autofit.Emcee#

Bases: AbstractMCMC

An Emcee non-linear search.

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

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 (Optional[str]) – The name of the search, controlling the last folder results are output.
path_prefix (Optional[str]) – The path of folders prefixing the name folder where results are output.
unique_tag (Optional[str]) – 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.
initializer (Optional[Initializer]) – Generates the initialize samples of non-linear parameter space (see autofit.non_linear.initializer).
auto_correlation_settings – Customizes and performs auto correlation calculations performed during and after the search.
number_of_cores (Optional[int]) – The number of cores sampling is performed using a Python multiprocessing Pool instance.
session (Optional[Session]) – An SQLalchemy session instance so the results of the model-fit are written to an SQLite database.

Methods

`check_model`
`config_dict_with_test_mode_settings_from`
`copy_with_paths`
`exact_fit`	rtype `Tuple`[`MeanField`, `Status`]
`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.
`make_pool`	Make the pool instance used to parallelize a NonLinearSearch alongside a set of unique ids for every process in the pool.
`make_sneakier_pool`	rtype `SneakierPool`
`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`
`post_fit_output`	Cleans up the output folderds after a completed non-linear search.
`pre_fit_output`	Outputs attributes of fit before the non-linear search begins.
`remove_state_files`
`result_via_completed_fit`	Returns the result of the non-linear search of a completed model-fit.
`samples_from`	Loads the samples of a non-linear search from its output files.
`samples_via_csv_from`	Returns a Samples object from the samples.csv and samples_info.json files.
`samples_via_internal_from`	Returns a Samples object from the emcee internal results.
`start_resume_fit`	Start a non-linear search from scratch, or resumes one which was previously terminated mid-way through.

Attributes

`auto_correlations`
`backend`	The Emcee hdf5 backend, which provides access to all samples, likelihoods, etc.
`backend_filename`
`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`	rtype `Dict`
`config_type`
`logger`	Log 'msg % args' with severity 'DEBUG'.
`name`
`paths`	rtype `Optional`[`AbstractPaths`]
`samples_cls`
`samples_info`
`timer`
`using_mpi`	Whether the search is being performing using MPI for parallelisation or not.

samples_via_internal_from(model)[source]#

Returns a Samples object from the emcee internal results.

The samples contain all information on the parameter space sampling (e.g. the parameters, log likelihoods, etc.).

The internal search results are converted from the native format used by the search to lists of values (e.g. parameter_lists, log_likelihood_list).

Parameters: model – Maps input vectors of unit parameter values to physical values and model instances via priors.

property backend: HDFBackend#

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

Return type: HDFBackend