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scripts/06_plot.py
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189
scripts/06_plot.py
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#!/usr/bin/env python
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"""
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НАЗВАНИЕ:
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05_srctool.py
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НАЗНАЧЕНИЕ:
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Запускает scrtool для самого широкого канала 0.2-10 кэВ, чтобы спектры имели самое полное покрытие по энергиям. Список источников берется из 0.3-2.3 кэВ.
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ВЫЗОВ:
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esass
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./05_srctool.py
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УПРАВЛЕНИЕ:
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Требуется запуск предыдущего скрипта 04_mosaics.py
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ПАРАМЕТРЫ:
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index=4 : Выбранный энергетический диапазон
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ВЫВОД:
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Выходные файлы записываются в директорию outfile_dir/srctool_dir
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ИСТОРИЯ:
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Роман Кривонос, ИКИ РАН, krivonos@cosmos.ru
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Март 2023
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"""
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from astropy.wcs import WCS
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from astropy.io import fits
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import sys, os, os.path, time, subprocess
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from pathlib import Path
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import numpy as np
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import glob
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from os.path import dirname
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import inspect
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import uds
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import matplotlib.pyplot as plt
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from uds.utils import *
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from uds.config import *
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from uds.sherpa import *
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""" find UDS root dir """
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#root_path=dirname(dirname(dirname(inspect.getfile(uds))))
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"""
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ftools does not like long file path names,
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for this reason, we use relative path here
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"""
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root_path='..'
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print("UDS root path: {}".format(root_path))
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infile_dir=root_path+'/data/processed'
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outfile_dir=root_path+'/products'
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create_folder(outfile_dir)
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srctool_dir="{}/{}".format(outfile_dir,"srctool-products")
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create_folder(srctool_dir)
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outkey="tm0"
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outfile_srctool="{}_SrcTool_".format(outkey)
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do_flux_distr = False
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do_sens_curve = True
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index=0
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catalog = "{}_SourceCatalog_en{}.main.selected.csv".format(os.path.join(outfile_dir,outkey), eband[0])
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if not (os.path.isfile(catalog)==True):
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print("{} not found, run 05_srctool.py?".format(catalog))
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sys.exit()
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if(do_flux_distr==True):
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data, logbins = get_log_distr(infile=catalog, field='ml_rate', minval=1e-3, maxval=2)
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fig, ax = plt.subplots()
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for axis in ['top','bottom','left','right']:
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ax.spines[axis].set_linewidth(1)
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ax.tick_params(axis="both", width=1, labelsize=14)
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plt.hist(data, bins=logbins, histtype='step', color='blue', linewidth=1, linestyle='solid')
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plt.xlabel('Count rate (counts s$^{-1}$)',fontsize=14, fontweight='normal')
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plt.ylabel('Number',fontsize=14, fontweight='normal')
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plt.grid(visible=True)
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plt.xscale('log')
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plt.yscale('log')
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plt.savefig(catalog.replace("main.selected.csv", "ml_rate.png"), bbox_inches='tight')
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plt.close(fig)
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data, logbins = get_log_distr(infile=catalog, field='ml_flux', minval=1e-15, maxval=2e-12)
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fig, ax = plt.subplots()
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for axis in ['top','bottom','left','right']:
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ax.spines[axis].set_linewidth(1)
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ax.tick_params(axis="both", width=1, labelsize=14)
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plt.hist(data, bins=logbins, histtype='step', color='blue', linewidth=1, linestyle='solid')
