x.replace('oRLO1', 'oRLO1-reverse')) + + # TODO: do the above split for unstable MT as well + + def save_intrinsic_pop(self, path='./intrinsic_population_type.h5', pop='DCO'): """Save intrinsic population. diff --git a/posydon/utils/common_functions.py b/posydon/utils/common_functions.py index 427e27573f..7c5a94065d 100644 --- a/posydon/utils/common_functions.py +++ b/posydon/utils/common_functions.py @@ -1545,11 +1545,14 @@ def cumulative_mass_transfer_string(cumulative_integers): result += "no_RLO" else: if not added_case_word: - result += "case" + result += "case_" added_case_word = True else: result += "/" - result += MT_CASE_TO_STR[integer] + if integer in MT_CASE_TO_STR: + result += MT_CASE_TO_STR[integer] + '1' # from star 1 + else: + result += MT_CASE_TO_STR[integer-10] + '2' # from star 2 return result diff --git a/posydon/visualization/combine_TF.py b/posydon/visualization/combine_TF.py index dd9cb1c11b..9b285d8ae4 100644 --- a/posydon/visualization/combine_TF.py +++ b/posydon/visualization/combine_TF.py @@ -13,119 +13,134 @@ # TODO: rename the varaible, these are termination flags that indicates # the star has reached the end of the evolution -TF1_POOL_STABLE = ['Primary has depleted central carbon', - 'Secondary has depleted central carbon', - 'Primary got stopped before central carbon depletion', - 'Secondary got stopped before central carbon depletion', - 'Primary enters pair-instability regime', - 'Secondary enters pair-instability regime', - 'Primary enters pulsational pair-instability regime', - 'Secondary enters pulsational pair-instability regime', - 'offcenter neon ignition for primary', - 'offcenter neon ignition for secondary', - 'envelope_mass_limit', - 'gamma_center_limit', - 'Reached TPAGB', - 'max_age'] +TF1_POOL_STABLE = [ + 'Primary has depleted central carbon', + 'Secondary has depleted central carbon', + 'Primary got stopped before central carbon depletion', + 'Secondary got stopped before central carbon depletion', + 'Primary enters pair-instability regime', + 'Secondary enters pair-instability regime', + 'Primary enters pulsational pair-instability regime', + 'Secondary enters pulsational pair-instability regime', + # 'offcenter neon ignition for primary', + # 'offcenter neon ignition for secondary', + 'envelope_mass_limit', + 'gamma_center_limit', + # 'Reached TPAGB', + 'max_age' + ] TF1_POOL_UNSTABLE = [ - 'Unstable', 'overflow from L2 (D_L2) distance for q(=Macc/Mdon)>1, donor is star 1', 'overflow from L2 (R_L2) surface for q(=Macc/Mdon)<1, donor is star 1', 'overflow from L2 (R_L2) surface for q(=Macc/Mdon)>1, donor is star 1', 'overflow from L2 (D_L2) distance for q(=Macc/Mdon)<1, donor is star 1', 'overflow from L2 (R_L2) surface for q(=Macc/Mdon)<1, donor is star 2', 'overflow from L2 (R_L2) surface for q(=Macc/Mdon)>1, donor is star 2', - 'reached maximum mass transfer rate: 10.0d0', 'Reached maximum mass transfer rate: 1d-1', - 'Reached the critical mt rate', 'Reached maximum mass transfer rate: Exceeded photon trapping radius', 'Both stars fill their Roche Lobe and at least one of them is off MS', - 'Terminate due to L2 overflow during case A'] - -TF1_POOL_INITIAL_RLO = ['Initial RLOF', - 'forced_initial_RLO', - 'overflow from L1 at ZAMS', - 'Terminate because of overflowing initial model', - 'overflow from L2 point for q>1 at ZAMS', - 'overflow from L2 surface for q<1 at ZAMS', - 'overflow from L2 surface for q>1 at ZAMS', - 'ignored_no_BH', - 'ignored_scrubbed'] - -TF1_POOL_ERROR = ['logQ_limit', - 'logQ_min_limit', - 'min timestep', - 'min_timestep_limit', - 'cluster timelimit', - 'reach cluster timelimit', - 'no termination code', - 'fe_core_infall_limit'] + 'Terminate due to L2 overflow during case A' + ] + +TF1_POOL_INITIAL_RLO = [ + 'forced_initial_RLO', + 'Terminate because of overflowing initial model', + ] + +TF1_POOL_ERROR = [ + 'logQ_min_limit', + 'min_timestep_limit', + 'reach cluster timelimit', + ] TF2_POOL_NO_RLO = [ 'no_RLOF', - 'undetermined_flag_mass_transfer_from_evolutionary_states_from_star1' -] + ] TF2_POOL_INITIAL_RLO = [ 'initial_RLOF', - 'ignored_scrubbed', - 'ignored_no_BH' -] + ] -TF2_UNSTABLE_L2 = [ +TF2_POOL_UNSTABLE = [ "L2_RLOF" -] - + ] + +TF2_POOL_UNKNOWN = [ + 'None' + ] + +TF2_POOL_CONT = [ + 'contact_during_MS' + ] + +TF2_POOL_A = [ + 'case_A1', + 'case_A2' + ] + +TF2_POOL_AB = [ + 'case_A1/B1', + 'case_A2/B2', + 'case_A1/B1/C1', + 'case_A2/B2/C2', + 'case_A1/B1/C1/BB1', + 'case_A2/B2/C2/BB2', + 'case_A1/B1/A1', + 'case_A2/B2/A2' + ] + +TF2_POOL_AC = [ + 'case_A1/C1', + 'case_A2/C2' + ] + +TF2_POOL_ABB = [ + 'case_A1/BB1', + 'case_A2/BB2' + ] + +TF2_POOL_B = [ + 'case_B1', + 'case_B2' + ] + +TF2_POOL_C = [ + 'case_C1', + 'case_C2', + ] + +TF2_POOL_BC = [ + 'case_B1/C1', + 'case_B2/C2' + ] + +TF2_POOL_BB = [ + 'case_A1/B1/BB1', + 'case_A2/B2/BB2', + 'case_B1/BB1', + 'case_B2/BB2', + 'case_B1/C1/BB1', + 'case_B2/C2/BB2', + 'case_BA1/BB1', + 'case_BA2/BB2', + 'case_C1/BB1', + 'case_C2/BB2', + 'case_BB1', + 'case_BB2' + ] + +TF2_POOL_BA = [ + 'case_BA1', + 'case_BA2' + ] def combine_TF12(IC, TF2, verbose=False): """Get the combination of interpolation classion and termination flag 2.""" N = len(IC) TF12 = np.array(['unknown']*N, dtype='U25') - # INITIAL RLO - TF2_pool_SCONT = ['contact_during_MS'] - - TF2_pool_SA = ['caseA_from_star1', - 'caseA_from_star2'] - - TF2_pool_SAB = ['caseA/B_from_star1', - 'caseA/B_from_star2', - 'caseA/B/C_from_star1', - 'caseA/B/C/BB_from_star1', - 'caseA/B/A_from_star1'] - - TF2_pool_SAC = ['caseA/C_from_star1'] - - TF2_pool_SABB = ['caseA/BB_from_star1', - 'caseA/BB_from_star2'] - - TF2_pool_SB = ['caseB_from_star1', - 'caseB_from_star2'] - - TF2_pool_SC = ['caseC_from_star1', - 'caseC_from_star2'] - - TF2_pool_SBC = ['caseB/C_from_star1', - 'caseB/C_from_star2'] - - TF2_pool_SBB = ['caseB/C/BB_from_star1', - 'caseB/C/BB_from_star2', - 'caseB/BB_from_star1', - 'caseB/BB_from_star2', - 'caseA/B/BB_from_star1', - 'caseA/B/BB_from_star2', - 'caseBA/BB_from_star1', - 'caseB/A_from_star1', - 'caseC/BB_from_star1', - 'caseBB_from_star1', - 'caseBB_from_star2'] - - TF2_pool_SBA = ['caseBA_from_star1', - 'caseBA_from_star2'] - - TF2_unknown = ['?_from_star1', '?_from_star2'] - + # mask unknown cases during history TF2 TF2 = [(tf[1:] if tf.startswith("?case") else tf) for tf in TF2] for i in range(N): @@ -136,52 +151,57 @@ def combine_TF12(IC, TF2, verbose=False): elif IC[i] == 'no_MT': TF12[i] = 'no_RLOF' elif IC[i] == 'stable_MT': - if TF2[i] in TF2_pool_SCONT: + if '1' in TF2[i] and '2' in TF2[i]: + TF12[i] = 'Reverse stable MT' + elif TF2[i] in TF2_POOL_CONT: TF12[i] = 'Stable contact' - elif TF2[i] in TF2_pool_SA: + elif TF2[i] in TF2_POOL_A: TF12[i] = 'Stable case A' - elif TF2[i] in TF2_pool_SAB: + elif TF2[i] in TF2_POOL_AB: TF12[i] = 'Stable case AB' - elif TF2[i] in TF2_pool_SAC: + elif TF2[i] in TF2_POOL_AC: TF12[i] = 'Stable case AC' - elif TF2[i] in TF2_pool_SABB: + elif TF2[i] in TF2_POOL_ABB: TF12[i] = 'Stable case ABB' - elif TF2[i] in TF2_pool_SB: + elif TF2[i] in TF2_POOL_B: TF12[i] = 'Stable case B' - elif TF2[i] in TF2_pool_SC: + elif TF2[i] in TF2_POOL_C: TF12[i] = 'Stable case C' - elif TF2[i] in TF2_pool_SBC: + elif TF2[i] in TF2_POOL_BC: TF12[i] = 'Stable case BC' - elif TF2[i] in TF2_pool_SBB: + elif TF2[i] in TF2_POOL_BB: TF12[i] = 'Stable case BB' - elif TF2[i] in TF2_pool_SBA: + elif TF2[i] in TF2_POOL_BA: TF12[i] = 'Stable case BA' elif IC[i] == 'unstable_MT': - if TF2[i] in TF2_pool_SCONT: + if '1' in TF2[i] and '2' in TF2[i]: + TF12[i] = 'Reverse unstable MT' + elif TF2[i] in TF2_POOL_CONT: TF12[i] = 'Unstable contact' - elif TF2[i] in TF2_pool_SA: + elif TF2[i] in TF2_POOL_A: TF12[i] = 'Unstable case A' - elif TF2[i] in TF2_pool_SAB: + elif TF2[i] in TF2_POOL_AB: TF12[i] = 'Unstable case AB' - elif TF2[i] in TF2_pool_SAC: + elif TF2[i] in TF2_POOL_AC: TF12[i] = 'Unstable case AC' - elif TF2[i] in TF2_pool_SABB: + elif TF2[i] in TF2_POOL_ABB: TF12[i] = 'Unstable case ABB' - elif TF2[i] in TF2_pool_SB: + elif TF2[i] in TF2_POOL_B: TF12[i] = 'Unstable case B' - elif TF2[i] in TF2_pool_SC: + elif TF2[i] in TF2_POOL_C: TF12[i] = 'Unstable case C' - elif TF2[i] in TF2_pool_SBB: + elif TF2[i] in TF2_POOL_BB: TF12[i] = 'Unstable case BB' - elif TF2[i] in TF2_pool_SBC: + elif TF2[i] in TF2_POOL_BC: TF12[i] = 'Unstable case BC' - elif TF2[i] in TF2_UNSTABLE_L2: + elif TF2[i] in TF2_POOL_UNSTABLE: TF12[i] = "Unstable L2 RLOF" - elif TF2[i] in TF2_pool_SBA: + # reverse MT case + elif TF2[i] in TF2_POOL_BA: TF12[i] = 'Unstable case BA' # catch if something is missing from the logic - if TF12[i] == 'unknown' or TF2[i] in TF2_unknown: + if TF12[i] == 'unknown': if verbose: print(i, IC[i], TF2[i]) diff --git a/posydon/visualization/plot_defaults.py b/posydon/visualization/plot_defaults.py index 8428227ed4..8e540e7cde 100644 --- a/posydon/visualization/plot_defaults.py +++ b/posydon/visualization/plot_defaults.py @@ -235,74 +235,141 @@ 'L2_RLOF': ['^', 1, 'black', 'L2 RLOF'], - 'caseA_from_star1': - ['s', 2, 'tab:blue', 'case A from star1'], - 'caseA/B_from_star1': - ['s', 2, 'tab:green', 'case A/B from star1'], - 'caseA/B/A_from_star1': - ['s', 2, 'lightgrey', 'case A/B/A from star1'], - 'caseA/B/C_from_star1': - ['s', 2, 'yellow', 'case A/B/C from star1'], - 'caseA/C_from_star1': - ['s', 2, 'yellow', 'case A/C from star1'], - 'caseA/B/BB_from_star1': - ['s', 2, 'tab:red', 'case A/B/BB from star1'], - 'caseA/B/C/BB_from_star1': - ['s', 2, 'tab:cyan', 'case A/B/C/BB from star1'], - 'caseB_from_star1': - ['s', 2, 'tab:purple', 'case B from star1'], - 'caseB/A_from_star1': - ['s', 2, 'tab:olive', 'case B/A from star1'], - 'caseB/BB_from_star1': - ['s', 2, 'tab:pink', 'case B/BB from star1'], - 'caseB/C/BB_from_star1': - ['s', 2, 'tab:gray', 'case B/C/BB from star1'], - 'caseB/C_from_star1': - ['s', 2, 'tab:orange', 'case B/C from star1'], - 'caseC_from_star1': - ['s', 2, 'black', 'case C from star1'], - 'caseC/BB_from_star1': - ['s', 2, 'brown', 'case C/BB from star1'], - - 'caseA_from_star2': - ['o', 2, 'tab:blue', 'case A from star2'], - 'caseA/B_from_star2': - ['o', 2, 'tab:green', 'case A/B from star2'], - 'caseA/B/A_from_star2': - ['s', 2, 'lightgrey', 'case A/B/A from star2'], - 'caseA/B/C_from_star2': - ['s', 2, 'yellow', 'case A/B/C from star2'], - 'caseA/B/BB_from_star2': - ['o', 2, 'tab:red', 'case A/B/BB from star2'], - 'caseA/B/C/BB_from_star2': - ['o', 2, 'tab:cyan', 'case A/B/C/BB from star2'], - 'caseB_from_star2': - ['o', 2, 'tab:purple', 'case B from star2'], - 'caseB/A_from_star2': - ['o', 2, 'tab:olive', 'case B/A from star2'], - 'caseB/BB_from_star2': - ['o', 2, 'tab:pink', 'case B/BB from star2'], - 'caseB/C/BB_from_star2': - ['o', 2, 'tab:gray', 'case B/C/BB from star2'], - 'caseB/C_from_star2': - ['o', 2, 'tab:orange', 'case B/C from star2'], - 'caseC_from_star2': - ['o', 2, 'black', 'case C from star2'], - 'caseC/BB_from_star2': - ['s', 2, 'brown', 'case C/BB from star2'], - - 'caseBA_from_star1': - ['s', 2, 'tab:blue', 'case BA from star1'], - 'caseBB_from_star1': - ['s', 2, 'tab:green', 'case BB from star1'], - 'caseBA/BB_from_star1': - ['s', 2, 'tab:red', 'case BA/BB from star1'], - - '_from_star1': - ['x', 1, 'black', 'unknown'], - '_from_star2': - ['x', 1, 'black', 'unknown'], - + 'case_A1': + ['s', 2, 'tab:blue', 'case A1'], + 'case_A1/B1': + ['s', 2, 'tab:green', 'case A1/B1'], + 'case_A1/B1/A1': + ['s', 2, 'lightgrey', 'case A1/B1/A1'], + 'case_A1/B1/C1': + ['s', 2, 'yellow', 'case A1/B1/C1'], + 'case_A1/C1': + ['s', 2, 'yellow', 'case A1/C1'], + 'case_A1/B1/BB1': + ['s', 2, 'tab:red', 'case A1/B1/BB1'], + 'case_A1/B1/C1/BB1': + ['s', 2, 'tab:cyan', 'case A1/B1/C1/BB1'], + 'case_B1': + ['s', 2, 'tab:purple', 'case B1'], + 'case_B1/BB1': + ['s', 2, 'tab:pink', 'case B1/BB1'], + 'case_B1/C1/BB1': + ['s', 2, 'tab:gray', 'case B1/C1/BB1'], + 'case_B1/C1': + ['s', 2, 'tab:orange', 'case B1/C1'], + 'case_C1': + ['s', 2, 'black', 'case C1'], + 'case_C1/BB1': + ['s', 2, 'brown', 'case C1/BB1'], + 'case_BA1': + ['s', 2, 'tab:blue', 'case BA1'], + 'case_BB1': + ['s', 2, 'tab:green', 'case BB1'], + 'case_BA1/BB1': + ['s', 2, 'tab:red', 'case BA1/BB1'], + + 'case_A2': + ['o', 2, 'tab:blue', 'case A2'], + 'case_A2/B2': + ['o', 2, 'tab:green', 'case A2/B2'], + 'case_A2/B2/A2': + ['o', 2, 'lightgrey', 'case A2/B2/A2'], + 'case_A2/B2/C2': + ['o', 2, 'yellow', 'case A2/B2/C2'], + 'case_A2/C2': + ['o', 2, 'yellow', 'case A2/C2'], + 'case_A2/B2/BB2': + ['o', 2, 'tab:red', 'case A2/B2/BB2'], + 'case_A2/B2/C2/BB2': + ['o', 2, 'tab:cyan', 'case A2/B2/C2/BB2'], + 'case_B2': + ['o', 2, 'tab:purple', 'case B2'], + 'case_B2/BB2': + ['o', 2, 'tab:pink', 'case B2/BB2'], + 'case_B2/C2/BB2': + ['o', 2, 'tab:gray', 'case B2/C2/BB2'], + 'case_B2/C2': + ['o', 2, 'tab:orange', 'case B2/C2'], + 'case_C2': + ['o', 2, 'black', 'case C2'], + 'case_C2/BB2': + ['o', 2, 'brown', 'case C2/BB2'], + 'case_BA2': + ['o', 2, 'tab:blue', 'case BA2'], + 'case_BB2': + ['o', 2, 'tab:green', 'case BB2'], + 'case_BA2/BB2': + ['o', 2, 'tab:red', 'case BA1/BB2'], + + 'case_A1/A2': + ['>', 2, 'tab:blue', 'case A1/A2'], + 'case_A1/A2/A1': + ['>', 2, 'tab:green', 'case A1/A2/A1'], + 'case_A1/A2/B1': + ['>', 2, 'tab:gray', 'case A1/A2/B1'], + 'case_A1/A2/B1/B2': + ['>', 2, 'tab:orange', 'case A1/A2/B1/B2'], + 'case_A1/B1/BB1': + ['>', 2, 'tab:purple', 'case A1/B1/BB1'], + 'case_A1/A2/B2': + ['>', 2, 'tab:red', 'case A1/A2/B2'], + 'case_A1/B1/A2': + ['>', 2, 'tab:pink', 'case A1/B1/B2'], + 'case_A1/B1/A2/B2': + ['>', 2, 'tab:olive', 'case A1/B1/A2/B2'], + 'case_A1/B1/A2/B1/B2': + ['>', 2, 'yellow', 'case A1/B1/A2/B1/B2'], + 'case_A1/B1/B2': + ['>', 2, 'brown', 'case A1/B1/B2'], + 'case_A1/B1/B2/B1': + ['>', 2, 'black', 'case A1/B1/B2/B1'], + 'case_A1/B2': + ['>', 2, 'tab:cyan', 'case A1/B1/C1/BB1'], + 'case_B1/A2': + ['<', 2, 'tab:blue', 'case B1/A2'], + 'case_B1/A2/B2': + ['<', 2, 'tab:green', 'case B1/A2/B2'], + 'case_B1/B2': + ['<', 2, 'tab:gray', 'case B1/B2'], + 'case_B1/B2/B1': + ['<', 2, 'tab:red', 'case B1/B2/B1'], + 'case_B1/B2/BB1': + ['<', 2, 'brown', 'case B1/B2/BB1'], + 'case_B1/B2/B1/B2/B1/B2/B1/B2/B1': + ['<', 2, 'tab:pink', 'case B1/B2/B1/B2/B1/B2/B1/B2/B1'], + 'case_B1/B2/B1/C1': + ['<', 2, 'tab:olive', 'case B1/B2/B1/C1'], + 'case_B1/B2/C1': + ['<', 2, 'tab:purple', 'case B1/B2/C1'], + 'case_B1/B2/C1/BB1': + ['<', 2, 'black', 'case B1/B2/C1/BB1'], + 'case_B1/C2': + ['<', 2, 'tab:cyan', 'case B1/C1'], + 'case_B2/B1': + ['v', 2, 'tab:blue', 'case B2/B1'], + 'case_B2/B1/C1': + ['v', 2, 'tab:green', 'case B2/B1/C1'], + 'case_B2/C1': + ['v', 2, 'tab:orange', 'case B2/C1'], + 'case_B1/A2/B2/C1': + ['v', 2, 'tab:red', 'case B1/A2/B2/C1'], + 'case_A2/B1/B2': + ['v', 2, 'tab:pink', 'case A2/B1/B2'], + 'case_A1/B1/B2/C1': + ['v', 2, 'tab:purple', 'case A1/B1/B2/C1'], + 'case_A1/B1/B2/B1/C1': + ['v', 2, 'tab:olive', 'case A1/B1/B2/B1/C1'], + 'case_A1/B1/A2/B2/C1': + ['v', 2, 'brown', 'case A1/B1/A2/B2/C1'], + 'case_A1/B2/C1': + ['v', 2, 'yellow', 'case A1/B2/C1'], + 'case_A1/A2/B2/C1': + ['v', 2, 'black', 'case A1/A2/B2/C1'], + 'case_A2/A1': + ['v', 2, 'tab:gray', 'case A2/A1'], + + 'None': + ['x', 1, 'tab:red', 'failed'], }, 'termination_flag_3': { @@ -443,6 +510,10 @@ ['x', 1, 'red', 'Not converged'], 'unknown': ['+', 1, 'green', 'unknown'], + 'Reverse stable MT': + ['s', 2, 'tab:olive', 'Stable reverse mass-transfer phase'], + 'Reverse unstable MT': + ['D', 1, 'tab:olive', 'Unstable reverse mass-transfer phase'], }, 'debug': { 'terminate due to primary depleting carbon (inverse sn?)': diff --git a/posydon/visualization/plot_pop.py b/posydon/visualization/plot_pop.py index 95e98f5e53..49a53e10f8 100644 --- a/posydon/visualization/plot_pop.py +++ b/posydon/visualization/plot_pop.py @@ -16,23 +16,22 @@ plt.style.use(os.path.join(PATH_TO_POSYDON, "posydon/visualization/posydon.mplstyle")) -COLORS = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', - 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan'] -COLORS *= 10 +cm = plt.cm.get_cmap('tab20') +COLORS = [cm.colors[i] for i in range(len(cm.colors)) if i%2==0] + [cm.colors[i] for i in range(len(cm.colors)) if i%2==1] -def plot_merger_rate_density(z, rate_density, ylim=(1e-1,4e2), zmax=10., show=True, - path=None, channels=False, GWTC3=False, label='DCO', **kwargs): +def plot_merger_rate_density(z, rate_density, zmax=10., channels=False, + GWTC3=False, label='DCO', **kwargs): plt.plot(z[z Date: Thu, 12 Oct 2023 10:25:25 -0400 Subject: [PATCH 129/319] Single stars updates (#110) * started the step_isolated files (2 is for superclass with detached) and first changes in detached and in flow * allowed for different psygrid for isoalted step, selected lists of stellar states, still need to solve absence of logR for mergers * deleted old step_isolated * made step_merging * integrated the step_merged correctly in the flow * before intorducing the option for not using log_R as a matching criteria, having the option do decide the parameters of the matching * added most merger cases, still need to solve logR avsence in matching * finished step_merged combinations * made the new disrupted_step * added spin of merged product * made step_initially_single too and changes of structure after discussion with Konstantinos * latest changes * Eirini's change,name of mist0 to match_to_single_star * making lists for each matching * changes in lambda minimize function to a more general function that can take an arbitrary list * typo * added the option of the different list of attributes in the step_merged * matching should work the same for all subclasses now * syntax errors prob fixed * fixing runtime errors in the new files * some more fixing * Some print statements * change the flowchart to not go to sterp_disurpted if one of the events is detached is called * prints * typo in flowchart * Friday's work * . * fix init orbit * disrupted bianry evolves until CC2 * step SN updated to expect a 2nd CC in an already disrupted system. Matching for postMS and maybe HeStars may still need work * matching fixing * slightly