TSMapEstimator#

class gammapy.estimators.TSMapEstimator(model=None, kernel_width=None, downsampling_factor=None, n_sigma=1, n_sigma_ul=2, threshold=None, rtol=0.01, selection_optional=None, energy_edges=None, sum_over_energy_groups=True, n_jobs=None, parallel_backend=None)[source]#

Bases: gammapy.estimators.core.Estimator, gammapy.utils.parallel.ParallelMixin

Compute TS map from a MapDataset using different optimization methods.

The map is computed fitting by a single parameter norm fit. The fit is simplified by finding roots of the derivative of the fit statistics using various root finding algorithms. The approach is described in Appendix A in Stewart (2009).

Parameters

modelSkyModel

Source model kernel. If set to None, assume spatail model: point source model, PointSpatialModel. spectral model: PowerLawSpectral Model of index 2

kernel_widthAngle

Width of the kernel to use: the kernel will be truncated at this size

n_sigmaint

Number of sigma for flux error. Default is 1.

n_sigma_ulint

Number of sigma for flux upper limits. Default is 2.

downsampling_factorint

Sample down the input maps to speed up the computation. Only integer values that are a multiple of 2 are allowed. Note that the kernel is not sampled down, but must be provided with the downsampled bin size.

thresholdfloat (None)

If the TS value corresponding to the initial flux estimate is not above this threshold, the optimizing step is omitted to save computing time.

rtolfloat (0.01)

Relative precision of the flux estimate. Used as a stopping criterion for the norm fit.

selection_optionallist of str

Which maps to compute besides TS, sqrt(TS), flux and symmetric error on flux. Available options are:

“all”: all the optional steps are executed

“errn-errp”: estimate asymmetric error on flux.

“ul”: estimate upper limits on flux.

Default is None so the optional steps are not executed.

energy_edgesQuantity

Energy edges of the maps bins.

sum_over_energy_groupsbool

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

n_jobsint

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

parallel_backend{“multiprocessing”, “ray”}

Which backend to use for multiprocessing. Defaults to BACKEND_DEFAULT.

Notes

Negative \(TS\) values are defined as following:

\[\begin{split}TS = \left \{ \begin{array}{ll} -TS \text{ if } F < 0 \\ TS \text{ else} \end{array} \right.\end{split}\]

Where \(F\) is the fitted flux norm.

References

[Stewart2009]

Examples

>>> import astropy.units as u
>>> from gammapy.estimators import TSMapEstimator
>>> from gammapy.datasets import MapDataset
>>> from gammapy.modeling.models import (SkyModel, PowerLawSpectralModel,PointSpatialModel)
>>> spatial_model = PointSpatialModel()
>>> spectral_model = PowerLawSpectralModel(amplitude="1e-22 cm-2 s-1 keV-1", index=2)
>>> model = SkyModel(spatial_model=spatial_model, spectral_model=spectral_model)
>>> dataset = MapDataset.read("$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc.fits.gz")
>>> estimator = TSMapEstimator(
            model, kernel_width="1 deg",energy_edges=[10, 100] * u.GeV, downsampling_factor=4
        )
>>> maps = estimator.run(dataset)
>>> print(maps)
FluxMaps
--------

  geom                   : WcsGeom
  axes                   : ['lon', 'lat', 'energy']
  shape                  : (400, 200, 1)
  quantities             : ['ts', 'norm', 'niter', 'norm_err', 'npred', 'npred_excess', 'stat', 'stat_null', 'success']  # noqa: E501
  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
`n_jobs`	Number of jobs (int)
`parallel_backend`	Parallel backend (str)
`selection_all`	Which quantities are computed
`selection_optional`
`tag`

Methods Summary

`copy`()	Copy estimator
`estimate_fit_input_maps`(dataset)	Estimate fit input maps
`estimate_flux_default`(dataset[, kernel, ...])	Estimate default flux map using a given kernel.
`estimate_flux_map`(dataset)	Estimate flux and ts maps for single dataset
`estimate_kernel`(dataset)	Get the convolution kernel for the input dataset.
`estimate_mask_default`(dataset)	Compute default mask where to estimate TS values.
`estimate_pad_width`(dataset[, kernel])	Estimate pad width of the dataset
`run`(dataset)	Run TS map estimation.

Attributes Documentation

config_parameters#: Config parameters

n_jobs#: Number of jobs (int)

parallel_backend#: Parallel backend (str)

selection_all#: Which quantities are computed

selection_optional#

tag = 'TSMapEstimator'#

Methods Documentation

copy()#: Copy estimator

estimate_fit_input_maps(dataset)[source]#

Estimate fit input maps

Parameters

datasetMapDataset: Map dataset

Returns

mapsdict of Map: Maps dict

estimate_flux_default(dataset, kernel=None, exposure=None)[source]#

Estimate default flux map using a given kernel.

Parameters

datasetMapDataset: Input dataset.
kernelWcsNDMap: Source model kernel.
exposureWcsNDMap: Exposure map on reconstructed energy.

Returns

fluxWcsNDMap: Approximate flux map.

estimate_flux_map(dataset)[source]#

Estimate flux and ts maps for single dataset

Parameters

datasetMapDataset: Map dataset

estimate_kernel(dataset)[source]#

Get the convolution kernel for the input dataset.

Convolves the model with the IRFs at the center of the dataset, or at the nearest position with non-zero exposure.

Parameters

datasetMapDataset: Input dataset.

Returns

kernelMap: Kernel map

static estimate_mask_default(dataset)[source]#

Compute default mask where to estimate TS values.

Parameters

datasetMapDataset: Input dataset.

Returns

maskWcsNDMap: Mask map.

estimate_pad_width(dataset, kernel=None)[source]#

Estimate pad width of the dataset

Parameters

datasetMapDataset: Input MapDataset.
kernelWcsNDMap: Source model kernel.

Returns

pad_widthtuple: Padding width

run(dataset)[source]#

Run TS map estimation.

Requires a MapDataset with counts, exposure and background_model properly set to run.

Parameters

datasetMapDataset: Input MapDataset.

Returns

mapsdict

Dictionary containing result maps. Keys are:

ts : delta TS map

sqrt_ts : sqrt(delta TS), or significance map

flux : flux map

flux_err : symmetric error map

flux_ul : upper limit map