def rename_files(files, key, keypattern, oldpattern, newpattern, exact=0):
'''Quick little function that lets you search a list of files for
a particular key (e.g., OBJECT) and if it matches the keypattern
(e.g., 'flat'), it applies imrename using the provided oldpattern
and newpattern to do the renaming (e.g. 'a' and 'comp'). If you
want an exact match for the key pattern, specify exact=1'''
list = get_header(files, key, str)
if exact: keypattern = r"\A" + keypattern + r"\Z"
for name in list:
if re.search(keypattern, list[name]):
iraf.imrename(oldnames=name,
newnames=re.sub(oldpattern, newpattern, name))
def _scale_image(self, in_image):
"""
Converts from [e] to [e/s]
@param in_image: the input image
@type in_image: string
"""
# unlearn some iraf tasks
iraf.unlearn('imarith')
# get a random filename
tmpfile1 = get_random_filename('t', '.fits')
# divide by exposure time
iraf.imarith(operand1=in_image,
op="/",
operand2=self.exptime,
result=tmpfile1)
# delete the old image;
# rename the new to the old name
os.unlink(in_image)
iraf.imrename(oldnames=tmpfile1, newnames=in_image)
def irafflatten():
os.system('cp /Users/rfinn/clusters/spitzer/flatsexfiles/* .')
infile=open('InputImageList.txt','r')
outfile=open('FlatImageList.txt','w')
sky=[]
for line in infile:
im=line[0:(len(line)-1)]
mask='mask'+im
skyim='s'+im
outline='f'+line
iraf.imgets(im,'DRIBKGND')
t=iraf.imgets.value
sky.append(float(t))
outfile.write(outline)
#get object positions using sextractor
iraf.imarith(im,'-',t,skyim)#subtract sky before running sextractor (otherwise it doesn't detect any objects - don't know why...)
s='sex '+skyim
os.system(s)
x=[]
y=[]
catfile=open('test.cat','r')
for line in catfile:
if line.find('#') > -1:
continue
t=line.split()
x.append(float(t[10]))
y.append(float(t[11]))
catfile.close()
x=N.array(x,'f')
y=N.array(y,'f')
try:#create image of ones same size as image
iraf.imarith(im,'/',im,'mask')
except:
iraf.imdelete('mask')
iraf.imarith(im,'/',im,'mask')
print "masking objects"
for j in range(len(x)): #mask objects and radius around each position using imreplace, radius=10 pix
for k in range(11):
y1=int(y[j]+5-k)
if y1 < 1:
continue
if y1 > 128:
continue
xmin=int(x[j]-5)
if xmin < 1:
xmin=1
xmax=int(x[j]+5)
if xmax > 128:
xmax=128
s='mask['+str(xmin)+':'+str(xmax)+","+str(y1)+":"+str(y1)+"]"
iraf.imreplace(s,0.)
iraf.imrename('mask',mask)
print "updating BPM field in header"
iraf.hedit(im,fields='BPM',value=mask,add='yes',verify='no',update='yes')
outfile.close()
infile.close()
avesky=N.average(N.array(sky,'f'))
lthresh=avesky-1.
hthresh=avesky+.6
iraf.imcombine('@InputImageList.txt','flat',combine='average',reject='ccdclip',scale='none',zero='mode',lthreshold=lthresh,hthreshold=hthresh,lsigma=2.,hsigma=2.,rdnoise=5.,gain=5.,blank=1.,grow=12.,masktype='badvalue',maskvalue='0')
t=iraf.imstat('flat',fields='mean',format='no',Stdout=1)
ave=float(t[0])
iraf.imarith('flat','/',ave,'nflat')
iraf.imarith('@InputImageList.txt','/','nflat','@FlatImageList.txt')
def saofocus(inpat,prepfile=yes,endhow="never",endwhen="",
clobber=globclob,verbose=globver):
""" derive best focus from an SAOFOCUS run on P60 """
# Defaults
twait=30
reduced={}
bpmname="BPM.pl"
saokey="IMGTYPE"
saore="SAOFOCUS"
fseqkey="SAOFVALS"
sigma=2.0
satval=50000.0
masksfx="mask"
pix=0.3787 # arcsec per pixel
mindiff=2 # minimum tolerable diff b/ best and worst seeing (pix)
# Parse end-condition "time"
if endhow=="time":
re1=re.search("^(\d+):(\d+)",endwhen)
if re1:
tnow=time.gmtime()
# Force UT calculation w/ ignoring of DST issues
reftime=time.mktime([tnow[0],tnow[1],tnow[2],
int(re1.group(1)),int(re1.group(2)),0,
tnow[6],tnow[7],0]) - time.timezone
if reftime<time.time():
reftime+=86400
if verbose:
print "Running until %s" % \
time.strftime("%d %b %H:%M",time.gmtime(reftime))
else:
print "Failed to parse %s as UT time" % endwhen
print "Running until stopped..."
endhow="never"
##################################################
# Big Loop
done=no
while not done:
# Parse inputs
allfiles=glob.glob(inpat)
newfiles=[]
for image in allfiles:
if not reduced.has_key(image):
# Interested only in SAOFOCUS images
saoval=get_head(image,saokey)
if re.search(saore,saoval,re.I):
reduced[image]=no
newfiles.append(image)
else:
# Skip
reduced[image]=yes
for image in newfiles:
if verbose:
print "Processing new SAOFOCUS image %s" % image
# Only attempt reduction once for each image
reduced[image]=yes
# Demosaic
ccdproc=iraf.ccdproc
ccdproc.overscan=yes
ccdproc.trim=yes
ccdproc.zerocor=no
iraf.iqmosaic(image,outpfx="",biassec="!BIASSEC",
trimsec="!TRIMSEC",joinkeys="DETSEC",
splitkeys="AMPSEC,CCDSEC,TRIM,OVERSCAN",
clobber=yes,verbose=verbose)
# Fix the WCS that IRAF has now clobbered
iraf.add_wcs(image,instrument="p60new")
# Get some important keywords
[raoff,decoff,saonseq,saoshift] = \
get_head(image,['RAOFF','DECOFF','NUMFOC',
'SAOSHIFT'])
# Type conversions
saonseq=int(saonseq)
saoshift=float(saoshift)
# No bias-subtraction or flat-fielding
# Subtract stair-step background if requested
if prepfile:
qtmp=iraf.mktemp("saofoc")+".fits"
iraf.imcopy(image,qtmp,verbose=no)
for iamp in [-1,1]:
for istep in range(saonseq):
qsec=iraf.mktemp("saofsc")+".fits"
y1=1024+iamp*(1+istep*saoshift)+max(0,iamp)
y2=y1+iamp*(saoshift-1)
if istep==0:
y1-=iamp
elif istep==saonseq-1:
y2=1+max(0,iamp)*2047
ymin=min(y1,y2)
ymax=max(y1,y2)
statsec='[*,%d:%d]' % (ymin,ymax)
iraf.iterstat(image+statsec,nsigrej=5,
maxiter=10,prin=no,verbose=no)
medreg=float(iraf.iterstat.median)
iraf.imarith(image+statsec,'-',medreg,
qsec,verbose=no,noact=no)
iraf.imcopy(qsec,qtmp+statsec,verbose=no)
iraf.imdel(qsec,verify=no,go_ahead=yes)
iraf.imdel(image,verify=no,go_ahead=yes)
iraf.imrename(qtmp,image,verbose=no)
# No renaming of the file
# No Bad Pixel Masking or rudimentary WCS
# Object-detection
doobj=1
if check_head(image,'STARFILE'):
if os.path.exists(get_head(image,'STARFILE')):
doobj=0
if doobj:
iraf.iqobjs(image,sigma,satval,skyval="0.0",
masksfx=masksfx,wtimage="none",minlim=no,
clobber=yes,verbose=verbose)
# Establish our "region of interest" for pixels
pixrough=800
pixminx=1024-float(decoff)/pix-pixrough/2
pixmaxx=pixminx+pixrough
pixmaxy=1023
pixminy=pixmaxy-float(raoff)/pix-(1+saonseq)*saoshift-pixrough/2
# The array of focus positions (from low Y to high Y)
farrkeys=[]
for i in range(saonseq):
farrkeys.append("SAOFOC%02d" % (i+1))
farr=get_head(image,farrkeys)
# Starlist from Sextractor
starfile=get_head(image,'STARFILE')
stars=Starlist(starfile)
stars.mag_sort()
# Find some of the stars in the sequence
xvals=[]
yvals=[]
good1=[]
for star in stars:
# We're going in order from bright to faint
if star.xval>pixminx and star.xval<pixmaxx and \
star.yval<pixmaxy and star.yval>pixminy and \
star.elong<3:
good1.append(star)
xvals.append(star.xval)
yvals.append(star.yval)
if len(good1)>=1.2*saonseq:
break
# Sanity checking
if len(xvals)<1:
print "No stars found in search region"
update_head(image,'SAOFOCUS',0,
"saofocus processing failed for this image")
continue
# Guess for x is median of the xvals of the goodstars
xctr=median(xvals,pick=1)
# First guess for y is median of the goodstars that fall
# within +/- (pixfine) pix of this X value
pixfine=8
yval2=[]
good2=[]
for star in good1:
if abs(star.xval-xctr)<pixfine:
good2.append(star)
yval2.append(star.yval)
# Sanity checking
if len(yval2)<1:
print "No stars found in refined search region"
update_head(image,'SAOFOCUS',0,
"saofocus processing failed for this image")
continue
yctr=median(yval2,pick=1)
# This will be our reference star
usestar=good2[yval2.index(yctr)]
[refx,refy]=[usestar.xval,usestar.yval]
# Identify additional stars, working out from the
# reference star
usestars=[usestar]
# Search increasing & decreasing in Y
# (we use a bigger box in Y, and correct for the last star
# position, because of potential for tracking errors)
for isgn in [+1,-1]:
shifty=refy
for i in range(1,saonseq):
shifty += isgn*saoshift
tgtstars=stars.starsinbox([[refx-pixfine,refx+pixfine],
[shifty-1.5*pixfine,
shifty+1.5*pixfine]])
if len(tgtstars)>0:
minmag=99.99
# Pick the brightest in the box
for tstar in tgtstars:
if tstar.mag<minmag:
svstar=tstar
minmag=tstar.mag
usestars.append(svstar)
shifty=svstar.yval
else:
# Quit searching when there are no stars in box
break
# Exclude faint stars if we have too many
# (could also drop stars until we reach the number, but
# then we would want to check that all the y-values are in
# strict succession...)
magrough=2.0
if len(usestars)>saonseq:
usemags=[]
for star in usestars:
usemags.append(star.mag)
minmag=min(usemags)
use2=[]
for star in usestars:
if star.mag<minmag+magrough:
use2.append(star)
if len(use2)==saonseq:
usestars=use2
# Check for the right number
if len(usestars)!=saonseq:
print "Failed to get right number of target stars"
update_head(image,'SAOFOCUS',0,
"saofocus processing failed for this image")
continue
# Construct the array of x- and y-values
xval3=[]
yval3=[]
for star in usestars:
xval3.append(star.xval)
yval3.append(star.yval)
# Choose a single x-value for the run
xctr2="%.3f" % median(xval3)
# Increasing y-value means increasing index in farr
yval3.sort()
ylis=','.join(map(str,yval3))
flis=','.join(map(str,farr))
# Run iqfocus
iraf.iqfocus(image,xctr2,ylis,flis,method="acorr",
boxsize=saoshift,focuskey="BESTFOC",
seekey="SEEING",fseekey="SAOSEE",
update=yes,clobber=yes,verbose=verbose)
# Check for successful iqfocus run
if not check_head(image,'BESTFOC'):
print "iqfocus run failed to find good focus"
update_head(image,'SAOFOCUS',0,
"saofocus processing failed for this image")
continue
# Grab focus positions and seeing values
bestfoc,saosee=get_head(image,['BESTFOC','SAOSEE'])
# Some information
if verbose:
print "Focus settings: "+flis
print "Seeing values: "+saosee
print "Best focus: %.3f" % bestfoc
# Check for best-focus "near edge" condition
for i in range(saonseq):
if ("%.3f" % bestfoc)==("%.3f" % float(farr[i])):
ibest=i
break
if (saonseq>3 and (ibest==0 or ibest==saonseq-1)) or \
(saonseq>5 and (ibest==1 or ibest==saonseq-2)):
update_head(image,'SAOEDGE',1,
"Best seeing is near edge of focus run")
# Check for insufficient variation across the focus run
maxsee=0
minsee=0.5*saoshift
saoseevals=eval(saosee)
for seeval in saoseevals:
minsee=min(minsee,seeval)
maxsee=max(maxsee,seeval)
if (maxsee-minsee) < mindiff:
print "Failed to observe significant variation "+\
"across focus run"
update_head(image,'SAOFOCUS',0,
"saofocus processing failed for this image")
continue
# Convert seeing value to arcsec
seepix=get_head(image,'SEEPIX')
update_head(image,'BESTSEE',seepix,
"Best seeing in pixels for this series")
try:
seeasec=float(seepix)*pix
update_head(image,'SEEING',seeasec,
"Best seeing in arcsec for this series")
except:
print "Failed to convert seeing to arcsec for %s" % image
# Successful processing
update_head(image,'SAOFOCUS',1,
"saofocus processing succeeded for this image")
##############################
# Test end conditions
if endhow=="never":
done=no
elif endhow=="once":
done=yes
elif endhow=="time":
if time.time()>reftime:
done=yes
# Wait a little while
if not done:
time.sleep(twait)
def flatten(self,qombos,flat=None,flatscale=no):
if type(qombos) is StringType:
qombo=qombos
qombos=[qombo]
# Loop over input list of qombos
for qombo in qombos:
# Check for presence of calibration files
obj,filt=qombo_split(qombo)
if not self.check_calib(filt):
self.calib()
# Corresponding files
offiles=self.science_images(qombo)
if len(offiles)==0:
continue
# Make a text ".lis" file to record this
putlines(qombo+'.lis',offiles,clobber=yes)
# Rescale flatfield if requested
inflat=self.flatpre+filt+'.fits'
outflat=self.flatpre+qombo+'.fits'
check_exist(outflat,"w",clobber=self.clobber)
if flatscale:
nirc_flatscale(offiles,inflat,outflat,clipfrac=0.02,
clobber=yes)
else:
iraf.imcopy(inflat,outflat,verbose=no)
check_exist(outflat,"r")
# Clip flatfield and update BPM
iraf.iqclip(outflat,lthresh=self.flatlow,
hthresh=self.flathigh,bookend=no,
replace=1.0,maskin='!BPM',maskval=1,
maskout=self.bpmsfx)
# Rename the updated BPM
newbpm1=get_head(outflat,'BPM')
newbpm2=newbpm1.replace(self.flatpre,'')
check_exist(newbpm2,'w',clobber=self.clobber)
iraf.imrename(newbpm1,newbpm2,verbose=no)
update_head(outflat,'BPM',newbpm2)
# Further preprocessing
for image in offiles:
# Keyword updates
update_head(image,[self.filtset,'BPM'],
[filt,newbpm2])
# Flatfielding
iraf.iqflatten(image,outflat,outpfx=self.flatpfx,
normflat=no,statsec=self.statsec,
subsky=yes,vignflat=no,clipflat=no,
clobber=yes,verbose=no)
# Set bad pixels to zero
image2=self.flatpfx+image
iraf.iqmask(image2,mask='!BPM',method='constant',value=0.0,
clobber=yes,verbose=no)
# Write Useful Keywords
update_head(image2,'PROCESSD',1,
"Image has been processed by iqnirc")
update_head(image2,'PROCVER',version,
"Version number of iqnirc used")
update_head(image2,'ZEROPT','INDEF',
"Zero-point relative to 2MASS or INDEF")
update_head(image2,'PROCPROB','None',
"Problems encountered in iqnirc processing")
# List of flatfielded images
offiles2=pfx_list(offiles,self.flatpfx)
oflist2=','.join(offiles2)
# Object-detection
iraf.iqobjs(oflist2,self.sigma,self.satval,
skyval="!SKYBKG",masksfx=self.masksfx,
wtimage="",minlim=no,
clobber=yes,verbose=no)
# Attempt WCS refinement
iraf.iqwcs(oflist2,objkey=self.objkey,rakey='RA',
deckey='DEC',pixscl=self.pix,pixtol=0.05,
starfile='!STARFILE',catalog='ir',
diffuse=yes,clobber=yes,verbose=self.verbose)
# Done with Step #3
update_head(offiles2,'IQWRCSTP',3,
"Stage of IQWIRC processing")
#! /usr/bin/env python
from pylab import *
from pyraf import iraf
import glob
files = glob.glob('*.fits')
for file in files:
iraf.imgets(image=file, param='FILENAME') #get RA of image
originalName = iraf.imgets.value
#print file, originalName
iraf.imrename(file, originalName)
print "All Done!"
def fastfocus(inpat,
prepfile=yes,
endhow="never",
endwhen="",
clobber=globclob,
verbose=globver):
""" derive best focus from an SAOFOCUS run on P60 """
# Defaults
twait = 30
reduced = {}
bpmname = "BPM.pl"
saokey = "IMGTYPE"
saore = "SAOFOCUS"
fseqkey = "SAOFVALS"
sigma = 10.0
satval = 50000.0
masksfx = "mask"
pix = 0.3787 # arcsec per pixel
mindiff = 2 # minimum tolerable diff b/ best and worst seeing (pix)
# Parse end-condition "time"
if endhow == "time":
re1 = re.search("^(\d+):(\d+)", endwhen)
if re1:
tnow = time.gmtime()
# Force UT calculation w/ ignoring of DST issues
reftime = time.mktime([
tnow[0], tnow[1], tnow[2],
int(re1.group(1)),
int(re1.group(2)), 0, tnow[6], tnow[7], 0
]) - time.timezone
if reftime < time.time():
reftime += 86400
if verbose:
print "Running until %s" % \
time.strftime("%d %b %H:%M",time.gmtime(reftime))
else:
print "Failed to parse %s as UT time" % endwhen
print "Running until stopped..."
