MapDatasetOnOff

class gammapy.datasets.MapDatasetOnOff(models=None, counts=None, counts_off=None, acceptance=None, acceptance_off=None, exposure=None, mask_fit=None, psf=None, edisp=None, name=None, mask_safe=None, gti=None, meta_table=None)

Bases: gammapy.datasets.MapDataset

Map dataset for on-off likelihood fitting.

Parameters

modelsModels

Source sky models.

countsWcsNDMap

Counts cube

counts_offWcsNDMap

Ring-convolved counts cube

acceptanceWcsNDMap

Acceptance from the IRFs

acceptance_offWcsNDMap

Acceptance off

exposureWcsNDMap

Exposure cube

mask_fitWcsNDMap

Mask to apply to the likelihood for fitting.

psfPSFKernel

PSF kernel

edispEDispKernel

Energy dispersion

mask_safeWcsNDMap

Mask defining the safe data range.

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.

namestr

Name of the dataset.

See also

MapDatasetOn, SpectrumDataset, FluxPointsDataset

Attributes Summary

alpha	Exposure ratio between signal and background regions
background	Computed as alpha * n_off
background_model
counts	A lazy FITS data descriptor.
data_shape	Shape of the counts or background data (tuple)
edisp	A lazy FITS data descriptor.
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(geom[, energy_axis_true, migra_axis, …])	Create a MapDataset object with zero filled maps.
cutout(position, width[, mode, name])	Cutout map dataset.
downsample(factor[, axis_name, name])	Downsample map dataset.
fake(npred_background[, random_state])	Simulate fake counts (on and off) for the current model and reduced IRFs.
from_dict(data[, lazy, cache])	Create from dicts and models list generated from YAML serialization.
from_geoms(geom, geom_exposure, geom_psf, …)	Create a MapDatasetOnOff object swith zero filled maps according to the specified geometries
from_hdulist(hdulist[, name])	Create map dataset from list of HDUs.
from_map_dataset(dataset, acceptance, …[, …])	Create on off dataset from a map dataset.
info_dict([in_safe_data_range])	Basic info dict with summary statistics
npred()	Predicted source and background counts
npred_background()	Prediced background counts estimated from the marginalized likelihood estimate.
npred_off()	Predicted counts in the off region
npred_signal([model])	“Model predicted signal counts.
pad()	Pad the spatial dimensions of the dataset.
plot_residuals([ax_spatial, ax_spectral, …])	Plot spatial and spectral residuals in two panels.
plot_residuals_spatial([ax, method, …])	Plot spatial residuals.
plot_residuals_spectral([ax, method, region])	Plot spectral residuals.
read(filename[, name, lazy, cache])	Read map dataset from file.
resample_energy_axis(energy_axis[, name])	Resample MapDatasetOnOff over reconstructed energy edges.
reset_data_cache()	Reset data cache to free memory space
residuals([method])	Compute residuals map.
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 likelihood 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_map_dataset([name])	Convert a MapDatasetOnOff to MapDataset The background model template is taken as alpha*counts_off
to_spectrum_dataset(on_region[, …])	Return a ~gammapy.datasets.SpectrumDatasetOnOff from on_region.
write(filename[, overwrite])	Write map dataset to file.

Attributes Documentation

alpha

Exposure ratio between signal and background regions

See WStat : Poisson data with background measurement

Returns

alphaMap

Alpha map

background

Computed as alpha * n_off

See WStat : Poisson data with background measurement

Returns

backgroundMap

Background map

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.

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 = 'wstat'

tag = 'MapDatasetOnOff'

Methods Documentation

apply_mask_safe()

Apply mask safe to the dataset

copy(name=None)

A deep copy.

classmethod create(geom, energy_axis_true=None, migra_axis=None, rad_axis=None, binsz_irf=None, reference_time='2000-01-01', name=None, meta_table=None, **kwargs)

Create a MapDataset object with zero filled maps.

Parameters

geomWcsGeom

Reference target geometry in reco energy, used for counts and background maps

energy_axis_trueMapAxis

True energy axis used for IRF maps

migra_axisMapAxis

If set, this provides the migration axis for the energy dispersion map. If not set, an EDispKernelMap is produced instead. Default is None

rad_axisMapAxis

Rad axis for the psf map

binsz_irffloat

IRF Map pixel size in degrees.

reference_timeTime

the reference time to use in GTI definition

namestr

Name of the returned dataset.

meta_tableTable

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

Returns

empty_mapsMapDataset

A MapDataset containing zero filled maps

cutout(position, width, mode='trim', name=None)

Cutout map dataset.

Parameters

positionSkyCoord

Center position of the cutout region.

widthtuple of Angle

Angular sizes of the region in (lon, lat) in that specific order. If only one value is passed, a square region is extracted.

mode{‘trim’, ‘partial’, ‘strict’}

Mode option for Cutout2D, for details see Cutout2D.

namestr

Name of the new dataset.

Returns

cutoutMapDatasetOnOff

Cutout map dataset.

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

datasetMapDatasetOnOff

Downsampled map dataset.

