def _medFilterMaskedWgtIm(self, imname, medfiltsize=5):
""" Method to median filter an image, intended to be the
detection weight image. Default filter size is 5 pix.
It first makes a mask from the input image so that no input
zero pixels (which are specially flagged as having no weight)
become nonzero after filtering.
"""
tmpMed = '_tmpMED.fits'
tmpMask = '_tmpMask.fits'
iraf.flpr()
iraf.flpr(iraf.median)
iraf.flpr(iraf.imcalc)
iraf.unlearn(iraf.median)
iraf.median.input = imname
iraf.median.output = tmpMed
iraf.median.xwindow = medfiltsize
iraf.median.ywindow = medfiltsize
iraf.median.mode = 'h'
iraf.median()
iraf.unlearn(iraf.imcalc)
iraf.imcalc(imname, tmpMask, "if im1 .eq. 0 then 0 else 1")
_instring = tmpMed+','+tmpMask
iraf.imcalc(_instring, tmpMed, "im1 * im2")
os.rename(tmpMed,imname)
os.remove(tmpMask)
iraf.flpr(iraf.median)
iraf.flpr(iraf.imcalc)
iraf.flpr()
return
def bootstrapZP(image,cat='zp.cat',refcat='ref.cat',refzp=1.0):
(ra,dec,mag,star)=np.loadtxt(cat,usecols=(0,1,2,3),unpack=True,
dtype=np.dtype([('ra','<f10'),('dec','<f10'),('mag','<f10'),
('star','<f10')]))
(rra,rdec,rmag,rstar)=np.loadtxt(refcat,usecols=(0,1,2,3),unpack=True,
dtype=np.dtype([('rra','<f10'),('rdec','<f10'),('rmag','<f10'),
('rstar','<f10')]))
#Grab only stars from the reference catalog
refgood=np.where((rstar >= 0.98) & (rmag != 99.0) & (rmag > 17.0) & (rmag < 22.5))
refcat=SkyCoord(ra=rra[refgood]*u.degree,dec=rdec[refgood]*u.degree)
#Sort through and remove anything that is not a star and is not isolated
#from other sources in the input catalog
catgood=np.where((star >= 0.98) & (mag != 99.0))
cat=SkyCoord(ra=ra[catgood]*u.degree,dec=dec[catgood]*u.degree)
idx,d2d,_=cat.match_to_catalog_sky(refcat)
_,d2d2,_=cat.match_to_catalog_sky(cat,2)
final=np.where((d2d.arcsec < 0.5) & (d2d2.arcsec >= 5.0))
diff=rmag[refgood][idx][final]-mag[catgood][final]
imgZP=np.mean(diff)
print '\n\tUsing '+str(len(diff))+' stars to calculate ZP...'
print '\tMean ZP: '+str(round(imgZP,3))+' mag\n'
scaleFactor=10.0**(0.4*(refzp-imgZP))
iraf.unlearn('imcalc')
iraf.imcalc(image,image[:-5]+'_scaled.fits','im1*'+str(scaleFactor))
def mkScaleImage(image):
hdr=pf.open(image)[0].header
(mult,add)=(hdr['MSCSCALE'],hdr['MSCZERO'])
iraf.unlearn('imcalc')
if os.path.exists(image[:-5]+'_scale.fits'):
os.remove(image[:-5]+'_scale.fits')
iraf.imcalc(image,image[:-5]+'_scale.fits','(im1+'+str(add)+')*'+str(mult))
def mkWeightMap(image,search=''):
if search != '':
imname=glob.glob(search)[0]
else:
imname=image
iraf.unlearn('imcalc')
iraf.imcalc(imname,'weight.fits','if im1 .eq. 0 then 0.0 else 1.0')
def doImcalc(self, input, output, operation): ''' Simple interface for IRAF task imcalc. Can be used for simple arithmetic operations between images. This could be rewritten with PyFits and NumPy to give broader selection of operations. ''' self.input = input self.output = output self.operation = operation I.imcalc(self.input, self.output, self.operation)
def doImcalc(self, input, output, operation):
'''
Simple interface for IRAF task imcalc. Can be used for simple
arithmetic operations between images. This could be rewritten with
PyFits and NumPy to give broader selection of operations.
'''
self.input = input
self.output = output
self.operation = operation
I.imcalc(self.input, self.output, self.operation)
def make_covar_map(idmap, catalog):
"""
Given the IDMAP image, replace the ID by the covariance index of the object,
in order to visualize the distribution of covariance indices in the image.
"""
c = sextractor(catalog)
cvdict = {}
for i in range(len(c)):
cvdict[c.objectid[i]] = c.maxcvratio[i]
# Now make covariance id map
covar_idmap = 'covar_' + idmap
if os.path.exists(covar_idmap):
os.remove(covar_idmap)
iraf.imcalc(idmap, covar_idmap, 'im1*1.0', pixtype='double')
#os.system('cp %s %s' % (idmap, covar_idmap))
h = pyfits.open(covar_idmap, mode='update')
data = h[0].data
datacopy = np.copy(data)
for k, v in cvdict.iteritems():
datacopy[data == k] = v
h[0].data = datacopy
h.flush()
h.close()
def scaleZP(inputList,reference,logfile='mosaic_log'):
refZP=pf.open(reference)[0].header['ZEROPT']
refZPerr=pf.open(reference)[0].header['ZEROERR']
print '\nNormalizing all images in stack to common zeropoint...'
newStack=[]
for x in inputList:
copy=x[:-5]+'_tmp.fits'
hdu=pf.open(x,mode='update')
imgZP=hdu[0].header['ZEROPT']
hdu[0].header['ZEROPT']=refZP
hdu[0].header['ZEROERR']=refZPerr
hdu.flush()
hdu.close()
scaleFactor=10.0**(-0.4*(refZP-imgZP))
iraf.unlearn('imcalc')
iraf.imcalc(x,copy,'im1*'+str(scaleFactor),verbose='yes')
newStack.append(copy)
return (newStack,refZP,refZPerr)
def _do_driz_cr(self, simplefits, mef, Ncombed):
""" find cosmic rays using iraf.dither.driz_cr """
iraf.unlearn(iraf.driz_cr)
inputfits = '_dz_' + simplefits # WZ
#os.rename(simplefits,'temp.fits') #WZ
#os.rename(inputfits,simplefits)
iraf.driz_cr.inlist = simplefits
iraf.driz_cr.group = 0
#pdb.set_trace()
if self.crlower:
iraf.driz_cr.SNR = "4.4 2.0"
iraf.driz_cr.scale = "0.6 0.4"
else:
# iraf.driz_cr.SNR = "5.0 3.0"
# iraf.driz_cr.SNR = "4.8 3.0" #<-- changed from 5.0 to 4.8
# iraf.driz_cr.SNR = "4.7 2.7"
#iraf.driz_cr.SNR = "4.7 2.6"
#iraf.driz_cr.SNR = "4.7 2.4"
# iraf.driz_cr.scale = "0.9 0.5"
# 15/Apr/2002, jpb: changing yet again - does this help?
# iraf.driz_cr.scale = "1.0 0.6"
# iraf.driz_cr.scale = "1.2 0.8"
# 18/Jun/2002 now that the blot rotation problem is fixed...
# iraf.driz_cr.scale = "1.0 0.6"
# iraf.driz_cr.scale = "1.2 0.6"
#iraf.driz_cr.scale = "1.3 0.6"
# ARGH, still problems with centers of some stars
# has to do with the smooth of the derivative in driz/blotting
# going with this 14/Nov/2002:
# iraf.driz_cr.SNR = "4.5 2.4" tweak again 18/March/2003
iraf.driz_cr.SNR = "4.5 2.1"
iraf.driz_cr.scale = "1.7 0.7"
if self.verbose:
print "Using driz_cr S/N rejection thresholds: ", iraf.driz_cr.SNR
print "and derivative scales: ", iraf.driz_cr.scale
self.logfile.write(' pyblot driz_cr rej thresh: ' + iraf.driz_cr.SNR +
' deriv scales: ' + iraf.driz_cr.scale)
iraf.driz_cr.backg = self.skyKey
# get gain, readnoise from image header
_tmp_hdr_gain_, _tmp_single_rn = _gain_rn(mef, self.logfile, ext=0)
# increase readnoise by sqrt of # of constituent raw images
iraf.driz_cr.rn = math.sqrt(1.0 * Ncombed) * _tmp_single_rn
# only use the header gain if hdrGain has been set
if self.hdrGain:
iraf.driz_cr.gain = _tmp_hdr_gain_
else:
iraf.driz_cr.gain = 1.0
del _tmp_hdr_gain_, _tmp_single_rn
iraf.driz_cr.mode = 'h'
print '\n ***> driz_cr using gain,rn_tot = ', iraf.driz_cr.gain, iraf.driz_cr.rn
print ' ***> (NCOMBINE = ' + str(Ncombed) + ')'
plmask = string.split(simplefits, '.')[0] + '_cr.pl'
fitsmask = string.split(simplefits, '.')[0] + '_cr.fits'
try:
os.remove(plmask)
except:
pass
try:
os.remove(fitsmask)
except:
pass
if self.verbose:
# print ' running driz_cr() on ',simplefits
print ' running driz_cr() on ', inputfits #WZ
print ' output: ', plmask
#pdb.set_trace()
iraf.driz_cr()
#os.rename(simplefits,inputfile) # WZ
#os.rename('temp.fits',simplefits)
# convert mask from pixel list to short integer fits
iraf.unlearn(iraf.imcalc)
iraf.imcalc.pixtype = 'short'
# following line is not right -- drizzle uses 1=good, 0=bad
#iraf.imcalc(plmask, fitsmask, "if im1 .gt. 0 then 0 else 1")
if self.verbose:
print ' converting', plmask, 'to', fitsmask
iraf.imcalc(plmask, fitsmask, "if im1 .eq. 0 then 0 else 1")
# the pixel-list version can be removed immediately
if self.clean_up:
os.remove(plmask)
# finally, save the mask name in a dictionary
nzero = numMaskZeros(fitsmask)
self.crmasks[simplefits] = (fitsmask, nzero)
if self.verbose:
print 'Masked ' + str(nzero) + ' CR pixels in ' + simplefits
return fitsmask
def _medDriz(self, parList, usemask=1):
"""median-combined separate drizzle images. there is an iraf dependant kludge
in here involving the inputString which the a string of input files for imcombine.
iraf cannot apparently handle a string over a certain size (guessing 512 char).
