def reidentify():
iraf.twodspec()
iraf.longslit()
iraf.reidentify(reference='Lamp',
images='Lamp',
interactive='no',
section='column',
newaps='yes',
override='yes',
refit='yes',
trace='no',
step=10,
nsum=10,
shift=0.0,
search=0.0,
nlost=5,
cradius=7.0,
threshold=0.0,
addfeatures='no',
coordlist=cdherb_file,
match=-3.0,
maxfeatures=50,
minsep=2.0,
database='database')
iraf.flpr()
def transform(lst):
f = open(lst)
l = f.readlines()
f.close()
namelst = ['ftb' + i for i in l]
outputlst = ['wftb' + i for i in l]
f = open("temp1.txt", 'w')
for i in namelst:
f.write(i + '\n')
f.close()
f = open("temp2.txt", 'w')
for i in outputlst:
f.write(i + '\n')
f.close()
iraf.twodspec()
iraf.longslit(dispaxis=2)
#
iraf.transform(input='@temp1.txt',
output='@temp2.txt',
minput='',
moutput='',
fitnames='LampLamp',
database='database',
interptype='spline3',
flux='yes')
def wal(lstfile):
iraf.noao()
iraf.twodspec()
iraf.longslit()
iraf.identify(images = 'Lamp'
, section = 'middle column', database = 'database'
, coordlist = 'linelists$idhenear.dat', units = '', nsum = 10
, match = -3.0, maxfeatures = 50, zwidth = 100.0
, ftype = 'emission', fwidth = 20.0, cradius = 5.0
, threshold = 0.0, minsep = 2.0, function = 'chebyshev'
, order = 6, sample = '*', niterate = 0
, low_reject = 3.0, high_reject = 3.0, grow = 0.0
, autowrite = False, graphics = 'stdgraph', cursor = ''
, crval = '', cdelt = '')
iraf.reidentify(reference = 'Lamp'
, images = 'Lamp', interactive = 'no', section = 'column'
, newaps = True, override = True, refit = True, trace = False
, step = 10, nsum = 10, shift = 0.0, search = 0.0, nlost = 5
, cradius = 7.0, threshold = 0.0, addfeatures = False
, coordlist = 'linelists$idhenear.dat', match = -3.0, maxfeatures = 50
, minsep = 2.0, database = 'database', logfiles = 'logfile'
, plotfile = '', verbose = False, graphics = 'stdgraph', cursor = ''
, answer = 'yes', crval = '', cdelt = '', mode = 'al')
iraf.fitcoords(images = 'Lamp'
, fitname = 'Lamp', interactive = True, combine = False, database = 'database'
, deletions = 'deletions.db', function = 'chebyshev', xorder = 6
, yorder = 6, logfiles = 'STDOUT,logfile', plotfile = 'plotfile'
, graphics = 'stdgraph', cursor = '', mode = 'al')
iraf.longslit(dispaxis = 2)
iraf.transform(input = '%ftbo%ftbo%@' + lstfile
, output = '%wftbo%wftbo%@' + lstfile, minput = '', moutput = ''
, fitnames = 'LampLamp', database = 'database', interptype = 'spline3'
, flux = True)
def identify():
iraf.twodspec()
iraf.longslit()
iraf.identify(images='Lamp.fits',
section='middle column',
database='database',
coordlist=cdherb_file,
nsum=10,
match=-3.0,
maxfeatures=50,
zwidth=100.0,
ftype='emission',
fwidth=20.0,
cradius=7.0,
threshold=0.0,
minsep=2.0,
function='chebyshev',
order=6,
sample='*',
niterate=0,
low_reject=3.0,
high_reject=3.0,
grow=0.0,
autowrite='no')
iraf.flpr()
def standard():
stdpath = os.path.split(os.path.realpath(__file__))[0] + os.sep + 'standarddir' + os.sep
print('standard dir is ' + stdpath)
extpath = os.path.split(os.path.realpath(__file__))[0] + os.sep + 'LJextinct.dat'
iraf.noao()
iraf.twodspec()
iraf.longslit(dispaxis = 2, nsum = 1, observatory = 'Lijiang',
extinction = extpath, caldir = stdpath)
for objname in stdgroup:
stdname, stdmag, stdmagband = get_std_name(objname)
print('the standard star is ' + stdname)
stdmag = float(stdmag)
outname1 = 'stdawftbo' + stdgroup[objname][0]
inname = ''
for tmpname in stdgroup[objname]:
inname = inname + 'awftbo' + tmpname + ','
inname = inname[0:-1]
iraf.standard(input = inname
, output = outname1, samestar = True, beam_switch = False
, apertures = '', bandwidth = 30.0, bandsep = 20.0
, fnuzero = 3.6800000000000E-20, extinction = extpath
, caldir = stdpath, observatory = ')_.observatory'
, interact = True, graphics = 'stdgraph', cursor = ''
, star_name = stdname, airmass = '', exptime = ''
, mag = stdmag, magband = stdmagband, teff = '', answer = 'yes')
for name in stdgroup:
inpar = 'stdawftbo' + stdgroup[name][0]
iraf.sensfunc(standards = inpar, sensitivity = 'sensawftbo' + stdgroup[name][0],
extinction = extpath, function = 'spline3', order = 9)
def transform(lst):
f = open(lst)
l = f.readlines()
f.close()
namelst = ['ftbo' + i for i in l]
outputlst = ['wftbo' + i for i in l]
# namelst = [i.split('.')[0] + 'otbf.fits' for i in l]
# outputlst = [i.split('.')[0] + 'otbfw.fits' for i in l]
f = open("temp1.txt", 'w')
for i in namelst:
f.write(i + '\n')
f.close()
f = open("temp2.txt", 'w')
for i in outputlst:
f.write(i + '\n')
f.close()
iraf.twodspec()
iraf.longslit(dispaxis=2)
# for i in namelst:
# print '#' * 30, i, '===>', i.split('.')[0] + 'w.fits'
# iraf.transform(input = i, output = i.split('.')[0] + 'w.fits',
# minput = '', moutput = '', fitnames = 'LampLamp',
# database = 'database', interptype = 'spline3',
# flux = 'yes')
iraf.transform(input='@temp1.txt',
output='@temp2.txt',
minput='',
moutput='',
fitnames='LampLamp',
database='database',
interptype='spline3',
flux='yes')
def standard(namelst):
iraf.noao()
iraf.twodspec()
iraf.longslit(dispaxis=2, nsum=1, observatory=func.obs.name,
extinction=extpath, caldir=stdpath)
if os.path.isfile('Std'):
print('remove file Std')
os.remove('Std')
for std_fitsname in namelst:
stdname, stdmag, stdmagband = func.standard_star_info(std_fitsname)
print(colored('the standard star is ' + stdname, 'green'))
wid, sep = get_band_width_sep(std_fitsname)
airmas = pyfits.getval(std_fitsname, 'airmass')
exposure = pyfits.getval(std_fitsname, 'exptime')
iraf.standard(input=std_fitsname, output='Std', samestar=True,
beam_switch=False, apertures='', bandwidth=wid,
bandsep=sep, # 30.0 20.0
fnuzero=3.6800000000000E-20, extinction=extpath,
caldir=stdpath, observatory=func.obs.name, interact=True,
graphics='stdgraph', cursor='', star_name=stdname,
airmass=airmas, exptime=exposure, mag=stdmag,
magband=stdmagband, teff='', answer='yes')
if os.path.isfile('Sens.fits'):
print('remove file Sens.fits')
os.remove('Sens.fits')
iraf.sensfunc(standards='Std', sensitivity='Sens',
extinction=extpath, function='spline3', order=9)
iraf.splot('Sens')
def normalise_flats(flatdir):
"""
Normalise flats
"""
print 'In directory ' + flatdir
print 'Normalising combinined flats...'