endhow = "never"
# Open fake OCS
fakeocs = ocs_ooriented(TEST=1)
fakeocs.read_foctable()
##################################################
# Big Loop
done = no
while not done:
# Parse inputs
allfiles = glob.glob(inpat)
newfiles = []
for image in allfiles:
if not reduced.has_key(image):
# Interested only in SAOFOCUS images
saoval = get_head(image, saokey)
if re.search(saore, saoval, re.I):
reduced[image] = no
newfiles.append(image)
else:
# Skip
reduced[image] = yes
for image in newfiles:
if verbose:
print "Processing new SAOFOCUS image %s" % image
# Only attempt reduction once for each image
reduced[image] = yes
# Demosaic
ccdproc = iraf.ccdproc
ccdproc.overscan = yes
ccdproc.trim = yes
ccdproc.zerocor = no
iraf.iqmosaic(image,
outpfx="",
biassec="!BIASSEC",
trimsec="!TRIMSEC",
joinkeys="DETSEC",
splitkeys="AMPSEC,CCDSEC,TRIM,OVERSCAN",
clobber=yes,
verbose=verbose)
# Fix the WCS that IRAF has now clobbered
iraf.add_wcs(image, instrument="p60new")
# Get some important keywords
[raoff,decoff,saonseq,saoshift] = \
get_head(image,['RAOFF','DECOFF','NUMFOC',
'SAOSHIFT'])
# Type conversions
saonseq = int(saonseq)
saoshift = float(saoshift)
# No bias-subtraction or flat-fielding
# Subtract stair-step background if requested
if prepfile:
qtmp = iraf.mktemp("saofoc") + ".fits"
iraf.imcopy(image, qtmp, verbose=no)
for iamp in [-1, 1]:
for istep in range(saonseq):
qsec = iraf.mktemp("saofsc") + ".fits"
y1 = 1024 + iamp * (1 + istep * saoshift) + max(
0, iamp)
y2 = y1 + iamp * (saoshift - 1)
if istep == 0:
y1 -= iamp
elif istep == saonseq - 1:
y2 = 1 + max(0, iamp) * 2047
ymin = min(y1, y2)
ymax = max(y1, y2)
statsec = '[*,%d:%d]' % (ymin, ymax)
iraf.iterstat(image + statsec,
nsigrej=5,
maxiter=10,
prin=no,
verbose=no)
medreg = float(iraf.iterstat.median)
iraf.imarith(image + statsec,
'-',
medreg,
qsec,
verbose=no,
noact=no)
iraf.imcopy(qsec, qtmp + statsec, verbose=no)
iraf.imdel(qsec, verify=no, go_ahead=yes)
iraf.imdel(image, verify=no, go_ahead=yes)
iraf.imrename(qtmp, image, verbose=no)
# No renaming of the file
# No Bad Pixel Masking or rudimentary WCS
# Object-detection
doobj = 1
if check_head(image, 'STARFILE'):
if os.path.exists(get_head(image, 'STARFILE')):
doobj = 0
if doobj:
iraf.iqobjs(image,
sigma,
satval,
skyval="0.0",
masksfx=masksfx,
wtimage="none",
minlim=no,
clobber=yes,
verbose=verbose)
# Establish our "region of interest" for pixels
pixrough = 800
pixminx = 1024 - float(decoff) / pix - pixrough / 2
pixmaxx = pixminx + pixrough
pixmaxy = 1023
pixminy = pixmaxy - float(raoff) / pix - (
1 + saonseq) * saoshift - pixrough / 2
# The array of focus positions (from low Y to high Y)
farrkeys = []
for i in range(saonseq):
farrkeys.append("SAOFOC%02d" % (i + 1))
farr = get_head(image, farrkeys)
# Starlist from Sextractor
starfile = get_head(image, 'STARFILE')
stars = Starlist(starfile)
stars.mag_sort()
# Find some of the stars in the sequence
xvals = []
yvals = []
good1 = []
for star in stars:
# We're going in order from bright to faint
if star.xval>pixminx and star.xval<pixmaxx and \
star.yval<pixmaxy and star.yval>pixminy and \
star.elong<3:
good1.append(star)
xvals.append(star.xval)
yvals.append(star.yval)
if len(good1) >= 1.2 * saonseq:
break
# Sanity checking
if len(xvals) < 1:
print "No stars found in search region"
update_head(image, 'SAOFOCUS', 0,
"saofocus processing failed for this image")
continue
# Guess for x is median of the xvals of the goodstars
xctr = median(xvals, pick=1)
# First guess for y is median of the goodstars that fall
# within +/- (pixfine) pix of this X value
#pixfine=10
pixfine = 20
yval2 = []
good2 = []
for star in good1:
if abs(star.xval - xctr) < pixfine:
good2.append(star)
yval2.append(star.yval)
# Sanity checking
if len(yval2) < 1:
print "No stars found in refined search region"
update_head(image, 'SAOFOCUS', 0,
"saofocus processing failed for this image")
continue
yctr = median(yval2, pick=1)
# This will be our reference star
usestar = good2[yval2.index(yctr)]
[refx, refy] = [usestar.xval, usestar.yval]
# Identify additional stars, working out from the
# reference star
usestars = [usestar]
# Search increasing & decreasing in Y
# (we use a bigger box in Y, and correct for the last star
# position, because of potential for tracking errors)
for isgn in [+1, -1]:
shifty = refy
for i in range(1, saonseq):
shifty += isgn * saoshift
tgtstars = stars.starsinbox(
[[refx - pixfine, refx + pixfine],
[shifty - pixfine, shifty + pixfine]])
#[shifty-1.5*pixfine,
#shifty+1.5*pixfine]])
if len(tgtstars) > 0:
minmag = 99.99
# Pick the brightest in the box
for tstar in tgtstars:
if tstar.mag < minmag:
svstar = tstar
minmag = tstar.mag
usestars.append(svstar)
shifty = svstar.yval
else:
# Quit searching when there are no stars in box
break
# Exclude faint stars if we have too many
# (could also drop stars until we reach the number, but
# then we would want to check that all the y-values are in
# strict succession...)
magrough = 2.0
if len(usestars) > saonseq:
usemags = []
for star in usestars:
usemags.append(star.mag)
minmag = min(usemags)
use2 = []
for star in usestars:
if star.mag < minmag + magrough:
use2.append(star)
if len(use2) == saonseq:
usestars = use2
# Check for the right number
if len(usestars) != saonseq:
print "Failed to get right number of target stars"
update_head(image, 'SAOFOCUS', 0,
"saofocus processing failed for this image")
continue
# Construct the array of x- and y-values
xval3 = []
yval3 = []
for star in usestars:
xval3.append(star.xval)
yval3.append(star.yval)
# Choose a single x-value for the run
xctr2 = "%.3f" % median(xval3)
# Increasing y-value means increasing index in farr
yval3.sort()
ylis = ','.join(map(str, yval3))
flis = ','.join(map(str, farr))
# Run iqfocus
iraf.iqfocus(image,
xctr2,
ylis,
flis,
method="acorr",
boxsize=saoshift,
focuskey="BESTFOC",
seekey="SEEPIX",
fseekey="SAOSEE",
update=yes,
clobber=yes,
verbose=verbose)
# Check for successful iqfocus run
if not check_head(image, 'BESTFOC'):
print "iqfocus run failed to find good focus"
update_head(image, 'SAOFOCUS', 0,
"saofocus processing failed for this image")
continue
# Grab focus positions and seeing values
bestfoc, saosee = get_head(image, ['BESTFOC', 'SAOSEE'])
# Some information
if verbose:
print "Focus settings: " + flis
print "Seeing values: " + saosee
print "Best focus: %.3f" % bestfoc
# Check for best-focus "near edge" condition
for i in range(saonseq):
if ("%.3f" % bestfoc) == ("%.3f" % float(farr[i])):
ibest = i
break
if (saonseq>3 and (ibest==0 or ibest==saonseq-1)) or \
(saonseq>5 and (ibest==1 or ibest==saonseq-2)):
update_head(image, 'SAOEDGE', 1,
"Best seeing is near edge of focus run")
# Check for insufficient variation across the focus run
maxsee = 0
minsee = 0.5 * saoshift
saoseevals = eval(saosee)
for seeval in saoseevals:
minsee = min(minsee, seeval)
maxsee = max(maxsee, seeval)
if (maxsee - minsee) < mindiff:
print "Failed to observe significant variation "+\
"across focus run"
update_head(image, 'SAOFOCUS', 0,
"saofocus processing failed for this image")
continue
# Convert seeing value to arcsec
seepix = get_head(image, 'SEEPIX')
update_head(image, 'BESTSEE', seepix,
"Best seeing in pixels for this series")
try:
seeasec = float(seepix) * pix
update_head(image, 'SEEING', seeasec,
"Best seeing in arcsec for this series")
except:
print "Failed to convert seeing to arcsec for %s" % image
# Successful processing
update_head(image, 'SAOFOCUS', 1,
"saofocus processing succeeded for this image")
# Get other necessary keywords
[filter, btubtemp, airmass] = get_head(image, \
['FILTER','BTUBTEMP','AIRMASS'])
# Update focus file
fakeocs.focus = [filter, bestfoc, float(btubtemp), float(airmass)]
fakeocs.write_foctable()
##############################
# Test end conditions
if endhow == "never":
done = no
elif endhow == "once":
done = yes
elif endhow == "time":
if time.time() > reftime:
done = yes
# Wait a little while
if not done:
time.sleep(twait)
def iqp60(inpat,endhow="never",endwhen="",focmode="all",
logfile="",clobber=globclob,verbose=globver):
""" intelligent processing of P60 data files """
# Defaults
twait=30
reduced={}
filtkey="FILTER"
biaskey="IMGTYPE"
biasre="BIAS"
biasroot="Bias"
biaspfx="b"
flatkey="IMGTYPE"
flatre="DOMEFLAT"
flatpre="Flat-"
flatpfx="f"
bpm0root="BPM0"
bpmroot="BPM"
binkey="CCDSUM"
detseckey="DETSEC"
binroikey="BINROI"
binroidef="A"
nfocus=10
focuskey="IMGTYPE"
focusre="^FOCUS"
fseqkey="FOCUSSEQ"
skipkey="IMGTYPE"
skipre="SAOFOCUS"
statsec=""
reqkeys=['IMGTYPE','OBJECT','FILTER','BINROI',
'CCDSUM','DETSEC','ROISEC']
sigma=3.0
minraw=0.0
maxraw=65535.0
satval=50000.0
masksfx="mask"
rawpix=0.3787
zptkey="ZEROPT"
zpukey="ZEROPTU"
# For now:
#catmags={'B':'BMAG','V':'BMAG','g':'BMAG',
# 'R':'RMAG','r':'RMAG','I':'IMAG',
# 'i':'IMAG','ip':'IMAG','z':'IMAG',
# 'zp':'IMAG','zl':'IMAG','zs':'IMAG'}
## In the future, once Sloan mags are written to the ubcone output:
catmags={'B':'BMAG','V':'VMAG','g':'GPMAG',
'R':'RMAG','r':'RPMAG','I':'IMAG',
'i':'IMAG','ip':'IPMAG','z':'ZPMAG',
'zp':'ZPMAG','zl':'ZPMAG','zs':'ZPMAG',
'upr':'UPMAG','gpr':'GPMAG','rpr':'RPMAG',
'ipr':'IPMAG','zpr':'ZPMAG'}
exptmkey="EXPTIME"
exptmstd=60.0
minzpts={}
minskys={}
fwhm0=1.5
gain=2.3
readn=7.5
crsfx="crm"
statfile="Status_Science.txt"
# Parse end-condition "time"
if endhow=="time":
re1=re.search("^(\d+):(\d+)",endwhen)
if re1:
tnow=time.gmtime()
# Force UT calculation w/ ignoring of DST issues
reftime=time.mktime([tnow[0],tnow[1],tnow[2],
int(re1.group(1)),int(re1.group(2)),0,
tnow[6],tnow[7],0]) - time.timezone
if reftime<time.time():
reftime+=86400
if verbose:
print "Running until %s" % \
time.strftime("%d %b %H:%M",time.gmtime(reftime))
else:
print "Failed to parse %s as UT time" % endwhen
print "Running until stopped..."
endhow="never"
# Parse focmode setting
fonly=0
if focmode=="nofocus":
skipre="FOCUS"
elif focmode=="focus":
fonly=1
skipre="SCIENCE"
# Parse logfile setting
if len(logfile)>0:
check_exist(logfile,'w',yes)
try:
log=open(logfile,'w')
sys.stdout=log
except:
print "Failed to open logfile %s for writing" % logfile
# Setup ccdproc options
ccdproc=iraf.ccdred.ccdproc
ccdproc.overscan=yes
ccdproc.trim=yes
ccdproc.zerocor=no
ccdproc.interactive=no
ccdproc.function="chebyshev"
ccdproc.order=3
ccdproc.sample="*"
ccdproc.naverage=3
ccdproc.niterate=2
ccdproc.low_reject=3.0
ccdproc.high_reject=3.0
ccdproc.grow=0.0
# Demosaic all the files
allfiles=glob.glob(inpat)
alllist=','.join(allfiles)
iraf.iqmosaic(alllist,outpfx="",biassec="!BIASSEC",
trimsec="!TRIMSEC",joinkeys="CCDSIZE,DETSEC,ROISEC",
splitkeys="AMPSEC,CCDSEC,TRIM,OVERSCAN,CCDPROC",
clobber=yes,verbose=verbose)
iraf.add_wcs(alllist,instrument="p60new",binkey=binkey)
# Set ccdproc params for bias-subtraction only
ccdproc.overscan=no
ccdproc.trim=no
ccdproc.zerocor=yes
# Determine if we need to make calibration files
dobias=no
doflats=no
for image in allfiles:
# Criteria for bias image
biasval,flatval=get_head(image,[biaskey,flatkey])
if re.search(biasre,biasval,re.I):
dobias=yes
reduced[image]=yes
elif re.search(flatre,flatval,re.I):
doflats=yes
reduced[image]=yes
# Calibrations
if (dobias or doflats) and not fonly:
iraf.iqcals(alllist,dobias=dobias,biasproc=no,
biaskey=biaskey,biasre=biasre,biasroot=biasroot,
doflats=doflats,flatproc=yes,flatkey=flatkey,
flatre=flatre,filtkey=filtkey,flatpre=flatpre,
flatscale="mode",statsec="",normflat=yes,
dobpm=no,bpmroot=bpmroot,classkey=binroikey,
classdef=binroidef,mosaic=no,
clobber=clobber,verbose=verbose)
# Delete binroi-specific BPMs and flatfields
oldbpms=glob.glob(bpmroot+'-'+'*.pl')
for oldbpm in oldbpms:
iraf.imdel(oldbpm,verify=no,go_ahead=yes)
oldflats=glob.glob(flatpre+'*-*.fits')
for oldflat in oldflats:
iraf.imdel(oldflat,verify=no,go_ahead=yes)
# Clip all flatfields and update BPM as we go
bpm0name=bpm0root+'.pl'
bpmname=bpmroot+'.pl'
if os.path.exists(bpmname):
check_exist(bpm0name,'w',clobber=yes)
iraf.imrename(bpmname,bpm0name,verbose=no)
allflats=glob.glob(flatpre+'*.fits')
for flatimg in allflats:
iraf.iqclip(flatimg,lthresh=0.75,hthresh=1.25,bookend=no,
replace=1.0,maskin=bpm0name,maskout=bpmname,
maskval=1,clobber=yes,verbose=no)
iraf.imdel(bpm0name,verify=no,go_ahead=yes)
iraf.imrename(bpmname,bpm0name,verbose=no)
update_head(flatimg,"CLIPFLAT",1,"Has flatfield been clipped?")
update_head(flatimg,"CLIPREPL",1.0,
"Replacement value for clipped pixels")
iraf.imrename(bpm0name,bpmname,verbose=no)
##################################################
# Lists of focusfiles
allfocus=[]
focusfiles=[]
# Should additional calibrations be taken later on...
biasfiles=[]
flatfiles=[]
# Big Loop
done=no
while not done:
# Parse inputs
allfiles=glob.glob(inpat)
newfiles=[]
for image in allfiles:
if not reduced.has_key(image):
# Exclude Bias & Flats
if re.search(biasroot,image,re.I) or \
re.search(flatpre,image,re.I):
reduced[image]=yes
continue
# Exclude calibration files (for now)
biasval,flatval=get_head(image,[biaskey,flatkey])
if re.search(biasre,biasval,re.I):
biasfiles.append(image)
reduced[image]=yes
elif re.search(flatre,flatval,re.I):
flatfiles.append(image)
reduced[image]=yes
# Exclude "skippable" files
skipval=get_head(image,skipkey)
if re.search(skipre,skipval,re.I):
reduced[image]=yes
# Queue file for processing
else:
reduced[image]=no
newfiles.append(image)
elif not reduced[image]:
# Attempted reduction before, but failed?
newfiles.append(image)
if newfiles:
# Brief pause to avoid file conflicts (?)
time.sleep(3.0)
# Demosaic
newlist=','.join(newfiles)
ccdproc.overscan=yes
ccdproc.trim=yes
ccdproc.zerocor=no
iraf.iqmosaic(newlist,outpfx="",biassec="!BIASSEC",
trimsec="!TRIMSEC",joinkeys="DETSEC",
splitkeys="AMPSEC,CCDSEC,TRIM,OVERSCAN",
clobber=yes,verbose=verbose)
iraf.add_wcs(newlist,instrument="p60new",binkey=binkey)
# Reset ccdproc params for bias-subtraction only
ccdproc.overscan=no
ccdproc.biassec=""
ccdproc.trim=no
ccdproc.trimsec=""
ccdproc.zerocor=yes
ccdproc.flatcor=no
##############################
# Process new images
for image in newfiles:
# Required keyword check
if not check_head(image,reqkeys):
print "Image %s is missing required keywords" % image
continue
# Attempt processing only once per image
reduced[image]=yes
if verbose:
print "Reducing new image %s" % image
# Track useful status information
scistat={}
# Prepare for processing
pix=rawpix
binroi=binroidef
biasname=biasroot+'.fits'
bpmname=bpmroot+'.pl'
filt=get_head(image,filtkey)
flatname=flatpre+filt+".fits"
# Calibration files for BINROI setting
if check_head(image,binroikey):
binroi=get_head(image,binroikey)
if binroi != binroidef:
# Get the right calibration files
fullbias=biasname
biasname=biasroot+'-'+binroi+'.fits'
fullbpm =bpmroot+'.pl'
bpmname =bpmroot+'-'+binroi+'.pl'
fullflat=flatname
flatname=flatpre+filt+'-'+binroi+".fits"
# Details of this binning/roi combo
[binval,detsec]=get_head(image,[binkey,detseckey])
binning=p60parsebin(binval)
roi=p60parseroi(detsec)
# Pixel scale (no anisotropic binning, sorry)
pix=binning[0]*rawpix
# Check for existance of bias; make it if we have to
if not os.path.exists(biasname):
print ("Failed to find bias for %s=%s; "+ \
"constructing one from full-frame bias") % \
(binroikey,binroi)
p60roiflat(fullbias,biasname,binning,roi)
# Produce the flatfield if necessary
if not os.path.exists(flatname):
p60roiflat(fullflat,flatname,binning,roi)
# Produce the bad-pixel mask if necessary
if not os.path.exists(bpmname):
# Get BPM into FITS format
bpm1=iraf.mktemp("iqbpm")+".fits"
iraf.imcopy(fullbpm,bpm1,verbose=no)
# Adjust BPM for binning/ROI
bpm2=iraf.mktemp("iqbpm")+".fits"
p60roibpm(bpm1,bpm2,binning,roi)
# Convert BPM to PL format
iraf.imcopy(bpm2,bpmname,verbose=no)
# Delete temporary files
iraf.imdel(bpm1,verify=no,go_ahead=yes)
iraf.imdel(bpm2,verify=no,go_ahead=yes)
# Bias subtraction
check_exist(biasname,"r")
ccdproc.zero=biasname
image1=biaspfx+image
check_exist(image1,'w',clobber)
iraf.ccdproc(image,output=image1,Stdout=1)
# Flatfielding
check_exist(flatname,"r")
iraf.iqflatten(image1,flatname,outpfx=flatpfx,
normflat=no,clipflat=no,
statsec=statsec,subsky=yes,
vignflat=no,clobber=yes,verbose=no)
imagef=flatpfx+image1
# Clip (bookend) flattened & sky-subtracted image
skybkg=float(get_head(image1,'SKYBKG'))
iraf.iqclip(image1,lthresh=minraw-skybkg,hthresh=maxraw-skybkg,
bookend=yes,maskin="",maskout="",clobber=yes,
verbose=no)
# Rename the file, if everything is well-behaved
reim=re.search('^%s%s(.+[^r])r?\.fits' % (flatpfx,biaspfx),
imagef,re.I)
if reim:
image2=reim.group(1)+'p.fits'
check_exist(image2,"w",yes)
iraf.imrename(imagef,image2,verbose=verbose)
else:
image2=imagef
# Set bad pixels to zero
update_head(image2,'BPM',bpmname,"Bad Pixel Mask")
iraf.iqmask(image2,mask='!BPM',method='constant',value='0.0',
clobber=yes,verbose=no)
# Defringing happens later, in bunches
# Cosmic Ray Rejection
tmpimg=iraf.mktemp("iqcr")+".fits"
check_exist("%s_%s.fits" % (image2[:15],crsfx),"w",clobber)
iraf.lacos_im(image2,tmpimg,"%s_%s.fits" % (image2[:15],crsfx),
gain=gain,readn=readn,skyval=skybkg,sigclip=4.5,
sigfrac=0.5,objlim=1.0,niter=1)
iraf.imdel(image2,verify=no,go_ahead=yes)
iraf.imcopy(tmpimg,image2,verbose=no)
iraf.imdel(tmpimg,verify=no,go_ahead=yes)
# Object-detection
if scistat.has_key("SEEING_%s" % file):
try:
fwhm=float(scistat["SEEING_%s" % file])
except:
fwhm=fwhm0
else:
fwhm=fwhm0
iraf.iqobjs(image2,sigma,satval,skyval="!SKYBKG",masksfx=masksfx,
wtimage="",fwhm=fwhm,pix=rawpix,aperture=2*fwhm/rawpix,
gain=gain,minlim=no,clobber=yes,verbose=no)
# Track some status information
nstars=get_head(image2,'NSTARS')
scistat['NSTARS']=nstars
if minskys.has_key(filt):
minsky=minskys[filt]
