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@ -8,7 +8,7 @@ The preprocessed data will be continuously updated until INTEGRAL stops register
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# INSTALL
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## Step 1. (optionally) Creation of Python virtual environment
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## Step 1. Creation of Python virtual environment
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```
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mkdir ~/work; cd ~/work
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python3 -m venv venv
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98
figures/cutoffpl_gb.r
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98
figures/cutoffpl_gb.r
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@ -0,0 +1,98 @@
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#
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# 2 data sets
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# in Xspec make ipl->wdata
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# then split file to four parts as lecr1/2/3/4
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#
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library(sfsmisc)
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up=2.0
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#xgrid <- c(30,50,70,100);
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Emin=25.0
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Emax=180.0
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postscript('cutoffpl_gb.eps', 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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par(lwd=2)
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layout(matrix(c(1,2), 2, 1, byrow = TRUE), heights = c(1.5,1), TRUE)
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leftx=5.3
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par(mar=c(0.1, leftx, 1, 1))
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par(cex.lab=1.6)
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par(cex.axis=1.6)
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xlim <- c(Emin,Emax)
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ylim <- c(0.2,2)
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cl="black"
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a <- read.table('./cutoffpl_gb_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, 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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#abline(v=xgrid, col="lightgray", lty="dotted")
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grid()
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###################################################################################
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##
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## Plot model components
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##
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###################################################################################
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lines(a$x,a$cutoffpl,pch=1,bg=cl,col="blue",lwd=3,lty="longdash")
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lines(a$x,a$pow,pch=1,bg=cl,col="red",lwd=3,lty="dotdash")
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lines(a$x,a$total,pch=1,bg=cl,col="black")
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#
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# Plot upper limits
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#
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upx=a$x[(a$y/a$dy)<up]
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upy=a$y[(a$y/a$dy)<up]
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updx=a$dx[(a$y/a$dy)<up]
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updy=a$dy[(a$y/a$dy)<up]
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segments(upx-updx,updy*up,upx+updx,updy*up,col=cl)
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segments(upx,updy*up/2,upx,updy*up,col=cl)
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points(upx,updy*up/2,pch=25,bg=cl,col=cl)
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px=a$x[(a$y/a$dy)>=up]
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py=a$y[(a$y/a$dy)>=up]
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pdx=a$dx[(a$y/a$dy)>=up]
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pdy=a$dy[(a$y/a$dy)>=up]
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segments(px,py-pdy,px,py+pdy,col=cl)
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segments(px-pdx,py,px+pdx,py,col=cl)
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text(100, 1.5, "GB",cex=2.5)
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###################################################################################
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par(mar=c(4.5, leftx, 0.1, 1))
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ylim <- c(-3.1,3.1)
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cl="black"
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a <- read.table("./cutoffpl_gb_delchi.dat", col.names=c("x","dx","y","dy"))
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plot(a$x, a$y, pch=3, ylim=ylim, xlim=xlim, ylab=expression(Delta~chi), xlab="",type="p",log="x",xaxt = 'n')
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grid()
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segments(a$x,a$y-a$dy,a$x,a$y+a$dy,col=cl)
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segments(a$x-a$dx,a$y,a$x+a$dx,a$y,col=cl)
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#abline(v=xgrid, col="lightgray", lty="dotted")
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#grid()
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#abline(h=0, col = "black",lty=2)
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#dev.off()
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#abline(v=xgrid, col="lightgray", lty="dotted")
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abline(h=0, col = "black",lty=2)
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mtext(side = 1, text = "Energy, keV", line = 4, cex=1.6)
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#axis(1, mgp=c(3, 1.5, 0))
