TSMapEstimator

class gammapy.estimators.TSMapEstimator(model=None, kernel_width='0.2 deg', downsampling_factor=None, method='root brentq', error_method='covar', error_sigma=1, ul_method='covar', ul_sigma=2, threshold=None, rtol=0.001)[source]

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 described in Appendix A in Stewart (2009).

Parameters
modelSkyModel

Source model kernel. If set to None, assume point source model, PointSpatialModel.

kernel_widthAngle

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

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.

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.

get_kernel(self, dataset)

Set the convolution kernel for the input dataset.

mask_default(exposure, background, kernel)

Compute default mask where to estimate TS values.

run(self, dataset[, steps])

Run TS map estimation.

sqrt_ts(map_ts)

Compute sqrt(TS) map.

Methods Documentation

static flux_default(dataset, kernel)[source]

Estimate default flux map using a given kernel.

Parameters
datasetMapDataset

Input dataset.

kernelndarray

Source model kernel.

Returns
flux_approxWcsNDMap

Approximate flux map (2D).

get_kernel(self, dataset)[source]

Set the convolution kernel for the input dataset.

Convolves the model with the PSFKernel at the center of the dataset. If no PSFMap or PSFKernel is found the dataset, the model is used without convolution.

static mask_default(exposure, background, kernel)[source]

Compute default mask where to estimate TS values.

Parameters
exposureMap

Input exposure map.

backgroundMap

Input background map.

kernelndarray

Source model kernel.

Returns
maskgammapy.maps.WcsNDMap

Mask map.

run(self, dataset, steps='all')[source]

Run TS map estimation.

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

Parameters
datasetMapDataset

Input MapDataset.

stepslist of str or ‘all’

Which maps to compute. Available options are:

  • “ts”: estimate delta TS and significance (sqrt_ts)

  • “flux-err”: estimate symmetric error on flux.

  • “flux-ul”: estimate upper limits on flux.

By default all steps are executed.

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

static sqrt_ts(map_ts)[source]

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.