SpectrumDatasetOnOff#

class gammapy.datasets.SpectrumDatasetOnOff(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, meta=None)[source]#

Bases: PlotMixin, MapDatasetOnOff

Spectrum dataset for 1D on-off likelihood fitting. It bundles together the binned on and off counts, the binned IRFs as well as the on and off acceptances.

A fit mask can be added to exclude bins during the analysis.

It uses the Wstat statistic (see wstat).

For more information see Datasets (DL4).

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.
`energy_range`	Energy range maps defined by the mask_safe and mask_fit.
`energy_range_fit`	Energy range maps defined by the mask_fit only.
`energy_range_safe`	Energy range maps defined by the mask_safe only.
`energy_range_total`	Largest energy range among all pixels, defined by mask_safe and mask_fit.
`evaluators`	Model evaluators.
`excess`	Observed excess: counts-background.
`exposure`	A lazy FITS data descriptor.
`geoms`	Map geometries.
`gti`
`mask`	Combined fit and safe mask.
`mask_fit`	A lazy FITS data descriptor.
`mask_fit_image`	Reduced fit mask.
`mask_image`	Reduced mask.
`mask_safe`	A lazy FITS data descriptor.
`mask_safe_edisp`	Safe mask for edisp maps.
`mask_safe_image`	Reduced safe mask.
`mask_safe_psf`	Safe mask for PSF maps.
`meta`
`meta_table`
`models`	Models set on the dataset (`Models`).
`name`
`psf`	A lazy FITS data descriptor.
`stat_type`
`tag`

Methods Summary

`copy`([name])	A deep copy.
`create`(geom[, energy_axis_true, migra_axis, ...])	Create a MapDataset object with zero filled maps.
`cutout`(args, *kwargs)	Not supported for `SpectrumDatasetOnOff`.
`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, **kwargs)	Create spectrum dataset from dict.
`from_geoms`(geom[, geom_exposure, geom_psf, ...])	Create an empty `MapDatasetOnOff` object according to the specified geometries.
`from_hdulist`(hdulist[, name, format])	Create map dataset from list of HDUs.
`from_map_dataset`(dataset, acceptance, ...[, ...])	Create on off dataset from a map dataset.
`from_spectrum_dataset`(**kwargs)	Create a SpectrumDatasetOnOff from a `SpectrumDataset` dataset.
`info_dict`([in_safe_data_range])	Basic info dict with summary statistics.
`npred`()	Total predicted source and background counts.
`npred_background`()	Predicted background counts estimated from the marginalized likelihood estimate.
`npred_off`()	Predicted counts in the off region; mu_bkg/alpha.
`npred_signal`([model_names, stack])	Model predicted signal counts.
`pad`()	Not implemented for MapDatasetOnOff.
`peek`([figsize])	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_masks`([ax, kwargs_fit, kwargs_safe])	Plot safe mask and fit mask.
`plot_residuals`([ax_spatial, ax_spectral, ...])	Plot spatial and spectral residuals in two panels.
`plot_residuals_spatial`(args, *kwargs)	Not supported for `SpectrumDatasetOnOff`.
`plot_residuals_spectral`([ax, method, region])	Plot spectral residuals.
`read`(filename[, format, checksum])	Read 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[, nan_to_num])	Stack another dataset in place.
`stat_array`()	Statistic function value per bin given the current model parameters.
`stat_sum`()	Total statistic function value given the current model parameters.
`to_dict`()	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 a MapDataset.
`to_masked`([name, nan_to_num])	Return masked dataset.
`to_region_map_dataset`(region[, name])	Integrate the map dataset in a given region.
`to_spectrum_dataset`([name])	Convert a SpectrumDatasetOnOff to a SpectrumDataset.
`write`(filename[, overwrite, format, checksum])	Write spectrum dataset on off 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.

energy_range#: Energy range maps defined by the mask_safe and mask_fit.

energy_range_fit#: Energy range maps defined by the mask_fit only.

energy_range_safe#: Energy range maps defined by the mask_safe only.

energy_range_total#: Largest energy range among all pixels, defined by mask_safe and mask_fit.

evaluators#: Model evaluators.

excess#: Observed excess: counts-background.

exposure#

A lazy FITS data descriptor.

Parameters:

cachebool: Whether to cache the data.

geoms#

Map geometries.

Returns:

geomsdict: Dictionary of map geometries involved in the dataset.

gti = None#

mask#: Combined fit and safe mask.

mask_fit#

A lazy FITS data descriptor.

