This pull request is the first part of migrating the indexing and broadcasting logic from xarray.core.variable to xarray.namedarray. I intend to open follow-up pull requests to address additional changes related to this refactoring, as outlined in the proposal for decoupling lazy indexing functionality from NamedArray.

• [ ] Closes #xxxx
• [ ] Tests added
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

• [ ] Towards #8856
• [ ] Tests added
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

this PR builds towards

• https://github.com/pydata/xarray/pull/8870
• https://github.com/pydata/xarray/pull/8856

the primary objective is to partially address

1. Implement fall back .oindex, .vindex properties on BackendArray base class. These will simply rewrap the key tuple with the appropriate *Indexer object, and pass it on to __getitem__ or __setitem__. These methods will also raise DeprecationWarning so that external backends will know to migrate to .oindex, and .vindex over the next year.

• [ ] Closes #xxxx
• [ ] Tests added
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

As noted in https://github.com/pydata/xarray/issues/8909, the use of .oindex and .vindex properties in coding/* appears to have broken some backends (e.g. scipy). This PR reverts those changes. We plan to bundle these changes into a separate backends feature branch (see this comment, which will be merged once we are confident about its impact on downstream dependencies.

• [ ] Closes #8909
• [ ] Tests added
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

• [ ] Closes #xxxx
• [ ] Tests added
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

• [ ] Towards #8856
• [ ] Tests added
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

this is another follow-up to previous Prs

• 8780

• 8750

• 8790

, continuing the proposal outlined in plan for decoupling lazy indexing functionality from NamedArray

Cc @maxrjones for visibility

• [ ] Closes #xxxx
• [ ] Tests added
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

this is a follow-up to previous PRs (#8780 and #8750), continuing the efforts outlined in the plan for decoupling lazy indexing functionality from NamedArray. the primary focus of this PR is the removal of vectorized and orthogonal indexing logic from the __getitem__ method. Now, __getitem__ exclusively handles basic indexers, aligning with points 3 and 4 of the plan:

• as per point 3, lazy indexing arrays will now implement __getitem__ solely for basic indexing. For orthogonal indexing, .oindex will be used, and for vectorized indexing, .vindex will be utilized.
• following point 4, IndexingAdapter classes have been updated to consistently implement __getitem__, .oindex, and .vindex

• [ ] Closes #xxxx
• [ ] Tests added
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

• 8827 follow up to fix https://readthedocs.org/projects/xray/builds/23743522/

What is your issue?

Recent changes in Dask's tokenization process (https://github.com/dask/dask/pull/10876) seem to have introduced unexpected behavior in Xarray's test suite. This has led to CI failures, specifically in tests related to tokenization.

• https://github.com/pydata/xarray/actions/runs/8069874717/job/22045898877

```python ---------- coverage: platform linux, python 3.12.2-final-0 ----------- Coverage XML written to file coverage.xml

=========================== short test summary info ============================ FAILED xarray/tests/test_dask.py::test_token_identical[obj0-<lambda>1] - AssertionError: assert 'bbd9679bdaf2...d3db65e29a72d' == '6352792990cf...e8004a9055314'

• 6352792990cfe23adb7e8004a9055314

• bbd9679bdaf284c371cd3db65e29a72d FAILED xarray/tests/test_dask.py::test_token_identical[obj0-<lambda>2] - AssertionError: assert 'bbd9679bdaf2...d3db65e29a72d' == '6352792990cf...e8004a9055314'

• 6352792990cfe23adb7e8004a9055314

• bbd9679bdaf284c371cd3db65e29a72d FAILED xarray/tests/test_dask.py::test_token_identical[obj1-<lambda>1] - AssertionError: assert 'c520b8516da8...0e9e0d02b79d0' == '9e2ab1c44990...6ac737226fa02'

• 9e2ab1c44990adb4fb76ac737226fa02

• c520b8516da8b6a98c10e9e0d02b79d0 FAILED xarray/tests/test_dask.py::test_token_identical[obj1-<lambda>2] - AssertionError: assert 'c520b8516da8...0e9e0d02b79d0' == '9e2ab1c44990...6ac737226fa02'

• 9e2ab1c44990adb4fb76ac737226fa02

• c520b8516da8b6a98c10e9e0d02b79d0 = 4 failed, 16293 passed, 628 skipped, 90 xfailed, 71 xpassed, 213 warnings in 472.07s (0:07:52) = Error: Process completed with exit code 1. ```

previously, the following code snippet would pass, verifying the consistency of tokenization in Xarray objects:

```python In [1]: import xarray as xr, numpy as np

In [2]: def make_da(): ...: da = xr.DataArray( ...: np.ones((10, 20)), ...: dims=["x", "y"], ...: coords={"x": np.arange(10), "y": np.arange(100, 120)}, ...: name="a", ...: ).chunk({"x": 4, "y": 5}) ...: da.x.attrs["long_name"] = "x" ...: da.attrs["test"] = "test" ...: da.coords["c2"] = 0.5 ...: da.coords["ndcoord"] = da.x * 2 ...: da.coords["cxy"] = (da.x * da.y).chunk({"x": 4, "y": 5}) ...: ...: return da ...:

In [3]: da = make_da()

In [4]: import dask.base

In [5]: assert dask.base.tokenize(da) == dask.base.tokenize(da.copy(deep=False))

In [6]: assert dask.base.tokenize(da) == dask.base.tokenize(da.copy(deep=True))

In [9]: dask.version Out[9]: '2023.3.0' ```

However, post-update in Dask version '2024.2.1', the same code fails:

```python In [55]: ...: def make_da(): ...: da = xr.DataArray( ...: np.ones((10, 20)), ...: dims=["x", "y"], ...: coords={"x": np.arange(10), "y": np.arange(100, 120)}, ...: name="a", ...: ).chunk({"x": 4, "y": 5}) ...: da.x.attrs["long_name"] = "x" ...: da.attrs["test"] = "test" ...: da.coords["c2"] = 0.5 ...: da.coords["ndcoord"] = da.x * 2 ...: da.coords["cxy"] = (da.x * da.y).chunk({"x": 4, "y": 5}) ...: ...: return da ...:

In [56]: da = make_da() ```

```python In [57]: assert dask.base.tokenize(da) == dask.base.tokenize(da.copy(deep=False))

AssertionError Traceback (most recent call last) Cell In[57], line 1 ----> 1 assert dask.base.tokenize(da) == dask.base.tokenize(da.copy(deep=False))

AssertionError:

In [58]: dask.base.tokenize(da) Out[58]: 'bbd9679bdaf284c371cd3db65e29a72d'

In [59]: dask.base.tokenize(da.copy(deep=False)) Out[59]: '6352792990cfe23adb7e8004a9055314'

In [61]: dask.version Out[61]: '2024.2.1' ```

additionally, a deeper dive into dask.base.normalize_token() across the two Dask versions revealed that the latest version includes additional state or metadata in tokenization that was not present in earlier versions.

