File: test_cftimeindex_resample.py

package info (click to toggle)
python-xarray 2025.09.0-1
  • links: PTS, VCS
  • area: main
  • in suites: forky, sid
  • size: 11,552 kB
  • sloc: python: 115,929; makefile: 260; sh: 47
file content (300 lines) | stat: -rw-r--r-- 9,218 bytes parent folder | download
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
from __future__ import annotations

import datetime
from typing import TypedDict

import numpy as np
import pandas as pd
import pytest

import xarray as xr
from xarray.coding.cftime_offsets import (
    CFTIME_TICKS,
    Day,
    _new_to_legacy_freq,
    to_offset,
)
from xarray.coding.cftimeindex import CFTimeIndex
from xarray.core.resample_cftime import CFTimeGrouper
from xarray.tests import has_pandas_3

cftime = 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", "21YS"),
    ("6h", "3h"),
    ("6h", "12h"),
    ("6h", "400min"),
    ("3D", "D"),
    ("3D", "6D"),
    ("11D", "MS"),
    ("3MS", "MS"),
    ("3MS", "6MS"),
    ("3MS", "85D"),
    ("7ME", "3ME"),
    ("7ME", "14ME"),
    ("7ME", "2QS-APR"),
    ("43QS-AUG", "21QS-AUG"),
    ("43QS-AUG", "86QS-AUG"),
    ("43QS-AUG", "11YE-JUN"),
    ("11QE-JUN", "5QE-JUN"),
    ("11QE-JUN", "22QE-JUN"),
    ("11QE-JUN", "51MS"),
    ("3YS-MAR", "YS-MAR"),
    ("3YS-MAR", "6YS-MAR"),
    ("3YS-MAR", "14QE-FEB"),
    ("7YE-MAY", "3YE-MAY"),
    ("7YE-MAY", "14YE-MAY"),
    ("7YE-MAY", "85ME"),
]


def has_tick_resample_freq(freqs):
    resample_freq, _ = freqs
    resample_freq_as_offset = to_offset(resample_freq)
    return isinstance(resample_freq_as_offset, CFTIME_TICKS)


def has_non_tick_resample_freq(freqs):
    return not has_tick_resample_freq(freqs)


FREQS_WITH_TICK_RESAMPLE_FREQ = list(filter(has_tick_resample_freq, FREQS))
FREQS_WITH_NON_TICK_RESAMPLE_FREQ = list(filter(has_non_tick_resample_freq, FREQS))


def compare_against_pandas(
    da_datetimeindex,
    da_cftimeindex,
    freq,
    closed=None,
    label=None,
    offset=None,
    origin=None,
) -> None:
    if isinstance(origin, tuple):
        origin_pandas = pd.Timestamp(datetime.datetime(*origin))
        origin_cftime = cftime.DatetimeGregorian(*origin)
    else:
        origin_pandas = origin
        origin_cftime = origin

    try:
        result_datetimeindex = da_datetimeindex.resample(
            time=freq,
            closed=closed,
            label=label,
            offset=offset,
            origin=origin_pandas,
        ).mean()
    except ValueError:
        with pytest.raises(ValueError):
            da_cftimeindex.resample(
                time=freq,
                closed=closed,
                label=label,
                origin=origin_cftime,
                offset=offset,
            ).mean()
    else:
        result_cftimeindex = da_cftimeindex.resample(
            time=freq,
            closed=closed,
            label=label,
            origin=origin_cftime,
            offset=offset,
        ).mean()
    # TODO (benbovy - flexible indexes): update when CFTimeIndex is a xarray Index subclass
    result_cftimeindex["time"] = (
        result_cftimeindex.xindexes["time"]
        .to_pandas_index()
        .to_datetimeindex(time_unit="ns")
    )
    xr.testing.assert_identical(result_cftimeindex, result_datetimeindex)


def da(index) -> xr.DataArray:
    return xr.DataArray(
        np.arange(100.0, 100.0 + index.size), coords=[index], dims=["time"]
    )


@pytest.mark.parametrize(
    "freqs", FREQS_WITH_TICK_RESAMPLE_FREQ, ids=lambda x: "{}->{}".format(*x)
)
@pytest.mark.parametrize("closed", [None, "left", "right"])
@pytest.mark.parametrize("label", [None, "left", "right"])
@pytest.mark.parametrize("offset", [None, "5s"], ids=lambda x: f"{x}")
def test_resample_with_tick_resample_freq(freqs, closed, label, offset) -> None:
    initial_freq, resample_freq = freqs
    start = "2000-01-01T12:07:01"
    origin = "start"

    datetime_index = pd.date_range(
        start=start, periods=5, freq=_new_to_legacy_freq(initial_freq)
    )
    cftime_index = xr.date_range(
        start=start, periods=5, freq=initial_freq, use_cftime=True
    )
    da_datetimeindex = da(datetime_index)
    da_cftimeindex = da(cftime_index)

    compare_against_pandas(
        da_datetimeindex,
        da_cftimeindex,
        resample_freq,
        closed=closed,
        label=label,
        offset=offset,
        origin=origin,
    )