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### Log axis using sfsmisc ###
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atx=c(8,30,50,80,100,150)
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eaxis(1, at = atx, labels = pretty10exp(atx, sub10=c(1,100), drop.1=TRUE), las=0)
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abline(v=atx, col="lightgray", lty="dotted")
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###
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dev.off()
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99
figures/cutoffpl_l+20.r
Normal file
99
figures/cutoffpl_l+20.r
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@ -0,0 +1,99 @@
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#
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# 2 data sets
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# in Xspec make ipl->wdata
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# then split file to four parts as lecr1/2/3/4
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#
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library(sfsmisc)
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up=2.0
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#xgrid <- c(30,50,70,100);
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Emin=25.0
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Emax=180.0
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postscript('cutoffpl_l+20.eps', 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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par(lwd=2)
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layout(matrix(c(1,2), 2, 1, byrow = TRUE), heights = c(1.5,1), TRUE)
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leftx=5.3
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par(mar=c(0.1, leftx, 1, 1))
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par(cex.lab=1.6)
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par(cex.axis=1.6)
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xlim <- c(Emin,Emax)
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ylim <- c(0.05,1.0)
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cl="black"
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a <- read.table('./cutoffpl_l+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,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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#abline(v=xgrid, col="lightgray", lty="dotted")
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grid()
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###################################################################################
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##
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## Plot model components
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##
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###################################################################################
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lines(a$x,a$cutoffpl,pch=1,bg=cl,col="blue",lwd=3,lty="longdash")
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lines(a$x,a$pow,pch=1,bg=cl,col="red",lwd=3,lty="dotdash")
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lines(a$x,a$total,pch=1,bg=cl,col="black")
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#
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# Plot upper limits
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#
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upx=a$x[(a$y/a$dy)<up]
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upy=a$y[(a$y/a$dy)<up]
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updx=a$dx[(a$y/a$dy)<up]
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updy=a$dy[(a$y/a$dy)<up]
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segments(upx-updx,updy*up,upx+updx,updy*up,col=cl)
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segments(upx,updy*up/2,upx,updy*up,col=cl)
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points(upx,updy*up/2,pch=25,bg=cl,col=cl)
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px=a$x[(a$y/a$dy)>=up]
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py=a$y[(a$y/a$dy)>=up]
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pdx=a$dx[(a$y/a$dy)>=up]
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pdy=a$dy[(a$y/a$dy)>=up]
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segments(px,py-pdy,px,py+pdy,col=cl)
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segments(px-pdx,py,px+pdx,py,col=cl)
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#legend( 35.0,1e-2,c("FPMA","FPMB"), lty=c(1,1), lwd=c(2.5,2.5),col=c("black","red"),cex=1.7)
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text(100, 0.75, "L+20",cex=2.5)
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###################################################################################
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par(mar=c(4.5, leftx, 0.1, 1))
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ylim <- c(-3.1,3.1)
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cl="black"
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a <- read.table("./cutoffpl_l+20_delchi.dat", col.names=c("x","dx","y","dy"))
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plot(a$x, a$y, pch=3, ylim=ylim, xlim=xlim, ylab=expression(Delta~chi), xlab="",type="p",log="x",xaxt = 'n')
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grid()
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segments(a$x,a$y-a$dy,a$x,a$y+a$dy,col=cl)
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segments(a$x-a$dx,a$y,a$x+a$dx,a$y,col=cl)
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#abline(v=xgrid, col="lightgray", lty="dotted")
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#grid()
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#abline(h=0, col = "black",lty=2)
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#dev.off()
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#abline(v=xgrid, col="lightgray", lty="dotted")
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abline(h=0, col = "black",lty=2)
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mtext(side = 1, text = "Energy, keV", line = 4, cex=1.6)
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#axis(1, mgp=c(3, 1.5, 0))
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### Log axis using sfsmisc ###
