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.

Added 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]