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merged 2 commits into from
Jan 22, 2025
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Update time coding tests to assert exact equality #9961

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52b3068
Update time coding tests to assert exact equality
spencerkclark Jan 18, 2025
ae9d9d5
Merge branch 'main' into cftime-assert-equal
kmuehlbauer Jan 20, 2025
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3 changes: 3 additions & 0 deletions doc/whats-new.rst
View file
Original file line number Diff line number Diff line change
@@ -76,6 +76,9 @@ Documentation

Internal Changes
~~~~~~~~~~~~~~~~
- Updated time coding tests to assert exact equality rather than equality with
a tolerance, since xarray's minimum supported version of cftime is greater
than 1.2.1 (:pull:`9961`). By `Spencer Clark <https://github.com/spencerkclark>`_.

.. _whats-new.2025.01.1:

14 changes: 4 additions & 10 deletions xarray/tests/test_backends.py
View file
Original file line number Diff line number Diff line change
@@ -619,16 +619,13 @@
dtype = actual.t.dtype
expected_decoded_t = expected_decoded_t.astype(dtype)
expected_decoded_t0 = expected_decoded_t0.astype(dtype)
abs_diff = abs(actual.t.values - expected_decoded_t)
assert (abs_diff <= np.timedelta64(1, "s")).all()
assert_array_equal(actual.t.values, expected_decoded_t)
assert (
actual.t.encoding["units"]
== "days since 0001-01-01 00:00:00.000000"
)
assert actual.t.encoding["calendar"] == expected_calendar

abs_diff = abs(actual.t0.values - expected_decoded_t0)
assert (abs_diff <= np.timedelta64(1, "s")).all()
assert_array_equal(actual.t0.values, expected_decoded_t0)
assert actual.t0.encoding["units"] == "days since 0001-01-01"
assert actual.t.encoding["calendar"] == expected_calendar

@@ -4709,11 +4706,8 @@
expected_decoded_t0 = np.array([date_type(1, 1, 1)])

with self.roundtrip(expected) as actual:
abs_diff = abs(actual.t.values - expected_decoded_t)
assert (abs_diff <= np.timedelta64(1, "s")).all()

abs_diff = abs(actual.t0.values - expected_decoded_t0)
assert (abs_diff <= np.timedelta64(1, "s")).all()
assert_array_equal(actual.t.values, expected_decoded_t)
assert_array_equal(actual.t0.values, expected_decoded_t0)

def test_write_store(self) -> None:
# Override method in DatasetIOBase - not applicable to dask
@@ -5215,7 +5209,7 @@
yield actual, expected

def test_cmp_local_file(self) -> None:
with self.create_datasets() as (actual, expected):

Check failure on line 5212 in xarray/tests/test_backends.py

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TestPydapOnline.test_cmp_local_file

webob.exc.HTTPError: 503 Service Unavailable
<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML 2.0//EN">
<html><head>
<title>503 Service Unavailable</title>
</head><body>
<h1>Service Unavailable</h1>
<p>The server is temporarily unable to service your
request due to maintenance downtime or capacity
problems. Please try again later.</p>
</body></html>
assert_equal(actual, expected)

# global attributes should be global attributes on the dataset
@@ -5249,7 +5243,7 @@

def test_compatible_to_netcdf(self) -> None:
# make sure it can be saved as a netcdf
with self.create_datasets() as (actual, expected):

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webob.exc.HTTPError: 503 Service Unavailable
<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML 2.0//EN">
<html><head>
<title>503 Service Unavailable</title>
</head><body>
<h1>Service Unavailable</h1>
<p>The server is temporarily unable to service your
request due to maintenance downtime or capacity
problems. Please try again later.</p>
</body></html>
with create_tmp_file() as tmp_file:
actual.to_netcdf(tmp_file)
with open_dataset(tmp_file) as actual2:
@@ -5258,7 +5252,7 @@

@requires_dask
def test_dask(self) -> None:
with self.create_datasets(chunks={"j": 2}) as (actual, expected):

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TestPydapOnline.test_dask

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<html><head>
<title>503 Service Unavailable</title>
</head><body>
<h1>Service Unavailable</h1>
<p>The server is temporarily unable to service your
request due to maintenance downtime or capacity
problems. Please try again later.</p>
</body></html>
assert_equal(actual, expected)


139 changes: 55 additions & 84 deletions xarray/tests/test_coding_times.py
View file
Original file line number Diff line number Diff line change
@@ -65,36 +65,36 @@
_ALL_CALENDARS = sorted(_NON_STANDARD_CALENDARS_SET.union(_STANDARD_CALENDARS))
_NON_STANDARD_CALENDARS = sorted(_NON_STANDARD_CALENDARS_SET)
