def runall(prefixes,cat):
for prefix in prefixes:
images=glob.glob(prefix)
iraf.imgets(images[0],'FILTER')
filter=str(iraf.imgets.value)
for im in images:
runsextractor(im)
t=im.split('.')
if cat < 0.1:
out='sdssmatch-'+str(t[0])
refcat='sdsscoords.dat'
s='cat '+out+' >> sdssmatch'+filter
plotsdsspos(im)
if cat > .1:
out='2massmatch-'+str(t[0])
refcat='2masscoords.dat'
s='cat '+out+' >> 2massmatch'+filter
plot2masspos(im)
#iraf.xyxymatch(input='testxy.cat',reference='sdsscoords.dat',output=out,tolerance=20.,refpoints='refpoints',interactive='yes')
iraf.xyxymatch(input='testxy.cat',reference=refcat,output=out,tolerance=10.,refpoints='refpoints',interactive='no')
os.system(s)
infile='sdssmatch'+filter
infile='2massmatch'+filter
iraf.geomap(input=infile,database='PiscesBok',transform=filter,xmin=1.,xmax=1024,ymin=1.,ymax=1024.)
def getpositions(ximage, yimage, isoarea, im):
dmin = 30. #minimum distance to object + sqrt(isoarea)
iraf.imgets(image=im, param='naxis1') #get RA of image
t = float(iraf.imgets.value)
xmax = t
xcenter = t / 2.
iraf.imgets(image=im, param='naxis2') #get RA of image
t = float(iraf.imgets.value)
ymax = t
ycenter = t / 2.
xpos = []
ypos = []
da = N.sqrt(isoarea)
d = N.zeros(len(ximage), 'f')
#print len(d)
i = 0
while i < npoints:
xtemp = random.uniform(0, 1) * xmax
ytemp = random.uniform(0, 1) * ymax
dcenter = N.sqrt((xtemp - xcenter)**2 + (ytemp - ycenter)**2)
if (dcenter < 500.):
j = 0
d = N.sqrt((ximage - xtemp)**2 + (yimage - ytemp)**2) - da
#for j in range(len(ximage)):
#print j,len(ximage),len(yimage),len(da),len(d)
#d[j] = N.sqrt((ximage[j]-xtemp)**2+(yimage[j]-ytemp)**2)-da[j]
if (min(d) > dmin):
xpos.append(xtemp)
ypos.append(ytemp)
i = i + 1
xpos = N.array(xpos, 'f')
ypos = N.array(ypos, 'f')
return xpos, ypos
def getpositionsLCS(ximage,yimage,isoarea,im,coverage_map):
iraf.imgets(image=im,param='naxis1')#get RA of image
t=float(iraf.imgets.value)
xmax=t
xcenter=t/2.
iraf.imgets(image=im,param='naxis2')#get RA of image
t=float(iraf.imgets.value)
ymax=t
ycenter=t/2.
xpos=[]
ypos=[]
da = N.sqrt(isoarea)
d = N.zeros(len(ximage),'f')
#print len(d)
i=0
covimage=pyfits.open(coverage_map)
cov_data=(covimage[0].data)
covimage.close()
while i < npoints:
xtemp=random.uniform(0,1)*xmax
ytemp=random.uniform(0,1)*ymax
# check coverage map to make sure point is on science area
# coverage map values > 6 are ok
if cov_data[ytemp,xtemp] > 6:
d = N.sqrt((ximage-xtemp)**2+(yimage-ytemp)**2)
if (min(d) > dmin):
xpos.append(xtemp)
ypos.append(ytemp)
i=i+1
#print 'found a good place to measure sky!',i,npoints
xpos=N.array(xpos,'f')
ypos=N.array(ypos,'f')
return xpos,ypos
def rotate24():
for i in range(len(images24)):
#for i in range(1):
iraf.imgets(
image=images24[i],
param='CROTA2') #[deg] Orientation of axis 2 (W of N, +=CW)
print iraf.imgets.value
rot24 = float(iraf.imgets.value)
iraf.imgets(
image=images[i],
param='ORIENTAT') #position angle of image y axis (deg. e of n)
print iraf.imgets.value
rotacs = float(iraf.imgets.value)
angle = -1 * rot24 - rotacs
outimage = rimages24[i]
iraf.rotate(input=images24[i], output=outimage, rotation=angle)
def geton24imageflag(self):
iraf.imgets(image=self.image24,param='CD1_1')#get x plate scale on rotated image
#print iraf.imgets.value
xplate=abs(float(iraf.imgets.value))#deg/pixel
dpix=deltaCutout/3600./xplate/2. # requires a 50 arcsec buffer b/w center of galaxy and edge of image
iraf.imgets(image=self.image24,param='naxis1')#get x value corresponding to RA
xpixmax=(int(iraf.imgets.value))#deg/pixel
