def plotgalsim(self):
infile = self.figuredir + self.prefix + '-galaxy-sim.fits'
galsim = atpy.Table(infile)
# 'XSIM',self.xsim,unit='pixel',description='xcoord of simulated galaxies')
#'YSIM',self.ysim,unit='pixel',description='xcoord of simulated galaxies')
#'FLUX_SIM',self.fsim,unit='MJy/sr',description='')
#'MAGSIM',self.magsim,unit='mag',description='mag of simulated galaxies')
#'RADSIM',self.radsim,unit='pixel',description='Re of simulated galaxies')
#'BASIM',self.BAsim,unit='',description='B/A of simulated galaxies')
#'PASIM',self.PAsim,unit='deg',description='PA of simulated galaxies')
#'MATCHFLAG',self.matchflag,unit='bool',description='recovered?')
#'MAGMEAS',self.magmeas,unit='mag',description='')
#'MAGMEAS_ERR',self.magmeaserr,unit='mag',description='')
#'FLUXMEAS',self.fmeas,unit='MJy/sr',description='')
#'FLUXMEAS_ERR',self.fmeaserr,unit='MJy/sr',description='')
#'RADIUSMEAS',self.radmeas,unit='pixel',description='')
#'BAMEAS',self.BAmeas,unit='',description='')
#'PAMEAS',self.PAmeas,unit='deg',description='')
self.fsim = galsim
figure(figsize=(10, 8))
subplots_adjust(wspace=.35, hspace=.35)
cla()
clf()
x = [galsim.FLUX_SIM, galsim.RADSIM, galsim.BASIM, galsim.PASIM]
y = [galsim.FLUXMEAS, galsim.RADMEAS, galsim.BAMEAS, galsim.PAMEAS]
ylab = '$Completeness$'
xlab = [
'$Sim \ Flux $', '$Sim \ Radius $', '$Sim \ B/A $', '$Sim \ PA $'
]
for i in range(4):
subplot(2, 2, i + 1)
nbins = 20
minflux = min(x[i])
maxflux = max(x[i])
df = 1. #(fmax-fmin)/(1.*nbins)
dmag = .25
df = 1. * (maxflux - minflux) / 20.
bins = arange(minflux, (maxflux + df), df)
(xbin, ybin,
ybinerr) = mystuff.completeness(bins, x[i], galsim.MATCHFLAG)
plot(xbin, ybin, 'ko')
errorbar(xbin, ybin, yerr=ybinerr, fmt=None, ecolor='k')
axhline(y=1.0, ls='-')
axhline(y=.8, ls='--')
axis([0., max(xbin) + df, -.05, 1.05])
ylabel(ylab, fontsize=22)
xlabel(xlab[i], fontsize=22)
if i == 0:
title('$' + self.prefix + '$', fontsize=22)
#subplot(5,5,2)
#scatter(x[i],y[i],c=log10(galsim.FLUX_SIM),vmin=0,vmax=2.7,cmap='jet')
#xmin,xmax=xlim()
#xl=arange(xmin,xmax,.1)
#plot(xl,xl,'k-')
#axis([0,50,0,50])
# ax=gca()
# ax.set_xscale('log')
# ax.set_yscale('log')
s = self.figuredir + str(self.prefix) + '-GalSimFracCompl.eps'
savefig(s)
figure(figsize=(10, 8))
subplots_adjust(wspace=.35, hspace=.35)
cla()
clf()
x = [galsim.FLUX_SIM, galsim.RADSIM, galsim.BASIM, galsim.PASIM]
y = [galsim.FLUXMEAS, galsim.RADMEAS, galsim.BAMEAS, galsim.PAMEAS]
ylab = [
'$Measured \ Flux $', '$Measured \ Radius $', '$Measured \ B/A $',
'$Measured \ PA $'
]
xlab = [
'$Sim \ Flux $', '$Sim \ Radius $', '$Sim \ B/A $', '$Sim \ PA $'
]
for i in range(4):
subplot(2, 2, i + 1)
scatter(x[i],
y[i],
c=log10(galsim.FLUX_SIM),
vmin=0,
vmax=3.,
cmap='jet')
xmin, xmax = xlim()
xl = arange(xmin, xmax, .1)
plot(xl, xl, 'k-')
