cf.Data.diff

Data.diff(axis=-1, n=1, inplace=False)

Calculate the n-th discrete difference along the given axis.

The first difference is given by x[i+1] - x[i] along the given axis, higher differences are calculated by using diff recursively.

The shape of the output is the same as the input except along the given axis, where the dimension is smaller by n. The data type of the output is the same as the type of the difference between any two elements of the input.

New in version 3.2.0.

Parameters

axis: int, optional

The axis along which the difference is taken. By default the last axis is used. The axis argument is an integer that selects the axis coresponding to the given position in the list of axes of the data array.

n: int, optional

The number of times values are differenced. If zero, the input is returned as-is. By default n is 1.

inplace: bool, optional

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

Returns

Data or None

The n-th differences, or None if the operation was in-place.

Examples:

>>> d = cf.Data(numpy.arange(12.).reshape(3, 4))
>>> d[1, 1] = 4.5
>>> d[2, 2] = 10.5
>>> print(d.array)
[[ 0.   1.   2.   3. ]
 [ 4.   4.5  6.   7. ]
 [ 8.   9.  10.5 11. ]]
>>> print(d.diff().array)
[[1.  1.  1. ]
 [0.5 1.5 1. ]
 [1.  1.5 0.5]]
>>> print(d.diff(n=2).array)
[[ 0.   0. ]
 [ 1.  -0.5]
 [ 0.5 -1. ]]
>>> print(d.diff(axis=0).array)
[[4.  3.5 4.  4. ]
 [4.  4.5 4.5 4. ]]
>>> print(d.diff(axis=0, n=2).array)
[[0.  1.  0.5 0. ]]
>>> d[1, 2] = cf.masked
>>> print(d.array)
[[0.0 1.0  2.0  3.0]
 [4.0 4.5   --  7.0]
 [8.0 9.0 10.5 11.0]]
>>> print(d.diff().array)
[[1.0 1.0 1.0]
 [0.5  --  --]
 [1.0 1.5 0.5]]
>>> print(d.diff(n=2).array)
[[0.0  0.0]
 [ --   --]
 [0.5 -1.0]]
>>> print(d.diff(axis=0).array)
[[4.0 3.5 -- 4.0]
 [4.0 4.5 -- 4.0]]
>>> print(d.diff(axis=0, n=2).array)
[[0.0 1.0 -- 0.0]]