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

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.

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_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.