def distmatch(sexlist,
catlist,
maxrad=180,
minrad=10,
reqmatch=3,
patolerance=1.2,
uncpa=-1,
showmatches=0,
fastmatch=1):
if reqmatch < 2:
print 'Warning: reqmatch >=3 suggested'
if patolerance <= 0:
print 'PA tolerance cannot be negative!!!'
patolerance = abs(patolerance)
if uncpa < 0: uncpa = 720
declist = []
for s in sexlist:
declist.append(s.dec_rad)
avdec_rad = astrometrystats.median(declist) # faster distance computation
rascale = numpy.cos(avdec_rad) # will mess up meridian crossings, however
#Calculates distances between objects in same list for both image catalog and reference catalog
(sexdists, sexmatchids) = calcdist(sexlist, maxrad, minrad, rascale)
(catdists, catmatchids) = calcdist(catlist, maxrad, minrad, rascale)
# Now look for matches in the reference catalog to distances in the image catalog.
countgreatmatches = 0
smatch = []
cmatch = []
mpa = []
offset = []
offpa = []
nmatch = []
primarymatchs = []
primarymatchc = []
#For each sextractor source A
for si in range(len(sexdists)):
sexdistarr = sexdists[si]
sexidarr = sexmatchids[si]
if len(sexdistarr) < 2: continue
#For each catalog source B
for ci in range(len(catdists)):
catdistarr = catdists[ci]
catidarr = catmatchids[ci]
if len(catdistarr) < 2: continue
match = 0
smatchin = []
cmatchin = []
#For each sextractor source A, use the distances to other sextractor sources
#that are within the radius range
for sj in range(len(sexdistarr)):
sexdist = sexdistarr[sj]
newmatch = 1
#For each catalog source B, use the distances to other catalog sources
#that are within the radius range and compare the ratio to
#(sextractor source A - sextractor sources) to (catalog source B - catalog sources)
#If within 3% then save the match (only count 1 match per catalog row)
for cj in range(len(catdistarr)):
catdist = catdistarr[cj]
if abs((sexdist / catdist) - 1.0) < 0.03:
match += newmatch
newmatch = 0 #further matches before the next sj loop indicate degeneracies
smatchin.append(sexmatchids[si][sj])
cmatchin.append(catmatchids[ci][cj])
#If number of matches is above or equal to required number of matches for sextractor source A
#(i.e. sextractor source A's distance to other sources makes it a likely candidate to be a catalog source)
#Find difference in position angle between matched angle between sextractor source A and matched source with
#catalog source B and matched catalog source.
#Remove values that are larger than user specified value or if above position angle tolerance.
if match >= reqmatch:
dpa = []
# Here, dpa[n] is the mean rotation of the PA from the primary star of this match
# to the stars in its match RELATIVE TO those same angles for those same stars
# in the catalog. Therefore it is a robust measurement of the rotation.
for i in range(len(smatchin)):
ddpa = posangle(sexlist[si],
sexlist[smatchin[i]]) - posangle(
catlist[ci], catlist[cmatchin[i]])
while ddpa > 200:
ddpa -= 360.
while ddpa < -160:
ddpa += 360.
dpa.append(ddpa)
#If user was confident the initial PA was right, remove bad PA's right away
for i in range(len(smatchin) - 1, -1, -1):
if abs(dpa[i]) > uncpa:
del smatchin[i]
del cmatchin[i]
del dpa[i]
if len(smatchin) < 2: continue
#Finds mode of difference position angle but allowing values to be +/- patolerance
dpamode = astrometrystats.most(dpa,
vmin=patolerance * 3,
vmax=patolerance * 3)
#Remove deviant matches by PA
for i in range(len(smatchin) - 1, -1, -1):
if abs(dpa[i] - dpamode) > patolerance:
del smatchin[i]
del cmatchin[i]
del dpa[i]
if len(smatchin) < 2: continue
#Finds the number of degenerate matches
ndegeneracies = len(smatchin) - len(
astrometrystats.unique(smatchin)) + len(cmatchin) - len(
astrometrystats.unique(cmatchin))
# this isn't quite accurate (overestimates if degeneracies are mixed up)
#Save values
mpa.append(dpamode)
primarymatchs.append(si)
primarymatchc.append(ci)
smatch.append(smatchin)
cmatch.append(cmatchin)
nmatch.append(len(smatchin) - ndegeneracies)
#If the number of unique sextractor sources is greater than 6, count as a great match
if (len(smatchin) - ndegeneracies > 6): countgreatmatches += 1
#Breaks loop if over 16 great matches are found and fastmatch
if countgreatmatches > 16 and fastmatch == 1:
break #save processing time
#If no matches found end program and return empty lists
nmatches = len(smatch)
if (nmatches == 0):
print 'Found no potential matches of any sort (including pairs).'