• old version python In [29]: dask.base.normalize_token((type(da), da._variable, da._coords, da._name)) Out[29]: ('tuple', [xarray.core.dataarray.DataArray, ('tuple', [xarray.core.variable.Variable, ('tuple', ['x', 'y']), 'xarray-<this-array>-14cc91345e4b75c769b9032d473f6f6e', ('list', [('tuple', ['test', 'test'])])]), ('list', [('tuple', ['c2', ('tuple', [xarray.core.variable.Variable, ('tuple', []), (0.5, dtype('float64')), ('list', [])])]), ('tuple', ['cxy', ('tuple', [xarray.core.variable.Variable, ('tuple', ['x', 'y']), 'xarray-<this-array>-8e98950eca22c69d304f0a48bc6c2df9', ('list', [])])]), ('tuple', ['ndcoord', ('tuple', [xarray.core.variable.Variable, ('tuple', ['x']), 'xarray-ndcoord-82411ea5e080aa9b9f554554befc2f39', ('list', [])])]), ('tuple', ['x', ('tuple', [xarray.core.variable.IndexVariable, ('tuple', ['x']), ['x', ('603944b9792513fa0c686bb494a66d96c667f879', dtype('int64'), (10,), (8,))], ('list', [('tuple', ['long_name', 'x'])])])]), ('tuple', ['y', ('tuple', [xarray.core.variable.IndexVariable, ('tuple', ['y']), ['y', ('fc411db876ae0f4734dac8b64152d5c6526a537a', dtype('int64'), (20,), (8,))], ('list', [])])])]), 'a'])

• most recent version

python In [44]: dask.base.normalize_token((type(da), da._variable, da._coords, da._name)) Out[44]: ('tuple', [('7b61e7593a274e48', []), ('tuple', [('215b115b265c420c', []), ('tuple', ['x', 'y']), 'xarray-<this-array>-980383b18aab94069bdb02e9e0956184', ('dict', [('tuple', ['test', 'test'])])]), ('dict', [('tuple', ['c2', ('tuple', [('__seen', 2), ('tuple', []), ('6825817183edbca7', ['48cb5e118059da42']), ('dict', [])])]), ('tuple', ['cxy', ('tuple', [('__seen', 2), ('tuple', ['x', 'y']), 'xarray-<this-array>-6babb4e95665a53f34a3e337129d54b5', ('dict', [])])]), ('tuple', ['ndcoord', ('tuple', [('__seen', 2), ('tuple', ['x']), 'xarray-ndcoord-8636fac37e5e6f4401eab2aef399f402', ('dict', [])])]), ('tuple', ['x', ('tuple', [('abc1995cae8530ae', []), ('tuple', ['x']), ['x', ('99b2df4006e7d28a', ['04673d65c892b5ba'])], ('dict', [('tuple', ['long_name', 'x'])])])]), ('tuple', ['y', ('tuple', [('__seen', 25), ('tuple', ['y']), ['y', ('88974ea603e15c49', ['a6c0f2053e85c87e'])], ('dict', [])])])]), 'a'])

Cc @dcherian / @crusaderky for visibility

• [ ] #8750 follow up
• [ ] Tests added
• [x] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

this pull request represents an initial effort to refactor the indexing logic within indexing.py by separating it from variable.py. to enhance the flexibility of explicitly indexed array classes, this PR introduces orthogonal ~~and vectorized~~ indexing methods—.oindex[key] ~~and .vindex[key[~~. these changes are a part of a broader initiative aimed at enabling indexing support for namedarray

• [x] Towards https://github.com/pydata/xarray/issues/8238
• [ ] Tests added
• [x] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

edit 1: - to minimize the blast radius of this PR, i decided to just focus on oindex[key] method in this PR. i intend to introduce vindex[key] in a separate PR

TODO:

• [x] https://github.com/pydata/xarray/pull/8750#discussion_r1491169460
• [ ] Enable passing the underlying tuple key

this pull request removes the "unnecessary optimization" introduced in https://github.com/pydata/xarray/pull/3588 that attempts to rewrite the indexer key for BasicIndexer and OuterIndexer

• [x] Closes #8377
• [ ] Tests added
• [x] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

This PR contains the design document for "named-array" a proposed new package aimed at providing a lightweight, efficient array structure with named dimensions. the main objective of named-array is to provide a more accessible version of Xarray's Variable class without the heavy dependencies like Pandas.

~~## proposed name~~

~~The "Xarray-lite" name is a placeholder for the time being. we have considered two main candidates for the package's name: "narray" and "yarray." feedback on the proposed names or other suggestions is welcome.~~

Related issues:

• https://github.com/pydata/xarray/issues/5081
• https://github.com/pydata/xarray/issues/3981

this PR copies the dtypes module from the xarray.core package to the xarray.namedarray package. It also recreates the ReprObject class from the utils module within the xarray.namedarray package

These changes are part of the ongoing work in: - https://github.com/pydata/xarray/pull/8244

• [ ] towards #8238
• [ ] Tests added
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

• ref: https://github.com/xarray-contrib/xarray.dev/issues/66#issuecomment-1664561089

Cc @jhamman

really like how good these look on both light/dark backgrounds:

What happened?

i'm experiencing compatibility issues when using _arrayfunction_or_api and _sparsearrayfunction_or_api with the sparse arrays with dtype=object. specifically, runtime checks using isinstance with these protocols are failing, despite the sparse array object appearing to meet the necessary criteria (attributes and methods).

What did you expect to happen?

i expected that since COO arrays from the sparse library provide the necessary attributes and methods, they would pass the isinstance checks with the defined protocols.

```python In [56]: from xarray.namedarray._typing import _arrayfunction_or_api, _sparsearrayfunc ...: tion_or_api

In [57]: import xarray as xr, sparse, numpy as np, sparse, pandas as pd ```

• numeric dtypes work

```python In [58]: x = np.random.random((10))

In [59]: x[x < 0.9] = 0

In [60]: s = sparse.COO(x)

In [61]: isinstance(s, _arrayfunction_or_api) Out[61]: True

In [62]: s Out[62]: <COO: shape=(10,), dtype=float64, nnz=0, fill_value=0.0> ```

• string dtypes work

```python In [63]: p = sparse.COO(np.array(['a', 'b']))

In [64]: p Out[64]: <COO: shape=(2,), dtype=<U1, nnz=2, fill_value=>

In [65]: isinstance(s, _arrayfunction_or_api) Out[65]: True ``` - object dtype doesn't work

```python In [66]: q = sparse.COO(np.array(['a', 'b']).astype(object))

In [67]: isinstance(s, _arrayfunction_or_api) Out[67]: True

In [68]: isinstance(q, _arrayfunction_or_api)

TypeError Traceback (most recent call last) File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/sparse/_umath.py:606, in _Elemwise._get_func_coords_data(self, mask) 605 try: --> 606 func_data = self.func(func_args, dtype=self.dtype, *self.kwargs) 607 except TypeError:

TypeError: real() got an unexpected keyword argument 'dtype'

During handling of the above exception, another exception occurred:

TypeError Traceback (most recent call last) File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/sparse/_umath.py:611, in _Elemwise._get_func_coords_data(self, mask) 610 out = np.empty(func_args[0].shape, dtype=self.dtype) --> 611 func_data = self.func(func_args, out=out, *self.kwargs) 612 except TypeError:

