Backends

Backends are used to store sampler information as it runs.

Regular Backend

class eryn.backends.Backend(store_missing_leaves=nan, dtype=None)

Bases: object

A simple default backend that stores the chain in memory

Parameters:

store_missing_leaves (double, optional) – Number to store for leaves that are not used in a specific step. (default: np.nan)
dtype (dtype, optional) – Dtype to use for data storage. If None, program uses np.float64. (default: None)

accepted

Number of accepted steps for within-model moves. The shape is (ntemps, nwalkers).

Type:: 2D int np.ndarray

betas

Inverse temperature latter at each step. Shape is (nsteps, ntemps). This keeps track of adjustable temperatures.

Type:: 2D double np.ndarray

blobs

Stores extra blob information returned from likelihood function. Shape is (nsteps, ntemps, nwalkers, nblobs).

Type:: 4D double np.ndarray

branch_names

List of branch names.

Type:: list of str

chain

Dictionary with branch_names as keys. The values are 5D double np.ndarray arrays with shape (nsteps, ntemps, nwalkers, nleaves_max, ndim). These are the locations of walkers over the MCMC run.

Type:: dict

dtype

Dtype to use for data storage.

Type:: dtype

inds

Keys are branch_names. Values are 4D bool np.ndarray of shape (nsteps, ntemps, nwalkers, nleaves_max). This array details which leaves are used in the current step. This is really only relevant for reversible jump.

Type:: dict

initiailized

If True, backend object has been initialized.

Type:: bool

iteration

Current index within the data storage arrays.

Type:: int

log_prior

Log of the prior values. Shape is (nsteps, nwalkers, ntemps).

Type:: 3D double np.ndarray

log_like

Log of the likelihood values. Shape is (nsteps, nwalkers, ntemps).

Type:: 3D double np.ndarray

move_info

Dictionary containing move info.

Type:: dict

move_keys

List of keys for move_info.

Type:: list

nbranches

Number of branches.

Type:: int

ndims

Dimensionality of each branch.

Type:: dict

nleaves_max

Maximum allowable leaves for each branch.

Type:: dict

nwalkers

The size of the ensemble (per temperature).

Type:: int

ntemps

Number of rungs in the temperature ladder.

Type:: int

reset_args

Arguments to reset backend.

Type:: tuple

reset_kwargs

Keyword arguments to reset backend.

Type:: dict

rj

If True, reversible-jump techniques are used.

Type:: bool

rj_accepted

Number of accepted steps for between-model moves. The shape is (ntemps, nwalkers).

Type:: 2D int np.ndarray

store_missing_leaves

Number to store for leaves that are not used in a specific step.

Type:: double

reset_base(): Allows for simple reset based on previous inputs

reset(nwalkers, ndims, nleaves_max=1, ntemps=1, branch_names=None, nbranches=1, rj=False, moves=None, **info)

Clear the state of the chain and empty the backend

Parameters:

nwalkers (int) – The size of the ensemble (per temperature).
ndims (int, list of ints, or dict) – The number of dimensions for each branch. If dict, keys should be the branch names and values the associated dimensionality.
nleaves_max (int, list of ints, or dict, optional) – Maximum allowable leaf count for each branch. It should have the same length as the number of branches. If dict, keys should be the branch names and values the associated maximal leaf value. (default: 1)
ntemps (int, optional) – Number of rungs in the temperature ladder. (default: 1)
branch_names (str or list of str, optional) – Names of the branches used. If not given, branches will be names model_0, …, model_n for n branches. (default: None)
nbranches (int, optional) – Number of branches. This is only used if branch_names is None. (default: 1)
rj (bool, optional) – If True, reversible-jump techniques are used. (default: False)
moves (list, optional) – List of all of the move classes input into the sampler. (default: None)
**info (dict, optional) – Any other key-value pairs to be added as attributes to the backend.

has_blobs(): Returns True if the model includes blobs

get_value(name, thin=1, discard=0, slice_vals=None)

Returns a requested value to user.

This function helps to streamline the backend for both basic and hdf backend.

Parameters:

name (str) – Name of value requested.
thin (int, optional) – Take only every thin steps from the chain. (default: 1)
discard (int, optional) – Discard the first discard steps in the chain as burn-in. (default: 0)
slice_vals (indexing np.ndarray or slice, optional) – Ignored for non-HDFBackend.

Returns:

Values requested.

