TSMapEstimator

class gammapy.detect.TSMapEstimator(method='root brentq', error_method='covar', error_sigma=1, ul_method='covar', ul_sigma=2, threshold=None, rtol=0.001)

Bases: object

Compute TS map from a MapDataset using different optimization methods.

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

Parameters

methodstr (‘root’)

The following options are available:

• 'root brentq' (default)

  Fit amplitude by finding the roots of the the derivative of the fit statistics using the brentq method.

• 'root newton'

  Fit amplitude by finding the roots of the the derivative of the fit statistics using Newton’s method.

• 'leastsq iter'

  Fit the amplitude by an iterative least square fit, that can be solved analytically.

error_method[‘covar’, ‘conf’]

Error estimation method.

error_sigmaint (1)

Sigma for flux error.

ul_method[‘covar’, ‘conf’]

Upper limit estimation method.

ul_sigmaint (2)

Sigma for flux upper limits.

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 (0.001)

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

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 amplitude.

References

[Stewart2009]

Methods Summary

flux_default(dataset, kernel)	Estimate default flux map using a given kernel.
mask_default(exposure, background, kernel)	Compute default mask where to estimate TS values.
run(self, dataset, kernel[, which, …])	Run TS map estimation.
sqrt_ts(map_ts)	Compute sqrt(TS) map.

Methods Documentation

static flux_default(dataset, kernel)

Estimate default flux map using a given kernel.

Parameters

datasetMapDataset

Input dataset.

kernelKernel2D

Source model kernel.

Returns

flux_approxWcsNDMap

Approximate flux map (2D).

static mask_default(exposure, background, kernel)

Compute default mask where to estimate TS values.

Parameters

exposureMap

Input exposure map.

backgroundMap

Input background map.

kernelastropy.convolution.Kernel2D

Source model kernel.

Returns

maskgammapy.maps.WcsNDMap

Mask map.

run(self, dataset, kernel, which='all', downsampling_factor=None)

Run TS map estimation.

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

Parameters

kernelastropy.convolution.Kernel2D or 2D ndarray

Source model kernel.

whichlist of str or ‘all’

Which maps to compute.

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.

Returns

mapsdict

Result maps.

static sqrt_ts(map_ts)

Compute sqrt(TS) map.

Compute sqrt(TS) as defined by:

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

Parameters

map_tsgammapy.maps.WcsNDMap

Input TS map.

Returns

sqrt_tsgammapy.maps.WcsNDMap

Sqrt(TS) map.