# Fit¶

class gammapy.utils.fitting.Fit[source]

Bases: object

Abstract Fit base class.

Methods Summary

 confidence(parameter[, backend, sigma]) Estimate confidence interval. covariance([backend]) Estimate the covariance matrix. likelihood_contour() Compute likelihood contour. likelihood_profile(parameter[, values, …]) Compute likelihood profile. minos_contour(x, y[, numpoints, sigma]) Compute MINOS contour. minos_profile() Compute MINOS profile. optimize([backend]) Run the optimization. run([optimize_opts, covariance_opts]) Run all fitting steps. total_stat(parameters) Total likelihood given the current model parameters

Methods Documentation

confidence(parameter, backend='minuit', sigma=1, **kwargs)[source]

Estimate confidence interval.

Extra kwargs are passed to the backend. E.g. iminuit.Minuit.minos supports a maxcall option.

Parameters: backend : str Which backend to use (see gammapy.utils.fitting.registry) parameter : Parameter Parameter of interest sigma : float Number of standard deviations for the confidence level result : dict Dictionary with keys “errp”, ‘errn”, “success” and “nfev”.
covariance(backend='minuit')[source]

Estimate the covariance matrix.

Assumes that the model parameters are already optimised.

Parameters: backend : str Which backend to use (see gammapy.utils.fitting.registry) result : CovarianceResult Results
likelihood_contour()[source]

Compute likelihood contour.

The method used is to vary two parameters, keeping all others fixed. So this is taking a “slice” or “scan” of the likelihood.

See also: Fit.minos_contour

Parameters: TODO TODO
likelihood_profile(parameter, values=None, bounds=2, nvalues=11)[source]

Compute likelihood profile.

The method used is to vary one parameter, keeping all others fixed. So this is taking a “slice” or “scan” of the likelihood.

See also: Fit.minos_profile.

Parameters: parameter : Parameter Parameter of interest 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. results : dict Dictionary with keys “values” and “likelihood”.
minos_contour(x, y, numpoints=10, sigma=1.0)[source]

Compute MINOS contour.

Calls iminuit.Minuit.mncontour.

This is a contouring algorithm for a 2D function which is not simply the likelihood function. That 2D function is given at each point (par_1, par_2) by re-optimising all other free parameters, and taking the likelihood at that point.

Very compute-intensive and slow.

Parameters: x, y : Parameter Parameters of interest numpoints : int Number of contour points sigma : float Number of standard deviations for the confidence level result : dict Dictionary with keys “x”, “y” (Numpy arrays with contour points) and a boolean flag “success”. The result objects from mncontour are in the additional keys “x_info” and “y_info”.
minos_profile()[source]

Compute MINOS profile.

The method used is to vary one parameter, then re-optimise all other free parameters and to take the likelihood at that point.

See also: Fit.likelihood_profile

Calls iminuit.Minuit.mnprofile

optimize(backend='minuit', **kwargs)[source]

Run the optimization.

Parameters: backend : str Which backend to use (see gammapy.utils.fitting.registry) **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: fit_result : FitResult Results
run(optimize_opts=None, covariance_opts=None)[source]

Run all fitting steps.

Parameters: optimize_opts : dict Options passed to Fit.optimize. covariance_opts : dict Options passed to Fit.covariance. fit_result : FitResult Results
total_stat(parameters)[source]

Total likelihood given the current model parameters