Is your feature request related to a problem?

Now that python 3.11 has been released, we should start to support it officially.

Describe the solution you'd like

I guess the first step would be to replace python 3.10 as a maximum version in the tests and see what crashes (and get lucky).

Describe alternatives you've considered

No response

Additional context

No response

What is your issue?

The doctest is currently failing with

E UserWarning: h5py is running against HDF5 1.14.3 when it was built against 1.14.2, this may cause problems

What is your issue?

According to the array api standard, the shape property returns tuple[int | None, ...]. Currently we only support tuple[int, ...]

This will actually raise some errors if a duckarray actually returns some None. E.g. NamedArray.size will fail.

(On a side note: dask arrays actually use NaN instead of None for some reason.... Only advantage of this is that NamedArray.size will actually also return NaN instead of raising...)

What happened?

I have a DataArray with a single dimension and multiple (Pandas)Indexes assigned to various coordinates for efficient indexing using sel.

Edit: the problem is even worse than originally described below: such a DataArray breaks all alignment and it's basically unusable...

When I try to add an additional coordinate without any index (I simply use the tuple[dimension, values] way) I get a ValueError about aligning with conflicting indexes.

If the original DataArray only has a single (Pandas)Index everything works as expected.

What did you expect to happen?

I expected that I can simply assign new coordinates without an index.

Minimal Complete Verifiable Example

```Python import xarray as xr

da = xr.DataArray( [1, 2, 3], dims="t", coords={ "a": ("t", [3, 4, 5]), "b": ("t", [5, 6, 7]) } )

set one index

da2 = da.set_xindex("a")

set second index (same dimension, maybe thats a problem?)

da3 = da2.set_xindex("b")

this works

da2.coords["c"] = ("t", [2, 3, 4])

this does not

da3.coords["c"] = ("t", [2, 3, 4]) ```

MVCE confirmation

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

Relevant log output

ValueError: cannot re-index or align objects with conflicting indexes found for the following dimensions: 't' (2 conflicting indexes) Conflicting indexes may occur when - they relate to different sets of coordinate and/or dimension names - they don't have the same type - they may be used to reindex data along common dimensions

Anything else we need to know?

No response

Environment

I have not yet tried this with a newer version of xarray....

What is your issue?

The coverage was reduced from ~94% to ~68% after merging #7126 See https://app.codecov.io/gh/pydata/xarray or our badge

I think this is because the unittests never included the tests directory while mypy does. And codecov uses the sum of both coverage reports to come up with its number.

Adding the flag to the badge also does not seem to help?

Not sure how or even if that is possible to solve, maybe we need to ask in codecov?

What is your issue?

It seems that the readthedocs build is failing since some upstream update. pydata-sphinx-theme seems to be incompatible with the sphinx-book-theme.

Maybe we have to pin to a specific or a maximum version for now.

What is your issue?

Since cfgrib 0.9.9 (04/2021) it comes with its own xarray backend plugin (looks mainly like a copy of our internal version). We should deprecate our internal plugin.

The deprecation is complicated since we usually bind the minimum version to a minor step, but cfgrib seems to be on 0.9 since 4 years already. Maybye an exception like for netCDF4?

Anyway, if we decide to leave it as it is for now, this ticket is just a reminder to remove it someday :)

What is your issue?

In the latest CI runs it seems that test_backends.py::TestNCZarr::test_open_nczarr is failing with

KeyError: 'Zarr object is missing the attribute _ARRAY_DIMENSIONS and the NCZarr metadata, which are required for xarray to determine variable dimensions.'

I don't see an obvious reason for this, especially since the zarr version has not changed compared to some runs that were successful (2.13.6).

What is your issue?

Xarray raises a FutureWarning in its date_range, also observable in your tests. The precise warning is:

xarray/coding/cftime_offsets.py:1130: FutureWarning: Argument closed is deprecated in favor of inclusive.

You should discuss if you will adapt the new inclusive argument or add a workaround.

What is your issue?

It seems that some update in urllib3 causes our doctests to fail.

The reason seems to be that botocore uses an interesting construction to import deprecated urllib3 things: python try: # pyopenssl will be removed in urllib3 2.0, we'll fall back to ssl_ at that point. # This can be removed once our urllib3 floor is raised to >= 2.0. with warnings.catch_warnings(): warnings.simplefilter("ignore", category=DeprecationWarning) # Always import the original SSLContext, even if it has been patched from urllib3.contrib.pyopenssl import ( orig_util_SSLContext as SSLContext, ) except ImportError: from urllib3.util.ssl_ import

I assume that this fails because we use -Werror which translates the warning into an error which then is not ignored...

Not sure if this is an issue with botocore or we have to catch this?

What happened?

The plot accessors i.e. dataarray.plot of type _PlotMethods are missing static typing especially of function attributes. See #6947 for an example.

The problem is that many plotting methods are added using hooks via decorators, something that mypy does not understand.

What did you expect to happen?

As a quick fix: type the plot accessors as _PlotMethods | Any to avoid false positives in mypy.

Better to either restructure the accessor with static methods instead of hooks or figure out another way of telling static type checkers about these methods.

Anyway: mypy should not complain.

Minimal Complete Verifiable Example

