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)[source]#

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

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 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`([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_name])	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 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[, 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`()	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_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 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 = '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=None, reference_time='2000-01-01', name=None, meta_table=None, **kwargs)#

Create a MapDataset object with zero filled maps.

Parameters

geomWcsGeom: Reference target geometry in reco energy, used for counts and background maps
energy_axis_trueMapAxis: True energy axis used for IRF maps
migra_axisMapAxis: If set, this provides the migration axis for the energy dispersion map. If not set, an EDispKernelMap is produced instead. Default is None
rad_axisMapAxis: Rad axis for the psf map
binsz_irffloat: IRF Map pixel size in degrees.
reference_timeTime: the reference time to use in GTI definition
namestr: Name of the returned dataset.
meta_tableTable: Table listing information on observations used to create the dataset. One line per observation for stacked datasets.

Returns

empty_mapsMapDataset: A MapDataset containing zero filled maps

Examples

>>> from gammapy.datasets import MapDataset
>>> from gammapy.maps import WcsGeom, MapAxis

>>> energy_axis = MapAxis.from_energy_bounds(1.0, 10.0, 4, unit="TeV")
>>> energy_axis_true = MapAxis.from_energy_bounds(
            0.5, 20, 10, unit="TeV", name="energy_true"
        )
>>> geom = WcsGeom.create(
            skydir=(83.633, 22.014),
            binsz=0.02, width=(2, 2),
            frame="icrs",
            proj="CAR",
            axes=[energy_axis]
        )
>>> empty = MapDataset.create(geom=geom, energy_axis_true=energy_axis_true, name="empty")

cutout(*args, **kwargs)[source]#

Cutout map dataset.

Parameters

positionSkyCoord: Center position of the cutout region.
widthtuple of Angle: Angular sizes of the region in (lon, lat) in that specific order. If only one value is passed, a square region is extracted.
mode{‘trim’, ‘partial’, ‘strict’}: Mode option for Cutout2D, for details see Cutout2D.
namestr: Name of the new dataset.

Returns

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_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_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 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)[source]#

Plot spatial residuals.

The normalization used for the residuals computation can be controlled using the method parameter.

Parameters

axWCSAxes: Axes to plot on.
method{“diff”, “diff/model”, “diff/sqrt(model)”}: Normalization used to compute the residuals, see MapDataset.residuals.
smooth_kernel{“gauss”, “box”}: Kernel shape.
smooth_radius: `~astropy.units.Quantity`, str or float: Smoothing width given as quantity or float. If a float is given, it is interpreted as smoothing width in pixels.
**kwargsdict: Keyword arguments passed to imshow.

Returns

axWCSAxes: WCSAxes object.

Examples

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

plot_residuals_spectral(ax=None, method='diff', region=None, **kwargs)#

Plot spectral residuals.

The residuals are extracted from the provided region, and the normalization used for its computation can be controlled using the method parameter.

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

Parameters

axAxes: Axes to plot on.
method{“diff”, “diff/sqrt(model)”}: Normalization used to compute the residuals, see SpectrumDataset.residuals.
region: `~regions.SkyRegion` (required): Target sky region.
**kwargsdict: Keyword arguments passed to errorbar.

Returns

axAxes: Axes object.

Examples

>>> from gammapy.datasets import MapDataset
>>> dataset = MapDataset.read("$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz")
>>> kwargs = {"markerfacecolor": "blue", "markersize":8, "marker":'s'}
>>> 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()#: 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_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(*args, **kwargs)[source]#

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.