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scipy.ndimage.

# gaussian_laplace

scipy.ndimage.gaussian_laplace(input, sigma, output=None, mode='reflect', cval=0.0, *, axes=None, **kwargs)

  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.

    **kwargs

      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 Support for the array API standard for more information.

  Examples

      >>> from scipy import ndimage, datasets
      >>> import matplotlib.pyplot as plt
      >>> ascent = datasets.ascent()

      >>> 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

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

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