scistat["SKYBKG_%s" % filt]=skybkg/minsky
# Is it a focus frame?
focusval=get_head(image2,focuskey)
# Focus frame actions
if re.search(focusre,focusval,re.I):
focusfiles.append(image2)
# Find its place in the sequence
focusseq=get_head(image2,fseqkey)
ref=re.search("(\d+)of(\d+)",focusseq,re.I)
if ref:
ifocus=int(ref.group(1))
nfocus=int(ref.group(2))
# Non-focus frame actions
else:
# Make a new keyword for mosaic objects
objkey='OBJECT'
if check_head(image2,'MSCSIZE'):
mscsize=get_head(image2,'MSCSIZE')
mscdims=mscsize.split(',')
if int(mscdims[0])>1 or int(mscdims[1])>1:
object=get_head(image2,'OBJECT')
mscposn=get_head(image2,'MSCPOSN')
objmsc=object+'-'+mscposn
update_head(image2,'OBJMSC',objmsc,
'Object Name/Mosaic Position')
objkey='OBJMSC'
# Refine WCS (also calculates seeing in arcsec)
iraf.iqwcs(image2,objkey=objkey,rakey='RA',
deckey='DEC',pixtol=0.05,starfile='!STARFILE',
nstar=40,catalog='web',ubhost="localhost",
diffuse=yes,clobber=yes,verbose=verbose,
nstarmax=40)
# Retry if unsuccessful
if not check_head(image2,'IQWCS'):
iraf.iqwcs(image2,objkey=objkey,rakey='RA',
deckey='DEC',pixtol=0.05,starfile="!STARFILE",
nstar=40,catalog='web',ubhost="localhost",
diffuse=yes,clobber=yes,verbose=verbose,
nstarmax=1000)
# Calculate further quantities if WCS was successful
extinct='INDEF'
if check_head(image2,'IQWCS'):
scistat['WCSGOOD'] = 1
scistat["SEEING_%s" % filt]=get_head(image2,'SEEING')
[rapt,dcpt,lenx,leny]=get_head(image2,
['RA','DEC','NAXIS1','NAXIS2'])
[[ractr,dcctr]]=impix2wcs(image2,0.5*(1+float(lenx)),
0.5*(1+float(leny)))
coopt=astrocoords(rapt,dcpt)
cooctr=astrocoords(ractr,dcctr)
[offra,offdc]=radecdiff(coopt.radeg(),coopt.dcdeg(),
cooctr.radeg(),cooctr.dcdeg())
update_head(image2,'PTOFFSET',"%.2f %.2f" % \
(offra,offdc),
"Offset from nominal pointing (arcsec)")
scistat['PTOFFSET']="%.2f,%.2f" % (offra,offdc)
# Get zero-point against SDSS/NOMAD/USNO-B
if catmags.has_key(filt):
if check_head(image2,'OBJMSC'):
object=get_head(image2,'OBJMSC')
else:
object=get_head(image2,'OBJECT')
zpcat=""
# See if attempt already made for SDSS
if not (os.path.exists("%s.sdss" % object)):
getsdss(image2, "%s.sdss" % object)
# If successful, set to SDSS
sdssfile=open("%s.sdss" % object)
if (len(sdssfile.readlines())>2):
catalog="%s.sdss" % object
zpcat="SDSS"
# If SDSS unsuccessful, update USNO-B
if not zpcat:
update_usnob("%s.cat" % object,
outfile="%s.reg" % object)
catalog="%s.reg" % object
zpcat="USNO-B"
# Update image header
update_head(image2,"ZPCAT",zpcat)
# Run the zeropoint calculation
iraf.iqzeropt(image2,catmags[filt],
starfile="!STARFILE",catalog=catalog,
pixtol=3.0,useflags=yes,maxnum=50,
method="mean",rejout=1,fencelim=0.50,
sigma=1.5,maxfrac=0.25,
zptkey=zptkey,zpukey=zpukey,
clobber=yes,verbose=verbose)
# Use that to get an extinction estimate
zeropt,zeroptu,exptime=get_head(image2,[zptkey,
zpukey,exptmkey])
zeropt=float(zeropt)
zeroptu=float(zeroptu)
exptime=float(exptime)
scistat["ZEROPT_%s" % filt] = \
zeropt - 2.5*math.log10(exptime/exptmstd)
if minzpts.has_key(filt):
extinct = zeropt - \
2.5*math.log10(exptime/exptmstd) - \
minzpts[filt]
scistat["EXTINCT_%s" % filt]=extinct
# Fix the SEEING keyword if WCS fit was not successful
else:
scistat['WCSGOOD'] = 0
try:
seepix=float(get_head(image2,'SEEPIX'))
seeing= "%.3f" % (pix*seepix)
except:
seeing='INDEF'
update_head(image2,'SEEING',seeing,
"Estimated seeing in arcsec or INDEF")
# Write Archive Keywords
update_head(image2,'PROCESSD',1,
"Image has been processed by iqp60")
update_head(image2,'PROCVER',version,
"Version number of iqp60 used")
update_head(image2,'EXTINCT',extinct,
"Estimated mags extinction or INDEF")
update_head(image2,'PROCPROB','None',
"Problems encountered in iqp60 processing")
# Update status file
p60status(statfile,image2,scistat)
# Clean up
check_exist(image1,"w",yes)
##############################
# If a set of focus images have been processed, find best focus
if len(focusfiles)>=nfocus:
print "%d focus images have been processed" % len(focusfiles)
# Pick a starfile
focusfiles.sort()
medfile=focusfiles[len(focusfiles)/2]
starfile=get_head(medfile,'STARFILE')
# Update seeing values in all images based on good
# stars in the starfile.
ffiles=','.join(focusfiles)
iraf.iqseeing(ffiles,stars=starfile,
seekey="SEEING",method="acorr",useflags=yes,
skipstars=0,usestars=10,boxsize=64,
strictbox=no,imgsec="[940:1980,64:1980]",
update=yes,clobber=yes,verbose=no)
# Grab focus positions and seeing values
focx=[]
focy=[]
for image in focusfiles:
[fpos,seep]=list2float(get_head(image,['FOCUSPOS','SEEING']))
fpos=float(fpos)
seep=float(seep)
focx.append(fpos)
pix=rawpix
if check_head(image,binkey):
binval=get_head(image,binkey)
binels=binval.split()
binning=[int(binels[0]),int(binels[1])]
# Let's hope we don't ever do asymmetric binning
pix=binning[0]*rawpix
focy.append(pix*seep)
update_head(image,'SEEING',pix*seep,
"Estimated seeing in arcsec")
# Choose best-seeing as minimum of these
bestsee=min(focy)
bestfoc=focx[focy.index(bestsee)]
# This was too much verbosity for Brad
if verbose and 0:
print "Focus settings: "+liststr(focx,"%.2f")
print "Seeing values: "+liststr(focy,"%.1f")
print
print "Best focus: %.2f" % bestfoc
# Expunge the focusfiles list
allfocus.append(focusfiles)
focusfiles=[]
##############################
# Test end conditions
if endhow=="never":
done=no
elif endhow=="once":
done=yes
elif endhow=="time":
if time.time()>reftime:
done=yes
# Wait a little while
if not done:
time.sleep(twait)
def floydsautoredu(files,
_interactive,
_dobias,
_doflat,
_listflat,
_listbias,
_listarc,
_cosmic,
_ext_trace,
_dispersionline,
liststandard,
listatmo,
_automaticex,
_classify=False,
_verbose=False,
smooth=1,
fringing=1):
import floyds
import string, re, os, glob, sys, pickle
from numpy import array, arange, mean, pi, arccos, sin, cos, argmin
import pyfits
from pyraf import iraf
import datetime
os.environ["PYRAF_BETA_STATUS"] = "1"
iraf.set(direc=floyds.__path__[0] + '/')
_extinctdir = 'direc$standard/extinction/'
_tel = floyds.util.readkey3(
floyds.util.readhdr(re.sub('\n', '', files[0])), 'TELID')
if _tel == 'fts':
_extinction = 'ssoextinct.dat'
_observatory = 'sso'
elif _tel == 'ftn':
_extinction = 'maua.dat'
_observatory = 'cfht'
else:
sys.exit('ERROR: observatory not recognised')
dv = floyds.util.dvex()
scal = pi / 180.
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.longslit(_doprint=0)
iraf.specred(_doprint=0)
toforget = ['ccdred.flatcombine','ccdred.zerocombine','ccdproc','specred.apall','longslit.identify','longslit.reidentify',\
'specred.standard','longslit.fitcoords','specred.transform','specred.response']
for t in toforget:
iraf.unlearn(t)
iraf.longslit.dispaxi = 2
iraf.longslit.mode = 'h'
iraf.identify.fwidth = 7
iraf.identify.order = 2
iraf.specred.dispaxi = 2
iraf.specred.mode = 'h'
iraf.ccdproc.darkcor = 'no'
iraf.ccdproc.fixpix = 'no'
iraf.ccdproc.trim = 'no'
iraf.ccdproc.flatcor = 'no'
iraf.ccdproc.overscan = 'no'
iraf.ccdproc.zerocor = 'no'
iraf.ccdproc.biassec = ''
iraf.ccdproc.ccdtype = ''
iraf.ccdred.instrument = "/dev/null"
if _verbose:
iraf.ccdred.verbose = 'yes'
iraf.specred.verbose = 'yes'
else:
iraf.specred.verbose = 'no'
iraf.ccdred.verbose = 'no'
now = datetime.datetime.now()
datenow = now.strftime('20%y%m%d%H%M')
MJDtoday = 55928 + (datetime.date.today() -
datetime.date(2012, 01, 01)).days
outputlist = []
hdra = floyds.util.readhdr(re.sub('\n', '', files[0]))
_gain = floyds.util.readkey3(hdra, 'gain')
_rdnoise = floyds.util.readkey3(hdra, 'ron')
std, rastd, decstd, magstd = floyds.util.readstandard(
'standard_floyds_mab.txt')
_naxis2 = hdra.get('NAXIS2')
_naxis1 = hdra.get('NAXIS1')
if not _naxis1: _naxis1 = 2079
if not _naxis2:
if not hdr0.get('HDRVER'): _naxis1 = 511
else: _naxis1 = 512
_overscan = '[2049:' + str(_naxis1) + ',1:' + str(_naxis2) + ']'
_biassecblu = '[380:2048,325:' + str(_naxis2) + ']'
_biassecred = '[1:1800,1:350]'
lista = {}
objectlist = {}
biaslist = {}
flatlist = {}
flatlistd = {}
arclist = {}
max_length = 14
for img in files:
hdr0 = floyds.util.readhdr(img)
if floyds.util.readkey3(hdr0, 'naxis2') >= 500:
if 'blu' not in lista: lista['blu'] = []
if 'red' not in lista: lista['red'] = []
_object0 = floyds.util.readkey3(hdr0, 'object')
_object0 = re.sub(':', '', _object0) # colon
_object0 = re.sub('/', '', _object0) # slash
_object0 = re.sub('\s', '', _object0) # any whitespace
_object0 = re.sub('\(', '', _object0) # open parenthesis
_object0 = re.sub('\[', '', _object0) # open square bracket
_object0 = re.sub('\)', '', _object0) # close parenthesis
_object0 = re.sub('\]', '', _object0) # close square bracket
if len(_object0) > max_length:
_object0 = _object0[:max_length]
_date0 = floyds.util.readkey3(hdr0, 'date-night')
_tel = floyds.util.readkey3(hdr0, 'TELID')
_type = floyds.util.readkey3(hdr0, 'OBSTYPE')
if not _type: _type = floyds.util.readkey3(hdr0, 'imagetyp')
_slit = floyds.util.readkey3(hdr0, 'slit')
if _type:
_type = _type.lower()
if _type in ['sky', 'spectrum', 'expose']:
nameoutb = str(_object0) + '_' + _tel + '_' + str(
_date0) + '_blue_' + str(_slit) + '_' + str(MJDtoday)
nameoutr = str(_object0) + '_' + _tel + '_' + str(
_date0) + '_red_' + str(_slit) + '_' + str(MJDtoday)
elif _type in ['lamp', 'arc', 'l']:
nameoutb = 'arc_' + str(_object0) + '_' + _tel + '_' + str(
_date0) + '_blue_' + str(_slit) + '_' + str(MJDtoday)
nameoutr = 'arc_' + str(_object0) + '_' + _tel + '_' + str(
_date0) + '_red_' + str(_slit) + '_' + str(MJDtoday)
elif _type in ['flat', 'f', 'lampflat', 'lamp-flat']:
nameoutb = 'flat_' + str(_object0) + '_' + _tel + '_' + str(
_date0) + '_blue_' + str(_slit) + '_' + str(MJDtoday)
nameoutr = 'flat_' + str(_object0) + '_' + _tel + '_' + str(
_date0) + '_red_' + str(_slit) + '_' + str(MJDtoday)
else:
nameoutb = str(_type.lower(
)) + '_' + str(_object0) + '_' + _tel + '_' + str(
_date0) + '_blue_' + str(_slit) + '_' + str(MJDtoday)
nameoutr = str(_type.lower(
)) + '_' + str(_object0) + '_' + _tel + '_' + str(
_date0) + '_red_' + str(_slit) + '_' + str(MJDtoday)
bimg = floyds.util.name_duplicate(img, nameoutb, '')
rimg = floyds.util.name_duplicate(img, nameoutr, '')
####
floyds.util.delete(bimg)
floyds.util.delete(rimg)
iraf.imcopy(img, bimg, verbose='no')
iraf.imcopy(img, rimg, verbose='no')
aaa = iraf.hedit(bimg,
'CCDSEC',
delete='yes',
update='yes',
verify='no',
Stdout=1)
aaa = iraf.hedit(bimg,
'TRIMSEC',
delete='yes',
update='yes',
verify='no',
Stdout=1)
aaa = iraf.hedit(rimg,
'CCDSEC',
delete='yes',
update='yes',
verify='no',
Stdout=1)
aaa = iraf.hedit(rimg,
'TRIMSEC',
delete='yes',
update='yes',
verify='no',
Stdout=1)
iraf.ccdproc(bimg,output='', overscan="yes", trim="yes", zerocor='no', flatcor='no', zero='', ccdtype='',\
fixpix='no', trimsec=_biassecblu, biassec=_overscan, readaxi='line', Stdout=1)
iraf.ccdproc(rimg,output='', overscan="yes", trim="yes", zerocor='no', flatcor='no', zero='', ccdtype='',\
fixpix='no', trimsec=_biassecred, biassec=_overscan, readaxi='line', Stdout=1)
floyds.util.updateheader(bimg, 0,
{'GRISM': ['blu', ' blue order']})
floyds.util.updateheader(rimg, 0,
{'GRISM': ['red', ' blue order']})
floyds.util.updateheader(
bimg, 0, {'arcfile': [img, 'file name in the archive']})
floyds.util.updateheader(
rimg, 0, {'arcfile': [img, 'file name in the archive']})
lista['blu'].append(bimg)
lista['red'].append(rimg)
else:
print 'warning type not defined'
for arm in lista.keys():
for img in lista[arm]:
print img
hdr = floyds.util.readhdr(img)
_type = floyds.util.readkey3(hdr, 'OBSTYPE')
if _type == 'EXPOSE':
_type = floyds.util.readkey3(hdr, 'imagetyp')
if not _type: _type = 'EXPOSE'
if _type == 'EXPOSE':
print 'warning obstype still EXSPOSE, are this old data ? run manually floydsfixheader'
_slit = floyds.util.readkey3(hdr, 'slit')
_grpid = floyds.util.readkey3(hdr, 'grpid')
if _type.lower() in ['flat', 'f', 'lamp-flat', 'lampflat']:
if (arm, _slit) not in flatlist: flatlist[arm, _slit] = {}
if _grpid not in flatlist[arm, _slit]:
flatlist[arm, _slit][_grpid] = [img]
else:
flatlist[arm, _slit][_grpid].append(img)
elif _type.lower() in ['lamp', 'l', 'arc']:
if (arm, _slit) not in arclist: arclist[arm, _slit] = {}
if _grpid not in arclist[arm, _slit]:
arclist[arm, _slit][_grpid] = [img]
else:
arclist[arm, _slit][_grpid].append(img)
elif _type in ['bias', 'b']:
if arm not in biaslist: biaslist[arm] = []
biaslist[arm].append(img)
elif _type.lower() in ['sky', 's', 'spectrum']:
try:
_ra = float(floyds.util.readkey3(hdr, 'RA'))
_dec = float(floyds.util.readkey3(hdr, 'DEC'))
except:
ra00 = string.split(floyds.util.readkey3(hdr, 'RA'), ':')
ra0, ra1, ra2 = float(ra00[0]), float(ra00[1]), float(
ra00[2])
_ra = ((ra2 / 60. + ra1) / 60. + ra0) * 15.
