bilby.core.sampler.ultranest.Ultranest

class bilby.core.sampler.ultranest.Ultranest(likelihood, priors, outdir='outdir', label='label', use_ratio=False, plot=False, exit_code=77, skip_import_verification=False, temporary_directory=True, callback_interval=10, **kwargs)[source]

Bases: _TemporaryFileSamplerMixin, NestedSampler

bilby wrapper of ultranest (https://johannesbuchner.github.io/UltraNest/index.html)

All positional and keyword arguments (i.e., the args and kwargs) passed to run_sampler will be propagated to ultranest.ReactiveNestedSampler.run or ultranest.NestedSampler.run, see documentation for those classes for further help. Under Other Parameters, we list commonly used kwargs and the bilby defaults. If the number of live points is specified the ultranest.NestedSampler will be used, otherwise the ultranest.ReactiveNestedSampler will be used.

Parameters:

num_live_points: int: The number of live points, note this can also equivalently be given as one of [nlive, nlives, n_live_points, num_live_points]. If not given then the ultranest.ReactiveNestedSampler will be used, which does not require the number of live points to be specified.
show_status: Bool: If true, print information information about the convergence during
resume: bool: If true, resume run from checkpoint (if available)
step_sampler:: An UltraNest step sampler object. This defaults to None, so the default stepping behaviour is used.

__init__(likelihood, priors, outdir='outdir', label='label', use_ratio=False, plot=False, exit_code=77, skip_import_verification=False, temporary_directory=True, callback_interval=10, **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`
`sampler_function_kwargs`
`sampler_init_kwargs`
`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 = None: 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.