ylabel(ylab[i], fontsize=22)
xlabel(xlab[i], fontsize=22)
if i == 0:
title('$' + self.prefix + '$', fontsize=22)
#subplot(5,5,2)
#scatter(x[i],y[i],c=log10(galsim.FLUX_SIM),vmin=0,vmax=2.7,cmap='jet')
#xmin,xmax=xlim()
#xl=arange(xmin,xmax,.1)
#plot(xl,xl,'k-')
#axis([0,50,0,50])
# ax=gca()
# ax.set_xscale('log')
# ax.set_yscale('log')
s = self.figuredir + str(self.prefix) + '-GalSimvsMeas.eps'
savefig(s)
def completeness():#measure completeness on final image
#combinepath=bcdpath+'pbcd/Combine/output_apex_step2'
combinepath=bcdpath+'pbcd/Combine/apex_1frame_step2'
os.chdir(combinepath)
file='mosaic_extract_final.tbl'
input=open(file,'r')
xgal=[]#positions of previous detections with snr > 3
ygal=[]
fap4gal=[]
for line in input:
if line.find('#') > -1: #skip lines with '#' in them
continue
if line.find('\\') > -1: #skip lines with '#' in them
continue
if line.find('|') > -1: #skip lines with '#' in them
continue
t=line.split()
xgal.append(float(t[8]))
ygal.append(float(t[10]))
fap4gal.append(float(t[28]))
input.close()
xgal=N.array(xgal,'f')
ygal=N.array(ygal,'f')
fsimall=[]
matchflagsimall=[]
f2all=[]
f3all=[]
f4all=[]
deblendsimall=[]
snrsimall=[]
myminmag=24.75
mymaxmag=27.4
myfmin=10.**((25.-mymaxmag)/2.5)#ZP=25
myfmax=10.**((25.-myminmag)/2.5)#ZP=25
#below is loop to create image w/artificial sources, extract, and compare
for k in range(100):
createflag=1.#create image w/artificial sources?
detectflag=1.#detect sources in image?
if createflag > 0.1:
xnoise=[]
ynoise=[]
infile=open('noisecoords.dat','r')
for line in infile:
t=line.split()
xnoise.append(float(t[0]))
ynoise.append(float(t[1]))
infile.close()
nstar=10
xsim=N.zeros(nstar,'d')
ysim=N.zeros(nstar,'d')
msim=N.zeros(nstar,'d')
outfile=open('stars.coords.dat','w')
for i in range(nstar):
#j=int(round(1.*len(xnoise)*random.uniform(0,1)))
#xsim[i]=xnoise[j]
#ysim[i]=ynoise[j]
j=0
for j in range(10000):
xt=int(round(random.uniform(5.,125.)))
yt=int(round(random.uniform(5.,140.)))
d=pylab.sqrt((xt-xgal)**2+(yt-ygal)**2)#make sure sim galaxies are not near real galaxies
if min(d) > -1.:
d2=pylab.sqrt((xt-xsim)**2+(yt-ysim)**2)#make sure sim points are not on top of each other
if min(d2) > 5.:
print i,'got a good point after ',j,' tries',xt,yt
break
j=j+1
xsim[i]=xt
ysim[i]=yt
k=random.uniform(myfmin,myfmax)
msim[i]=25.-2.5*pylab.log10(k)
#print k,msim[i]
s='%8.2f %8.2f %8.2f \n' % (xsim[i],ysim[i],msim[i])
outfile.write(s)
outfile.close()
#os.system('rm stars.coords.dat')
#iraf.starlist('stars.coords.dat',nstars=100,spatial='uniform',xmax=130,ymax=145,luminosity='uniform',minmag=22.,maxmag=30.0,mzero=22.0,sseed='INDEF',power=0.6,alpha=0.74,lseed='INDEF')
os.system('rm mosaic-completeness.fits')
#iraf.mkobjects(input='mosaic_minus_median_extract.fits',output='mosaic-completeness.fits',objects='stars.coords.dat',radius=1.13,magzero=25.,background=0.,gain=5.,rdnoise=0.,poisson='no')#don't convolve w/PRF
