cogwheel.sampling.PyMultiNest

class cogwheel.sampling.PyMultiNest(posterior, run_kwargs=None, sample_prior=False, dir_permissions=493, file_permissions=420)

Bases: Sampler

Sample a posterior or prior using PyMultiNest.

Parameters:

posteriorcogwheel.posterior.Posterior

Implements the prior and likelihood.

run_kwargsdict

Keyword arguments for the sampler or its run method. Allowed keys depend on the particular sampler used.

sample_priorFalse

Deprecated, will raise ValueError if it is not False.

dir_permissions, file_permissionsoctal

Directory and file permissions.

Methods

completed	Return whether the run completed successfully.
get_init_dict	Keyword arguments to instantiate the class.
get_module_name	Name of the module that defines the instance's class.
get_rundir	Return a pathlib.Path object with a new run directory, following a standardized naming scheme for output directories.
load_evidence	Define for sampling classes which compute evidence.
load_samples	Return a pandas.DataFrame with the posterior samples.
reinstantiate	Return a new instance of the class, possibly updating init_kwargs.
run	Make a directory to save results and run sampler.
run_kwargs_options	Return list of possible parameters to configure sampler, along with their default values.
submit_condor	Submit a parameter estimation run using the HTCondor scheduler.
submit_lsf
submit_slurm
to_json	Write class instance to json file.

Attributes

DEFAULT_RUN_KWARGS
JSON_FILENAME
PROFILING_FILENAME
sampled_params	Like .posterior.prior.sampled_params but the folded parameters have 'folded_' prepended.
subclass_registry

static completed(rundir) → bool

Return whether the run completed successfully.

get_init_dict(**kwargs)

Keyword arguments to instantiate the class.

get_module_name()

Name of the module that defines the instance’s class.

get_rundir(parentdir)

Return a pathlib.Path object with a new run directory, following a standardized naming scheme for output directories. Directory will be of the form {parentdir}/{prior_name}/{eventname}/{RUNDIR_PREFIX}{run_id}

Parameters:

parentdiros.PathLike

Path to a directory where to store parameter estimation data.

load_evidence()

Define for sampling classes which compute evidence.

Return a dict with the following items:

• ‘log_ev’ = log evidence from sampling

• ‘log_ev_std’ = log standard deviation of evidence

If using nested importance sampling (NIS), should also have:

• ‘log_ev_NIS’ = log evidence from nested importance sampling

• ‘log_ev_std_NIS’ = log standard deviation of NIS evidence

load_samples()

Return a pandas.DataFrame with the posterior samples.

reinstantiate(**new_init_kwargs)

Return a new instance of the class, possibly updating init_kwargs.

Values not passed will be taken from the current instance.

run(rundir)

Make a directory to save results and run sampler.

Parameters:

rundiros.PathLike

Directory where to save output, will create if needed.

run_kwargs_options()

Return list of possible parameters to configure sampler, along with their default values.

Refer to each sampler’s documentation for details. Sampler parameters not listed here are handled automatically by cogwheel.

Returns:

list of inspect.Parameter

property sampled_params

Like .posterior.prior.sampled_params but the folded parameters have ‘folded_’ prepended.

Returns:

list of str

submit_condor(rundir, request_cpus=1, request_memory='8G', request_disk='1G', resuming=False, postprocess=True, **submit_kwargs)

Submit a parameter estimation run using the HTCondor scheduler.

This method generates ‘submit.sub’ and ‘executable.sh’ files, the user should provide any instructions for the submit file as **submit_kwargs.

Parameters:

rundirstr, os.PathLike

Run directory, e.g. from self.get_rundir

request_cpus, request_memory, request_diskint or str

Specifications in the HTCondor submit file.

resumingbool

Whether to attempt resuming a previous run if rundir already exists.

postprocessbool

Whether to perform convergence tests to the run after sampling. See postprocessing.postprocess_rundir.

**submit_kwargs

Further options to include in the HTCondor submit file. Do not pass executable, output, error, log, queue, which will be dealt with automatically.

submit_lsf(rundir, n_hours_limit=48, memory_per_task='32GB', resuming=False, bsub_cmds=(), postprocess=True)

Parameters:

rundirstr, os.PathLike

Run directory, e.g. from self.get_rundir

n_hours_limitint

Number of hours to allocate for the job.

memory_per_taskstr

Determines the memory and number of cpus.

resumingbool

Whether to attempt resuming a previous run if rundir already exists.

bsub_cmdstuple of str

Strings with BSUB commands.

postprocessbool

Whether to perform convergence tests to the run after sampling. See postprocessing.postprocess_rundir.

submit_slurm(rundir, n_hours_limit=48, memory_per_task='32G', resuming=False, sbatch_cmds=(), postprocess=True)

Parameters:

rundirstr, os.PathLike

Run directory, e.g. from self.get_rundir

n_hours_limitint

Number of hours to allocate for the job.

memory_per_taskstr

Determines the memory and number of cpus.

resumingbool

Whether to attempt resuming a previous run if rundir already exists.

sbatch_cmdstuple of str

Strings with SBATCH commands.

postprocessbool

Whether to perform convergence tests to the run after sampling. See postprocessing.postprocess_rundir.

to_json(dirname, basename=None, *, dir_permissions=493, file_permissions=420, overwrite=False)

Write class instance to json file.

It can then be loaded with read_json.