numpy.ma.outer(a, b)

numpy.ma.masked_greater(x, value, copy=True)

MaskedArray.soften_mask()

numpy.ma.cumsum(self, axis=None, dtype=None, out=None) = Return the cumulative sum of the elements along the given axis. The cumulative

numpy.ma.masked_inside(x, v1, v2, copy=True)

numpy.ma.ediff1d(arr, to_end=None, to_begin=None)

numpy.ma.clip(a, a_min, a_max, out=None)

numpy.ma.soften_mask(self) = Force the mask to soft. Whether the mask of a masked array is hard or soft is determined

numpy.ma.masked_all(shape, dtype=)

numpy.ma.anom(self, axis=None, dtype=None) = Compute the anomalies (deviations from the arithmetic mean) along the given axis.