EventList#

class gammapy.data.EventList(table, meta=None)[source]#

Bases: object

Event list.

Event list data is stored as table (Table) data member.

The most important reconstructed event parameters are available as the following columns:

TIME - Mission elapsed time (sec)
RA, DEC - ICRS system position (deg)
ENERGY - Energy (usually MeV for Fermi and TeV for IACTs)

Note that TIME is usually sorted, but sometimes it is not. E.g. when simulating data, or processing it in certain ways. So generally any analysis code should assume TIME is not sorted.

Other optional (columns) that are sometimes useful for high level analysis:

GLON, GLAT - Galactic coordinates (deg)
DETX, DETY - Field of view coordinates (deg)

Note that when reading data for analysis you shouldn’t use those values directly, but access them via properties which create objects of the appropriate class:

time for TIME
radec for RA, DEC
energy for ENERGY
galactic for GLON, GLAT

Parameters:

tableTable: Event list table.
metaEventListMetaData: The metadata. Default is None.

Examples

>>> from gammapy.data import EventList
>>> events = EventList.read("$GAMMAPY_DATA/cta-1dc/data/baseline/gps/gps_baseline_110380.fits")
>>> print(events)
EventList
---------

  Instrument       : None
  Telescope        : CTA
  Obs. ID          : 110380

  Number of events : 106217
  Event rate       : 59.273 1 / s

  Time start       : 59235.5
  Time stop        : 59235.52074074074

  Min. energy      : 3.00e-02 TeV
  Max. energy      : 1.46e+02 TeV
  Median energy    : 1.02e-01 TeV

  Max. offset      : 5.0 deg

Attributes Summary

`altaz`	ALT / AZ position computed from RA / DEC as a `SkyCoord` object.
`altaz_frame`	ALT / AZ frame as an `AltAz` object.
`altaz_from_table`	ALT / AZ position from table as a `SkyCoord` object.
`energy`	Event energies as a `Quantity`.
`galactic`	Event Galactic sky coordinates as a `SkyCoord` object.
`galactic_median`	Median position as a `SkyCoord` object.
`is_pointed_observation`	Whether observation is pointed.
`observation_dead_time_fraction`	Dead-time fraction as a float.
`observation_live_time_duration`	Live-time duration in seconds as a `Quantity`.
`observation_time_duration`	Observation time duration in seconds as a `Quantity`.
`observation_time_start`	Observation start time as a `Time` object.
`observation_time_stop`	Observation stop time as a `Time` object.
`observatory_earth_location`	Observatory location as an `EarthLocation` object.
`offset`	Event offset from the array pointing position as an `Angle`.
`offset_from_median`	Event offset from the median position as an `Angle`.
`pointing_radec`	Pointing RA / DEC sky coordinates as a `SkyCoord` object.
`radec`	Event RA / DEC sky coordinates as a `SkyCoord` object.
`time`	Event times as a `Time` object.
`time_ref`	Time reference as a `Time` object.

Methods Summary

`check`([checks])	Run checks.
`copy`()	Copy event list (`EventList`).
`from_stack`(event_lists, **kwargs)	Stack (concatenate) list of event lists.
`map_coord`(geom)	Event map coordinates for a given geometry.
`peek`([allsky])	Quick look plots.
`plot_energy`([ax])	Plot counts as a function of energy.
`plot_energy_offset`([ax, center])	Plot counts histogram with energy and offset axes.
`plot_image`([ax, allsky])	Quick look counts map sky plot.
`plot_offset2_distribution`([ax, center, ...])	Plot offset^2 distribution of the events.
`plot_time`([ax])	Plot an event rate time curve.
`read`(filename[, hdu, checksum])	Read from FITS file.
`select_energy`(energy_range)	Select events in energy band.
`select_mask`(mask)	Select events inside a mask (`EventList`).
`select_offset`(offset_band)	Select events in offset band.
`select_parameter`(parameter, band)	Select events with respect to a specified parameter.
`select_rad_max`(rad_max[, position])	Select energy dependent offset.
`select_region`(regions[, wcs])	Select events in given region.
`select_row_subset`(row_specifier)	Select table row subset.
`select_time`(time_interval)	Select events in time interval.
`stack`(other)	Stack with another EventList in place.
`to_table_hdu`([format])	Convert event list to a `BinTableHDU`.
`write`(filename[, gti, overwrite, format, ...])	Deprecated since version v1.2.

