generated from erosita/uds
init
This commit is contained in:
@@ -1,4 +1,4 @@
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# uds
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# Arches cluster, a search for non-thermal emission
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Содержит код для обработки данных XMM-Newton для звездного скопления Арки (Arches cluster).
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File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@@ -1,15 +0,0 @@
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В данной директории находится каталог 4XMM-DR12
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Источник: http://xmmssc.irap.omp.eu/Catalogue/4XMM-DR12/4XMM_DR12.html
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Для вырезки источников для работы с полем UDS была использована эта команда:
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```
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ftselect '4XMM_DR12cat_slim_v1.0.fits[1]' 4XMM_DR12cat_slim_v1.0_UDS.fits 'SC_RA > 32.75 && SC_RA < 36.31 && SC_DEC > -6.55 && SC_DEC < -3.0' clobber=yes
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ftselect '4XMM_DR12cat_v1.0.fits[1]' 4XMM_DR12cat_v1.0_UDS.fits 'SC_RA > 32.75 && SC_RA < 36.31 && SC_DEC > -6.55 && SC_DEC < -3.0' clobber=yes
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Demo with DR13:
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ftselect '4XMM_slim_DR13cat_v1.0.fits[1]' 4XMM_slim_DR13cat_v1.0_DEMO.fits 'SC_RA > 25.75 && SC_RA < 50.31 && SC_DEC > -16.55 && SC_DEC < 13.0' clobber=yes
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```
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library(ggplot2)
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# see https://r-graph-gallery.com/4-barplot-with-error-bar.html
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png(file = "barplot.png")
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# create dummy data
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data <- data.frame(
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name=c('','yy','zz','ff','DR12'),
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flux=c(1,2,3,4,5),
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err=c(1,0.2,3,2,4)
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)
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# Most basic error bar
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ggplot(data) +
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geom_bar(aes(x=name, y=flux), stat="identity", fill="skyblue", alpha=0.7) +
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geom_errorbar( aes(x=name, ymin=flux-err, ymax=flux+err), width=0.4, colour="black", alpha=0.9, linewidth=2)
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dev.off()
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@@ -1,40 +0,0 @@
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options(digits = 7) # knitr sometimes decreases number of digits shown
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require(stats)
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xgrid <- c(1e-17,1e-16,1e-15,1e-14,1e-13,1e-12);
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postscript(paste("../../products/eUDS_4XMM-DR12.flux.eps",sep=""), horizontal = FALSE, onefile = FALSE, paper = "special",width = 9.0, height = 6.0)
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par(oma=c(1,1,1,1))
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#layout(matrix(c(1,2), 2, 1, byrow = TRUE), heights = c(1.5,1), TRUE)
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par(mar=c(4.3, 4.3, 1, 1))
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par(cex.lab=1.2)
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par(cex.axis=1.2)
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a <- read.table("../../products/eUDS_4XMM-DR12.flux.cvs", col.names=c("eflux","eerr","xflux","xerr"))
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xlim <- c(1e-15,1e-12)
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ylim <- c(1e-16,1e-12)
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plot(NULL, pch=21, bg="gold",ylim=ylim, xlim=xlim, main="", ylab="eUDS, erg/s/cm2",type="p",xaxt="n",xlab="4XMM-DR12, erg/s/cm2", log="xy")
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axis(side=1, at=xgrid, labels=xgrid)
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segments(a$xflux,a$eflux-a$eerr,a$xflux,a$eflux+a$eerr)
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segments(a$xflux-a$xerr,a$eflux,a$xflux+a$xerr,a$eflux)
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#segments(a$xlo,d,a$xhi,d)
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points(a$xflux,a$eflux,pch=21, bg="gold")
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lines(c(1e-17,1e-12),c(1e-17,1e-12))
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lines(c(1e-17,1e-12),10*c(1e-17,1e-12),lt=2)
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lines(c(1e-17,1e-12),0.1*c(1e-17,1e-12),lt=2)
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grid()
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abline(h=0, col = "black",lty=2)
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abline(v=xgrid, col="lightgray", lty="dotted")
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dev.off()
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@@ -1,19 +0,0 @@
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from astropy.io import fits
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import sys
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#filename='4XMM_DR12cat_slim_v1.0_UDS.fits.catalog'
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filename='4XMM_slim_DR13cat_v1.0_DEMO.fits'
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fout=filename.replace(".fits.catalog", ".names.reg")
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hdul = fits.open(filename)
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#hdul.info()
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tbdata = hdul[1].data
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hdul.close()
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#with open("./{}".format(fout), 'w') as writer:
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for rec in tbdata:
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print("fk5;circle({}, {}, 0.008)".format(rec['sc_ra'],rec['sc_dec']))
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#writer.write("fk5;circle({}, {}, {}) # text={{{}}}\n".format(rec['sc_ra'],rec['sc_dec'],rec['sc_poserr']/3600,rec['IAUNAME']))
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Binary file not shown.
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В данной директории находится каталог Gaia-unWISE из статьи https://ui.adsabs.harvard.edu/abs/2019MNRAS.489.4741S/abstract
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Каталог был получен с этого ресурса: https://zenodo.org/record/6837642#.ZAcufYBByRR
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Для вырезки источников для работы с полем UDS была использована эта команда:
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```
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ftselect 'Gaia_unWISE_AGNs.fits[1]' Gaia_unWISE_UDS.fits 'RA > 32.75 && RA < 36.31 && DEC > -6.55 && DEC < -3.0' clobber=yes
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```
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File diff suppressed because one or more lines are too long
@@ -1,15 +0,0 @@
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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from astropy.io import fits
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hdul = fits.open('mcxc.fits.catalog')
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tbdata = hdul[1].data
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hdul.close()
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with open("../../products/mcxc.reg", 'w') as writer:
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for rec in tbdata:
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writer.write("fk5;point({}, {}) # color=magenta text={{{}}}\n".format(rec['RAJ2000'],
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rec['DEJ2000'],
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rec['OName'],))
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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from astropy.io import fits
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hdul = fits.open('nustar.fits.catalog')
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tbdata = hdul[1].data
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hdul.close()
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with open("../../products/nustar_UDS.reg", 'w') as writer:
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for rec in tbdata:
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writer.write("fk5;circle({}, {}, 0.0025) # color=magenta text={{{}}}\n".format(rec['RAJ2000'], rec['DEJ2000'], rec['NuSTAR']))
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@@ -1,3 +1,5 @@
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Эта директория содержит данные (списки событий) телескопа еРОЗИТА.
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Эта директория содержит данные (списки событий) телескопа XMM.
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archive -- оригинальные списки событий
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processed -- откалиброванные списки событий
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raw -- оригинальные файлы с камер телескопа
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@@ -1,4 +0,0 @@
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В данной директории находится каталог Subaru/XMM-Newton deep survey (SXDS) https://ui.adsabs.harvard.edu/abs/2008ApJS..179..124U/abstract
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Каталог был получен с этого ресурса: https://cdsarc.cds.unistra.fr/viz-bin/cat/J/ApJS/179/124
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from astropy.io import fits
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import sys
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filename='SXDS.fits.catalog'
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fout=filename.replace(".fits.catalog", ".reg")
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hdul = fits.open(filename)
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#hdul.info()
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tbdata = hdul[1].data
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with open("../../products/{}".format(fout), 'w') as writer:
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for rec in tbdata:
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text="{}{}".format(rec['__UWS2008_'],rec['Note'])
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#writer.write("fk5;point({}, {})\n".format(rec['sc_ra'],rec['sc_dec']))
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writer.write("fk5;circle({}, {}, {}) # text={{{}}}\n".format(rec['RAJ2000'],rec['DEJ2000'],rec['e_pos']/3600,text))
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@@ -1,10 +0,0 @@
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В данной директории находится каталог скоплений галактик X-CLASS из статьи https://ui.adsabs.harvard.edu/abs/2021A%26A...652A..12K/abstract
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Каталог был получен с этого ресурса: https://vizier.cds.unistra.fr/viz-bin/VizieR-3?-source=J/A%2bA/652/A12/table3
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Для вырезки источников для работы с полем UDS была использована эта команда:
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```
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ftselect 'x-class.fits[1]' x-class_UDS.fits 'RAJ2000 > 32.75 && RAJ2000 < 36.31 && DEJ2000 > -6.55 && DEJ2000 < -3.0' clobber=yes
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```
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@@ -1,18 +0,0 @@
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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from astropy.io import fits
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hdul = fits.open('x-class_UDS.fits.catalog')
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tbdata = hdul[1].data
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hdul.close()
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with open("../../products/x-class_UDS.fits.reg", 'w') as writer:
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for rec in tbdata:
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writer.write("fk5;circle({}, {}, {}) # color=magenta text={{{} {:.2f} {:.2f}}}\n".format(rec['RAJ2000'],
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rec['DEJ2000'],
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rec['extent']/3600,
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rec['XClass'],
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rec['MLdet'],
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rec['MLext'],))
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File diff suppressed because one or more lines are too long
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method leven 10 0.01
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abund wilm
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xsect vern
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cosmo 70 0 0.73
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xset delta 0.01
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systematic 0
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model TBabs*powerlaw
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0.0123819 1 0 0 100000 1e+06
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2 -1 -3 -2 9 10
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0.000106473 0.01 0 0 1e+20 1e+24
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bayes off
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@@ -1,11 +0,0 @@
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method leven 10 0.01
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abund wilm
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xsect vern
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cosmo 70 0 0.73
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xset delta 0.01
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systematic 0
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model TBabs*powerlaw
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0.00327282 1 0 0 100000 1e+06
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2 -1 -3 -2 9 10
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0.000121635 0.01 0 0 1e+20 1e+24
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bayes off
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@@ -1,11 +0,0 @@
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method leven 10 0.01
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abund wilm
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xsect vern
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cosmo 70 0 0.73
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xset delta 0.01
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systematic 0
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model TBabs*powerlaw
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0.00240283 1 0 0 100000 1e+06
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2 -1 -3 -2 9 10
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0.000297459 0.01 0 0 1e+20 1e+24
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bayes off
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@@ -1,11 +0,0 @@
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method leven 10 0.01
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abund wilm
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xsect vern
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cosmo 70 0 0.73
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xset delta 0.01
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systematic 0
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model TBabs*powerlaw
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0.0320991 1 0 0 100000 1e+06
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2 -1 -3 -2 9 10
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0.000123814 0.01 0 0 1e+20 1e+24
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bayes off
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@@ -1,11 +0,0 @@
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method leven 10 0.01
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abund wilm
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xsect vern
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cosmo 70 0 0.73
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xset delta 0.01
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systematic 0
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model TBabs*powerlaw
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0.02 -1 0 0 100000 1e+06
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2 -1 -3 -2 9 10
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4.83094e-05 0.01 0 0 1e+20 1e+24
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bayes off
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@@ -1,8 +0,0 @@
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data tm0_SrcTool_020_SourceSpec_00005.fits.grp
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ign bad
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ign **-0.2
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ign 10.-**
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cpd /xs
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setpl en
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pl lda
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@@ -1,61 +0,0 @@
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#!/usr/bin/env python
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"""Печатает информацию о наблюдениях
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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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from uds.utils import *
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from uds.config import *
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outkey="mosa_tm0"
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""" find UDS root dir """
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root_path=dirname(dirname(dirname(inspect.getfile(uds))))
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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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index=5
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events=[]
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expmaps=[]
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totexp=0.0
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for tmkey in keylist_tm.keys():
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print("TM{} in work... init events".format(tmkey))
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for datakey in keylist_tm[tmkey]:
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#print("--> {}".format(datakey))
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""" Запускаем полностью в холостом режиме, нам нужно получить только названия файлов """
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outfile_evtool,outfile_expmap=init_events(key=datakey, eband_index=eband[index],
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infile_dir=infile_dir,
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outfile_dir=outfile_dir,
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do_init=False,
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do_obsmode=False,
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do_center=False,
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do_evtool=False,
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do_expmap=False,
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ra_cen=ra_cen, de_cen=de_cen,
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emin_kev=emin_kev[index],
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emax_kev=emax_kev[index])
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events.append(outfile_evtool)
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expmaps.append(outfile_expmap)
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tstart, tstop = read_tstart_tstop(infile=outfile_evtool)
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totexp=totexp+(tstop-tstart)
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print("{}, {} -- {}, {} -- {}, {:.2f} ks".format(outfile_evtool,tstart,tstop,
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mission2date_utc(tstart),
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mission2date_utc(tstop),
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(tstop-tstart)/1000))
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print("\n***\n*** Total exposure: {:.1f} ks\n***".format(totexp))
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@@ -1,58 +0,0 @@
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#!/usr/bin/env python
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"""Создает начальные списки событий и помещает их в uds/data/processed
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Оригинальные файлы со списками событий задаются в файлах uds/data/evtlists/*.txt
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"""
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import os
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import inspect
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from os.path import dirname
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import uds
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from uds.config import *
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from uds.utils 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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print("UDS root path: {}".format(root_path))
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el=root_path+'/data/evtlists/'
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pr=root_path+'/data/processed/'
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region="box({},{},4d,4d,0)".format(ra_cen,de_cen)
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""" Selection region """
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"""
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# TM1
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do_evtool_esass(evlist=el+'tm1.txt', outfile=pr+'tm1_obs_1.fits', gti='621296896. 621304128.', rmlock=True)
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# TM5
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do_evtool_esass(evlist=el+'tm5.txt', outfile=pr+'tm5_obs_1.fits', gti='620606016. 620614848.', rmlock=True)
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do_evtool_esass(evlist=el+'tm5.txt', outfile=pr+'tm5_obs_2.fits', gti='620676992. 620689792.', rmlock=True)
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# TM6
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do_evtool_esass(evlist=el+'tm6.txt', outfile=pr+'tm6_park_1.fits', gti='620174080. 620178002.620032', rmlock=True)
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do_evtool_esass(evlist=el+'tm6.txt', outfile=pr+'tm6_scan_1.fits', gti='620178002.620032 620192246.62720', rmlock=True)
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do_evtool_esass(evlist=el+'tm6.txt', outfile=pr+'tm6_park_2.fits', gti='620192448. 620194624.', rmlock=True)
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do_evtool_esass(evlist=el+'tm6.txt', outfile=pr+'tm6_scan_2.fits', gti='620194666.606144 620208904.673408', rmlock=True)
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do_evtool_esass(evlist=el+'tm6.txt', outfile=pr+'tm6_park_3.fits', gti='620209162.670976 620211316.650304', rmlock=True)
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do_evtool_esass(evlist=el+'tm6.txt', outfile=pr+'tm6_scan_3.fits', gti='620211328. 620225600.', rmlock=True)
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do_evtool_esass(evlist=el+'tm6.txt', outfile=pr+'tm6_park_4.fits', gti='620225853.609024 620227974.68832', rmlock=True)
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do_evtool_esass(evlist=el+'tm6.txt', outfile=pr+'tm6_scan_4.fits', gti='620227904. 620242176.', rmlock=True)
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do_evtool_esass(evlist=el+'tm6.txt', outfile=pr+'tm6_obs_1.fits', gti='620242432. 620258368.', rmlock=True)
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||||
do_evtool_esass(evlist=el+'tm6.txt', outfile=pr+'tm6_obs_2.fits', gti='620607424. 620614656.', rmlock=True)
|
||||
do_badpix_tm6(filename=pr+'tm6_obs_2.fits')
|
||||
|
||||
do_evtool_esass(evlist=el+'tm6.txt', outfile=pr+'tm6_obs_3.fits', gti='620676992. 620690368.', rmlock=True)
|
||||
do_badpix_tm6(filename=pr+'tm6_obs_3.fits')
|
||||
"""
|
||||
# TM7
|
||||
do_evtool_esass(evlist=el+'tm7.txt', outfile=pr+'tm7_obs_1.fits', gti='621043136. 621052416.', emin=0.2, emax=10.0, region=region, rmlock=True)
|
||||
#do_evtool_esass(evlist=el+'tm7.txt', outfile=pr+'tm7_obs_2.fits', gti='621110272. 621117952.', region=region, rmlock=True)
|
||||
|
||||
|
||||
@@ -1,95 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
"""Создает объедененный список событий и помещает его в uds/products
|
||||
|
||||
Этот список событий нужен, в основном для извлечения спектров с помощью srctool
|
||||
"""
|
||||
|
||||
from astropy.wcs import WCS
|
||||
from astropy.io import fits
|
||||
import sys, os, os.path, time, subprocess
|
||||
from pathlib import Path
|
||||
import numpy as np
|
||||
import glob
|
||||
from os.path import dirname
|
||||
import inspect
|
||||
import uds
|
||||
|
||||
from uds.utils import *
|
||||
from uds.config import *
|
||||
|
||||
outkey="mosa_tm0"
|
||||
|
||||
""" find UDS root dir """
|
||||
|
||||
#root_path=dirname(dirname(dirname(inspect.getfile(uds))))
|
||||
"""
|
||||
ftools does not like long file path names, for this reason, we use relative path here
|
||||
"""
|
||||
root_path='..'
