From fe1e9d068461f6af7ee56f7b56ce9a4b1c4a8a43 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Wed, 23 Jul 2025 09:02:38 +0200 Subject: [PATCH 01/56] Added boilerplate structure --- .gitignore | 2 ++ diabayes/SVI.py | 0 diabayes/__init__.py | 0 diabayes/forward_models.py | 0 diabayes/inversion.py | 0 pyproject.toml | 38 +++++++++++++++++++++++++++++++++++++ test/test_forward_models.py | 0 7 files changed, 40 insertions(+) create mode 100644 diabayes/SVI.py create mode 100644 diabayes/__init__.py create mode 100644 diabayes/forward_models.py create mode 100644 diabayes/inversion.py create mode 100644 pyproject.toml create mode 100644 test/test_forward_models.py diff --git a/.gitignore b/.gitignore index b7faf40..388b0fe 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,5 @@ +scratch/ + # Byte-compiled / optimized / DLL files __pycache__/ *.py[codz] diff --git a/diabayes/SVI.py b/diabayes/SVI.py new file mode 100644 index 0000000..e69de29 diff --git a/diabayes/__init__.py b/diabayes/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/diabayes/forward_models.py b/diabayes/forward_models.py new file mode 100644 index 0000000..e69de29 diff --git a/diabayes/inversion.py b/diabayes/inversion.py new file mode 100644 index 0000000..e69de29 diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..65facdd --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,38 @@ +[project] +name = "diabayes" +version = "0.0.0" +requires-python = "> 3.0" +authors = [ + { name = "Martijn van den Ende", email = "martijnende@gmail.com" }, +] +dependencies = [ + "jax", + "equinox", + "diffrax", + "optax", + "optimistix", + "matplotlib", + "ipympl", + "ipython", + "jupyter", +] + +[project.optional-dependencies] +dev = ["black", "isort", "twine"] +gpu = ["jax[cuda12]"] + +[build-system] +requires = ["setuptools"] +build-backend = "setuptools.build_meta" + +[tool.setuptools] +packages = ["diabayes"] + +[tool.isort] +profile = "black" + +[tool.pytest.ini_options] +minversion = "6.0" +addopts = "-ra -q" +testpaths = ["test"] +pythonpath = ["scripts"] \ No newline at end of file diff --git a/test/test_forward_models.py b/test/test_forward_models.py new file mode 100644 index 0000000..e69de29 From 74a50f529b9bfc91cef08d1c7ada0fdf5bbd7849 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Wed, 23 Jul 2025 13:52:30 +0200 Subject: [PATCH 02/56] Forward model working, but problem JIT caching --- .gitignore | 1 + README.md | 21 ++++++++- diabayes/__init__.py | 8 ++++ diabayes/forward_models.py | 60 +++++++++++++++++++++++++ diabayes/solver.py | 87 +++++++++++++++++++++++++++++++++++++ diabayes/typedefs.py | 65 +++++++++++++++++++++++++++ pyproject.toml | 2 + test/test_forward_models.py | 10 +++++ 8 files changed, 253 insertions(+), 1 deletion(-) create mode 100644 diabayes/solver.py create mode 100644 diabayes/typedefs.py diff --git a/.gitignore b/.gitignore index 388b0fe..34a0c87 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,5 @@ scratch/ +.vscode/ # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/README.md b/README.md index 97993d5..9c63dad 100644 --- a/README.md +++ b/README.md @@ -1 +1,20 @@ -# diabayes \ No newline at end of file +# DiaBayes: rock friction inversion tools + +Documentation | [Installation](#installation) | [How to cite?](#how-to-cite) + +## Installation + +Installing the latest version with CPU support from GitHub: +```bash +pip install git@github.com:martijnende/diabayes.git +``` +Installing from a local repository with Nvidia GPU support (CUDA version 12): +```bash +cd /path/to/diabase && pip install .[gpu] +``` +If you plan to contribute to the development of this package, please include the development tools: +```bash +pip install .[gpu,dev] +``` + +## How to cite? \ No newline at end of file diff --git a/diabayes/__init__.py b/diabayes/__init__.py index e69de29..71caaa6 100644 --- a/diabayes/__init__.py +++ b/diabayes/__init__.py @@ -0,0 +1,8 @@ +from diabayes.typedefs import ( + BlockConstants, + Constants, + Params, + RSFConstants, + RSFParams, + Variables, +) diff --git a/diabayes/forward_models.py b/diabayes/forward_models.py index e69de29..b2aa895 100644 --- a/diabayes/forward_models.py +++ b/diabayes/forward_models.py @@ -0,0 +1,60 @@ +from typing import Callable + +import equinox as eqx +import jax.numpy as jnp +from jaxtyping import Array, Float + +from diabayes.typedefs import ( + BlockConstants, + Constants, + Params, + RSFConstants, + RSFParams, + SpringBlockConstants, + Variables, +) + + +@eqx.filter_jit +def rsf(variables: Variables, params: RSFParams, constants: RSFConstants) -> Float: + Omega = params.b * jnp.log(variables.state * constants.v0 / params.Dc) + return constants.v0 * jnp.exp((variables.mu - constants.mu0 - Omega) / params.a) + + +@eqx.filter_jit +def ageing_law( + variables: Variables, params: RSFParams, constants: RSFConstants +) -> Float: + return 1 - variables.v * variables.state / params.Dc + + +@eqx.filter_jit +def springblock(variables: Variables, constants: SpringBlockConstants) -> Float: + return constants.k * (constants.v_lp - variables.v) + + +class Forward: + + def __init__( + self, + friction_model: Callable[[Variables, Params, Constants], Float], + state_evolution: Callable[[Variables, Params, Constants], Float], + block_type: Callable[[Variables, BlockConstants], Float], + ) -> None: + self.friction_model = friction_model + self.state_evolution = state_evolution + self.stress_transfer = block_type + pass + + @eqx.filter_jit + def __call__( + self, + variables: Variables, + params: Params, + friction_constants: Constants, + block_constants: BlockConstants, + ) -> Variables: + variables.v = self.friction_model(variables, params, friction_constants) + dstate = self.state_evolution(variables, params, friction_constants) + dmu = self.stress_transfer(variables, block_constants) + return Variables(mu=dmu, state=dstate) diff --git a/diabayes/solver.py b/diabayes/solver.py new file mode 100644 index 0000000..71c1b93 --- /dev/null +++ b/diabayes/solver.py @@ -0,0 +1,87 @@ +from typing import Tuple, Union + +import diffrax as dfx +import jax +from jaxtyping import Array, Float + +from diabayes.forward_models import Forward +from diabayes.typedefs import BlockConstants, Constants, Params, Variables + +jax.config.update("jax_enable_x64", True) + + +class ODESolver: + + forward_model: Forward + rtol: float + atol: float + checkpoints: int + + def __init__( + self, + forward_model: Forward, + rtol: float = 1e-10, + atol: float = 1e-12, + checkpoints: int = 100, + ) -> None: + self.forward_model = forward_model + self.rtol = rtol + self.atol = atol + self.checkpoints = checkpoints + pass + + def _forward_wrapper( + self, + t: Float, + variables: Variables, + # params: Params, + # friction_constants: Constants, + # block_constants: BlockConstants, + args: Tuple[Params, Constants, BlockConstants], + ) -> Variables: + params, friction_constants, block_constants = args + return self.forward_model( + variables=variables, + params=params, + friction_constants=friction_constants, + block_constants=block_constants, + ) + + def solve_forward( + self, + t: Float[Array, "Nt"], + y0: Variables, + params: Params, + friction_constants: Constants, + block_constants: BlockConstants, + adjoint: Union[None, dfx.AbstractAdjoint] = None, + ) -> dfx.Solution: + + term = dfx.ODETerm(self._forward_wrapper) + t0 = t.min() + t1 = t.max() + dt0 = t[1] - t[0] + saveat = dfx.SaveAt(ts=t) + args = (params, friction_constants, block_constants) + + controller = dfx.PIDController(rtol=self.rtol, atol=self.atol) + if adjoint is None: + adjoint = dfx.RecursiveCheckpointAdjoint(checkpoints=self.checkpoints) + assert isinstance(adjoint, dfx.AbstractAdjoint) + + sol = dfx.diffeqsolve( + terms=term, + solver=dfx.Tsit5(), + t0=t0, + t1=t1, + dt0=dt0, + saveat=saveat, + y0=y0, + args=args, + stepsize_controller=controller, + adjoint=adjoint, + ) + + assert sol is not None + + return sol diff --git a/diabayes/typedefs.py b/diabayes/typedefs.py new file mode 100644 index 0000000..45c44ec --- /dev/null +++ b/diabayes/typedefs.py @@ -0,0 +1,65 @@ +from dataclasses import dataclass +from typing import Union + +from jax import tree_util +from jaxtyping import Float + + +@tree_util.register_pytree_node_class +@dataclass(frozen=False) +class Variables: + mu: Float + state: Float + v: Float = 0.0 + + def tree_flatten(self): + return (self.mu, self.state), self.v + + @classmethod + def tree_unflatten(cls, aux_data, children): + return cls(mu=children[0], state=children[1], v=aux_data) + + +# @tree_util.register_pytree_node_class +# @dataclass(frozen=False) +# class RSFParams: +# a: Float +# b: Float +# Dc: Float + +# def tree_flatten(self): +# return (self.a, self.b, self.Dc), None + +# @classmethod +# def tree_unflatten(cls, aux_data, children): +# return cls(*children) + + +@dataclass(frozen=True) +class RSFParams: + a: Float + b: Float + Dc: Float + + +@dataclass(frozen=True) +class CNSParams: + phi_c: Float + Z: Float + + +@dataclass(frozen=True) +class RSFConstants: + v0: Float + mu0: Float + + +@dataclass(frozen=True) +class SpringBlockConstants: + k: Float + v_lp: Float + + +Params = Union[RSFParams, CNSParams] +Constants = Union[RSFConstants] +BlockConstants = Union[SpringBlockConstants] diff --git a/pyproject.toml b/pyproject.toml index 65facdd..adb46a7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,6 +7,7 @@ authors = [ ] dependencies = [ "jax", + "jaxtyping", "equinox", "diffrax", "optax", @@ -15,6 +16,7 @@ dependencies = [ "ipympl", "ipython", "jupyter", + "pytest", ] [project.optional-dependencies] diff --git a/test/test_forward_models.py b/test/test_forward_models.py index e69de29..304a14c 100644 --- a/test/test_forward_models.py +++ b/test/test_forward_models.py @@ -0,0 +1,10 @@ +import jax.numpy as jnp + +from diabayes.forward_models import ageing_law, rsf, springblock # type:ignore + + +class TestForwardModels: + + def test_rate_and_state(self): ... + + def test_spring_block(self): ... From 8c821a181e0e0dbcc4a8d2ce1d93f00aeb856709 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Wed, 23 Jul 2025 14:11:54 +0200 Subject: [PATCH 03/56] Fixed JIT caching --- diabayes/__init__.py | 1 + diabayes/forward_models.py | 16 +++++++++------- diabayes/solver.py | 3 --- diabayes/typedefs.py | 22 +++------------------- 4 files changed, 13 insertions(+), 29 deletions(-) diff --git a/diabayes/__init__.py b/diabayes/__init__.py index 71caaa6..77e9a63 100644 --- a/diabayes/__init__.py +++ b/diabayes/__init__.py @@ -4,5 +4,6 @@ Params, RSFConstants, RSFParams, + SpringBlockConstants, Variables, ) diff --git a/diabayes/forward_models.py b/diabayes/forward_models.py index b2aa895..2187b0f 100644 --- a/diabayes/forward_models.py +++ b/diabayes/forward_models.py @@ -23,14 +23,16 @@ def rsf(variables: Variables, params: RSFParams, constants: RSFConstants) -> Flo @eqx.filter_jit def ageing_law( - variables: Variables, params: RSFParams, constants: RSFConstants + v: Float, variables: Variables, params: RSFParams, constants: RSFConstants ) -> Float: - return 1 - variables.v * variables.state / params.Dc + return 1 - v * variables.state / params.Dc @eqx.filter_jit -def springblock(variables: Variables, constants: SpringBlockConstants) -> Float: - return constants.k * (constants.v_lp - variables.v) +def springblock( + v: Float, variables: Variables, constants: SpringBlockConstants +) -> Float: + return constants.k * (constants.v_lp - v) class Forward: @@ -54,7 +56,7 @@ def __call__( friction_constants: Constants, block_constants: BlockConstants, ) -> Variables: - variables.v = self.friction_model(variables, params, friction_constants) - dstate = self.state_evolution(variables, params, friction_constants) - dmu = self.stress_transfer(variables, block_constants) + v = self.friction_model(variables, params, friction_constants) + dstate = self.state_evolution(v, variables, params, friction_constants) + dmu = self.stress_transfer(v, variables, block_constants) return Variables(mu=dmu, state=dstate) diff --git a/diabayes/solver.py b/diabayes/solver.py index 71c1b93..c622439 100644 --- a/diabayes/solver.py +++ b/diabayes/solver.py @@ -34,9 +34,6 @@ def _forward_wrapper( self, t: Float, variables: Variables, - # params: Params, - # friction_constants: Constants, - # block_constants: BlockConstants, args: Tuple[Params, Constants, BlockConstants], ) -> Variables: params, friction_constants, block_constants = args diff --git a/diabayes/typedefs.py b/diabayes/typedefs.py index 45c44ec..e33d244 100644 --- a/diabayes/typedefs.py +++ b/diabayes/typedefs.py @@ -6,33 +6,17 @@ @tree_util.register_pytree_node_class -@dataclass(frozen=False) +@dataclass(frozen=True) class Variables: mu: Float state: Float - v: Float = 0.0 def tree_flatten(self): - return (self.mu, self.state), self.v + return (self.mu, self.state), None @classmethod def tree_unflatten(cls, aux_data, children): - return cls(mu=children[0], state=children[1], v=aux_data) - - -# @tree_util.register_pytree_node_class -# @dataclass(frozen=False) -# class RSFParams: -# a: Float -# b: Float -# Dc: Float - -# def tree_flatten(self): -# return (self.a, self.b, self.Dc), None - -# @classmethod -# def tree_unflatten(cls, aux_data, children): -# return cls(*children) + return cls(*children) @dataclass(frozen=True) From d851b52680a634b5fa3483d9bc42d246f36a5ed9 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Wed, 23 Jul 2025 15:17:12 +0200 Subject: [PATCH 04/56] Implemented first unit tests --- diabayes/__init__.py | 10 +------- diabayes/forward_models.py | 22 ++++++++-------- diabayes/solver.py | 10 ++++---- diabayes/typedefs.py | 31 +++++++++++++++++----- test/__init__.py | 0 test/aux.py | 22 ++++++++++++++++ test/test_forward_models.py | 51 ++++++++++++++++++++++++++++++++++--- test/test_solvers.py | 34 +++++++++++++++++++++++++ 8 files changed, 146 insertions(+), 34 deletions(-) create mode 100644 test/__init__.py create mode 100644 test/aux.py create mode 100644 test/test_solvers.py diff --git a/diabayes/__init__.py b/diabayes/__init__.py index 77e9a63..99ece2e 100644 --- a/diabayes/__init__.py +++ b/diabayes/__init__.py @@ -1,9 +1 @@ -from diabayes.typedefs import ( - BlockConstants, - Constants, - Params, - RSFConstants, - RSFParams, - SpringBlockConstants, - Variables, -) +from diabayes.typedefs import RSFConstants, RSFParams, SpringBlockConstants, Variables diff --git a/diabayes/forward_models.py b/diabayes/forward_models.py index 2187b0f..b467d63 100644 --- a/diabayes/forward_models.py +++ b/diabayes/forward_models.py @@ -2,16 +2,18 @@ import equinox as eqx import jax.numpy as jnp -from jaxtyping import Array, Float +from jaxtyping import Float from diabayes.typedefs import ( - BlockConstants, - Constants, - Params, RSFConstants, RSFParams, SpringBlockConstants, Variables, + _BlockConstants, + _Constants, + _FrictionModel, + _Params, + _StateEvolution, ) @@ -39,9 +41,9 @@ class Forward: def __init__( self, - friction_model: Callable[[Variables, Params, Constants], Float], - state_evolution: Callable[[Variables, Params, Constants], Float], - block_type: Callable[[Variables, BlockConstants], Float], + friction_model: _FrictionModel, + state_evolution: _StateEvolution, + block_type: Callable[[Float, Variables, _BlockConstants], Float], ) -> None: self.friction_model = friction_model self.state_evolution = state_evolution @@ -52,9 +54,9 @@ def __init__( def __call__( self, variables: Variables, - params: Params, - friction_constants: Constants, - block_constants: BlockConstants, + params: _Params, + friction_constants: _Constants, + block_constants: _BlockConstants, ) -> Variables: v = self.friction_model(variables, params, friction_constants) dstate = self.state_evolution(v, variables, params, friction_constants) diff --git a/diabayes/solver.py b/diabayes/solver.py index c622439..1ae4c97 100644 --- a/diabayes/solver.py +++ b/diabayes/solver.py @@ -5,7 +5,7 @@ from jaxtyping import Array, Float from diabayes.forward_models import Forward -from diabayes.typedefs import BlockConstants, Constants, Params, Variables +from diabayes.typedefs import Variables, _BlockConstants, _Constants, _Params jax.config.update("jax_enable_x64", True) @@ -34,7 +34,7 @@ def _forward_wrapper( self, t: Float, variables: Variables, - args: Tuple[Params, Constants, BlockConstants], + args: Tuple[_Params, _Constants, _BlockConstants], ) -> Variables: params, friction_constants, block_constants = args return self.forward_model( @@ -48,9 +48,9 @@ def solve_forward( self, t: Float[Array, "Nt"], y0: Variables, - params: Params, - friction_constants: Constants, - block_constants: BlockConstants, + params: _Params, + friction_constants: _Constants, + block_constants: _BlockConstants, adjoint: Union[None, dfx.AbstractAdjoint] = None, ) -> dfx.Solution: diff --git a/diabayes/typedefs.py b/diabayes/typedefs.py index e33d244..69d1b38 100644 --- a/diabayes/typedefs.py +++ b/diabayes/typedefs.py @@ -1,5 +1,5 @@ from dataclasses import dataclass -from typing import Union +from typing import Callable, Union from jax import tree_util from jaxtyping import Float @@ -26,6 +26,12 @@ class RSFParams: Dc: Float +@dataclass(frozen=True) +class RSFConstants: + v0: Float + mu0: Float + + @dataclass(frozen=True) class CNSParams: phi_c: Float @@ -33,9 +39,9 @@ class CNSParams: @dataclass(frozen=True) -class RSFConstants: - v0: Float - mu0: Float +class CNSConstants: + phi_c: Float + Z: Float @dataclass(frozen=True) @@ -44,6 +50,17 @@ class SpringBlockConstants: v_lp: Float -Params = Union[RSFParams, CNSParams] -Constants = Union[RSFConstants] -BlockConstants = Union[SpringBlockConstants] +# These typedefs should only be used for type checking, +# and should not be instantiated. +_Params = Union[RSFParams, CNSParams] +_Constants = Union[RSFConstants] +_BlockConstants = Union[SpringBlockConstants] + +_FrictionModel = Union[ + Callable[[Variables, RSFParams, RSFConstants], Float], + Callable[[Variables, CNSParams, CNSConstants], Float], +] +_StateEvolution = Union[ + Callable[[Float, Variables, RSFParams, RSFConstants], Float], + Callable[[Float, Variables, CNSParams, CNSConstants], Float], +] diff --git a/test/__init__.py b/test/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/test/aux.py b/test/aux.py new file mode 100644 index 0000000..a10dea9 --- /dev/null +++ b/test/aux.py @@ -0,0 +1,22 @@ +import diabayes as db + + +def init_params(): + + a = 0.01 + b = 0.9 * a + Dc = 1e-5 + + k = 1e3 + v0 = 1e-6 + v1 = 1e-5 + + mu0 = 0.6 + theta0 = Dc / v0 + + variables = db.Variables(mu=mu0, state=theta0) + params = db.RSFParams(a=a, b=b, Dc=Dc) + constants = db.RSFConstants(v0=v0, mu0=mu0) + block_constants = db.SpringBlockConstants(k=k, v_lp=v1) + + return variables, params, constants, block_constants diff --git a/test/test_forward_models.py b/test/test_forward_models.py index 304a14c..0ddb648 100644 --- a/test/test_forward_models.py +++ b/test/test_forward_models.py @@ -1,10 +1,55 @@ import jax.numpy as jnp -from diabayes.forward_models import ageing_law, rsf, springblock # type:ignore +import diabayes as db +import diabayes.forward_models as db_models + +from .aux import init_params class TestForwardModels: - def test_rate_and_state(self): ... + def test_rate_and_state(self): + + variables, params, constants, block_constants = init_params() + + # Steady-state tests + + dtheta = db_models.ageing_law(constants.v0, variables, params, constants) + assert jnp.allclose(dtheta, 0.0) + + v = db_models.rsf(variables, params, constants) + assert jnp.allclose(v, constants.v0) + + # Asymptotic tests + + v = 1e-20 * block_constants.v_lp + dtheta = db_models.ageing_law(v, variables, params, constants) + assert jnp.allclose(dtheta, 1.0) + + variables2 = db.Variables(mu=0.0, state=variables.state) + v = db_models.rsf(variables2, params, constants) + assert jnp.allclose(v, 0.0) + + variables2 = db.Variables(mu=variables.mu, state=1e20) + v = db_models.rsf(variables2, params, constants) + assert jnp.allclose(v, 0.0) + + def test_spring_block(self): + + variables, _, constants, block_constants = init_params() + + # Steady-state tests + + block_constants2 = db.SpringBlockConstants(block_constants.k, v_lp=constants.v0) + dmu = db_models.springblock(constants.v0, variables, block_constants2) + assert jnp.allclose(dmu, 0.0) + + # Asymptotic tests + + v = 1e-20 * block_constants.v_lp + dmu = db_models.springblock(v, variables, block_constants) + assert jnp.allclose(dmu, block_constants.k * block_constants.v_lp) - def test_spring_block(self): ... + v = 1e20 * block_constants.v_lp + dmu = db_models.springblock(v, variables, block_constants) + assert jnp.allclose(dmu, -block_constants.k * v) diff --git a/test/test_solvers.py b/test/test_solvers.py new file mode 100644 index 0000000..52afff8 --- /dev/null +++ b/test/test_solvers.py @@ -0,0 +1,34 @@ +import jax.numpy as jnp + +import diabayes as db +from diabayes.forward_models import Forward, ageing_law, rsf, springblock +from diabayes.solver import ODESolver + +from .aux import init_params + + +class TestSolvers: + + def test_forward_solver(self): + + variables, params, constants, block_constants = init_params() + forward = Forward(rsf, ageing_law, springblock) + solver = ODESolver(forward) + + t = jnp.linspace(0.0, 100.0, 1000) + result = solver.solve_forward(t, variables, params, constants, block_constants) + + assert result is not None + assert result.ys is not None + + # Steady-state tests + + mu_final = result.ys.mu[-1] + mu_pred = constants.mu0 + (params.a - params.b) * jnp.log( + block_constants.v_lp / constants.v0 + ) + assert jnp.allclose(mu_final, mu_pred) + + state_final = result.ys.state[-1] + state_pred = params.Dc / block_constants.v_lp + assert jnp.allclose(state_final, state_pred) From 31a789670e9283e0f939c7293d1eb13c485eece3 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Wed, 23 Jul 2025 15:40:49 +0200 Subject: [PATCH 05/56] Added Github actions workflow (untested) Fixed minor type checking bug --- .github/workflows/python-test.yml | 34 +++++++++++++++++++++++++++++++ diabayes/forward_models.py | 8 ++++---- diabayes/typedefs.py | 21 ++++++++++--------- pyproject.toml | 1 + 4 files changed, 50 insertions(+), 14 deletions(-) create mode 100644 .github/workflows/python-test.yml diff --git a/.github/workflows/python-test.yml b/.github/workflows/python-test.yml new file mode 100644 index 0000000..6228900 --- /dev/null +++ b/.github/workflows/python-test.yml @@ -0,0 +1,34 @@ +name: Python Unit Tests + +on: + push: + branches: [ "main", "dev" ] + pull_request: + branches: [ "main", "dev" ] + +permissions: + contents: read + +jobs: + build: + + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + - name: Set up Python 3.10 + uses: actions/setup-python@v5 + with: + python-version: "3.10" + - name: Install dependencies + run: | + pip install .[dev] + - name: Lint with flake8 + run: | + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: Test with pytest + run: | + pytest \ No newline at end of file diff --git a/diabayes/forward_models.py b/diabayes/forward_models.py index b467d63..4451971 100644 --- a/diabayes/forward_models.py +++ b/diabayes/forward_models.py @@ -5,15 +5,15 @@ from jaxtyping import Float from diabayes.typedefs import ( + FrictionModel, RSFConstants, RSFParams, SpringBlockConstants, + StateEvolution, Variables, _BlockConstants, _Constants, - _FrictionModel, _Params, - _StateEvolution, ) @@ -41,8 +41,8 @@ class Forward: def __init__( self, - friction_model: _FrictionModel, - state_evolution: _StateEvolution, + friction_model: FrictionModel, + state_evolution: StateEvolution, block_type: Callable[[Float, Variables, _BlockConstants], Float], ) -> None: self.friction_model = friction_model diff --git a/diabayes/typedefs.py b/diabayes/typedefs.py index 69d1b38..352dfd6 100644 --- a/diabayes/typedefs.py +++ b/diabayes/typedefs.py @@ -1,5 +1,5 @@ from dataclasses import dataclass -from typing import Callable, Union +from typing import Any, Callable, Protocol, Union from jax import tree_util from jaxtyping import Float @@ -50,17 +50,18 @@ class SpringBlockConstants: v_lp: Float +class FrictionModel(Protocol): + def __call__(self, variables: Variables, params: Any, constants: Any) -> Float: ... + + +class StateEvolution(Protocol): + def __call__( + self, v: Float, variables: Variables, params: Any, constants: Any + ) -> Float: ... + + # These typedefs should only be used for type checking, # and should not be instantiated. _Params = Union[RSFParams, CNSParams] _Constants = Union[RSFConstants] _BlockConstants = Union[SpringBlockConstants] - -_FrictionModel = Union[ - Callable[[Variables, RSFParams, RSFConstants], Float], - Callable[[Variables, CNSParams, CNSConstants], Float], -] -_StateEvolution = Union[ - Callable[[Float, Variables, RSFParams, RSFConstants], Float], - Callable[[Float, Variables, CNSParams, CNSConstants], Float], -] diff --git a/pyproject.toml b/pyproject.toml index adb46a7..515f975 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,6 +17,7 @@ dependencies = [ "ipython", "jupyter", "pytest", + "flake8", ] [project.optional-dependencies] From ec6710336068ab85476c201d634b612fc96a7a5f Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Wed, 23 Jul 2025 15:43:41 +0200 Subject: [PATCH 06/56] Removed flake from workflow --- .github/workflows/python-test.yml | 6 ------ 1 file changed, 6 deletions(-) diff --git a/.github/workflows/python-test.yml b/.github/workflows/python-test.yml index 6228900..6e2c12b 100644 --- a/.github/workflows/python-test.yml +++ b/.github/workflows/python-test.yml @@ -23,12 +23,6 @@ jobs: - name: Install dependencies run: | pip install .[dev] - - name: Lint with flake8 - run: | - # stop the build if there are Python syntax errors or undefined names - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics - # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - name: Test with pytest run: | pytest \ No newline at end of file From 864726d931e6247261a3f99da7f1ffda2775c48e Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Wed, 23 Jul 2025 15:54:32 +0200 Subject: [PATCH 07/56] Fixed duplicate code actions --- .github/workflows/python-test.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/python-test.yml b/.github/workflows/python-test.yml index 6e2c12b..b9dd177 100644 --- a/.github/workflows/python-test.yml +++ b/.github/workflows/python-test.yml @@ -2,9 +2,9 @@ name: Python Unit Tests on: push: - branches: [ "main", "dev" ] + branches: [ "main" ] pull_request: - branches: [ "main", "dev" ] + branches: [ "main" ] permissions: contents: read From beded664019798e4fd5e9ab7626e620283d0e949 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Wed, 23 Jul 2025 16:33:11 +0200 Subject: [PATCH 08/56] Added documentation logic --- diabayes/typedefs.py | 2 +- docs/Makefile | 20 +++++++++++++++ docs/make.bat | 35 ++++++++++++++++++++++++++ docs/source/conf.py | 43 ++++++++++++++++++++++++++++++++ docs/source/diabayes.rst | 53 ++++++++++++++++++++++++++++++++++++++++ docs/source/index.rst | 19 ++++++++++++++ docs/source/modules.rst | 7 ++++++ pyproject.toml | 2 +- 8 files changed, 179 insertions(+), 2 deletions(-) create mode 100644 docs/Makefile create mode 100644 docs/make.bat create mode 100644 docs/source/conf.py create mode 100644 docs/source/diabayes.rst create mode 100644 docs/source/index.rst create mode 100644 docs/source/modules.rst diff --git a/diabayes/typedefs.py b/diabayes/typedefs.py index 352dfd6..a496803 100644 --- a/diabayes/typedefs.py +++ b/diabayes/typedefs.py @@ -1,5 +1,5 @@ from dataclasses import dataclass -from typing import Any, Callable, Protocol, Union +from typing import Any, Protocol, Union from jax import tree_util from jaxtyping import Float diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000..d0c3cbf --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 0000000..747ffb7 --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=source +set BUILDDIR=build + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +if "%1" == "" goto help + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/source/conf.py b/docs/source/conf.py new file mode 100644 index 0000000..6b8aba6 --- /dev/null +++ b/docs/source/conf.py @@ -0,0 +1,43 @@ +# Configuration file for the Sphinx documentation builder. +# +# For the full list of built-in configuration values, see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Project information ----------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information + +project = "DiaBayes" +copyright = "%Y, Martijn van den Ende" +author = "Martijn van den Ende" + +# -- General configuration --------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration + +extensions = [ + "sphinx_design", + "sphinx_copybutton", + "sphinx.ext.autodoc", + "sphinx.ext.napoleon", + "sphinx.ext.intersphinx", + "sphinx.ext.viewcode", +] + +pygments_style = "sphinx" +napoleon_numpy_docstring = True + +templates_path = ["_templates"] +exclude_patterns = [] + + +# -- Options for HTML output ------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output + +html_theme = "pydata_sphinx_theme" +html_static_path = ["_static"] + +# Configuration for intersphinx. +intersphinx_mapping = { + "python": ("https://docs.python.org/3/", None), + "numpy": ("https://numpy.org/doc/stable", None), + "xarray": ("https://docs.xarray.dev/en/stable", None), +} diff --git a/docs/source/diabayes.rst b/docs/source/diabayes.rst new file mode 100644 index 0000000..d822693 --- /dev/null +++ b/docs/source/diabayes.rst @@ -0,0 +1,53 @@ +diabayes package +================ + +Submodules +---------- + +diabayes.SVI module +------------------- + +.. automodule:: diabayes.SVI + :members: + :show-inheritance: + :undoc-members: + +diabayes.forward\_models module +------------------------------- + +.. automodule:: diabayes.forward_models + :members: + :show-inheritance: + :undoc-members: + +diabayes.inversion module +------------------------- + +.. automodule:: diabayes.inversion + :members: + :show-inheritance: + :undoc-members: + +diabayes.solver module +---------------------- + +.. automodule:: diabayes.solver + :members: + :show-inheritance: + :undoc-members: + +diabayes.typedefs module +------------------------ + +.. automodule:: diabayes.typedefs + :members: + :show-inheritance: + :undoc-members: + +Module contents +--------------- + +.. automodule:: diabayes + :members: + :show-inheritance: + :undoc-members: diff --git a/docs/source/index.rst b/docs/source/index.rst new file mode 100644 index 0000000..4009172 --- /dev/null +++ b/docs/source/index.rst @@ -0,0 +1,19 @@ +.. DiaBayes documentation master file, created by + sphinx-quickstart on Wed Jul 23 16:21:03 2025. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +DiaBayes documentation +====================== + +Add your content using ``reStructuredText`` syntax. See the +`reStructuredText `_ +documentation for details. + + +.. toctree:: + :maxdepth: 2 + :caption: API reference + + diabayes + diff --git a/docs/source/modules.rst b/docs/source/modules.rst new file mode 100644 index 0000000..0cf652b --- /dev/null +++ b/docs/source/modules.rst @@ -0,0 +1,7 @@ +diabayes +======== + +.. toctree:: + :maxdepth: 4 + + diabayes diff --git a/pyproject.toml b/pyproject.toml index 515f975..ea64532 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,12 +17,12 @@ dependencies = [ "ipython", "jupyter", "pytest", - "flake8", ] [project.optional-dependencies] dev = ["black", "isort", "twine"] gpu = ["jax[cuda12]"] +docs = ["pydata-sphinx-theme", "sphinx-design", "sphinx-copybutton", "sphinx"] [build-system] requires = ["setuptools"] From f140dcb211c4eb238d4ce8b2f7ba6c92d61fb29a Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Wed, 23 Jul 2025 16:45:54 +0200 Subject: [PATCH 09/56] Created actions workflow for docs --- .github/workflows/build-docs.yml | 63 ++++++++++++++++++++++++++++++++ 1 file changed, 63 insertions(+) create mode 100644 .github/workflows/build-docs.yml diff --git a/.github/workflows/build-docs.yml b/.github/workflows/build-docs.yml new file mode 100644 index 0000000..9079cdd --- /dev/null +++ b/.github/workflows/build-docs.yml @@ -0,0 +1,63 @@ +name: build_docs + +on: + push: + branches: ["main", "dev"] + +jobs: + build_docs: + runs-on: ubuntu-latest + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.10" + - name: Install dependencies + run: | + pip install make sphinx sphinx-design sphinx-copybutton pydata-sphinx-theme + + - name: Remove all .rst files except index.rst + run: | + find docs/source -type f -name "*.rst" ! -name "index.rst" -exec rm -f {} \; + + - name: Generate .rst files from package + run: | + sphinx-apidoc -o docs/source diabayes + + - name: Build Sphinx HTML documentation + run: | + cd docs + make html + + - name: Upload documentation artifact + uses: actions/upload-artifact@v4 + with: + name: sphinx-html + path: docs/build/html + + # Publish built docs to gh-pages branch + - name: Commit documentation changes + run: | + git clone https://${{ secrets.GH_TOKEN }}@github.com/martijnende/diabayes.git --branch gh-pages --single-branch gh-pages + cd gh-pages + git clean -fdx # Remove old untracked files and directories + cp -r ../docs/build/html/* . # Copy new HTML files to gh-pages + touch .nojekyll + git config --local user.email "action@github.com" + git config --local user.name "GitHub Action" + git add . + git commit -m "Update documentation" -a || true + # The above command will fail if no changes were present, so we ignore + # that. + env: + GH_PAT: ${{ secrets.GH_TOKEN }} + + - name: Push changes + uses: ad-m/github-push-action@master + with: + branch: gh-pages + directory: gh-pages + GH_PAT: ${{ secrets.GH_TOKEN }} \ No newline at end of file From 919a479e5134ecd6e1ea68edb852907cd31f8bbd Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Wed, 23 Jul 2025 17:56:46 +0200 Subject: [PATCH 10/56] Levenberg-Marquardt inversion working --- .github/workflows/build-docs.yml | 44 ++++++++++++++--------------- diabayes/inversion.py | 16 +++++++++++ diabayes/solver.py | 48 ++++++++++++++++++++++++++++++++ diabayes/typedefs.py | 8 ++++++ 4 files changed, 94 insertions(+), 22 deletions(-) diff --git a/.github/workflows/build-docs.yml b/.github/workflows/build-docs.yml index 9079cdd..94cff5e 100644 --- a/.github/workflows/build-docs.yml +++ b/.github/workflows/build-docs.yml @@ -39,25 +39,25 @@ jobs: path: docs/build/html # Publish built docs to gh-pages branch - - name: Commit documentation changes - run: | - git clone https://${{ secrets.GH_TOKEN }}@github.com/martijnende/diabayes.git --branch gh-pages --single-branch gh-pages - cd gh-pages - git clean -fdx # Remove old untracked files and directories - cp -r ../docs/build/html/* . # Copy new HTML files to gh-pages - touch .nojekyll - git config --local user.email "action@github.com" - git config --local user.name "GitHub Action" - git add . - git commit -m "Update documentation" -a || true - # The above command will fail if no changes were present, so we ignore - # that. - env: - GH_PAT: ${{ secrets.GH_TOKEN }} - - - name: Push changes - uses: ad-m/github-push-action@master - with: - branch: gh-pages - directory: gh-pages - GH_PAT: ${{ secrets.GH_TOKEN }} \ No newline at end of file + # - name: Commit documentation changes + # run: | + # git clone https://${{ secrets.GH_TOKEN }}@github.com/martijnende/diabayes.git --branch gh-pages --single-branch gh-pages + # cd gh-pages + # git clean -fdx # Remove old untracked files and directories + # cp -r ../docs/build/html/* . # Copy new HTML files to gh-pages + # touch .nojekyll + # git config --local user.email "action@github.com" + # git config --local user.name "GitHub Action" + # git add . + # git commit -m "Update documentation" -a || true + # # The above command will fail if no changes were present, so we ignore + # # that. + # env: + # GH_PAT: ${{ secrets.GH_TOKEN }} + + # - name: Push changes + # uses: ad-m/github-push-action@master + # with: + # branch: gh-pages + # directory: gh-pages + # GH_PAT: ${{ secrets.GH_TOKEN }} \ No newline at end of file diff --git a/diabayes/inversion.py b/diabayes/inversion.py index e69de29..2353ad3 100644 --- a/diabayes/inversion.py +++ b/diabayes/inversion.py @@ -0,0 +1,16 @@ +import diffrax as dfx +import equinox as eqx +import jax +import jax.numpy as jnp +import jax.random as jr +import optax +import optimistix as optx +from jax import lax +from jaxtyping import Array, Float + +from diabayes import Variables, _Params + +""" +- Implement Levenberg-Marquardt +- +""" diff --git a/diabayes/solver.py b/diabayes/solver.py index 1ae4c97..56a7940 100644 --- a/diabayes/solver.py +++ b/diabayes/solver.py @@ -1,7 +1,9 @@ from typing import Tuple, Union import diffrax as dfx +import equinox as eqx import jax +import optimistix as optx from jaxtyping import Array, Float from diabayes.forward_models import Forward @@ -82,3 +84,49 @@ def solve_forward( assert sol is not None return sol + + @eqx.filter_jit + def _residuals( + self, + params: _Params, + t: Float[Array, "N"], + mu: Float[Array, "N"], + friction_constants: _Constants, + block_constants: _BlockConstants, + ) -> Float[Array, "N"]: + adjoint = dfx.ForwardMode() + theta0 = params.Dc / friction_constants.v0 + y0 = Variables(mu=mu[0], state=theta0) + result = self.solve_forward( + t, y0, params, friction_constants, block_constants, adjoint + ) + mu_hat = result.ys.mu + return mu - mu_hat + + def initial_inversion( + self, + t: Float[Array, "Nt"], + mu: Float[Array, "Nt"], + params: _Params, + friction_constants: _Constants, + block_constants: _BlockConstants, + verbose: bool = False, + ) -> optx.Solution: + + if verbose: + verbose_opts = frozenset(["step", "loss"]) + else: + verbose_opts = None + + options = {"autodiff_mode": "fwd"} + + _residuals = lambda params, mu: self._residuals( + params, t, mu, friction_constants, block_constants + ) + + lm_solver = optx.LevenbergMarquardt(rtol=1e-5, atol=1e-5, verbose=verbose_opts) + sol = optx.least_squares( + _residuals, lm_solver, params, args=mu, options=options + ) + + return sol diff --git a/diabayes/typedefs.py b/diabayes/typedefs.py index a496803..9874def 100644 --- a/diabayes/typedefs.py +++ b/diabayes/typedefs.py @@ -19,12 +19,20 @@ def tree_unflatten(cls, aux_data, children): return cls(*children) +@tree_util.register_pytree_node_class @dataclass(frozen=True) class RSFParams: a: Float b: Float Dc: Float + def tree_flatten(self): + return (self.a, self.b, self.Dc), None + + @classmethod + def tree_unflatten(cls, aux_data, children): + return cls(*children) + @dataclass(frozen=True) class RSFConstants: From e5cf71ed4797c1a49fd56435e53cff833dfc2a68 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Thu, 24 Jul 2025 11:10:32 +0200 Subject: [PATCH 11/56] Implemented test for LM inversion --- diabayes/solver.py | 8 +++++--- diabayes/typedefs.py | 7 +++++++ test/test_solvers.py | 29 +++++++++++++++++++++++++++++ 3 files changed, 41 insertions(+), 3 deletions(-) diff --git a/diabayes/solver.py b/diabayes/solver.py index 56a7940..1a8c5b2 100644 --- a/diabayes/solver.py +++ b/diabayes/solver.py @@ -95,12 +95,14 @@ def _residuals( block_constants: _BlockConstants, ) -> Float[Array, "N"]: adjoint = dfx.ForwardMode() - theta0 = params.Dc / friction_constants.v0 + # TODO: replace this with something like `get_steadystate()` + # because this is different for e.g. CNS + theta0 = params.Dc / friction_constants.v0 # type:ignore y0 = Variables(mu=mu[0], state=theta0) result = self.solve_forward( t, y0, params, friction_constants, block_constants, adjoint ) - mu_hat = result.ys.mu + mu_hat = result.ys.mu # type:ignore return mu - mu_hat def initial_inversion( @@ -116,7 +118,7 @@ def initial_inversion( if verbose: verbose_opts = frozenset(["step", "loss"]) else: - verbose_opts = None + verbose_opts = frozenset([]) options = {"autodiff_mode": "fwd"} diff --git a/diabayes/typedefs.py b/diabayes/typedefs.py index 9874def..52bf311 100644 --- a/diabayes/typedefs.py +++ b/diabayes/typedefs.py @@ -1,6 +1,7 @@ from dataclasses import dataclass from typing import Any, Protocol, Union +import jax.numpy as jnp from jax import tree_util from jaxtyping import Float @@ -18,6 +19,9 @@ def tree_flatten(self): def tree_unflatten(cls, aux_data, children): return cls(*children) + def to_array(self): + return jnp.array([self.mu, self.state]) + @tree_util.register_pytree_node_class @dataclass(frozen=True) @@ -33,6 +37,9 @@ def tree_flatten(self): def tree_unflatten(cls, aux_data, children): return cls(*children) + def to_array(self): + return jnp.array([self.a, self.b, self.Dc]) + @dataclass(frozen=True) class RSFConstants: diff --git a/test/test_solvers.py b/test/test_solvers.py index 52afff8..c82d722 100644 --- a/test/test_solvers.py +++ b/test/test_solvers.py @@ -32,3 +32,32 @@ def test_forward_solver(self): state_final = result.ys.state[-1] state_pred = params.Dc / block_constants.v_lp assert jnp.allclose(state_final, state_pred) + + def test_levenberg_marquardt(self): + + variables, params, constants, block_constants = init_params() + forward = Forward(rsf, ageing_law, springblock) + solver = ODESolver(forward) + + t = jnp.linspace(0.0, 100.0, 1000) + result = solver.solve_forward(t, variables, params, constants, block_constants) + + assert result is not None + assert result.ys is not None + + params2 = db.RSFParams(a=params.a * 1.1, b=params.b * 0.9, Dc=params.Dc * 5) + mu = result.ys.mu + + result_inv = solver.initial_inversion( + t, mu, params2, constants, block_constants, verbose=False + ) + params_inv = result_inv.value + result2 = solver.solve_forward( + t, variables, params_inv, constants, block_constants + ) + + assert result2 is not None + assert result2.ys is not None + + assert jnp.allclose(params.to_array(), params_inv.to_array()) + assert jnp.allclose(result.ys.to_array(), result2.ys.to_array()) From 8f7b18c75cd5d75997ee6e83a776484c6e7d65e6 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Thu, 24 Jul 2025 12:30:57 +0200 Subject: [PATCH 12/56] Created a basic example notebook --- examples/simple_inversion.ipynb | 557 ++++++++++++++++++++++++++++++++ test/test_solvers.py | 20 ++ 2 files changed, 577 insertions(+) create mode 100644 examples/simple_inversion.ipynb diff --git a/examples/simple_inversion.ipynb b/examples/simple_inversion.ipynb new file mode 100644 index 0000000..e7aa5b3 --- /dev/null +++ b/examples/simple_inversion.ipynb @@ -0,0 +1,557 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "6c146450", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib widget" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "73cbf145", + "metadata": {}, + "outputs": [], + "source": [ + "import jax.numpy as jnp\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import diabayes as db\n", + "from diabayes.forward_models import Forward, rsf, ageing_law, springblock\n", + "from diabayes.solver import ODESolver" + ] + }, + { + "cell_type": "markdown", + "id": "73ea42dd", + "metadata": {}, + "source": [ + "In this example, we will be running a simple forward simulation of a non-inertial spring-block governed by classical rate-and-state friction (RSF). The forward simulation serves as a set of \"friction observations\" that we will then invert for the rate-and-state parameters ($a,b,D_c$). If everything goes well, the inverted parameters are identical to the ones we started with." + ] + }, + { + "cell_type": "markdown", + "id": "f60e4390", + "metadata": {}, + "source": [ + "## 1. Forward modelling" + ] + }, + { + "cell_type": "markdown", + "id": "be69a38d", + "metadata": {}, + "source": [ + "First we define the RSF and spring-block parameters, and assume that we start at steady-state:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f81d9956", + "metadata": {}, + "outputs": [], + "source": [ + "# RSF parameters\n", + "a = 0.01\n", + "b = 0.9 * a\n", + "Dc = 1e-5\n", + "\n", + "# Spring-block parameters: stiffness, initial velocity, final velocity\n", + "k = 1e3\n", + "v0 = 1e-6\n", + "v1 = 1e-5\n", + "\n", + "# Initial friction and state\n", + "mu0 = 0.6\n", + "theta0 = Dc / v0" + ] + }, + { + "cell_type": "markdown", + "id": "56391625", + "metadata": {}, + "source": [ + "Next, we describe our forward model, which is composed of a friction law (classical RSF), a state evolution law (ageing law), and the stress transfer model (non-inertial spring-block). We also define the solver, which includes both the forward and inverse solver routines." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9c9b1a41", + "metadata": {}, + "outputs": [], + "source": [ + "forward = Forward(friction_model=rsf, state_evolution=ageing_law, block_type=springblock)\n", + "solver = ODESolver(forward_model=forward)" + ] + }, + { + "cell_type": "markdown", + "id": "355429e9", + "metadata": {}, + "source": [ + "The last step before running a forward simulation, is to pack all of the parameters and initial conditions into appropriate \"containers\". These containers act like `namedtuple`s; you can initialise one by providing the parameters in the right order, or as keyword arguments (for example, `params = db.RSFParams(a, b, Dc)` returns the same as `params = db.RSFParams(Dc=Dc, a=a, b=b)`). You can access individual entries by their name, e.g. `b = params.b`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "89636489", + "metadata": {}, + "outputs": [], + "source": [ + "# Initial values of friction and state\n", + "y0 = db.Variables(mu=mu0, state=theta0)\n", + "# RSF parameters\n", + "params = db.RSFParams(a=a, b=b, Dc=Dc)\n", + "# RSF constants\n", + "constants = db.RSFConstants(v0=v0, mu0=mu0)\n", + "# Spring-block constants\n", + "block_constants = db.SpringBlockConstants(k=k, v_lp=v1)\n", + "\n", + "# The time samples for which we want a solution\n", + "t = jnp.linspace(0, 15., 1000)\n", + "\n", + "# Run the forward simulation\n", + "result = solver.solve_forward(\n", + " t, y0, params=params,\n", + " friction_constants=constants,\n", + " block_constants=block_constants\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "14114ce1", + "metadata": {}, + "source": [ + "You may notice that executing this cell for the first time will take a few seconds, but that the second time it runs must faster. This is because the forward simulations are _just-in-time_ (JIT) compiled and optimised. This needs to be done once for the initial call, after which the underlying code runs much faster for subsequent calls. This may not be optimal for a single forward simulation, but it greatly speeds up the (Bayesian) inversion procedure, which may incur thousands of forward calls.\n", + "\n", + "The result of this forward simulation (`result`) is a [`diffrax.Solution`](https://docs.kidger.site/diffrax/api/solution/#diffrax.Solution) object. It contains a lot of information about the forward run, but in most cases you would only be interested in the solution of the differential equation (the time series of friction and state). This solution is stored in `result.ys`, which is a `db.Variables` object similar to `y0` defined above. Hence the friction and state can be accessed as `result.ys.mu` and `result.ys.state`:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b58d2518", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'time [s]')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "78c4826e61b145fdaf73c9507f7b6e1f", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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", + "text/html": [ + "\n", + "
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For this particular toy example it is not super useful to invert for the parameters that you already know, but in reality you would replace `mu` with your laboratory measurements, for which the RSF parameters are not known.\n", + "\n", + "To start simple, we begin with a _maximum-likelihood_ inversion, which tries to find a (local) minimum in the misfit between the measured and predicted friction values. The parameters that correspond with the best fit may not be unique (there could be multiple combinations of parameters that yield the same misfit), and this kind of inversion does not offer a good sense of the uncertainties and trade-offs in the inverted parameters. For this we'd need to do a _Bayesian inversion_, which will come a bit later. Regardless, the maximum likelihood inversion gives us a good starting point, and might already be sufficient for some applications." + ] + }, + { + "cell_type": "markdown", + "id": "1facf775", + "metadata": {}, + "source": [ + "In the cell below, we generate simulate noisy measurements by adding some Gaussian noise to the friction curve obtained above. We also define an initial guess of the RSF parameters that is a bit different from the true parameter values, which in practice is likely to be the case." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d4c47b4b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step: 0, Loss on this step: 0.057334728304379204\n", + "Step: 1, Loss on this step: 0.008662664862567759\n", + "Step: 2, Loss on this step: 0.04407865522592208\n", + "Step: 3, Loss on this step: 0.0407632034212465\n", + "Step: 4, Loss on this step: 0.03979882593967461\n", + "Step: 5, Loss on this step: 0.054827800646387044\n", + "Step: 6, Loss on this step: 0.1547759225714206\n", + "Step: 7, Loss on this step: 0.005761228910415207\n", + "Step: 8, Loss on this step: 0.003753402302866818\n", + "Step: 9, Loss on this step: 0.0029963676711166837\n", + "Step: 10, Loss on this step: 0.00267729420881892\n", + "Step: 11, Loss on this step: 0.0020681032556638637\n", + "Step: 12, Loss on this step: 0.0017316281478101186\n", + "Step: 13, Loss on this step: 0.0013765930624384474\n", + "Step: 14, Loss on this step: 0.0011562860256277836\n", + "Step: 15, Loss on this step: 0.000991121834253768\n", + "Step: 16, Loss on this step: 0.0008735269805232258\n", + "Step: 17, Loss on this step: 0.0007841762570394665\n", + "Step: 18, Loss on this step: 0.000620184372538452\n", + "Step: 19, Loss on this step: 0.0005421354645256971\n", + "Step: 20, Loss on this step: 0.0004803631736445323\n", + "Step: 21, Loss on this step: 0.0004674834252644044\n", + "Step: 22, Loss on this step: 0.0004670353172771068\n", + "Step: 23, Loss on this step: 0.0004670333632110958\n" + ] + } + ], + "source": [ + "import jax.random as jr\n", + "\n", + "# The JAX random number generator\n", + "key = jr.PRNGKey(42)\n", + "key, split_key = jr.split(key)\n", + "noise = jr.normal(split_key, shape=(len(t),))\n", + "\n", + "# You can play around with the noise amplitude to make\n", + "# the inversion more or less challenging...\n", + "noise_amplitude = 1e-3\n", + "\n", + "mu_measured = mu + noise * noise_amplitude\n", + "\n", + "# Initial guess of the parameters\n", + "params_guess = db.RSFParams(\n", + " a = 1.1 * a,\n", + " b = 1.2 * b,\n", + " Dc = 5 * Dc\n", + ")\n", + "\n", + "# Do the maximum-likelihood inversion\n", + "# Set verbose=True to get intermediate messages\n", + "# of the solver progress\n", + "inv_result = solver.initial_inversion(\n", + " t, mu_measured,\n", + " params=params_guess,\n", + " friction_constants=constants,\n", + " block_constants=block_constants,\n", + " verbose=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "e38a32d4", + "metadata": {}, + "source": [ + "The result of the inversion (`inv_result`) is an [`optimistix.Solution`](https://docs.kidger.site/optimistix/api/solution/#optimistix.Solution) object, similar to the `diffrax.Solution` object we've seen before. The inverted parameters are accessed through `inv_result.value`, which contains a `RSFParams` object.\n", + "\n", + "We can get these parameters and run another forward simulation to see if we obtained an acceptable fit with the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "97c8272e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Inverted parameters: RSFParams(a=Array(0.00990386, dtype=float64), b=Array(0.00891232, dtype=float64), Dc=Array(1.03470351e-05, dtype=float64))\n", + "True parameters: RSFParams(a=0.01, b=0.009000000000000001, Dc=1e-05)\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f451002621234864949fc77c60e19d38", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "params_inv = inv_result.value\n", + "\n", + "result_predicted = solver.solve_forward(t, y0, params_inv, constants, block_constants)\n", + "\n", + "print(f\"Inverted parameters: {params_inv}\")\n", + "print(f\"True parameters: {params}\")\n", + "\n", + "plt.close(\"all\")\n", + "fig, ax = plt.subplots(figsize=(9, 5), constrained_layout=True)\n", + "ax.plot(t, mu_measured)\n", + "ax.plot(t, result_predicted.ys.mu)" + ] + }, + { + "cell_type": "markdown", + "id": "fbac4483", + "metadata": {}, + "source": [ + "As you can see, even with a substantial noise level we were able to recover the true parameters. We can do the inversion again with a different initial guess to see how much this starting point affects the solver convergence:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a0fbfb01", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step: 0, Loss on this step: 0.03734159365807644\n", + "Step: 1, Loss on this step: 0.008450562771825366\n", + "Step: 2, Loss on this step: 0.011316614163166748\n", + "Step: 3, Loss on this step: 0.010433715747893148\n", + "Step: 4, Loss on this step: 0.008123443543690061\n", + "Step: 5, Loss on this step: 0.007670866529293636\n", + "Step: 6, Loss on this step: 0.7705983105364462\n", + "Step: 7, Loss on this step: 1.113562431993166\n", + "Step: 8, Loss on this step: 0.011455439575389807\n", + "Step: 9, Loss on this step: 0.004275817159058383\n", + "Step: 10, Loss on this step: 0.0018859801476722431\n", + "Step: 11, Loss on this step: 0.0015430926093998685\n", + "Step: 12, Loss on this step: 0.0012603736034531433\n", + "Step: 13, Loss on this step: 0.0010728469502646895\n", + "Step: 14, Loss on this step: 0.0009327820011862342\n", + "Step: 15, Loss on this step: 0.0008297305074442885\n", + "Step: 16, Loss on this step: 0.0007508047726027415\n", + "Step: 17, Loss on this step: 0.000604485828538301\n", + "Step: 18, Loss on this step: 0.0004910520582135309\n", + "Step: 19, Loss on this step: 0.00046785413197942606\n", + "Step: 20, Loss on this step: 0.00046703693253931757\n", + "Step: 21, Loss on this step: 0.0004670333642627893\n" + ] + } + ], + "source": [ + "params_guess2 = db.RSFParams(\n", + " a = 0.9 * a,\n", + " b = 0.5 * b,\n", + " Dc = 2 * Dc\n", + ")\n", + "\n", + "inv_result2 = solver.initial_inversion(\n", + " t, mu_measured,\n", + " params=params_guess2,\n", + " friction_constants=constants,\n", + " block_constants=block_constants,\n", + " verbose=True\n", + ")\n", + "\n", + "params_inv2 = inv_result2.value\n", + "result_predicted2 = solver.solve_forward(t, y0, params_inv2, constants, block_constants)" + ] + }, + { + "cell_type": "markdown", + "id": "85119cb0", + "metadata": {}, + "source": [ + "Visual inspection of the results:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1da6bb44", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "edae397ca436499487b20a9916728c07", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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+ "text/html": [ + "\n", + "
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\n", + " Figure\n", + "
\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.close(\"all\")\n", + "fig, ax = plt.subplots(figsize=(9, 5), constrained_layout=True)\n", + "ax.plot(t, mu_measured, alpha=0.3)\n", + "ax.plot(t, result_predicted.ys.mu)\n", + "ax.plot(t, result_predicted2.ys.mu)" + ] + }, + { + "cell_type": "markdown", + "id": "d82ff351", + "metadata": {}, + "source": [ + "Quantitative comparison:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "7ca97ff1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t\tInitial guess 1\tInversion 1\tInitial guess 2\tInversion 2\tTrue values\n", + "a\t\t1.10e-02\t9.90e-03\t9.00e-03\t9.90e-03\t1.00e-02\n", + "b\t\t1.08e-02\t8.91e-03\t4.50e-03\t8.91e-03\t9.00e-03\n", + "Dc\t\t5.00e-05\t1.03e-05\t2.00e-05\t1.03e-05\t1.00e-05\n" + ] + } + ], + "source": [ + "print(\"\\t\\tInitial guess 1\\tInversion 1\\tInitial guess 2\\tInversion 2\\tTrue values\")\n", + "print(f\"a\\t\\t{params_guess.a:.2e}\\t{params_inv.a:.2e}\\t{params_guess2.a:.2e}\\t{params_inv2.a:.2e}\\t{params.a:.2e}\")\n", + "print(f\"b\\t\\t{params_guess.b:.2e}\\t{params_inv.b:.2e}\\t{params_guess2.b:.2e}\\t{params_inv2.b:.2e}\\t{params.b:.2e}\")\n", + "print(f\"Dc\\t\\t{params_guess.Dc:.2e}\\t{params_inv.Dc:.2e}\\t{params_guess2.Dc:.2e}\\t{params_inv2.Dc:.2e}\\t{params.Dc:.2e}\")" + ] + }, + { + "cell_type": "markdown", + "id": "ac6b7d65", + "metadata": {}, + "source": [ + "As you can see, the inversion for both initial guesses converged to the same final result. This is not a given, and results may vary depending on the level of noise and other factors, like slip weakening or other phenomena that are not accounted for by the friction model.\n", + "\n", + "You can also see that the inverted parameters are not exactly the same as those that we started with. Because of the influence of the noise, this is not unexpected. But how \"good\" are the inverted results? Is the ~10% mismatch between the inversion and the ground truth acceptable, given the level of noise that we have? In other words, does the ground truth fall within the uncertainty of the inversion estimate? And are there other combinations of parameters that would also produce an acceptable fit to the observations?\n", + "\n", + "These are the kind of questions that can be answered with Bayesian inversion." + ] + }, + { + "cell_type": "markdown", + "id": "db953902", + "metadata": {}, + "source": [ + "## 3. Bayesian inversion" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "diabayes", + "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": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/test/test_solvers.py b/test/test_solvers.py index c82d722..debd192 100644 --- a/test/test_solvers.py +++ b/test/test_solvers.py @@ -10,6 +10,10 @@ class TestSolvers: def test_forward_solver(self): + """ + Test to see whether the ODE solver runs correctly, + and whether the steady-state result is as expected. + """ variables, params, constants, block_constants = init_params() forward = Forward(rsf, ageing_law, springblock) @@ -34,24 +38,38 @@ def test_forward_solver(self): assert jnp.allclose(state_final, state_pred) def test_levenberg_marquardt(self): + """ + Test for the Levenberg-Marquardt maximum-likelihood + inversion solver. A forward simulation generates the + friction "observation" for a given set of parameters. + Then the true parameters are pertubed and an inversion + is performed. The test checks whether the inverted + parameters are identical to the initial ones, and + whether the friction curves are identical. + """ variables, params, constants, block_constants = init_params() forward = Forward(rsf, ageing_law, springblock) solver = ODESolver(forward) + # Forward simulation to generate "observations" t = jnp.linspace(0.0, 100.0, 1000) result = solver.solve_forward(t, variables, params, constants, block_constants) assert result is not None assert result.ys is not None + # Perturb parameters (initial guess parameters) params2 = db.RSFParams(a=params.a * 1.1, b=params.b * 0.9, Dc=params.Dc * 5) mu = result.ys.mu + # Invert parameters result_inv = solver.initial_inversion( t, mu, params2, constants, block_constants, verbose=False ) params_inv = result_inv.value + + # Reproduce friction curve result2 = solver.solve_forward( t, variables, params_inv, constants, block_constants ) @@ -59,5 +77,7 @@ def test_levenberg_marquardt(self): assert result2 is not None assert result2.ys is not None + # Check that inverted parameters and resulting friction + # curves are identical to the original ones assert jnp.allclose(params.to_array(), params_inv.to_array()) assert jnp.allclose(result.ys.to_array(), result2.ys.to_array()) From 98a75aba62e602822f4a9bd520cc48c5231d9cd4 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Thu, 24 Jul 2025 15:13:38 +0200 Subject: [PATCH 13/56] Added basic SVI functionalities (not tested) --- diabayes/SVI.py | 79 ++++++++++++++++++++++++++++++++++++++++++++ diabayes/typedefs.py | 10 +++++- pyproject.toml | 1 + 3 files changed, 89 insertions(+), 1 deletion(-) diff --git a/diabayes/SVI.py b/diabayes/SVI.py index e69de29..d5bb9a5 100644 --- a/diabayes/SVI.py +++ b/diabayes/SVI.py @@ -0,0 +1,79 @@ +from typing import Callable + +import equinox as eqx +import jax +import jax.numpy as jnp +import optax +from jax import lax +from jax_tqdm import scan_tqdm # type:ignore +from jaxtyping import Array, Float + +from diabayes.typedefs import Variables, _Params + +Particles = Float[Array, "N M"] +Gradients = Float[Array, "N M"] + + +@eqx.filter_jit +def _distance(x: Particles) -> Float[Array, "N N"]: + return jnp.square(x[:, None] - x[None]).sum(axis=-1) + + +@eqx.filter_value_and_grad +def _exponential_kernel(theta: Particles, h: float) -> Float[Array, "N N"]: + """Radial basis distance between the particles `theta`""" + pairwise_dists_sq = _distance(theta) + return jnp.exp(-pairwise_dists_sq / h) + + +@eqx.filter_jit +def _median_trick_h(theta: Particles) -> Float: + """ + Compute the scaling factor `h` proportional to the + squared median of the RMS distance between all pairs + of particles. + """ + pairwise_dists_sq = _distance(theta) + # Replace this one with jnp.nanmedian to avoid spreading NaNs? + med_sq = jnp.median(jnp.sqrt(pairwise_dists_sq)) + h = med_sq**2 / jnp.log(theta.shape[0] + 1) + return h + + +@eqx.filter_jit +def compute_phi(theta: Particles, gradp: Gradients, gradq: Gradients) -> Gradients: + """ + Compute the Stein variational gradients for a set of + particles (`theta`) and the gradients of the log-likelihood + function (`gradp`) and the log-prior (`gradq`). + """ + h = _median_trick_h(theta) + K, grad_K = _exponential_kernel(theta, h) + grad_theta = (jnp.matmul(K, gradp) + jnp.matmul(K, gradq) + grad_K) / gradp.shape[0] + return -grad_theta + + +@eqx.filter_value_and_grad +def _log_likelihood( + log_params: _Params, + mu_obs: Float[Array, "Nt"], + sigma: Float, + forward_fn: Callable[[_Params], Variables], +) -> Float: + # Transform the log_params by taking exponential + params = type(log_params).from_array(jnp.exp(log_params.to_array())) + # Forward pass to get friction curve + mu_hat = forward_fn(params).mu + # Log-likelihood of residuals + p = -(jnp.square(mu_obs - mu_hat).mean() / (2 * sigma**2)) + return p + + +""" +vmap _log_likelihood along particle axis. Since _log_likelihood is +decorated with eqx.filter_value_and_grad, it returns the log- +likelihood and its gradient (hence it needs 2 values for the out_axes). +""" +mapped_log_likelihood = jax.vmap( + _log_likelihood, in_axes=(0, None, None, None), out_axes=(0, 0) +) diff --git a/diabayes/typedefs.py b/diabayes/typedefs.py index 52bf311..8aaa648 100644 --- a/diabayes/typedefs.py +++ b/diabayes/typedefs.py @@ -1,5 +1,5 @@ from dataclasses import dataclass -from typing import Any, Protocol, Union +from typing import Any, Iterable, Protocol, Union import jax.numpy as jnp from jax import tree_util @@ -22,6 +22,10 @@ def tree_unflatten(cls, aux_data, children): def to_array(self): return jnp.array([self.mu, self.state]) + @classmethod + def from_array(cls, x: Iterable): + return cls(*x) + @tree_util.register_pytree_node_class @dataclass(frozen=True) @@ -40,6 +44,10 @@ def tree_unflatten(cls, aux_data, children): def to_array(self): return jnp.array([self.a, self.b, self.Dc]) + @classmethod + def from_array(cls, x: Iterable): + return cls(*x) + @dataclass(frozen=True) class RSFConstants: diff --git a/pyproject.toml b/pyproject.toml index ea64532..0c200bc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,6 +7,7 @@ authors = [ ] dependencies = [ "jax", + "jax-tqdm", "jaxtyping", "equinox", "diffrax", From a124bb466ecf76c6c54c07d1ca0536c85c26271a Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Thu, 24 Jul 2025 15:29:05 +0200 Subject: [PATCH 14/56] Generalised typedefs a bit --- diabayes/typedefs.py | 49 ++++++++++++++++++++++---------------------- 1 file changed, 24 insertions(+), 25 deletions(-) diff --git a/diabayes/typedefs.py b/diabayes/typedefs.py index 8aaa648..1f3568d 100644 --- a/diabayes/typedefs.py +++ b/diabayes/typedefs.py @@ -6,30 +6,36 @@ from jaxtyping import Float -@tree_util.register_pytree_node_class -@dataclass(frozen=True) -class Variables: - mu: Float - state: Float - - def tree_flatten(self): - return (self.mu, self.state), None +class Container: @classmethod def tree_unflatten(cls, aux_data, children): return cls(*children) - def to_array(self): - return jnp.array([self.mu, self.state]) - @classmethod def from_array(cls, x: Iterable): - return cls(*x) + return cls.tree_unflatten(None, x) + + def tree_flatten(self): + raise NotImplementedError + + def to_array(self): + return jnp.array(self.tree_flatten()[0]) + + +@tree_util.register_pytree_node_class +@dataclass(frozen=True) +class Variables(Container): + mu: Float + state: Float + + def tree_flatten(self): + return (self.mu, self.state), None @tree_util.register_pytree_node_class @dataclass(frozen=True) -class RSFParams: +class RSFParams(Container): a: Float b: Float Dc: Float @@ -37,17 +43,6 @@ class RSFParams: def tree_flatten(self): return (self.a, self.b, self.Dc), None - @classmethod - def tree_unflatten(cls, aux_data, children): - return cls(*children) - - def to_array(self): - return jnp.array([self.a, self.b, self.Dc]) - - @classmethod - def from_array(cls, x: Iterable): - return cls(*x) - @dataclass(frozen=True) class RSFConstants: @@ -55,11 +50,15 @@ class RSFConstants: mu0: Float +@tree_util.register_pytree_node_class @dataclass(frozen=True) -class CNSParams: +class CNSParams(Container): phi_c: Float Z: Float + def tree_flatten(self): + return (self.phi_c, self.Z), None + @dataclass(frozen=True) class CNSConstants: From 83ebf872bd5f0068c528eddcfe257f0ba2ab8fe7 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Thu, 24 Jul 2025 18:53:38 +0200 Subject: [PATCH 15/56] Updated docs actions now that repo is public --- .github/workflows/build-docs.yml | 44 ++++++++++++++++---------------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/.github/workflows/build-docs.yml b/.github/workflows/build-docs.yml index 94cff5e..9079cdd 100644 --- a/.github/workflows/build-docs.yml +++ b/.github/workflows/build-docs.yml @@ -39,25 +39,25 @@ jobs: path: docs/build/html # Publish built docs to gh-pages branch - # - name: Commit documentation changes - # run: | - # git clone https://${{ secrets.GH_TOKEN }}@github.com/martijnende/diabayes.git --branch gh-pages --single-branch gh-pages - # cd gh-pages - # git clean -fdx # Remove old untracked files and directories - # cp -r ../docs/build/html/* . # Copy new HTML files to gh-pages - # touch .nojekyll - # git config --local user.email "action@github.com" - # git config --local user.name "GitHub Action" - # git add . - # git commit -m "Update documentation" -a || true - # # The above command will fail if no changes were present, so we ignore - # # that. - # env: - # GH_PAT: ${{ secrets.GH_TOKEN }} - - # - name: Push changes - # uses: ad-m/github-push-action@master - # with: - # branch: gh-pages - # directory: gh-pages - # GH_PAT: ${{ secrets.GH_TOKEN }} \ No newline at end of file + - name: Commit documentation changes + run: | + git clone https://${{ secrets.GH_TOKEN }}@github.com/martijnende/diabayes.git --branch gh-pages --single-branch gh-pages + cd gh-pages + git clean -fdx # Remove old untracked files and directories + cp -r ../docs/build/html/* . # Copy new HTML files to gh-pages + touch .nojekyll + git config --local user.email "action@github.com" + git config --local user.name "GitHub Action" + git add . + git commit -m "Update documentation" -a || true + # The above command will fail if no changes were present, so we ignore + # that. + env: + GH_PAT: ${{ secrets.GH_TOKEN }} + + - name: Push changes + uses: ad-m/github-push-action@master + with: + branch: gh-pages + directory: gh-pages + GH_PAT: ${{ secrets.GH_TOKEN }} \ No newline at end of file From 43d01bbac5d6f7ea874bd3888db96335362bc1d2 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Thu, 24 Jul 2025 19:07:53 +0200 Subject: [PATCH 16/56] Changed workflow titles for badges --- .github/workflows/build-docs.yml | 2 +- .github/workflows/python-test.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build-docs.yml b/.github/workflows/build-docs.yml index 9079cdd..2e42d67 100644 --- a/.github/workflows/build-docs.yml +++ b/.github/workflows/build-docs.yml @@ -1,4 +1,4 @@ -name: build_docs +name: documentation on: push: diff --git a/.github/workflows/python-test.yml b/.github/workflows/python-test.yml index b9dd177..66bfde3 100644 --- a/.github/workflows/python-test.yml +++ b/.github/workflows/python-test.yml @@ -1,4 +1,4 @@ -name: Python Unit Tests +name: tests on: push: From fb6b62c2690d4e99eac89e1a465e3cb67cf827da Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Thu, 24 Jul 2025 19:26:02 +0200 Subject: [PATCH 17/56] Updated README to include status badges --- README.md | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 9c63dad..8640ea8 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,16 @@ # DiaBayes: rock friction inversion tools -Documentation | [Installation](#installation) | [How to cite?](#how-to-cite) +[![GitHub Release](https://img.shields.io/github/release/martijnende/diabase.svg?style=flat)]() +[![tests](https://github.com/martijnende/diabayes/actions/workflows/python-test.yml/badge.svg)](https://github.com/martijnende/diabayes/actions/workflows/python-test.yml) +[![documentation](https://github.com/martijnende/diabayes/actions/workflows/build-docs.yml/badge.svg)](https://martijnende.github.io/diabayes) +[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) + + +[Documentation](https://martijnende.github.io/diabayes) | [Example usage](#example-usage) | [Installation](#installation) | [How to cite?](#how-to-cite) + +## Example usage + +See `examples/simple_example.ipynb` for a self-contained Jupyter notebook that illustrates forward and inverse modelling. ## Installation @@ -17,4 +27,6 @@ If you plan to contribute to the development of this package, please include the pip install .[gpu,dev] ``` -## How to cite? \ No newline at end of file +## How to cite? + +A publication describing this software package is underway. Until then, feel free to refer to this repository. \ No newline at end of file From 765c1bad9c3cfce79ba6c441f16107d07498b695 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Fri, 25 Jul 2025 11:02:36 +0200 Subject: [PATCH 18/56] Added forward_models docstrings --- diabayes/SVI.py | 17 ++++++++ diabayes/forward_models.py | 88 ++++++++++++++++++++++++++++++++++++++ diabayes/inversion.py | 16 ------- docs/source/conf.py | 2 + 4 files changed, 107 insertions(+), 16 deletions(-) delete mode 100644 diabayes/inversion.py diff --git a/diabayes/SVI.py b/diabayes/SVI.py index d5bb9a5..9bf060c 100644 --- a/diabayes/SVI.py +++ b/diabayes/SVI.py @@ -46,6 +46,23 @@ def compute_phi(theta: Particles, gradp: Gradients, gradq: Gradients) -> Gradien Compute the Stein variational gradients for a set of particles (`theta`) and the gradients of the log-likelihood function (`gradp`) and the log-prior (`gradq`). + + Parameters + ---------- + theta : Particles + The set of invertible parameters ("particles") + of shape (Nparticles, Ndimensions) + gradp, gradq : Gradients + The gradients of the log-likelihood (`gradp`) and the + log-prior (`gradq`) with respect to the invertible parameters. + Has a shape (Nparticles, Ndimensions) + + Returns + ------- + grad_theta : Gradients + The directional gradients for the particle updates, e.g. + `new_theta = theta + step_size * grad_theta`. Has the same + shape as `theta` and `gradp, gradq`. """ h = _median_trick_h(theta) K, grad_K = _exponential_kernel(theta, h) diff --git a/diabayes/forward_models.py b/diabayes/forward_models.py index 4451971..472d245 100644 --- a/diabayes/forward_models.py +++ b/diabayes/forward_models.py @@ -19,6 +19,27 @@ @eqx.filter_jit def rsf(variables: Variables, params: RSFParams, constants: RSFConstants) -> Float: + r""" + The classical rate-and-state friction law, given by: + + .. math:: + + v(\mu, \theta) = v_0 \exp \left( \frac{1}{a} \left[ \mu - \mu_0 - b \log \left( \frac{v_0 \theta}{D_c} \right) \right] \right) + + Parameters + ---------- + variables : Variables + The friction coefficient `mu` and state parameter `theta` + params : RSFParams + The rate-and-state parameters `a`, `b`, and `D_c` + constants : RSFConstants + The constant parameters `mu0` and `v0` + + Returns + ------- + v : Float + The instantaneous slip rate in the same units as `v0` + """ Omega = params.b * jnp.log(variables.state * constants.v0 / params.Dc) return constants.v0 * jnp.exp((variables.mu - constants.mu0 - Omega) / params.a) @@ -27,6 +48,29 @@ def rsf(variables: Variables, params: RSFParams, constants: RSFConstants) -> Flo def ageing_law( v: Float, variables: Variables, params: RSFParams, constants: RSFConstants ) -> Float: + r""" + The conventional ageing law state evolution formulation: + + .. math:: + + \frac{\mathrm{d}\theta}{\mathrm{d}t} = 1 - \frac{v \theta}{D_c} + + Parameters + ---------- + v : Float + Instantaneous fault slip rate [m/s]. + variables : Variables + The friction coefficient `mu` and state parameter `theta` + params : RSFParams + The rate-and-state parameters `a`, `b`, and `D_c` + constants : RSFConstants + The constant parameters `mu0` and `v0` + + Returns + ------- + dtheta : Float + The rate of change of the state variable `theta` [s/s] + """ return 1 - v * variables.state / params.Dc @@ -34,10 +78,54 @@ def ageing_law( def springblock( v: Float, variables: Variables, constants: SpringBlockConstants ) -> Float: + r""" + A conventional (non-inertial) spring-block loading formulation: + + .. math:: + + \frac{\mathrm{d} \mu}{\mathrm{d} t} = k \left ( v_{lp} - v(t) \right) + + Parameters + ---------- + v : Float + Instantaneous fault slip rate [m/s]. + variables : Variables + The friction coefficient `mu` and state parameter `theta`. This argument + is not used, but included for call signature consistency. + constants : SpringBlockConstants + The constant parameters stiffness `k` (units of "friction per metre") + and load-point velocity `v_lp` (same units as `v`). + + Returns + ------- + dmu : Float + The rate of change of the friction coefficient `mu` [1/s] + """ return constants.k * (constants.v_lp - v) class Forward: + r""" + The `Forward` class assembles the various components that comprise + a forward model, such that `Forward.__call__` takes some variables + and returns the rate of change of these variables, i.e.: + + .. math:: + + \frac{\mathrm{d} \vec{X}}{\mathrm{d}t} = f \left( \vec{X} \right) + + This forward ODE can then be solved by any ODE solver. + + The `Forward` class is instantiated by providing a friction model + (of type `FrictionModel`), a "state" evolution law (of type `StateEvolution`), + and a stress transfer model. + + Examples + -------- + >>> from diabayes.forward_models import ageing_law, rsf, springblock, Forward + >>> foward_model = Forward(rsf, ageing_law, springblock) + >>> X_dot = forward_model(variables=..., params=..., friction_constants=..., block_constants=...) + """ def __init__( self, diff --git a/diabayes/inversion.py b/diabayes/inversion.py deleted file mode 100644 index 2353ad3..0000000 --- a/diabayes/inversion.py +++ /dev/null @@ -1,16 +0,0 @@ -import diffrax as dfx -import equinox as eqx -import jax -import jax.numpy as jnp -import jax.random as jr -import optax -import optimistix as optx -from jax import lax -from jaxtyping import Array, Float - -from diabayes import Variables, _Params - -""" -- Implement Levenberg-Marquardt -- -""" diff --git a/docs/source/conf.py b/docs/source/conf.py index 6b8aba6..08d9dc1 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -18,8 +18,10 @@ "sphinx_copybutton", "sphinx.ext.autodoc", "sphinx.ext.napoleon", + "sphinx.ext.doctest", "sphinx.ext.intersphinx", "sphinx.ext.viewcode", + "sphinx.ext.mathjax", ] pygments_style = "sphinx" From 308d5136a785db82df7e03a4c7e5748b614c88c9 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Fri, 25 Jul 2025 11:15:18 +0200 Subject: [PATCH 19/56] Updated documentation building workflow to fix sphinx-apidoc --- .github/workflows/build-docs.yml | 2 +- .gitignore | 2 ++ docs/source/diabayes.rst | 53 -------------------------------- docs/source/index.rst | 2 +- 4 files changed, 4 insertions(+), 55 deletions(-) delete mode 100644 docs/source/diabayes.rst diff --git a/.github/workflows/build-docs.yml b/.github/workflows/build-docs.yml index 2e42d67..c08fb32 100644 --- a/.github/workflows/build-docs.yml +++ b/.github/workflows/build-docs.yml @@ -25,7 +25,7 @@ jobs: - name: Generate .rst files from package run: | - sphinx-apidoc -o docs/source diabayes + sphinx-apidoc -o docs/source/api diabayes - name: Build Sphinx HTML documentation run: | diff --git a/.gitignore b/.gitignore index 34a0c87..77ab8e8 100644 --- a/.gitignore +++ b/.gitignore @@ -73,6 +73,8 @@ instance/ # Sphinx documentation docs/_build/ +docs/build/ +docs/source/api # PyBuilder .pybuilder/ diff --git a/docs/source/diabayes.rst b/docs/source/diabayes.rst deleted file mode 100644 index d822693..0000000 --- a/docs/source/diabayes.rst +++ /dev/null @@ -1,53 +0,0 @@ -diabayes package -================ - -Submodules ----------- - -diabayes.SVI module -------------------- - -.. automodule:: diabayes.SVI - :members: - :show-inheritance: - :undoc-members: - -diabayes.forward\_models module -------------------------------- - -.. automodule:: diabayes.forward_models - :members: - :show-inheritance: - :undoc-members: - -diabayes.inversion module -------------------------- - -.. automodule:: diabayes.inversion - :members: - :show-inheritance: - :undoc-members: - -diabayes.solver module ----------------------- - -.. automodule:: diabayes.solver - :members: - :show-inheritance: - :undoc-members: - -diabayes.typedefs module ------------------------- - -.. automodule:: diabayes.typedefs - :members: - :show-inheritance: - :undoc-members: - -Module contents ---------------- - -.. automodule:: diabayes - :members: - :show-inheritance: - :undoc-members: diff --git a/docs/source/index.rst b/docs/source/index.rst index 4009172..5d8dd39 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -15,5 +15,5 @@ documentation for details. :maxdepth: 2 :caption: API reference - diabayes + api/modules From 667033e35ebbde34c778b6886370ba35d3eca2e2 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Fri, 25 Jul 2025 11:31:28 +0200 Subject: [PATCH 20/56] Changed workflow logic for better reuse --- .github/actions/setup/install.yml | 10 ++++++++++ .github/workflows/build-docs.yml | 8 +++++--- .github/workflows/python-test.yml | 4 +--- pyproject.toml | 8 +++++++- 4 files changed, 23 insertions(+), 7 deletions(-) create mode 100644 .github/actions/setup/install.yml diff --git a/.github/actions/setup/install.yml b/.github/actions/setup/install.yml new file mode 100644 index 0000000..a446430 --- /dev/null +++ b/.github/actions/setup/install.yml @@ -0,0 +1,10 @@ +name: Setup project +description: Checkout code, install dependencies + +runs: + using: "composite" + steps: + - name: Install dependencies + shell: bash + run: | + pip install .[dev,docs] \ No newline at end of file diff --git a/.github/workflows/build-docs.yml b/.github/workflows/build-docs.yml index c08fb32..9b219a9 100644 --- a/.github/workflows/build-docs.yml +++ b/.github/workflows/build-docs.yml @@ -15,9 +15,11 @@ jobs: uses: actions/setup-python@v5 with: python-version: "3.10" - - name: Install dependencies - run: | - pip install make sphinx sphinx-design sphinx-copybutton pydata-sphinx-theme + - uses: ./.github/actions/setup + + # - name: Install dependencies + # run: | + # pip install make sphinx sphinx-design sphinx-copybutton pydata-sphinx-theme - name: Remove all .rst files except index.rst run: | diff --git a/.github/workflows/python-test.yml b/.github/workflows/python-test.yml index 66bfde3..9000f79 100644 --- a/.github/workflows/python-test.yml +++ b/.github/workflows/python-test.yml @@ -20,9 +20,7 @@ jobs: uses: actions/setup-python@v5 with: python-version: "3.10" - - name: Install dependencies - run: | - pip install .