numpy.ma.dot¶

numpy.ma.
dot
(a, b, strict=False, out=None)[source]¶ Return the dot product of two arrays.
This function is the equivalent of
numpy.dot
that takes masked values into account. Note that strict and out are in different position than in the method version. In order to maintain compatibility with the corresponding method, it is recommended that the optional arguments be treated as keyword only. At some point that may be mandatory.Note
Works only with 2D arrays at the moment.
Parameters:  a, b : masked_array_like
Inputs arrays.
 strict : bool, optional
Whether masked data are propagated (True) or set to 0 (False) for the computation. Default is False. Propagating the mask means that if a masked value appears in a row or column, the whole row or column is considered masked.
 out : masked_array, optional
Output argument. This must have the exact kind that would be returned if it was not used. In particular, it must have the right type, must be Ccontiguous, and its dtype must be the dtype that would be returned for dot(a,b). This is a performance feature. Therefore, if these conditions are not met, an exception is raised, instead of attempting to be flexible.
New in version 1.10.2.
See also
numpy.dot
 Equivalent function for ndarrays.
Examples
>>> a = np.ma.array([[1, 2, 3], [4, 5, 6]], mask=[[1, 0, 0], [0, 0, 0]]) >>> b = np.ma.array([[1, 2], [3, 4], [5, 6]], mask=[[1, 0], [0, 0], [0, 0]]) >>> np.ma.dot(a, b) masked_array( data=[[21, 26], [45, 64]], mask=[[False, False], [False, False]], fill_value=999999) >>> np.ma.dot(a, b, strict=True) masked_array( data=[[, ], [, 64]], mask=[[ True, True], [ True, False]], fill_value=999999)