So, we now write out a temp file, imcombine_input, which is just a list of the
input files imcombine is use. We use the iraf idiom input = "@file" to get this
task to run. So far it seems to work.
"""
drizList = []
maskList = []
iraf.flpr('imcombine')
iraf.unlearn(iraf.imcalc)
iraf.imcalc.pixtype = 'short'
self.logfile.write("Entered _medDriz for asn number %d." %
(self.runNum))
for ii in range(len(parList)):
for jj in range(ii + 1, len(parList)):
if parList[ii]['outdata'] == parList[jj]['outdata']:
raise KeyError, "requested to median stack same images!"
drizList.append(parList[ii]['outdata'])
if usemask:
plmask = string.split(parList[ii]['outcontext'],
'.')[0] + '.pl'
try:
os.remove(plmask)
except:
pass
if self.verbose:
print 'making', plmask, 'from', parList[ii][
'outcontext'], '...'
iraf.imcalc(parList[ii]['outcontext'], plmask,
"if im1 .eq. 0 then 0 else 1")
maskList.append(plmask)
self.removeList.append(plmask)
# construct input list and add masks info to the headers
inputString = drizList[0]
# ***> NOTE! If list too big, imcombine crashes!
# ***> NOTE! define 76 as a safe maximum, or 80...
MAX_IM = 80
NumTot = len(drizList)
NumIm = min(NumTot, MAX_IM)
for ii in range(NumIm):
if ii > 0:
inputString = inputString + ',' + drizList[ii]
if usemask:
fUtil.fixHeader(drizList[ii], [('BPM', maskList[ii])])
if self.verbose:
print 'median stacking: ', inputString
if usemask: print ' with masks: ', maskList
#if that all checks out, go ahead and median
iraf.unlearn(iraf.imcombine)
# 15/Apr/2002, jpb: want to keep all the medriz's around for CR-rej debugging
outfile = 'medriz_' + str(self.runNum) + '.fits'
# self.removeList.append(outfile)
try:
os.remove(outfile)
except:
pass
# temp file for iraf input because the list might be too big.
filekludge = open("imcombine_input", "w")
newinputList = inputString.split(',')
if (NumIm != len(newinputList)):
errtxt = "ERROR! Error: NumIm != len(newinputList) in _medDriz ?!"
print errtxt
self.logfile.write(errtxt)
for item in newinputList:
filekludge.write(item + "\n")
filekludge.close()
#iraf.imcombine.input = inputString # this is what we used to do.
iraf.imcombine.input = "@imcombine_input"
iraf.imcombine.output = outfile
# iraf.imcombine.plfile = ''
iraf.imcombine.sigma = ''
iraf.imcombine.combine = 'median'
iraf.imcombine.reject = 'minmax'
iraf.imcombine.outtype = 'real'
iraf.imcombine.offsets = 'none'
if usemask:
iraf.imcombine.masktype = 'badvalue'
else:
iraf.imcombine.masktype = 'none'
iraf.imcombine.maskvalue = 0.
iraf.imcombine.scale = 'exposure'
iraf.imcombine.expname = 'EXPTIME'
iraf.imcombine.nkeep = 1
# paradoxically, this is not what we want
# NumIm = len(drizList)/self.imNsci
# imcombine considers the total number of images being
# stacked, not the number at any given point!
# NOTE: nhigh must be >= NumIm/2 if cr rejection to be done everywhere
if NumIm == 1: #1,2
iraf.imcombine.nlow = 0
iraf.imcombine.nhigh = 0
elif NumIm == 2: # 2
iraf.imcombine.nlow = 0
iraf.imcombine.nhigh = 1
elif NumIm == 3: # 3
iraf.imcombine.nlow = 0
iraf.imcombine.nhigh = 2
elif NumIm == 4: # 4 HRC; 2 WFC
iraf.imcombine.nlow = 0
iraf.imcombine.nhigh = 3
elif NumIm == 5: # 5 HRC
iraf.imcombine.nlow = 1
iraf.imcombine.nhigh = 3
elif NumIm == 6: # 6 HRC; 3 WFC
iraf.imcombine.nlow = 1
iraf.imcombine.nhigh = 4
elif NumIm == 7: # 7 HRC
iraf.imcombine.nlow = 1
iraf.imcombine.nhigh = 5
elif NumIm < 10: # 8,9 HRC; 4 WFC
iraf.imcombine.nlow = 1
iraf.imcombine.nhigh = 6
elif NumIm < 12: # 10,11 HRC; 5 WFC
iraf.imcombine.nlow = 2
iraf.imcombine.nhigh = 7
# next added/changed
else:
iraf.imcombine.nlow = (NumIm + 2) / 4 - 1
iraf.imcombine.nhigh = 3 * NumIm / 4
iraf.imcombine.mode = 'h'
self.logfile.write(self.modName+' calling imcombine. come in imcombine. NumIm/nlo/nhi: '+\
str(NumIm)+' '+str(iraf.imcombine.nlow)+' '+str(iraf.imcombine.nhigh)+\
(' [NumTot=%d]'%(NumTot)))
iraf.imcombine()
if self.verbose:
print 'NumIm = %d nlow,high: %d %d' % (
NumIm, iraf.imcombine.nlow, iraf.imcombine.nhigh)
print 'median image', outfile, 'created'
self.logfile.write('median image ' + outfile +
' created. Removing imcombine_input temp file.')
print "removing imcombine_input temp file."
try:
os.remove("imcombine_input")
except:
pass
return outfile
process="no")
# creating master flat
unlearn(iraf.flatcombine)
print "> creating master flat image"
iraf.flatcombine("Flat*.fit",
output="master_flat_temp.fits",
combine="median",
reject="crreject",
ccdtype="flat",
process="no",
delete="no",
scale="median")
# removing 0s and negative values with standard ones (-9.0)
iraf.imcalc("master_flat_temp.fits",
output="master_flat.fits",
equals="if (im1.le.0.0) then -9.0 else im1",
verbose="no")
# creating the necessary lists for dark and flat processing
rd = open('rawdata.irafl', 'w')
cd = open('cordata.irafl', 'w')
for im in data:
# print im
name, ext = os.path.splitext(im)
# print name, ext
rd.write(name + ext + "\n")
cd.write(name + '_cor' + ext + '\n')
# print name+'_cor'+ext+'\n'
rd.close()
def stacking(cllist,zpofflist,ref,zprefoff=0.0,stackname='stack',shiftsize=400):
"""
"""
#Reset the IRAF tasks used in this routine.
iraf.unlearn('imcalc')
iraf.unlearn('imcombine')
iraf.unlearn('imreplace')
iraf.unlearn('xyxymatch')
iraf.unlearn('geomap')
iraf.unlearn('geotran')
iraf.unlearn('imcopy')
#Find reference image in reference directory. Check to make
#sure that it is actually the image and not the mask file!
#Grab the mask for adding to the mask list now.
(refimg,refmask,expmap)=classify(ref+'/tu*.fits')
zpref=pf.open(refimg)[0].header['MAGZERO']
# zprefoff=NewfirmZPoffset[ref.split('/')[-1]]
zprefoff=float(zprefoff)
#Get 2MASS PSC positions for reference cluster image.
catalog=get2masspsc(refimg)
foo=file_check(ref+'/2mass_ref_stars.cdt',delete=True)
foo=open(ref+'/2mass_ref_stars.cdt','w')
for y in catalog:
data=y.split()
foo.write(data[6]+'\t'+data[7]+'\n')
foo.close()
#Create lists for files to be input into the stacking routine.
foo=file_check('matchlist',delete=True)
foo=file_check('scalelist',delete=True)
foo=file_check('shiftlist',delete=True)
foo=file_check('masklist',delete=True)
foo=file_check('shiftmask',delete=True)
foo=file_check('expmaplist',delete=True)
(matchlist,scalelist,shiftlist,masklist,
shiftmask,finalmasks,stacklist,stackmask,
finalmasks2,expmaplist,shiftexp,expmaplist2)=(open('matchlist','w'),open('scalelist','w'),
open('shiftlist','w'),open('masklist','w'),
open('shiftmask','w'),open('finalmasks','w'),
open('stacklist','w'),open('stackmask','w'),
open('finalmasks2','w'),open('expmaplist','w'),
open('shiftexp','w'),open('expmaplist2','w'))
(xsize,ysize)=(np.array([]),np.array([]))
#Step through all of the input cluster directories.
i=0
for x in cllist:
#Find the image, mask, and exposure map files. Get zeropoints and
#scale image to the reference image.
scaleimg=x+'/scaled_to_'+ref.split('/')[-1]+'.fits'
foo=file_check(scaleimg,delete=True)
(img,mask,expmap)=classify(x+'/tu*.fits')
imgzp=pf.open(img)[0].header['MAGZERO']
(xs,ys)=(pf.open(img)[0].header['NAXIS1'],pf.open(img)[0].header['NAXIS2'])
(xsize,ysize)=(np.append(xsize,xs),np.append(ysize,ys))
imgzpoff=float(zpofflist[i])
# imgzpoff=NewfirmZPoffset[x.split('/')[-1]]
scale=scalecounts(imgzp+imgzpoff,zpref+zprefoff)
iraf.imcalc(img,scaleimg,'im1*'+str(scale))
#Get X,Y pixel positions of 2MASS sources from the 2MASS PSC
#in the image. Use these to compute shifts relative to the
#reference image using IRAF task geomap.
foo=file_check(x+'/2mass_ref_stars.cdt',delete=True)
foo=open(x+'/2mass_ref_stars.cdt','w')
catalog=get2masspsc(scaleimg)
for y in catalog:
data=y.split()
foo.write(data[6]+'\t'+data[7]+'\n')
foo.close()
#Match the 2MASS PSC positions with stars in the reference
#image using xyxymatch. The matched source list is then fed
#into geomap to get the X and Y shifts.