improved the verbose text of the matching * print errors * changes to match_to_single_star * generalized alternative matching * for this PR, step merged does nothing * matching happening only when needed (once) * step merged and intially single go to step end for now * testing cases of ZAMS stars not needed to match * binary fraction * minor changes in isolated or initially single * else statement, self.companion * typo * deleted the option of fidning ZAMS through logR * syntax error fixing * resolving merging issues * went back to grid_Hrich and grid_strippedHe * ini file * ini * surf_avg_omega_div_omega_crit is not always stored, so it is no longer called for and passed to solve_ivp. It is calculated in the Matt+2015 magnetic braking calculation from stellar properties now, as it is only needed there. * del grid from detached params * ini * Removed additional instances where interp1d_sec[surf_avg_omega_div_omega_crit](t - t_offset_sec) was called. * changing the random * removed superfluous commas in defining detached step properties * Adding step_isolated,step_disrupted in the STEP_NAMES_LOADING_GRIDS so the metallicity can propagate in ther kwargs * fix in ini file * I think I went the POSYDON-MESA-INLIST submodule back * changed class names according to CamelCase convention * time profiling * Corrections to the detached verbose with timestamp. * no matching for the CO star, using m0,t0 of the previously matched star * Giving additional option 1 in verbose to print only the matching parameters * printing of more timing profiling * Step 1 in cleaning detached step. * Step 2 in cleaing detached step. * Removing duplicate code in square difference function. * Deciding on which column are going to use relative distance during matching. * Deleting obsolete transform method in detached step. * Removing unnecessary transform when scaling columns in detached step. * Using stored scalers and removing some useless self.grid assignments * adding massless in the star states in flow chart * Adding massless_remant and a compact binary state list for the detached. Adding initially single stars in the step names for metallisity * fixing the primary.co in some elif * fixing the infite loop created by the flow chart * metallicity = 1 as default * in the flow, a CO+massless goes to step_end * going to step initially single only after ZAMS * pulled merged step from manos_merged_step * pulled the isolated step and the population defaults for all substeps (disrupted, initially_single, merged) * pulled step_SN, flow, and step_detached * added step_merged in steps that need a metallicity (in binarypopulations.py) * IsolatedStep changes * fixing the STAR_STATES_ALL in the flow_chart * properties_massless_remnant in common function * convert_massless_remenant in merged * properties massless in SingleStar class instead * corrected massless_remnant dictionary * fixing the state after CC1 in merged and single stars * set initial period and separation for single star equal to nan * deleting some unnecessary comments * deleting repeated dictionaries in detached * reverting the changes in MESA-INLISTS * adding an independent mass distrubution for the single stars * Implementing varying binary fraction based on the m1 * changing the name of the scheme and erasing some old stuff * Update step_detached.py * Update step_detached.py * Update binarystar.py * Update binarypopulation.py * Update binarypopulation.py * Update independent_sample.py Fixed typo in reference * Update binarypopulation.py * Update binarypopulation.py * Update binarypopulation.py * Fixing the spacing in the if else conditions * raising an error for m1<0.8 * adding an else statement to the m1 checks * adding kwargs to ini --------- Co-authored-by: ezapartas Co-authored-by: Eirini Kasdagli Co-authored-by: sg Co-authored-by: kkovlakas Co-authored-by: tassos25 Co-authored-by: Jeff Andrews --- posydon/popsyn/binarypopulation.py | 19 ++++---- posydon/popsyn/defaults.py | 6 ++- posydon/popsyn/independent_sample.py | 50 ++++++++++++++++++++ posydon/popsyn/population_params_default.ini | 4 +- 4 files changed, 67 insertions(+), 12 deletions(-) diff --git a/posydon/popsyn/binarypopulation.py b/posydon/popsyn/binarypopulation.py index b0e8323bd2..2476d7c634 100644 --- a/posydon/popsyn/binarypopulation.py +++ b/posydon/popsyn/binarypopulation.py @@ -43,7 +43,8 @@ from posydon.binary_evol.simulationproperties import SimulationProperties from posydon.popsyn.star_formation_history import get_formation_times -from posydon.popsyn.independent_sample import generate_independent_samples +from posydon.popsyn.independent_sample import (generate_independent_samples, + binary_fraction_value) from posydon.utils.common_functions import (orbital_period_from_separation, orbital_separation_from_period) from posydon.popsyn.defaults import default_kwargs @@ -92,8 +93,6 @@ def __init__(self, **kwargs): self.kwargs = default_kwargs.copy() for key, arg in kwargs.items(): self.kwargs[key] = arg - # Have a binary fraction change the number_of binaries. - self.binary_fraction = self.kwargs.get('binary_fraction') self.number_of_binaries = self.kwargs.get('number_of_binaries') self.population_properties = self.kwargs.get('population_properties', @@ -724,7 +723,8 @@ def __init__(self, sampler=generate_independent_samples, self.kwargs = kwargs.copy() self.sampler = sampler self.star_formation = kwargs.get('star_formation', 'burst') - self.binary_fraction = kwargs.get('binary_fraction', 1) + self.binary_fraction_generator = binary_fraction_value + def reset_rng(self): """Reset the RNG with the stored entropy.""" self._num_gen = 0 @@ -749,7 +749,6 @@ def get_binary_by_iter(self, n=1, **kwargs): def draw_initial_samples(self, orbital_scheme='separation', **kwargs): """Generate all random varibles.""" - binary_fraction = self.kwargs.get('binary_fraction', 1) if not ('RNG' in kwargs.keys()): kwargs['RNG'] = self.RNG # a, e, M_1, M_2, P @@ -767,9 +766,11 @@ def draw_initial_samples(self, orbital_scheme='separation', **kwargs): N_binaries = len(orbital_period) formation_times = get_formation_times(N_binaries, **kwargs) + #Get the binary_fraction + binary_fraction = self.binary_fraction_generator(m1=m1, **kwargs) + # indices indices = np.arange(self._num_gen, self._num_gen+N_binaries, 1) - output_dict = { 'binary_index': indices, 'binary_fraction':binary_fraction, @@ -799,12 +800,13 @@ def draw_initial_binary(self, **kwargs): sampler_kwargs = kwargs.copy() sampler_kwargs['number_of_binaries'] = 1 sampler_kwargs['RNG'] = kwargs.get('RNG', self.RNG) + # Randomly generated variables output = self.draw_initial_samples(**sampler_kwargs) default_index = output['binary_index'].item() - # Randomly generated variables + binary_fraction = output['binary_fraction'] - if self.RNG.uniform() < self.binary_fraction: + if self.RNG.uniform() < binary_fraction: formation_time = output['time'].item() separation = output['separation'].item() orbital_period = output['orbital_period'].item() @@ -854,7 +856,6 @@ def draw_initial_binary(self, **kwargs): orbital_period = np.nan eccentricity = np.nan m1 = output['S1_mass'].item() - m2 = output['S2_mass'].item() Z_div_Zsun = kwargs.get('metallicity', 1.) zams_table = {2.: 2.915e-01, 1.: 2.703e-01, diff --git a/posydon/popsyn/defaults.py b/posydon/popsyn/defaults.py index 1a7a850588..396db9667c 100644 --- a/posydon/popsyn/defaults.py +++ b/posydon/popsyn/defaults.py @@ -15,8 +15,6 @@ # Size of the population 'number_of_binaries': 100, - #Binary fraction - 'binary_fraction' : 1, 'metallicity' : 1.0, # in Zsolar # Star Formation History @@ -55,4 +53,8 @@ 'secondary_mass_scheme': 'flat_mass_ratio', 'secondary_mass_min': 0.35, 'secondary_mass_max': 120.0, + + #Binary fraction + 'binary_fraction_const' : 1, + 'binary_fraction_scheme' : 'const',#'Moe_17' } diff --git a/posydon/popsyn/independent_sample.py b/posydon/popsyn/independent_sample.py index d0a2a8c8c1..c6737f3edd 100644 --- a/posydon/popsyn/independent_sample.py +++ b/posydon/popsyn/independent_sample.py @@ -358,3 +358,53 @@ def generate_secondary_masses(primary_masses, secondary_masses = primary_masses return secondary_masses + +def binary_fraction_value(binary_fraction_const=1,binary_fraction_scheme = 'const',m1 = None,**kwargs): + """ + Getting the binary fraction depending on the scheme. The two possible option are a constant binary fraction + and a binary fraction based on the values given in Moe and Di Stefano (2017). + + Parameters: + -------------------- + binary scheme: string + Determines if the value of the binary fraction will be constant or not + binary fraction const: int + Gives the value the constant value of the binary if the constant scheme is choosen. + + Returns + ------------------ + binary fraction: int + + """ + binary_fraction_scheme_options = ['const','Moe_17'] + + # Input parameter checks + if binary_fraction_scheme not in binary_fraction_scheme_options: + raise ValueError("You must provide an allowed binary fraction scheme.") + + + if binary_fraction_scheme == 'const': + binary_fraction = binary_fraction_const + + elif binary_fraction_scheme == 'Moe_17': + if m1 is None: + raise ValueError("There was not a primary mass provided in the inputs. Unable to return a binary fraction") + elif m1 < 0.8: + raise ValueError("The scheme doesn't support values of m1 less than 0.8") + elif m1 <= 2 and m1 >= 0.8: + binary_fraction = 0.4 + elif m1 <= 5 and m1 > 2: + binary_fraction = 0.59 + elif m1<=9 and m1 > 5 : + binary_fraction = 0.76 + elif m1<= 16 and m1 > 9: + binary_fraction = 0.84 + elif m1 > 16: + binary_fraction = 0.94 + else: + raise ValueError(f'There primary mass provided {m1} is not supported by the Moe_17 scheme.') + else: + pass + return binary_fraction + + diff --git a/posydon/popsyn/population_params_default.ini b/posydon/popsyn/population_params_default.ini index d08f9f5fbe..7f070a8101 100644 --- a/posydon/popsyn/population_params_default.ini +++ b/posydon/popsyn/population_params_default.ini @@ -277,7 +277,9 @@ # `None` uses system entropy (recommended) number_of_binaries = 100 # int - binary_fraction = 1 + binary_fraction_scheme = 'const' + #'const' 'Moe_17' + binary_fraction_const = 1 # float 0< fraction <=1 star_formation = 'constant' # 'constant' 'burst' 'custom_linear' 'custom_log10' 'custom_linear_histogram' 'custom_log10_histogram' From 4eaac73490ab49187e8ab31656ef0f71827bd5d8 Mon Sep 17 00:00:00 2001 From: Simone Bavera <32518238+ssbvr@users.noreply.github.com> Date: Thu, 19 Oct 2023 16:45:38 +0200 Subject: [PATCH 130/319] Introducing POSYDON v2 documentation (#144) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * remove wrong code for kick in eccentric orbit * introducing step_CO-HeMS_RLO * spelling * minor change to catch collapsing stars states in step * add interp_class_CO_HeMS_RLO to binarystar.py * fix bug on characters lenght of step_names * PR88 defaul params ini change * minor changes * fix two small bugs * fix SN MODEL properties for nearest_neighbour interpolation * update TF2 and comibined TF12 * TMP debug edit to plot2D * add TF2 labels * debug * add new TF2 * debug code * remove debug option plot2d * add mt_history in post_processing.py * add mt_history column to be classified * move mt_history code in post_processing.py * debug post processing * debug * debug * add support for mt_history in pop synth * increase lenght mt_history oneline dataframe * synthetic population now identifies formation channels with contact and reverse MT * save ram in synthetic population pop synth evolution * update color palette * debug * introduce v2 documentation structure * introduce posydon v2 documentation structure * introduction section template * template for the overall structure * revert git file changes * revemove vscode settings * add api page * update documentation * update git ignore * update git ignore * upload notebooks * update docs * add step 1 tutorials * add two new tutorials * add webapp and database pages * add step 2 and 3 roadmap templates * add VHD page * add team picture * add pop params page * add component overview pages * add bib ref POSYDON * add new pages star/binaries in-depth docs * Documentation edits (#148) * numbering correction * Optional library installation code changed to include quotation marks. This makes sure the code instructions work on bash and zsh. * added available reference links * Add reference links to website + internal documentation * references added * typo in posydon website link * rewrote one item * update tutorials * Updating introduction, contact and user guide pages. (#149) * Adding members to the "Current members" section. * Typo fixed * Adding comment to improve the user experience * Adding latex format for scientific notation * Fixing typos * Change location of config to avoid module conflict * Updating contact information * Update reference to posydon.config * add missing reference to posydon.config * Adding comments to tutorial notebooks * Warning the user to follow tutorials in order * debug * add step 2 and 3 roadmap files * API restructure + small changes (#151) * DS_Store added to .gitignore * note on adding paths * numbering of tutorial titles * Add additional content to 1. tutorial * auto update copyright year to compile year * Restructured docs - Moved the docs into a _source folder - Altered makefile to account for new directory - Added the POSYDON logo + custom css to match the background - API construction using templates (sphinx-apidoc templates included) * resolved merge conflict * add step rerun and single HMS grids * Remove justify + posydon.config docs header (#152) * DS_Store added to .gitignore * note on adding paths * numbering of tutorial titles * Add additional content to 1. tutorial * auto update copyright year to compile year * Restructured docs - Moved the docs into a _source folder - Altered makefile to account for new directory - Added the POSYDON logo + custom css to match the background - API construction using templates (sphinx-apidoc templates included) * resolved merge conflict * remove jusity of section in css. This causes weird placement of whitespace in code function * remove unnecessary posydon.config header * typo fix * retain justify text for paragraphs * add full pipeline tutorial and single star processing tutorial * minor fixes * add plot 1D tutorial and started to add titles to notebooks so that they show up as a structured list in the tutorial and example pages * add plot 2D tutorial and add sections in all notebooks * Small TOC and cross-reference fixes (#154) * preserve default function values * double copy of index.rst * clean TOC structure of tutorials * clean up TOC structure of In-depth components overview * readd API reference * reference correct page for population parameters * cross-reference fixed and warning fixed * direct link to published work * allow for notebook depth * add pop synth debug tutorial * Matthias additions to the docs (#159) * add logo as png and let sphinx decide image type depending on builder * use png for html logo, too; correct typo * improvements on getting-started section * Matthias documentation additions (2) (#160) * add missing file [components-overview/mesa_grids/dynamic.rst] * check the fixed grid [fixed.rst] * reorder the In-Depth Components Overview to go from base up [index.rst] * correct citations in docstrings [step_CEE.py, step_detached.py, step_SN.py, common_functions.py] * Updating collabotation team info (#164) --------- Co-authored-by: Max <14039563+maxbriel@users.noreply.github.com> Co-authored-by: Jaime Román-Garza Co-authored-by: mkruckow <122798003+mkruckow@users.noreply.github.com> --- .gitignore | 7 +- docs/Makefile | 7 +- docs/PSyGrid/PSyGrid.rst | 479 ---- docs/PSyGrid/pngs/slurm_job.png | Bin 22429 -> 0 bytes docs/_source/_static/custom.css | 17 + .../posydon.active_learning.psy_cris.rst | 56 + ...ctive_learning.psy_cris.synthetic_data.rst | 25 + .../api_reference/posydon.active_learning.rst | 14 + .../api_reference/posydon.binary_evol.CE.rst | 17 + .../api_reference/posydon.binary_evol.DT.rst | 57 + 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PAPEROPT_a4 = -D latex_paper_size=a4 PAPEROPT_letter = -D latex_paper_size=letter -ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . +ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) $(SOURCEDIR) # the i18n builder cannot share the environment and doctrees with the others -I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . +I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) $(SOURCEDIR) .PHONY: help help: @@ -53,7 +54,7 @@ clean: rm -rf $(BUILDDIR)/* .PHONY: html -html: apidoc +html: $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." diff --git a/docs/PSyGrid/PSyGrid.rst b/docs/PSyGrid/PSyGrid.rst deleted file mode 100644 index 6b808319a5..0000000000 --- a/docs/PSyGrid/PSyGrid.rst +++ /dev/null @@ -1,479 +0,0 @@ -.. _PSyGrid: - -############################### -The PSyGrid Object -############################### - -The PSyGrid class is the "bridge" connecting MESA and POSYDON. It: - -- encapsulates the data from a MESA grid in a compact form -- saves the data in an HDF5 file -- allows the user to access the data in a memory-efficient way - -To use PSyGrid import it using: - -.. code-block:: python - - from posydon.grids.psygrid import PSyGrid - - -Creating a PSyGrid object -========================= - -A PSyGrid HDF5 file can be created by using the ``create()`` method on a newly -constructed PSyGrid object. - -Basic example -------------- -The simplest method is to provide the paths of the folder of the MESA runs -output, and the HDF5 file to be created. - -.. code-block:: python - - grid = PSyGrid() - grid.create(MESA_runs_directory, psygrid_hdf5_path) - -Now, the PSyGrid object is ready to be used for accessing and plotting data. - -Output options --------------- -Use the option ``verbose`` to see more details while the PSyGrid object is -being built. - -Set ``overwrite`` to ``True`` if the HDF5 