Improve CoregPipeline.apply() and .fit() performance by merging matrices

[erikmannerfelt]

If parts or all steps of a pipeline can be described as matrices, the current apply loop is inefficient and might induce resampling-related errors:

xdem/xdem/coreg.py

Line 1531 in 82b87eb

dem_mod = coreg.apply(dem_mod, transform)

A better approach would be to merge transformation matrices (when possible) and therefore do fewer resampling iterations, potentially lowering the error and the processing time.

A conceptual example is:

1. A) BiasCorr (matrixable)
2. B) ICP (matrixable)
3. C) NuthKaab (matrixable)
4. D) Deramp(degree=3) (not matrixable)

Currently, the pipeline would modify the DEM four times. By combining the matrices of A, B and C, only two modifications are needed:

1. A-C
2. D

This could be done by first implementing a property of Coreg, for example called is_rigid which is True if it can be represented as a rigid transform (4x4 matrix). Then, the following pseudocode could work:

dem_mod = dem.copy()

matrices: list[np.ndarray] = []
for coreg in self.pipeline:
    if coreg.is_rigid:
        # Append the matrix to the list of matrices to be applied at the same time.
        matrices.append(coreg.to_matrix())
    else:   # This marks when matrices can no longer be added, and need to be applied before the non-rigid apply is done
        # Use matrix-magic to combine the matrices
        combined_matrix = combine_matrix(matrices)
        # Use more matrix-magic to transform the DEM
        apply_matrix(dem_mod, combined_matrix)
        # Reset the matrices list
        matrices.clear()
        # Apply the non-rigid transform
        coreg.apply(dem_mod, transform=transform)

if len(matrices) > 0:
     # do the same as above to apply all the matrices

where the combine_matrix() and apply_matrix() functions need to be created.