numpy.ufunc.accumulate

ufunc.accumulate(array, axis=0, dtype=None, out=None)

Accumulate the result of applying the operator to all elements.

For a one-dimensional array, accumulate produces results equivalent to:

r = np.empty(len(A))
t = op.identity        # op = the ufunc being applied to A's  elements
for i in range(len(A)):
    t = op(t, A[i])
    r[i] = t
return r

For example, add.accumulate() is equivalent to np.cumsum().

For a multi-dimensional array, accumulate is applied along only one axis (axis zero by default; see Examples below) so repeated use is necessary if one wants to accumulate over multiple axes.

Parameters:

array : array_like

The array to act on.

axis : int, optional

The axis along which to apply the accumulation; default is zero.

dtype : data-type code, optional

The data-type used to represent the intermediate results. Defaults to the data-type of the output array if such is provided, or the the data-type of the input array if no output array is provided.

out : ndarray, optional

A location into which the result is stored. If not provided a freshly-allocated array is returned.

Returns:

r : ndarray

The accumulated values. If out was supplied, r is a reference to out.

Examples

1-D array examples:

>>> np.add.accumulate([2, 3, 5])
array([ 2,  5, 10])
>>> np.multiply.accumulate([2, 3, 5])
array([ 2,  6, 30])

2-D array examples:

>>> I = np.eye(2)
>>> I
array([[ 1.,  0.],
       [ 0.,  1.]])

Accumulate along axis 0 (rows), down columns:

>>> np.add.accumulate(I, 0)
array([[ 1.,  0.],
       [ 1.,  1.]])
>>> np.add.accumulate(I) # no axis specified = axis zero
array([[ 1.,  0.],
       [ 1.,  1.]])

Accumulate along axis 1 (columns), through rows:

>>> np.add.accumulate(I, 1)
array([[ 1.,  1.],
       [ 0.,  1.]])