bilby.core.sampler.cpnest.Cpnest

class bilby.core.sampler.cpnest.Cpnest(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 cpnest (https://github.com/johnveitch/cpnest)

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

Parameters:
nlive: int

The number of live points, note this can also equivalently be given as one of [npoints, nlives, n_live_points]

seed: int (1234)

Initialised random seed

nthreads: int, (1)

Number of threads to use

maxmcmc: int (1000)

The maximum number of MCMC steps to take

verbose: Bool (True)

If true, print information information about the convergence during

resume: Bool (True)

Whether or not to resume from a previous run

output: str

Where to write the CPNest, by default this is {self.outdir}/cpnest_{self.label}/

__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_expected_outputs([outdir, label])

Get lists of the expected outputs directories and files.

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])

Overwrites the base class to make sure that CPNest terminates properly as CPNest handles all the multiprocessing internally.

Attributes

abbreviation

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

sampler_name

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 fixed_parameter_keys

list: List of parameter keys that are not being sampled

classmethod get_expected_outputs(outdir=None, label=None)[source]

Get lists of the expected outputs directories and files.

These are used by bilby_pipe when transferring files via HTCondor. Both can be empty. Defaults to a single directory: "{outdir}/{name}_{label}/", where name is abbreviation if it is defined for the sampler class, otherwise it defaults to sampler_name.

Parameters:
outdirstr

The output directory.

labelstr

The label for the run.

Returns:
list

List of file names.

list

List of directory names.

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]

Overwrites the base class to make sure that CPNest terminates properly as CPNest handles all the multiprocessing internally.