Return type:

dict or np.ndarray

get_chain(**kwargs)

Get the stored chain of MCMC samples

Parameters:

thin (int, optional) – Take only every thin steps from the chain. (default: 1)
discard (int, optional) – Discard the first discard steps in the chain as burn-in. (default: 0)
slice_vals (indexing np.ndarray or slice, optional) – This is only available in eryn.backends.hdfbackend. If provided, slice the array directly from the HDF5 file with slice = slice_vals. thin and discard will be ignored if slice_vals is not None. This is particularly useful if files are very large and the user only wants a small subset of the overall array. (default: None)

Returns:

MCMC samples: The dictionary contains np.ndarrays of samples across the branches.

Return type:

dict

get_autocorr_thin_burn()

Return the discard and thin values based on the autocorrelation length.

The discard is determined as 2 times the maximum correlation length among parameters. The thin is determined using 1/2 times the minimum correlation legnth among parameters.

Returns:

Information on thin and burn: (discard, thin)

Return type:

tuple

get_inds(**kwargs)

Get the stored chain of MCMC samples

Parameters:

thin (int, optional) – Take only every thin steps from the chain. (default: 1)
discard (int, optional) – Discard the first discard steps in the chain as burn-in. (default: 0)
slice_vals (indexing np.ndarray or slice, optional) – This is only available in eryn.backends.hdfbackend. If provided, slice the array directly from the HDF5 file with slice = slice_vals. thin and discard will be ignored if slice_vals is not None. This is particularly useful if files are very large and the user only wants a small subset of the overall array. (default: None)

Returns:

The inds associated with the MCMC samples.: The dictionary contains np.ndarrays of inds across the branches indicated which leaves were used at each step.

Return type:

dict

get_nleaves(**kwargs)

Get the number of leaves for each walker

Parameters:

thin (int, optional) – Take only every thin steps from the chain. (default: 1)
discard (int, optional) – Discard the first discard steps in the chain as burn-in. (default: 0)
slice_vals (indexing np.ndarray or slice, optional) – This is only available in eryn.backends.hdfbackend. If provided, slice the array directly from the HDF5 file with slice = slice_vals. thin and discard will be ignored if slice_vals is not None. This is particularly useful if files are very large and the user only wants a small subset of the overall array. (default: None)

Returns:

nleaves on each branch.

The number of leaves on each branch associated with the MCMC samples: within each branch.

Return type:

dict

get_blobs(**kwargs)

Get the chain of blobs for each sample in the chain

Parameters:

thin (int, optional) – Take only every thin steps from the chain. (default: 1)
discard (int, optional) – Discard the first discard steps in the chain as burn-in. (default: 0)
slice_vals (indexing np.ndarray or slice, optional) – This is only available in eryn.backends.hdfbackend. If provided, slice the array directly from the HDF5 file with slice = slice_vals. thin and discard will be ignored if slice_vals is not None. This is particularly useful if files are very large and the user only wants a small subset of the overall array. (default: None)

Returns:

The chain of blobs.

Return type:

double np.ndarray[nsteps, ntemps, nwalkers, nblobs]

get_log_like(**kwargs)

Get the chain of log Likelihood values evaluated at the MCMC samples

Parameters:

thin (int, optional) – Take only every thin steps from the chain. (default: 1)
discard (int, optional) – Discard the first discard steps in the chain as burn-in. (default: 0)
slice_vals (indexing np.ndarray or slice, optional) – This is only available in eryn.backends.hdfbackend. If provided, slice the array directly from the HDF5 file with slice = slice_vals. thin and discard will be ignored if slice_vals is not None. This is particularly useful if files are very large and the user only wants a small subset of the overall array. (default: None)

Returns:

The chain of log likelihood values.

Return type:

double np.ndarray[nsteps, ntemps, nwalkers]

get_log_prior(**kwargs)

Get the chain of log Prior evaluated at the MCMC samples

Parameters:

thin (int, optional) – Take only every thin steps from the chain. (default: 1)
discard (int, optional) – Discard the first discard steps in the chain as burn-in. (default: 0)
slice_vals (indexing np.ndarray or slice, optional) – This is only available in eryn.backends.hdfbackend. If provided, slice the array directly from the HDF5 file with slice = slice_vals. thin and discard will be ignored if slice_vals is not None. This is particularly useful if files are very large and the user only wants a small subset of the overall array. (default: None)

Returns:

The chain of log prior values.

