cube - Map cube analysis


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:


gammapy.cube Package

Sky cubes (3-dimensional: energy, lon, lat).


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.


AdaptiveRingBackgroundEstimator(r_in, …[, …]) Adaptive ring background algorithm.
EDispMap(edisp_map, exposure_map) Energy dispersion map.
MapDataset([model, counts, exposure, …]) 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.
RingBackgroundEstimator(r_in, width) Ring background method for cartesian coordinates.