file already exists. - -By default, any warnings produced are collected and shown at the end -(``warn="end"``) of the process. Use ``warn="normal"`` to see the warning -when they occur, or ``warn="suppress"`` to hide them completely. - -.. code-block:: python - - grid = PSyGrid() - grid.create(MESA_runs_directory, psygrid_hdf5_path, - verbose=True, overwrite=True, warn="suppress") - - -Setting the properties of the grid ----------------------------------- -PSyGrid provides a compact form of the initial MESA grid. To define how many runs should be included (e.g. for testing purposes), -and which data columns are needed, use the following code block as a guide. - -.. code-block:: python - - DEFAULT_BINARY_HISTORY_COLS = ['star_1_mass', 'star_2_mass', 'period_days', 'binary_separation', 'age', 'rl_relative_overflow_1', 'rl_relative_overflow_2', 'lg_mtransfer_rate'] - DEFAULT_STAR_HISTORY_COLS = ['star_age', 'star_mass', 'he_core_mass', 'c_core_mass', 'center_h1', 'center_he4', 'c12_c12', 'center_c12', 'log_LH', 'log_LHe', 'log_LZ', 'log_Lnuc', 'surface_h1', 'log_Teff', 'log_L'] - DEFAULT_PROFILE_COLS = ['radius', 'mass', 'logRho', 'omega'] - - GRIDPROPERTIES = { - # file loading parameters - "description": "", # description text - "max_number_of_runs": None, - "format": "hdf5", - # resampling parameters - "n_downsample_history": None, # False/None = no hist. resampling - "n_downsample_profile": None, # False/None = no prof. resampling - "history_resample_columns": DEFAULT_HISTORY_DOWNSAMPLE_COLS, - "profile_resample_columns": DEFAULT_PROFILE_DOWNSAMPLE_COLS, - # columns to pass to the grid - "star1_history_saved_columns": DEFAULT_STAR_HISTORY_COLS, - "star2_history_saved_columns": DEFAULT_STAR_HISTORY_COLS, - "binary_history_saved_columns": DEFAULT_BINARY_HISTORY_COLS, - "star1_profile_saved_columns": DEFAULT_PROFILE_COLS, - "star2_profile_saved_columns": DEFAULT_PROFILE_COLS, - # Initial/Final value arrays - "initial_value_columns": None, - "final_value_columns": None, - } - - grid = PSyGrid() - grid.create(MESA_runs_directory, psygrid_path, **GRIDPROPERTIES) - - -Loading an existing PSyGrid object -================================== - -To load a PSyGrid object from an HDF5 file you only need its path: - -.. code-block:: python - - grid = PSyGrid('/home/mydata/my_MESA_grid') - -Closing/deleting a PSyGrid object -================================== -As with files in Python, it is preferable to "close" PSyGrid objects after -handling them. - -The ``.close()`` method closes the HDF5 file associated to the PSyGrid object. -However, the object continues to exist and hold all the metadata. - -.. code-block:: python - - grid.close() - -In the case that a PSyGrid object is not needed anymore, -use the ``del`` keyword. Note that this also closes the HDF5 file. - -.. code-block:: python - - del grid - - - -Grid engineering (advanced use case scenario) -============================================= - -When confronted with a large parameter space to cover, we advise that the -parameter space be split into multiple directories for separate grid slices, -composed of a few thousand runs each. These separate grids can be combined -together afterwards. - -Here we preset an advanced use of the ``PSyGrid`` object meant to be used on a -HPC facility with Slurm. Our experience is that grid manipulation is most -easily performed with a Jupyter notebook using Slurm magic commands which can -be installed with - -.. code-block:: python - - !pip install git+https://github.com/NERSC/slurm-magic.git - -directly from the Jupyter notebook. - -Here we illustrate how to post process an example HMS-HMS grid composed of -three slices at fixed mass ratios of 0.50, 0.70, and 0.90. - -Creating grid slices --------------------- - -The following script allows to post process the raw MESA data into a ``PSyGrid`` -object either in its ``ORIGINAL`` form (without any downsampling) or to use -the ``LITE`` downsampling presented in Fragos et al. (2022). - -.. code-block:: python - - %%writefile create_individual_psygrid_files.py - import os - import sys - import numpy as np - from posydon.grids.psygrid import (PSyGrid, DEFAULT_HISTORY_DS_EXCLUDE, - DEFAULT_PROFILE_DS_EXCLUDE, - EXTRA_COLS_DS_EXCLUDE) - - if __name__ == "__main__": - - # directory with MESA data - path = '/working_dir/' - - # MESA grid slices to post process - grid_names = ['grid_q_0.50','grid_q_0.70','grid_q_0.90'] - - # choose grid slice - i = int(sys.argv[1]) - print('Job array index:',i) - grid_name = grid_names[i] - - # choose the compression - grid_type = str(sys.argv[2]) - if grid_type == 'ORIGINAL': - history_DS_error = None - profile_DS_error = None - history_DS_exclude = DEFAULT_HISTORY_DS_EXCLUDE - profile_DS_exclude = DEFAULT_PROFILE_DS_EXCLUDE - elif grid_type == 'LITE': - history_DS_error = 0.1 - profile_DS_error = 0.1 - history_DS_exclude = EXTRA_COLS_DS_EXCLUDE - profile_DS_exclude = EXTRA_COLS_DS_EXCLUDE - else: - raise ValueError('grid_type = %s not supported!'%grid_type) - - print('Creating psygrid for',grid_name, '...') - grid = PSyGrid(verbose=True) - grid.create(path+"%s"%grid_name, - "./"+grid_type+"/%s.h5"%grid_name, - overwrite=True, history_DS_error=history_DS_error, - profile_DS_error=profile_DS_error, - history_DS_exclude=history_DS_exclude, - profile_DS_exclude=profile_DS_exclude, - compression="gzip9", start_at_RLO=False, - ) - -The script above creates a script with the name -``create_individual_psygrid_files.py`` which we can run with the following -Slurm magic command. -IMPORTANT: we will run the script with both options compressions options -LITE and ORIGINAL. The uncompressed version of the grid will be used in a later -step -Before running the script we need to create a ``logs`` directory where we will -store Slurm output messages, and two directories where we will store the two -different ``PSyGrid`` object outputs, i.e. ``ORIGINAL`` and ``LITE``. -Once the post processing of the data is complete, we will be notified by email -from Slurm. - - -.. code-block:: python - - %%sbatch - #!/bin/bash - #SBATCH --mail-type=ALL - #SBATCH --mail-user=my_email - #SBATCH --account=b1119 - #SBATCH --partition=posydon-priority - #SBATCH --array=0-2 - #SBATCH --ntasks-per-node 1 - #SBATCH --mem-per-cpu=8G - #SBATCH --time=24:00:00 - #SBATCH --job-name="psygrid" - #SBATCH --output=/working_dir/grid_%a.out - - srun python /working_dir/create_individual_psygrid_files.py $SLURM_ARRAY_TASK_ID LITE - -The above code cell will return the Slurm job id which we can use to verify that -our script is working correctly. In this specific example each grid slice is -composed by 2800 MESA HMS-HMS simulations. The post processing -will take roughly one hour per grid slice. - -.. code-block:: python - - %squeue -j 3761056 - -.. image:: pngs/slurm_job.png - -Rerun subsample of grid slices ------------------------------- - -Because of convergence issues, a non-negligible fraction of the MESA -simulations did not converge. The grid architect would now like to rerun the -subsample of the grid with convergence problem changing one or more MESA inlist -flags. This can be easily done with the ``.rerun()`` method. - -.. code-block:: python - - grid = PSyGrid('/working_dir/LITE/grid_0.70.h5') - grid.rerun('/working_dir/grid_0.70_rerun/', - termination_flags=['min_timestep_limit','reach cluster timelimit'], - new_mesa_flag={'opacity_max':0.5}) - grid.close() - -The above script loads the LITE version of the ``PSyGrid`` object -``grid_q_0.70.h5``, generates a new ``grid.csv`` file containing the initial -points runs with given ``termination_flags`` (see TF1 of plot2D documentation) -and additional columns corresponding to the new MESA flags specified in the -dictionary ``new_mesa_flag``, e.g. limiting the opacity maximal value of a star. - -Alternatively, the grid architect might care to select a subsample of the MESA -runs with some user defined logic, e.g. to generate a patch of the grid which -addresses a change that affect a portion of the parameter space to rerun with a -new MESA inlist commit (see running MESA documentation). This can be specified -with the ``runs_to_rerun`` option of the ``.rerun`` method which expects a list -of indices of tracks to rerun. - - -Combine grid slices -------------------- - -Let assume that we now have the three original grid slices -(``grid_q_0.50.h5,grid_q_0.70.h5,grid_q_0.90.h5``), -three additional reruns of them addressing convergence issues -(``grid_q_0.50_rerun.h5,grid_q_0.70_rerun.h5,grid_q_0.90_rerun.h5``), -and a grid slice extension which cover wider larger orbital periods not covered -in the main grids (``grid_p_extension.h5``) which we want to combine in a -single file. - -The function ``join_grids`` does exactly this for us taking care of replacing -older runs with newer one. The grid layering follows the list order, -namely the last grid will be layered last. -Here is how we can combine the grid slices - -.. code-block:: python - - %%writefile combine_grid_slices.py - import os - import sys - import numpy as np - from posydon.grids.psygrid import PSyGrid, join_grids - - if __name__ == "__main__": - - # choose the compression - grid_type = str(sys.argv[1]) - - path = '/working_dir/'+grid_type+'/' - - # grid slices to combine - grid_names = ['grid_q_0.50.h5','grid_q_0.70.h5','grid_q_0.90.h5', - 'grid_q_0.50_rerun.h5','grid_q_0.70_rerun.h5', - 'grid_q_0.90_rerun.h5','grid_p_extension.h5' - ] - grid_paths = [path+name for name in grid_names] - - print('Combining the grids:') - print(grid_paths) - print('') - join_grids(grid_paths,path+'grid_combined.h5') - print('DONE!') - -The above script is run for both grid compressions ``LITE`` and ``ORIGINAL`` -with the following cell. - -.. code-block:: python - - %%sbatch - #!/bin/bash - #SBATCH --mail-type=ALL - #SBATCH --mail-user=my_email - #SBATCH --account=b1119 - #SBATCH --partition=posydon-priority - #SBATCH --ntasks-per-node 1 - #SBATCH --mem-per-cpu=8G - #SBATCH --time=24:00:00 - #SBATCH --job-name="psygrid" - #SBATCH --output=/working_dir/logs/combine_grid_slices.out - - srun python /working_dir/combine_grid_slices.py LITE - - -Add post process quantities ---------------------------- - -There is a list of quantities that ``POSYDON`` requires to be precomputed -on the original grids. These quantities include, e.g., core-collapse -and common envelope properties. - -.. code-block:: python - - %%writefile post_process_grid.py - import os - import sys - from shutil import copyfile - import numpy as np - import pickle - from posydon.grids.psygrid import PSyGrid - from posydon.grids.post_processing import post_process_grid, add_post_processed_quantities - - - if __name__ == "__main__": - - path = '/working_dir/' - - # chose the grid given the job_array index - grid_name = 'grid_combined.h5' - grid_name_processed = 'grid_combined_processed.h5' - columns_name = 'post_processed_EXTRA_COLUMNS.pkl' - dirs_name = 'post_processed_MESA_dirs.txt' - - # copy file, it will be overwritten when we add columns - if os.path.exists(path+'ORIGINAL/'+grid_name_processed): - print('Post processed grid file alredy exist, removing it...') - os.remove(path+'ORIGINAL/'+grid_name_processed) - copyfile(path+'ORIGINAL/'+grid_name, path+'ORIGINAL/'+grid_name_processed) - - grid_ORIGINAL = PSyGrid(path+'ORIGINAL/'+grid_name_processed) - MESA_dirs_EXTRA_COLUMNS, EXTRA_COLUMNS = post_process_grid(grid_ORIGINAL, - index=None, star_2_CO=False, verbose=False) - - # save processed quantities - if os.path.exists(path+'ORIGINAL/'+columns_name): - print('EXTRA COLUMNS file alredy exist, removing it...') - os.remove(path+'ORIGINAL/'+columns_name) - with open(path+'ORIGINAL/'+columns_name, 'wb') as handle: - pickle.dump(EXTRA_COLUMNS, handle, protocol=pickle.HIGHEST_PROTOCOL) - if os.path.exists(path+'ORIGINAL/'+dirs_name): - print('MESA dirs file alredy exist, removing it...') - os.remove(path+'ORIGINAL/'+dirs_name) - with open(path+'ORIGINAL/'+dirs_name, 'w') as filehandle: - for listitem in MESA_dirs_EXTRA_COLUMNS: - filehandle.write('%s\n'%listitem) - - print('Add post porcessed columns to ORIGIN grid...') - add_post_processed_quantities(grid_ORIGINAL, MESA_dirs_EXTRA_COLUMNS, - EXTRA_COLUMNS, verbose=False) - grid_ORIGINAL.close() - - # copy file, it will be overwritten when we add columns - if os.path.exists(path+'LITE/'+grid_name_processed): - print('Post processed grid file alredy exist, removing it...') - os.remove(path+'LITE/'+grid_name_processed) - copyfile(path+'LITE/'+grid_name, path+'LITE/'+grid_name_processed) - - grid_LITE = PSyGrid(path+'LITE/'+grid_name_processed) - print('Add post porcessed columns to LITE grid...') - add_post_processed_quantities(grid_LITE, MESA_dirs_EXTRA_COLUMNS, - EXTRA_COLUMNS, verbose=False) - grid_LITE.close() - print('Done!') - -The script can be run with the following Slurm magic commad. - -.. code-block:: python - - %%sbatch - #!/bin/bash - #SBATCH --mail-type=ALL - #SBATCH --mail-user=my_email - #SBATCH --account=b1119 - #SBATCH --partition=posydon-priority - #SBATCH --ntasks-per-node 1 - #SBATCH --mem-per-cpu=8G - #SBATCH --time=24:00:00 - #SBATCH --job-name="psygrid" - #SBATCH --output=/working_dir/logs/post_process_grid.out - - export PATH_TO_POSYDON=/add_your_path/ - srun python /working_dir/post_process_grid.py - - -Splitting the grid into chunks ------------------------------- - -Some data sharing services limit the file size. E.g. ``git-lfs`` limits -the maximum file size to be around 2 GB. The following script splits the -post processed grid into chunks of files of 2 GB size. - -.. code-block:: python - - %%writefile split_grid.py - import os - import numpy as np - from posydon.grids.psygrid import PSyGrid, join_grids - - if __name__ == "__main__": - - path = '/working_dir/' - - git_lfs_dir = 'git-lfs_data_format' - - files = os.listdir(path+git_lfs_dir) - if files: - print('Remove old grid in git_lfs_dir ...') - for file in files: - if '.h5' in file: - os.remove(path+git_lfs_dir+file) - - grid_names = [path+'LITE/grid_combined_processed.h5'] - print('') - join_grids(grid_names,path+git_lfs_dir+'/grid_%d.h5') - print('DONE!') - -The above script can be run with the following Slurm magic command. - -.. code-block:: python - - %%sbatch - #!/bin/bash - #SBATCH --mail-type=ALL - #SBATCH --mail-user=my_email - #SBATCH --account=b1119 - #SBATCH --partition=posydon-priority - #SBATCH --ntasks-per-node 1 - #SBATCH --mem-per-cpu=8G - #SBATCH --time=24:00:00 - #SBATCH 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+ + +.. toctree:: + :maxdepth: 2 + :titlesonly: + + posydon.active_learning.psy_cris.synthetic_data + + + +posydon.active\_learning.psy\_cris.classify +------------------------------------------- + +.. automodule:: posydon.active_learning.psy_cris.classify + :members: + :undoc-members: + :show-inheritance: + +posydon.active\_learning.psy\_cris.data +--------------------------------------- + +.. automodule:: posydon.active_learning.psy_cris.data + :members: + :undoc-members: + :show-inheritance: + +posydon.active\_learning.psy\_cris.regress +------------------------------------------ + +.. automodule:: posydon.active_learning.psy_cris.regress + :members: + :undoc-members: + :show-inheritance: + +posydon.active\_learning.psy\_cris.sample +----------------------------------------- + +.. automodule:: posydon.active_learning.psy_cris.sample + :members: + :undoc-members: + :show-inheritance: + +posydon.active\_learning.psy\_cris.utils +---------------------------------------- + +.. automodule:: posydon.active_learning.psy_cris.utils + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_source/api_reference/posydon.active_learning.psy_cris.synthetic_data.rst b/docs/_source/api_reference/posydon.active_learning.psy_cris.synthetic_data.rst new file mode 100644 index 0000000000..6286a3ba22 --- /dev/null +++ b/docs/_source/api_reference/posydon.active_learning.psy_cris.synthetic_data.rst @@ -0,0 +1,25 @@ +posydon.active\_learning.psy\_cris.synthetic\_data +================================================== + +.. automodule:: posydon.active_learning.psy_cris.synthetic_data + :members: + :undoc-members: + :show-inheritance: + + + +posydon.active\_learning.psy\_cris.synthetic\_data.synth\_data\_2D +------------------------------------------------------------------ + +.. automodule:: posydon.active_learning.psy_cris.synthetic_data.synth_data_2D + :members: + :undoc-members: + :show-inheritance: + +posydon.active\_learning.psy\_cris.synthetic\_data.synth\_data\_3D +------------------------------------------------------------------ + +.. automodule:: posydon.active_learning.psy_cris.synthetic_data.synth_data_3D + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_source/api_reference/posydon.active_learning.rst b/docs/_source/api_reference/posydon.active_learning.rst new file mode 100644 index 0000000000..2ba80b47c6 --- /dev/null +++ b/docs/_source/api_reference/posydon.active_learning.rst @@ -0,0 +1,14 @@ +posydon.active\_learning +======================== + +.. automodule:: posydon.active_learning + :members: + :undoc-members: + :show-inheritance: + + +.. toctree:: + :maxdepth: 2 + :titlesonly: + + posydon.active_learning.psy_cris diff --git a/docs/_source/api_reference/posydon.binary_evol.CE.rst b/docs/_source/api_reference/posydon.binary_evol.CE.rst new file mode 100644 index 0000000000..a42181bd63 --- /dev/null +++ b/docs/_source/api_reference/posydon.binary_evol.CE.rst @@ -0,0 +1,17 @@ +posydon.binary\_evol.CE +======================= + +.. automodule:: posydon.binary_evol.CE + :members: + :undoc-members: + :show-inheritance: + + + +posydon.binary\_evol.CE.step\_CEE +--------------------------------- + +.. automodule:: posydon.binary_evol.CE.step_CEE + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_source/api_reference/posydon.binary_evol.DT.rst b/docs/_source/api_reference/posydon.binary_evol.DT.rst new file mode 100644 index 0000000000..5cb520682c --- /dev/null +++ b/docs/_source/api_reference/posydon.binary_evol.DT.rst @@ -0,0 +1,57 @@ +posydon.binary\_evol.DT +======================= + +.. automodule:: posydon.binary_evol.DT + :members: + :undoc-members: + :show-inheritance: + + + +posydon.binary\_evol.DT.double\_CO +---------------------------------- + +.. automodule:: posydon.binary_evol.DT.double_CO + :members: + :undoc-members: + :show-inheritance: + +posydon.binary\_evol.DT.step\_detached +-------------------------------------- + +.. automodule:: posydon.binary_evol.DT.step_detached + :members: + :undoc-members: + :show-inheritance: + +posydon.binary\_evol.DT.step\_disrupted +--------------------------------------- + +.. automodule:: posydon.binary_evol.DT.step_disrupted + :members: + :undoc-members: + :show-inheritance: + +posydon.binary\_evol.DT.step\_initially\_single +----------------------------------------------- + +.. automodule:: posydon.binary_evol.DT.step_initially_single + :members: + :undoc-members: + :show-inheritance: + +posydon.binary\_evol.DT.step\_isolated +-------------------------------------- + +.. automodule:: posydon.binary_evol.DT.step_isolated + :members: + :undoc-members: + :show-inheritance: + +posydon.binary\_evol.DT.step\_merged +------------------------------------ + +.. automodule:: posydon.binary_evol.DT.step_merged + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_source/api_reference/posydon.binary_evol.MESA.rst b/docs/_source/api_reference/posydon.binary_evol.MESA.rst new file mode 100644 index 0000000000..d05d7b45b2 --- /dev/null +++ b/docs/_source/api_reference/posydon.binary_evol.MESA.rst @@ -0,0 +1,17 @@ +posydon.binary\_evol.MESA +========================= + +.. automodule:: posydon.binary_evol.MESA + :members: + :undoc-members: + :show-inheritance: + + + +posydon.binary\_evol.MESA.step\_mesa +------------------------------------ + +.. automodule:: posydon.binary_evol.MESA.step_mesa + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_source/api_reference/posydon.binary_evol.SN.rst b/docs/_source/api_reference/posydon.binary_evol.SN.rst new file mode 100644 index 0000000000..56258eb8e6 --- /dev/null +++ b/docs/_source/api_reference/posydon.binary_evol.SN.rst @@ -0,0 +1,25 @@ +posydon.binary\_evol.SN +======================= + +.. automodule:: posydon.binary_evol.SN + :members: + :undoc-members: + :show-inheritance: + + + +posydon.binary\_evol.SN.profile\_collapse +----------------------------------------- + +.. automodule:: posydon.binary_evol.SN.profile_collapse + :members: + :undoc-members: + :show-inheritance: + +posydon.binary\_evol.SN.step\_SN +-------------------------------- + +.. automodule:: posydon.binary_evol.SN.step_SN + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_source/api_reference/posydon.binary_evol.rst b/docs/_source/api_reference/posydon.binary_evol.rst new file mode 100644 index 0000000000..14984acac5 --- /dev/null +++ b/docs/_source/api_reference/posydon.binary_evol.rst @@ -0,0 +1,59 @@ +posydon.binary\_evol +==================== + +.. automodule:: posydon.binary_evol + :members: + :undoc-members: + :show-inheritance: + + +.. toctree:: + :maxdepth: 2 + :titlesonly: + + posydon.binary_evol.CE + posydon.binary_evol.DT + posydon.binary_evol.MESA + posydon.binary_evol.SN + + + +posydon.binary\_evol.binarystar +------------------------------- + +.. automodule:: posydon.binary_evol.binarystar + :members: + :undoc-members: + :show-inheritance: + +posydon.binary\_evol.flow\_chart +-------------------------------- + +.. automodule:: posydon.binary_evol.flow_chart + :members: + :undoc-members: + :show-inheritance: + +posydon.binary\_evol.simulationproperties +----------------------------------------- + +.. automodule:: posydon.binary_evol.simulationproperties + :members: + :undoc-members: + :show-inheritance: + +posydon.binary\_evol.singlestar +------------------------------- + +.. automodule:: posydon.binary_evol.singlestar + :members: + :undoc-members: + :show-inheritance: + +posydon.binary\_evol.step\_end +------------------------------ + +.. automodule:: posydon.binary_evol.step_end + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_source/api_reference/posydon.grids.rst b/docs/_source/api_reference/posydon.grids.rst new file mode 100644 index 0000000000..1b5ca9cbf3 --- /dev/null +++ b/docs/_source/api_reference/posydon.grids.rst @@ -0,0 +1,73 @@ +posydon.grids +============= + +.. automodule:: posydon.grids + :members: + :undoc-members: + :show-inheritance: + + + +posydon.grids.MODELS +-------------------- + +.. automodule:: posydon.grids.MODELS + :members: + :undoc-members: + :show-inheritance: + +posydon.grids.downsampling +-------------------------- + +.. automodule:: posydon.grids.downsampling + :members: + :undoc-members: + :show-inheritance: + +posydon.grids.downsampling\_report +---------------------------------- + +.. automodule:: posydon.grids.downsampling_report + :members: + :undoc-members: + :show-inheritance: + +posydon.grids.io +---------------- + +.. automodule:: posydon.grids.io + :members: + :undoc-members: + :show-inheritance: + +posydon.grids.post\_processing +------------------------------ + +.. automodule:: posydon.grids.post_processing + :members: + :undoc-members: + :show-inheritance: + +posydon.grids.psygrid +--------------------- + +.. automodule:: posydon.grids.psygrid + :members: + :undoc-members: + :show-inheritance: + +posydon.grids.scrubbing +----------------------- + +.. automodule:: posydon.grids.scrubbing + :members: + :undoc-members: + :show-inheritance: + +posydon.grids.termination\_flags +-------------------------------- + +.. automodule:: posydon.grids.termination_flags + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_source/api_reference/posydon.interpolation.rst b/docs/_source/api_reference/posydon.interpolation.rst new file mode 100644 index 0000000000..1f34dda88d --- /dev/null +++ b/docs/_source/api_reference/posydon.interpolation.rst @@ -0,0 +1,57 @@ +posydon.interpolation +===================== + +.. automodule:: posydon.interpolation + :members: + :undoc-members: + :show-inheritance: + + + +posydon.interpolation.IF\_interpolation +--------------------------------------- + +.. automodule:: posydon.interpolation.IF_interpolation + :members: + :undoc-members: + :show-inheritance: + +posydon.interpolation.constraints +--------------------------------- + +.. automodule:: posydon.interpolation.constraints + :members: + :undoc-members: + :show-inheritance: + +posydon.interpolation.data\_scaling +----------------------------------- + +.. automodule:: posydon.interpolation.data_scaling + :members: + :undoc-members: + :show-inheritance: + +posydon.interpolation.eep +------------------------- + +.. automodule:: posydon.interpolation.eep + :members: + :undoc-members: + :show-inheritance: + +posydon.interpolation.interpolation +----------------------------------- + +.. automodule:: posydon.interpolation.interpolation + :members: + :undoc-members: + :show-inheritance: + +posydon.interpolation.profile\_interpolation +-------------------------------------------- + +.. automodule:: posydon.interpolation.profile_interpolation + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_source/api_reference/posydon.popsyn.rst b/docs/_source/api_reference/posydon.popsyn.rst new file mode 100644 index 0000000000..0e0b49a610 --- /dev/null +++ b/docs/_source/api_reference/posydon.popsyn.rst @@ -0,0 +1,97 @@ +posydon.popsyn +============== + +.. automodule:: posydon.popsyn + :members: + :undoc-members: + :show-inheritance: + + + +posydon.popsyn.GRB +------------------ + +.. automodule:: posydon.popsyn.GRB + :members: + :undoc-members: + :show-inheritance: + +posydon.popsyn.analysis +----------------------- + +.. automodule:: posydon.popsyn.analysis + :members: + :undoc-members: + :show-inheritance: + +posydon.popsyn.binarypopulation +------------------------------- + +.. automodule:: posydon.popsyn.binarypopulation + :members: + :undoc-members: + :show-inheritance: + +posydon.popsyn.defaults +----------------------- + +.. automodule:: posydon.popsyn.defaults + :members: + :undoc-members: + :show-inheritance: + +posydon.popsyn.independent\_sample +---------------------------------- + +.. automodule:: posydon.popsyn.independent_sample + :members: + :undoc-members: + :show-inheritance: + +posydon.popsyn.io +----------------- + +.. automodule:: posydon.popsyn.io + :members: + :undoc-members: + :show-inheritance: + +posydon.popsyn.normalized\_pop\_mass +------------------------------------ + +.. automodule:: posydon.popsyn.normalized_pop_mass + :members: + :undoc-members: + :show-inheritance: + +posydon.popsyn.rate\_calculation +-------------------------------- + +.. automodule:: posydon.popsyn.rate_calculation + :members: + :undoc-members: + :show-inheritance: + +posydon.popsyn.selection\_effects +--------------------------------- + +.. automodule:: posydon.popsyn.selection_effects + :members: + :undoc-members: + :show-inheritance: + +posydon.popsyn.star\_formation\_history +--------------------------------------- + +.. automodule:: posydon.popsyn.star_formation_history + :members: + :undoc-members: + :show-inheritance: + +posydon.popsyn.synthetic\_population +------------------------------------ + +.. automodule:: posydon.popsyn.synthetic_population + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_source/api_reference/posydon.rst b/docs/_source/api_reference/posydon.rst new file mode 100644 index 0000000000..0e06dbddb1 --- /dev/null +++ b/docs/_source/api_reference/posydon.rst @@ -0,0 +1,14 @@ +POSYDON +======= + +.. toctree:: + :maxdepth: 2 + :titlesonly: + + posydon.active_learning + posydon.binary_evol + posydon.grids + posydon.interpolation + posydon.popsyn + posydon.utils + posydon.visualization \ No newline at end of file diff --git a/docs/_source/api_reference/posydon.utils.rst b/docs/_source/api_reference/posydon.utils.rst new file mode 100644 index 0000000000..2f720929b5 --- /dev/null +++ b/docs/_source/api_reference/posydon.utils.rst @@ -0,0 +1,49 @@ +posydon.utils +============= + +.. automodule:: posydon.utils + :members: + :undoc-members: + :show-inheritance: + + + +posydon.utils.common\_functions +------------------------------- + +.. automodule:: posydon.utils.common_functions + :members: + :undoc-members: + :show-inheritance: + +posydon.utils.configfile +------------------------ + +.. automodule:: posydon.utils.configfile + :members: + :undoc-members: + :show-inheritance: + +posydon.utils.constants +----------------------- + +.. automodule:: posydon.utils.constants + :members: + :undoc-members: + :show-inheritance: + +posydon.utils.data\_download +---------------------------- + +.. automodule:: posydon.utils.data_download + :members: + :undoc-members: + :show-inheritance: + +posydon.utils.gridutils +----------------------- + +.. automodule:: posydon.utils.gridutils + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_source/api_reference/posydon.visualization.VH_diagram.rst b/docs/_source/api_reference/posydon.visualization.VH_diagram.rst new file mode 100644 index 0000000000..7084ed923b --- /dev/null +++ b/docs/_source/api_reference/posydon.visualization.VH_diagram.rst @@ -0,0 +1,81 @@ +posydon.visualization.VH\_diagram +================================= + +.. automodule:: posydon.visualization.VH_diagram + :members: + :undoc-members: + :show-inheritance: + + + +posydon.visualization.VH\_diagram.GraphVisualizer +------------------------------------------------- + +.. automodule:: posydon.visualization.VH_diagram.GraphVisualizer + :members: + :undoc-members: + :show-inheritance: + +posydon.visualization.VH\_diagram.MainWindow +-------------------------------------------- + +.. automodule:: posydon.visualization.VH_diagram.MainWindow + :members: + :undoc-members: + :show-inheritance: + +posydon.visualization.VH\_diagram.MathTextLabel +----------------------------------------------- + +.. automodule:: posydon.visualization.VH_diagram.MathTextLabel + :members: + :undoc-members: + :show-inheritance: + +posydon.visualization.VH\_diagram.OptionsWindow +----------------------------------------------- + +.. automodule:: posydon.visualization.VH_diagram.OptionsWindow + :members: + :undoc-members: + :show-inheritance: + +posydon.visualization.VH\_diagram.ParseDataFrame +------------------------------------------------ + +.. automodule:: posydon.visualization.VH_diagram.ParseDataFrame + :members: + :undoc-members: + :show-inheritance: + +posydon.visualization.VH\_diagram.Presenter +------------------------------------------- + +.. automodule:: posydon.visualization.VH_diagram.Presenter + :members: + :undoc-members: + :show-inheritance: + +posydon.visualization.VH\_diagram.PresenterMode +----------------------------------------------- + +.. automodule:: posydon.visualization.VH_diagram.PresenterMode + :members: + :undoc-members: + :show-inheritance: + +posydon.visualization.VH\_diagram.PresenterMultiple +--------------------------------------------------- + +.. automodule:: posydon.visualization.VH_diagram.PresenterMultiple + :members: + :undoc-members: + :show-inheritance: + +posydon.visualization.VH\_diagram.SimulationModel +------------------------------------------------- + +.. automodule:: posydon.visualization.VH_diagram.SimulationModel + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_source/api_reference/posydon.visualization.rst b/docs/_source/api_reference/posydon.visualization.rst new file mode 100644 index 0000000000..10aae7df82 --- /dev/null +++ b/docs/_source/api_reference/posydon.visualization.rst @@ -0,0 +1,72 @@ +posydon.visualization +===================== + +.. automodule:: posydon.visualization + :members: + :undoc-members: + :show-inheritance: + + +.. toctree:: + :maxdepth: 2 + :titlesonly: + + posydon.visualization.VH_diagram + + + +posydon.visualization.VHdiagram +------------------------------- + +.. automodule:: posydon.visualization.VHdiagram + :members: + :undoc-members: + :show-inheritance: + +posydon.visualization.combine\_TF +--------------------------------- + +.. automodule:: posydon.visualization.combine_TF + :members: + :undoc-members: + :show-inheritance: + +posydon.visualization.interpolation +----------------------------------- + +.. automodule:: posydon.visualization.interpolation + :members: + :undoc-members: + :show-inheritance: + +posydon.visualization.plot1D +---------------------------- + +.. automodule:: posydon.visualization.plot1D + :members: + :undoc-members: + :show-inheritance: + +posydon.visualization.plot2D +---------------------------- + +.. automodule:: posydon.visualization.plot2D + :members: + :undoc-members: + :show-inheritance: + +posydon.visualization.plot\_defaults +------------------------------------ + +.. automodule:: posydon.visualization.plot_defaults + :members: + :undoc-members: + :show-inheritance: + +posydon.visualization.plot\_pop +------------------------------- + +.. automodule:: posydon.visualization.plot_pop + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_source/components-overview/machine-learning-components.rst b/docs/_source/components-overview/machine-learning-components.rst new file mode 100644 index 0000000000..8c7b5925bf --- /dev/null +++ b/docs/_source/components-overview/machine-learning-components.rst @@ -0,0 +1,32 @@ +.. _machine-learning: + +Machine Learning Components +--------------------------- + + + +Intial Final Classification & Interpolation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +.. toctree:: + :maxdepth: 2 + + machine_learning/initial_final_interp + + + +Profile Interpolation +~~~~~~~~~~~~~~~~~~~~~ + +.. warning:: + + This feature is experimental and may not be suited for scientific use. If you want to us this feature, please install the relevant dependencies with `pip install -e .