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# scipy.ndimage.laplace

`scipy.ndimage.``laplace`(input, output=None, mode='reflect', cval=0.0)

  N-dimensional Laplace filter based on approximate second derivatives.

  Parameters: | input : array_like Input array to filter. 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

  Examples

      >>> from scipy import ndimage, misc
      >>> import matplotlib.pyplot as plt
      >>> 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
      >>> ascent = misc.ascent()
      >>> result = ndimage.laplace(ascent)
      >>> ax1.imshow(ascent)
      >>> ax2.imshow(result)
      >>> plt.show()

  (Source code)