Data Management¶

Classes¶

Gammapy helps with data management using a multi-layered set of classes. The job of the DataStore is to make it easy and fast to locate files and select subsets of observations.

Getting Started¶

The following example demonstrates how data management is done in Gammapy. It uses a test data set, which is available in the gammapy-extra repository. Please clone this repository and navigate to gammapy-extra/datasets/. The folder hess-crab4-hd-hap-prod2 contains IRFs and simulated event lists for 4 observations of the Crab nebula. It also contains two index files:

• Observation table observations.fits.gz
• File table files.fits.gz

These files tell gammapy which observations are contained in the data set and where the event list and IRF files are located for each observation (for more information see Data formats).

Data Store¶

Exploring the data using the DataStore class works like this

>>> from gammapy.data import DataStore
>>> data_store = DataStore.from_dir('\$GAMMAPY_EXTRA/datasets/hess-crab4-hd-hap-prod2')
>>> data_store.info()
Data store summary info:
name: noname
base_dir: hess-crab4
observations: 4
files: 16
>>> data_store.obs(obs_id=23592).location(hdu_class='events').path(abs_path=False)
'hess-crab4/hess_events_simulated_023592.fits'


In addition, the DataStore class has convenience properties and methods that actually load the data and IRFs and return objects of the appropriate class

>>> event_list = data_store.obs(obs_id=23592).events
>>> type(event_list)
TODO
>>> aeff2d = data_store.obs(obs_id=23592).aeff
>>> type(aeff2d)
<class 'gammapy.irf.effective_area_table.EffectiveAreaTable2D'>