def saltclean(images,
outpath,
obslogfile=None,
gaindb=None,
xtalkfile=None,
geomfile=None,
subover=True,
trim=True,
masbias=None,
subbias=False,
median=False,
function='polynomial',
order=5,
rej_lo=3,
rej_hi=3,
niter=5,
interp='linear',
clobber=False,
logfile='salt.log',
verbose=True):
"""SALTCLEAN will provide basic CCD reductions for a set of data. It will
sort the data, and first process the biases, flats, and then the science
frames. It will record basic quality control information about each of
the steps.
"""
plotover = False
#start logging
with logging(logfile, debug) as log:
# Check the input images
infiles = saltio.argunpack('Input', images)
# create list of output files
outpath = saltio.abspath(outpath)
#does the gain database file exist
if gaindb:
dblist = saltio.readgaindb(gaindb)
else:
dblist = []
# does crosstalk coefficient data exist
if xtalkfile:
xtalkfile = xtalkfile.strip()
xdict = saltio.readxtalkcoeff(xtalkfile)
else:
xdict = None
#does the mosaic file exist--raise error if no
saltio.fileexists(geomfile)
# Delete the obslog file if it already exists
if os.path.isfile(obslogfile) and clobber: saltio.delete(obslogfile)
#read in the obsveration log or create it
if os.path.isfile(obslogfile):
msg = 'The observing log already exists. Please either delete it or run saltclean with clobber=yes'
raise SaltError(msg)
else:
headerDict = obslog(infiles, log)
obsstruct = createobslogfits(headerDict)
saltio.writefits(obsstruct, obslogfile)
#create the list of bias frames and process them
filename = obsstruct.data.field('FILENAME')
detmode = obsstruct.data.field('DETMODE')
ccdtype = obsstruct.data.field('CCDTYPE')
#set the bias list of objects
biaslist = filename[ccdtype == 'ZERO']
masterbias_dict = {}
for img in infiles:
if os.path.basename(img) in biaslist:
#open the image
struct = fits.open(img)
bimg = outpath + 'bxgp' + os.path.basename(img)
#print the message
if log:
message = 'Processing Zero frame %s' % img
log.message(message, with_stdout=verbose)
#process the image
struct = clean(struct,
createvar=False,
badpixelstruct=None,
mult=True,
dblist=dblist,
xdict=xdict,
subover=subover,
trim=trim,
subbias=False,
bstruct=None,
median=median,
function=function,
order=order,
rej_lo=rej_lo,
rej_hi=rej_hi,
niter=niter,
plotover=plotover,
log=log,
verbose=verbose)
#write the file out
# housekeeping keywords
fname, hist = history(
level=1,
wrap=False,
exclude=['images', 'outimages', 'outpref'])
saltkey.housekeeping(struct[0], 'SPREPARE',
'Images have been prepared', hist)
saltkey.new('SGAIN', time.asctime(time.localtime()),
'Images have been gain corrected', struct[0])
saltkey.new('SXTALK', time.asctime(time.localtime()),
'Images have been xtalk corrected', struct[0])
saltkey.new('SBIAS', time.asctime(time.localtime()),
'Images have been de-biased', struct[0])
# write FITS file
saltio.writefits(struct, bimg, clobber=clobber)
saltio.closefits(struct)
#add files to the master bias list
masterbias_dict = compareimages(struct,
bimg,
masterbias_dict,
keylist=biasheader_list)
#create the master bias frame
for i in masterbias_dict.keys():
bkeys = masterbias_dict[i][0]
blist = masterbias_dict[i][1:]
mbiasname = outpath + createmasterbiasname(blist, bkeys)
bfiles = ','.join(blist)
saltcombine(bfiles, mbiasname, method='median', reject='sigclip', mask=False,
weight=False, blank=0, scale=None, statsec=None, lthresh=3, \
hthresh=3, clobber=False, logfile=logfile,verbose=verbose)
#create the list of flatfields and process them
flatlist = filename[ccdtype == 'FLAT']
masterflat_dict = {}
for img in infiles:
if os.path.basename(img) in flatlist:
#open the image
struct = fits.open(img)
fimg = outpath + 'bxgp' + os.path.basename(img)
#print the message