iraf.imgets(image=self.image24,param='naxis2')#get x value corresponding to RA
ypixmax=(int(iraf.imgets.value))#deg/pixel
# write RA and Dec to ascii file
t = atpy.Table()
t.add_column('RA',self.ndat.RA)
t.add_column('DEC',self.ndat.DEC)
outfile=self.nsadir+self.prefix+'_RADEC.txt'
out1=open(outfile,'w')
for i in range(len(self.ndat.RA)):
s='%12.8f %12.8f \n'%(self.ndat.RA[i],self.ndat.DEC[i])
out1.write(s)
out1.close()
# transform RA and Dec to pixels using wcsctran
print 'transforming 24um coords'
outcoords=self.nsadir+str(self.prefix)+'xy24.txt'
if os.path.exists(outcoords):
os.remove(outcoords)
iraf.imcoords.wcsctran(image=self.image24,input=outfile,output=outcoords,inwcs='world',outwcs='logical',verbose='no')
# read in pixel coordinates
self.mipscoords=atpy.Table(outcoords,type='ascii')
x=self.mipscoords['col1']
y=self.mipscoords['col2']
self.On24ImageFlag= (x > dpix) & (x < (xpixmax-dpix)) & (y > dpix) & (y < (ypixmax-dpix))
print self.prefix,': # on 24um image = ',sum(self.On24ImageFlag)
def makecutouts():
cutouts = []
for i in range(len(images)):
#for i in range(1):
#print i,prefix,prefix[i]
outcoords = str(prefix[i]) + '.xy'
iraf.imgets(image=images[i],
param='CD2_1') #get x value corresponding to RA
xplate = abs(float(iraf.imgets.value)) #deg/pixel
dpix = delta / 3600. / xplate / 2.
print dpix
#delta=100.
infile = open(outcoords, 'r')
for line in infile:
if line.find('#') > -1:
continue
if len(line) < 2:
continue
x, y, id = line.split()
x = float(x)
y = float(y)
xmin = int(round(x - dpix))
xmax = int(round(x + dpix))
ymin = int(round(y - dpix))
ymax = int(round(y + dpix))
s = images[i] + '[%i:%i,%i:%i]' % (xmin, xmax, ymin, ymax)
print s
#print ra[i],dec[i]
outim = id + 'cutout.fits'
print outim
iraf.imcopy(s, outim)
cutouts.append(outim)
infile.close()
return cutouts
def makecutouts24(delta):
cutouts24 = []
images24 = rimages24
for i in range(len(images24)):
#for i in range(1):
#print i,prefix,prefix[i]
outcoords = str(prefix[i]) + '.xy24'
#iraf.imgets(image=images24[i],param='CDELT1')#get x plate scale
iraf.imgets(image=images24[i],
param='CD2_1') #get x plate scale on rotated image
print iraf.imgets.value
xplate = abs(float(iraf.imgets.value)) #deg/pixel
dpix = delta / 3600. / xplate / 2.
iraf.imgets(image=images24[i],
param='naxis1') #get x value corresponding to RA
xpixmax = (int(iraf.imgets.value)) #deg/pixel
iraf.imgets(image=images24[i],
param='naxis2') #get x value corresponding to RA
ypixmax = (int(iraf.imgets.value)) #deg/pixel
infile = open(outcoords, 'r')
#print outcoords
for line in infile:
#print images24[i],line,outcoords
if line.find('#') > -1:
continue
if len(line) < 2:
continue
x, y, id = line.split()
x = float(x)
y = float(y)
#delta=100.
xmin = int(round(x - dpix))
xmax = int(round(x + dpix))
ymin = int(round(y - dpix))
ymax = int(round(y + dpix))
if xmin < 1:
xmin = 1
if ymin < 1:
ymin = 1
if xmax > xpixmax:
xmax = xpixmax
if ymax > ypixmax:
ymax = ypixmax
s = images24[i] + '[%i:%i,%i:%i]' % (xmin, xmax, ymin, ymax)
print s
#print ra[i],dec[i]
outim = id + 'cutout24.fits'
print outim
iraf.imcopy(s, outim)
cutouts24.append(outim)
infile.close()
return cutouts24
def imgets(img,field): iraf.flprcache() iraf.flprcache() iraf.imgets(img,field) return iraf.imgets.value
#! /usr/bin/env python
from pylab import *
from pyraf import iraf
import glob
files = glob.glob('*.fits')
for file in files:
iraf.imgets(image=file, param='FILENAME') #get RA of image
originalName = iraf.imgets.value
#print file, originalName
iraf.imrename(file, originalName)
print "All Done!"