|
||||
|
||||
|
||||
print("UDS root path: {}".format(root_path))
|
||||
|
||||
infile_dir=root_path+'/data/processed'
|
||||
outfile_dir=root_path+'/products'
|
||||
|
||||
create_folder(outfile_dir)
|
||||
|
||||
index=3 # select energy band
|
||||
|
||||
do_init = True
|
||||
do_merge = True
|
||||
do_rate = False
|
||||
do_adapt = True # requires CIAO
|
||||
|
||||
vign=False
|
||||
vignetting = 'vign' if (vign==True) else 'novign'
|
||||
|
||||
events=[]
|
||||
expmaps=[]
|
||||
bkgmaps=[]
|
||||
for tmkey in keylist_tm.keys():
|
||||
print("TM{} in work... init events".format(tmkey))
|
||||
for datakey in keylist_tm[tmkey]:
|
||||
#if not ("scan" in datakey):
|
||||
# continue
|
||||
print("--> {}".format(datakey))
|
||||
""" Подготавливаем списки событий индивидуальных наблюдений """
|
||||
outfile_evtool,outfile_expmap=init_events(key=datakey, eband_index=eband[index],
|
||||
infile_dir=infile_dir,
|
||||
outfile_dir=outfile_dir,
|
||||
do_init=do_init,
|
||||
do_obsmode=False,
|
||||
do_center=False,
|
||||
do_evtool=True,
|
||||
do_expmap=False,
|
||||
vign=vign,
|
||||
ra_cen=ra_cen, de_cen=de_cen,
|
||||
emin_kev=emin_kev[index],
|
||||
emax_kev=emax_kev[index])
|
||||
events.append(outfile_evtool)
|
||||
expmaps.append(outfile_expmap)
|
||||
bkgmaps.append("{}_BackMap3_en{}.fits".format(os.path.join(outfile_dir,datakey), eband[0]))
|
||||
|
||||
""" Собираем общий список событий """
|
||||
outfile_evtool="{}_EventList_en{}.fits".format(os.path.join(outfile_dir,outkey), eband[index])
|
||||
|
||||
outfile_expmap="{}_ExposureMap_en{}.{}.fits".format(os.path.join(outfile_dir,outkey), eband[index], vignetting)
|
||||
outfile_bkgmap="{}_BackMap_en{}.{}.fits".format(os.path.join(outfile_dir,outkey), eband[index], vignetting)
|
||||
|
||||
if(do_merge==True):
|
||||
do_evtool_esass(events=events, outfile=outfile_evtool)
|
||||
do_fimgmerge_ftools(maps=expmaps, outfile=outfile_expmap)
|
||||
do_fimgmerge_ftools(maps=bkgmaps, outfile=outfile_bkgmap)
|
||||
|
||||
outfile_rate="{}_RateMap_en{}.{}.fits".format(os.path.join(outfile_dir,outkey), eband[index], vignetting)
|
||||
if(do_rate==True):
|
||||
make_rate_map(cntmap=outfile_evtool, expmap=outfile_expmap, outfile=outfile_rate)
|
||||
|
||||
|
||||
function='gaussian'
|
||||
outfile_adapt="{}_ImageAdapt_en{}.{}.{}.fits".format(os.path.join(outfile_dir,outkey), eband[index], function, vignetting)
|
||||
if(do_adapt==True):
|
||||
do_adapt_ciao(infile=outfile_evtool, outfile=outfile_adapt, expmap=outfile_expmap, function=function, expcut=100)
|
||||
|
||||
|
||||
@@ -1,424 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
"""
|
||||
НАЗВАНИЕ:
|
||||
|
||||
01_init_obs.py
|
||||
|
||||
|
||||
НАЗНАЧЕНИЕ:
|
||||
|
||||
Подготавливает списки событий в разных энергетических диапазонах.
|
||||
Производит списки источников в наждом наблюдении и делает астрокоррекцию с помощью wcs_match/wcs_update
|
||||
|
||||
ВЫЗОВ:
|
||||
|
||||
conda activate ciao-4.15
|
||||
esass
|
||||
./01_init_obs.py
|
||||
|
||||
|
||||
УПРАВЛЕНИЕ:
|
||||
|
||||
Запуск отдельных команд управляется переменными, например: do_init = True
|
||||
Выбранные энергетические диапазоны управляется массивом eband_selected
|
||||
|
||||
ПАРАМЕТРЫ:
|
||||
eband_selected : Выбранные энергетические диапазоны
|
||||
|
||||
|
||||
ВЫВОД:
|
||||
|
||||
Выходные файлы записываются в директорию outfile_dir
|
||||
|
||||
|
||||
ИСТОРИЯ:
|
||||
Роман Кривонос, ИКИ РАН, krivonos@cosmos.ru
|
||||
Март 2023
|
||||
|
||||
"""
|
||||
|
||||
from astropy.wcs import WCS
|
||||
from astropy.io import fits
|
||||
import sys, os, os.path, time, subprocess
|
||||
from pathlib import Path
|
||||
import numpy as np
|
||||
import glob
|
||||
from multiprocessing import Pool
|
||||
|
||||
from os.path import dirname
|
||||
import inspect
|
||||
import uds
|
||||
|
||||
from uds.utils import *
|
||||
from uds.config import *
|
||||
|
||||
|
||||
""" find UDS root dir """
|
||||
root_path=dirname(dirname(dirname(inspect.getfile(uds))))
|
||||
print("UDS root path: {}".format(root_path))
|
||||
|
||||
infile_dir=root_path+'/data/processed'
|
||||
outfile_dir=root_path+'/products'
|
||||
create_folder(outfile_dir)
|
||||
|
||||
run_Pool=False
|
||||
|
||||
do_init = False
|
||||
do_ermask = False
|
||||
|
||||
do_erbox1 = False # local mode
|
||||
do_erbackmap1 = False #
|
||||
do_erbox2 = False # map mode, with background map
|
||||
do_erbackmap2 = False #
|
||||
do_erbox3 = False # map mode, with background map
|
||||
do_erbackmap3 = False #
|
||||
|
||||
do_ermldet = False
|
||||
do_catprep = False
|
||||
do_cross_match = False
|
||||
|
||||
do_astro_corr = False # search optimal shift
|
||||
do_astro_update = True
|
||||
|
||||
do_wcs_match = False # Chandra task -- DEPRECATED
|
||||
do_wcs_update = False # Chandra task -- DEPRECATED
|
||||
|
||||
eband_selected=[5]
|
||||
|
||||
vign=True
|
||||
vignetting = 'vign' if (vign==True) else 'novign'
|
||||
|
||||
|
||||
def runme(datakey):
|
||||
""" runs datakey over energy bands """
|
||||
events=[]
|
||||
expmaps=[]
|
||||
outfile_boxlist1=[]
|
||||
outfile_boxlist2=[]
|
||||
outfile_boxlist3=[]
|
||||
outfile_backmap1=[]
|
||||
outfile_backmap2=[]
|
||||
outfile_backmap3=[]
|
||||
cheesemask=[]
|
||||
bkgimage=[]
|
||||
srcmaps=[]
|
||||
|
||||
print(datakey)
|
||||
print('module name:', __name__)
|
||||
print('parent process:', os.getppid())
|
||||
print('process id:', os.getpid())
|
||||
|
||||
for ii in range(len(eband_selected)):
|
||||
index=eband_selected[ii]
|
||||
print("\t>>> Energy band en{} -- {}-{} keV".format(eband[index],emin_kev[index],emax_kev[index]))
|
||||
outfile_evtool, outfile_expmap = init_events(key=datakey, eband_index=eband[index],
|
||||
infile_dir=infile_dir,
|
||||
outfile_dir=outfile_dir,
|
||||
do_init=do_init,
|
||||
do_obsmode=True,
|
||||
do_center=True,
|
||||
do_evtool=True,
|
||||
do_expmap=True,
|
||||
vign=vign,
|
||||
ra_cen=ra_cen, de_cen=de_cen,
|
||||
emin_kev=emin_kev[index],
|
||||
emax_kev=emax_kev[index])
|
||||
expmaps.append(outfile_expmap)
|
||||
events.append(outfile_evtool)
|
||||
|
||||
|
||||
# After astrometry-corrected files (*.attcorr.fits) are obtained, one can take them as original, in order to check the full chain:
|
||||
#events.append(outfile_evtool.replace(".fits", ".attcorr.fits"))
|
||||
|
||||
|
||||
""" Detmask """
|
||||
|
||||
detmask="{}_DetectionMask{}".format(os.path.join(outfile_dir,datakey), outfile_post)
|
||||
if(do_ermask==True):
|
||||
cmd=["ermask",
|
||||
"expimage=%s" %(expmaps[0]), # use the first exposure maps calculated for that skyfield, independent of the energy band
|
||||
"detmask=%s" %(detmask),
|
||||
"threshold1=0.01",
|
||||
"threshold2=10.0",
|
||||
"regionfile_flag=no"
|
||||
]
|
||||
remove_file(detmask)
|
||||
print((" ").join(cmd))
|
||||
os.system((" ").join(cmd))
|
||||
|
||||
for ii in range(len(eband_selected)):
|
||||
index=eband_selected[ii]
|
||||
print("\t>>> Energy band en{} -- {}-{} keV".format(eband[index],emin_kev[index],emax_kev[index]))
|
||||
|
||||
""" erbox in local mode """
|
||||
outfile_boxlist1.append("{}_BoxList1_en{}{}".format(os.path.join(outfile_dir,datakey), eband[index], outfile_post))
|
||||
if(do_erbox1==True):
|
||||
""" erbox in local mode """
|
||||
cmd=["erbox",
|
||||
"images=%s" %(events[ii]),
|
||||
"boxlist=%s" %(outfile_boxlist1[ii]),
|
||||
"expimages=%s" %(expmaps[ii]),
|
||||
"detmasks=%s" %(detmask),
|
||||
"emin=%s" %(emin_ev[index]),
|
||||
"emax=%s" %(emax_ev[index]),
|
||||
"ecf=1.0",
|
||||
"nruns=2",
|
||||
"likemin=6.0",
|
||||
"boxsize=4",
|
||||
"compress_flag=N",
|
||||
"bkgima_flag=N",
|
||||
"expima_flag=Y",
|
||||
"detmask_flag=Y"
|
||||
]
|
||||
|
||||
remove_file(outfile_boxlist1[ii])
|
||||
|
||||
print((" ").join(cmd))
|
||||
os.system((" ").join(cmd))
|
||||
save_ds9reg(outfile_boxlist1[ii])
|
||||
|
||||
|
||||
outfile_backmap1.append("{}_BackMap1_en{}{}".format(os.path.join(outfile_dir,datakey), eband[index], outfile_post))
|
||||
cheese_mask="{}_CheeseMask1_en{}{}".format(os.path.join(outfile_dir,datakey), eband[index], outfile_post)
|
||||
if(do_erbackmap1==True):
|
||||
""" back map 1 """
|
||||
cmd=["erbackmap",
|
||||
"image=%s" %(events[ii]),
|
||||
"expimage=%s" %(expmaps[ii]),
|
||||
"boxlist=%s" %(outfile_boxlist1[ii]),
|
||||
"detmask=%s" %(detmask),
|
||||
"emin=%s" %(emin_ev[index]),
|
||||
"emax=%s" %(emax_ev[index]),
|
||||
"bkgimage=%s" %(outfile_backmap1[ii]),
|
||||
"cheesemask=%s" %(cheese_mask),
|
||||
"idband=1",
|
||||
"scut=0.001",
|
||||
"mlmin=6",
|
||||
"maxcut=0.5",
|
||||
"fitmethod=smooth smoothval=15",
|
||||
"snr=40.",
|
||||
]
|
||||
|
||||
remove_file(cheese_mask)
|
||||
remove_file(outfile_backmap1[ii])
|
||||
|
||||
os.system((" ").join(cmd))
|
||||
print((" ").join(cmd))
|
||||
|
||||
outfile_boxlist2.append("{}_BoxList2_en{}{}".format(os.path.join(outfile_dir,datakey), eband[index], outfile_post))
|
||||
if(do_erbox2==True):
|
||||
""" erbox in background mode """
|
||||
cmd=["erbox",
|
||||
"images=%s" %(events[ii]),
|
||||
"boxlist=%s" %(outfile_boxlist2[ii]),
|
||||
"expimages=%s" %(expmaps[ii]),
|
||||
"detmasks=%s" %(detmask),
|
||||
"emin=%s" %(emin_ev[index]),
|
||||
"emax=%s" %(emax_ev[index]),
|
||||
"ecf=1.0",
|
||||
"nruns=2",
|
||||
"likemin=4.0",
|
||||
"boxsize=4",
|
||||
"compress_flag=N",
|
||||
"bkgima_flag=Y",
|
||||
"bkgimages={}".format(outfile_backmap1[ii]),
|
||||
"expima_flag=Y",
|
||||
"detmask_flag=Y"
|
||||
]
|
||||
|
||||
remove_file(outfile_boxlist2[ii])
|
||||
|
||||
print((" ").join(cmd))
|
||||
os.system((" ").join(cmd))
|
||||
save_ds9reg(outfile_boxlist2[ii])
|
||||
|
||||
|
||||
outfile_backmap2.append("{}_BackMap2_en{}{}".format(os.path.join(outfile_dir,datakey), eband[index], outfile_post))
|
||||
cheese_mask="{}_CheeseMask2_en{}{}".format(os.path.join(outfile_dir,datakey), eband[index], outfile_post)
|
||||
if(do_erbackmap2==True):
|
||||
""" back map 2 """
|
||||
cmd=["erbackmap",
|
||||
"image=%s" %(events[ii]),
|
||||
"expimage=%s" %(expmaps[ii]),
|
||||
"boxlist=%s" %(outfile_boxlist2[ii]),
|
||||
"detmask=%s" %(detmask),
|
||||
"emin=%s" %(emin_ev[index]),
|
||||
"emax=%s" %(emax_ev[index]),
|
||||
"bkgimage=%s" %(outfile_backmap2[ii]),
|
||||
"cheesemask=%s" %(cheese_mask),
|
||||
"idband=1",
|
||||
"scut=0.001",
|
||||
"mlmin=6",
|
||||
"maxcut=0.5",
|
||||
"fitmethod=smooth smoothval=15",
|
||||
"snr=40.",
|
||||
]
|
||||
|
||||
remove_file(cheese_mask)
|
||||
remove_file(outfile_backmap2[ii])
|
||||
|
||||
os.system((" ").join(cmd))
|
||||
print((" ").join(cmd))
|
||||
|
||||
outfile_boxlist3.append("{}_BoxList3_en{}{}".format(os.path.join(outfile_dir,datakey), eband[index], outfile_post))
|
||||
if(do_erbox3==True):
|
||||
""" erbox in map mode FINAL """
|
||||
cmd=["erbox",
|
||||
"images=%s" %(events[ii]),
|
||||
"boxlist=%s" %(outfile_boxlist3[ii]),
|
||||
"expimages=%s" %(expmaps[ii]),
|
||||
"detmasks=%s" %(detmask),
|
||||
"emin=%s" %(emin_ev[index]),
|
||||
"emax=%s" %(emax_ev[index]),
|
||||
"ecf=1.0",
|
||||
"nruns=2",
|
||||
"likemin=4.0",
|
||||
"boxsize=4",
|
||||
"compress_flag=N",
|
||||
"bkgima_flag=Y",
|
||||
"bkgimages={}".format(outfile_backmap2[ii]),
|
||||
"expima_flag=Y",
|
||||
"detmask_flag=Y"
|
||||
]
|
||||
|
||||
remove_file(outfile_boxlist3[ii])
|
||||
|
||||
print((" ").join(cmd))
|
||||
os.system((" ").join(cmd))
|
||||
save_ds9reg(outfile_boxlist3[ii])
|
||||
|
||||
outfile_backmap3.append("{}_BackMap3_en{}{}".format(os.path.join(outfile_dir,datakey), eband[index], outfile_post))
|
||||
cheese_mask="{}_CheeseMask3_en{}{}".format(os.path.join(outfile_dir,datakey), eband[index], outfile_post)
|
||||
if(do_erbackmap3==True):
|
||||
""" back map 3 FINAL """
|
||||
cmd=["erbackmap",
|
||||
"image=%s" %(events[ii]),
|
||||
"expimage=%s" %(expmaps[ii]),
|
||||
"boxlist=%s" %(outfile_boxlist3[ii]),
|
||||
"detmask=%s" %(detmask),
|
||||
"emin=%s" %(emin_ev[index]),
|
||||
"emax=%s" %(emax_ev[index]),
|
||||
"bkgimage=%s" %(outfile_backmap3[ii]),
|
||||
"cheesemask=%s" %(cheese_mask),
|
||||
"idband=1",
|
||||
"scut=0.001",
|
||||
"mlmin=6",
|
||||
"maxcut=0.5",
|
||||
"fitmethod=smooth smoothval=15",
|
||||
"snr=40.",
|
||||
]
|
||||
|
||||
remove_file(cheese_mask)
|
||||
remove_file(outfile_backmap3[ii])
|
||||
|
||||
os.system((" ").join(cmd))
|
||||
print((" ").join(cmd))
|
||||
|
||||
mllist="{}_MaxLikSourceList_en{}{}".format(os.path.join(outfile_dir,datakey), eband[index], outfile_post)
|
||||
srcmap="{}_SourceMap_en{}{}".format(os.path.join(outfile_dir,datakey), eband[index], outfile_post)
|
||||
|
||||
cmd=["ermldet",
|
||||
"mllist=%s" %(mllist),
|
||||
"boxlist=%s" %(outfile_boxlist3[ii]),
|
||||
"images=%s" %(events[ii]),
|
||||
"expimages=%s" %(expmaps[ii]),
|
||||
"detmasks=%s" %(detmask),
|
||||
"bkgimages=%s" %(outfile_backmap3[ii]),
|
||||
"emin=%s" %(emin_ev[index]),
|
||||
"emax=%s" %(emax_ev[index]),
|
||||
"hrdef=",
|
||||
"ecf={}".format(ecf[index]),
|
||||
"likemin=5.",
|
||||
"extlikemin=6.",
|
||||
"compress_flag=N",
|
||||
"cutrad=15.",
|
||||
"multrad=20.",
|
||||
"extmin=2.0",
|
||||
"extmax=15.0",
|
||||
#"bkgima_flag=Y", looks outdated
|
||||
"expima_flag=Y",
|
||||
"detmask_flag=Y",
|
||||
"extentmodel=beta",
|
||||
"thres_flag=N",
|
||||
"thres_col=like",
|
||||
"thres_val=30.",
|
||||
"nmaxfit=4",
|
||||
"nmulsou=2",
|
||||
"fitext_flag=yes",
|
||||
"srcima_flag=yes",
|
||||
"srcimages=%s" %(srcmap)
|
||||
|
||||
]
|
||||
|
||||
if(do_ermldet==True):
|
||||
test_exe('ermldet')
|
||||
remove_file(mllist)
|
||||
remove_file(srcmap)
|
||||
|
||||
os.system((" ").join(cmd))
|
||||
print((" ").join(cmd))
|
||||
save_ermldet_ds9reg(mllist,scale=60*60)
|
||||
|
||||
catprep="{}_SourceCatalog_en{}{}".format(os.path.join(outfile_dir,datakey), eband[index], outfile_post)
|
||||
catprep_en0="{}_SourceCatalog_en{}{}".format(os.path.join(outfile_dir,datakey), eband[0], outfile_post)
|
||||
if(do_catprep==True):
|
||||
cmd=["catprep",
|
||||
"infile={}".format(mllist),
|
||||
"outfile={}".format(catprep),]
|
||||
remove_file(catprep)
|
||||
os.system((" ").join(cmd))
|
||||
print((" ").join(cmd))
|
||||
save_catprep_ds9reg(catprep,scale=60*60)
|
||||
|
||||
if(do_cross_match==True):
|
||||
crossmatch_shu2019(catprep,dlmin=10,refimage=events[ii],crval=wcslist[datakey],
|
||||
catalog=root_path+"/data/Gaia_unWISE/Gaia_unWISE_UDS.fits.catalog",errlim=5.0)
|
||||
|
||||
if(do_astro_corr==True and eband[index]=='0'):
|
||||
""" run astro_corr for 0.3-2.3 keV only """
|
||||
wcs_astro_corr(catprep)
|
||||
#wcs_match_ciao(catprep, method='rst',radius=12,residlim=0,residtype=0,residfac=1)
|
||||
|
||||
if(do_astro_update==True):
|
||||
""" run astro_corr for 0.3-2.3 keV only """
|
||||
attcorr=wcs_update_shift(events[ii],flog=catprep_en0.replace(".fits", ".shift.log"))
|
||||
do_evtool_esass(evfile=attcorr,outfile=attcorr,rmlock=False, do_center=True, ra_cen=ra_cen, de_cen=de_cen)
|
||||
|
||||
|
||||
if(do_wcs_match==True and eband[index]=='0'):
|
||||
""" run wcs_match for 0.3-2.3 keV only """
|
||||
wcs_match_ciao(catprep, method='trans',radius=12,residlim=5)
|
||||
#wcs_match_ciao(catprep, method='rst',radius=12,residlim=0,residtype=0,residfac=1)
|
||||
|
||||
if(do_wcs_update==True):
|
||||
""" use 0.3-2.3 keV transform matrix for all other bands """
|
||||
attcorr=wcs_update_ciao(events[ii],crval=wcslist[datakey],transformfile=catprep_en0.replace(".fits", ".xfm"),clean=False)
|
||||
do_evtool_esass(evfile=attcorr,outfile=attcorr,rmlock=False, do_center=True, ra_cen=ra_cen, de_cen=de_cen)
|
||||
|
||||
|
||||
"""
|
||||
# individual run, testing
|
||||
runme("tm7_obs_1")
|
||||
runme("tm5_obs_1")
|
||||
runme("tm6_scan_1")
|
||||
"""
|
||||
|
||||
|
||||
|
||||
if(run_Pool==True):
|
||||
# parallel run
|
||||
items=[]
|
||||
for tmkey in keylist_tm.keys():
|
||||
for datakey in keylist_tm[tmkey]:
|
||||
items.append(datakey)
|
||||
|
||||
with Pool() as pool:
|
||||
pool.map(runme, items)
|
||||
|
||||
else:
|
||||
# conventional run
|
||||
for tmkey in keylist_tm.keys():
|
||||
for datakey in keylist_tm[tmkey]:
|
||||
print("--> {}".format(datakey))
|
||||
runme(datakey)
|
||||
#sys.exit()
|
||||
@@ -1,458 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
НАЗВАНИЕ:
|
||||
|
||||
04_mosaics.py
|
||||
|
||||
|
||||
НАЗНАЧЕНИЕ:
|
||||
|
||||
Собирает мозайки в разных энергетических диапазонах.
