1. This moves the region detection code into ZarrStore so we only open the store once.
2. Instead of opening the store as a dataset, construct a pd.Index directly to "auto"-infer the region.

The diff is large mostly because a bunch of code moved from backends/api.py to backends/zarr.py

Builds on #8997

Expose Grouper and Resampler as public API

TODO: - [ ] Consider avoiding IndexVariable

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

@benbovy this seems to be the root cause of #8646, the variable name in Dataset._variables does not match IndexVariable.name.

A good number of tests seem to fail though, so not sure if this is a good chck.

• [ ] 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] Closes #8343
• [x] Tests added
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [x] New functions/methods are listed in api.rst

The intended pattern is: ```python

after_init = initialize_zarr(store, ds, region_dims=("x",))
for i in range(ds.sizes["x"]):
    after_init.isel(x=[i]).to_zarr(store, region={"x": slice(i, i + 1)})

```

cc @slevang

Not for merging, just proof that it can be done nicely :)

Now builds on #8840 ~Builds on an older version of #8507~

Try it out!

```python import xarray as xr from xarray.core.groupers import SeasonGrouper, SeasonResampler

ds = xr.tutorial.open_dataset("air_temperature")

custom seasons!

ds.air.groupby(time=SeasonGrouper(["JF", "MAM", "JJAS", "OND"])).mean()

ds.air.resample(time=SeasonResampler(["DJF", "MAM", "JJAS", "ON"])).count() ```

Use as_indexable instead of NumpyIndexingAdapter

• [x] Closes #8090
• [x] Tests added
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

• xref #3937
• [ ] Tests added
• [ ] Passes pre-commit run --all-files
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

Initial proof-of-concept. Suggestions to improve this are very welcome.

Here's some convenient testing code ``` python
import xarray as xr

ds = xr.tutorial.open_dataset('rasm').load() month_length = ds.time.dt.days_in_month weights = month_length.groupby('time.season') / month_length.groupby('time.season').sum()

actual = ds.weighted(month_length).groupby("time.season").mean() expected = (ds * weights).groupby('time.season').sum(skipna=False) xr.testing.assert_allclose(actual, expected) ```

I've added info to the repr python ds.weighted(month_length).groupby("time.season") WeightedDatasetGroupBy, grouped over 'season' 4 groups with labels 'DJF', 'JJA', 'MAM', 'SON'. weighted along dimensions: time by 'days_in_month'

• [x] Tests added

Fixes yet another bug with GroupBy reductions. We weren't assigning the group index when reducing by a subset of dimensions present on the grouper

This will only pass when flox 0.7.1 reaches conda-forge.

• [x] Closes #4288
• [ ] Tests added
• [x] Passes pre-commit run --all-files
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [ ] New functions/methods are listed in api.rst

Refactors line plotting to use a _plot1d decorator.

Next i'll use this decorator on hist so we can "facet" and "hue" histograms.

see #4288

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

This adds plotting methods to GroupBy objects so that it's easy to plot each group as a facet. I'm finding this super helpful in my current research project.

It's pretty self-contained, mostly just adding map_groupby* methods to FacetGrid. But that's because I make GroupBy mimic the underlying DataArray by adding coords, attrs and __getitem__.

This still needs more tests but I would like feedback on the feature and the implementation.

Example

``` python import numpy as np import xarray as xr

time = np.arange(80) da = xr.DataArray(5 * np.sin(2np.pitime/10), coords={"time": time}, dims="time") da["period"] = da.time.where((time % 10) == 0).ffill("time")/10 da.plot() ```

python da.groupby("period").plot(col="period", col_wrap=4)

python da = da.expand_dims(y=10) da.groupby("period").plot(col="period", col_wrap=4, sharex=False, sharey=True, robust=True)