with warnings.catch_warnings(): warnings.simplefilter("ignore") min_dt = Timestamp(1900, 1, 1).to_pydatetime() max_dt = Timestamp(1900, 1, 1).to_pydatetime() gen_date_range = st.builds( pd.date_range, start=st.datetimes( # TODO: Choose the min/max values more systematically min_value=Timestamp(1900, 1, 1).to_pydatetime(), max_value=Timestamp(2100, 1, 1).to_pydatetime(), ), periods=st.integers(min_value=2, max_value=100), freq=st.sampled_from("Y Q M D H T s ms us ns".split()), tz=st.one_of(st.none(), dateutil_timezones(), pytz_timezones()), ) gen_random_datetime = st.datetimes( min_value=min_dt, max_value=max_dt, timezones=st.one_of(st.none(), dateutil_timezones(), pytz_timezones()), ) # The strategy for each type is registered in conftest.py, as they don't carry # enough runtime information (e.g. type hints) to infer how to build them. gen_yqm_offset = st.one_of( *map( st.from_type, [ MonthBegin,
with warnings.catch_warnings(): warnings.simplefilter('ignore') min_dt = pd.Timestamp(1900, 1, 1).to_pydatetime(), max_dt = pd.Timestamp(1900, 1, 1).to_pydatetime(), gen_date_range = st.builds( pd.date_range, start=st.datetimes( # TODO: Choose the min/max values more systematically min_value=pd.Timestamp(1900, 1, 1).to_pydatetime(), max_value=pd.Timestamp(2100, 1, 1).to_pydatetime() ), periods=st.integers(min_value=2, max_value=100), freq=st.sampled_from('Y Q M D H T s ms us ns'.split()), tz=st.one_of(st.none(), dateutil_timezones(), pytz_timezones()), ) gen_random_datetime = st.datetimes( min_value=min_dt, max_value=max_dt, timezones=st.one_of(st.none(), dateutil_timezones(), pytz_timezones()) ) # The strategy for each type is registered in conftest.py, as they don't carry # enough runtime information (e.g. type hints) to infer how to build them. gen_yqm_offset = st.one_of(*map(st.from_type, [ MonthBegin, MonthEnd, BMonthBegin, BMonthEnd, QuarterBegin, QuarterEnd, BQuarterBegin, BQuarterEnd, YearBegin, YearEnd, BYearBegin, BYearEnd ]))