if os.path.exists( os.path.join(flatdir,'nFlat.fits') ):
os.remove(os.path.join(flatdir,'nFlat.fits') )
print 'Removing file ' + os.path.join(flatdir,'nFlat.fits')
iraf.twodspec(_doprint=0)
iraf.longslit(_doprint=0)
iraf.response.setParam('calibration', os.path.join(flatdir,'Flat.fits'))
iraf.response.setParam('normalization', os.path.join( flatdir, 'Flat.fits' ))
iraf.response.setParam('response', os.path.join( flatdir, 'nFlat') )
iraf.response.setParam('low_reject', 3.)
iraf.response.setParam('high_reject', 3.)
iraf.response.setParam('order',40)
iraf.response()
return None
def calibrate(namelst):
iraf.noao()
iraf.twodspec()
iraf.longslit(dispaxis=2,
nsum=1,
observatory='ca',
extinction=extpath,
caldir=stdpath)
for fitname in namelst:
outname = 'mark_' + fitname
if os.path.isfile(outname):
print('remove file ' + outname)
os.remove(outname)
iraf.calibrate(input=fitname,
output=outname,
extinct='yes',
flux='yes',
extinction=extpath,
ignoreaps='yes',
sensitivity='Sens',
fnu='no')
iraf.splot(images=outname)
iraf.flpr()
def apall_config(
config_file,
config_default='/Users/ando/andry/research/Make_software/saltrss/apall_conf.yml'
):
config = yaml.safe_load(open(config_default))
custom_config = {}
if os.path.exists(config_file):
custom_config = yaml.safe_load(open(config_file))
config = config_merge(custom_config, config)
sections = [
'iraf.apall',
]
iraf.noao(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.onedspec(_doprint=0)
iraf.apextract(_doprint=0)
iraf.apextract.unlearn()
iraf.apall.unlearn()
iraf.apsum.unlearn()
iraf.apextract.setParam('dispaxis', 1)
for section_name in sections:
params = config[section_name].items()
for param_id in params:
eval(section_name).setParam(param_id[0], param_id[1])
return config
def fitcoord_edge_each(fname, overwrite=False):
nteractive = 'yes'
database = 'database'
function = 'chebyshev'
xorder = 2
yorder = 7
logfiles = 'STDOUT,fitcoord_edge.log'
interactive = 'yes'
cursor = ''
# cursor = filibdir+'fitcoord_edge.cur'
idfile = database + '/id' + fname
if not os.path.isfile(idfile):
print('\t Edge identification files do not exist. ' + idfile)
return
fcfile = database + '/fc' + fname
if os.path.isfile(fcfile) and not overwrite:
print('\t Edge fitcoord files already exist. ' + fcfile)
print('\t This procedure is skipped.')
return
# Not to display items in IRAF packages
sys.stdout = open('/dev/null', 'w')
iraf.noao()
iraf.twodspec()
iraf.longslit()
sys.stdout = sys.__stdout__ # Back to the stadard output
iraf.fitcoord(fname, fitname='', interactive=interactive, \
combine='no', database=database, deletions='',\
function = function,xorder=xorder, yorder=yorder,\
logfiles=logfiles, graphics='stdgraph', cursor=cursor)
return
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 combine_flat(lstfile):
if os.path.isfile('Halogen.fits'):
print 'remove Halogen.fits'
os.remove('Halogen.fits')
if os.path.isfile('Resp.fits'):
print 'remove Resp.fits'
os.remove('Resp.fits')
iraf.noao()
iraf.imred()
iraf.ccdred()
iraf.flatcombine(input='tbo//@' + lstfile,
output='Halogen',
combine='average',
reject='crreject',
ccdtype='',
process=False,
subsets=False,
delete=False,
clobber=False,
scale='mode',
statsec='',
nlow=1,
nhigh=1,
nkeep=1,
mclip=True,
lsigma=3.0,
hsigma=3.0,
rdnoise='rdnoise',
gain='gain',
snoise=0.0,
pclip=-0.5,
blank=1.0)
iraf.twodspec()
iraf.longslit(dispaxis=2,
nsum=1,
observatory='Lijiang',
extinction=func.config_path + os.sep + 'LJextinct.dat',
caldir=func.std_path + os.sep,
interp='poly5')
iraf.response(calibration='Halogen',
normalization='Halogen',
response='Resp',
interactive=True,
threshold='INDEF',
sample='*',
naverage=1,
function='spline3',
order=25,
low_reject=10.0,
high_reject=10.0,
niterate=1,
grow=0.0,
graphics='stdgraph',
cursor='')
def fitcoords():
iraf.twodspec()
iraf.longslit()
iraf.fitcoords(images='Lamp',
fitname='Lamp',
interactive='yes',
combine='no',
database='database',
deletions='deletions.db',
function='chebyshev',
xorder=6,
yorder=6)
def sensfunc():
iraf.twodspec()
iraf.longslit(dispaxis=2,
nsum=1,
observatory='Lijiang',
extinction='onedstds$LJextinct.dat',
caldir='onedstds$ctiocal/')
iraf.sensfunc(standards='std',
sensitivity='sens',
extinction='onedstds$LJextinct.dat',
function='spline3',
order=9)
def standard(namelst):
iraf.noao()
iraf.twodspec()
iraf.longslit(dispaxis=2,
nsum=1,
observatory='ca',
