FluxPointsEstimator

class gammapy.estimators.FluxPointsEstimator(energy_edges=<Quantity [ 1., 10.] TeV>, sum_over_energy_groups=False, n_jobs=None, parallel_backend=None, **kwargs)

Bases: FluxEstimator, ParallelMixin

Flux 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 for details.

The method is also described in the Fermi-LAT catalog paper or the H.E.S.S. Galactic Plane Survey paper

Parameters:

sourcestr or int

For which source in the model to compute the flux points.

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, optional

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.

energy_edgeslist of Quantity, optional

Edges of the flux points energy bins. The resulting bin edges won’t be exactly equal to the input ones, but rather the closest values to the energy axis edges of the parent dataset. Default is None: apply the estimator in each energy bin of the parent dataset. For further explanation see Estimators (DL4 to DL5, and DL6).

fitFit

Fit instance specifying the backend and fit options.

reoptimizebool

If True the free parameters of the other models are fitted in each bin independently, together with the norm of the source of interest (but the other parameters of the source of interest are kept frozen). If False only the norm of the source of interest if fitted, and all other parameters are frozen at their current values.

sum_over_energy_groupsbool

Whether to sum over the energy groups or fit the norm on the full energy grid.

n_jobsint

Number of processes used in parallel for the computation. Default is one, unless N_JOBS_DEFAULT was modified. The number of jobs is limited to the number of physical CPUs.

parallel_backend{“multiprocessing”, “ray”}

Which backend to use for multiprocessing.

normParameter or dict

Norm parameter used for the fit Default is None and a new parameter is created automatically, with value=1, name=”norm”, scan_min=0.2, scan_max=5, and scan_n_values = 11. By default the min and max are not set and derived from the source model, unless the source model does not have one and only one norm parameter. If a dict is given the entries should be a subset of Parameter arguments.

Attributes Summary

config_parameters	Configuration parameters.
n_jobs	Number of jobs as an integer.
parallel_backend	Parallel backend as a string.
selection_optional
tag

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 statistic 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

Configuration parameters.

n_jobs

Number of jobs as an integer.

parallel_backend

Parallel backend as a string.

selection_optional

tag = 'FluxPointsEstimator'

Methods Documentation

copy()

Copy estimator.

estimate_best_fit(datasets, parameter)

Estimate parameter asymmetric errors.

Parameters:

datasetsDatasets

Datasets.

parameterParameter

For which parameter to get the value.

Returns:

resultdict

Dictionary 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

Dictionary 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:

datasetsDatasets

Datasets.

parameterParameter

For which parameter to get the value.

Returns:

resultdict

Dictionary 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)

Estimate flux point for a single energy group.

Parameters:

datasetsDatasets

Datasets.

energy_min, energy_maxQuantity

Energy bounds to compute the flux point for.

Returns:

resultdict

Dictionary with results for the flux point.

static estimate_npred(datasets)

Estimate npred for the flux point.

Parameters:

datasetsDatasets

Datasets.

Returns:

resultdict

Dictionary 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

Dictionary with an array with one entry per dataset with the sum of the masked npred excess.

estimate_scan(datasets, parameter)

Estimate parameter statistic scan.

Parameters:

datasetsDatasets

The datasets used to estimate the model parameter.

parameterParameter

For which parameter to get the value.

Returns:

resultdict

Dictionary 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:

datasetsDatasets

Datasets.

parameterParameter

For which parameter to get the value.

Returns:

resultdict

Dictionary with the test statistic 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.

• “stat_null” : total stat corresponding to the null hypothesis

estimate_ul(datasets, parameter)

Estimate parameter ul.

Parameters:

datasetsDatasets

The datasets used to estimate the model parameter.

parameterParameter

For which parameter to get the value.

Returns:

resultdict

Dictionary with the parameter upper limits. Entries are:

• parameter.name_ul : upper limit on parameter value.

get_scale_model(models)

Set scale model.

Parameters:

modelsModels

Models.

Returns:

modelScaleSpectralModel

Scale spectral model.

run(datasets)

Run the flux point estimator for all energy groups.

Parameters:

datasetsDatasets

Datasets.

Returns:

flux_pointsFluxPoints

Estimated flux points.