TestReduce1D.test_topk[nan] AssertionError: Left and right DataArray objects are not identical Differing values: L array([-4.]) R array([nan])

TestReduce1D.test_topk[obj] AssertionError: Left and right DataArray objects are not identical Differing values: L array([1.0], dtype=object) R array([-4.])

TestReduce1D.test_topk[datetime] numpy.core._exceptions._UFuncNoLoopError: ufunc 'negative' did not contain a loop with signature matching types <class 'numpy.dtype[datetime64]'> -> None

Process completed with exit code 1.

test_where TypeError: `condition` must be have a boolean data type

TestReduce1D.test_topk[nan] AssertionError: Left and right DataArray objects are not identical Differing values: L array([-4.]) R array([nan])

TestReduce1D.test_topk[obj] AssertionError: Left and right DataArray objects are not identical Differing values: L array([1.0], dtype=object) R array([-4.])

TestReduce1D.test_topk[datetime] numpy._core._exceptions._UFuncNoLoopError: ufunc 'negative' did not contain a loop with signature matching types <class 'numpy.dtypes.DateTime64DType'> -> None

Process completed with exit code 1.

test_where TypeError: `condition` must be have a boolean data type

TestReduce1D.test_topk[nan] AssertionError: Left and right DataArray objects are not identical Differing values: L array([-4.]) R array([nan])

TestReduce1D.test_topk[obj] AssertionError: Left and right DataArray objects are not identical Differing values: L array([1.0], dtype=object) R array([-4.])

TestReduce1D.test_topk[datetime] numpy._core._exceptions._UFuncNoLoopError: ufunc 'negative' did not contain a loop with signature matching types <class 'numpy.dtypes.DateTime64DType'> -> None

Process completed with exit code 1.

test_where TypeError: `condition` must be have a boolean data type

TestReduce1D.test_topk[nan] AssertionError: Left and right DataArray objects are not identical Differing values: L array([-4.]) R array([nan])

TestReduce1D.test_topk[obj] AssertionError: Left and right DataArray objects are not identical Differing values: L array([1.0], dtype=object) R array([-4.])

TestReduce1D.test_topk[datetime] numpy._core._exceptions._UFuncNoLoopError: ufunc 'negative' did not contain a loop with signature matching types <class 'numpy.dtypes.DateTime64DType'> -> None

Process completed with exit code 1.

test_where TypeError: `condition` must be have a boolean data type

TestReduce1D.test_topk[nan] AssertionError: Left and right DataArray objects are not identical Differing values: L array([-4.]) R array([nan])

TestReduce1D.test_topk[obj] AssertionError: Left and right DataArray objects are not identical Differing values: L array([1.0], dtype=object) R array([-4.])

TestReduce1D.test_topk[datetime] numpy._core._exceptions._UFuncNoLoopError: ufunc 'negative' did not contain a loop with signature matching types <class 'numpy.dtypes.DateTime64DType'> -> None

Process completed with exit code 1.

TestReduce1D.test_topk[nan] AssertionError: Left and right DataArray objects are not identical Differing values: L array([-4.]) R array([nan])

TestReduce1D.test_topk[obj] AssertionError: Left and right DataArray objects are not identical Differing values: L array([1.0], dtype=object) R array([-4.])

TestReduce1D.test_topk[datetime] numpy.core._exceptions._UFuncNoLoopError: ufunc 'negative' did not contain a loop with signature matching types <class 'numpy.dtype[datetime64]'> -> None

Process completed with exit code 1.

test_where TypeError: `condition` must be have a boolean data type

TestReduce1D.test_topk[nan] AssertionError: Left and right DataArray objects are not identical Differing values: L array([-4.]) R array([nan])

TestReduce1D.test_topk[obj] AssertionError: Left and right DataArray objects are not identical Differing values: L array([1.0], dtype=object) R array([-4.])

