code: def GetCovariance(kernel, kernel_params, time, errors):
      '''
      Returns the covariance matrix for a given light curve
      segment.
  
      :param array_like kernel_params: A list of kernel parameters \
            (white noise amplitude, red noise amplitude, and red noise timescale)
      :param array_like time: The time array (*N*)
      :param array_like errors: The data error array (*N*)
  
      :returns: The covariance matrix :py:obj:`K` (*N*,*N*)
  
      '''
  
      # NOTE: We purposefully compute the covariance matrix
      # *without* the GP white noise term
      K = np.diag(errors ** 2)
      K += GP(kernel, kernel_params, white=False).get_matrix(time)
      return f GetCovariance(kernel, kernel_params, time, errors):
      '''
      Returns the covariance matrix for a given light curve
      segment.
  
      :param array_like kernel_params: A list of kernel parameters \
            (white noise amplitude, red noise amplitude, and red noise timescale)
      :param array_like time: The time array (*N*)
      :param array_like errors: The data error array (*N*)
  
      :returns: The covariance matrix :py:obj:`K` (*N*,*N*)
  
      '''
  
      # NOTE: We purposefully compute the covariance matrix
      # *without* the GP white noise term
      K = np.diag(errors ** 2)
      K += GP(kernel, kernel_params, white=False).get_matrix(time)
      return K
docs: Returns the covariance matrix for a given light curve
      segment.
  
      :param array_like kernel_params: A list of kernel parameters \
            (white noise amplitude, red noise amplitude, and red noise timescale)
      :param array_like time: The time array (*N*)
      :param array_like errors: The data error array (*N*)
  
      :returns: The covariance matrix :py:obj:`K` (*N*,*N*)