|
||||
|
||||
ВЫЗОВ:
|
||||
|
||||
esass
|
||||
./04_mosaics.py
|
||||
|
||||
|
||||
УПРАВЛЕНИЕ:
|
||||
|
||||
Запуск отдельных команд управляется переменными, например: do_init = True
|
||||
Выбранный энергетический диапазон управляется переменной index
|
||||
forced=True делает принудительную фотометрию
|
||||
|
||||
ПАРАМЕТРЫ:
|
||||
|
||||
index : Выбранный энергетический диапазон
|
||||
|
||||
|
||||
ВЫВОД:
|
||||
|
||||
Выходные файлы записываются в директорию outfile_dir
|
||||
|
||||
|
||||
ИСТОРИЯ:
|
||||
|
||||
Роман Кривонос, ИКИ РАН, krivonos@cosmos.ru
|
||||
Март 2023
|
||||
|
||||
"""
|
||||
|
||||
from astropy.wcs import WCS
|
||||
from astropy.io import fits
|
||||
import sys, os, os.path, time, subprocess
|
||||
#from pathlib import Path
|
||||
import numpy as np
|
||||
import glob
|
||||
from os.path import dirname
|
||||
import inspect
|
||||
import pickle
|
||||
|
||||
|
||||
|
||||
import uds
|
||||
|
||||
from uds.utils import *
|
||||
from uds.config import *
|
||||
|
||||
|
||||
""" find UDS root dir """
|
||||
#root_path=dirname(dirname(dirname(inspect.getfile(uds))))
|
||||
"""
|
||||
ftools does not like long file path names,
|
||||
for this reason, we use relative path here
|
||||
"""
|
||||
root_path='..'
|
||||
print("UDS root path: {}".format(root_path))
|
||||
|
||||
infile_dir=root_path+'/data/processed'
|
||||
outfile_dir=root_path+'/products'
|
||||
create_folder(outfile_dir)
|
||||
|
||||
local_run = False
|
||||
|
||||
outkey="tm0"
|
||||
|
||||
do_init = True
|
||||
do_merge = True
|
||||
do_detmask = True
|
||||
do_expmap = True
|
||||
do_erbox1 = True # local mode
|
||||
do_erbackmap1 = True #
|
||||
do_erbox2 = True # map mode, with background map
|
||||
do_erbackmap2 = True #
|
||||
do_erbox3 = True # map mode, with background map
|
||||
do_erbackmap3 = True #
|
||||
do_ersensmap = False
|
||||
do_ermldet = True
|
||||
do_fixcat = False # only for index=0
|
||||
do_fixxmm = False # prepare forced photometry, only for index=0
|
||||
do_apetool = False
|
||||
do_catprep = True
|
||||
do_filter_catalog = False
|
||||
|
||||
do_cross_match = False
|
||||
|
||||
index=3
|
||||
|
||||
forced=False
|
||||
""" If forced=True, take input catalog from energy range en0 """
|
||||
|
||||
comm='' # for 4XMM-DR12 forced photometry use '-xmm'
|
||||
|
||||
|
||||
vign=True
|
||||
vignetting = 'vign' if (vign==True) else 'novign'
|
||||
|
||||
attcorr=True
|
||||
|
||||
events=[]
|
||||
expmaps=[]
|
||||
bkgmaps=[]
|
||||
for tmkey in keylist_tm.keys():
|
||||
print("TM{} in work... init events".format(tmkey))
|
||||
for datakey in keylist_tm[tmkey]:
|
||||
print("--> {}".format(datakey))
|
||||
""" Подготавливаем списки событий индивидуальных наблюдений """
|
||||
outfile_evtool,outfile_expmap=init_events(key=datakey,attcorr=attcorr,
|
||||
eband_index=eband[index],
|
||||
infile_dir=infile_dir,
|
||||
outfile_dir=outfile_dir,
|
||||
do_init=do_init,
|
||||
do_obsmode=False,
|
||||
do_center=False,
|
||||
do_evtool=True,
|
||||
do_expmap=False,
|
||||
vign=vign,
|
||||
ra_cen=ra_cen, de_cen=de_cen,
|
||||
emin_kev=emin_kev[index],
|
||||
emax_kev=emax_kev[index])
|
||||
events.append(outfile_evtool)
|
||||
expmaps.append(outfile_expmap)
|
||||
|
||||
|
||||
""" Собираем общий список событий """
|
||||
outfile_evtool="{}_EventList_en{}.fits".format(os.path.join(outfile_dir,outkey),
|
||||
eband[index])
|
||||
|
||||
if(do_merge==True):
|
||||
do_evtool_esass(events=events, outfile=outfile_evtool)
|
||||
|
||||
|
||||
""" makes detmask from TM exposures """
|
||||
detmask="{}/{}_DetectorMask_en{}{}".format(outfile_dir,
|
||||
outkey,
|
||||
eband[index],
|
||||
outfile_post)
|
||||
if(do_detmask==True):
|
||||
create_detmask_merged(expmaps,detmask,minval=100)
|
||||
|
||||
"""
|
||||
Собираем общую карту экспозиции, обратите внимание на коэффициент 7.
|
||||
Экспозиция рассчитывается на 7 телескопов.
|
||||
outfile_expmap="{}_ExposureMap_en{}.{}.fits".format(os.path.join(outfile_dir,outkey), eband[index], vignetting)
|
||||
outfile_bkgmap="{}_BackMap_en{}.{}.fits".format(os.path.join(outfile_dir,outkey), eband[index], vignetting)
|
||||
"""
|
||||
outfile_expmap="{}_ExposureMap_en{}.{}{}".format(os.path.join(outfile_dir,outkey),
|
||||
eband[index],vignetting,
|
||||
outfile_post)
|
||||
if(do_expmap==True):
|
||||
create_expmap_merged(expmaps,outfile_expmap,scale=7.0)
|
||||
|
||||
outfile_boxlist1="{}/{}_BoxList1_en{}{}".format(outfile_dir,outkey, eband[index], outfile_post)
|
||||
if(do_erbox1==True):
|
||||
cmd=["erbox",
|
||||
"images=\'{}\'".format(outfile_evtool),
|
||||
"boxlist=%s" %(outfile_boxlist1),
|
||||
"expimages=\'{}\'".format(outfile_expmap),
|
||||
"detmasks=\'{}\'".format(detmask),
|
||||
"emin=\'{}\'".format(emin_ev[index]),
|
||||
"emax=\'{}\'".format(emax_ev[index]),
|
||||
"ecf=\'{}\'".format(ecf[index]),
|
||||
"nruns=2",
|
||||
"likemin=6.0",
|
||||
"boxsize=4",
|
||||
"compress_flag=N",
|
||||
"bkgima_flag=N",
|
||||
"expima_flag=Y",
|
||||
"detmask_flag=Y"
|
||||
]
|
||||
remove_file(outfile_boxlist1)
|
||||
print((" ").join(cmd))
|
||||
os.system((" ").join(cmd))
|
||||
save_ds9reg(outfile_boxlist1)
|
||||
|
||||
""" Background map 1 """
|
||||
outfile_backmap1="{}_BackMap1_en{}{}".format(os.path.join(outfile_dir,outkey), eband[index], outfile_post)
|
||||
cheese_mask="{}_CheeseMask1_en{}{}".format(os.path.join(outfile_dir,outkey), eband[index], outfile_post)
|
||||
if(do_erbackmap1==True):
|
||||
do_erbackmap_esass(outfile_evtool,outfile_expmap,outfile_boxlist1,detmask,emin_ev[index],emax_ev[index],
|
||||
outfile_backmap1,cheese_mask)
|
||||
|
||||
outfile_boxlist2="{}/{}_BoxList2_en{}{}".format(outfile_dir,outkey, eband[index], outfile_post)
|
||||
if(do_erbox2==True):
|
||||
cmd=["erbox",
|
||||
"images=\'{}\'".format(outfile_evtool),
|
||||
"boxlist=%s" %(outfile_boxlist2),
|
||||
"expimages=\'{}\'".format(outfile_expmap),
|
||||
"detmasks=\'{}\'".format(detmask),
|
||||
"emin=\'{}\'".format(emin_ev[index]),
|
||||
"emax=\'{}\'".format(emax_ev[index]),
|
||||
"ecf=\'{}\'".format(ecf[index]),
|
||||
"nruns=2",
|
||||
"likemin=6.0",
|
||||
"boxsize=4",
|
||||
"compress_flag=N",
|
||||
"bkgima_flag=Y",
|
||||
"bkgimages={}".format(outfile_backmap1),
|
||||
"expima_flag=Y",
|
||||
"detmask_flag=Y"
|
||||
]
|
||||
remove_file(outfile_boxlist2)
|
||||
print((" ").join(cmd))
|
||||
os.system((" ").join(cmd))
|
||||
save_ds9reg(outfile_boxlist2)
|
||||
|
||||
""" Background map 2 """
|
||||
outfile_backmap2="{}_BackMap2_en{}{}".format(os.path.join(outfile_dir,outkey), eband[index], outfile_post)
|
||||
cheese_mask="{}_CheeseMask2_en{}{}".format(os.path.join(outfile_dir,outkey), eband[index], outfile_post)
|
||||
if(do_erbackmap2==True):
|
||||
do_erbackmap_esass(outfile_evtool,outfile_expmap,outfile_boxlist2,detmask,emin_ev[index],emax_ev[index],
|
||||
outfile_backmap2,cheese_mask)
|
||||
|
||||
outfile_boxlist3="{}/{}_BoxList3_en{}{}".format(outfile_dir,outkey, eband[index], outfile_post)
|
||||
if(do_erbox3==True):
|
||||
cmd=["erbox",
|
||||
"images=\'{}\'".format(outfile_evtool),
|
||||
"boxlist=%s" %(outfile_boxlist3),
|
||||
"expimages=\'{}\'".format(outfile_expmap),
|
||||
"detmasks=\'{}\'".format(detmask),
|
||||
"emin=\'{}\'".format(emin_ev[index]),
|
||||
"emax=\'{}\'".format(emax_ev[index]),
|
||||
"ecf=\'{}\'".format(ecf[index]),
|
||||
"nruns=2",
|
||||
"likemin=6.0",
|
||||
"boxsize=4",
|
||||
"compress_flag=N",
|
||||
"bkgima_flag=Y",
|
||||
"bkgimages={}".format(outfile_backmap2),
|
||||
"expima_flag=Y",
|
||||
"detmask_flag=Y"
|
||||
]
|
||||
remove_file(outfile_boxlist3)
|
||||
print((" ").join(cmd))
|
||||
os.system((" ").join(cmd))
|
||||
save_ds9reg(outfile_boxlist3)
|
||||
|
||||
""" Background map 3 """
|
||||
outfile_backmap3="{}_BackMap3_en{}.{}{}".format(os.path.join(outfile_dir,outkey), eband[index], vignetting, outfile_post)
|
||||
cheese_mask="{}_CheeseMask3_en{}.{}{}".format(os.path.join(outfile_dir,outkey), eband[index], vignetting, outfile_post)
|
||||
if(do_erbackmap3==True):
|
||||
boxlist3 = outfile_boxlist3 if(forced == False) else "{}/{}_BoxList3_en{}{}".format(outfile_dir,outkey, eband[0], outfile_post)
|
||||
do_erbackmap_esass(outfile_evtool,outfile_expmap,boxlist3,detmask,emin_ev[index],emax_ev[index],
|
||||
outfile_backmap3,cheese_mask)
|
||||
|
||||
if(forced==True):
|
||||
mllist="{}_MaxLikSourceList_en{}.forced{}{}".format(os.path.join(outfile_dir,outkey), eband[index], comm, outfile_post)
|
||||
srcmap="{}_SourceMap_en{}.forced{}{}".format(os.path.join(outfile_dir,outkey), eband[index], comm, outfile_post)
|
||||
|
||||
""" for en1,2,3,6 give mllist from en0 as input """
|
||||
#boxlist3="{}_MaxLikSourceList_en{}.forced{}{}".format(os.path.join(outfile_dir,outkey), eband[0], comm, outfile_post)
|
||||
#if(index==0):
|
||||
boxlist3="{}_MaxLikSourceList_en{}.fixed{}{}".format(os.path.join(outfile_dir,outkey), eband[0], comm, outfile_post)
|
||||
if not (os.path.exists(boxlist3)):
|
||||
print("{} not found. Run do_fixcat=True, index=0, forced=False".format(boxlist3))
|
||||
sys.exit()
|
||||
|
||||
add_specific_columns(boxlist3)
|
||||
fitpos_flag="fitpos_flag=no"
|
||||
fitext_flag="fitext_flag=no"
|
||||
nmulsou = "nmulsou=1"
|
||||
nmaxfit="nmaxfit=10"
|
||||
multrad="multrad=15."
|
||||
cutrad="cutrad=15."
|
||||
if(index == 3 or index == 6):
|
||||
""" for hard band take unvignetted background """
|
||||
outfile_backmap3="{}_BackMap3_en{}.{}{}".format(os.path.join(outfile_dir,outkey), eband[index], "novign", outfile_post)
|
||||
|
||||
else:
|
||||
mllist="{}_MaxLikSourceList_en{}{}".format(os.path.join(outfile_dir,outkey), eband[index], outfile_post)
|
||||
srcmap="{}_SourceMap_en{}{}".format(os.path.join(outfile_dir,outkey), eband[index], outfile_post)
|
||||
boxlist3 = outfile_boxlist3
|
||||
fitpos_flag="fitpos_flag=yes"
|
||||
fitext_flag="fitext_flag=yes"
|
||||
nmulsou = "nmulsou=2"
|
||||
nmaxfit="nmaxfit=4"
|
||||
multrad="multrad=20."
|
||||
cutrad="cutrad=20."
|
||||
""" allow ermpldet to split sources (no more than two) """
|
||||
|
||||
cmd=["ermldet",
|
||||
"mllist={}".format(mllist),
|
||||
"boxlist=%s" %(boxlist3),
|
||||
"images=\'{}\'".format(outfile_evtool),
|
||||
"expimages=\'{}\'".format(outfile_expmap),
|
||||
"detmasks=\'{}\'".format(detmask),
|
||||
"bkgimages=\'{}\'".format(outfile_backmap3),
|
||||
"emin=\'{}\'".format(emin_ev[index]),
|
||||
"emax=\'{}\'".format(emax_ev[index]),
|
||||
"ecf=\'{}\'".format(ecf[index]),
|
||||
"hrdef=",
|
||||
"likemin=0.",
|
||||
"extlikemin=5.",
|
||||
"compress_flag=N",
|
||||
cutrad,
|
||||
multrad,
|
||||
"extmin=2.0",
|
||||
"extmax=35.0",
|
||||
#"bkgima_flag=Y", looks outdated
|
||||
"expima_flag=Y",
|
||||
"detmask_flag=Y",
|
||||
"shapelet_flag=yes",
|
||||
"photon_flag=yes",
|
||||
"extentmodel=beta",
|
||||
"thres_flag=N",
|
||||
"thres_col=like",
|
||||
"thres_val=30.",
|
||||
nmaxfit,
|
||||
nmulsou,
|
||||
fitpos_flag,
|
||||
fitext_flag,
|
||||
"srcima_flag=yes",
|
||||
"srcimages=\'{}\'".format(srcmap)
|
||||
]
|
||||
|
||||
if(do_ersensmap==True):
|
||||
detlike=10
|
||||
create_sensmap(sensmap="{}_SensitivityMap_dl{}_en{}{}".format(os.path.join(outfile_dir,outkey), detlike,
|
||||
eband[index], outfile_post),
|
||||
areatab="{}_AreaTable_dl{}_en{}{}".format(os.path.join(outfile_dir,outkey),
|
||||
detlike, eband[index], outfile_post),
|
||||
expmap=outfile_expmap, backmap=outfile_backmap3,detlike=detlike,
|
||||
detmask=detmask, emin=emin_ev[index], emax=emax_ev[index],ecf=ecf[index])
|
||||
"""
|
||||
detlike=6
|
||||
create_sensmap(sensmap="{}_SensitivityMap_dl{}_en{}{}".format(os.path.join(outfile_dir,outkey), detlike,
|
||||
eband[index], outfile_post),
|
||||
areatab="{}_AreaTable_dl{}_en{}{}".format(os.path.join(outfile_dir,outkey),
|
||||
detlike, eband[index], outfile_post),
|
||||
expmap=outfile_expmap, backmap=outfile_backmap3,detlike=detlike,
|
||||
detmask=detmask, emin=emin_ev[index], emax=emax_ev[index],ecf=ecf[index])
|
||||
"""
|
||||
|
||||
if(do_ermldet==True):
|
||||
test_exe('ermldet')
|
||||
if(vign==False):
|
||||
print('Run ermldet with vignetted exposure!')