print 'The algorithm is probably not finding enough real stars to solve the field. Check seeing.'
return [], [], []
#Get rid of matches that don't pass the reqmatch cut (2nd cut after removing bad position angles)
for i in range(len(primarymatchs) - 1, -1, -1):
if nmatch[i] < reqmatch:
del mpa[i]
del primarymatchs[i]
del primarymatchc[i]
del smatch[i]
del cmatch[i]
del nmatch[i]
#If no remaining matches then exit program
if len(smatch) < 1:
print 'Found no matching clusters of reqmatch =', reqmatch
return [], [], []
#If we still have lots of matches, get rid of those with the minimum number of submatches
#(that is, increase reqmatch by 1)
minmatch = min(nmatch)
countnotmin = 0
#Finds how many matches have more than the minimum require matches
for n in nmatch:
if n > minmatch: countnotmin += 1
#If the number of matches is above 16 and there are more than 3 sources with more than the
#required number of matches, then delete any source with the bare minimum number of matches
if len(nmatch) > 16 and countnotmin > 3:
print 'Too many matches: increasing reqmatch to', reqmatch + 1
for i in range(len(primarymatchs) - 1, -1, -1):
if nmatch[i] == minmatch:
del mpa[i]
del primarymatchs[i]
del primarymatchc[i]
del smatch[i]
del cmatch[i]
del nmatch[i]
nmatches = len(
smatch
) # recalculate with the new reqmatch and with prunes supposedly removed
print 'Found', nmatches, 'candidate matches.'
# Kill the bad matches
rejects = 0
# Use only matches with a consistent PA (finds mode counting those within 3 patolerance)
offpa = astrometrystats.most(mpa,
vmin=3 * patolerance,
vmax=3 * patolerance)
#Removes values with rotational positional offsets that are above tolerance, then removes
#values that are above 2 sigma of those values
if len(smatch) > 2:
#Coarse iteration for anything away from the mode
for i in range(len(primarymatchs) - 1, -1, -1):
if abs(mpa[i] - offpa) > patolerance:
del mpa[i]
del primarymatchs[i]
del primarymatchc[i]
del smatch[i]
del cmatch[i]
del nmatch[i]
rejects += 1
medpa = astrometrystats.median(mpa)
stdevpa = astrometrystats.stdev(mpa)
refinedtolerance = (
2.0 * stdevpa
) #VLT Changed from arbitrary value of 2.2 from original script
#Fine iteration to flag outliers now that we know most are reliable
for i in range(len(primarymatchs) - 1, -1, -1):
if abs(mpa[i] - offpa) > refinedtolerance:
del mpa[i]
del primarymatchs[i]
del primarymatchc[i]
del smatch[i]
del cmatch[i]
del nmatch[i]
rejects += 1 #these aren't necessarily bad, just making more manageable.
# New verification step: calculate distances and PAs between central stars of matches
ndistflags = [0] * len(primarymatchs)
for v in range(2): #two iterations
if len(primarymatchs) == 0: break
#Find distances between central stars of matches and compare sextractor source distances to catalog sources
for i in range(len(primarymatchs)):
for j in range(len(primarymatchs)):
if i == j: continue
si = primarymatchs[i]
ci = primarymatchc[i]
sj = primarymatchs[j]
cj = primarymatchc[j]
sexdistij = distance(sexlist[si], sexlist[sj])
catdistij = distance(catlist[ci], catlist[cj])
try:
if abs((sexdistij / catdistij) - 1.0) > 0.03:
ndistflags[i] += 1
except: # (occasionally will get divide by zero)
pass
#Delete bad clusters that were flagged for every match
ntestmatches = len(primarymatchs)
for i in range(ntestmatches - 1, -1, -1):
if ndistflags[
i] == ntestmatches - 1: #if every comparison is bad, this is a bad match
del mpa[i]
del primarymatchs[i]
del primarymatchc[i]
del smatch[i]
del cmatch[i]
del nmatch[i]
rejects += 1
print 'Rejected', rejects, 'bad matches.'
