SpectrumDataset

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

Bases: gammapy.datasets.MapDataset

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

exposureRegionNDMap

Effective area

edispEDispKernelMap

Energy dispersion kernel.

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

meta_tableTable

Table listing informations on observations used to create the dataset. One line per observation for stacked datasets.

See also

SpectrumDatasetOnOff, FluxPointsDataset, MapDataset

Attributes Summary

background	A lazy FITS data descriptor.
background_model
counts	A lazy FITS data descriptor.
data_shape	Shape of the counts or background data (tuple)
edisp	A lazy FITS data descriptor.
energy_range	Energy range defined by the safe mask
evaluators	Model evaluators
excess	Excess
exposure	A lazy FITS data descriptor.
geoms	Map geometries
mask	Combined fit and safe mask
mask_fit	A lazy FITS data descriptor.
mask_safe	A lazy FITS data descriptor.
mask_safe_edisp	Mask safe for edisp maps
mask_safe_image	Reduced mask safe
mask_safe_psf	Mask safe for psf maps
models	Models (Models).
name
psf	A lazy FITS data descriptor.
stat_type
tag

Methods Summary

apply_mask_safe()	Apply mask safe to the dataset
copy([name])	A deep copy.
create(e_reco[, e_true, region, …])	Creates empty spectrum dataset.
cutout(*args, **kwargs)	Returns self
downsample(factor[, axis_name, name])	Downsample map dataset.
fake([random_state])	Simulate fake counts for the current model and reduced IRFs.
from_dict()	Create from dicts and models list generated from YAML serialization.
from_geoms(geom, geom_exposure, geom_psf, …)	Create a MapDataset object with zero filled maps according to the specified geometries
from_hdulist()	Create map dataset from list of HDUs.
info_dict([in_safe_data_range])	Info dict with summary statistics, summed over energy
npred()	Predicted source and background counts
npred_background()	Predicted background counts
npred_signal([model])	“Model predicted signal counts.
pad(*args, **kwargs)	Returns self
peek([fig])	Quick-look summary plots.
plot_counts([ax, kwargs_counts, …])	Plot counts and background.
plot_excess([ax, kwargs_excess, …])	Plot excess and predicted signal.
plot_fit([ax_spectrum, ax_residuals, …])	Plot spectrum and residuals in two panels.
plot_residuals([ax, method])	Plot spectrum residuals.
plot_residuals_spatial([ax, method, …])	Plot spatial residuals.
plot_residuals_spectral([ax, method, region])	Plot spectral residuals.
read()	Read map dataset from file.
resample_energy_axis(energy_axis[, name])	Resample MapDataset over new reco energy axis.
reset_data_cache()	Reset data cache to free memory space
residuals([method])	Compute the spectral residuals.
slice_by_energy(energy_min, energy_max[, name])	Select and slice datasets in energy range
slice_by_idx(slices[, name])	Slice sub dataset.
stack(other)	Stack another dataset in place.
stat_array()	Likelihood per bin given the current model parameters
stat_sum()	Total statistic given the current model parameters.
to_dict([filename])	Convert to dict for YAML serialization.
to_hdulist()	Convert map dataset to list of HDUs.
to_image([name])	Create images by summing over the reconstructed energy axis.
to_spectrum_dataset(*args, **kwargs)	Returns self
write()	Write map dataset to file.

Attributes Documentation

background

A lazy FITS data descriptor.

Parameters

cachebool

Whether to cache the data.

background_model

counts

A lazy FITS data descriptor.

Parameters

cachebool

Whether to cache the data.

data_shape

Shape of the counts or background data (tuple)

edisp

A lazy FITS data descriptor.

Parameters

cachebool

Whether to cache the data.

energy_range

Energy range defined by the safe mask

evaluators

Model evaluators

excess

Excess

exposure

A lazy FITS data descriptor.

Parameters

cachebool

Whether to cache the data.

geoms

Map geometries

Returns

geomsdict

Dict of map geometries involved in the dataset.

mask

Combined fit and safe mask

mask_fit

A lazy FITS data descriptor.

Parameters

cachebool

Whether to cache the data.

mask_safe

A lazy FITS data descriptor.