|
||||
sys.exit()
|
||||
remove_file(mllist)
|
||||
remove_file(srcmap)
|
||||
print(cmd)
|
||||
runme(cmd, local_run=local_run)
|
||||
print(cmd)
|
||||
save_ermldet_ds9reg(mllist,scale=60*60,label='det_like')
|
||||
save_ermldet_ds9reg(mllist,scale=60*60,label='id_src')
|
||||
|
||||
correct_fluxerr_ermldet_forced(mllist)
|
||||
if(forced==True):
|
||||
result = check_ermldet_forced(mllist)
|
||||
# for a some reason, for an arbitrary energy band, ermldet break order of sources. Do this forced correction.
|
||||
if(result == False):
|
||||
correct_srcid_ermldet_forced(mllist)
|
||||
|
||||
if(do_fixcat==True):
|
||||
if not index == 0:
|
||||
print("ERROR: You can fix only reference catalog for en0.")
|
||||
sys.exit()
|
||||
if forced == True:
|
||||
print("ERROR: You can fix only non-forced catalog for en0.")
|
||||
sys.exit()
|
||||
|
||||
srcs_remove=[341,446,346,96]
|
||||
srcs_add = {'4XMM J021738.8-051257':[34.4117002, -5.2159135, 0.624],# 341
|
||||
'4XMM J021733.8-051311':[34.3910215,-5.2199877,2.247],# 341
|
||||
'4XMM J021929.4-051220':[34.8725460,-5.2056849,1.074],#446
|
||||
'4XMM J021930.7-051225':[34.8782267,-5.2072112,0.624],# 446
|
||||
'4XMM J021945.2-045331':[34.9383593,-4.8919843,1.538],#346
|
||||
'4XMM J021929.4-043224':[34.8728586,-4.5400022,0.659555],#96
|
||||
#'4XMM J021929.4-043224':[34.8728586,-4.5400022, 0.660],
|
||||
#'4XMM J021831.8-050059':[34.6328841,-5.0163909,1.529],
|
||||
#'4XMM J022131.1-050027':[35.3797879,-5.0075498,0.941],
|
||||
#'4XMM J022129.5-045914':[35.3732136,-4.9874025,0.332],
|
||||
#'4XMM J022026.3-050251':[35.1098619,-5.0476199,0.551],
|
||||
#'4XMM J021925.4-042647':[34.8559099,-4.4465007,1.366],
|
||||
#'4XMM J021910.9-045108':[34.7954311,-4.8522901,0.898],
|
||||
#'4XMM J021945.2-045331':[34.9383593,-4.8919843,1.538],
|
||||
#'4XMM J021733.8-051311':[34.3910215,-5.2199877,2.247],
|
||||
}
|
||||
fix_catalog(mllist=mllist,refimage=outfile_evtool, srcs_remove=srcs_remove, srcs_add=srcs_add)
|
||||
"""
|
||||
Note that fix_catalog added ID_SRC to each XMM source.
|
||||
Next, we save forced XMM sources (with new ID_SRC!) for later catalog compilation
|
||||
"""
|
||||
with open(mllist.replace(".fits", ".xmm.pickle"), 'wb') as f:
|
||||
pickle.dump(srcs_add, f)
|
||||
|
||||
if(do_fixxmm==True):
|
||||
if not index == 0:
|
||||
print("ERROR: You can fix only reference catalog for en0.")
|
||||
sys.exit()
|
||||
if forced == True:
|
||||
print("ERROR: You can fix only non-forced catalog for en0.")
|
||||
sys.exit()
|
||||
|
||||
fix_xmm_sources(mllist=mllist,refimage=outfile_evtool, xmm_catalog='../data/4XMM-DR12/4XMM_DR12cat_slim_v1.0_UDS.fits.catalog')
|
||||
|
||||
|
||||
if(do_apetool==True):
|
||||
psfmap="{}_PsfMap{}".format(os.path.join(outfile_dir,outkey), outfile_post)
|
||||
#remove_file(psfmap)
|
||||
#cmd=["apetool",
|
||||
# "images=\'{}\'".format(outfile_evtool),
|
||||
# "psfmaps=\'{}\'".format(psfmap),
|
||||
# "psfmapflag=yes",]
|
||||
#runme(cmd, local_run=local_run)
|
||||
|
||||
cmd=["apetool",
|
||||
"mllist={}".format(mllist),
|
||||
"apelistout={}".format(mllist), # give the same file
|
||||
"images=\'{}\'".format(outfile_evtool),
|
||||
"expimages=\'{}\'".format(outfile_expmap),
|
||||
"detmasks=\'{}\'".format(detmask),
|
||||
"bkgimages=\'{}\'".format(outfile_backmap3),
|
||||
"emin=\'{}\'".format(emin_ev[index]),
|
||||
"emax=\'{}\'".format(emax_ev[index]),
|
||||
"srcimages=\'{}\'".format(srcmap),
|
||||
"psfmaps={}".format(psfmap),
|
||||
"psfmapflag=no",
|
||||
"stackflag=no",
|
||||
"apexflag=yes",
|
||||
"apesenseflag=no",
|
||||
"eefextract=0.65",
|
||||
"cutrad=15",
|
||||
"eindex=1",]
|
||||
runme(cmd, local_run=local_run)
|
||||
|
||||
if(forced==True):
|
||||
catprep="{}_SourceCatalog_en{}.forced{}{}".format(os.path.join(outfile_dir,outkey), eband[index], comm, outfile_post)
|
||||
else:
|
||||
catprep="{}_SourceCatalog_en{}{}".format(os.path.join(outfile_dir,outkey), eband[index], outfile_post)
|
||||
|
||||
if(do_catprep==True):
|
||||
cmd=["catprep",
|
||||
"infile={}".format(mllist),
|
||||
"outfile={}".format(catprep),]
|
||||
remove_file(catprep)
|
||||
runme(cmd, local_run=local_run)
|
||||
|
||||
|
||||
if(do_filter_catalog==True):
|
||||
#filter_mllist(mllist,expcut=5000.0,dlcut=10.0,dlmin=10,dlmax=10000)
|
||||
""" works the same """
|
||||
filter_catprep(catprep,expcut=5000.0,dlmin=10,dlmax=10000,outkey='bright')
|
||||
#filter_catprep(catprep,expcut=5000.0,dlmin=6,dlmax=10,outkey='faint')
|
||||
|
||||
if(do_cross_match==True):
|
||||
crossmatch_shu2019(catprep,dlmin=10,refimage=outfile_evtool,crval=[ra_cen, de_cen],
|
||||
catalog=root_path+"/data/Gaia_unWISE/Gaia_unWISE_UDS.fits.catalog")
|
||||
@@ -1,286 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
"""
|
||||
НАЗВАНИЕ:
|
||||
|
||||
05_srctool.py
|
||||
|
||||
|
||||
НАЗНАЧЕНИЕ:
|
||||
|
||||
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.
|
||||
|
||||
ВЫЗОВ:
|
||||
|
||||
esass
|
||||
./05_srctool.py
|
||||
|
||||
|
||||
УПРАВЛЕНИЕ:
|
||||
|
||||
Требуется запуск предыдущего скрипта 04_mosaics.py
|
||||
|
||||
ПАРАМЕТРЫ:
|
||||
|
||||
index=4 : Выбранный энергетический диапазон
|
||||
|
||||
|
||||
ВЫВОД:
|
||||
|
||||
Выходные файлы записываются в директорию outfile_dir/srctool_dir
|
||||
|
||||
|
||||
ИСТОРИЯ:
|
||||
|
||||
Роман Кривонос, ИКИ РАН, krivonos@cosmos.ru
|
||||
Март 2023
|
||||
|
||||
"""
|
||||
|
||||
from astropy.wcs import WCS
|
||||
from astropy.io import fits
|
||||
import sys, os, os.path, time, subprocess
|
||||
from pathlib import Path
|
||||
import numpy as np
|
||||
import glob
|
||||
from os.path import dirname
|
||||
import inspect
|
||||
import pickle
|
||||
|
||||
import uds
|
||||
|
||||
from uds.utils import *
|
||||
from uds.config import *
|
||||
from uds.sherpa import *
|
||||
|
||||
|
||||
""" find UDS root dir """
|
||||
#root_path=dirname(dirname(dirname(inspect.getfile(uds))))
|
||||
"""
|
||||
ftools does not like long file path names,
|
||||
for this reason, we use relative path here
|
||||
"""
|
||||
root_path='..'
|
||||
print("UDS root path: {}".format(root_path))
|
||||
|
||||
infile_dir=root_path+'/data/processed'
|
||||
outfile_dir=root_path+'/products'
|
||||
create_folder(outfile_dir)
|
||||
|
||||
srctool_dir="{}/{}".format(outfile_dir,"srctool-products")
|
||||
create_folder(srctool_dir)
|
||||
|
||||
outkey="tm0"
|
||||
|
||||
outfile_srctool="{}_SrcTool_".format(outkey)
|
||||
|
||||
do_init = False
|
||||
do_merge = False
|
||||
do_srctool = True
|
||||
do_grppha = False
|
||||
do_ecf_calc = False # for all bands
|
||||
do_ecf_print = False # for all bands
|
||||
do_flux_calc = False # for all bands
|
||||
|
||||
do_catalog = False
|
||||
do_extended = False
|
||||
do_ds9reg = False
|
||||
do_euds_final = False
|
||||
do_euds_dr12 = False # crossmatch eUDS with DR12
|
||||
do_euds_stat = False
|
||||
do_euds_cds = False
|
||||
|
||||
do_xmm_catalog = False
|
||||
do_xmm_final = False
|
||||
do_xmm_xmatch = False
|
||||
do_xmm_ds9reg = False
|
||||
do_xmm_cds = False
|
||||
do_euds_cosmatch = False
|
||||
|
||||
do_cross_check = False # Check whether all E3,E6 sources are detected in E0
|
||||
|
||||
|
||||
index=5
|
||||
""" чтобы спектры покрывали все энергии работаем в диапазоне 5 """
|
||||
|
||||
vign=True
|
||||
vignetting = 'vign' if (vign==True) else 'novign'
|
||||
|
||||
events=[]
|
||||
expmaps=[]
|
||||
bkgmaps=[]
|
||||
for tmkey in keylist_tm.keys():
|
||||
print("TM{} in work... init events".format(tmkey))
|
||||
for datakey in keylist_tm[tmkey]:
|
||||
print("--> {}".format(datakey))
|
||||
""" Подготавливаем списки событий индивидуальных наблюдений """
|
||||
outfile_evtool,outfile_expmap=init_events(key=datakey,attcorr=True,
|
||||
eband_index=eband[index],
|
||||
infile_dir=infile_dir,
|
||||
outfile_dir=outfile_dir,
|
||||
do_init=do_init,
|
||||
do_obsmode=False,
|
||||
do_center=False,
|
||||
do_evtool=False,
|
||||
do_expmap=False,
|
||||
vign=vign,
|
||||
ra_cen=ra_cen, de_cen=de_cen,
|
||||
emin_kev=emin_kev[index],
|
||||
emax_kev=emax_kev[index])
|
||||
events.append(outfile_evtool)
|
||||
expmaps.append(outfile_expmap)
|
||||
|
||||
|
||||
""" Собираем общий список событий """
|
||||
outfile_evtool="{}_EventList_en{}.fits".format(os.path.join(outfile_dir,outkey),
|
||||
eband[index])
|
||||
|
||||
if(do_merge==True):
|
||||
do_evtool_esass(events=events, outfile=outfile_evtool)
|
||||
|
||||
|
||||
suffix_srctool=".fits"
|
||||
""" Output filename suffix - all output filenames appended with this string.
|
||||
If suffix contains no filename extension (does not contain a "."), then ".fits"
|
||||
is also appended to the filename. """
|
||||
|
||||
catprep="{}_SourceCatalog_en{}{}".format(os.path.join(outfile_dir,outkey), eband[0], outfile_post)
|
||||
""" take source catalog from 0.3-2.3 keV band """
|
||||
|
||||
if not (os.path.isfile(catprep)==True):
|
||||
print("{} not found, run 04_mosaics.py?".format(catprep))
|
||||
sys.exit()
|
||||
|
||||
|
||||
if(do_srctool==True):
|
||||
test_exe('srctool')
|
||||
cmd=['srctool',
|
||||
"todo=\'SPEC RMF ARF\'",
|
||||
"insts=\'1 5 6 7\'",
|
||||
"eventfiles={}".format(outfile_evtool),
|
||||
"prefix=\'{}\'".format(os.path.join(srctool_dir,outfile_srctool)),
|
||||
"suffix=\'{}\'".format(suffix_srctool),
|
||||
"srccoord={}".format("../products/eUDS_for_srctool_test.fits"),
|
||||
# the same as original file eUDS.fits, but changed ML_CTS --> ML_CTS_0, ML_BKG --> ML_BKG_0, ML_EXP --> ML_EXP_0
|
||||
#"srcreg=\'fk5;circle * * 60s\'",
|
||||
#"backreg=\'fk5;annulus * * 90s 120s\'",
|
||||
"srcreg=AUTO",
|
||||
"backreg=AUTO",
|
||||
"clobber=yes",]
|
||||
#print((" ").join(cmd))
|
||||
#os.system((" ").join(cmd))
|
||||
#print((" ").join(cmd))
|
||||
runme(cmd, local_run=True)
|
||||
|
||||
if(do_grppha==True):
|
||||
group_spectra("{}/*020_SourceSpec_*.fits".format(srctool_dir))
|
||||
|
||||
ecfout="{}_SampleFlux_v1.pickle".format(os.path.join(outfile_dir,outkey))
|
||||
|
||||
if(do_ecf_calc==True):
|
||||
calc_ecf("{}/tm0_SrcTool_020_ARF_?????.fits".format(srctool_dir),
|
||||
catprep=catprep, emin=emin_kev, emax=emax_kev, eband=eband, outfile=ecfout, simnum=10000)
|
||||
|
||||
if(do_ecf_print==True):
|
||||
print_ecf(infile=ecfout, emin=emin_kev, emax=emax_kev, eband=eband, skipfrac=10.0)
|
||||
|
||||
fluxout="{}_SherpaFlux.pickle".format(os.path.join(outfile_dir,outkey))
|
||||
if(do_flux_calc==True):
|
||||
calc_flux("{}/tm0_SrcTool_020_ARF_?????.fits".format(srctool_dir),
|
||||
catprep=catprep, emin=emin_kev, emax=emax_kev, eband=eband, outfile=ecfout, simnum=100)
|
||||
|
||||
|
||||
|
||||
#index=0
|
||||
|
||||
catprep="{}_SourceCatalog_en{}{}".format(os.path.join(outfile_dir,outkey), eband[index], outfile_post)
|
||||
rawcat="{}_SourceCatalog_en{}.pickle".format(os.path.join(outfile_dir,outkey), eband[index])
|
||||
if(do_catalog==True):
|
||||
forced_xmm_sources="{}_MaxLikSourceList_en{}.xmm.pickle".format(os.path.join(outfile_dir,outkey), eband[index])
|
||||
with open(forced_xmm_sources, 'rb') as f:
|
||||
print("Reading forced XMM sources from {}".format(forced_xmm_sources))
|
||||
srcs_forced = pickle.load(f)
|
||||
print()
|
||||
print(srcs_forced)
|
||||
print()
|
||||
|
||||
make_euds_catalog(infile='../products/tm0_SourceCatalog_en0.forced.fits', rawcat=rawcat, dlmin=10.0, dlmax=100000, ext_like=1000,
|
||||
emin=emin_kev[index], emax=emax_kev[index], eband=eband[index],
|
||||
infile_en00cat=catprep,
|
||||
infile_en01cat='../products/tm0_SourceCatalog_en1.forced.fits',
|
||||
infile_en02cat='../products/tm0_SourceCatalog_en2.forced.fits',
|
||||
infile_en03cat='../products/tm0_SourceCatalog_en3.forced.fits',
|
||||
infile_en06cat='../products/tm0_SourceCatalog_en6.forced.fits',
|
||||
infile_en00sens='../products/tm0_SensitivityMap_dl10_en0.fits',
|
||||
infile_en01sens='../products/tm0_SensitivityMap_dl10_en1.fits',
|
||||
infile_en02sens='../products/tm0_SensitivityMap_dl10_en2.fits',
|
||||
infile_en03sens='../products/tm0_SensitivityMap_dl10_en3.fits',
|
||||
infile_en06sens='../products/tm0_SensitivityMap_dl10_en6.fits',
|
||||
srcs_forced=srcs_forced,
|
||||
)
|
||||
|
||||
|
||||
if(do_extended==True):
|
||||
make_extended(infile=rawcat,outreg="{}_ExtendedCat_en{}.reg".format(os.path.join(outfile_dir,outkey), eband[index]))
|
||||
|
||||
if(do_ds9reg==True):
|
||||
#make_final_ds9reg(infile=rawcat,outreg="{}_FinalCat_dl10.reg".format(os.path.join(outfile_dir,outkey)))
|
||||
make_final_ds9reg(infile=rawcat,scale=(60*60)/10,outreg="{}_FinalCat_dl10_talk.reg".format(os.path.join(outfile_dir,outkey)))
|
||||
|
||||
if(do_euds_final==True):
|
||||
""" make final eUDS catalog """
|
||||
final_euds_catalog(infile=rawcat, outfile_fits='../products/eUDS.fits')
|
||||
|
||||
if(do_euds_dr12==True):
|
||||
crossmatch_dr12('../products/eUDS.fits', catalog=root_path+"/data/4XMM-DR12/4XMM_DR12cat_slim_v1.0_UDS.fits.catalog", devmax=15)
|
||||
|
||||
|
||||
|
||||
if(do_euds_stat==True):
|
||||
make_euds_stat(infile="../products/eUDS.fits",fluxlim=5e-14)
|
||||
|
||||
if(do_euds_cds==True):
|
||||
make_euds_cds(infile="../products/eUDS.fits",outfile='../products/eUDS.cds')
|
||||
|
||||
if(do_cross_check==True):
|
||||
""" cross check final eUDS catalog """
|
||||
cross_check_euds(infile=catprep, euds='../products/eUDS.fits', outkey="../products/en{}_FinalCat_dl10".format(index))
|
||||
|
||||
if(do_xmm_catalog==True):
|
||||
""" complile raw forced XMM catalog """
|
||||
make_xmm_catalog(infile_en00cat='../products/tm0_SourceCatalog_en0.forced-xmm.fits',
|
||||
infile_en01cat='../products/tm0_SourceCatalog_en1.forced-xmm.fits',
|
||||
infile_en02cat='../products/tm0_SourceCatalog_en2.forced-xmm.fits',
|
||||
infile_en03cat='../products/tm0_SourceCatalog_en3.forced-xmm.fits',
|
||||
infile_en06cat='../products/tm0_SourceCatalog_en6.forced-xmm.fits',
|
||||
forced_xmm_sources='../products/tm0_MaxLikSourceList_en0.fixed-xmm.pickle',
|
||||
outfile='../products/tm0_4XMM-DR12.pickle')
|
||||
|
||||
|
||||
if(do_xmm_final==True):
|
||||
""" make final XMM-forced catalog """
|
||||
final_xmm_catalog(infile='../products/tm0_4XMM-DR12.pickle', outfile_fits='../products/eUDS_4XMM-DR12.fits')
|
||||
|
||||
if(do_xmm_xmatch==True):
|
||||
""" cross-match XMM-forced catalog
|
||||
outfile_cvs contains 0.3-2.3 keV eUDS flux (col1=flux,col2=err) vs. 4XMM-DR12 flux (col3=flux, col4=err)
|
||||
XMM flux was converted from 0.2-2.0 keV to 0.3-2.3 keV using wabs*powerlow with wabs=0.02 gamma=2.0
|
||||
"""
|
||||
final_xmm_xmatch(infile='../products/eUDS_4XMM-DR12.fits',
|
||||
xmmslim='../data/4XMM-DR12/4XMM_DR12cat_slim_v1.0_UDS.fits.catalog',
|
||||
xmmfull='../data/4XMM-DR12/4XMM_DR12cat_v1.0_UDS.fits.catalog',
|
||||
xmmlim=2e-14,
|
||||
outfile_flux="../products/eUDS_4XMM-DR12.flux.csv")
|
||||
|
||||
if(do_xmm_ds9reg==True):
|
||||
""" show XMM-forced catalog """
|
||||
make_xmm_ds9reg_confused(infile='../products/eUDS_4XMM-DR12.fits', outfile='../products/eUDS_4XMM-DR12.confused.reg')
|
||||
|
||||
""" obsolete """
|
||||
if(do_xmm_cds==True):
|
||||
make_xmm_cds(infile="../products/eUDS_4XMM-DR12.fits", outfile='../products/eUDS_4XMM-DR12.cds')
|
||||
|
||||
|
||||
|
||||
if(do_euds_cosmatch==True):
|
||||
""" prepare eUDS catalog for CosMatch (Mescheryakov) """
|
||||
make_euds_cosmatch(infile='../products/eUDS.fits', outfile='../products/eUDS-CosMatch.fits')
|
||||
@@ -1,445 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
"""
|
||||
НАЗВАНИЕ:
|
||||
|
||||
05_srctool.py
|
||||
|
||||
|
||||
НАЗНАЧЕНИЕ:
|
||||
|
||||
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.