if log:
message = 'Processing Flat frame %s' % img
log.message(message, with_stdout=verbose)
#process the image
struct = clean(struct,
createvar=False,
badpixelstruct=None,
mult=True,
dblist=dblist,
xdict=xdict,
subover=subover,
trim=trim,
subbias=False,
bstruct=None,
median=median,
function=function,
order=order,
rej_lo=rej_lo,
rej_hi=rej_hi,
niter=niter,
plotover=plotover,
log=log,
verbose=verbose)
#write the file out
# housekeeping keywords
fname, hist = history(
level=1,
wrap=False,
exclude=['images', 'outimages', 'outpref'])
saltkey.housekeeping(struct[0], 'SPREPARE',
'Images have been prepared', hist)
saltkey.new('SGAIN', time.asctime(time.localtime()),
'Images have been gain corrected', struct[0])
saltkey.new('SXTALK', time.asctime(time.localtime()),
'Images have been xtalk corrected', struct[0])
saltkey.new('SBIAS', time.asctime(time.localtime()),
'Images have been de-biased', struct[0])
# write FITS file
saltio.writefits(struct, fimg, clobber=clobber)
saltio.closefits(struct)
#add files to the master bias list
masterflat_dict = compareimages(struct,
fimg,
masterflat_dict,
keylist=flatheader_list)
#create the master flat frame
for i in masterflat_dict.keys():
fkeys = masterflat_dict[i][0]
flist = masterflat_dict[i][1:]
mflatname = outpath + createmasterflatname(flist, fkeys)
ffiles = ','.join(flist)
saltcombine(ffiles, mflatname, method='median', reject='sigclip', mask=False,
weight=False, blank=0, scale=None, statsec=None, lthresh=3, \
hthresh=3, clobber=False, logfile=logfile,verbose=verbose)
#process the science data
for img in infiles:
nimg = os.path.basename(img)
#print nimg, nimg in flatlist, nimg in biaslist
if not (nimg in biaslist):
#open the image
struct = fits.open(img)
simg = outpath + 'bxgp' + os.path.basename(img)
#print the message
if log:
message = 'Processing science frame %s' % img
log.message(message, with_stdout=verbose)
#process the image
struct = clean(struct,
createvar=False,
badpixelstruct=None,
mult=True,
dblist=dblist,
xdict=xdict,
subover=subover,
trim=trim,
subbias=False,
bstruct=None,
median=median,
function=function,
order=order,
rej_lo=rej_lo,
rej_hi=rej_hi,
niter=niter,
plotover=plotover,
log=log,
verbose=verbose)
#write the file out
# housekeeping keywords
fname, hist = history(
level=1,
wrap=False,
exclude=['images', 'outimages', 'outpref'])
saltkey.housekeeping(struct[0], 'SPREPARE',
'Images have been prepared', hist)
saltkey.new('SGAIN', time.asctime(time.localtime()),
'Images have been gain corrected', struct[0])
saltkey.new('SXTALK', time.asctime(time.localtime()),
'Images have been xtalk corrected', struct[0])
saltkey.new('SBIAS', time.asctime(time.localtime()),
'Images have been de-biased', struct[0])
# write FITS file
saltio.writefits(struct, simg, clobber=clobber)
saltio.closefits(struct)
#mosaic the files--currently not in the proper format--will update when it is
if not saltkey.fastmode(saltkey.get('DETMODE', struct[0])):
mimg = outpath + 'mbxgp' + os.path.basename(img)
saltmosaic(images=simg,
outimages=mimg,
outpref='',
geomfile=geomfile,
interp=interp,
cleanup=True,
clobber=clobber,
logfile=logfile,
verbose=verbose)
#remove the intermediate steps
saltio.delete(simg)
def saltclean(images, outpath, obslogfile=None, gaindb=None,xtalkfile=None,
geomfile=None,subover=True,trim=True,masbias=None,
subbias=False, median=False, function='polynomial', order=5,rej_lo=3,
rej_hi=3,niter=5,interp='linear', clobber=False, logfile='salt.log',
verbose=True):
"""SALTCLEAN will provide basic CCD reductions for a set of data. It will
sort the data, and first process the biases, flats, and then the science
frames. It will record basic quality control information about each of
the steps.
