API

This page details the methods and classes provided by the dynesty module.

Top-Level Interface

The top-level interface (defined natively upon initialization) that provides access to the two main sampler “super-classes” via NestedSampler() and DynamicNestedSampler().

class dynesty.dynesty.DynamicNestedSampler(loglikelihood, prior_transform, ndim, nlive=500, bound='multi', sample='auto', periodic=None, reflective=None, update_interval=None, first_update=None, rstate=None, queue_size=None, pool=None, use_pool=None, logl_args=None, logl_kwargs=None, ptform_args=None, ptform_kwargs=None, enlarge=None, bootstrap=None, walks=None, facc=0.5, slices=None, ncdim=None, blob=False, history_filename=None, save_evaluation_history=False)

Bases: DynamicSampler

The main class for performing dynamic nested sampling. It inherits all the methods from dynesty.dynamicsampler.DynamicSampler

Initializes a sampler object for Dynamic Nested Sampling.

Parameters:

loglikelihoodfunction

Function returning ln(likelihood) given parameters as a 1-d numpy array of length ndim.

prior_transformfunction

Function translating a unit cube to the parameter space according to the prior. The input is a 1-d numpy array with length ndim, where each value is in the range [0, 1). The return value should also be a 1-d numpy array with length ndim, where each value is a parameter. The return value is passed to the loglikelihood function. For example, for a 2 parameter model with flat priors in the range [0, 2), the function would be:

def prior_transform(u):
    return 2.0 * u

ndimint

Number of parameters returned by prior_transform and accepted by loglikelihood.

nliveint, optional

Number of “live” points. Larger numbers result in a more finely sampled posterior (more accurate evidence), but also a larger number of iterations required to converge. Default is 500.

bound{'none', 'single', 'multi', 'balls', 'cubes'}, optional

Method used to approximately bound the prior using the current set of live points. Conditions the sampling methods used to propose new live points. Choices are no bound ('none'), a single bounding ellipsoid ('single'), multiple bounding ellipsoids ('multi'), balls centered on each live point ('balls'), and cubes centered on each live point ('cubes'). Default is 'multi'.

sample{'auto', 'unif', 'rwalk', 'slice', 'rslice'},

optional Method used to sample uniformly within the likelihood constraint, conditioned on the provided bounds. Unique methods available are: uniform sampling within the bounds('unif'), random walks with fixed proposals ('rwalk'), multivariate slice sampling along preferred orientations ('slice'), “random” slice sampling along all orientations ('rslice'), 'auto' selects the sampling method based on the dimensionality of the problem (from ndim). When ndim < 10, this defaults to 'unif'. When 10 <= ndim <= 20, this defaults to 'rwalk'. When ndim > 20, this defaults to`’rslice’ `'slice' is provided as alternative for`’rslice’ Default is `'auto'.

periodiciterable, optional

A list of indices for parameters with periodic boundary conditions. These parameters will not have their positions constrained to be within the unit cube, enabling smooth behavior for parameters that may wrap around the edge. Default is None (i.e. no periodic boundary conditions).

reflectiveiterable, optional

A list of indices for parameters with reflective boundary conditions. These parameters will not have their positions constrained to be within the unit cube, enabling smooth behavior for parameters that may reflect at the edge. Default is None (i.e. no reflective boundary conditions).

update_intervalint or float, optional

If an integer is passed, only update the proposal distribution every update_interval-th likelihood call. If a float is passed, update the proposal after every round(update_interval * nlive)-th likelihood call. Larger update intervals larger can be more efficient when the likelihood function is quick to evaluate. Default behavior is to target a roughly constant change in prior volume, with 1.5 for 'unif', 0.15 * walks for 'rwalk'. 0.9 * ndim * slices for 'slice', 2.0 * slices for 'rslice'.

first_updatedict, optional

A dictionary containing parameters governing when the sampler should first update the bounding distribution from the unit cube ('none') to the one specified by sample. Options are the minimum number of likelihood calls ('min_ncall') and the minimum allowed overall efficiency in percent ('min_eff'). Defaults are 2 * nlive and 10., respectively.

rstateGenerator, optional

Generator instance. If not given, the

global random state of the random module will be used.

queue_sizeint, optional

Carry out likelihood evaluations in parallel by queueing up new live point proposals using (at most) queue_size many threads. Each thread independently proposes new live points until the proposal distribution is updated. If no value is passed, this defaults to pool.size (if a pool has been provided) and 1 otherwise (no parallelism).

pooluser-provided pool, optional

Use this pool of workers to execute operations in parallel.

use_pooldict, optional

A dictionary containing flags indicating where a pool should be used to execute operations in parallel. These govern whether prior_transform is executed in parallel during initialization ('prior_transform'), loglikelihood is executed in parallel during initialization ('loglikelihood'), live points are proposed in parallel during a run ('propose_point'), and bounding distributions are updated in parallel during a run ('update_bound'). Default is True for all options.

live_pointslist of 3 ndarray each with shape (nlive, ndim)

A set of live points used to initialize the nested sampling run. Contains live_u, the coordinates on the unit cube, live_v, the transformed variables, and live_logl, the associated loglikelihoods. By default, if these are not provided the initial set of live points will be drawn uniformly from the unit ndim-cube. WARNING: It is crucial that the initial set of live points have been sampled from the prior. Failure to provide a set of valid live points will result in incorrect results.

logl_argsiterable, optional

Additional arguments that can be passed to loglikelihood.

logl_kwargsdict, optional

Additional keyword arguments that can be passed to loglikelihood.

ptform_argsiterable, optional

Additional arguments that can be passed to prior_transform.

ptform_kwargsdict, optional

Additional keyword arguments that can be passed to prior_transform.

enlargefloat, optional

Enlarge the volumes of the specified bounding object(s) by this fraction. The preferred method is to determine this organically using bootstrapping. If bootstrap > 0, this defaults to 1.0. If bootstrap = 0, this instead defaults to 1.25.

bootstrapint, optional

Compute this many bootstrapped realizations of the bounding objects. Use the maximum distance found to the set of points left out during each iteration to enlarge the resulting volumes. Can lead to unstable bounding ellipsoids. Default is None (no bootstrap unless the sampler is uniform). If bootstrap is set to zero, bootstrap is disabled.

walksint, optional

For the 'rwalk' sampling option, the minimum number of steps (minimum 2) before proposing a new live point. Default is 25.

faccfloat, optional

The target acceptance fraction for the 'rwalk' sampling option. Default is 0.5. Bounded to be between [1. / walks, 1.].

slicesint, optional

For the 'slice', 'rslice' sampling options, the number of times to execute a “slice update” before proposing a new live point. Default is 3 for 'slice' and 3+ndim for rslice. Note that 'slice' cycles through all dimensions when executing a “slice update”.

ncdim: int, optional

The number of clustering dimensions. The first ncdim dimensions will be sampled using the sampling method, the remaining dimensions will just sample uniformly from the prior distribution. If this is None (default), this will default to ndim.

blob: bool, optional

The default value is False. If it is true, then the log-likelihood should return the tuple of logl and a numpy-array “blob” that will stored as part of the chain. That blob can contain auxiliary information computed inside the likelihood function.

Returns:

samplera dynesty.DynamicSampler instance

An initialized instance of the dynamic nested sampler.

class dynesty.dynesty.NestedSampler(loglikelihood, prior_transform, ndim, nlive=500, bound='multi', sample='auto', periodic=None, reflective=None, update_interval=None, first_update=None, rstate=None, queue_size=None, pool=None, use_pool=None, live_points=None, logl_args=None, logl_kwargs=None, ptform_args=None, ptform_kwargs=None, enlarge=None, bootstrap=None, walks=None, facc=0.5, slices=None, ncdim=None, blob=False, save_evaluation_history=False, history_filename=None)

Bases: Sampler

The main class performing the static nested sampling. It inherits all the methods of the dynesty.sampler.Sampler.

Initializes and returns a sampler object for Static Nested Sampling.

Parameters:

loglikelihoodfunction

Function returning ln(likelihood) given parameters as a 1-d numpy array of length ndim.

prior_transformfunction

Function translating a unit cube to the parameter space according to the prior. The input is a 1-d numpy array with length ndim, where each value is in the range [0, 1). The return value should also be a 1-d numpy array with length ndim, where each value is a parameter. The return value is passed to the loglikelihood function. For example, for a 2 parameter model with flat priors in the range [0, 2), the function would be:

def prior_transform(u):
    return 2.0 * u

ndimint

Number of parameters returned by prior_transform and accepted by loglikelihood.

nliveint, optional

Number of “live” points. Larger numbers result in a more finely sampled posterior (more accurate evidence), but also a larger number of iterations required to converge. Default is 500.

bound{'none', 'single', 'multi', 'balls', 'cubes'}, optional

Method used to approximately bound the prior using the current set of live points. Conditions the sampling methods used to propose new live points. Choices are no bound ('none'), a single bounding ellipsoid ('single'), multiple bounding ellipsoids ('multi'), balls centered on each live point ('balls'), and cubes centered on each live point ('cubes'). Default is 'multi'.

sample{'auto', 'unif', 'rwalk', 'slice', 'rslice'},

optional Method used to sample uniformly within the likelihood constraint, conditioned on the provided bounds. Unique methods available are: uniform sampling within the bounds('unif'), random walks with fixed proposals ('rwalk'), multivariate slice sampling along preferred orientations ('slice'), “random” slice sampling along all orientations ('rslice'), 'auto' selects the sampling method based on the dimensionality of the problem (from ndim). When ndim < 10, this defaults to 'unif'. When 10 <= ndim <= 20, this defaults to 'rwalk'. When ndim > 20, this defaults to`’rslice’ `'slice' is provided as alternative for`’rslice’ Default is `'auto'.

periodiciterable, optional

A list of indices for parameters with periodic boundary conditions. These parameters will not have their positions constrained to be within the unit cube, enabling smooth behavior for parameters that may wrap around the edge. Default is None (i.e. no periodic boundary conditions).

reflectiveiterable, optional

A list of indices for parameters with reflective boundary conditions. These parameters will not have their positions constrained to be within the unit cube, enabling smooth behavior for parameters that may reflect at the edge. Default is None (i.e. no reflective boundary conditions).

update_intervalint or float, optional

If an integer is passed, only update the proposal distribution every update_interval-th likelihood call. If a float is passed, update the proposal after every round(update_interval * nlive)-th likelihood call. Larger update intervals larger can be more efficient when the likelihood function is quick to evaluate. Default behavior is to target a roughly constant change in prior volume, with 1.5 for 'unif', 0.15 * walks for 'rwalk'. 0.9 * ndim * slices for 'slice', 2.0 * slices for 'rslice'.

first_updatedict, optional

A dictionary containing parameters governing when the sampler should first update the bounding distribution from the unit cube ('none') to the one specified by sample. Options are the minimum number of likelihood calls ('min_ncall') and the minimum allowed overall efficiency in percent ('min_eff'). Defaults are 2 * nlive and 10., respectively.

rstateGenerator, optional

Generator instance. If not given, the

global random state of the random module will be used.

queue_sizeint, optional

Carry out likelihood evaluations in parallel by queueing up new live point proposals using (at most) queue_size many threads. Each thread independently proposes new live points until the proposal distribution is updated. If no value is passed, this defaults to pool.size (if a pool has been provided) and 1 otherwise (no parallelism).

pooluser-provided pool, optional

Use this pool of workers to execute operations in parallel.

use_pooldict, optional

A dictionary containing flags indicating where a pool should be used to execute operations in parallel. These govern whether prior_transform is executed in parallel during initialization ('prior_transform'), loglikelihood is executed in parallel during initialization ('loglikelihood'), live points are proposed in parallel during a run ('propose_point'), and bounding distributions are updated in parallel during a run ('update_bound'). Default is True for all options.

live_pointslist of 3 ndarray each with shape (nlive, ndim)

A set of live points used to initialize the nested sampling run. Contains live_u, the coordinates on the unit cube, live_v, the transformed variables, and live_logl, the associated loglikelihoods. By default, if these are not provided the initial set of live points will be drawn uniformly from the unit ndim-cube. WARNING: It is crucial that the initial set of live points have been sampled from the prior. Failure to provide a set of valid live points will result in incorrect results.

logl_argsiterable, optional

Additional arguments that can be passed to loglikelihood.

logl_kwargsdict, optional

Additional keyword arguments that can be passed to loglikelihood.

ptform_argsiterable, optional

Additional arguments that can be passed to prior_transform.

ptform_kwargsdict, optional

Additional keyword arguments that can be passed to prior_transform.

enlargefloat, optional

Enlarge the volumes of the specified bounding object(s) by this fraction. The preferred method is to determine this organically using bootstrapping. If bootstrap > 0, this defaults to 1.0. If bootstrap = 0, this instead defaults to 1.25.

bootstrapint, optional

Compute this many bootstrapped realizations of the bounding objects. Use the maximum distance found to the set of points left out during each iteration to enlarge the resulting volumes. Can lead to unstable bounding ellipsoids. Default is None (no bootstrap unless the sampler is uniform). If bootstrap is set to zero, bootstrap is disabled.

walksint, optional

For the 'rwalk' sampling option, the minimum number of steps (minimum 2) before proposing a new live point. Default is 25.

faccfloat, optional

The target acceptance fraction for the 'rwalk' sampling option. Default is 0.5. Bounded to be between [1. / walks, 1.].

slicesint, optional

For the 'slice', 'rslice' sampling options, the number of times to execute a “slice update” before proposing a new live point. Default is 3 for 'slice' and 3+ndim for rslice. Note that 'slice' cycles through all dimensions when executing a “slice update”.

ncdim: int, optional

The number of clustering dimensions. The first ncdim dimensions will be sampled using the sampling method, the remaining dimensions will just sample uniformly from the prior distribution. If this is None (default), this will default to ndim.

blob: bool, optional

The default value is False. If it is true, then the log-likelihood should return the tuple of logl and a numpy-array “blob” that will stored as part of the chain. That blob can contain auxiliary information computed inside the likelihood function.

