Hi numpy users, This email is regarding the discussion on how np.ma.polyfit should deal with non-uniform mask across columns in 2D data array. Github Issue#9193 [ https://github.com/numpy/numpy/issues/9193 ]
Current behaviour of np.ma.polyfit is to union combine all the column masks across all the columns and do the polynomial fit for all columns using this same mask. This has the potentially undesired behaviour of masking out lots of data across the columns if just one column has a certain row masked out. Since the polynomial fit of data in each column is independent, it seems to be an undesired behaviour. We were thinking to change this behaviour by fitting columns with different mask, separately. What are the opinions on this in regards to backward compatibility? Was there any usecase for the previous behaviour? Also, any implementation ideas/suggestions on the plan currently being discussed in Issue#9193 page. Thanks, -cheers Joe -- /--------------------------------------------------------------- "GNU/Linux: because a PC is a terrible thing to waste" - GNU Generation ************************************************ Joe Philip Ninan Postdoctoral Researcher 406 Davey Lab, Dept. of Astronomy & Astrophysics The Pennsylvania State University University Park, PA-16802 ------------------------------------------------------------ Website: https://indiajoe.gitlab.io/ My GnuPG Public Key: https://indiajoe.gitlab.io/files/JPN_public.key
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