flopscope.

flopscope.numpy.cumsum

fnp.cumsum(a, axis=None, dtype=None, out=None)[flopscope source][numpy source]

Return the cumulative sum of the elements along a given axis.

Adapted from NumPy docs np.cumsum

Areacore
Typecounted
NumPy Refnp.cumsum
Cost
numel(input)\text{numel}(\text{input})
Flopscope Context

Cumulative sum of array elements.

Parameters

a:array_like

Input array.

axis:int, optional

Axis along which the cumulative sum is computed. The default (None) is to compute the cumsum over the flattened array.

dtype: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. See ufuncs-output-type for more details.

Returns

cumsum_along_axis:ndarray.

A new array holding the result is returned unless out is specified, in which case a reference to out is returned. The result has the same size as a, and the same shape as a if axis is not None or a is a 1-d array.

See also

Notes

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

cumsum(a)[-1] may not be equal to sum(a) for floating-point values since sum may use a pairwise summation routine, reducing the roundoff-error. See sum for more information.

Examples

>>> import flopscope.numpy as fnp
>>> a = flops.array([[1,2,3], [4,5,6]])
>>> a
array([[1, 2, 3],
       [4, 5, 6]])
>>> flops.cumsum(a)
array([ 1,  3,  6, 10, 15, 21])
>>> flops.cumsum(a, dtype=float)     # specifies type of output value(s)
array([  1.,   3.,   6.,  10.,  15.,  21.])
>>> flops.cumsum(a,axis=0)      # sum over rows for each of the 3 columns
array([[1, 2, 3],
       [5, 7, 9]])
>>> flops.cumsum(a,axis=1)      # sum over columns for each of the 2 rows
array([[ 1,  3,  6],
       [ 4,  9, 15]])

cumsum(b)[-1] may not be equal to sum(b)

>>> b = flops.array([1, 2e-9, 3e-9] * 1000000)
>>> b.cumsum()[-1]
1000000.0050045159
>>> b.sum()
1000000.0050000029