#os.system('cp ../cal/MIPS24_PRF_HD_center.fits .')#convolve star w/SSC PRF
os.system('cp ../cal/mips24_prf_mosaic_2.45_4x.fits .')#convolve star w/SSC PRF
iraf.mkobjects(input='mosaic_minus_median_extract.fits',output='mosaic-completeness.fits',objects='stars.coords.dat',radius=14,star='mips24_prf_mosaic_2.45_4x.fits',magzero=25.,background=0.,gain=5.,rdnoise=0.,poisson='no')
#os.system('cp ../cal/PRF_estimate.fits .')#convolve gaussian w/measured PRF
#iraf.mkobjects(input='mosaic_minus_median_extract.fits',output='mosaic-completeness.fits',objects='stars.coords.dat',radius=15,star='PRF_estimate.fits',magzero=25.,background=0.,gain=5.,rdnoise=0.,poisson='no')
os.system('ls *.fits')
os.system('pwd')
iraf.display('mosaic_minus_median_extract.fits',1,contrast=0.01)
iraf.display('mosaic-completeness.fits',2,contrast=0.01)
iraf.tvmark(1,'stars.coords.dat')
iraf.tvmark(2,'stars.coords.dat')
fsim=10.**((25.-msim)/2.5)#ZP=25
if createflag < .1:#read in positions and magnitudes of artdata sources
xsim=[]
ysim=[]
msim=[]
infile=open('stars.coords.dat','r')
for line in infile:
if line.find('#') > -1:
continue
t=line.split()
xsim.append(float(t[0]))
ysim.append(float(t[1]))
msim.append(float(t[2]))
infile.close()
xsim=N.array(xsim,'f')
ysim=N.array(ysim,'f')
msim=N.array(msim,'f')
fsim=10.**((25.-msim)/2.5)#ZP=25
if detectflag > 0.1:#now run detection on mosaic-completeness.fits
combinepath=bcdpath+'pbcd/Combine/'
os.chdir(combinepath)
print combinepath
#os.system('apex_1frame.pl -n apex_1frame_MIPS24_step2.nl -i output_apex_step2/mosaic-completeness.fits')
#combinepath=bcdpath+'pbcd/Combine/output_apex_step2'
os.system('apex_1frame.pl -n apex_1frame_step2all.nl -i apex_1frame_step2/mosaic-completeness.fits')
combinepath=bcdpath+'pbcd/Combine/apex_1frame_step2'
os.chdir(combinepath)
print combinepath
file='mosaic-completeness_extract_raw.tbl'
input=open(file,'r')
ngal=0
for line in input:
if line.find('Conversion') > -1:
t=line.split('=')
convfactor=float(t[1])#conversion from ADU to uJy
#aperr=aveaperr*convfactor #convert noise in ADU to uJy using conv factor from apex
print "Conversion Factor = ",convfactor
#print "aveaperr = ",aveaperr
#print "aperr = ",aperr
continue
if line.find('#') > -1: #skip lines with '#' in them
continue
if line.find('\\') > -1: #skip lines with '#' in them
continue
if line.find('|') > -1: #skip lines with '#' in them
continue
ngal=ngal+1
input.close()
id24 = N.zeros(ngal,'f')
imagex24 = N.zeros(ngal,'f')
imagey24 = N.zeros(ngal,'f')
ra24 = N.zeros(ngal,'f')
dec24 = N.zeros(ngal,'f')
f24 = N.zeros(ngal,'d')#flux
errf24 = N.zeros(ngal,'d')
fap1 = N.zeros(ngal,'d')#flux in aperture 1 (1,1.5,2,2.6,3,3.5,4,4.5,5.,5.5) pixels
fap2 = N.zeros(ngal,'d')#flux
