Lens Modeling#

Analysis#

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

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

AnalysisImaging

Fits a lens model to a dataset via a non-linear search.

AnalysisInterferometer

Analysis classes are used by PyAutoFit to fit a model to a dataset via a non-linear search.

Non-linear Searches#

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

PyAutoLens currently supports three types of non-linear search algorithms: nested samplers, Markov Chain Monte Carlo (MCMC) and optimizers.

Nautilus

A Nautilus non-linear search.

DynestyDynamic

A Dynesty non-linear search, using a dynamically changing number of live points.

Emcee

An Emcee non-linear search.

PySwarmsLocal

A PySwarms Particle Swarm Optimizer global non-linear search.

PySwarmsGlobal

A PySwarms Particle Swarm Optimizer global non-linear search.

Priors#

The priors of parameters of every component of a model, 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 prior with a uniform distribution, defined between a lower limit and upper limit.

LogUniformPrior

A prior with a log base 10 uniform distribution, defined between a lower limit and upper limit.

LogGaussianPrior

A prior with a log base 10 uniform distribution, defined between a lower limit and upper limit.

Adapt#