@docfiller
def gaussian_laplace(input, sigma, output=None, mode='reflect', cval=0.0, **kwargs):
 input = numpy.asarray(input)
 def derivative2(input, axis, output, mode, cval, sigma, **kwargs):
  order = ([0] * input.ndim)
  order[axis] = 2
  return gaussian_filter(input, sigma, order, output, mode, cval, **kwargs)
 return generic_laplace(input, derivative2, output, mode, cval, extra_arguments=(sigma,), extra_keywords=kwargs)

@ docfiller def gaussian_laplace ( input , sigma , output = None , mode = 'reflect' , cval = 0.0 , ** kwargs ) : input = numpy . asarray ( input ) def derivative2 ( input , axis , output , mode , cval , sigma , ** kwargs ) : order = ( [ 0 ] * input . ndim ) order [ axis ] = 2 return gaussian_filter ( input , sigma , order , output , mode , cval , ** kwargs ) return generic_laplace ( input , derivative2 , output , mode , cval , extra_arguments = ( sigma , ) , extra_keywords = kwargs )
multidimensional laplace filter using gaussian second derivatives .