def find_ghost_centers(fnlist, tolerance=3, thresh=4, guess=None):
"""This routine finds the most likely optical center for a series of images
by attempting to match ghost reflections of stars with their stellar
counterparts. It can be rather slow if the fields are very dense with
stars, but is also much more likely to succeed in locating the correct
center in dense fields.
Images should have already been aperture masked at this point. If they
haven't, run 'aperture_mask' on them first.
It is useful if the images have had their seeing FWHMs measured. If they
have, these values (in pixels) should be in the fits headers as "FPFWHM".
If not, the FWHM is assumed to be 5 pixels (and this is not ideal).
The routine creates a number of temporary files while running, which are
deleted at the end of the routine. Interrupting the routine while running
is probably not a great idea.
The routine returns a list of image centers as well as appending these to
the fits headers as "fpxcen" and "fpycen"
Inputs:
fnlist -> List of strings, each one containing the path to a fits image.
The images should be roughly aligned to one another or the
routine will not work.
tolerance -> Optional. How close two pixels can be and be "close enough"
Default is 3 pixels.
thresh -> Optional. Level above sky background variation to look for objs.
Default is 4.5 (times SkySigma). Decrease if center positions
aren't being found accurately. Increase for crowded fields to
decrease computation time.
guess -> Optional. If you already have an idea of where the center should
be, you'd put it here. Should be a 2-long iterable, with guess[0]
being the X center guess, and guess[1] being the y center guess.
Outputs:
xcenlist -> List of image center X coordinates
ycenlist -> List of image center Y coordinates
"""
# Get image FWHMs
fwhm = np.empty(len(fnlist))
firstimage = FPImage(fnlist[0])
toggle = firstimage.fwhm
axcen = firstimage.axcen
aycen = firstimage.aycen
arad = firstimage.arad
firstimage.close()
if axcen is None:
exit("Error! Images have not yet been aperture-masked! Do this first!")
if toggle is None:
print "Warning: FWHMs have not been measured!"
print "Assuming 5 pixel FWHM for all images."
fwhm = 5.*np.ones(len(fnlist))
else:
for i in range(len(fnlist)):
image = FPImage(fnlist[i])
fwhm[i] = image.fwhm
image.close()
# Get sky background levels
skyavg = np.empty(len(fnlist))
skysig = np.empty(len(fnlist))
for i in range(len(fnlist)):
image = FPImage(fnlist[i])
skyavg[i], skysig[i], _skyvar = image.skybackground()
image.close()
# Identify the stars in each image
xlists = []
ylists = []
maglists = []
print "Identifying stars and ghosts in each image..."
for i in range(len(fnlist)):
xlists.append([])
ylists.append([])
maglists.append([])
image = FPImage(fnlist[i])
axcen = image.axcen
aycen = image.aycen
arad = image.arad
sources = daofind(image.inty-skyavg[i],
fwhm=fwhm[i],
threshold=thresh*skysig[i]).as_array()
for j in range(len(sources)):
# Masks for center and edge of image
cenmask = ((sources[j][1]-axcen)**2 +
(sources[j][2]-aycen)**2 > (0.05*arad)**2)
edgemask = ((sources[j][1]-axcen)**2 +
(sources[j][2]-aycen)**2 < (0.95*arad)**2)
if np.logical_and(cenmask, edgemask):
xlists[i].append(sources[j][1])
ylists[i].append(sources[j][2])
maglists[i].append(sources[j][-1])
image.close()
if guess is None:
# Use the found stars to come up with a center guess for each image
xcen = np.zeros(len(fnlist))
ycen = np.zeros(len(fnlist))
goodcen = np.zeros(len(fnlist))
for i in range(len(fnlist)):
N = len(xlists[i])
xcenarray = np.zeros(((N*N-N)/2))
ycenarray = np.zeros(((N*N-N)/2))
dist2array = np.zeros(((N*N-N)/2))
sub1array = np.zeros(((N*N-N)/2))
index = 0
# All "possible" centers for all possible pairs of stars
for j in range(N):
for k in range(j+1, N):
xcenarray[index] = xlists[i][j]+xlists[i][k]
ycenarray[index] = ylists[i][j]+ylists[i][k]
index = index+1
xcenarray, ycenarray = 0.5*xcenarray, 0.5*ycenarray
# Cross check the various possible centers against each other
for j in range(len(xcenarray)):
dist2array = ((xcenarray-xcenarray[j])**2 +
(ycenarray-ycenarray[j])**2)
sub1array[j] = np.sum(dist2array < tolerance**2)
