issues

• [ ] 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

• [ ] 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

• [x] Tests added
• [x] User visible changes (including notable bug fixes) are documented in whats-new.rst

```python import matplotlib.pyplot as plt import xarray as xr

airtemps = xr.tutorial.open_dataset("air_temperature") air = airtemps.air - 273.15 air1d = air.isel(lat=10, lon=10)

plt.figure() air1d.plot() ``` Before:

After:

• [ ] 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

• [ ] 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

Use https://pypi.anaconda.org/scientific-python-nightly-wheels/simple/ instead.

• [x] Closes #7969

A simple refactor to make it easier to retrieve the proper paths that open_mfdataset uses and passes on the engine.

I've been thinking how to make use of DataTree and one idea I wanted to try was: * Open file (using_find_absolute_path). * Get all groups in the file. * For each group run xr.open_mfdataset(..., group=group)

Fixes typing errors when using newest mypy version.

• [x] Closes #7270

• [x] Closes #7313
• [x] Tests added
• [x] User visible changes (including notable bug fixes) are documented in whats-new.rst

• Workaround for Pull Request Labeler The PR labeler keeps removing manually added labels. xref: https://github.com/actions/labeler/issues/112
• ASV benchmarks starts also when topic-performance label is added now. Bot is allowed to change this one, but not run-benchmarks.

Fix some issues with scatter plots: * Always use markersize widths for scatter. * Fix issue with .values_unique not returning the same values as .values * Added more type hints to _Normalize, some rework had to be done to make mypy pass.

xref: #6778

Clean up a little in preparation for #6059.

• [ ] 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

xref: #6244

Inspired by #5734, remove the non-bottleneck build and instead use xr.set_options on the relevant tests. This makes the report much more readable and reduces testing time quite a bit since everything isn't accelerated by bottleneck.

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

Minor change to align with other booleans.

ref #5582

• [ ] 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

While trying to figure out which types are allowed in #5678 I felt that the set_options docstring was rather hard to read. Moving it to typical numpy docstring style helped at least for me.

Allow controlling the randomness of the dataset. It's difficult to catch issues with the dataset if it always changes each run.

Make sure name is a string. Use the same .format method as in _get_units_from_attrs to convert to string.

• [x] Closes #6826
• [x] Tests added
• [x] User visible changes (including notable bug fixes) are documented in whats-new.rst

Copy pasting the examples in http://xarray.pydata.org/en/stable/internals/how-to-add-new-backend.html resulted in crashes. Make the docs copy/paste friendly by removing the self arguments.

Example: ```python expected = xr.Dataset( dict(a=2 * np.arange(5)), coords=dict(x=("x", np.arange(5), dict(units="s"))) )

class CustomBackend(xr.backends.BackendEntrypoint): def open_dataset( self, filename_or_obj, drop_variables=None, **kwargs, ): return expected.copy(deep=True)

xr.open_dataset("fake_filename", engine=CustomBackend) TypeError: open_dataset() missing 1 required positional argument: 'filename_or_obj' This works if self is removed:python expected = xr.Dataset( dict(a=2 * np.arange(5)), coords=dict(x=("x", np.arange(5), dict(units="s"))) )

class CustomBackend(xr.backends.BackendEntrypoint): def open_dataset( filename_or_obj, drop_variables=None, **kwargs, ): return expected.copy(deep=True)

xr.open_dataset("fake_filename", engine=CustomBackend) <xarray.Dataset> Dimensions: (a: 5, x: 5) Coordinates: * a (a) int32 0 2 4 6 8 * x (x) int32 0 1 2 3 4 Data variables: empty ```

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