Ide nowcasting

[pulkkins]

This branch implements the Lagrangian INtegro-Difference equation model with Autoregression (LINDA) developed by Pulkkinen et al. (2021).

Remaining issues that will be addressed in the next version:

• The timesteps argument is required to be an integer. Supplying the nowcast timesteps in a list is not yet supported.
• There are several functions that overlap with existing methods implemented in pysteps. For instance, _estimate_ar*_params, _iterate_ar_model and _window_tukey can be replaced with existing methods or moved outside linda.py.

[pulkkins]

I noticed some duplication in the code, for example you reimplemented the Tukey tapering window even though we already have it available in the utils.tapering module. Probably not something urgent to be done now, but I would strongly recommend to try to reduce code duplication by using methods already available in pysteps. If for some reasons the existing methods do not cover your needs, we should first try to extend or modify them instead of creating new ones (especially now that V2 #216 will give us more freedom for breaking changes). Otherwise, the risk is to get to a point of having a code base that is impossible to maintain...

What do you think, @pulkkins ? Is there something we can do now or is it better to close this PR and keep this in mind for a small refactoring in V2?

Let's do that in V2. The purpose of this pull request is to implement the first usable version of LINDA. It can be refined later.

[dnerini]

With the changes introduced in #225, were you thinking of adding a default max_num_features ? Something around 20?

[pulkkins]

With the changes introduced in #225, were you thinking of adding a default max_num_features ? Something around 20?

Done.

[dnerini]

Very nice, well done @pulkkins ! If you agree, id like to do one last profiling with the default parameters and, if everything looks good, I think we can merge.

Edit: here it is for the same case as above (640x710 domain, 12 time steps).

	Sequential	Parallel
Parameters	25 blobs, 10 members, 1 worker	25 blobs, 10 members, 10 workers
Total time (forecast loop)	11 min (ca. 4 seconds / member / time step)	4 min (ca. 0.5 seconds / member / time step)
Peak memory	1.2 GB	4.2 GB

[dnerini]

The RTD built for this branch can be found here:
https://pysteps.readthedocs.io/en/ide_nowcasting/