def wl_cal(filename, outname, cals):
print 'run wavelength calibration...'
if os.path.isfile(outname):
print 'file %s is already exist' % outname
else:
iraf.imred()
iraf.specred()
print 'The calibration file is listed below:'
for i in xrange(len(cals)):
print i, cals[i], get_fits_objname(cals[i])
valget = 0
if len(cals) > 1:
valget = raw_input('Which one are you want to use?:')
while type(eval(valget)) != int:
valget = raw_input('input is not a int type, please reinput:')
valget = int(valget)
iraf.refspectra(input = filename
, references = cals[valget], apertures = '', refaps = ''
, ignoreaps = True, select = 'interp', sort = ''
, group = '', time = False, timewrap = 17.0
, override = False, confirm = True, assign = True
, logfiles = 'STDOUT,logfile', verbose = False, answer = 'yes')
print 'make file ' + outname + '...'
iraf.dispcor(input = filename
, output = outname, linearize = True, database = 'database'
, table = '', w1 = 'INDEF', w2 = 'INDEF', dw = 'INDEF'
, nw = 'INDEF', log = False, flux = True, blank = 0.0
, samedisp = False, ignoreaps = True
, confirm = False, listonly = False, verbose = True
, logfile = '')
print 'splot %s' % outname
iraf.splot(images = outname)
def initialize_iraf():
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
iraf.specred(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.longslit(_doprint=0)
return
def scalewavelenght(calspec):
''' Creates a wavelenght solution for 'calspec' '''
iraf.imred()
iraf.specred()
linelist = str(raw_input('Enter file with list of lines (linelists$thar.dat) : '))
if linelist == '':
linelist = 'linelists$thar.dat'
iraf.specred.identify.coordlist = linelist
iraf.specred.identify(images=calspec)
def scalewavelenght(calspec):
''' Creates a wavelenght solution for 'calspec' '''
iraf.imred()
iraf.specred()
linelist = str(
raw_input('Enter file with list of lines (linelists$thar.dat) : '))
if linelist == '':
linelist = 'linelists$thar.dat'
iraf.specred.identify.coordlist = linelist
iraf.specred.identify(images=calspec)
def flatresponse(input='Flat', output='nFlat'):
''' normalize Flat to correct illumination patterns'''
iraf.imred()
iraf.ccdred()
iraf.specred()
iraf.ccdred.combine.unlearn()
iraf.ccdred.ccdproc.unlearn()
iraf.specred.response.unlearn()
iraf.specred.response.interactive = True
iraf.specred.response.function = 'chebyshev'
iraf.specred.response.order = 1
iraf.specred.response(calibration=input, normalization=input,
response=output)
def findaperture(img, _interactive=False):
# print "LOGX:: Entering `findaperture` method/function in %(__file__)s" %
# globals()
import re
import string
import os
from pyraf import iraf
import ntt
iraf.noao(_doprint=0, Stdout=0)
iraf.imred(_doprint=0, Stdout=0)
iraf.specred(_doprint=0, Stdout=0)
toforget = ['specred.apfind']
for t in toforget:
iraf.unlearn(t)
iraf.specred.databas = 'database'
iraf.specred.dispaxi = 2
iraf.specred.apedit.thresho = 0
dv = ntt.dvex()
grism = ntt.util.readkey3(ntt.util.readhdr(img), 'grism')
if _interactive:
_interac = 'yes'
_edit = 'yes'
else:
_interac = 'no'
_edit = 'no'
if os.path.isfile('database/ap' + re.sub('.fits', '', img)):
ntt.util.delete('database/ap' + re.sub('.fits', '', img))
xx = iraf.specred.apfind(img,
interac=_interac,
find='yes',
recenter='yes',
edit=_edit,
resize='no',
aperture=1,
Stdout=1,
nfind=1,
line=dv['line'][grism],
nsum=50,
mode='h')
try:
for line in open('database/ap' + re.sub('.fits', '', img)):
if "center" in line:
center = float(string.split(line)[1])
except:
center = 9999
return center
def flatresponse(input='Flat', output='nFlat'):
''' normalize Flat to correct illumination patterns'''
iraf.imred()
iraf.ccdred()
iraf.specred()
iraf.ccdred.combine.unlearn()
iraf.ccdred.ccdproc.unlearn()
iraf.specred.response.unlearn()
iraf.specred.response.interactive = True
iraf.specred.response.function = 'chebyshev'
iraf.specred.response.order = 1
iraf.specred.response(calibration=input,
normalization=input,
response=output)
def runapall(imagesre, gain='gain', rdnoise='rdnoise'):
'''Extract aperture spectra for science images ...'''
imageslist = glob.glob(imagesre)
imagesin = ', '.join(imageslist)
# load packages
iraf.imred()
iraf.ccdred()
iraf.specred()
# unlearn previous settings
iraf.ccdred.combine.unlearn()
iraf.ccdred.ccdproc.unlearn()
iraf.specred.apall.unlearn()
# setup and run task
iraf.specred.apall.format = 'onedspec'
iraf.specred.apall.readnoise = rdnoise
iraf.specred.apall.gain = gain
iraf.specred.apall(input=imagesin)
def runapall(imagesre, gain='gain', rdnoise='rdnoise'):
'''Extract aperture spectra for science images ...'''
imageslist = glob.glob(imagesre)
imagesin = ', '.join(imageslist)
# load packages
iraf.imred()
iraf.ccdred()
iraf.specred()
# unlearn previous settings
iraf.ccdred.combine.unlearn()
iraf.ccdred.ccdproc.unlearn()
iraf.specred.apall.unlearn()
# setup and run task
iraf.specred.apall.format = 'onedspec'
iraf.specred.apall.readnoise = rdnoise
iraf.specred.apall.gain = gain
iraf.specred.apall(input=imagesin)
def continumsub(imagefile, _order1, _order2):
# print "LOGX:: Entering `continumsub` method/function in %(__file__)s" %
# globals()
from pyraf import iraf
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.specred(_doprint=0)
toforget = ['specred.continuum']
for t in toforget:
iraf.unlearn(t)
delete('tsky.fits')
iraf.specred.continuum(imagefile, output='tsky.fits', type='difference',
interact='no', function='legendre', niterat=300, low_rej=3, high_re=2, sample='*',
order=_order1, ask='YES')
delete(imagefile)
iraf.continuum('tsky.fits', output=imagefile, type='difference',
interact='no', function='spline1', overrid='yes', niterat=10, low_rej=3, high_re=1, sample='*',
order=_order2, ask='YES')
delete('tsky.fits')
return imagefile
def findaperture(img, _interactive=False):
# print "LOGX:: Entering `findaperture` method/function in %(__file__)s" %
# globals()
import re
import string
import os
from pyraf import iraf
import ntt
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.specred(_doprint=0)
toforget = ['specred.apfind']
for t in toforget:
iraf.unlearn(t)
iraf.specred.databas = 'database'
iraf.specred.dispaxi = 2
iraf.specred.apedit.thresho = 0
dv = ntt.dvex()
grism = ntt.util.readkey3(ntt.util.readhdr(img), 'grism')
if _interactive:
_interac = 'yes'
_edit = 'yes'
else:
_interac = 'no'
_edit = 'no'
if os.path.isfile('database/ap' + re.sub('.fits', '', img)):
ntt.util.delete('database/ap' + re.sub('.fits', '', img))
xx = iraf.specred.apfind(img, interac=_interac, find='yes', recenter='yes', edit=_edit, resize='no',
aperture=1, Stdout=1, nfind=1, line=dv['line'][grism], nsum=50, mode='h')
try:
for line in open('database/ap' + re.sub('.fits', '', img)):
if "center" in line:
center = float(string.split(line)[1])
except:
center = 9999
return center
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 extractspectrum(img, dv, inst, _interactive, _type, automaticex=False):
# print "LOGX:: Entering `extractspectrum` method/function in
# %(__file__)s" % globals()
import glob
import os
import string
import sys
import re
import datetime
import numpy as np
MJDtoday = 55927 + (datetime.date.today() - datetime.date(2012, 01, 01)).days
from pyraf import iraf
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.specred(_doprint=0)
toforget = ['specred.apall', 'specred.transform']
for t in toforget:
iraf.unlearn(t)
hdr = readhdr(img)
iraf.specred.dispaxi = inst.get('dispaxis')
imgex = re.sub('.fits', '_ex.fits', img)
imgfast = re.sub(string.split(img, '_')[-2] + '_', '', img)
if not os.path.isfile(imgex) and not os.path.isfile(
'database/ap' + re.sub('.fits', '', img)) and not os.path.isfile(
'database/ap' + re.sub('.fits', '', imgfast)):
_new = 'yes'
_extract = 'yes'
else:
if automaticex:
if _interactive in ['Yes', 'yes', 'YES', 'y', 'Y']:
answ = 'x'
while answ not in ['o', 'n', 's']:
answ = raw_input(
'\n### New extraction [n], extraction with old parameters [o], skip extraction [s] ? [o]')
if not answ:
answ = 'o'
if answ == 'o':
_new, _extract = 'no', 'yes'
elif answ == 'n':
_new, _extract = 'yes', 'yes'
else:
_new, _extract = 'yes', 'no'
else:
_new, _extract = 'no', 'yes'
else:
if _interactive in ['Yes', 'yes', 'YES', 'y', 'Y']:
answ = 'x'
while answ not in ['y', 'n']:
answ = raw_input(
'\n### do you want to extract again [[y]/n] ? ')
if not answ:
answ = 'y'
if answ == 'y':
_new, _extract = 'yes', 'yes'
else:
_new, _extract = 'yes', 'no'
else:
_new, _extract = 'yes', 'yes'
if _extract == 'yes':
delete(imgex)
dist = 200
_reference = ''
_fittrac = 'yes'
_trace = 'yes'
if _new == 'no':
if not os.path.isfile('database/ap' + re.sub('.fits', '', img)):
repstringinfile('database/ap' + re.sub('.fits', '', imgfast),
'database/ap' +
re.sub('.fits', '', img), re.sub(
'.fits', '', imgfast),
re.sub('.fits', '', img))
_find = 'no'
_recenter = 'no'
_edit = 'no'
_trace = 'no'
_fittrac = 'no'
_mode = 'h'
_resize = 'no'
_review = 'no'
iraf.specred.mode = 'h'
_interactive = 'no'
else:
iraf.specred.mode = 'q'
_mode = 'q'
_find = 'yes'
_recenter = 'yes'
_edit = 'yes'
_review = 'yes'
_resize = dv[_type]['_resize']
# _interactive = False
_recenter = 'no'
_resize = 'no'
_edit = 'yes'
_trace = 'yes'
_fittrace = 'no'
_review = 'yes'
iraf.specred.apall(img, output=imgex, referen=_reference, trace=_trace, fittrac=_fittrac, find=_find,
recenter=_recenter, edit=_edit,
nfind=1, backgro='fit', lsigma=4, usigma=4,
format='multispec', extras='yes',
b_function='legendre', b_sample=dv[_type]['_b_sample'], clean='yes', pfit='fit1d',
lower=dv[_type]['_lower'], upper=dv[_type][
'_upper'], t_niter=dv[_type]['_t_niter'],
width=dv[_type]['_width'],
radius=dv[_type]['_radius'], line='INDEF', nsum=dv[
_type]['_nsum'], t_step=dv[_type]['_t_step'],
t_nsum=dv[_type]['_t_nsum'],
t_nlost=dv[_type]['_t_nlost'], t_sample=dv[
_type]['_t_sample'], resize=_resize,
t_order=dv[_type]['_t_order'],
weights=dv[_type]['_weights'], interactive=_interactive, review=_review, mode=_mode)
else:
print '\n### skipping new extraction'
return imgex
#!/usr/bin/env python
#
# A script to reduce data from the Coude Spectrograph from the 1.6m telescope
# of the Observatorio do Pico dos Dias - Brazil
#
# Load Python standard modules
import glob
# Load third-party modules
import ds9
from pyraf import iraf
print 'Loading IRAF packages ...'
iraf.imred()
iraf.ccdred()
iraf.specred()
print 'unlearning previous settings...'
iraf.ccdred.unlearn()
iraf.ccdred.ccdproc.unlearn()
iraf.ccdred.combine.unlearn()
iraf.ccdred.zerocombine.unlearn()
iraf.ccdred.flatcombine.unlearn()
iraf.specred.response.unlearn()
# regular expression of files (e.g bias_00*.fits, flat-2000jan01_?.*)
theflat = str(raw_input('Enter flat image: '))
iraf.specred.extinction = ''
iraf.specred.caldir = ''
iraf.specred.observatory = 'lna'
def main():
description = "> Performs pre-reduction steps"
usage = "%prog \t [option] \n Recommended syntax: %prog -i -c"
parser = OptionParser(usage=usage, description=description, version="0.1")
option, args = parser.parse_args()
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.longslit(_doprint=0)
iraf.onedspec(_doprint=0)
iraf.specred(_doprint=0)
iraf.ccdred.verbose = 'no'
iraf.specred.verbose = 'no'
iraf.ccdproc.darkcor = 'no'
iraf.ccdproc.fixpix = 'no'
iraf.ccdproc.flatcor = 'no'
iraf.ccdproc.zerocor = 'no'
iraf.ccdproc.ccdtype = ''
iraf.longslit.mode = 'h'
iraf.specred.mode = 'h'
iraf.noao.mode = 'h'
iraf.ccdred.instrument = "ccddb$kpno/camera.dat"
mkarc = raw_input("Make arc? ([y]/n): ")
mkflat = raw_input("Make flat? ([y]/n): ")
if len(args) > 1:
files = []
sys.argv.append('--help')
option, args = parser.parse_args()
sys.exit()
elif len(args) == 1:
files = util.readlist(args[0])
sys.exit()
else:
listfile = glob.glob('*.fits')
files_science = []
files_arc = []
files_dflat = []
#print 'checking your files ...'
for img in listfile:
_type = ''
hdr0 = util.readhdr(img)
_type = util.readkey3(hdr0, 'object')
if 'flat' in _type.lower():
files_dflat.append(img)
elif 'arc' not in _type.lower() and 'arc' not in img.lower():
files_science.append(img)
if mkarc != 'n':
mkarc_b = raw_input(
"List blue arc files to combine (.fits will be added): "
).split()
mkarc_r = raw_input(
"List red arc files to combine (.fits will be added): ").split(
)
for arc in mkarc_b:
files_arc.append(arc + '.fits')
for arc in mkarc_r:
files_arc.append(arc + '.fits')
if mkarc != 'n':
list_arc_b = []
list_arc_r = []
for arcs in files_arc:
if instruments.blue_or_red(arcs)[0] == 'blue':
list_arc_b.append(arcs)
elif instruments.blue_or_red(arcs)[0] == 'red':
list_arc_r.append(arcs)
else:
sys.exit()
if mkflat != 'n':
list_flat_b = []
list_flat_r = []
for dflats in files_dflat:
if instruments.blue_or_red(dflats)[0] == 'blue':
list_flat_b.append(dflats)
elif instruments.blue_or_red(dflats)[0] == 'red':
list_flat_r.append(dflats)
else:
sys.exit()
# make pre_reduced if it doesn't exist
if not os.path.isdir('pre_reduced/'):
os.mkdir('pre_reduced/')
# log the existing processed files (need to verify this works if pre_reduced is empty...)
pfiles = []
new_files = []
for root, dirnames, filenames in os.walk('pre_reduced'):
for file in filenames:
if file.startswith('to'):
pfiles.append(file)
print(pfiles)
# loop over each image in pre_reduced
for img in listfile:
hdr = util.readhdr(img)
targ = util.readkey3(hdr, 'object')
# if file is not not a processed file, run the overscan+trim code
if 'to' + img not in pfiles:
# if the file is a science file, grab the name for later
if 'arc' not in targ.lower() and 'flat' not in targ.lower():
new_files.append(img)
print('Adding data for: ' + targ)
inst = instruments.blue_or_red(img)[1]
iraf.specred.dispaxi = inst.get('dispaxis')
iraf.longslit.dispaxi = inst.get('dispaxis')
_biassec0 = inst.get('biassec')
_trimsec0 = inst.get('trimsec')
######################################################################
#
# JB: this chunk of code needs attention
# It seems incredibly hacky for anything but Kast...
#
# overscan
if not img.startswith('o') and inst.get('observatory') == 'lick':
if os.path.isfile('pre_reduced/o' + img):
os.remove('pre_reduced/o' + img)
util.kastbias(img, 'pre_reduced/o' + img)
elif not img.startswith('o') and inst.get('observatory') != 'lick':
if os.path.isfile('pre_reduced/o' + img):
os.remove('pre_reduced/o' + img)
os.system('cp ' + img + ' ' + 'pre_reduced/' + img)
# trim
if not img.startswith('t') and inst.get('observatory') == 'lick':
if os.path.isfile('pre_reduced/to' + img):
os.remove('pre_reduced/to' + img)
iraf.ccdproc('pre_reduced/o' + img,
output='pre_reduced/to' + img,
overscan='no',
trim='yes',
zerocor="no",
flatcor="no",
readaxi='line',
trimsec=str(_trimsec0),
Stdout=1)
elif not img.startswith('t') and inst.get('observatory') != 'lick':
if os.path.isfile('pre_reduced/to' + img):
os.remove('pre_reduced/to' + img)
iraf.ccdproc('pre_reduced/' + img,
output='pre_reduced/to' + img,
overscan='yes',
trim='yes',
zerocor="no",
flatcor="no",
readaxi='line',
trimsec=str(_trimsec0),
biassec=str(_biassec0),
Stdout=1)
# combine the arcs
if mkarc != 'n':
# blue arcs
if len(list_arc_b) > 0:
if len(list_arc_b) == 1:
arc_blue = list_arc_b[0]
os.system('cp ' + 'pre_reduced/to' + arc_blue + ' ' +
'pre_reduced/ARC_blue.fits')
else:
arc_str = ''
for arc in list_arc_b:
arc_str = arc_str + 'pre_reduced/to' + arc + ','
if os.path.isfile('pre_reduced/ARC_blue.fits'):
os.remove('pre_reduced/ARC_blue.fits')
iraf.imcombine(arc_str, output='pre_reduced/ARC_blue.fits')
# red arcs
if len(list_arc_r) > 0:
if len(list_arc_r) == 1:
arc_red = list_arc_r[0]
os.system('cp ' + 'pre_reduced/to' + arc_red + ' ' +
'pre_reduced/ARC_red.fits')
else:
arc_str = ''
for arc in list_arc_r:
arc_str = arc_str + 'pre_reduced/to' + arc + ','
if os.path.isfile('pre_reduced/ARC_red.fits'):
os.remove('pre_reduced/ARC_red.fits')
iraf.imcombine(arc_str, output='pre_reduced/ARC_red.fits')
# combine the flats
if mkflat != 'n':
inter = 'yes'
# blue flats
if len(list_flat_b) > 0:
br, inst = instruments.blue_or_red(list_flat_b[0])
iraf.specred.dispaxi = inst.get('dispaxis')
if len(list_flat_b) == 1:
# Flat_blue = 'pre_reduced/to'+ list_flat_b[0]
Flat_blue = list_flat_b[0]
else:
flat_str = ''
for flat in list_flat_b:
flat_str = flat_str + 'pre_reduced/to' + flat + ','
#subsets = 'no'
if os.path.isfile('pre_reduced/toFlat_blue'):
os.remove('pre_reduced/toFlat_blue')
iraf.flatcombine(flat_str,
output='pre_reduced/toFlat_blue',
ccdtype='',
rdnoise=3.7,
subsets='no',
process='no')
Flat_blue = 'Flat_blue.fits'
#What is the output here? Check for overwrite
iraf.specred.response('pre_reduced/to' + Flat_blue,
normaliz='pre_reduced/to' + Flat_blue,
response='pre_reduced/RESP_blue',
interac=inter,
thresho='INDEF',
sample='*',
naverage=2,
function='legendre',
low_rej=3,
high_rej=3,
order=60,
niterat=20,
grow=0,
graphic='stdgraph')
# red flats
if len(list_flat_r) > 0:
br, inst = instruments.blue_or_red(list_flat_r[0])
iraf.specred.dispaxi = inst.get('dispaxis')
if len(list_flat_r) == 1:
# Flat_red = 'pre_reduced/to' + list_flat_r[0]
Flat_red = list_flat_r[0]
else:
flat_str = ''
for flat in list_flat_r:
flat_str = flat_str + 'pre_reduced/to' + flat + ','
if os.path.isfile('pre_reduced/toFlat_red'):
os.remove('pre_reduced/toFlat_red')
iraf.flatcombine(flat_str,
output='pre_reduced/toFlat_red',
ccdtype='',
rdnoise=3.8,
subsets='yes',
process='no')
Flat_red = 'Flat_red.fits'
#What is the output here? Check for overwrite
iraf.specred.response('pre_reduced/to' + Flat_red,
normaliz='pre_reduced/to' + Flat_red,
response='pre_reduced/RESP_red',
interac=inter,
thresho='INDEF',
sample='*',
naverage=2,
function='legendre',
low_rej=3,
high_rej=3,
order=80,
niterat=20,
grow=0,
graphic='stdgraph')
# science files should have 't' in front now
# this just gets the base name, to prefix assumed below
if new_files is not None:
files_science = new_files
# get all the science objects for the night
science_targets = []
for obj in files_science:
hdr = util.readhdr(obj)
_type = util.readkey3(hdr, 'object')
science_targets.append(_type)
# make a dir for each sci object
science_targets = set(science_targets)
for targ in science_targets:
if not os.path.isdir('pre_reduced/' + targ + '/'):
os.mkdir('pre_reduced/' + targ + '/')
# copy the files into the obj dir
for obj in files_science:
hdr = util.readhdr(obj)
targ = util.readkey3(hdr, 'object')
if not obj.startswith('to'):
os.system('cp ' + 'pre_reduced/to' + obj + ' ' + 'pre_reduced/' +
targ + '/')
else:
os.system('cp ' + 'pre_reduced/' + obj + ' ' + 'pre_reduced/' +
targ + '/')
rawfiles = glob.glob('*.fits')
ofiles = glob.glob('pre_reduced/o' + '*.fits')
tfiles = glob.glob('pre_reduced/to' + '*.fits')
# delete raw files from the pre_reduced dir
# there shouldn't be any there though?
# maybe if the overscan isn't implemented for that detector
for img in rawfiles:
util.delete('pre_reduced/' + img)
# delete the ofiles from pre_reduced dir
for img in ofiles:
util.delete(img)
def reduce(imglist,files_arc, _cosmic, _interactive_extraction,_arc):
import string
import os
import re
import sys
os.environ["PYRAF_BETA_STATUS"] = "1"
try: from astropy.io import fits as pyfits
except: import pyfits
import numpy as np
import util
import instruments
import combine_sides as cs
import cosmics
from pyraf import iraf
dv = util.dvex()
scal = np.pi / 180.
if not _interactive_extraction:
_interactive = False
else:
_interactive = True
if not _arc:
_arc_identify = False
else:
_arc_identify = True
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.longslit(_doprint=0)
iraf.onedspec(_doprint=0)
iraf.specred(_doprint=0)
iraf.disp(inlist='1', reference='1')
toforget = ['ccdproc', 'imcopy', 'specred.apall', 'longslit.identify', 'longslit.reidentify', 'specred.standard',
'longslit.fitcoords', 'onedspec.wspectext']
for t in toforget:
iraf.unlearn(t)
iraf.ccdred.verbose = 'no'
iraf.specred.verbose = 'no'
iraf.ccdproc.darkcor = 'no'
iraf.ccdproc.fixpix = 'no'
iraf.ccdproc.flatcor = 'no'
iraf.ccdproc.zerocor = 'no'
iraf.ccdproc.ccdtype = ''
iraf.longslit.mode = 'h'
iraf.specred.mode = 'h'
iraf.noao.mode = 'h'
iraf.ccdred.instrument = "ccddb$kpno/camera.dat"
list_arc_b = []
list_arc_r = []
for arcs in files_arc:
hdr = util.readhdr(arcs)
if util.readkey3(hdr, 'VERSION') == 'kastb':
list_arc_b.append(arcs)
elif util.readkey3(hdr, 'VERSION') == 'kastr':
list_arc_r.append(arcs)
else:
print util.readkey3(hdr, 'VERSION') + 'not in database'
sys.exit()
asci_files = []
newlist = [[],[]]
print '\n### images to reduce :',imglist
#raise TypeError
for img in imglist:
if 'b' in img:
newlist[0].append(img)
elif 'r' in img:
newlist[1].append(img)
if len(newlist[1]) < 1:
newlist = newlist[:-1]
for imgs in newlist:
hdr = util.readhdr(imgs[0])
if util.readkey3(hdr, 'VERSION') == 'kastb':
inst = instruments.kast_blue
elif util.readkey3(hdr, 'VERSION') == 'kastr':
inst = instruments.kast_red
else:
print util.readkey3(hdr, 'VERSION') + 'not in database'
sys.exit()
iraf.specred.dispaxi = inst.get('dispaxis')
iraf.longslit.dispaxi = inst.get('dispaxis')
_gain = inst.get('gain')
_ron = inst.get('read_noise')
iraf.specred.apall.readnoi = _ron
iraf.specred.apall.gain = _gain
_object0 = util.readkey3(hdr, 'OBJECT')
_date0 = util.readkey3(hdr, 'DATE-OBS')
_biassec0 = inst.get('biassec')
_trimsec0 = inst.get('trimsec')
_object0 = re.sub(' ', '', _object0)
_object0 = re.sub('/', '_', _object0)
nameout0 = str(_object0) + '_' + inst.get('name') + '_' + str(_date0)
nameout0 = util.name_duplicate(imgs[0], nameout0, '')
timg = nameout0
print '\n### now processing :',timg,' for -> ',inst.get('name')
if len(imgs) > 1:
img_str = ''
for i in imgs:
img_str = img_str + i + ','
iraf.imcombine(img_str, output=timg)
else:
img = imgs[0]
if os.path.isfile(timg):
os.system('rm -rf ' + timg)
iraf.imcopy(img, output=timg)
zero_file = inst.get('archive_zero_file')
os.system('cp ' + zero_file + ' .')
