get_best_2comp_model

mufasa.master_fitter.get_best_2comp_model(reg)

Retrieve the best-fit model cube for the given Region object.

The method selects between one- and two-component fits based on log-likelihood values.

Parameters:

reg (Region) – The Region object containing the original spectral cube and the fitted model results.

Returns:

A 3D array representing the data cube of the best-fit model for each pixel, where the third dimension corresponds to the spectral axis.

Return type:

numpy.ndarray

Notes

The best-fit model is determined using log-likelihood thresholds:

• One-component fits are selected if their relative log-likelihood (lnk10) exceeds 5.

• Two-component fits are selected if their relative log-likelihood (lnk21) exceeds 5 and their relative log-likelihood compared to the noise model (lnk20) also exceeds 5.

The function combines the model cubes for one- and two-component fits into a single “best” model cube by assigning the appropriate model to each spatial pixel.

Examples

>>> from region import Region
>>> reg = Region('input_cube.fits', 'output_root', fittype='nh3')
>>> best_model = get_best_2comp_model(reg)
>>> # Returns a 3D NumPy array representing the best-fit model cube.