Fit

class gammapy.utils.fitting.Fit

Bases: object

Abstract Fit base class.

Methods Summary

likelihood_profile(model, parameter[, …])	Compute likelihood profile for a single parameter of the model.
likelihood_profiles(model[, parameters])	Compute likelihood profiles for multiple parameters.
optimize([backend])	Run the optimization
run([steps, optimize_opts, profile_opts])	Run all fitting steps.
total_stat(parameters)	Total likelihood given the current model parameters

Methods Documentation

likelihood_profile(model, parameter, values=None, bounds=2, nvalues=11)

Compute likelihood profile for a single parameter of the model.

Parameters:

model : SpectralModel

Model to compute the likelihood profile for.

parameter : str

Parameter to calculate profile for

values : Quantity (optional)

Parameter values to evaluate the likelihood for.

bounds : int or tuple of float

When an int is passed the bounds are computed from bounds * sigma from the best fit value of the parameter, where sigma corresponds to the one sigma error on the parameter. If a tuple of floats is given those are taken as the min and max values and nvalues are linearly spaced between those.

nvalues : int

Number of parameter grid points to use.

Returns:

likelihood_profile : dict

Dict of parameter values and likelihood values.

likelihood_profiles(model, parameters='all')

Compute likelihood profiles for multiple parameters.

Parameters:

model : SpectralModel or SkyModel

Model to compute the likelihood profile for.

parameters : list of str or “all”

For which parameters to compute likelihood profiles.

optimize(backend='minuit', **kwargs)

Run the optimization

Parameters:

backend : {“minuit”, “sherpa”}

Which fitting backend to use.

**kwargs : dict

Keyword arguments passed to the optimizer. For the "minuit" backend see https://iminuit.readthedocs.io/en/latest/api.html#iminuit.Minuit for a detailed description of the available options. For the "sherpa" backend you can from the options method = {"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

Returns:

fit_result : dict

Optimize info dict with the best fit model and additional information.

run(steps='all', optimize_opts=None, profile_opts=None)

Run all fitting steps.

Parameters:

steps : {“all”, “optimize”, “errors”, “profiles”}

Which fitting steps to run.

optimize_opts : dict

Options passed to Fit.optimize.

profile_opts : dict

Options passed to Fit.likelihood_profiles.

Returns:

fit_result : FitResult

Fit result object with the best fit model and additional information.

total_stat(parameters)

Total likelihood given the current model parameters