def __sexFindFWHM(self, tries, threshold, deblendmin):
focpos = []
fwhm = []
fwhm_min = None
fwhm_MinimumX = None
keys = tries.keys()
keys.sort()
sextr = sextractor.Sextractor(threshold=threshold,
deblendmin=deblendmin)
for k in keys:
try:
sextr.runSExtractor(tries[k])
fwhm, fwhms, nstars = sextr.calculate_FWHM(
min_stars, self.filterGalaxies)
except Exception, ex:
self.log('W', 'offset {0}: {1}'.format(k, ex))
continue
self.log(
'I',
'offset {0} fwhm {1} with {2} stars'.format(k, fwhm, nstars))
focpos.append(k)
fwhm.append(fwhm)
if (fwhm_min is None or fwhm < fwhm_min):
fwhm_MinimumX = k
fwhm_min = fwhm
def findBestFWHM(self,
tries,
defaultFit=H3,
min_stars=95,
ds9display=False,
threshold=2.7,
deblendmin=0.03):
# X is FWHM, Y is offset value
self.focpos = []
self.fwhm = []
fwhm_min = None
self.fwhm_MinimumX = None
keys = tries.keys()
keys.sort()
sextr = sextractor.Sextractor(threshold=threshold,
deblendmin=deblendmin)
for k in keys:
try:
sextr.runSExtractor(tries[k])
fwhm, fwhms, nstars = sextr.calculate_FWHM(
min_stars, self.filterGalaxies)
except Exception, ex:
self.log('W', 'offset {0}: {1}'.format(k, ex))
continue
self.log(
'I',
'offset {0} fwhm {1} with {2} stars'.format(k, fwhm, nstars))
focpos.append(k)
fwhm.append(fwhm)
if (fwhm_min is None or fwhm < fwhm_min):
fwhm_MinimumX = k
fwhm_min = fwhm
def processImage(fn,
d,
obs_id=None,
threshold=2.7,
pr=False,
ds9cat=None,
bysegments=False,
stars=[]):
"""Process image, print its FWHM. Works with multi extension images.
"""
ff = pyfits.fitsopen(fn)
rts2 = rts2comm.Rts2Comm()
if d:
d.set('file mosaicimage iraf ' + fn)
sexcols = [
'X_IMAGE', 'Y_IMAGE', 'MAG_BEST', 'FLAGS', 'CLASS_STAR', 'FWHM_IMAGE',
'A_IMAGE', 'B_IMAGE', 'EXT_NUMBER', 'FLUX_BEST', 'BACKGROUND'
]
c = sextractor.Sextractor(
sexcols,
threshold=threshold,
sexconfig='/home/observer/findfwhm/Sextractor/focus.sex',
starnnw='/home/observer/findfwhm/Sextractor/default.nnw')
c.runSExtractor(fn)
c.sortObjects(2)
for st in stars:
# append distance - none and star number - to star list
st.append(None)
st.append(None)
# dump Sextractor to DS9 catalogue
if ds9cat:
cat = open(ds9cat, 'w')
cat.write('\t'.join(sexcols) + '\n')
for x in c.objects:
cat.write('\t'.join(map(lambda y: str(y), x)) + '\n')
cat.close()
seg_fwhms = map(lambda x: FWHM(), range(0, len(ff) + 1))
for x in c.objects:
if pr:
print '\t'.join(map(lambda y: str(y), x))
segnum = int(x[8])
for st in stars:
if st[0] == segnum:
dist = math.sqrt((x[0] - st[1])**2 + (x[1] - st[2])**2)
if st[4] is None or st[4] > dist:
st[4] = dist
st[5] = x
if x[3] == 0 and x[4] != 0:
if d:
d.set(
'regions',
'tile {0}\nimage; circle {1} {2} 10 # color=green'.format(
segnum, x[0], x[1]))
seg_fwhms[0].addFWHM(x[5], x[6], x[7])
seg_fwhms[segnum].addFWHM(x[5], x[6], x[7])
elif d:
d.set(
'regions',
'# tile {0}\nphysical; point {1} {2} # point = x 5 color=red'.
format(segnum, x[0], x[1]))
# average results
map(FWHM.average, seg_fwhms)
# default suffix
defsuffix = '_KCAM'
try:
defsuffix = '_' + ff[0].header['CCD_NAME']
except KeyError, er:
pass
def runOnImage(self,fn,partial_len=None,interactive=False,sequences_num=15,mag_limit_num=7):
"""
Run algorithm on image. Extract sources with sextractor, and
pass them through sequence finding algorithm, and fit focusing position.
"""
c = sextractor.Sextractor(['NUMBER','X_IMAGE','Y_IMAGE','MAG_BEST','FLAGS','CLASS_STAR','FWHM_IMAGE','A_IMAGE','B_IMAGE'],sexpath='/usr/bin/sextractor',sexconfig='/usr/share/sextractor/default.sex',starnnw='/usr/share/sextractor/default.nnw')
c.runSExtractor(fn)
self.objects = c.objects
# sort by flux/brightness
self.objects.sort(cmp=lambda x,y:cmp(x[3],y[3]))
print 'from {0} extracted {1} sources'.format(fn,len(c.objects))
d = None
if interactive:
d = ds9()
# display in ds9
d.set('file {0}'.format(fn))
for x in self.objects:
d.set('regions','image; point {0} {1} # point=x 5 color=red'.format(x[1],x[2]))
sequences = []
usednum = []
for x in self.objects:
# do not examine already used objects..
if x[0] in usednum:
continue
# find object in a row..
b = self.findRowObjects(x,partial_len)
if b is None:
continue
sequences.append(b)
if d:
d.set('regions select none')
d.set('regions','image; circle {0} {1} 20 # color=yellow tag = sel'.format(x[1],x[2]))
for obj in b:
usednum.append(obj[0])
if d:
print 'best mag: ',x[3]
d.set('regions select group sel')
d.set('regions delete select')
for obj in b:
if obj[0] is None:
d.set('regions','image; point {0} {1} # point=boxcircle 15 color = red'.format(obj[1],obj[2]))
else:
d.set('regions','image; circle {0} {1} 10 # color = green'.format(obj[1],obj[2]))
if len(sequences) > sequences_num:
break
# if enough sequences were found, process them and try to fit results
if len(sequences) > sequences_num:
# get median of FWHM from each sequence
fwhm=[]
for x in range(0,len(self.shifts) + 1):
fa = []
for o in sequences:
if o[x][0] is not None:
fa.append(o[x][6])
# if length of magnitude estimates is greater then limit..
if len(fa) >= mag_limit_num:
m = numpy.median(fa)
fwhm.append(m)
else:
if interactive:
print 'removing focuser position, because not enough matches were found',self.focpos[x]
self.focpos.remove(self.focpos[x])
# fit it
foc = focusing.Focusing()
res,ftype = foc.doFitOnArrays(fwhm,self.focpos,focusing.H2)
if interactive:
print res,ftype
foc.plotFit(res,ftype)