```Python import xarray as xr

da = xr.DataArray([[1,2,3], [4,5,6]], dims=["x", "y"]) da.plot.contourf(x="x", y="y")

mypy complains:

error: "_PlotMethods" has no attribute "contourf"

```

MVCE confirmation

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

Relevant log output

No response

Anything else we need to know?

No response

Environment

On mobile, can edit it later if required. Newest xarray should have this problem, before the accessor was Any.

What happened?

We used Data{set,Array}.rename{_vars}({coord: dim_coord}) to make a coordinate a dimension coordinate (instead of set_index). This results in the coordinate correctly being displayed as a dimension coordinate (with the *) but it does not create an index, such that further operations like sel fail with a strange KeyError.

What did you expect to happen?

I expect one of two things to be true:

1. rename{_vars} does not allow setting dimension coordinates (raises Error and tells you to use set_index)
2. rename{_vars} checks for this occasion and sets the index correctly

Minimal Complete Verifiable Example

```python import xarray as xr

data = xr.DataArray([5, 6, 7], coords={"c": ("x", [1, 2, 3])}, dims="x")

<xarray.DataArray (x: 3)>

array([5, 6, 7])

Coordinates:

c (x) int64 1 2 3

Dimensions without coordinates: x

data_renamed = data.rename({"c": "x"})

<xarray.DataArray (x: 3)>

array([5, 6, 7])

Coordinates:

* x (x) int64 1 2 3

data_renamed.indexes

Empty

data_renamed.sel(x=2)

KeyError: 'no index found for coordinate x'

if we use set_index it works

data_indexed = data.set_index({"x": "c"})

looks the same as data_renamed!

<xarray.DataArray (x: 3)>

array([1, 2, 3])

Coordinates:

* x (x) int64 1 2 3

data_indexed.indexes

x: Int64Index([1, 2, 3], dtype='int64', name='x')

```

Relevant log output

No response

Anything else we need to know?

No response

Environment

INSTALLED VERSIONS

commit: None python: 3.9.1 (default, Jan 13 2021, 15:21:08) [GCC 4.8.5 20150623 (Red Hat 4.8.5-44)] python-bits: 64 OS: Linux OS-release: 3.10.0-1160.49.1.el7.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: ('en_US', 'UTF-8') libhdf5: 1.12.0 libnetcdf: 4.7.4

xarray: 0.20.2 pandas: 1.3.5 numpy: 1.21.5 scipy: 1.7.3 netCDF4: 1.5.8 pydap: None h5netcdf: None h5py: None Nio: None zarr: None cftime: 1.5.1.1 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: None distributed: None matplotlib: 3.5.1 cartopy: None seaborn: None numbagg: None fsspec: None cupy: None pint: None sparse: None setuptools: 49.2.1 pip: 22.0.2 conda: None pytest: 6.2.5 IPython: 8.0.0 sphinx: None

What happened: http://xarray.pydata.org/en/stable/generated/xarray.DataArray.rename.html#xarray.DataArray.rename states that:

Returns a new DataArray with renamed coordinates or a new name.

What you expected to happen: It should state: "Returns a new DataArray with renamed coordinates, dimensions or a new name." Since it definitely can do that.

Minimal example xr.DataArray([1, 2, 3]).rename({"dim_0": "new"})

Further While at it: Dataset.rename als does not mention explicitly that you can rename coordinates.

What happened?

I tried to reduce some dimensions using where (sel did not work in this case) and shorten the dimensions with "drop=True". This works fine on DataArrays and Datasets with only a single dimension but fails as soon as you have a Dataset with two dimensions on different variables. The dimensions are left untouched and you have NaNs in the data, just as if you were using "drop=False" (see example).

I am actually not sure what the expected behavior is, maybe I am wrong and it is correct due to some broadcasting rules?

What did you expect to happen?

I expected that relevant dims are shortened. If the ds.where with "drop=False" all variables along a dimenions have some NaNs, then using "drop=True" I expect these dimensions to be shortened and the NaNs removed.

Minimal Complete Verifiable Example

