Package: src:python-xarray
Version: 2025.01.2-1
Severity: serious
Tags: ftbfs trixie sid
Dear maintainer:
During a rebuild of all packages in unstable, your package failed to build:
--------------------------------------------------------------------------------
[...]
debian/rules clean
dh clean --buildsystem=pybuild --with python3
debian/rules override_dh_auto_clean
make[1]: Entering directory '/<<PKGBUILDDIR>>'
dh_auto_clean
pybuild --clean -i python{version} -p 3.13
I: pybuild base:311: python3.13 setup.py clean
/usr/lib/python3/dist-packages/setuptools_scm/_integration/setuptools.py:92:
UserWarning: version of None already set
warnings.warn(f"version of {dist_name} already set")
/usr/lib/python3/dist-packages/setuptools/dist.py:330: InformationOnly:
Normalizing '2025.01.2' to '2025.1.2'
self.metadata.version = self._normalize_version(self.metadata.version)
running clean
removing '/<<PKGBUILDDIR>>/.pybuild/cpython3_3.13_xarray/build' (and everything
under it)
'build/bdist.linux-x86_64' does not exist -- can't clean it
[... snipped ...]
else:
raise ValueError(
f"unknown setting for chunked array handling in
apply_ufunc: {dask}"
)
else:
if vectorize:
func = _vectorize(
func, signature, output_dtypes=output_dtypes,
exclude_dims=exclude_dims
)
> result_data = func(*input_data)
E RuntimeWarning: invalid value encountered in sqrt
xarray/core/computation.py:824: RuntimeWarning
______________ test_weighted_operations_keep_attr[None-False-std] ______________
operation = 'std', as_dataset = False, keep_attrs = None
@pytest.mark.parametrize(
"operation",
("sum_of_weights", "sum", "mean", "sum_of_squares", "var", "std",
"quantile"),
)
@pytest.mark.parametrize("as_dataset", (True, False))
@pytest.mark.parametrize("keep_attrs", (True, False, None))
def test_weighted_operations_keep_attr(operation, as_dataset, keep_attrs):
weights = DataArray(np.random.randn(2, 2), attrs=dict(attr="weights"))
data = DataArray(np.random.randn(2, 2))
if as_dataset:
data = data.to_dataset(name="data")
data.attrs = dict(attr="weights")
kwargs = {"keep_attrs": keep_attrs}
if operation == "quantile":
kwargs["q"] = 0.5
> result = getattr(data.weighted(weights), operation)(**kwargs)
xarray/tests/test_weighted.py:743:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
xarray/core/weighted.py:517: in std
return self._implementation(
xarray/core/weighted.py:546: in _implementation
dataset = dataset.map(func, dim=dim, **kwargs)
xarray/core/dataset.py:7172: in map
k: maybe_wrap_array(v, func(v, *args, **kwargs))
xarray/core/weighted.py:316: in _weighted_std
return cast("T_DataArray", np.sqrt(self._weighted_var(da, dim, skipna)))
xarray/core/arithmetic.py:83: in __array_ufunc__
return apply_ufunc(
xarray/core/computation.py:1271: in apply_ufunc
return apply_dataarray_vfunc(
xarray/core/computation.py:313: in apply_dataarray_vfunc
result_var = func(*data_vars)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
func = <ufunc 'sqrt'>, signature = _UFuncSignature([()], [()])
exclude_dims = frozenset(), dask = 'allowed', output_dtypes = None
def apply_variable_ufunc(
func,
*args,
signature: _UFuncSignature,
exclude_dims=frozenset(),
dask="forbidden",
output_dtypes=None,
vectorize=False,
keep_attrs="override",
dask_gufunc_kwargs=None,
) -> Variable | tuple[Variable, ...]:
"""Apply a ndarray level function over Variable and/or ndarray
objects."""