TestReduce1D.test_topk[datetime] numpy._core._exceptions._UFuncNoLoopError: ufunc 'negative' did not contain a loop with signature matching types <class 'numpy.dtypes.DateTime64DType'> -> None

Process completed with exit code 1.

test_where TypeError: `condition` must be have a boolean data type

TestReduce1D.test_topk[nan] AssertionError: Left and right DataArray objects are not identical Differing values: L array([-4.]) R array([nan])

TestReduce1D.test_topk[obj] AssertionError: Left and right DataArray objects are not identical Differing values: L array([1.0], dtype=object) R array([-4.])

TestReduce1D.test_topk[datetime] numpy._core._exceptions._UFuncNoLoopError: ufunc 'negative' did not contain a loop with signature matching types <class 'numpy.dtypes.DateTime64DType'> -> None

Process completed with exit code 1.

test_mypy_pipe_lambda_noarg_return_type

test_mypy_pipe_lambda_posarg_return_type

test_mypy_pipe_lambda_chaining_return_type

test_mypy_pipe_lambda_missing_arg

test_mypy_pipe_lambda_extra_arg

test_mypy_pipe_function_missing_posarg

test_mypy_pipe_function_extra_posarg

test_mypy_pipe_function_missing_kwarg

test_mypy_pipe_function_missing_keyword

test_mypy_pipe_function_unexpected_keyword

test_where TypeError: `condition` must be have a boolean data type

TestReduce1D.test_topk[nan] AssertionError: Left and right DataArray objects are not identical Differing values: L array([-4.]) R array([nan])

TestReduce1D.test_topk[obj] AssertionError: Left and right DataArray objects are not identical Differing values: L array([1.0], dtype=object) R array([-4.])

TestReduce1D.test_topk[datetime] numpy._core._exceptions._UFuncNoLoopError: ufunc 'negative' did not contain a loop with signature matching types <class 'numpy.dtypes.DateTime64DType'> -> None

Process completed with exit code 1.

test_mypy_pipe_lambda_noarg_return_type

test_mypy_pipe_lambda_posarg_return_type

test_mypy_pipe_lambda_chaining_return_type

test_mypy_pipe_lambda_missing_arg

test_mypy_pipe_lambda_extra_arg

test_mypy_pipe_function_missing_posarg

test_mypy_pipe_function_extra_posarg

test_mypy_pipe_function_missing_kwarg

test_mypy_pipe_function_missing_keyword

test_mypy_pipe_function_unexpected_keyword

test_where TypeError: `condition` must be have a boolean data type

TestReduce1D.test_topk[nan] AssertionError: Left and right DataArray objects are not identical Differing values: L array([-4.]) R array([nan])

TestReduce1D.test_topk[obj] AssertionError: Left and right DataArray objects are not identical Differing values: L array([1.0], dtype=object) R array([-4.])

TestReduce1D.test_topk[datetime] numpy._core._exceptions._UFuncNoLoopError: ufunc 'negative' did not contain a loop with signature matching types <class 'numpy.dtypes.DateTime64DType'> -> None

Process completed with exit code 1.

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')``.

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')``.

Degrees of freedom <= 0 for slice.

Degrees of freedom <= 0 for slice.

Degrees of freedom <= 0 for slice.

Degrees of freedom <= 0 for slice.

Degrees of freedom <= 0 for slice.

Degrees of freedom <= 0 for slice.

Degrees of freedom <= 0 for slice.

Degrees of freedom <= 0 for slice.

Input 'save-always' has been deprecated with message: save-always does not work as intended and will be removed in a future release. A separate `actions/cache/restore` step should be used instead. See https://github.com/actions/cache/tree/main/save#always-save-cache for more details.

deallocating CachingFileManager(<class 'netCDF4._netCDF4.Dataset'>, '/tmp/pytest-of-runner/pytest-0/popen-gw3/test_download_from_github0/xarray_tutorial_data/4c5c606840191d085ac934c9665f7387-tiny.nc', mode='r', kwargs={'clobber': True, 'diskless': False, 'persist': False, 'format': 'NETCDF4'}, manager_id='89462830-2e99-47d9-ae49-04ba6d0fd8a4'), but file is not already closed. This may indicate a bug.

