ASmoothMapEstimator

class gammapy.estimators.ASmoothMapEstimator(scales, kernel=<class 'astropy.convolution.kernels.Gaussian2DKernel'>, method='lima', threshold=5)

Bases: object

Adaptively smooth counts image.

Achieves a roughly constant significance of features across the whole image.

Algorithm based on https://ui.adsabs.harvard.edu/abs/2006MNRAS.368…65E

The algorithm was slightly adapted to also allow Li & Ma to estimate the significance of a feature in the image.

Parameters

scalesQuantity

Smoothing scales.

kernelastropy.convolution.Kernel

Smoothing kernel.

method{‘asmooth’, ‘lima’}

Significance estimation method.

thresholdfloat

Significance threshold.

Methods Summary

estimate_maps(self, counts, background[, …])	Run adaptive smoothing on input Maps.
get_scales(n_scales[, factor, kernel])	Create list of Gaussian widths.
kernels(self, pixel_scale)	Ring kernels according to the specified method.
run(self, dataset)	Run adaptive smoothing on input MapDataset.

Methods Documentation

estimate_maps(self, counts, background, exposure=None)

Run adaptive smoothing on input Maps.

Parameters

countsMap

counts map

backgroundMap

estimated background counts map

exposureMap

exposure map. If set, it will produce a flux smoothed map.

Returns

imagesdict of WcsNDMap

Smoothed images; keys are:

• ‘counts’

• ‘background’

• ‘flux’ (optional)

• ‘scales’

• ‘significance’.

static get_scales(n_scales, factor=1.4142135623730951, kernel=<class 'astropy.convolution.kernels.Gaussian2DKernel'>)

Create list of Gaussian widths.

kernels(self, pixel_scale)

Ring kernels according to the specified method.

Parameters

pixel_scaleAngle

Sky image pixel scale

Returns

kernelslist

List of Kernel

run(self, dataset)

Run adaptive smoothing on input MapDataset. The latter should have

Parameters

datasetMapDataset or MapDatasetOnOff

the input dataset (with one bin in energy at most)

Returns

——-

imagesdict of WcsNDMap

Smoothed images; keys are:

• ‘counts’

• ‘background’

• ‘flux’ (optional)

• ‘scales’

• ‘significance’.