Returns:

samplersampler from nestedsamplers

An initialized instance of the chosen sampler specified via bound.

class dynesty.dynesty._function_wrapper(func, args, kwargs, name='input')

Bases: object

A hack to make functions pickleable when args or kwargs are also included. Based on the implementation in emcee.

Bounding

Bounding classes used when proposing new live points, along with a number of useful helper functions. Bounding objects include:

UnitCube:

The unit N-cube (unconstrained draws from the prior).

Ellipsoid:

Bounding ellipsoid.

MultiEllipsoid:

A set of (possibly overlapping) bounding ellipsoids.

RadFriends:

A set of (possibly overlapping) balls centered on each live point.

SupFriends:

A set of (possibly overlapping) cubes centered on each live point.

class dynesty.bounding.Bound(ndim)

Bases: object

This is is master class for the all the bounds listing the key methods required from the bound

contains(x)

Checks if unit cube contains the point x.

get_random_axes(rstate)

sample(rstate=None)

Draw a sample uniformly distributed within the unit cube.

Returns:

xndarray with shape (ndim,)

A coordinate within the unit cube.

samples(nsamples, rstate=None)

Draw nsamples samples randomly distributed within the unit cube.

Returns:

xndarray with shape (nsamples, ndim)

A collection of coordinates within the unit cube.

scale_to_logvol(rstate)

update(points, rstate=None, bootstrap=0, pool=None)

class dynesty.bounding.Ellipsoid(ndim, ctr=None, cov=None, am=None, axes=None)

Bases: Bound

An N-dimensional ellipsoid defined by:

(x - v)^T A (x - v) = 1

where the vector v is the center of the ellipsoid and A is a symmetric, positive-definite N x N matrix.

Parameters:

ctrndarray with shape (N,)

Coordinates of ellipsoid center.

covndarray with shape (N, N)

Covariance matrix describing the axes.

contains(x)

Checks if ellipsoid contains x.

distance(x)

Compute the normalized distance to x from the center of the ellipsoid.

distance_many(x)

Compute the normalized distance to x from the center of the ellipsoid.

get_random_axes(rstate)

major_axis_endpoints()

Return the endpoints of the major axis.

sample(rstate=None)

Draw a sample uniformly distributed within the ellipsoid.

Returns:

xndarray with shape (ndim,)

A coordinate within the ellipsoid.

samples(nsamples, rstate=None)

Draw nsamples samples uniformly distributed within the ellipsoid.

Returns:

xndarray with shape (nsamples, ndim)

A collection of coordinates within the ellipsoid.

scale_to_logvol(logvol)

Scale ellipsoid to a target volume.

unitcube_overlap(ndraws=10000, rstate=None)

Using ndraws Monte Carlo draws, estimate the fraction of overlap between the ellipsoid and the unit cube.

update(points, rstate=None, bootstrap=0, pool=None, mc_integrate=False)

Update the ellipsoid to bound the collection of points.

Parameters:

pointsndarray with shape (npoints, ndim)

The set of points to bound.

rstateGenerator, optional

Generator instance.

bootstrapint, optional

The number of bootstrapped realizations of the ellipsoid. The maximum distance to the set of points “left out” during each iteration is used to enlarge the resulting volumes. Default is 0.

pooluser-provided pool, optional

Use this pool of workers to execute operations in parallel.

mc_integratebool, optional

Whether to use Monte Carlo methods to compute the effective overlap of the final ellipsoid with the unit cube. Default is False.

class dynesty.bounding.MultiEllipsoid(ndim, ells=None, ctrs=None, covs=None)

Bases: Bound

A collection of M N-dimensional ellipsoids.

Parameters:

ellslist of Ellipsoid objects with length M, optional

A set of Ellipsoid objects that make up the collection of N-ellipsoids. Used to initialize MultiEllipsoid if provided.

ctrsndarray with shape (M, N), optional

Collection of coordinates of ellipsoid centers. Used to initialize MultiEllipsoid if ams is also provided.

covsndarray with shape (M, N, N), optional

Collection of matrices describing the axes of the ellipsoids. Used to initialize MultiEllipsoid if ctrs also provided.

__update_arrays()

Update internal arrays to ensure that in sync with ells

contains(x)

Checks if the set of ellipsoids contains x.

get_random_axes(rstate)

major_axis_endpoints()

Return the endpoints of the major axis of each ellipsoid.

monte_carlo_logvol(ndraws=10000, rstate=None, return_overlap=True)

Using ndraws Monte Carlo draws, estimate the log volume of the union of ellipsoids. If return_overlap=True, also returns the estimated fractional overlap with the unit cube.

overlap(x, j=None)

Checks how many ellipsoid(s) x falls within, skipping the j-th ellipsoid.

sample(rstate=None, return_q=False)

Sample a point uniformly distributed within the union of ellipsoids.

Returns:

xndarray with shape (ndim,)

A coordinate within the set of ellipsoids.

idxint

The index of the ellipsoid x was sampled from.

qint, optional

The number of ellipsoids x falls within.

samples(nsamples, rstate=None)

Draw nsamples samples uniformly distributed within the union of ellipsoids.

Returns:

xsndarray with shape (nsamples, ndim)

A collection of coordinates within the set of ellipsoids.

scale_to_logvol(logvol)

Scale ellipoids to a corresponding set of target volumes or to a new total volume If the argument is scalar this is assumed to be new logvol, if it’s iterable it’s assumed to be array of log volumes

update(points, rstate=None, bootstrap=0, pool=None, mc_integrate=False)

Update the set of ellipsoids to bound the collection of points.

Parameters:

pointsndarray with shape (npoints, ndim)

The set of points to bound.

rstateGenerator, optional

Generator instance.

bootstrapint, optional

The number of bootstrapped realizations of the ellipsoids. The maximum distance to the set of points “left out” during each iteration is used to enlarge the resulting volumes. Default is 0.

pooluser-provided pool, optional

Use this pool of workers to execute operations in parallel.

mc_integratebool, optional

Whether to use Monte Carlo methods to compute the effective volume and fractional overlap of the final union of ellipsoids with the unit cube. Default is False.

within(x, j=None)

Checks which ellipsoid(s) x falls within, skipping the j-th ellipsoid if need be.

class dynesty.bounding.RadFriends(ndim, cov=None)

Bases: Bound

A collection of N-balls of identical size centered on each live point. Importantly this class requires that the centers of the balls are set in the .ctrs attribute.

Parameters:

ndimint

The number of dimensions of each ball.

covndarray with shape (ndim, ndim), optional

Covariance structure (correlation and size) of each ball.

_get_covariance_from_all_points(points)

Compute covariance using all points.

_get_covariance_from_clusters(points)

Compute covariance from re-centered clusters.

contains(x)

Check if the set of balls contains x.

get_random_axes(rstate)

monte_carlo_logvol(ndraws=10000, rstate=None, return_overlap=True)

Using ndraws Monte Carlo draws, estimate the log volume of the union of balls. If return_overlap=True, also returns the estimated fractional overlap with the unit cube.

overlap(x)

Check how many balls x falls within.

sample(rstate=None, return_q=False)

Sample a point uniformly distributed within the union of balls.

Returns:

xndarray with shape (ndim,)

A coordinate within the set of balls.

qint, optional

The number of balls x falls within.

samples(nsamples, rstate=None)

Draw nsamples samples uniformly distributed within the union of balls.

Returns:

xsndarray with shape (nsamples, ndim)

A collection of coordinates within the set of balls.

scale_to_logvol(logvol)

Scale ball to encompass a target volume.

update(points, rstate=None, bootstrap=0, pool=None, mc_integrate=False, use_clustering=True)

Update the radii of our balls.

Parameters:

pointsndarray with shape (npoints, ndim)

The set of points to bound.

rstateGenerator, optional

Generator instance.

bootstrapint, optional

The number of bootstrapped realizations of the ellipsoids. The maximum distance to the set of points “left out” during each iteration is used to enlarge the resulting volumes. Default is 0.

pooluser-provided pool, optional

Use this pool of workers to execute operations in parallel.

mc_integratebool, optional

Whether to use Monte Carlo methods to compute the effective volume and fractional overlap of the final union of balls with the unit cube. Default is False.

use_clusteringbool, optional

Whether to use clustering to avoid issues with widely-seperated modes. Default is True.

within(x)

Check which balls x falls within.

class dynesty.bounding.SupFriends(ndim, cov=None)

Bases: Bound

A collection of N-cubes of identical size centered on each live point. Importantly this class requires that the centers of the cubes are set in the .ctrs attribute.

Parameters:

ndimint

The number of dimensions of the cube.

covndarray with shape (ndim, ndim), optional

Covariance structure (correlation and size) of each cube.

_get_covariance_from_all_points(points)

Compute covariance using all points.

_get_covariance_from_clusters(points)

Compute covariance from re-centered clusters.

contains(x)

Checks if the set of cubes contains x.

get_random_axes(rstate)

monte_carlo_logvol(ndraws=10000, rstate=None, return_overlap=True)

Using ndraws Monte Carlo draws, estimate the log volume of the union of cubes. If return_overlap=True, also returns the estimated fractional overlap with the unit cube.

overlap(x)

Checks how many cubes x falls within, skipping the j-th cube.

sample(rstate=None, return_q=False)

Sample a point uniformly distributed within the union of cubes.

Returns:

xndarray with shape (ndim,)

A coordinate within the set of cubes.

qint, optional

The number of cubes x falls within.

samples(nsamples, rstate=None)

Draw nsamples samples uniformly distributed within the union of cubes.

Returns:

xsndarray with shape (nsamples, ndim)

A collection of coordinates within the set of cubes.

scale_to_logvol(logvol)

Scale cube to encompass a target volume.

update(points, rstate=None, bootstrap=0, pool=None, mc_integrate=False, use_clustering=True)

Update the half-side-lengths of our cubes.

Parameters:

pointsndarray with shape (npoints, ndim)

The set of points to bound.

rstateGenerator, optional

Generator instance.

bootstrapint, optional

The number of bootstrapped realizations of the ellipsoids. The maximum distance to the set of points “left out” during each iteration is used to enlarge the resulting volumes. Default is 0.

pooluser-provided pool, optional

Use this pool of workers to execute operations in parallel.

mc_integratebool, optional

Whether to use Monte Carlo methods to compute the effective volume and fractional overlap of the final union of cubes with the unit cube. Default is False.

use_clusteringbool, optional

Whether to use clustering to avoid issues with widely-seperated modes. Default is True.

within(x)

Checks which cubes x falls within.

class dynesty.bounding.UnitCube(ndim)

Bases: Bound

An N-dimensional unit cube.

Parameters:

ndimint

The number of dimensions of the unit cube.

contains(x)

Checks if unit cube contains the point x.

get_random_axes(rstate)

sample(rstate=None)

Draw a sample uniformly distributed within the unit cube.

Returns:

xndarray with shape (ndim,)

A coordinate within the unit cube.

samples(nsamples, rstate=None)

Draw nsamples samples randomly distributed within the unit cube.

Returns:

xndarray with shape (nsamples, ndim)

A collection of coordinates within the unit cube.

scale_to_logvol(logvol)

update(points, rstate=None, bootstrap=0, pool=None)

Filler function.

dynesty.bounding._bounding_ellipsoids(points, ell, scale=None)

Internal method used to compute a set of bounding ellipsoids when a bounding ellipsoid for the entire set has already been calculated.

Parameters:

pointsndarray with shape (npoints, ndim)

A set of coordinates.

ellEllipsoid

The bounding ellipsoid containing points.

Returns:

ellslist of Ellipsoid objects

List of Ellipsoid objects used to bound the collection of points. Used to initialize the MultiEllipsoid object returned in bounding_ellipsoids().

dynesty.bounding._ellipsoid_bootstrap_expand(args)

Internal method used to compute the expansion factor for a bounding ellipsoid or ellipsoids based on bootstrapping. The argument is a tuple: multi: boolean flag if we are doing multiell or single ell decomposition points: 2d array of points rseed: seed to initialize the random generator

dynesty.bounding._friends_bootstrap_radius(args)

Internal method used to compute the radius (half-side-length) for each ball (cube) used in RadFriends (SupFriends) using bootstrapping.

dynesty.bounding._friends_leaveoneout_radius(points, ftype)

Internal method used to compute the radius (half-side-length) for each ball (cube) used in RadFriends (SupFriends) using leave-one-out (LOO) cross-validation.

dynesty.bounding.bounding_ellipsoid(points)

Calculate the bounding ellipsoid containing a collection of points.

Parameters:

pointsndarray with shape (npoints, ndim)

A set of coordinates.

Returns:

ellipsoidEllipsoid

The bounding Ellipsoid object.

dynesty.bounding.bounding_ellipsoids(points)

Calculate a set of ellipsoids that bound the collection of points.

Parameters:

pointsndarray with shape (npoints, ndim)

A set of coordinates.

Returns:

mellMultiEllipsoid object

The MultiEllipsoid object used to bound the collection of points.

dynesty.bounding.logvol_prefactor(n, p=2.0)

Returns the ln(volume constant) for an n-dimensional sphere with an \(L^p\) norm. The constant is defined as:

lnf = n * ln(2.) + n * LogGamma(1./p + 1) - LogGamma(n/p + 1.)

By default the p=2. norm is used (i.e. the standard Euclidean norm).

dynesty.bounding.randsphere(n, rstate=None)

Draw a point uniformly within an n-dimensional unit sphere.

Sampling

Functions for proposing new live points used by Sampler DynamicSampler.

class dynesty.internal_samplers.InternalSampler(**kwargs)

Bases: object

Base class for all internal samplers.

This class is not meant to be used directly. The basic interface of the class is to provide sampling that can be distributed over workers.