photometry.correct_coordinates([corrbyhand[i]["filename_incl_path"]], radius=10., twomassmag = 17.)
photometry.correct_coordinates([corrbyhand[i]["filename_incl_path"]], radius=10., twomassmag = 17.)
iraf.centerpars.cbox = 9.
iraf.centerpars.maxshift = 1.
# apply the coordinate transformations done for the individual images also to their respective masks. This is not strictly necessary, because the mask trimming and dividing is done on a pixel-by-pixel basis, but it's nicer this way when you want to look at the mask in ds9 and compare things by eye.
trimpath = input_info.resultfolder + '*YSO*/*_wcs_unnormed.fits'
trimlist = glob.glob(trimpath)
trimlist.sort()
masklist = deepcopy(trimlist)
for i in np.arange(0, len(masklist)): masklist[i] = masklist[i].replace('_wcs_unnormed.fits', '_coadd.weight.fits')
for f in masklist:
image = f.replace('_coadd.weight.fits','_wcs_unnormed.fits')
for headerkeyword in ['RADECSYS', 'CTYPE1', 'CRVAL1', 'CRPIX1', 'CD1_1', 'CD1_2', 'CTYPE2', 'CRVAL2', 'CRPIX2', 'CD2_1', 'CD2_2']:
iraf.imgets(image, headerkeyword)
iraf.hedit(images=f, fields=headerkeyword, value=iraf.imgets.value, verify="no")
# now trim the images so that only parts with good exposure are retained:
trimpath = input_info.resultfolder + '*YSO*/*_wcs_unnormed.fits'
trimlist = glob.glob(trimpath)
trimlist.sort()
masklist = deepcopy(trimlist)
for i in np.arange(0, len(masklist)): masklist[i] = masklist[i].replace('_wcs_unnormed.fits', '_coadd.weight.fits')
print "trimming..."
for i in np.arange(0, len(masklist)):
#for i in np.arange(0, 1):
"Is everything ok? Press 'enter' to continue, 'e' to exit.\t")
# option to exit
if getout == 'e' or getout == 'E':
print "\nexiting then... Bye!\n"
raise SystemExit
# preparing header fields (input of imagetyp, exptime/darktime)
print "> preparing the image headers"
#l.write("\n> preparing the image headers")
unlearn(iraf.ccdhedit)
iraf.ccdhedit("Dark*.fit", parameter="imagetyp", value="dark")
iraf.ccdhedit("Flat*.fit", parameter="imagetyp", value="flat")
iraf.ccdhedit(rname + "_*.fit", parameter="imagetyp", value="object")
for im in glob.glob('*.fit*'):
unlearn(iraf.imgets)
iraf.imgets(im, param="inttime")
iraf.hedit(im,
fields="exptime",
value=iraf.imgets.value,
add="no",
addonly="yes",
delete="no",
verify="no",
show="no",
update="yes")
for im in glob.glob("Dark*.fit"):
unlearn(iraf.imgets)
iraf.imgets(im, param="inttime")
iraf.hedit(im,
fields="darktime",
value=iraf.imgets.value,
def autoalign(images):
ra = []
dec = []
xcenter = []
ycenter = []
dx = []
dy = []
xpscale = []
ypscale = []
for im in images:
iraf.imgets(image=im, param='CRVAL1') #get RA of image in deg
t = iraf.imgets.value
ra.append(float(t))
iraf.imgets(image=im, param='CRPIX1') #get pix of RA
t = iraf.imgets.value
xcenter.append(float(t))
iraf.imgets(image=im, param='CD1_1') #get pix of RA
t = iraf.imgets.value
xpscale.append(float(t))
iraf.imgets(image=im, param='CRVAL2') #get RA of image
t = iraf.imgets.value
dec.append(float(t))
iraf.imgets(image=im, param='CRPIX2') #get RA of image
