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import datetime
import numpy as np
import pandas as pd
import pytest
import xarray as xr
from xarray.core.resample_cftime import CFTimeGrouper
pytest.importorskip("cftime")
# Create a list of pairs of similar-length initial and resample frequencies
# that cover:
# - Resampling from shorter to longer frequencies
# - Resampling from longer to shorter frequencies
# - Resampling from one initial frequency to another.
# These are used to test the cftime version of resample against pandas
# with a standard calendar.
FREQS = [
("8003D", "4001D"),
("8003D", "16006D"),
("8003D", "21AS"),
("6H", "3H"),
("6H", "12H"),
("6H", "400T"),
("3D", "D"),
("3D", "6D"),
("11D", "MS"),
("3MS", "MS"),
("3MS", "6MS"),
("3MS", "85D"),
("7M", "3M"),
("7M", "14M"),
("7M", "2QS-APR"),
("43QS-AUG", "21QS-AUG"),
("43QS-AUG", "86QS-AUG"),
("43QS-AUG", "11A-JUN"),
("11Q-JUN", "5Q-JUN"),
("11Q-JUN", "22Q-JUN"),
("11Q-JUN", "51MS"),
("3AS-MAR", "AS-MAR"),
("3AS-MAR", "6AS-MAR"),
("3AS-MAR", "14Q-FEB"),
("7A-MAY", "3A-MAY"),
("7A-MAY", "14A-MAY"),
("7A-MAY", "85M"),
]
def da(index):
return xr.DataArray(
np.arange(100.0, 100.0 + index.size), coords=[index], dims=["time"]
)
@pytest.mark.parametrize("freqs", FREQS, ids=lambda x: "{}->{}".format(*x))
@pytest.mark.parametrize("closed", [None, "left", "right"])
@pytest.mark.parametrize("label", [None, "left", "right"])
@pytest.mark.parametrize("base", [24, 31])
def test_resample(freqs, closed, label, base):
initial_freq, resample_freq = freqs
start = "2000-01-01T12:07:01"
index_kwargs = dict(start=start, periods=5, freq=initial_freq)
datetime_index = pd.date_range(**index_kwargs)
cftime_index = xr.cftime_range(**index_kwargs)
loffset = "12H"
try:
da_datetime = (
da(datetime_index)
.resample(
time=resample_freq,
closed=closed,
label=label,
base=base,
loffset=loffset,
)
.mean()
)
except ValueError:
with pytest.raises(ValueError):
da(cftime_index).resample(
time=resample_freq,
closed=closed,
label=label,
base=base,
loffset=loffset,
).mean()
else:
da_cftime = (
da(cftime_index)
.resample(
time=resample_freq,
closed=closed,
label=label,
base=base,
loffset=loffset,
)
.mean()
)
da_cftime["time"] = da_cftime.indexes["time"].to_datetimeindex()
xr.testing.assert_identical(da_cftime, da_datetime)
@pytest.mark.parametrize(
("freq", "expected"),
[
("S", "left"),
("T", "left"),
("H", "left"),
("D", "left"),
("M", "right"),
("MS", "left"),
("Q", "right"),
("QS", "left"),
("A", "right"),
("AS", "left"),
],
)
def test_closed_label_defaults(freq, expected):
assert CFTimeGrouper(freq=freq).closed == expected
assert CFTimeGrouper(freq=freq).label == expected
@pytest.mark.filterwarnings("ignore:Converting a CFTimeIndex")
@pytest.mark.parametrize(
"calendar", ["gregorian", "noleap", "all_leap", "360_day", "julian"]
)
def test_calendars(calendar):
# Limited testing for non-standard calendars
freq, closed, label, base = "8001T", None, None, 17
loffset = datetime.timedelta(hours=12)
xr_index = xr.cftime_range(
start="2004-01-01T12:07:01", periods=7, freq="3D", calendar=calendar
)
pd_index = pd.date_range(start="2004-01-01T12:07:01", periods=7, freq="3D")
da_cftime = (
da(xr_index)
.resample(time=freq, closed=closed, label=label, base=base, loffset=loffset)
.mean()
)
da_datetime = (
da(pd_index)
.resample(time=freq, closed=closed, label=label, base=base, loffset=loffset)
.mean()
)
da_cftime["time"] = da_cftime.indexes["time"].to_datetimeindex()
xr.testing.assert_identical(da_cftime, da_datetime)
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