The key methods are prepare_sampler, sample and tune.

The prepare_sampler method constructs the list of SamplerArguments objects.

The sample method is called by the workers to sample a new point. Importantly the sample method is a static method and does not have access to the class instance.

The tune method is called by the parent process to adjust things, such as scale of the proposal distribution. The tune method is not a static method and has access to the class instance.

Initialize the internal sampler.

Importantely this sets up the sampler_kwargs that is being passed to each .sample() call

Parameters:

kwargsdict

A dictionary of additional method-specific parameters. This common keywords:

nonboundedarray

Array of boolean values indicating which dimensions are non-bounded.

periodicarray

Array of boolean values indicating which dimensions are periodic.

reflectivearray

Array of boolean values indicating which dimensions are reflective.

ndim: int

Number of dimensions.

_new_from_template(template_kwargs)

Create a new sampler from a template.

property citations

prepare_sampler(loglstar=None, points=None, axes=None, seeds=None, prior_transform=None, loglikelihood=None, nested_sampler=None)

Prepare the list of arguments for sampling.

Parameters:

loglstarfloat

Ln(likelihood) bound.

pointsndarray with shape (n, ndim)

Initial sample points.

axesndarray with shape (ndim, ndim)

Axes used to propose new points.

seedsndarray with shape (n,)

Random number generator seeds.

prior_transformfunction

Function transforming a sample from the a unit cube to the parameter space of interest according to the prior.

loglikelihoodfunction

Function returning ln(likelihood) given parameters as a 1-d numpy array of length ndim.

nested_samplerSampler

The nested sampler object used to sample.

Returns:

arglist:

List of SamplerArgument objects containing the parameters needed for sampling.

static sample(args)

Sample a new live point.

Parameters:

argsSamplerArgument

The arguments needed for sampling.

Returns:

undarray with shape (ndim,)

Position of the final proposed point within the unit cube.

vndarray with shape (ndim,)

Position of the final proposed point in the target parameter space.

loglfloat

Ln(likelihood) of the final proposed point.

ncint

Number of function calls used to generate the sample.

tuning_infodict

Collection of ancillary quantities used to tune scale.

tune(tuning_info, update=False)

Accumulate sampling info and optionally update the proposal scale and other tuning parameters.

Parameters:

tuning_infodict

Dictionary containing the sampling information.

updatebool

Whether to update the proposal scale or not (default: False).

property update_bound_interval_ratio

How often to force updating the bounds The value is in units of ncall per nlive. I.e. the value of 10 means for N live points, the bound will be updated every 10 * N calls

class dynesty.internal_samplers.RSliceSampler(**kwargs)

Bases: InternalSampler

Initialize the internal sampler.

Importantely this sets up the sampler_kwargs that is being passed to each .sample() call

Parameters:

kwargsdict

A dictionary of additional method-specific parameters. This common keywords:

nonboundedarray

Array of boolean values indicating which dimensions are non-bounded.

periodicarray

Array of boolean values indicating which dimensions are periodic.

reflectivearray

Array of boolean values indicating which dimensions are reflective.

ndim: int

Number of dimensions.

property citations

static sample(args)

Return a new live point proposed by a series of random slices away from an existing live point. Standard “random” implementation where each slice is along a random direction based on the provided axes.

Parameters:

undarray with shape (ndim,)

Position of the initial sample. This is a copy of an existing live point.

loglstarfloat

Ln(likelihood) bound.

axesndarray with shape (ndim, ndim)

Axes used to propose new slice directions.

scalefloat

Value used to scale the provided axes.

prior_transformfunction

Function transforming a sample from the a unit cube to the parameter space of interest according to the prior.

loglikelihoodfunction

Function returning ln(likelihood) given parameters as a 1-d numpy array of length ndim.

kwargsdict

A dictionary of additional method-specific parameters.

Returns:

undarray with shape (ndim,)

Position of the final proposed point within the unit cube.

vndarray with shape (ndim,)

Position of the final proposed point in the target parameter space.

loglfloat

Ln(likelihood) of the final proposed point.

ncint

Number of function calls used to generate the sample.

tuning_infodict

Collection of ancillary quantities used to tune scale.

tune(sampler_info, update=True)

Accumulate sampling info and optionally update the proposal scale and other tuning parameters.

Parameters:

tuning_infodict

Dictionary containing the sampling information.

updatebool

Whether to update the proposal scale or not (default: False).

property update_bound_interval_ratio

How often to force updating the bounds The value is in units of ncall per nlive. I.e. the value of 10 means for N live points, the bound will be updated every 10 * N calls

class dynesty.internal_samplers.RWalkSampler(**kwargs)

Bases: InternalSampler

Initialize the internal sampler.

Importantely this sets up the sampler_kwargs that is being passed to each .sample() call

Parameters:

kwargsdict

A dictionary of additional method-specific parameters. This common keywords:

nonboundedarray

Array of boolean values indicating which dimensions are non-bounded.

periodicarray

Array of boolean values indicating which dimensions are periodic.

reflectivearray

Array of boolean values indicating which dimensions are reflective.

ndim: int

Number of dimensions.

property citations

static sample(args)

Return a new live point proposed by random walking away from an existing live point.

Parameters:

undarray with shape (ndim,)

Position of the initial sample. This is a copy of an existing live point.

loglstarfloat

Ln(likelihood) bound.

axesndarray with shape (ndim, ndim)

Axes used to propose new points. For random walks new positions are proposed using the Ellipsoid whose shape is defined by axes.

scalefloat

Value used to scale the provided axes.

prior_transformfunction

Function transforming a sample from the a unit cube to the parameter space of interest according to the prior.

loglikelihoodfunction

Function returning ln(likelihood) given parameters as a 1-d numpy array of length ndim.

kwargsdict

A dictionary of additional method-specific parameters.

Returns:

undarray with shape (ndim,)

Position of the final proposed point within the unit cube.

vndarray with shape (ndim,)

Position of the final proposed point in the target parameter space.

loglfloat

Ln(likelihood) of the final proposed point.

ncint

Number of function calls used to generate the sample.

tuning_infodict

Collection of ancillary quantities used to tune scale.

tune(tuning_info, update=True)

Update the random walk proposal scale based on the current number of accepted/rejected steps. For rwalk the scale is important because it determines the speed of diffusion of points. I.e. if scale is too large, the proposal efficiency will be very low so it’s likely that we’ll only do one random walk step at the time, thus producing very correlated chain. The keyword update determines if we are just accumulating the number of steps or actually adjusting the scale

property update_bound_interval_ratio

How often to force updating the bounds The value is in units of ncall per nlive. I.e. the value of 10 means for N live points, the bound will be updated every 10 * N calls

class dynesty.internal_samplers.SliceSampler(**kwargs)

Bases: InternalSampler

Initialize the internal sampler.

Importantely this sets up the sampler_kwargs that is being passed to each .sample() call

Parameters:

kwargsdict

A dictionary of additional method-specific parameters. This common keywords:

nonboundedarray

Array of boolean values indicating which dimensions are non-bounded.

periodicarray

Array of boolean values indicating which dimensions are periodic.

reflectivearray

Array of boolean values indicating which dimensions are reflective.

ndim: int

Number of dimensions.

property citations

static sample(args)

Return a new live point proposed by a series of random slices away from an existing live point. Standard “Gibbs-like” implementation where a single multivariate “slice” is a combination of ndim univariate slices through each axis.

Parameters:

undarray with shape (ndim,)

Position of the initial sample. This is a copy of an existing live point.

loglstarfloat

Ln(likelihood) bound.

axesndarray with shape (ndim, ndim)

Axes used to propose new points. For slices new positions are proposed along the arthogonal basis defined by axes.

scalefloat

Value used to scale the provided axes.

prior_transformfunction

Function transforming a sample from the a unit cube to the parameter space of interest according to the prior.

loglikelihoodfunction

Function returning ln(likelihood) given parameters as a 1-d numpy array of length ndim.

kwargsdict

A dictionary of additional method-specific parameters.

Returns:

undarray with shape (ndim,)

Position of the final proposed point within the unit cube.

vndarray with shape (ndim,)

Position of the final proposed point in the target parameter space.

loglfloat

Ln(likelihood) of the final proposed point.

ncint

Number of function calls used to generate the sample.

tuning_infodict

Collection of ancillary quantities used to tune scale.

tune(sampler_info, update=True)

Accumulate sampling info and optionally update the proposal scale and other tuning parameters.

Parameters:

tuning_infodict

Dictionary containing the sampling information.

updatebool

Whether to update the proposal scale or not (default: False).

property update_bound_interval_ratio

How often to force updating the bounds The value is in units of ncall per nlive. I.e. the value of 10 means for N live points, the bound will be updated every 10 * N calls

class dynesty.internal_samplers.UniformBoundSampler(**kwargs)

Bases: InternalSampler

Uniformly sample within a bounding proposal distribution.

Initialize the internal sampler.

Importantely this sets up the sampler_kwargs that is being passed to each .sample() call

Parameters:

kwargsdict

A dictionary of additional method-specific parameters. This common keywords:

nonboundedarray

Array of boolean values indicating which dimensions are non-bounded.

periodicarray

Array of boolean values indicating which dimensions are periodic.

reflectivearray

Array of boolean values indicating which dimensions are reflective.

ndim: int

Number of dimensions.

prepare_sampler(loglstar=None, points=None, axes=None, seeds=None, prior_transform=None, loglikelihood=None, nested_sampler=None)

Prepare the list of arguments for sampling.

This is is overriding the base class method and providing the bound itself to the sampler through kwargs.

static sample(args)

Return a new live point sampling uniformly within the boundary.

Parameters:

undarray with shape (ndim,)

Initial sample (not used)

loglstarfloat

Ln(likelihood) bound.

axesndarray with shape (ndim, ndim)

Axes used to propose new points. (not used)

scalefloat

Value used to scale the provided axes. (not used)

prior_transformfunction

Function transforming a sample from the a unit cube to the parameter space of interest according to the prior.

loglikelihoodfunction

Function returning ln(likelihood) given parameters as a 1-d numpy array of length ndim.

kwargsdict

A dictionary of additional method-specific parameters. This method requires keywords: bound (dynesty.bounding object) ndim (number of dimensions) n_cluster (number of dimensions that are clustered) nonbounded array

Returns:

undarray with shape (ndim,)

Position of the final proposed point within the unit cube.

vndarray with shape (ndim,)

Position of the final proposed point in the target parameter space.

loglfloat

Ln(likelihood) of the final proposed point.

ncint

Number of function calls used to generate the sample.

tuning_infodict

Collection of ancillary quantities used to tune scale.

class dynesty.internal_samplers.UnitCubeSampler(**kwargs)

Bases: InternalSampler

Initialize the internal sampler.

Importantely this sets up the sampler_kwargs that is being passed to each .sample() call

Parameters:

kwargsdict

A dictionary of additional method-specific parameters. This common keywords:

nonboundedarray

Array of boolean values indicating which dimensions are non-bounded.

periodicarray

Array of boolean values indicating which dimensions are periodic.

reflectivearray

Array of boolean values indicating which dimensions are reflective.

ndim: int

Number of dimensions.

prepare_sampler(loglstar=None, points=None, axes=None, seeds=None, prior_transform=None, loglikelihood=None, nested_sampler=None)

Prepare the list of arguments for sampling.

Parameters:

loglstarfloat

Ln(likelihood) bound.

pointsndarray with shape (n, ndim)

Initial sample points.

axesndarray with shape (ndim, ndim)

Axes used to propose new points.

seedsndarray with shape (n,)

Random number generator seeds.

prior_transformfunction

Function transforming a sample from the a unit cube to the parameter space of interest according to the prior.

loglikelihoodfunction

Function returning ln(likelihood) given parameters as a 1-d numpy array of length ndim.

nested_samplerSampler

The nested sampler object used to sample.

Returns:

arglist:

List of SamplerArgument objects containing the parameters needed for sampling.

static sample(args)

Return a new live point sampling uniformly within the boundary.

Parameters:

undarray with shape (ndim,)

Initial sample (not used)

loglstarfloat

Ln(likelihood) bound.

axesndarray with shape (ndim, ndim)

Axes used to propose new points. (not used)

scalefloat

Value used to scale the provided axes. (not used)

prior_transformfunction

Function transforming a sample from the a unit cube to the parameter space of interest according to the prior.

loglikelihoodfunction

Function returning ln(likelihood) given parameters as a 1-d numpy array of length ndim.

kwargsdict

A dictionary of additional method-specific parameters. This method requires keywords: bound (dynesty.bounding object) ndim (number of dimensions) n_cluster (number of dimensions that are clustered) nonbounded array

Returns:

undarray with shape (ndim,)

Position of the final proposed point within the unit cube.

vndarray with shape (ndim,)

Position of the final proposed point in the target parameter space.

loglfloat

Ln(likelihood) of the final proposed point.

ncint

Number of function calls used to generate the sample.

tuning_infodict

Collection of ancillary quantities used to tune scale.

Baseline Sampler

The base Sampler class containing various helpful functions. All other samplers inherit this class either explicitly or implicitly.

class dynesty.sampler.Sampler(loglikelihood, prior_transform, ndim, live_points, sampling, bounding, ncdim=None, rstate=None, pool=None, use_pool=None, queue_size=None, bound_update_interval=None, first_bound_update=None, bound_bootstrap=None, bound_enlarge=None, blob=False, cite=None, logvol_init=0)

Bases: object

The basic sampler object that performs the actual nested sampling. It is used for the initial sampling and also for each batch.