zero_file = string.split(zero_file, '/')[-1]
flat_file = inst.get('archive_flat_file')
os.system('cp ' + flat_file + ' .')
flat_file = string.split(flat_file, '/')[-1]
iraf.ccdproc(timg, output='', overscan='yes', trim='yes', zerocor="no", flatcor="no", readaxi='line',
trimsec=str(_trimsec0),biassec=str(_biassec0), Stdout=1)
iraf.ccdproc(timg, output='', overscan='no', trim='no', zerocor="yes", flatcor="no", readaxi='line',
zero=zero_file,order=3, Stdout=1)
iraf.ccdproc(timg, output='', overscan='no', trim='no', zerocor="no", flatcor="yes", readaxi='line',
flat=flat_file, Stdout=1)
img = timg
#raw_input("Press Enter to continue...")
print '\n### starting cosmic removal'
if _cosmic:
array, header = cosmics.fromfits(img)
c = cosmics.cosmicsimage(array, gain=inst.get('gain'), readnoise=inst.get('read_noise'), sigclip = 4.5, sigfrac = 0.5, objlim = 1.0)
c.run(maxiter = 4)
cosmics.tofits('cosmic_' + img, c.cleanarray, header)
print '\n### cosmic removal finished'
img='cosmic_' + img
if inst.get('name') == 'kast_blue':
arcfile = list_arc_b[0]
elif inst.get('name') == 'kast_red':
arcfile = list_arc_r[0]
if not arcfile.endswith(".fits"):
arcfile=arcfile+'.fits'
if os.path.isfile(arcfile):
util.delete('t' + arcfile)
iraf.ccdproc(arcfile, output= 't' + arcfile, overscan='yes', trim='yes', zerocor="no", flatcor="no",
readaxi='line', trimsec=str(_trimsec0), biassec=str(_biassec0), Stdout=1)
arcfile = 't' + arcfile
else:
print '\n### warning no arcfile \n exit '
sys.exit()
if not os.path.isdir('database/'):
os.mkdir('database/')
if _arc_identify:
arc_ex=re.sub('.fits', '.ms.fits', arcfile)
print '\n### arcfile : ',arcfile
print '\n### arcfile extraction : ',arc_ex
iraf.specred.apall(arcfile, output='', line = 'INDEF', nsum=10, interactive='no', extract='yes',find='yes', nfind=1 ,format='multispec', trace='no',back='no',recen='no')
iraf.longslit.identify(images=arc_ex, section=inst.get('section'),coordli=inst.get('line_list'),function = 'spline3',order=3, mode='h')
else:
arcref = inst.get('archive_arc_extracted')
arcrefid = inst.get('archive_arc_extracted_id')
os.system('cp ' + arcref + ' .')
arcref = string.split(arcref, '/')[-1]
os.system('cp ' + arcrefid + ' ./database')
arc_ex=re.sub('.fits', '.ms.fits', arcfile)
print '\n### arcfile : ',arcfile
print '\n### arcfile extraction : ',arc_ex
print '\n### arc referenece : ',arcref
iraf.specred.apall(arcfile, output=arc_ex, line = 'INDEF', nsum=10, interactive='no', extract='yes',find='yes', nfind=1 ,format='multispec', trace='no',back='no',recen='no')
iraf.longslit.reidentify(referenc=arcref, images=arc_ex, interac='NO', section=inst.get('section'),
coordli=inst.get('line_list'), shift='INDEF', search='INDEF',
mode='h', verbose='YES', step=0,nsum=5, nlost=2, cradius=10, refit='yes',overrid='yes',newaps='no')
#print '\n### checking sky lines '
#_skyfile = inst.get('sky_file')
#shift = util.skyfrom2d(img, _skyfile,'True')
#print '\n### I found a shift of : ',shift
print '\n### extraction using apall'
result = []
hdr_image = util.readhdr(img)
_type=util.readkey3(hdr_image, 'object')
if _type.startswith("arc") or _type.startswith("dflat") or _type.startswith("Dflat") or _type.startswith("Dbias") or _type.startswith("Bias"):
print '\n### warning problem \n exit '
sys.exit()
else:
imgex = util.extractspectrum(
img, dv, inst, _interactive, 'obj')
print '\n### applying wavelength solution'
iraf.disp(inlist=imgex, reference=arc_ex)
sensfile = inst.get('archive_sens')
os.system('cp ' + sensfile + ' .')
sensfile = string.split(sensfile, '/')[-1]
if sensfile:
print '\n### sensitivity function : ',sensfile
imgf = re.sub('.fits', '_f.fits', img)
_extinction = inst.get('extinction_file')
_observatory = inst.get('observatory')
_exptime = util.readkey3(hdr, 'EXPTIME')
_airmass = util.readkey3(hdr, 'AIRMASS')
util.delete(imgf)
dimgex='d'+imgex
iraf.specred.calibrate(input=dimgex, output=imgf, sensiti=sensfile, extinct='yes',
extinction=_extinction,flux='yes', ignorea='yes', airmass=_airmass, exptime=_exptime,
fnu='no')
imgout = imgf
imgasci = re.sub('.fits', '.asci', imgout)
errasci = re.sub('.fits', '_err.asci', imgout)
util.delete(imgasci)
iraf.onedspec.wspectext(imgout + '[*,1,1]', imgasci, header='no')
iraf.onedspec.wspectext(imgout + '[*,1,4]', errasci, header='no')
spec = np.transpose(np.genfromtxt(imgasci))
err = np.transpose(np.genfromtxt(errasci))
util.delete(errasci)
final = np.transpose([spec[0], spec[1], err[1]])
np.savetxt(imgasci, final)
result = result + [imgout, imgasci]
result = result + [imgex] + [timg]
asci_files.append(imgasci)
if not os.path.isdir(_object0 + '/'):
os.mkdir(_object0 + '/')
for img in result:
os.system('mv ' + img + ' ' + _object0 + '/')
else:
for img in result:
os.system('mv ' + img + ' ' + _object0 + '/')
if not _arc_identify:
util.delete(arcref)
util.delete(sensfile)
util.delete(zero_file)
util.delete(flat_file)
util.delete(arc_ex)
util.delete(arcfile)
util.delete('logfile')
util.delete(dimgex)
util.delete('cosmic_*')
print '\n### now i will merge ...'
if len(asci_files) > 1:
final = cs.combine_blue_red(asci_files[0], asci_files[1], _object0)
print '\n### final result in folder ',_object0,' is ',_object0+'_merged.asci'
return result
def efoscspec1Dredu(files,
_interactive,
_ext_trace,
_dispersionline,
liststandard,
listatmo0,
_automaticex,
_verbose=False):
# print "LOGX:: Entering `efoscspec1Dredu` method/function in
# %(__file__)s" % globals()
import ntt
try:
import pyfits
except:
from astropy.io import fits as pyfits
import re
import string
import sys
import os
import numpy as np
os.environ["PYRAF_BETA_STATUS"] = "1"
_extinctdir = 'direc$standard/extinction/'
_extinction = 'lasilla2.txt'
_observatory = 'lasilla'
import datetime
now = datetime.datetime.now()
datenow = now.strftime('20%y%m%d%H%M')
MJDtoday = 55927 + (datetime.date.today() -
datetime.date(2012, 01, 01)).days
dv = ntt.dvex()
scal = np.pi / 180.
_gain = ntt.util.readkey3(ntt.util.readhdr(re.sub('\n', '', files[0])),
'gain')
_rdnoise = ntt.util.readkey3(ntt.util.readhdr(re.sub('\n', '', files[0])),
'ron')
std, rastd, decstd, magstd = ntt.util.readstandard(
'standard_efosc_mab.txt')
objectlist = {}
for img in files:
hdr = ntt.util.readhdr(img)
img = re.sub('\n', '', img)
ntt.util.correctcard(img)
_ra = ntt.util.readkey3(hdr, 'RA')
_dec = ntt.util.readkey3(hdr, 'DEC')
_object = ntt.util.readkey3(hdr, 'object')
_grism = ntt.util.readkey3(hdr, 'grism')
_filter = ntt.util.readkey3(hdr, 'filter')
_slit = ntt.util.readkey3(hdr, 'slit')
dd = np.arccos(
np.sin(_dec * scal) * np.sin(decstd * scal) +
np.cos(_dec * scal) * np.cos(decstd * scal) * np.cos(
(_ra - rastd) * scal)) * ((180 / np.pi) * 3600)
if min(dd) < 100:
_type = 'stdsens'
else:
_type = 'obj'
if min(dd) < 100:
ntt.util.updateheader(img, 0,
{'stdname': [std[np.argmin(dd)], '']})
ntt.util.updateheader(
img, 0, {'magstd': [float(magstd[np.argmin(dd)]), '']})
if _type not in objectlist:
objectlist[_type] = {}
if (_grism, _filter, _slit) not in objectlist[_type]:
objectlist[_type][_grism, _filter, _slit] = [img]
else:
objectlist[_type][_grism, _filter, _slit].append(img)
from pyraf import iraf
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.specred(_doprint=0)
iraf.imutil(_doprint=0)
toforget = ['imutil.imcopy', 'specred.sarith', 'specred.standard']
for t in toforget:
iraf.unlearn(t)
iraf.specred.verbose = 'no'
iraf.specred.dispaxi = 2
iraf.set(direc=ntt.__path__[0] + '/')
sens = {}
print objectlist
outputfile = []
if 'obj' in objectlist.keys():
tpe = 'obj'
elif 'stdsens' in objectlist.keys():
tpe = 'stdsens'
else:
sys.exit('error: no objects and no standards in the list')
for setup in objectlist[tpe]:
extracted = []
listatmo = []
if setup not in sens:
sens[setup] = []
if tpe == 'obj':
print '\n### setup= ', setup, '\n### objects= ', objectlist['obj'][
setup], '\n'
for img in objectlist['obj'][setup]:
# hdr=readhdr(img)
print '\n\n### next object= ', img, ' ', ntt.util.readkey3(
ntt.util.readhdr(img), 'object'), '\n'
if os.path.isfile(re.sub('.fits', '_ex.fits', img)):
if ntt.util.readkey3(
ntt.util.readhdr(re.sub('.fits', '_ex.fits', img)),
'quality') == 'Rapid':
ntt.util.delete(re.sub('.fits', '_ex.fits', img))
imgex = ntt.util.extractspectrum(img,
dv,
_ext_trace,
_dispersionline,
_interactive,
'obj',
automaticex=_automaticex)
if not os.path.isfile(imgex):
sys.exit('### error, extraction not computed')
if not ntt.util.readkey3(ntt.util.readhdr(imgex), 'shift') and \
ntt.util.readkey3(ntt.util.readhdr(imgex), 'shift') != 0.0:
# if not readkey3(readhdr(imgex),'shift'):
ntt.efoscspec1Ddef.checkwavestd(imgex, _interactive)
extracted.append(imgex)
if imgex not in outputfile:
outputfile.append(imgex)
ntt.util.updateheader(
imgex, 0, {'FILETYPE': [22107, 'extracted 1D spectrum ']})
ntt.util.updateheader(
imgex, 0, {
'PRODCATG': [
'SCIENCE.' + ntt.util.readkey3(
ntt.util.readhdr(imgex), 'tech').upper(),
'Data product category'
]
})
ntt.util.updateheader(imgex, 0,
{'TRACE1': [img, 'Originating file']})
if os.path.isfile('database/ap' +
re.sub('_ex.fits', '', imgex)):
if 'database/ap' + re.sub('_ex.fits', '',
imgex) not in outputfile:
outputfile.append('database/ap' +
re.sub('_ex.fits', '', imgex))
print '\n### all object with this setup extracted\n'
if liststandard:
standardlist = liststandard
_type = 'stdfromdreducer'
else:
try:
standardlist = objectlist['stdsens'][setup]
_type = 'stdsens'
except:
standardlist = ''
_type = ''
if _type == 'stdfromdreducer' and len(extracted) >= 1:
_outputsens2 = ntt.util.searchsens(extracted[0], standardlist)[0]
print '\n### using standard from reducer ' + str(_outputsens2)
elif _type not in ['stdsens', 'stdfromdreducer'
] and len(extracted) >= 1:
_outputsens2 = ntt.util.searchsens(extracted[0], '')[0]
os.system('cp ' + _outputsens2 + ' .')
_outputsens2 = string.split(_outputsens2, '/')[-1]
print '\n### no standard in the list, using standard from archive'
else:
for simg in standardlist:
print '\n### standard for setup ' + \
str(setup) + ' = ', simg, ' ', ntt.util.readkey3(
ntt.util.readhdr(simg), 'object'), '\n'
simgex = ntt.util.extractspectrum(simg,
dv,
False,
False,
_interactive,
'std',
automaticex=_automaticex)
ntt.util.updateheader(
simgex, 0, {'FILETYPE': [22107, 'extracted 1D spectrum']})
ntt.util.updateheader(
simgex, 0, {
'PRODCATG': [
'SCIENCE.' + ntt.util.readkey3(
ntt.util.readhdr(simgex), 'tech').upper(),
'Data product category'
]
})
ntt.util.updateheader(simgex, 0,
{'TRACE1': [simg, 'Originating file']})
if not ntt.util.readkey3(ntt.util.readhdr(simgex), 'shift') and \
ntt.util.readkey3(ntt.util.readhdr(simgex), 'shift') != 0.0:
# if not readkey3(readhdr(simgex),'shift'):
ntt.efoscspec1Ddef.checkwavestd(simgex, _interactive)
atmofile = ntt.efoscspec1Ddef.telluric_atmo(
simgex) # atmo file2
ntt.util.updateheader(atmofile, 0,
{'TRACE1': [simgex, 'Originating file']})
ntt.util.updateheader(
atmofile, 0,
{'FILETYPE': [21211, 'telluric correction 1D spectrum ']})
if tpe != 'obj' and atmofile not in outputfile:
outputfile.append(atmofile)
if not listatmo0:
listatmo.append(atmofile)
sens[setup].append(simgex)
if simgex not in outputfile:
outputfile.append(simgex)
if setup[0] == 'Gr13' and setup[1] == 'Free':
if os.path.isfile(re.sub('Free', 'GG495', simg)):
print '\n### extract standard frame with blocking filter to correct for second order contamination\n'
simg2 = re.sub('Free', 'GG495', simg)
simgex2 = ntt.util.extractspectrum(
simg2,
dv,
False,
False,
_interactive,
'std',
automaticex=_automaticex)
ntt.util.updateheader(
simgex2, 0,
{'FILETYPE': [22107, 'extracted 1D spectrum']})
ntt.util.updateheader(
simgex2, 0, {
'PRODCATG': [
'SCIENCE.' + ntt.util.readkey3(
ntt.util.readhdr(simgex2),
'tech').upper(),
'Data product category'
]
})
if not ntt.util.readkey3(ntt.util.readhdr(simgex2), 'shift') and \
ntt.util.readkey3(ntt.util.readhdr(simgex2), 'shift') != 0.0:
# if not readkey3(readhdr(simgex2),'shift'):
ntt.efoscspec1Ddef.checkwavestd(
simgex2, _interactive)
ntt.util.updateheader(
simgex2, 0,
{'TRACE1': [simg2, 'Originating file']})
print '\n### standard available: ', sens[setup]
if tpe == 'obj':
if len(sens[setup]) > 1:
goon = 'no'
while goon != 'yes':
stdused = raw_input(
'\n### more than one standard for this setup, which one do you want to use ['
+ sens[setup][0] + '] ?')
if not stdused:
stdused = sens[setup][0]
if os.path.isfile(stdused):
goon = 'yes'
else:
stdused = sens[setup][0]
stdvec = [stdused]
else:
stdvec = sens[setup]
for stdused in stdvec:
stdusedclean = re.sub('_ex', '_clean', stdused)
ntt.util.delete(stdusedclean)
iraf.specred.sarith(input1=stdused,
op='/',
input2=atmofile,
output=stdusedclean,
format='multispec')
_outputsens2 = ntt.efoscspec1Ddef.sensfunction(
stdusedclean, 'spline3', 16, _interactive)
ntt.util.updateheader(
_outputsens2, 0,
{'FILETYPE': [21212, 'sensitivity function']})
ntt.util.updateheader(
_outputsens2, 0, {'TRACE1': [stdused, 'Originating file']})
if setup[0] == 'Gr13' and setup[1] == 'Free':
if os.path.isfile(re.sub('Free', 'GG495', stdused)):
print '\n### compute sensitivity function of grim 13 with blocking filter ' \
'to correct for second order contamination \n'
stdused2 = re.sub('Free', 'GG495', stdused)
if not ntt.util.readkey3(ntt.util.readhdr(stdused2),
'STDNAME'):
ntt.util.updateheader(
stdused2, 0, {
'STDNAME': [
ntt.util.readkey3(
ntt.util.readhdr(stdused),
'STDNAME'), ''
]
})
atmofile2 = ntt.efoscspec1Ddef.telluric_atmo(
stdused2) # atmo file2
stdusedclean2 = re.sub('_ex', '_clean', stdused2)
ntt.util.delete(stdusedclean2)
iraf.specred.sarith(input1=stdused2,
op='/',
input2=atmofile2,
output=stdusedclean2,
format='multispec')
_outputsens3 = ntt.efoscspec1Ddef.sensfunction(
stdusedclean2, 'spline3', 16, _interactive)
ntt.util.updateheader(
_outputsens3, 0,
{'FILETYPE': [21212, 'sensitivity function']})
ntt.util.updateheader(
_outputsens3, 0,
{'TRACE1': [stdused2, 'Originating file']})
_outputsens2 = correctsens(_outputsens2, _outputsens3)
if _outputsens2 not in outputfile:
outputfile.append(_outputsens2)
if _outputsens2 and tpe == 'obj':
####################################################
for img in objectlist['obj'][setup]: # flux calibrate 2d images
imgd = fluxcalib2d(img, _outputsens2)
ntt.util.updateheader(
imgd, 0, {
'FILETYPE':
[22209, '2D wavelength and flux calibrated spectrum ']
})
ntt.util.updateheader(imgd, 0,
{'TRACE1': [img, 'Originating files']})
iraf.hedit(imgd,
'PRODCATG',
delete='yes',
update='yes',
verify='no')
if imgd not in outputfile:
outputfile.append(imgd)
####################################################
# flux calib in the standard way
if not listatmo and listatmo0:
listatmo = listatmo0[:]
for _imgex in extracted:
_airmass = ntt.util.readkey3(ntt.util.readhdr(_imgex),
'airmass')
_exptime = ntt.util.readkey3(ntt.util.readhdr(_imgex),
'exptime')
_imgf = re.sub('_ex.fits', '_f.fits', _imgex)
ntt.util.delete(_imgf)
qqq = iraf.specred.calibrate(input=_imgex,
output=_imgf,
sensiti=_outputsens2,
extinct='yes',
flux='yes',
extinction=_extinctdir +
_extinction,
observatory=_observatory,
airmass=_airmass,
ignorea='yes',
exptime=_exptime,
fnu='no')
hedvec = {
'SENSFUN': [_outputsens2, ''],
'FILETYPE':
[22208, '1D wavelength and flux calibrated spectrum', ''],
# 'SNR':[ntt.util.StoN(_imgf,50),'Average signal to noise ratio per pixel'],
'SNR': [
ntt.util.StoN2(_imgf, False),
'Average signal to noise ratio per pixel'
],
'BUNIT':
['erg/cm2/s/Angstrom', 'Physical unit of array values'],
'TRACE1': [_imgex, 'Originating file'],
'ASSON1': [
re.sub('_f.fits', '_2df.fits', _imgf),
'Name of associated file'
],
'ASSOC1':
['ANCILLARY.2DSPECTRUM', 'Category of associated file']
}
ntt.util.updateheader(_imgf, 0, hedvec)
if _imgf not in outputfile:
outputfile.append(_imgf)
if listatmo:
atmofile = ntt.util.searcharc(_imgex, listatmo)[0]
if atmofile:
_imge = re.sub('_f.fits', '_e.fits', _imgf)
ntt.util.delete(_imge)
iraf.specred.sarith(input1=_imgf,
op='/',
input2=atmofile,
output=_imge,
w1='INDEF',
w2='INDEF',
format='multispec')
try:
iraf.imutil.imcopy(input=_imgf + '[*,1,2]',
output=_imge + '[*,1,2]',
verbose='no')
except:
pass
try:
iraf.imutil.imcopy(input=_imgf + '[*,1,3]',
output=_imge + '[*,1,3]',
verbose='no')
except:
pass
try:
iraf.imutil.imcopy(input=_imgf + '[*,1,4]',
output=_imge + '[*,1,4]',
verbose='no')
except:
pass
if _imge not in outputfile:
outputfile.append(_imge)
ntt.util.updateheader(
_imge, 0, {
'FILETYPE': [
22210,
'1D, wave, flux calib, telluric corr.'
]
})
if atmofile not in outputfile:
outputfile.append(atmofile)
ntt.util.updateheader(_imge, 0,
{'ATMOFILE': [atmofile, '']})
ntt.util.updateheader(
_imge, 0, {'TRACE1': [_imgf, 'Originating file']})
imgin = _imge
else:
imgin = _imgf
else:
imgin = _imgf
imgasci = re.sub('.fits', '.asci', imgin)
ntt.util.delete(imgasci)
iraf.onedspec(_doprint=0)
iraf.onedspec.wspectext(imgin + '[*,1,1]',
imgasci,
header='no')
if imgasci not in outputfile:
outputfile.append(imgasci)
print '\n### adding keywords for phase 3 ....... '
for img in outputfile:
if str(img)[-5:] == '.fits':
try:
ntt.util.phase3header(img) # phase 3 definitions
ntt.util.updateheader(img, 0, {'quality': ['Final', '']})
except:
print 'Warning: ' + img + ' is not a fits file'
try:
if int(re.sub('\.', '', str(pyfits.__version__))[:2]) <= 30:
aa = 'HIERARCH '
else:
aa = ''
except:
aa = ''
imm = pyfits.open(img, mode='update')
hdr = imm[0].header
if aa + 'ESO DPR CATG' in hdr:
hdr.pop(aa + 'ESO DPR CATG')
if aa + 'ESO DPR TECH' in hdr:
hdr.pop(aa + 'ESO DPR TECH')
if aa + 'ESO DPR TYPE' in hdr:
hdr.pop(aa + 'ESO DPR TYPE')
imm.flush()
imm.close()
print outputfile
reduceddata = ntt.rangedata(outputfile)
f = open(
'logfile_spec1d_' + str(reduceddata) + '_' + str(datenow) +
'.raw.list', 'w')
for img in outputfile:
try:
f.write(ntt.util.readkey3(ntt.util.readhdr(img), 'arcfile') + '\n')
except:
pass
f.close()
return outputfile, 'logfile_spec1d_' + str(reduceddata) + '_' + str(
datenow) + '.raw.list'
def sensfunction(standardfile, _function, _order, _interactive):
# print "LOGX:: Entering `sensfunction` method/function in %(__file__)s" %
# globals()
import re
import os
import sys
import ntt
import datetime
try:
import pyfits # added later
except:
from astropy.io import fits as pyfits
from pyraf import iraf
import numpy as np
MJDtoday = 55927 + (datetime.date.today() -
datetime.date(2012, 01, 01)).days
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.specred(_doprint=0)
toforget = ['specred.scopy', 'specred.sensfunc', 'specred.standard']
for t in toforget:
iraf.unlearn(t)
iraf.specred.scopy.format = 'multispec'
iraf.specred.verbose = 'no'
hdrs = ntt.util.readhdr(standardfile)
try:
_outputsens = 'sens_' + str(ntt.util.readkey3(hdrs, 'date-night')) + '_' + \
str(ntt.util.readkey3(hdrs, 'grism')) + '_' + str(ntt.util.readkey3(hdrs, 'filter')) + '_' + \
re.sub('.dat', '', ntt.util.readkey3(
hdrs, 'stdname')) + '_' + str(MJDtoday)
except:
sys.exit('Error: missing header -stdname- in standard ' +
str(standardfile) + ' ')
_outputsens = ntt.util.name_duplicate(standardfile, _outputsens, '')
if os.path.isfile(_outputsens):
if _interactive.lower() != 'yes':
ntt.util.delete(_outputsens)
else:
answ = raw_input(
'sensitivity function already computed, do you want to do it again [[y]/n] ? '
)
if not answ:
answ = 'y'
if answ.lower() in ['y', 'yes']:
ntt.util.delete(_outputsens)
if not os.path.isfile(_outputsens):
iraf.set(direc=ntt.__path__[0] + '/')
_caldir = 'direc$standard/MAB/'
_extinctdir = 'direc$standard/extinction/'
_observatory = 'lasilla'
_extinction = 'lasilla2.txt'
refstar = 'm' + \
re.sub('.dat', '', pyfits.open(standardfile)
[0].header.get('stdname'))
_airmass = ntt.util.readkey3(hdrs, 'airmass')
_exptime = ntt.util.readkey3(hdrs, 'exptime')
_outputstd = 'std_' + str(ntt.util.readkey3(hdrs, 'grism')) + '_' + \
str(ntt.util.readkey3(hdrs, 'filter')) + '.fits'
ntt.util.delete(_outputstd)
ntt.util.delete(_outputsens)
iraf.specred.standard(input=standardfile,
output=_outputstd,
extinct=_extinctdir + _extinction,
caldir=_caldir,
observa=_observatory,
star_nam=refstar,
airmass=_airmass,
exptime=_exptime,
interac=_interactive)
iraf.specred.sensfunc(standard=_outputstd,
sensitiv=_outputsens,
extinct=_extinctdir + _extinction,
ignorea='yes',
observa=_observatory,
functio=_function,
order=_order,
interac=_interactive)
data, hdr = pyfits.getdata(standardfile, 0, header=True) # added later
data1, hdr1 = pyfits.getdata(_outputsens, 0,
header=True) # added later
ntt.util.delete(_outputsens) # added later
pyfits.writeto(_outputsens, np.float32(data1), hdr) # added later
return _outputsens
def efoscspec1Dredu(files, _interactive, _ext_trace, _dispersionline, liststandard, listatmo0, _automaticex,
_verbose=False):
# print "LOGX:: Entering `efoscspec1Dredu` method/function in
# %(__file__)s" % globals()
import ntt
try: import pyfits
except: from astropy.io import fits as pyfits
import re
import string
import sys
import os
import numpy as np
os.environ["PYRAF_BETA_STATUS"] = "1"
_extinctdir = 'direc$standard/extinction/'
_extinction = 'lasilla2.txt'
_observatory = 'lasilla'
import datetime
now = datetime.datetime.now()
datenow = now.strftime('20%y%m%d%H%M')
MJDtoday = 55927 + (datetime.date.today() - datetime.date(2012, 01, 01)).days
dv = ntt.dvex()
scal = np.pi / 180.