deallocating CachingFileManager(<class 'netCDF4._netCDF4.Dataset'>, '/tmp/pytest-of-runner/pytest-0/popen-gw3/test_download_from_github0/xarray_tutorial_data/4c5c606840191d085ac934c9665f7387-tiny.nc', mode='r', kwargs={'clobber': True, 'diskless': False, 'persist': False, 'format': 'NETCDF4'}, manager_id='89462830-2e99-47d9-ae49-04ba6d0fd8a4'), but file is not already closed. This may indicate a bug.

deallocating CachingFileManager(<class 'netCDF4._netCDF4.Dataset'>, '/tmp/pytest-of-runner/pytest-0/popen-gw3/test_download_from_github_load0/xarray_tutorial_data/4c5c606840191d085ac934c9665f7387-tiny.nc', mode='r', kwargs={'clobber': True, 'diskless': False, 'persist': False, 'format': 'NETCDF4'}, manager_id='7e5ee387-1122-4169-b405-1b920a082e17'), but file is not already closed. This may indicate a bug.

deallocating CachingFileManager(<class 'netCDF4._netCDF4.Dataset'>, '/tmp/pytest-of-runner/pytest-0/popen-gw3/test_download_from_github_load0/xarray_tutorial_data/4c5c606840191d085ac934c9665f7387-tiny.nc', mode='r', kwargs={'clobber': True, 'diskless': False, 'persist': False, 'format': 'NETCDF4'}, manager_id='19cee389-3067-4850-bd39-c9cdf61ff616'), but file is not already closed. This may indicate a bug.

deallocating CachingFileManager(<class 'netCDF4._netCDF4.Dataset'>, '/tmp/pytest-of-runner/pytest-0/popen-gw3/test_download_from_github_load0/xarray_tutorial_data/4c5c606840191d085ac934c9665f7387-tiny.nc', mode='r', kwargs={'clobber': True, 'diskless': False, 'persist': False, 'format': 'NETCDF4'}, manager_id='7e5ee387-1122-4169-b405-1b920a082e17'), but file is not already closed. This may indicate a bug.

deallocating CachingFileManager(<class 'netCDF4._netCDF4.Dataset'>, '/tmp/pytest-of-runner/pytest-0/popen-gw3/test_download_from_github_load0/xarray_tutorial_data/4c5c606840191d085ac934c9665f7387-tiny.nc', mode='r', kwargs={'clobber': True, 'diskless': False, 'persist': False, 'format': 'NETCDF4'}, manager_id='19cee389-3067-4850-bd39-c9cdf61ff616'), but file is not already closed. This may indicate a bug.

PyDAP was unable to determine the DAP protocol defaulting to DAP2 which is consider legacy and may result in slower responses. For more, see go to https://www.opendap.org/faq-page.

PyDAP was unable to determine the DAP protocol defaulting to DAP2 which is consider legacy and may result in slower responses. For more, see go to https://www.opendap.org/faq-page.

Call to deprecated method findAll. (Replaced by find_all) -- Deprecated since version 4.0.0.

Call to deprecated method findAll. (Replaced by find_all) -- Deprecated since version 4.0.0.

Input 'save-always' has been deprecated with message: save-always does not work as intended and will be removed in a future release. A separate `actions/cache/restore` step should be used instead. See https://github.com/actions/cache/tree/main/save#always-save-cache for more details.

Unexpected input(s) 'file', valid inputs are ['base_sha', 'binary', 'codecov_yml_path', 'commit_parent', 'directory', 'disable_file_fixes', 'disable_search', 'disable_safe_directory', 'disable_telem', 'dry_run', 'env_vars', 'exclude', 'fail_ci_if_error', 'files', 'flags', 'force', 'git_service', 'gcov_args', 'gcov_executable', 'gcov_ignore', 'gcov_include', 'handle_no_reports_found', 'job_code', 'name', 'network_filter', 'network_prefix', 'os', 'override_branch', 'override_build', 'override_build_url', 'override_commit', 'override_pr', 'plugins', 'report_code', 'report_type', 'root_dir', 'run_command', 'skip_validation', 'slug', 'swift_project', 'token', 'url', 'use_legacy_upload_endpoint', 'use_oidc', 'use_pypi', 'verbose', 'version', 'working-directory']

Input 'save-always' has been deprecated with message: save-always does not work as intended and will be removed in a future release. A separate `actions/cache/restore` step should be used instead. See https://github.com/actions/cache/tree/main/save#always-save-cache for more details.

