SpectrumDataset

class gammapy.datasets.SpectrumDataset(models=None, counts=None, livetime=None, aeff=None, edisp=None, background=None, mask_safe=None, mask_fit=None, name=None, gti=None)

Bases: gammapy.datasets.Dataset

Spectrum dataset for likelihood fitting.

The spectrum dataset bundles reduced counts data, with a spectral model, background model and instrument response function to compute the fit-statistic given the current model and data.

Parameters

modelsModels

Fit model

countsRegionNDMap

Counts spectrum

livetimeQuantity

Livetime

aeffEffectiveAreaTable

Effective area

edispEDispKernel

Energy dispersion

backgroundRegionNDMap

Background to use for the fit.

mask_safeRegionNDMap

Mask defining the safe data range.

mask_fitRegionNDMap

Mask to apply to the likelihood for fitting.

namestr

Dataset name.

gtiGTI

GTI of the observation or union of GTI if it is a stacked observation

See also

SpectrumDatasetOnOff, FluxPointsDataset, gammapy.cube.MapDataset

Attributes Summary

data_shape	Shape of the counts data
energy_range	Energy range defined by the safe mask
evaluators	Model evaluators
excess	Excess (counts - alpha * counts_off)
exposure	Excess (aeff * livetime)
mask	Combined fit and safe mask
mask_safe
models	Models (gammapy.modeling.models.Models).
name
stat_type
tag

Methods Summary

copy(self[, name])	A deep copy.
create(e_reco[, e_true, region, …])	Creates empty spectrum dataset.
fake(self[, random_state])	Simulate fake counts for the current model and reduced irfs.
info_dict(self[, in_safe_energy_range])	Info dict with summary statistics, summed over energy
npred(self)	Return npred map (model + background)
npred_sig(self)	Predicted counts from source model (RegionNDMap).
peek(self[, figsize])	Quick-look summary plots.
plot_counts(self[, ax])	Plot predicted and detected counts.
plot_fit(self)	Plot counts and residuals in two panels.
plot_residuals(self[, method, ax])	Plot residuals.
residuals(self[, method])	Compute the spectral residuals.
stack(self, other)	Stack this dataset with another one.
stat_array(self)	Likelihood per bin given the current model parameters
stat_sum(self)	Total statistic given the current model parameters.

Attributes Documentation

data_shape

Shape of the counts data

energy_range

Energy range defined by the safe mask

evaluators

Model evaluators

excess

Excess (counts - alpha * counts_off)

exposure

Excess (aeff * livetime)

mask

Combined fit and safe mask

mask_safe

models

Models (gammapy.modeling.models.Models).

name

stat_type = 'cash'

tag = 'SpectrumDataset'

Methods Documentation

copy(self, name=None)

A deep copy.

classmethod create(e_reco, e_true=None, region=None, reference_time='2000-01-01', name=None)

Creates empty spectrum dataset.

Empty containers are created with the correct geometry. counts, background and aeff are zero and edisp is diagonal.

The safe_mask is set to False in every bin.

Parameters

e_recoQuantity

edges of counts vector

e_trueQuantity

edges of effective area table. If not set use reco energy values. Default : None

regionSkyRegion

Region to define the dataset for.

reference_timeTime

reference time of the dataset, Default is “2000-01-01”

fake(self, random_state='random-seed')

Simulate fake counts for the current model and reduced irfs.

This method overwrites the counts defined on the dataset object.

Parameters

random_state{int, ‘random-seed’, ‘global-rng’, RandomState}

Defines random number generator initialisation. Passed to get_random_state.

info_dict(self, in_safe_energy_range=True)

Info dict with summary statistics, summed over energy

Parameters

in_safe_energy_rangebool

Whether to sum only in the safe energy range

Returns

info_dictdict

Dictionary with summary info.

npred(self)

Return npred map (model + background)

npred_sig(self)

Predicted counts from source model (RegionNDMap).

peek(self, figsize=(16, 4))

Quick-look summary plots.

plot_counts(self, ax=None)

Plot predicted and detected counts.

Parameters

axAxes

Axes object.

Returns

axAxes

Axes object.

plot_fit(self)

Plot counts and residuals in two panels.

Calls plot_counts and plot_residuals.

plot_residuals(self, method='diff', ax=None, **kwargs)

Plot residuals.

Parameters

axAxes

Axes object.

method{“diff”, “diff/model”, “diff/sqrt(model)”}

Normalization used to compute the residuals, see SpectrumDataset.residuals()

**kwargsdict

Keywords passed to RegionNDMap.plot()

Returns

axAxes

Axes object.

residuals(self, method='diff')

Compute the spectral residuals.

Parameters

method{“diff”, “diff/model”, “diff/sqrt(model)”}

Method used to compute the residuals. Available options are:

• diff (default): data - model

• diff/model: (data - model) / model

• diff/sqrt(model): (data - model) / sqrt(model)

Returns

residualsRegionNDMap

Residual spectrum

stack(self, other)

Stack this dataset with another one.

Safe mask is applied to compute the stacked counts vector. Counts outside each dataset safe mask are lost.

Stacking is performed in-place.

The stacking of 2 datasets is implemented as follows. Here, \(k\) denotes a bin in reconstructed energy and \(j = {1,2}\) is the dataset number

The mask_safe of each dataset is defined as:

\[\begin{split}\epsilon_{jk} =\left\{\begin{array}{cl} 1, & \mbox{if bin k is inside the energy thresholds}\\ 0, & \mbox{otherwise} \end{array}\right.\end{split}\]

Then the total counts and model background bkg are computed according to:

\[ \begin{align}\begin{aligned}\overline{\mathrm{n_{on}}}_k = \mathrm{n_{on}}_{1k} \cdot \epsilon_{1k} + \mathrm{n_{on}}_{2k} \cdot \epsilon_{2k}\\\overline{bkg}_k = bkg_{1k} \cdot \epsilon_{1k} + bkg_{2k} \cdot \epsilon_{2k}\end{aligned}\end{align} \]

The stacked safe_mask is then:

\[\overline{\epsilon_k} = \epsilon_{1k} OR \epsilon_{2k}\]

Please refer to the IRFStacker for the description of how the IRFs are stacked.

Parameters

otherSpectrumDataset

the dataset to stack to the current one

stat_array(self)

Likelihood per bin given the current model parameters

stat_sum(self)

Total statistic given the current model parameters.