Parameters:

cachebool: Whether to cache the data.

mask_fit_image#: Reduced fit mask.

mask_image#: Reduced mask.

mask_safe#

A lazy FITS data descriptor.

Parameters:

cachebool: Whether to cache the data.

mask_safe_edisp#: Safe mask for edisp maps.

mask_safe_image#: Reduced safe mask.

mask_safe_psf#: Safe mask for PSF maps.

meta#

meta_table = None#

models#: Models set on the dataset (Models).

name#

psf#

A lazy FITS data descriptor.

Parameters:

cachebool: Whether to cache the data.

stat_type = 'wstat'#

tag = 'SpectrumDatasetOnOff'#

Methods Documentation

copy(name=None)#

A deep copy.

Parameters:

namestr, optional: Name of the copied dataset. Default is None.

Returns:

datasetDataset: Copied datasets.

classmethod create(geom, energy_axis_true=None, migra_axis=None, rad_axis=None, binsz_irf=<Quantity 0.2 deg>, reference_time='2000-01-01', name=None, meta_table=None, reco_psf=False, **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, optional: True energy axis used for IRF maps. Default is None.
migra_axisMapAxis, optional: 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, optional: Rad axis for the PSF map. Default is None.
binsz_irffloat: IRF Map pixel size in degrees. Default is BINSZ_IRF_DEFAULT.
reference_timeTime: The reference time to use in GTI definition. Default is “2000-01-01”.
namestr, optional: Name of the returned dataset. Default is None.
meta_tableTable, optional: Table listing information on observations used to create the dataset. One line per observation for stacked datasets. Default is None.
reco_psfbool: Use reconstructed energy for the PSF geometry. Default is False.

Returns:

empty_mapsMapDataset: A MapDataset containing zero filled maps.

Examples

>>> from gammapy.datasets import MapDataset
>>> from gammapy.maps import WcsGeom, MapAxis

>>> energy_axis = MapAxis.from_energy_bounds(1.0, 10.0, 4, unit="TeV")
>>> energy_axis_true = MapAxis.from_energy_bounds(
...            0.5, 20, 10, unit="TeV", name="energy_true"
...        )
>>> geom = WcsGeom.create(
...            skydir=(83.633, 22.014),
...            binsz=0.02, width=(2, 2),
...            frame="icrs",
...            proj="CAR",
...            axes=[energy_axis]
...        )
>>> empty = MapDataset.create(geom=geom, energy_axis_true=energy_axis_true, name="empty")

cutout(*args, **kwargs)[source]#: Not supported for SpectrumDatasetOnOff.

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, optional: Which non-spatial axis to downsample. By default, only spatial axes are downsampled. Default is None.
namestr, optional: Name of the downsampled dataset. Default is None.

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:

npred_backgroundMap: Expected number of background counts in the on region.
random_state{int, ‘random-seed’, ‘global-rng’, RandomState}: Defines random number generator initialisation. Passed to get_random_state. Default is “random-seed”.

classmethod from_dict(data, **kwargs)[source]#

Create spectrum dataset from dict. Reads file from the disk as specified in the dict.

Parameters:

datadict: Dictionary containing data to create dataset from.

Returns:

datasetSpectrumDatasetOnOff: Spectrum dataset on off.

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

Create an empty MapDatasetOnOff object according to the specified geometries.

Parameters:

geomgammapy.maps.WcsGeom: Geometry for the counts, counts_off, acceptance and acceptance_off maps.
geom_exposuregammapy.maps.WcsGeom, optional: Geometry for the exposure map. Default is None.
geom_psfgammapy.maps.WcsGeom, optional: Geometry for the PSF map. Default is None.
geom_edispgammapy.maps.WcsGeom, optional: Geometry for the energy dispersion kernel map. If geom_edisp has a migra axis, this will create an EDispMap instead. Default is None.
reference_timeTime: The reference time to use in GTI definition. Default is “2000-01-01”.
namestr, optional: Name of the returned dataset. Default is None.
**kwargsdict, optional: Keyword arguments to be passed.

Returns:

empty_mapsMapDatasetOnOff: A MapDatasetOnOff containing zero filled maps.

classmethod from_hdulist(hdulist, name=None, format='gadf')#

Create map dataset from list of HDUs.

Parameters:

hdulistHDUList: List of HDUs.
namestr, optional: Name of the new dataset. Default is None.
format{“gadf”}: Format the hdulist is given in. Default is “gadf”.