_gain = ntt.util.readkey3(ntt.util.readhdr(
re.sub('\n', '', files[0])), 'gain')
_rdnoise = ntt.util.readkey3(
ntt.util.readhdr(re.sub('\n', '', files[0])), 'ron')
std, rastd, decstd, magstd = ntt.util.readstandard(
'standard_efosc_mab.txt')
objectlist = {}
for img in files:
hdr = ntt.util.readhdr(img)
img = re.sub('\n', '', img)
ntt.util.correctcard(img)
_ra = ntt.util.readkey3(hdr, 'RA')
_dec = ntt.util.readkey3(hdr, 'DEC')
_object = ntt.util.readkey3(hdr, 'object')
_grism = ntt.util.readkey3(hdr, 'grism')
_filter = ntt.util.readkey3(hdr, 'filter')
_slit = ntt.util.readkey3(hdr, 'slit')
dd = np.arccos(np.sin(_dec * scal) * np.sin(decstd * scal) + np.cos(_dec * scal) *
np.cos(decstd * scal) * np.cos((_ra - rastd) * scal)) * ((180 / np.pi) * 3600)
if min(dd) < 100:
_type = 'stdsens'
else:
_type = 'obj'
if min(dd) < 100:
ntt.util.updateheader(
img, 0, {'stdname': [std[np.argmin(dd)], '']})
ntt.util.updateheader(
img, 0, {'magstd': [float(magstd[np.argmin(dd)]), '']})
if _type not in objectlist:
objectlist[_type] = {}
if (_grism, _filter, _slit) not in objectlist[_type]:
objectlist[_type][_grism, _filter, _slit] = [img]
else:
objectlist[_type][_grism, _filter, _slit].append(img)
from pyraf import iraf
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.specred(_doprint=0)
iraf.imutil(_doprint=0)
toforget = ['imutil.imcopy', 'specred.sarith', 'specred.standard']
for t in toforget:
iraf.unlearn(t)
iraf.specred.verbose = 'no'
iraf.specred.dispaxi = 2
iraf.set(direc=ntt.__path__[0] + '/')
sens = {}
print objectlist
outputfile = []
if 'obj' in objectlist.keys():
tpe = 'obj'
elif 'stdsens' in objectlist.keys():
tpe = 'stdsens'
else:
sys.exit('error: no objects and no standards in the list')
for setup in objectlist[tpe]:
extracted = []
listatmo = []
if setup not in sens:
sens[setup] = []
if tpe == 'obj':
print '\n### setup= ', setup, '\n### objects= ', objectlist['obj'][setup], '\n'
for img in objectlist['obj'][setup]:
# hdr=readhdr(img)
print '\n\n### next object= ', img, ' ', ntt.util.readkey3(ntt.util.readhdr(img), 'object'), '\n'
if os.path.isfile(re.sub('.fits', '_ex.fits', img)):
if ntt.util.readkey3(ntt.util.readhdr(re.sub('.fits', '_ex.fits', img)), 'quality') == 'Rapid':
ntt.util.delete(re.sub('.fits', '_ex.fits', img))
imgex = ntt.util.extractspectrum(img, dv, _ext_trace, _dispersionline, _interactive, 'obj',
automaticex=_automaticex)
if not os.path.isfile(imgex):
sys.exit('### error, extraction not computed')
if not ntt.util.readkey3(ntt.util.readhdr(imgex), 'shift') and \
ntt.util.readkey3(ntt.util.readhdr(imgex), 'shift') != 0.0:
# if not readkey3(readhdr(imgex),'shift'):
ntt.efoscspec1Ddef.checkwavestd(imgex, _interactive)
extracted.append(imgex)
if imgex not in outputfile:
outputfile.append(imgex)
ntt.util.updateheader(
imgex, 0, {'FILETYPE': [22107, 'extracted 1D spectrum ']})
ntt.util.updateheader(imgex, 0, {
'PRODCATG': ['SCIENCE.' +
ntt.util.readkey3(ntt.util.readhdr(imgex), 'tech').upper(), 'Data product category']})
ntt.util.updateheader(
imgex, 0, {'TRACE1': [img, 'Originating file']})
if os.path.isfile('database/ap' + re.sub('_ex.fits', '', imgex)):
if 'database/ap' + re.sub('_ex.fits', '', imgex) not in outputfile:
outputfile.append(
'database/ap' + re.sub('_ex.fits', '', imgex))
print '\n### all object with this setup extracted\n'
if liststandard:
standardlist = liststandard
_type = 'stdfromdreducer'
else:
try:
standardlist = objectlist['stdsens'][setup]
_type = 'stdsens'
except:
standardlist = ''
_type = ''
if _type == 'stdfromdreducer' and len(extracted) >= 1:
_outputsens2 = ntt.util.searchsens(extracted[0], standardlist)[0]
print '\n### using standard from reducer ' + str(_outputsens2)
elif _type not in ['stdsens', 'stdfromdreducer'] and len(extracted) >= 1:
_outputsens2 = ntt.util.searchsens(extracted[0], '')[0]
os.system('cp ' + _outputsens2 + ' .')
_outputsens2 = string.split(_outputsens2, '/')[-1]
print '\n### no standard in the list, using standard from archive'
else:
for simg in standardlist:
print '\n### standard for setup ' + \
str(setup) + ' = ', simg, ' ', ntt.util.readkey3(
ntt.util.readhdr(simg), 'object'), '\n'
simgex = ntt.util.extractspectrum(
simg, dv, False, False, _interactive, 'std', automaticex=_automaticex)
ntt.util.updateheader(
simgex, 0, {'FILETYPE': [22107, 'extracted 1D spectrum']})
ntt.util.updateheader(simgex, 0, {
'PRODCATG': [
'SCIENCE.' + ntt.util.readkey3(ntt.util.readhdr(simgex), 'tech').upper(), 'Data product category']})
ntt.util.updateheader(
simgex, 0, {'TRACE1': [simg, 'Originating file']})
if not ntt.util.readkey3(ntt.util.readhdr(simgex), 'shift') and \
ntt.util.readkey3(ntt.util.readhdr(simgex), 'shift') != 0.0:
# if not readkey3(readhdr(simgex),'shift'):
ntt.efoscspec1Ddef.checkwavestd(simgex, _interactive)
atmofile = ntt.efoscspec1Ddef.telluric_atmo(
simgex) # atmo file2
ntt.util.updateheader(
atmofile, 0, {'TRACE1': [simgex, 'Originating file']})
ntt.util.updateheader(
atmofile, 0, {'FILETYPE': [21211, 'telluric correction 1D spectrum ']})
if tpe != 'obj' and atmofile not in outputfile:
outputfile.append(atmofile)
if not listatmo0:
listatmo.append(atmofile)
sens[setup].append(simgex)
if simgex not in outputfile:
outputfile.append(simgex)
if setup[0] == 'Gr13' and setup[1] == 'Free':
if os.path.isfile(re.sub('Free', 'GG495', simg)):
print '\n### extract standard frame with blocking filter to correct for second order contamination\n'
simg2 = re.sub('Free', 'GG495', simg)
simgex2 = ntt.util.extractspectrum(simg2, dv, False, False, _interactive, 'std',
automaticex=_automaticex)
ntt.util.updateheader(
simgex2, 0, {'FILETYPE': [22107, 'extracted 1D spectrum']})
ntt.util.updateheader(simgex2, 0, {
'PRODCATG': ['SCIENCE.' +
ntt.util.readkey3(
ntt.util.readhdr(simgex2), 'tech').upper(), 'Data product category']})
if not ntt.util.readkey3(ntt.util.readhdr(simgex2), 'shift') and \
ntt.util.readkey3(ntt.util.readhdr(simgex2), 'shift') != 0.0:
# if not readkey3(readhdr(simgex2),'shift'):
ntt.efoscspec1Ddef.checkwavestd(
simgex2, _interactive)
ntt.util.updateheader(
simgex2, 0, {'TRACE1': [simg2, 'Originating file']})
print '\n### standard available: ', sens[setup]
if tpe == 'obj':
if len(sens[setup]) > 1:
goon = 'no'
while goon != 'yes':
stdused = raw_input(
'\n### more than one standard for this setup, which one do you want to use [' + sens[setup][
0] + '] ?')
if not stdused:
stdused = sens[setup][0]
if os.path.isfile(stdused):
goon = 'yes'
else:
stdused = sens[setup][0]
stdvec = [stdused]
else:
stdvec = sens[setup]
for stdused in stdvec:
stdusedclean = re.sub('_ex', '_clean', stdused)
ntt.util.delete(stdusedclean)
iraf.specred.sarith(
input1=stdused, op='/', input2=atmofile, output=stdusedclean, format='multispec')
_outputsens2 = ntt.efoscspec1Ddef.sensfunction(
stdusedclean, 'spline3', 16, _interactive)
ntt.util.updateheader(_outputsens2, 0, {'FILETYPE': [
21212, 'sensitivity function']})
ntt.util.updateheader(
_outputsens2, 0, {'TRACE1': [stdused, 'Originating file']})
if setup[0] == 'Gr13' and setup[1] == 'Free':
if os.path.isfile(re.sub('Free', 'GG495', stdused)):
print '\n### compute sensitivity function of grim 13 with blocking filter ' \
'to correct for second order contamination \n'
stdused2 = re.sub('Free', 'GG495', stdused)
if not ntt.util.readkey3(ntt.util.readhdr(stdused2), 'STDNAME'):
ntt.util.updateheader(stdused2, 0, {
'STDNAME': [ntt.util.readkey3(ntt.util.readhdr(stdused), 'STDNAME'), '']})
atmofile2 = ntt.efoscspec1Ddef.telluric_atmo(
stdused2) # atmo file2
stdusedclean2 = re.sub('_ex', '_clean', stdused2)
ntt.util.delete(stdusedclean2)
iraf.specred.sarith(input1=stdused2, op='/', input2=atmofile2, output=stdusedclean2,
format='multispec')
_outputsens3 = ntt.efoscspec1Ddef.sensfunction(
stdusedclean2, 'spline3', 16, _interactive)
ntt.util.updateheader(_outputsens3, 0, {'FILETYPE': [
21212, 'sensitivity function']})
ntt.util.updateheader(
_outputsens3, 0, {'TRACE1': [stdused2, 'Originating file']})
_outputsens2 = correctsens(_outputsens2, _outputsens3)
if _outputsens2 not in outputfile:
outputfile.append(_outputsens2)
if _outputsens2 and tpe == 'obj':
####################################################
for img in objectlist['obj'][setup]: # flux calibrate 2d images
imgd = fluxcalib2d(img, _outputsens2)
ntt.util.updateheader(
imgd, 0, {'FILETYPE': [22209, '2D wavelength and flux calibrated spectrum ']})
ntt.util.updateheader(
imgd, 0, {'TRACE1': [img, 'Originating files']})
iraf.hedit(imgd, 'PRODCATG', delete='yes',
update='yes', verify='no')
if imgd not in outputfile:
outputfile.append(imgd)
####################################################
# flux calib in the standard way
if not listatmo and listatmo0:
listatmo = listatmo0[:]
for _imgex in extracted:
_airmass = ntt.util.readkey3(
ntt.util.readhdr(_imgex), 'airmass')
_exptime = ntt.util.readkey3(
ntt.util.readhdr(_imgex), 'exptime')
_imgf = re.sub('_ex.fits', '_f.fits', _imgex)
ntt.util.delete(_imgf)
qqq = iraf.specred.calibrate(input=_imgex, output=_imgf, sensiti=_outputsens2, extinct='yes',
flux='yes',
extinction=_extinctdir + _extinction, observatory=_observatory,
airmass=_airmass, ignorea='yes', exptime=_exptime, fnu='no')
hedvec = {'SENSFUN': [_outputsens2, ''],
'FILETYPE': [22208, '1D wavelength and flux calibrated spectrum', ''],
# 'SNR':[ntt.util.StoN(_imgf,50),'Average signal to noise ratio per pixel'],
'SNR': [ntt.util.StoN2(_imgf, False), 'Average signal to noise ratio per pixel'],
'BUNIT': ['erg/cm2/s/Angstrom', 'Physical unit of array values'],
'TRACE1': [_imgex, 'Originating file'],
'ASSON1': [re.sub('_f.fits', '_2df.fits', _imgf), 'Name of associated file'],
'ASSOC1': ['ANCILLARY.2DSPECTRUM', 'Category of associated file']}
ntt.util.updateheader(_imgf, 0, hedvec)
if _imgf not in outputfile:
outputfile.append(_imgf)
if listatmo:
atmofile = ntt.util.searcharc(_imgex, listatmo)[0]
if atmofile:
_imge = re.sub('_f.fits', '_e.fits', _imgf)
ntt.util.delete(_imge)
iraf.specred.sarith(input1=_imgf, op='/', input2=atmofile, output=_imge, w1='INDEF', w2='INDEF',
format='multispec')
try:
iraf.imutil.imcopy(
input=_imgf + '[*,1,2]', output=_imge + '[*,1,2]', verbose='no')
except:
pass
try:
iraf.imutil.imcopy(
input=_imgf + '[*,1,3]', output=_imge + '[*,1,3]', verbose='no')
except:
pass
try:
iraf.imutil.imcopy(
input=_imgf + '[*,1,4]', output=_imge + '[*,1,4]', verbose='no')
except:
pass
if _imge not in outputfile:
outputfile.append(_imge)
ntt.util.updateheader(
_imge, 0, {'FILETYPE': [22210, '1D, wave, flux calib, telluric corr.']})
if atmofile not in outputfile:
outputfile.append(atmofile)
ntt.util.updateheader(
_imge, 0, {'ATMOFILE': [atmofile, '']})
ntt.util.updateheader(
_imge, 0, {'TRACE1': [_imgf, 'Originating file']})
imgin = _imge
else:
imgin = _imgf
else:
imgin = _imgf
imgasci = re.sub('.fits', '.asci', imgin)
ntt.util.delete(imgasci)
iraf.onedspec(_doprint=0)
iraf.onedspec.wspectext(
imgin + '[*,1,1]', imgasci, header='no')
if imgasci not in outputfile:
outputfile.append(imgasci)
print '\n### adding keywords for phase 3 ....... '
for img in outputfile:
if str(img)[-5:] == '.fits':
try:
ntt.util.phase3header(img) # phase 3 definitions
ntt.util.updateheader(img, 0, {'quality': ['Final', '']})
except:
print 'Warning: ' + img + ' is not a fits file'
try:
if int(re.sub('\.', '', str(pyfits.__version__))[:2]) <= 30:
aa = 'HIERARCH '
else:
aa = ''
except:
aa = ''
imm = pyfits.open(img, mode='update')
hdr = imm[0].header
if aa + 'ESO DPR CATG' in hdr:
hdr.pop(aa + 'ESO DPR CATG')
if aa + 'ESO DPR TECH' in hdr:
hdr.pop(aa + 'ESO DPR TECH')
if aa + 'ESO DPR TYPE' in hdr:
hdr.pop(aa + 'ESO DPR TYPE')
imm.flush()
imm.close()
print outputfile
reduceddata = ntt.rangedata(outputfile)
f = open('logfile_spec1d_' + str(reduceddata) +
'_' + str(datenow) + '.raw.list', 'w')
for img in outputfile:
try:
f.write(ntt.util.readkey3(ntt.util.readhdr(img), 'arcfile') + '\n')
except:
pass
f.close()
return outputfile, 'logfile_spec1d_' + str(reduceddata) + '_' + str(datenow) + '.raw.list'
def sensfunction(standardfile, _function, _order, _interactive):
# print "LOGX:: Entering `sensfunction` method/function in %(__file__)s" %
# globals()
import re
import os
import sys
import ntt
import datetime
try: import pyfits # added later
except: from astropy.io import fits as pyfits
from pyraf import iraf
import numpy as np
MJDtoday = 55927 + (datetime.date.today() - datetime.date(2012, 01, 01)).days
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.specred(_doprint=0)
toforget = ['specred.scopy', 'specred.sensfunc', 'specred.standard']
for t in toforget:
iraf.unlearn(t)
iraf.specred.scopy.format = 'multispec'
iraf.specred.verbose = 'no'
hdrs = ntt.util.readhdr(standardfile)
try:
_outputsens = 'sens_' + str(ntt.util.readkey3(hdrs, 'date-night')) + '_' + \
str(ntt.util.readkey3(hdrs, 'grism')) + '_' + str(ntt.util.readkey3(hdrs, 'filter')) + '_' + \
re.sub('.dat', '', ntt.util.readkey3(
hdrs, 'stdname')) + '_' + str(MJDtoday)
except:
sys.exit('Error: missing header -stdname- in standard ' +
str(standardfile) + ' ')
_outputsens = ntt.util.name_duplicate(standardfile, _outputsens, '')
if os.path.isfile(_outputsens):
if _interactive.lower() != 'yes':
ntt.util.delete(_outputsens)
else:
answ = raw_input(
'sensitivity function already computed, do you want to do it again [[y]/n] ? ')
if not answ:
answ = 'y'
if answ.lower() in ['y', 'yes']:
ntt.util.delete(_outputsens)
if not os.path.isfile(_outputsens):
iraf.set(direc=ntt.__path__[0] + '/')
_caldir = 'direc$standard/MAB/'
_extinctdir = 'direc$standard/extinction/'
_observatory = 'lasilla'
_extinction = 'lasilla2.txt'
refstar = 'm' + \
re.sub('.dat', '', pyfits.open(standardfile)
[0].header.get('stdname'))
_airmass = ntt.util.readkey3(hdrs, 'airmass')
_exptime = ntt.util.readkey3(hdrs, 'exptime')
_outputstd = 'std_' + str(ntt.util.readkey3(hdrs, 'grism')) + '_' + \
str(ntt.util.readkey3(hdrs, 'filter')) + '.fits'
ntt.util.delete(_outputstd)
ntt.util.delete(_outputsens)
iraf.specred.standard(input=standardfile, output=_outputstd, extinct=_extinctdir + _extinction,
caldir=_caldir, observa=_observatory, star_nam=refstar, airmass=_airmass,
exptime=_exptime, interac=_interactive)
iraf.specred.sensfunc(standard=_outputstd, sensitiv=_outputsens, extinct=_extinctdir + _extinction,
ignorea='yes', observa=_observatory, functio=_function, order=_order,
interac=_interactive)
data, hdr = pyfits.getdata(standardfile, 0, header=True) # added later
data1, hdr1 = pyfits.getdata(
_outputsens, 0, header=True) # added later
ntt.util.delete(_outputsens) # added later
pyfits.writeto(_outputsens, np.float32(data1), hdr) # added later
return _outputsens
def lickshane1Dredu(files,
_interactive,
_ext_trace,
_dispersionline,
_automaticex,
_verbose=False):
import lickshane
import datetime
import os
import re
import string
import sys
liststandard = ''
listatmo0 = ''
# os.environ["PYRAF_BETA_STATUS"] = "1"
_extinctdir = 'direc$standard/extinction/'
_extinction = 'lick.dat'
_observatory = 'lick'
now = datetime.datetime.now()
datenow = now.strftime('20%y%m%d%H%M')
MJDtoday = 55927 + (datetime.date.today() -
datetime.date(2012, 01, 01)).days
scal = np.pi / 180.
dv = lickshane.util.dvex()
std, rastd, decstd, magstd = lickshane.util.readstandard(
'standard_lick_mab.txt')
objectlist = {}
for img in files:
hdr = lickshane.util.readhdr(img)
img = re.sub('\n', '', img)
lickshane.util.correctcard(img)
_ra = lickshane.util.readkey3(hdr, 'RA')
_dec = lickshane.util.readkey3(hdr, 'DEC')
_object = lickshane.util.readkey3(hdr, 'object')
_grism = lickshane.util.readkey3(hdr, 'grism')
_slit = lickshane.util.readkey3(hdr, 'slit')
dd = np.arccos(
np.sin(_dec * scal) * np.sin(decstd * scal) +
np.cos(_dec * scal) * np.cos(decstd * scal) * np.cos(
(_ra - rastd) * scal)) * ((180 / np.pi) * 3600)
if min(dd) < 100:
_type = 'stdsens'
else:
_type = 'obj'
print img, _type
if min(dd) < 100:
lickshane.util.updateheader(img, 0,
{'stdname': [std[np.argmin(dd)], '']})
lickshane.util.updateheader(
img, 0, {'magstd': [float(magstd[np.argmin(dd)]), '']})
if _type not in objectlist:
objectlist[_type] = {}
if (_grism, _slit) not in objectlist[_type]:
objectlist[_type][_grism, _slit] = [img]
else:
objectlist[_type][_grism, _slit].append(img)
from pyraf import iraf
iraf.set(stdimage='imt2048')
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.specred(_doprint=0)
iraf.imutil(_doprint=0)
toforget = ['imutil.imcopy', 'specred.sarith', 'specred.standard']
for t in toforget:
iraf.unlearn(t)
iraf.specred.verbose = 'no'
iraf.specred.dispaxi = 2
iraf.set(direc=lickshane.__path__[0] + '/')
sens = {}
print objectlist
outputfile = []
if 'obj' in objectlist.keys():
tpe = 'obj'
elif 'stdsens' in objectlist.keys():
tpe = 'stdsens'
else:
sys.exit('error: no objects and no standards in the list')
for setup in objectlist[tpe]:
extracted = []
listatmo = []
if setup not in sens:
sens[setup] = []
if tpe == 'obj':
print '\n### setup= ', setup, '\n### objects= ', objectlist['obj'][
setup], '\n'
for img in objectlist['obj'][setup]:
# hdr=readhdr(img)
print '\n\n### next object= ', img, ' ', lickshane.util.readkey3(
lickshane.util.readhdr(img), 'object'), '\n'
#_automaticex = ''
imgex = lickshane.lickshane1Ddef.extractspectrum(
img,
dv,
_ext_trace,
_dispersionline,
_interactive,
'obj',
automaticex=_automaticex)
if not os.path.isfile(imgex):
sys.exit('### error, extraction not computed')
if not lickshane.util.readkey3(lickshane.util.readhdr(imgex), 'shift') and \
lickshane.util.readkey3(lickshane.util.readhdr(imgex), 'shift') != 0.0:
if setup in ['300_7500']:
lickshane.lickshane1Ddef.checkwavestd(
imgex, _interactive)
else:
print 'wave check using teluric not possible'
extracted.append(imgex)
if imgex not in outputfile:
outputfile.append(imgex)
lickshane.util.updateheader(
imgex, 0, {'TRACE1': [img, 'Originating file']})
if os.path.isfile('database/ap' +
re.sub('_ex.fits', '', imgex)):
if 'database/ap' + re.sub('_ex.fits', '',
imgex) not in outputfile:
outputfile.append('database/ap' +
re.sub('_ex.fits', '', imgex))
print '\n### all object with this setup extracted\n'
if liststandard:
standardlist = liststandard
_type = 'stdfromdreducer'
else:
try:
standardlist = objectlist['stdsens'][setup]
_type = 'stdsens'
except:
standardlist = ''
_type = ''
if _type == 'stdfromdreducer' and len(extracted) >= 1:
_outputsens2 = lichshane.util.searchsens(extracted[0],
standardlist)[0]
print '\n### using standard from reducer ' + str(_outputsens2)
elif _type not in ['stdsens', 'stdfromdreducer'
] and len(extracted) >= 1:
_outputsens2 = lickshane.util.searchsens(extracted[0], '')[0]
os.system('cp ' + _outputsens2 + ' .')