Parameters:

loglikelihoodfunction

Function returning ln(likelihood) given parameters as a 1-d numpy array of length ndim.

prior_transformfunction

Function transforming a sample from the a unit cube to the parameter space of interest according to the prior.

ndimint, optional

Number of parameters accepted by prior_transform.

live_pointslist of 3 or 4 ndarray

Each with shape (nlive, ndim) for the first three arrays. If blob=True, a fourth array of blobs (arbitrary shape) may be included.

sampling{'unif', 'rwalk', 'slice', 'rslice'}

Sampling Method used to sample uniformly within the likelihood constraint, conditioned on the provided bounds.

bound_update_intervalint

Only update the bounding distribution every update_interval-th likelihood call.

first_updatedict

A dictionary containing parameters governing when the sampler should first update the bounding distribution from the unit cube to the one specified by the user.

rstateGenerator

Generator instance.

queue_size: int

Carry out likelihood evaluations in parallel by queueing up new live point proposals using (at most) this many threads/members.

pool: pool

Use this pool of workers to execute operations in parallel.

use_pooldict, optional

A dictionary containing flags indicating where the provided pool should be used to execute operations in parallel.

kwargsdict, optional

A dictionary of additional parameters.

ncdim: int, optional

The number of clustering dimensions. The first ncdim dimensions will be sampled using the sampling method, the remaining dimensions will just sample uniformly from the prior distribution. If this is None (default), this will default to ndim.

logvol_init: float, optional

The initial log of volume when starting sampling. This is relevant when the log(L) is finite only within a fraction of prior volume.

Initializes and returns a sampler object for Static Nested Sampling.

Parameters:

loglikelihoodfunction

Function returning ln(likelihood) given parameters as a 1-d numpy array of length ndim.

prior_transformfunction

Function translating a unit cube to the parameter space according to the prior. The input is a 1-d numpy array with length ndim, where each value is in the range [0, 1). The return value should also be a 1-d numpy array with length ndim, where each value is a parameter. The return value is passed to the loglikelihood function. For example, for a 2 parameter model with flat priors in the range [0, 2), the function would be:

def prior_transform(u):
    return 2.0 * u

ndimint

Number of parameters returned by prior_transform and accepted by loglikelihood.

nliveint, optional

Number of “live” points. Larger numbers result in a more finely sampled posterior (more accurate evidence), but also a larger number of iterations required to converge. Default is 500.

bound{'none', 'single', 'multi', 'balls', 'cubes'}, optional

Method used to approximately bound the prior using the current set of live points. Conditions the sampling methods used to propose new live points. Choices are no bound ('none'), a single bounding ellipsoid ('single'), multiple bounding ellipsoids ('multi'), balls centered on each live point ('balls'), and cubes centered on each live point ('cubes'). Default is 'multi'.

sample{'auto', 'unif', 'rwalk', 'slice', 'rslice'},

optional Method used to sample uniformly within the likelihood constraint, conditioned on the provided bounds. Unique methods available are: uniform sampling within the bounds('unif'), random walks with fixed proposals ('rwalk'), multivariate slice sampling along preferred orientations ('slice'), “random” slice sampling along all orientations ('rslice'), 'auto' selects the sampling method based on the dimensionality of the problem (from ndim). When ndim < 10, this defaults to 'unif'. When 10 <= ndim <= 20, this defaults to 'rwalk'. When ndim > 20, this defaults to`’rslice’ `'slice' is provided as alternative for`’rslice’ Default is `'auto'.

periodiciterable, optional

A list of indices for parameters with periodic boundary conditions. These parameters will not have their positions constrained to be within the unit cube, enabling smooth behavior for parameters that may wrap around the edge. Default is None (i.e. no periodic boundary conditions).

reflectiveiterable, optional

A list of indices for parameters with reflective boundary conditions. These parameters will not have their positions constrained to be within the unit cube, enabling smooth behavior for parameters that may reflect at the edge. Default is None (i.e. no reflective boundary conditions).

update_intervalint or float, optional

If an integer is passed, only update the proposal distribution every update_interval-th likelihood call. If a float is passed, update the proposal after every round(update_interval * nlive)-th likelihood call. Larger update intervals larger can be more efficient when the likelihood function is quick to evaluate. Default behavior is to target a roughly constant change in prior volume, with 1.5 for 'unif', 0.15 * walks for 'rwalk'. 0.9 * ndim * slices for 'slice', 2.0 * slices for 'rslice'.

first_updatedict, optional

A dictionary containing parameters governing when the sampler should first update the bounding distribution from the unit cube ('none') to the one specified by sample. Options are the minimum number of likelihood calls ('min_ncall') and the minimum allowed overall efficiency in percent ('min_eff'). Defaults are 2 * nlive and 10., respectively.

rstateGenerator, optional

Generator instance. If not given, the

global random state of the random module will be used.

queue_sizeint, optional

Carry out likelihood evaluations in parallel by queueing up new live point proposals using (at most) queue_size many threads. Each thread independently proposes new live points until the proposal distribution is updated. If no value is passed, this defaults to pool.size (if a pool has been provided) and 1 otherwise (no parallelism).

pooluser-provided pool, optional

Use this pool of workers to execute operations in parallel.

use_pooldict, optional

A dictionary containing flags indicating where a pool should be used to execute operations in parallel. These govern whether prior_transform is executed in parallel during initialization ('prior_transform'), loglikelihood is executed in parallel during initialization ('loglikelihood'), live points are proposed in parallel during a run ('propose_point'), and bounding distributions are updated in parallel during a run ('update_bound'). Default is True for all options.

live_pointslist of 3 ndarray each with shape (nlive, ndim)

A set of live points used to initialize the nested sampling run. Contains live_u, the coordinates on the unit cube, live_v, the transformed variables, and live_logl, the associated loglikelihoods. By default, if these are not provided the initial set of live points will be drawn uniformly from the unit ndim-cube. WARNING: It is crucial that the initial set of live points have been sampled from the prior. Failure to provide a set of valid live points will result in incorrect results.

logl_argsiterable, optional

Additional arguments that can be passed to loglikelihood.

logl_kwargsdict, optional

Additional keyword arguments that can be passed to loglikelihood.

ptform_argsiterable, optional

Additional arguments that can be passed to prior_transform.

ptform_kwargsdict, optional

Additional keyword arguments that can be passed to prior_transform.

enlargefloat, optional

Enlarge the volumes of the specified bounding object(s) by this fraction. The preferred method is to determine this organically using bootstrapping. If bootstrap > 0, this defaults to 1.0. If bootstrap = 0, this instead defaults to 1.25.

bootstrapint, optional

Compute this many bootstrapped realizations of the bounding objects. Use the maximum distance found to the set of points left out during each iteration to enlarge the resulting volumes. Can lead to unstable bounding ellipsoids. Default is None (no bootstrap unless the sampler is uniform). If bootstrap is set to zero, bootstrap is disabled.

walksint, optional

For the 'rwalk' sampling option, the minimum number of steps (minimum 2) before proposing a new live point. Default is 25.

faccfloat, optional

The target acceptance fraction for the 'rwalk' sampling option. Default is 0.5. Bounded to be between [1. / walks, 1.].

slicesint, optional

For the 'slice', 'rslice' sampling options, the number of times to execute a “slice update” before proposing a new live point. Default is 3 for 'slice' and 3+ndim for rslice. Note that 'slice' cycles through all dimensions when executing a “slice update”.

ncdim: int, optional

The number of clustering dimensions. The first ncdim dimensions will be sampled using the sampling method, the remaining dimensions will just sample uniformly from the prior distribution. If this is None (default), this will default to ndim.

blob: bool, optional

The default value is False. If it is true, then the log-likelihood should return the tuple of logl and a numpy-array “blob” that will stored as part of the chain. That blob can contain auxiliary information computed inside the likelihood function.

Returns:

samplersampler from nestedsamplers

An initialized instance of the chosen sampler specified via bound.

_fill_queue(loglstar)

Sequentially add new live point proposals to the queue.

_get_point_value(loglstar)

Grab the first live point proposal in the queue.

_new_point(loglstar)

Propose points until a new point that satisfies the log-likelihood constraint loglstar is found.

_remove_live_points()

Remove the final set of live points if they were previously added to the current set of dead points.

add_final_live(print_progress=True, print_func=None)

A wrapper that executes the loop adding the final live points. Adds the final set of live points to the pre-existing sequence of dead points from the current nested sampling run.

Parameters:

print_progressbool, optional

Whether or not to output a simple summary of the current run that updates with each iteration. Default is True.

print_funcfunction, optional

A function that prints out the current state of the sampler. If not provided, the default results.print_fn() is used.

add_live_points()

Add the remaining set of live points to the current set of dead points. Instantiates a generator that will be called by the user. Returns the same outputs as sample().

property citations

Return list of papers that should be cited given the specified configuration of the sampler.

property n_effective

Estimate the effective number of posterior samples using the Kish Effective Sample Size (ESS) where ESS = sum(wts)^2 / sum(wts^2). Note that this is len(wts) when wts are uniform and 1 if there is only one non-zero element in wts.

propose_live(*args)

Return a live point/axes to be used by other sampling methods. If args is not empty, it contains the subset of indices of points to sample from.

reset()

Re-initialize the sampler.

static restore(fname, pool=None)

Restore the sampler from a file. It is assumed that the file was created using .save() method of a sampler or as a result of checkpointing during run_nested()

Parameters:

fname: string

Filename of the save file.

pool: object(optional)

The multiprocessing pool-like object that supports map() calls that will be used in the restored object.

property results

Saved results from the nested sampling run. If bounding distributions were saved, those are also returned.

run_nested(maxiter=None, maxcall=None, dlogz=None, logl_max=inf, add_live=True, print_progress=True, print_func=None, save_bounds=True, checkpoint_file=None, checkpoint_every=60, resume=False)

A wrapper that executes the main nested sampling loop. Iteratively replace the worst live point with a sample drawn uniformly from the prior until the provided stopping criteria are reached.

Parameters:

maxiterint, optional

Maximum number of iterations. Iteration may stop earlier if the termination condition is reached. Default is sys.maxsize (no limit).

maxcallint, optional

Maximum number of likelihood evaluations. Iteration may stop earlier if termination condition is reached. Default is sys.maxsize (no limit).

dlogzfloat, optional

Iteration will stop when the estimated contribution of the remaining prior volume to the total evidence falls below this threshold. Explicitly, the stopping criterion is ln(z + z_est) - ln(z) < dlogz, where z is the current evidence from all saved samples and z_est is the estimated contribution from the remaining volume. If add_live is True, the default is 1e-3 * (nlive - 1) + 0.01. Otherwise, the default is 0.01.

logl_maxfloat, optional

Iteration will stop when the sampled ln(likelihood) exceeds the threshold set by logl_max. Default is no bound (np.inf).

add_livebool, optional

Whether or not to add the remaining set of live points to the list of samples at the end of each run. Default is True.

print_progressbool, optional

Whether or not to output a simple summary of the current run that updates with each iteration. Default is True.

print_funcfunction, optional

A function that prints out the current state of the sampler. If not provided, the default results.print_fn() is used.

save_boundsbool, optional

Whether or not to save past bounding distributions used to bound the live points internally. Default is True.

checkpoint_file: string, optional

if not None The state of the sampler will be saved into this file every checkpoint_every seconds

checkpoint_every: float, optional

The number of seconds between checkpoints that will save the internal state of the sampler. The sampler will also be saved in the end of the run irrespective of checkpoint_every.

sample(maxiter=None, maxcall=None, dlogz=0.01, logl_max=inf, add_live=True, save_bounds=True, resume=False)

The main nested sampling loop. Iteratively replace the worst live point with a sample drawn uniformly from the prior until the provided stopping criteria are reached. Instantiates a generator that will be called by the user.

Parameters:

maxiterint, optional

Maximum number of iterations. Iteration may stop earlier if the termination condition is reached. Default is sys.maxsize (no limit).

maxcallint, optional

Maximum number of likelihood evaluations. Iteration may stop earlier if termination condition is reached. Default is sys.maxsize (no limit).

dlogzfloat, optional

Iteration will stop when the estimated contribution of the remaining prior volume to the total evidence falls below this threshold. Explicitly, the stopping criterion is ln(z + z_est) - ln(z) < dlogz, where z is the current evidence from all saved samples and z_est is the estimated contribution from the remaining volume. Default is 0.01.

logl_maxfloat, optional

Iteration will stop when the sampled ln(likelihood) exceeds the threshold set by logl_max. Default is no bound (np.inf).

add_livebool, optional

Whether or not to add the remaining set of live points to the list of samples when calculating n_effective. Default is True.

save_boundsbool, optional

Whether or not to save past distributions used to bound the live points internally. Default is True.

Returns:

worstint

Index of the live point with the worst likelihood. This is our new dead point sample.

ustarndarray with shape (ndim,)

Position of the sample.

vstarndarray with shape (ndim,)

Transformed position of the sample.

loglstarfloat

Ln(likelihood) of the sample.

logvolfloat

Ln(prior volume) within the sample.

logwtfloat

Ln(weight) of the sample.

logzfloat

Cumulative ln(evidence) up to the sample (inclusive).

logzvarfloat

Estimated cumulative variance on logz (inclusive).

hfloat

Cumulative information up to the sample (inclusive).

ncint

Number of likelihood calls performed before the new live point was accepted.

worst_itint

Iteration when the live (now dead) point was originally proposed.

boundidxint

Index of the bound the dead point was originally drawn from.

bounditerint

Index of the bound being used at the current iteration.

efffloat

The cumulative sampling efficiency (in percent).

delta_logzfloat

The estimated remaining evidence expressed as the ln(ratio) of the current evidence.

save(fname)

Save the state of the sampler in a file

Parameters:

fname: string

Filename of the save file.

update_bound(subset=slice(None, None, None))

Update the bounds using the current set of live points.

update_bound_if_needed(loglstar, ncall=None, force=False)

Here we update the bound depending on the situation The arguments are the loglstar and number of calls if force is true we update the bound no matter what

Dynamic Nested Sampler

Contains the dynamic nested sampler class DynamicSampler used to dynamically allocate nested samples. Note that DynamicSampler implicitly wraps a sampler from nestedsamplers. Also contains the weight function weight_function() and stopping function stopping_function(). These are used by default within DynamicSampler if corresponding functions are not provided by the user.

class dynesty.dynamicsampler.DynamicSampler(loglikelihood, prior_transform, ndim, sampling, bounding, nlive0=None, ncdim=None, rstate=None, pool=None, use_pool=None, queue_size=None, bound_update_interval_ratio=None, first_bound_update=None, bound_bootstrap=None, bound_enlarge=None, blob=None, cite=None)

Bases: object

A dynamic nested sampler that allocates live points adaptively during a single run according to a specified weight function until a specified stopping criteria is reached.