nmatches = len(primarymatchs)
print 'Found', nmatches, 'good matches.'
#If no remaining matches, return empty lists
if nmatches == 0:
return [], [], []
#Returns pixel scale (great circle distance in catalog [ra, dec]/cartesian distance in sextractor source [x,y])
pixscalelist = []
if len(primarymatchs) >= 2:
for i in range(len(primarymatchs) - 1):
for j in range(i + 1, len(primarymatchs)):
si = primarymatchs[i]
ci = primarymatchc[i]
sj = primarymatchs[j]
cj = primarymatchc[j]
try:
pixscalelist.append(
distance(catlist[ci], catlist[cj]) /
imdistance(sexlist[si], sexlist[sj]))
except:
pass
pixelscale = astrometrystats.median(pixscalelist)
pixelscalestd = astrometrystats.stdev(pixscalelist)
if len(primarymatchs) >= 3:
print 'Refined pixel scale measurement: %.4f"/pix (+/- %.4f)' % (
pixelscale, pixelscalestd)
else:
print 'Refined pixel scale measurement: %.4f"/pix' % pixelscale
#If showmatches keyword set then print which objects match
for i in range(len(primarymatchs)):
si = primarymatchs[i]
ci = primarymatchc[i]
print '%3i' % si, 'matches', '%3i' % ci, ' (dPA =%7.3f)' % mpa[i],
#Keyword set in main program
if showmatches:
print
if len(smatch[i]) < 16:
print ' ', si, '-->', smatch[i],
if len(smatch[i]) >= 7: print
print ' ', ci, '-->', cmatch[i]
else:
print ' ', si, '-->', smatch[i][0:10], '+', len(
smatch[i]) - 10, 'more'
print ' ', ci, '-->', cmatch[i][
0:10], '+' #, len(cmatch[i])-10, ' more'
if i + 1 >= 10 and len(primarymatchs) - 10 > 0:
print(len(primarymatchs) - 10), 'additional matches not shown.'
break
else:
print ':', str(len(smatch[i])).strip(), 'rays'
#Create region files for DS9 with the sextractor sources (matchlines.im.reg) and catalog (matchlines.wcs.reg)
out = open('matchlines.im.reg', 'w')
i = -1
color = 'red'
out.write(
'# Region file format: DS9 version 4.0\nglobal color=' + color +
' font="helvetica 10 normal" select=1 highlite=1 edit=1 move=1 delete=1 include=1 fixed=0 source\n'
)
out.write('image\n')
for i in range(len(primarymatchs)):
si = primarymatchs[i]
for j in range(len(smatch[i])):
sj = smatch[i][j]
out.write(
"line(%.3f,%.3f,%.3f,%.3f) # line=0 0\n" %
(sexlist[si].x, sexlist[si].y, sexlist[sj].x, sexlist[sj].y))
out.close()
out = open('matchlines.wcs.reg', 'w')
i = -1
color = 'green'
out.write(
'# Region file format: DS9 version 4.0\nglobal color=' + color +
' font="helvetica 10 normal" select=1 highlite=1 edit=1 move=1 delete=1 include=1 fixed=0 source\n'
)
out.write('fk5\n')
for i in range(len(primarymatchs)):
ci = primarymatchc[i]
for j in range(len(smatch[i])):
cj = cmatch[i][j]
out.write("line(%.5f,%.5f,%.5f,%.5f) # line=0 0\n" %
(catlist[ci].ra, catlist[ci].dec, catlist[cj].ra,
catlist[cj].dec))
out.close()
#future project: if not enough, go to the secondary offsets
#Returns sextractor sources and catalog sources that appear to have matches along with the mode of the position angle
return (primarymatchs, primarymatchc, mpa)
def sextract(
sexfilename, nxpix, nypix, border=3, corner=12, minfwhm=1.5, maxfwhm=25, maxellip=0.5, saturation=-1, sexpath=""
):
if maxellip == -1:
maxellip = 0.5
if saturation > 0:
sexsaturation = saturation
else:
sexsaturation = 9e5
# Runs sextractor and reads in sextractor output file (temp.cat)
try:
# Sextract the image !
print sexpath + "sex " + sexfilename + " -c sex.config -SATUR_LEVEL " + str(sexsaturation)
os.system(sexpath + "sex " + sexfilename + " -c sex.config -SATUR_LEVEL " + str(sexsaturation))
except:
print " Error: Problem running sextractor"
print " Check that program is installed and runs at command line using " + sexpath + "sex"
sys.exit(1)
# Read in the sextractor catalog
try:
cat = numpy.loadtxt("temp.cat", dtype="float", comments="#")
except:
print "Cannot load sextractor output file!"
sys.exit(1)
if len(cat) == 0:
print "Sextractor catalog is empty: try a different catalog?"
sys.exit(1)
minx = border
miny = border
maxx = nxpix - border # This should be generalized
maxy = nypix - border
# Separate columns from sextractor output
x = cat[:, 0]
y = cat[:, 1]
ra = cat[:, 2]
dec = cat[:, 3]
mag = cat[:, 4]
magerr = cat[:, 5]
ellip = cat[:, 6]
fwhm = cat[:, 7]
flag = cat[:, 8]
# Initial filtering creates mask that will remove borders, corners,
# values above max ellipticity (galaxies), and within fwhm constraints
mask = (
(ellip <= maxellip)
& (fwhm >= minfwhm)
& (fwhm <= maxfwhm)
& (x >= minx)
& (x <= maxx)
& (y >= miny)
& (y <= maxy)
& (x + y >= corner)
& (x + nypix - y >= corner)
& (nxpix - x >= corner)
& (nxpix - x + nypix - y >= corner)
)
# Removes flagged values if saturation level set
if saturation > 0:
mask = mask & (flag == 0)
# VLT Removed code from autoastrometry.py line 387-432 that rules out false detections
# If too many false positives need to rewrite section in more coherent way
# OC: Might also be good to screen for false detections created by bad columns/rows
fwhmlist = fwhm[mask]
# Calculates the 20% value of the sextractor masked FWHM and the mode
# to distinguish stars from galaxies (removed by ellipticity) and cosmic rays
if len(fwhmlist) > 5:
sfwhmlist = sorted(fwhmlist)
fwhm20 = sfwhmlist[len(fwhmlist) / 5] # percentile values
fwhmmode = astrometrystats.most(sfwhmlist, vmax=0, vmin=0)
else:
fwhmmode = minfwhm
fwhm20 = minfwhm
# Creates new FWHM cutoff to distinguish stars from cosmic rays
# This method done from trial and error from original program to calculate typical cutoffs between stars and cosmic
# OC: if CR's are bigger and more common than stars, this is dangerous...
refinedminfwhm = numpy.median([0.75 * fwhmmode, 0.9 * fwhm20, minfwhm])
print "Refined min FWHM:", refinedminfwhm, "pix"
# Masks out fwhm with more stringent fwhm condition and creates newmask
# that combines the new masked out fwhm values
refwhmmask = fwhm > refinedminfwhm
newmask = refwhmmask & mask
# Create array with bad values removed and then sort by magnitude (column 4)
goodsext = cat[newmask]
sortedgoodsext = goodsext[goodsext[:, 4].argsort()]
print len(fwhmlist), "objects detected in image (" + str(len(fwhmlist) - len(sortedgoodsext)) + " discarded)"
# Save RA, DEC, and mag into class and save class objects into list
goodsexlist = []
for value in sortedgoodsext:
# RA = column 2, DEC = column 3, mag = column 4 with columns starting at 0
indObj = SexObj(value)
goodsexlist.append(indObj)
return goodsexlist
def sextract(sexfilename,
nxpix,
nypix,
border=3,
corner=12,
minfwhm=1.5,
maxfwhm=25,
maxellip=0.5,
saturation=-1,
sexpath='',
quiet=False):
if maxellip == -1: maxellip = 0.5
if saturation > 0:
sexsaturation = saturation
else:
sexsaturation = 9e5
#Runs sextractor and reads in sextractor output file (temp.cat)
try:
