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)

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.
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.
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([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)	Plot spatial residuals.
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.
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)	Return a ~gammapy.datasets.SpectrumDataset from on_region.
write(filename[, overwrite])	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

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 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.

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

Name of the copied dataset

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

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.

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)

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

cutoutMapDataset

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

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.

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

geom_psfGeom

geometry for the psf map

geom_edispGeom

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

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

Name of the new dataset.

format{“gadf”}

Format the hdulist is given in.

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

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_names: list of str

List of name of SkyModel for which to compute the npred. If none, all the SkyModel predicted counts are computed

stack: bool

Whether to stack the npred maps upon each other.

Returns

npred_sig: gammapy.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.

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 safe mask and fit mask.

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

Read a dataset from file.

Parameters

filenamestr

Filename to read from.

namestr

Name of the new dataset.

lazybool

Whether to lazy load data into memory

cachebool

Whether to cache the data after loading.

format{“gadf”}

Format of the dataset file.

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.

name: str

Name of the new dataset.

Returns

dataset: MapDataset 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)

**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

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

other: `~gammapy.datasets.MapDataset` or `~gammapy.datasets.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_num: bool

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

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.

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_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 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

Name of the new dataset.

Returns

datasetMapDataset

the resulting reduced dataset

to_spectrum_dataset(*args, **kwargs)

Return a ~gammapy.datasets.SpectrumDataset from on_region.

Counts and background are summed in the on_region. Exposure is taken from the average exposure.

The energy dispersion kernel is obtained at the on_region center. Only regions with centers are supported.

The model is not exported to the ~gammapy.datasets.SpectrumDataset. It must be set after the dataset extraction.

Parameters

on_regionSkyRegion

the input ON region on which to extract the spectrum

containment_correctionbool

Apply containment correction for point sources and circular on regions

namestr

Name of the new dataset.

Returns

datasetSpectrumDataset

the resulting reduced dataset

write(filename, overwrite=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

Overwrite file if it exists.