_outputsens2 = string.split(_outputsens2, '/')[-1]
print '\n### no standard in the list, using standard from archive'
else:
for simg in standardlist:
print '\n### standard for setup ' + \
str(setup) + ' = ', simg, ' ', lickshane.util.readkey3(
lickshane.util.readhdr(simg), 'object'), '\n'
simgex = lickshane.lickshane1Ddef.extractspectrum(
simg,
dv,
False,
False,
_interactive,
'std',
automaticex=_automaticex)
lickshane.util.updateheader(
simgex, 0, {'TRACE1': [simg, 'Originating file']})
if not lickshane.util.readkey3(
lickshane.util.readhdr(simgex),
'shift') and lickshane.util.readkey3(
lickshane.util.readhdr(simgex), 'shift') != 0.0:
lickshane.lickshane1Ddef.checkwavestd(simgex, _interactive)
print simgex
atmofile = lickshane.lickshane1Ddef.telluric_atmo(
simgex) # atmo file2
print atmofile
lickshane.util.updateheader(
atmofile, 0, {'TRACE1': [simgex, 'Originating file']})
if tpe != 'obj' and atmofile not in outputfile:
outputfile.append(atmofile)
if not listatmo0:
listatmo.append(atmofile)
sens[setup].append(simgex)
if simgex not in outputfile:
outputfile.append(simgex)
print '\n### standard available: ', sens[setup]
if tpe == 'obj':
if len(sens[setup]) > 1:
goon = 'no'
while goon != 'yes':
stdused = raw_input(
'\n### more than one standard for this setup, which one do you want to use ['
+ sens[setup][0] + '] ?')
if not stdused:
stdused = sens[setup][0]
if os.path.isfile(stdused):
goon = 'yes'
else:
stdused = sens[setup][0]
stdvec = [stdused]
else:
stdvec = sens[setup]
for stdused in stdvec:
stdusedclean = re.sub('_ex', '_clean', stdused)
lickshane.util.delete(stdusedclean)
iraf.specred.sarith(input1=stdused,
op='/',
input2=atmofile,
output=stdusedclean,
format='multispec')
_outputsens2 = lickshane.lickshane1Ddef.sensfunction(
stdusedclean, 'spline3', 16, _interactive)
lickshane.util.updateheader(
_outputsens2, 0, {'TRACE1': [stdused, 'Originating file']})
if _outputsens2 not in outputfile:
outputfile.append(_outputsens2)
if _outputsens2 and tpe == 'obj':
####################################################
for img in objectlist['obj'][setup]: # flux calibrate 2d images
imgd = fluxcalib2d(img, _outputsens2)
lickshane.util.updateheader(
imgd, 0, {'TRACE1': [img, 'Originating files']})
if imgd not in outputfile:
outputfile.append(imgd)
####################################################
# flux calib in the standard way
if not listatmo and listatmo0:
listatmo = listatmo0[:]
for _imgex in extracted:
_airmass = lickshane.util.readkey3(
lickshane.util.readhdr(_imgex), 'airmass')
_exptime = lickshane.util.readkey3(
lickshane.util.readhdr(_imgex), 'exptime')
_imgf = re.sub('_ex.fits', '_f.fits', _imgex)
lickshane.util.delete(_imgf)
qqq = iraf.specred.calibrate(input=_imgex,
output=_imgf,
sensiti=_outputsens2,
extinct='yes',
flux='yes',
extinction=_extinctdir +
_extinction,
observatory=_observatory,
airmass=_airmass,
ignorea='yes',
exptime=_exptime,
fnu='no')
hedvec = {
'SENSFUN': [_outputsens2, ''],
# 'SNR': [lickshane.util.StoN2(_imgf, False), 'Average signal to noise ratio per pixel'],
'BUNIT':
['erg/cm2/s/Angstrom', 'Physical unit of array values'],
'TRACE1': [_imgex, 'Originating file'],
'ASSON1': [
re.sub('_f.fits', '_2df.fits', _imgf),
'Name of associated file'
],
'ASSOC1':
['ANCILLARY.2DSPECTRUM', 'Category of associated file']
}
lickshane.util.updateheader(_imgf, 0, hedvec)
if _imgf not in outputfile:
outputfile.append(_imgf)
if listatmo:
atmofile = lickshane.util.searcharc(_imgex, listatmo)[0]
if atmofile:
_imge = re.sub('_f.fits', '_e.fits', _imgf)
lickshane.util.delete(_imge)
iraf.specred.sarith(input1=_imgf,
op='/',
input2=atmofile,
output=_imge,
w1='INDEF',
w2='INDEF',
format='multispec')
try:
iraf.imutil.imcopy(input=_imgf + '[*,1,2]',
output=_imge + '[*,1,2]',
verbose='no')
except:
pass
try:
iraf.imutil.imcopy(input=_imgf + '[*,1,3]',
output=_imge + '[*,1,3]',
verbose='no')
except:
pass
try:
iraf.imutil.imcopy(input=_imgf + '[*,1,4]',
output=_imge + '[*,1,4]',
verbose='no')
except:
pass
if _imge not in outputfile:
outputfile.append(_imge)
if atmofile not in outputfile:
outputfile.append(atmofile)
lickshane.util.updateheader(
_imge, 0, {'ATMOFILE': [atmofile, '']})
lickshane.util.updateheader(
_imge, 0, {'TRACE1': [_imgf, 'Originating file']})
imgin = _imge
else:
imgin = _imgf
else:
imgin = _imgf
imgasci = re.sub('.fits', '.asci', imgin)
lickshane.util.delete(imgasci)
iraf.onedspec(_doprint=0)
iraf.onedspec.wspectext(imgin + '[*,1,1]',
imgasci,
header='no')
if imgasci not in outputfile:
outputfile.append(imgasci)
return objectlist, 'ddd'
version="%prog " + str(fdst.__version__))
parser.add_option("-v", "--verbose",dest="verbose",\
action="store_true",default=False,
help='Print tasks description')
option, args = parser.parse_args()
##############################################################
from pyraf import iraf
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.longslit(_doprint=0)
iraf.onedspec(_doprint=0)
iraf.specred(_doprint=0)
toforget = [
'ccdproc', 'imcopy', 'specred.apall', 'longslit.identify',
'longslit.reidentify', 'specred.standard', 'longslit.fitcoords',
'onedspec.wspectext'
]
for t in toforget:
iraf.unlearn(t)
##################### opening setup ##########################
print ''
question = raw_input('Do you have a list of spectra ? ([yes]/no) ')
if not question:
question = 'yes'
def efoscfastredu(imglist, _listsens, _listarc, _ext_trace, _dispersionline, _cosmic, _interactive):
# print "LOGX:: Entering `efoscfastredu` method/function in %(__file__)s"
# % globals()
import string
import os
import re
import sys
os.environ["PYRAF_BETA_STATUS"] = "1"
try: from astropy.io import fits as pyfits
except: import pyfits
from ntt.util import readhdr, readkey3
import ntt
import numpy as np
dv = ntt.dvex()
scal = np.pi / 180.
if not _interactive:
_interactive = False
_inter = 'NO'
else:
_inter = 'YES'
from pyraf import iraf
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.longslit(_doprint=0)
iraf.onedspec(_doprint=0)
iraf.specred(_doprint=0)
toforget = ['ccdproc', 'imcopy', 'specred.apall', 'longslit.identify', 'longslit.reidentify', 'specred.standard',
'longslit.fitcoords', 'onedspec.wspectext']
for t in toforget:
iraf.unlearn(t)
iraf.ccdred.verbose = 'no' # not print steps
iraf.specred.verbose = 'no' # not print steps
iraf.ccdproc.darkcor = 'no'
iraf.ccdproc.fixpix = 'no'
iraf.ccdproc.flatcor = 'no'
iraf.ccdproc.zerocor = 'no'
iraf.ccdproc.ccdtype = ''
_gain = ntt.util.readkey3(ntt.util.readhdr(imglist[0]), 'gain')
_ron = ntt.util.readkey3(ntt.util.readhdr(imglist[0]), 'ron')
iraf.specred.apall.readnoi = _ron
iraf.specred.apall.gain = _gain
iraf.specred.dispaxi = 2
iraf.longslit.dispaxi = 2
iraf.longslit.mode = 'h'
iraf.specred.mode = 'h'
iraf.noao.mode = 'h'
iraf.ccdred.instrument = "ccddb$kpno/camera.dat"
iraf.set(direc=ntt.__path__[0] + '/')
for img in imglist:
hdr = ntt.util.readhdr(img)
_tech = ntt.util.readkey3(hdr, 'tech')
if _tech != 'SPECTRUM':
sys.exit('error: ' + str(img) + ' is not a spectrum ')
print '\n#### image name = ' + img + '\n'
_grism0 = readkey3(hdr, 'grism')
_filter0 = readkey3(hdr, 'filter')
_slit0 = readkey3(hdr, 'slit')
_object0 = readkey3(hdr, 'object')
_date0 = readkey3(hdr, 'date-night')
setup = (_grism0, _filter0, _slit0)
_biassec0 = '[3:1010,1026:1029]'
if _grism0 == 'Gr16':
_trimsec0 = '[100:950,1:950]'
elif _grism0 == 'Gr13':
if _filter0 == 'Free':
_trimsec0 = '[100:950,1:1015]'
elif _filter0 == 'GG495':
_trimsec0 = '[100:950,208:1015]'
elif _filter0 == 'OG530':
_trimsec0 = '[100:950,300:1015]'
elif _grism0 == 'Gr11':
_trimsec0 = '[100:950,5:1015]'
else:
_trimsec0 = '[100:950,5:1015]'
_object0 = re.sub(' ', '', _object0)
_object0 = re.sub('/', '_', _object0)
nameout0 = 't' + str(_object0) + '_' + str(_date0)
for _set in setup:
nameout0 = nameout0 + '_' + _set
nameout0 = ntt.util.name_duplicate(img, nameout0, '')
timg = nameout0
if os.path.isfile(timg):
os.system('rm -rf ' + timg)
iraf.imcopy(img, output=timg)
iraf.ccdproc(timg, output='', overscan='no', trim='yes', zerocor="no", flatcor="no", readaxi='column',
trimsec=str(_trimsec0), biassec=_biassec0, Stdout=1)
img = timg
if _listarc:
arcfile = ntt.util.searcharc(img, _listarc)[0]
else:
arcfile = ''
if not arcfile:
arcfile = ntt.util.searcharc(img, '')[0]
else:
iraf.ccdproc(arcfile, output='t' + arcfile, overscan='no', trim='yes', zerocor="no", flatcor="no",
readaxi='column', trimsec=str(_trimsec0), biassec=str(_biassec0), Stdout=1)
arcfile = 't' + arcfile
if _cosmic:
# print cosmic rays rejection
ntt.cosmics.lacos(img, output='', gain=_gain, readn=_ron, xorder=9, yorder=9, sigclip=4.5, sigfrac=0.5,
objlim=1, verbose=True, interactive=False)
print '\n### cosmic rays rejections ........ done '
if not arcfile:
print '\n### warning no arcfile \n exit '
else:
arcref = ntt.util.searcharc(img, '')[0]
if arcfile[0] == '/':
os.system('cp ' + arcfile + ' ' +
string.split(arcfile, '/')[-1])
arcfile = string.split(arcfile, '/')[-1]
arcref = string.split(arcref, '/')[-1]
if arcref:
os.system('cp ' + arcref + ' .')
arcref = string.split(arcref, '/')[-1]
if not os.path.isdir('database/'):
os.mkdir('database/')
if os.path.isfile(ntt.util.searcharc(img, '')[1] + '/database/id' + re.sub('.fits', '', arcref)):
os.system('cp ' + ntt.util.searcharc(img, '')[1] + '/database/id' + re.sub('.fits', '',
arcref) + ' database/')
iraf.longslit.reidentify(referenc=arcref, images=arcfile, interac=_inter, section='column 10',
coordli='direc$standard/ident/Lines_HgCdHeNeAr600.dat', overrid='yes', step=0,
newaps='no', nsum=5, nlost=2, mode='h', verbose='no')
else:
iraf.longslit.identify(images=arcfile, section='column 10',
coordli='direc$standard/ident/Lines_HgCdHeNeAr600.dat', nsum=10, fwidth=7,
order=3, mode='h')
iraf.longslit.reident(referenc=arcfile, images=arcfile, interac='NO', section='column 10',
coordli='direc$standard/ident/Lines_HgCdHeNeAr600.dat', overrid='yes', step=10,
newaps='yes', nsum=5, nlost=2, mode='h', verbose='no')
qqq = iraf.longslit.fitcoords(images=re.sub('.fits', '', arcfile), fitname=re.sub('.fits', '', arcfile),
interac='no', combine='yes', databas='database',
function='legendre', yorder=4, logfile='logfile', plotfil='', mode='h')
iraf.specred.transform(input=img, output=img, minput='', fitnames=re.sub('.fits', '', arcfile),
databas='database',
x1='INDEF', x2='INDEF', y1='INDEF', y2='INDEF', flux='yes', mode='h',
logfile='logfile')
# ###################### check wavelength calibration ############
_skyfile = ntt.__path__[
0] + '/standard/ident/sky_' + setup[0] + '_' + setup[1] + '.fits'
shift = ntt.efoscspec2Ddef.skyfrom2d(img, _skyfile)
print '\n### check in wavelengh performed ...... spectrum shifted of ' + str(shift) + ' Angstrom \n'
zro = pyfits.open(img)[0].header.get('CRVAL2')
ntt.util.updateheader(img, 0, {'CRVAL2': [zro + int(shift), '']})
std, rastd, decstd, magstd = ntt.util.readstandard(
'standard_efosc_mab.txt')
hdrt = readhdr(img)
_ra = readkey3(hdrt, 'RA')
_dec = readkey3(hdrt, 'DEC')
_object = readkey3(hdrt, 'object')
dd = np.arccos(np.sin(_dec * scal) * np.sin(decstd * scal) + np.cos(_dec * scal) *
np.cos(decstd * scal) * np.cos((_ra - rastd) * scal)) * ((180 / np.pi) * 3600)
if min(dd) < 100:
_type = 'stdsens'
ntt.util.updateheader(
img, 0, {'stdname': [std[np.argmin(dd)], '']})
ntt.util.updateheader(
img, 0, {'magstd': [float(magstd[np.argmin(dd)]), '']})
else:
_type = 'obj'
print '\n### EXTRACTION USING IRAF TASK APALL \n'
result = []
if _type == 'obj':
imgex = ntt.util.extractspectrum(
img, dv, _ext_trace, _dispersionline, _interactive, _type)
ntt.util.updateheader(
imgex, 0, {'FILETYPE': [22107, 'extracted 1D spectrum ']})
ntt.util.updateheader(imgex, 0, {
'PRODCATG': ['SCIENCE.' + readkey3(readhdr(imgex), 'tech').upper(), 'Data product category']})
ntt.util.updateheader(imgex, 0, {'TRACE1': [img, '']})
result.append(imgex)
if _listsens:
sensfile = ntt.util.searchsens(img, _listsens)[0]
else:
sensfile = ''
if not sensfile:
sensfile = ntt.util.searchsens(img, '')[0]
if sensfile:
imgf = re.sub('.fits', '_f.fits', img)
_extinctdir = 'direc$standard/extinction/'
_extinction = 'extinction_lasilla.dat'
_observatory = 'lasilla'
_exptime = readkey3(hdrt, 'exptime')
_airmass = readkey3(hdrt, 'airmass')
ntt.util.delete(imgf)
iraf.specred.calibrate(input=imgex, output=imgf, sensiti=sensfile, extinct='yes',
flux='yes', ignorea='yes', extinction=_extinctdir + _extinction,
observatory=_observatory, airmass=_airmass, exptime=_exptime,
fnu='no')
hedvec = {'SENSFUN': [string.split(sensfile, '/')[-1], 'sensitivity function'],
'FILETYPE': [22208, '1D wavelength and flux calibrated spectrum '],
'SNR': [ntt.util.StoN2(imgf, False), 'Average S/N ratio'],
'BUNIT': ['erg/cm2/s/Angstrom', 'Flux Calibration Units'], 'TRACE1': [imgex, '']}
ntt.util.updateheader(imgf, 0, hedvec)
imgout = imgf
imgd = ntt.efoscspec1Ddef.fluxcalib2d(img, sensfile)
ntt.util.updateheader(
imgd, 0, {'FILETYPE': [22209, '2D wavelength and flux calibrated spectrum ']})
ntt.util.updateheader(imgd, 0, {'TRACE1': [img, '']})
imgasci = re.sub('.fits', '.asci', imgout)
ntt.util.delete(imgasci)
iraf.onedspec.wspectext(
imgout + '[*,1,1]', imgasci, header='no')
result = result + [imgout, imgd, imgasci]
else:
imgex = ntt.util.extractspectrum(
img, dv, _ext_trace, _dispersionline, _interactive, 'std')
imgout = ntt.efoscspec1Ddef.sensfunction(
imgex, 'spline3', 6, _inter)
result = result + [imgout]
for img in result:
if img[-5:] == '.fits':
ntt.util.phase3header(img) # phase 3 definitions
ntt.util.airmass(img) # phase 3 definitions
ntt.util.updateheader(
img, 0, {'quality': ['Rapid', 'Final or Rapid reduction']})
return result
def sofispecreduction(files, _interactive, _doflat, listflat, _docross, _verbose=False):
# print "LOGX:: Entering `sofispecreduction` method/function in
# %(__file__)s" % globals()
import ntt
from ntt.util import delete, readhdr, readkey3, correctcard, rangedata
import string, re, sys, os, glob
try:
from astropy.io import fits as pyfits
except:
import pyfits
from pyraf import iraf
from numpy import argmin, array, min, isnan, arange, mean, sum
from numpy import sqrt, pi
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', 'ccdproc', 'specred.apall', 'longslit.identify', 'longslit.reidentify',
'longslit.fitcoords', 'specred.transform', 'specred.response', 'imutil.hedit']
for t in toforget:
iraf.unlearn(t)
iraf.longslit.dispaxi = 2
iraf.longslit.mode = 'h'
iraf.specred.dispaxi = 2
iraf.specred.mode = 'h'
iraf.ccdproc.darkcor = 'no'
iraf.ccdproc.fixpix = 'no'
iraf.ccdproc.flatcor = 'no'
iraf.ccdproc.zerocor = 'no'
iraf.ccdproc.overscan = 'no'
iraf.ccdproc.ccdtype = ''
iraf.ccdred.instrument = "/dev/null"
iraf.set(direc=ntt.__path__[0] + '/')
if _interactive:
_interact = 'yes'
else:
_interact = 'no'
if _verbose:
iraf.ccdred.verbose = 'yes'
iraf.specred.verbose = 'yes'
else:
iraf.specred.verbose = 'no'
iraf.ccdred.verbose = 'no'
import datetime
import time
now = datetime.datetime.now()
datenow = now.strftime('20%y%m%d%H%M')
MJDtoday = 55927 + (datetime.date.today() - datetime.date(2012, 01, 01)).days
# if they are not sorted the fieldlist dict could crash
files = ntt.sofiphotredudef.sortbyJD(files)
outputlist = []
setup = []
fieldlist = {}
OBID = {}
RA = {}
DEC = {}
objects = {}
flats = {}
lamps1 = {}
_rdnoise = readkey3(readhdr(re.sub('\n', '', files[0])), 'ron')
_gain = readkey3(readhdr(re.sub('\n', '', files[0])), 'gain')
for img in files:
img = re.sub('\n', '', img)
hdr = readhdr(img)
_object = readkey3(hdr, 'object')
_filter = readkey3(hdr, 'filter')
_date = readkey3(hdr, 'date-night')
_exptime = readkey3(hdr, 'exptime')
_grism = readkey3(hdr, 'grism')
_obsmode = readkey3(hdr, 'obsmode')
_type = ''
if _grism.lower() not in ['gr', 'gb']:
_type = 'image'
if not _type:
if _object.lower() == 'flat':
_type = 'flat'
if _date not in flats:
flats[_date] = {}
if _grism not in flats[_date]:
flats[_date][_grism] = [img]
else:
flats[_date][_grism].append(img)
elif _object.lower() == 'lamp':
_lampid = (readkey3(hdr, 'esoid'), readkey3(hdr, 'grism'))
if _lampid not in lamps1:
lamps1[_lampid] = [None, None]
if readkey3(hdr, 'lamp1') == 'Xenon':
lamps1[_lampid][0] = img
else:
lamps1[_lampid][1] = img
_type = 'lamp'
# if readkey3(hdr,'lamp1')=='Xenon':
# _type='lamp'
# if _grism not in lamps:
# lamps[_grism]=[img]
# else:
# lamps[_grism].append(img)
# else:
# _type='notgood'
if not _type:
_ra = readkey3(hdr, 'RA')
_dec = readkey3(hdr, 'DEC')
_object_name = readkey3(hdr, 'object')
_OBID = (readkey3(hdr, 'esoid'), _grism)
if string.count(_object_name, '/') or string.count(_object_name, '.') or string.count(_object_name, ' '):
nameobj = string.split(_object_name, '/')[0]
nameobj = string.split(nameobj, ' ')[0]
nameobj = string.split(nameobj, '.')[0]
else:
nameobj = _object_name
if _grism not in fieldlist:
fieldlist[_grism] = {}
if _OBID not in OBID:
count = 1
nameobj0 = nameobj + '_' + str(count)
answ = 'yes'
while answ == 'yes':
if nameobj0 in fieldlist[_grism]:
count = count + 1
nameobj0 = nameobj + '_' + str(count)
else:
answ = 'no'
fieldlist[_grism][nameobj0] = []
OBID[readkey3(hdr, 'esoid'), _grism] = nameobj0
fieldlist[_grism][nameobj0].append(img)
if _verbose:
print img
print _type, _object, _filter
print 'lamps', lamps1
lamps = {}
for _lampid in lamps1:
lamp = ''
output = 'arc_' + str(_lampid[0]) + '_' + str(_lampid[1]) + '.fits'
if lamps1[_lampid][0] and lamps1[_lampid][1]:
print lamps1[_lampid][0], lamps1[_lampid][1]
# try:
ntt.util.delete(output)
iraf.imarith(lamps1[_lampid][0], '-', lamps1[_lampid]
[1], result=output, verbose='yes')
# except:
# print 'warning, lamp file not ON/OFF'
# os.system('cp '+lamps1[_lampid][0]+' '+output)
lamp = output
elif lamps1[_lampid][0] and not lamps1[_lampid][1]:
os.system('cp ' + lamps1[_lampid][0] + ' ' + output)
lamp = output
if lamp:
if _lampid[1] not in lamps:
lamps[_lampid[1]] = [lamp]
else:
lamps[_lampid[1]].append(lamp)
if _verbose:
print '\n### FIELDS\n', fieldlist
print '\n### OBID\n', OBID
print '\n### FLATS\n', flats
print '\n### LAMPS\n', lamps
# if not flats:
# sys.exit('\n### error: spectroscopic flat not available, add flats in the directory and try again')
# if not lamps:
# sys.exit('\n### error: spectroscopic lamp not available, add lamps in
# the directory and try again')
if not listflat:
print '\n### list of available spectroscopic flats (ON,OFF):'
for _date in flats:
for _grism in flats[_date]:
for img in flats[_date][_grism]:
if pyfits.open(img)[0].data.mean() >= 2000:
print img, _grism, _date, 'ON ? '
else:
print img, _grism, _date, 'OFF ? '
for _date in flats:
for _grism in flats[_date]:
flat = {'ON': [], 'OFF': []}
for img in flats[_date][_grism]:
_type = ''
if readkey3(hdr, 'lamp3'):
print '\n### header lamp3 found: flat ON ', str(img)
_type = 'ON'
else:
if pyfits.open(img)[0].data.mean() >= 2000:
_type = 'ON'
else:
_type = 'OFF'
aa, bb, cc = ntt.util.display_image(img, 1, '', '', False)
print '\n### number of flat already selected (ON,OFF): \n ### please select same number ' \
'of ON and OFF flats \n' + \
str(len(flat['ON'])) + ' ' + str(len(flat['OFF']))
print '\n### image ' + str(img)
answ = raw_input(
'ON/OFF/REJECT/STOP [' + str(_type) + '] ok (ON[n]/OFF[f]/r/s) [' + _type + '] ? ')
if not answ:
answ = _type
if answ in ['ON', 'on', 'n']:
_type = 'ON'
if answ in ['OFF', 'off', 'f']:
_type = 'OFF'
if answ in ['s', 'S', 'STOP', 'stop', 'Stop']:
_type = 'stop'
if answ in ['r', 'R', 'reject']:
_type = 'r'
if _type in ['ON', 'OFF']:
flat[_type].append(img)
elif _type == 'stop':
if len(flat['ON']) == len(flat['OFF']) and len(flat['OFF']) >= 2:
break
elif len(flat['ON']) == len(flat['OFF']) and len(flat['OFF']) == 0:
break
else:
print '\n### Warning: you can stop only if the numbers of ON and OFF are the same'
print len(flat['ON']), len(flat['OFF'])
if len(flat['ON']) == len(flat['OFF']) and len(flat['OFF']) >= 2:
ff = open('_flatlist', 'w')
for ii in range(0, len(flat['OFF'])):
delete('flat_' + str(_date) + '_' + str(_grism) +
'_' + str(MJDtoday) + '_' + str(ii) + '.fits')
iraf.imarith(flat['ON'][ii], '-', flat['OFF'][ii],
result='flat_' + str(_date) + '_' + str(_grism) + '_' + str(MJDtoday) + '_' + str(
ii) + '.fits', verbose='no')
ff.write(
'flat_' + str(_date) + '_' + str(_grism) + '_' + str(MJDtoday) + '_' + str(ii) + '.fits\n')
ff.close()
masterflat = 'flat_' + \
str(_date) + '_' + str(_grism) + \
'_' + str(MJDtoday) + '.fits'
delete(masterflat)
_order = '80'
iraf.ccdred.flatcombine(input='@_flatlist', output=masterflat, combine='median', rdnoise=_rdnoise,
gain=_gain, ccdtype='')
hdr = readhdr(masterflat)
matching = [s for s in hdr.keys() if "IMCMB" in s]
for imcmb in matching:
aaa = iraf.hedit(masterflat, imcmb, delete='yes', update='yes',
verify='no', Stdout=1)
delete('_flatlist')
print masterflat
correctcard(masterflat)
if masterflat not in outputlist:
outputlist.append(masterflat)
ntt.util.updateheader(masterflat, 0, {'FILETYPE': [41102, 'flat field'],
'SINGLEXP': [False, 'TRUE if resulting from single exposure'],
'M_EPOCH': [False, 'TRUE if resulting from multiple epochs']})
print '\n### master flat ........... done '
delete('n' + masterflat)
iraf.specred.response(masterflat, normaliz=masterflat + '[100:900,*]',
response='n' + masterflat, interac=_interact, thresho='INDEF', sample='*',
naverage=2,
function='spline3', low_rej=3, high_rej=3, order=_order, niterat=20, grow=0,
graphic='stdgraph', mode='q')
listflat.append('n' + masterflat)
if 'n' + masterflat not in outputlist:
outputlist.append('n' + masterflat)
ntt.util.updateheader('n' + masterflat, 0, {'FILETYPE': [41203, 'normalized flat field'],
'TRACE1': [masterflat, 'Originating file']})
# ntt.util.updateheader('n'+masterflat,0,{'TRACE1':[masterflat,'']})
flattot = flat['ON'] + flat['OFF']
num = 0
for img in flattot:
num = num + 1
ntt.util.updateheader(masterflat, 0, {
'PROV' + str(num): [readkey3(readhdr(img), 'ARCFILE'), 'Originating file'],
'TRACE' + str(num): [readkey3(readhdr(img), 'ARCFILE'), 'Originating file']})
ntt.util.updateheader('n' + masterflat, 0, {
'PROV' + str(num): [readkey3(readhdr(img), 'ARCFILE'), 'Originating file']})
if listflat:
print '\n### flat available:\n### ' + str(listflat), '\n'
elif len(flat['ON']) == len(flat['OFF']) and len(flat['OFF']) == 0:
print '\n### no good flats in this set ......'