Return type:

double np.ndarray[nsteps, ntemps, nwalkers]

get_log_posterior(temper: bool = False, **kwargs)

Get the chain of log posterior values evaluated at the MCMC samples

Parameters:

temper (bool, optional) – Apply tempering to the posterior values. (default: False)
thin (int, optional) – Take only every thin steps from the chain. (default: 1)
discard (int, optional) – Discard the first discard steps in the chain as burn-in. (default: 0)
slice_vals (indexing np.ndarray or slice, optional) – This is only available in eryn.backends.hdfbackend. If provided, slice the array directly from the HDF5 file with slice = slice_vals. thin and discard will be ignored if slice_vals is not None. This is particularly useful if files are very large and the user only wants a small subset of the overall array. (default: None)

Returns:

The chain of log prior values.

Return type:

double np.ndarray[nsteps, ntemps, nwalkers]

get_betas(**kwargs)

Get the chain of inverse temperatures

Parameters:

thin (int, optional) – Take only every thin steps from the chain. (default: 1)
discard (int, optional) – Discard the first discard steps in the chain as burn-in. (default: 0)
slice_vals (indexing np.ndarray or slice, optional) – This is only available in eryn.backends.hdfbackend. If provided, slice the array directly from the HDF5 file with slice = slice_vals. thin and discard will be ignored if slice_vals is not None. This is particularly useful if files are very large and the user only wants a small subset of the overall array. (default: None)

Returns:

The chain of temperatures.

Return type:

double np.ndarray[nsteps, ntemps]

get_a_sample(it)

Access a sample in the chain

Parameters:: it (int) – iteration of State to return.
Returns:: eryn.state.State object containing the sample from the chain.
Return type:: State
Raises:: AttributeError – Backend is not initialized.

get_last_sample()

Access the most recent sample in the chain

Returns:: eryn.state.State object containing the last sample from the chain.
Return type:: State

get_autocorr_time(discard=0, thin=1, all_temps=False, multiply_thin=True, **kwargs)

Compute an estimate of the autocorrelation time for each parameter

Parameters:

discard (int, optional) – Discard the first discard steps in the chain as burn-in. (default: 0)
thin (int, optional) – Use only every thin steps from the chain. The returned estimate is multiplied by thin so the estimated time is in units of steps, not thinned steps. (default: 1)
all_temps (bool, optional) – If True, calculate autocorrelation across all temperatures. If False, calculate autocorrelation across the minumum temperature chain (usually T=1). (default: False)
multiply_thin (bool, optional) – into the autocorrelation length. (default: True)

Other arguments are passed directly to emcee.autocorr.integrated_time().

Returns:

autocorrelation times: The dictionary contains autocorrelation times for all parameters as 1D double np.ndarrays as values with associated branch_names as keys.

Return type:

dict

get_evidence_estimate(discard=0, thin=1, return_error=True, method='therodynamic', **ss_kwargs)

Get an estimate of the evidence

This function gets the sample information and uses thermodynamic_integration_log_evidence() or stepping_stone_log_evidence() to compute the evidence estimate.

Parameters:

discard (int, optional) – Discard the first discard steps in the chain as burn-in. (default: 0)
thin (int, optional) – Use only every thin steps from the chain. The returned estimate is multiplied by thin so the estimated time is in units of steps, not thinned steps. (default: 1)
return_error (bool, optional) – If True, return the error associated with the log evidence estimate. (default: True)
method (string, optional) – Method to compute the evidence. Available methods are the ‘thermodynamic’ and ‘stepping-stone’ (default: thermodynamic)

Returns:

Evidence estimate: If requesting the error on the estimate, will receive a tuple: (logZ, dlogZ). Otherwise, just a double value of logZ.

Return type:

double or tuple

get_gelman_rubin_convergence_diagnostic(discard=0, thin=1, doprint=True, **psrf_kwargs)

The Gelman - Rubin convergence diagnostic. A general approach to monitoring convergence of MCMC output of multiple walkers. The function makes a comparison of within-chain and between-chain variances. A large deviation between these two variances indicates non-convergence, and the output [Rhat] deviates from unity.

Based on a. Brooks, SP. and Gelman, A. (1998) General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics, 7, 434-455 b. Gelman, A and Rubin, DB (1992) Inference from iterative simulation using multiple sequences, Statistical Science, 7, 457-511.

