RGDR class implementation

[BSchilperoort]

This PR implements RGDR as a class, with the previously implemented DBSCAN functionality integrated into the class, and with plotting methods available for users to play with the RGDR configuration.

Note that this is a standalone implementation of RGDR, without any knowledge about splits/folds implemented explicitly.

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Example:

from s2spy import RGDR
rgdr = RGDR(target_timeseries) # instantiate the class

# Fit & apply the RGDR clustering to the training data
clustered_train_data = rgdr.fit(precursor_field_train_data) 

# Apply the clustering to test data
clustered_test_data = rgdr.transform(precursor_field_test_data)

Closes #38

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[BSchilperoort]

Hi Yang, thanks for the review. RGDR does feel cluttered, however it currently only contains the following;

• spherical_area
• cluster_area
• remove_small_area_clusters
• masked_spherical_dbscan
• _pearsonr_nan
• correlation
• RGDR
  • __init__
  • plot_correlation
  • plot_clusters
  • fit
  • transform

I think the size and structure of the file is fine at the moment. We could move out the correlation method along with the pearsonr_nan function, but that would indeed be appropriate to do when implementing different correlation/regression methods.

We could refactor it to something like;

• spherical_area → utils.py
• cluster_area → utils.py
• remove_small_area_clusters → utils.py
• masked_spherical_dbscan
• _pearsonr_nan → correlation.py
• correlation → correlation.py
• RGDR
  • __init__
  • plot_correlation
  • plot_clusters
  • fit
  • transform

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