"""
plotover=False
#start logging
with logging(logfile,debug) as log:
# Check the input images
infiles = saltio.argunpack ('Input',images)
# create list of output files
outpath=saltio.abspath(outpath)
#does the gain database file exist
if gaindb:
dblist= saltio.readgaindb(gaindb)
else:
dblist=[]
# does crosstalk coefficient data exist
if xtalkfile:
xtalkfile = xtalkfile.strip()
xdict = saltio.readxtalkcoeff(xtalkfile)
else:
xdict=None
#does the mosaic file exist--raise error if no
saltio.fileexists(geomfile)
# Delete the obslog file if it already exists
if os.path.isfile(obslogfile) and clobber: saltio.delete(obslogfile)
#read in the obsveration log or create it
if os.path.isfile(obslogfile):
msg='The observing log already exists. Please either delete it or run saltclean with clobber=yes'
raise SaltError(msg)
else:
headerDict=obslog(infiles, log)
obsstruct=createobslogfits(headerDict)
saltio.writefits(obsstruct, obslogfile)
#create the list of bias frames and process them
filename=obsstruct.data.field('FILENAME')
detmode=obsstruct.data.field('DETMODE')
ccdtype=obsstruct.data.field('CCDTYPE')
#set the bias list of objects
biaslist=filename[ccdtype=='ZERO']
masterbias_dict={}
for img in infiles:
if os.path.basename(img) in biaslist:
#open the image
struct=pyfits.open(img)
bimg=outpath+'bxgp'+os.path.basename(img)
#print the message
if log:
message='Processing Zero frame %s' % img
log.message(message, with_stdout=verbose)
#process the image
struct=clean(struct, createvar=False, badpixelstruct=None, mult=True,
dblist=dblist, xdict=xdict, subover=subover, trim=trim, subbias=False,
bstruct=None, median=median, function=function, order=order,
rej_lo=rej_lo, rej_hi=rej_hi, niter=niter, plotover=plotover, log=log,
verbose=verbose)
#write the file out
# housekeeping keywords
fname, hist=history(level=1, wrap=False, exclude=['images', 'outimages', 'outpref'])
saltkey.housekeeping(struct[0],'SPREPARE', 'Images have been prepared', hist)
saltkey.new('SGAIN',time.asctime(time.localtime()),'Images have been gain corrected',struct[0])
saltkey.new('SXTALK',time.asctime(time.localtime()),'Images have been xtalk corrected',struct[0])
saltkey.new('SBIAS',time.asctime(time.localtime()),'Images have been de-biased',struct[0])
# write FITS file
saltio.writefits(struct,bimg, clobber=clobber)
saltio.closefits(struct)
#add files to the master bias list
masterbias_dict=compareimages(struct, bimg, masterbias_dict, keylist=biasheader_list)
#create the master bias frame
for i in masterbias_dict.keys():
bkeys=masterbias_dict[i][0]
blist=masterbias_dict[i][1:]
mbiasname=outpath+createmasterbiasname(blist, bkeys)
bfiles=','.join(blist)
saltcombine(bfiles, mbiasname, method='median', reject='sigclip', mask=False,
weight=False, blank=0, scale=None, statsec=None, lthresh=3, \
hthresh=3, clobber=False, logfile=logfile,verbose=verbose)
#create the list of flatfields and process them
flatlist=filename[ccdtype=='FLAT']
masterflat_dict={}
for img in infiles:
if os.path.basename(img) in flatlist:
#open the image
struct=pyfits.open(img)
fimg=outpath+'bxgp'+os.path.basename(img)
#print the message
if log:
message='Processing Flat frame %s' % img
log.message(message, with_stdout=verbose)
#process the image
struct=clean(struct, createvar=False, badpixelstruct=None, mult=True,
dblist=dblist, xdict=xdict, subover=subover, trim=trim, subbias=False,
bstruct=None, median=median, function=function, order=order,
rej_lo=rej_lo, rej_hi=rej_hi, niter=niter, plotover=plotover, log=log,
verbose=verbose)
#write the file out
# housekeeping keywords
fname, hist=history(level=1, wrap=False, exclude=['images', 'outimages', 'outpref'])
saltkey.housekeeping(struct[0],'SPREPARE', 'Images have been prepared', hist)
saltkey.new('SGAIN',time.asctime(time.localtime()),'Images have been gain corrected',struct[0])
saltkey.new('SXTALK',time.asctime(time.localtime()),'Images have been xtalk corrected',struct[0])
saltkey.new('SBIAS',time.asctime(time.localtime()),'Images have been de-biased',struct[0])
# write FITS file
saltio.writefits(struct,fimg, clobber=clobber)
saltio.closefits(struct)
#add files to the master bias list
masterflat_dict=compareimages(struct, fimg, masterflat_dict, keylist=flatheader_list)
#create the master flat frame
for i in masterflat_dict.keys():
fkeys=masterflat_dict[i][0]
flist=masterflat_dict[i][1:]
mflatname=outpath+createmasterflatname(flist, fkeys)
