Moves

Proposal distributions for MCMC.

Move Parent Class

class eryn.moves.Move(temperature_control=None, periodic=None, gibbs_sampling_setup=None, prevent_swaps=False, skip_supp_names_update=[], is_rj=False, use_gpu=False, random_seed=None, **kwargs)

Bases: object

Parent class for proposals or “moves”

Parameters:

• temperature_control (tempering.TemperatureControl, optional) – This object controls the tempering. It is passed to the parent class to moves so that all proposals can share and use temperature settings. (default: None)

• ( (periodic) – class:eryn.utils.PeriodicContainer, optional): This object holds periodic information and methods for periodic parameters. It is passed to the parent class to moves so that all proposals can share and use periodic information. (default: ``None`)

• gibbs_sampling_setup (str, tuple, dict, or list, optional) – This sets the Gibbs Sampling setup if desired. The Gibbs sampling setup is completely customizable down to the leaf and parameters. All of the separate Gibbs sampling splits will be run within 1 call to this proposal. If None, run all branches and all parameters. If str, run all parameters within the branch given as the string. To enter a branch with a specific set of parameters, you can provide a 2-tuple with the first entry as the branch name and the second entry as a 2D boolean array of shape (nleaves_max, ndim) that indicates which leaves and/or parameters you want to run. None can also be entered in the second entry if all parameters are to be run. A dictionary is also possible with keys as branch names and values as the same 2D boolean array of shape (nleaves_max, ndim) that indicates which leaves and/or parameters you want to run. None can also be entered in the value of the dictionary if all parameters are to be run. If multiple keys are provided in the dictionary, those branches will be run simultaneously in the proposal as one iteration of the proposing loop. The final option is a list. This is how you make sure to run all the Gibbs splits. Each entry of the list can be a string, 2-tuple, or dictionary as described above. The list controls the order in which all of these splits are run. (default: None)

• prevent_swaps (bool, optional) – If True, do not perform temperature swaps in this move.

• skip_supp_names_update (list, optional) – List of names (str), that can be in any eryn.state.BranchSupplemental, to skip when updating states (Move.update()). This is useful if a large amount of memory is stored in the branch supplementals.

• is_rj (bool, optional) – If using RJ, this should be True. (default: False)

• use_gpu (bool, optional) – If True, use CuPy for computations. Use NumPy if use_gpu == False. (default: False)

• random_seed (int, optional) – Set the random seed in CuPy/NumPy if not None. (default: None)

Raises:

ValueError – Incorrect inputs.

Note

All kwargs are stored as attributes.

num_proposals

the number of times this move has been run. This is needed to compute the acceptance fraction.

Type:

int

gibbs_sampling_setup

All of the Gibbs sampling splits as described above.

Type:

list

xp

NumPy or CuPy.

Type:

obj

use_gpu

Whether Cupy (True) is used or not (False).

Type:

bool

gibbs_sampling_setup_iterator(all_branch_names)

Iterate through the gibbs splits as a generator

Parameters:

all_branch_names (list) – List of all branch names.

Yields:

2-tuple –

Gibbs sampling split.

First entry is the branch names to run and the second entry is the index into the leaves/parameters for this Gibbs split.

Raises:

ValueError – Incorrect inputs.

setup_proposals(branch_names_run, inds_run, branches_coords, branches_inds)

Setup proposals when gibbs sampling.

Get inputs into the proposal including Gibbs split information.

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• branches_coords (dict) – Dictionary of coordinate arrays for all branches.

• branches_inds (dict) – Dictionary of inds arrays for all branches.

Returns:

(coords, inds, at_least_one_proposal)

• Coords including Gibbs sampling info.

• inds including Gibbs sampling info.

• at_least_one_proposal is boolean. It is passed out to

  indicate there is at least one leaf available for the requested branch names.

Return type:

tuple

cleanup_proposals_gibbs(branch_names_run, inds_run, q, branches_coords, new_inds=None, branches_inds=None, new_branch_supps=None, branches_supplemental=None)

Set all not Gibbs-sampled parameters back

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• q (dict) – Dictionary of new coordinate arrays for all proposal branches.

• branches_coords (dict) – Dictionary of old coordinate arrays for all branches.

• new_inds (dict, optional) – Dictionary of new inds arrays for all proposal branches.

• branches_inds (dict, optional) – Dictionary of old inds arrays for all branches.

• new_branch_supps (dict, optional) – Dictionary of new branches supplemental for all proposal branches.

• branches_supplemental (dict, optional) – Dictionary of old branches supplemental for all branches.

ensure_ordering(correct_key_order, q, new_inds, new_branch_supps)

Ensure proper order of key in dictionaries.

Parameters:

• correct_key_order (list) – Keys in correct order.

• q (dict) – Dictionary of new coordinate arrays for all branches.

• new_inds (dict) – Dictionary of new inds arrays for all branches.

• new_branch_supps (dict or None) – Dictionary of new branches supplemental for all proposal branches.

Returns:

(q, new_inds, new_branch_supps) in correct key order.

Return type:

Tuple

fix_logp_gibbs(branch_names_run, inds_run, logp, inds)

Set any walker with no leaves to have logp = -np.inf

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• logp (np.ndarray) – Log of the prior going into final posterior computation.

• inds (dict) – Dictionary of inds arrays for all branches.

property accepted

Accepted counts for this move.

property acceptance_fraction

Acceptance fraction for this move.

property temperature_control

Temperature controller

compute_log_posterior_basic(logl, logp)

Compute the log of posterior

\(\log{P} = \log{L} + \log{p}\)

This method is to mesh with the tempered log posterior computation.

Parameters:

• logl (np.ndarray[ntemps, nwalkers]) – Log-likelihood values.

• logp (np.ndarray[ntemps, nwalkers]) – Log-prior values.

Returns:

Log-Posterior values.

Return type:

np.ndarray[ntemps, nwalkers]

tune(state, accepted)

Tune a proposal

This is a place holder for tuning.

Parameters:

• state (eryn.state.State) – Current state of sampler.

• accepted (np.ndarray[ntemps, nwalkers]) – Accepted values for last pass through proposal.

update(old_state, new_state, accepted, subset=None)

Update a given subset of the ensemble with an accepted proposal

This class was updated from emcee to handle the added structure of Eryn.

Parameters:

• old_state (eryn.state.State) – State with current information. New information is added to this state.

• new_state (eryn.state.State) – State with information from proposed points.

• accepted (np.ndarray[ntemps, nwalkers]) – A vector of booleans indicating which walkers were accepted.

• subset (np.ndarray[ntemps, nwalkers], optional) – A boolean mask indicating which walkers were included in the subset. This can be used, for example, when updating only the primary ensemble in a RedBlueMove. (default: None)

Returns:

old_state with accepted points added from new_state.

Return type:

eryn.state.State

Metropolis-Hastings Moves

MH Parent Class

class eryn.moves.MHMove(**kwargs)

Bases: Move

A general Metropolis-Hastings proposal

Concrete implementations can be made by providing a get_proposal method. For standard Gaussian Metropolis moves, moves.GaussianMove can be used.

get_proposal(branches_coords, random, branches_inds=None, **kwargs)

Get proposal

Parameters:

• branches_coords (dict) – Keys are branch_names and values are np.ndarray[ntemps, nwalkers, nleaves_max, ndim] representing coordinates for walkers.

• random (object) – Current random state object.

• branches_inds (dict, optional) – Keys are branch_names and values are np.ndarray[ntemps, nwalkers, nleaves_max] representing which leaves are currently being used. (default: None)

• **kwargs (ignored) – This is added for compatibility. It is ignored in this function.

Returns:

(Proposed coordinates, factors) -> (dict, np.ndarray)

Return type:

tuple

Raises:

NotImplementedError – If proposal is not implemented in a subclass.

setup(branches_coords)

Any setup for the proposal.

Parameters:

branches_coords (dict) – Keys are branch_names. Values are np.ndarray[ntemps, nwalkers, nleaves_max, ndim]. These are the curent coordinates for all the walkers.

propose(model, state)

Use the move to generate a proposal and compute the acceptance

Parameters:

• model (eryn.model.Model) – Carrier of sampler information.

• state (State) – Current state of the sampler.

Returns:

State of sampler after proposal is complete.

Return type:

State

property acceptance_fraction

Acceptance fraction for this move.

property accepted

Accepted counts for this move.

cleanup_proposals_gibbs(branch_names_run, inds_run, q, branches_coords, new_inds=None, branches_inds=None, new_branch_supps=None, branches_supplemental=None)

Set all not Gibbs-sampled parameters back

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• q (dict) – Dictionary of new coordinate arrays for all proposal branches.

• branches_coords (dict) – Dictionary of old coordinate arrays for all branches.

• new_inds (dict, optional) – Dictionary of new inds arrays for all proposal branches.

• branches_inds (dict, optional) – Dictionary of old inds arrays for all branches.

• new_branch_supps (dict, optional) – Dictionary of new branches supplemental for all proposal branches.

• branches_supplemental (dict, optional) – Dictionary of old branches supplemental for all branches.

compute_log_posterior_basic(logl, logp)

Compute the log of posterior

\(\log{P} = \log{L} + \log{p}\)

This method is to mesh with the tempered log posterior computation.

Parameters:

• logl (np.ndarray[ntemps, nwalkers]) – Log-likelihood values.

• logp (np.ndarray[ntemps, nwalkers]) – Log-prior values.

Returns:

Log-Posterior values.

Return type:

np.ndarray[ntemps, nwalkers]

ensure_ordering(correct_key_order, q, new_inds, new_branch_supps)

Ensure proper order of key in dictionaries.

Parameters:

• correct_key_order (list) – Keys in correct order.

• q (dict) – Dictionary of new coordinate arrays for all branches.

• new_inds (dict) – Dictionary of new inds arrays for all branches.

• new_branch_supps (dict or None) – Dictionary of new branches supplemental for all proposal branches.

Returns:

(q, new_inds, new_branch_supps) in correct key order.

Return type:

Tuple

fix_logp_gibbs(branch_names_run, inds_run, logp, inds)

Set any walker with no leaves to have logp = -np.inf

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• logp (np.ndarray) – Log of the prior going into final posterior computation.

• inds (dict) – Dictionary of inds arrays for all branches.

gibbs_sampling_setup_iterator(all_branch_names)

Iterate through the gibbs splits as a generator

Parameters:

all_branch_names (list) – List of all branch names.

Yields:

2-tuple –

Gibbs sampling split.

First entry is the branch names to run and the second entry is the index into the leaves/parameters for this Gibbs split.

Raises:

ValueError – Incorrect inputs.

setup_proposals(branch_names_run, inds_run, branches_coords, branches_inds)

Setup proposals when gibbs sampling.

Get inputs into the proposal including Gibbs split information.

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• branches_coords (dict) – Dictionary of coordinate arrays for all branches.

• branches_inds (dict) – Dictionary of inds arrays for all branches.

Returns:

(coords, inds, at_least_one_proposal)

• Coords including Gibbs sampling info.

• inds including Gibbs sampling info.

• at_least_one_proposal is boolean. It is passed out to

  indicate there is at least one leaf available for the requested branch names.

Return type:

tuple

property temperature_control

Temperature controller

tune(state, accepted)

Tune a proposal

This is a place holder for tuning.

Parameters:

• state (eryn.state.State) – Current state of sampler.

• accepted (np.ndarray[ntemps, nwalkers]) – Accepted values for last pass through proposal.

update(old_state, new_state, accepted, subset=None)

Update a given subset of the ensemble with an accepted proposal

This class was updated from emcee to handle the added structure of Eryn.

Parameters:

• old_state (eryn.state.State) – State with current information. New information is added to this state.

• new_state (eryn.state.State) – State with information from proposed points.

• accepted (np.ndarray[ntemps, nwalkers]) – A vector of booleans indicating which walkers were accepted.

• subset (np.ndarray[ntemps, nwalkers], optional) – A boolean mask indicating which walkers were included in the subset. This can be used, for example, when updating only the primary ensemble in a RedBlueMove. (default: None)

Returns:

old_state with accepted points added from new_state.

Return type:

eryn.state.State

Implemented MH Moves

class eryn.moves.GaussianMove(cov_all, mode='vector', factor=None, **kwargs)

Bases: MHMove

A Metropolis step with a Gaussian proposal function.

This class is heavily based on the same class in emcee.

Parameters:

• cov (dict) – The covariance of the proposal function. The keys are branch names and the values are covariance information. This information can be provided as a scalar, vector, or matrix and the proposal will be assumed isotropic, axis-aligned, or general, respectively.

• mode (str, optional) – Select the method used for updating parameters. This can be one of "vector", "random", or "sequential". The "vector" mode updates all dimensions simultaneously, "random" randomly selects a dimension and only updates that one, and "sequential" loops over dimensions and updates each one in turn. (default: "vector")

• factor (float, optional) – If provided the proposal will be made with a standard deviation uniformly selected from the range exp(U(-log(factor), log(factor))) * cov. This is invalid for the "vector" mode. (default: None)

• **kwargs (dict, optional) – Kwargs for parent classes. (default: {})

Raises:

ValueError – If the proposal dimensions are invalid or if any of any of the other arguments are inconsistent.

get_proposal(branches_coords, random, branches_inds=None, **kwargs)

Get proposal from Gaussian distribution

Parameters:

• branches_coords (dict) – Keys are branch_names and values are np.ndarray[ntemps, nwalkers, nleaves_max, ndim] representing coordinates for walkers.

