cf.Field.cumsum

Field.cumsum(axis, masked_as_zero=False, coordinate=None, inplace=False)[source]

Return the field cumulatively summed along the given axis.

The cell bounds of the axis are updated to describe the range over which the sums apply, and a new “sum” cell method construct is added to the resulting field construct.

New in version 3.0.0.

See also

collapse, convolution_filter, moving_window, sum

Parameters
axis:

Select the domain axis over which the cumulative sums are to be calculated, defined by that which would be selected by passing the given axis description to a call of the field construct’s domain_axis method. For example, for a value of 'X', the domain axis construct returned by f.domain_axis('X') is selected.

inplace: bool, optional

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

coordinate: deprecated at version 3.14.0

Set how the cell coordinate values for the summed axis are defined, relative to the new cell bounds.

masked_as_zero: deprecated at version 3.14.0

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

Returns
Field or None

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

Examples

>>> f = cf.example_field(2)
>>> print(f)
Field: air_potential_temperature (ncvar%air_potential_temperature)
------------------------------------------------------------------
Data            : air_potential_temperature(time(36), latitude(5), longitude(8)) K
Cell methods    : area: mean
Dimension coords: time(36) = [1959-12-16 12:00:00, ..., 1962-11-16 00:00:00]
                : latitude(5) = [-75.0, ..., 75.0] degrees_north
                : longitude(8) = [22.5, ..., 337.5] degrees_east
                : air_pressure(1) = [850.0] hPa
>>> print(f.dimension_coordinate('T').bounds[[0, -1]].datetime_array)
[[cftime.DatetimeGregorian(1959-12-01 00:00:00)
  cftime.DatetimeGregorian(1960-01-01 00:00:00)]
 [cftime.DatetimeGregorian(1962-11-01 00:00:00)
  cftime.DatetimeGregorian(1962-12-01 00:00:00)]]
>>> print(f.array[:, 0, 0])
[210.7 305.3 249.4 288.9 231.1 200.  234.4 289.2 204.3 203.6 261.8 256.2
 212.3 231.7 255.1 213.9 255.8 301.2 213.3 200.1 204.6 203.2 244.6 238.4
 304.5 269.8 267.9 282.4 215.  288.7 217.3 307.1 299.3 215.9 290.2 239.9]
>>> g = f.cumsum('T')
>>> print(g)
Field: air_potential_temperature (ncvar%air_potential_temperature)
------------------------------------------------------------------
Data            : air_potential_temperature(time(36), latitude(5), longitude(8)) K
Cell methods    : area: mean time(36): sum
Dimension coords: time(36) = [1959-12-16 12:00:00, ..., 1962-11-16 00:00:00]
                : latitude(5) = [-75.0, ..., 75.0] degrees_north
                : longitude(8) = [22.5, ..., 337.5] degrees_east
                : air_pressure(1) = [850.0] hPa
>>> print(g.dimension_coordinate('T').bounds[[0, -1]].datetime_array)
[[cftime.DatetimeGregorian(1959-12-01 00:00:00)
  cftime.DatetimeGregorian(1960-01-01 00:00:00)]
 [cftime.DatetimeGregorian(1959-12-01 00:00:00)
  cftime.DatetimeGregorian(1962-12-01 00:00:00)]]
>>> print(g.array[:, 0, 0])
[ 210.7  516.   765.4 1054.3 1285.4 1485.4 1719.8 2009.  2213.3 2416.9
 2678.7 2934.9 3147.2 3378.9 3634.  3847.9 4103.7 4404.9 4618.2 4818.3
 5022.9 5226.1 5470.7 5709.1 6013.6 6283.4 6551.3 6833.7 7048.7 7337.4
 7554.7 7861.8 8161.1 8377.  8667.2 8907.1]