dec00 = string.split(floyds.util.readkey3(hdr, 'DEC'), ':')
dec0, dec1, dec2 = float(dec00[0]), float(dec00[1]), float(
dec00[2])
if '-' in str(dec0):
_dec = (-1) * ((dec2 / 60. + dec1) / 60. +
((-1) * dec0))
else:
_dec = (dec2 / 60. + dec1) / 60. + dec0
dd = arccos(
sin(_dec * scal) * sin(decstd * scal) +
cos(_dec * scal) * cos(decstd * scal) * cos(
(_ra - rastd) * scal)) * ((180 / pi) * 3600)
if _verbose:
print _ra, _dec
print std[argmin(dd)], min(dd)
if min(dd) < 5200: _typeobj = 'std'
else: _typeobj = 'obj'
if min(dd) < 5200:
floyds.util.updateheader(
img, 0, {'stdname': [std[argmin(dd)], '']})
floyds.util.updateheader(
img, 0, {'magstd': [float(magstd[argmin(dd)]), '']})
if _typeobj not in objectlist: objectlist[_typeobj] = {}
if (arm, _slit) not in objectlist[_typeobj]:
objectlist[_typeobj][arm, _slit] = [img]
else:
objectlist[_typeobj][arm, _slit].append(img)
if _verbose:
print 'object'
print objectlist
print 'flat'
print flatlist
print 'bias'
print biaslist
print 'arc'
print arclist
if liststandard and 'std' in objectlist.keys():
print 'external standard, raw standard not used'
del objectlist['std']
sens = {}
outputfile = {}
atmo = {}
for tpe in objectlist:
if tpe not in outputfile: outputfile[tpe] = {}
for setup in objectlist[tpe]:
if setup not in sens: sens[setup] = []
print '\n### setup= ', setup, '\n### objects= ', objectlist[tpe][
setup], '\n'
for img in objectlist[tpe][setup]:
print '\n\n### next object= ', img, ' ', floyds.util.readkey3(
floyds.util.readhdr(img), 'object'), '\n'
hdr = floyds.util.readhdr(img)
archfile = floyds.util.readkey3(hdr, 'arcfile')
_gain = floyds.util.readkey3(hdr, 'gain')
_rdnoise = floyds.util.readkey3(hdr, 'ron')
_grism = floyds.util.readkey3(hdr, 'grism')
_grpid = floyds.util.readkey3(hdr, 'grpid')
if archfile not in outputfile[tpe]:
outputfile[tpe][archfile] = []
##################### flat ###############
if _listflat: flatgood = _listflat # flat list from reducer
elif setup in flatlist:
if _grpid in flatlist[setup]:
print '\n###FLAT WITH SAME GRPID'
flatgood = flatlist[setup][
_grpid] # flat in the raw data
else:
flatgood = []
for _grpid0 in flatlist[setup].keys():
for ii in flatlist[setup][_grpid0]:
flatgood.append(ii)
else:
flatgood = []
if len(flatgood) != 0:
if len(flatgood) > 1:
f = open('_oflatlist', 'w')
for fimg in flatgood:
print fimg
f.write(fimg + '\n')
f.close()
floyds.util.delete('flat' + img)
iraf.ccdred.flatcombine('"@_oflatlist"',
output='flat' + img,
combine='average',
reject='none',
ccdtype=' ',
rdnoise=_rdnoise,
gain=_gain,
process='no',
Stdout=1)
floyds.util.delete('_oflatlist')
flatfile = 'flat' + img
elif len(flatgood) == 1:
os.system('cp ' + flatgood[0] + ' flat' + img)
flatfile = 'flat' + img
else:
flatfile = ''
########################## find arcfile #######################
arcfile = ''
if _listarc:
arcfile = [floyds.util.searcharc(img, _listarc)[0]
][0] # take arc from list
if not arcfile and setup in arclist.keys():
if _grpid in arclist[setup]:
print '\n###ARC WITH SAME GRPID'
arcfile = arclist[setup][
_grpid] # flat in the raw data
else:
arcfile = []
for _grpid0 in arclist[setup].keys():
for ii in arclist[setup][_grpid0]:
arcfile.append(ii)
if arcfile:
if len(arcfile) > 1: # more than one arc available
print arcfile
# _arcclose=floyds.util.searcharc(imgex,arcfile)[0] # take the closest in time
_arcclose = floyds.sortbyJD(arcfile)[
-1] # take the last arc of the sequence
if _interactive.upper() in ['YES', 'Y']:
for ii in floyds.floydsspecdef.sortbyJD(arcfile):
print '\n### ', ii
arcfile = raw_input(
'\n### more than one arcfile available, which one to use ['
+ str(_arcclose) + '] ? ')
if not arcfile: arcfile = _arcclose
else: arcfile = _arcclose
else: arcfile = arcfile[0]
else: print '\n### Warning: no arc found'
################################################################### rectify
if setup[0] == 'red':
fcfile = floyds.__path__[
0] + '/standard/ident/fcrectify_' + _tel + '_red'
fcfile1 = floyds.__path__[
0] + '/standard/ident/fcrectify1_' + _tel + '_red'
print fcfile
else:
fcfile = floyds.__path__[
0] + '/standard/ident/fcrectify_' + _tel + '_blue'
fcfile1 = floyds.__path__[
0] + '/standard/ident/fcrectify1_' + _tel + '_blue'
print fcfile
print img, arcfile, flatfile
img0 = img
if img and not img in outputfile[tpe][archfile]:
outputfile[tpe][archfile].append(img)
if arcfile and arcfile not in outputfile[tpe][archfile]:
outputfile[tpe][archfile].append(arcfile)
if flatfile and flatfile not in outputfile[tpe][archfile]:
outputfile[tpe][archfile].append(flatfile)
img, arcfile, flatfile = floyds.floydsspecdef.rectifyspectrum(
img, arcfile, flatfile, fcfile, fcfile1, 'no', _cosmic)
print outputfile
if img and not img in outputfile[tpe][archfile]:
outputfile[tpe][archfile].append(img)
if arcfile and arcfile not in outputfile[tpe][archfile]:
outputfile[tpe][archfile].append(arcfile)
if flatfile and flatfile not in outputfile[tpe][archfile]:
outputfile[tpe][archfile].append(flatfile)
print outputfile
################################################################### check wavecalib
if tpe == 'std' or floyds.util.readkey3(
floyds.util.readhdr(img), 'exptime') < 300:
if setup[0] == 'red':
print '\n### check standard wave calib'
#print img
data, hdr = pyfits.getdata(img, 0, header=True)
y = data.mean(1)
import numpy as np
if np.argmax(y) < 80 and np.argmax(y) > 15:
y2 = data[np.argmax(y) - 3:np.argmax(y) +
3].mean(0)
yy2 = data[np.argmax(y) - 9:np.argmax(y) -
3].mean(0)
floyds.util.delete('_std.fits')
pyfits.writeto('_std.fits', np.float32(y2 - yy2),
hdr)
#print '_std.fits',_interactive
shift = floyds.floydsspecdef.checkwavestd(
'_std.fits', _interactive, 2)
zro = hdr['CRVAL1']
floyds.util.updateheader(
img, 0, {'CRVAL1': [zro + int(shift), '']})
floyds.util.updateheader(
img, 0, {'shift': [float(shift), '']})
floyds.util.delete('_std.fits')
else:
print 'object not found'
else:
print '\n### warning check in wavelength not possible for short exposure in the blu range '
else:
print '\n### check object wave calib'
_skyfile = floyds.__path__[
0] + '/standard/ident/sky_' + setup[0] + '.fits'
data, hdr = pyfits.getdata(img, 0, header=True)
y = data.mean(1)
import numpy as np
if np.argmax(y) < 80 and np.argmax(y) > 15:
yy1 = data[10:np.argmax(y) - 9].mean(0)
yy2 = data[np.argmax(y) + 9:-10].mean(0)
floyds.util.delete('_sky.fits')
pyfits.writeto('_sky.fits', np.float32(yy1 + yy2), hdr)
shift = floyds.floydsspecdef.checkwavelength_obj(
'_sky.fits', _skyfile, _interactive, 2)
floyds.util.delete('_sky.fits')
zro = hdr['CRVAL1']
floyds.util.updateheader(
img, 0, {'CRVAL1': [zro + int(shift), '']})
floyds.util.updateheader(img, 0,
{'shift': [float(shift), '']})
else:
print 'object not found'
#################################################### flat field
if img and flatfile and setup[0] == 'red':
imgn = 'n' + img
hdr1 = floyds.readhdr(img)
hdr2 = floyds.readhdr(flatfile)
_grpid1 = floyds.util.readkey3(hdr1, 'grpid')
_grpid2 = floyds.util.readkey3(hdr2, 'grpid')
if _grpid1 == _grpid2:
print flatfile, img, setup[0]
imgn = floyds.fringing_classicmethod2(
flatfile, img, 'no', '*', 15, setup[0])
else:
print 'Warning flat not the same OB'
imgex = floyds.floydsspecdef.extractspectrum(
img,
dv,
_ext_trace,
_dispersionline,
_interactive,
tpe,
automaticex=_automaticex)
floyds.delete('flat' + imgex)
iraf.specred.apsum(flatfile,output='flat'+imgex,referen=img,interac='no',find='no',recente='no',resize='no',\
edit='no',trace='no',fittrac='no',extract='yes',extras='no',review='no',backgro='none')
fringingmask = floyds.normflat('flat' + imgex)
print '\n### fringing correction'
print imgex, fringingmask
imgex, scale, shift = floyds.correctfringing_auto(
imgex, fringingmask) # automatic correction
shift = int(
.5 + float(shift) /
3.5) # shift from correctfringing_auto in Angstrom
print '\n##### flat scaling: ', str(scale), str(shift)
########################################################
datax, hdrx = pyfits.getdata(flatfile, 0, header=True)
xdim = hdrx['NAXIS1']
ydim = hdrx['NAXIS2']
iraf.specred.apedit.nsum = 15
iraf.specred.apedit.width = 100.
iraf.specred.apedit.line = 1024
iraf.specred.apfind.minsep = 20.
iraf.specred.apfind.maxsep = 1000.
iraf.specred.apresize.bkg = 'no'
iraf.specred.apresize.ylevel = 0.5
iraf.specred.aptrace.nsum = 10
iraf.specred.aptrace.step = 10
iraf.specred.aptrace.nlost = 10
floyds.util.delete('n' + flatfile)
floyds.util.delete('norm.fits')
floyds.util.delete('n' + img)
floyds.util.delete(re.sub('.fits', 'c.fits', flatfile))
iraf.imcopy(flatfile + '[500:' + str(xdim) + ',*]',
re.sub('.fits', 'c.fits', flatfile),
verbose='no')
iraf.imarith(flatfile,
'/',
flatfile,
'norm.fits',
verbose='no')
flatfile = re.sub('.fits', 'c.fits', flatfile)
floyds.util.delete('n' + flatfile)
iraf.unlearn(iraf.specred.apflatten)
floyds.floydsspecdef.aperture(flatfile)
iraf.specred.apflatten(flatfile,output='n'+flatfile,interac=_interactive,find='no',recenter='no', resize='no',edit='no',trace='no',\
fittrac='no',fitspec='no', flatten='yes', aperture='',\
pfit='fit2d',clean='no',function='legendre',order=15,sample = '*', mode='ql')
iraf.imcopy('n' + flatfile,
'norm.fits[500:' + str(xdim) + ',*]',
verbose='no')
floyds.util.delete('n' + flatfile)
floyds.util.delete('n' + img)
iraf.imrename('norm.fits',
'n' + flatfile,
verbose='no')
imgn = floyds.floydsspecdef.applyflat(
img, 'n' + flatfile, 'n' + img, scale, shift)
else:
imgn = ''
if imgn and not imgn in outputfile[tpe][archfile]:
outputfile[tpe][archfile].append(imgn)
################################################### 2D flux calib
print '####### ' + imgn
hdr = floyds.util.readhdr(img)
_sens = ''
if liststandard:
_sens = floyds.util.searchsens(
img,
liststandard)[0] # search in the list from reducer
if not _sens:
try:
_sens = floyds.util.searchsens(
img, sens[setup])[0] # search in the reduced data
except:
_sens = floyds.util.searchsens(
img, '')[0] # search in tha archive
if _sens:
if _sens[0] == '/':
os.system('cp ' + _sens + ' .')
_sens = string.split(_sens, '/')[-1]
imgd = fluxcalib2d(img, _sens)
if imgn: imgdn = fluxcalib2d(imgn, _sens)
else: imgdn = ''
if _sens not in outputfile[tpe][archfile]:
outputfile[tpe][archfile].append(_sens)
else:
imgdn = ''
print '\n### do 2D calibration'
else:
imgd = ''
imgdn = ''
################ extraction ####################################
if imgdn:
try:
imgdnex = floyds.floydsspecdef.extractspectrum(
imgdn,
dv,
_ext_trace,
_dispersionline,
_interactive,
tpe,
automaticex=_automaticex)
except:
imgdnex = ''
else:
imgdnex = ''
if imgd:
try:
imgdex = floyds.floydsspecdef.extractspectrum(
imgd,
dv,
_ext_trace,
_dispersionline,
_interactive,
tpe,
automaticex=_automaticex)
except:
imgdex = ''
else:
imgdex = ''
if imgd and not imgd in outputfile[tpe][archfile]:
outputfile[tpe][archfile].append(imgd)
if imgdn and not imgdn in outputfile[tpe][archfile]:
outputfile[tpe][archfile].append(imgdn)
if imgdnex and imgdnex not in outputfile[tpe][archfile]:
outputfile[tpe][archfile].append(imgdnex)
if imgdex and imgdex not in outputfile[tpe][archfile]:
outputfile[tpe][archfile].append(imgdex)
if tpe == 'std':
if imgn:
try:
imgnex = floyds.floydsspecdef.extractspectrum(
imgn,
dv,
_ext_trace,
_dispersionline,
_interactive,
tpe,
automaticex=_automaticex)
except:
imgnex = ''
elif img:
try:
imgnex = floyds.floydsspecdef.extractspectrum(
img,
dv,
_ext_trace,
_dispersionline,
_interactive,
tpe,
automaticex=_automaticex)
except:
imgnex = ''
if imgnex:
hdrs = floyds.util.readhdr(imgnex)
_tel = floyds.util.readkey3(hdrs, 'TELID')
try:
_outputsens2='sens_'+_tel+'_'+str(floyds.util.readkey3(hdrs,'date-night'))+'_'+str(floyds.util.readkey3(hdrs,'grism'))+\
'_'+re.sub('.dat','',floyds.util.readkey3(hdrs,'stdname'))+'_'+str(MJDtoday)
except:
sys.exit(
'Error: missing header -stdname- in standard '
+ str(standardfile) + ' ')
print '\n### compute sensitivity function and atmofile'
if setup[0] == 'red':
atmofile = floyds.floydsspecdef.telluric_atmo(
imgnex)
#print atmofile
if atmofile and atmofile not in outputfile[tpe][
archfile]:
outputfile[tpe][archfile].append(atmofile)
stdusedclean = re.sub('_ex.fits', '_clean.fits',
imgnex)
floyds.util.delete(stdusedclean)
_function = 'spline3'
iraf.specred.sarith(input1=imgnex,
op='/',
input2=atmofile,
output=stdusedclean,
format='multispec')
try:
_outputsens2 = floyds.floydsspecdef.sensfunction(
stdusedclean, _outputsens2, _function, 8,
_interactive)
except:
print 'Warning: problem computing sensitivity function'
_outputsens2 = ''
if setup not in atmo: atmo[setup] = [atmofile]
else: atmo[setup].append(atmofile)
else:
_function = 'spline3'
try:
_outputsens2 = floyds.floydsspecdef.sensfunction(
imgnex, _outputsens2, _function, 12,
_interactive, '3400:4700') #,3600:4300')
except:
print 'Warning: problem computing sensitivity function'
_outputsens2 = ''
if _outputsens2 and _outputsens2 not in outputfile[
tpe][archfile]:
outputfile[tpe][archfile].append(_outputsens2)
###################################################
print outputfile
if 'obj' in outputfile:
for imm in outputfile['obj']:
lista = outputfile['obj'][imm]
lista1 = []
for i in lista:
if '_ex.fits' in i:
lista1.append(i)
lista.pop(lista.index(i))
done = []
for img in lista1:
_output = ''
if img not in done:
if '_red_' in img:
redfile = img
if re.sub('_red_', '_blue_', redfile)[1:] in lista1:
bluefile = lista1[lista1.index(
re.sub('_red_', '_blue_', redfile)[1:])]
_output = re.sub('_red_', '_merge_', redfile)
try:
_output = floyds.floydsspecdef.combspec(
bluefile,
redfile,
_output,
scale=True,
num=None)
lista.append(_output)
done.append(redfile)
done.append(bluefile)
floyds.util.delete(bluefile)
floyds.util.delete(redfile)
floyds.util.delete(redfile[1:])
except:
done.append(redfile)
lista.append(redfile)
else:
done.append(redfile)
lista.append(redfile)
elif '_blue_' in img:
bluefile = img
if 'n' + re.sub('_blue_', '_red_', bluefile) in lista1:
redfile = lista1[lista1.index(
'n' + re.sub('_blue_', '_red_', bluefile))]
_output = re.sub('_red_', '_merge_', redfile)
try:
_output = floyds.floydsspecdef.combspec(
bluefile,
redfile,
_output,
scale=True,
num=None)
lista.append(_output)
done.append(redfile)
done.append(bluefile)
floyds.util.delete(bluefile)
floyds.util.delete(redfile)
floyds.util.delete(redfile[1:])
except:
done.append(bluefile)
lista.append(bluefile)
else:
done.append(bluefile)
lista.append(bluefile)
outputfile['obj'][imm] = lista
readme = floyds.floydsspecauto.writereadme()
return outputfile, readme
def flatten(self, qombos, flat=None, flatscale=no):
if type(qombos) is StringType:
qombo = qombos
qombos = [qombo]
# Loop over input list of qombos
for qombo in qombos:
# Check for presence of calibration files
obj, filt = qombo_split(qombo)
if not self.check_calib(filt):
self.calib()
# Corresponding files
offiles = self.science_images(qombo)
if len(offiles) == 0:
continue
# Make a text ".lis" file to record this
putlines(qombo + '.lis', offiles, clobber=yes)
# Rescale flatfield if requested
inflat = self.flatpre + filt + '.fits'
outflat = self.flatpre + qombo + '.fits'
check_exist(outflat, "w", clobber=self.clobber)
if flatscale:
nirc_flatscale(offiles,
inflat,
outflat,
clipfrac=0.02,
clobber=yes)
else:
iraf.imcopy(inflat, outflat, verbose=no)
check_exist(outflat, "r")
# Clip flatfield and update BPM
iraf.iqclip(outflat,
lthresh=self.flatlow,
hthresh=self.flathigh,
bookend=no,
replace=1.0,
maskin='!BPM',
maskval=1,
maskout=self.bpmsfx)
# Rename the updated BPM
newbpm1 = get_head(outflat, 'BPM')
newbpm2 = newbpm1.replace(self.flatpre, '')
check_exist(newbpm2, 'w', clobber=self.clobber)
iraf.imrename(newbpm1, newbpm2, verbose=no)
update_head(outflat, 'BPM', newbpm2)
# Further preprocessing
for image in offiles:
# Keyword updates
update_head(image, [self.filtset, 'BPM'], [filt, newbpm2])
# Flatfielding
iraf.iqflatten(image,
outflat,
outpfx=self.flatpfx,
normflat=no,
statsec=self.statsec,
subsky=yes,
vignflat=no,
clipflat=no,
clobber=yes,
verbose=no)
# Set bad pixels to zero
image2 = self.flatpfx + image
iraf.iqmask(image2,
mask='!BPM',
method='constant',
value=0.0,
clobber=yes,
verbose=no)
# Write Useful Keywords
update_head(image2, 'PROCESSD', 1,
"Image has been processed by iqnirc")
update_head(image2, 'PROCVER', version,
"Version number of iqnirc used")
update_head(image2, 'ZEROPT', 'INDEF',
"Zero-point relative to 2MASS or INDEF")
update_head(image2, 'PROCPROB', 'None',
"Problems encountered in iqnirc processing")
# List of flatfielded images
offiles2 = pfx_list(offiles, self.flatpfx)
oflist2 = ','.join(offiles2)
# Object-detection
iraf.iqobjs(oflist2,
self.sigma,
self.satval,
skyval="!SKYBKG",
masksfx=self.masksfx,
wtimage="",
minlim=no,
clobber=yes,
verbose=no)
# Attempt WCS refinement
iraf.iqwcs(oflist2,
objkey=self.objkey,
rakey='RA',
deckey='DEC',
pixscl=self.pix,
pixtol=0.05,
starfile='!STARFILE',
catalog='ir',
diffuse=yes,
clobber=yes,
verbose=self.verbose)
# Done with Step #3
update_head(offiles2, 'IQWRCSTP', 3, "Stage of IQWIRC processing")
def floydsautoredu(files,_interactive,_dobias,_doflat,_listflat,_listbias,_listarc,_cosmic,_ext_trace,_dispersionline,liststandard,listatmo,_automaticex,_classify=False,_verbose=False,smooth=1,fringing=1):
import floyds
import string,re,os,glob,sys,pickle
from numpy import array, arange, mean,pi,arccos,sin,cos,argmin
from astropy.io import fits
from pyraf import iraf
import datetime
os.environ["PYRAF_BETA_STATUS"] = "1"
iraf.set(direc=floyds.__path__[0]+'/')
_extinctdir='direc$standard/extinction/'
_tel=floyds.util.readkey3(floyds.util.readhdr(re.sub('\n','',files[0])),'TELID')
if _tel=='fts':
_extinction='ssoextinct.dat'
_observatory='sso'
elif _tel=='ftn':
_extinction='maua.dat'
_observatory='cfht'
else: sys.exit('ERROR: observatory not recognised')
dv=floyds.util.dvex()
scal=pi/180.
