MapDataset¶
-
class
gammapy.datasets.
MapDataset
(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.Dataset
Perform sky model likelihood fit on maps.
- Parameters
- models
Models
Source sky models.
- counts
WcsNDMap
Counts cube
- exposure
WcsNDMap
Exposure cube
- background
WcsNDMap
Background cube
- mask_fit
WcsNDMap
Mask to apply to the likelihood for fitting.
- psf
PSFKernel
orPSFMap
PSF kernel
- edisp
EDispKernel
orEDispMap
Energy dispersion kernel
- mask_safe
WcsNDMap
Mask defining the safe data range.
- gti
GTI
GTI of the observation or union of GTI if it is a stacked observation
- meta_table
Table
Table listing informations on observations used to create the dataset. One line per observation for stacked datasets.
- models
See also
Attributes Summary
A lazy FITS data descriptor.
A lazy FITS data descriptor.
Shape of the counts or background data (tuple)
A lazy FITS data descriptor.
Model evaluators
Excess
A lazy FITS data descriptor.
Map geometries
Combined fit and safe mask
A lazy FITS data descriptor.
A lazy FITS data descriptor.
Mask safe for edisp maps
Reduced mask safe
Mask safe for psf maps
Models (
Models
).A lazy FITS data descriptor.
Methods Summary
Apply mask safe to the dataset
copy
([name])A deep copy.
create
(geom[, energy_axis_true, migra_axis, …])Create a MapDataset object with zero filled maps.
cutout
(position, width[, mode, name])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])Create map dataset from list of HDUs.
info_dict
([in_safe_data_range])Info dict with summary statistics, summed over energy
npred
()Predicted source and background counts
Predicted background counts
npred_signal
([model])“Model predicted signal counts.
pad
(pad_width[, mode, name])Pad the spatial dimensions of the dataset.
plot_residuals
([ax_spatial, ax_spectral, …])Plot spatial and spectral residuals in two panels.
plot_residuals_spatial
([ax, method, …])Plot spatial residuals.
plot_residuals_spectral
([ax, method, region])Plot spectral residuals.
read
(filename[, name, lazy, cache])Read map dataset from file.
resample_energy_axis
(energy_axis[, name])Resample MapDataset over new reco energy axis.
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)Stack another dataset in place.
Likelihood per bin given the current model parameters
stat_sum
()Total likelihood given the current model parameters.
to_dict
([filename])Convert to dict for YAML serialization.
Convert map dataset to list of HDUs.
to_image
([name])Create images by summing over the reconstructed energy axis.
to_spectrum_dataset
(on_region[, …])Return a ~gammapy.datasets.SpectrumDataset from on_region.
write
(filename[, overwrite])Write map 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.
-
evaluators
¶ Model evaluators
-
excess
¶ Excess
-
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_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
-
name
¶
-
psf
¶ A lazy FITS data descriptor.
- Parameters
- cachebool
Whether to cache the data.
-
stat_type
= 'cash'¶
-
tag
= 'MapDataset'¶
Methods Documentation
-
copy
(name=None)¶ A deep copy.
-
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)[source]¶ Create a MapDataset object with zero filled maps.
- Parameters
- geom
WcsGeom
Reference target geometry in reco energy, used for counts and background maps
- energy_axis_true
MapAxis
True energy axis used for IRF maps
- migra_axis
MapAxis
If set, this provides the migration axis for the energy dispersion map. If not set, an EDispKernelMap is produced instead. Default is None
- rad_axis
MapAxis
Rad axis for the psf map
- binsz_irffloat
IRF Map pixel size in degrees.
- reference_time
Time
the reference time to use in GTI definition
- namestr
Name of the returned dataset.
- meta_table
Table
Table listing informations on observations used to create the dataset. One line per observation for stacked datasets.
- geom
- Returns
- empty_maps
MapDataset
A MapDataset containing zero filled maps
- empty_maps
-
cutout
(position, width, mode='trim', name=None)[source]¶ Cutout map dataset.
- Parameters
- position
SkyCoord
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.
- position
- Returns
- cutout
MapDataset
Cutout map dataset.
- cutout
-
downsample
(factor, axis_name=None, name=None)[source]¶ 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
- dataset
MapDataset
orSpectrumDataset
Downsampled map dataset.
- dataset
-
fake
(random_state='random-seed')[source]¶ 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
.
- random_state{int, ‘random-seed’, ‘global-rng’,
-
classmethod
from_dict
(data, lazy=False, cache=True)[source]¶ Create from dicts and models list generated from YAML serialization.
-
classmethod
from_geoms
(geom, geom_exposure, geom_psf, geom_edisp, reference_time='2000-01-01', name=None, **kwargs)[source]¶ Create a MapDataset object with zero filled maps according to the specified geometries
- Parameters
- geom
Geom
geometry for the counts and background maps
- geom_exposure
Geom
geometry for the exposure map
- geom_psf
Geom
geometry for the psf map
- geom_edisp
Geom
geometry for the energy dispersion kernel map. If geom_edisp has a migra axis, this wil create an EDispMap instead.