ffiles=','.join(flist)
saltcombine(ffiles, mflatname, method='median', reject='sigclip', mask=False,
weight=False, blank=0, scale=None, statsec=None, lthresh=3, \
hthresh=3, clobber=False, logfile=logfile,verbose=verbose)
#process the science data
for img in infiles:
nimg=os.path.basename(img)
if not nimg in flatlist or not nimg in biaslist:
#open the image
struct=pyfits.open(img)
simg=outpath+'bxgp'+os.path.basename(img)
#print the message
if log:
message='Processing science frame %s' % img
log.message(message, with_stdout=verbose)
#process the image
struct=clean(struct, createvar=False, badpixelstruct=None, mult=True,
dblist=dblist, xdict=xdict, subover=subover, trim=trim, subbias=False,
bstruct=None, median=median, function=function, order=order,
rej_lo=rej_lo, rej_hi=rej_hi, niter=niter, plotover=plotover, log=log,
verbose=verbose)
#write the file out
# housekeeping keywords
fname, hist=history(level=1, wrap=False, exclude=['images', 'outimages', 'outpref'])
saltkey.housekeeping(struct[0],'SPREPARE', 'Images have been prepared', hist)
saltkey.new('SGAIN',time.asctime(time.localtime()),'Images have been gain corrected',struct[0])
saltkey.new('SXTALK',time.asctime(time.localtime()),'Images have been xtalk corrected',struct[0])
saltkey.new('SBIAS',time.asctime(time.localtime()),'Images have been de-biased',struct[0])
# write FITS file
saltio.writefits(struct,simg, clobber=clobber)
saltio.closefits(struct)
#mosaic the files--currently not in the proper format--will update when it is
if not saltkey.fastmode(saltkey.get('DETMODE', struct[0])):
mimg=outpath+'mbxgp'+os.path.basename(img)
saltmosaic(images=simg, outimages=mimg,outpref='',geomfile=geomfile,
interp=interp,cleanup=True,clobber=clobber,logfile=logfile,
verbose=verbose)
#remove the intermediate steps
saltio.delete(simg)
def hrsclean(images, outpath, obslogfile=None, subover=True, trim=True, masbias=None,
subbias=True, median=False, function='polynomial', order=5, rej_lo=3, rej_hi=3,
niter=5, interp='linear', clobber=False, logfile='salt.log',verbose=True):
"""Convert MEF HRS data into a single image. If variance frames and BPMs, then
convert them to the same format as well. Returns an MEF image but that is
combined into a single frame
"""
with logging(logfile,debug) as log:
# Check the input images
infiles = saltio.argunpack ('Input',images)
# create list of output files
outpath=saltio.abspath(outpath)
if saltio.checkfornone(obslogfile) is None:
raise SaltError('Obslog file is required')
# Delete the obslog file if it already exists
if (os.path.isfile(obslogfile) and clobber) or not os.path.isfile(obslogfile):
if os.path.isfile(obslogfile): saltio.delete(obslogfile)
#read in the obsveration log or create it
headerDict=obslog(infiles, log)
obsstruct=createobslogfits(headerDict)
saltio.writefits(obsstruct, obslogfile)
else:
obsstruct=saltio.openfits(obslogfile)
#create the list of bias frames and process them
filename=obsstruct.data.field('FILENAME')
detmode=obsstruct.data.field('DETMODE')
ccdtype=obsstruct.data.field('OBJECT')
biaslist=filename[ccdtype=='Bias']
masterbias_dict={}
if log: log.message('Processing Bias Frames')
for img in infiles:
if os.path.basename(img) in biaslist:
#open the image
struct=pyfits.open(img)
bimg=outpath+'bgph'+os.path.basename(img)
#print the message
if log:
message='Processing Zero frame %s' % img
log.message(message, with_stdout=verbose, with_header=False)
#process the image
struct=clean(struct, createvar=False, badpixelstruct=None, mult=True,
subover=subover, trim=trim, subbias=False, imstack=False,
bstruct=None, median=median, function=function, order=order,
rej_lo=rej_lo, rej_hi=rej_hi, niter=niter, log=log,
verbose=verbose)
#write the file out
# housekeeping keywords
fname, hist=history(level=1, wrap=False, exclude=['images', 'outimages', 'outpref'])
saltkey.housekeeping(struct[0],'HPREPARE', 'Images have been prepared', hist)
saltkey.new('HGAIN',time.asctime(time.localtime()),'Images have been gain corrected',struct[0])
#saltkey.new('HXTALK',time.asctime(time.localtime()),'Images have been xtalk corrected',struct[0])
saltkey.new('HBIAS',time.asctime(time.localtime()),'Images have been de-biased',struct[0])
# write FITS file
saltio.writefits(struct,bimg, clobber=clobber)
saltio.closefits(struct)
#add files to the master bias list
masterbias_dict=compareimages(struct, bimg, masterbias_dict, keylist=hrsbiasheader_list)
