sdapy.model_fitters.fit_model

class sdapy.model_fitters.fit_model(x_data, y_data, yerr_data, filters=None)

Fits data with analytic models.

Parameters

x_dataarray

Independent values, e.g. (rest frame) phase relative to peak

y_dataarray

Dependent values, e.g. fluxes

yerr_dataarray, int

Dependent value errors, e.g. flux errors

filtersarray

If filters available, will fit for each band simultaneously.

Methods

continue_chains(t_data, f_data, f_unc_data, ...)	Run MCMC for longer than initial fit
filter_samples(samples, lnprob[, limit])	get samplings with good chi square
get_par([filt, quant, parname])	get bestfit and quantiles of a fitted parameter
get_random_samples([limit, plotnsamples])	get parameters for the random samplings
lnlikelihood1(theta, f, t, f_err, func, ...)	define likelhood for MCMC
lnlikelihood2(theta, f, t, f_err, filters, ...)	define likelhood for MCMC, with filters
lnposterior1(theta, f, t, f_err, func, ...)	define posterior for MCMC
lnposterior2(theta, f, t, f_err, filters, ...)	define posterior for MCMC, with filters
lnprior1(theta, bounds)	define prior for MCMC
lnprior2(theta, filters, cl, bounds)	define prior for MCMC, with filters
predict([x_pred, step, returnv, quant])	re-produce the data with the fitted models
predict_random([limit, plotnsamples, ...])	re-produce the data randomly with the fitted models
run(bestv, bounds[, filt])	run fitting
run_mcmc(t_data, f_data, f_unc_data, bestv, ...)	Initial MCMC fit
run_scipy(func, p0, bounds, x, y, yerr[, ...])	run model fitting with scipy packages
save_corner(figpath[, datadir, filts, ...])	generate corner plots
set_peak()	get peak infos of the fitting
train([opt_routine, fit_mean, nwalkers, ...])	Fits data with analytic models.

__init__(x_data, y_data, yerr_data, filters=None)

Methods

__init__(x_data, y_data, yerr_data[, filters])
continue_chains(t_data, f_data, f_unc_data, ...)	Run MCMC for longer than initial fit
filter_samples(samples, lnprob[, limit])	get samplings with good chi square
get_par([filt, quant, parname])	get bestfit and quantiles of a fitted parameter
get_random_samples([limit, plotnsamples])	get parameters for the random samplings
lnlikelihood1(theta, f, t, f_err, func, ...)	define likelhood for MCMC
lnlikelihood2(theta, f, t, f_err, filters, ...)	define likelhood for MCMC, with filters
lnposterior1(theta, f, t, f_err, func, ...)	define posterior for MCMC
lnposterior2(theta, f, t, f_err, filters, ...)	define posterior for MCMC, with filters
lnprior1(theta, bounds)	define prior for MCMC
lnprior2(theta, filters, cl, bounds)	define prior for MCMC, with filters
predict([x_pred, step, returnv, quant])	re-produce the data with the fitted models
predict_random([limit, plotnsamples, ...])	re-produce the data randomly with the fitted models
run(bestv, bounds[, filt])	run fitting
run_mcmc(t_data, f_data, f_unc_data, bestv, ...)	Initial MCMC fit
run_scipy(func, p0, bounds, x, y, yerr[, ...])	run model fitting with scipy packages
save_corner(figpath[, datadir, filts, ...])	generate corner plots
set_peak()	get peak infos of the fitting
train([opt_routine, fit_mean, nwalkers, ...])	Fits data with analytic models.