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
]))
