autofit.DynestyDynamic#

class autofit.DynestyDynamic(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, **kwargs)[source]#

Bases: AbstractDynesty

A Dynesty non-linear search, using a dynamically changing 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

`check_model`
`check_pool`
`config_dict_with_test_mode_settings_from`
`copy_with_paths`
`exact_fit`
`fit`	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.
`fit_sequential`	Fit multiple analyses contained within the analysis sequentially.
`fitness_function_from_model_and_analysis`
`iterations_from`	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`	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`	Create a pool for multiprocessing that uses slight-of-hand to avoid copying the fitness function between processes multiple times.
`optimise`	Perform optimisation for expectation propagation.
`perform_update`	Perform an update of the non-linear search's model-fitting results.
`perform_visualization`	Perform visualization of the non-linear search's model-fitting results.
`plot_results`
`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`	Run the Dynesty sampler, which could be either the static of dynamic sampler.
`sampler_from`	Returns an instance of the Dynesty dynamic sampler set up using the input variables of this class.
`samples_from`	Create a Samples object from this non-linear search's output files on the hard-disk and model.
`write_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`	A property that is only computed once per instance and then replaces itself with an ordinary attribute.
`config_dict_search`	A property that is only computed once per instance and then replaces itself with an ordinary attribute.
`config_dict_settings`
`config_type`
`logger`	Log 'msg % args' with severity 'DEBUG'.
`name`
`paths`
`samples_cls`
`timer`
`total_live_points`

samples_from(model)[source]#

Create a Samples object from this non-linear search’s output files on the hard-disk and model.

For Dynesty, all information that we need is available from the instance of the dynesty sampler.

Parameters:: model – The model which generates instances for different points in parameter space. This maps the points from unit cube values to physical values via the priors.

sampler_from(model: AbstractPriorModel, fitness_function, checkpoint_exists: bool, pool: Optional, queue_size: Optional[int])[source]#

Returns an instance of the Dynesty dynamic sampler set up using the input variables of this class.

If no existing dynesty sampler exist on hard-disk (located via a checkpoint_file) a new instance is created with which sampler is performed. If one does exist, the dynesty restore() function is used to create the instance of the sampler.

Dynesty samplers with a multiprocessing pool may be created by inputting a dynesty Pool object, however non pooled instances can also be created by passing pool=None and queue_size=None.

Parameters:

model – The model which generates instances for different points in parameter space.
fitness_function – An instance of the fitness class used to evaluate the likelihood of each model.
pool – A dynesty Pool object which performs likelihood evaluations over multiple CPUs.
queue_size – The number of CPU’s over which multiprocessing is performed, determining how many samples are stored in the dynesty queue for samples.