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atx=c(8,30,50,80,100,150)
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eaxis(1, at = atx, labels = pretty10exp(atx, sub10=c(1,100), drop.1=TRUE), las=0)
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abline(v=atx, col="lightgray", lty="dotted")
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###
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dev.off()
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99
figures/cutoffpl_l-20.r
Normal file
99
figures/cutoffpl_l-20.r
Normal file
@ -0,0 +1,99 @@
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#
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# 2 data sets
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# in Xspec make ipl->wdata
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# then split file to four parts as lecr1/2/3/4
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#
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library(sfsmisc)
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up=2.0
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#xgrid <- c(30,50,70,100);
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Emin=25.0
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Emax=180.0
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postscript('cutoffpl_l-20.eps', 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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par(lwd=2)
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layout(matrix(c(1,2), 2, 1, byrow = TRUE), heights = c(1.5,1), TRUE)
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leftx=5.3
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par(mar=c(0.1, leftx, 1, 1))
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par(cex.lab=1.6)
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par(cex.axis=1.6)
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xlim <- c(Emin,Emax)
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ylim <- c(0.05,1)
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cl="black"
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a <- read.table('./cutoffpl_l-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, 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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#abline(v=xgrid, col="lightgray", lty="dotted")
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grid()
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###################################################################################
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##
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## Plot model components
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##
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###################################################################################
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lines(a$x,a$cutoffpl,pch=1,bg=cl,col="blue",lwd=3,lty="longdash")
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lines(a$x,a$pow,pch=1,bg=cl,col="red",lwd=3,lty="dotdash")
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lines(a$x,a$total,pch=1,bg=cl,col="black")
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#
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# Plot upper limits
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#
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upx=a$x[(a$y/a$dy)<up]
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upy=a$y[(a$y/a$dy)<up]
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updx=a$dx[(a$y/a$dy)<up]
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updy=a$dy[(a$y/a$dy)<up]
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segments(upx-updx,updy*up,upx+updx,updy*up,col=cl)
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segments(upx,updy*up/2,upx,updy*up,col=cl)
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points(upx,updy*up/2,pch=25,bg=cl,col=cl)
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px=a$x[(a$y/a$dy)>=up]
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py=a$y[(a$y/a$dy)>=up]
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pdx=a$dx[(a$y/a$dy)>=up]
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pdy=a$dy[(a$y/a$dy)>=up]
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segments(px,py-pdy,px,py+pdy,col=cl)
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segments(px-pdx,py,px+pdx,py,col=cl)
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#legend( 35.0,1e-2,c("FPMA","FPMB"), lty=c(1,1), lwd=c(2.5,2.5),col=c("black","red"),cex=1.7)
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text(100, 0.75, "L-20",cex=2.5)
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###################################################################################
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par(mar=c(4.5, leftx, 0.1, 1))
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ylim <- c(-3.1, 3.1)
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cl="black"
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a <- read.table("./cutoffpl_l-20_delchi.dat", col.names=c("x","dx","y","dy"))
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plot(a$x, a$y, pch=3, ylim=ylim, xlim=xlim, ylab=expression(Delta~chi), xlab="",type="p",log="x",xaxt = 'n')
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grid()
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segments(a$x,a$y-a$dy,a$x,a$y+a$dy,col=cl)
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segments(a$x-a$dx,a$y,a$x+a$dx,a$y,col=cl)
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#abline(v=xgrid, col="lightgray", lty="dotted")
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#grid()
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#abline(h=0, col = "black",lty=2)
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#dev.off()
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#abline(v=xgrid, col="lightgray", lty="dotted")
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abline(h=0, col = "black",lty=2)
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mtext(side = 1, text = "Energy, keV", line = 4, cex=1.6)
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#axis(1, mgp=c(3, 1.5, 0))
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### Log axis using sfsmisc ###
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atx=c(8,30,50,80,100,150)
