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.
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.
predict_counts() Predict counts for all observations.
run([optimize_opts, covariance_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.

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

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
Returns:

result : dict

Dictionary with keys “errp”, ‘errn”, “success” and “nfev”.
covariance(backend='minuit')

Estimate the covariance matrix.

Assumes that the model parameters are already optimised.

Parameters:

backend : str

Which backend to use (see gammapy.utils.fitting.registry)
Returns:

result : CovarianceResult

Results
likelihood_contour()

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
Returns:TODO
likelihood_profile(parameter, values=None, bounds=2, nvalues=11)

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.
Returns:

results : dict

Dictionary with keys “values” and “likelihood”.
minos_contour(x, y, numpoints=10, sigma=1.0)

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
Returns:

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()

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)

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:
Returns:

fit_result : FitResult

Results
predict_counts()[source]

Predict counts for all observations.

The result is stored as predicted_counts attribute.

run(optimize_opts=None, covariance_opts=None)

Run all fitting steps.

Parameters:

optimize_opts : dict

Options passed to Fit.optimize.

covariance_opts : dict

Options passed to Fit.covariance.
Returns:

fit_result : FitResult

Results
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