t = iraf.imgets.value
ycenter.append(float(t))
iraf.imgets(image=im, param='CD2_2') #get RA of image
t = iraf.imgets.value
ypscale.append(float(t))
dra = (ra - ra[0]) * N.cos(
N.pi / 180. * dec) #correct delta ra for cos declination
ddec = (dec - dec[0])
dx = dra / xpscale + (xcenter - xcenter[0])
dy = dra / ypscale + (ycenter - ycenter[0])
xshift = dx
yshift = -1. * dy
outfile = open('shifts', 'w')
for i in range(len(xshift)):
s = '%8.3f %8.3f \n' % (xshift[i], yshift[i])
outfile.write(s)
outfile.close()
def writegmoscat(gmosim):
iraf.imgets(image=gmosim, param='i_naxis1')
t = iraf.imgets.value
gxmax = (float(t))
iraf.imgets(image=gmosim, param='i_naxis2')
t = iraf.imgets.value
gymax = (float(t))
gxmin = 1
gymin = 1
outfile2 = prefix + '-GMOSxy.dat' #file containing x and y pixels values on gmos image
gmosx = []
gmosy = []
input = open(outfile2, 'r')
for line in input:
t = line.split()
gmosx.append(float(t[0]))
gmosy.append(float(t[1]))
input.close()
gmosx = N.array(gmosx, 'f')
gmosy = N.array(gmosy, 'f')
#get gmos ra and dec positions from x,y pixels values
radecfile = prefix + '-GMOSradec.dat'
iraf.wcsctran(outfile2,
radecfile,
image=gmosim,
inwcs='physical',
outwcs='world',
columns='1 2',
verbose='no')
infile = open(radecfile, 'r')
gmosra = []
gmosdec = []
for line in infile:
if line.find('#') > -1:
continue
t = line.split()
gmosra.append(float(t[0]))
gmosdec.append(float(t[1]))
infile.close()
gmosra = N.array(gmosra, 'f')
gmosdec = N.array(gmosdec, 'f')
s = prefix + '.reg'
dsfile = open(s, 'w')
s = "global color=green font='helvetica 10 normal' select=1 highlite=1 edit=1 move=1 delete=1 include=1 fixed=0 source\n"
dsfile.write(s)
s = "fk5 \n"
dsfile.write(s)
infile = '/Users/rfinn/clusters/spitzer/Mastertables/' + prefix + 'mastertable24.dat'
gmoscat = prefix + 'mastertable24GMOS.dat'
input = open(infile, 'r')
output = open(gmoscat, 'w')
i = 0
for line in input:
if line.find('#') > -1: #skip lines with '#' in them
output.write(line)
continue
if line.find('\\') > -1: #skip lines with '#' in them
output.write(line)
continue
if line.find('|') > -1: #skip lines with '#' in them
output.write(line)
continue
if gmosx[i] < 1.:
print "bad x value: ", gmosx[i], 1, gxmax
i = i + 1
continue
if gmosx[i] > gxmax:
print "bad x value: ", gmosx[i], 1, gxmax
i = i + 1
continue
if gmosy[i] < 1.:
print "bad y value (too small): ", i, gmosy[i], 1, gymax
i = i + 1
continue
if gmosy[i] > gymax:
print "bad y value: ", i, gmosy[i], 1, gymax
i = i + 1
continue
line = line[0:(len(line) - 1)] #get rid of \n at end of line
s = " %10.3f %10.3f %13.8f %13.8f \n" % (gmosx[i], gmosy[i], gmosra[i],
gmosdec[i])
outline = line + s #append gmos x and y pixels to the end of the line
output.write(outline)
string1 = "circle(%12.8f, %12.8f, 3\") \n" % (gmosra[i], gmosdec[i])
dsfile.write(string1)
i = i + 1
input.close()
dsfile.close()
def imgets(img, field):
iraf.flprcache()
iraf.flprcache()
iraf.imgets(img, field)
return iraf.imgets.value
def run(funcname,outname):