• random (object) – Current random state object.

• branches_inds (dict, optional) – Keys are branch_names and values are np.ndarray[ntemps, nwalkers, nleaves_max] representing which leaves are currently being used. (default: None)

• **kwargs (ignored) – This is added for compatibility. It is ignored in this function.

Returns:

(Proposed coordinates, factors) -> (dict, np.ndarray)

Return type:

tuple

property acceptance_fraction

Acceptance fraction for this move.

property accepted

Accepted counts for this move.

cleanup_proposals_gibbs(branch_names_run, inds_run, q, branches_coords, new_inds=None, branches_inds=None, new_branch_supps=None, branches_supplemental=None)

Set all not Gibbs-sampled parameters back

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• q (dict) – Dictionary of new coordinate arrays for all proposal branches.

• branches_coords (dict) – Dictionary of old coordinate arrays for all branches.

• new_inds (dict, optional) – Dictionary of new inds arrays for all proposal branches.

• branches_inds (dict, optional) – Dictionary of old inds arrays for all branches.

• new_branch_supps (dict, optional) – Dictionary of new branches supplemental for all proposal branches.

• branches_supplemental (dict, optional) – Dictionary of old branches supplemental for all branches.

compute_log_posterior_basic(logl, logp)

Compute the log of posterior

\(\log{P} = \log{L} + \log{p}\)

This method is to mesh with the tempered log posterior computation.

Parameters:

• logl (np.ndarray[ntemps, nwalkers]) – Log-likelihood values.

• logp (np.ndarray[ntemps, nwalkers]) – Log-prior values.

Returns:

Log-Posterior values.

Return type:

np.ndarray[ntemps, nwalkers]

ensure_ordering(correct_key_order, q, new_inds, new_branch_supps)

Ensure proper order of key in dictionaries.

Parameters:

• correct_key_order (list) – Keys in correct order.

• q (dict) – Dictionary of new coordinate arrays for all branches.

• new_inds (dict) – Dictionary of new inds arrays for all branches.

• new_branch_supps (dict or None) – Dictionary of new branches supplemental for all proposal branches.

Returns:

(q, new_inds, new_branch_supps) in correct key order.

Return type:

Tuple

fix_logp_gibbs(branch_names_run, inds_run, logp, inds)

Set any walker with no leaves to have logp = -np.inf

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• logp (np.ndarray) – Log of the prior going into final posterior computation.

• inds (dict) – Dictionary of inds arrays for all branches.

gibbs_sampling_setup_iterator(all_branch_names)

Iterate through the gibbs splits as a generator

Parameters:

all_branch_names (list) – List of all branch names.

Yields:

2-tuple –

Gibbs sampling split.

First entry is the branch names to run and the second entry is the index into the leaves/parameters for this Gibbs split.

Raises:

ValueError – Incorrect inputs.

propose(model, state)

Use the move to generate a proposal and compute the acceptance

Parameters:

• model (eryn.model.Model) – Carrier of sampler information.

• state (State) – Current state of the sampler.

Returns:

State of sampler after proposal is complete.

Return type:

State

setup(branches_coords)

Any setup for the proposal.

Parameters:

branches_coords (dict) – Keys are branch_names. Values are np.ndarray[ntemps, nwalkers, nleaves_max, ndim]. These are the curent coordinates for all the walkers.

setup_proposals(branch_names_run, inds_run, branches_coords, branches_inds)

Setup proposals when gibbs sampling.

Get inputs into the proposal including Gibbs split information.

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• branches_coords (dict) – Dictionary of coordinate arrays for all branches.

• branches_inds (dict) – Dictionary of inds arrays for all branches.

Returns:

(coords, inds, at_least_one_proposal)

• Coords including Gibbs sampling info.

• inds including Gibbs sampling info.

• at_least_one_proposal is boolean. It is passed out to

  indicate there is at least one leaf available for the requested branch names.

Return type:

tuple

property temperature_control

Temperature controller

tune(state, accepted)

Tune a proposal

This is a place holder for tuning.

Parameters:

• state (eryn.state.State) – Current state of sampler.

• accepted (np.ndarray[ntemps, nwalkers]) – Accepted values for last pass through proposal.

update(old_state, new_state, accepted, subset=None)

Update a given subset of the ensemble with an accepted proposal

This class was updated from emcee to handle the added structure of Eryn.

Parameters:

• old_state (eryn.state.State) – State with current information. New information is added to this state.

• new_state (eryn.state.State) – State with information from proposed points.

• accepted (np.ndarray[ntemps, nwalkers]) – A vector of booleans indicating which walkers were accepted.

• subset (np.ndarray[ntemps, nwalkers], optional) – A boolean mask indicating which walkers were included in the subset. This can be used, for example, when updating only the primary ensemble in a RedBlueMove. (default: None)

Returns:

old_state with accepted points added from new_state.

Return type:

eryn.state.State

class eryn.moves.DistributionGenerate(generate_dist, *args, **kwargs)

Bases: MHMove

Generate proposals from a distribution

Parameters:

generate_dist (dict) – Dictionary with keys as branch names and items as ProbDistContainer objects that have logpdf and rvs methods.

get_proposal(branches_coords, random, branches_inds=None, **kwargs)

Make a proposal

Parameters:

• branches_coords (dict) – Keys are branch_names and values are np.ndarray[ntemps, nwalkers, nleaves_max, ndim] representing coordinates for walkers.

• random (object) – Current random state object.

• branches_inds (dict, optional) – Keys are branch_names and values are np.ndarray[ntemps, nwalkers, nleaves_max] representing which leaves are currently being used. (default: None)

• **kwargs (ignored) – This is added for compatibility. It is ignored in this function.

Returns:

Tuple containing proposal information.

First entry is the new coordinates as a dictionary with keys as branch_names and values as ``double `` np.ndarray[ntemps, nwalkers, nleaves_max, ndim] containing proposed coordinates. Second entry is the factors associated with the proposal necessary for detailed balance. This is effectively any term in the detailed balance fraction. +log of factors if in the numerator. -log of factors if in the denominator.

Return type:

tuple

property acceptance_fraction

Acceptance fraction for this move.

property accepted

Accepted counts for this move.

cleanup_proposals_gibbs(branch_names_run, inds_run, q, branches_coords, new_inds=None, branches_inds=None, new_branch_supps=None, branches_supplemental=None)

Set all not Gibbs-sampled parameters back

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• q (dict) – Dictionary of new coordinate arrays for all proposal branches.

• branches_coords (dict) – Dictionary of old coordinate arrays for all branches.

• new_inds (dict, optional) – Dictionary of new inds arrays for all proposal branches.

• branches_inds (dict, optional) – Dictionary of old inds arrays for all branches.

• new_branch_supps (dict, optional) – Dictionary of new branches supplemental for all proposal branches.

• branches_supplemental (dict, optional) – Dictionary of old branches supplemental for all branches.

compute_log_posterior_basic(logl, logp)

Compute the log of posterior

\(\log{P} = \log{L} + \log{p}\)

This method is to mesh with the tempered log posterior computation.

Parameters:

• logl (np.ndarray[ntemps, nwalkers]) – Log-likelihood values.

• logp (np.ndarray[ntemps, nwalkers]) – Log-prior values.

Returns:

Log-Posterior values.

Return type:

np.ndarray[ntemps, nwalkers]

ensure_ordering(correct_key_order, q, new_inds, new_branch_supps)

Ensure proper order of key in dictionaries.

Parameters:

• correct_key_order (list) – Keys in correct order.

• q (dict) – Dictionary of new coordinate arrays for all branches.

• new_inds (dict) – Dictionary of new inds arrays for all branches.

• new_branch_supps (dict or None) – Dictionary of new branches supplemental for all proposal branches.

Returns:

(q, new_inds, new_branch_supps) in correct key order.

Return type:

Tuple

fix_logp_gibbs(branch_names_run, inds_run, logp, inds)

Set any walker with no leaves to have logp = -np.inf

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• logp (np.ndarray) – Log of the prior going into final posterior computation.

• inds (dict) – Dictionary of inds arrays for all branches.

gibbs_sampling_setup_iterator(all_branch_names)

Iterate through the gibbs splits as a generator

Parameters:

all_branch_names (list) – List of all branch names.

Yields:

2-tuple –

Gibbs sampling split.

First entry is the branch names to run and the second entry is the index into the leaves/parameters for this Gibbs split.

Raises:

ValueError – Incorrect inputs.

propose(model, state)

Use the move to generate a proposal and compute the acceptance

Parameters:

• model (eryn.model.Model) – Carrier of sampler information.

• state (State) – Current state of the sampler.

Returns:

State of sampler after proposal is complete.

Return type:

State

setup(branches_coords)

Any setup for the proposal.

Parameters:

branches_coords (dict) – Keys are branch_names. Values are np.ndarray[ntemps, nwalkers, nleaves_max, ndim]. These are the curent coordinates for all the walkers.

setup_proposals(branch_names_run, inds_run, branches_coords, branches_inds)

Setup proposals when gibbs sampling.

Get inputs into the proposal including Gibbs split information.

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• branches_coords (dict) – Dictionary of coordinate arrays for all branches.

• branches_inds (dict) – Dictionary of inds arrays for all branches.

Returns:

(coords, inds, at_least_one_proposal)

• Coords including Gibbs sampling info.

• inds including Gibbs sampling info.

• at_least_one_proposal is boolean. It is passed out to

  indicate there is at least one leaf available for the requested branch names.

Return type:

tuple

property temperature_control

Temperature controller

tune(state, accepted)

Tune a proposal

This is a place holder for tuning.

Parameters:

• state (eryn.state.State) – Current state of sampler.

• accepted (np.ndarray[ntemps, nwalkers]) – Accepted values for last pass through proposal.

update(old_state, new_state, accepted, subset=None)

Update a given subset of the ensemble with an accepted proposal

This class was updated from emcee to handle the added structure of Eryn.

Parameters:

• old_state (eryn.state.State) – State with current information. New information is added to this state.

• new_state (eryn.state.State) – State with information from proposed points.

• accepted (np.ndarray[ntemps, nwalkers]) – A vector of booleans indicating which walkers were accepted.

• subset (np.ndarray[ntemps, nwalkers], optional) – A boolean mask indicating which walkers were included in the subset. This can be used, for example, when updating only the primary ensemble in a RedBlueMove. (default: None)

Returns:

old_state with accepted points added from new_state.

Return type:

eryn.state.State

Red-Blue Moves

Red-Blue Parent Class

class eryn.moves.RedBlueMove(nsplits=2, randomize_split=True, live_dangerously=False, **kwargs)

Bases: Move, ABC

An abstract red-blue ensemble move with parallelization as described in Foreman-Mackey et al. (2013).

This class is heavily based on the original from emcee.

Parameters:

• nsplits (int, optional) – The number of sub-ensembles to use. Each sub-ensemble is updated in parallel using the other sets as the complementary ensemble. The default value is 2 and you probably won’t need to change that.

• randomize_split (bool, optional) – Randomly shuffle walkers between sub-ensembles. The same number of walkers will be assigned to each sub-ensemble on each iteration. (default: True)

• live_dangerously (bool, optional) – By default, an update will fail with a RuntimeError if the number of walkers is smaller than twice the dimension of the problem because the walkers would then be stuck on a low dimensional subspace. This can be avoided by switching between the stretch move and, for example, a Metropolis-Hastings step. If you want to do this and suppress the error, set live_dangerously = True. Thanks goes (once again) to @dstndstn for this wonderful terminology. (default: False)

• **kwargs (dict, optional) – Kwargs for parent classes.

setup(branches_coords)

Any setup for the proposal.

Parameters:

branches_coords (dict) – Keys are branch_names. Values are np.ndarray[ntemps, nwalkers, nleaves_max, ndim]. These are the curent coordinates for all the walkers.

classmethod get_proposal(sample, complement, random, gibbs_ndim=None)

Make a proposal

Parameters:

• sample (dict) – Keys are branch_names. Values are np.ndarray[ntemps, nwalkers, nleaves_max, ndim].

• complement (dict) – Keys are branch_names. Values are lists of other other np.ndarray[ntemps, nwalkers - subset size, nleaves_max, ndim] from all other subsets. This is the compliment whose coords are used to form the proposal for the sample subset.

• random (object) – Current random state of the sampler.

• gibbs_ndim (int or np.ndarray, optional) – If Gibbs sampling, this indicates the true dimension. If given as an array, must have shape (ntemps, nwalkers). See the tutorial for more information. (default: None)

Returns:

Tuple contained proposal information.