TypeError: real() got an unexpected keyword argument 'out'

During handling of the above exception, another exception occurred:

ValueError Traceback (most recent call last) Cell In[68], line 1 ----> 1 isinstance(q, _arrayfunction_or_api)

File ~/mambaforge/envs/xarray-tests/lib/python3.9/typing.py:1149, in _ProtocolMeta.instancecheck(cls, instance) 1147 return True 1148 if cls._is_protocol: -> 1149 if all(hasattr(instance, attr) and 1150 # All methods can be blocked by setting them to None. 1151 (not callable(getattr(cls, attr, None)) or 1152 getattr(instance, attr) is not None) 1153 for attr in _get_protocol_attrs(cls)): 1154 return True 1155 return super().instancecheck(instance)

File ~/mambaforge/envs/xarray-tests/lib/python3.9/typing.py:1149, in <genexpr>(.0) 1147 return True 1148 if cls._is_protocol: -> 1149 if all(hasattr(instance, attr) and 1150 # All methods can be blocked by setting them to None. 1151 (not callable(getattr(cls, attr, None)) or 1152 getattr(instance, attr) is not None) 1153 for attr in _get_protocol_attrs(cls)): 1154 return True 1155 return super().instancecheck(instance)

File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/sparse/_sparse_array.py:900, in SparseArray.real(self) 875 @property 876 def real(self): 877 """The real part of the array. 878 879 Examples (...) 898 numpy.real : NumPy equivalent function. 899 """ --> 900 return self.array_ufunc(np.real, "call", self)

File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/sparse/_sparse_array.py:340, in SparseArray.array_ufunc(self, ufunc, method, inputs, kwargs) 337 inputs = tuple(reversed(inputs_transformed)) 339 if method == "call": --> 340 result = elemwise(ufunc, inputs, kwargs) 341 elif method == "reduce": 342 result = SparseArray._reduce(ufunc, *inputs, kwargs)

File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/sparse/_umath.py:49, in elemwise(func, args, kwargs) 12 def elemwise(func, args, kwargs): 13 """ 14 Apply a function to any number of arguments. 15 (...) 46 it is necessary to convert Numpy arrays to :obj:COO objects. 47 """ ---> 49 return _Elemwise(func, *args, kwargs).get_result()

File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/sparse/_umath.py:480, in _Elemwise.get_result(self) 477 if not any(mask): 478 continue --> 480 r = self._get_func_coords_data(mask) 482 if r is not None: 483 coords_list.append(r[0])

File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/sparse/_umath.py:613, in _Elemwise._get_func_coords_data(self, mask) 611 func_data = self.func(func_args, out=out, self.kwargs) 612 except TypeError: --> 613 func_data = self.func(func_args, **self.kwargs).astype(self.dtype) 615 unmatched_mask = ~equivalent(func_data, self.fill_value) 617 if not unmatched_mask.any():

ValueError: invalid literal for int() with base 10: 'a'

In [69]: q Out[69]: <COO: shape=(2,), dtype=object, nnz=2, fill_value=0> ```

the failing case appears to be a well know issue

• https://github.com/pydata/sparse/issues/104

Minimal Complete Verifiable Example

```Python In [69]: q Out[69]: <COO: shape=(2,), dtype=object, nnz=2, fill_value=0>

In [70]: n = xr.NamedArray(data=q, dims=['x']) ```

MVCE confirmation

• [ ] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
• [ ] Complete example — the example is self-contained, including all data and the text of any traceback.
• [ ] Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
• [ ] New issue — a search of GitHub Issues suggests this is not a duplicate.
• [ ] Recent environment — the issue occurs with the latest version of xarray and its dependencies.

Relevant log output

```Python In [71]: n.data Out[71]: <COO: shape=(2,), dtype=object, nnz=2, fill_value=0>

In [72]: n Out[72]: --------------------------------------------------------------------------- TypeError Traceback (most recent call last) File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/sparse/_umath.py:606, in _Elemwise._get_func_coords_data(self, mask) 605 try: --> 606 func_data = self.func(func_args, dtype=self.dtype, *self.kwargs) 607 except TypeError:

TypeError: real() got an unexpected keyword argument 'dtype'

During handling of the above exception, another exception occurred:

TypeError Traceback (most recent call last) File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/sparse/_umath.py:611, in _Elemwise._get_func_coords_data(self, mask) 610 out = np.empty(func_args[0].shape, dtype=self.dtype) --> 611 func_data = self.func(func_args, out=out, *self.kwargs) 612 except TypeError:

TypeError: real() got an unexpected keyword argument 'out'

During handling of the above exception, another exception occurred:

ValueError Traceback (most recent call last) File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/IPython/core/formatters.py:708, in PlainTextFormatter.call(self, obj) 701 stream = StringIO() 702 printer = pretty.RepresentationPrinter(stream, self.verbose, 703 self.max_width, self.newline, 704 max_seq_length=self.max_seq_length, 705 singleton_pprinters=self.singleton_printers, 706 type_pprinters=self.type_printers, 707 deferred_pprinters=self.deferred_printers) --> 708 printer.pretty(obj) 709 printer.flush() 710 return stream.getvalue()

File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/IPython/lib/pretty.py:410, in RepresentationPrinter.pretty(self, obj) 407 return meth(obj, self, cycle) 408 if cls is not object \ 409 and callable(cls.dict.get('repr')): --> 410 return _repr_pprint(obj, self, cycle) 412 return _default_pprint(obj, self, cycle) 413 finally:

File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/IPython/lib/pretty.py:778, in repr_pprint(obj, p, cycle) 776 """A pprint that just redirects to the normal repr function.""" 777 # Find newlines and replace them with p.break() --> 778 output = repr(obj) 779 lines = output.splitlines() 780 with p.group():

File ~/devel/pydata/xarray/xarray/namedarray/core.py:987, in NamedArray.repr(self) 986 def repr(self) -> str: --> 987 return formatting.array_repr(self)

File ~/mambaforge/envs/xarray-tests/lib/python3.9/reprlib.py:21, in recursive_repr.<locals>.decorating_function.<locals>.wrapper(self) 19 repr_running.add(key) 20 try: ---> 21 result = user_function(self) 22 finally: 23 repr_running.discard(key)

File ~/devel/pydata/xarray/xarray/core/formatting.py:665, in array_repr(arr) 658 name_str = "" 660 if ( 661 isinstance(arr, Variable) 662 or _get_boolean_with_default("display_expand_data", default=True) 663 or isinstance(arr.variable._data, MemoryCachedArray) 664 ): --> 665 data_repr = short_data_repr(arr) 666 else: 667 data_repr = inline_variable_array_repr(arr.variable, OPTIONS["display_width"])

File ~/devel/pydata/xarray/xarray/core/formatting.py:633, in short_data_repr(array) 631 if isinstance(array, np.ndarray): 632 return short_array_repr(array) --> 633 elif isinstance(internal_data, _arrayfunction_or_api): 634 return limit_lines(repr(array.data), limit=40) 635 elif getattr(array, "_in_memory", None):