_CF_DATETIME_NUM_DATES_UNITS = [
(np.arange(10), "days since 2000-01-01"),
(np.arange(10).astype("float64"), "days since 2000-01-01"),
(np.arange(10).astype("float32"), "days since 2000-01-01"),
(np.arange(10).reshape(2, 5), "days since 2000-01-01"),
(12300 + np.arange(5), "hours since 1680-01-01 00:00:00"),
(np.arange(10), "days since 2000-01-01", "s"),
(np.arange(10).astype("float64"), "days since 2000-01-01", "s"),
(np.arange(10).astype("float32"), "days since 2000-01-01", "s"),
(np.arange(10).reshape(2, 5), "days since 2000-01-01", "s"),
(12300 + np.arange(5), "hours since 1680-01-01 00:00:00", "s"),
# here we add a couple minor formatting errors to test
# the robustness of the parsing algorithm.
(12300 + np.arange(5), "hour since 1680-01-01 00:00:00"),
(12300 + np.arange(5), "Hour since 1680-01-01 00:00:00"),
(12300 + np.arange(5), " Hour since 1680-01-01 00:00:00 "),
(10, "days since 2000-01-01"),
([10], "daYs since 2000-01-01"),
([[10]], "days since 2000-01-01"),
([10, 10], "days since 2000-01-01"),
(np.array(10), "days since 2000-01-01"),
(0, "days since 1000-01-01"),
([0], "days since 1000-01-01"),
([[0]], "days since 1000-01-01"),
(np.arange(2), "days since 1000-01-01"),
(np.arange(0, 100000, 20000), "days since 1900-01-01"),
(np.arange(0, 100000, 20000), "days since 1-01-01"),
(17093352.0, "hours since 1-1-1 00:00:0.0"),
([0.5, 1.5], "hours since 1900-01-01T00:00:00"),
(0, "milliseconds since 2000-01-01T00:00:00"),
(0, "microseconds since 2000-01-01T00:00:00"),
(np.int32(788961600), "seconds since 1981-01-01"), # GH2002
(12300 + np.arange(5), "hour since 1680-01-01 00:00:00.500000"),
(164375, "days since 1850-01-01 00:00:00"),
(164374.5, "days since 1850-01-01 00:00:00"),
([164374.5, 168360.5], "days since 1850-01-01 00:00:00"),
(12300 + np.arange(5), "hour since 1680-01-01 00:00:00", "s"),
(12300 + np.arange(5), "Hour since 1680-01-01 00:00:00", "s"),
(12300 + np.arange(5), " Hour since 1680-01-01 00:00:00 ", "s"),
(10, "days since 2000-01-01", "s"),
([10], "daYs since 2000-01-01", "s"),
([[10]], "days since 2000-01-01", "s"),
([10, 10], "days since 2000-01-01", "s"),
(np.array(10), "days since 2000-01-01", "s"),
(0, "days since 1000-01-01", "s"),
([0], "days since 1000-01-01", "s"),
([[0]], "days since 1000-01-01", "s"),
(np.arange(2), "days since 1000-01-01", "s"),
(np.arange(0, 100000, 20000), "days since 1900-01-01", "s"),
(np.arange(0, 100000, 20000), "days since 1-01-01", "s"),
(17093352.0, "hours since 1-1-1 00:00:0.0", "s"),
([0.5, 1.5], "hours since 1900-01-01T00:00:00", "s"),
(0, "milliseconds since 2000-01-01T00:00:00", "s"),
(0, "microseconds since 2000-01-01T00:00:00", "s"),
(np.int32(788961600), "seconds since 1981-01-01", "s"), # GH2002
(12300 + np.arange(5), "hour since 1680-01-01 00:00:00.500000", "us"),
(164375, "days since 1850-01-01 00:00:00", "s"),
(164374.5, "days since 1850-01-01 00:00:00", "s"),
([164374.5, 168360.5], "days since 1850-01-01 00:00:00", "s"),
]
_CF_DATETIME_TESTS = [
num_dates_units + (calendar,)
@@ -122,9 +122,15 @@ def _all_cftime_date_types():
@requires_cftime
@pytest.mark.filterwarnings("ignore:Ambiguous reference date string")
@pytest.mark.filterwarnings("ignore:Times can't be serialized faithfully")
@pytest.mark.parametrize(["num_dates", "units", "calendar"], _CF_DATETIME_TESTS)