#create the master bias frame
for i in list(masterbias_dict.keys()):
bkeys=masterbias_dict[i][0]
blist=masterbias_dict[i][1:]
mbiasname=outpath+createmasterbiasname(blist, bkeys, x1=5, x2=13)
bfiles=','.join(blist)
saltcombine(bfiles, mbiasname, method='median', reject='sigclip', mask=False,
weight=False, blank=0, scale=None, statsec=None, lthresh=3, \
hthresh=3, clobber=False, logfile=logfile,verbose=verbose)
#apply full reductions to the science data
for img in infiles:
nimg=os.path.basename(img)
if not nimg in biaslist:
#open the image
struct=pyfits.open(img)
simg=outpath+'mbgph'+os.path.basename(img)
#print the message
if log:
message='Processing science frame %s' % img
log.message(message, with_stdout=verbose)
#get master bias frame
masterbias=get_masterbias(struct, masterbias_dict, keylist=hrsbiasheader_list)
if masterbias:
subbias=True
bstruct=saltio.openfits(masterbias)
else:
subbias=False
bstruct=None
#process the image
struct=clean(struct, createvar=False, badpixelstruct=None, mult=True,
subover=subover, trim=trim, subbias=subbias, imstack=True,
bstruct=bstruct, median=median, function=function, order=order,
rej_lo=rej_lo, rej_hi=rej_hi, niter=niter, log=log,
verbose=verbose)
#write the file out
# housekeeping keywords
fname, hist=history(level=1, wrap=False, exclude=['images', 'outimages', 'outpref'])
saltkey.housekeeping(struct[0],'HPREPARE', 'Images have been prepared', hist)
saltkey.new('HGAIN',time.asctime(time.localtime()),'Images have been gain corrected',struct[0])
#saltkey.new('HXTALK',time.asctime(time.localtime()),'Images have been xtalk corrected',struct[0])
saltkey.new('HBIAS',time.asctime(time.localtime()),'Images have been de-biased',struct[0])
# write FITS file
saltio.writefits(struct,simg, clobber=clobber)
saltio.closefits(struct)
return
def saltadvance(images, outpath, obslogfile=None, gaindb=None,xtalkfile=None,
geomfile=None,subover=True,trim=True,masbias=None,
subbias=False, median=False, function='polynomial', order=5,rej_lo=3,
rej_hi=3,niter=5,interp='linear', sdbhost='',sdbname='',sdbuser='', password='',
clobber=False, cleanup=True, logfile='salt.log', verbose=True):
"""SALTADVANCE provides advanced data reductions for a set of data. It will
sort the data, and first process the biases, flats, and then the science
frames. It will record basic quality control information about each of
the steps.
"""
plotover=False
#start logging
with logging(logfile,debug) as log:
# Check the input images
infiles = saltio.argunpack ('Input',images)
infiles.sort()
# create list of output files
outpath=saltio.abspath(outpath)
#log into the database
sdb=saltmysql.connectdb(sdbhost, sdbname, sdbuser, password)
#does the gain database file exist
if gaindb:
dblist= saltio.readgaindb(gaindb)
else:
dblist=[]
# does crosstalk coefficient data exist
if xtalkfile:
xtalkfile = xtalkfile.strip()
xdict = saltio.readxtalkcoeff(xtalkfile)
else:
xdict=None
#does the mosaic file exist--raise error if no
saltio.fileexists(geomfile)
# Delete the obslog file if it already exists
if os.path.isfile(obslogfile) and clobber: saltio.delete(obslogfile)
#read in the obsveration log or create it
if os.path.isfile(obslogfile):
msg='The observing log already exists. Please either delete it or run saltclean with clobber=yes'
raise SaltError(msg)
else:
headerDict=obslog(infiles, log)
obsstruct=createobslogfits(headerDict)
saltio.writefits(obsstruct, obslogfile)
#create the list of bias frames and process them
filename=obsstruct.data.field('FILENAME')
detmode=obsstruct.data.field('DETMODE')
obsmode=obsstruct.data.field('OBSMODE')
ccdtype=obsstruct.data.field('CCDTYPE')
propcode=obsstruct.data.field('PROPID')
masktype=obsstruct.data.field('MASKTYP')
#set the bias list of objects
biaslist=filename[(ccdtype=='ZERO')*(propcode=='CAL_BIAS')]
masterbias_dict={}
for img in infiles:
if os.path.basename(img) in biaslist:
#open the image
struct=fits.open(img)
bimg=outpath+'bxgp'+os.path.basename(img)
#print the message
if log:
message='Processing Zero frame %s' % img
log.message(message, with_stdout=verbose)
#process the image
struct=clean(struct, createvar=True, badpixelstruct=None, mult=True,
dblist=dblist, xdict=xdict, subover=subover, trim=trim, subbias=False,
bstruct=None, median=median, function=function, order=order,
rej_lo=rej_lo, rej_hi=rej_hi, niter=niter, plotover=plotover, log=log,
verbose=verbose)
