ASmoothMapEstimator#

class gammapy.estimators.ASmoothMapEstimator(scales=None, kernel=<class 'astropy.convolution.kernels.Gaussian2DKernel'>, spectrum=None, method='lima', threshold=5, energy_edges=None)[source]#

Bases: gammapy.estimators.core.Estimator

Adaptively smooth counts image.

Achieves a roughly constant sqrt_ts 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 sqrt_ts of a feature in the image.

Parameters

scalesQuantity: Smoothing scales.
kernelastropy.convolution.Kernel: Smoothing kernel.
spectrumSpectralModel: Spectral model assumption
method{‘asmooth’, ‘lima’}: Significance estimation method.
thresholdfloat: Significance threshold.

Examples

>>> import astropy.units as u
>>> import numpy as np
>>> from gammapy.estimators import ASmoothMapEstimator
>>> from gammapy.datasets import MapDataset
>>> dataset = MapDataset.read("$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz")
>>> scales = u.Quantity(np.arange(0.1, 1, 0.1), unit="deg")
>>> smooth = ASmoothMapEstimator(threshold=3, scales=scales, energy_edges=[1, 10] * u.TeV)
>>> images = smooth.run(dataset)

Attributes Summary

`config_parameters`	Config parameters
`selection_optional`
`tag`

Methods Summary

`copy`()	Copy estimator
`estimate_maps`(dataset)	Run adaptive smoothing on input Maps.
`get_kernels`(pixel_scale)	Get kernels according to the specified method.
`get_scales`(n_scales[, factor, kernel])	Create list of Gaussian widths.
`run`(dataset)	Run adaptive smoothing on input MapDataset.
`selection_all`()	Which quantities are computed

Attributes Documentation

config_parameters#: Config parameters

selection_optional#

tag = 'ASmoothMapEstimator'#

Methods Documentation

copy()#: Copy estimator

estimate_maps(dataset)[source]#

Run adaptive smoothing on input Maps.

Parameters

datasetMapDataset: Dataset

Returns

imagesdict of WcsNDMap

Smoothed images; keys are:

‘counts’
‘background’
‘flux’ (optional)
‘scales’
‘sqrt_ts’.

get_kernels(pixel_scale)[source]#

Get kernels according to the specified method.

Parameters

pixel_scaleAngle: Sky image pixel scale

Returns

kernelslist: List of Kernel

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

Create list of Gaussian widths.

Parameters

n_scalesint: Number of scales
factorfloat: Incremental factor

Returns

scalesndarray: Scale array

run(dataset)[source]#

Run adaptive smoothing on input MapDataset.

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’
‘sqrt_ts’.

selection_all()[source]#: Which quantities are computed