@pytest.mark.parametrize(
    "freqs", FREQS_WITH_NON_TICK_RESAMPLE_FREQ, ids=lambda x: "{}->{}".format(*x)
)
@pytest.mark.parametrize("closed", [None, "left", "right"])
@pytest.mark.parametrize("label", [None, "left", "right"])
def test_resample_with_non_tick_resample_freq(freqs, closed, label) -> None:
    initial_freq, resample_freq = freqs
    resample_freq_as_offset = to_offset(resample_freq)
    if isinstance(resample_freq_as_offset, Day) and not has_pandas_3:
        pytest.skip("Only valid for pandas >= 3.0")
    start = "2000-01-01T12:07:01"

    # Set offset and origin to their default values since they have no effect
    # on resampling data with a non-tick resample frequency.
    offset = None
    origin = "start_day"

    datetime_index = pd.date_range(
        start=start, periods=5, freq=_new_to_legacy_freq(initial_freq)
    )
    cftime_index = xr.date_range(
        start=start, periods=5, freq=initial_freq, use_cftime=True
    )
    da_datetimeindex = da(datetime_index)
    da_cftimeindex = da(cftime_index)

    compare_against_pandas(
        da_datetimeindex,
        da_cftimeindex,
        resample_freq,
        closed=closed,
        label=label,
        offset=offset,
        origin=origin,
    )


@pytest.mark.parametrize(
    ("freq", "expected"),
    [
        ("s", "left"),
        ("min", "left"),
        ("h", "left"),
        ("D", "left"),
        ("ME", "right"),
        ("MS", "left"),
        ("QE", "right"),
        ("QS", "left"),
        ("YE", "right"),
        ("YS", "left"),
    ],
)
def test_closed_label_defaults(freq, expected) -> None:
    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: str) -> None:
    # Limited testing for non-standard calendars
    freq, closed, label = "8001min", None, None
    xr_index = xr.date_range(
        start="2004-01-01T12:07:01",
        periods=7,
        freq="3D",
        calendar=calendar,
        use_cftime=True,
    )
    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).mean()
    da_datetime = da(pd_index).resample(time=freq, closed=closed, label=label).mean()
    # TODO (benbovy - flexible indexes): update when CFTimeIndex is a xarray Index subclass
    new_pd_index = da_cftime.xindexes["time"].to_pandas_index()
    assert isinstance(new_pd_index, CFTimeIndex)  # shouldn't that be a pd.Index?
    da_cftime["time"] = new_pd_index.to_datetimeindex(time_unit="ns")
    xr.testing.assert_identical(da_cftime, da_datetime)


class DateRangeKwargs(TypedDict):
    start: str
    periods: int
    freq: str


@pytest.mark.parametrize("closed", ["left", "right"])
@pytest.mark.parametrize(
    "origin",
    ["start_day", "start", "end", "end_day", "epoch", (1970, 1, 1, 3, 2)],
    ids=lambda x: f"{x}",
)
def test_origin(closed, origin) -> None:
    initial_freq, resample_freq = ("3h", "9h")
    start = "1969-12-31T12:07:01"
    index_kwargs: DateRangeKwargs = dict(start=start, periods=12, freq=initial_freq)
    datetime_index = pd.date_range(**index_kwargs)
    cftime_index = xr.date_range(**index_kwargs, use_cftime=True)
    da_datetimeindex = da(datetime_index)
    da_cftimeindex = da(cftime_index)

    compare_against_pandas(
        da_datetimeindex,
        da_cftimeindex,
        resample_freq,
        closed=closed,
        origin=origin,
    )


@pytest.mark.parametrize("offset", ["foo", "5MS", 10])
def test_invalid_offset_error(offset: str | int) -> None:
    cftime_index = xr.date_range("2000", periods=5, use_cftime=True)
    da_cftime = da(cftime_index)
    with pytest.raises(ValueError, match="offset must be"):
        da_cftime.resample(time="2h", offset=offset)  # type: ignore[arg-type]


def test_timedelta_offset() -> None:
    timedelta = datetime.timedelta(seconds=5)
    string = "5s"

    cftime_index = xr.date_range("2000", periods=5, use_cftime=True)
    da_cftime = da(cftime_index)

    timedelta_result = da_cftime.resample(time="2h", offset=timedelta).mean()
    string_result = da_cftime.resample(time="2h", offset=string).mean()
    xr.testing.assert_identical(timedelta_result, string_result)


@pytest.mark.parametrize(("option", "value"), [("offset", "5s"), ("origin", "start")])
def test_non_tick_option_warning(option, value) -> None:
    cftime_index = xr.date_range("2000", periods=5, use_cftime=True)
    da_cftime = da(cftime_index)
    kwargs = {option: value}
    with pytest.warns(RuntimeWarning, match=option):
        da_cftime.resample(time="ME", **kwargs)