[dev] + - uses: ./.github/actions/setup - name: Test with pytest run: | pytest \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 0c200bc..72f1afc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,7 +23,13 @@ dependencies = [ [project.optional-dependencies] dev = ["black", "isort", "twine"] gpu = ["jax[cuda12]"] -docs = ["pydata-sphinx-theme", "sphinx-design", "sphinx-copybutton", "sphinx"] +docs = [ + "make", + "pydata-sphinx-theme", + "sphinx", + "sphinx-copybutton", + "sphinx-design" +] [build-system] requires = ["setuptools"] From 3d74ff29b87ad896f779873726ddc1799b5a8e51 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Fri, 25 Jul 2025 11:32:47 +0200 Subject: [PATCH 21/56] Fixed file naming github actions --- .github/actions/setup/{install.yml => action.yml} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename .github/actions/setup/{install.yml => action.yml} (100%) diff --git a/.github/actions/setup/install.yml b/.github/actions/setup/action.yml similarity index 100% rename from .github/actions/setup/install.yml rename to .github/actions/setup/action.yml From a70e0eb856762cf676ac262a0cc65bd211ce4f81 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Fri, 25 Jul 2025 16:01:43 +0200 Subject: [PATCH 22/56] Probably going to remove the Particles typedef logic later --- diabayes/SVI.py | 24 +++-- diabayes/solver.py | 20 +++- diabayes/typedefs.py | 177 ++++++++++++++++++++++++++++++-- examples/simple_inversion.ipynb | 22 ++-- test/test_solvers.py | 2 +- test/test_typedefs.py | 64 ++++++++++++ 6 files changed, 280 insertions(+), 29 deletions(-) create mode 100644 test/test_typedefs.py diff --git a/diabayes/SVI.py b/diabayes/SVI.py index 9bf060c..9d2e8fe 100644 --- a/diabayes/SVI.py +++ b/diabayes/SVI.py @@ -1,4 +1,4 @@ -from typing import Callable +from typing import Callable, TypeAlias import equinox as eqx import jax @@ -10,24 +10,24 @@ from diabayes.typedefs import Variables, _Params -Particles = Float[Array, "N M"] -Gradients = Float[Array, "N M"] +ParticleArray = Float[Array, "N M"] +GradientArray: TypeAlias = ParticleArray @eqx.filter_jit -def _distance(x: Particles) -> Float[Array, "N N"]: +def _distance(x: ParticleArray) -> Float[Array, "N N"]: return jnp.square(x[:, None] - x[None]).sum(axis=-1) @eqx.filter_value_and_grad -def _exponential_kernel(theta: Particles, h: float) -> Float[Array, "N N"]: +def _exponential_kernel(theta: ParticleArray, h: float) -> Float[Array, "N N"]: """Radial basis distance between the particles `theta`""" pairwise_dists_sq = _distance(theta) return jnp.exp(-pairwise_dists_sq / h) @eqx.filter_jit -def _median_trick_h(theta: Particles) -> Float: +def _median_trick_h(theta: ParticleArray) -> Float: """ Compute the scaling factor `h` proportional to the squared median of the RMS distance between all pairs @@ -36,12 +36,14 @@ def _median_trick_h(theta: Particles) -> Float: pairwise_dists_sq = _distance(theta) # Replace this one with jnp.nanmedian to avoid spreading NaNs? med_sq = jnp.median(jnp.sqrt(pairwise_dists_sq)) - h = med_sq**2 / jnp.log(theta.shape[0] + 1) + h = med_sq**2 / jnp.log(len(theta) + 1) return h @eqx.filter_jit -def compute_phi(theta: Particles, gradp: Gradients, gradq: Gradients) -> Gradients: +def compute_phi( + theta: ParticleArray, gradp: GradientArray, gradq: GradientArray +) -> GradientArray: """ Compute the Stein variational gradients for a set of particles (`theta`) and the gradients of the log-likelihood @@ -49,10 +51,10 @@ def compute_phi(theta: Particles, gradp: Gradients, gradq: Gradients) -> Gradien Parameters ---------- - theta : Particles + theta : ParticleArray The set of invertible parameters ("particles") of shape (Nparticles, Ndimensions) - gradp, gradq : Gradients + gradp, gradq : GradientArray The gradients of the log-likelihood (`gradp`) and the log-prior (`gradq`) with respect to the invertible parameters. Has a shape (Nparticles, Ndimensions) @@ -66,7 +68,7 @@ def compute_phi(theta: Particles, gradp: Gradients, gradq: Gradients) -> Gradien """ h = _median_trick_h(theta) K, grad_K = _exponential_kernel(theta, h) - grad_theta = (jnp.matmul(K, gradp) + jnp.matmul(K, gradq) + grad_K) / gradp.shape[0] + grad_theta = (jnp.matmul(K, gradp) + jnp.matmul(K, gradq) + grad_K) / len(gradp) return -grad_theta diff --git a/diabayes/solver.py b/diabayes/solver.py index 1a8c5b2..18607c0 100644 --- a/diabayes/solver.py +++ b/diabayes/solver.py @@ -7,7 +7,13 @@ from jaxtyping import Array, Float from diabayes.forward_models import Forward -from diabayes.typedefs import Variables, _BlockConstants, _Constants, _Params +from diabayes.typedefs import ( + BayesianSolution, + Variables, + _BlockConstants, + _Constants, + _Params, +) jax.config.update("jax_enable_x64", True) @@ -105,7 +111,7 @@ def _residuals( mu_hat = result.ys.mu # type:ignore return mu - mu_hat - def initial_inversion( + def max_likelihood_inversion( self, t: Float[Array, "Nt"], mu: Float[Array, "Nt"], @@ -132,3 +138,13 @@ def initial_inversion( ) return sol + + def bayesian_inversion( + self, + t: Float[Array, "Nt"], + mu: Float[Array, "Nt"], + params: _Params, + friction_constants: _Constants, + block_constants: _BlockConstants, + verbose: bool = False, + ) -> BayesianSolution: ... diff --git a/diabayes/typedefs.py b/diabayes/typedefs.py index 1f3568d..b6b927a 100644 --- a/diabayes/typedefs.py +++ b/diabayes/typedefs.py @@ -1,9 +1,26 @@ from dataclasses import dataclass -from typing import Any, Iterable, Protocol, Union - +from typing import ( + Any, + Iterable, + NamedTuple, + Protocol, + Sequence, + Tuple, + TypeAlias, + Union, +) + +import jax import jax.numpy as jnp +import jax.random as jr from jax import tree_util -from jaxtyping import Float +from jaxtyping import Array, Float + +""" +-------------------- +Parameter containers +-------------------- +""" class Container: @@ -72,6 +89,20 @@ class SpringBlockConstants: v_lp: Float +# These typedefs should only be used for type checking, +# and should not be instantiated. +_Params = Union[RSFParams, CNSParams] +_Constants = Union[RSFConstants] +_BlockConstants = Union[SpringBlockConstants] + + +""" +------------------- +Function signatures +------------------- +""" + + class FrictionModel(Protocol): def __call__(self, variables: Variables, params: Any, constants: Any) -> Float: ... @@ -82,8 +113,138 @@ def __call__( ) -> Float: ... -# These typedefs should only be used for type checking, -# and should not be instantiated. -_Params = Union[RSFParams, CNSParams] -_Constants = Union[RSFConstants] -_BlockConstants = Union[SpringBlockConstants] +""" +-------------- +SVI containers +-------------- +""" + + +@tree_util.register_pytree_node_class +@dataclass(frozen=True) +class Particles: + + _particles: Union[Tuple[RSFParams, ...], Tuple[CNSParams, ...]] + + def __getitem__(self, idx): + # Check if the requested selection is of the kind `x[1, 3]` + if isinstance(idx, tuple): + assert ( + len(idx) == 2 + ), "Particles do not support item selection with more than 2 indices" + i, j = idx + return self._particles[i].to_array()[j] + # Else the selection is of the kind `x[4]` + return self._particles[idx] + + def __iter__(self): + return iter(self._particles) + + def __len__(self): + return len(self._particles) + + @classmethod + def tree_unflatten(cls, aux_data, children): + raise NotImplementedError + + @classmethod + def from_array(cls, x: Iterable): + return cls.tree_unflatten(None, x) + + def tree_flatten(self): + return self._particles, None + + def to_array(self): + return jnp.array([p.tree_flatten()[0] for p in self._particles]) + + @classmethod + def generate( + cls, N: int, loc: Float[Array, "M"], scale: Float[Array, "M"], key: jax.Array + ): + assert len(loc) == len(scale) + x = jr.normal(key, shape=(N, len(loc))) * scale[None] + loc[None] + return cls.from_array(x) + + +@tree_util.register_pytree_node_class +@dataclass(frozen=True) +class RSFParticles(Particles): + + @classmethod + def tree_unflatten(cls, aux_data, children): + x = tuple(RSFParams(*child) for child in children) + return cls(x) + + +@tree_util.register_pytree_node_class +@dataclass(frozen=True) +class Chains(Container): + + _chains: Sequence[Particles] + + def __getitem__(self, idx): + + # Check if the requested selection is of the kind `x[1, 3]` or `x[1, 3, 2]` + if isinstance(idx, tuple): + assert ( + 1 < len(idx) <= 3 + ), "Chains do not support item selection with more than 3 indices" + + if len(idx) == 2: + i, j = idx + return self._chains[i][j] + + i, j, k = idx + return self._chains[i][j].to_array(k) + + # Else the selection is of the kind `x[4]` + return self._chains[idx] + + def __iter__(self): + return iter(self._chains) + + def __len__(self): + return len(self._chains) + + def tree_flatten(self): + return self._chains, None + + def to_array(self): + return jnp.array([c.to_array() for c in self._chains]) + + +# Alias gradients for semantic clarity +Gradients: TypeAlias = Particles + + +class Statistics(NamedTuple): + mean: Float + median: Float + std: Float + q5: Float + q95: Float + + +class RSFStatistics(NamedTuple): + a: Statistics + b: Statistics + Dc: Statistics + + +class CNSStatistics(NamedTuple): + a: Statistics + b: Statistics + Dc: Statistics + + +@dataclass +class BayesianSolution: + + chains: Chains + statistics: Union[RSFStatistics, CNSStatistics] + + def __init__(self, chains: Chains): + self.chains = chains + final_state = chains[-1] + # TODO: final_state contains N particles + # diff --git a/examples/simple_inversion.ipynb b/examples/simple_inversion.ipynb index e7aa5b3..038a4fb 100644 --- a/examples/simple_inversion.ipynb +++ b/examples/simple_inversion.ipynb @@ -154,7 +154,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "78c4826e61b145fdaf73c9507f7b6e1f", + "model_id": "f4194f2e7e0545ae92cc4842f2203c89", "version_major": 2, "version_minor": 0 }, @@ -218,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "d4c47b4b", "metadata": {}, "outputs": [ @@ -277,7 +277,7 @@ "# Do the maximum-likelihood inversion\n", "# Set verbose=True to get intermediate messages\n", "# of the solver progress\n", - "inv_result = solver.initial_inversion(\n", + "inv_result = solver.max_likelihood_inversion(\n", " t, mu_measured,\n", " params=params_guess,\n", " friction_constants=constants,\n", @@ -313,7 +313,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 8, @@ -323,7 +323,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f451002621234864949fc77c60e19d38", + "model_id": "ef4a71096e3f409fb63ef0fb0498884e", "version_major": 2, "version_minor": 0 }, @@ -370,7 +370,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "a0fbfb01", "metadata": {}, "outputs": [ @@ -410,7 +410,7 @@ " Dc = 2 * Dc\n", ")\n", "\n", - "inv_result2 = solver.initial_inversion(\n", + "inv_result2 = solver.max_likelihood_inversion(\n", " t, mu_measured,\n", " params=params_guess2,\n", " friction_constants=constants,\n", @@ -531,6 +531,14 @@ "source": [ "## 3. Bayesian inversion" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a9fbf61", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/test/test_solvers.py b/test/test_solvers.py index debd192..84cb7d3 100644 --- a/test/test_solvers.py +++ b/test/test_solvers.py @@ -64,7 +64,7 @@ def test_levenberg_marquardt(self): mu = result.ys.mu # Invert parameters - result_inv = solver.initial_inversion( + result_inv = solver.max_likelihood_inversion( t, mu, params2, constants, block_constants, verbose=False ) params_inv = result_inv.value diff --git a/test/test_typedefs.py b/test/test_typedefs.py new file mode 100644 index 0000000..67a1f45 --- /dev/null +++ b/test/test_typedefs.py @@ -0,0 +1,64 @@ +import jax.numpy as jnp + +import diabayes as db +from diabayes.typedefs import Chains, RSFParams, RSFParticles, RSFStatistics, Variables + + +class TestTypedefs: + + def test_basic_containers(self): + + a = 1.0 + b = 2.0 + c = 3.0 + + assert ( + Variables(a, b) + == Variables(mu=a, state=b) + == Variables.from_array(jnp.array([a, b])) + == Variables.tree_unflatten(None, jnp.array([a, b])) + ) + variables = Variables(mu=a, state=b) + assert jnp.allclose( + variables.to_array(), jnp.array(variables.tree_flatten()[0]) + ) + + assert ( + RSFParams(a, b, c) + == RSFParams(a=a, b=b, Dc=c) + == RSFParams.from_array(jnp.array([a, b, c])) + == RSFParams.tree_unflatten(None, jnp.array([a, b, c])) + ) + params = RSFParams(a=a, b=b, Dc=c) + assert jnp.allclose(params.to_array(), jnp.array(params.tree_flatten()[0])) + + def test_SVI_containers(self): + + import jax.random as jr + + key = jr.PRNGKey(42) + + # Test Particles + + key, split_key = jr.split(key) + Nparticles = 100 + loc = jnp.array([1.0, 2.0, 3.0]) + scale = jnp.ones(3) + particles = RSFParticles.generate(Nparticles, loc, scale, split_key) + + assert len(particles) == Nparticles + + x = particles.to_array() + assert x.shape[1] == len(loc) + + rtol = 2 / jnp.sqrt(Nparticles) + assert jnp.allclose(x.mean(axis=0), loc, rtol=rtol) + assert jnp.allclose(x.std(axis=0), scale, rtol=rtol) + + assert jnp.allclose(x, RSFParticles.from_array(x).to_array()) + + # Test Chains + + pass + + def test_Bayesian_statistics(self): ... From c951bf75590df7fb28d6e448e66c463f1eadbcb2 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Tue, 29 Jul 2025 14:59:54 +0200 Subject: [PATCH 23/56] Started implementing SVI, but bugs around every corner --- diabayes/SVI.py | 74 ++++++++----- diabayes/solver.py | 163 ++++++++++++++++++++++++++++- diabayes/typedefs.py | 6 ++ docs/source/diabayes.rst | 45 ++++++++ examples/simple_inversion.ipynb | 180 +++++++++++++++++++------------- pyproject.toml | 2 +- test/test_solvers.py | 27 +++-- 7 files changed, 379 insertions(+), 118 deletions(-) create mode 100644 docs/source/diabayes.rst diff --git a/diabayes/SVI.py b/diabayes/SVI.py index 9d2e8fe..3c90133 100644 --- a/diabayes/SVI.py +++ b/diabayes/SVI.py @@ -1,4 +1,4 @@ -from typing import Callable, TypeAlias +from typing import Callable, Tuple, TypeAlias import equinox as eqx import jax @@ -15,43 +15,45 @@ @eqx.filter_jit -def _distance(x: ParticleArray) -> Float[Array, "N N"]: - return jnp.square(x[:, None] - x[None]).sum(axis=-1) +def _distance(x1: ParticleArray, x2: ParticleArray) -> Float[Array, "N N"]: + return jnp.square(x1 - x2).sum(axis=-1) -@eqx.filter_value_and_grad -def _exponential_kernel(theta: ParticleArray, h: float) -> Float[Array, "N N"]: - """Radial basis distance between the particles `theta`""" - pairwise_dists_sq = _distance(theta) - return jnp.exp(-pairwise_dists_sq / h) +@eqx.filter_jit +def _exponential_kernel( + x1: ParticleArray, x2: ParticleArray, h: float +) -> Float[Array, "N N"]: + """Radial basis distance between two particles `x1` and `x2`""" + dist_sq = _distance(x1, x2) + return jnp.exp(-dist_sq / h) @eqx.filter_jit -def _median_trick_h(theta: ParticleArray) -> Float: +def _median_trick_h(x: ParticleArray) -> Float: """ Compute the scaling factor `h` proportional to the squared median of the RMS distance between all pairs of particles. """ - pairwise_dists_sq = _distance(theta) + dist_sq = _distance(x, x) # Replace this one with jnp.nanmedian to avoid spreading NaNs? - med_sq = jnp.median(jnp.sqrt(pairwise_dists_sq)) - h = med_sq**2 / jnp.log(len(theta) + 1) + med_sq = jnp.median(jnp.sqrt(dist_sq)) + h = med_sq**2 / jnp.log(len(x) + 1) return h @eqx.filter_jit def compute_phi( - theta: ParticleArray, gradp: GradientArray, gradq: GradientArray + x: ParticleArray, gradp: GradientArray, gradq: GradientArray ) -> GradientArray: """ Compute the Stein variational gradients for a set of - particles (`theta`) and the gradients of the log-likelihood + particles (`x`) and the gradients of the log-likelihood function (`gradp`) and the log-prior (`gradq`). Parameters ---------- - theta : ParticleArray + x : ParticleArray The set of invertible parameters ("particles") of shape (Nparticles, Ndimensions) gradp, gradq : GradientArray @@ -61,30 +63,44 @@ def compute_phi( Returns ------- - grad_theta : Gradients + grad_x : Gradients The directional gradients for the particle updates, e.g. - `new_theta = theta + step_size * grad_theta`. Has the same - shape as `theta` and `gradp, gradq`. + `new_x = x + step_size * grad_x`. Has the same + shape as `x` and `gradp, gradq`. """ - h = _median_trick_h(theta) - K, grad_K = _exponential_kernel(theta, h) - grad_theta = (jnp.matmul(K, gradp) + jnp.matmul(K, gradq) + grad_K) / len(gradp) - return -grad_theta + h = _median_trick_h(x) + map_over_a = (0, None, None) + map_over_b = (None, 0, None) + + map1 = jax.vmap(_exponential_kernel, map_over_a, 0) + kernel = jax.vmap(map1, map_over_b, 1) + K = kernel(x, x, h) + + map1 = jax.vmap(jax.grad(_exponential_kernel), map_over_a, 0) + grad_kernel = jax.vmap(map1, map_over_b, 1) + grad_K = grad_kernel(x, x, h).sum(axis=0) + + grad_x = (jnp.matmul(K, gradp) + jnp.matmul(K, gradq) + grad_K) / len(gradp) + return -grad_x @eqx.filter_value_and_grad def _log_likelihood( - log_params: _Params, + log_params: Float[Array, "..."], mu_obs: Float[Array, "Nt"], - sigma: Float, - forward_fn: Callable[[_Params], Variables], + noise_std: Float, + v0: Float, + forward_fn: Callable[[Float[Array, "2"], Float[Array, "..."]], Variables], ) -> Float: # Transform the log_params by taking exponential - params = type(log_params).from_array(jnp.exp(log_params.to_array())) + params = jnp.exp(log_params) + # Get initial values + # TODO: replace with `get_steady_state` or something + y0 = jnp.array([mu_obs[0], params[2] / v0]) # Forward pass to get friction curve - mu_hat = forward_fn(params).mu + mu_hat = forward_fn(y0, params).mu # Log-likelihood of residuals - p = -(jnp.square(mu_obs - mu_hat).mean() / (2 * sigma**2)) + p = -(jnp.square(mu_obs - mu_hat).mean() / (2 * noise_std**2)) return p @@ -94,5 +110,5 @@ def _log_likelihood( likelihood and its gradient (hence it needs 2 values for the out_axes). """ mapped_log_likelihood = jax.vmap( - _log_likelihood, in_axes=(0, None, None, None), out_axes=(0, 0) + _log_likelihood, in_axes=(0, None, None, None, None), out_axes=(0, 0) ) diff --git a/diabayes/solver.py b/diabayes/solver.py index 18607c0..2c64908 100644 --- a/diabayes/solver.py +++ b/diabayes/solver.py @@ -1,14 +1,23 @@ -from typing import Tuple, Union +from time import time_ns +from typing import Any, Callable, Tuple, Union import diffrax as dfx import equinox as eqx import jax +import jax.numpy as jnp +import jax.random as jr +import optax import optimistix as optx +from jax import lax +from jax_tqdm import scan_tqdm from jaxtyping import Array, Float +from scipy.integrate import solve_ivp from diabayes.forward_models import Forward from diabayes.typedefs import ( BayesianSolution, + RSFParams, + RSFParticles, Variables, _BlockConstants, _Constants, @@ -24,6 +33,7 @@ class ODESolver: rtol: float atol: float checkpoints: int + learning_rate: float = 1e-2 def __init__( self, @@ -38,6 +48,61 @@ def __init__( self.checkpoints = checkpoints pass + def solve_forward( + self, + t: Float[Array, "Nt"], + y0: Variables, + params: _Params, + friction_constants: _Constants, + block_constants: _BlockConstants, + ) -> Any: + """ + Solve a forward problem using SciPy's `solve_ivp` routine. + While this routine doesn't propagate any gradients, it is + much faster to initialise and to perform a single forward + run. Hence for playing around with different parameters, + it is preferred over a JITed JAX implementation. + + Parameters + ---------- + t : Float[Array, "Nt"] + A vector of time samples where a solution is requested + y0 : Variables + The initial values (fricton and state) wrapped in a + `Variables` container. + params : _Params + The (invertible) parameters that govern the dynamics, + wrapped in a `Params` container. + friction_constants : _Constants + A container object containing the friction constants + block_constants : _BlockConstants + A container object containing the block constants + + Returns + ------- + result : Variables + Solution time series of friction and state + """ + + _forward = lambda t, y, *args: self.forward_model( + Variables.from_array(y), *args + ).to_array() + + result = solve_ivp( + fun=_forward, + t_span=(t.min(), t.max()), + y0=y0.to_array(), + t_eval=t, + args=(params, friction_constants, block_constants), + rtol=self.rtol, + atol=self.atol, + ) + + assert result.y is not None + + return Variables.from_array(result.y) + + @eqx.filter_jit def _forward_wrapper( self, t: Float, @@ -52,7 +117,30 @@ def _forward_wrapper( block_constants=block_constants, ) - def solve_forward( + @eqx.filter_jit + def _forward_wrapper_SVI( + self, + y0: Float[Array, "2"], + params: Float[Array, "..."], + t: Float[Array, "Nt"], + friction_constants: _Constants, + block_constants: _BlockConstants, + ) -> Variables: + result = self._solve_forward( + t=t, + y0=Variables.from_array(y0), + params=RSFParams.from_array(params), + friction_constants=friction_constants, + block_constants=block_constants, + ) + + assert result is not None + assert result.ys is not None + + return result.ys + + @eqx.filter_jit + def _solve_forward( self, t: Float[Array, "Nt"], y0: Variables, @@ -105,7 +193,7 @@ def _residuals( # because this is different for e.g. CNS theta0 = params.Dc / friction_constants.v0 # type:ignore y0 = Variables(mu=mu[0], state=theta0) - result = self.solve_forward( + result = self._solve_forward( t, y0, params, friction_constants, block_constants, adjoint ) mu_hat = result.ys.mu # type:ignore @@ -143,8 +231,73 @@ def bayesian_inversion( self, t: Float[Array, "Nt"], mu: Float[Array, "Nt"], + noise_std: Float, params: _Params, friction_constants: _Constants, block_constants: _BlockConstants, - verbose: bool = False, - ) -> BayesianSolution: ... + Nparticles: int = 1000, + Nsteps: int = 150, + key: Union[None, int, jax.Array] = None, + ) -> None: + + assert isinstance( + params, RSFParams + ), "Bayesian inversion is only implemented for RSF" + + if key is None: + key = jr.PRNGKey(time_ns()) + elif isinstance(key, int): + key = jr.PRNGKey(key) + + key, split_key = jr.split(key) + + scale = jnp.ones(len(params)) * 0.1 + inv_scale = 1 / (jnp.sqrt(2) * scale) + log_params = jnp.log(params.to_array()) + + # Sample particles from a log-normal distribution + log_particles = RSFParticles.generate( + N=Nparticles, loc=log_params, scale=scale, key=split_key + ).to_array() + + # Instantiate optimiser + opt = optax.adam(learning_rate=self.learning_rate) + opt_state = opt.init(log_particles) + + from functools import partial + + forward_fn = partial( + self._forward_wrapper_SVI, + t=t, + friction_constants=friction_constants, + block_constants=block_constants, + ) + + from diabayes.SVI import compute_phi, mapped_log_likelihood + + @scan_tqdm(Nsteps) + def body_fun(carry, i): + params, state = carry + loss, gradp = mapped_log_likelihood( + params, mu, noise_std, friction_constants.v0, forward_fn + ) + """ + Sometimes the adjoint back-propagation becomes unstable, + producing NaNs in the gradients. By setting the NaN-gradients + to zero, the particle will be attracted towards the prior. + This is fine, because in the next step the gradients will + likely be stable again and the particle will continue + to be attracted by the maximum likelihood + """ + nan_count = jnp.isnan(gradp).sum() / gradp.shape[1] + gradp = jnp.asarray(jnp.where(jnp.isnan(gradp), 0.0, gradp)) + gradq = -2 * (params - log_params) * inv_scale + phi = compute_phi(params, gradp, gradq) + updates, state = opt.update(phi, state, params) + params = optax.apply_updates(params, updates) + return (params, state), (loss.mean(), nan_count, params) + + carry = (log_particles, opt_state) + _, (loss, nan_count, states) = lax.scan(body_fun, carry, jnp.arange(Nsteps)) + + return states diff --git a/diabayes/typedefs.py b/diabayes/typedefs.py index b6b927a..26b0d97 100644 --- a/diabayes/typedefs.py +++ b/diabayes/typedefs.py @@ -25,6 +25,12 @@ class Container: + def __len__(self): + return len(self.tree_flatten()[0]) + + def __iter__(self): + return iter(self.tree_flatten()[0]) + @classmethod def tree_unflatten(cls, aux_data, children): return cls(*children) diff --git a/docs/source/diabayes.rst b/docs/source/diabayes.rst new file mode 100644 index 0000000..3ebc2b3 --- /dev/null +++ b/docs/source/diabayes.rst @@ -0,0 +1,45 @@ +diabayes package +================ + +Submodules +---------- + +diabayes.SVI module +------------------- + +.. automodule:: diabayes.SVI + :members: + :show-inheritance: + :undoc-members: + +diabayes.forward\_models module +------------------------------- + +.. automodule:: diabayes.forward_models + :members: + :show-inheritance: + :undoc-members: + +diabayes.solver module +---------------------- + +.. automodule:: diabayes.solver + :members: + :show-inheritance: + :undoc-members: + +diabayes.typedefs module +------------------------ + +.. automodule:: diabayes.typedefs + :members: + :show-inheritance: + :undoc-members: + +Module contents +--------------- + +.. automodule:: diabayes + :members: + :show-inheritance: + :undoc-members: diff --git a/examples/simple_inversion.ipynb b/examples/simple_inversion.ipynb index 038a4fb..311e087 100644 --- a/examples/simple_inversion.ipynb +++ b/examples/simple_inversion.ipynb @@ -130,9 +130,7 @@ "id": "14114ce1", "metadata": {}, "source": [ - "You may notice that executing this cell for the first time will take a few seconds, but that the second time it runs must faster. This is because the forward simulations are _just-in-time_ (JIT) compiled and optimised. This needs to be done once for the initial call, after which the underlying code runs much faster for subsequent calls. This may not be optimal for a single forward simulation, but it greatly speeds up the (Bayesian) inversion procedure, which may incur thousands of forward calls.\n", - "\n", - "The result of this forward simulation (`result`) is a [`diffrax.Solution`](https://docs.kidger.site/diffrax/api/solution/#diffrax.Solution) object. It contains a lot of information about the forward run, but in most cases you would only be interested in the solution of the differential equation (the time series of friction and state). This solution is stored in `result.ys`, which is a `db.Variables` object similar to `y0` defined above. Hence the friction and state can be accessed as `result.ys.mu` and `result.ys.state`:" + "The result of this forward simulation (`result`) is a `db.Variables` object similar to `y0` defined above. Hence the friction and state can be accessed as `result.ys.mu` and `result.ys.state`:" ] }, { @@ -154,18 +152,18 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f4194f2e7e0545ae92cc4842f2203c89", + "model_id": "5186eadfb9524b1589ab373bb90f7473", "version_major": 2, "version_minor": 0 }, - "image/png": 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", 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", 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\n", " " ], @@ -178,8 +176,8 @@ } ], "source": [ - "mu = result.ys.mu\n", - "theta = result.ys.state\n", + "mu = result.mu\n", + "theta = result.state\n", "\n", "plt.close(\"all\")\n", "fig, axes = plt.subplots(nrows=2, figsize=(9, 5), constrained_layout=True, sharex=\"all\")\n", @@ -218,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "d4c47b4b", "metadata": {}, "outputs": [ @@ -226,30 +224,29 @@ "name": "stdout", "output_type": "stream", "text": [ - "Step: 0, Loss on this step: 0.057334728304379204\n", - "Step: 1, Loss on this step: 0.008662664862567759\n", - "Step: 2, Loss on this step: 0.04407865522592208\n", - "Step: 3, Loss on this step: 0.0407632034212465\n", - "Step: 4, Loss on this step: 0.03979882593967461\n", - "Step: 5, Loss on this step: 0.054827800646387044\n", - "Step: 6, Loss on this step: 0.1547759225714206\n", - "Step: 7, Loss on this step: 0.005761228910415207\n", - "Step: 8, Loss on this step: 0.003753402302866818\n", - "Step: 9, Loss on this step: 0.0029963676711166837\n", - "Step: 10, Loss on this step: 0.00267729420881892\n", - "Step: 11, Loss on this step: 0.0020681032556638637\n", - "Step: 12, Loss on this step: 0.0017316281478101186\n", - "Step: 13, Loss on this step: 0.0013765930624384474\n", - "Step: 14, Loss on this step: 0.0011562860256277836\n", - "Step: 15, Loss on this step: 0.000991121834253768\n", - "Step: 16, Loss on this step: 0.0008735269805232258\n", - "Step: 17, Loss on this step: 0.0007841762570394665\n", - "Step: 18, Loss on this step: 0.000620184372538452\n", - "Step: 19, Loss on this step: 0.0005421354645256971\n", - "Step: 20, Loss on this step: 0.0004803631736445323\n", - "Step: 21, Loss on this step: 0.0004674834252644044\n", - "Step: 22, Loss on this step: 0.0004670353172771068\n", - "Step: 23, Loss on this step: 0.0004670333632110958\n" + "Step: 0, Loss on this step: 0.05701778953668869\n", + "Step: 1, Loss on this step: 0.008426669395963007\n", + "Step: 2, Loss on this step: 0.04533961434363544\n", + "Step: 3, Loss on this step: 0.04219218180410471\n", + "Step: 4, Loss on this step: 0.04089619022651131\n", + "Step: 5, Loss on this step: 0.05387010819163528\n", + "Step: 6, Loss on this step: 0.12880864148248167\n", + "Step: 7, Loss on this step: 0.005859894402973482\n", + "Step: 8, Loss on this step: 0.0036073091782483696\n", + "Step: 9, Loss on this step: 0.0029065271595560523\n", + "Step: 10, Loss on this step: 0.00232008817640738\n", + "Step: 11, Loss on this step: 0.0019266369568930897\n", + "Step: 12, Loss on this step: 0.0015456980054288673\n", + "Step: 13, Loss on this step: 0.0012871461506268778\n", + "Step: 14, Loss on this step: 0.0010871856614614634\n", + "Step: 15, Loss on this step: 0.0009450841274790639\n", + "Step: 16, Loss on this step: 0.0008377017682057023\n", + "Step: 17, Loss on this step: 0.0006440217127184301\n", + "Step: 18, Loss on this step: 0.0005527392653225283\n", + "Step: 19, Loss on this step: 0.0004820365076114968\n", + "Step: 20, Loss on this step: 0.00046755116549630034\n", + "Step: 21, Loss on this step: 0.0004670631428968824\n", + "Step: 22, Loss on this step: 0.00046706112968350824\n" ] } ], @@ -306,14 +303,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Inverted parameters: RSFParams(a=Array(0.00990386, dtype=float64), b=Array(0.00891232, dtype=float64), Dc=Array(1.03470351e-05, dtype=float64))\n", + "Inverted parameters: RSFParams(a=Array(0.00986196, dtype=float64), b=Array(0.00886787, dtype=float64), Dc=Array(1.04567191e-05, dtype=float64))\n", "True parameters: RSFParams(a=0.01, b=0.009000000000000001, Dc=1e-05)\n" ] }, { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 8, @@ -323,18 +320,18 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ef4a71096e3f409fb63ef0fb0498884e", + "model_id": "a4f58642232641b7b6c3c8136ceb4c95", "version_major": 2, "version_minor": 0 }, - "image/png": 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", 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\n", - " \n", + " \n", "
\n", " " ], @@ -356,8 +353,8 @@ "\n", "plt.close(\"all\")\n", "fig, ax = plt.subplots(figsize=(9, 5), constrained_layout=True)\n", - "ax.plot(t, mu_measured)\n", - "ax.plot(t, result_predicted.ys.mu)" + "ax.plot(t, mu_measured, alpha=0.3)\n", + "ax.plot(t, result_predicted.mu)" ] }, { @@ -370,7 +367,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "a0fbfb01", "metadata": {}, "outputs": [ @@ -378,28 +375,28 @@ "name": "stdout", "output_type": "stream", "text": [ - "Step: 0, Loss on this step: 0.03734159365807644\n", - "Step: 1, Loss on this step: 0.008450562771825366\n", - "Step: 2, Loss on this step: 0.011316614163166748\n", - "Step: 3, Loss on this step: 0.010433715747893148\n", - "Step: 4, Loss on this step: 0.008123443543690061\n", - "Step: 5, Loss on this step: 0.007670866529293636\n", - "Step: 6, Loss on this step: 0.7705983105364462\n", - "Step: 7, Loss on this step: 1.113562431993166\n", - "Step: 8, Loss on this step: 0.011455439575389807\n", - "Step: 9, Loss on this step: 0.004275817159058383\n", - "Step: 10, Loss on this step: 0.0018859801476722431\n", - "Step: 11, Loss on this step: 0.0015430926093998685\n", - "Step: 12, Loss on this step: 0.0012603736034531433\n", - "Step: 13, Loss on this step: 0.0010728469502646895\n", - "Step: 14, Loss on this step: 0.0009327820011862342\n", - "Step: 15, Loss on this step: 0.0008297305074442885\n", - "Step: 16, Loss on this step: 0.0007508047726027415\n", - "Step: 17, Loss on this step: 0.000604485828538301\n", - "Step: 18, Loss on this step: 0.0004910520582135309\n", - "Step: 19, Loss on this step: 0.00046785413197942606\n", - "Step: 20, Loss on this step: 0.00046703693253931757\n", - "Step: 21, Loss on this step: 0.0004670333642627893\n" + "Step: 0, Loss on this step: 0.037112978439809007\n", + "Step: 1, Loss on this step: 0.008446597639457603\n", + "Step: 2, Loss on this step: 0.011230654700420253\n", + "Step: 3, Loss on this step: 0.010351276459970582\n", + "Step: 4, Loss on this step: 0.00805469116699875\n", + "Step: 5, Loss on this step: 0.007736147358686476\n", + "Step: 6, Loss on this step: 0.5659830665660537\n", + "Step: 7, Loss on this step: 0.8313167481375603\n", + "Step: 8, Loss on this step: 0.011655851340145167\n", + "Step: 9, Loss on this step: 0.004206803455487899\n", + "Step: 10, Loss on this step: 0.0018982842003712366\n", + "Step: 11, Loss on this step: 0.0015535046629491392\n", + "Step: 12, Loss on this step: 0.001263024993119862\n", + "Step: 13, Loss on this step: 0.0010727452050579983\n", + "Step: 14, Loss on this step: 0.0009311492053747009\n", + "Step: 15, Loss on this step: 0.0008275058544759351\n", + "Step: 16, Loss on this step: 0.000748352946798722\n", + "Step: 17, Loss on this step: 0.000602385717104706\n", + "Step: 18, Loss on this step: 0.0004903515239977218\n", + "Step: 19, Loss on this step: 0.00046783355546733746\n", + "Step: 20, Loss on this step: 0.0004670642991533489\n", + "Step: 21, Loss on this step: 0.0004670611303524343\n" ] } ], @@ -439,7 +436,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 10, @@ -449,18 +446,18 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "edae397ca436499487b20a9916728c07", + "model_id": "59eba0d8eb8f4285ab73e2f82f70b0e8", "version_major": 2, "version_minor": 0 }, - "image/png": 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", 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", "text/html": [ "\n", "
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\n", " " ], @@ -476,8 +473,8 @@ "plt.close(\"all\")\n", "fig, ax = plt.subplots(figsize=(9, 5), constrained_layout=True)\n", "ax.plot(t, mu_measured, alpha=0.3)\n", - "ax.plot(t, result_predicted.ys.mu)\n", - "ax.plot(t, result_predicted2.ys.mu)" + "ax.plot(t, result_predicted.mu, lw=3)\n", + "ax.plot(t, result_predicted2.mu)" ] }, { @@ -499,9 +496,9 @@ "output_type": "stream", "text": [ "\t\tInitial guess 1\tInversion 1\tInitial guess 2\tInversion 2\tTrue values\n", - "a\t\t1.10e-02\t9.90e-03\t9.00e-03\t9.90e-03\t1.00e-02\n", - "b\t\t1.08e-02\t8.91e-03\t4.50e-03\t8.91e-03\t9.00e-03\n", - "Dc\t\t5.00e-05\t1.03e-05\t2.00e-05\t1.03e-05\t1.00e-05\n" + "a\t\t1.10e-02\t9.86e-03\t9.00e-03\t9.86e-03\t1.00e-02\n", + "b\t\t1.08e-02\t8.87e-03\t4.50e-03\t8.87e-03\t9.00e-03\n", + "Dc\t\t5.00e-05\t1.05e-05\t2.00e-05\t1.05e-05\t1.00e-05\n" ] } ], @@ -534,9 +531,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "6a9fbf61", "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "multiple values for argument 't'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[12]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m states = \u001b[43msolver\u001b[49m\u001b[43m.\u001b[49m\u001b[43mbayesian_inversion\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 2\u001b[39m \u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m=\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmu\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmu_measured\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnoise_std\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnoise_amplitude\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[32m 3\u001b[39m \u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m=\u001b[49m\u001b[43mparams_inv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfriction_constants\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconstants\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[32m 4\u001b[39m \u001b[43m \u001b[49m\u001b[43mblock_constants\u001b[49m\u001b[43m=\u001b[49m\u001b[43mblock_constants\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[32m 5\u001b[39m \u001b[43m \u001b[49m\u001b[43mNparticles\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mNsteps\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m42\u001b[39;49m\n\u001b[32m 6\u001b[39m \u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/diabayes/solver.py:272\u001b[39m, in \u001b[36mODESolver.bayesian_inversion\u001b[39m\u001b[34m(self, t, mu, noise_std, params, friction_constants, block_constants, Nparticles, Nsteps, key)\u001b[39m\n\u001b[32m 269\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m (params, state), (loss.mean(), nan_count, params)\n\u001b[32m 271\u001b[39m carry = (log_particles, opt_state)\n\u001b[32m--> \u001b[39m\u001b[32m272\u001b[39m _, (loss, nan_count, states) = \u001b[43mlax\u001b[49m\u001b[43m.\u001b[49m\u001b[43mscan\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbody_fun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcarry\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43marange\u001b[49m\u001b[43m(\u001b[49m\u001b[43mNsteps\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 274\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m states\n", + " \u001b[31m[... skipping hidden 10 frame]\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax_tqdm/scan_pbar.py:65\u001b[39m, in \u001b[36mscan_tqdm.._scan_tqdm..wrapper_progress_bar\u001b[39m\u001b[34m(carry, x)\u001b[39m\n\u001b[32m 63\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 64\u001b[39m carry, x = update_progress_bar((carry, x), iter_num, \u001b[32m0\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m65\u001b[39m result = \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcarry\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 66\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m close_tqdm(result, iter_num, \u001b[32m0\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/diabayes/solver.py:252\u001b[39m, in \u001b[36mODESolver.bayesian_inversion..body_fun\u001b[39m\u001b[34m(carry, i)\u001b[39m\n\u001b[32m 249\u001b[39m \u001b[38;5;129m@scan_tqdm\u001b[39m(Nsteps)\n\u001b[32m 250\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mbody_fun\u001b[39m(carry, i):\n\u001b[32m 251\u001b[39m params, state = carry\n\u001b[32m--> \u001b[39m\u001b[32m252\u001b[39m loss, gradp = \u001b[43mmapped_log_likelihood\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 253\u001b[39m \u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmu\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnoise_std\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfriction_constants\u001b[49m\u001b[43m.\u001b[49m\u001b[43mv0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mforward_fn\u001b[49m\n\u001b[32m 254\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 255\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 256\u001b[39m \u001b[33;03m Sometimes the adjoint back-propagation becomes unstable, \u001b[39;00m\n\u001b[32m 257\u001b[39m \u001b[33;03m producing NaNs in the gradients. By setting the NaN-gradients\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 261\u001b[39m \u001b[33;03m to be attracted by the maximum likelihood\u001b[39;00m\n\u001b[32m 262\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m 263\u001b[39m nan_count = jnp.isnan(gradp).sum() / gradp.shape[\u001b[32m1\u001b[39m]\n", + " \u001b[31m[... skipping hidden 19 frame]\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/diabayes/SVI.py:88\u001b[39m, in \u001b[36m_log_likelihood\u001b[39m\u001b[34m(log_params, mu_obs, noise_std, v0, forward_fn)\u001b[39m\n\u001b[32m 86\u001b[39m y0 = jnp.array([mu_obs[\u001b[32m0\u001b[39m], params[\u001b[32m2\u001b[39m] / v0])\n\u001b[32m 87\u001b[39m \u001b[38;5;66;03m# Forward pass to get friction curve\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m88\u001b[39m mu_hat = \u001b[43mforward_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43my0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m.mu\n\u001b[32m 89\u001b[39m \u001b[38;5;66;03m# Log-likelihood of residuals\u001b[39;00m\n\u001b[32m 90\u001b[39m p = -(jnp.square(mu_obs - mu_hat).mean() / (\u001b[32m2\u001b[39m * noise_std**\u001b[32m2\u001b[39m))\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/equinox/_module/_prebuilt.py:81\u001b[39m, in \u001b[36mPartial.__call__\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 68\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, *args: Any, **kwargs: Any) -> _Return:\n\u001b[32m 69\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Call the wrapped `self.func`.\u001b[39;00m\n\u001b[32m 70\u001b[39m \n\u001b[32m 71\u001b[39m \u001b[33;03m **Arguments:**\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 79\u001b[39m \u001b[33;03m The result of the wrapped function.\u001b[39;00m\n\u001b[32m 80\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m81\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mkeywords\u001b[49m\u001b[43m)\u001b[49m\n", + " \u001b[31m[... skipping hidden 4 frame]\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/inspect.py:3195\u001b[39m, in \u001b[36mSignature.bind\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 3190\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mbind\u001b[39m(\u001b[38;5;28mself\u001b[39m, /, *args, **kwargs):\n\u001b[32m 3191\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Get a BoundArguments object, that maps the passed `args`\u001b[39;00m\n\u001b[32m 3192\u001b[39m \u001b[33;03m and `kwargs` to the function's signature. Raises `TypeError`\u001b[39;00m\n\u001b[32m 3193\u001b[39m \u001b[33;03m if the passed arguments can not be bound.\u001b[39;00m\n\u001b[32m 3194\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m3195\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_bind\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/inspect.py:3134\u001b[39m, in \u001b[36mSignature._bind\u001b[39m\u001b[34m(self, args, kwargs, partial)\u001b[39m\n\u001b[32m 3131\u001b[39m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[32m 3133\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m param.name \u001b[38;5;129;01min\u001b[39;00m kwargs \u001b[38;5;129;01mand\u001b[39;00m param.kind != _POSITIONAL_ONLY:\n\u001b[32m-> \u001b[39m\u001b[32m3134\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[32m 3135\u001b[39m \u001b[33m'\u001b[39m\u001b[33mmultiple values for argument \u001b[39m\u001b[38;5;132;01m{arg!r}\u001b[39;00m\u001b[33m'\u001b[39m.format(\n\u001b[32m 3136\u001b[39m arg=param.name)) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 3138\u001b[39m arguments[param.name] = arg_val\n\u001b[32m 3140\u001b[39m \u001b[38;5;66;03m# Now, we iterate through the remaining parameters to process\u001b[39;00m\n\u001b[32m 3141\u001b[39m \u001b[38;5;66;03m# keyword arguments\u001b[39;00m\n", + "\u001b[31mTypeError\u001b[39m: multiple values for argument 't'" + ] + } + ], + "source": [ + "states = solver.bayesian_inversion(\n", + " t=t, mu=mu_measured, noise_std=noise_amplitude, \n", + " params=params_inv, friction_constants=constants, \n", + " block_constants=block_constants, \n", + " Nparticles=100, Nsteps=100, key=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3998245f", + "metadata": {}, "outputs": [], "source": [] } diff --git a/pyproject.toml b/pyproject.toml index 72f1afc..3c9ec87 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,7 @@ authors = [ ] dependencies = [ "jax", - "jax-tqdm", + "jax_tqdm", "jaxtyping", "equinox", "diffrax", diff --git a/test/test_solvers.py b/test/test_solvers.py index 84cb7d3..e68390c 100644 --- a/test/test_solvers.py +++ b/test/test_solvers.py @@ -1,3 +1,4 @@ +import jax import jax.numpy as jnp import diabayes as db @@ -6,6 +7,8 @@ from .aux import init_params +jax.config.update("jax_enable_x64", True) + class TestSolvers: @@ -21,19 +24,27 @@ def test_forward_solver(self): t = jnp.linspace(0.0, 100.0, 1000) result = solver.solve_forward(t, variables, params, constants, block_constants) + result_jax = solver._solve_forward( + t, variables, params, constants, block_constants + ) assert result is not None - assert result.ys is not None + assert result_jax is not None + assert result_jax.ys is not None + + diff = result.to_array() - result_jax.ys.to_array() + + assert jnp.allclose(result.to_array(), result_jax.ys.to_array()) # Steady-state tests - mu_final = result.ys.mu[-1] + mu_final = result.mu[-1] mu_pred = constants.mu0 + (params.a - params.b) * jnp.log( block_constants.v_lp / constants.v0 ) assert jnp.allclose(mu_final, mu_pred) - state_final = result.ys.state[-1] + state_final = result.state[-1] state_pred = params.Dc / block_constants.v_lp assert jnp.allclose(state_final, state_pred) @@ -53,15 +64,12 @@ def test_levenberg_marquardt(self): solver = ODESolver(forward) # Forward simulation to generate "observations" - t = jnp.linspace(0.0, 100.0, 1000) + t = jnp.linspace(0.0, 20.0, 1000) result = solver.solve_forward(t, variables, params, constants, block_constants) - assert result is not None - assert result.ys is not None - # Perturb parameters (initial guess parameters) params2 = db.RSFParams(a=params.a * 1.1, b=params.b * 0.9, Dc=params.Dc * 5) - mu = result.ys.mu + mu = result.mu # Invert parameters result_inv = solver.max_likelihood_inversion( @@ -75,9 +83,8 @@ def test_levenberg_marquardt(self): ) assert result2 is not None - assert result2.ys is not None # Check that inverted parameters and resulting friction # curves are identical to the original ones assert jnp.allclose(params.to_array(), params_inv.to_array()) - assert jnp.allclose(result.ys.to_array(), result2.ys.to_array()) + assert jnp.allclose(result.to_array(), result2.to_array()) From 9ddcbbe5d1f4f67714cf6e84e14ce7d1ae570303 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Thu, 31 Jul 2025 11:54:10 +0200 Subject: [PATCH 24/56] Basic SVI working, still working on interface --- diabayes/SVI.py | 2 +- diabayes/solver.py | 8 +++++--- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/diabayes/SVI.py b/diabayes/SVI.py index 3c90133..02510c5 100644 --- a/diabayes/SVI.py +++ b/diabayes/SVI.py @@ -35,7 +35,7 @@ def _median_trick_h(x: ParticleArray) -> Float: squared median of the RMS distance between all pairs of particles. """ - dist_sq = _distance(x, x) + dist_sq = jnp.square(x[:, None] - x[None]).sum(axis=-1) # Replace this one with jnp.nanmedian to avoid spreading NaNs? med_sq = jnp.median(jnp.sqrt(dist_sq)) h = med_sq**2 / jnp.log(len(x) + 1) diff --git a/diabayes/solver.py b/diabayes/solver.py index 2c64908..95bd228 100644 --- a/diabayes/solver.py +++ b/diabayes/solver.py @@ -9,7 +9,7 @@ import optax import optimistix as optx from jax import lax -from jax_tqdm import scan_tqdm +from jax_tqdm import scan_tqdm # type:ignore from jaxtyping import Array, Float from scipy.integrate import solve_ivp @@ -238,7 +238,7 @@ def bayesian_inversion( Nparticles: int = 1000, Nsteps: int = 150, key: Union[None, int, jax.Array] = None, - ) -> None: + ): assert isinstance( params, RSFParams @@ -298,6 +298,8 @@ def body_fun(carry, i): return (params, state), (loss.mean(), nan_count, params) carry = (log_particles, opt_state) - _, (loss, nan_count, states) = lax.scan(body_fun, carry, jnp.arange(Nsteps)) + _, (loss, nan_count, states) = lax.scan( + body_fun, carry, jnp.arange(Nsteps) # type:ignore + ) return states From 0e04b044954d479bf23749bda8b343f98d69809d Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Thu, 31 Jul 2025 17:00:38 +0200 Subject: [PATCH 25/56] Implemented SVI user interface (plots etc.) --- diabayes/solver.py | 8 +- diabayes/typedefs.py | 224 ++++++++++++++++++++++++++++++++++--------- 2 files changed, 184 insertions(+), 48 deletions(-) diff --git a/diabayes/solver.py b/diabayes/solver.py index 95bd228..e7127a4 100644 --- a/diabayes/solver.py +++ b/diabayes/solver.py @@ -1,3 +1,4 @@ +from functools import partial from time import time_ns from typing import Any, Callable, Tuple, Union @@ -14,6 +15,7 @@ from scipy.integrate import solve_ivp from diabayes.forward_models import Forward +from diabayes.SVI import compute_phi, mapped_log_likelihood from diabayes.typedefs import ( BayesianSolution, RSFParams, @@ -264,8 +266,6 @@ def bayesian_inversion( opt = optax.adam(learning_rate=self.learning_rate) opt_state = opt.init(log_particles) - from functools import partial - forward_fn = partial( self._forward_wrapper_SVI, t=t, @@ -273,8 +273,6 @@ def bayesian_inversion( block_constants=block_constants, ) - from diabayes.SVI import compute_phi, mapped_log_likelihood - @scan_tqdm(Nsteps) def body_fun(carry, i): params, state = carry @@ -302,4 +300,4 @@ def body_fun(carry, i): body_fun, carry, jnp.arange(Nsteps) # type:ignore ) - return states + return BayesianSolution(states, loss, nan_count) diff --git a/diabayes/typedefs.py b/diabayes/typedefs.py index 26b0d97..89ea7a4 100644 --- a/diabayes/typedefs.py +++ b/diabayes/typedefs.py @@ -1,4 +1,5 @@ from dataclasses import dataclass +from time import time_ns from typing import ( Any, Iterable, @@ -13,9 +14,12 @@ import jax import jax.numpy as jnp import jax.random as jr +import matplotlib.pyplot as plt from jax import tree_util from jaxtyping import Array, Float +from diabayes.solver import ODESolver + """ -------------------- Parameter containers @@ -182,62 +186,60 @@ def tree_unflatten(cls, aux_data, children): return cls(x) -@tree_util.register_pytree_node_class -@dataclass(frozen=True) -class Chains(Container): - - _chains: Sequence[Particles] - - def __getitem__(self, idx): +# Alias gradients for semantic clarity +Gradients: TypeAlias = Particles - # Check if the requested selection is of the kind `x[1, 3]` or `x[1, 3, 2]` - if isinstance(idx, tuple): - assert ( - 1 < len(idx) <= 3 - ), "Chains do not support item selection with more than 3 indices" +""" +----------------- +Inversion results +----------------- +""" - if len(idx) == 2: - i, j = idx - return self._chains[i][j] +Chains = Float[Array, "Nsteps Nparticles Nparams"] - i, j, k = idx - return self._chains[i][j].to_array(k) - # Else the selection is of the kind `x[4]` - return self._chains[idx] +class Statistics(NamedTuple): + mean: Float + median: Float + std: Float + q5: Float + q95: Float - def __iter__(self): - return iter(self._chains) + @classmethod + def from_array(cls, x: Float[Array, "..."]): + mean = jnp.mean(x) + std = jnp.std(x) + q5, median, q95 = jnp.quantile(x, jnp.array([0.05, 0.5, 0.95])) + return cls(mean=mean, median=median, std=std, q5=q5, q95=q95) - def __len__(self): - return len(self._chains) + def get(self, x: str) -> Float: + return getattr(self, x) - def tree_flatten(self): - return self._chains, None - def to_array(self): - return jnp.array([c.to_array() for c in self._chains]) +class ParamStatistics(NamedTuple): + @classmethod + def from_state(cls, state): + cov = jnp.cov(state.T) + stats = tuple(Statistics.from_array(param) for param in state.T) + return cls(*(stats + (cov,))) # type:ignore -# Alias gradients for semantic clarity -Gradients: TypeAlias = Particles + def get(self, x: str) -> Statistics: + return getattr(self, x) + def get_param_names(self) -> Tuple: + return self._fields[:-1] -class Statistics(NamedTuple): - mean: Float - median: Float - std: Float - q5: Float - q95: Float +class RSFStatistics(ParamStatistics): -class RSFStatistics(NamedTuple): a: Statistics b: Statistics Dc: Statistics + cov: Float[Array, "N N"] -class CNSStatistics(NamedTuple): +class CNSStatistics(ParamStatistics): a: Statistics b: Statistics Dc: Statistics @@ -247,10 +249,146 @@ class CNSStatistics(NamedTuple): class BayesianSolution: chains: Chains - statistics: Union[RSFStatistics, CNSStatistics] + log_likelihood: Float[Array, "Nsteps"] + nan_count: Float[Array, "Nsteps"] + statistics: Union[RSFStatistics, CNSStatistics, None] = None + + def __init__( + self, + chains: Chains, + log_likelihood: Float[Array, "Nsteps"], + nan_count: Float[Array, "Nsteps"], + ): + self.chains = jnp.exp(chains) + self.final_state = jnp.exp(chains[-1]) + self.log_likelihood = log_likelihood + self.nan_count = nan_count + self.statistics = RSFStatistics.from_state(self.final_state) + + def plot_convergence(self): + + assert self.statistics is not None + + params = self.statistics.get_param_names() + nparams = len(params) + + plt.close("all") + fig, axes = plt.subplots( + nrows=nparams + 1, + figsize=(6, (nparams + 1) * 3), + constrained_layout=True, + sharex="all", + ) + + ax = axes[0] + ax.plot(self.log_likelihood) + ax.set_ylabel("log-likelihood") + + for i, (ax, chains) in enumerate( + zip(axes[1:], self.chains.transpose((2, 0, 1))) + ): + ax.plot(chains, c="k", alpha=0.1, lw=1) + ax.set_ylabel(f"Parameter {i+1}") + + ax.set_xlabel("step") + + return fig + + def cornerplot(self, nbins=20): + + assert self.statistics is not None + params = self.statistics.get_param_names() + nparams = len(params) + state = self.final_state.T + + plt.close("all") + fig, axes = plt.subplots( + nrows=nparams, + ncols=nparams, + figsize=(8, 5), + constrained_layout=True, + sharex="col", + ) + + for i, (axrow, param1) in enumerate(zip(axes, params)): + axrow[0].set_ylabel(param1) + for j, (ax, param2) in enumerate(zip(axrow, params)): + if j > i: + fig.delaxes(ax) + continue + + stat1 = self.statistics.get(param1) + stat2 = self.statistics.get(param2) + ax.axvline(stat2.median, ls=":", c="C1") + + if i == j: + ax.hist(state[i], bins=nbins, alpha=0.7) + else: + ax.scatter(state[j], state[i], c="k", alpha=0.1, s=10) + + xerr = [[stat2.median - stat2.q5], [stat2.q95 - stat2.median]] + yerr = [[stat1.median - stat1.q5], [stat1.q95 - stat1.median]] + ax.errorbar( + stat2.median, + stat1.median, + xerr=xerr, + yerr=yerr, + fmt="o", + c="C1", + capsize=5, + ) + + if i == nparams - 1: + ax.set_xlabel(param2) + + return fig + + def __repr__(self) -> str: + + assert self.statistics is not None + + params = self.statistics.get_param_names() + stats = self.statistics.get(params[0])._fields + nl = "\n" + tab = "\t" + gutter = 2 * tab + header = gutter.join(params) + s = "Bayesian inversion results" + nl + s += "-" * 60 + nl * 2 + s += f"Number of particles:\t{self.chains.shape[1]}" + nl + s += f"Number of iterations:\t{self.chains.shape[0]}" + nl + s += f"Number of parameters:\t{self.chains.shape[2]} {params}" + nl * 2 + s += gutter + header + nl + for stat in stats: + param_stats = tab.join( + f"{self.statistics.get(param).get(stat):.2e}" for param in params + ) + s += stat + 2 * tab + param_stats + nl + s += nl + "-" * 60 + return s + + def sample( + self, + solver: ODESolver, + y0: Variables, + t: Float[Array, "Nt"], + friction_constants: _Constants, + block_constants: _BlockConstants, + nsamples: int = 10, + rng: Union[None, int, jax.Array] = None, + ): + + if rng is None: + key = jr.PRNGKey(time_ns()) + elif isinstance(rng, int): + key = jr.PRNGKey(rng) + elif isinstance(rng, jax.Array): + key = rng + + key, split_key = jr.split(key) + samples = jr.choice(split_key, self.final_state, shape=(nsamples,), axis=0) - def __init__(self, chains: Chains): - self.chains = chains - final_state = chains[-1] - # TODO: final_state contains N particles - # + sample_results = jax.vmap( + solver._forward_wrapper_SVI, in_axes=(None, 0, None, None, None), out_axes=0 + )(y0.to_array(), samples, t, friction_constants, block_constants) + return samples, sample_results From 4f1f6033029d37f3b56c9f4d8e06cb72352b51bb Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Thu, 31 Jul 2025 17:05:15 +0200 Subject: [PATCH 26/56] Fixed circular import --- diabayes/typedefs.py | 15 ++------------- 1 file changed, 2 insertions(+), 13 deletions(-) diff --git a/diabayes/typedefs.py b/diabayes/typedefs.py index 89ea7a4..eedfff0 100644 --- a/diabayes/typedefs.py +++ b/diabayes/typedefs.py @@ -1,15 +1,6 @@ from dataclasses import dataclass from time import time_ns -from typing import ( - Any, - Iterable, - NamedTuple, - Protocol, - Sequence, - Tuple, - TypeAlias, - Union, -) +from typing import Any, Iterable, NamedTuple, Protocol, Tuple, TypeAlias, Union import jax import jax.numpy as jnp @@ -18,8 +9,6 @@ from jax import tree_util from jaxtyping import Array, Float -from diabayes.solver import ODESolver - """ -------------------- Parameter containers @@ -369,7 +358,7 @@ def __repr__(self) -> str: def sample( self, - solver: ODESolver, + solver, y0: Variables, t: Float[Array, "Nt"], friction_constants: _Constants, From 0274b5f100b2b7e51e573a7c98c13836b44517a2 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Thu, 31 Jul 2025 18:25:13 +0200 Subject: [PATCH 27/56] Added SVI to example notebook (with several minor fixes) --- diabayes/solver.py | 10 +- diabayes/typedefs.py | 11 +- examples/simple_inversion.ipynb | 370 ++++++++++++++++++++++++-------- 3 files changed, 290 insertions(+), 101 deletions(-) diff --git a/diabayes/solver.py b/diabayes/solver.py index e7127a4..6309444 100644 --- a/diabayes/solver.py +++ b/diabayes/solver.py @@ -239,17 +239,19 @@ def bayesian_inversion( block_constants: _BlockConstants, Nparticles: int = 1000, Nsteps: int = 150, - key: Union[None, int, jax.Array] = None, + rng: Union[None, int, jax.Array] = None, ): assert isinstance( params, RSFParams ), "Bayesian inversion is only implemented for RSF" - if key is None: + if rng is None: key = jr.PRNGKey(time_ns()) - elif isinstance(key, int): - key = jr.PRNGKey(key) + elif isinstance(rng, int): + key = jr.PRNGKey(rng) + else: + key = rng key, split_key = jr.split(key) diff --git a/diabayes/typedefs.py b/diabayes/typedefs.py index eedfff0..b897013 100644 --- a/diabayes/typedefs.py +++ b/diabayes/typedefs.py @@ -2,6 +2,7 @@ from time import time_ns from typing import Any, Iterable, NamedTuple, Protocol, Tuple, TypeAlias, Union +import equinox as eqx import jax import jax.numpy as jnp import jax.random as jr @@ -205,7 +206,7 @@ def get(self, x: str) -> Float: return getattr(self, x) -class ParamStatistics(NamedTuple): +class ParamStatistics(eqx.Module): @classmethod def from_state(cls, state): @@ -217,7 +218,7 @@ def get(self, x: str) -> Statistics: return getattr(self, x) def get_param_names(self) -> Tuple: - return self._fields[:-1] + return tuple(self.__dataclass_fields__.keys())[:-1] class RSFStatistics(ParamStatistics): @@ -273,11 +274,11 @@ def plot_convergence(self): ax.plot(self.log_likelihood) ax.set_ylabel("log-likelihood") - for i, (ax, chains) in enumerate( - zip(axes[1:], self.chains.transpose((2, 0, 1))) + for i, (ax, chains, param) in enumerate( + zip(axes[1:], self.chains.transpose((2, 0, 1)), params) ): ax.plot(chains, c="k", alpha=0.1, lw=1) - ax.set_ylabel(f"Parameter {i+1}") + ax.set_ylabel(f"Parameter {param}") ax.set_xlabel("step") diff --git a/examples/simple_inversion.ipynb b/examples/simple_inversion.ipynb index 311e087..637aea2 100644 --- a/examples/simple_inversion.ipynb +++ b/examples/simple_inversion.ipynb @@ -135,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 16, "id": "b58d2518", "metadata": {}, "outputs": [ @@ -145,25 +145,25 @@ "Text(0.5, 0, 'time [s]')" ] }, - "execution_count": 6, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5186eadfb9524b1589ab373bb90f7473", + "model_id": "bde9df638e3749c3817a5297b14edcbe", "version_major": 2, "version_minor": 0 }, - "image/png": 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", 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", 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16, Loss on this step: 0.0008377017682057023\n", - "Step: 17, Loss on this step: 0.0006440217127184301\n", - "Step: 18, Loss on this step: 0.0005527392653225283\n", - "Step: 19, Loss on this step: 0.0004820365076114968\n", - "Step: 20, Loss on this step: 0.00046755116549630034\n", - "Step: 21, Loss on this step: 0.0004670631428968824\n", - "Step: 22, Loss on this step: 0.00046706112968350824\n" + "Step: 0, Loss on this step: 0.05733472830988543\n", + "Step: 1, Loss on this step: 0.008662664872409683\n", + "Step: 2, Loss on this step: 0.044078655206233705\n", + "Step: 3, Loss on this step: 0.040763203396466045\n", + "Step: 4, Loss on this step: 0.03979882592590013\n", + "Step: 5, Loss on this step: 0.05482780070276433\n", + "Step: 6, Loss on this step: 0.15477592354300435\n", + "Step: 7, Loss on this step: 0.00576122891182264\n", + "Step: 8, Loss on this step: 0.0037534023188489493\n", + "Step: 9, Loss on this step: 0.0029963676892875604\n", + "Step: 10, Loss on this step: 0.0026772942526744076\n", + "Step: 11, Loss on this step: 0.0020681032777418494\n", + "Step: 12, Loss on this step: 0.001731628170095942\n", + "Step: 13, Loss on this step: 0.001376593074536853\n", + "Step: 14, Loss on this step: 0.0011562860341618624\n", + "Step: 15, Loss on this step: 0.000991121839986473\n", + "Step: 16, Loss on this step: 0.0008735269848152941\n", + "Step: 17, Loss on this step: 0.0007841762603306585\n", + "Step: 18, Loss on this step: 0.0006201843741583328\n", + "Step: 19, Loss on this step: 0.0005421354653486064\n", + "Step: 20, Loss on this step: 0.00048036317386252825\n", + "Step: 21, Loss on this step: 0.0004674834253528182\n", + "Step: 22, Loss on this step: 0.00046703531736108617\n", + "Step: 23, Loss on this step: 0.0004670333632948055\n" ] } ], @@ -295,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 17, "id": "97c8272e", "metadata": {}, "outputs": [ @@ -303,35 +304,35 @@ "name": "stdout", "output_type": "stream", "text": [ - "Inverted parameters: RSFParams(a=Array(0.00986196, dtype=float64), b=Array(0.00886787, dtype=float64), Dc=Array(1.04567191e-05, dtype=float64))\n", + "Inverted parameters: RSFParams(a=Array(0.00990386, dtype=float64), b=Array(0.00891232, dtype=float64), Dc=Array(1.03470351e-05, dtype=float64))\n", "True parameters: RSFParams(a=0.01, b=0.009000000000000001, Dc=1e-05)\n" ] }, { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 8, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a4f58642232641b7b6c3c8136ceb4c95", + "model_id": "0797bf57735a453c9395d18bc77fce98", "version_major": 2, "version_minor": 0 }, - "image/png": 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", + "image/png": 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QRfO3dCzA7HSNpXx8VhklIiIiWjwYENKiFYqYB25GxvgobJIh1AZRVgGhmkaX0QL3KRScfAr2pudQFfQm3dYZHIKz9xBKew+h8fQ/IeQoQffyG9Gx6s8RzK/TbZtJz1LGg0RERESLBwNCWrQyHX8Xo52CYmLi+In1Ydmiy6hinX0rGnoXKz/4Ccr73ogvC+ZUYaDuKnjLNmKkdB2CuVU4b/lShMJhtHX1IdfXibLRM8jreQsVPa/CEXKj8fTPsbTpcfQu+wKaL9iJUG51/LFTdRU1PiciIiIiWvgYENKiNdnpG7TxUix40lYejVhkCAFANgRbjrF+nPfeg6g99+vovgUR4TVfgH/jbTiqrAEE/TBfGRJEm4SwswxhZxnUuovRtvRGCEoE5b1/wNKmf0ZZ/5uoa/sVqjpfQOvaO9G+5i+hijYcPjuU1vNjQEhERES0eDAgpEVrshlCrVjspJ0rMJQsINR0N61u34/zj38H9lB0WovuZV9Cy8e+gY0bNkKOKEC7O+H+hTl2eMfC8d9jRWVU0YbBuqswWHcViobexeoTf4eSoRM47/0fobL7FXyw+UEEChrSek7amjijwQjO9o9iRWU+CnPsad2fiIiIiOYPVhmlRWsa4kHTDKFVl1EgGhCKET8+9ub/xAVv3gN7yANv6Xoc2boPH23+ewQKGiAIQkKwWlHoxEVLS1Cca9dVFjUrMuot34ijn3kaH176Q0TsBSgZOo5PvPRFVHS/Et9mVVUBcuySaRu1hXJOtEersB47N5z8hSAiIiKieYkBIS1a05ohTKOoDADYPOfw8YM3obb9N1AECWc/9g28ffXT8JZt0LQrMVjNd0goL3ACME5Mb/EcBAE9jX+MN6/5NdzlF8MWHsXG17ejoelxlObZ0FiRn9B9NcbtD+PNliF4/GEEwtF5ECNJglwiIiIimr8YENKileEUfqZkVYWiqLouo1YBYfHgMWz87R+h0HMaoZwKHLvq39D6sW9gw7JK2G0Tb0Vh/J+W1WT0UoqgNlDQgGNX/Ss6V9wEASrWnNiDZUf/Dhhvt5XRQATH2l1J901ERERE8x8DQlrEJoIps/kE06GqwDmXX7dMMYkHK7p/h4t//xewh9zwlG3Ae5/7NTwVmwAAlYVOXXAqCIAhHtRlDLW3tcFhrsOiC6hox6lN30PTxnujbfng58CBv4Ysy0mfm9nzICIiIqKFhQEhLVraIExOki1LRlVVnO0f1S0zVumsbn8OG964E5IcxEDtVTh25b+itKYRAOKZQW1GUBSEhC6j2sDPKjisLc7BhiXF5g0VBLSvuR0fXbIHKgTg7X/CmuPfy3jSwYiswOMPp96QiIiIiOYFVhmlRUvbDVMbDzb3j0BRgdXVhSn3YRZIasfmVXX8Fuvf+msIqoLuxj/CyUt+AFW0oyTXjs0rykwLuwhCQoJQP24Q5t1HBcMdnXYRwbA+zRfaeDOEJWXAr+9Ew9lfIJRTjtaPfSPl84x5u20YvmAE6+uLUVOck/b9iIiIiCg7MUNIhImsXiAso23Qj/Yhf9L5BGO0FUUrCqNFX2Jj8yq7XsL6N3dCUBV0Lf8yPvr4A1DF6NQNgiCgMMcOu5T4FoxmCIWEZTHaVZImzWkMIp22xGDzgvpi4MI/AT73YwDAyg9/hrqzT6d8nkA0Gxqb3L7XG0jrPkRERESU3RgQ0qKlHTeoDQgnlqXeR2h8HJ4oAnnjY/gUNVpAZv3heyCqMnqWfREnN31fN8m8saCNClW3LiFDaDFu0Dj2UJs9dNoS397xtZf8JVrW3gkAWHt8N8r63kjxTIHIJLvVEhEREVH2YkBIi5Y2vIl1/QxoulgaxwKaiRVesUtiPNiyu1uw8fXtkJQQBuquxkcffwB2h35Sd2MVUd06kzGEunGDhm21+zSOKUzc98TtlvV3o3vZlyCoCtYfvgc5vk7LNgGceoKIiIhoIWJASIuWNt6LJb/8oYjp+lRsoghBAOwBF84/eBscITc8ZRfg/U88BFW0ocBp6L6ZYsoLY8AoWk07Ieq7kmrbXJrvwMaGEv1+NfddVpGPU5d8H97S9XCE3Njwxl0QI9ZdQUNpdKElIiIiovklaUD48MMPo7GxETk5Odi8eTOOHDmSdGdutxs7duxAbW0tnE4nVq9ejQMHDsTXv/baa7jhhhtQV1cHQRDwzDPPJOzjL/7iL8YzJBM/119/vW4bl8uFm2++GUVFRSgpKcHtt9+O0dHRhH0RpSs27m9M12U0/YjQLgmAHMbGQ3cid7Qd/vwlePdT/w+KLRcAkO/U129KNQdisgwhdN1H9dtENHNF2EQBBZrHNe7zvOpCfPL8evRs+yeEnGUocn+E1Sd+YNkmszGVXe4xNPfzvUdEREQ0X1kGhE8//TR27tyJ3bt34/jx49i4cSOuu+469Pf3m24fCoVwzTXXoK2tDfv27cPp06fx2GOPob6+Pr6Nz+fDxo0b8fDDDydt1PXXX4+enp74z7//+7/r1t9888348MMP8dJLL+G5557Da6+9hq997WuZPG8iaDuNmo8hzCQgFFFx6PsoGTyOiL0QJz79GEI5FfH1+Q59QGgsGpOK1bhBY5XRcETV/W41RUVMjl1COL8umsmEgCUt/4HKzhdM22BWUfVktxdtgz5ORUFEREQ0T1lOO/HQQw/hjjvuwG233QYAeOSRR7B//37s3bsX3/rWtxK237t3L1wuFw4dOgS7PTpeqrGxUbfNtm3bsG3btpSNcjqdqKmpMV138uRJPP/883j77bdxySWXAAB+9rOf4bOf/Sx+9KMfoa6uLuX+iQB998rYVBHacXKZTMxe3vprlLy/FwDw4Sd+BH/RSt36AkOGMFU4KBqiN6txg6Khymi+sWuqbh/mj6qoKoarP4lz5/8VGk89hnVHv403yzYimKd/D8pJAmR2JyUiIiKan0wzhKFQCMeOHcPWrVsnNhRFbN26FYcPHzbd0bPPPostW7Zgx44dqK6uxvr167Fnzx7Ismy6fTKvvvoqqqqqsGbNGmzfvh1DQ0PxdYcPH0ZJSUk8GASArVu3QhRFvPXWWxk/Fi1e2vAmFvzplqWZIcz3NKHutXsBAK1rt2Og9qqEbYzzDaZKECZ2GTWfXsLYZbS8wIn19cX4xMpyk52aP1Ys8Xf2Y3fDW7oe9pAH647cmzCIUpshzCy/SURERETZyjQgHBwchCzLqK6u1i2vrq5Gb2+v6Y5aWlqwb98+yLKMAwcO4L777sOPf/xj/OAH1mOSzFx//fX4l3/5Fxw8eBD/5//8H/z+97/Htm3b4oFlb28vqqqqdPex2WwoKyuzbFswGITX69X9EOmLyqiWy5IRI35sOPQNiJExjDVcjrMf+6b5dqL11BFmjGutxhyKhiqjAFBTnBPPSFpVJ9VaXpEPAKirKMaHW34MWcpBef9h1LX+Ured8eVQM6m6Q0RERERZybLLaKYURUFVVRUeffRRSJKETZs2oaurCw8++CB2796d9n6++tWvxm9fcMEF2LBhA1auXIlXX30VV1999aTa9sADD+C73/3upO5LC5d27j+z8XHpTLu3+sQPkT/Sikh+LYau+7+A17zLpigIEEUBcppTNxjHGFqNOUw1RlDbTdRqH8W5dly5phI2ScSr3hU4u/5/YPW7P8Tqd3+IoZrLoRbVIRRREl4j7byEKhgcEhEREc1HphnCiooKSJKEvr4+3fK+vj7LsX21tbVYvXo1JGnihHjt2rXo7e1FKBSadANXrFiBiooKNDc3AwBqamoSCttEIhG4XC7Ltu3atQsejyf+09HRMen20MKhTXDFbmsDG1VVk2bBKroOYknLUwAA93U/hZpn0k1znCgIkHQFYJK3Ld0MoWTRlTTdx4mxSeL44whoP+9WeMo2whYexfnHdiPfEX1Pa8cQDowE8fvTA+ntnIiIiIiylmlA6HA4sGnTJhw8eDC+TFEUHDx4EFu2bDHd0WWXXYbm5mYomkocTU1NqK2thcPhmHQDOzs7MTQ0hNraWgDAli1b4Ha7cezYsfg2r7zyChRFwebNm0334XQ6UVRUpPshMisqA12XUessoWNsAOuO/i8AwLnVt0FpvDzpY4lChl1GjWMINeGedjyimE6f0AxIogCIEj76+ANQRDsqe36H6p7o54CSTsqUiIiIiOYVy2kndu7cicceewxPPPEETp48ie3bt8Pn88Wrjt5yyy3YtWtXfPvt27fD5XLh7rvvRlNTE/bv3489e/Zgx44d8W1GR0dx4sQJnDhxAgDQ2tqKEydOoL29Pb7+b/7mb/Dmm2+ira0NBw8exBe/+EWsWrUK1113HYBo1vH666/HHXfcgSNHjuCNN97AXXfdha9+9ausMEqTFh9DaFhmOo5QVbH26P+GIziMkeI1aL7gf0IShaTZOEEQDOP9EnaZsL3ud807VRIFXLGmEleuqTSMEUxsgMX0hUnaGf3fV7wK59b8FQCg+tB3IUYCui6iCRgrEhEREc1LlmMIb7rpJgwMDOD+++9Hb28vLrzwQjz//PPxQjPt7e0QxYmz1IaGBrzwwgu45557sGHDBtTX1+Puu+/GvffeG9/m6NGjuOqqiQqMO3fuBADceuutePzxxyFJEt577z088cQTcLvdqKurw7XXXovvf//7cDqd8fs9+eSTuOuuu3D11VdDFEXceOON+OlPfzp9rwotCvruofr/gWhAaBYP1rQ/i8qeV6GIdnzwiR9DlRywiSIEwXzqBUmKRln68X6pwzNBmGiPcWv7eBdPbRBo1q10KvMdtq79OpZ2Pgv7SAcaTz0Kf/nfWt5vcDSEsnxHvOspEREREc0PSYvK3HXXXbjrrrtM17366qsJy7Zs2YI333zTcn9XXnll0jFZubm5eOEF80mxtcrKyvCLX/wi5XZEyegPxcTjUlWB070jumX2wBBWv/N3AICODd+Er3g1gGjQZxV6OW2JwVu62bpYGy27mGY4FjEVSRNVrl9eC+n6PcAvb8WyU4/i1LqvAKL5ON1u9xj8oQguaSzL8BGJiIiIaC7xcj4RzIvKyIqKPm9At92ad74PR8iNkZLz0bf+a/HltiRdRmNj/rQZPNGqSoyGvkJoys0zHpdovo+J2yW5DmDdFxFo+DQkJYS6t76X9L5ufzj1AxARERFRVmFASIuWNkN4bsiP9iG/bplxzFxF10HUdByAIkSLrgi2iWJJkiiYjuEDgBzbeECYRhCok0YRmpTTTmRcaMYQhAoCPFf9HRTBhtKOl1Hab90DgIiIiIjmHwaEtGgZ585r6htBKDIxDlA7754UHsX5x78DAGhf/ZcYKf2Y7r6SYJ0hrCyMjn/NNDbTFYRJo8eoaVGZzCPCuFgQqpavQdfKmwAAq977UWIFHCIiIiKatxgQ0qKVKq6JaKZQWf7R/0XOWB/8+Q1o+dg3ErYVTbqMXri0BBsaiicCwgyDM0FXldQqQzi9007op8YY/18EWtftgGzLQ7HrPVR1vTj1ByIiIiKirMCAkMhCRI5GjPmeZixtehwAcPqib0Ox5aR1/wKnDVWFE9smiweXlecBAKqKJqrpaiedt+ptmk4WcWLbyQWkoiAglFOBc6ujU86sfP8hCArHCxIREREtBAwIadFK1fExoqiAqmLNO9+DqEYwUHc1huompk0xZhiN4/xshiguWdGXpWV5uHRFGdbXFceXOe0Tb0+r7OI0JwhN9xELTM+tuR0hZxnyR1pR1/qf0/BoRERERDTXGBDSopVsChQgOoawuuMAyvrfhCw5cfrC/5X0/sZgSjIEhMkCNkEQUJRj1xWeiU1XAVhnCI37mCqzfcQCWdlegNa1dwIAVnz4M4iRsSk/HhERERHNLQaEtGilyhAqwVGc9+4PAQBt538dgYKGpPfXBlOimBhcpZoWwig2XYVx37rHTDExvb59GT38xH41nxKdK7+KsfwlcAYGUN/y9OR2SERERERZgwEhLVqpisrUfPRz5Iz1IZC/BOfOvyPl/bXxllnwl2lAps0QWpmGpKB+fybLtJlOVXKg9fyvAwAaTz0GMRIwuQcRERERzRcMCGkRs44IHWP9WPLRowCA9ov/ForkTNimerwATJ4zmsnTzwk4HQGhlHojjUwzkJPdb0/jHyGQVxvNErb+x4w8JhERERHNDgaEtGglyxCu+PCnsEX88JRtxHDj50y3aSzPxwVLinHJsjIAqbtvZhqwVRY6keeQ4tNWmMlkl+lsarY/Y7u1WcJlpx6FKAfTbwQRERERZRUGhLRoWcWD+Z4m1LfuAwA0bbwXomT+NhFFAdVFOXDEunZq4iZpGjKEkihgy8pybGwosdwm06kkUjHbn7E4DgB0L/8yArnVyBnrRy0rjhIRERHNWwwIadGyyhCe9+7fQ1AV9NdfC0/lJWlV+ARSdxmNzUloT2NsYLL9pPP400kUEoNCVXKg7fyvAQCWn/p/EOTQzDw4EREREc0oBoS0aJlNO1HW+wYqel+DIthwZsNfA0g/KEs1SXxZvgMfbyzDlhXlk2nurDBrtyAIaCjLS1jeveIrCOZUIsffg5qO52ahdUREREQ03RgQEsWoCla99yAAoHPVn2KssBFA+mP/tIGjWTdLACjOs090MZ1taTyNfKfNdPmS0tyEZYrkRPt5twAAlp3eC6gqFCXVZB5ERERElE0YENKiZQxdqrpeQpH7I0Rs+Whdd2d8uSgAn1xVjjU1hUn3p592YvramYxdEuCwidEfi7GOmVhWlofGinx8vLFMt1w7J6JW18o/QcSWjwJPE8p7/wAl1VweRERERJRVGBDSoqWLXRQZKz74/wAA7av/AmHnREAkCgLyHDbTbpNaqcYQzgRBEPCpVRX41KqKaXlMURSwqqoAxXn2tLaPOIrQteK/AQCWnf4nMEFIRERENL8wIKRFS9XkCGs69qPA24ywvQjtq2/TbZd2l1HdtBOzlCJENIgT00hJTrUiaa7DPEvYft5fQBEklPW/CbX7hOk2H3V7cbLHO6XHJyIiIqLpx4CQFp0+bwBvNA/CH5QBAIISxooPfwYAOLfmdkQcRbrtxTTfJdoYcLa6jM6mi5eWoqY4J2F5ML8OfQ3RuRptb/4scX1ERrd7DF3DYxga5ZyFRERERNmEASEtOu93ejAWkuO/17Y9g7zRcwg5S9ExXiRFK/2iMpnfZz7JdUhYX19suu7cmr8EAIgnfw2423XrtF1zO4fHZqx9RERERJQ5BoS0qAlyCMs/ehgA0Hb+1yHb8xO2EXVjA5Psa5oniZ9u0xWjfryxDBctLdEtGy1dh6GqLRBUGXj757p12kIzY2EZRERERJQ9GBDSolbf+kvk+rsRzKlC58o/Nd0m7XkIF3iGMKY4z47yAmfC8s7z/jx64/gTGHC5cXZgFIA+QxiRWXWGiIiIKJswIKRFS5BDaDz1KACgde1/h2JLHB8H6IO75BnCxakkz45ch4SB2qsgFzUAY8MYOPQkWgd86PMGMOwPxbcNK8octpSIiIiIjBgQ0qJVe+5Z5Ph7EMypRPf41AlAYhEZXZfRJGGfkGbguBAJAgBRQvt4lrWh+V8BVcX7nR6c6hmJbyfLnLyeiIiIKJswIKQFxeUL4aNuL8Kygg6XH384M4CBkYnKlup4/0VBiaDx1P8DEC2IokgTXSCNQZ+u+2eaGcJs7DI6ky2KPd9zS2+ELOWg0H0SxYPHTLdllpCIiIgoezAgpAXlvU43ut1jeLfDjQ6XH8Gwgnc73IjI0SAkPD6Grarz+WhlUUcJulZ8NWE/2ixhOnP8AcaJ6Sf/HOYbQZgICCPOEvQuvQHAeJbQBMcREhEREWUPBoS0oMSCDbc/rFseiEQDwoiiAKqC5ScfAQB0rP4Li8qi2knmJ5Yni/PSLT6zUKypKYTDJmJNTZHuNeoYLy5T1fkinP7ehPsxICQiIiLKHgwIaUEpybPHb/s1cw3KioqRQBjhiIrK7oMo8DQhYi9Ax6o/S9iHCtUQEGrHBqaZLZxM42fYdAesDWV5uHx1JQqcNt2+R0vOx3DFJRBVGXVt/5VwP3YZJSIiIsoeDAhpUXi/04O3Wlx4u3UIjR/9IwCgY9WfIeIoMt3eqvtnuiHVIksWwtirtmvlTQCAupZfAop+7sGwzICQiIiIKFswIKQFIRRREIzIsCpgGRifEL2s7xCKhz+ALOWi/by/sNyfpIno7JoBhbXF0akpinLtCffRW1wRobGITn/9dQg7ipHr70J53xu6dewySkRERJQ9GBDSvKeqKl5rGsAfmgYhp5jSYNnpxwAAXSu+gnBOmeV22i6QkjRxe2VlATYsKcZFS0uSPk42Zghnsk3GgFCx5aBn2ZcAAPUtT+vWMUNIRERElD0YENK8px0rGIzIltsVDH+E8r5DUAQJ7atvTbpPbRdIm+YXURRQVZQDu5T8rZOF8eCMctgSX4+uFV8BAFR0vwLHWH98eaqgnYiIiIhmDwNCmvdGApH4bTVJrLGsaS8AoH/J9QjkL7HcTlX1GUKbcab6NCy2iqNOk4DQV3we3BUXR4vLtP5nfDnjQSIiIqLswYCQ5r2RwMQUE1bZJ6e/B9XtBwBEJ6JPRdFElrY05yHUyqZwMJa9qyhwzthjOO3mHyWxLGFd6y8BNdpVVEkWtRMRERHRrGJASPPeSDCScpulTU9AVCNwVW7GSNkFKbfXBi3pTkyvlU0Jws0rynDBkmIsK8ubscdwWHSh7VuyDWF7IfJ8nSjrOwyAASERERFRNmFASPNeqqqVUmgkXtjk3Pm3p7XPqcYsQhblCJ02CdVFOZMKbNN+DLtkulyx5aJ32RcBTBSXYTxIRERElD0YENK8lyrjVN/yH7BFfBgtWoWhmsshpREYTTaLVV+aixy7hNqSnEndf74yG0MYE+s2Wtn1MuwBFzOERERERFmEASHNe0qSKiWCEsbSM48DANpX/yUgiGkGhJNry9raInzqvIqUVUgXGrtk/rrmOSWMlpwPT+l6iGoENe3PsagMERERURZZXGettCDJSTJOVZ0vIGesD8GcCvQs+wKA1EViVJXj3CbDmCV02kVcUF8MAOhp/CMAQG3bf/G1JSIiIsoiDAhp3kuWcWo4868AgM6VfwJVcgAAbGlk71QGLRnLcejHEaoq4lnDvqWfhyLaUeT+CM6hk3PRPCIiIiIywYCQ5j2rLqNFrvdQMvQOFNGOrhVfjS9Pq8uoMm3NWzTMxhGK4+VWw85SuJdcDQAob/7PhO2IiIiIaG4wIKR5z6oLYiw72NewDaHcyvhybZfRScw5TxactsRKo9rpN1zn3QgAKDv7DCCnniqEiIiIiGZe0tPhhx9+GI2NjcjJycHmzZtx5MiRpDtzu93YsWMHamtr4XQ6sXr1ahw4cCC+/rXXXsMNN9yAuro6CIKAZ555Rnf/cDiMe++9FxdccAHy8/NRV1eHW265Bd3d3brtGhsbIQiC7ueHP/xhhk+d5rNgRIbLF4KiqKbTGDgCg6juiB57Hav+XLdOmyG0Kv6yoaEYogisHx8DR6kZM4TFufZ4hhAARhquRMhZBkdgEDh7cLabR0REREQmLAPCp59+Gjt37sTu3btx/PhxbNy4Eddddx36+/tNtw+FQrjmmmvQ1taGffv24fTp03jsscdQX18f38bn82Hjxo14+OGHTffh9/tx/Phx3HfffTh+/Dj+67/+C6dPn8YXvvCFhG2/973voaenJ/7zjW98I9PnTvPEyR4vzg6M6pa90+7G8XPD6PUGTO9Tf/ZpiEoY3vKN8JZv1K3TBoE2ixRhVWEOrlxdhZrixTV9xFQ47ROv5bLyPKytLdIFhKLkQO/S6Hs5cvzJWW8fERERESWyWa146KGHcMcdd+C2224DADzyyCPYv38/9u7di29961sJ2+/duxculwuHDh2C3W4HEM3kaW3btg3btm2zbExxcTFeeukl3bJ/+Id/wKWXXor29nYsXbo0vrywsBA1NTWpnyHNayOBMLqGxwAAKysL4stHA9Euh21DvoT7CHIIS87+OwCg9/y/SFivzRDaJOvxhDM5kftCVFngRGWhE/lOG1ZVRf9W2uI8ggB0N34JS888DrHpABTfMMT80rlqLhERERHBIkMYCoVw7NgxbN26dWJDUcTWrVtx+PBh0x09++yz2LJlC3bs2IHq6mqsX78ee/bsgSzLU2qgx+OBIAgoKSnRLf/hD3+I8vJyXHTRRXjwwQcRiViPSQoGg/B6vbofmh/kFJPW+YOJx1dV14twBvoRzKmEuzHxAoRuDCFjvmkjCAI2NpTEg8HYshhREDBaug4jxWsgKmEoH+ybi2YSERERkYZpQDg4OAhZllFdXa1bXl1djd7eXtMdtbS0YN++fZBlGQcOHMB9992HH//4x/jBD34w6cYFAgHce++9+JM/+RMUFRXFl3/zm9/EU089hd/97nf4+te/jj179uBv//ZvLffzwAMPoLi4OP7T0NAw6TbR7NKOD0x3KoiJqSa+CsnhTFgvifoghWZH7KXuafxjAID43lNz2BoiIiIiApJ0Gc2UoiioqqrCo48+CkmSsGnTJnR1deHBBx/E7t27M95fOBzGV77yFaiqin/8x3/Urdu5c2f89oYNG+BwOPD1r38dDzzwAJzOxABg165duvt4vV4GhfOENgRU1WhQEZGt54QwTjVRZDJGUNsVNJ0pKGh6CIi+1r1LP4/z3vs/ELuOAq5WoGz5HLeMiIiIaPEyzRBWVFRAkiT09fXplvf19VmO26utrcXq1ashSROl59euXYve3l6EQqGMGhULBs+dO4eXXnpJlx00s3nzZkQiEbS1tZmudzqdKCoq0v3Q/KDNCsZuhWXrTOGSM/8GYGKqCZtJwKddwgzh7InF5qHcSriqtkR/+WAfzg354PZn9hlBRERERNPDNCB0OBzYtGkTDh6cKA2vKAoOHjyILVu2mO7osssuQ3NzMxTNjN5NTU2ora2Fw+FIu0GxYPDMmTN4+eWXUV5envI+J06cgCiKqKqqSvtxaH5QTLqMhi1mjbcHhzVTTfwZgNQZQAaEs0f7Wvcu/TwAIPLuL3GmdwRH24ahqmra3YKJiIiIaHpYTjuxc+dOPPbYY3jiiSdw8uRJbN++HT6fL1519JZbbsGuXbvi22/fvh0ulwt33303mpqasH//fuzZswc7duyIbzM6OooTJ07gxIkTAIDW1lacOHEC7e3tAKLB4Je//GUcPXoUTz75JGRZRm9vry7LePjwYfzkJz/Bu+++i5aWFjz55JO455578Gd/9mcoLWXFwvmsxzOWkClSkZghjFhkCGvbnoGkhDBSshbesuhUE2YZQi12GZ095fkTF4b666+FIjpgGzqNAs/p6LKRIP5wZhAfdHnmqolEREREi47lGMKbbroJAwMDuP/++9Hb24sLL7wQzz//fLzQTHt7O0TN+KyGhga88MILuOeee7BhwwbU19fj7rvvxr333hvf5ujRo7jqqqviv8fG9d166614/PHH0dXVhWeffRYAcOGFF+ra87vf/Q5XXnklnE4nnnrqKXznO99BMBjE8uXLcc899+jGCNL84w2E8WFXtPrr1nWaYka6DGH0f9MxhKqK+pZokZLOlX8Sr2CSOkM4+TZTej51XgUCYRlFOfb4MtlRiMHaK1HV9SJq2n+D5pLzcbLHi4isotcTwPr64jlsMREREdHikbSozF133YW77rrLdN2rr76asGzLli148803Lfd35ZVXJu0S1tjYmLLL2MUXX5z0MWh+CoQnpo9QVRWyosImifouo4h1GU08RkoH3kL+SCsitvx4d0RAP/F8jl2CIABVhU58NL6Mcw3OvBy7hBy7lLC8d9kNqOp6EdXt+9F8wf+EINgQuwIQkRXYJMsODEREREQ0TXjGRVlBO77s6LlhvHp6AMGIDEVbVGb8ZjiSmCGsPxvNDvYuvQGyfWIePEkz8fwFS4px2aoKXaAhcQzhnBmsuQIRewFy/d0oHjyuWxc0+RsTERER0fRjQEhZQRsQevxhAEC/N6gLCGMihqIyjsAgqrpeAgB0rfyqbp12DKFuQvrxI7+sIP2CRzS9FFsO+uuvAQDUtD8HWfN31WaMiYiIiGjmMCCkrGDWczOiqIaJ6aP/G4cQ1rb+J0QlDE/ZRoyUrtOtkyzmHPzUqkpcuqJMN66NZl/v0hsAANUdB6BGwvHl77S7EWKWkIiIiGjGMSCkrGA2dDQiK5AVbZVRVfd/9BcF9S1PAxgvJmOg7RKqDQgdNpHBYBYYrvoEgs5yOEJulPW9oVvXNuSbo1YRERERLR4MCCkrmJUSCsuq6RhCbY/Rsr43kOfrRMRRiL6GbQn70HZFtZqCYnV1IUryGBzOBVW0oa/hswCi3Ua1BkeDc9EkIiIiokWFASFlBbPqshFF0VUZdflCaBkY1QWJS87+OwBgeNWXodhyE/YhjB/hoggIFgVklpbn4ZLGsim0nqaid1m022hl18sQI2Px5f6gzLGERERERDOMASFlBZOZJBCWVV2geLp3BC0DPvR6AgAAp78XFd2/AwC4191sut9YVtAhJU57QNnBW7YR/vwlsEX8qOx+BZWFTtht0Y+miNmBQURERETThgEhZQXVpNNoWFZMA8WY2rZfQVRlDFdsglyxxnSbPIcNa2oKsba2cLqaSlPw8eUmmVhBiHcbrep8HqIgxIsMyQwIiYiIiGYUA0LKDibn/bKiWgcEqoK61n0AgO4V/003AX2uQ8LHl5fhU+dVAAAayvJQXuCc9iZT5opz7WisyE9Y3j8+/rOi5/eQIr54MSCzrsRERERENH0YEFJWMC8qo5jOQwgApQNvI8/XgYgtH31LrtcVjBEQDTxy7Owmmo2WluUhz6n/24yUrIM/vwGSHEBhx0GI439PZgiJiIiIZhYDQsoK5tNOqKbLAaCu9ZcAgN6ln4diy9NNKQHz2jGUJRw2EZ9cWYGa4pyJhYIQrxJb3HIgXh2W8SARERHRzGJASFnBbAwhAITkxCqTUmgEVZ0vAAC6l38ZACCKAmK9RkWLaqKUXYx/p1i30YKOV2CX/QBgmSEmIiIiounBgJCyglUmKBhREpbVtD8HSQ5itOg8eMs2AABEwXpaiXSdV10AIDovIc08m6T/e8W6jYqRAIo7o9VjGRASERERzSwGhJQVrIqHmAWEdW3RYjI9K78MjAeBoiDEC5FMNkO4rDwfl6+uxNLyvEndnzKT8HfSdBsta9sPgGMIiYiIiGYaA0LKClaJIFnWr8h3n0ax630ooh0Dy78UXy4IEwHGVBKFDhvfErNFN+5zXKzbaFHH7yBG/FASrwcQERER0TTi2S/NK/XjU00M1H0GSm5FfLl27jqOIJwfJJPIfaRkHcJFyyDKAVT0vMouo0REREQzjAEhZYV0zvsFOYSac78GAHQvvxGaqQejAaE49QwhzR5JMvlDCQJ8q24AAFR3PA/Z5MAIhGUcPjuELvfYTDeRiIiIaMFjQEhZIZ1MUGX3QThCbgRyqzFU/WldhkkUtGPSGBHOB2YZQgAYO+/zAICKnleBkA8AMDQaxEggDABo6huBLxjByW7vrLSTiIiIaCFjQEhZIZ2OgXXj3UV7Gv8YECXdGDRB22WU8eC8IFp8+oQrNyBctAySHEBu60H4QxG80+7GWy0uAECEhWaIiIiIpg0DQsoKVlVGAaAgxwbHWB/K+94AAHQ3/jEAxLuIAuMZwliX0RlsJ00fqwyhIE50Gy04+xz8ocS5KImIiIhoejAgpKyQLOfjsImoOfccBFWBu+JijBUuAwBDl1FBU2WUIeF8YFZlFIgG9rFuowXtByFFJsYKKswOEhEREU0rBoSUFYwJQpskoDDHhnV1RRBUFXXnfgUA6Fn2pfg2kmgMCKO3GQ7OD9q/n7bAjCgAkaoNGMurhygH4Dj3anxdRFHTKkBEREREROlhQEhZQn+Wn+ewYfOKctSV5CJv+CQKPE2QRUd84nJAP7H5dM1DSLNHGxDaNQMKBQgQJRH9S66JrmvaH1/HieqJiIiIphcDQsoKxvN8bW/CsjP/CQAYrLsaEUdxfLm+qIwmIGSOcF7QBvQOm6hZHu0O3F9/LQDA1vwCBDkEAIhwpnoiIiKiacWAkLKCsRtgPFaQIyg5G517sKfxS+bbIBpcSJyHcF7RBvQ27ZyEQrQCqaf8IoRzKiAGPSgdOAIgliFklpCIiIhoujAgpKygJpzkjwcIZ1+BPTCIkLMMQzWfMtsCgH4MIc0P2qJADkmbIRwvECRKcDVEu41Wdb0EgFNOEBEREU03BoQ0Z1oHffhofHJxY4YwHty9++8AgN6lN0AV7bpt9BnCieqiIlOE84IoClhTU4jzqgtQ4LRNLBeEeLA4tDTabbSy6yVAkTmGkIiIiGiaMSCkOXO2fxTd7jF4xsLmlSPH3MCpaEERY3fRKP3E9LEsk677IWW1hrI8LCvP1/3NBEwE9T0lH4fiLIIzMIhi1wlWGSUiIiKaZgwIac7JigrFcJavAsBHvwbkIAKlqzFSsk633iwJWFuSg9XVhVhaljdzjaUZYTd0GbXbon9gVXJgoPYqAEBV54uQZUaDRERERNOJASHNOdUk5aOqAN59CgDgOe/LCRGgWUBol0QsLc9Djl2aiWbSDNIGhIIYnXakpjgHANBbtxVAtNtoRJZ19zM7doiIiIgofQwIaU4omrFgKhLHEDpG2oH2QwAEeFd/KeH+0fGCDAYWCmOXUQCoHQ8Ih2o+DVnKQZ6vE9LAh7r7newZYVBIRERENAUMCGnGqaqKDpcf3kB4YplmvaKqCVVGy8/+KnpjxZWQC2oT9slRgguLdmL62PhB53imV7HlYajm0wCAvLMHdPfrdo+hxxOYpVYSERERLTwMCGnG9XoDON07giMtrvgyXVZHNWQIVRVlzf8Vvb3xT0wnmhcFgcVFFhBthjD2Z3VqJqvvXxKtNlrY+tuEvHAwwsnqiYiIiCaLASHNuNFAJGGZarit/b146ARyRs4B9nxg7edNxwtyaomFxSYmdhm1SyJiicPB2iuhCDbkDDfB4T6ruy+PBCIiIqLJY0BIM84sdtNWFVVV/e817b+J3lj7ecCRb3rCLwhAvmbuOprfBEHAqqoCNJTl6f6uDinabTTiKMZw1ScAAKXtLxruO3vtJCIiIlpoGBDSnNB295TVibnlBCWCqo7fRn+54L9Fl5mc8AtCtOjIedUF+Hhj2Qy3lmZDY0U+1tQU6pY57YndRkvPvaDbxqxLMRERERGlhwEhzYLEE3ZtQBitOBpdUNr/JpzBIYSdZcCKKy3vDzWaVVpWno/iPPu0t5iyg0MzHcVA3dVQIaBw6F04/T3x5cwQEhEREU0eA0KaE9qqoqqmqEysu+hQ42cBKRromZ3w+0Ny4kJacCTN2MJQbiVGqzYBAKq6XpqrJhEREREtKAwIacaZBXQJXUYBiJEAqjqj48MGG78wcf8Zbh9lL231UQAYXnodAKCy6+X4MoXlZomIiIgmjQEhzTizgE57Et/tHoPHH0ZFz6uwRXwYy6vHSOXFE/fXRJQ1xTkoyLEljDWjhUlbfRQAhhrGxxEOHIE9OAwAUBgPEhEREU0aA0KacYJJilB7Dj823v0z1l20b+lnoQqJ0xAAQJ5DwidWlKOhLG8mmkpZxji9SKCgASPFayCoCip6XgXADCERERHRVDAgpFl1pm8EsqImTCpvC3njJ/i9S2/QrdPGBHaJh+xiYhP1f29FVTFQvxXARLdRlQEhERER0aQlPbt++OGH0djYiJycHGzevBlHjhxJujO3240dO3agtrYWTqcTq1evxoEDB+LrX3vtNdxwww2oq6uDIAh45plnEvahqiruv/9+1NbWIjc3F1u3bsWZM2d027hcLtx8880oKipCSUkJbr/9doyOjmbwtGk2aXM854b8aBvyJZzEV3W+CFEJI1C6GoGy8/GxumLN/Sf24LQxIFxMDPEgZGUiICzvfR1iJMAuo0RERERTYHl2/fTTT2Pnzp3YvXs3jh8/jo0bN+K6665Df3+/6fahUAjXXHMN2trasG/fPpw+fRqPPfYY6uvr49v4fD5s3LgRDz/8sGWD/v7v/x4//elP8cgjj+Ctt95Cfn4+rrvuOgQCgfg2N998Mz788EO89NJLeO655/Daa6/ha1/72mSeP82B0UAkIUNYPd5dVF3/ZVx5fjWKcyemkmCGcPEyZghlRcVIyToE8mohyWMo6z/MLqNEREREU2CzWvHQQw/hjjvuwG233QYAeOSRR7B//37s3bsX3/rWtxK237t3L1wuFw4dOgS7PXoy39jYqNtm27Zt2LZtm2VjVFXFT37yE3z729/GF7/4RQDAv/zLv6C6uhrPPPMMvvrVr+LkyZN4/vnn8fbbb+OSSy4BAPzsZz/DZz/7WfzoRz9CXV1dZq8Azah+bwDN/frs7cSsg1GOsX6U9b8JAAivvRG5SfZnZ4ZwUTHLEEIQMFD3GTQ0P4nKrpfhXmf9mUJEREREyZmeXYdCIRw7dgxbt26d2FAUsXXrVhw+fNh0R88++yy2bNmCHTt2oLq6GuvXr8eePXsgy+nPF9fa2ore3l7d4xYXF2Pz5s3xxz18+DBKSkriwSAAbN26FaIo4q233jLdbzAYhNfr1f3Q7Hiv05OwTFFVXZfR6o7fQoAKd/lFQNmypPuzS5yEYjExZghjBuqinxEV3a9AzeAzhoiIiIj0TM+2BgcHIcsyqqurdcurq6vR29truqOWlhbs27cPsizjwIEDuO+++/DjH/8YP/jBD9JuTGzfyR63t7cXVVVVuvU2mw1lZWWWbXvggQdQXFwc/2loaEi7TTT9VFU/VUCsumjv0s+bTlER0WzsYJfRRUUym8QSwHDlxxG2F8IZHEJO//FZbhURERHRwjFtZ9eKoqCqqgqPPvooNm3ahJtuugn/+3//bzzyyCPT9RCTtmvXLng8nvhPR0fHXDdpUVNVFep4p9HckXModr0HVRDR37At2iXQICIr8dtmU1jQwiVZZIRVyYGh2isAAIVtL81mk4iIiIgWFNOAsKKiApIkoa+vT7e8r68PNTU1pjuqra3F6tWrIUlSfNnatWvR29uLUCiUVmNi+072uDU1NQmFbSKRCFwul2XbnE4nioqKdD80d1QgXlSmpmM/AMBVtQWhnArYTDKAYZlFQxYrqwwhAAzUXQ0AKG5/cbaaQ0RERLTgmAaEDocDmzZtwsGDB+PLFEXBwYMHsWXLFtMdXXbZZWhuboaiTGRzmpqaUFtbC4fDkVZjli9fjpqaGt3jer1evPXWW/HH3bJlC9xuN44dOxbf5pVXXoGiKNi8eXNaj0NzS9HMQ1jV8dvojfVfxsfqi1DgTKxzxHGDi5ckWv/tB2uvgCLakeNpAQaaZrFVRERERAuHZZfRnTt34rHHHsMTTzyBkydPYvv27fD5fPGqo7fccgt27doV33779u1wuVy4++670dTUhP3792PPnj3YsWNHfJvR0VGcOHECJ06cABAtInPixAm0t7cDiHYH/B//43/gBz/4AZ599lm8//77uOWWW1BXV4cvfelLAKJZx+uvvx533HEHjhw5gjfeeAN33XUXvvrVr7LC6DwRrTKqIs/bjELPaSiiHaUX/xFqi83rizZW5KO6KAcbG0pmtZ0096wCwoIcG2R7AYYrxy8Cnd4/i60iIiIiWjgsp5246aabMDAwgPvvvx+9vb248MIL8fzzz8cLvrS3t0PUVABsaGjACy+8gHvuuQcbNmxAfX097r77btx7773xbY4ePYqrrroq/vvOnTsBALfeeisef/xxAMDf/u3fwufz4Wtf+xrcbjc+9alP4fnnn0dOTk78fk8++STuuusuXH311RBFETfeeCN++tOfTs8rQtPGOPl8jKKqUFSguuN5AIC37lMoyS+13I9dEnHBkmLL9bRwWQWEG5YUo2t4DP31W1He9zpw6gCUT/4PnOkfRUWBA+UFTgCAZyyMgZEAllcUJM02EhERES1Wgmp11r6Aeb1eFBcXw+PxcDzhDJIVFb871Z+w3GkXUeC04bx916DAewbqF/8vhItunoMW0nzw8kd9CcuuWFOJ0UAEH5w8iU8/dzkAAZ1/eQKnRqNZ5q3rqnX3bazIx6qqgllrMxEREVE2MouDWMOfZoxica0hGFYQ6PoQBd4zUEQHhPM/N8sto/nEZjKGVBQEiIKAYF4NRsouAKDCfvZ5y334Q5EZbCERERHR/MWAkGaM2RQSMdUdBwAAo0suB3JLZqlFNB99+rxKfOq8Ct0yUQCE8U+voSXRSeoL2qyrjQqmM1wSEREREQNCmjFWGUKoKqrHq4t6Vnx+FltE85EkCsixS7plwniGEAAGllwDAMjtfB1S2Ge6D05fSURERGSOASHNGKsEYb6nCfkjLZBFB0Ybr5ndRtGCEasRM1q4CihthCgHUdb3OoDk2WkiIiIimsCAkGaM1Ul5LDs4VPNpqA4W9aHJiWUIFQBYEx2HWtX1MgAgLCum2xIRERGRHgNCmjGmBWxVFdWd0YCwr+GzEHkE0iTFgjxVBZTV2wAA5T2/h6BEcKp3RHdBIhYP9nsDaB/yz3pbiYiIiLIVT8dpxphlCAs8p5A/0gpZcmKw7ioW+6BJs0tCPNAL1V8KOacUjpAbJYPHMDgSRLd7LL5tbLv3Oj1o6huBNxCegxYTERERZR8GhDRjZJMM4UR30Ssg2wtY7IMmTRAE2KXoR1hIFTG6LFpttLLrIACgfyQwsa3hwkMoou9SSkRERLRYMSCkGZMQD2qqi/Y1RLv4iQwIaQoctuhHWDiiYGS8QFFl98uAqmLYN5EF5HFGREREZI4BIc0YY5fRQvdJ5I2egyzlYLD2yvGlPFOnyYsFhCFZgbfucsiSE7m+ThR4Tuu2M2airWZEISIiIlpsGBDSjDHOQ1g1Phn9YO2VkO35c9EkWmAc411GWwd86PaJcFV9EsBEt9EJgq7IkQpGhEREREQAA0KaQYp2mJaqono8IIx1F40u5ok5ZUab7YtlCP0hGQAwUD8+jrD7YMJ9eKgRERERJWJASBkLRZS0Jv7WFpUpHP4Aeb5OyFIuBmuviC/n/OGUKV1AKOk/wgbrroIKAUXDH8Dp75m4D8CcIBEREZEJBoSUkWBExmtNA3ijeTDlttouo9UdzwMABuquhGLLM92GKFOxDGFMKKcCnvKLAOi7japgNpqIiIjIDANCyojbH63cqC3bHwjLaBv0ISzrS/krsfSfqqKq6wUAQP+S6/Xb8CSdMqSdQiLfaUtYP7TkagD6bqOqasgQ8rAjIiIiAsCAkDJkVhP07TYXmvtHcaZvVLc8OB40FnhOIW+0HbLkxFDN5bptGA9SprRdRotz7bBJ+qNyeOm1AIDS/rdgC3nHl6o81oiIiIhMMCCkzJhEhMFwNPBz+0O65YFwtNBHVeeLAAB/w5Wor6nUbcMMIU3VJ1aU634PlayAr3AFRDWC8t7XAEQvPGiPNY5dJSIiIopiQEjTxjieKzAeKMYCQvX8GyAaJoTjiTllyngM5dglrK0riv9uE0UM1I93G+16GUBiD1FeiCAiIiKKYkBIGdGO31IM0Zw2IFQUFcGIjDxvCwq8Z6CIdgRXXpswQXhZnmNG20sLx/m1hRBFYH19ccI6uzhxYEmigP76awAAFT2/hyBHM9faGJDhIBEREVEUA0LKiDagU9Ro0Bdj10wBEIwoUFWgqjNaTMZV9Qkgp0TX4/Rj9UVYUpo7002mBWJJaR6uWlOFsvzEiwiSJiC0iQK8ZRsQzKmELeJD6cBb40VlNF1GmZomIiIiAsCAkDKkDehUTHQLNYqPH+yKdhftX3IdBAGoK4kGgOUFDtQW50IUzcrUEJkTjCnmcXZNdloSBUAQMVAX7TZa1fUyVBaVISIiIjLFgJAyoj2nNmYIteOywrKCnNEOFA1/CFUQMVC3FQKi472uXFOJCxtKZq3NtPDZDF1GAWCgfiuA6HyEqqIkHLtERERExICQMqQbh6UCYVk1XSerKqq6XgIADFd8HOGcsnh2xyaJlpkeosmQTAJCV9UnELHlwxnoR07/u7qJ6dljlIiIiCiKASFlRDcOS1Uhy/rfY2RFjVcX7V8SnReOISDNFKdNQmNFHhor8uNjWVXJgcHaKwAABW3P64JAVVWhqioisnmXZyIiIqLFggEhZUZzUq2o0Uyg9vc4bw9Kho4DAAbGKz4yKUgzaVVVIVZVFeguPMS6jRa2vag7dlsGfDh4sh+vnh5AmEEhERERLWIMCCkjxnFYsmKeIcw5+1sAwEjFRQjm1QDQT1lBNFO0Fx4Ga66AItqR426GMNRkuv2wLzRLLSMiIiLKPgwIKSO6MYSKPgjUjtHKO3sAADC87PqJOzAepFmgvfAgOwqjU54AsJ35ren2EivdEhER0SLGgJAyogsAYcwQjt/wDSG3+00AgHf5tvh6dhml2WA8zgbqot1G7WcOmG7PgJCIiIgWMwaElBF9l1HoA8LY7dP7IagyRkrWQilZFl/P026aDQkBYf1nAAD2nmNwjPXPQYuIiIiIshcDQsqIvnS/qssYxmPDj54FEJ2MXpt94VQTNBu0x1meQ0Iotxq+ygsBAJXdr8xRq4iIiIiyEwNCyoiqqzKq7zKqqiow5gZaXgUA9C25Nj4FAMAMIc0ObQ/QWHDoWRad+qSy6+WE7TknIRERES1mDAhp0lQViRnCMy8CShhjJavgL1plyBDOQSNp0dEWlYkdfu6l0YCwrP8wpPCobntt1puIiIhosWFASBlJzBBO/C6rKgLv/xoAMLz0OgCATRsQMkdIs0B74SF2QSJQvBKR0pUQlTDKe1/Tbc9wkIiIiBYzBoSUERX6jKC2y6gc8MPWGh2jNdQQzcjYtF1GGQ/SLBBMuoyqEBBYGa14W2XoNsoEIRERES1mDAgpI9rxVqqhqExZ/2HYIn4EcmswUvIxAPoMIdFsMOsyCgBjK6JzYlb0vApBnpiMXmWOkIiIiBYxBoQ0aaohQ1jZ9RIAYKD+akTGF3MMIc027XEmxn9REaq9GMGcCtjCoygdOBLfZmg0hKNtLviCkdltKBEREVEWYEBIGVEM007EA0JFRkX37wAAA/VbEYpEBxcyQ0izTXvExQJCVY12Gx2ouxqAvtpo1/AY3P4wPurxzmYziYiIiLICA0LKiL6ozESGsNh1As7gEML2QgxXXhrfRpsh5Fgtmg2iJkUojn/CqYgefwP1WwEAld0vA6qiu184ov+diIiIaDFgQEgZ0hSRUSZOoGMZl8Haq6CK9vhy7TyEErOFNAvMuozGLka4qrYgYstHzlg/yjwf6u4n8vgkIiKiRYgBIWVEm+ULy2p8Yaxy40D91brt7ZKINTWFWFNTqAsOiWaKvqhMLCBUo91GJQcGay8HANT2HNTdjxcsiIiIaDHiGTplRNvrs2t4DABQMHIWeaPnoIh2DNV8Or5eEKIn2Q1leWgoy5vlltJiJWg+1WIx3mgwgqa+EQDAQP01AICSjhd19xNZ9YiIiIgWIQaElBGzcYBV3dHsoKv6k5DtBfHlNmYEaQ7oisqI+i6jADBYcwUU0Y5cdzPyvC0T2zIeJCIiokUo6Rn7ww8/jMbGRuTk5GDz5s04cuRIss3hdruxY8cO1NbWwul0YvXq1Thw4EDa+2xra4MgCKY/v/zlL+Pbma1/6qmnJvP8KUNmc7YtG4hWF+2v26pbbucZNs0BQUjsMqolOwoxXLkZAFDZPdFtlF1GiYiIaDGyDAiffvpp7Ny5E7t378bx48exceNGXHfddejv7zfdPhQK4ZprrkFbWxv27duH06dP47HHHkN9fX3a+2xoaEBPT4/u57vf/S4KCgqwbds23eP98z//s267L33pS9PwclAyvmAEna4x3TKnvxdSzztQIWCw7jO6dTzBprkgCua3tdzLrgWgn36CXUaJiIhoMbIMCB966CHccccduO2227Bu3To88sgjyMvLw969e02337t3L1wuF5555hlcdtllaGxsxBVXXIGNGzemvU9JklBTU6P7+dWvfoWvfOUrKCgo0D1eSUmJbrucnJzpeD0oicNnhxKWVXS/AgAYqbgQodxK3Tp2GaW5YFZURqumOAfLP/llAEDx0Ak4xqIXpOJzahIREREtIqZn7KFQCMeOHcPWrRNdAEVRxNatW3H48GHTHT377LPYsmULduzYgerqaqxfvx579uyBLMuT3uexY8dw4sQJ3H777QnrduzYgYqKClx66aXYu3cv1CST3AWDQXi9Xt0PpWfYF8JH3V6EZfM52uLjB5dek7DOLjHjQrNPN+2ESYrQYRMhltTDX3URBKjxLKHCiTKJiIhoETINCAcHByHLMqqrq3XLq6ur0dvba7qjlpYW7Nu3D7Is48CBA7jvvvvw4x//GD/4wQ8mvc+f//znWLt2LT75yU/qln/ve9/Df/zHf+Cll17CjTfeiDvvvBM/+9nPLJ/kAw88gOLi4vhPQ0OD5bakd+zcMLrdY2gZ8CWss4W8KO17EwDgWXpt4nqRGUKaW2ZdRmPLRhqj3dCrOl8AADBBSERERIuRbbp2pCgKqqqq8Oijj0KSJGzatAldXV148MEHsXv37oz3NzY2hl/84he47777EtZpl1100UXw+Xx48MEH8c1vftN0X7t27cLOnTvjv3u9XgaFGQpG5IRl5b2vQVQjQMUahEtWAv6wbr2NGUKaA2KKojKxOqQjK7ah+sgelA4cgT04DDWvapZaSERERJQ9TFM4FRUVkCQJfX19uuV9fX2oqakx3VFtbS1Wr14NSZLiy9auXYve3l6EQqGM97lv3z74/X7ccsstKZ/E5s2b0dnZiWAwaLre6XSiqKhI90OZMTuxjhfkOP+zpl3zWFOG5oL2UDUNB8cXRkoaMVJyPkRVRmXXQWYIiYiIaFEyDQgdDgc2bdqEgwcnSrIrioKDBw9iy5Ytpju67LLL0NzcDEWZGGvW1NSE2tpaOByOjPf585//HF/4whdQWVmZsM7oxIkTKC0thdPpTLktTY5xfJUgh1DR8/voL+d/HhIrNFKWECx/iYodygIE9NdfBwCo6nqBYwiJiIhoUbIc5LVz50489thjeOKJJ3Dy5Els374dPp8Pt912GwDglltuwa5du+Lbb9++HS6XC3fffTeampqwf/9+7NmzBzt27Eh7nzHNzc147bXX8Fd/9VcJ7frNb36Df/qnf8IHH3yA5uZm/OM//iP27NmDb3zjG1N+MciaMXtS1v8mbBEfQrlVQN3FnGKCsoZ2HkLBJCKMBX6CAPQ1RAPCsr5DEAIsNkVERESLj+UYwptuugkDAwO4//770dvbiwsvvBDPP/98vChMe3s7RE3RkIaGBrzwwgu45557sGHDBtTX1+Puu+/Gvffem/Y+Y/bu3YslS5bg2msTC5XY7XY8/PDDuOeee6CqKlatWhWfzoKmV0RTWdRYkj/WXdS99BpUiWLSsVpEs0l7bcKs0m08IATgL1qF0aKVKPCeRUnnK8C6xIrGRERERAuZoCabr2GB8nq9KC4uhsfj4XjCJAJhGa+fGUxcoSr49G8uhzPQj9NbH8eaT/0RTveOoMPl1222ZWU58p3TVreIKG29ngBkVUVpnh2HmvXzZ9aW5OBjdcVo7h9B26AfK97/CVac/L8YXHItKv7ql3PUYiIiIqKZZxYHcV4AsmQ192CR6304A/2I2PLhrt4MANDOQX/R0hJ8enUFg0GaMzXFOagvyTXvMho/rKPr+pdEeyKU9rwGhBKnVyEiIiJayBgQkiVjN9GYWHfRwdrLoYjRQj7aLqOSKMBpk0zvSzSbzHoya8cQAsBoyVr48xsgyQHgzEuz2DoiIiKiuceAkCyFZYuAsOd3AIDBuquhIrqNtqiMwIqjlMVkzRjC6A0B/UuixWVw8tk5aRMRERHRXGFASJYiSmKX0RxfJwo8TVAFCcN1V2Bd7XjfY91k4LPWRKKkzK5N5Dts4+smVsYCQrXpBSAcmJW2EREREWUDBoRkKWKSIazsfiW6rv5SfGrDeSjJcwDQZwjNK44SzT7tGMI8h4SGsjysqMwHoL9w4S27AIHcGgihUaDld7PdTCIiIqI5w4CQLEUMYwglUUBFd/RkObTyWl2GhQEhZSPtoViS58CamkLYxysg6QrOCGK8uAw+YrdRIiIiWjwYEJKOPxSJF5MxzkjikEdROnAEABBZdb1unTYIZDxI2chmmJPQeJzGxxGePgDI4VlqFREREdHcYkBIcZ6xMA41D+HNlui8bcYOoxW9b0BUwvAVNEItX6Vbp+1+x4CQsoX2UJRSDG51l18MNa8SCLiB1tdmtF1ERERE2YIBIcX1e6PFNMZCMgDAkCBE+fj4wcG6qxLuqy8qw4iQsoO2W7NNTJ4hhCghsuZz0dsfPTOzDSMiIiLKEgwIKS4xjtNEhIqM4s5XAQCDdZ9Jel8GhJQtkmUIzaZHCa/5QvTGyd/Eu422DfpwbogT1hMREdHCxICQLGkzhMWuE7AHXQjbi+CuuBjGOeu1BTo47QRlC23MZxP1H3dmh6m/bguQXwWMDQMtryIUUdDcP4ozfaPxsbVERERECwkDwkXK4w8jIhvnGdSfImtPf2PTTQzVXg5VtCcUnLHbODE9ZR/tsWiIB3XBotMeXdk3GgbWfTG68MNf6ebibB30IRiRZ6ytRERERHOBAeEi1OcN4O02F460upJup435YtNNDIx3FzUmS/IcNqysKsDauqJpbSvRdEnMEE5EhLXFuQCAgZEg8LE/ii48+Rzk0MQk9W2DPrzb4Zn5hhIRERHNIgaEi1CvJ3qS6w8lz3ao4znC3NF2FHiboQoS1JVXw2kXUZbvSNh+eUU+6ktyp7/BRNMg1y7pftdmCAtzbACAiKwCS7cAhbVA0APRMEm9d4zTURAREdHCwoCQ4rQnyC5fCLHecrHs4FjtpdhwXiM+taoiZQl/omxx0dISXLCkGLkOQ0Coua2tQCpDANZ9Kbr85DMz30AiIiKiOcSAcBFKZ4jf8XPD6BufhqJifPzgaOM14/dnMEjzR3mBE9VFOYkrtAVnpImPQkVVgfV/DACwN/8WYiRgvCcRERHRgsGAkOKMYZ6sqJBCIygdeBsAMNa4dfYbRTRDtGMIJVGIF52RFRVY8nGguAFi2IfyXk5ST0RERAsXA0JKqrzvDxDVCHyFyxEpXTnXzSGaNqKgvx2bP1NR1WgafbzaaHXHgbloHhEREdGsYEBIcWZdQWPTTQzUfcZ03jai+Up7vAsQ4gFhfL7B8W6jld2/gxjxz3r7iIiIiGYDA8JFSEgztBOUCMp7ot3lBus+Ez9hJloItEezICBeKElWVBxpdeFoqBHhomWQ5DFU9Px+bhpJRERENMMYEJKl4qF34Ai5EXYUw1N+EQNCWrBEYSJDOBaW4R0Lwz0WQe+S6wGw2ygREREtXAwIKc4Y7sWmmxisuQKqaEurOinRfKFqbouaDGFEnljTVb8NAFDR8yqk8OhsNo+IiIhoVjAgJEux8YODdVcBSG+6CqL5SBQExGaeGA1G4stHS9bCV7gckhxEVdeLc9Q6IiIiopnDgJDitBmT3JFzyB9pgSLYMFhzOQCwyygtKKo6ccQLmiqjXcNj0K7oXRatNlpz7jez2j4iIiKi2cCAcBGyiuu0J8ix7KC78hLIjkIADAhpYdFeABEEId5l1Kh36ecBAGX9h+EY60coophupyiq6XIiIiKibMaAkOK0p7MVPdHxgwN1n4kvYzxIC5nVBY+xgqVwl18EQVVQ074frzUNTExNMS4sK/h90wDeaR+ejaYSERERTRsGhIucNisYuymFRlAycBQAMFh7VXw9A0JaSJw2/cdfsgx477IvAABq2qPdRsOygnc73HirZQiKoqJ/JAhZUTE0Gpq5BhMRERHNAAaEi5xq0sutvO91iGoEvsLlGCtcFl+e7vyFRPNBYY4d6+qKcPGyUgCw7DIKAH0N26AINhQNf4A871kEwjIGRoIYCUTgHguzuygRERHNWwwIFzlFlyGM3o5Nwj1Ye6Vu2yTny0TzUl1JLsryHQAQrzJqJuwsw1DNpwFEi8scbZvoGhoIy5b3U1UVR9tc+LDbMz0NJiIiIppmDAgXOdV4W1VQHg8Ir9BtK7DPKC1gqZJ8vctuAADUtD+rS60HI4ppph0AvGMRuP1h9LgD09VMIiIiomnFgHCRUwxnskXDH8AZHELElg93xSVz1Cqi2aedkN7MQN3ViNjykefrRPHQO/HlYyE54X1ERERENF8wIFzktOexqop4dtBVfRlUyTFHrSKafcbKoUaKLRf99dcAAGrOPRtfPhaO6DLtVuMJOc6QiIiIshEDwkVIMVQWVVUV3kAYsqKioudVAIndRQGwpAwtaCpSB2yxaqPVHQcgKGEAQCCs6N9Tmu0FzScsw0EiIiLKRgwIFzBZUdHh8icUvdBlBaGiuX8UR1pcGOztQLHrfQDAYO3ls9lUojm3srIg5TbDVZ9AMKcCjpAb5b2vA4hOQaF9T2mDQ+1FFHYrJSIiomzEgHABaxkYxeneEbzV6tIt13VvU4FzQ34AQHnvawAAb8k6hHKrZ6uZRFkh32nDpSvKkm6jijb0NXweAFDb9gyA6NhD7XyeVoEfA0IiIiLKRgwIF7CB0SAAIBxRdMvNppoAoKkueqXp/px2Hi60sNnSmFulu/FLAIDK7pdhC7oBRCuNxljFfYwHiYiIstNYSEZz/0jSqaQWMp7hL2RpnJjG6lwISjjeBW6w7krd9udVF+CCJcXIc9hmoJFE2UNMY2qV0dJ1GCk5H6ISRk37cwCsA0LdtC4MCImIiLLSsXPDaBv044OuxTlvMAPCBcz6/DPxjLV48B3YwyMIOUvhLb1At3VZvgPVRTkz0USirCKlkSEEgO7GGwEAdW3/BQAIaq4oGos2mS0nIiKi7BHLDLr94TluydxgQLiApdN1LXaSWjHeXXSo5tOAKM1004iykpRGhhCITlKviHYUDX+AfPdpXYZQsUgRMiAkIiKibMSAcAGzKqOvnQ4tosQCwlcBmI8fFNI8SSaa78QUGcLSfDsAIOwsw2DtVQCAurb/1M1hqOsmCvPlRERERNmCAeECpg38FEVF+5Af/lBEV0gmGJGR4+tCgfcMVEHEUPWnEvaTZi86ogVPEic+MruX/zEAoPbcs/E5CQFA1dRw0iUL9bWdiIiIiLJC0oDw4YcfRmNjI3JycrB582YcOXIk6c7cbjd27NiB2tpaOJ1OrF69GgcOHMhon1deeSUEQdD9/Pf//t9127S3t+Nzn/sc8vLyUFVVhb/5m79BJBLJ5HkvCtrAr2XQh6a+ERxqHtJlKoIRJV5d1F1+ESLOktltJNE8ou1SOlRzeXROwqAr3uUagOUk9W+3ueBJc2xCWFbQMjCKsdDirHZGREREs8cyIHz66aexc+dO7N69G8ePH8fGjRtx3XXXob+/33T7UCiEa665Bm1tbdi3bx9Onz6Nxx57DPX19Rnv84477kBPT0/85+///u/j62RZxuc+9zmEQiEcOnQITzzxBB5//HHcf//9U30tFhztyahnLBS/rT1hDYaVifGDtVeY7kcAU4REAKDtPa2KNvQs+yIAoLb1PyeWa7ZXDeMG3+10p/U4H3V70TLgw9ttrtQbExEREU2BZUD40EMP4Y477sBtt92GdevW4ZFHHkFeXh727t1ruv3evXvhcrnwzDPP4LLLLkNjYyOuuOIKbNy4MeN95uXloaamJv5TVFQUX/fiiy/io48+wr/927/hwgsvxLZt2/D9738fDz/8MEKhEEjDatCStitpaAxl/YcBWM8/yCGEtBgV5KSeZqWnMdpttKLn97AHhgAkLx6T7jhClz/6WRaKsJ8pERERzSzTgDAUCuHYsWPYunXrxIaiiK1bt+Lw4cOmO3r22WexZcsW7NixA9XV1Vi/fj327NkDWZYz3ueTTz6JiooKrF+/Hrt27YLf74+vO3z4MC644AJUV1fHl1133XXwer348MMPJ/ESLFxWRWW0S3O7D0GSAwjk1mC0eM3sNIwoi33qvApsWlaKzcvLcOmKMtQUW0+54is+D56yCyCqEdS2PQPAussowPG4RKR3dmAU3e6xuW4GES1yppfABwcHIcuyLugCgOrqapw6dcp0Ry0tLXjllVdw880348CBA2hubsadd96JcDiM3bt3p73PP/3TP8WyZctQV1eH9957D/feey9Onz6N//qv6Hxfvb29pvuIrTMTDAYRDAbjv3u9XtPtFhrFIrmgPWEt7PgdAGCw9grLVCAzhLSY5Ngl5NijU68U5dh1k9WbvRe6ln8Fxa73Ud/6H2hf85foGh7DsC+MNTWFCVO/pDPxPREtDh5/GK0DPgBAXUnuHLeGiBaz1H2i0qQoCqqqqvDoo49CkiRs2rQJXV1dePDBB7F79+609/O1r30tfvuCCy5AbW0trr76apw9exYrV66cVNseeOABfPe7353UfReKYZ+mCqI6caOo4xUA1t1FAZ7E0uKmPfyN42klUUDf0s9h9bs/RP5IK0oH3sKQ8AkMIYTKQmdClj7ttxLnqCCaVwZGghgYCeL8msKU09fEhGR2CSei7GDaZbSiogKSJKGvr0+3vK+vDzU1NaY7qq2txerVqyFJE5Oar127Fr29vQiFQpPaJwBs3rwZANDc3AwAqKmpMd1HbJ2ZXbt2wePxxH86OjosH28xiJ1r5o20IGe0A4poh6t6y5y2iShbJQviHDYRsr0AvUtvAADUn306vs5sLGG6F1esunsTUXZ6t8ONbvcYOob9qTcex/c5EWUL04DQ4XBg06ZNOHjwYHyZoig4ePAgtmwxDxwuu+wyNDc3Q9H0U2xqakJtbS0cDsek9gkAJ06cABANOAFgy5YteP/993WVSV966SUUFRVh3bp1pvtwOp0oKirS/SxmscqHscnohys3Q7HlWW7P/CBRlHE8YXWREwDQtfImAEBV14uwB6KVQW2ikJDpY7adaGELhJn1o8VDUXhRY6GwrDK6c+dOPPbYY3jiiSdw8uRJbN++HT6fD7fddhsA4JZbbsGuXbvi22/fvh0ulwt33303mpqasH//fuzZswc7duxIe59nz57F97//fRw7dgxtbW149tlnccstt+Dyyy/Hhg0bAADXXnst1q1bhz//8z/Hu+++ixdeeAHf/va3sWPHDjidzhl5kRaaWOIiNt1Esu6iAE9iaXHTdhMtzLHhslUV+MTKcqyrK8LyigIAwEjpx+ApXQ9RCaOu7b/i92NRGaL5SVVVfNDlwbkh3ww+yMztmmimvdM+jD80DyLCrs8LguUYwptuugkDAwO4//770dvbiwsvvBDPP/98vIBLe3s7RHEinmxoaMALL7yAe+65Bxs2bEB9fT3uvvtu3HvvvWnv0+Fw4OWXX8ZPfvIT+Hw+NDQ04MYbb8S3v/3t+D4kScJzzz2H7du3Y8uWLcjPz8ett96K733ve9P+4ixUKlRI4VGUDBwFMF5QRmPDkmK81+mZi6YRZTUBQK4j2i2+wKn/+Oxa+VUUH/026luexrk1t0OFmlBURki3yyhPFInm1MBoEL2eAHo9wLLy/LTvx+un84OqqujxBFCSZ0eeY9rKaSwqQ6PR6ZFcvhCqiqyrcdP8kPRdcNddd+Guu+4yXffqq68mLNuyZQvefPPNpA+YbJ8NDQ34/e9/n/T+ALBs2TIcOHAg5XZkTlWBir43IKoR+AqXY6xwmW69cUA8v+CIopIFdH0Nn8PqEw8gb/QcSvvfhNr42YQxQswQEs0P8ix0heN1n7nTOTyG070jAICt66pTbE3J8DheGCy7jNLCpapA+Xh30aGaKxLWS4IxIORZLBGQfDytbM9H77IvAADqW56CisRMH7tfE5EZld0CZpXbH069EdEiwoBwgUr65aKqKO99DUBid1GAGUEiK2bvjWXl0YJMhTk2dK6IFZd5GRjtMxlDyC6jRBTF9/ncYYXX6cPjeGFgQLhAJevtUuA5jZyxfshSLtyVlySsN861RkRRZtny86oLccWaSpTmOzBaug6eso0QlTCc7/2byf1no5VENFWzfZLLk2oimksMCBcosznQYsp7/wAAcFVthiJFK7NKkm72bYg8MojSZpfE+PjAjvP+HADgPPE4TnUO6bZjQEhEMdosFePB5EaDEXS5x6atay0D8OnDbOvCwNP+BSrZgPjynmh30aGaT8eX2TTVLgQBsDEiJAKQyZdd9D3Ut+R6BHMqYfP1oqrzRf2++L1JRJSxN88O4WS3F33e4Fw3hWhB4ln/AmV14imFR1EyeAwAMKQZPyhpA0LoA0QiSi32llElB7rGxxI2nPnXOWwR0fymqipaBkbh9odm/7Fn4zFU7W1eLUrHSGB6isHw1Z4a7fHKQ3dhYEC4QFl1GS3rPwxRjcBfsAxjBUvjy+3SxKEgCAJsEg8NosnqXPlVKKIdJUPHUej6YK6bQzQvdQ6PoWXAh6Ntw3PdlBk3G+fUYVnBaDAyC49ECx2DwIWHZ/0LVEQ2f7eW90THD/qXXqVbbswQSswQEmXEH5Ljt0O5Vehbsg0A0NA8kSXklyhR+rzTlA3KVtP9cXCmbwS9noDl+jeaB/Hm2SF4xrL7de3zBuDymWeFp2scNjOyU6Na3Kb5iwHhAhWSlcSFmukmxNXX6FYZxxDaJQaERJloKM3TnazEisvUtD8HeyBaXIaD74nSNxuTw6cjVfAwHcHFVHcxOBrEuSE/PujyWG4Tu1A8NJq94/DGQjLe7/Tg+LmFnxWez/RdRrPjfUpTw4BwgQqbBIR5I2eR6++GLDqgLLtMt05bREaAwKIyRBkqzrPjM+dXobIwWrnXW74RnrINEJUw6lv+A0Dqk75+bwDvtPNEiAgAwhY9XWZDJmOkJns+rHuMKV4sCkVMLgJbMJs+J1sEI3KKLaan7Qxhpoav38LDs/4FyqzLaMV4dVF35aWwOwt062ySocooM4REGRMEwZAlvAUAsOTskxDk1IUx3uv0YGh09gtoEGUjbYawc9iftZkIbasy+ebUF5WZrtZMTp83gG732Nw2ggAAEVmByxfK2uPdaJ40k1JgQLhAmXUZjc0/OFh7eULAZxwzmGOTZq5xRAuYoDkljE5BUYWcsX7UdDzHq6pEGYgoE99jp3pG0Dk8+YBFUVScHRiFx5/5+LlU79tk8/7Olqm0QFVVvN/pwUfd3jQydLPHLCCavjGE07OfmfBeV7TLbOugb66bYimbX7/pMF+C8enEgHCBMnYZFSN+lA4cARCdf9AYABrHEC4pzUVVkRNr64pmvrFEC4j2hEWVHGhffSsAYNmpn0NVsudkiyjbGXu6uCcRzMV0DPvROuDD222ujO+begzh5No0V+ecxphKO1TTqiDdXJiL10dVVTT3j1oWtUlXc/8oXj8zOKkA2zXeSyR2ASQiK/CHsqs67EIfD3+kNfPPifmOAeECZQwIS/vfgqiEMZa/BP7CFQnzDOqrjAoQRQEblpSgviR3VtpLtFB1rvgqIrZ8FHjPoLDz1bluDi0SwYiM9iG/6Xjy+cJYVGYqJ6G+YGYn5rrunKm21WyRSQuVDMYpziT9WMbsNn2DWcyfabcngLZB35SL2rQN+hAIR9+DyfhDEcusdez4eLPFhUPNQ9M2B+N00B6v2ZAhn24jgUjWFLWaLQwIFyjjYPyK8eqiQzWfBgQhIUOo/X2hX/khmknGLk2yoxA95/0pAKD6/UcARCdX7nBl75gomv/e7fCgqW8EH3Z757opk5YQEE7h7TKTdVQmOxZQX7p/ap8FU/ksUXTtn/x+hn0hvNUyfdNazMWn49g0Z+JSPYdDzUN4u82FQDjxgkVEVtHvDcTX9Y9kZ3VYfo0tDAwIFyjjVeH4+MGaywFEi19oC4k6JBGSFA0UHZyUnmjSBJNr2N3n3wpFtKOw9wjQ8TbeanHhdO8Ier3Wc4YRTYV3/KR8MEtPImebmEFE2Dnsx+nekfjvmVQZzSSwm6kLQkqKzIbxpdC2eSpJkWPnhjESiOD4NFVKNh9DOE1VRi2ep3F5ICynfD0n8zhGo0HzQPS9zolpRKbSjum20DOEQPrPq38kgHNDieM9w7ICn8XfNRvxzH8BaR304WRP9GqwNiDMHWlD3mg7FNGO4arN8eXaL0hREHDFeZW4fHVlVpekJsp2ZjO2hPNq0bv0CwCAyOs/iS/3js2fLwuiuTaV085MvtZO9Yzofk8V5Om6jE42QzjFc+pMurgmu+90BB3yNI1DnIswQ/uY/lAEr58ZxJstQzPzWKo2EE/9bOUsCrwm200aiAZQY6HsH0+fzsstKyre6/DgTN9oQvD3RvMgDp/Nrq6+yTAgXEDO9o+ia3gMvZ4AguGJgDDWXdRdsQmyfWK6CclQSEYUE7uSElFmzDKEogCcW3M7AEA6vR95I60A2D2byIpZYDKVjJr2XZnpfjLKEI7fVhQ1ZXA1lSAu+X5TZAgNn1FKhoHJbIk1xfh8pqMSqtWz1D7UwHh23T+F4CXZZ7zuaaUZfGSjTA6ZgZEg3uvw4I3mwZlr0DRJ5/tZW3woYvj7xAo0TbVA0WxhQLgAtbuig5iLcu0AgPKe2PjBy3XbSYI2IGQgSGTGZpbyS8L0rSQAvuJVGKi9CgJULD39cwCAMn/rfdACp6qq6bimqTg7MIq+NLtJm2VDppYhnHhjTveJtXZvE4VAhvDamYGkQeFMdRk122uyx9KuysYslLZJHS4//tA0iLYZmpJhugv9JNuH7rHS2Fc2fV/oL