# Sextract the image !
if quiet == False:
print sexpath + "sex " + sexfilename + " -c sex.config -SATUR_LEVEL " + str(
sexsaturation)
os.system(sexpath + "sex " + sexfilename +
" -c sex.config -SATUR_LEVEL " + str(sexsaturation))
except:
print ' Error: Problem running sextractor'
print ' Check that program is installed and runs at command line using ' + sexpath + 'sex'
sys.exit(1)
# Read in the sextractor catalog
try:
cat = numpy.loadtxt('temp.cat', dtype='float', comments='#')
except:
print 'Cannot load sextractor output file!'
sys.exit(1)
if len(cat) == 0:
print 'Sextractor catalog is empty: try a different catalog?'
sys.exit(1)
minx = border
miny = border
maxx = nxpix - border # This should be generalized
maxy = nypix - border
#Separate columns from sextractor output
x = cat[:, 0]
y = cat[:, 1]
ra = cat[:, 2]
dec = cat[:, 3]
mag = cat[:, 4]
magerr = cat[:, 5]
ellip = cat[:, 6]
fwhm = cat[:, 7]
flag = cat[:, 8]
a_imag = cat[:, 9]
b_imag = cat[:, 10]
#Initial filtering creates mask that will remove borders, corners,
#values above max ellipticity (galaxies), and within fwhm constraints
mask = (ellip <= maxellip) & (fwhm >= minfwhm) & (fwhm <= maxfwhm) \
& (x >= minx) & (x <= maxx) & (y >= miny) & (y <= maxy) \
& (x+y >= corner) & (x+nypix-y >= corner) \
& (nxpix-x >= corner) & (nxpix-x+nypix-y >= corner) \
& (a_imag > 1) & (b_imag > 1)
#Removes flagged values if saturation level set
if saturation > 0:
mask = mask & (flag == 0)
#VLT Removed code from autoastrometry.py line 387-432 that rules out false detections
#If too many false positives need to rewrite section in more coherent way
#OC: Might also be good to screen for false detections created by bad columns/rows
fwhmlist = fwhm[mask]
#Calculates the 20% value of the sextractor masked FWHM and the mode
#to distinguish stars from galaxies (removed by ellipticity) and cosmic rays
if len(fwhmlist) > 5:
sfwhmlist = sorted(fwhmlist)
fwhm20 = sfwhmlist[len(fwhmlist) / 5] #percentile values
fwhmmode = astrometrystats.most(sfwhmlist, vmax=0, vmin=0)
else:
fwhmmode = minfwhm
fwhm20 = minfwhm
#Creates new FWHM cutoff to distinguish stars from cosmic rays
#This method done from trial and error from original program to calculate typical cutoffs between stars and cosmic
#OC: if CR's are bigger and more common than stars, this is dangerous...
refinedminfwhm = numpy.median([0.75 * fwhmmode, 0.9 * fwhm20, minfwhm])
if quiet == False:
print 'Refined min FWHM:', refinedminfwhm, 'pix'
#Masks out fwhm with more stringent fwhm condition and creates newmask
#that combines the new masked out fwhm values
refwhmmask = fwhm > refinedminfwhm
newmask = refwhmmask & mask
#Create array with bad values removed and then sort by magnitude (column 4)
goodsext = cat[newmask]
sortedgoodsext = goodsext[goodsext[:, 4].argsort()]
if quiet == False:
print len(fwhmlist), 'objects detected in image (' + str(
len(fwhmlist) - len(sortedgoodsext)) + ' discarded)'
#Save RA, DEC, and mag into class and save class objects into list
goodsexlist = []
for value in sortedgoodsext:
#RA = column 2, DEC = column 3, mag = column 4 with columns starting at 0
indObj = SexObj(value)
goodsexlist.append(indObj)
return goodsexlist
def distmatch(sexlist, catlist, maxrad=180, minrad=10, reqmatch=3, patolerance=1.2,uncpa=-1, showmatches=0, fastmatch=1):
if reqmatch < 2:
print 'Warning: reqmatch >=3 suggested'
if patolerance <= 0:
print 'PA tolerance cannot be negative!!!'