extinction=extpath,
caldir=stdpath)
std_fitsname = namelst[0]
stdname, stdmag, stdmagband = standard_star_info(std_fitsname)
wid, sep = get_band_width_sep(stdname)
print('<<<<<the standard star is ', stdname, '>>>>>')
print std_fitsname
if os.path.isfile('Std'):
print('remove file Std')
os.remove('Std')
iraf.standard(
input=std_fitsname,
output='Std',
samestar=True,
beam_switch=False,
apertures='',
bandwidth=wid,
bandsep=sep, # 30.0 20.0
fnuzero=3.6800000000000E-20,
extinction=extpath,
caldir=stdpath,
observatory='ca',
interact=True,
graphics='stdgraph',
cursor='',
star_name=stdname,
airmass='',
exptime='',
mag=stdmag,
magband=stdmagband,
teff='',
answer='yes')
if os.path.isfile('Sens.fits'):
print('remove file Sens.fits')
os.remove('Sens.fits')
iraf.sensfunc(standards='Std',
sensitivity='Sens',
extinction=extpath,
function='spline3',
order=15)
iraf.splot('Sens')
iraf.flpr()
def cor_airmass(lstfile):
f = open(lstfile)
l = f.readlines()
f.close()
l = [tmp.split('\n')[0] for tmp in l]
fitlst = ['awftbo' + tmp for tmp in l]
for fitname in fitlst:
if os.path.isfile(fitname):
fit = pyfits.open(fitname)
objname = fit[0].header['object'].replace('_', ' ').split()[0]
print(fitname + ' ' + objname)
objname_new = find_normal_objname(objname)
if len(objname_new) == 0:
objname_new = raw_input('please input object name:')
radec = findradec(objname_new)
if len(radec) == 0:
radec = raw_input('please input ra dec of objname:')
radec = radec.split()
fitextnum = len(fit)
fit.close()
for lay in range(fitextnum):
airold = iraf.hselect(images = fitname + '[%i]' % lay, fields = 'airmass', expr = 'yes', Stdout = 1)
airold = float(airold[0])
print(fitname + ' ' + objname + ' ' + str(lay) + ' airmass old: ' + str(airold))
fitnamelay = fitname + '[%i]' % lay
iraf.hedit(images = fitnamelay, fields = 'airold',
value = airold, add = 'yes', addonly = 'yes', delete = 'no',
verify = 'no', show = 'yes', update = 'yes')
iraf.hedit(images = fitnamelay, fields = 'sname',
value = objname_new, add = 'yes', addonly = 'yes', delete = 'no',
verify = 'no', show = 'yes', update = 'yes')
iraf.hedit(images = fitnamelay, fields = 'RA',
value = radec[0], add = 'yes', addonly = 'yes', delete = 'no',
verify = 'no', show = 'yes', update = 'yes')
iraf.hedit(images = fitnamelay, fields = 'DEC',
value = radec[1], add = 'yes', addonly = 'yes', delete = 'no',
verify = 'no', show = 'yes', update = 'yes')
iraf.twodspec()
stdpath = os.path.split(os.path.realpath(__file__))[0] + os.sep + 'standarddir' + os.sep
iraf.longslit(dispaxis = 2, nsum = 1, observatory = 'Lijiang',
extinction = 'onedstds$LJextinct.dat', caldir = stdpath)
iraf.setairmass(images = fitnamelay,
observatory = 'Lijiang', intype = 'beginning',
outtype = 'effective', ra = 'ra', dec = 'dec',
equinox = 'epoch', st = 'lst', ut = 'date-obs',
date = 'date-obs', exposure = 'exptime', airmass = 'airmass',
utmiddle = 'utmiddle', scale = 750.0, show = 'yes',
override = 'yes', update = 'yes')
print('name airmass_new airmass_old')
iraf.hselect(fitnamelay, fields = '$I,airmass,airold',
expr = 'yes')
def fitcoord_dispersion(basenames, overwrite=False):
print('\n#############################')
print('Getting the dispersion map.')
# entering the channel image directory.
print('\t Entering the channel image directory, \"'+fi.chimagedir+'\".')
os.chdir(fi.chimagedir)
database='database'
function='chebyshev'
xorder=3
yorder=5
logfiles='STDOUT,fitcoord_dispersion.log'
# for multi-comparison images
combine = 'yes'
# Not to display items in IRAF packages
sys.stdout = open('/dev/null', 'w')
iraf.noao()
iraf.twodspec()
iraf.longslit()
sys.stdout = sys.__stdout__ # Back to the stadard output
for i in range(1,25):
fcfile = basenames[0] + '.ch%02d'%i
if os.path.isfile(database + '/fc' + fcfile) and overwrite == False:
print('\t FC file already exits: ' + fcfile)
else:
if os.path.isfile(database + '/fc' + fcfile) and overwrite == True:
print('Removing ' + fcfile)
try:
os.remove(database + '/fc' + fcfile)
except:
pass
infiles = ''
for basename in basenames:
infiles = infiles + basename + '.ch%02d,'%i
iraf.fitcoord(infiles[:len(infiles)-1], fitname=fcfile, interactive='yes', \
combine=combine, database=database, deletions='',\
function = function,xorder=xorder, yorder=yorder,\
logfiles=logfiles, graphics='stdgraph', cursor='')
print('Going back to the original directory.')
os.chdir('..')