4Kk/0J5xuZHcASk9/cfHJ3okp9NXXongwHhPKOqasqrY7GxIxUFDoiRQHy6icFaQ0AoaruMTnNDiRaIZeV5KM13pD0Fi9lbKdY9u+38OwAAdW2/gtPXrR+7M8+/TGhhOdkzgtfPDKLXMz3jXF2+EFoHfHhfMyYqGbNM1XTFKtMd9Bjbqigq/CEZEVmFP0lQre8yOtWiMtbtMa43yjQI6vcGcKh5EN4Z6Apnlm3VNikUiUZFzf2jpvdvH/LjVK91ISV/KAJFUdMeQziTlBR/M6PsCtbNb6c28Q157NwwOlx+y/GT84H2opkxQzjfMCCcZ050uPGHpsGEKl5mJ5M5dgklg29DkgMI5FbDV3Sebr3dNvHnZ4aQyJxdErFpWWnaU7CYvZViAaGn8hK4qj4BUQlj+an/N6mB+aqqzusvUJodU+3+3+2OzoHWMmB+4p2p2Il8uszfDlM54Zq4b6YZwky7jJplDFPeL6MWmewrxThG7aLEojIT0nlt3uv0wB+S8UFXesF9JswePZNurE19I+h0jcHtT8xEDY4Gcah5CO90uHUBuPY5T2VsnJl0A/F0Xvfs6s47uW7G2mNv2BfC6d4RvHl2ZsZoTlVaQboyudchGzEgnGeG4hOW6ue2MTsMJVFARU+0uuhQzeUJ3wLaaqLMEBJNl8Q3k/at17LuLgBAXes+2Ea648vTvfr7YbcXb54dQocr+fxWtLjN92t8050h1J5vJzv5jpjM25hyLJEusFPTzrhN5xjiVPFE0i6jmqecyUmt1es4lWNPNXntJvN3N8vWdI1P9D5sGNP1u1P98SIn031On+4Ywh5PAG+2DCWdgD6bepHos9tz1owZlW5RmRhmCGlOGD+IzT7EBQEo7/09AGDI0F0UAJzaDOE0TvdKtJiZZwgnbrurLoWrcjNEJYyKd/8hvjzdL9VYF742kzLXRDHzvUDYdI/zSzcbY3VSpygqOof9puMqjdk5fUBo/VjaMWFTrzKafoYwcZ2263r6j2l13jClgFB3W9X9n9F+TO6in29Zr2M48QLbTM8Tqz1OPP4wRgMRfJRk3tBsCjgmO+3EfPpUSudZaS/kTld13bnCgHCeMn4wmL0fbZ525I+0QhEkuKq2JKx32qT47fl+NZkoW5jNd2bskt36sWiWsOz0f8Dd04rTvSMJc4emwos4i4+qqjjZ441350xGmoMP9UBYRn8aRWPSyXSYbTKV0y1toJMsIDR7H6oq0DLow6meERw/N5y4b0PXT12RlmRFZWC44xTo25DpGMKJ25mMU5uJQ8x0DGGaTdIFxSavgT3JHMuxoGbaM4RpdhmNCSX5HsiqLokmf6d0zKfhSelcENC+v+U0LwRlKwaE85TxC9Xsg8LZ9jsAgKf8IkQciQUx7Db9tBNENHUWRUZ1hqs2Y7jyUohKCCMv/z06XH6cG2IX0Lk2FpJxtM2F/pHpKaQy3fq8QXQNjyXNIsRM94lXICybjsvSOt07gvc6PRjSVN4zI6tqyhMms/VT6zJqfuJmFDG5yq8C6BoPws2mIDCOp0q3aqd+DKF+u15PAKd6vTh0djDl657YhsT1yccypj8OSluwRXuEaQvMTOVi1VTG8KU6PnQZQsO2sQsGVo8/Eyf4mVYNzaZpJ+b7lEmqquq6h5t+3qSxH11AqB2LOg9fHgaE81Q6XQccrQcBAEM1nzZdb9eNIWRESDQdkhWV0Wr52DcAAPWtv4TT35swqW3MfLzSOF+d7PXC7Q/jvY7pL5YxHXxJxhcZ6U9+zY+hUETB2YHR5JNEC9Gs2etnBnG0bTjpGKdYdiPVvG3vdXrwhzODpuP1YkwzhFN4L1hl7RRFxQddnnhX7LDJWbqqqggnKYpjbJX2ZHkyJ/HBSLRgS6drDP6gjGMmWclkbUgdbOt/11W7TNJetz+ETpcmOz1+iIVlBUdaXAnLpyr2PNIuuGXyS49nLD5m0KZ5T0QMf2ezKS6sbltpHfTh7TZX2r09TJ+XCljNdJTOed9oMJJxAScgWn013SlhgPnfZfTouWG8enogXrXf9E+Rxsto1RU9q7K5aWJAOE+lGkMoyCHY2l8HAAzVXmG6D7vIgJBoupldHTd7ew1XbYa3+tJoxdGT/2i5v3n4vTJvJTvpzwaZdCvW9o6zOpH8sNuD1gEfjp5zma4HopnBtzQn+8FwkiBu/HHMur1pv6OGfSGEIgr6R6wziaZFZSy3Ts3qxK3LPYZeTyBeMdMqQ5iMPnBQ0y5gYxVwGNugqsDJHm98onQzxjntVFXF4Ggwfswky0Ymyy76gpF4htK4LvZZl2l396RMetGm+xlofA28gTA+7PLGA2rt57CxzWZTXBibNTgaTFrh+Wz/KDz+sG6OxJFABO93ekzvZ3WMW2X3U41R84ciePPsEN44m/mk74eah/B+pyft3hH6CxDR/7vcY0mPUcC6N9pULvYoipr8opYJjz+a0R4ctc6+p8qCqqpq2RV9Pn5tMyCcp1KNISwZPAoh7EMwpwIjJefr1sXekE67tqgMEU2HdDOEAHB2/d0AgLqW/4DT2zKTzaIFILMr/6knYh8eP9FPHuTp59pKduU79jBm7cz0irlZpmoqF0dki4DQGLyaBYTa5+OwJZ42GQu6aJ9rsm6BVhOTmz3NruExvNvhtt6XtkCNAnS4xnCi3R0f85hsWgrtr8Zj5fDZoXhm2FinKPaxZvx8m8r5hFmgke6f3Rhg+4Oy5Xrj32VivKL5o7n9IZxod8enSFBVFf0jAdNjXdt91heMZt5SjT3VSvb6JQucvGPRoFOW1UlXJPWOpTe3pLGbsS8Ywclub9JjNJlkzQ1FFLyfpCv6220uvNGcXtdqo9jzMHv4VJ83xvNwqwzhfLmoy4Bwnko1hrC8NzbdxKcBwfzPnGOXsLq6EGtqCiHO84p0RPPRUPnHMVB7JURVRv3xH5tuM0++SxaEbCp44PGH8WbLEFyaEvmZZWKm1m3RSrJdxU6uTAPCDJNI5kVlptJl1Pz1SOw+mTwgNN234bYu25fkiRu7eaqqioGRoGkl01T0waWKbk+0a+dIIDK+f/PHNd7XKnAfCUTSfn9M5X2kGl9MpJ890o//UxPGbybbi2IaGEz8NmSYqqJzeAzvdXhwtC0xux57zbXMjiGrcbLJXr9kL4X2YsXYJI6hqMz/dirSv1hlNb402QWjpr4R9HkDeKfdbbo+9nr3eDIf+x0IK2gd9JkXk0pxX+PnqmwRBM6X73AGhPOU8UA0HnAV2oDQQPtZs7Q8Dw1ledPdPKJFK5MMIQA0X/A/oUJAZftvUeR6L2G91cnQbMQurYO+RTXfYRbFgzjW7sJoIKLLLIQi6Z9apFvYJFPJgrJ4hjBFl1GtQFiOd99Kuf0UnoZ1N87kF1eN25u9lsbMVLpFWozv7W5PAO92uHHC4sQXiBY+SjaOM9aGVOcIxu1jrNobUdSEncTeLpM9vNqH/AlZH2NQF/0/PfrnkV7xPeM64z5ijEH6wHi7Y+NltY9llmU2f8zEZSqSF1xK97WwGpM+XYwXM7SfnZPp/pnsb5NucJt212LNC9826MPZ/lGc6h0x2V/yHRrbLGsu/kx2jOVcYkC4QGgHvzr9vSjwNEGFAFf1ZXPXKKJFyOzqrlWRAADwlaxBz7IvAgBWvfejhG+1VF8lvmAkrVL/mRoLyTjbP4rTJl+UNPPMEkuTHauVzvxYr58ZTKt6abJzm9gJfCZdRl8/M4i321zwGLqqTfdJlO6EPUmG0OxRtdubvZb67pj6MYR93qBl8RxjWJrO+/iN5kEcaXUlrSOgqKrJsJKJ3ztcft2YNv19ze8jy2pirYLxzzrjRYJ0rqu4/SE09Y0kZH0mW9zDeF9FSWxv8mM38Zb2/gFDt2qHYQqLycwRaDqGMDHuNqxP7wKDL8MxdTHpXhQzXgTRZv6SvRTWYwit7zPZ7q9WzC7qDJt0NzV72LGQjPc7PfCMhRMzhJpDhF1Gadad6RvBh90e3QdyWd8b0Rt1F2PtysaE+3D+MqKZY9b7OtV7rmX93VBEO8r630RZ3+u6dam+TA6fHUqr1H+mIrqrnfPkG22Ksv2TURsQpjpJ0q61yhBqj8tAWE5rfsN0xhDGKveZrbMS6xrb5w2gfyRgGhBP5SjUPr42g2Pcp9mxLhsaIyv6MVqJ8xBOLBgLyabZh+hjmd9OJSKr8WPh3JAPLl8ooQ2J2bGJ26GIEh8LZ3xs2eJ5RRQl4bWKndwb255OUGHVxVCfeYotS3xxzB7DOE4ysat06mDKapyhMUOordKuquqkumVbFZWZbIZQuy7TIisxVn864/GUMD2H5o6TuZhjdp8ezxhePzNo2gV3Ksz+VjaTL26z4+7dTjf6vAG8bXJRRvudqQ8I58f3JwPCeSwiKzg35EePO6B7w8TGD2LVZ1BVlIOLlpbo75jtZz1E85hZ8JdqiG4gvx4dq24GAKw+8UMIysT7Od1xU+40iwFMxjz5PlvwMhmXMlNdlpKd98YeR1GQkBVL1QZZiQY573d68F6HB019Zl24Mm/vxH21GULzrl0ef9i08qkx++P2h/BqUz+a+0cT9q0YMoQA4lNaANET6+Ptwzg7MDqlE8WwrKB/JIAzfaM4fm5Yty+vSfYi2QGjD0zNM2SyYt2VcTLPQjstin5MZ+Ljp90VUBvYqmrCSXny8a/j/+v2N/GbMYC1SRPtD8lK0rGiqR5Tv0xN+nzTXZfJe177mpv1cAlGZPyheTBeiTd6J81jZdA114pZkPZhlzej8bTpfleaNU8y+5I22U7bFTcxQ2h+oSmLpo9MigHhPKYdpxGMfVgpMsr6DgEAhJVXAwBK8xzId9pmvX1Ei5HZlWurIgHr6orQWBEdw9u69k6EHCUo8J5B/dmn4ttYfbca9ziZk0tVVVOORwLmz6D4qcqmMYSppDrp0p1YZ3iuapbhm9hXel3WjOMIU7VXSSPLMpWiMlZjCLX7fLvNlVCZ0rg9ALzT7oaiID69gO6pqYnvRW2X8YHRIFyjIbQO+AxdRjN7brKi6tqqbWLLgC9h++RjP/WBn9nysJwYUMWDKGNXUsOn00ggjLdahuK9GAZHg3hPE1yEZfMAPbbunY7hhDabZggNwaT2EIyO7Uy8z8T2iY+fbtYvGFGmLUNobEPCumRZzkm+P1K1/dyQH+GIoruwYTjkdQsmc51jNoMmsx4TdsmkerDJfXXHx/gvsYsDuiy9RffRbMaAcB7TXrGKfYEXuT+EI+RGxF4ALLkEACCKArasLI9vm+9gcEg0U8xiCqtAo64kF0tKowFhxFmClvFpKFZ8+FPYgu6MHncyX6jvdXpwqHlI90VvZr50eZm67I0IjX+DVH8Sq0xHKoGwjD80Wc9jlnysz8RtY0YlVVCazgl1MKzgTN9IWhcxEtqmyxBmFixncrKvIvFEMscmIRCWcbLHC7emgI4xkMxEREnMgCVtV9IgY4JVpcSwrCQEHOaVORM/784N+TESiOCddjdUVcWJdrduLKbV+DsV0cJW6V7Q0L2cxgwh0guKdeMm0xyvFwwrkxxDaLbf5PdJ962cyUe2th1mn4Bm3U+N2Ujta5u8kJLV8ql/x6S7C7P3s1mGMNX+YvuxjV/xscquz5evTwaE85j2CmwsrV7eGx1/5KraAkh23fYfX16G6qIcXFBfPHuNJFpsMqwyqr0y2bXiJowWr4Yj5MaKD3+WsG2yL02zL2F/KJK02lxsEuH2FJVE58n32YJm/POmzhBO3M4koHGbVPxM53GNx2bQGBCmkSFMJ3A9N+RPWoUz2f5j0q0CGd8+xetnPPkzPo8ch4STPV50DY/pqvYmFD3JpE2yGq9wmc5906mwCVh3O4woSsKDpNulU9u9ctjk+IpYZAhVNbNCSvqsTGK2M53ulroLKVaBqqH7aTAip3yPmX0FmP1NtMsy7rGgfe0yOJqs5sOM8ZlcgFH1D2ZZnTX9Nhh+n8GUYbpznKbTzR2YOL6tuvQzQ0jTzviFq70COxaK3i4bDwiHqj+VcP/iXDsuWFKMXIc0g60kWtzMxhAm+16XRAGNFdHpX65cV4fmi/83AGDJ2V8g33Mm7S/axImWVRxqHsLhs0OWVQ61bUhmnnyfTVk2dxnN9E+QbCJyfyiCbveY6UljOoGb+XL978YMYapjSFHSP5H0h2SoqoqhUesKnkbax7cq/mAl2cm+xx82nAgmdq0UAPhMuqJqT0wz/ft+0OXRZfZTd7dNFHt8q4sH2uWhiHWXUePOjS+p9oKY2ZiwsCZA12UooVrPW2dadEgfABoD3eQBYeJrYfWSGvcVlhOruhql6uKq3XeM2YXE2PrRYATvdrgxEtBknC32k0qqeShTZwjTD4CsAlXjfYwXlCz3N4kvJ/PqribLNLfd/hCOtw/r1k9kCBOr7c6XIFCLAeE8kti3fmJBICxDCo+ieOgEAGCoJjEgJKKZN5mgYlVVIdbUFEaDw49/Fv6V2yCqMtYc/x5UNb2TV+O6cBpdsmLMK6xpb8+/L7eFxnjCkvKEQ3uSb9j2UPMQPur2TqqSZ+xQSjbtAZB5hlBWk8+/ZnSiw4132t1oHUwcL2fGapxcOpJlFN9uc+lK1keDBeNrY/65oH1f9nujYwsnK9lzsipUIseDIPMTWV1RlaRdRvXLrbazamds2bmh6Jxwmh0lnbJHUVT4ghF0uPxQFP1zVJTEsaLJA5XEtlt1TTYGm2FZSTm1i1lgm+owTFZJ9b0ONwZGgjimmafUKkOVilVRH7P9mu3fOHei2TQhgbCMswOjCdN3xCR+fqRXTGYyiUSzrsBmx2XsOXUO+/Fupyfh/Rn7jnXYogepYjFucL4EhwwI5xHjIWW8AlvWfxiiGoG/YBkCBQ2z1zAiistkDKGZkjwH8j7/fyBLOSgbeAvSexMFZjIZm2F1pd9MqvET8+T7bMqyMUEYL+1vWJ5BPJjRCYlVVy0p3u1PxfudHvzuVL8u22N8DOP3k+mE7tr1SvIqkEZD4ydn6VTXNQZEqjrx/phqhlDbltj+YruMBTPGibvN9ts3xblEk130kRXV9KJO7PGtjhVt+8IRJbFb3/jvid2ZDb+nKLARlhX4QxGc6RvVLVeRfMoeRVVx+OwQTveOoMs9lnASnslnYPy+mu2sLgSohucRTqfKaJpdRrWSDTWIdRfWT6FiHtjJSvR10lUJ1bXD/DaQXpdd1SRDGJEVfNTtxdBoEN5AGG80D6J1wKfrMm3ch1a6GcJJTXFhsmvTzyc1WiH4VM8Iwibtib02sYAwep/E44hVRmnaJWYADAFhb3T+QWYHieZOsi/xtJUuw7kLvgkAcLxyP+CLzhmWrDtTwueD5lsvWXEEIP2S2zT7YoeT8e+byRjCTMbjWO02lkVW1IngpXN4Yt5C4/1iY9w7XH4M+0KmmQf9XH6ZZQhjnLbUpzFmTz8sK2gf8qPfm3r+zkymFIjIKtqGolnL2GeBopp/LkymMqUV41yJujYpKtqHEk/EIyZBsaIg3h33aNuwbvugobunoqpw+0MJVUCTZbPNnrMvFMGh5qGE5aqafMoe7a5GApGELpPGIDnZ4RXbNJ0/iVmX0ZRjCM0eM1UMadrNNPq/dlymaRs1t4dGg/AFI5bFw4zvQy3teaa2Pcassv67SUXLoA/d7jG80+7GkRZX+gH5uKBFJjFm2BdCy8BoRkF/jFXwZ7ZM2yXXKPbaOG0Tw7BUk+OIGUKadqmuoMTmHxyq+fRsNYmIDNKNBysLnUnXd51/G0aK10AYcwEv3Q8gsRtKsi9y7ZVjs5MV7dirVJPyzpPvsymzmh5kLsXaZPwbpPqT6MezpP94Vt2DpXhAqDvtnrhluNvgSBB/ODOA070jOHZuOGVFRW2G0GlP/9QknWNT2+bY85AV1XSuw1T3T0fsZDZWfVBR1em5UJREsuCibchnWiwoIivo9wYSxjcqKvBBtzdhe2N3P1VVcbRtOHH8suF+xmDBaHDEvKusCjXpe1K7L4dNTJkhTDplw3gQnM68d8YAKCJPVBmVLAI1s+eR7LgSRfPsaOweDrNpElTz29rA2Ji5N7bD+LcMRwyvYbybsb5N2tdWVtWMJ5NPGIOcIjN57NwwWgaiQefEPtJ7n5pdIDPtMpriU9Y0Q2jSlvny/cmAcB4xHpzaKze5o+3I83VAEWwYrrx0tptGROPMPvy1X+zlBQ6sqSnEurqi5DuS7Di16XvR2yf+DWh7PeGqo76bjv7LXhvwmX0Bak8Skl2JBhbPGEKzUznPWBidw8mrsM4kq9Nhs3E6VjLJRFltGg9uNI/rGYvgnfZhjATCpidj2qv8pidhuhPRiXFZZnOCWbc3+ckuMPH8BWFi35lME2A8SU53Xt9Yl9FoQJj2w02b2OO7fOYBV5d7DO91ejBsWK+oqulxpz35BqyD0OQZwsTtrYIwVU1+gU17UdwhiYZuemrCGMJkJ+aqGp1fMl3G8ZWxx8qxmRftM3saydojQDB97mf6RhAIy7Bp3iPx4kD6vU+0z2SKMi3d+9DY9Vs2XgRIbJNq7DOqIu1iT/HHNbwf060u6wtad1u3YjqONc2sYYwgTHy+aYNzs6lY5su0TUk/dR9++GE0NjYiJycHmzdvxpEjR5LuzO12Y8eOHaitrYXT6cTq1atx4MCBtPfpcrnwjW98A2vWrEFubi6WLl2Kb37zm/B49P2eBUFI+Hnqqaew0CV0ydG8yWPZQU/FxZDtBbPZLCLSSPXR77CJaCjLS+uk11NxEYIX/kX0l2e/ASU4McZGVfVfgMO+EF5rGkDX+ElbOEn2EEg+GbTxecyT77MZ8XarC6d6RuJTdGipqooOlx/eJN2KpkoQzDJzqYP0VFkZK1bBo1mGcNgXwtBoCEfPDadRndRsmT6rEPs9VdVbs/2e6RvBa00DGBxN/DvF9isKQryrXaYnrACwvDIfjRX5+Fh9ios54ybmJ5ub7LMUH8Rovr7Hbd6F8N0Ot2VwDQAVKXo3GA+FyRb0SbXlh4YxccYLZKmmA9HKqF2GfUVkNf55mmOR3Tbbe9L3jMXhMjQawnudHmi/PmKBsWp4vjFjmoDbNEOoWWRskjEwM5tqxFghWFEzu+Ci3a/V41qZTE+IyVQZTdx+Yhynfgzh+GMombdrrlmekTz99NPYuXMndu/ejePHj2Pjxo247rrr0N/fb7p9KBTCNddcg7a2Nuzbtw+nT5/GY489hvr6+rT32d3dje7ubvzoRz/CBx98gMcffxzPP/88br/99oTH++d//mf09PTEf770pS9N8aWYf7TjGmLzD3L8INHcSnU1MFmRBLPtAlfeDxTVA64WOF/9/sTjWFzxPjne1Uu2KOUeo61Cat6dL3lAuRAlO2c3m8+xfySI070jONLimrE2KWr0ZDPxJDv5/SYb0FudGMdLq5usluWJ7p4OizF9qU7CZE3t+sxCp+idzo2PkTN2A+31BOLj00RRgD0WEE7iLK3QacOqqgLT7npmtEH0XGQIpUkGoanmoky132TdmzP5LLGqjhqjnYcxcZoJQyEhpA4w026X4XJMRJnoMuq0yBAmXNBR1aRzxAqwfh94x8IY9k38jWIZVuPzNa4HzIu1JKuIaQzM1Pj/SbLAqprR/JHR++t/T3ZBQteeFBe+VMPQCqvtTCsup3msOnVdRhMD5nmfIXzooYdwxx134LbbbsO6devwyCOPIC8vD3v37jXdfu/evXC5XHjmmWdw2WWXobGxEVdccQU2btyY9j7Xr1+P//zP/8QNN9yAlStX4jOf+Qz+7u/+Dr/5zW8QiejfOCUlJaipqYn/5OTkTMfrkdUSTgjGD2BBCaO0/00AE/MPFuSk16WFiKbXdH30x8+5HEXAF6KT1DuP/1P8vR69Kmv9aNqiMmZfdrrJoE1abbbnYV8Ih5oHLbugLSTpfImPWpzQDY0G0eMZM12XKVlW8fvTAwkVD1O1TzWcoKX9eBlkCM0ezyYKplMFmJ0gahdFT+ijt62yacV59oRlxuYagxVtZUVRmMjapXvCqSWOvwbpjgeUNIV4ZjtDKIqaKqczsO9UZCVacEZR9CfkyTJx1UX68zjtmLVUVJiNSbUOEKfC2C5Fmeg+aDX+1fg8hnwh+EOyZXEYURDSvjISNvmA1z6cduyn2XGv71pr2HcaGULjPhRFTTpVi5lUXVVjzg6M6iqV6sc/Jj7msXPD+EPzoK596caq6V4zskuipqLw+P+THMM9l0yP3FAohGPHjmHr1q0TG4oitm7disOHD5vu6Nlnn8WWLVuwY8cOVFdXY/369dizZw9kWZ70PgHA4/GgqKgINps+wNmxYwcqKipw6aWXYu/evfMmAp8Kqy5CFe53YYv4EHKWonD5JqyvL8bFS0tnuXVEBExf98qJIv8qsOpqYNNtAIB1b/8vSOFo19FkAWEkVYZQ0Z/QGOmvGkf/P3ZuGP6QjOOaua/mkqqqk+r6F9M57EezZt4zbfY2WZYjxipT8k67Gx92eS0DxskwdkvNJEM4HV1GYyeuVvuKV6S0OIk1O0GUDcdg7DgVBeBT51VgQ0OxbvsL6vW/R++n32+y7qaSIMS7ameawQAmsqTpZvtsc5ghFCDEA9fpPj1KJyBu6hvB0bZhnOkf1U8vkqQxptPfpNkmszHV+uzR9GVqtBcvYmJZOKuqt4qiD8ZiRVcqCpymzzsaD6Z30MSDEIuslHbcoFmGMFmWLRTR/x6RVYQiSsLfZapFVIx/m7BFQNk64MPp3oleAMa/uZHbH0Y4oui6/Mc+dxrK8rCxoSRZq1I1G0D0uI39reJdRudhhtA0jTQ4OAhZllFdXa1bXl1djVOnTpnuqKWlBa+88gpuvvlmHDhwAM3NzbjzzjsRDoexe/fuSe1zcHAQ3//+9/G1r31Nt/x73/sePvOZzyAvLw8vvvgi7rzzToyOjuKb3/ym6X6CwSCCwYmDwetNrJ41H1gdU5X90ekmXNWXwWaTUFO88LOlRNmqPN+BfKcNhTm2eJlv7flT2omC2Pxzsff9td+HfOZl5Ho7sPrEHpz8+J6kV9vDJkVlovOhjVdZtJi/ylSWfp8daXVhJBDBp1dXWHbVMjMWkmGXBJzqiZ5YlOc7UJrv0JdVT2M/2hM57Wsb4w9FUJBmAZJUEoo7ZPA30wZNqU5OrKZYiHcZTf6oGVXTTKyMq8T3kWOXkGOXAGgzfKkDhmSZOFEU4LBNvstobN/pPseJgEyFqCoQI2MQlRAEVYagKsD4/9EfGRj/P7pu/Pb4bHyqIIz/LwIQoQoAIEKySYgo448jiFAhAoIASZLgRA7GQgIEwQFBkaAKUmaToo4rzbfruiiKQjQLbDxUivPs8Ix3N+0an5Kkw+XX9VhKNgWKMVuWatyfViAsw65JXSZ8NqrT2WXU+nkk+xx6vXkAnz6vEnZpoiKqTTLPBKYbDAKZjQE3zRCq1p8PxgsnR1pdgABUFujHkU51mgVjAGU275+ZdMdKf9DlgWcsjDU1hfHtcu1S0u7fGT2N+HyxakJbsvTrM8G09StUFAVVVVV49NFHIUkSNm3ahK6uLjz44IPYvXt3xvvzer343Oc+h3Xr1uE73/mObt19990Xv33RRRfB5/PhwQcftAwIH3jgAXz3u9/NuA3ZxupgL+mOTTfxKThms0FElEAUBWxZWQ4AlvM+pSN+xTG2wFmIrit+jIbf3IT61n1wVV8GufHPLO+vPeFVVBXHzg0jFFHwiRVlEAQh5RVd/XiU7PxKi11lHxwNob4kN637jAYjePPskK5rlzcQRmm+/tNTUVVIKU7KRE1AGFHU+Pi0GLOAvcPlh9Muoqowswt3xhO5VCcrVl2WUsVBVt8zUrzKaPIdTCkgVGIZQqtudOb70J7ExoL0/pEAvGP6DK2oyRCGIgpskpCYuVRk2MNe2ENu2EIe2MIjsIV9yFH9KPQACI9CCI5iTW8/bBEfpPAopIgfkhyEqAQhyqHx/4OwqWGsjkRvi+r0ZYunQhHtUEQ7VMEW/V+0QTVZpogOKJITiuSE3ZkL0Z4Db0SCIjpRUJAPZ1hCRHBAkXIgj2+Xm5cHR1iK308Ro/87c/MQiUhQpBxAzIcoS1BER0JwasyUdbr98AdTTwMBRAvk9GDi89Z4YSNVlVErDpto+t6z2lWyKVMUBRgYCaKuJDfeFqvATxDSj90nKluaf6ZrP4e03Uvd/hC8YxHd+mFfGCOBMApzot2zjQFhbFvjd5ts+L7JlPY+VtlBM8nGuhs/qzpcfqypKYxnSe02AUKS7s/RSzHJxTKM8R49Jtna+TIG3zQgrKiogCRJ6Ovr0y3v6+tDTU2N6Y5qa2tht9shSRNXR9auXYve3l6EQqGM9jkyMoLrr78ehYWF+NWvfgW7PXHcgNbmzZvx/e9/H8FgEE5nYvWrXbt2YefOnfHfvV4vGhoaku5zvrAHXcgdfB9AdPxgXRbOo0W0WOU5JfiDMioLnfFuLum+RWPbxb7wAmEZTbkXQl7737H85D9i7bH7MLL+MgAlpvfXTlQdUdR4afnRYASFOfbU89ip5rfnu1jXIe2UCMZ52ACTLqMmL4L2TykrKuyG5IAx2BgYL0IDAFvXWQeEZo+VcUBocUKY6uTEauyPTTN/XzKZdI007ir22FbvEdPuoKo+exrb4r2O8cyiIsMRHIIzMIBS1zCKu9xo7O1AkTKMSv8wpMAw7CE37CHPeBDoHc/KWRMATMcZhCJIgCBGM3ux2zD8Pv5iCKoazRpC+78KAUr0f1WJvhjj6+KZRgNRCUNUJlcVV1tbtWxSe9CTNUGjLDlhc+SiUrVDkXKgSA7IUo4usIxuF1umD0Z160UnVJsTsjixDD4ZQigMQbFBFdPPhZgFhEiSudR2GS0vcGB5Rf5Ed2poA8LYxQ/zoENWVNP5Yc3EWmJ2ES+hoIrm91i7ch36D663WlzYuq463o50GKfhyJQ2fo8FoTZJQHGuHUOj1mPWtRc+jW21anns7+m0Jb/klyzwB6JjaWPzCUcvYk2MW9UHqkl2kkVM3xUOhwObNm3CwYMH49U7FUXBwYMHcdddd5nu6LLLLsMvfvELKIoCcfxKYlNTE2pra+FwRK+8prNPr9eL6667Dk6nE88++2xaxWJOnDiB0tJS02AQAJxOp+W6+cTsoCrrOwQBKkaK1yCUWzWZ3iBENEM+sbwcEUW1rLyYjtjbPnZVs+Vj30DpwFsoGTyOgue+DuGyf4Uq6bNbg6NB3VVW7VXeWPYlWWW56OOm7vKiqiqCEWW8a9/0UVUVfd5o4DYbXeD9oQjaBn26q97pZEX1V7UTXwfjCYpxHjcrZueaxpOsVIGd1XiiVCd4VmO8pDQDQkUFlpXno3XAl3S76Lbm3dOsvscEQ1dFQQnDPtIDufVD1LadRI6vG8XhXkBxYfNgJxyBATiCroTAqCRly4CIvQBhRwki9gJEbPnILShGTn4x4CgAnIVo8QqQbfmI2PIh2/MNgUo0EKkpL0GnV4YiOlBUVIihgABVtMeDPSA6hcOgyZQmQDT70OcNJO1pUJRrh3csGuDFLkABQI5dQigcAuQIBCUMQYlAVMIQ1PH/Y8vU6HrRuI0cgqiEIMpBlDpkOBHGsHcEohxEiUPB2JgPajigy4jmCGHIwbH47+J41lSSQ+O/B3TBtiQHIclBIBaf+jCjvZw+Pv6/IkjxoFMRNUGmFA0yo8uif0PJkYsgHLpljr4S1PiAgGqHasuBLDqginbAZofNUYmSAR9UwYacsTyUOMuR5/WMZ2EdCClOoEIFwqHo38CirbJJF3QrpvMQjv9ilYXXGgtZZ2HTDWa0nwuZFpSJPo7mMypeoErEhQ0lCMkK/tA0aH4/XXf4ieWjwYiuoJRWbEylwyYmDQhTfcZq/z7xC7jx+05sN6/HEALAzp07ceutt+KSSy7BpZdeip/85Cfw+Xy47bZoYYNbbrkF9fX1eOCBBwAA27dvxz/8wz/g7rvvxje+8Q2cOXMGe/bs0XXjTLVPr9eLa6+9Fn6/H//2b/8Gr9cbH+9XWVkJSZLwm9/8Bn19ffjEJz6BnJwcvPTSS9izZw/++q//esZepKxhckwZp5vIpLvO/9/em0fJcZb3v99ael+nu2dfNCON9tXWZkkGA3YwxD9yuZDg5DjG1+TmnHDkg5ccH7PEJjeAHSAYAjg45heW/BISEhITIATHGOPgRZYsWba1WPsuzb70bL1W3T+6q/ut6tq6p0fdo34+5+iop+qtqqe2t97nfTaCIBYWnufg1Mzy2i87kWMmmcGpoWmEvbmhksyLeGv747jhv38HjoHX0f/WX+HEpk+rtj14fkKlnKQzpZ2HlQXQSKFgOXw5joHJBK5f0oSIr3pDueODxUxyUb/TsmZjOb2e3rlMzKZLUu3bSSrDtlEGQez+tYOvsVl72Vn1lLISC6GtPZXuz2psYlV2wipjaUaS0Bf1YXwmVXJNtXFnWuuFcmy+aB6HIzEG7/RZeKfOAldGsPb8MbhmLsE9cxmuxFBB2VurkSPA/JY5HilXDFlfMxyhdgzJYcjeGJKOEBKOMDhvBDN8AGlXGBlnGGlnKDfAZ1jXGVJNTpw+ovZ20iMa8WFOyinGaacDUqbUMqd1M1Zt73Nics78mWGtpl6nWFAIeQ6QIACCAAj2JsQFntO9/1LIjaDbgTP5kh59zT4MTCZKFInmgAujM0nVPRYEDgLH5Z5fWYaTzyKbShQURiGbyCuNSfSFBVwYGgefTYCXUhAyCbVymVcwA0IGfDaJxNwM46rL7C+bzCuzzHGk4nXk5Sz4zAyQsZ60MGKpybotmr936rRZnv8HAEtLXHYdkHgRnOBEhsu79XIiZD4XB1r8l7Mk+zwuwOVEcxrwpGTIHA9ecACHveDAY2U8nWvPCxAFETjlB3gRS0fn1PvjBeTiUzkgHgI4Di0jM0hnAVkphMFxud8cV2wLDoFBJ9rmMgDHw+kQ0JrJOVwq63PWbzC/mX0B8E84gRkfAIBPpBEbmobLwYOTgnDKEmKXGeWO6YNEgYNPmSzjACAMyDIuDsThyUjwaLZJS0E0XY4DkOGWQshKMpoH4qXfEFmGf8wByBJaC1m1ZaxsDeDYQM6LQOABJAKALKN1MI50RoI45QUcPIKTc+iYSQKynDOSrdQ3ptUThgrh7bffjuHhYTzyyCMYGBjApk2b8Itf/KKQFOb8+fMFSyAAdHd345lnnsH999+PDRs2oLOzE/feey8eeugh2/s8cOAAXn31VQBAf3+/Sp4zZ86gt7cXDocDTzzxBO6//37Isoz+/v5COYtridHpJCbm0lga8xVmIUpmrGUZkcGcQjiWVwhJHSSI+sa+y2iu4YnBXBZMJV4u5HVgVuzCka2PYuPL92DJ8e8h3rQeg0s+oNqerT2VUpWYyP9vaSHU/82iWC5ODU8j4quGE1mOKSajZiZb6op5NbAzqatSCHUG0trBtTa5i5EFQNdCqFEIzWL5tEpvOQkfrMpOWFoIpdxESMzvKlEI3Q5BFROm7IrPzMIfP4ngwAUERk4hlroIzJ4DRk/jpqR6ll+dlg45i1yoC5PONiS8HZCDXejoWYY3JtxIuZuRdDcj5YoAfC7hWl/Mh6OnRiEIxeRKrGXNCDulFrTk7nHufholsTGLIcvFqJp3GKxC6GFfFA7Y0BXCmxf1rSR6iIK+QqjIo8BznO7kczEzJmMFZOOVOQ5ZzoGsQ0TW4S85j0x7EKOCtbwdYQ9EgcP50VnLtgqbuoM4eWkUibmZggJaVDQTEJT4z2wCDjkFOZ2AkE3CL2Zy22QVS2cCMY+M+NQUuEwSopwCMglwUgYi0vALMmbm5sBJaQhyBi4ui3Q6WbDC6rnrzseNl8Wf/8ciwNi92UypVVhWxvG7ymhrhh/AJuZvTvO3HVaZrNvI/BY1f+uhvX7rddqs0Pzdnv8H5F3Db1vECiEA3HPPPYYuor/+9a9Llu3YsQN79uwxPaDZPt/1rndZmlbf97734X3ve59pm2uB189PAAD8LrFQm0d7aXzxE3DPDUEWPZiI5eakyqk3RRBE/WIWR8VzwHDXezG5+R6E9n8Ta177DGaCyzDdtEZ3G1XG0UICgiL61i/7ViUzl6NK0CbEsWIhHCOymmQlemKwsilJLNRKonHcnyQDRsYhvXO2G8ujJ6tUxvU0thDytuRQzllPYfAJWfATR+GfPAHf5AmEpk/CPX4M3pmLhvuTwSHhbcdsoBfR7tU4m40h7m5HwtuJhLcDWV8MK9pCOHo5503kdQloWRLB6PHhkn2xSWWyWbnw3Ngp4G7H+0ZrYcsyCqFVMp6S49ksccGWXWHj1zhwaAm6EfHPYcwkBovFIfCq2FoFWVYnUOKgH8/JgSt5F3lOc01MFE678ac8D9vxdQoSOGRFNzIu85wUQC4xjHIdmgMuVckCANjUE8aRS5PIZGUE3GJhsq7J58DmJRG8krce+90iblgaxQuMNVnkgXctj+DwxVEMj09hWdSJS6NxZFLJEpfeoFPGzOxcwZ03Fxeq+SdJaPGLiPkEDMdnMTY1B07OwsFJ6Iu4kUqncXF0Kp/JNgMeEjpDToxNzSGRTGn2lS3EpLYFXYAsYWgqkZvYyC/Pxa3K+U6GiV0FCr8LLtrMdgIPSFmlbXE7j8hhLi1BFHj48rGM6ayM2XQWIs/B5xQBjsPkHKswF++9rHngQh4HOHCYTKTz/aB6vcspIJnOguM4hNwOSACzb3VbpyhABodUVi58ZMJeJ8Zn0wDHgef4fDIyDuNzaWQlIOR1wiEIiCczSOatpIIoVCXmdqGh6uV1DjvLr+1GowO57KLykp2Q8i4h5QwaCIKoX4yGOyLPFQZuQ1seRPrSG4gN/AYbX9qNvb/1b0i7Sj89rEKofKurmWW0kiLfZljFNwLlD7AV7G6159Qogh7zwSO7r4LLKLPMrD+WZRnprIwjl+NoD7tVWUfLtU6ayQVoizeXv9+ctcqebMqxhLkRRK+8isD4YQQmjsI/eRze6XO58go6JF1RJELLMO3rgattBWI9a4BoP54f8EISc9fmljWtuHJqFDNsfUepWOJAOT+j0hk8n3PRVFxXlXMxq11Y2NaGQugQeGQlxgIq5baTIOtO1sYCLuOJH5slLthYYbatckrlhJEYuWZLsqxSmjkO0Guay4ypthDmavZZP9CCUXYVHcopycBiN9sje810ayPKxWdHZC6EqDEj63tecIDggCx6kHFykL0BpOa8SDlLn1lBU+7DCHezD7FmPyaHpnBhJGc1dYo8+lY0I5lI4/TpMVX76bDHMp65LZ9U5q2jg5BlwCHytktB6LGuM6Qb06csV5RpABiZnMPhS3FE/M5CTe39bw9a9l0AcMOyKPwuEa8fG9KNZYz6nRidTiHkdWBrbwTZrIT9x0onj4Bc/Ho6K6mS2ty0shmv59t7nAJ29ccAAG/n+6UVrQF4nAIuT8xheCqJVe0BdDV5rQWvA0ghXERorafRgVz9QXnZzYVllRTbJQji6mF3KGNmIVSS1CQlDodueBzbfvlheKfPY8PLn8CBd36nJMmMSiGEEutWXG8Z9F6FeaasJOPMyDRagrl4JDPUcWbWIs23ZpcRcdWstE5slUm8oNGy4rbA6ZFpDE8lMTyVVGUdtTNwPTU8jc4mj8GAVdb8nVOgeZ6zPShmMXIRVHbuTAwhOH4EgfFDCI4fAX7+Njrjl9Cp0zztCGI6tBwzoRVINK3ApL8f08HlSLsjhTIQffkBLgBII+pYPb3zZe9TRpKM3V45DhzHwS0KmGWs2rwthdCySa6mHPPIxAJODE8nAcgqmdZ1huB1CfA7RRwfmirdEYrnaaXQscqvut6pPQsji1E8o6zZD1v0Xot2aVayV+7B9BkD0BJ0YSifaIrjyu+SWCXOVA5efQ56Ik3Mpgr3k71m2mfTzKtA+V9bXkKxKJeDDODk0DTOjhRdaIvHyf3NKnTxhD33VFku3juR5zAfp1ajSRdFhx6fSWP/uXFc3xMuXFvVJITGFdkIq9qvinKnWNPNnrncZIZGXpVM7PLc/8fzcbaKS7qdyaZ6gRTCRQT7XPKZBMIj+3J/LHsPMJr7WUl2J4Ig6hH9D4nI85DzoUKJdBYZZwhv7PobbH3udjQN78X61z6FN7d9GWyBJZWCJSv/MxZCneOoLYT6sCnZs5Js+vE7OZRLFHN2ZLaQ0twI1ppi5AZfD7WdWMVLUbqNMnpqLZrHB6dUHiAsds4tK8m4PDGH7kjp7LPe1hlJhrNShZAvDtb5zCyCY4cQGn0d4dGDCI69AVeiNAOgDA6zgV5MNa3FVHgNpsKr0Ld2K/aPFOvPaa0OyvfLbJBmkV8IkiybuCXm9ut2qhVCPZfR63rCOHQ5XpDPTsZH1sK2qj2AloC7UGZEue79LX5VchqjyQzlGlgdtjPswenhGUT8TpViW4mF0Gj+QhvvmrMQ6scQao9n12vJKlpSa/0s9zHOSLItTwYOakul3nmeY2IXWaug1sKqayGU1YqWdjJC4NnamPbunSzLKmVQJYOiuDIKnV3DAXvv5qvYGLn4svd1fCaFybl04XvFHtPuYzybzKozSunAcUBPvt80Oy3lXqm2Ndyn5rnPliq19Q4phHUO+7FgO5fwyGsQskkkPG1wNa8ENzYMWQaavFSaniDqmXLrEGrJxRDmBh6JfJzLTGgF3tz5TWz6zR+j5dxP0e9pw8kND+pur3zgVPFsOoM2oyLHLCLPQXGmSWclCLxx9pfRGf3U+nrYiXmrVB+sNAW4/mx/8beSCEfPjTTXVr0Ds1ICdr1hJ+fSugkj2EMpcW0ZSYITfHk1sWQZnunzaJ58A85jR7Dt1B74J4+B17h9yhyPmWA/4uE1mGpai5XX3Ygh7wq8Naxut7opBowWlUcjt1+zQZpgkd1FkozroCkDTLco6C5nCXkcaAm4Cu6oVgNigedUbaI+tj5ZcVLG77Y37FL2ZdVf9EZ9CHudCLpFjDCubcp25SiEKldchhILoY7iB+jHELJYuR2ayapex5XUzrNCiTG1QiuC1YCetRCKGgur0bsmyYyFEOpxHqsQ2r11ui7/hWPlJ1nyz2dWku0rhMyOzbLh2kEw2F57zy+OzxX6RpU1TuOKbMSsjXh2r1MsZO02m+iRUTqhoVLgmZ9G3YMd74N6gRTCxQTzXCrxg6NtN6KT57FzWQxjsym0Bxe+ZhdBELVD4DkIeYVQqacEAGNtu3B0y+exdt8n0fv2t5HwtOPi8j8s2V75vllZAO3EELJLrSxP5cQZ2oohrFCxq2aYNSvC5FxaleAD0BaEL2e/9hobuX6xyreSOVKJNTO7bpyUQWD8MJqG9yE88hpCo6/DmRwvrFcKkyc8bZiMXofJ6EZMRjdhKrwakphL8L6hKwQE3eCmEgDUMUPasZehJc/EMmInmUjawlNGq0zoKSLaZVaH5fPJnhSUzbW71u7GeOLHWDbtcZWSL6yurAx0y7Hs9ES8BYsmiyzLGgsdZ2ghNDuaU6hcIWSPx3NAR8iN2WQGGUlWxZDOF60MooVJml3vsBFDqCwvZNjl1Eq0yipmctyI3wmXyOPKREK3b1H6EEWpExiF0E4sHgCNpW6hLITqv9mJMrvXAshlME6ks5hN5SY1zBRIl8NeymBZx2XUCKtY4MUAKYSLCPa5VOoPjrffiE7kPnCdTo/udgRB1BP2PhBGgyMlqQxQVEgU101h8x0YlofR/NpXsOr1v0DW4cWV3g+pti/EEDI9ynQig8m5NEJMEhW2vzHSIdjlltknbbqzS5qYIzuDF7vKoSzLhm6alaBN1jIxl1bFR+pld7W3X3vtZpNZpLOSylUtkc7i8KWiNURJQKQoq6wcXDaF4PghNA3vRXh4H8IjByBqarNJvAOz0fXw9+/EZGwT3pRXIOlt05VnXWcILflJST2lzq61yqyZU7QezCUt7rHHYW0h1MZ2WckuaGLgCi62mu3sFhs32t5KhsJxCvspru+KeHBxzFh56mryIOAWMTyVVLlFep2ixlpjfM3MujdLK5PJarWynYsFXd4awOh0ckEVQqvnjVV0tFYwRSlTlBWFXFyl4h4NxPwuXBibhcvBoyvsxfHBKdOEVl6ngE1dYZwZzb2rZkm/lPha3uCeKXAcEPW7MDKVLJaYYeIc7UZtGtWy1Du22yGYvg+CaoLD/Lh+t4hEOmtr4lHrIaAgChz6W/x4+0puUkSG+eScuo8zUHjJQkgsBMpz6ZodgD9+AjI4jLftqq1QBEEsCEYzi2xSGYWAW8T6zhBEgcfJrfdjbnIYPSf+Hmv2fRoS78Rgz/8qtFX6Ee03e9+ZMVVsn6rkgoGMdrNXlqOEaWMG7VgI7apahy/HS1LI28XKigrklF52cJaV5EJsZXkKof22U4lMwUIElLpLKgPWjCQDmSTEC6+g7+ivER7ei/Do6xCyatfVtDOEidgWjDdvxURsM6bCqxEO+rB5SQSZ6SSS+ZJIerAGEu04qDfms22tMhtEGWXCZDFyGVWeaa1iop8xk1MN+KwGpFpPVqMYPrvjQ7suo3rbsMfldZYZwXFcMa1+nuaAC/0tftU7bJRUxizZDGB978yuDaej7GqXVwNeY1iyUmJZN1GH5gSUPnH70ghmkhm8fn6ikGSncAguF1fqdQpoDrjgEnn4XAKCHgcOM26uSmZcAFjbEVLF9er1vZIEXJqYK8QWGll1FWQZWNUWwItTyZLENwLP2XbR5w0UQsUSWszOymHzkibDjMDKNqx8ZnjzVn87pdeMLIQeh4CuJi8cAo+3Lk5ClvWzAytokwHpQUlliAVBGWhEBnPZReOR9ZDcTbUUiSCIMrE7fjH6kIg6CiHPcQXXJVHgcXzTZ8Bnk+g6/UOsffVByJyIoe5c/dZCHUKLDyf7TTdqa8dl9O2BuMoqYXX+2sGEkeWxrCypeczi9ipBe85ZuTSjohJbWZ7LqP228bm0SiFUXV5Zhm/qNAKnfoXQ3leAi6+gOTOHZqZJytWEidhWjDdvw3jLVkyHVqoSEgHFgZllHJ2OhQwAlrf6sSTqs32fzA5jx2VUr5aeev/2rHascm/HQqhnUdSKq7WcGl0Su0llVDLoJOHQs1pawbZa3R6EQ+BVlhee079HRrGFCtoYOy32XUbV7qPVhOM41XPqEsxjFVm5tO6l7rwl2iHwCHudhXsiyXIhflZR1NjkUFF/Lv6UPTWlfAlQfD2V9UYWwrMjRWu/lUIIFJ+ZwqQhk+2T7b9YxU6LwOlnI1Us6IqC1d/ih8cpYCZp3Cew8lpNkikKoZLZ2exMXQZWX8WVvHBdZfveGmYePYsFUgjrHPYZU94HxV10tO0dVe8MCYKoD4wGTwLPlXxk2H5CFHK+bm9v/v/g4TOInvw3rN9zH45kH8WV3g8ZWgjNMHYZZaxhBo20LmpWLljaD7/Rsa3qKFYb3cQNmmV6SVKSGQluh1CW1c+q7iPL+GwKvfAV/uaSU2i++AyiAy8iOvAbeGYvq9qnPc0YjW3BRPM2jDdvw0yw31LrsKsQGikfyj3nNFYCO/vRYsdl1MgKrBxWJSdvbI1njRdWsUDa95UvWPhKNEL1MQwuht2yE3rb5A6Tv2fM9natFWrFVvmfNYcYFaY3zwJrZSE0O1W9+Mzc7+pbCFnvdodofxJEeQa29kVwangay1v86rb5hDFZTQyhEaUTDGplR9nWTtci8BwE2f77KzPZenlerSSzil3JPgxusdYFu1gWxVgmlTymkufcmoFcdk+riSdtvUiFgiu5ohjDIuba4DdLOe9vrSGFsA4xnY2XsgUL4WjbjRUXaCUIojbYfWONZxz5EoWQbVsYdHE8Lr/zr5CWgLbT/4a1ez8JMTUFqf2efMvSfkapVQdoXEZtxBCyH85kJovxmTRaAi6j0zNEq1MZl50oXTaVSEPgucLgoJroKWnawYLe4EGxrMjllIgtQ8EdnUpg8OjLaB16CTj5S/gu7sNGJguoxDsx3rwF0tL34GRgO2ZCK8ozO6E4+LdUCFmFROU+qlYUrdy6zMSz4zJqhSr5Cjg4DJRM9p5bXTKXKOh+j52C1tqqOQZzKfqafTgznLPqVKIQauP82P+ByhJc6FkqDV1GOfMssFbWEvtZRtnlprssG57nVPVDrZ439p1QksqEPI5CQXXVvvPnsJcpFF/J94DT3BM7CiHHmd9/h8irZJFldUIalaeGidBGx+A4TlepNXsk2WtrpeR5mURRVvHsWjfg5oALw1PJgpVWudZsrKcV5DJKLAhms/GBiSNwpiaQcfgRj2yAZ/E8awRBlIHR4EkUzF1/3EzCDInjcXrnXyIlBNBz4ntYefALmHTMAL/957oKVVaWoQwL7HwG2QEza005eH4CU4kMemO+km2sLJPaj7nhDK3KZTSneL2aH2hp6xweOD9uWOLALnqhLsoeFauXJJf232mdZC5WWLV0zg2hefBFxAZfROjKi3CmJgrrOAAzgT6Mtr0Do203IrT63Tg9IaM16MZMvDKXWbsJTozc+uzE2rCYTXRWmv7eKfLoavLkZVBbuwwLsqvc5MyP63YIugkttBZNs/0sa/YjmZZweWLO0OVUGbzqodcvVFLLjYXXUUw5AwshwKkUAq+m3uN8lHlVLGQZcZFlH0ezOzMldl1nSHU/rVxi9UQ1tRCCPc9SGZVN7cTNGcUBel0Cupu8iPicqnPJubUWZcywyagsjmOEnWy+RvsyO0WOy7mBKv1wxqCv72/xYy6dVbnYA7nMyFlJLrj8Ks+wZJGRlb1eV2vCYiEhhbAOMau7Fc1bB8eat0PmHVV3lyAIYmExyx7HYvRhdQh8braVSTLAfozYTKHJjASnKOL4pk8h7Qxh2eG/Rmjf14DZc8CmRwGo42OM3DDt1AJk20wlcqm/L46XFku2Uoy0ipuRIqdOKiMjwZTgYAtpp7MSxpj6bJViZiEsuIJJckk7RUkoSyHUNOWyKYRHXkN04EW0Dr8Ez9hR1fqMww9x2buA/psx3v4O7J8MFtZFXX4AU0hlK8+uqhh9yoshZLYvGTiZXwuzw9hVKnqiXsTn0pjIJ0h5x/IY46ZWbMfBOE6sHFdkt4PXre9WohBq1mufi5jfiZHpJKL5QSurFCxt9qEn4sWvjw3ryqBnnWGv/XysFeymsmxSmJ453fawB0G3iNfziYhM7x1nZSFUNVUds5rkYgjVf+shChzaQm5MzBb7lkosoJzZJWHfIR13YEU2K4sYoH/Pwl4HVrUH4XeJ+TZsn6q2EGpdRo3eYbOEUHpuv2aXzG5SGaeY+yay/bDebVsS9ereT47jVMq8cty05rqGvQ7NdsYyAbnrtpjG6KQQ1iFGz70sA5HBlwEAY6257KKLafaBIBqZG5ZFMZ3IIOa350ZprBBy+fU8pLxGqP3mbOwO48iVOJY2+3Ip2TkOZ9buRtLTgtUHPgvu8NNYPXAKB3b+DVKelsJ27GyoqjC9jhyyJoGK3qBEr9SElQuOdrbbaPabXao9tCyrazRWA1aMRDqLyxNzhcyLIs8jk83qnluqYCEs51gSvFNnEB34DSIDLyIy9CqELBuLyQEdmyAvvRn7nddjMrIRu1a2YyiexPFBdR05ZaCTLKMOpBZF0bByOSyNeTL/zW7HXjozxcWuQijynHoQbSADx3ElcWJKcgmzWM4tvU04dCleeAbcDqEwEcJSaiE0l7sl6C6U7gDUCkNr0G1aF48932JB8uJ62zGEFiVDZMj69xHq+CyeyyVI2dQTht8lYtqg8D3b3nid/edpPtjdn9KOdU+3GvwbXbNy5dIqU0YKITvZkJVkVTKVkNeBLb0RtSwlFkK5cBxZ1c5YTrM+Qp0pttTyXLIvm8+r4patKIRGFkLbJV/yl4mtmbmqPYCWgHGdb13r7yIboJNCWIcYjpfSMwiP7AeQK0INLL4HjiAaFb9LLMzE2sFotpljBudKNjftR7U54MJNgVwuySsTRTfBy0t/D03dq9D+i/8X/tE3sf3Z/xuHbvgKxltuAACcGp7GyrYAHAJvmcVTu8iuNcWqrqDWYnJxbA4hjwPtIY9hO618GUnGiydGAORSvlebNy9OFup7AcWBi57LaCGG0OoCJeLAmf8BTj2H5uPPoj1+QbU66W7OxY3334z2634b8EXBAUifGoWczGA6mSlRBoGiQminPhfL6o4gjubT3hcsa1YWQgP3RD13N+127OSB2UDQ1CWNsZo7BB4xnwtj06mS+nCqwTXUcX49US/6m3PJQMyU+LDXiS29TYXnLFdTrbRdaQxhed9sI9dbK5THrdLtS+TQXHd9CyEHgddaklCYBLPK/mo2YJ+PC3I52B1SKfI4RR67+mOGyVRY9HR5+wpo6TJFqWL7IhbWYp1l4sNzshh9X3LPTs4FXlEINVZTEzXW3GVUfRzAvE+x61WhuIAW6ifOMzxAew6CwKGryVvSjm2ldx8XU1F6gBTCusRoVtJ16VXwUhpz3g7M+nsB2J9dIghicWHpnmczNkhrKZvtuAH4419h9vsfgXfyBK5/4f/B6TX34Mzqj2NgMgGOy9W5UimEOvvVLmMteU6RN1VAJM3gRL2udNnhS/EShdBMvtlU0RIxH8uYSi7mgNoBmKJ05VxG1SgWpJIxiiwhMH4E0YHfIDrwG+BHBwEpJ7eIXEH4idjmQizgdGgVwHFYEvUCvkBhNz6XgJlkBrNJfYuoMijRs9aawd4eq2dxZVtOHtZ6ZxhTo7MvxcKqYGUF3NoXQSYrFVwR2e2S+QfIIfBoDbrgEDk0edUxQyr3R6jLBfAcdBMrWWGUyt6o5pmC1aDXyPXWCnYwr2A2QPW7yxgOyvr74jTH095rM/dIwMpCqD5Ocbm1xbkceI6zleWXlcfjNC9NoaAnk90so3oWbivlle1LM5KsmmQ0Tv5SlFNRrARefU3sWnJL1i2QAaMtlLPc5azTWWQkaV6Zp7XnYKd0hL6FsHIZagEphHWIema++Nt94X8AAGOtOwtP32LyTyYIwj7lKIRmTftiPrx5cRI8zyGdkXKD3MhSvPH+p9Hz6p+j88yPsOzw1xEZ2oMjWx/FhLMPgMZlVOfjWlKDj9F4jBJssNvyBtNZdhIk5GRSy8duxiqB1VIIzcQqWghLGyXyVpGsLMM5N4zo4IuIDLyI6OBLcCbH1I0jy4D+mzHceiMOieuRdZQm5dF2+YqLnmEa+Aq/EUYJLfRga6hZHVc3sYRm4GQ1AAsZxOGKPI8kctdbid/RTiSYyQYAaUZxtnoS3Q4B7WE3RJ6HQ+B1FVmthVB7aKvHvdJM4oWER8wyvT7F6xLQGnSjM1y8Ts0BF44PTsFn4NEgQ3+wy3GAyGh9WqXD6lk0G88IBhqhrmVGY3FmcYi8yhWwVIbSZRu6QrgwPgunIGAwn5ipkrGXnuHKbDfsvde1ipYhQlZSu/kaWwg5AHLOZVQutlXJrpHFrru3kVKvR8TvLMTRGtEWcsPrFBBwO1THzkpySV/cFSntB4zltLaGA8Yu8oXtFtn4nBTCOsToA1FUCHcVlpHHKEFcm+h9hNTxQOwa444g6nfh3atacGJwCudGZwv9S0Zw4+jWRzHevA2rDvw5mob34oZnPoBz1z0ILL1f0w/ZcRnNpeg+Pjht6MJkvLcidrOBytq/NDF+Csl0dWIJzSw5IqMQsooql03BO3QA8tkDaD72S3SOHFJtlxF9GGu5AaNt78DKXR8EH80p44mxWWQHSt0/83tVH7tgndQf5FY6K2812LHC6LC6LqPaGfkKs1E6RQ4z+QScZtlI2cOVuBuzCqGNyYm1HaHC7yURLyZm02gNFuOESxRCbWF6i/1X6h6pJ7ves+B2CFjWrK6X53EKeMeKWKGMgt42usk5wKn6KO3mZo9iwGWebMswBlSnrdbirLCrPwanyOP5t4eMj6OzRyWu8+RQ8Z2s5LXSc2W0e0/1rKLlvJc9Ea/Gymeg5OT/V7uMqp8nraKqVgiNZdBm9zWiO+IteB2Y0d/iV2XVVvrhTFZt440FXFjVFoRdtN9eo7qF7Enonc5iKjkBkEJYl6iTOeR/Tw3CNXoUMjiMte4orF9MRS8JgrCP3keIrfElaJI3WKF8jCUZOD44VYjnGej9ICZj12PNvk+jaXgvlr72F8Dgf8O95RHA0Q/ApoVQljE8ncSFsdLMolqykgyHgZeVMmiycvvSZkRl+805RglMWMQt2cVs4F6wEEoyuNET6D7+n4gNvYTQ0KsQM7nrocx1x5vW5t1A34HJ6CbIfG4gvDzcDOWOmp239l4Xa2bZa18iu8Ahq2NNYT8tXpsucertzSwQGhmqNHBiLXRmSiUrg3LmopCzKkX8RatEuW5nosBj85Im1TKeV2cELrUQmh9EPYa2f52U3Vaq2LvE0nu+tTeCuXQWIY9DN6MqoE0qY20hXNbiRyKdLVFKASDqd2I0nyHY0GXUotQGi0vkLSdIJuZSJvedUUoreGZ1lfQKXEbLMRDyPLC9LwqfS8QQU3bG6BopGURzFsKi2/HSmB/HB6fQHnbrJk9Sb2+9zuxZtntpS2L9FIWwjPqBdo5vp3+ipDLEgqDrMnr61wCAqaY1SLuKSRJIISSIaxO9SUnWkqL6XcYHS09hm/P3YP+7/h5dp/4Jy9/8MoQLe7Dswm1wL/09nFr3AGSUugRqkSS1u12lKK6PomDu2qWNIWQn3+eY2md62UbNarkBwOYlTXh7YAozTFZEo/GFIzmGptMHED71KzQPvQTnzBWsZNYnXVFg2c2Id74TRzybkXZH9c9H9dv4OmoVqsKsuIGF0OrZcIsCZrKlAzwOHDZ2hzGdzCBqMzOu4TEY7V9PnPk/NTlYZcRO3A978BuWRhGfS6M5UDzXeeamKMBmBNZ+s6t17loU2dXlCkopZwwR8joQQm4CwyHw2L40AkkG9p0puj6rvBhsKIRBt4g+nXqlK1oD6Il6cXxwCqImfb+VzEbWYeValBRaR64cUHwujZjfhYFJ/XqdRomS7KLn1m22GyODWiEhi4175xaFgusv2xcYzZcou5RRnJzjOQ49US8ifid8TgH7zo4zcnFgn2K7LqNm2FWktM+XcuxTQ9Oq5eUqh9qyTtq+RJmo6FG5yqvb+Fwi2oLGWUnrEVII6xBZ7/fp5wHk4wcZSB8kiGsTPQshWweJ/fDqzeZrsRw8cDwu9t+Bub734rrjXwP31r+i6/S/oO38f2Jywx8Bt9yvaq79xmrdJc2QZBnDU0kcG5jC2o4gmphYEcXd06lRCLOSjLl0FoPxBFoCLhzJZ8BUZGGPzVoI9WIIrZQkjjOx5GSSaBrak48DfBmB8cPgWK8OwYWx2GbE22/EXM87cdnVjxVtISQzWaRHja2nRrHjJbIZnItRZj2r++5xCirFt3AcLpcdklWQymVnfxQZSdYohKXyzCcBBAt7X7WumkYoyrfbIajkBMqrHWmGyBczAmvP3uoQjkqUXBRlD7odaAu5DbOgzmcIEXA7kNFYCgUTxa0cJUo57RWtOddB9hnVnseNy2PYe2asELds9X7v6o8hkcli7+miIrt5SRMG8n3LFQOFUFuupFz0XlG7CrmetdDOtkp8HaCNOzdyGc0tZ+OylbZKlmzVpgYeC3rYzXhrO/Oqjcy3laLEUurtd1N3GIm0pEomxDbxOgXsWKY/8VfPkEJYh6iTJeTfylM5hXC0dZfKlYoUQoK4NmE/Qsta/Ehmslga8+uud1tkMgTsD8YygQ7gw/8bJ3tuR8vLf47g+CFED3wdOPw9LFt6By7034GUp6VktjvnpmPvGJIMvHFhAgBw8OIE3r2yWAtRUQi1s/yzqQz2nhmDLANnhmdU6y6Mzaozi6bNk8qwAw6HyGNpzIfh6aSqgH0hTiebRHD0DbRP7If869cgX9iLzVm1dTEVXY0r0R2Id9yIrk034/XLSXicQi4d+vicbjmI0mtiL5mJts+fj0IoCBzCHgdGdKyl1fi0sDXaistygyjFRRMwV7yU8hfLW0tdCrUIqvi1+Z9BtRRV84Gw+UF4nsM7VzQXftuFVR7XdebiHNl3pFpoFSOV0lESQ2hffu1+zVxn3Q4BPlcxkZVxzFcOp8iX1IcUeE6VWEdXJrZ9JQqhzjtq957qulja2JRNwKS2EBq5jOb+f/38eEGZNIsFDbpFjM8UY8bNzsdmCGHFuTHaQ26c15l0q+Q1FjgOWSjeKtpnkSvJLGs3PrKeIYWwDmH7jFRWgjx0FNz0ACTBhcnYZpUrFbmMEsS1T1vQXfIBYge/diyEduOPOABTiTTO+jbg7C3/hubLv8TKo9+Ee+wo+o5+C0uO/W8Mdv824NoNyEsKX/mRqaTKVRMwjgNUFUzWuJnOpXJ9W2vQjYnZ4kDj1dOajJwaRhlljlWO9NxO2Q+8g+fQHfEWimbzmQTEcy+i8+3nsfzyHgRHD0KQivvmACTdMYy17sRo640Ya92Jvr5lOJFPAjN4OVloF/Q4cGmcLShvDzNLq1FKdCOF0Gjgt7E7jKjPicuT+vIt1LdlWbMfrSE3gm4HfnlkEIC5QtgZ9qAl4NLN4Lm2M4jDl4qW4iavE1P+TFn1Ps2UvtagC6eHM/C6yo+hZGGfN62iY0fp1CovZqzvCuHMyAxWt9tLojHf+2xksdb+BuwpP8ta/BibSaLdxN1Or3yF3UyX86HSBD8K5VqcVbX/dI5n5zSDnuK7YMdCqNzQTFbG+EzKoG3x75VtQZwbnSnUuzVX9Gxa/ip8JgN5a7iRy285sNfKVtkJ5vdiHZeTQliPMJ3AxbE5BM//FzoAzLRugyS44BY4pPMTfYstixFBEPa5YVkU6YykW+eKHf8b1UBjsZ3NjuPw2rnxwkbDnb8Fx5r/hVUTv0b8V3+N8OgBtJ/7D+D//AduCPbjypIPYmDJ7yDpbStxPdzUHcZMMltiIZM1Olo8kUbQ7YAkyQULYUvQBb9LxH5FlipS+GDLMtxTZ4E3f4W24y+h4/J+BCbeBi+lwdqjku4YptpuwFTrNgyEt2AmuEx1QY3q5gXKqO22/9w4tvdFIAp8WTPavKWFUH87h8CB5zlbKdWrCc9zCLo1GSWt3CYNrm97yIOZZAZnR3JWAZ7jcF1PU1nymI3Re6M++N0iwh7z9PdWmA0QfS5RNfExX1qDbrSWEbs03/vMKnkyZHtKB4NW2e2L+XRjClWZLXX2w95GtQJufI+7I15cGJtFr+Z4RhMy6lIs5V+49V0hvHlh0nZ7y5IkdmIIGTdotfXWyEJYulzbR7BNnAKP1W3BokJoIpK6dq6Ja6mObKxHgRnVUsbY3Qg2CgpaZb5dDJBCWIdoEwo4z70AAJjoyJWbcAo8ZpEbNJFCSBDXLn6XCBiEcLF1/uzMvNt3N5NLrHYyx0Fe/X/hNW4HgmNvovvE36Pl4jPwx09i+Vt/heVv/RXiTWsx0v5uDHe8B1NNawCOB8dxugME7Uz53tNjeM+qlkLsn8BzcImCLcunbWQJnpmL8E8eQ/TsaYQvvIbg6BtwpiYAAKwaIfnbMNGyDYNNWzDevA2zgT743A5kGYWVRXe8wAF+HZdJI+ZSWVwYn0NfzGceQ6i5nlYWQm2CBHY5YB1LpEVx4bTKAlsO80neosq+WOXPIc9zaAnMPzGEmYWhv8UPgefKUuIqpVr3ywxVYXqLG7KqPaCKcTPDKlsqe27sepco6L6zALCi1Y+OsNu2RZlVNCtRPFoCbrx7lQsnh6ZtZWNmqdRCyE6mCDaUFr3lpfHUahnUiYtMFD2bXpV67rhbeyO4NDGHqURa5aKqReveCcCwfIoZrAzlFqZfbNlFFUghrENUbgLZFMLD+wAAl5puAKB+wSt50AmCWPwYKQBG2P1E6e2WTdoSj2zA4e1/hbev+yxaL/4X2s/+GOGR/QiOH0Zw/DCWHvkmUs4wJqPXwT2wC+nmzRCkJcg6/MwxSg8ym84WlFwXExO5viuEty7an1WHlIV77gq8U+fgnT4L/+Rx+CeOwT95DGJmprQ57wTfvgHjkY246FuLeGQD1q/fiLPDM6qYQlmWDTN56vXDuZpsHPpb/Ign0hiKG2c1VVASdJi5jGpn1gsWQstt1OuVMYu2WHWh7IfBp6Uz7EF70I3Dl+OFIt3zRYZccPXSWmusmG+ij6uB2cStQ+ALiVNqQbUvmdpCaN62q8k6e7EeejKz7wzbN7ocvKFCyHGcrkJq9Cax1sxKh14Cz5nW6lPLYVGSxEavbuTCa7RnvXeoVPmVDdeZy6w/eXPDsij2nhktTFrpPTc+l4gVrQGcGZkxVQi18oS8Dlvxx1rsxFuyXAMhhKQQ1iPsYCk0ehBiZhYpVwTT4VUA1DMgerMhBEFc+/Q1+zA6k0S3zUGV0Yz2tqURVbY9I0VTuzTrDODy0o/g8tKPwJEYRezKr9F8+VeIDL4EZ2oCzVeeB648Dy+AVgAJbzumg/2YCS6Dc7APLakwkp5WpNwxZBwBzM55wIs5cyg7O9sadIPrknHo7BCEzCzEzDTE1CRciWG45obgmhuCMzEM19wgvNPn4Jm5CF7SHzBIvAPTweUQ2tfhomsFJqMbIbetw/blHRgZnMJgPiEBx3G65QEM4/R0+mFlc0XBefHEiOHAVHuN7Q2pchTLThhvxSZIUCgWty4uC3ochbgh01ggnrOVyMguPpeINe1BdIY9qiQYdlDH7lRNpKpSL548XqeAJp8T6ayE6XwtuXJqG1ohyzkFd1V7ABzHmdaCnA96MrNPt16pmWqgUgjnoUlXMnGhN99T7m7sHFfvUdVa7FQWQt54Xcl+DN4Dv0tEX8xfKBdhJueSiBeyLCNmkP1YlWlY5LG1N6LbzgquTAuhXXfYeoYUwjqEfZ+igy8ByJebyE/ZOlXFdxfng0cQxPwIuh24aUWL7cGm0Tcq6HagyecozLrqWe9k2TghgtcpwB/swBX3h3Gl78PgsikEJo4iPLIffXOHwF96DcLMANyzV+CevYLYwG+A48AGnX3Jggvv4h0AOGYKXkZzagbvke0P8iTegVl/D+b8SzAd7Ef7ii14PdmJ2UAvIDiwsi2AC1dycY1+oTSVOseVDjnTWclwsGOnxMH2pRGMTqdweXJOZXnUw7wwPaf7t15xeaNt2GXs89MWchcUQit6Yz7MprJoC1Xu6rh9aQQX826yPM+pyo/YZbFbCCuhUnddjuOweUkT5lJZvHRypKoysVRq+TPDKqELez3YzMJtQTcmZ9OFWnzzgX3P53NH7SqT1jGE8xCiDMpRfs1EVruMavux4m/TWoY8h6XNxhY/lVvsPK6PVrG0bL+ArutXC1II6xC2E4jkFcLR1l2FZeQyShAEUN5A0+wjxfY5el6RuRqD+tvyPIcNXWHsOT2K6UQGsuBEPLoR8ehGtPZFMJPM4PiZ8/BNnYJv8iR8U2cQkUaQmbgE99wgnIkRCNl8QoJsEmLWvARCVvAg7Qwi6W5GytOS+9/djKSnpaAEJjxtAF+MP+xa2YyZY8OFfaniznSPUnq9jBIaCEKpNbF0b7l+uy3kNszqCRTvg5nblbbLN5u9XtkW0N2GXcbO8PudIlqCLswks/BZxD86BB4bu8OmbawIuB1Y3V6eRVCLOrZsXrtaMOrFQqigjneqnRzlwBm4Gyqw70yz31VQAruaPPA6BQTLsTzbmPgxs8hb0RZ049TQtKqubCVUYqXsingwNp1Cq4GFTc8LQvuMmJ25mYVfJa9GdHXCHpMDWCDMM85TD73yOVrUcZSLE1II6xClYxNTkwiOHwIAjLEKoUgWQoIgysNstpkdA0iyrJuExAilB3KJPKa167jcRznjCmPStRmTsc0AUJIanJMyiDpT6PRmcPzSGEIeEes6QkXZHR78+swcsqJXpejZhR0YyLL6g60oh9qxit3BhGiQqbMit7B8329emF69XyNlo7/Fj+5IzlKjl6Sh6DLKDGR4YENXuByRa476vtXn97CcgvJ2WNWeS+zT11xevKUeVXUZrdqe5nfwnogXXqeAsNcJjuMQ9Rtk5ioTdtBfbgw3i8cp4KaVzfN+LtitV7YFcGzAut7pqjbzciR6p1XiPq/T5h0rYkhmJN0EPco41czKq/XQqBS2r5vPRAzr3m/HQsjeSyo7QVSP/MsWGdoDTpYwE1iKpLcNQO6lYR88o3TcBEEQLGYz2q1BF+JzOZfRrCyD5zhIzAjLzEKoLLZjJVPQFouXeRFJ0Y1MwIu5QBAevxOINan2k3UOGspvRtjrKJlx1vtecxbrjeA5TndGu5IxQcFCaOoyqj2OfhZRVbZHHQELLqOLPF36QmYZrRatQTdODE4j6p9f+QqFzrAHMb+z4iy8Vu6X9QhrAbRSYnmeQ8s8srZaJXMBcjWi50M1xm7sO16tsaBeQis7Co5eVuiOsAfTyUzRU8Gkr2H7qPkocqw1s5z6nVqs4r1Lj1v//ZAVpBDWIcq4LTJQ6i4q8JwqlqfaM48EQVybSCYKYU/EC57jcGxgCrIsg+d5sFPuMowHScp+jaxkel2Ukk0z6ndiSdSHA+fGIclFRagavVqTz4G1HSE4Bb5kxlnP/UztRqrvBqoHz3H5bIUiphIZy/Zmyp5idTAbkOpdZ4HnIWk0Qqs0/TxX2m4xwi8C5cbtEPDuVS1VdWmdT0mWerWkmqEuZ1G6/mpbJ+djIbSLVQwhz3NY3uqHJEM3yVMlipWuhVDrMmoh2IbuECZn0+hv8av6VbM+lV0zL5dR5hh2YruNMIqZt3PcxWohJPNSHTGXymJyNl1IH67ED461qeMH2ViWhcriRRDEtUVJMXAGjivWQZMkIK2x4I1Np3D4clx3W2UAYWh101MIGSVS+fjLslz4CFfjgyrwPNwOQdc6phfKom1lVwRl0LVFk82ukjMYmExgZDppUYewdM9Bd+ncrlVsnbKfeotvKxd1wfL6PReB5+om6c1CiWGlKMwHJXvp6o6g7rjnatRYBICeaM4Ne5lJYpOryZKoD30xn365iIoUQmsLodWlbgm4sbw1UFoix8QttJz6lWaUmwzGiHWdIYgChw3dIevGWPz9KEAKYV3x6plR7Ds7htlkBp7p8/DOXIDEiRhv3lZoIzJ1ogiCIOwS8jpw/ZImw/VWH7TJWYNSDiZKHMfpD9IVhVCxruX2w1gIdURhZ6lvXB7D6g7zWBgz7wk991CtG519hTAvnw23VL12Wg6enzBdr7e9XtwfO64zu7fszPZi/7RcA2Oyq0K1L5M/PyHROg83TTt0NXnRGfborrPj5lkNVrQGcNPKZkQqyIhbLuw5VZJxtBIPMj2FUC8GuRL0k3kp64q/q6UQzseNtiXgxrtWtqAlYO+ZJoWQqCrKbEYyIyEy+DIAYDK6UVXQWeC5eWW3IgiicWEHMQLP4cblscLfet+zdZ3Ws6OKR4Ouyyg43YGK4jLKxt/JKMYpmpVJAHIueJ1hj+nsrWnCLV1rpvGMtRlKO7vWn5VtAXicAkImGQbN3JX05JpfzA2z7SL/tNSLBa7eqfZ12tYbwTtWxKpS2qFSqmkhtNrX1crdoMo4n48/NcoKa9VnVnLMwn7KqDVohpl7t6q24XxcRqtkISz7uJrEZYsRUgjrCFdBIcwW3UWZ+EEg92K25+s+zTdlMUEQjUd7ONd/bOgKwe0oxiHpDRI9Tus4JblgISxdl7MQ6m2T+591o5Nki3ILZQ54zGbH1dspCp26jd0xCTsAUQ/W9PfgdYrY1R9DV5O+pQMorw6hHWaS9hIkuKpYbL4WlHNlFIXc66o8Fm+xUm21mee5ecU0VoNFOga3TUfIjQ1dIexcFtNdr3dPK8lCbyd2rlJrrN2+a14TXKpEO1dvgohVmsuNP6wXKKlMHeEUBABpJJNpRIb2ANBRCDkOPpeId65ovqoPO0EQ1wZrO0Lob/HbGsC5bMywKrlMjKwOZtYIgS8qkumMhDMjM/ltStt2hD04OzKjsqyZ9YB2Exjoxj6W4TKqzpzHQRmaWm1vJp/ZcKIS445VxrxtSyNIZyTVBMFipJyYqfWdIVwcnzNVzK9VrkVDanvIjfOjs+XVGzSgXsbzrBgcZ545Ve+etlXgwmvr3Cu2EBrH+6qyyFbLZbRGRTZJISTmjWLedgy/AUdqEmlHAPHIelUbxSx9NU3hBEFcW9idzbejECoYKThWShu7nZIwS29fS2M+BNwimrxFt1ezgYNoMhjQ205bz87uoIS1opYzjjFrapacw+g6a0tPsLtY1R7A4UtxOES+JGEQYJ5w6FrF7RDQ31IfiUGuNrV0reW4hVG4+pv9CHkcVYntawm6MBRPIqCTrKleYfuFzUuakJVlRK9CnGM5cEyXbOYyOh9YzxChRkaTxRrVtXie9gZAUfKaruTiB8dbtkPm1bfoWpzZIwiiPvA4BcylitakcgaOhi6jJrvIJZXR366kLc+VJK0wVTZNdNlSh9HS49s589agG73RYnHw3PWSLWUrttVndDpluM7ICCbyPFLaYoR52kMetATcGJ9NWSatIYiFZKEUQr3+oVJWtwfR5E2gJVidgvaVUmnWVofIo2kB4zkrvX2VekWUQ86S6kI6KyFQo5jWhcy2u5CQQlhHKAph1CB+ELg2MhkRBFGfbOuL4IVjwxVtq1+YnjMtBZArO1F5MgSzZmZ9pTqjqH5SGDvK8PoudVIbs7TqZjKUg9G1EQUOKaYMojbOR+A5xPwuXNcTrmnyD6KxYd2q6xWHwKM74q21GGVhVvS9HHqiXpwfnTVto504tIvZ8NVRxbGtXtblq0l9P93GkN9hHeEUeAjpGYRGXwegLkivQAohQRALhUPgbSXZ2NkfLVlmWO+PWaztv3gDhdBuL2elbNrZTtdCCOPBi5nlsZxBWaWp1Y3i5LTxfyGDWKqo37XoYwWJRQwNYWxTjmLBdifzUUiWt/ixtS9SKFGkl5hmTXsQbSE3tmpqr1qh6h81u20OuNAWcmNVe6B8oesMaZH6jNI0YR3hFHmER/aBl9KY83Zizr+kpA3pgwRBLCTamlNKbJogcHAJPDrCHnidItpCbgxMJtCUj1PRdRmFpr6Upo6qwJSdUG1n10JoopyZ1s6y2D3HGVs2zVwzy+me9dpq4wDLYVVbAAcvTKAl4EbY60DYW1/xQwtF0EPDmMVEzOfCYDyx6DPaXg2CbhFjJu7jLPOp3cfCcVxhMmlrb0Q307TbIdgqSVS6b+a3pgfkOK6ifdYji1QfJIWwnnAKPKIDufjBsdaduj5FVGeJIIiFRGtZ27wkgtPD01jeGoCfcTVc1RZA1O9EzJ+Ls9EvTK/+7AscB7a8Pc9ZJ3ipFDMLoVk9LKBUkWUReQ5GQzTOZAa8VAZ9V1mpwvl9r1M0TEl/LeN1irhhWZSybi8SVrUH4HeLFWXAbDT6Yv5cTFzAOpaRffqrFcNmViu1EtivQaWlK4iFw3SK5oknnkBvby/cbje2b9+OvXv3mu5sYmICu3fvRnt7O1wuF1asWIGf//znZe0zkUhg9+7diEaj8Pv9+PCHP4zBwUFVm/Pnz+O2226D1+tFS0sLHnzwQWQyGSx2OA6F+oOjbaXuooDFrDdBEMQ8acvXOVVcR0MeB67raVIpgwAgCjzaQ55CkWZjxaq4Qutuqbg/liy32c+ZjXvMYwhL059rWxsqhCZFqc1mwK1YbDFL9YTfJda8Dh5hD4fAoy/ms1XjtNEReA7Lmv0I2MgCvBiGhte6h9vqjiAcIo81HcFai1IRhl+2H/7wh3jggQfw2c9+FgcOHMDGjRtx6623YmhoSLd9KpXCb/3Wb+Hs2bP40Y9+hGPHjuHb3/42Ojs7y9rn/fffj5/+9Kf413/9V7zwwgu4fPkyPvShDxXWZ7NZ3HbbbUilUnj55Zfx/e9/H9/73vfwyCOPVON61BR+egD++AnI4DDeskO/zWJ46wmCWLR0hj3Y2B3GliXlxYfoTVZxnHoQoG2j/F3iPmTzmGYz4eYxhGoZS9YbWC4B82LPlfbPG7pCWN7ir5v6ZwRBLC7Y/sqs5E4tEQUefc0+9Ma81+QETmfYg5tWNBvGb9c7hk/N448/jj/+4z/G3XffjTVr1uDJJ5+E1+vFd77zHd323/nOdzA2NoYf//jH2LVrF3p7e3HTTTdh48aNtvc5OTmJv/u7v8Pjjz+O97znPdi8eTO++93v4uWXX8aePblC7f/93/+NI0eO4B/+4R+wadMmvP/978fnPvc5PPHEE0il7Pla1yvCmRcAAFNNa5F2Nem2qdP3nCCIawSO49AccJVd69TIZZRFL6mMvgz2jmmmQJkWprcRQ2jkjWFW7NjKFVV9kOLPqN8FnufIjYogiIpZ1xnCyrZAXVtflzX70d+y+BPHXIvoftlSqRT279+PW265pdiQ53HLLbfglVde0d3RT37yE+zYsQO7d+9Ga2sr1q1bh0cffRTZbNb2Pvfv3490Oq1qs2rVKvT09BTavPLKK1i/fj1aW1sLbW699VbE43EcPny40utQF/BnngcAjLbuNGxDWUYJgqhHDAumq1xGNQph/k+tImTX0mZWPkG0mWW0sEyzyEghNq1vWGH3rGxWaUIZgiCItpCbXM+JitH9tI2MjCCbzaqULgBobW3FwMCA7o5Onz6NH/3oR8hms/j5z3+Ohx9+GF/5ylfw+c9/3vY+BwYG4HQ6EQ6HTdvo7UNZp0cymUQ8Hlf9qztkGchbCPXqDyqQyyhBEPWIWcZPhRKX0bzSVqmrpFPksas/plsqw34dQv02LgOF0Cw2sJykMkbyEARBEMTVpmoOiJIkoaWlBU899RQ2b96M22+/HZ/5zGfw5JNPVusQFfPYY48hFAoV/nV3d9dapFIySXCb78Z4y3ZMxDYbNiOFkCCIesSob2IXl7iMGlkVy/CE8DgF3XgU26UrDBQ8IwuhNrmOel/2YS2YlD2aIAiCqCW6X7xYLAZBEEqyew4ODqKtrU13R+3t7VixYgUEofhhXr16NQYGBpBKpWzts62tDalUChMTE6Zt9PahrNPjU5/6FCYnJwv/Lly4oNuupjjcwLs/hdff838gC8b1o8hjlCCIesQo5o5VuLQKoJGFsPxuztrEyCaD0c+Iql7oMMgm2tXkQW/Mhy29pXHe6sL05mfhc4nojnixrMVv2o4gCIIgFhrdL57T6cTmzZvx3HPPFZZJkoTnnnsOO3boZ7/ctWsXTp48CYkJgjh+/Dja29vhdDpt7XPz5s1wOByqNseOHcP58+cLbXbs2IG33npLlZn02WefRTAYxJo1a3Rlc7lcCAaDqn/1itVMMcUQEgRRjxh1XerC9Op1duIO7ZBMWwffXdfThIBbxPVLmublacHzHPpb/LqF3/kyXUZXtgXQF/NVLAtBEARBVANDl9EHHngA3/72t/H9738fR48excc//nHMzMzg7rvvBgB89KMfxac+9alC+49//OMYGxvDvffei+PHj+M///M/8eijj2L37t229xkKhfBHf/RHeOCBB/D8889j//79uPvuu7Fjxw7ccMMNAID3vve9WLNmDe6880688cYbeOaZZ/Bnf/Zn2L17N1wu6+Kd9Y7VQIVciwiCqEcMXUZN2hhNcJXbzc2ls5ZtQh4Hti+NIuJzlm2B9LoE9DX7sLXPvBTHfLvnJt/iTFdOLF5ojpkgCAAwDIa4/fbbMTw8jEceeQQDAwPYtGkTfvGLXxQSuJw/fx48M93b3d2NZ555Bvfffz82bNiAzs5O3HvvvXjooYds7xMAvvrVr4LneXz4wx9GMpnErbfeir/5m78prBcEAT/72c/w8Y9/HDt27IDP58Ndd92Fv/iLv6jqhakVVp0zWQgJgqhHjLomVWF6po1ZPb9yFatyk9KwMikZTs2OmZVkLGu2du20k6zGjHWdIbx1cRITs+nyNyaIMlje6sfAZAJLomShJggC4GSzyr7XKPF4HKFQCJOTk3XnPvrSyRHMpdSz3W0hNwYmEwCAnf1ReJ3GSQ0IgiBqgSTJ+NXbQ6plt6xpVS3va/bhzPAMAMDtEHDj8hgA4JdH1HHhG7vDaA7Y9/h4+eQIZjX95i1rWg1aq2VtD7uxtiOEwXgCb12cVG17bGAKF8ZmsaYjiI6wx1KOI5fjuDwxBwDojnixsq38eluj00m8fn7CcL3ZeREEQRCEFXp6EGkWdYbepHJXk6egEFKWUYIg6hE7RebZ/quaFsJNPWFcGp9Dd8SLK5MJxPzGibm0+1emRPVqGq5o9aMn4rVd6JmNkay0qyYvEIIgCOJqQwphnaEXI8guo8ECQRCLCSOXUbO+rNyJL69TxPLWnDXOTpIWvX7W7xJxXU8YLoegamdXGQSqM2Gnp1iLAgcZwKoKLI4EQRAEYQUphHWG3hhJFZdy9UQhCIIoi2UtfpwamjZcb1aCQt2uNkT980tMxhn8Lge98h0RnxPrO0OUVIwgCIJYEKpWmJ6oDnqzwx6HgLDXgSafE6JBbSyCIIha0xfzweUw7qNYl0ozC+Fi1Xu4MstO6GGceXWRXhSCIAii7iELYZ2hNxYQOA5bes3TnRMEQSwmjArZA4tX+amG2Iv01AmCIIhFDJmb6gz9GMIaCEIQBLGAmPVrizVUWu0GW9lJUOIwgiAI4mpDCmGdoTcUWKyz5QRBEEaYu4xevT6vmoWXhCq4jDoEHs0BFyIWmVIJgiAIolqQQlhnsLPDvTEv1nbWV51EgiCIasAqhBu6Qqp1i9VCKDClNOZzChu7w7i+p2n+AhEEQRCEDUghrDPYWeUlUR/aQ9bFkAmCIBYD6hIUxd8tQTd29keL7a5inlEZ1TMRmsVFzoereT0IgiCIxoMUwjqDdV+iWBKCIK4lfExNP7cmGylfBXfLWsNaPavp9up3U/43giAIYuGgr0wds1jdpgiCIFi29kUwm8og7HViTUcQYzMpdGi8H2o1AVbNGEKRr47LqMLWvghGppNYEvFWYW8EQRAEoQ8phHUGOzahZDIEQVwLhDwOhDwOAEBH2IOOcKkr/LXQ3enVkZ0P7HUjCIIgiIWCXEYJgiCImsNa1xzC4vw0qSyE14CCSxAEQTQGZCGsM+Rq+i8RBEFcZSpNgMJxHN65ohkyZNOSFNWmmj2uKoaQEsEQBEEQiwRSCAmCIIi6wCkuTsugAqsQVjN7KUEQBEEsJIv763sNQkMIgiCIq0c1vTLYshNZiXpzgiAIYnFACiFBEARBVAE2qYxE7v8EQRDEIoEUQoIgCIKoMlmp1hIQBEEQhD1IIawzaFKZIAji6rFQXS65jBIEQRCLBVIICYIgCKLKkMsoQRAEsVgghbDOcC3yLHsEQRCLifACFX4nCyFBEASxWKCyE3XG8lY/MpKMzrCn1qIQBEGUTcTnxOWJOYhCfdfh29kfxeh0asH62qtZS5EgCIIg5gMphHWGSxSwqTtcazEIgiAqYkWrHz6XgJaAu9aimOJ1ivBGqv8JXNcZwsXxWfS3+Ku+b4IgCIJYCEghJAiCIKqGKPBYEvXVWoya0RZyoy1U38owQRAEQbBQwBpBEARBEARBEESDQgohQRAEQRAEQRBEg0IKIUEQBEEQBEEQRINCCiFBEARBEARBEESDQgohQRAEQRAEQRBEg0IKIUEQBEEQBEEQRINCCiFBEARBEARBEESDQgohQRAEQRAEQRBEg0IKIUEQBEEQBEEQRINCCiFBEARBEARBEESDQgohQRAEQRAEQRBEg0IKIUEQBEEQBEEQRINCCiFBEARBEARBEESDQgohQRAEQRAEQRBEg0IKIUEQBEEQBEEQRINCCiFBEARBEARBEESDQgohQRAEQRAEQRBEg0IKIUEQBEEQBEEQRIMi1lqAWiDLMgAgHo/XWBKCIAiCIAiCIIirg6L/KPoQ0KAK4dTUFACgu7u7xpIQBEEQBEEQBEFcXaamphAKhQAAnMyqhw2CJEm4fPkyAoEAOI6rtTgF4vE4uru7ceHCBQSDwVqLQ4DuST1C96T+oHtSf9A9qT/ontQfdE/qD7onC48sy5iamkJHRwd4Phc92JAWQp7n0dXVVWsxDAkGg/QS1Bl0T+oPuif1B92T+oPuSf1B96T+oHtSf9A9WVgUy6ACJZUhCIIgCIIgCIJoUEghJAiCIAiCIAiCaFBIIawjXC4XPvvZz8LlctVaFCIP3ZP6g+5J/UH3pP6ge1J/0D2pP+ie1B90T2pDQyaVIQiCIAiCIAiCIMhCSBAEQRAEQRAE0bCQQkgQBEEQBEEQBNGgkEJIEARBEARBEATRoJBCSBAEQRAEQRAE0aCQQlhHPPHEE+jt7YXb7cb27duxd+/eWovUsDz22GPYunUrAoEAWlpa8MEPfhDHjh2rtVgEw1/+5V+C4zjcd999tRalobl06RL+8A//ENFoFB6PB+vXr8drr71Wa7Ealmw2i4cffhh9fX3weDxYtmwZPve5z4Hyx109/ud//gcf+MAH0NHRAY7j8OMf/1i1XpZlPPLII2hvb4fH48Ett9yCEydO1EbYBsHsnqTTaTz00ENYv349fD4fOjo68NGPfhSXL1+uncANgNV7wvInf/In4DgOX/va166afI0GKYR1wg9/+EM88MAD+OxnP4sDBw5g48aNuPXWWzE0NFRr0RqSF154Abt378aePXvw7LPPIp1O473vfS9mZmZqLRoBYN++ffjbv/1bbNiwodaiNDTj4+PYtWsXHA4H/uu//gtHjhzBV77yFTQ1NdVatIbli1/8Ir71rW/hm9/8Jo4ePYovfvGL+NKXvoRvfOMbtRatYZiZmcHGjRvxxBNP6K7/0pe+hK9//et48skn8eqrr8Ln8+HWW29FIpG4ypI2Dmb3ZHZ2FgcOHMDDDz+MAwcO4N///d9x7Ngx/M7v/E4NJG0crN4Thaeffhp79uxBR0fHVZKsQZGJumDbtm3y7t27C39ns1m5o6NDfuyxx2ooFaEwNDQkA5BfeOGFWovS8ExNTcnLly+Xn332Wfmmm26S77333lqL1LA89NBD8o033lhrMQiG2267Tf7Yxz6mWvahD31IvuOOO2okUWMDQH766acLf0uSJLe1tclf/vKXC8smJiZkl8sl/9M//VMNJGw8tPdEj71798oA5HPnzl0doRoco3ty8eJFubOzUz506JC8ZMkS+atf/epVl61RIAthHZBKpbB//37ccssthWU8z+OWW27BK6+8UkPJCIXJyUkAQCQSqbEkxO7du3Hbbbep3heiNvzkJz/Bli1b8Hu/93toaWnBddddh29/+9u1Fquh2blzJ5577jkcP34cAPDGG2/gxRdfxPvf//4aS0YAwJkzZzAwMKDqv0KhELZv307f+zpicnISHMchHA7XWpSGRZIk3HnnnXjwwQexdu3aWotzzSPWWgACGBkZQTabRWtrq2p5a2sr3n777RpJRShIkoT77rsPu3btwrp162otTkPzz//8zzhw4AD27dtXa1EIAKdPn8a3vvUtPPDAA/j0pz+Nffv24ROf+AScTifuuuuuWovXkHzyk59EPB7HqlWrIAgCstksvvCFL+COO+6otWgEgIGBAQDQ/d4r64jakkgk8NBDD+EP/uAPEAwGay1Ow/LFL34RoijiE5/4RK1FaQhIISQIC3bv3o1Dhw7hxRdfrLUoDc2FCxdw77334tlnn4Xb7a61OARykyVbtmzBo48+CgC47rrrcOjQITz55JOkENaIf/mXf8E//uM/4gc/+AHWrl2LgwcP4r777kNHRwfdE4KwIJ1O4yMf+QhkWca3vvWtWovTsOzfvx9//dd/jQMHDoDjuFqL0xCQy2gdEIvFIAgCBgcHVcsHBwfR1tZWI6kIALjnnnvws5/9DM8//zy6urpqLU5Ds3//fgwNDeH666+HKIoQRREvvPACvv71r0MURWSz2VqL2HC0t7djzZo1qmWrV6/G+fPnayQR8eCDD+KTn/wkfv/3fx/r16/HnXfeifvvvx+PPfZYrUUjgMI3nb739YeiDJ47dw7PPvssWQdryG9+8xsMDQ2hp6en8L0/d+4c/vRP/xS9vb21Fu+ahBTCOsDpdGLz5s147rnnCsskScJzzz2HHTt21FCyxkWWZdxzzz14+umn8atf/Qp9fX21Fqnhufnmm/HWW2/h4MGDhX9btmzBHXfcgYMHD0IQhFqL2HDs2rWrpBzL8ePHsWTJkhpJRMzOzoLn1Z92QRAgSVKNJCJY+vr60NbWpvrex+NxvPrqq/S9ryGKMnjixAn88pe/RDQarbVIDc2dd96JN998U/W97+jowIMPPohnnnmm1uJdk5DLaJ3wwAMP4K677sKWLVuwbds2fO1rX8PMzAzuvvvuWovWkOzevRs/+MEP8B//8R8IBAKF2I5QKASPx1Nj6RqTQCBQEsPp8/kQjUYptrNG3H///di5cyceffRRfOQjH8HevXvx1FNP4amnnqq1aA3LBz7wAXzhC19AT08P1q5di9dffx2PP/44Pvaxj9VatIZhenoaJ0+eLPx95swZHDx4EJF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\n", " " ], @@ -375,28 +376,28 @@ "name": "stdout", "output_type": "stream", "text": [ - "Step: 0, Loss on this step: 0.037112978439809007\n", - "Step: 1, Loss on this step: 0.008446597639457603\n", - "Step: 2, Loss on this step: 0.011230654700420253\n", - "Step: 3, Loss on this step: 0.010351276459970582\n", - "Step: 4, Loss on this step: 0.00805469116699875\n", - "Step: 5, Loss on this step: 0.007736147358686476\n", - "Step: 6, Loss on this step: 0.5659830665660537\n", - "Step: 7, Loss on this step: 0.8313167481375603\n", - "Step: 8, Loss on this step: 0.011655851340145167\n", - "Step: 9, Loss on this step: 0.004206803455487899\n", - "Step: 10, Loss on this step: 0.0018982842003712366\n", - "Step: 11, Loss on this step: 0.0015535046629491392\n", - "Step: 12, Loss on this step: 0.001263024993119862\n", - "Step: 13, Loss on this step: 0.0010727452050579983\n", - "Step: 14, Loss on this step: 0.0009311492053747009\n", - "Step: 15, Loss on this step: 0.0008275058544759351\n", - "Step: 16, Loss on this step: 0.000748352946798722\n", - "Step: 17, Loss on this step: 0.000602385717104706\n", - "Step: 18, Loss on this step: 0.0004903515239977218\n", - "Step: 19, Loss on this step: 0.00046783355546733746\n", - "Step: 20, Loss on this step: 0.0004670642991533489\n", - "Step: 21, Loss on this step: 0.0004670611303524343\n" + "Step: 0, Loss on this step: 0.037341593662781856\n", + "Step: 1, Loss on this step: 0.008450562781209037\n", + "Step: 2, Loss on this step: 0.011316614188600499\n", + "Step: 3, Loss on this step: 0.010433715770249097\n", + "Step: 4, Loss on this step: 0.008123443559005811\n", + "Step: 5, Loss on this step: 0.0076708665776726625\n", + "Step: 6, Loss on this step: 0.7705982639065795\n", + "Step: 7, Loss on this step: 1.1135623707166982\n", + "Step: 8, Loss on this step: 0.011455439618007345\n", + "Step: 9, Loss on this step: 0.004275817134790895\n", + "Step: 10, Loss on this step: 0.0018859801576182802\n", + "Step: 11, Loss on this step: 0.0015430926186676386\n", + "Step: 12, Loss on this step: 0.0012603736088423983\n", + "Step: 13, Loss on this step: 0.0010728469540741393\n", + "Step: 14, Loss on this step: 0.0009327820039051789\n", + "Step: 15, Loss on this step: 0.0008297305095412023\n", + "Step: 16, Loss on this step: 0.000750804774247332\n", + "Step: 17, Loss on this step: 0.0006044858293821533\n", + "Step: 18, Loss on this step: 0.000491052058431897\n", + "Step: 19, Loss on this step: 0.0004678541320678155\n", + "Step: 20, Loss on this step: 0.0004670369326232863\n", + "Step: 21, Loss on this step: 0.000467033364346502\n" ] } ], @@ -429,35 +430,35 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 18, "id": "1da6bb44", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 10, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": 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", + "image/png": 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\n", " " ], @@ -496,9 +497,9 @@ "output_type": "stream", "text": [ "\t\tInitial guess 1\tInversion 1\tInitial guess 2\tInversion 2\tTrue values\n", - "a\t\t1.10e-02\t9.86e-03\t9.00e-03\t9.86e-03\t1.00e-02\n", - "b\t\t1.08e-02\t8.87e-03\t4.50e-03\t8.87e-03\t9.00e-03\n", - "Dc\t\t5.00e-05\t1.05e-05\t2.00e-05\t1.05e-05\t1.00e-05\n" + "a\t\t1.10e-02\t9.90e-03\t9.00e-03\t9.90e-03\t1.00e-02\n", + "b\t\t1.08e-02\t8.91e-03\t4.50e-03\t8.91e-03\t9.00e-03\n", + "Dc\t\t5.00e-05\t1.03e-05\t2.00e-05\t1.03e-05\t1.00e-05\n" ] } ], @@ -529,6 +530,29 @@ "## 3. Bayesian inversion" ] }, + { + "cell_type": "markdown", + "id": "00830e3d", + "metadata": {}, + "source": [ + "Bayesian inversion methods acknowledge that our data are affected by noise, and that our models are not perfect. Hence, instead of looking for a single set of parameters that can describe the measurements, we will be calculating the probability that a given set of parameters describes the observations. By randomly sampling from a _prior distribution_ of parameters, we can approximate the _posterior distribution_ of the parameters given the observations.\n", + "\n", + "There are numerous Bayesian sampling methods out there, each with their own performance characteristics in terms of computational cost, stability, and accuracy. A particularly suitable method for friction curve inversion is a particle-based method called _Stein Variational Inference_ (SVI). The basic outline of this method, is that each particle represents a set of model parameters, and that the collection of particles can move around in this parameter space. The particles are attracted towards the peak of the maximum-likelihood (the parameters that we inverted above), but they are also attracted towards the peak of the prior distribution, and they mutually repel one another based on their inter-particle distance. By iteratively updating the particle positions using gradient descent, the collection of particles eventually settles down into an equilibrium state. This equilibrium state is the posterior probability distribution over the inverted parameters.\n", + "\n", + "All of the mathematics and numerical operations that come with SVI are swept under a rug that can be called with `solver.bayesian_inversion`. This routine takes the following inputs:\n", + "\n", + "- `t`: the time at which the measurements are sampled\n", + "- `mu`: the friction measurements\n", + "- `noise_std`: an estimate of the standard deviation of the friction noise. Note that a larger value will permit a wider range of parameter values. Empirically, dividing the noise amplitude by 2 gives somewhat more acceptable results.\n", + "- `params`: the maximum-likelihood parameter values (obtained from the inversion above)\n", + "- `friction_constants`, `block_constants`: the constant parameters that will not be inverted for\n", + "- `Nparticles`: the number of particles in the swarm. A higher number gives more robust statistics, at the expense of a higher computational cost (though this doesn't scale proportionally due to significant GPU/CPU transmission overhead etc.). A minimum value around 1000 is recommended.\n", + "- `Nsteps`: the number of gradient descent steps to take. This number should be large enough to achieve a (quasi-)equilibrium. We will explore this in more detail later.\n", + "- `rng`: a random seed or a `jax.random.PRNGKey` (or `None`) to control the random initial state of the particle swarm. If `None`, the initialisation will be different every time you run the inversion, so it is recommended to fix the random seed to ensure reproducibililty.\n", + "\n", + "On a modest GPU, the Bayesian sampling with the parameters below takes around 1-2 minutes. Part of this time is spent doing the JIT compilation, so a second run would likely be a little bit faster." + ] + }, { "cell_type": "code", "execution_count": 12, @@ -536,43 +560,205 @@ "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "multiple values for argument 't'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[12]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m states = \u001b[43msolver\u001b[49m\u001b[43m.\u001b[49m\u001b[43mbayesian_inversion\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 2\u001b[39m \u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m=\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmu\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmu_measured\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnoise_std\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnoise_amplitude\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[32m 3\u001b[39m \u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m=\u001b[49m\u001b[43mparams_inv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfriction_constants\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconstants\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[32m 4\u001b[39m \u001b[43m \u001b[49m\u001b[43mblock_constants\u001b[49m\u001b[43m=\u001b[49m\u001b[43mblock_constants\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[32m 5\u001b[39m \u001b[43m \u001b[49m\u001b[43mNparticles\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mNsteps\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m42\u001b[39;49m\n\u001b[32m 6\u001b[39m \u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/diabayes/solver.py:272\u001b[39m, in \u001b[36mODESolver.bayesian_inversion\u001b[39m\u001b[34m(self, t, mu, noise_std, params, friction_constants, block_constants, Nparticles, Nsteps, key)\u001b[39m\n\u001b[32m 269\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m (params, state), (loss.mean(), nan_count, params)\n\u001b[32m 271\u001b[39m carry = (log_particles, opt_state)\n\u001b[32m--> \u001b[39m\u001b[32m272\u001b[39m _, (loss, nan_count, states) = \u001b[43mlax\u001b[49m\u001b[43m.\u001b[49m\u001b[43mscan\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbody_fun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcarry\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43marange\u001b[49m\u001b[43m(\u001b[49m\u001b[43mNsteps\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 274\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m states\n", - " \u001b[31m[... skipping hidden 10 frame]\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax_tqdm/scan_pbar.py:65\u001b[39m, in \u001b[36mscan_tqdm.._scan_tqdm..wrapper_progress_bar\u001b[39m\u001b[34m(carry, x)\u001b[39m\n\u001b[32m 63\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 64\u001b[39m carry, x = update_progress_bar((carry, x), iter_num, \u001b[32m0\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m65\u001b[39m result = \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcarry\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 66\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m close_tqdm(result, iter_num, \u001b[32m0\u001b[39m)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/diabayes/solver.py:252\u001b[39m, in \u001b[36mODESolver.bayesian_inversion..body_fun\u001b[39m\u001b[34m(carry, i)\u001b[39m\n\u001b[32m 249\u001b[39m \u001b[38;5;129m@scan_tqdm\u001b[39m(Nsteps)\n\u001b[32m 250\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mbody_fun\u001b[39m(carry, i):\n\u001b[32m 251\u001b[39m params, state = carry\n\u001b[32m--> \u001b[39m\u001b[32m252\u001b[39m loss, gradp = \u001b[43mmapped_log_likelihood\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 253\u001b[39m \u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmu\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnoise_std\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfriction_constants\u001b[49m\u001b[43m.\u001b[49m\u001b[43mv0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mforward_fn\u001b[49m\n\u001b[32m 254\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 255\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 256\u001b[39m \u001b[33;03m Sometimes the adjoint back-propagation becomes unstable, \u001b[39;00m\n\u001b[32m 257\u001b[39m \u001b[33;03m producing NaNs in the gradients. By setting the NaN-gradients\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 261\u001b[39m \u001b[33;03m to be attracted by the maximum likelihood\u001b[39;00m\n\u001b[32m 262\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m 263\u001b[39m nan_count = jnp.isnan(gradp).sum() / gradp.shape[\u001b[32m1\u001b[39m]\n", - " \u001b[31m[... skipping hidden 19 frame]\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/diabayes/SVI.py:88\u001b[39m, in \u001b[36m_log_likelihood\u001b[39m\u001b[34m(log_params, mu_obs, noise_std, v0, forward_fn)\u001b[39m\n\u001b[32m 86\u001b[39m y0 = jnp.array([mu_obs[\u001b[32m0\u001b[39m], params[\u001b[32m2\u001b[39m] / v0])\n\u001b[32m 87\u001b[39m \u001b[38;5;66;03m# Forward pass to get friction curve\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m88\u001b[39m mu_hat = \u001b[43mforward_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43my0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m.mu\n\u001b[32m 89\u001b[39m \u001b[38;5;66;03m# Log-likelihood of residuals\u001b[39;00m\n\u001b[32m 90\u001b[39m p = -(jnp.square(mu_obs - mu_hat).mean() / (\u001b[32m2\u001b[39m * noise_std**\u001b[32m2\u001b[39m))\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/equinox/_module/_prebuilt.py:81\u001b[39m, in \u001b[36mPartial.__call__\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 68\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, *args: Any, **kwargs: Any) -> _Return:\n\u001b[32m 69\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Call the wrapped `self.func`.\u001b[39;00m\n\u001b[32m 70\u001b[39m \n\u001b[32m 71\u001b[39m \u001b[33;03m **Arguments:**\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 79\u001b[39m \u001b[33;03m The result of the wrapped function.\u001b[39;00m\n\u001b[32m 80\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m81\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mkeywords\u001b[49m\u001b[43m)\u001b[49m\n", - " \u001b[31m[... skipping hidden 4 frame]\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/inspect.py:3195\u001b[39m, in \u001b[36mSignature.bind\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 3190\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mbind\u001b[39m(\u001b[38;5;28mself\u001b[39m, /, *args, **kwargs):\n\u001b[32m 3191\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Get a BoundArguments object, that maps the passed `args`\u001b[39;00m\n\u001b[32m 3192\u001b[39m \u001b[33;03m and `kwargs` to the function's signature. Raises `TypeError`\u001b[39;00m\n\u001b[32m 3193\u001b[39m \u001b[33;03m if the passed arguments can not be bound.\u001b[39;00m\n\u001b[32m 3194\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m3195\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_bind\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/inspect.py:3134\u001b[39m, in \u001b[36mSignature._bind\u001b[39m\u001b[34m(self, args, kwargs, partial)\u001b[39m\n\u001b[32m 3131\u001b[39m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[32m 3133\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m param.name \u001b[38;5;129;01min\u001b[39;00m kwargs \u001b[38;5;129;01mand\u001b[39;00m param.kind != _POSITIONAL_ONLY:\n\u001b[32m-> \u001b[39m\u001b[32m3134\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[32m 3135\u001b[39m \u001b[33m'\u001b[39m\u001b[33mmultiple values for argument \u001b[39m\u001b[38;5;132;01m{arg!r}\u001b[39;00m\u001b[33m'\u001b[39m.format(\n\u001b[32m 3136\u001b[39m arg=param.name)) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 3138\u001b[39m arguments[param.name] = arg_val\n\u001b[32m 3140\u001b[39m \u001b[38;5;66;03m# Now, we iterate through the remaining parameters to process\u001b[39;00m\n\u001b[32m 3141\u001b[39m \u001b[38;5;66;03m# keyword arguments\u001b[39;00m\n", - "\u001b[31mTypeError\u001b[39m: multiple values for argument 't'" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a97b9b15dbbb4185b0fb058d810d23c3", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Running for 100 iterations: 0%| | 0/100 [00:00\n", + "
\n", + " Figure\n", + "
\n", + " \n", + " \n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bayesian_result.plot_convergence();" + ] + }, + { + "cell_type": "markdown", + "id": "b0bcae9f", + "metadata": {}, + "source": [ + "This seems to be ok. Next, we can visualise the parameter posterior distributions in a _corner plot_ representation. In this representation, each parameter is plotted against the other parameters, which gives you a sense of the trade-offs between them. The error bars in each panel indicate the median and the 5/95th percentile range for each parameter, although these summary statistics may not necessarily be representative for all parameter combinations. The diagonal of the corner plot is populated with histrograms of a given parameter." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "0f47a596", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ddb158ecd07a4aafb27cf70f14ad640c", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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If the predictions don't follow the observations well enough, then perhaps `noise_amplitude` was over-estimated." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "948b8061", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Friction [-]')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5e564430e74e4cb982bb6c8fe93e991c", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "samples, sample_results = bayesian_result.sample(solver, y0, t, constants, block_constants, nsamples=100)\n", + "\n", + "plt.close(\"all\")\n", + "fig, ax = plt.subplots(figsize=(9, 5), constrained_layout=True)\n", + "ax.plot(t, sample_results.mu.T, c=\"k\", alpha=0.2)\n", + "ax.scatter(t, mu_measured, c=\"C1\", s=10, zorder=len(samples) + 1, ec=\"k\", lw=1, alpha=0.7)\n", + "ax.plot([], [], c=\"k\", label=\"Prediction\")\n", + "ax.scatter([], [], c=\"C1\", ec=\"k\", lw=1, label=\"Observation\")\n", + "ax.legend()\n", + "ax.set_xlabel(\"Time [s]\")\n", + "ax.set_ylabel(\"Friction [-]\")" + ] } ], "metadata": { From 4fa001447bf2a4507b36342f24ce63d7e4d55d1a Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Fri, 1 Aug 2025 09:56:33 +0200 Subject: [PATCH 28/56] Clean-up of unused imports --- diabayes/SVI.py | 7 ++----- diabayes/solver.py | 2 +- test/test_solvers.py | 2 -- test/test_typedefs.py | 3 +-- 4 files changed, 4 insertions(+), 10 deletions(-) diff --git a/diabayes/SVI.py b/diabayes/SVI.py index 02510c5..6f28d17 100644 --- a/diabayes/SVI.py +++ b/diabayes/SVI.py @@ -1,14 +1,11 @@ -from typing import Callable, Tuple, TypeAlias +from typing import Callable, TypeAlias import equinox as eqx import jax import jax.numpy as jnp -import optax -from jax import lax -from jax_tqdm import scan_tqdm # type:ignore from jaxtyping import Array, Float -from diabayes.typedefs import Variables, _Params +from diabayes.typedefs import Variables ParticleArray = Float[Array, "N M"] GradientArray: TypeAlias = ParticleArray diff --git a/diabayes/solver.py b/diabayes/solver.py index 6309444..3dcb520 100644 --- a/diabayes/solver.py +++ b/diabayes/solver.py @@ -1,6 +1,6 @@ from functools import partial from time import time_ns -from typing import Any, Callable, Tuple, Union +from typing import Any, Tuple, Union import diffrax as dfx import equinox as eqx diff --git a/test/test_solvers.py b/test/test_solvers.py index e68390c..e27bd69 100644 --- a/test/test_solvers.py +++ b/test/test_solvers.py @@ -32,8 +32,6 @@ def test_forward_solver(self): assert result_jax is not None assert result_jax.ys is not None - diff = result.to_array() - result_jax.ys.to_array() - assert jnp.allclose(result.to_array(), result_jax.ys.to_array()) # Steady-state tests diff --git a/test/test_typedefs.py b/test/test_typedefs.py index 67a1f45..35e748a 100644 --- a/test/test_typedefs.py +++ b/test/test_typedefs.py @@ -1,7 +1,6 @@ import jax.numpy as jnp -import diabayes as db -from diabayes.typedefs import Chains, RSFParams, RSFParticles, RSFStatistics, Variables +from diabayes.typedefs import RSFParams, RSFParticles, Variables class TestTypedefs: From b77693d8f7e63793826190b68e63b416be0c8040 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Fri, 1 Aug 2025 15:35:33 +0200 Subject: [PATCH 29/56] Added extended documentation structure --- .github/workflows/build-docs.yml | 10 +---- docs/source/community.md | 3 ++ docs/source/conf.py | 9 +++- docs/source/diabayes.rst | 45 ------------------- docs/source/getting-started.md | 13 ++++++ docs/source/index.md | 44 ++++++++++++++++++ docs/source/index.rst | 19 -------- docs/source/modules.rst | 7 --- docs/source/user-guide/adding_features.md | 1 + docs/source/user-guide/advanced_usage.md | 1 + .../forward/chen-niemeijer-spiers.md | 1 + docs/source/user-guide/forward/index.md | 10 +++++ .../user-guide/forward/rate-and-state.md | 1 + .../source/user-guide/forward/spring-block.md | 1 + docs/source/user-guide/index.md | 11 +++++ docs/source/user-guide/inversion/bayesian.md | 1 + docs/source/user-guide/inversion/index.md | 9 ++++ .../user-guide/inversion/max-likelihood.md | 1 + pyproject.toml | 3 +- 19 files changed, 108 insertions(+), 82 deletions(-) create mode 100644 docs/source/community.md delete mode 100644 docs/source/diabayes.rst create mode 100644 docs/source/getting-started.md create mode 100644 docs/source/index.md delete mode 100644 docs/source/index.rst delete mode 100644 docs/source/modules.rst create mode 100644 docs/source/user-guide/adding_features.md create mode 100644 docs/source/user-guide/advanced_usage.md create mode 100644 docs/source/user-guide/forward/chen-niemeijer-spiers.md create mode 100644 docs/source/user-guide/forward/index.md create mode 100644 docs/source/user-guide/forward/rate-and-state.md create mode 100644 docs/source/user-guide/forward/spring-block.md create mode 100644 docs/source/user-guide/index.md create mode 100644 docs/source/user-guide/inversion/bayesian.md create mode 100644 docs/source/user-guide/inversion/index.md create mode 100644 docs/source/user-guide/inversion/max-likelihood.md diff --git a/.github/workflows/build-docs.yml b/.github/workflows/build-docs.yml index 9b219a9..2864776 100644 --- a/.github/workflows/build-docs.yml +++ b/.github/workflows/build-docs.yml @@ -17,14 +17,6 @@ jobs: python-version: "3.10" - uses: ./.github/actions/setup - # - name: Install dependencies - # run: | - # pip install make sphinx sphinx-design sphinx-copybutton pydata-sphinx-theme - - - name: Remove all .rst files except index.rst - run: | - find docs/source -type f -name "*.rst" ! -name "index.rst" -exec rm -f {} \; - - name: Generate .rst files from package run: | sphinx-apidoc -o docs/source/api diabayes @@ -32,7 +24,7 @@ jobs: - name: Build Sphinx HTML documentation run: | cd docs - make html + make clean && make html - name: Upload documentation artifact uses: actions/upload-artifact@v4 diff --git a/docs/source/community.md b/docs/source/community.md new file mode 100644 index 0000000..8b39d5c --- /dev/null +++ b/docs/source/community.md @@ -0,0 +1,3 @@ +# DiaBayes Community + +## How to cite? \ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py index 08d9dc1..af5777a 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -14,9 +14,10 @@ # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration extensions = [ + "myst_parser", "sphinx_design", "sphinx_copybutton", - "sphinx.ext.autodoc", + "sphinx.ext.autosummary", "sphinx.ext.napoleon", "sphinx.ext.doctest", "sphinx.ext.intersphinx", @@ -26,6 +27,7 @@ pygments_style = "sphinx" napoleon_numpy_docstring = True +html_show_sourcelink = False templates_path = ["_templates"] exclude_patterns = [] @@ -43,3 +45,8 @@ "numpy": ("https://numpy.org/doc/stable", None), "xarray": ("https://docs.xarray.dev/en/stable", None), } + +html_sidebars = { + "getting-started": [], # Prevent an empty navigation bar from appearing + "community": [], +} diff --git a/docs/source/diabayes.rst b/docs/source/diabayes.rst deleted file mode 100644 index 3ebc2b3..0000000 --- a/docs/source/diabayes.rst +++ /dev/null @@ -1,45 +0,0 @@ -diabayes package -================ - -Submodules ----------- - -diabayes.SVI module -------------------- - -.. automodule:: diabayes.SVI - :members: - :show-inheritance: - :undoc-members: - -diabayes.forward\_models module -------------------------------- - -.. automodule:: diabayes.forward_models - :members: - :show-inheritance: - :undoc-members: - -diabayes.solver module ----------------------- - -.. automodule:: diabayes.solver - :members: - :show-inheritance: - :undoc-members: - -diabayes.typedefs module ------------------------- - -.. automodule:: diabayes.typedefs - :members: - :show-inheritance: - :undoc-members: - -Module contents ---------------- - -.. automodule:: diabayes - :members: - :show-inheritance: - :undoc-members: diff --git a/docs/source/getting-started.md b/docs/source/getting-started.md new file mode 100644 index 0000000..baf68a5 --- /dev/null +++ b/docs/source/getting-started.md @@ -0,0 +1,13 @@ +# Getting started + +## Installation guide + +## Variables, parameters, constants + +```python +x = 0 +``` + +## Forward models + +## Inversion \ No newline at end of file diff --git a/docs/source/index.md b/docs/source/index.md new file mode 100644 index 0000000..947c498 --- /dev/null +++ b/docs/source/index.md @@ -0,0 +1,44 @@ +# DiaBayes documentation + +DiaBayes (pronounced like the mafic rock type *diabase*) is a collection of Python tools for forward and inverse modelling of rock friction experiments. It is designed to be modular in terms of the physical components that define a forward problem, so that the user can mix and match different types of physics, e.g. classical rate-and-state friction combined with a more exotic state evolution law, or combining the *Chen-Niemeijer-Spiers* model with an inertial spring-block. These models can be solved in a forward sense (generating a friction time series), or in the inverse sense (estimating the parameters that generated your measured friction). To greatly accelerate the maximum-likelihood and Bayesian inversion, DiaBayes is optimised for GPU execution using JAX and several high-performance libraries (Diffrax, Optax, and Optimistix, to name a few). + +See the getting started section for a quick overview of the installation options and basic usage. A self-contained example Jupyter Notebook is included in the GitHub repo (`examples/simple_inversion.ipynb`). Keep reading for more detailed background on the modelling procedures and implementation. + +````{grid} 1 2 2 2 +:gutter: 4 +:padding: 2 2 0 0 + +```{grid-item-card} Getting Started +:link: getting-started +:link-type: doc +The _Getting Started_ guide covers the installation options and a few practical example usage cases. Start here if you're new to DiaBayes. +``` + +```{grid-item-card} User Guide +:link: user-guide/index +:link-type: doc +The _User Guide_ explains a number of core concepts in depth, with detailed descriptions of commonly-used functionalities. +``` + +```{grid-item-card} API Reference +:link: api/index +:link-type: doc +The _API Reference Guide_ is auto-generated based on the docstrings of all the DiaBayes classes and routines. +``` + +```{grid-item-card} DiaBayes Community +:link: community +:link-type: doc +Found a bug? Want to contribute? How to cite? Go here to find out more. +``` +```` + +```{toctree} +:maxdepth: 1 +:hidden: + +getting-started +user-guide/index +api/index +community +``` diff --git a/docs/source/index.rst b/docs/source/index.rst deleted file mode 100644 index 5d8dd39..0000000 --- a/docs/source/index.rst +++ /dev/null @@ -1,19 +0,0 @@ -.. DiaBayes documentation master file, created by - sphinx-quickstart on Wed Jul 23 16:21:03 2025. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -DiaBayes documentation -====================== - -Add your content using ``reStructuredText`` syntax. See the -`reStructuredText `_ -documentation for details. - - -.. toctree:: - :maxdepth: 2 - :caption: API reference - - api/modules - diff --git a/docs/source/modules.rst b/docs/source/modules.rst deleted file mode 100644 index 0cf652b..0000000 --- a/docs/source/modules.rst +++ /dev/null @@ -1,7 +0,0 @@ -diabayes -======== - -.. toctree:: - :maxdepth: 4 - - diabayes diff --git a/docs/source/user-guide/adding_features.md b/docs/source/user-guide/adding_features.md new file mode 100644 index 0000000..cb4217e --- /dev/null +++ b/docs/source/user-guide/adding_features.md @@ -0,0 +1 @@ +# Adding custom physics \ No newline at end of file diff --git a/docs/source/user-guide/advanced_usage.md b/docs/source/user-guide/advanced_usage.md new file mode 100644 index 0000000..7802a20 --- /dev/null +++ b/docs/source/user-guide/advanced_usage.md @@ -0,0 +1 @@ +# Advanced use cases \ No newline at end of file diff --git a/docs/source/user-guide/forward/chen-niemeijer-spiers.md b/docs/source/user-guide/forward/chen-niemeijer-spiers.md new file mode 100644 index 0000000..6d1c9bf --- /dev/null +++ b/docs/source/user-guide/forward/chen-niemeijer-spiers.md @@ -0,0 +1 @@ +# Chen-Niemeijer-Spiers \ No newline at end of file diff --git a/docs/source/user-guide/forward/index.md b/docs/source/user-guide/forward/index.md new file mode 100644 index 0000000..6e34323 --- /dev/null +++ b/docs/source/user-guide/forward/index.md @@ -0,0 +1,10 @@ +# Forward modelling + +```{toctree} +:hidden: +:maxdepth: 1 + +rate-and-state +chen-niemeijer-spiers +spring-block +``` \ No newline at end of file diff --git a/docs/source/user-guide/forward/rate-and-state.md b/docs/source/user-guide/forward/rate-and-state.md new file mode 100644 index 0000000..d34e04d --- /dev/null +++ b/docs/source/user-guide/forward/rate-and-state.md @@ -0,0 +1 @@ +# Rate-and-state friction \ No newline at end of file diff --git a/docs/source/user-guide/forward/spring-block.md b/docs/source/user-guide/forward/spring-block.md new file mode 100644 index 0000000..2fcd15e --- /dev/null +++ b/docs/source/user-guide/forward/spring-block.md @@ -0,0 +1 @@ +# Spring-blocks \ No newline at end of file diff --git a/docs/source/user-guide/index.md b/docs/source/user-guide/index.md new file mode 100644 index 0000000..5c5f65f --- /dev/null +++ b/docs/source/user-guide/index.md @@ -0,0 +1,11 @@ +# User guide + +```{toctree} +:hidden: +:maxdepth: 1 + +forward/index +inversion/index +advanced_usage +adding_features +``` \ No newline at end of file diff --git a/docs/source/user-guide/inversion/bayesian.md b/docs/source/user-guide/inversion/bayesian.md new file mode 100644 index 0000000..6a129e5 --- /dev/null +++ b/docs/source/user-guide/inversion/bayesian.md @@ -0,0 +1 @@ +# Bayesian inversion \ No newline at end of file diff --git a/docs/source/user-guide/inversion/index.md b/docs/source/user-guide/inversion/index.md new file mode 100644 index 0000000..52071e9 --- /dev/null +++ b/docs/source/user-guide/inversion/index.md @@ -0,0 +1,9 @@ +# Inverse modelling + +```{toctree} +:hidden: +:maxdepth: 1 + +max-likelihood +bayesian +``` \ No newline at end of file diff --git a/docs/source/user-guide/inversion/max-likelihood.md b/docs/source/user-guide/inversion/max-likelihood.md new file mode 100644 index 0000000..540d078 --- /dev/null +++ b/docs/source/user-guide/inversion/max-likelihood.md @@ -0,0 +1 @@ +# Maximum-likelihood inversion \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 3c9ec87..34c6486 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,10 +25,11 @@ dev = ["black", "isort", "twine"] gpu = ["jax[cuda12]"] docs = [ "make", + "myst-nb", "pydata-sphinx-theme", "sphinx", "sphinx-copybutton", - "sphinx-design" + "sphinx-design", ] [build-system] From a9935d4211adbebfc6685e9ad52f4c7c9662729c Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Fri, 1 Aug 2025 17:11:55 +0200 Subject: [PATCH 30/56] Populated the getting started page --- docs/source/community.md | 8 +- docs/source/conf.py | 7 +- docs/source/getting-started.md | 129 ++++++++++++++++++++++++++++++++- 3 files changed, 138 insertions(+), 6 deletions(-) diff --git a/docs/source/community.md b/docs/source/community.md index 8b39d5c..c2a2bcc 100644 --- a/docs/source/community.md +++ b/docs/source/community.md @@ -1,3 +1,9 @@ # DiaBayes Community -## How to cite? \ No newline at end of file +## Frequently Asked Questions + +## How to cite? + +A publication describing this software package is underway. Until then, feel free to refer to the GitHub repository: https://github.com/martijnende/diabayes + +## Useful resources diff --git a/docs/source/conf.py b/docs/source/conf.py index af5777a..c9ba4f9 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -17,7 +17,7 @@ "myst_parser", "sphinx_design", "sphinx_copybutton", - "sphinx.ext.autosummary", + "sphinx.ext.autodoc", "sphinx.ext.napoleon", "sphinx.ext.doctest", "sphinx.ext.intersphinx", @@ -39,6 +39,11 @@ html_theme = "pydata_sphinx_theme" html_static_path = ["_static"] +myst_enable_extensions = [ + "dollarmath", + "amsmath", +] + # Configuration for intersphinx. intersphinx_mapping = { "python": ("https://docs.python.org/3/", None), diff --git a/docs/source/getting-started.md b/docs/source/getting-started.md index baf68a5..00c2b30 100644 --- a/docs/source/getting-started.md +++ b/docs/source/getting-started.md @@ -2,12 +2,133 @@ ## Installation guide -## Variables, parameters, constants +It is recommended to create a dedicated Python environment using either `conda` or `venv`. DiaBayes will install numerous JAX-related packages that might conflict with your existing Python environments, especially when GPU support is enabled (which installs CUDA binaries). To create a new environment using `conda`, use: +```bash +conda create -n diabayes python=3.11 pip +conda activate diabayes +``` +You can then install the latest version using `pip` directly from GitHub: +```bash +pip install "git+https://github.com/martijnende/diabayes.git#egg=diabayes" +``` +To enable Nvidia GPU support (CUDA version 12): +```bash +pip install "git+https://github.com/martijnende/diabayes.git#egg=diabayes[gpu]" +``` +If you plan to make direct changes to the code base, you can clone the repository and install from the local repository: +```bash +git clone https://github.com/martijnende/diabayes.git +cd diabayes +pip install -e .[gpu] +``` +Note that you'll need to repeat `pip install` every time you make changes. If you also plan to push these changes to the original DiaBayes project, please include the development and documentation tools: +```bash +pip install .[gpu,docs,dev] +``` +The `dev` packages include [`black`](https://github.com/psf/black) and [`isort`](https://github.com/PyCQA/isort), which (re-)structure your code to a consistent format whenever you execute these in the repository root directory. + +## Basic usage + +### Variables, parameters, constants + +DiaBayes can accommodate several physical models that each come with their own set of parameters. To assist in the bookkeeping of the variables, parameters, and constants, DiaBayes uses special containers that allow access to their contents by name. For example, variables are declared as: +```python +import diabayes as db +y = db.Variables(mu=0.6, state=10.5) +``` +The container items can then be accessed through `y.mu` and `y.state`. If you'we used Python's `namedtuple` collection before then this should feel familiar. Invertible parameters and constants are declared in a similar way: +```python +# RSF parameters +params = db.RSFParams(a=a, b=b, Dc=Dc) +# RSF constants +constants = db.RSFConstants(v0=v0, mu0=mu0) +# Spring-block constants +block_constants = db.SpringBlockConstants(k=k, v_lp=v1) +``` + +### Forward models + +The physics that underlies your experiment is encoded by a _forward model_. A DiaBayes `Forward` model is composed of three components: + +1. A friction law, which is an equation that takes the friction and "state" as an input, and returns the instanteneous slip rate as an output, i.e. $v(t) = f(\mu(t), \theta(t))$. +2. A state evolution law, which returns the time derivative of the "state" variable. +3. A stress transfer model, which describes how the local slip rate results in a change in shear stress on the fault interface. +These three components can be mixed and matched according to the user's needs. For example, one could compile a forward model with regularised rate-and-state friction, the slip law for state evolution, and a non-inertial spring-block analogue for the stress transfer. Or one could pick the _Chen-Niemeijer-Spiers_ friction model with its corresponding state (porosity) evolution law coupled with an inertial spring-block to simulate stick-slip motion. See the [User Guide on forward modelling](user-guide/forward/index) for more information about the various forward model components that have been implemented. + +Here is an example of a classical rate-and-state friction model combined with the ageing law and a non-inertial spring-block: ```python -x = 0 +from diabayes.forward_models import Forward, rsf, ageing_law, springblock +forward = Forward(friction_model=rsf, state_evolution=ageing_law, block_type=springblock) ``` -## Forward models +This forward model can then be handed over to a solver that takes care of the forward (and inverse) modelling: +```python +from diabayes.solver import ODESolver +solver = ODESolver(forward_model=forward) +``` + +The `ODESolver` class contains a number of public and private methods that take care of unpacking the various containers (`Variables`, `RSFConstants`, etc.) and casting the forward models in a common format to make them compatible for an ODE solver. Getting a forward solution from DiaBayes is a single function call: +```python +result = solver.solve_forward( + t=t, y0=y0, params=params, + friction_constants=constants, + block_constants=block_constants +) +``` +This `result` is a `Variables` container that contains the time series of the relevant variables (e.g. friction and state for rate-and-state friction) that can be accessed e.g. through `result.mu`, `result.state`, etc. + +### Inversion + +DiaBayes currently has two flavours if inversion implemented. The _maximum-likelihood_ inversion uses the Levenberg-Marquardt method with adjoint back-propagation through the ODE to search for a single set of physical parameters that minimise the misfit between the predicted friction curve and the measured one. The second flavour is a form of _Bayesian inversion_, which estimates the probability that a given set of parameters describes the observed friction within the measurement uncertainties. See the [User Guide on inverse modelling](user-guide/inversion/index) for an in-depth discussion of these two inversion approaches. + +Performing a maximum-likelihood inversion involves a single function call that has a similar signature as the forward solver: +```python +mu_measured = ... +initial_guess_params = ... +inv_result = solver.max_likelihood_inversion( + t=t, mu=mu_measured, + params=initial_guess_params, + friction_constants=constants, + block_constants=block_constants +) +``` + +The return value `inv_result` is an [`optimistix.Solution`](https://docs.kidger.site/optimistix/api/solution/#optimistix.Solution) object that contains the maximum-likelihood solution of the parameters, as well as diagnostic information. This maximum-likelihood estimate can then be used for subsequent Bayesian inversion to estimate the uncertainties and trade-offs in the parameters: +```python +params_inv = inv_result.value +noise_amplitude = 1e-3 # Estimate of the measurement uncertainty +bayesian_result = solver.bayesian_inversion( + t=t, mu=mu_measured, noise_std=noise_amplitude, + params=params_inv, friction_constants=constants, + block_constants=block_constants, + Nparticles=1500, Nsteps=100, rng=42 +) +``` +In this example, the posterior distribution is estimated by means of a particle swarm consisting of 1500 particles, which converge to an equilibrium over 100 steps. + +The return value of `bayesian_inversion` is a `BayesianSolution` object, which not only contains the posterior samples, but also several diagnostic and visualisation routines. To evaluate whether the inversion converged to an equilibrium state, use: +```python +bayesian_result.plot_convergence(); +``` +To visualise the posterior distributions over the inverted parameters, use: +```python +bayesian_result.cornerplot(); +``` +To draw samples from the posterior distribution (`samples`), and their corresponding friction time series (`sample_results`), use: +```python +samples, sample_results = bayesian_result.sample( + solver, y0, t, + constants, + block_constants, + nsamples=100 +) +``` + +## Examples + +See `examples/simple_example.ipynb` for a basic tutorial on how to use DiaBayes in practice. + +## Getting help -## Inversion \ No newline at end of file +If it is unclear on how to use the DiaBayes API, if you encountered a bug, or if you want to contribute a new feature, head over to the [DiaBayes Community page](community). There you will find useful links and resources, frequently encountered issues, and suggestions. \ No newline at end of file From 6391c20ed4b6a4b8c2d201a8090a0671a07a4907 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Fri, 1 Aug 2025 18:26:37 +0200 Subject: [PATCH 31/56] Included infrastructure for references --- docs/source/conf.py | 20 +++++++++++--------- docs/source/references.bib | 6 ++++++ docs/source/user-guide/forward/index.md | 21 +++++++++++++++++++++ docs/source/user-guide/index.md | 6 +++++- pyproject.toml | 3 +++ 5 files changed, 46 insertions(+), 10 deletions(-) create mode 100644 docs/source/references.bib diff --git a/docs/source/conf.py b/docs/source/conf.py index c9ba4f9..8f4c1e9 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -17,17 +17,25 @@ "myst_parser", "sphinx_design", "sphinx_copybutton", + "sphinxcontrib.bibtex", "sphinx.ext.autodoc", "sphinx.ext.napoleon", "sphinx.ext.doctest", - "sphinx.ext.intersphinx", "sphinx.ext.viewcode", "sphinx.ext.mathjax", + "sphinx_togglebutton", + "hoverxref.extension", ] pygments_style = "sphinx" napoleon_numpy_docstring = True -html_show_sourcelink = False + +bibtex_default_style = "unsrt" # or plain, alpha, unsrt, abbrv +bibtex_bibfiles = ["references.bib"] +hoverxref_role_types = { + "cite": "tooltip", # Show tooltip for :cite: role + "numref": "tooltip", # Show tooltip for :numref: role +} templates_path = ["_templates"] exclude_patterns = [] @@ -38,19 +46,13 @@ html_theme = "pydata_sphinx_theme" html_static_path = ["_static"] +html_show_sourcelink = False myst_enable_extensions = [ "dollarmath", "amsmath", ] -# Configuration for intersphinx. -intersphinx_mapping = { - "python": ("https://docs.python.org/3/", None), - "numpy": ("https://numpy.org/doc/stable", None), - "xarray": ("https://docs.xarray.dev/en/stable", None), -} - html_sidebars = { "getting-started": [], # Prevent an empty navigation bar from appearing "community": [], diff --git a/docs/source/references.bib b/docs/source/references.bib new file mode 100644 index 0000000..0434d34 --- /dev/null +++ b/docs/source/references.bib @@ -0,0 +1,6 @@ +@phdthesis{kidger2021, + title={{O}n {N}eural {D}ifferential {E}quations}, + author={Patrick Kidger}, + year={2021}, + school={University of Oxford}, +} diff --git a/docs/source/user-guide/forward/index.md b/docs/source/user-guide/forward/index.md index 6e34323..9aa25a8 100644 --- a/docs/source/user-guide/forward/index.md +++ b/docs/source/user-guide/forward/index.md @@ -1,5 +1,23 @@ # Forward modelling +A fundamental aspect of any quantitative science discipline is to make observations. These observations are generated by some physical (or chemical, biological, ...) process. A _forward model_ is a mathematical description of this process, which is often expressed as a _differential equation_; while the observations that we make can look extremely complex (e.g. planetary objects orbiting a star), the dynamics underlying this behaviour are often much simpler to describe (Newton's laws of motion). We can then generate synthetic observations by solving the dynamics forward in time, given some set of parameters and initial conditions. + +Quantitatively, the dynamics of a system with multiple variables or dimensions can be written as: + +```{math} +:label: ODE +\frac{\mathrm{d} \vec{X}}{\mathrm{d} t} = f\left( t, \vec{X}, \dots \right) +``` + +The synthetic observations are then generated by integrating Eq. {eq}`ODE` over time $t \in \left[ t_0, t \right)$: +```{math} +:label: ODE_solution +\vec{X}(t) = \vec{X}(t=t_0) + \int_{t_0}^{t} f\left(t', \vec{X}, \dots \right) \mathrm{d} t' +``` + +There exists a plethora of strategies to perform the integration as indicated above in a stable and accurate manner: Runge-Kutta, Rosenbrock, LSODA, ... DiaBayes uses the [Diffrax](https://docs.kidger.site/diffrax/) library {cite}`kidger2021`, which implements several of these solvers in JAX. A good general-purpose solver is the [`Tsit5`](https://docs.kidger.site/diffrax/api/solvers/ode_solvers/#diffrax.Tsit5) algorithm, which is the default for DiaBayes. + + ```{toctree} :hidden: :maxdepth: 1 @@ -7,4 +25,7 @@ rate-and-state chen-niemeijer-spiers spring-block +``` + +```{bibliography} ``` \ No newline at end of file diff --git a/docs/source/user-guide/index.md b/docs/source/user-guide/index.md index 5c5f65f..f693bfd 100644 --- a/docs/source/user-guide/index.md +++ b/docs/source/user-guide/index.md @@ -1,4 +1,8 @@ -# User guide +# User Guide + +This guide aims to provide more theoretical background on the various methods that are used, as well as their implementation in DiaBayes. If you are considering to develop new features, or if you simply wish to understand how things work, this is a good place to start. + +The guide is not structured in a particular order, and so it is not necessarily meant to be read from top to bottom. The individual chapters will contain occasional cross-references to other chapters if prior knowledge is assumed. ```{toctree} :hidden: diff --git a/pyproject.toml b/pyproject.toml index 34c6486..e1a49f8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,7 +29,10 @@ docs = [ "pydata-sphinx-theme", "sphinx", "sphinx-copybutton", + "sphinx-togglebutton", + "sphinx-hoverxref", "sphinx-design", + "sphinxcontrib-bibtex", ] [build-system] From 6ea77fda181dfcab4dcb6ee30528d2cbe05d293e Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Tue, 5 Aug 2025 18:09:12 +0200 Subject: [PATCH 32/56] Added forward models documentation page --- docs/source/references.bib | 12 ++++++ docs/source/user-guide/forward/index.md | 56 +++++++++++++++++++++++++ 2 files changed, 68 insertions(+) diff --git a/docs/source/references.bib b/docs/source/references.bib index 0434d34..e9c9c31 100644 --- a/docs/source/references.bib +++ b/docs/source/references.bib @@ -4,3 +4,15 @@ @phdthesis{kidger2021 year={2021}, school={University of Oxford}, } + +@article{marone1997, + title = {On the rate of frictional healing and the constitutive law for time- and slip-dependent friction}, + volume = {34}, + ISSN = {1365-1609}, + DOI = {10.1016/s1365-1609(97)00054-3}, + number = {3–4}, + journal = {International Journal of Rock Mechanics and Mining Sciences}, + publisher = {Elsevier BV}, + author = {Marone, Chris}, + year = {1997}, +} diff --git a/docs/source/user-guide/forward/index.md b/docs/source/user-guide/forward/index.md index 9aa25a8..d5d4467 100644 --- a/docs/source/user-guide/forward/index.md +++ b/docs/source/user-guide/forward/index.md @@ -17,6 +17,60 @@ The synthetic observations are then generated by integrating Eq. {eq}`ODE` over There exists a plethora of strategies to perform the integration as indicated above in a stable and accurate manner: Runge-Kutta, Rosenbrock, LSODA, ... DiaBayes uses the [Diffrax](https://docs.kidger.site/diffrax/) library {cite}`kidger2021`, which implements several of these solvers in JAX. A good general-purpose solver is the [`Tsit5`](https://docs.kidger.site/diffrax/api/solvers/ode_solvers/#diffrax.Tsit5) algorithm, which is the default for DiaBayes. +## Friction models + +A typical forward model describing a rock friction experiment comprises 3 components: a _friction law_ that describes the slip rate of the fault in response to a given state of stress (or "friction"), a _state evolution law_ that controls the time- and history-dependent internal state of the fault, and a model that describes how the externally imposed stress is transmitted to the fault (usually through elasticity). To make this more concrete, let's consider a classical spring-block model governed by rate-and-state friction {cite}`marone1997`: + +```{math} +:label: RSF +\frac{\mathrm{d} \vec{X}}{\mathrm{d} t} = \begin{cases} +\dot{\mu} = k \left(v_{lp} - v(\mu, \theta) \right) \quad \text{(spring-block stress transfer)} \\ +\dot{\theta} = 1 - \frac{v \theta}{D_c} \quad \text{(Ageing law state evolution)} +\end{cases} +``` +with +```{math} +v(\mu, \theta) = v_0 \exp \left( \frac{1}{a} \left[ \mu - \mu_0 - b \ln \left( \frac{v_0 \theta}{D_c} \right) \right] \right) +``` +In the above expressions, $a$, $b$, and $D_c$ are frictional parameters that are often of interest, and [can be inverted for](../inversion/index) from measured friction curves ($\mu(t)$). The friction parameters $\mu_0$ and $v_0$ are typically taken as constants as they can be inferred directly from the data. The spring-block stiffness $k$ and the load-point velocity $v_{lp}$ are determined by the experimental set-up. The forward model is completed by a description of the evolution of the state $\theta$ (here the Ageing law was chosen). Integrating Eq. {eq}`RSF` over time yields $\vec{X}(t) = \left[ \mu(t), \theta(t) \right]^\intercal$. + +The formulations above are merely a classical example of a friction forward model. There are several other models for the friction law, state evolution, and the stress transfer, that can be used (and in some cases combined in different ways). For more background on the various models that have been implemented, see the sections on [rate-and-state](rate-and-state) models, the [Chen-Niemeijer-Spiers](chen-niemeijer-spiers) model, and [spring-block](spring-block) models. + +The remainder of this section is dedicated to the practical implementation of forward models in DiaBayes. + +## Implementation in DiaBayes + +DiaBayes provides a generalised interface that takes the three (compatible) forward components as an input, and connects them together into a form equivalent to Eq. {eq}`ODE`. This interface is accessed through the `diabayes.forward_models.Forward` class. The forward model expressed by Eq. {eq}`RSF` is constructed as follows: +```python +from diabayes.forward_models import Forward, rsf, ageing_law, springblock +forward = Forward(friction_model=rsf, state_evolution=ageing_law, block_type=springblock) +``` +The `Forward` class implements a `__call__` method that, when given the appropriate variables ($\vec{X}$), parameters ($\left\{ a, b, D_c \right\}$), friction constants ($\left\{ \mu_0, v_0 \right\}$), and spring-block constants ($\left\{ k, v_{lp} \right\}$), yields $\mathrm{d} \vec{X} / \mathrm{d} t$. The `forward` class instance is passed to an ODE solver, which will successively call `forward(...)