Parameters:

loglikelihoodfunction

Function returning ln(likelihood) given parameters as a 1-d numpy array of length ndim.

prior_transformfunction

Function transforming a sample from the a unit cube to the parameter space of interest according to the prior.

ndimint, optional

Number of parameters accepted by prior_transform.

bound{'none', 'single', 'multi', 'balls', 'cubes'}, optional

Method used to approximately bound the prior using the current set of live points. Conditions the sampling methods used to propose new live points.

method{'unif', 'rwalk',

'slice', 'rslice'}, optional Method used to sample uniformly within the likelihood constraint, conditioned on the provided bounds.

update_intervalint

Only update the bounding distribution every update_interval-th likelihood call.

first_updatedict

A dictionary containing parameters governing when the sampler should first update the bounding distribution from the unit cube to the one specified by the user.

rstateGenerator

Generator instance.

queue_size: int

Carry out likelihood evaluations in parallel by queueing up new live point proposals using (at most) this many threads/members.

pool: pool

Use this pool of workers to execute operations in parallel.

use_pooldict

A dictionary containing flags indicating where the provided pool should be used to execute operations in parallel.

ncdim: int

Number of clustered dimensions

nlive0: int

Default number of live points to use

kwargsdict, optional

A dictionary of additional parameters (described below).

__get_update_interval(update_interval, nlive)

add_batch(nlive=500, dlogz=0.01, mode='weight', wt_function=None, wt_kwargs=None, maxiter=None, maxcall=None, logl_bounds=None, save_bounds=True, print_progress=True, print_func=None, stop_val=None, resume=False, checkpoint_file=None, checkpoint_every=None)

Allocate an additional batch of (nested) samples based on the combined set of previous samples using the specified weight function.

Parameters:

nliveint, optional

The number of live points used when adding additional samples in the batch. Default is 500.

mode: string, optional

How to allocate a new batch. The possible values are ‘auto’, ‘weight’, ‘full’, ‘manual’ ‘weight’ means to use the weight_function to decide the optimal logl range. ‘full’ means sample the whole posterior again ‘auto’ means choose automatically, which currently means using ‘weight’ ‘manual’ means that logl_bounds need to be explicitely specified

wt_functionfunc, optional

A cost function that takes a Results instance and returns a log-likelihood range over which a new batch of samples should be generated. The default function simply computes a weighted average of the posterior and evidence information content as:

weight = pfrac * pweight + (1. - pfrac) * zweight

wt_kwargsdict, optional

Extra arguments to be passed to the weight function.

maxiterint, optional

Maximum number of iterations allowed. Default is sys.maxsize (no limit).

maxcallint, optional

Maximum number of likelihood evaluations allowed. Default is sys.maxsize (no limit).

logl_boundstuple of size (2,), optional

The ln(likelihood) bounds used to bracket the run. If None, the provided wt_function will be used to determine the bounds (this is the default behavior).

save_boundsbool, optional

Whether or not to save distributions used to bound the live points internally during dynamic live point allocations. Default is True.

print_progressbool, optional

Whether to output a simple summary of the current run that updates each iteration. Default is True.

print_funcfunction, optional

A function that prints out the current state of the sampler. If not provided, the default results.print_fn() is used.

stop_valfloat, optional

The value of the stopping criteria to be passed to print_func(). Used internally within run_nested() to keep track of progress.

resume: bool, optional

If resume is set to true, we will try to resume a previously interrupted run

checkpoint_file: string, optional

if not None The state of the sampler will be saved into this file every checkpoint_every seconds

checkpoint_every: float, optional

The number of seconds between checkpoints that will save the internal state of the sampler. If this is None, we we will use the timer created in run_nested()

property citations

Return list of papers that should be cited given the specified configuration of the sampler.

combine_runs()

Merge the most recent run into the previous (combined) run by “stepping through” both runs simultaneously.

property n_effective

Estimate the effective number of posterior samples using the Kish Effective Sample Size (ESS) where ESS = sum(wts)^2 / sum(wts^2). Note that this is len(wts) when wts are uniform and 1 if there is only one non-zero element in wts.

reset()

Re-initialize the sampler.

static restore(fname, pool=None)

Restore the dynamic sampler from a file. It is assumed that the file was created using .save() method of DynamicNestedSampler or as a result of checkpointing during run_nested()

Parameters:

fname: string

Filename of the save file.

pool: object(optional)

The multiprocessing pool-like object that supports map() calls that will be used in the restored object.

property results

Saved results from the dynamic nested sampling run. All saved bounds are also returned.

run_nested(nlive_init=None, maxiter_init=None, maxcall_init=None, dlogz_init=0.01, logl_max_init=inf, nlive_batch=None, wt_function=None, wt_kwargs=None, maxiter_batch=None, maxcall_batch=None, maxiter=None, maxcall=None, maxbatch=None, n_effective=None, stop_function=None, stop_kwargs=None, use_stop=True, save_bounds=True, print_progress=True, print_func=None, live_points=None, resume=False, checkpoint_file=None, checkpoint_every=60)

The main dynamic nested sampling loop. After an initial “baseline” run using a constant number of live points, dynamically allocates additional (nested) samples to optimize a specified weight function until a specified stopping criterion is reached.

Parameters:

nlive_initint, optional

The number of live points used during the initial (“baseline”) nested sampling run. Default is the number provided at initialization

maxiter_initint, optional

Maximum number of iterations for the initial baseline nested sampling run. Iteration may stop earlier if the termination condition is reached. Default is sys.maxsize (no limit).

maxcall_initint, optional

Maximum number of likelihood evaluations for the initial baseline nested sampling run. Iteration may stop earlier if the termination condition is reached. Default is sys.maxsize (no limit).

dlogz_initfloat, optional

The baseline run will stop when the estimated contribution of the remaining prior volume to the total evidence falls below this threshold. Explicitly, the stopping criterion is ln(z + z_est) - ln(z) < dlogz, where z is the current evidence from all saved samples and z_est is the estimated contribution from the remaining volume. The default is 0.01.

logl_max_initfloat, optional

The baseline run will stop when the sampled ln(likelihood) exceeds this threshold. Default is no bound (np.inf).

nlive_batchint, optional

The number of live points used when adding additional samples from a nested sampling run within each batch. Default is the number provided at init

wt_functionfunc, optional

A cost function that takes a Results instance and returns a log-likelihood range over which a new batch of samples should be generated. The default function simply computes a weighted average of the posterior and evidence information content as:

weight = pfrac * pweight + (1. - pfrac) * zweight

wt_kwargsdict, optional

Extra arguments to be passed to the weight function.

maxiter_batchint, optional

Maximum number of iterations for the nested sampling run within each batch. Iteration may stop earlier if the termination condition is reached. Default is sys.maxsize (no limit).

maxcall_batchint, optional

Maximum number of likelihood evaluations for the nested sampling run within each batch. Iteration may stop earlier if the termination condition is reached. Default is sys.maxsize (no limit).

maxiterint, optional

Maximum number of iterations allowed. Default is sys.maxsize (no limit).

maxcallint, optional

Maximum number of likelihood evaluations allowed. Default is sys.maxsize (no limit).

maxbatchint, optional

Maximum number of batches allowed. Default is sys.maxsize (no limit).

n_effective: int, optional

Minimum number of effective posterior samples needed during the entire run. If the estimated “effective sample size” (ESS) exceeds this number, sampling will terminate. Default is max(10000, ndim^2)

stop_functionfunc, optional

A function that takes a Results instance and returns a boolean indicating that we should terminate the run because we’ve collected enough samples.

stop_kwargsfloat, optional

Extra arguments to be passed to the stopping function.

use_stopbool, optional

Whether to evaluate our stopping function after each batch. Disabling this can improve performance if other stopping criteria such as maxcall are already specified. Default is True.

save_boundsbool, optional

Whether or not to save distributions used to bound the live points internally during dynamic live point allocation. Default is True.

print_progressbool, optional

Whether to output a simple summary of the current run that updates each iteration. Default is True.

print_funcfunction, optional

A function that prints out the current state of the sampler. If not provided, the default results.print_fn() is used.

live_points: list of 3 `~numpy.ndarray` each with shape (nlive, ndim)

and optionally list of blobs associated with these likelihood calls (if blob=True in the sampler) A set of live points used to initialize the nested sampling run. Contains live_u, the coordinates on the unit cube, live_v, the transformed variables, and live_logl, the associated loglikelihoods. By default, if these are not provided the initial set of live points will be drawn from the unit ndim-cube. WARNING: It is crucial that the initial set of live points have been sampled from the prior. Failure to provide a set of valid live points will lead to incorrect results.

resume: bool, optional

If resume is set to true, we will try to resume a previously interrupted run

checkpoint_file: string, optional

if not None The state of the sampler will be saved into this file every checkpoint_every seconds

checkpoint_every: float, optional

The number of seconds between checkpoints that will save the internal state of the sampler

sample_batch(dlogz=0.01, nlive_new=None, update_interval=None, logl_bounds=None, maxiter=None, maxcall=None, save_bounds=True, resume=False)

Generate an additional series of nested samples that will be combined with the previous set of dead points. Works by hacking the internal sampler object. Instantiates a generator that will be called by the user.

Parameters:

nlive_newint

Number of new live points to be added. Default is 500.

update_intervalint or float, optional

If an integer is passed, only update the bounding distribution every update_interval-th likelihood call. If a float is passed, update the bound after every round(update_interval * nlive)-th likelihood call. Larger update intervals can be more efficient when the likelihood function is quick to evaluate. If no value is provided, defaults to the value passed during initialization.

logl_boundstuple of size (2,), optional

The ln(likelihood) bounds used to bracket the run. If None, the default bounds span the entire range covered by the original run.

maxiterint, optional

Maximum number of iterations. Iteration may stop earlier if the termination condition is reached. Default is sys.maxsize (no limit).

maxcallint, optional

Maximum number of likelihood evaluations. Iteration may stop earlier if termination condition is reached. Default is sys.maxsize (no limit).

save_boundsbool, optional

Whether or not to save past distributions used to bound the live points internally. Default is True.

dlogzfloat, optional

The stopping point in terms of remaining delta(logz)

Returns:

worstint

Index of the live point with the worst likelihood. This is our new dead point sample. Negative values indicate the index of a new live point generated when initializing a new batch.

ustarndarray with shape (ndim,)

Position of the sample.

vstarndarray with shape (ndim,)

Transformed position of the sample.

loglstarfloat

Ln(likelihood) of the sample.

ncint

Number of likelihood calls performed before the new live point was accepted.

worst_itint

Iteration when the live (now dead) point was originally proposed.

boundidxint

Index of the bound the dead point was originally drawn from.

bounditerint

Index of the bound being used at the current iteration.

efffloat

The cumulative sampling efficiency (in percent).

sample_initial(nlive=None, update_interval=None, first_update=None, maxiter=None, maxcall=None, logl_max=inf, dlogz=0.01, live_points=None, resume=False)

Generate a series of initial samples from a nested sampling run using a fixed number of live points using an internal sampler from nestedsamplers. Instantiates a generator that will be called by the user.

Parameters:

nliveint, optional

The number of live points to use for the baseline nested sampling run. Default is either nlive0 parameter of 500

update_intervalint or float, optional

If an integer is passed, only update the bounding distribution every update_interval-th likelihood call. If a float is passed, update the bound after every round(update_interval * nlive)-th likelihood call. Larger update intervals can be more efficient when the likelihood function is quick to evaluate. If no value is provided, defaults to the value passed during initialization.

first_updatedict, optional

A dictionary containing parameters governing when the sampler will first update the bounding distribution from the unit cube ('none') to the one specified by sample.

maxiterint, optional

Maximum number of iterations. Iteration may stop earlier if the termination condition is reached. Default is sys.maxsize (no limit).

maxcallint, optional

Maximum number of likelihood evaluations. Iteration may stop earlier if termination condition is reached. Default is sys.maxsize (no limit).

dlogzfloat, optional

Iteration will stop when the estimated contribution of the remaining prior volume to the total evidence falls below this threshold. Explicitly, the stopping criterion is ln(z + z_est) - ln(z) < dlogz, where z is the current evidence from all saved samples and z_est is the estimated contribution from the remaining volume. The default is 0.01.

logl_maxfloat, optional

Iteration will stop when the sampled ln(likelihood) exceeds the threshold set by logl_max. Default is no bound (np.inf).

live_points: list of 3 `~numpy.ndarray` each with shape (nlive, ndim)

and optionally list of blobs associated with these likelihood calls (if blob=True in the sampler) A set of live points used to initialize the nested sampling run. Contains live_u, the coordinates on the unit cube, live_v, the transformed variables, and live_logl, the associated loglikelihoods. By default, if these are not provided the initial set of live points will be drawn from the unit ndim-cube. WARNING: It is crucial that the initial set of live points have been sampled from the prior. Failure to provide a set of valid live points will lead to incorrect results.

