# 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¶

 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.