Builds on top of #4972 to add tests for Variable/DataArray/Dataset constructors and properties when wrapping duck arrays.

Adds a file xarray/tests/duckarrays/base/constructors.py which contains new test base classes.

Also uses those new base classes to test Sparse array integration (not yet tried for pint integration).

• [x] Closes part of #6894
• [ ] Tests added (tests for tests?? Maybe...)
• [ ] 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?

7019 generalized most of xarrays internals to be able to use any chunked array type that we can create a ChunkManagerEntrypoint for. Most functions now go through this (e.g. apply_ufunc), but I did not redirect xarray.map_blocks to go through ChunkManagerEntrypoint.

This redirection works by dispatching to high-level dask.array primitives such as dask.array.apply_gufunc, dask.array.blockwise, and dask.array.map_blocks. However the current implementation of xarray.map_blocks is much lower-level, building a custom HLG, so it was not obvious how to swap it out.

Describe the solution you'd like

I would like to either:

1) Replace the current internals of xarray.map_blocks with a simple call to ChunkManagerEntrypoint.map_blocks. This would be the cleanest separation of concerns we could do here. Presumably there is some obvious reason why this cannot or should not be done, but I have yet to understand what that reason is. (either @dcherian or @tomwhite can you enlighten me perhaps? 🙏)

2) (More likely) refactor so that the existing guts of xarray.map_blocks are only called from the ChunkManagerEntrypoint, and a non-dask chunked array (i.e. cubed, but in theory other types too) would be able to specify how it wants to perform the map_blocks.

Describe alternatives you've considered

Leaving it as the status quo breaks the nice abstraction and separation of concerns that #7019 introduced.

Additional context

Split off from https://github.com/pydata/xarray/issues/8414

Add suggestions @paigem made in #7439, as well as fix a few small formatting things and broken links.

I would like to merge this so that it can be helpful for the new contributors we will hopefully get through Outreachy.

• [x] Closes #7439
• [ ] ~~Tests added~~
• [x] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] ~~New functions/methods are listed in api.rst~~

What is your issue?

I just saw this new typing feature in python 3.11, and I'm wondering whether / where we could usefully use this? The feature is parametrizing Generics with arbitrary numbers of TypeVars, which allows you to have Array types whose static typing behaviour is a function of their shape. (But we could possibly use it for a tuple of dims too...) We might use it to do things like:

• Specify that a function expects an array of a certain dimensionality
• Overload methods based on the array dimensionality (e.g. .plot for 1D vs 2D arrays)
• (If they implement Shape Arithmetic) Type hint how certain methods will change the output shape?

@headtr1ck @max-sixty any thoughts?

Release notes:

rst This release brings improvements to plotting of categorical data, the ability to specify how attributes are combined in xarray operations, a new high-level :py:func:`unify_chunks` function, as well as various deprecations, bug fixes, and minor improvements.

• [x] Closes #5588

xarray.plot.pcolormesh() fails when you pass the matplotlib.pyplot.pcolormesh() keyword argument shading='gouraud' to it.

Code Sample, a copy-pastable example if possible

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

lon, lat = np.meshgrid(np.linspace(-20, 20, 5), np.linspace(0, 30, 4)) lon += lat/10 lat += lon/10

da = xr.DataArray(np.arange(20).reshape(4, 5), dims=['y', 'x'], coords = {'lat': (('y', 'x'), lat), 'lon': (('y', 'x'), lon)})

da.plot.pcolormesh('lon', 'lat', shading='gouraud') plt.show() ```

Problem description

This gives an error:

Traceback (most recent call last): File "mwe.py", line 17, in <module> da.plot.pcolormesh('lon', 'lat', shading='gouraud') File "/home/tegn500/Documents/Work/Code/xarray/xarray/plot/plot.py", line 721, in plotmethod return newplotfunc(**allargs) File "/home/tegn500/Documents/Work/Code/xarray/xarray/plot/plot.py", line 662, in newplotfunc **kwargs) File "/home/tegn500/Documents/Work/Code/xarray/xarray/plot/plot.py", line 864, in pcolormesh primitive = ax.pcolormesh(x, y, z, **kwargs) File "/home/tegn500/anaconda3/envs/py36/lib/python3.6/site-packages/matplotlib/__init__.py", line 1805, in inner return func(ax, *args, **kwargs) File "/home/tegn500/anaconda3/envs/py36/lib/python3.6/site-packages/matplotlib/axes/_axes.py", line 5971, in pcolormesh X, Y, C = self._pcolorargs('pcolormesh', *args, allmatch=allmatch) File "/home/tegn500/anaconda3/envs/py36/lib/python3.6/site-packages/matplotlib/axes/_axes.py", line 5559, in _pcolorargs C.shape, Nx, Ny, funcname)) TypeError: Dimensions of C (4, 5) are incompatible with X (6) and/or Y (5); see help(pcolormesh)

Expected Output

This should give almost the same image as in the documentation, just with smoother shading:

• [x] Closes #3150
• [x] Tests added

• [x] Closes #3091
• [x] Tests added