First entry is the new coordinates as a dictionary with keys as branch_names and values as np.ndarray[ntemps, nwalkers, nleaves_max, ndim] of new coordinates. Second entry is the factors associated with the proposal necessary for detailed balance. This is effectively any term in the detailed balance fraction. +log of factors if in the numerator. -log of factors if in the denominator.

Return type:

tuple

propose(model, state)

Use the move to generate a proposal and compute the acceptance

Parameters:

• model (eryn.model.Model) – Carrier of sampler information.

• state (State) – Current state of the sampler.

Returns:

(state, accepted)

The first return is the state of the sampler after the move. The second return value is the accepted count array.

Return type:

tuple

property acceptance_fraction

Acceptance fraction for this move.

property accepted

Accepted counts for this move.

cleanup_proposals_gibbs(branch_names_run, inds_run, q, branches_coords, new_inds=None, branches_inds=None, new_branch_supps=None, branches_supplemental=None)

Set all not Gibbs-sampled parameters back

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• q (dict) – Dictionary of new coordinate arrays for all proposal branches.

• branches_coords (dict) – Dictionary of old coordinate arrays for all branches.

• new_inds (dict, optional) – Dictionary of new inds arrays for all proposal branches.

• branches_inds (dict, optional) – Dictionary of old inds arrays for all branches.

• new_branch_supps (dict, optional) – Dictionary of new branches supplemental for all proposal branches.

• branches_supplemental (dict, optional) – Dictionary of old branches supplemental for all branches.

compute_log_posterior_basic(logl, logp)

Compute the log of posterior

\(\log{P} = \log{L} + \log{p}\)

This method is to mesh with the tempered log posterior computation.

Parameters:

• logl (np.ndarray[ntemps, nwalkers]) – Log-likelihood values.

• logp (np.ndarray[ntemps, nwalkers]) – Log-prior values.

Returns:

Log-Posterior values.

Return type:

np.ndarray[ntemps, nwalkers]

ensure_ordering(correct_key_order, q, new_inds, new_branch_supps)

Ensure proper order of key in dictionaries.

Parameters:

• correct_key_order (list) – Keys in correct order.

• q (dict) – Dictionary of new coordinate arrays for all branches.

• new_inds (dict) – Dictionary of new inds arrays for all branches.

• new_branch_supps (dict or None) – Dictionary of new branches supplemental for all proposal branches.

Returns:

(q, new_inds, new_branch_supps) in correct key order.

Return type:

Tuple

fix_logp_gibbs(branch_names_run, inds_run, logp, inds)

Set any walker with no leaves to have logp = -np.inf

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• logp (np.ndarray) – Log of the prior going into final posterior computation.

• inds (dict) – Dictionary of inds arrays for all branches.

gibbs_sampling_setup_iterator(all_branch_names)

Iterate through the gibbs splits as a generator

Parameters:

all_branch_names (list) – List of all branch names.

Yields:

2-tuple –

Gibbs sampling split.

First entry is the branch names to run and the second entry is the index into the leaves/parameters for this Gibbs split.

Raises:

ValueError – Incorrect inputs.

setup_proposals(branch_names_run, inds_run, branches_coords, branches_inds)

Setup proposals when gibbs sampling.

Get inputs into the proposal including Gibbs split information.

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• branches_coords (dict) – Dictionary of coordinate arrays for all branches.

• branches_inds (dict) – Dictionary of inds arrays for all branches.

Returns:

(coords, inds, at_least_one_proposal)

• Coords including Gibbs sampling info.

• inds including Gibbs sampling info.

• at_least_one_proposal is boolean. It is passed out to

  indicate there is at least one leaf available for the requested branch names.

Return type:

tuple

property temperature_control

Temperature controller

tune(state, accepted)

Tune a proposal

This is a place holder for tuning.

Parameters:

• state (eryn.state.State) – Current state of sampler.

• accepted (np.ndarray[ntemps, nwalkers]) – Accepted values for last pass through proposal.

update(old_state, new_state, accepted, subset=None)

Update a given subset of the ensemble with an accepted proposal

This class was updated from emcee to handle the added structure of Eryn.

Parameters:

• old_state (eryn.state.State) – State with current information. New information is added to this state.

• new_state (eryn.state.State) – State with information from proposed points.

• accepted (np.ndarray[ntemps, nwalkers]) – A vector of booleans indicating which walkers were accepted.

• subset (np.ndarray[ntemps, nwalkers], optional) – A boolean mask indicating which walkers were included in the subset. This can be used, for example, when updating only the primary ensemble in a RedBlueMove. (default: None)

Returns:

old_state with accepted points added from new_state.

Return type:

eryn.state.State

Implemented Red-Blue Moves

class eryn.moves.StretchMove(a=2.0, return_gpu=False, random_seed=None, **kwargs)

Bases: RedBlueMove

Affine-Invariant Proposal

A Goodman & Weare (2010) “stretch move” with parallelization as described in Foreman-Mackey et al. (2013).

This class was originally implemented in emcee.

Parameters:

• a (double, optional) – The stretch scale parameter. (default: 2.0)

• return_gpu (bool, optional) – If use_gpu == True and return_gpu == True, the returned arrays will be returned as CuPy arrays. (default: False)

• kwargs (dict, optional) – Additional keyword arguments passed down through :class:`RedRedBlueMove`_.

a

The stretch scale parameter.

Type:

double

return_gpu

Whether the array being returned is in Cupy (True) or NumPy (False).

Type:

bool

adjust_factors(factors, ndims_old, ndims_new)

Adjust the factors based on changing dimensions.

factors is adjusted in place.

Parameters:

• factors (xp.ndarray) – Array of factors values. It is adjusted in place.

• ndims_old (int or xp.ndarray) – Old dimension. If given as an xp.ndarray, must be broadcastable with factors.

• ndims_new (int or xp.ndarray) – New dimension. If given as an xp.ndarray, must be broadcastable with factors.

choose_c_vals(c, Nc, Ns, ntemps, random_number_generator, **kwargs)

Get the compliment array

The compliment represents the points that are used to move the actual points whose position is changing.

Parameters:

• c (np.ndarray) – Possible compliment values with shape (ntemps, Nc, nleaves_max, ndim).

• Nc (int) – Length of the ...: the subset of walkers proposed to move now (usually nwalkers/2).

• Ns (int) – Number of generation points.

• ntemps (int) – Number of temperatures.

• random_number_generator (object) – Random state object.

• **kwargs (ignored) – Ignored here. For modularity.

Returns:

Compliment values to use with shape (ntemps, Ns, nleaves_max, ndim).

Return type:

np.ndarray

get_new_points(name, s, c_temp, Ns, branch_shape, branch_i, random_number_generator)

Get mew points in stretch move.

Takes compliment and uses it to get new points for those being proposed.

Parameters:

• name (str) – Branch name.

• s (np.ndarray) – Points to be moved with shape (ntemps, Ns, nleaves_max, ndim).

• c (np.ndarray) – Compliment to move points with shape (ntemps, Ns, nleaves_max, ndim).

• Ns (int) – Number to generate.

• branch_shape (tuple) – Full branch shape.

• branch_i (int) – Which branch in the order is being run now. This ensures that the randomly generated quantity per walker remains the same over branches.

• random_number_generator (object) – Random state object.

Returns:

New proposed points with shape (ntemps, Ns, nleaves_max, ndim).

Return type:

np.ndarray

get_proposal(s_all, c_all, random, gibbs_ndim=None, **kwargs)

Generate stretch proposal

Parameters:

• s_all (dict) – Keys are branch_names and values are coordinates for which a proposal is to be generated.

• c_all (dict) – Keys are branch_names and values are lists. These lists contain all the complement array values.

• random (object) – Random state object.

• gibbs_ndim (int or np.ndarray, optional) – If Gibbs sampling, this indicates the true dimension. If given as an array, must have shape (ntemps, nwalkers). See the tutorial for more information. (default: None)

Returns:

First entry is new positions. Second entry is detailed balance factors.

Return type:

tuple

Raises:

ValueError – Issues with dimensionality.

property acceptance_fraction

Acceptance fraction for this move.

property accepted

Accepted counts for this move.

cleanup_proposals_gibbs(branch_names_run, inds_run, q, branches_coords, new_inds=None, branches_inds=None, new_branch_supps=None, branches_supplemental=None)

Set all not Gibbs-sampled parameters back

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• q (dict) – Dictionary of new coordinate arrays for all proposal branches.

• branches_coords (dict) – Dictionary of old coordinate arrays for all branches.

• new_inds (dict, optional) – Dictionary of new inds arrays for all proposal branches.

• branches_inds (dict, optional) – Dictionary of old inds arrays for all branches.

• new_branch_supps (dict, optional) – Dictionary of new branches supplemental for all proposal branches.

• branches_supplemental (dict, optional) – Dictionary of old branches supplemental for all branches.

compute_log_posterior_basic(logl, logp)

Compute the log of posterior

\(\log{P} = \log{L} + \log{p}\)

This method is to mesh with the tempered log posterior computation.

Parameters:

• logl (np.ndarray[ntemps, nwalkers]) – Log-likelihood values.

• logp (np.ndarray[ntemps, nwalkers]) – Log-prior values.

Returns:

Log-Posterior values.

Return type:

np.ndarray[ntemps, nwalkers]

ensure_ordering(correct_key_order, q, new_inds, new_branch_supps)

Ensure proper order of key in dictionaries.

Parameters:

• correct_key_order (list) – Keys in correct order.

• q (dict) – Dictionary of new coordinate arrays for all branches.

• new_inds (dict) – Dictionary of new inds arrays for all branches.

• new_branch_supps (dict or None) – Dictionary of new branches supplemental for all proposal branches.

Returns:

(q, new_inds, new_branch_supps) in correct key order.

Return type:

Tuple

fix_logp_gibbs(branch_names_run, inds_run, logp, inds)

Set any walker with no leaves to have logp = -np.inf

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• logp (np.ndarray) – Log of the prior going into final posterior computation.

• inds (dict) – Dictionary of inds arrays for all branches.

gibbs_sampling_setup_iterator(all_branch_names)

Iterate through the gibbs splits as a generator

Parameters:

all_branch_names (list) – List of all branch names.

Yields:

2-tuple –

Gibbs sampling split.

First entry is the branch names to run and the second entry is the index into the leaves/parameters for this Gibbs split.

Raises:

ValueError – Incorrect inputs.

propose(model, state)

Use the move to generate a proposal and compute the acceptance

Parameters:

• model (eryn.model.Model) – Carrier of sampler information.

• state (State) – Current state of the sampler.

Returns:

(state, accepted)

The first return is the state of the sampler after the move. The second return value is the accepted count array.

Return type:

tuple

setup(branches_coords)

Any setup for the proposal.

Parameters:

branches_coords (dict) – Keys are branch_names. Values are np.ndarray[ntemps, nwalkers, nleaves_max, ndim]. These are the curent coordinates for all the walkers.

setup_proposals(branch_names_run, inds_run, branches_coords, branches_inds)

Setup proposals when gibbs sampling.

Get inputs into the proposal including Gibbs split information.

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• branches_coords (dict) – Dictionary of coordinate arrays for all branches.

• branches_inds (dict) – Dictionary of inds arrays for all branches.

Returns:

(coords, inds, at_least_one_proposal)

• Coords including Gibbs sampling info.

• inds including Gibbs sampling info.

• at_least_one_proposal is boolean. It is passed out to

  indicate there is at least one leaf available for the requested branch names.

Return type:

tuple

property temperature_control

Temperature controller

tune(state, accepted)

Tune a proposal

This is a place holder for tuning.

Parameters:

• state (eryn.state.State) – Current state of sampler.

• accepted (np.ndarray[ntemps, nwalkers]) – Accepted values for last pass through proposal.

update(old_state, new_state, accepted, subset=None)

Update a given subset of the ensemble with an accepted proposal

This class was updated from emcee to handle the added structure of Eryn.

Parameters:

• old_state (eryn.state.State) – State with current information. New information is added to this state.

• new_state (eryn.state.State) – State with information from proposed points.

• accepted (np.ndarray[ntemps, nwalkers]) – A vector of booleans indicating which walkers were accepted.

• subset (np.ndarray[ntemps, nwalkers], optional) – A boolean mask indicating which walkers were included in the subset. This can be used, for example, when updating only the primary ensemble in a RedBlueMove. (default: None)

Returns:

old_state with accepted points added from new_state.

Return type:

eryn.state.State

Group Moves

Group Parent Class

class eryn.moves.GroupMove(nfriends=None, n_iter_update=100, live_dangerously=False, **kwargs)

Bases: Move, ABC

A “group” ensemble move based on the eryn.moves.RedBlueMove.

In moves like the eryn.moves.StretchMove, the complimentary group for which the proposal is used is chosen from the current points in the ensemble. In “group” moves the complimentary group is a stationary group that is updated every n_iter_update iterations. This update is performed with the last set of coordinates to maintain detailed balance.