from xarray.core.formatting import short_array_repr
from xarray.core.variable import Variable, as_compatible_data
dim_sizes = unified_dim_sizes(
(a for a in args if hasattr(a, "dims")), exclude_dims=exclude_dims
)
broadcast_dims = tuple(
dim for dim in dim_sizes if dim not in signature.all_core_dims
)
output_dims = [broadcast_dims + out for out in
signature.output_core_dims]
input_data = [
(
broadcast_compat_data(arg, broadcast_dims, core_dims)
if isinstance(arg, Variable)
else arg
)
for arg, core_dims in zip(args, signature.input_core_dims,
strict=True)
]
if any(is_chunked_array(array) for array in input_data):
if dask == "forbidden":
raise ValueError(
"apply_ufunc encountered a chunked array on an "
"argument, but handling for chunked arrays has not "
"been enabled. Either set the ``dask`` argument "
"or load your data into memory first with "
"``.load()`` or ``.compute()``"
)
elif dask == "parallelized":
chunkmanager = get_chunked_array_type(*input_data)
numpy_func = func
if dask_gufunc_kwargs is None:
dask_gufunc_kwargs = {}
else:
dask_gufunc_kwargs = dask_gufunc_kwargs.copy()
allow_rechunk = dask_gufunc_kwargs.get("allow_rechunk", None)
if allow_rechunk is None:
for n, (data, core_dims) in enumerate(
zip(input_data, signature.input_core_dims, strict=True)
):
if is_chunked_array(data):
# core dimensions cannot span multiple chunks
for axis, dim in enumerate(core_dims,
start=-len(core_dims)):
if len(data.chunks[axis]) != 1:
raise ValueError(
f"dimension {dim} on {n}th function
argument to "
"apply_ufunc with dask='parallelized'
consists of "
"multiple chunks, but is also a core
dimension. To "
"fix, either rechunk into a single
array chunk along "
f"this dimension, i.e.,
``.chunk(dict({dim}=-1))``, or "
"pass ``allow_rechunk=True`` in
``dask_gufunc_kwargs`` "
"but beware that this may significantly
increase memory usage."
)
dask_gufunc_kwargs["allow_rechunk"] = True
output_sizes = dask_gufunc_kwargs.pop("output_sizes", {})
if output_sizes:
output_sizes_renamed = {}
for key, value in output_sizes.items():
if key not in signature.all_output_core_dims:
raise ValueError(
f"dimension '{key}' in 'output_sizes' must
correspond to output_core_dims"
)
output_sizes_renamed[signature.dims_map[key]] = value
dask_gufunc_kwargs["output_sizes"] = output_sizes_renamed
for key in signature.all_output_core_dims:
if (
key not in signature.all_input_core_dims or key in
exclude_dims
) and key not in output_sizes:
raise ValueError(
f"dimension '{key}' in 'output_core_dims' needs
corresponding (dim, size) in 'output_sizes'"
)
def func(*arrays):
res = chunkmanager.apply_gufunc(
numpy_func,
signature.to_gufunc_string(exclude_dims),
*arrays,
vectorize=vectorize,
output_dtypes=output_dtypes,
**dask_gufunc_kwargs,
)
return res
elif dask == "allowed":
pass
else:
raise ValueError(
f"unknown setting for chunked array handling in
apply_ufunc: {dask}"
)
else:
if vectorize:
func = _vectorize(
func, signature, output_dtypes=output_dtypes,
exclude_dims=exclude_dims
)
> result_data = func(*input_data)
E RuntimeWarning: invalid value encountered in sqrt
xarray/core/computation.py:824: RuntimeWarning
_______________ test_weighted_operations_keep_attr_da_in_ds[std] _______________
operation = 'std'
@pytest.mark.parametrize(
"operation",
("sum_of_weights", "sum", "mean", "sum_of_squares", "var", "std",
"quantile"),
)