` to obtain the time derivatives for integration. + +To enable the assembly of a forward model from the three components, each component needs to follow a specific call signature. These are: +```python +def friction_model(variables: Variables, parameters: _Params, friction_constants: _Constants) -> Float: + velocity: Float = ... + return velocity + +def state_evolution(variables: Variables, parameters: _Params, friction_constants: _Constants) -> Float: + state_change: Float = ... + return state_change + +def block_type(velocity: Float, variables: Variables, constants: _BlockConstants) -> Float: + friction_change: Float = ... + return friction_change +``` +The type annotations `Variables`, `_Params`, `_Constants`, and `_BlockConstants` are defined in `diabayes.typedefs`, and they represent special containers holding the expected items that can be accessed by name. The contents of the containers are specific to a given model (as each model has its own variables and parameters). Note that each function above returns a scalar; vectorised computations are handled at a higher level by [`vmap`](https://docs.jax.dev/en/latest/_autosummary/jax.vmap.html)ing the forward model. + +Any new implementations of physics (see [Adding custom physics](../adding_features)) will have to obey these call signatures to ensure a consistent computational framework. + +The forward model is passed to the ODE solver upon instantiating the `ODESolver` class: +```python +from diabayes.solver import ODESolver +solver = ODESolver(forward_model=forward) +``` +The `ODESolver` class implements several wrappers around `Forward.__call__` to make it compatible with SciPy's [`solve_ivp`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.solve_ivp.html) routine for single forward calculations, or the (`vmap`ped) [`diffrax.diffeqsolve`](https://docs.kidger.site/diffrax/api/diffeqsolve/) routine for parallelised forward passes during inversion. The inversion methods implemented by this class are discussed in the [Inverse modelling](../inversion/index) section. ```{toctree} :hidden: @@ -27,5 +81,7 @@ chen-niemeijer-spiers spring-block ``` +```{rubric} References +``` ```{bibliography} ``` \ No newline at end of file From 124869dc79bc24a4902492f72f8f9da14d13bb1c Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Wed, 6 Aug 2025 12:01:14 +0200 Subject: [PATCH 33/56] Added inverse models, fixed referencing --- docs/source/conf.py | 9 +++----- docs/source/references.bib | 16 ++++++++++++++ docs/source/user-guide/forward/index.md | 6 ++--- docs/source/user-guide/inversion/index.md | 27 +++++++++++++++++++++++ pyproject.toml | 1 - 5 files changed, 49 insertions(+), 10 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 8f4c1e9..04c14a6 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -24,18 +24,15 @@ "sphinx.ext.viewcode", "sphinx.ext.mathjax", "sphinx_togglebutton", - "hoverxref.extension", ] pygments_style = "sphinx" napoleon_numpy_docstring = True -bibtex_default_style = "unsrt" # or plain, alpha, unsrt, abbrv +bibtex_default_style = "plain" # or plain, alpha, unsrt, abbrv +bibtex_reference_style = "label" bibtex_bibfiles = ["references.bib"] -hoverxref_role_types = { - "cite": "tooltip", # Show tooltip for :cite: role - "numref": "tooltip", # Show tooltip for :numref: role -} +bibtex_cite_id_filter = "docname" templates_path = ["_templates"] exclude_patterns = [] diff --git a/docs/source/references.bib b/docs/source/references.bib index e9c9c31..e0bfc0b 100644 --- a/docs/source/references.bib +++ b/docs/source/references.bib @@ -1,3 +1,11 @@ +@misc{chen2018_neuralode, + doi = {10.48550/ARXIV.1806.07366}, + author = {Chen, Ricky T. Q. and Rubanova, Yulia and Bettencourt, Jesse and Duvenaud, David}, + title = {Neural Ordinary Differential Equations}, + publisher = {arXiv}, + year = {2018}, +} + @phdthesis{kidger2021, title={{O}n {N}eural {D}ifferential {E}quations}, author={Patrick Kidger}, @@ -5,6 +13,14 @@ @phdthesis{kidger2021 school={University of Oxford}, } +@misc{kingma2014, + doi = {10.48550/ARXIV.1412.6980}, + author = {Kingma, Diederik P. and Ba, Jimmy}, + title = {Adam: A Method for Stochastic Optimization}, + publisher = {arXiv}, + year = {2014}, +} + @article{marone1997, title = {On the rate of frictional healing and the constitutive law for time- and slip-dependent friction}, volume = {34}, diff --git a/docs/source/user-guide/forward/index.md b/docs/source/user-guide/forward/index.md index d5d4467..8659b06 100644 --- a/docs/source/user-guide/forward/index.md +++ b/docs/source/user-guide/forward/index.md @@ -15,11 +15,11 @@ The synthetic observations are then generated by integrating Eq. {eq}`ODE` over \vec{X}(t) = \vec{X}(t=t_0) + \int_{t_0}^{t} f\left(t', \vec{X}, \dots \right) \mathrm{d} t' ``` -There exists a plethora of strategies to perform the integration as indicated above in a stable and accurate manner: Runge-Kutta, Rosenbrock, LSODA, ... DiaBayes uses the [Diffrax](https://docs.kidger.site/diffrax/) library {cite}`kidger2021`, which implements several of these solvers in JAX. A good general-purpose solver is the [`Tsit5`](https://docs.kidger.site/diffrax/api/solvers/ode_solvers/#diffrax.Tsit5) algorithm, which is the default for DiaBayes. +There exists a plethora of strategies to perform the integration as indicated above in a stable and accurate manner: Runge-Kutta, Rosenbrock, LSODA, ... DiaBayes uses the [Diffrax](https://docs.kidger.site/diffrax/) library{footcite}`kidger2021`, which implements several of these solvers in JAX. A good general-purpose solver is the [`Tsit5`](https://docs.kidger.site/diffrax/api/solvers/ode_solvers/#diffrax.Tsit5) algorithm, which is the default for DiaBayes. ## Friction models -A typical forward model describing a rock friction experiment comprises 3 components: a _friction law_ that describes the slip rate of the fault in response to a given state of stress (or "friction"), a _state evolution law_ that controls the time- and history-dependent internal state of the fault, and a model that describes how the externally imposed stress is transmitted to the fault (usually through elasticity). To make this more concrete, let's consider a classical spring-block model governed by rate-and-state friction {cite}`marone1997`: +A typical forward model describing a rock friction experiment comprises 3 components: a _friction law_ that describes the slip rate of the fault in response to a given state of stress (or "friction"), a _state evolution law_ that controls the time- and history-dependent internal state of the fault, and a model that describes how the externally imposed stress is transmitted to the fault (usually through elasticity). To make this more concrete, let's consider a classical spring-block model governed by rate-and-state friction{footcite}`marone1997`: ```{math} :label: RSF @@ -83,5 +83,5 @@ spring-block ```{rubric} References ``` -```{bibliography} +```{footbibliography} ``` \ No newline at end of file diff --git a/docs/source/user-guide/inversion/index.md b/docs/source/user-guide/inversion/index.md index 52071e9..55b7ccd 100644 --- a/docs/source/user-guide/inversion/index.md +++ b/docs/source/user-guide/inversion/index.md @@ -1,9 +1,36 @@ # Inverse modelling +Given a set of observations $\mu_{obs}(t)$ (i.e., a _friction curve_), and a suitable [forward model](../forward/index) with parameters $\mathcal{P}$, what are the parameter values that can explain the observations? We could choose some random $\mathcal{P}$, run a forward simulation, and see if the forward solution $\mu(t) = \mathrm{Forward}[t, \mathcal{P}]$ matches $\mu_{obs}(t)$. If not, change $\mathcal{P}$ a little bit and try again, until an acceptable agreement between $\mu(t)$ and $\mu_{obs}(t)$ is found. + +Of course, we can do this more systematically. Define an objective function (or "loss" function in Machine/Deep Learning jargon): +```{math} +:label: loss_fn +\mathcal{L} = \frac{1}{T} \int_0^T \left(\mu_{obs} - \mathrm{Forward}[t, \mathcal{P}] \right)^2 \mathrm{d} t +``` +In practice, the integral over $t$ is replaced by a summation (since $\mu_{obs}$ is sampled at discrete time intervals), but for the theory it makes sense to keep the integral for now. The "best" parameter values are those that result in the smallest value of $\mathcal{L}$, i.e.: +```{math} +\mathcal{P}^* = \underset{\mathcal{P}}{\arg \min} \, \left\{ \frac{1}{T} \int_0^T \left(\mu_{obs} - \mathrm{Forward}[t, \mathcal{P}] \right)^2 \mathrm{d} t \right\} +``` + +A classical approach to finding $\mathcal{P}^*$ is performing gradient descent on Eq. {eq}`loss_fn`: we compute the gradient of $\mathcal{L}$ with respect to $\mathcal{P}$, and then take a small step down the gradient. By repeating this procedure multiple times, we expect to eventually arrive at the (local) minimum, where the gradients are zero (and so we automatically stop there). Quantitatively, this reads: +```{math} +\mathcal{P}^{i+1} = \mathcal{P}^i - \eta \left.\frac{\mathrm{d} \mathcal{L}}{\mathrm{d} \mathcal{P}} \right\vert_{\mathcal{P}^i} +``` +There are more advanced gradient-descent algorithms, like ADAM{footcite}`kingma2014` (as used by DiaBayes), but they are all just different flavours of this general concept. + +The challenge now is to find the derivative term on the right-hand side. By virtue of the [Leibniz integral rule](https://en.wikipedia.org/wiki/Leibniz_integral_rule), the total derivative of $\mathcal{L}$ becomes a partial derivative of $\mathrm{Forward}[t, \mathcal{P}]$ with respect to $\mathcal{P}$ inside the integral. But how does one differentiate the solution of an ODE with respect to its parameters? The solution at any point $t_i$ depends on the entire history from $t = t_0$ up to $t_i$, and so the derivatives at time $t_i$ depend on the derivatives at $t_{i-1}, t_{i-2}, \dots$. Fortunately, there is an elegant approach to solve this recursive problem, which is called the [_adjoint state method_](https://en.wikipedia.org/wiki/Adjoint_state_method) (or simply "adjoint method"). + +It is beyond the scope of the user guide to explain this method here, but there are numerous lecture notes and blog posts out there that describe this approach in the context of continuous-time ODEs (and also Neural ODEs{footcite}`chen2018_neuralode`). Conveniently, this method is built into the solvers of the [Diffrax](https://docs.kidger.site/diffrax/) library{footcite}`kidger2021`, and so in the spirit of the underlying JAX library, any operations involving an ODE solution are differentiable with respect to its parameters. DiaBayes builds further on this, integrating the gradients coming out of the adjoint back-propagation into gradient-based inversion algorithms. DiaBayes implements two flavours of inversion methods, which are the [maximum-likelihood inversion](max-likelihood) and [Bayesian inversion](bayesian) that are discussed in the next sections. + ```{toctree} :hidden: :maxdepth: 1 max-likelihood bayesian +``` + +```{rubric} References +``` +```{footbibliography} ``` \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index e1a49f8..c5e3379 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -30,7 +30,6 @@ docs = [ "sphinx", "sphinx-copybutton", "sphinx-togglebutton", - "sphinx-hoverxref", "sphinx-design", "sphinxcontrib-bibtex", ] From 142a4e4681d9f6362121f43ff2837e096b85ebc4 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Wed, 6 Aug 2025 16:14:13 +0200 Subject: [PATCH 34/56] Populated RSF and spring-block documentation pages --- docs/source/conf.py | 1 - docs/source/references.bib | 44 ++++++++--- docs/source/user-guide/forward/index.md | 2 +- .../user-guide/forward/rate-and-state.md | 60 ++++++++++++++- .../source/user-guide/forward/spring-block.md | 76 ++++++++++++++++++- .../source/user-guide/friction_experiments.md | 13 ++++ docs/source/user-guide/index.md | 1 + .../user-guide/inversion/max-likelihood.md | 3 +- 8 files changed, 185 insertions(+), 15 deletions(-) create mode 100644 docs/source/user-guide/friction_experiments.md diff --git a/docs/source/conf.py b/docs/source/conf.py index 04c14a6..c55fa07 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -32,7 +32,6 @@ bibtex_default_style = "plain" # or plain, alpha, unsrt, abbrv bibtex_reference_style = "label" bibtex_bibfiles = ["references.bib"] -bibtex_cite_id_filter = "docname" templates_path = ["_templates"] exclude_patterns = [] diff --git a/docs/source/references.bib b/docs/source/references.bib index e0bfc0b..3faa6e8 100644 --- a/docs/source/references.bib +++ b/docs/source/references.bib @@ -21,14 +21,38 @@ @misc{kingma2014 year = {2014}, } -@article{marone1997, - title = {On the rate of frictional healing and the constitutive law for time- and slip-dependent friction}, - volume = {34}, - ISSN = {1365-1609}, - DOI = {10.1016/s1365-1609(97)00054-3}, - number = {3–4}, - journal = {International Journal of Rock Mechanics and Mining Sciences}, - publisher = {Elsevier BV}, - author = {Marone, Chris}, - year = {1997}, +@article{lapusta2000, + title = {Elastodynamic analysis for slow tectonic loading with spontaneous rupture episodes on faults with rate‐ and state‐dependent friction}, + volume = {105}, + ISSN = {0148-0227}, + DOI = {10.1029/2000jb900250}, + number = {B10}, + journal = {Journal of Geophysical Research: Solid Earth}, + publisher = {American Geophysical Union (AGU)}, + author = {Lapusta, Nadia and Rice, James R. and Ben‐Zion, Yehuda and Zheng, Gutuan}, + year = {2000}, } + +@article{marone1998, + title = {LABORATORY-DERIVED FRICTION LAWS AND THEIR APPLICATION TO SEISMIC FAULTING}, + volume = {26}, + ISSN = {1545-4495}, + DOI = {10.1146/annurev.earth.26.1.643}, + number = {1}, + journal = {Annual Review of Earth and Planetary Sciences}, + publisher = {Annual Reviews}, + author = {Marone, Chris}, + year = {1998}, +} + +@article{ruina1983, + title = {Slip instability and state variable friction laws}, + volume = {88}, + ISSN = {0148-0227}, + DOI = {10.1029/jb088ib12p10359}, + number = {B12}, + journal = {Journal of Geophysical Research: Solid Earth}, + publisher = {American Geophysical Union (AGU)}, + author = {Ruina, Andy}, + year = {1983}, +} \ No newline at end of file diff --git a/docs/source/user-guide/forward/index.md b/docs/source/user-guide/forward/index.md index 8659b06..5ca329d 100644 --- a/docs/source/user-guide/forward/index.md +++ b/docs/source/user-guide/forward/index.md @@ -19,7 +19,7 @@ There exists a plethora of strategies to perform the integration as indicated ab ## Friction models -A typical forward model describing a rock friction experiment comprises 3 components: a _friction law_ that describes the slip rate of the fault in response to a given state of stress (or "friction"), a _state evolution law_ that controls the time- and history-dependent internal state of the fault, and a model that describes how the externally imposed stress is transmitted to the fault (usually through elasticity). To make this more concrete, let's consider a classical spring-block model governed by rate-and-state friction{footcite}`marone1997`: +A typical forward model describing a rock friction experiment comprises 3 components: a _friction law_ that describes the slip rate of the fault in response to a given state of stress (or "friction"), a _state evolution law_ that controls the time- and history-dependent internal state of the fault, and a model that describes how the externally imposed stress is transmitted to the fault (usually through elasticity). To make this more concrete, let's consider a classical spring-block model governed by rate-and-state friction{footcite}`marone1998`: ```{math} :label: RSF diff --git a/docs/source/user-guide/forward/rate-and-state.md b/docs/source/user-guide/forward/rate-and-state.md index d34e04d..2ccf3a6 100644 --- a/docs/source/user-guide/forward/rate-and-state.md +++ b/docs/source/user-guide/forward/rate-and-state.md @@ -1 +1,59 @@ -# Rate-and-state friction \ No newline at end of file +# Rate-and-state friction + +Rate-and-state friction (RSF) is a well-established formulation that describes a wide range of laboratory and natural phenomena. It is therefore commonly used to model laboratory friction experiments and natural earthquakes{footcite}`marone1998`. The formulation itself is empirical, and the governing parameters have long been subject of debate over their physical interpretation. Nonetheless, there is sufficient consistency between the parameters for different types of experiments and conditions to warrant a comparison, and analysis of their various dependencies (most notably temperature, but also stress, slip rate, chemistry, etc.). The RSF formulation consists of a friction law with a characteristic exponential dependency, and is accompanied by a state evolution law that prescribes the time-evolution of a state parameter (usually denoted by $\theta$) that keeps track of the slip history. + +DiaBayes currently implements the two most common flavours of rate-and-state friction, as well as two state evolution laws. + +## Classical rate-and-state friction + +The classical RSF law is often written as: +```{math} +:label: RSF +\mu(v, \theta) = \mu_0 + a \ln \left( \frac{v}{v_0} \right) + b \ln \left( \frac{v_0 \theta}{D_c} \right) +``` +with $\mu$ the instantaneous friction coefficient, which is a function of the instantaneous slip rate $v$ and "state" $\theta$. The governing parameters are the reference value of friction $\mu_0$ at the reference slip rate $v_0$, the direct effect parameter $a$, the evolution effect parameter $b$, and the characteristic slip distance $D_c$. In a laboratory setting, $mu_0$ and $v_0$ are often taken directly from a steady-state measurement, leaving only $a$, $b$, and $D_c$ as the parameters of interest. + +In DiaBayes, Eq. {eq}`RSF` is re-written in terms of $v$: +```{math} +:label: RSF2 +v = v_0 \exp \left( \frac{1}{a} \left[ \mu - \mu_0 - b \ln \left( \frac{v_0 \theta}{D_c} \right) \right] \right) +``` + +The implementation of the classical RSF formulation is available through `diabayes.forward_models.rsf`. + +## Regularised rate-and-state friction + +Owing to the logarithms used in the classical RSF formulation, the slip rate $v$ is required to be strictly positive. This requirement can be problematic in simulations where the shear stress (and corresonding slip rate) can change sign or become zero. To alleviate this, a regularised form{footcite}`lapusta2000` can be adopted with the same asymptotic behaviour as Eq. {eq}`RSF2`: +```{math} +:label: RSF_reg +v = 2 v_0 \sinh \left\{ \frac{\mu}{a} \exp \left( - \frac{1}{a} \left[\mu_0 + b \ln \left( \frac{v_0 \theta}{D_c} \right) \right] \right) \right\} +``` + +The implementation of the regularised RSF formulation is available through `diabayes.forward_models.rsf_regular`. + +## Ageing law state evolution + +The _ageing law_ derives its name from the fact that the incremental growth of state is proportional to time ("age"), and decays with slip: +```{math} +:label: ageing_law +\frac{\mathrm{d} \theta}{\mathrm{d} t} = 1 - \frac{v \theta}{D_c} +``` +At steady-state $\dot{\theta} = 0$, hence correspondingly $\theta_{ss} = D_c v^{-1}$. Since the state variable cannot be directly observed in laboratory experiment, it is often assumed that prior to a velocity-step, the initial value of state is $\theta(t=0) = \theta_{ss}$. + +The implementation of the ageing law is available through `diabayes.forward_models.ageing_law`. + +## Slip law state evolution + +An alternative formulation to the ageing law is the _slip law_, which derives its name from the fact that it exclusively evolves with slip: +```{math} +:label: slip_law +\frac{\mathrm{d} \theta}{\mathrm{d} t} = - \frac{v \theta}{D_c} \ln \left(\frac{v \theta}{D_c} \right) +``` +Like for the ageing law, the slip law is characterised by a steady-state value of $\theta_{ss} = D_c v^{-1}$. + +The implementation of the slip law is available through `diabayes.forward_models.slip_law`. + +```{rubric} References +``` +```{footbibliography} +``` \ No newline at end of file diff --git a/docs/source/user-guide/forward/spring-block.md b/docs/source/user-guide/forward/spring-block.md index 2fcd15e..525eb39 100644 --- a/docs/source/user-guide/forward/spring-block.md +++ b/docs/source/user-guide/forward/spring-block.md @@ -1 +1,75 @@ -# Spring-blocks \ No newline at end of file +# Spring-blocks + +While the friction law (and state evolution law) describes how a fault responds to a particular state of stress, an additional descriptor is needed to connect the fault response back to the state of stress (i.e., mechanical feedback). For natural faults it is often necessary to come up with an elastic model that includes complexities like a free surface and fault non-planarities. For laboratory experiments, however, it is often sufficient to represent the experimental set-up with a _spring-block_ mechanical analogue. A frictional interface is formed between a rigid "block" placed on top of a surface, and the block is pulled horizontally by a spring, the other end of which is being translated at a constant rate ($v_{lp}$). For $t > 0$, stretching of the spring transmits a force to the block. This force is resisted by friction ($\mu$) at the interface. The friction law then describes the rate of translation of the block ($v = f(\mu, \dots)$), which results in shortening of the spring, resulting in a reduction in friction, etc. + +The most basic spring-block formulation is effectively zero-dimensional, meaning that there is no internal deformation of the block or variation of stress/slip at the interface. More advanced formulations can account for such internal deformation, which can lead to e.g. rupture propagation across the interface. DiaBayes currently only implements two types of zero-dimensional spring-block analogues. + +## Basic spring-block + +The force balance describing the most basic spring-block analogue reads as follows: +```{math} +:label: spring-block +\frac{\mathrm{d} \mu}{\mathrm{d} t} = k \left( v_{lp} - v \right) +``` +where $k$ is the stiffness of the spring (with units of $[m^{-1}]$), and $v_{lp}$ is the rate of translation of the spring's endpoint (the "load-point velocity"). The response of the fault to a given state of stress is given by the friction law, i.e. $v = f(\mu, \dots)$. The difference $v_{lp} - v$ is the rate of stretching of the spring, which, combined with the stiffness $k$, gives the rate of stress increase on the interface. + +The force balance (Eq. {eq}`spring-block`) is then combined with the friction law $f$ and the state evolution law $g$ to furnish a complete description of the forward model, i.e.: +```{math} +:label: forward +\frac{\mathrm{d} \vec{X}}{\mathrm{d} t} = \begin{cases} +\dot{\mu} = k \left(v_{lp} - f(\mu, \dots) \right) \\ +\dot{\theta} = g(v, \theta, \dots) +\end{cases} +``` + +The basic spring-block model is available through `diabayes.forward_models.springblock`. + +```{note} +For certain values of $k$ below a given threshold (which is dictated by the friction law), a _frictional instability_ can develop{footcite}`ruina1983`, also known as _stick-slip_. This instability results from a force imbalance that can potentially increase indefinitely for a simple spring-block analogue, meaning that the stick-slip cycles will continue to grow until the numerical solver fails to resolve the (near-infinite) acceleration. To correctly model stick-slip cycles in DiaBayes, select either an _inertial spring-block_ (see below) or pick an appropriate friction law like the [_Chen-Niemeijer-Spiers_ model](chen-niemeijer-spiers). +``` + +## Inertial spring-block + +For a basic spring-block analogue, a normal stress is imposed to the block to act as a certain "weight" pressing down on the frictional interface. However, the block itself is assumed to be massless (non-inertial), leading to a simplified force balance. For small accelerations, like one would encounter for small velocity-steps or slide-hold-slide experiments, this simplification is sufficiently accurate (i.e., the inertial effects are negligible). For more rapid accelerations, like one would encounter in stick-slip sequences, inertia becomes a significant contributor to the force balance. When accounting for a finite (and dimensionless) mass $M$ of the block, the governing force balance becomes: +```{math} +:label: inertial_spring-block +M \frac{\mathrm{d} v}{\mathrm{d} t} = k \left(v_{lp}t - x \right) - \mu(\dots) +``` +Since DiaBayes defines a friction law as $v = f(\mu, \dots)$ and not $\mu = f'(v, \dots)$, the above expression is in principle incompatible with the DiaBayes modelling strategy. However, it is possible to rewrite it using the partial derivatives of $v$ with respect to the variables of the friction law (usually friction $\mu$ and state $\theta$): +```{math} +:label: inertial_spring-block_partials +M \left( \frac{\partial v}{\partial \mu} \frac{\mathrm{d} \mu}{\mathrm{d} t} + \frac{\partial v}{\partial \theta} \frac{\mathrm{d} \theta}{\mathrm{d} t} + \dots \right) = k \left(v_{lp}t - x \right) - \mu +``` +The $\dots$ denote any other variables that might be associated with a particular friction law (normal stress, temperature, ...), though this would represent a much more [advanced physics scenario](../advanced_usage). Rewriting Eq. {eq}`inertial_spring-block_partials` in terms of $\dot{\mu}$ yields a more familiar form of the force balance: +```{math} +:label: inertial_spring-block_partials2 +\frac{\mathrm{d} \mu}{\mathrm{d} t} = \left[ \frac{\partial v}{\partial \mu} \right]^{-1} \left( \frac{1}{M} \left[ k \left( v_{lp} t - x \right) - \mu \right] - \frac{\partial v}{\partial \theta} \frac{\mathrm{d} \theta}{\mathrm{d} t} - \dots \right) +``` +To the forward model is completed with the somewhat trivial expression for the position of the block, $\mathrm{d}x / \mathrm{d}t = v(\mu, \theta, \dots)$, which, all together, gives: +```{math} +\frac{\mathrm{d} \vec{X}}{\mathrm{d} t} = \begin{cases} +\dot{\mu} = \dots \quad \text{(inertial force balance)} \\ +\dot{\theta} = g(v, \theta, \dots) \quad \text{(state evolution)} \\ +\dot{x} = v(\mu, \theta, \dots) \quad \text{(friction law)} +\end{cases} +``` +Correspondingly, the solution vector gains an extra component, $\vec{X}(t) = \left[ \mu(t), \theta(t), x(t) \right]^\intercal$. + +In order for a friction model to be compatible with an inertial spring-block, it must implement a `_partials` method to compute the partial derivatives of $v$ with respect to the relevant variables. This method takes as an argument the vector $\vec{X}$ and returns $\partial v / \partial \mu$ and the sum of the remaining partial derivative terms ($(\partial v / \partial \theta) \dot{\theta} + \dots$). + +The inertial spring-block model is available through `diabayes.forward_models.inertial_springblock`. + +```{hint} +To calculate $M$ for your experimental set-up, estimate the mass of the forcing block that is moving (i.e., driven by a shear piston), multiply this by the gravitational acceleration (9.8 m/s²) and divide this by the product of the interface contact area times applies normal stress: + +$$ +M = \frac{m g}{A \sigma_n} +$$ + +``` + + +```{rubric} References +``` +```{footbibliography} +``` \ No newline at end of file diff --git a/docs/source/user-guide/friction_experiments.md b/docs/source/user-guide/friction_experiments.md new file mode 100644 index 0000000..036dbd0 --- /dev/null +++ b/docs/source/user-guide/friction_experiments.md @@ -0,0 +1,13 @@ +# Rock friction experiments + +This section briefly summarises a few key concepts in rock friction experimentation, which are presumed to be well understood for the remainder of this User Guide. + +- Effective friction coefficient (ignoring friction) + +## Velocity-step tests + +- Up and down steps + +## Stick-slip instabilities + +## Slide-hold-slide tests \ No newline at end of file diff --git a/docs/source/user-guide/index.md b/docs/source/user-guide/index.md index f693bfd..d61fd01 100644 --- a/docs/source/user-guide/index.md +++ b/docs/source/user-guide/index.md @@ -8,6 +8,7 @@ The guide is not structured in a particular order, and so it is not necessarily :hidden: :maxdepth: 1 +friction_experiments forward/index inversion/index advanced_usage diff --git a/docs/source/user-guide/inversion/max-likelihood.md b/docs/source/user-guide/inversion/max-likelihood.md index 540d078..bddfc7b 100644 --- a/docs/source/user-guide/inversion/max-likelihood.md +++ b/docs/source/user-guide/inversion/max-likelihood.md @@ -1 +1,2 @@ -# Maximum-likelihood inversion \ No newline at end of file +# Maximum-likelihood inversion + From ea530faeae1f7a8fb54a80afff4158ff456d9a5f Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Wed, 6 Aug 2025 18:56:35 +0200 Subject: [PATCH 35/56] Documented max-likelihood and Bayesian inversion (partially) --- .../source/user-guide/forward/spring-block.md | 4 +- docs/source/user-guide/inversion/bayesian.md | 39 ++++++++++++++++++- .../user-guide/inversion/max-likelihood.md | 19 +++++++++ pyproject.toml | 2 +- 4 files changed, 61 insertions(+), 3 deletions(-) diff --git a/docs/source/user-guide/forward/spring-block.md b/docs/source/user-guide/forward/spring-block.md index 525eb39..172ee40 100644 --- a/docs/source/user-guide/forward/spring-block.md +++ b/docs/source/user-guide/forward/spring-block.md @@ -60,12 +60,14 @@ In order for a friction model to be compatible with an inertial spring-block, it The inertial spring-block model is available through `diabayes.forward_models.inertial_springblock`. ```{hint} -To calculate $M$ for your experimental set-up, estimate the mass of the forcing block that is moving (i.e., driven by a shear piston), multiply this by the gravitational acceleration (9.8 m/s²) and divide this by the product of the interface contact area times applies normal stress: +To calculate $M$ for your experimental set-up, estimate the mass of the forcing block that is moving (i.e., driven by a shear piston), multiply this by the gravitational acceleration (9.8 m/s²) and divide this by the product of the interface contact area times the interface normal stress: $$ M = \frac{m g}{A \sigma_n} $$ +If only the weight of the block is pressing down on the interface (and no external normal load is applied), then $M = 1$. + ``` diff --git a/docs/source/user-guide/inversion/bayesian.md b/docs/source/user-guide/inversion/bayesian.md index 6a129e5..a8e1da6 100644 --- a/docs/source/user-guide/inversion/bayesian.md +++ b/docs/source/user-guide/inversion/bayesian.md @@ -1 +1,38 @@ -# Bayesian inversion \ No newline at end of file +# Bayesian inversion + +Bayesian inversion refers to a family of inversion methods that aim to estimate the _probability_ that a given set of parameters $\mathcal{P}$ can explain the measured data $\mu_{obs}$. This probability is expressed in terms of the _posterior distribution_ over the parameters, which comprises a likelihood term and a prior probability term, usually written as: +```{math} +:label: Bayes +p\left(\mathcal{P} | \mu_{obs} \right) \propto p\left(\mu_{obs} | \mathcal{P} \right) p\left(\mathcal{P}\right) +``` +For those new to Bayesian statistics: $p\left(a|b \right)$ is read as "the probability that $a$ is true given that $b$ is true" (loosely speaking). The first term on the right-hand side is called the _likelihood_ term, and the second term is the _prior_ probability (or simply "prior"). The likelihood term can be estimated by running a forward simulation (with a given $\mathcal{P}$) and calculating the mismatch with the observations; the maximum-likelihood inversion discussed in [the previous section](max-likelihood) aims to find $\mathcal{P}$ that maximises this term. However, Bayesian inversion also takes into account that one might have a-priori information about $\mathcal{P}$ itself, before observing any data (the prior). For example, we might expect $\mathcal{P}$ to be positive, or be constrained by independent measurements, etc. Lastly, the proportionality constant, which quantifies the probability of observing the data, is unknown but irrelevant for the inversion. + +## Gradients of the posterior + +In practice, most Bayesian inversion methods sample the logaritm of $p$, hence Eq. {eq}`Bayes` becomes: +```{math} +:label: logBayes +\ln p\left(\mathcal{P} | \mu_{obs} \right) \propto \ln p\left(\mu_{obs} | \mathcal{P} \right) + \ln p\left(\mathcal{P}\right) +``` +To permit an informed exploration of the posterior distribution, gradient information is used to guide the search direction within the parameter space. To see how our forward simulations fit into this, let's consider a normal (Gaussian) likelihood distribution: +```{math} +:label: Gaussian +p\left(\mu_{obs} | \mathcal{P} \right) \propto \prod_t \exp \left( - \left[ \frac{\mu_{obs}(t) - \mathrm{Forward}\left[t, \mathcal{P}\right]}{\sqrt{2} \nu} \right]^2 \right) +``` +To avoid confusion with the normal stress (often denoted $\sigma$), the standard deviation (also often denoted by $\sigma$) is here denoted by $\nu$. This standard deviation is expected to be proportional to the measurement noise amplitude in $\mu_{obs}$. The logarithm of Eq. {eq}`Gaussian` turns the product into a summation: +```{math} +:label: log_Gaussian +\ln p\left(\mu_{obs} | \mathcal{P} \right) \propto - \sum_t \left[ \frac{\mu_{obs}(t) - \mathrm{Forward}\left[t, \mathcal{P}\right]}{\sqrt{2} \nu} \right]^2 +``` +Lastly, the derivative of the log-likelihood with respect to $\mathcal{P}$ can then be found by virtue of the chain rule, and differentiation through the ODE solution: +```{math} +:label: grad_log_Gaussian +\frac{\partial \ln p\left(\mu_{obs} | \mathcal{P} \right)}{\partial \mathcal{P}} \propto \sum_t \frac{\mu_{obs}(t) - \mathrm{Forward}\left[t, \mathcal{P}\right]}{2 \nu^2} \frac{\partial \mathrm{Forward}}{\partial \mathcal{P}} +``` +Because of the differentiability of the DiaBayes forward models, the gradient term on the right-hand side can be precisely calculated. A similar procedure is applied to obtain the gradients of the prior distribution. + +By doing gradient descent on $-\partial \ln p\left(\mathcal{P} | \mu_{obs} \right) / \partial \mathcal{P}$, one arrives as the maximum a-posteriori estimate, which strikes a balance between the likelihood and prior terms. But this is not what we want: the point of doing Bayesian inversion, is to gain insight into the _distribution_ of parameter probabilities, not just a single estimate thereof. To estimate the posterior distribution, one needs to perform some kind of sampling of the neighbourhood around the maximum posterior. There are numerous methods that can do this, many of which have been implemented into the [Blackjax](https://blackjax-devs.github.io/blackjax/) library. However, DiaBayes implements its own Bayesian sampler: _Stein Variational Inference_. + + +## Stein Variational Inference + diff --git a/docs/source/user-guide/inversion/max-likelihood.md b/docs/source/user-guide/inversion/max-likelihood.md index bddfc7b..49fa8f3 100644 --- a/docs/source/user-guide/inversion/max-likelihood.md +++ b/docs/source/user-guide/inversion/max-likelihood.md @@ -1,2 +1,21 @@ # Maximum-likelihood inversion +For a given measured friction curve ($\mu_{obs}$), it is likely that a single set of parameters $\mathcal{P}$ produces the best possible agreement between a forward simulation $\mu = \mathrm{Forward}[t, \mathcal{P}]$ and the (noisy) measurements. This single optimal estimate of $\mathcal{P}$ is called the _maximum-likelihood estimate_, and it can be obtained through standard inversion schemes. + +For the least-squares objective function discussed in [the previous section](index), the [Levenberg-Marquardt algorithm](https://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm) is one of the fastest converging methods on the market. DiaBayes utilises the [`LevenbergMarquardt`](https://docs.kidger.site/optimistix/api/least_squares/#optimistix.LevenbergMarquardt) implementation of the [Optimistix](https://docs.kidger.site/optimistix/) library, which interfaces directly with the [`diffrax.diffeqsolve`](https://docs.kidger.site/diffrax/api/diffeqsolve/) routine that is used to solve the forward problem. + +Concretely, performing a maximum-likelihood inversion in DiaBayes is fairly straightforward. Assuming that a solver has already been instantiated with an appropriate forward model (see the [Getting Started guide](../../getting-started)), and given some measured friction curve `mu_measured` and initial guess parameters `params_guess`, the inversion can be performed with a single function call: +```python +inv_result = solver.max_likelihood_inversion( + t, mu_measured, + params=params_guess, + friction_constants=constants, + block_constants=block_constants, + verbose=True +) + +params_inverted = inv_result.value +``` +Depending on the number of data samples and the complexity of the forward model, this inversion should take anywhere from a few seconds to at most one minute. + +In many cases, the maximum-likelihood result is sufficient for the analysis of an experiment. However, this approach provides no information on the uncertainty in the inverted parameters, and the potential trade-offs between them. For a complete evaluation of the admissible range of parameters, consider performing a Bayesian inversion (hint: see next section). \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index c5e3379..b31c1df 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "diabayes" -version = "0.0.0" +version = "0.1.0" requires-python = "> 3.0" authors = [ { name = "Martijn van den Ende", email = "martijnende@gmail.com" }, From da633e348b22236b1ac3121edaefd52161e00b3b Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Thu, 7 Aug 2025 12:28:56 +0200 Subject: [PATCH 36/56] Added SVI to docs --- docs/source/references.bib | 8 +++ .../source/user-guide/forward/spring-block.md | 18 +++---- docs/source/user-guide/inversion/bayesian.md | 51 ++++++++++++++++--- docs/source/user-guide/inversion/index.md | 6 ++- 4 files changed, 65 insertions(+), 18 deletions(-) diff --git a/docs/source/references.bib b/docs/source/references.bib index 3faa6e8..f5b7c2b 100644 --- a/docs/source/references.bib +++ b/docs/source/references.bib @@ -33,6 +33,14 @@ @article{lapusta2000 year = {2000}, } +@misc{liu2016, + doi = {10.48550/ARXIV.1608.04471}, + author = {Liu, Qiang and Wang, Dilin}, + title = {Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm}, + publisher = {arXiv}, + year = {2016}, +} + @article{marone1998, title = {LABORATORY-DERIVED FRICTION LAWS AND THEIR APPLICATION TO SEISMIC FAULTING}, volume = {26}, diff --git a/docs/source/user-guide/forward/spring-block.md b/docs/source/user-guide/forward/spring-block.md index 172ee40..6bb868b 100644 --- a/docs/source/user-guide/forward/spring-block.md +++ b/docs/source/user-guide/forward/spring-block.md @@ -8,14 +8,14 @@ The most basic spring-block formulation is effectively zero-dimensional, meaning The force balance describing the most basic spring-block analogue reads as follows: ```{math} -:label: spring-block +:label: eq:spring-block \frac{\mathrm{d} \mu}{\mathrm{d} t} = k \left( v_{lp} - v \right) ``` where $k$ is the stiffness of the spring (with units of $[m^{-1}]$), and $v_{lp}$ is the rate of translation of the spring's endpoint (the "load-point velocity"). The response of the fault to a given state of stress is given by the friction law, i.e. $v = f(\mu, \dots)$. The difference $v_{lp} - v$ is the rate of stretching of the spring, which, combined with the stiffness $k$, gives the rate of stress increase on the interface. -The force balance (Eq. {eq}`spring-block`) is then combined with the friction law $f$ and the state evolution law $g$ to furnish a complete description of the forward model, i.e.: +The force balance (Eq. {eq}`eq:spring-block`) is then combined with the friction law $f$ and the state evolution law $g$ to furnish a complete description of the forward model, i.e.: ```{math} -:label: forward +:label: eq:forward \frac{\mathrm{d} \vec{X}}{\mathrm{d} t} = \begin{cases} \dot{\mu} = k \left(v_{lp} - f(\mu, \dots) \right) \\ \dot{\theta} = g(v, \theta, \dots) @@ -32,15 +32,15 @@ For certain values of $k$ below a given threshold (which is dictated by the fric For a basic spring-block analogue, a normal stress is imposed to the block to act as a certain "weight" pressing down on the frictional interface. However, the block itself is assumed to be massless (non-inertial), leading to a simplified force balance. For small accelerations, like one would encounter for small velocity-steps or slide-hold-slide experiments, this simplification is sufficiently accurate (i.e., the inertial effects are negligible). For more rapid accelerations, like one would encounter in stick-slip sequences, inertia becomes a significant contributor to the force balance. When accounting for a finite (and dimensionless) mass $M$ of the block, the governing force balance becomes: ```{math} -:label: inertial_spring-block +:label: eq:inertial_spring-block M \frac{\mathrm{d} v}{\mathrm{d} t} = k \left(v_{lp}t - x \right) - \mu(\dots) ``` Since DiaBayes defines a friction law as $v = f(\mu, \dots)$ and not $\mu = f'(v, \dots)$, the above expression is in principle incompatible with the DiaBayes modelling strategy. However, it is possible to rewrite it using the partial derivatives of $v$ with respect to the variables of the friction law (usually friction $\mu$ and state $\theta$): ```{math} -:label: inertial_spring-block_partials +:label: eq:inertial_spring-block_partials M \left( \frac{\partial v}{\partial \mu} \frac{\mathrm{d} \mu}{\mathrm{d} t} + \frac{\partial v}{\partial \theta} \frac{\mathrm{d} \theta}{\mathrm{d} t} + \dots \right) = k \left(v_{lp}t - x \right) - \mu ``` -The $\dots$ denote any other variables that might be associated with a particular friction law (normal stress, temperature, ...), though this would represent a much more [advanced physics scenario](../advanced_usage). Rewriting Eq. {eq}`inertial_spring-block_partials` in terms of $\dot{\mu}$ yields a more familiar form of the force balance: +The $\dots$ denote any other variables that might be associated with a particular friction law (normal stress, temperature, ...), though this would represent a much more [advanced physics scenario](../advanced_usage). Rewriting Eq. {eq}`eq:inertial_spring-block_partials` in terms of $\dot{\mu}$ yields a more familiar form of the force balance: ```{math} :label: inertial_spring-block_partials2 \frac{\mathrm{d} \mu}{\mathrm{d} t} = \left[ \frac{\partial v}{\partial \mu} \right]^{-1} \left( \frac{1}{M} \left[ k \left( v_{lp} t - x \right) - \mu \right] - \frac{\partial v}{\partial \theta} \frac{\mathrm{d} \theta}{\mathrm{d} t} - \dots \right) @@ -60,13 +60,13 @@ In order for a friction model to be compatible with an inertial spring-block, it The inertial spring-block model is available through `diabayes.forward_models.inertial_springblock`. ```{hint} -To calculate $M$ for your experimental set-up, estimate the mass of the forcing block that is moving (i.e., driven by a shear piston), multiply this by the gravitational acceleration (9.8 m/s²) and divide this by the product of the interface contact area times the interface normal stress: +To calculate $M$ for your experimental set-up, estimate the mass of the forcing block that is moving (i.e., driven by a shear piston) and divide this by the product of the interface contact area times the interface normal stress: $$ -M = \frac{m g}{A \sigma_n} +M = \frac{m}{A \sigma_n} $$ -If only the weight of the block is pressing down on the interface (and no external normal load is applied), then $M = 1$. +If only the weight of the block is pressing down on the interface (and no external normal load is applied), then $M = 1 / g = 1 / 9.8 \approx 0.1$. ``` diff --git a/docs/source/user-guide/inversion/bayesian.md b/docs/source/user-guide/inversion/bayesian.md index a8e1da6..52a02e5 100644 --- a/docs/source/user-guide/inversion/bayesian.md +++ b/docs/source/user-guide/inversion/bayesian.md @@ -2,37 +2,72 @@ Bayesian inversion refers to a family of inversion methods that aim to estimate the _probability_ that a given set of parameters $\mathcal{P}$ can explain the measured data $\mu_{obs}$. This probability is expressed in terms of the _posterior distribution_ over the parameters, which comprises a likelihood term and a prior probability term, usually written as: ```{math} -:label: Bayes +:label: eq:Bayes p\left(\mathcal{P} | \mu_{obs} \right) \propto p\left(\mu_{obs} | \mathcal{P} \right) p\left(\mathcal{P}\right) ``` For those new to Bayesian statistics: $p\left(a|b \right)$ is read as "the probability that $a$ is true given that $b$ is true" (loosely speaking). The first term on the right-hand side is called the _likelihood_ term, and the second term is the _prior_ probability (or simply "prior"). The likelihood term can be estimated by running a forward simulation (with a given $\mathcal{P}$) and calculating the mismatch with the observations; the maximum-likelihood inversion discussed in [the previous section](max-likelihood) aims to find $\mathcal{P}$ that maximises this term. However, Bayesian inversion also takes into account that one might have a-priori information about $\mathcal{P}$ itself, before observing any data (the prior). For example, we might expect $\mathcal{P}$ to be positive, or be constrained by independent measurements, etc. Lastly, the proportionality constant, which quantifies the probability of observing the data, is unknown but irrelevant for the inversion. ## Gradients of the posterior -In practice, most Bayesian inversion methods sample the logaritm of $p$, hence Eq. {eq}`Bayes` becomes: +In practice, most Bayesian inversion methods sample the logaritm of $p$, hence Eq. {eq}`eq:Bayes` becomes: ```{math} -:label: logBayes +:label: eq:logBayes \ln p\left(\mathcal{P} | \mu_{obs} \right) \propto \ln p\left(\mu_{obs} | \mathcal{P} \right) + \ln p\left(\mathcal{P}\right) ``` To permit an informed exploration of the posterior distribution, gradient information is used to guide the search direction within the parameter space. To see how our forward simulations fit into this, let's consider a normal (Gaussian) likelihood distribution: ```{math} -:label: Gaussian +:label: eq:Gaussian p\left(\mu_{obs} | \mathcal{P} \right) \propto \prod_t \exp \left( - \left[ \frac{\mu_{obs}(t) - \mathrm{Forward}\left[t, \mathcal{P}\right]}{\sqrt{2} \nu} \right]^2 \right) ``` -To avoid confusion with the normal stress (often denoted $\sigma$), the standard deviation (also often denoted by $\sigma$) is here denoted by $\nu$. This standard deviation is expected to be proportional to the measurement noise amplitude in $\mu_{obs}$. The logarithm of Eq. {eq}`Gaussian` turns the product into a summation: +To avoid confusion with the normal stress (often denoted $\sigma$), the standard deviation (also often denoted by $\sigma$) is here denoted by $\nu$. This standard deviation is expected to be proportional to the measurement noise amplitude in $\mu_{obs}$. The logarithm of Eq. {eq}`eq:Gaussian` turns the product into a summation: ```{math} -:label: log_Gaussian +:label: eq:log_Gaussian \ln p\left(\mu_{obs} | \mathcal{P} \right) \propto - \sum_t \left[ \frac{\mu_{obs}(t) - \mathrm{Forward}\left[t, \mathcal{P}\right]}{\sqrt{2} \nu} \right]^2 ``` Lastly, the derivative of the log-likelihood with respect to $\mathcal{P}$ can then be found by virtue of the chain rule, and differentiation through the ODE solution: ```{math} -:label: grad_log_Gaussian +:label: eq:grad_log_Gaussian \frac{\partial \ln p\left(\mu_{obs} | \mathcal{P} \right)}{\partial \mathcal{P}} \propto \sum_t \frac{\mu_{obs}(t) - \mathrm{Forward}\left[t, \mathcal{P}\right]}{2 \nu^2} \frac{\partial \mathrm{Forward}}{\partial \mathcal{P}} ``` Because of the differentiability of the DiaBayes forward models, the gradient term on the right-hand side can be precisely calculated. A similar procedure is applied to obtain the gradients of the prior distribution. -By doing gradient descent on $-\partial \ln p\left(\mathcal{P} | \mu_{obs} \right) / \partial \mathcal{P}$, one arrives as the maximum a-posteriori estimate, which strikes a balance between the likelihood and prior terms. But this is not what we want: the point of doing Bayesian inversion, is to gain insight into the _distribution_ of parameter probabilities, not just a single estimate thereof. To estimate the posterior distribution, one needs to perform some kind of sampling of the neighbourhood around the maximum posterior. There are numerous methods that can do this, many of which have been implemented into the [Blackjax](https://blackjax-devs.github.io/blackjax/) library. However, DiaBayes implements its own Bayesian sampler: _Stein Variational Inference_. +By doing gradient descent on $-\partial \ln p\left(\mu_{obs} | \mathcal{P} \right) / \partial \mathcal{P}$, one arrives as the maximum a-posteriori estimate, which strikes a balance between the likelihood and prior terms. But this is not what we want: the point of doing Bayesian inversion, is to gain insight into the _distribution_ of parameter probabilities, not just a single estimate thereof. To estimate the posterior distribution, one needs to perform some kind of sampling of the neighbourhood around the maximum posterior. There are numerous methods that can do this, many of which have been implemented into the [Blackjax](https://blackjax-devs.github.io/blackjax/) library. However, DiaBayes implements its own Bayesian sampler: _Stein Variational Inference_. ## Stein Variational Inference +Stein Variational Inference (SVI) through _Stein Variational Gradient Descent_{footcite}`liu2016` is a particle-swarm optimisation method in which each particle represents one sample from the posterior distribution. The particles can move around in a space with the same number of dimensions as the number of invertible parameters. They are attracted towards the maximum in the likelihood and the prior propabilities (through gradient descent on the negative log-likelihood and log-prior, see Eq. {eq}`eq:grad_log_Gaussian`), and they repel one another based on their mutual distance. Eventually the particle swarm will find an equilibrium, after which the particle positions are fixed. This final distribution of the particles approximates the posterior distribution. + +To get a statistically significant sample of the posterior distribution, we need $N \gg 1$ particles, which scales with the number of invertible parameters. For an inversion of a classical rate-and-state friction model with 3 parameters, a sample size of $N = 1000$ is a good starting point. And thanks to the `jax.vmap` functionality we can run $N$ forward simulations in parallel on a GPU, so that doing the inversion with $N=100$ is not necessarily much faster than with $N=1000$. + +To find the equilibrium state, we randomly initialise the swarm of $N$ particles in a ball around the maximum-likelihood estimate (see [the previous section](max-likelihood)), and allow for $T$ update steps to converge towards the equilibrium state. During each step, we first calculate an "effective gradient" as defined by the SVI method: +```{math} +:label: eq:SVI +\begin{split} +\phi\left(\mathcal{P}_n \right) = \frac{1}{N} \sum_{i=1}^N \underbrace{\kappa \left(\mathcal{P}_n, \mathcal{P}_i \right) \frac{\partial \ln p \left(\mu_{obs} | \mathcal{P}_i \right)}{\partial \mathcal{P}_i}}_{\text{attraction to likelihood}} \\ ++ \underbrace{\kappa \left(\mathcal{P}_n, \mathcal{P}_i \right) \frac{\partial \ln p \left(\mathcal{P}_i \right)}{\partial \mathcal{P}_i}}_{\text{attraction to prior}} \\ ++ \underbrace{\kappa \left(\mathcal{P}_n, \mathcal{P}_i \right)}_{\text{repulsion}} +\end{split} +``` +Here, $\kappa(a, b)$ denotes a function (the "kernel") that equals to 1 when $a = b$, and decays to 0 when $a$ and $b$ get farther removed from each other. DiaBayes uses a standard radial basis function combined with a Euclidean distance metric: +```{math} +:label: eq:kernel +\kappa(a, b) = \exp \left(- \frac{1}{h} ||a - b||_2^2 \right) +``` +where $||x||_2$ denotes the Euclidean norm of a vector, and $h$ is calculated for the entire particle swarm using [the median trick](https://en.wikipedia.org/wiki/Median_trick). Then, each particle position is updated using a gradient-descent approach. For classical gradient descent, the update step looks like this: +```{math} +:label: eq:SVI_update +\mathcal{P}_n^{k+1} = \mathcal{P}_n^{k} - \eta \phi\left(\mathcal{P}_n^k \right) +``` +However, DiaBayes employs the [Adam](https://optax.readthedocs.io/en/latest/api/optimizers.html#optax.adam){footcite}`kingma2014` implementation of the [Optax](https://optax.readthedocs.io/en/latest/index.html) library for improved convergence, which employs a slightly different formulation. + +After having converged over $T$ steps, the final positions of $\left\{ \mathcal{P} \right\}_{n=1}^N$ represent the posterior probability distribution over the parameters. + +## Usage in DiaBayes + +... + +```{rubric} References +``` +```{footbibliography} +``` \ No newline at end of file diff --git a/docs/source/user-guide/inversion/index.md b/docs/source/user-guide/inversion/index.md index 55b7ccd..c79cf61 100644 --- a/docs/source/user-guide/inversion/index.md +++ b/docs/source/user-guide/inversion/index.md @@ -16,7 +16,11 @@ A classical approach to finding $\mathcal{P}^*$ is performing gradient descent o ```{math} \mathcal{P}^{i+1} = \mathcal{P}^i - \eta \left.\frac{\mathrm{d} \mathcal{L}}{\mathrm{d} \mathcal{P}} \right\vert_{\mathcal{P}^i} ``` -There are more advanced gradient-descent algorithms, like ADAM{footcite}`kingma2014` (as used by DiaBayes), but they are all just different flavours of this general concept. +There are more advanced gradient-descent algorithms, like Adam{footcite}`kingma2014` (as used by DiaBayes), but they are all just different flavours of this general concept. + +```{caution} +When the number of invertible parameter exceeds 1, $\mathcal{P}$ becomes a vector. A more precise notation would be to use a vector arrow $\vec{\mathcal{P}}$ to distinguish it from a scalar, but for ease of reading (and writing...) I will drop the arrow everywhere in these sections. +``` The challenge now is to find the derivative term on the right-hand side. By virtue of the [Leibniz integral rule](https://en.wikipedia.org/wiki/Leibniz_integral_rule), the total derivative of $\mathcal{L}$ becomes a partial derivative of $\mathrm{Forward}[t, \mathcal{P}]$ with respect to $\mathcal{P}$ inside the integral. But how does one differentiate the solution of an ODE with respect to its parameters? The solution at any point $t_i$ depends on the entire history from $t = t_0$ up to $t_i$, and so the derivatives at time $t_i$ depend on the derivatives at $t_{i-1}, t_{i-2}, \dots$. Fortunately, there is an elegant approach to solve this recursive problem, which is called the [_adjoint state method_](https://en.wikipedia.org/wiki/Adjoint_state_method) (or simply "adjoint method"). From eddf9122a56302372564512f1f36e9bc5ee22192 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Thu, 7 Aug 2025 15:01:03 +0200 Subject: [PATCH 37/56] Finalised SVI docs --- docs/source/user-guide/inversion/bayesian.md | 33 ++++++++++++++++++-- 1 file changed, 30 insertions(+), 3 deletions(-) diff --git a/docs/source/user-guide/inversion/bayesian.md b/docs/source/user-guide/inversion/bayesian.md index 52a02e5..9a157e6 100644 --- a/docs/source/user-guide/inversion/bayesian.md +++ b/docs/source/user-guide/inversion/bayesian.md @@ -61,11 +61,38 @@ where $||x||_2$ denotes the Euclidean norm of a vector, and $h$ is calculated fo ``` However, DiaBayes employs the [Adam](https://optax.readthedocs.io/en/latest/api/optimizers.html#optax.adam){footcite}`kingma2014` implementation of the [Optax](https://optax.readthedocs.io/en/latest/index.html) library for improved convergence, which employs a slightly different formulation. -After having converged over $T$ steps, the final positions of $\left\{ \mathcal{P} \right\}_{n=1}^N$ represent the posterior probability distribution over the parameters. +After having converged over $T$ steps, the final positions of the set of particles $\left\{ \mathcal{P} \right\}_{n=1}^N$ represent the posterior probability distribution over the parameters. -## Usage in DiaBayes +## Bayesian analysis in DiaBayes -... +Concretely, if one wants to perform a Bayesian inversion of the forward model parameters, it only requires a single function call, e.g.: +```python +bayesian_result = solver.bayesian_inversion( + t=t, mu=mu_measured, noise_std=noise_std, + params=params_init, friction_constants=constants, + block_constants=block_constants, + Nparticles=1500, Nsteps=100, rng=42 +) +``` +The function call signature is very similar to that of the `diabayes.ODEsolver.max_likelihood_solver` routine, with a few additional arguments specific to the SVI method. The `examples/simple_inversion.ipynb` example notebook describes these arguments in detail, which will not be repeated here. This notebook also demonstrates how to use the various analysis tools that accompany the `bayesian_inversion` method, such as convergence checking and uncertainty visualisation. + +What is important to clarify here, is that the prior distribution is currently not "properly" defined, at least not in the strict meaning of the term. In many (or even the majority) of Bayesian geophysics studies, the prior distribution is assumed to be uniform (also known as a "flat prior"). The corresponding gradients of this prior hence vanish ($\partial \ln p\left(\mathcal{P} \right) / \partial \mathcal{P} = 0$), causing the particles to settle around the likelihood distribution. This is not necessarily a bad strategy, because often there are no independent constraints on the parameters of the friction model, and a uniform prior accurately reflects this notion of "we don't know what to expect". On the other hand, we actually do know a bit more that: both empirically and theoretically, the rate-and-state friction parameters $a$ and $D_c$ are known to be strictly positive, and we expect them to exhibit a certain amplitude (e.g., $10^{-4} < a < 10^{-1}$). To somewhat account for this additional knowledge, DiaBayes opts to define a prior conditioned on the maximum-likelihood estimate $\mathcal{P}^*$, i.e.: +```{math} +p\left( \mathcal{P} \right) \approx q \left( \mathcal{P} | \mathcal{P}^* \right) +``` +Because of this conditioning on $\mathcal{P}^*$, which itself in conditioned on the data $\mu_{obs}$, this choice of a prior cannot be considered a _true_ prior. Nonetheless, it is a convenient choice to somewhat restrict the posterior distribution to parameter values that one would consider reasonable (assuming that the maximum-likelihood inversion found reasonable parameter values). + +A second important clarification, is that currently DiaBayes solves the Bayesian problem in log-parameter space, i.e. $\mathcal{P} = \ln \mathcal{Q}$, with $\mathcal{Q}$ representing the forward model parameters like $a$, $b$, $D_c$ (in the case of rate-and-state friction). This strategy ensures that all the parameters $\mathcal{Q}$ are strictly positive, which greatly improves the stability of the inversion; forward simulations with $a < 0$ are not only non-physical, they are also highly unstable. And simulations with $D_c < 0$ are not defined at all (logarithm of a negative number). Moreover, some parameters can vary by orders of magnitude, and hence solving the problem in log-space is a sensible choice. The flip-side of this strategy, is that some parameters that are normally not restricted to be positive (like the rate-and-state $b$ parameter) cannot become negative, even if it is demanded by the observed data. + +```{warning} +TL;DR of the above: +- The DiaBayes prior distribution is conditioned on the maximum-likelihood solution. +- The inversion is done on log-transformed parameters, hence the resulting values are strictly positive. +``` + +```{admonition} Future work +Future versions of DiaBayes will address the above restrictions by allowing for user-defined prior distribution functions. +``` ```{rubric} References ``` From c38f2d7a8fcae025979ce9b0adbae3978bbafb81 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Thu, 7 Aug 2025 15:11:49 +0200 Subject: [PATCH 38/56] Some typo corrections --- docs/source/getting-started.md | 4 ++-- docs/source/user-guide/forward/rate-and-state.md | 2 +- docs/source/user-guide/inversion/bayesian.md | 4 ++-- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/source/getting-started.md b/docs/source/getting-started.md index 00c2b30..02dc3cf 100644 --- a/docs/source/getting-started.md +++ b/docs/source/getting-started.md @@ -36,7 +36,7 @@ DiaBayes can accommodate several physical models that each come with their own s import diabayes as db y = db.Variables(mu=0.6, state=10.5) ``` -The container items can then be accessed through `y.mu` and `y.state`. If you'we used Python's `namedtuple` collection before then this should feel familiar. Invertible parameters and constants are declared in a similar way: +The container items can then be accessed through `y.mu` and `y.state`. If you've used Python's `namedtuple` collection before then this should feel familiar. Invertible parameters and constants are declared in a similar way: ```python # RSF parameters params = db.RSFParams(a=a, b=b, Dc=Dc) @@ -50,7 +50,7 @@ block_constants = db.SpringBlockConstants(k=k, v_lp=v1) The physics that underlies your experiment is encoded by a _forward model_. A DiaBayes `Forward` model is composed of three components: -1. A friction law, which is an equation that takes the friction and "state" as an input, and returns the instanteneous slip rate as an output, i.e. $v(t) = f(\mu(t), \theta(t))$. +1. A friction law, which is an equation that takes the friction and "state" as an input, and returns the instantaneous slip rate as an output, i.e. $v(t) = f(\mu(t), \theta(t))$. 2. A state evolution law, which returns the time derivative of the "state" variable. 3. A stress transfer model, which describes how the local slip rate results in a change in shear stress on the fault interface. diff --git a/docs/source/user-guide/forward/rate-and-state.md b/docs/source/user-guide/forward/rate-and-state.md index 2ccf3a6..6e31c6a 100644 --- a/docs/source/user-guide/forward/rate-and-state.md +++ b/docs/source/user-guide/forward/rate-and-state.md @@ -23,7 +23,7 @@ The implementation of the classical RSF formulation is available through `diabay ## Regularised rate-and-state friction -Owing to the logarithms used in the classical RSF formulation, the slip rate $v$ is required to be strictly positive. This requirement can be problematic in simulations where the shear stress (and corresonding slip rate) can change sign or become zero. To alleviate this, a regularised form{footcite}`lapusta2000` can be adopted with the same asymptotic behaviour as Eq. {eq}`RSF2`: +Owing to the logarithms used in the classical RSF formulation, the slip rate $v$ is required to be strictly positive. This requirement can be problematic in simulations where the shear stress (and corresponding slip rate) can change sign or become zero. To alleviate this, a regularised form{footcite}`lapusta2000` can be adopted with the same asymptotic behaviour as Eq. {eq}`RSF2`: ```{math} :label: RSF_reg v = 2 v_0 \sinh \left\{ \frac{\mu}{a} \exp \left( - \frac{1}{a} \left[\mu_0 + b \ln \left( \frac{v_0 \theta}{D_c} \right) \right] \right) \right\} diff --git a/docs/source/user-guide/inversion/bayesian.md b/docs/source/user-guide/inversion/bayesian.md index 9a157e6..54bceab 100644 --- a/docs/source/user-guide/inversion/bayesian.md +++ b/docs/source/user-guide/inversion/bayesian.md @@ -9,7 +9,7 @@ For those new to Bayesian statistics: $p\left(a|b \right)$ is read as "the proba ## Gradients of the posterior -In practice, most Bayesian inversion methods sample the logaritm of $p$, hence Eq. {eq}`eq:Bayes` becomes: +In practice, most Bayesian inversion methods sample the logarithm of $p$, hence Eq. {eq}`eq:Bayes` becomes: ```{math} :label: eq:logBayes \ln p\left(\mathcal{P} | \mu_{obs} \right) \propto \ln p\left(\mu_{obs} | \mathcal{P} \right) + \ln p\left(\mathcal{P}\right) @@ -36,7 +36,7 @@ By doing gradient descent on $-\partial \ln p\left(\mu_{obs} | \mathcal{P} \righ ## Stein Variational Inference -Stein Variational Inference (SVI) through _Stein Variational Gradient Descent_{footcite}`liu2016` is a particle-swarm optimisation method in which each particle represents one sample from the posterior distribution. The particles can move around in a space with the same number of dimensions as the number of invertible parameters. They are attracted towards the maximum in the likelihood and the prior propabilities (through gradient descent on the negative log-likelihood and log-prior, see Eq. {eq}`eq:grad_log_Gaussian`), and they repel one another based on their mutual distance. Eventually the particle swarm will find an equilibrium, after which the particle positions are fixed. This final distribution of the particles approximates the posterior distribution. +Stein Variational Inference (SVI) through _Stein Variational Gradient Descent_{footcite}`liu2016` is a particle-swarm optimisation method in which each particle represents one sample from the posterior distribution. The particles can move around in a space with the same number of dimensions as the number of invertible parameters. They are attracted towards the maximum in the likelihood and the prior probabilities (through gradient descent on the negative log-likelihood and log-prior, see Eq. {eq}`eq:grad_log_Gaussian`), and they repel one another based on their mutual distance. Eventually the particle swarm will find an equilibrium, after which the particle positions are fixed. This final distribution of the particles approximates the posterior distribution. To get a statistically significant sample of the posterior distribution, we need $N \gg 1$ particles, which scales with the number of invertible parameters. For an inversion of a classical rate-and-state friction model with 3 parameters, a sample size of $N = 1000$ is a good starting point. And thanks to the `jax.vmap` functionality we can run $N$ forward simulations in parallel on a GPU, so that doing the inversion with $N=100$ is not necessarily much faster than with $N=1000$. From 4ab56403e9ef5e72130bbaae9f10c26d016d8ca7 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Thu, 7 Aug 2025 17:00:43 +0200 Subject: [PATCH 39/56] Started populating rock friction docs --- .../img/steady-state_niemeijer2006_dark.svg | 1348 +++++++++++++++++ .../img/steady-state_niemeijer2006_light.svg | 1348 +++++++++++++++++ docs/source/references.bib | 24 + .../source/user-guide/forward/spring-block.md | 4 +- .../source/user-guide/friction_experiments.md | 34 +- 5 files changed, 2752 insertions(+), 6 deletions(-) create mode 100644 docs/source/_static/img/steady-state_niemeijer2006_dark.svg create mode 100644 docs/source/_static/img/steady-state_niemeijer2006_light.svg diff --git a/docs/source/_static/img/steady-state_niemeijer2006_dark.svg b/docs/source/_static/img/steady-state_niemeijer2006_dark.svg new file mode 100644 index 0000000..7950f98 --- /dev/null +++ b/docs/source/_static/img/steady-state_niemeijer2006_dark.svg @@ -0,0 +1,1348 @@ + + + + + + + + 2025-08-07T16:55:01.022996 + image/svg+xml + + + Matplotlib v3.8.0, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/source/references.bib b/docs/source/references.bib index f5b7c2b..3922b48 100644 --- a/docs/source/references.bib +++ b/docs/source/references.bib @@ -53,6 +53,18 @@ @article{marone1998 year = {1998}, } +@article{Niemeijer2006, + title = {Velocity dependence of strength and healing behaviour in simulated phyllosilicate-bearing fault gouge}, + volume = {427}, + ISSN = {0040-1951}, + DOI = {10.1016/j.tecto.2006.03.048}, + number = {1–4}, + journal = {Tectonophysics}, + publisher = {Elsevier BV}, + author = {Niemeijer, A.R. and Spiers, C.J.}, + year = {2006}, +} + @article{ruina1983, title = {Slip instability and state variable friction laws}, volume = {88}, @@ -63,4 +75,16 @@ @article{ruina1983 publisher = {American Geophysical Union (AGU)}, author = {Ruina, Andy}, year = {1983}, +} + +@article{vandenende2019, + title = {An investigation into the role of time-dependent cohesion in interseismic fault restrengthening}, + volume = {9}, + ISSN = {2045-2322}, + DOI = {10.1038/s41598-019-46241-5}, + number = {1}, + journal = {Scientific Reports}, + publisher = {Springer Science and Business Media LLC}, + author = {van den Ende, M. P. A. and Niemeijer, A. R.}, + year = {2019}, } \ No newline at end of file diff --git a/docs/source/user-guide/forward/spring-block.md b/docs/source/user-guide/forward/spring-block.md index 6bb868b..4e30c92 100644 --- a/docs/source/user-guide/forward/spring-block.md +++ b/docs/source/user-guide/forward/spring-block.md @@ -1,6 +1,6 @@ # Spring-blocks -While the friction law (and state evolution law) describes how a fault responds to a particular state of stress, an additional descriptor is needed to connect the fault response back to the state of stress (i.e., mechanical feedback). For natural faults it is often necessary to come up with an elastic model that includes complexities like a free surface and fault non-planarities. For laboratory experiments, however, it is often sufficient to represent the experimental set-up with a _spring-block_ mechanical analogue. A frictional interface is formed between a rigid "block" placed on top of a surface, and the block is pulled horizontally by a spring, the other end of which is being translated at a constant rate ($v_{lp}$). For $t > 0$, stretching of the spring transmits a force to the block. This force is resisted by friction ($\mu$) at the interface. The friction law then describes the rate of translation of the block ($v = f(\mu, \dots)$), which results in shortening of the spring, resulting in a reduction in friction, etc. +While the friction law (and state evolution law) describes how a fault responds to a particular state of stress, an additional descriptor is needed to connect the fault response back to the state of stress (i.e., mechanical feedback). For natural faults it is often necessary to come up with an elastic model that includes complexities like a free surface and fault non-planarities. For laboratory experiments, however, it is often sufficient to represent the experimental set-up with a _spring-block_ mechanical analogue. A frictional interface is formed between a rigid "block" placed on top of a surface, and the block is pulled horizontally by a spring, the other end of which is being translated at a constant rate ($v_{lp}$). For $t > 0$, stretching of the spring transmits a force to the block. This force is resisted by friction ($\mu$) at the interface. The friction law then describes the rate of translation of the block ($v = f(\mu, \dots)$), which results in shortening of the spring, resulting in a reduction in friction, etc. See [this YouTube video](https://youtu.be/AylMEWgVAGA?si=joC_juqVs1OhIHhK) (courtesy of Bochum university) that demonstrates this principle with a physical model. The most basic spring-block formulation is effectively zero-dimensional, meaning that there is no internal deformation of the block or variation of stress/slip at the interface. More advanced formulations can account for such internal deformation, which can lead to e.g. rupture propagation across the interface. DiaBayes currently only implements two types of zero-dimensional spring-block analogues. @@ -55,7 +55,7 @@ To the forward model is completed with the somewhat trivial expression for the p ``` Correspondingly, the solution vector gains an extra component, $\vec{X}(t) = \left[ \mu(t), \theta(t), x(t) \right]^\intercal$. -In order for a friction model to be compatible with an inertial spring-block, it must implement a `_partials` method to compute the partial derivatives of $v$ with respect to the relevant variables. This method takes as an argument the vector $\vec{X}$ and returns $\partial v / \partial \mu$ and the sum of the remaining partial derivative terms ($(\partial v / \partial \theta) \dot{\theta} + \dots$). +In order for a friction model to be compatible with an inertial spring-block, it must implement a `_partials` method to compute the partial derivatives of $v$ with respect to the relevant variables. This method takes as an argument the vector $\vec{X}$ and returns $\partial v / \partial \mu$ and the sum of the remaining partial derivative terms ($(\partial v / \partial \theta) \dot{\theta} + \dots$). If a `_partials` method is absent, a `NotImplementedError` exception will be raised upon instantiating `diabayes.forward_models.Forward`. The inertial spring-block model is available through `diabayes.forward_models.inertial_springblock`. diff --git a/docs/source/user-guide/friction_experiments.md b/docs/source/user-guide/friction_experiments.md index 036dbd0..de6fe01 100644 --- a/docs/source/user-guide/friction_experiments.md +++ b/docs/source/user-guide/friction_experiments.md @@ -2,12 +2,38 @@ This section briefly summarises a few key concepts in rock friction experimentation, which are presumed to be well understood for the remainder of this User Guide. -- Effective friction coefficient (ignoring friction) +First of all, "friction" is a term that is used rather loosely to refer to the shear resistance of a fault interface. While this term suggests purely brittle deformation, it is not necessarily implied that non-brittle processes do not contribute to the observed behaviour. In fact, detailed analyses of microstructure samples and theoretical modelling suggests that "non-brittle" processes (like fluid-rock chemical reactions, or crystal plasticity) play a dominant role in controlling the shear resistance of faults, even at room ambient conditions (see e.g. the [Chen-Niemeijer-Spiers model](forward/chen-niemeijer-spiers)). Hence, while remaining entirely agnostic to the underlying processes, "friction" in most cases simply refers to the ratio of the shear stress to normal stress. When interpreted in terms of [Coulomb mechanics](https://en.wikipedia.org/wiki/Charles-Augustin_de_Coulomb#Contributions_to_earth_pressure_theory), this informal definition also implies that cohesion is negligible. In other words, assuming the following criterion for fault failure: +```{math} +:label: eq:coulomb_friction +\tau = \mu \sigma_n + C +``` +it is often assumed that the cohesion $C=0$, such that the friction coefficient $\mu = \tau / \sigma_n$. This is sometimes more precisely referred to as an _effective friction coefficient_ (typically denoted $\mu'$). Whether the assumption of $C=0$ holds true in practice remains to be tested, especially when longer time-scales are considered{footcite}`vandenende2019`. -## Velocity-step tests +What is also not explicit in Eq. {eq}`eq:coulomb_friction`, but is well-known in the community, is that the friction coefficient $\mu$ is not a constant; it depends notably on the rate of deformation (sliding) of the fault, temperature, time, fault material, and many other factors. There are several models that predict some of these dependencies (see [Forward models](forward/index)), known as "friction models". The friction models are parameterised, and it is the purpose of many friction studies to systematically measure/infer these parameters across a range of experimental conditions. Below, I describe common experimental techniques to study fault "friction". + +## Steady-state tests + +One of the most straightfoward means of investigating the variation of the friction coefficient, is by deforming a laboratory fault until steady-state sliding is achieved. In this context, "steady-state" means no change in the sliding rate of the fault (zero time derivative) as well as no change in the measured shear stress. The resulting value of the (effective) friction coefficient is recorded, and the experiment is repeated under different conditions, e.g. a change in temperature, normal stress, or externally imposed sliding rate. + +```{image} ../_static/img/steady-state_niemeijer2006_dark.svg +:class: only-dark +:align: center +``` +```{image} ../_static/img/steady-state_niemeijer2006_light.svg +:class: only-light +:align: center +``` +(Steady-state friction data of a halite-muscovite gouge mixture, deformed at room temperature and a normal load of 5 MPa. Data from _Niemeijer & Spiers_{footcite}`niemeijer2006`) -- Up and down steps +One of the most significant observations (also shown in the figure above), is that the steady-state friction coefficient varies systematically with the fault slip rate. Under some conditions, the friction increases with increasing slip rate, a phenomenon that is called _velocity-strengthening_ (because the fault becomes "stronger" with increasing sliding velocity). The converse case is called _velocity-weakening_. It is widely thought that velocity-weakening is a prerequisite for earthquakes in nature. + +## Velocity-step tests ## Stick-slip instabilities -## Slide-hold-slide tests \ No newline at end of file +## Slide-hold-slide tests + +```{rubric} References +``` +```{footbibliography} +``` \ No newline at end of file From 61f11e67c83a332741c1a0e22b030147395dda5d Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Tue, 12 Aug 2025 12:32:19 +0200 Subject: [PATCH 40/56] Prior to recasting state evolution formalism --- docs/source/references.bib | 2 +- docs/source/user-guide/adding_features.md | 6 +++++- docs/source/user-guide/advanced_usage.md | 6 +++++- docs/source/user-guide/forward/chen-niemeijer-spiers.md | 6 +++++- docs/source/user-guide/friction_experiments.md | 5 +++++ 5 files changed, 21 insertions(+), 4 deletions(-) diff --git a/docs/source/references.bib b/docs/source/references.bib index 3922b48..195ae87 100644 --- a/docs/source/references.bib +++ b/docs/source/references.bib @@ -53,7 +53,7 @@ @article{marone1998 year = {1998}, } -@article{Niemeijer2006, +@article{niemeijer2006, title = {Velocity dependence of strength and healing behaviour in simulated phyllosilicate-bearing fault gouge}, volume = {427}, ISSN = {0040-1951}, diff --git a/docs/source/user-guide/adding_features.md b/docs/source/user-guide/adding_features.md index cb4217e..ccc248b 100644 --- a/docs/source/user-guide/adding_features.md +++ b/docs/source/user-guide/adding_features.md @@ -1 +1,5 @@ -# Adding custom physics \ No newline at end of file +# Adding custom physics + +```{admonition} Future work +To be written +``` \ No newline at end of file diff --git a/docs/source/user-guide/advanced_usage.md b/docs/source/user-guide/advanced_usage.md index 7802a20..214a9cb 100644 --- a/docs/source/user-guide/advanced_usage.md +++ b/docs/source/user-guide/advanced_usage.md @@ -1 +1,5 @@ -# Advanced use cases \ No newline at end of file +# Advanced use cases + +```{admonition} Future work +To be written +``` \ No newline at end of file diff --git a/docs/source/user-guide/forward/chen-niemeijer-spiers.md b/docs/source/user-guide/forward/chen-niemeijer-spiers.md index 6d1c9bf..c3ee60a 100644 --- a/docs/source/user-guide/forward/chen-niemeijer-spiers.md +++ b/docs/source/user-guide/forward/chen-niemeijer-spiers.md @@ -1 +1,5 @@ -# Chen-Niemeijer-Spiers \ No newline at end of file +# Chen-Niemeijer-Spiers + +```{admonition} Future work +To be written +``` \ No newline at end of file diff --git a/docs/source/user-guide/friction_experiments.md b/docs/source/user-guide/friction_experiments.md index de6fe01..98384a8 100644 --- a/docs/source/user-guide/friction_experiments.md +++ b/docs/source/user-guide/friction_experiments.md @@ -15,6 +15,7 @@ What is also not explicit in Eq. {eq}`eq:coulomb_friction`, but is well-known in One of the most straightfoward means of investigating the variation of the friction coefficient, is by deforming a laboratory fault until steady-state sliding is achieved. In this context, "steady-state" means no change in the sliding rate of the fault (zero time derivative) as well as no change in the measured shear stress. The resulting value of the (effective) friction coefficient is recorded, and the experiment is repeated under different conditions, e.g. a change in temperature, normal stress, or externally imposed sliding rate. +--- ```{image} ../_static/img/steady-state_niemeijer2006_dark.svg :class: only-dark :align: center @@ -25,10 +26,14 @@ One of the most straightfoward means of investigating the variation of the frict ``` (Steady-state friction data of a halite-muscovite gouge mixture, deformed at room temperature and a normal load of 5 MPa. Data from _Niemeijer & Spiers_{footcite}`niemeijer2006`) +--- + One of the most significant observations (also shown in the figure above), is that the steady-state friction coefficient varies systematically with the fault slip rate. Under some conditions, the friction increases with increasing slip rate, a phenomenon that is called _velocity-strengthening_ (because the fault becomes "stronger" with increasing sliding velocity). The converse case is called _velocity-weakening_. It is widely thought that velocity-weakening is a prerequisite for earthquakes in nature. ## Velocity-step tests +One remarkable feature of many frictional materials, is that they display a rather curious transient response to an instantaneous step in the external loading velocity. This is most clearly seen in experiments where a fault is driven at a constant rate until steady-state is reached, after which this rate is stepwise increased or decreased. This protocol is called a "velocity-step test". The study of the transient frictional response induced by a velocity-step has laid the foundation for much of our understanding of fault friction and earthquakes. + ## Stick-slip instabilities ## Slide-hold-slide tests From ddad42cd0036b7705e06b0603420eea55b9163a9 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Mon, 18 Aug 2025 15:07:32 +0200 Subject: [PATCH 41/56] Implemented base Variables class and tested differentiability --- diabayes/forward_models.py | 111 +++++++++++++++++++++++--- diabayes/typedefs.py | 158 +++++++++++++++++++++++++++---------- 2 files changed, 215 insertions(+), 54 deletions(-) diff --git a/diabayes/forward_models.py b/diabayes/forward_models.py index 472d245..11fe980 100644 --- a/diabayes/forward_models.py +++ b/diabayes/forward_models.py @@ -1,15 +1,18 @@ -from typing import Callable +from dataclasses import make_dataclass +from typing import Tuple, Union import equinox as eqx import jax.numpy as jnp -from jaxtyping import Float +from jaxtyping import Array, Float from diabayes.typedefs import ( FrictionModel, RSFConstants, RSFParams, SpringBlockConstants, + StateDict, StateEvolution, + StressTransfer, Variables, _BlockConstants, _Constants, @@ -40,7 +43,7 @@ def rsf(variables: Variables, params: RSFParams, constants: RSFConstants) -> Flo v : Float The instantaneous slip rate in the same units as `v0` """ - Omega = params.b * jnp.log(variables.state * constants.v0 / params.Dc) + Omega = params.b * jnp.log(variables.theta * constants.v0 / params.Dc) return constants.v0 * jnp.exp((variables.mu - constants.mu0 - Omega) / params.a) @@ -71,12 +74,17 @@ def ageing_law( dtheta : Float The rate of change of the state variable `theta` [s/s] """ - return 1 - v * variables.state / params.Dc + return 1 - v * variables.theta / params.Dc @eqx.filter_jit def springblock( - v: Float, variables: Variables, constants: SpringBlockConstants + t: Float, + v: Float, + v_partials: Float[Array, "..."], + variables: Variables, + dstate: Variables, + constants: SpringBlockConstants, ) -> Float: r""" A conventional (non-inertial) spring-block loading formulation: @@ -104,6 +112,26 @@ def springblock( return constants.k * (constants.v_lp - v) +@eqx.filter_jit +def inertial_springblock( + t: Float, + v: Float, + v_partials: Variables, + variables: Variables, + dstate: Float[Array, "..."], + constants: SpringBlockConstants, +) -> Float: + r""" + An inertial spring-block loading formulation: + """ + mass_term = constants.k * (constants.v_lp * t - variables.slip) - variables.mu + # The partials_term contains the summation of the partial derivatives of + # v with respect to some variable y, times the time-derivative of y + # The first partial derivative is v with respect to mu, and is excluded. + partials_term = jnp.dot(v_partials.state.to_array(), dstate) + return (mass_term - partials_term) / v_partials.mu + + class Forward: r""" The `Forward` class assembles the various components that comprise @@ -130,23 +158,84 @@ class Forward: def __init__( self, friction_model: FrictionModel, - state_evolution: StateEvolution, - block_type: Callable[[Float, Variables, _BlockConstants], Float], + state_evolution: Union[StateEvolution, Tuple[StateEvolution]], + stress_transfer: StressTransfer, ) -> None: + # Set the friction model and stress transfer model (easy...) self.friction_model = friction_model - self.state_evolution = state_evolution - self.stress_transfer = block_type + self.stress_transfer = stress_transfer + # If a StatEvolution function is passed directly, make it a tuple + if not isinstance(state_evolution, tuple): + state_evolution = (state_evolution,) + + self.state_evolution_fns = state_evolution + # Do ast inspection of variables + # Validate number of state_evolution items vs. ast inspected state values + # Create Variables object for user to populate + + # Compile a function that calls each provided StateEvolution and + # stacks the results in an array. This way, the "state" can host + # an arbitrary number of variables (porosity, temperature, slip, ...) + self.state_evolution = eqx.filter_jit( + lambda v, variables, params, constants: jnp.stack( + [fi(v, variables, params, constants) for fi in state_evolution] + ) + ) pass + def create_variables(self) -> Variables: + + import ast + import inspect + + accessed = set() + + def get_accessed_attrs(func, arg_name): + tree = ast.parse(inspect.getsource(func)) + accessed = set() + + class Visitor(ast.NodeVisitor): + def visit_Attribute(self, node: ast.Attribute): + if isinstance(node.value, ast.Name) and node.value.id == arg_name: + accessed.add(node.attr) + self.generic_visit(node) + + Visitor().visit(tree) + return accessed + + for func in ( + self.friction_model, + self.stress_transfer, + *self.state_evolution_fns, + ): + accessed.update(get_accessed_attrs(func, "variables")) + + assert "mu" in accessed + accessed.remove("mu") + + assert len(accessed) == len(self.state_evolution_fns) + + # state = dict(zip(accessed, -jnp.ones(len(accessed)))) + state_obj = StateDict(keys=tuple(accessed), vals=-1.0 * jnp.ones(len(accessed))) + variables = Variables( + mu=jnp.asarray([-1.0], dtype=jnp.float64), state=state_obj + ) + return variables + @eqx.filter_jit def __call__( self, + t: Float, variables: Variables, params: _Params, friction_constants: _Constants, block_constants: _BlockConstants, ) -> Variables: - v = self.friction_model(variables, params, friction_constants) + v, v_derivs = eqx.filter_value_and_grad(self.friction_model)( + variables, params, friction_constants + ) + print(v) + print(v_derivs) dstate = self.state_evolution(v, variables, params, friction_constants) - dmu = self.stress_transfer(v, variables, block_constants) + dmu = self.stress_transfer(t, v, v_derivs, variables, dstate, block_constants) return Variables(mu=dmu, state=dstate) diff --git a/diabayes/typedefs.py b/diabayes/typedefs.py index b897013..643a8df 100644 --- a/diabayes/typedefs.py +++ b/diabayes/typedefs.py @@ -1,6 +1,6 @@ -from dataclasses import dataclass +import dataclasses as dcs from time import time_ns -from typing import Any, Iterable, NamedTuple, Protocol, Tuple, TypeAlias, Union +from typing import Any, Iterable, Mapping, NamedTuple, Protocol, Tuple, TypeAlias, Union import equinox as eqx import jax @@ -17,83 +17,143 @@ """ -class Container: +class StateDict(eqx.Module): + keys: tuple[str, ...] = eqx.field(static=True) + vals: Float[Array, "..."] - def __len__(self): - return len(self.tree_flatten()[0]) + def __getitem__(self, k: str) -> Array: + i = self.keys.index(k) + return self.vals[i] - def __iter__(self): - return iter(self.tree_flatten()[0]) + def __str__(self) -> str: + return "hi" - @classmethod - def tree_unflatten(cls, aux_data, children): - return cls(*children) + def replace_values(self, **kwargs) -> "StateDict": + mapping = dict(zip(self.keys, self.vals)) + mapping.update(kwargs) + return StateDict(self.keys, jnp.array([mapping[k] for k in self.keys])) - @classmethod - def from_array(cls, x: Iterable): - return cls.tree_unflatten(None, x) - def tree_flatten(self): - raise NotImplementedError +class Variables(eqx.Module): + mu: jax.Array + state: StateDict - def to_array(self): - return jnp.array(self.tree_flatten()[0]) + def __getattr__(self, name: str): + # NOTE: __getattr__ is only called when __getattribute__, + # lookup fails, i.e, it looks for self.name before + # checking self.state.name + if name in self.state.keys: + return self.state[name] + raise AttributeError(f"{type(self).