Returns:

datasetMapDatasetOnOff: 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, optional: 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. Default is None.
namestr, optional: Name of the returned dataset. Default is None.

Returns:

datasetMapDatasetOnOff: Map dataset on off.

classmethod from_spectrum_dataset(**kwargs)[source]#

Create a SpectrumDatasetOnOff from a SpectrumDataset dataset.

Parameters:

datasetSpectrumDataset: Spectrum dataset defining counts, edisp, exposure etc.
acceptancearray or float: Relative background efficiency in the on region.
acceptance_offarray or float: Relative background efficiency in the off region.
counts_offRegionNDMap: Off counts spectrum. If the dataset provides a background model, and no off counts are defined. The off counts are deferred from counts_off / alpha.

Returns:

datasetSpectrumDatasetOnOff: Spectrum 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. Default is True.

Returns:

info_dictdict: Dictionary with summary info.

npred()#

Total predicted source and background counts.

Returns:

npredMap: Total predicted counts.

npred_background()#

Predicted 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; mu_bkg/alpha.

See WStat : Poisson data with background measurement.

Returns:

npred_offMap: Predicted off counts.

npred_signal(model_names=None, stack=True)#

Model predicted signal counts.

If a list of model name is passed, predicted counts from these components are returned. If stack is set to True, a map of the sum of all the predicted counts is returned. If stack is set to False, a map with an additional axis representing the models is returned.

Parameters:

model_nameslist of str: List of name of SkyModel for which to compute the npred. If none, all the SkyModel predicted counts are computed.
stackbool: Whether to stack the npred maps upon each other.

Returns:

npred_siggammapy.maps.Map: Map of the predicted signal counts.

pad()#: Not implemented for MapDatasetOnOff.

peek(figsize=(16, 4))#

Quick-look summary plots.

Parameters:

figsizetuple: Size of the figure. Default is (16, 4).

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

Plot counts and background.

Parameters:

axAxes, optional: Axes to plot on. Default is None.
kwargs_countsdict, optional: Keyword arguments passed to hist for the counts. Default is None.
kwargs_backgrounddict, optional: Keyword arguments passed to hist for the background. Default is None.
**kwargsdict, optional: 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.

The error bars are computed with a symmetric assumption on the excess.

Parameters:

axAxes, optional: Axes to plot on. Default is None.
kwargs_excessdict, optional: Keyword arguments passed to errorbar for the excess. Default is None.
kwargs_npred_signaldict, optional: Keyword arguments passed to hist for the predicted signal. Default is None.
**kwargsdict, optional: Keyword arguments passed to both plot methods.

Returns:

axAxes: Axes object.

Examples

>>> #Creating a spectral dataset
>>> from gammapy.datasets import SpectrumDatasetOnOff
>>> from gammapy.modeling.models import PowerLawSpectralModel, SkyModel
>>> filename = "$GAMMAPY_DATA/joint-crab/spectra/hess/pha_obs23523.fits"
>>> dataset = SpectrumDatasetOnOff.read(filename)
>>> p = PowerLawSpectralModel()
>>> dataset.models = SkyModel(spectral_model=p)
>>> #Plot the excess in blue and the npred in black dotted lines
>>> kwargs_excess = {"color": "blue", "markersize":8, "marker":'s', }
>>> kwargs_npred_signal = {"color": "black", "ls":"--"}
>>> dataset.plot_excess(kwargs_excess=kwargs_excess, kwargs_npred_signal=kwargs_npred_signal)  

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_spectral.

Parameters:

ax_spectrumAxes, optional: Axes to plot spectrum on. Default is None.
ax_residualsAxes, optional: Axes to plot residuals on. Default is None.
kwargs_spectrumdict, optional: Keyword arguments passed to plot_excess. Default is None.
kwargs_residualsdict, optional: Keyword arguments passed to plot_residuals_spectral. Default is None.

Returns:

ax_spectrum, ax_residualsAxes: Spectrum and residuals plots.

