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)

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 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
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 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 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 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
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 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

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)

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

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.

classmethod from_dict(data, **kwargs)

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 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

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)

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 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
>>> 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

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
>>> 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}  # noqa: E501
>>> kwargs_residuals = {"color": "black", "markersize":4, "marker":'s', }  # optional configuration  # noqa: E501
>>> 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
>>> 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

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"}  # noqa: E501
>>> dataset.plot_residuals(kwargs_spatial=kwargs_spatial, kwargs_spectral=kwargs_spectral) 

plot_residuals_spatial(*args, **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.

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'}
>>> dataset.plot_residuals_spectral(method="diff/sqrt(model)", **kwargs) 

classmethod read(filename, format='ogip', **kwargs)

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()

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)

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')

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.