def test_weighted_operations_keep_attr_da_in_ds(operation):
# GH #3595
weights = DataArray(np.random.randn(2, 2))
data = DataArray(np.random.randn(2, 2), attrs=dict(attr="data"))
data = data.to_dataset(name="a")
kwargs = {"keep_attrs": True}
if operation == "quantile":
kwargs["q"] = 0.5
> result = getattr(data.weighted(weights), operation)(**kwargs)
xarray/tests/test_weighted.py:768:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
xarray/core/weighted.py:517: in std
return self._implementation(
xarray/core/weighted.py:554: in _implementation
return self.obj.map(func, dim=dim, **kwargs)
xarray/core/dataset.py:7172: in map
k: maybe_wrap_array(v, func(v, *args, **kwargs))
xarray/core/weighted.py:316: in _weighted_std
return cast("T_DataArray", np.sqrt(self._weighted_var(da, dim, skipna)))
xarray/core/arithmetic.py:83: in __array_ufunc__
return apply_ufunc(
xarray/core/computation.py:1271: in apply_ufunc
return apply_dataarray_vfunc(
xarray/core/computation.py:313: in apply_dataarray_vfunc
result_var = func(*data_vars)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
func = <ufunc 'sqrt'>, signature = _UFuncSignature([()], [()])
exclude_dims = frozenset(), dask = 'allowed', output_dtypes = None
def apply_variable_ufunc(
func,
*args,
signature: _UFuncSignature,
exclude_dims=frozenset(),
dask="forbidden",
output_dtypes=None,
vectorize=False,
keep_attrs="override",
dask_gufunc_kwargs=None,
) -> Variable | tuple[Variable, ...]:
"""Apply a ndarray level function over Variable and/or ndarray
objects."""
from xarray.core.formatting import short_array_repr
from xarray.core.variable import Variable, as_compatible_data
dim_sizes = unified_dim_sizes(
(a for a in args if hasattr(a, "dims")), exclude_dims=exclude_dims
)
broadcast_dims = tuple(
dim for dim in dim_sizes if dim not in signature.all_core_dims
)
output_dims = [broadcast_dims + out for out in
signature.output_core_dims]
input_data = [
(
broadcast_compat_data(arg, broadcast_dims, core_dims)
if isinstance(arg, Variable)
else arg
)
for arg, core_dims in zip(args, signature.input_core_dims,
strict=True)
]
if any(is_chunked_array(array) for array in input_data):
if dask == "forbidden":
raise ValueError(
"apply_ufunc encountered a chunked array on an "
"argument, but handling for chunked arrays has not "
"been enabled. Either set the ``dask`` argument "
"or load your data into memory first with "
"``.load()`` or ``.compute()``"
)
elif dask == "parallelized":
chunkmanager = get_chunked_array_type(*input_data)
numpy_func = func
if dask_gufunc_kwargs is None:
dask_gufunc_kwargs = {}
else:
dask_gufunc_kwargs = dask_gufunc_kwargs.copy()
allow_rechunk = dask_gufunc_kwargs.get("allow_rechunk", None)
if allow_rechunk is None:
for n, (data, core_dims) in enumerate(
zip(input_data, signature.input_core_dims, strict=True)
):
if is_chunked_array(data):
# core dimensions cannot span multiple chunks
for axis, dim in enumerate(core_dims,
start=-len(core_dims)):
if len(data.chunks[axis]) != 1:
raise ValueError(
f"dimension {dim} on {n}th function
argument to "
"apply_ufunc with dask='parallelized'
consists of "
"multiple chunks, but is also a core
dimension. To "
"fix, either rechunk into a single
array chunk along "
f"this dimension, i.e.,
``.chunk(dict({dim}=-1))``, or "
"pass ``allow_rechunk=True`` in
``dask_gufunc_kwargs`` "
"but beware that this may significantly
increase memory usage."
