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Numpy functions may not do what you think

Numpy has the ability to mask arrays and ignore their values for certain computations, called “masked arrays”. They contain a .mask attribute which is a boolean array, True where the value should be masked and False otherwise.

Numpy also comes with a suite of functions which can handle this masking naturally. Typically for a function in the np. namespace, there is a masked-array-aware version under the namespace:

np.median  =>
np.average =>

A crucial thing to remember however is that standard numpy functions ignore the mask for a masked array.

This caught me out when sigma clipping values using astropy.stats.sigma_clip - which masks out values outside the sigma range. To ignore the sigma clipped values I should have used instead of np.median.