patolerance = abs(patolerance)
if uncpa < 0: uncpa = 720
declist = []
for s in sexlist:
declist.append(s.dec_rad)
avdec_rad = astrometrystats.median(declist) # faster distance computation
rascale = numpy.cos(avdec_rad) # will mess up meridian crossings, however
#Calculates distances between objects in same list for both image catalog and reference catalog
(sexdists, sexmatchids) = calcdist(sexlist, maxrad, minrad, rascale)
(catdists, catmatchids) = calcdist(catlist, maxrad, minrad, rascale)
# Now look for matches in the reference catalog to distances in the image catalog.
countgreatmatches = 0
smatch = []
cmatch = []
mpa = []
offset = []
offpa = []
nmatch = []
primarymatchs = []
primarymatchc = []
#For each sextractor source A
for si in range(len(sexdists)):
sexdistarr = sexdists[si]
sexidarr = sexmatchids[si]
if len(sexdistarr) < 2: continue
#For each catalog source B
for ci in range(len(catdists)):
catdistarr = catdists[ci]
catidarr = catmatchids[ci]
if len(catdistarr) < 2: continue
match = 0
smatchin = []
cmatchin = []
#For each sextractor source A, use the distances to other sextractor sources
#that are within the radius range
for sj in range(len(sexdistarr)):
sexdist = sexdistarr[sj]
newmatch = 1
#For each catalog source B, use the distances to other catalog sources
#that are within the radius range and compare the ratio to
#(sextractor source A - sextractor sources) to (catalog source B - catalog sources)
#If within 3% then save the match (only count 1 match per catalog row)
for cj in range(len(catdistarr)):
catdist = catdistarr[cj]
if abs((sexdist/catdist)-1.0) < 0.03:
match += newmatch
newmatch = 0 #further matches before the next sj loop indicate degeneracies
smatchin.append(sexmatchids[si][sj])
cmatchin.append(catmatchids[ci][cj])
#If number of matches is above or equal to required number of matches for sextractor source A
#(i.e. sextractor source A's distance to other sources makes it a likely candidate to be a catalog source)
#Find difference in position angle between matched angle between sextractor source A and matched source with
#catalog source B and matched catalog source.
#Remove values that are larger than user specified value or if above position angle tolerance.
if match >= reqmatch:
dpa = []
# Here, dpa[n] is the mean rotation of the PA from the primary star of this match
# to the stars in its match RELATIVE TO those same angles for those same stars
# in the catalog. Therefore it is a robust measurement of the rotation.
for i in range(len(smatchin)):
ddpa = posangle(sexlist[si],sexlist[smatchin[i]]) - posangle(catlist[ci],catlist[cmatchin[i]])
while ddpa > 200: ddpa -= 360.
while ddpa < -160: ddpa += 360.
dpa.append(ddpa)
#If user was confident the initial PA was right, remove bad PA's right away
for i in range(len(smatchin)-1,-1,-1):
if abs(dpa[i]) > uncpa:
del smatchin[i]
del cmatchin[i]
del dpa[i]
if len(smatchin) < 2: continue
#Finds mode of difference position angle but allowing values to be +/- patolerance
dpamode = astrometrystats.most(dpa, vmin=patolerance*3, vmax=patolerance*3)
#Remove deviant matches by PA
for i in range(len(smatchin)-1,-1,-1):
if abs(dpa[i] - dpamode) > patolerance:
del smatchin[i]
del cmatchin[i]
del dpa[i]
if len(smatchin) < 2: continue
#Finds the number of degenerate matches
ndegeneracies = len(smatchin)-len(astrometrystats.unique(smatchin)) + len(cmatchin)-len(astrometrystats.unique(cmatchin))
# this isn't quite accurate (overestimates if degeneracies are mixed up)
#Save values
mpa.append(dpamode)
primarymatchs.append(si)
primarymatchc.append(ci)
smatch.append(smatchin)
cmatch.append(cmatchin)
nmatch.append(len(smatchin)-ndegeneracies)
#If the number of unique sextractor sources is greater than 6, count as a great match
if (len(smatchin)-ndegeneracies > 6): countgreatmatches += 1
#Breaks loop if over 16 great matches are found and fastmatch
if countgreatmatches > 16 and fastmatch == 1: break #save processing time
#If no matches found end program and return empty lists
nmatches = len(smatch)
if (nmatches == 0):
print 'Found no potential matches of any sort (including pairs).'