Attributes Documentation

altaz#: ALT / AZ position computed from RA / DEC as a SkyCoord object.

altaz_frame#: ALT / AZ frame as an AltAz object.

altaz_from_table#: ALT / AZ position from table as a SkyCoord object.

energy#: Event energies as a Quantity.

galactic#

Event Galactic sky coordinates as a SkyCoord object.

Always computed from RA / DEC using Astropy.

galactic_median#: Median position as a SkyCoord object.

is_pointed_observation#: Whether observation is pointed.

observation_dead_time_fraction#

Dead-time fraction as a float.

This is a keyword related to IACTs. Defined as dead-time over observation time.

Dead-time is defined as the time during the observation where the detector didn’t record events: http://en.wikipedia.org/wiki/Dead_time https://ui.adsabs.harvard.edu/abs/2004APh….22..285F

The dead-time fraction is used in the live-time computation, which in turn is used in the exposure and flux computation.

observation_live_time_duration#

Live-time duration in seconds as a Quantity.

The dead-time-corrected observation time.

In Fermi-LAT it is automatically provided in the header of the event list.
In IACTs is computed as t_live = t_observation * (1 - f_dead) where f_dead is the dead-time fraction.

observation_time_duration#

Observation time duration in seconds as a Quantity.

This is a keyword related to IACTs. The wall time, including dead-time.

observation_time_start#: Observation start time as a Time object.

observation_time_stop#: Observation stop time as a Time object.

observatory_earth_location#: Observatory location as an EarthLocation object.

offset#: Event offset from the array pointing position as an Angle.

offset_from_median#: Event offset from the median position as an Angle.

pointing_radec#: Pointing RA / DEC sky coordinates as a SkyCoord object.

radec#: Event RA / DEC sky coordinates as a SkyCoord object.

time#

Event times as a Time object.

Notes

Times are automatically converted to 64-bit floats. With 32-bit floats times will be incorrect by a few seconds when e.g. adding them to the reference time.

time_ref#: Time reference as a Time object.

Methods Documentation

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

Run checks.

This is a generator that yields a list of dicts.

copy()[source]#: Copy event list (EventList).

classmethod from_stack(event_lists, **kwargs)[source]#

Stack (concatenate) list of event lists.

Calls vstack.

Parameters:

event_listslist: List of EventList to stack.
**kwargsdict, optional: Keyword arguments passed to vstack.

map_coord(geom)[source]#

Event map coordinates for a given geometry.

Parameters:

geomGeom: Geometry.

Returns:

coordMapCoord: Coordinates.

peek(allsky=False)[source]#

Quick look plots.

Parameters:

allskybool, optional: Whether to look at the events all-sky. Default is False.

plot_energy(ax=None, **kwargs)[source]#

Plot counts as a function of energy.

Parameters:

axAxes, optional: Matplotlib axes. Default is None
**kwargsdict, optional: Keyword arguments passed to hist.

Returns:

axAxes: Matplotlib axes.

plot_energy_offset(ax=None, center=None, **kwargs)[source]#

Plot counts histogram with energy and offset axes.

Parameters:

axAxis, optional: Plot axis. Default is None.
centerSkyCoord, optional: Sky coord from which offset is computed. Default is None.
**kwargsdict, optional: Keyword arguments forwarded to pcolormesh.

Returns:

axAxis: Plot axis.

plot_image(ax=None, allsky=False)[source]#

Quick look counts map sky plot.

Parameters:

axAxes, optional: Matplotlib axes.
allskybool, optional: Whether to plot on an all sky geom. Default is False.

plot_offset2_distribution(ax=None, center=None, max_percentile=98, **kwargs)[source]#

Plot offset^2 distribution of the events.

