`scipy.ndimage._filters:gaussian_laplace`

source: /scipy/ndimage/_filters.py :1073

Signature

   def     gaussian_laplace (    input    ,    sigma    ,    output    =  None   ,    mode    =  reflect   ,    cval    =  0.0   ,  * ,     axes    =  None   ,   ** kwargs      )

Summary

Multidimensional Laplace filter using Gaussian second derivatives.

Parameters

input : array_like

The input array.

sigma : scalar or sequence of scalars

The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes.

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.

axes : tuple of int or None

The axes over which to apply the filter. If sigma or mode tuples are provided, their length must match the number of axes.

Extra keyword arguments will be passed to gaussian_filter().

Returns

gaussian_laplace : ndarray: Filtered array. Has the same shape as input.

Notes

Array API Standard Support

gaussian_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.

Examples

from scipy import ndimage, datasets
import matplotlib.pyplot as plt

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ascent = datasets.ascent()

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fig = plt.figure()
plt.gray()  # show the filtered result in grayscale
ax1 = fig.add_subplot(121)  # left side
ax2 = fig.add_subplot(122)  # right side

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result = ndimage.gaussian_laplace(ascent, sigma=1)
ax1.imshow(result)

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result = ndimage.gaussian_laplace(ascent, sigma=3)
ax2.imshow(result)

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plt.show()

✓

Aliases

scipy.ndimage.gaussian_laplace