Returns:

worstint

Index of the live point with the worst likelihood. This is our new dead point sample.

ustarndarray with shape (ndim,)

Position of the sample.

vstarndarray with shape (ndim,)

Transformed position of the sample.

loglstarfloat

Ln(likelihood) of the sample.

logvolfloat

Ln(prior volume) within the sample.

logwtfloat

Ln(weight) of the sample.

logzfloat

Cumulative ln(evidence) up to the sample (inclusive).

logzvarfloat

Estimated cumulative variance on logz (inclusive).

hfloat

Cumulative information up to the sample (inclusive).

ncint

Number of likelihood calls performed before the new live point was accepted.

worst_itint

Iteration when the live (now dead) point was originally proposed.

boundidxint

Index of the bound the dead point was originally drawn from.

bounditerint

Index of the bound being used at the current iteration.

efffloat

The cumulative sampling efficiency (in percent).

delta_logzfloat

The estimated remaining evidence expressed as the ln(ratio) of the current evidence.

save(fname)

Save the state of the dynamic sampler in a file

Parameters:

fname: string

Filename of the save file.

dynesty.dynamicsampler.stopping_function(results, args=None, rstate=None, mapper=None, return_vals=False)

The default stopping function utilized by DynamicSampler. Zipped parameters are passed to the function via args. Assigns the run a stopping value based on a weighted average of the stopping values for the posterior and evidence:

stop = pfrac * stop_post + (1.- pfrac) * stop_evid

The evidence stopping value is based on the estimated evidence error (i.e. standard deviation) relative to a given threshold:

stop_evid = evid_std / evid_thresh

The posterior stopping value is based on the estimated effective number of samples.

stop_post = target_n_effective / n_effective

Estimates of the mean and standard deviation are computed using n_mc realizations of the input using a provided 'error' keyword (either 'jitter' or 'resample', which call related functions jitter_run() and resample_run() in dynesty.utils, respectively.

Returns the boolean stop <= 1. If True, the DynamicSampler will stop adding new samples to our results.

Parameters:

resultsResults instance

Results instance.

argsdictionary of keyword arguments, optional

Arguments used to set the stopping values. Default values are pfrac = 1.0, evid_thresh = 0.1, target_n_effective = 10000, n_mc = 0, error = 'jitter', and approx = True.

rstateGenerator, optional

Generator instance.

mappermap function, optional

An alias to a map-like function. This allows users to pass functions from pools (e.g., pool.map) to compute realizations in parallel. By default the standard map function is used.

return_valsbool, optional

Whether to return the stopping value (and its components). Default is False.

Returns:

stop_flagbool

Boolean flag indicating whether we have passed the desired stopping criteria.

stop_valstuple of shape (3,), optional

The individual stopping values (stop_post, stop_evid, stop) used to determine the stopping criteria.

dynesty.dynamicsampler.weight_function(results, args=None, return_weights=False)

The default weight function utilized by DynamicSampler. Zipped parameters are passed to the function via args. Assigns each point a weight based on a weighted average of the posterior and evidence information content:

weight = pfrac * pweight + (1. - pfrac) * zweight

where pfrac is the fractional importance placed on the posterior, the evidence weight zweight is based on the estimated remaining posterior mass, and the posterior weight pweight is the sample’s importance weight.

Returns a set of log-likelihood bounds set by the earliest/latest samples where weight > maxfrac * max(weight), with additional left/right padding based on pad.

Parameters:

resultsResults instance

Results instance.

argsdictionary of keyword arguments, optional

Arguments used to set the log-likelihood bounds used for sampling, as described above. Default values are pfrac = 0.8, maxfrac = 0.8, and pad = 1.

return_weightsbool, optional

Whether to return the individual weights (and their components) used to compute the log-likelihood bounds. Default is False.

Returns:

logl_boundstuple with shape (2,)

Log-likelihood bounds (logl_min, logl_max) determined by the weights.

weightstuple with shape (3,), optional

The individual weights (pweight, zweight, weight) used to determine logl_bounds.

Sampling Results

Utilities for handling results.

class dynesty.results.Results(key_values)

Bases: object

Contains the full output of a run along with a set of helper functions for summarizing the output. The object is meant to be unchangeable record of the static or dynamic nested run.

Results attributes (name, type, description, array size):

(‘logl’, ‘array[float]’, ‘Log likelihood’, ‘niter’)

(‘samples_it’, ‘array[int]’, ‘the sampling iteration when the sample was proposed (e.g., iteration 570)’, ‘niter’)

(‘samples_id’, ‘array[int]’, ‘The unique ID of the sample XXX (within nlive points)’, None)

(‘samples_n’, ‘array[int]’, ‘The number of live points at the point when the sample was proposed’, ‘niter’)

(‘samples_u’, ‘array[float]’, ‘The coordinates of live points in then unit cube coordinate system’, ‘niter,ndim’)

(‘samples_v’, ‘array[float]’, ‘The coordinates of live points’, ‘niter,ndim’)

(‘samples’, ‘array’, ‘the location (in original coordinates). Identical to samples_v’, ‘niter,ndim’)

(‘niter’, ‘int’, ‘number of iterations’, None)

(‘ncall’, ‘int’, ‘Total number likelihood calls’, None)

(‘logz’, ‘array’, ‘Array of cumulative log(Z) integrals’, ‘niter’)

(‘logzerr’, ‘array’, ‘Array of uncertainty of log(Z)’, ‘niter’)

(‘logwt’, ‘array’, ‘Array of log-posterior weights’, ‘niter’)

(‘eff’, ‘float’, ‘Sampling efficiency’, None)

(‘nlive’, ‘int’, ‘Number of live points for a static run’, None)

(‘logvol’, ‘array[float]’, ‘Logvolumes of dead points’, ‘niter’)

(‘information’, ‘array[float]’, ‘Information Integral H’, ‘niter’)

(‘bound’, ‘array[object]’, ‘the set of bounding objects used to condition proposals for the base run’, ‘nbound’)

(‘bound_iter’, ‘array[int]’, ‘index of the bound being used for an iteration that generated the point’, ‘niter’)

(‘samples_bound’, ‘array[int]’, ‘The index of the bound that the corresponding sample was drawn from’, ‘niter’)

(‘samples_batch’, ‘array[int]’, ‘Tracks the batch during which the samples were proposed’, ‘niter’)

(‘batch_logl_bounds’, ‘array[tuple]’, ‘The log-likelihood bounds used to run a batch.’, ‘nbatch’)

(‘batch_nlive’, ‘array[int]’, ‘The number of live points used for given batch’, ‘nbatch’)

(‘scale’, ‘array[float]’, ‘Scalar scale applied for proposals’, ‘niter’)

(‘blob’, ‘array[]’, ‘The auxiliary blobs computed by the log-likelihood function’, ‘niter’)

(‘proposal_stats’, ‘array[]’, ‘Information from the inner sampler’, ‘niter’)

Initialize the results using the list of key value pairs or a dictionary Results([(‘logl’, [1, 2, 3]), (‘samples_it’,[1,2,3])]) Results(dict(logl=[1, 2, 3], samples_it=[1,2,3]))

_ALLOWED = {'batch_logl_bounds', 'batch_nlive', 'blob', 'bound', 'bound_iter', 'eff', 'information', 'logl', 'logvol', 'logwt', 'logz', 'logzerr', 'ncall', 'niter', 'nlive', 'proposal_stats', 'samples', 'samples_batch', 'samples_bound', 'samples_id', 'samples_it', 'samples_n', 'samples_u', 'samples_v', 'scale'}

asdict()

Return contents of the Results object as dictionary

copy()

return a copy of the object all numpy arrays will be copied too

importance_weights()

Return the importance weights for the each sample.

isdynamic()

Return true if the results was constructed using dynamic nested sampling run with (potentially) variable number of live-points

items()

Return the list of items in the results object as list of key,value pairs

keys()

Return the list of attributes/keys stored in Results

samples_equal(rstate=None)

Return the equally weighted samples in random order.

summary()

Return a formatted string giving a quick summary of the results.

dynesty.results.print_fn(itresult, niter, ncall, add_live_it=None, dlogz=None, stop_val=None, nbatch=None, logl_min=-inf, logl_max=inf, pbar=None)

The default function used to print out results in real time.

Parameters:

itresultIteratorResult or IteratorResultShort

Single iterator output record from the sampler loop that is used for progress/status printing.

niterint

The current iteration of the sampler.

ncallint

The total number of function calls at the current iteration.

add_live_itint, optional

If the last set of live points are being added explicitly, this quantity tracks the sorted index of the current live point being added.

dlogzfloat, optional

The evidence stopping criterion. If not provided, the provided stopping value will be used instead.

stop_valfloat, optional

The current stopping criterion (for dynamic nested sampling). Used if the dlogz value is not specified.

nbatchint, optional

The current batch (for dynamic nested sampling).

logl_minfloat, optional

The minimum log-likelihood used when starting sampling. Default is -np.inf.

logl_maxfloat, optional

The maximum log-likelihood used when stopping sampling. Default is np.inf.

Useful Helper Functions

A collection of useful functions.

class dynesty.utils.DelayTimer(delay)

Bases: object

Utility class that allows us to detect a certain time has passed

Initialise the time with delay of dt seconds

Parameters:

delay: float

The number of seconds in the timer

is_time()

Returns true if more than self.dt seconds has passed since the initialization or last call of successful is_time()

Returns:

ret: bool

True if specified amout of time has passed since the initialization or last successful is_time() call

class dynesty.utils.LogLikelihood(loglikelihood, ndim, blob=False, history_filename=None, save_evaluation_history=False)

Bases: object

Class that calls the likelihood function (using a pool if provided) Also if requested it saves the history of evaluations

Initialize the object.

Parameters:

loglikelihood: function

ndim: int

Dimensionality

history_filename: string

The filename where the history will go

blob: boolean

if True we expect the logl output to be a tuple of logl value and a blob, otherwise it’ll be logl value only

save_evaluation_history: bool

if True, evaluation history (intermediate points) will also be saved. This automatically enables basic history saving.

append_evaluation_history(evaluation_history)

Append evaluation history from samplers to the centralized storage. This method is called by samplers to add their evaluation history.

Parameters:

evaluation_historylist of SamplerHistoryItem

The evaluation history from a sampler

finalize_history()

Finalize and save any remaining history data to file. Call this at the end of sampling to ensure all data is saved.

history_init()

Initialize the hdf5 storage of evaluations

history_save()

Save the actual history from an internal buffer into the file

class dynesty.utils.LoglOutput(v, blob_flag)

Bases: object

Class that encapsulates the output of the likelihood function. The reason we need this wrapper is to preserve the blob associated with the likelihood function.

Initialize the object

Parameters:

v: float or tuple

if blob_flag is true v have to be a tuple of logl and blob if it is False v is just logl

blob_flag: boolean

flag to mark whether the v has a blob or not

class dynesty.utils.RunRecord(dynamic=False)

Bases: object

This is the dictionary like class that saves the results of the nested run so it is basically a collection of various lists of quantities

If dynamic is true. We initialize the class for a dynamic nested run

append(newD)

append new information to the RunRecord in the form a dictionary i.e. run.append(dict(batch=3, niter=44))

keys()

dynesty.utils.jitter_run(res, rstate=None, approx=False)

Probes statistical uncertainties on a nested sampling run by explicitly generating a realization of the prior volume associated with each sample (dead point). Companion function to resample_run().

Parameters:

resResults instance

The Results instance taken from a previous nested sampling run.

rstateGenerator, optional

Generator instance.

approxbool, optional

Whether to approximate all sets of uniform order statistics by their associated marginals (from the Beta distribution). Default is False.

Returns:

new_resResults instance

A new Results instance with corresponding weights based on our “jittered” prior volume realizations.

dynesty.utils.kld_error(res, error='jitter', rstate=None, return_new=False, approx=False)

Computes the Kullback-Leibler (KL) divergence from the discrete probability distribution defined by res to the discrete probability distribution defined by a realization of res.

Parameters:

resResults instance

Results instance for the distribution we are computing the KL divergence from.

error{'jitter', 'resample'}, optional

The error method employed, corresponding to jitter_run() or resample_run(). Default is 'jitter'.

rstateGenerator, optional

Generator instance.

return_newbool, optional

Whether to return the realization of the run used to compute the KL divergence. Default is False.

approxbool, optional

Whether to approximate all sets of uniform order statistics by their associated marginals (from the Beta distribution). Default is False.

Returns:

kldndarray with shape (nsamps,)

The cumulative KL divergence defined from res to a random realization of res.

new_resResults instance, optional

The Results instance corresponding to the random realization we computed the KL divergence to.

dynesty.utils.mean_and_cov(samples, weights)

Compute the weighted mean and covariance of the samples.

Parameters:

samplesndarray with shape (nsamples, ndim)

2-D array containing data samples. This ordering is equivalent to using rowvar=False in cov.

weightsndarray with shape (nsamples,)

1-D array of sample weights.

Returns:

meanndarray with shape (ndim,)

Weighted sample mean vector.

covndarray with shape (ndim, ndim)

Weighted sample covariance matrix.

Notes

Implements the formulae found here.

dynesty.utils.merge_runs(res_list, print_progress=True)

Merges a set of runs with differing (possibly variable) numbers of live points into one run.

Parameters:

res_listlist of Results instances

A list of Results instances returned from previous runs.

print_progressbool, optional

Whether to output the current progress to stderr. Default is True.

Returns:

combined_resResults instance

The Results instance for the combined run.

dynesty.utils.quantile(x, q, weights=None)

Compute (weighted) quantiles from an input set of samples.

Parameters:

xndarray with shape (nsamps,)

Input samples.

qndarray with shape (nquantiles,)

The list of quantiles to compute from [0., 1.].

weightsndarray with shape (nsamps,), optional

The associated weight from each sample.

Returns:

quantilesndarray with shape (nquantiles,)

The weighted sample quantiles computed at q.

dynesty.utils.resample_equal(samples, weights, rstate=None)

Resample a set of points from the weighted set of inputs such that they all have equal weight. The points are also randomly shuffled.

Each input sample appears in the output array either floor(weights[i] * nsamples) or ceil(weights[i] * nsamples) times, with floor or ceil randomly selected (weighted by proximity).