return
def set_airmass(fn):
fit = pyfits.open(fn)
size = len(fit)
for i, hdu in enumerate(fit):
if 'AIRMASS' in hdu.header:
airmassold = hdu.header['AIRMASS']
print('%s[%d] airmassold = %f' % (fn, i, airmassold), )
if 'AIROLD' in hdu.header:
airold = hdu.header['AIROLD']
print('%s[%d] AIROLD = %f' % (fn, i, airold))
print(
'AIROLD keyword alreay exist, the airmass old will not saved'
)
else:
iraf.hedit(images=fn + '[%d]' % i,
fields='AIROLD',
value=airmassold,
add='Yes',
addonly='Yes',
delete='No',
verify='No',
show='Yes',
update='Yes')
fit.close()
ra, dec = get_ra_dec(fn)
set_ra_dec(fn, ra, dec)
iraf.twodspec()
iraf.longslit(dispaxis=2, nsum=1, observatory='ca', caldir=stdpath)
for i in range(size):
iraf.setairmass(images=fn,
observatory='ca',
intype='beginning',
outtype='effective',
ra='ra',
dec='dec',
equinox='epoch',
st='lst',
ut='date-obs',
date='date-obs',
exposure='exptime',
airmass='airmass',
utmiddle='utmiddle',
scale=750.0,
show='yes',
override='yes',
update='yes')
def apall(lstfile):
iraf.noao()
iraf.twodspec()
iraf.apextract(dispaxis = 2, database = 'database')
f = open(lstfile)
l = f.readlines()
f.close()
l = [tmp.split('\n')[0] for tmp in l]
path = os.getcwd()
for i in l:
infile = 'wftbo' + i
outfile = 'awftbo' + i
while True:
if os.path.isfile(outfile):
print('remove ' + outfile)
os.remove(outfile)
delfile = path + os.sep + 'database/ap' + infile[0:-5]
if os.path.isfile(delfile):
print('remove ' + delfile)
os.remove(delfile)
iraf.apall(input = infile
, output = outfile, apertures = 2, format = 'multispec'
, references = '', profiles = '', interactive = True
, find = True, recenter = True, resize = False
, edit = True, trace = True, fittrace = True
, extract = True, extras = True, review = True
, line = 'INDEF', nsum = 10, lower = -15.0
, upper = 15.0, apidtable = '', b_function = 'chebyshev'
, b_order = 2, b_sample = '-50:-26,26:50', b_naverage = -25
, b_niterate = 1, b_low_reject = 3.0, b_high_reject = 3.0
, b_grow = 0.0, width = 5.0, radius = 10.0
, threshold = 0.0, nfind = 2, minsep = 5.0
, maxsep = 100000.0, order = 'increasing', aprecenter = ''
, npeaks = 'INDEF', shift = True, llimit ='INDEF'
, ulimit = 'INDEF', ylevel = 0.1, peak = True
, bkg = True, r_grow = 0.0, avglimits = False
, t_nsum = 20, t_step = 10, t_nlost = 3, t_function = 'legendre'
, t_order = 7, t_sample = '*', t_naverage = 1
, t_niterate = 1, t_low_reject = 3.0, t_high_reject = 3.0
, t_grow = 0.0, background = 'fit', skybox = 1
, weights = 'variance', pfit = 'fit1d', clean = True
, saturation = 'INDEF', readnoise = 9.4, gain = 0.35
, lsigma = 4.0, usigma = 4.0, nsubaps = 1)
iraf.flpr()
sspecplot.sspecplot(outfile)
getval = raw_input('Are you need repeat apall,may be clean should be close(r/n)')
if getval != 'r':
break
def FitcoordsTask(self, ArcFile, Fits_Folder):
iraf.noao(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.longslit(_doprint=0)
FitcoordsConf = self.FitcoordsAttributes(ArcFile, Fits_Folder)
#Display the equivalent command in IRAF
Command = self.printIrafCommand('fitcords', FitcoordsConf)
print '--- Using the command'
print Command
iraf.twodspec.longslit.fitcoords(**FitcoordsConf)
return
def ReidentifyTask(self, ArcFile, Fits_Folder, ):
iraf.noao(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.longslit(_doprint=0)
ReidentifyConf = self.ReidentifyAttributes(ArcFile, Fits_Folder)
#Display the equivalent command in IRAF
Command = self.printIrafCommand('reidentify', ReidentifyConf)
print '--- Using the command'
print Command
iraf.twodspec.longslit.reidentify(**ReidentifyConf)
return
def identify_gap(infile, overwrite=False):
print('\n#############################')
print('Identifying the spectrum gaps.')
database = 'database'
idfile = database + '/id' + os.path.splitext(infile)[0]
if os.path.exists(idfile):
if overwrite:
try:
os.remove(idfile)
except:
pass
else:
print('\t ID file already exists: '+idfile)
print('\t This precedure is skipped.')
