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
]))
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,
import unittest
from datetime import date, datetime, time, timezone
from hypothesis import given, strategies as st
from hypothesis.extra.dateutil import timezones as dateutil_timezones
TIME_ZONES_STRATEGY = st.one_of(
st.sampled_from([None, timezone.utc]), dateutil_timezones()
)
class TestDatetime(unittest.TestCase):
# Surrogate characters have been particularly problematic here in the past,
# so we give them a boost by combining strategies pending an upstream
# feature (https://github.com/HypothesisWorks/hypothesis/issues/1401)
@unittest.skipIf(not hasattr(datetime, "fromisoformat"), reason="new in 3.7")
@given(
dt=st.datetimes(timezones=TIME_ZONES_STRATEGY),
sep=st.characters() | st.characters(whitelist_categories=["Cs"]),
)
def test_fromisoformat_auto(self, dt, sep):
"""Test isoformat with timespec="auto"."""
dtstr = dt.isoformat(sep=sep, timespec="auto")
dt_rt = datetime.fromisoformat(dtstr)
self.assertEqual(dt, dt_rt)
@unittest.skipIf(not hasattr(datetime, "fromisocalendar"), reason="new in 3.8")
@given(dt=st.datetimes())
def test_fromisocalendar_date_datetime(self, dt):
isocalendar = dt.isocalendar()
dt_rt = datetime.fromisocalendar(*isocalendar)