Parameters:

• nfriends (int, optional) – The number of friends to draw from as the complimentary ensemble. This group is determined from the stationary group. If None, it will be set to the number of walkers. (default: None)

• n_iter_update (int, optional) – Number of iterations to run before updating the stationary distribution. (default: 100).

• live_dangerously (bool, optional) – If True, allow for n_iter_update == 1. (deafault: False)

kwargs are passed to Move class.

find_friends(name, s, s_inds=None, branch_supps=None)

Function for finding friends.

Parameters:

• name (str) – Branch name for proposal coordinates.

• s (np.ndarray) – Coordinates array for the points to be moved.

• s_inds (np.ndarray, optional) – inds arrays that represent which leaves are present. (default: None)

• branch_supps (dict, optional) – Keys are branch_names and values are BranchSupplemental objects. For group proposals, branch_supps are the best device for passing and tracking useful information. (default: None)

Returns:

Complimentary values.

Return type:

np.ndarray

choose_c_vals(name, s, s_inds=None, branch_supps=None)

Get the complimentary values.

setup(branches)

Any setup necessary for the proposal

setup_friends(branches)

Setup anything for finding friends.

Parameters:

branches (dict) – Dictionary with all the current branches in the sampler.

fix_friends(branches)

Fix any friends that were born through RJ.

This function is not required. If not implemented, it will just return immediately.

Parameters:

branches (dict) – Dictionary with all the current branches in the sampler.

classmethod get_proposal(s_all, random, gibbs_ndim=None, s_inds_all=None, **kwargs)

Generate group stretch proposal coordinates

Parameters:

• s_all (dict) – Keys are branch_names and values are coordinates for which a proposal is to be generated.

• random (object) – Random state object.

• gibbs_ndim (int or np.ndarray, optional) – If Gibbs sampling, this indicates the true dimension. If given as an array, must have shape (ntemps, nwalkers). See the tutorial for more information. (default: None)

• s_inds_all (dict, optional) – Keys are branch_names and values are inds arrays indicating which leaves are currently used. (default: None)

Returns:

First entry is new positions. Second entry is detailed balance factors.

Return type:

tuple

Raises:

ValueError – Issues with dimensionality.

propose(model, state)

Use the move to generate a proposal and compute the acceptance

Parameters:

• model (eryn.model.Model) – Carrier of sampler information.

• state (State) – Current state of the sampler.

Returns:

(state, accepted)

The first return is the state of the sampler after the move. The second return value is the accepted count array.

Return type:

tuple

property acceptance_fraction

Acceptance fraction for this move.

property accepted

Accepted counts for this move.

cleanup_proposals_gibbs(branch_names_run, inds_run, q, branches_coords, new_inds=None, branches_inds=None, new_branch_supps=None, branches_supplemental=None)

Set all not Gibbs-sampled parameters back

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• q (dict) – Dictionary of new coordinate arrays for all proposal branches.

• branches_coords (dict) – Dictionary of old coordinate arrays for all branches.

• new_inds (dict, optional) – Dictionary of new inds arrays for all proposal branches.

• branches_inds (dict, optional) – Dictionary of old inds arrays for all branches.

• new_branch_supps (dict, optional) – Dictionary of new branches supplemental for all proposal branches.

• branches_supplemental (dict, optional) – Dictionary of old branches supplemental for all branches.

compute_log_posterior_basic(logl, logp)

Compute the log of posterior

\(\log{P} = \log{L} + \log{p}\)

This method is to mesh with the tempered log posterior computation.

Parameters:

• logl (np.ndarray[ntemps, nwalkers]) – Log-likelihood values.

• logp (np.ndarray[ntemps, nwalkers]) – Log-prior values.

Returns:

Log-Posterior values.

Return type:

np.ndarray[ntemps, nwalkers]

ensure_ordering(correct_key_order, q, new_inds, new_branch_supps)

Ensure proper order of key in dictionaries.

Parameters:

• correct_key_order (list) – Keys in correct order.

• q (dict) – Dictionary of new coordinate arrays for all branches.

• new_inds (dict) – Dictionary of new inds arrays for all branches.

• new_branch_supps (dict or None) – Dictionary of new branches supplemental for all proposal branches.

Returns:

(q, new_inds, new_branch_supps) in correct key order.

Return type:

Tuple

fix_logp_gibbs(branch_names_run, inds_run, logp, inds)

Set any walker with no leaves to have logp = -np.inf

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• logp (np.ndarray) – Log of the prior going into final posterior computation.

• inds (dict) – Dictionary of inds arrays for all branches.

gibbs_sampling_setup_iterator(all_branch_names)

Iterate through the gibbs splits as a generator

Parameters:

all_branch_names (list) – List of all branch names.

Yields:

2-tuple –

Gibbs sampling split.

First entry is the branch names to run and the second entry is the index into the leaves/parameters for this Gibbs split.

Raises:

ValueError – Incorrect inputs.

setup_proposals(branch_names_run, inds_run, branches_coords, branches_inds)

Setup proposals when gibbs sampling.

Get inputs into the proposal including Gibbs split information.

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• branches_coords (dict) – Dictionary of coordinate arrays for all branches.

• branches_inds (dict) – Dictionary of inds arrays for all branches.

Returns:

(coords, inds, at_least_one_proposal)

• Coords including Gibbs sampling info.

• inds including Gibbs sampling info.

• at_least_one_proposal is boolean. It is passed out to

  indicate there is at least one leaf available for the requested branch names.

Return type:

tuple

property temperature_control

Temperature controller

tune(state, accepted)

Tune a proposal

This is a place holder for tuning.

Parameters:

• state (eryn.state.State) – Current state of sampler.

• accepted (np.ndarray[ntemps, nwalkers]) – Accepted values for last pass through proposal.

update(old_state, new_state, accepted, subset=None)

Update a given subset of the ensemble with an accepted proposal

This class was updated from emcee to handle the added structure of Eryn.

Parameters:

• old_state (eryn.state.State) – State with current information. New information is added to this state.

• new_state (eryn.state.State) – State with information from proposed points.

• accepted (np.ndarray[ntemps, nwalkers]) – A vector of booleans indicating which walkers were accepted.

• subset (np.ndarray[ntemps, nwalkers], optional) – A boolean mask indicating which walkers were included in the subset. This can be used, for example, when updating only the primary ensemble in a RedBlueMove. (default: None)

Returns:

old_state with accepted points added from new_state.

Return type:

eryn.state.State

Implemented Group Moves

class eryn.moves.GroupStretchMove(**kwargs)

Bases: GroupMove, StretchMove

Proposal like stretch with stationary compliment.

This move uses the stretch proposal method and math, but the compliment of walkers used to propose a new point is chosen from a stationary group rather than the current walkers in the ensemble.

This move allows for “stretch”-like proposal to be used in Reversible Jump MCMC.

Parameters:

**kwargs (dict, optional) – Keyword arguments passed to GroupMove and StretchMove.

get_proposal(s_all, random, gibbs_ndim=None, s_inds_all=None, branch_supps=None, **kwargs)

Generate group stretch proposal coordinates

Parameters:

• s_all (dict) – Keys are branch_names and values are coordinates for which a proposal is to be generated.

• random (object) – Random state object.

• gibbs_ndim (int or np.ndarray, optional) – If Gibbs sampling, this indicates the true dimension. If given as an array, must have shape (ntemps, nwalkers). See the tutorial for more information. (default: None)

• s_inds_all (dict, optional) – Keys are branch_names and values are inds arrays indicating which leaves are currently used. (default: None)

• branch_supps (dict, optional) – Keys are branch_names and values are BranchSupplemental objects. For the group stretch, branch_supps are the best device for passing and tracking useful information. (default: None)

Returns:

First entry is new positions. Second entry is detailed balance factors.

Return type:

tuple

Raises:

ValueError – Issues with dimensionality.

property acceptance_fraction

Acceptance fraction for this move.

property accepted

Accepted counts for this move.

adjust_factors(factors, ndims_old, ndims_new)

Adjust the factors based on changing dimensions.

factors is adjusted in place.

Parameters:

• factors (xp.ndarray) – Array of factors values. It is adjusted in place.

• ndims_old (int or xp.ndarray) – Old dimension. If given as an xp.ndarray, must be broadcastable with factors.

• ndims_new (int or xp.ndarray) – New dimension. If given as an xp.ndarray, must be broadcastable with factors.

choose_c_vals(name, s, s_inds=None, branch_supps=None)

Get the complimentary values.

cleanup_proposals_gibbs(branch_names_run, inds_run, q, branches_coords, new_inds=None, branches_inds=None, new_branch_supps=None, branches_supplemental=None)

Set all not Gibbs-sampled parameters back

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• q (dict) – Dictionary of new coordinate arrays for all proposal branches.

• branches_coords (dict) – Dictionary of old coordinate arrays for all branches.

• new_inds (dict, optional) – Dictionary of new inds arrays for all proposal branches.

• branches_inds (dict, optional) – Dictionary of old inds arrays for all branches.

• new_branch_supps (dict, optional) – Dictionary of new branches supplemental for all proposal branches.

• branches_supplemental (dict, optional) – Dictionary of old branches supplemental for all branches.

compute_log_posterior_basic(logl, logp)

Compute the log of posterior

\(\log{P} = \log{L} + \log{p}\)

This method is to mesh with the tempered log posterior computation.

Parameters:

• logl (np.ndarray[ntemps, nwalkers]) – Log-likelihood values.

• logp (np.ndarray[ntemps, nwalkers]) – Log-prior values.

Returns:

Log-Posterior values.

Return type:

np.ndarray[ntemps, nwalkers]

ensure_ordering(correct_key_order, q, new_inds, new_branch_supps)

Ensure proper order of key in dictionaries.

Parameters:

• correct_key_order (list) – Keys in correct order.

• q (dict) – Dictionary of new coordinate arrays for all branches.

• new_inds (dict) – Dictionary of new inds arrays for all branches.

• new_branch_supps (dict or None) – Dictionary of new branches supplemental for all proposal branches.

Returns:

(q, new_inds, new_branch_supps) in correct key order.

Return type:

Tuple

find_friends(name, s, s_inds=None, branch_supps=None)

Function for finding friends.

Parameters:

• name (str) – Branch name for proposal coordinates.

• s (np.ndarray) – Coordinates array for the points to be moved.

• s_inds (np.ndarray, optional) – inds arrays that represent which leaves are present. (default: None)

• branch_supps (dict, optional) – Keys are branch_names and values are BranchSupplemental objects. For group proposals, branch_supps are the best device for passing and tracking useful information. (default: None)

Returns:

Complimentary values.

Return type:

np.ndarray

fix_friends(branches)

Fix any friends that were born through RJ.

This function is not required. If not implemented, it will just return immediately.

Parameters:

branches (dict) – Dictionary with all the current branches in the sampler.

fix_logp_gibbs(branch_names_run, inds_run, logp, inds)

Set any walker with no leaves to have logp = -np.inf

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• logp (np.ndarray) – Log of the prior going into final posterior computation.

• inds (dict) – Dictionary of inds arrays for all branches.

get_new_points(name, s, c_temp, Ns, branch_shape, branch_i, random_number_generator)

Get mew points in stretch move.

Takes compliment and uses it to get new points for those being proposed.

Parameters:

• name (str) – Branch name.

• s (np.ndarray) – Points to be moved with shape (ntemps, Ns, nleaves_max, ndim).

• c (np.ndarray) – Compliment to move points with shape (ntemps, Ns, nleaves_max, ndim).

• Ns (int) – Number to generate.

• branch_shape (tuple) – Full branch shape.

• branch_i (int) – Which branch in the order is being run now. This ensures that the randomly generated quantity per walker remains the same over branches.

• random_number_generator (object) – Random state object.

Returns:

New proposed points with shape (ntemps, Ns, nleaves_max, ndim).

Return type:

np.ndarray

gibbs_sampling_setup_iterator(all_branch_names)

Iterate through the gibbs splits as a generator

Parameters:

all_branch_names (list) – List of all branch names.

Yields:

2-tuple –

Gibbs sampling split.

First entry is the branch names to run and the second entry is the index into the leaves/parameters for this Gibbs split.

Raises:

ValueError – Incorrect inputs.

propose(model, state)

Use the move to generate a proposal and compute the acceptance

Parameters:

• model (eryn.model.Model) – Carrier of sampler information.

• state (State) – Current state of the sampler.

Returns:

(state, accepted)

The first return is the state of the sampler after the move. The second return value is the accepted count array.

Return type:

tuple

setup(branches)

Any setup necessary for the proposal

setup_friends(branches)

Setup anything for finding friends.

Parameters:

branches (dict) – Dictionary with all the current branches in the sampler.

setup_proposals(branch_names_run, inds_run, branches_coords, branches_inds)

Setup proposals when gibbs sampling.

Get inputs into the proposal including Gibbs split information.

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• branches_coords (dict) – Dictionary of coordinate arrays for all branches.

• branches_inds (dict) – Dictionary of inds arrays for all branches.

Returns:

(coords, inds, at_least_one_proposal)

• Coords including Gibbs sampling info.

• inds including Gibbs sampling info.