Parameters:

C (np.ndarray[nwalkers, nsamples, ndim]) – The parameter traces. The MCMC chains.
doprint (bool, optional) – Flag to print the results on screen.

discard (int, optional): Discard the first discard steps in: the chain as burn-in. (default: 0)
thin (int, optional): Use only every thin steps from the: chain. The returned estimate is multiplied by thin so the estimated time is in units of steps, not thinned steps. (default: 1)

doprint (bool, optional): Flag to print a table with the results, per temperature.

Returns

dict: Rhat_all_branches:: Returns an estimate of the Gelman-Rubin convergence diagnostic Rhat, per temperature, stored in a dictionary, per branch name.

property shape

The dimensions of the ensemble

Returns:

Shape of samples: Keys are branch_names and values are tuples with shapes of individual branches: (ntemps, nwalkers, nleaves_max, ndim).

Return type:

dict

grow(ngrow, blobs)

Expand the storage space by some number of samples

Parameters:

ngrow (int) – The number of steps to grow the chain.
blobs (None or np.ndarray) – The current array of blobs. This is used to compute the dtype for the blobs array.

get_move_info()

Get move information.

Returns:: Keys are move names and values are dictionaries with information on the moves.
Return type:: dict

save_step(state, accepted, rj_accepted=None, swaps_accepted=None, moves_accepted_fraction=None)

Save a step to the backend

Parameters:

state (State) – The State of the ensemble.
accepted (ndarray) – An array of boolean flags indicating whether or not the proposal for each walker was accepted.
rj_accepted (ndarray, optional) – An array of the number of accepted steps for the reversible jump proposal for each walker. If self.rj is True, then rj_accepted must be an array with rj_accepted.shape == accepted.shape. If self.rj is False, then rj_accepted must be None, which is the default.
swaps_accepted (ndarray, optional) – 1D array with number of swaps accepted for the in-model step. (default: None)
moves_accepted_fraction (dict, optional) – Dict of acceptance fraction arrays for all of the moves in the sampler. This dict must have the same keys as self.move_keys. (default: None)

get_info(discard=0, thin=1)

Get an output info dictionary

This dictionary could be used for diagnostics or plotting.

Parameters:

discard (int, optional) – Discard the first discard steps in the chain as burn-in. (default: 0)
thin (int, optional) – Use only every thin steps from the chain. The returned estimate is multiplied by thin so the estimated time is in units of steps, not thinned steps. (default: 1)

Returns:

Information for diagnostics: Dictionary that contains much of the information for diagnostic checks or plotting.

Return type:

dict

HDF Backend

class eryn.backends.HDFBackend(filename, name='mcmc', read_only=False, dtype=None, compression=None, compression_opts=None, store_missing_leaves=nan)

Bases: Backend

A backend that stores the chain in an HDF5 file using h5py

Note

You must install h5py to use this backend.

Parameters:

filename (str) – The name of the HDF5 file where the chain will be saved.
name (str, optional) – The name of the group where the chain will be saved. (default: "mcmc")
read_only (bool, optional) – If True, the backend will throw a RuntimeError if the file is opened with write access. (default: False)
dtype (dtype, optional) – Dtype to use for data storage. If None, program uses np.float64. (default: None)
compression (str, optional) – Compression type for h5 file. See more information in the h5py documentation. (default: None)
compression_opts (int, optional) –
Compression level for h5 file. See more information in the h5py documentation. (default: None)
store_missing_leaves (double, optional) – Number to store for leaves that are not used in a specific step. (default: np.nan)

property initialized: Check if backend file has been initialized properly.

open(mode='r')

Opens the h5 file in the proper mode.

Parameters:: mode (str, optional) – Mode to open h5 file.
Returns:: Opened file.
Return type:: H5 file object
Raises:: RuntimeError – If backend is opened for writing when it is read-only.

reset(nwalkers, ndims, nleaves_max=1, ntemps=1, branch_names=None, nbranches=1, rj=False, moves=None, **info)

Clear the state of the chain and empty the backend

Parameters:

nwalkers (int) – The size of the ensemble
ndims (int, list of ints, or dict) – The number of dimensions for each branch. If dict, keys should be the branch names and values the associated dimensionality.
nleaves_max (int, list of ints, or dict, optional) – Maximum allowable leaf count for each branch. It should have the same length as the number of branches. If dict, keys should be the branch names and values the associated maximal leaf value. (default: 1)
ntemps (int, optional) – Number of rungs in the temperature ladder. (default: 1)
branch_names (str or list of str, optional) – Names of the branches used. If not given, branches will be names model_0, …, model_n for n branches. (default: None)
nbranches (int, optional) – Number of branches. This is only used if branch_names is None. (default: 1)
rj (bool, optional) – If True, reversible-jump techniques are used. (default: False)
moves (list, optional) – List of all of the move classes input into the sampler. (default: None)
**info (dict, optional) – Any other key-value pairs to be added as attributes to the backend. These are also added to the HDF5 file.

