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  elementsforiinrange(len(A)):t=op(t,A[i])r[i]=treturnr

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

For a multi-dimensional array, accumulate is applied along only oneaxis (axis zero by default; see Examples below) so repeated use isnecessary 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. Defaultsto the data-type of the output array if such is provided, or thethe data-type of the input array if no output array is provided. out:ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If not provided orNone,a freshly-allocated array is returned. For consistency withufunc.__call__, if given as a keyword, this may be wrapped in a1-element tuple. Changed in version 1.13.0:Tuples are allowed for keyword argument.

Returns:

r:ndarray The accumulated values. Ifout was supplied,r is a reference toout.

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)>>>Iarray([[ 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 zeroarray([[ 1.,  0.],       [ 1.,  1.]])

Accumulate along axis 1 (columns), through rows:

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