__name__} has no attribute {name!r}") + def set_values(self, **kwargs) -> "Variables": + mu = jnp.asarray(kwargs.pop("mu")) + return dcs.replace(self, mu=mu, state=self.state.replace_values(**kwargs)) -@tree_util.register_pytree_node_class -@dataclass(frozen=True) -class Variables(Container): - mu: Float - state: Float + def __repr__(self) -> str: + state_str = ", ".join( + f"{k}={v}" for k, v in zip(self.state.keys, self.state.vals) + ) + return f"Variables(mu={self.mu}, {state_str})" - def tree_flatten(self): - return (self.mu, self.state), None +# class Container: -@tree_util.register_pytree_node_class -@dataclass(frozen=True) -class RSFParams(Container): +# def __len__(self): +# return len(self.tree_flatten()[0]) + +# def __iter__(self): +# return iter(self.tree_flatten()[0]) + +# @classmethod +# def tree_unflatten(cls, aux_data, children): +# return cls(*children) + +# @classmethod +# def from_array(cls, x: Iterable, aux: Any = None): +# return cls.tree_unflatten(aux, x) + +# def tree_flatten(self): +# raise NotImplementedError + +# def to_array(self): +# return jnp.array(self.tree_flatten()[0]) + + +# # TODO: replace tree_util stuff with eqx.Module... +# @tree_util.register_pytree_node_class +# @dataclass(frozen=True) +# class Variables(Container): +# mu: Float +# state: Union[Float, Container] + +# def tree_flatten(self): +# # If the state is a scalar, return it directly +# if not isinstance(self.state, Container): +# return (self.mu, self.state), None +# # Else, tree_flatten the state first +# else: +# return (self.mu, *self.state.tree_flatten()[0]), type(self.state) + +# @classmethod +# def tree_unflatten(cls, aux_data: Union[None, Container], children: Iterable): +# children = list(children) +# mu = children[0] +# if aux_data is None: +# state = children[1] +# else: +# state = aux_data.tree_unflatten(None, children[1:]) +# return cls(mu=mu, state=state) + +# def __getattr__(self, name: str) -> Any: +# if isinstance(self.state, Container): +# return getattr(self.state, name) +# if name in ("state", "theta"): +# return self.state +# raise AttributeError() + + +class RSFParams(eqx.Module): a: Float b: Float Dc: Float - def tree_flatten(self): - return (self.a, self.b, self.Dc), None - -@dataclass(frozen=True) +@dcs.dataclass(frozen=True) class RSFConstants: v0: Float mu0: Float -@tree_util.register_pytree_node_class -@dataclass(frozen=True) -class CNSParams(Container): +class CNSParams(eqx.Module): phi_c: Float Z: Float - def tree_flatten(self): - return (self.phi_c, self.Z), None - -@dataclass(frozen=True) +@dcs.dataclass(frozen=True) class CNSConstants: phi_c: Float Z: Float -@dataclass(frozen=True) +@dcs.dataclass(frozen=True) class SpringBlockConstants: k: Float v_lp: Float +@dcs.dataclass(frozen=True) +class InertialSpringBlockConstants: + k: Float + v_lp: Float + M: Float + + # These typedefs should only be used for type checking, # and should not be instantiated. _Params = Union[RSFParams, CNSParams] _Constants = Union[RSFConstants] -_BlockConstants = Union[SpringBlockConstants] +_BlockConstants = Union[SpringBlockConstants, InertialSpringBlockConstants] """ @@ -113,6 +173,18 @@ def __call__( ) -> Float: ... +class StressTransfer(Protocol): + def __call__( + self, + t: Float, + v: Float, + v_derivs: Variables, + variables: Variables, + dstate: Float[Array, "..."], + constants: Any, + ) -> Float: ... + + """ -------------- SVI containers @@ -121,7 +193,7 @@ def __call__( @tree_util.register_pytree_node_class -@dataclass(frozen=True) +@dcs.dataclass(frozen=True) class Particles: _particles: Union[Tuple[RSFParams, ...], Tuple[CNSParams, ...]] @@ -167,7 +239,7 @@ def generate( @tree_util.register_pytree_node_class -@dataclass(frozen=True) +@dcs.dataclass(frozen=True) class RSFParticles(Particles): @classmethod @@ -235,7 +307,7 @@ class CNSStatistics(ParamStatistics): Dc: Statistics -@dataclass +@dcs.dataclass class BayesianSolution: chains: Chains From 37a97a951947149faa6053b1b8cfa025024cf1c8 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Mon, 18 Aug 2025 15:24:10 +0200 Subject: [PATCH 42/56] A few small fixes before adjusting solver wrappers --- diabayes/forward_models.py | 24 ++++++++++++++++-------- 1 file changed, 16 insertions(+), 8 deletions(-) diff --git a/diabayes/forward_models.py b/diabayes/forward_models.py index 11fe980..5790653 100644 --- a/diabayes/forward_models.py +++ b/diabayes/forward_models.py @@ -128,7 +128,7 @@ def inertial_springblock( # The partials_term contains the summation of the partial derivatives of # v with respect to some variable y, times the time-derivative of y # The first partial derivative is v with respect to mu, and is excluded. - partials_term = jnp.dot(v_partials.state.to_array(), dstate) + partials_term = jnp.dot(v_partials.state.vals, dstate) return (mass_term - partials_term) / v_partials.mu @@ -169,9 +169,6 @@ def __init__( state_evolution = (state_evolution,) self.state_evolution_fns = state_evolution - # Do ast inspection of variables - # Validate number of state_evolution items vs. ast inspected state values - # Create Variables object for user to populate # Compile a function that calls each provided StateEvolution and # stacks the results in an array. This way, the "state" can host @@ -191,6 +188,7 @@ def create_variables(self) -> Variables: accessed = set() def get_accessed_attrs(func, arg_name): + """Helper routine to automatically extract the variable names""" tree = ast.parse(inspect.getsource(func)) accessed = set() @@ -203,6 +201,8 @@ def visit_Attribute(self, node: ast.Attribute): Visitor().visit(tree) return accessed + # Loop over the forward model components and evaluate + # which variable names are accessed for func in ( self.friction_model, self.stress_transfer, @@ -210,12 +210,15 @@ def visit_Attribute(self, node: ast.Attribute): ): accessed.update(get_accessed_attrs(func, "variables")) + # Ensure that we have at least one function that touches mu assert "mu" in accessed accessed.remove("mu") + # Ensure that the number of state variables equals the number + # of state evolution functions assert len(accessed) == len(self.state_evolution_fns) - # state = dict(zip(accessed, -jnp.ones(len(accessed)))) + # Initialise values to -1 state_obj = StateDict(keys=tuple(accessed), vals=-1.0 * jnp.ones(len(accessed))) variables = Variables( mu=jnp.asarray([-1.0], dtype=jnp.float64), state=state_obj @@ -231,11 +234,16 @@ def __call__( friction_constants: _Constants, block_constants: _BlockConstants, ) -> Variables: + # Calculate v and its partial derivatives with respect to + # the variables (mu, state1, state2, ...) v, v_derivs = eqx.filter_value_and_grad(self.friction_model)( variables, params, friction_constants ) - print(v) - print(v_derivs) + # Rate of change of state variables dstate = self.state_evolution(v, variables, params, friction_constants) + # Rate of change of mu (stress transfer) dmu = self.stress_transfer(t, v, v_derivs, variables, dstate, block_constants) - return Variables(mu=dmu, state=dstate) + # Create a new state container for dstate + state_obj = StateDict(variables.state.keys, dstate) + # Create a new variables container + return Variables(mu=dmu, state=state_obj) From c4cf8f9187d9e950c5eb98442ed4c51f4bd885e8 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Mon, 18 Aug 2025 15:47:13 +0200 Subject: [PATCH 43/56] New Variables class working with SciPy solve_ivp --- diabayes/solver.py | 6 ++++-- diabayes/typedefs.py | 9 +++++++++ 2 files changed, 13 insertions(+), 2 deletions(-) diff --git a/diabayes/solver.py b/diabayes/solver.py index 3dcb520..b5c8127 100644 --- a/diabayes/solver.py +++ b/diabayes/solver.py @@ -86,8 +86,10 @@ def solve_forward( Solution time series of friction and state """ + keys = y0.state.keys + _forward = lambda t, y, *args: self.forward_model( - Variables.from_array(y), *args + t, Variables.from_array(y, keys), *args ).to_array() result = solve_ivp( @@ -102,7 +104,7 @@ def solve_forward( assert result.y is not None - return Variables.from_array(result.y) + return Variables.from_array(result.y, keys) @eqx.filter_jit def _forward_wrapper( diff --git a/diabayes/typedefs.py b/diabayes/typedefs.py index 643a8df..c81fe3d 100644 --- a/diabayes/typedefs.py +++ b/diabayes/typedefs.py @@ -56,6 +56,15 @@ def __repr__(self) -> str: ) return f"Variables(mu={self.mu}, {state_str})" + def to_array(self) -> Float[Array, "..."]: + return jnp.hstack([self.mu, self.state.vals]) + + @classmethod + def from_array(cls, x: Float[Array, "..."], keys: tuple[str, ...]) -> "Variables": + mu = jnp.asarray(x[0]) + state_obj = StateDict(keys=keys, vals=x[1:]) + return cls(mu=mu, state=state_obj) + # class Container: From c0b98b6d0af2daae90263841bb24e5323212eca3 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Mon, 18 Aug 2025 17:28:31 +0200 Subject: [PATCH 44/56] Inertial spring-block working, but equations are extremely stiff... --- diabayes/forward_models.py | 14 ++++++++++++-- diabayes/solver.py | 2 ++ 2 files changed, 14 insertions(+), 2 deletions(-) diff --git a/diabayes/forward_models.py b/diabayes/forward_models.py index 5790653..00485cc 100644 --- a/diabayes/forward_models.py +++ b/diabayes/forward_models.py @@ -7,6 +7,7 @@ from diabayes.typedefs import ( FrictionModel, + InertialSpringBlockConstants, RSFConstants, RSFParams, SpringBlockConstants, @@ -77,6 +78,13 @@ def ageing_law( return 1 - v * variables.theta / params.Dc +@eqx.filter_jit +def slip_rate( + v: Float, variables: Variables, params: RSFParams, constants: RSFConstants +) -> Float: + return v + + @eqx.filter_jit def springblock( t: Float, @@ -119,12 +127,14 @@ def inertial_springblock( v_partials: Variables, variables: Variables, dstate: Float[Array, "..."], - constants: SpringBlockConstants, + constants: InertialSpringBlockConstants, ) -> Float: r""" An inertial spring-block loading formulation: """ - mass_term = constants.k * (constants.v_lp * t - variables.slip) - variables.mu + mass_term = ( + constants.k * (constants.v_lp * t - variables.slip) - variables.mu + ) / constants.M # The partials_term contains the summation of the partial derivatives of # v with respect to some variable y, times the time-derivative of y # The first partial derivative is v with respect to mu, and is excluded. diff --git a/diabayes/solver.py b/diabayes/solver.py index b5c8127..5c89250 100644 --- a/diabayes/solver.py +++ b/diabayes/solver.py @@ -57,6 +57,7 @@ def solve_forward( params: _Params, friction_constants: _Constants, block_constants: _BlockConstants, + method: str = "RK45", ) -> Any: """ Solve a forward problem using SciPy's `solve_ivp` routine. @@ -100,6 +101,7 @@ def solve_forward( args=(params, friction_constants, block_constants), rtol=self.rtol, atol=self.atol, + method=method, ) assert result.y is not None From 5c4ea8c62e6d337fb5f6715b594f4e373c534c96 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Wed, 20 Aug 2025 11:47:47 +0200 Subject: [PATCH 45/56] Changed automatic state variable discovery to user-defined assignment --- diabayes/forward_models.py | 67 +++++++++++++++++++------------------- diabayes/typedefs.py | 60 ++-------------------------------- 2 files changed, 35 insertions(+), 92 deletions(-) diff --git a/diabayes/forward_models.py b/diabayes/forward_models.py index 00485cc..384542c 100644 --- a/diabayes/forward_models.py +++ b/diabayes/forward_models.py @@ -1,5 +1,5 @@ from dataclasses import make_dataclass -from typing import Tuple, Union +from typing import Callable, Dict, Iterable, Tuple, Union import equinox as eqx import jax.numpy as jnp @@ -168,35 +168,41 @@ class Forward: def __init__( self, friction_model: FrictionModel, - state_evolution: Union[StateEvolution, Tuple[StateEvolution]], + state_evolution: Dict[str, Callable], stress_transfer: StressTransfer, ) -> None: # Set the friction model and stress transfer model (easy...) self.friction_model = friction_model self.stress_transfer = stress_transfer - # If a StatEvolution function is passed directly, make it a tuple - if not isinstance(state_evolution, tuple): - state_evolution = (state_evolution,) - self.state_evolution_fns = state_evolution + state_obj = StateDict( + keys=tuple(state_evolution.keys()), + vals=-1.0 * jnp.ones(len(state_evolution)), + ) + variables = Variables( + mu=jnp.asarray([-1.0], dtype=jnp.float64), state=state_obj + ) + self.variables = variables - # Compile a function that calls each provided StateEvolution and + # Compile a function that calls each provided state_evolution item and # stacks the results in an array. This way, the "state" can host # an arbitrary number of variables (porosity, temperature, slip, ...) self.state_evolution = eqx.filter_jit( lambda v, variables, params, constants: jnp.stack( - [fi(v, variables, params, constants) for fi in state_evolution] + [ + fi(v, variables, params, constants) + for _, fi in state_evolution.items() + ] ) ) pass - def create_variables(self) -> Variables: + @staticmethod + def inspect(fn: Union[Callable, Tuple[Callable, ...]]) -> None: import ast import inspect - accessed = set() - def get_accessed_attrs(func, arg_name): """Helper routine to automatically extract the variable names""" tree = ast.parse(inspect.getsource(func)) @@ -211,29 +217,22 @@ def visit_Attribute(self, node: ast.Attribute): Visitor().visit(tree) return accessed - # Loop over the forward model components and evaluate - # which variable names are accessed - for func in ( - self.friction_model, - self.stress_transfer, - *self.state_evolution_fns, - ): - accessed.update(get_accessed_attrs(func, "variables")) - - # Ensure that we have at least one function that touches mu - assert "mu" in accessed - accessed.remove("mu") - - # Ensure that the number of state variables equals the number - # of state evolution functions - assert len(accessed) == len(self.state_evolution_fns) - - # Initialise values to -1 - state_obj = StateDict(keys=tuple(accessed), vals=-1.0 * jnp.ones(len(accessed))) - variables = Variables( - mu=jnp.asarray([-1.0], dtype=jnp.float64), state=state_obj - ) - return variables + if not isinstance(fn, Iterable): + fn = (fn,) + + accessed = set() + + for fn_i in fn: + accessed.update(get_accessed_attrs(fn_i, "variables")) + + if "mu" in accessed: + accessed.remove("mu") + + print("Auto-detected the following state variable names:") + print(accessed) + + def set_initial_values(self, **kwargs) -> None: + self.variables = self.variables.set_values(**kwargs) @eqx.filter_jit def __call__( diff --git a/diabayes/typedefs.py b/diabayes/typedefs.py index c81fe3d..db9cd79 100644 --- a/diabayes/typedefs.py +++ b/diabayes/typedefs.py @@ -66,62 +66,6 @@ def from_array(cls, x: Float[Array, "..."], keys: tuple[str, ...]) -> "Variables return cls(mu=mu, state=state_obj) -# class Container: - -# def __len__(self): -# return len(self.tree_flatten()[0]) - -# def __iter__(self): -# return iter(self.tree_flatten()[0]) - -# @classmethod -# def tree_unflatten(cls, aux_data, children): -# return cls(*children) - -# @classmethod -# def from_array(cls, x: Iterable, aux: Any = None): -# return cls.tree_unflatten(aux, x) - -# def tree_flatten(self): -# raise NotImplementedError - -# def to_array(self): -# return jnp.array(self.tree_flatten()[0]) - - -# # TODO: replace tree_util stuff with eqx.Module... -# @tree_util.register_pytree_node_class -# @dataclass(frozen=True) -# class Variables(Container): -# mu: Float -# state: Union[Float, Container] - -# def tree_flatten(self): -# # If the state is a scalar, return it directly -# if not isinstance(self.state, Container): -# return (self.mu, self.state), None -# # Else, tree_flatten the state first -# else: -# return (self.mu, *self.state.tree_flatten()[0]), type(self.state) - -# @classmethod -# def tree_unflatten(cls, aux_data: Union[None, Container], children: Iterable): -# children = list(children) -# mu = children[0] -# if aux_data is None: -# state = children[1] -# else: -# state = aux_data.tree_unflatten(None, children[1:]) -# return cls(mu=mu, state=state) - -# def __getattr__(self, name: str) -> Any: -# if isinstance(self.state, Container): -# return getattr(self.state, name) -# if name in ("state", "theta"): -# return self.state -# raise AttributeError() - - class RSFParams(eqx.Module): a: Float b: Float @@ -187,10 +131,10 @@ def __call__( self, t: Float, v: Float, - v_derivs: Variables, + v_partials: Variables, variables: Variables, dstate: Float[Array, "..."], - constants: Any, + constants: _BlockConstants, ) -> Float: ... From c9dcdba01ec0486e80f5efc6a642f67984db42bc Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Thu, 21 Aug 2025 12:13:37 +0200 Subject: [PATCH 46/56] Made unit tests compatible with new data structure --- diabayes/forward_models.py | 19 +++++---- diabayes/solver.py | 15 ++++--- diabayes/typedefs.py | 55 +++++++++++++++++++----- test/aux.py | 7 +++- test/test_forward_models.py | 20 ++++++--- test/test_solvers.py | 84 ++++++++++++++++++------------------- test/test_typedefs.py | 77 ++++++++++++++++------------------ 7 files changed, 163 insertions(+), 114 deletions(-) diff --git a/diabayes/forward_models.py b/diabayes/forward_models.py index 384542c..ab8c730 100644 --- a/diabayes/forward_models.py +++ b/diabayes/forward_models.py @@ -1,5 +1,4 @@ -from dataclasses import make_dataclass -from typing import Callable, Dict, Iterable, Tuple, Union +from typing import Callable, Dict, Generic, Iterable, Tuple, TypeVar, Union import equinox as eqx import jax.numpy as jnp @@ -12,7 +11,6 @@ RSFParams, SpringBlockConstants, StateDict, - StateEvolution, StressTransfer, Variables, _BlockConstants, @@ -20,6 +18,8 @@ _Params, ) +BC = TypeVar("BC", bound=_BlockConstants) + @eqx.filter_jit def rsf(variables: Variables, params: RSFParams, constants: RSFConstants) -> Float: @@ -45,7 +45,8 @@ def rsf(variables: Variables, params: RSFParams, constants: RSFConstants) -> Flo The instantaneous slip rate in the same units as `v0` """ Omega = params.b * jnp.log(variables.theta * constants.v0 / params.Dc) - return constants.v0 * jnp.exp((variables.mu - constants.mu0 - Omega) / params.a) + v = constants.v0 * jnp.exp((variables.mu - constants.mu0 - Omega) / params.a) + return jnp.squeeze(v) @eqx.filter_jit @@ -89,9 +90,9 @@ def slip_rate( def springblock( t: Float, v: Float, - v_partials: Float[Array, "..."], + v_partials: Variables, variables: Variables, - dstate: Variables, + dstate: Float[Array, "..."], constants: SpringBlockConstants, ) -> Float: r""" @@ -142,7 +143,7 @@ def inertial_springblock( return (mass_term - partials_term) / v_partials.mu -class Forward: +class Forward(Generic[BC]): r""" The `Forward` class assembles the various components that comprise a forward model, such that `Forward.__call__` takes some variables @@ -169,7 +170,7 @@ def __init__( self, friction_model: FrictionModel, state_evolution: Dict[str, Callable], - stress_transfer: StressTransfer, + stress_transfer: StressTransfer[BC], ) -> None: # Set the friction model and stress transfer model (easy...) self.friction_model = friction_model @@ -241,7 +242,7 @@ def __call__( variables: Variables, params: _Params, friction_constants: _Constants, - block_constants: _BlockConstants, + block_constants: BC, ) -> Variables: # Calculate v and its partial derivatives with respect to # the variables (mu, state1, state2, ...) diff --git a/diabayes/solver.py b/diabayes/solver.py index 5c89250..8352b42 100644 --- a/diabayes/solver.py +++ b/diabayes/solver.py @@ -40,7 +40,7 @@ class ODESolver: def __init__( self, forward_model: Forward, - rtol: float = 1e-10, + rtol: float = 1e-8, atol: float = 1e-12, checkpoints: int = 100, ) -> None: @@ -117,6 +117,7 @@ def _forward_wrapper( ) -> Variables: params, friction_constants, block_constants = args return self.forward_model( + t=t, variables=variables, params=params, friction_constants=friction_constants, @@ -126,16 +127,18 @@ def _forward_wrapper( @eqx.filter_jit def _forward_wrapper_SVI( self, - y0: Float[Array, "2"], - params: Float[Array, "..."], + y0: Variables, + params: _Params, t: Float[Array, "Nt"], friction_constants: _Constants, block_constants: _BlockConstants, ) -> Variables: + # TODO: previously y0 and params were arrays, not + # PyTrees. Does this logic still work? result = self._solve_forward( t=t, - y0=Variables.from_array(y0), - params=RSFParams.from_array(params), + y0=y0, + params=params, friction_constants=friction_constants, block_constants=block_constants, ) @@ -197,6 +200,8 @@ def _residuals( adjoint = dfx.ForwardMode() # TODO: replace this with something like `get_steadystate()` # because this is different for e.g. CNS + # TODO: y0 needs to be defined consistent with the new + # "state" variable structure... theta0 = params.Dc / friction_constants.v0 # type:ignore y0 = Variables(mu=mu[0], state=theta0) result = self._solve_forward( diff --git a/diabayes/typedefs.py b/diabayes/typedefs.py index db9cd79..1a86ab9 100644 --- a/diabayes/typedefs.py +++ b/diabayes/typedefs.py @@ -1,6 +1,16 @@ import dataclasses as dcs from time import time_ns -from typing import Any, Iterable, Mapping, NamedTuple, Protocol, Tuple, TypeAlias, Union +from typing import ( + Any, + Iterable, + Mapping, + NamedTuple, + Protocol, + Tuple, + TypeAlias, + TypeVar, + Union, +) import equinox as eqx import jax @@ -19,15 +29,12 @@ class StateDict(eqx.Module): keys: tuple[str, ...] = eqx.field(static=True) - vals: Float[Array, "..."] + vals: jax.Array def __getitem__(self, k: str) -> Array: i = self.keys.index(k) return self.vals[i] - def __str__(self) -> str: - return "hi" - def replace_values(self, **kwargs) -> "StateDict": mapping = dict(zip(self.keys, self.vals)) mapping.update(kwargs) @@ -57,12 +64,38 @@ def __repr__(self) -> str: return f"Variables(mu={self.mu}, {state_str})" def to_array(self) -> Float[Array, "..."]: - return jnp.hstack([self.mu, self.state.vals]) + mu = jnp.asarray(jnp.squeeze(self.mu)) + state = jnp.asarray(jnp.squeeze(self.state.vals)) + + # First case: mu and state are scalars + # Result shape (2,) + if mu.ndim == state.ndim == 0: + return jnp.hstack([mu, state]) + # Second case: mu is scalar, state is vector + # Result shape (1+n,) + elif mu.ndim == 0 and state.ndim == 1: + return jnp.hstack([mu, *state]) + # Third case: mu and state are time series of scalars + # Result shape: (2, t) + elif mu.ndim == state.ndim == 1: + return jnp.vstack([mu[None, :], state[None, :]]) + # Fourth case: mu is time series of scalars, + # state is time series of vectors (t, n) + # Result shape: (1+n, t) + elif mu.ndim == 1 and state.ndim == 2: + return jnp.vstack([mu[None, :], state]) + # Other combinations of shapes should not exist + else: + raise ValueError(f"Unsupported shapes: mu={mu.shape}, state={state.shape}") @classmethod def from_array(cls, x: Float[Array, "..."], keys: tuple[str, ...]) -> "Variables": - mu = jnp.asarray(x[0]) - state_obj = StateDict(keys=keys, vals=x[1:]) + mu = jnp.atleast_1d(x[0]) + if x.ndim == 2: + state = jnp.atleast_2d(x[1:]) + else: + state = jnp.atleast_1d(x[1:]) + state_obj = StateDict(keys=keys, vals=state) return cls(mu=mu, state=state_obj) @@ -107,7 +140,7 @@ class InertialSpringBlockConstants: _Params = Union[RSFParams, CNSParams] _Constants = Union[RSFConstants] _BlockConstants = Union[SpringBlockConstants, InertialSpringBlockConstants] - +BC = TypeVar("BC", bound=_BlockConstants, contravariant=True) """ ------------------- @@ -126,7 +159,7 @@ def __call__( ) -> Float: ... -class StressTransfer(Protocol): +class StressTransfer(Protocol[BC]): def __call__( self, t: Float, @@ -134,7 +167,7 @@ def __call__( v_partials: Variables, variables: Variables, dstate: Float[Array, "..."], - constants: _BlockConstants, + constants: BC, ) -> Float: ... diff --git a/test/aux.py b/test/aux.py index a10dea9..eded549 100644 --- a/test/aux.py +++ b/test/aux.py @@ -1,4 +1,7 @@ +import jax.numpy as jnp + import diabayes as db +from diabayes.typedefs import StateDict def init_params(): @@ -14,7 +17,9 @@ def init_params(): mu0 = 0.6 theta0 = Dc / v0 - variables = db.Variables(mu=mu0, state=theta0) + state_obj = StateDict(keys=("theta",), vals=jnp.atleast_1d(theta0)) + + variables = db.Variables(mu=jnp.atleast_1d(mu0), state=state_obj) params = db.RSFParams(a=a, b=b, Dc=Dc) constants = db.RSFConstants(v0=v0, mu0=mu0) block_constants = db.SpringBlockConstants(k=k, v_lp=v1) diff --git a/test/test_forward_models.py b/test/test_forward_models.py index 0ddb648..5249c64 100644 --- a/test/test_forward_models.py +++ b/test/test_forward_models.py @@ -1,7 +1,10 @@ +from functools import partial + import jax.numpy as jnp import diabayes as db import diabayes.forward_models as db_models +from diabayes.typedefs import StateDict from .aux import init_params @@ -26,11 +29,12 @@ def test_rate_and_state(self): dtheta = db_models.ageing_law(v, variables, params, constants) assert jnp.allclose(dtheta, 1.0) - variables2 = db.Variables(mu=0.0, state=variables.state) + variables2 = db.Variables(mu=jnp.asarray(0.0), state=variables.state) v = db_models.rsf(variables2, params, constants) assert jnp.allclose(v, 0.0) - variables2 = db.Variables(mu=variables.mu, state=1e20) + state_obj = StateDict(keys=("theta",), vals=jnp.atleast_1d(1e20)) + variables2 = db.Variables(mu=variables.mu, state=state_obj) v = db_models.rsf(variables2, params, constants) assert jnp.allclose(v, 0.0) @@ -40,16 +44,22 @@ def test_spring_block(self): # Steady-state tests + springblock = partial( + db_models.springblock, t=0.0, v_partials=variables, dstate=jnp.asarray(0.0) + ) + block_constants2 = db.SpringBlockConstants(block_constants.k, v_lp=constants.v0) - dmu = db_models.springblock(constants.v0, variables, block_constants2) + dmu = springblock( + v=constants.v0, variables=variables, constants=block_constants2 + ) assert jnp.allclose(dmu, 0.0) # Asymptotic tests v = 1e-20 * block_constants.v_lp - dmu = db_models.springblock(v, variables, block_constants) + dmu = springblock(v=v, variables=variables, constants=block_constants) assert jnp.allclose(dmu, block_constants.k * block_constants.v_lp) v = 1e20 * block_constants.v_lp - dmu = db_models.springblock(v, variables, block_constants) + dmu = springblock(v=v, variables=variables, constants=block_constants) assert jnp.allclose(dmu, -block_constants.k * v) diff --git a/test/test_solvers.py b/test/test_solvers.py index e27bd69..9a5cb32 100644 --- a/test/test_solvers.py +++ b/test/test_solvers.py @@ -19,7 +19,7 @@ def test_forward_solver(self): """ variables, params, constants, block_constants = init_params() - forward = Forward(rsf, ageing_law, springblock) + forward = Forward(rsf, {"theta": ageing_law}, springblock) solver = ODESolver(forward) t = jnp.linspace(0.0, 100.0, 1000) @@ -42,47 +42,47 @@ def test_forward_solver(self): ) assert jnp.allclose(mu_final, mu_pred) - state_final = result.state[-1] + state_final = result.theta[-1] state_pred = params.Dc / block_constants.v_lp assert jnp.allclose(state_final, state_pred) - def test_levenberg_marquardt(self): - """ - Test for the Levenberg-Marquardt maximum-likelihood - inversion solver. A forward simulation generates the - friction "observation" for a given set of parameters. - Then the true parameters are pertubed and an inversion - is performed. The test checks whether the inverted - parameters are identical to the initial ones, and - whether the friction curves are identical. - """ - - variables, params, constants, block_constants = init_params() - forward = Forward(rsf, ageing_law, springblock) - solver = ODESolver(forward) - - # Forward simulation to generate "observations" - t = jnp.linspace(0.0, 20.0, 1000) - result = solver.solve_forward(t, variables, params, constants, block_constants) - - # Perturb parameters (initial guess parameters) - params2 = db.RSFParams(a=params.a * 1.1, b=params.b * 0.9, Dc=params.Dc * 5) - mu = result.mu - - # Invert parameters - result_inv = solver.max_likelihood_inversion( - t, mu, params2, constants, block_constants, verbose=False - ) - params_inv = result_inv.value - - # Reproduce friction curve - result2 = solver.solve_forward( - t, variables, params_inv, constants, block_constants - ) - - assert result2 is not None - - # Check that inverted parameters and resulting friction - # curves are identical to the original ones - assert jnp.allclose(params.to_array(), params_inv.to_array()) - assert jnp.allclose(result.to_array(), result2.to_array()) + # def test_levenberg_marquardt(self): + # """ + # Test for the Levenberg-Marquardt maximum-likelihood + # inversion solver. A forward simulation generates the + # friction "observation" for a given set of parameters. + # Then the true parameters are pertubed and an inversion + # is performed. The test checks whether the inverted + # parameters are identical to the initial ones, and + # whether the friction curves are identical. + # """ + + # variables, params, constants, block_constants = init_params() + # forward = Forward(rsf, {"theta": ageing_law}, springblock) + # solver = ODESolver(forward) + + # # Forward simulation to generate "observations" + # t = jnp.linspace(0.0, 20.0, 1000) + # result = solver.solve_forward(t, variables, params, constants, block_constants) + + # # Perturb parameters (initial guess parameters) + # params2 = db.RSFParams(a=params.a * 1.1, b=params.b * 0.9, Dc=params.Dc * 5) + # mu = result.mu + + # # Invert parameters + # result_inv = solver.max_likelihood_inversion( + # t, mu, params2, constants, block_constants, verbose=False + # ) + # params_inv = result_inv.value + + # # Reproduce friction curve + # result2 = solver.solve_forward( + # t, variables, params_inv, constants, block_constants + # ) + + # assert result2 is not None + + # # Check that inverted parameters and resulting friction + # # curves are identical to the original ones + # assert jnp.allclose(params.to_array(), params_inv.to_array()) + # assert jnp.allclose(result.to_array(), result2.to_array()) diff --git a/test/test_typedefs.py b/test/test_typedefs.py index 35e748a..0b83bd0 100644 --- a/test/test_typedefs.py +++ b/test/test_typedefs.py @@ -1,63 +1,58 @@ import jax.numpy as jnp -from diabayes.typedefs import RSFParams, RSFParticles, Variables +from diabayes.typedefs import RSFParams, RSFParticles, StateDict, Variables class TestTypedefs: def test_basic_containers(self): - a = 1.0 - b = 2.0 - c = 3.0 + x = {"a": 1.0, "b": 2.0, "c": 3.0} + keys = tuple(x.keys()) - assert ( - Variables(a, b) - == Variables(mu=a, state=b) - == Variables.from_array(jnp.array([a, b])) - == Variables.tree_unflatten(None, jnp.array([a, b])) - ) - variables = Variables(mu=a, state=b) - assert jnp.allclose( - variables.to_array(), jnp.array(variables.tree_flatten()[0]) - ) + state_obj = StateDict(keys=keys, vals=jnp.array(list(x.values()))) + variables = Variables(mu=jnp.array([0.6]), state=state_obj) - assert ( - RSFParams(a, b, c) - == RSFParams(a=a, b=b, Dc=c) - == RSFParams.from_array(jnp.array([a, b, c])) - == RSFParams.tree_unflatten(None, jnp.array([a, b, c])) - ) - params = RSFParams(a=a, b=b, Dc=c) - assert jnp.allclose(params.to_array(), jnp.array(params.tree_flatten()[0])) + vars_array = variables.to_array() + variables2 = Variables.from_array(vars_array, keys) - def test_SVI_containers(self): + assert jnp.allclose(vars_array, variables2.to_array()) - import jax.random as jr + x2 = {"mu": 0.2, "a": 1.0, "b": 2.0, "c": 5.0} + variables3 = variables.set_values(**x2) - key = jr.PRNGKey(42) + assert jnp.allclose(jnp.array(list(x2.values())), variables3.to_array()) - # Test Particles + x2.pop("mu") + assert tuple(x2.keys()) == variables3.state.keys - key, split_key = jr.split(key) - Nparticles = 100 - loc = jnp.array([1.0, 2.0, 3.0]) - scale = jnp.ones(3) - particles = RSFParticles.generate(Nparticles, loc, scale, split_key) + # def test_SVI_containers(self): - assert len(particles) == Nparticles + # import jax.random as jr - x = particles.to_array() - assert x.shape[1] == len(loc) + # key = jr.PRNGKey(42) - rtol = 2 / jnp.sqrt(Nparticles) - assert jnp.allclose(x.mean(axis=0), loc, rtol=rtol) - assert jnp.allclose(x.std(axis=0), scale, rtol=rtol) + # # Test Particles - assert jnp.allclose(x, RSFParticles.from_array(x).to_array()) + # key, split_key = jr.split(key) + # Nparticles = 100 + # loc = jnp.array([1.0, 2.0, 3.0]) + # scale = jnp.ones(3) + # particles = RSFParticles.generate(Nparticles, loc, scale, split_key) - # Test Chains + # assert len(particles) == Nparticles - pass + # x = particles.to_array() + # assert x.shape[1] == len(loc) - def test_Bayesian_statistics(self): ... + # rtol = 2 / jnp.sqrt(Nparticles) + # assert jnp.allclose(x.mean(axis=0), loc, rtol=rtol) + # assert jnp.allclose(x.std(axis=0), scale, rtol=rtol) + + # assert jnp.allclose(x, RSFParticles.from_array(x).to_array()) + + # # Test Chains + + # pass + + # def test_Bayesian_statistics(self): ... From 642fe24b145f854ffab3dfaba79e8c084a847aa8 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Mon, 25 Aug 2025 14:47:56 +0200 Subject: [PATCH 47/56] Finally got LM inversion test working... --- diabayes/forward_models.py | 4 +- diabayes/solver.py | 12 ++---- diabayes/typedefs.py | 16 ++++++- test/test_solvers.py | 85 ++++++++++++++++++++------------------ 4 files changed, 67 insertions(+), 50 deletions(-) diff --git a/diabayes/forward_models.py b/diabayes/forward_models.py index ab8c730..0379b4b 100644 --- a/diabayes/forward_models.py +++ b/diabayes/forward_models.py @@ -233,7 +233,9 @@ def visit_Attribute(self, node: ast.Attribute): print(accessed) def set_initial_values(self, **kwargs) -> None: - self.variables = self.variables.set_values(**kwargs) + # Change this shit back it doesn't work and breaks everything + scalars = {k: float(jnp.asarray(v).item()) for k, v in kwargs.items()} + self.variables = self.variables.set_values(**scalars) @eqx.filter_jit def __call__( diff --git a/diabayes/solver.py b/diabayes/solver.py index 8352b42..8aaaa84 100644 --- a/diabayes/solver.py +++ b/diabayes/solver.py @@ -194,26 +194,22 @@ def _residuals( params: _Params, t: Float[Array, "N"], mu: Float[Array, "N"], + y0: Variables, friction_constants: _Constants, block_constants: _BlockConstants, ) -> Float[Array, "N"]: adjoint = dfx.ForwardMode() - # TODO: replace this with something like `get_steadystate()` - # because this is different for e.g. CNS - # TODO: y0 needs to be defined consistent with the new - # "state" variable structure... - theta0 = params.Dc / friction_constants.v0 # type:ignore - y0 = Variables(mu=mu[0], state=theta0) result = self._solve_forward( t, y0, params, friction_constants, block_constants, adjoint ) - mu_hat = result.ys.mu # type:ignore + mu_hat = jnp.squeeze(result.ys.mu) # type:ignore return mu - mu_hat def max_likelihood_inversion( self, t: Float[Array, "Nt"], mu: Float[Array, "Nt"], + y0: Variables, params: _Params, friction_constants: _Constants, block_constants: _BlockConstants, @@ -228,7 +224,7 @@ def max_likelihood_inversion( options = {"autodiff_mode": "fwd"} _residuals = lambda params, mu: self._residuals( - params, t, mu, friction_constants, block_constants + params, t, mu, y0, friction_constants, block_constants ) lm_solver = optx.LevenbergMarquardt(rtol=1e-5, atol=1e-5, verbose=verbose_opts) diff --git a/diabayes/typedefs.py b/diabayes/typedefs.py index 1a86ab9..6b69e5e 100644 --- a/diabayes/typedefs.py +++ b/diabayes/typedefs.py @@ -36,6 +36,8 @@ def __getitem__(self, k: str) -> Array: return self.vals[i] def replace_values(self, **kwargs) -> "StateDict": + # Why is it so fucking problematic to set a JAX array? + # How many dozens of hours do I need to spend to set a fucking array? mapping = dict(zip(self.keys, self.vals)) mapping.update(kwargs) return StateDict(self.keys, jnp.array([mapping[k] for k in self.keys])) @@ -54,7 +56,7 @@ def __getattr__(self, name: str): raise AttributeError(f"{type(self).__name__} has no attribute {name!r}") def set_values(self, **kwargs) -> "Variables": - mu = jnp.asarray(kwargs.pop("mu")) + mu = jnp.atleast_1d(kwargs.pop("mu")) return dcs.replace(self, mu=mu, state=self.state.replace_values(**kwargs)) def __repr__(self) -> str: @@ -72,6 +74,7 @@ def to_array(self) -> Float[Array, "..."]: if mu.ndim == state.ndim == 0: return jnp.hstack([mu, state]) # Second case: mu is scalar, state is vector + # (i.e., multiple state variables) # Result shape (1+n,) elif mu.ndim == 0 and state.ndim == 1: return jnp.hstack([mu, *state]) @@ -90,11 +93,22 @@ def to_array(self) -> Float[Array, "..."]: @classmethod def from_array(cls, x: Float[Array, "..."], keys: tuple[str, ...]) -> "Variables": + """ + Import a JAX array to instantiate the class. The array `x` can either be an + array of scalars (size n for n variables), or an array of time series of + shape `(n, t)`. The first element in the array (i.e., `x[0]`) is assumed to + be the friction coefficient `mu`. The remaining elements are the state + variables, matching the number and order of the `keys` tuple. + """ + # The first element is assumed to be mu mu = jnp.atleast_1d(x[0]) + # If x is 2D: time series if x.ndim == 2: state = jnp.atleast_2d(x[1:]) + # Else: scalars else: state = jnp.atleast_1d(x[1:]) + # Map `state` to `keys` state_obj = StateDict(keys=keys, vals=state) return cls(mu=mu, state=state_obj) diff --git a/test/test_solvers.py b/test/test_solvers.py index 9a5cb32..0d7ac20 100644 --- a/test/test_solvers.py +++ b/test/test_solvers.py @@ -46,43 +46,48 @@ def test_forward_solver(self): state_pred = params.Dc / block_constants.v_lp assert jnp.allclose(state_final, state_pred) - # def test_levenberg_marquardt(self): - # """ - # Test for the Levenberg-Marquardt maximum-likelihood - # inversion solver. A forward simulation generates the - # friction "observation" for a given set of parameters. - # Then the true parameters are pertubed and an inversion - # is performed. The test checks whether the inverted - # parameters are identical to the initial ones, and - # whether the friction curves are identical. - # """ - - # variables, params, constants, block_constants = init_params() - # forward = Forward(rsf, {"theta": ageing_law}, springblock) - # solver = ODESolver(forward) - - # # Forward simulation to generate "observations" - # t = jnp.linspace(0.0, 20.0, 1000) - # result = solver.solve_forward(t, variables, params, constants, block_constants) - - # # Perturb parameters (initial guess parameters) - # params2 = db.RSFParams(a=params.a * 1.1, b=params.b * 0.9, Dc=params.Dc * 5) - # mu = result.mu - - # # Invert parameters - # result_inv = solver.max_likelihood_inversion( - # t, mu, params2, constants, block_constants, verbose=False - # ) - # params_inv = result_inv.value - - # # Reproduce friction curve - # result2 = solver.solve_forward( - # t, variables, params_inv, constants, block_constants - # ) - - # assert result2 is not None - - # # Check that inverted parameters and resulting friction - # # curves are identical to the original ones - # assert jnp.allclose(params.to_array(), params_inv.to_array()) - # assert jnp.allclose(result.to_array(), result2.to_array()) + def test_levenberg_marquardt(self): + """ + Test for the Levenberg-Marquardt maximum-likelihood + inversion solver. A forward simulation generates the + friction "observation" for a given set of parameters. + Then the true parameters are pertubed and an inversion + is performed. The test checks whether the inverted + parameters are identical to the initial ones, and + whether the friction curves are identical. + """ + + variables, params, constants, block_constants = init_params() + forward = Forward(rsf, {"theta": ageing_law}, springblock) + forward.set_initial_values(mu=variables.mu, theta=variables.theta) + + solver = ODESolver(forward) + + y0 = forward.variables + + # Forward simulation to generate "observations" + t = jnp.linspace(0.0, 10.0, 1000) + result = solver.solve_forward(t, y0, params, constants, block_constants) + mu = result.mu + + # Perturb parameters (initial guess parameters) + params2 = db.RSFParams(a=params.a * 1.1, b=params.b * 1.2, Dc=params.Dc * 0.9) + + # Invert parameters + result_inv = solver.max_likelihood_inversion( + t, mu, y0, params2, constants, block_constants, verbose=True + ) + params_inv = result_inv.value + + # Reproduce friction curve + result2 = solver.solve_forward(t, y0, params_inv, constants, block_constants) + + assert result2 is not None + + params_array = jax.flatten_util.ravel_pytree(params)[0] # type:ignore + params_inv_array = jax.flatten_util.ravel_pytree(params_inv)[0] # type:ignore + + # Check that inverted parameters and resulting friction + # curves are identical to the original ones + assert jnp.allclose(params_array, params_inv_array) + assert jnp.allclose(result.mu, result2.mu) From b250f8d9fbe7ba6a30138f92c084f4b55ff1578a Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Mon, 25 Aug 2025 18:11:19 +0200 Subject: [PATCH 48/56] Modified example to match new data structure. LM unstable --- diabayes/solver.py | 6 +- examples/simple_inversion.ipynb | 127 ++++++++++++-------------------- 2 files changed, 54 insertions(+), 79 deletions(-) diff --git a/diabayes/solver.py b/diabayes/solver.py index 8aaaa84..f989a93 100644 --- a/diabayes/solver.py +++ b/diabayes/solver.py @@ -229,7 +229,11 @@ def max_likelihood_inversion( lm_solver = optx.LevenbergMarquardt(rtol=1e-5, atol=1e-5, verbose=verbose_opts) sol = optx.least_squares( - _residuals, lm_solver, params, args=mu, options=options + _residuals, + lm_solver, + params, + args=mu, + options=options, ) return sol diff --git a/examples/simple_inversion.ipynb b/examples/simple_inversion.ipynb index 637aea2..b0193af 100644 --- a/examples/simple_inversion.ipynb +++ b/examples/simple_inversion.ipynb @@ -86,7 +86,8 @@ "metadata": {}, "outputs": [], "source": [ - "forward = Forward(friction_model=rsf, state_evolution=ageing_law, block_type=springblock)\n", + "state_dict = {\"theta\": ageing_law}\n", + "forward = Forward(friction_model=rsf, state_evolution=state_dict, stress_transfer=springblock)\n", "solver = ODESolver(forward_model=forward)" ] }, @@ -95,7 +96,9 @@ "id": "355429e9", "metadata": {}, "source": [ - "The last step before running a forward simulation, is to pack all of the parameters and initial conditions into appropriate \"containers\". These containers act like `namedtuple`s; you can initialise one by providing the parameters in the right order, or as keyword arguments (for example, `params = db.RSFParams(a, b, Dc)` returns the same as `params = db.RSFParams(Dc=Dc, a=a, b=b)`). You can access individual entries by their name, e.g. `b = params.b`." + "The last step before running a forward simulation, is to pack all of the parameters and initial conditions into appropriate \"containers\". These containers act like `namedtuple`s; you can initialise one by providing the parameters in the right order, or as keyword arguments (for example, `params = db.RSFParams(a, b, Dc)` returns the same as `params = db.RSFParams(Dc=Dc, a=a, b=b)`). You can access individual entries by their name, e.g. `b = params.b`. \n", + "\n", + "Setting variables (i.e., the quantities that change over time) works similarly, but the structure of their container depends on the physics that was provided to the `Forward` class. Fortunately, this is handled automatically by providing the appropriate initial values to the `Forward` class object." ] }, { @@ -106,7 +109,8 @@ "outputs": [], "source": [ "# Initial values of friction and state\n", - "y0 = db.Variables(mu=mu0, state=theta0)\n", + "forward.set_initial_values(mu=mu0, theta=theta0)\n", + "y0 = forward.variables\n", "# RSF parameters\n", "params = db.RSFParams(a=a, b=b, Dc=Dc)\n", "# RSF constants\n", @@ -130,12 +134,12 @@ "id": "14114ce1", "metadata": {}, "source": [ - "The result of this forward simulation (`result`) is a `db.Variables` object similar to `y0` defined above. Hence the friction and state can be accessed as `result.ys.mu` and `result.ys.state`:" + "The result of this forward simulation (`result`) is a `db.Variables` object similar to `y0` defined above. Hence the friction and state can be accessed as `result.ys.mu` and `result.ys.theta`:" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 6, "id": "b58d2518", "metadata": {}, "outputs": [ @@ -145,14 +149,14 @@ "Text(0.5, 0, 'time [s]')" ] }, - "execution_count": 16, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bde9df638e3749c3817a5297b14edcbe", + "model_id": "701bafc9b90f4f0d881387e7c26b5f8a", "version_major": 2, "version_minor": 0 }, @@ -177,7 +181,7 @@ ], "source": [ "mu = result.mu\n", - "theta = result.state\n", + "theta = result.theta\n", "\n", "plt.close(\"all\")\n", "fig, axes = plt.subplots(nrows=2, figsize=(9, 5), constrained_layout=True, sharex=\"all\")\n", @@ -216,7 +220,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "id": "d4c47b4b", "metadata": {}, "outputs": [ @@ -224,30 +228,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Step: 0, Loss on this step: 0.05733472830988543\n", - "Step: 1, Loss on this step: 0.008662664872409683\n", - "Step: 2, Loss on this step: 0.044078655206233705\n", - "Step: 3, Loss on this step: 0.040763203396466045\n", - "Step: 4, Loss on this step: 0.03979882592590013\n", - "Step: 5, Loss on this step: 0.05482780070276433\n", - "Step: 6, Loss on this step: 0.15477592354300435\n", - "Step: 7, Loss on this step: 0.00576122891182264\n", - "Step: 8, Loss on this step: 0.0037534023188489493\n", - "Step: 9, Loss on this step: 0.0029963676892875604\n", - "Step: 10, Loss on this step: 0.0026772942526744076\n", - "Step: 11, Loss on this step: 0.0020681032777418494\n", - "Step: 12, Loss on this step: 0.001731628170095942\n", - "Step: 13, Loss on this step: 0.001376593074536853\n", - "Step: 14, Loss on this step: 0.0011562860341618624\n", - "Step: 15, Loss on this step: 0.000991121839986473\n", - "Step: 16, Loss on this step: 0.0008735269848152941\n", - "Step: 17, Loss on this step: 0.0007841762603306585\n", - "Step: 18, Loss on this step: 0.0006201843741583328\n", - "Step: 19, Loss on this step: 0.0005421354653486064\n", - "Step: 20, Loss on this step: 0.00048036317386252825\n", - "Step: 21, Loss on this step: 0.0004674834253528182\n", - "Step: 22, Loss on this step: 0.00046703531736108617\n", - "Step: 23, Loss on this step: 0.0004670333632948055\n" + "Step: 0, Loss on this step: 0.0031062619344770547\n", + "Step: 1, Loss on this step: 0.0014327439927961727\n", + "Step: 2, Loss on this step: 0.0005079012385201983\n", + "Step: 3, Loss on this step: 0.0004554938162316355\n", + "Step: 4, Loss on this step: 0.0004549483107437401\n", + "Step: 5, Loss on this step: 0.0004549482405273438\n" ] } ], @@ -269,14 +255,14 @@ "params_guess = db.RSFParams(\n", " a = 1.1 * a,\n", " b = 1.2 * b,\n", - " Dc = 5 * Dc\n", + " Dc = 2 * Dc\n", ")\n", "\n", "# Do the maximum-likelihood inversion\n", "# Set verbose=True to get intermediate messages\n", "# of the solver progress\n", "inv_result = solver.max_likelihood_inversion(\n", - " t, mu_measured,\n", + " t, mu_measured, y0,\n", " params=params_guess,\n", " friction_constants=constants,\n", " block_constants=block_constants,\n", @@ -296,7 +282,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 12, "id": "97c8272e", "metadata": {}, "outputs": [ @@ -304,35 +290,35 @@ "name": "stdout", "output_type": "stream", "text": [ - "Inverted parameters: RSFParams(a=Array(0.00990386, dtype=float64), b=Array(0.00891232, dtype=float64), Dc=Array(1.03470351e-05, dtype=float64))\n", + "Inverted parameters: RSFParams(a=f64[], b=f64[], Dc=f64[])\n", "True parameters: RSFParams(a=0.01, b=0.009000000000000001, Dc=1e-05)\n" ] }, { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 17, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0797bf57735a453c9395d18bc77fce98", + "model_id": "7f5716b5e7d74485953ccb4b93c14adc", "version_major": 2, "version_minor": 0 }, - "image/png": 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", + "image/png": 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\n", - " \n", + " \n", "
\n", " " ], @@ -368,7 +354,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 14, "id": "a0fbfb01", "metadata": {}, "outputs": [ @@ -376,40 +362,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "Step: 0, Loss on this step: 0.037341593662781856\n", - "Step: 1, Loss on this step: 0.008450562781209037\n", - "Step: 2, Loss on this step: 0.011316614188600499\n", - "Step: 3, Loss on this step: 0.010433715770249097\n", - "Step: 4, Loss on this step: 0.008123443559005811\n", - "Step: 5, Loss on this step: 0.0076708665776726625\n", - "Step: 6, Loss on this step: 0.7705982639065795\n", - "Step: 7, Loss on this step: 1.1135623707166982\n", - "Step: 8, Loss on this step: 0.011455439618007345\n", - "Step: 9, Loss on this step: 0.004275817134790895\n", - "Step: 10, Loss on this step: 0.0018859801576182802\n", - "Step: 11, Loss on this step: 0.0015430926186676386\n", - "Step: 12, Loss on this step: 0.0012603736088423983\n", - "Step: 13, Loss on this step: 0.0010728469540741393\n", - "Step: 14, Loss on this step: 0.0009327820039051789\n", - "Step: 15, Loss on this step: 0.0008297305095412023\n", - "Step: 16, Loss on this step: 0.000750804774247332\n", - "Step: 17, Loss on this step: 0.0006044858293821533\n", - "Step: 18, Loss on this step: 0.000491052058431897\n", - "Step: 19, Loss on this step: 0.0004678541320678155\n", - "Step: 20, Loss on this step: 0.0004670369326232863\n", - "Step: 21, Loss on this step: 0.000467033364346502\n" + "Step: 0, Loss on this step: 0.00879827890106595\n", + "Step: 1, Loss on this step: 0.005383346705837146\n", + "Step: 2, Loss on this step: 0.0027221340210402474\n", + "Step: 3, Loss on this step: 0.0007582650668123534\n", + "Step: 4, Loss on this step: 0.00045876325933946424\n", + "Step: 5, Loss on this step: 0.00045495330751144623\n", + "Step: 6, Loss on this step: 0.00045494824098784144\n" ] } ], "source": [ "params_guess2 = db.RSFParams(\n", " a = 0.9 * a,\n", - " b = 0.5 * b,\n", - " Dc = 2 * Dc\n", + " b = 0.7 * b,\n", + " Dc = 1.5 * Dc\n", ")\n", "\n", "inv_result2 = solver.max_likelihood_inversion(\n", - " t, mu_measured,\n", + " t, mu_measured, y0,\n", " params=params_guess2,\n", " friction_constants=constants,\n", " block_constants=block_constants,\n", @@ -430,35 +401,35 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 15, "id": "1da6bb44", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 18, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "365520a360164c389033b60f5113b6d9", + "model_id": "ed1ac1c5009e4aa3950a5475e6e5d021", "version_major": 2, "version_minor": 0 }, - "image/png": 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", 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CWZWqx6LA7Q8nDiLJ4yMiIiKiqY0BIU1bIUnG8VYXKvJsuks9JKMNCCP/F5LsV5MVbQApSEHM+eBXKKn9Lf4r147fzaiErz8bWBkK4xaPByu9PlwYDMOfPQvenDmw5xRAEo3wOzth9bXB3n4S9UYBO7Js+Ed2FppMJvxnfh6ey83BVx1v4a7tt6Dx4gfRNO/zAICT7W54AqkDQlaMEhEREU0fDAhp2jrd2YcOVwAdrgBWLSxL+3baOYSJ0VNI1YBGTR0M5vR+iAvf+haOBZvw1apStBkjv4oX+wO41+nCNUGgY8YadCy8AW+ULYdstAMAFlTmIhCSUN/pAQDYhCCsbYdwW+MW3HtuG/aawvhNfh6OW8z4/wrzsS0riB998FNc0rYbmP0szvWk0w2VESERERHRdMGAkKatYFg/cIsXHx+FdDKE6iAqWUCo9JeMVtZvxqx3foCf52fhz0WlAICqUBgP9vRipWSFdPm30XvR5/BhZ+LERIMgQFRPWDTZ0Ft2BXrLrkDtJRswo+45/NfJ32Kb2Y3HCvNx0mLG5yvLsaH7MD791DXI+cgv4S5YmNHjJSIiIqKpi+sQ0rQlDnGFd/USFAMLxw/sD0n6EZUSDqHizf+F3KPfx5fKCvHn3Mji83c53djc2oX5M7+AfWteQ3DZ/YC1QP8cUDQNbNRfS6ZsnF3wNey/+VVcVnIz/n6uFdd6vAgJAn5UXISHjX1Y9PpdKGjfl/LxMUNIRERENH0wIKRpa4jxoCaDFg2e1J1HwzoZQjHsxeK9X8eZhj/jzspynLCYUShJeLq1Hevkcrx//WacXvQ/IZlzIAhC0rFJsqIJZA06B4bNeTj+kf+Nhqt+h/9wAf/W0wtRUfBiTjYeLLLj/De/ipJz25M+PgaERERERNMHA0KatoaaIVSLxk6qeBDBuIDQFOjF0l1fxGHnQXytvBROgwEX+wN4obkNldV34uD1f0Zf/gWqcSVf1TDHatI+BjH5Y+gtW46DN/wNtxvPw6/bO2GTZey12/CvpQWoeevfkgaF6p44fYEw3m1ywO0ffO4hEREREU0+DAhp2hqBeFA3Q6guGTUGHLj0jS9he+g0vlVajJAgYJXHi//scKDrsp+idsl3oRjMceMSEoLV4hwLlszMR57NpAkCU8SDAICgrQyHV/4e84uuxVNtHciRZLxrtWBdaTHmvvU/Udi2N+E26kY5Rxsd6HQHcPhs7+BPBhERERFNOgwIadoa0QyhTlMZY9CFS3d/Gf+UmvDvxYVQBAF3uNx41BHC8Wv/C22zbksyrsRgNctsQFF2ZGkM9S4hjcegGMx47/LHUVz1KfyurR25koR3rRb8j+JcLNj7deR2v6s53uEN4UB9N5zeEPwhCQAQTjIvkoiIiIgmNwaENG0Nll1Lh6QokGVFUzIakmSIYT8u2XMfXgudwb8XFwIAvuh04X/6LHjn2j/CW3Zp7PjF1fkwGQd+FYX+/9TUgd9gcwh1iQacWPpDZM34FP5vf/noPrsNjxTasWjv12HxtmkO7/OHcbixJ71zExEREdGkxYCQprGBYEpvPcF0KApwtser2SaHJVz01v/AYd8J/KA/GPy804V1Piveufa/4cmbB4MqGi3JsWiCU0FAwiTCZJ1F1cGhzWxIPVhBwIlLf4DzZt2MX7Z3wqgo+Gd2Fp61BrB471qIYZ/2caS3KgcRERERTWIMCGnaUgdh6sXmM6EoCk539Gm2lb/1Y7R2vYGHSoqgCAI+7XLjmz4zjqz8L3iyZgIAynKtABDLDKozgqJOl1F14JcsOKzIs2LRjLzUAxYNED/1/3B55XJ8tzuSAfx1QT7eDtRj4aHvDroIYViS4fSywQwRERHRVMGAkKYtdRmmOh6s63Cjtt2d1jniA8nq2mfhP/ksvlFWgoAo4mqvD992hXH06mfgy5kVOy7fZsKyOYW4Ym6RzrgSu4xq5g1Cv3xUiLuhxZT4612cY4HVagU+/TvcrBTjLmfkcW4oKYK/ZRsq6/+U8vG+3dCLtxt60Ob0pzyOiIiIiCYHBoREGGgK4w9JaOjyorHbq7ueYDx1R9Ea9yGUv/cTrC0vQa/BgIWBAH7a5cSHK56AJ2+e5naCICDHaoLJkPgrGMkQCgnbBm47sF1dehofRFqMiSWkF1f1ZxDthTh65ZN4wB3GR3x++EQR3yotRs3RHyPLWav7WBVFgScQBgC0uRgQEhEREU0FDAhp2lLPG1QHhAPbBj9HUIocb/O3Yebr38D3igpw1mRCRTiMJ9o70XDZRvSWXp5wu/iGNgoUzb6EDGGSeYPxcw/V2UOLMfHXW31eb+5c1H5kI37S2Y0CScIJixm/yLPj4v3/BjGcGPCFh1hWS0REREQTFwNCmrbU4U209NMfGsgKymk0mpFlQJCCuHjvN/CMJYRdWXaYZQW/aO+E84J1saUlTHHBWXwXUc0+nTmEmnmDcceqzxk/pzDx3NrvO2esRmD2Z/G/O7sBAH/My8GR0DnM+eD/S7gtl54gIiIimnoYENK0pY73oskvbzCsuz+V+e8+ive9J/Hrgkg55ve6e1BSdCXOLFwXOybbEle+OchqEfEBozYrqFp2QtSWkqrHXJBlxuLqfO15VbedVWQHANRd8hAWW+bgC04XAOCHxYUoqHsGeV3vaG4bTKOEloiIiIgml5QB4RNPPIGamhpYrVYsW7YMBw8eTHkyh8OBdevWoaKiAhaLBfPnz8fWrVtj+3fv3o1bb70VlZWVEAQBL774YsI5vvSlL/VnSAb+3XTTTZpjenp6cNdddyE3Nxf5+fm499570dfXl3AuonTJ/RGhT1MyOnhEWNL8Gixnno91FL3d3YebUIgPlj0GCAO/XlkWo+Z2g62BmCpDCE35qPaYsGqtCKMoIFt1v/HnPK8sB1eeV4zSwjx8sOxnuN/pQU0whA6jEf9RkIeFb39HUzqqN6ey2eFDXQd/94iIiIgmq6QB4QsvvID169fjkUcewZEjR7B48WKsXr0aHR0duscHg0HccMMNaGhowObNm3Hy5Ek8/fTTqKqqih3j8XiwePFiPPHEEykHddNNN6G1tTX2749//KNm/1133YUPPvgAr776Kl5++WXs3r0bX/3qVzN53ERQF43qzyFMHRCafZ04/9B38d2SQjgMBiwIBPHtXg/ev/IJhM25mmOzzNqAML5pzGCSzRuM7zIaCiua75MtURFlNRmgKIAnbx6aF34T/97VDUFR8FJONg6HWzH7+P+NHau3NMfxFhcaujxcioKIiIhokjIm2/H444/jvvvuwz333AMAePLJJ7FlyxZs2rQJ3/nOdxKO37RpE3p6erBv3z6YTCYAQE1NjeaYNWvWYM2aNYMOymKxoLy8XHff8ePHsW3bNrz99tu47LLLAAC/+tWvcPPNN+NnP/sZKisrBz0/EaAtr5T6v1HPk0u5MLuiYOHb38GfLWEcsOXAKsv4SWcXGpb8AO78C4C422bHZQgHCwfFuOgt2bxBMa7LaFZ8aarmHPr3Gg18G8+/F5c1v4q7XY14Ni8XG4sK8Zfa36J11ifgzZ0Te470sJyUiIiIaHLSzRAGg0EcPnwYq1atGjhQFLFq1Srs379f90QvvfQSli9fjnXr1qGsrAwXXXQRNm7cCEmSdI9PZdeuXSgtLcX555+PtWvXoru7O7Zv//79yM/PjwWDALBq1SqIooi33nor4/ui6Usd3kSDP822FAHQjLrn0NN9AL8oyAcAfKvHgbLqG9Ay+zO6gaTVpA3UBksQJpaM6i8vEV8yWpRtwUVVebhcZ33DZFFoNPGniEZ8+JFH8a8OD8rDYTSbjPhtrh0XHPkBoCiaDGFm+U0iIiIimqh0A8Kuri5IkoSysjLN9rKyMrS1temeqL6+Hps3b4YkSdi6dSu+//3v4+c//zl+/OMfZzSgm266Cb///e+xY8cO/PSnP8Ubb7yBNWvWxALLtrY2lJaWam5jNBpRWFiYdGyBQAAul0vzj0jbVEZJui2ezX0Ws479FN8pLUJQFHCN14dPSlnoue4/kkZ6oph86Qg98XuTzTnUlIz236o8zxrLSCbrTqo2uzgLAFBVYIOv4Dx0nfclfKe7FwDwu7xcOHoPoazx5YQmO0q6XXeIiIiIaMJKWjKaKVmWUVpaiqeeegoGgwFLly5Fc3MzHnvsMTzyyCNpn+fOO++MfX3xxRdj0aJFmDt3Lnbt2oXrr79+SGN79NFH8cMf/nBIt6WpS732n978ON1l9xQFCw59D7/NseKU2YxCScIPO7vhuPW/odiKAZdb975EQYAoCpDSXLohfo5hsjmHg80RVJeJJjtHns2EleeXwGgQ0e7y48zCr+OKppdxtdeH3XYbHi0qwC+PPYaOSz6uuZ16XUIFDA6JiIiIJiPdDGFxcTEMBgPa29s129vb25PO7auoqMD8+fNhMAyUxi1YsABtbW0IBoNDHuCcOXNQXFyMuro6AEB5eXlCY5twOIyenp6kY9uwYQOcTmfsX1NT05DHQ1OHOsEV/Vod2CiKkpAFq6z/EzodR/Db/EjTmO929cA753MIzb4uZRmoKAgwaBrApB5buhlCQ5JS0nTvJ8poEPvvR4BkykLtJd/Fd7p7YFIUHLDZcAgOZB99OnZ8pzuAN052pndyIiIiIpqwdANCs9mMpUuXYseOHbFtsixjx44dWL58ue6JVqxYgbq6OsiqCVS1tbWoqKiA2Wwe8gDPnTuH7u5uVFRUAACWL18Oh8OBw4cPx47ZuXMnZFnGsmXLdM9hsViQm5ur+Uek11QGmpJRbZbQ4m3D3GM/xQ9KChEWBFzn8eJKFKLu4v8B4yDrSIhChiWj8XMIVeGeej6imE5NaAai6xp2Vt0Ae+FHcZczkvH8eWE+co78GmZ/1/DvhIiIiIgmjKTLTqxfvx5PP/00nn32WRw/fhxr166Fx+OJdR29++67sWHDhtjxa9euRU9PDx544AHU1tZiy5Yt2LhxI9atG1icu6+vD0ePHsXRo0cBAGfOnMHRo0fR2NgY2/+tb30LBw4cQENDA3bs2IGPf/zjmDdvHlavXg0gknW86aabcN999+HgwYPYu3cv7r//ftx5553sMEpDFptDGLctNo9QUXD+kR/ieZuA9y0W5EgyvtvdixOX/QiSKQsGUUiZjRMEIW6+n1b8dLyEklHVb6pBFHDN+SVYeX5J3BzBxAEkWb4wxTgHvji1+CF8xelCniThtNmMl6zA7A9+rX9DVowSERERTUpJA8I77rgDP/vZz/Dwww/jkksuwdGjR7Ft27ZYo5nGxka0trbGjq+ursb27dvx9ttvY9GiRfjmN7+JBx54QLNExaFDh7BkyRIsWbIEQCToXLJkCR5++GEAgMFgwLFjx3Dbbbdh/vz5uPfee7F06VLs2bMHFosldp7nnnsOF1xwAa6//nrcfPPNuPLKK/HUU0+N7DNDU562PFT7fyASEEa/L27ZiVDHLvy6IA8A8D96eqHMvA095VcBAIyimHSOnsEQ2a6d7zd4eJYq+WcyiDAaRE0QqJekHM56h30FC2G+4NP4V0ekCdOv8/NRcOZPsLvqEm7X1RfUXbieiIiIiCa2lE1l7r//ftx///26+3bt2pWwbfny5Thw4EDS861cuTJlZ0KbzYbt27enGhIAoLCwEH/4wx8GPY4oFe1LMfF1qSjAyTY3xLAf5x/933iosAB+UcRlPj8+FrLg3cu+GzvWYEi2yh9gMUY+d9E0eEljfIIwMMakJaYZzkUcjEEVVS6qzoOt6hF85pcv4Q+5ITSZTHg2NwufOfZzHLvyN5rbtTh88AbDuKymMMN7JCIiIqLxlDRDSDSd6DWVkWQF7S4/ak48hUNyF17PssOoKPhudy9OXbIBim0g+DGmKBmNzvlTZ/DEQeYcAvEdQgd/DJnOS9Q/x8DX+TYzkDcDgSVfxb/1OAAAv8/LgantdeT0vJ9wW4c3NPgdEBEREdGEwoCQpi11hvBstxeN3V7NtrCswNbXiIoTT+EnRQUAgM+53CjKW4y2mbdpgjqDKOjO4QMAq7E/IEwjCNRIownNoMtOZNxoJjEI7fvIN3BN2IIFgSC8oohn8nIw58MkcwmJiIiIaFJhQEjTVvzaebXtbgTDA/PgJFnB/KMb8V85VjSaTCgOS/iaow8nL304IdIyCMkzhCU5kfmvmcZmmoYwaVSM6jaVyTwijIkGoaI1F03nfxlrHU4AwB9zc2Bo24WcnveGfG4iIiIimhgYENK0lWI6KwDA0rgLUvsbeLp/zcH/0dMLx9zPoS//goRjRZ2S0Utm5mNRdd5AQJhhcCZoupImyxCO7LIT2qUx+v8vAk3zPo8VYSsWBgLwiSKeycvFnA9+Nfw7JCIiIqJxxYCQSI8soezA/8YvC/LhF0Vc6vdjVdiG+gu/mfYpsi1GlOZYY9+nigdnFdkBAKW5A9101YvOJ6s2TSeLOHDs0ALSyGL12Th7/r34em8kS/h8bjbE9t3I7TmW0TmJiIiIaGJhQEjTVqoEYcXZv+NMXx3+kZMFAPh2twP1Fz+IsDl34PZxJ4if5xe/WH2qpi8zC+346JxCXFSZF9tmMQ38eibLLo5wglD3HNHA9Ny8u3C5bMOFqixh0nUJiYiIiGhSYEBI01ayJVDEsA9z3n8cPy+MNJK5uc+DWbYatNTcnvL28cGUIS4gTBWwCYKAXKtJ03gmulwFkDxDGH+O4dI7RzSQlUxZaDz/vliW8IXcbJjbdyPLWTvs+yUiIiKi8cGAkKatZBnCmbXP4G24cNBmhVlW8M1eB04t+jYgGlLeXh1MiWJicDXYshDxostVxJ9bc5+DLEyvHV9Gdz9wXtVfiaZ5n8MyyYr5gSB8oogXcrIx68R/Du3ERERERDTuGBDStKWXIDT7uzDjxFOx7OBdLjeySpaju/yqQW+vjrf0gr9MAzJ1hjCZEUgKas+ns02d6ZSNdjTP+zzucboAAM/l5SC/6WVYvK0jOxAiIiIiGhMMCGkaS4wI53zwK/zDBtSbTciTJNzrdOHspRt0I6+y/gYwdkskk6ddE3AkAkLD4AepZJqBHOp5m+Z9Hqv8MirCYfQYDHg5y4qZtc+Myn0TERER0ehiQEjTVnyGz+Y+i/wzf8YT+fkAgLUOJzwzPwF/0QLd29cUZeHiGXm4bFYhgMHLNzMN2EpyLLCbDbFlK/Rkcsp0DtU7X/y4Q9ZCdM6+HV/szxI+m5eD8vo/wRh0pj8YIiIiIpoQGBDStBWfH5zz4a/xQo4N3UYDqkJh3O4J4/RF/6Zp9KImigLKcq0wR0s7VYcZRiBDaBAFLJ9bhMXV+UmPyXQpicHonS++OQ4ANM6/B5/o8yFPktBoMmG3WcaM038c0bEQERER0ehjQEjTljpDaHfVIbvpZfwuL7KsxNcdTrTN+zwC9vK0OnwCg5eMRtckNKUxNzDVedK5/5EkColBoS97JtxVq3Gnqw8A8Lu8XMw49V9AODg6gyAiIiKiUcGAkKYt9bIRcz74FZ7LzYLTYEBNMITVfuDsBV8BkH5QNtgi8YVZZnykphDL5xQNZ9ijSm/cgiCgutCesP3sBffhsy43zLKC96wWnFScwId/H4NREhEREdFIYUBI01624wRszdvw+9yB7GDLeXcjZInMDUx37p86cNQrswSAPLtpoMR0rKXxMLIsRt3tMwpsCdvcBRdCLLwUazweAMAfcnOgvPX/hjVEIiIiIhpbDAhp2ormB+d88Ev8PjcXboOIecEgrgsacPb8L8eOEwXginlFOL88J+X5tMtOjPx49ZgMAsxGMfLPMPxf51mFdtQUZ+EjNYWa7eo1EdWazrsbn3O5AQCvZNnR1XYYaD487HEQERER0dhgQEjTlqIAOT3vw9S6E/+dFwn21vU6cW7+lxE258WOEwUBdrNRt2xSbbA5hKNBEARcOa8YV84rHpH7FEUB80qzkWc3pXV8Z9UqzDEU4RJ/AGFBwJ9zcoC3nhr2OIiIiIhobDAgpGlLgYI5H/wSv8vLhVcUsSAQxFVhCxrnf1FzXNolo5plJ8YoRYhIEJesE6racDuS2syJWUJFNOLc3M/irv4s4Z9yshH64K9AX6fmuA9bXDje6hrW/RMRERHRyGNASNNOu8uPvXVdMLS9B2P7bjyfmw0AWNfrQOP5X4FkytYcL6b5W6KOAceqZHQsXTqzAOV51oTtzXPuwEpfGKXhMLqNBmy3GoHDz8T2B8ISWhw+NPf60N0XGMMRExEREdFgGBDStPPeOSd8QQk1x/8fnsvNga8/O3i5bEPTeZ9POD79pjKZ32YysZkNuKgqL2F7yFqInpkfw2fckSUo/pibAxz6LSCFAWiX9zjX6xuTsRIRERFRehgQ0rRkd52GvXk7/pAbmTt4n8OJpvO+BNmYOE9Q1MwNTH7OkV4kfqSNVIz6kZpCLJmZr9nWdN4X8GlXH4yKgmNWC04EuoBTrwAAZFVE6AtJIzMIIiIiIhoRDAhpWqo58RReyM2G2yBidjCEa0IGnJt3l+6xaa9DOMUzhFF5dhOKsi2abe6CC2HKvwjXe7wAgM052fDs/08A2gxhWFJARERERBMHA0Kadqyec8hr/Af+q7+z6FecLjTP+zzC5lzd49XBXeoM4fSUbzfBZjagec4d+HR/2eiW7CygaRc6z9Wh1xuMHRuS5XEaJRERERHpYUBI086sE/+JF7Ot6DEYUBUK40afjMbzvhTbH99ERlMymiLsE9IMHKciQQDaZt6CS8MmVIdC6BNFvGK3wr1vE060umPHSZICWWaWkIiIiGiiYEBIU0qPJ4gPW1wISTKaerzYc6oTne6BzpaKqxUlZzbjd3mRbOCXnS50zLkDIevAQuzxQZ+m/DPNDOFELBkdzRGJggDZaEf7rNtwe3+WcHNONirPbIYghzXHMktIRERENHEwIKQp5dg5B1ocPrzb5EBTjxeBkIx3mxwIS5EgRN7/BLbaTWg3GlESDuNWTwBnz/9ywnnUWcJ01vgD4hemH9bDmFQEYSAAbp7zL/i42xNrLnNW6kFR227N8ZxHSERERDRxMCCkKSUabDi8Ic12f1gG/C7gyDOx7OAXnW5013wSAXt5wnnUGT4xvQRh2s1nporzy3NgNoo4vzw39hz1FSyEKf9CXOuNLC+xOScbVaef19yOASERERHRxMGAkKaUfLsp9rU3OLDEgSQr8B98BnvEMBrMJuRIMm53e3D2/K8knEOBEhcQqucGppktHMrgR9lIB6zVhXZcPb8E2Raj5tzNc+7Ap12RstGXs7OQ3b4bZl97bD9LRomIiIgmDgaENC2839gF5cBv8Gx/Z9FPu/vgrbwevpwa3eOTlX+mG1JNs2ShJovaXn0LLgsbUBkKo08U8brNioqz/4jtD0kMCImIiIgmCgaENCUEwzICYQnJGljmntmK+nAXDtmsMCoKPudy4+z59yY9n0EV0ZlUEwor8qyR89lMCbfRml4RoTqLKpmy0DVjNW7t8wAAXsrOQkXDX2MLErJklIiIiGjiYEBIk56iKNhd24k9tV2Q9CJCRcGsk7+NZQfX9HlhzV8EZ/GlSc+pLoE0GAa+nluSjUUz8rBkZn7KMU3EDOFojim+q2pLzadwW39AuN9mhcdzBjm97wNghpCIiIhoImFASJOeeq5gICwl7C/oPAi3+wReybIDAL7ocqFRp7OomroE0qj6RhQFlOZaYTKk/tWZgPHgqDIbtc+Ho+QylFjKcanfD1kQ8HK2HZUNfwUA/aCdiIiIiMYFA0Ka9Nz+gXXuFJ1YY+bJ3+K/c3MgCQIu9/lQbSpHR+WqpOdTFG2G0Bi/Un0aplvHUUtcQAhBROusj+M2d7RsNBuljVsgSMGkZb1ERERENPYYENKk5/YPLDERn32yu+pgaX8Df83JBgB8yelG4/x7ANGQ8pyyKrI0prkOodpECgej2bvibMuo3YfFlPinpLXmk7jR44VVllFvNqEWXpS0vq55bomIiIhofDEgpEnPHQgn3Tfz1H/hr9nZ8Ioi5gWD+IhkQcvsTw16TnXQku7C9GoTKUG4bE4hLp6Rh1mF9lG7D7NOCa0veybCRUtxXf+ahH/PyUJFw98YEBIRERFNIAwIadJL1rXSGHShtOFFPJ8baSbzBacbzXPuhGwcPDAabswiTKAcocVoQFmudUiBbdr3YdLPuLbWfAof7y8b/WeWHTmtb0D0dI7aOIiIiIgoMwwIadJLlnGqPPMXvGkBmk1G5EkSbvL6cW7eXTCkERgNNYtVVWCD1WRARb51SLefrBLmEPZrr74Jl4WA0nAYLoMBu21m5NW9OLaDIyIiIqKkGBDSpCfrLjUhY8bp5/CH/uzgp9weuCtvRMBenmZAOLSxLKjIxZXnFQ/ahXSqMRlE3efVkp2H7hk3xpageCknG/mn/jLWwyMiIiKiJKbXVStNSZJONq+o9Q20Blrwls0KUVFwp9uNpvM+D2DwJjGKMvQM4XQWnyW0mERcXJWH1ppPxhap32uzIug4AXSeHI8hEhEREVEcBoQ06ell86rr/ht/7M8OXuv1ISf7PDiKLwMAGNPI3ikMCDNmNWvnESoKYBAF9JRcjipDAS4IBBEWhMh6kO9tHqdREhEREZEaA0Ka9OJLRu2uepg73sRL2VkAgM+53Gia94VY68+0SkblkR/nVKc3j1AUBEA0oL36ZtzsGWgug/c3D79zDxERERENGwNCmvTiyzur6/4bL2Znw9e/1MRi2Ya2mbfG9qtLRoew5jwlYTEmdhqNLr/RNvNjuKnPCwA4bLWgzXkWaDkylsMjIiIiIh0pL4efeOIJ1NTUwGq1YtmyZTh48GDKkzkcDqxbtw4VFRWwWCyYP38+tm7dGtu/e/du3HrrraisrIQgCHjxxRc1tw+FQnjooYdw8cUXIysrC5WVlbj77rvR0tKiOa6mpgaCIGj+/eQnP8nwodNkFghL6PEEIcuKJtFkCPWhrOFveD43shD9Z119aJ39GcjGga6f6gxhsuYvi6rzIIrARVV5o/MApqD4DGGezRTJEAJwFS5Cob0Kl/r9UAQB27PswHtsLkNEREQ03pIGhC+88ALWr1+PRx55BEeOHMHixYuxevVqdHR06B4fDAZxww03oKGhAZs3b8bJkyfx9NNPo6qqKnaMx+PB4sWL8cQTT+iew+v14siRI/j+97+PI0eO4K9//StOnjyJ2267LeHYH/3oR2htbY39+8Y3vpHpY6dJ4nirC6c7+zTb3ml04MjZXrS5/JrtlQ1/xQGThCaTCTmSjFs8Ppyb9znNMeog0JgkRViaY8XK+aUoz5tey0cMh8U08FzOKrJjQUVuLCCEIMAx9+O4uT9LuCU7C3j/L4AsjcdQiYiIiKifMdmOxx9/HPfddx/uueceAMCTTz6JLVu2YNOmTfjOd76TcPymTZvQ09ODffv2wWQyAYhk8tTWrFmDNWvWJB1MXl4eXn31Vc22X//61/joRz+KxsZGzJw5M7Y9JycH5eXlgz9CmtTc/hCae30AgLkl2bHtff4wAKCh2zNwsKJgRt1z+D/RpSb6+uCvuh7+rIEPJQBthtBoSD6fcDQXcp+KSrItKMmxIMtixLzSyM9K3ZzHMfc23HjsCfykqADHLWacCbRg9tm9wOyrx2vIRERERNOebnokGAzi8OHDWLVq1cCBoohVq1Zh//79uid66aWXsHz5cqxbtw5lZWW46KKLsHHjRkjS8DIATqcTgiAgPz9fs/0nP/kJioqKsGTJEjz22GMIh8NJzxEIBOByuTT/aHKQBlkQ0BsYeH0VdL6Fbl8T9tptEBQFd7jc6FjwxYTbaOYQMuYbMYIgYHF1fiwYjG6LChWeD2PufFzui2R1/5mVBbz35zEfJxEREREN0A0Iu7q6IEkSysrKNNvLysrQ1tame6L6+nps3rwZkiRh69at+P73v4+f//zn+PGPfzzkwfn9fjz00EP47Gc/i9zc3Nj2b37zm3j++efx+uuv42tf+xo2btyIb3/720nP8+ijjyIvLy/2r7q6eshjorGlnh842FIQVaefx+acSDByhc+PAnsNvJXLE44zaAJCRoRjRRCAtuqP4ea+gW6jyocvAeHAOI+MiIiIaPpKWjKaKVmWUVpaiqeeegoGgwFLly5Fc3MzHnvsMTzyyCMZny8UCuFf/uVfoCgKfvOb32j2rV+/Pvb1okWLYDab8bWvfQ2PPvooLBZLwrk2bNiguY3L5WJQOEmoQ0BFiQQVYSlxTQiTvxv5za/i7zNKAQCfcfehef5XYTAYAGizx+pS0HSWoKCRIUBA+8xbcN37P4dFltFgNuFDuQsX1u0ALrh5vIdHRERENC3pZgiLi4thMBjQ3t6u2d7e3p503l5FRQXmz5/ffwEesWDBArS1tSEYDGY0qGgwePbsWbz66qua7KCeZcuWIRwOo6GhQXe/xWJBbm6u5h9NDuqsYPSrkJSYKaw88xe8bjOhx2BAaTiMKwMyWms+oSkPjVJvYYZw7Igi4M+qQrDoUqz0RuaFbsvKgufIn+DwZvY3goiIiIhGhm5AaDabsXTpUuzYsSO2TZZl7NixA8uXJ5bgAcCKFStQV1cHWbWid21tLSoqKmA2m9MeUDQYPHXqFF577TUUFRUNepujR49CFEWUlpamfT80Ocg6JaOh+FXjFRlV9S/gz/3lop90e9A9Yw3C5rxBM4AMCMdO9Llur74FN3ki3UZfybLDXP8Kjpxug6Iog5YFExEREdHISrrsxPr16/H000/j2WefxfHjx7F27Vp4PJ5Y19G7774bGzZsiB2/du1a9PT04IEHHkBtbS22bNmCjRs3Yt26dbFj+vr6cPToURw9ehQAcObMGRw9ehSNjY0AIsHgpz/9aRw6dAjPPfccJElCW1ubJsu4f/9+/OIXv8C7776L+vp6PPfcc3jwwQfx+c9/HgUFBSP+BNHYaXX6EjJFChIzhOG4DGFh+360B1vxts0KUVFwu7sPzXPvBADdDKEaS0bHTlFW5IOhjhk34gpfADZZRovJiFoxhMK2N9HhDmDPqS683+wc55ESERERTR9J5xDecccd6OzsxMMPP4y2tjZccskl2LZtW6zRTGNjI0TVGm7V1dXYvn07HnzwQSxatAhVVVV44IEH8NBDD8WOOXToEK699trY99F5fV/84hfxzDPPoLm5GS+99BIA4JJLLtGM5/XXX8fKlSthsVjw/PPP4wc/+AECgQBmz56NBx98UDNHkCYflz+ED5oj3V9XLVQ1M9JkCCP/j59DWFX/PH7fnx28yudHTvZcOIuWABg84GM8OPquPK8Y/pCEXGtkOZqgrRSBoktxtfcMtmdnYXuWHZ89tw3HZ65CWFLQ5vTjoqq8cR41ERER0fSQsqnM/fffj/vvv193365duxK2LV++HAcOHEh6vpUrV6YsCaupqRm0ZOzSSy9NeR80OflDA8tHKIoCSVZgNIjaklFES0YHNpp9Hcht2YG/z4gEkZ9xuXHugrWR7jPQLjxvNRkgCEBpjgUf9m/jWoOjz2oywGoyaLZ1VN+EG07+HNuzs/Bqlg3faNmBU3IQQCRoDEsyjIakBQxERERENEJ4xUUTgnou36Gzvdh1shOBsARZ3VSm/8tQeCBDWHnmL3jNZobTYEB5OIzlQQGtsz4e229QLTx/8Yw8rJhXrAk0DJxDOC46qm7EVT4/rLKMcyYTTgkBFLTti+0PhBM7yRIRERHRyGNASBOCOiB0ekMAgA5XQBMQRoWjTWVkCVX1f8Lm3Ei56KfcfeiceQskc07sWPUcQs2C9P2v/MLs9Bse0cgJ2MsRLFqCq/oXqX8ly47is1tj+9UZYyIiIiIaPQwIaULQq9wMy0rcwvSR/0enEBa1v4nmUAeOWK0wKAo+5fagec4dmnMYkqw5eOW8Enx0TmFsXhuNvfYZN+HG/m6jr2bZUdS8A4IUaSr0TqMDQWYJiYiIiEYdA0KaEPSmjoYlGZKs7jKqaP5fVf9n/CUnCwBwtdcHe858uAoXac6hLglVB4Rmo8hgcJx1zFiNq70+WGQZjSYT6gUfCjv2x/Y3dHvGcXRERERE0wMDQpoQ9FoJhSRFdw6hLAMmfw/yWnbi5exIQPhpdx/Ozbkj1kwmSl2KmmwJivllOci3MzgcawF7BYKFi3Glqmy09Nz22P6uvsB4DY2IiIho2mBASBOCXnfZsCxruoz2eIKo7+yDrCioOPt3vGEzwWEwoCQcxuVBBe0zP5ZwDqH/FS6KgJCkgczMIjsuqykckcdBmemYcRNuiJaN2u0obn4NghyZQ+oNSJxLSERERDTKGBDShCDrpAhDkqIJFE+2uVHf6UGbw4fKM5vxt/61Bz/e54F39s0Im3MTzhHNCpoNhoR9NP46ZqzGNV4fzLKCBrMJZxUPatxHYDJG/jSF9V4YRERERDRiGBDShKDoFI2GJFk3UMztfQ99nnrss1kBAJ9we+BacKfuee1mI84vz8GCihzd/TS2PjJbm4n1Z1VBKrgYV/h8ACJlowUNW2JNhiQGhERERESjigEhTQw61/2SrOgGBJVn/oJ/ZGdBFgRc6vejxFKO4IwrYvttZgM+MrsQV55XDACoLrSjKNsyakOn9OXZTKgpztJs64jrNprbsB0GJVIqqldKTEREREQjhwEhTQj6TWXkhHUIxbAPZY3/wN/6u4t+0u1Ba83tMKpKQgVEAg+riWWiE9HMQjvsloGfTfuM1Vjp9cGkKKg3m3BWdiO/6xAAZgiJiIiIRhsDQpoQ9JedUBK2lza/gncNITSZTLDLMm7w+NBS80nNkhLQ7x1DE4TZKOKKucUoz4uU/Pqzq6HkLcCy/m6jO+12FDa9CkB/bikRERERjRwGhDQh6M0hBICgpO0yWXnmL/hb/1ITazxe+EuvQCCrEqIoQIx2FE3STZQmFvXPqbPqBlznjZSN7syyoaDxFUBREjLERERERDSyGBDShJAsExQIy7GvbX2NMHe+hVez7ACAT7j70DL7dgCAKCRfViJd55VFupbOL2MDmrFgNAz8vDqqVuFarw+CouB9iwW9/nbkOD5kQEhEREQ0yhgQ0oSQrHmIOiCsaPgbtmXb4RNFzA6GsFCxo6PqBgCRbJOhPyAcaoZwVlEWrp5fgplF9iHdnjKj/jl58ubDbpuBxYEgAOB1uw0l517lHEIiIiKiUcaAkCaEZIkgSerfIUuobPgr/pYdyeJ9sq8PHTW3QTGYAQCCMBBgDCdRaDbyV2KsaOd9CuisXIXrPQNlo6Utr0GWk9yYiIiIiEYEr35pUijofAstoU4cs1pgUBTc2udB59zPxPaLghBbu44zCCcHQ1zkHi0bBYBDVisk9ymIvfXjMTQiIiKiaYMBIU0Ig00Vq2h4ES/3N5O50ueHJWc+fMULY/tFQYAoDj9DSGPHYND+oJxFS1BuyMO8YBBhQcBumw3W+m0Jt/OHJOw/3Y1mh2+shkpEREQ0ZTEgpAkhVfMQMexFcfMrsYDwY30etM76hCbDJArqOWmMCCeD+AwhRAO6Kq/DdZ5IoPd6lh1Z/QFhd18Abn8IAFDb7oYnEMbxFteYjpeIiIhoKmJASBNCqgRhafOreNcoodVoRI4k4xqfH20zb9XMQRPUJaOMBycFUeevT0fVqtjyE2/arED7YXh7WvBOowNv1fcAAMJsNENEREQ0YhgQ0oSQrMsoAMxo/Dte6s8O3uj1wlO6AkFbSaxEFOjPEEZLRkd3qDRCEjKEAHrLrsAFihnl4TB8ooi3rBZIJ7aOw+iIiIiIpgcGhDQhJAsHLd42mNv3x9YevNXtQWvNJwEgrmRUUHUZZUg4GWi6jPaTDRYEagbKRndm2WCt++fAfmYHiYiIiEYUA0KaEOIThEaDgByrEYscr2KXzQKPKKIqFMbFkhGdldcD0AYU7DI6+ah/fuoGM4F5N8fKRnfZbcDZ3TCE+gBEykW5Vj0RERHRyGFASBOE9irfbjZi2exC5NX+JVYu+rE+D7pm3ATZaAOgXdh8pNYhpLGjDghNqgmFwdmrcGlQRq4koddgwLsmAUVtuwGAC9UTERERjTAGhDQhxF/niwKAtvfQ1X0C+21WAMCtfR601nwidoy2qYwqIGSOcFJQB/Rm48CfIsGaC7l6BVb2r0m4w25DafNrAIAwV6onIiIiGlEMCGlCiC8DFAQA7z6PLVlZkAUBi/0BlJpL0VvyEe0x/URBiAWIzBBODuqA3qhek1AAAufdjOv6A8JddhsKW3dDkEP9GUJmCYmIiIhGCgNCmhCU+It8WQLe+3Ns7cFb+zxom3UbIKgySarD1XMIaXJQNwUyGwZ+rqIgIDz3Riz3+WFSFJwzmdAEL/K7DnPJCSIiIqIRxoCQxs2ZLg8+7F9cPD5DmN+yByeDvThhMcOoKLjJ40XrrI9rjtFmCAe6i4pMEU4Koijg/PIcnFeWjWyLcWC7IEDIrUI4fyE+6vMDiGQJi1t2cg4hERER0QhjQEjj5nRHH1ocPjh9oYSAsOD032LZwZVeH5B/Eby5c+POoF2YPppl0pQf0oRWXWjHrKIszc9MQCQo7Kq4Ftf2l42+YbehpOV1hCWZXUaJiIiIRhADQhp3kqxAVl3lG0J9yDm7HVuyI2sPfqzPkzI7GFWRb8X8shzMLLSP6nhp5JniSkZNRgGdldfhmv6A8KjFAr+3CULXqfEaIhEREdGUxICQxp0Sl/IpPbcdb5sFdBqNyJMkXOULoX3mLZpj9AJCk0HEzCI7rCbDaA6XRoE6IBTEyLIjWTVLkW8uwgWBIBRBwB67DZb67Zrbxb92iIiIiCgzDAhpXMiquWAKtHMIyxtfxpasSJbvJo8XgZnXImQp1Nw+Ml+QwcBUEV8yCgAV+TZ0VQxkCd+w22A/86rmdsdb3QwKiYiIiIaBASGNOkVR0NTjhcsfGtim2i8rSqzLqNnXCXvnfuzoDwhv7vPCdd4nE87JWYJTi3ph+mhTIIvJgM7KgXmEe21WiO2HYPD3xo5tcfjQ6vSP7WCJiIiIphAGhDTq2lx+nGxz42B9T2ybJqujDGQIy879E29aLfCIIirCYVyimOCZdUPCOUVBYHORKUSdIYz+WC1GEb2lyzFfElESDsMrijhkNSOv+XXNbQNhLlZPRERENFQMCGnU9fnDCduUuK+j35c1voyt/d1Fb+rzQrzgFsBsS7g9l5aYWoxiYsmoySACZit6y1bgau/A8hN5ja9pbstXAhEREdHQMSCkUacXu6m7iipK5HtbXyMMPe9ity0SAN7s8QAXfVr3gl8QgCzV2nU0uQmCgHml2agutGt+rmaDAV2V10WWHkFkHmFuyx4IUlB12zEfLhEREdGUwYCQxoW63FNSFCgKUNa4BTuy7AiKAuYEQzjfkAPMvVb3gl8QgIo8K84ry8ZHagoTD6BJp6Y4C+eX52i2WUwiuipWYpnfD6sso9VoRL0QQEHn27FjBOYIiYiIiIaMASGNgcQLdnVAGOk4qqC8aQv+2d9MZo3HA+HCTwAGk+7toUSySrOKspBnN43KqGn8mQ0igrYSBAsW4XLfQNloccvO2DHMEBIRERENHQNCGhcKtCWj1p4T8LvrcMBmBRDpLoqLPwNA/4LfG5TGZJw0vgz9cws7K6/DNb7+slGbDSUtO8GuQkRERETDx4CQRp1eQBdfMlpQ/w+8kmWHLAi4KBBAmbkEqL48cvsxGidNPNHuo12V18bWI3zPaoEn0IosZy0A7XxUIiIiIsoMA0IadXoBnfoivqXXi4L6l2LdRW/u86Kr5lagf206QRVRludZkW01Jsw1o6kp2n20L+98ZFvKcVEgAECVJQQgMx4kIiIiGjIGhDTqBJ0Uofoa3tx6GN2BVrxrtUBQFKz2eNE1+2MDt1cdazcbcPmcIlQX2kdvwDRhxJYXEQR0VV4XyxLusttQ3BpZj5AZQiIiIqKhY0BIY+pUuxuSrGhKRssb/4FtWZHs4Ef9AWTZZ8FTsDC2Xx1Pmgx8yU4nRnHg592pWn7igM0KS88xmP1dUBgQEhEREQ1ZyqvrJ554AjU1NbBarVi2bBkOHjyY8mQOhwPr1q1DRUUFLBYL5s+fj61bt8b27969G7feeisqKyshCAJefPHFhHMoioKHH34YFRUVsNlsWLVqFU6dOqU5pqenB3fddRdyc3ORn5+Pe++9F319fRk8bBpL6gzf2W4vGro9sYt4QQ6jtOmf2Jrd3120z4OOmo/hwqp81e0HzmAxMiCcTlTxIHpLPoq5shnl4TD8ooiDVjOKW3axZJSIiIhoGJJeXb/wwgtYv349HnnkERw5cgSLFy/G6tWr0dHRoXt8MBjEDTfcgIaGBmzevBknT57E008/jaqqqtgxHo8HixcvxhNPPJF0QP/xH/+BX/7yl3jyySfx1ltvISsrC6tXr4bf748dc9ddd+GDDz7Aq6++ipdffhm7d+/GV7/61aE8fhoHff5wLENY0HEAjbILp8xmGBUFq7xezF75JeTZBpaSYIZw+lJnCBWDGb0VV8WVje5kySgRERHRMBiT7Xj88cdx33334Z577gEAPPnkk9iyZQs2bdqE73znOwnHb9q0CT09Pdi3bx9MpsjFfE1NjeaYNWvWYM2aNUkHoygKfvGLX+B73/sePv7xjwMAfv/736OsrAwvvvgi7rzzThw/fhzbtm3D22+/jcsuuwwA8Ktf/Qo333wzfvazn6GysjKzZ4BGVYfLj7oObfZWwcAcwvLGl7G5Pzt4pdeHrNLFQNHcpOczMUM4rYhxP+7Oimux8r1deCE3B7vtNvyv1n3oCvoA5I7H8IiIiIgmPd2r62AwiMOHD2PVqlUDB4oiVq1ahf379+ue6KWXXsLy5cuxbt06lJWV4aKLLsLGjRshSemvF3fmzBm0tbVp7jcvLw/Lli2L3e/+/fuRn58fCwYBYNWqVRBFEW+99ZbueQOBAFwul+YfjY1j55wJ22RFgaIoEKQgiptfw7b+xehv9ngRXviplOczGbgIxXRijIsIuyquwWX+AGyyjA6jEafEEOwtB8ZpdERERESTn25A2NXVBUmSUFZWptleVlaGtrY23RPV19dj8+bNkCQJW7duxfe//338/Oc/x49//OO0BxM9d6r7bWtrQ2lpqWa/0WhEYWFh0rE9+uijyMvLi/2rrq5Oe0w08hQlslRAYcc+1Ak+NJlMsMgyrvH6EL7gEwnHh1WTxMwsGZ1WDHEdakOWQvgLl2C5L1JC/obdhuzG18ZjaERERERTwohdXcuyjNLSUjz11FNYunQp7rjjDnz3u9/Fk08+OVJ3MWQbNmyA0+mM/WtqahrvIU1riqJAgYKypn/ilf7s4NU+P4KFSxDOrkg4PizJsa/1lrCgqcugkxHuqlwZm0e4225DbuNOgPMIiYiIiIZENyAsLi6GwWBAe3u7Znt7ezvKy8t1T1RRUYH58+fDYDDEti1YsABtbW0IBoNpDSZ67lT3W15entDYJhwOo6enJ+nYLBYLcnNzNf9o/CgAlFAAxc2vYXt/QHijx4v26jUw6mQAQxIv9qer+AwhEFl+4mpfJCB832KB09cCdHw41kMjIiIimhJ0A0Kz2YylS5dix44dsW2yLGPHjh1Yvny57olWrFiBuro6yPJANqe2thYVFRUwm81pDWb27NkoLy/X3K/L5cJbb70Vu9/ly5fD4XDg8OHDsWN27twJWZaxbNmytO6HxpcsKzCffQOnBD/OmUywyjKu9vqQ/5FPI9uS2OeI8wanL4OY+LP35J6HLEsFLgoEAAB7bDagdttYD42IiIhoSkhaMrp+/Xo8/fTTePbZZ3H8+HGsXbsWHo8n1nX07rvvxoYNG2LHr127Fj09PXjggQdQW1uLLVu2YOPGjVi3bl3smL6+Phw9ehRHjx4FEGkic/ToUTQ2NgKIlAP+27/9G3784x/jpZdewnvvvYe7774blZWV+MQnPgEgknW86aabcN999+HgwYPYu3cv7r//ftx5553sMDpJKAAstS9he/9i9Ff5/LDNuBxlVXN0j68pzkJZrhWLq/PHbpA0IegFhBAEOKuvx9Wq5SdwkgEhERER0VAkXXbijjvuQGdnJx5++GG0tbXhkksuwbZt22INXxobGyGqOgBWV1dj+/btePDBB7Fo0SJUVVXhgQcewEMPPRQ75tChQ7j22mtj369fvx4A8MUvfhHPPPMMAODb3/42PB4PvvrVr8LhcODKK6/Etm3bYLVaY7d77rnncP/99+P666+HKIq4/fbb8ctf/nJknhEaMUqSeV1K2A/L6W14pTQHALC6zwPh0k8kPY/JIOLiGXmjMUSa4HQDQgD5S27Fyr/+Ef+3IB8HbFYEGt+Gyd2JUx4rirPNKMq2AACcvhA63X7MLs5Oei4iIiKi6UxQkl21T2Eulwt5eXlwOp2cTziKJFnB6yc6ErZXtL8O7P8G7qwqh02W8XpjC7Ie/ADIZYaXEr32YXvCtmvm5sDws7m4oTwfHUYjftPWgQUrfop3CiPrnK5aWKa5bU1xFuaVZo/doImIiIgmIL04iD38adTIST5rKGzYEmsmc7XXh6zqyxkMUlJGnTmkoskGqeZaTdmouf6VpOfwBsOjNj4iIiKiyYwBIY0aSU4MCEUpgOKWnbGAcLXHC1z4ybEeGk0iV51XgivPK9ZsEwUgPO9GrFQtP2FvegOCpN/RWADLRYmIiIj0MCCkUaOXISxsexMnhSBaTEbYZBkrfAFg4W3jMDqaLAyiAKvJoNkmCALk827ER/0BWGQZrUYjTiOAgq63dc/B5SuJiIiI9DEgpFGjkyBEWdM/Y9nBa7w+KOUfAXL0148kSkXILkWw4GJc7o8sP/GG3Ybill0A9LPTRERERJSIASGNmviL8ki56A68oioXdc352HgMjaYAURDQVTEwjzASEO4EFAUhSU44loiIiIgSMSCkURPfwLaobQ9OiCFNuWjf3JvHaXQ02YmCgM7KgYDwmMUMn68ZdvdpnGhzaz6QiMaDHS4/Gru94zFcIiIiogmJASGNmvgMYWnTP7E9O5IdXOn1wV98GWR72XgMjaYAk0GAp2AB8s3FWBAIQhEEvGm3oaRlF7rcAbQ4fLFjowHhsXNO1La74fKHxmnURERERBMLA0IaNZIqQyiG/QndRdur17DZBw2ZIAgwGQ36ZaMAOtz+gWPjuowGw9qSUiIiIqLpigEhjRp1xWhR2258aAihzWiEXZZxhT+AjqobITIgpGEwG0V0Vl4bW35in82KrO4jMAYc6PUMZAH5OiMiIiLSx4CQRo26ZLTs3LZYM5mVXh98RR9B0FYCcH04GgazUURv6XIskA0oCkvwiCIOW0woatujOS4+E62zIgoRERHRtMSAkEZNdB1CUQqgqOV1vNofEN7o8aKj+qbxHBpNEWaDCNlohbPsClzti5aN2lHSXzY6QNA0OVLAiJCIiIgIYEBIo0jun6ZV2L4PtWIIrcZId9ErfJFyUSCxEynRYNTZPrMx8iesvfwaXNNfNrrLbkNh2x4IckhzG77UiIiIiBIxIKSMBcNyWgt/R5vKlJ57Ba9l2QAAV3l98Bct6S8X1V+8nigVTUBoiPwJ66pYieU+P0yKgmaTEefgRV7XOwO3AZgTJCIiItLBgJAyEghL2F3bib11XYMeKysKBDmEopYdeM0eKRdd5fWhY8ZqzTFEQxXNEAbs5QjnL8RHfZHOorvsNk3ZqAJmo4mIiIj0MCCkjDi8kTI8ddt+f0hCQ5cHIUnbyl+WFeR3vo0mxYMGswkmRcFVXh86q24YOIYX6ZQh9RISWRZj7OuuipWxstE37DYUt74e26cocRlCvuyIiIiIADAgpAzp9QR9u6EHdR19ONXep9keCMsoPfcKXu0vF73C54eUfyH8WVWxYxgPUqbUJaN5NhOMhsiGzsrrcE1/Y5mjFgvCnrOwuRv6j1T4WiMiIiLSwYCQMqMTEQZCkcygwxvUbPcHQyhtfhU7+stFr/d4EZz/Mc0xzBDScF0+pwgA4C64EEXGQpwXDEIWBOyxWVHSEskSKor2tca5q0REREQRDAhpxETnc0WZWg6hI9yLkxYzDIqCa70+eOferDmGF+aUKTFuUUGryYAFlbmAIKKrYqDb6G5V2Wj8y4wfRBARERFFMCCkjKjnb8lx0Zw6IJRlBflnt8W6i17mDyC74DyEC+ZqblNoN4/iaGkquaAiB6IIXFSVl7DPJEZel12V18UCwjdtNmR1HoIh6AagLU9mOEhEREQUwYCQMqJOzsiKgkBYin1vMgy8nAIhKbLcRH+56A0eLwLnfUxTcXphVS5mFNhGe8g0RcwosOPa80tRmJX4IYKhPyDsKV2OC8MCCiQJboOIYxYDitr39DeVUZWMMjVNREREBIABIWVIHdApAPwhWfe4UPM7cAbacMxqgaAouM7rRXD+LajMjwSARdlmVOTZIIp6bWqI9AmC/uvF1J+dlkxZ8Fcsx1WqbqMlLTuhsKkMERERkS4GhJQR9TV1fIZQPS/LcOIf2GmPBH+XBALIss2AXHIhrCYDVp5fgkuq88doxDQdGFUfLHhqVg0sP2Gzoah1NxRJSnjtEhEREREDQsqQZh6WAoQkRXefpW4rXsuKdhf1oaPqRghi5OVmNIhJMz1EQ2FQBYTemlW4wueHUVHQYDahRe6Dtf2IZmF6VowSERERRTAgpIxo5mEpCiRJ+z0AoOMEXI7TOGy1AABWeb3omLFadw1DopFgMRpQU2xHTXEWhPyZUHLn4zK/H0Ck22j22Vc1QaCiKFAUBWFJv+SZiIiIaLpgQEiZUV1Uywog6WVdjv8Dr9ttkAUBCwJBFJmK4Sq8GEwK0miaV5qDeaXZEAB0VV6La7yRgPANuw05jTs0r936Tg92HO/ArpOdCDEoJCIiommMASFlJH4eliTrZAiP/z1WLnqDx4vOqhsAQdQsWUE0WgQB6Kq4NjaP8IjVgqDzFARHg+7xvZ7gGI6OiIiIaGJhQEgZ0cwhlLXNORRFAXrOwN3+Pg7YrACA6/vLRQGA8SCNBQECnIWLUGbIxZxgCGFBwD6bDcbTr+geb2CnWyIiIprGGBBSRjQBIOIzhACO/wNv2G0ICwLmBEOoNuTCUbwUAFgySmNCEACIBnRVXDPQbdTOgJCIiIhIDwNCyoi2ZBTagFBWgOP/wI7+ctFVHi9cs26EIhoBMEFIYyP6wYO6bHSPzQqxaR8Mob5xHBkRERHRxMOAkDKibd2vaNce9HbC33wIe1Xlon2z18T2c6kJGgvR11l3+ZW4OCQjV5LgNBhwzCigsH3vOI+OiIiIaGJhQEgZUTRdRrUlo/lNr+GA1QKfKKIiHMZ5shnBmVfG9jMcpLEQrQCVTNnwln4UV/qi3UatKGnZlXA81yQkIiKi6YwBIQ2ZomjnFBY2vYqdWTYAwLUeH7orroZossb2M0FIY0HdzdZRfT1WquYRFrXuinRDUlFnvYmIiIimGwaElJHEDGHka0PIg5y2fXjDHgkIr/NGlpswqhp2cNkJGgvqDx7c1dfhCp8PBkXBabMZHZIDuT3vaY5nOEhERETTGQNCyogCbVfRaMloUfubeM8koMdgQI4k45KAhO7yq2E0DLzEmCGksaB+nQXzamDInoMl/gAAYLfdhpKWnZrjmSAkIiKi6YwBIWVEPd9KUTWVKWl+Fa/3Zwev8fngK78cYXOuJkNINBbUmWhRALoqrtOUjRa3vq45XmGOkIiIiKYxBoQ0ZEp/hlCQQyhqfUM1f9CLzqrrAWjXeGOGkMaC+nUmCgK6Klfial8kIHzbaoXoPAmLtzV2THdfEIcaeuAJhMd6qERERETjjgEhZUSOW3ZCkhXkdx5Ck+JBo8kEs6xghc+PtvLrAIAZQhpz6lecKAhwFi1BtZiNWaEQwoKA/TYrilsGsoTNvT44vCF82Ooa+8ESERERjTMGhJQRbVOZSIawpGUHdtoji9Ev8/sh5V+IgL0CgDZDyLlaNBZEVYpQFAFFNMJZdQ2u7i8b3WW3oSSubBQAQmE5YRsRERHRVMeAkDI0ENVJsgwoCkqaX4vNH4x0F10VO8akaipjYLaQxkB8ySgA9M64Htf0B4R77Dbkte+HBX7N7US+PomIiGgaYkBIGVFn+UKSgmzHcbgC7XjPaoGgKFjp9aGj8vrYMSaDiPPLc3B+eY4mOCQaLdqmMpGvHZVXY0kgjGxZRo/BgA9NQHnXAc3t+IEFERERTUe8QqeMqKs+m3t9KG1+Dbv6s4OLAkHYrVXw5M0HEMnUGEQB1YV2VBfax2G0NB0Jqr9q0RjPqdjRV3wZVqi6jRac05aNiux6RERERNMQA0LKSPw8wJKWHbFy0Wu9/d1F+y+sjcwI0jjQNJXpjwgVBeiqXBkrG91tsyHv3A5AGZg3yAQhERERTUcpr9ifeOIJ1NTUwGq1YtmyZTh48GDKkzkcDqxbtw4VFRWwWCyYP38+tm7dmvY5GxoaIAiC7r8///nPseP09j///PNDefyUIfWabVbPOcB5Am/ZrACA67w+dFbdENtv4hU2jQNBSCwZBYCuyutwpc8PUVFw0mJGV6AbOb0fxvazZJSIiIimo6QB4QsvvID169fjkUcewZEjR7B48WKsXr0aHR0duscHg0HccMMNaGhowObNm3Hy5Ek8/fTTqKqqSvuc1dXVaG1t1fz74Q9/iOzsbKxZs0Zzf7/73e80x33iE58YgaeDUvEEwjjX44t9X9K8E3vtNoQFATXBEKqEbDiLlsT28wKbxoMo6H/tzZkNs30mFgcCAIDddhtKWnaqjuXrlYiIiKafpAHh448/jvvuuw/33HMPFi5ciCeffBJ2ux2bNm3SPX7Tpk3o6enBiy++iBUrVqCmpgbXXHMNFi9enPY5DQYDysvLNf/+9re/4V/+5V+QnZ2tub/8/HzNcVardSSeD0ph/+luzfclLa9hp6q7aFfldVBEY2w/S0ZpPOg1lYnqqrw2Vjb6ht2G4tZdsX2SzHVRiIiIaPrRvWIPBoM4fPgwVq0aWD5AFEWsWrUK+/fv1z3RSy+9hOXLl2PdunUoKyvDRRddhI0bN0KSpCGf8/Dhwzh69CjuvffehH3r1q1DcXExPvrRj2LTpk1QUixyFwgE4HK5NP8oPb2eID5scSEkaddoMwYcyOp8G3ti8wd9kfmDKiYDMy409jTLToh6AWFkuYm3rFYYHR/A7GsHAMhcKJOIiIimId2AsKurC5IkoaysTLO9rKwMbW1tuieqr6/H5s2bIUkStm7diu9///v4+c9/jh//+MdDPudvf/tbLFiwAFdccYVm+49+9CP86U9/wquvvorbb78dX//61/GrX/0q6YN89NFHkZeXF/tXXV2d9FjSOny2Fy0OH+o7PZrtxa27cNhqQp8ooigs4cKwiO6yKzXHGEVmCGl8xVctO4qXokawoSoURlAU8JbNiuKWXQAAJgiJiIhoOhqxK3ZZllFaWoqnnnoKS5cuxR133IHvfve7ePLJJ4d0Pp/Phz/84Q+62cHvf//7WLFiBZYsWYKHHnoI3/72t/HYY48lPdeGDRvgdDpj/5qamoY0puksEJY035e07IiVi670+tBXeSVko01zjJEZQhoHYpKmMgCgiCb4Zq7UlI2W9JeNpqoyICIiIpqqdAPC4uJiGAwGtLe3a7a3t7ejvLxc90QVFRWYP38+DAZDbNuCBQvQ1taGYDCY8Tk3b94Mr9eLu+++e9AHsWzZMpw7dw6B/mYR8SwWC3JzczX/KDOai2wpgMK2PbHlJq7zeuGcdaPObcZseEQx6hhQ7yXorVmFa3zR5SesyG/fCzHsZ4aQiIiIpiXdgNBsNmPp0qXYsWNHbJssy9ixYweWL1+ue6IVK1agrq4Osjww16y2thYVFRUwm80Zn/O3v/0tbrvtNpSUlAz6II4ePYqCggJYLJZBj6WhUc+vKmjfh1oxjA6jETZZxrJAEH0zV6W4NdHYEZJ+E+GZeS0u8wdhl2V0Go2oNUgo6DzAOYREREQ0LSUtGV2/fj2efvppPPvsszh+/DjWrl0Lj8eDe+65BwBw9913Y8OGDbHj165di56eHjzwwAOora3Fli1bsHHjRqxbty7tc0bV1dVh9+7d+MpXvpIwrn/84x/4z//8T7z//vuoq6vDb37zG2zcuBHf+MY3hv1kUHLq7ElJyw7syIpkB6/0+WGZcTmQVTxOIyPSUq9DKOhEhGFLAczVy3CFL9Jc5g27HcUtuyAzRUhERETTkDHZjjvuuAOdnZ14+OGH0dbWhksuuQTbtm2LNYVpbGyEqGoaUl1dje3bt+PBBx/EokWLUFVVhQceeAAPPfRQ2ueM2rRpE2bMmIEbb0wsQzSZTHjiiSfw4IMPQlEUzJs3L7acBY2ssKqzaKwlvyKjpOV17Crq7y7q8QKX3JxkDTfWjNLYU5cq63W6lRUFmL8a1xw4htey7HjDbsWXW1/HWQaERERENA0JyjTspOByuZCXlwen08n5hCn4QxLePNWl2ZbT8x6qd/0LVldXQVQUvNHYjPyvv42T4TI09Xg1xy6fW4QsS9LPHIhGTZvTD0lRUGA3YV+ddv3MinwrLjS2ouvJ5bhuZhUUQcCOxmY03fgili67epxGTERERDT69OIgrgtAScWvPQgAJS078YYtkh28JBCAJXsWUDwP6jXol8zMx1XzixkM0rgpz7OiKt+mWzIqywBKzkde9gxcHAgCAHbbrSho3jnGoyQiIiIafwwIKSlJp4SuuOV1vNHfXfQarw+9MyKL0atLRg2iAIvRkHBborGmV8ksKwogCPDMWjWw/ITNhiIGhERERDQNMSCkpEKSNiC0eNtgcB7HQZsVQGT9wZ4Z1wGIBIFRgu58QqKJQeqvkvfNviG2/MQBmxXmnveAvo7xHBoRERHRmGNASEmFZW3JaHHrLuy3WRESBFSHQpgBO2ZcvBJA/GLgYzlKouT0PpvIMkdKmf1Vl2OObEZ5OAy/KOJtqxlK7bYxHiERERHR+GJASEmF4zKExS07NeWipvNvRH5OFgBthlC/4yjR2FPPIbSbDagutGNOSeQ1Kxot6Cm/MlY2utNuB05sGZdxEhEREY0XBoSUVFg1h1AM+5DfsR+7VQGhcP6a2H4GhDQRqV+K+XYzzi/Pgam/A5IAAZ1Vq3C9J9Id9/UsG6TTrwMB93gMlYiIiGhcMCAkDW8wHGsmo16RpLB9Hz40KugxGJAty7g0EAbmXR/brw4CGQ/SRGSMW5NQEICuimtxaUBCriShx2DAUROAU6+OzwCJiIiIxgEDQopx+kLYV9eNA/WRddvUBaPFrQPdRVd4fRBnLANsBbH96nmDDAhpolC/FA06k1vD5ly4y5ZjZX/Z6A67DTj+jzEaHREREdH4Y0BIMR0uPwDAF5QAALEEoSKjpGVXbP3Ba7w+hOau1txW21SGESFNDOqOt0YxMUMIAJ1VN2BVf0D4WpYdyqlXgJB/zMZIRERENJ4YEFJMYhwXiQhzej9Ad7gbtRYzREXBVT4/QvNWJ70tA0KaKFJlCKPBYmfVKlzuC8Amy2gzGvEhgkD9rthxDV0enO32jMFoiYiIiMYeA0JKKpohLGnZGcsOXhIIwJQ1C+GCuZpj1d0cuewETRTqzyaMovbPXXRX0FoMf9GluCpaNpo1UDYaDMuo6+jDqfa+2NxaIiIioqmEAeE05fSGEJbkuK3aSC56+Vvc8rpmuYmuipWahjMAYDJyYXqaeNSvxbh4UBMs9sxaPVA2arcDJ7cCUlizFueZLg8CYWlUx0tEREQ01hgQTkPtLj/ebujBwTM9KY9TFMDibYPBeRwHbVYAwEqvD12V1yE+WWI3GzG3NBsLKnNHa9hEw5KYIRyICA0Lb8NVXh9MioIzZhPqwy6gcZ8mK9jQ5cG7Tc4xGy8RERHRWGBAOA21OSMNM7zB1NkOBQqKW3fhgM2KkCBgRiiEasEOX8VlKMwyJxw/uzgLVfm2URkz0XDZTAbN9+oMoa2kBnL+Qiz3RX43XrPbgeP/gByXRHf5QqM9TCIiIqIxxYCQYjQldJ4gZBkobtmJXf3loiu9Ppjm34gV8yt0W/gTTURLZubj4hl5sJnjAkLV10ZRQEfVjVjVv0j9a1l24PjLkGSWiBIREdHUxoBwGkpnit+Rs73o7OlFfsd+7FbNH8T8mzhHkCaVomwLynKtiTvUDWcMIjpm3ICVXh9ERcFxixnNvnag5fDYDZSIiIhoHDAgpJj4MC+vbR8+NCroMRiQLcu4NBAGzls1LmMjGmmaOYSiAH/+PJizanCZPwAA2GG3w1y7ZbyGR0RERDQmGBBSUsUtO2PdRVd4fZAqPgrYCsZ5VEQjQxS0X4uCgI4ZN+L6/rLRHVk2WE5tHVh/hYiIiGgKYkBIMZpSUEVGScuu2PqD1/h88NYwO0hTh/r1LkCIBIRVN+K6/uUn3rFY4Ow7iyxn7XgNkYiIiGjUMSCchoSE4tBEOb0foDvcjVqLGaKi4CqvH77ZN4zB6IjGhvq3QBAiZaPuggtRmlWBRf4AFEHATrsdpc2vjNsYiYiIiEYbA0LSVdLyeiw7eEkgAJN9JuTC88Z5VESjQxQiGUIIArxzb8Z13kjZ6KtZNpSd2z7OoyMiIiIaPQwIKUadMSlufT02f/Aarw9dldem1Z2UaLJQzwwU+zOEAOCZezNu9ETKRt+2WhF01yHLWTcOIyQiIiIafQwIKYHF2waj40MctEVa9UcCwusYENKUJQoCDP1/DXsKl6DEVIiFgQAkQcBrWXaUNbHbKBEREU1NDAgpJpoxKWp9A/ttVoQEATNCIVTDBkfxpZGSOqIpQlF1DxX6u4wCQLMjgI4Za7C6v9vo9iw7yprYbZSIiIimJgaE01CyuC56gVzc9gb29M8fvNrrR0/5VVBEEwNCmlLU4Z0gCLGSUQBor745FhAeslrg855FtvMkACAYlnXPJ8sMGImIiGjyYUBIMQoAQQqioH0f9tgj5aJX+3zorrgGQPJAkmgqUH/g4Sy6BIXmMizyByALAl6192cJAeyu7YQUF/yFJBlv1HbincbeMR0zERER0XAxIJzm1GVzigIUdL2NejGEDqMRVlnGUn8AXeVXA2BASFOLxaj986fJgAsC2qsHyka3ZWvLRkOSjHebHHirvhuyrKDDHYAkK+juC47Z+ImIiIhGAgPCaS5+WlRR6xvY3V8uuswfQKDgYoSsRQDSW7+QaLLIsZqwsDIXl84qAABNySgAdFSvwY39AeE7Fgvcvmbk9H4AAPCHJHS6A3D7w3D4QiwXJSIiokmLAeE0J2syhAqKW98YKBf1+tBVsTK2X2Q8SFNMZb4NhVlmAIh1GY1yFVyMXGsllvj9UAQBr2QNlI0eahgoDfWHpKTnVxQFhxp68EGLc+QHT0RERDQCGBBOc+q8htF5BmHPWbxrsQAArvT50NU/fxCINN4gmqoSknxxZaORbqP/TEirB8Jy0gakLl8YDm8IrQ7/KIyYiIiIaPgYEE5z6gxh1tmd2GuzQhYEzAsGUWQsgLvgwnEcHdHYCUuJUV1H9c24weODoCh412qBI9CG3J5jmmN8QUnze0REREQ0mTAgnObU17FZjTuxxx6ZP3iV14/u8qsBgS8Rmh7iO4cCgDt/AbJs1VjqDwCIZgm1i9T7QmFNpj3ZfELOMyQiIqKJiFf705Ac11lUURS4XA6YWw5gry0yf/CquHJRAGwpQ1OaAp2ATRDQXn0zbtJ0G/0noAysRegPydrfKfXNVX9hGQ4SERHRRMSAcAqTZAVNPd6EphfqrKACBXUdfag/+E98aFTQazAgR5KxKBBGd9mVYzxiovEztyRbd3v7zJtxvccLUVHwgcWCzlA38rsOx/aHJO0cQnVwqP4QhWWlRERENBExIJzC6jv7cLLNjbfO9Gi2a8rbFOBstzfSXbR/uYnlPh+8xUshmXPGcLRE4yvLYsRH5xQmbPfkngdr9mx81B9pDPPPLDvKG1+O7Q9LimY9z2SBHwNCIiIimogYEE5hnX2ReU+hsKzZHr/UBBQFxa27sDs6f9Dn1yw3EWUx8eVCU5tRb20VQUDbzNtwS1+kbPTl7CyUNm2FIA0sQh9Q/Y4li/sYDxIREU1MvqCEug53yqWkpjJe4U9laVyYygqQ5ToFd6Adxy2R9diu9GrnD55Xlo2LZ+TBbjaO5miJxp2YZGmVtpkfwyqPFxZZxhmzCafgRXHb7tj+ZAGh+leQASEREdHEdPhsLxq6vHi/eXquG8yAcApLfv2pvWItbn0Db/aXi14YCCDLWg5P7rzYIYVZZpTlWkdtnEQThUEvQwjAn12NUNGluNbrAxDJEpaffSm2P6D6RDG+aZPediIiIpo4oplBhzc0ziMZHwwIp7B0Stfk/nLR6HITV3v7y0W5CD1NQ4YUr/u2WR/Hx/o8AIB/ZmUhv2UnDEE3AG2GUE6SImRASERERBMRA8IpTLeNPiJlolGS1wF71xHsiy434fWhO365CQaHNE2ISTKEANA+4yZcHpCQL0noNhpwyCKi7Nx2ANo1DDVlotDfTkRERDRRMCCcwtSBnywraOz2whsMazoiCvU7ccxihEcUUShJuCAM9JRerjlPimtkomkjbMmHr/q62JqEL2dnobzxpYTjVEsUaucTygmHEhEREY27lAHhE088gZqaGlitVixbtgwHDx5MeTKHw4F169ahoqICFosF8+fPx9atWzM658qVKyEIgubfv/7rv2qOaWxsxC233AK73Y7S0lJ861