Parameters

cachebool

Whether to cache the data.

mask_safe_edisp

Mask safe for edisp maps

mask_safe_image

Reduced mask safe

mask_safe_psf

Mask safe for psf maps

models

Models (Models).

name

psf

A lazy FITS data descriptor.

Parameters

cachebool

Whether to cache the data.

stat_type = 'cash'

tag = 'SpectrumDataset'

Methods Documentation

apply_mask_safe()

Apply mask safe to the dataset

copy(name=None)

A deep copy.

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

counts energy axis. Its name must be “energy”.

e_trueMapAxis

effective area table energy axis. Its name must be “energy-true”. 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”

meta_tableTable

Table listing informations on observations used to create the dataset. One line per observation for stacked datasets.

cutout(*args, **kwargs)

Returns self

downsample(factor, axis_name=None, name=None)

Downsample map dataset.

The PSFMap and EDispKernelMap are not downsampled, except if a corresponding axis is given.

Parameters

factorint

Downsampling factor.

axis_namestr

Which non-spatial axis to downsample. By default only spatial axes are downsampled.

namestr

Name of the downsampled dataset.

Returns

datasetMapDataset or SpectrumDataset

Downsampled map dataset.

fake(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.

from_dict()

Create from dicts and models list generated from YAML serialization.

classmethod from_geoms(geom, geom_exposure, geom_psf, geom_edisp, reference_time='2000-01-01', name=None, **kwargs)

Create a MapDataset object with zero filled maps according to the specified geometries

Parameters

geomGeom

geometry for the counts and background maps

geom_exposureGeom

geometry for the exposure map

geom_psfGeom

geometry for the psf map

geom_edispGeom

geometry for the energy dispersion kernel map. If geom_edisp has a migra axis, this wil create an EDispMap instead.

reference_timeTime

the reference time to use in GTI definition

namestr

Name of the returned dataset.

Returns

datasetMapDataset or SpectrumDataset

A dataset containing zero filled maps

from_hdulist()

Create map dataset from list of HDUs.

Parameters

hdulistHDUList

List of HDUs.

namestr

Name of the new dataset.

Returns

datasetMapDataset

Map dataset.

info_dict(in_safe_data_range=True)

Info dict with summary statistics, summed over energy

Parameters

in_safe_data_rangebool

Whether to sum only in the safe energy range

Returns

info_dictdict

Dictionary with summary info.

npred()

Predicted source and background counts

Returns

npredMap

Total predicted counts

npred_background()

Predicted background counts

The predicted background counts depend on the parameters of the FoVBackgroundModel defined in the dataset.

Returns

npred_backgroundMap

Predicted counts from the background.

npred_signal(model=None)

“Model predicted signal counts.

If a model is passed, predicted counts from that component is returned. Else, the total signal counts are returned.

Parameters

model: `~gammapy.modeling.models.SkyModel`, optional

Sky model to compute the npred for. If none, the sum of all components (minus the background model) is returned

Returns

npred_sig: gammapy.maps.Map

Map of the predicted signal counts

pad(*args, **kwargs)

Returns self

peek(fig=None)

Quick-look summary plots.

Parameters

figFigure

Figure to add AxesSubplot on.

Returns

ax1, ax2, ax3AxesSubplot

Counts, effective area and energy dispersion subplots.

plot_counts(ax=None, kwargs_counts=None, kwargs_background=None, **kwargs)

Plot counts and background.

Parameters

axAxes

Axes to plot on.

kwargs_counts: dict

Keyword arguments passed to hist for the counts.

kwargs_background: dict

Keyword arguments passed to hist for the background.

**kwargs: dict

Keyword arguments passed to both hist.

Returns

axAxes

Axes object.

plot_excess(ax=None, kwargs_excess=None, kwargs_npred_signal=None, **kwargs)

Plot excess and predicted signal.

Parameters

axAxes

Axes to plot on.

kwargs_excess: dict

Keyword arguments passed to errorbar for the excess.

kwargs_npred_signaldict

Keyword arguments passed to hist for the predicted signal.

**kwargs: dict

Keyword arguments passed to both plot methods.