|
||||
|
||||
ВЫЗОВ:
|
||||
|
||||
esass
|
||||
./05_srctool.py
|
||||
|
||||
|
||||
УПРАВЛЕНИЕ:
|
||||
|
||||
Требуется запуск предыдущего скрипта 04_mosaics.py
|
||||
|
||||
ПАРАМЕТРЫ:
|
||||
|
||||
index=4 : Выбранный энергетический диапазон
|
||||
|
||||
|
||||
ВЫВОД:
|
||||
|
||||
Выходные файлы записываются в директорию outfile_dir/srctool_dir
|
||||
|
||||
|
||||
ИСТОРИЯ:
|
||||
|
||||
Роман Кривонос, ИКИ РАН, krivonos@cosmos.ru
|
||||
Март 2023
|
||||
|
||||
"""
|
||||
|
||||
from astropy.wcs import WCS
|
||||
from astropy.io import fits
|
||||
import sys, os, os.path, time, subprocess
|
||||
from pathlib import Path
|
||||
import numpy as np
|
||||
import glob
|
||||
from os.path import dirname
|
||||
import inspect
|
||||
import uds
|
||||
from scipy.stats import norm
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from uds.utils import *
|
||||
from uds.config import *
|
||||
from uds.sherpa import *
|
||||
|
||||
|
||||
""" find UDS root dir """
|
||||
#root_path=dirname(dirname(dirname(inspect.getfile(uds))))
|
||||
"""
|
||||
ftools does not like long file path names,
|
||||
for this reason, we use relative path here
|
||||
"""
|
||||
root_path='..'
|
||||
print("UDS root path: {}".format(root_path))
|
||||
|
||||
infile_dir=root_path+'/data/processed'
|
||||
outfile_dir=root_path+'/products'
|
||||
create_folder(outfile_dir)
|
||||
|
||||
srctool_dir="{}/{}".format(outfile_dir,"srctool-products")
|
||||
create_folder(srctool_dir)
|
||||
|
||||
outkey="tm0"
|
||||
|
||||
outfile_srctool="{}_SrcTool_".format(outkey)
|
||||
|
||||
do_flux_distr = False
|
||||
do_sens_curve = False
|
||||
do_4xmm_ratio = False
|
||||
do_euds_radec_err = False
|
||||
do_euds_dr12_diff = False
|
||||
do_euds_dr12_stat = True
|
||||
|
||||
do_print_ecf = False
|
||||
|
||||
index=0
|
||||
|
||||
catalog = "{}_SourceCatalog_en{}.main.selected.csv".format(os.path.join(outfile_dir,outkey), eband[0])
|
||||
|
||||
if not (os.path.isfile(catalog)==True):
|
||||
print("{} not found, run 05_srctool.py?".format(catalog))
|
||||
sys.exit()
|
||||
|
||||
if(do_flux_distr==True):
|
||||
|
||||
|
||||
data, logbins, mean, median = get_log_distr(infile=catalog, field='ml_rate', minval=1e-3, maxval=2)
|
||||
fig, ax = plt.subplots()
|
||||
for axis in ['top','bottom','left','right']:
|
||||
ax.spines[axis].set_linewidth(1)
|
||||
ax.tick_params(axis="both", width=1, labelsize=14)
|
||||
plt.hist(data, bins=logbins, histtype='step', color='blue', linewidth=1, linestyle='solid')
|
||||
plt.xlabel('Count rate (counts s$^{-1}$)',fontsize=14, fontweight='normal')
|
||||
plt.ylabel('Number',fontsize=14, fontweight='normal')
|
||||
plt.grid(visible=True)
|
||||
plt.xscale('log')
|
||||
plt.yscale('log')
|
||||
plt.savefig(catalog.replace("main.selected.csv", "ml_rate.png"), bbox_inches='tight')
|
||||
plt.close(fig)
|
||||
|
||||
|
||||
data, logbins, mean, median = get_log_distr(infile=catalog, field='ml_flux', minval=1e-15, maxval=2e-12)
|
||||
fig, ax = plt.subplots()
|
||||
for axis in ['top','bottom','left','right']:
|
||||
ax.spines[axis].set_linewidth(1)
|
||||
ax.tick_params(axis="both", width=1, labelsize=14)
|
||||
plt.hist(data, bins=logbins, histtype='step', color='blue', linewidth=1, linestyle='solid')
|
||||
plt.xlabel('Energy flux (erg s$^{-1}$ cm$^{-2}$)',fontsize=14, fontweight='normal')
|
||||
plt.ylabel('Number',fontsize=14, fontweight='normal')
|
||||
plt.grid(visible=True)
|
||||
plt.xscale('log')
|
||||
plt.yscale('log')
|
||||
plt.savefig(catalog.replace("main.selected.csv", "ml_flux.png"), bbox_inches='tight')
|
||||
plt.close(fig)
|
||||
|
||||
if(do_sens_curve==True):
|
||||
coeff = 2.4336e-13 / 3.4012e-13 # see below
|
||||
|
||||
areatab="{}_AreaTable_dl10_en{}{}".format(os.path.join(outfile_dir,outkey), eband[index], outfile_post)
|
||||
hdul = fits.open(areatab)
|
||||
tbdata = hdul[1].data
|
||||
""" convert limflux from 0.3-2.3 keV to 0.5-2 keV """
|
||||
limflux_dl10 = tbdata['LIMFLUX']*coeff
|
||||
area_dl10 = tbdata['SKY_AREA']
|
||||
hdul.close()
|
||||
|
||||
areatab="{}_AreaTable_dl6_en{}{}".format(os.path.join(outfile_dir,outkey), eband[index], outfile_post)
|
||||
hdul = fits.open(areatab)
|
||||
tbdata = hdul[1].data
|
||||
""" convert limflux from 0.3-2.3 keV to 0.5-2 keV """
|
||||
limflux_dl6 = tbdata['LIMFLUX']*coeff
|
||||
area_dl6 = tbdata['SKY_AREA']
|
||||
hdul.close()
|
||||
|
||||
fig, ax = plt.subplots()
|
||||
for axis in ['top','bottom','left','right']:
|
||||
ax.spines[axis].set_linewidth(1)
|
||||
ax.tick_params(axis="both", width=1, labelsize=14)
|
||||
ax.set_xlim([3e-16,1e-13])
|
||||
ax.set_ylim([0.3,160])
|
||||
|
||||
plt.plot(limflux_dl10,area_dl10, color="black", linestyle='solid',label="eUDS (DL10)")
|
||||
plt.plot(limflux_dl6,area_dl6, color="black", linestyle='dashed', label="eUDS (DL6)")
|
||||
|
||||
df = pandas.read_csv("../data/surveys/eFEDS.dat",header=None,names=['limflux','area'])
|
||||
plt.plot(df['limflux'],df['area'], color="brown", linestyle='solid',label="eFEDS (DL6)")
|
||||
|
||||
|
||||
df = pandas.read_csv("../data/surveys/cosmos-legacy.dat",header=None,names=['limflux','area'])
|
||||
plt.plot(df['limflux'],df['area'], color="blue", linestyle='solid',label="COSMOS Legacy")
|
||||
|
||||
df = pandas.read_csv("../data/surveys/CDWFS.dat",header=None,names=['limflux','area'])
|
||||
plt.plot(df['limflux'],df['area'], color="green", linestyle='solid', label="CDWFS")
|
||||
|
||||
df = pandas.read_csv("../data/surveys/XMM-RM.dat",header=None,names=['limflux','area'])
|
||||
plt.plot(df['limflux'],df['area'], color="magenta", linestyle='solid',label="XMM-RM")
|
||||
|
||||
df = pandas.read_csv("../data/surveys/XMM-XXL-N.dat",header=None,names=['limflux','area'])
|
||||
plt.plot(df['limflux'],df['area'], color="red", linestyle='solid',label="XMM-XXL-N")
|
||||
|
||||
|
||||
ax.legend()
|
||||
|
||||
#plt.hist(data, bins=logbins, histtype='step', color='blue', linewidth=1, linestyle='solid')
|
||||
plt.xlabel('Limiting flux (0.5-2 keV, erg s$^{-1}$ cm$^{-2}$)',fontsize=14, fontweight='normal')
|
||||
plt.ylabel('Area (deg$^{2}$)',fontsize=14, fontweight='normal')
|
||||
plt.grid(visible=True)
|
||||
plt.xscale('log')
|
||||
plt.yscale('log')
|
||||
png="{}_AreaTable_en{}.png".format(os.path.join(outfile_dir,outkey), eband[index])
|
||||
plt.savefig(png, bbox_inches='tight')
|
||||
plt.close(fig)
|
||||
|
||||
"""
|
||||
========================================================================
|
||||
Model TBabs<1>*powerlaw<2> Source No.: 1 Active/On
|
||||
Model Model Component Parameter Unit Value
|
||||
par comp
|
||||
1 1 TBabs nH 10^22 2.00000E-02 frozen
|
||||
2 2 powerlaw PhoIndex 2.00000 frozen
|
||||
3 2 powerlaw norm 1.14851E-04 +/- 3.83988E-06
|
||||
________________________________________________________________________
|
||||
|
||||
Model Flux 0.00028334 photons (3.4012e-13 ergs/cm^2/s) range (0.30000 - 2.3000 keV)
|
||||
Model Flux 0.0001619 photons (2.4336e-13 ergs/cm^2/s) range (0.50000 - 2.0000 keV)
|
||||
"""
|
||||
if(do_4xmm_ratio==True):
|
||||
filename="../products/eUDS_4XMM-DR12.flux.csv"
|
||||
|
||||
data, logbins, mean, median = get_log_distr(infile=filename, field='ratio', minval=8e-2, maxval=60, nbin=60)
|
||||
print("Median {}".format(median))
|
||||
print(" Mean {}".format(mean))
|
||||
print("Ntotal {}".format(len(data)))
|
||||
fig, ax = plt.subplots()
|
||||
#plt.figure(figsize=(5,5))
|
||||
#plt.figure().set_figheight(3.6)
|
||||
#plt.rcParams['figure.figsize'] = [4, 4]
|
||||
for axis in ['top','bottom','left','right']:
|
||||
ax.spines[axis].set_linewidth(1)
|
||||
ax.tick_params(axis="both", width=1, labelsize=14)
|
||||
plt.hist(data, bins=logbins, histtype='step', color='blue', linewidth=1, linestyle='solid')
|
||||
plt.axvline(x = median, color = 'black', label = 'Median value', linestyle='dashed')
|
||||
plt.xlabel('Energy flux ratio',fontsize=14, fontweight='normal')
|
||||
plt.ylabel('Number',fontsize=14, fontweight='normal')
|
||||
plt.grid(visible=True)
|
||||
plt.xscale('log')
|
||||
plt.yscale('log')
|
||||
plt.savefig(filename.replace(".csv", ".distr.png"), bbox_inches='tight')
|
||||
plt.close(fig)
|
||||
|
||||
df = pandas.read_csv(filename)
|
||||
fig, ax = plt.subplots()
|
||||
#plt.rcParams['figure.figsize'] = [4, 4]
|
||||
#plt.figure(figsize=(5,5))
|
||||
#plt.figure().set_figheight(4)
|
||||
for axis in ['top','bottom','left','right']:
|
||||
ax.spines[axis].set_linewidth(1)
|
||||
ax.tick_params(axis="both", width=1, labelsize=14)
|
||||
plt.plot(np.array([1e-17,1e-11]), np.array([1e-17,1e-11]), linestyle = 'solid', color='black')
|
||||
plt.plot(np.array([1e-17,1e-11]), 10*np.array([1e-17,1e-11]), linestyle = 'dashed', color='black')
|
||||
plt.plot(np.array([1e-17,1e-11]), 0.1*np.array([1e-17,1e-11]), linestyle = 'dotted', color='black')
|
||||
#plt.plot(np.array([1e-17,1e-11]), median*np.array([1e-17,1e-11]), linestyle = 'dashed', color='blue')
|
||||
plt.errorbar(df['dr12_flux'],df['euds_flux'], yerr=df['euds_flux_err'], xerr=df['dr12_flux_err'], linestyle='None', color='black', label='Errors')
|
||||
plt.plot(df['dr12_flux'],df['euds_flux'], marker="o", linewidth=1, linestyle='None', markerfacecolor='Gold',markeredgecolor="black",)
|
||||
#plt.axvline(x = median, color = 'black', label = 'Median value', linestyle='dashed')
|
||||
plt.xlabel('4XMM-DR12 Energy flux (erg s$^{-1}$ cm$^{-2}$)',fontsize=14, fontweight='normal')
|
||||
plt.ylabel('eUDS Energy flux (erg s$^{-1}$ cm$^{-2}$)',fontsize=14, fontweight='normal')
|
||||
plt.grid(visible=True)
|
||||
plt.xlim(2e-16,1e-12)
|
||||
plt.ylim(1e-15,2e-12)
|
||||
plt.xscale('log')
|
||||
plt.yscale('log')
|
||||
plt.savefig(filename.replace(".csv", ".png"), bbox_inches='tight')
|
||||
plt.close(fig)
|
||||
|
||||
if(do_euds_radec_err==True):
|
||||
filename="../products/eUDS.fits"
|
||||
|
||||
ecat={}
|
||||
hdul = fits.open(filename)
|
||||
etab = hdul[1].data
|
||||
hdul.close()
|
||||
|
||||
x=[]
|
||||
y=[]
|
||||
for s in etab:
|
||||
if ("XMM" in s['DR12_IAU_NAME']):
|
||||
print("Skip ",s['DR12_IAU_NAME'])
|
||||
continue
|
||||
if (s['EXT_LIKE']>0.0):
|
||||
print("Skip extended ",s['ID_SRC'])
|
||||
continue
|
||||
x.append(s['DET_LIKE'])
|
||||
y.append(s['RADEC_ERR'])
|
||||
|
||||
|
||||
fig, ax = plt.subplots()
|
||||
for axis in ['top','bottom','left','right']:
|
||||
ax.spines[axis].set_linewidth(1)
|
||||
ax.tick_params(axis="both", width=1, labelsize=14)
|
||||
ax.set_xlim([10,10000])
|
||||
ax.set_ylim([0.1,40])
|
||||
|
||||
plt.plot(x,y,".", color="gold", markersize=12, markeredgewidth=1.5, markeredgecolor="black")
|
||||
|
||||
plt.xlabel('DET_LIKE',fontsize=14, fontweight='normal')
|
||||
plt.ylabel('RADEC_ERR (arcsec)',fontsize=14, fontweight='normal')
|
||||
plt.grid(visible=True)
|
||||
plt.xscale('log')
|
||||
plt.yscale('log')
|
||||
png="../products/en0_radec_err.png"
|
||||
plt.savefig(png, bbox_inches='tight')
|
||||
plt.close(fig)
|
||||
|
||||
stat={}
|
||||
hdul = fits.open("../products/eUDS.dr12.cross.fits")
|
||||
tab = hdul[1].data
|
||||
hdul.close()
|
||||
|
||||
print(len(np.unique(tab['ID'])))
|
||||
for rec in tab:
|
||||
sid=rec['ID']
|
||||
if sid in stat.keys():
|
||||
stat[sid]=stat[sid]+1
|
||||
else:
|
||||
stat[sid]=1
|
||||
|
||||
unique=0
|
||||
double=0
|
||||
triple=0
|
||||
for key in stat.keys():
|
||||
if(stat[key]==1):
|
||||
unique=unique+1
|
||||
if(stat[key]==2):
|
||||
double=double+1
|
||||
if(stat[key]==3):
|
||||
triple=triple+1
|
||||
print("unique: {}, double: {}. triple: {}".format(unique, double, triple))
|
||||
|
||||
|
||||
if(do_euds_dr12_diff==True):
|
||||
# read forced first
|
||||
# fieldnames = ['euds_det_like', 'euds_flux', 'euds_flux_err', 'dr12_flux', 'dr12_flux_err', 'ratio']
|
||||
filename="../products/eUDS_4XMM-DR12.flux.csv"
|
||||
df = pandas.read_csv(filename)
|
||||
# calculate difference similar to Bruner
|
||||
|
||||
threshold=5e-14
|
||||
|
||||
# forced catalog contains CONF flag, take it to remove from histogram
|
||||
hdul = fits.open("../products/eUDS_4XMM-DR12.fits")
|
||||
forced = hdul[1].data
|
||||
fcat={}
|
||||
for rec in forced:
|
||||
key=rec['DR12_SRCID']
|
||||
fcat[key]={'conf':rec['CONF'],}
|
||||
|
||||
# read cross-match results (not forced)
|
||||
hdul = fits.open('../products/eUDS.dr12.cross.fits')
|
||||
tb = hdul[1].data
|
||||
cross_diff=[]
|
||||
cross_ratio=[]
|
||||
mean=0.0
|
||||
count=0
|
||||
for ind,s in enumerate(tb):
|
||||
key=rec['DR12_SRCID']
|
||||
#if not (tb[ind]['dr12_flux']>threshold and tb[ind]['src_flux']>threshold):
|
||||