Input 'save-always' has been deprecated with message: save-always does not work as intended and will be removed in a future release. A separate `actions/cache/restore` step should be used instead. See https://github.com/actions/cache/tree/main/save#always-save-cache for more details.

invalid value encountered in cast

invalid value encountered in cast

invalid value encountered in cast

invalid value encountered in cast

The experimental Zarr V3 implementation in this version of Zarr-Python is not in alignment with the final V3 specification. This version will be removed in Zarr-Python 3 in favor of a spec compliant version.

The experimental Zarr V3 implementation in this version of Zarr-Python is not in alignment with the final V3 specification. This version will be removed in Zarr-Python 3 in favor of a spec compliant version.

invalid value encountered in cast

invalid value encountered in cast

The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.

The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.

Input 'save-always' has been deprecated with message: save-always does not work as intended and will be removed in a future release. A separate `actions/cache/restore` step should be used instead. See https://github.com/actions/cache/tree/main/save#always-save-cache for more details.

invalid value encountered in cast

invalid value encountered in cast

invalid value encountered in cast

invalid value encountered in cast

The experimental Zarr V3 implementation in this version of Zarr-Python is not in alignment with the final V3 specification. This version will be removed in Zarr-Python 3 in favor of a spec compliant version.

The experimental Zarr V3 implementation in this version of Zarr-Python is not in alignment with the final V3 specification. This version will be removed in Zarr-Python 3 in favor of a spec compliant version.

invalid value encountered in cast

invalid value encountered in cast

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')``.

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')``.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Input 'save-always' has been deprecated with message: save-always does not work as intended and will be removed in a future release. A separate `actions/cache/restore` step should be used instead. See https://github.com/actions/cache/tree/main/save#always-save-cache for more details.

deallocating CachingFileManager(<class 'h5netcdf.core.File'>, <fsspec.implementations.local.LocalFileOpener object at 0x7f3e5194fcd0>, mode='r', kwargs={'invalid_netcdf': None, 'decode_vlen_strings': True, 'driver': None, 'phony_dims': 'access'}, manager_id='d1ab80b4-c83d-4b38-9756-928a517801c8'), but file is not already closed. This may indicate a bug.

deallocating CachingFileManager(<class 'h5netcdf.core.File'>, <fsspec.implementations.local.LocalFileOpener object at 0x7f3e5ddeb6a0>, mode='r', kwargs={'invalid_netcdf': None, 'decode_vlen_strings': True, 'driver': None, 'phony_dims': 'access'}, manager_id='0f836585-16b8-4061-93b8-80dacd49e2fd'), but file is not already closed. This may indicate a bug.

deallocating CachingFileManager(<class 'h5netcdf.core.File'>, <fsspec.implementations.local.LocalFileOpener object at 0x7f3e5194fcd0>, mode='r', kwargs={'invalid_netcdf': None, 'decode_vlen_strings': True, 'driver': None, 'phony_dims': 'access'}, manager_id='d1ab80b4-c83d-4b38-9756-928a517801c8'), but file is not already closed. This may indicate a bug.

deallocating CachingFileManager(<class 'h5netcdf.core.File'>, <fsspec.implementations.local.LocalFileOpener object at 0x7f3e5ddeb6a0>, mode='r', kwargs={'invalid_netcdf': None, 'decode_vlen_strings': True, 'driver': None, 'phony_dims': 'access'}, manager_id='0f836585-16b8-4061-93b8-80dacd49e2fd'), but file is not already closed. This may indicate a bug.

invalid value encountered in cast

invalid value encountered in cast

invalid value encountered in cast

invalid value encountered in cast

The experimental Zarr V3 implementation in this version of Zarr-Python is not in alignment with the final V3 specification. This version will be removed in Zarr-Python 3 in favor of a spec compliant version.

