autofit.Emcee

class autofit.Emcee(name: Optional[str] = None, path_prefix: Optional[str] = None, unique_tag: Optional[str] = None, prior_passer: Optional[autofit.non_linear.abstract_search.PriorPasser] = None, initializer: Optional[autofit.non_linear.initializer.Initializer] = None, auto_correlations_settings=<autofit.non_linear.mcmc.auto_correlations.AutoCorrelationsSettings object>, iterations_per_update: int = None, number_of_cores: int = None, session: Optional[sqlalchemy.orm.session.Session] = None, **kwargs)

__init__(name: Optional[str] = None, path_prefix: Optional[str] = None, unique_tag: Optional[str] = None, prior_passer: Optional[autofit.non_linear.abstract_search.PriorPasser] = None, initializer: Optional[autofit.non_linear.initializer.Initializer] = None, auto_correlations_settings=<autofit.non_linear.mcmc.auto_correlations.AutoCorrelationsSettings object>, iterations_per_update: int = None, number_of_cores: int = None, session: Optional[sqlalchemy.orm.session.Session] = None, **kwargs)

An Emcee non-linear search.

For a full description of Emcee, checkout its Github and readthedocs webpages:

https://github.com/dfm/emcee

https://emcee.readthedocs.io/en/stable/

If you use Emcee as part of a published work, please cite the package following the instructions under the Attribution section of the GitHub page.

Parameters:

• name – The name of the search, controlling the last folder results are output.
• path_prefix – The path of folders prefixing the name folder where results are output.
• unique_tag – The name of a unique tag for this model-fit, which will be given a unique entry in the sqlite database and also acts as the folder after the path prefix and before the search name.
• prior_passer – Controls how priors are passed from the results of this NonLinearSearch to a subsequent non-linear search.
• initializer – Generates the initialize samples of non-linear parameter space (see autofit.non_linear.initializer).
• auto_correlations_settings (AutoCorrelationsSettings) – Customizes and performs auto correlation calculations performed during and after the search.
• number_of_cores (int) – The number of cores Emcee sampling is performed using a Python multiprocessing Pool instance. If 1, a pool instance is not created and the job runs in serial.
• session – An SQLalchemy session instance so the results of the model-fit are written to an SQLite database.

Methods

__init__(name, path_prefix, unique_tag, …)	An Emcee non-linear search.
copy_with_paths(paths)
fit(model, analysis[, info, pickle_files, …])	Fit a model, M with some function f that takes instances of the class represented by model M and gives a score for their fitness.
fitness_function_from_model_and_analysis(…)
make_pool()	Make the pool instance used to parallelize a NonLinearSearch alongside a set of unique ids for every process in the pool.
make_sneaky_pool(fitness_function)	Create a pool for multiprocessing that uses slight-of-hand to avoid copying the fitness function between processes multiple times.
optimise(factor, model_approx, status)	Perform optimisation for expectation propagation.
perform_update(model, analysis, during_analysis)	Perform an update of the NonLinearSearch results, which occurs every iterations_per_update of the non-linear search.
plot_results(samples)
remove_state_files()
samples_from(model)
samples_via_results_from(model)

Attributes

backend	The Emcee hdf5 backend, which provides access to all samples, likelihoods, etc.
backend_filename
config_dict_run
config_dict_search
config_dict_settings
config_type
logger
name
paths
samples_cls
timer

backend

The Emcee hdf5 backend, which provides access to all samples, likelihoods, etc. of the non-linear search.

The sampler is described in the “Results” section at https://dynesty.readthedocs.io/en/latest/quickstart.html