)
dask_gufunc_kwargs["allow_rechunk"] = True
output_sizes = dask_gufunc_kwargs.pop("output_sizes", {})
if output_sizes:
output_sizes_renamed = {}
for key, value in output_sizes.items():
if key not in signature.all_output_core_dims:
raise ValueError(
f"dimension '{key}' in 'output_sizes' must
correspond to output_core_dims"
)
output_sizes_renamed[signature.dims_map[key]] = value
dask_gufunc_kwargs["output_sizes"] = output_sizes_renamed
for key in signature.all_output_core_dims:
if (
key not in signature.all_input_core_dims or key in
exclude_dims
) and key not in output_sizes:
raise ValueError(
f"dimension '{key}' in 'output_core_dims' needs
corresponding (dim, size) in 'output_sizes'"
)
def func(*arrays):
res = chunkmanager.apply_gufunc(
numpy_func,
signature.to_gufunc_string(exclude_dims),
*arrays,
vectorize=vectorize,
output_dtypes=output_dtypes,
**dask_gufunc_kwargs,
)
return res
elif dask == "allowed":
pass
else:
raise ValueError(
f"unknown setting for chunked array handling in
apply_ufunc: {dask}"
)
else:
if vectorize:
func = _vectorize(
func, signature, output_dtypes=output_dtypes,
exclude_dims=exclude_dims
)
> result_data = func(*input_data)
E RuntimeWarning: invalid value encountered in sqrt
xarray/core/computation.py:824: RuntimeWarning
=============================== warnings summary ===============================
xarray/tests/test_backends.py: 722 warnings
/usr/lib/python3/dist-packages/zarr/api/asynchronous.py:203: UserWarning:
Consolidated metadata is currently not part in the Zarr format 3 specification.
It may not be supported by other zarr implementations and may change in the
future.
warnings.warn(
xarray/tests/test_backends.py: 896 warnings
/usr/lib/python3/dist-packages/zarr/codecs/vlen_utf8.py:44: UserWarning: The
codec `vlen-utf8` is currently not part in the Zarr format 3 specification. It
may not be supported by other zarr implementations and may change in the future.
return cls(**configuration_parsed)
xarray/tests/test_backends.py: 171 warnings
/usr/lib/python3/dist-packages/zarr/core/array.py:3989: UserWarning: The
dtype `<U1` is currently not part in the Zarr format 3 specification. It may
not be supported by other zarr implementations and may change in the future.
meta = AsyncArray._create_metadata_v3(
xarray/tests/test_backends.py: 217 warnings
/usr/lib/python3/dist-packages/zarr/codecs/vlen_utf8.py:99: UserWarning: The
codec `vlen-bytes` is currently not part in the Zarr format 3 specification. It
may not be supported by other zarr implementations and may change in the future.
return cls(**configuration_parsed)
xarray/tests/test_backends.py::TestZarrDictStore::test_zero_dimensional_variable[3]
xarray/tests/test_backends.py::TestZarrDirectoryStore::test_zero_dimensional_variable[3]
xarray/tests/test_backends.py::TestZarrWriteEmpty::test_zero_dimensional_variable[3]
/usr/lib/python3/dist-packages/zarr/core/array.py:3989: UserWarning: The
dtype `|S6` is currently not part in the Zarr format 3 specification. It may
not be supported by other zarr implementations and may change in the future.
meta = AsyncArray._create_metadata_v3(
xarray/tests/test_backends.py::TestZarrDictStore::test_zero_dimensional_variable[3]
xarray/tests/test_backends.py::TestZarrDirectoryStore::test_zero_dimensional_variable[3]
xarray/tests/test_backends.py::TestZarrWriteEmpty::test_zero_dimensional_variable[3]
/usr/lib/python3/dist-packages/zarr/core/array.py:3989: UserWarning: The
dtype `<U6` is currently not part in the Zarr format 3 specification. It may
not be supported by other zarr implementations and may change in the future.
meta = AsyncArray._create_metadata_v3(
xarray/tests/test_backends.py: 18 warnings
/usr/lib/python3/dist-packages/zarr/core/array.py:3989: UserWarning: The
dtype `|S4` is currently not part in the Zarr format 3 specification. It may
not be supported by other zarr implementations and may change in the future.
meta = AsyncArray._create_metadata_v3(
xarray/tests/test_backends.py: 48 warnings
/usr/lib/python3/dist-packages/zarr/core/array.py:3989: UserWarning: The
dtype `StringDType()` is currently not part in the Zarr format 3 specification.