• at_least_one_proposal is boolean. It is passed out to

  indicate there is at least one leaf available for the requested branch names.

Return type:

tuple

property temperature_control

Temperature controller

tune(state, accepted)

Tune a proposal

This is a place holder for tuning.

Parameters:

• state (eryn.state.State) – Current state of sampler.

• accepted (np.ndarray[ntemps, nwalkers]) – Accepted values for last pass through proposal.

update(old_state, new_state, accepted, subset=None)

Update a given subset of the ensemble with an accepted proposal

This class was updated from emcee to handle the added structure of Eryn.

Parameters:

• old_state (eryn.state.State) – State with current information. New information is added to this state.

• new_state (eryn.state.State) – State with information from proposed points.

• accepted (np.ndarray[ntemps, nwalkers]) – A vector of booleans indicating which walkers were accepted.

• subset (np.ndarray[ntemps, nwalkers], optional) – A boolean mask indicating which walkers were included in the subset. This can be used, for example, when updating only the primary ensemble in a RedBlueMove. (default: None)

Returns:

old_state with accepted points added from new_state.

Return type:

eryn.state.State

Reversible Jump Moves

Reversible Jump Parent Class

class eryn.moves.ReversibleJumpMove(nleaves_max=None, nleaves_min=None, dr=None, dr_max_iter=5, tune=False, fix_change=None, **kwargs)

Bases: Move

An abstract reversible jump move.

Parameters:

• nleaves_max (dict) – Maximum number(s) of leaves for each model. Keys are branch_names and values are nleaves_max for each branch. This is a keyword argument, nut it is required.

• nleaves_min (dict) – Minimum number(s) of leaves for each model. Keys are branch_names and values are nleaves_min for each branch. This is a keyword argument, nut it is required.

• tune (bool, optional) – If True, tune proposal. (Default: False)

• fix_change (int or None, optional) – Fix the change in the number of leaves. Make them all add a leaf or remove a leaf. This can be useful for some search functions. Options are +1 or -1. (default: None)

tune(state, accepted)

Tune a proposal

This is a place holder for tuning.

Parameters:

• state (eryn.state.State) – Current state of sampler.

• accepted (np.ndarray[ntemps, nwalkers]) – Accepted values for last pass through proposal.

setup(branches_coords)

Any setup for the proposal.

Parameters:

branches_coords (dict) – Keys are branch_names. Values are np.ndarray[ntemps, nwalkers, nleaves_max, ndim]. These are the curent coordinates for all the walkers.

get_proposal(all_coords, all_inds, nleaves_min_all, nleaves_max_all, random, **kwargs)

Make a proposal

Parameters:

• all_coords (dict) – Keys are branch_names. Values are np.ndarray[ntemps, nwalkers, nleaves_max, ndim]. These are the curent coordinates for all the walkers.

• all_inds (dict) – Keys are branch_names. Values are np.ndarray[ntemps, nwalkers, nleaves_max]. These are the boolean arrays marking which leaves are currently used within each walker.

• nleaves_min_all (dict) – Minimum values of leaf ount for each model. Must have same order as all_cords.

• nleaves_max_all (dict) – Maximum values of leaf ount for each model. Must have same order as all_cords.

• random (object) – Current random state of the sampler.

• **kwargs (ignored) – For modularity.

Returns:

Tuple containing proposal information.

First entry is the new coordinates as a dictionary with keys as branch_names and values as double `` np.ndarray[ntemps, nwalkers, nleaves_max, ndim] containing proposed coordinates. Second entry is the new ``inds array with boolean values flipped for added or removed sources. Third entry is the factors associated with the proposal necessary for detailed balance. This is effectively any term in the detailed balance fraction. +log of factors if in the numerator. -log of factors if in the denominator.

Return type:

tuple

Raises:

NotImplementedError – If this proposal is not implemented by a subclass.

get_model_change_proposal(inds, random, nleaves_min, nleaves_max)

Helper function for changing the model count by 1.

This helper function works with nested models where you want to add or remove one leaf at a time.

Parameters:

• inds (np.ndarray) – inds values for this specific branch with shape (ntemps, nwalkers, nleaves_max).

• random (object) – Current random state of the sampler.

• nleaves_min (int) – Minimum allowable leaf count for this branch.

• nleaves_max (int) – Maximum allowable leaf count for this branch.

Returns:

Keys are "+1" and "-1". Values are indexing information.

"+1" and "-1" indicate if a source is being added or removed, respectively. The indexing information is a 2D array with shape (number changing, 3). The length 3 is the index into each of the (ntemps, nwalkers, nleaves_max).

Return type:

dict

property acceptance_fraction

Acceptance fraction for this move.

property accepted

Accepted counts for this move.

cleanup_proposals_gibbs(branch_names_run, inds_run, q, branches_coords, new_inds=None, branches_inds=None, new_branch_supps=None, branches_supplemental=None)

Set all not Gibbs-sampled parameters back

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• q (dict) – Dictionary of new coordinate arrays for all proposal branches.

• branches_coords (dict) – Dictionary of old coordinate arrays for all branches.

• new_inds (dict, optional) – Dictionary of new inds arrays for all proposal branches.

• branches_inds (dict, optional) – Dictionary of old inds arrays for all branches.

• new_branch_supps (dict, optional) – Dictionary of new branches supplemental for all proposal branches.

• branches_supplemental (dict, optional) – Dictionary of old branches supplemental for all branches.

compute_log_posterior_basic(logl, logp)

Compute the log of posterior

\(\log{P} = \log{L} + \log{p}\)

This method is to mesh with the tempered log posterior computation.

Parameters:

• logl (np.ndarray[ntemps, nwalkers]) – Log-likelihood values.

• logp (np.ndarray[ntemps, nwalkers]) – Log-prior values.

Returns:

Log-Posterior values.

Return type:

np.ndarray[ntemps, nwalkers]

ensure_ordering(correct_key_order, q, new_inds, new_branch_supps)

Ensure proper order of key in dictionaries.

Parameters:

• correct_key_order (list) – Keys in correct order.

• q (dict) – Dictionary of new coordinate arrays for all branches.

• new_inds (dict) – Dictionary of new inds arrays for all branches.

• new_branch_supps (dict or None) – Dictionary of new branches supplemental for all proposal branches.

Returns:

(q, new_inds, new_branch_supps) in correct key order.

Return type:

Tuple

fix_logp_gibbs(branch_names_run, inds_run, logp, inds)

Set any walker with no leaves to have logp = -np.inf

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• logp (np.ndarray) – Log of the prior going into final posterior computation.

• inds (dict) – Dictionary of inds arrays for all branches.

gibbs_sampling_setup_iterator(all_branch_names)

Iterate through the gibbs splits as a generator

Parameters:

all_branch_names (list) – List of all branch names.

Yields:

2-tuple –

Gibbs sampling split.

First entry is the branch names to run and the second entry is the index into the leaves/parameters for this Gibbs split.

Raises:

ValueError – Incorrect inputs.

propose(model, state)

Use the move to generate a proposal and compute the acceptance

Parameters:

• model (eryn.model.Model) – Carrier of sampler information.

• state (State) – Current state of the sampler.

Returns:

State of sampler after proposal is complete.

Return type:

State

setup_proposals(branch_names_run, inds_run, branches_coords, branches_inds)

Setup proposals when gibbs sampling.

Get inputs into the proposal including Gibbs split information.

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• branches_coords (dict) – Dictionary of coordinate arrays for all branches.

• branches_inds (dict) – Dictionary of inds arrays for all branches.

Returns:

(coords, inds, at_least_one_proposal)

• Coords including Gibbs sampling info.

• inds including Gibbs sampling info.

• at_least_one_proposal is boolean. It is passed out to

  indicate there is at least one leaf available for the requested branch names.

Return type:

tuple

property temperature_control

Temperature controller

update(old_state, new_state, accepted, subset=None)

Update a given subset of the ensemble with an accepted proposal

This class was updated from emcee to handle the added structure of Eryn.

Parameters:

• old_state (eryn.state.State) – State with current information. New information is added to this state.

• new_state (eryn.state.State) – State with information from proposed points.

• accepted (np.ndarray[ntemps, nwalkers]) – A vector of booleans indicating which walkers were accepted.

• subset (np.ndarray[ntemps, nwalkers], optional) – A boolean mask indicating which walkers were included in the subset. This can be used, for example, when updating only the primary ensemble in a RedBlueMove. (default: None)

Returns:

old_state with accepted points added from new_state.

Return type:

eryn.state.State

Implemented Reversible Jump Moves

class eryn.moves.DistributionGenerateRJ(generate_dist, *args, **kwargs)

Bases: ReversibleJumpMove

Generate Revesible-Jump proposals from a distribution.

The prior can be entered as the generate_dist to generate proposals directly from the prior.

Parameters:

• generate_dist (dict) – Keys are branch names and values are ProbDistContainer objects that have logpdf and rvs methods. If you

• *args (tuple, optional) – Additional arguments to pass to parent classes.

• **kwargs (dict, optional) – Keyword arguments passed to parent classes.

get_model_change_proposal(inds, random, nleaves_min, nleaves_max)

Helper function for changing the model count by 1.

This helper function works with nested models where you want to add or remove one leaf at a time.

Parameters:

• inds (np.ndarray) – inds values for this specific branch with shape (ntemps, nwalkers, nleaves_max).

• random (object) – Current random state of the sampler.

• nleaves_min (int) – Minimum allowable leaf count for this branch.

• nleaves_max (int) – Maximum allowable leaf count for this branch.

Returns:

Keys are "+1" and "-1". Values are indexing information.

"+1" and "-1" indicate if a source is being added or removed, respectively. The indexing information is a 2D array with shape (number changing, 3). The length 3 is the index into each of the (ntemps, nwalkers, nleaves_max).

Return type:

dict

get_proposal(all_coords, all_inds, nleaves_min_all, nleaves_max_all, random, **kwargs)

Make a proposal

Parameters:

• all_coords (dict) – Keys are branch_names. Values are np.ndarray[ntemps, nwalkers, nleaves_max, ndim]. These are the curent coordinates for all the walkers.

• all_inds (dict) – Keys are branch_names. Values are np.ndarray[ntemps, nwalkers, nleaves_max]. These are the boolean arrays marking which leaves are currently used within each walker.

• nleaves_min_all (dict) – Minimum values of leaf ount for each model. Must have same order as all_cords.

• nleaves_max_all (dict) – Maximum values of leaf ount for each model. Must have same order as all_cords.

• random (object) – Current random state of the sampler.

• **kwargs (ignored) – For modularity.

Returns:

Tuple containing proposal information.

First entry is the new coordinates as a dictionary with keys as branch_names and values as double `` np.ndarray[ntemps, nwalkers, nleaves_max, ndim] containing proposed coordinates. Second entry is the new ``inds array with boolean values flipped for added or removed sources. Third entry is the factors associated with the proposal necessary for detailed balance. This is effectively any term in the detailed balance fraction. +log of factors if in the numerator. -log of factors if in the denominator.

Return type:

tuple

property acceptance_fraction

Acceptance fraction for this move.

property accepted

Accepted counts for this move.

cleanup_proposals_gibbs(branch_names_run, inds_run, q, branches_coords, new_inds=None, branches_inds=None, new_branch_supps=None, branches_supplemental=None)

Set all not Gibbs-sampled parameters back

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• q (dict) – Dictionary of new coordinate arrays for all proposal branches.

• branches_coords (dict) – Dictionary of old coordinate arrays for all branches.

• new_inds (dict, optional) – Dictionary of new inds arrays for all proposal branches.

• branches_inds (dict, optional) – Dictionary of old inds arrays for all branches.

• new_branch_supps (dict, optional) – Dictionary of new branches supplemental for all proposal branches.

• branches_supplemental (dict, optional) – Dictionary of old branches supplemental for all branches.

compute_log_posterior_basic(logl, logp)

Compute the log of posterior

\(\log{P} = \log{L} + \log{p}\)

This method is to mesh with the tempered log posterior computation.

Parameters:

• logl (np.ndarray[ntemps, nwalkers]) – Log-likelihood values.

• logp (np.ndarray[ntemps, nwalkers]) – Log-prior values.

Returns:

Log-Posterior values.

Return type:

np.ndarray[ntemps, nwalkers]

ensure_ordering(correct_key_order, q, new_inds, new_branch_supps)

Ensure proper order of key in dictionaries.

Parameters:

• correct_key_order (list) – Keys in correct order.

• q (dict) – Dictionary of new coordinate arrays for all branches.

• new_inds (dict) – Dictionary of new inds arrays for all branches.

• new_branch_supps (dict or None) – Dictionary of new branches supplemental for all proposal branches.

Returns:

(q, new_inds, new_branch_supps) in correct key order.

Return type:

Tuple

fix_logp_gibbs(branch_names_run, inds_run, logp, inds)

Set any walker with no leaves to have logp = -np.inf

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• logp (np.ndarray) – Log of the prior going into final posterior computation.

