text: # -*- coding: utf-8 -*-
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  import numpy as np
  from math import ceil
  
  def gaussian_kernel(sigma, cutoff=4, force_odd_size=False):
      """
      Generates a Gaussian convolution kernel.
  
      :param sigma: Standard Deviation of the Gaussian curve.
      :param cutoff: Parameter tuning the truncation of the Gaussian.
          The higher cutoff, the biggest the array will be (and the closest to
          a "true" Gaussian function).
      :param force_odd_size: when set to True, the resulting array will always
          have an odd size, regardless of the values of "sigma" and "cutoff".
      :return: a numpy.ndarray containing the truncated Gaussian function.
          The array size is 2*c*s+1 where c=cutoff, s=sigma.
  
      Nota: due to the quick decay of the Gaussian function, small values of the
          "cutoff" parameter are usually fine. The energy difference between a
          Gaussian truncated to [-c, c] and a "true" one is
              erfc(c/(sqrt(2)*s))
          so choosing cutoff=4*sigma keeps the truncation error below 1e-4.
      """
      size = int(ceil(2 * cutoff * sigma + 1))
      if force_odd_size and size % 2 == 0:
          size += 1
      x = np.arange(size) - (size - 1.0) / 2.0
      g = np.exp(-(x / sigma) ** 2 / 2.0)
      g /= g.sum()
      return g
  