if not (tb[ind]['dr12_flux']>threshold):
|
||||
continue
|
||||
ape_flux=(tb[ind]['ape_cts']-tb[ind]['ape_bkg'])/tb[ind]['ape_exp']/ecf[index]
|
||||
|
||||
cross_dr12_flux=(tb[ind]['dr12_flux'])
|
||||
cross_dr12_flux_error=(tb[ind]['dr12_flux_error'])
|
||||
cross_euds_flux=(tb[ind]['src_flux'])
|
||||
#cross_euds_flux=ape_flux
|
||||
cross_euds_flux_error=(tb[ind]['src_flux_error'])
|
||||
|
||||
print(cross_euds_flux,cross_euds_flux_error,tb[ind]['ape_cts'],tb[ind]['det_like'])
|
||||
|
||||
d=(cross_dr12_flux - cross_euds_flux) / np.sqrt(cross_dr12_flux_error**2 + cross_euds_flux_error**2)
|
||||
#if(d < -15):
|
||||
# continue
|
||||
r=cross_euds_flux/cross_dr12_flux
|
||||
cross_diff.append(d)
|
||||
cross_ratio.append(r)
|
||||
|
||||
#if(d < -15.0):
|
||||
#print("GGG",d,r,cross_euds_flux,cross_dr12_flux)
|
||||
#print(tb[ind]['DR12_NAME'],tb[ind]['DR12_RA'],tb[ind]['DR12_DEC'])
|
||||
mean=mean + (cross_euds_flux/cross_dr12_flux)
|
||||
count=count+1
|
||||
|
||||
|
||||
|
||||
print("min={:.2f}, max={:.2f}, mean={:.2f}, median={:.2f}, std={:.2f} N={}".format(min(cross_diff),
|
||||
max(cross_diff),
|
||||
np.mean(cross_diff),
|
||||
np.median(cross_diff),
|
||||
np.std(cross_diff),
|
||||
len(cross_diff)))
|
||||
print("ratio mean",mean/count)
|
||||
print("ratio median",np.median(cross_ratio))
|
||||
|
||||
|
||||
|
||||
nbin=50
|
||||
bins=np.linspace(-30,30, nbin)
|
||||
|
||||
fig, ax = plt.subplots()
|
||||
for axis in ['top','bottom','left','right']:
|
||||
ax.spines[axis].set_linewidth(1)
|
||||
ax.tick_params(axis="both", width=1, labelsize=14)
|
||||
plt.hist(cross_diff, bins=bins,histtype='step', color='green', linewidth=1, linestyle='solid', density=True)
|
||||
|
||||
#x = np.arange(-10, 10, 0.001)
|
||||
#plot normal distribution with mean 0 and standard deviation 1
|
||||
#plt.plot(x, norm.pdf(x, 0, 1), color='red', linewidth=2)
|
||||
|
||||
plt.ylabel('Relative fraction',fontsize=14, fontweight='normal')
|
||||
plt.xlabel('(F$_{XMM}$-F$_{eUDS}$)/$\sqrt{\Delta F_{XMM}^{2}+\Delta F_{eUDS}^{2}}$',fontsize=14, fontweight='normal')
|
||||
plt.grid(visible=True)
|
||||
plt.xscale('linear')
|
||||
plt.yscale('linear')
|
||||
ax.set_xlim([-31, 31])
|
||||
ax.set_ylim([0, 0.2])
|
||||
plt.savefig("../products/cross-match_diff.png", bbox_inches='tight')
|
||||
plt.close(fig)
|
||||
|
||||
|
||||
nbin=200
|
||||
bins=np.linspace(-1,1.0, nbin)
|
||||
|
||||
fig, ax = plt.subplots()
|
||||
for axis in ['top','bottom','left','right']:
|
||||
ax.spines[axis].set_linewidth(1)
|
||||
ax.tick_params(axis="both", width=1, labelsize=14)
|
||||
plt.hist(cross_ratio, bins=bins, histtype='step', color='green', linewidth=1, linestyle='solid', density=False)
|
||||
|
||||
#x = np.arange(-10, 10, 0.001)
|
||||
#plot normal distribution with mean 0 and standard deviation 1
|
||||
#plt.plot(x, norm.pdf(x, 0, 1), color='red', linewidth=2)
|
||||
|
||||
plt.xlabel('eUDS and 4XMM-DR12 flux ratio',fontsize=14, fontweight='normal')
|
||||
plt.ylabel('Relative fraction',fontsize=14, fontweight='normal')
|
||||
plt.grid(visible=True)
|
||||
plt.xscale('linear')
|
||||
plt.yscale('linear')
|
||||
plt.savefig("../products/cross-match_ratio.png", bbox_inches='tight')
|
||||
plt.close(fig)
|
||||
|
||||
|
||||
if(do_print_ecf==True):
|
||||
filename='../data/ECF/ecf_tbabspow_g2nh0.02.pkl'
|
||||
with open(filename, 'rb') as f:
|
||||
ecf_table = pickle.load(f)
|
||||
"""
|
||||
for key in table.keys():
|
||||
print("{} --> {}".format(key,table[key]))
|
||||
"""
|
||||
|
||||
print(ecf_table[(0.3,2.3)])
|
||||
print(ecf_table[(0.3,0.6)])
|
||||
print(ecf_table[(0.6,2.3)])
|
||||
print(ecf_table[(2.3,5.0)])
|
||||
print(ecf_table[(5.0,8.0)])
|
||||
print()
|
||||
print(ecf_table[(0.5,1.0)]) # 4XMM-DR12 EP2 band
|
||||
print(ecf_table[(1.0,2.0)]) # 4XMM-DR12 EP3 band
|
||||
|
||||
if(do_euds_dr12_stat==True):
|
||||
dr12_stat(infile='../products/eUDS_4XMM-DR12.fits',
|
||||
xmmslim='../data/4XMM-DR12/4XMM_DR12cat_slim_v1.0_UDS.fits.catalog',
|
||||
xmmlim=None,
|
||||
outfile_reg='../products/dr12_fluxlim.reg')
|
||||
@@ -1,44 +1,9 @@
|
||||
Подготовка рабочего окружения:
|
||||
```
|
||||
source <MY PATH>/venv/bin/activate.csh
|
||||
source <MY PATH>/eSASS4EDR/bin/esass-init.csh
|
||||
```
|
||||
|
||||
|
||||
### 01_init_events.py
|
||||
|
||||
Создает начальные списки событий и помещает их в ```uds/data/processed```
|
||||
Создает списки событий и помещает их в ```arches/data/processed```
|
||||
Оригинальные файлы со списками событий задаются в файлах ```uds/data/evtlists/*.txt```
|
||||
|
||||
### 02_merge_events.py
|
||||
|
||||
Создает объедененный список событий и помещает его в ```uds/products```. Этот список событий нужен, в основном для извлечения спектров с помощью ```srctool```.
|
||||
|
||||
Попутно этот скрипт унифицирует оригинальные списки событий для последующей обработки. А именно, корректируются слова OBS_MODE=POINING/SURVEY в зависимости от типа наблюдения и производится центрирование на одни и те же координаты с помощью команды ```radec2xy```.
|
||||
|
||||
Для запуска адаптивного сглаживания ```do_adapt = True``` требуется запустить окружение ```ciao```, так как нужна команда ```dmimgadapt```
|
||||
|
||||
```
|
||||
conda activate ciao-4.15
|
||||
source <MY PATH>/eSASS4EDR/bin/esass-init.csh
|
||||
```
|
||||
|
||||
### 03_init_obs.py
|
||||
|
||||
1) Подготавливает списки событий в разных энергетических диапазонах.
|
||||
2) Запускает ```erbox``` в три этапа, чтобы получить рабочий список источников для ```ermldet```.
|
||||
3) Запускает ```ermldet```
|
||||
4) Делает кросс-корреляцию с каталогом Gaia-unWISE ```do_cross_match=True```, которая создает три файла: ```.cross``` -- все пересечения, и ```.ref``` / ```.src``` -- входные каталоги для последующей команды ```wcs_match```
|
||||
5) Делает матрицу преобразования координат и корректирует списки событий. Для запуска команд```wcs_match/wcs_update``` требуется запустить окружение ```ciao``` (см. выше)
|
||||
|
||||
### 04_mosaics.py
|
||||
|
||||
Создает сборные изображения (мозайки) в разных энергетических диапазонах.
|
||||
|
||||
### 05_scrtool.py
|
||||
|
||||
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.
|
||||
|
||||
Вычисляет ECF для всех диапазонов.
|
||||
|
||||
Делает принудительную фотометрию в выбранных каналах (параметр```forced=True```). Внимание! ermldet из eSASS4EDR не делает ассимитричные ошибки на потоки. Мы запускаем более последнюю версию ermldet (v1.56/2.18 esass_200412 Jul 2 12:04:46 2022). Для этого используется параметр ```local_run=True```, который высвечивает какую команду надо запустить на другой машине и ждет ввода.
|
||||
@@ -1,683 +0,0 @@
|
||||
import cdspyreadme
|
||||
from astropy.io import fits
|
||||
from astropy.table import Table
|
||||
|
||||
def make_euds_cds(infile1=None,outfile1=None,infile2=None,outfile2=None,fluxlim=None):
|
||||
|
||||
print("Reading {}".format(infile1))
|
||||
|
||||
#########################
|
||||
""" Read eUDS catalog """
|
||||
#########################
|
||||
|
||||
tablemaker = cdspyreadme.CDSTablesMaker()
|
||||
|
||||
r = Table.read(infile1)
|
||||
tab=Table([
|
||||
r['ID_SRC'], # 0
|
||||
r['NAME'], # 1
|
||||
r['RA'], # 2
|
||||
r['DEC'], # 3
|
||||
r['RADEC_ERR'], # 4 Positional error (68% confidence)
|
||||
r['EXT'], # 5 Source extent
|
||||
r['EXT_ERR'], # 6 Extent uncertainty
|
||||
r['EXT_LIKE'], # 7
|
||||
r['DET_LIKE'], # 8
|
||||
r['ML_RATE'], # 9
|
||||
r['ML_RATE_ERR'], # 10
|
||||
r['ML_CTS'], # 11
|
||||
r['ML_CTS_ERR'], # 12
|
||||
r['ML_FLUX'], # 13
|
||||
r['ML_FLUX_ERR'], # 14
|
||||
r['ML_EXP'], # 15
|
||||
r['ML_BKG'], # 16
|
||||
r['DR12_IAU_NAME'] , # 17 4XMM J021738.8-051257
|
||||
r['DET_LIKE_1'] , # 18
|
||||
r['ML_RATE_1'] , # 19
|
||||
r['ML_RATE_ERR_1'] , # 20
|
||||
r['ML_CTS_1'] , # 21
|
||||
r['ML_CTS_ERR_1'] , # 22
|
||||
r['ML_FLUX_1'] , # 23
|
||||
r['ML_FLUX_ERR_1'] , # 24
|
||||
r['ML_EXP_1'] , # 25
|
||||
r['ML_BKG_1'] , # 26
|
||||
r['DET_LIKE_2'] , # 27
|
||||
r['ML_RATE_2'] , # 28
|
||||
r['ML_RATE_ERR_2'] , # 29
|
||||
r['ML_CTS_2'] , # 30
|
||||
r['ML_CTS_ERR_2'] , # 31
|
||||
r['ML_FLUX_2'] , # 32
|
||||
r['ML_FLUX_ERR_2'] , # 33
|
||||
r['ML_EXP_2'] , # 34
|
||||
r['ML_BKG_2'] , # 35
|
||||
r['DET_LIKE_3'] , # 36
|
||||
r['ML_RATE_3'] , # 37
|
||||
r['ML_RATE_ERR_3'] , # 38
|
||||
r['ML_CTS_3'] , # 39
|
||||
r['ML_CTS_ERR_3'] , # 40
|
||||
r['ML_FLUX_3'] , # 41
|
||||
r['ML_FLUX_ERR_3'] , # 42
|
||||
r['ML_EXP_3'] , # 43
|
||||
r['ML_BKG_3'] , # 44
|
||||
r['DET_LIKE_4'] , # 45
|
||||
r['ML_RATE_4'] , # 46
|
||||
r['ML_RATE_ERR_4'] , # 47
|
||||
r['ML_CTS_4'] , # 48
|
||||
r['ML_CTS_ERR_4'] , # 49
|
||||
r['ML_FLUX_4'] , # 50
|
||||
r['ML_FLUX_ERR_4'] , # 51
|
||||
r['ML_EXP_4'] , # 52
|
||||
r['ML_BKG_4'] , # 53
|
||||
])
|
||||
|
||||
table = tablemaker.addTable(tab, name=outfile1, description="eUDS X-ray catalog, converted from eUDS.fits")
|
||||
|
||||
col = table.get_column('ID_SRC') # 0
|
||||
col.name="SrcID"
|
||||
col.set_format("I3")
|
||||
col.description="Source ID (ID_SRC)"
|
||||
|
||||
col = table.get_column('NAME') # 1
|
||||
col.name="Name"
|
||||
col.set_format("A21")
|
||||
col.description="Source name, eUDS JHHMMSS.s+DDMMSS"
|
||||
|
||||
col = table.get_column('RA') # 2
|
||||
col.set_format("F10.6")
|
||||
col.name="RAdeg"
|
||||
col.description="RA (J2000) (RA)"
|
||||
|
||||
col = table.get_column('DEC') # 3
|
||||
col.name="DEdeg"
|
||||
col.set_format("F10.6")
|
||||
col.description="Dec (J2000) (RA)"
|
||||
|
||||
col = table.get_column('RADEC_ERR') # 4 Positional error (68% confidence)
|
||||
col.set_format("F6.3")
|
||||
col.name="ePos"
|
||||
col.description="Positional uncertainty (RADEC_ERR)"
|
||||
|
||||
col = table.get_column('EXT') # 5 Source extent
|
||||
col.set_format("F5.2")
|
||||
col.name="Ext"
|
||||
col.description="Source extent (EXT)"
|
||||
|
||||
col = table.get_column('EXT_ERR') # 6 Extent uncertainty
|
||||
col.set_format("F5.2")
|
||||
col.name="e_Ext"
|
||||
col.description="Extent error (EXT_ERR)"
|
||||
|
||||
col = table.get_column('EXT_LIKE') # 7
|
||||
col.set_format("F10.6")
|
||||
col.name="ExtLike"
|
||||
col.description="Extent likelihood (EXT_LIKE)"
|
||||
|
||||
col = table.get_column('DET_LIKE') # 8
|
||||
col.set_format("F12.6")
|
||||
col.name="DetLike"
|
||||
col.description="Detection likelihood measured by PSF-fitting; 0.3-2.3 keV (DET_LIKE)"
|
||||
|
||||
col = table.get_column('ML_RATE') # 9
|
||||
col.name="MLRate"
|
||||
col.set_format("F8.6")
|
||||
col.description="Source count rate measured by PSF-fitting; 0.3-2.3 keV (ML_RATE)"
|
||||
|
||||
col = table.get_column('ML_RATE_ERR') # 10
|
||||
col.name="e_MLRate"
|
||||
col.set_format("F8.6")
|
||||
col.description="1 sigma count rate error; 0.3-2.3 keV (ML_RATE_ERR)"
|
||||
|
||||
col = table.get_column('ML_CTS') # 11
|
||||
col.name="MLCts"
|
||||
col.set_format("F11.6")
|
||||
col.description="Source net counts measured from count rate; 0.3-2.3 keV (ML_CTS)"
|
||||
|
||||
col = table.get_column('ML_CTS_ERR') # 12
|
||||
col.name="e_MLCts"
|
||||
col.set_format("F11.6")
|
||||
col.description="1 sigma counts error; 0.3-2.3 keV (ML_CTS_ERR)"
|
||||
|
||||
col = table.get_column('ML_FLUX') # 13
|
||||
col.name="MLFlux"
|
||||
col.set_format("E12.6")
|
||||
col.description="Source flux converted from count rate; 0.3-2.3 keV (ML_FLUX)"
|
||||
|
||||
col = table.get_column('ML_FLUX_ERR') # 14
|
||||
col.name="e_MLFlux"
|
||||
col.set_format("E12.6")
|
||||
col.description="1 sigma flux error; 0.3-2.3 keV (ML_FLUX_ERR)"
|
||||
|
||||
col = table.get_column('ML_EXP') # 15
|
||||
col.name="MLExp"
|
||||
col.set_format("E12.6")
|
||||
col.description="Vignetted exposure value; 0.3-2.3 keV (ML_EXP)"
|
||||
|
||||
col = table.get_column('ML_BKG') # 16
|
||||
col.name="MLBkg"
|
||||
col.set_format("F12.6")
|
||||
col.description="Background at the source position; 0.3-2.3 keV (ML_BKG)"
|
||||
|
||||
col = table.get_column('DR12_IAU_NAME') # 17 4XMM J021738.8-051257
|
||||
col.name="DR12Name"
|
||||
col.set_format("A21")
|
||||
col.description="4XMM-DR12 source name used for forced photometry (DR12_IAU_NAME)"
|
||||
|
||||
col = table.get_column('DET_LIKE_1') # 18
|
||||
col.name="DetLike1"
|
||||
col.set_format("F12.6")
|
||||
col.description="Detection likelihood measured by PSF-fitting; 0.3-0.6 keV (DET_LIKE_1)"
|
||||
|
||||
col = table.get_column('ML_RATE_1') # 19
|
||||
col.name="MLRate1"
|
||||
col.set_format("F8.6")
|
||||
col.description="Source count rate measured by PSF-fitting; 0.3-0.6 keV (ML_RATE_1)"
|
||||
|