#update the database
updatedq(os.path.basename(img), struct, sdb)
#write the file out
# housekeeping keywords
fname, hist=history(level=1, wrap=False, exclude=['images', 'outimages', 'outpref'])
saltkey.housekeeping(struct[0],'SPREPARE', 'Images have been prepared', hist)
saltkey.new('SGAIN',time.asctime(time.localtime()),'Images have been gain corrected',struct[0])
saltkey.new('SXTALK',time.asctime(time.localtime()),'Images have been xtalk corrected',struct[0])
saltkey.new('SBIAS',time.asctime(time.localtime()),'Images have been de-biased',struct[0])
# write FITS file
saltio.writefits(struct,bimg, clobber=clobber)
saltio.closefits(struct)
#add files to the master bias list
masterbias_dict=compareimages(struct, bimg, masterbias_dict, keylist=biasheader_list)
#create the master bias frame
for i in masterbias_dict.keys():
bkeys=masterbias_dict[i][0]
blist=masterbias_dict[i][1:]
mbiasname=outpath+createmasterbiasname(blist, bkeys)
bfiles=','.join(blist)
saltcombine(bfiles, mbiasname, method='median', reject='sigclip', mask=False,
weight=False, blank=0, scale=None, statsec=None, lthresh=3, \
hthresh=3, clobber=False, logfile=logfile,verbose=verbose)
#create the list of flatfields and process them
flatlist=filename[ccdtype=='FLAT']
masterflat_dict={}
for img in infiles:
if os.path.basename(img) in flatlist:
#open the image
struct=fits.open(img)
fimg=outpath+'bxgp'+os.path.basename(img)
#print the message
if log:
message='Processing Flat frame %s' % img
log.message(message, with_stdout=verbose)
#process the image
struct=clean(struct, createvar=True, badpixelstruct=None, mult=True,
dblist=dblist, xdict=xdict, subover=subover, trim=trim, subbias=False,
bstruct=None, median=median, function=function, order=order,
rej_lo=rej_lo, rej_hi=rej_hi, niter=niter, plotover=plotover, log=log,
verbose=verbose)
#update the database
updatedq(os.path.basename(img), struct, sdb)
#write the file out
# housekeeping keywords
fname, hist=history(level=1, wrap=False, exclude=['images', 'outimages', 'outpref'])
saltkey.housekeeping(struct[0],'SPREPARE', 'Images have been prepared', hist)
saltkey.new('SGAIN',time.asctime(time.localtime()),'Images have been gain corrected',struct[0])
saltkey.new('SXTALK',time.asctime(time.localtime()),'Images have been xtalk corrected',struct[0])
saltkey.new('SBIAS',time.asctime(time.localtime()),'Images have been de-biased',struct[0])
# write FITS file
saltio.writefits(struct,fimg, clobber=clobber)
saltio.closefits(struct)
#add files to the master bias list
masterflat_dict=compareimages(struct, fimg, masterflat_dict, keylist=flatheader_list)
#create the master flat frame
for i in masterflat_dict.keys():
fkeys=masterflat_dict[i][0]
flist=masterflat_dict[i][1:]
mflatname=outpath+createmasterflatname(flist, fkeys)
ffiles=','.join(flist)
saltcombine(ffiles, mflatname, method='median', reject='sigclip', mask=False,
weight=False, blank=0, scale=None, statsec=None, lthresh=3, \
hthresh=3, clobber=False, logfile=logfile,verbose=verbose)
#process the arc data
arclist=filename[(ccdtype=='ARC') * (obsmode=='SPECTROSCOPY') * (masktype=='LONGSLIT')]
for i, img in enumerate(infiles):
nimg=os.path.basename(img)
if nimg in arclist:
#open the image
struct=fits.open(img)
simg=outpath+'bxgp'+os.path.basename(img)
obsdate=os.path.basename(img)[1:9]
#print the message
if log:
message='Processing ARC frame %s' % img
log.message(message, with_stdout=verbose)
struct=clean(struct, createvar=False, badpixelstruct=None, mult=True,
dblist=dblist, xdict=xdict, subover=subover, trim=trim, subbias=False,
bstruct=None, median=median, function=function, order=order,
rej_lo=rej_lo, rej_hi=rej_hi, niter=niter, plotover=plotover,
log=log, verbose=verbose)
# write FITS file
saltio.writefits(struct,simg, clobber=clobber)
saltio.closefits(struct)
#mosaic the images
mimg=outpath+'mbxgp'+os.path.basename(img)
saltmosaic(images=simg, outimages=mimg,outpref='',geomfile=geomfile,
interp=interp,cleanup=True,clobber=clobber,logfile=logfile,
verbose=verbose)
#remove the intermediate steps
saltio.delete(simg)
#measure the arcdata
arcimage=outpath+'mbxgp'+nimg
dbfile=outpath+obsdate+'_specid.db'
lamp = obsstruct.data.field('LAMPID')[i]
lamp = lamp.replace(' ', '')
lampfile = iraf.osfn("pysalt$data/linelists/%s.salt" % lamp)
print arcimage, lampfile, os.getcwd()
specidentify(arcimage, lampfile, dbfile, guesstype='rss',