• inds (dict) – Dictionary of inds arrays for all branches.

gibbs_sampling_setup_iterator(all_branch_names)

Iterate through the gibbs splits as a generator

Parameters:

all_branch_names (list) – List of all branch names.

Yields:

2-tuple –

Gibbs sampling split.

First entry is the branch names to run and the second entry is the index into the leaves/parameters for this Gibbs split.

Raises:

ValueError – Incorrect inputs.

propose(model, state)

Use the move to generate a proposal and compute the acceptance

Parameters:

• model (eryn.model.Model) – Carrier of sampler information.

• state (State) – Current state of the sampler.

Returns:

State of sampler after proposal is complete.

Return type:

State

setup(branches_coords)

Any setup for the proposal.

Parameters:

branches_coords (dict) – Keys are branch_names. Values are np.ndarray[ntemps, nwalkers, nleaves_max, ndim]. These are the curent coordinates for all the walkers.

setup_proposals(branch_names_run, inds_run, branches_coords, branches_inds)

Setup proposals when gibbs sampling.

Get inputs into the proposal including Gibbs split information.

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• branches_coords (dict) – Dictionary of coordinate arrays for all branches.

• branches_inds (dict) – Dictionary of inds arrays for all branches.

Returns:

(coords, inds, at_least_one_proposal)

• Coords including Gibbs sampling info.

• inds including Gibbs sampling info.

• at_least_one_proposal is boolean. It is passed out to

  indicate there is at least one leaf available for the requested branch names.

Return type:

tuple

property temperature_control

Temperature controller

tune(state, accepted)

Tune a proposal

This is a place holder for tuning.

Parameters:

• state (eryn.state.State) – Current state of sampler.

• accepted (np.ndarray[ntemps, nwalkers]) – Accepted values for last pass through proposal.

update(old_state, new_state, accepted, subset=None)

Update a given subset of the ensemble with an accepted proposal

This class was updated from emcee to handle the added structure of Eryn.

Parameters:

• old_state (eryn.state.State) – State with current information. New information is added to this state.

• new_state (eryn.state.State) – State with information from proposed points.

• accepted (np.ndarray[ntemps, nwalkers]) – A vector of booleans indicating which walkers were accepted.

• subset (np.ndarray[ntemps, nwalkers], optional) – A boolean mask indicating which walkers were included in the subset. This can be used, for example, when updating only the primary ensemble in a RedBlueMove. (default: None)

Returns:

old_state with accepted points added from new_state.

Return type:

eryn.state.State

Mutliple Try Moves

Mutliple Try Parent Class

class eryn.moves.MultipleTryMove(num_try=1, independent=False, symmetric=False, rj=False, use_gpu=None, **kwargs)

Bases: ABC

Generate multiple proposal tries.

This class should be inherited by another proposal class with the @classmethods overwritten. See eryn.moves.MTDistGenMove and MTDistGenRJ for examples.

Parameters:

• num_try (int, optional) – Number of tries. (default: 1)

• independent (bool, optional) – Set to True if the proposal is independent of the current points. (default: False).

• symmetric (bool, optional) – Set to True if the proposal is symmetric. (default: False).

• rj (bool, optional) – Set to True if this is a nested reversible jump proposal. (default: False).

• **kwargs (dict, optional) – for compatibility with other proposals.

• Raises – ValueError: Input issues.

classmethod special_like_func(generated_coords, *args, inds_leaves_rj=None, **kwargs)

Calculate the Likelihood for sampled points.

Parameters:

• generated_coords (np.ndarray) – Generated coordinates with shape (number of independent walkers, num_try).

• *args (tuple, optional) – additional arguments passed by overwriting the get_proposal function and passing args_like keyword argument.

• inds_leaves_rj (np.ndarray) – Index into each individual walker giving the leaf index associated with this proposal. Should only be used if self.rj is True. (default: None)

• **kwargs (tuple, optional) – additional keyword arguments passed by overwriting the get_proposal function and passing kwargs_like keyword argument.

Returns:

Likelihood values with shape (generated_coords.shape[0], num_try).

Return type:

np.ndarray

Raises:

NotImplementedError – Function not included.

classmethod special_prior_func(generated_coords, *args, **kwargs)

Calculate the Prior for sampled points.

Parameters:

• generated_coords (np.ndarray) – Generated coordinates with shape (number of independent walkers, num_try).

• *args (tuple, optional) – additional arguments passed by overwriting the get_proposal function and passing args_prior keyword argument.

• inds_leaves_rj (np.ndarray) – Index into each individual walker giving the leaf index associated with this proposal. Should only be used if self.rj is True. (default: None)

• **kwargs (tuple, optional) – additional keyword arguments passed by overwriting the get_proposal function and passing kwargs_prior keyword argument.

Returns:

Prior values with shape (generated_coords.shape[0], num_try).

Return type:

np.ndarray

Raises:

NotImplementedError – Function not included.

classmethod special_generate_func(random, size=1, *args, fill_tuple=None, fill_values=None, **kwargs)

Generate samples and calculate the logpdf of their proposal function.

Parameters:

• coords (np.ndarray) – Current coordinates of walkers.

• random (obj) – Random generator.

• *args (tuple, optional) – additional arguments passed by overwriting the get_proposal function and passing args_generate keyword argument.

• size (int, optional) – Number of tries to generate.

• fill_tuple (tuple, optional) – Length 2 tuple with the indexing of which values to fill when generating. Can be used for auxillary proposals or reverse RJ proposals. First index is the index into walkers and the second index is the index into the number of tries. (default: None)

• fill_values (np.ndarray) – values to fill associated with fill_tuple. Should have size (len(fill_tuple[0]), ndim). (default: None).

• **kwargs (tuple, optional) – additional keyword arguments passed by overwriting the get_proposal function and passing kwargs_generate keyword argument.

Returns:

(generated points, logpdf of generated points).

Return type:

tuple

Raises:

NotImplementedError – Function not included.

classmethod special_generate_logpdf(coords)

Get logpdf of generated coordinates.

Parameters:

coords (np.ndarray) – Current coordinates of walkers.

Returns:

logpdf of generated points.

Return type:

np.ndarray

Raises:

NotImplementedError – Function not included.

get_mt_log_posterior(ll, lp, betas=None)

Calculate the log of the posterior for all tries.

Parameters:

• ll (np.ndarray) – Log Likelihood values with shape (nwalkers, num_tries).

• lp (np.ndarray) – Log Prior values with shape (nwalkers, num_tries).

• betas (np.ndarray, optional) – Inverse temperatures to include in log Posterior computation. (default: None)

Returns:

Log of the Posterior with shape (nwalkers, num_tries).

Return type:

np.ndarray

readout_adjustment(out_vals, all_vals_prop, aux_all_vals)

Read out values from the proposal.

Allows the user to read out any values from the proposal that may be needed elsewhere. This function must be overwritten.

Parameters:

• out_vals (list) – [logP_out, ll_out, lp_out, log_proposal_pdf_out, log_sum_weights].

• all_vals_prop (list) – [logP, ll, lp, log_proposal_pdf, log_sum_weights].

• aux_all_vals (list) – [aux_logP, aux_ll, aux_lp, aux_log_proposal_pdf, aux_log_sum_weights].

get_mt_proposal(coords, random, args_generate=(), kwargs_generate={}, args_like=(), kwargs_like={}, args_prior=(), kwargs_prior={}, betas=None, ll_in=None, lp_in=None, inds_leaves_rj=None, inds_reverse_rj=None)

Make a multiple-try proposal

Here, nwalkers refers to all independent walkers which generally will mean nwalkers * ntemps in terms of the rest of the sampler.

Parameters:

• coords (np.ndarray) – Current coordinates of walkers.

• random (obj) – Random generator.

• args_generate (tuple, optional) – Additional *args to pass to generate function. Must overwrite get_proposal function to use these. (default: ())

• kwargs_generate (dict, optional) – Additional **kwargs to pass to generate function. (default: {}) Must overwrite get_proposal function to use these.

• args_like (tuple, optional) – Additional *args to pass to Likelihood function. Must overwrite get_proposal function to use these. (default: ())

• kwargs_like (dict, optional) – Additional **kwargs to pass to Likelihood function. Must overwrite get_proposal function to use these. (default: {})

• args_prior (tuple, optional) – Additional *args to pass to Prior function. Must overwrite get_proposal function to use these. (default: ())

• kwargs_prior (dict, optional) – Additional **kwargs to pass to Prior function. Must overwrite get_proposal function to use these. (default: {})

• betas (np.ndarray, optional) – Inverse temperatures passes to the proposal with shape (nwalkers,).

• ll_in (np.ndarray, optional) – Log Likelihood values coming in for current coordinates. Must be provided if self.rj is True. If self.rj is True, must be nested. Also, for all proposed removals, this value must be the Likelihood with the binary removed so all proposals are pretending to add a binary. Useful if self.independent is True. (default: None)

• lp_in (np.ndarray, optional) – Log Prior values coming in for current coordinates. Must be provided if self.rj is True. If self.rj is True, must be nested. Also, for all proposed removals, this value must be the Likelihood with the binary removed so all proposals are pretending to add a binary. Useful if self.independent is True. (default: None)

• inds_leaves_rj (np.ndarray, optional) – Array giving the leaf index of each incoming walker. Must be provided if self.rj is True. (default: None)

• inds_reverse_rj (np.ndarray, optional) – Array giving the walker index for which proposals are reverse proposal removing a leaf. Must be provided if self.rj is True. (default: None)

Returns:

(generated points, factors).

Return type:

tuple

Raises:

ValueError – Inputs are incorrect.

get_proposal(branches_coords, random, branches_inds=None, **kwargs)

Get proposal

Parameters:

• branches_coords (dict) – Keys are branch_names and values are np.ndarray[ntemps, nwalkers, nleaves_max, ndim] representing coordinates for walkers.

• random (object) – Current random state object.

• branches_inds (dict, optional) – Keys are branch_names and values are np.ndarray[ntemps, nwalkers, nleaves_max] representing which leaves are currently being used. (default: None)

• **kwargs (ignored) – This is added for compatibility. It is ignored in this function.

Returns:

(Proposed coordinates, factors) -> (dict, np.ndarray)

Return type:

tuple

Raises:

ValueError – Input issues.

Implemented Multiple Try Moves

class eryn.moves.MTDistGenMove(generate_dist, **kwargs)

Bases: MultipleTryMove, MHMove

Perform a multiple try MH move based on a distribution.

Distribution must be independent of the current point.

This is effectively an example of the mutliple try class inheritance structure.

Parameters:

• generate_dist (dict) – Keys are branch names and values are ProbDistContainer objects that have logpdf and rvs methods. If you

• *args (tuple, optional) – Additional arguments to pass to parent classes.

• **kwargs (dict, optional) – Keyword arguments passed to parent classes.

special_generate_logpdf(generated_coords)

Get logpdf of generated coordinates.

Parameters:

generated_coords (np.ndarray) – Current coordinates of walkers.

Returns:

logpdf of generated points.

Return type:

np.ndarray

special_generate_func(coords, random, size=1, fill_tuple=None, fill_values=None, **kwargs)

Generate samples and calculate the logpdf of their proposal function.

Parameters:

• coords (np.ndarray) – Current coordinates of walkers.

• random (obj) – Random generator.

• *args (tuple, optional) – additional arguments passed by overwriting the get_proposal function and passing args_generate keyword argument.

• size (int, optional) – Number of tries to generate.

• fill_tuple (tuple, optional) – Length 2 tuple with the indexing of which values to fill when generating. Can be used for auxillary proposals or reverse RJ proposals. First index is the index into walkers and the second index is the index into the number of tries. (default: None)

• fill_values (np.ndarray) – values to fill associated with fill_tuple. Should have size (len(fill_tuple[0]), ndim). (default: None).

• **kwargs (tuple, optional) – additional keyword arguments passed by overwriting the get_proposal function and passing kwargs_generate keyword argument.

Returns:

(generated points, logpdf of generated points).

Return type:

tuple

set_coords_and_inds(generated_coords)

Setup coordinates for prior and Likelihood

Parameters:

generated_coords (np.ndarray) – Generated coordinates with shape (number of independent walkers, num_try).

Returns:

Coordinates for Likelihood and Prior.

Return type:

dict

special_like_func(generated_coords, **kwargs)

Calculate the Likelihood for sampled points.

Parameters:

• generated_coords (np.ndarray) – Generated coordinates with shape (number of independent walkers, num_try).

• **kwargs (dict, optional) – For compatibility.

Returns:

Likelihood values with shape (generated_coords.shape[0], num_try).

Return type:

np.ndarray

special_prior_func(generated_coords, **kwargs)

Calculate the Prior for sampled points.

Parameters:

• generated_coords (np.ndarray) – Generated coordinates with shape (number of independent walkers, num_try).

• **kwargs (dict, optional) – For compatibility.

Returns:

Prior values with shape (generated_coords.shape[0], num_try).

