FluxProfileEstimator#

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

Bases: FluxPointsEstimator

Estimate flux profiles.

Parameters:

regionslist of SkyRegion: Regions to use.
spectral_modelSpectralModel, optional: Spectral model to compute the fluxes or brightness. Default is power-law with spectral index of 2.
n_jobsint, optional: 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”}, optional: Which backend to use for multiprocessing. Defaults to BACKEND_DEFAULT.
**kwargsdict, optional: 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
>>> filename = "$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc.fits.gz"
>>> dataset = MapDataset.read(filename, 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', 'stat_null', '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`	Configuration parameters.
`n_jobs`	Number of jobs as an integer.
`parallel_backend`	Parallel backend as a string.
`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 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 flux profile estimation.

Attributes Documentation

config_parameters#: Configuration parameters.

n_jobs#: Number of jobs as an integer.

parallel_backend#: Parallel backend as a string.

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

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)[source]#

Run flux profile estimation.

Parameters:

datasetslist of MapDataset: Map datasets.

Returns:

profileFluxPoints: Profile flux points.