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.longslit(_doprint=0)
iraf.specred(_doprint=0)
toforget = ['ccdred.flatcombine','ccdred.zerocombine','ccdproc','specred.apall','longslit.identify','longslit.reidentify',\
'specred.standard','longslit.fitcoords','specred.transform','specred.response']
for t in toforget: iraf.unlearn(t)
iraf.longslit.dispaxi=2
iraf.longslit.mode='h'
iraf.identify.fwidth=7
iraf.identify.order=2
iraf.specred.dispaxi=2
iraf.specred.mode='h'
iraf.ccdproc.darkcor='no'
iraf.ccdproc.fixpix='no'
iraf.ccdproc.trim='no'
iraf.ccdproc.flatcor='no'
iraf.ccdproc.overscan='no'
iraf.ccdproc.zerocor='no'
iraf.ccdproc.biassec=''
iraf.ccdproc.ccdtype=''
iraf.ccdred.instrument = "/dev/null"
if _verbose:
iraf.ccdred.verbose='yes'
iraf.specred.verbose='yes'
else:
iraf.specred.verbose='no'
iraf.ccdred.verbose='no'
now=datetime.datetime.now()
datenow=now.strftime('20%y%m%d%H%M')
MJDtoday=55928+(datetime.date.today()-datetime.date(2012, 01, 01)).days
outputlist=[]
hdra=floyds.util.readhdr(re.sub('\n','',files[0]))
_gain=floyds.util.readkey3(hdra,'gain')
_rdnoise=floyds.util.readkey3(hdra,'ron')
std,rastd,decstd,magstd=floyds.util.readstandard('standard_floyds_mab.txt')
_naxis2=hdra.get('NAXIS2')
_naxis1=hdra.get('NAXIS1')
if not _naxis1: _naxis1=2079
if not _naxis2:
if not hdr0.get('HDRVER'): _naxis1=511
else: _naxis1=512
_overscan='[2049:'+str(_naxis1)+',1:'+str(_naxis2)+']'
_biassecblu='[380:2048,325:'+str(_naxis2)+']'
_biassecred='[1:1800,1:350]'
lista={}
objectlist={}
biaslist={}
flatlist={}
flatlistd={}
arclist={}
max_length=14
for img in files:
hdr0=floyds.util.readhdr(img)
if floyds.util.readkey3(hdr0,'naxis2')>=500:
if 'blu' not in lista: lista['blu']=[]
if 'red' not in lista: lista['red']=[]
_object0=floyds.util.readkey3(hdr0,'object')
_object0 = re.sub(':', '', _object0) # colon
_object0 = re.sub('/', '', _object0) # slash
_object0 = re.sub('\s', '', _object0) # any whitespace
_object0 = re.sub('\(', '', _object0) # open parenthesis
_object0 = re.sub('\[', '', _object0) # open square bracket
_object0 = re.sub('\)', '', _object0) # close parenthesis
_object0 = re.sub('\]', '', _object0) # close square bracket
_object0 = _object0.replace(r'\t', '') # Any tab characters
_object0 = _object0.replace('*', '') # Any asterisks
if len(_object0) > max_length:
_object0 = _object0[:max_length]
_date0=floyds.util.readkey3(hdr0,'date-night')
_tel=floyds.util.readkey3(hdr0,'TELID')
_type=floyds.util.readkey3(hdr0,'OBSTYPE')
if not _type: _type=floyds.util.readkey3(hdr0,'imagetyp')
_slit=floyds.util.readkey3(hdr0,'slit')
if _type:
_type = _type.lower()
if _type in ['sky','spectrum','expose']:
nameoutb=str(_object0)+'_'+_tel+'_'+str(_date0)+'_blue_'+str(_slit)+'_'+str(MJDtoday)
nameoutr=str(_object0)+'_'+_tel+'_'+str(_date0)+'_red_'+str(_slit)+'_'+str(MJDtoday)
elif _type in ['lamp','arc','l']:
nameoutb='arc_'+str(_object0)+'_'+_tel+'_'+str(_date0)+'_blue_'+str(_slit)+'_'+str(MJDtoday)
nameoutr='arc_'+str(_object0)+'_'+_tel+'_'+str(_date0)+'_red_'+str(_slit)+'_'+str(MJDtoday)
elif _type in ['flat','f','lampflat','lamp-flat']:
nameoutb='flat_'+str(_object0)+'_'+_tel+'_'+str(_date0)+'_blue_'+str(_slit)+'_'+str(MJDtoday)
nameoutr='flat_'+str(_object0)+'_'+_tel+'_'+str(_date0)+'_red_'+str(_slit)+'_'+str(MJDtoday)
else:
nameoutb=str(_type.lower())+'_'+str(_object0)+'_'+_tel+'_'+str(_date0)+'_blue_'+str(_slit)+'_'+str(MJDtoday)
nameoutr=str(_type.lower())+'_'+str(_object0)+'_'+_tel+'_'+str(_date0)+'_red_'+str(_slit)+'_'+str(MJDtoday)
bimg=floyds.util.name_duplicate(img,nameoutb,'')
rimg=floyds.util.name_duplicate(img,nameoutr,'')
####
floyds.util.delete(bimg)
floyds.util.delete(rimg)
iraf.imcopy(img,bimg,verbose='no')
iraf.imcopy(img,rimg,verbose='no')
aaa=iraf.hedit(bimg,'CCDSEC',delete='yes',update='yes',verify='no',Stdout=1)
aaa=iraf.hedit(bimg,'TRIMSEC',delete='yes',update='yes',verify='no',Stdout=1)
aaa=iraf.hedit(rimg,'CCDSEC',delete='yes',update='yes',verify='no',Stdout=1)
aaa=iraf.hedit(rimg,'TRIMSEC',delete='yes',update='yes',verify='no',Stdout=1)
iraf.ccdproc(bimg,output='', overscan="yes", trim="yes", zerocor='no', flatcor='no', zero='', ccdtype='',\
fixpix='no', trimsec=_biassecblu, biassec=_overscan, readaxi='line', Stdout=1)
iraf.ccdproc(rimg,output='', overscan="yes", trim="yes", zerocor='no', flatcor='no', zero='', ccdtype='',\
fixpix='no', trimsec=_biassecred, biassec=_overscan, readaxi='line', Stdout=1)
floyds.util.updateheader(bimg,0,{'GRISM':['blu',' blue order']})
floyds.util.updateheader(rimg,0,{'GRISM':['red',' blue order']})
floyds.util.updateheader(bimg,0,{'arcfile':[img,'file name in the archive']})
floyds.util.updateheader(rimg,0,{'arcfile':[img,'file name in the archive']})
lista['blu'].append(bimg)
lista['red'].append(rimg)
else:
print 'warning type not defined'
for arm in lista.keys():
for img in lista[arm]:
print img
hdr=floyds.util.readhdr(img)
_type=floyds.util.readkey3(hdr,'OBSTYPE')
if _type=='EXPOSE':
_type=floyds.util.readkey3(hdr,'imagetyp')
if not _type: _type='EXPOSE'
if _type=='EXPOSE':
print 'warning obstype still EXPOSE, are this old data ? run manually floydsfixheader'
_slit=floyds.util.readkey3(hdr,'slit')
_grpid=floyds.util.readkey3(hdr,'grpid')
if _type.lower() in ['flat','f','lamp-flat','lampflat'] :
if (arm,_slit) not in flatlist: flatlist[arm,_slit]={}
if _grpid not in flatlist[arm,_slit]: flatlist[arm,_slit][_grpid]=[img]
else: flatlist[arm,_slit][_grpid].append(img)
elif _type.lower() in ['lamp','l','arc']:
if (arm,_slit) not in arclist: arclist[arm,_slit]={}
if _grpid not in arclist[arm,_slit]: arclist[arm,_slit][_grpid]=[img]
else: arclist[arm,_slit][_grpid].append(img)
elif _type in ['bias','b']:
if arm not in biaslist: biaslist[arm]=[]
biaslist[arm].append(img)
elif _type.lower() in ['sky','s','spectrum']:
try:
_ra=float(floyds.util.readkey3(hdr,'RA'))
_dec=float(floyds.util.readkey3(hdr,'DEC'))
except:
ra00=string.split(floyds.util.readkey3(hdr,'RA'),':')
ra0,ra1,ra2=float(ra00[0]),float(ra00[1]),float(ra00[2])
_ra=((ra2/60.+ra1)/60.+ra0)*15.
dec00=string.split(floyds.util.readkey3(hdr,'DEC'),':')
dec0,dec1,dec2=float(dec00[0]),float(dec00[1]),float(dec00[2])
if '-' in str(dec0): _dec=(-1)*((dec2/60.+dec1)/60.+((-1)*dec0))
else: _dec=(dec2/60.+dec1)/60.+dec0
dd=arccos(sin(_dec*scal)*sin(decstd*scal)+cos(_dec*scal)*cos(decstd*scal)*cos((_ra-rastd)*scal))*((180/pi)*3600)
if _verbose:
print _ra,_dec
print std[argmin(dd)],min(dd)
if min(dd)<5200: _typeobj='std'
else: _typeobj='obj'
if min(dd)<5200:
floyds.util.updateheader(img,0,{'stdname':[std[argmin(dd)],'']})
floyds.util.updateheader(img,0,{'magstd':[float(magstd[argmin(dd)]),'']})
if _typeobj not in objectlist: objectlist[_typeobj]={}
if (arm,_slit) not in objectlist[_typeobj]: objectlist[_typeobj][arm,_slit]=[img]
else: objectlist[_typeobj][arm,_slit].append(img)
if _verbose:
print 'object'
print objectlist
print 'flat'
print flatlist
print 'bias'
print biaslist
print 'arc'
print arclist
if liststandard and 'std' in objectlist.keys():
print 'external standard, raw standard not used'
del objectlist['std']
sens={}
outputfile={}
atmo={}
for tpe in objectlist:
if tpe not in outputfile: outputfile[tpe]={}
for setup in objectlist[tpe]:
if setup not in sens: sens[setup]=[]
print '\n### setup= ',setup,'\n### objects= ',objectlist[tpe][setup],'\n'
for img in objectlist[tpe][setup]:
print '\n\n### next object= ',img,' ',floyds.util.readkey3(floyds.util.readhdr(img),'object'),'\n'
hdr=floyds.util.readhdr(img)
archfile=floyds.util.readkey3(hdr,'arcfile')
_gain=floyds.util.readkey3(hdr,'gain')
_rdnoise=floyds.util.readkey3(hdr,'ron')
_grism=floyds.util.readkey3(hdr,'grism')
_grpid=floyds.util.readkey3(hdr,'grpid')
if archfile not in outputfile[tpe]: outputfile[tpe][archfile]=[]
##################### flat ###############
if _listflat: flatgood=_listflat # flat list from reducer
elif setup in flatlist:
if _grpid in flatlist[setup]:
print '\n###FLAT WITH SAME GRPID'
flatgood= flatlist[setup][_grpid] # flat in the raw data
else:
flatgood=[]
for _grpid0 in flatlist[setup].keys():
for ii in flatlist[setup][_grpid0]:
flatgood.append(ii)
else: flatgood=[]
if len(flatgood)!=0:
if len(flatgood)>1:
f=open('_oflatlist','w')
for fimg in flatgood:
print fimg
f.write(fimg+'\n')
f.close()
floyds.util.delete('flat'+img)
iraf.ccdred.flatcombine('"@_oflatlist"',output='flat'+img,combine='average',reject='none',ccdtype=' ',rdnoise=_rdnoise,gain=_gain, process='no', Stdout=1)
floyds.util.delete('_oflatlist')
flatfile='flat'+img
elif len(flatgood)==1:
os.system('cp '+flatgood[0]+' flat'+img)
flatfile='flat'+img
else: flatfile=''
########################## find arcfile #######################
arcfile=''
if _listarc: arcfile= [floyds.util.searcharc(img,_listarc)[0]][0] # take arc from list
if not arcfile and setup in arclist.keys():
if _grpid in arclist[setup]:
print '\n###ARC WITH SAME GRPID'
arcfile= arclist[setup][_grpid] # flat in the raw data
else:
arcfile=[]
for _grpid0 in arclist[setup].keys():
for ii in arclist[setup][_grpid0]:
arcfile.append(ii)
if arcfile:
if len(arcfile)>1: # more than one arc available
print arcfile
# _arcclose=floyds.util.searcharc(imgex,arcfile)[0] # take the closest in time
_arcclose=floyds.sortbyJD(arcfile)[-1] # take the last arc of the sequence
if _interactive.upper() in ['YES','Y']:
for ii in floyds.floydsspecdef.sortbyJD(arcfile):
print '\n### ',ii
arcfile=raw_input('\n### more than one arcfile available, which one to use ['+str(_arcclose)+'] ? ')
if not arcfile: arcfile=_arcclose
else: arcfile=_arcclose
else: arcfile=arcfile[0]
else: print '\n### Warning: no arc found'
################################################################### rectify
if setup[0]=='red':
fcfile=floyds.__path__[0]+'/standard/ident/fcrectify_'+_tel+'_red'
fcfile1=floyds.__path__[0]+'/standard/ident/fcrectify1_'+_tel+'_red'
print fcfile
else:
fcfile=floyds.__path__[0]+'/standard/ident/fcrectify_'+_tel+'_blue'
fcfile1=floyds.__path__[0]+'/standard/ident/fcrectify1_'+_tel+'_blue'
print fcfile
print img,arcfile,flatfile
img0=img
if img and img not in outputfile[tpe][archfile]: outputfile[tpe][archfile].append(img)
if arcfile and arcfile not in outputfile[tpe][archfile]: outputfile[tpe][archfile].append(arcfile)
if flatfile and flatfile not in outputfile[tpe][archfile]: outputfile[tpe][archfile].append(flatfile)
img,arcfile,flatfile=floyds.floydsspecdef.rectifyspectrum(img,arcfile,flatfile,fcfile,fcfile1,'no',_cosmic)
if img and img not in outputfile[tpe][archfile]: outputfile[tpe][archfile].append(img)
if arcfile and arcfile not in outputfile[tpe][archfile]: outputfile[tpe][archfile].append(arcfile)
if flatfile and flatfile not in outputfile[tpe][archfile]: outputfile[tpe][archfile].append(flatfile)
################################################################### check wavecalib
if tpe=='std' or floyds.util.readkey3(floyds.util.readhdr(img),'exptime') < 300:
if setup[0]=='red':
print '\n### check standard wave calib'
data, hdr = fits.getdata(img, 0, header=True)
y=data.mean(1)
import numpy as np
if np.argmax(y) < 80 and np.argmax(y) > 15:
y2=data[np.argmax(y)-3:np.argmax(y)+3].mean(0)
yy2=data[np.argmax(y)-9:np.argmax(y)-3].mean(0)
floyds.util.delete('_std.fits')
fits.writeto('_std.fits', np.float32(y2-yy2), hdr)
shift=floyds.floydsspecdef.checkwavestd('_std.fits',_interactive,2)
zro=hdr['CRVAL1']
floyds.util.updateheader(img,0,{'CRVAL1':[zro+int(shift),'']})
floyds.util.updateheader(img,0,{'shift':[float(shift),'']})
floyds.util.delete('_std.fits')
else:
print 'object not found'
else:
print '\n### warning check in wavelength not possible for short exposure in the blu range '
else:
print '\n### check object wave calib'
_skyfile=floyds.__path__[0]+'/standard/ident/sky_'+setup[0]+'.fits'
data, hdr = fits.getdata(img, 0, header=True)
y=data.mean(1)
import numpy as np
if np.argmax(y) < 80 and np.argmax(y) > 15:
yy1=data[10:np.argmax(y)-9].mean(0)
yy2=data[np.argmax(y)+9:-10].mean(0)
floyds.util.delete('_sky.fits')
fits.writeto('_sky.fits', np.float32(yy1+yy2), hdr)
shift=floyds.floydsspecdef.checkwavelength_obj('_sky.fits',_skyfile,_interactive,usethirdlayer=False)
floyds.util.delete('_sky.fits')
zro=hdr['CRVAL1']
floyds.util.updateheader(img,0,{'CRVAL1':[zro+int(shift),'']})
floyds.util.updateheader(img,0,{'shift':[float(shift),'']})
else: print 'object not found'
#################################################### flat field
if img and flatfile and setup[0]=='red':
imgn='n'+img
hdr1 = floyds.readhdr(img)
hdr2 = floyds.readhdr(flatfile)
_grpid1=floyds.util.readkey3(hdr1,'grpid')
_grpid2=floyds.util.readkey3(hdr2,'grpid')
if _grpid1==_grpid2:
print flatfile,img,setup[0]
imgn=floyds.fringing_classicmethod2(flatfile,img,'no','*',15,setup[0])
else:
print 'Warning flat not the same OB'
imgex=floyds.floydsspecdef.extractspectrum(img,dv,_ext_trace,_dispersionline,_interactive,tpe,automaticex=_automaticex)
floyds.delete('flat'+imgex)
iraf.specred.apsum(flatfile,output='flat'+imgex,referen=img,interac='no',find='no',recente='no',resize='no',\
edit='no',trace='no',fittrac='no',extract='yes',extras='no',review='no',backgro='none')
fringingmask=floyds.normflat('flat'+imgex)
print '\n### fringing correction'
print imgex,fringingmask
imgex,scale,shift=floyds.correctfringing_auto(imgex,fringingmask) # automatic correction
shift=int(.5+float(shift)/3.5) # shift from correctfringing_auto in Angstrom
print '\n##### flat scaling: ',str(scale),str(shift)
########################################################
datax, hdrx = fits.getdata(flatfile, 0, header=True)
xdim=hdrx['NAXIS1']
ydim=hdrx['NAXIS2']
iraf.specred.apedit.nsum=15
iraf.specred.apedit.width=100.
iraf.specred.apedit.line=1024
iraf.specred.apfind.minsep=20.
iraf.specred.apfind.maxsep=1000.
iraf.specred.apresize.bkg='no'
iraf.specred.apresize.ylevel=0.5
iraf.specred.aptrace.nsum=10
iraf.specred.aptrace.step=10
iraf.specred.aptrace.nlost=10
floyds.util.delete('n'+flatfile)
floyds.util.delete('norm.fits')
floyds.util.delete('n'+img)
floyds.util.delete(re.sub('.fits','c.fits',flatfile))
iraf.imcopy(flatfile+'[500:'+str(xdim)+',*]',re.sub('.fits','c.fits',flatfile),verbose='no')
iraf.imarith(flatfile,'/',flatfile,'norm.fits',verbose='no')
flatfile=re.sub('.fits','c.fits',flatfile)
floyds.util.delete('n'+flatfile)
iraf.unlearn(iraf.specred.apflatten)
floyds.floydsspecdef.aperture(flatfile)
iraf.specred.apflatten(flatfile,output='n'+flatfile,interac=_interactive,find='no',recenter='no', resize='no',edit='no',trace='no',\
fittrac='no',fitspec='no', flatten='yes', aperture='',\
pfit='fit2d',clean='no',function='legendre',order=15,sample = '*', mode='ql')
iraf.imcopy('n'+flatfile,'norm.fits[500:'+str(xdim)+',*]',verbose='no')
floyds.util.delete('n'+flatfile)
floyds.util.delete('n'+img)
iraf.imrename('norm.fits','n'+flatfile,verbose='no')
imgn=floyds.floydsspecdef.applyflat(img,'n'+flatfile,'n'+img,scale,shift)
else: imgn=''
if imgn and imgn not in outputfile[tpe][archfile]: outputfile[tpe][archfile].append(imgn)
################################################### 2D flux calib
hdr=floyds.util.readhdr(img)
_sens=''
if liststandard: _sens=floyds.util.searchsens(img,liststandard)[0] # search in the list from reducer
if not _sens:
try: _sens=floyds.util.searchsens(img,sens[setup])[0] # search in the reduced data
except: _sens=floyds.util.searchsens(img,'')[0] # search in tha archive
if _sens:
if _sens[0]=='/':
os.system('cp '+_sens+' .')