else:
sys.exit('\n### Error: number of ON and OFF not the same')
for _grism in fieldlist:
obj0 = fieldlist[_grism][fieldlist[_grism].keys()[0]][0]
# ############# arc #########################
if _grism not in lamps:
print '\n### take arc from archive '
arcfile = ntt.util.searcharc(obj0, '')[0]
if arcfile[0] == '/':
os.system('cp ' + arcfile + ' ' +
string.split(arcfile, '/')[-1])
arcfile = string.split(arcfile, '/')[-1]
lamps[_grism] = [arcfile]
if _grism in lamps:
arclist = lamps[_grism]
if arclist:
arcfile = ntt.util.searcharc(obj0, arclist)[0]
else:
arcfile = ntt.util.searcharc(obj0, '')[0]
print arcfile
if arcfile:
print arcfile
datea = readkey3(readhdr(arcfile), 'date-night')
if arcfile[0] == '/':
os.system('cp ' + arcfile + ' ' +
string.split(arcfile, '/')[-1])
arcfile = string.split(arcfile, '/')[-1]
if _doflat:
if listflat:
flat0 = ntt.util.searchflat(arcfile, listflat)[0]
else:
flat0 = ''
else:
flat0 = ''
if flat0:
_flatcor = 'yes'
else:
_flatcor = 'no'
_doflat = False
ntt.util.delete('arc_' + datea + '_' + _grism +
'_' + str(MJDtoday) + '.fits')
print arcfile, flat0, _flatcor, _doflat
if _doflat:
iraf.noao.imred.ccdred.ccdproc(arcfile,
output='arc_' + datea + '_' + _grism +
'_' +
str(MJDtoday) + '.fits',
overscan='no', trim='no', zerocor='no', flatcor=_flatcor, flat=flat0)
else:
os.system('cp ' + arcfile + ' ' + 'arc_' + datea +
'_' + _grism + '_' + str(MJDtoday) + '.fits')
iraf.noao.imred.ccdred.ccdproc('arc_' + datea + '_' + _grism + '_' + str(MJDtoday) + '.fits', output='',
overscan='no', trim='yes', zerocor='no', flatcor='no', flat='',
trimsec='[30:1000,1:1024]')
arcfile = 'arc_' + datea + '_' + \
_grism + '_' + str(MJDtoday) + '.fits'
ntt.util.correctcard(arcfile)
print arcfile
if arcfile not in outputlist:
outputlist.append(arcfile)
ntt.util.updateheader(arcfile, 0, {'FILETYPE': [41104, 'pre-reduced 2D arc'],
'SINGLEXP': [True, 'TRUE if resulting from single exposure'],
'M_EPOCH': [False, 'TRUE if resulting from multiple epochs'],
'PROV1': [readkey3(readhdr(arcfile), 'ARCFILE'), 'Originating file'],
'TRACE1': [readkey3(readhdr(arcfile), 'ARCFILE'),
'Originating file']})
arcref = ntt.util.searcharc(obj0, '')[0]
if not arcref:
identific = iraf.longslit.identify(images=arcfile, section='column 10',
coordli='direc$standard/ident/Lines_XeAr_SOFI.dat', nsum=10,
fwidth=7, order=3, mode='h', Stdout=1, verbose='yes')
else:
print arcref
os.system('cp ' + arcref + ' .')
arcref = string.split(arcref, '/')[-1]
if not os.path.isdir('database/'):
os.mkdir('database/')
if os.path.isfile(ntt.util.searcharc(obj0, '')[1] + '/database/id' + re.sub('.fits', '', arcref)):
os.system('cp ' + ntt.util.searcharc(obj0, '')[1] + '/database/id' + re.sub('.fits', '',
arcref) + ' database/')
print arcref, arcfile
# time.sleep(5)
# os.system('rm -rf database/idarc_20130417_GR_56975')
# raw_input('ddd')
identific = iraf.longslit.reidentify(referenc=arcref, images=arcfile, interac='NO', # _interact,
section='column 10', shift=0.0,
coordli='direc$standard/ident/Lines_XeAr_SOFI.dat',
overrid='yes', step=0, newaps='no', nsum=5, nlost=2,
mode='h', verbose='yes', Stdout=1)
# print identific
# raw_input('ddd')
identific = iraf.longslit.reidentify(referenc=arcref, images=arcfile, interac=_interact,
section='column 10', shift=1.0,
coordli='direc$standard/ident/Lines_XeAr_SOFI.dat',
overrid='yes', step=0, newaps='no', nsum=5, nlost=2,
mode='h', verbose='yes', Stdout=1)
# fitsfile = ntt.efoscspec2Ddef.continumsub('new3.fits', 6, 1)
# I need to run twice I don't know why
# print identific
# raw_input('ddd')
if _interactive:
answ = raw_input(
'\n### do you like the identification [[y]/n]')
if not answ:
answ = 'y'
else:
answ = 'y'
if answ in ['n', 'N', 'no', 'NO', 'No']:
yy1 = pyfits.open(arcref)[0].data[:, 10:20].mean(1)
xx1 = arange(len(yy1))
yy2 = pyfits.open(arcfile)[0].data[:, 10:20].mean(1)
xx2 = arange(len(yy2))
ntt.util.delete('_new3.fits')
hdu = pyfits.PrimaryHDU(yy1)
hdulist = pyfits.HDUList([hdu])
hdulist.writeto('_new3.fits')
fitsfile = ntt.efoscspec2Ddef.continumsub('_new3.fits', 4, 1)
yy1 = pyfits.open(fitsfile)[0].data
ntt.util.delete('_new3.fits')
hdu = pyfits.PrimaryHDU(yy2)
hdulist = pyfits.HDUList([hdu])
hdulist.writeto('_new3.fits')
fitsfile = ntt.efoscspec2Ddef.continumsub('_new3.fits', 4, 1)
yy2 = pyfits.open(fitsfile)[0].data
_shift = ntt.efoscspec2Ddef.checkwavelength_arc(
xx1, yy1, xx2, yy2, '', '') * (-1)
print arcref, arcfile, _shift
identific = iraf.longslit.reidentify(referenc=arcref, images=arcfile, interac='YES',
section='column 10', shift=_shift,
coordli='direc$standard/ident/Lines_XeAr_SOFI.dat',
overrid='yes', step=0, newaps='no', nsum=5, nlost=2,
mode='h', verbose='yes', Stdout=1)
answ = raw_input('\n### is it ok now ? [[y]/n] ')
if not answ:
answ = 'y'
if answ in ['n', 'N', 'no', 'NO', 'No']:
sys.exit(
'\n### Warning: line identification with some problems')
iraf.longslit.reidentify(referenc=arcfile, images=arcfile, interac='NO', section='column 10',
coordli='direc$standard/ident/Lines_XeAr_SOFI.dat', overrid='yes', step=10,
newaps='yes', nsum=5, nlost=2, mode='h', verbose='no')
iraf.longslit.fitcoords(images=re.sub('.fits', '', arcfile), fitname=re.sub('.fits', '', arcfile),
interac='no', combine='yes', databas='database',
function='legendre', yorder=4, logfile='', plotfil='', mode='h')
if identific:
_rms = float(identific[-1].split()[-1])
_num = float(identific[-1].split()[2].split('/')[0])
hdr = ntt.util.readhdr(arcfile)
hedvec = {'LAMRMS': [_rms * .1, 'residual RMS [nm]'],
'LAMNLIN': [_num, 'Nb of arc lines used in the fit of the wavel. solution'],
'SPEC_ERR': [(_rms * .1) / sqrt(float(_num)), 'statistical uncertainty'],
'SPEC_SYE': [0.1, 'systematic error']}
ntt.util.updateheader(arcfile, 0, hedvec)
else:
sys.exit('Warning: arcfile not found')
else:
print 'here'
# ########################################################################################################
for field in fieldlist[_grism]:
listaobj = fieldlist[_grism][field]
listaobj = ntt.sofiphotredudef.sortbyJD(listaobj)
listatemp = listaobj[:]
# ############## flat ######################
if listflat and _doflat:
flat0 = ntt.util.searchflat(listaobj[0], listflat)[0]
else:
flat0 = ''
if flat0:
_flatcor = 'yes'
else:
_flatcor = 'no'
########## crosstalk ###########################
listatemp2 = []
_date = readkey3(readhdr(listatemp[0]), 'date-night')
for img in listatemp:
# num2=listatemp.index(listasub[j])
imgout = field + '_' + str(_date) + '_' + str(_grism) + '_' + str(MJDtoday) + '_' + str(
listatemp.index(img)) + '.fits'
print '\n### input image: ' + str(img)
delete(imgout)
listatemp2.append(imgout)
if _docross:
print '### correct for cross talk ..... done'
ntt.sofiphotredudef.crosstalk(img, imgout)
correctcard(imgout)
ntt.util.updateheader(
imgout, 0, {'CROSSTAL': ['True', '']})
else:
os.system('cp ' + img + ' ' + imgout)
correctcard(imgout)
if _flatcor == 'yes':
print '### correct for flat field ..... done'
try:
iraf.noao.imred.ccdred.ccdproc(imgout, output='', overscan='no', trim='no', zerocor='no',
flatcor=_flatcor, flat=flat0)
except:
iraf.imutil.imreplace(
images=flat0, value=0.01, lower='INDEF', upper=0.01, radius=0)
iraf.noao.imred.ccdred.ccdproc(imgout, output='', overscan='no', trim='no', zerocor='no',
flatcor=_flatcor, flat=flat0)
iraf.noao.imred.ccdred.ccdproc(imgout, output='', overscan='no', trim='yes', zerocor='no',
flatcor='no', flat='', trimsec='[30:1000,1:1024]')
ntt.util.updateheader(
imgout, 0, {'FLATCOR': [flat0, 'flat correction']})
if imgout not in outputlist:
outputlist.append(imgout)
ntt.util.updateheader(imgout, 0, {'FILETYPE': [42104, 'pre-reduced frame'],
'SINGLEXP': [True, 'TRUE if resulting from single exposure'],
'M_EPOCH': [False, 'TRUE if resulting from multiple epochs'],
'PROV1': [readkey3(readhdr(imgout), 'ARCFILE'), 'Originating file'],
'TRACE1': [readkey3(readhdr(imgout), 'ARCFILE'), 'Originating file']})
print '### output image: ' + str(imgout)
listatemp = listatemp2[:]
######### differences object images #####################
listasub = ntt.sofispec2Ddef.findsubimage(listatemp)
reduced = []
print '\n### Select Frames to be subtracted (eg A-B, B-A, C-D, D-C, ....) '
print '### frame1 \t frame2 \t offset1 \t offset2 \t JD1 \t JD2\n'
if len(listatemp) >= 2 and len(listasub) >= 2:
for j in range(0, len(listatemp)):
print '### ', listatemp[j], listasub[j], str(readkey3(readhdr(listatemp[j]), 'xcum')), str(
readkey3(readhdr(listasub[j]), 'xcum')), \
str(readkey3(readhdr(listatemp[j]), 'JD')), str(
readkey3(readhdr(listatemp[j]), 'JD'))
if _interactive:
answ = raw_input('\n### ok [[y]/n] ? ')
if not answ:
answ = 'y'
else:
answ = 'y'
num1 = j
image1 = listatemp[j]
_date = readkey3(readhdr(image1), 'date-night')
if answ == 'y':
num2 = listatemp.index(listasub[j])
image2 = listasub[j]
else:
image2 = raw_input(
'which image do you want to subtract')
num2 = listatemp.index(image2)
imgoutsub = field + '_' + str(_date) + '_' + str(_grism) + '_' + str(MJDtoday) + '_' + str(
num1) + '_' + str(num2) + '.fits'
delete(imgoutsub)
iraf.images.imutil.imarith(
operand1=image1, op='-', operand2=image2, result=imgoutsub, verbose='no')
ntt.util.updateheader(imgoutsub, 0, {'skysub': [image2, 'sky image subtracted'],
'FILETYPE': [42115, 'pre-reduced frame sky subtracted'],
'TRACE1': [image1, 'Originating file'],
'PROV2': [readkey3(readhdr(image2), 'ARCFILE'),
'Originating file'],
'TRACE2': [image2, 'Originating file']})
reduced.append(imgoutsub)
if imgoutsub not in outputlist:
outputlist.append(imgoutsub)
######################## 2D wavelengh calibration ########
for img in reduced:
if arcfile:
hdra = ntt.util.readhdr(arcfile)
delete('t' + img)
iraf.specred.transform(input=img, output='t' + img, minput='',
fitnames=re.sub('.fits', '', arcfile), databas='database',
x1='INDEF', x2='INDEF', y1='INDEF', y2='INDEF', flux='yes', mode='h',
logfile='logfile')
ntt.util.updateheader('t' + img, 0,
{'ARC': [arcfile, ''], 'FILETYPE': [42106, 'wavelength calibrate 2D frames'],
'TRACE1': [img, 'Originating file']})
ntt.util.updateheader(
't' + img, 0, {'TRACE1': [img, 'Originating file']})
ntt.util.updateheader('t' + img, 0,
{'LAMRMS': [ntt.util.readkey3(hdra, 'LAMRMS'), 'residual RMS [nm]'],
'LAMNLIN': [ntt.util.readkey3(hdra, 'LAMNLIN'), 'number of arc lines'],
'SPEC_ERR': [ntt.util.readkey3(hdra, 'SPEC_ERR'), 'statistical uncertainty'],
'SPEC_SYE': [ntt.util.readkey3(hdra, 'SPEC_SYE'), 'systematic error']})
###########################
delete('t' + arcfile)
iraf.specred.transform(input=arcfile, output='t' + arcfile, minput='',
fitnames=re.sub('.fits', '', arcfile), databas='database',
x1='INDEF', x2='INDEF', y1='INDEF', y2='INDEF', flux='yes', mode='h',
logfile='logfile')
specred = ntt.util.spectraresolution2(arcfile, 50)
if specred:
ntt.util.updateheader(
't' + img, 0, {'SPEC_RES': [specred, 'Spectral resolving power']})
delete('t' + arcfile)
###########################
iraf.hedit('t' + img, 'TRACE2', delete='yes',
update='yes', verify='no', Stdout=1)
if 't' + img not in outputlist:
outputlist.append('t' + img)
print '\n### 2D frame t' + str(img) + ' wavelengh calibrated ............ done'
_skyfile = ntt.__path__[
0] + '/standard/ident/sky_' + _grism + '.fits' # check in wavelengh #########
hdr = ntt.util.readhdr(img)
if glob.glob(_skyfile) and readkey3(hdr, 'exptime') > 20.:
_original = readkey3(hdr, 'ORIGFILE')
_archive = readkey3(hdr, 'ARCFILE')
if os.path.isfile(_archive):
imgstart = _archive
elif os.path.isfile(_original):
imgstart = _original
else:
imgstart = ''
if imgstart:
delete('_tmp.fits')
print imgstart, arcfile
iraf.specred.transform(input=imgstart, output='_tmp.fits', minput='',
fitnames=re.sub('.fits', '', arcfile), databas='database',
x1='INDEF', x2='INDEF', y1='INDEF', y2='INDEF', flux='yes', mode='h',
logfile='logfile')
shift = ntt.sofispec2Ddef.skysofifrom2d('_tmp.fits', _skyfile)
zro = pyfits.open('_tmp.fits')[0].header.get('CRVAL2')
delete('_tmp.fits')
if _interactive:
answ = raw_input(
'do you want to correct the wavelengh calibration with this shift: ' + str(
shift) + ' [[y]/n] ? ')
if not answ:
answ = 'y'
else:
answ = 'y'
if answ.lower() in ['y', 'yes']:
ntt.util.updateheader('t' + img, 0,
{'CRVAL2': [zro + int(shift), ''], 'shift': [float(shift), '']})
# ntt.util.updateheader('t'+img,0,{'shift':[float(shift),'']})
print '\n### check wavelengh calibration with sky lines ..... done'
try:
hdrt = ntt.util.readhdr('t' + img)
wavelmin = float(readkey3(hdrt, 'CRVAL2')) + (0.5 - float(readkey3(hdrt, 'CRPIX2'))) * float(
readkey3(hdrt, 'CDELT2'))
wavelmax = float(readkey3(hdrt, 'CRVAL2')) + (
(float(readkey3(hdrt, 'NAXIS2')) + 0.5 - float(readkey3(hdrt, 'CRPIX2'))) * float(
readkey3(hdrt, 'CDELT2')))
hedvec = {}
hedvec['WAVELMIN'] = [
wavelmin * .1, '[nm] minimum wavelength']
hedvec['WAVELMAX'] = [
wavelmax * .1, ' [nm] maximum wavelength']
hedvec['XMIN'] = [wavelmin, '[A] minimum wavelength']
hedvec['XMAX'] = [wavelmax, '[A] maximum wavelength']
hedvec['SPEC_BW'] = [
(wavelmax * .1) - (wavelmin * .1), '[nm] Bandpass Width Wmax - Wmin']
hedvec['SPEC_VAL'] = [
((wavelmax * .1) + (wavelmin * .1)) / 2., '[nm] Mean Wavelength']
hedvec['SPEC_BIN'] = [
((wavelmax * .1) - (wavelmin * .1)) /
(float(readkey3(hdr, 'NAXIS2')) - 1),
'Wavelength bin size [nm/pix]']
hedvec['VOCLASS'] = ['SPECTRUM V1.0', 'VO Data Model']
hedvec['VOPUB'] = ['ESO/SAF',
'VO Publishing Authority']
# hedvec['APERTURE']=[float(re.sub('slit','',readkey3(hdrt,'slit'))),'aperture width']
ntt.util.updateheader('t' + img, 0, hedvec)
except:
pass
else:
print '\n### Warning: arc not found for the image ' + str(img) + ' with setup ' + str(_grism)
reduceddata = rangedata(outputlist)
print '\n### adding keywords for phase 3 ....... '
f = open('logfile_spec2d_' + str(reduceddata) +
'_' + str(datenow) + '.raw.list', 'w')
for img in outputlist:
if img[-4:] == 'fits':
hdr = readhdr(img)
# ###############################################
# cancel pc matrix
if 'PC1_1' in hdr.keys():
aaa = iraf.hedit(img, 'PC1_1', delete='yes',
update='yes', verify='no', Stdout=1)
if 'PC2_2' in hdr.keys():
aaa = iraf.hedit(img, 'PC2_2', delete='yes',
update='yes', verify='no', Stdout=1)
if 'PC1_2' in hdr.keys():
aaa = iraf.hedit(img, 'PC1_2', delete='yes',
update='yes', verify='no', Stdout=1)
if 'PC2_1' in hdr.keys():
aaa = iraf.hedit(img, 'PC2_1', delete='yes',
update='yes', verify='no', Stdout=1)
#################
# added for DR2
print img
if 'NCOMBINE' in hdr:
_ncomb = readkey3(hdr, 'NCOMBINE')
else:
_ncomb = 1.0
ntt.util.updateheader(
img, 0, {'DETRON ': [12, 'Readout noise per output (e-)']})
ntt.util.updateheader(img, 0, {'EFFRON': [12. * (1 / sqrt(readkey3(hdr, 'ndit') * _ncomb)) * sqrt(pi / 2),
'Effective readout noise per output (e-)']})
ntt.util.phase3header(img) # phase 3 definitions
############################
# change for DR2
############################
texp = float(readkey3(hdr, 'dit')) * float(readkey3(hdr, 'ndit'))
mjdend = float(readkey3(hdr, 'MJD-OBS')) + (float(readkey3(hdr, 'ndit')) * (
float(readkey3(hdr, 'dit')) + 1.8)) / (60. * 60. * 24.)
strtexp = time.strftime('%H:%M:%S', time.gmtime(texp))
_telapse = (mjdend - float(readkey3(hdr, 'MJD-OBS'))) * \
60. * 60 * 24.
# tmid=_telapse/2.
tmid = (mjdend + float(readkey3(hdr, 'MJD-OBS'))) / 2
ntt.util.updateheader(img, 0, {'quality': ['Final', 'fast or rapid reduction'],
'BUNIT': ['ADU', 'Physical unit of array values'],
'DIT': [readkey3(hdr, 'dit'), 'Detector Integration Time'],
'NDIT': [readkey3(hdr, 'ndit'), 'Number of sub-integrations'],
'TEXPTIME': [texp, 'Total integration time of all exposures (s)'],
'EXPTIME': [texp, 'Total integration time. ' + strtexp],
'MJD-END': [mjdend, 'End of observations (days)'],
'TELAPSE': [_telapse, 'Total elapsed time [days]'],
'TMID': [tmid, '[d] MJD mid exposure'],
'TITLE': [readkey3(hdr, 'object'), 'Dataset title'],
#'TITLE':[str(tmid)[0:9]+' '+str(readkey3(hdr,'object'))+' '+str(readkey3(hdr,'grism'))+' '+\
# str(readkey3(hdr,'filter'))+'
# '+str(readkey3(hdr,'slit')),'Dataset
# title'],\
'EXT_OBJ': [False, 'TRUE if extended'],
'CONTNORM': [False, 'spectrum normalized to the continuum'],
'TOT_FLUX': [False, 'TRUE if phot cond and all src flux is captured'],
'SPECSYS': ['TOPOCENT', 'Reference frame for spectral coordinate'],
'FLUXCAL': ['ABSOLUTE', 'type of flux calibration'],
'FLUXERR': [34.7, 'Fractional uncertainty of the flux [%]'],
'DISPELEM': ['Gr#' + re.sub('Gr', '', readkey3(hdr, 'grism')),
'Dispersive element name']})
if readkey3(hdr, 'tech'):
ntt.util.updateheader(
img, 0, {'PRODCATG': ['SCIENCE.IMAGE', 'Data product category']})
aaa = str(readkey3(hdr, 'arcfiles')) + '\n'
f.write(aaa)
try:
ntt.util.airmass(img) # phase 3 definitions
except:
print '\n### airmass not computed for image: ', img
else:
print img + ' is not a fits image'
f.close()
return outputlist, 'logfile_spec2d_' + str(reduceddata) + '_' + str(datenow) + '.raw.list'
def extractspectrum(img,
dv,
inst,
_interactive,
_type,
automaticex=False,
host_ex=False,
match_aperture='n'):
# print "LOGX:: Entering `extractspectrum` method/function in
# %(__file__)s" % globals()
import glob
import os
import string
import sys
import re
import datetime
import numpy as np
MJDtoday = 55927 + (datetime.date.today() -
datetime.date(2012, 0o1, 0o1)).days
from pyraf import iraf
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.specred(_doprint=0)
toforget = ['specred.apall', 'specred.transform']
for t in toforget:
iraf.unlearn(t)
hdr = readhdr(img)
iraf.specred.dispaxi = inst.get('dispaxis')
imgex = re.sub('.fits', '_ex.fits', img)
print(img)
imgfast = re.sub(string.split(img, '_')[-2] + '_', '', img)
if not os.path.isfile(imgex) and not os.path.isfile(
'database/ap' + re.sub('.fits', '', img)) and not os.path.isfile(
'database/ap' + re.sub('.fits', '', imgfast)):
_new = 'yes'
_extract = 'yes'
else:
if automaticex:
if _interactive in ['Yes', 'yes', 'YES', 'y', 'Y']:
answ = 'x'
while answ not in ['o', 'n', 's']:
answ = input(
'\n### New extraction [n], extraction with old parameters [o], skip extraction [s] ? [o]'
)
if not answ:
answ = 'o'
if answ == 'o':
_new, _extract = 'no', 'yes'
elif answ == 'n':
_new, _extract = 'yes', 'yes'
else:
_new, _extract = 'yes', 'no'
else:
_new, _extract = 'no', 'yes'
else:
if _interactive in ['Yes', 'yes', 'YES', 'y', 'Y']:
answ = 'x'
while answ not in ['y', 'n']:
answ = input(
'\n### do you want to extract again [[y]/n] ? ')
if not answ:
answ = 'y'
if answ == 'y':
_new, _extract = 'yes', 'yes'
else:
_new, _extract = 'yes', 'no'
else:
_new, _extract = 'yes', 'yes'
if _extract == 'yes':
delete(imgex)
dist = 200
_reference = ''
_fittrac = 'yes'
_trace = 'yes'
if _new == 'no':
if not os.path.isfile('database/ap' + re.sub('.fits', '', img)):
repstringinfile('database/ap' + re.sub('.fits', '', imgfast),
'database/ap' + re.sub('.fits', '', img),
re.sub('.fits', '', imgfast),
re.sub('.fits', '', img))
_find = 'no'
_recenter = 'no'
_edit = 'no'
_trace = 'no'
_fittrac = 'no'
_mode = 'h'
_resize = 'no'
_review = 'no'
iraf.specred.mode = 'h'
_interactive = 'no'
else:
iraf.specred.mode = 'q'
_mode = 'q'
_find = 'yes'
_recenter = 'yes'
_edit = 'yes'
_review = 'yes'
_resize = dv[_type]['_resize']
# _interactive = False
_recenter = 'no'
_resize = 'no'
_edit = 'yes'
_trace = 'yes'
_fittrace = 'no'
_review = 'yes'
_lower = dv[_type]['_lower']
_upper = dv[_type]['_upper']
_b_sample = dv[_type]['_b_sample']
if match_aperture == 'y':
aps = glob.glob('database/ap*')
for ap in aps:
print(ap.split('/')[-1])
ap_select = raw_input('Choose image to match apertures: ')
#this is just to make things automatic for now
if 'blue' in ap_select and 'lris' in inst.get('name'):
ap_match = ap_select.replace('blue', 'red')
ap_binning = 1.
elif 'red' in ap_select and 'lris' in inst.get('name'):
ap_match = ap_select.replace('red', 'blue')
ap_binning = 2.
elif 'blue' in ap_select and 'kast' in inst.get('name'):
ap_match = ap_select.replace('blue', 'red')
ap_binning = 1.
elif 'red' in ap_select and 'kast' in inst.get('name'):
ap_match = ap_select.replace('red', 'blue')
ap_binning = 1.
