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, norm=None, max_niter=100)[source]#
Bases:
Estimator
,ParallelMixin
Compute test statistic 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:
- model
SkyModel
Source model kernel. If set to None, assume spatail model: point source model, PointSpatialModel. spectral model: PowerLawSpectral Model of index 2
- kernel_width
Angle
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, optional
If the test statistic value corresponding to the initial flux estimate is not above this threshold, the optimizing step is omitted to save computing time. Default is None.
- rtolfloat
Relative precision of the flux estimate. Used as a stopping criterion for the norm fit. Default is 0.01.
- selection_optionallist of str, optional
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.
“stat_scan”: estimate likelihood profile
Default is None so the optional steps are not executed.
- energy_edgeslist of
Quantity
, optional Edges of the target maps 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).
- sum_over_energy_groupsbool
Whether to sum over the energy groups or fit the norm on the full energy cube.
- norm
Parameter
or dict Norm parameter used for the likelihood profile computation on a fixed norm range. Only used for “stat_scan” in
selection_optional
. Default is None and a new parameter is created automatically, with value=1, name=”norm”, scan_min=-100, scan_max=100, and values sampled such as we can probe a 0.1% relative error on the norm. If a dict is given the entries should be a subset ofParameter
arguments.- 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
.- max_niterint
Maximal number of iterations used by the root finding algorithm. Default is 100.
- model
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
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'] ref. model : pl n_sigma : 1 n_sigma_ul : 2 sqrt_ts_threshold_ul : 2 sed type init : likelihood
Attributes Summary
Configuration parameters.
Number of jobs as an integer.
Parallel backend as a string.
Which quantities are computed.
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
(datasets)Estimate flux and test statistic 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 test statistic values.
estimate_pad_width
(dataset[, kernel])Estimate pad width of the dataset.
run
(datasets)Run test statistic map estimation.
Attributes Documentation
- config_parameters#
Configuration parameters.
- n_jobs#
Number of jobs as an integer.
- parallel_backend#
Parallel backend as a string.
- 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:
- dataset
MapDataset
Map dataset.
- dataset
- Returns:
- mapsdict of
Map
Maps dictionary.
- mapsdict of
- estimate_flux_default(dataset, kernel=None, exposure=None)[source]#
Estimate default flux map using a given kernel.
- Parameters:
- dataset
MapDataset
Input dataset.
- kernel
WcsNDMap
Source model kernel.
- exposure
WcsNDMap
Exposure map on reconstructed energy.
- dataset
- Returns:
- flux
WcsNDMap
Approximate flux map.
- flux
- estimate_flux_map(datasets)[source]#
Estimate flux and test statistic maps for single dataset.
- Parameters:
- dataset
Datasets
orMapDataset
Map dataset or Datasets (list of MapDataset with the same spatial geometry).
- 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:
- dataset
MapDataset
Input dataset.
- dataset
- Returns:
- kernel
Map
Kernel map.
- kernel
- static estimate_mask_default(dataset)[source]#
Compute default mask where to estimate test statistic values.
- Parameters:
- dataset
MapDataset
Input dataset.
- dataset
- Returns:
- mask
WcsNDMap
Mask map.
- mask
- estimate_pad_width(dataset, kernel=None)[source]#
Estimate pad width of the dataset.
- Parameters:
- dataset
MapDataset
Input MapDataset.
- kernel
WcsNDMap
Source model kernel.
- dataset
- Returns:
- pad_widthtuple
Padding width.
- run(datasets)[source]#
Run test statistic map estimation.
Requires a MapDataset with counts, exposure and background_model properly set to run.
- Parameters:
- dataset
Datasets
orMapDataset
Map dataset or Datasets (list of MapDataset with the same spatial geometry).
- dataset
- 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.
Notes
The progress bar can be displayed for this function.