numpy.ufunc.accumulate¶

method

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

arrayarray_like: The array to act on.
axisint, optional: The axis along which to apply the accumulation; default is zero.
dtypedata-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.
outndarray, None, or tuple of ndarray and None, optional: A location into which the result is stored. If not provided or None, a freshly-allocated array is returned. For consistency with ufunc.__call__, if given as a keyword, this may be wrapped in a 1-element tuple.

Changed in version 1.13.0: Tuples are allowed for keyword argument.

Returns

rndarray: 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.]])

numpy.ufunc.__call__

numpy.ufunc.at