cf.Data.cumsum

Data.cumsum(axis, masked_as_zero=False, inplace=False)

Return the data cumulatively summed along the given axis.

New in version 3.0.0.

See also

sum

Parameters

axis: int, optional

Select the axis over which the cumulative sums are to be calculated.

masked_as_zero: bool, optional

If True then set missing data values to zero before calculating the cumulative sum. By default the output data will be masked at the same locations as the original data.

Note

Sums produced entirely from masked elements will always result in masked values in the output data, regardless of the setting of masked_as_zero.

inplace: bool, optional

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

New in version 3.3.0.

Returns

Data

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(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] = cf.masked
>>> d[2, 0:2] = cf.masked
>>> print(d.array)
[[--  1  3  6]
 [ 4 -- 10 17]
 [-- -- 10 21]]
>>> print(d.cumsum(axis=1).array)
[[--  1  3  6]
 [ 4 -- 10 17]
 [-- -- 10 21]]
>>> print(d.cumsum(axis=1, masked_as_zero=True).array)
[[--  1  3  6]
 [ 4  4 10 17]
 [-- -- 10 21]]