Merge branch 'main' into groupby-remove-index-variable

* main:
  Split out distributed writes in zarr docs (pydata#9132)
  Update zendoo badge link (pydata#9133)
  Support duplicate dimensions in `.chunk` (pydata#9099)
  Bump the actions group with 2 updates (pydata#9130)
  adjust repr tests to account for different platforms (pydata#9127) (pydata#9128)

.github/workflows/ci-additional.yaml

@@ -130,7 +130,7 @@ jobs:
           python -m mypy --install-types --non-interactive --cobertura-xml-report mypy_report xarray/
 
       - name: Upload mypy coverage to Codecov
-        uses: codecov/codecov-action@v4.4.1
+        uses: codecov/codecov-action@v4.5.0
         with:
           file: mypy_report/cobertura.xml
           flags: mypy
@@ -184,7 +184,7 @@ jobs:
           python -m mypy --install-types --non-interactive --cobertura-xml-report mypy_report xarray/
 
       - name: Upload mypy coverage to Codecov
-        uses: codecov/codecov-action@v4.4.1
+        uses: codecov/codecov-action@v4.5.0
         with:
           file: mypy_report/cobertura.xml
           flags: mypy39
@@ -245,7 +245,7 @@ jobs:
           python -m pyright xarray/
 
       - name: Upload pyright coverage to Codecov
-        uses: codecov/codecov-action@v4.4.1
+        uses: codecov/codecov-action@v4.5.0
         with:
           file: pyright_report/cobertura.xml
           flags: pyright
@@ -304,7 +304,7 @@ jobs:
           python -m pyright xarray/
 
       - name: Upload pyright coverage to Codecov
-        uses: codecov/codecov-action@v4.4.1
+        uses: codecov/codecov-action@v4.5.0
         with:
           file: pyright_report/cobertura.xml
           flags: pyright39

.github/workflows/ci.yaml

@@ -159,7 +159,7 @@ jobs:
           path: pytest.xml
 
       - name: Upload code coverage to Codecov
-        uses: codecov/codecov-action@v4.4.1
+        uses: codecov/codecov-action@v4.5.0
         with:
           file: ./coverage.xml
           flags: unittests

.github/workflows/pypi-release.yaml

@@ -88,7 +88,7 @@ jobs:
           path: dist
       - name: Publish package to TestPyPI
         if: github.event_name == 'push'
-        uses: pypa/gh-action-pypi-publish@v1.8.14
+        uses: pypa/gh-action-pypi-publish@v1.9.0
         with:
           repository_url: https://test.pypi.org/legacy/
           verbose: true
@@ -111,6 +111,6 @@ jobs:
           name: releases
           path: dist
       - name: Publish package to PyPI
-        uses: pypa/gh-action-pypi-publish@v1.8.14
+        uses: pypa/gh-action-pypi-publish@v1.9.0
         with:
           verbose: true

.github/workflows/upstream-dev-ci.yaml

@@ -146,7 +146,7 @@ jobs:
         run: |
           python -m mypy --install-types --non-interactive --cobertura-xml-report mypy_report
       - name: Upload mypy coverage to Codecov
-        uses: codecov/codecov-action@v4.4.1
+        uses: codecov/codecov-action@v4.5.0
         with:
           file: mypy_report/cobertura.xml
           flags: mypy

README.md

@@ -7,7 +7,7 @@
 [![Available on pypi](https://img.shields.io/pypi/v/xarray.svg)](https://pypi.python.org/pypi/xarray/)
 [![Formatted with black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/python/black)
 [![Checked with mypy](http://www.mypy-lang.org/static/mypy_badge.svg)](http://mypy-lang.org/)
-[![Mirror on zendoo](https://zenodo.org/badge/DOI/10.5281/zenodo.598201.svg)](https://doi.org/10.5281/zenodo.598201)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.11183201.svg)](https://doi.org/10.5281/zenodo.11183201)
 [![Examples on binder](https://img.shields.io/badge/launch-binder-579ACA.svg?logo=data:image/png;base64,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)](https://mybinder.org/v2/gh/pydata/xarray/main?urlpath=lab/tree/doc/examples/weather-data.ipynb)
 [![Twitter](https://img.shields.io/twitter/follow/xarray_dev?style=social)](https://twitter.com/xarray_dev)
 
@@ -46,15 +46,15 @@ provide a powerful and concise interface. For example:
 
