generated from erosita/uds
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@ -27,7 +27,7 @@ ylim <- c(0.1,1.5)
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cl="black"
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cl="black"
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a <- read.table('../cutoffpl_lon+20_eeuf.dat', col.names=c("x","dx","y","dy","total","cutoffpl","pow"))
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a <- read.table('../cutoffpl_lon+20_eeuf.dat', col.names=c("x","dx","y","dy","total","cutoffpl","pow"))
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plot(a$x, a$y, pch=3, bg="white", col="white", ylim=ylim, xlim=xlim,main="GC (cutoffpl+powerlaw)",ylab=expression("keV"^"2"~"(Phot. keV"^"-1"~"cm"^"-2"~"s"^"-1"*")"),type="p",xaxt = 'n',xlab="",log="xy")
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plot(a$x, a$y, pch=3, bg="white", col="white", ylim=ylim, xlim=xlim,main="LON+20 (cutoffpl+powerlaw)",ylab=expression("keV"^"2"~"(Phot. keV"^"-1"~"cm"^"-2"~"s"^"-1"*")"),type="p",xaxt = 'n',xlab="",log="xy")
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#axis(side=2, at=c(1e-6,1e-5, 1e-4, 1e-3, 1e-2), labels=expression('-6','-5','-4','-3','-2'))
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#axis(side=2, at=c(1e-6,1e-5, 1e-4, 1e-3, 1e-2), labels=expression('-6','-5','-4','-3','-2'))
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@ -61,7 +61,7 @@ segments(a$x-a$dx,a$y,a$x+a$dx,a$y,col=cl)
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mtext(side = 1, text = "Energy, keV", line = 4, cex=1.6)
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mtext(side = 1, text = "Energy, keV", line = 4, cex=1.6)
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legend( 90.0,0.08,c("GC","LON+20","LON-20","BGD"), lty=c(1,1,1,1), lwd=c(2.5,2.5,2.5,2.5),col=c("red","blue","green","black"),cex=1.7)
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legend( 85.0,0.08,c("GC","LON+20","LON-20","3C 273/Coma"), lty=c(1,1,1,1), lwd=c(2.5,2.5,2.5,2.5),col=c("red","blue","green","black"),cex=1.7)
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axis(1, mgp=c(3, 1.5, 0))
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axis(1, mgp=c(3, 1.5, 0))
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9
Spectra/load_gc06_polar.xcm
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9
Spectra/load_gc06_polar.xcm
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@ -0,0 +1,9 @@
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data GC06.sim.spec.pha
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setpl en
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cpd /xs
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setpl reb 2
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systematic 0
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setpl reb 2 2
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@polar_gc06_cflux
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pl eeuf delchi
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8
Spectra/load_lon+20_polar.xcm
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Spectra/load_lon+20_polar.xcm
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@ -0,0 +1,8 @@
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data LON+20.sim.spec.pha
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setpl en
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cpd /xs
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@polar_lon+20_cflux
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setpl reb 2 2
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pl eeuf delchi
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#steppar 9 -10 -12 100
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#new 9 -10.34
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8
Spectra/load_lon-20_polar.xcm
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Spectra/load_lon-20_polar.xcm
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@ -0,0 +1,8 @@
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data LON-20.sim.spec.pha
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setpl en
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cpd /xs
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@polar_lon-20_cflux
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setpl reb 2 2
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pl eeuf delchi
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#steppar 9 -10 -12 100
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#new 9 -10.34
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@ -5,11 +5,11 @@ cosmo 70 0 0.73
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xset delta 0.01
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xset delta 0.01
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systematic 0.05
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systematic 0.05
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model atable{polarmodel.fits} + cflux*powerlaw
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model atable{polarmodel.fits} + cflux*powerlaw
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0.67731 0.01 0 0 3 3
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0.656833 0.01 0 0 3 3
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3.02538e-09 0.01 0 0 1e+20 1e+24
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3.19989e-09 0.01 0 0 1e+20 1e+24
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30 -0.1 0 0 1e+06 1e+06
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30 -0.1 0 0 1e+06 1e+06
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80 -0.1 0 0 1e+06 1e+06
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80 -0.1 0 0 1e+06 1e+06
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-9.38934 0.01 -100 -100 100 100
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-9.19605 0.01 -100 -100 100 100
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1.55 -1 -3 -2 9 10
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1.55 -1 -3 -2 9 10
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1 -1 0 0 1e+20 1e+24
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1 -1 0 0 1e+20 1e+24
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bayes off
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bayes off
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18
Spectra/polar_gc06_cflux.xcm
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Spectra/polar_gc06_cflux.xcm
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@ -0,0 +1,18 @@
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method leven 10 0.01
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abund angr
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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 0.05
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model cflux*atable{polarmodel.fits} + cflux*powerlaw
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30 -0.1 0 0 1e+06 1e+06
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80 -0.1 0 0 1e+06 1e+06
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-9.01521 0.01 -100 -100 100 100
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0.657219 0.01 0 0 3 3
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1 -1 0 0 1e+20 1e+24
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30 -0.1 0 0 1e+06 1e+06
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80 -0.1 0 0 1e+06 1e+06
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-9.1965 0.01 -100 -100 100 100
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1.55 -1 -3 -2 9 10
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1 -1 0 0 1e+20 1e+24
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bayes off
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15
Spectra/polar_lon+20.xcm
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15
Spectra/polar_lon+20.xcm
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method leven 10 0.01
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abund angr
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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 0.05