guessfile='', automethod='Matchlines', function='legendre',
order=3, rstep=100, rstart='middlerow', mdiff=20, thresh=3,
startext=0, niter=5, smooth=3, inter=False, clobber=True, logfile=logfile,
verbose=verbose)
try:
ximg = outpath+'xmbxgp'+os.path.basename(arcimage)
specrectify(images=arcimage, outimages=ximg, outpref='', solfile=dbfile, caltype='line',
function='legendre', order=3, inttype='interp', w1=None, w2=None, dw=None,
nw=None, blank=0.0, conserve=True, nearest=True, clobber=True,
logfile=logfile, verbose=verbose)
except:
pass
#process the science data
for i, img in enumerate(infiles):
nimg=os.path.basename(img)
if not (nimg in flatlist or nimg in biaslist or nimg in arclist):
#open the image
struct=fits.open(img)
if struct[0].header['PROPID'].count('CAL_GAIN'): continue
simg=outpath+'bxgp'+os.path.basename(img)
#print the message
if log:
message='Processing science frame %s' % img
log.message(message, with_stdout=verbose)
#Check to see if it is RSS 2x2 and add bias subtraction
instrume=saltkey.get('INSTRUME', struct[0]).strip()
gainset = saltkey.get('GAINSET', struct[0])
rospeed = saltkey.get('ROSPEED', struct[0])
target = saltkey.get('OBJECT', struct[0]).strip()
exptime = saltkey.get('EXPTIME', struct[0])
obsmode = saltkey.get('OBSMODE', struct[0]).strip()
detmode = saltkey.get('DETMODE', struct[0]).strip()
masktype = saltkey.get('MASKTYP', struct[0]).strip()
xbin, ybin = saltkey.ccdbin( struct[0], img)
obsdate=os.path.basename(img)[1:9]
bstruct=None
crtype=None
thresh=5
mbox=11
bthresh=5.0,
flux_ratio=0.2
bbox=25
gain=1.0
rdnoise=5.0
fthresh=5.0
bfactor=2
gbox=3
maxiter=5
subbias=False
if instrume=='RSS' and gainset=='FAINT' and rospeed=='SLOW':
bfile='P%sBiasNM%ix%iFASL.fits' % (obsdate, xbin, ybin)
if os.path.exists(bfile):
bstruct=fits.open(bfile)
subbias=True
if detmode=='Normal' and target!='ARC' and xbin < 5 and ybin < 5:
crtype='edge'
thresh=5
mbox=11
bthresh=5.0,
flux_ratio=0.2
bbox=25
gain=1.0
rdnoise=5.0
fthresh=5.0
bfactor=2
gbox=3
maxiter=3
#process the image
struct=clean(struct, createvar=True, badpixelstruct=None, mult=True,
dblist=dblist, xdict=xdict, subover=subover, trim=trim, subbias=subbias,
bstruct=bstruct, median=median, function=function, order=order,
rej_lo=rej_lo, rej_hi=rej_hi, niter=niter, plotover=plotover,
crtype=crtype,thresh=thresh,mbox=mbox, bbox=bbox, \
bthresh=bthresh, flux_ratio=flux_ratio, gain=gain, rdnoise=rdnoise,
bfactor=bfactor, fthresh=fthresh, gbox=gbox, maxiter=maxiter,
log=log, verbose=verbose)
#update the database
updatedq(os.path.basename(img), struct, sdb)
#write the file out
# housekeeping keywords
fname, hist=history(level=1, wrap=False, exclude=['images', 'outimages', 'outpref'])
saltkey.housekeeping(struct[0],'SPREPARE', 'Images have been prepared', hist)
saltkey.new('SGAIN',time.asctime(time.localtime()),'Images have been gain corrected',struct[0])
saltkey.new('SXTALK',time.asctime(time.localtime()),'Images have been xtalk corrected',struct[0])
saltkey.new('SBIAS',time.asctime(time.localtime()),'Images have been de-biased',struct[0])
# write FITS file
saltio.writefits(struct,simg, clobber=clobber)
saltio.closefits(struct)
#mosaic the files--currently not in the proper format--will update when it is
if not saltkey.fastmode(saltkey.get('DETMODE', struct[0])):
mimg=outpath+'mbxgp'+os.path.basename(img)
saltmosaic(images=simg, outimages=mimg,outpref='',geomfile=geomfile,
interp=interp,fill=True, cleanup=True,clobber=clobber,logfile=logfile,
verbose=verbose)
#remove the intermediate steps
saltio.delete(simg)
#if the file is spectroscopic mode, apply the wavelength correction
if obsmode == 'SPECTROSCOPY' and masktype.strip()=='LONGSLIT':
dbfile=outpath+obsdate+'_specid.db'
try:
ximg = outpath+'xmbxgp'+os.path.basename(img)
specrectify(images=mimg, outimages=ximg, outpref='', solfile=dbfile, caltype='line',
function='legendre', order=3, inttype='interp', w1=None, w2=None, dw=None,
nw=None, blank=0.0, conserve=True, nearest=True, clobber=True,
logfile=logfile, verbose=verbose)
except Exception, e:
log.message('%s' % e)
#clean up the results
if cleanup:
#clean up the bias frames
for i in masterbias_dict.keys():
blist=masterbias_dict[i][1:]
for b in blist: saltio.delete(b)
#clean up the flat frames
for i in masterflat_dict.keys():
flist=masterflat_dict[i][1:]
for f in flist: saltio.delete(f)
def hrsclean(images,
outpath,
obslogfile=None,
subover=True,
trim=True,
masbias=None,
