Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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105 lines
3.2 KiB
105 lines
3.2 KiB
""" generic datetimelike tests """
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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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import pandas._testing as tm
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from .common import Base
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class DatetimeLike(Base):
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def test_argmax_axis_invalid(self):
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# GH#23081
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msg = r"`axis` must be fewer than the number of dimensions \(1\)"
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rng = self.create_index()
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with pytest.raises(ValueError, match=msg):
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rng.argmax(axis=1)
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with pytest.raises(ValueError, match=msg):
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rng.argmin(axis=2)
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with pytest.raises(ValueError, match=msg):
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rng.min(axis=-2)
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with pytest.raises(ValueError, match=msg):
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rng.max(axis=-3)
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def test_can_hold_identifiers(self):
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idx = self.create_index()
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key = idx[0]
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assert idx._can_hold_identifiers_and_holds_name(key) is False
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def test_shift_identity(self):
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idx = self.create_index()
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tm.assert_index_equal(idx, idx.shift(0))
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def test_str(self):
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# test the string repr
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idx = self.create_index()
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idx.name = "foo"
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assert not (f"length={len(idx)}" in str(idx))
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assert "'foo'" in str(idx)
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assert type(idx).__name__ in str(idx)
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if hasattr(idx, "tz"):
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if idx.tz is not None:
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assert idx.tz in str(idx)
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if hasattr(idx, "freq"):
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assert f"freq='{idx.freqstr}'" in str(idx)
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def test_view(self):
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i = self.create_index()
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i_view = i.view("i8")
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result = self._holder(i)
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tm.assert_index_equal(result, i)
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i_view = i.view(self._holder)
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result = self._holder(i)
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tm.assert_index_equal(result, i_view)
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def test_map_callable(self):
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index = self.create_index()
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expected = index + index.freq
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result = index.map(lambda x: x + x.freq)
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tm.assert_index_equal(result, expected)
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# map to NaT
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result = index.map(lambda x: pd.NaT if x == index[0] else x)
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expected = pd.Index([pd.NaT] + index[1:].tolist())
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tm.assert_index_equal(result, expected)
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@pytest.mark.parametrize(
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"mapper",
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[
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lambda values, index: {i: e for e, i in zip(values, index)},
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lambda values, index: pd.Series(values, index, dtype=object),
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],
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)
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def test_map_dictlike(self, mapper):
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index = self.create_index()
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expected = index + index.freq
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# don't compare the freqs
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if isinstance(expected, (pd.DatetimeIndex, pd.TimedeltaIndex)):
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expected = expected._with_freq(None)
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result = index.map(mapper(expected, index))
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tm.assert_index_equal(result, expected)
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expected = pd.Index([pd.NaT] + index[1:].tolist())
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result = index.map(mapper(expected, index))
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tm.assert_index_equal(result, expected)
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# empty map; these map to np.nan because we cannot know
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# to re-infer things
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expected = pd.Index([np.nan] * len(index))
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result = index.map(mapper([], []))
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tm.assert_index_equal(result, expected)
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def test_getitem_preserves_freq(self):
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index = self.create_index()
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assert index.freq is not None
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result = index[:]
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assert result.freq == index.freq
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