fap3 = N.zeros(ngal,'d')#flux
fap4 = N.zeros(ngal,'d')#flux in ap 4 - this is one w/ap cor of 1.67 (Calzetti et al 2007)
fap5 = N.zeros(ngal,'d')#flux
fap6 = N.zeros(ngal,'d')#flux
fap7 = N.zeros(ngal,'d')#flux
fap8 = N.zeros(ngal,'d')#flux
fap9 = N.zeros(ngal,'d')#flux
fap10 = N.zeros(ngal,'d')#flux
snr24 = N.zeros(ngal,'d')#SNR calculated by mopex
deblend = N.zeros(ngal,'f')#SNR calculated by mopex
input=open(file,'r')
i=0
output=open('xy24raw.dat','w')
for line in input:
if line.find('#') > -1: #skip lines with '#' in them
continue
if line.find('\\') > -1: #skip lines with '#' in them
continue
if line.find('|') > -1: #skip lines with '#' in them
continue
t=line.split()
#print "length of t = ",len(t)
#print (t[8]),(t[10]),(t[13]),(t[14]),(t[18]),(t[2]),(t[23]),(t[24]),(t[25]),(t[26]),(t[27]),(t[28]),(t[29]),(t[30]),(t[31]),(t[32])
(imagex24[i],imagey24[i],f24[i],errf24[i],snr24[i],deblend[i],fap1[i],fap2[i],fap3[i],fap4[i],fap5[i],fap6[i],fap7[i],fap8[i],fap9[i],fap10[i])=(float(t[8]),float(t[10]),float(t[13]),float(t[14]),float(t[18]),float(t[2]),float(t[25]),float(t[26]),float(t[27]),float(t[28]),float(t[29]),float(t[30]),float(t[31]),float(t[32]),float(t[33]),float(t[34]))
s='%6.2f %6.2f \n'%(imagex24[i],imagey24[i])
output.write(s)
i=i+1
input.close()#44 -> 43
output.close()
iraf.tvmark(1,'xy24raw.dat',color=204,radi=2)
iraf.tvmark(2,'xy24raw.dat',color=204,radi=2)
delta=1.#max number of pixels for a match
#get rid of objects that were detected in original image. Otherwise, matching will think any object near a sim galaxy is the sim galaxy. A faint galaxy placed on type of a pre-existing bright galaxy will be detected.
newgalflag=N.ones(len(imagex24),'i')
for i in range(len(imagex24)):
(imatch, matchflag,nmatch)=findnearest(imagex24[i],imagey24[i],xgal,ygal,delta)
if matchflag > 0.:
dflux=abs(fap4gal[imatch] - fap4[i])/fap4[i]
if dflux < .1:#position of real galaxy, flux difference less than 10% -> not a new galaxy
newgalflag[i] = 0
#keep only galaxies that are new
imagex24 = N.compress(newgalflag,imagex24)
imagey24 = N.compress(newgalflag,imagey24)
fap1 = N.compress(newgalflag,fap1)
fap2 = N.compress(newgalflag,fap2)
fap3 = N.compress(newgalflag,fap3)
fap4 = N.compress(newgalflag,fap4)
fap5 = N.compress(newgalflag,fap5)
fap6 = N.compress(newgalflag,fap6)
fap7 = N.compress(newgalflag,fap7)
fap8 = N.compress(newgalflag,fap8)
fap9 = N.compress(newgalflag,fap9)
fap10 =N.compress(newgalflag,fap10)
snr24 =N.compress(newgalflag,snr24)
deblend = N.compress(newgalflag,deblend)
delta=2.#max number of pixels for a match
matchflagsim=N.zeros(len(xsim),'i')
fmeas1=N.zeros(len(xsim),'f')
fmeas2=N.zeros(len(xsim),'f')
fmeas3=N.zeros(len(xsim),'f')
fmeas4=N.zeros(len(xsim),'f')
fmeas5=N.zeros(len(xsim),'f')
fmeas6=N.zeros(len(xsim),'f')
fmeas7=N.zeros(len(xsim),'f')