print 'The algorithm is probably not finding enough real stars to solve the field. Check seeing.'
return [], [], []
#Get rid of matches that don't pass the reqmatch cut (2nd cut after removing bad position angles)
for i in range(len(primarymatchs)-1,-1,-1):
if nmatch[i] < reqmatch:
del mpa[i]
del primarymatchs[i]
del primarymatchc[i]
del smatch[i]
del cmatch[i]
del nmatch[i]
#If no remaining matches then exit program
if len(smatch) < 1:
print 'Found no matching clusters of reqmatch =', reqmatch
return [], [], []
#If we still have lots of matches, get rid of those with the minimum number of submatches
#(that is, increase reqmatch by 1)
minmatch = min(nmatch)
countnotmin = 0
#Finds how many matches have more than the minimum require matches
for n in nmatch:
if n > minmatch: countnotmin += 1
#If the number of matches is above 16 and there are more than 3 sources with more than the
#required number of matches, then delete any source with the bare minimum number of matches
if len(nmatch) > 16 and countnotmin > 3:
print 'Too many matches: increasing reqmatch to', reqmatch+1
for i in range(len(primarymatchs)-1,-1,-1):
if nmatch[i] == minmatch:
del mpa[i]
del primarymatchs[i]
del primarymatchc[i]
del smatch[i]
del cmatch[i]
del nmatch[i]
nmatches = len(smatch) # recalculate with the new reqmatch and with prunes supposedly removed
print 'Found',nmatches,'candidate matches.'
# Kill the bad matches
rejects = 0
# Use only matches with a consistent PA (finds mode counting those within 3 patolerance)
offpa = astrometrystats.most(mpa, vmin=3*patolerance, vmax=3*patolerance)
#Removes values with rotational positional offsets that are above tolerance, then removes
#values that are above 2 sigma of those values
if len(smatch) > 2:
#Coarse iteration for anything away from the mode
for i in range(len(primarymatchs)-1,-1,-1):
if abs(mpa[i] - offpa) > patolerance:
del mpa[i]
del primarymatchs[i]
del primarymatchc[i]
del smatch[i]
del cmatch[i]
del nmatch[i]
rejects += 1
medpa = astrometrystats.median(mpa)
stdevpa = astrometrystats.stdev(mpa)
refinedtolerance = (2.0 * stdevpa) #VLT Changed from arbitrary value of 2.2 from original script
#Fine iteration to flag outliers now that we know most are reliable
for i in range(len(primarymatchs)-1,-1,-1):
if abs(mpa[i] - offpa) > refinedtolerance:
del mpa[i]
del primarymatchs[i]
del primarymatchc[i]
del smatch[i]
del cmatch[i]
del nmatch[i]
rejects += 1 #these aren't necessarily bad, just making more manageable.
# New verification step: calculate distances and PAs between central stars of matches
ndistflags = [0]*len(primarymatchs)
for v in range(2): #two iterations
if len(primarymatchs) == 0: break
#Find distances between central stars of matches and compare sextractor source distances to catalog sources
for i in range(len(primarymatchs)):
for j in range(len(primarymatchs)):
if i == j: continue
si = primarymatchs[i]
ci = primarymatchc[i]
sj = primarymatchs[j]
cj = primarymatchc[j]
sexdistij = distance(sexlist[si], sexlist[sj])
catdistij = distance(catlist[ci], catlist[cj])
try:
if abs((sexdistij/catdistij)-1.0) > 0.03:
ndistflags[i] += 1
except: # (occasionally will get divide by zero)
pass
#Delete bad clusters that were flagged for every match
ntestmatches = len(primarymatchs)
for i in range(ntestmatches-1,-1,-1):
if ndistflags[i] == ntestmatches-1: #if every comparison is bad, this is a bad match
del mpa[i]
del primarymatchs[i]
del primarymatchc[i]
del smatch[i]
del cmatch[i]
del nmatch[i]
rejects += 1
print 'Rejected', rejects, 'bad matches.'
nmatches = len(primarymatchs)
print 'Found', nmatches, 'good matches.'
