cf.Data.cumsum

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

Return the data cumulatively summed along the given axis.

Added 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.

Added in version 3.14.0.

inplace: bool, optional

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

Added 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]]