FluxProfileEstimator#

class gammapy.estimators.FluxProfileEstimator(regions, spectrum=None, **kwargs)[source]#

Bases: gammapy.estimators.points.sed.FluxPointsEstimator

Estimate flux profiles

Parameters
regionslist of SkyRegion

regions to use

spectrumSpectralModel (optional)

Spectral model to compute the fluxes or brightness. Default is power-law with spectral index of 2.

**kwargsdict

Keywords forwarded to the FluxPointsEstimator (see documentation there for further description of valid keywords)

Examples

This example shows how to compute a counts profile for the Fermi galactic center region:

>>> from astropy import units as u
>>> from astropy.coordinates import SkyCoord
>>> from gammapy.data import GTI
>>> from gammapy.estimators import FluxProfileEstimator
>>> from gammapy.utils.regions import make_orthogonal_rectangle_sky_regions
>>> from gammapy.datasets import MapDataset
>>> from gammapy.maps import RegionGeom

>>> # load example data
>>> dataset = MapDataset.read("$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc.fits.gz", name="fermi_dataset")

>>> # configuration
>>> dataset.gti = GTI.create("0s", "1e7s", "2010-01-01")

>>> # creation of the boxes and axis
>>> start_pos = SkyCoord("-1d", "0d", frame='galactic')
>>> end_pos = SkyCoord("1d", "0d", frame='galactic')

>>> regions = make_orthogonal_rectangle_sky_regions(start_pos=start_pos, end_pos=end_pos, wcs=dataset.counts.geom.wcs, height=2.*u.deg, nbin=21)

>>> # set up profile estimator and run
>>> prof_maker = FluxProfileEstimator(regions=regions, energy_edges=[10, 2000] * u.GeV)
>>> fermi_prof = prof_maker.run(dataset)
>>> print(fermi_prof)
FluxPoints
----------

  geom                   : RegionGeom
  axes                   : ['lon', 'lat', 'energy', 'projected-distance']
  shape                  : (1, 1, 1, 21)
  quantities             : ['norm', 'norm_err', 'ts', 'npred', 'npred_excess', 'stat', 'counts', 'success']
  ref. model             : pl
  n_sigma                : 1
  n_sigma_ul             : 2
  sqrt_ts_threshold_ul   : 2
  sed type init          : likelihood

Attributes Summary

config_parameters

Config parameters

projected_distance_axis

Get projected distance from the first region.

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 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 flux profile estimation

Attributes Documentation

config_parameters#

Config parameters

projected_distance_axis#

Get projected distance from the first region.

For normal region this is defined as the distance from the center of the region. For annulus shaped regions it is the mean between the inner and outer radius.

Returns
axisMapAxis

Projected distance axis

selection_optional#
tag = 'FluxProfileEstimator'#

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

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
datasetsDatasets

Datasets

parameterParameter

For which parameter to get the value

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)#

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

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
datasetsDatasets

The datasets used to estimate the model parameter

parameterParameter

For which parameter to get the value

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
datasetsDatasets

Datasets

parameterParameter

For which parameter to get the value

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
datasetsDatasets

The datasets used to estimate the model parameter

parameterParameter

For which parameter to get the value

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
modelsModels

Models

Returns
modelScaleSpectralModel

Scale spectral model

run(datasets)[source]#

Run flux profile estimation

Parameters
datasetslist of MapDataset

Map datasets.

Returns
profileFluxPoints

Profile flux points.