Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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228 lines
7.1 KiB
228 lines
7.1 KiB
from datetime import datetime
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import numpy as np
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import pytest
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import pandas as pd
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from pandas import DataFrame, Series
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import pandas._testing as tm
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from pandas.core.groupby.groupby import DataError
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from pandas.core.groupby.grouper import Grouper
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from pandas.core.indexes.datetimes import date_range
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from pandas.core.indexes.period import period_range
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from pandas.core.indexes.timedeltas import timedelta_range
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from pandas.core.resample import _asfreq_compat
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# a fixture value can be overridden by the test parameter value. Note that the
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# value of the fixture can be overridden this way even if the test doesn't use
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# it directly (doesn't mention it in the function prototype).
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# see https://docs.pytest.org/en/latest/fixture.html#override-a-fixture-with-direct-test-parametrization # noqa
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# in this module we override the fixture values defined in conftest.py
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# tuples of '_index_factory,_series_name,_index_start,_index_end'
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DATE_RANGE = (date_range, "dti", datetime(2005, 1, 1), datetime(2005, 1, 10))
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PERIOD_RANGE = (period_range, "pi", datetime(2005, 1, 1), datetime(2005, 1, 10))
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TIMEDELTA_RANGE = (timedelta_range, "tdi", "1 day", "10 day")
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all_ts = pytest.mark.parametrize(
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"_index_factory,_series_name,_index_start,_index_end",
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[DATE_RANGE, PERIOD_RANGE, TIMEDELTA_RANGE],
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)
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@pytest.fixture
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def create_index(_index_factory):
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def _create_index(*args, **kwargs):
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""" return the _index_factory created using the args, kwargs """
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return _index_factory(*args, **kwargs)
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return _create_index
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@pytest.mark.parametrize("freq", ["2D", "1H"])
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@pytest.mark.parametrize(
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"_index_factory,_series_name,_index_start,_index_end", [DATE_RANGE, TIMEDELTA_RANGE]
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)
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def test_asfreq(series_and_frame, freq, create_index):
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obj = series_and_frame
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result = obj.resample(freq).asfreq()
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new_index = create_index(obj.index[0], obj.index[-1], freq=freq)
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expected = obj.reindex(new_index)
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tm.assert_almost_equal(result, expected)
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@pytest.mark.parametrize(
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"_index_factory,_series_name,_index_start,_index_end", [DATE_RANGE, TIMEDELTA_RANGE]
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)
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def test_asfreq_fill_value(series, create_index):
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# test for fill value during resampling, issue 3715
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s = series
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result = s.resample("1H").asfreq()
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new_index = create_index(s.index[0], s.index[-1], freq="1H")
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expected = s.reindex(new_index)
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tm.assert_series_equal(result, expected)
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frame = s.to_frame("value")
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frame.iloc[1] = None
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result = frame.resample("1H").asfreq(fill_value=4.0)
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new_index = create_index(frame.index[0], frame.index[-1], freq="1H")
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expected = frame.reindex(new_index, fill_value=4.0)
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tm.assert_frame_equal(result, expected)
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@all_ts
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def test_resample_interpolate(frame):
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# # 12925
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df = frame
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tm.assert_frame_equal(
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df.resample("1T").asfreq().interpolate(), df.resample("1T").interpolate()
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)
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def test_raises_on_non_datetimelike_index():
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# this is a non datetimelike index
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xp = DataFrame()
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msg = (
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"Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, "
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"but got an instance of 'Index'"
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)
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with pytest.raises(TypeError, match=msg):
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xp.resample("A").mean()
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@all_ts
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@pytest.mark.parametrize("freq", ["M", "D", "H"])
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def test_resample_empty_series(freq, empty_series_dti, resample_method):
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# GH12771 & GH12868
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if resample_method == "ohlc":
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pytest.skip("need to test for ohlc from GH13083")
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s = empty_series_dti
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result = getattr(s.resample(freq), resample_method)()
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expected = s.copy()
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expected.index = _asfreq_compat(s.index, freq)