- reference_time
Time
the reference time to use in GTI definition
- namestr
Name of the returned dataset.
- geom
- Returns
- dataset
MapDataset
orSpectrumDataset
A dataset containing zero filled maps
- dataset
-
classmethod
from_hdulist
(hdulist, name=None, lazy=False)[source]¶ Create map dataset from list of HDUs.
- Parameters
- hdulist
HDUList
List of HDUs.
- namestr
Name of the new dataset.
- hdulist
- Returns
- dataset
MapDataset
Map dataset.
- dataset
-
info_dict
(in_safe_data_range=True)[source]¶ 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_background
()[source]¶ Predicted background counts
The predicted background counts depend on the parameters of the
FoVBackgroundModel
defined in the dataset.- Returns
- npred_background
Map
Predicted counts from the background.
- npred_background
-
npred_signal
(model=None)[source]¶ “Model predicted signal counts.
If a model is passed, predicted counts from that component is returned. Else, the total signal counts are returned.
- Parameters
- model: `~gammapy.modeling.models.SkyModel`, optional
Sky model 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
- npred_sig:
-
pad
(pad_width, mode='constant', name=None)[source]¶ 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
- dataset
MapDataset
Padded map dataset.
- dataset
-
plot_residuals
(ax_spatial=None, ax_spectral=None, kwargs_spatial=None, kwargs_spectral=None)[source]¶ Plot spatial and spectral residuals in two panels.
Calls
plot_residuals_spatial
andplot_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.- Parameters
- ax_spatial
WCSAxes
Axes to plot spatial residuals on.
- ax_spectral
Axes
Axes to plot spectral residuals on.
- kwargs_spatialdict
Keyword arguments passed to
plot_residuals_spatial
.- kwargs_spectraldict (
region
required) Keyword arguments passed to
plot_residuals_spectral
.
- ax_spatial
- Returns
-
plot_residuals_spatial
(ax=None, method='diff', smooth_kernel='gauss', smooth_radius='0.1 deg', **kwargs)[source]¶ Plot spatial residuals.
The normalization used for the residuals computation can be controlled using the method parameter.
- Parameters
- ax
WCSAxes
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
.
- ax
- Returns
- ax
WCSAxes
WCSAxes object.
- ax
-
plot_residuals_spectral
(ax=None, method='diff', region=None, **kwargs)[source]¶ 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.
- Parameters
- ax
Axes
Axes to plot on.
- method{“diff”, “diff/model”, “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
.
- ax
- Returns
- ax
Axes
Axes object.
- ax
-
classmethod
read
(filename, name=None, lazy=False, cache=True)[source]¶ Read map 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.
- Returns
- dataset
MapDataset
Map dataset.
- dataset
-
resample_energy_axis
(energy_axis, name=None)[source]¶ Resample MapDataset over new reco energy axis.
Counts are summed taking into account safe mask.
- Parameters
- energy_axis
MapAxis
New reconstructed energy axis.
- name: str
Name of the new dataset.
- energy_axis
- Returns
- dataset:
MapDataset
orSpectrumDataset
Resampled dataset .
- dataset:
-
residuals
(method='diff', **kwargs)[source]¶ 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
- residuals
gammapy.maps.Map
Residual map.
- residuals
-
slice_by_energy
(energy_min, energy_max, name=None)[source]¶ Select and slice datasets in energy range
- Parameters
- energy_min, energy_max
Quantity
Energy bounds to compute the flux point for.
- namestr
Name of the sliced dataset.
- energy_min, energy_max
- Returns
- dataset
MapDataset
Sliced Dataset
- dataset
-
slice_by_idx
(slices, name=None)[source]¶ 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
- dataset
MapDataset
orSpectrumDataset
Sliced dataset
- dataset
-
stack
(other)[source]¶ Stack another dataset in place.
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 backgroundbkg
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.
-
to_hdulist
()[source]¶ Convert map dataset to list of HDUs.
- Returns
- hdulist
HDUList
Map dataset list of HDUs.
- hdulist
-
to_image
(name=None)[source]¶ Create images by summing over the reconstructed energy axis.
- Parameters
- namestr
Name of the new dataset.
- Returns
- dataset
MapDataset
orSpectrumDataset
Dataset integrated over non-spatial axes.
- dataset
-
to_spectrum_dataset
(on_region, containment_correction=False, name=None)[source]¶ Return a ~gammapy.datasets.SpectrumDataset from on_region.
Counts and background are summed in the on_region.
Effective area is taken from the average exposure divided by the livetime. Here we assume it is the sum of the GTIs.
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_region
SkyRegion
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.
- on_region
- Returns
- dataset
SpectrumDataset
the resulting reduced dataset
- dataset