cf.Data.cumsum

Data.cumsum(axis=None, masked_as_zero=False, method='sequential', inplace=False)

Return the data cumulatively summed along the given axis.

New in version 3.0.0.

See also

diff, sum

Parameters

axis: int, optional

Select the axis over which the cumulative sums are to be calculated. By default the cumulative sum is computed over the flattened array.

method: str, optional

Choose which method to use to perform the cumulative sum. See dask.array.cumsum for details.

New in version 3.14.0.

inplace: bool, optional

If True then do the operation in-place and return None.

New in version 3.3.0.

masked_as_zero: deprecated at version 3.14.0

See the examples for the new behaviour when there are masked values.

Returns

Data or None

The data with the cumulatively summed axis, or None if the operation was in-place.

Examples

>>> d = cf.Data(numpy.arange(12).reshape(3, 4))
>>> print(d.array)
[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]
>>> print(d.cumsum().array)
[ 0  1  3  6 10 15 21 28 36 45 55 66]
>>> print(d.cumsum(axis=0).array)
[[ 0  1  2  3]
 [ 4  6  8 10]
 [12 15 18 21]]
>>> print(d.cumsum(axis=1).array)
[[ 0  1  3  6]
 [ 4  9 15 22]
 [ 8 17 27 38]]

>>> d[0, 0] = cf.masked
>>> d[1, [1, 3]] = cf.masked
>>> d[2, 0:2] = cf.masked
>>> print(d.array)
[[-- 1 2 3]
 [4 -- 6 --]
 [-- -- 10 11]]
>>> print(d.cumsum(axis=0).array)
[[-- 1 2 3]
 [4 -- 8 --]
 [-- -- 18 14]]
>>> print(d.cumsum(axis=1).array)
[[-- 1 3 6]
 [4 -- 10 --]
 [-- -- 10 21]]