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eaxis(1, at = atx, labels = pretty10exp(atx, sub10=c(1,100), drop.1=TRUE), las=0)
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abline(v=atx, col="lightgray", lty="dotted")
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###
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dev.off()
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98
figures/cutoffpl_scox1.r
Normal file
98
figures/cutoffpl_scox1.r
Normal file
@ -0,0 +1,98 @@
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#
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# 2 data sets
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# in Xspec make ipl->wdata
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# then split file to four parts as lecr1/2/3/4
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#
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library(sfsmisc)
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up=2.0
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#xgrid <- c(30,50,70,100);
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Emin=25.0
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Emax=180.0
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postscript('cutoffpl_scox1.eps', 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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par(lwd=2)
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layout(matrix(c(1,2), 2, 1, byrow = TRUE), heights = c(1.5,1), TRUE)
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leftx=5.3
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par(mar=c(0.1, leftx, 1, 1))
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par(cex.lab=1.6)
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par(cex.axis=1.6)
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xlim <- c(Emin,Emax)
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ylim <- c(0.01,10)
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cl="black"
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a <- read.table('./cutoffpl_scox1_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, 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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#abline(v=xgrid, col="lightgray", lty="dotted")
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grid()
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###################################################################################
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##
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## Plot model components
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##
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###################################################################################
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lines(a$x,a$cutoffpl,pch=1,bg=cl,col="blue",lwd=3,lty="longdash")
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lines(a$x,a$pow,pch=1,bg=cl,col="red",lwd=3,lty="dotdash")
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lines(a$x,a$total,pch=1,bg=cl,col="black")
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#
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# Plot upper limits
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#
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upx=a$x[(a$y/a$dy)<up]
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upy=a$y[(a$y/a$dy)<up]
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updx=a$dx[(a$y/a$dy)<up]
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updy=a$dy[(a$y/a$dy)<up]
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segments(upx-updx,updy*up,upx+updx,updy*up,col=cl)
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segments(upx,updy*up/2,upx,updy*up,col=cl)
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points(upx,updy*up/2,pch=25,bg=cl,col=cl)
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px=a$x[(a$y/a$dy)>=up]
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py=a$y[(a$y/a$dy)>=up]
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pdx=a$dx[(a$y/a$dy)>=up]
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pdy=a$dy[(a$y/a$dy)>=up]
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segments(px,py-pdy,px,py+pdy,col=cl)
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segments(px-pdx,py,px+pdx,py,col=cl)
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text(100, 1.5, "Sco X-1",cex=2.5)
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###################################################################################
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|
||||
par(mar=c(4.5, leftx, 0.1, 1))
|
||||
ylim <- c(-3.1,3.1)
|
||||
|
||||
cl="black"
|
||||
a <- read.table("./cutoffpl_scox1_delchi.dat", col.names=c("x","dx","y","dy"))
|
||||
plot(a$x, a$y, pch=3, ylim=ylim, xlim=xlim, ylab=expression(Delta~chi), xlab="",type="p",log="x",xaxt = 'n')
|
||||
grid()
|
||||
segments(a$x,a$y-a$dy,a$x,a$y+a$dy,col=cl)
|
||||
segments(a$x-a$dx,a$y,a$x+a$dx,a$y,col=cl)
|
||||
|
||||
#abline(v=xgrid, col="lightgray", lty="dotted")
|
||||
#grid()
|
||||
#abline(h=0, col = "black",lty=2)
|
||||
#dev.off()
|
||||
|
||||
#abline(v=xgrid, col="lightgray", lty="dotted")
|
||||
abline(h=0, col = "black",lty=2)
|
||||
|
||||
mtext(side = 1, text = "Energy, keV", line = 4, cex=1.6)
|
||||
|
||||
#axis(1, mgp=c(3, 1.5, 0))
|
||||
### Log axis using sfsmisc ###
|
||||
atx=c(8,30,50,80,100,150)
|
||||
eaxis(1, at = atx, labels = pretty10exp(atx, sub10=c(1,100), drop.1=TRUE), las=0)
|
||||
abline(v=atx, col="lightgray", lty="dotted")
|
||||
###
|
||||
|
||||
dev.off()
|
||||
98
figures/cutoffpl_solo_gb.r
Normal file
98
figures/cutoffpl_solo_gb.r
Normal file
@ -0,0 +1,98 @@
|
||||
#
|
||||
# 2 data sets
|
||||
# in Xspec make ipl->wdata
|
||||
# then split file to four parts as lecr1/2/3/4
|
||||
#
|
||||
|
||||
|
||||
library(sfsmisc)
|
||||
|
||||
|
||||
up=2.0
|
||||
#xgrid <- c(30,50,70,100);
|
||||
Emin=25.0
|
||||
Emax=180.0
|
||||
postscript('cutoffpl_solo_gb.eps', horizontal = FALSE, onefile = FALSE, paper = "special",width = 9.0, height = 6.0)
|
||||
par(oma=c(1,1,1,1))
|
||||
par(lwd=2)
|
||||
|
||||
layout(matrix(c(1,2), 2, 1, byrow = TRUE), heights = c(1.5,1), TRUE)
|
||||
leftx=5.3
|
||||
par(mar=c(0.1, leftx, 1, 1))
|
||||
par(cex.lab=1.6)
|
||||
par(cex.axis=1.6)
|
||||
xlim <- c(Emin,Emax)
|
||||
ylim <- c(0.2,2)
|
||||
|
||||
cl="black"