property nwalkers: Get nwalkers from h5 file.

property ntemps: Get ntemps from h5 file.

property rj: Get rj from h5 file.

property nleaves_max: Get nleaves_max from h5 file.

property ndims: Get ndims from h5 file.

property move_keys: Get move_keys from h5 file.

property branch_names: Get branch names from h5 file.

property nbranches: Get number of branches from h5 file.

property reset_args: Get reset_args from h5 file.

property reset_kwargs: Get reset_kwargs from h5 file.

has_blobs(): Returns True if the model includes blobs

get_value(name, thin=1, discard=0, slice_vals=None)

Returns a requested value to user.

This function helps to streamline the backend for both basic and hdf backend.

Parameters:

name (str) – Name of value requested.
thin (int, optional) – Take only every thin steps from the chain. (default: 1)
discard (int, optional) – Discard the first discard steps in the chain as burn-in. (default: 0)
slice_vals (indexing np.ndarray or slice, optional) – If provided, slice the array directly from the HDF5 file with slice = slice_vals. thin and discard will be ignored if slice_vals is not None. This is particularly useful if files are very large and the user only wants a small subset of the overall array. (default: None)

Returns:

Values requested.

Return type:

dict or np.ndarray

get_move_info()

Get move information.

Returns:: Keys are move names and values are dictionaries with information on the moves.
Return type:: dict

property shape

The dimensions of the ensemble

Returns:

Shape of samples: Keys are branch_names and values are tuples with shapes of individual branches: (ntemps, nwalkers, nleaves_max, ndim).

Return type:

dict

property iteration: Number of iterations stored in the hdf backend so far.

property accepted: Number of accepted moves per walker.

property rj_accepted: Number of accepted rj moves per walker.

property swaps_accepted: Number of accepted swaps.

property random_state: Get the random state

grow(ngrow, blobs)

Expand the storage space by some number of samples

Parameters:

ngrow (int) – The number of steps to grow the chain.
blobs (None or np.ndarray) – The current array of blobs. This is used to compute the dtype for the blobs array.

save_step(state, accepted, rj_accepted=None, swaps_accepted=None, moves_accepted_fraction=None)

Save a step to the backend

Parameters:

state (State) – The State of the ensemble.
accepted (ndarray) – An array of boolean flags indicating whether or not the proposal for each walker was accepted.
rj_accepted (ndarray, optional) – An array of the number of accepted steps for the reversible jump proposal for each walker. If self.rj is True, then rj_accepted must be an array with rj_accepted.shape == accepted.shape. If self.rj is False, then rj_accepted must be None, which is the default.
swaps_accepted (ndarray, optional) – 1D array with number of swaps accepted for the in-model step. (default: None)
moves_accepted_fraction (dict, optional) – Dict of acceptance fraction arrays for all of the moves in the sampler. This dict must have the same keys as self.move_keys. (default: None)

get_a_sample(it)

Access a sample in the chain

Parameters:: it (int) – iteration of State to return.
Returns:: eryn.state.State object containing the sample from the chain.
Return type:: State
Raises:: AttributeError – Backend is not initialized.

get_autocorr_thin_burn()

Return the discard and thin values based on the autocorrelation length.

The discard is determined as 2 times the maximum correlation length among parameters. The thin is determined using 1/2 times the minimum correlation legnth among parameters.

Returns:

Information on thin and burn: (discard, thin)

Return type:

tuple

get_autocorr_time(discard=0, thin=1, all_temps=False, multiply_thin=True, **kwargs)

Compute an estimate of the autocorrelation time for each parameter

Parameters:

discard (int, optional) – Discard the first discard steps in the chain as burn-in. (default: 0)
thin (int, optional) – Use only every thin steps from the chain. The returned estimate is multiplied by thin so the estimated time is in units of steps, not thinned steps. (default: 1)
all_temps (bool, optional) – If True, calculate autocorrelation across all temperatures. If False, calculate autocorrelation across the minumum temperature chain (usually T=1). (default: False)
multiply_thin (bool, optional) – into the autocorrelation length. (default: True)

Other arguments are passed directly to emcee.autocorr.integrated_time().