```python import xarray as xr

this works

ds = xr.Dataset({"a": ("x", [1, 2 ,3])}) ds.where(ds > 2, drop=True)

returns:

<xarray.Dataset>

Dimensions: (x: 1)

Dimensions without coordinates: x

Data variables:

a (x) float64 3.0

this doesn't

ds = xr.Dataset({"a": ("x", [1, 2 ,3]), "b": ("y", [2, 3, 4])}) ds.where(ds > 2, drop=True)

returns:

<xarray.Dataset>

Dimensions: (x: 3, y: 3)

Dimensions without coordinates: x, y

Data variables:

a (x) float64 nan nan 3.0

b (y) float64 nan 3.0 4.0

```

Relevant log output

No response

Anything else we need to know?

No response

Environment

INSTALLED VERSIONS

commit: None python: 3.9.1 (default, Jan 13 2021, 15:21:08) [GCC 4.8.5 20150623 (Red Hat 4.8.5-44)] python-bits: 64 OS: Linux OS-release: 3.10.0-1160.49.1.el7.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: ('en_US', 'UTF-8') libhdf5: 1.12.0 libnetcdf: 4.7.4

xarray: 0.20.2 pandas: 1.3.5 numpy: 1.21.5 scipy: 1.7.3 netCDF4: 1.5.8 pydap: None h5netcdf: None h5py: None Nio: None zarr: None cftime: 1.5.1.1 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: None distributed: None matplotlib: 3.5.1 cartopy: None seaborn: None numbagg: None fsspec: None cupy: None pint: None sparse: None setuptools: 49.2.1 pip: 22.0.2 conda: None pytest: 6.2.5 IPython: 8.0.0 sphinx: None

What is your issue?

In the recent typing improvements the engine argument for open_dataset was changed from Str to a Literal of xarrays internal engines. This will cause problems for all third party backend plugins.

We have several possibilities:

1. I don't know if there is a way to know installed backends at type checking time. Then we could add this support. (I doubt this is possible seeing how dynamic these imports are)
2. Is it possible for these plugins to tell type checkers that their engine is valid, i.e. change the type signature of xarrays function? Then we should add a how-to in the docu.
3. Else we should probably revert to using Str.

Any typing experts here that could help?

What happened?

When using DataArray/Dataset.isel with drop=True with a scalar DataArray as indexer (see example) resulting scalar coordinates do not get dropped. When using an integer the behavior is as expected.

What did you expect to happen?

I expect that using a scalar DataArray behaves the same as an integer.

Minimal Complete Verifiable Example

```Python import xarray as xr

da = xr.DataArray([1, 2, 3], dims="x", coord={"k": ("x", [0, 1, 2])})

<xarray.DataArray (x: 3)>

array([1, 2, 3])

Coordinates:

k (x) int32 0 1 2

da.isel({"x": 1}, drop=True)

works

<xarray.DataArray ()>

array(2)

da.isel({"x": xr.DataArray(1)}, drop=True)

does not drop "k" coordinate

<xarray.DataArray ()>

array(2)

Coordinates:

k int32 1

```

Relevant log output

No response

Anything else we need to know?

No response

Environment

What happened?

I have some polynomial coefficients and want to evaluate them at some values using xr.polyval.

As described in the docstring/docu I created a 1D coordinate DataArray and pass it to xr.polyval but it raises a KeyError (see example).

What did you expect to happen?

I expected that the polynomial would be evaluated at the given points.

Minimal Complete Verifiable Example

```Python import xarray as xr

coeffs = xr.DataArray([1, 2, 3], dims="degree")

With a "handmade" coordinate it fails:

coord = xr.DataArray([0, 1, 2], dims="x")

xr.polyval(coord, coeffs)

raises:

Traceback (most recent call last):

File "<stdin>", line 1, in <module>

File "xarray/core/computation.py", line 1847, in polyval

x = get_clean_interp_index(coord, coord.name, strict=False)

File "xarray/core/missing.py", line 252, in get_clean_interp_index

index = arr.get_index(dim)

File "xarray/core/common.py", line 404, in get_index

raise KeyError(key)