|
||||
a <- read.table('./cutoffpl_solo_gb_eeuf.dat', col.names=c("x","dx","y","dy","cutoffpl"))
|
||||
plot(a$x, a$y, pch=3, bg="white", col="white", ylim=ylim, xlim=xlim, ylab=expression("keV"^"2"~"(Phot. keV"^"-1"~"cm"^"-2"~"s"^"-1"*")"),type="p",xaxt = 'n',xlab="",log="xy")
|
||||
|
||||
#axis(side=2, at=c(1e-6,1e-5, 1e-4, 1e-3, 1e-2), labels=expression('-6','-5','-4','-3','-2'))
|
||||
|
||||
#abline(v=xgrid, col="lightgray", lty="dotted")
|
||||
grid()
|
||||
|
||||
###################################################################################
|
||||
##
|
||||
## Plot model components
|
||||
##
|
||||
###################################################################################
|
||||
|
||||
lines(a$x,a$cutoffpl,pch=1,bg=cl,col="blue", lwd=3, lty="longdash")
|
||||
#lines(a$x,a$pow,pch=1,bg=cl,col="red")
|
||||
#lines(a$x,a$total,pch=1,bg=cl,col="black")
|
||||
|
||||
|
||||
#
|
||||
# Plot upper limits
|
||||
#
|
||||
upx=a$x[(a$y/a$dy)<up]
|
||||
upy=a$y[(a$y/a$dy)<up]
|
||||
updx=a$dx[(a$y/a$dy)<up]
|
||||
updy=a$dy[(a$y/a$dy)<up]
|
||||
|
||||
segments(upx-updx,updy*up,upx+updx,updy*up,col=cl)
|
||||
segments(upx,updy*up/2,upx,updy*up,col=cl)
|
||||
points(upx,updy*up/2,pch=25,bg=cl,col=cl)
|
||||
|
||||
px=a$x[(a$y/a$dy)>=up]
|
||||
py=a$y[(a$y/a$dy)>=up]
|
||||
pdx=a$dx[(a$y/a$dy)>=up]
|
||||
pdy=a$dy[(a$y/a$dy)>=up]
|
||||
|
||||
segments(px,py-pdy,px,py+pdy,col=cl)
|
||||
segments(px-pdx,py,px+pdx,py,col=cl)
|
||||
|
||||
text(100, 1.5, "GB",cex=2.5)
|
||||
|
||||
###################################################################################
|
||||
|
||||
par(mar=c(4.5, leftx, 0.1, 1))
|
||||
ylim <- c(-4.5,4.5)
|
||||
|
||||
cl="black"
|
||||
a <- read.table("./cutoffpl_solo_gb_delchi.dat", col.names=c("x","dx","y","dy"))
|
||||
plot(a$x, a$y, pch=3, ylim=ylim, xlim=xlim, ylab=expression(Delta~chi), xlab="",type="p",log="x",xaxt = 'n')
|
||||
grid()
|
||||
segments(a$x,a$y-a$dy,a$x,a$y+a$dy,col=cl)
|
||||
segments(a$x-a$dx,a$y,a$x+a$dx,a$y,col=cl)
|
||||
|
||||
#abline(v=xgrid, col="lightgray", lty="dotted")
|
||||
#grid()
|
||||
#abline(h=0, col = "black",lty=2)
|
||||
#dev.off()
|
||||
|
||||
#abline(v=xgrid, col="lightgray", lty="dotted")
|
||||
abline(h=0, col = "black",lty=2)
|
||||
|
||||
mtext(side = 1, text = "Energy, keV", line = 4, cex=1.6)
|
||||
|
||||
#axis(1, mgp=c(3, 1.5, 0))
|
||||
### Log axis using sfsmisc ###
|
||||
atx=c(8,30,50,80,100,150)
|
||||
eaxis(1, at = atx, labels = pretty10exp(atx, sub10=c(1,100), drop.1=TRUE), las=0)
|
||||
abline(v=atx, col="lightgray", lty="dotted")
|
||||
###
|
||||
|
||||
dev.off()
|
||||
83
figures/galplane.r
Normal file
83
figures/galplane.r
Normal file
@ -0,0 +1,83 @@
|
||||
#
|
||||
# 2 data sets
|
||||
# in Xspec make ipl->wdata
|
||||
# then split file
|
||||
#
|
||||
|
||||
|
||||
|
||||
library(sfsmisc)
|
||||
|
||||
|
||||
|
||||
|
||||
up=-10.0
|
||||
|
||||
Emin=25.0
|
||||
Emax=180.0
|
||||
postscript('galplane.eps', horizontal = FALSE, onefile = FALSE, paper = "special",width = 9.0, height = 6.0)
|
||||
par(oma=c(1,1,1,1))
|
||||
par(lwd=2)
|
||||
|
||||
#layout(matrix(c(1,2), 2, 1, byrow = TRUE), heights = c(1.5,1), TRUE)
|
||||
|
||||
leftx=4.5
|
||||
par(mar=c(4., leftx, 0.1, 0.1))
|
||||
par(cex.lab=1.6)
|
||||
par(cex.axis=1.6)
|
||||
xlim <- c(Emin,Emax)
|
||||
|
||||
cl="black"
|
||||
|
||||
sc=1000.0
|
||||
|
||||
ylim <- c(-0.005,0.09)*sc
|
||||
|
||||
a <- read.table("./galplane_1.dat", col.names=c("x","dx","y","dy"))
|
||||
b <- read.table("./galplane_3.dat", col.names=c("x","dx","y","dy"))
|
||||
c <- read.table("./galplane_2.dat", col.names=c("x","dx","y","dy"))
|
||||
d <- read.table("./galplane_4.dat", col.names=c("x","dx","y","dy"))
|
||||
|
||||
|
||||
plot(a$x, a$y*sc, pch=0, ylim=ylim, xlim=xlim, ylab=expression("mCrab keV"^"-1"~"FOV"^"-1"), xaxt="no",log="x",xlab="",type="n")
|
||||
|
||||
### Log axis using sfsmisc ###
|
||||
atx=c(8,30,50,80,100,150)
|
||||
eaxis(1, at = atx, labels = pretty10exp(atx, sub10=c(1,100), drop.1=TRUE), las=0)
|
||||
abline(v=atx, col="lightgray", lty="dotted")
|
||||
###
|
||||
|
||||
grid()
|
||||
|
||||
cex=1.5
|
||||
|
||||
cl="black"
|
||||
segments(a$x,(a$y-a$dy)*sc,a$x,(a$y+a$dy)*sc,col=cl)
|
||||
segments(a$x-a$dx,(a$y)*sc,a$x+a$dx,a$y*sc,col=cl)
|
||||
points(a$x,a$y*sc,pch=21,col=cl,bg="white",cex=cex)
|
||||
|
||||
# L-20
|
||||
cl="blue"
|
||||
bias=1.01
|
||||
segments(c$x*bias,(c$y-c$dy)*sc,c$x*bias,(c$y+c$dy)*sc,col=cl)
|
||||
segments((c$x-c$dx)*bias,c$y*sc,(c$x+c$dx)*bias,c$y*sc,col=cl)
|
||||
points(c$x*bias,c$y*sc,pch=22,col=cl,bg="white",cex=cex)
|
||||
|
||||
# L+20
|
||||
cl="red"
|
||||
segments(b$x,(b$y-b$dy)*sc,b$x,(b$y+b$dy)*sc,col=cl)
|
||||
segments(b$x-b$dx,b$y*sc,b$x+b$dx,b$y*sc,col=cl)
|
||||
points(b$x,b$y*sc,pch=23,col=cl,bg="white",cex=cex)
|
||||
|
||||
# BKG
|
||||
cl="magenta"
|
||||
segments(d$x,(d$y-d$dy)*sc,d$x,(d$y+d$dy)*sc,col=cl)
|
||||
segments(d$x-d$dx,(d$y)*sc,d$x+d$dx,d$y*sc,col=cl)
|
||||
points(d$x,d$y*sc,pch=24,col=cl,bg="white", ,cex=cex)
|
||||
|
||||
|
||||
mtext(side = 1, text = "Energy, keV", line = 3, cex=1.6)
|
||||
|
||||
legend( 85.0,0.09*sc,c("GB","L+20","L-20","3C 273/Coma"), pch=c(21,22,23,24),bg=c("white","white","white","white"), col=c("black","blue","red","magenta"),cex=1.7)
|
||||
|
||||
dev.off()
|
||||
@ -68,6 +68,10 @@ grxe_err_cut=90
|
||||
crab_ra = 83.6287
|
||||
crab_dec = 22.014
|
||||
|
||||
""" Sco X-1 coordinates """
|
||||
sco_ra = 244.979455
|
||||
sco_dec = -15.640283
|
||||
|
||||
bands={
|
||||
'A01':' 25.00 60.00',
|
||||
'A02':' 60.00 80.00',
|
||||
|
||||
@ -520,3 +520,176 @@ def get_spec(df, grxe_err_cut=None, skey=None, enkey=None, sigma=3, n_bins = 60,
|
||||
hdus[1].add_checksum()
|
||||
|
||||
return sg_mean, sg_sem, skew_val, skew_err
|
||||
|
||||
|
||||
|
||||
|
||||
def get_spec_src(df, grxe_err_cut=None, skey=None, enkey=None, sigma=3, n_bins = 60, plotme=False, gaussfit=False, fout=None, nscw_min=10, bootstrap=False):
|
||||
src = np.array(df['SRC'])
|
||||
|
||||
|
||||
rev = np.array(df['REV'])
|
||||
phase = np.array(df['PHASE'])
|
||||
clean = np.array(df['CLEAN'])
|
||||
model = np.array(df['MODEL'])
|
||||
crab = np.array(df['CRAB'])
|
||||
resid = np.array(df['RESID'])
|
||||
if(plotme):
|
||||
print("get_spec_src: Initial size: {}".format(len(src)))
|
||||
|
||||
"""
|
||||
perc = np.percentile(src, grxe_err_cut, axis=0, keepdims=False)
|
||||
print("{} {}: {}% cut of SCO: {:.2f} mCrab".format(skey,enkey,grxe_err_cut,perc))
|
||||