The experimental Zarr V3 implementation in this version of Zarr-Python is not in alignment with the final V3 specification. This version will be removed in Zarr-Python 3 in favor of a spec compliant version.

Input 'save-always' has been deprecated with message: save-always does not work as intended and will be removed in a future release. A separate `actions/cache/restore` step should be used instead. See https://github.com/actions/cache/tree/main/save#always-save-cache for more details.

Input 'save-always' has been deprecated with message: save-always does not work as intended and will be removed in a future release. A separate `actions/cache/restore` step should be used instead. See https://github.com/actions/cache/tree/main/save#always-save-cache for more details.

NumPy will stop allowing conversion of out-of-bound Python integers to integer arrays. The conversion of 255 to int8 will fail in the future. For the old behavior, usually: np.array(value).astype(dtype)` will give the desired result (the cast overflows).

NumPy will stop allowing conversion of out-of-bound Python integers to integer arrays. The conversion of 255 to int8 will fail in the future. For the old behavior, usually: np.array(value).astype(dtype)` will give the desired result (the cast overflows).

NumPy will stop allowing conversion of out-of-bound Python integers to integer arrays. The conversion of 255 to int8 will fail in the future. For the old behavior, usually: np.array(value).astype(dtype)` will give the desired result (the cast overflows).

NumPy will stop allowing conversion of out-of-bound Python integers to integer arrays. The conversion of 255 to int8 will fail in the future. For the old behavior, usually: np.array(value).astype(dtype)` will give the desired result (the cast overflows).

NumPy will stop allowing conversion of out-of-bound Python integers to integer arrays. The conversion of 255 to int8 will fail in the future. For the old behavior, usually: np.array(value).astype(dtype)` will give the desired result (the cast overflows).

NumPy will stop allowing conversion of out-of-bound Python integers to integer arrays. The conversion of 255 to int8 will fail in the future. For the old behavior, usually: np.array(value).astype(dtype)` will give the desired result (the cast overflows).

NumPy will stop allowing conversion of out-of-bound Python integers to integer arrays. The conversion of 255 to int8 will fail in the future. For the old behavior, usually: np.array(value).astype(dtype)` will give the desired result (the cast overflows).

NumPy will stop allowing conversion of out-of-bound Python integers to integer arrays. The conversion of 255 to int8 will fail in the future. For the old behavior, usually: np.array(value).astype(dtype)` will give the desired result (the cast overflows).

NumPy will stop allowing conversion of out-of-bound Python integers to integer arrays. The conversion of 255 to int8 will fail in the future. For the old behavior, usually: np.array(value).astype(dtype)` will give the desired result (the cast overflows).

NumPy will stop allowing conversion of out-of-bound Python integers to integer arrays. The conversion of 255 to int8 will fail in the future. For the old behavior, usually: np.array(value).astype(dtype)` will give the desired result (the cast overflows).

invalid value encountered in cast

invalid value encountered in cast

invalid value encountered in cast

invalid value encountered in cast

The experimental Zarr V3 implementation in this version of Zarr-Python is not in alignment with the final V3 specification. This version will be removed in Zarr-Python 3 in favor of a spec compliant version.

The experimental Zarr V3 implementation in this version of Zarr-Python is not in alignment with the final V3 specification. This version will be removed in Zarr-Python 3 in favor of a spec compliant version.

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')``.

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')``.

The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.

The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.

Input 'save-always' has been deprecated with message: save-always does not work as intended and will be removed in a future release. A separate `actions/cache/restore` step should be used instead. See https://github.com/actions/cache/tree/main/save#always-save-cache for more details.

Input 'save-always' has been deprecated with message: save-always does not work as intended and will be removed in a future release. A separate `actions/cache/restore` step should be used instead. See https://github.com/actions/cache/tree/main/save#always-save-cache for more details.

invalid value encountered in cast

invalid value encountered in cast

invalid value encountered in cast

invalid value encountered in cast

invalid value encountered in cast

invalid value encountered in cast

The experimental Zarr V3 implementation in this version of Zarr-Python is not in alignment with the final V3 specification. This version will be removed in Zarr-Python 3 in favor of a spec compliant version.