File ~/mambaforge/envs/xarray-tests/lib/python3.9/typing.py:1149, in _ProtocolMeta.instancecheck(cls, instance) 1147 return True 1148 if cls._is_protocol: -> 1149 if all(hasattr(instance, attr) and 1150 # All methods can be blocked by setting them to None. 1151 (not callable(getattr(cls, attr, None)) or 1152 getattr(instance, attr) is not None) 1153 for attr in _get_protocol_attrs(cls)): 1154 return True 1155 return super().instancecheck(instance)

File ~/mambaforge/envs/xarray-tests/lib/python3.9/typing.py:1149, in <genexpr>(.0) 1147 return True 1148 if cls._is_protocol: -> 1149 if all(hasattr(instance, attr) and 1150 # All methods can be blocked by setting them to None. 1151 (not callable(getattr(cls, attr, None)) or 1152 getattr(instance, attr) is not None) 1153 for attr in _get_protocol_attrs(cls)): 1154 return True 1155 return super().instancecheck(instance)

File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/sparse/_sparse_array.py:900, in SparseArray.real(self) 875 @property 876 def real(self): 877 """The real part of the array. 878 879 Examples (...) 898 numpy.real : NumPy equivalent function. 899 """ --> 900 return self.array_ufunc(np.real, "call", self)

File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/sparse/_sparse_array.py:340, in SparseArray.array_ufunc(self, ufunc, method, inputs, kwargs) 337 inputs = tuple(reversed(inputs_transformed)) 339 if method == "call": --> 340 result = elemwise(ufunc, inputs, kwargs) 341 elif method == "reduce": 342 result = SparseArray._reduce(ufunc, *inputs, kwargs)

File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/sparse/_umath.py:49, in elemwise(func, args, kwargs) 12 def elemwise(func, args, kwargs): 13 """ 14 Apply a function to any number of arguments. 15 (...) 46 it is necessary to convert Numpy arrays to :obj:COO objects. 47 """ ---> 49 return _Elemwise(func, *args, kwargs).get_result()

File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/sparse/_umath.py:480, in _Elemwise.get_result(self) 477 if not any(mask): 478 continue --> 480 r = self._get_func_coords_data(mask) 482 if r is not None: 483 coords_list.append(r[0])

File ~/mambaforge/envs/xarray-tests/lib/python3.9/site-packages/sparse/_umath.py:613, in _Elemwise._get_func_coords_data(self, mask) 611 func_data = self.func(func_args, out=out, self.kwargs) 612 except TypeError: --> 613 func_data = self.func(func_args, **self.kwargs).astype(self.dtype) 615 unmatched_mask = ~equivalent(func_data, self.fill_value) 617 if not unmatched_mask.any():

ValueError: invalid literal for int() with base 10: 'a' ```

Anything else we need to know?

i was trying to replace instances of is_duck_array with the protocol runtime checks (as part of https://github.com/pydata/xarray/pull/8319), and i've come to a realization that these runtime checks are rigid to accommodate the diverse behaviors of different array types, and is_duck_array() the function-based approach might be more manageable.

@Illviljan, are there any changes that could be made to both protocols without making them too complex?

Environment

What is your issue?

A recent release of pytest (v8.0.0) appears to have broken our CI.

bash pytest 8.0.0 pyhd8ed1ab_0 conda-forge pytest-cov 4.1.0 pyhd8ed1ab_0 conda-forge pytest-env 1.1.3 pyhd8ed1ab_0 conda-forge pytest-github-actions-annotate-failures 0.2.0 pypi_0 pypi pytest-timeout 2.2.0 pyhd8ed1ab_0 conda-forge pytest-xdist 3.5.0 pyhd8ed1ab_0 conda-forge

Strangely, the issue doesn't seem to occur when using previous versions (e.g. v7.4.4). our last successful CI run used pytest v7.4.4

bash pytest 7.4.4 pyhd8ed1ab_0 conda-forge pytest-cov 4.1.0 pyhd8ed1ab_0 conda-forge pytest-env 1.1.3 pyhd8ed1ab_0 conda-forge pytest-github-actions-annotate-failures 0.2.0 pypi_0 pypi pytest-timeout 2.2.0 pyhd8ed1ab_0 conda-forge pytest-xdist 3.5.0 pyhd8ed1ab_0 conda-forge

i recreated the environment and successfully ran tests locally. the CI failures appear to be connected to the latest release of pytest. i haven't had a chance to do an in-depth exploration of the changes from pytest which could be influencing this disruption. so, i wanted to open an issue to track what is going on. in the meantime, i'm going to pin pytest to an earlier version.

any insights, especially from those familiar with changes in the pytest v8.0.0 update, are warmly welcomed.

• [ ] towards fixing #8681
• [ ] Tests added
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

this is the initial phase of our plan to split the .set_dims() method into two distinct methods .expand_dims() and .broadcast_to() as outlined in the design document. - [x] add expand_dims() - [x] add broadcast_to() - [x] add .transpose() and .T to NamedArray - [x] update whats-new.rst

Is your feature request related to a problem? Please describe.

I'm working on some caching/data-provenance functionality for xarray objects, and I realized that there's no standard/efficient way of computing hashes for xarray objects.

Describe the solution you'd like

It would be useful to have a configurable, reliable/standard .hexdigest() method on xarray objects. For example, zarr provides a digest method that returns you a digest/hash of the data.

```python In [16]: import zarr

In [17]: z = zarr.zeros(shape=(10000, 10000), chunks=(1000, 1000))

In [18]: z.hexdigest() # uses sha1 by default for speed Out[18]: '7162d416d26a68063b66ed1f30e0a866e4abed60'

In [20]: z.hexdigest(hashname='sha256') Out[20]: '46fc6e52fc1384e37cead747075f55201667dd539e4e72d0f372eb45abdcb2aa' ```

I'm thinking that an xarray's built-in hashing mechanism would provide a more reliable way to treat metadata such as global attributes, encoding, etc... during the hash computation...

Describe alternatives you've considered

So far, I am using joblib's default hasher: joblib.hash() function. However, I am in favor of having a configurable/built-in hasher that is aware of xarray's data model and quirks :)

```python In [1]: import joblib

In [2]: import xarray as xr

In [3]: ds = xr.tutorial.open_dataset('rasm')

In [5]: joblib.hash(ds, hash_name='sha1') Out[5]: '3e5e3f56daf81e9e04a94a3dff9fdca9638c36cf'

In [8]: ds.attrs = {}

In [9]: joblib.hash(ds, hash_name='sha1') Out[9]: 'daab25fe735657e76514040608fadc67067d90a0' ```

Additional context Add any other context about the feature request here.

• [x] Closes #2620
• [x] Tests added
• [x] Passes isort . && black . && mypy . && flake8
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

• [ ] #8365 follow up
• [ ] Tests added

• [ ] towards #8238
• [ ] Tests added
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

this pull request introduces the initial prototype for NamedArray as discussed in issue #8073. the intention is to approach the implementation in multiple phases through separate pull requests. the main objectives of this PR are as follows:

1. creation of a new class, xarray.named_array.NamedArray, which will encompass most of the necessary functionality for the lightweight version of xarray.Variable. As a result, xarray.Variable will inherit from this class and implement specific functionality related to xarray.