# Determine the locations of the "best" centers.
bestcenloc = np.where(sub1array == max(sub1array))[0]
# Now cross check JUST the best ones against each other
sub1array = np.zeros(len(bestcenloc))
xcenarray = xcenarray[bestcenloc]
ycenarray = ycenarray[bestcenloc]
for j in range(len(bestcenloc)):
dist2array = ((xcenarray-xcenarray[j])**2 +
(ycenarray-ycenarray[j])**2)
sub1array[j] = np.sum(dist2array < tolerance**2)
# Again, determine the locations of the "best" centers.
bestcenloc = np.where(sub1array == max(sub1array))[0]
xcen[i] = np.average(xcenarray[bestcenloc])
ycen[i] = np.average(ycenarray[bestcenloc])
# Cross-check the various image's centers against each other
for i in range(len(fnlist)):
dist2array = (xcen-xcen[i])**2 + (ycen-ycen[i])**2
goodcen[i] = np.sum(dist2array < tolerance**2)
# Determine where in the arrays the best fitting centers are
bestcenloc = np.where(goodcen == max(goodcen))[0]
bestxcen = np.average(xcen[bestcenloc])
bestycen = np.average(ycen[bestcenloc])
else:
# Forced guess:
bestxcen, bestycen = guess[0], guess[1]
# Now we want to improve the center for each image using the best guesses
xcenlist = np.zeros(len(fnlist))
ycenlist = np.zeros(len(fnlist))
objxs = []
objys = []
ghoxs = []
ghoys = []
for i in range(len(fnlist)):
# Where would the reflected objects be if they exist
refxlist = 2.*bestxcen - np.array(xlists[i])
refylist = 2.*bestycen - np.array(ylists[i])
# Populate lists of objects and ghosts based on this
objxs.append([])
objys.append([])
ghoxs.append([])
ghoys.append([])
for j in range(len(xlists[i])):
dist2list = ((xlists[i] - refxlist[j])**2 +
(ylists[i] - refylist[j])**2)
matchlist = dist2list < tolerance**2
if np.sum(matchlist) >= 1:
# We found a match! Now we need to know where the match is
matchloc = np.where(matchlist == 1)[0][0]
# Cool, now, is this thing brighter than the ghost?
if maglists[i][matchloc] < maglists[i][j]:
# It is! Record it:
objxs[i].append(xlists[i][matchloc])
objys[i].append(ylists[i][matchloc])
ghoxs[i].append(xlists[i][j])
ghoys[i].append(ylists[i][j])
# Calculate the centers based on the object / ghost coords
if len(objxs[i]) == 0:
xcenlist[i] = 0
ycenlist[i] = 0
else:
xcenlist[i] = 0.5*(np.average(objxs[i])+np.average(ghoxs[i]))
ycenlist[i] = 0.5*(np.average(objys[i])+np.average(ghoys[i]))
# Fill in the blanks with a "best guess"
xcenlist[xcenlist == 0] = np.average(xcenlist[xcenlist != 0])
ycenlist[ycenlist == 0] = np.average(ycenlist[ycenlist != 0])
xcenlist[np.isnan(xcenlist)] = bestxcen
ycenlist[np.isnan(ycenlist)] = bestycen
# Append the values to the image headers
for i in range(len(fnlist)):
image = FPImage(fnlist[i], update=True)
image.xcen = xcenlist[i]
image.ycen = ycenlist[i]
image.close()
# Manually verify ghost centers
while True:
yn = raw_input("Manually verify ghost centers? (Recommended) (y/n) ")
if "n" in yn or "N" in yn:
break
elif "y" in yn or "Y" in yn:
goodtog = verify_center(fnlist,
objxs, objys,
ghoxs, ghoys,
xcenlist, ycenlist)
if goodtog:
break
else:
exit("Centers not approved!")
return xcenlist, ycenlist