It may not be supported by other zarr implementations and may change in the
future.
meta = AsyncArray._create_metadata_v3(
xarray/tests/test_backends.py::TestZarrDictStore::test_roundtrip_string_data[3]
xarray/tests/test_backends.py::TestZarrDirectoryStore::test_roundtrip_string_data[3]
xarray/tests/test_backends.py::TestZarrWriteEmpty::test_roundtrip_string_data[3]
/usr/lib/python3/dist-packages/zarr/core/array.py:3989: UserWarning: The
dtype `<U4` is currently not part in the Zarr format 3 specification. It may
not be supported by other zarr implementations and may change in the future.
meta = AsyncArray._create_metadata_v3(
xarray/tests/test_backends.py: 33 warnings
/usr/lib/python3/dist-packages/zarr/core/array.py:3989: UserWarning: The
dtype `<U2` is currently not part in the Zarr format 3 specification. It may
not be supported by other zarr implementations and may change in the future.
meta = AsyncArray._create_metadata_v3(
xarray/tests/test_backends.py: 33 warnings
/usr/lib/python3/dist-packages/zarr/core/array.py:3989: UserWarning: The
dtype `|S2` is currently not part in the Zarr format 3 specification. It may
not be supported by other zarr implementations and may change in the future.
meta = AsyncArray._create_metadata_v3(
xarray/tests/test_backends.py::TestH5NetCDFFileObject::test_open_fileobj
/usr/lib/python3/dist-packages/_pytest/unraisableexception.py:85:
PytestUnraisableExceptionWarning: Exception ignored in: <function
CachingFileManager.__del__ at 0x7f4a3f4bafc0>
Traceback (most recent call last):
File
"/<<PKGBUILDDIR>>/.pybuild/cpython3_3.13_xarray/build/xarray/backends/file_manager.py",
line 255, in __del__
warnings.warn(
~~~~~~~~~~~~~^
f"deallocating {self}, but file is not already closed. "
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
...<2 lines>...
stacklevel=2,
^^^^^^^^^^^^^
)
^
RuntimeWarning: deallocating CachingFileManager(<class 'h5netcdf.core.File'>,
<_io.BufferedReader name='/tmp/tmpo1h3uc_h/temp-2487.nc'>, mode='r',
kwargs={'invalid_netcdf': None, 'decode_vlen_strings': True, 'driver': None},
manager_id='58f3b5b1-d353-4530-a4b1-d54ea76f5452'), but file is not already
closed. This may indicate a bug.
warnings.warn(pytest.PytestUnraisableExceptionWarning(msg))
xarray/tests/test_backends.py::test_h5netcdf_storage_options
/usr/lib/python3/dist-packages/_pytest/python.py:159: RuntimeWarning:
deallocating CachingFileManager(<class 'h5netcdf.core.File'>,
<fsspec.implementations.local.LocalFileOpener object at 0x7f4a2a2625f0>,
mode='r', kwargs={'invalid_netcdf': None, 'decode_vlen_strings': True,
'driver': None}, manager_id='5b831278-e650-4485-835b-9372ce9ae114'), but file
is not already closed. This may indicate a bug.