foo=file_check(x+'/2mass_matched.cdt',delete=True)
iraf.xyxymatch(x+'/2mass_ref_stars.cdt',ref+'/2mass_ref_stars.cdt',
x+'/2mass_matched.cdt','200.0',verbose='no')
#Append all of the names of the files for the input and output filename
#lists to be passed to IRAF tasks further down the line.
matchlist.write(x+'/2mass_matched.cdt\n')
scalelist.write(scaleimg+'\n')
foo=file_check(x+'/scaled_and_shifted.fits',delete=True)
shiftlist.write(x+'/scaled_and_shifted.fits['+str(shiftsize)+':'+\
str(int(np.max(xsize))+shiftsize)+','+str(shiftsize)+':'+\
str(int(np.max(ysize))+shiftsize)+']\n')
stacklist.write(x+'/scaled_and_shifted.fits\n')
file_check(x+'/mask_tmp.fits',delete=True)
file_check(x+'/expmap_tmp.fits',delete=True)
iraf.imarith(mask+'[1]','*',1000.0,x+'/mask_tmp.fits',pixtype='real')
iraf.imarith(expmap+'[1]','*',1.0,x+'/expmap_tmp.fits',pixtype='real')
offset=2.558435
file_check(x+'/mask_tmp2.fits',delete=True)
iraf.imcalc(x+'/mask_tmp.fits',x+'/mask_tmp2.fits','im1+'+str(offset))
os.remove(x+'/mask_tmp.fits')
masklist.write(x+'/mask_tmp2.fits\n')
file_check(x+'/mask_shift.fits',delete=True)
shiftmask.write(x+'/mask_shift.fits['+str(shiftsize)+':'+\
str(int(np.max(xsize))+shiftsize)+','+str(shiftsize)+':'+\
str(int(np.max(ysize))+shiftsize)+']\n')
stackmask.write(x+'/mask_shift.fits\n')
finalmasks.write(x+'/mask_final.fits\n')
finalmasks2.write(x+'/mask_final.fits[0]\n')
expmaplist.write(x+'/expmap_tmp.fits[0]\n')
shiftexp.write(x+'/expmap_shift.fits['+str(shiftsize)+':'+\
str(int(np.max(xsize))+shiftsize)+','+str(shiftsize)+':'+\
str(int(np.max(ysize))+shiftsize)+']\n')
expmaplist2.write(x+'/expmap_shift.fits\n')
i += 1
#Close all of the input and output filename lists to be passed to IRAF tasks.
matchlist.close()
scalelist.close()
stacklist.close()
masklist.close()
shiftmask.close()
finalmasks.close()
shiftlist.close()
stackmask.close()
finalmasks2.close()
expmaplist.close()
expmaplist2.close()
shiftexp.close()
#Get the shifts between all input files (including the reference) and the
#reference image itself.
foo=file_check('shift.db',delete=True)
iraf.geomap('@matchlist','shift.db',1.0,np.max(xsize),
1.0,np.max(ysize),fitgeometry='shift',interactive='no',
maxiter=2,function='legendre',verbose='no')
#Shift the input images (including the reference) and associated mask files
#to a common pixel grid. Add some padding around the individual frames (-99
#in the images, 1 in the bad pixel masks) to ensure that the images will
#combine properly.
(maxx,maxy)=(np.max(xsize)+shiftsize+100.0,np.max(ysize)+shiftsize+100.0)
iraf.geotran('@scalelist','@shiftlist','shift.db','@matchlist',geometry='linear',
boundary='constant',nlines=maxy,ncols=maxx,constant=-99.0)
iraf.geotran('@masklist','@shiftmask','shift.db','@matchlist',geometry='linear',
boundary='constant',nlines=maxy,ncols=maxx,constant=1000.0,
nxblock=10000,nyblock=10000)
iraf.geotran('@expmaplist','@shiftexp','shift.db','@matchlist',geometry='linear',
boundary='constant',nlines=maxy,ncols=maxx,constant=0.)
for x in cllist:
file_check(x+'/mask_final.fits',delete=True)
shutil.copy(x+'/mask_shift.fits',x+'/mask_final.fits')
iraf.hedit(x+'/scaled_and_shifted.fits[0]','BPM',x+'/mask_final.fits[0]',
add='yes',update='yes',verify='no')
iraf.imreplace('@finalmasks2',0,upper=offset)
iraf.imreplace('@finalmasks2',1,lower=offset)
file_check(stackname,delete=True)
file_check(stackname[:-5]+'_mask.pl',delete=True)
file_check(stackname[:-5]+'_expmap.fits',delete=True)
iraf.imcombine('@stacklist',stackname,bpmasks=stackname[:-5]+'_bpm',
masktype='goodval',reject='none',mclip='yes',lthresh='INDEF',hthresh='INDEF',
hsigma=10.0,lsigma='INDEF',nrejmasks=stackname[:-5]+'_nrej',
sigmas=stackname[:-5]+'_sigma',grow=2.5,nkeep=1,blank=-99.0,gain=8.0,rdnoise=35.0)
iraf.imcombine('@expmaplist2',stackname[:-5]+'_expmap.fits',combine='sum')
hdu=pf.open(stackname,mode='update')
hdu[0].header['BPM']=stackname.split('/')[-1][:-5]+'_mask.pl'
hdu[0].header['MAGZERO']=zpref+zprefoff
hdu.close()
#Fix the WCS information in the stacked image.
copyhead(stackname,refimg,offset=shiftsize)
applywcs(stackname,stackname[:-5]+'_wcs.fits')
trash=['matchlist','scalelist','shiftlist','masklist','shiftmask','finalmasks',
'shift.db','stacklist','finalmasks2','stackmask','tmp_wcs.fits','expmaplist',
'expmaplist2','shiftexp']
for x in trash:
os.remove(x)
def subtract_constant_bkgd(input_image, mask_image, output_image, growsig=0,
subreg=None, savemask=True, boxsize=40,
boxcenter=None, fitgauss=True, show_plot=False):
"""
Subtract a constant (median) background not masked by the mask_image, where
mask_image > 0 means the pixels are masked. The mask image should contain
at least the segmentations of sources, and could also define the subregion
of an image.
boxsize: the size of the square box centered around the object within which
one determines the median background
"""
#if subreg == None:
# img = pyfits.getdata(input_image)
# subreg = [1, img.shape[0], 1, img.shape[1]]
# output_image = os.getcwd() + '/' + os.path.split(output_image)[-1]
img1 = pyfits.getdata(input_image)
mask = pyfits.getdata(mask_image)
if growsig > 0:
mask = ssb.growmask(mask, growsig=growsig)
mask = np.where(mask, 1, 0)
maskout = os.path.splitext(mask_image)[0] + '_out.fits'
if os.path.exists(maskout):
os.remove(maskout)
hdu = pyfits.PrimaryHDU(mask)
hdu.writeto(maskout)
if subreg != None:
xmin, xmax, ymin, ymax = subreg
elif boxsize != None:
xc, yc = boxcenter
xmin = xc - boxsize / 2
xmax = xc + boxsize / 2
ymin = yc - boxsize / 2
ymax = yc + boxsize / 2
else:
xmin = 0
xmax = img1.shape[0]
ymin = 0
ymax = img1.shape[1]
print "Determining median background within the region:"
print "(xmin, xmax, ymin, ymax) = (%d, %d, %d, %d)" % (xmin, xmax, ymin, ymax)
mask_subreg = np.ones(img1.shape, 'int')
mask_subreg[ymin-1:ymax,xmin-1:xmax] = 0
mask = np.logical_or(mask, mask_subreg)
mask = np.where(mask==True, 1, 0)
img1_masked = np.ma.masked_array(img1, mask=mask)
bgpix = np.compress(mask.ravel()==0, img1.ravel())
y = np.histogram(bgpix, bins=30)
# figure out if the best-fit peak is too far away from the peak of the
# histogram, which suggests bimodality
ipeak = np.argsort(y[0])[-1]
# calculate the bin center of the peak of histogram
xmode = y[1][ipeak] + (y[1][1] - y[1][0]) / 2.
if fitgauss:
## Determin local background by fitting a Gaussian...
bkgd_median, bkgd_sig = gauss.fitgauss(bgpix, clip=True, clipsigma=3.0)
