diff --git a/environment.yml b/environment.yml index 7061885d..232469b8 100644 --- a/environment.yml +++ b/environment.yml @@ -16,6 +16,7 @@ dependencies: - r-essentials - pytest - pytest-cov +- twine - pip: - python-dotenv diff --git a/tidepool-analysis-tools/RELEASING.md b/tidepool-analysis-tools/RELEASING.md new file mode 100644 index 00000000..542ca436 --- /dev/null +++ b/tidepool-analysis-tools/RELEASING.md @@ -0,0 +1,6 @@ +## Publishing a PyPI release + +1. Create a release in github. +2. Update version_string in setup.py with the version from step 1. +3. Run the pypi_push.sh script + diff --git a/tidepool-analysis-tools/tests/fixtures/__init__.py b/tidepool-analysis-tools/__init__.py similarity index 100% rename from tidepool-analysis-tools/tests/fixtures/__init__.py rename to tidepool-analysis-tools/__init__.py diff --git a/tidepool-analysis-tools/pypi_push.sh b/tidepool-analysis-tools/pypi_push.sh new file mode 100644 index 00000000..a1e0867d --- /dev/null +++ b/tidepool-analysis-tools/pypi_push.sh @@ -0,0 +1,3 @@ +rm dist/* +python setup.py sdist +echo "RUN: twine upload dist/{VERSION}.tar.gz" \ No newline at end of file diff --git a/tidepool-analysis-tools/setup.cfg b/tidepool-analysis-tools/setup.cfg new file mode 100644 index 00000000..224a7795 --- /dev/null +++ b/tidepool-analysis-tools/setup.cfg @@ -0,0 +1,2 @@ +[metadata] +description-file = README.md \ No newline at end of file diff --git a/tidepool-analysis-tools/setup.py b/tidepool-analysis-tools/setup.py index 5ad5d567..a0ce7a33 100644 --- a/tidepool-analysis-tools/setup.py +++ b/tidepool-analysis-tools/setup.py @@ -20,9 +20,11 @@ with open("README.md", "r") as fh: long_description = fh.read() +version_string = "v0.0.1" + setuptools.setup( name="tidals", - version="0.0.1", + version=version_string, author="Ed Nykaza", author_email="ed@tidepool.org", description="Tidepool Data Analysis Tools", @@ -30,12 +32,22 @@ long_description_content_type="text/markdown", url="https://github.com/tidepool-org/data-analytics/tree/master/tidepool-analysis-tools", packages=setuptools.find_packages(), + download_url=( + 'https://github.com/tidepool-org/data-analytics/tarball/' + version_string + ), classifiers=[ - "Development Status :: 3 - Alpha" - "Programming Language :: Python :: 3", - "License :: OSI Approved :: BSD-2-Clause", - "Operating System :: OS Independent", + 'Development Status :: 3 - Alpha', + 'Intended Audience :: Developers', + 'License :: OSI Approved :: BSD-2-Clause', + 'Programming Language :: Python :: 3', + 'Programming Language :: Python :: 3.4', + 'Programming Language :: Python :: 3.5', + 'Programming Language :: Python :: 3.6', ], + install_requires=[ + 'numpy>=1.14.1', + 'pandas>=0.23.1', + ], ) diff --git a/tidepool-analysis-tools/tests/mocks/__init__.py b/tidepool-analysis-tools/tests/clean/__init__.py similarity index 100% rename from tidepool-analysis-tools/tests/mocks/__init__.py rename to tidepool-analysis-tools/tests/clean/__init__.py diff --git a/tidepool-analysis-tools/tests/clean/example_data.json b/tidepool-analysis-tools/tests/clean/example_data.json new file mode 100644 index 00000000..c96d4e41 --- /dev/null +++ b/tidepool-analysis-tools/tests/clean/example_data.json @@ -0,0 +1,36172 @@ +[ + { + "clockDriftOffset": 0, + "conversionOffset": 0, + "deliveryType": "scheduled", + "deviceId": "InsOmn-130346997", + "deviceTime": "2017-12-31T18:00:00", + "duration": 14400000, + "guid": "6b6b6e42-abcf-4554-b121-7074a0918484", + "id": "h16d0och05pod6m1ehr858caibmh0vee", + 