return
# Checking version consistency
if not fi.check_version_f(infile):
return
# Not to display items in IRAF packages
sys.stdout = open('/dev/null', 'w')
iraf.noao()
iraf.twodspec()
iraf.longslit()
sys.stdout = sys.__stdout__ # Back to the stadard output
binfct1 = fits.getval(infile, 'BIN-FCT1')
coordlist = fi.filibdir+'pseudoslitgap_binx'+str(binfct1)+'.dat'
iraf.identify(infile, section='middle line', database=database, \
coordlist=coordlist, units='', nsum=20,\
match=-15., ftype='absorption', fwidth=16./binfct1, \
cradius=5.,\
threshold=0., function='chebyshev', order=2, sample='*', \
niter=0, autowrite='no')
iraf.reidentify(infile, infile, interac='no', nsum=50, \
section='middle line', newaps='no', override='no',\
refit='yes', trace='yes', step=100, shift=0,\
nlost=20, cradius=5., threshold=0., addfeatures='no',\
coordlist=coordlist, match=-3., \
database=database, logfile='identify_gap.log', plotfile='', \
verbose='yes', cursor='')
return
def calibrate():
namelst = [i.split('\n')[0] for i in file(targetoutput)]
for i in namelst:
iraf.twodspec()
iraf.longslit(dispaxis=2,
nsum=1,
observatory='Lijiang',
extinction='onedstds$LJextinct.dat',
caldir='onedstds$ctiocal/')
iraf.calibrate(input=i,
output=i.split('.')[0] + 'f.fits',
extinct='yes',
flux='yes',
extinction='onedstds$LJextinct.dat',
ignoreaps='yes',
sensitivity='sens',
fnu='no')
def calibrate(lstfile):
stdpath = os.path.split(os.path.realpath(__file__))[0] + os.sep + 'standarddir' + os.sep
extpath = os.path.split(os.path.realpath(__file__))[0] + os.sep + 'LJextinct.dat'
iraf.noao()
iraf.twodspec()
iraf.longslit(dispaxis = 2, nsum = 1, observatory = 'Lijiang',
extinction = extpath, caldir = stdpath)
f = open(lstfile)
l = f.readlines()
f.close()
l = [tmp.split('\n')[0] for tmp in l]
for fitname in l:
stdobjname = select_std(fitname)
stdfitname = 'sensawftbo' + stdgroup[stdobjname][0]
iraf.calibrate(input = 'awftbo'+ fitname, output = 'mark_awftbo' + fitname,
extinct = 'yes', flux = 'yes', extinction = extpath,
ignoreaps = 'yes', sensitivity = stdfitname, fnu = 'no')
iraf.splot(images = 'mark_awftbo' + fitname)
def identify_each(inname, database='database', \
coordlist=fi.filibdir+'thar.300.dat', \
section_x=50, \
overwrite=False):
# Not to display items in IRAF packages
sys.stdout = open('/dev/null', 'w')
iraf.noao()
iraf.twodspec()
iraf.longslit()
sys.stdout = sys.__stdout__ # Back to the stadard output
idfile = database + '/id' + inname
if os.path.isfile(idfile) and overwrite == False:
print('ID file already exists. '+idfile)
else:
if os.path.isfile(idfile) and overwrite == True:
print('Removing ' + idfile)
try:
os.remove(idfile)
except:
pass
# Creating the "section" parameter
section = 'y '+str(section_x)
iraf.identify(inname, section=section,
database=database, coordlist=coordlist, units='',
nsum=nsum, match=match, ftype='emission', fwidth=fwidth,
cradius=cradius, threshold=threshold,
function='chebyshev', order=order, sample='*',
niter=niter, autowrite=autowrite, cursor='')
iraf.reidentify(inname, inname,
interac='no', section=section, newaps=newaps,
override=override, refit=refit, trace=trace,
step=step, shift=0, nlost=nlost, cradius=cradius,
threshold=threshold, addfeatures=addfeatures,
coordlist=coordlist, match=match, database=database,
logfile=logfile, plotfile='', verbose=verbose,
cursor='')
return
def apall(ilst, olst):
iraf.noao()
iraf.twodspec()
iraf.apextract(dispaxis=1, database='database')
for i, infile in enumerate(ilst):
outfile = olst[i]
laper, raper, back_samp = -7, 7, '-30:-15,15:30'
while True:
if os.path.isfile(outfile):
print('remove ' + outfile)
os.remove(outfile)
delfile = os.getcwd()+os.sep+'database/ap'+infile[0:-5]
if os.path.isfile(delfile):
print('remove ' + delfile)
os.remove(delfile)
iraf.apall(input=infile, output=outfile, apertures=2,
format='multispec', references='', profiles='',
interactive=True, find=True, recenter=True,
resize=False, edit=True, trace=True, fittrace=True,
extract=True, extras=True, review=True, line='INDEF',
nsum=10, lower=laper, upper=raper, apidtable='',
b_function='chebyshev', b_order=2, b_sample=back_samp,
b_naverage=-25, b_niterate=1, b_low_reject=3.0,
b_high_reject=3.0, b_grow=0.0, width=5.0, radius=10.0,
threshold=0.0, nfind=2, minsep=5.0, maxsep=100000.0,
order='increasing', aprecenter='', npeaks='INDEF',
shift=True, llimit='INDEF', ulimit='INDEF', ylevel=0.1,
peak=True, bkg=True, r_grow=0.0, avglimits=False,
t_nsum=20, t_step=10, t_nlost=3, t_function='legendre',
t_order=12, t_sample='*', t_naverage=1, t_niterate=1,
t_low_reject=3.0, t_high_reject=3.0, t_grow=0.0,
background='median', skybox=1, weights='none',
pfit='fit1d', clean=True, saturation='INDEF',
readnoise='CCDRON', gain='CCDGAIN', lsigma=4.0, usigma=4.0,
nsubaps=1)
iraf.flpr()
getval = raw_input(('Are you need repeat apall,'
'may be clean should be close(r/n)'))
if getval != 'r':
break
def TransformTask(self, InputFile, OutputFile, Fits_Folder, ArcFile, Suffix = 'a'):
iraf.noao(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.longslit(_doprint=0)
#Incase no output name is given, we generate one with the provided "preffix" (The defaul format is a_std_wolf.dat)
if OutputFile == None:
OutputFile = self.outputNameGenerator(InputFile, Suffix)
TransConf = self.TransformAttributes(InputFile, OutputFile, Fits_Folder, ArcFile)
#Display the equivalent command in IRAF
Command = self.printIrafCommand('transform', TransConf)
print '--- Using the command'
print Command
iraf.twodspec.longslit.transform(**TransConf)
return OutputFile
def standard():
f = open(standoutput)
l = f.readlines()
f.close()
namelst = [i.split('\n')[0] for i in l]
temp = ''
for i in namelst:
temp = temp + i + ','
temp = temp[0:-1]
# for i in xrange(len(namelst)):
# iraf.hselect(images = namelst[i], fields = '$I,object', expr = 'yes')
# standname = raw_input('please input standard star name:')
# print 'standard star name:', standname
iraf.twodspec()
iraf.longslit(dispaxis=2,
nsum=1,
observatory='Lijiang',
extinction='onedstds$LJextinct.dat',
caldir='onedstds$ctiocal/')
for i in xrange(len(namelst)):
print '+' * 10, namelst[i]
iraf.hselect(images=namelst[i], fields='$I,object', expr='yes')
standname = raw_input('please input standard star name:')
print 'standard star name:', standname
#iraf.standard(input = namelst[i], output = namelst[i].split('.')[0] + '.std',
# samestar = 'yes', interact = 'yes', star_name = standname, airmass = '',
# exptime = '', extinction = 'onedstds$LJextinct.dat',
# caldir = 'onedstds$ctiocal/')
iraf.standard(input=namelst[i],
output='std',
samestar='yes',
interact='yes',
star_name=standname,
airmass='',
exptime='',
extinction='onedstds$LJextinct.dat',
caldir='onedstds$ctiocal/')
def combine_flat(filename):
outname = filename.replace('.lst','.fits')
print 'run function flatcombine...'