The experimental Zarr V3 implementation in this version of Zarr-Python is not in alignment with the final V3 specification. This version will be removed in Zarr-Python 3 in favor of a spec compliant version.

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')``.

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')``.

Input 'save-always' has been deprecated with message: save-always does not work as intended and will be removed in a future release. A separate `actions/cache/restore` step should be used instead. See https://github.com/actions/cache/tree/main/save#always-save-cache for more details.

invalid value encountered in cast

invalid value encountered in cast

invalid value encountered in cast

invalid value encountered in cast

The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.

The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.

The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.

The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.

The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.

The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.

Input 'save-always' has been deprecated with message: save-always does not work as intended and will be removed in a future release. A separate `actions/cache/restore` step should be used instead. See https://github.com/actions/cache/tree/main/save#always-save-cache for more details.

Input 'save-always' has been deprecated with message: save-always does not work as intended and will be removed in a future release. A separate `actions/cache/restore` step should be used instead. See https://github.com/actions/cache/tree/main/save#always-save-cache for more details.

Input 'save-always' has been deprecated with message: save-always does not work as intended and will be removed in a future release. A separate `actions/cache/restore` step should be used instead. See https://github.com/actions/cache/tree/main/save#always-save-cache for more details.

invalid value encountered in cast

invalid value encountered in cast

invalid value encountered in cast

invalid value encountered in cast

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')``.

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')``.

The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.

The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.

The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.

The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.

artifacts/Test results for Linux-3.10/pytest.xml [took 0s] artifacts/Test results for Linux-3.12 all-but-dask/pytest.xml [took 0s] artifacts/Test results for Linux-3.13 all-but-numba/pytest.xml [took 0s] artifacts/Test results for Linux-3.13/pytest.xml [took 0s] artifacts/Test results for Windows-3.10/pytest.xml [took 0s] artifacts/Test results for Windows-3.13/pytest.xml [took 0s] artifacts/Test results for macOS-3.10/pytest.xml [took 0s] artifacts/Test results for macOS-3.13/pytest.xml [took 0s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 16s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 16s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 16s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 16s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 16s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 15s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 17s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 15s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 17s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 16s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 15s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 15s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 17s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 16s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 15s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 15s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 15s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 17s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 15s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 16s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 16s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 15s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 17s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 15s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 16s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 15s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 15s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 15s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 15s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 15s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 17s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 15s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 15s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 15s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 bare-minimum/pytest.xml [took 0s] artifacts/Test results for Linux-3.10 min-all-deps/pytest.xml [took 0s] artifacts/Test results for Linux-3.10/pytest.xml [took 0s] artifacts/Test results for Linux-3.12 all-but-dask/pytest.xml [took 0s] artifacts/Test results for Linux-3.13 all-but-numba/pytest.xml [took 0s] artifacts/Test results for Linux-3.13/pytest.xml [took 0s] artifacts/Test results for Windows-3.10/pytest.xml [took 0s] artifacts/Test results for Windows-3.13/pytest.xml [took 0s] artifacts/Test results for macOS-3.10/pytest.xml [took 0s] artifacts/Test results for macOS-3.13/pytest.xml [took 0s]

artifacts/Test results for Linux-3.10 bare-minimum/pytest.xml [took 0s] artifacts/Test results for Linux-3.10 min-all-deps/pytest.xml [took 0s] artifacts/Test results for Linux-3.10/pytest.xml [took 0s] artifacts/Test results for Linux-3.12 all-but-dask/pytest.xml [took 0s] artifacts/Test results for Linux-3.13 all-but-numba/pytest.xml [took 0s] artifacts/Test results for Linux-3.13/pytest.xml [took 0s] artifacts/Test results for Windows-3.10/pytest.xml [took 0s] artifacts/Test results for Windows-3.13/pytest.xml [took 0s] artifacts/Test results for macOS-3.10/pytest.xml [took 0s] artifacts/Test results for macOS-3.13/pytest.xml [took 0s]