 - Apply operations over dimensions by name: `x.sum('time')`.
 - Select values by label instead of integer location:
-    `x.loc['2014-01-01']` or `x.sel(time='2014-01-01')`.
+  `x.loc['2014-01-01']` or `x.sel(time='2014-01-01')`.
 - Mathematical operations (e.g., `x - y`) vectorize across multiple
-    dimensions (array broadcasting) based on dimension names, not shape.
+  dimensions (array broadcasting) based on dimension names, not shape.
 - Flexible split-apply-combine operations with groupby:
-    `x.groupby('time.dayofyear').mean()`.
+  `x.groupby('time.dayofyear').mean()`.
 - Database like alignment based on coordinate labels that smoothly
-    handles missing values: `x, y = xr.align(x, y, join='outer')`.
+  handles missing values: `x, y = xr.align(x, y, join='outer')`.
 - Keep track of arbitrary metadata in the form of a Python dictionary:
-    `x.attrs`.
+  `x.attrs`.
 
 ## Documentation
 
@@ -73,12 +73,12 @@ page](https://docs.xarray.dev/en/stable/contributing.html).
 ## Get in touch
 
 - Ask usage questions ("How do I?") on
-    [GitHub Discussions](https://github.com/pydata/xarray/discussions).
+  [GitHub Discussions](https://github.com/pydata/xarray/discussions).
 - Report bugs, suggest features or view the source code [on
-    GitHub](https://github.com/pydata/xarray).
+  GitHub](https://github.com/pydata/xarray).
 - For less well defined questions or ideas, or to announce other
-    projects of interest to xarray users, use the [mailing
-    list](https://groups.google.com/forum/#!forum/xarray).
+  projects of interest to xarray users, use the [mailing
+  list](https://groups.google.com/forum/#!forum/xarray).
 
 ## NumFOCUS
 
@@ -114,7 +114,7 @@ Licensed under the Apache License, Version 2.0 (the "License"); you
 may not use this file except in compliance with the License. You may
 obtain a copy of the License at
 
- <https://www.apache.org/licenses/LICENSE-2.0>
+<https://www.apache.org/licenses/LICENSE-2.0>
 
 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,

doc/user-guide/io.rst

@@ -741,6 +741,65 @@ instance and pass this, as follows:
 .. _Google Cloud Storage: https://cloud.google.com/storage/
 .. _gcsfs: https://github.com/fsspec/gcsfs
 
+.. _io.zarr.distributed_writes:
+
+Distributed writes
+~~~~~~~~~~~~~~~~~~
+
+Xarray will natively use dask to write in parallel to a zarr store, which should
+satisfy most moderately sized datasets. For more flexible parallelization, we
+can use ``region`` to write to limited regions of arrays in an existing Zarr
+store.
+
+To scale this up to writing large datasets, first create an initial Zarr store
+without writing all of its array data. This can be done by first creating a
+``Dataset`` with dummy values stored in :ref:`dask <dask>`, and then calling
+``to_zarr`` with ``compute=False`` to write only metadata (including ``attrs``)
+to Zarr:
+
+.. ipython:: python
+    :suppress:
+
+    ! rm -rf path/to/directory.zarr
+
+.. ipython:: python
+
+    import dask.array
+
+    # The values of this dask array are entirely irrelevant; only the dtype,
+    # shape and chunks are used
+    dummies = dask.array.zeros(30, chunks=10)
+    ds = xr.Dataset({"foo": ("x", dummies)}, coords={"x": np.arange(30)})
+    path = "path/to/directory.zarr"
+    # Now we write the metadata without computing any array values
+    ds.to_zarr(path, compute=False)
+
+Now, a Zarr store with the correct variable shapes and attributes exists that
+can be filled out by subsequent calls to ``to_zarr``.
+Setting ``region="auto"`` will open the existing store and determine the
+correct alignment of the new data with the existing dimensions, or as an
+explicit mapping from dimension names to Python ``slice`` objects indicating
+where the data should be written (in index space, not label space), e.g.,
+
+.. ipython:: python
+
+    # For convenience, we'll slice a single dataset, but in the real use-case
+    # we would create them separately possibly even from separate processes.
+    ds = xr.Dataset({"foo": ("x", np.arange(30))}, coords={"x": np.arange(30)})
+    # Any of the following region specifications are valid
+    ds.isel(x=slice(0, 10)).to_zarr(path, region="auto")
+    ds.isel(x=slice(10, 20)).to_zarr(path, region={"x": "auto"})
+    ds.isel(x=slice(20, 30)).to_zarr(path, region={"x": slice(20, 30)})
+
+Concurrent writes with ``region`` are safe as long as they modify distinct
+chunks in the underlying Zarr arrays (or use an appropriate ``lock``).
+
+As a safety check to make it harder to inadvertently override existing values,
+if you set ``region`` then *all* variables included in a Dataset must have
+dimensions included in ``region``. Other variables (typically coordinates)
+need to be explicitly dropped and/or written in a separate calls to ``to_zarr``
+with ``mode='a'``.
+
 Zarr Compressors and Filters
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -767,37 +826,6 @@ For example:
     Not all native zarr compression and filtering options have been tested with
     xarray.
 
