bilby.core.sampler.ptmcmc.PTMCMCSampler

class bilby.core.sampler.ptmcmc.PTMCMCSampler(likelihood, priors, outdir='outdir', label='label', use_ratio=False, plot=False, skip_import_verification=False, **kwargs)[source]

Bases: MCMCSampler

bilby wrapper of PTMCMC (https://github.com/jellis18/PTMCMCSampler/)

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

Parameters:

Niter: int (2*10**4 + 1): The number of mcmc steps
burn: int (5 * 10**3): If given, the fixed number of steps to discard as burn-in
thin: int (1): The number of steps before saving the sample to the chain
custom_proposals: dict (None): Add dictionary of proposals to the array of proposals, this must be in the form of a dictionary with the name of the proposal, then a list containing the jump function and the weight e.g {‘name’ : [function , weight]} see (https://github.com/rgreen1995/PTMCMCSampler/blob/master/examples/simple.ipynb) and (http://jellis18.github.io/PTMCMCSampler/PTMCMCSampler.html#ptmcmcsampler-ptmcmcsampler-module) for examples and more info.
logl_grad: func (None): Gradient of likelihood if known (default = None)
logp_grad: func (None): Gradient of prior if known (default = None)
verbose: bool (True): Update current run-status to the screen

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

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

Methods

`__init__`(likelihood, priors[, outdir, ...])
`calc_likelihood_count`()
`calculate_autocorrelation`(samples[, c])	Uses the emcee.autocorr module to estimate the autocorrelation
`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)	Parameters:
`log_prior`(theta)	Parameters:
`print_nburn_logging_info`()	Prints logging info as to how nburn was calculated
`prior_transform`(theta)	Prior transform method that is passed into the external sampler.
`run_sampler`(args, *kwargs)	A template method to run in subclasses
`write_current_state`()	TODO: implement a checkpointing method
`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
`custom_proposals`
`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.
`nburn_equiv_kwargs`
`ndim`	int: Number of dimensions of the search parameter space
`npool`
`npool_equiv_kwargs`
`nwalkers_equiv_kwargs`
`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

calculate_autocorrelation(samples, c=3)[source]

Uses the emcee.autocorr module to estimate the autocorrelation

Parameters:

samples: array_like: A chain of samples.
c: float: The minimum number of autocorrelation times needed to trust the estimate (default: 3). See emcee.autocorr.integrated_time.

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]

Parameters:

theta: list: List of values for the likelihood parameters

Returns:

float: Log-likelihood or log-likelihood-ratio given the current: likelihood.parameter values

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

print_nburn_logging_info()[source]: Prints logging info as to how nburn was calculated

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

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()[source]: TODO: implement a checkpointing method

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.