# img_binning = hdr.get('BINNING', None).strip()
# if img_binning != None:
# img_binning = float(img_binning.split(',')[0])
img_binning = inst.get('spatial_binning')
# ap_binning = raw_input('Enter aperture spatial binning [1]: ') or 1
# ap_binning = float(ap_binning)
delete('database/' + ap_match)
new_apfile = 'database/' + ap_match
ismainap = False
aps = glob.glob('../master_files/ap*')
for ap in aps:
print(ap.split('/')[-1])
center_ap = raw_input('Choose master aperture for center: ')
#get center of main reference aperture (should be a std)
with open('../master_files/' + center_ap) as c_ap:
c_ap_data = c_ap.readlines()
for i, c_line in enumerate(c_ap_data):
if 'center' in c_line:
center_ap_loc = float(c_line.split()[2])
with open('database/' + ap_select) as old_file:
ap_data = old_file.readlines()
lows, highs, b_samps = get_relevant_ap_data(
ap_data, ap_binning, img_binning, inst)
with open(new_apfile, 'w') as new_file:
for i in range(len(lows)):
low = str(lows[i])
high = str(highs[i])
for c_line in c_ap_data:
if 'begin' in c_line and 'aperture' in c_line:
new_file.write(
c_line.replace(
(c_line.split()[2] + ' 1'),
c_line.split()[2] + ' ' +
str(i + 1)))
elif 'begin' not in c_line and 'aperture' in c_line:
new_file.write(
c_line.replace(c_line.split()[1],
str(i + 1)))
elif 'low' in c_line and 'reject' not in c_line:
new_file.write(
c_line.replace(c_line.split()[2], low))
elif 'high' in c_line and 'reject' not in c_line:
new_file.write(
c_line.replace(c_line.split()[2],
high))
elif 'sample' in c_line:
b_sample = str(b_samps[i])
new_file.write(
c_line.replace(
c_line.split('sample')[1].split(
'\n')[0], ' ' + b_sample))
else:
new_file.write(c_line)
_find = 'yes'
_recenter = 'yes'
_reference = ap_match[2:]
# _reference = 'BD262606_lris_red_1'
# use_diff_aperture = raw_input('Trace different aperture? y/[n]: ')
# if use_diff_aperture == 'y':
# aps = glob.glob('../master_files/ap*')
# for ap in aps:
# print (ap.split('/')[-1])
# ap_select = raw_input('Choose ref image from list above: ')
# os.system('cp ' + ' ../master_files/' + ap_select + ' database/'+ap_select)
# _reference = ap_select[2:]
# _fittrac = 'no'
# _trace = 'no'
if host_ex:
_recenter = 'n'
_trace = 'n'
_fittrac = 'n'
_find = 'n'
iraf.specred.apall(img,
output=imgex,
referen=_reference,
trace=_trace,
fittrac=_fittrac,
find=_find,
recenter=_recenter,
edit=_edit,
nfind=1,
backgro='fit',
lsigma=3,
usigma=3,
b_order=dv[_type]['_b_order'],
format='multispec',
extras='yes',
b_function='chebyshev',
b_sample=_b_sample,
clean='yes',
pfit='fit1d',
lower=_lower,
upper=_upper,
t_niter=dv[_type]['_t_niter'],
width=dv[_type]['_width'],
radius=dv[_type]['_radius'],
line=inst.get('approx_extract_column', 'INDEF'),
nsum=dv[_type]['_nsum'],
t_step=dv[_type]['_t_step'],
t_nsum=dv[_type]['_t_nsum'],
t_nlost=dv[_type]['_t_nlost'],
t_sample=dv[_type]['_t_sample'],
resize=_resize,
t_order=dv[_type]['_t_order'],
weights=dv[_type]['_weights'],
interactive=_interactive,
review=_review,
mode=_mode)
else:
print('\n### skipping new extraction')
return imgex
def sofispec1Dredu(files, _interactive, _ext_trace, _dispersionline, _automaticex, _verbose=False):
# print "LOGX:: Entering `sofispec1Dredu` method/function in %(__file__)s"
# % globals()
import re
import string
import sys
import os
os.environ["PYRAF_BETA_STATUS"] = "1"
import ntt
try: import pyfits
except: from astropy.io import fits as pyfits
import numpy as np
import datetime
import pylab as pl
from pyraf import iraf
dv = ntt.dvex()
now = datetime.datetime.now()
datenow = now.strftime('20%y%m%d%H%M')
MJDtoday = 55927 + (datetime.date.today() - datetime.date(2012, 01, 01)).days
scal = np.pi / 180.
hdr0 = ntt.util.readhdr(re.sub('\n', '', files[0]))
_gain = ntt.util.readkey3(hdr0, 'gain')
_rdnoise = ntt.util.readkey3(hdr0, 'ron')
std_sun, rastd_sun, decstd_sun, magstd_sun = ntt.util.readstandard(
'standard_sofi_sun.txt')
std_vega, rastd_vega, decstd_vega, magstd_vega = ntt.util.readstandard(
'standard_sofi_vega.txt')
std_phot, rastd_phot, decstd_phot, magstd_phot = ntt.util.readstandard(
'standard_sofi_phot.txt')
outputfile = []
objectlist, RA, DEC = {}, {}, {}
for img in files:
img = re.sub('\n', '', img)
hdr = ntt.util.readhdr(img)
_ra = ntt.util.readkey3(hdr, 'RA')
_dec = ntt.util.readkey3(hdr, 'DEC')
_grism = ntt.util.readkey3(hdr, 'grism')
_filter = ntt.util.readkey3(hdr, 'filter')
_slit = ntt.util.readkey3(hdr, 'slit')
cc_sun = np.arccos(np.sin(_dec * scal) * np.sin(decstd_sun * scal) + np.cos(_dec * scal) *
np.cos(decstd_sun * scal) * np.cos((_ra - rastd_sun) * scal)) * ((180 / np.pi) * 3600)
cc_vega = np.arccos(np.sin(_dec * scal) * np.sin(decstd_vega * scal) + np.cos(_dec * scal) *
np.cos(decstd_vega * scal) * np.cos((_ra - rastd_vega) * scal)) * ((180 / np.pi) * 3600)
cc_phot = np.arccos(np.sin(_dec * scal) * np.sin(decstd_phot * scal) + np.cos(_dec * scal) *
np.cos(decstd_phot * scal) * np.cos((_ra - rastd_phot) * scal)) * ((180 / np.pi) * 3600)
if min(cc_sun) < 100:
_type = 'sun'
elif min(cc_phot) < 100:
_type = 'stdp'
elif min(cc_vega) < 100:
_type = 'vega'
else:
_type = 'obj'
if min(cc_phot) < 100:
if _verbose:
print img, 'phot', str(min(cc_phot)), str(std_phot[np.argmin(cc_phot)])
ntt.util.updateheader(img, 0, {'stdname': [std_phot[np.argmin(cc_phot)], ''],
'magstd': [float(magstd_phot[np.argmin(cc_phot)]), '']})
# ntt.util.updateheader(img,0,{'magstd':[float(magstd_phot[argmin(cc_phot)]),'']})
elif min(cc_sun) < 100:
if _verbose:
print img, 'sun', str(min(cc_sun)), str(std_sun[np.argmin(cc_sun)])
ntt.util.updateheader(img, 0, {'stdname': [std_sun[np.argmin(cc_sun)], ''],
'magstd': [float(magstd_sun[np.argmin(cc_sun)]), '']})
# ntt.util.updateheader(img,0,{'magstd':[float(magstd_sun[argmin(cc_sun)]),'']})
elif min(cc_vega) < 100:
if _verbose:
print img, 'vega', str(min(cc_vega)), str(std_vega[np.argmin(cc_vega)])
ntt.util.updateheader(img, 0, {'stdname': [std_vega[np.argmin(cc_vega)], ''],
'magstd': [float(magstd_vega[np.argmin(cc_vega)]), '']})
# ntt.util.updateheader(img,0,{'magstd':[float(magstd_vega[argmin(cc_vega)]),'']})
else:
if _verbose:
print img, 'object'
_OBID = (ntt.util.readkey3(hdr, 'esoid'))
if _type not in objectlist:
objectlist[_type] = {}
if _grism not in objectlist[_type]:
objectlist[_type][_grism] = {}
if _OBID not in objectlist[_type][_grism]:
objectlist[_type][_grism][_OBID] = []
objectlist[_type][_grism][_OBID].append(img)
if 'stdp' not in objectlist:
print '### warning: not photometric standard'
else:
print '### photometric standard in the list of object'
if 'sun' not in objectlist:
print '### warning: not telluric G standard (sun type)'
else:
print '### telluric G standard (sun type) in the list of object'
if 'vega' not in objectlist:
print '### warning: not telluric A standard (vega type)'
else:
print '### telluric A standard (vega type) in the list of object'
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.specred(_doprint=0)
iraf.immatch(_doprint=0)
iraf.imutil(_doprint=0)
toforget = ['specred.apall', 'specred.transform']
for t in toforget:
iraf.unlearn(t)
iraf.specred.apall.readnoi = _rdnoise
iraf.specred.apall.gain = _gain
iraf.specred.dispaxi = 2
for _type in objectlist:
for setup in objectlist[_type]:
for _ID in objectlist[_type][setup]:
listmerge = objectlist[_type][setup][_ID]
listmerge = ntt.sortbyJD(listmerge)
_object = ntt.util.readkey3(
ntt.util.readhdr(listmerge[0]), 'object')
if string.count(_object, '/') or string.count(_object, '.') or string.count(_object, ' '):
nameobj = string.split(_object, '/')[0]
nameobj = string.split(nameobj, ' ')[0]
nameobj = string.split(nameobj, '.')[0]
else:
nameobj = _object
_date = ntt.util.readkey3(
ntt.util.readhdr(listmerge[0]), 'date-night')
outputimage = nameobj + '_' + _date + \
'_' + setup + '_merge_' + str(MJDtoday)
outputimage = ntt.util.name_duplicate(
listmerge[0], outputimage, '')
print '### setup= ', setup, ' name field= ', nameobj, ' merge image= ', outputimage, '\n'
#################
# added to avoid crashing with a single frame
# header will not be updated with all info
#################
if len(listmerge)==1:
ntt.util.delete(outputimage)
iraf.imutil.imcopy(listmerge[0], output=outputimage, verbose='no')
answ= 'n'
else:
if os.path.isfile(outputimage) and _interactive:
answ = raw_input(
'combine frame of dithered spectra already created. Do you want to make it again [[y]/n] ? ')
if not answ:
answ = 'y'
else:
answ = 'y'
#################
if answ in ['Yes', 'y', 'Y', 'yes']:
if _interactive:
automaticmerge = raw_input(
'\n### Do you want to try to find the dither bethween frames automatically [[y]/n]')
if not automaticmerge:
automaticmerge = 'yes'
elif automaticmerge.lower() in ['y', 'yes']:
automaticmerge = 'yes'
else:
automaticmerge = 'no'
else:
automaticmerge = 'yes'
if automaticmerge == 'yes':
offset = 0
offsetvec = []
_center0 = ntt.sofispec1Ddef.findaperture(
listmerge[0], False)
_offset0 = ntt.util.readkey3(
ntt.util.readhdr(listmerge[0]), 'xcum')
print '\n### Try to merge spectra considering their offset along x axes .......'
f = open('_offset', 'w')
for img in listmerge:
_center = ntt.sofispec1Ddef.findaperture(
img, False)
_center2 = (
float(_center) + (float(_offset0) - float(_center0))) * (-1)
_offset = (-1) * \
ntt.util.readkey3(
ntt.util.readhdr(img), 'xcum')
if abs(_center2 - _offset) >= 20:
automaticmerge = 'no'
break
else:
offset3 = _center2
offsetvec.append(offset3)
line = str(offset3) + ' 0\n'
f.write(line)
f.close()
if automaticmerge == 'yes':
print '### automatic merge .......... done'
else:
print '\n### warning: try identification of spectra position in interactive way '
offset = 0
offsetvec = []
_z1, _z2, goon = ntt.util.display_image(
listmerge[0], 1, '', '', False)
print '\n### find aperture on first frame and use it as reference position of ' \
'the spectra (mark with ' + '"' + 'm' + '"' + ')'
_center0 = ntt.sofispec1Ddef.findaperture(
listmerge[0], True)
_offset0 = ntt.util.readkey3(
ntt.util.readhdr(listmerge[0]), 'xcum')
print '\n### find the aperture on all the spectra frames (mark with ' + '"' + 'm' + '"' + ')'
f = open('_offset', 'w')
for img in listmerge:
print '\n### ', img
_z1, _z2, goon = ntt.util.display_image(
img, 1, '', '', False)
_center = ntt.sofispec1Ddef.findaperture(img, True)
_center2 = (
float(_center) + (float(_offset0) - float(_center0))) * (-1)
_offset = (-1) * \
ntt.util.readkey3(
ntt.util.readhdr(img), 'xcum')
print '\n### position from dither header: ' + str(_offset)
print '### position identified interactively: ' + str(_center2)
offset3 = raw_input(
'\n### which is the right position [' + str(_center2) + '] ?')
if not offset3:
offset3 = _center2
offsetvec.append(offset3)
line = str(offset3) + ' 0\n'
f.write(line)
f.close()
print offsetvec
start = int(max(offsetvec) - min(offsetvec))
print start
f = open('_goodlist', 'w')
print listmerge
for img in listmerge:
f.write(img + '\n')
f.close()
ntt.util.delete(outputimage)
ntt.util.delete('_output.fits')
yy1 = pyfits.open(listmerge[0])[0].data[:, 10]
iraf.immatch.imcombine('@_goodlist', '_output', combine='sum', reject='none', offset='_offset',
masktyp='', rdnoise=_rdnoise, gain=_gain, zero='mode', Stdout=1)
_head = pyfits.open('_output.fits')[0].header
if _head['NAXIS1'] < 1024:
stop = str(_head['NAXIS1'])
else:
stop = '1024'
iraf.imutil.imcopy(
'_output[' + str(start) + ':'+stop+',*]', output=outputimage, verbose='no')
print outputimage
print len(listmerge)
hdr1 = ntt.util.readhdr(outputimage)
ntt.util.updateheader(outputimage, 0,
{'SINGLEXP': [False, 'TRUE if resulting from single exposure'],
'M_EPOCH': [False, 'TRUE if resulting from multiple epochs'],
'EXPTIME': [ntt.util.readkey3(hdr1, 'EXPTIME') * len(listmerge),
'Total integration time per pixel (s)'],
'TEXPTIME': [float(ntt.util.readkey3(hdr1, 'TEXPTIME')) * len(listmerge),
'Total integration time of all exposures (s)'],
'APERTURE': [2.778e-4 * float(re.sub('long_slit_', '',
ntt.util.readkey3(hdr1, 'slit'))),
'[deg] Aperture diameter'],
'NOFFSETS': [2, 'Number of offset positions'],
'NUSTEP': [0, 'Number of microstep positions'],
'NJITTER': [int(ntt.util.readkey3(hdr1, 'NCOMBINE') / 2),
'Number of jitter positions']})
hdr = ntt.util.readhdr(outputimage)
matching = [s for s in hdr.keys() if "IMCMB" in s]
for imcmb in matching:
aaa = iraf.hedit(outputimage, imcmb, delete='yes', update='yes',
verify='no', Stdout=1)
if 'SKYSUB' in hdr.keys():
aaa = iraf.hedit(outputimage, 'SKYSUB', delete='yes', update='yes',
verify='no', Stdout=1)
mjdend = []
mjdstart = []
num = 0
for img in listmerge:
num = num + 1
hdrm = ntt.util.readhdr(img)
ntt.util.updateheader(outputimage, 0,
{'PROV' + str(num):
[ntt.util.readkey3(
hdrm, 'ARCFILE'), 'Originating file'],
'TRACE' + str(num): [img, 'Originating file']})
mjdend.append(ntt.util.readkey3(hdrm, 'MJD-END'))
mjdstart.append(ntt.util.readkey3(hdrm, 'MJD-OBS'))
_dateobs = ntt.util.readkey3(ntt.util.readhdr(
listmerge[np.argmin(mjdstart)]), 'DATE-OBS')
_telapse = (max(mjdend) - min(mjdstart)) * \
60. * 60 * 24. # *86400
_tmid = (max(mjdend) + min(mjdstart)) / 2
_title = str(_tmid)[0:9] + ' ' + str(ntt.util.readkey3(hdr, 'object')) + ' ' + str(
ntt.util.readkey3(hdr, 'grism')) + ' ' + \
str(ntt.util.readkey3(hdr, 'filter')) + \
' ' + str(ntt.util.readkey3(hdr, 'slit'))
ntt.util.updateheader(outputimage, 0,
{'MJD-OBS': [min(mjdstart), 'MJD start'],
'MJD-END': [max(mjdend), 'MJD end'],
'TELAPSE': [_telapse, 'Total elapsed time [days]'],
'TMID': [_tmid, '[d] MJD mid exposure'],
'TITLE': [_title, 'Dataset title'],
'DATE-OBS': [_dateobs, 'Date of observation']})
# missing: merge airmass
else:
print '\n### skip making again combined spectrum'
objectlist[_type][setup][_ID] = [outputimage]
print '\n### setup= ', setup, ' name field= ', nameobj, ' merge image= ', outputimage, '\n'
if outputimage not in outputfile:
outputfile.append(outputimage)
ntt.util.updateheader(outputimage, 0, {'FILETYPE': [
42116, 'combine 2D spectra frame']})
if _verbose:
if 'obj' in objectlist:
print objectlist['obj']
if 'stdp' in objectlist:
print objectlist['stdp']
if 'sun' in objectlist:
print objectlist['sun']
if 'vega' in objectlist:
print objectlist['vega']
if 'obj' not in objectlist.keys():
sys.exit('\n### error: no objects in the list')
sens = {}
print '\n############################################\n### extract the spectra '
# print objectlist
for setup in objectlist['obj']:
reduced = []
for _ID in objectlist['obj'][setup]:
for img in objectlist['obj'][setup][_ID]:
hdr = ntt.util.readhdr(img)
print '\n### next object\n ', img, ntt.util.readkey3(hdr, 'object')
_grism = ntt.util.readkey3(hdr, 'grism')
_exptimeimg = ntt.util.readkey3(hdr, 'exptime')
_JDimg = ntt.util.readkey3(hdr, 'JD')
imgex = ntt.util.extractspectrum(img, dv, _ext_trace, _dispersionline, _interactive, 'obj',
automaticex=_automaticex)
if imgex not in outputfile:
outputfile.append(imgex)
ntt.util.updateheader(imgex, 0, {'FILETYPE': [42107, 'extracted 1D wave calib'],
'PRODCATG': ['SCIENCE.' + ntt.util.readkey3(hdr, 'tech').upper(),
'Data product category']})
hdr = ntt.util.readhdr(imgex)
matching = [s for s in hdr.keys() if "TRACE" in s]
for imcmb in matching:
aaa = iraf.hedit(imgex, imcmb, delete='yes',
update='yes', verify='no', Stdout=1)
ntt.util.updateheader(
imgex, 0, {'TRACE1': [img, 'Originating file']})
if os.path.isfile('database/ap' + re.sub('_ex.fits', '', imgex)):
if 'database/ap' + re.sub('_ex.fits', '', imgex) not in outputfile:
outputfile.append(
'database/ap' + re.sub('_ex.fits', '', imgex))
########################### telluric standard #############
if 'sun' in objectlist and setup in objectlist['sun']:
_type = 'sun'
elif 'vega' in objectlist and setup in objectlist['vega']:
_type = 'vega'
else:
_type = 'none'
if _type in ['sun', 'vega']:
stdref = ntt.__path__[
0] + '/standard/fits/' + str(_type) + '.fits'
stdvec, airmassvec, JDvec = [], [], []
for _ID in objectlist[_type][setup]:
for std in objectlist[_type][setup][_ID]:
_airmassstd = ntt.util.readkey3(
ntt.util.readhdr(std), 'airmass')
_JDstd = ntt.util.readkey3(
ntt.util.readhdr(std), 'JD')
JDvec.append(abs(_JDstd - _JDimg))
stdvec.append(std)
airmassvec.append(_airmassstd)
stdtelluric = stdvec[np.argmin(JDvec)]
_exptimestd = ntt.util.readkey3(
ntt.util.readhdr(stdtelluric), 'exptime')
_magstd = ntt.util.readkey3(
ntt.util.readhdr(stdtelluric), 'magstd')
print '\n\n ##### closer standard for telluric corrections #### \n\n'
print stdtelluric, airmassvec[np.argmin(JDvec)]
stdtelluric_ex = ntt.util.extractspectrum(stdtelluric, dv, False, False, _interactive, 'std',
automaticex=_automaticex)
if stdtelluric_ex not in outputfile:
outputfile.append(stdtelluric_ex)
ntt.util.updateheader(stdtelluric_ex, 0, {'FILETYPE': [
42107, 'extracted 1D wave calib ']})
ntt.util.updateheader(stdtelluric_ex, 0, {'PRODCATG': [
'SCIENCE.' + ntt.util.readkey3(
ntt.util.readhdr(stdtelluric_ex), 'tech').upper(), 'Data product category']})
hdr = ntt.util.readhdr(stdtelluric_ex)
matching = [s for s in hdr.keys() if "TRACE" in s]
for imcmb in matching:
aaa = iraf.hedit(
stdtelluric_ex, imcmb, delete='yes', update='yes', verify='no', Stdout=1)
ntt.util.updateheader(stdtelluric_ex, 0, {'TRACE1': [
stdtelluric, 'Originating file']})
###########################################################
# SN tellurich calibration
imgf = re.sub('_ex.fits', '_f.fits', imgex)
imgf, senstelluric = ntt.sofispec1Ddef.calibrationsofi(imgex, stdtelluric_ex, stdref, imgf,
_interactive)
if imgf not in outputfile:
outputfile.append(imgf)
if senstelluric not in outputfile:
outputfile.append(senstelluric)
ntt.util.updateheader(imgf, 0, {'FILETYPE': [42208, '1D wave calib, tell cor.'],
# 'SNR': [ntt.util.StoN(imgf, 50),
'SNR': [ntt.util.StoN2(imgf, False),
'Average signal to noise ratio per pixel'],
'TRACE1': [imgex, 'Originating file'],
'ASSON1': [re.sub('_f.fits', '_2df.fits', imgf),
'Name of associated file'],
'ASSOC1': ['ANCILLARY.2DSPECTRUM', 'Category of associated file']})
###########################################################
imgd = ntt.efoscspec1Ddef.fluxcalib2d(
img, senstelluric) # flux calibration 2d images
ntt.util.updateheader(
imgd, 0, {'FILETYPE': [42209, '2D wavelength and flux calibrated spectrum']})
iraf.hedit(imgd, 'PRODCATG', delete='yes',
update='yes', verify='no')
hdrd = ntt.util.readhdr(imgd)
matching = [s for s in hdrd.keys() if "TRACE" in s]
for imcmb in matching:
aaa = iraf.hedit(
imgd, imcmb, delete='yes', update='yes', verify='no', Stdout=1)
ntt.util.updateheader(
imgd, 0, {'TRACE1': [img, 'Originating file']})
if imgd not in outputfile:
outputfile.append(imgd)
###############################################################
if 'stdp' in objectlist and setup in objectlist['stdp']:
print '\n ##### photometric calibration ######\n '
standardfile = []
for _ID in objectlist['stdp'][setup]:
for stdp in objectlist['stdp'][setup][_ID]:
stdp_ex = ntt.util.extractspectrum(stdp, dv, False, _dispersionline, _interactive, 'std',
automaticex=_automaticex)
standardfile.append(stdp_ex)
if stdp_ex not in outputfile:
outputfile.append(stdp_ex)
ntt.util.updateheader(stdp_ex, 0, {
'FILETYPE': [42107, 'extracted 1D wave calib'],
'TRACE1': [stdp_ex, 'Originating file'],
'PRODCATG': ['SCIENCE.' + ntt.util.readkey3(ntt.util.readhdr(stdp_ex), 'tech').upper(),
'Data product category']})
print '\n### ', standardfile, ' \n'
if len(standardfile) >= 2:
standardfile0 = raw_input(
'which one do you want to use [' + str(standardfile[0]) + '] ? ')
if not standardfile0:
standardfile0 = standardfile[0]
else:
standardfile0 = standardfile[0]
print standardfile0
stdpf = re.sub('_ex.fits', '_f.fits', standardfile0)
stdpf, senstelluric2 = ntt.sofispec1Ddef.calibrationsofi(standardfile0, stdtelluric_ex, stdref,
stdpf, _interactive)
if stdpf not in outputfile:
outputfile.append(stdpf)
ntt.util.updateheader(stdpf, 0, {'FILETYPE': [42208, '1D wave calib, tell cor'],
'TRACE1': [stdp, 'Originating file']})
stdname = ntt.util.readkey3(
ntt.util.readhdr(standardfile0), 'stdname')
standardfile = ntt.__path__[
0] + '/standard/flux/' + stdname
xx, yy = ntt.util.ReadAscii2(standardfile)
crval1 = pyfits.open(stdpf)[0].header.get('CRVAL1')
cd1 = pyfits.open(stdpf)[0].header.get('CD1_1')
datastdpf, hdrstdpf = pyfits.getdata(stdpf, 0, header=True)
xx1 = np.arange(len(datastdpf[0][0]))
aa1 = crval1 + (xx1) * cd1
yystd = np.interp(aa1, xx, yy)
rcut = np.compress(
((aa1 < 13000) | (aa1 > 15150)) & ((11700 < aa1) | (aa1 < 11000)) & (aa1 > 10000) &
((aa1 < 17800) | (aa1 > 19600)) & (aa1 < 24000), datastdpf[0][0] / yystd)
aa11 = np.compress(
((aa1 < 13000) | (aa1 > 15150)) & ((11700 < aa1) | (aa1 < 11000)) & (aa1 > 10000) &
((aa1 < 17800) | (aa1 > 19600)) & (aa1 < 24000), aa1)
yy1clean = np.interp(aa1, aa11, rcut)
aa1 = np.array(aa1)
yy1clean = np.array(yy1clean)
A = np.ones((len(rcut), 2), dtype=float)
A[:, 0] = aa11
result = np.linalg.lstsq(A, rcut) # result=[zero,slope]
p = [result[0][1], result[0][0]]
yfit = ntt.util.pval(aa1, p)
pl.clf()
pl.ion()
pl.plot(aa1, datastdpf[0][0] / yystd,
color='red', label='std')
pl.plot(aa1, yfit, color='blue', label='fit')
pl.legend(numpoints=1, markerscale=1.5)
# sens function sofi spectra
outputsens = 'sens_' + stdpf
ntt.util.delete(outputsens)
datastdpf[0][0] = yfit
pyfits.writeto(outputsens, np.float32(datastdpf), hdrstdpf)
#################
imgsc = re.sub('_ex.fits', '_sc.fits', imgex)
ntt.util.delete(imgsc)
crval2 = pyfits.open(imgf)[0].header.get('CRVAL1')
cd2 = pyfits.open(imgf)[0].header.get('CD1_1')
dataf, hdrf = pyfits.getdata(imgf, 0, header=True)
xx2 = np.arange(len(dataf[0][0]))
aa2 = crval2 + (xx2) * cd2
yyscale = np.interp(aa2, aa1, yfit)
dataf[0][0] = dataf[0][0] / yyscale
dataf[1][0] = dataf[1][0] / yyscale
dataf[2][0] = dataf[2][0] / yyscale
dataf[3][0] = dataf[3][0] / yyscale
pyfits.writeto(imgsc, np.float32(dataf), hdrf)
ntt.util.updateheader(imgsc, 0, {'SENSPHOT': [outputsens, 'sens used to flux cal'],
'FILETYPE': [42208, '1D wave,flux calib, tell cor'],
'TRACE1': [imgf, 'Originating file']})
# ntt.util.updateheader(imgsc,0,{'FILETYPE':[42208,'1D wave,flux calib, tell cor']})
# ntt.util.updateheader(imgsc,0,{'TRACE1':[imgf,'']})
print '\n### flux calibrated spectrum= ', imgf, ' with the standard= ', stdpf
if imgsc not in outputfile:
outputfile.append(imgsc)
else:
print '\n### photometric calibrated not performed \n'
print '\n### adding keywords for phase 3 ....... '
reduceddata = ntt.util.rangedata(outputfile)
f = open('logfile_spec1d_' + str(reduceddata) +
'_' + str(datenow) + '.raw.list', 'w')
for img in outputfile:
if str(img)[-5:] == '.fits':
hdr = ntt.util.readhdr(img)
# added for DR2
if 'NCOMBINE' in hdr:
_ncomb = ntt.util.readkey3(hdr, 'NCOMBINE')
else:
_ncomb = 1.0
_effron = 12. * \
(1 / np.sqrt(ntt.util.readkey3(hdr, 'ndit') * _ncomb)) * \
np.sqrt(np.pi / 2)
try:
ntt.util.phase3header(img) # phase 3 definitions
ntt.util.updateheader(img, 0, {'quality': ['Final', ''],
'EFFRON': [_effron, 'Effective readout noise per output (e-)']})
f.write(ntt.util.readkey3(
ntt.util.readhdr(img), 'arcfile') + '\n')
except:
print 'Warning: ' + img + ' is not a fits file'
f.close()
return outputfile, 'logfile_spec1d_' + str(reduceddata) + '_' + str(datenow) + '.raw.list'
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
# Global python packages
import pyraf
from pyraf import iraf
import os, shutil, math, subprocess
import pyfits
# Local python packages
from iqutils import *
# IRAF modules
iraf.images()
iraf.noao()
iraf.imred()
iraf.ccdred()
iraf.specred()
iraf.rvsao()
# IRAF variables
yes = iraf.yes
no = iraf.no
INDEF = iraf.INDEF
hedit = iraf.hedit
imgets = iraf.imgets
imcombine = iraf.imcombine
# PyRAF setup
pyrafdir = "python/pyraf/"
pyrafdir_key = 'PYRAFPARS'
if os.environ.has_key(pyrafdir_key):
pardir = os.environ[pyrafdir_key]
def efoscfastredu(imglist, _listsens, _listarc, _ext_trace, _dispersionline,
_cosmic, _interactive):
# print "LOGX:: Entering `efoscfastredu` method/function in %(__file__)s"
# % globals()
import string
import os
import re
import sys
os.environ["PYRAF_BETA_STATUS"] = "1"
try:
from astropy.io import fits as pyfits
except:
import pyfits
from ntt.util import readhdr, readkey3
import ntt
import numpy as np
dv = ntt.dvex()
scal = np.pi / 180.