||||
col = table.get_column('ML_RATE_ERR_1') # 20
|
||||
col.name="e_MLRate1"
|
||||
col.set_format("F8.6")
|
||||
col.description="1 sigma count rate error; 0.3-0.6 keV (ML_RATE_ERR_1)"
|
||||
|
||||
col = table.get_column('ML_CTS_1') # 21
|
||||
col.name="MLCts1"
|
||||
col.set_format("F11.6")
|
||||
col.description="Source net counts measured from count rate; 0.3-0.6 keV (ML_CTS_1)"
|
||||
|
||||
col = table.get_column('ML_CTS_ERR_1') # 22
|
||||
col.name="e_MLCts1"
|
||||
col.set_format("F11.6")
|
||||
col.description="1 sigma counts error; 0.3-0.6 keV (ML_CTS_ERR_1)"
|
||||
|
||||
col = table.get_column('ML_FLUX_1') # 23
|
||||
col.name="MLFlux1"
|
||||
col.set_format("E12.6")
|
||||
col.description="Source flux converted from count rate; 0.3-0.6 keV (ML_FLUX_1)"
|
||||
|
||||
col = table.get_column('ML_FLUX_ERR_1') # 24
|
||||
col.name="e_MLFlux1"
|
||||
col.set_format("E12.6")
|
||||
col.description="1 sigma flux error; 0.3-0.6 keV (ML_FLUX_ERR_1)"
|
||||
|
||||
col = table.get_column('ML_EXP_1') # 25
|
||||
col.name="MLExp1"
|
||||
col.set_format("E12.6")
|
||||
col.description="Vignetted exposure value; 0.3-0.6 keV (ML_EXP_1)"
|
||||
|
||||
col = table.get_column('ML_BKG_1') # 26
|
||||
col.name="MLBkg1"
|
||||
col.set_format("F12.6")
|
||||
col.description="Background at the source position; 0.3-0.6 keV (ML_BKG_1)"
|
||||
|
||||
col = table.get_column('DET_LIKE_2') # 27
|
||||
col.name="DetLike2"
|
||||
col.set_format("F12.6")
|
||||
col.description="Detection likelihood measured by PSF-fitting; 0.6-2.3 keV (DET_LIKE_2)"
|
||||
|
||||
col = table.get_column('ML_RATE_2') # 28
|
||||
col.name="MLRate2"
|
||||
col.set_format("F8.6")
|
||||
col.description="Source count rate measured by PSF-fitting; 0.6-2.3 keV (ML_RATE_2)"
|
||||
|
||||
col = table.get_column('ML_RATE_ERR_2') # 29
|
||||
col.name="e_MLRate2"
|
||||
col.set_format("F8.6")
|
||||
col.description="1 sigma count rate error; 0.6-2.3 keV (ML_RATE_ERR_2)"
|
||||
|
||||
col = table.get_column('ML_CTS_2') # 30
|
||||
col.name="MLCts2"
|
||||
col.set_format("F11.6")
|
||||
col.description="Source net counts measured from count rate; 0.6-2.3 keV (ML_CTS_2)"
|
||||
|
||||
col = table.get_column('ML_CTS_ERR_2') # 31
|
||||
col.name="e_MLCts2"
|
||||
col.set_format("F11.6")
|
||||
col.description="1 sigma counts error; 0.6-2.3 keV (ML_CTS_ERR_2)"
|
||||
|
||||
col = table.get_column('ML_FLUX_2') # 32
|
||||
col.name="MLFlux2"
|
||||
col.set_format("E12.6")
|
||||
col.description="Source flux converted from count rate; 0.6-2.3 keV (ML_FLUX_2)"
|
||||
|
||||
col = table.get_column('ML_FLUX_ERR_2') # 33
|
||||
col.name="e_MLFlux2"
|
||||
col.set_format("E12.6")
|
||||
col.description="1 sigma flux error; 0.6-2.3 keV (ML_FLUX_ERR_2)"
|
||||
|
||||
col = table.get_column('ML_EXP_2') # 34
|
||||
col.name="MLExp2"
|
||||
col.set_format("E12.6")
|
||||
col.description="Vignetted exposure value; 0.6-2.3 keV (ML_EXP_2)"
|
||||
|
||||
col = table.get_column('ML_BKG_2') # 35
|
||||
col.name="MLBkg2"
|
||||
col.set_format("F12.6")
|
||||
col.description="Background at the source position; 0.6-2.3 keV (ML_BKG_2)"
|
||||
|
||||
col = table.get_column('DET_LIKE_3') # 36
|
||||
col.name="DetLike3"
|
||||
col.set_format("F12.6")
|
||||
col.description="Detection likelihood measured by PSF-fitting; 2.3-5 keV (DET_LIKE_3)"
|
||||
|
||||
col = table.get_column('ML_RATE_3') # 37
|
||||
col.name="MLRate3"
|
||||
col.set_format("F8.6")
|
||||
col.description="Source count rate measured by PSF-fitting; 2.3-5 keV (ML_RATE_3)"
|
||||
|
||||
col = table.get_column('ML_RATE_ERR_3') # 38
|
||||
col.name="e_MLRate3"
|
||||
col.set_format("F8.6")
|
||||
col.description="1 sigma count rate error; 2.3-5 keV (ML_RATE_ERR_3)"
|
||||
|
||||
col = table.get_column('ML_CTS_3') # 39
|
||||
col.name="MLCts3"
|
||||
col.set_format("F11.6")
|
||||
col.description="Source net counts measured from count rate; 2.3-5 keV (ML_CTS_3)"
|
||||
|
||||
col = table.get_column('ML_CTS_ERR_3') # 40
|
||||
col.name="e_MLCts3"
|
||||
col.set_format("F11.6")
|
||||
col.description="1 sigma counts error; 2.3-5 keV (ML_CTS_ERR_3)"
|
||||
|
||||
col = table.get_column('ML_FLUX_3') # 41
|
||||
col.name="MLFlux3"
|
||||
col.set_format("E12.6")
|
||||
col.description="Source flux converted from count rate; 2.3-5 keV (ML_FLUX_3)"
|
||||
|
||||
col = table.get_column('ML_FLUX_ERR_3') # 42
|
||||
col.name="e_MLFlux3"
|
||||
col.set_format("E12.6")
|
||||
col.description="1 sigma flux error; 2.3-5 keV (ML_FLUX_ERR_3)"
|
||||
|
||||
col = table.get_column('ML_EXP_3') # 43
|
||||
col.name="MLExp3"
|
||||
col.set_format("E12.6")
|
||||
col.description="Vignetted exposure value; 2.3-5 keV (ML_EXP_3)"
|
||||
|
||||
col = table.get_column('ML_BKG_3') # 44
|
||||
col.name="MLBkg3"
|
||||
col.set_format("F12.6")
|
||||
col.description="Background at the source position; 2.3-5 keV (ML_BKG_3)"
|
||||
|
||||
col = table.get_column('DET_LIKE_4') # 45
|
||||
col.name="DetLike4"
|
||||
col.set_format("F12.6")
|
||||
col.description="Detection likelihood measured by PSF-fitting; 5-8 keV (DET_LIKE_4)"
|
||||
|
||||
col = table.get_column('ML_RATE_4') # 46
|
||||
col.name="MLRate4"
|
||||
col.set_format("F8.6")
|
||||
col.description="Source count rate measured by PSF-fitting; 5-8 keV (ML_RATE_4)"
|
||||
|
||||
col = table.get_column('ML_RATE_ERR_4') # 47
|
||||
col.name="e_MLRate4"
|
||||
col.set_format("F8.6")
|
||||
col.description="1 sigma count rate error; 5-8 keV (ML_RATE_ERR_4)"
|
||||
|
||||
col = table.get_column('ML_CTS_4') # 48
|
||||
col.name="MLCts4"
|
||||
col.set_format("F11.6")
|
||||
col.description="Source net counts measured from count rate; 5-8 keV (ML_CTS_4)"
|
||||
|
||||
col = table.get_column('ML_CTS_ERR_4') # 49
|
||||
col.name="e_MLCts4"
|
||||
col.set_format("F11.6")
|
||||
col.description="1 sigma counts error; 5-8 keV (ML_CTS_ERR_4)"
|
||||
|
||||
col = table.get_column('ML_FLUX_4') # 50
|
||||
col.name="MLFLux4"
|
||||
col.set_format("E12.6")
|
||||
col.description="Source flux converted from count rate; 5-8 keV (ML_FLUX_4)"
|
||||
|
||||
col = table.get_column('ML_FLUX_ERR_4') # 51
|
||||
col.name="e_MLFlux4"
|
||||
col.set_format("E12.6")
|
||||
col.description="1 sigma flux error; 5-8 keV (ML_FLUX_ERR_4)"
|
||||
|
||||
col = table.get_column('ML_EXP_4') # 52
|
||||
col.name="MLExp4"
|
||||
col.set_format("E12.6")
|
||||
col.description="Vignetted exposure value; 5-8 keV (ML_EXP_4)"
|
||||
|
||||
col = table.get_column('ML_BKG_4') # 53
|
||||
col.name="MLBkg4"
|
||||
col.set_format("F12.6")
|
||||
col.description="Background at the source position; 5-8 keV (ML_BKG_4)"
|
||||
|
||||
#####################################
|
||||
""" Read 4XMM-DR12 forced catalog """
|
||||
#####################################
|
||||
print("Reading {}".format(infile2))
|
||||
|
||||
r = Table.read(infile2)
|
||||
tab2=Table([
|
||||
r['DR12_IAU_NAME'], # 0
|
||||
r['DR12_SRCID'], # 1
|
||||
r['DR12_RA'], # 2
|
||||
r['DR12_DEC'], # 3
|
||||
r['DR12_RADEC_ERR'], # 4
|
||||
r['DET_LIKE_0'] , # 5
|
||||
r['DET_LIKE_1'] , # 6
|
||||
r['DET_LIKE_2'] , # 7
|
||||
r['DET_LIKE_3'] , # 8
|
||||
r['DET_LIKE_4'] , # 9
|
||||
r['ML_RATE_0'] , # 10
|
||||
r['ML_RATE_1'] , # 11
|
||||
r['ML_RATE_2'] , # 12
|
||||
r['ML_RATE_3'] , # 13
|
||||
r['ML_RATE_4'] , # 14
|
||||
r['ML_RATE_ERR_0'] , # 15
|
||||
r['ML_RATE_ERR_1'] , # 16
|
||||
r['ML_RATE_ERR_2'] , # 17
|
||||
r['ML_RATE_ERR_3'] , # 18
|
||||
r['ML_RATE_ERR_4'] , # 19
|
||||
r['ML_CTS_0'] , # 20
|
||||
r['ML_CTS_1'] , # 21
|
||||
r['ML_CTS_2'] , # 22
|
||||
r['ML_CTS_3'] , # 23
|
||||
r['ML_CTS_4'] , # 24
|
||||
r['ML_CTS_ERR_0'] , # 25
|
||||
r['ML_CTS_ERR_1'] , # 26
|
||||
r['ML_CTS_ERR_2'] , # 27
|
||||
r['ML_CTS_ERR_3'] , # 28
|
||||
r['ML_CTS_ERR_4'] , # 29
|
||||
r['ML_FLUX_0'] , # 30
|
||||
r['ML_FLUX_1'] , # 31
|
||||
r['ML_FLUX_2'] , # 32
|
||||
r['ML_FLUX_3'] , # 33
|
||||
r['ML_FLUX_4'] , # 34
|
||||
r['ML_FLUX_ERR_0'] , # 35
|
||||
r['ML_FLUX_ERR_1'] , # 36
|
||||
r['ML_FLUX_ERR_2'] , # 37
|
||||
r['ML_FLUX_ERR_3'] , # 38
|
||||
r['ML_FLUX_ERR_4'] , # 39
|
||||
r['ML_EXP_0'] , # 40
|
||||
r['ML_EXP_1'] , # 41
|
||||
r['ML_EXP_2'] , # 42
|
||||
r['ML_EXP_3'] , # 43
|
||||
r['ML_EXP_4'] , # 44
|
||||
r['ML_BKG_0'] , # 45
|
||||
r['ML_BKG_1'] , # 46
|
||||
r['ML_BKG_2'] , # 47
|
||||
r['ML_BKG_3'] , # 48
|
||||
r['ML_BKG_4'] , # 49
|
||||
r['CONF'], # 50
|
||||
])
|
||||
|
||||
table2 = tablemaker.addTable(tab2, name=outfile2, description="eUDS forced photometry of 4XMM-DR12 sources, converted from eUDS_4XMM-DR12.fits")
|
||||
|
||||
col = table2.get_column('DR12_IAU_NAME') # 0
|
||||
col.name="DR12Name"
|
||||
col.set_format("A21")
|
||||
col.description="4XMM-DR12 source IAU name (DR12_IAU_NAME)"
|
||||
|
||||
col = table2.get_column('DR12_SRCID') # 1
|
||||
col.name="DR12SrcID"
|
||||
col.set_format("I15")
|
||||
col.description="Source ID (64-bit integer, DR12_SRCID)"
|
||||
|
||||
col = table2.get_column('DR12_RA') # 2
|
||||
col.name="RAdeg"
|
||||
col.set_format("F10.6")
|
||||
col.description="Right ascension (J2000, DR12_RA)"
|
||||
|
||||
col = table2.get_column('DR12_DEC') # 3
|
||||
col.name="DEdeg"
|
||||
col.set_format("F10.6")
|
||||
col.description="Declination (J2000, DR12_DEC)"
|
||||
|
||||
col = table2.get_column('DR12_RADEC_ERR') # 4
|
||||
col.name="ePos"
|
||||
col.set_format("F6.3")
|
||||
col.description="Positional error (DR12_RADEC_ERR)"
|
||||
|
||||
col = table2.get_column('DET_LIKE_0') # 5
|
||||
col.name="DetLike0"
|
||||
col.set_format("F12.6")
|
||||
col.description="Detection likelihood; 0.3-2.3 keV (DET_LIKE_0)"
|
||||
|
||||
col = table2.get_column('DET_LIKE_1') # 6
|
||||
col.name="DetLike1"
|
||||
col.set_format("F12.6")
|
||||
col.description="Detection likelihood; 0.3-0.6 keV (DET_LIKE_1)"
|
||||
|
||||
col = table2.get_column('DET_LIKE_2') # 7
|
||||
col.name="DetLike2"
|
||||
col.set_format("F12.6")
|
||||
col.description="Detection likelihood; 0.6-2.3 keV (DET_LIKE_2)"
|
||||
|
||||
col = table2.get_column('DET_LIKE_3') # 8
|
||||
col.name="DetLike3"
|
||||
col.set_format("F12.6")
|
||||
col.description="Detection likelihood; 2.3-5 keV (DET_LIKE_3)"
|
||||
|
||||
col = table2.get_column('DET_LIKE_4') # 9
|
||||
col.name="DetLike4"
|
||||
col.set_format("F12.6")
|
||||
col.description="Detection likelihood; 5-8 keV (DET_LIKE_4)"
|
||||
|
||||
col = table2.get_column('ML_RATE_0') # 10
|
||||
col.name="MLRate0"
|
||||
col.set_format("F10.6")
|
||||
col.description="Source count rate measured by PSF-fitting; 0.3-2.3 keV (ML_RATE_0)"
|
||||
|
||||
col = table2.get_column('ML_RATE_1') # 11
|
||||
col.name="MLRate1"
|
||||
col.set_format("F10.6")
|
||||
col.description="Source count rate measured by PSF-fitting; 0.3-0.6 keV (ML_RATE_1)"
|
||||
|
||||
col = table2.get_column('ML_RATE_2') # 12
|
||||
col.name="MLRate2"
|
||||
col.set_format("F10.6")
|
||||
col.description="Source count rate measured by PSF-fitting; 0.6-2.3 keV (ML_RATE_2)"
|
||||
|
||||
col = table2.get_column('ML_RATE_3') # 13
|
||||
col.name="MLRate3"
|
||||
col.set_format("F10.6")
|
||||
col.description="Source count rate measured by PSF-fitting; 2.3-5 keV (ML_RATE_3)"
|
||||
|
||||
col = table2.get_column('ML_RATE_4') # 14
|
||||
col.name="MLRate4"
|
||||
col.set_format("F10.6")
|
||||
col.description="Source count rate measured by PSF-fitting; 5-8 keV (ML_RATE_4)"
|
||||
|
||||
col = table2.get_column('ML_RATE_ERR_0') # 15
|
||||
col.name="e_MLRate0"
|
||||
col.set_format("F10.6")
|
||||
col.description="1 sigma count rate error; 0.3-2.3 keV (ML_RATE_ERR_0)"
|
||||
|
||||
col = table2.get_column('ML_RATE_ERR_1') # 16
|
||||
col.name="e_MLRate1"
|
||||
col.set_format("F10.6")
|
||||
col.description="1 sigma count rate error; 0.3-0.6 keV (ML_RATE_ERR_1)"
|
||||
|
||||
col = table2.get_column('ML_RATE_ERR_2') # 17
|
||||
col.name="e_MLRate2"
|
||||
col.set_format("F10.6")
|
||||
col.description="1 sigma count rate error; 0.6-2.3 keV (ML_RATE_ERR_2)"
|
||||
|
||||
col = table2.get_column('ML_RATE_ERR_3') # 18
|
||||
col.name="e_MLRate3"
|
||||
col.set_format("F10.6")
|
||||
col.description="1 sigma count rate error; 2.3-5 keV (ML_RATE_ERR_3)"
|
||||
|
||||
col = table2.get_column('ML_RATE_ERR_4') # 19
|
||||
col.name="e_MLRate4"
|
||||
col.set_format("F10.6")
|
||||
col.description="1 sigma count rate error; 5-8 keV (ML_RATE_ERR_4)"
|
||||
|
||||
col = table2.get_column('ML_CTS_0') # 20
|
||||
col.name="MLCts0"
|
||||
col.set_format("F11.6")
|
||||
col.description="Source net counts measured from count rate; 0.3-2.3 keV (ML_CTS_0)"
|
||||
|
||||
col = table2.get_column('ML_CTS_1') # 21