-.. _io.zarr.consolidated_metadata:
-
-Consolidated Metadata
-~~~~~~~~~~~~~~~~~~~~~
-
-Xarray needs to read all of the zarr metadata when it opens a dataset.
-In some storage mediums, such as with cloud object storage (e.g. `Amazon S3`_),
-this can introduce significant overhead, because two separate HTTP calls to the
-object store must be made for each variable in the dataset.
-By default Xarray uses a feature called
-*consolidated metadata*, storing all metadata for the entire dataset with a
-single key (by default called ``.zmetadata``). This typically drastically speeds
-up opening the store. (For more information on this feature, consult the
-`zarr docs on consolidating metadata <https://zarr.readthedocs.io/en/latest/tutorial.html#consolidating-metadata>`_.)
-
-By default, xarray writes consolidated metadata and attempts to read stores
-with consolidated metadata, falling back to use non-consolidated metadata for
-reads. Because this fall-back option is so much slower, xarray issues a
-``RuntimeWarning`` with guidance when reading with consolidated metadata fails:
-
-    Failed to open Zarr store with consolidated metadata, falling back to try
-    reading non-consolidated metadata. This is typically much slower for
-    opening a dataset. To silence this warning, consider:
-
-    1. Consolidating metadata in this existing store with
-       :py:func:`zarr.consolidate_metadata`.
-    2. Explicitly setting ``consolidated=False``, to avoid trying to read
-       consolidate metadata.
-    3. Explicitly setting ``consolidated=True``, to raise an error in this case
-       instead of falling back to try reading non-consolidated metadata.
-
 .. _io.zarr.appending:
 
 Modifying existing Zarr stores
@@ -856,59 +884,6 @@ order, e.g., for time-stepping a simulation:
     )
     ds2.to_zarr("path/to/directory.zarr", append_dim="t")
 
-Finally, you can use ``region`` to write to limited regions of existing arrays
-in an existing Zarr store. This is a good option for writing data in parallel
-from independent processes.
-
-To scale this up to writing large datasets, the first step is creating an
-initial Zarr store without writing all of its array data. This can be done by
-first creating a ``Dataset`` with dummy values stored in :ref:`dask <dask>`,
-and then calling ``to_zarr`` with ``compute=False`` to write only metadata
-(including ``attrs``) to Zarr:
-
-.. ipython:: python
-    :suppress:
-
-    ! rm -rf path/to/directory.zarr
-
-.. ipython:: python
-
-    import dask.array
-
-    # The values of this dask array are entirely irrelevant; only the dtype,
-    # shape and chunks are used
-    dummies = dask.array.zeros(30, chunks=10)
-    ds = xr.Dataset({"foo": ("x", dummies)}, coords={"x": np.arange(30)})
-    path = "path/to/directory.zarr"
-    # Now we write the metadata without computing any array values
-    ds.to_zarr(path, compute=False)
-
-Now, a Zarr store with the correct variable shapes and attributes exists that
-can be filled out by subsequent calls to ``to_zarr``.
-Setting ``region="auto"`` will open the existing store and determine the
-correct alignment of the new data with the existing coordinates, or as an
-explicit mapping from dimension names to Python ``slice`` objects indicating
-where the data should be written (in index space, not label space), e.g.,
-
-.. ipython:: python
-
-    # For convenience, we'll slice a single dataset, but in the real use-case
-    # we would create them separately possibly even from separate processes.
-    ds = xr.Dataset({"foo": ("x", np.arange(30))}, coords={"x": np.arange(30)})
-    # Any of the following region specifications are valid
-    ds.isel(x=slice(0, 10)).to_zarr(path, region="auto")
-    ds.isel(x=slice(10, 20)).to_zarr(path, region={"x": "auto"})
-    ds.isel(x=slice(20, 30)).to_zarr(path, region={"x": slice(20, 30)})
-
-Concurrent writes with ``region`` are safe as long as they modify distinct
-chunks in the underlying Zarr arrays (or use an appropriate ``lock``).
-
-As a safety check to make it harder to inadvertently override existing values,
-if you set ``region`` then *all* variables included in a Dataset must have
-dimensions included in ``region``. Other variables (typically coordinates)
-need to be explicitly dropped and/or written in a separate calls to ``to_zarr``
-with ``mode='a'``.
-
 .. _io.zarr.writing_chunks:
 