[ml]`. + +POSYDON support an experimental feature that allows the user to interpolate the final MESA profile of a supported binary MESA grid. + + +.. toctree:: + :maxdepth: 2 + + machine_learning/ProfileInterpolator \ No newline at end of file diff --git a/docs/interpolation/Profile-Interpolator/ProfileInterpolator.rst b/docs/_source/components-overview/machine_learning/ProfileInterpolator.rst similarity index 50% rename from docs/interpolation/Profile-Interpolator/ProfileInterpolator.rst rename to docs/_source/components-overview/machine_learning/ProfileInterpolator.rst index 1772bef48d..c2322a85b0 100644 --- a/docs/interpolation/Profile-Interpolator/ProfileInterpolator.rst +++ b/docs/_source/components-overview/machine_learning/ProfileInterpolator.rst @@ -1,15 +1,12 @@ - -.. _ProfileInterpolator: +.. _profile-interpolation: ########################### Profile Interpolation ########################### +We demonstrate the profile interpolator which delivers predictions on internal stellar structure at the end of MESA evolution. -We demonstrate the profile interpolator which delivers predictions -on internal stellar structure at the end of MESA evolution. -To use the profile interpolator we first import the ``ProfileInterpolator`` -and ``CompileData`` objects from the POSYDON library. +To use the profile interpolator we first import the ``ProfileInterpolator`` and ``CompileData`` objects from the POSYDON library. .. code-block:: python @@ -17,18 +14,9 @@ and ``CompileData`` objects from the POSYDON library. Extracting Profile Data from .h5 Files for Training Interpolators -=============================== - -To extract profile data from existing grids, we pass arguments -``train_path`` and ``test_path`` pointing to .h5 files -containing instances of the ``PSyGrid`` class from which the -training and testing grids can be loaded. These can be constructed -by running one's own simulations or by loading a precomputed simulation -stored in the data directory of the POSYDON repository. -The ``profile_names`` argument denotes which profiles will be extracted -from the grid and saved to ``filename`` using the function ``save``. -The Boolean argument ``hms_s2`` should be set to True only to extract star 2 -profiles from the HMS-HMS grid. +=================================================================== + +To extract profile data from existing grids, we pass arguments ``train_path`` and ``test_path`` pointing to .h5 files containing instances of the ``PSyGrid`` class from which the training and testing grids can be loaded. These can be constructed by running one's own simulations or by loading a precomputed simulation stored in the data directory of the POSYDON repository. The ``profile_names`` argument denotes which profiles will be extracted from the grid and saved to ``filename`` using the function ``save``. The Boolean argument ``hms_s2`` should be set to True only to extract star 2 profiles from the HMS-HMS grid. .. code-block:: python @@ -42,11 +30,9 @@ profiles from the HMS-HMS grid. Loading a Pretrained Interpolator -=============================== +================================= -To load a pretrained interpolator we instantiate a ``ProfileInterpolator`` object -instance and then use the ``load`` function which specifies the path to a -.pkl file from which the pretrained interpolator can be loaded. +To load a pretrained interpolator we instantiate a ``ProfileInterpolator`` object instance and then use the ``load`` function which specifies the path to a .pkl file from which the pretrained interpolator can be loaded. .. code-block:: python @@ -57,20 +43,7 @@ instance and then use the ``load`` function which specifies the path to a Training the Interpolator ========================= -The interpolator is trained on profiles stored in a .pkl file (passed with -argument ``filename``) generated with the ``CompileData`` class. -The function ``load_profiles`` also randomly splits the training data into -a training set and validation set based on the argument ``valid_split``, which -specifies the percentage of the training data that should be used for validation. -The profile interpolation is anchored to POSYDON's IF interpolation, -and so we pass the name of the interpolator file to the ``train`` function -using the ``IF_interpolator`` argument. -The four following arguments specify the numbers of epochs and level of patience -for callback that will be used for training the neural networks in the density -and composition models. The ``loss_history`` argument provides an option to -return the loss histories for the composition and density model training. -Once again, the Boolean argument ``hms_s2`` should be set to True to -build an interpolator for star 2 profiles from the HMS-HMS grid. +The interpolator is trained on profiles stored in a .pkl file (passed with argument ``filename``) generated with the ``CompileData`` class. The function ``load_profiles`` also randomly splits the training data into a training set and validation set based on the argument ``valid_split``, which specifies the percentage of the training data that should be used for validation. The profile interpolation is anchored to POSYDON's IF interpolation, and so we pass the name of the interpolator file to the ``train`` function using the ``IF_interpolator`` argument. The four following arguments specify the numbers of epochs and level of patience for callback that will be used for training the neural networks in the density and composition models. The ``loss_history`` argument provides an option to return the loss histories for the composition and density model training. Once again, the Boolean argument ``hms_s2`` should be set to True to build an interpolator for star 2 profiles from the HMS-HMS grid. .. code-block:: python @@ -89,20 +62,13 @@ build an interpolator for star 2 profiles from the HMS-HMS grid. Using the Interpolator ====================== -Once the interpolator has been trained or loaded from a .pkl file it can be used -to predict profiles for sets of initial conditions passed through argument ``inputs``. -These initial conditions must be in log space and in shape (N,3) for N binaries. -The order of the coordinates is star 1 mass, star 2 mass, period. The prediction -function returns four arrays containing the profiles' coordinates along mass -enclosed, log density, Hydrogen mass fraction, and Helium mass fraction. All -profiles share the same coordinates. +Once the interpolator has been trained or loaded from a .pkl file it can be used to predict profiles for sets of initial conditions passed through argument ``inputs``. These initial conditions must be in log space and in shape (N,3) for N binaries. The order of the coordinates is star 1 mass, star 2 mass, period. The prediction function returns four arrays containing the profiles' coordinates along mass enclosed, log density, Hydrogen mass fraction, and Helium mass fraction. All profiles share the same coordinates. .. code-block:: python mass_coords, density_profiles, h_profiles, he_profiles = model.predict(inputs) -Finally a trained interpolator can be easily saved by specifying a path to a .pkl file -where the interpolator will be saved to. +Finally a trained interpolator can be easily saved by specifying a path to a .pkl file where the interpolator will be saved to. .. code-block:: python @@ -111,8 +77,7 @@ where the interpolator will be saved to. Evaluating on Testing Data ========================== -To evaluate the interpolator on the testing grid, we can pull the testing data -out of the ``ProfileInterpolator`` class as follows: +To evaluate the interpolator on the testing grid, we can pull the testing data out of the ``ProfileInterpolator`` class as follows: .. code-block:: python diff --git a/docs/interpolation/IF-Interpolator/IFInterpolator.rst b/docs/_source/components-overview/machine_learning/initial_final_interp.rst similarity index 96% rename from docs/interpolation/IF-Interpolator/IFInterpolator.rst rename to docs/_source/components-overview/machine_learning/initial_final_interp.rst index b25bdd4069..82191ce381 100644 --- a/docs/interpolation/IF-Interpolator/IFInterpolator.rst +++ b/docs/_source/components-overview/machine_learning/initial_final_interp.rst @@ -1,10 +1,8 @@ +.. _initial-final-interp: -.. _IFInterpolator: - -########################### -Initial-Final Interpolation -########################### - +########################################### +Intial Final Classification & Interpolation +########################################### We showcase the initial-final interpolator which plays a critical role in the evolving binary populations. To use the initial-final interpolator we first @@ -16,7 +14,7 @@ import the ``IFInterpolator`` object from the POSYDON library. from posydon.interpolation.IF_interpolation import IFInterpolator Loading a Pretrained Interpolator -=============================== +================================== To load a pretrained interpolator we need to pass in the ``filename`` argument into the ``IFInterpolator`` diff --git a/docs/_source/components-overview/mesa-grids.rst b/docs/_source/components-overview/mesa-grids.rst new file mode 100644 index 0000000000..40fc7c6f11 --- /dev/null +++ b/docs/_source/components-overview/mesa-grids.rst @@ -0,0 +1,37 @@ +.. _mesa-grids-api: + +MESA Grids API +=============== + + +Running Grids +-------------- + + +Running fix type grids +~~~~~~~~~~~~~~~~~~~~~~ + +Learn how to run fix type grids using the POSYDON API. + +.. toctree:: + + mesa_grids/fixed + +Running dynamic type grids +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Learn how to run dynamic type grids using the POSYDON API. TODO + +.. toctree:: + + mesa_grids/dynamic + + +Ini file documetation +~~~~~~~~~~~~~~~~~~~~~ + +Learn more about the MESA ini file content and how to use it in your own code. + +.. toctree:: + + mesa_grids/inifile \ No newline at end of file diff --git a/docs/run_mesa_grids/dynamic/dynamic.rst b/docs/_source/components-overview/mesa_grids/dynamic.rst similarity index 68% rename from docs/run_mesa_grids/dynamic/dynamic.rst rename to docs/_source/components-overview/mesa_grids/dynamic.rst index a2210337ac..acc31929ed 100644 --- a/docs/run_mesa_grids/dynamic/dynamic.rst +++ b/docs/_source/components-overview/mesa_grids/dynamic.rst @@ -1,5 +1,7 @@ .. _dynamic_grid: - + ####################### -Run a Dynamic MESA Grid +Run a dynamic MESA grid ####################### + +TODO diff --git a/docs/run_mesa_grids/fixed/fixed.rst b/docs/_source/components-overview/mesa_grids/fixed.rst similarity index 93% rename from docs/run_mesa_grids/fixed/fixed.rst rename to docs/_source/components-overview/mesa_grids/fixed.rst index 419f428d10..52dc17dcf5 100644 --- a/docs/run_mesa_grids/fixed/fixed.rst +++ b/docs/_source/components-overview/mesa_grids/fixed.rst @@ -64,13 +64,13 @@ named `example_grid.ini`) in our example_grid directory. Now, with our `grid.csv` and `example_grid.ini` files ready to go, we use a POSYDON script `posydon-setup-grid` to generate all the necessary files to run the grid using slurm. Note the different arguments here. We are designating -that: 1) we are using a fixed grid, 2) the name of the .ini file, and 3) that -our job scheduler is slurm. We may expand compatibility for other job +that: 1) we are using a fixed grid, 2) that our job scheduler is slurm, and 3) +the name of the .ini file. We may expand compatibility for other job schedulers in the future, but for now slurm is the only implemented option. .. code-block:: - posydon-setup-grid --grid-type fixed --inifile example_grid.ini --submission-type slurm + posydon-setup-grid --grid-type fixed --submission-type slurm --inifile example_grid.ini This will take a minute to run. You will note that it includes a pull from the POSYDON GitHub repository to ensure the correct, designated version of the MESA @@ -91,7 +91,7 @@ Finally, we are ready to submit our jobs with: Once the grid of runs is completed, we recommend you use our provided PSyGrid functionality to interpret and collate the individual binary runs -(link for documentation: :ref:`PSyGrid`.) +(link for documentation: :mod:`posydon.grids.psygrid`.) Non-default evolutionary parameters diff --git a/docs/run_mesa_grids/inifile.rst b/docs/_source/components-overview/mesa_grids/inifile.rst similarity index 98% rename from docs/run_mesa_grids/inifile.rst rename to docs/_source/components-overview/mesa_grids/inifile.rst index 834e8772d2..365ade76bd 100644 --- a/docs/run_mesa_grids/inifile.rst +++ b/docs/_source/components-overview/mesa_grids/inifile.rst @@ -5,7 +5,7 @@ ######################################### How to write a configuration file -================================= +================================== The `posydon-setup-grid` command-line executable cannot run without a configuration file. This inifile is meant to specify all aspects of the MESA @@ -241,8 +241,8 @@ This section designates all the parameters for MESA makefiles and fortran files. This section designates the run parameters for a grid. -==================== ======================================================== -==================== ======================================================== +==================== ======================================================== +==================== ======================================================== .. code-block:: ini diff --git a/docs/_source/components-overview/pop_syn/binary_population.rst b/docs/_source/components-overview/pop_syn/binary_population.rst new file mode 100644 index 0000000000..54ed4fc400 --- /dev/null +++ b/docs/_source/components-overview/pop_syn/binary_population.rst @@ -0,0 +1,4 @@ +.. _binary-population: + +The Binary Population Object +============================ \ No newline at end of file diff --git a/docs/BinaryStar/BinaryStar.rst b/docs/_source/components-overview/pop_syn/binary_star.rst similarity index 78% rename from docs/BinaryStar/BinaryStar.rst rename to docs/_source/components-overview/pop_syn/binary_star.rst index e5f9003b9b..fed7c9599b 100644 --- a/docs/BinaryStar/BinaryStar.rst +++ b/docs/_source/components-overview/pop_syn/binary_star.rst @@ -1,15 +1,10 @@ -.. _BinaryStar: +.. _binary-star: + -###################### The BinaryStar object -###################### +====================== -The BinaryStar object is composed of two SingleStar objects and contains the -current and past states of the binary. Only parameters in the BINARYPROPERTIES -list are stored in the history. The current parameter value of the star object -is accessed as, e.g. `binary.orbital_period` while his past history with -`binary.orbital_period_history`. The two stars are accesses as, e.g. -`binary.star_1.mass` while his past history with `binary.star_1.mass_history`. +The BinaryStar object is composed of two SingleStar objects and contains the current and past states of the binary. Only parameters in the BINARYPROPERTIES list are stored in the history. The current parameter value of the star object is accessed as, e.g. `binary.orbital_period` while his past history with `binary.orbital_period_history`. The two stars are accesses as, e.g. `binary.star_1.mass` while his past history with `binary.star_1.mass_history`. To use BinaryStar object import it using: @@ -20,10 +15,10 @@ To use BinaryStar object import it using: Creating a BinaryStar object -============================ +---------------------------- BINARYPROPERTIES ----------------- +~~~~~~~~~~~~~~~~ The binary properties are defined as follows @@ -40,8 +35,10 @@ The binary properties are defined as follows `V_sys`, "Velocity of the centre of mass of the binary [Vx, Vy, Vz] in km/s." `mass_transfer_case`, "Mass transfer case, see MT case options." +TODO: add missing properties + State options -------------- +~~~~~~~~~~~~~ Binary states are defined according to the following table: @@ -57,8 +54,10 @@ Binary states are defined according to the following table: `disrupted`, "The binary was disrupted." `MaxTimeChanged`, "Max time of the evolution was reached." +TODO: update properties + Event options -------------- +~~~~~~~~~~~~~ Binary events are defined according to the following table: @@ -74,10 +73,12 @@ Binary events are defined according to the following table: `None`, "No event occurred." `END`, "The binary evolution was stopped." +TODO: update properties + Mass Transfer case ------------------- +~~~~~~~~~~~~~~~~~~ -The mass transfer cases are defined according to the following table: TODO: add the table below +The mass transfer cases are stored in `mt_history_GRIDTYPE` and are defined according to the following table: TODO: add the table below .. csv-table:: Mass transfer cases :header: "State", "Description" @@ -85,8 +86,13 @@ The mass transfer cases are defined according to the following table: TODO: add `None`, "The binary is not Roche Lobe overflowing." + +TODO: update properties + + Basic example -------------- +~~~~~~~~~~~~~ + The simplest method is to provide the two star objects and `kwargs` of the initial binary parameters. .. code-block:: python diff --git a/docs/advanced_pop_options/custom_hooks.rst b/docs/_source/components-overview/pop_syn/custom_hooks.rst similarity index 90% rename from docs/advanced_pop_options/custom_hooks.rst rename to docs/_source/components-overview/pop_syn/custom_hooks.rst index d1a212662e..6d403ab2ef 100644 --- a/docs/advanced_pop_options/custom_hooks.rst +++ b/docs/_source/components-overview/pop_syn/custom_hooks.rst @@ -1,18 +1,12 @@ -.. _custom_hooks: +.. _custom-hooks: ################## Evolutionary Hooks ################## -During the evolution of a binary, one may be interested in having access to -the ``BinaryStar`` instance before, during, and after the evolutionary loop. -You may check the attributes, or add your own data to the binary during the -evolution. -We use *hooks* to pass functions that are called during the evolution -of all binaries. +During the evolution of a binary, one may be interested in having access to the ``BinaryStar`` instance before, during, and after the evolutionary loop. You may check the attributes, or add your own data to the binary during the evolution. We use *hooks* to pass functions that are called during the evolution of all binaries. -The evolutionary loop is in ``BinaryStar.evolve``, and the pseudo code looks -like this: +The evolutionary loop is in ``BinaryStar.evolve``, and the pseudo code looks like this: .. code-block:: python @@ -28,9 +22,7 @@ like this: post_evolve(binary) -All hooks functions and classes should be provided in the -``SimulationProperties`` with the name ``extra_hooks``. -Then ``extra_hooks`` points to a ``list`` of ``tuples``, containing either: +All hooks functions and classes should be provided in the ``SimulationProperties`` with the name ``extra_hooks``. Then ``extra_hooks`` points to a ``list`` of ``tuples``, containing either: #. The corresponding extra step name and a callable or, @@ -105,9 +97,7 @@ The options for step names and their arguments include: 2. Passing extra hooks classes ============================== -For more complex tasks, you can provide a class deriving from ``EvolveHooks``. -We have implemented two examples of hooks classes which add step timing and -step names to the history of a binary in ``simulationproperties.py``: +For more complex tasks, you can provide a class deriving from ``EvolveHooks``. We have implemented two examples of hooks classes which add step timing and step names to the history of a binary in ``simulationproperties.py``: .. code-block:: python :linenos: @@ -185,8 +175,7 @@ The source for ``StepNamesHooks``: return binary -You can simply combine multiple hooks classes and functions together by passing them -in the list. +You can simply combine multiple hooks classes and functions together by passing them in the list. .. code-block:: python diff --git a/docs/_source/components-overview/pop_syn/flow_chart.rst b/docs/_source/components-overview/pop_syn/flow_chart.rst new file mode 100644 index 0000000000..add2b93c34 --- /dev/null +++ b/docs/_source/components-overview/pop_syn/flow_chart.rst @@ -0,0 +1,8 @@ +.. _flow-chart: + +The Flow Chart Object +--------------------- + +Along with the BinaryStar object the flow chart object is the most