idx = np.where(src < perc)
|
||||
|
||||
# in some cases grxe_err is just one number
|
||||
if (len(src[idx])>nscw_min):
|
||||
src=src[idx]
|
||||
|
||||
if(plotme):
|
||||
print("get_spec_scr: {} percentage error cut size: {}".format(grxe_err_cut, len(src)))
|
||||
|
||||
clean=clean[idx]
|
||||
model=model[idx]
|
||||
crab=crab[idx]
|
||||
rev=rev[idx]
|
||||
phase=phase[idx]
|
||||
resid=resid[idx]
|
||||
|
||||
|
||||
idx = filtered_src.mask==False
|
||||
src=src[idx]
|
||||
|
||||
clean=clean[idx]
|
||||
model=model[idx]
|
||||
crab=crab[idx]
|
||||
rev=rev[idx]
|
||||
phase=phase[idx]
|
||||
resid=resid[idx]
|
||||
|
||||
|
||||
"""
|
||||
filtered_data = sigma_clip(src, sigma=sigma, maxiters=10, return_bounds=True)
|
||||
filtered_src = filtered_data[0]
|
||||
filtered_min = filtered_data[1]
|
||||
filtered_max = filtered_data[2]
|
||||
|
||||
skew_val = skew(src)
|
||||
skew_err = np.sqrt(6/len(src))
|
||||
print("{} SKEW: {:.5f} {:.5f}".format(enkey,skew_val, skew_err))
|
||||
|
||||
#print("length orig: {} taken: {} filtered: {}".format(len(src),len(src[filtered_src.mask==False]),len(src[filtered_src.mask==True])))
|
||||
|
||||
sg_mean, sg_med, sg_std = sigma_clipped_stats(src, sigma=sigma, maxiters=10)
|
||||
sg_sem = sem(src)
|
||||
|
||||
if(plotme):
|
||||
print("{}: mean {:.2f} med {:.2f} std {:.2f} sem {:.2f} N={}".format(enkey, sg_mean, sg_med, sg_std, sg_sem, len(src)))
|
||||
|
||||
if(gaussfit==True):
|
||||
k=1.1
|
||||
n, bins, patches = plt.hist(src, n_bins, density=True, facecolor='green', alpha=0.75,
|
||||
range=[filtered_min*k, filtered_max*k])
|
||||
# add a 'best fit' line
|
||||
area = np.sum(n * np.diff(bins))
|
||||
xdata = bins[:-1]+np.diff(bins)/2
|
||||
ydata = n
|
||||
(mu, sg) = norm.fit(src)
|
||||
|
||||
if(plotme==True):
|
||||
print("Initial Gaiss fit: mu={:.2f} sigma={:.2f}".format(mu,sg))
|
||||
|
||||
y_peak = norm.pdf(mu, mu, sg)
|
||||
plt.xlim(filtered_min*k, filtered_max*k)
|
||||
plt.ylim(y_peak*area*0.001,y_peak*area*10)
|
||||
fraction=0.1
|
||||
|
||||
params=[
|
||||
y_peak*area, # height
|
||||
mu, # mu
|
||||
sg, # sigma
|
||||
0.0, # const
|
||||
0.0,
|
||||
]
|
||||
|
||||
#pfit, perr = fit_curvefit(params, xdata, ydata, const_gaussian_ff)
|
||||
pfit, perr = fit_leastsq(params, xdata, ydata, const_gaussian_ff)
|
||||
#print(pfit)
|
||||
#print(perr)
|
||||
|
||||
#pfit, perr = fit_leastsq(params, xdata, ydata, double_gaussian2_ff)
|
||||
#pfit, perr = fit_curvefit(params, xdata, ydata, double_gaussian_ff)
|
||||
#plt.plot(bins, double_gaussian_ff(bins, pfit), c='r' )
|
||||
|
||||
sg_mean = pfit[1]
|
||||
sg_sem = perr[1]
|
||||
if(plotme):
|
||||
print("Const-Gauss fit: {:.2f} {:.2f}".format(sg_mean,sg_sem))
|
||||
|
||||
#sg_mean = mu
|
||||
#sg_sem = sg/np.sqrt(len(grxe))
|
||||
|
||||
#sg_sem = perr[1]
|
||||
#print("Narrow Gaiss fit: mu={:.2f} sigma={:.2f}".format(sg_mean,sg_sem))
|
||||
#print(" Wide Gaiss fit: mu={:.2f} sigma={:.2f}".format(pfit[4],pfit[5]))
|
||||
|
||||
if(plotme):
|
||||
plt.yscale("log")
|
||||
k=1.1
|
||||
n, bins, patches = plt.hist(src, n_bins, density=True, facecolor='green', alpha=0.75,
|
||||
range=[filtered_min*k, filtered_max*k])
|
||||
#plt.hist(grxe, bins=n_bins, range=[filtered_min*k, filtered_max*k])
|
||||
#plt.hist(grxe[filtered_grxe.mask], bins=n_bins, range=[filtered_min*k, filtered_max*k])
|
||||
plt.axvline(sg_mean, color="black")
|
||||
plt.axvline(sg_mean+sg_sem, color="black", linestyle="dashed")
|
||||
plt.axvline(sg_mean-sg_sem, color="black", linestyle="dashed")
|
||||
plt.axvline(sg_mean+sg_std, color="blue", linestyle="dashed")
|
||||
plt.axvline(sg_mean-sg_std, color="blue", linestyle="dashed")
|
||||
plt.xlabel("Flux, mCrab")
|
||||
plt.ylabel('Probability')
|
||||
area = np.sum(n * np.diff(bins))
|
||||
xdata = bins[:-1]+np.diff(bins)/2
|
||||
ydata = n
|
||||
#plt.plot(xdata, ydata, c='m' )
|
||||
|
||||
if(gaussfit==True):
|
||||
# add a 'best fit' line
|
||||
(mu, sg) = norm.fit(src)
|
||||
print("Gauss fit: {:.2f} {:.2f}".format(mu,sg))
|
||||
y = norm.pdf(bins, mu, sg)
|
||||
l = plt.plot(bins, y*area, 'r--', linewidth=2)
|
||||
plt.plot(bins, const_gaussian_ff(bins, pfit), c='black' )
|
||||
#plt.plot(bins, double_gaussian2_ff(bins, pfit), c='black' )
|
||||
#plt.plot(bins, one_gaussian_ff(bins, pfit[:3]), c='b' )
|
||||
#plt.plot(bins, one_gaussian_ff(bins, [pfit[3],pfit[1],pfit[4]]), c='m' )
|
||||
|
||||
|
||||
plt.title("[DATA] {}: {}".format(skey, enkey))
|
||||
plt.grid(True)
|
||||
plt.show()
|
||||
|
||||
if(fout != None):
|
||||
coldefs = fits.ColDefs([
|
||||
fits.Column(name='REV', format='J', unit='', array=rev),
|
||||
fits.Column(name='CLEAN', format='D', unit='cts/s', array=clean),
|
||||
fits.Column(name='PHASE', format='D', unit='', array=phase),
|
||||
fits.Column(name='MODEL', format='D', unit='cts/s', array=model),
|
||||
fits.Column(name='CRAB', format='D', unit='cts/s', array=crab),
|
||||
fits.Column(name='RESID', format='D', unit='cts/s', array=resid),
|
||||
fits.Column(name='SRC', format='D', unit='cts/s', array=src),
|
||||
])
|
||||
|
||||
|
||||
hdu = fits.BinTableHDU.from_columns(coldefs, name='SRC')
|
||||
hdu.header['MISSION'] = ('INTEGRAL', '')
|
||||
hdu.header['INSTITUT'] = ('IKI', 'Affiliation')
|
||||
hdu.header['AUTHOR'] = ('Roman Krivonos', 'Responsible person')
|
||||
hdu.header['EMAIL'] = ('krivonos@cosmos.ru', 'E-mail')
|
||||
#hdu.add_checksum()
|
||||
print(hdu.columns)
|
||||
hdu.writeto(fout, overwrite=True)
|
||||
|
||||
with fits.open(fout, mode='update') as hdus:
|
||||
hdus[1].add_checksum()
|
||||
|
||||
return sg_mean, sg_sem, skew_val, skew_err
|
||||
|
||||
209
scripts/00_scox1_lightcurve.py
Executable file
209
scripts/00_scox1_lightcurve.py
Executable file
@ -0,0 +1,209 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
__author__ = "Roman Krivonos"
|
||||
__copyright__ = "Space Research Institute (IKI)"
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from astropy.io import fits
|
||||
from astropy import wcs
|
||||
import matplotlib.pyplot as plt
|
||||
import math, sys, os
|
||||
import pickle
|
||||
from numpy.polynomial import Polynomial
|
||||
from astropy.table import Table, Column
|
||||
|
||||
from sklearn.linear_model import LinearRegression
|
||||
from sklearn.linear_model import HuberRegressor
|
||||
from sklearn.linear_model import RANSACRegressor
|
||||
from sklearn.linear_model import TheilSenRegressor
|
||||
from sklearn.model_selection import cross_val_score
|
||||