The distribution shown in this plot is for this quantity:

offset = center.separation(events.radec).deg
offset2 = offset ** 2

Note that this method is just for a quicklook plot.

If you want to do computations with the offset or offset^2 values, you can use the line above. As an example, here’s how to compute the 68% event containment radius using numpy.percentile:

import numpy as np
r68 = np.percentile(offset, q=68)

Parameters:

axAxes, optional: Matplotlib axes. Default is None.
centerastropy.coordinates.SkyCoord, optional: Center position for the offset^2 distribution. Default is the observation pointing position.
max_percentilefloat, optional: Define the percentile of the offset^2 distribution used to define the maximum offset^2 value. Default is 98.
**kwargsdict, optional: Extra keyword arguments are passed to hist.

Returns:

axAxes: Matplotlib axes.

Examples

Load an example event list:

>>> from gammapy.data import EventList
>>> from astropy import units as u
>>> filename = "$GAMMAPY_DATA/hess-dl3-dr1/data/hess_dl3_dr1_obs_id_023523.fits.gz"
>>> events = EventList.read(filename)

>>> #Plot the offset^2 distribution wrt. the observation pointing position
>>> #(this is a commonly used plot to check the background spatial distribution):
>>> events.plot_offset2_distribution() 
Plot the offset^2 distribution wrt. the Crab pulsar position (this is
commonly used to check both the gamma-ray signal and the background
spatial distribution):

>>> import numpy as np
>>> from astropy.coordinates import SkyCoord
>>> center = SkyCoord(83.63307, 22.01449, unit='deg')
>>> bins = np.linspace(start=0, stop=0.3 ** 2, num=30) * u.deg ** 2
>>> events.plot_offset2_distribution(center=center, bins=bins) 

Note how we passed the bins option of matplotlib.pyplot.hist to control the histogram binning, in this case 30 bins ranging from 0 to (0.3 deg)^2.

plot_time(ax=None, **kwargs)[source]#

Plot an event rate time curve.

Parameters:

axAxes, optional: Matplotlib axes. Default is None.
**kwargsdict, optional: Keyword arguments passed to errorbar.

Returns:

axAxes: Matplotlib axes.

classmethod read(filename, hdu='EVENTS', checksum=False, **kwargs)[source]#

Read from FITS file.

Format specification: EVENTS

Parameters:

filenamepathlib.Path, str: Filename
hdustr: Name of events HDU. Default is “EVENTS”.
checksumbool: If True checks both DATASUM and CHECKSUM cards in the file headers. Default is False.

select_energy(energy_range)[source]#

Select events in energy band.

Parameters:

energy_rangeQuantity: Energy range [energy_min, energy_max).

Returns:

event_listEventList: Copy of event list with selection applied.

Examples

>>> from astropy import units as u
>>> from gammapy.data import EventList
>>> filename = "$GAMMAPY_DATA/fermi_3fhl/fermi_3fhl_events_selected.fits.gz"
>>> event_list = EventList.read(filename)
>>> energy_range =[1, 20] * u.TeV
>>> event_list = event_list.select_energy(energy_range=energy_range)

select_mask(mask)[source]#

Select events inside a mask (EventList).

Parameters:

maskMap: Mask.

Returns:

event_listEventList: Copy of event list with selection applied.

Examples

>>> from gammapy.data import EventList
>>> from gammapy.maps import WcsGeom, Map
>>> geom = WcsGeom.create(skydir=(0,0), width=(4, 4), frame="galactic")
>>> mask = geom.region_mask("galactic;circle(0, 0, 0.5)")
>>> filename = "$GAMMAPY_DATA/cta-1dc/data/baseline/gps/gps_baseline_110380.fits"
>>> events = EventList.read(filename)
>>> masked_event = events.select_mask(mask)
>>> len(masked_event.table)
5594

select_offset(offset_band)[source]#

Select events in offset band.

Parameters:

offset_bandAngle: offset band [offset_min, offset_max).

Returns:

event_listEventList: Copy of event list with selection applied.

