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FluxPointFit

class gammapy.spectrum.FluxPointFit(model, data, stat='chi2')[source]

Bases: gammapy.utils.fitting.Fit

Fit a set of flux points with a parametric model.

Parameters:

model : SpectralModel

Spectral model (with fit start parameters)

data : FluxPoints

Flux points.

Examples

Load flux points from file and fit with a power-law model:

from astropy import units as u
from gammapy.spectrum import FluxPoints, FluxPointFit
from gammapy.spectrum.models import PowerLaw

filename = '$GAMMAPY_EXTRA/test_datasets/spectrum/flux_points/diff_flux_points.fits'
flux_points = FluxPoints.read(filename)

model = PowerLaw(
    index=2. * u.Unit(''),
    amplitude=1e-12 * u.Unit('cm-2 s-1 TeV-1'),
    reference=1. * u.TeV,
)

fitter = FluxPointFit(model, flux_points)
result = fitter.run()
print(result['best_fit_model'])

Attributes Summary

stat

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

Attributes Documentation

stat

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:
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)[source]

Total likelihood given the current model parameters