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b/tidepool-analysis-tools/tests/clean/test_clean.py new file mode 100644 index 00000000..c4ce558b --- /dev/null +++ b/tidepool-analysis-tools/tests/clean/test_clean.py @@ -0,0 +1,100 @@ + +from tidals.clean.clean import remove_duplicates, round_time, flatten_json +import pandas as pd +from pandas.util import testing as tm +import pytest + + +def test_remove_duplicates(valid_df): + + duplicated_data = [["jjety", "AB9960", "response1", "response2", "Sun Nov 19 23:59:59 2018", + pd.to_datetime("2018-11-20 00:00:00")], + ["jjety", "AB9958", "response1", "response2", "Sun Nov 19 23:56:59 2018", + pd.to_datetime("2018-11-19 23:55:00")], + ["hqoh", "AB9953", "response1", "response2", "Sun Nov 19 23:29:59 2018", + pd.to_datetime("2018-11-19 23:30:00")], + ["hhawe", "AB8769", "response1", "response2", "Sun Nov 19 23:20:01 2018", + pd.to_datetime("2018-11-19 23:20:00")], + ["hhawe", "AB8769", "response1", "response2", "Sun Nov 19 23:20:01 2018", + pd.to_datetime("2018-11-19 23:20:00")]] + + duplicated_df = pd.DataFrame(duplicated_data, columns=['userID', 'studyID',"getData.response1", "getData.response2", + "time", "roundedTime"]) + pandas_drop_df = duplicated_df.drop_duplicates('time') + + clean_df, duplicate_count = remove_duplicates(duplicated_df, duplicated_df["time"]) + + tm.assert_frame_equal(valid_df, clean_df) + tm.assert_frame_equal(valid_df, pandas_drop_df) + assert duplicate_count == 1 + + +def test_round_time(valid_df): + raw_data = [["hhawe", "AB8769", "response1", "response2", "Sun Nov 19 23:20:01 2018"], + ["hqoh", "AB9953", "response1", "response2", "Sun Nov 19 23:29:59 2018"], + ["jjety", "AB9958", "response1", "response2", "Sun Nov 19 23:56:59 2018"], + ["jjety", "AB9960", "response1", "response2", "Sun Nov 19 23:59:59 2018"]] + + + raw_df = pd.DataFrame(raw_data, columns=['userID', 'studyID', "getData.response1", "getData.response2", "time"]) + rounded_df = round_time(raw_df) + tm.assert_frame_equal(valid_df, rounded_df) + + +def test_round_time_across_month(valid_df): + raw_data = [["hhawe", "AB8769", "newyear", "response2", "2018-11-30 23:59:10"]] + raw_df = pd.DataFrame(raw_data, columns=['userID', 'studyID', "getData.response1", "getData.response2", "time"]) + rounded_df = round_time(raw_df) + expected_time = pd.Timestamp('2018-12-01') + + rounded_new_year = rounded_df.iloc[0]['roundedTime'] + assert( pd.Timestamp(rounded_new_year) == expected_time) + + +def test_round_time_across_year(): + raw_data = [["hhawe", "AB8769", "newyear", "response2", "2018-12-31 23:59:10"]] + raw_df = pd.DataFrame(raw_data, columns=['userID', 'studyID', "getData.response1", "getData.response2", "time"]) + rounded_df = round_time(raw_df) + expected_time = pd.Timestamp('2019-01-01') + + rounded_new_year = rounded_df.iloc[0]['roundedTime'] + assert( pd.Timestamp(rounded_new_year) == expected_time) + + +def test_round_time_early_hour(): + raw_data = [["hhawe", "AB8769", "newyear", "response2", "2018-11-29 00:01:10"]] + raw_df = pd.DataFrame(raw_data, columns=['userID', 'studyID', "getData.response1", "getData.response2", "time"]) + rounded_df = round_time(raw_df) + expected_time = pd.Timestamp('2018-11-29') + + rounded_new_year = rounded_df.iloc[0]['roundedTime'] + assert( pd.Timestamp(rounded_new_year) == expected_time) + + +def test_round_invalid_date(): + raw_data = [["hhawe", "AB8769", "newyear", "response2", "2018-11-31 00:01:10"]] + raw_df = pd.DataFrame(raw_data, columns=['userID', 'studyID', "getData.response1", "getData.response2", "time"]) + with pytest.raises(ValueError) as excinfo: + round_time(raw_df) + + assert("day is out of range for month" == excinfo.value.args[0]) + +def test_round_invalid_time(): + raw_data = [["hhawe", "AB8769", "newyear", "response2", "2018-11-31 25:01:10"]] + raw_df = pd.DataFrame(raw_data, columns=['userID', 'studyID', "getData.response1", "getData.response2", "time"]) + with pytest.raises(ValueError) as excinfo: + round_time(raw_df) + + assert("day is out of range for month" == excinfo.value.args[0]) + +@pytest.mark.skip(reason='need a valid dataframe example') +def test_flatten_json(): + df = pd.read_csv('') + flattened_df = flatten_json(df) + + + + + + + diff --git a/tidepool-analysis-tools/tests/fixtures/clean_setup.py b/tidepool-analysis-tools/tests/conftest.py similarity index 59% rename from tidepool-analysis-tools/tests/fixtures/clean_setup.py rename to tidepool-analysis-tools/tests/conftest.py index a8a8d276..f7e05a66 100644 --- a/tidepool-analysis-tools/tests/fixtures/clean_setup.py +++ b/tidepool-analysis-tools/tests/conftest.py @@ -1,5 +1,7 @@ import pytest import pandas as pd +import os +from distutils import dir_util @pytest.fixture() @@ -9,8 +11,13 @@ def valid_df(): ["hqoh", "AB9953", "response1", "response2", "Sun Nov 19 23:29:59 2018", pd.to_datetime("2018-11-19 23:30:00")], ["hhawe", "AB8769", "response1", "response2", "Sun Nov 19 23:20:01 2018", pd.to_datetime("2018-11-19 23:20:00")]] - valid_df = pd.DataFrame(check_data, columns=['userID', 'studyID', "getData.response1", "getData.response2", "time", "roundedTime"]) + return pd.DataFrame(check_data, columns=['userID', 'studyID', "getData.response1", "getData.response2", "time", "roundedTime"]) - return valid_df +@pytest.fixture() +def current_path_load(): + return os.path.dirname(os.path.realpath(__file__)) + "/load" +@pytest.fixture() +def current_path_clean(): + return os.path.dirname(os.path.realpath(__file__)) + "/clean" diff --git a/tidepool-analysis-tools/tests/load/raw_data.csv b/tidepool-analysis-tools/tests/load/raw_data.csv new file mode 100644 index 00000000..0428a443 --- /dev/null +++ b/tidepool-analysis-tools/tests/load/raw_data.csv @@ -0,0 +1,5 @@ +string,datetime,number +one,11/20/18 0:00,74857 +two,11/19/18 23:55,968225 +three,11/19/18 23:30,946412 +four,11/19/18 23:20,536377 \ No newline at end of file diff --git a/tidepool-analysis-tools/tests/load/raw_data.json b/tidepool-analysis-tools/tests/load/raw_data.json new file mode 100644 index 00000000..c16fa0f5 --- /dev/null +++ b/tidepool-analysis-tools/tests/load/raw_data.json @@ -0,0 +1,20 @@ +{ + "string": { + "0": "one", + "1": "two", + "2": "three", + "3": "four" + }, + "datetime": { + "0": 1542672000000, + "1": 1542671700000, + "2": 1542670200000, + "3": 1542669600000 + }, + "number": { + "0": 74857, + "1": 968225, + "2": 946412, + "3": 536377 + } +} \ No newline at end of file diff --git a/tidepool-analysis-tools/tests/load/raw_data.xlsx b/tidepool-analysis-tools/tests/load/raw_data.xlsx new file mode 100644 index 00000000..88f4a0a4 Binary files /dev/null and b/tidepool-analysis-tools/tests/load/raw_data.xlsx differ diff --git a/tidepool-analysis-tools/tests/load/test_load.py b/tidepool-analysis-tools/tests/load/test_load.py new file mode 100644 index 00000000..2a29270d --- /dev/null +++ b/tidepool-analysis-tools/tests/load/test_load.py @@ -0,0 +1,67 @@ + +from tidals.load.load import load_data +import pandas