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# scipy.ndimage.gaussian_laplace

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

  Multidimensional Laplace filter using gaussian second derivatives.

  Parameters:

  input : array_like

  > Input array to filter.

  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, optional

  > The output parameter passes an array in which to store the filter output. Output array should have different name as compared to input array to avoid aliasing errors.

  mode : str or sequence, optional

  > The mode parameter determines how the array borders are handled. Valid modes are {‘reflect’, ‘constant’, ‘nearest’, ‘mirror’, ‘wrap’}. cval is the value used when mode is equal to ‘constant’. A list of modes with length equal to the number of axes can be provided to specify different modes for different axes. Default is ‘reflect’

  cval : scalar, optional

  > Value to fill past edges of input if mode is ‘constant’. Default is 0.0

  Extra keyword arguments will be passed to gaussian_filter().

  Examples

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

  (Source code)