Parameters:

samplesndarray with shape (nsamples,)

Set of unequally weighted samples.

weightsndarray with shape (nsamples,)

Corresponding weight of each sample.

rstateGenerator, optional

Generator instance.

Returns:

equal_weight_samplesndarray with shape (nsamples,)

New set of samples with equal weights in random order.

Notes

Implements the systematic resampling method described in Hol, Schon, and Gustafsson (2006).

Examples

>>> x = np.array([[1., 1.], [2., 2.], [3., 3.], [4., 4.]])
>>> w = np.array([0.6, 0.2, 0.15, 0.05])
>>> utils.resample_equal(x, w)
array([[ 1.,  1.],
       [ 1.,  1.],
       [ 1.,  1.],
       [ 3.,  3.]])

dynesty.utils.resample_run(res, rstate=None, return_idx=False)

Probes sampling uncertainties on a nested sampling run using bootstrap resampling techniques to generate a realization of the (expected) prior volume(s) associated with each sample (dead point). This effectively splits a nested sampling run with K particles (live points) into a series of K “strands” (i.e. runs with a single live point) which are then bootstrapped to construct a new “resampled” run. Companion function to jitter_run().

Parameters:

resResults instance

The Results instance taken from a previous nested sampling run.

rstateGenerator, optional

Generator instance.

return_idxbool, optional

Whether to return the list of resampled indices used to construct the new run. Default is False.

Returns:

new_resResults instance

A new Results instance with corresponding samples and weights based on our “bootstrapped” samples and (expected) prior volumes.

dynesty.utils.reweight_run(res, logp_new, logp_old=None)

Reweight a given run based on a new target distribution.

Parameters:

resResults instance

The Results instance taken from a previous nested sampling run.

logp_newndarray with shape (nsamps,)

New target distribution evaluated at the location of the samples.

logp_oldndarray with shape (nsamps,)

Old target distribution evaluated at the location of the samples. If not provided, the logl values from res will be used.

Returns:

new_resResults instance

A new Results instance with corresponding weights based on our reweighted samples.

dynesty.utils.unitcheck(u, nonbounded=None)

Check whether u is inside the unit cube. Given a masked array nonbounded, also allows periodic boundaries conditions to exceed the unit cube.

dynesty.utils.unravel_run(res, print_progress=True)

Unravels a run with K live points into K “strands” (a nested sampling run with only 1 live point). WARNING: the anciliary quantities provided with each unraveled “strand” are only valid if the point was initialized from the prior.

Parameters:

resResults instance

The Results instance taken from a previous nested sampling run.

print_progressbool, optional

Whether to output the current progress to stderr. Default is True.

Returns:

new_reslist of Results instances

A list of new Results instances for each individual strand.

Plotting Utilities

A set of built-in plotting functions to help visualize dynesty nested sampling Results.

dynesty.plotting._hist2d(x, y, smooth=0.02, span=None, weights=None, levels=None, ax=None, color='gray', plot_datapoints=False, plot_density=True, plot_contours=True, no_fill_contours=False, fill_contours=True, contour_kwargs=None, contourf_kwargs=None, data_kwargs=None)

Internal function called by cornerplot() used to generate a a 2-D histogram/contour of samples.

Parameters:

xinterable with shape (nsamps,)

Sample positions in the first dimension.

yiterable with shape (nsamps,)

Sample positions in the second dimension.

spaniterable with shape (ndim,), optional

A list where each element is either a length-2 tuple containing lower and upper bounds or a float from (0., 1.] giving the fraction of (weighted) samples to include. If a fraction is provided, the bounds are chosen to be equal-tailed. An example would be:

span = [(0., 10.), 0.95, (5., 6.)]

Default is 0.999999426697 (5-sigma credible interval).

weightsiterable with shape (nsamps,)

Weights associated with the samples. Default is None (no weights).

levelsiterable, optional

The contour levels to draw. Default are [0.5, 1, 1.5, 2]-sigma.

axAxes, optional

An axes instance on which to add the 2-D histogram. If not provided, a figure will be generated.

colorstr, optional

The matplotlib-style color used to draw lines and color cells and contours. Default is 'gray'.

plot_datapointsbool, optional

Whether to plot the individual data points. Default is False.

plot_densitybool, optional

Whether to draw the density colormap. Default is True.

plot_contoursbool, optional

Whether to draw the contours. Default is True.

no_fill_contoursbool, optional

Whether to add absolutely no filling to the contours. This differs from fill_contours=False, which still adds a white fill at the densest points. Default is False.

fill_contoursbool, optional

Whether to fill the contours. Default is True.

contour_kwargsdict

Any additional keyword arguments to pass to the contour method.

contourf_kwargsdict

Any additional keyword arguments to pass to the contourf method.

data_kwargsdict

Any additional keyword arguments to pass to the plot method when adding the individual data points.

dynesty.plotting.boundplot(results, dims, it=None, idx=None, prior_transform=None, periodic=None, reflective=None, ndraws=5000, color='gray', plot_kwargs=None, labels=None, label_kwargs=None, max_n_ticks=5, use_math_text=False, show_live=False, live_color='darkviolet', live_kwargs=None, span=None, fig=None)

Return the bounding distribution used to propose either (1) live points at a given iteration or (2) a specific dead point during the course of a run, projected onto the two dimensions specified by dims.

Parameters:

resultsResults instance

A Results instance from a nested sampling run.

dimslength-2 tuple

The dimensions used to plot the bounding.

itint, optional

If provided, returns the bounding distribution at the specified iteration of the nested sampling run. Note that this option and `idx` are mutually exclusive.

idxint, optional

If provided, returns the bounding distribution used to propose the dead point at the specified iteration of the nested sampling run. Note that this option and `it` are mutually exclusive.

prior_transformfunc, optional

The function transforming samples within the unit cube back to samples in the native model space. If provided, the transformed bounding distribution will be plotted in the native model space.

periodiciterable, optional

A list of indices for parameters with periodic boundary conditions. These parameters will not have their positions constrained to be within the unit cube, enabling smooth behavior for parameters that may wrap around the edge. Default is None (i.e. no periodic boundary conditions).

reflectiveiterable, optional

A list of indices for parameters with reflective boundary conditions. These parameters will not have their positions constrained to be within the unit cube, enabling smooth behavior for parameters that may reflect at the edge. Default is None (i.e. no reflective boundary conditions).

ndrawsint, optional

The number of random samples to draw from the bounding distribution when plotting. Default is 5000.

colorstr, optional

The color of the points randomly sampled from the bounding distribution. Default is 'gray'.

plot_kwargsdict, optional

Extra keyword arguments used when plotting the bounding draws.

labelsiterable with shape (ndim,), optional

A list of names for each parameter. If not provided, the default name used when plotting will follow \(x_i\) style.

label_kwargsdict, optional

Extra keyword arguments that will be sent to the set_xlabel and set_ylabel methods.

max_n_ticksint, optional

Maximum number of ticks allowed. Default is 5.

use_math_textbool, optional

Whether the axis tick labels for very large/small exponents should be displayed as powers of 10 rather than using e. Default is False.

show_livebool, optional

Whether the live points at a given iteration (for it) or associated with the bounding (for idx) should be highlighted. Default is False. In the dynamic case, only the live points associated with the batch used to construct the relevant bound are plotted.

live_colorstr, optional

The color of the live points. Default is 'darkviolet'.

live_kwargsdict, optional

Extra keyword arguments used when plotting the live points.

spaniterable with shape (2,), optional

A list where each element is a length-2 tuple containing lower and upper bounds. Default is None (no bound).

fig(Figure, Axes), optional

If provided, overplot the draws onto the provided figure. Otherwise, by default an internal figure is generated.

Returns:

bounding_plot(Figure, Axes)

Output plot of the bounding distribution.

dynesty.plotting.cornerbound(results, it=None, idx=None, dims=None, prior_transform=None, periodic=None, reflective=None, ndraws=5000, color='gray', plot_kwargs=None, labels=None, label_kwargs=None, max_n_ticks=5, use_math_text=False, show_live=False, live_color='darkviolet', live_kwargs=None, span=None, fig=None)

Return the bounding distribution used to propose either (1) live points at a given iteration or (2) a specific dead point during the course of a run, projected onto all pairs of dimensions.

Parameters:

resultsResults instance

A Results instance from a nested sampling run.

itint, optional

If provided, returns the bounding distribution at the specified iteration of the nested sampling run. Note that this option and `idx` are mutually exclusive.

idxint, optional

If provided, returns the bounding distribution used to propose the dead point at the specified iteration of the nested sampling run. Note that this option and `it` are mutually exclusive.

dimsiterable of shape (ndim,), optional

The subset of dimensions that should be plotted. If not provided, all dimensions will be shown.

prior_transformfunc, optional

The function transforming samples within the unit cube back to samples in the native model space. If provided, the transformed bounding distribution will be plotted in the native model space.

periodiciterable, optional

A list of indices for parameters with periodic boundary conditions. These parameters will not have their positions constrained to be within the unit cube, enabling smooth behavior for parameters that may wrap around the edge. Default is None (i.e. no periodic boundary conditions).

reflectiveiterable, optional

A list of indices for parameters with reflective boundary conditions. These parameters will not have their positions constrained to be within the unit cube, enabling smooth behavior for parameters that may reflect at the edge. Default is None (i.e. no reflective boundary conditions).

ndrawsint, optional

The number of random samples to draw from the bounding distribution when plotting. Default is 5000.

colorstr, optional

The color of the points randomly sampled from the bounding distribution. Default is 'gray'.

plot_kwargsdict, optional

Extra keyword arguments used when plotting the bounding draws.

labelsiterable with shape (ndim,), optional

A list of names for each parameter. If not provided, the default name used when plotting will be in \(x_i\) style.

label_kwargsdict, optional

Extra keyword arguments that will be sent to the set_xlabel and set_ylabel methods.

max_n_ticksint, optional

Maximum number of ticks allowed. Default is 5.

use_math_textbool, optional

Whether the axis tick labels for very large/small exponents should be displayed as powers of 10 rather than using e. Default is False.

show_livebool, optional

Whether the live points at a given iteration (for it) or associated with the bounding (for idx) should be highlighted. Default is False. In the dynamic case, only the live points associated with the batch used to construct the relevant bound are plotted.

live_colorstr, optional

The color of the live points. Default is 'darkviolet'.

live_kwargsdict, optional

Extra keyword arguments used when plotting the live points.

spaniterable with shape (2,), optional

A list where each element is a length-2 tuple containing lower and upper bounds. Default is None (no bound).

fig(Figure, Axes), optional

If provided, overplot the draws onto the provided figure. Otherwise, by default an internal figure is generated.

Returns:

cornerbound(Figure, Axes)

Output corner plot of the bounding distribution.

dynesty.plotting.cornerplot(results, dims=None, span=None, quantiles=(0.025, 0.5, 0.975), color='black', smooth=0.02, quantiles_2d=None, hist_kwargs=None, hist2d_kwargs=None, labels=None, label_kwargs=None, show_titles=False, title_quantiles=(0.025, 0.5, 0.975), title_fmt='.2f', title_kwargs=None, truths=None, truth_color='red', truth_kwargs=None, max_n_ticks=5, top_ticks=False, use_math_text=False, verbose=False, fig=None)

Generate a corner plot of the 1-D and 2-D marginalized posteriors.

Parameters:

resultsResults instance

A Results instance from a nested sampling run. Compatible with results derived from nestle.

dimsiterable of shape (ndim,), optional

The subset of dimensions that should be plotted. If not provided, all dimensions will be shown.

spaniterable with shape (ndim,), optional

A list where each element is either a length-2 tuple containing lower and upper bounds or a float from (0., 1.] giving the fraction of (weighted) samples to include. If a fraction is provided, the bounds are chosen to be equal-tailed. An example would be:

span = [(0., 10.), 0.95, (5., 6.)]

Default is 0.999999426697 (5-sigma credible interval).

quantilesiterable, optional

A list of fractional quantiles to overplot on the 1-D marginalized posteriors as vertical dashed lines. Default is [0.025, 0.5, 0.975] (spanning the 95%/2-sigma credible interval).

colorstr or iterable with shape (ndim,), optional

A matplotlib-style color (either a single color or a different value for each subplot) used when plotting the histograms. Default is 'black'.

smoothfloat or iterable with shape (ndim,), optional

The standard deviation (either a single value or a different value for each subplot) for the Gaussian kernel used to smooth the 1-D and 2-D marginalized posteriors, expressed as a fraction of the span. Default is 0.02 (2% smoothing). If an integer is provided instead, this will instead default to a simple (weighted) histogram with bins=smooth.

quantiles_2diterable with shape (nquant,), optional

The quantiles used for plotting the smoothed 2-D distributions. If not provided, these default to 0.5, 1, 1.5, and 2-sigma contours roughly corresponding to quantiles of [0.1, 0.4, 0.65, 0.85].

hist_kwargsdict, optional

Extra keyword arguments to send to the 1-D (smoothed) histograms.

hist2d_kwargsdict, optional

Extra keyword arguments to send to the 2-D (smoothed) histograms.

labelsiterable with shape (ndim,), optional

A list of names for each parameter. If not provided, the default name used when plotting will follow \(x_i\) style.

label_kwargsdict, optional

Extra keyword arguments that will be sent to the set_xlabel and set_ylabel methods.

show_titlesbool, optional

Whether to display a title above each 1-D marginalized posterior showing the 0.5 quantile along with the upper/lower bounds associated with the 0.025 and 0.975 (95%/2-sigma credible interval) quantiles. Default is False.

title_quantilesiterable, optional

A list of fractional quantiles to use in the title. Default is [0.025, 0.5, 0.975] (median plus 95%/2-sigma credible interval).

title_fmtstr, optional

The format string for the quantiles provided in the title. Default is '.2f'.

title_kwargsdict, optional

Extra keyword arguments that will be sent to the set_title command.

truthsiterable with shape (ndim,), optional

A list of reference values that will be overplotted on the traces and marginalized 1-D posteriors as solid horizontal/vertical lines. Individual values can be exempt using None. Default is None.

truth_colorstr or iterable with shape (ndim,), optional

A matplotlib-style color (either a single color or a different value for each subplot) used when plotting truths. Default is 'red'.

truth_kwargsdict, optional

Extra keyword arguments that will be used for plotting the vertical and horizontal lines with truths.

max_n_ticksint, optional

Maximum number of ticks allowed. Default is 5.

top_ticksbool, optional

Whether to label the top (rather than bottom) ticks. Default is False.

use_math_textbool, optional

Whether the axis tick labels for very large/small exponents should be displayed as powers of 10 rather than using e. Default is False.

verbosebool, optional

Whether to print the values of the computed quantiles associated with each parameter. Default is False.

fig(Figure, Axes), optional

If provided, overplot the traces and marginalized 1-D posteriors onto the provided figure. Otherwise, by default an internal figure is generated.

