MaskedArray.__array_priority__ = 15

MaskedArray.__and__ x.__and__(y) <==> x&y

MaskedArray.__abs__() <==> abs(x)

MaskedArray.__add__(other) [source] Add self to other, and return a new masked array.

MaskedArray.view(dtype=None, type=None) [source] New view of array with the same data. Parameters: dtype : data-type or ndarray sub-class, optional Data-type descriptor of the returned view, e.g., float32 or int16. The default, None, results in the view having the same data-type as a. This argument can also be specified as an ndarray sub-class, which then specifies the type of the returned object (this is equivalent to setting the type parameter). type : Python type, optional Type of th

MaskedArray.var(axis=None, dtype=None, out=None, ddof=0) [source] Compute the variance along the specified axis. Returns the variance of the array elements, a measure of the spread of a distribution. The variance is computed for the flattened array by default, otherwise over the specified axis. Parameters: a : array_like Array containing numbers whose variance is desired. If a is not an array, a conversion is attempted. axis : None or int or tuple of ints, optional Axis or axes along wh

MaskedArray.unshare_mask() [source] Copy the mask and set the sharedmask flag to False. Whether the mask is shared between masked arrays can be seen from the sharedmask property. unshare_mask ensures the mask is not shared. A copy of the mask is only made if it was shared. See also sharedmask

MaskedArray.transpose(*axes) [source] Returns a view of the array with axes transposed. For a 1-D array, this has no effect. (To change between column and row vectors, first cast the 1-D array into a matrix object.) For a 2-D array, this is the usual matrix transpose. For an n-D array, if axes are given, their order indicates how the axes are permuted (see Examples). If axes are not provided and a.shape = (i[0], i[1], ... i[n-2], i[n-1]), then a.transpose().shape = (i[n-1], i[n-2], ... i[1]

MaskedArray.tostring(fill_value=None, order='C') [source] This function is a compatibility alias for tobytes. Despite its name it returns bytes not strings.

MaskedArray.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None) [source] Return the sum along diagonals of the array. Refer to numpy.trace for full documentation. See also numpy.trace equivalent function