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plt.xlabel('Energy flux (erg s$^{-1}$ cm$^{-2}$)',fontsize=14, fontweight='normal')
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plt.ylabel('Number',fontsize=14, fontweight='normal')
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plt.grid(visible=True)
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plt.xscale('log')
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plt.yscale('log')
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plt.savefig(catalog.replace("main.selected.csv", "ml_flux.png"), bbox_inches='tight')
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plt.close(fig)
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if(do_sens_curve==True):
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coeff = 2.4336e-13 / 3.4012e-13 # see below
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areatab="{}_AreaTable_dl10_en{}{}".format(os.path.join(outfile_dir,outkey), eband[index], outfile_post)
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hdul = fits.open(areatab)
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tbdata = hdul[1].data
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""" convert limflux from 0.3-2.3 keV to 0.5-2 keV """
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limflux_dl10 = tbdata['LIMFLUX']*coeff
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area_dl10 = tbdata['SKY_AREA']
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hdul.close()
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areatab="{}_AreaTable_dl6_en{}{}".format(os.path.join(outfile_dir,outkey), eband[index], outfile_post)
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hdul = fits.open(areatab)
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tbdata = hdul[1].data
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""" convert limflux from 0.3-2.3 keV to 0.5-2 keV """
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limflux_dl6 = tbdata['LIMFLUX']*coeff
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area_dl6 = tbdata['SKY_AREA']
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hdul.close()
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fig, ax = plt.subplots()
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for axis in ['top','bottom','left','right']:
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ax.spines[axis].set_linewidth(1)
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ax.tick_params(axis="both", width=1, labelsize=14)
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ax.set_xlim([3e-16,1e-13])
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ax.set_ylim([0.3,160])
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df = pandas.read_csv("../data/surveys/eFEDS.dat",header=None,names=['limflux','area'])
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plt.plot(df['limflux'],df['area'], color="brown", linestyle='solid',label="eFEDS (DL6)")
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plt.plot(limflux_dl10,area_dl10, color="black", linestyle='solid',label="eUDS (DL10)")
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plt.plot(limflux_dl6,area_dl6, color="black", linestyle='dashed', label="eUDS (DL6)")
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df = pandas.read_csv("../data/surveys/cosmos-legacy.dat",header=None,names=['limflux','area'])
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plt.plot(df['limflux'],df['area'], color="blue", linestyle='solid',label="COSMOS Legacy")
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df = pandas.read_csv("../data/surveys/CDWFS.dat",header=None,names=['limflux','area'])
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plt.plot(df['limflux'],df['area'], color="green", linestyle='solid', label="CDWFS")
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df = pandas.read_csv("../data/surveys/XMM-RM.dat",header=None,names=['limflux','area'])
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plt.plot(df['limflux'],df['area'], color="magenta", linestyle='solid',label="XMM-RM")
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df = pandas.read_csv("../data/surveys/XMM-XXL-N.dat",header=None,names=['limflux','area'])
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plt.plot(df['limflux'],df['area'], color="red", linestyle='solid',label="XMM-XXL-N")
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ax.legend()
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#plt.hist(data, bins=logbins, histtype='step', color='blue', linewidth=1, linestyle='solid')
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plt.xlabel('Limiting flux (0.5-2 keV, erg s$^{-1}$ cm$^{-2}$)',fontsize=14, fontweight='normal')
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plt.ylabel('Area (deg$^{2}$)',fontsize=14, fontweight='normal')
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plt.grid(visible=True)
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plt.xscale('log')
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plt.yscale('log')
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png="{}_AreaTable_en{}.png".format(os.path.join(outfile_dir,outkey), eband[index])
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plt.savefig(png, bbox_inches='tight')
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plt.close(fig)
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"""
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========================================================================
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Model TBabs<1>*powerlaw<2> Source No.: 1 Active/On
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Model Model Component Parameter Unit Value
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par comp
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1 1 TBabs nH 10^22 2.00000E-02 frozen
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2 2 powerlaw PhoIndex 2.00000 frozen
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3 2 powerlaw norm 1.14851E-04 +/- 3.83988E-06
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________________________________________________________________________
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Model Flux 0.00028334 photons (3.4012e-13 ergs/cm^2/s) range (0.30000 - 2.3000 keV)
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Model Flux 0.0001619 photons (2.4336e-13 ergs/cm^2/s) range (0.50000 - 2.0000 keV)
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"""
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