vfQjgczuRxTwvqwK++y4Padjf21XVrMhWmM69hd//8wSu9PjhLl0E22sZ4pESTg3PeJ2NlozvsNlg6D8LibdUck2yR+rcbeuBMc25CSJJR39kHX3B6djsjIiKisZM0IHzhhRewfv16PPLIIzhy5AgWL16M1atXo6OjQ/f4YDCIG264AQ0NDdi8eTNOnjyJp59+GlVVVRmf87777kNra2vs33/8x3/E9kmShFtuuQXBYBD79u3Ds88+i2eeeQYPP/zwcJ+LKUd9Mer0DbTIj12wKjLsZ1/HHnt/uajPn1AuCgACmCIkAoC+mddhgWxBdSgEnyjidbtVsyYhEN9ZVFso+u45R1r382GLC/WdHrzd0DP4wURERETDkDQgfPzxx3HffffhnnvuwcKFC/Hkk0/Cbrdj06ZNusdv2rQJPT09ePHFF7FixQrU1NTgmmuuweLFizM+p91uR3l5eexfbm5ubN8rr7yCDz/8EP/93/+NSy65BGvWrMG///u/44knnkAwGASpJJu01L89t/d9tId6cdpshkFRsNzn011/kFMIaTrKtiYus6IYreiccRM+plqTUN1tFEjdPCbdeYQ93sjfsmCYdaZEREQ0unQDwmAwiMOHD2PVqlUDB4oiVq1ahf379+ue6KWXXsLy5cuxbt06lJWV4aKLLsLGjRshSVLG53zuuedQXFyMiy66CBs2bIDX643t279/Py6++GKUlZXFtq1evRoulwsffPDBEJ6CqStZU5no1qLWN7Cnv1x0cSAAY1YNfNkzx2h0RBPTlecVY+msAiybXYiPzilEeZ52yZXWWR/HLf1lo/ttVgT6TiGn98PY/mQlowDn4xKR1unOPrQ4fOM9DCKa5nRXGu/q6oIkSZqgCwDKyspw4sQJ3RPV19dj586duOuuu7B161bU1dXh61//OkKhEB555JG0z/m5z30Os2bNQmVlJY4dO4aHHnoIJ0+exF//+lcAQFtbm+45ovv0BAIBBAKB2Pcul0v3uKlGTpJciF6wqpebuMrrR1fFLbrHM0NI04nVZIDVZAAA5FpNmsXqBQFwlFyGC82lWOwP4F2rBf/IzsL1Z/4Cd8FCAEBzrw+9nhDOL89JWPol2cL3RDT9OL0hnOmMfLhUmc+5+0Q0fnQDwqGQZRmlpaV46qmnYDAYsHTpUjQ3N+Oxxx7DI488kvZ5vvrVr8a+vvjii1FRUYHrr78ep0+fxty5c4c0tkcffRQ//OEPh3TbqaLXM9DMQlEAs78Llt73cXBmZI7n1V4funTmDwK8iKXpTf3yFyAAgojWmk/ik43P4F2rBX/LzsZdjS+hbvG3IRss6O4LohtBlORYErL0af8qcY0Kokml0x1ApzuAC8pzUi5foxaUWBJORBODbslocXExDAYD2tvbNdvb29tRXl6ue6KKigrMnz8fBoMhtm3BggVoa2tDMBgc0jkBYNmyZQCAuro6AEB5ebnuOaL79GzYsAFOpzP2r6mpKen9TQcKgKK2PXjbaoFfFFEWDmO2Yoaj+LLxHhrRhKMXxLXWfBKrPV7YZBlnzCYcF3wobtmhOUZvLmG6H64kK/cmoonp3SYHWhw+NPV6Bz+4H3/PiWii0A0IzWYzli5dih07Bi5wZFnGjh07sHz5ct0TrVixAnV1dZBVdYq1tbWoqKiA2Wwe0jkB4OjRowAiAScALF++HO+9956mM+mrr76K3NxcLFy4UPccFosFubm5mn/TmaIoKGrdhd2xclEfesuugGIw6x7P/CBRRHQ+oS97JoIly3BD/5qEf8vORuWZv2iONYpCQqaP2Xaiqc0fYtaPpg9Z5ocaU0XSLqPr16/H008/jWeffRbHjx/H2rVr4fF4cM899wAA7r77bmzYsCF2/Nq1a9HT04MHHngAtbW12LJlCzZu3Ih169alfc7Tp0/j3//933H48GE0NDTgpZdewt13342rr74aixYtAgDceOONWLhwIb7whS/g3Xffxfbt2/G9730P69atg8ViGZUnacqRwihs24s3bQPLTSQrFwV4EUvTm3rZlRyrESvmFePyuUUQlnwen+hvLrMt2w5b+17NmoQCBDaVIZqkFEXB+81OnO32jOKdjN6piUbbO4292FPXhTBLn6eEpHMI77jjDnR2duLhhx9GW1sbLrnkEmzbti3WwKWxsRGiOBBPVldXY/v27XjwwQexaNEiVFVV4YEHHsBDDz2U9jnNZjNee+01/OIXv4DH40F1dTVuv/12fO9734udw2Aw4OWXX8batWuxfPlyZGVl4Ytf/CJ+9KMfjfiTM1Vl97yLFsWLc6Z8GBUFl/v8OFx+dWz/ohl5OHbOOY4jJJqYBAA2c39Z/NLbcdmu/4XqUAhNJhN2ZNmwsOFFNCxcCyBSDhZfNSqkWzLKC0WicdXZF0Cb0482JzCrKCvt2/Hz08lBURS0Ov3It5tgN49YO41ppbsvsjxSjyeI0lzrIEfTRJfyt+D+++/H/fffr7tv165dCduWL1+OAwcOpLzDVOesrq7GG2+8kfL2ADBr1ixs3bp10ONIX1HLbuztX4x+qT8AKXc+AvaB+ZfxE+L5BkcUoQnozHYIF30Kn6j7K35VmI+/ZWdh1ZnNaFjwNUAQoSiJc4SYISSaHKQxKIXj5z7j51yvDyfb3ACAVQvLBjmaUuHreGpIWjJKU1dh62682b/+4AqfD92q7CAAGIT4gJBXsUSAznzaJV/AbX0eCIqCQzYrugOtyO88BCDyJsllJ4goHQrLAsaUwxsa/CCiaYQB4RSV7M3F5O+GxfEB3rZG5lte6fWju+IqzTG8ZiXSl/C7UbUUBblzcYXPDwB4MTsLlWc2A4j8DibOIWTJKBFF8Pd8/LDD68jh63hqYEA4RSWrdilqfxOHrRYERBGl4TBqFDMcRZdqjhHYV5RIV0K2XBBguewLseYyL+ZkoejcNhiDTt3LDX7YQjQ5jPVFLi+qiWg8MSCcovTWQAOAotY9mu6ivaWXQzGYYTBoVt+GyFcGUXoW34nrfEEUSBI6jEbstQioaHgRDm8Ix1tcmkMZEBJRlDpLxXgwtb5AGM0O34iV1jIAHznMtk4NvOyfonQnxCsyitr3DMwf9A7MHzSqul0IAmBkREgEII03u+xSmBfcik+4I1nCP+VkY8bpP6Khsy/xXHzfJCLK2IHT3Tje4kK7KzDeQyGaknjVP0XpXXjm9r6PDsmFBrMJBkXBMv/A/EGDOiCENkAkokFcdi8+4450rNtrt6HHdxYFnW+N86CIJjdFUVDf2QeHNzj29z0W96Gov+anRelw+0emGQyf7eFRv1750p0aGBBOUXolo0Wte7CvPzu4OBCAIWs2/FkzAAAmw8BLQRAEGA18aRClreZKlOXMxgqvDwDw55wczKj74zgPimhyO9frQ32nB4caesd7KKNuLK6pQ5KMvkB4DO6JpjoGgVMPr/qnqLCkExC27cGe/vmDV3r9muUm4jOEBmYIidInCOi+4C78iztSJvpiThbyml+F2depOYxvokTpc41QNmiiGuk/B6fa3Whz+pPu31vXhQOnu+H0Tezntd3lR49HPys8UvOwmZEdHiXJ1zR5MSCcooKSrPneGHDA3nMUb/UHhCt8PnSplpuIn0NoMjAgJMqE/SN3Y0VAQVk4jF6DATvs5tgSFFGcfE+UvrFYHD4dgwUPIxFcDPcUXX0BnO324v1mZ9Jjoh8Ud/dN3Hl4vqCE9845ceTs1M8KT2baktGJ8XtKw8OAcIoKxQWEhR378I7FBJ8ooigs4XzZCEfJR2P71U1kBAhsKkOUobzCYpgv/jRu788S/ik3G1X1LwCyFDtmsPfNDpcf7zTyQogIAEI6lS5jJZM5UkO9HtbcxzA/LAqG5cEP6pewfM4EEghLgxwxMmNnCDM8fP6mHl71T1HxJaPFrXuwN9pd1OeDPHMFZIMltt9oiOsyygwhUcaEj3wZt7s9MCgKjlitaAp1orjtjbRvf+ycE919Y99Ag2giUmcIz/V6J2wmQj2qTN45tU1lRmo0Q9Pu8qPF4RvfQRAAICzJ6PEEJ+zrPd4kGSYNggHhFKUpGVUUFLXtwZv2aLmoH/Kc6zTHx88ZtBoNoz5GoimnaimyCxbi2v7mMn/KzUb1qf+O7eb7JlH6wvLA+9iJVjfO9Q49YJFlBac7++D0Zj5/brDf22Tr/o6l4YxAURS8d86JD1tcaWToxo5eQDRycwhH5jyj4VhzpGT2TJdnvIeS1ER+/kbCZAnGRxIDwilKXTKa7TyB3lA3TpnNEBQFV/j8wHk3aI6Pn0M4o8CG0lwLFlTmjtmYiaaC3gVfwJ2uyBIUL2Vnwdi5F9nuOgDT802GaKjiK10cQwjmopp6vTjT6cHbDT0Z33bwOYRDG9N4/TmIj6nUUzX1GtKNl/F4fhRFQV1HX9KmNumq6+jDm6e6hhRg9/RXiUQ/AAlLMrzBidUddqrPhz94JvO/E5MdA8IpSh0QRpabiGQHLw4EYbZVwVA0V3O8tsuoAFEUsGhGPqrybWMzYKIpwjHvE7hEtmF+IAifKOIvOdmYWfssAGYIaewEwhIau70J88knk/imMsO5CPUEMrsw15RzDnas6ohMRihnME9xNGnnMk5sIzeZRf+Rtjj9aOjyDLupTUOXB/5Q5HcwFW8wnDRrHX19HKjvwb667hFbg3EkqF+vEyFDPtLc/vCEaWo1VhgQTlHqyfjFbbvxpj06f9CP7vKrYIhbZ1AdEE71T36IRpXJipa5n8Xn+7OEf8jNQWHDizAFBj5xdPtDaOqZuHOiaPJ7t8mJ2nY3PmhxjfdQhiwhIBzGr8to9lEZ6lxAbev+4f0tGM7fElkz/qGfp9cTxFv1I7esxXj8dfSNcCZusMewr64bbzf0wB9K/MAiLCnocPlj+zrcE7M7LN/GpgYGhFNU9FNhQ6gPWV1HcMDav/6gz4fuimsgCALUjUTNBhEGgwCDKMDMRemJhkyAgKZ5d+EmbxCFkoR2oxGvWw2Ycfr52BvnW/U9ONnmRpsr+ZphRMPh6r8o75qgF5FjTcwgIjzX68XJNnfs+0y6jGYS2I3WB0LyIJmN+KdCPebhJEUOn+2F2x/GkRHqlKw/h3CEuowmeZzx2/0hadDncyj3E68voB+IHjs3sIzIcMYx0qZ6hhBI/3F1uP0425043zMkyfAk+blORLzyn0LOdHlwvDXyaXA0ICzs2I/3zQa4DSLyJAkLQgp6+5ebUL9BioKAa84rwdXzSyZ0S2qiiU4UgaCtFI7qm2NzCf8rNwdVdc9BkPyauSAu3+R5syAab8O57Mzkbe1Eq1vz/WBBnqZkdKgZwmFeU2dS4prqtiMRdEgjNA9xPMIM9X16g2G8eaoLB+q7R+e+FHUgPvijlSZQ4DXUMmkgEkD5ghOneVEy6TzdkqzgWJMTp9r7EoK/vXVd2H96YpX6psKAcAo53dGH5l4f2px+BEKRgLCodXesu+gVPj9cxZdBMmUBiJs3KACiKCR0GyWizAj9s1wa59+Df3H1wSwreM9qwQnFCcuJv2Nf3cDFBcuzifTpBSbDyaip39kyPU9GGcL+r2VZGTS4Gk4Ql/q8g2QI42biyRkGJmMlOpT4xzMSnVCTPUr1XXX2Z9e9wwheUv2N1zysNIOPiSiTl0ynO4BjTU7sresavQGNkHTen9XNh8JxP59og6bhNigaKwwIp6DGnsgk5lyrMbLcRP/6g1f6/OiuuDp2nEFQB4QMBIn0GMXM/kxGf5XcBQshFl2GWzyRUpLf5+ViZu0zmndPefL2+6ApTlEU3XlNw3G6sw/taZZJ62VDhpchHHiPG+kLa/XZBhqBdGP3qc6UQeFolYzqnTXVfal3TcQslHpITT1e7KntQsMoLckw0o1+Up1Dc19pnGsivV9oPwRJ/4ly+iZHcASk9/Pv6hsoyZ9IJb1DwYBwklEUZdBPx6JzRypCjfD423DcYgYAXOHzobtcFRCK6pLRURgs0RQwq8iOgixz2kuwqH+VGud/CZ93RsrPdthtcPfVoqDzYGy/Zu7OJH8zoanleKsbb57qQptzZOa59niCONPpwXuqOVGp6GWqRipWGemgJ36ssqzAG5QQlhR4UwTV2pLR4TaVST6e+P3xMg2COlx+7KvrgmsUSuH0sq3qIQXDkaiorqNP9/aN3V6caEveSMkbDEOWlbTnEI4meZCfWbyJFazrfz24gXfIw2d70dTjTTp/cjJQf2gWnyGcbBgQTjJHmxzYU9uV0MVL72Iyt3kX9veXiy4IBJFtLoUnd15sv8k48ONnhpBIn8kgYumsgrSXYFH/KnVWXIsZlgpc7vNBFgT8V24OZp14KrZ/KBPzFUWZ1G+gNDaGW/7f4oisgVbfqX/hnanohXy69H8dhnPBNXDbTDOEmZaM6mUMB71dRiPSOdcg8xjVmxKbygxI57k5ds4Jb1DC+83pBfeZ0Lv3TMpYa9vdONfjg8ObmInq6gtgX1033mlyaAJw9WMeztw4PekG4uk87xOrnHdoZcbq116vJ4iTbW4cOD06czSHK60gXR7a8zARMSCcZLpjC5Zq17bRexlaz76OPbFyUR+6Kq7W/Daqu4kyQ0g0UlS/TKIBjed9Eff0Zwn/kpMNsWMvcno/ADC0Uq0PWlw4cLobTT2p17ei6W2yf8Y30hlC9fV2qovvsM66jYPOJdIEdkraGbeRnEM8WDyRsmRU9ZAzuahN9jwO57Wn6Dx3Q/m562VrmvsXeu+Nm9P1+omOWJOTkb6mT3cOYavTjwP13SkXoJ9IVSTa7Pa4DWNUpdtUJooZQhoX8X+I4/+Ii2EvDOf2Y3//gvQrvJH1B9Us6gzhCC73SjSdxV8Mtcz+NC5TsrAwEIBPFPFcbg5qjkeyhEPpThgt4WvQaXNNFDXZG4SN9Dy/dLMxyS7qZFnBuV6v7rzK+N9jbUCY/L7Uc8KG32U0/Qxh4r6hzWtOdt0wrIBQ87Wi+X9G59G5iXa9Za2m3sQP2EZ7nVj168TpDaHPH8aHKdYNnUgBx1CXnZhMf5XSeVTqD3JHqrvueGFAOEnF/2GI/30s6HgLHxoBh8GAbFnGRUEJPaVXaI6xGA2xryf7p8lEE0X8emey0YbOC7+Mrzgib/R/zM2BvXk77K56KEpkbtXJNndsqZh08UOc6UdRFBxvdcXKOVMxjMMfdX9IQkcaTWPSyXToHTKcyy11oJMqINT7PVQUoL7LgxOtbhw525t47rjST03mP1VTGcTdcBi0Y8h0DuHA15nMUxuNl5juHMI0h6QJinWeA1OKNZajQc2IZwjTLBmNCqZ4H5hQJYk6P6d0TKbpSel8IKD+/ZbS/CBoomJAOEnFv6HG/6EobtuDvf3ZweU+P7zFSyCZczTHmIzaZSeIaPj0fpW6L/g8rgkZMDsYgtsg4s+5WZh18j8hKwqO9E+sP9vNEtDx5gtKONTQgw73yDRSGWntrgCae30pswhRI33h5Q9JuvOy1E62uXHsnBPdqs57eiRFGfSCSW//8EpG9S/c4oV1PuVXADT3B+F6SxDEz6dKtxRcO4dQe1yb048TbS7sO9016POeOIbE/annMqY/D0rdsEX9ClM3mBnOh1XDmcM32OtDkyGMOzb6gUGy+x+NC/xMu4ZOpGUnJvuSSYqiaMrDdf/epHEeTUConos6CZ8eBoST1GClA+rlJlZ4fQnlooD207L4rAYRDY3er5JizUfz3LvwZWfkYur3ubkoOPsiRHdz7Jj4RW1jt52M7yyT1PE2FxzeEI41jXyzjJHgSTG/KJ724lf/NRQMyzjd2Zd6kWghkjV781QXDjX0ppzjFM1uDLZu27FzTuw51aU7Xy9KN0M4jN+FZFk7WVbwfrMzVood0rlKVxQFoRRNceJHpb5YHspFfCAcadhyrscHb0DCYZ2sZKoxDB5sa7/XdLtMMV6HN4hzParsdP9LLCTJOFjfk7B9uKKPI+2GWzrftDp9sTmDRtXvRDju56y3xEWyr5M50+XB2w09aVd76D4uBUi20lE6JaN9gXDGDZyASPfVdJeEASZ/yeihs73YdbIz1rVf90eRxtOYrBR9QmVz08SAcJJKNYfQ5j6LoLcJ7/cvN7HC59csNxFlEhkQEo00vU/HBSGyBMVNvjAqwmF0Gw34e5YVZcd+M+j5JuH7yqSV6qJ/IsikrFhdHZfsQvKDFifOdHpw6GyP7n4gkhl8S3WxHwilCOL670ev7E39HtXrCSIYltHhTp5J1G0qk/TowSW7cGt2+NDm9Mc6ZibLEKaiDRyUtBvYJAs44segKMDxVldsoXQ98WvaKYqCrr5A7DWTKhuZKrvoCYRjGcr4fdG/dZmWu6ekU0Wb7t/A+OfA5Q/hg2ZXLKBWX+bEjzlWnppiWF19gZQdnk939MHpDWnWSHT7w3jvnFP3dsle48my+4PNUfMGwzhwuht7T2e+6Pu+um68d86ZdnWE9gOIyP+bHb6Ur1EgeTXacD7skWUl9YdaOpzeSEa7qy959n2wLKiiKElL0Sfj2zYDwkkq1RzCovY3ccBmhSwImBcMosBUAHf+BbH90V9Ii0ndVIaIRoLeG54oCAhZi9A5+zO4p38u4X/m5yLv1AuweFoATM43EBpbmX3yP/hC7L39F/qpgzztWlupPvmO3o3eODP9xFwvUzWcD0ekJAFhfPCqFxCqH4/ZmHjZFN/QRf1YU5UFJluYXO9hNvf68G6TI/m51A1qZKCpx4ejjY7YnMdUDazU38a/Vvaf7o5lhuP7FEX/1sV/oDyc6wm9QCPdH3t8gO0NSEn3x/9cBuYr6t+bwxvE0UZHbIkERVHQ4fbrvtbV5bOeQCTzNtjcU7VUz1+qwMnliwSdkqQMuSOpy5fe2pLxZcaeQBjHW1wpX6OppBpuMCzjvRSl6G839GBvXXql1fGij0Pv7gf7exN/HZ4sQzhZPtRlQDhJpZpDWNT2Jvb1zx+8wudHT9kKQEj8UVtNBswvy8H55TkQJ3lHOqLJoOGCr+KTngDKwmF0GI34a7YFs49HsoTJPmWfJO8lU8JEanjg9IZwoL4bPaoW+ZllYoZXtphMqlNFL650A8IMk0j6TWWGUzKq/3wklk+mDgh1zx33tSbbl+KBx5d5KoqCTndAt5PpYLTBpYIWZ6S00+0P959f/37jb5sscHf7w2n/fgzn90iJfzKRfvZIO/9PSZi/meossm5gMPBdd9xSFed6fTjW5MShhsTsevQ5V9N7DSWbJ5vq+Uv1VKg/rPAN4TUUkfnPTkH6H1Ylm1+a6gOj2nY32l1+vNPo0N0ffb5bnZnP/faHZJzp8ug3kxrktvF/V6UkQeBkeQ9nQDhJxb8Qo98Jcgj5HQdiDWWu8CUuN6H+WzOzyI7qQvtoDpVoWkmWIQSAgL0cHXPuxFejWcK8PBQ0/BW2vsakWZpkF0NjEbuc6fJMq/UOJ1A8iMONPejzhzWZhWA4/UuLdBubZCpVUBbLEA5SMqrmD0mx8q1Bjx/Gw0hexpn8w1W94/Wey/jMVLpNWuJ/t1ucfrzb5MDRJBe+QKTxUap5nNExJLtGSHZ8VLLxhmUl4STRX5ehvrwau70JWZ/4oC7y//RoH8fgzff09sWfIyo+SO/sH3d0vqz6vvSyzPr3mbhNQeqGS+k+F8nmpI+U+A8z1H87h1L+mepnk25wm3ZpseqJb+jy4HRHH060uXXOl/qE8WOWVB/+DHWO5XhiQDhFRCe/5nUfRaMQQIfRCLOsYKk/gO6yFeM7OKJpRO/TXXWTgIYFX8Nt3hCqQmF0GQ3YnG3F7A+TzyUc7K3EEwin1eo/U76ghNMdfTip80ZJo08vsTTUuVrprI/15qmutLqXprq2iV7AZ1Iy+uapLrzd0ANnXKnaSF9EaS7YU2QI9e5Vfbzec6ktx9TOIWx3BZI2z4kPS9P5Pd5b14WDZ3pS9hGQFUVnWsnA9009Xs2cNu1t9W8jSUrCzyT6ty7+Q4J0PldxeIOobXcnZH2G2twj/raynDje1K/dxK/Ut/fHfWBnjlvCYihrBOrOIUyMu+P2p/cBgyfDOXVR6X4oFv8hiDrzl+qpSD6HMPlthlr+mozehzq9OuWmenfrC0p475wTTl8oMUOoeomwZJTG3Kl2Nz5occb+IEfKRSPdRZcG/DCUXISQtUhzG65fRjR69Kqv1b9zQVsp2ud+Dl9zRJpYbMrPRW7j32FzN+ieb7A3k/2nu9Nq9Z+psObTzknyjjZME/0vozogHOwiSb03WYZQ/br0h6S01jdMZw5htHOf3r5koqWx7S4/Otx+3YB4OK9C9f2rMzjx59R7rUtxg5Fk7RytxHUIBzb4gpJu9iFyX/pfDyYsKbHXwtluD3o8wYQxJGbHBr4OhuXYXLj4+5aSPK6wLCc8V9GL+/ixpxNUJCsx1GaeotsSnxzdbs5x8yQTS6UHD6aSzTOMzxCqu7QrijKksuxkTWWGmiFU78u0yUpUsh9d/OspYXkO1Q2H8mGO3m1anT68eapLtwR3OPR+VkadN26919275xxod/nxts6HMur3TG1AODnePxkQTmJhScbZbi9aHf7YL0yhav3BFV4/TOddjyUz87U3nOhXPUSTmN4HLvHvNQ0XfBU3+2RUh0LoMRjwxxw75r7/f3TPl+68KUeazQCGYpK8n015mcxLGa2SpVTXvdH7kWUkZMUGG4MkR4Kc9845cazJidp2vRKuzMc7cFt1hlC/tMvpDel2Po3P/ji8Qeyq7UBdR1/CueW4DCGA2JIWQOTC+khjL0539g3rQjEkyehw+3GqvQ9HzvZqzuXSyV6kesFoA1P9DJkkJy9lHMqjUC+Lop3TmXj/aZcCqgNbRUm4KE89/7X//5rzDXwXH8AaDaoP+SQ55VzRwe5Tu01J+XjT3ZfJ77z6OdercAmEJeyp64p14o3cSHVfGZTmJqMXpH3Q7MpoPm2675V6wzPofZKrc5y6FDcxQ6j/QdMEWj4yJQaEk5h6nkYgLMMU6IHF8QEOWy0AIvMHMe96FNjNyLIYx2uYRNOK3ifX8W+yIWsRWs/7PL7eO5AlNDdvR273uwm3TfbeGn83Q7m4VBRl0PlIwOSZFD9cE2kO4WAGu+jSXFhneK2ql+EbOFd6JWvx8wgHG6+cRpZlOE1lks0hVJ/z7YaehM6U8ccDwDuNDsgyYssLxDdCif9dVJeMd/YF0NMXxJlOT1zJaGaPTZIVzVjVQ6zv9CQcn3rupzbw09sekhIDqlgQFV9KGvfXye0P4a367lgVQ1dfAMdUwUVI0g/Qo/veaepNGLNuhjAumFS/BCNzOxNvM3B84v2nm/ULhOURyxDGjyFhX6os5xB/PwYb+9luL0JhWfPBRtxLXrNhKJ9zjGXQpFcxYTLodA/Wua3m9dH/TfTDAU2WPkn56ETGgHASU39iFQhLKGzfh3csZvhFEaXhMObBBFQvgygKWD53oGw0y8zgkGi06MUUehcvDeffh5slExYEgugTRfy//Dyc9+5Ph5wGGcob6rFzTuyr69a80euZLCUvwzdxI8L4n8FgP5JkmY7B+EMS9tQmX8cs9Vyfga/jMyqDBaXpXFAHQjJOtbvT+hAjYWyaDGFmwXImF/sKEi8krUYD/CEJx1tdcKga6MQHkpkIy4kZsJTjShlkDEjWKTEkyQkBh35nzsS/d2e7vXD7w3in0QFFUXC00aGZi5ls/p2CSGOrdD/Q0Dyd8RlCpBcUa+ZNpjlfLxCShziHUO+8qW+T7q9yJn+y1ePQ+wuoV34an41UP7epGykl2z7895h0T6H3+6yXIRzsfNHzGPs/8UmWXZ8sb58MCCcx9Sew/pCEora92N8/f3C5zw+h5irAaIkd85HZhSjLteLiqrwxHyvRtJGiy6ha2JIP8epvYX1P5NPvP+Vmw+04iuKWnUlPnepNU+9N2BsMp+w2F11EuHGQTqKT5P1sSov/8Q6eIRz4OpOAxqHT8TOd+41/bQbiA8I0MoTpBK5nu70pu3CmOn9Uul0gY8cP8vzFX/zFPw6r2YDjrS409/o0XXsTmp5kMiZJiXW4TOe26XTYBJKXHYZlOeFO0i3pVJdX9uq8vsJJMoSKklkjJW1WJjHbmU65peaDlGSBalz5aSAsDfo7pvehoN7PRL0t44oF9XOXwasp2XqYUR6dD2AU7Z0l7c6a/hjivh/FlGG6a5ymU+YODLy+k5X0M0NIIy7+DVf9CawvIKGwfY9muQnMvU5zfJ7NhItn5MFmNoz+YImmKb05hEnf1z9yHy63lOFKrw9hQcAvCvNx3nuPQZAH3oDTfaNNXGhZwb66buw/3Z20y2GU7vwJzblS7p4yJnLJaKY/glQLkXuDYbQ4fLoXjekEbvrbtd/HZwgHew3JcvoXkt6gBEVR0N2XvINnPPX9J2v+kEyqi32nNxR3IZhYWikA8OiUoqovTDP9+b7f7NRk9gcvt00Uvf9kHx6otwfDyUtG408e/5SqPxDTmxMWUgXomgwllOTr1uk2HdIGgPGBbuqAMPG5SPaUxp8rJCV2dY03WImr+txReh8kRvf3BcJ4t8kBt1+VcU5ynsEMtg7l4BnC9AOgZIFq/G3iP1BKer4hvDnpd3fV2ab62uEN4khjr2b/QIYwsdvuZAkC1RgQTiKJtfUDGwzdJ+EOdqHWYoagKFjeP3+QiMZWRkGFyQpc/zAe7HFAUBS8mmXHqcA5LOl6KXZIum8y8ftCaZRkRel3WFN/Pfne3Kaa+AuWQS841Bf5ccfuq+vGhy2uIXXyjL6UUi17AGSeIZSU1OuvxTva5MA7jQ6c6UqcL6cn2Ty5dKTKKL7d0KNpWR8JFuKfG/2/C+rfyw5XZG7hUKV6TMkalURfF8lK3TRNVVKWjGq3Jzsu2Tij2852R9aEU51IM/8yniwr8ATCaOrxQpa1j1GWE+eKpg5UEseerDQ5PtgMSfKgS7voBbaDvQxTdVI91uRApzuAw6p1SpNlqAaTrKmP3nn1zh+/dqLeMiH+kITTnX0Jy3dEJf79SK+ZzFASiXqlwHqvy+hjOtfrxbvnnAm/n9H3WLMx8iKVk8wbnCzBIQPCSST+JaX+BLaobU+sXHRBMAirtRwomjeGoyMiIP05hDEX3Y75xRfi432RC9v/KCxA/ls/hTXsAJC8hCte/K5kn/TrGWz+xCR5Pxu2iZggjLX2j9ueQTyY0QVJslItQ6zsT8F755x4/USHJtsTfx/xGULdBd3V++XUXSDjdfdfnKXTXTc+IFKUgd+P4WYI1WOJni96ymgwE79wt95524e5lmiqD30kWdH9UCd6/8leK+rxhcJyYllf//eJ5cxx3w/SYCMkyfAGwzjV3qfZriD1MlmyomD/6W6cbHOj2eFLuAjP5G9g7Laq45J9EKDEPY5QOl1G0ywZVdPLEEZFy4W1S6joB3aSHHmeNF1CNePQ/xpIr2RX0ckQhiUZH7a40N0XgMsfwt66Lpzp9GhKpuPPoZZuhnBIS1zonFr375MS6RB8otWNkM54os9NNCCM3CbxdcQuozTiEjMA6oDwTeyLLjfh86O7/MqJXf9ENEWlehPXv4EI3PhjfLPXAbss4z2rBX83BDHn2P8BkLwMKPHiLO7vg+pdL1VzBCD9lts09qIvp/ifbyZzCDOZj5PstNEssqwMBC/negfWLYy/XXSOe1OPF72eoG7mQbuWX2YZwiiLcfDLGL2HH5JkNHZ70eEafP3OTJYUCEsKGrojH+5E/xbIiv7fhaF0pkwmfq1EzZhkBY3diRfiYZ2gWJYRK8c91NCrOT4QV+4pKwoc3mBCF9BU2Wy9x+wJhrGvrjthu6Lor+s6cN6Br93+cMLfyvggOdXLK3poOj8SvZLRQecQ6t3nYDGkbplp5P/qeZm6Y1R93d0XgCcQTto8LP73UE19nakeT3xWOf6Dy/ouD1ocPrzT6MDB+p70A/J+gSSZxKheTxD1nX0ZBf1RyYI/vW3qktx40efGYhyYhqXovI6YIaQRl+wTFDHsR27X29jfHxAu9/nRU3blWA+PiJD+5zAlOQMNnzD7KpRc8InYMhS/KMxHVv0LyOl5P2U5V6o3cvUnx3oXK+q5V4MtyjtJ3s+GTW8NrvEWHVP8z2CwH4m21Dj9+0tWHmyIBYSay+6Br+Ju1uUOYM+pTpxsc+P/b+/N4+Wmzvv/j7aRZp87d1+978YGbDC2CRBCIClNS9M2aUMJJfmmryROQ6DlR5YCaRbI8s3SJjSU/Jrl14SENC0JSSiEgNkxBhsDxmAb79vd93tnlc7vjxnNSBpJo7n32jPX93nzMjNXOjp6tIx0nvNsO44Mlc2oaLQQypL3oYmXe9Mos34cqsZsax2W294L+mBWzz6oMVb5RFGFuCkXhwcmbJMFZVUNvaPJkvhGjQG7T46WtLe6+zHG8NLhodL4Zct25bwc+sfsXWUZmOtv0tiXT+TLWghdSzbklWAvde+sClBWLWYZFRwUNbvjcLuveN7eOqpv4bMrk8DsvxsVY6vl3iqH9VpmspZzWHAzNstkPLcqYxUXky+JQS5jmdxxZAgH+3JKZ7EPb79TuwkyW5fRMk9ZWwuhjSyz5f1JCuEswnpz6jdjrP8lvCVoGBQEBDQNa1MZDDZvrIaIBDHnsXv4G1/s9SEflrWEsbItYm505ZfwgYSKhelcsfp/i0Wx7OUvgBkCMqyzjmY3HfPL3qjw2b0AjYMEt5loYO7EENoN5UYSGRwfcs/CejpxGg7bxek4UYklyqlpQbkx7HckkcXLR4cwlszYDsaMs/y2gzDTQLQYl2VXE8xZXvfBLlA8fo4r9l1JmQDrINlrXV/dZTSnEHre3Yyh739wwl7hOjGcwKvHRzBkWa8xZnvfGQffgLMS6m4hLG3vpIQx5j7BZnQr9Am8xU2PlcQQug3MGcvVl/SKNb5S35ci2iftszsMN3k4cLbHvr9nDMmMCtHwGykkBzL3XpTPUqLMiul3aHX9Vq2TAKUyMavPKIPnZE+F/Vp+j16zy06knN3WnbCNY/VoNdThuOLzzaic25VimS1lm1yfunfffTfmz58PRVGwYcMGbN++3bWz4eFhbNmyBa2trZBlGUuXLsVDDz3kuc/BwUH8/d//PZYtWwa/34+uri588pOfxMiI2e+Z47iSfz//+c8rPfZZR4lLTv5HXt/9DJ7Nxw9emEhiMr4GWV/0TItHEATKW258Io/OeKB00Btth3TJLfjMwCAA4OeREHpGX4f46s8KTdzS2w9NpPHUvj6cyA/aMi7WQ8C9GLT1OGbJ++y08OKhQbx5aqxQosMIYwzHBicx6uJWNF04zs4yV15JL2eVccJJebSzEA5NpDEwnsZLR4Y8ZCe1W2a2Kuh/l8t6a9fv/p4xPLWvD/3jpddJ75fnuIKrXaUDVgBY0BjE/IYgVrVHyjeGsT5ZdazPQiGI0X79qWF7F8JXjg07KtcA0GD0brDBeitMNaFPuZavW2LirBNk5cqBGKlILktfWZUVnqeKg3XbrnfX34zD7TIwnsarx0dgfH3oijGzHK9OwqBw21oIDYusIlkVM7tSI9YMwRqrbMLF2K/Tfp2YiifEVLKMlrYvxnGaYwjz+9Aql6vaOCqE999/P26++Wbccccd2LlzJ9auXYurrroKvb29tu3T6TTe+c534vDhw/jlL3+JvXv34vvf/z7a29s993ny5EmcPHkS//f//l/s3r0bP/rRj/Dwww/jwx/+cMn+fvjDH+LUqVOFf9dcc800T8XsQ49rqO95puAuuoncRQmiqpSbDXRLkoCNW3BRoANXjk9A4zh8oSEO3+O3A2M9AGBJU28/4/1G3tVLdUjlrmPMQmrvzueuUJ6NuI3Z7eo59o6lsLd7DNsPDp42mTSWG2yWDrLdt5uqQu80MC6kVrdZrapFd0+fQ0xfuUGYashdX5nqlNvoSD5GzuoG2j2SLMSn8TwHSVcIpzBKC8siFjeFbN317DAq0dWwEApTVELL1aIs16+be3MlzxKn7Kg6xjqMpWUmLImEUF7B9CyXZTomqxVdRmUHC2HJhA5jrjViOTj/DkYTGQxNFK+RbmG1Hq91PWCfrMUtI6ZVMWOFTxcrMGMV1Y/MbW/+221CwiRPmYkvZgmtcGpnm3HZ470qm1xGSxXmWW8h/OY3v4mPfOQjuOGGG7By5Urcc889CAQC+MEPfmDb/gc/+AEGBwfxq1/9Cps3b8b8+fNx6aWXYu3atZ77XL16Nf77v/8b73nPe7Bo0SJcfvnl+PKXv4zf/OY3yGbNP5xYLIaWlpbCP0VRZuJ81DQlAwIN8CV6wI/ux04lN2O3KZ9QJqR4c2khCGJmmdajX5SBd38NtwwOI6hpeFWRcb+sAv97S67vkllZ570Zk8rYvexMxaBtpLbreWgijefe6nd0QTub8PISH3cY0A2Mp3BqJGG7rlJUleHJvX0lGQ/LyccsAzTP+6vAQmi3P5HnbEsF2A0QjYtyA/rcdydrWjQglSyzimtVVoyZFXmuaLXzOuA0wufPgdd4QMGQiOdMWwh53pDl9DT0XQ5VyyWc0TTzgNzNEtccMY/jjDFr5WCwi0l1VhCng1UuTSu6DzrFv1qPY2Aijcm06pgchuc4zzMjGZsHvHF3xthPu/ve7Fpr6duDhdDah6Yx11ItdpRzVdU50DduylRqjn8s3eeOI0N4+q1+k3xedVWvc0aSwBsyCuc/pxjDXU1stYZ0Oo0dO3bgM5/5TGEZz/O44oor8Pzzz9t29OCDD2Ljxo3YsmULfv3rX6OxsREf+MAHcOutt0IQhCn1CQAjIyOIRCIQRbOoW7Zswf/5P/8HCxcuxEc/+lHccMMNNZkQYCaxG7TVdz+LlxQZWY5DeyaLdj4A+Zy3IR4OVEFCgiCmPehYcgVaVvwZbjryML7UEMe/1MXw9n2/Rdsbv4XW+U5TUzeFMFvOQqiZBzRWzLPGuU+95tXOI0O4YmWzp8M5nbB8nJBYQcyZkeNDk0hmNCxuCgEwW2+t8Ut2Z9rJUqLHIoUVCSGP8WblsLqlVmIhnAmXUX3g6tRXISOlw2vYboCoWu5B/T7lOeDiJQ0YTWbw6rGiQndOexTP7O839WEdBLq5mwocV3DVtrVgMA1CdhJiehRiZgxSZhRCNgFeTUJQkwiM8ACXgZCewMLugcJyIZsExzLgtCw4poLXsuBYFjLP0JVJg2dZSJyKBZkMOGPwp+n+0Ytbw7KeA+NEMF6AxolgvJj/W4Rm+F5Ylm8LXoLgU5DURPA+P9KQoPEyVEGGJvigCTI0Xs59Fr77cuv15bwP4EstXl4U4n09YzgxlEBnPGAuL+JyL9qWvym7pxx2MdVm69HMWWqMkxc6uhXOKeutpuWUMd2CriddaQjJ6BtLlfzucvqgtzFtQQlxsEoZ4wbtLIRuVrZ01vx3VmXgoJVcl+kmUbFem4yDQnmoz1x31HrNrejW7r6xFFqiuQkH/Vx3xgOIB3145diwk1Rl5QZy923uWhUnHTSHa1HL2L6p+vv7oaoqmpvNL/zm5ma8+eabth0dPHgQjz/+OK699lo89NBDeOutt/Dxj38cmUwGd9xxx5T67O/vxxe/+EX83d/9nWn5F77wBVx++eUIBAL4/e9/j49//OMYHx/HJz/5Sdt+UqkUUqliXMHoaGn2rNmA3T1V3/MMHszHD25KJJBo34yWutAZlowgCJ36oA9BWURYEQtpvo3jJ0/zVu/+Kq7518fwUDKJnYqCL9TH8b3f/QO0658yNXObbc/YJJXJ1UPLZ1l0qF9lS42+z7YfGsRYMou3LW1wdNWyI5FWIQkc3jyVcy+sD/pQF/SVVQCtGAewxnOrM5nOzphCWJLcoYJrZnI1LjM4cSqxUHAZdd9rRdk0SzPjaoU+FEmAIgkAjBa+8gpD4RpkEsBEP8KDe+FLDcCXGkQoO4yINozYcA8ibBx1k8N55W8cYib3qYJhgucxynMY53kkOB4TPIckx+Eol/tMchySPIckxxf+znJAluOgIveZBaByXO67vk4GNIfTw+nWUeOy/D8BDAIDRMYgaYDAGETkPxkgovhpXCYxQGYMMmPwGT4Vw9+mdVpxmZTft8ZLYIKMLC9DExRoggxBDqAVPqiFZQpUQYYoB5BkeaVSUNCVbx9XAlA0CZogwx8IQcuK0AQZan5b/bvEONNMTLm4PyPJjArJYLoseTaymXQZtbdGAc4uowDwzFt9eNuSRkhCMSOqKNhbAr0qg0BlMeC2FkLm/HywTpxsPzQIcEBjyBxHOt0yC1YFyq7unx3lXEZ1dp8YwUgig2Ut4UI7vyS4un9XdBiFerGsRJYafX2WMGN+hZqmoampCffeey8EQcC6detw4sQJfP3rX8cdd9xRcX+jo6O4+uqrsXLlSnz+8583rbvtttsK38877zxMTEzg61//uqNCeNddd+Gf//mfK5ah1ii52ZmGeM+zeK6hWH9wcvWlCFdBNoIgcvA8h42L6gHAse5TWYINOLr+Nnx++/+Dv2hrxbMBPx6cGMBlj34WOPfOQjM3hdAYI6Uxhh1HhpDOarhoYRwcx5Wd0TXHo9TmK02fZe8fT6M95ve0zXgqi20HBkyuXaPJDOqCPlM7jTEIZQZlvEEhzGqsEJ+mY3d9jg1OQpZ4NIUrC3OwDuTKDVacXJbKWRadBlVCIcuoewfTUgg13ULo5EYHgGnwpQYhT3ZDSZxCON0LdmAEq44dhJLoRiDZA6QGoKXHMcrzaBR4DAoCTvA8hgQBg/m/R3geYzyP8QCPMZ7DGB/GOB/FpBd/yDmCrGnwGZRKhamQ2QQUNp5bpjEoWQY5k1My5bF8O01vb/6UNYYsY/AXlFGt2EbLKaFLgIJCqSuaHfnP3N9K3qpZ/K4rlmlBRmf+OyQFGU4u9MFLjfCPMGSyvKHv3PaML3VF1vGJvO1vz+lX4FYyRdNylqq2mL/w+3VS/DjOewmjYmZL+2e68TlkdC8dnkxjNJE1rR+ayGAsmUFYyZ0Tq0Kot7W+21TL+6ZSjNs4WQftcIt1tz6rjg1OYllLuGAllUQOnMvPnaG81+7azhhgaGdnrZ0tMfi2CmFDQwMEQUBPT49peU9PD1paWmw7am1thSRJEITi7MiKFSvQ3d2NdDpdUZ9jY2N417vehXA4jAceeACS5PxjBYANGzbgi1/8IlKpFGS5NPvVZz7zGdx8882Fv0dHR9HZ2ena52wgPLQH/eooDvvCEBjDhYkkBjovq7ZYBEHkCcgCJlMqGsMy9nbnrFFeX/IDC/8Uiw/8Ch8b3oV/icfwlfo6XHDwATTVX4zezj8C4K4QGgtVZzVWSC0/nsoirEjl69gx+++zHT1bqLEkgrUOG2CTGMPmJBgvpaoxSBbjgNVNsi+fhAYArljprBDa7atihdBhQFhucOIU+yMa6ve5UUnyFGtX+r6FzChw8hAwdAjz3twN/8RRBMaPQvj9SVw+egpJlkW3KKBHEHBIFPFo/nuPKKInLKA/FsWwUAd1GmEkfk1DSNMQNCg3hX+aBtmk2OSsbCLLWfNEVrTiiQZrnpD/zDmXFWGWTwBgupUMMFgbgSw4qPnPDFf8XlwGqMi1TXMc0hyHVP5f2uEz9x1IWZThFM8jBcBbtcbpwxmVR/27lobCUsVlGoOs6uuMCqdWsHzq64x9nWAMQcZQb1FWBQAaJ5gslUaFk5MUpDnZtF45EENHkkMKvoLCqbf3a42o70lDFRREgn60xsPY0z1ZcO8dYUG0cXHwEylIqQzEdBZCNguNCWCcWHhBqBqzrQ9rR+H+sZnEK0moYvhbd/P2+8wPrhcODhZCArxmX7WW4agUo2OCroSKAoeoX8LAuHPMunHi0yqrk+T6s1QW3af83BR/IBdLq9cTzk1iFeNWzYqqSyc1hK1C6PP5sG7dOjz22GOF7J2apuGxxx7DJz7xCduONm/ejPvuuw+apoHPP1T27duH1tZW+Hy5mVcvfY6OjuKqq66CLMt48MEHPSWL2bVrF+rq6myVQQCQZdlx3WzCelPVdz+N5/LZRdekUuBD86FGZ7+iSxBnCxctqEdWY46ZF13hOLyx7gv44KPvwdZkCq8qMj7bUI97dtyBkfrzkQq0OMbj9I+nTLOsxlle3frillkOsMw2O4jIGEMqq+Vd+2YOxhh6RnOKmx73cTqZTGdxuH/CNOvtxSpqntUuPQ/WAYq1jpsTdpfVOsgqp9iZFXrnQZMVp3tK8KgQagyYVx8sifWxwmkZSENH0dj9JoKjBxAcO4Do5BEsGDkCLj2Mw6KI45KIY6KIY5KI46KI40ER3ZFmjFUQMxpWNcQ1FXFVRVzVUJf/jGoawpqGSF7xC2sawhpDKP+3BCArhZAVA9AEPyQlCEkOAJIfkPzoSfB5BcGfUxZ4KR/LJ+S+cwLqwgEMTGrQeBEBv4zxNACOBwMHLn9/hRQJ48n8gNd0ahk64wGMTaYwOpnMxydmwWkqeJYBp6ngWBYKryGdSYPXspB4DWr+uwgVTE2BV9Pg1RR4LZX7VFPgtdwyQU0W/9YyYAAyQFFJ5IsKY5KzfOf1ZbztuvLbFZcVlF+OQ4LjMDPpmLwh2iqQCShswmTt1K2gPsagDNlbQmXGMPpqcZ0Ghh4GNDMGIe/CK4JhmAFNjKEtv/+FKE4uFeJEeRHgRahcTpHUeBGMlwwxowIYx4NxPPw+CfD50JxlCGcYwPHgBQHYpgDgsWYiA+TbC6IIhBSAF7ByJJVfzoFxQu7e5Hgw8MDRMAAOnSPJwrtEv05FabnCR0iRIOU9NkKKBDmZNcx+cihUtiyZpMn9HVQkIKIA4CBkVSwYmoTEc+isU6BqGo7mswhzer5YlvuUBA7ZrAaAgecYcDjnKZJWNQyMJbE0v66wzWE/uoYmoWkawvtzesHy0WShP4CBy38P+gQADOFC8rBcH7oMHAC8LgOMYflYEpqmQXlVAngOLckMoqlMrh0HYIcPeP9/2sbk1gqOLqM333wzrr/+eqxfvx4XXnghvv3tb2NiYgI33HADAOCDH/wg2tvbcddddwEAPvaxj+G73/0ubrzxRvz93/899u/fjzvvvNPkxlmuz9HRUVx55ZWYnJzET37yE4yOjhbi/RobGyEIAn7zm9+gp6cHF110ERRFwaOPPoo777wT//iP/3jaTlLNYFUIe57Fc/n4wY2JJAabr6zIXYcgiNMLz3PwWWZ5vcaGcABSwTaMXnonvrL1JvxFewt2+BX81J/ANdtvxcuX/tBxcL7r6LBJOclkS9uVswA6KRRGXj85iu6RJM6fV4e4xeVyOuzrKWaSqw/5yhYqr+SpZ3csw5OZklT7JRZC276K33XrlrF/a2mDwUlv2VntlLISC6Gnnkr7K2dZLFd2olzG0qymYUF9EEMT6dw51VQExg8hPPwmwqP7ERg5gMDYAfjHj2KA03BQErHbJ+GQJOGwJOFYUwDdYhhamXdZSNPQnM2iOauiWVXzn7m/G1QV9XnlT+BEpJU4MnIcCDRCrm9CdzYMFqhDWgwjJYTA+2OY5IMYlULIShFkpTCyYtA0eFvdHjVNTry2p8dOLBNcQxDH+nOKcSwg2ZZzaI0pjvUAVyxvQm//OE70T9quB4C6oK9g/W8Iy+jPW8ADPsFUlqEsmgoJGbBMEryWgqArk1oKjYqGOj6LUwND8KtJtAQYxsbGoaYmweeVSkFNIihkkE5Ogs8m8wl3UhC0ZF4pLS4zfteV0CRvVCB5WwXSpEjy5mXWdWnTOt60LmO4t7Ich3GOw3iVPYVFxgrWZJEZY0IBiTGIyEJk2UK8KJ9fxwPgGQOfzH0XwMAxQMgC/DDLrYehHQBhPGeNFQDweh/It833zQ/k4lqN5S+M6p2RkvXMfrlx23LbMACMA9hRGNWv4jp9PYwWdg6su7SN6e8BQ1+Dpf2Y+rLpw9QXADaeiwEt/J0wrufAuNx5//LxQYBpAGahQvj+978ffX19uP3229Hd3Y1zzz0XDz/8cCEpzNGjRwuWQADo7OzEI488gptuuglr1qxBe3s7brzxRtx6662e+9y5cydeeOEFAMDixYtN8hw6dAjz58+HJEm4++67cdNNN4ExhsWLFxfKWZxNDIynMJzIYGFDsBAkb5yxFjLjCPbvxLbOnLvt5kQSAy1vQ6wawhIE4Rmvczb6735X7Eqsbr0Knx54Arc31uO7dTFsPPkS5u39D6htNztub6w9lTaVmMh/lrUQ2n83olvUDvSNIx6Mux9QBYwZMmpm1VJXzDOBFzdZk0Joo0hZlStrchenzNi2FkKLQugWy2dVeitJ+FCu7ISbhVDITCA4vB/8wFEsO7IL2qlXERzZhyGk8abPh5d8Eg5KEg4GJRysa8a4S7yeX9PQns2iM5NFh+GzPZtFU1ZFADzS/iakAh2Q453oQRwpfwu4aDsWLViEbb0C0nIcWSlS+NG1RBUsaAhi34EBCEIxuZLu2u3GVEILc9c4dz2d6h66xZDlYlTdHxjGxEZ+4w+FA9Z0RPHq8RGbrWzgBfCShBRfapEPRhVIfgn9cs55NNwUQu9IsqSOXlNExsB42nSP+MRcApWCOzDPFdczraAsSiyFpfUSjp/oK1guBTWJoJpEuGDNzCmRUUmFqCUxPj5hr2hmUoUMsHx+ucRSQCYJnmWhomgFdVM8jdbOEmuok6Jq2CaLYkKhLPKfDr95t3XE7EZgDF/u1xXC2sU1qcwnPvEJRxfRJ554omTZxo0bsW3bNtcduvV52WWXlc2A9q53vQvvete7XNucDehpy0OyWKjNYzw1dX3b8YaPx5jAI6KqWJFheKbxQoTPpmAfgpjDFL1tOLy57vN41yPvwVMTk/hDMIBbmhpw3+5vIbH4IkA8p2xfpoyjeoyDYb299cu7VSlRiSXCA9aEOOU4HeMolbGy58Aom56d06wkOsf9aQxwKEFme8xeY3nsZNUqOJ/OFkLetJ5XUwgNv4HowKuIDL6CyOBr8I0fwRFJxG99Evb5fHhTlrC3vR6Dgr1GzzOGzmwWC9IZLMxksDCTRVcmg85sFvUawEU6MCjPRyLaiUSoE4uXnYNXxiM4LjUhLTcAvABB4LC0OYz9J3PeRAFZwLzOOCYTfaX744plJ1SVFe4bLwXcvXjfmJQd5O4hXSEsl4ynZH+Fmofu7Yz1RI0lDzhwaIooiIcSGHSJwTIiCbwptlaHMXMCJQ72JSI4cCW/RZ6znBPjeeB4aKICTVQAgQOri2BiovzEUkfcD5/A42AZt2Qjazqj2N8zjmQyZbJqWpVJn5pCmEtDTSUgqElERBWTk+MmJbXRzzAyOgoum4SPpcEyiYLSGeAySCYmcoooy0JkKjQ1A57llGcGmDLRmpVF5JVI43dr29wyFYDGcdCQy1yrIf8duXhTfT8sH1PKAKjGdiXf83/n+2b5dVbrmA5z/Cy6kerLndta+9Rdh3XrZNGaaLVUcvkOdGdUW0smM25b2lepJdOmLxsraUlfJW1y1lbr8loPxqfq5TWOcZbfeCvVdz+N3+bjBy9KJDFWfz5UKVjRoIEgiNrFOK7K+qLYs+FruP2pG/C67MNRScLnG2L42v9+FL4rHkDa3+Tal1Eh1CcpZzLL6FSKfLtRLr4RqHyAreN1q20HBhDxuyc0M/ZVcBk1LHN7HjPGkFEZ9pwcRWtMMWUdrdQ66SYXYC3eXHm/HDQIQ/vRevgpRAZfRWTgFQRH9uKowLBblvGq7MPuiA/76zuQtlEUeMawIJPBEl3xS+eUv3mZDJjSgInwIkzEFyFTtxgj/i5MdC5Fw8IVgOjDToNr5uKVzZg8MIC0wTKlqgwnhopRZ5rmXDqD5wFJ4MDzuXb6sbrVLixs60EhlAQeqlZ8Z2tabjsNzNbVtiEsO05m6Epquf0aY4WNbfVDqiSMxMk1W2PMpDRzHGDXlON0zwbz79fLpI7Ac559vyspyWBEYwyMF6HyIahSCKUOvDn8PqEwyaVGlZKMmkpnDHtOjEDVmMlltzEsY21nDM/k79mALGDTogY8vqcnF3vGshCh4dLFMew/MYi+4XEsqpdxYnAU2Uy+jqWWUx45LYuIzJCaTEHQcu6inJYp1LjkmApOy6lxjUERjSEJfaOTGBxLgWMqRJ5hYb0fqUwGxwYmcu2ZBjANYZnHRDIFMA1c/l/ue151ZCo6ornYuJPDOXdlnss/R5iuDFnUusIltqzPx+NF/RJGErnzxBna6st9Aoe6QO55m8yoGE1kIUkC6gI+ABx6xlIGt1EO4AzqG6fvkUNjRIEkCDg1kgBjnCnukXG5cjbJDINPEtAUVqAyhpPDSRRqfub7BjgEZBEaAxKFSRIOHfUBHBtMAuAgijw66gIAOBwfzsVaxkMyRIHHWErDRFpFQ1hB1J87hlqOHwRIIZxVGGer493P4rmwXn8wicEFmwE4FNslCKJm8DqUsY7jhpouwuCKj+Ob+/8dH2xrxh+CAdyXHMKfbLsJOy/9cS4JgQMmhTD/ovYSI2jYaNqoGsOh/nE0RRREFHdFyzied1JgjCJNt2aXE6MJ45CxdEOzhbB0vZObYG5b4GD/OPrGUugbS5myjnoZQB/oG0d7nd++mLdle91CxfOcN4urmkZk6DXU9b2IWN9LiA7swog2jl5ZxmOyD6/JPrzeYZ/cJaBpWJZOY1k6g+Wp3OfiTAZQGoGmNeiR52MishjDkUXojy/BBF8slCQKHLIqQ6QuCIj2Mal2x2u8TllNc3Z75ThwHAdFNMfX8Z4UwrJNcjXlDLdMQ9iHvvEUAGaSaXV7FAFZQMgnYl+vfQ5PgfemEBqVX2NTrqBQlpdbx1o2RYdZ+uHAOcplXapqzNNvjuec+wRy7qi9+URTHFf5I8mgy7jLwVti3mxEGp4susUaz5n13jTtj+PAOAlZDoASQUbWkPb7oUXCSGViBcusblEGAD4oYWjCSW0tIjcGMcKAw/0TQGtumShwWLisCclEBocPDUIS+UJtv5AiYjyZdekR6FjZDMYY9rzRC8CsJE+FtZ0x7LYpAL+mM4rXjuXcmuuCPpzfFUP/cAJvnhpDY1hGXb6sw543ez0ZPNZ0RtEUVrB3b69jxmQgdz81dcQAjWHvm72ObTJqMUM3AHQsb8Jb+fYBn4COxQ0AgBMHBzCWzOJAvp0gcDl39PYIolFvJZGqDSmEswj91lbGj0GdPILXGjoA5BTCo80XA3BOGU4QxGyjdCRyaOUWXDT6Gm4deAlfaojjW/EYVnW/iiWvfAX7zvsnALk6WFa3L5OCxfRPg4XQZu9mC6E9xhpdqsZcLS1v9eYSxRzunyykNHfCaE1xSmJSC7WdjIqXrnQ7ZfS0WjT39YyZPECMeDk2VWM4OZxAZzxQKpdN+6zG4HNQCIXMOKIDL6Ou7yXE+l9CeOAVHBE07FAU7FRk7GwJ4ZQYK9lO1jSsTKdxTir3b1UqjVaVQWxYikTjShzzLcZ43XK8EF2BjBLH+fPqsP/IUGF7SeQBg3VZf3+5KQblEoxqjDkOHHUlSbEkXLFzGT2vK4bdJ0cLg2ineE8jRgvb8tZwbmCaLzOin/fFTSFTchqnyQz9HJTbbXvMj4N9E4iHfCbFdioWQqfxtjXeNWchtHEZ5Ur359VryeRaZ4PV+lnpzz+rMU+eDFzB4pTD7jiPDBST/IiG4FKrhdU2NpvlzmdB6bP0L/CcYRzn7doxxnDYIfGQ/tyReK4wV+HVcGC8dl6s6G44ldAwXtehiTRGEpnC+8q4T6+38WRKRbli3BwHdOWfm26HpV8r07aOfVruez1mdhbFhZJCWOMYXxb6w6W+51lsVxSoHIcF6Qxa5Bj2xFcCQN68ThBEreI9qYzdQh697/wO/uwX78Ku8Qn8NhTEPzQ14CeHfoqO0HwcX/I3CCsSUpmUY7/6C84Uz2YzaHMqcmxE5Dnoc6cZVYPg4hIzMOEskxUvMW9T1QfLWkMdtytdZjxteiIcOzfSXFtzB1Y3NKd+3RhJZGBXaMi4Kz2uLatp8IGHxgA+O4m6vpdQ1/s84r0vwD+8B3slEU8pMnYoMl7uaMSwJe6PYwyLMhmsTukKYAqL0lmkI4sxGl+Dkfq1OFm3CvujS3H5OV0YGUni6IkRUx9+S3YgJ7dft0GaUCa7i6Y510HTB5iKKNguNxL1S2gKywV31HIDYoHnTG3qg8b6ZMVJmZDibdil91XueTG/PohYwIeIIqLfECuob1eJQmhNEqNTYiG0UfwA+xhCI0YrlR1usprXcSW188rxRj7GtBxWEcoN6I0WQtFiYXX6HWus+DzIKcJmK6P+3PB66Wxd/gv7yk+y5O9PVWPeFUJDx07WY68IDttbr/nxoUTh2cibJiHMrshOeMmsG/CJiOXHym4TPQylExq82VReXO7QjRfvg1qBFMLZRP6+rO9+Gr/Ixw9uTiTALbwCmxY3YXAyjdbI6a/ZRRBE9eAC9Rj8o/8Xn/vvP8VBScQeWcYnmpvw/736ZSRCXRCjV7puX0jwV8YC6CWG0Li0nFWrkjhDTzGEU1TsZjLM2ijCSCJjSvABWAvCV9Kvt8ajSXt3MqPyLQoctGwa2pEXgBPPILp/K9526iXsFTn8wa9gu1/Grq42JCyKlqJpWJNK4/xkCucnkzgnlYbsb8JQ3TqMtK7BaP1aPFu3GqoUMm23piMKwD4rp3Xs5WjJc7GMeCnWnSnjKWNVJuwUEeuycrvlea5EaTJ+FpZbtnOMIeSdZbPuVy/5Yjzn+kC3EstOVzxQsGgaYYxZLHSco4XQbW8+YeoKoXF/PAe0RRVMprLIauYY0ulilUEsY5I2rpf48hZCfbl+6/OcWYk2WcVc9hsP+SCLPE4NJ22fLfozRFfqBINCWC6OuCCnyVJ3uiyE5r+NE2VezwWAfGygisl0blLDTYGUJW8pg5nhOpWjXCzwbIAUwlkEA8BpWdT1Po/nmiMAcvUHsfgd8PsEtPtmh58yQcxtvL0gnAZHIs8B7efj8Pov4zsvfRp/3daMgz4J/9gYx3e2fQqnWv4H4OY79luIITS8LMeTWYwkMogakqgY34NO+olxeTnXMK/u7Jol5sjL4MWrcsgYc3TTnArWZC3DiYwpPtIuu6u3fr21m0ypyKiayVUtmVHx+vERBEf3I97zHJr6tyHU/QJOIIXf+hVs8yvYbhP/F1Y1nJ/KKX/nJ1NYmcogFVuG4Yb1SLVvQOj8d6CXr8erx0asYhRY3R5FU35S0k6p82qtcmvmE8sP5lJlrrHVUumo3JgsAGUshJYYOP27dTsvrqdu25eTobCfQj/F9R1xP44POitPHXV+hBURfWMpk1tkwCdarDXO58zt8VbWyuSy2qxs52JBlzSHMTCeOq0KYbn7zajoWK1gulKmKys6ubhK3T0aaAjJODY4CVni0RELYF/PmGtCq4BPwLkdMRwayGVZdUv6pcfX8g7XTIfjgPpQro5locSMbsXkcnvxgjXTrnG5FUUSXH8PgmmCw32/IUVEMqN6mni0egjoiAKHxU0hvHkqNynC4D45Z37GOSi8ZCEkTgeMAZHBV3CCJXFSikNiDOuTKWDh26stGkEQM4zTzKLAc/CJPLrn/SkCY4fx3bfuxfWtzdjm9+OuqIp/+t3f4ORl9yER6ioEthvR32/Wd/aLhwZNsX2mkgsOMnrNXlmJEmaNGfRiIfSqar1+chR9Y95dV42Us6ICOaXXODhTNVaIraxMIfTediyZzVmIMkng0FPg3/gdLn7zYYyle7FNUfCffgXbWqPoEc2v+7CqYX0yiQ3JJC5IpLBQBcbrzsFw6zoMNazHcw3rkPXlJh7rghIQjUMYdz93RgOJdRw0vyHo2VrlNohyyoRpxMllVL+nrYqJfcZMzjTgKzcgtVpEnWL4vI4PvbqM2m1j3C9vs8wJjuMQC/gwNFm0PDeGZSxuCpl+w05JZdySzQDlr53bueFslF3r8pmAtxiWyimxRjdRyXIA+jNxw8I4JlJZvHx0uJBkp7ALLhdXGvAJaAzLkEUeQVlAxC/hdYObq54ZFwBWtUXzFmnOtB/rvk8MJwqxhU5WXR3GgOUtYTwzlio8fzSDddHrI4l3UAh1S6jejyhwWDevzjEjsL6NUT43Anmrv1PMuREnC6FfEtBRF4Ak8Hjt+AgYs88OrGNO4mTfZrqxl2cSUghnEQwM9d3P4vm8u+h5yRS0yBIg0lplyQiC8IrX8YvTi0TMK4QAcHDVJ7F67BC+1rsVNzY34H/CIYS1UXz8yb/FjrffBynegXHVHBdUqENY5sVpfKc7tfXiMvpm96jJKlHu+K2DCSfLY0VZUvO4xe1NBesxq6w0o6IeW1mZy6i3dr5EL7SXfg2c3Ap28Ans4TJ4MhDAE3V+vCF3mNpKjOG8ZAoXJXJK4PK0hsn6NRjs2IShpg14Kr42Vw/OBn1gVjaOzsZCBgBLmkOYVx/0fJ3cduPFZdSulp65f29WO6Ny78VCaGdRtIprtZw6nRKvSWVMMtgk4bCzWpbD2GpFawSSwJssLzxnf42cYgt1rDF2Vry7jJrdR2cSjuNM96nsUEPTTi6re6mSt0RLAo9YwFe4JhpjhfhZXVEzJoeqD+XiT42HppcvAQAuvxt9vZOF8HB/sU5jOYUQKN4zhUnDvIwCx5meX0bFzorAcbblPHQLuq5gLW4Kwe8TMJFyfiYY5S03SaYrhHpmZ7cjlR2svroreeG8Mu/eGq4ePbMEUghrHOM9xhgQ73muoBBuTCQx3HoxQg7bEgQxe3EaPAk8V3zJcBz2XPAVbHj2Q/h8/xu4vbEeP45GEB4axrVP3YBDV/8CE1zYojzlPmdCQTFl1HRoZHVRK+eCZX3xO+27XB3FmcY2cYNlmV2SlFRWgyIJFVn9HF3AGEN4+A00nHwcDae2wje0Gy8oCp4I+PF0Swy9BisgxxhWpNM5BTCRwnmpFHwNKzDUuQnHYhfg2cYLSuL/nPCqEDopH/o15yxWAi/9WPHiMupkBdZ3a5KTd7bGG40X5WKBrL9XvmDhK9EIzftwOBley07YbZPbTf6aGbb3aq0wK7b6p9Ec4lSY3j0LbDkLoduh2sVn5r7PvIXQ6FQhid4nQfR74IIFcRzoG8eSJvPvS08Yo1piCJ0onWAwKzv6tl4eLQLPQWDux2K2yBWz9fK8WUk2KnYlfThcYqsLdrEsirNMJnlcJc+5NQO57J7lJp5EByELruS6Ygx3RZRz+G6kkt9vtSGFsAZxvJlTowgMvoIXu3IWQV0h7LBvTRBEDeL19eA848ibZh01UcGRq36IP/v9BzA+cBhfq6/Dd+tiCAz04i8f/muMX/YjTEgNxfaF50vpc0avVQdYXEY9xBAaX5yprIqhiQyawnK5wyyVwbIv57ITpcvGkhkIPFcYHMwkdkqadbBgN3jQLSuskhKxhm74bBJ1vc+j8eRWNJzaiuF0P54K+PFkwI8XutqRMgxuApqGTYkkLplM4G2TCQSVFqS63o1jsQtxctnbcSgRrECIIvrgv6xCaFRITO6jZkWxnFuX2xjKi8toOUzJV8Dlyl/YYLzm5cZ1sijYxk36LPJaT6HxVCxoDOJQX86qMxWF0BrnZ/wEppbgws5S6egyyrlngS1nLfGeZdS43LXLiuF5zlQ/tNz9ZvxN6Ellon4J53fVlfadP4btBwcLy6byPuAs18SLQshx7tdfEnmTLIyZE9KYPDVchHbaB1eIsTUrtW63pPHcllPyAoZEUeXi2a1uwI1hGX1jqYKVVj/XxljPcpDLKHFacLr/fMefwx6fgHGeR1RVsTTLsKP5gjMrHEEQZwSnwZMolLr+aHIU+JsHcN0P343xoR78W10MX6uvQ3rwFD7wh7/GS5f8GKlgGwB3C6HKGPRhgZfXoHHAbLSm7Do6jLFkFvMbShWQcpZJ68vccYbWYvVMZzW8kB9oWesc7jw65FjiwCt2oS56j7rVS2Olz289sUwlFkJuvAdtB36NxlNbEet5DvsEDb8O+PFknR9vyO2mtm2ZLC6dTOCyRALrk2nwbRfg0IK3YX/b5ZiILMKCphB6+ibQLClAYmous14TnDi59XmJtTHilmV0qunvfSKPjjp/XgaztcuxILvJTc59v4ok2Ca0sFo03fpZ1BhCKqPh5HDC0eVUH7zaYTf4nEotNyO8jWLKOVgIAc6kEAQs9R6no8ybYiEriIuseD+W7tyU2NXtUdP1LOcSayeqq4UQxuMslVHf1EvcnFMcYEAW0FkXQDzoMx1Lzq21KGPWsLHbUbopQF6y+Tr15XaIHJdzA9Wfw1mHZ/3iphASGbWQlVdnTUcUqsYKLr/6PayVychqPF9nasLidEIKYQ3iNHCQjzxVcBfdkEhirP58MGlqM74EQVQHt+xxRpxerJLA52ZbDUkGeI4DQo3AB3+Nj/7wXVCHRvDvdVF8O16HyaFhfHjrB7Dr0h9iMryg8GK1e8w4uWF6qQVobDOWzMUtHh8qLZZcTjGyKm5Oipw5qQxDMlsceBoLaWdUDYOG+mxTxc1CWHAF01hJO11JcD1uxoCTu4B9DwN7/xfB7lew26/gFwE/nmqLo8/iCromlcZlkwlcMpnAAubDUOslaLr0GmDxOzHAQjhydLjQXrdapNWpZ1fVjT6VxRAati8ZOLnfA2678apUdNUHMJrIYDifIOVtSxoMbmrFdhyc48QqcUVWJN62vluJQmhZb70vGkI+9I+nUJ8ftBqVgoWNQXTFA3hib5+tDHbWGeO5n461wrgpYy6F6Q2H2xrzI6KIeDl/P7peO66chdDU1LTPmSQXQ2j+2w5R4NASVTA8WXy2TMUCyrmdEuNvyMYdWJetnEUMsL9msYCE5a0RhGQx38b4TDVbCK0uo06/YbeEUHZuv26nzGtSGZ+Yeycan8N2l21efcD2enIcZ1Lm9f1mLOc1FpAs2znLBOTO20y7NJ9OSCGsQZzue+Xok9imxw8mkxict2lWzT4QxFzmokX1GE9m0RDy5kbprBBy+fU8tLxGWHjnRNvB/e1D+OiP/wT+wT58O16He+uimBgZw42PvR+vb/outKbLAdgrKMbZUFNhehs5mCWBit2gxK7URDkXHOtst9Pst3GpddeM5dxWZxKjGMmMipPDiULmRZHnkVVV22NLFyyE5uV8NoF477ZCPGB3egBPBfx4IuDHdhtX0M2JJC6dTODiyQTqo13A8vdCW/IuPJFcBI334eIlDegdTWFfz7BpP/pAJ1VBHUgruqJRzuWwNObJ/btxO+Opc1NcvCqEIs+ZB9EOMnAcVxInpieXcEvnv35+HXafGC3cA4okFCZCjJRaCN3lbooohdIdgFlhaI4ornXxjMdbLEheXO85hrBMyRAGZn8dYY7P4rlcgpRzu2IIySLGHQrfG9s7r/N+P00Hr/3p7Yzu6eUG/07nrFK5rMqUk0JofMarGjMlU4kGJKyfHzfLUmIhZIX9MFM7ZzndnhHmTLGllueSvjzer7pbtq4QOlkIPZd8yZ8mY83M5a1hNIWd63zbWn9n2QCdFMIaxHb8M3Ic6ZGDeDWWixjcmEjiRMvmWXfDEcRcJSSLhZlYLzjNNnOGwbmezc30Uo11QvzQw/jwf14Df/9R3NUQx0+jYZwQJ3HX0x9CQvw6cPHf2j5nDvSNY1lLGJLAl83iaV3k1ZpSrq6gVVE9PphA1C+hNep3bGeVL6sxPLO/H0Au5ftM8+rxkUJ9L6A4cLFzGS3EEDIGebIbDaeeQMPJrYj1Po83BQ2/yruCvmlxBW3Pu4JeOpnAulQak/Xno3/h2zG28o9Qv+w8gOPAA/AfGMBEKovxVBb7ekqLiusKoZf6XEZWtEXwRj7tfcGyVs5C6OCeaOfuZt3OOHngNhB0dUkzWM0lgUdDUMbgeLqkPpxpcA1znF9XfQCLG3PJQNwML7GAD+vn1xXus1xNtdJ2pTGElb2znVxvy6Hfh1PdvkQOy3m3txByEHirJQmFSbBy2V/dBuzTcUGuBK9DKl0en8hj8+IGx2QqRux0ee8KaOkyXakyPouMGC3WqiE+PCeL0/sld+/kXOB1hdBiNXVRY91dRs37AdyfKV7d7HUX0EL9xGmGB1iPQRA4dNQFStoZW9ldx9lUlB4ghbAmsZ2VPLAVLyoKshyHzkwGTXwIb8RWIXbGpSMI4kxQ1j3PLTYo3Az87e/wgZ+8F/HevfhcQz2eCAbwIVHEd7beDEzsBxZ8AoB5hNI9kgTH5epcmRRCm/1blxkteT6Rd1VANMvgxLyudNnrJ0ZLFEI3+SbTRUvEdCxjJrkMO7QOwHSlK+cyahRSA3/qZWDf86h74yGs63sNz/sV/Dzgx1Nt9eg3FEjmGMPaVDoXDziZwDzIGGh5G/qXXI5tLZcgI9cBAObVBUwXPCgLmEhlMZmyt4jqgxI7a60bxstT7l5c1hIGYLbeOcbU2PSlW1h1ylkBL1gQR1bVCq6Ixu1S+RtIEng0R2RIIoe6gDlmyOT+CHO5AJ6DbWKlcjilsneqeaZTbtDr5HpbDuNgXsdtgBpSKhgOMvu+OMv+rNfazT0SKGchNO+nuLy8xbkSeI5ztQzbyeP3uZem0LGTyWuWUTsLdznl1fgszWrMNMnonPylKKeuWAm8+Zx4teSWrDtNBoyWaM5yl7NOq8hq2rQyT1uPwUvpCHsL4dRlqAakENYgdinicfAJU7mJwaaLAF6YVf7JBEF4pxKF0LZpIA787e9w+f0fwn8cfxw3NjfiDdmH97e14Gsvfx+r9j+LVy76NlIBcx1TPebK5DJaJt4QMM/KOiXYMG7LO8wye0mQkJPJLJ9xM6MSOFMKoZtYRQshAzKTOTfQk4+j8eQTGMoM4P6CK2gH0oaLFcxnBb0snxW0LjoPWPHn6G19O54SVoLxpfGm1ke+7qLnmAZ+iu8Ip4QWdhhrqJXbr21iCcvAqdwALOoQhyvyPFLIXW89fsc6keAmGwBkDIpzuTtRkQS0xhSIPA9J4G0VWauF0Lrrcre7mzXGjULCI8Myu2dKQBbQHFHQHiuep8awjH09Ywg6eDQw2A92OQ4QDVqfVekody+6jWcEB43Q1jJjsTgbkUTe5ApYKkPpsjUdURwbmoRPENAzmiwrqxN2hiu3bozX3tYqWoEIqmZ283W2EHIAWM5llBXbmmS3yOLV3dtJqbcjHvIV4midaIkqCPgEhBXJtG9VYyXvp4546XPAWc7y1nDA2UW+sN0sG5+TQliDlLwgNA04+AS21RkUwvmbAcyuDEYEQXjH7iVkjgcyrnF4EPiC8F17H8599Hb89MXv4e+bG/GWz4ePtDRhy9ABfPD3f4q967+Evo4rS3oyP4e8uIzmUnTv6xl3dGFy7q2I12ygzPqXJcZPJ5WZmVhCN0uOP9GN9rceQXvfk/Cfeg68oOEBvx9Pxv3Ya+MKetlkApdOTmJdKoOJ+vPRv/ByRC/+C6BxKcBxSA1OgnWXun/msMxeF6yT9oPcqc7KlxvslMNpt7Yuo9YZ+Slmo/SJHCbyCTjdspEad1fibmxUCD1MTqxqixa+z4sHMDyZQXOkGCdcohBaC9OX6X+q7pF2stvdC4okYFGjuV6e3yfgbUsbCgmJ7LaxTc4BzvSMsm7udiuGZfdkW44xoDZtrRZnnc2LG+ATeWx9s9d5PzY96nGdb/UWf5NT+VnZuTJ6vaZ2VtFKfpdd8YDFyueg5OQ/zS6j5vvJqqiaFUJnGazZfZ3ojAcKXgduLG4KQZGK1ll9Iimrmm28DWEZy1siZfvTsb57neoWGg/C7nBmU8kJgBTCmsSczIEBPbvRnRrCQV87eMZwQTKJ3c2bAMx8QDVBELWB3UvIWONLsCRvcIQXgKu+DJ/chZ88/Tl8pS6MX4VD+E48hhcTCXzhhRvRfOxd2Hv+bcjIcduBnicLIWPoG0/h2GBpZlErqsYgOXhZ6YOmcm5f1oyoxudmwqAEJsvELXnFJIqmIjr4ai4e8NQTEEbexPN+BT8L+PFUaz0GDK6gPGM4N5XCJXlX0E74MdhyCfqXvB3PtVyCrBwDACyobyw8z92O23qtizWzvLW3IggcVBtrivE2CHh0iTNv72aBsMgwQwMno4XOTak0yqAfuSjkrErxUNEqUanbmSjwWDevzrSM580ZgUsthO47MY+hvZ8nvdupKvayWHrNL5gfRyKjIuqXbDOqAtakMuUthIuaQkhm1BKlFADqQz4M5DMEO7qMlim1YUQW+bITJMOJtMt1NyilU7hnbZX0KbiMVmIg5Hlgw4J6BGURvaPFsjNO50jPIJqzEBbdjhc2hLCvZwytMcU2eZJ5+/Lr3O5lr6e2JNZPVwgrqB/oZf9enk+UVIY4LZS4jB7cWsguuiqVhuTvQCLUBYAUQoI4W7GblDRaUkzfPbx4xlZdi0PSEnzmuU/i/OQA7qyvwza/H3/W0Yp/HHwCf/zwNrx17qcxvvS9ANxj9OzQNLO73VTRXR9Fwd21yyqfcfI9Yah9Zpdt1K2WGwCsm1eHN7vHMGHIisgnR4DXHgP2/x6X7P09etQxPBVQ8JTfjxfndSBjuB4hTcOmfG3AiyeTiETnY3zBn+Gt2GYcblhn6wrKTN+dz6NVoSrMijtYCMvdG4ooYEItHeBx4LC2M4bxVBb1HjPjOu7DoP3biTP9uyaHURnxEvdj3PlFC+sxmsigMVw81mnmpihgzAhsfWfP1LFb0WU3lysopZIxRDQgIYrcvSsJPDYsjENjwIuHioXWTV4MHhTCiCJigU290qXNYXTVB7CvZwyiJX1/OZmdrMP6uSgptI5cOaDRRAYNIRndI/b1Op0SJXnFzq3brRsng1ohIYuHa6eIQsH11/gscJov0btkKE7O8RyHrvoA4iEfgj4BLx4eMsjFwXgXe3UZdcOrImW9v/R9H+gdNy2vVDm0lnWyPkv0iYouk6u8uU1QFtEScc5KWouQQliDMOt3Q/zgRckkBpvfXlhP+iBBnJ3YWQiNdZCML1672XwrPMdhrG4Vtr/zAWx+8bP4xcnHcXtDPXYpMv65oR7/m0ji0zs/i3kHfwZc8w0wLCxsa/c+tS7TmPdZWY0x9I2lsLd7DKvaIqgzxIro7p4+i0KoagyJjIqe0SSawjL25DNg6rIY9220ENrFEJZTkjgO4MAQHNlfyAoaHHgZ22URTwb8eKrBj8M+s0tTRyaTdwVN4LxUFsnG9Rhb/g680XAJOpasRSqrYmjA2XpqGztuJ5vDsThl1is3aPT7BJPiW9gPl8sOaVSQKmXT4npkNWZRCL1ZoKeC8bpaXTWd0JVvRRJMcgLesxyWQ+SLGYGtR19uF9JUlFwUZY8oElqiimMW1OkMIcKKhKzFUii4KG6VKFH6YS9tzv3OjPeo9TguXtKA7YcGC3HL5X7fmxc3IJlVsf1gUZFdN68O3flnyykHhdBarqRS7H6iXhVyO2uhl231+DrAGnfu5DJa9FKwZqrVs2SbNnXwWLDDa8Zbz5lXPWS+nSp6LKVdv+d2xpDMaKZkQsYmAZ+AjYvqZ0yWMwUphDWIaVCVSUA78hy2teVuro2JJIZWbCqsJoWQIM5OjC+hRU0hpLIqFjaEbNcrZTIZAsUXVtYXwaubvoOWI7/G91/+Eu73T+I7dVFs9yv4y/YWvH/0ED7+H+9A17w/wsTSj2EiusTWYmWd7c656Xg7No0BrxwbBgDsOj6Mty9rKqzTFULrLP9kOovthwbBGHCob8K07tjgpDmzaMY9qYxxwCGJPBY2BNE3nsJY/ynEe56Df8+LOP/gExhN9uKZgB9PBfx4vqsV48bBOWM4L5lzBb0kkUAHAhhuuxSBjVfjefF8SOF6xIM+TA4lbMtBlJ4Tb8lMrM/86SiEgsAh5pfQb2MtnYlXi7FGW3FZbhClu2gC7oqXXv5iSXOpS6EVwRS/Nv0jmClF1X0g7L4TnudwydLGwnevGJXH1e25OEfjb2SmsCpGJqWjJIbQu/zWft1cZxVJQFAuJrJyjvnK4RP5kvqQAs+ZEuvYymRsPxWF0OY36vWa2rpYetjUmIDJbCF0chnNfb58dKigTLrFgkYUEUMTxZhxt+PxGEI45dwYrVEFR20m3abyMxY4Dip0bxXrvciVZJb1Gh9Zy5BCWIMYnxnCie3Yz6kYFAT4NQ1rk2m82FpUCMlllCDOfloiSskLyDj49WIhNA0oOA7d86/BYPMm/NGO23H5iSfxjXgdHgsGcF80jN+Egrhu6Clc+/uHMNlxFRLrPw7W/DZTf1YFpH8sZXLVzO+mbPyhNX4tkc4N6pojSiHjKQC8YJjNt0OPNbLKZud2qr/g+WwCjf070XloBxr3PgYMvYmdiozvKgqejynYK3eYtourKi6eTOCSyQQ2JZLgwovQ3/onCKx6N57ml5tcQX3IuaGdGEq4ym2Hm6XVKSW6k0LoNPBb2xlDfdCHkyP28p2ud8uixhCaowoiioQ/7OkB4K4Qtsf8aArLthk8V7VH8PqJoqW4LuDDWChbUb1PN6WvOSLjYF8WAbnyGEojxgGlVdHxonRalRc3zumI4lD/BFa0ekuiMd3r7GSxtn4HvCk/i5pCGJxIodXF3c6ufIXXTJfTYaoJfnQqtTibav/Z7M/LYUb8xd+CFwuhfkGzKsPQRNqhbfHvZS0RHBmYwKnhpGWNnbweLX9TvCfDeWu4k8tvJRjPlaeyE4bvs3VcTgphLWJ4CGhvPV6IH1yfTCERXw0uUAfka07NtixGBEF456JF9chkNds6V8bxv1MNNCN276i0vwmvbP4eGk5txZ277sSroz34an0d3vL58G91MfxnJIJrR5/D+x58BNi+Ds0df4W+9ndCExVT3JCO1fXw3M4YJlJqiYWMWXS00WQGEUWCprGChbApIiMki9hxZAgzhZgeRbR/Bxr3v4qmo88hNLQb+0TgP/wKnvf78bKlLAQArEylcMlkEpdMJrBC5TDcuAH9yy7DK22XIRnMKYyr26NgJ0ZK9heuoLbbjiND2LAgDlHgK5rR5staCO23kwQOPM95Sqk+k/A8h4hiiaEs5zbp4P7ZGvVjIpXF4f6cVYDnOJzXVVeRPG5j9Pn1QYQUETG/e/r7crgNEIOyaJr4mC7NEQXNFcQuTfc6G5U8BuZN6TBgVXYXNARtYwpNmS1t+jFeRrMC7nyNO+MBHBucxHzL/pwmZMylWCo/ced0RPHqsdLnhBNlS5J4iSE0uEGbrbdOFsLS5dZnhLGJT+CxoiVSVAhdRDLXznVxLbWRzehR4MZMKWPGbgQPBQXLZb6dDZBCWIMY3bPiPc8V4wcTSQy2b4ZP4DEJUggJ4mwnJIuAQwiXsc6fl5l3xzYch/62yzHQfDG69v0I9+/9dzwujeKeWAQHfD7cUxfF/xuL4MqJg3j/rs/ibTtuR1/Hu9A9748x1HihbYKUYtec7QDBOlO+/eAgLl/eVIj9E3gOsih4snw6wjQExo8gPLgbsYGdiPW9BHlkH/bIEh5QZOxUFLzc0YRRwbyP5mwWGxNJbEwksSGRRFBpRn/rH2HsvMuxvWkjJlmpcmA7XuCAkI3LpBOJtIpjQwksaAi6xxBazmc5C6E1QYJxOVA+lsiK7sJZLgtsJUwneYsp++IMvw55nkNTePqJIdwsDIubQhB4riIlbqrM1PVyw1SYvswFWd4aNsW4uVEuW6rx2IzrZVEwlaExsrQ5hLaY4tmibFQ0p6J4NIUVvH25jLd6xz1lYzYyVQuhcTJF8KC02C0vzYxrlsGcuMhF0fPoVWnnjnvB/DhODCcwlsyYXFStWN07ATiWT3HDKEOlhelnW3ZRHVIIaxD9xyalBuEb3oMdXbk6VhsTSQw2bzb9wKdyoxMEMftxUgCcKPeKYoIPR1b8HcZWX4elr9yL+/f/GE9IKn4SjWCXIuOhUBAPhYJoz2Rx9fAf8O4TD2I1FAw1X4z+1ksw3LA+l/3Yw6DDznVqMqMWlFzZEBN5TkcUrx13n1XntAz840cQHn4TkcHdiAztRnh4D4bVSeyRfXhSlrHTL+O1WDuSlmdmQNNwYSKJi5I5JbCTycD8i9G9eCP2xjZgMrwQ4DgEfALSqgbYzFLbPYdzNdk4LG4KYTSZQe+oc1ZTHT1Bh5vLqHVmvWAhLLuNeb0+ZrEWqy6U/XB4tbTH/GiNKHj95GihSPd0YWAFVy+rtaYc0030cSZwm7iVBL6QOKUazPQpM1sI3dt21AXcGzhgJ7PxN2N8NsoS76gQchxnq5A6/ZKM1sypDr0EnnOt1WeWo0xJEg+2KCcXXqee7X5Dpcovc1znLrP95M1Fi+qx/dBAYdLK7r4JyiKWNodxqH/CVSG0yhMNSJ7ij614ibc0chaEEJJCWIvog6W63m14RfYhyfNozGaxQBPxZP15aDHMgNjNhhAEcfazoDGIgYkUOj0OqpxmtC9cGDdl20tLYRxcfSOOLv1bLHrrPnz/wM9wUD2Fn0XC+H0wgBOSiHvrori3Lor2TBYXJ7Zh8+tP4NxkCkEpipH68zFatxoTkUUQgueCC3SV7NNOmZ1MZwsyGmdnmyMKuE7gtcN9kBM9UCZPwT95AoGxQwiOHkBw9ACU8aMY4Bn2+SQ8J/uwx+fD6y0R9Ijxkv3UqSrOS6ZwfjKFdckUlmZU+NrXYaBzEw5GLsSh+rW4cHEzBvomMGlItmJMw25FsHkO64egKzjP7O93HJga92H8tMO6p2LZCeetjAkSdIrFrYvLIn6pEDfkGgvEc54SGXklKItY2RpBe8xvSoLhBXPszoyJNKPUiidPwCegLuhDRtUwnq8lV0ltw3IwllNwl7eGwXGcay3I6WAns/Hutis1MxOYFMJpaNJTmbiwm++ptBsv+7W7Va0WO5OFkHdeV9KPw+8gJItY0BAqlItwk3NePADGGBocsh+bMg2LPC6YX/oO8AJXoYXQqztsLUMKYQ2i/57qu5/FfxXcRVMYblwPJvhM6bRJISSIuUlEkXDp0ibPg02nd1REkVAXlAqzrvqEVNYXxeGVH8OR5R/B4qEn8c8Hfo7PHHkaTwYU/C4UxPOKghOSiPulMO6P5Cwc8zIZrE2+jJXHXsCCTBYT2zNo1RgalAaklUaklQak5Qb498awOMVDFfzQeAk8U+E7xEHiNSwenkCATQAvTQKJISAxhIbxXrx9vBfDPIduUcApUcRhScRBScKhkIRD8RZTBtDCMTOGBZksVqXSOC+VxLpkCl0qD7X1fJysPw9DjevxStt6XLB8PoZ6xzCSj0XjuNIhZ0bVHAc7XkocbFgYx8B4GidHEhg0JMCxw70wPWf7t11xeadtjMuM909LVCkohOWY3xDEZFpFS3Tqro4bFsZxPO8my/OcqfyIV2a7hXAqTNVdl+M4rJtXh0RaxbNv9c+oTEamavlzo1xCF+P5MGYWbokoGJnMFGrxTQfj73w6V9SrMlk+hnAaQlRAJcqvm8hml1Hrc6z43bWWIc9hYaOzxc/kFjuN82NVLMu2P42u62cKUghrEMZy/4v3PIdtsdzLdmMygcFFmwGAXEYJggBQ2UDT7SVlHHhYY80YL2J4/h+h+aL3Y8cru7Ho2O9w59HfQex9HdsVBU8FFOxQFBz0STgi5f49aNjer2lozqqoV/vQkO1GPKUhMKJBYQx+jUEEgwoOWQ6Fz3Gex0j+36jAYzAq4FR9O1IuzzueMXRlsliZTmNVKo2V6TRWpNIQ5QZInetwQDkHA43rcTh+DpZ1NOBAvo5huBDnZz5B1vPllNBAEDjbAZN1iSTwaIkqjlk9geJ1cHO7sp4Ct9nrZS1h222My4wz/CGfiKaIjImUimCZ+EdJ4LG2M+baphxhRcKK1sosglbMsWXT6uq0USsWQh1zvFP15KgEzsHdUMf4m2kMyQUlsKPOj4BPQKQSy7OHiR83i3w5WiIKDvSOm+rKToWpWCk74n4MjqfR7GBhs/OCsN4jbkfuZuE3yWsR3Zywx2UHZRCmGedph135HCvmOMrZCSmENQgDg3/8CFLJU9jjy8UPbkikcKA5rxCKZCEkCKIy3GabjWMAjTHbJCQAkAq04OiyD+Posg/Dl+hB06mn8InepxEf2ImRRA9ekX14RZZxwCfhkCThqCQiwfM47ONxGNMb/Og0ZFW0ZrPoyGaxIJPBwnQGCzNZzMtkwJRGjNWtxmjjaozWrcaO+Gqk/c14+/ImHHmzt9CH8ampW5WsYxWvgwnRIVPnlNzC8kMt98L05n6dlI3FTSF0xnOWGrskDUWXUcNAhgfWdMQqEbnquNWnqxUqKSjvheWtucQ+Cxori7e0Y0ZdRmesp+ntvCseQMAnIBbwgeM41IccMnNViHHQX2kMtxG/T8ClyxqnfV8Yt17WEsbe7vL1Tpe3uJcjsTuskjhBmzZvW9qAVFazTdCjj1PdrLzmdVM/L8Zn3XQmYozu/V4shMZrSWUniJmD5bKLvqDIYByHxek0olIcE9Gl4DjzjeeUjpsgCMKI24x2c0TGaCLnMqoyBp7joBlGWBorLTqf9jfj5MK/xOjKv8ZFC+I4+cZriB95AdcM7EJg9ACCgwchJI7hpCiiTxTQL+T+DQo8EhyHJMchyfPIIPciEhiDlP8MagxRTUVU0xBVNcQ0DS1ZFc3ZLHgxgGSgFYlgJybiizAZXoThyCKciCxE1hctObZYQCqZcbZ7X3Nl1jvBc5ztjPZUxgQFC6Gry6h1P/ZZRE3ZHm0EtIvXnI3DmNOZZXSmaI4o2N8zjvrQ9MpX6LTH/GgI+aachbec+2UtYrQAllNieZ5D0zSytpZL5gIgl2BqGszE2M34G5+psaBdQisvCo5dVui2mB/jqWzRU8HlWWN8Rk1HkTNaMyup32mlXLx36X5r/zlUDlIIaxCNAfU9z+J+vx9AvtxE8yUAl5uJNmbom+mZR4Igzk40F4WwKx4Az3HY2z0Gxhh4nodxyp3BeZCkaQzgOKjReeie14zueX9SWHdRh4Rwz1sYPHIQXcl+LE72w5cahIIU1NQk/FwKAUHDcIqBF30IKDJGkhrEQBSNjc2Avy7/L45tAwoOBNqQlSKe3rh1QQmr2qLwCXzJjLOd+5lJsYC9G6gdPMflsxWKGEtmy7Z3U/Z0q4PbgNRusCTwPDSLRlguTT/PlbabjfCzQLlRJAFvX940oy6t0ynJUquWVDfM5SxK159p6+R0LIReKRdDyPMcljSHoDHYJnmaimJlayG0uoyWEWxNZxQjkxksbgqZnqtuz1Tjmmm5jBr24SW22wm7TNhe90sWQmLaJNK5tOuqmkG8dxu25VNRb0wkMbh4E4DcLJAxluV0ZfEiCOLsoqQYuAGOy9VB29s9Bk1DiXIxOJ7G6/mYOyv6AMLW6iZHkG1Zg4HMPNNyv09AIq2iKSKjKx7Aq4eHEPAJ6KoP4M1TY2gMy2i0xKeN7+kpf5AGBJ43FWU2yWUTymIV3+s7XR90rZ8fx1YHt1SvdI8k0RJVytQhLO05oogYsCSqKRdbp/dTa/FtlWIuWF67x1JL5/l0jVfLKQrToVz20jNRYxEAuuoDODowiUUuiU3OJPPqc27DI4nSUgxTqYdnpwiVlpZwpyms2NbvNE/ecJZ1M6NQVZoMxonV7VHsOTmKlW3uLrZ2+52tkDZRQ7xwaAAvHh6EdmwHelgCJyQRImNYn0xhsCmnEIqGOlEEQRBeiQYknD+vznF9uRfayKR97Sd9AGGbWIWzH6Tr7qu6dS3XT3FQZzceMM5SX7ykASvKvKjdvCfs3EOtbnTeFcK8fB7cUu3aWdl1dNh1vd32dnF/xnGd27U1zmzP9lfLWTAmOyPM9GkKKTnbQvM03DS90FEXQHvMb7vOi5vnTLC0OYxLlzUiPoWMuJViPKapZBydigeZnUJoF4M8FcxeGNZ1xe8zpRBOx422KazgsmVNtoptuf3OVkghrCH02Qzf0afwfL7cxNpkClp4EVKBFgC5m2462a0Igpi7GAcxAs/h4iUNhb/t3mer20tj8qzoBdFtE6uAsx2o6AXYjfF3DMU4RbcyCUDOBa895seaTmf5XBNu2cYQOs9Yu6G385oIYVlLGH6fgKhLhkE3dyU7uaYXc2PYdpa/Wmq17EStMdPn6cL5cbxtacOMlHaYKjNpISzX15nK3WCUI56PP3XKClvumTmVfRb6qaDWoBtu7t2m2obTcRmdIQthxfs1HNCZslbPNKQQ1hBy/uYNHH+6oBBuTCYxmM8uCuR+mK35uk/TTVlMEMTcozVfymZNR9TkUmk3SPT7yscpsYKFsHRdzkJot03uU+AtFkK3cgsVDnjcZsfN2+kKnbmN1zGJcQBiHqzZ9xDwidi8uAEddfaWDqCyOoRemEh5S5Agz2Cx+WpQyZnRFfKAPPVYvNnKTKvNPM9NK6ZxJpilY3DPtEUVrOmIYtOiBtv1dtd0KlnovcTOTdUa6/XZNa0JLlOinTM3QWRUmiuNP6wVKIawhvAJAoTMMJS+l/FCRzOAXPzgQPOmQhue4xCURVyytPGM3uwEQZwdrGqLYnFTyNMATvYww6qHGzpZHdysEQJfVCQzWQ2H+ify25S2bYv5cbh/wmRZc3sCek1gYBv7WIHLqDlzHgd9aFpuezf53IYTUzHulMuYd+HCODJZzTHmcrZQSczUOe1RHB9KuCrmZytnoyG1Narg6MBkZfUGHaiV8bxRDI5zz5xqd01bpuDC6+nYp2whdI73NWWRnSmX0SoV2SSFkJg2PpFHrG873pA4jAk8wqqGFWkNzzReUGijm6XPpCmcIIizC6+z+V4UQh0nBaec0mbcTk+YZdfXwoYgwoqIukDR7dVt4CC6DAbstrPWs/M6KDFaUSsZx7g1dUvO4XSeraUnjF0sbw3j9ROjkEQemWxpuny3hENnK4okYHFTbSQGOdNU07WW406PwrW4MYSoX5qR2L6miIze0RTCyuwZIhufC+vm1UFlDPVnIM6xEjjDI9nNZXQ6GD1DhCoZTWZrVNfsudvnAD6Rh7/nOTys5GZ1LkwmMV5/LlSp+NI6G2f2CIKoDfTsnzqVDBwdXUZdusgllbHfrqQtz5UkrXBVNl102VKH0dL9ezny5oiC+fXF4uC588XKylZsa481Y6gRJyOYyPNIW4sR5mmN+tEUVjA0mS6btIYgTienSyG0ez5MlRWtEdQFkmiKzExB+6ky1aytksij7jTGc0718k3VK6IScpZUGRlVQ7hKMa2nM9vu6YQUwhrCJ/KI9jyL5wP5+MFEEoNdm01tzoZMRgRB1CYXLojjyb19U9rWNsto/j8nBN6+3p/XWBO3Zm7PSnNGUfukMF6U4XM6zElt3NKqu8lQCU7nRhQ4pA1lEK1xPgLPoSEk47yuWFWTfxBzG6Nbda0iCTw644Fqi1ERbkXfK0Evq+GGdeLQK27DV2kGx7Z2WZfPJLV9dztDfoc1hJLoBT92ALuU3KzUxkQSAy2kEBIEcWaQBN5Tko1Ni+tLltnFb3EcTKMT6/OLd1AIvT7lyimbXraztRDCefDiZnmsZFA21dTqTnFy1vi/qEMsVX1InvWxgsQshoYwnqlEsTA+TqajkCxpCuGCBfFCiSK7xDQrWyNoiSq4YH68or5Nz0dLt41hGS1RBctbw5ULXWNos9RnlKYJawj/8afxkiIjy3Foz2TRwvmxv261qQ3pgwRBnE6sNaf02DRB4CALPNpifgR8IlqiCrpHkqjLx6nYuozCUl/KUkdVMJSdMG3n1ULoopy51s4q0z3HOVs23VwzK3k827W1xgFWwvKWMHYdG0ZTWEEsICEWqK34odNFxE/DmNlEQ1BGz2hy1me0PRNEFBGDLu7jRqZTu88Ix3GFyaQL5sdtM00rkuCpJFFp34bvlicgx3FT6rMWmaX6ICmEtYTvyJOFchMXJZIYbroIjDdfIqqzRBDE6cRqWVs3L46DfeNY0hxGyOBquLwljPqQDw2hnEeDfWF682tf4DgYy9vzXPkEL1PFzULoVg8LKFVkjYg8B6chGucyA14qg72rrDbF+f2AT3RMSX82E/CJuGhRPWXdniUsbw0jpIhTyoA511jQEMrFxIXLxzIa7/6ZimFzq5U6FYxvg6mWriBOH65TNHfffTfmz58PRVGwYcMGbN++3bWz4eFhbNmyBa2trZBlGUuXLsVDDz1UUZ/JZBJbtmxBfX09QqEQ/vzP/xw9PT2mNkePHsXVV1+NQCCApqYm3HLLLchms5jVMAbh0JPYpiuEySQGDeUmdFxnvQmCIKZJS77Oqe46GvVLOK+rzqQMAoAo8GiN+gtFmp0Vq+IKq7ul7v5Ystzjc85t3OMeQ1ia/tza2lEhdClK7TYDXo7ZFrNUS4Rksep18AhvSAKPBQ1BTzVO5zoCz2FRYwhhD1mAZ8PQ8Gz3cFvRFoEk8ljZFqm2KFPC8c12//334+abb8Ydd9yBnTt3Yu3atbjqqqvQ29tr2z6dTuOd73wnDh8+jF/+8pfYu3cvvv/976O9vb2iPm+66Sb85je/wX/913/hySefxMmTJ/He9763sF5VVVx99dVIp9N47rnn8OMf/xg/+tGPcPvtt8/E+agevW+gP9mHt3w+cIzhokQSA82bS5rNlFsAQRCEHe0xP9Z2xrB+XmXxIXaTVRxnHgRY2+h/l7gPedyn20y4ewyhWcaS9Q6WS8C92PNUn89rOqJY0hSqmfpnBEHMLozPK7eSO9VEFHgsaAxifkPgrJzAaY/5cenSRsf47VrH8a755je/iY985CO44YYbsHLlStxzzz0IBAL4wQ9+YNv+Bz/4AQYHB/GrX/0Kmzdvxvz583HppZdi7dq1nvscGRnBf/zHf+Cb3/wmLr/8cqxbtw4//OEP8dxzz2Hbtm0AgN///vfYs2cPfvKTn+Dcc8/Fu9/9bnzxi1/E3XffjXTam691TXJwa8E6uCKdhqy0IhGaV9KsRn/nBEGcJXAch8awXHGtUyeXUSN2SWXsZfC2TzcFyrUwvYcYQidvDLdix+VcUc07KX6tD8ngeY7cqAiCmDKr26NY1hKuaevrosYQFjfN/sQxZyO2b7Z0Oo0dO3bgiiuuKDbkeVxxxRV4/vnnbTt68MEHsXHjRmzZsgXNzc1YvXo17rzzTqiq6rnPHTt2IJPJmNosX74cXV1dhTbPP/88zjnnHDQ3NxfaXHXVVRgdHcXrr78+1fNQfQ5sxfOKH0C+3ETzJtsRBWUZJQiiFnEsmG5yGbUohPk/rYqQV0ubW/kE0WOW0cIyyyInhdi1vuEUH8/6ZlNNKEMQBNESVcj1nJgytq+2/v5+qKpqUroAoLm5Gd3d3bYdHTx4EL/85S+hqioeeugh3HbbbfjGN76BL33pS5777O7uhs/nQywWc21j14e+zo5UKoXR0VHTv5oimwI78iy2+Z3LTeiQyyhBELWIW8ZPnRKX0bzSNlVXSZ/IY/PiBttSGd7rENq3kR0UQrfYwEqSyjjJQxAEQRBnmhlzQNQ0DU1NTbj33nuxbt06vP/978fnPvc53HPPPTO1iylz1113IRqNFv51dnZWWyQzx7YjnZ3EX4xN4IJEEmtTaQw1XWTblBRCgiBqEadnk3Fxicuok1WxAk8Iv0+wjUfxXLrCQcFzshBak+uY+/KO0YJJ2aMJgiCIamL7xmtoaIAgCCXZPXt6etDS0mLbUWtrK5YuXQpBKL6YV6xYge7ubqTTaU99trS0IJ1OY3h42LWNXR/6Ojs+85nPYGRkpPDv2LFjtu2qRtu5kN/3U3x8yV/iuxMBpGMrkZHtEzqQxyhBELWIU8ydUeGyKoBOFsLKH3PlTYzGZDD2GVHNCyWHbKIddX7Mbwhi/fy6knXmwvTuRxGURXTGA1jUFHJtRxAEQRCnG9s3ns/nw7p16/DYY48VlmmahsceewwbN2607Wjz5s146623oBmCIPbt24fW1lb4fD5Pfa5btw6SJJna7N27F0ePHi202bhxI1577TVTZtJHH30UkUgEK1eutJVNlmVEIhHTv5pCDgMr/hi4+ht44Y8fxY7LfuLYlGIICYKoRZyMXObC9OZ1XuIOvZDKlA++O6+rDmFFxPnz6qblacHzHBY3hWwLv/MVuowuawljQUNwyrIQBEEQxEzg6DJ688034/vf/z5+/OMf44033sDHPvYxTExM4IYbbgAAfPCDH8RnPvOZQvuPfexjGBwcxI033oh9+/bhd7/7He68805s2bLFc5/RaBQf/vCHcfPNN2Pr1q3YsWMHbrjhBmzcuBEXXZRzobzyyiuxcuVKXHfddXjllVfwyCOP4J/+6Z+wZcsWyHL54p21Ds9xUCXnAQK5FhEEUYs4uoy6tHGa4Kr0MZfIqGXbRP0SNiysRzzoq9gCGZAFLGgM4oIF7qU4pvt4rgvOznTlxOyF5pgJggAAx2CI97///ejr68Ptt9+O7u5unHvuuXj44YcLCVyOHj0K3jDd29nZiUceeQQ33XQT1qxZg/b2dtx444249dZbPfcJAN/61rfA8zz+/M//HKlUCldddRX+7d/+rbBeEAT89re/xcc+9jFs3LgRwWAQ119/Pb7whS/M6ImpFuUezmQhJAiiFnF6NJkK0xvauNXzq1SxqjQpjVEmPcOp2z5VjWFRY3nXTi/JatxY3R7Fa8dHMDyZqXxjgqiAJc0hdI8kMa+eLNQEQQAcc6vse5YyOjqKaDSKkZGRmnMfffatfiTS5tnulqiC7pEkAGDT4noEfM5JDQiCIKqBpjE8/mavadkVK5tNyxc0BnGobwIAoEgCLl7SAAD4wx5zXPjazhgaw949Pp57qx+TlufmFSubHVqbZW2NKVjVFkXPaBKvHR8xbbu3ewzHBiexsi2Ctpi/rBx7To7i5HACANAZD2BZS+X1tgbGU3j56LDjerfjIgiCIIhy2OlBpFnUGHaTyh11/oJCSFlGCYKoRbwUmTc+v2bSQnhuVwwnhhLojAdwaiSJhlBpfJ9T//qUqF1Nw6XNIXTFA54LPRtjJKf6qCYvEIIgCOJMQwphjWEXI2hcRoMFgiBmE04uo27PskonvgI+EUuac9Y4L0la7J6zIVnEeV0xyJJgaudVGQRmZsLOTrEWBQ4MwPIpWBwJgiAIohykENYYdmMkU1zKmROFIAiiIhY1hXCgd9xxvVsJCnO76lAfml5iMs7heyXYle+IB304pz1KScUIgiCI08KMFaYnZga72WG/JCAWkFAX9EF0qI1FEARRbRY0BCFLzs8oo0ulm4Vwtuo9XIVlJ+xwzrw6S08KQRAEUfOQhbDGsBsLCByH9fPd050TBEHMJpwK2QOzV/mZCbFn6aETBEEQsxgyN9UY9jGEVRCEIAjiNOL2XJutodJmN9ipHQQlDiMIgiDONKQQ1hh2Q4HZOltOEAThhLvL6Jl75s1k4SVhBlxGJYFHY1hGvEymVIIgCIKYKUghrDGMs8PzGwJY1V5bdRIJgiBmAqNCuKYjalo3Wy2EgqGUxnQOYW1nDOd31U1fIIIgCILwACmENYZxVnlefRCt0fLFkAmCIGYD5hIUxe9NEQWbFtcX253BPKMMM2cidIuLnA5n8nwQBEEQcw9SCGsMo/sSxZIQBHE2ETTU9FMs2Uj5GXC3rDZGq+dMur2GFMr/RhAEQZw+6C1Tw8xWtymCIAgjFyyIYzKdRSzgw8q2CAYn0mizeD9UawJsJmMIRX5mXEZ1LlgQR/94CvPigRnojSAIgiDsIYWwxjCOTSiZDEEQZwNRv4SoXwIAtMX8aIuVusKfDY87uzqy08F43giCIAjidEEuowRBEETVMVrXJGF2vppMFsKzQMElCIIg5gZkIawx2Ez6LxEEQZxhppoAheM4XLK0EQzMtSTFTDOTT1xTDCElgiEIgiBmCaQQEgRBEDWBT5ydlkEdo0I4k9lLCYIgCOJ0MrvfvmchNIQgCII4c8ykV4ax7ISq0dOcIAiCmB2QQkgQBEEQM4AxqYxG7v8EQRDELIEUQoIgCIKYYVSt2hIQBEEQhDdIIawxaFKZIAjizHG6HrnkMkoQBEHMFkghJAiCIIgZhlxGCYIgiNkCKYQ1hjzLs+wRBEHMJmKnqfA7WQgJgiCI2QKVnagxljSHkNUY2mP+aotCEARRMfGgDyeHExCF2q7Dt2lxPQbG06ftWXsmaykSBEEQxHQghbDGkEUB53bGqi0GQRDElFjaHEJQFtAUVqotiisBn4hAfOZfgavbozg+NInFTaEZ75sgCIIgTgekEBIEQRAzhijwmFcfrLYYVaMlqqAlWtvKMEEQBEEYoYA1giAIgiAIgiCIOQophARBEARBEARBEHMUUggJgiAIgiAIgiDmKKQQEgRBEARBEARBzFFIISQIgiAIgiAIgpijkEJIEARBEARBEAQxRyGFkCAIgiAIgiAIYo5CCiFBEARBEARBEMQchRRCgiAIgiAIgiCIOQophARBEARBEARBEHMUUggJgiAIgiAIgiDmKKQQEgRBEARBEARBzFFIISQIgiAIgiAIgpijkEJIEARBEARBEAQxRyGFkCAIgiAIgiAIYo5CCiFBEARBEARBEMQchRRCgiAIgiAIgiCIOQophARBEARBEARBEHMUsdoCVAPGGABgdHS0ypIQBEEQBEEQBEGcGXT9R9eHgDmqEI6NjQEAOjs7qywJQRAEQRAEQRDEmWVsbAzRaBQAwDGjejhH0DQNJ0+eRDgcBsdx1RanwOjoKDo7O3Hs2DFEIpFqi0OArkktQtek9qBrUnvQNak96JrUHnRNag+6JqcfxhjGxsbQ1tYGns9FD85JCyHP8+jo6Ki2GI5EIhH6EdQYdE1qD7omtQddk9qDrkntQdek9qBrUnvQNTm96JZBHUoqQxAEQRAEQRAEMUchhZAgCIIgCIIgCGKOQgphDSHLMu644w7IslxtUYg8dE1qD7omtQddk9qDrkntQdek9qBrUnvQNakOczKpDEEQBEEQBEEQBEEWQoIgCIIgCIIgiDkLKYQEQRAEQRAEQRBzFFIICYIgCIIgCIIg5iikEBIEQRAEQRAEQcxRSCGsIe6++27Mnz8fiqJgw4YN2L59e7VFmrPcdddduOCCCxAOh9HU1IRrrrkGe/furbZYhIGvfOUr4DgOn/rUp6otypzmxIkT+Ju/+RvU19fD7/fjnHPOwUsvvVRtseYsqqritttuw4IFC+D3+7Fo0SJ88YtfBOWPO3M89dRTeM973oO2tjZwHIdf/epXpvWMMdx+++1obW2F3+/HFVdcgf3791dH2DmC2zXJZDK49dZbcc455yAYDKKtrQ0f/OAHcfLkyeoJPAco9zsx8tGPfhQcx+Hb3/72GZNvrkEKYY1w//334+abb8Ydd9yBnTt3Yu3atbjqqqvQ29tbbdHmJE8++SS2bNmCbdu24dFHH0Umk8GVV16JiYmJaotGAHjxxRfx7//+71izZk21RZnTDA0NYfPmzZAkCf/7v/+LPXv24Bvf+Abq6uqqLdqc5atf/Sq+973v4bvf/S7eeOMNfPWrX8XXvvY1fOc736m2aHOGiYkJrF27Fnfffbft+q997Wv413/9V9xzzz144YUXEAwGcdVVVyGZTJ5hSecObtdkcnISO3fuxG233YadO3fif/7nf7B37178yZ/8SRUknTuU+53oPPDAA9i2bRva2trOkGRzFEbUBBdeeCHbsmVL4W9VVVlbWxu76667qigVodPb28sAsCeffLLaosx5xsbG2JIlS9ijjz7KLr30UnbjjTdWW6Q5y6233souvvjiaotBGLj66qvZhz70IdOy9773vezaa6+tkkRzGwDsgQceKPytaRpraWlhX//61wvLhoeHmSzL7Gc/+1kVJJx7WK+JHdu3b2cA2JEjR86MUHMcp2ty/Phx1t7eznbv3s3mzZvHvvWtb51x2eYKZCGsAdLpNHbs2IErrriisIzneVxxxRV4/vnnqygZoTMyMgIAiMfjVZaE2LJlC66++mrT74WoDg8++CDWr1+Pv/zLv0RTUxPOO+88fP/736+2WHOaTZs24bHHHsO+ffsAAK+88gqeeeYZvPvd766yZAQAHDp0CN3d3abnVzQaxYYNG+h9X0OMjIyA4zjEYrFqizJn0TQN1113HW655RasWrWq2uKc9YjVFoAA+vv7oaoqmpubTcubm5vx5ptvVkkqQkfTNHzqU5/C5s2bsXr16mqLM6f5+c9/jp07d+LFF1+stigEgIMHD+J73/sebr75Znz2s5/Fiy++iE9+8pPw+Xy4/vrrqy3enOTTn/40RkdHsXz5cgiCAFVV8eUvfxnXXntttUUjAHR3dwOA7fteX0dUl2QyiVtvvRV//dd/jUgkUm1x5ixf/epXIYoiPvnJT1ZblDkBKYQEUYYtW7Zg9+7deOaZZ6otypzm2LFjuPHGG/Hoo49CUZRqi0MgN1myfv163HnnnQCA8847D7t378Y999xDCmGV+MUvfoGf/vSnuO+++7Bq1Srs2rULn/rUp9DW1kbXhCDKkMlk8L73vQ+MMXzve9+rtjhzlh07duBf/uVfsHPnTnAcV21x5gTkMloDNDQ0QBAE9PT0mJb39PSgpaWlSlIRAPCJT3wCv/3tb7F161Z0dHRUW5w5zY4dO9Db24vzzz8foihCFEU8+eST+Nd//VeIoghVVast4pyjtbUVK1euNC1bsWIFjh49WiWJiFtuuQWf/vSn8Vd/9Vc455xzcN111+Gmm27CXXfdVW3RCKDwTqf3fe2hK4NHjhzBo48+StbBKvL000+jt7cXXV1dhff9kSNH8A//8A+YP39+tcU7KyGFsAbw+XxYt24dHnvsscIyTdPw2GOPYePGjVWUbO7CGMMnPvEJPPDAA3j88cexYMGCaos053nHO96B1157Dbt27Sr8W79+Pa699lrs2rULgiBUW8Q5x+bNm0vKsezbtw/z5s2rkkTE5OQkeN78ahcEAZqmVUkiwsiCBQvQ0tJiet+Pjo7ihRdeoPd9FdGVwf379+MPf/gD6uvrqy3SnOa6667Dq6++anrft7W14ZZbbsEjjzxSbfHOSshltEa4+eabcf3112P9+vW48MIL8e1vfxsTExO44YYbqi3anGTLli2477778Otf/xrhcLgQ2xGNRuH3+6ss3dwkHA6XxHAGg0HU19dTbGeVuOmmm7Bp0ybceeedeN/73oft27fj3nvvxb333ltt0eYs73nPe/DlL38ZXV1dWLVqFV5++WV885vfxIc+9KFqizZnGB8fx1tvvVX4+9ChQ9i1axfi8Ti6urrwqU99Cl/60pewZMkSLFiwALfddhva2tpwzTXXVE/osxy3a9La2oq/+Iu/wM6dO/Hb3/4WqqoW3vnxeBw+n69aYp/VlPudWJVySZLQ0tKCZcuWnWlR5wbVTnNKFPnOd77Durq6mM/nYxdeeCHbtm1btUWaswCw/ffDH/6w2qIRBqjsRPX5zW9+w1avXs1kWWbLly9n9957b7VFmtOMjo6yG2+8kXV1dTFFUdjChQvZ5z73OZZKpaot2pxh69attu+P66+/njGWKz1x2223sebmZibLMnvHO97B9u7dW12hz3LcrsmhQ4cc3/lbt26ttuhnLeV+J1ao7MTphWOMsTOkexIEQRAEQRAEQRA1BMUQEgRBEARBEARBzFFIISQIgiAIgiAIgpijkEJIEARBEARBEAQxRyGFkCAIgiAIgiAIYo5CCiFBEARBEARBEMQchRRCgiAIgiAIgiCIOcr/D5TEthz0RDrhAAAAAElFTkSuQmCC", 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\n", " " ], @@ -488,7 +459,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 16, "id": "7ca97ff1", "metadata": {}, "outputs": [ @@ -497,9 +468,9 @@ "output_type": "stream", "text": [ "\t\tInitial guess 1\tInversion 1\tInitial guess 2\tInversion 2\tTrue values\n", - "a\t\t1.10e-02\t9.90e-03\t9.00e-03\t9.90e-03\t1.00e-02\n", - "b\t\t1.08e-02\t8.91e-03\t4.50e-03\t8.91e-03\t9.00e-03\n", - "Dc\t\t5.00e-05\t1.03e-05\t2.00e-05\t1.03e-05\t1.00e-05\n" + "a\t\t1.10e-02\t9.93e-03\t9.00e-03\t9.93e-03\t1.00e-02\n", + "b\t\t1.08e-02\t8.94e-03\t6.30e-03\t8.94e-03\t9.00e-03\n", + "Dc\t\t2.00e-05\t1.00e-05\t1.50e-05\t1.00e-05\t1.00e-05\n" ] } ], From a9f68ded84be896f94c640197e3385f1552d67df Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Tue, 26 Aug 2025 15:57:27 +0200 Subject: [PATCH 49/56] Managed to make SVI work, but converges to the wrong results! --- diabayes/SVI.py | 16 +-- diabayes/solver.py | 29 ++-- diabayes/typedefs.py | 117 ++++++---------- examples/simple_inversion.ipynb | 240 ++++++++++++++++++++++---------- test/aux.py | 2 +- test/test_typedefs.py | 36 ++--- 6 files changed, 254 insertions(+), 186 deletions(-) diff --git a/diabayes/SVI.py b/diabayes/SVI.py index 6f28d17..55e0635 100644 --- a/diabayes/SVI.py +++ b/diabayes/SVI.py @@ -5,7 +5,7 @@ import jax.numpy as jnp from jaxtyping import Array, Float -from diabayes.typedefs import Variables +from diabayes.typedefs import Variables, _Params ParticleArray = Float[Array, "N M"] GradientArray: TypeAlias = ParticleArray @@ -83,19 +83,15 @@ def compute_phi( @eqx.filter_value_and_grad def _log_likelihood( - log_params: Float[Array, "..."], + log_params: _Params, mu_obs: Float[Array, "Nt"], noise_std: Float, - v0: Float, - forward_fn: Callable[[Float[Array, "2"], Float[Array, "..."]], Variables], + forward_fn: Callable[[_Params], Variables], ) -> Float: # Transform the log_params by taking exponential - params = jnp.exp(log_params) - # Get initial values - # TODO: replace with `get_steady_state` or something - y0 = jnp.array([mu_obs[0], params[2] / v0]) + params = type(log_params).from_array(jnp.exp(log_params.to_array())) # Forward pass to get friction curve - mu_hat = forward_fn(y0, params).mu + mu_hat = forward_fn(params).mu # Log-likelihood of residuals p = -(jnp.square(mu_obs - mu_hat).mean() / (2 * noise_std**2)) return p @@ -107,5 +103,5 @@ def _log_likelihood( likelihood and its gradient (hence it needs 2 values for the out_axes). """ mapped_log_likelihood = jax.vmap( - _log_likelihood, in_axes=(0, None, None, None, None), out_axes=(0, 0) + _log_likelihood, in_axes=(0, None, None, None), out_axes=(0, 0) ) diff --git a/diabayes/solver.py b/diabayes/solver.py index f989a93..5fa110f 100644 --- a/diabayes/solver.py +++ b/diabayes/solver.py @@ -19,7 +19,6 @@ from diabayes.typedefs import ( BayesianSolution, RSFParams, - RSFParticles, Variables, _BlockConstants, _Constants, @@ -127,8 +126,8 @@ def _forward_wrapper( @eqx.filter_jit def _forward_wrapper_SVI( self, - y0: Variables, params: _Params, + y0: Variables, t: Float[Array, "Nt"], friction_constants: _Constants, block_constants: _BlockConstants, @@ -243,6 +242,7 @@ def bayesian_inversion( t: Float[Array, "Nt"], mu: Float[Array, "Nt"], noise_std: Float, + y0: Variables, params: _Params, friction_constants: _Constants, block_constants: _BlockConstants, @@ -264,14 +264,16 @@ def bayesian_inversion( key, split_key = jr.split(key) - scale = jnp.ones(len(params)) * 0.1 + Nparams = len(params.__dict__.keys()) + + scale = jnp.ones(Nparams) * 0.1 inv_scale = 1 / (jnp.sqrt(2) * scale) log_params = jnp.log(params.to_array()) # Sample particles from a log-normal distribution - log_particles = RSFParticles.generate( + log_particles = RSFParams.generate( N=Nparticles, loc=log_params, scale=scale, key=split_key - ).to_array() + ) # Instantiate optimiser opt = optax.adam(learning_rate=self.learning_rate) @@ -279,6 +281,7 @@ def bayesian_inversion( forward_fn = partial( self._forward_wrapper_SVI, + y0=y0, t=t, friction_constants=friction_constants, block_constants=block_constants, @@ -287,9 +290,7 @@ def bayesian_inversion( @scan_tqdm(Nsteps) def body_fun(carry, i): params, state = carry - loss, gradp = mapped_log_likelihood( - params, mu, noise_std, friction_constants.v0, forward_fn - ) + loss, gradp = mapped_log_likelihood(params, mu, noise_std, forward_fn) """ Sometimes the adjoint back-propagation becomes unstable, producing NaNs in the gradients. By setting the NaN-gradients @@ -298,10 +299,14 @@ def body_fun(carry, i): likely be stable again and the particle will continue to be attracted by the maximum likelihood """ - nan_count = jnp.isnan(gradp).sum() / gradp.shape[1] - gradp = jnp.asarray(jnp.where(jnp.isnan(gradp), 0.0, gradp)) - gradq = -2 * (params - log_params) * inv_scale - phi = compute_phi(params, gradp, gradq) + params_array = params.to_array().T + gradp_array = gradp.to_array().T + nans = jnp.isnan(gradp_array) + nan_count = nans.sum() / nans.shape[1] + gradp_array = jnp.asarray(jnp.where(nans, 0.0, gradp_array)) + gradq_array = -2 * (params_array - log_params) * inv_scale + phi_array = compute_phi(params_array, gradp_array, gradq_array) + phi = type(params).from_array(phi_array.T) updates, state = opt.update(phi, state, params) params = optax.apply_updates(params, updates) return (params, state), (loss.mean(), nan_count, params) diff --git a/diabayes/typedefs.py b/diabayes/typedefs.py index 6b69e5e..5dd4ed0 100644 --- a/diabayes/typedefs.py +++ b/diabayes/typedefs.py @@ -113,10 +113,48 @@ def from_array(cls, x: Float[Array, "..."], keys: tuple[str, ...]) -> "Variables return cls(mu=mu, state=state_obj) -class RSFParams(eqx.Module): - a: Float - b: Float - Dc: Float +class Params(eqx.Module): + + def __init__(self, *args, **kwargs): + field_names = [f.name for f in dcs.fields(self)] + for key, val in zip(field_names, args): + kwargs[key] = val + + for key, val in kwargs.items(): + object.__setattr__(self, key, jnp.asarray(val)) + + for name in field_names: + if not hasattr(self, name): + object.__setattr__(self, name, jnp.asarray(-1.0)) + + def __getitem__(self, idx): + x = dict((key.name, getattr(self, key.name)[idx]) for key in dcs.fields(self)) + return type(self)(**x) + + def __len__(self): + key = dcs.fields(self)[0].name + return len(getattr(self, key)) + + def to_array(self): + return jnp.squeeze(jnp.array(jax.tree_util.tree_flatten(self)[0])) + + @classmethod + def from_array(cls, x): + return cls(*x) + + @classmethod + def generate( + cls, N: int, loc: Float[Array, "M"], scale: Float[Array, "M"], key: jax.Array + ): + assert len(loc) == len(scale) + x = jr.normal(key, shape=(N, len(loc))) * scale + loc + return cls.from_array(x.T) + + +class RSFParams(Params): + a: Float[Array, "..."] + b: Float[Array, "..."] + Dc: Float[Array, "..."] @dcs.dataclass(frozen=True) @@ -125,7 +163,7 @@ class RSFConstants: mu0: Float -class CNSParams(eqx.Module): +class CNSParams(Params): phi_c: Float Z: Float @@ -185,72 +223,6 @@ def __call__( ) -> Float: ... -""" --------------- -SVI containers --------------- -""" - - -@tree_util.register_pytree_node_class -@dcs.dataclass(frozen=True) -class Particles: - - _particles: Union[Tuple[RSFParams, ...], Tuple[CNSParams, ...]] - - def __getitem__(self, idx): - # Check if the requested selection is of the kind `x[1, 3]` - if isinstance(idx, tuple): - assert ( - len(idx) == 2 - ), "Particles do not support item selection with more than 2 indices" - i, j = idx - return self._particles[i].to_array()[j] - # Else the selection is of the kind `x[4]` - return self._particles[idx] - - def __iter__(self): - return iter(self._particles) - - def __len__(self): - return len(self._particles) - - @classmethod - def tree_unflatten(cls, aux_data, children): - raise NotImplementedError - - @classmethod - def from_array(cls, x: Iterable): - return cls.tree_unflatten(None, x) - - def tree_flatten(self): - return self._particles, None - - def to_array(self): - return jnp.array([p.tree_flatten()[0] for p in self._particles]) - - @classmethod - def generate( - cls, N: int, loc: Float[Array, "M"], scale: Float[Array, "M"], key: jax.Array - ): - assert len(loc) == len(scale) - x = jr.normal(key, shape=(N, len(loc))) * scale[None] + loc[None] - return cls.from_array(x) - - -@tree_util.register_pytree_node_class -@dcs.dataclass(frozen=True) -class RSFParticles(Particles): - - @classmethod - def tree_unflatten(cls, aux_data, children): - x = tuple(RSFParams(*child) for child in children) - return cls(x) - - -# Alias gradients for semantic clarity -Gradients: TypeAlias = Particles - """ ----------------- Inversion results @@ -317,10 +289,11 @@ class BayesianSolution: def __init__( self, - chains: Chains, + x: Params, log_likelihood: Float[Array, "Nsteps"], nan_count: Float[Array, "Nsteps"], ): + chains = x.to_array().transpose(1, 2, 0) self.chains = jnp.exp(chains) self.final_state = jnp.exp(chains[-1]) self.log_likelihood = log_likelihood diff --git a/examples/simple_inversion.ipynb b/examples/simple_inversion.ipynb index b0193af..b76bd45 100644 --- a/examples/simple_inversion.ipynb +++ b/examples/simple_inversion.ipynb @@ -156,7 +156,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "701bafc9b90f4f0d881387e7c26b5f8a", + "model_id": "a59aaf45f11f4877a52b07e869cdecef", "version_major": 2, "version_minor": 0 }, @@ -220,7 +220,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "id": "d4c47b4b", "metadata": {}, "outputs": [ @@ -228,12 +228,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Step: 0, Loss on this step: 0.0031062619344770547\n", - "Step: 1, Loss on this step: 0.0014327439927961727\n", - "Step: 2, Loss on this step: 0.0005079012385201983\n", - "Step: 3, Loss on this step: 0.0004554938162316355\n", - "Step: 4, Loss on this step: 0.0004549483107437401\n", - "Step: 5, Loss on this step: 0.0004549482405273438\n" + "Step: 0, Loss on this step: 0.0031062619344770495\n", + "Step: 1, Loss on this step: 0.0014327439927960947\n", + "Step: 2, Loss on this step: 0.0005079012385201731\n", + "Step: 3, Loss on this step: 0.0004554938162316278\n", + "Step: 4, Loss on this step: 0.00045494831074373895\n", + "Step: 5, Loss on this step: 0.0004549482405273416\n" ] } ], @@ -282,7 +282,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "id": "97c8272e", "metadata": {}, "outputs": [ @@ -291,23 +291,23 @@ "output_type": "stream", "text": [ "Inverted parameters: RSFParams(a=f64[], b=f64[], Dc=f64[])\n", - "True parameters: RSFParams(a=0.01, b=0.009000000000000001, Dc=1e-05)\n" + "True parameters: RSFParams(a=weak_f64[], b=weak_f64[], Dc=weak_f64[])\n" ] }, { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 12, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7f5716b5e7d74485953ccb4b93c14adc", + "model_id": "e28f8ad8e00f41f2ad6ca84c057dad14", "version_major": 2, "version_minor": 0 }, @@ -354,7 +354,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 9, "id": "a0fbfb01", "metadata": {}, "outputs": [ @@ -362,13 +362,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Step: 0, Loss on this step: 0.00879827890106595\n", - "Step: 1, Loss on this step: 0.005383346705837146\n", - "Step: 2, Loss on this step: 0.0027221340210402474\n", - "Step: 3, Loss on this step: 0.0007582650668123534\n", - "Step: 4, Loss on this step: 0.00045876325933946424\n", - "Step: 5, Loss on this step: 0.00045495330751144623\n", - "Step: 6, Loss on this step: 0.00045494824098784144\n" + "Step: 0, Loss on this step: 0.00879827890106592\n", + "Step: 1, Loss on this step: 0.005383346705836939\n", + "Step: 2, Loss on this step: 0.0027221340210401073\n", + "Step: 3, Loss on this step: 0.000758265066812316\n", + "Step: 4, Loss on this step: 0.0004587632593394646\n", + "Step: 5, Loss on this step: 0.00045495330751144526\n", + "Step: 6, Loss on this step: 0.00045494824098784334\n" ] } ], @@ -401,24 +401,24 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 10, "id": "1da6bb44", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 15, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ed1ac1c5009e4aa3950a5475e6e5d021", + "model_id": "51bcc146fbe241f9bddc6261f94b7ccc", "version_major": 2, "version_minor": 0 }, @@ -459,7 +459,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 11, "id": "7ca97ff1", "metadata": {}, "outputs": [ @@ -533,7 +533,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a97b9b15dbbb4185b0fb058d810d23c3", + "model_id": "47957f8ca97f43cfacc9dc56845af50a", "version_major": 2, "version_minor": 0 }, @@ -556,11 +556,11 @@ "Number of parameters:\t3 ('a', 'b', 'Dc')\n", "\n", "\t\ta\t\tb\t\tDc\n", - "mean\t\t9.78e-03\t8.78e-03\t1.05e-05\n", - "median\t\t9.74e-03\t8.77e-03\t1.04e-05\n", - "std\t\t8.62e-04\t8.57e-04\t1.33e-06\n", - "q5\t\t8.42e-03\t7.42e-03\t8.53e-06\n", - "q95\t\t1.12e-02\t1.02e-02\t1.29e-05\n", + "mean\t\t6.25e-03\t4.77e-03\t2.46e-05\n", + "median\t\t6.05e-03\t4.57e-03\t2.53e-05\n", + "std\t\t9.05e-04\t9.88e-04\t6.24e-06\n", + "q5\t\t5.05e-03\t3.54e-03\t1.30e-05\n", + "q95\t\t7.93e-03\t6.69e-03\t3.33e-05\n", "\n", "------------------------------------------------------------\n" ] @@ -569,7 +569,7 @@ "source": [ "bayesian_result = solver.bayesian_inversion(\n", " t=t, mu=mu_measured, noise_std=noise_amplitude * 0.5, \n", - " params=params_inv, friction_constants=constants, \n", + " y0=y0, params=params_inv, friction_constants=constants, \n", " block_constants=block_constants, \n", " Nparticles=1500, Nsteps=100, rng=42\n", ")\n", @@ -589,25 +589,25 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 13, "id": "0e54cb25", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6e706bd6bdde49d1b68b135400ed532e", + "model_id": "cadb4af6b0f44e7abd71f8a7ed63eb29", "version_major": 2, "version_minor": 0 }, - "image/png": 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", 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", + "image/png": 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", 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\n", - " " - ], - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "ValueError", + "evalue": "Terms are not compatible with solver! Got:\nODETerm(\n vector_field=Partial(\n func=_JitWrapper(\n fn='ODESolver._forward_wrapper',\n filter_warning=False,\n donate_first=False,\n donate_rest=False\n ),\n args=(,),\n keywords={}\n )\n)\nbut expected:\ndiffrax.AbstractTerm\nNote that terms are checked recursively: if you scroll up you may find a root-cause error that is more specific.", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/core.py:990\u001b[39m, in \u001b[36mTracer.__getattr__\u001b[39m\u001b[34m(self, name)\u001b[39m\n\u001b[32m 989\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m990\u001b[39m attr = \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m.aval, name)\n\u001b[32m 991\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", + "\u001b[31mAttributeError\u001b[39m: 'ShapedArray' object has no attribute 'b'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/diffrax/_integrate.py:170\u001b[39m, in \u001b[36m_assert_term_compatible.._check\u001b[39m\u001b[34m(term_cls, term, term_contr_kwargs, yi)\u001b[39m\n\u001b[32m 169\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m170\u001b[39m vf_type = \u001b[43meqx\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfilter_eval_shape\u001b[49m\u001b[43m(\u001b[49m\u001b[43mterm\u001b[49m\u001b[43m.\u001b[49m\u001b[43mvf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43myi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 171\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/equinox/_eval_shape.py:38\u001b[39m, in \u001b[36mfilter_eval_shape\u001b[39m\u001b[34m(fun, *args, **kwargs)\u001b[39m\n\u001b[32m 37\u001b[39m dynamic, static = partition((fun, args, kwargs), _filter)\n\u001b[32m---> \u001b[39m\u001b[32m38\u001b[39m dynamic_out, static_out = \u001b[43mjax\u001b[49m\u001b[43m.\u001b[49m\u001b[43meval_shape\u001b[49m\u001b[43m(\u001b[49m\u001b[43mft\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpartial\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_fn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstatic\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdynamic\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 39\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m combine(dynamic_out, static_out.value)\n", + " \u001b[31m[... skipping hidden 1 frame]\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api.py:2848\u001b[39m, in \u001b[36meval_shape\u001b[39m\u001b[34m(fun, *args, **kwargs)\u001b[39m\n\u001b[32m 2847\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m: fun = partial(fun)\n\u001b[32m-> \u001b[39m\u001b[32m2848\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mjit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtrace\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m.eval_shape()\n", + " \u001b[31m[... skipping hidden 1 frame]\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:300\u001b[39m, in \u001b[36mjit_trace\u001b[39m\u001b[34m(jit_func, *args, **kwargs)\u001b[39m\n\u001b[32m 298\u001b[39m \u001b[38;5;129m@api_boundary\u001b[39m\n\u001b[32m 299\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mjit_trace\u001b[39m(jit_func, *args, **kwargs) -> stages.Traced:\n\u001b[32m--> \u001b[39m\u001b[32m300\u001b[39m p, args_flat = \u001b[43m_infer_params\u001b[49m\u001b[43m(\u001b[49m\u001b[43mjit_func\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_fun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjit_func\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_jit_info\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 301\u001b[39m donate_argnums = \u001b[38;5;28mtuple\u001b[39m(i \u001b[38;5;28;01mfor\u001b[39;00m i, d \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(p.params[\u001b[33m'\u001b[39m\u001b[33mdonated_invars\u001b[39m\u001b[33m'\u001b[39m]) \u001b[38;5;28;01mif\u001b[39;00m d)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:629\u001b[39m, in \u001b[36m_infer_params\u001b[39m\u001b[34m(fun, ji, args, kwargs)\u001b[39m\n\u001b[32m 628\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m629\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_infer_params_internal\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mji\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:653\u001b[39m, in \u001b[36m_infer_params_internal\u001b[39m\u001b[34m(fun, ji, args, kwargs)\u001b[39m\n\u001b[32m 652\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m entry.pjit_params \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m653\u001b[39m p, args_flat = \u001b[43m_infer_params_impl\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 654\u001b[39m \u001b[43m \u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mji\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mctx_mesh\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdbg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_avals\u001b[49m\u001b[43m=\u001b[49m\u001b[43mavals\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 655\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m p.params[\u001b[33m'\u001b[39m\u001b[33mjaxpr\u001b[39m\u001b[33m'\u001b[39m].jaxpr.is_high:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:552\u001b[39m, in \u001b[36m_infer_params_impl\u001b[39m\u001b[34m(***failed resolving arguments***)\u001b[39m\n\u001b[32m 547\u001b[39m in_shardings_flat, in_layouts_flat = _process_in_axis_resources(\n\u001b[32m 548\u001b[39m in_shardings_treedef, in_shardings_leaves,\n\u001b[32m 549\u001b[39m ji.in_layouts_treedef, ji.in_layouts_leaves,\n\u001b[32m 550\u001b[39m in_avals, in_tree, flat_fun.debug_info, device_or_backend_set, have_kwargs)\n\u001b[32m--> \u001b[39m\u001b[32m552\u001b[39m jaxpr, consts, jaxpr_for_top_jit, out_avals = \u001b[43m_create_pjit_jaxpr\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 553\u001b[39m \u001b[43m \u001b[49m\u001b[43mflat_fun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mIgnoreKey\u001b[49m\u001b[43m(\u001b[49m\u001b[43mji\u001b[49m\u001b[43m.\u001b[49m\u001b[43minline\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 554\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m config.mutable_array_checks.value:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/linear_util.py:472\u001b[39m, in \u001b[36mcache..memoized_fun\u001b[39m\u001b[34m(fun, *args)\u001b[39m\n\u001b[32m 471\u001b[39m start = time.time()\n\u001b[32m--> \u001b[39m\u001b[32m472\u001b[39m ans = \u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 473\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m do_explain:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:1350\u001b[39m, in \u001b[36m_create_pjit_jaxpr\u001b[39m\u001b[34m(***failed resolving arguments***)\u001b[39m\n\u001b[32m 1349\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1350\u001b[39m jaxpr, global_out_avals, consts = \u001b[43mpe\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtrace_to_jaxpr_dynamic\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1352\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m config.debug_key_reuse.value:\n\u001b[32m 1353\u001b[39m \u001b[38;5;66;03m# Import here to avoid circular imports\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/profiler.py:354\u001b[39m, in \u001b[36mannotate_function..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 353\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m TraceAnnotation(name, **decorator_kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m354\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/interpreters/partial_eval.py:2337\u001b[39m, in \u001b[36mtrace_to_jaxpr_dynamic\u001b[39m\u001b[34m(fun, in_avals, keep_inputs, lower, auto_dce)\u001b[39m\n\u001b[32m 2336\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m core.set_current_trace(trace):\n\u001b[32m-> \u001b[39m\u001b[32m2337\u001b[39m ans = \u001b[43mfun\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcall_wrapped\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43min_tracers\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2338\u001b[39m _check_returned_jaxtypes(fun.debug_info, ans)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/linear_util.py:212\u001b[39m, in \u001b[36mWrappedFun.call_wrapped\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 211\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Calls the transformed function\"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m212\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mf_transformed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api_util.py:73\u001b[39m, in \u001b[36mflatten_fun\u001b[39m\u001b[34m(f, store, in_tree, *args_flat)\u001b[39m\n\u001b[32m 72\u001b[39m py_args, py_kwargs = tree_unflatten(in_tree, args_flat)\n\u001b[32m---> \u001b[39m\u001b[32m73\u001b[39m ans = \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43mpy_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mpy_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 74\u001b[39m ans, out_tree = tree_flatten(ans)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/linear_util.py:397\u001b[39m, in \u001b[36m_get_result_paths_thunk\u001b[39m\u001b[34m(_fun, _store, *args, **kwargs)\u001b[39m\n\u001b[32m 395\u001b[39m \u001b[38;5;129m@transformation_with_aux2\u001b[39m\n\u001b[32m 396\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_get_result_paths_thunk\u001b[39m(_fun: Callable, _store: Store, *args, **kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m397\u001b[39m ans = \u001b[43m_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 398\u001b[39m result_paths = \u001b[38;5;28mtuple\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mresult\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m_clean_keystr_arg_names(path)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m path, _ \u001b[38;5;129;01min\u001b[39;00m generate_key_paths(ans))\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/equinox/_eval_shape.py:33\u001b[39m, in \u001b[36mfilter_eval_shape.._fn\u001b[39m\u001b[34m(_static, _dynamic)\u001b[39m\n\u001b[32m 32\u001b[39m _fun, _args, _kwargs = combine(_static, _dynamic)\n\u001b[32m---> \u001b[39m\u001b[32m33\u001b[39m _out = \u001b[43m_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43m_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 34\u001b[39m _dynamic_out, _static_out = partition(_out, _filter)\n", + " \u001b[31m[... skipping hidden 1 frame]\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/diffrax/_term.py:194\u001b[39m, in \u001b[36mODETerm.vf\u001b[39m\u001b[34m(self, t, y, args)\u001b[39m\n\u001b[32m 193\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mvf\u001b[39m(\u001b[38;5;28mself\u001b[39m, t: RealScalarLike, y: Y, args: Args) -> _VF:\n\u001b[32m--> \u001b[39m\u001b[32m194\u001b[39m out = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mvector_field\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 195\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m jtu.tree_structure(out) != jtu.tree_structure(y):\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/equinox/_module/_prebuilt.py:81\u001b[39m, in \u001b[36mPartial.__call__\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Call the wrapped `self.func`.\u001b[39;00m\n\u001b[32m 70\u001b[39m \n\u001b[32m 71\u001b[39m \u001b[33;03m**Arguments:**\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 79\u001b[39m \u001b[33;03mThe result of the wrapped function.\u001b[39;00m\n\u001b[32m 80\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m81\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mkeywords\u001b[49m\u001b[43m)\u001b[49m\n", + " \u001b[31m[... skipping hidden 3 frame]\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:260\u001b[39m, in \u001b[36m_cpp_pjit..cache_miss\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 256\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mre-tracing function \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mjit_info.fun_sourceinfo\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m for \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 257\u001b[39m \u001b[33m\"\u001b[39m\u001b[33m`jit`, but \u001b[39m\u001b[33m'\u001b[39m\u001b[33mno_tracing\u001b[39m\u001b[33m'\u001b[39m\u001b[33m is set\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 259\u001b[39m outs, out_flat, out_tree, args_flat, jaxpr, executable, pgle_profiler = \\\n\u001b[32m--> \u001b[39m\u001b[32m260\u001b[39m \u001b[43m_python_pjit_helper\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjit_info\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 262\u001b[39m maybe_fastpath_data = _get_fastpath_data(\n\u001b[32m 263\u001b[39m executable, out_tree, args_flat, out_flat, jaxpr.effects, jaxpr.consts,\n\u001b[32m 264\u001b[39m jit_info.abstracted_axes, pgle_profiler)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:137\u001b[39m, in \u001b[36m_python_pjit_helper\u001b[39m\u001b[34m(fun, jit_info, *args, **kwargs)\u001b[39m\n\u001b[32m 136\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_python_pjit_helper\u001b[39m(fun: Callable, jit_info: PjitInfo, *args, **kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m137\u001b[39m p, args_flat = \u001b[43m_infer_params\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjit_info\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 139\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m arg \u001b[38;5;129;01min\u001b[39;00m args_flat:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:629\u001b[39m, in \u001b[36m_infer_params\u001b[39m\u001b[34m(fun, ji, args, kwargs)\u001b[39m\n\u001b[32m 628\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m629\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_infer_params_internal\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mji\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:653\u001b[39m, in \u001b[36m_infer_params_internal\u001b[39m\u001b[34m(fun, ji, args, kwargs)\u001b[39m\n\u001b[32m 652\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m entry.pjit_params \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m653\u001b[39m p, args_flat = \u001b[43m_infer_params_impl\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 654\u001b[39m \u001b[43m \u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mji\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mctx_mesh\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdbg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_avals\u001b[49m\u001b[43m=\u001b[49m\u001b[43mavals\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 655\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m p.params[\u001b[33m'\u001b[39m\u001b[33mjaxpr\u001b[39m\u001b[33m'\u001b[39m].jaxpr.is_high:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:552\u001b[39m, in \u001b[36m_infer_params_impl\u001b[39m\u001b[34m(***failed resolving arguments***)\u001b[39m\n\u001b[32m 547\u001b[39m in_shardings_flat, in_layouts_flat = _process_in_axis_resources(\n\u001b[32m 548\u001b[39m in_shardings_treedef, in_shardings_leaves,\n\u001b[32m 549\u001b[39m ji.in_layouts_treedef, ji.in_layouts_leaves,\n\u001b[32m 550\u001b[39m in_avals, in_tree, flat_fun.debug_info, device_or_backend_set, have_kwargs)\n\u001b[32m--> \u001b[39m\u001b[32m552\u001b[39m jaxpr, consts, jaxpr_for_top_jit, out_avals = \u001b[43m_create_pjit_jaxpr\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 553\u001b[39m \u001b[43m \u001b[49m\u001b[43mflat_fun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mIgnoreKey\u001b[49m\u001b[43m(\u001b[49m\u001b[43mji\u001b[49m\u001b[43m.\u001b[49m\u001b[43minline\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 554\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m config.mutable_array_checks.value:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/linear_util.py:472\u001b[39m, in \u001b[36mcache..memoized_fun\u001b[39m\u001b[34m(fun, *args)\u001b[39m\n\u001b[32m 471\u001b[39m start = time.time()\n\u001b[32m--> \u001b[39m\u001b[32m472\u001b[39m ans = \u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 473\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m do_explain:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:1350\u001b[39m, in \u001b[36m_create_pjit_jaxpr\u001b[39m\u001b[34m(***failed resolving arguments***)\u001b[39m\n\u001b[32m 1349\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1350\u001b[39m jaxpr, global_out_avals, consts = \u001b[43mpe\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtrace_to_jaxpr_dynamic\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1352\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m config.debug_key_reuse.value:\n\u001b[32m 1353\u001b[39m \u001b[38;5;66;03m# Import here to avoid circular imports\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/profiler.py:354\u001b[39m, in \u001b[36mannotate_function..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 353\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m TraceAnnotation(name, **decorator_kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m354\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/interpreters/partial_eval.py:2337\u001b[39m, in \u001b[36mtrace_to_jaxpr_dynamic\u001b[39m\u001b[34m(fun, in_avals, keep_inputs, lower, auto_dce)\u001b[39m\n\u001b[32m 2336\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m core.set_current_trace(trace):\n\u001b[32m-> \u001b[39m\u001b[32m2337\u001b[39m ans = \u001b[43mfun\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcall_wrapped\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43min_tracers\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2338\u001b[39m _check_returned_jaxtypes(fun.debug_info, ans)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/linear_util.py:212\u001b[39m, in \u001b[36mWrappedFun.call_wrapped\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 211\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Calls the transformed function\"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m212\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mf_transformed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api_util.py:73\u001b[39m, in \u001b[36mflatten_fun\u001b[39m\u001b[34m(f, store, in_tree, *args_flat)\u001b[39m\n\u001b[32m 72\u001b[39m py_args, py_kwargs = tree_unflatten(in_tree, args_flat)\n\u001b[32m---> \u001b[39m\u001b[32m73\u001b[39m ans = \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43mpy_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mpy_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 74\u001b[39m ans, out_tree = tree_flatten(ans)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api_util.py:314\u001b[39m, in \u001b[36m_argnames_partial\u001b[39m\u001b[34m(_fun, _fixed_kwargs, *args, **dyn_kwargs)\u001b[39m\n\u001b[32m 313\u001b[39m kwargs = \u001b[38;5;28mdict\u001b[39m({k: v.val \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m _fixed_kwargs.val.items()}, **dyn_kwargs)\n\u001b[32m--> \u001b[39m\u001b[32m314\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api_util.py:288\u001b[39m, in \u001b[36m_argnums_partial\u001b[39m\u001b[34m(_fun, _dyn_argnums, _fixed_args, *dyn_args, **kwargs)\u001b[39m\n\u001b[32m 287\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mnext\u001b[39m(fixed_args_, sentinel) \u001b[38;5;129;01mis\u001b[39;00m sentinel\n\u001b[32m--> \u001b[39m\u001b[32m288\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/linear_util.py:397\u001b[39m, in \u001b[36m_get_result_paths_thunk\u001b[39m\u001b[34m(_fun, _store, *args, **kwargs)\u001b[39m\n\u001b[32m 395\u001b[39m \u001b[38;5;129m@transformation_with_aux2\u001b[39m\n\u001b[32m 396\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_get_result_paths_thunk\u001b[39m(_fun: Callable, _store: Store, *args, **kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m397\u001b[39m ans = \u001b[43m_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 398\u001b[39m result_paths = \u001b[38;5;28mtuple\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mresult\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m_clean_keystr_arg_names(path)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m path, _ \u001b[38;5;129;01min\u001b[39;00m generate_key_paths(ans))\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/equinox/_jit.py:55\u001b[39m, in \u001b[36m_filter_jit_cache..fun_wrapped\u001b[39m\u001b[34m(dynamic_donate, dynamic_nodonate, static)\u001b[39m\n\u001b[32m 54\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m dummy_arg \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m55\u001b[39m out = \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 56\u001b[39m dynamic_out, static_out = partition(out, is_array)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/mnt/Pollux/Sapienza/RSFinversion/diabayes/diabayes/solver.py:118\u001b[39m, in \u001b[36mODESolver._forward_wrapper\u001b[39m\u001b[34m(self, t, variables, args)\u001b[39m\n\u001b[32m 117\u001b[39m params, friction_constants, block_constants = args\n\u001b[32m--> \u001b[39m\u001b[32m118\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mforward_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 119\u001b[39m \u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m=\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 120\u001b[39m \u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 121\u001b[39m \u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m=\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 122\u001b[39m \u001b[43m \u001b[49m\u001b[43mfriction_constants\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfriction_constants\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 123\u001b[39m \u001b[43m \u001b[49m\u001b[43mblock_constants\u001b[49m\u001b[43m=\u001b[49m\u001b[43mblock_constants\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 124\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/equinox/_module/_prebuilt.py:81\u001b[39m, in \u001b[36mPartial.__call__\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Call the wrapped `self.func`.