Non-Linear Searches

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

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

Examples / Tutorials:

• readthedocs: example using non-linear searches.

• autofit_workspace: simple tutorial

• autofit_workspace: complex tutorial

• HowToFit: introduction chapter (detailed step-by-step examples)

Nested Samplers

DynestyDynamic([name, path_prefix, ...])	A Dynesty non-linear search, using a dynamically changing number of live points.
DynestyStatic([name, path_prefix, ...])	A Dynesty NonLinearSearch using a static number of live points.
UltraNest([name, path_prefix, unique_tag, ...])	An UltraNest non-linear search.

MCMC

Emcee(name, path_prefix, unique_tag, initializer)	An Emcee non-linear search.
Zeus(name, path_prefix, unique_tag, initializer)	An Zeus non-linear search.

Optimizers

PySwarmsLocal([name, path_prefix, ...])	A PySwarms Particle Swarm Optimizer global non-linear search.
PySwarmsGlobal([name, path_prefix, ...])	A PySwarms Particle Swarm Optimizer global non-linear search.

There are also a number of tools which are used to customize the behaviour of non-linear searches in PyAutoFit, including directory output structure, parameter sample initialization and MCMC auto correlation analysis.

Tools

DirectoryPaths([name, path_prefix, ...])	Manages the path structure for NonLinearSearch output, for analyses both not using and using the search API.
DatabasePaths(session[, name, path_prefix, ...])	Manages the path structure for NonLinearSearch output, for analyses both not using and using the search API.
Result(samples_summary[, paths, samples, ...])	The result of a non-linear search.
InitializerBall(lower_limit, upper_limit)	The Initializer creates the initial set of samples in non-linear parameter space that can be passed into a NonLinearSearch to define where to begin sampling.
InitializerPrior()	The Initializer creates the initial set of samples in non-linear parameter space that can be passed into a NonLinearSearch to define where to begin sampling.
AutoCorrelationsSettings([...])	Class for performing and customizing AutoCorrelation calculations, which are used:

PyAutoFit can perform a parallelized grid-search of non-linear searches, where a subset of parameters in the model are fitted in over a discrete grid.

Examples / Tutorials:

• readthedocs: example using a non-linear search grid search.

• autofit_workspace: example using a non-linear search grid search

GridSearch

SearchGridSearch	alias of GridSearch
GridSearchResult(samples, ...[, parent])	The sample of a grid search.