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bundles / scipy 1.17.1 / scipy / ndimage / _morphology / morphological_laplace

function

scipy.ndimage._morphology:morphological_laplace

source: /scipy/ndimage/_morphology.py :1754

Signature

def   morphological_laplace ( input size = None footprint = None structure = None output = None mode = reflect cval = 0.0 origin = 0 * axes = None )

Summary

Multidimensional morphological laplace.

Parameters

input : array_like

Input.

size : tuple of ints

Shape of a flat and full structuring element used for the mathematical morphology operations. Optional if footprint or structure is provided.

footprint : array of ints, optional

Positions of non-infinite elements of a flat structuring element used for the morphology operations.

structure : array of ints, optional

Structuring element used for the morphology operations. structure may be a non-flat structuring element. The structure array applies offsets to the pixels in a neighborhood (the offset is additive during dilation and subtractive during erosion)

output : ndarray, optional

An output array can optionally be provided.

mode : {'reflect','constant','nearest','mirror', 'wrap'}, optional

The mode parameter determines how the array borders are handled. For 'constant' mode, values beyond borders are set to be cval. Default is 'reflect'.

cval : scalar, optional

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

origin : origin, optional

The origin parameter controls the placement of the filter.

axes : tuple of int or None

The axes over which to apply the filter. If None, input is filtered along all axes. If an origin tuple is provided, its length must match the number of axes.

Returns

morphological_laplace : ndarray

Output

Notes

Array API Standard Support

morphological_laplace 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.morphological_laplace