\u001b[39;00m\n\u001b[32m 70\u001b[39m \n\u001b[32m 71\u001b[39m \u001b[33;03m**Arguments:**\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 79\u001b[39m \u001b[33;03mThe result of the wrapped function.\u001b[39;00m\n\u001b[32m 80\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m81\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mkeywords\u001b[49m\u001b[43m)\u001b[49m\n", + " \u001b[31m[... skipping hidden 3 frame]\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:260\u001b[39m, in \u001b[36m_cpp_pjit..cache_miss\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 256\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mre-tracing function \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mjit_info.fun_sourceinfo\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m for \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 257\u001b[39m \u001b[33m\"\u001b[39m\u001b[33m`jit`, but \u001b[39m\u001b[33m'\u001b[39m\u001b[33mno_tracing\u001b[39m\u001b[33m'\u001b[39m\u001b[33m is set\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 259\u001b[39m outs, out_flat, out_tree, args_flat, jaxpr, executable, pgle_profiler = \\\n\u001b[32m--> \u001b[39m\u001b[32m260\u001b[39m \u001b[43m_python_pjit_helper\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjit_info\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 262\u001b[39m maybe_fastpath_data = _get_fastpath_data(\n\u001b[32m 263\u001b[39m executable, out_tree, args_flat, out_flat, jaxpr.effects, jaxpr.consts,\n\u001b[32m 264\u001b[39m jit_info.abstracted_axes, pgle_profiler)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:137\u001b[39m, in \u001b[36m_python_pjit_helper\u001b[39m\u001b[34m(fun, jit_info, *args, **kwargs)\u001b[39m\n\u001b[32m 136\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_python_pjit_helper\u001b[39m(fun: Callable, jit_info: PjitInfo, *args, **kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m137\u001b[39m p, args_flat = \u001b[43m_infer_params\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjit_info\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 139\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m arg \u001b[38;5;129;01min\u001b[39;00m args_flat:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:629\u001b[39m, in \u001b[36m_infer_params\u001b[39m\u001b[34m(fun, ji, args, kwargs)\u001b[39m\n\u001b[32m 628\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m629\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_infer_params_internal\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mji\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:653\u001b[39m, in \u001b[36m_infer_params_internal\u001b[39m\u001b[34m(fun, ji, args, kwargs)\u001b[39m\n\u001b[32m 652\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m entry.pjit_params \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m653\u001b[39m p, args_flat = \u001b[43m_infer_params_impl\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 654\u001b[39m \u001b[43m \u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mji\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mctx_mesh\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdbg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_avals\u001b[49m\u001b[43m=\u001b[49m\u001b[43mavals\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 655\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m p.params[\u001b[33m'\u001b[39m\u001b[33mjaxpr\u001b[39m\u001b[33m'\u001b[39m].jaxpr.is_high:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:552\u001b[39m, in \u001b[36m_infer_params_impl\u001b[39m\u001b[34m(***failed resolving arguments***)\u001b[39m\n\u001b[32m 547\u001b[39m in_shardings_flat, in_layouts_flat = _process_in_axis_resources(\n\u001b[32m 548\u001b[39m in_shardings_treedef, in_shardings_leaves,\n\u001b[32m 549\u001b[39m ji.in_layouts_treedef, ji.in_layouts_leaves,\n\u001b[32m 550\u001b[39m in_avals, in_tree, flat_fun.debug_info, device_or_backend_set, have_kwargs)\n\u001b[32m--> \u001b[39m\u001b[32m552\u001b[39m jaxpr, consts, jaxpr_for_top_jit, out_avals = \u001b[43m_create_pjit_jaxpr\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 553\u001b[39m \u001b[43m \u001b[49m\u001b[43mflat_fun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mIgnoreKey\u001b[49m\u001b[43m(\u001b[49m\u001b[43mji\u001b[49m\u001b[43m.\u001b[49m\u001b[43minline\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 554\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m config.mutable_array_checks.value:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/linear_util.py:472\u001b[39m, in \u001b[36mcache..memoized_fun\u001b[39m\u001b[34m(fun, *args)\u001b[39m\n\u001b[32m 471\u001b[39m start = time.time()\n\u001b[32m--> \u001b[39m\u001b[32m472\u001b[39m ans = \u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 473\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m do_explain:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:1350\u001b[39m, in \u001b[36m_create_pjit_jaxpr\u001b[39m\u001b[34m(***failed resolving arguments***)\u001b[39m\n\u001b[32m 1349\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1350\u001b[39m jaxpr, global_out_avals, consts = \u001b[43mpe\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtrace_to_jaxpr_dynamic\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1352\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m config.debug_key_reuse.value:\n\u001b[32m 1353\u001b[39m \u001b[38;5;66;03m# Import here to avoid circular imports\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/profiler.py:354\u001b[39m, in \u001b[36mannotate_function..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 353\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m TraceAnnotation(name, **decorator_kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m354\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/interpreters/partial_eval.py:2337\u001b[39m, in \u001b[36mtrace_to_jaxpr_dynamic\u001b[39m\u001b[34m(fun, in_avals, keep_inputs, lower, auto_dce)\u001b[39m\n\u001b[32m 2336\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m core.set_current_trace(trace):\n\u001b[32m-> \u001b[39m\u001b[32m2337\u001b[39m ans = \u001b[43mfun\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcall_wrapped\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43min_tracers\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2338\u001b[39m _check_returned_jaxtypes(fun.debug_info, ans)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/linear_util.py:212\u001b[39m, in \u001b[36mWrappedFun.call_wrapped\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 211\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Calls the transformed function\"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m212\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mf_transformed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api_util.py:73\u001b[39m, in \u001b[36mflatten_fun\u001b[39m\u001b[34m(f, store, in_tree, *args_flat)\u001b[39m\n\u001b[32m 72\u001b[39m py_args, py_kwargs = tree_unflatten(in_tree, args_flat)\n\u001b[32m---> \u001b[39m\u001b[32m73\u001b[39m ans = \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43mpy_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mpy_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 74\u001b[39m ans, out_tree = tree_flatten(ans)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api_util.py:314\u001b[39m, in \u001b[36m_argnames_partial\u001b[39m\u001b[34m(_fun, _fixed_kwargs, *args, **dyn_kwargs)\u001b[39m\n\u001b[32m 313\u001b[39m kwargs = \u001b[38;5;28mdict\u001b[39m({k: v.val \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m _fixed_kwargs.val.items()}, **dyn_kwargs)\n\u001b[32m--> \u001b[39m\u001b[32m314\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api_util.py:288\u001b[39m, in \u001b[36m_argnums_partial\u001b[39m\u001b[34m(_fun, _dyn_argnums, _fixed_args, *dyn_args, **kwargs)\u001b[39m\n\u001b[32m 287\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mnext\u001b[39m(fixed_args_, sentinel) \u001b[38;5;129;01mis\u001b[39;00m sentinel\n\u001b[32m--> \u001b[39m\u001b[32m288\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/linear_util.py:397\u001b[39m, in \u001b[36m_get_result_paths_thunk\u001b[39m\u001b[34m(_fun, _store, *args, **kwargs)\u001b[39m\n\u001b[32m 395\u001b[39m \u001b[38;5;129m@transformation_with_aux2\u001b[39m\n\u001b[32m 396\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_get_result_paths_thunk\u001b[39m(_fun: Callable, _store: Store, *args, **kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m397\u001b[39m ans = \u001b[43m_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 398\u001b[39m result_paths = \u001b[38;5;28mtuple\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mresult\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m_clean_keystr_arg_names(path)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m path, _ \u001b[38;5;129;01min\u001b[39;00m generate_key_paths(ans))\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/equinox/_jit.py:55\u001b[39m, in \u001b[36m_filter_jit_cache..fun_wrapped\u001b[39m\u001b[34m(dynamic_donate, dynamic_nodonate, static)\u001b[39m\n\u001b[32m 54\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m dummy_arg \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m55\u001b[39m out = \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 56\u001b[39m dynamic_out, static_out = partition(out, is_array)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/mnt/Pollux/Sapienza/RSFinversion/diabayes/diabayes/forward_models.py:251\u001b[39m, in \u001b[36mForward.__call__\u001b[39m\u001b[34m(self, t, variables, params, friction_constants, block_constants)\u001b[39m\n\u001b[32m 240\u001b[39m \u001b[38;5;129m@eqx\u001b[39m.filter_jit\n\u001b[32m 241\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__call__\u001b[39m(\n\u001b[32m 242\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 249\u001b[39m \u001b[38;5;66;03m# Calculate v and its partial derivatives with respect to\u001b[39;00m\n\u001b[32m 250\u001b[39m \u001b[38;5;66;03m# the variables (mu, state1, state2, ...)\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m251\u001b[39m v, v_derivs = \u001b[43meqx\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfilter_value_and_grad\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfriction_model\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 252\u001b[39m \u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfriction_constants\u001b[49m\n\u001b[32m 253\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 254\u001b[39m \u001b[38;5;66;03m# Rate of change of state variables\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/equinox/_ad.py:71\u001b[39m, in \u001b[36m_ValueAndGradWrapper.__call__\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 70\u001b[39m diff_x, nondiff_x = partition(x, is_inexact_array)\n\u001b[32m---> \u001b[39m\u001b[32m71\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfun_value_and_grad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdiff_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnondiff_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + " \u001b[31m[... skipping hidden 1 frame]\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api.py:479\u001b[39m, in \u001b[36mvalue_and_grad..value_and_grad_f\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 478\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_aux:\n\u001b[32m--> \u001b[39m\u001b[32m479\u001b[39m ans, vjp_py = \u001b[43m_vjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf_partial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43mdyn_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 480\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api.py:2143\u001b[39m, in \u001b[36m_vjp\u001b[39m\u001b[34m(fun, has_aux, *primals)\u001b[39m\n\u001b[32m 2142\u001b[39m flat_fun, out_tree = flatten_fun_nokwargs(fun, in_tree)\n\u001b[32m-> \u001b[39m\u001b[32m2143\u001b[39m out_primals, vjp = \u001b[43mad\u001b[49m\u001b[43m.\u001b[49m\u001b[43mvjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mflat_fun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprimals_flat\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2144\u001b[39m out_tree = out_tree()\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/interpreters/ad.py:309\u001b[39m, in \u001b[36mvjp\u001b[39m\u001b[34m(traceable, primals, has_aux)\u001b[39m\n\u001b[32m 308\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_aux:\n\u001b[32m--> \u001b[39m\u001b[32m309\u001b[39m out_primals, pvals, jaxpr, consts = \u001b[43mlinearize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtraceable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43mprimals\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 310\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/interpreters/ad.py:283\u001b[39m, in \u001b[36mlinearize\u001b[39m\u001b[34m(traceable, *primals, **kwargs)\u001b[39m\n\u001b[32m 282\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m config.use_direct_linearize.value:\n\u001b[32m--> \u001b[39m\u001b[32m283\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdirect_linearize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtraceable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprimals\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhas_aux\u001b[49m\u001b[43m=\u001b[49m\u001b[43mhas_aux\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 284\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_aux:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/interpreters/ad.py:256\u001b[39m, in \u001b[36mdirect_linearize\u001b[39m\u001b[34m(traceable, primals, kwargs, has_aux, tag)\u001b[39m\n\u001b[32m 255\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m256\u001b[39m ans = \u001b[43mtraceable\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcall_wrapped\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43mtracers\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 257\u001b[39m aux = \u001b[38;5;28;01mNone\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/linear_util.py:212\u001b[39m, in \u001b[36mWrappedFun.call_wrapped\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 211\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Calls the transformed function\"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m212\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mf_transformed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api_util.py:90\u001b[39m, in \u001b[36mflatten_fun_nokwargs\u001b[39m\u001b[34m(f, store, in_tree, *args_flat)\u001b[39m\n\u001b[32m 89\u001b[39m py_args = tree_unflatten(in_tree, args_flat)\n\u001b[32m---> \u001b[39m\u001b[32m90\u001b[39m ans = \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43mpy_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 91\u001b[39m ans, out_tree = tree_flatten(ans)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api_util.py:288\u001b[39m, in \u001b[36m_argnums_partial\u001b[39m\u001b[34m(_fun, _dyn_argnums, _fixed_args, *dyn_args, **kwargs)\u001b[39m\n\u001b[32m 287\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mnext\u001b[39m(fixed_args_, sentinel) \u001b[38;5;129;01mis\u001b[39;00m sentinel\n\u001b[32m--> \u001b[39m\u001b[32m288\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/linear_util.py:397\u001b[39m, in \u001b[36m_get_result_paths_thunk\u001b[39m\u001b[34m(_fun, _store, *args, **kwargs)\u001b[39m\n\u001b[32m 395\u001b[39m \u001b[38;5;129m@transformation_with_aux2\u001b[39m\n\u001b[32m 396\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_get_result_paths_thunk\u001b[39m(_fun: Callable, _store: Store, *args, **kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m397\u001b[39m ans = \u001b[43m_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 398\u001b[39m result_paths = \u001b[38;5;28mtuple\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mresult\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m_clean_keystr_arg_names(path)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m path, _ \u001b[38;5;129;01min\u001b[39;00m generate_key_paths(ans))\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/equinox/_ad.py:54\u001b[39m, in \u001b[36m_ValueAndGradWrapper.__call__..fun_value_and_grad\u001b[39m\u001b[34m(_diff_x, _nondiff_x, *_args, **_kwargs)\u001b[39m\n\u001b[32m 53\u001b[39m _x = combine(_diff_x, _nondiff_x)\n\u001b[32m---> \u001b[39m\u001b[32m54\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43m_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", + " \u001b[31m[... skipping hidden 3 frame]\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:260\u001b[39m, in \u001b[36m_cpp_pjit..cache_miss\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 256\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mre-tracing function \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mjit_info.fun_sourceinfo\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m for \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 257\u001b[39m \u001b[33m\"\u001b[39m\u001b[33m`jit`, but \u001b[39m\u001b[33m'\u001b[39m\u001b[33mno_tracing\u001b[39m\u001b[33m'\u001b[39m\u001b[33m is set\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 259\u001b[39m outs, out_flat, out_tree, args_flat, jaxpr, executable, pgle_profiler = \\\n\u001b[32m--> \u001b[39m\u001b[32m260\u001b[39m \u001b[43m_python_pjit_helper\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjit_info\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 262\u001b[39m maybe_fastpath_data = _get_fastpath_data(\n\u001b[32m 263\u001b[39m executable, out_tree, args_flat, out_flat, jaxpr.effects, jaxpr.consts,\n\u001b[32m 264\u001b[39m jit_info.abstracted_axes, pgle_profiler)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:137\u001b[39m, in \u001b[36m_python_pjit_helper\u001b[39m\u001b[34m(fun, jit_info, *args, **kwargs)\u001b[39m\n\u001b[32m 136\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_python_pjit_helper\u001b[39m(fun: Callable, jit_info: PjitInfo, *args, **kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m137\u001b[39m p, args_flat = \u001b[43m_infer_params\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjit_info\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 139\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m arg \u001b[38;5;129;01min\u001b[39;00m args_flat:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:629\u001b[39m, in \u001b[36m_infer_params\u001b[39m\u001b[34m(fun, ji, args, kwargs)\u001b[39m\n\u001b[32m 628\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m629\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_infer_params_internal\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mji\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:653\u001b[39m, in \u001b[36m_infer_params_internal\u001b[39m\u001b[34m(fun, ji, args, kwargs)\u001b[39m\n\u001b[32m 652\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m entry.pjit_params \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m653\u001b[39m p, args_flat = \u001b[43m_infer_params_impl\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 654\u001b[39m \u001b[43m \u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mji\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mctx_mesh\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdbg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_avals\u001b[49m\u001b[43m=\u001b[49m\u001b[43mavals\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 655\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m p.params[\u001b[33m'\u001b[39m\u001b[33mjaxpr\u001b[39m\u001b[33m'\u001b[39m].jaxpr.is_high:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:552\u001b[39m, in \u001b[36m_infer_params_impl\u001b[39m\u001b[34m(***failed resolving arguments***)\u001b[39m\n\u001b[32m 547\u001b[39m in_shardings_flat, in_layouts_flat = _process_in_axis_resources(\n\u001b[32m 548\u001b[39m in_shardings_treedef, in_shardings_leaves,\n\u001b[32m 549\u001b[39m ji.in_layouts_treedef, ji.in_layouts_leaves,\n\u001b[32m 550\u001b[39m in_avals, in_tree, flat_fun.debug_info, device_or_backend_set, have_kwargs)\n\u001b[32m--> \u001b[39m\u001b[32m552\u001b[39m jaxpr, consts, jaxpr_for_top_jit, out_avals = \u001b[43m_create_pjit_jaxpr\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 553\u001b[39m \u001b[43m \u001b[49m\u001b[43mflat_fun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mIgnoreKey\u001b[49m\u001b[43m(\u001b[49m\u001b[43mji\u001b[49m\u001b[43m.\u001b[49m\u001b[43minline\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 554\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m config.mutable_array_checks.value:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/linear_util.py:472\u001b[39m, in \u001b[36mcache..memoized_fun\u001b[39m\u001b[34m(fun, *args)\u001b[39m\n\u001b[32m 471\u001b[39m start = time.time()\n\u001b[32m--> \u001b[39m\u001b[32m472\u001b[39m ans = \u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 473\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m do_explain:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:1350\u001b[39m, in \u001b[36m_create_pjit_jaxpr\u001b[39m\u001b[34m(***failed resolving arguments***)\u001b[39m\n\u001b[32m 1349\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1350\u001b[39m jaxpr, global_out_avals, consts = \u001b[43mpe\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtrace_to_jaxpr_dynamic\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1352\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m config.debug_key_reuse.value:\n\u001b[32m 1353\u001b[39m \u001b[38;5;66;03m# Import here to avoid circular imports\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/profiler.py:354\u001b[39m, in \u001b[36mannotate_function..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 353\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m TraceAnnotation(name, **decorator_kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m354\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/interpreters/partial_eval.py:2337\u001b[39m, in \u001b[36mtrace_to_jaxpr_dynamic\u001b[39m\u001b[34m(fun, in_avals, keep_inputs, lower, auto_dce)\u001b[39m\n\u001b[32m 2336\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m core.set_current_trace(trace):\n\u001b[32m-> \u001b[39m\u001b[32m2337\u001b[39m ans = \u001b[43mfun\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcall_wrapped\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43min_tracers\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2338\u001b[39m _check_returned_jaxtypes(fun.debug_info, ans)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/linear_util.py:212\u001b[39m, in \u001b[36mWrappedFun.call_wrapped\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 211\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Calls the transformed function\"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m212\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mf_transformed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api_util.py:73\u001b[39m, in \u001b[36mflatten_fun\u001b[39m\u001b[34m(f, store, in_tree, *args_flat)\u001b[39m\n\u001b[32m 72\u001b[39m py_args, py_kwargs = tree_unflatten(in_tree, args_flat)\n\u001b[32m---> \u001b[39m\u001b[32m73\u001b[39m ans = \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43mpy_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mpy_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 74\u001b[39m ans, out_tree = tree_flatten(ans)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api_util.py:314\u001b[39m, in \u001b[36m_argnames_partial\u001b[39m\u001b[34m(_fun, _fixed_kwargs, *args, **dyn_kwargs)\u001b[39m\n\u001b[32m 313\u001b[39m kwargs = \u001b[38;5;28mdict\u001b[39m({k: v.val \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m _fixed_kwargs.val.items()}, **dyn_kwargs)\n\u001b[32m--> \u001b[39m\u001b[32m314\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api_util.py:288\u001b[39m, in \u001b[36m_argnums_partial\u001b[39m\u001b[34m(_fun, _dyn_argnums, _fixed_args, *dyn_args, **kwargs)\u001b[39m\n\u001b[32m 287\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mnext\u001b[39m(fixed_args_, sentinel) \u001b[38;5;129;01mis\u001b[39;00m sentinel\n\u001b[32m--> \u001b[39m\u001b[32m288\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/linear_util.py:397\u001b[39m, in \u001b[36m_get_result_paths_thunk\u001b[39m\u001b[34m(_fun, _store, *args, **kwargs)\u001b[39m\n\u001b[32m 395\u001b[39m \u001b[38;5;129m@transformation_with_aux2\u001b[39m\n\u001b[32m 396\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_get_result_paths_thunk\u001b[39m(_fun: Callable, _store: Store, *args, **kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m397\u001b[39m ans = \u001b[43m_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 398\u001b[39m result_paths = \u001b[38;5;28mtuple\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mresult\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m_clean_keystr_arg_names(path)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m path, _ \u001b[38;5;129;01min\u001b[39;00m generate_key_paths(ans))\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/equinox/_jit.py:55\u001b[39m, in \u001b[36m_filter_jit_cache..fun_wrapped\u001b[39m\u001b[34m(dynamic_donate, dynamic_nodonate, static)\u001b[39m\n\u001b[32m 54\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m dummy_arg \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m55\u001b[39m out = \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 56\u001b[39m dynamic_out, static_out = partition(out, is_array)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/mnt/Pollux/Sapienza/RSFinversion/diabayes/diabayes/forward_models.py:47\u001b[39m, in \u001b[36mrsf\u001b[39m\u001b[34m(variables, params, constants)\u001b[39m\n\u001b[32m 26\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33mr\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 27\u001b[39m \u001b[33;03mThe classical rate-and-state friction law, given by:\u001b[39;00m\n\u001b[32m 28\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 45\u001b[39m \u001b[33;03m The instantaneous slip rate in the same units as `v0`\u001b[39;00m\n\u001b[32m 46\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m47\u001b[39m Omega = \u001b[43mparams\u001b[49m\u001b[43m.\u001b[49m\u001b[43mb\u001b[49m * jnp.log(variables.theta * constants.v0 / params.Dc)\n\u001b[32m 48\u001b[39m v = constants.v0 * jnp.exp((variables.mu - constants.mu0 - Omega) / params.a)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/core.py:992\u001b[39m, in \u001b[36mTracer.__getattr__\u001b[39m\u001b[34m(self, name)\u001b[39m\n\u001b[32m 991\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[32m--> \u001b[39m\u001b[32m992\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[32m 993\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.\u001b[34m__class__\u001b[39m.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m has no attribute \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 994\u001b[39m ) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01merr\u001b[39;00m\n\u001b[32m 995\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "\u001b[31mAttributeError\u001b[39m: DynamicJaxprTracer has no attribute b", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/diffrax/_integrate.py:200\u001b[39m, in \u001b[36m_assert_term_compatible\u001b[39m\u001b[34m(t, y, args, terms, term_structure, contr_kwargs)\u001b[39m\n\u001b[32m 199\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m jax.numpy_dtype_promotion(\u001b[33m\"\u001b[39m\u001b[33mstandard\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m--> \u001b[39m\u001b[32m200\u001b[39m \u001b[43mjtu\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtree_map\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_check\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mterm_structure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mterms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontr_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 201\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 202\u001b[39m \u001b[38;5;66;03m# ValueError may also arise from mismatched tree structures\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/tree_util.py:361\u001b[39m, in \u001b[36mtree_map\u001b[39m\u001b[34m(f, tree, is_leaf, *rest)\u001b[39m\n\u001b[32m 360\u001b[39m all_leaves = [leaves] + [treedef.flatten_up_to(r) \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m rest]\n\u001b[32m--> \u001b[39m\u001b[32m361\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtreedef\u001b[49m\u001b[43m.\u001b[49m\u001b[43munflatten\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43mxs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mxs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mzip\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43mall_leaves\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/tree_util.py:361\u001b[39m, in \u001b[36m\u001b[39m\u001b[34m(.0)\u001b[39m\n\u001b[32m 360\u001b[39m all_leaves = [leaves] + [treedef.flatten_up_to(r) \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m rest]\n\u001b[32m--> \u001b[39m\u001b[32m361\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m treedef.unflatten(\u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43mxs\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m xs \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(*all_leaves))\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/diffrax/_integrate.py:172\u001b[39m, in \u001b[36m_assert_term_compatible.._check\u001b[39m\u001b[34m(term_cls, term, term_contr_kwargs, yi)\u001b[39m\n\u001b[32m 171\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m--> \u001b[39m\u001b[32m172\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mError while tracing \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mterm\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.vf: \u001b[39m\u001b[33m\"\u001b[39m + \u001b[38;5;28mstr\u001b[39m(e))\n\u001b[32m 173\u001b[39m vf_type_compatible = eqx.filter_eval_shape(\n\u001b[32m 174\u001b[39m better_isinstance, vf_type, vf_type_expected\n\u001b[32m 175\u001b[39m )\n", + "\u001b[31mValueError\u001b[39m: Error while tracing ODETerm(\n vector_field=Partial(\n func=_JitWrapper(\n fn='ODESolver._forward_wrapper',\n filter_warning=False,\n donate_first=False,\n donate_rest=False\n ),\n args=(,),\n keywords={}\n )\n).vf: DynamicJaxprTracer has no attribute b", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[15]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m samples, sample_results = \u001b[43mbayesian_result\u001b[49m\u001b[43m.\u001b[49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[43msolver\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconstants\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mblock_constants\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnsamples\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m100\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 3\u001b[39m plt.close(\u001b[33m\"\u001b[39m\u001b[33mall\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 4\u001b[39m fig, ax = plt.subplots(figsize=(\u001b[32m9\u001b[39m, \u001b[32m5\u001b[39m), constrained_layout=\u001b[38;5;28;01mTrue\u001b[39;00m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/mnt/Pollux/Sapienza/RSFinversion/diabayes/diabayes/typedefs.py:426\u001b[39m, in \u001b[36mBayesianSolution.sample\u001b[39m\u001b[34m(self, solver, y0, t, friction_constants, block_constants, nsamples, rng)\u001b[39m\n\u001b[32m 423\u001b[39m key, split_key = jr.split(key)\n\u001b[32m 424\u001b[39m samples = jr.choice(split_key, \u001b[38;5;28mself\u001b[39m.final_state, shape=(nsamples,), axis=\u001b[32m0\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m426\u001b[39m sample_results = \u001b[43mjax\u001b[49m\u001b[43m.\u001b[49m\u001b[43mvmap\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 427\u001b[39m \u001b[43m \u001b[49m\u001b[43msolver\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_forward_wrapper_SVI\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_axes\u001b[49m\u001b[43m=\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout_axes\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m0\u001b[39;49m\n\u001b[32m 428\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43my0\u001b[49m\u001b[43m.\u001b[49m\u001b[43mto_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msamples\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfriction_constants\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mblock_constants\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 429\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m samples, sample_results\n", + " \u001b[31m[... skipping hidden 7 frame]\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/equinox/_module/_prebuilt.py:81\u001b[39m, in \u001b[36mPartial.__call__\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 68\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, *args: Any, **kwargs: Any) -> _Return:\n\u001b[32m 69\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Call the wrapped `self.func`.\u001b[39;00m\n\u001b[32m 70\u001b[39m \n\u001b[32m 71\u001b[39m \u001b[33;03m **Arguments:**\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 79\u001b[39m \u001b[33;03m The result of the wrapped function.\u001b[39;00m\n\u001b[32m 80\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m81\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mkeywords\u001b[49m\u001b[43m)\u001b[49m\n", + " \u001b[31m[... skipping hidden 18 frame]\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/mnt/Pollux/Sapienza/RSFinversion/diabayes/diabayes/solver.py:137\u001b[39m, in \u001b[36mODESolver._forward_wrapper_SVI\u001b[39m\u001b[34m(self, params, y0, t, friction_constants, block_constants)\u001b[39m\n\u001b[32m 126\u001b[39m \u001b[38;5;129m@eqx\u001b[39m.filter_jit\n\u001b[32m 127\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_forward_wrapper_SVI\u001b[39m(\n\u001b[32m 128\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 135\u001b[39m \u001b[38;5;66;03m# TODO: previously y0 and params were arrays, not\u001b[39;00m\n\u001b[32m 136\u001b[39m \u001b[38;5;66;03m# PyTrees. Does this logic still work?\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m137\u001b[39m result = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_solve_forward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 138\u001b[39m \u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m=\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 139\u001b[39m \u001b[43m \u001b[49m\u001b[43my0\u001b[49m\u001b[43m=\u001b[49m\u001b[43my0\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 140\u001b[39m \u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m=\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 141\u001b[39m \u001b[43m \u001b[49m\u001b[43mfriction_constants\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfriction_constants\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 142\u001b[39m \u001b[43m \u001b[49m\u001b[43mblock_constants\u001b[49m\u001b[43m=\u001b[49m\u001b[43mblock_constants\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 143\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 145\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 146\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m result.ys \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/equinox/_module/_prebuilt.py:81\u001b[39m, in \u001b[36mPartial.__call__\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 68\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, *args: Any, **kwargs: Any) -> _Return:\n\u001b[32m 69\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Call the wrapped `self.func`.\u001b[39;00m\n\u001b[32m 70\u001b[39m \n\u001b[32m 71\u001b[39m \u001b[33;03m **Arguments:**\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 79\u001b[39m \u001b[33;03m The result of the wrapped function.\u001b[39;00m\n\u001b[32m 80\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m81\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mkeywords\u001b[49m\u001b[43m)\u001b[49m\n", + " \u001b[31m[... skipping hidden 18 frame]\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/mnt/Pollux/Sapienza/RSFinversion/diabayes/diabayes/solver.py:173\u001b[39m, in \u001b[36mODESolver._solve_forward\u001b[39m\u001b[34m(self, t, y0, params, friction_constants, block_constants, adjoint)\u001b[39m\n\u001b[32m 170\u001b[39m adjoint = dfx.RecursiveCheckpointAdjoint(checkpoints=\u001b[38;5;28mself\u001b[39m.checkpoints)\n\u001b[32m 171\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(adjoint, dfx.AbstractAdjoint)\n\u001b[32m--> \u001b[39m\u001b[32m173\u001b[39m sol = \u001b[43mdfx\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdiffeqsolve\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 174\u001b[39m \u001b[43m \u001b[49m\u001b[43mterms\u001b[49m\u001b[43m=\u001b[49m\u001b[43mterm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 175\u001b[39m \u001b[43m \u001b[49m\u001b[43msolver\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdfx\u001b[49m\u001b[43m.\u001b[49m\u001b[43mTsit5\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 176\u001b[39m \u001b[43m \u001b[49m\u001b[43mt0\u001b[49m\u001b[43m=\u001b[49m\u001b[43mt0\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 177\u001b[39m \u001b[43m \u001b[49m\u001b[43mt1\u001b[49m\u001b[43m=\u001b[49m\u001b[43mt1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 178\u001b[39m \u001b[43m \u001b[49m\u001b[43mdt0\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdt0\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 179\u001b[39m \u001b[43m \u001b[49m\u001b[43msaveat\u001b[49m\u001b[43m=\u001b[49m\u001b[43msaveat\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 180\u001b[39m \u001b[43m \u001b[49m\u001b[43my0\u001b[49m\u001b[43m=\u001b[49m\u001b[43my0\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 181\u001b[39m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m=\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 182\u001b[39m \u001b[43m \u001b[49m\u001b[43mstepsize_controller\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcontroller\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 183\u001b[39m \u001b[43m \u001b[49m\u001b[43madjoint\u001b[49m\u001b[43m=\u001b[49m\u001b[43madjoint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 184\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 186\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m sol \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 188\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m sol\n", + " \u001b[31m[... skipping hidden 18 frame]\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/diffrax/_integrate.py:1103\u001b[39m, in \u001b[36mdiffeqsolve\u001b[39m\u001b[34m(terms, solver, t0, t1, dt0, y0, args, saveat, stepsize_controller, adjoint, event, max_steps, throw, progress_meter, solver_state, controller_state, made_jump, discrete_terminating_event)\u001b[39m\n\u001b[32m 1100\u001b[39m terms = MultiTerm(*terms)\n\u001b[32m 1102\u001b[39m \u001b[38;5;66;03m# Error checking for term compatibility\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1103\u001b[39m \u001b[43m_assert_term_compatible\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1104\u001b[39m \u001b[43m \u001b[49m\u001b[43mt0\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1105\u001b[39m \u001b[43m \u001b[49m\u001b[43my0\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1106\u001b[39m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1107\u001b[39m \u001b[43m \u001b[49m\u001b[43mterms\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1108\u001b[39m \u001b[43m \u001b[49m\u001b[43msolver\u001b[49m\u001b[43m.\u001b[49m\u001b[43mterm_structure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1109\u001b[39m \u001b[43m \u001b[49m\u001b[43msolver\u001b[49m\u001b[43m.\u001b[49m\u001b[43mterm_compatible_contr_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1110\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1112\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_sde(terms):\n\u001b[32m 1113\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(solver, (AbstractItoSolver, AbstractStratonovichSolver)):\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/diffrax/_integrate.py:205\u001b[39m, in \u001b[36m_assert_term_compatible\u001b[39m\u001b[34m(t, y, args, terms, term_structure, contr_kwargs)\u001b[39m\n\u001b[32m 203\u001b[39m pretty_term = wl.pformat(terms)\n\u001b[32m 204\u001b[39m pretty_expected = wl.pformat(term_structure)\n\u001b[32m--> \u001b[39m\u001b[32m205\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 206\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mTerms are not compatible with solver! Got:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mpretty_term\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33mbut expected:\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 207\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mpretty_expected\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33mNote that terms are checked recursively: if you \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 208\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mscroll up you may find a root-cause error that is more specific.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 209\u001b[39m ) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01me\u001b[39;00m\n", + "\u001b[31mValueError\u001b[39m: Terms are not compatible with solver! Got:\nODETerm(\n vector_field=Partial(\n func=_JitWrapper(\n fn='ODESolver._forward_wrapper',\n filter_warning=False,\n donate_first=False,\n donate_rest=False\n ),\n args=(,),\n keywords={}\n )\n)\nbut expected:\ndiffrax.AbstractTerm\nNote that terms are checked recursively: if you scroll up you may find a root-cause error that is more specific." + ] } ], "source": [ @@ -730,6 +816,14 @@ "ax.set_xlabel(\"Time [s]\")\n", "ax.set_ylabel(\"Friction [-]\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "650c36d8", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/test/aux.py b/test/aux.py index eded549..fd2a2c4 100644 --- a/test/aux.py +++ b/test/aux.py @@ -20,7 +20,7 @@ def init_params(): state_obj = StateDict(keys=("theta",), vals=jnp.atleast_1d(theta0)) variables = db.Variables(mu=jnp.atleast_1d(mu0), state=state_obj) - params = db.RSFParams(a=a, b=b, Dc=Dc) + params = db.RSFParams(*jnp.array([a, b, Dc])[:, None]) constants = db.RSFConstants(v0=v0, mu0=mu0) block_constants = db.SpringBlockConstants(k=k, v_lp=v1) diff --git a/test/test_typedefs.py b/test/test_typedefs.py index 0b83bd0..c5611f3 100644 --- a/test/test_typedefs.py +++ b/test/test_typedefs.py @@ -26,33 +26,33 @@ def test_basic_containers(self): x2.pop("mu") assert tuple(x2.keys()) == variables3.state.keys - # def test_SVI_containers(self): + def test_SVI_containers(self): - # import jax.random as jr + import jax.random as jr - # key = jr.PRNGKey(42) + key = jr.PRNGKey(42) - # # Test Particles + # Test Particles - # key, split_key = jr.split(key) - # Nparticles = 100 - # loc = jnp.array([1.0, 2.0, 3.0]) - # scale = jnp.ones(3) - # particles = RSFParticles.generate(Nparticles, loc, scale, split_key) + key, split_key = jr.split(key) + Nparticles = 100 + loc = jnp.array([1.0, 2.0, 3.0]) + scale = jnp.ones(3) + particles = RSFParams.generate(Nparticles, loc, scale, split_key) - # assert len(particles) == Nparticles + assert len(particles) == Nparticles - # x = particles.to_array() - # assert x.shape[1] == len(loc) + x = particles.to_array() + assert x.shape[0] == len(loc) - # rtol = 2 / jnp.sqrt(Nparticles) - # assert jnp.allclose(x.mean(axis=0), loc, rtol=rtol) - # assert jnp.allclose(x.std(axis=0), scale, rtol=rtol) + rtol = 2 / jnp.sqrt(Nparticles) + assert jnp.allclose(x.mean(axis=1), loc, rtol=rtol) + assert jnp.allclose(x.std(axis=1), scale, rtol=rtol) - # assert jnp.allclose(x, RSFParticles.from_array(x).to_array()) + assert jnp.allclose(x, RSFParams.from_array(x).to_array()) - # # Test Chains + # Test Chains - # pass + pass # def test_Bayesian_statistics(self): ... From 4b50462560107d22812e28dfcc90cbc1e1555a6c Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Tue, 26 Aug 2025 17:16:12 +0200 Subject: [PATCH 50/56] SVI and everything finally working again... --- diabayes/SVI.py | 2 +- diabayes/solver.py | 4 +- diabayes/typedefs.py | 15 ++- examples/simple_inversion.ipynb | 210 +++++++++----------------------- test/aux.py | 2 +- test/test_typedefs.py | 2 +- 6 files changed, 72 insertions(+), 163 deletions(-) diff --git a/diabayes/SVI.py b/diabayes/SVI.py index 55e0635..9b39e10 100644 --- a/diabayes/SVI.py +++ b/diabayes/SVI.py @@ -91,7 +91,7 @@ def _log_likelihood( # Transform the log_params by taking exponential params = type(log_params).from_array(jnp.exp(log_params.to_array())) # Forward pass to get friction curve - mu_hat = forward_fn(params).mu + mu_hat = jnp.squeeze(forward_fn(params).mu) # Log-likelihood of residuals p = -(jnp.square(mu_obs - mu_hat).mean() / (2 * noise_std**2)) return p diff --git a/diabayes/solver.py b/diabayes/solver.py index 5fa110f..f17b75c 100644 --- a/diabayes/solver.py +++ b/diabayes/solver.py @@ -132,8 +132,6 @@ def _forward_wrapper_SVI( friction_constants: _Constants, block_constants: _BlockConstants, ) -> Variables: - # TODO: previously y0 and params were arrays, not - # PyTrees. Does this logic still work? result = self._solve_forward( t=t, y0=y0, @@ -277,7 +275,7 @@ def bayesian_inversion( # Instantiate optimiser opt = optax.adam(learning_rate=self.learning_rate) - opt_state = opt.init(log_particles) + opt_state = opt.init(log_particles) # type:ignore forward_fn = partial( self._forward_wrapper_SVI, diff --git a/diabayes/typedefs.py b/diabayes/typedefs.py index 5dd4ed0..a8e72fe 100644 --- a/diabayes/typedefs.py +++ b/diabayes/typedefs.py @@ -282,6 +282,7 @@ class CNSStatistics(ParamStatistics): @dcs.dataclass class BayesianSolution: + log_params: _Params chains: Chains log_likelihood: Float[Array, "Nsteps"] nan_count: Float[Array, "Nsteps"] @@ -289,15 +290,17 @@ class BayesianSolution: def __init__( self, - x: Params, + log_params: _Params, log_likelihood: Float[Array, "Nsteps"], nan_count: Float[Array, "Nsteps"], ): - chains = x.to_array().transpose(1, 2, 0) + self.log_params = log_params + chains = log_params.to_array().transpose(1, 2, 0) self.chains = jnp.exp(chains) self.final_state = jnp.exp(chains[-1]) self.log_likelihood = log_likelihood self.nan_count = nan_count + # TODO: need to generalise this... self.statistics = RSFStatistics.from_state(self.final_state) def plot_convergence(self): @@ -421,9 +424,11 @@ def sample( key = rng key, split_key = jr.split(key) - samples = jr.choice(split_key, self.final_state, shape=(nsamples,), axis=0) + inds = jr.choice(split_key, jnp.arange(self.chains.shape[1]), shape=(nsamples,)) + log_samples = self.log_params[-1][inds] + samples = type(log_samples)(*jnp.exp(log_samples.to_array())) sample_results = jax.vmap( - solver._forward_wrapper_SVI, in_axes=(None, 0, None, None, None), out_axes=0 - )(y0.to_array(), samples, t, friction_constants, block_constants) + solver._forward_wrapper_SVI, in_axes=(0, None, None, None, None), out_axes=0 + )(samples, y0, t, friction_constants, block_constants) return samples, sample_results diff --git a/examples/simple_inversion.ipynb b/examples/simple_inversion.ipynb index b76bd45..0603cb2 100644 --- a/examples/simple_inversion.ipynb +++ b/examples/simple_inversion.ipynb @@ -156,7 +156,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a59aaf45f11f4877a52b07e869cdecef", + "model_id": "29ba954137114c05bf9cb586b2311d82", "version_major": 2, "version_minor": 0 }, @@ -297,7 +297,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 8, @@ -307,7 +307,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e28f8ad8e00f41f2ad6ca84c057dad14", + "model_id": "9ebb733530bc4535a97dbb2ecf5c9e2d", "version_major": 2, "version_minor": 0 }, @@ -401,24 +401,24 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "1da6bb44", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "51bcc146fbe241f9bddc6261f94b7ccc", + "model_id": "2bb050cfb54a45f9a14b5f9af80434fe", "version_major": 2, "version_minor": 0 }, @@ -459,7 +459,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "7ca97ff1", "metadata": {}, "outputs": [ @@ -526,14 +526,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "6a9fbf61", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "47957f8ca97f43cfacc9dc56845af50a", + "model_id": "78e669728de540ef8f000946533bda71", "version_major": 2, "version_minor": 0 }, @@ -556,11 +556,11 @@ "Number of parameters:\t3 ('a', 'b', 'Dc')\n", "\n", "\t\ta\t\tb\t\tDc\n", - "mean\t\t6.25e-03\t4.77e-03\t2.46e-05\n", - "median\t\t6.05e-03\t4.57e-03\t2.53e-05\n", - "std\t\t9.05e-04\t9.88e-04\t6.24e-06\n", - "q5\t\t5.05e-03\t3.54e-03\t1.30e-05\n", - "q95\t\t7.93e-03\t6.69e-03\t3.33e-05\n", + "mean\t\t9.83e-03\t8.82e-03\t1.02e-05\n", + "median\t\t9.84e-03\t8.82e-03\t1.01e-05\n", + "std\t\t6.24e-04\t7.02e-04\t1.33e-06\n", + "q5\t\t8.79e-03\t7.67e-03\t8.22e-06\n", + "q95\t\t1.08e-02\t9.99e-03\t1.26e-05\n", "\n", "------------------------------------------------------------\n" ] @@ -589,25 +589,25 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "0e54cb25", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cadb4af6b0f44e7abd71f8a7ed63eb29", + "model_id": "50647692fe564379bbfdb8ec80c3b073", "version_major": 2, "version_minor": 0 }, - "image/png": 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", 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", 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", 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Got:\nODETerm(\n vector_field=Partial(\n func=_JitWrapper(\n fn='ODESolver._forward_wrapper',\n filter_warning=False,\n donate_first=False,\n donate_rest=False\n ),\n args=(,),\n keywords={}\n )\n)\nbut expected:\ndiffrax.AbstractTerm\nNote that terms are checked recursively: if you scroll up you may find a root-cause error that is more specific.", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/core.py:990\u001b[39m, in \u001b[36mTracer.__getattr__\u001b[39m\u001b[34m(self, name)\u001b[39m\n\u001b[32m 989\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m990\u001b[39m attr = \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m.aval, name)\n\u001b[32m 991\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", - "\u001b[31mAttributeError\u001b[39m: 'ShapedArray' object has no attribute 'b'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/diffrax/_integrate.py:170\u001b[39m, in \u001b[36m_assert_term_compatible.._check\u001b[39m\u001b[34m(term_cls, term, term_contr_kwargs, yi)\u001b[39m\n\u001b[32m 169\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m170\u001b[39m vf_type = \u001b[43meqx\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfilter_eval_shape\u001b[49m\u001b[43m(\u001b[49m\u001b[43mterm\u001b[49m\u001b[43m.\u001b[49m\u001b[43mvf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43myi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 171\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/equinox/_eval_shape.py:38\u001b[39m, in \u001b[36mfilter_eval_shape\u001b[39m\u001b[34m(fun, *args, **kwargs)\u001b[39m\n\u001b[32m 37\u001b[39m dynamic, static = partition((fun, args, kwargs), _filter)\n\u001b[32m---> \u001b[39m\u001b[32m38\u001b[39m dynamic_out, static_out = \u001b[43mjax\u001b[49m\u001b[43m.\u001b[49m\u001b[43meval_shape\u001b[49m\u001b[43m(\u001b[49m\u001b[43mft\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpartial\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_fn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstatic\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdynamic\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 39\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m combine(dynamic_out, static_out.value)\n", - " \u001b[31m[... skipping hidden 1 frame]\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api.py:2848\u001b[39m, in \u001b[36meval_shape\u001b[39m\u001b[34m(fun, *args, **kwargs)\u001b[39m\n\u001b[32m 2847\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m: fun = partial(fun)\n\u001b[32m-> \u001b[39m\u001b[32m2848\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mjit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtrace\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m.eval_shape()\n", - " \u001b[31m[... skipping hidden 1 frame]\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:300\u001b[39m, in \u001b[36mjit_trace\u001b[39m\u001b[34m(jit_func, *args, **kwargs)\u001b[39m\n\u001b[32m 298\u001b[39m \u001b[38;5;129m@api_boundary\u001b[39m\n\u001b[32m 299\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mjit_trace\u001b[39m(jit_func, *args, **kwargs) -> stages.Traced:\n\u001b[32m--> \u001b[39m\u001b[32m300\u001b[39m p, args_flat = \u001b[43m_infer_params\u001b[49m\u001b[43m(\u001b[49m\u001b[43mjit_func\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_fun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjit_func\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_jit_info\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 301\u001b[39m donate_argnums = \u001b[38;5;28mtuple\u001b[39m(i \u001b[38;5;28;01mfor\u001b[39;00m i, d \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(p.params[\u001b[33m'\u001b[39m\u001b[33mdonated_invars\u001b[39m\u001b[33m'\u001b[39m]) \u001b[38;5;28;01mif\u001b[39;00m d)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:629\u001b[39m, in \u001b[36m_infer_params\u001b[39m\u001b[34m(fun, ji, args, kwargs)\u001b[39m\n\u001b[32m 628\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m629\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_infer_params_internal\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mji\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:653\u001b[39m, in \u001b[36m_infer_params_internal\u001b[39m\u001b[34m(fun, ji, args, kwargs)\u001b[39m\n\u001b[32m 652\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m entry.pjit_params \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m653\u001b[39m p, args_flat = \u001b[43m_infer_params_impl\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 654\u001b[39m \u001b[43m \u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mji\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mctx_mesh\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdbg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_avals\u001b[49m\u001b[43m=\u001b[49m\u001b[43mavals\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 655\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m p.params[\u001b[33m'\u001b[39m\u001b[33mjaxpr\u001b[39m\u001b[33m'\u001b[39m].jaxpr.is_high:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:552\u001b[39m, in \u001b[36m_infer_params_impl\u001b[39m\u001b[34m(***failed resolving arguments***)\u001b[39m\n\u001b[32m 547\u001b[39m in_shardings_flat, in_layouts_flat = _process_in_axis_resources(\n\u001b[32m 548\u001b[39m in_shardings_treedef, in_shardings_leaves,\n\u001b[32m 549\u001b[39m ji.in_layouts_treedef, ji.in_layouts_leaves,\n\u001b[32m 550\u001b[39m in_avals, in_tree, flat_fun.debug_info, device_or_backend_set, have_kwargs)\n\u001b[32m--> \u001b[39m\u001b[32m552\u001b[39m jaxpr, consts, jaxpr_for_top_jit, out_avals = \u001b[43m_create_pjit_jaxpr\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 553\u001b[39m \u001b[43m \u001b[49m\u001b[43mflat_fun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mIgnoreKey\u001b[49m\u001b[43m(\u001b[49m\u001b[43mji\u001b[49m\u001b[43m.\u001b[49m\u001b[43minline\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 554\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m config.mutable_array_checks.value:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/linear_util.py:472\u001b[39m, in \u001b[36mcache..memoized_fun\u001b[39m\u001b[34m(fun, *args)\u001b[39m\n\u001b[32m 471\u001b[39m start = time.time()\n\u001b[32m--> \u001b[39m\u001b[32m472\u001b[39m ans = \u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 473\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m do_explain:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:1350\u001b[39m, in \u001b[36m_create_pjit_jaxpr\u001b[39m\u001b[34m(***failed resolving arguments***)\u001b[39m\n\u001b[32m 1349\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1350\u001b[39m jaxpr, global_out_avals, consts = \u001b[43mpe\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtrace_to_jaxpr_dynamic\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1352\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m config.debug_key_reuse.value:\n\u001b[32m 1353\u001b[39m \u001b[38;5;66;03m# Import here to avoid circular imports\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/profiler.py:354\u001b[39m, in \u001b[36mannotate_function..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 353\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m TraceAnnotation(name, **decorator_kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m354\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/interpreters/partial_eval.py:2337\u001b[39m, in \u001b[36mtrace_to_jaxpr_dynamic\u001b[39m\u001b[34m(fun, in_avals, keep_inputs, lower, auto_dce)\u001b[39m\n\u001b[32m 2336\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m core.set_current_trace(trace):\n\u001b[32m-> \u001b[39m\u001b[32m2337\u001b[39m ans = \u001b[43mfun\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcall_wrapped\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43min_tracers\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2338\u001b[39m _check_returned_jaxtypes(fun.debug_info, ans)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/linear_util.py:212\u001b[39m, in \u001b[36mWrappedFun.call_wrapped\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 211\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Calls the transformed function\"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m212\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mf_transformed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api_util.py:73\u001b[39m, in \u001b[36mflatten_fun\u001b[39m\u001b[34m(f, store, in_tree, *args_flat)\u001b[39m\n\u001b[32m 72\u001b[39m py_args, py_kwargs = tree_unflatten(in_tree, args_flat)\n\u001b[32m---> \u001b[39m\u001b[32m73\u001b[39m ans = \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43mpy_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mpy_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 74\u001b[39m ans, out_tree = tree_flatten(ans)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/linear_util.py:397\u001b[39m, in \u001b[36m_get_result_paths_thunk\u001b[39m\u001b[34m(_fun, _store, *args, **kwargs)\u001b[39m\n\u001b[32m 395\u001b[39m \u001b[38;5;129m@transformation_with_aux2\u001b[39m\n\u001b[32m 396\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_get_result_paths_thunk\u001b[39m(_fun: Callable, _store: Store, *args, **kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m397\u001b[39m ans = \u001b[43m_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 398\u001b[39m result_paths = \u001b[38;5;28mtuple\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mresult\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m_clean_keystr_arg_names(path)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m path, _ \u001b[38;5;129;01min\u001b[39;00m generate_key_paths(ans))\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/equinox/_eval_shape.py:33\u001b[39m, in \u001b[36mfilter_eval_shape.._fn\u001b[39m\u001b[34m(_static, _dynamic)\u001b[39m\n\u001b[32m 32\u001b[39m _fun, _args, _kwargs = combine(_static, _dynamic)\n\u001b[32m---> \u001b[39m\u001b[32m33\u001b[39m _out = \u001b[43m_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43m_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 34\u001b[39m _dynamic_out, _static_out = partition(_out, _filter)\n", - " \u001b[31m[... skipping hidden 1 frame]\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/diffrax/_term.py:194\u001b[39m, in \u001b[36mODETerm.vf\u001b[39m\u001b[34m(self, t, y, args)\u001b[39m\n\u001b[32m 193\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mvf\u001b[39m(\u001b[38;5;28mself\u001b[39m, t: RealScalarLike, y: Y, args: Args) -> _VF:\n\u001b[32m--> \u001b[39m\u001b[32m194\u001b[39m out = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mvector_field\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 195\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m jtu.tree_structure(out) != jtu.tree_structure(y):\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/equinox/_module/_prebuilt.py:81\u001b[39m, in \u001b[36mPartial.__call__\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Call the wrapped `self.func`.\u001b[39;00m\n\u001b[32m 70\u001b[39m \n\u001b[32m 71\u001b[39m \u001b[33;03m**Arguments:**\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 79\u001b[39m \u001b[33;03mThe result of the wrapped function.\u001b[39;00m\n\u001b[32m 80\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m81\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mkeywords\u001b[49m\u001b[43m)\u001b[49m\n", - " \u001b[31m[... skipping hidden 3 frame]\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:260\u001b[39m, in \u001b[36m_cpp_pjit..cache_miss\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 256\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mre-tracing function \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mjit_info.fun_sourceinfo\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m for \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 257\u001b[39m \u001b[33m\"\u001b[39m\u001b[33m`jit`, but \u001b[39m\u001b[33m'\u001b[39m\u001b[33mno_tracing\u001b[39m\u001b[33m'\u001b[39m\u001b[33m is set\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 259\u001b[39m outs, out_flat, out_tree, args_flat, jaxpr, executable, pgle_profiler = \\\n\u001b[32m--> \u001b[39m\u001b[32m260\u001b[39m \u001b[43m_python_pjit_helper\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjit_info\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 262\u001b[39m maybe_fastpath_data = _get_fastpath_data(\n\u001b[32m 263\u001b[39m executable, out_tree, args_flat, out_flat, jaxpr.effects, jaxpr.consts,\n\u001b[32m 264\u001b[39m jit_info.abstracted_axes, pgle_profiler)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:137\u001b[39m, in \u001b[36m_python_pjit_helper\u001b[39m\u001b[34m(fun, jit_info, *args, **kwargs)\u001b[39m\n\u001b[32m 136\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_python_pjit_helper\u001b[39m(fun: Callable, jit_info: PjitInfo, *args, **kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m137\u001b[39m p, args_flat = \u001b[43m_infer_params\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjit_info\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 139\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m arg \u001b[38;5;129;01min\u001b[39;00m args_flat:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:629\u001b[39m, in \u001b[36m_infer_params\u001b[39m\u001b[34m(fun, ji, args, kwargs)\u001b[39m\n\u001b[32m 628\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m629\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_infer_params_internal\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mji\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:653\u001b[39m, in \u001b[36m_infer_params_internal\u001b[39m\u001b[34m(fun, ji, args, kwargs)\u001b[39m\n\u001b[32m 652\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m entry.pjit_params \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m653\u001b[39m p, args_flat = \u001b[43m_infer_params_impl\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 654\u001b[39m \u001b[43m \u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mji\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mctx_mesh\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdbg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_avals\u001b[49m\u001b[43m=\u001b[49m\u001b[43mavals\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 655\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m p.params[\u001b[33m'\u001b[39m\u001b[33mjaxpr\u001b[39m\u001b[33m'\u001b[39m].jaxpr.is_high:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:552\u001b[39m, in \u001b[36m_infer_params_impl\u001b[39m\u001b[34m(***failed resolving arguments***)\u001b[39m\n\u001b[32m 547\u001b[39m in_shardings_flat, in_layouts_flat = _process_in_axis_resources(\n\u001b[32m 548\u001b[39m in_shardings_treedef, in_shardings_leaves,\n\u001b[32m 549\u001b[39m ji.in_layouts_treedef, ji.in_layouts_leaves,\n\u001b[32m 550\u001b[39m in_avals, in_tree, flat_fun.debug_info, device_or_backend_set, have_kwargs)\n\u001b[32m--> \u001b[39m\u001b[32m552\u001b[39m jaxpr, consts, jaxpr_for_top_jit, out_avals = \u001b[43m_create_pjit_jaxpr\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 553\u001b[39m \u001b[43m \u001b[49m\u001b[43mflat_fun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mIgnoreKey\u001b[49m\u001b[43m(\u001b[49m\u001b[43mji\u001b[49m\u001b[43m.\u001b[49m\u001b[43minline\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 554\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m config.mutable_array_checks.value:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/linear_util.py:472\u001b[39m, in \u001b[36mcache..memoized_fun\u001b[39m\u001b[34m(fun, *args)\u001b[39m\n\u001b[32m 471\u001b[39m start = time.time()\n\u001b[32m--> \u001b[39m\u001b[32m472\u001b[39m ans = \u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 473\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m do_explain:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:1350\u001b[39m, in \u001b[36m_create_pjit_jaxpr\u001b[39m\u001b[34m(***failed resolving arguments***)\u001b[39m\n\u001b[32m 1349\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1350\u001b[39m jaxpr, global_out_avals, consts = \u001b[43mpe\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtrace_to_jaxpr_dynamic\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1352\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m config.debug_key_reuse.value:\n\u001b[32m 1353\u001b[39m \u001b[38;5;66;03m# Import here to avoid circular imports\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/profiler.py:354\u001b[39m, in \u001b[36mannotate_function..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 353\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m TraceAnnotation(name, **decorator_kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m354\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/interpreters/partial_eval.py:2337\u001b[39m, in \u001b[36mtrace_to_jaxpr_dynamic\u001b[39m\u001b[34m(fun, in_avals, keep_inputs, lower, auto_dce)\u001b[39m\n\u001b[32m 2336\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m core.set_current_trace(trace):\n\u001b[32m-> \u001b[39m\u001b[32m2337\u001b[39m ans = \u001b[43mfun\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcall_wrapped\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43min_tracers\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2338\u001b[39m _check_returned_jaxtypes(fun.debug_info, ans)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/linear_util.py:212\u001b[39m, in \u001b[36mWrappedFun.call_wrapped\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 211\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Calls the transformed function\"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m212\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mf_transformed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api_util.py:73\u001b[39m, in \u001b[36mflatten_fun\u001b[39m\u001b[34m(f, store, in_tree, *args_flat)\u001b[39m\n\u001b[32m 72\u001b[39m py_args, py_kwargs = tree_unflatten(in_tree, args_flat)\n\u001b[32m---> \u001b[39m\u001b[32m73\u001b[39m ans = \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43mpy_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mpy_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 74\u001b[39m ans, out_tree = tree_flatten(ans)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api_util.py:314\u001b[39m, in \u001b[36m_argnames_partial\u001b[39m\u001b[34m(_fun, _fixed_kwargs, *args, **dyn_kwargs)\u001b[39m\n\u001b[32m 313\u001b[39m kwargs = \u001b[38;5;28mdict\u001b[39m({k: v.val \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m _fixed_kwargs.val.items()}, **dyn_kwargs)\n\u001b[32m--> \u001b[39m\u001b[32m314\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api_util.py:288\u001b[39m, in \u001b[36m_argnums_partial\u001b[39m\u001b[34m(_fun, _dyn_argnums, _fixed_args, *dyn_args, **kwargs)\u001b[39m\n\u001b[32m 287\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mnext\u001b[39m(fixed_args_, sentinel) \u001b[38;5;129;01mis\u001b[39;00m sentinel\n\u001b[32m--> \u001b[39m\u001b[32m288\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/linear_util.py:397\u001b[39m, in \u001b[36m_get_result_paths_thunk\u001b[39m\u001b[34m(_fun, _store, *args, **kwargs)\u001b[39m\n\u001b[32m 395\u001b[39m \u001b[38;5;129m@transformation_with_aux2\u001b[39m\n\u001b[32m 396\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_get_result_paths_thunk\u001b[39m(_fun: Callable, _store: Store, *args, **kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m397\u001b[39m ans = \u001b[43m_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 398\u001b[39m result_paths = \u001b[38;5;28mtuple\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mresult\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m_clean_keystr_arg_names(path)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m path, _ \u001b[38;5;129;01min\u001b[39;00m generate_key_paths(ans))\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/equinox/_jit.py:55\u001b[39m, in \u001b[36m_filter_jit_cache..fun_wrapped\u001b[39m\u001b[34m(dynamic_donate, dynamic_nodonate, static)\u001b[39m\n\u001b[32m 54\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m dummy_arg \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m55\u001b[39m out = \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 56\u001b[39m dynamic_out, static_out = partition(out, is_array)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/mnt/Pollux/Sapienza/RSFinversion/diabayes/diabayes/solver.py:118\u001b[39m, in \u001b[36mODESolver._forward_wrapper\u001b[39m\u001b[34m(self, t, variables, args)\u001b[39m\n\u001b[32m 117\u001b[39m params, friction_constants, block_constants = args\n\u001b[32m--> \u001b[39m\u001b[32m118\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mforward_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 119\u001b[39m \u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m=\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 120\u001b[39m \u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 121\u001b[39m \u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m=\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 122\u001b[39m \u001b[43m \u001b[49m\u001b[43mfriction_constants\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfriction_constants\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 123\u001b[39m \u001b[43m \u001b[49m\u001b[43mblock_constants\u001b[49m\u001b[43m=\u001b[49m\u001b[43mblock_constants\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 124\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/equinox/_module/_prebuilt.py:81\u001b[39m, in \u001b[36mPartial.__call__\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 69\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Call the wrapped `self.func`.\u001b[39;00m\n\u001b[32m 70\u001b[39m \n\u001b[32m 71\u001b[39m \u001b[33;03m**Arguments:**\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 79\u001b[39m \u001b[33;03mThe result of the wrapped function.\u001b[39;00m\n\u001b[32m 80\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m81\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mkeywords\u001b[49m\u001b[43m)\u001b[49m\n", - " \u001b[31m[... skipping hidden 3 frame]\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:260\u001b[39m, in \u001b[36m_cpp_pjit..cache_miss\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 256\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mre-tracing function \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mjit_info.fun_sourceinfo\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m for \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 257\u001b[39m \u001b[33m\"\u001b[39m\u001b[33m`jit`, but \u001b[39m\u001b[33m'\u001b[39m\u001b[33mno_tracing\u001b[39m\u001b[33m'\u001b[39m\u001b[33m is set\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 259\u001b[39m outs, out_flat, out_tree, args_flat, jaxpr, executable, pgle_profiler = \\\n\u001b[32m--> \u001b[39m\u001b[32m260\u001b[39m \u001b[43m_python_pjit_helper\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjit_info\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 262\u001b[39m maybe_fastpath_data = _get_fastpath_data(\n\u001b[32m 263\u001b[39m executable, out_tree, args_flat, out_flat, jaxpr.effects, jaxpr.consts,\n\u001b[32m 264\u001b[39m jit_info.abstracted_axes, pgle_profiler)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:137\u001b[39m, in \u001b[36m_python_pjit_helper\u001b[39m\u001b[34m(fun, jit_info, *args, **kwargs)\u001b[39m\n\u001b[32m 136\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_python_pjit_helper\u001b[39m(fun: Callable, jit_info: PjitInfo, *args, **kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m137\u001b[39m p, args_flat = \u001b[43m_infer_params\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjit_info\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 139\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m arg \u001b[38;5;129;01min\u001b[39;00m args_flat:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:629\u001b[39m, in \u001b[36m_infer_params\u001b[39m\u001b[34m(fun, ji, args, kwargs)\u001b[39m\n\u001b[32m 628\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m629\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_infer_params_internal\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mji\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:653\u001b[39m, in \u001b[36m_infer_params_internal\u001b[39m\u001b[34m(fun, ji, args, kwargs)\u001b[39m\n\u001b[32m 652\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m entry.pjit_params \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m653\u001b[39m p, args_flat = \u001b[43m_infer_params_impl\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 654\u001b[39m \u001b[43m \u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mji\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mctx_mesh\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdbg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_avals\u001b[49m\u001b[43m=\u001b[49m\u001b[43mavals\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 655\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m p.params[\u001b[33m'\u001b[39m\u001b[33mjaxpr\u001b[39m\u001b[33m'\u001b[39m].jaxpr.is_high:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:552\u001b[39m, in \u001b[36m_infer_params_impl\u001b[39m\u001b[34m(***failed resolving arguments***)\u001b[39m\n\u001b[32m 547\u001b[39m in_shardings_flat, in_layouts_flat = _process_in_axis_resources(\n\u001b[32m 548\u001b[39m in_shardings_treedef, in_shardings_leaves,\n\u001b[32m 549\u001b[39m ji.in_layouts_treedef, ji.in_layouts_leaves,\n\u001b[32m 550\u001b[39m in_avals, in_tree, flat_fun.debug_info, device_or_backend_set, have_kwargs)\n\u001b[32m--> \u001b[39m\u001b[32m552\u001b[39m jaxpr, consts, jaxpr_for_top_jit, out_avals = \u001b[43m_create_pjit_jaxpr\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 553\u001b[39m \u001b[43m \u001b[49m\u001b[43mflat_fun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mIgnoreKey\u001b[49m\u001b[43m(\u001b[49m\u001b[43mji\u001b[49m\u001b[43m.\u001b[49m\u001b[43minline\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 554\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m config.mutable_array_checks.value:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/linear_util.py:472\u001b[39m, in \u001b[36mcache..memoized_fun\u001b[39m\u001b[34m(fun, *args)\u001b[39m\n\u001b[32m 471\u001b[39m start = time.time()\n\u001b[32m--> \u001b[39m\u001b[32m472\u001b[39m ans = \u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 473\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m do_explain:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:1350\u001b[39m, in \u001b[36m_create_pjit_jaxpr\u001b[39m\u001b[34m(***failed resolving arguments***)\u001b[39m\n\u001b[32m 1349\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1350\u001b[39m jaxpr, global_out_avals, consts = \u001b[43mpe\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtrace_to_jaxpr_dynamic\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1352\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m config.debug_key_reuse.value:\n\u001b[32m 1353\u001b[39m \u001b[38;5;66;03m# Import here to avoid circular imports\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/profiler.py:354\u001b[39m, in \u001b[36mannotate_function..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 353\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m TraceAnnotation(name, **decorator_kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m354\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/interpreters/partial_eval.py:2337\u001b[39m, in \u001b[36mtrace_to_jaxpr_dynamic\u001b[39m\u001b[34m(fun, in_avals, keep_inputs, lower, auto_dce)\u001b[39m\n\u001b[32m 2336\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m core.set_current_trace(trace):\n\u001b[32m-> \u001b[39m\u001b[32m2337\u001b[39m ans = \u001b[43mfun\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcall_wrapped\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43min_tracers\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2338\u001b[39m _check_returned_jaxtypes(fun.debug_info, ans)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/linear_util.py:212\u001b[39m, in \u001b[36mWrappedFun.call_wrapped\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 211\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Calls the transformed function\"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m212\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mf_transformed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api_util.py:73\u001b[39m, in \u001b[36mflatten_fun\u001b[39m\u001b[34m(f, store, in_tree, *args_flat)\u001b[39m\n\u001b[32m 72\u001b[39m py_args, py_kwargs = tree_unflatten(in_tree, args_flat)\n\u001b[32m---> \u001b[39m\u001b[32m73\u001b[39m ans = \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43mpy_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mpy_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 74\u001b[39m ans, out_tree = tree_flatten(ans)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api_util.py:314\u001b[39m, in \u001b[36m_argnames_partial\u001b[39m\u001b[34m(_fun, _fixed_kwargs, *args, **dyn_kwargs)\u001b[39m\n\u001b[32m 313\u001b[39m kwargs = \u001b[38;5;28mdict\u001b[39m({k: v.val \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m _fixed_kwargs.val.items()}, **dyn_kwargs)\n\u001b[32m--> \u001b[39m\u001b[32m314\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api_util.py:288\u001b[39m, in \u001b[36m_argnums_partial\u001b[39m\u001b[34m(_fun, _dyn_argnums, _fixed_args, *dyn_args, **kwargs)\u001b[39m\n\u001b[32m 287\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mnext\u001b[39m(fixed_args_, sentinel) \u001b[38;5;129;01mis\u001b[39;00m sentinel\n\u001b[32m--> \u001b[39m\u001b[32m288\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/linear_util.py:397\u001b[39m, in \u001b[36m_get_result_paths_thunk\u001b[39m\u001b[34m(_fun, _store, *args, **kwargs)\u001b[39m\n\u001b[32m 395\u001b[39m \u001b[38;5;129m@transformation_with_aux2\u001b[39m\n\u001b[32m 396\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_get_result_paths_thunk\u001b[39m(_fun: Callable, _store: Store, *args, **kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m397\u001b[39m ans = \u001b[43m_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 398\u001b[39m result_paths = \u001b[38;5;28mtuple\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mresult\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m_clean_keystr_arg_names(path)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m path, _ \u001b[38;5;129;01min\u001b[39;00m generate_key_paths(ans))\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/equinox/_jit.py:55\u001b[39m, in \u001b[36m_filter_jit_cache..fun_wrapped\u001b[39m\u001b[34m(dynamic_donate, dynamic_nodonate, static)\u001b[39m\n\u001b[32m 54\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m dummy_arg \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m55\u001b[39m out = \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 56\u001b[39m dynamic_out, static_out = partition(out, is_array)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/mnt/Pollux/Sapienza/RSFinversion/diabayes/diabayes/forward_models.py:251\u001b[39m, in \u001b[36mForward.__call__\u001b[39m\u001b[34m(self, t, variables, params, friction_constants, block_constants)\u001b[39m\n\u001b[32m 240\u001b[39m \u001b[38;5;129m@eqx\u001b[39m.filter_jit\n\u001b[32m 241\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__call__\u001b[39m(\n\u001b[32m 242\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 249\u001b[39m \u001b[38;5;66;03m# Calculate v and its partial derivatives with respect to\u001b[39;00m\n\u001b[32m 250\u001b[39m \u001b[38;5;66;03m# the variables (mu, state1, state2, ...)\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m251\u001b[39m v, v_derivs = \u001b[43meqx\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfilter_value_and_grad\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfriction_model\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 252\u001b[39m \u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfriction_constants\u001b[49m\n\u001b[32m 253\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 254\u001b[39m \u001b[38;5;66;03m# Rate of change of state variables\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/equinox/_ad.py:71\u001b[39m, in \u001b[36m_ValueAndGradWrapper.__call__\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 70\u001b[39m diff_x, nondiff_x = partition(x, is_inexact_array)\n\u001b[32m---> \u001b[39m\u001b[32m71\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfun_value_and_grad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdiff_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnondiff_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - " \u001b[31m[... skipping hidden 1 frame]\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api.py:479\u001b[39m, in \u001b[36mvalue_and_grad..value_and_grad_f\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 478\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_aux:\n\u001b[32m--> \u001b[39m\u001b[32m479\u001b[39m ans, vjp_py = \u001b[43m_vjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf_partial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43mdyn_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 480\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api.py:2143\u001b[39m, in \u001b[36m_vjp\u001b[39m\u001b[34m(fun, has_aux, *primals)\u001b[39m\n\u001b[32m 2142\u001b[39m flat_fun, out_tree = flatten_fun_nokwargs(fun, in_tree)\n\u001b[32m-> \u001b[39m\u001b[32m2143\u001b[39m out_primals, vjp = \u001b[43mad\u001b[49m\u001b[43m.\u001b[49m\u001b[43mvjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mflat_fun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprimals_flat\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2144\u001b[39m out_tree = out_tree()\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/interpreters/ad.py:309\u001b[39m, in \u001b[36mvjp\u001b[39m\u001b[34m(traceable, primals, has_aux)\u001b[39m\n\u001b[32m 308\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_aux:\n\u001b[32m--> \u001b[39m\u001b[32m309\u001b[39m out_primals, pvals, jaxpr, consts = \u001b[43mlinearize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtraceable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43mprimals\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 310\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/interpreters/ad.py:283\u001b[39m, in \u001b[36mlinearize\u001b[39m\u001b[34m(traceable, *primals, **kwargs)\u001b[39m\n\u001b[32m 282\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m config.use_direct_linearize.value:\n\u001b[32m--> \u001b[39m\u001b[32m283\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdirect_linearize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtraceable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprimals\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhas_aux\u001b[49m\u001b[43m=\u001b[49m\u001b[43mhas_aux\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 284\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_aux:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/interpreters/ad.py:256\u001b[39m, in \u001b[36mdirect_linearize\u001b[39m\u001b[34m(traceable, primals, kwargs, has_aux, tag)\u001b[39m\n\u001b[32m 255\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m256\u001b[39m ans = \u001b[43mtraceable\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcall_wrapped\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43mtracers\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 257\u001b[39m aux = \u001b[38;5;28;01mNone\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/linear_util.py:212\u001b[39m, in \u001b[36mWrappedFun.call_wrapped\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 211\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Calls the transformed function\"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m212\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mf_transformed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api_util.py:90\u001b[39m, in \u001b[36mflatten_fun_nokwargs\u001b[39m\u001b[34m(f, store, in_tree, *args_flat)\u001b[39m\n\u001b[32m 89\u001b[39m py_args = tree_unflatten(in_tree, args_flat)\n\u001b[32m---> \u001b[39m\u001b[32m90\u001b[39m ans = \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43mpy_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 91\u001b[39m ans, out_tree = tree_flatten(ans)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api_util.py:288\u001b[39m, in \u001b[36m_argnums_partial\u001b[39m\u001b[34m(_fun, _dyn_argnums, _fixed_args, *dyn_args, **kwargs)\u001b[39m\n\u001b[32m 287\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mnext\u001b[39m(fixed_args_, sentinel) \u001b[38;5;129;01mis\u001b[39;00m sentinel\n\u001b[32m--> \u001b[39m\u001b[32m288\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/linear_util.py:397\u001b[39m, in \u001b[36m_get_result_paths_thunk\u001b[39m\u001b[34m(_fun, _store, *args, **kwargs)\u001b[39m\n\u001b[32m 395\u001b[39m \u001b[38;5;129m@transformation_with_aux2\u001b[39m\n\u001b[32m 396\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_get_result_paths_thunk\u001b[39m(_fun: Callable, _store: Store, *args, **kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m397\u001b[39m ans = \u001b[43m_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 398\u001b[39m result_paths = \u001b[38;5;28mtuple\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mresult\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m_clean_keystr_arg_names(path)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m path, _ \u001b[38;5;129;01min\u001b[39;00m generate_key_paths(ans))\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/equinox/_ad.py:54\u001b[39m, in \u001b[36m_ValueAndGradWrapper.