DataStore#

class gammapy.data.DataStore(hdu_table=None, obs_table=None)[source]#

Bases: object

IACT data store.

The data selection and access happens using an observation and an HDU index file as described at Data storage.

For a usage example see cta.html

Parameters

hdu_tableHDUIndexTable: HDU index table
obs_tableObservationTable: Observation index table

Examples

Here’s an example how to create a DataStore to access H.E.S.S. data:

>>> from gammapy.data import DataStore
>>> data_store = DataStore.from_dir('$GAMMAPY_DATA/hess-dl3-dr1')
>>> data_store.info() 
Data store:
HDU index table:
BASE_DIR: /Users/ASinha/Gammapy-dev/gammapy-data/hess-dl3-dr1
Rows: 630
OBS_ID: 20136 -- 47829
HDU_TYPE: ['aeff', 'bkg', 'edisp', 'events', 'gti', 'psf']
HDU_CLASS: ['aeff_2d', 'bkg_3d', 'edisp_2d', 'events', 'gti', 'psf_table']


Observation table:
Observatory name: 'N/A'
Number of observations: 105

Attributes Summary

`DEFAULT_HDU_TABLE`	Default HDU table filename.
`DEFAULT_OBS_TABLE`	Default observation table filename.
`obs_ids`	Return the sorted obs_ids contained in the datastore.

Methods Summary

`check`([checks])	Check index tables and data files.
`copy_obs`(obs_id, outdir[, hdu_class, ...])	Create a new `DataStore` containing a subset of observations.
`from_dir`(base_dir[, hdu_table_filename, ...])	Create from a directory.
`from_events_files`(events_paths[, irfs_paths])	Create from a list of event filenames.
`from_file`(filename[, hdu_hdu, hdu_obs])	Create a Datastore from a FITS file.
`get_observations`([obs_id, skip_missing, ...])	Generate a `Observations`.
`info`([show])	Print some info.
`obs`(obs_id[, required_irf])	Access a given `Observation`.

Attributes Documentation

DEFAULT_HDU_TABLE = 'hdu-index.fits.gz'#: Default HDU table filename.

DEFAULT_OBS_TABLE = 'obs-index.fits.gz'#: Default observation table filename.

obs_ids#: Return the sorted obs_ids contained in the datastore.

Methods Documentation

check(checks='all')[source]#

Check index tables and data files.

This is a generator that yields a list of dicts.

copy_obs(obs_id, outdir, hdu_class=None, verbose=False, overwrite=False)[source]#

Create a new DataStore containing a subset of observations.

Parameters

obs_idarray-like, ObservationTable: List of observations to copy
outdirstr, Path: Directory for the new store
hdu_classlist of str: see gammapy.data.HDUIndexTable.VALID_HDU_CLASS
verbosebool: Print copied files
overwritebool: Overwrite

classmethod from_dir(base_dir, hdu_table_filename=None, obs_table_filename=None)[source]#

Create from a directory.

Parameters

base_dirstr, Path: Base directory of the data files.
hdu_table_filenamestr, Path: Filename of the HDU index file. May be specified either relative to base_dir or as an absolute path. If None, the default filename will be looked for.
obs_table_filenamestr, Path: Filename of the observation index file. May be specified either relative to base_dir or as an absolute path. If None, the default filename will be looked for.

Returns

data_storeDataStore: Data store

Examples

>>> from gammapy.data import DataStore
>>> data_store = DataStore.from_dir('$GAMMAPY_DATA/hess-dl3-dr1')

classmethod from_events_files(events_paths, irfs_paths=None)[source]#

Create from a list of event filenames.

HDU and observation index tables will be created from the EVENTS header.