result = testfunction(**testargs)
xarray/tests/test_backends.py::test_h5netcdf_storage_options
/usr/lib/python3/dist-packages/_pytest/python.py:159: RuntimeWarning:
deallocating CachingFileManager(<class 'h5netcdf.core.File'>,
<fsspec.implementations.local.LocalFileOpener object at 0x7f4a2a263100>,
mode='r', kwargs={'invalid_netcdf': None, 'decode_vlen_strings': True,
'driver': None}, manager_id='4b681f13-bb3a-48e9-80af-2783a46dd492'), but file
is not already closed. This may indicate a bug.
result = testfunction(**testargs)
xarray/tests/test_coding_times.py::test_roundtrip_datetime64_nanosecond_precision[1677-09-21T00:12:43.145225-us-int64-None-False]
xarray/tests/test_coding_times.py::test_roundtrip_datetime64_nanosecond_precision[1970-01-01T00:00:01.000001-us-int64-None-False]
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.13_xarray/build/xarray/tests/test_coding_times.py:1295:
UserWarning: Converting non-nanosecond precision datetime values to nanosecond
precision. This behavior can eventually be relaxed in xarray, as it is an
artifact from pandas which is now beginning to support non-nanosecond precision
values. This warning is caused by passing non-nanosecond np.datetime64 or
np.timedelta64 values to the DataArray or Variable constructor; it can be
silenced by converting the values to nanosecond precision ahead of time.
var = Variable(["time"], times, encoding=encoding)
xarray/tests/test_computation.py::test_unified_dim_sizes
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.13_xarray/build/xarray/namedarray/core.py:264:
UserWarning: Duplicate dimension names present: dimensions {'x'} appear more
than once in dims=('x', 'x'). We do not yet support duplicate dimension names,
but we do allow initial construction of the object. We recommend you rename the
dims immediately to become distinct, as most xarray functionality is likely to
fail silently if you do not. To rename the dimensions you will need to set the
``.dims`` attribute of each variable, ``e.g. var.dims=('x0', 'x1')``.
self._dims = self._parse_dimensions(dims)
xarray/tests/test_conventions.py: 1 warning
xarray/tests/test_dataset.py: 1 warning
xarray/tests/test_rolling.py: 36 warnings
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.13_xarray/build/xarray/core/duck_array_ops.py:234:
RuntimeWarning: invalid value encountered in cast
return data.astype(dtype, **kwargs)
xarray/tests/test_dask.py: 12 warnings
/usr/lib/python3/dist-packages/numpy/_core/numeric.py:442: RuntimeWarning:
invalid value encountered in cast
multiarray.copyto(res, fill_value, casting='unsafe')
xarray/tests/test_dataset.py::TestDataset::test_to_and_from_dict_with_nan_nat[array]
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.13_xarray/build/xarray/tests/test_dataset.py:5170:
UserWarning: Converting non-nanosecond precision datetime values to nanosecond
precision. This behavior can eventually be relaxed in xarray, as it is an
artifact from pandas which is now beginning to support non-nanosecond precision
values. This warning is caused by passing non-nanosecond np.datetime64 or
np.timedelta64 values to the DataArray or Variable constructor; it can be
silenced by converting the values to nanosecond precision ahead of time.
roundtripped = Dataset.from_dict(ds.to_dict(data=data))
xarray/tests/test_duck_array_ops.py::test_cftime_datetime_mean[True]
/usr/lib/python3/dist-packages/dask/array/core.py:5083: DeprecationWarning:
Bitwise inversion '~' on bool is deprecated and will be removed in Python 3.16.