Examples

>>> #Creating a spectral dataset
>>> from gammapy.datasets import SpectrumDatasetOnOff
>>> from gammapy.modeling.models import PowerLawSpectralModel, SkyModel
>>> filename = "$GAMMAPY_DATA/joint-crab/spectra/hess/pha_obs23523.fits"
>>> dataset = SpectrumDatasetOnOff.read(filename)
>>> p = PowerLawSpectralModel()
>>> dataset.models = SkyModel(spectral_model=p)
>>> # optional configurations
>>> kwargs_excess = {"color": "blue", "markersize":8, "marker":'s', }
>>> kwargs_npred_signal = {"color": "black", "ls":"--"}
>>> kwargs_spectrum = {"kwargs_excess":kwargs_excess, "kwargs_npred_signal":kwargs_npred_signal}
>>> kwargs_residuals = {"color": "black", "markersize":4, "marker":'s', }
>>> dataset.plot_fit(kwargs_residuals=kwargs_residuals, kwargs_spectrum=kwargs_spectrum)  

plot_masks(ax=None, kwargs_fit=None, kwargs_safe=None)#

Plot safe mask and fit mask.

Parameters:

axAxes, optional: Axes to plot on. Default is None.
kwargs_fitdict, optional: Keyword arguments passed to plot_mask() for mask fit. Default is None.
kwargs_safedict, optional: Keyword arguments passed to plot_mask() for mask safe. Default is None.

Returns:

axAxes: Axes object.

Examples

>>> # Reading a spectral dataset
>>> from gammapy.datasets import SpectrumDatasetOnOff
>>> filename = "$GAMMAPY_DATA/joint-crab/spectra/hess/pha_obs23523.fits"
>>> dataset = SpectrumDatasetOnOff.read(filename)
>>> dataset.mask_fit = dataset.mask_safe.copy()
>>> dataset.mask_fit.data[40:46] = False  # setting dummy mask_fit for illustration
>>> # Plot the masks on top of the counts histogram
>>> kwargs_safe = {"color":"green", "alpha":0.2} #optinonal arguments to configure
>>> kwargs_fit = {"color":"pink", "alpha":0.2}
>>> ax=dataset.plot_counts()  
>>> dataset.plot_masks(ax=ax, kwargs_fit=kwargs_fit, kwargs_safe=kwargs_safe)  

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. If no region is passed, the residuals are computed for the entire map.

Parameters:

ax_spatialWCSAxes, optional: Axes to plot spatial residuals on. Default is None.
ax_spectralAxes, optional: Axes to plot spectral residuals on. Default is None.
kwargs_spatialdict, optional: Keyword arguments passed to plot_residuals_spatial. Default is None.
kwargs_spectraldict, optional: Keyword arguments passed to plot_residuals_spectral. The region should be passed as a dictionary key. Default is None.

Returns:

ax_spatial, ax_spectralWCSAxes, Axes: Spatial and spectral residuals plots.

Examples

>>> from regions import CircleSkyRegion
>>> from astropy.coordinates import SkyCoord
>>> import astropy.units as u
>>> from gammapy.datasets import MapDataset
>>> dataset = MapDataset.read("$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz")
>>> reg = CircleSkyRegion(SkyCoord(0,0, unit="deg", frame="galactic"), radius=1.0 * u.deg)
>>> kwargs_spatial = {"cmap": "RdBu_r", "vmin":-5, "vmax":5, "add_cbar": True}
>>> kwargs_spectral = {"region":reg, "markerfacecolor": "blue", "markersize": 8, "marker": "s"}
>>> dataset.plot_residuals(kwargs_spatial=kwargs_spatial, kwargs_spectral=kwargs_spectral) 

plot_residuals_spatial(*args, **kwargs)[source]#: Not supported for SpectrumDatasetOnOff.

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.

The error bars are computed using the uncertainty on the excess with a symmetric assumption.

Parameters:

axAxes, optional: Axes to plot on. Default is None.
method{“diff”, “diff/sqrt(model)”}: Normalization used to compute the residuals, see SpectrumDataset.residuals. Default is “diff”.
regionSkyRegion (required): Target sky region. Default is None.
**kwargsdict, optional: Keyword arguments passed to errorbar.

Returns:

axAxes: Axes object.

Examples

>>> from gammapy.datasets import MapDataset
>>> dataset = MapDataset.read("$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz")
>>> kwargs = {"markerfacecolor": "blue", "markersize":8, "marker":'s'}
>>> dataset.plot_residuals_spectral(method="diff/sqrt(model)", **kwargs) 

classmethod read(filename, format='ogip', checksum=False, **kwargs)[source]#

Read from file.

For OGIP formats, filename is the name of a PHA file. The BKG, ARF, and RMF file names must be set in the PHA header and the files must be present in the same folder. For details, see OGIPDatasetReader.read.

For the GADF format, a MapDataset serialisation is used.