KeyError: None

If one adds a name to the coord that is called like the dimension:

coord2 = xr.DataArray([0, 1, 2], dims="x", name="x")

xr.polyval(coord2, coeffs)

works

```

Relevant log output

No response

Anything else we need to know?

I assume that the "standard" workflow is to obtain the coord argument from an existing DataArrays coordinate, where the name would be correctly set already. However, that is not clear from the description, and also prevents my "manual" workflow.

It could be that the problem will be solved by replacing the coord DataArray argument by an explicit Index in the future.

Environment

Is your feature request related to a problem?

When using sortby with ascending=False on a DataArray/Dataset without an explicit index, the data gets correctly reversed, but it is not possible to tell anymore which ordering the data has.

If an explicit index (like [0, 1, 2]) exists, it gets correctly reordered and allowes correct aligning.

Describe the solution you'd like

For consistency with aligning xarray should create a new index that indicates that the data has been reordered, i.e. [2, 1, 0].

Only downside: this will break code that relies on non-existent indexes.

Describe alternatives you've considered

No response

Additional context

No response

What happened?

When grouping and reducing an array or dataset over a multi-index the coordinates that make up the multi-index get renamed to "{name_of_multiindex}_level_{i}".

It only works correctly when the Multiindex is a "homogenous grid", i.e. as obtained by stacking.

What did you expect to happen?

I expect that all coordinates keep their initial names.

Minimal Complete Verifiable Example

```Python import xarray as xr

this works:

d = xr.DataArray(range(4), dims="t", coords={"x": ("t", [0, 0, 1, 1]), "y": ("t", [0, 1, 0, 1])}) dd = d.set_index({"t": ["x", "y"]})

returns

<xarray.DataArray (t: 4)>

array([0, 1, 2, 3])

Coordinates:

* t (t) MultiIndex

- x (t) int64 0 0 1 1

- y (t) int64 0 1 0 1

dd.groupby("t").mean(...)

returns

<xarray.DataArray (t: 4)>

array([0., 1., 2., 3.])

Coordinates:

* t (t) MultiIndex

- x (t) int64 0 0 1 1

- y (t) int64 0 1 0 1

this does not work

d2 = xr.DataArray(range(6), dims="t", coords={"x": ("t", [0, 0, 1, 1, 0, 1]), "y": ("t", [0, 1, 0, 1, 0, 0])}) dd2 = d2.set_index({"t": ["x", "y"]})

returns

<xarray.DataArray (t: 6)>

array([0, 1, 2, 3, 4, 5])

Coordinates:

* t (t) MultiIndex

- x (t) int64 0 0 1 1 0 1

- y (t) int64 0 1 0 1 0 0

dd2.groupby("t").mean(...)

returns

<xarray.DataArray (t: 4)>

array([2. , 1. , 3.5, 3. ])

Coordinates:

* t (t) MultiIndex

- t_level_0 (t) int64 0 0 1 1

- t_level_1 (t) int64 0 1 0 1

```

Relevant log output

No response

Anything else we need to know?

No response

Environment

INSTALLED VERSIONS

commit: None python: 3.9.1 (default, Jan 13 2021, 15:21:08) [GCC 4.8.5 20150623 (Red Hat 4.8.5-44)] python-bits: 64 OS: Linux OS-release: 3.10.0-1160.49.1.el7.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: ('en_US', 'UTF-8') libhdf5: 1.12.0 libnetcdf: 4.7.4

xarray: 0.21.1 pandas: 1.4.0 numpy: 1.21.5 scipy: 1.7.3 netCDF4: 1.5.8 pydap: None h5netcdf: None h5py: None Nio: None zarr: None cftime: 1.5.1.1 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: None distributed: None matplotlib: 3.5.1 cartopy: None seaborn: None numbagg: None fsspec: None cupy: None pint: None sparse: None setuptools: 49.2.1 pip: 22.0.3 conda: None pytest: 6.2.5 IPython: 8.0.0 sphinx: None

Is your feature request related to a problem? Please describe. I have a dataset where I do not know which of two dimensions (lets call them a and b) exists in this dataset (So either it has dims ("a", "other") or ("b", "other")). I would like to make sure that this dimension is first using transpose, but currently this is only possible using if or try statements. Just using ds.transpose("a", "b", "other") raises a ValueError arguments to transpose XXX must be permuted dataset dimensions YYY.

Describe the solution you'd like It would be nice if I could just use ds.transpose("a", "b", "other", missing_dims="ignore") similar to how DataArray.transpose handles it.

Describe alternatives you've considered Currently I'm also using ds.map(lambda x: x.transpose("a", "b", "other", missing_dims="ignore")), which could (maybe?) replace the current implementation of the transpose.

While at it, transpose_coords could also be exposed to Dataset.transpose.