subbias=True,
median=False,
function='polynomial',
order=5,
rej_lo=3,
rej_hi=3,
niter=5,
interp='linear',
clobber=False,
logfile='salt.log',
verbose=True):
"""Convert MEF HRS data into a single image. If variance frames and BPMs, then
convert them to the same format as well. Returns an MEF image but that is
combined into a single frame
"""
with logging(logfile, debug) as log:
# Check the input images
infiles = saltio.argunpack('Input', images)
# create list of output files
outpath = saltio.abspath(outpath)
if saltio.checkfornone(obslogfile) is None:
raise SaltError('Obslog file is required')
# Delete the obslog file if it already exists
if (os.path.isfile(obslogfile)
and clobber) or not os.path.isfile(obslogfile):
if os.path.isfile(obslogfile): saltio.delete(obslogfile)
#read in the obsveration log or create it
headerDict = obslog(infiles, log)
obsstruct = createobslogfits(headerDict)
saltio.writefits(obsstruct, obslogfile)
else:
obsstruct = saltio.openfits(obslogfile)
#create the list of bias frames and process them
filename = obsstruct.data.field('FILENAME')
detmode = obsstruct.data.field('DETMODE')
ccdtype = obsstruct.data.field('OBJECT')
biaslist = filename[ccdtype == 'Bias']
masterbias_dict = {}
if log: log.message('Processing Bias Frames')
for img in infiles:
if os.path.basename(img) in biaslist:
#open the image
struct = pyfits.open(img)
bimg = outpath + 'bgph' + os.path.basename(img)
#print the message
if log:
message = 'Processing Zero frame %s' % img
log.message(message,
with_stdout=verbose,
with_header=False)
#process the image
struct = clean(struct,
createvar=False,
badpixelstruct=None,
mult=True,
subover=subover,
trim=trim,
subbias=False,
imstack=False,
bstruct=None,
median=median,
function=function,
order=order,
rej_lo=rej_lo,
rej_hi=rej_hi,
niter=niter,
log=log,
verbose=verbose)
#write the file out
# housekeeping keywords
fname, hist = history(
level=1,
wrap=False,
exclude=['images', 'outimages', 'outpref'])
saltkey.housekeeping(struct[0], 'HPREPARE',
'Images have been prepared', hist)
saltkey.new('HGAIN', time.asctime(time.localtime()),
'Images have been gain corrected', struct[0])
#saltkey.new('HXTALK',time.asctime(time.localtime()),'Images have been xtalk corrected',struct[0])
saltkey.new('HBIAS', time.asctime(time.localtime()),
'Images have been de-biased', struct[0])
# write FITS file
saltio.writefits(struct, bimg, clobber=clobber)
saltio.closefits(struct)
#add files to the master bias list
masterbias_dict = compareimages(struct,
bimg,
masterbias_dict,
keylist=hrsbiasheader_list)
#create the master bias frame
for i in masterbias_dict.keys():
bkeys = masterbias_dict[i][0]
blist = masterbias_dict[i][1:]
mbiasname = outpath + createmasterbiasname(
blist, bkeys, x1=5, x2=13)
bfiles = ','.join(blist)
saltcombine(bfiles, mbiasname, method='median', reject='sigclip', mask=False,
weight=False, blank=0, scale=None, statsec=None, lthresh=3, \
hthresh=3, clobber=False, logfile=logfile,verbose=verbose)
#apply full reductions to the science data
for img in infiles:
nimg = os.path.basename(img)
if not nimg in biaslist:
#open the image
struct = pyfits.open(img)
simg = outpath + 'mbgph' + os.path.basename(img)
#print the message
if log:
message = 'Processing science frame %s' % img
log.message(message, with_stdout=verbose)
#get master bias frame
masterbias = get_masterbias(struct,
masterbias_dict,
keylist=hrsbiasheader_list)
if masterbias:
subbias = True
bstruct = saltio.openfits(masterbias)
else:
subbias = False
bstruct = None
#process the image
struct = clean(struct,
createvar=False,
badpixelstruct=None,
mult=True,
subover=subover,
trim=trim,
subbias=subbias,
imstack=True,
bstruct=bstruct,
median=median,
function=function,
order=order,
rej_lo=rej_lo,
rej_hi=rej_hi,
niter=niter,
log=log,
verbose=verbose)
#write the file out
# housekeeping keywords
fname, hist = history(
level=1,
wrap=False,
exclude=['images', 'outimages', 'outpref'])
saltkey.housekeeping(struct[0], 'HPREPARE',
'Images have been prepared', hist)
saltkey.new('HGAIN', time.asctime(time.localtime()),
'Images have been gain corrected', struct[0])
#saltkey.new('HXTALK',time.asctime(time.localtime()),'Images have been xtalk corrected',struct[0])
saltkey.new('HBIAS', time.asctime(time.localtime()),
'Images have been de-biased', struct[0])
# write FITS file
saltio.writefits(struct, simg, clobber=clobber)
saltio.closefits(struct)
return