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.

AnalysisImagingCI(dataset, clocker, ...)

Fits a CTI model to a charge injection imaging dataset via a non-linear search.

AnalysisDataset1D(dataset, clocker, ...)

Fits a CTI model to a 1D CTI dataset via a non-linear search.

Settings#

Input into an Analysis class to customize the behaviour of a CTI model-fit performed via a non-linear search.

SettingsCTI1D([total_density_range])

Controls the modeling settings of CTI clocking in 1D.

SettingsCTI2D([...])

Controls the modeling settings of CTI clocking in 2D.

Non-linear Searches#

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

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

Nautilus([name, path_prefix, unique_tag, ...])

A Nautilus non-linear search.

DynestyDynamic([name, path_prefix, ...])

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

Emcee(name, path_prefix, unique_tag, initializer)

An Emcee non-linear search.

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.

Priors#

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

UniformPrior([lower_limit, upper_limit, id_])

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

GaussianPrior(mean, sigma[, lower_limit, ...])

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

LogUniformPrior([lower_limit, upper_limit, id_])

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

LogGaussianPrior(mean, sigma[, lower_limit, ...])

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