wtlike 0.1.2

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

Currently in pre-alpha, and must be cloned. This includes the nbdev support as well.

Demo

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

from wtlike import *
wtl = WtLike('BL Lac', bins=(0,0,7)) # how to define 7-day bins for the full dataset. Default is (0,0,1).
wtl.plot_flux();

photons and exposure for BL Lac: Saving to cache with key "BL Lac__data"
Assembling photon data and exposure for source BL Lac from folder "/home/burnett/wtlike_data/data_files", with 664 files, last=week_673.pkl
	Using weeks (None, None)
........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
Week at 2018-04-03 14:23 has only 0 photons
.
Week at 2018-04-05 01:08 has only 0 photons
.
Week at 2018-04-12 01:05 has only 0 photons
.
Week at 2018-04-19 01:05 has only 0 photons
..........
Week at 2018-06-28 00:56 has only 0 photons
.......
Week at 2018-08-16 00:58 has only 0 photons
........
Week at 2018-10-11 00:59 has only 0 photons
....................................................................................................................................
Load weights from file /mnt/c/Users/thbur/OneDrive/fermi/wtlike-data/weight_files/BL_Lac_weights.pkl
	Found: P88Y6076 at (92.60, -10.44)
	Applyng weights: 0 / 404789 photon pixels are outside weight region
	95324 weights set to NaN
BBanalysis: Source BL Lac with:
	 data:       404,789 photons from   2008-08-04 to 2021-04-28
	 exposure: 3,171,944 intervals from 2008-08-04 to 2021-04-28
Bin photon data into 664.0 1-week bins from 54683.0 to 59331.0
Loaded 656 / 664 cells with at least 1 photons and exposure > 1e-06 for light curve analysis

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

wtl2 = wtl.view((-5,0, 1/24)) # for the last 5 days, 1-hour bins
wtl2.plot_flux(fmt='o'); # Accepts plt.plot args, e.g. xlim, ylim, etc.

Bin photon data into 120.0 1-hour bins from 59328.0 to 59333.0
Loaded 107 / 120 cells with at least 1 photons and exposure > 1e-06 for light curve analysis

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

wtl2.plot_BB(fmt='o');

Partitioned 107 cells into 8 blocks, using LikelihoodFitness 
Loaded 8 / 8 cells for fitting

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

wtl2.bb_table()

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; }

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</style>

	t	tw	n	flux	ts	errors	limit
0	59328.73	1.46	312	0.33	88.3	(-0.049, 0.051)	0.41
1	59330.02	1.12	362	0.76	231.8	(-0.078, 0.08)	0.90
2	59330.83	0.50	290	1.83	439.6	(-0.155, 0.162)	2.10
3	59331.17	0.17	229	7.32	727.3	(-0.563, 0.588)	8.33
4	59331.31	0.12	103	3.67	242.3	(-0.445, 0.475)	4.50
5	59331.67	0.58	236	1.01	200.0	(-0.116, 0.121)	1.22
6	59332.35	0.79	213	0.48	87.9	(-0.075, 0.079)	0.61
7	59332.88	0.25	101	1.56	130.9	(-0.222, 0.238)	1.97

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.