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model atable{polarmodel.fits} + cflux*powerlaw
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0.6479 0.01 0 0 3 3
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1.33563e-09 0.01 0 0 1e+20 1e+24
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30 -0.1 0 0 1e+06 1e+06
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80 -0.1 0 0 1e+06 1e+06
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-9.52474 0.01 -100 -100 100 100
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1.55 -1 -3 -2 9 10
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1 -1 0 0 1e+20 1e+24
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bayes off
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18
Spectra/polar_lon+20_cflux.xcm
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18
Spectra/polar_lon+20_cflux.xcm
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@ -0,0 +1,18 @@
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method leven 10 0.01
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abund angr
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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 0.05
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model cflux*atable{polarmodel.fits} + cflux*powerlaw
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30 -0.1 0 0 1e+06 1e+06
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80 -0.1 0 0 1e+06 1e+06
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-9.42255 0.01 -100 -100 100 100
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0.647891 0.01 0 0 3 3
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1 -1 0 0 1e+20 1e+24
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30 -0.1 0 0 1e+06 1e+06
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80 -0.1 0 0 1e+06 1e+06
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-9.52474 0.01 -100 -100 100 100
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1.55 -1 -3 -2 9 10
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1 -1 0 0 1e+20 1e+24
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bayes off
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18
Spectra/polar_lon-20_cflux.xcm
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Spectra/polar_lon-20_cflux.xcm
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method leven 10 0.01
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abund angr
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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 0.05
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model cflux*atable{polarmodel.fits} + cflux*powerlaw
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30 -0.1 0 0 1e+06 1e+06
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80 -0.1 0 0 1e+06 1e+06
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-9.31443 0.01 -100 -100 100 100
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0.718932 0.01 0 0 3 3
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1 -1 0 0 1e+20 1e+24
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30 -0.1 0 0 1e+06 1e+06
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80 -0.1 0 0 1e+06 1e+06
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-9.57592 0.01 -100 -100 100 100
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1.55 -1 -3 -2 9 10
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1 -1 0 0 1e+20 1e+24
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bayes off
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@ -153,6 +153,6 @@ sigma=3
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modelrxte="allsky/modelrxte_ait_3to20keV_flux_2deg.fits"
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modelrxte="allsky/modelrxte_ait_3to20keV_flux_2deg.fits"
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# Galactic longitude profile
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# Galactic longitude profile
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lon_max=180.0
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lon_max=173.0
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lon_nbin=100
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lon_nbin=100
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lat_max=10.0
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lat_max=10.0
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@ -65,8 +65,9 @@ ign=ignored_rev.tolist()
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glon, step = np.linspace(-lon_max, lon_max, num=lon_nbin, endpoint=False,retstep=True)
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glon, step = np.linspace(-lon_max, lon_max, num=lon_nbin, endpoint=False,retstep=True)
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#print(glon,step)
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print("STEP",step)
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#sys.exit()
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#
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#
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# initiate 2d arrays
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# initiate 2d arrays
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@ -1,4 +1,4 @@
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./03_grxe_galprof.py E01 51
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./03_grxe_galprof.py E01 21
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./03_grxe_galprof.py E13 17
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./03_grxe_galprof.py E13 21
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./03_grxe_galprof.py E14 21
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./03_grxe_galprof.py E14 21
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@ -26,7 +26,9 @@ fn="detcnts.E13.ALL.resid.galprof.fits"
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dat = Table.read(profdir+fn, unit_parse_strict='silent')
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dat = Table.read(profdir+fn, unit_parse_strict='silent')
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df3 = dat.to_pandas().sort_values(by=['LON1'])
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df3 = dat.to_pandas().sort_values(by=['LON1'])
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#df_cobe = pd.read_csv('../data/cobe_ibis_resp_lon.dat', header=None)
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df_cobe = pd.read_csv('../data/cobe_ibis_resp_lon.dat', sep=' ', header=None)
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print(df_cobe)
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fig, (ax1, ax2, ax3) = plt.subplots(3, sharex=True, figsize=(9, 7), dpi=100)
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fig, (ax1, ax2, ax3) = plt.subplots(3, sharex=True, figsize=(9, 7), dpi=100)
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#fig.suptitle('Vertically stacked subplots')
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#fig.suptitle('Vertically stacked subplots')
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@ -65,6 +67,7 @@ ax2.yaxis.set_minor_locator(ticker.MultipleLocator(0.2))
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ax3.yaxis.set_minor_locator(ticker.MultipleLocator(0.2))
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ax3.yaxis.set_minor_locator(ticker.MultipleLocator(0.2))
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lon=(df1['LON1']+df1['LON2'])/2
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lon=(df1['LON1']+df1['LON2'])/2
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ax1.plot(df_cobe[0],df_cobe[2]/3, color='red', linewidth=4)
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ax1.errorbar(lon, df1['GRXE_SIM_FLUX']/scale,
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ax1.errorbar(lon, df1['GRXE_SIM_FLUX']/scale,
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yerr=df1['GRXE_SIM_ERROR']/scale,
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yerr=df1['GRXE_SIM_ERROR']/scale,
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xerr=(df1['LON2']-df1['LON1'])/2,
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xerr=(df1['LON2']-df1['LON1'])/2,
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