# output as SExtractor format
#iraf.ls('obj*_out.fits',Stdout='tmplist')
outlist = glob.glob('obj*_out.fits')
f = open('tmplist','wb')
for t in outlist:
f.write('%s\n' % t)
f.close()
if funcname=="sersic":
f2=open(outname,"w")
colnames = ['OBJNUM','XCENT','XCENTERR','YCENT','YCENTERR','MAG',
'MAGERR','RE','REERR','N','NERR','AR','ARERR','PA','PAERR',
'CHISQ','CHISQNU']
writeheader(f2,colnames)
#if funcname=="devauc":
# f2=open(outname,"w")
# colnames=['#OBJNUM','XCENT1','YCENT1','MAG1','MAG1ERR','RE1','RE1ERR',
# 'AR1','AR1ERR','PA1','PA1ERR','XCENT2','YCENT2','MAG2',
# 'MAG2ERR','RS2','RS2ERR','AR2','AR2ERR','PA2','PA2ERR'
# 'CHISQ','CHISQNU']
# writeheader(f2,colnames)
f=open('tmplist')
objnum = []
xcent = []
xcenterr = []
ycent = []
ycenterr = []
mag = []
magerr = []
re = []
reerr = []
sersicn = []
sersicnerr = []
axratio = []
axratioerr = []
pa = []
paerr = []
chisq = []
chisqnu = []
for line in f.readlines():
id = line.split()[0]
imgroot=id+"[2]"
comp1=funcname
if comp1=="sersic":
iraf.imgets.saveParList(filename="imgets.par")
# figure out how many components there are
n = 1
ifcomp = 1
while ifcomp:
compstr = 'COMP_%d' % n
iraf.imgets(imgroot,compstr)
if iraf.imgets.value == 'sersic': # nth fitted component found
xcstrn = '%d_XC' % n # x center
iraf.imgets(imgroot,xcstrn)
hxcentn=str(iraf.imgets.value)
hxcentn=hxcentn.split('+/-')
if len(hxcentn) == 1:
xcentn = hxcentn[0]
xcenterrn = '1.e-6' # there's no x center error information
else:
xcentn,xcenterrn=hxcentn
xcent += [xcentn]
xcenterr += [xcenterrn]
ycstrn = '%d_YC' % n # y center
iraf.imgets(imgroot,ycstrn)
hycentn=str(iraf.imgets.value)
hycentn=hycentn.split('+/-')
if len(hycentn) == 1:
ycentn = hycentn[0]
ycenterrn = '1.e-6' # there's no y center error information
else:
ycentn,ycenterrn=hycentn
ycent += [ycentn]
ycenterr += [ycenterrn]
magstrn = '%d_MAG' % n # mag
iraf.imgets(imgroot,magstrn)
hmagn=str(iraf.imgets.value)
magn,magerrn=hmagn.split('+/-')
mag += [magn]
magerr += [magerrn]
restrn = '%d_RE' % n # effective radius r_e
iraf.imgets(imgroot,restrn)
hren=str(iraf.imgets.value)
ren,reerrn=hren.split('+/-')
re += [ren]
reerr += [reerrn]
sersicnstrn = '%d_N' % n # sersic n
iraf.imgets(imgroot,sersicnstrn)
hsersicnn=str(iraf.imgets.value)
sersicnn,sersicnerrn=hsersicnn.split('+/-')
sersicn += [sersicnn]
sersicnerr += [sersicnerrn]
axratiostrn = '%d_AR' % n # axial ratio
iraf.imgets(imgroot,axratiostrn)
haxration=str(iraf.imgets.value)
axration,axratioerrn=haxration.split('+/-')
axratio += [axration]
axratioerr += [axratioerrn]
pastrn = '%d_PA' % n # position angle
iraf.imgets(imgroot,pastrn)
hpan=str(iraf.imgets.value)
pan,paerrn=hpan.split('+/-')
pa += [pan]
paerr += [paerrn]
iraf.imgets(imgroot,'CHISQ') # chi-square (same for the same image)
hchisq=str(iraf.imgets.value)
chisq += [hchisq]
iraf.imgets(imgroot,'CHI2NU') # reduced chi-square (same for the same image)
hchisqnu=str(iraf.imgets.value)
chisqnu += [hchisqnu]
iraf.imgets(imgroot,'OBJNUM') # object number (same for the same image!)
hobjnum=str(iraf.imgets.value)
objnum += [hobjnum]
