This merge request adds handling of masks and unequal length timeseries. It touches topics of issues (#9 (closed), #15 (closed), #50, and #51 (closed)).
I would like to solicit input on the way this is achieved. Here is what's happening:
First, for every gridpoint, Midas removes all masked points from both reference and model. If all points are masked, the output point is masked. If after the removal the number of points is unequal between reference and model, Midas removes points from the longer array so that it shrinks to the length of the shorter one. This is done by selecting points randomly from a uniform distribution with a fixed seed for the random number generator. While this was done to handle masks, it also deals with the situation of unequal lengths.
Do you think this is reasonable?