|
||||
col.name="MLCts1"
|
||||
col.set_format("F11.6")
|
||||
col.description="Source net counts measured from count rate; 0.3-0.6 keV (ML_CTS_1)"
|
||||
|
||||
col = table2.get_column('ML_CTS_2') # 22
|
||||
col.name="MLCts2"
|
||||
col.set_format("F11.6")
|
||||
col.description="Source net counts measured from count rate; 0.6-2.3 keV (ML_CTS_2)"
|
||||
|
||||
col = table2.get_column('ML_CTS_3') # 23
|
||||
col.name="MLCts3"
|
||||
col.set_format("F11.6")
|
||||
col.description="Source net counts measured from count rate; 2.3-5 keV (ML_CTS_3)"
|
||||
|
||||
col = table2.get_column('ML_CTS_4') # 24
|
||||
col.name="MLCts4"
|
||||
col.set_format("F11.6")
|
||||
col.description="Source net counts measured from count rate; 5-8 keV (ML_CTS_4)"
|
||||
|
||||
col = table2.get_column('ML_CTS_ERR_0') # 25
|
||||
col.name="e_MLCts0"
|
||||
col.set_format("F12.6")
|
||||
col.description="1 sigma counts error; 0.3-2.3 keV (ML_CTS_ERR_0)"
|
||||
|
||||
col = table2.get_column('ML_CTS_ERR_1') # 26
|
||||
col.name="e_MLCts1"
|
||||
col.set_format("F12.6")
|
||||
col.description="1 sigma counts error; 0.3-0.6 keV (ML_CTS_ERR_1)"
|
||||
|
||||
col = table2.get_column('ML_CTS_ERR_2') # 27
|
||||
col.name="e_MLCts2"
|
||||
col.set_format("F12.6")
|
||||
col.description="1 sigma counts error; 0.6-2.3 keV (ML_CTS_ERR_2)"
|
||||
|
||||
col = table2.get_column('ML_CTS_ERR_3') # 28
|
||||
col.name="e_MLCts3"
|
||||
col.set_format("F12.6")
|
||||
col.description="1 sigma counts error; 2.3-5 keV (ML_CTS_ERR_3)"
|
||||
|
||||
col = table2.get_column('ML_CTS_ERR_4') # 29
|
||||
col.name="e_MLCts4"
|
||||
col.set_format("F12.6")
|
||||
col.description="1 sigma counts error; 5-8 keV (ML_CTS_ERR_4)"
|
||||
|
||||
col = table2.get_column('ML_FLUX_0') # 30
|
||||
col.name="MLFlux0"
|
||||
col.set_format("E12.6")
|
||||
col.description="Source flux converted from count rate; 0.3-2.3 keV (ML_FLUX_0)"
|
||||
|
||||
col = table2.get_column('ML_FLUX_1') # 31
|
||||
col.name="MLFlux1"
|
||||
col.set_format("E12.6")
|
||||
col.description="Source flux converted from count rate; 0.3-0.6 keV (ML_FLUX_1)"
|
||||
|
||||
col = table2.get_column('ML_FLUX_2') # 32
|
||||
col.name="MLFlux2"
|
||||
col.set_format("E12.6")
|
||||
col.description="Source flux converted from count rate; 0.6-2.3 keV (ML_FLUX_2)"
|
||||
|
||||
col = table2.get_column('ML_FLUX_3') # 33
|
||||
col.name="MLFlux3"
|
||||
col.set_format("E12.6")
|
||||
col.description="Source flux converted from count rate; 2.3-5 keV (ML_FLUX_3)"
|
||||
|
||||
col = table2.get_column('ML_FLUX_4') # 34
|
||||
col.name="MLFlux4"
|
||||
col.set_format("E12.6")
|
||||
col.description="Source flux converted from count rate; 5-8 keV (ML_FLUX_4)"
|
||||
|
||||
col = table2.get_column('ML_FLUX_ERR_0') # 35
|
||||
col.name="e_MLFlux0"
|
||||
col.set_format("E12.6")
|
||||
col.description="1 sigma flux error; 0.3-2.3 keV (ML_FLUX_ERR_0)"
|
||||
|
||||
col = table2.get_column('ML_FLUX_ERR_1') # 36
|
||||
col.name="e_MLFlux1"
|
||||
col.set_format("E12.6")
|
||||
col.description="1 sigma flux error; 0.3-0.6 keV (ML_FLUX_ERR_1)"
|
||||
|
||||
col = table2.get_column('ML_FLUX_ERR_2') # 37
|
||||
col.name="e_MLFlux2"
|
||||
col.set_format("E12.6")
|
||||
col.description="1 sigma flux error; 0.6-2.3 keV (ML_FLUX_ERR_2)"
|
||||
|
||||
col = table2.get_column('ML_FLUX_ERR_3') # 38
|
||||
col.name="e_MLFlux3"
|
||||
col.set_format("E12.6")
|
||||
col.description="1 sigma flux error; 2.3-5 keV (ML_FLUX_ERR_3)"
|
||||
|
||||
col = table2.get_column('ML_FLUX_ERR_4') # 39
|
||||
col.name="e_MLFlux4"
|
||||
col.set_format("E12.6")
|
||||
col.description="1 sigma flux error; 5-8 keV (ML_FLUX_ERR_4)"
|
||||
|
||||
col = table2.get_column('ML_EXP_0') # 40
|
||||
col.name="MLExp0"
|
||||
col.set_format("E10.6")
|
||||
col.description="Vignetted exposure value; 0.3-2.3 keV (ML_EXP_0)"
|
||||
|
||||
col = table2.get_column('ML_EXP_1') # 41
|
||||
col.name="MLExp1"
|
||||
col.set_format("E10.6")
|
||||
col.description="Vignetted exposure value; 0.3-0.6 keV (ML_EXP_1)"
|
||||
|
||||
col = table2.get_column('ML_EXP_2') # 42
|
||||
col.name="MLExp2"
|
||||
col.set_format("E10.6")
|
||||
col.description="Vignetted exposure value; 0.6-2.3 keV (ML_EXP_2)"
|
||||
|
||||
col = table2.get_column('ML_EXP_3') # 43
|
||||
col.name="MLExp3"
|
||||
col.set_format("E10.6")
|
||||
col.description="Vignetted exposure value; 2.3-5 keV (ML_EXP_3)"
|
||||
|
||||
col = table2.get_column('ML_EXP_4') # 44
|
||||
col.name="MLExp4"
|
||||
col.set_format("E10.6")
|
||||
col.description="Vignetted exposure value; 5-8 keV (ML_EXP_4)"
|
||||
|
||||
col = table2.get_column('ML_BKG_0') # 45
|
||||
col.name="MLBkg0"
|
||||
col.set_format("F12.6")
|
||||
col.description="Background at the source position; 0.3-2.3 keV (ML_BKG_0)"
|
||||
|
||||
col = table2.get_column('ML_BKG_1') # 46
|
||||
col.name="MLBkg1"
|
||||
col.set_format("F12.6")
|
||||
col.description="Background at the source position; 0.3-0.6 keV (ML_BKG_1)"
|
||||
|
||||
col = table2.get_column('ML_BKG_2') # 47
|
||||
col.name="MLBkg2"
|
||||
col.set_format("F12.6")
|
||||
col.description="Background at the source position; 0.6-2.3 keV (ML_BKG_2)"
|
||||
|
||||
col = table2.get_column('ML_BKG_3') # 48
|
||||
col.name="MLBkg3"
|
||||
col.set_format("F12.6")
|
||||
col.description="Background at the source position; 2.3-5 keV (ML_BKG_3)"
|
||||
|
||||
col = table2.get_column('ML_BKG_4') # 49
|
||||
col.name="MLBkg4"
|
||||
col.set_format("F12.6")
|
||||
col.description="Background at the source position; 5-8 keV (ML_BKG_4)"
|
||||
|
||||
col = table2.get_column('CONF') # 50
|
||||
col.name="Conf"
|
||||
col.set_format("I1")
|
||||
col.description="True if source is located within 60 arcsec from another source with 0.3-2.3 keV flux > 1E−14 erg/s/cm**2 (CONF)"
|
||||
|
||||
tablemaker.writeCDSTables()
|
||||
|
||||
# Customize ReadMe output
|
||||
|
||||
tablemaker.title = "eUDS: The SRG/eROSITA X-ray Survey of the UKIDSS Ultra Deep Survey Field. Catalogue of Sources."
|
||||
tablemaker.author = 'Krivonos R.'
|
||||
tablemaker.authors="Gilfanov M., Medvedev P., Sazonov S., Sunyaev R."
|
||||
tablemaker.date = 2024
|
||||
tablemaker.bibcode = "2024MNRAS.528.1264K"
|
||||
tablemaker.keywords = "catalogues - surveys - X-ray: general"
|
||||
tablemaker.abstract = "The eROSITA X-ray telescope on board the Spectrum-Roentgen-Gamma (SRG) spacecraft observed the field of the UKIDSS Ultra-Deep Survey (UDS) in August-September 2019, during its flight to Sun-Earth L2 point. The resulting eROSITA UDS (or eUDS) survey was thus the first eROSITA X-ray imaging survey, which demonstrated the capability of the telescope to perform uniform observations of large sky areas. With a moderate single-camera exposure of 150 ks, eUDS covered ∼5 deg^2^ with the limiting flux ranging between 4*10^-15^ and 5*10^-14^erg/cm^2^/s in the 0.3-2.3 keV band. We present a catalogue of 647 sources detected at likelihood >10 (∼4sigma) during the eUDS. The catalogue provides information on the source fluxes in the main energy band 0.3-2.3 keV and forced photometry in a number of bands between 0.3 and 8 keV. Using the deeper 4XMM-DR12 catalogue, we have identified 22 strongly variable objects that have brightened or faded by at least a factor of ten during the eROSITA observations compared to previous observations by XMM-Newton. We also provide a catalogue of 22 sources detected by eROSITA in the hard energy band of 2.3-5 keV."
|
||||
tablemaker.more_description = "The catalogue (fits/eUDS.fits) contains 647 sources detected in the 0.3-2.3 keV band using eSASS adopting a detection likelihood threshold of 10. The catalogue provides information on the source fluxes in the main energy band 0: 0.3-2.3 keV and forced photometry in 1: 0.3-0.6, 2: 0.6-2.3, 3: 2.3-5.0 and 4: 5.0-8.0 keV bands. The catalogue of forced PSF-fitting fluxes (fits/eUDS_4XMM-DR12.fits) contains 3603 XMM-Newton sources from 4XMM-DR12 catalogue with the eUDS exposure of more than 100 s. The catalogue provides information on the source forced photometry in the energy bands 0: 0.3-2.3, 1: 0.3-0.6, 2: 0.6-2.3, 3: 2.3-5.0 and 4: 5.0-8.0 keV. To warn about the potential impact of spatial confusion with a nearby bright source, we added a confusion flag (see original paper for details)."
|
||||
|
||||
tablemaker.putRef("J/A+A/661/A1","The eFEDS X-ray catalogs (Brunner+, 2022)")
|
||||
tablemaker.putRef("J/A+A/661/A38","SRG/ART-XC 1st year all-sky X-ray survey (Pavlinsky+, 2022)")
|
||||
tablemaker.putRef("https://www.srg.cosmos.ru/eUDS","eUDS catalog")
|
||||
#tablemaker.makeReadMe()
|
||||
with open("ReadMe.tmp","w") as fd:
|
||||
tablemaker.makeReadMe(out=fd)
|
||||
|
||||
make_euds_cds(infile1="../uds/products/eUDS.fits",outfile1='table1.dat',
|
||||
infile2="../uds/products/eUDS_4XMM-DR12.fits",outfile2='table2.dat')
|
||||
@@ -1,55 +0,0 @@
|
||||
import glob, sys
|
||||
from astropy.io import fits
|
||||
import statistics
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import csv
|
||||
colnames=['Match', 'Ref', 'Dup', 'RA', 'Dec', 'Incl']
|
||||
|
||||
flist=glob.glob('../products/tm[1,5,6,7]*en0*src.fits')
|
||||
total1=0
|
||||
total2=0
|
||||
|
||||
for f in flist:
|
||||
|
||||
hdul = fits.open(f)
|
||||
try:
|
||||
table=hdul[1].data['RADEC_ERR']
|
||||
except:
|
||||
continue
|
||||
hdul.close()
|
||||
|
||||
dref=f.replace("src.fits","ref.fits")
|
||||
hdul = fits.open(dref)
|
||||
try:
|
||||
rtable=hdul[1].data['RA']
|
||||
except:
|
||||
continue
|
||||
hdul.close()
|
||||
|
||||
err=[]
|
||||
err10=[]
|
||||
dfile=f.replace("shu2019.src.fits","xfm.log.dat.awk")
|
||||
with open(dfile) as csvfile:
|
||||
spamreader = csv.reader(csvfile, delimiter=' ')
|
||||
total=0
|
||||
for row in spamreader:
|
||||
if(row[5] == 'N'):
|
||||
continue
|
||||
for ii,values in np.ndenumerate(rtable):
|
||||
if(int(row[2]) == int(ii[0])):
|
||||
total=total+1
|
||||
#print(">>",row[5],rtable[ii],table[ii],ii[0])
|
||||
if(table[ii]>0.0):
|
||||
err.append(table[ii])
|
||||
if(table[ii]>10.0):
|
||||
err10.append(table[ii])
|
||||
|
||||
total1=total1+total
|
||||
total2=total2+len(err)
|
||||
#print("{} MIN {:.2f} MAX {:.2f} ({}/{})".format(f[12:15],min(err),max(err),len(err10),len(err)))
|
||||
print("{} MIN {:.2f} MAX {:.2f} ({}/{})".format(f,min(err),max(err),len(err10),len(err)))
|
||||
#sys.exit()
|
||||
|
||||
|
||||
#print(total1,total2)
|
||||
@@ -1,67 +0,0 @@
|
||||
import glob, sys
|
||||
from astropy.io import fits
|
||||
import statistics
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import csv
|
||||
from uds.config import *
|
||||
|
||||
colnames=['Match', 'Ref', 'Dup', 'RA', 'Dec', 'Incl']
|
||||
|
||||
flist=glob.glob('../products/tm[1,5,6,7]*en0*.xfm')
|
||||
total1=0
|
||||
total2=0
|
||||
|
||||
for f in flist:
|
||||
|
||||
pos = f.find("_SourceCatalog")
|
||||
key=f[12:pos]
|
||||
|
||||
hdul = fits.open(f)
|
||||
try:
|
||||
tab=hdul[1].data
|
||||
except:
|
||||
continue
|
||||
hdul.close()
|
||||
|
||||
dsrc=f.replace(".xfm",".shu2019.src.fits")
|
||||
hdul = fits.open(dsrc)
|
||||
try:
|
||||
hdr=hdul[0].header
|
||||
cdelt=hdr['CDELT2']*3600
|
||||
except:
|
||||
continue
|
||||
hdul.close()
|
||||
|
||||
a11=tab[0]['a11']
|
||||
a12=tab[0]['a12']
|
||||
a22=tab[0]['a22']
|
||||
a21=tab[0]['a21']
|
||||
x_scale=tab[0]['x_scale']
|
||||
y_scale=tab[0]['y_scale']
|
||||
|
||||
t1=tab[0]['t1']*cdelt
|
||||
t2=tab[0]['t2']*cdelt
|
||||
|
||||
sx1=np.sign(a11)*np.sqrt(a11**2 + a12**2)
|
||||
sy1=np.sign(a22)*np.sqrt(a21**2 + a22**2)
|
||||
|
||||
sx2=np.sign(a11)*np.sqrt(a11**2 + a21**2)
|
||||
sy2=np.sign(a22)*np.sqrt(a12**2 + a22**2)
|
||||
|
||||
a1 = np.arctan2(a12/sx1,a11/sy2) * 180 / np.pi * 60
|
||||
a2 = np.arctan2(a12/sx1,a22/sy2) * 180 / np.pi * 60
|
||||
|
||||
b1 = np.arctan2(a21,a11) * 180 / np.pi * 60
|
||||
b2 = np.arctan2(a21,a22) * 180 / np.pi * 60
|
||||
|
||||
det=a11*a22-a12*a21
|
||||
|
||||
alpha=355*np.pi/180
|
||||
a3 = np.arctan2(np.sin(alpha),np.cos(alpha)) * 180 / np.pi
|
||||
|
||||
print("{:+.8} {:+.8}".format(a11,a12))
|
||||
print("{:+.8} {:+.8}".format(a21,a22))
|
||||
|
||||
#print("{} d {:.10f} s {:.8f} {:.8f} {:.8f} {:.8f} | {:+.8f} {:+.8f} {:+.8f} {:+.8f} t {:+.2f} {:+.2f}".format(obslist[key],np.sqrt(det)*cdelt,sx1*cdelt,sx2*cdelt,sy1*cdelt,sy2*cdelt,a1,a2,b1,b2,t1,t2))
|
||||
#print("{} d {:.10f} s {:.8f} {:.8f} {:.8f} {:.8f} | {:+.8f} {:+.8f} {:+.8f} {:+.8f} t {:+.2f} {:+.2f}".format(obslist[key],np.sqrt(det)*cdelt,sx1*cdelt,sx2*cdelt,sy1*cdelt,sy2*cdelt,a1,a2,b1,b2,t1,t2))
|
||||
Reference in New Issue
Block a user