# process other attributes...
n += 1 # jump to next possible component
else:
ifcomp = 0
# the last n will be 1 greater than the number of components
# now start to print out the entries
for i in range(n-1):
f2.write("%s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s\n"
%(objnum[i],xcent[i],xcenterr[i],ycent[i],ycenterr[i],mag[i],magerr[i],
re[i],reerr[i],sersicn[i],sersicnerr[i],axratio[i],axratioerr[i],
pa[i],paerr[i],chisq[i],chisqnu[i]))
f.close()
f2.close()
iraf.delete(files="tmplist", verify="no")
def getgain(im):
iraf.imgets(image=im, param='GAIN')
t = float(iraf.imgets.value)
return t
def getheader(im, keyword):
iraf.imgets(image=im, param=keyword)
t = float(iraf.imgets.value)
return t
def rotate24():
#for i in range(len(images24)):
for i in range(3,6):
#for i in range(1):
iraf.imgets(image=images24[i],param='CROTA2')#[deg] Orientation of axis 2 (W of N, +=CW)
def createartcat(self,
nstar=100,
covimageflag=0,
covimage='null',
ncov=6,
galcatflag=0):
# add sources at random positions on image
# even if a previous source is there, we need to know how crowding affects completeness
# if there is a covereage map, like in the case of the rotated 24um image
# need to take into account that some areas of the image have no exposure
# ncov = min number of images
iraf.imgets(image=self.inputimage, param='naxis1') #get RA of image
xmax = float(iraf.imgets.value)
iraf.imgets(image=self.inputimage, param='naxis2') #get RA of image
ymax = float(iraf.imgets.value)
xpos = []
ypos = []
mag = []
rad = []
BA = []
PA = []
if covimageflag:
covimage = pyfits.open(covimage)
cov_data = (covimage[0].data)
covimage.close()
i = 0
while i < nstar:
xtemp = int(round(1 + rand() * (xmax - 2)))
ytemp = int(round(1 + rand() * (ymax - 2)))
if covimageflag:
# check coverage map to make sure point is on science area
# coverage map values > 6 are ok
if cov_data[ytemp, xtemp] < ncov:
continue
xpos.append(xtemp)
ypos.append(ytemp)
# create magnitudes that sample linearly w/flux
#flux=fmin+rand()*(fmax-fmin)
#m=zp-2.5*log10(flux)
if galcatflag:
m = minmaggal + rand() * (maxmaggal - minmaggal)
else:
m = minmag + rand() * (maxmag - minmag)
mag.append(m)
r = rmin + rand() * (rmax - rmin)
rad.append(r)
b = BAmin + rand() * (BAmax - BAmin)
BA.append(b)
p = PAmin + rand() * (PAmax - PAmin)
PA.append(p)
i = i + 1
self.xpos = array(xpos, 'f')
self.ypos = array(ypos, 'f')
mag = array(mag, 'f')
rad = array(rad, 'f')
BA = array(BA, 'f')
PA = array(PA, 'f')
if galcatflag:
outf = 'galaxies.coords.dat'
else:
outf = 'stars.coords.dat'
outfile = open(self.imagedir + outf, 'w')
for i in range(len(self.xpos)):
if galcatflag:
# x, y, mag, model (expdisk/devauc) radius B/A pa
s = '%8.2f %8.2f %8.2f expdisk %6.2f %5.2f %6.2f\n' % (
self.xpos[i], self.ypos[i], mag[i], rad[i], BA[i], PA[i])
else:
s = '%8.2f %8.2f %8.2f \n' % (self.xpos[i], self.ypos[i],
mag[i])
outfile.write(s)
outfile.close()
def plot2masspos(im):
infile=open('fp_2mass.fp_psc1374.tbl','r')
dra=[]
ddec=[]
iraf.imgets(image=im,param='RA')#get RA of image
t=iraf.imgets.value
t1=t.split(':')
for i in range(len(t1)):#convert to floats
t1[i]=float(t1[i])
RAcenter=15.*(t1[0]+t1[1]/60.+t1[2]/3600.)#convert to degrees
iraf.imgets(image=im,param='DEC')#get RA of image
t=iraf.imgets.value
t1=t.split(':')
for i in range(len(t1)):#convert to floats
t1[i]=float(t1[i])
DECcenter=(t1[0]+t1[1]/60.+t1[2]/3600.)#convert to degrees
for line in infile:
if line.find('\\') > -1:
continue
if line.find('=') > -1:
continue
if line.find('|') > -1:
continue
t=line.split()
if (float(t[6]) >15.):
continue
ra=(float(t[1])-RAcenter)*N.cos(N.pi/180.*float(t[2]))#correct delta ra for cos declination
dec=(DECcenter-float(t[2]))#makes east to the left
ra=ra*3600.#convert to arcsec
dec=dec*3600.
dra.append(ra)#save offsets from center position
ddec.append(dec)#save offsets from center
dra=N.array(dra,'f')
ddec=N.array(ddec,'f')
infile.close()
xcenter=512
ycenter=512
plate=0.5#arcsec/pixel
#convert offsets to pixels on pisces at 90"
x=dra/plate
y=ddec/plate
#center at (512,512) in pixel coordinates
x=x+xcenter
y=y+ycenter
#reflect about y axis and then swap x & y to match the orientation of pisces
x=imxmax-x#reflect about x
yt=x#swap x & t
xt=y
xt=imxmax-xt
dy=-26.