Return type:

np.ndarray

property acceptance_fraction

Acceptance fraction for this move.

property accepted

Accepted counts for this move.

cleanup_proposals_gibbs(branch_names_run, inds_run, q, branches_coords, new_inds=None, branches_inds=None, new_branch_supps=None, branches_supplemental=None)

Set all not Gibbs-sampled parameters back

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• q (dict) – Dictionary of new coordinate arrays for all proposal branches.

• branches_coords (dict) – Dictionary of old coordinate arrays for all branches.

• new_inds (dict, optional) – Dictionary of new inds arrays for all proposal branches.

• branches_inds (dict, optional) – Dictionary of old inds arrays for all branches.

• new_branch_supps (dict, optional) – Dictionary of new branches supplemental for all proposal branches.

• branches_supplemental (dict, optional) – Dictionary of old branches supplemental for all branches.

compute_log_posterior_basic(logl, logp)

Compute the log of posterior

\(\log{P} = \log{L} + \log{p}\)

This method is to mesh with the tempered log posterior computation.

Parameters:

• logl (np.ndarray[ntemps, nwalkers]) – Log-likelihood values.

• logp (np.ndarray[ntemps, nwalkers]) – Log-prior values.

Returns:

Log-Posterior values.

Return type:

np.ndarray[ntemps, nwalkers]

ensure_ordering(correct_key_order, q, new_inds, new_branch_supps)

Ensure proper order of key in dictionaries.

Parameters:

• correct_key_order (list) – Keys in correct order.

• q (dict) – Dictionary of new coordinate arrays for all branches.

• new_inds (dict) – Dictionary of new inds arrays for all branches.

• new_branch_supps (dict or None) – Dictionary of new branches supplemental for all proposal branches.

Returns:

(q, new_inds, new_branch_supps) in correct key order.

Return type:

Tuple

fix_logp_gibbs(branch_names_run, inds_run, logp, inds)

Set any walker with no leaves to have logp = -np.inf

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• logp (np.ndarray) – Log of the prior going into final posterior computation.

• inds (dict) – Dictionary of inds arrays for all branches.

get_mt_log_posterior(ll, lp, betas=None)

Calculate the log of the posterior for all tries.

Parameters:

• ll (np.ndarray) – Log Likelihood values with shape (nwalkers, num_tries).

• lp (np.ndarray) – Log Prior values with shape (nwalkers, num_tries).

• betas (np.ndarray, optional) – Inverse temperatures to include in log Posterior computation. (default: None)

Returns:

Log of the Posterior with shape (nwalkers, num_tries).

Return type:

np.ndarray

get_mt_proposal(coords, random, args_generate=(), kwargs_generate={}, args_like=(), kwargs_like={}, args_prior=(), kwargs_prior={}, betas=None, ll_in=None, lp_in=None, inds_leaves_rj=None, inds_reverse_rj=None)

Make a multiple-try proposal

Here, nwalkers refers to all independent walkers which generally will mean nwalkers * ntemps in terms of the rest of the sampler.

Parameters:

• coords (np.ndarray) – Current coordinates of walkers.

• random (obj) – Random generator.

• args_generate (tuple, optional) – Additional *args to pass to generate function. Must overwrite get_proposal function to use these. (default: ())

• kwargs_generate (dict, optional) – Additional **kwargs to pass to generate function. (default: {}) Must overwrite get_proposal function to use these.

• args_like (tuple, optional) – Additional *args to pass to Likelihood function. Must overwrite get_proposal function to use these. (default: ())

• kwargs_like (dict, optional) – Additional **kwargs to pass to Likelihood function. Must overwrite get_proposal function to use these. (default: {})

• args_prior (tuple, optional) – Additional *args to pass to Prior function. Must overwrite get_proposal function to use these. (default: ())

• kwargs_prior (dict, optional) – Additional **kwargs to pass to Prior function. Must overwrite get_proposal function to use these. (default: {})

• betas (np.ndarray, optional) – Inverse temperatures passes to the proposal with shape (nwalkers,).

• ll_in (np.ndarray, optional) – Log Likelihood values coming in for current coordinates. Must be provided if self.rj is True. If self.rj is True, must be nested. Also, for all proposed removals, this value must be the Likelihood with the binary removed so all proposals are pretending to add a binary. Useful if self.independent is True. (default: None)

• lp_in (np.ndarray, optional) – Log Prior values coming in for current coordinates. Must be provided if self.rj is True. If self.rj is True, must be nested. Also, for all proposed removals, this value must be the Likelihood with the binary removed so all proposals are pretending to add a binary. Useful if self.independent is True. (default: None)

• inds_leaves_rj (np.ndarray, optional) – Array giving the leaf index of each incoming walker. Must be provided if self.rj is True. (default: None)

• inds_reverse_rj (np.ndarray, optional) – Array giving the walker index for which proposals are reverse proposal removing a leaf. Must be provided if self.rj is True. (default: None)

Returns:

(generated points, factors).

Return type:

tuple

Raises:

ValueError – Inputs are incorrect.

get_proposal(branches_coords, random, branches_inds=None, **kwargs)

Get proposal

Parameters:

• branches_coords (dict) – Keys are branch_names and values are np.ndarray[ntemps, nwalkers, nleaves_max, ndim] representing coordinates for walkers.

• random (object) – Current random state object.

• branches_inds (dict, optional) – Keys are branch_names and values are np.ndarray[ntemps, nwalkers, nleaves_max] representing which leaves are currently being used. (default: None)

• **kwargs (ignored) – This is added for compatibility. It is ignored in this function.

Returns:

(Proposed coordinates, factors) -> (dict, np.ndarray)

Return type:

tuple

Raises:

ValueError – Input issues.

gibbs_sampling_setup_iterator(all_branch_names)

Iterate through the gibbs splits as a generator

Parameters:

all_branch_names (list) – List of all branch names.

Yields:

2-tuple –

Gibbs sampling split.

First entry is the branch names to run and the second entry is the index into the leaves/parameters for this Gibbs split.

Raises:

ValueError – Incorrect inputs.

propose(model, state)

Use the move to generate a proposal and compute the acceptance

Parameters:

• model (eryn.model.Model) – Carrier of sampler information.

• state (State) – Current state of the sampler.

Returns:

State of sampler after proposal is complete.

Return type:

State

readout_adjustment(out_vals, all_vals_prop, aux_all_vals)

Read out values from the proposal.

Allows the user to read out any values from the proposal that may be needed elsewhere. This function must be overwritten.

Parameters:

• out_vals (list) – [logP_out, ll_out, lp_out, log_proposal_pdf_out, log_sum_weights].

• all_vals_prop (list) – [logP, ll, lp, log_proposal_pdf, log_sum_weights].

• aux_all_vals (list) – [aux_logP, aux_ll, aux_lp, aux_log_proposal_pdf, aux_log_sum_weights].

setup(branches_coords)

Any setup for the proposal.

Parameters:

branches_coords (dict) – Keys are branch_names. Values are np.ndarray[ntemps, nwalkers, nleaves_max, ndim]. These are the curent coordinates for all the walkers.

setup_proposals(branch_names_run, inds_run, branches_coords, branches_inds)

Setup proposals when gibbs sampling.

Get inputs into the proposal including Gibbs split information.

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• branches_coords (dict) – Dictionary of coordinate arrays for all branches.

• branches_inds (dict) – Dictionary of inds arrays for all branches.

Returns:

(coords, inds, at_least_one_proposal)

• Coords including Gibbs sampling info.

• inds including Gibbs sampling info.

• at_least_one_proposal is boolean. It is passed out to

  indicate there is at least one leaf available for the requested branch names.

Return type:

tuple

property temperature_control

Temperature controller

tune(state, accepted)

Tune a proposal

This is a place holder for tuning.

Parameters:

• state (eryn.state.State) – Current state of sampler.

• accepted (np.ndarray[ntemps, nwalkers]) – Accepted values for last pass through proposal.

update(old_state, new_state, accepted, subset=None)

Update a given subset of the ensemble with an accepted proposal

This class was updated from emcee to handle the added structure of Eryn.

Parameters:

• old_state (eryn.state.State) – State with current information. New information is added to this state.

• new_state (eryn.state.State) – State with information from proposed points.

• accepted (np.ndarray[ntemps, nwalkers]) – A vector of booleans indicating which walkers were accepted.

• subset (np.ndarray[ntemps, nwalkers], optional) – A boolean mask indicating which walkers were included in the subset. This can be used, for example, when updating only the primary ensemble in a RedBlueMove. (default: None)

Returns:

old_state with accepted points added from new_state.

Return type:

eryn.state.State

Implemented Multiple Try RJ Moves

class eryn.moves.MTDistGenMoveRJ(generate_dist, *args, **kwargs)

Bases: MultipleTryMoveRJ, DistributionGenerateRJ

special_generate_logpdf(generated_coords)

Get logpdf of generated coordinates.

Parameters:

generated_coords (np.ndarray) – Current coordinates of walkers.

Returns:

logpdf of generated points.

Return type:

np.ndarray

special_generate_func(coords, random, size=1, fill_tuple=None, fill_values=None)

Generate samples and calculate the logpdf of their proposal function.

Parameters:

• coords (np.ndarray) – Current coordinates of walkers.

• random (obj) – Random generator.

• *args (tuple, optional) – additional arguments passed by overwriting the get_proposal function and passing args_generate keyword argument.

• size (int, optional) – Number of tries to generate.

• fill_tuple (tuple, optional) – Length 2 tuple with the indexing of which values to fill when generating. Can be used for auxillary proposals or reverse RJ proposals. First index is the index into walkers and the second index is the index into the number of tries. (default: None)

• fill_values (np.ndarray) – values to fill associated with fill_tuple. Should have size (len(fill_tuple[0]), ndim). (default: None).

• **kwargs (tuple, optional) – additional keyword arguments passed by overwriting the get_proposal function and passing kwargs_generate keyword argument.

Returns:

(generated points, logpdf of generated points).

Return type:

tuple

set_coords_and_inds(generated_coords, inds_leaves_rj=None)

Setup coordinates for prior and Likelihood

Parameters:

• generated_coords (np.ndarray) – Generated coordinates with shape (number of independent walkers, num_try).

• inds_leaves_rj (np.ndarray) – Index into each individual walker giving the leaf index associated with this proposal. Should only be used if self.rj is True. (default: None)

Returns:

Coordinates for Likelihood and Prior.

Return type:

dict

special_like_func(generated_coords, inds_leaves_rj=None, **kwargs)

Calculate the Likelihood for sampled points.

Parameters:

• generated_coords (np.ndarray) – Generated coordinates with shape (number of independent walkers, num_try).

• inds_leaves_rj (np.ndarray) – Index into each individual walker giving the leaf index associated with this proposal. Should only be used if self.rj is True. (default: None)

Returns:

Likelihood values with shape (generated_coords.shape[0], num_try).

Return type:

np.ndarray

special_prior_func(generated_coords, inds_leaves_rj=None, **kwargs)

Calculate the Prior for sampled points.

Parameters:

• generated_coords (np.ndarray) – Generated coordinates with shape (number of independent walkers, num_try).

• inds_leaves_rj (np.ndarray) – Index into each individual walker giving the leaf index associated with this proposal. Should only be used if self.rj is True. (default: None)

Returns:

Prior values with shape (generated_coords.shape[0], num_try).

Return type:

np.ndarray

property acceptance_fraction

Acceptance fraction for this move.

property accepted

Accepted counts for this move.

cleanup_proposals_gibbs(branch_names_run, inds_run, q, branches_coords, new_inds=None, branches_inds=None, new_branch_supps=None, branches_supplemental=None)

Set all not Gibbs-sampled parameters back

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• q (dict) – Dictionary of new coordinate arrays for all proposal branches.

• branches_coords (dict) – Dictionary of old coordinate arrays for all branches.

• new_inds (dict, optional) – Dictionary of new inds arrays for all proposal branches.

• branches_inds (dict, optional) – Dictionary of old inds arrays for all branches.

• new_branch_supps (dict, optional) – Dictionary of new branches supplemental for all proposal branches.

• branches_supplemental (dict, optional) – Dictionary of old branches supplemental for all branches.

compute_log_posterior_basic(logl, logp)

Compute the log of posterior

\(\log{P} = \log{L} + \log{p}\)

This method is to mesh with the tempered log posterior computation.

Parameters:

• logl (np.ndarray[ntemps, nwalkers]) – Log-likelihood values.

• logp (np.ndarray[ntemps, nwalkers]) – Log-prior values.

Returns:

Log-Posterior values.

Return type:

np.ndarray[ntemps, nwalkers]

ensure_ordering(correct_key_order, q, new_inds, new_branch_supps)

Ensure proper order of key in dictionaries.

Parameters:

• correct_key_order (list) – Keys in correct order.

• q (dict) – Dictionary of new coordinate arrays for all branches.

• new_inds (dict) – Dictionary of new inds arrays for all branches.

• new_branch_supps (dict or None) – Dictionary of new branches supplemental for all proposal branches.

Returns:

(q, new_inds, new_branch_supps) in correct key order.