as pd + + +def test_load_data_csv(current_path_load): + check_data = [["one", "11/20/18 0:00", 74857], + ["two", "11/19/18 23:55", 968225], + ["three", "11/19/18 23:30", 946412], + ["four", "11/19/18 23:20", 536377]] + check_dataframe = pd.DataFrame(check_data, columns=['string', 'datetime', "number"]) + output_dataframe, file_name = load_data(current_path_load + "/raw_data.csv") + + column_count = 3 + row_count = 4 + + pd.testing.assert_frame_equal(output_dataframe, check_dataframe) + assert(column_count == output_dataframe.shape[1]) + assert(row_count == output_dataframe.shape[0]) + assert(file_name == 'raw_data') + + +def test_load_data_json(current_path_load): + check_data = [["one", pd.to_datetime("2018-11-20 00:00:00"), 74857], + ["two", pd.to_datetime("2018-11-19 23:55:00"), 968225], + ["three", pd.to_datetime("2018-11-19 23:30:00"), 946412], + ["four", pd.to_datetime("2018-11-19 23:20:00"), 536377]] + check_dataframe = pd.DataFrame(check_data, columns=['string', 'datetime', "number"]) + output_dataframe, file_name = load_data(current_path_load + "/raw_data.json") + + column_count = 3 + row_count = 4 + + pd.testing.assert_frame_equal(output_dataframe, check_dataframe) + assert(column_count == output_dataframe.shape[1]) + assert(row_count == output_dataframe.shape[0]) + assert (file_name == 'raw_data') + + +def test_load_data_xlsx(current_path_load): + check_data = [[1, "one", pd.to_datetime("11/20/18 0:00"), 74857], + [2, "two", pd.to_datetime("11/19/18 23:55"), 968225], + [3, "three", pd.to_datetime("11/19/18 23:30"), 946412], + [4, "four", pd.to_datetime("11/19/18 23:20"), 536377]] + check_dataframe = pd.DataFrame(check_data, columns=['jsonRowIndex', 'string', 'datetime', "number"]) + check_dataframe = check_dataframe.set_index('jsonRowIndex') + output_dataframe, file_name = load_data(current_path_load + "/raw_data.xlsx") + + column_count = 3 + row_count = 4 + + pd.testing.assert_frame_equal(output_dataframe, check_dataframe) + assert(column_count == output_dataframe.shape[1]) + assert(row_count == output_dataframe.shape[0]) + assert(file_name == 'raw_data') + + + + + + + + + + + + diff --git a/tidepool-analysis-tools/tests/unit_tests/__init__.py b/tidepool-analysis-tools/tests/unit_tests/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tidepool-analysis-tools/tests/unit_tests/test_clean.py b/tidepool-analysis-tools/tests/unit_tests/test_clean.py deleted file mode 100644 index 2952c4ec..00000000 --- a/tidepool-analysis-tools/tests/unit_tests/test_clean.py +++ /dev/null @@ -1,37 +0,0 @@ - -from tidals.clean.clean import remove_duplicates, round_time -import pandas as pd -from pandas.util import testing as tm -import pytest -from tests.fixtures.clean_setup import valid_df - - -def test_remove_duplicates(valid_df): - - duplicated_data = [["jjety", "AB9960", "response1", "response2", "Sun Nov 19 23:59:59 2018", pd.to_datetime("2018-11-20 00:00:00")], - ["jjety", "AB9958", "response1", "response2", "Sun Nov 19 23:56:59 2018", pd.to_datetime("2018-11-19 23:55:00")], - ["hqoh", "AB9953", "response1", "response2", "Sun Nov 19 23:29:59 2018", pd.to_datetime("2018-11-19 23:30:00")], - ["hhawe", "AB8769", "response1", "response2", "Sun Nov 19 23:20:01 2018", pd.to_datetime("2018-11-19 23:20:00")], - ["hhawe", "AB8769", "response1", "response2", "Sun Nov 19 23:20:01 2018", pd.to_datetime("2018-11-19 23:20:00")]] - - duplicated_df = pd.DataFrame(duplicated_data, columns=['userID', 