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bundles / scipy latest / scipy / ndimage / _filters / generic_gradient_magnitude

function

scipy.ndimage._filters:generic_gradient_magnitude

source: /scipy/ndimage/_filters.py :1140

Signature

def   generic_gradient_magnitude ( input derivative output = None mode = reflect cval = 0.0 extra_arguments = () extra_keywords = None * axes = None )

Summary

Gradient magnitude using a provided gradient function.

Parameters

input : array_like

The input array.

derivative : callable

Callable with the following signature

derivative(input, axis, output, mode, cval,
           *extra_arguments, **extra_keywords)

See extra_arguments, extra_keywords below. derivative can assume that input and output are ndarrays. Note that the output from derivative is modified inplace; be careful to copy important inputs before returning them.

output : array or dtype, optional

The array in which to place the output, or the dtype of the returned array. By default an array of the same dtype as input will be created.

mode : str or sequence, optional

The mode parameter determines how the input array is extended when the filter overlaps a border. By passing a sequence of modes with length equal to the number of dimensions of the input array, different modes can be specified along each axis. Default value is 'reflect'. The valid values and their behavior is as follows:

'reflect' (d c b a | a b c d | d c b a)

The input is extended by reflecting about the edge of the last pixel. This mode is also sometimes referred to as half-sample symmetric.

'constant' (k k k k | a b c d | k k k k)

The input is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter.

'nearest' (a a a a | a b c d | d d d d)

The input is extended by replicating the last pixel.

'mirror' (d c b | a b c d | c b a)

The input is extended by reflecting about the center of the last pixel. This mode is also sometimes referred to as whole-sample symmetric.

'wrap' (a b c d | a b c d | a b c d)

The input is extended by wrapping around to the opposite edge.

For consistency with the interpolation functions, the following mode names can also be used:

'grid-constant'

This is a synonym for 'constant'.

'grid-mirror'

This is a synonym for 'reflect'.

'grid-wrap'

This is a synonym for 'wrap'.

cval : scalar, optional

Value to fill past edges of input if mode is 'constant'. Default is 0.0.

extra_keywords : dict, optional

dict of extra keyword arguments to pass to passed function.

extra_arguments : sequence, optional

Sequence of extra positional arguments to pass to passed function.

axes : tuple of int or None

The axes over which to apply the filter. If a mode tuple is provided, its length must match the number of axes.

Returns

generic_gradient_magnitude : ndarray

Filtered array. Has the same shape as input.

Notes

Array API Standard Support

generic_gradient_magnitude has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

====================  ====================  ====================
Library               CPU                   GPU
====================  ====================  ====================
NumPy                 ✅                     n/a                 
CuPy                  n/a                   ✅                   
PyTorch               ✅                     ⛔                   
JAX                   ⚠️ no JIT
Dask                  ⚠️ computes graph     n/a                 
====================  ====================  ====================

See dev-arrayapi for more information.

Aliases

  • scipy.ndimage.generic_gradient_magnitude