2. removal of all imports of internal Xarray classes and utilities from the xarray.named_array sub-package. the aim is to eliminate dependencies on anything within xarray.core. This will facilitate the future goal of moving named_array into its own standalone package, with xarray then depending on it.

3. [ ] Towards #3981

4. [x] Tests added
5. [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst
6. [ ] New functions/methods are listed in api.rst

None is not really practical in current xarray so not allowing it as a dimension is probably the easiest path, but type hinting will not be correct.

I want dims to have a type hint that is consistent, easy to read and understand. In a dream world it would look something like this: python InputDim = Hashable # Single dimension InputDims = Iterable[InputDim , ...] # multiple dimensions InputDimOrDims = Union[InputDim, InputDims] # Single or multiple dimensions

Then we can easily go through our xarray methods and easily replace dim and dims arguments.

Hashable could be fine in NamedArray, we haven't introduced None as a typical default value there yet.

But it wouldn't be easy in xarray because we use None as default value a lot, which will (I suspect) lead to a bunch of refactoring and deprecations. I haven't tried it maybe it's doable?

Another idea is to try and make a HashableExcludingNone: python HashableExcludingNone = Union[int, str, tuple, ...] # How many more Hashables are there? InputDim = HashableExcludingNone # Single dimension InputDims = Iterable[InputDim , ...] # multiple dimensions InputDimOrDims = Union[InputDim, InputDims] # Single or multiple dimensions I suspect this is harder than it seems.

Another idea is drop the idea of Hashable and just allow a few common ones that are used: python InputDim = str # Single dimension InputDims = tuple[InputDim , ...] # multiple dimensions InputDimOrDims = Union[InputDim, InputDims] # Single or multiple dimensions Very clear! I think a few users (and maintainers) will be sad because of the lack of flexibility though.

No easy paths, and trying to be backwards compatible is very demotivating.

Originally posted by @Illviljan in https://github.com/pydata/xarray/pull/8075#discussion_r1330437962

• [x] Closes #6649
• [ ] Tests added
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

This PR introduces small changes to enhance code maintainability...

• [x] Passes pre-commit run --all-files

• [ ] Addresses https://github.com/pydata/xarray/pull/5139#issuecomment-817362317
• [x] Passes pre-commit run --all-files

Here's a snippet of how this looks like when a pull request is created on the same main repository:

Cc @keewis

Libraries, including xarray, register backends via entrypoints. Xarray adds an EntryPoint "group" in its setup.py. Third party libraries add items to that group in their setup.pys.

Xarray and the third-party library don't need to talk to each other directly to register a backend. They talk through pkg_resources.

For instance, hvplot/holoviews would add an EntryPoint for xarray_plotting_backends in its setup.py:

python entry_points={"xarray_plotting_backends": ['holoviews = hvplot:plotting']}

and xarray users should be able to achieve what @jsignell proposed in #3553:

```python import xarray as xr air = xr.tutorial.open_dataset('air_temperature').load().air xr.set_options(plotting_backend='holoviews')

air.isel(time=500).plot() ```

• [ ] Closes #3553
• [x] Tests added
• [x] Passes black . && mypy . && flake8
• [ ] Fully documented, including whats-new.rst for all changes and api.rst for new API

• [x] Closes #7299
• [x] Tests added
• [x] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

This PR removes the existing team page from the docs and links to the newly created https://xarray.dev/team on the landing page

• [ ] Addresses https://github.com/pydata/xarray/pull/6884#issuecomment-1207094758
• [ ] Tests added
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

• [ ] #6530 follow-up

This PR supersedes https://github.com/pydata/xarray/pull/4540. This is still a very rough draft :)

• Closes
• [x] Closes #4473
• [x] Closes #4498
• [x] Closes #659
• [x] Closes #2237
• [x] xref https://github.com/pangeo-data/pangeo/issues/271
• [x] Tests added
• [x] Passes pre-commit run --all-files
• [x] User visible changes (including notable bug fixes) are documented in whats-new.rst

TODO: - [x] flox vs legacy tests with nans - [x] test passing flox-specific kwargs like engine and method.

Currently, when xarray.coding.times.encode_cf_datetime() is called, it always casts the input to a NumPy array. This is not what I would expect when the input is a dask array. I am wondering if we could make this operation lazy when the input is a dask array?

https://github.com/pydata/xarray/blob/01462d65c7213e5e1cddf36492c6a34a7e53ce55/xarray/coding/times.py#L352-L354

```python In [46]: import numpy as np

In [47]: import xarray as xr

In [48]: import pandas as pd

In [49]: times = pd.date_range("2000-01-01", "2001-01-01", periods=11)

In [50]: time_bounds = np.vstack((times[:-1], times[1:])).T

In [51]: arr = xr.DataArray(time_bounds).chunk()

In [52]: arr
Out[52]: <xarray.DataArray (dim_0: 10, dim_1: 2)> dask.array<xarray-\<this-array>, shape=(10, 2), dtype=datetime64[ns], chunksize=(10, 2), chunktype=numpy.ndarray> Dimensions without coordinates: dim_0, dim_1

In [53]: xr.coding.times.encode_cf_datetime(arr)
Out[53]: (array([[ 0, 52704], [ 52704, 105408], [105408, 158112], [158112, 210816], [210816, 263520], [263520, 316224], [316224, 368928], [368928, 421632], [421632, 474336], [474336, 527040]]), 'minutes since 2000-01-01 00:00:00', 'proleptic_gregorian')

```

Cc @jhamman

What happened:

While using xr.save_mfdataset() function with compute=False I noticed that the function returns a dask.delayed object, but it doesn't actually defer the computation i.e. it actually writes datasets right away.

What you expected to happen:

I expect the datasets to be written when I explicitly call .compute() on the returned delayed object.

Minimal Complete Verifiable Example:

```python In [2]: import xarray as xr

In [3]: ds = xr.tutorial.open_dataset('rasm', chunks={})

In [4]: ds Out[4]: <xarray.Dataset> Dimensions: (time: 36, x: 275, y: 205) Coordinates: * time (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00 xc (y, x) float64 dask.array<chunksize=(205, 275), meta=np.ndarray> yc (y, x) float64 dask.array<chunksize=(205, 275), meta=np.ndarray> Dimensions without coordinates: x, y Data variables: Tair (time, y, x) float64 dask.array<chunksize=(36, 205, 275), meta=np.ndarray> Attributes: title: /workspace/jhamman/processed/R1002RBRxaaa01a/l... institution: U.W. source: RACM R1002RBRxaaa01a output_frequency: daily output_mode: averaged convention: CF-1.4 references: Based on the initial model of Liang et al., 19... comment: Output from the Variable Infiltration Capacity... nco_openmp_thread_number: 1 NCO: "4.6.0" history: Tue Dec 27 14:15:22 2016: ncatted -a dimension...