_sens=string.split(_sens,'/')[-1]
imgd=fluxcalib2d(img,_sens)
if imgn: imgdn=fluxcalib2d(imgn,_sens)
else: imgdn=''
if _sens not in outputfile[tpe][archfile]: outputfile[tpe][archfile].append(_sens)
else: imgdn=''
print '\n### do 2D calibration'
else:
imgd=''
imgdn=''
################ extraction ####################################
if imgdn:
try:
imgdnex=floyds.floydsspecdef.extractspectrum(imgdn,dv,_ext_trace,_dispersionline,_interactive,tpe,automaticex=_automaticex)
except Exception as e:
print 'failed to extract', imgdn
print e
imgdnex=''
else:
imgdnex=''
if imgd:
try:
imgdex=floyds.floydsspecdef.extractspectrum(imgd,dv,_ext_trace,_dispersionline,_interactive,tpe,automaticex=_automaticex)
except Exception as e:
print 'failed to extract', imgd
print e
imgdex=''
else:
imgdex=''
if imgd and imgd not in outputfile[tpe][archfile]: outputfile[tpe][archfile].append(imgd)
if imgdn and imgdn not in outputfile[tpe][archfile]: outputfile[tpe][archfile].append(imgdn)
if imgdnex and imgdnex not in outputfile[tpe][archfile]: outputfile[tpe][archfile].append(imgdnex)
if imgdex and imgdex not in outputfile[tpe][archfile]: outputfile[tpe][archfile].append(imgdex)
if tpe=='std':
if imgn:
try:
imgnex=floyds.floydsspecdef.extractspectrum(imgn,dv,_ext_trace,_dispersionline,_interactive,tpe,automaticex=_automaticex)
except Exception as e:
print 'failed to extract', imgn
print e
imgnex=''
elif img:
try:
imgnex=floyds.floydsspecdef.extractspectrum(img,dv,_ext_trace,_dispersionline,_interactive,tpe,automaticex=_automaticex)
except Exception as e:
print 'failed to extract', img
print e
imgnex=''
if imgnex:
hdrs=floyds.util.readhdr(imgnex)
_tel=floyds.util.readkey3(hdrs,'TELID')
try:
_outputsens2='sens_'+_tel+'_'+str(floyds.util.readkey3(hdrs,'date-night'))+'_'+str(floyds.util.readkey3(hdrs,'grism'))+\
'_'+re.sub('.dat','',floyds.util.readkey3(hdrs,'stdname'))+'_'+str(MJDtoday)
except: sys.exit('Error: missing header -stdname- in standard '+str(standardfile)+' ')
print '\n### compute sensitivity function and atmofile'
if setup[0]=='red':
atmofile=floyds.floydsspecdef.telluric_atmo(imgnex)
if atmofile and atmofile not in outputfile[tpe][archfile]: outputfile[tpe][archfile].append(atmofile)
stdusedclean=re.sub('_ex.fits','_clean.fits',imgnex)
floyds.util.delete(stdusedclean)
_function='spline3'
iraf.specred.sarith(input1=imgnex,op='/',input2=atmofile,output=stdusedclean, format='multispec')
try:
_outputsens2=floyds.floydsspecdef.sensfunction(stdusedclean,_outputsens2,_function,8,_interactive)
except:
print 'Warning: problem computing sensitivity function'
_outputsens2=''
if setup not in atmo: atmo[setup]=[atmofile]
else: atmo[setup].append(atmofile)
else:
_function='spline3'
try:
_outputsens2=floyds.floydsspecdef.sensfunction(imgnex,_outputsens2,_function,12,_interactive,'3400:4700')#,3600:4300')
except:
print 'Warning: problem computing sensitivity function'
_outputsens2=''
if _outputsens2 and _outputsens2 not in outputfile[tpe][archfile]: outputfile[tpe][archfile].append(_outputsens2)
###################################################
if 'obj' in outputfile:
for imm in outputfile['obj']:
lista = []
tt_red = ''
ntt_red = ''
tt_blue = ''
for f in outputfile['obj'][imm]:
if '_ex.fits' in f and '_blue_' in f:
tt_blue = f
elif '_ex.fits' in f and f[:3] == 'ntt':
ntt_red = f
elif '_ex.fits' in f and f[:2] == 'tt':
tt_red = f
else:
lista.append(f)
merged = ntt_red.replace('_red_', '_merge_')
if tt_blue and ntt_red:
floyds.floydsspecdef.combspec2(tt_blue, ntt_red, merged, scale=True, num=None)
if os.path.isfile(merged):
lista.append(merged)
floyds.util.delete(tt_blue)
floyds.util.delete(tt_red)
floyds.util.delete(ntt_red)
else:
if tt_blue: lista.append(tt_blue)
if tt_red: lista.append(tt_red)
if ntt_red: lista.append(ntt_red)
outputfile['obj'][imm] = lista
readme=floyds.floydsspecauto.writereadme()
return outputfile,readme
def mscimage(input=None, output=None, format='image', pixmask=no,verbose=')_.verbose',wcssource='image',reference='',ra=INDEF,dec=INDEF,scale=INDEF,rotation=INDEF,blank=0.0,interpolant='poly5',minterpolant='linear',boundary='reflect',constant=0.0,fluxconserve=no,ntrim=8,nxblock=INDEF,nyblock=INDEF,interactive=no,nx=10,ny=20,fitgeometry='general',xxorder=4,xyorder=4,xxterms='half',yxorder=4,yyorder=4,yxterms='half',fd_in='',fd_ext='',fd_coord='',mode='ql',DOLLARnargs=0,taskObj=None):
Vars = IrafParList('mscimage')
Vars.addParam(makeIrafPar(input, datatype='string', name='input', mode='a',prompt='List of input mosaic exposures'))
Vars.addParam(makeIrafPar(output, datatype='string', name='output',mode='a',prompt='List of output images'))
Vars.addParam(makeIrafPar(format, datatype='string', name='format',enum=['image', 'mef'],mode='h',prompt='Output format (image|mef)'))
Vars.addParam(makeIrafPar(pixmask, datatype='bool', name='pixmask',mode='h',prompt='Create pixel mask?'))
Vars.addParam(makeIrafPar(verbose, datatype='bool', name='verbose',mode='h',prompt='Verbose output?\n\n# Output WCS parameters'))
Vars.addParam(makeIrafPar(wcssource, datatype='string', name='wcssource',enum=['image', 'parameters', 'match'],mode='h',prompt='Output WCS source (image|parameters|match)'))
Vars.addParam(makeIrafPar(reference, datatype='file', name='reference',mode='h',prompt='Reference image'))
Vars.addParam(makeIrafPar(ra, datatype='real', name='ra', max=24.0,min=0.0,mode='h',prompt='RA of tangent point (hours)'))
Vars.addParam(makeIrafPar(dec, datatype='real', name='dec', max=90.0,min=-90.0,mode='h',prompt='DEC of tangent point (degrees)'))
Vars.addParam(makeIrafPar(scale, datatype='real', name='scale', mode='h',prompt='Scale (arcsec/pixel)'))
Vars.addParam(makeIrafPar(rotation, datatype='real', name='rotation',max=360.0,min=-360.0,mode='h',prompt='Rotation of DEC from N to E (degrees)\n\n# Resampling parmeters'))
Vars.addParam(makeIrafPar(blank, datatype='real', name='blank', mode='h',prompt='Blank value'))
Vars.addParam(makeIrafPar(interpolant, datatype='string',name='interpolant',mode='h',prompt='Interpolant for data'))
Vars.addParam(makeIrafPar(minterpolant, datatype='string',name='minterpolant',mode='h',prompt='Interpolant for mask'))
Vars.addParam(makeIrafPar(boundary, datatype='string', name='boundary',enum=['nearest', 'constant', 'reflect', 'wrap'],mode='h',prompt='Boundary extension'))
Vars.addParam(makeIrafPar(constant, datatype='real', name='constant',mode='h',prompt='Constant boundary extension value'))
Vars.addParam(makeIrafPar(fluxconserve, datatype='bool',name='fluxconserve',mode='h',prompt='Preserve flux per unit area?'))
Vars.addParam(makeIrafPar(ntrim, datatype='int', name='ntrim', min=0,mode='h',prompt='Edge trim in each extension'))
Vars.addParam(makeIrafPar(nxblock, datatype='int', name='nxblock',mode='h',prompt='X dimension of working block size in pixels'))
Vars.addParam(makeIrafPar(nyblock, datatype='int', name='nyblock',mode='h',prompt='Y dimension of working block size in pixels\n\n# Geometric mapping parameters'))
Vars.addParam(makeIrafPar(interactive, datatype='bool', name='interactive',mode='h',prompt='Fit mapping interactively?'))
Vars.addParam(makeIrafPar(nx, datatype='int', name='nx', mode='h',prompt='Number of x grid points'))
Vars.addParam(makeIrafPar(ny, datatype='int', name='ny', mode='h',prompt='Number of y grid points'))
Vars.addParam(makeIrafPar(fitgeometry, datatype='string',name='fitgeometry',enum=['shift', 'xyscale', 'rotate', 'rscale', 'rxyscale', 'general'],mode='h',prompt='Fitting geometry'))
Vars.addParam(makeIrafPar(xxorder, datatype='int', name='xxorder', min=2,mode='h',prompt='Order of x fit in x'))
Vars.addParam(makeIrafPar(xyorder, datatype='int', name='xyorder', min=2,mode='h',prompt='Order of x fit in y'))
Vars.addParam(makeIrafPar(xxterms, datatype='string', name='xxterms',mode='h',prompt='X fit cross terms type'))
Vars.addParam(makeIrafPar(yxorder, datatype='int', name='yxorder', min=2,mode='h',prompt='Order of y fit in x'))
Vars.addParam(makeIrafPar(yyorder, datatype='int', name='yyorder', min=2,mode='h',prompt='Order of y fit in y'))
Vars.addParam(makeIrafPar(yxterms, datatype='string', name='yxterms',mode='h',prompt='Y fit cross terms type\n\n'))
Vars.addParam(makeIrafPar(fd_in, datatype='struct', name='fd_in',list_flag=1,mode='h',prompt=''))
Vars.addParam(makeIrafPar(fd_ext, datatype='struct', name='fd_ext',list_flag=1,mode='h',prompt=''))
Vars.addParam(makeIrafPar(fd_coord, datatype='struct', name='fd_coord',list_flag=1,mode='h',prompt=''))
Vars.addParam(makeIrafPar(mode, datatype='string', name='mode', mode='h',prompt=''))
Vars.addParam(makeIrafPar(DOLLARnargs, datatype='int', name='$nargs',mode='h'))
Vars.addParam(makeIrafPar(None, datatype='file', name='in', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='out', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='ref', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='pl', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='image', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='trimsec', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='outsec', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='plsec', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='inlists', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='extlist', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='pllist', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='coord', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='db', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='wcsref', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='outtemp', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='pltemp', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='nc', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='nl', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='ncref', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='nlref', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='cmin', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='cmax', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='lmin', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='lmax', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='nimage', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='nimages', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='nxblk', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='nyblk', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='x', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='y', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='rval', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='xmin', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='xmax', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='ymin', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='ymax', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='crpix1', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='crpix2', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='string', name='extname',mode='u'))
Vars.addParam(makeIrafPar(None, datatype='string', name='str', mode='u'))
iraf.cache('mscextensions', 'mscgmask')
Vars.inlists = iraf.mktemp('tmp$iraf')
Vars.extlist = iraf.mktemp('tmp$iraf')
Vars.pllist = iraf.mktemp('tmp$iraf')
Vars.coord = iraf.mktemp('tmp$iraf')
Vars.db = iraf.mktemp('tmp$iraf')
Vars.outtemp = iraf.mktemp('tmp')
Vars.wcsref = iraf.mktemp('tmp')
Vars.pltemp = iraf.mktemp('tmp')
iraf.joinlists(Vars.input, Vars.output, output = Vars.inlists, delim = ' ',short=yes,type = 'image')
Vars.fd_in = Vars.inlists
while (iraf.fscan(locals(), 'Vars.fd_in', 'Vars.PYin', 'Vars.out') != EOF):
if (iraf.imaccess(Vars.out)):
iraf.printf('Warning: Image already exists (%s)\n', Vars.out)
continue
if (Vars.pixmask):
Vars.pl = Vars.out
Vars.nc = iraf.strlen(Vars.pl)
if (Vars.nc > 5 and iraf.substr(Vars.pl, Vars.nc - 4, Vars.nc) == '.fits'):
Vars.pl = iraf.substr(Vars.pl, 1, Vars.nc - 5)
elif (Vars.nc > 4 and iraf.substr(Vars.out, Vars.nc - 3, Vars.nc) == '.imh'):
Vars.pl = iraf.substr(Vars.pl, 1, Vars.nc - 4)
Vars.pl = Vars.pl + '_bpm'
if (Vars.format == 'image' and iraf.imaccess(Vars.pl)):
iraf.printf('Warning: Mask already exists (%s)\n', Vars.pl)
continue
else:
Vars.pl = ''
iraf.mscextensions(Vars.PYin, output = 'file', index = '0-',extname = '',extver = '',lindex = no,lname = yes,lver = no,ikparams = '',Stdout=Vars.extlist)
Vars.nimages = int(iraf.mscextensions.nimages)
Vars.nimage = 0
if (Vars.nimages < 1):
iraf.printf("WARNING: No input image data found in `%s'.\
",Vars.PYin)
iraf.delete(Vars.extlist, verify = no)
continue
if (not iraf.imaccess(Vars.wcsref)):
Vars.ref = Vars.reference
if (Vars.wcssource == 'match'):
Vars.wcsref = Vars.ref
else:
iraf.mscwtemplate('@' + Vars.extlist, Vars.wcsref,wcssource = Vars.wcssource,reference = Vars.ref,ra = Vars.ra,dec = Vars.dec,scale = Vars.scale,rotation = Vars.rotation,projection = '',verbose = Vars.verbose)
Vars.fd_ext = Vars.extlist
while (iraf.fscan(locals(), 'Vars.fd_ext', 'Vars.image') != EOF):
Vars.nimage = Vars.nimage + 1
if (Vars.nimages > 1):
Pipe1 = iraf.hselect(Vars.image, 'extname', yes, Stdout=1)
iraf.scan(locals(), 'Vars.extname', Stdin=Pipe1)
del Pipe1
if (iraf.nscan() == 0):
Vars.extname = 'im' + str(Vars.nimage)
Pipe1 = iraf.printf('%s[%s,append]\n', Vars.outtemp,Vars.extname,Stdout=1)
iraf.scan(locals(), 'Vars.outsec', Stdin=Pipe1)
del Pipe1
Pipe1 = iraf.printf('%s%s\n', Vars.pl, Vars.extname, Stdout=1)
iraf.scan(locals(), 'Vars.plsec', Stdin=Pipe1)
del Pipe1
else:
Vars.extname = ''
Vars.outsec = Vars.outtemp
Vars.plsec = Vars.pl
if (Vars.pixmask and iraf.imaccess(Vars.plsec)):
iraf.delete(Vars.coord, verify = no)
iraf.delete(Vars.db, verify = no)
iraf.printf('Warning: Mask already exists (%s)\n', Vars.plsec)
continue
if (Vars.verbose):
iraf.printf('Resampling %s ...\n', Vars.image)
Pipe1 = iraf.hselect(Vars.image, 'naxis1,naxis2', yes, Stdout=1)
iraf.scan(locals(), 'Vars.nc', 'Vars.nl', Stdin=Pipe1)
del Pipe1
Vars.cmin = 1 + Vars.ntrim
Vars.cmax = Vars.nc - Vars.ntrim
Vars.lmin = 1 + Vars.ntrim
Vars.lmax = Vars.nl - Vars.ntrim
Pipe1 = iraf.printf('[%d:%d,%d:%d]\n', Vars.cmin, Vars.cmax,Vars.lmin,Vars.lmax,Stdout=1)
iraf.scan(locals(), 'Vars.trimsec', Stdin=Pipe1)
del Pipe1
if (Vars.wcssource == 'match'):
Pipe1 = iraf.hselect(Vars.ref, 'naxis1,naxis2', yes, Stdout=1)
iraf.scan(locals(), 'Vars.ncref', 'Vars.nlref', Stdin=Pipe1)
del Pipe1
Vars.xmin = (Vars.ncref - 1.) / (Vars.nx - 1.)
Vars.ymin = (Vars.nlref - 1.) / (Vars.ny - 1.)
Vars.ymax = 1
while (Vars.ymax <= Vars.nlref + 1):
Vars.xmax = 1
while (Vars.xmax <= Vars.ncref + 1):
iraf.clPrint(Vars.xmax, Vars.ymax, Vars.xmax,Vars.ymax,StdoutAppend=Vars.coord)
Vars.xmax = Vars.xmax + Vars.xmin
Vars.ymax = Vars.ymax + Vars.ymin
iraf.mscctran(Vars.coord, Vars.db, Vars.ref, 'logical','world',columns = '3 4',units = '',formats = '%.4H %.3h',min_sigdigit = 10,verbose = no)
iraf.delete(Vars.coord, verify=no)
iraf.wcsctran(Vars.db, Vars.coord, Vars.image + Vars.trimsec,inwcs = 'world',outwcs = 'logical',columns = '3 4',units = 'hours native',formats = '',min_sigdigit = 10,verbose = no)
iraf.delete(Vars.db, verify=no)
else:
Vars.nc = Vars.cmax - Vars.cmin + 1
Vars.nl = Vars.lmax - Vars.lmin + 1
Vars.xmin = (Vars.nc - 1.) / (Vars.nx - 1.)
Vars.ymin = (Vars.nl - 1.) / (Vars.ny - 1.)
Vars.ymax = 1
while (Vars.ymax <= Vars.nl + 1):
Vars.xmax = 1
while (Vars.xmax <= Vars.nc + 1):
iraf.clPrint(Vars.xmax, Vars.ymax, Vars.xmax,Vars.ymax,StdoutAppend=Vars.coord)
Vars.xmax = Vars.xmax + Vars.xmin
Vars.ymax = Vars.ymax + Vars.ymin
iraf.mscctran(Vars.coord, Vars.db, Vars.image + Vars.trimsec,'logical','world',columns = '1 2',units = '',formats = '%.4H %.3h',min_sigdigit = 10,verbose = no)
iraf.delete(Vars.coord, verify=no)
iraf.wcsctran(Vars.db, Vars.coord, Vars.wcsref,inwcs = 'world',outwcs = 'logical',columns = '1 2',units = 'hours native',formats = '',min_sigdigit = 10,verbose = no)
iraf.delete(Vars.db, verify=no)
Vars.xmax = 0.
Vars.xmin = 1.
Vars.ymax = 0.
Vars.ymin = 1.
Vars.fd_coord = Vars.coord
while (iraf.fscan(locals(), 'Vars.fd_coord', 'Vars.x', 'Vars.y') != EOF):
if (iraf.nscan() < 2):
continue
if (Vars.xmax < Vars.xmin):
Vars.xmin = Vars.x
Vars.xmax = Vars.x
Vars.ymin = Vars.y
Vars.ymax = Vars.y
else:
Vars.xmin = float(iraf.minimum(Vars.x, Vars.xmin))
Vars.xmax = float(iraf.maximum(Vars.x, Vars.xmax))
Vars.ymin = float(iraf.minimum(Vars.y, Vars.ymin))
Vars.ymax = float(iraf.maximum(Vars.y, Vars.ymax))
Vars.fd_coord = ''
if (Vars.xmax <= Vars.xmin or Vars.ymax <= Vars.ymin):
iraf.error(1, 'No overlap for matching reference')
Vars.cmin = int(iraf.nint(Vars.xmin - 1.5))
Vars.cmax = int(iraf.nint(Vars.xmax + 1.5))
Vars.lmin = int(iraf.nint(Vars.ymin - 1.5))
Vars.lmax = int(iraf.nint(Vars.ymax + 1.5))
iraf.geomap(Vars.coord, Vars.db, Vars.cmin, Vars.cmax, Vars.lmin,Vars.lmax,transforms = '',results = '',fitgeometry = Vars.fitgeometry,function = 'chebyshev',xxorder = Vars.xxorder,xyorder = Vars.xyorder,xxterms = Vars.xxterms,yxorder = Vars.yxorder,yyorder = Vars.yyorder,yxterms = Vars.yxterms,reject = INDEF,calctype = 'double',verbose = no,interactive = Vars.interactive,graphics = 'stdgraph',cursor = '')
if (Vars.wcssource == 'match'):
Vars.cmin = 1
Vars.lmin = 1
Vars.cmax = Vars.ncref
Vars.lmax = Vars.nlref
if (Vars.nxblock == INDEF):
Vars.nxblk = Vars.cmax - Vars.cmin + 3
else:
Vars.nxblk = Vars.nxblock
if (Vars.nyblock == INDEF):
Vars.nyblk = Vars.lmax - Vars.lmin + 3
else:
Vars.nyblk = Vars.nyblock
iraf.geotran(Vars.image + Vars.trimsec, Vars.outsec, Vars.db,Vars.coord,geometry = 'geometric',xin = INDEF,yin = INDEF,xshift = INDEF,yshift = INDEF,xout = INDEF,yout = INDEF,xmag = INDEF,ymag = INDEF,xrotation = INDEF,yrotation = INDEF,xmin = Vars.cmin,xmax = Vars.cmax,ymin = Vars.lmin,ymax = Vars.lmax,xsample = 10.,ysample = 10.,xscale = 1.,yscale = 1.,ncols = INDEF,nlines = INDEF,interpolant = Vars.interpolant,boundary = 'constant',constant = Vars.constant,fluxconserve = Vars.fluxconserve,nxblock = Vars.nxblk,nyblock = Vars.nyblk,verbose = no)
iraf.wcscopy(Vars.outsec, Vars.wcsref, verbose=no)
Vars.xmin = 0.
