FluxPointsEstimator#
- class gammapy.estimators.FluxPointsEstimator(energy_edges=<Quantity [ 1., 10.] TeV>, sum_over_energy_groups=False, **kwargs)[source]#
Bases:
gammapy.estimators.flux.FluxEstimatorFlux points estimator.
Estimates flux points for a given list of datasets, energies and spectral model.
To estimate the flux point the amplitude of the reference spectral model is fitted within the energy range defined by the energy group. This is done for each group independently. The amplitude is re-normalized using the “norm” parameter, which specifies the deviation of the flux from the reference model in this energy group. See https://gamma-astro-data-formats.readthedocs.io/en/latest/spectra/binned_likelihoods/index.html # noqa: E501 for details.
The method is also described in the Fermi-LAT catalog paper https://ui.adsabs.harvard.edu/abs/2015ApJS..218…23A or the HESS Galactic Plane Survey paper https://ui.adsabs.harvard.edu/abs/2018A%26A…612A…1H
- Parameters
- energy_edges
Quantity Energy edges of the flux point bins.
- sourcestr or int
For which source in the model to compute the flux points.
- norm_minfloat
Minimum value for the norm used for the fit statistic profile evaluation.
- norm_maxfloat
Maximum value for the norm used for the fit statistic profile evaluation.
- norm_n_valuesint
Number of norm values used for the fit statistic profile.
- norm_values
numpy.ndarray Array of norm values to be used for the fit statistic profile.
- n_sigmaint
Number of sigma to use for asymmetric error computation. Default is 1.
- n_sigma_ulint
Number of sigma to use for upper limit computation. Default is 2.
- selection_optionallist of str
Which additional quantities to estimate. Available options are:
“all”: all the optional steps are executed
“errn-errp”: estimate asymmetric errors on flux.
“ul”: estimate upper limits.
“scan”: estimate fit statistic profiles.
Default is None so the optional steps are not executed.
- fit
Fit Fit instance specifying the backend and fit options.
- reoptimizebool
Re-optimize other free model parameters. Default is True.
- sum_over_energy_groupsbool
Whether to sum over the energy groups or fit the norm on the full energy grid.
- energy_edges
Attributes Summary
Config parameters
Methods Summary
copy()Copy estimator
estimate_best_fit(datasets, parameter)Estimate parameter asymmetric errors
estimate_counts(datasets)Estimate counts for the flux point.
estimate_errn_errp(datasets, parameter)Estimate parameter asymmetric errors
estimate_flux_point(datasets, energy_min, ...)Estimate flux point for a single energy group.
estimate_npred(datasets)Estimate npred for the flux point.
estimate_npred_excess(datasets)Estimate npred excess for the source.
estimate_scan(datasets, parameter)Estimate parameter stat scan.
estimate_ts(datasets, parameter)Estimate parameter ts
estimate_ul(datasets, parameter)Estimate parameter ul.
get_scale_model(models)Set scale model
run(datasets)Run the flux point estimator for all energy groups.
Attributes Documentation
- config_parameters#
Config parameters
- selection_optional#
- tag = 'FluxPointsEstimator'#
Methods Documentation
- copy()#
Copy estimator
- estimate_best_fit(datasets, parameter)#
Estimate parameter asymmetric errors
- Parameters
- datasets
Datasets Datasets
- parameter
Parameter For which parameter to get the value
- datasets
- Returns
- resultdict
Dict with the various parameter estimation values. Entries are:
parameter.name: best fit parameter value
“stat”: best fit total stat.
“success”: boolean flag for fit success
parameter.name_err: covariance-based error estimate on parameter value
- static estimate_counts(datasets)#
Estimate counts for the flux point.
- Parameters
- datasetsDatasets
Datasets
- Returns
- resultdict
Dict with an array with one entry per dataset with the sum of the masked counts.
- estimate_errn_errp(datasets, parameter)#
Estimate parameter asymmetric errors
- Parameters
- datasets
Datasets Datasets
- parameter
Parameter For which parameter to get the value
- datasets
- Returns
- resultdict
Dict with the parameter asymmetric errors. Entries are:
{parameter.name}_errp : positive error on parameter value
{parameter.name}_errn : negative error on parameter value
- estimate_flux_point(datasets, energy_min, energy_max)[source]#
Estimate flux point for a single energy group.
- Parameters
- datasets
Datasets Datasets
- energy_min, energy_max
Quantity Energy bounds to compute the flux point for.
- datasets
- Returns
- resultdict
Dict with results for the flux point.
- static estimate_npred(datasets)#
Estimate npred for the flux point.
- Parameters
- datasetsDatasets
Datasets
- Returns
- resultdict
Dict with an array with one entry per dataset with the sum of the masked npred.
- estimate_npred_excess(datasets)#
Estimate npred excess for the source.
- Parameters
- datasetsDatasets
Datasets
- Returns
- resultdict
Dict with an array with one entry per dataset with the sum of the masked npred excess.
- estimate_scan(datasets, parameter)#
Estimate parameter stat scan.
- Parameters
- datasets
Datasets The datasets used to estimate the model parameter
- parameter
Parameter For which parameter to get the value
- datasets
- Returns
- resultdict
Dict with the parameter fit scan values. Entries are:
parameter.name_scan : parameter values scan
“stat_scan” : fit statistic values scan
- estimate_ts(datasets, parameter)#
Estimate parameter ts
- Parameters
- datasets
Datasets Datasets
- parameter
Parameter For which parameter to get the value
- datasets
- Returns
- resultdict
Dict with the TS of the best fit value compared to the null hypothesis. Entries are:
“ts” : fit statistic difference with null hypothesis
“npred” : predicted number of counts per dataset
- estimate_ul(datasets, parameter)#
Estimate parameter ul.
- Parameters
- datasets
Datasets The datasets used to estimate the model parameter
- parameter
Parameter For which parameter to get the value
- datasets
- Returns
- resultdict
Dict with the parameter ULs. Entries are:
parameter.name_ul : upper limit on parameter value
- get_scale_model(models)#
Set scale model
- Parameters
- models
Models Models
- models
- Returns
- model
ScaleSpectralModel Scale spectral model
- model
- run(datasets)[source]#
Run the flux point estimator for all energy groups.
- Parameters
- datasets
Datasets Datasets
- datasets
- Returns
- flux_points
FluxPoints Estimated flux points.
- flux_points