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)[source]

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)[source]

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')[source]

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)[source]

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)[source]

Return npred map (model + background)

npred_sig(self)[source]

Predicted counts from source model (RegionNDMap).

peek(self, figsize=(16, 4))[source]

Quick-look summary plots.

plot_counts(self, ax=None)[source]

Plot predicted and detected counts.

Parameters
axAxes

Axes object.

Returns
axAxes

Axes object.

plot_fit(self)[source]

Plot counts and residuals in two panels.

Calls plot_counts and plot_residuals.

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

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')[source]

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)[source]

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)[source]

Likelihood per bin given the current model parameters

stat_sum(self)

Total statistic given the current model parameters.