bundles / scipy 1.17.1 / 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_likeThe input array.
derivative: callableCallable with the following signature
derivative(input, axis, output, mode, cval, *extra_arguments, **extra_keywords)
See
extra_arguments,extra_keywordsbelow.derivativecan assume thatinputandoutputare ndarrays. Note that the output fromderivativeis modified inplace; be careful to copy important inputs before returning them.output: array or dtype, optionalThe 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, optionalThe
modeparameter 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
cvalparameter.'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, optionalValue to fill past edges of input if
modeis 'constant'. Default is 0.0.extra_keywords: dict, optionaldict of extra keyword arguments to pass to passed function.
extra_arguments: sequence, optionalSequence of extra positional arguments to pass to passed function.
axes: tuple of int or NoneThe axes over which to apply the filter. If a
modetuple is provided, its length must match the number of axes.
Returns
generic_gradient_magnitude: ndarrayFiltered 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-arrayapifor more information.
Aliases
-
scipy.ndimage.generic_gradient_magnitude