get_all_lnk_maps#

mufasa.UltraCube.get_all_lnk_maps(ucube, ncomp_max=2, rest_model_mask=True, multicore=True)[source]#

Compute log-likelihood ratio maps for model comparisons up to a specified number of components.

This function calculates log-likelihood ratio (lnk) maps for comparing spectral models with different numbers of components, based on the Akaike Information Criterion corrected for finite sample sizes (AICc).

Parameters:

  • ucube (UltraCube) – An instance of the UltraCube class containing the spectral data and fitted models.

  • ncomp_max (int, optional) – The maximum number of components to include in the model comparison. Default is 2.

  • rest_model_mask (bool, optional) – If True, resets and updates the master model mask in ucube for components being compared. Default is True.

  • multicore (bool, optional) – Whether to enable parallel processing for calculations. Default is True.

Returns:

lnk_maps – Log-likelihood ratio maps for the model comparisons. The returned maps include: - lnk10: Comparison between 1-component and 0-component models. - lnk20 (if ncomp_max >= 2): Comparison between 2-component and 0-component models. - lnk21 (if ncomp_max >= 2): Comparison between 2-component and 1-component models.

Return type:

tuple of numpy.ndarray

Notes

  • Log-likelihood ratios are calculated using AICc values for each model as:

    \[\ln(\mathcal{L}_A / \mathcal{L}_B) = (AICc_B - AICc_A) / 2\]

    where \(\mathcal{L}_A\) and \(\mathcal{L}_B\) are the likelihoods of models A and B, respectively.

  • If ncomp_max is greater than 2, the function does not compute higher-order comparisons and simply returns the log-likelihood maps for up to 2 components.

Raises:

ValueError – If ncomp_max is less than 1 or if model data for the required number of components is missing in ucube.

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