Returns:

autocorrelation times: The dictionary contains autocorrelation times for all parameters as 1D double np.ndarrays as values with associated branch_names as keys.

Return type:

dict

get_betas(**kwargs)

Get the chain of inverse temperatures

Parameters:

thin (int, optional) – Take only every thin steps from the chain. (default: 1)
discard (int, optional) – Discard the first discard steps in the chain as burn-in. (default: 0)
slice_vals (indexing np.ndarray or slice, optional) – This is only available in eryn.backends.hdfbackend. If provided, slice the array directly from the HDF5 file with slice = slice_vals. thin and discard will be ignored if slice_vals is not None. This is particularly useful if files are very large and the user only wants a small subset of the overall array. (default: None)

Returns:

The chain of temperatures.

Return type:

double np.ndarray[nsteps, ntemps]

get_blobs(**kwargs)

Get the chain of blobs for each sample in the chain

Parameters:

thin (int, optional) – Take only every thin steps from the chain. (default: 1)
discard (int, optional) – Discard the first discard steps in the chain as burn-in. (default: 0)
slice_vals (indexing np.ndarray or slice, optional) – This is only available in eryn.backends.hdfbackend. If provided, slice the array directly from the HDF5 file with slice = slice_vals. thin and discard will be ignored if slice_vals is not None. This is particularly useful if files are very large and the user only wants a small subset of the overall array. (default: None)

Returns:

The chain of blobs.

Return type:

double np.ndarray[nsteps, ntemps, nwalkers, nblobs]

get_chain(**kwargs)

Get the stored chain of MCMC samples

Parameters:

thin (int, optional) – Take only every thin steps from the chain. (default: 1)
discard (int, optional) – Discard the first discard steps in the chain as burn-in. (default: 0)
slice_vals (indexing np.ndarray or slice, optional) – This is only available in eryn.backends.hdfbackend. If provided, slice the array directly from the HDF5 file with slice = slice_vals. thin and discard will be ignored if slice_vals is not None. This is particularly useful if files are very large and the user only wants a small subset of the overall array. (default: None)

Returns:

MCMC samples: The dictionary contains np.ndarrays of samples across the branches.

Return type:

dict

get_evidence_estimate(discard=0, thin=1, return_error=True, method='therodynamic', **ss_kwargs)

Get an estimate of the evidence

This function gets the sample information and uses thermodynamic_integration_log_evidence() or stepping_stone_log_evidence() to compute the evidence estimate.

Parameters:

discard (int, optional) – Discard the first discard steps in the chain as burn-in. (default: 0)
thin (int, optional) – Use only every thin steps from the chain. The returned estimate is multiplied by thin so the estimated time is in units of steps, not thinned steps. (default: 1)
return_error (bool, optional) – If True, return the error associated with the log evidence estimate. (default: True)
method (string, optional) – Method to compute the evidence. Available methods are the ‘thermodynamic’ and ‘stepping-stone’ (default: thermodynamic)

Returns:

Evidence estimate: If requesting the error on the estimate, will receive a tuple: (logZ, dlogZ). Otherwise, just a double value of logZ.

Return type:

double or tuple

get_gelman_rubin_convergence_diagnostic(discard=0, thin=1, doprint=True, **psrf_kwargs)

The Gelman - Rubin convergence diagnostic. A general approach to monitoring convergence of MCMC output of multiple walkers. The function makes a comparison of within-chain and between-chain variances. A large deviation between these two variances indicates non-convergence, and the output [Rhat] deviates from unity.

Based on a. Brooks, SP. and Gelman, A. (1998) General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics, 7, 434-455 b. Gelman, A and Rubin, DB (1992) Inference from iterative simulation using multiple sequences, Statistical Science, 7, 457-511.

Parameters:

C (np.ndarray[nwalkers, nsamples, ndim]) – The parameter traces. The MCMC chains.
doprint (bool, optional) – Flag to print the results on screen.

discard (int, optional): Discard the first discard steps in: the chain as burn-in. (default: 0)
thin (int, optional): Use only every thin steps from the: chain. The returned estimate is multiplied by thin so the estimated time is in units of steps, not thinned steps. (default: 1)

doprint (bool, optional): Flag to print a table with the results, per temperature.