Vars.ymin = 0.
Pipe1 = iraf.hselect(Vars.outsec, 'crpix1,crpix2', yes, Stdout=1)
iraf.scan(locals(), 'Vars.xmin', 'Vars.ymin', Stdin=Pipe1)
del Pipe1
Vars.xmin = Vars.xmin - Vars.cmin + 1
Vars.ymin = Vars.ymin - Vars.lmin + 1
if (Vars.nimage == 1):
Vars.crpix1 = Vars.xmin
Vars.crpix2 = Vars.ymin
else:
Vars.crpix1 = float(iraf.maximum(Vars.crpix1, Vars.xmin))
Vars.crpix2 = float(iraf.maximum(Vars.crpix2, Vars.ymin))
iraf.hedit(Vars.outsec, 'crpix1', Vars.xmin, add=yes, verify=no,show=no,update=yes)
iraf.hedit(Vars.outsec, 'crpix2', Vars.ymin, add=yes, verify=no,show=no,update=yes)
if (Vars.pixmask):
Pipe1 = iraf.printf('%s%s\n', Vars.pl, Vars.extname, Stdout=1)
iraf.scan(locals(), 'Vars.plsec', Stdin=Pipe1)
del Pipe1
iraf.mscgmask(Vars.image + Vars.trimsec, Vars.pltemp + '.pl','BPM',mval = 10000)
iraf.geotran(Vars.pltemp, Vars.plsec + '.fits', Vars.db,Vars.coord,geometry = 'geometric',xin = INDEF,yin = INDEF,xshift = INDEF,yshift = INDEF,xout = INDEF,yout = INDEF,xmag = INDEF,ymag = INDEF,xrotation = INDEF,yrotation = INDEF,xmin = Vars.cmin,xmax = Vars.cmax,ymin = Vars.lmin,ymax = Vars.lmax,xsample = 10.,ysample = 10.,interpolant = Vars.minterpolant,boundary = 'constant',constant = 20000.,fluxconserve = no,nxblock = Vars.nxblk,nyblock = Vars.nyblk,verbose = no)
iraf.imdelete(Vars.pltemp, verify=no)
iraf.mscpmask(Vars.plsec + '.fits', Vars.plsec + '.pl')
iraf.imdelete(Vars.plsec + '.fits', verify=no)
iraf.hedit(Vars.outsec, 'BPM', Vars.plsec + '.pl', add=yes,show=no,verify=no,update=yes)
iraf.wcscopy(Vars.plsec, Vars.outsec, verbose=no)
iraf.clPrint(Vars.plsec, StdoutAppend=Vars.pllist)
else:
iraf.hedit(Vars.outsec, 'BPM', PYdel=yes, add=no, addonly=no,show=no,verify=no,update=yes)
iraf.delete(Vars.coord, verify = no)
iraf.delete(Vars.db, verify = no)
Vars.fd_ext = ''
iraf.delete(Vars.extlist, verify = no)
if (Vars.nimages > 1 and Vars.format == 'image'):
if (Vars.verbose):
iraf.printf('Creating image %s ...\n', Vars.out)
iraf.mscextensions(Vars.outtemp, output = 'file', index = '',extname = '',extver = '',lindex = no,lname = yes,lver = no,ikparams = '',Stdout=Vars.extlist)
if (Vars.pixmask):
iraf.combine('@' + Vars.pllist, Vars.pltemp + '.pl',headers = '',bpmasks = Vars.pl,rejmasks = '',nrejmasks = '',expmasks = '',sigmas = '',imcmb = '',ccdtype = '',amps = no,subsets = no,delete = no,combine = 'average',reject = 'none',project = no,outtype = 'real',outlimits = '',offsets = 'wcs',masktype = 'none',maskvalue = '0',blank = 0.,scale = 'none',zero = 'none',weight = 'none',statsec = '',lthreshold = INDEF,hthreshold = 0.99,nlow = 1,nhigh = 1,nkeep = 1,mclip = yes,lsigma = 3.,hsigma = 3.,rdnoise = '0.',gain = '1.',snoise = '0.',sigscale = 0.1,pclip = - 0.5,grow = 0.,Stdout='dev$null')
iraf.imdelete(Vars.pltemp, verify=no)
iraf.combine('@' + Vars.extlist, Vars.out, headers = '',bpmasks = '',rejmasks = '',nrejmasks = '',expmasks = '',sigmas = '',imcmb = '',ccdtype = '',amps = no,subsets = no,delete = no,combine = 'average',reject = 'none',project = no,outtype = 'real',outlimits = '',offsets = 'wcs',masktype = 'badvalue',maskvalue = '2',blank = 0.,scale = 'none',zero = 'none',weight = 'none',statsec = '',lthreshold = INDEF,hthreshold = INDEF,nlow = 1,nhigh = 1,nkeep = 1,mclip = yes,lsigma = 3.,hsigma = 3.,rdnoise = '0.',gain = '1.',snoise = '0.',sigscale = 0.1,pclip = - 0.5,grow = 0.,Stdout='dev$null')
iraf.hedit(Vars.out, 'BPM', Vars.pl, add=yes, verify=no,show=no,update=yes)
iraf.hedit(Vars.pl, 'IMCMB???,PROCID??', add=no, addonly=no,PYdel=yes,update=yes,verify=no,show=no)
else:
iraf.combine('@' + Vars.extlist, Vars.out, headers = '',bpmasks = '',rejmasks = '',nrejmasks = '',expmasks = '',sigmas = '',imcmb = '',ccdtype = '',amps = no,subsets = no,delete = no,combine = 'average',reject = 'none',project = no,outtype = 'real',outlimits = '',offsets = 'wcs',masktype = 'none',maskvalue = '2',blank = 0.,scale = 'none',zero = 'none',weight = 'none',statsec = '',lthreshold = INDEF,hthreshold = INDEF,nlow = 1,nhigh = 1,nkeep = 1,mclip = yes,lsigma = 3.,hsigma = 3.,rdnoise = '0.',gain = '1.',snoise = '0.',sigscale = 0.1,pclip = - 0.5,grow = 0.,Stdout='dev$null')
Pipe2 = iraf.hselect('@' + Vars.extlist, 'gain', yes, Stdout=1)
Pipe1 = iraf.average(data_value = 0., Stdin=Pipe2, Stdout=1)
del Pipe2
iraf.scan(locals(), 'Vars.rval', Stdin=Pipe1)
del Pipe1
iraf.hedit(Vars.out, 'gain', Vars.rval, add=yes, PYdel=no,update=yes,verify=no,show=no)
Pipe2 = iraf.hselect('@' + Vars.extlist, 'rdnoise', yes, Stdout=1)
Pipe1 = iraf.average(data_value = 0., Stdin=Pipe2, Stdout=1)
del Pipe2
iraf.scan(locals(), 'Vars.rval', Stdin=Pipe1)
del Pipe1
iraf.hedit(Vars.out, 'rdnoise', Vars.rval, add=yes, PYdel=no,update=yes,verify=no,show=no)
iraf.hedit(Vars.out, 'IMCMB???,PROCID??', add=no, addonly=no,PYdel=yes,update=yes,verify=no,show=no)
iraf.hedit(Vars.out,'NEXTEND,DETSEC,CCDSEC,AMPSEC,IMAGEID,DATASEC,TRIMSEC,BIASSEC',add=no,addonly=no,PYdel=yes,update=yes,verify=no,show=no)
iraf.imdelete(Vars.outtemp, verify=no)
if (iraf.access(Vars.pllist)):
iraf.imdelete('@' + Vars.pllist, verify=no)
iraf.delete(Vars.pllist, verify=no)
iraf.delete(Vars.extlist, verify = no)
elif (Vars.nimages > 1):
iraf.imrename(Vars.outtemp, Vars.out, verbose=no)
iraf.mscextensions(Vars.out, output = 'file', index = '',extname = '',extver = '',lindex = no,lname = yes,lver = no,ikparams = '',Stdout=Vars.extlist)
Vars.fd_ext = Vars.extlist
while (iraf.fscan(locals(), 'Vars.fd_ext', 'Vars.image') != EOF):
Pipe1 = iraf.hselect(Vars.image, 'naxis1,naxis2,crpix1,crpix2',yes,Stdout=1)
iraf.scan(locals(), 'Vars.nc', 'Vars.nl', 'Vars.xmin','Vars.ymin',Stdin=Pipe1)
del Pipe1
Vars.cmin = int(iraf.nint(Vars.crpix1 - Vars.xmin + 1))
Vars.lmin = int(iraf.nint(Vars.crpix2 - Vars.ymin + 1))
Vars.cmax = Vars.nc + Vars.cmin - 1
Vars.lmax = Vars.nl + Vars.lmin - 1
Pipe1 = iraf.printf('[%d:%d,%d:%d]\n', Vars.cmin, Vars.cmax,Vars.lmin,Vars.lmax,Stdout=1)
iraf.scan(locals(), 'Vars.str', Stdin=Pipe1)
del Pipe1
iraf.hedit(Vars.image, 'DETSEC', Vars.str, add=yes, verify=no,show=no,update=yes)
iraf.hedit(Vars.image, 'DTM1_1', 1., add=yes, verify=no,show=no,update=yes)
iraf.hedit(Vars.image, 'DTM2_2', 1., add=yes, verify=no,show=no,update=yes)
Vars.cmin = Vars.cmin - 1
Vars.lmin = Vars.lmin - 1
iraf.hedit(Vars.image, 'DTV1', Vars.cmin, add=yes, verify=no,show=no,update=yes)
iraf.hedit(Vars.image, 'DTV2', Vars.lmin, add=yes, verify=no,show=no,update=yes)
iraf.hedit(Vars.image,'CCDSUM,CCDSEC,AMPSEC,ATM1_1,ATM2_2,ATV1,ATV2',PYdel=yes,add=no,addonly=no,verify=no,show=no,update=yes)
Vars.fd_ext = ''
iraf.delete(Vars.extlist, verify=no)
else:
iraf.imrename(Vars.outsec, Vars.out, verbose=no)
if (iraf.access(Vars.pllist)):
iraf.delete(Vars.pllist, verify=no)
Vars.fd_in = ''
iraf.delete(Vars.inlists, verify = no)
if (Vars.wcssource != 'match' and iraf.imaccess(Vars.wcsref)):
iraf.imdelete(Vars.wcsref, verify=no)
src.delete('listtmp*')
if glob.glob(out2):
_date = src.date(out2, _header, _telescope)
_ut = src.UT(out2, _header, _telescope)
if not out:
out = src.objects(out2, _header, _telescope)
out = re.sub('-', '', out)
if not out:
out = 'SN'
src.delete(out + '_' + _date + '_' +
str('0' + string.split(_ut, ':')[0])[-2:] + '_' + fil +
str(system0) + '.fits')
iraf.imrename(out2,
out + '_' + _date + '_' +
str('0' + string.split(_ut, ':')[0])[-2:] + '_' + fil +
str(system0) + '.fits',
verbose='yes')
src.delete("tmp*")
src.delete("_templ.co*")
src.delete(coordinatelist + "_templ.coo")
src.delete(coordinatelist + ".tv")
src.close_program(logincl)
#x0,y0,z0=string.split(xycoo[0])
#hdulist = pyfits.open('tmp_imcomb.fits')
#wcs = pywcs.WCS(hdulist[0].header)
#pix = wcs.wcs_pix2sky(x0,y0, 1)
def mscimage(input=None,
output=None,
format='image',
pixmask=no,
verbose=')_.verbose',
wcssource='image',
reference='',
ra=INDEF,
dec=INDEF,
scale=INDEF,
rotation=INDEF,
blank=0.0,
interpolant='poly5',
minterpolant='linear',
boundary='reflect',
constant=0.0,
fluxconserve=no,
ntrim=8,
nxblock=INDEF,
nyblock=INDEF,
interactive=no,
nx=10,
ny=20,
fitgeometry='general',
xxorder=4,
xyorder=4,
xxterms='half',
yxorder=4,
yyorder=4,
yxterms='half',
fd_in='',
fd_ext='',
fd_coord='',
mode='ql',
DOLLARnargs=0,
taskObj=None):
Vars = IrafParList('mscimage')
Vars.addParam(
makeIrafPar(input,
datatype='string',
name='input',
mode='a',
prompt='List of input mosaic exposures'))
Vars.addParam(
makeIrafPar(output,
datatype='string',
name='output',
mode='a',
prompt='List of output images'))
Vars.addParam(
makeIrafPar(format,
datatype='string',
name='format',
enum=['image', 'mef'],
mode='h',
prompt='Output format (image|mef)'))
Vars.addParam(
makeIrafPar(pixmask,
datatype='bool',
name='pixmask',
mode='h',
prompt='Create pixel mask?'))
Vars.addParam(
makeIrafPar(verbose,
datatype='bool',
name='verbose',
mode='h',
prompt='Verbose output?\n\n# Output WCS parameters'))
Vars.addParam(
makeIrafPar(wcssource,
datatype='string',
name='wcssource',
enum=['image', 'parameters', 'match'],
mode='h',
prompt='Output WCS source (image|parameters|match)'))
Vars.addParam(
makeIrafPar(reference,
datatype='file',
name='reference',
mode='h',
prompt='Reference image'))
Vars.addParam(
makeIrafPar(ra,
datatype='real',
name='ra',
max=24.0,
min=0.0,
mode='h',
prompt='RA of tangent point (hours)'))
Vars.addParam(
makeIrafPar(dec,
datatype='real',
name='dec',
max=90.0,
min=-90.0,
mode='h',
prompt='DEC of tangent point (degrees)'))
Vars.addParam(
makeIrafPar(scale,
datatype='real',
name='scale',
mode='h',
prompt='Scale (arcsec/pixel)'))
Vars.addParam(
makeIrafPar(
rotation,
datatype='real',
name='rotation',
max=360.0,
min=-360.0,
mode='h',
prompt=
'Rotation of DEC from N to E (degrees)\n\n# Resampling parmeters'))
Vars.addParam(
makeIrafPar(blank,
datatype='real',
name='blank',
mode='h',
prompt='Blank value'))
Vars.addParam(
makeIrafPar(interpolant,
datatype='string',
name='interpolant',
mode='h',
prompt='Interpolant for data'))
Vars.addParam(
makeIrafPar(minterpolant,
datatype='string',
name='minterpolant',
mode='h',
prompt='Interpolant for mask'))
Vars.addParam(
makeIrafPar(boundary,
datatype='string',
name='boundary',
enum=['nearest', 'constant', 'reflect', 'wrap'],
mode='h',
prompt='Boundary extension'))
Vars.addParam(
makeIrafPar(constant,
datatype='real',
name='constant',
mode='h',
prompt='Constant boundary extension value'))
Vars.addParam(
makeIrafPar(fluxconserve,
datatype='bool',
name='fluxconserve',
mode='h',
prompt='Preserve flux per unit area?'))
Vars.addParam(
makeIrafPar(ntrim,
datatype='int',
name='ntrim',
min=0,
mode='h',
prompt='Edge trim in each extension'))
Vars.addParam(
makeIrafPar(nxblock,
datatype='int',
name='nxblock',
mode='h',
prompt='X dimension of working block size in pixels'))
Vars.addParam(
makeIrafPar(
nyblock,
datatype='int',
name='nyblock',
mode='h',
prompt=
'Y dimension of working block size in pixels\n\n# Geometric mapping parameters'
))
Vars.addParam(
makeIrafPar(interactive,
datatype='bool',
name='interactive',
mode='h',
prompt='Fit mapping interactively?'))
Vars.addParam(
makeIrafPar(nx,
datatype='int',
name='nx',
mode='h',
prompt='Number of x grid points'))
Vars.addParam(
makeIrafPar(ny,
datatype='int',
name='ny',
mode='h',
prompt='Number of y grid points'))
Vars.addParam(
makeIrafPar(fitgeometry,
datatype='string',
name='fitgeometry',
enum=[
'shift', 'xyscale', 'rotate', 'rscale', 'rxyscale',
'general'
],
mode='h',
prompt='Fitting geometry'))
Vars.addParam(
makeIrafPar(xxorder,
datatype='int',
name='xxorder',
min=2,
mode='h',
prompt='Order of x fit in x'))
Vars.addParam(
makeIrafPar(xyorder,
datatype='int',
name='xyorder',
min=2,
mode='h',
prompt='Order of x fit in y'))
Vars.addParam(
makeIrafPar(xxterms,
datatype='string',
name='xxterms',
mode='h',
prompt='X fit cross terms type'))
Vars.addParam(
makeIrafPar(yxorder,
datatype='int',
name='yxorder',
min=2,
mode='h',
prompt='Order of y fit in x'))
Vars.addParam(
makeIrafPar(yyorder,
datatype='int',
name='yyorder',
min=2,
mode='h',
prompt='Order of y fit in y'))
Vars.addParam(
makeIrafPar(yxterms,
datatype='string',
name='yxterms',
mode='h',
prompt='Y fit cross terms type\n\n'))
Vars.addParam(
makeIrafPar(fd_in,
datatype='struct',
name='fd_in',
list_flag=1,
mode='h',
prompt=''))
Vars.addParam(
makeIrafPar(fd_ext,
datatype='struct',
name='fd_ext',
list_flag=1,
mode='h',
prompt=''))
Vars.addParam(
makeIrafPar(fd_coord,
datatype='struct',
name='fd_coord',
list_flag=1,
mode='h',
prompt=''))
Vars.addParam(
makeIrafPar(mode, datatype='string', name='mode', mode='h', prompt=''))
Vars.addParam(
makeIrafPar(DOLLARnargs, datatype='int', name='$nargs', mode='h'))
Vars.addParam(makeIrafPar(None, datatype='file', name='in', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='out', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='ref', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='pl', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='image', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='trimsec', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='outsec', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='plsec', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='inlists', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='extlist', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='pllist', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='coord', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='db', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='wcsref', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='outtemp', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='file', name='pltemp', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='nc', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='nl', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='ncref', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='nlref', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='cmin', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='cmax', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='lmin', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='lmax', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='nimage', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='nimages', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='nxblk', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='int', name='nyblk', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='x', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='y', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='rval', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='xmin', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='xmax', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='ymin', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='ymax', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='crpix1', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='real', name='crpix2', mode='u'))
Vars.addParam(
makeIrafPar(None, datatype='string', name='extname', mode='u'))
Vars.addParam(makeIrafPar(None, datatype='string', name='str', mode='u'))
iraf.cache('mscextensions', 'mscgmask')
Vars.inlists = iraf.mktemp('tmp$iraf')
Vars.extlist = iraf.mktemp('tmp$iraf')
Vars.pllist = iraf.mktemp('tmp$iraf')
Vars.coord = iraf.mktemp('tmp$iraf')
Vars.db = iraf.mktemp('tmp$iraf')
Vars.outtemp = iraf.mktemp('tmp')
Vars.wcsref = iraf.mktemp('tmp')
Vars.pltemp = iraf.mktemp('tmp')
iraf.joinlists(Vars.input,
Vars.output,
output=Vars.inlists,
delim=' ',
short=yes,
type='image')
Vars.fd_in = Vars.inlists
while (iraf.fscan(locals(), 'Vars.fd_in', 'Vars.PYin', 'Vars.out') != EOF):
if (iraf.imaccess(Vars.out)):
iraf.printf('Warning: Image already exists (%s)\n', Vars.out)
continue
if (Vars.pixmask):
Vars.pl = Vars.out
Vars.nc = iraf.strlen(Vars.pl)
if (Vars.nc > 5
and iraf.substr(Vars.pl, Vars.nc - 4, Vars.nc) == '.fits'):
Vars.pl = iraf.substr(Vars.pl, 1, Vars.nc - 5)
elif (Vars.nc > 4
and iraf.substr(Vars.out, Vars.nc - 3, Vars.nc) == '.imh'):
Vars.pl = iraf.substr(Vars.pl, 1, Vars.nc - 4)
Vars.pl = Vars.pl + '_bpm'
if (Vars.format == 'image' and iraf.imaccess(Vars.pl)):
iraf.printf('Warning: Mask already exists (%s)\n', Vars.pl)
continue
else:
Vars.pl = ''
iraf.mscextensions(Vars.PYin,
output='file',
index='0-',
extname='',
extver='',
lindex=no,
lname=yes,
lver=no,
ikparams='',
Stdout=Vars.extlist)
Vars.nimages = int(iraf.mscextensions.nimages)
Vars.nimage = 0
if (Vars.nimages < 1):
iraf.printf("WARNING: No input image data found in `%s'.\
", Vars.PYin)
iraf.delete(Vars.extlist, verify=no)
continue
if (not iraf.imaccess(Vars.wcsref)):
Vars.ref = Vars.reference
if (Vars.wcssource == 'match'):
Vars.wcsref = Vars.ref
else:
iraf.mscwtemplate('@' + Vars.extlist,
Vars.wcsref,
wcssource=Vars.wcssource,
reference=Vars.ref,
ra=Vars.ra,
dec=Vars.dec,
scale=Vars.scale,
rotation=Vars.rotation,
projection='',
verbose=Vars.verbose)
Vars.fd_ext = Vars.extlist
while (iraf.fscan(locals(), 'Vars.fd_ext', 'Vars.image') != EOF):
Vars.nimage = Vars.nimage + 1
if (Vars.nimages > 1):
Pipe1 = iraf.hselect(Vars.image, 'extname', yes, Stdout=1)
iraf.scan(locals(), 'Vars.extname', Stdin=Pipe1)
del Pipe1
if (iraf.nscan() == 0):
Vars.extname = 'im' + str(Vars.nimage)
Pipe1 = iraf.printf('%s[%s,append]\n',
Vars.outtemp,
Vars.extname,
Stdout=1)
iraf.scan(locals(), 'Vars.outsec', Stdin=Pipe1)
del Pipe1
Pipe1 = iraf.printf('%s%s\n', Vars.pl, Vars.extname, Stdout=1)
iraf.scan(locals(), 'Vars.plsec', Stdin=Pipe1)
del Pipe1
else:
Vars.extname = ''
Vars.outsec = Vars.outtemp
Vars.plsec = Vars.pl
if (Vars.pixmask and iraf.imaccess(Vars.plsec)):
iraf.delete(Vars.coord, verify=no)
iraf.delete(Vars.db, verify=no)
iraf.printf('Warning: Mask already exists (%s)\n', Vars.plsec)
continue
if (Vars.verbose):
iraf.printf('Resampling %s ...\n', Vars.image)
Pipe1 = iraf.hselect(Vars.image, 'naxis1,naxis2', yes, Stdout=1)
iraf.scan(locals(), 'Vars.nc', 'Vars.nl', Stdin=Pipe1)
del Pipe1
Vars.cmin = 1 + Vars.ntrim
Vars.cmax = Vars.nc - Vars.ntrim
Vars.lmin = 1 + Vars.ntrim
Vars.lmax = Vars.nl - Vars.ntrim
Pipe1 = iraf.printf('[%d:%d,%d:%d]\n',
Vars.cmin,
Vars.cmax,
Vars.lmin,
Vars.lmax,
Stdout=1)
iraf.scan(locals(), 'Vars.trimsec', Stdin=Pipe1)
del Pipe1
if (Vars.wcssource == 'match'):
Pipe1 = iraf.hselect(Vars.ref, 'naxis1,naxis2', yes, Stdout=1)
iraf.scan(locals(), 'Vars.ncref', 'Vars.nlref', Stdin=Pipe1)
del Pipe1
Vars.xmin = (Vars.ncref - 1.) / (Vars.nx - 1.)