important building block of POSYDON, such that it is featured in the center of the POSYDON logo. The flow chart object maps the evolution of a BinaryStar object to its corresponing evolutionary step. + +TODO: continue the description of the flow chart object \ No newline at end of file diff --git a/docs/_source/components-overview/pop_syn/pop_syn_debug.rst b/docs/_source/components-overview/pop_syn/pop_syn_debug.rst new file mode 100644 index 0000000000..97dd05b7c3 --- /dev/null +++ b/docs/_source/components-overview/pop_syn/pop_syn_debug.rst @@ -0,0 +1,4 @@ +.. _pop-syn-debugging: + +Debugging POSYDON population synthesis models +============================================= \ No newline at end of file diff --git a/docs/_source/components-overview/pop_syn/population_params.rst b/docs/_source/components-overview/pop_syn/population_params.rst new file mode 100644 index 0000000000..f2a5f9880f --- /dev/null +++ b/docs/_source/components-overview/pop_syn/population_params.rst @@ -0,0 +1,420 @@ +.. _pop-params-guide: + +================================================ +POSDYON Population Synthesis Configuration Guide +================================================ + +This documentation provides a detailed overview of the configuration options available in the Posydon software package. + +TODO: fill each table cell with a description of the parameter and the options + +Environment Variables +--------------------- + +The environment variable `PATH_TO_POSYDON` will be read from your shell session. + +.. list-table:: + :widths: 50 50 + :header-rows: 1 + + * - Parameter + - Description/Value + * - `PATH_TO_POSYDON` + - `` + +SimulationProperties +-------------------- + +TODO: add description + +Flow Chart +~~~~~~~~~~ + +The flow chart is the core of POSYDON. It controls the mapping between a POSYDON binary object and its step evolution, see the :ref:`Flow Chart Object ` page for more details. + +.. list-table:: + :widths: 50 50 + :header-rows: 1 + + * - Parameter + - Description/Value + * - `import` + - ['posydon.binary_evol.flow_chart', 'flow_chart'] + * - absolute_import = None + - 'package' (kwarg for importlib.import_module) + +Step MESA (HMS-HMS, CO-HMS_RLO, CO-HeMS, CO-HeMS_RLO) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The MESA step is the most important step of POSYDON as it leverages the POSYDON MESA grids to evolve the binary object according to one of the supported MESA binary-star grids. + +.. list-table:: + :widths: 50 50 + :header-rows: 1 + + * - Parameter + - Description/Value + * - `import` + - ['posydon.binary_evol.MESA.step_mesa', 'MS_MS_step'] + * - absolute_import = None + - 'package' (kwarg for importlib.import_module) + * - `interpolation_path` + - None (found by default) + * - `interpolation_filename` + - None (found by default) + * - `interpolation_method` + - '1NN_1NN' ('nearest_neighbour', 'linear3c_kNN', '1NN_1NN' are options) + * - `save_initial_conditions` + - True (only for interpolation_method='nearest_neighbour') + * - `track_interpolation` + - False + * - `stop_method` + - 'stop_at_max_time' ('stop_at_end', 'stop_at_max_time', 'stop_at_condition' are options) + * - `stop_star` + - 'star_1' (only for stop_method='stop_at_condition', 'star_1' and 'star_2' are options) + * - `stop_var_name` + - None (only for stop_method='stop_at_condition', string) + * - `stop_value` + - None (only for stop_method='stop_at_condition', float) + * - `stop_interpolate` + - True + * - `verbose` + - False + + +Step Detached +~~~~~~~~~~~~~ + +TODO: add description + +.. list-table:: + :widths: 50 50 + :header-rows: 1 + + * - Parameter + - Description/Value + * - `import` + - ['posydon.binary_evol.DT.step_detached', 'detached_step'] + * - `absolute_import` + - None ('package' kwarg for importlib.import_module) + * - `matching_method` + - 'minimize' (options 'minimize' 'root') + * - `do_wind_loss` + - True + * - `do_tides` + - True + * - `do_gravitational_radiation` + - True + * - `do_magnetic_braking` + - True + * - `do_stellar_evolution_and_spin_from_winds` + - True + * - `RLO_orbit_at_orbit_with_same_am` + - False + * - `verbose` + - False + +Step Disrupted +~~~~~~~~~~~~~~ + +TODO: add description + +.. list-table:: + :widths: 50 50 + :header-rows: 1 + + * - Parameter + - Description/Value + * - `import` + - ['posydon.binary_evol.DT.step_disrupted','DisruptedStep'] + +Step Merged +~~~~~~~~~~~ + +TODO: add description + +.. list-table:: + :widths: 50 50 + :header-rows: 1 + + * - Parameter + - Description/Value + * - `import` + - ['posydon.binary_evol.DT.step_merged','MergedStep'] + +Step Initially Single +~~~~~~~~~~~~~~~~~~~~~ + +TODO: add description + +.. list-table:: + :widths: 50 50 + :header-rows: 1 + + * - Parameter + - Description/Value + * - `import` + - ['posydon.binary_evol.DT.step_initially_single','InitiallySingleStep'] + +Step Common Envelope +~~~~~~~~~~~~~~~~~~~~ + +TODO: add description + +.. list-table:: + :widths: 50 50 + :header-rows: 1 + + * - Parameter + - Description/Value + * - `import` + - ['posydon.binary_evol.CE.step_CEE', 'StepCEE'] + * - `absolute_import` + - None('package' kwarg for importlib.import_module) + * - `prescription` + - 'alpha-lambda' + * - `common_envelope_efficiency` + - 1.0 (float in [0, inf]) + * - `common_envelope_option_for_lambda` + - 'lambda_from_grid_final_values' (options are: (1) 'default_lambda', (2) 'lambda_from_grid_final_values', (3) 'lambda_from_profile_gravitational', (4) 'lambda_from_profile_gravitational_plus_internal', (5) 'lambda_from_profile_gravitational_plus_internal_minus_recombination') + * - `common_envelope_lambda_default` + - 0.5 (float in [0, inf] used only for option (1)) + * - `common_envelope_option_for_HG_star` + - 'optimistic' (options are 'optimistic', 'pessimistic') + * - `common_envelope_alpha_thermal` + - 1.0 (float in [0, inf] used only for option for (4), (5)) + * - `core_definition_H_fraction` + - 0.1 (options are 0.01, 0.1, 0.3) + * - `core_definition_He_fraction` + - 0.1 + * - `CEE_tolerance_err` + - 0.001 (float in [0, inf]) + * - `common_envelope_option_after_succ_CEE` + - 'core_not_replaced_noMT' (options are 'core_not_replaced_noMT' 'core_replaced_noMT', 'core_not_replaced_stableMT' 'core_not_replaced_windloss') + * - `verbose` + - False + +Step Supernova +~~~~~~~~~~~~~~ + +TODO: add description + +.. list-table:: + :widths: 50 50 + :header-rows: 1 + + * - Parameter + - Description/Value + * - `import` + - ['posydon.binary_evol.SN.step_SN', 'StepSN'] + * - `absolute_import` + - None ('package' kwarg for importlib.import_module) + * - `mechanism` + - 'Patton&Sukhbold20-engine' (options are: 'direct', Fryer+12-rapid', 'Fryer+12-delayed', 'Sukhbold+16-engine', 'Patton&Sukhbold20-engine') + * - `engine` + - 'N20' (options are 'N20' for 'Sukhbold+16-engine', 'Patton&Sukhbold20-engine' or None for the others) + * - `PISN` + - 'Marchant+19' (options are None, "Marchant+19") + * - `ECSN` + - "Podsiadlowksi+04" (options are "Tauris+15", "Podsiadlowksi+04") + * - `conserve_hydrogen_envelope` + - True + * - `max_neutrino_mass_loss` + - 0.5 (float in [0,inf]) + * - `max_NS_mass` + - 2.5 (float in [0,inf]) + * - `use_interp_values` + - True + * - `use_profiles` + - True + * - `use_core_masses` + - True + * - `approx_at_he_depletion` + - False + * - `kick` + - True + * - `kick_normalisation` + - 'one_over_mass' (options are "one_minus_fallback", "one_over_mass", "NS_one_minus_fallback_BH_one", "one", "zero") + * - `sigma_kick_CCSN_NS` + - 265.0 (float in [0,inf]) + * - `sigma_kick_CCSN_BH` + - 265.0 (float in [0,inf]) + * - `sigma_kick_ECSN` + - 20.0 (float in [0,inf]) + * - `verbose` + - False + +Step Double Compact Object +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +TODO: add description + +.. list-table:: + :widths: 50 50 + :header-rows: 1 + + * - Parameter + - Description/Value + * - `import` + - ['posydon.binary_evol.DT.double_CO', 'DoubleCO'] + * - `absolute_import` + - None ('package' kwarg for importlib.import_module) + * - `n_o_steps_interval` + - None + +Step End +~~~~~~~~ + +TODO: add description + +.. list-table:: + :widths: 50 50 + :header-rows: 1 + + * - Parameter + - Description/Value + * - `import` + - ['posydon.binary_evol.step_end', 'step_end'] + * - `absolute_import` + - None ('package' kwarg for importlib.import_module) + +Extra Hooks +~~~~~~~~~~~ + +TODO: add description + +.. list-table:: + :widths: 50 50 + :header-rows: 1 + + * - Parameter + - Description/Value + * - `import` + - ['posydon.binary_evol.simulationproperties', 'TimingHooks'] + * - `absolute_import_1` + - None + * - `kwargs_1` + - {} + * - `import` + - ['posydon.binary_evol.simulationproperties', 'StepNamesHooks'] + * - `absolute_import_2` + - None + * - `kwargs_2` + - {} + +BinaryPopulation +---------------- + +TODO: add description + +BinaryPopulation Options +~~~~~~~~~~~~~~~~~~~~~~~~ + +TODO: add description + +.. list-table:: + :widths: 50 50 + :header-rows: 1 + + * - Parameter + - Description/Value + * - `optimize_ram` + - True (save population in batches) + * - `ram_per_cpu` + - None (set maximum ram per cpu before batch saving in GB) + * - `dump_rate` + - 1000 (batch save after evolving N binaries) + * - `temp_directory` + - 'batches' (folder for keeping batch files) + * - `tqdm` + - False (progress bar) + * - `breakdown_to_df` + - True (convert BinaryStars into DataFrames after evolution) + * - `use_MPI` + - True ( if True evolve with MPI, equivalent to the following: from mpi4py import MPI, comm = MPI.COMM_WORLD) + * - `metallicity` + - [2., 1., 0.45, 0.2, 0.1, 0.01, 0.001, 0.0001] (In units of solar metallicity) + * - `entropy` + - `None` (Random Number Generation: uses system entropy (recommended)) + * - `number_of_binaries` + - 1000000 (int) + * - `star_formation` + - 'burst' (options are 'constant' 'burst' 'custom_linear' 'custom_log10' 'custom_linear_histogram' 'custom_log10_histogram') + * - `max_simulation_time` + - 13.8e9 (float in [0,inf]) + * - `binary_fraction` + - 1 (float 0< fraction <=1) + * - `primary_mass_scheme` + - 'Kroupa2001' (options are 'Salpeter', 'Kroupa1993', 'Kroupa2001') + * - `primary_mass_min` + - 6.5 (float in [0,300]) + * - `primary_mass_max` + - 250.0 (float in [0,300]) + * - `secondary_mass_scheme` + - 'flat_mass_ratio' (options are 'flat_mass_ratio', 'q=1') + * - `secondary_mass_min` + - 0.35 (float in [0,300]) + * - `secondary_mass_max` + - 250.0 (float in [0,300]) + * - `orbital_scheme`` + - 'period' (options are 'separation', 'period') + * - `orbital_period_scheme` + - 'Sana+12_period_extended' (used only for orbital_scheme = 'period') + * - `orbital_period_min` + - 0.75 (float i [0,inf]) + * - `orbital_period_max` + - 6000.0 (float i [0,inf]) + * - `#orbital_separation_scheme` + - 'log_uniform' (used only for orbital_scheme = 'separation', 'log_uniform', 'log_normal') + * - `#orbital_separation_min` + - 5.0 (float i [0,inf]) + * - `#orbital_separation_max` + - 1e5 (float i [0,inf]) + * - `#log_orbital_separation_mean` + - None (float i [0,inf] used only for orbital_separation_scheme ='log_normal') + * - `#log_orbital_separation_sigma` + - None (float i [0,inf] used only for orbital_separation_scheme ='log_normal') + * - `eccentricity_scheme` + - 'zero' (options are 'zero', 'thermal', 'uniform') + + + +Saving Output +------------- + +TODO: add description + +BinaryStar Output +~~~~~~~~~~~~~~~~~ + +TODO: add description + +.. list-table:: + :widths: 50 50 + :header-rows: 1 + + * - Parameter + - Description/Value + * - `extra_columns` + - {'step_names':'string', 'step_times':'float64'} ('step_times' with from posydon.binary_evol.simulationproperties import TimingHooks) + * - `only_select_columns` + - ['state', 'event', 'time', 'orbital_period', 'eccentricity', 'lg_mtransfer_rate'] (all options: 'state', 'event', 'time', 'separation', 'orbital_period', 'eccentricity', 'V_sys', 'rl_relative_overflow_1', 'rl_relative_overflow_2', 'lg_mtransfer_rate', 'mass_transfer_case', 'trap_radius', 'acc_radius', 't_sync_rad_1', 't_sync_conv_1', 't_sync_rad_2', 't_sync_conv_2', 'nearest_neighbour_distance') + + +SingleStar 1 and 2 Output +~~~~~~~~~~~~~~~~~~~~~~~~~ + +TODO: add description + +.. list-table:: + :widths: 50 50 + :header-rows: 1 + + * - Parameter + - Description/Value + * - `include_S1` + - True + * - `only_select_columns` + - ['state', 'mass', 'log_R', 'log_L', 'lg_mdot', 'he_core_mass', 'he_core_radius', 'co_core_mass', 'co_core_radius', 'center_h1', 'center_he4', 'surface_h1', 'surface_he4', 'surf_avg_omega_div_omega_crit', 'spin',] (options are: 'state', 'metallicity', 'mass', 'log_R', 'log_L', 'lg_mdot', 'lg_system_mdot', 'lg_wind_mdot', 'he_core_mass', 'he_core_radius', 'c_core_mass', 'c_core_radius', 'o_core_mass', 'o_core_radius', 'co_core_mass', 'co_core_radius', 'center_h1', 'center_he4', 'center_c12', 'center_n14', 'center_o16', 'surface_h1', 'surface_he4', 'surface_c12', 'surface_n14', 'surface_o16', 'log_LH', 'log_LHe', 'log_LZ', 'log_Lnuc', 'c12_c12', 'center_gamma', 'avg_c_in_c_core', 'surf_avg_omega', 'surf_avg_omega_div_omega_crit', 'total_moment_of_inertia', 'log_total_angular_momentum', 'spin', 'conv_env_top_mass', 'conv_env_bot_mass', 'conv_env_top_radius', 'conv_env_bot_radius', 'conv_env_turnover_time_g', 'conv_env_turnover_time_l_b', 'conv_env_turnover_time_l_t', 'envelope_binding_energy', 'mass_conv_reg_fortides', 'thickness_conv_reg_fortides', 'radius_conv_reg_fortides', 'lambda_CE_1cent', 'lambda_CE_10cent', 'lambda_CE_30cent', 'lambda_CE_pure_He_star_10cent', 'profile') + * - `scalar_names` + - [ 'natal_kick_array', 'SN_type', 'f_fb', 'spin_orbit_tilt', 'm_disk_accreted', 'm_disk_radiated'] diff --git a/docs/SingleStar/SingleStar.rst b/docs/_source/components-overview/pop_syn/single_star.rst similarity index 89% rename from docs/SingleStar/SingleStar.rst rename to docs/_source/components-overview/pop_syn/single_star.rst index f0c15f0a4e..f882692e41 100644 --- a/docs/SingleStar/SingleStar.rst +++ b/docs/_source/components-overview/pop_syn/single_star.rst @@ -1,13 +1,10 @@ -.. _SingleStar: +.. _single-star: + -###################### The SingleStar object -###################### +===================== -The Single Star object contains the current and past states of the star. -Only parameters in the STARPROPERTIES list are stored in the history. -The current parameter value of the star object is accessed as, e.g. `star.mass` while -its past history with `star.mass_history`. +The Single Star object contains the current and past states of the star. Only parameters in the STARPROPERTIES list are stored in the history. The current parameter value of the star object is accessed as, e.g. `star.mass` while its past history with `star.mass_history`. To use SingleStar object import it using: @@ -17,10 +14,10 @@ To use SingleStar object import it using: Creating a SingleStar object -============================ +---------------------------- STARPROPERTIES --------------- +~~~~~~~~~~~~~~ The star properties are defined as follows @@ -62,8 +59,11 @@ The star properties are defined as follows `spin`, "Angular momentum of the star in g*cm^2*s^-1 or dimensionless BH spin." `profile`, "Stellar profile from MESA." + +TODO: add missing properties + State options -------------- +~~~~~~~~~~~~~ Star states are defined on their relative position on the HR diagram as follows: @@ -80,8 +80,11 @@ Star states are defined on their relative position on the HR diagram as follows: `NS`, "The star is a Neutron Star." `BH`, "The star is a Black Hole." +TODO: update the states to POSYDON v2.0.0 + Basic example -------------- +~~~~~~~~~~~~~ + The simplest method is to provide `kwargs` of the initial stellar parameters. .. code-block:: python diff --git a/docs/_source/components-overview/pop_syn/synthetic_population.rst b/docs/_source/components-overview/pop_syn/synthetic_population.rst new file mode 100644 index 0000000000..a722897520 --- /dev/null +++ b/docs/_source/components-overview/pop_syn/synthetic_population.rst @@ -0,0 +1,4 @@ +.. _synthetic-population: + +The Synthetic Population Object +=============================== \ No newline at end of file diff --git a/docs/_source/components-overview/processing-pipeline.rst b/docs/_source/components-overview/processing-pipeline.rst new file mode 100644 index 0000000000..9ab99b282a --- /dev/null +++ b/docs/_source/components-overview/processing-pipeline.rst @@ -0,0 +1,4 @@ +.. _processing-pipeline: + +Processing Pipeline +------------------- \ No newline at end of file diff --git a/docs/_source/components-overview/stellar-binary-simulation.rst b/docs/_source/components-overview/stellar-binary-simulation.rst new file mode 100644 index 0000000000..ebe4fe7db9 --- /dev/null +++ b/docs/_source/components-overview/stellar-binary-simulation.rst @@ -0,0 +1,117 @@ +.. _stellar-binary-simulation: + +################################ +Stellar & Binary-star Simulation +################################ + + + + +POSYDON: Building Populations with Object-Oriented Design +========================================================= + +POSYDON provide a fully customizable framework to population synthesis modeling. By leveraging the power and flexibility of object-oriented design, it offers a modular and scalable architecture that enables researchers to customize, extend, and integrate various astrophysical processes with ease. This section delves into the intricacies of the POSYDON hierarchy, outlining the key classes, their interrelationships, and their roles in crafting sophisticated population synthesis models. + +The Single Star Object +---------------------- + +The `SingleStar` object contains the stellar properties and its evolutionary history. The single star object is passed to the :class:`~.binary_evol.BinaryStar` class to create a `BinaryStar` object. + +.. toctree:: + :maxdepth: 1 + + pop_syn/single_star + +---- + +The Binary Star Object +---------------------- + +The `BinaryStar` object contains the binary properties and its evolutionary history as well as two `SingleStar`. The binary star