time - Time analysis

Introduction

gammapy.time contains methods for time-based analysis, e.g. from AGN, binaries or pulsars. There is also gammapy.utils.time, which contains low-level helper functions for time conversions e.g. following the

Getting Started

Lightcurve

This section introduces the LightCurve class.

Read a table that contains a lightcurve:

>>> from astropy.table import Table
>>> url = 'https://github.com/gammapy/gamma-cat/raw/master/input/data/2006/2006A%2526A...460..743A/tev-000119-lc.ecsv'
>>> table = Table.read(url, format='ascii.ecsv')

Create a LightCurve object:

>>> from gammapy.time import LightCurve
>>> lc = LightCurve(table)

LightCurve is a simple container that stores the LC table, and provices a few conveniences, like creating time objects and a quick-look plot:

>>> lc.time[:2].iso
['2004-05-23 01:47:08.160' '2004-05-23 02:17:31.200']
>>> lc.plot()

Variability test

The exptest function can be used to compute the significance of variability (compared to the null hypothesis of constant rate) for a list of event time differences.

Here’s an example how to use the random_times helper function to simulate a TimeDelta array for a given constant rate and use exptest to assess the level of variability (0.11 sigma in this case, not variable):

>>> from astropy.units import Quantity
>>> from gammapy.time import random_times, exptest
>>> rate = Quantity(10, 'Hz')
>>> time_delta = random_times(size=100, rate=rate, return_diff=True, random_state=0)
>>> mr = exptest(time_delta)
>>> print(mr)
0.11395763079

See examples/example_exptest.py for a longer example.

TODO: apply this to the 2FHL events and check which sources are variable as a nice example.

from gammapy.data import EventList
from gammapy.time import exptest
events = EventList.read('$GAMMAPY_DATA/fermi_2fhl/2fhl_events.fits.gz ', hdu='EVENTS')
# TODO: cone select events for 2FHL catalog sources, compute mr for each and print 10 most variable sources

Other codes

Where possible we shouldn’t duplicate timing analysis functionality that’s already available elsewhere. In some cases it’s enough to refer gamma-ray astronomers to other packages, sometimes a convenience wrapper for our data formats or units might make sense.

Some references (please add what you find useful

• https://github.com/astroML/gatspy
• https://github.com/matteobachetti/MaLTPyNT
• https://github.com/nanograv/PINT
• http://www.astroml.org/modules/classes.html#module-astroML.time_series
• http://www.astroml.org/book_figures/chapter10/index.html
• http://docs.astropy.org/en/latest/api/astropy.stats.bayesian_blocks.html
• https://github.com/samconnolly/DELightcurveSimulation
• https://github.com/cokelaer/spectrum
• https://github.com/YSOVAR/YSOVAR
• https://nbviewer.ipython.org/github/YSOVAR/Analysis/blob/master/TimeScalesinYSOVAR.ipynb

Using gammapy.time

• Period detection and plotting

Reference/API

gammapy.time Package

Time-based analysis.

Functions

exptest(time_delta)	Compute the level of variability for a certain period of time.
plot_periodogram(time, flux, periods, power)	Plot a light curve and its periodogram.
random_times(size, rate[, dead_time, …])	Make random times assuming a Poisson process.
robust_periodogram(time, flux[, flux_err, …])	Compute a light curve’s period.

Classes

LightCurve(table)	Lightcurve container.
LightCurveEstimator(spec_extract)	Light curve estimator.

gammapy.time.models Module

Classes

PhaseCurveTableModel(table, time_0, phase_0, …)	Temporal phase curve model.
LightCurveTableModel(table)	Temporal light curve model.