from sklearn.model_selection import RepeatedKFold
|
||||
|
||||
|
||||
from astropy.coordinates import SkyCoord # High-level coordinates
|
||||
from astropy.coordinates import ICRS, Galactic, FK4, FK5 # Low-level frames
|
||||
from astropy.coordinates import Angle, Latitude, Longitude # Angles
|
||||
import astropy.units as u
|
||||
|
||||
#from statsmodels.robust.scale import huber
|
||||
from astropy.stats import sigma_clip
|
||||
from numpy import absolute
|
||||
from numpy import arange
|
||||
|
||||
from ridge.utils import *
|
||||
from ridge.config import *
|
||||
|
||||
if not os.path.exists(proddir):
|
||||
os.makedirs(proddir)
|
||||
|
||||
#enkey = sys.argv[1]
|
||||
enkey='A01'
|
||||
inkey="ALL"
|
||||
|
||||
fn="detcnts.{}.{}.resid.fits".format(enkey,inkey)
|
||||
dat = Table.read(proddir+fn, unit_parse_strict='silent')
|
||||
df = dat.to_pandas()
|
||||
|
||||
with open(proddir+"detcnts.{}.ignored_scw.pkl".format(enkey), 'rb') as fp:
|
||||
ignored_scw = pickle.load(fp)
|
||||
|
||||
sco_crd = SkyCoord(sco_ra, sco_dec, frame=FK5(), unit="deg")
|
||||
|
||||
plotme=False
|
||||
|
||||
npix = 50
|
||||
sw = 30.0 # deg
|
||||
pix = sw/npix # pixel size in degrees
|
||||
|
||||
|
||||
crabmodel={}
|
||||
rota_arr=[]
|
||||
a0=[]
|
||||
b0=[]
|
||||
a_full0=[]
|
||||
b_full0=[]
|
||||
b_est0=[]
|
||||
err0=[]
|
||||
rev0=[]
|
||||
|
||||
totx=[]
|
||||
toty=[]
|
||||
|
||||
for i,rec in df.iterrows():
|
||||
obsid = rec['OBSID']#.decode("utf-8")
|
||||
|
||||
if (obsid in ignored_scw):
|
||||
print("Skip ScW",obsid)
|
||||
continue
|
||||
|
||||
# accumulate full data set
|
||||
for rev in range(revmin,revmax):
|
||||
|
||||
|
||||
df0 = df.query('SRC > 0.0 & REV == {} & PHASE > {} & PHASE < {} & SCO_SEP < {}'.format(rev,phmin,phmax,crab_sep_max))
|
||||
nobs=len(df0)
|
||||
if not (nobs> crab_nmax):
|
||||
continue
|
||||
|
||||
print(rev,nobs)
|
||||
|
||||
for n in df0['SCO_SEP'].values:
|
||||
totx.append(n)
|
||||
|
||||
for n in df0['SRC'].values:
|
||||
toty.append(n)
|
||||
|
||||
|
||||
x = np.array(totx)
|
||||
y = np.array(toty)
|
||||
x = x.reshape((-1, 1))
|
||||
model = LinearRegression()
|
||||
#model = TheilSenRegressor()
|
||||
results = evaluate_model(x, y, model)
|
||||
a_full,b_full,err_full = plot_best_fit(x, y, model)
|
||||
if(plotme):
|
||||
plot_ab(x, y, a_full, b_full, err_full, title="REGRESSION")
|
||||
|
||||
|
||||
|
||||
# go over orbits
|
||||
poly_x=[]
|
||||
poly_y=[]
|
||||
|
||||
ntotal=0
|
||||
nrev=0
|
||||
for rev in range(revmin,revmax):
|
||||
df0 = df.query('SRC > 0.0 & REV == {} & PHASE > {} & PHASE < {} & SCO_SEP < {}'.format(rev,phmin,phmax,crab_sep_max))
|
||||
nobs=len(df0)
|
||||
if not (nobs> crab_nmax):
|
||||
continue
|
||||
#cen_ra = np.array(df0['RA'].values)
|
||||
#cen_dec = np.array(df0['DEC'].values)
|
||||
print("*** Orbit ",rev)
|
||||
x = np.array(df0['SCO_SEP'].values)
|
||||
y = np.array(df0['SRC'].values)
|
||||
|
||||
#rota_deg = np.array(df0['ROTA'].values)
|
||||
#rota = np.array(df0['ROTA'].values) * np.pi / 180. # in radians
|
||||
#detx = np.cos(rota)*x
|
||||
#dety = np.sin(rota)*x
|
||||
|
||||
x = x.reshape((-1, 1))
|
||||
model = LinearRegression()
|
||||
#model = TheilSenRegressor()
|
||||
results = evaluate_model(x, y, model)
|
||||
a,b,err = plot_best_fit(x, y, model)
|
||||
|
||||
b_est = np.mean(y - a_full*x)
|
||||
|
||||
a_full0.append(a_full)
|
||||
b_full0.append(b_full)
|
||||
b_est0.append(b_est)
|
||||
|
||||
|
||||
a0.append(a)
|
||||
b0.append(b)
|
||||
err0.append(err)
|
||||
rev0.append(rev)
|
||||
|
||||
poly_x.append(rev)
|
||||
poly_y.append(b_est)
|
||||
|
||||
|
||||
crabmodel[rev]={'a':a_full, 'b':b_est, 'err':err}
|
||||
if(plotme):
|
||||
plot_ab(x, y, a_full, b_est, err, title="REGRESSION rev {}".format(rev))
|
||||
|
||||
|
||||
|
||||
print("ax+b: a={:.2e}, b={:.2e}, std={:.2e}\n".format(a,b,err))
|
||||
ntotal=ntotal+nobs
|
||||
nrev=nrev+1
|
||||
|
||||
print("Orbits: {}, obs: {}".format(nrev,ntotal))
|
||||
|
||||
npoly=4
|
||||
|
||||
z = np.polyfit(poly_x, poly_y, npoly)
|
||||
|
||||
p = np.poly1d(z)
|
||||
poly_z=[]
|
||||
for t in poly_x:
|
||||
poly_z.append(p(t))
|
||||
plt.scatter(poly_x, poly_y)
|
||||
plt.plot(poly_x, poly_z, color='r')
|
||||
plt.title("Crab detector count rate evolution")
|
||||
plt.ylabel("Crab count rate cts/s/pix")
|
||||
plt.xlabel("INTEGRAL orbit")
|
||||
#plt.show()
|
||||
plt.savefig(proddir+fn.replace(".fits",".sco_rate.png"))
|
||||
|
||||
indices = sorted(
|
||||
range(len(rev0)),
|
||||
key=lambda index: rev0[index]
|
||||
)
|
||||
|
||||
coldefs = fits.ColDefs([
|
||||
fits.Column(name='REV', format='J', unit='', array=[rev0[index] for index in indices]),
|
||||
fits.Column(name='A', format='D', unit='', array=[a0[index] for index in indices]),
|
||||
fits.Column(name='B', format='D', unit='', array=[b0[index] for index in indices]),
|
||||
fits.Column(name='ERR', format='D', unit='', array=[err0[index] for index in indices]),
|
||||
fits.Column(name='A_FULL', format='D', unit='', array=[a_full0[index] for index in indices]),
|
||||
fits.Column(name='B_FULL', format='D', unit='', array=[b_full0[index] for index in indices]),
|
||||
fits.Column(name='B_EST', format='D', unit='', array=[b_est0[index] for index in indices]),
|
||||
fits.Column(name='B_POLY', format='D', unit='', array=[poly_z[index] for index in indices]),
|
||||
])
|
||||
|
||||
fout = fn.replace(".fits",".scox1.fits")
|
||||
hdu = fits.BinTableHDU.from_columns(coldefs, name='GRXE')
|
||||
hdu.header['MISSION'] = ('INTEGRAL', '')
|
||||
hdu.header['TELESCOP'] = ('IBIS', '')
|
||||
hdu.header['INSTITUT'] = ('IKI', 'Affiliation')
|
||||
hdu.header['AUTHOR'] = ('Roman Krivonos', 'Responsible person')
|
||||
hdu.header['EMAIL'] = ('krivonos@cosmos.ru', 'E-mail')
|
||||
#hdu.add_checksum()
|
||||
print(hdu.columns)
|
||||
hdu.writeto(proddir+fout, overwrite=True)
|
||||
|
||||
|
||||
@ -36,6 +36,8 @@ from numpy import arange
|
||||
from ridge.utils import *
|
||||
from ridge.config import *
|
||||
|
||||
sco_crd = SkyCoord(sco_ra, sco_dec, frame=FK5(), unit="deg")
|
||||
|
||||
plotme=False
|
||||
enkey = sys.argv[1]
|
||||
outkey = sys.argv[2]
|
||||
@ -71,6 +73,8 @@ clean = data.field('clean')
|
||||
phase = data.field('phase')
|
||||
texp = data.field('exposure')
|
||||
|
||||
src = data.field('src') # for Sco X-1 testing
|
||||
|
||||
obsid0=[]
|
||||
rev0=[]
|
||||
phase0=[]
|
||||
@ -90,8 +94,10 @@ lat0=[]
|
||||
base0=[]
|
||||
c0=[]
|
||||
texp0=[]
|
||||
src0=[]
|
||||
sco_sep0=[]
|
||||