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

Simulate fake counts (on and off) 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.

classmethod from_dict(data, lazy=False, cache=True)

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 MapDatasetOnOff object swith zero filled maps according to the specified geometries

Parameters

geomgammapy.maps.WcsGeom

geometry for the counts, counts_off, acceptance and acceptance_off maps

geom_exposuregammapy.maps.WcsGeom

geometry for the exposure map

geom_psfgammapy.maps.WcsGeom

geometry for the psf map

geom_edispgammapy.maps.WcsGeom

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

empty_mapsMapDatasetOnOff

A MapDatasetOnOff containing zero filled maps

classmethod from_hdulist(hdulist, name=None)

Create map dataset from list of HDUs.

Parameters

hdulistHDUList

List of HDUs.

namestr

Name of the new dataset.

Returns

datasetMapDataset

Map dataset.

classmethod from_map_dataset(dataset, acceptance, acceptance_off, counts_off=None, name=None)

Create on off dataset from a map dataset.

Parameters

datasetMapDataset

Spectrum dataset defining counts, edisp, aeff, livetime etc.

acceptanceMap

Relative background efficiency in the on region.

acceptance_offMap

Relative background efficiency in the off region.

counts_offMap

Off counts map . If the dataset provides a background model, and no off counts are defined. The off counts are deferred from counts_off / alpha.

namestr

Name of the returned dataset.

Returns

datasetMapDatasetOnOff

Map dataset on off.

info_dict(in_safe_data_range=True)

Basic info dict with summary statistics

If a region is passed, then a spectrum dataset is extracted, and the corresponding info returned.

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()

Prediced background counts estimated from the marginalized likelihood estimate.

See WStat : Poisson data with background measurement

Returns

npred_backgroundMap

Predicted background counts

npred_off()

Predicted counts in the off region

See WStat : Poisson data with background measurement

Returns

npred_offMap

Predicted off counts

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()

Pad the spatial dimensions of the dataset.

The padding only applies to counts, masks, background and exposure.

Counts, background and masks are padded with zeros, exposure is padded with edge value.

Parameters

pad_width{sequence, array_like, int}

Number of pixels padded to the edges of each axis.

namestr

Name of the padded dataset.

Returns

datasetMapDataset

Padded map dataset.

plot_residuals(ax_spatial=None, ax_spectral=None, kwargs_spatial=None, kwargs_spectral=None)

Plot spatial and spectral residuals in two panels.

Calls plot_residuals_spatial and plot_residuals_spectral. The spectral residuals are extracted from the provided region, and the normalization used for its computation can be controlled using the method parameter. The region outline is overlaid on the residuals map.

Parameters

ax_spatialWCSAxes

Axes to plot spatial residuals on.

ax_spectralAxes

Axes to plot spectral residuals on.

kwargs_spatialdict

Keyword arguments passed to plot_residuals_spatial.

kwargs_spectraldict (region required)

Keyword arguments passed to plot_residuals_spectral.

Returns

ax_spatial, ax_spectralWCSAxes, Axes

Spatial and spectral residuals plots.

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.

classmethod read(filename, name=None, lazy=False, cache=True)

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 MapDatasetOnOff over reconstructed energy edges.

Counts are summed taking into account safe mask.

Parameters

energy_axisMapAxis

New reco energy axis.

name: str

Name of the new dataset.

Returns

dataset: SpectrumDataset

Resampled spectrum dataset .

reset_data_cache()

Reset data cache to free memory space

residuals(method='diff', **kwargs)

Compute residuals map.

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)

**kwargsdict

Keyword arguments forwarded to Map.smooth()

Returns

residualsgammapy.maps.Map

Residual map.

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

map_outMap

Sliced map object.

stack(other)

Stack another dataset in place.

The acceptance of the stacked dataset is normalized to 1, and the stacked acceptance_off is scaled so that:

\[\alpha_\text{stacked} = \frac{1}{a_\text{off}} = \frac{\alpha_1\text{OFF}_1 + \alpha_2\text{OFF}_2}{\text{OFF}_1 + OFF_2}\]

Parameters

otherMapDatasetOnOff

Other dataset

stat_array()

Likelihood per bin given the current model parameters

stat_sum()

Total likelihood 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_map_dataset(name=None)

Convert a MapDatasetOnOff to MapDataset The background model template is taken as alpha*counts_off

Parameters

name: str

Name of the new dataset

Returns

dataset: MapDataset

Map dataset with cash statistics

to_spectrum_dataset(on_region, containment_correction=False, name=None)

Return a ~gammapy.datasets.SpectrumDatasetOnOff from on_region.

Counts and OFF counts are summed in the on_region.

Acceptance is the average of all acceptances while acceptance OFF is taken such that number of excess is preserved in the on_region.

Effective area is taken from the average exposure divided by the livetime. Here we assume it is the sum of the GTIs.

The energy dispersion kernel is obtained at the on_region center. Only regions with centers are supported.

The model is not exported to the ~gammapy.dataset.SpectrumDataset. It must be set after the dataset extraction.

Parameters

on_regionSkyRegion

the input ON region on which to extract the spectrum

containment_correctionbool

Apply containment correction for point sources and circular on regions

namestr

Name of the new dataset.

Returns

datasetSpectrumDatasetOnOff

the resulting reduced dataset

write(filename, overwrite=False)

Write map dataset to file.

Parameters

filenamestr

Filename to write to.

overwritebool

Overwrite file if it exists.