Fit

class gammapy.modeling.Fit(backend='minuit', optimize_opts=None, covariance_opts=None, confidence_opts=None, store_trace=False)

Bases: object

Fit class.

The fit class provides a uniform interface to multiple fitting backends. Currently available: “minuit”, “sherpa” and “scipy”

Parameters

backend{“minuit”, “scipy” “sherpa”}

Global backend used for fitting, default : minuit

optimize_optsdict

Keyword arguments passed to the optimizer. For the "minuit" backend see https://iminuit.readthedocs.io/en/stable/reference.html#iminuit.Minuit for a detailed description of the available options. If there is an entry ‘migrad_opts’, those options will be passed to iminuit.Minuit.migrad().

For the "sherpa" backend you can from the options:

• "simplex"

• "levmar"

• "moncar"

• "gridsearch"

Those methods are described and compared in detail on http://cxc.cfa.harvard.edu/sherpa/methods/index.html. The available options of the optimization methods are described on the following pages in detail:

• http://cxc.cfa.harvard.edu/sherpa/ahelp/neldermead.html

• http://cxc.cfa.harvard.edu/sherpa/ahelp/montecarlo.html

• http://cxc.cfa.harvard.edu/sherpa/ahelp/gridsearch.html

• http://cxc.cfa.harvard.edu/sherpa/ahelp/levmar.html

For the "scipy" backend the available options are described in detail here: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html

covariance_optsdict

Covariance options passed to the given backend.

confidence_optsdict

Extra arguments passed to the backend. E.g. iminuit.Minuit.minos supports a maxcall option. For the scipy backend confidence_opts are forwarded to brentq. If the confidence estimation fails, the bracketing interval can be adapted by modifying the the upper bound of the interval (b) value.

store_tracebool

Whether to store the trace of the fit

Attributes Summary

minuit

Iminuit object

Methods Summary

confidence(datasets, parameter[, sigma, ...])	Estimate confidence interval.
covariance(datasets)	Estimate the covariance matrix.
optimize(datasets)	Run the optimization.
run(datasets)	Run all fitting steps.
stat_contour(datasets, x, y[, numpoints, sigma])	Compute stat contour.
stat_profile(datasets, parameter[, reoptimize])	Compute fit statistic profile.
stat_surface(datasets, x, y[, reoptimize])	Compute fit statistic surface.

Attributes Documentation

Methods Documentation