print 'make file', outname
iraf.flatcombine(input = 'tbo//@' + filename
, output = outname, combine = 'average', reject = 'avsigclip'
, ccdtype = '', process = False, subsets = True
, delete = False, clobber = False, scale = 'mode'
, statsec = '', nlow = 1, nhigh = 1, nkeep = 1
, mclip = True, lsigma = 3.0, hsigma = 3.0
, rdnoise = 9.4, gain = 0.35, snoise = 0.0
, pclip = -0.5, blank = 1.0)
iraf.noao()
iraf.twodspec()
iraf.longslit()
print 'run function response...'
print 'make file', 're' + outname
iraf.response(calibration = outname
, normalization = outname, response = 're' + outname
, interactive = True, threshold = 'INDEF', sample = '*'
, naverage = 1, function = 'spline3', order = 7
, low_reject = 0.0, high_reject = 0.0, niterate = 1
, grow = 0.0, graphics = 'stdgraph', cursor = '')
print 'run function illumination...'
print 'make file', 'il' + outname
iraf.illumination(images = 're' + outname
, illuminations = 'il' + outname, interactive = False
, bins = '', nbins = 5, sample = '*', naverage = 1
, function = 'spline3', order = 1, low_reject = 0.0
, high_reject = 0.0, niterate = 1, grow = 0.0
, interpolator = 'poly3', graphics = 'stdgraph', cursor = '')
print 'run function imarith...'
print 'make file', 'per' + outname
iraf.imarith(operand1 = 're' + outname
, op = '/', operand2 = 'il' + outname, result = 'per' + outname
, title = '', divzero = 0.0, hparams = '', pixtype = ''
, calctype = '', verbose = True, noact = False)
return outname, 're' + outname, 'il' + outname, 'per' + outname
def gen_Resp_2016():
iraf.twodspec()
iraf.longslit(dispaxis=2,
nsum=1,
observatory='observatory',
extinction=func.extinction_file,
caldir=func.std_path + os.sep,
interp='poly5')
iraf.response(calibration='Halogen',
normalization='Halogen',
response='Resp',
interactive=True,
threshold='INDEF',
sample='*',
naverage=1,
function='spline3',
order=45,
low_reject=10.0,
high_reject=10.0,
niterate=1,
grow=0.0,
graphics='stdgraph',
cursor='')
def ApallTask(self, InputFile, OutputFile, Fits_Folder, line, nsum = None, lower_lim = None, upper_lim = None, extras = 'yes', Suffix = 'bg'):
iraf.noao(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.apextract(_doprint=0)
#Incase no output name is given, we generate one with the provided "preffix" (The defaul format is a_std_wolf.dat)
if OutputFile == None:
OutputFile = self.outputNameGenerator(InputFile + '[1]', Suffix)
ApallConf = self.ApallAttributes(InputFile, OutputFile, Fits_Folder, line, nsum, lower_lim, upper_lim, extras)
#Display the equivalent command in IRAF
Command = self.printIrafCommand('apall', ApallConf)
print '--- Using the command'
print Command
# p = Popen(iraf.twodspec.apextract.apall(**ApallConf), shell=True, stdout=PIPE, stderr=STDOUT)
#Run the task
iraf.twodspec.apextract.apall(**ApallConf)
return OutputFile
def combine_flat(lstfile):
iraf.noao()
iraf.imred()
iraf.ccdred()
iraf.flatcombine(input = 'tbo//@' + lstfile
, output = 'Halogen', combine = 'average', reject = 'crreject'
, ccdtype = '', process = False, subsets = False
, delete = False, clobber = False, scale = 'mode'
, statsec = '', nlow = 1, nhigh = 1, nkeep = 1
, mclip = True, lsigma = 3.0, hsigma = 3.0
, rdnoise = 'rdnoise', gain = 'gain', snoise = 0.0
, pclip = -0.5, blank = 1.0)
script_path = os.path.split(os.path.realpath(__file__))[0]
iraf.twodspec()
iraf.longslit(dispaxis = 2, nsum = 1, observatory = 'observatory'
, extinction = script_path + os.sep + 'LJextinct.dat'
, caldir = script_path + os.sep + 'standarddir' + os.sep, interp = 'poly5')
iraf.response(calibration = 'Halogen'
, normalization = 'Halogen', response = 'Resp'
, interactive = True, threshold = 'INDEF', sample = '*'
, naverage = 1, function = 'spline3', order = 25
, low_reject = 10.0, high_reject = 10.0, niterate = 1
, grow = 0.0, graphics = 'stdgraph', cursor = '')
def identify_edge(infile, overwrite=False):
print('\n#############################')
print('Identifying the edges.')
binfct1 = fits.getval(infile, 'BIN-FCT1')
coordlist = fi.filibdir + 'edge' + str(binfct1) + '.dat'
section = 'middle line'
verbose = 'yes'
nsum = 50
match = -10.
fwidth = 6. / binfct1
cradius = 20. / binfct1
threshold = 0.
function = 'chebyshev'
order = 2
niter = 0
autowrite = 'yes'
newaps = 'yes'
override = 'yes'
refit = 'no'
trace = 'yes'
step = 50
shift = 0
nlost = 0
minsep = 60. / binfct1
addfeatures = 'no'
database = 'database'
logfile = 'identify_edge.log'
# Not to display items in IRAF packages
sys.stdout = open('/dev/null', 'w')
iraf.noao()
iraf.twodspec()
iraf.longslit()
sys.stdout = sys.__stdout__ # Back to the stadard output
# entering the channel image directory.
# os.chdir() does not change the directory for pyraf only in this function.
print('\t Entering the channel image directory, \"' + fi.chimagedir +
'\".')
iraf.cd(fi.chimagedir)
basename = fits.getval('../' + infile, 'FRAMEID')
idfile = database + '/id' + basename + '.ch01edge'
if os.path.isfile(idfile) and not overwrite:
print('\t Edge identification files already exist, ' + idfile \
+ '. Skipping.')
