#exclude acquisition images
if targets[i]!=0:
#get data and prepare arrays for xhi^2 and scaling ratios
im = fits.getdata('results/'+fileNames[i][:-5]+'.reg.fits')
Chi = np.ones(n)*np.inf
ss = np.zeros(n)
#calculate noise in the image
sig = iS.calcNoiseProfile(im)
for j in range(n):
#loop over images of the other target
if (targets[i]==1 and targets[j]==2) or (targets[i]==2 and targets[j]==1):
tim = fits.getdata('results/'+fileNames[j][:-5]+'.reg.fits')
#find scaling ratio and chi^2
ss[j] = iS.findRatio(im,tim,mask)
Chi[j] = np.sum(((im - ss[j]*tim)/sig)**2) ### what to use for sigma???
#progress bar
percent1 = float(i) / n
hashes1 = '#' * int(round(percent1 * 20))
spaces1 = ' ' * (20 - len(hashes1))
percent2 = float(j) / n
hashes2 = '#' * int(round(percent2 * 20))
spaces2 = ' ' * (20 - len(hashes2))
sys.stdout.write("\rImages: [{0}] {1}%\tTemplates: [{2}] {3}%".format(hashes1 + spaces1, int(round(percent1 * 100)),hashes2+spaces2, int(round(percent2*100))))
sys.stdout.flush()
#find and write min chi^2
best = np.argmin(Chi)
f.write(fileNames[i]+'\t'+fileNames[best]+'\n')
if (np.sqrt((i-o)**2+(j-o)**2)<30 and np.sqrt((i-o)**2+(j-o)**2)>10):
mask[j,i]=1.
n = np.size(targets)
for i in range(n):
#exclude acquisition images
if targets[i]!=0:
im = fits.getdata('results/'+fileNames[i][:-5]+'.reg.fits')
#load appropriate psf
if targets[i]==1:
psf = ROXs42B
if targets[i]==2:
psf = ROXs12
#find scaling ratio, subtract and output image
s = iS.findRatio(im,psf,mask)#(o,o),15,15)
im = im - s*psf
fits.writeto('results/'+fileNames[i][:-5]+'.adi.fits',im)
#progress bar
percent = float(i) / n
hashes = '#' * int(round(percent * 20))
spaces = ' ' * (20 - len(hashes))
sys.stdout.write("\rPercent: [{0}] {1}%".format(hashes + spaces, int(round(percent * 100))))
sys.stdout.flush()
sys.stdout.write("\n")
#register and stack adi images
positions = ascii.read('starPositions.txt')
positions['x'] = np.ones(n)*512