# if np.abs(bkgd_median - xmode) >= 0.5 * bkgd_sig:
# do something...?
else:
## ... or by just calculating the median & standard deviation
bkgd_median = np.ma.median(img1_masked)
bkgd_sig = np.ma.std(img1_masked)
num_bkgd = np.sum(mask == 0)
print "Median sky background: %.6f" % bkgd_median
print "1-sigma of sky background: %.6f" % bkgd_sig
print "Number of sky pixels for determining background: %d" % num_bkgd
if os.path.exists(output_image):
os.remove(output_image)
iraf.imcalc(input_image, output_image, 'im1 - %f' % bkgd_median)
h = pyfits.open(output_image, mode='update')
h[0].header['MEDBKGD'] = bkgd_median
h[0].header['SIGBKGD'] = bkgd_sig
h[0].header['NUMBKGD'] = num_bkgd
h.flush()
h.close()
# Show background pixel histogram
print "Number of background pixels:", len(bgpix)
fig = plt.figure()
ax = fig.add_subplot(111)
y = ax.hist(bgpix, bins=y[1], histtype='step', lw=2.0)
# also plot the best-fit gaussian
x = np.linspace(bgpix.min(), bgpix.max(), num=100)
g = gauss.gauss(x, bkgd_median, bkgd_sig)
g = g * np.max(y[0]) / g.max()
ax.plot(x, g, color='red', lw=2.0)
bkgd_str = "median=%.2e\nsigma=%.2e" % (bkgd_median, bkgd_sig)
ax.text(0.95, 0.95, bkgd_str, transform=ax.transAxes, ha='right', va='top',
bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
ymax = ax.get_ylim()[1]
ax.plot([bkgd_median, bkgd_median], [0., ymax], ls='--', lw=2.0, color='black')
plt.title('Background pixel values', size=24)
if not show_plot:
plt.close('all')
if savemask:
maskname = os.path.splitext(output_image)[0] + '_mask.fits'
if os.path.exists(maskname):
os.remove(maskname)
hdu2 = pyfits.PrimaryHDU(mask)
hdu2.writeto(maskname)
return fig
def combine(do_cti=False, doreduce=True, doshifts=True):
if do_cti:
os.system('stis_cti --crds_update')
if doreduce:
# Defringing didn't seem to converge because of the low S/N
stistools.ocrreject.ocrreject('oc0102070_flc.fits',
'oc0102070_crc.fits')
iraf.normspflat(inflat='oc0102070_crc.fits',
outflat='oc0102070_nsp.fits',
do_cal='no')
iraf.imcalc(input='oc0102070_nsp.fits',
output='temp_nsp.fits',
equals='if(x .lt. 250) then 1 else im1')
iraf.imcopy('temp_nsp.fits[1][1:250,*]',
'oc0102070_nsp.fits[1][1:250,*]')
#iraf.defringe('oc0102060_flc.fits', 'oc0102070_nsp.fits', 'oc0102060_dfr.fits')
#for each image
for im in ['oc0102050_flc', 'oc0102060_flc']:
outbase = 'blue'
if im[:-4] == 'oc0102060':
outbase = 'red'
#reset the aperture table to the newer file (we maybe should check this)
pyfits.setval(im + '.fits',
'APERTAB',
value='oref$y2r1559to_apt.fits')
pyfits.setval(im + '.fits',
'SPTRCTAB',
value='oref$qa31608go_1dt.fits')
# fixpix any negative values. In principle some of this noise
# could be real, but I have found that is often not the case
hdu = fits.open(im + '.fits')
mask1 = hdu[1].data < -20
mask2 = hdu[4].data < -20
hdu.close()
fits.writeto(outbase + 'mask1.fits',
mask1.astype('i'),
clobber=True)
fits.writeto(outbase + 'mask2.fits',
mask2.astype('i'),
clobber=True)
iraf.unlearn(iraf.fixpix)
iraf.fixpix(im + '[1]', outbase + 'mask1.fits')
iraf.unlearn(iraf.fixpix)
iraf.fixpix(im + '[4]', outbase + 'mask2.fits')
# Subtract off the median value
hdu = fits.open(im + '.fits', mode='update')
hdu[1].data -= np.median(hdu[1].data)
hdu[4].data -= np.median(hdu[4].data)
readnoise1 = 1.4826 * np.median(np.abs(hdu[1].data))
readnoise2 = 1.4826 * np.median(np.abs(hdu[4].data))
# Cosmic ray reject both images using scrappy
# Make sure to treat the noise in a sensible way
crmask1, clean1 = detect_cosmics(hdu[1].data,
readnoise=readnoise1,
sigclip=5,
objlim=5,
sigfrac=0.8,
fsmode='median',
psfmodel='gaussy',
psffwhm=2.,
cleantype='idw')
crmask2, clean2 = detect_cosmics(hdu[4].data,
readnoise=readnoise2,
sigclip=5,
objlim=5,
sigfrac=0.8,
fsmode='median',
psfmodel='gaussy',
psffwhm=2.,
cleantype='idw')
hdu.flush()
hdu.close()
fits.writeto(outbase + '_crmask1.fits',
crmask1.astype('i'),
clobber=True)
fits.writeto(outbase + '_crmask2.fits',
crmask2.astype('i'),
clobber=True)
# Run fixpix on the frames
iraf.unlearn(iraf.fixpix)
iraf.fixpix(im + '[1]', outbase + '_crmask1.fits')
iraf.unlearn(iraf.fixpix)
iraf.fixpix(im + '[4]', outbase + '_crmask2.fits')
if outbase == 'red':
iraf.mkfringeflat('oc0102060_flc.fits',
'oc0102070_nsp.fits',
'oc0102070_frr.fits',
beg_scale=0.6,
end_scale=1.5,
scale_step=0.01,
beg_shift=-3.0,
end_shift=3.0,
shift_step=0.05)
iraf.defringe('oc0102060_flc.fits', 'oc0102070_frr.fits',
'oc0102060_dfr.fits')
#Run x2d on the flt frame
stistools.x2d.x2d(input='oc0102060_dfr.fits',
output=im[:-4] + 'x2d.fits')
else:
stistools.x2d.x2d(input='oc0102050_flc.fits',
output=im[:-4] + 'x2d.fits')
h = pyfits.open(im[:-4] + 'x2d.fits', mode='update')
#Replace all of the bad pixels in the image by -666 based on the DQ array
#save them to a new file
#Throw away bad reference file pixels and saturated pixels. None of the other error codes
#were in the first file so I haven't included them here, but we might want to
d = h[3].data
badpix = logical_and(
bitwise_and(d, 256) == 256,
bitwise_and(d, 512) == 512)
h[1].data[badpix] = -10000
d = h[6].data
badpix = logical_and(
bitwise_and(d, 256) == 256,
bitwise_and(d, 512) == 512)
h[4].data[badpix] = -10000
h.flush()
# Trim the images
for i in range(1, 7):
h[i].data = h[i].data[100:-100, 100:-100]
h[i].header['CRPIX1'] -= 100
h[i].header['CRPIX2'] -= 100
h.flush()
h.close()
# Combine the images
iraf.unlearn(iraf.imcombine)
iraf.imcombine(input=im[:-4] + 'x2d[1],' + im[:-4] + 'x2d[4]',
output=outbase + '_com.fits',
reject='crreject')
hdu = pyfits.open(outbase + '_com.fits')
mask = hdu[0].data == 0.0
hdu.close()
fits.writeto(outbase + '_mask.fits',
mask.astype('i'),
clobber=True)
iraf.unlearn(iraf.fixpix)
iraf.fixpix(outbase + '_com.fits', outbase + '_mask.fits')
iraf.unlearn(iraf.apall)
iraf.apall(input=outbase + '_com',
output='13dh_' + outbase,
review='no',
nsum=-500,
b_order=1,
b_function='legendre',
b_niterate=30,
b_naverage=-21,
nfind=1,
t_order=3,
background='median',
weights='variance',
skybox=100)
iraf.splot(outbase + '[SCI]')
def subBack(image,bkg):
iraf.unlearn('imcalc')
if os.path.exists(image[:-5]+'_bkgsub.fits'):
os.remove(image[:-5]+'_bkgsub.fits')
iraf.imcalc(image+','+bkg,image[:-5]+'_bkgsub.fits','im1-im2')
def sigmap(inlist,
sigmap,
expmap='none',
whtmap='none',
inpref='',
ffpref='',
objmask='none',
reject='sigclip',
fscale=False,
fbase=100,
fhead='F1',
gain=5.6):
# check output image
if os.access(sigmap, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing image (%s)' % sigmap
return 1
if os.access(expmap, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing image (%s)' % expmap
return 1
if os.access(whtmap, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing image (%s)' % whtmap
return 1
# check input image list
inimg_arr = check_input(inlist, inpref)
if isinstance(inimg_arr, int):
return 1
# check input image list
ffimg_arr = check_input2(inlist, ffpref)
if isinstance(ffimg_arr, int):
return 1
# get array size
im = pyfits.open(inimg_arr[0])
nx = im[0].header['NAXIS1']
ny = im[0].header['NAXIS2']
im.close()
# check geomap data
dx = []
dy = []
gmp_arr = []
gmp2_arr = []
dbs_arr = []
for i in range(len(inimg_arr)):
fname, ext = os.path.splitext(inimg_arr[i])
gmp = fname + '.gmp'
gmp2 = fname + '.gmp2'
dbs = fname + '.dbs'
gmp_arr.append(gmp)
gmp2_arr.append(gmp2)
dbs_arr.append(dbs)
if not os.access(gmp, os.R_OK):
print >> sys.stderr, 'geomap file (%s) does not exist' % (gmp)
return 1
if not os.access(dbs, os.R_OK):
print >> sys.stderr, 'database file (%s) does not exist' % (dbs)
return 1
if not os.access(gmp2, os.R_OK):
print >> sys.stderr, 'modified geomap file (%s) does not exist' % (
gmp2)
return 1
fgmp = open(gmp)
nl = 1
dx_ave = 0.0
dy_ave = 0.0
for line in fgmp:
if not line.startswith('#'):
param = line[:-1].split()
if len(param) != 4:
print >> sys.stderr, 'Invalid format in line %d of %s: %s' % (
nl, gmp, line[:-1])
fgmp.close()
return 1
else:
if isfloat(param[0]) == False or isfloat(
param[1]) == False or isfloat(
param[2]) == False or isfloat(
param[3]) == False:
print >> sys.stderr, 'failed to decode line %d of %s: %s' % (
nl, gmp, line[:-1])
fgmp.close()
return 1
else:
dx_ave += float(param[0]) - float(param[2])
dy_ave += float(param[1]) - float(param[3])