This returns the bitwise inversion of the underlying int object and is usually
not what you expect from negating a bool. Use the 'not' operator for boolean
negation or ~int(x) if you really want the bitwise inversion of the underlying
int.
result = function(*args, **kwargs)
xarray/tests/test_strategies.py: 10 warnings
/usr/lib/python3/dist-packages/numpy/_core/fromnumeric.py:86: RuntimeWarning:
invalid value encountered in reduce
return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
xarray/tests/test_strategies.py::TestReduction::test_mean
xarray/tests/test_strategies.py::TestReduction::test_mean
xarray/tests/test_strategies.py::TestReduction::test_mean
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.13_xarray/build/xarray/tests/test_strategies.py:277:
RuntimeWarning: Mean of empty slice
expected = np.nanmean(var.data, axis=reduction_axes)
xarray/tests/test_variable.py::TestIndexVariable::test_to_index_multiindex_level
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.13_xarray/build/xarray/tests/test_variable.py:2435:
FutureWarning: the `pandas.MultiIndex` object(s) passed as 'x' coordinate(s)
or data variable(s) will no longer be implicitly promoted and wrapped into
multiple indexed coordinates in the future (i.e., one coordinate for each
multi-index level + one dimension coordinate). If you want to keep this
behavior, you need to first wrap it explicitly using `mindex_coords =
xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as
coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`,
`dataset.assign_coords(mindex_coords)` or
`dataarray.assign_coords(mindex_coords)`.
ds = Dataset(coords={"x": midx})
xarray/tests/test_weighted.py::test_weighted_quantile_3D[None-True-q1-a]
xarray/tests/test_weighted.py::test_weighted_quantile_3D[None-True-q2-a]
/usr/lib/python3/dist-packages/numpy/lib/_nanfunctions_impl.py:1620:
RuntimeWarning: All-NaN slice encountered
return fnb._ureduce(a,
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
=========================== short test summary info ============================
FAILED
xarray/tests/test_backends.py::TestZarrDictStore::test_hidden_zarr_keys[2]
FAILED
xarray/tests/test_backends.py::TestZarrDirectoryStore::test_hidden_zarr_keys[2]
FAILED
xarray/tests/test_backends.py::TestZarrWriteEmpty::test_hidden_zarr_keys[2]
FAILED xarray/tests/test_groupby.py::test_dask_da_groupby_quantile - Failed: ...
FAILED
xarray/tests/test_weighted.py::test_weighted_operations_keep_attr[False-True-std]
FAILED
xarray/tests/test_weighted.py::test_weighted_operations_keep_attr[False-False-std]
FAILED
xarray/tests/test_weighted.py::test_weighted_operations_keep_attr[None-True-std]
FAILED
xarray/tests/test_weighted.py::test_weighted_operations_keep_attr[None-False-std]
FAILED
xarray/tests/test_weighted.py::test_weighted_operations_keep_attr_da_in_ds[std]
= 9 failed, 16789 passed, 1293 skipped, 73 xfailed, 107 xpassed, 2221 warnings
in 234.58s (0:03:54) =
E: pybuild pybuild:389: test: plugin distutils failed with: exit code=1: cd
/<<PKGBUILDDIR>>/.pybuild/cpython3_3.13_xarray/build; python3.13 -m pytest
# cleanup after tests
find . -name 'tmp.zarr*' -delete
find: cannot delete â./.pybuild/cpython3_3.13_xarray/build/tmp.zarr.zipâ:
Directory not empty
make[1]: *** [debian/rules:43: override_dh_auto_test] Error 1
make[1]: Leaving directory '/<<PKGBUILDDIR>>'
make: *** [debian/rules:19: binary] Error 2
dpkg-buildpackage: error: debian/rules binary subprocess returned exit status 2
--------------------------------------------------------------------------------
The above is just how the build ends and not necessarily the most relevant part.
If required, the full build log is available here:
https://people.debian.org/~sanvila/build-logs/202503/
About the archive rebuild: The build was made on virtual machines from AWS,
using sbuild and a reduced chroot with only build-essential packages.
If you could not reproduce the bug please contact me privately, as I
am willing to provide ssh access to a virtual machine where the bug is
fully reproducible.
If this is really a bug in one of the build-depends, please use
reassign and add an affects on src:python-xarray, so that this is still
visible in the BTS web page for this package.
Thanks.