 Specifying chunks in a zarr store
@@ -978,6 +953,38 @@ length of each dimension by using the shorthand chunk size ``-1``:
 The number of chunks on Tair matches our dask chunks, while there is now only a single
 chunk in the directory stores of each coordinate.
 
+.. _io.zarr.consolidated_metadata:
+
+Consolidated Metadata
+~~~~~~~~~~~~~~~~~~~~~
+
+Xarray needs to read all of the zarr metadata when it opens a dataset.
+In some storage mediums, such as with cloud object storage (e.g. `Amazon S3`_),
+this can introduce significant overhead, because two separate HTTP calls to the
+object store must be made for each variable in the dataset.
+By default Xarray uses a feature called
+*consolidated metadata*, storing all metadata for the entire dataset with a
+single key (by default called ``.zmetadata``). This typically drastically speeds
+up opening the store. (For more information on this feature, consult the
+`zarr docs on consolidating metadata <https://zarr.readthedocs.io/en/latest/tutorial.html#consolidating-metadata>`_.)
+
+By default, xarray writes consolidated metadata and attempts to read stores
+with consolidated metadata, falling back to use non-consolidated metadata for
+reads. Because this fall-back option is so much slower, xarray issues a
+``RuntimeWarning`` with guidance when reading with consolidated metadata fails:
+
+    Failed to open Zarr store with consolidated metadata, falling back to try
+    reading non-consolidated metadata. This is typically much slower for
+    opening a dataset. To silence this warning, consider:
+
+    1. Consolidating metadata in this existing store with
+       :py:func:`zarr.consolidate_metadata`.
+    2. Explicitly setting ``consolidated=False``, to avoid trying to read
+       consolidate metadata.
+    3. Explicitly setting ``consolidated=True``, to raise an error in this case
+       instead of falling back to try reading non-consolidated metadata.
+
+
 .. _io.iris:
 
 Iris

doc/whats-new.rst

@@ -22,7 +22,8 @@ v2024.06.1 (unreleased)
 
 New Features
 ~~~~~~~~~~~~
-
+- Allow chunking for arrays with duplicated dimension names (:issue:`8759`, :pull:`9099`).
+  By `Martin Raspaud <https://github.com/mraspaud>`_.
 
 Breaking changes
 ~~~~~~~~~~~~~~~~
@@ -73,7 +74,6 @@ Bug fixes
   support arbitrary kwargs such as ``order`` for polynomial interpolation (:issue:`8762`).
   By `Nicolas Karasiak <https://github.com/nkarasiak>`_.
 
-
 Documentation
 ~~~~~~~~~~~~~
 - Add link to CF Conventions on packed data and sentence on type determination in the I/O user guide (:issue:`9041`, :pull:`9045`).

xarray/namedarray/core.py

@@ -812,7 +812,12 @@ def chunk(
             chunks = either_dict_or_kwargs(chunks, chunks_kwargs, "chunk")
 
         if is_dict_like(chunks):
-            chunks = {self.get_axis_num(dim): chunk for dim, chunk in chunks.items()}
+            # This method of iteration allows for duplicated dimension names, GH8579
+            chunks = {
+                dim_number: chunks[dim]
+                for dim_number, dim in enumerate(self.dims)
+                if dim in chunks
+            }
 
         chunkmanager = guess_chunkmanager(chunked_array_type)
 

xarray/tests/test_dask.py

@@ -638,6 +638,13 @@ def counting_get(*args, **kwargs):
 
         assert count[0] == 1
 
+    def test_duplicate_dims(self):
+        data = np.random.normal(size=(4, 4))
+        arr = DataArray(data, dims=("x", "x"))
+        chunked_array = arr.chunk({"x": 2})
+        assert chunked_array.chunks == ((2, 2), (2, 2))
+        assert chunked_array.chunksizes == {"x": (2, 2)}
+
     def test_stack(self):
         data = da.random.normal(size=(2, 3, 4), chunks=(1, 3, 4))
         arr = DataArray(data, dims=("w", "x", "y"))