object is passed to the :class:`~.populations.BinaryPopulation` class to create a population synthesis model. + +.. toctree:: + :maxdepth: 1 + :titlesonly: + + pop_syn/binary_star + + +---- + +The Binary Population Object +---------------------------- + +The `BinaryPopulation` object contains a list of `BinaryStar` objects and the `SimulationProperties` object which contains the information about the population synthesis model. + +.. toctree:: + :maxdepth: 1 + + pop_syn/binary_population + + +---- + + +The Simulation Properties object +-------------------------------- + + +POSDYON Population Synthesis Configuration Guide +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +The `SimulationProperties` objects contains the POSYDON population synthesis parameter configurations determining the evolution of each binaries in the `BinaryPopulation` class. The following guide will walk you through the configuration parameters of a POSYDON binary population synthesis model. + +.. toctree:: + :maxdepth: 1 + + pop_syn/population_params + + +The Flow Chart Object +~~~~~~~~~~~~~~~~~~~~~ + +The flow chart object is the main object that is used to configure the population synthesis model. It is a dictionary that contains all the parameters that are used to configure the model. The flow chart object is passed to the :class:`~.populations.BinaryPopulation` class to create a population synthesis model. + +.. toctree:: + :maxdepth: 1 + + pop_syn/flow_chart + + +Evolutionary Hooks +~~~~~~~~~~~~~~~~~~ + +The evolutionary hooks allows the use to execute code between POSYDON steps. The following guide will walk you through the evolutionary hooks that are available in POSYDON. + +.. toctree:: + :maxdepth: 1 + + pop_syn/custom_hooks + +---- + + +The Synthetic Population Object +------------------------------- + +The `SyntheticPopulation` object contains a collection of `BinaryPopulation` objects run, e.g. at different metallicities or with different model assumptions. It also provide the POSYDON API interface to analyse, process, and visualize the results of a POSYDON population synthesis model. + +.. toctree:: + :maxdepth: 1 + + pop_syn/synthetic_population + +---- + +Debugging POSYDON population synthesis models +============================================= + +POSYDON provides a set of tools to debug population synthesis models. The following guide will walk you through the debugging tools that are available in POSYDON. + +.. toctree:: + :maxdepth: 1 + + pop_syn/pop_syn_debug + + diff --git a/docs/conf.py b/docs/_source/conf.py similarity index 91% rename from docs/conf.py rename to docs/_source/conf.py index 2cd40f727a..60f464bd6b 100644 --- a/docs/conf.py +++ b/docs/_source/conf.py @@ -1,9 +1,6 @@ # posydon documentation build configuration file, created by # sphinx-quickstart on Thu Apr 21 14:05:08 2016. # -# This file is execfile()d with the current directory set to its -# containing dir. -# # Note that not all possible configuration values are present in this # autogenerated file. # @@ -13,35 +10,54 @@ import sys import os import re +import datetime from PSphinxTheme import utils from posydon import __version__ as posydon_version -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -sys.path.insert(0, os.path.abspath('.')) +# Rewrite posydon_version for docs developement +posydon_version = 'v2.0.0-dev' -# -- General configuration ------------------------------------------------ -# If your documentation needs a minimal Sphinx version, state it here. -# needs_sphinx = '1.0' +# -- Project information ----------------------------------------------------- + +project = u'POSYDON' +copyright =f'{datetime.datetime.now().year}, Tassos Fragos' +author = u'POSYDON Collaboration' +# The short X.Y version. +version = posydon_version +# The full version, including alpha/beta/rc tags. +release = posydon_version + + +# -- Path setup ---------------------------------------------------------- +# Point towards the posydon source code directory +# autodoc uses this to automatically generate documentation from docstrings +sys.path.insert(0, os.path.abspath('../../posydon/')) + +# -- General configuration ------------------------------------------------ # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.autodoc', + 'sphinx.ext.viewcode', + 'sphinx.ext.napoleon', + 'numpydoc', + 'sphinx.ext.githubpages', + 'sphinx.ext.duration', + 'sphinx.ext.autosummary', 'sphinx.ext.doctest', 'sphinx.ext.intersphinx', 'sphinx.ext.todo', 'sphinx.ext.coverage', 'sphinx.ext.mathjax', 'sphinx.ext.ifconfig', - 'sphinx.ext.viewcode', - 'sphinx.ext.githubpages', - 'sphinx.ext.napoleon' + 'nbsphinx', ] +autodoc_preserve_defaults = True + # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] @@ -103,26 +119,12 @@ # The master toctree document. master_doc = 'index' -# General information about the project. -project = u'posydon' -copyright = u'2022, Tassos Fragos' -author = u'POSYDON Collaboration' - -# The version info for the project you're documenting, acts as replacement for -# |version| and |release|, also used in various other places throughout the -# built documents. -# -# The short X.Y version. -version = posydon_version -# The full version, including alpha/beta/rc tags. -release = posydon_version - # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. -language = None +language = 'en' # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: @@ -139,7 +141,7 @@ # If true, the current module name will be prepended to all description # unit titles (such as .. function::). -# add_module_names = True +add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. @@ -191,7 +193,10 @@ # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] + +html_static_path = ['_static'] +# html_static_path is added before the below files. +html_css_files = ['custom.css'] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied @@ -230,7 +235,7 @@ html_show_sphinx = False # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. -# html_show_copyright = True +html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the @@ -258,6 +263,13 @@ # Output file base name for HTML help builder. htmlhelp_basename = 'posydondoc' +# Add logo to the sidebar +html_logo = 'posydon_logo.png' +html_theme_options = { + 'logo_only': True, + 'display_version': True, +} + # -- Options for LaTeX output --------------------------------------------- latex_elements = { @@ -345,3 +357,6 @@ 'matplotlib': ('http://matplotlib.org/', None), 'astropy': ('http://docs.astropy.org/en/stable/', None), } + +# skip jupyter notebook execution +nbsphinx_execute = 'never' diff --git a/docs/_source/contact-support/contact-information.rst b/docs/_source/contact-support/contact-information.rst new file mode 100644 index 0000000000..4ac17756f3 --- /dev/null +++ b/docs/_source/contact-support/contact-information.rst @@ -0,0 +1,41 @@ +.. _contact_info: + +Contact Information +------------------- + +We're committed to assisting the POSYDON community. If you have questions or need assistance, please refer to the following channels: + +Official Website +~~~~~~~~~~~~~~~~ + +For the latest news, updates, and comprehensive information about POSYDON, visit our official website: + +- `POSYDON Official Website `_ + + +Report Issues or Contribute +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +If you've encountered a bug or would like to suggest improvements, consider contributing directly on our GitHub repository: + +- `POSYDON on GitHub `_. + +When reporting issues, please ensure you provide sufficient information and context to help us understand and address the issue efficiently. + +Mailing List +~~~~~~~~~~~~ + +Before reaching out, we recommend checking our FAQ section on :ref:`installation ` and :ref:`code usage `. If your query is not addressed there, join our mailing list where you can ask questions and seek support from the POSYDON community: + +- Join the `POSYDON mailing list `_. + +Please be respectful and specific in your questions. This will help the community provide more accurate and timely responses. + +It is also advised to write a short description on how do you plan to use POSYDON. This will help us to understand the impact of this tool in different fields. + +Feedback +~~~~~~~~ + +We continually strive to improve POSYDON, and your feedback is invaluable. If you have any suggestions, praise, or constructive criticism, please share it with us through the `POSYDON mailing list `_. + +Thank you for being part of the POSYDON community! diff --git a/docs/_source/contributing/code-style-guidelines.rst b/docs/_source/contributing/code-style-guidelines.rst new file mode 100644 index 0000000000..14ac698e01 --- /dev/null +++ b/docs/_source/contributing/code-style-guidelines.rst @@ -0,0 +1,61 @@ +.. _code-style: + +Code Style Guidelines +--------------------- + +Adhering to a consistent code style helps maintain readability and eases collaboration. Here are the guidelines you should follow when contributing to POSYDON. + +General Principles +~~~~~~~~~~~~~~~~~~ +1. **Clarity and Readability**: Code should be easy to understand. Opt for clarity over cleverness. +2. **Consistency**: Follow the style of the existing codebase. When in doubt, consistency with the existing code is more important than adhering to external style guides. +3. **Comments**: Write meaningful comments, especially for complex logic. Avoid obvious comments. + +Python Style Guide +~~~~~~~~~~~~~~~~~~ +We follow the `PEP 8 `_ style guide for Python code with some exceptions and additions as outlined below. + +1. **Indentation**: Use 4 spaces per indentation level. +2. **Line Length**: Limit lines to 79 characters. +3. **Imports**: Group imports in the following order: standard libraries, third-party libraries, and local application imports. Each group should be separated by a blank line. +4. **Whitespace**: Avoid extraneous whitespace in the following situations: + - Immediately inside parentheses, brackets, or braces. + - Immediately before a comma, colon, or semicolon. +5. **Comments**: Comments should be complete sentences and should be used sparingly, i.e., only when necessary to explain complex pieces of logic or decisions that could seem non-obvious to other developers. + +Naming Conventions +~~~~~~~~~~~~~~~~~~ +1. **Functions and Variables**: Use `snake_case`. +2. **Classes and Exceptions**: Use `CamelCase`. +3. **Constants**: Use `UPPER_SNAKE_CASE`. +4. **Module-level globals** (like loggers or constants): Prefix with an underscore `_`. + +Docstrings +~~~~~~~~~~ +We adhere to the `NumPy docstring style guide `_. Every function, class, and module should have a docstring that adheres to this style. This helps in generating consistent and readable documentation. Ensure: + +1. Every module, class, and function/method has an accompanying docstring. +2. Always describe parameters, return types, and raised exceptions. +3. Include examples in function/method docstrings when possible. + +Testing +~~~~~~~ +Ensure that your code does not introduce new bugs: + +1. Write unit tests for your new features. +2. Run all the tests to make sure they pass. +3. Follow the existing testing conventions in the codebase. + +Commit Guidelines +~~~~~~~~~~~~~~~~~ +1. Commit messages should be concise and descriptive. +2. Use the present tense ("Add feature" not "Added feature"). +3. Use the imperative mood ("Move cursor to..." not "Moves cursor to..."). +4. Limit the first line to 72 characters or less. +5. Reference issues and pull requests liberally after the first line. + +Final Thoughts +~~~~~~~~~~~~~~ +Before submitting your code, review these guidelines and check your code against them. Taking the time to ensure your code adheres to these standards will make the review process smoother and faster. + +Thank you for your contribution and for making POSYDON's codebase clean and consistent! diff --git a/docs/_source/contributing/how-to-contribute.rst b/docs/_source/contributing/how-to-contribute.rst new file mode 100644 index 0000000000..5e7bff52b4 --- /dev/null +++ b/docs/_source/contributing/how-to-contribute.rst @@ -0,0 +1,64 @@ +.. _how-to-contribute: + +How To Contribute +----------------- + +We're thrilled that you're interested in contributing to POSYDON! Whether you're a developer, a scientist, or an enthusiast, your contributions can help improve this project. Below are some guidelines to get you started. + +Prerequisites +~~~~~~~~~~~~~ + +Before contributing, it's essential to familiarize yourself with: + +- The POSYDON codebase and its structure. +- The development workflow and the tools used. +- Our code of conduct. + +Setting Up Your Development Environment +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +1. **Fork the Repository**: Navigate to `POSYDON's GitHub page ` and click the 'Fork' button to create a copy of the repository in your personal GitHub account. +2. **Clone Your Fork**: Once forked, clone the repository to your local machine: + +.. code-block:: bash + + git clone https://github.com/your-username/POSYDON.git + +3. **Install Dependencies**: Navigate to the cloned repository and install necessary dependencies: + +.. code-block:: bash + + pip install . + +Making a Contribution +~~~~~~~~~~~~~~~~~~~~~ +1. **Choose an Issue**: Look for open issues on our GitHub page. It's a good idea to comment on an issue you're interested in to let the maintainers know you're planning to work on it. +2. **Create a New Branch**: Before making changes, create a new branch for your contribution: + +.. code-block:: bash + + git checkout -b your-branch-name + +3. **Make Your Changes**: Once you've made changes, test them thoroughly to ensure they don't introduce new bugs. +4. **Commit and Push Your Changes**: Commit your changes with a meaningful commit message, and then push them to your fork: + +.. code-block:: bash + + git commit -m "Your meaningful commit message" + git push origin your-branch-name + +5. **Submit a Pull Request (PR)**: Go to your fork on GitHub and click on 'Compare & pull request'. Fill out the PR form and submit it. Wait for the maintainers to review your changes. + +Code of Conduct +~~~~~~~~~~~~~~~ +We value our community's safety and well-being. Please ensure that your contributions and interactions adhere to our code of conduct. Any form of harassment, bullying, or inappropriate behavior will not be tolerated. + +Feedback and Questions +~~~~~~~~~~~~~~~~~~~~~~ +If you have feedback or questions, please don't hesitate to reach out, see the :ref:`contact us ` page or check our FAQ section on :ref:`code usage `. + +Additional Resources +~~~~~~~~~~~~~~~~~~~~ +- **POSYDON Documentation**: Dive deeper into POSYDON's features, architecture, and best practices. +- **Community Chat/Forum**: Engage with other community members, seek help, share your ideas, and discuss relevant topics. + +Thank you for your interest in contributing to POSYDON. Together, we can achieve great things! diff --git a/docs/_source/contributing/reporting-issues.rst b/docs/_source/contributing/reporting-issues.rst new file mode 100644 index 0000000000..4f04e29605 --- /dev/null +++ b/docs/_source/contributing/reporting-issues.rst @@ -0,0 +1,52 @@ +.. _report-issues: + +Reporting issues +---------------- + +If you encounter any problems while using POSYDON, we encourage you to report them. By doing so, you can help us improve the software for everyone. This page provides guidelines on how to report issues effectively. + +Before Reporting an Issue +~~~~~~~~~~~~~~~~~~~~~~~~~ + +1. **Check the FAQ**: Before submitting a new issue, please review our [FAQ](link-to-faq-page) to see if your problem has been addressed before. + +2. **Update to the Latest Version**: Ensure that you're using the latest version of POSYDON. Some issues may have been resolved in newer releases. + +3. **Search Existing Issues**: Look through the `issues on our GitHub repository `_ to check if someone else has already reported the same problem. If you find a match, you can comment on it with any additional information you have. + +How to Report an Issue +~~~~~~~~~~~~~~~~~~~~~~~ + +1. **Be Specific**: The title of the issue should be descriptive. Instead of writing "Program crashes," write "Program crashes when processing XYZ data." + +2. **Provide Detailed Information**: + - **Operating System and Version**: E.g., "Windows 10 version 1903" or "Ubuntu 20.04". + - **POSYDON Version**: Mention the version you are using. + - **Steps to Reproduce**: List the steps you took that led to the issue. + - **Expected Behavior vs. Observed Behavior**: Explain what you expected to happen and what actually occurred. + - **Error Messages**: If there was an error message, please include it (preferably in a text format or as a screenshot). + +3. **Include Screenshots or Logs**: If applicable, add screenshots or logs to provide a visual context. This can be especially helpful for UI issues or visual glitches. + +4. **Label the Issue**: If you're familiar with GitHub, label your issue (e.g., "bug", "enhancement", "documentation", etc.). If you're unsure, don't worry; our team will label it appropriately after review. + +5. **Be Respectful**: Remember that POSYDON is an open-source project. The people helping you are volunteers. Always approach the conversation with respect and patience. + +Submitting the Issue +~~~~~~~~~~~~~~~~~~~~~ +Once you've gathered all the necessary information: + +1. Navigate to the `POSYDON GitHub Issues page `_. +2. Click on the "New issue" button. +3. Fill out the template provided (if any) or use the guidelines above. +4. Click "Submit" to create the issue. + +After Reporting +~~~~~~~~~~~~~~~~ +Once the issue is reported: + +1. **Stay Engaged**: Respond to any questions or feedback from the development team. Your insights can help expedite the resolution. +2. **Updates**: If you find more information or discover a workaround, update the issue with a new comment. +3. **Resolution**: Once the issue is resolved, it'll be closed. You'll