cube - Map cube analysis¶
Introduction¶
The gammapy.cube sub-package contains functions and classes to make maps
(counts, exposure, background), as well as to compute an effective PSF and
energy dispersion for a given set of observations.
It also contains classes that represent cube models (sky maps with an energy axis), and classes to evaluate and fit those models to data.
Getting Started¶
TODO: what to show here?
Using gammapy.cube¶
Gammapy tutorial notebooks that show examples using gammapy.cube:
Reference/API¶
gammapy.cube Package¶
Sky cubes (3-dimensional: energy, lon, lat).
Functions¶
fill_map_counts(counts_map, events) |
Fill events into a counts map. |
make_edisp_map(edisp, pointing, geom, max_offset) |
Make a edisp map for a single observation |
make_map_background_irf(pointing, ontime, …) |
Compute background map from background IRFs. |
make_map_exposure_true_energy(pointing, …) |
Compute exposure map. |
make_psf_map(psf, pointing, geom, max_offset) |
Make a psf map for a single observation |
simulate_dataset(skymodel, geom, pointing, irfs) |
Simulate a 3D dataset. |
Classes¶
EDispMap(edisp_map, exposure_map) |
Energy dispersion map. |
MapDataset(model[, counts, exposure, mask, …]) |
Perform sky model likelihood fit on maps. |
MapEvaluator([model, exposure, psf, edisp, …]) |
Sky model evaluation on maps. |
MapMaker(geom, offset_max[, geom_true, …]) |
Make maps from IACT observations. |
MapMakerObs(observation, geom, offset_max[, …]) |
Make maps for a single IACT observation. |
MapMakerRing(geom, offset_max[, …]) |
Make maps from IACT observations. |
PSFKernel(psf_kernel_map) |
PSF kernel for Map. |
PSFMap(psf_map[, exposure_map]) |
Class containing the Map of PSFs and allowing to interact with it. |
gammapy.cube.models Module¶
Classes¶
SkyModelBase([params]) |
Sky model base class |
SkyModels(skymodels) |
Collection of SkyModel |
SkyModel(spatial_model, spectral_model[, name]) |
Sky model component. |
SkyDiffuseCube(map[, norm, meta, interp_kwargs]) |
Cube sky map template model (3D). |
BackgroundModel(background[, norm, tilt, …]) |
Background model. |
BackgroundModels(models) |
Background models. |