nl += 1
#print inimg_arr[i],nl
dx.append(dx_ave / (nl - 1))
dy.append(dy_ave / (nl - 1))
if len(inimg_arr) != len(dx):
print >> sys.stderr, 'number of input images does not match with that of offsets'
return 1
# check object mask
if objmask.lower() == 'none':
objmask = ''
else:
objmask_arr = check_inpref(objmask, inimg_arr)
if isinstance(objmask_arr, int):
return 1
# prepare for temporary file
tmp = tempfile.NamedTemporaryFile(suffix='', prefix='', dir='/tmp')
tmp_prefix = tmp.name
tmp.close()
# get large array size and combined image size
ret = get_large_region(nx, ny, dx, dy)
if len(ret) != 6:
print >> sys.stderr, 'failed to get large array size'
return 1
x_size = ret[0]
y_size = ret[1]
xcmin = ret[2]
xcmax = ret[3]
ycmin = ret[4]
ycmax = ret[5]
# calculate image region in the large format
xmin = int((x_size - nx) / 2) + 1
xmax = nx + int((x_size - nx) / 2)
ymin = int((y_size - ny) / 2) + 1
ymax = ny + int((y_size - ny) / 2)
# copy image to larger format and shift image #
iraf.unlearn('geomap')
iraf.unlearn('geotran')
# for exposure time weight and flux scaling
expt_arr = []
flux_scale_arr = []
for i in range(len(inimg_arr)):
# load original frame
img = pyfits.open(inimg_arr[i])
# for exposure time weight
try:
t = float(img[0].header['EXP1TIME'])
coadd = float(img[0].header['COADDS'])
expt_arr.append(t * coadd)
except KeyError:
print >> sys.stderr, 'can not read exposure time from the header of %s' % inimg_arr[
i]
img.close()
return 1
# for flux scaling and weight
if fscale:
try:
flux = float(img[0].header[fhead])
except KeyError:
print >> sys.stderr, 'can not read flux keyword (%s) from the header of %s' % (
fhead, inimg_arr[i])
img.close()
return 1
flux_scale_arr.append(fbase / flux)
else:
flux_scale_arr.append(1.0)
img.close()
# preparing weighted variance map for each image
inverse_var_list = tmp_prefix + '_obj.lst'
if os.access(inverse_var_list, os.R_OK):
os.remove(inverse_var_list)
finverse_var = open(inverse_var_list, 'w')
for i in range(len(inimg_arr)):
# mask frame
msk = np.ones((y_size, x_size))
msk[ymin - 1:ymax, xmin - 1:xmax] = 0
hdu = pyfits.PrimaryHDU(msk)
msk_img = pyfits.HDUList([hdu])
msk_fits = tmp_prefix + 'mask' + os.path.basename(inimg_arr[i])
msktr_fits = tmp_prefix + 'masktr' + os.path.basename(inimg_arr[i])
if os.access(msk_fits, os.R_OK):
os.remove(msk_fits)
if os.access(msktr_fits, os.R_OK):
os.remove(msktr_fits)
msk_img.writeto(msk_fits)
msk_img.close()
# transform mask geometry
iraf.geotran(msk_fits,
msktr_fits,
dbs_arr[i],
gmp2_arr[i],
geometr='linear',
boundar='constant',
constant=1)
os.remove(msk_fits)
convert_maskfits_int(msktr_fits, msktr_fits)
# load original frame
ffimg = pyfits.open(ffimg_arr[i])
img = pyfits.open(inimg_arr[i])
# object frame
inverse_var = np.zeros((y_size, x_size))
#print np.median(ffimg[0].data), ffimg_arr[i], gain, expt_arr[i], flux_scale_arr[i]
#print np.median(np.sqrt(ffimg[0].data / (gain * expt_arr[i]))), flux_scale_arr[i]
inverse_var[ymin - 1:ymax, xmin -
1:xmax] = (np.sqrt(ffimg[0].data / (gain * expt_arr[i])) *
flux_scale_arr[i])**-2
hdu = pyfits.PrimaryHDU(inverse_var)
inverse_var_img = pyfits.HDUList([hdu])
inverse_var_img[0].header = img[0].header
inverse_var_img[0].header['bpm'] = msktr_fits
inverse_var_img[0].header.update('EXPTIME', expt_arr[i])
#inverse_var_img[0].header.update('MASKSCAL', expt_arr[i])
#inverse_var_img[0].header.update('MASKZERO', expt_arr[i])
#print 'EXPT = %f' % (expt_arr[i])
inverse_var_fits = tmp_prefix + 'var' + os.path.basename(inimg_arr[i])
inverse_vartr_fits = tmp_prefix + 'vartr' + os.path.basename(
inimg_arr[i])
if os.access(inverse_var_fits, os.R_OK):
os.remove(inverse_var_fits)
if os.access(inverse_vartr_fits, os.R_OK):
os.remove(inverse_vartr_fits)
inverse_var_img.writeto(inverse_var_fits)
inverse_var_img.close()
iraf.geotran(inverse_var_fits,
inverse_vartr_fits,
dbs_arr[i],
gmp2_arr[i],
geometr='linear',
boundar='constant',
constant=0)
finverse_var.write('%s\n' % inverse_vartr_fits)
ffimg.close()
# close file handlers
finverse_var.close()
# sum weighted variance images
tmp_inverse_var_sum = tmp_prefix + 'inverse_var.fits'
if os.access(tmp_inverse_var_sum, os.R_OK):
os.remove(tmp_inverse_var_sum)
tmp_sigma = tmp_prefix + 'sigma.fits'
if os.access(tmp_sigma, os.R_OK):
os.remove(tmp_sigma)
tmp_exp = tmp_prefix + 'exp.fits'
if os.access(tmp_exp, os.R_OK):
os.remove(tmp_exp)
if expmap != 'none':
#iraf.hselect('@'+inverse_var_list,"$I,EXPTIME,MASKSCAL,MASKZERO","yes")
iraf.imcombine('@' + inverse_var_list,
tmp_inverse_var_sum,
expmasks=tmp_exp,
combine='sum',
reject=reject,
masktype='!BPM',
maskvalue=0.0,
expname='EXPTIME')
else:
iraf.imcombine('@' + inverse_var_list,
tmp_inverse_var_sum,
combine='sum',
reject=reject,
masktype='!BPM',
maskvalue=0.0)
# calculate sigma
iraf.stsdas()
iraf.imcalc(tmp_inverse_var_sum,
tmp_sigma,
'sqrt(1.0/im1)',
pixtype='double')
# cut image
iraf.unlearn('imcopy')
cut_sig = '%s[%d:%d,%d:%d]' % (tmp_sigma, xcmin, xcmax, ycmin, ycmax)
iraf.imcopy(cut_sig, sigmap)
if expmap != 'none':
cut_exp = '%s[%d:%d,%d:%d]' % (tmp_exp, xcmin, xcmax, ycmin, ycmax)
iraf.imcopy(cut_exp, expmap)
# calc weight map
if whtmap != 'none':
cut_wht = '%s[%d:%d,%d:%d]' % (tmp_inverse_var_sum, xcmin, xcmax,
ycmin, ycmax)
iraf.imcopy(cut_wht, whtmap)
# delete temporary object files
os.remove(inverse_var_list)
# remove all temporary files
remove_temp_all(tmp_prefix)
return 0
def imshiftcomb(inlist,
outimg,
fitgeom='shift',
inpref='',
objmask='none',
combine='average',
reject='none',
fscale=False,
fbase=100,
fhead='F1',
second=False,
first_pref='sdfr',
second_pref='sdf2r',
indep=False,
sigmap='none',
expmap='none',
whtmap='none',
gain=5.6,
ffpref=''):
# check output image
if os.access(outimg, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing image (%s)' % outimg
return 1
# check input image list
inimg_arr = check_input(inlist, inpref)
if isinstance(inimg_arr, int):
return 1
# check input image list
if sigmap != 'none':
ffimg_arr = check_input2(inlist, ffpref)
if isinstance(ffimg_arr, int):
return 1
# check optional output image
if sigmap != 'none':
if os.access(sigmap, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing image (%s)' % sigmap
return 1
if expmap != 'none':
if os.access(expmap, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing image (%s)' % expmap
return 1
if whtmap != 'none':
if os.access(whtmap, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing image (%s)' % whtmap
return 1
# get array size
im = pyfits.open(inimg_arr[0])
nx = im[0].header['NAXIS1']
ny = im[0].header['NAXIS2']
im.close()
# check geomap data
dx = []
dy = []
gmp_arr = []
gmp2_arr = []
dbs_arr = []
for i in range(len(inimg_arr)):
fname, ext = os.path.splitext(inimg_arr[i])
gmp = fname + '.gmp'
gmp2 = fname + '.gmp2'
dbs = fname + '.dbs'
gmp_arr.append(gmp)
gmp2_arr.append(gmp2)
dbs_arr.append(dbs)
if not os.access(gmp, os.R_OK):
print >> sys.stderr, 'geomap file (%s) does not exist' % (gmp)
return 1
if os.access(dbs, os.R_OK):
print >> sys.stderr, 'database file (%s) is already exist' % (dbs)
return 1
if os.access(gmp2, os.R_OK):
print >> sys.stderr, 'modified geomap file (%s) is already exist' % (
gmp2)
return 1
fgmp = open(gmp)
nl = 1
dx_ave = 0.0
dy_ave = 0.0
for line in fgmp:
if not line.startswith('#'):
param = line[:-1].split()
if len(param) != 4:
print >> sys.stderr, 'Invalid format in line %d of %s: %s' % (
nl, gmp, line[:-1])
fgmp.close()
return 1
else:
if isfloat(param[0]) == False or isfloat(
param[1]) == False or isfloat(
param[2]) == False or isfloat(
param[3]) == False:
print >> sys.stderr, 'failed to decode line %d of %s: %s' % (
nl, gmp, line[:-1])
fgmp.close()
return 1
else:
dx_ave += float(param[0]) - float(param[2])
dy_ave += float(param[1]) - float(param[3])
nl += 1
#print inimg_arr[i],nl
dx.append(dx_ave / (nl - 1))
dy.append(dy_ave / (nl - 1))
if len(inimg_arr) != len(dx):
print >> sys.stderr, 'number of input images does not match with that of offsets'
return 1
#print 'debug'
#print dx, max(dx), min(dx)
#print dy, max(dy), min(dy)
# check object mask
if objmask.lower() == 'none':
objmask = ''
else:
objmask_arr = check_inpref(objmask, inimg_arr)
if isinstance(objmask_arr, int):
return 1
# independent run flag
if indep:
second = True
# prepare for temporary file
tmp = tempfile.NamedTemporaryFile(suffix='', prefix='', dir='/tmp')
tmp_prefix = tmp.name
tmp.close()
# calculate image median for zero shift
iraf.unlearn('imstat')
iraf.unlearn('mimstat')
bgmed = []
for i in range(len(inimg_arr)):
if objmask == '':
ret = iraf.imstat(inimg_arr[i],
format='no',
fields='midpt',
nclip=50,
lsigma=3.,
usigma=3.,
Stdout=1)
else:
if not second:
ret = iraf.mimstat(inimg_arr[i],
imasks=objmask_arr[i] + '[pl]',
format='no',
fields='midpt',
nclip=50,
lsigma=3.,
usigma=3.,
Stdout=1)
else:
ret = iraf.mimstat(inimg_arr[i],
imasks=objmask_arr[i],
format='no',
fields='midpt',
nclip=50,
lsigma=3.,
usigma=3.,
Stdout=1)
if len(ret) == 1:
bgmed.append(-1.0 * float(ret[0]))
else:
fout.close()
remove_temp_all(tmp_prefix)
print >> sys.stderr, 'failed to calculate median of the background in %s' % inimg_arr[
i]
return 1
# get large array size and combined image size
ret = get_large_region(nx, ny, dx, dy)
if len(ret) != 6:
print >> sys.stderr, 'failed to get large array size'
return 1
x_size = ret[0]
y_size = ret[1]
xcmin = ret[2]
xcmax = ret[3]
ycmin = ret[4]
ycmax = ret[5]
# calculate image region in the large format
xmin = int((x_size - nx) / 2) + 1
xmax = nx + int((x_size - nx) / 2)
ymin = int((y_size - ny) / 2) + 1
ymax = ny + int((y_size - ny) / 2)
#print 'debug'
#print x_size, y_size, xcmin, xcmax, ycmin, ycmax
#print xmin, xmax, ymin, ymax
# copy image to larger format and shift image #
iraf.unlearn('geomap')
iraf.unlearn('geotran')
obj_list = tmp_prefix + '_obj.lst'
if os.access(obj_list, os.R_OK):
os.remove(obj_list)
fobj = open(obj_list, 'w')
# for exposure time weight
expweight = tmp_prefix + '_exp.lst'
if os.access(expweight, os.R_OK):
os.remove(expweight)
fexp = open(expweight, 'w')
# for zero offset
zeroshift = tmp_prefix + '_zeroshift.dat'
if os.access(zeroshift, os.R_OK):
os.remove(zeroshift)
fzero = open(zeroshift, 'w')
# save the original fit geometry
fitgeom_org = fitgeom
# preparing for the sigma list and mask
if sigmap == 'none':
tmp_rejmask = ''
else:
tmp_rejmask = tmp_prefix + 'rejmask.fits'
if os.access(tmp_rejmask, os.R_OK):
os.remove(tmp_rejmask)
inverse_var_list = tmp_prefix + '_var.lst'
if os.access(inverse_var_list, os.R_OK):
os.remove(inverse_var_list)
finverse_var = open(inverse_var_list, 'w')
for i in range(len(inimg_arr)):
# restore the original fit geometry
fitgeom = fitgeom_org
# geometry transformation
fgmp = open(gmp_arr[i])
fgmp2 = open(gmp2_arr[i], 'w')
nobj = 0
for line in fgmp:
if not line.startswith('#'):
param = line[:-1].split()
xref = float(param[0]) + xmin - 1
yref = float(param[1]) + ymin - 1
xin = float(param[2]) + xmin - 1
yin = float(param[3]) + ymin - 1
fgmp2.write('%.3f %.3f %.3f %.3f\n' % (xref, yref, xin, yin))
nobj += 1
fgmp.close()
fgmp2.close()
# check number of objects
if i == 0 and nobj == 1 and fitgeom == 'rotate':
print 'Warning: Number of reference objects is not enought to measure the rotation'
print 'Warning: Only shift applied for all images'
fitgeom = 'shift'
fitgeom_org = 'shift'
if nobj == 1 and fitgeom == 'rotate':
print 'Warning: Number of objects in %s is not enought to measure the rotation' % (
inimg_arr[i])
print 'Warning: Only shift applied for %s' % (inimg_arr[i])
fitgeom = 'shift'
# mapping geometry
iraf.geomap(gmp2_arr[i],
dbs_arr[i],
1,
x_size,
1,
y_size,
fitgeom=fitgeom,
interac='no')
# mask frame
msk = np.ones((y_size, x_size))
msk[ymin - 1:ymax, xmin - 1:xmax] = 0
hdu = pyfits.PrimaryHDU(msk)
msk_img = pyfits.HDUList([hdu])
msk_fits = tmp_prefix + 'mask' + os.path.basename(inimg_arr[i])
msktr_fits = tmp_prefix + 'masktr' + os.path.basename(inimg_arr[i])
if os.access(msk_fits, os.R_OK):
os.remove(msk_fits)
if os.access(msktr_fits, os.R_OK):
os.remove(msktr_fits)
msk_img.writeto(msk_fits)
msk_img.close()
# transform mask geometry
iraf.geotran(msk_fits,
msktr_fits,
dbs_arr[i],
gmp2_arr[i],
geometr='linear',
boundar='constant',
constant=1)
os.remove(msk_fits)
convert_maskfits_int(msktr_fits, msktr_fits)
# load original frame
img = pyfits.open(inimg_arr[i])
if sigmap != 'none':
ffimg = pyfits.open(ffimg_arr[i])
# for exposure time weight
try:
t = float(img[0].header['EXP1TIME'])
coadd = float(img[0].header['COADDS'])
expt = t * coadd
except KeyError:
print >> sys.stderr, 'can not read exposure time from the header of %s' % inimg_arr[
i]
img.close()
return 1
# for flux scaling and weight
if fscale:
try:
flux = float(img[0].header[fhead])
except KeyError:
print >> sys.stderr, 'can not read flux keyword (%s) from the header of %s' % (
fhead, inimg_arr[i])
img.close()
return 1
flux_scale = fbase / flux
weight = expt * (1.0 / flux_scale)**2
else:
flux_scale = 1.0
weight = expt
fzero.write('%f\n' % (bgmed[i] * flux_scale))
fexp.write('%f\n' % weight)
# object frame
obj = np.zeros((y_size, x_size))
obj[ymin - 1:ymax, xmin - 1:xmax] = img[0].data * flux_scale
hdu = pyfits.PrimaryHDU(obj)
obj_img = pyfits.HDUList([hdu])
obj_img[0].header = img[0].header
obj_img[0].header['bpm'] = msktr_fits
obj_img[0].header['expmap'] = expt
obj_fits = tmp_prefix + 'obj' + os.path.basename(inimg_arr[i])
objtr_fits = tmp_prefix + 'objtr' + os.path.basename(inimg_arr[i])
if os.access(obj_fits, os.R_OK):
os.remove(obj_fits)
obj_img.writeto(obj_fits)
obj_img.close()
iraf.geotran(obj_fits,
objtr_fits,
dbs_arr[i],
gmp2_arr[i],
geometr='linear',
boundar='constant',
constant=0)
fobj.write('%s\n' % objtr_fits)
img.close()
if sigmap != 'none':
inverse_var = np.zeros((y_size, x_size))
inverse_var[ymin - 1:ymax, xmin -
1:xmax] = (np.sqrt(ffimg[0].data / (gain * expt)) *
flux_scale)**-2
hdu_var = pyfits.PrimaryHDU(inverse_var)
inverse_var_img = pyfits.HDUList([hdu_var])
inverse_var_img[0].header = img[0].header
inverse_var_img[0].header['bpm2'] = '%s[*,*,%d]' % (tmp_rejmask,
i + 1)
inverse_var_fits = tmp_prefix + 'var' + os.path.basename(
inimg_arr[i])
inverse_vartr_fits = tmp_prefix + 'vartr' + os.path.basename(
inimg_arr[i])
if os.access(inverse_var_fits, os.R_OK):
os.remove(inverse_var_fits)
if os.access(inverse_vartr_fits, os.R_OK):
os.remove(inverse_vartr_fits)
inverse_var_img.writeto(inverse_var_fits)
inverse_var_img.close()
iraf.geotran(inverse_var_fits,
inverse_vartr_fits,
dbs_arr[i],
gmp2_arr[i],
geometr='linear',
boundar='constant',
constant=0)
finverse_var.write('%s\n' % inverse_vartr_fits)
ffimg.close()
# close file handlers
fobj.close()
fexp.close()
fzero.close()
if sigmap != 'none':
finverse_var.close()
# combine image
comb_img = tmp_prefix + '_comb.fits'
if os.access(comb_img, os.R_OK):
os.remove(comb_img)
if expmap == 'none':
tmp_expmap = ''
else:
tmp_expmap = tmp_prefix + 'expmap.fits'
if os.access(tmp_expmap, os.R_OK):
os.remove(tmp_expmap)
iraf.unlearn('imcombine')
try:
iraf.imcombine('@' + obj_list,
comb_img,
sigma='',
rejmask=tmp_rejmask,
expmasks=tmp_expmap,
combine=combine,
reject=reject,
masktype='!BPM',
maskvalue=0.0,
zero='@' + zeroshift,
weight='@' + expweight,
expname='EXPMAP')
except:
if os.access(comb_img, os.R_OK):
os.remove(comb_img)
if expmap != 'none':
if os.access(tmp_expmap, os.R_OK):
os.remove(tmp_expmap)
iraf.imcombine('@' + obj_list,
comb_img,
sigma='',
rejmask='',
expmasks=tmp_expmap,
combine=combine,
reject=reject,
masktype='!BPM',
maskvalue=0.0,
zero='@' + zeroshift,
weight='@' + expweight,
expname='EXPMAP')
if sigmap != 'none':
tmp_inverse_var_sum = tmp_prefix + 'inverse_var.fits'
if os.access(tmp_inverse_var_sum, os.R_OK):
os.remove(tmp_inverse_var_sum)
iraf.imcombine('@' + inverse_var_list,
tmp_inverse_var_sum,
combine='sum',
reject='none',
masktype='!BPM',
maskvalue=0.0)
iraf.stsdas()
tmp_sigma = tmp_prefix + 'sigma.fits'
if os.access(tmp_sigma, os.R_OK):
os.remove(tmp_sigma)
iraf.imcalc(tmp_inverse_var_sum,
tmp_sigma,
'sqrt(1.0/im1)',
pixtype='double')
# cut image
iraf.unlearn('imcopy')
cut_img = '%s[%d:%d,%d:%d]' % (comb_img, xcmin, xcmax, ycmin, ycmax)
iraf.imcopy(cut_img, outimg)
if expmap != 'none':
cut_exp = '%s[%d:%d,%d:%d]' % (tmp_expmap, xcmin, xcmax, ycmin, ycmax)
iraf.imcopy(cut_exp, expmap)
if sigmap != 'none':
cut_sigma = '%s[%d:%d,%d:%d]' % (tmp_sigma, xcmin, xcmax, ycmin, ycmax)
iraf.imcopy(cut_sigma, sigmap)
if whtmap != 'none':
cut_wht = '%s[%d:%d,%d:%d]' % (tmp_inverse_var_sum, xcmin, xcmax,
