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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

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()

fit = FluxPointFit(model, flux_points)
result = fit.run()
print(result)
print(result.model)

Attributes Summary

stat

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

Attributes Documentation

stat

Methods Documentation

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
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]

Total likelihood given the current model parameters