src.superphot_plus.samplers.dynesty_sampler

MCMC sampling using dynesty.

Module Contents

Classes

DynestySampler

"MCMC sampling using dynesty.

Functions

run_mcmc(lightcurve[, priors, rstate])

Runs dynesty importance nested sampling on a single light curve; returns set
class DynestySampler[source]

Bases: superphot_plus.samplers.sampler.Sampler

“MCMC sampling using dynesty.

run_single_curve(lightcurve: superphot_plus.lightcurve.Lightcurve, priors: superphot_plus.surveys.fitting_priors.MultibandPriors, rstate=None, **kwargs) → superphot_plus.posterior_samples.PosteriorSamples[source]

Perform model fitting using dynesty on a single light curve.

This function runs the dynesty importance nested sampling algorithm on a single light curve.

Parameters:

  • lightcurve (Lightcurve object) – The light curve of interest.

  • rstate (int, optional) – Random state that is seeded. if none, use machine entropy.

  • plot (bool, optional) – Flag to enable/disable plotting. Defaults to False.

  • rstate – Random state that is seeded. if none, use machine entropy.

Returns:

samples – Return the MCMC samples or None if the fitting is skipped or encounters an error.

Return type:

PosteriorSamples

run_multi_curve(lightcurves, priors, rstate=None, **kwargs) → List[superphot_plus.posterior_samples.PosteriorSamples][source]

Not yet implemented.

run_mcmc(lightcurve, priors=Survey.ZTF().priors, rstate=None)[source]

Runs dynesty importance nested sampling on a single light curve; returns set of equally weighted posteriors (sets of fit parameters).

Parameters:

  • lightcurve (Lightcurve object) – The lightcurve of interest

  • priors (str, optional) – Prior information. Defaults to ZTF.

  • rstate (int, optional) – Random state that is seeded. if none, use machine entropy.

Returns:

Equally weighted posteriors, or None if the data is invalid.

Return type:

PosteriorSamples or None