dx=20.
for i in range(1000):
yt=yt+dy
xt=xt+dx
outfile=open('2masscoords.dat','w')
for i in range(len(x)):
s="%8.2f %8.2f \n"%(xt[i],yt[i])
outfile.write(s)
outfile.close()
iraf.display(im,2)
iraf.tvmark(2,'testxy.cat',color=207,radii=5)
iraf.tvmark(2,'2masscoords.dat',color=206,radii=7.)
try:
flag= raw_input("how does it look? 1=keep, 0=adjust positions \n")
flag=float(flag)
except ValueError:
print "Sorry, didn't get that. Let's try one more time."
flag= raw_input("how does it look? 1=keep, 0=adjust positions \n")
flag=float(flag)
if flag > .1:
break
dx=raw_input("enter new x offset for sdss coords\n")
dx=float(dx)
dy=raw_input("enter new y offset for sdss coords\n")
dy=float(dy)
def irafflatten():
os.system('cp /Users/rfinn/clusters/spitzer/flatsexfiles/* .')
infile=open('InputImageList.txt','r')
outfile=open('FlatImageList.txt','w')
sky=[]
for line in infile:
im=line[0:(len(line)-1)]
mask='mask'+im
skyim='s'+im
outline='f'+line
iraf.imgets(im,'DRIBKGND')
t=iraf.imgets.value
sky.append(float(t))
outfile.write(outline)
#get object positions using sextractor
iraf.imarith(im,'-',t,skyim)#subtract sky before running sextractor (otherwise it doesn't detect any objects - don't know why...)
s='sex '+skyim
os.system(s)
x=[]
y=[]
catfile=open('test.cat','r')
for line in catfile:
if line.find('#') > -1:
continue
t=line.split()
x.append(float(t[10]))
y.append(float(t[11]))
catfile.close()
x=N.array(x,'f')
y=N.array(y,'f')
try:#create image of ones same size as image
iraf.imarith(im,'/',im,'mask')
except:
iraf.imdelete('mask')
iraf.imarith(im,'/',im,'mask')
print "masking objects"
for j in range(len(x)): #mask objects and radius around each position using imreplace, radius=10 pix
for k in range(11):
y1=int(y[j]+5-k)
if y1 < 1:
continue
if y1 > 128:
continue
xmin=int(x[j]-5)
if xmin < 1:
xmin=1
xmax=int(x[j]+5)
if xmax > 128:
xmax=128
s='mask['+str(xmin)+':'+str(xmax)+","+str(y1)+":"+str(y1)+"]"
iraf.imreplace(s,0.)
iraf.imrename('mask',mask)
print "updating BPM field in header"
iraf.hedit(im,fields='BPM',value=mask,add='yes',verify='no',update='yes')
outfile.close()
infile.close()
avesky=N.average(N.array(sky,'f'))
lthresh=avesky-1.
hthresh=avesky+.6
iraf.imcombine('@InputImageList.txt','flat',combine='average',reject='ccdclip',scale='none',zero='mode',lthreshold=lthresh,hthreshold=hthresh,lsigma=2.,hsigma=2.,rdnoise=5.,gain=5.,blank=1.,grow=12.,masktype='badvalue',maskvalue='0')
t=iraf.imstat('flat',fields='mean',format='no',Stdout=1)
ave=float(t[0])
iraf.imarith('flat','/',ave,'nflat')
iraf.imarith('@InputImageList.txt','/','nflat','@FlatImageList.txt')
def getxyfromradec(im, ra, dec):
iraf.imgets(image=im, param='CRVAL1') #get RA of image in deg
t = iraf.imgets.value
RAcenter = float(t)
iraf.imgets(image=im, param='CRVAL2') #get RA of image
t = iraf.imgets.value
DECcenter = float(t)
dra = (RAcenter - ra) * cos(
pi / 180. * dec) #correct delta ra for cos declination
ddec = (dec - DECcenter)
#ra=ra*3600.#convert to arcsec
#dec=dec*3600.