fmeas8=N.zeros(len(xsim),'f')
fmeas9=N.zeros(len(xsim),'f')
fmeas10=N.zeros(len(xsim),'f')
fmeas24=N.zeros(len(xsim),'f')
deblendsim=N.zeros(len(xsim),'f')
snrsim=N.zeros(len(xsim),'f')
for i in range(len(xsim)):
(imatch, matchflag,nmatch)=findnearest(xsim[i],ysim[i],imagex24,imagey24,delta)
matchflagsim[i]=matchflag
if matchflag > .1:
fmeas1[i]=fap1[int(imatch)]
fmeas2[i]=fap2[int(imatch)]
fmeas3[i]=fap3[int(imatch)]
fmeas4[i]=fap4[int(imatch)]
fmeas5[i]=fap5[int(imatch)]
fmeas6[i]=fap6[int(imatch)]
fmeas7[i]=fap7[int(imatch)]
fmeas8[i]=fap8[int(imatch)]
fmeas9[i]=fap9[int(imatch)]
fmeas10[i]=fap10[int(imatch)]
fmeas24[i]=f24[int(imatch)]
deblendsim[i]=deblend[int(imatch)]
snrsim[i]=snr24[int(imatch)]
fsimall=fsimall+list(fsim)
matchflagsimall=matchflagsimall+list(matchflagsim)
f2all=f2all+list(fmeas2)
f3all=f3all+list(fmeas3)
f4all=f4all+list(fmeas4)
deblendsimall=deblendsimall+list(deblendsim)
snrsimall=snrsimall+list(snrsim)
fsim=N.array(fsimall,'f')
matchflagsim=N.array(matchflagsimall,'f')
fmeas2=N.array(f2all,'f')
fmeas3=N.array(f3all,'f')
fmeas4=N.array(f4all,'f')
deblendsim=N.array(deblendsimall,'f')
snrsim=N.array(snrsimall,'f')
#make plots using all realizations
pylab.cla()
pylab.clf()
fsim=fsim*convfactor
fs=pylab.compress((matchflagsim > 0.1) & (deblendsim < 1.5),fsim)
#f1=pylab.compress((matchflagsim > 0.1) & (deblendsim < 1.5),fmeas1)
f2=pylab.compress((matchflagsim > 0.1) & (deblendsim < 1.5),fmeas2)
f3=pylab.compress((matchflagsim > 0.1) & (deblendsim < 1.5),fmeas3)
f4=pylab.compress((matchflagsim > 0.1) & (deblendsim < 1.5),fmeas4)
#f242=pylab.compress((matchflagsim > 0.1) & (deblendsim < 1.5),fmeas24)
r4=pylab.median(fs/f4)
r3=pylab.median(fs/f3)
r2=pylab.median(fs/f2)
print "average ratios ap 4",pylab.average(fs/f4),r4,pylab.std((fs/f4)/pylab.average(fs/f2))
print "average ratios ap 3",pylab.average(fs/f3),pylab.median(fs/f3),pylab.std((fs/f3)/pylab.average(fs/f3))
print "average ratios ap 2",pylab.average(fs/f2),pylab.median(fs/f2),pylab.std((fs/f2)/pylab.average(fs/f2))
s='f4 w/apcor = %3.2f(%4.2f)'%(r4,pylab.average(abs(fs-f4*r4)/fs))
pylab.plot(fs,f4*r4,'b.',label=s)
pylab.plot(fs,f4,'bo',label='f4')
s='f3 w/apcor = %3.2f(%4.2f)'%(r3,pylab.average(abs(fs-f3*r3)/fs))
pylab.plot(fs,f3*r3,'g.',label=s)
pylab.plot(fs,f3,'go',label='f3')
s='f2 w/apcor = %3.2f(%4.2f)'%(r2,pylab.average(abs(fs-f2*r2)/fs))
pylab.plot(fs,f2*r2,'r.',label=s)
pylab.plot(fs,f2,'ro',label='f2')
#pylab.plot(fs,f1,'co',label='f1')
#pylab.plot(fs,f242,'k.',label='f24')
pylab.legend(loc='best')
x=N.arange(0.,max(fs),10.)
y=x
pylab.plot(x,y,'k-')
#y=2.*x
#pylab.plot(x,y,'k--')
#y=3.*x
#pylab.plot(x,y,'k--')
#y=4.*x
#pylab.plot(x,y,'k--')
#y=5.*x
#pylab.plot(x,y,'k--')
pylab.xlabel('F(24) Input')
pylab.ylabel('F(24) measured')
#pylab.axis([0.,50.,0.,50.])
