mcmc

Monte-Carlo Markov Chain methods.

run_mcmc(spectro, positions, simcfg, …[, …])	Monte-Carlo Markov Chain exploration of parameter space, using emcee.
plot_mcmc_chains(chains, parameters)	Plot MCMC-chains (nwalkers, nsteps, ndim).
plot_mcmc_corner(chains, parameters[, burnin])	corner plot of MCMC parameters.
mcmc_best_params(chains, parameters[, burnin])	Compute best parameter estimates and assymetric 1-sigma errors from marginalized distributions.

spectrogrism.mcmc.run_mcmc(spectro, positions, simcfg, optparams, lnprior, modes=None, nwalkers=10, nsteps=500, outfile='chains.dat')

Monte-Carlo Markov Chain exploration of parameter space, using emcee.

Warning

very preliminary implementation

Parameters

• spectro (Spectrograph) – spectrograph

• positions (DetectorPositions) – target positions

• simcfg (SimConfig) – simulation configuration

• optparams (list) – optical parameters to be probed

• lnprior (function) – log-likelihood prior function

• modes (list) – adjusted observing modes (default: simulated modes)

• nwalkers (int) – the number of walkers will be 2*len(optparams)*nwalkers

• nsteps (int) – number of MCMC-steps

• outfile (str) – incremental file output

Returns

Monte-Carlo Markov Chains array (nwalkers, nsteps, ndim)

Return type

emcee.EnsembleSampler.chain

Raises

KeyError – unknown optical parameter

Reference: Foreman-Mackey et al. 2012 2013PASP..125..306F

spectrogrism.mcmc.plot_mcmc_chains(chains, parameters)

Plot MCMC-chains (nwalkers, nsteps, ndim).

spectrogrism.mcmc.plot_mcmc_corner(chains, parameters, burnin=0)

corner plot of MCMC parameters.

Reference: Foreman-Mackey 2016

spectrogrism.mcmc.mcmc_best_params(chains, parameters, burnin=0)

Compute best parameter estimates and assymetric 1-sigma errors from marginalized distributions. Return {name: (median, low_err, high_err)}.