EventListLAT¶

class gammapy.data.EventListLAT(table)[source]¶

Bases: gammapy.data.EventListBase

Event list for Fermi-LAT dataset

Fermi-LAT data products https://fermi.gsfc.nasa.gov/ssc/data/analysis/documentation/Cicerone/Cicerone_Data/LAT_DP.html Data format specification (columns) https://fermi.gsfc.nasa.gov/ssc/data/analysis/documentation/Cicerone/Cicerone_Data/LAT_Data_Columns.html

For further information, see the base class: EventListBase.

Parameters:	table : `Table` Event list table

Examples

To load an example Fermi-LAT event list:

>>> from gammapy.data import EventListLAT
>>> filename = "$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-events.fits.gz"
>>> events = EventListLAT.read(filename)

Attributes Summary

`energy`	Event energies (`Quantity`).
`galactic`	Event Galactic sky coordinates (`SkyCoord`).
`observation_dead_time_fraction`	Dead-time fraction (float).
`observation_live_time_duration`	Live-time duration in seconds (`Quantity`).
`observation_time_end`	Observation stop time (`Time`).
`observation_time_start`	Observation start time (`Time`).
`offset`	Event offset from the array pointing position (`Angle`).
`pointing_radec`	Pointing RA / DEC sky coordinates (`SkyCoord`).
`radec`	Event RA / DEC sky coordinates (`SkyCoord`).
`time`	Event times (`Time`).
`time_ref`	Time reference (`Time`)

Methods Summary

`check`([checks])	Run checks.
`filter_circular_region`(region)	Create selection mask for event in given circular regions.
`plot_energy`([ax, ebounds])	Plot counts as a function of energy.
`plot_energy_offset`([ax])	Plot counts histogram with energy and offset axes.
`plot_image`()	Quick look counts map sky plot.
`plot_offset2_distribution`([ax, center])	Plot offset^2 distribution of the events.
`plot_time`([ax])	Plots an event rate time curve.
`read`(filename, **kwargs)	Read from FITS file.
`select_circular_region`(region)	Select events in circular regions.
`select_energy`(energy_band)	Select events in energy band.
`select_parameter`(parameter, band)	Select events with respect to a specified parameter
`select_row_subset`(row_specifier)	Select table row subset.
`select_sky_box`(lon_lim, lat_lim[, frame])	Select events in sky box.
`select_sky_cone`(center, radius)	Select events in sky circle.
`select_sky_ring`(center, inner_radius, …)	Select events in ring region on the sky.
`select_time`(time_interval)	Select events in time interval.
`stack`(event_lists, **kwargs)	Stack (concatenate) list of event lists.

Attributes Documentation

energy¶: Event energies (Quantity).

galactic¶

Event Galactic sky coordinates (SkyCoord).

Always computed from RA / DEC using Astropy.

observation_dead_time_fraction¶

Dead-time fraction (float).

Defined as dead-time over observation time.

Dead-time is defined as the time during the observation where the detector didn’t record events: https://en.wikipedia.org/wiki/Dead_time https://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 (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_end¶: Observation stop time (Time).

observation_time_start¶: Observation start time (Time).

offset¶: Event offset from the array pointing position (Angle).

pointing_radec¶: Pointing RA / DEC sky coordinates (SkyCoord).

radec¶: Event RA / DEC sky coordinates (SkyCoord).

time¶

Event times (Time).

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 (Time)

Methods Documentation

check(checks='all')¶

Run checks.

This is a generator that yields a list of dicts.

filter_circular_region(region)¶

Create selection mask for event in given circular regions.

