dask.array.cumsum¶
- dask.array.cumsum(x, axis=None, dtype=None, out=None, method='sequential')[source]¶
Return the cumulative sum of the elements along a given axis.
This docstring was copied from numpy.cumsum.
Some inconsistencies with the Dask version may exist.
Dask added an additional keyword-only argument
method
.- method{‘sequential’, ‘blelloch’}, optional
Choose which method to use to perform the cumsum. Default is ‘sequential’.
‘sequential’ performs the cumsum of each prior block before the current block.
‘blelloch’ is a work-efficient parallel cumsum. It exposes parallelism by first taking the sum of each block and combines the sums via a binary tree. This method may be faster or more memory efficient depending on workload, scheduler, and hardware. More benchmarking is necessary.
- Parameters
- aarray_like (Not supported in Dask)
Input array.
- axisint, optional
Axis along which the cumulative sum is computed. The default (None) is to compute the cumsum over the flattened array.
- dtypedtype, 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.
- outndarray, 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 Output type determination for more details.
- Returns
- cumsum_along_axisndarray.
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 tosum(a)
for floating-point values sincesum
may use a pairwise summation routine, reducing the roundoff-error. See sum for more information.Examples
>>> a = np.array([[1,2,3], [4,5,6]]) >>> a array([[1, 2, 3], [4, 5, 6]]) >>> np.cumsum(a) array([ 1, 3, 6, 10, 15, 21]) >>> np.cumsum(a, dtype=float) # specifies type of output value(s) array([ 1., 3., 6., 10., 15., 21.])
>>> np.cumsum(a,axis=0) # sum over rows for each of the 3 columns array([[1, 2, 3], [5, 7, 9]]) >>> np.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 tosum(b)
>>> b = np.array([1, 2e-9, 3e-9] * 1000000) >>> b.cumsum()[-1] 1000000.0050045159 >>> b.sum() 1000000.0050000029