|
||||
hdulist = fits.open(datadir+modelrxte)
|
||||
hdulist = fits.open(modelsdir+modelrxte)
|
||||
w = wcs.WCS(hdulist[0].header)
|
||||
smap = hdulist[0].data
|
||||
sx=int(hdulist[0].header['NAXIS1'])
|
||||
@ -191,6 +197,12 @@ for i, row in df.iterrows():
|
||||
rev0.append(orbit)
|
||||
lon0.append(row['LON'])
|
||||
lat0.append(row['LAT'])
|
||||
src0.append(1000*(float(row['SRC'])/p(orbit)))
|
||||
|
||||
ra=float(row['RA'])
|
||||
dec=float(row['DEC'])
|
||||
sc = SkyCoord(ra, dec, frame=FK5(), unit="deg")
|
||||
sco_sep0.append(sco_crd.separation(sc).deg)
|
||||
|
||||
lon=row['LON']
|
||||
lat=row['LAT']
|
||||
@ -276,6 +288,7 @@ coldefs = fits.ColDefs([
|
||||
fits.Column(name='TEXP', format='D', unit='', array=[texp0[index] for index in indices]),
|
||||
fits.Column(name='PHASE', format='D', unit='', array=[phase0[index] for index in indices]),
|
||||
fits.Column(name='CLEAN', format='D', unit='cts/s', array=[clean0[index] for index in indices]),
|
||||
fits.Column(name='SRC', format='D', unit='cts/s', array=[src0[index] for index in indices]),
|
||||
fits.Column(name='MODEL', format='D', unit='cts/s', array=[model0[index] for index in indices]),
|
||||
fits.Column(name='MODEL_ERR', format='D', unit='', array=[model_err0[index] for index in indices]),
|
||||
fits.Column(name='RESID', format='D', unit='cts/s', array=[resid0[index] for index in indices]),
|
||||
@ -288,6 +301,7 @@ coldefs = fits.ColDefs([
|
||||
fits.Column(name='C', format='D', unit='', array=[c0[index] for index in indices]),
|
||||
fits.Column(name='DS9X', format='D', unit='', array=[ds9x[index] for index in indices]),
|
||||
fits.Column(name='DS9Y', format='D', unit='', array=[ds9y[index] for index in indices]),
|
||||
fits.Column(name='SCO_SEP', format='D', unit='', array=[sco_sep0[index] for index in indices]),
|
||||
])
|
||||
|
||||
fout = fn.replace(".fits",".{}.resid.fits".format(outkey))
|
||||
|
||||
@ -1,14 +1,14 @@
|
||||
for band in A01 A02 A03
|
||||
do
|
||||
./02_grxe_resid.py $band ALL
|
||||
./02_grxe_resid.py $band BKG
|
||||
./02_grxe_resid.py $band GAL
|
||||
#./02_grxe_resid.py $band BKG
|
||||
#./02_grxe_resid.py $band GAL
|
||||
done
|
||||
|
||||
for band in B01 B02 B03 B04 B05 B06 B07 B08 B09 B10 B11 B12 B13 B14 B15 B16 B17 B18 B19 B20 B21
|
||||
do
|
||||
./02_grxe_resid.py $band ALL
|
||||
./02_grxe_resid.py $band BKG
|
||||
./02_grxe_resid.py $band GAL
|
||||
#./02_grxe_resid.py $band BKG
|
||||
#./02_grxe_resid.py $band GAL
|
||||
done
|
||||
|
||||
|
||||
125
scripts/03_scox1_spec.py
Executable file
125
scripts/03_scox1_spec.py
Executable file
@ -0,0 +1,125 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
__author__ = "Roman Krivonos"
|
||||
__copyright__ = "Space Research Institute (IKI)"
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from astropy.io import fits
|
||||
from astropy.table import Table, Column
|
||||
from astropy import units as u
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import math, sys, os
|
||||
import pickle
|
||||
|
||||
from sklearn.linear_model import LinearRegression
|
||||
from sklearn.linear_model import HuberRegressor
|
||||
from sklearn.linear_model import RANSACRegressor
|
||||
from sklearn.linear_model import TheilSenRegressor
|
||||
from sklearn.model_selection import cross_val_score
|
||||
from sklearn.model_selection import RepeatedKFold
|
||||
|
||||
#from statsmodels.robust.scale import huber
|
||||
from astropy.stats import sigma_clip
|
||||
from astropy.stats import sigma_clipped_stats
|
||||
|
||||
from scipy.stats import norm
|
||||
from scipy.stats import describe
|
||||
from scipy.stats import sem
|
||||
import subprocess
|
||||
|
||||
from numpy import absolute
|
||||
from numpy import arange
|
||||
|
||||
from ridge.utils import *
|
||||
from ridge.config import *
|
||||
|
||||
inkey="ALL"
|
||||
|
||||
skey="SCOX1"
|
||||
|
||||
|
||||
plotme=False
|
||||
|
||||
|
||||
ebands0={'B01':[0.0,0.0],
|
||||
'B02':[0.0,0.0],
|
||||
'B03':[0.0,0.0],
|
||||
'B04':[0.0,0.0],
|
||||
'B05':[0.0,0.0],
|
||||
'B06':[0.0,0.0],
|
||||
'B07':[0.0,0.0],
|
||||
'B08':[0.0,0.0],
|
||||
'B09':[0.0,0.0],
|
||||
'B10':[0.0,0.0],
|
||||
'B11':[0.0,0.0],
|
||||
'B12':[0.0,0.0],
|
||||
'B13':[0.0,0.0],
|
||||
'B14':[0.0,0.0],
|
||||
'B15':[0.0,0.0],
|
||||
'B16':[0.0,0.0],
|
||||
'B17':[0.0,0.0],
|
||||
'B18':[0.0,0.0],
|
||||
'B19':[0.0,0.0],
|
||||
'B20':[0.0,0.0],
|
||||
'B21':[0.0,0.0],
|
||||
}
|
||||
|
||||
"""
|
||||
ebands0={'A01':[0.0,0.0],
|
||||
'A02':[0.0,0.0],
|
||||
'A03':[0.0,0.0],
|
||||
}
|
||||
"""
|
||||
|
||||
mcrab=u.def_unit('mCrab')
|
||||
ctss=u.def_unit('cts/s')
|
||||
u.add_enabled_units([mcrab,ctss])
|
||||
|
||||
if not os.path.exists(specdir):
|
||||
os.makedirs(specdir)
|
||||
|
||||
fitsdir = "{}fits/".format(specdir)
|
||||
if not os.path.exists(fitsdir):
|
||||
os.makedirs(fitsdir)
|
||||
|
||||
for enkey in ebands0.keys():
|
||||
|
||||
fn="detcnts.{}.{}.resid.fits".format(enkey,inkey)
|
||||
dat = Table.read(proddir+fn, unit_parse_strict='silent')
|
||||
df = dat.to_pandas()
|
||||
|
||||
#crab_sep_max=2.0
|
||||
query = 'PHASE > {} & PHASE < {} & SCO_SEP < {}'.format(phmin,phmax,crab_sep_max)
|
||||
df = df.query(query)
|
||||
print("{}: {} N={}".format(enkey, query, df.shape[0]))
|
||||
|
||||
t = Table.from_pandas(df)
|
||||
t.write("{}fits/SCOX1.{}.fits".format(specdir,enkey),overwrite=True)
|
||||
|
||||
if not (df.shape[0]>0):
|
||||
print("continue")
|
||||
continue
|
||||
|
||||
sg_mean,sg_sem,skew_val,skew_err = get_spec_src(df, sigma=3, grxe_err_cut=grxe_err_cut, enkey=enkey, plotme=True, gaussfit=True)
|
||||
ebands0[enkey]=[sg_mean,sg_sem]
|
||||
|
||||
|
||||
|
||||
|
||||
fspec="{}{}.spec".format(specdir,skey)
|
||||
with open(fspec, 'w') as fp:
|
||||
for enkey in ebands0.keys():
|
||||
flux=ebands0[enkey][0]
|
||||
err=ebands0[enkey][1]
|
||||
print("[DATA] {}: {} {:.6f} {:.6f}".format(skey,enkey,flux,err))
|
||||
fp.write("0 {} {:.6f} {:.6f} 0.0\n".format(bands[enkey],flux,err))
|
||||
subprocess.run(["perl", "do_pha_igr_v3_mCrab.pl", fspec])
|
||||
|
||||
|
||||
try:
|
||||
for remfile in ["cols","cols1","cols2","header",]:
|
||||
os.remove(remfile)
|
||||
except OSError:
|
||||
pass
|
||||
@ -3,7 +3,7 @@
|
||||
__author__ = "Roman Krivonos"
|
||||
__copyright__ = "Space Research Institute (IKI)"
|
||||
|
||||
""" Работает только в архиве """
|
||||
""" TO BE RUN IN ARCHIVE ONLY """
|
||||
|
||||
import re
|
||||
import sys
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user