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.

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))