ycmin, ycmax)
iraf.imcopy(cut_wht, whtmap)
# delete temporary object files
remove_temp_all(tmp_prefix + 'obj')
os.remove(obj_list)
os.remove(comb_img)
# record relative offset between input images and combined image and rotation
for i in range(len(inimg_arr)):
im = pyfits.open(inimg_arr[i], mode='update')
if second:
if indep:
# calculate offset
dxc = xcmin - xmin - dx[i]
dyc = ycmin - ymin - dy[i]
# retrieve rotation
rot = 0.0
fdbs = open(dbs_arr[i])
for line in fdbs:
param = line[:-1].split()
if param[0] == 'xrotation':
rot = float(param[1])
if rot > 180.0:
rot = rot - 360.0
im[0].header['dx'] = dx[i]
im[0].header['dy'] = dy[i]
im[0].header['dxc'] = dxc
im[0].header['dyc'] = dyc
im[0].header['rotation'] = rot
else:
# check number of objects in the geomap file
nobj = 0
fgmp = open(gmp_arr[0])
for line in fgmp:
nobj += 1
im1 = pyfits.open(inimg_arr[i].replace(second_pref,
first_pref),
mode='update')
for j in range(nobj):
key = 'XC%d' % (j + 1)
im[0].header[key] = float(im1[0].header[key])
key = 'YC%d' % (j + 1)
im[0].header[key] = float(im1[0].header[key])
key = 'PEAK%d' % (j + 1)
im[0].header[key] = float(im1[0].header[key])
key = 'FWHM%d' % (j + 1)
im[0].header[key] = float(im1[0].header[key])
key = 'DX'
im[0].header[key] = float(im1[0].header[key])
key = 'DY'
im[0].header[key] = float(im1[0].header[key])
key = 'DXC'
im[0].header[key] = float(im1[0].header[key])
key = 'DYC'
im[0].header[key] = float(im1[0].header[key])
key = 'ROTATION'
im[0].header[key] = float(im1[0].header[key])
im1.close()
else:
# calculate offset
dxc = xcmin - xmin - dx[i]
dyc = ycmin - ymin - dy[i]
# retrieve rotation
rot = 0.0
fdbs = open(dbs_arr[i])
for line in fdbs:
param = line[:-1].split()
if param[0] == 'xrotation':
rot = float(param[1])
if rot > 180.0:
rot = rot - 360.0
im[0].header['dx'] = dx[i]
im[0].header['dy'] = dy[i]
im[0].header['dxc'] = dxc
im[0].header['dyc'] = dyc
im[0].header['rotation'] = rot
im.close()
# remove all temporary files
remove_temp_all(tmp_prefix)
return 0
def make_iracpsf(psfname,
oversample=10,
interp='sinc',
iracpix=0.6,
radius=3.0):
"""
Make an oversample IRAC PSF given the PSF image in the IRAC pixel scale,
for use in TFIT.
psfname: the input IRAC PSF
oversample: the oversampling factor (default is 10)
interp: the algorithm of interpolation (default is linear)
other options are nearest, poly3, poly5, spline3, sinc, lsinc,
and drizzle.
iracpix: the pixel scale (in arcsec) of the input IRAC PSF (default is 0.6)
radius: the radius of the circularized PSF in arcsec (beyond which is set to 0)
"""
newscale = int(round(iracpix / oversample * 1000))
# the new pixel scale in milli-arcsec
interp = interp.replace('[', '_')
interp = interp.replace(']', '_')
psfroot = os.path.splitext(psfname)[0]
psfroot = psfroot + '_%dmas' % newscale
psfroot = psfroot + '_%s' % interp
size0 = pyfits.getdata(psfname).shape[0]
size1 = size0 * oversample
# First, run iraf.imlintran
factor = 1. / oversample
print "factor", factor
if os.path.exists('temp1.fits'):
os.remove('temp1.fits')
iraf.imlintran(psfname,
'temp1.fits',
0.,
0.,
factor,
factor,
ncols=size1,
nlines=size1,
interpolant=interp,
fluxconserve='yes')
# Second, circularize the PSF, assume that the input PSF has equal numbers
# of rows and columns
imgsize = pyfits.getdata('temp1.fits').shape[0]
imgcenter = (imgsize + 1) / 2
print "imgcenter", imgcenter
pixrad = radius / iracpix * oversample
pixrad = np.round(pixrad)
mathstr = 'if ((x-%d)**2 + (y-%d)**2) < %f**2 then im1 else 0' % (
imgcenter, imgcenter, pixrad)
print "mathstr", mathstr
if os.path.exists('temp2.fits'):
os.remove('temp2.fits')
iraf.imcalc('temp1.fits', 'temp2.fits', mathstr)
# Third, trim the borders; shed border with units of 100 pixels
nborder = ((imgsize - 2 * pixrad) / 2) / 100
print "nborder:", nborder
if nborder > 0:
begin = nborder * 100 + 1
end = imgsize - nborder * 100 - 1
iraf.imcopy('temp2.fits[%d:%d,%d:%d]' % (begin, end, begin, end),
psfroot + '.fits')
else:
os.system('mv temp2.fits %s' % (psfroot + '.fits'))
os.system('rm temp*.fits')
def irac_pipeline(drz_img, unc_img, channel, rmsmode='unc', pixscale=0.6, gain=3.7,
zeromask=None, bgmask=None, source_threshold=3,
mask_growsig=5, subreg='none', aperture_width=7,
nnear=23, constant_bkgd=False, cluster='bullet',
inmask_checkrms='ch1_pf0.01_cut_mask.fits'):
"""
drz_img: the file name of the IRAC science mosaic (in MJy/sr)
unc_img: the file name of the IRAC uncertainty image (in MJy/sr)
channel: the IRAC channel name (either 'ch1' or 'ch2')
pixscale: the pixel scale of the input IRAC mosaic in arcsec/pixel
gain: the CCD gain of the IRAC mosaic
zeromask: the image name that provides which pixels to be zeroed out at the
end of this pipeline, both in the drz image and the RMS image
bgmask: user-provided mask for background subtraction (optional)
subreg: provide a list (or array) of four numbers (xmin, xmax, ymin, ymax)
that specifies a region in which to measure RMS. If None, default
to the entire image?
rmsmode: either 'cov' (use the coverage map to calculate the RMS map) or
'unc' (use the uncertainty map from the MOPEX pipeline)
"""
clean_pipeline(drz_img)
print "Step 1: convert image units from MJy/sr to DN/sec..."
drz_new1 = os.path.split(drz_img)[-1][:-5] + '_MJy.fits' # without converting to DN/sec
iraf.imcopy(drz_img, drz_new1)
if zeromask != None:
# zero out the masked pixel before entering background subtraction stage
iraf.imcalc("%s,%s" % (drz_new1, zeromask), "temp1.fits",
"if im2 > 0 then 0. else im1")
os.remove(drz_new1)
os.system('mv temp1.fits %s' % drz_new1)
# Run background-subtraction step
print "Step 2: subtract background from the science mosaic..."
print "See ch1_sub_bkgd.param for explanations of each parameter."
# Use the default parameters here... can maybe fiddle with these numbers
# later?
drz_root2 = os.getcwd() + '/' + os.path.split(drz_new1)[-1][:-5]
drz_new3 = drz_root2 + '_bgcorr.fits'
if constant_bkgd == True:
print "Subtracting a constant background within the designated region..."
if bgmask == None:
print "Error: must supply a background mask if one wants to subtract a constant background..."
sys.exit()
else:
subtract_constant_bkgd(drz_new1, bgmask, drz_new3,
growsig=mask_growsig)
else:
bkgd_parfile = os.path.split(drz_new1)[-1][:-5] + '_bkgd.param'
print "bkgd_parfile", bkgd_parfile
f = open(os.getcwd() + '/' + bkgd_parfile, 'wb')
#drz_root2 = drz_new1[:-5]
f.write('IMAGE \t %s\n' % drz_new1)
f.write('OUT_MASK \t %s\n' % (drz_root2 + '_bgmask.fits'))
f.write('OUT_BKGD \t %s\n' % (drz_root2 + '_bkgd.fits'))
f.write('OUT_BKGD_SUBTRACTED \t %s\n' % (drz_root2 + '_bgcorr.fits'))
f.write('OUT_NEWCOLLAGE \t %s\n' % (drz_root2 + '_bkgdcoll.fits'))
f.write('NNEAR \t %d\n' % nnear)
f.write('SOURCE_THRESHOLD \t %f\n' % float(source_threshold))
f.write('MASK_GROWSIG \t %f\n' % float(mask_growsig))
f.write('DQEXT \t 0\n')
f.write('CRUDESUB \t 1\n')
if subreg == None:
f.write('SUBREG \t %d,%d,%d,%d\n' % tuple(subreg_default[cluster]))
elif subreg == "none":
f.write('SUBREG \t none\n')
else:
f.write('SUBREG \t %d,%d,%d,%d' % tuple(subreg))
f.write('\n')
f.write('DILATE_FACTOR \t 2\n')
f.write('DILATE_THRESH \t 300\n')
f.write('DILATE_TOT_THRESH \t 500\n')
f.write('DILATE_SATURATED \t 1\n')
f.write('GAIN \t %f\n' % gain)
f.write('OUT_LABEL \t %s\n' % (drz_root2 + '_bgmask_labels.fits'))
f.write('\n')
f.write('MAKE_RMS \t 0\n')
f.write('OUT_RMS \t %s\n' % (drz_root2 + '_rms_estimated.fits'))
f.write('RMS_SMOOTH \t 100\n')
f.write('\n')
f.write('OUT_CRUDE \t %s\n' % (drz_root2 + '_simple_bgcorr.fits'))
f.write('OUT_FILT \t %s\n' % (drz_root2 + '_bkgd.filt.fits'))
if bgmask != None:
f.write('IN_MASK \t %s\n' % bgmask)
f.write('APERTURE_WIDTH \t %d\n' % aperture_width)
f.write('\n')
f.close()
ssb.run(bkgd_parfile)
# Make an image of background pixels after subtraction
#if os.path.exists(drz_root2+'_bgpix.fits'):
# os.remove(drz_root2+'_bgpix.fits')
#iraf.imcalc('%s,%s' % (drz_root2+'_bgcorr.fits','ch1_pf0.01_cut_seg.fits'),
# drz_root2+'_bgpix.fits','if im2 > 0 then -99.0 else im1')
if zeromask != None:
# again, zero out the masked pixels in the background-subtracted image
print "zero out the masked pixels in the background-subtracted image..."
