wtlike 0.1.5

Time-dependent analysis of point sources in Fermi-LAT data

The wtlike package

Code for generating fermi-LAT light curves.

GitHub Links

• this document
• repository

Context

This package has code that was adapted to the nbdev code/tests/documentation environment from the github package lat-timing to manage light curves of Fermi-LAT sources.
It is based on a paper by Matthew Kerr, which derives the weighted likelihood formalism used here, specifically with the Bayesian Block to detect and characterize variability of a gamma-ray source.

Also, I've ported some code from my jupydoc documentation package supporting enhanced documentation combining Markdown and code, such that the Markdown reflects execution of the code.

Installation

Istall from pip:

pip install wtlike

Demo

The following code cell loads the data for the BL Lac blazar, and plots by default, a weekly light curve for the full fermi mission

from wtlike import *
weekly = WtLike('BL Lac') # how to define 7-day bins for the full dataset.
weekly.plot(ylim=(-0.8,15)); #plot takes plt.plot args.

SourceData: photons and exposure for BL Lac: Restoring from cache with key "BL Lac_data"
WtLike: Source BL Lac with:
	 data:       310,969 photons from   2008-08-04 to 2021-05-06
	 exposure: 3,177,752 intervals from 2008-08-04 to 2021-05-06
CellData: Bin photon data into 665 1-week bins from 54683.0 to 59338.0
LightCurve: select 656 cells for fitting with e>0.5 & n>2

The variable weekly has lots of capabilities. To examine a subset of the data at the end of the current data, we use view to create a new WtLike object and plot it.

len(weekly.cells)

665

hourly_at_end = weekly.view((-5,0, 1/24)) # for the last 5 days, 1-hour bins
hourly_at_end.plot(); # Accepts plt.plot args, e.g. xlim, ylim, etc.

CellData: Bin photon data into 120 1-hour bins from 59335.0 to 59340.0
LightCurve: select 81 cells for fitting with e>0.5 & n>2

Or, to do a Bayesian Block partition with these 1-hour bins, perform fits, and overplot the result, just run the following.

hourly_at_end.plot_BB(fmt='o');

Partitioned 81 cells into 4 blocks, using LikelihoodFitness 
LightCurve: Loaded 4 / 4 cells for fitting

Finally, let's look at the values plotted above:

hourly_at_end.bb_table()

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	t	tw	n	flux	ts	errors	limit
0	59335.42	0.83	178	6.70	404.1	(-0.655, 0.689)	7.89
1	59336.69	1.71	205	2.38	170.0	(-0.308, 0.323)	2.93
2	59338.02	0.96	222	8.70	573.6	(-0.734, 0.767)	10.01
3	59339.23	1.46	217	4.48	369.4	(-0.434, 0.454)	5.25

Input data

There are three data sources which wtlike needs to function:

• The photon/spacecraft data
• A table of weights for each source
• An effective area IRF table

These must be found under a folder, which by default is ~/wtlike_data. In that folder there must be (perhaps links to) three folders named data_files, weight_files, aeff_files. A copy of what I'm using is at /afs/slac/g/glast/users/burnett/wtlike_data

Module summary

Configuration config

Implements basic configuration information, Config, a cache system Cache, point source info PointSource, and time conversion

Photon and Spacecraft Data data_man

This module manages conversion of the weekly FT1 (photons) and FT2 (spacecraft) files, downloaded from GSFC, to a folder containing pickled files, each with tables of photons, space craft data, and a list of GTI times derived from the FT1 file. The total size of this is 2.8 GB. A class WeeklyData exports the results.

Source data source_data

The module depends on a specific source. It extracts the photons within a disk, and calculates the exposure for this direction. It assumes that a weigtht analysis has been done for this source, which it uses to apply a weight to each photon. This is handled by the class SourceData.

Cell data cell_data

The next step is to define a set of time bins. This module, implementing the class CellData(SourceData), creates a set of cells.

The light-curve light_curve

The the class LightCurve(CellData) uses the set of cell defined by its super class, and evaluates the likelihood for each. This function is represented by a Poisson-like function for further analysis. It creates a table with this information for plotting a light curve.

Bayesian Blocks bayesian

THis module defines the class BBanalysis(LightCurve). INheriting from LightCurve, it adds Bayesian block capability. It is the class returned by `from wtlike import WtLike'

Simulation simulation

A light curve can be also generated with a simulation.