@pytest.mark.parametrize(
["num_dates", "units", "minimum_resolution", "calendar"], _CF_DATETIME_TESTS
)
def test_cf_datetime(
num_dates, units, calendar, time_unit: PDDatetimeUnitOptions
num_dates,
units: str,
minimum_resolution: PDDatetimeUnitOptions,
calendar: str,
time_unit: PDDatetimeUnitOptions,
) -> None:
import cftime

@@ -137,25 +143,23 @@ def test_cf_datetime(
actual = decode_cf_datetime(num_dates, units, calendar, time_unit=time_unit)

if actual.dtype.kind != "O":
expected = cftime_to_nptime(expected, time_unit=time_unit)

abs_diff = np.asarray(abs(actual - expected)).ravel()
abs_diff = pd.to_timedelta(abs_diff.tolist()).to_numpy()
if np.timedelta64(1, time_unit) > np.timedelta64(1, minimum_resolution):
expected_unit = minimum_resolution
else:
expected_unit = time_unit
expected = cftime_to_nptime(expected, time_unit=expected_unit)
Comment on lines +146 to +150
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I needed to add this logic specifically for this test:

   (12300 + np.arange(5), "hour since 1680-01-01 00:00:00.500000", "us"),

This is because the reference date required the dates be decoded to microsecond resolution (really only millisecond resolution would be required, but we follow pandas's lead here). Otherwise for time_unit="s" we would truncate precision when converting the cftime objects to np.datetime64 values, which prevented asserting exact equality.


# once we no longer support versions of netCDF4 older than 1.1.5,
# we could do this check with near microsecond accuracy:
# https://github.com/Unidata/netcdf4-python/issues/355
assert (abs_diff <= np.timedelta64(1, "s")).all()
assert_array_equal(actual, expected)
encoded1, _, _ = encode_cf_datetime(actual, units, calendar)

assert_duckarray_allclose(num_dates, encoded1)
assert_array_equal(num_dates, encoded1)

if hasattr(num_dates, "ndim") and num_dates.ndim == 1 and "1000" not in units:
# verify that wrapping with a pandas.Index works
# note that it *does not* currently work to put
# non-datetime64 compatible dates into a pandas.Index
encoded2, _, _ = encode_cf_datetime(pd.Index(actual), units, calendar)
assert_duckarray_allclose(num_dates, encoded2)
assert_array_equal(num_dates, encoded2)


@requires_cftime
@@ -206,11 +210,7 @@ def test_decode_cf_datetime_non_iso_strings() -> None:
]
for num_dates, units in cases:
actual = decode_cf_datetime(num_dates, units)
abs_diff = abs(actual - expected.values)
# once we no longer support versions of netCDF4 older than 1.1.5,
# we could do this check with near microsecond accuracy:
# https://github.com/Unidata/netcdf4-python/issues/355
assert (abs_diff <= np.timedelta64(1, "s")).all()
assert_array_equal(actual, expected)


@requires_cftime
@@ -220,7 +220,7 @@ def test_decode_standard_calendar_inside_timestamp_range(
) -> None:
import cftime

units = "days since 0001-01-01"
units = "hours since 0001-01-01"
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Using encoding units of "days" unnecessarily led the times to be encoded with floats, which prevented asserting exact equality in this test. Testing floating point time decoding was not the point of this test, so I changed to encoding the times with units of "hours" instead.

times = pd.date_range(
"2001-04-01-00", end="2001-04-30-23", unit=time_unit, freq="h"
)