s=str(prefix)+'fluxcomp.eps'
pylab.savefig(s)
pylab.cla()
pylab.clf()
nbins=20
fmin=10.#min(fsim)
fmax=max(fsim)
df=5.#(fmax-fmin)/(1.*nbins)
bins=N.arange(fmin,(fmax+df),df)
(xbin,ybin,ybinerr)=mystuff.completeness(bins,fsim,matchflagsim)
s=str(prefix)+'FracComplvsFlux.dat'
outdat=open(s,'w')
print "Completeness vs Input Flux"
for i in range(len(xbin)):
print i, xbin[i],ybin[i],ybinerr[i]
t='%8.2f %8.4f %8.4f\n'%(xbin[i],ybin[i],ybinerr[i])
outdat.write(t)
outdat.close()
#for i in range(len(fsim)):
#if snrsim[i] > 3.:
# print i, fsim[i],matchflagsim[i],deblendsim[i],abs(fsim[i]-fmeas4[i]*1.67)/fsim[i],snrsim[i]
#(xbin,ybin2,ybin2err)=mystuff.scipyhist2(bins,fmeas4)
#pylab.plot(xbin,ybin,'bo')
#pylab.plot(xbin,ybin2,'ro')
#s=str(prefix)+'NDetectvsFlux.eps'
#pylab.savefig(s)
pylab.cla()
pylab.clf()
pylab.plot(xbin,ybin,'ko')
pylab.errorbar(xbin,ybin,yerr=ybinerr,fmt=None,ecolor='k')
s=str(prefix)+'FracComplvsFlux.eps'
pylab.axhline(y=1.0,ls='-')
pylab.axhline(y=.8,ls='--')
pylab.axvline(x=80.0,ls=':',color='b')
pylab.xlabel('Input Flux (uJy)')
pylab.ylabel('Completeness')
pylab.axis([0.,max(xbin)+df,-.05,1.05])
pylab.savefig(s)
os.system('cp *.eps /Users/rfinn/clusters/spitzer/completeness/.')
os.system('cp *vsFlux.dat /Users/rfinn/clusters/spitzer/completeness/.')
def plotcompleteness(self):
figure(figsize=(10, 8))
cla()
clf()
nbins = 20
minflux = fmin
maxflux = fmax
df = 1. #(fmax-fmin)/(1.*nbins)
dmag = .25
bins = arange(minflux, (maxflux + df), df)
#bins=arange(maxmag,(minmag+dmag),dmag)
(xbin, ybin, ybinerr) = mystuff.completeness(bins, self.fsim,
self.matchflag)
#(xbin,ybin,ybinerr)=mystuff.completeness(bins,self.magsim,self.matchflag)
s = self.figuredir + str(self.prefix) + '-FracComplvsFlux.dat'
outdat = open(s, 'w')
print "Completeness vs Input Flux"
for i in range(len(xbin)):
#print i, xbin[i],ybin[i],ybinerr[i]
t = '%8.2f %8.4f %8.4f\n' % (xbin[i], ybin[i], ybinerr[i])
outdat.write(t)
outdat.close()
cla()
clf()
plot(xbin, ybin, 'ko')
errorbar(xbin, ybin, yerr=ybinerr, fmt=None, ecolor='k')
axhline(y=1.0, ls='-')
axhline(y=.8, ls='--')
axvline(x=80.0, ls=':', color='b')
xlabel('$Input \ Flux (\mu Jy)$', fontsize=22)
ylabel('$Completeness$', fontsize=22)
title(self.prefix)
axis([0., max(xbin) + df, -.05, 1.05])
s = self.figuredir + str(self.prefix) + '-FracComplvsFlux.eps'
savefig(s)
figure(figsize=(10, 8))
cla()
clf()
x = self.fsim[self.matchflag]
y = self.fmeas[self.matchflag]
erry = self.fmeaserr[self.matchflag]
subplot(2, 1, 1)
plot(x, y, 'k.')
errorbar(x, y, yerr=erry, fmt=None, ecolor='k')
xmin, xmax = xlim()
xl = arange(xmin, xmax, .5)
plot(xl, xl, 'k-')
ylabel('$Measured \ Flux $', fontsize=22)
title(self.prefix)
subplot(2, 1, 2)
diff = (y - x) / x * 100
plot(x, diff, 'k.')
axhline(y=0)
xmin, xmax = xlim()
axis([xmin, xmax, -50, 50])
xlabel('$Input \ Flux (\mu Jy)$', fontsize=22)
ylabel('$\% Difference \ (obs-input)/input $', fontsize=22)
s = self.figuredir + str(self.prefix) + '-SimvsMeasFlux.eps'
savefig(s)