IRFs are found only if you have a CALDB environment variable set, and if the EVENTS files contain the following keys:

TELESCOP (example: TELESCOP = CTA)
CALDB (example: CALDB = 1dc)
IRF (example: IRF = South_z20_50h)

This method is useful specifically if you want to load data simulated with ctobssim

Parameters

events_pathslist of str or Path: List of paths to the events files
irfs_pathsstr, Path, or list of str or Path: Path to the IRFs file. If a list is provided it must be the same length than events_paths. If None the events files have to contain CALDB and IRF header keywords to locate the IRF files, otherwise the IRFs are assumed to be contained in the events files.

Returns

data_storeDataStore: Data store

Examples

This is how you can access a single event list:

>>> from gammapy.data import DataStore
>>> import os
>>> os.environ["CALDB"] = os.environ["GAMMAPY_DATA"] + "/cta-1dc/caldb"
>>> path = "$GAMMAPY_DATA/cta-1dc/data/baseline/gps/gps_baseline_110380.fits"
>>> data_store = DataStore.from_events_files([path])
>>> observations = data_store.get_observations()

You can now analyse this data as usual (see any Gammapy tutorial).

If you have multiple event files, you have to make the list. Here’s an example using Path.glob to get a list of all events files in a given folder:

>>> import os
>>> from pathlib import Path
>>> path = Path(os.environ["GAMMAPY_DATA"]) / "cta-1dc/data"
>>> paths = list(path.rglob("*.fits"))
>>> data_store = DataStore.from_events_files(paths)
>>> observations = data_store.get_observations()

>>> #Note that you have a lot of flexibility to select the observations you want,
>>> # by having a few lines of custom code to prepare ``paths``, or to select a
>>> # subset via a method on the ``data_store`` or the ``observations`` objects.
>>> # If you want to generate HDU and observation index files, write the tables to disk::

>>> data_store.hdu_table.write("hdu-index.fits.gz") 
>>> data_store.obs_table.write("obs-index.fits.gz") 

classmethod from_file(filename, hdu_hdu='HDU_INDEX', hdu_obs='OBS_INDEX')[source]#

Create a Datastore from a FITS file.

The FITS file must contain both index files.

Parameters

filenamestr, Path: FITS filename
hdu_hdustr or int: FITS HDU name or number for the HDU index table
hdu_obsstr or int: FITS HDU name or number for the observation index table

Returns

data_storeDataStore: Data store

get_observations(obs_id=None, skip_missing=False, required_irf='full-enclosure')[source]#

Generate a Observations.

Parameters

obs_idlist

Observation IDs (default of None means “all”) If not given, all observations ordered by OBS_ID are returned. This is not necessarily the order in the obs_table.

skip_missingbool, optional

Skip missing observations, default: False

required_irflist of str or str

Runs will be added to the list of observations only if the required HDUs are present. Otherwise, the given run will be skipped The list can include the following options:

"events" : Events
"gti" : Good time intervals
"aeff" : Effective area
"bkg" : Background
"edisp": Energy dispersion
"psf" : Point Spread Function
"rad_max" : Maximal radius

Alternatively single string can be used as shortcut:

"full-enclosure" : includes ["events", "gti", "aeff", "edisp", "psf", "bkg"]
"point-like" : includes ["events", "gti", "aeff", "edisp"]
"all-optional" : no HDUs are required, only warnings will be emitted for missing HDUs among all possibilities.

Returns

observationsObservations: Container holding a list of Observation

info(show=True)[source]#: Print some info.

obs(obs_id, required_irf='full-enclosure')[source]#

Access a given Observation.

Parameters

obs_idint

Observation ID.

required_irflist of str or str

The list can include the following options:

"events" : Events
"gti" : Good time intervals
"aeff" : Effective area
"bkg" : Background
"edisp": Energy dispersion
"psf" : Point Spread Function
"rad_max" : Maximal radius

Alternatively single string can be used as shortcut:

"full-enclosure" : includes ["events", "gti", "aeff", "edisp", "psf", "bkg"]
"point-like" : includes ["events", "gti", "aeff", "edisp"]

Returns

observationObservation: Observation container