`src.superphot_plus.samplers.iminuit_sampler`

Gradient slope fitting using iminuit.

Module Contents

Classes

IminuitSampler

Negative log-likelihood optimization with iminuit's migrad.

Functions

run_fit(lightcurve[, priors, rstate])

Runs iminuit fit for a single light curve

class IminuitSampler[source]

Bases: superphot_plus.samplers.sampler.Sampler

Negative log-likelihood optimization with iminuit’s migrad.

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 iminuit on a single light curve.

This function runs a gradient slope fitting algorithm with iminuit package. It returns 100 parameter cubes sampled from a multivariate Gaussian distribution centered at the best-fit parameters and with a covariance matrix given by the iminuit.

Parameters:

lightcurve (Lightcurve object) – The light curve of interest.
priors (MultibandPriors) – Prior distribution.
rstate (int, optional)

Returns:

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

Return type:

PosteriorSamples

abstract run_multi_curve(lightcurves, priors, **kwargs) → List[superphot_plus.posterior_samples.PosteriorSamples][source]: Not yet implemented.

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