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tm.assert_index_equal(result.index, expected.index)
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assert result.index.freq == expected.index.freq
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tm.assert_series_equal(result, expected, check_dtype=False)
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@all_ts
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@pytest.mark.parametrize("freq", ["M", "D", "H"])
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@pytest.mark.parametrize("resample_method", ["count", "size"])
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def test_resample_count_empty_series(freq, empty_series_dti, resample_method):
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# GH28427
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result = getattr(empty_series_dti.resample(freq), resample_method)()
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index = _asfreq_compat(empty_series_dti.index, freq)
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expected = pd.Series([], dtype="int64", index=index, name=empty_series_dti.name)
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tm.assert_series_equal(result, expected)
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@all_ts
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@pytest.mark.parametrize("freq", ["M", "D", "H"])
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def test_resample_empty_dataframe(empty_frame_dti, freq, resample_method):
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# GH13212
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df = empty_frame_dti
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# count retains dimensions too
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result = getattr(df.resample(freq), resample_method)()
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if resample_method != "size":
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expected = df.copy()
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else:
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# GH14962
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expected = Series([], dtype=object)
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expected.index = _asfreq_compat(df.index, freq)
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tm.assert_index_equal(result.index, expected.index)
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assert result.index.freq == expected.index.freq
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tm.assert_almost_equal(result, expected, check_dtype=False)
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# test size for GH13212 (currently stays as df)
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@all_ts
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@pytest.mark.parametrize("freq", ["M", "D", "H"])
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def test_resample_count_empty_dataframe(freq, empty_frame_dti):
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# GH28427
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empty_frame_dti["a"] = []
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result = empty_frame_dti.resample(freq).count()
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index = _asfreq_compat(empty_frame_dti.index, freq)
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expected = pd.DataFrame({"a": []}, dtype="int64", index=index)
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tm.assert_frame_equal(result, expected)
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@all_ts
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@pytest.mark.parametrize("freq", ["M", "D", "H"])
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def test_resample_size_empty_dataframe(freq, empty_frame_dti):
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# GH28427
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empty_frame_dti["a"] = []
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result = empty_frame_dti.resample(freq).size()
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index = _asfreq_compat(empty_frame_dti.index, freq)
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expected = pd.Series([], dtype="int64", index=index)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("index", tm.all_timeseries_index_generator(0))
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@pytest.mark.parametrize("dtype", [float, int, object, "datetime64[ns]"])
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def test_resample_empty_dtypes(index, dtype, resample_method):
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# Empty series were sometimes causing a segfault (for the functions
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# with Cython bounds-checking disabled) or an IndexError. We just run
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# them to ensure they no longer do. (GH #10228)
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empty_series_dti = Series([], index, dtype)
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try:
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getattr(empty_series_dti.resample("d"), resample_method)()
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except DataError:
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# Ignore these since some combinations are invalid
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# (ex: doing mean with dtype of np.object)
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pass
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@all_ts
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def test_apply_to_empty_series(empty_series_dti):
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# GH 14313
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s = empty_series_dti
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for freq in ["M", "D", "H"]:
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result = s.resample(freq).apply(lambda x: 1)
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expected = s.resample(freq).apply(np.sum)
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tm.assert_series_equal(result, expected, check_dtype=False)
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@all_ts
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def test_resampler_is_iterable(series):
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# GH 15314
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freq = "H"
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tg = Grouper(freq=freq, convention="start")
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grouped = series.groupby(tg)
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resampled = series.resample(freq)
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for (rk, rv), (gk, gv) in zip(resampled, grouped):
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assert rk == gk
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tm.assert_series_equal(rv, gv)
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@all_ts
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def test_resample_quantile(series):
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# GH 15023
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s = series
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q = 0.75
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freq = "H"
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result = s.resample(freq).quantile(q)
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expected = s.resample(freq).agg(lambda x: x.quantile(q)).rename(s.name)
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tm.assert_series_equal(result, expected)
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