Vars.ymin = (Vars.nlref - 1.) / (Vars.ny - 1.)
Vars.ymax = 1
while (Vars.ymax <= Vars.nlref + 1):
Vars.xmax = 1
while (Vars.xmax <= Vars.ncref + 1):
iraf.clPrint(Vars.xmax,
Vars.ymax,
Vars.xmax,
Vars.ymax,
StdoutAppend=Vars.coord)
Vars.xmax = Vars.xmax + Vars.xmin
Vars.ymax = Vars.ymax + Vars.ymin
iraf.mscctran(Vars.coord,
Vars.db,
Vars.ref,
'logical',
'world',
columns='3 4',
units='',
formats='%.4H %.3h',
min_sigdigit=10,
verbose=no)
iraf.delete(Vars.coord, verify=no)
iraf.wcsctran(Vars.db,
Vars.coord,
Vars.image + Vars.trimsec,
inwcs='world',
outwcs='logical',
columns='3 4',
units='hours native',
formats='',
min_sigdigit=10,
verbose=no)
iraf.delete(Vars.db, verify=no)
else:
Vars.nc = Vars.cmax - Vars.cmin + 1
Vars.nl = Vars.lmax - Vars.lmin + 1
Vars.xmin = (Vars.nc - 1.) / (Vars.nx - 1.)
Vars.ymin = (Vars.nl - 1.) / (Vars.ny - 1.)
Vars.ymax = 1
while (Vars.ymax <= Vars.nl + 1):
Vars.xmax = 1
while (Vars.xmax <= Vars.nc + 1):
iraf.clPrint(Vars.xmax,
Vars.ymax,
Vars.xmax,
Vars.ymax,
StdoutAppend=Vars.coord)
Vars.xmax = Vars.xmax + Vars.xmin
Vars.ymax = Vars.ymax + Vars.ymin
iraf.mscctran(Vars.coord,
Vars.db,
Vars.image + Vars.trimsec,
'logical',
'world',
columns='1 2',
units='',
formats='%.4H %.3h',
min_sigdigit=10,
verbose=no)
iraf.delete(Vars.coord, verify=no)
iraf.wcsctran(Vars.db,
Vars.coord,
Vars.wcsref,
inwcs='world',
outwcs='logical',
columns='1 2',
units='hours native',
formats='',
min_sigdigit=10,
verbose=no)
iraf.delete(Vars.db, verify=no)
Vars.xmax = 0.
Vars.xmin = 1.
Vars.ymax = 0.
Vars.ymin = 1.
Vars.fd_coord = Vars.coord
while (iraf.fscan(locals(), 'Vars.fd_coord', 'Vars.x', 'Vars.y') !=
EOF):
if (iraf.nscan() < 2):
continue
if (Vars.xmax < Vars.xmin):
Vars.xmin = Vars.x
Vars.xmax = Vars.x
Vars.ymin = Vars.y
Vars.ymax = Vars.y
else:
Vars.xmin = float(iraf.minimum(Vars.x, Vars.xmin))
Vars.xmax = float(iraf.maximum(Vars.x, Vars.xmax))
Vars.ymin = float(iraf.minimum(Vars.y, Vars.ymin))
Vars.ymax = float(iraf.maximum(Vars.y, Vars.ymax))
Vars.fd_coord = ''
if (Vars.xmax <= Vars.xmin or Vars.ymax <= Vars.ymin):
iraf.error(1, 'No overlap for matching reference')
Vars.cmin = int(iraf.nint(Vars.xmin - 1.5))
Vars.cmax = int(iraf.nint(Vars.xmax + 1.5))
Vars.lmin = int(iraf.nint(Vars.ymin - 1.5))
Vars.lmax = int(iraf.nint(Vars.ymax + 1.5))
iraf.geomap(Vars.coord,
Vars.db,
Vars.cmin,
Vars.cmax,
Vars.lmin,
Vars.lmax,
transforms='',
results='',
fitgeometry=Vars.fitgeometry,
function='chebyshev',
xxorder=Vars.xxorder,
xyorder=Vars.xyorder,
xxterms=Vars.xxterms,
yxorder=Vars.yxorder,
yyorder=Vars.yyorder,
yxterms=Vars.yxterms,
reject=INDEF,
calctype='double',
verbose=no,
interactive=Vars.interactive,
graphics='stdgraph',
cursor='')
if (Vars.wcssource == 'match'):
Vars.cmin = 1
Vars.lmin = 1
Vars.cmax = Vars.ncref
Vars.lmax = Vars.nlref
if (Vars.nxblock == INDEF):
Vars.nxblk = Vars.cmax - Vars.cmin + 3
else:
Vars.nxblk = Vars.nxblock
if (Vars.nyblock == INDEF):
Vars.nyblk = Vars.lmax - Vars.lmin + 3
else:
Vars.nyblk = Vars.nyblock
iraf.geotran(Vars.image + Vars.trimsec,
Vars.outsec,
Vars.db,
Vars.coord,
geometry='geometric',
xin=INDEF,
yin=INDEF,
xshift=INDEF,
yshift=INDEF,
xout=INDEF,
yout=INDEF,
xmag=INDEF,
ymag=INDEF,
xrotation=INDEF,
yrotation=INDEF,
xmin=Vars.cmin,
xmax=Vars.cmax,
ymin=Vars.lmin,
ymax=Vars.lmax,
xsample=10.,
ysample=10.,
xscale=1.,
yscale=1.,
ncols=INDEF,
nlines=INDEF,
interpolant=Vars.interpolant,
boundary='constant',
constant=Vars.constant,
fluxconserve=Vars.fluxconserve,
nxblock=Vars.nxblk,
nyblock=Vars.nyblk,
verbose=no)
iraf.wcscopy(Vars.outsec, Vars.wcsref, verbose=no)
Vars.xmin = 0.
Vars.ymin = 0.
Pipe1 = iraf.hselect(Vars.outsec, 'crpix1,crpix2', yes, Stdout=1)
iraf.scan(locals(), 'Vars.xmin', 'Vars.ymin', Stdin=Pipe1)
del Pipe1
Vars.xmin = Vars.xmin - Vars.cmin + 1
Vars.ymin = Vars.ymin - Vars.lmin + 1
if (Vars.nimage == 1):
Vars.crpix1 = Vars.xmin
Vars.crpix2 = Vars.ymin
else:
Vars.crpix1 = float(iraf.maximum(Vars.crpix1, Vars.xmin))
Vars.crpix2 = float(iraf.maximum(Vars.crpix2, Vars.ymin))
iraf.hedit(Vars.outsec,
'crpix1',
Vars.xmin,
add=yes,
verify=no,
show=no,
update=yes)
iraf.hedit(Vars.outsec,
'crpix2',
Vars.ymin,
add=yes,
verify=no,
show=no,
update=yes)
if (Vars.pixmask):
Pipe1 = iraf.printf('%s%s\n', Vars.pl, Vars.extname, Stdout=1)
iraf.scan(locals(), 'Vars.plsec', Stdin=Pipe1)
del Pipe1
iraf.mscgmask(Vars.image + Vars.trimsec,
Vars.pltemp + '.pl',
'BPM',
mval=10000)
iraf.geotran(Vars.pltemp,
Vars.plsec + '.fits',
Vars.db,
Vars.coord,
geometry='geometric',
xin=INDEF,
yin=INDEF,
xshift=INDEF,
yshift=INDEF,
xout=INDEF,
yout=INDEF,
xmag=INDEF,
ymag=INDEF,
xrotation=INDEF,
yrotation=INDEF,
xmin=Vars.cmin,
xmax=Vars.cmax,
ymin=Vars.lmin,
ymax=Vars.lmax,
xsample=10.,
ysample=10.,
interpolant=Vars.minterpolant,
boundary='constant',
constant=20000.,
fluxconserve=no,
nxblock=Vars.nxblk,
nyblock=Vars.nyblk,
verbose=no)
iraf.imdelete(Vars.pltemp, verify=no)
iraf.mscpmask(Vars.plsec + '.fits', Vars.plsec + '.pl')
iraf.imdelete(Vars.plsec + '.fits', verify=no)
iraf.hedit(Vars.outsec,
'BPM',
Vars.plsec + '.pl',
add=yes,
show=no,
verify=no,
update=yes)
iraf.wcscopy(Vars.plsec, Vars.outsec, verbose=no)
iraf.clPrint(Vars.plsec, StdoutAppend=Vars.pllist)
else:
iraf.hedit(Vars.outsec,
'BPM',
PYdel=yes,
add=no,
addonly=no,
show=no,
verify=no,
update=yes)
iraf.delete(Vars.coord, verify=no)
iraf.delete(Vars.db, verify=no)
Vars.fd_ext = ''
iraf.delete(Vars.extlist, verify=no)
if (Vars.nimages > 1 and Vars.format == 'image'):
if (Vars.verbose):
iraf.printf('Creating image %s ...\n', Vars.out)
iraf.mscextensions(Vars.outtemp,
output='file',
index='',
extname='',
extver='',
lindex=no,
lname=yes,
lver=no,
ikparams='',
Stdout=Vars.extlist)
if (Vars.pixmask):
iraf.combine('@' + Vars.pllist,
Vars.pltemp + '.pl',
headers='',
bpmasks=Vars.pl,
rejmasks='',
nrejmasks='',
expmasks='',
sigmas='',
imcmb='',
ccdtype='',
amps=no,
subsets=no,
delete=no,
combine='average',
reject='none',
project=no,
outtype='real',
outlimits='',
offsets='wcs',
masktype='none',
maskvalue='0',
blank=0.,
scale='none',
zero='none',
weight='none',
statsec='',
lthreshold=INDEF,
hthreshold=0.99,
nlow=1,
nhigh=1,
nkeep=1,
mclip=yes,
lsigma=3.,
hsigma=3.,
rdnoise='0.',
gain='1.',
snoise='0.',
sigscale=0.1,
pclip=-0.5,
grow=0.,
Stdout='dev$null')
iraf.imdelete(Vars.pltemp, verify=no)
iraf.combine('@' + Vars.extlist,
Vars.out,
headers='',
bpmasks='',
rejmasks='',
nrejmasks='',
expmasks='',
sigmas='',
imcmb='',
ccdtype='',
amps=no,
subsets=no,
delete=no,
combine='average',
reject='none',
project=no,
outtype='real',
outlimits='',
offsets='wcs',
masktype='badvalue',
maskvalue='2',
blank=0.,
scale='none',
zero='none',
weight='none',
statsec='',
lthreshold=INDEF,
hthreshold=INDEF,
nlow=1,
nhigh=1,
nkeep=1,
mclip=yes,
lsigma=3.,
hsigma=3.,
rdnoise='0.',
gain='1.',
snoise='0.',
sigscale=0.1,
pclip=-0.5,
grow=0.,
Stdout='dev$null')
iraf.hedit(Vars.out,
'BPM',
Vars.pl,
add=yes,
verify=no,
show=no,
update=yes)
iraf.hedit(Vars.pl,
'IMCMB???,PROCID??',
add=no,
addonly=no,
PYdel=yes,
update=yes,
verify=no,
show=no)
else:
iraf.combine('@' + Vars.extlist,
Vars.out,
headers='',
bpmasks='',
rejmasks='',
nrejmasks='',
expmasks='',
sigmas='',
imcmb='',
ccdtype='',
amps=no,
subsets=no,
delete=no,
combine='average',
reject='none',
project=no,
outtype='real',
outlimits='',
offsets='wcs',
masktype='none',
maskvalue='2',
blank=0.,
scale='none',
zero='none',
weight='none',
statsec='',
lthreshold=INDEF,
hthreshold=INDEF,
nlow=1,
nhigh=1,
nkeep=1,
mclip=yes,
lsigma=3.,
hsigma=3.,
rdnoise='0.',
gain='1.',
snoise='0.',
sigscale=0.1,
pclip=-0.5,
grow=0.,
Stdout='dev$null')
Pipe2 = iraf.hselect('@' + Vars.extlist, 'gain', yes, Stdout=1)
Pipe1 = iraf.average(data_value=0., Stdin=Pipe2, Stdout=1)
del Pipe2
iraf.scan(locals(), 'Vars.rval', Stdin=Pipe1)
del Pipe1
iraf.hedit(Vars.out,
'gain',
Vars.rval,
add=yes,
PYdel=no,
update=yes,
verify=no,
show=no)
Pipe2 = iraf.hselect('@' + Vars.extlist, 'rdnoise', yes, Stdout=1)
Pipe1 = iraf.average(data_value=0., Stdin=Pipe2, Stdout=1)
del Pipe2
iraf.scan(locals(), 'Vars.rval', Stdin=Pipe1)
del Pipe1
iraf.hedit(Vars.out,
'rdnoise',
Vars.rval,
add=yes,
PYdel=no,
update=yes,
verify=no,
show=no)
iraf.hedit(Vars.out,
'IMCMB???,PROCID??',
add=no,
addonly=no,
PYdel=yes,
update=yes,
verify=no,
show=no)
iraf.hedit(
Vars.out,
'NEXTEND,DETSEC,CCDSEC,AMPSEC,IMAGEID,DATASEC,TRIMSEC,BIASSEC',
add=no,
addonly=no,
PYdel=yes,
update=yes,
verify=no,
show=no)
iraf.imdelete(Vars.outtemp, verify=no)
if (iraf.access(Vars.pllist)):
iraf.imdelete('@' + Vars.pllist, verify=no)
iraf.delete(Vars.pllist, verify=no)
iraf.delete(Vars.extlist, verify=no)
elif (Vars.nimages > 1):
iraf.imrename(Vars.outtemp, Vars.out, verbose=no)
iraf.mscextensions(Vars.out,
output='file',
index='',
extname='',
extver='',
lindex=no,
lname=yes,
lver=no,
ikparams='',
Stdout=Vars.extlist)
Vars.fd_ext = Vars.extlist
while (iraf.fscan(locals(), 'Vars.fd_ext', 'Vars.image') != EOF):
Pipe1 = iraf.hselect(Vars.image,
'naxis1,naxis2,crpix1,crpix2',
yes,
Stdout=1)
iraf.scan(locals(),
'Vars.nc',
'Vars.nl',
'Vars.xmin',
'Vars.ymin',
Stdin=Pipe1)
del Pipe1
Vars.cmin = int(iraf.nint(Vars.crpix1 - Vars.xmin + 1))
Vars.lmin = int(iraf.nint(Vars.crpix2 - Vars.ymin + 1))
Vars.cmax = Vars.nc + Vars.cmin - 1
Vars.lmax = Vars.nl + Vars.lmin - 1
Pipe1 = iraf.printf('[%d:%d,%d:%d]\n',
Vars.cmin,
Vars.cmax,
Vars.lmin,
Vars.lmax,
Stdout=1)
iraf.scan(locals(), 'Vars.str', Stdin=Pipe1)
del Pipe1
iraf.hedit(Vars.image,
'DETSEC',
Vars.str,
add=yes,
verify=no,
show=no,
update=yes)
iraf.hedit(Vars.image,
'DTM1_1',
1.,
add=yes,
verify=no,
show=no,
update=yes)
iraf.hedit(Vars.image,
'DTM2_2',
1.,
add=yes,
verify=no,
show=no,
update=yes)
Vars.cmin = Vars.cmin - 1
Vars.lmin = Vars.lmin - 1
iraf.hedit(Vars.image,
'DTV1',
Vars.cmin,
add=yes,
verify=no,
show=no,
update=yes)
iraf.hedit(Vars.image,
'DTV2',
Vars.lmin,
add=yes,
verify=no,
show=no,
update=yes)
iraf.hedit(Vars.image,
'CCDSUM,CCDSEC,AMPSEC,ATM1_1,ATM2_2,ATV1,ATV2',
PYdel=yes,
add=no,
addonly=no,
verify=no,
show=no,
update=yes)
Vars.fd_ext = ''
iraf.delete(Vars.extlist, verify=no)
else:
iraf.imrename(Vars.outsec, Vars.out, verbose=no)
if (iraf.access(Vars.pllist)):
iraf.delete(Vars.pllist, verify=no)
Vars.fd_in = ''
iraf.delete(Vars.inlists, verify=no)
if (Vars.wcssource != 'match' and iraf.imaccess(Vars.wcsref)):
iraf.imdelete(Vars.wcsref, verify=no)