Modeling

Analysis

The Analysis objects define the log_likelihood_function of how a galaxy model is fitted to a dataset.

It acts as an interface between the data, model and the non-linear search.

AnalysisImaging	Fits a galaxy model to an imaging dataset via a non-linear search.
AnalysisInterferometer	Fits a galaxy model to an interferometer dataset via a non-linear search.
AnalysisEllipse	Fits a model made of ellipses to an imaging dataset via a non-linear search.

Non-linear Searches

A non-linear search is an algorithm which fits a model to data.

PyAutoGalaxy currently supports three types of non-linear search algorithms: nested samplers, Markov Chain Monte Carlo (MCMC) and Maximum Likelihood Estimaotrs (MLE).

Nautilus	A Nautilus non-linear search.
LBFGS	Abstract wrapper for the BFGS and L-BFGS scipy non-linear searches.
BFGS	Abstract wrapper for the BFGS and L-BFGS scipy non-linear searches.
DynestyDynamic	A Dynesty non-linear search, using a dynamically changing number of live points.
Emcee	An Emcee non-linear search.

Priors

The priors of parameters of every component of a mdoel, which is fitted to data, are customized using Prior objects.

UniformPrior	A prior with a uniform distribution, defined between a lower limit and upper limit.
GaussianPrior	A Gaussian prior defined by a normal distribution.
LogUniformPrior	A prior with a log base 10 uniform distribution, defined between a lower limit and upper limit.
LogGaussianPrior	A prior for a variable whose logarithm is gaussian distributed.

Adapt

AdaptImages

Contains the adapt-images which are used to make a pixelization's mesh and regularization adapt to the reconstructed galaxy's morphology.