else:
if os.path.isfile(idfile) and overwrite:
print('\t Removing ' + idfile)
try:
os.remove(idfile)
except:
pass
print('\t Identifying: ' + basename + '.ch01edge.fits')
iraf.identify(basename + '.ch01edge',
section=section,
database=database,
coordlist=coordlist,
units='',
nsum=nsum,
match=match,
maxfeat=2,
ftype='emission',
fwidth=fwidth,
cradius=cradius,
threshold=threshold,
function=function,
order=order,
sample='*',
niter=niter,
autowrite=autowrite)
print('\t Reidentifying: ' + basename + '.ch01edge.fits')
iraf.reidentify(basename + '.ch01edge',
basename + '.ch01edge',
interac='no',
section=section,
newaps=newaps,
override=override,
refit=refit,
trace=trace,
step=step,
nsum=nsum,
shift=shift,
nlost=nlost,
cradius=cradius,
threshold=threshold,
addfeatures=addfeatures,
coordlist=coordlist,
match=match,
maxfeat=2,
minsep=minsep,
database=database,
logfile=logfile,
plotfile='',
verbose=verbose,
cursor='')
for i in range(2, 25):
print('\t Reidentifying: ' + basename + '.ch%02dedge.fits' % i)
idfile = database + '/id' + basename + '.ch%02dedge' % i
if os.path.isfile(idfile) and not overwrite:
print('\t Edge identification files already exist, ' + idfile +
'. Skipping.')
else:
if os.path.isfile(idfile) and overwrite:
print('\t Removing ' + idfile)
try:
os.remove(idfile)
except:
pass
# treatment for VPH650
if i == 12:
disperser = fits.getval(basename + '.ch12edge.fits',
'DISPERSR')
if disperser == 'SCFCGRHD65':
nlost = 1
if i == 13:
disperser = fits.getval(basename + '.ch12edge.fits',
'DISPERSR')
if disperser == 'SCFCGRHD65':
nlost = 0
iraf.reidentify(basename+'.ch%02dedge'%(i-1), \
basename+'.ch%02dedge'%i, \
interac='no', section=section, newaps=newaps, \
override=override, refit=refit, trace=trace, \
step=0.0, nsum=nsum, shift=shift, nlost=nlost, \
cradius=cradius, threshold=threshold, \
addfeatures=addfeatures, coordlist=coordlist, \
match=match, maxfeat=2, minsep=minsep, \
database=database, logfile=logfile, \
plotfile='', verbose=verbose, cursor='')
#Check the result
iraf.identify(basename+'.ch%02dedge'%i, section=section, \
database=database, coordlist=coordlist, units='', \
nsum=nsum, match=match, maxfeat=2,ftype='emission', \
fwidth=fwidth, cradius=cradius, threshold=threshold, \
function=function, order=order, sample='*', \
niter=niter, autowrite=autowrite)
iraf.reidentify(basename+'.ch%02dedge'%i, \
basename+'.ch%02dedge'%i, \
interac='no', section=section, newaps=newaps, \
override=override, refit=refit, trace=trace, \
step=step, nsum=nsum, shift=shift, nlost=nlost, \
cradius=cradius, threshold=threshold, \
addfeatures=addfeatures, coordlist=coordlist, \
match=match, maxfeat=2, minsep=minsep, \
database=database, logfile=logfile, \
plotfile='', verbose=verbose, cursor='')
print('\t Go back to the original directory.')
iraf.cd('..')
disperser = fits.getval(fi.chimagedir + basename + '.ch12edge.fits',
'DISPERSR')
if disperser == 'SCFCGRHD65':
correct_ch12_edge(basename, overwrite=overwrite)
return
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 main():
iraf.noao()
iraf.imred()
iraf.ccdred()
iraf.crutil()
iraf.twodspec()
iraf.apextract()
name = os.popen('ls object*.lst').readlines()
name = [i.split('\n')[0] for i in name]
if len(name) == 0:
print 'no object, the script end'
for i in name:
print 'open file ' + i + '...'
f = open(i)
l = f.readlines()
f.close()
l = [i2.split('\n')[0] for i2 in l]
for i2 in l:
print i2
objs = []
calibs = []
standards = []
fno = l.index('object') + 1
while fno < len(l):
if '.fits' not in l[fno]:
break
objs.append('ftbo' + l[fno])
fno += 1
fno = l.index('calibration') + 1
while fno < len(l):
if '.fits' not in l[fno]:
break
calibs.append('ftbo' + l[fno])
fno += 1
fno = l.index('standard') + 1
while fno < len(l):
if '.fits' not in l[fno]:
break
standards.append('ftbo' + l[fno])
fno += 1
print '=' * 20, 'reject cosmic ray', '=' * 20
#cobj = shift_align(objs)
#cstandard = shift_align(standards)
cobjs = []
cstandards = []
for eobj in objs:
cobjs.append(cos_clear([eobj]))
for eobj in standards:
cstandards.append(cos_clear([eobj]))
print 'The fits having rejected cosmic ray is: '
print cobjs
print cstandards
print '=' * 20, 'get spectrum line', '=' * 20
acobjs = []
acstandards = []
for cobj in cobjs:
acobjs.append(get_spec_line(cobj))
for cobj in cstandards:
acstandards.append(get_spec_line(cobj))
acalibs = []
for i3 in calibs:
acalibs.append(get_cal_spec_line(i3, cobj))
print '=' * 20, 'wavelength identify', '=' * 20
iacalibs = wl_identy(acalibs)
print '=' * 20, 'wavelength calibrate', '=' * 20
inamepart = i.replace('.','_').split('_')
objnames = []
stdobjnames = []
for numb in xrange(len(acobjs)):
objnames.append( 'iac' + inamepart[1] + '_' + inamepart[2] + '_' + inamepart[3] +
'_' + str(numb) + '.fits')
for numb in xrange(len(acstandards)):
stdobjnames.append('iacstd' + inamepart[1] + '_' + inamepart[2] + '_' + inamepart[3] +
'_' + str(numb) + '.fits')
for numb in xrange(len(objnames)):
wl_cal(acobjs[numb], objnames[numb], iacalibs)
for numb in xrange(len(stdobjnames)):
wl_cal(acstandards[numb], stdobjnames[numb], iacalibs)
print '=' * 20, 'flux calibrate', '=' * 20
for objname in objnames:
final_objname = flux_cal_new(objname, stdobjnames)
print 'the final object name is', final_objname
global iraf
from pyraf import iraf
import numpy as np
import pyfits
from glob import glob
import os
iraf.pysalt()
iraf.saltspec()
iraf.saltred()
iraf.set(clobber='YES')
iraf.noao()
iraf.twodspec()
iraf.longslit()
def tofits(filename, data, hdr=None, clobber=False):
"""simple pyfits wrapper to make saving fits files easier."""
