SpectrumDataset

class gammapy.datasets.SpectrumDataset(models=None, counts=None, exposure=None, background=None, psf=None, edisp=None, mask_safe=None, mask_fit=None, gti=None, meta_table=None, name=None, meta=None)

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

Main dataset for spectrum fitting (1D analysis). It bundles together binned counts, background, IRFs into RegionNDMap (a Map with only one spatial bin). A safe mask and a fit mask can be added to exclude bins during the analysis. If models are assigned to it, it can compute the predicted number of counts and the statistic function, here the Cash statistic (see cash).

For more information see Datasets (DL4).

Attributes Summary

background	A lazy FITS data descriptor.
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 SpectrumDataset
downsample(factor[, axis_name, name])	Downsample map dataset.
fake([random_state])	Simulate fake counts for the current model and reduced IRFs.
from_dict(data[, lazy, cache])	Create from dicts and models list generated from YAML serialization.
from_geoms(geom[, geom_exposure, geom_psf, ...])	Create a MapDataset object with zero filled maps according to the specified geometries.
from_hdulist(hdulist[, name, lazy, format])	Create map dataset from list of HDUs.
info_dict([in_safe_data_range])	Info dict with summary statistics, summed over energy.
npred()	Total predicted source and background counts.
npred_background()	Predicted background counts.
npred_signal([model_names, stack])	Model predicted signal counts.
pad(pad_width[, mode, name])	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 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 SpectrumDataset
plot_residuals_spectral([ax, method, region])	Plot spectral residuals.
read(filename[, name, lazy, cache, format, ...])	Read a dataset from file.
resample_energy_axis(energy_axis[, name])	Resample MapDataset over new reco energy axis.
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 and priors.
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_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(*args, **kwargs)	Not supported for SpectrumDataset
write(filename[, overwrite, checksum])	Write Dataset to file.

Attributes Documentation

background

A lazy FITS data descriptor.

Parameters

cachebool

Whether to cache the data.

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 = 'cash'

tag = 'SpectrumDataset'

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)

Not supported for SpectrumDataset

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

datasetMapDataset or SpectrumDataset

Downsampled map dataset.

fake(random_state='random-seed')

Simulate fake counts 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. Default is “random-seed”.

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

Create from dicts and models list generated from YAML serialization.

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

Create a MapDataset object with zero filled maps according to the specified geometries.

Parameters

geomGeom

Geometry for the counts and background maps.

geom_exposureGeom

Geometry for the exposure map. Default is None.

geom_psfGeom

Geometry for the PSF map. Default is None.

geom_edispGeom

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

Name of the returned dataset. Default is None.

kwargsdict, optional

Keyword arguments to be passed.

Returns

datasetMapDataset or SpectrumDataset

A dataset containing zero filled maps.

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

Create map dataset from list of HDUs.

Parameters

hdulistHDUList

List of HDUs.

namestr, optional

Name of the new dataset. Default is None.

lazybool

Whether to lazy load data into memory. Default is False.

format{“gadf”}

Format the hdulist is given in. Default is “gadf”.

Returns

datasetMapDataset

Map dataset.

info_dict(in_safe_data_range=True)

Info dict with summary statistics, summed over energy.

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.

The predicted background counts depend on the parameters of the FoVBackgroundModel defined in the dataset.

Returns

npred_backgroundMap

Predicted counts from the background.

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(pad_width, mode='constant', name=None)

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.

modestr

Pad mode. Default is “constant”.

namestr, optional

Name of the padded dataset. Default is None.

Returns

datasetMapDataset

Padded map dataset.

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)

Not supported for SpectrumDataset

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, name=None, lazy=False, cache=True, format='gadf', checksum=False)

Read a dataset from file.

Parameters

filenamestr

Filename to read from.

namestr, optional

Name of the new dataset. Default is None.

lazybool

Whether to lazy load data into memory. Default is False.

cachebool

Whether to cache the data after loading. Default is True.

format{“gadf”}

Format of the dataset file. Default is “gadf”.

checksumbool

If True checks both DATASUM and CHECKSUM cards in the file headers. Default is False.

Returns

datasetMapDataset

Map dataset.

resample_energy_axis(energy_axis, name=None)

Resample MapDataset over new reco energy axis.

Counts are summed taking into account safe mask.

Parameters

energy_axisMapAxis

New reconstructed energy axis.

namestr, optional

Name of the new dataset. Default is None.

Returns

datasetMapDataset or SpectrumDataset

Resampled 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

datasetMapDataset or SpectrumDataset

Sliced dataset.

Examples

>>> from gammapy.datasets import MapDataset
>>> dataset = MapDataset.read("$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz")
>>> slices = {"energy": slice(0, 3)} #to get the first 3 energy slices
>>> sliced = dataset.slice_by_idx(slices)
>>> print(sliced.geoms["geom"])
WcsGeom
        axes       : ['lon', 'lat', 'energy']
        shape      : (320, 240, 3)
        ndim       : 3
        frame      : galactic
        projection : CAR
        center     : 0.0 deg, 0.0 deg
        width      : 8.0 deg x 6.0 deg
        wcs ref    : 0.0 deg, 0.0 deg

stack(other, nan_to_num=True)

Stack another dataset in place. The original dataset is modified.

Safe mask is applied to compute the stacked counts data. Counts outside each dataset safe mask are lost.

The stacking of 2 datasets is implemented as follows. Here, \(k\) denotes a bin in reconstructed energy and \(j = {1,2}\) is the dataset number.

The mask_safe of each dataset is defined as:

\[\begin{split}\epsilon_{jk} =\left\{\begin{array}{cl} 1, & \mbox{if bin k is inside the thresholds}\\ 0, & \mbox{otherwise} \end{array}\right.\end{split}\]

Then the total counts and model background bkg are computed according to:

\[ \begin{align}\begin{aligned}\overline{\mathrm{n_{on}}}_k = \mathrm{n_{on}}_{1k} \cdot \epsilon_{1k} + \mathrm{n_{on}}_{2k} \cdot \epsilon_{2k}.\\\overline{bkg}_k = bkg_{1k} \cdot \epsilon_{1k} + bkg_{2k} \cdot \epsilon_{2k}.\end{aligned}\end{align} \]

The stacked safe_mask is then:

\[\overline{\epsilon_k} = \epsilon_{1k} OR \epsilon_{2k}.\]

Parameters

otherMapDataset or MapDatasetOnOff

Map dataset to be stacked with this one. If other is an on-off dataset alpha * counts_off is used as a background model.

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 and priors.

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, optional

Name of the new dataset. Default is None.

Returns

datasetMapDataset or SpectrumDataset

Dataset integrated over non-spatial axes.

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(*args, **kwargs)

Not supported for SpectrumDataset

write(filename, overwrite=False, checksum=False)

Write Dataset to file.

A MapDataset is serialised using the GADF format with a WCS geometry. A SpectrumDataset uses the same format, with a RegionGeom.

Parameters

filenamestr

Filename to write to.

overwritebool, optional

Overwrite existing file. Default is False.

checksumbool

When True adds both DATASUM and CHECKSUM cards to the headers written to the file. Default is False.