bilby.core.sampler.pymultinest.Pymultinest

class bilby.core.sampler.pymultinest.Pymultinest(likelihood, priors, outdir='outdir', label='label', use_ratio=False, plot=False, exit_code=77, skip_import_verification=False, temporary_directory=True, **kwargs)[source]

Bases: _TemporaryFileSamplerMixin, NestedSampler

bilby wrapper of pymultinest (https://github.com/JohannesBuchner/PyMultiNest)

All positional and keyword arguments (i.e., the args and kwargs) passed to run_sampler will be propagated to pymultinest.run, see documentation for that class for further help. Under Other Parameters, we list commonly used kwargs and the bilby defaults.

Parameters:

npoints: int: The number of live points, note this can also equivalently be given as one of [nlive, nlives, n_live_points]
importance_nested_sampling: bool, (False): If true, use importance nested sampling
sampling_efficiency: float or {‘parameter’, ‘model’}, (‘parameter’): Defines the sampling efficiency
verbose: Bool: If true, print information information about the convergence during
resume: bool: If true, resume run from checkpoint (if available)

__init__(likelihood, priors, outdir='outdir', label='label', use_ratio=False, plot=False, exit_code=77, skip_import_verification=False, temporary_directory=True, **kwargs)[source]

__call__(*args, **kwargs): Call self as a function.

Methods

`__init__`(likelihood, priors[, outdir, ...])
`calc_likelihood_count`()
`check_draw`(theta[, warning])	Checks if the draw will generate an infinite prior or likelihood
`get_initial_points_from_prior`([npoints])	Method to draw a set of live points from the prior
`get_random_draw_from_prior`()	Get a random draw from the prior distribution
`log_likelihood`(theta)	Since some nested samplers don't call the log_prior method, evaluate the prior constraint here.
`log_prior`(theta)	Parameters:
`prior_transform`(theta)	Prior transform method that is passed into the external sampler.
`reorder_loglikelihoods`(...)	Reorders the stored log-likelihood after they have been reweighted
`run_sampler`(args, *kwargs)	A template method to run in subclasses
`write_current_state`()
`write_current_state_and_exit`([signum, frame])	Make sure that if a pool of jobs is running only the parent tries to checkpoint and exit.

Attributes

`check_point_equiv_kwargs`
`constraint_parameter_keys`	list: List of parameters providing prior constraints
`default_kwargs`
`external_sampler_name`
`fixed_parameter_keys`	list: List of parameter keys that are not being sampled
`hard_exit`
`kwargs`	dict: Container for the kwargs.
`ndim`	int: Number of dimensions of the search parameter space
`npoints_equiv_kwargs`
`npool`
`npool_equiv_kwargs`
`outputfiles_basename`
`sampling_seed_equiv_kwargs`
`sampling_seed_key`	Name of keyword argument for setting the sampling for the specific sampler.
`search_parameter_keys`	list: List of parameter keys that are being sampled
`short_name`
`temporary_outputfiles_basename`
`walks_equiv_kwargs`

check_draw(theta, warning=True)[source]

Checks if the draw will generate an infinite prior or likelihood

Also catches the output of numpy.nan_to_num.

Parameters:

theta: array_like: Parameter values at which to evaluate likelihood
warning: bool: Whether or not to print a warning

Returns:

bool, cube (nlive,: True if the likelihood and prior are finite, false otherwise

property constraint_parameter_keys: list: List of parameters providing prior constraints

property fixed_parameter_keys: list: List of parameter keys that are not being sampled

get_initial_points_from_prior(npoints=1)[source]

Method to draw a set of live points from the prior

This iterates over draws from the prior until all the samples have a finite prior and likelihood (relevant for constrained priors).

Parameters:

npoints: int: The number of values to return

Returns:

unit_cube, parameters, likelihood: tuple of array_like: unit_cube (nlive, ndim) is an array of the prior samples from the unit cube, parameters (nlive, ndim) is the unit_cube array transformed to the target space, while likelihood (nlive) are the likelihood evaluations.

get_random_draw_from_prior()[source]

Get a random draw from the prior distribution

Returns:

draw: array_like: An ndim-length array of values drawn from the prior. Parameters with delta-function (or fixed) priors are not returned

property kwargs: dict: Container for the kwargs. Has more sophisticated logic in subclasses

log_likelihood(theta)[source]

Since some nested samplers don’t call the log_prior method, evaluate the prior constraint here.

Parameters:

theta: array_like: Parameter values at which to evaluate likelihood

Returns:

float: log_likelihood

log_prior(theta)[source]

Parameters:

theta: list: List of sampled values on a unit interval

Returns:

float: Joint ln prior probability of theta

property ndim: int: Number of dimensions of the search parameter space

prior_transform(theta)[source]

Prior transform method that is passed into the external sampler.

Parameters:

theta: list: List of sampled values on a unit interval

Returns:

list: Properly rescaled sampled values

static reorder_loglikelihoods(unsorted_loglikelihoods, unsorted_samples, sorted_samples)[source]

Reorders the stored log-likelihood after they have been reweighted

This creates a sorting index by matching the reweights result.samples against the raw samples, then uses this index to sort the loglikelihoods

Parameters:

sorted_samples, unsorted_samples: array-like: Sorted and unsorted values of the samples. These should be of the same shape and contain the same sample values, but in different orders
unsorted_loglikelihoods: array-like: The loglikelihoods corresponding to the unsorted_samples

Returns:

sorted_loglikelihoods: array-like: The loglikelihoods reordered to match that of the sorted_samples

run_sampler(*args, **kwargs)[source]: A template method to run in subclasses

sampling_seed_key = 'seed': Name of keyword argument for setting the sampling for the specific sampler. If a specific sampler does not have a sampling seed option, then it should be left as None.

property search_parameter_keys: list: List of parameter keys that are being sampled

write_current_state_and_exit(signum=None, frame=None)[source]

Make sure that if a pool of jobs is running only the parent tries to checkpoint and exit. Only the parent has a ‘pool’ attribute.

For samplers that must hard exit (typically due to non-Python process) use os._exit that cannot be excepted. Other samplers exiting can be caught as a SystemExit.