Config features and better `unstack_dates`

[aulemahal]

Pull Request Checklist:

• [ ] This PR addresses an already opened issue (for bug fixes / features)
  • This PR fixes #xyz
• [x] (If applicable) Documentation has been added / updated (for bug fixes / features)
• [ ] HISTORY.rst has been updated (with summary of main changes)
  • [ ] Link to issue (:issue:number) and pull request (:pull:number) has been added

What kind of change does this PR introduce?

• Add a CONFIG.set method. Each time an item of CONFIG is accessed, a copy is returned, in order to avoid errors. This adds back the possibility to edit the configuration programmatically.
• Larger scope of load_config so it accepts key=value entries. This is meant to make it easier to parse from the CLI.
• Rewrite of a condition in extract_dataset, the changed line is supposed to be equivalent to the old one, given how those variables are set above, but it also avoids a bug when a var_name is not given in variables_and_freq.
• to_dataset_dict only prints a progress bar if there are more than 1 dataset. Avoids useless output in trivial cases.

Rewrite of unstack_dates

Instead of using xarray,s unstack, I try here to perform a reshape operation. This is much more efficient with dask, but it did necessitate heavy changes.

• This new version will fail if xr.infer_freq(ds) fails. I assumed this is almost never the case for the use we make of this function.
• The seasons argument is now a dictionary with the month number as keys, not a "%m-%d" date. The function currently only works for coarser-than-month frequencoes, so this simply made the code easier to write. Also, even if we expand to accept daily freqs, the seasons arg wouldn't have any meaning I think (the new dim would be dayofyear anyway, no ?).
• Reshape is not a xarray method, the data's one is used instead, necessitating some DataArray/Dataset manipulation.
• And most importantest: to be able to perform a reshape on dask arrays, the chunks are realigned using flox beforehand, ensuring the chunks frontiers fall on yearly timesteps.
• Flox as a new hard dependency.

Does this PR introduce a breaking change?

unstack_dates has lost some flexibility : data must pass xr.infer_freq. flox as a dependency. No more progress bar printed by extract_dataset for single datasets.

Other information:

I struggled to run the notebooks. But I'll try to do it soon, as a test for breaking changes.

[aulemahal]

@juliettelavoie For illustration. I take all QS-DEC indicators of ScenGen and construct an ensemble (dims: (experiment: 2, realization: 11, time: 605, lat: 320, lon: 416)). I then unstack the seasons and perform a spatial mean over 33 regions + percentiles over realizations.

The "stacked" dataset has 71508 tasks (len(ds.__dask_graph__().keys())).

Before: unstack_seasons took 1470 ms and added 118 560 tasks. The final compute took 12 min 19s.

After (this branch): unstack_seasons took 381 ms (0.25x) add added 24 730 tasks (0.21x). The final compute took 5 min 9s (0.42x).

All that said, I realize the code is more opaque... I can try to make it clearer, but I felt the performance boost was worth it.

[aulemahal]

Latest pushes fix my fix and all notebooks should run. As per produce_horizon, its use of unstack_dates should not be affected.