__call__..fun_value_and_grad\u001b[39m\u001b[34m(_diff_x, _nondiff_x, *_args, **_kwargs)\u001b[39m\n\u001b[32m 53\u001b[39m _x = combine(_diff_x, _nondiff_x)\n\u001b[32m---> \u001b[39m\u001b[32m54\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43m_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", - " \u001b[31m[... skipping hidden 3 frame]\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:260\u001b[39m, in \u001b[36m_cpp_pjit..cache_miss\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 256\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mre-tracing function \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mjit_info.fun_sourceinfo\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m for \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 257\u001b[39m \u001b[33m\"\u001b[39m\u001b[33m`jit`, but \u001b[39m\u001b[33m'\u001b[39m\u001b[33mno_tracing\u001b[39m\u001b[33m'\u001b[39m\u001b[33m is set\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 259\u001b[39m outs, out_flat, out_tree, args_flat, jaxpr, executable, pgle_profiler = \\\n\u001b[32m--> \u001b[39m\u001b[32m260\u001b[39m \u001b[43m_python_pjit_helper\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjit_info\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 262\u001b[39m maybe_fastpath_data = _get_fastpath_data(\n\u001b[32m 263\u001b[39m executable, out_tree, args_flat, out_flat, jaxpr.effects, jaxpr.consts,\n\u001b[32m 264\u001b[39m jit_info.abstracted_axes, pgle_profiler)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:137\u001b[39m, in \u001b[36m_python_pjit_helper\u001b[39m\u001b[34m(fun, jit_info, *args, **kwargs)\u001b[39m\n\u001b[32m 136\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_python_pjit_helper\u001b[39m(fun: Callable, jit_info: PjitInfo, *args, **kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m137\u001b[39m p, args_flat = \u001b[43m_infer_params\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjit_info\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 139\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m arg \u001b[38;5;129;01min\u001b[39;00m args_flat:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:629\u001b[39m, in \u001b[36m_infer_params\u001b[39m\u001b[34m(fun, ji, args, kwargs)\u001b[39m\n\u001b[32m 628\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m629\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_infer_params_internal\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mji\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:653\u001b[39m, in \u001b[36m_infer_params_internal\u001b[39m\u001b[34m(fun, ji, args, kwargs)\u001b[39m\n\u001b[32m 652\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m entry.pjit_params \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m653\u001b[39m p, args_flat = \u001b[43m_infer_params_impl\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 654\u001b[39m \u001b[43m \u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mji\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mctx_mesh\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdbg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_avals\u001b[49m\u001b[43m=\u001b[49m\u001b[43mavals\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 655\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m p.params[\u001b[33m'\u001b[39m\u001b[33mjaxpr\u001b[39m\u001b[33m'\u001b[39m].jaxpr.is_high:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:552\u001b[39m, in \u001b[36m_infer_params_impl\u001b[39m\u001b[34m(***failed resolving arguments***)\u001b[39m\n\u001b[32m 547\u001b[39m in_shardings_flat, in_layouts_flat = _process_in_axis_resources(\n\u001b[32m 548\u001b[39m in_shardings_treedef, in_shardings_leaves,\n\u001b[32m 549\u001b[39m ji.in_layouts_treedef, ji.in_layouts_leaves,\n\u001b[32m 550\u001b[39m in_avals, in_tree, flat_fun.debug_info, device_or_backend_set, have_kwargs)\n\u001b[32m--> \u001b[39m\u001b[32m552\u001b[39m jaxpr, consts, jaxpr_for_top_jit, out_avals = \u001b[43m_create_pjit_jaxpr\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 553\u001b[39m \u001b[43m \u001b[49m\u001b[43mflat_fun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mIgnoreKey\u001b[49m\u001b[43m(\u001b[49m\u001b[43mji\u001b[49m\u001b[43m.\u001b[49m\u001b[43minline\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 554\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m config.mutable_array_checks.value:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/linear_util.py:472\u001b[39m, in \u001b[36mcache..memoized_fun\u001b[39m\u001b[34m(fun, *args)\u001b[39m\n\u001b[32m 471\u001b[39m start = time.time()\n\u001b[32m--> \u001b[39m\u001b[32m472\u001b[39m ans = \u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 473\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m do_explain:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/pjit.py:1350\u001b[39m, in \u001b[36m_create_pjit_jaxpr\u001b[39m\u001b[34m(***failed resolving arguments***)\u001b[39m\n\u001b[32m 1349\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1350\u001b[39m jaxpr, global_out_avals, consts = \u001b[43mpe\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtrace_to_jaxpr_dynamic\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1352\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m config.debug_key_reuse.value:\n\u001b[32m 1353\u001b[39m \u001b[38;5;66;03m# Import here to avoid circular imports\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/profiler.py:354\u001b[39m, in \u001b[36mannotate_function..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 353\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m TraceAnnotation(name, **decorator_kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m354\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/interpreters/partial_eval.py:2337\u001b[39m, in \u001b[36mtrace_to_jaxpr_dynamic\u001b[39m\u001b[34m(fun, in_avals, keep_inputs, lower, auto_dce)\u001b[39m\n\u001b[32m 2336\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m core.set_current_trace(trace):\n\u001b[32m-> \u001b[39m\u001b[32m2337\u001b[39m ans = \u001b[43mfun\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcall_wrapped\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43min_tracers\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2338\u001b[39m _check_returned_jaxtypes(fun.debug_info, ans)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/linear_util.py:212\u001b[39m, in \u001b[36mWrappedFun.call_wrapped\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 211\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Calls the transformed function\"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m212\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mf_transformed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api_util.py:73\u001b[39m, in \u001b[36mflatten_fun\u001b[39m\u001b[34m(f, store, in_tree, *args_flat)\u001b[39m\n\u001b[32m 72\u001b[39m py_args, py_kwargs = tree_unflatten(in_tree, args_flat)\n\u001b[32m---> \u001b[39m\u001b[32m73\u001b[39m ans = \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43mpy_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mpy_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 74\u001b[39m ans, out_tree = tree_flatten(ans)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api_util.py:314\u001b[39m, in \u001b[36m_argnames_partial\u001b[39m\u001b[34m(_fun, _fixed_kwargs, *args, **dyn_kwargs)\u001b[39m\n\u001b[32m 313\u001b[39m kwargs = \u001b[38;5;28mdict\u001b[39m({k: v.val \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m _fixed_kwargs.val.items()}, **dyn_kwargs)\n\u001b[32m--> \u001b[39m\u001b[32m314\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/api_util.py:288\u001b[39m, in \u001b[36m_argnums_partial\u001b[39m\u001b[34m(_fun, _dyn_argnums, _fixed_args, *dyn_args, **kwargs)\u001b[39m\n\u001b[32m 287\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mnext\u001b[39m(fixed_args_, sentinel) \u001b[38;5;129;01mis\u001b[39;00m sentinel\n\u001b[32m--> \u001b[39m\u001b[32m288\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/linear_util.py:397\u001b[39m, in \u001b[36m_get_result_paths_thunk\u001b[39m\u001b[34m(_fun, _store, *args, **kwargs)\u001b[39m\n\u001b[32m 395\u001b[39m \u001b[38;5;129m@transformation_with_aux2\u001b[39m\n\u001b[32m 396\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_get_result_paths_thunk\u001b[39m(_fun: Callable, _store: Store, *args, **kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m397\u001b[39m ans = \u001b[43m_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 398\u001b[39m result_paths = \u001b[38;5;28mtuple\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mresult\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m_clean_keystr_arg_names(path)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m path, _ \u001b[38;5;129;01min\u001b[39;00m generate_key_paths(ans))\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/equinox/_jit.py:55\u001b[39m, in \u001b[36m_filter_jit_cache..fun_wrapped\u001b[39m\u001b[34m(dynamic_donate, dynamic_nodonate, static)\u001b[39m\n\u001b[32m 54\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m dummy_arg \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m55\u001b[39m out = \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 56\u001b[39m dynamic_out, static_out = partition(out, is_array)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/mnt/Pollux/Sapienza/RSFinversion/diabayes/diabayes/forward_models.py:47\u001b[39m, in \u001b[36mrsf\u001b[39m\u001b[34m(variables, params, constants)\u001b[39m\n\u001b[32m 26\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33mr\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 27\u001b[39m \u001b[33;03mThe classical rate-and-state friction law, given by:\u001b[39;00m\n\u001b[32m 28\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 45\u001b[39m \u001b[33;03m The instantaneous slip rate in the same units as `v0`\u001b[39;00m\n\u001b[32m 46\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m47\u001b[39m Omega = \u001b[43mparams\u001b[49m\u001b[43m.\u001b[49m\u001b[43mb\u001b[49m * jnp.log(variables.theta * constants.v0 / params.Dc)\n\u001b[32m 48\u001b[39m v = constants.v0 * jnp.exp((variables.mu - constants.mu0 - Omega) / params.a)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/core.py:992\u001b[39m, in \u001b[36mTracer.__getattr__\u001b[39m\u001b[34m(self, name)\u001b[39m\n\u001b[32m 991\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[32m--> \u001b[39m\u001b[32m992\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[32m 993\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.\u001b[34m__class__\u001b[39m.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m has no attribute \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 994\u001b[39m ) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01merr\u001b[39;00m\n\u001b[32m 995\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "\u001b[31mAttributeError\u001b[39m: DynamicJaxprTracer has no attribute b", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/diffrax/_integrate.py:200\u001b[39m, in \u001b[36m_assert_term_compatible\u001b[39m\u001b[34m(t, y, args, terms, term_structure, contr_kwargs)\u001b[39m\n\u001b[32m 199\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m jax.numpy_dtype_promotion(\u001b[33m\"\u001b[39m\u001b[33mstandard\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m--> \u001b[39m\u001b[32m200\u001b[39m \u001b[43mjtu\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtree_map\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_check\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mterm_structure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mterms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontr_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 201\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 202\u001b[39m \u001b[38;5;66;03m# ValueError may also arise from mismatched tree structures\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/tree_util.py:361\u001b[39m, in \u001b[36mtree_map\u001b[39m\u001b[34m(f, tree, is_leaf, *rest)\u001b[39m\n\u001b[32m 360\u001b[39m all_leaves = [leaves] + [treedef.flatten_up_to(r) \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m rest]\n\u001b[32m--> \u001b[39m\u001b[32m361\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtreedef\u001b[49m\u001b[43m.\u001b[49m\u001b[43munflatten\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43mxs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mxs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mzip\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43mall_leaves\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/jax/_src/tree_util.py:361\u001b[39m, in \u001b[36m\u001b[39m\u001b[34m(.0)\u001b[39m\n\u001b[32m 360\u001b[39m all_leaves = [leaves] + [treedef.flatten_up_to(r) \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m rest]\n\u001b[32m--> \u001b[39m\u001b[32m361\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m treedef.unflatten(\u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43mxs\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m xs \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(*all_leaves))\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/diffrax/_integrate.py:172\u001b[39m, in \u001b[36m_assert_term_compatible.._check\u001b[39m\u001b[34m(term_cls, term, term_contr_kwargs, yi)\u001b[39m\n\u001b[32m 171\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m--> \u001b[39m\u001b[32m172\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mError while tracing \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mterm\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.vf: \u001b[39m\u001b[33m\"\u001b[39m + \u001b[38;5;28mstr\u001b[39m(e))\n\u001b[32m 173\u001b[39m vf_type_compatible = eqx.filter_eval_shape(\n\u001b[32m 174\u001b[39m better_isinstance, vf_type, vf_type_expected\n\u001b[32m 175\u001b[39m )\n", - "\u001b[31mValueError\u001b[39m: Error while tracing ODETerm(\n vector_field=Partial(\n func=_JitWrapper(\n fn='ODESolver._forward_wrapper',\n filter_warning=False,\n donate_first=False,\n donate_rest=False\n ),\n args=(,),\n keywords={}\n )\n).vf: DynamicJaxprTracer has no attribute b", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[15]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m samples, sample_results = \u001b[43mbayesian_result\u001b[49m\u001b[43m.\u001b[49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[43msolver\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconstants\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mblock_constants\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnsamples\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m100\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 3\u001b[39m plt.close(\u001b[33m\"\u001b[39m\u001b[33mall\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 4\u001b[39m fig, ax = plt.subplots(figsize=(\u001b[32m9\u001b[39m, \u001b[32m5\u001b[39m), constrained_layout=\u001b[38;5;28;01mTrue\u001b[39;00m)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/mnt/Pollux/Sapienza/RSFinversion/diabayes/diabayes/typedefs.py:426\u001b[39m, in \u001b[36mBayesianSolution.sample\u001b[39m\u001b[34m(self, solver, y0, t, friction_constants, block_constants, nsamples, rng)\u001b[39m\n\u001b[32m 423\u001b[39m key, split_key = jr.split(key)\n\u001b[32m 424\u001b[39m samples = jr.choice(split_key, \u001b[38;5;28mself\u001b[39m.final_state, shape=(nsamples,), axis=\u001b[32m0\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m426\u001b[39m sample_results = \u001b[43mjax\u001b[49m\u001b[43m.\u001b[49m\u001b[43mvmap\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 427\u001b[39m \u001b[43m \u001b[49m\u001b[43msolver\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_forward_wrapper_SVI\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_axes\u001b[49m\u001b[43m=\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout_axes\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m0\u001b[39;49m\n\u001b[32m 428\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43my0\u001b[49m\u001b[43m.\u001b[49m\u001b[43mto_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msamples\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfriction_constants\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mblock_constants\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 429\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m samples, sample_results\n", - " \u001b[31m[... skipping hidden 7 frame]\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/equinox/_module/_prebuilt.py:81\u001b[39m, in \u001b[36mPartial.__call__\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 68\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, *args: Any, **kwargs: Any) -> _Return:\n\u001b[32m 69\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Call the wrapped `self.func`.\u001b[39;00m\n\u001b[32m 70\u001b[39m \n\u001b[32m 71\u001b[39m \u001b[33;03m **Arguments:**\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 79\u001b[39m \u001b[33;03m The result of the wrapped function.\u001b[39;00m\n\u001b[32m 80\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m81\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mkeywords\u001b[49m\u001b[43m)\u001b[49m\n", - " \u001b[31m[... skipping hidden 18 frame]\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32m/mnt/Pollux/Sapienza/RSFinversion/diabayes/diabayes/solver.py:137\u001b[39m, in \u001b[36mODESolver._forward_wrapper_SVI\u001b[39m\u001b[34m(self, params, y0, t, friction_constants, block_constants)\u001b[39m\n\u001b[32m 126\u001b[39m \u001b[38;5;129m@eqx\u001b[39m.filter_jit\n\u001b[32m 127\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_forward_wrapper_SVI\u001b[39m(\n\u001b[32m 128\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 135\u001b[39m \u001b[38;5;66;03m# TODO: previously y0 and params were arrays, not\u001b[39;00m\n\u001b[32m 136\u001b[39m \u001b[38;5;66;03m# PyTrees. Does this logic still work?\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m137\u001b[39m result = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_solve_forward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 138\u001b[39m \u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m=\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 139\u001b[39m \u001b[43m \u001b[49m\u001b[43my0\u001b[49m\u001b[43m=\u001b[49m\u001b[43my0\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 140\u001b[39m \u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m=\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 141\u001b[39m \u001b[43m \u001b[49m\u001b[43mfriction_constants\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfriction_constants\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 142\u001b[39m \u001b[43m \u001b[49m\u001b[43mblock_constants\u001b[49m\u001b[43m=\u001b[49m\u001b[43mblock_constants\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 143\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 145\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 146\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m result.ys \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/equinox/_module/_prebuilt.py:81\u001b[39m, in \u001b[36mPartial.__call__\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 68\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, *args: Any, **kwargs: Any) -> _Return:\n\u001b[32m 69\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Call the wrapped `self.func`.\u001b[39;00m\n\u001b[32m 70\u001b[39m \n\u001b[32m 71\u001b[39m \u001b[33;03m **Arguments:**\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 79\u001b[39m \u001b[33;03m The result of the wrapped function.\u001b[39;00m\n\u001b[32m 80\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m81\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mkeywords\u001b[49m\u001b[43m)\u001b[49m\n", - " \u001b[31m[... skipping hidden 18 frame]\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32m/mnt/Pollux/Sapienza/RSFinversion/diabayes/diabayes/solver.py:173\u001b[39m, in \u001b[36mODESolver._solve_forward\u001b[39m\u001b[34m(self, t, y0, params, friction_constants, block_constants, adjoint)\u001b[39m\n\u001b[32m 170\u001b[39m adjoint = dfx.RecursiveCheckpointAdjoint(checkpoints=\u001b[38;5;28mself\u001b[39m.checkpoints)\n\u001b[32m 171\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(adjoint, dfx.AbstractAdjoint)\n\u001b[32m--> \u001b[39m\u001b[32m173\u001b[39m sol = \u001b[43mdfx\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdiffeqsolve\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 174\u001b[39m \u001b[43m \u001b[49m\u001b[43mterms\u001b[49m\u001b[43m=\u001b[49m\u001b[43mterm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 175\u001b[39m \u001b[43m \u001b[49m\u001b[43msolver\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdfx\u001b[49m\u001b[43m.\u001b[49m\u001b[43mTsit5\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 176\u001b[39m \u001b[43m \u001b[49m\u001b[43mt0\u001b[49m\u001b[43m=\u001b[49m\u001b[43mt0\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 177\u001b[39m \u001b[43m \u001b[49m\u001b[43mt1\u001b[49m\u001b[43m=\u001b[49m\u001b[43mt1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 178\u001b[39m \u001b[43m \u001b[49m\u001b[43mdt0\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdt0\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 179\u001b[39m \u001b[43m \u001b[49m\u001b[43msaveat\u001b[49m\u001b[43m=\u001b[49m\u001b[43msaveat\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 180\u001b[39m \u001b[43m \u001b[49m\u001b[43my0\u001b[49m\u001b[43m=\u001b[49m\u001b[43my0\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 181\u001b[39m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m=\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 182\u001b[39m \u001b[43m \u001b[49m\u001b[43mstepsize_controller\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcontroller\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 183\u001b[39m \u001b[43m \u001b[49m\u001b[43madjoint\u001b[49m\u001b[43m=\u001b[49m\u001b[43madjoint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 184\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 186\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m sol \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 188\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m sol\n", - " \u001b[31m[... skipping hidden 18 frame]\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/diffrax/_integrate.py:1103\u001b[39m, in \u001b[36mdiffeqsolve\u001b[39m\u001b[34m(terms, solver, t0, t1, dt0, y0, args, saveat, stepsize_controller, adjoint, event, max_steps, throw, progress_meter, solver_state, controller_state, made_jump, discrete_terminating_event)\u001b[39m\n\u001b[32m 1100\u001b[39m terms = MultiTerm(*terms)\n\u001b[32m 1102\u001b[39m \u001b[38;5;66;03m# Error checking for term compatibility\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1103\u001b[39m \u001b[43m_assert_term_compatible\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1104\u001b[39m \u001b[43m \u001b[49m\u001b[43mt0\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1105\u001b[39m \u001b[43m \u001b[49m\u001b[43my0\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1106\u001b[39m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1107\u001b[39m \u001b[43m \u001b[49m\u001b[43mterms\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1108\u001b[39m \u001b[43m \u001b[49m\u001b[43msolver\u001b[49m\u001b[43m.\u001b[49m\u001b[43mterm_structure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1109\u001b[39m \u001b[43m \u001b[49m\u001b[43msolver\u001b[49m\u001b[43m.\u001b[49m\u001b[43mterm_compatible_contr_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1110\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1112\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_sde(terms):\n\u001b[32m 1113\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(solver, (AbstractItoSolver, AbstractStratonovichSolver)):\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/diabayes/lib/python3.11/site-packages/diffrax/_integrate.py:205\u001b[39m, in \u001b[36m_assert_term_compatible\u001b[39m\u001b[34m(t, y, args, terms, term_structure, contr_kwargs)\u001b[39m\n\u001b[32m 203\u001b[39m pretty_term = wl.pformat(terms)\n\u001b[32m 204\u001b[39m pretty_expected = wl.pformat(term_structure)\n\u001b[32m--> \u001b[39m\u001b[32m205\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 206\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mTerms are not compatible with solver! Got:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mpretty_term\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33mbut expected:\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 207\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mpretty_expected\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33mNote that terms are checked recursively: if you \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 208\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mscroll up you may find a root-cause error that is more specific.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 209\u001b[39m ) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01me\u001b[39;00m\n", - "\u001b[31mValueError\u001b[39m: Terms are not compatible with solver! Got:\nODETerm(\n vector_field=Partial(\n func=_JitWrapper(\n fn='ODESolver._forward_wrapper',\n filter_warning=False,\n donate_first=False,\n donate_rest=False\n ),\n args=(,),\n keywords={}\n )\n)\nbut expected:\ndiffrax.AbstractTerm\nNote that terms are checked recursively: if you scroll up you may find a root-cause error that is more specific." - ] + "data": { + "text/plain": [ + "Text(0, 0.5, 'Friction [-]')" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "51bb761ec3144a8fa8c4782dc8fef0a4", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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\n", + " Figure\n", + "
\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -808,7 +722,7 @@ "\n", "plt.close(\"all\")\n", "fig, ax = plt.subplots(figsize=(9, 5), constrained_layout=True)\n", - "ax.plot(t, sample_results.mu.T, c=\"k\", alpha=0.2)\n", + "ax.plot(t, jnp.squeeze(sample_results.mu).T, c=\"k\", alpha=0.2)\n", "ax.scatter(t, mu_measured, c=\"C1\", s=10, zorder=len(samples) + 1, ec=\"k\", lw=1, alpha=0.7)\n", "ax.plot([], [], c=\"k\", label=\"Prediction\")\n", "ax.scatter([], [], c=\"C1\", ec=\"k\", lw=1, label=\"Observation\")\n", @@ -816,14 +730,6 @@ "ax.set_xlabel(\"Time [s]\")\n", "ax.set_ylabel(\"Friction [-]\")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "650c36d8", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/test/aux.py b/test/aux.py index fd2a2c4..eded549 100644 --- a/test/aux.py +++ b/test/aux.py @@ -20,7 +20,7 @@ def init_params(): state_obj = StateDict(keys=("theta",), vals=jnp.atleast_1d(theta0)) variables = db.Variables(mu=jnp.atleast_1d(mu0), state=state_obj) - params = db.RSFParams(*jnp.array([a, b, Dc])[:, None]) + params = db.RSFParams(a=a, b=b, Dc=Dc) constants = db.RSFConstants(v0=v0, mu0=mu0) block_constants = db.SpringBlockConstants(k=k, v_lp=v1) diff --git a/test/test_typedefs.py b/test/test_typedefs.py index c5611f3..e6f4841 100644 --- a/test/test_typedefs.py +++ b/test/test_typedefs.py @@ -1,6 +1,6 @@ import jax.numpy as jnp -from diabayes.typedefs import RSFParams, RSFParticles, StateDict, Variables +from diabayes.typedefs import RSFParams, StateDict, Variables class TestTypedefs: From 24c44867de1080a8a1f74a026a6bc61bf3caa1c2 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Thu, 28 Aug 2025 11:37:35 +0200 Subject: [PATCH 51/56] Updated documentation to account for new data structure --- diabayes/typedefs.py | 6 +- docs/source/getting-started.md | 56 +++++++++++-------- docs/source/user-guide/forward/index.md | 32 ++++++++--- .../source/user-guide/forward/spring-block.md | 4 +- docs/source/user-guide/inversion/bayesian.md | 2 +- .../user-guide/inversion/max-likelihood.md | 2 +- examples/simple_inversion.ipynb | 10 ++-- 7 files changed, 69 insertions(+), 43 deletions(-) diff --git a/diabayes/typedefs.py b/diabayes/typedefs.py index a8e72fe..ff132bf 100644 --- a/diabayes/typedefs.py +++ b/diabayes/typedefs.py @@ -152,9 +152,9 @@ def generate( class RSFParams(Params): - a: Float[Array, "..."] - b: Float[Array, "..."] - Dc: Float[Array, "..."] + a: Union[Float, Float[Array, "..."]] + b: Union[Float, Float[Array, "..."]] + Dc: Union[Float, Float[Array, "..."]] @dcs.dataclass(frozen=True) diff --git a/docs/source/getting-started.md b/docs/source/getting-started.md index 02dc3cf..836d614 100644 --- a/docs/source/getting-started.md +++ b/docs/source/getting-started.md @@ -29,29 +29,19 @@ The `dev` packages include [`black`](https://github.com/psf/black) and [`isort`] ## Basic usage -### Variables, parameters, constants - -DiaBayes can accommodate several physical models that each come with their own set of parameters. To assist in the bookkeeping of the variables, parameters, and constants, DiaBayes uses special containers that allow access to their contents by name. For example, variables are declared as: -```python -import diabayes as db -y = db.Variables(mu=0.6, state=10.5) -``` -The container items can then be accessed through `y.mu` and `y.state`. If you've used Python's `namedtuple` collection before then this should feel familiar. Invertible parameters and constants are declared in a similar way: -```python -# RSF parameters -params = db.RSFParams(a=a, b=b, Dc=Dc) -# RSF constants -constants = db.RSFConstants(v0=v0, mu0=mu0) -# Spring-block constants -block_constants = db.SpringBlockConstants(k=k, v_lp=v1) +```{note} +To clarify the notation used throughout the documentation: +- A _variable_ is a quantity that potentially varies over time. +- A _parameter_ is a constant quantity that one might want to invert for. +- A _constant_ is a user-defined constant quantity that remains untouched throughout inversion procedures. ``` ### Forward models The physics that underlies your experiment is encoded by a _forward model_. A DiaBayes `Forward` model is composed of three components: -1. A friction law, which is an equation that takes the friction and "state" as an input, and returns the instantaneous slip rate as an output, i.e. $v(t) = f(\mu(t), \theta(t))$. -2. A state evolution law, which returns the time derivative of the "state" variable. +1. A friction law, which is an equation that takes the friction and one or more "state" variables as an input, and returns the instantaneous slip rate as an output, i.e. $v(t) = f(\mu(t), \theta(t), \phi(t), \dots)$. +2. A state evolution law, which returns the time derivative of the "state" variable(s). 3. A stress transfer model, which describes how the local slip rate results in a change in shear stress on the fault interface. These three components can be mixed and matched according to the user's needs. For example, one could compile a forward model with regularised rate-and-state friction, the slip law for state evolution, and a non-inertial spring-block analogue for the stress transfer. Or one could pick the _Chen-Niemeijer-Spiers_ friction model with its corresponding state (porosity) evolution law coupled with an inertial spring-block to simulate stick-slip motion. See the [User Guide on forward modelling](user-guide/forward/index) for more information about the various forward model components that have been implemented. @@ -59,8 +49,10 @@ These three components can be mixed and matched according to the user's needs. F Here is an example of a classical rate-and-state friction model combined with the ageing law and a non-inertial spring-block: ```python from diabayes.forward_models import Forward, rsf, ageing_law, springblock -forward = Forward(friction_model=rsf, state_evolution=ageing_law, block_type=springblock) +state_dict = {"theta": ageing_law} +forward = Forward(friction_model=rsf, state_evolution=state_dict, block_type=springblock) ``` +The `rsf` friction model uses only a single state variable (`theta`), but other friction models could accept several. The structure of the `state_dict` is `{"variable_name1": variable_evolution1, "variable_name2": variable_evolution2, ...}`. In other words, every state variable has a unique name and exactly one function that describes the evolution of this variable. The variable names need to correspond with what is expected by the friction law; see the documentation of each friction law for a description. This forward model can then be handed over to a solver that takes care of the forward (and inverse) modelling: ```python @@ -68,7 +60,27 @@ from diabayes.solver import ODESolver solver = ODESolver(forward_model=forward) ``` -The `ODESolver` class contains a number of public and private methods that take care of unpacking the various containers (`Variables`, `RSFConstants`, etc.) and casting the forward models in a common format to make them compatible for an ODE solver. Getting a forward solution from DiaBayes is a single function call: +Since the forward/inverse models need to accommodate any kind of physics that come with their own quantities, DiaBayes implements special containers that allows access to these quantities by name (rather than by position inside an array, which is not known ahead of time). For constant quantities (parameters and constants), the user can import the relevant containers and set these quantities directly: +```python +import diabayes as db + +# RSF parameters +params = db.RSFParams(a=a, b=b, Dc=Dc) +# RSF constants +constants = db.RSFConstants(v0=v0, mu0=mu0) +# Spring-block constants +block_constants = db.SpringBlockConstants(k=k, v_lp=v1) +``` + +Time-dependent variables are a bit special, and so they need to be set through the `Forward` class: +```python +theta0 = Dc / v0 +forward.set_initial_values(mu=mu0, theta=theta0) +y0 = forward.variables +``` +Since we initially provided just one state evolution equation, there is only one state variable to specify in addition to the friction coefficient. In this example `forward.variables` points to a `Variables` class, and we can access the various quantities like `params.Dc`, `block_constants.k`, `y0.theta`, etc. + +The last step is to get a forward solution from DiaBayes, which is a single function call: ```python result = solver.solve_forward( t=t, y0=y0, params=params, @@ -76,7 +88,7 @@ result = solver.solve_forward( block_constants=block_constants ) ``` -This `result` is a `Variables` container that contains the time series of the relevant variables (e.g. friction and state for rate-and-state friction) that can be accessed e.g. through `result.mu`, `result.state`, etc. +This `result` is a `Variables` container that contains the time series of the relevant variables (e.g. friction and state for rate-and-state friction) that can be accessed e.g. through `result.mu`, `result.theta`, etc. ### Inversion @@ -88,7 +100,7 @@ mu_measured = ... initial_guess_params = ... inv_result = solver.max_likelihood_inversion( t=t, mu=mu_measured, - params=initial_guess_params, + y0=y0, params=initial_guess_params, friction_constants=constants, block_constants=block_constants ) @@ -100,7 +112,7 @@ params_inv = inv_result.value noise_amplitude = 1e-3 # Estimate of the measurement uncertainty bayesian_result = solver.bayesian_inversion( t=t, mu=mu_measured, noise_std=noise_amplitude, - params=params_inv, friction_constants=constants, + y0=y0, params=params_inv, friction_constants=constants, block_constants=block_constants, Nparticles=1500, Nsteps=100, rng=42 ) diff --git a/docs/source/user-guide/forward/index.md b/docs/source/user-guide/forward/index.md index 5ca329d..49be0be 100644 --- a/docs/source/user-guide/forward/index.md +++ b/docs/source/user-guide/forward/index.md @@ -5,11 +5,11 @@ A fundamental aspect of any quantitative science discipline is to make observati Quantitatively, the dynamics of a system with multiple variables or dimensions can be written as: ```{math} -:label: ODE +:label: eqn:ODE \frac{\mathrm{d} \vec{X}}{\mathrm{d} t} = f\left( t, \vec{X}, \dots \right) ``` -The synthetic observations are then generated by integrating Eq. {eq}`ODE` over time $t \in \left[ t_0, t \right)$: +The synthetic observations are then generated by integrating Eq. {eq}`eqn:ODE` over time $t \in \left[ t_0, t \right)$: ```{math} :label: ODE_solution \vec{X}(t) = \vec{X}(t=t_0) + \int_{t_0}^{t} f\left(t', \vec{X}, \dots \right) \mathrm{d} t' @@ -22,7 +22,7 @@ There exists a plethora of strategies to perform the integration as indicated ab A typical forward model describing a rock friction experiment comprises 3 components: a _friction law_ that describes the slip rate of the fault in response to a given state of stress (or "friction"), a _state evolution law_ that controls the time- and history-dependent internal state of the fault, and a model that describes how the externally imposed stress is transmitted to the fault (usually through elasticity). To make this more concrete, let's consider a classical spring-block model governed by rate-and-state friction{footcite}`marone1998`: ```{math} -:label: RSF +:label: eqn:RSF \frac{\mathrm{d} \vec{X}}{\mathrm{d} t} = \begin{cases} \dot{\mu} = k \left(v_{lp} - v(\mu, \theta) \right) \quad \text{(spring-block stress transfer)} \\ \dot{\theta} = 1 - \frac{v \theta}{D_c} \quad \text{(Ageing law state evolution)} @@ -32,7 +32,7 @@ with ```{math} v(\mu, \theta) = v_0 \exp \left( \frac{1}{a} \left[ \mu - \mu_0 - b \ln \left( \frac{v_0 \theta}{D_c} \right) \right] \right) ``` -In the above expressions, $a$, $b$, and $D_c$ are frictional parameters that are often of interest, and [can be inverted for](../inversion/index) from measured friction curves ($\mu(t)$). The friction parameters $\mu_0$ and $v_0$ are typically taken as constants as they can be inferred directly from the data. The spring-block stiffness $k$ and the load-point velocity $v_{lp}$ are determined by the experimental set-up. The forward model is completed by a description of the evolution of the state $\theta$ (here the Ageing law was chosen). Integrating Eq. {eq}`RSF` over time yields $\vec{X}(t) = \left[ \mu(t), \theta(t) \right]^\intercal$. +In the above expressions, $a$, $b$, and $D_c$ are frictional parameters that are often of interest, and [can be inverted for](../inversion/index) from measured friction curves ($\mu(t)$). The friction parameters $\mu_0$ and $v_0$ are typically taken as constants as they can be inferred directly from the data. The spring-block stiffness $k$ and the load-point velocity $v_{lp}$ are determined by the experimental set-up. The forward model is completed by a description of the evolution of the state $\theta$ (here the Ageing law was chosen). Integrating Eq. {eq}`eqn:RSF` over time yields $\vec{X}(t) = \left[ \mu(t), \theta(t) \right]^\intercal$. The formulations above are merely a classical example of a friction forward model. There are several other models for the friction law, state evolution, and the stress transfer, that can be used (and in some cases combined in different ways). For more background on the various models that have been implemented, see the sections on [rate-and-state](rate-and-state) models, the [Chen-Niemeijer-Spiers](chen-niemeijer-spiers) model, and [spring-block](spring-block) models. @@ -40,24 +40,38 @@ The remainder of this section is dedicated to the practical implementation of fo ## Implementation in DiaBayes -DiaBayes provides a generalised interface that takes the three (compatible) forward components as an input, and connects them together into a form equivalent to Eq. {eq}`ODE`. This interface is accessed through the `diabayes.forward_models.Forward` class. The forward model expressed by Eq. {eq}`RSF` is constructed as follows: +DiaBayes provides a generalised interface that takes the three (compatible) forward components as an input, and connects them together into a form equivalent to Eq. {eq}`eqn:ODE`. This interface is accessed through the `diabayes.forward_models.Forward` class. The forward model expressed by Eq. {eq}`eqn:RSF` is constructed as follows: ```python from diabayes.forward_models import Forward, rsf, ageing_law, springblock -forward = Forward(friction_model=rsf, state_evolution=ageing_law, block_type=springblock) +forward = Forward( + friction_model=rsf, + state_evolution={"theta": ageing_law}, + block_type=springblock +) ``` The `Forward` class implements a `__call__` method that, when given the appropriate variables ($\vec{X}$), parameters ($\left\{ a, b, D_c \right\}$), friction constants ($\left\{ \mu_0, v_0 \right\}$), and spring-block constants ($\left\{ k, v_{lp} \right\}$), yields $\mathrm{d} \vec{X} / \mathrm{d} t$. The `forward` class instance is passed to an ODE solver, which will successively call `forward(...)` to obtain the time derivatives for integration. To enable the assembly of a forward model from the three components, each component needs to follow a specific call signature. These are: ```python -def friction_model(variables: Variables, parameters: _Params, friction_constants: _Constants) -> Float: +def friction_model( + variables: Variables, parameters: + _Params, friction_constants: _Constants + ) -> Float: velocity: Float = ... return velocity -def state_evolution(variables: Variables, parameters: _Params, friction_constants: _Constants) -> Float: +def state_evolution( + velocity: Float, variables: Variables, + parameters: _Params, friction_constants: _Constants + ) -> Float: state_change: Float = ... return state_change -def block_type(velocity: Float, variables: Variables, constants: _BlockConstants) -> Float: +def block_type( + time: Float, velocity: Float, velocity_derivs: Variables, + variables: Variables, state_rate: Float[Array, "..."], + constants: _BlockConstants + ) -> Float: friction_change: Float = ... return friction_change ``` diff --git a/docs/source/user-guide/forward/spring-block.md b/docs/source/user-guide/forward/spring-block.md index 4e30c92..0554d67 100644 --- a/docs/source/user-guide/forward/spring-block.md +++ b/docs/source/user-guide/forward/spring-block.md @@ -53,9 +53,7 @@ To the forward model is completed with the somewhat trivial expression for the p \dot{x} = v(\mu, \theta, \dots) \quad \text{(friction law)} \end{cases} ``` -Correspondingly, the solution vector gains an extra component, $\vec{X}(t) = \left[ \mu(t), \theta(t), x(t) \right]^\intercal$. - -In order for a friction model to be compatible with an inertial spring-block, it must implement a `_partials` method to compute the partial derivatives of $v$ with respect to the relevant variables. This method takes as an argument the vector $\vec{X}$ and returns $\partial v / \partial \mu$ and the sum of the remaining partial derivative terms ($(\partial v / \partial \theta) \dot{\theta} + \dots$). If a `_partials` method is absent, a `NotImplementedError` exception will be raised upon instantiating `diabayes.forward_models.Forward`. +Correspondingly, the solution vector gains an extra component, $\vec{X}(t) = \left[ \mu(t), \theta(t), x(t) \right]^\intercal$. Also notice that the invertial spring-block requires the partial derivatives of $\partial v$ with respect to $\mu$ and the various state variables. These are computed automatically through the JAX's automatic differentiation routines, and so they do not need to be implemented in analytical form by the user. This is especially helpful if the friction law does not have a convenient explicit derivative (e.g. a Neural Network, or a conditional switch). The inertial spring-block model is available through `diabayes.forward_models.inertial_springblock`. diff --git a/docs/source/user-guide/inversion/bayesian.md b/docs/source/user-guide/inversion/bayesian.md index 54bceab..0de5bcc 100644 --- a/docs/source/user-guide/inversion/bayesian.md +++ b/docs/source/user-guide/inversion/bayesian.md @@ -69,7 +69,7 @@ Concretely, if one wants to perform a Bayesian inversion of the forward model pa ```python bayesian_result = solver.bayesian_inversion( t=t, mu=mu_measured, noise_std=noise_std, - params=params_init, friction_constants=constants, + y0, params=params_init, friction_constants=constants, block_constants=block_constants, Nparticles=1500, Nsteps=100, rng=42 ) diff --git a/docs/source/user-guide/inversion/max-likelihood.md b/docs/source/user-guide/inversion/max-likelihood.md index 49fa8f3..e8177af 100644 --- a/docs/source/user-guide/inversion/max-likelihood.md +++ b/docs/source/user-guide/inversion/max-likelihood.md @@ -8,7 +8,7 @@ Concretely, performing a maximum-likelihood inversion in DiaBayes is fairly stra ```python inv_result = solver.max_likelihood_inversion( t, mu_measured, - params=params_guess, + y0=y0, params=params_guess, friction_constants=constants, block_constants=block_constants, verbose=True diff --git a/examples/simple_inversion.ipynb b/examples/simple_inversion.ipynb index 0603cb2..c609e01 100644 --- a/examples/simple_inversion.ipynb +++ b/examples/simple_inversion.ipynb @@ -103,14 +103,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "89636489", "metadata": {}, "outputs": [], "source": [ - "# Initial values of friction and state\n", - "forward.set_initial_values(mu=mu0, theta=theta0)\n", - "y0 = forward.variables\n", "# RSF parameters\n", "params = db.RSFParams(a=a, b=b, Dc=Dc)\n", "# RSF constants\n", @@ -118,6 +115,10 @@ "# Spring-block constants\n", "block_constants = db.SpringBlockConstants(k=k, v_lp=v1)\n", "\n", + "# Initial values of friction and state\n", + "forward.set_initial_values(mu=mu0, theta=theta0)\n", + "y0 = forward.variables\n", + "\n", "# The time samples for which we want a solution\n", "t = jnp.linspace(0, 15., 1000)\n", "\n", @@ -515,6 +516,7 @@ "- `t`: the time at which the measurements are sampled\n", "- `mu`: the friction measurements\n", "- `noise_std`: an estimate of the standard deviation of the friction noise. Note that a larger value will permit a wider range of parameter values. Empirically, dividing the noise amplitude by 2 gives somewhat more acceptable results.\n", + "- `y0`: the initial values of the variables (friction, state, ...)\n", "- `params`: the maximum-likelihood parameter values (obtained from the inversion above)\n", "- `friction_constants`, `block_constants`: the constant parameters that will not be inverted for\n", "- `Nparticles`: the number of particles in the swarm. A higher number gives more robust statistics, at the expense of a higher computational cost (though this doesn't scale proportionally due to significant GPU/CPU transmission overhead etc.). A minimum value around 1000 is recommended.\n", From b9de1f8012f8639aad397fa109adacea7e57cf3d Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Thu, 28 Aug 2025 12:10:36 +0200 Subject: [PATCH 52/56] Figured out how to use autosummary/autodoc --- .gitignore | 2 +- diabayes/forward_models.py | 3 +++ docs/source/api/forward_models.md | 41 +++++++++++++++++++++++++++++++ docs/source/api/index.md | 7 ++++++ docs/source/conf.py | 2 +- 5 files changed, 53 insertions(+), 2 deletions(-) create mode 100644 docs/source/api/forward_models.md create mode 100644 docs/source/api/index.md diff --git a/.gitignore b/.gitignore index 77ab8e8..93a3820 100644 --- a/.gitignore +++ b/.gitignore @@ -74,7 +74,7 @@ instance/ # Sphinx documentation docs/_build/ docs/build/ -docs/source/api +docs/source/_autosummary # PyBuilder .pybuilder/ diff --git a/diabayes/forward_models.py b/diabayes/forward_models.py index 0379b4b..93e24e8 100644 --- a/diabayes/forward_models.py +++ b/diabayes/forward_models.py @@ -83,6 +83,9 @@ def ageing_law( def slip_rate( v: Float, variables: Variables, params: RSFParams, constants: RSFConstants ) -> Float: + r""" + Evolve slip from slip rate + """ return v diff --git a/docs/source/api/forward_models.md b/docs/source/api/forward_models.md new file mode 100644 index 0000000..16b1d64 --- /dev/null +++ b/docs/source/api/forward_models.md @@ -0,0 +1,41 @@ +```{eval-rst} +.. currentmodule:: diabayes.forward_models +``` + +# Forward models + +```{eval-rst} +.. autosummary:: + :toctree: ../_autosummary + + Forward +``` + +## Friction laws + +```{eval-rst} +.. autosummary:: + :toctree: ../_autosummary + + rsf +``` + +## State evolution models + +```{eval-rst} +.. autosummary:: + :toctree: ../_autosummary + + ageing_law + slip_rate +``` + +## Stress-transfer models + +```{eval-rst} +.. autosummary:: + :toctree: ../_autosummary + + springblock + intertial_springblock +``` \ No newline at end of file diff --git a/docs/source/api/index.md b/docs/source/api/index.md new file mode 100644 index 0000000..3fb4e6b --- /dev/null +++ b/docs/source/api/index.md @@ -0,0 +1,7 @@ +# API Reference + +```{toctree} +:maxdepth: 1 + +forward_models +``` \ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py index c55fa07..725bcea 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -18,7 +18,7 @@ "sphinx_design", "sphinx_copybutton", "sphinxcontrib.bibtex", - "sphinx.ext.autodoc", + "sphinx.ext.autosummary", "sphinx.ext.napoleon", "sphinx.ext.doctest", "sphinx.ext.viewcode", From 411b219c57fc2d4ae0d18c4f366fa14e06b8ca9e Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Thu, 28 Aug 2025 18:16:26 +0200 Subject: [PATCH 53/56] Docstrings forward models --- diabayes/forward_models.py | 185 ++++++++++++++++++++---------- docs/source/api/forward_models.md | 4 +- 2 files changed, 130 insertions(+), 59 deletions(-) diff --git a/diabayes/forward_models.py b/diabayes/forward_models.py index 93e24e8..8fe8227 100644 --- a/diabayes/forward_models.py +++ b/diabayes/forward_models.py @@ -24,7 +24,7 @@ @eqx.filter_jit def rsf(variables: Variables, params: RSFParams, constants: RSFConstants) -> Float: r""" - The classical rate-and-state friction law, given by: + The classical rate-and-state friction law .. math:: @@ -33,16 +33,16 @@ def rsf(variables: Variables, params: RSFParams, constants: RSFConstants) -> Flo Parameters ---------- variables : Variables - The friction coefficient `mu` and state parameter `theta` + The friction coefficient ``mu`` and state parameter ``theta`` params : RSFParams - The rate-and-state parameters `a`, `b`, and `D_c` + The rate-and-state parameters ``a``, ``b``, and ``D_c`` constants : RSFConstants - The constant parameters `mu0` and `v0` + The constant parameters ``mu0`` and ``v0`` Returns ------- v : Float - The instantaneous slip rate in the same units as `v0` + The instantaneous slip rate in the same units as ``v0`` """ Omega = params.b * jnp.log(variables.theta * constants.v0 / params.Dc) v = constants.v0 * jnp.exp((variables.mu - constants.mu0 - Omega) / params.a) @@ -54,7 +54,7 @@ def ageing_law( v: Float, variables: Variables, params: RSFParams, constants: RSFConstants ) -> Float: r""" - The conventional ageing law state evolution formulation: + The conventional ageing law state evolution formulation .. math:: @@ -65,26 +65,54 @@ def ageing_law( v : Float Instantaneous fault slip rate [m/s]. variables : Variables - The friction coefficient `mu` and state parameter `theta` + The friction coefficient ``mu`` and state parameter ``theta`` params : RSFParams - The rate-and-state parameters `a`, `b`, and `D_c` + The rate-and-state parameters, including ``D_c`` constants : RSFConstants - The constant parameters `mu0` and `v0` + The constant parameters ``mu0`` and ``v0`` (not used) Returns ------- dtheta : Float - The rate of change of the state variable `theta` [s/s] + The rate of change of the state variable [s/s] """ return 1 - v * variables.theta / params.Dc +@eqx.filter_jit +def aging_law(*args, **kwargs): + """ + Alias for the ``ageing_law`` function + """ + return ageing_law(*args, **kwargs) + + @eqx.filter_jit def slip_rate( v: Float, variables: Variables, params: RSFParams, constants: RSFConstants ) -> Float: r""" Evolve slip from slip rate + + .. math:: + + \frac{\mathrm{d} x}{\mathrm{d} t} = v + + Parameters + ---------- + v : Float + Instantaneous fault slip rate [m/s] + variables : Variables + The friction coefficient ``mu`` and state parameter ``theta`` (not used) + params : RSFParams + The rate-and-state parameters (not used) + constants : RSFConstants + The constant parameters ``mu0`` and ``v0`` (not used) + + Returns + ------- + v : Float + The rate of change of slip [m/s] """ return v @@ -99,7 +127,7 @@ def springblock( constants: SpringBlockConstants, ) -> Float: r""" - A conventional (non-inertial) spring-block loading formulation: + A conventional (non-inertial) spring-block loading formulation .. math:: @@ -110,16 +138,16 @@ def springblock( v : Float Instantaneous fault slip rate [m/s]. variables : Variables - The friction coefficient `mu` and state parameter `theta`. This argument - is not used, but included for call signature consistency. + The instantaneous variables. This argument is not used, + but included for call signature consistency. constants : SpringBlockConstants - The constant parameters stiffness `k` (units of "friction per metre") - and load-point velocity `v_lp` (same units as `v`). + The constant parameters stiffness ``k`` (units of "friction per metre") + and load-point velocity ``v_lp`` (same units as ``v``). Returns ------- dmu : Float - The rate of change of the friction coefficient `mu` [1/s] + The rate of change of the friction coefficient [1/s] """ return constants.k * (constants.v_lp - v) @@ -134,7 +162,48 @@ def inertial_springblock( constants: InertialSpringBlockConstants, ) -> Float: r""" - An inertial spring-block loading formulation: + An inertial spring-block loading formulation + + .. math:: + \frac{\mathrm{d} \mu}{\mathrm{d} t} = \left[ \frac{\partial v}{\partial \mu} \right]^{-1} \left( \frac{1}{M} \left[ k \left( v_{lp} t - x \right) - \mu \right] - \frac{\partial v}{\partial \theta} \frac{\mathrm{d} \theta}{\mathrm{d} t} - \dots \right) + + The acceleration term in the classical inertial spring-block formulation + is decomposed into its partial derivatives, avoiding the need for solving + a second-order ODE. These partial derivatives (``v_partials``) are computed + using the JAX autodiff framework. + + Notes + ----- + This formulation is rather stiff, and for certain parameter values could + lead to extremely small time steps necessary to maintain numerical + accuracy. It is recommended to use a conventional (non-inenrtial) + ``springblock`` formulation for basic velocity-steps and slide-hold-slide + simuilations. Inertia is only really needed for stick-slip simulations. + + Parameters + ---------- + t : Float + Current value of time [s] + v : Float + Instantaneous fault slip rate [m/s] + v_partials : Variables + The partial derivatives of slip rate to the relevant variables + (friction and state variables) + variables: Variables + The instantaneous values of the variables: friction (``mu``), + slip (``slip``), and other state variables (not used) + dstate : Array + The time derivatives of the state variables. The radiation term + is ``v_partials @ dstate`` (excluding ``mu``) + constants : InertialSpringBlockConstants + The spring-block constants containing the mass term ``M``, the + stiffness ``k``, and the load-point velocity ``v_lp`` + + Returns + ------- + dmu : Float + The rate of change of the friction coefficient [1/s] + """ mass_term = ( constants.k * (constants.v_lp * t - variables.slip) - variables.mu @@ -148,9 +217,9 @@ def inertial_springblock( class Forward(Generic[BC]): r""" - The `Forward` class assembles the various components that comprise - a forward model, such that `Forward.__call__` takes some variables - and returns the rate of change of these variables, i.e.: + The ``Forward`` class assembles the various components that comprise + a forward model, such that ``Forward.__call__`` takes some variables + and returns the rate of change of these variables .. math:: @@ -158,14 +227,13 @@ class Forward(Generic[BC]): This forward ODE can then be solved by any ODE solver. - The `Forward` class is instantiated by providing a friction model - (of type `FrictionModel`), a "state" evolution law (of type `StateEvolution`), - and a stress transfer model. + The ``Forward`` class is instantiated by providing a friction model, + a "state" evolution law, and a stress transfer model. Examples -------- >>> from diabayes.forward_models import ageing_law, rsf, springblock, Forward - >>> foward_model = Forward(rsf, ageing_law, springblock) + >>> foward_model = Forward(rsf, {"theta": ageing_law}, springblock) >>> X_dot = forward_model(variables=..., params=..., friction_constants=..., block_constants=...) """ @@ -201,42 +269,17 @@ def __init__( ) pass - @staticmethod - def inspect(fn: Union[Callable, Tuple[Callable, ...]]) -> None: - - import ast - import inspect - - def get_accessed_attrs(func, arg_name): - """Helper routine to automatically extract the variable names""" - tree = ast.parse(inspect.getsource(func)) - accessed = set() - - class Visitor(ast.NodeVisitor): - def visit_Attribute(self, node: ast.Attribute): - if isinstance(node.value, ast.Name) and node.value.id == arg_name: - accessed.add(node.attr) - self.generic_visit(node) - - Visitor().visit(tree) - return accessed - - if not isinstance(fn, Iterable): - fn = (fn,) - - accessed = set() - - for fn_i in fn: - accessed.update(get_accessed_attrs(fn_i, "variables")) - - if "mu" in accessed: - accessed.remove("mu") + def set_initial_values(self, **kwargs) -> None: + """ + Set the initial values of the ``variables``. This sets the + values as ``self.variables``, which is a ``Variables`` object. - print("Auto-detected the following state variable names:") - print(accessed) + Parameters + ---------- + **kwargs : dict + Key-value pairs of variable names and corresponding values - def set_initial_values(self, **kwargs) -> None: - # Change this shit back it doesn't work and breaks everything + """ scalars = {k: float(jnp.asarray(v).item()) for k, v in kwargs.items()} self.variables = self.variables.set_values(**scalars) @@ -249,6 +292,29 @@ def __call__( friction_constants: _Constants, block_constants: BC, ) -> Variables: + """ + Calculate the rate of change of the variables, defining the ODE + to be solved. + + Parameters + ---------- + t : Float + Current value of time [s] + variables : diabayes.Variables + The instantaneous values of the variables for which the time + derivative will be computed + params : _Params + The forward model (invertible) parameters + friction_constants : _Constants + The forward model constants + block_constants : _BlockConstants + The constants associated with the stress transfer + + Returns + ------- + dvars : Variables + The instantaneous rate of change of the ``variables`` + """ # Calculate v and its partial derivatives with respect to # the variables (mu, state1, state2, ...) v, v_derivs = eqx.filter_value_and_grad(self.friction_model)( @@ -262,3 +328,6 @@ def __call__( state_obj = StateDict(variables.state.keys, dstate) # Create a new variables container return Variables(mu=dmu, state=state_obj) + + # Alias for documentation + call = __call__ diff --git a/docs/source/api/forward_models.md b/docs/source/api/forward_models.md index 16b1d64..7e9605e 100644 --- a/docs/source/api/forward_models.md +++ b/docs/source/api/forward_models.md @@ -11,6 +11,7 @@ Forward ``` + ## Friction laws ```{eval-rst} @@ -27,6 +28,7 @@ :toctree: ../_autosummary ageing_law + aging_law slip_rate ``` @@ -37,5 +39,5 @@ :toctree: ../_autosummary springblock - intertial_springblock + inertial_springblock ``` \ No newline at end of file From 136c3e66f59a4faa6fda8a45d68d0dffb202fdc6 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Fri, 29 Aug 2025 12:28:53 +0200 Subject: [PATCH 54/56] Added many more docstrings --- diabayes/SVI.py | 17 +-- diabayes/__init__.py | 8 +- diabayes/solver.py | 136 +++++++++++++++++++++- diabayes/typedefs.py | 13 +-- docs/source/api/index.md | 2 + docs/source/api/solver.md | 15 +++ docs/source/api/svi.md | 12 ++ docs/source/community.md | 21 ++++ docs/source/user-guide/adding_features.md | 6 +- docs/source/user-guide/advanced_usage.md | 6 +- 10 files changed, 212 insertions(+), 24 deletions(-) create mode 100644 docs/source/api/solver.md create mode 100644 docs/source/api/svi.md diff --git a/diabayes/SVI.py b/diabayes/SVI.py index 9b39e10..b87b0fd 100644 --- a/diabayes/SVI.py +++ b/diabayes/SVI.py @@ -13,6 +13,7 @@ @eqx.filter_jit def _distance(x1: ParticleArray, x2: ParticleArray) -> Float[Array, "N N"]: + """Euclidean distance between two particles `x1` and `x2`""" return jnp.square(x1 - x2).sum(axis=-1) @@ -45,25 +46,25 @@ def compute_phi( ) -> GradientArray: """ Compute the Stein variational gradients for a set of - particles (`x`) and the gradients of the log-likelihood - function (`gradp`) and the log-prior (`gradq`). + particles (``x``) and the gradients of the log-likelihood + function (``gradp``) and the log-prior (``gradq``). Parameters ---------- x : ParticleArray The set of invertible parameters ("particles") - of shape (Nparticles, Ndimensions) + of shape ``(Nparticles, Ndimensions)`` gradp, gradq : GradientArray - The gradients of the log-likelihood (`gradp`) and the - log-prior (`gradq`) with respect to the invertible parameters. - Has a shape (Nparticles, Ndimensions) + The gradients of the log-likelihood (``gradp``) and the + log-prior (``gradq``) with respect to the invertible parameters. + Has a shape ``(Nparticles, Ndimensions)`` Returns ------- grad_x : Gradients The directional gradients for the particle updates, e.g. - `new_x = x + step_size * grad_x`. Has the same - shape as `x` and `gradp, gradq`. + ``new_x = x + step_size * grad_x``. Has the same + shape as ``x`` and ``gradp, gradq``. """ h = _median_trick_h(x) map_over_a = (0, None, None) diff --git a/diabayes/__init__.py b/diabayes/__init__.py index 99ece2e..83a18bb 100644 --- a/diabayes/__init__.py +++ b/diabayes/__init__.py @@ -1 +1,7 @@ -from diabayes.typedefs import RSFConstants, RSFParams, SpringBlockConstants, Variables +from diabayes.typedefs import ( + BayesianSolution, + RSFConstants, + RSFParams, + SpringBlockConstants, + Variables, +) diff --git a/diabayes/solver.py b/diabayes/solver.py index f17b75c..f215409 100644 --- a/diabayes/solver.py +++ b/diabayes/solver.py @@ -29,6 +29,27 @@ class ODESolver: + """ + The main solver class that contains forward and inverse modelling + methods. + + Attributes + ---------- + forward_model : Forward + An instantiated ``diabayes.Forward`` class, including + friction law, state evolution equations, and stress + transfer equation + rtol, atol : float + The relative and absolute tolerances used by the ODE solver + checkpoints : int + The number of checkpoints to use to compute the (adjoint) + gradients through the ODE routine. A higher number increases + stability and speed, at the expense of more GPU memory + learning_rate : float + The initial learning rate provided to the Adam algorithm + for the Stein Variational Inference. The default value of + ``1e-2`` seems like a sensible choice for most models. + """ forward_model: Forward rtol: float @@ -43,6 +64,20 @@ def __init__( atol: float = 1e-12, checkpoints: int = 100, ) -> None: + """ + Parameters + ---------- + forward_model : Forward + An instantiated ``diabayes.Forward`` class, including + friction law, state evolution equations, and stress + transfer equation + rtol, atol : float + The relative and absolute tolerances used by the ODE solver + checkpoints : int + The number of checkpoints to use to compute the (adjoint) + gradients through the ODE routine. A higher number increases + stability and speed, at the expense of more GPU memory + """ self.forward_model = forward_model self.rtol = rtol self.atol = atol @@ -212,6 +247,49 @@ def max_likelihood_inversion( block_constants: _BlockConstants, verbose: bool = False, ) -> optx.Solution: + r""" + Minimises the least-squares residuals between the observed friction + curve and the parameterised one, using the Levenberg-Marquardt + algorithm. + + Parameters + ---------- + t : Array + A vector or time values (in units of seconds). The time steps + do not need to be uniform + mu : Array + The observed friction curve sampled at ``t`` + y0 : Variables + The initial values for the modelled friction and any state + variables + params : _Params + The initial guess for the invertible parameters that characterise + the forward problem. These need to be sufficiently close to the + "true" values for the algorithm to converge + friction_constants : _Constants + The non-invertible constants that characterise the forward + problem + block_constants : _BlockConstants + The stress transfer constants (e.g. stiffness and loading rate) + verbose : bool + Whether or not to output detailed progress of the inversion. + Defaults to ``False`` + + Returns + ------- + sol : optimistix.Solution + The inversion result, including various diagnostics. The + inverted parameter values can be accessed as ``sol.values`` + + Notes + ----- + If an error is produced in the first iteration step, it is quite + possible that the initial guess parameters were too far off from + the "true" values (i.e., the mismatch between the observed and + modelled friction curves is too large), breaking the Gauss-Newton + step of the Levenberg-Marquardt algorithm. Initial manual tuning + is recommended. + """ if verbose: verbose_opts = frozenset(["step", "loss"]) @@ -247,7 +325,63 @@ def bayesian_inversion( Nparticles: int = 1000, Nsteps: int = 150, rng: Union[None, int, jax.Array] = None, - ): + ) -> BayesianSolution: + """ + A Bayesian inversion routine using the Stein Variational Inference + method. + + Parameters + ---------- + t : Array + A vector or time values (in units of seconds). The time steps + do not need to be uniform + mu : Array + The observed friction curve sampled at ``t`` + noise_std : Float + An estimate of the standard deviation of the noise in the + measured friction curve. A conservative value is recommended, + i.e. if the noise has a standard deviation of $10^{-3}$, a good + starting point would be to set ``noise_std = 0.5e-3`` + y0 : Variables + The initial values for the modelled friction and any state + variables + params : _Params + The initial guess for the invertible parameters that characterise + the forward problem. It is recommended to use the result from + ``ODESolver.max_likelihood_inversion``. The prior distribution + will be centered around this initial guess + friction_constants : _Constants + The non-invertible constants that characterise the forward + problem + block_constants : _BlockConstants + The stress transfer constants (e.g. stiffness and loading rate) + Nparticles : int + The number of particles (= posterior samples) to include. A higher + value gives a more accurate estimation of the posterior + distribution, at a higher computational cost. + Nsteps : int + The number of iterations before convergence is expected to be + achieved. It is recommended to start with a value of 100 and then + see if an equilibrium was actually achieved. Increasing this value + beyond the point of equilibrium does not do anything. + rng: None, int, jax.random.PRNGKey + The random seed used to initialise the particle swarm distribution. + If ``None``, the current time will be used as a seed, which leads + to different result for each realisation. When an integer is + provided, a new ``jax.random.PRNGKey`` is generated. + + Returns + ------- + result : diabayes.BayesianSolution + The result of the inversion incapsulated in a ``BayesianSolution`` + container, which provides access to the full convergence chains, + as well as diagnostic and visualisation routines. + + Notes + ----- + This method is currently only compatible with standard rate-and-state + friction models. + """ assert isinstance( params, RSFParams diff --git a/diabayes/typedefs.py b/diabayes/typedefs.py index ff132bf..4ebe630 100644 --- a/diabayes/typedefs.py +++ b/diabayes/typedefs.py @@ -1,23 +1,12 @@ import dataclasses as dcs from time import time_ns -from typing import ( - Any, - Iterable, - Mapping, - NamedTuple, - Protocol, - Tuple, - TypeAlias, - TypeVar, - Union, -) +from typing import Any, NamedTuple, Protocol, Tuple, TypeVar, Union import equinox as eqx import jax import jax.numpy as jnp import jax.random as jr import matplotlib.pyplot as plt -from jax import tree_util from jaxtyping import Array, Float """ diff --git a/docs/source/api/index.md b/docs/source/api/index.md index 3fb4e6b..e98a777 100644 --- a/docs/source/api/index.md +++ b/docs/source/api/index.md @@ -4,4 +4,6 @@ :maxdepth: 1 forward_models +svi.md +solver.md ``` \ No newline at end of file diff --git a/docs/source/api/solver.md b/docs/source/api/solver.md new file mode 100644 index 0000000..5dfafd9 --- /dev/null +++ b/docs/source/api/solver.md @@ -0,0 +1,15 @@ +```{eval-rst} +.. currentmodule:: diabayes.solver +``` + +# Solver routines + +```{eval-rst} +.. autosummary:: + :toctree: ../_autosummary + + ODESolver + ODESolver.bayesian_inversion + ODESolver.max_likelihood_inversion + ODESolver.solve_forward +``` \ No newline at end of file diff --git a/docs/source/api/svi.md b/docs/source/api/svi.md new file mode 100644 index 0000000..2d8917c --- /dev/null +++ b/docs/source/api/svi.md @@ -0,0 +1,12 @@ +```{eval-rst} +.. currentmodule:: diabayes.SVI +``` + +# Stein Variational Inference + +```{eval-rst} +.. autosummary:: + :toctree: ../_autosummary + + compute_phi +``` \ No newline at end of file diff --git a/docs/source/community.md b/docs/source/community.md index c2a2bcc..ab62726 100644 --- a/docs/source/community.md +++ b/docs/source/community.md @@ -2,8 +2,29 @@ ## Frequently Asked Questions +### The maximum-likelihood inversion crashes before converging + +To ensure that everything is wired-up correctly, check that you are able to run a forward simulation with your specified forward model. If that works well, make sure that the initial guess of the parameters produces a friction curve that is sufficiently close to the observed friction. For parameter estimates that are too far off, the Gauss-Newton step of the Levenberg-Marquardt algorithm may fail. + +### I am getting a mysterious error + +Under the hood, most of the computations are handled by JAX, which compiles and ports the code to a GPU or multi-core CPU. One disadvantage of this automatic optimisation and acceleration, is that the error messages that are being produced tend to be rather cryptic, and often the final ``Exception`` is not at all informative of the code segment that causes trouble. To facilitate debugging, you can turn off the _Just-in-Time_ (JIT) compiler by including the following at the top of your script: +```python +import jax +jax.config.update("jax_disable_jit", True) +``` +If NaNs are produced, it may also help to turn on NaN debugging: +```python +jax.config.update("jax_debug_nans", True) +``` +Note that ``jax.config`` should be set only at the start of your script before any JAX routines are called. The configuration will stay enabled until the script terminates. + ## How to cite? A publication describing this software package is underway. Until then, feel free to refer to the GitHub repository: https://github.com/martijnende/diabayes +## How to contribute? + +If you have ideas for new features to include, you can submit an issue on [GitHub](https://github.com/martijnende/diabayes) to discuss it. See [the User Guide](user-guide/adding_features.md) for general guidelines on how to extend DiaBayes with new physics. Contributed example Jupyter Notebooks and documentation improvements are also welcome. + ## Useful resources diff --git a/docs/source/user-guide/adding_features.md b/docs/source/user-guide/adding_features.md index ccc248b..f0e85c3 100644 --- a/docs/source/user-guide/adding_features.md +++ b/docs/source/user-guide/adding_features.md @@ -1,5 +1,9 @@ # Adding custom physics ```{admonition} Future work -To be written +To be written. This will include + +- Friction laws +- State equations +- Stress transfer ``` \ No newline at end of file diff --git a/docs/source/user-guide/advanced_usage.md b/docs/source/user-guide/advanced_usage.md index 214a9cb..27c6885 100644 --- a/docs/source/user-guide/advanced_usage.md +++ b/docs/source/user-guide/advanced_usage.md @@ -1,5 +1,9 @@ # Advanced use cases ```{admonition} Future work -To be written +To be written. This page will include + +- Combining multiple state equations +- Stick-slip inversion +- Neural Networks ``` \ No newline at end of file From 1f76806d7597f57bc1657f6f798190b8090b6b99 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Fri, 29 Aug 2025 16:36:20 +0200 Subject: [PATCH 55/56] Docstrings of containers --- docs/source/api/index.md | 5 +++-- docs/source/api/typedefs.md | 24 ++++++++++++++++++++++++ docs/source/conf.py | 8 ++++++++ 3 files changed, 35 insertions(+), 2 deletions(-) create mode 100644 docs/source/api/typedefs.md diff --git a/docs/source/api/index.md b/docs/source/api/index.md index e98a777..cea646e 100644 --- a/docs/source/api/index.md +++ b/docs/source/api/index.md @@ -3,7 +3,8 @@ ```{toctree} :maxdepth: 1 -forward_models -svi.md +typedefs.md +forward_models.md solver.md +svi.md ``` \ No newline at end of file diff --git a/docs/source/api/typedefs.md b/docs/source/api/typedefs.md new file mode 100644 index 0000000..3a93411 --- /dev/null +++ b/docs/source/api/typedefs.md @@ -0,0 +1,24 @@ +```{eval-rst} +.. currentmodule:: diabayes.typedefs +``` + +# Data containers + +## Variables + +```{eval-rst} +.. autosummary:: + :toctree: ../_autosummary + + Variables + StateDict +``` + +## Inversion results + +```{eval-rst} +.. autosummary:: + :toctree: ../_autosummary + + BayesianSolution +``` diff --git a/docs/source/conf.py b/docs/source/conf.py index 725bcea..3199526 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -36,6 +36,14 @@ templates_path = ["_templates"] exclude_patterns = [] +autodoc_class_content = "class" +autodoc_member_order = "groupwise" +autodoc_default_options = { + "members": True, + "undoc-members": True, + "inherited-members": True, +} + # -- Options for HTML output ------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output From 366970b790002370ba83fd4ba370405c8b056076 Mon Sep 17 00:00:00 2001 From: Martijn van den Ende Date: Fri, 29 Aug 2025 16:46:07 +0200 Subject: [PATCH 56/56] Forgot to include .py in commit... --- diabayes/solver.py | 50 ++++------- diabayes/typedefs.py | 202 +++++++++++++++++++++++++++++++++++++++++-- 2 files changed, 215 insertions(+), 37 deletions(-) diff --git a/diabayes/solver.py b/diabayes/solver.py index f215409..045391a 100644 --- a/diabayes/solver.py +++ b/diabayes/solver.py @@ -32,30 +32,30 @@ class ODESolver: """ The main solver class that contains forward and inverse modelling methods. - - Attributes - ---------- - forward_model : Forward - An instantiated ``diabayes.Forward`` class, including - friction law, state evolution equations, and stress - transfer equation - rtol, atol : float - The relative and absolute tolerances used by the ODE solver - checkpoints : int - The number of checkpoints to use to compute the (adjoint) - gradients through the ODE routine. A higher number increases - stability and speed, at the expense of more GPU memory - learning_rate : float - The initial learning rate provided to the Adam algorithm - for the Stein Variational Inference. The default value of - ``1e-2`` seems like a sensible choice for most models. """ forward_model: Forward + """ + An instantiated ``diabayes.Forward`` class, including + friction law, state evolution equations, and stress + transfer equation + """ rtol: float + """The absolute tolerance used by the ODE solver""" atol: float + """The absolute tolerance used by the ODE solver""" checkpoints: int + """ + The number of checkpoints to use to compute the (adjoint) + gradients through the ODE routine. A higher number increases + stability and speed, at the expense of more GPU memory + """ learning_rate: float = 1e-2 + """ + The initial learning rate provided to the Adam algorithm + for the Stein Variational Inference. The default value of + ``1e-2`` seems like a sensible choice for most models. + """ def __init__( self, @@ -64,20 +64,6 @@ def __init__( atol: float = 1e-12, checkpoints: int = 100, ) -> None: - """ - Parameters - ---------- - forward_model : Forward - An instantiated ``diabayes.Forward`` class, including - friction law, state evolution equations, and stress - transfer equation - rtol, atol : float - The relative and absolute tolerances used by the ODE solver - checkpoints : int - The number of checkpoints to use to compute the (adjoint) - gradients through the ODE routine. A higher number increases - stability and speed, at the expense of more GPU memory - """ self.forward_model = forward_model self.rtol = rtol self.atol = atol @@ -94,7 +80,7 @@ def solve_forward( method: str = "RK45", ) -> Any: """ - Solve a forward problem using SciPy's `solve_ivp` routine. + Solve a forward problem using SciPy's ``solve_ivp`` routine. While this routine doesn't propagate any gradients, it is much faster to initialise and to perform a single forward run. Hence for playing around with different parameters, diff --git a/diabayes/typedefs.py b/diabayes/typedefs.py index 4ebe630..4d8fc78 100644 --- a/diabayes/typedefs.py +++ b/diabayes/typedefs.py @@ -17,24 +17,74 @@ class StateDict(eqx.Module): + """ + A container class for state variables. A user would typically not + interact with this class directly (only through ``Variables`` and + ``Forward.set_initial_values``). + + Examples + -------- + >>> from diabayes.typedefs import StateDict + >>> import jax.numpy as jnp + >>> keys = ("x", "y") + >>> vals = jnp.array([1.0, -5.1]) + >>> state_dict = StateDict(keys=keys, vals=vals) + """ + keys: tuple[str, ...] = eqx.field(static=True) + """A tuple of key names (strings) corresponding with the state variable names""" vals: jax.Array + """An array of values corresponding (in order) with the variables defined by ``keys``""" def __getitem__(self, k: str) -> Array: + """Get the value of the variable ``k``""" i = self.keys.index(k) return self.vals[i] def replace_values(self, **kwargs) -> "StateDict": - # Why is it so fucking problematic to set a JAX array? - # How many dozens of hours do I need to spend to set a fucking array? + """ + Update the values of the state variables. Since immutability is + required for JIT caching, this method will return a copy of the + class with the updated values. + + Examples + -------- + >>> from diabayes.typedefs import StateDict + >>> import jax.numpy as jnp + >>> state_dict = StateDict(keys=("x",), vals=jnp.asarray(1.0)) + >>> state_dict = state_dict.replace_values({"x": jnp.asarray(2.0)}) + + Parameters + ---------- + **kwargs : dict + Key-value pairs to update. These can overwrite existing values + or create entirely new ones. + + Returns + ------- + state_dict : StateDict + A copy of the current StateDict with updated values + """ mapping = dict(zip(self.keys, self.vals)) mapping.update(kwargs) return StateDict(self.keys, jnp.array([mapping[k] for k in self.keys])) class Variables(eqx.Module): + """ + The main container class for variables (friction and state). It + contains additional convenience routines for mapping between + this class object and JAX arrays. + + Users would typically not instantiate a ``Variables`` object + directly; instead, it is created through the ``Forward.set_initial_values`` + method and retreived as ``Forward.variables``. + """ + mu: jax.Array + """The friction coefficient""" state: StateDict + """A container with the various state variables""" def __getattr__(self, name: str): # NOTE: __getattr__ is only called when __getattribute__, @@ -45,6 +95,18 @@ def __getattr__(self, name: str): raise AttributeError(f"{type(self).__name__} has no attribute {name!r}") def set_values(self, **kwargs) -> "Variables": + """ + Set the values of friction and the state variables through key-value + pair arguments. The state parameters are provided individually as + key-value pairs as well. Because this class is immutable, ``set_values`` + returns a copy of the class with updated values. + + Examples + -------- + >>> state_dict = StateDict(keys=("x", "y"), vals=jnp.array([0.1, 0.2])) + >>> variables = Variables(mu=jnp.asarray(0.6), state=state_dict) + >>> variables = variables.set_values(mu=..., x=..., y=...) + """ mu = jnp.atleast_1d(kwargs.pop("mu")) return dcs.replace(self, mu=mu, state=self.state.replace_values(**kwargs)) @@ -55,6 +117,24 @@ def __repr__(self) -> str: return f"Variables(mu={self.mu}, {state_str})" def to_array(self) -> Float[Array, "..."]: + """ + Convert the container values to a JAX array. The order of the output + follows the order of `StateDict.keys`, with the first item being + the friction coefficient. For ``n`` state variables, the output is + an array of shape ``(1+n,)`` for scalars, and ``(t, 1+n)`` for + time series. + + Examples + -------- + >>> scalars = Variables(...) + >>> mu = scalars.to_array()[0] # friction is first element + >>> timeseries = Variables(...) + >>> mu = timeseries.to_array()[:, 0] + >>> all_final_values = timeseries.to_array()[-1, :] + + Of course, in this example one could simply do ``scalars.mu`` + and ``timeseries.mu`` to extract ``mu`` directly. + """ mu = jnp.asarray(jnp.squeeze(self.mu)) state = jnp.asarray(jnp.squeeze(self.state.vals)) @@ -218,8 +298,6 @@ def __call__( ----------------- """ -Chains = Float[Array, "Nsteps Nparticles Nparams"] - class Statistics(NamedTuple): mean: Float @@ -270,12 +348,36 @@ class CNSStatistics(ParamStatistics): @dcs.dataclass class BayesianSolution: + """ + The result of a Bayesian inversion. This data class stores the + particle trajectories (equivalent of MCMC chains), the final + equilibrum state, and various statistcs, as well as helper + routines to diagnose or visualise the results. + + Examples + -------- + >>> result = solver.bayesian_inversion(...) + >>> print(result.nan_count) # Check for NaN values + >>> print(result.statistics.Dc.median) # Get the median of Dc + >>> result.plot_convergence() # Visually inspect convergence + >>> result.cornerplot(nbins=15) # Create a cornerplot + """ log_params: _Params - chains: Chains + """ + The particles representing the (log-valued) parameters, + including all the iteration steps + """ + chains: Float[Array, "Nsteps Nparticles Nparams"] + """The particle trajectories (to inspect convergence)""" log_likelihood: Float[Array, "Nsteps"] + """The log-likelihood evolution""" nan_count: Float[Array, "Nsteps"] + """The number of NaNs encountered during each iteration step""" + final_state: Float[Array, "Nparticles Nparams"] + """The final state of the particle swarm""" statistics: Union[RSFStatistics, CNSStatistics, None] = None + """Pre-computed statistics of the parameters""" def __init__( self, @@ -284,15 +386,31 @@ def __init__( nan_count: Float[Array, "Nsteps"], ): self.log_params = log_params + # Convert the inversion result to an array and transpose chains = log_params.to_array().transpose(1, 2, 0) + # Undo the log-transform and store chains self.chains = jnp.exp(chains) + # Undo the log-transform and store final state self.final_state = jnp.exp(chains[-1]) + # Store the log-likelihood self.log_likelihood = log_likelihood + # Store the number of NaNs encountered during each step self.nan_count = nan_count # TODO: need to generalise this... self.statistics = RSFStatistics.from_state(self.final_state) def plot_convergence(self): + """ + Helper routine to visualise the convergence of the inversion. + Convergence is achieved when the particles settle in an + equilibrium position (i.e., they stop moving). + + Returns + ------- + fig : matplotlib.pyplot.figure + The figure object that can be manipulated after creation + (e.g. to save to disk) + """ assert self.statistics is not None @@ -322,6 +440,27 @@ def plot_convergence(self): return fig def cornerplot(self, nbins=20): + """ + Create a corner plot from the particle positions. For `N` + parameters, a corner plot is the lower half of an `N x N` + grid of subplots. On the diagonal, the histogram of the + marginal posterior over the `i-th` parameter is plotted. + Below the diagonal, the `(i,j)`-th panel is a scatter + plot of parameter `i` against parameter `j`, which shows + the co-variance between the two quantities. + + Parameters + ---------- + nbins : int + The number of bins to use for the histograms on the + diagonal of the corner plot + + Returns + ------- + fig : matplotlib.pyplot.figure + The figure object that can be manipulated after creation + (e.g. to save to disk) + """ assert self.statistics is not None params = self.statistics.get_param_names() @@ -371,6 +510,7 @@ def cornerplot(self, nbins=20): return fig def __repr__(self) -> str: + """Print out nicely-formatted statistics""" assert self.statistics is not None @@ -404,6 +544,58 @@ def sample( nsamples: int = 10, rng: Union[None, int, jax.Array] = None, ): + """ + Draw random samples from the posterior distribution. Each + particle should represent one realisation of a plausible + friction curve. For ``nsamples`` realisations requested by a + user, it is more beneficial to `vmap` the forward simulations + rather than compute them one by one. + + The resulting friction curves can be used for validation + purposes (does the posterior match the data?). + + Examples + -------- + >>> result = solver.bayesian_inversion(...) + >>> samples, sample_results = result.sample( + ... solver=solver, y0=y0, t=t, + ... friction_constants=constants, + ... block_constants=block_constants, + ... nsamples=20, rng=42) + ... ) + + Parameters + ---------- + solver : ODESolver + The ``ODESolver`` class instantiated with the ``Forward`` + model (typically the same one as used for the inversion) + y0 : Variables + The initial values of friction and state + t : Array + The time samples at which a solution is requested. This + does not need to be the same as the time samples recorded + in the experiment (from which the observed friction curve + is obtained) + friction_constants : _Constants + The friction and state model constants + block_constants : _BlockConstants + The stress transfer constants + nsamples : int + The number of random samples for which a solution should + be computed + rng: None, int, jax.random.PRNGKey + The random seed used to sample from the particle distribution. + If ``None``, the current time will be used as a seed, which leads + to different result for each realisation. When an integer is + provided, a new ``jax.random.PRNGKey`` is generated. + + Returns + ------- + samples : _Params + The samples drawn from the posterior distribution + sample_results : Variables + The forward simulation results corresponding with ``samples`` + """ if rng is None: key = jr.PRNGKey(time_ns())