be notified via the GitHub notification system. + +Thank you for helping make POSYDON better for everyone! diff --git a/docs/_source/getting-started/installation-guide.rst b/docs/_source/getting-started/installation-guide.rst new file mode 100644 index 0000000000..3df0269af9 --- /dev/null +++ b/docs/_source/getting-started/installation-guide.rst @@ -0,0 +1,264 @@ +.. _installation-guide: + +Installation Guide +------------------ + +This guide will help you step by step in installing POSYDON. We recommend using Anaconda, a package manager, to manage the installation and dependencies, ensuring a smooth setup process. + +.. contents:: Table of Contents + :local: + +Using Anaconda (Recommended) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +1. **Install Anaconda** + + If you haven't already, download and install Anaconda from `Anaconda's official website `_. + +2. **Create a New Conda Environment** + + Open your terminal or Anaconda prompt and create a new environment called ``posydon_env`` using Python 3.11 (the ``-y`` automatically answers all confirmations with yes): + + .. code-block:: bash + + conda create --name posydon_env python=3.11 -y + + Activate the new environment: + + .. code-block:: bash + + conda activate posydon_env + +3. **Install POSYDON** + + .. warning:: + This documentation describes the POSYDON v2.0.0 code which is not yet available on Anaconda. Please use the development version for now. See :ref:`Using the Development Version ` for more details. + + With the environment activated, install the POSYDON code from the POSYDON channel using (by default the installation will take place in the current directory, hence please navigate to your desired location first or use the ``-p`` option to specify a path): + + .. code-block:: bash + + conda install -c posydon posydon + +.. _posydon-env: + +4. **Download the Dataset** + + .. warning:: + The POSYDON v2.0.0 dataset is not yet available on Zenodo. The above instructions currently point to the POSYDON v1.0.0 dataset release. Please refer to the development version of the dataset available on Northwestern and UNIGE HPC facilities for now. To have access to latest pre-release dataset (230914) you must be a POSYDON core developer, please refer to the #developers Slack channel. + + You can use POSYDON's built-in API command (by default the downloaded data will be saved in the current directory, hence please navigate to your desired location first): + + .. code-block:: bash + + get-posydon-data + + Alternatively, you can manually download the dataset from Zenodo using the provided `link `_. (TODO: update link to v2) + +5. **Set Environment Variables** + + Export the required paths (please change the location names accordingly to your installation): + + .. code-block:: bash + + export PATH_TO_POSYDON=/path/to/your/posydon/installation + export PATH_TO_POSYDON_DATA=/path/where/you/want/to/store/data + + .. note:: + You can add these lines to your ``~/.bashrc`` or ``~/.bash_profile`` or your shell equivalent to ensure the environment variables are set every time you open a new terminal. + +.. _dev-version: + +Using the Development Version +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +For users interested in the latest features and developments, you can install POSYDON directly from its GitHub repository: + +1. **Clone the Repository** + + In your terminal or command prompt (by default the repository will be placed in the current directory, hence please navigate to your desired location first): + + .. code-block:: bash + + git clone https://github.com/POSYDON-code/POSYDON.git + +2. **Install the Development Version** + + .. warning:: + If you are installing POSYDON on a Mac with Apple M1 or M2 chips, you should first install `hdf5` and `pytables` through conda with `conda install hdf5 pytables`, before following the instractions below. + + Navigate to the cloned repository's directory: + + .. code-block:: bash + + cd POSYDON + + Install the software as an editable package using `pip`: + + .. code-block:: bash + + pip install -e . + +3. **Set Environment Variables and Download Data** + + Refer back to the recommended installation steps, starting from :ref:`point 4 `, to download the required dataset and set the necessary environment variables. + + +Using POSYDON on HPC Facilities +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +(TODO: check whether it is still needed) +If you are planning to run POSYDON's population synthesis on a High-Performance Computing (HPC) facility, it's essential to have `mpi4py` installed to enable parallel computations. + +1. **Install mpi4py via Anaconda (Recommended)**: + + .. code-block:: bash + + conda install mpi4py + +2. **Alternatively, via pip**: + + .. code-block:: bash + + pip install ".[hpc]" + + .. warning:: + Users have reported issues when trying to install `mpi4py` via pip. If you encounter any issues, try installing `mpi4py` through Anaconda. If you cannot solve the issue, please refer to the :ref:`Troubleshooting Guide ` or seek support from the community or developers, see the :ref:`contact us ` page. + +Machine Learning Modules Installation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +For users who wish to utilize POSYDON's latest machine learning features: + +1. **Navigate to your POSYDON directory** (where the `setup.py` is located) and run: + + .. code-block:: bash + + pip install ".[ml]" + + +Installing Experimental Visualization Libraries +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +POSYDON provides experimental visualization libraries to enhance the experience of data analysis and results visualization. While these libraries offer advanced features, please note that they might still be in development and could be subject to changes. + +To install these experimental visualization libraries + +1. **Navigate to your POSYDON directory** (where the `setup.py` is located) and run: + + .. code-block:: bash + + pip install ".[vis]" + + After installing these libraries, you can access various visualization tools and features integrated within POSYDON. Ensure to consult the documentation or any guides associated with these features for their optimal usage. + + .. note:: + As these are experimental features, feedback, and bug reports regarding the visualization tools are highly appreciated. It will aid the development and optimization of these features for future stable releases. + + +Documentation Installation & Compilation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +If you're interested in building the POSYDON documentation locally: + +1. **Install Documentation Modules**: + + Navigate to your POSYDON directory and install the required documentation modules: + + .. code-block:: bash + + pip install ".[doc]" + +2. **Compile the Documentation**: + + Once you have the required modules installed, you can build the documentation using Sphinx: + + .. code-block:: bash + + cd docs + make html + +3. **Install Pandoc via Anaconda** + + .. warning:: + If you are installing POSYDON on a Mac with Apple M1 or M2 chips, you should install `pandoc` through brew with `brew install pandoc`. + + .. code-block:: bash + + conda install pandoc + +4. **Open the Compiled Documentation**: + + After successfully building the documentation, you can view it in your preferred browser. Navigate to the build directory and open the `index.html`: + + .. code-block:: bash + + open _build/html/index.html + + .. note:: + The `open` command works on macOS. If you're using a different OS, you might need to open the `index.html` using your file manager or use a different command. + + +Installing Jupyter for Tutorials +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Our tutorials are provided as Jupyter notebooks. If you want to run these notebooks interactively, you will need to have either Jupyter Lab or Jupyter Notebook installed. + +1. **Using Anaconda (Recommended)** + + + If you have already installed Anaconda as suggested earlier in the installation guide, installing Jupyter Lab or Notebook is straightforward: + + .. code-block:: bash + + conda install -c conda-forge jupyterlab + + Or, for the classic Jupyter Notebook: + + .. code-block:: bash + + conda install -c conda-forge notebook + +2. **Alternatively, via pip** + + + If you prefer using `pip`, you can also install Jupyter Lab or Notebook using the following commands: + + .. code-block:: bash + + pip install jupyterlab + + Or, for the classic Jupyter Notebook: + + .. code-block:: bash + + pip install notebook + +3. **After Installation** + + + Once installed, you can start Jupyter Lab or Notebook by running: + + .. code-block:: bash + + jupyter lab + + Or: + + .. code-block:: bash + + jupyter notebook + + From the terminal or command prompt. This will open a browser window where you can navigate to the downloaded notebooks and run them interactively. + + .. note:: + Remember to navigate to the directory containing the Jupyter notebooks or you won't see them listed in the Jupyter interface. + + +Additional Notes +~~~~~~~~~~~~~~~~~ + +- After installation, ensure you verify the setup by following our :ref:`Verification Guide `. +- Always ensure you activate the `posydon_env` environment before running POSYDON. +- If you encounter issues during the installation, consult our :ref:`Troubleshooting Guide ` or seek support from the community or developers, see the :ref:`contact us ` page. + diff --git a/docs/_source/getting-started/prerequisites.rst b/docs/_source/getting-started/prerequisites.rst new file mode 100644 index 0000000000..af270f97eb --- /dev/null +++ b/docs/_source/getting-started/prerequisites.rst @@ -0,0 +1,60 @@ +Prerequisites +------------- + +Before proceeding with the installation and usage of POSYDON, there are certain prerequisites that must be met. This page outlines all the necessary requirements for a smooth experience with the tool. + +Operating System +~~~~~~~~~~~~~~~~ + +POSYDON has been tested on the following operating systems: + +- Linux (Ubuntu 20.04 and newer, CentOS 7, etc.) +- macOS (10.14 and newer) +- Windows 10 + +(Note: Adapt and expand the list based on the actual compatibility of POSYDON) + +Software Dependencies +~~~~~~~~~~~~~~~~~~~~~ + +1. Python 3.11 +2. [List other software dependencies here, if any.] + +(Note: Expand on any specific versions or configurations, if necessary.) + +Hardware Requirements +~~~~~~~~~~~~~~~~~~~~~ + +**Storage**: + +In order to run a population synthesis model with POSYDON, you must ensure that you have approximately **40GB** of free storage space. This is crucial for downloading the lite MESA simulation library, interpolation objects, and other auxiliary files used by the code. + +Memory: + +- Minimum: 8GB RAM +- Recommended: 16GB RAM + +(Note: Adjust memory requirements based on your knowledge of POSYDON's demands.) + +CPU: + +- Multi-core processor recommended for optimal performance, e.g. HPC cluster. + +(Note: Include more specific CPU details if necessary.) + +Network: + +- A stable internet connection for downloading necessary libraries and datasets. + +Installation Steps +~~~~~~~~~~~~~~~~~~ + +Proceed to :ref:`Installation Guide ` to understand the steps required to get POSYDON up and running. + +Final Notes +~~~~~~~~~~~ + +Ensure all the above prerequisites are met before initiating the setup for POSYDON to ensure a seamless and hassle-free experience. + +If you encounter any issues, please refer to the :ref:`Troubleshooting Guide ` or :ref:`contact us `. + diff --git a/docs/_source/getting-started/verification.rst b/docs/_source/getting-started/verification.rst new file mode 100644 index 0000000000..d358735d0f --- /dev/null +++ b/docs/_source/getting-started/verification.rst @@ -0,0 +1,36 @@ +.. _verification: + +Verification +------------ + +After installing POSYDON, it's essential to verify that the software is functioning correctly. This page provides steps to help you confirm that your installation is successful and the software is operating as intended. + +1. **Initial Check** + + - Start POSYDON. You should see [describe the expected initial interface or message]. + - Check for any error messages in the console or log files. + +2. **Run a Test Simulation** + + Follow the steps below to run a basic test simulation: + + a. [Step-by-step instructions to start a basic simulation. This could be a pre-defined scenario or test case included with the software.] + + b. Once the simulation is complete, review the results. They should match [provide a brief description of expected results or show an example output]. + +3. **Check the Version** + + - In the POSYDON interface (or console), go to [specific location or command] to check the software version. Ensure it matches the version you intended to install. + +4. **Additional Diagnostics** (if applicable) + + - [Any other diagnostic tools or steps that can help verify the software's integrity or functionality.] + +**Troubleshooting** + +If you encounter any discrepancies during verification: + +- Review the :ref:`Installation Guide ` to ensure all steps were followed correctly. +- Consult the :ref:`Troubleshooting Guide ` for common issues and solutions. +- If you continue to experience issues, :ref:`contact us `. + diff --git a/docs/_source/index.rst b/docs/_source/index.rst new file mode 100644 index 0000000000..2628c0da11 --- /dev/null +++ b/docs/_source/index.rst @@ -0,0 +1,140 @@ +============================== +POSYDON Documentation +============================== + + +.. image:: posydon_logo.* + :width: 800 + + + +Welcome to POSYDON! +------------------- + +Welcome to the official documentation of **POSYDON** - *POpulation SYnthesis with Detailed binary-evolution simulatiONs*. Whether you're a new user, a contributor, or just exploring, we're delighted to have you here! + +About POSYDON +------------- + +POSYDON is a next-generation single and binary-star population synthesis tool. Our vision is to provide researchers and enthusiasts with a state-of-the-art platform to delve into the intricacies of stellar structures and binary evolutions using MESA. With full stellar structure modeling, advanced machine learning techniques, and a modular architecture, POSYDON stands at the forefront of astrophysical simulations. + +To stay up to date with the latest news about POSYDON, check out our `official website `_ for more details. + +How is this Documentation Structured? +------------------------------------- + +- **Introductions** Discover what POSYDON is, its objectives, and its science scope. +- **Getting Started:** A quick guide to get POSYDON up and running on your machine. +- **User Guides:** Detailed guides on using various features. +- **Tutorials and Examples:** Step-by-step walkthroughs of typical use-cases. +- **In-Depth Components Overview:** Delve deep into the core components of POSYDON. +- **API Reference:** A comprehensive reference for developers. +- **Troubleshooting and FAQs:** Answers to common questions and problems. +- **Contributing to POSYDON:** Join our community and help us grow! +- **Release Notes and Changelog:** Stay updated with our version history. +- **Contact and Support:** Reach out to us with your queries or feedback. + +.. - **POSYDON Workflow:** Learn about how to conduct simulations from start to finish. + +.. toctree:: + :maxdepth: 1 + :caption: Introduction + + introduction-acknowledgements/intro + introduction-acknowledgements/collaborative-team + Publications + +.. toctree:: + :maxdepth: 1 + :caption: Getting Started + + getting-started/prerequisites + getting-started/installation-guide + getting-started/verification + + +.. toctree:: + :maxdepth: 1 + :caption: User Guides + + user-guides/user-roadmap + user-guides/web-application + user-guides/database-query-system + + +.. toctree:: + :maxdepth: 1 + :titlesonly: + :caption: Tutorials and Examples + + tutorials-examples/population-synthesis/binary-pop-syn + tutorials-examples/generating-datasets/generating-datasets + tutorials-examples/MESA-grids/running-grids + + +.. toctree:: + :maxdepth: 1 + :titlesonly: + :caption: In-Depth Components Overview + + components-overview/mesa-grids + components-overview/processing-pipeline + components-overview/machine-learning-components + components-overview/stellar-binary-simulation + +.. .. toctree:: +.. :maxdepth: 1 +.. :caption: POSYDON Workflow + +.. posydon-workflow/population-specification +.. posydon-workflow/stellar-evolution-choices +.. posydon-workflow/mesa-evolutionary-tracks +.. posydon-workflow/interacting-binary-grids +.. posydon-workflow/running-simulations + +.. toctree:: + :maxdepth: 1 + :caption: API Reference + + api_reference/posydon + +.. toctree:: + :maxdepth: 1 + :caption: Troubleshooting and FAQs + + troubleshooting-faqs/installation-issues + troubleshooting-faqs/code-questions + + +.. toctree:: + :maxdepth: 1 + :caption: Contributing to POSYDON + + contributing/how-to-contribute + contributing/code-style-guidelines + contributing/reporting-issues + + +.. toctree:: + :maxdepth: 1 + :caption: Release Notes and Changelog + + release-notes/version-history + release-notes/major-updates + + +.. toctree:: + :maxdepth: 1 + :caption: Contact and Support + + contact-support/contact-information + + +Acknowledgments +--------------- + +**Team Members:** POSYDON is being led and developed by a dedicated team of astrophysicists and computer scientists. At the helm are Principal Investigators Tassos Fragos (Université de Genève) and Vicky Kalogera (Northwestern University), along with many talented individuals. You can read more about the team and they research on :ref:`Collaborative Team ` page. + +**Funding Agencies:** The POSYDON project is supported primarily by two sources: the Swiss National Science Foundation Professorship grant (PI Fragos) and the Gordon and Betty Moore Foundation (PI Kalogera). + +**Licensing:** POSYDON is licensed under "BSD 3-Clause". 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