Returns:

cornerplot(Figure, Axes)

Output corner plot.

dynesty.plotting.cornerpoints(results, dims=None, thin=1, span=None, cmap='plasma', color=None, kde=True, nkde=1000, plot_kwargs=None, labels=None, label_kwargs=None, truths=None, truth_color='red', truth_kwargs=None, max_n_ticks=5, use_math_text=False, fig=None)

Generate a (sub-)corner plot of (weighted) samples.

Parameters:

resultsResults instance

A Results instance from a nested sampling run. Compatible with results derived from nestle.

dimsiterable of shape (ndim,), optional

The subset of dimensions that should be plotted. If not provided, all dimensions will be shown.

thinint, optional

Thin the samples so that only each thin-th sample is plotted. Default is 1 (no thinning).

spaniterable with shape (ndim,), optional

A list where each element is either a length-2 tuple containing lower and upper bounds or a float from (0., 1.] giving the fraction of (weighted) samples to include. If a fraction is provided, the bounds are chosen to be equal-tailed. An example would be:

span = [(0., 10.), 0.95, (5., 6.)]

Default is 1. for all parameters (no bound).

cmapstr, optional

A matplotlib-style colormap used when plotting the points, where each point is colored according to its weight. Default is 'plasma'.

colorstr, optional

A matplotlib-style color used when plotting the points. This overrides the cmap option by giving all points the same color. Default is None (not used).

kdebool, optional

Whether to use kernel density estimation to estimate and plot the PDF of the importance weights as a function of log-volume (as opposed to the importance weights themselves). Default is True.

nkdeint, optional

The number of grid points used when plotting the kernel density estimate. Default is 1000.

plot_kwargsdict, optional

Extra keyword arguments that will be used for plotting the points.

labelsiterable with shape (ndim,), optional

A list of names for each parameter. If not provided, the default name used when plotting will follow \(x_i\) style.

label_kwargsdict, optional

Extra keyword arguments that will be sent to the set_xlabel and set_ylabel methods.

truthsiterable with shape (ndim,), optional

A list of reference values that will be overplotted on the traces and marginalized 1-D posteriors as solid horizontal/vertical lines. Individual values can be exempt using None. Default is None.

truth_colorstr or iterable with shape (ndim,), optional

A matplotlib-style color (either a single color or a different value for each subplot) used when plotting truths. Default is 'red'.

truth_kwargsdict, optional

Extra keyword arguments that will be used for plotting the vertical and horizontal lines with truths.

max_n_ticksint, optional

Maximum number of ticks allowed. Default is 5.

use_math_textbool, optional

Whether the axis tick labels for very large/small exponents should be displayed as powers of 10 rather than using e. Default is False.

fig(Figure, Axes), optional

If provided, overplot the points onto the provided figure object. Otherwise, by default an internal figure is generated.

Returns:

cornerpoints(Figure, Axes)

Output (sub-)corner plot of (weighted) samples.

dynesty.plotting.runplot(results, span=None, logplot=False, kde=True, nkde=1000, color='blue', plot_kwargs=None, label_kwargs=None, lnz_error=True, lnz_truth=None, truth_color='red', truth_kwargs=None, max_x_ticks=8, max_y_ticks=3, use_math_text=True, mark_final_live=True, fig=None)

Plot live points, ln(likelihood), ln(weight), and ln(evidence) as a function of ln(prior volume).

Parameters:

resultsResults instance

A Results instance from a nested sampling run.

spaniterable with shape (4,), optional

A list where each element is either a length-2 tuple containing lower and upper bounds or a float from (0., 1.] giving the fraction below the maximum. If a fraction is provided, the bounds are chosen to be equal-tailed. An example would be:

span = [(0., 10.), 0.001, 0.2, (5., 6.)]

Default is (0., 1.05 * max(data)) for each element.

logplotbool, optional

Whether to plot the evidence on a log scale. Default is False.

kdebool, optional

Whether to use kernel density estimation to estimate and plot the PDF of the importance weights as a function of log-volume (as opposed to the importance weights themselves). Default is True.

nkdeint, optional

The number of grid points used when plotting the kernel density estimate. Default is 1000.

colorstr or iterable with shape (4,), optional

A matplotlib-style color (either a single color or a different value for each subplot) used when plotting the lines in each subplot. Default is 'blue'.

plot_kwargsdict, optional

Extra keyword arguments that will be passed to plot.

label_kwargsdict, optional

Extra keyword arguments that will be sent to the set_xlabel and set_ylabel methods.

lnz_errorbool, optional

Whether to plot the 1, 2, and 3-sigma approximate error bars derived from the ln(evidence) error approximation over the course of the run. Default is True.

lnz_truthfloat, optional

A reference value for the evidence that will be overplotted on the evidence subplot if provided.

truth_colorstr or iterable with shape (ndim,), optional

A matplotlib-style color used when plotting lnz_truth. Default is 'red'.

truth_kwargsdict, optional

Extra keyword arguments that will be used for plotting lnz_truth.

max_x_ticksint, optional

Maximum number of ticks allowed for the x axis. Default is 8.

max_y_ticksint, optional

Maximum number of ticks allowed for the y axis. Default is 4.

use_math_textbool, optional

Whether the axis tick labels for very large/small exponents should be displayed as powers of 10 rather than using e. Default is False.

mark_final_livebool, optional

Whether to indicate the final addition of recycled live points (if they were added to the resulting samples) using a dashed vertical line. Default is True.

fig(Figure, Axes), optional

If provided, overplot the run onto the provided figure. Otherwise, by default an internal figure is generated.

Returns:

runplot(Figure, Axes)

Output summary plot.

dynesty.plotting.traceplot(results, span=None, quantiles=(0.025, 0.5, 0.975), smooth=0.02, thin=1, dims=None, post_color='blue', post_kwargs=None, kde=True, nkde=1000, trace_cmap='plasma', trace_color=None, trace_kwargs=None, connect=False, connect_highlight=10, connect_color='red', connect_kwargs=None, max_n_ticks=5, use_math_text=False, labels=None, label_kwargs=None, show_titles=False, title_quantiles=(0.025, 0.5, 0.975), title_fmt='.2f', title_kwargs=None, truths=None, truth_color='red', truth_kwargs=None, verbose=False, fig=None)

Plot traces and marginalized posteriors for each parameter.

Parameters:

resultsResults instance

A Results instance from a nested sampling run. Compatible with results derived from nestle.

spaniterable with shape (ndim,), optional

A list where each element is either a length-2 tuple containing lower and upper bounds or a float from (0., 1.] giving the fraction of (weighted) samples to include. If a fraction is provided, the bounds are chosen to be equal-tailed. An example would be:

span = [(0., 10.), 0.95, (5., 6.)]

Default is 0.999999426697 (5-sigma credible interval) for each parameter.

quantilesiterable, optional

A list of fractional quantiles to overplot on the 1-D marginalized posteriors as vertical dashed lines. Default is [0.025, 0.5, 0.975] (the 95%/2-sigma credible interval).

smoothfloat or iterable with shape (ndim,), optional

The standard deviation (either a single value or a different value for each subplot) for the Gaussian kernel used to smooth the 1-D marginalized posteriors, expressed as a fraction of the span. Default is 0.02 (2% smoothing). If an integer is provided instead, this will instead default to a simple (weighted) histogram with bins=smooth.

thinint, optional

Thin the samples so that only each thin-th sample is plotted. Default is 1 (no thinning).

dimsiterable of shape (ndim,), optional

The subset of dimensions that should be plotted. If not provided, all dimensions will be shown.

post_colorstr or iterable with shape (ndim,), optional

A matplotlib-style color (either a single color or a different value for each subplot) used when plotting the histograms. Default is 'blue'.

post_kwargsdict, optional

Extra keyword arguments that will be used for plotting the marginalized 1-D posteriors.

kdebool, optional

Whether to use kernel density estimation to estimate and plot the PDF of the importance weights as a function of log-volume (as opposed to the importance weights themselves). Default is True.

nkdeint, optional

The number of grid points used when plotting the kernel density estimate. Default is 1000.

trace_cmapstr or iterable with shape (ndim,), optional

A matplotlib-style colormap (either a single colormap or a different colormap for each subplot) used when plotting the traces, where each point is colored according to its weight. Default is 'plasma'.

trace_colorstr or iterable with shape (ndim,), optional

A matplotlib-style color (either a single color or a different color for each subplot) used when plotting the traces. This overrides the trace_cmap option by giving all points the same color. Default is None (not used).

trace_kwargsdict, optional

Extra keyword arguments that will be used for plotting the traces.

connectbool, optional

Whether to draw lines connecting the paths of unique particles. Default is False.

connect_highlightint or iterable, optional

If connect=True, highlights the paths of a specific set of particles. If an integer is passed, connect_highlight random particle paths will be highlighted. If an iterable is passed, then the particle paths corresponding to the provided indices will be highlighted.

connect_colorstr, optional

The color of the highlighted particle paths. Default is 'red'.

connect_kwargsdict, optional

Extra keyword arguments used for plotting particle paths.

max_n_ticksint, optional

Maximum number of ticks allowed. Default is 5.

use_math_textbool, optional

Whether the axis tick labels for very large/small exponents should be displayed as powers of 10 rather than using e. Default is False.

labelsiterable with shape (ndim,), optional

A list of names for each parameter. If not provided, the default name used when plotting will follow \(x_i\) style.

label_kwargsdict, optional

Extra keyword arguments that will be sent to the set_xlabel and set_ylabel methods.

show_titlesbool, optional

Whether to display a title above each 1-D marginalized posterior showing the 0.5 quantile along with the upper/lower bounds associated with the 0.025 and 0.975 (95%/2-sigma credible interval) quantiles. Default is False.

title_quantilesiterable, optional

A list of fractional quantiles to use in the title. Default is [0.025, 0.5, 0.975] (median plus 95%/2-sigma credible interval).

title_fmtstr, optional

The format string for the quantiles provided in the title. Default is '.2f'.

title_kwargsdict, optional

Extra keyword arguments that will be sent to the set_title command.

truthsiterable with shape (ndim,), optional

A list of reference values that will be overplotted on the traces and marginalized 1-D posteriors as solid horizontal/vertical lines. Individual values can be exempt using None. Default is None.

truth_colorstr or iterable with shape (ndim,), optional

A matplotlib-style color (either a single color or a different value for each subplot) used when plotting truths. Default is 'red'.

truth_kwargsdict, optional

Extra keyword arguments that will be used for plotting the vertical and horizontal lines with truths.

verbosebool, optional

Whether to print the values of the computed quantiles associated with each parameter. Default is False.

fig(Figure, Axes), optional

If provided, overplot the traces and marginalized 1-D posteriors onto the provided figure. Otherwise, by default an internal figure is generated.

Returns:

traceplot(Figure, Axes)

Output trace plot.

Parallelization helpers

The wrapper around multiprocessing pool that can be helpful with dynesty since it avoids some overhead that one would get with standard pool

class dynesty.pool.Pool(njobs, loglike, prior_transform, logl_args=None, logl_kwargs=None, ptform_args=None, ptform_kwargs=None)

Bases: object

The multiprocessing pool wrapper class It is intended to be used as a context manager for dynesty sampler only.

Parameters:

njobs: int

The number of multiprocessing jobs/processes

loglike: function

ln(likelihood) function

prior_transform: function

Function transforming from a unit cube to the parameter space of interest according to the prior

logl_args: tuple(optional)

The optional arguments to be added to the likelihood function call. Note that if you specify the additional arguments here, you do not need to provide them again to the sampler.

logl_kwargs: tuple(optional)

The optional keywords to be added to the likelihood function call

ptform_args: tuple(optional)

The optional arguments to be added to the prior transform function call

ptform_kwargs: tuple(optional)

The optional keywords to be added to the prior transform function call

Examples

To use the dynesty pool you have to use it with the context manager:

with dynesty.pool.Pool(16, loglike, prior_transform) as pool:
    dns = DynamicNestedSampler(pool.loglike, pool.prior_transform,
                               ndim, pool=pool)

Also note that you have to provide the .loglike/.prior_transform attributes from the pool object to the Nested samper rather than your original functions!

If your likelihood function takes additional arguments, it is better to pass them when creating the pool, rather then to nested sampler:

with dynesty.pool.Pool(16, loglike, prior_transform,
                                    logl_args=(...) ) as pool:
    dns = DynamicNestedSampler(pool.loglike, pool.prior_transform,
                               ndim, pool=pool)

as this way they will not need to be pickled and unpickled every function call.

Note though that if you specify logl_args, and ptform_args when creating the Pool AND in the sampler those will be concatenated

Attributes:

loglike: function

ln(likelihood) function

prior_transform: function

Function transforming from a unit cube to the parameter space of interest according to the prior

close()

join()

map(F, x)

Apply the function F to the list x

Parameters:

F: function

x: iterable

property size

Return the number of processes in the pool