if not _interactive:
_interactive = False
_inter = 'NO'
else:
_inter = 'YES'
from pyraf import iraf
iraf.noao(_doprint=0, Stdout=0)
iraf.imred(_doprint=0, Stdout=0)
iraf.ccdred(_doprint=0, Stdout=0)
iraf.twodspec(_doprint=0, Stdout=0)
iraf.longslit(_doprint=0, Stdout=0)
iraf.onedspec(_doprint=0, Stdout=0)
iraf.specred(_doprint=0, Stdout=0)
toforget = [
'ccdproc', 'imcopy', 'specred.apall', 'longslit.identify',
'longslit.reidentify', 'specred.standard', 'longslit.fitcoords',
'onedspec.wspectext'
]
for t in toforget:
iraf.unlearn(t)
iraf.ccdred.verbose = 'no' # not print steps
iraf.specred.verbose = 'no' # not print steps
iraf.ccdproc.darkcor = 'no'
iraf.ccdproc.fixpix = 'no'
iraf.ccdproc.flatcor = 'no'
iraf.ccdproc.zerocor = 'no'
iraf.ccdproc.ccdtype = ''
_gain = ntt.util.readkey3(ntt.util.readhdr(imglist[0]), 'gain')
_ron = ntt.util.readkey3(ntt.util.readhdr(imglist[0]), 'ron')
iraf.specred.apall.readnoi = _ron
iraf.specred.apall.gain = _gain
iraf.specred.dispaxi = 2
iraf.longslit.dispaxi = 2
iraf.longslit.mode = 'h'
iraf.specred.mode = 'h'
iraf.noao.mode = 'h'
iraf.ccdred.instrument = "ccddb$kpno/camera.dat"
iraf.set(direc=ntt.__path__[0] + '/')
for img in imglist:
hdr = ntt.util.readhdr(img)
_tech = ntt.util.readkey3(hdr, 'tech')
if _tech != 'SPECTRUM':
sys.exit('error: ' + str(img) + ' is not a spectrum ')
print '\n#### image name = ' + img + '\n'
_grism0 = readkey3(hdr, 'grism')
_filter0 = readkey3(hdr, 'filter')
_slit0 = readkey3(hdr, 'slit')
_object0 = readkey3(hdr, 'object')
_date0 = readkey3(hdr, 'date-night')
setup = (_grism0, _filter0, _slit0)
_biassec0 = '[3:1010,1026:1029]'
if _grism0 == 'Gr16':
_trimsec0 = '[100:950,1:950]'
elif _grism0 == 'Gr13':
if _filter0 == 'Free':
_trimsec0 = '[100:950,1:1015]'
elif _filter0 == 'GG495':
_trimsec0 = '[100:950,208:1015]'
elif _filter0 == 'OG530':
_trimsec0 = '[100:950,300:1015]'
elif _grism0 == 'Gr11':
_trimsec0 = '[100:950,5:1015]'
else:
_trimsec0 = '[100:950,5:1015]'
_object0 = re.sub(' ', '', _object0)
_object0 = re.sub('/', '_', _object0)
nameout0 = 't' + str(_object0) + '_' + str(_date0)
for _set in setup:
nameout0 = nameout0 + '_' + _set
nameout0 = ntt.util.name_duplicate(img, nameout0, '')
timg = nameout0
if os.path.isfile(timg):
os.system('rm -rf ' + timg)
iraf.imcopy(img, output=timg)
iraf.ccdproc(timg,
output='',
overscan='no',
trim='yes',
zerocor="no",
flatcor="no",
readaxi='column',
trimsec=str(_trimsec0),
biassec=_biassec0,
Stdout=1)
img = timg
if _listarc:
arcfile = ntt.util.searcharc(img, _listarc)[0]
else:
arcfile = ''
if not arcfile:
arcfile = ntt.util.searcharc(img, '')[0]
else:
iraf.ccdproc(arcfile,
output='t' + arcfile,
overscan='no',
trim='yes',
zerocor="no",
flatcor="no",
readaxi='column',
trimsec=str(_trimsec0),
biassec=str(_biassec0),
Stdout=1)
arcfile = 't' + arcfile
if _cosmic:
# print cosmic rays rejection
ntt.cosmics.lacos(img,
output='',
gain=_gain,
readn=_ron,
xorder=9,
yorder=9,
sigclip=4.5,
sigfrac=0.5,
objlim=1,
verbose=True,
interactive=False)
print '\n### cosmic rays rejections ........ done '
if not arcfile:
print '\n### warning no arcfile \n exit '
else:
arcref = ntt.util.searcharc(img, '')[0]
if arcfile[0] == '/':
os.system('cp ' + arcfile + ' ' +
string.split(arcfile, '/')[-1])
arcfile = string.split(arcfile, '/')[-1]
arcref = string.split(arcref, '/')[-1]
if arcref:
os.system('cp ' + arcref + ' .')
arcref = string.split(arcref, '/')[-1]
if not os.path.isdir('database/'):
os.mkdir('database/')
if os.path.isfile(
ntt.util.searcharc(img, '')[1] + '/database/id' +
re.sub('.fits', '', arcref)):
os.system('cp ' + ntt.util.searcharc(img, '')[1] +
'/database/id' + re.sub('.fits', '', arcref) +
' database/')
iraf.longslit.reidentify(
referenc=arcref,
images=arcfile,
interac=_inter,
section='column 10',
coordli='direc$standard/ident/Lines_HgCdHeNeAr600.dat',
overrid='yes',
step=0,
newaps='no',
nsum=5,
nlost=2,
mode='h',
verbose='no')
else:
iraf.longslit.identify(
images=arcfile,
section='column 10',
coordli='direc$standard/ident/Lines_HgCdHeNeAr600.dat',
nsum=10,
fwidth=7,
order=3,
mode='h')
iraf.longslit.reident(
referenc=arcfile,
images=arcfile,
interac='NO',
section='column 10',
coordli='direc$standard/ident/Lines_HgCdHeNeAr600.dat',
overrid='yes',
step=10,
newaps='yes',
nsum=5,
nlost=2,
mode='h',
verbose='no')
qqq = iraf.longslit.fitcoords(images=re.sub('.fits', '', arcfile),
fitname=re.sub('.fits', '', arcfile),
interac='no',
combine='yes',
databas='database',
function='legendre',
yorder=4,
logfile='logfile',
plotfil='',
mode='h')
iraf.specred.transform(input=img,
output=img,
minput='',
fitnames=re.sub('.fits', '', arcfile),
databas='database',
x1='INDEF',
x2='INDEF',
y1='INDEF',
y2='INDEF',
flux='yes',
mode='h',
logfile='logfile')
# ###################### check wavelength calibration ############
_skyfile = ntt.__path__[0] + '/standard/ident/sky_' + setup[
0] + '_' + setup[1] + '.fits'
shift = ntt.efoscspec2Ddef.skyfrom2d(img, _skyfile)
print '\n### check in wavelengh performed ...... spectrum shifted of ' + str(
shift) + ' Angstrom \n'
zro = pyfits.open(img)[0].header.get('CRVAL2')
ntt.util.updateheader(img, 0, {'CRVAL2': [zro + int(shift), '']})
std, rastd, decstd, magstd = ntt.util.readstandard(
'standard_efosc_mab.txt')
hdrt = readhdr(img)
_ra = readkey3(hdrt, 'RA')
_dec = readkey3(hdrt, 'DEC')
_object = readkey3(hdrt, 'object')
dd = np.arccos(
np.sin(_dec * scal) * np.sin(decstd * scal) +
np.cos(_dec * scal) * np.cos(decstd * scal) * np.cos(
(_ra - rastd) * scal)) * ((180 / np.pi) * 3600)
if min(dd) < 100:
_type = 'stdsens'
ntt.util.updateheader(img, 0,
{'stdname': [std[np.argmin(dd)], '']})
ntt.util.updateheader(
img, 0, {'magstd': [float(magstd[np.argmin(dd)]), '']})
else:
_type = 'obj'
print '\n### EXTRACTION USING IRAF TASK APALL \n'
result = []
if _type == 'obj':
imgex = ntt.util.extractspectrum(img, dv, _ext_trace,
_dispersionline, _interactive,
_type)
ntt.util.updateheader(
imgex, 0, {'FILETYPE': [22107, 'extracted 1D spectrum ']})
ntt.util.updateheader(
imgex, 0, {
'PRODCATG': [
'SCIENCE.' +
readkey3(readhdr(imgex), 'tech').upper(),
'Data product category'
]
})
ntt.util.updateheader(imgex, 0, {'TRACE1': [img, '']})
result.append(imgex)
if _listsens:
sensfile = ntt.util.searchsens(img, _listsens)[0]
else:
sensfile = ''
if not sensfile:
sensfile = ntt.util.searchsens(img, '')[0]
if sensfile:
imgf = re.sub('.fits', '_f.fits', img)
_extinctdir = 'direc$standard/extinction/'
_extinction = 'extinction_lasilla.dat'
_observatory = 'lasilla'
_exptime = readkey3(hdrt, 'exptime')
_airmass = readkey3(hdrt, 'airmass')
ntt.util.delete(imgf)
iraf.specred.calibrate(input=imgex,
output=imgf,
sensiti=sensfile,
extinct='yes',
flux='yes',
ignorea='yes',
extinction=_extinctdir +
_extinction,
observatory=_observatory,
airmass=_airmass,
exptime=_exptime,
fnu='no')
hedvec = {
'SENSFUN': [
string.split(sensfile, '/')[-1],
'sensitivity function'
],
'FILETYPE':
[22208, '1D wavelength and flux calibrated spectrum '],
'SNR':
[ntt.util.StoN2(imgf, False), 'Average S/N ratio'],
'BUNIT':
['erg/cm2/s/Angstrom', 'Flux Calibration Units'],
'TRACE1': [imgex, '']
}
ntt.util.updateheader(imgf, 0, hedvec)
imgout = imgf
imgd = ntt.efoscspec1Ddef.fluxcalib2d(img, sensfile)
ntt.util.updateheader(
imgd, 0, {
'FILETYPE': [
22209,
'2D wavelength and flux calibrated spectrum '
]
})
ntt.util.updateheader(imgd, 0, {'TRACE1': [img, '']})
imgasci = re.sub('.fits', '.asci', imgout)
ntt.util.delete(imgasci)
iraf.onedspec.wspectext(imgout + '[*,1,1]',
imgasci,
header='no')
result = result + [imgout, imgd, imgasci]
else:
imgex = ntt.util.extractspectrum(img, dv, _ext_trace,
_dispersionline, _interactive,
'std')
imgout = ntt.efoscspec1Ddef.sensfunction(
imgex, 'spline3', 6, _inter)
result = result + [imgout]
for img in result:
if img[-5:] == '.fits':
ntt.util.phase3header(img) # phase 3 definitions
ntt.util.airmass(img) # phase 3 definitions
ntt.util.updateheader(
img, 0, {'quality': ['Rapid', 'Final or Rapid reduction']})
return result
def pre_reduction_dev(*args,**kwargs):
# parse kwargs
VERBOSE = kwargs.get('VERBOSE')
CLOBBER = kwargs.get('CLOBBER')
FAKE_BASIC_2D = kwargs.get('FAKE_BASIC_2D')
FULL_CLEAN = kwargs.get('FULL_CLEAN')
FAST = kwargs.get('FAST')
CONFIG_FILE = kwargs.get('CONFIG_FILE')
MAKE_ARCS = kwargs.get('MAKE_ARCS')
MAKE_FLATS = kwargs.get('MAKE_FLATS')
QUICK = kwargs.get('QUICK')
RED_AMP_BAD = kwargs.get('RED_AMP_BAD')
BLUE_AMP_BAD = kwargs.get('BLUE_AMP_BAD')
HOST = kwargs.get('HOST')
TRIM = kwargs.get('TRIM')
FIX_AMP_OFFSET = kwargs.get('FIX_AMP_OFFSET')
RED_DIR = kwargs.get('RED_DIR')
# init iraf stuff
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.longslit(_doprint=0)
iraf.onedspec(_doprint=0)
iraf.specred(_doprint=0)
iraf.ccdred.verbose = 'no'
iraf.specred.verbose = 'no'
iraf.ccdproc.darkcor = 'no'
iraf.ccdproc.fixpix = 'no'
iraf.ccdproc.flatcor = 'no'
iraf.ccdproc.zerocor = 'no'
iraf.ccdproc.ccdtype = ''
iraf.longslit.mode = 'h'
iraf.specred.mode = 'h'
iraf.noao.mode = 'h'
iraf.ccdred.instrument = "ccddb$kpno/camera.dat"
prereddir= os.path.join(RED_DIR, 'pre_reduced/')
# set up config
if CONFIG_FILE:
with open(CONFIG_FILE,'r') as fin:
configDict = json.load(fin)
else:
STANDARD_STAR_LIBRARY = mu.construct_standard_star_library()
observations = sorted(glob.glob('*.fits'))
#TODO: Better first pass at config file, std have exptime < 250s(?)
# CDK - updated this and placed it after arm/inst_name definition to
# better generalize configDict
configDict = {}
for obsfile in observations:
arm, inst_dict = instruments.blue_or_red(obsfile)
inst_name = inst_dict.get('instrument').upper()
for key in ['SCI','STD','CAL_ARC','CAL_FLAT','CAL']:
if key not in configDict.keys():
configDict[key]={}
if arm.upper() not in configDict[key].keys():
configDict[key][arm.upper()]={}
if 'CAL_' in key:
full_key = key.replace('CAL_','CALIBRATION_')
configDict[key][arm.upper()]={full_key: []}
# CDK - added this to explicitly skip files that dont need to be
# reduced.
if not mu.needs_to_be_reduced(obsfile): continue
hdu = fits.open(obsfile)
if 'lris' in inst_dict.get('name'):
hdu[0].header['OBJECT']=hdu[0].header['TARGNAME']
hdu.writeto(obsfile, overwrite=True)
use_ext = inst_dict['use_ext']
header = hdu[use_ext].header
imageType = mu.determine_image_type(header, inst_name, STANDARD_STAR_LIBRARY)
channel, inst_dict = instruments.blue_or_red(obsfile)
obj = header.get('OBJECT').strip()
if imageType == 'SCI' or imageType == 'STD':
if obj in configDict[imageType][channel.upper()].keys():
configDict[imageType][channel.upper()][obj].append(obsfile)
else:
configDict[imageType][channel.upper()][obj] = [obsfile]
if imageType == 'CAL_ARC' and 'foc' not in obsfile:
configDict[imageType][channel.upper()]['CALIBRATION_ARC'].append(obsfile)
if imageType == 'CAL_FLAT':
configDict[imageType][channel.upper()]['CALIBRATION_FLAT'].append(obsfile)
with open('custom_config.json','w') as fout:
fout.write(json.dumps(configDict,indent=4))
outStr = '\n\nOk, not config supplied, so I wrote a first pass custom_config.json\n'
outStr += 'Use at your own risk! Manually edit if needed and run again with -c custom_config.json\n'
outStr += 'You can manually add files to the appropriate lists and rerun.\n'
outStr += 'WARNING: make sure you rename your config file, or it could get overwritten!\n\n'
print(outStr)
sys.exit(1)
if not FAST:
# do visual inspection of frames via ds9 windows
usrResp = ''
while usrResp != 'C':
promptStr = '\nYou\'ve opted to display images before kicking off the reduction.\n'
promptStr += 'At this point you may:\n'
promptStr += ' (D)isplay the current state of the reduction config\n'
promptStr += ' (C)ontinue with these files as is\n'
promptStr += ' (R)emove a file from the current config\n'
promptStr += ' (A)dd a file to the current config\n'
promptStr += ' (Q)uit the whole thing. \n'
promptStr += 'I recommend you (D)isplay and remove unwanted frames from your config file,\n'
promptStr += '(Q)uit, and then rerun with the updated config file.\nCommand: '
usrRespOrig = raw_input(promptStr)
try:
usrResp = usrRespOrig.strip().upper()
except Exception as e:
usrResp = 'nothing'
# (D)isplay all the images in the lists
if usrResp == 'D':
blueArcList = configDict['CAL_ARC']['BLUE']['CALIBRATION_ARC']
redArcList = configDict['CAL_ARC']['RED']['CALIBRATION_ARC']
blueFlatList = configDict['CAL_FLAT']['BLUE']['CALIBRATION_FLAT']
redFlatList = configDict['CAL_FLAT']['RED']['CALIBRATION_FLAT']
blueStdList = []
redStdList = []
blueSciList = []
redSciList = []
for targ,imgList in configDict['STD']['BLUE'].items():
for file in imgList:
blueStdList.append(file)
for targ,imgList in configDict['STD']['RED'].items():
for file in imgList:
redStdList.append(file)
for targ,imgList in configDict['SCI']['BLUE'].items():
for file in imgList:
blueSciList.append(file)
for targ,imgList in configDict['SCI']['RED'].items():
for file in imgList:
redSciList.append(file)
blueArcDS9 = show_ds9_list(blueArcList,instanceName='BlueArcs')
redArcDS9 = show_ds9_list(redArcList,instanceName='RedArcs')
blueFlatDS9 = show_ds9_list(blueFlatList,instanceName='BlueFlats')
redFlatDS9 = show_ds9_list(redFlatList,instanceName='RedFlats')
blueStdDS9 = show_ds9_list(blueStdList,instanceName='BlueStandards')
redStdDS9 = show_ds9_list(redStdList,instanceName='RedStandards')
blueSciDS9 = show_ds9_list(blueSciList,instanceName='BlueScience')
redSciDS9 = show_ds9_list(redSciList,instanceName='RedScience')
if usrResp == 'R':
configDict = user_adjust_config(configDict,operation='REMOVE')
if usrResp == 'A':
configDict = user_adjust_config(configDict,operation='ADD')
if usrResp == 'Q':
print('Okay, quitting pre_reduction...')
