McmcSampler

McmcSampler#

class jetset.mcmc.McmcSampler(model_minimizer)[source]#

Bases: object

Run and manage emcee sampling for a fitted JetSeT model.

Notes

Wraps an input ModelMinimizer, clones its model state, stores sampler outputs, and provides serialization-safe state handling for chain analysis and plotting.

Attributes Summary

labels

Labels.

par_table

Par table.

Methods Summary

corner_plot([comp_name, quantiles, levels, ...])

Corner plot.

get_par(par_name_or_idx[, comp_name, get_index])

Return par.

get_par_quantiles(par_name[, comp_name, ...])

Return par quantiles.

get_sample(par_name[, comp_name])

Return sample.

get_trace(par_name[, comp_name])

Return trace.

load(file_name)

Load object state from disk.

plot_chain([par_name, comp_name, log_plot])

Plot chain.

plot_model([sed_data, fit_range, size, ...])

Plot model.

plot_par(par_name[, comp_name, nbins, ...])

Plot par.

reset_to_mcmc_best_fit([verbose])

Reset to mcmc best fit.

reset_to_minimizer_best_fit()

Reset sampled parameter values to minimizer best-fit values.

run_sampler([nwalkers, steps, pos, burnin, ...])

Run sampler.

save(name)

Save object state to disk.

set_bounds([bound, bound_rel, ...])

Set bounds.

set_labels([use_labels_dict])

Set labels.

set_plot_label(par_name, plot_label[, comp_name])

Set plot label.

show_pars([getstring, names_list, sort_key])

Display pars.

Attributes Documentation

labels#

Labels.

Returns:

Requested value.

Return type:

object

par_table#

Par table.

Returns:

Requested value.

Return type:

object

Methods Documentation

corner_plot(comp_name=None, quantiles=(0.16, 0.5, 0.84), levels=None, title_kwargs={}, **kwargs)[source]#

Corner plot.

Parameters:

  • comp_name (str, optional) – Model-component name.

  • quantiles (tuple, optional) – Quantiles to evaluate/report.

  • levels (object, optional) – Contour levels for corner plots.

  • title_kwargs (dict, optional) – Keyword arguments forwarded to plot-title rendering.

  • **kwargs (dict) – Additional keyword arguments.

Returns:

Computed value.

Return type:

object

get_par(par_name_or_idx, comp_name=None, get_index=False)[source]#

Return par.

Parameters:

  • par_name_or_idx (object) – Parameter identifier by name or index.

  • comp_name (object, optional) – Model-component name.

  • get_index (bool, optional) – If True, also return the index of the selected item.

Returns:

Requested value.

Return type:

object

get_par_quantiles(par_name, comp_name=None, quantiles=(0.16, 0.5, 0.84))[source]#

Return par quantiles.

Parameters:

  • par_name (str) – Parameter name.

  • comp_name (object, optional) – Model-component name.

  • quantiles (tuple, optional) – Quantiles to evaluate/report.

Returns:

Requested value.

Return type:

object

get_sample(par_name, comp_name=None)[source]#

Return sample.

Parameters:

  • par_name (str) – Parameter name.

  • comp_name (object, optional) – Model-component name.

Returns:

Requested value.

Return type:

object

get_trace(par_name, comp_name=None)[source]#

Return trace.

Parameters:

  • par_name (str) – Parameter name.

  • comp_name (object, optional) – Model-component name.

Returns:

Requested value.

Return type:

object

classmethod load(file_name)[source]#

Load object state from disk.

Parameters:

file_name (object) – Input/output file path.

Returns:

Loaded object.

Return type:

object

plot_chain(par_name=None, comp_name=None, log_plot=False)[source]#

Plot chain.

Parameters:

  • par_name (str, optional) – Parameter name.

  • comp_name (object, optional) – Model-component name.

  • log_plot (bool, optional) – If True, use logarithmic plot scaling where applicable.

Returns:

Plot object or generated visualization.

Return type:

object

plot_model(sed_data=None, fit_range=None, size=100, frame='obs', density=False, quantiles=None, get_model=False, plot_mcmc_best_fit_model=False, rnd_seed=0)[source]#

Plot model.

Parameters:

  • sed_data (object, optional) – Observational SED data container.

  • fit_range ([float,float], optional) – Range for fit.

  • size (int, optional) – Number of samples or sample size.

  • frame (str, optional) – Reference frame for data/model values.

  • density (bool, optional) – If True, use density representation instead of integrated quantity.

  • quantiles (object, optional) – Quantiles to evaluate/report.

  • get_model (bool, optional) – If True, return model values.

  • plot_mcmc_best_fit_model (bool, optional) – If True, overlay MCMC best-fit model in plots.

  • rnd_seed (int, optional) – Random seed used for reproducible sampling.

Returns:

Plot object or generated visualization.

Return type:

object

plot_par(par_name, comp_name=None, nbins=20, log_plot=False, quantiles=(0.16, 0.5, 0.84), figsize=None)[source]#

Plot par.

Parameters:

  • par_name (str) – Parameter name.

  • comp_name (object, optional) – Model-component name.

  • nbins (int, optional) – Number of bins for histogram estimates.

  • log_plot (bool, optional) – If True, use logarithmic plot scaling where applicable.

  • quantiles (tuple, optional) – Quantiles to evaluate/report.

  • figsize (object, optional) – Matplotlib figure size.

Returns:

Plot object or generated visualization.

Return type:

object

reset_to_mcmc_best_fit(verbose=True)[source]#

Reset to mcmc best fit.

Parameters:

verbose (bool, optional) – If True, print additional information.

reset_to_minimizer_best_fit()[source]#

Reset sampled parameter values to minimizer best-fit values.

Notes

This updates only the internal parameter dictionary used by the sampler helper; it does not run a new minimization.

run_sampler(nwalkers=None, steps=100, pos=None, burnin=50, use_UL=False, threads=None, walker_start_bound=0.005, loglog=False, progress='notebook')[source]#

Run sampler.

Parameters:

  • nwalkers (int, optional) – Number of MCMC walkers.

  • steps (int, optional) – Number of MCMC steps.

  • pos (object, optional) – Initial walker positions.

  • burnin (int, optional) – Number of burn-in steps to discard.

  • use_UL (bool, optional) – If True, enable ul.

  • threads (object, optional) – Number of worker threads/processes.

  • walker_start_bound (float, optional) – Initial spread factor for walker starting points.

  • loglog (bool, optional) – If True, operate in log10 space.

  • progress (str, optional) – If True, display sampling progress.

save(name)[source]#

Save object state to disk.

Parameters:

name (object) – Name identifier.

set_bounds(bound=0.2, bound_rel=False, preserve_fit_range=True)[source]#

Set bounds.

Parameters:

  • bound (float, optional) – Absolute parameter-bound span.

  • bound_rel (bool, optional) – Relative parameter-bound span.

  • preserve_fit_range (bool, optional) – Range for preserve fit.

set_labels(use_labels_dict=None)[source]#

Set labels.

Parameters:

use_labels_dict (object, optional) – If True, enable labels dict.

set_plot_label(par_name, plot_label, comp_name=None)[source]#

Set plot label.

Parameters:

  • par_name (object) – Parameter name.

  • plot_label (object) – Custom label used in plots.

  • comp_name (object, optional) – Model-component name.

show_pars(getstring=False, names_list=None, sort_key=None)[source]#

Display pars.

Parameters:

  • getstring (bool, optional) – If True, return text output instead of printing.

  • names_list (object, optional) – Ordered list of parameter/component names.

  • sort_key (object, optional) – Key used to sort table-like outputs.

Returns:

Computed value.

Return type:

object