new_contents: # coding: utf-8
  # /*##########################################################################
  #
  # Copyright (c) 2016-2017 European Synchrotron Radiation Facility
  #
  # Permission is hereby granted, free of charge, to any person obtaining a copy
  # of this software and associated documentation files (the "Software"), to deal
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  # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  # copies of the Software, and to permit persons to whom the Software is
  # furnished to do so, subject to the following conditions:
  #
  # The above copyright notice and this permission notice shall be included in
  # all copies or substantial portions of the Software.
  #
  # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
  # THE SOFTWARE.
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  # ###########################################################################*/
  
  from __future__ import absolute_import, division, unicode_literals
  
  __authors__ = ['Marius Retegan']
  __license__ = 'MIT'
  __date__ = '04/10/2017'
  
  import numpy as np
  
  MIN_KERNEL_SUM = 1e-8
  
  
  def gaussian_kernel1d(sigma=None, truncate=6):
      size = int(2 * truncate * sigma)
      if size % 2 == 0:
          size = size + 1
      x = np.arange(size)
      # print('The size of the kernel is: {}'.format(size))
      mu = np.median(x)
      # The prefactor 1 / (sigma * np.sqrt(2 * np.pi))
      # drops in the normalization.
      kernel = np.exp(-0.5 * ((x - mu)**2 / sigma**2))
      if kernel.sum() < MIN_KERNEL_SUM:
          raise Exception(
              'The kernel can\'t be normalized, because its sum is close to '
              'zero. The sum of the kernel is < {0}'.format(MIN_KERNEL_SUM))
      kernel /= kernel.sum()
      return kernel
  
  
  def gaussian_kernel2d(sigma=None, truncate=(6, 6)):
      if sigma.size != 2 or len(truncate) != 2:
          raise Exception('Sigma and the truncation parameter don\'t have the '
                          'required dimenstion.')
      kernel_x = gaussian_kernel1d(sigma[0], truncate[0])
      kernel_y = gaussian_kernel1d(sigma[1], truncate[1])
      kernel = np.outer(kernel_y, kernel_x)
      return kernel
  
  
  def convolve_fft(array, kernel):
      """
      Convolve an array with a kernel using FFT.
      Implemntation based on the convolve_fft function from astropy.
  
      https://github.com/astropy/astropy/blob/master/astropy/convolution/convolve.py
      """
  
      array = np.asarray(array, dtype=np.complex)
      kernel = np.asarray(kernel, dtype=np.complex)
  
      if array.ndim != kernel.ndim:
          raise ValueError("Image and kernel must have same number of "
                           "dimensions")
  
      array_shape = array.shape
      kernel_shape = kernel.shape
      new_shape = np.array(array_shape) + np.array(kernel_shape)
  
      array_slices = []
      kernel_slices = []
      for (new_dimsize, array_dimsize, kernel_dimsize) in zip(
              new_shape, array_shape, kernel_shape):
          center = new_dimsize - (new_dimsize + 1) // 2
          array_slices += [slice(center - array_dimsize // 2,
                           center + (array_dimsize + 1) // 2)]
          kernel_slices += [slice(center - kernel_dimsize // 2,
                            center + (kernel_dimsize + 1) // 2)]
  
      if not np.all(new_shape == array_shape):
          big_array = np.zeros(new_shape, dtype=np.complex)
          big_array[array_slices] = array
      else:
          big_array = array
  
      if not np.all(new_shape == kernel_shape):
          big_kernel = np.zeros(new_shape, dtype=np.complex)
          big_kernel[kernel_slices] = kernel
      else:
          big_kernel = kernel
  
      array_fft = np.fft.fftn(big_array)
      kernel_fft = np.fft.fftn(np.fft.ifftshift(big_kernel))
  
      rifft = np.fft.ifftn(array_fft * kernel_fft)
  
      return rifft[array_slices].real
  
  
  def broaden(array, fwhm=None, kind='gaussian'):
      fwhm = np.array(fwhm)
      if kind == 'gaussian':
          sigma = fwhm / (2 * np.sqrt(2 * np.log(2)))
          if fwhm.size == 1:
              kernel = gaussian_kernel1d(sigma)
          elif fwhm.size == 2:
              kernel = gaussian_kernel2d(sigma)
      else:
          print('Unvailable type of broadening.')
          return array
  
      return convolve_fft(array, kernel)
  
subject: add gaussian broadening scheme from crispy