numpy.ma.cumsum

numpy.ma.cumsum(self, axis=None, dtype=None, out=None)

Return the cumulative sum of the elements along the given axis. The cumulative sum is calculated over the flattened array by default, otherwise over the specified axis.

Masked values are set to 0 internally during the computation. However, their position is saved, and the result will be masked at the same locations.

Parameters :

axis : {None, -1, int}, optional

Axis along which the sum is computed. The default (axis = None) is to compute over the flattened array. axis may be negative, in which case it counts from the last to the first axis.

dtype : {None, dtype}, optional

Type of the returned array and of the accumulator in which the elements are summed. If dtype is not specified, it defaults to the dtype of a, unless a has an integer dtype with a precision less than that of the default platform integer. In that case, the default platform integer is used.

out : ndarray, optional

Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output but the type will be cast if necessary.

Returns :

cumsum : ndarray.

A new array holding the result is returned unless out is specified, in which case a reference to out is returned.

Notes

The mask is lost if out is not a valid MaskedArray !

Arithmetic is modular when using integer types, and no error is raised on overflow.

Examples

>>> marr = np.ma.array(np.arange(10), mask=[0,0,0,1,1,1,0,0,0,0])
>>> print marr.cumsum()
[0 1 3 -- -- -- 9 16 24 33]