artifacts/Test results for Linux-3.10 bare-minimum/pytest.xml [took 0s] artifacts/Test results for Linux-3.10 min-all-deps/pytest.xml [took 0s] artifacts/Test results for Linux-3.10/pytest.xml [took 0s] artifacts/Test results for Linux-3.12 all-but-dask/pytest.xml [took 0s] artifacts/Test results for Linux-3.13 all-but-numba/pytest.xml [took 0s] artifacts/Test results for Linux-3.13/pytest.xml [took 0s] artifacts/Test results for Windows-3.10/pytest.xml [took 0s] artifacts/Test results for Windows-3.13/pytest.xml [took 0s] artifacts/Test results for macOS-3.10/pytest.xml [took 0s] artifacts/Test results for macOS-3.13/pytest.xml [took 0s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 23s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 25s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 16s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 16s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 16s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 17s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 16s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 16s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 16s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 16s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 16s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 17s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 16s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 16s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 16s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 16s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 17s]

artifacts/Test results for Linux-3.10 mypy/pytest.xml [took 16s] artifacts/Test results for Linux-3.13 mypy/pytest.xml [took 18s]

There are 2518 skipped tests, see "Raw output" for the list of skipped tests 1 to 582.

There are 2518 skipped tests, see "Raw output" for the list of skipped tests 583 to 1254.

There are 2518 skipped tests, see "Raw output" for the list of skipped tests 1255 to 1975.

There are 2518 skipped tests, see "Raw output" for the list of skipped tests 1976 to 2518.

There are 22540 tests, see "Raw output" for the list of tests 1 to 868.

There are 22540 tests, see "Raw output" for the list of tests 869 to 1547.

There are 22540 tests, see "Raw output" for the list of tests 1548 to 2195.

There are 22540 tests, see "Raw output" for the list of tests 2196 to 2833.

There are 22540 tests, see "Raw output" for the list of tests 2834 to 3507.

There are 22540 tests, see "Raw output" for the list of tests 3508 to 4050.

There are 22540 tests, see "Raw output" for the list of tests 4051 to 4631.

There are 22540 tests, see "Raw output" for the list of tests 4632 to 5281.

There are 22540 tests, see "Raw output" for the list of tests 5282 to 5940.

There are 22540 tests, see "Raw output" for the list of tests 5941 to 6737.

There are 22540 tests, see "Raw output" for the list of tests 6738 to 7485.

There are 22540 tests, see "Raw output" for the list of tests 7486 to 8225.

There are 22540 tests, see "Raw output" for the list of tests 8226 to 8885.

There are 22540 tests, see "Raw output" for the list of tests 8886 to 9636.

There are 22540 tests, see "Raw output" for the list of tests 9637 to 10497.

There are 22540 tests, see "Raw output" for the list of tests 10498 to 11378.

There are 22540 tests, see "Raw output" for the list of tests 11379 to 12142.

There are 22540 tests, see "Raw output" for the list of tests 12143 to 12956.

There are 22540 tests, see "Raw output" for the list of tests 12957 to 13768.

There are 22540 tests, see "Raw output" for the list of tests 13769 to 14626.

There are 22540 tests, see "Raw output" for the list of tests 14627 to 15387.

There are 22540 tests, see "Raw output" for the list of tests 15388 to 16013.

There are 22540 tests, see "Raw output" for the list of tests 16014 to 16634.

There are 22540 tests, see "Raw output" for the list of tests 16635 to 17223.

There are 22540 tests, see "Raw output" for the list of tests 17224 to 17915.

There are 22540 tests, see "Raw output" for the list of tests 17916 to 18544.

There are 22540 tests, see "Raw output" for the list of tests 18545 to 19368.

There are 22540 tests, see "Raw output" for the list of tests 19369 to 20042.

There are 22540 tests, see "Raw output" for the list of tests 20043 to 20741.

There are 22540 tests, see "Raw output" for the list of tests 20742 to 21489.

There are 22540 tests, see "Raw output" for the list of tests 21490 to 22302.