Note

You are not reading the most up to date version of Gammapy documentation.
Access the latest stable version v1.3 or the list of Gammapy releases.

FluxPointsDataset

class gammapy.spectrum.FluxPointsDataset(model, data, mask_fit=None, likelihood='chi2', mask_safe=None)

Bases: gammapy.utils.fitting.Dataset

Fit a set of flux points with a parametric model.

Parameters:

model : SpectralModel

Spectral model

data : FluxPoints

Flux points.

mask_fit : numpy.ndarray

Mask to apply to the likelihood for fitting.

likelihood : {“chi2”, “chi2assym”}

Likelihood function to use for the fit.

mask_safe : numpy.ndarray

Mask defining the safe data range.

Examples

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

from astropy import units as u
from gammapy.spectrum import FluxPoints, FluxPointsDataset
from gammapy.utils.fitting import Fit
from gammapy.spectrum.models import PowerLaw

filename = '$GAMMAPY_DATA/tests/spectrum/flux_points/diff_flux_points.fits'
flux_points = FluxPoints.read(filename)

model = PowerLaw()

dataset = FluxPointsDataset(model, flux_points)
fit = Fit(dataset)
result = fit.run()
print(result)
print(result.parameters.to_table())

Attributes Summary

mask	Combined fit and safe mask

Methods Summary

copy(self)	A deep copy.
data_shape(self)	Shape of the flux points data
flux_pred(self)	Compute predicted flux.
likelihood(self)	Total likelihood given the current model parameters.
likelihood_per_bin(self)	Likelihood per bin given the current model parameters
peek(self, \*\*kwargs)	Plot flux points, best fit model and residuals.
plot_residuals(self[, ax])	Plot flux point residuals.
plot_spectrum(self[, ax, fp_kwargs, …])	Plot spectrum including flux points and model.
residuals(self)	Compute flux point residuals

Attributes Documentation

mask

Combined fit and safe mask

Methods Documentation

copy(self)

A deep copy.

data_shape(self)

Shape of the flux points data

flux_pred(self)

Compute predicted flux.

likelihood(self)

Total likelihood given the current model parameters.

likelihood_per_bin(self)

Likelihood per bin given the current model parameters

peek(self, **kwargs)

Plot flux points, best fit model and residuals.

plot_residuals(self, ax=None, **kwargs)

Plot flux point residuals.

Parameters:

ax : Axes

Axes object.

**kwargs : dict

Keyword arguments passed to errorbar.

Returns:

ax : Axes

Axes object.

plot_spectrum(self, ax=None, fp_kwargs=None, model_kwargs=None)

Plot spectrum including flux points and model.

Parameters:

ax : Axes

Axes object.

fp_kwargs : dict

Keyword arguments passed to FluxPoints.plot.

model_kwargs : dict

Keywords passed to SpectralModel.plot and SpectralModel.plot_error

Returns:

ax : Axes

Axes object.

residuals(self)

Compute flux point residuals

Defined as (data - model) / model.

Returns:

residuals : ndarray

Flux point residuals