In [5]: path = "test.nc"

In [7]: ls -ltrh test.nc ls: cannot access test.nc: No such file or directory

In [8]: tasks = xr.save_mfdataset(datasets=[ds], paths=[path], compute=False)

In [9]: tasks Out[9]: Delayed('list-aa0b52e0-e909-4e65-849f-74526d137542')

In [10]: ls -ltrh test.nc -rw-r--r-- 1 abanihi ncar 14K Jul 8 10:29 test.nc ```

Anything else we need to know?:

Environment:

xr.Dataset reduce methods such as mean drop non-numeric data variables prior to the reduction. However, as far as I can tell this info isn't mentioned anywhere in the documentation/docstrings. I think this would be useful information to include here for example:

```python In [47]: import xarray as xr

In [48]: import numpy as np, pandas as pd

In [50]: ds['foo'] = xr.DataArray(np.arange(6).reshape(2, 3), dims=['x', 'y'])

In [53]: ds['bar'] = xr.DataArray(pd.date_range(start='2000', periods=6).values.reshape(2, 3), dims=['x', 'y'])

In [54]: ds Out[54]: <xarray.Dataset> Dimensions: (x: 2, y: 3) Dimensions without coordinates: x, y Data variables: foo (x, y) int64 0 1 2 3 4 5 bar (x, y) datetime64[ns] 2000-01-01 2000-01-02 ... 2000-01-06 ```

```python In [55]: ds.mean('x') Out[55]: <xarray.Dataset> Dimensions: (y: 3) Dimensions without coordinates: y Data variables: foo (y) float64 1.5 2.5 3.5

In [56]: ds.bar.mean('x') Out[56]: <xarray.DataArray 'bar' (y: 3)> array(['2000-01-02T12:00:00.000000000', '2000-01-03T12:00:00.000000000', '2000-01-04T12:00:00.000000000'], dtype='datetime64[ns]') Dimensions without coordinates: y ```

• [x] Addresses https://github.com/pydata/xarray/issues/6027#issuecomment-1048737617

Cc @benbovy & @max-sixty

This PR ensures all CI workflows are only enabled on the main repository. A few workflows were still being triggered on forks.

• [x] Closes #6222
• [ ] Tests added
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

• [ ] Closes #6027

Preview:

We've been using pre-commit for the last month or so, and it seems to be working fine. This PR removes the redundant pre-commit hooks GHA workflow.

• [ ] Towards https://github.com/pydata/xarray/issues/5940

This fixes https://github.com/pydata/xarray/issues/5232#issuecomment-833729171

Currently, the CI runs both on a user's fork and pydata/xarray. This leads to duplicate runs and the user is flooded with GitHub notifications in case of failures... This PR addresses this issue by making sure the workflows are triggered on pydata/xarray only.

It turns out that pushes on main branch result in an invalid package version. To address this issue, I am disabling the push event trigger on the release workflow

Cc @keewis

```html Uploading xarray-0.17.1.dev108+gf5e4fd5f-py3-none-any.whl

0%| | 0.00/796k [00:00<?, ?B/s] 73%|███████▎ | 584k/796k [00:00<00:00, 3.39MB/s] 100%|██████████| 796k/796k [00:00<00:00, 1.52MB/s] Content received from server:

400 '0.17.1.dev108+gf5e4fd5f' is an invalid value for Version. Error: Can't use PEP 440 local versions. See packaging.python.org/specifications/core-metadata for more information.

HTTPError: 400 Bad Request from test.pypi.org/legacy '0.17.1.dev108+gf5e4fd5f' is an invalid value for Version. Error: Can't use PEP 440 local versions. See packaging.python.org/specifications/core-metadata for more information. ```

This addresses https://github.com/pydata/xarray/issues/5232#issuecomment-830242266, and will (hopefully) reduce the time it takes to cut a new release.

• [x] Passes pre-commit run --all-files

Follow up PR for #5174

• [x] Tests added
• [x] Passes pre-commit run --all-files

• [x] Closes #5248
• [x] Passes pre-commit run --all-files

As I understand it, the issue described in #5248 is caused by bad indentation... With this PR, we get the following:

@max-sixty, let me know if there are other instances that need to be fixed as part of this PR....

• [x] Passes pre-commit run --all-files

• This PR reduces the max-width for .container divs so as to address issues with RTD's multiple version badges.

• Latest: https://xarray.pydata.org/en/latest/index.html: the rtd badge box doesn't work

• This PR: https://xray--5039.org.readthedocs.build/en/5039/index.html: appears to be working fine

• Fixes CSS properties to enable appropriate toggle/responsiveness when using small screens. Unfortunately, this doesn't seem to have fixed the problem on small screens:

• [x] Passes pre-commit run --all-files

• [ ] Closes #xxxx
• [x] Passes pre-commit run --all-files
• [x] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

The styfle/cancel-workflow-action requires a token with write permissions in order to cancel duplicate runs. It turns out that the default GitHub token access made available to pull requests from forks has read permissions only. To address this issue, this PR introduces a new workflow that uses a special workflow_run trigger which allows the workflow to run to run with a token with write permissions.

• [x] Passes pre-commit run --all-files

Cc @keewis

As discussed in today's dev meeting, it'd be convenient to migrate all our CIs from azure pipelines to GitHub Actions.

• [x] Consolidate all style/type checks (isort, black, mypy, flake8) into a single workflow and use the pre-commit GitHub action.
• [x] Create a main CI workflow and parametrize it for different settings
• [x] Add minimum version policy workflow
• [x] Add Doctests workflow
• [x] Remove azure pipelines config
• [x] Update docs: remove references to azure pipelines

As discussed in today's dev meeting, this PR modifies the push and pull_request event triggers by making sure the upstream dev CI is triggered by cron schedule by default.

• [ ] Passes isort . && black . && mypy . && flake8
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

• [x] Closes #2840
• [x] Tests added
• [x] Passes isort . && black . && mypy . && flake8
• [x] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

• [x] Closes #4603
• [x] Closes #4643
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst

Currently, xarray has a room on Gitter: https://gitter.im/pydata/xarray. This room works fine for discussions outside of the codebase. However, Gitter has a few disadvantages:

• The contents are not indexed by search engines
• Searching through existing discussions is almost impossible
• Linking to prior conversations in the room is also complicated

A few months ago, GitHub announced GitHub discussions which is meant to serve as a forum for discussions outside of the codebase. I am of the opinion that GitHub discussions is a better alternative to Gitter. I am wondering if xarray folks would be interested in enabling GitHub discussion on this repo, and migrating general discussions from Gitter to GitHub discussions?

GitHub Discussions is still in beta, but projects can request early access here

Here is a list of a few projects with beta access:

• https://github.com/vercel/vercel/discussions
• https://github.com/KaTeX/KaTeX/discussions
• https://github.com/vercel/next.js/discussions

Towards https://github.com/pydata/xarray/issues/4549

• [ ] Closes #xxxx
• [ ] Tests added
• [x] Passes isort . && black . && mypy . && flake8
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

This GitHub action will run tests against the upstream dependencies. When the tests fail, the GH action will create an issue (with "CI" label). The body of the created issue includes:

• test summary report
• a link to the failed workflow run

Here's a couple screenshots from a toy repository:

• [x] Closes #4574
• [x] Tests added
• [x] Passes isort . && black . && mypy . && flake8
• [x] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

Is your feature request related to a problem? Please describe.

Currently, when a user wants to write multiple netCDF files in parallel with xarray and dask, they can take full advantage of xr.save_mfdataset() function. This function in its current state works fine, but the existing API requires that - the user generates file paths themselves - the user maps each chunk or dataset to a corresponding output file

A few months ago, I wrote a blog post showing how to save an xarray dataset backed by dask into multiple netCDF files, and since then I've been meaning to request a new feature to make this process convenient for users.

Describe the solution you'd like

Would it be useful to actually refactor the existing xr.save_mfdataset() to automatically save an xarray object backed by dask arrays to multiple files without needing to create paths ourselves? Today, this can be achieved via xr.map_blocks. In other words, is it possible to have something analogous to to_zarr(....) but for netCDF:

```python ds.save_mfdataset(prefix="directory/my-dataset")

or

xr.save_mfdataset(ds, prefix="directoy/my-dataset")

```

---->

```bash

directory/my-dataset-chunk-1.nc directory/my-dataset-chunk-2.nc directory/my-dataset-chunk-3.nc .... ```

What happened:

While computing averaged time using time_bounds via times = bounds.mean('d2'), I get weird results (see example below). It's my understanding that this is a bug, but I don't know yet where it's coming from. I should note that in addition to getting wrong time values, the resulting time values are not monotonically increasing even though my time bounds are.

What you expected to happen:

Correct averaged time values

Minimal Complete Verifiable Example:

```python In [1]: import xarray as xr

In [2]: import numpy as np

In [3]: dates = xr.cftime_range(start='0400-01', end='2101-01', freq='120Y', calendar='noleap')

In [4]: bounds = xr.DataArray(np.vstack([dates[:-1], dates[1:]]).T, dims=['time', 'd2'])

In [5]: bounds
Out[5]: <xarray.DataArray (time: 14, d2: 2)> array([[cftime.DatetimeNoLeap(400, 12, 31, 0, 0, 0, 0), cftime.DatetimeNoLeap(520, 12, 31, 0, 0, 0, 0)], [cftime.DatetimeNoLeap(520, 12, 31, 0, 0, 0, 0), cftime.DatetimeNoLeap(640, 12, 31, 0, 0, 0, 0)], [cftime.DatetimeNoLeap(640, 12, 31, 0, 0, 0, 0), cftime.DatetimeNoLeap(760, 12, 31, 0, 0, 0, 0)], [cftime.DatetimeNoLeap(760, 12, 31, 0, 0, 0, 0), cftime.DatetimeNoLeap(880, 12, 31, 0, 0, 0, 0)], [cftime.DatetimeNoLeap(880, 12, 31, 0, 0, 0, 0), cftime.DatetimeNoLeap(1000, 12, 31, 0, 0, 0, 0)], [cftime.DatetimeNoLeap(1000, 12, 31, 0, 0, 0, 0), cftime.DatetimeNoLeap(1120, 12, 31, 0, 0, 0, 0)], [cftime.DatetimeNoLeap(1120, 12, 31, 0, 0, 0, 0), cftime.DatetimeNoLeap(1240, 12, 31, 0, 0, 0, 0)], [cftime.DatetimeNoLeap(1240, 12, 31, 0, 0, 0, 0), cftime.DatetimeNoLeap(1360, 12, 31, 0, 0, 0, 0)], [cftime.DatetimeNoLeap(1360, 12, 31, 0, 0, 0, 0), cftime.DatetimeNoLeap(1480, 12, 31, 0, 0, 0, 0)], [cftime.DatetimeNoLeap(1480, 12, 31, 0, 0, 0, 0), cftime.DatetimeNoLeap(1600, 12, 31, 0, 0, 0, 0)], [cftime.DatetimeNoLeap(1600, 12, 31, 0, 0, 0, 0), cftime.DatetimeNoLeap(1720, 12, 31, 0, 0, 0, 0)], [cftime.DatetimeNoLeap(1720, 12, 31, 0, 0, 0, 0), cftime.DatetimeNoLeap(1840, 12, 31, 0, 0, 0, 0)], [cftime.DatetimeNoLeap(1840, 12, 31, 0, 0, 0, 0), cftime.DatetimeNoLeap(1960, 12, 31, 0, 0, 0, 0)], [cftime.DatetimeNoLeap(1960, 12, 31, 0, 0, 0, 0), cftime.DatetimeNoLeap(2080, 12, 31, 0, 0, 0, 0)]], dtype=object) Dimensions without coordinates: time, d2

In [6]: bounds.mean('d2')
Out[6]: <xarray.DataArray (time: 14)> array([cftime.DatetimeNoLeap(460, 12, 31, 0, 0, 0, 0), cftime.DatetimeNoLeap(580, 12, 31, 0, 0, 0, 0), cftime.DatetimeNoLeap(116, 1, 21, 0, 25, 26, 290448), cftime.DatetimeNoLeap(236, 1, 21, 0, 25, 26, 290448), cftime.DatetimeNoLeap(356, 1, 21, 0, 25, 26, 290448), cftime.DatetimeNoLeap(476, 1, 21, 0, 25, 26, 290448), cftime.DatetimeNoLeap(596, 1, 21, 0, 25, 26, 290448), cftime.DatetimeNoLeap(131, 2, 11, 0, 50, 52, 580897), cftime.DatetimeNoLeap(251, 2, 11, 0, 50, 52, 580897), cftime.DatetimeNoLeap(371, 2, 11, 0, 50, 52, 580897), cftime.DatetimeNoLeap(491, 2, 11, 0, 50, 52, 580897), cftime.DatetimeNoLeap(611, 2, 11, 0, 50, 52, 580897), cftime.DatetimeNoLeap(146, 3, 4, 1, 16, 18, 871345), cftime.DatetimeNoLeap(266, 3, 4, 1, 16, 18, 871345)], dtype=object) Dimensions without coordinates: time

```

Anything else we need to know?:

Environment:

• [x] Closes #3609
• [x] Tests added
• [x] Passes black . && mypy . && flake8
• [x] Fully documented, including whats-new.rst for all changes and api.rst for new API