TODO: Extend to support generic regions

Parameters:	region : list of `SkyRegion` List of sky regions
Returns:	index_array : `numpy.ndarray` Index array of selected events

plot_energy(ax=None, ebounds=None, **kwargs)¶: Plot counts as a function of energy.

plot_energy_offset(ax=None)¶: Plot counts histogram with energy and offset axes.

plot_image()[source]¶: Quick look counts map sky plot.

plot_offset2_distribution(ax=None, center=None, **kwargs)¶

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:	ax : `Axes` (optional) Axes center : `astropy.coordinates.SkyCoord` Center position for the offset^2 distribution. Default is the observation pointing position. **kwargs : Extra keyword arguments are passed to `matplotlib.pyplot.hist`.
Returns:	ax : `Axes` Axes

Examples

Load an example event list:

>>> from gammapy.data import EventList
>>> events = EventList.read('$GAMMAPY_DATA/hess-dl3-dr1/data/hess_dl3_dr1_obs_id_023523.fits.gz')

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)
>>> 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)¶

Plots an event rate time curve.

Parameters:	ax : `Axes` or None Axes
Returns:	ax : `Axes` Axes

classmethod read(filename, **kwargs)¶

Read from FITS file.

Format specification: EVENTS

Parameters:	filename : `Path`, str Filename

select_circular_region(region)¶

Select events in circular regions.

TODO: Extend to support generic regions

Parameters:	region : `CircleSkyRegion` or list of `CircleSkyRegion` (List of) sky region(s)
Returns:	event_list : `EventList` Copy of event list with selection applied.

select_energy(energy_band)¶

Select events in energy band.

Parameters:	energy_band : `Quantity` Energy band `[energy_min, energy_max)`
Returns:	event_list : `EventList` Copy of event list with selection applied.

Examples

>>> from astropy.units import Quantity
>>> from gammapy.data import EventList
>>> event_list = EventList.read('events.fits')
>>> energy_band = Quantity([1, 20], 'TeV')
>>> event_list = event_list.select_energy()

select_parameter(parameter, band)¶

Select events with respect to a specified parameter

Parameters:	parameter : str Parameter used for the selection. Must be present in `self.table`. band : tuple or `astropy.units.Quantity` Min and max value for the parameter to be selected (min <= parameter < max). If parameter is not dimensionless you have to provide a Quantity.
Returns:	event_list : `EventList` Copy of event list with selection applied.

Examples

>>> from gammapy.data import EventList
>>> event_list = EventList.read('events.fits')
>>> phase_region = (0.3, 0.5)
>>> event_list = event_list.select_parameter(parameter='PHASE', band=phase_region)

select_row_subset(row_specifier)¶

Select table row subset.

Parameters:	row_specifier : slice, int, or array of ints Specification for rows to select, passed on to `self.table[row_specifier]`.
Returns:	event_list : `EventList` New event list with table row subset selected

Examples

Use a boolean mask as row_specifier:

mask = events.table[‘FOO’] > 42 events2 = events.select_row_subset(mask)

Use row index array as row_specifier:

idx = np.where(events.table[‘FOO’] > 42)[0] events2 = events.select_row_subset(idx)

select_sky_box(lon_lim, lat_lim, frame='icrs')¶

Select events in sky box.

TODO: move gammapy.catalog.select_sky_box to gammapy.utils.

select_sky_cone(center, radius)¶

Select events in sky circle.

Parameters:	center : `SkyCoord` Sky circle center radius : `Angle` Sky circle radius
Returns:	event_list : `EventList` Copy of event list with selection applied.

select_sky_ring(center, inner_radius, outer_radius)¶

Select events in ring region on the sky.

Parameters:	center : `SkyCoord` Sky ring center inner_radius, outer_radius : `Angle` Sky ring inner and outer radius
Returns:	event_list : `EventList` Copy of event list with selection applied.

select_time(time_interval)¶

Select events in time interval.

Parameters:	time_interval : `astropy.time.Time` Start time (inclusive) and stop time (exclusive) for the selection.
Returns:	events : `EventList` Copy of event list with selection applied.

classmethod stack(event_lists, **kwargs)¶

Stack (concatenate) list of event lists.

Calls vstack.

Parameters:	event_lists : list list of `EventList` to stack