Examples

>>> from gammapy.data import EventList
>>> import astropy.units as u
>>> filename = "$GAMMAPY_DATA/cta-1dc/data/baseline/gps/gps_baseline_110380.fits"
>>> events = EventList.read(filename)
>>> selected_events = events.select_offset([0.3, 0.9]*u.deg)
>>> len(selected_events.table)
12688

select_parameter(parameter, band)[source]#

Select events with respect to a specified parameter.

Parameters:

parameterstr: Parameter used for the selection. Must be present in self.table.
bandtuple or astropy.units.Quantity: Minimum and maximum value for the parameter to be selected (minimum <= parameter < maximum). If parameter is not dimensionless, a Quantity is required.

Returns:

event_listEventList: Copy of event list with selection applied.

Examples

>>> from astropy import units as u
>>> from gammapy.data import EventList
>>> filename = "$GAMMAPY_DATA/fermi_3fhl/fermi_3fhl_events_selected.fits.gz"
>>> event_list = EventList.read(filename)
>>> zd = (0, 30) * u.deg
>>> event_list = event_list.select_parameter(parameter='ZENITH_ANGLE', band=zd)
>>> print(len(event_list.table))
123944

select_rad_max(rad_max, position=None)[source]#

Select energy dependent offset.

Parameters:

rad_maxRadMax2D: Rad max definition.
positionSkyCoord, optional: Center position. Default is the pointing position.

Returns:

event_listEventList: Copy of event list with selection applied.

select_region(regions, wcs=None)[source]#

Select events in given region.

Parameters:

regionsstr or Region or list of Region: Region or list of regions (pixel or sky regions accepted). A region can be defined as a string in the DS9 format as well. See http://ds9.si.edu/doc/ref/region.html for details.
wcsWCS, optional: World coordinate system transformation. Default is None.

Returns:

event_listEventList: Copy of event list with selection applied.

select_row_subset(row_specifier)[source]#

Select table row subset.

Parameters:

row_specifierslice or int or array of int: Specification for rows to select, passed to self.table[row_specifier].

Returns:

event_listEventList: New event list with table row subset selected.

Examples

>>> from gammapy.data import EventList
>>> import numpy as np
>>> filename = "$GAMMAPY_DATA/cta-1dc/data/baseline/gps/gps_baseline_110380.fits"
>>> events = EventList.read(filename)
>>> #Use a boolean mask as ``row_specifier``:
>>> mask = events.table['MC_ID'] == 1
>>> events2 = events.select_row_subset(mask)
>>> print(len(events2.table))
97978
>>> #Use row index array as ``row_specifier``:
>>> idx = np.where(events.table['MC_ID'] == 1)[0]
>>> events2 = events.select_row_subset(idx)
>>> print(len(events2.table))
97978

select_time(time_interval)[source]#

Select events in time interval.

Parameters:

time_intervalastropy.time.Time: Start time (inclusive) and stop time (exclusive) for the selection.

Returns:

eventsEventList: Copy of event list with selection applied.

stack(other)[source]#

Stack with another EventList in place.

Calls vstack.

Parameters:

otherEventList: Event list to stack to self.

to_table_hdu(format='gadf')[source]#

Convert event list to a BinTableHDU.

Parameters:

formatstr, optional: Output format, currently only “gadf” is supported. Default is “gadf”.

Returns:

hduastropy.io.fits.BinTableHDU: EventList converted to FITS representation.

write(filename, gti=None, overwrite=False, format='gadf', checksum=False)[source]#

Deprecated since version v1.2: To write an EventList utilise Observation.write() with include_irfs=False

Write the event list to a FITS file.

If a GTI object is provided, it is saved into a second extension in the file.

Parameters:

filenamepathlib.Path or str: Filename.
gtiGTI, optional: Good Time Intervals object to save to the same file. Default is None.
overwritebool, optional: Overwrite existing file. Default is False.
formatstr, optional: FITS format convention. Default is “gadf”.
checksumbool, optional: When True adds both DATASUM and CHECKSUM cards to the headers written to the file. Default is False.