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)[source]#

Bases: PlotMixin, 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)[source]#

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)[source]#

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, np.int64(240), np.int64(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      : (np.int64(320), np.int64(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 the other dataset to compute the stacked counts data. Counts outside the safe mask are lost.

Note that the masking is not applied to the current dataset. If masking needs to be applied to it, use to_masked() first.

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:

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

For details, see Stacking Multiple Datasets.

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)[source]#

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.