Parameters:

filenamePath or str: OGIP PHA file to read.
format{“ogip”, “ogip-sherpa”, “gadf”}: Format to use. Default is “ogip”.
checksumbool: If True checks both DATASUM and CHECKSUM cards in the file headers. Default is False.
kwargsdict, optional: Keyword arguments passed to MapDataset.read.

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.
namestr, optional: Name of the new dataset. Default is None.

Returns:

datasetSpectrumDataset: 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).

Default is “diff”.

**kwargsdict, optional

Keyword arguments forwarded to Map.smooth().

Returns:

residualsgammapy.maps.Map: Residual map.

slice_by_energy(energy_min=None, energy_max=None, name=None)#

Select and slice datasets in energy range.

Parameters:

energy_min, energy_maxQuantity, optional: Energy bounds to compute the flux point for. Default is None.
namestr, optional: Name of the sliced dataset. Default is None.

Returns:

datasetMapDataset: Sliced Dataset.

Examples

>>> from gammapy.datasets import MapDataset
>>> dataset = MapDataset.read("$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz")
>>> sliced = dataset.slice_by_energy(energy_min="1 TeV", energy_max="5 TeV")
>>> sliced.data_shape
(3, np.int64(240), np.int64(320))

slice_by_idx(slices, name=None)#

Slice sub dataset.

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

Parameters:

slicesdict: Dictionary 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, optional: Name of the sliced dataset. Default is None.

Returns:

map_outMap: Sliced map object.

stack(other, nan_to_num=True)#

Stack another dataset in place.

Safe mask is applied to the other dataset to compute the stacked counts data, counts outside the safe mask are lost (as for stack).

The acceptance of the stacked dataset is obtained by stacking the acceptance weighted by the other mask_safe onto the current unweighted acceptance.

Note that the masking is not applied to the current dataset. If masking needs to be applied to it, use to_masked() first.

The stacked acceptance_off is scaled so that:

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

For details, see Stacking Multiple Datasets.

Parameters:

otherMapDatasetOnOff: Other dataset.
nan_to_numbool: Non-finite values are replaced by zero if True. Default is True.

stat_array()#: Statistic function value per bin given the current model parameters.

stat_sum()#

Total statistic function value given the current model parameters.

If the off counts are passed as None and the elements of the safe mask are False, zero will be returned. Otherwise, the stat sum will be calculated and returned.

to_dict()[source]#: 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, optional: Name of the new dataset. Default is None.

Returns:

datasetMapDataset or SpectrumDataset: Dataset integrated over non-spatial axes.

to_map_dataset(name=None)#

Convert a MapDatasetOnOff to a MapDataset.

The background model template is taken as alpha * counts_off.

Parameters:

namestr, optional: Name of the new dataset. Default is None.

Returns:

datasetMapDataset: Map dataset with cash statistics.

to_masked(name=None, nan_to_num=True)#

Return masked dataset.

Parameters:

namestr, optional: Name of the masked dataset. Default is None.
nan_to_numbool: Non-finite values are replaced by zero if True. Default is True.

Returns:

datasetMapDataset or SpectrumDataset: Masked dataset.

to_region_map_dataset(region, name=None)#

Integrate the map dataset in a given region.

Counts and background of the dataset are integrated in the given region, taking the safe mask into account. The exposure is averaged in the region again taking the safe mask into account. The PSF and energy dispersion kernel are taken at the center of the region.

Parameters:

regionSkyRegion: Region from which to extract the spectrum.
namestr, optional: Name of the new dataset. Default is None.

Returns:

datasetMapDataset: The resulting reduced dataset.

to_spectrum_dataset(name=None)[source]#

Convert a SpectrumDatasetOnOff to a SpectrumDataset. The background model template is taken as alpha*counts_off.

Parameters:

namestr, optional: Name of the new dataset. Default is None.

Returns:

datasetSpectrumDataset: SpectrumDataset with Cash statistic.

write(filename, overwrite=False, format='ogip', checksum=False)[source]#

Write spectrum dataset on off to file.

Can be serialised either as a MapDataset with a RegionGeom following the GADF specifications, or as per the OGIP format. For OGIP formats specs, see OGIPDatasetWriter.

Parameters:

filenamePath or str: Filename to write to.
overwritebool, optional: Overwrite existing file. Default is False.
format{“ogip”, “ogip-sherpa”, “gadf”}: Format to use. Default is “ogip”.
checksumbool: When True adds both DATASUM and CHECKSUM cards to the headers written to the file. Default is False.