luis-astro-analysis 0.2.1

A small package assisting in data analysis for Xray astronomy

The fits_util library

Introduction

This package exists to streamline certain parts of astronomical X-ray data analysis. For now, the main focus lies on the creation of Colour-Colour / Colour-Intensity plots from raw photon event data, as well as the successive (interactive) categorization of these data into branches, using the plots mentioned. Furthermore, the package features a method to effortlessly calculate the cross-correlation curve from two light curves.

Lastly, the package features a collection of useful functions, mostly made for data-extraction of satellite data contained inside fits-files. For now, the scope of this project remains rather compact, featuring support for .evt-files, .pha-files and the qdp-format.

As the data-extraction differs from mission to mission, higher level functions have been prepared in mission-specific files (i.e. ninja.py, tess.py). These will be expanded as my need for them arises.

Examples of how to use the most important features of this package are shown below.

Dependencies

This Package depends on the following:

• astropy
• numpy
• pandas
• matplotlib
• seaborn

Documentation

As of now, there is no documentation apart from the doc strings. I did my best to briefly state what each function does, for any further information The user is encouraged to look into the source code himself.

Examples

CCI_Analyzer

Below I have provided the method I currently use to analyse NinjaSat data:

from luis_astro_analysis import cci_analyzer as cci
from luis_astro_analysis import fits_util as f

ninja_path = "/path/to/ninja.evt"
gti_path = "/path/to/ninja.gti"
rmf_path = "/path/to/ninja.rmf"

ninja_bands = {
    "soft": (2, 4),
    "med": (4, 8),
    "hard": (8, 20),
}

# Load and format event data using fits_util
ninja_evts = f.read_mjd_channel(ninja_path)
ninja_rmf = f.read_rmf_ebounds(rmf_path)
ninja_evts = ninja_evts.merge(ninja_rmf, on="channel", how="left")

# initialize CCI_Analyzer
CCI = cci.CCI_Analyzer()

# load data
CCI.load_dataframe(ninja_evts)
CCI.load_gti_from_file(gti_path)

# set analysis parameters
CCI.set_colorbands(ninja_bands) # initial colorbands
CCI.set_binsize(binsize_sec=600) # initial binsize
CCI.set_interactive_bin_bounds(1, 1000) # boundaries for binsize (sec)
CCI.set_color_ratio_error_threshold(0.5) # error threshold for color data

# gti filtering
CCI.refine_gti() #recommended for NinjaSat data
CCI.select_good_time()

# interactive selection
CCI.select_bins_and_colors()
CCI.select_branches()

# results
CCI.plot_color_intensity()
CCI.plot_color_color()

Cross-Correlator

Below is an example of how to calculate the direct cross-correlation of two lightcurves using the Cross-Correlator class:

from luis_astro_analysis import Cross-Correlator as croco
from luis_astro_analysis import fits_util as f
from luis_astro_analysis import ninja
from luis_astro_analysis import tess

ninja_path = "/path/to/ninja.evt"
gti_path = "/path/to/ninja.gti"
rmf_path = "/path/to/ninja.rmf"
tess_path = "/path/to/tess.evt"

binsize_sec = 20 # same as TESS

gti = f.read_stdgti_mjd(gti_path)
gti = f.refine_gti(gti, f.read_evt_mjd(ninja_path))

ninja_lc = ninja.extract_lightcurve(ninja_path, binsize_sec, gti=gti, rmf=rmf, drop_zeros=True)

tess_lc = tess.extract_lightcurve(tess_path)
tess_lc = tess_lc.loc[tess_lc["mjd"] > ninja_lc["mjd"].min()]

CC = croco.Cross_Correlator()

CC.load_dataframe(tess_lc, "Tess", 1)
CC.load_dataframe(ninja_lc, "Ninja", 2)
CC.load_gti(gti)
df = CC.interactive_dccf(tau_max_init=5000, d_tau_init=20)