As pointed out in https://github.com/jbusecke/cmip6_preprocessing/issues/5, xarray appears to be very strict during the encoding and decoding steps even when there are (harmless) discrepancies between missing_value and _FillValue. For instance, when dtypes of missing_value and _FillValue are different, xarray gives up:

```python In [74]: from xarray.coding import variables

In [75]: import numpy as np

In [76]: import xarray as xr

In [77]: original = xr.Variable( ...: ("x",), ...: [0.0, -1.0, 1.0], ...: encoding={"_FillValue": np.float32(1e20), "missing_value": np.float64(1e20)}, ...: )

In [78]: coder = variables.CFMaskCoder()

In [79]: encoded = coder.encode(original)

ValueError Traceback (most recent call last) <ipython-input-79-9fbc3632e28b> in <module> ----> 1 encoded = coder.encode(original)

/glade/work/abanihi/devel/pangeo/xarray/xarray/coding/variables.py in encode(self, variable, name) 156 raise ValueError( 157 "Variable {!r} has multiple fill values {}. " --> 158 "Cannot encode data. ".format(name, [fv, mv]) 159 ) 160

ValueError: Variable None has multiple fill values [1e+20, 1e+20]. Cannot encode data. ```

• [ ] Closes #xxxx
• [x] Tests added
• [x] Passes black . && mypy . && flake8
• [x] Fully documented, including whats-new.rst for all changes and api.rst for new API

Cc @dcherian

• Don't need dayofwk=-1 for cftime>=1.0.4

• [x] Closes #3426 and Fixes #3427
• [ ] Tests added
• [x] Passes black . && mypy . && flake8
• [x] Fully documented, including whats-new.rst for all changes and api.rst for new API

I just updated to the latest cftime release, and all of a sudden sel() appears to be broken:

```python In [1]: import xarray as xr

In [2]: import cftime

In [3]: ds = xr.tutorial.load_dataset('rasm')

In [4]: ds
Out[4]: <xarray.Dataset> Dimensions: (time: 36, x: 275, y: 205) Coordinates: * time (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00 xc (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91 yc (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51 Dimensions without coordinates: x, y Data variables: Tair (time, y, x) float64 nan nan nan nan nan ... 29.8 28.66 28.19 28.21 Attributes: title: /workspace/jhamman/processed/R1002RBRxaaa01a/l... institution: U.W. source: RACM R1002RBRxaaa01a output_frequency: daily output_mode: averaged convention: CF-1.4 references: Based on the initial model of Liang et al., 19... comment: Output from the Variable Infiltration Capacity... nco_openmp_thread_number: 1 NCO: "4.6.0" history: Tue Dec 27 14:15:22 2016: ncatted -a dimension...

In [5]: ds.sel(time=slice("1980", "1982"))

ValueError Traceback (most recent call last) <ipython-input-5-2c26e36a673a> in <module> ----> 1 ds.sel(time=slice("1980", "1982"))

~/opt/miniconda3/envs/intake-esm-dev/lib/python3.7/site-packages/xarray/core/dataset.py in sel(self, indexers, method, tolerance, drop, **indexers_kwargs) 1998 indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "sel") 1999 pos_indexers, new_indexes = remap_label_indexers( -> 2000 self, indexers=indexers, method=method, tolerance=tolerance 2001 ) 2002 result = self.isel(indexers=pos_indexers, drop=drop)

~/opt/miniconda3/envs/intake-esm-dev/lib/python3.7/site-packages/xarray/core/coordinates.py in remap_label_indexers(obj, indexers, method, tolerance, **indexers_kwargs) 390 391 pos_indexers, new_indexes = indexing.remap_label_indexers( --> 392 obj, v_indexers, method=method, tolerance=tolerance 393 ) 394 # attach indexer's coordinate to pos_indexers

~/opt/miniconda3/envs/intake-esm-dev/lib/python3.7/site-packages/xarray/core/indexing.py in remap_label_indexers(data_obj, indexers, method, tolerance) 259 coords_dtype = data_obj.coords[dim].dtype 260 label = maybe_cast_to_coords_dtype(label, coords_dtype) --> 261 idxr, new_idx = convert_label_indexer(index, label, dim, method, tolerance) 262 pos_indexers[dim] = idxr 263 if new_idx is not None:

~/opt/miniconda3/envs/intake-esm-dev/lib/python3.7/site-packages/xarray/core/indexing.py in convert_label_indexer(index, label, index_name, method, tolerance) 123 _sanitize_slice_element(label.start), 124 _sanitize_slice_element(label.stop), --> 125 _sanitize_slice_element(label.step), 126 ) 127 if not isinstance(indexer, slice):

~/opt/miniconda3/envs/intake-esm-dev/lib/python3.7/site-packages/pandas/core/indexes/base.py in slice_indexer(self, start, end, step, kind) 5032 slice(1, 3) 5033 """ -> 5034 start_slice, end_slice = self.slice_locs(start, end, step=step, kind=kind) 5035 5036 # return a slice

~/opt/miniconda3/envs/intake-esm-dev/lib/python3.7/site-packages/pandas/core/indexes/base.py in slice_locs(self, start, end, step, kind) 5246 start_slice = None 5247 if start is not None: -> 5248 start_slice = self.get_slice_bound(start, "left", kind) 5249 if start_slice is None: 5250 start_slice = 0

~/opt/miniconda3/envs/intake-esm-dev/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_slice_bound(self, label, side, kind) 5158 # For datetime indices label may be a string that has to be converted 5159 # to datetime boundary according to its resolution. -> 5160 label = self._maybe_cast_slice_bound(label, side, kind) 5161 5162 # we need to look up the label

~/opt/miniconda3/envs/intake-esm-dev/lib/python3.7/site-packages/xarray/coding/cftimeindex.py in _maybe_cast_slice_bound(self, label, side, kind) 336 pandas.tseries.index.DatetimeIndex._maybe_cast_slice_bound""" 337 if isinstance(label, str): --> 338 parsed, resolution = _parse_iso8601_with_reso(self.date_type, label) 339 start, end = _parsed_string_to_bounds(self.date_type, resolution, parsed) 340 if self.is_monotonic_decreasing and len(self) > 1:

~/opt/miniconda3/envs/intake-esm-dev/lib/python3.7/site-packages/xarray/coding/cftimeindex.py in _parse_iso8601_with_reso(date_type, timestr) 114 # 1.0.3.4. 115 replace["dayofwk"] = -1 --> 116 return default.replace(**replace), resolution 117 118

cftime/_cftime.pyx in cftime._cftime.datetime.replace()

ValueError: Replacing the dayofyr or dayofwk of a datetime is not supported.

```

Output of xr.show_versions()

Expected Output

I can confirm that everything works just fine with an older version of cftime:

```python In [4]: ds.sel(time=slice("1980", "1982"))
Out[4]: <xarray.Dataset> Dimensions: (time: 28, x: 275, y: 205) Coordinates: * time (time) object 1980-09-16 12:00:00 ... 1982-12-17 00:00:00 xc (y, x) float64 ... yc (y, x) float64 ... Dimensions without coordinates: x, y Data variables: Tair (time, y, x) float64 ... Attributes: title: /workspace/jhamman/processed/R1002RBRxaaa01a/l... institution: U.W. source: RACM R1002RBRxaaa01a output_frequency: daily output_mode: averaged convention: CF-1.4 references: Based on the initial model of Liang et al., 19... comment: Output from the Variable Infiltration Capacity... nco_openmp_thread_number: 1 NCO: "4.6.0" history: Tue Dec 27 14:15:22 2016: ncatted -a dimension... In [5]: import cftime

In [6]: cftime.version
Out[6]: '1.0.3.4'

In [7]: xr.version
Out[7]: '0.14.0' ```