#dra=N.array(dra,'f')
#ddec=N.array(ddec,'f')
iraf.imgets(image=im, param='CRPIX1') #get x value corresponding to RA
t = float(iraf.imgets.value)
xcenter = t
iraf.imgets(image=im, param='CRPIX2') #get y value corresponding to dec
t = float(iraf.imgets.value)
ycenter = t
iraf.imgets(image=im, param='CD1_1') #get x value corresponding to RA
xplate = abs(float(iraf.imgets.value)) #deg/pixel
iraf.imgets(image=im, param='CD2_2') #get x value corresponding to RA
yplate = abs(float(iraf.imgets.value)) #deg/pixel
#convert offsets to pixels on pisces at 90"
x = dra / xplate + xcenter
y = ddec / yplate + ycenter
return x, y
prefix = ['D17s', 'EGSdJ', 'EGSdn', 'EGSsn', 'standA']
iraf.images()
iraf.images.imutil()
iraf.immatch()
for name in prefix:
i = 0
s = name + "*.fits"
files = glob.glob(s)
for file in files:
(pre, junk) = file.split('.')
index = pre[(len(pre) - 4):len(pre)]
iraf.imgets(image=file, param='FILTER') #get RA of image
filter = iraf.imgets.value
if my.beginsWith('D17s', file) > .1:
outname = 'D17s' + str(
file[7]) + "-" + str(filter) + "-" + str(index) + ".fits"
s = "cp " + file + " " + outname + " \n"
os.system(s)
if my.beginsWith('EGSd', file) > .1:
outname = 'EGS' + "-" + str(filter) + "-n1-" + str(index) + ".fits"
s = "cp " + file + " " + outname + " \n"
os.system(s)
if my.beginsWith('EGSs', file) > .1:
outname = 'EGSs' + str(int(file[7]) + 1) + "-" + str(
filter) + "-" + str(index) + ".fits"
def alignimages(im1, im2):
runsex = 1 #run sextractor to get object catalogs
auto = 0.
t = im1.split('.')
prefixim1 = t[0]
t = prefixim1.split('pre')
fieldid = t[0] #saves cl1018 or whatever as fieldid
t = im2.split('.')
prefixim2 = t[0]
filexy1 = prefixim1 + '-xy.cat'
filexy2 = prefixim2 + '-xy.cat'
print "aligning images for ", fieldid
if runsex > 0:
runsextractor(im1, auto)
infile = open('test.cat', 'r') #keep only sources w/in useable field
outfile = open(filexy1, 'w')
for line in infile: #keep only targets w/in 500 pixels of center
if line.find('#') > -1:
continue
t = line.split()
out = '%8.4f %8.4f \n' % (float(t[10]), float(t[11]))
outfile.write(out)
infile.close()
outfile.close()
print "running sextractor on ", im2
runsextractor(im2, auto)
infile = open('test.cat', 'r') #keep only sources w/in useable field
outfile = open(filexy2, 'w')
for line in infile: #keep only targets w/in 500 pixels of center
if line.find('#') > -1:
continue
t = line.split()
out = '%8.4f %8.4f \n' % (float(t[10]), float(t[11]))
outfile.write(out)
infile.close()
outfile.close()
match = fieldid + '-match'
dbase = fieldid + '-match-geomap'
iraf.display(im1, 1, fill='yes')
iraf.display(im2, 2, fill='yes')
reffile = fieldid + 'refpoints' #make these individually. First line w/3points in gmos image (x1 y1 x2 y2 x3 y3). 2nd line w/same points in ediscs I-band
flag = raw_input("Should we create a refpoints file (0=no, 1=yes)?\n")
flag = int(flag)
if flag > 0.1:
s = 'cp cl1018refpoints ' + reffile
os.system(s)
print "open ", reffile, " in emacs, delete 2 lines"
print "Enter x1 y1 x2 y2 x3 y3 for ref image (gmos)"
print "Enter x1 y1 x2 y2 x3 y3 for 2nd image (vlt)"
iraf.imexam()
iraf.xyxymatch(filexy2,
filexy1,
match,
tolerance=15,
refpoints=reffile,
matching='tolerance',
interactive='no')
iraf.imgets(image=im1, param='i_naxis1')
t = iraf.imgets.value
im1xmax = (float(t))
iraf.imgets(image=im1, param='i_naxis2')
t = iraf.imgets.value
im1ymax = (float(t))
iraf.geomap(input=match,
database=dbase,
function='polynomial',
xmin=1,
xmax=im1xmax,
ymin=1,
ymax=im1ymax,
xxorder=4,
xyorder=4,
yyorder=4,
yxorder=4,
transform='gmos',
interactive='yes')
transformim = 1
if transformim > 0:
outim2 = 'g' + im2
iraf.geotran(im2, output=outim2, database=dbase, transform='gmos')
iraf.display(im2, 1, fill='yes')
iraf.display(outim2, 2, fill='yes')
iraf.display(im1, 3, fill='yes')
