bilby.core.sampler.emcee.Emcee

class bilby.core.sampler.emcee.Emcee(likelihood, priors, outdir='outdir', label='label', use_ratio=False, plot=False, skip_import_verification=False, pos0=None, nburn=None, burn_in_fraction=0.25, resume=True, burn_in_act=3, **kwargs)[source]

Bases: MCMCSampler

bilby wrapper emcee (https://github.com/dfm/emcee)

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

Parameters:

nwalkers: int, (500): The number of walkers
nsteps: int, (100): The number of steps
nburn: int (None): If given, the fixed number of steps to discard as burn-in. These will be discarded from the total number of steps set by nsteps and therefore the value must be greater than nsteps. Else, nburn is estimated from the autocorrelation time
burn_in_fraction: float, (0.25): The fraction of steps to discard as burn-in in the event that the autocorrelation time cannot be calculated
burn_in_act: float: The number of autocorrelation times to discard as burn-in
a: float (2): The proposal scale factor
verbose: bool: Whether to print diagnostic information during the analysis

__init__(likelihood, priors, outdir='outdir', label='label', use_ratio=False, plot=False, skip_import_verification=False, pos0=None, nburn=None, burn_in_fraction=0.25, resume=True, burn_in_act=3, **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_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)	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_chains_to_file`(sample)
`write_current_state`()	Writes a pickle file of the sampler to disk using dill
`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

`abbreviation`
`check_point_equiv_kwargs`
`checkpoint_info`	Defines various things related to checkpointing and storing data
`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.
`nburn`
`nburn_equiv_kwargs`
`ndim`	int: Number of dimensions of the search parameter space
`npool`
`npool_equiv_kwargs`
`nsteps`
`nwalkers`
`nwalkers_equiv_kwargs`
`sampler`	Returns the emcee sampler object
`sampler_chain`
`sampler_function_kwargs`
`sampler_init_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
`stored_chain`	Read the stored zero-temperature chain data in from disk
`stored_loglike`	Returns the log-likelihood stored on disk
`stored_logprior`	Returns the log-prior stored on disk
`stored_samples`	Returns the samples stored on disk

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 checkpoint_info

Defines various things related to checkpointing and storing data

Returns:

checkpoint_info: named_tuple: An object with attributes sampler_file, chain_file, and chain_template. The first two give paths to where the sampler and chain data is stored, the last a formatted-str-template with which to write the chain data to disk

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]

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

property sampler

Returns the emcee sampler object

If, already initialized, returns the stored _sampler value. Otherwise, first checks if there is a pickle file from which to load. If there is not, then initialize the sampler and set the initial random draw

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

property stored_chain: Read the stored zero-temperature chain data in from disk

property stored_loglike: Returns the log-likelihood stored on disk

property stored_logprior: Returns the log-prior stored on disk

property stored_samples: Returns the samples stored on disk

write_current_state()[source]

Writes a pickle file of the sampler to disk using dill

Overwrites the stored sampler chain with one that is truncated to only the completed steps

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.