autofit.DynestyStatic

class autofit.DynestyStatic(name: Optional[str] = None, path_prefix: Optional[str] = None, unique_tag: Optional[str] = None, prior_passer=None, iterations_per_update: Optional[int] = None, number_of_cores: Optional[int] = None, session: Optional[Session] = None, **kwargs)

__init__(name: Optional[str] = None, path_prefix: Optional[str] = None, unique_tag: Optional[str] = None, prior_passer=None, iterations_per_update: Optional[int] = None, number_of_cores: Optional[int] = None, session: Optional[Session] = None, **kwargs)

A Dynesty NonLinearSearch using a static number of live points.

For a full description of Dynesty, checkout its GitHub and readthedocs webpages:

https://github.com/joshspeagle/dynesty https://dynesty.readthedocs.io/en/latest/index.html

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.

• iterations_per_update – The number of iterations performed between every Dynesty back-up (via dumping the Dynesty instance as a pickle).

• number_of_cores – 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, ...])	A Dynesty NonLinearSearch using a static number of live points.
check_model(model)
check_pool(uses_pool, pool)
config_dict_with_test_mode_settings_from(...)
copy_with_paths(paths)
exact_fit(factor_approx[, status])
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(...)
iterations_from(sampler)	Returns the next number of iterations that a dynesty call will use and the total number of iterations that have been performed so far.
live_points_init_from(model, fitness_function)	By default, dynesty live points are generated via the sampler's in-built initialization.
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_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)
read_uses_pool()	If a Dynesty fit does not use a parallel pool, and is then resumed using one, this causes significant slow down.
remove_state_files()
run_sampler(sampler)	Run the Dynesty sampler, which could be either the static of dynamic sampler.
sampler_from(model, fitness_function, ...)	Returns an instance of the Dynesty static sampler set up using the input variables of this class.
samples_from(model)	Create a Samples object from this non-linear search's output files on the hard-disk and model.
write_uses_pool(uses_pool)	If a Dynesty fit does not use a parallel pool, and is then resumed using one, this causes significant slow down.

Attributes

checkpoint_file	The path to the file used by dynesty for checkpointing.
config_dict_run
config_dict_search
config_dict_settings
config_type
logger	Log 'msg % args' with severity 'DEBUG'.
name
paths
samples_cls
timer
total_live_points