Returns

dict: Rhat_all_branches:: Returns an estimate of the Gelman-Rubin convergence diagnostic Rhat, per temperature, stored in a dictionary, per branch name.

get_inds(**kwargs)

Get the stored chain of MCMC samples

Parameters:

thin (int, optional) – Take only every thin steps from the chain. (default: 1)
discard (int, optional) – Discard the first discard steps in the chain as burn-in. (default: 0)
slice_vals (indexing np.ndarray or slice, optional) – This is only available in eryn.backends.hdfbackend. If provided, slice the array directly from the HDF5 file with slice = slice_vals. thin and discard will be ignored if slice_vals is not None. This is particularly useful if files are very large and the user only wants a small subset of the overall array. (default: None)

Returns:

The inds associated with the MCMC samples.: The dictionary contains np.ndarrays of inds across the branches indicated which leaves were used at each step.

Return type:

dict

get_info(discard=0, thin=1)

Get an output info dictionary

This dictionary could be used for diagnostics or plotting.

Parameters:

discard (int, optional) – Discard the first discard steps in the chain as burn-in. (default: 0)
thin (int, optional) – Use only every thin steps from the chain. The returned estimate is multiplied by thin so the estimated time is in units of steps, not thinned steps. (default: 1)

Returns:

Information for diagnostics: Dictionary that contains much of the information for diagnostic checks or plotting.

Return type:

dict

get_last_sample()

Access the most recent sample in the chain

Returns:: eryn.state.State object containing the last sample from the chain.
Return type:: State

get_log_like(**kwargs)

Get the chain of log Likelihood values evaluated at the MCMC samples

Parameters:

thin (int, optional) – Take only every thin steps from the chain. (default: 1)
discard (int, optional) – Discard the first discard steps in the chain as burn-in. (default: 0)
slice_vals (indexing np.ndarray or slice, optional) – This is only available in eryn.backends.hdfbackend. If provided, slice the array directly from the HDF5 file with slice = slice_vals. thin and discard will be ignored if slice_vals is not None. This is particularly useful if files are very large and the user only wants a small subset of the overall array. (default: None)

Returns:

The chain of log likelihood values.

Return type:

double np.ndarray[nsteps, ntemps, nwalkers]

get_log_posterior(temper: bool = False, **kwargs)

Get the chain of log posterior values evaluated at the MCMC samples

Parameters:

temper (bool, optional) – Apply tempering to the posterior values. (default: False)
thin (int, optional) – Take only every thin steps from the chain. (default: 1)
discard (int, optional) – Discard the first discard steps in the chain as burn-in. (default: 0)
slice_vals (indexing np.ndarray or slice, optional) – This is only available in eryn.backends.hdfbackend. If provided, slice the array directly from the HDF5 file with slice = slice_vals. thin and discard will be ignored if slice_vals is not None. This is particularly useful if files are very large and the user only wants a small subset of the overall array. (default: None)

Returns:

The chain of log prior values.

Return type:

double np.ndarray[nsteps, ntemps, nwalkers]

get_log_prior(**kwargs)

Get the chain of log Prior evaluated at the MCMC samples

Parameters:

thin (int, optional) – Take only every thin steps from the chain. (default: 1)
discard (int, optional) – Discard the first discard steps in the chain as burn-in. (default: 0)
slice_vals (indexing np.ndarray or slice, optional) – This is only available in eryn.backends.hdfbackend. If provided, slice the array directly from the HDF5 file with slice = slice_vals. thin and discard will be ignored if slice_vals is not None. This is particularly useful if files are very large and the user only wants a small subset of the overall array. (default: None)

Returns:

The chain of log prior values.

Return type:

double np.ndarray[nsteps, ntemps, nwalkers]

get_nleaves(**kwargs)

Get the number of leaves for each walker

Parameters:

thin (int, optional) – Take only every thin steps from the chain. (default: 1)
discard (int, optional) – Discard the first discard steps in the chain as burn-in. (default: 0)
slice_vals (indexing np.ndarray or slice, optional) – This is only available in eryn.backends.hdfbackend. If provided, slice the array directly from the HDF5 file with slice = slice_vals. thin and discard will be ignored if slice_vals is not None. This is particularly useful if files are very large and the user only wants a small subset of the overall array. (default: None)

Returns:

nleaves on each branch.

The number of leaves on each branch associated with the MCMC samples: within each branch.

Return type:

dict

reset_base(): Allows for simple reset based on previous inputs