#If no remaining matches, return empty lists
if nmatches == 0:
return [], [], []
#Returns pixel scale (great circle distance in catalog [ra, dec]/cartesian distance in sextractor source [x,y])
pixscalelist = []
if len(primarymatchs) >= 2:
for i in range(len(primarymatchs)-1):
for j in range(i+1,len(primarymatchs)):
si = primarymatchs[i]
ci = primarymatchc[i]
sj = primarymatchs[j]
cj = primarymatchc[j]
try:
pixscalelist.append(distance(catlist[ci],catlist[cj])/imdistance(sexlist[si],sexlist[sj]))
except:
pass
pixelscale = astrometrystats.median(pixscalelist)
pixelscalestd = astrometrystats.stdev(pixscalelist)
if len(primarymatchs) >= 3:
print 'Refined pixel scale measurement: %.4f"/pix (+/- %.4f)' % (pixelscale, pixelscalestd)
else:
print 'Refined pixel scale measurement: %.4f"/pix' % pixelscale
#If showmatches keyword set then print which objects match
for i in range(len(primarymatchs)):
si = primarymatchs[i]
ci = primarymatchc[i]
print '%3i' % si, 'matches', '%3i' % ci, ' (dPA =%7.3f)' % mpa[i],
#Keyword set in main program
if showmatches:
print
if len(smatch[i]) < 16:
print ' ', si, '-->', smatch[i],
if len(smatch[i]) >= 7: print
print ' ', ci, '-->', cmatch[i]
else:
print ' ', si, '-->', smatch[i][0:10], '+', len(smatch[i])-10, 'more'
print ' ', ci, '-->', cmatch[i][0:10], '+'#, len(cmatch[i])-10, ' more'
if i+1 >= 10 and len(primarymatchs)-10 > 0:
print (len(primarymatchs)-10), 'additional matches not shown.'
break
else:
print ':', str(len(smatch[i])).strip(), 'rays'
#Create region files for DS9 with the sextractor sources (matchlines.im.reg) and catalog (matchlines.wcs.reg)
out = open('matchlines.im.reg','w')
i = -1
color='red'
out.write('# Region file format: DS9 version 4.0\nglobal color='+color+' font="helvetica 10 normal" select=1 highlite=1 edit=1 move=1 delete=1 include=1 fixed=0 source\n')
out.write('image\n')
for i in range(len(primarymatchs)):
si = primarymatchs[i]
for j in range(len(smatch[i])):
sj = smatch[i][j]
out.write("line(%.3f,%.3f,%.3f,%.3f) # line=0 0\n" % (sexlist[si].x, sexlist[si].y, sexlist[sj].x, sexlist[sj].y))
out.close()
out = open('matchlines.wcs.reg','w')
i = -1
color='green'
out.write('# Region file format: DS9 version 4.0\nglobal color='+color+' font="helvetica 10 normal" select=1 highlite=1 edit=1 move=1 delete=1 include=1 fixed=0 source\n')
out.write('fk5\n')
for i in range(len(primarymatchs)):
ci = primarymatchc[i]
for j in range(len(smatch[i])):
cj = cmatch[i][j]
out.write("line(%.5f,%.5f,%.5f,%.5f) # line=0 0\n" % (catlist[ci].ra, catlist[ci].dec, catlist[cj].ra, catlist[cj].dec))
out.close()
#future project: if not enough, go to the secondary offsets
#Returns sextractor sources and catalog sources that appear to have matches along with the mode of the position angle
return (primarymatchs, primarymatchc, mpa)
