SpectrumFit¶

class gammapy.spectrum.SpectrumFit(obs_list, model, stat='wstat', forward_folded=True, fit_range=None)[source]¶

Bases: gammapy.utils.fitting.Fit

Orchestrate a 1D counts spectrum fit.

After running the run() method, the fit results are available in result(). For usage examples see Spectral Fitting

Parameters:

obs_list : SpectrumObservationList, SpectrumObservation

Observation(s) to fit

model : SpectralModel

Source model with initial parameter values. Should return counts if forward_folded is False and a flux otherwise

stat : {‘wstat’, ‘cash’}

Fit statistic

forward_folded : bool, default: True

Fold model with the IRFs given in obs_list

fit_range : tuple of Quantity

The intersection between the fit range and the observation thresholds will be used. If you want to control which bins are taken into account in the fit for each observation, use quality()

Attributes Summary

`bins_in_fit_range`	Bins participating in the fit for each observation.
`fit_range`	Fit range.
`obs_list`	Observations participating in the fit
`predicted_counts`	Current value of predicted counts.
`result`	Bundle fit results into `SpectrumFitResult`.
`statval`	Current value of statval.
`true_fit_range`	True fit range for each observation.

Methods Summary

`calc_statval`()	Calc statistic for all observations.
`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
`predict_counts`()	Predict counts for all observations.
`run`([steps, optimize_opts, profile_opts])	Run all fitting steps.
`total_stat`(parameters)	Statistic summed over all bins and all observations.

Attributes Documentation

bins_in_fit_range¶: Bins participating in the fit for each observation.

fit_range¶: Fit range.

obs_list¶: Observations participating in the fit

predicted_counts¶

Current value of predicted counts.

For each observation a tuple to counts for the on and off region is returned.

result¶

Bundle fit results into SpectrumFitResult.

Parameters:

parameters : Parameters

Best fit parameters

statval¶

Current value of statval.

For each observation the statval per bin is returned.

true_fit_range¶

True fit range for each observation.

True fit range is the fit range set in the SpectrumFit with observation threshold taken into account.

Methods Documentation

calc_statval()[source]¶

Calc statistic for all observations.

The result is stored as attribute statval, bin outside the fit range are set to 0.

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.

predict_counts()[source]¶

Predict counts for all observations.

The result is stored as predicted_counts attribute.

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)[source]¶

Statistic summed over all bins and all observations.

This is the likelihood function that is passed to the optimizers

Parameters:

parameters : Parameters

Model parameters