@@ -233,11 +233,7 @@ def test_decode_standard_calendar_inside_timestamp_range(
# representable with nanosecond resolution.
actual = decode_cf_datetime(time, units, calendar=calendar, time_unit=time_unit)
assert actual.dtype == np.dtype(f"=M8[{time_unit}]")
abs_diff = abs(actual - expected)
# once we no longer support versions of netCDF4 older than 1.1.5,
# we could do this check with near microsecond accuracy:
# https://github.com/Unidata/netcdf4-python/issues/355
assert (abs_diff <= np.timedelta64(1, "s")).all()
assert_array_equal(actual, expected)


@requires_cftime
@@ -256,11 +252,7 @@ def test_decode_non_standard_calendar_inside_timestamp_range(calendar) -> None:

actual = decode_cf_datetime(non_standard_time, units, calendar=calendar)
assert actual.dtype == expected_dtype
abs_diff = abs(actual - expected)
# once we no longer support versions of netCDF4 older than 1.1.5,
# we could do this check with near microsecond accuracy:
# https://github.com/Unidata/netcdf4-python/issues/355
assert (abs_diff <= np.timedelta64(1, "s")).all()
assert_array_equal(actual, expected)


@requires_cftime
@@ -287,11 +279,7 @@ def test_decode_dates_outside_timestamp_range(
warnings.filterwarnings("ignore", "Unable to decode time axis")
actual = decode_cf_datetime(time, units, calendar=calendar, time_unit=time_unit)
assert all(isinstance(value, expected_date_type) for value in actual)
abs_diff = abs(actual - expected)
# once we no longer support versions of netCDF4 older than 1.1.5,
# we could do this check with near microsecond accuracy:
# https://github.com/Unidata/netcdf4-python/issues/355
assert (abs_diff <= np.timedelta64(1, "us")).all()
assert_array_equal(actual, expected)


@requires_cftime
@@ -367,14 +355,8 @@ def test_decode_standard_calendar_multidim_time_inside_timestamp_range(
mdim_time, units, calendar=calendar, time_unit=time_unit
)
assert actual.dtype == np.dtype(f"=M8[{time_unit}]")

abs_diff1 = abs(actual[:, 0] - expected1)
abs_diff2 = abs(actual[:, 1] - expected2)
# once we no longer support versions of netCDF4 older than 1.1.5,
# we could do this check with near microsecond accuracy:
# https://github.com/Unidata/netcdf4-python/issues/355
assert (abs_diff1 <= np.timedelta64(1, "s")).all()
assert (abs_diff2 <= np.timedelta64(1, "s")).all()
assert_array_equal(actual[:, 0], expected1)
assert_array_equal(actual[:, 1], expected2)


@requires_cftime
@@ -409,13 +391,8 @@ def test_decode_nonstandard_calendar_multidim_time_inside_timestamp_range(
actual = decode_cf_datetime(mdim_time, units, calendar=calendar)

assert actual.dtype == expected_dtype
abs_diff1 = abs(actual[:, 0] - expected1)
abs_diff2 = abs(actual[:, 1] - expected2)
# once we no longer support versions of netCDF4 older than 1.1.5,
# we could do this check with near microsecond accuracy:
# https://github.com/Unidata/netcdf4-python/issues/355
assert (abs_diff1 <= np.timedelta64(1, "s")).all()
assert (abs_diff2 <= np.timedelta64(1, "s")).all()
assert_array_equal(actual[:, 0], expected1)
assert_array_equal(actual[:, 1], expected2)


@requires_cftime
@@ -455,14 +432,8 @@ def test_decode_multidim_time_outside_timestamp_range(
dtype = np.dtype(f"=M8[{time_unit}]")

assert actual.dtype == dtype

abs_diff1 = abs(actual[:, 0] - expected1)
abs_diff2 = abs(actual[:, 1] - expected2)
# once we no longer support versions of netCDF4 older than 1.1.5,
# we could do this check with near microsecond accuracy:
# https://github.com/Unidata/netcdf4-python/issues/355
assert (abs_diff1 <= np.timedelta64(1, "s")).all()
assert (abs_diff2 <= np.timedelta64(1, "s")).all()
assert_array_equal(actual[:, 0], expected1)
assert_array_equal(actual[:, 1], expected2)


@requires_cftime

Unchanged files with check annotations Beta

View file
xarray/namedarray/core.py
attrs: _AttrsLike = None,
):
self._data = data
self._dims = self._parse_dimensions(dims)

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self._attrs = dict(attrs) if attrs else None
def __init_subclass__(cls, **kwargs: Any) -> None:
View file
xarray/core/duck_array_ops.py
xp = get_array_namespace(data)
if xp == np:
# numpy currently doesn't have a astype:
return data.astype(dtype, **kwargs)

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return xp.astype(data, dtype, **kwargs)
return data.astype(dtype, **kwargs)
View file
xarray/coding/variables.py
# otherwise numpy unsigned ints will silently cast to the signed counterpart
fill_value = fill_value.item()
# passes if provided fill value fits in encoded on-disk type
new_fill = encoded_dtype.type(fill_value)

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    np.array(value).astype(dtype)`
will give the desired result (the cast overflows).

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For the old behavior, usually:
    np.array(value).astype(dtype)`
will give the desired result (the cast overflows).

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except OverflowError:
encoded_kind_str = "signed" if encoded_dtype.kind == "i" else "unsigned"
warnings.warn(