'studyID',"getData.response1", "getData.response2", "time", "roundedTime"]) - pandas_drop_df = duplicated_df.drop_duplicates('time') - - clean_df, duplicate_count = remove_duplicates(duplicated_df, duplicated_df["time"]) - - tm.assert_frame_equal(valid_df, clean_df) - tm.assert_frame_equal(valid_df, pandas_drop_df) - assert duplicate_count == 1 - - -def test_round_time(valid_df): - raw_data = [["hhawe", "AB8769", "response1", "response2", "Sun Nov 19 23:20:01 2018"], - ["hqoh", "AB9953", "response1", "response2", "Sun Nov 19 23:29:59 2018"], - ["jjety", "AB9958", "response1", "response2", "Sun Nov 19 23:56:59 2018"], - ["jjety", "AB9960", "response1", "response2", "Sun Nov 19 23:59:59 2018"]] - - - raw_df = pd.DataFrame(raw_data, columns=['userID', 'studyID', "getData.response1", "getData.response2", "time"]) - rounded_df = round_time(raw_df) - tm.assert_frame_equal(valid_df, rounded_df) - diff --git a/tidepool-analysis-tools/tidals/__init__.py b/tidepool-analysis-tools/tidals/__init__.py index b52f1a3b..0ef413da 100644 --- a/tidepool-analysis-tools/tidals/__init__.py +++ b/tidepool-analysis-tools/tidals/__init__.py @@ -8,5 +8,5 @@ license: BSD-2-Clause """ -from .clean import clean -from .load import load +from tidals.clean.clean import * +from tidals.load.load import * diff --git a/tidepool-analysis-tools/tidals/clean/clean.py b/tidepool-analysis-tools/tidals/clean/clean.py index 9a4f1836..e8e96679 100644 --- a/tidepool-analysis-tools/tidals/clean/clean.py +++ b/tidepool-analysis-tools/tidals/clean/clean.py @@ -7,6 +7,11 @@ license: BSD-2-Clause """ +import pandas as pd +import numpy as np + + + def remove_duplicates(df, criteriaDF): nBefore = len(df) df = df.loc[~(criteriaDF.duplicated())] @@ -18,7 +23,7 @@ def remove_duplicates(df, criteriaDF): def round_time(df, timeIntervalMinutes=5, timeField="time", roundedTimeFieldName="roundedTime", verbose=False): - import pandas as pd + # A general purpose round time function that rounds the # "time" field to nearest minutes # INPUTS: @@ -65,15 +70,13 @@ def round_time(df, timeIntervalMinutes=5, timeField="time", def remove_brackets(df, fieldName): if fieldName in list(df): - df.loc[df[fieldName].notnull(), fieldName] = \ - df.loc[df[fieldName].notnull(), fieldName].str[0] + df[fieldName] = df[fieldName].apply(lambda x: x[1:-1]) + # df.loc[df[fieldName].notnull(), fieldName] = df.loc[df[fieldName].notnull(), fieldName].str[0] return df def flatten_json(df): - import pandas as pd - import numpy as np # remove [] from annotations field df = remove_brackets(df, "annotations") diff --git a/tidepool-analysis-tools/tidals/load/load.py b/tidepool-analysis-tools/tidals/load/load.py index 313d9abb..28df550e 100644 --- a/tidepool-analysis-tools/tidals/load/load.py +++ b/tidepool-analysis-tools/tidals/load/load.py @@ -7,21 +7,22 @@ license: BSD-2-Clause """ +import pandas as pd +import os +import sys + def load_json(dataPathAndName): - import pandas as pd df = pd.read_json(dataPathAndName, orient="records") return df def load_csv(dataPathAndName): - import pandas as pd df = pd.read_csv(dataPathAndName, low_memory=False) return df def load_xlsx(dataPathAndName): # load xlsx - import pandas as pd df = pd.read_excel(dataPathAndName, sheet_name=None, ignore_index=True) cdf = pd.concat(df.values(), ignore_index=True) cdf = cdf.set_index('jsonRowIndex') @@ -29,8 +30,6 @@ def load_xlsx(dataPathAndName): def load_data(inputFile): - import os - import sys if os.path.isfile(inputFile): if os.stat(inputFile).st_size > 2: if inputFile[-4:] == "json":