iraf.imcalc("%s,%s" % (drz_new3, zeromask), "temp2.fits",
"if im2 > 0 then 0. else im1")
os.remove(drz_new3)
os.system('mv temp2.fits %s' % drz_new3)
# print "Step 3: re-scale the uncertainty image"
if rmsmode == 'cov':
print "Step 3: make RMS map from coverage map, then re-scale the uncertainty image"
cov_img = unc_img
rms_new1 = drz_img[:-9] + '_rms.fits'
if os.path.exists(rms_new1):
os.remove(rms_new1)
iraf.imcalc(cov_img, rms_new1,
"if im1 == 0. then 1000. else 1./sqrt(im1*%f)" % exptimes[channel])
# use coverage map (in units of seconds) as the weight map to calculate
# the RMS map
unc_new1 = rms_new1 # to comply with previous version
else:
print "Step 3: use the uncertainty map as the RMS map, and use FLUXCONV to convert to DN/sec "
print "(the important point is to convert the RMS map to the same unit as the science map)"
#unc_new1 = unc_img[:-5] + '_DNS.fits'
unc_new1 = unc_img[:-5] + '_MJy.fits'
#iraf.imcalc(unc_img, unc_new1, 'im1/%.4f' % (factor * fluxconv[channel]))
#iraf.imcalc(unc_img, unc_new1, 'im1/%.4f' % (fluxconv[channel]))
iraf.imcopy(unc_img, unc_new1) # without converting to DN/sec
try:
inmask = pyfits.getdata(inmask_checkrms)
except:
inmask = None
rmscheck.checkrms(pyfits.getdata(drz_new3), pyfits.getdata(unc_new1),
inmask=inmask, growsig=0.2)
try:
unc_scale = float(raw_input('Now enter the scaling factor for the uncertainty image: '))
except:
print 'Need to enter a number here.'
sys.exit()
unc_new2 = unc_new1[:-5] + '_scaled.fits'
if os.path.exists(unc_new2):
os.remove(unc_new2)
print "Multiply the uncertainty image by a factor of %f" % unc_scale
iraf.imcalc(unc_new1, unc_new2, 'im1*%f' % unc_scale)
print "Step 4: make the source-weighted RMS map..."
# Here both the science image and the (re-scaled) RMS map are in units of
# DN/sec
#mathstr = 'sqrt(im1**2 + im2/%f)' % gain
#mathstr = 'sqrt(im1**2 + im2)' # a different weighting scheme
#mathstr = 'sqrt((im1*%f)**2 + im2*%f) / %f' % (gain, gain, gain)
#mathstr = 'sqrt((im1*%f)**2 + im2) / %f' % (gain, gain)
mathstr = 'im1 * 1.0'
# convert everything into photon units
unc_new4 = unc_new2[:-5] + '_src.fits'
if os.path.exists(unc_new4):
os.remove(unc_new4)
iraf.imcalc('%s,%s' % (unc_new2,drz_new3), unc_new4, mathstr)
#iraf.imreplace(unc_new4, 1000., lo=0., up=0.)
iraf.imcalc('%s,%s' % (unc_new4,unc_new2), 'temp_unc.fits',
'if im1 <= 0. then im2 else im1')
os.system('mv temp_unc.fits %s' % unc_new4)
# Extra steps
print "All steps finished. A summary:"
print "Background-subtracted science mosaic: %s" % drz_new3
print "Source-weighted, re-scaled RMS map: %s" % unc_new4
print "All images converted into DN/sec."
def combine(do_cti=False, doreduce=True, doshifts=True):
if do_cti:
os.system('stis_cti --crds_update')
if doreduce:
# Defringing didn't seem to converge because of the low S/N
stistools.ocrreject.ocrreject('oc0102070_flc.fits','oc0102070_crc.fits')
iraf.normspflat(inflat='oc0102070_crc.fits',outflat='oc0102070_nsp.fits', do_cal='no')
iraf.imcalc(input='oc0102070_nsp.fits', output='temp_nsp.fits', equals='if(x .lt. 250) then 1 else im1')
iraf.imcopy('temp_nsp.fits[1][1:250,*]', 'oc0102070_nsp.fits[1][1:250,*]')
#iraf.defringe('oc0102060_flc.fits', 'oc0102070_nsp.fits', 'oc0102060_dfr.fits')
#for each image
for im in ['oc0102050_flc','oc0102060_flc']:
outbase = 'blue'
if im[:-4] == 'oc0102060':
outbase = 'red'
#reset the aperture table to the newer file (we maybe should check this)
pyfits.setval(im +'.fits','APERTAB',value='oref$y2r1559to_apt.fits')
pyfits.setval(im +'.fits', 'SPTRCTAB', value='oref$qa31608go_1dt.fits')
# fixpix any negative values. In principle some of this noise
# could be real, but I have found that is often not the case
hdu = fits.open(im+ '.fits')
mask1 = hdu[1].data < -20
mask2 = hdu[4].data < -20
hdu.close()
fits.writeto(outbase+'mask1.fits', mask1.astype('i'), clobber=True)
fits.writeto(outbase+'mask2.fits', mask2.astype('i'), clobber=True)
iraf.unlearn(iraf.fixpix)
iraf.fixpix(im+'[1]', outbase+'mask1.fits')
iraf.unlearn(iraf.fixpix)
iraf.fixpix(im+'[4]', outbase+'mask2.fits')
# Subtract off the median value
hdu = fits.open(im+ '.fits', mode='update')
hdu[1].data -= np.median(hdu[1].data)
hdu[4].data -= np.median(hdu[4].data)
readnoise1 = 1.4826 * np.median(np.abs(hdu[1].data))
readnoise2 = 1.4826 * np.median(np.abs(hdu[4].data))
# Cosmic ray reject both images using scrappy
# Make sure to treat the noise in a sensible way
crmask1, clean1 = detect_cosmics(hdu[1].data, readnoise=readnoise1,
sigclip=5, objlim=5, sigfrac=0.8,
fsmode='median', psfmodel='gaussy',
psffwhm=2., cleantype='idw')
crmask2, clean2 = detect_cosmics(hdu[4].data, readnoise=readnoise2,
sigclip=5, objlim=5, sigfrac=0.8,
fsmode='median', psfmodel='gaussy',
psffwhm=2., cleantype='idw')
hdu.flush()
hdu.close()
fits.writeto(outbase + '_crmask1.fits', crmask1.astype('i'), clobber=True)
fits.writeto(outbase + '_crmask2.fits', crmask2.astype('i'), clobber=True)
# Run fixpix on the frames
iraf.unlearn(iraf.fixpix)
iraf.fixpix(im+'[1]', outbase+'_crmask1.fits')
iraf.unlearn(iraf.fixpix)
iraf.fixpix(im+'[4]', outbase+'_crmask2.fits')
if outbase=='red':
iraf.mkfringeflat('oc0102060_flc.fits', 'oc0102070_nsp.fits', 'oc0102070_frr.fits',
beg_scale=0.6, end_scale=1.5, scale_step=0.01,
beg_shift=-3.0, end_shift=3.0,shift_step=0.05)
iraf.defringe('oc0102060_flc.fits', 'oc0102070_frr.fits', 'oc0102060_dfr.fits')
#Run x2d on the flt frame
stistools.x2d.x2d(input='oc0102060_dfr.fits',output=im[:-4]+'x2d.fits')
else:
stistools.x2d.x2d(input='oc0102050_flc.fits',output=im[:-4]+'x2d.fits')
h = pyfits.open(im[:-4]+'x2d.fits', mode='update')
#Replace all of the bad pixels in the image by -666 based on the DQ array
#save them to a new file
#Throw away bad reference file pixels and saturated pixels. None of the other error codes
#were in the first file so I haven't included them here, but we might want to
d = h[3].data
badpix = logical_and(bitwise_and(d,256) == 256,bitwise_and(d,512) == 512)
h[1].data[badpix] = -10000
d = h[6].data
badpix = logical_and(bitwise_and(d,256) == 256,bitwise_and(d,512) == 512)
h[4].data[badpix] = -10000
h.flush()
# Trim the images
for i in range(1,7):
h[i].data = h[i].data[100:-100, 100:-100]
h[i].header['CRPIX1'] -= 100
h[i].header['CRPIX2'] -= 100
h.flush()
h.close()
# Combine the images
iraf.unlearn(iraf.imcombine)
iraf.imcombine(input=im[:-4]+'x2d[1],'+im[:-4]+'x2d[4]', output=outbase+'_com.fits',
reject='crreject')
hdu = pyfits.open(outbase +'_com.fits')
mask = hdu[0].data == 0.0
hdu.close()
fits.writeto(outbase+'_mask.fits', mask.astype('i'), clobber=True)
iraf.unlearn(iraf.fixpix)
iraf.fixpix(outbase+'_com.fits', outbase+'_mask.fits')
iraf.unlearn(iraf.apall)
iraf.apall(input=outbase+'_com',output='13dh_'+outbase, review='no',
nsum = -500, b_order = 1,
b_function='legendre',b_niterate=30, b_naverage = -21,
nfind=1,t_order=3,background='median',weights='variance',
skybox=100 )
iraf.splot(outbase+'[SCI]')