sys.exit(1)
# pre_reduced does not exist, needs to be made
if not os.path.isdir(prereddir):
os.mkdir(prereddir)
if QUICK:
file =glob.glob('*.fits')[0]
inst = instruments.blue_or_red(file)[1]
if 'kast' in inst['name']:
b_inst = instruments.kast_blue
r_inst = instruments.kast_red
if 'lris' in inst['name']:
b_inst = instruments.lris_blue
r_inst = instruments.lris_red
if 'goodman' in inst['name']:
b_inst = instruments.goodman_blue
r_inst = instruments.goodman_red
if not os.path.isdir(prereddir+'master_files/'):
os.mkdir(prereddir+'master_files/')
b_arcsol = b_inst.get('archive_arc_extracted_id')
b_resp = b_inst.get('archive_flat_file')
r_arcsol = r_inst.get('archive_arc_extracted_id')
r_resp = r_inst.get('archive_flat_file')
if os.path.isdir(prereddir+'master_files/'):
os.system('cp ' + b_arcsol + ' ' + 'pre_reduced/master_files/')
os.system('cp ' + b_resp + ' ' + 'pre_reduced/')
os.system('cp ' + r_arcsol + ' ' + 'pre_reduced/master_files/')
os.system('cp ' + r_resp + ' ' + 'pre_reduced/')
# pre_reduced exists, but we want to clobber/do a clean reduction
elif FULL_CLEAN:
promptStr = 'Do you really want to wipe pre_reduced? [y/n]: '
usrRespOrig = raw_input(promptStr)
if usrRespOrig and usrRespOrig[0].strip().upper() == 'Y':
# remove all pre_reduced files
shutil.rmtree(prereddir)
os.mkdir(prereddir)
# pre_reduced exists, need to document what is there
else:
# get existing pre_reduced files
preRedFiles = glob.glob(prereddir+'*.fits')
# loop over raw files in configDict, if the destination exists, do nothing
# # otherwise, do the bias/reorient/trim/output/etc
for imgType,typeDict in configDict.items():
for chan,objDict in typeDict.items():
for obj,fileList in objDict.items():
for rawFile in fileList:
# try:
# res = basic_2d_proc(rawFile,CLOBBER=CLOBBER)
# if res != 0:
# raise ValueError('Something bad happened in basic_2d_proc on {}'.format(rawFile))
# except (Exception,ValueError) as e:
# print('Exception (basic_2d): {}'.format(e))
if not FAKE_BASIC_2D:
inst = instruments.blue_or_red(rawFile)[1]
if inst['name'] == 'lris_blue' or inst['name'] == 'lris_red':
fix_lris_header(rawFile)
# res = keck_basic_2d.main([rawFile])
if imgType != 'CAL_FLAT':
print (imgType)
res = keck_basic_2d.main([rawFile], TRIM=TRIM, ISDFLAT=False, RED_AMP_BAD=RED_AMP_BAD, MASK_MIDDLE_RED=False, MASK_MIDDLE_BLUE=False, FIX_AMP_OFFSET=FIX_AMP_OFFSET, BLUE_AMP_BAD=BLUE_AMP_BAD)
else:
print (imgType)
res = keck_basic_2d.main([rawFile], TRIM=TRIM, ISDFLAT = True, RED_AMP_BAD=RED_AMP_BAD, MASK_MIDDLE_RED=False, MASK_MIDDLE_BLUE=False, FIX_AMP_OFFSET=FIX_AMP_OFFSET, BLUE_AMP_BAD=BLUE_AMP_BAD)
else:
res = basic_2d_proc(rawFile,imgType=imgType,CLOBBER=CLOBBER)
else:
# here we're faking the basic 2D reduction because we've done
# specialized 2D reduction (e.g., keck_basic_2d)
res = 0
if res != 0:
raise ValueError('Something bad happened in basic_2d_proc on {}'.format(rawFile))
# move the std and sci files into their appropriate directories
try:
res = reorg_files(configDict,CLOBBER=CLOBBER)
if res != 0:
raise ValueError('Something bad happened in reorg_files')
except (Exception,ValueError) as e:
print('Exception (reorg): {}'.format(e))
### some blocks of code from the original pre_reduction ###
# combine the arcs
if MAKE_ARCS:
# CDK - generalized arc creation method
for key in configDict['CAL_ARC'].keys():
list_arc = configDict['CAL_ARC'][key]['CALIBRATION_ARC']
if len(list_arc)>0:
first = prereddir+'to{}'.format(list_arc[0])
br, inst = instruments.blue_or_red(first)
destFile = prereddir+'ARC_{0}.fits'.format(key.lower())
util.make_arc(list_arc, destFile, inst, iraf)
# combine the flats
if MAKE_FLATS and 'lris' in inst['name']:
list_flat_b = configDict['CAL_FLAT']['BLUE']['CALIBRATION_FLAT']
list_flat_r = configDict['CAL_FLAT']['RED']['CALIBRATION_FLAT']
inter = 'yes'
b_amp1_list = []
b_amp2_list = []
r_amp1_list = []
r_amp2_list = []
predir = prereddir+'to'
for flat in list_flat_b:
suffix = '.'.join(flat.split('.')[1:])
b_amp1_file = flat.split('.')[0]+'_amp1.'+suffix
b_amp2_file = flat.split('.')[0]+'_amp2.'+suffix
if os.path.exists(predir+b_amp1_file): b_amp1_list.append(b_amp1_file)
if os.path.exists(predir+b_amp2_file): b_amp2_list.append(b_amp2_file)
for flat in list_flat_r:
suffix = '.'.join(flat.split('.')[1:])
r_amp1_file = flat.split('.')[0]+'_amp1.'+suffix
r_amp2_file = flat.split('.')[0]+'_amp2.'+suffix
print(r_amp1_file)
if os.path.exists(predir+r_amp1_file): r_amp1_list.append(r_amp1_file)
if os.path.exists(predir+r_amp2_file): r_amp2_list.append(r_amp2_file)
# blue flats
if len(list_flat_b) > 0:
# br, inst = instruments.blue_or_red(list_flat_b[0])
br, inst = instruments.blue_or_red(prereddir+'to{}'.format(b_amp1_list[0]))
dispaxis = inst.get('dispaxis')
iraf.specred.dispaxi = dispaxis
Flat_blue_amp1 = prereddir+'toFlat_blue_amp1.fits'
Flat_blue_amp2 = prereddir+'toFlat_blue_amp2.fits'
flat_list_amp1 = []
for flat in b_amp1_list:
flat_list_amp1.append(prereddir+'to'+ flat)
if os.path.isfile(Flat_blue_amp1):
os.remove(Flat_blue_amp1)
# first, combine all the flat files into a master flat
res = combine_flats(flat_list_amp1,OUTFILE=Flat_blue_amp1,MEDIAN_COMBINE=True)
# run iraf response
iraf.specred.response(Flat_blue_amp1,
normaliz=Flat_blue_amp1,
response=prereddir+'RESP_blue_amp1',
interac=inter, thresho='INDEF',
sample='*', naverage=2, function='legendre',
low_rej=5,high_rej=5, order=60, niterat=20,
grow=0, graphic='stdgraph')
# finally, inspect the flat and mask bad regions
res = inspect_flat([prereddir+'RESP_blue_amp1.fits'],
OUTFILE=prereddir+'RESP_blue_amp1.fits', DISPAXIS=dispaxis)
hdu_amp1 = fits.open(prereddir+'RESP_blue_amp1.fits')
amp1_flatten = np.asarray(hdu_amp1[0].data).flatten()
shape1=hdu_amp1[0].data.shape
dim1 = shape1[0]
dim2 = shape1[1]
concat_amps = amp1_flatten
if not BLUE_AMP_BAD:
flat_list_amp2 = []
for flat in b_amp2_list:
flat_list_amp2.append(prereddir+'to'+ flat)
if os.path.isfile(Flat_blue_amp2):
os.remove(Flat_blue_amp2)
res = combine_flats(flat_list_amp2,OUTFILE=Flat_blue_amp2,MEDIAN_COMBINE=True)
iraf.specred.response(Flat_blue_amp2,
normaliz=Flat_blue_amp2,
response=prereddir+'RESP_blue_amp2',
interac=inter, thresho='INDEF',
sample='*', naverage=2, function='legendre',
low_rej=5,high_rej=5, order=60, niterat=20,
grow=0, graphic='stdgraph')
res = inspect_flat([prereddir+'RESP_blue_amp2.fits'],
OUTFILE=prereddir+'RESP_blue_amp2.fits', DISPAXIS=dispaxis)
hdu_amp2 = fits.open(prereddir+'RESP_blue_amp2.fits')
amp2_flatten = np.asarray(hdu_amp2[0].data).flatten()
shape2=hdu_amp2[0].data.shape
dim1+=shape2[0]
concat_amps = np.concatenate([concat_amps, amp2_flatten])
print('Output blue response dimensions:',dim1,dim2)
resp_blue_data = np.reshape(concat_amps, (dim1, dim2))
header = hdu_amp1[0].header
if os.path.exists(prereddir+'RESP_blue.fits'):
os.remove(prereddir+'RESP_blue.fits')
hdu = fits.PrimaryHDU(resp_blue_data,header)
hdu.writeto(prereddir+'RESP_blue.fits',output_verify='ignore')
resp_files = ['RESP_blue_amp1.fits','RESP_blue_amp2.fits']
for file in resp_files:
if os.path.exists(prereddir+file):
os.remove(prereddir+file)
# red flats
if len(list_flat_r) > 0:
# br, inst = instruments.blue_or_red(list_flat_r[0])
br, inst = instruments.blue_or_red(prereddir+'to{}'.format(r_amp1_list[0]))
dispaxis = inst.get('dispaxis')
iraf.specred.dispaxi = dispaxis
Flat_red_amp1 = prereddir+'toFlat_red_amp1.fits'
Flat_red_amp2 = prereddir+'toFlat_red_amp2.fits'
flat_list_amp1 = []
for flat in r_amp1_list:
flat_list_amp1.append(prereddir+'to'+ flat)
if os.path.isfile(Flat_red_amp1):
os.remove(Flat_red_amp1)
flat_list_amp2 = []
for flat in r_amp2_list:
flat_list_amp2.append(prereddir+'to'+ flat)
if os.path.isfile(Flat_red_amp2):
os.remove(Flat_red_amp2)
amp2flag = len(flat_list_amp2)>0
# first, combine all the flat files into a master flat
if amp2flag:
res = combine_flats(flat_list_amp1,OUTFILE=Flat_red_amp1,MEDIAN_COMBINE=True)
res = combine_flats(flat_list_amp2,OUTFILE=Flat_red_amp2,MEDIAN_COMBINE=True)
else:
res = combine_flats(flat_list_amp1,OUTFILE=Flat_red_amp1,MEDIAN_COMBINE=True)
#What is the output here? Check for overwrite
iraf.specred.response(Flat_red_amp1,
normaliz=Flat_red_amp1,
response='pre_reduced/RESP_red_amp1',
interac=inter, thresho='INDEF',
sample='*', naverage=2, function='legendre',
low_rej=5,high_rej=5, order=80, niterat=20,
grow=0, graphic='stdgraph')
if amp2flag:
iraf.specred.response(Flat_red_amp2,
normaliz=Flat_red_amp2,
response='pre_reduced/RESP_red_amp2',
interac=inter, thresho='INDEF',
sample='*', naverage=2, function='legendre',
low_rej=5,high_rej=5, order=80, niterat=20,
grow=0, graphic='stdgraph')
# finally, inspect the flat and mask bad regions
if amp2flag:
res = inspect_flat([prereddir+'RESP_red_amp1.fits'],
OUTFILE=prereddir+'RESP_red_amp1.fits', DISPAXIS=dispaxis)
res = inspect_flat([prereddir+'RESP_red_amp2.fits'],
OUTFILE=prereddir+'RESP_red_amp2.fits', DISPAXIS=dispaxis)
else:
res = inspect_flat([prereddir+'RESP_red_amp1.fits'],
OUTFILE=prereddir+'RESP_red.fits', DISPAXIS=dispaxis)
if amp2flag:
hdu_amp1 = fits.open(prereddir+'RESP_red_amp1.fits')
hdu_amp2 = fits.open(prereddir+'RESP_red_amp2.fits')
head = hdu_amp1[0].header
shape1 = hdu_amp1[0].data.shape
shape2 = hdu_amp2[0].data.shape
amp1_flatten = np.asarray(hdu_amp1[0].data).flatten()
amp2_flatten = np.asarray(hdu_amp2[0].data).flatten()
concat_amps = np.concatenate([amp2_flatten, amp1_flatten])
xbin, ybin = [int(ibin) for ibin in head['BINNING'].split(',')]
resp_red_data = np.reshape(concat_amps, (shape1[0]+shape2[0],shape1[1]))
resp_red_data[278:294,:] = 1.
header = hdu_amp1[0].header
if os.path.isfile(prereddir+'RESP_red.fits'):
os.remove(prereddir+'RESP_red.fits')
hdu = fits.PrimaryHDU(resp_red_data,header)
hdu.writeto(prereddir+'RESP_red.fits',output_verify='ignore')
os.remove(prereddir+'RESP_red_amp1.fits')
os.remove(prereddir+'RESP_red_amp2.fits')
else:
os.remove(prereddir+'RESP_red_amp1.fits')
elif MAKE_FLATS:
# CDK - generalized arc creation method
for key in configDict['CAL_FLAT'].keys():
list_flat = configDict['CAL_FLAT'][key]['CALIBRATION_FLAT']
if len(list_flat)>0:
first = prereddir+'to{}'.format(list_flat[0])
br, inst = instruments.blue_or_red(first)
dispaxis = inst.get('dispaxis')
iraf.specred.dispaxi = dispaxis
inter = True
Flat_out = prereddir+'toFlat_{0}.fits'.format(br.lower())
dum = prereddir+'dummy_{0}.fits'.format(br.lower())
resp = prereddir+'RESP_{0}.fits'.format(br.lower())
flat_list = []
norm_list = []
for flat in list_flat:
flat_list.append(prereddir+'to'+flat)
norm = flat.replace('.fits','_norm.fits')
norm_list.append(prereddir+'to'+norm)
for file in [Flat_out, dum, resp]:
if os.path.exists(file):
os.remove(Flat_out)
# first, combine all the flat files into a master flat
res = combine_flats(flat_list,OUTFILE=Flat_out,
MEDIAN_COMBINE=True)
# combine all the flat files for norm region
res = combine_flats(norm_list,OUTFILE=dum, MEDIAN_COMBINE=True)
iraf.specred.response(Flat_out, normaliz=dum, response=resp,
interac=inter, thresho='INDEF', sample='*', naverage=2,
function='legendre', low_rej=5,high_rej=5, order=60,
niterat=20, grow=0, graphic='stdgraph')
# finally, inspect the flat and mask bad regions
res = inspect_flat([resp], OUTFILE=resp, DISPAXIS=dispaxis)
for flat in norm_list:
os.remove(flat)
if HOST:
host_gals.make_host_metadata(configDict)
return 0
def reduce(imglist, files_arc, files_flat, _cosmic, _interactive_extraction,
_arc):
import string
import os
import re
import sys
import pdb
os.environ["PYRAF_BETA_STATUS"] = "1"
try:
from astropy.io import fits as pyfits
except:
import pyfits
import numpy as np
import util
import instruments
import combine_sides as cs
import cosmics
from pyraf import iraf
dv = util.dvex()
scal = np.pi / 180.
if not _interactive_extraction:
_interactive = False
else:
_interactive = True
if not _arc:
_arc_identify = False
else:
_arc_identify = True
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.longslit(_doprint=0)
iraf.onedspec(_doprint=0)
iraf.specred(_doprint=0)
iraf.disp(inlist='1', reference='1')
toforget = [
'ccdproc', 'imcopy', 'specred.apall', 'longslit.identify',
'longslit.reidentify', 'specred.standard', 'longslit.fitcoords',
'onedspec.wspectext'
]
for t in toforget:
iraf.unlearn(t)
iraf.ccdred.verbose = 'no'
iraf.specred.verbose = 'no'
iraf.ccdproc.darkcor = 'no'
iraf.ccdproc.fixpix = 'no'
iraf.ccdproc.flatcor = 'no'
iraf.ccdproc.zerocor = 'no'
iraf.ccdproc.ccdtype = ''
iraf.longslit.mode = 'h'
iraf.specred.mode = 'h'
iraf.noao.mode = 'h'
iraf.ccdred.instrument = "ccddb$kpno/camera.dat"
list_arc_b = []
list_arc_r = []
for arcs in files_arc:
hdr = util.readhdr(arcs)
br, inst = instruments.blue_or_red(arcs)
if br == 'blue':
list_arc_b.append(arcs)
elif br == 'red':
list_arc_r.append(arcs)
else:
errStr = '{} '.format(str(util.readkey3(hdr, 'VERSION')))
errStr += 'not in database'
print(errStr)
sys.exit()
asci_files = []
newlist = [[], []]
print('\n### images to reduce :', imglist)
#raise TypeError
for img in imglist:
if 'b' in img:
newlist[0].append(img)
elif 'r' in img:
newlist[1].append(img)
if len(newlist[1]) < 1:
newlist = newlist[:-1]
elif len(newlist[0]) < 1:
newlist = newlist[1:]
else:
sides = raw_input("Reduce which side? ([both]/b/r): ")
if sides == 'b':
newlist = newlist[:-1]
elif sides == 'r':
newlist = newlist[1:]
for imgs in newlist:
hdr = util.readhdr(imgs[0])
br, inst = instruments.blue_or_red(imgs[0])
if br == 'blue':
flat_file = '../RESP_blue'
elif br == 'red':
flat_file = '../RESP_red'
else:
errStr = 'Not in intrument list'
print(errStr)
sys.exit()
iraf.specred.dispaxi = inst.get('dispaxis')
iraf.longslit.dispaxi = inst.get('dispaxis')
_gain = inst.get('gain')
_ron = inst.get('read_noise')
iraf.specred.apall.readnoi = _ron
iraf.specred.apall.gain = _gain
_object0 = util.readkey3(hdr, 'OBJECT')
_date0 = util.readkey3(hdr, 'DATE-OBS')
_object0 = re.sub(' ', '', _object0)
_object0 = re.sub('/', '_', _object0)
nameout0 = str(_object0) + '_' + inst.get('name') + '_' + str(_date0)
nameout0 = util.name_duplicate(imgs[0], nameout0, '')
timg = nameout0
print('\n### now processing :', timg, ' for -> ', inst.get('name'))
if len(imgs) > 1:
img_str = ''
for i in imgs:
img_str = img_str + i + ','
iraf.imcombine(img_str, output=timg)
else:
img = imgs[0]
if os.path.isfile(timg):
os.system('rm -rf ' + timg)
iraf.imcopy(img, output=timg)
# should just do this by hand
iraf.ccdproc(timg,
output='',
overscan='no',
trim='no',
zerocor="no",
flatcor="yes",
readaxi='line',
flat=flat_file,
Stdout=1)
img = timg
#raw_input("Press Enter to continue...")
if _cosmic:
print('\n### starting cosmic removal')
array, header = cosmics.fromfits(img)
c = cosmics.cosmicsimage(array,
gain=inst.get('gain'),
readnoise=inst.get('read_noise'),
sigclip=5,
sigfrac=0.5,
objlim=2.0)
c.run(maxiter=5)
cosmics.tofits('cosmic_' + img, c.cleanarray, header)
img = 'cosmic_' + img
print('\n### cosmic removal finished')
else:
print(
'\n### No cosmic removal, saving normalized image for inspection???'
)
if inst.get('arm') == 'blue' and len(list_arc_b) > 0:
arcfile = list_arc_b[0]
elif inst.get('arm') == 'red' and len(list_arc_r) > 0:
arcfile = list_arc_r[0]
else:
arcfile = None
if arcfile is not None and not arcfile.endswith(".fits"):
arcfile = arcfile + '.fits'
if not os.path.isdir('database/'):
os.mkdir('database/')
if _arc_identify:
os.system('cp ' + arcfile + ' .')
arcfile = string.split(arcfile, '/')[-1]
arc_ex = re.sub('.fits', '.ms.fits', arcfile)
arcref = inst.get('archive_arc_extracted')
arcref_img = string.split(arcref, '/')[-1]
arcref_img = arcref_img.replace('.ms.fits', '')
arcrefid = inst.get('archive_arc_extracted_id')
os.system('cp ' + arcref + ' .')
arcref = string.split(arcref, '/')[-1]
os.system('cp ' + arcrefid + ' ./database')
aperture = inst.get('archive_arc_aperture')
os.system('cp ' + aperture + ' ./database')
print('\n### arcfile : ', arcfile)
print('\n### arcfile extraction : ', arc_ex)
print('\n### arc reference : ', arcref)
# read for some meta data to get the row right
tmpHDU = pyfits.open(arcfile)
header = tmpHDU[0].header
try:
spatialBin = int(header['binning'].split(',')[0])
except KeyError:
spatialBin = 1
apLine = 700 // spatialBin
iraf.specred.apall(arcfile,
output=arc_ex,
ref=arcref_img,
line=apLine,
nsum=10,
interactive='no',
extract='yes',
find='yes',
nfind=1,
format='multispec',
trace='no',
back='no',
recen='no')
iraf.longslit.reidentify(referenc=arcref,
images=arc_ex,
interac='NO',
section=inst.get('section'),
coordli=inst.get('line_list'),
shift='INDEF',
search='INDEF',
mode='h',
verbose='YES',
step=0,
nsum=5,
nlost=2,
cradius=10,
refit='yes',
overrid='yes',
newaps='no')
print('\n### extraction using apall')
result = []
hdr_image = util.readhdr(img)
_type = util.readkey3(hdr_image, 'object')
if (_type.startswith("arc") or _type.startswith("dflat")
or _type.startswith("Dflat") or _type.startswith("Dbias")
or _type.startswith("Bias")):
print('\n### warning problem \n exit ')
sys.exit()
else:
imgex = util.extractspectrum(img, dv, inst, _interactive, 'obj')
print('\n### applying wavelength solution')
print(arc_ex)
iraf.disp(inlist=imgex, reference=arc_ex)
result = result + [imgex] + [timg]
# asci_files.append(imgasci)
if not os.path.isdir(_object0 + '_ex/'):
os.mkdir(_object0 + '_ex/')
if not _arc_identify:
util.delete(arcref)
else:
util.delete(arcfile)
util.delete(arc_ex)
util.delete(img)
util.delete(imgex)
util.delete(arcref)
util.delete('logfile')
#if _cosmic:
#util.delete(img[7:])
#util.delete("cosmic_*")
os.system('mv ' + 'd' + imgex + ' ' + _object0 + '_ex/')
use_sens = raw_input('Use archival flux calibration? [y]/n ')
if use_sens != 'no':
sensfile = inst.get('archive_sens')
os.system('cp ' + sensfile + ' ' + _object0 + '_ex/')
bstarfile = inst.get('archive_bstar')
os.system('cp ' + bstarfile + ' ' + _object0 + '_ex/')
return result
def extractspectrum(img,
dv,
_ext_trace,
_dispersionline,
_interactive,
_type,
automaticex=False):
# print "LOGX:: Entering `extractspectrum` method/function in
# %(__file__)s" % globals()
import glob
import os
import string
import sys
import re
import lickshane
import datetime
MJDtoday = 55927 + (datetime.date.today() -
datetime.date(2012, 01, 01)).days
from pyraf import iraf
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.specred(_doprint=0)
toforget = ['specred.apall', 'specred.transform']
for t in toforget:
iraf.unlearn(t)
dv = lickshane.util.dvex()
hdr = lickshane.util.readhdr(img)
_gain = lickshane.util.readkey3(hdr, 'gain')
_rdnoise = lickshane.util.readkey3(hdr, 'ron')
_grism = lickshane.util.readkey3(hdr, 'grism')
_instrument = lickshane.util.readkey3(hdr, 'version')
imgex = re.sub('.fits', '_ex.fits', img)
imgfast = re.sub(string.split(img, '_')[-2] + '_', '', img)
# imgfast=re.sub(str(MJDtoday)+'_','',img)
if not os.path.isfile(imgex) and not os.path.isfile(
'database/ap' + re.sub('.fits', '', img)) and not os.path.isfile(
'database/ap' + re.sub('.fits', '', imgfast)):
_new = 'yes'
_extract = 'yes'
else:
if automaticex:
if _interactive in ['Yes', 'yes', 'YES', 'y', 'Y']:
answ = 'x'
while answ not in ['o', 'n', 's']:
answ = raw_input(
'\n### New extraction [n], extraction with old parameters [o], skip extraction [s] ? [o]'
)
if not answ:
answ = 'o'
if answ == 'o':
_new, _extract = 'no', 'yes'
elif answ == 'n':
_new, _extract = 'yes', 'yes'
else:
_new, _extract = 'yes', 'no'
else:
_new, _extract = 'no', 'yes'
else:
if _interactive in ['Yes', 'yes', 'YES', 'y', 'Y']:
answ = 'x'
while answ not in ['y', 'n']:
answ = raw_input(
'\n### do you want to extract again [[y]/n] ? ')
if not answ:
answ = 'y'
if answ == 'y':
_new, _extract = 'yes', 'yes'
else:
_new, _extract = 'yes', 'no'
else:
_new, _extract = 'yes', 'yes'
if _extract == 'yes':
lickshane.util.delete(imgex)
if _dispersionline:
question = 'yes'
while question == 'yes':
_z1, _z2, goon = lickshane.util.display_image(
img, 1, '', '', False)
dist = raw_input(
'\n### At which line do you want to extract the spectrum ['
+ str(dv['line'][_grism]) + '] ? ')
if not dist:
dist = 400
try:
dist = int(dist)
question = 'no'
except:
print '\n### input not valid, try again:'
else:
dist = dv['line'][_grism]
if _ext_trace in ['yes', 'Yes', 'YES', True]:
lista = glob.glob('*ex.fits')
if lista:
for ii in lista:
print ii
_reference = raw_input(
'\### which object do you want to use for the trace [' +
str(lista[0]) + '] ? ')
if not _reference:
_reference = lista[0]
_reference = re.sub('_ex', '', _reference)
_fittrac = 'no'
_trace = 'no'
else:
sys.exit('\n### error: no extracted spectra in the directory')
else:
_reference = ''
_fittrac = 'yes'
_trace = 'yes'
if _new == 'no':
if not os.path.isfile('database/ap' + re.sub('.fits', '', img)):
lickshane.util.repstringinfile(
'database/ap' + re.sub('.fits', '', imgfast),
'database/ap' + re.sub('.fits', '', img),
re.sub('.fits', '', imgfast), re.sub('.fits', '', img))
_find = 'no'
_recenter = 'no'
_edit = 'no'
_trace = 'no'
_fittrac = 'no'
_mode = 'h'
_resize = 'no'
_review = 'no'
iraf.specred.mode = 'h'
_interactive = 'no'
else:
iraf.specred.mode = 'q'
_mode = 'q'
_find = 'yes'
_recenter = 'yes'
_edit = 'yes'
_review = 'yes'
_resize = dv[_type]['_resize']
if _instrument == 'kastb':
iraf.specred.dispaxi = 1
elif _instrument == 'kastr':
iraf.specred.dispaxi = 2
iraf.specred.apall(img,
output=imgex,
referen=_reference,
trace=_trace,
fittrac=_fittrac,
find=_find,
recenter=_recenter,
edit=_edit,
nfind=1,
extract='yes',
backgro='fit',
gain=_gain,
readnoi=_rdnoise,
lsigma=4,
usigma=4,
format='multispec',
b_function='legendre',
b_sample=dv[_type]['_b_sample'],
clean='yes',
pfit='fit1d',
lower=dv[_type]['_lower'],
upper=dv[_type]['_upper'],
t_niter=dv[_type]['_t_niter'],
width=dv[_type]['_width'],
radius=dv[_type]['_radius'],
line=dist,
nsum=dv[_type]['_nsum'],
t_step=dv[_type]['_t_step'],
t_nsum=dv[_type]['_t_nsum'],
t_nlost=dv[_type]['_t_nlost'],
t_sample=dv[_type]['_t_sample'],
resize=_resize,
t_order=dv[_type]['_t_order'],
weights=dv[_type]['_weights'],
interactive=_interactive,
review=_review,
mode=_mode)
lickshane.util.repstringinfile(
'database/ap' + re.sub('.fits', '', img),
'database/ap' + re.sub('.fits', '', imgfast),
re.sub('.fits', '', img), re.sub('.fits', '', imgfast))
else:
print '\n### skipping new extraction'
return imgex
