122 lines
3.5 KiB
Python
122 lines
3.5 KiB
Python
"""
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Behavioral based tests for offsets and date_range.
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This file is adapted from https://github.com/pandas-dev/pandas/pull/18761 -
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which was more ambitious but less idiomatic in its use of Hypothesis.
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You may wish to consult the previous version for inspiration on further
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tests, or when trying to pin down the bugs exposed by the tests below.
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"""
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import warnings
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from hypothesis import assume, given, strategies as st
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from hypothesis.errors import Flaky
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from hypothesis.extra.dateutil import timezones as dateutil_timezones
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from hypothesis.extra.pytz import timezones as pytz_timezones
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import pytest
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import pytz
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import pandas as pd
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from pandas import Timestamp
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from pandas.tseries.offsets import (
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BMonthBegin,
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BMonthEnd,
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BQuarterBegin,
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BQuarterEnd,
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BYearBegin,
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BYearEnd,
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MonthBegin,
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MonthEnd,
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QuarterBegin,
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QuarterEnd,
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YearBegin,
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YearEnd,
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)
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# ----------------------------------------------------------------
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# Helpers for generating random data
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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min_dt = Timestamp(1900, 1, 1).to_pydatetime()
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max_dt = Timestamp(1900, 1, 1).to_pydatetime()
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gen_date_range = st.builds(
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pd.date_range,
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start=st.datetimes(
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# TODO: Choose the min/max values more systematically
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min_value=Timestamp(1900, 1, 1).to_pydatetime(),
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max_value=Timestamp(2100, 1, 1).to_pydatetime(),
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),
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periods=st.integers(min_value=2, max_value=100),
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freq=st.sampled_from("Y Q M D H T s ms us ns".split()),
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tz=st.one_of(st.none(), dateutil_timezones(), pytz_timezones()),
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)
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gen_random_datetime = st.datetimes(
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min_value=min_dt,
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max_value=max_dt,
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timezones=st.one_of(st.none(), dateutil_timezones(), pytz_timezones()),
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)
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# The strategy for each type is registered in conftest.py, as they don't carry
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# enough runtime information (e.g. type hints) to infer how to build them.
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gen_yqm_offset = st.one_of(
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*map(
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st.from_type,
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[
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MonthBegin,
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MonthEnd,
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BMonthBegin,
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BMonthEnd,
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QuarterBegin,
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QuarterEnd,
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BQuarterBegin,
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BQuarterEnd,
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YearBegin,
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YearEnd,
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BYearBegin,
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BYearEnd,
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],
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)
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)
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# ----------------------------------------------------------------
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# Offset-specific behaviour tests
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@pytest.mark.arm_slow
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@given(gen_random_datetime, gen_yqm_offset)
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def test_on_offset_implementations(dt, offset):
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assume(not offset.normalize)
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# check that the class-specific implementations of is_on_offset match
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# the general case definition:
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# (dt + offset) - offset == dt
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try:
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compare = (dt + offset) - offset
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except pytz.NonExistentTimeError:
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# dt + offset does not exist, assume(False) to indicate
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# to hypothesis that this is not a valid test case
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assume(False)
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assert offset.is_on_offset(dt) == (compare == dt)
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@pytest.mark.xfail(strict=False, raises=Flaky, reason="unreliable test timings")
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@given(gen_yqm_offset)
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def test_shift_across_dst(offset):
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# GH#18319 check that 1) timezone is correctly normalized and
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# 2) that hour is not incorrectly changed by this normalization
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assume(not offset.normalize)
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# Note that dti includes a transition across DST boundary
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dti = pd.date_range(
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start="2017-10-30 12:00:00", end="2017-11-06", freq="D", tz="US/Eastern"
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)
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assert (dti.hour == 12).all() # we haven't screwed up yet
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res = dti + offset
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assert (res.hour == 12).all()
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