bilby.core.sampler.nessai.Nessai

class bilby.core.sampler.nessai.Nessai(likelihood, priors, outdir='outdir', label='label', use_ratio=False, plot=False, skip_import_verification=False, injection_parameters=None, meta_data=None, result_class=None, likelihood_benchmark=False, soft_init=False, exit_code=130, npool=1, **kwargs)[source]

Bases: NestedSampler

bilby wrapper of nessai (https://github.com/mj-will/nessai)

All positional and keyword arguments passed to run_sampler are propagated to nessai.flowsampler.FlowSampler

See the documentation for an explanation of the different kwargs.

Documentation: https://nessai.readthedocs.io/

__init__(likelihood, priors, outdir='outdir', label='label', use_ratio=False, plot=False, skip_import_verification=False, injection_parameters=None, meta_data=None, result_class=None, likelihood_benchmark=False, soft_init=False, exit_code=130, npool=1, **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_nessai_model`()	Get the model for nessai.
`get_nested_samples`()	Get the nested samples dataframe
`get_posterior_weights`()	Get the posterior weights for the nested samples
`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
`split_kwargs`()	Split kwargs into configuration and run time kwargs
`update_result`()	Update the result object.
`write_current_state`()	Write the current state of the sampler
`write_current_state_and_exit`([signum, frame])	Overwrites the base class to make sure that `Nessai` terminates properly.

Attributes

`check_point_equiv_kwargs`
`constraint_parameter_keys`	list: List of parameters providing prior constraints
`default_kwargs`	Default kwargs for nessai.
`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`
`run_kwargs_list`	List of kwargs used in the run method of `FlowSampler`
`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
`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 default_kwargs

Default kwargs for nessai.

Retrieves default values from nessai directly and then includes any bilby specific defaults. This avoids the need to update bilby when the defaults change or new kwargs are added to nessai.

Includes the following kwargs that are specific to bilby:

nessai_log_level: allows setting the logging level in nessai
nessai_logging_stream: allows setting the logging stream
nessai_plot: allows toggling the plotting in FlowSampler.run

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_nessai_model()[source]: Get the model for nessai.

get_nested_samples()[source]: Get the nested samples dataframe

get_posterior_weights()[source]: Get the posterior weights for the nested samples

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

property run_kwargs_list: List of kwargs used in the run method of FlowSampler

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

split_kwargs()[source]: Split kwargs into configuration and run time kwargs

update_result()[source]: Update the result object.

write_current_state()[source]: Write the current state of the sampler

write_current_state_and_exit(signum=None, frame=None)[source]: Overwrites the base class to make sure that Nessai terminates properly.