Returns

axAxes

Axes object.

plot_fit(ax_spectrum=None, ax_residuals=None, kwargs_spectrum=None, kwargs_residuals=None)

Plot spectrum and residuals in two panels.

Calls plot_excess and plot_residuals.

Parameters

ax_spectrumAxes

Axes to plot spectrum on.

ax_residualsAxes

Axes to plot residuals on.

kwargs_spectrumdict

Keyword arguments passed to plot_excess.

kwargs_residualsdict

Keyword arguments passed to plot_residuals.

Returns

ax_spectrum, ax_residualsAxes

Spectrum and residuals plots.

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

Plot spectrum residuals.

Parameters

axAxes

Axes to plot on.

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

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

**kwargsdict

Keyword arguments passed to errorbar.

Returns

axAxes

Axes object.

plot_residuals_spatial(ax=None, method='diff', smooth_kernel='gauss', smooth_radius='0.1 deg', **kwargs)

Plot spatial residuals.

The normalization used for the residuals computation can be controlled using the method parameter.

Parameters

axWCSAxes

Axes to plot on.

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

Normalization used to compute the residuals, see MapDataset.residuals.

smooth_kernel{“gauss”, “box”}

Kernel shape.

smooth_radius: `~astropy.units.Quantity`, str or float

Smoothing width given as quantity or float. If a float is given, it is interpreted as smoothing width in pixels.

**kwargsdict

Keyword arguments passed to imshow.

Returns

axWCSAxes

WCSAxes object.

plot_residuals_spectral(ax=None, method='diff', region=None, **kwargs)

Plot spectral residuals.

The residuals are extracted from the provided region, and the normalization used for its computation can be controlled using the method parameter.

Parameters

axAxes

Axes to plot on.

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

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

region: `~regions.SkyRegion` (required)

Target sky region.

**kwargsdict

Keyword arguments passed to errorbar.

Returns

axAxes

Axes object.

read()

Read map dataset from file.

Parameters

filenamestr

Filename to read from.

namestr

Name of the new dataset.

lazybool

Whether to lazy load data into memory

cachebool

Whether to cache the data after loading.

Returns

datasetMapDataset

Map dataset.

resample_energy_axis(energy_axis, name=None)

Resample MapDataset over new reco energy axis.

Counts are summed taking into account safe mask.

Parameters

energy_axisMapAxis

New reconstructed energy axis.

name: str

Name of the new dataset.

Returns

dataset: MapDataset or SpectrumDataset

Resampled dataset .

reset_data_cache()

Reset data cache to free memory space

residuals(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

slice_by_energy(energy_min, energy_max, name=None)

Select and slice datasets in energy range

Parameters

energy_min, energy_maxQuantity

Energy bounds to compute the flux point for.

namestr

Name of the sliced dataset.

Returns

datasetMapDataset

Sliced Dataset

slice_by_idx(slices, name=None)

Slice sub dataset.

The slicing only applies to the maps that define the corresponding axes.

Parameters

slicesdict

Dict of axes names and integers or slice object pairs. Contains one element for each non-spatial dimension. For integer indexing the corresponding axes is dropped from the map. Axes not specified in the dict are kept unchanged.

namestr

Name of the sliced dataset.

Returns

datasetMapDataset or SpectrumDataset

Sliced dataset

stack(other)

Stack another dataset in place.

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

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:

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

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:

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

Parameters

other: `~gammapy.datasets.MapDataset` or `~gammapy.datasets.MapDatasetOnOff`

Map dataset to be stacked with this one. If other is an on-off dataset alpha * counts_off is used as a background model.

stat_array()

Likelihood per bin given the current model parameters

stat_sum()

Total statistic given the current model parameters.

to_dict(filename='')

Convert to dict for YAML serialization.

to_hdulist()

Convert map dataset to list of HDUs.

Returns

hdulistHDUList

Map dataset list of HDUs.

to_image(name=None)

Create images by summing over the reconstructed energy axis.

Parameters

namestr

Name of the new dataset.

Returns

datasetMapDataset or SpectrumDataset

Dataset integrated over non-spatial axes.

to_spectrum_dataset(*args, **kwargs)

Returns self

write()

Write map dataset to file.

Parameters

filenamestr

Filename to write to.

overwritebool

Overwrite file if it exists.