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)[source]#

Bases: gammapy.datasets.spectrum.PlotMixin, gammapy.datasets.map.MapDatasetOnOff

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 full mask (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 the full mask (mask_safe and mask_fit).
`evaluators`	Model evaluators
`excess`	Observed excess: counts-background
`exposure`	A lazy FITS data descriptor.
`geoms`	Map geometries
`mask`	Combined fit and safe mask
`mask_fit`	A lazy FITS data descriptor.
`mask_fit_image`	Reduced mask fit
`mask_image`	Reduced mask
`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 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)	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, **kwargs)	Create spectrum dataset from dict.
`from_geoms`(geom, geom_exposure[, geom_psf, ...])	Create a MapDatasetOnOff object switch zero filled maps 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 spectrum dataseton off from another 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 (mu_bkg) estimated from the marginalized likelihood estimate.
`npred_off`()	Predicted counts in the off region; mu_bkg/alpha
`npred_signal`([model_name])	Model predicted signal counts.
`pad`()	Pad the spatial dimensions of the dataset.
`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 mask safe and mask fit
`plot_residuals`([ax_spatial, ax_spectral, ...])	Plot spatial and spectral residuals in two panels.
`plot_residuals_spatial`(args, *kwargs)	Plot spatial residuals.
`plot_residuals_spectral`([ax, method, region])	Plot spectral residuals.
`read`(filename[, format])	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`()	Likelihood per bin given the current model parameters
`stat_sum`()	Total likelihood 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 MapDataset The background model template is taken as alpha * counts_off
`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 The background model template is taken as alpha*counts_off
`write`(filename[, overwrite, format])	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 full mask (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 the full mask (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: 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_fit_image#: Reduced mask fit

mask_image#: Reduced mask

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 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: Name of the copied dataset

Returns

datasetDataset: Copied datasets.

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 information on observations used to create the dataset. One line per observation for stacked datasets.

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]#

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, **kwargs)[source]#

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

Parameters

datadict: Dict containing data to create dataset from.

Returns

datasetSpectrumDatasetOnOff: Spectrum dataset on off.

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

Create a MapDatasetOnOff object switch 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 will 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, format='gadf')#

Create map dataset from list of HDUs.

Parameters

hdulistHDUList: List of HDUs.
namestr: Name of the new dataset.
format{“gadf”}: Format the hdulist is given in.

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: 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.

classmethod from_spectrum_dataset(**kwargs)[source]#

Create spectrum dataseton off from another 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

Returns

info_dictdict: Dictionary with summary info.

npred()#

Total predicted source and background counts

Returns

npredMap: Total predicted counts

npred_background()#

Predicted background counts (mu_bkg) 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_name=None)#

Model predicted signal counts.

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

Parameters

model_name: str: Name of SkyModel for which 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.

peek(figsize=(16, 4))#

Quick-look summary plots.

Parameters

figsizetuple: Size of the figure.

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.

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

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.

Examples

>>> #Creating a spectral dataset
>>> from gammapy.datasets import SpectrumDatasetOnOff
>>> from gammapy.modeling.models import PowerLawSpectralModel, SkyModel
>>> dataset = SpectrumDatasetOnOff.read(f"$GAMMAPY_DATA/joint-crab/spectra/hess/pha_obs23523.fits")
>>> 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: 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_spectral.

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
>>> dataset = SpectrumDatasetOnOff.read(f"$GAMMAPY_DATA/joint-crab/spectra/hess/pha_obs23523.fits")
>>> 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', } #optional configuration
>>> dataset.plot_fit(kwargs_residuals=kwargs_residuals, kwargs_spectrum=kwargs_spectrum)  

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

Plot mask safe and mask fit

Parameters

axAxes: Axes to plot on.
kwargs_fit: dict: Keyword arguments passed to plot_mask() for mask fit.
kwargs_safe: dict: Keyword arguments passed to plot_mask() for mask safe.

Returns

axAxes: Axes object.

Examples

>>> #Reading a spectral dataset
>>> from gammapy.datasets import SpectrumDatasetOnOff
>>> dataset = SpectrumDatasetOnOff.read(f"$GAMMAPY_DATA/joint-crab/spectra/hess/pha_obs23523.fits")
>>> 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: 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: Keyword arguments passed to plot_residuals_spectral. The region should be passed as a dictionary key

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]#

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.

Examples

>>> from gammapy.datasets import MapDataset
>>> dataset = MapDataset.read("$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz")
>>> kwargs = {"cmap": "RdBu_r", "vmin":-5, "vmax":5, "add_cbar": True}
>>> dataset.plot_residuals_spatial(method="diff/sqrt(model)", **kwargs) 

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: Axes to plot on.
method{“diff”, “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.

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', } #plot big blue squares
>>> dataset.plot_residuals_spectral(method="diff/sqrt(model)", **kwargs) 

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

Read from file

For OGIP formats, filename is assumed to the name of a PHA file where BKG file, ARF, and RMF names must be set in the PHA header and 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.
kwargsarguments 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.
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=None, energy_max=None, 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

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, 240, 320)

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, nan_to_num=True)#

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
nan_to_num: bool: Non-finite values are replaced by zero if True (default).

stat_array()#: Likelihood per bin given the current model parameters

stat_sum()#: Total likelihood given the current model parameters.

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: 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_masked(name=None, nan_to_num=True)#

Return masked dataset

Parameters

namestr: Name of the masked dataset.
nan_to_num: bool: Non-finite values are replaced by zero if True (default).

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 accounts. 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: Name of the new dataset.

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

name: str: Name of the new dataset

Returns

dataset: SpectrumDataset: SpectrumDatset with cash statistics

write(filename, overwrite=False, format='ogip')[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: Overwrite existing file.
format{“ogip”, “ogip-sherpa”, “gadf”}: Format to use.