pygrb-lc 0.1.7

Python package for GRB analysis

pygrb_lc

Package to load and handle GRB (Gamma-ray bursts) light curves, their furie transformations and various other functions.

To install run the following command

pip install --upgrade pygrb_lc

or

pip3 install --upgrade pygrb_lc

Light curves

Main object in this package is LightCurve object and its children. Start by creating one

from pygrb_lc.light_curves import LightCurve # base class for ligth curve

lc = LightCurve()
print(lc)

It is created but empty, semplest way to provide data is by data argument, that requires numpy.ndarray with 2 (time, signal) or 4 (time, time_err, signal, signal_err) columns

import numpy as np

data = np.loadtxt('test.txt')
lc = LightCurve(data = data)
print(lc)

Base class can rebin data, subtract polynomial function, filter peaks, set intervals and load and save data to LIGHT_CURVE_SAVE folder in pickle format.

There are specific classes for INTERAL/SPI-ACS and Fermi/GBM instruments. They are able to load actual data from web, you need to specify it in loading_method parameter

from pygrb_lc.light_curves import SPI_ACS_LightCurve, GBM_LightCurve

lc1 = SPI_ACS_LightCurve('2020-01-01 00:00:00', 500, loading_method = 'web')
lc2 = GBM_LightCurve('2020-01-01 00:00:00', ['na'], duration = 500, loading_method = 'web')

Furie transformations

Furie transformation is performed by FurieLightCurve class. It requires LightCurve object as an argument

from pygrb_lc.furie import FurieLightCurve

lc = LightCurve(data = np.loadtxt('test.txt'))
flc = FurieLightCurve(lc, interval_t90 = (0, 10))

flc.plot()

All classes have plot method, you can provide matplotlib.pyplot.Axes object as an argument to plot on existing plot

fig,(ax1,ax2) = plt.subplots(2,1)
lc.plot(ax = ax1)
flc.plot(ax = ax2)

Catalog

Catalog class is an extension of pandas.DataFrame. It provides simplicity of manipulations and visualization and extend it with crossmatching functionality. Easiest way to start using it is to create it from existing tabular data. It is important to provide event_column argument, it is the name of column that will be used for crossmatching and comparison

from pygrb_lc.catalogs import Catalog
import pandas as pd
import numpy as np

data = np.loadtxt('test.txt')
cat = Catalog(data, columns = ['datetime','duration'], event_column = 'datetime')
cat.find_event(pd.Timestamp('2023-01-01 00:00:00'), precision = 10)

precision (in seconds) controls uncertainity that is allowed for the event. If there is no such event method will return None. There is a method of crossmatching two catalogs crossmatch, it works like inner join for SQL tables, but compares only corresponding event_column columns and allows uncertainity via precision. precision = 0 equals to exact match.

Roadmap

Add support of LightCurve class in Catalog Add support of spectra (based on current photon_data in GBM_LightCurve class) Add typical functions for approximation: Band function, power law, etc. and their interaction with LightCurve and FurieLightCurve classes.