Return type:

Tuple

fix_logp_gibbs(branch_names_run, inds_run, logp, inds)

Set any walker with no leaves to have logp = -np.inf

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• logp (np.ndarray) – Log of the prior going into final posterior computation.

• inds (dict) – Dictionary of inds arrays for all branches.

get_model_change_proposal(inds, random, nleaves_min, nleaves_max)

Helper function for changing the model count by 1.

This helper function works with nested models where you want to add or remove one leaf at a time.

Parameters:

• inds (np.ndarray) – inds values for this specific branch with shape (ntemps, nwalkers, nleaves_max).

• random (object) – Current random state of the sampler.

• nleaves_min (int) – Minimum allowable leaf count for this branch.

• nleaves_max (int) – Maximum allowable leaf count for this branch.

Returns:

Keys are "+1" and "-1". Values are indexing information.

"+1" and "-1" indicate if a source is being added or removed, respectively. The indexing information is a 2D array with shape (number changing, 3). The length 3 is the index into each of the (ntemps, nwalkers, nleaves_max).

Return type:

dict

get_mt_log_posterior(ll, lp, betas=None)

Calculate the log of the posterior for all tries.

Parameters:

• ll (np.ndarray) – Log Likelihood values with shape (nwalkers, num_tries).

• lp (np.ndarray) – Log Prior values with shape (nwalkers, num_tries).

• betas (np.ndarray, optional) – Inverse temperatures to include in log Posterior computation. (default: None)

Returns:

Log of the Posterior with shape (nwalkers, num_tries).

Return type:

np.ndarray

get_mt_proposal(coords, random, args_generate=(), kwargs_generate={}, args_like=(), kwargs_like={}, args_prior=(), kwargs_prior={}, betas=None, ll_in=None, lp_in=None, inds_leaves_rj=None, inds_reverse_rj=None)

Make a multiple-try proposal

Here, nwalkers refers to all independent walkers which generally will mean nwalkers * ntemps in terms of the rest of the sampler.

Parameters:

• coords (np.ndarray) – Current coordinates of walkers.

• random (obj) – Random generator.

• args_generate (tuple, optional) – Additional *args to pass to generate function. Must overwrite get_proposal function to use these. (default: ())

• kwargs_generate (dict, optional) – Additional **kwargs to pass to generate function. (default: {}) Must overwrite get_proposal function to use these.

• args_like (tuple, optional) – Additional *args to pass to Likelihood function. Must overwrite get_proposal function to use these. (default: ())

• kwargs_like (dict, optional) – Additional **kwargs to pass to Likelihood function. Must overwrite get_proposal function to use these. (default: {})

• args_prior (tuple, optional) – Additional *args to pass to Prior function. Must overwrite get_proposal function to use these. (default: ())

• kwargs_prior (dict, optional) – Additional **kwargs to pass to Prior function. Must overwrite get_proposal function to use these. (default: {})

• betas (np.ndarray, optional) – Inverse temperatures passes to the proposal with shape (nwalkers,).

• ll_in (np.ndarray, optional) – Log Likelihood values coming in for current coordinates. Must be provided if self.rj is True. If self.rj is True, must be nested. Also, for all proposed removals, this value must be the Likelihood with the binary removed so all proposals are pretending to add a binary. Useful if self.independent is True. (default: None)

• lp_in (np.ndarray, optional) – Log Prior values coming in for current coordinates. Must be provided if self.rj is True. If self.rj is True, must be nested. Also, for all proposed removals, this value must be the Likelihood with the binary removed so all proposals are pretending to add a binary. Useful if self.independent is True. (default: None)

• inds_leaves_rj (np.ndarray, optional) – Array giving the leaf index of each incoming walker. Must be provided if self.rj is True. (default: None)

• inds_reverse_rj (np.ndarray, optional) – Array giving the walker index for which proposals are reverse proposal removing a leaf. Must be provided if self.rj is True. (default: None)

Returns:

(generated points, factors).

Return type:

tuple

Raises:

ValueError – Inputs are incorrect.

get_proposal(branches_coords, branches_inds, nleaves_min_all, nleaves_max_all, random, **kwargs)

Make a proposal

Parameters:

• all_coords (dict) – Keys are branch_names. Values are np.ndarray[ntemps, nwalkers, nleaves_max, ndim]. These are the curent coordinates for all the walkers.

• all_inds (dict) – Keys are branch_names. Values are np.ndarray[ntemps, nwalkers, nleaves_max]. These are the boolean arrays marking which leaves are currently used within each walker.

• nleaves_min_all (list) – Minimum values of leaf ount for each model. Must have same order as all_cords.

• nleaves_max_all (list) – Maximum values of leaf ount for each model. Must have same order as all_cords.

• random (object) – Current random state of the sampler.

• **kwargs (ignored) – For modularity.

Returns:

Tuple containing proposal information.

First entry is the new coordinates as a dictionary with keys as branch_names and values as double `` np.ndarray[ntemps, nwalkers, nleaves_max, ndim] containing proposed coordinates. Second entry is the new ``inds array with boolean values flipped for added or removed sources. Third entry is the factors associated with the proposal necessary for detailed balance. This is effectively any term in the detailed balance fraction. +log of factors if in the numerator. -log of factors if in the denominator.

Return type:

tuple

gibbs_sampling_setup_iterator(all_branch_names)

Iterate through the gibbs splits as a generator

Parameters:

all_branch_names (list) – List of all branch names.

Yields:

2-tuple –

Gibbs sampling split.

First entry is the branch names to run and the second entry is the index into the leaves/parameters for this Gibbs split.

Raises:

ValueError – Incorrect inputs.

propose(model, state)

Use the move to generate a proposal and compute the acceptance

Parameters:

• model (eryn.model.Model) – Carrier of sampler information.

• state (State) – Current state of the sampler.

Returns:

State of sampler after proposal is complete.

Return type:

State

readout_adjustment(out_vals, all_vals_prop, aux_all_vals)

Read out values from the proposal.

Allows the user to read out any values from the proposal that may be needed elsewhere. This function must be overwritten.

Parameters:

• out_vals (list) – [logP_out, ll_out, lp_out, log_proposal_pdf_out, log_sum_weights].

• all_vals_prop (list) – [logP, ll, lp, log_proposal_pdf, log_sum_weights].

• aux_all_vals (list) – [aux_logP, aux_ll, aux_lp, aux_log_proposal_pdf, aux_log_sum_weights].

setup(branches_coords)

Any setup for the proposal.

Parameters:

branches_coords (dict) – Keys are branch_names. Values are np.ndarray[ntemps, nwalkers, nleaves_max, ndim]. These are the curent coordinates for all the walkers.

setup_proposals(branch_names_run, inds_run, branches_coords, branches_inds)

Setup proposals when gibbs sampling.

Get inputs into the proposal including Gibbs split information.

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• branches_coords (dict) – Dictionary of coordinate arrays for all branches.

• branches_inds (dict) – Dictionary of inds arrays for all branches.

Returns:

(coords, inds, at_least_one_proposal)

• Coords including Gibbs sampling info.

• inds including Gibbs sampling info.

• at_least_one_proposal is boolean. It is passed out to

  indicate there is at least one leaf available for the requested branch names.

Return type:

tuple

property temperature_control

Temperature controller

tune(state, accepted)

Tune a proposal

This is a place holder for tuning.

Parameters:

• state (eryn.state.State) – Current state of sampler.

• accepted (np.ndarray[ntemps, nwalkers]) – Accepted values for last pass through proposal.

update(old_state, new_state, accepted, subset=None)

Update a given subset of the ensemble with an accepted proposal

This class was updated from emcee to handle the added structure of Eryn.

Parameters:

• old_state (eryn.state.State) – State with current information. New information is added to this state.

• new_state (eryn.state.State) – State with information from proposed points.

• accepted (np.ndarray[ntemps, nwalkers]) – A vector of booleans indicating which walkers were accepted.

• subset (np.ndarray[ntemps, nwalkers], optional) – A boolean mask indicating which walkers were included in the subset. This can be used, for example, when updating only the primary ensemble in a RedBlueMove. (default: None)

Returns:

old_state with accepted points added from new_state.

Return type:

eryn.state.State

Utility Moves

class eryn.moves.CombineMove(moves, *args, verbose=False, **kwargs)

Bases: Move

Move that combines specific moves in order.

Parameters:

• moves (list) – List of moves, similar to how moves is submitted to eryn.ensemble.EnsembleSampler. If weights are provided, they will be ignored.

• *args (tuple, optional) – args to be passed to Move.

• verbose (bool, optional) – If True, use tqdm to show progress throught steps. This can be very helpful when debugging.

• **kwargs (dict, optional) – kwargs to be passed to Move.

property accepted

Accepted counts for each move.

property acceptance_fraction

get acceptance fraction averaged over all moves

property acceptance_fraction_separate

get acceptance fraction from each move

property temperature_control

temperature controller

cleanup_proposals_gibbs(branch_names_run, inds_run, q, branches_coords, new_inds=None, branches_inds=None, new_branch_supps=None, branches_supplemental=None)

Set all not Gibbs-sampled parameters back

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• q (dict) – Dictionary of new coordinate arrays for all proposal branches.

• branches_coords (dict) – Dictionary of old coordinate arrays for all branches.

• new_inds (dict, optional) – Dictionary of new inds arrays for all proposal branches.

• branches_inds (dict, optional) – Dictionary of old inds arrays for all branches.

• new_branch_supps (dict, optional) – Dictionary of new branches supplemental for all proposal branches.

• branches_supplemental (dict, optional) – Dictionary of old branches supplemental for all branches.

compute_log_posterior_basic(logl, logp)

Compute the log of posterior

\(\log{P} = \log{L} + \log{p}\)

This method is to mesh with the tempered log posterior computation.

Parameters:

• logl (np.ndarray[ntemps, nwalkers]) – Log-likelihood values.

• logp (np.ndarray[ntemps, nwalkers]) – Log-prior values.

Returns:

Log-Posterior values.

Return type:

np.ndarray[ntemps, nwalkers]

ensure_ordering(correct_key_order, q, new_inds, new_branch_supps)

Ensure proper order of key in dictionaries.

Parameters:

• correct_key_order (list) – Keys in correct order.

• q (dict) – Dictionary of new coordinate arrays for all branches.

• new_inds (dict) – Dictionary of new inds arrays for all branches.

• new_branch_supps (dict or None) – Dictionary of new branches supplemental for all proposal branches.

Returns:

(q, new_inds, new_branch_supps) in correct key order.

Return type:

Tuple

fix_logp_gibbs(branch_names_run, inds_run, logp, inds)

Set any walker with no leaves to have logp = -np.inf

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• logp (np.ndarray) – Log of the prior going into final posterior computation.

• inds (dict) – Dictionary of inds arrays for all branches.

gibbs_sampling_setup_iterator(all_branch_names)

Iterate through the gibbs splits as a generator

Parameters:

all_branch_names (list) – List of all branch names.

Yields:

2-tuple –

Gibbs sampling split.

First entry is the branch names to run and the second entry is the index into the leaves/parameters for this Gibbs split.

Raises:

ValueError – Incorrect inputs.

setup_proposals(branch_names_run, inds_run, branches_coords, branches_inds)

Setup proposals when gibbs sampling.

Get inputs into the proposal including Gibbs split information.

Parameters:

• branch_names_run (list) – List of branch names to run concurrently.

• inds_run (list) – List of inds arrays including Gibbs sampling information.

• branches_coords (dict) – Dictionary of coordinate arrays for all branches.

• branches_inds (dict) – Dictionary of inds arrays for all branches.

Returns:

(coords, inds, at_least_one_proposal)

• Coords including Gibbs sampling info.

• inds including Gibbs sampling info.

• at_least_one_proposal is boolean. It is passed out to

  indicate there is at least one leaf available for the requested branch names.

Return type:

tuple

tune(state, accepted)

Tune a proposal

This is a place holder for tuning.

Parameters:

• state (eryn.state.State) – Current state of sampler.

• accepted (np.ndarray[ntemps, nwalkers]) – Accepted values for last pass through proposal.

update(old_state, new_state, accepted, subset=None)

Update a given subset of the ensemble with an accepted proposal

This class was updated from emcee to handle the added structure of Eryn.

Parameters:

• old_state (eryn.state.State) – State with current information. New information is added to this state.

• new_state (eryn.state.State) – State with information from proposed points.

• accepted (np.ndarray[ntemps, nwalkers]) – A vector of booleans indicating which walkers were accepted.

• subset (np.ndarray[ntemps, nwalkers], optional) – A boolean mask indicating which walkers were included in the subset. This can be used, for example, when updating only the primary ensemble in a RedBlueMove. (default: None)

Returns:

old_state with accepted points added from new_state.

Return type:

eryn.state.State

property periodic

periodic parameter information

propose(model, state)

Propose a combined move.

Parameters:

• model (eryn.model.Model) – Carrier of sampler information.

• state (State) – Current state of the sampler.

Returns:

(state, accepted)

The first return is the state of the sampler after the move. The second return value is the accepted count array for each walker counting for all proposals.

Return type:

tuple