docs: def noise_normalization_from_noise_map_and_mask(noise_map, mask):
      """Compute the noise-map normalization terms of a list of masked 1D noise-maps, summing the noise_map vale in every
      pixel as:
  
      [Noise_Term] = sum(log(2*pi*[Noise]**2.0))
  
      Parameters
      ----------
      noise_map : np.ndarray
          The masked noise-map of the observed data.
      mask : np.ndarray
          The mask applied to the noise-map, where *False* entries are included in the calculation.
      """
      return np.sum(np.log(2 * np.pi * noise_map[np.asarray(mask) == 0] ** 2.0))