from pyfits import PrimaryHDU, HDUList
hdu = PrimaryHDU(data)
if hdr is not None:
hdu.header = hdr
hdulist = HDUList([hdu])
hdulist.writeto(filename, clobber=clobber, output_verify='ignore')
def get_ims(fs, imtype):
imtypekeys = {'sci': 'OBJECT', 'arc': 'ARC', 'flat': 'FLAT'}
ims = []
grangles = []
for f in fs:
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 load_modules():
# Define a function to load all of the modules so that they don't' import
# unless we need them
global iraf
from pyraf import iraf
iraf.pysalt()
iraf.saltspec()
iraf.saltred()
iraf.set(clobber='YES')
global sys
import sys
global os
import os
global shutil
import shutil
global glob
from glob import glob
global pyfits
import pyfits
global np
import numpy as np
global lacosmicx
import lacosmicx
global interp
from scipy import interp
global signal
from scipy import signal
global ndimage
from scipy import ndimage
global interpolate
from scipy import interpolate
global WCS
from astropy.wcs import WCS
global optimize
from scipy import optimize
global ds9
import ds9
global GaussianProcess
from sklearn.gaussian_process import GaussianProcess
global pandas
import pandas
iraf.onedspec()
iraf.twodspec()
iraf.longslit()
iraf.apextract()
iraf.imutil()
def correct_airmass():
stdout = os.popen("ls Y*otbfmsw.fits").readlines()
namelst = [i.split('\n')[0] for i in stdout]
for i in xrange(len(namelst)):
fits = pyfits.open(namelst[i])
extnum = len(fits)
objname = fits[0].header['object']
fits.close()
print '#' * 50
print namelst[i], objname
name = raw_input('please input the name of object:')
ra, dec = findradec(name)
print name, ra, dec
for j in xrange(extnum):
stdout = iraf.hselect(images=namelst[i] + '[%i]' % j,
fields='airmass',
expr='yes',
Stdout=1)
airold = float(stdout[0])
print '+' * 5, namelst[i], 'ext:', j, 'airmass_old:', airold
iraf.hedit(images=namelst[i] + '[%i]' % j,
fields='airold',
value=airold,
add='yes',
addonly='yes',
delete='no',
verify='no',
show='yes',
update='yes')
iraf.hedit(images=namelst[i] + '[%i]' % j,
fields='sname',
value=name,
add='yes',
addonly='yes',
delete='no',
verify='no',
show='yes',
update='yes')
iraf.hedit(images=namelst[i] + '[%i]' % j,
fields='sname',
value=name,
add='yes',
addonly='yes',
delete='no',
verify='no',
show='yes',
update='yes')
iraf.hedit(images=namelst[i] + '[%i]' % j,
fields='ra',
value=ra,
add='yes',
addonly='yes',
delete='no',
verify='no',
show='yes',
update='yes')
iraf.hedit(images=namelst[i] + '[%i]' % j,
fields='dec',
value=dec,
add='yes',
addonly='yes',
delete='no',
verify='no',
show='yes',
update='yes')
iraf.twodspec()
iraf.longslit(dispaxis=2,
nsum=1,
observatory='Lijiang',
extinction='onedstds$LJextinct.dat',
caldir='onedstds$spec50cal/')
iraf.setairmass(images=namelst[i] + '[%i]' % j,
observatory='Lijiang',
intype='beginning',
outtype='effective',
ra='ra',
dec='dec',
equinox='epoch',
st='lst',
ut='date-obs',
date='date-obs',
exposure='exptime',
airmass='airmass',
utmiddle='utmiddle',
scale=750.0,
show='yes',
override='yes',
update='yes')
print 'name airmass_new airmass_old'
iraf.hselect(images=namelst[i] + '[%i]' % j,
fields='$I,airmass,airold',
expr='yes')
def main():
description = "> Performs pre-reduction steps"
usage = "%prog \t [option] \n Recommended syntax: %prog -i -c"
parser = OptionParser(usage=usage, description=description, version="0.1")
option, args = parser.parse_args()
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.longslit(_doprint=0)
iraf.onedspec(_doprint=0)
iraf.specred(_doprint=0)
iraf.ccdred.verbose = 'no'
iraf.specred.verbose = 'no'
iraf.ccdproc.darkcor = 'no'
iraf.ccdproc.fixpix = 'no'
iraf.ccdproc.flatcor = 'no'
iraf.ccdproc.zerocor = 'no'
iraf.ccdproc.ccdtype = ''
iraf.longslit.mode = 'h'
iraf.specred.mode = 'h'
iraf.noao.mode = 'h'
iraf.ccdred.instrument = "ccddb$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 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 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
from pyraf import iraf #from iraf import * import os,glob,time,re,pickle,math,matplotlib,aplpy from numpy import * from scipy.optimize import leastsq,fsolve#,fmin_slsqp from scipy.integrate import quad from scipy.stats import * from datetime import datetime, date, time import matplotlib.pyplot as plt from datetime import timedelta from numpy.random import randint import pyfits iraf.tables() iraf.noao() iraf.imred() iraf.twodspec() iraf.onedspec() iraf.ccdred() iraf.apextract() iraf.longslit() iraf.plot() iraf.stsdas() iraf.nebular() from time import strftime ############################################################# ########Setup a list of lines and fitting regions ############################################################# #the list of lines
def telluric_atmo(imgstd):
# print "LOGX:: Entering `telluric_atmo` method/function in %(__file__)s"
# % globals()
import numpy as np
import ntt
from pyraf import iraf
try: import pyfits
except: from astropy.io import fits as pyfits
iraf.images(_doprint=0)
iraf.noao(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.longslit(_doprint=0)
iraf.onedspec(_doprint=0)
toforget = ['imfilter.gauss', 'specred.apall', 'longslit.identify', 'longslit.reidentify', 'specred.standard',
'onedspec.wspectext']
for t in toforget:
iraf.unlearn(t)
_grism = ntt.util.readkey3(ntt.util.readhdr(imgstd), 'grism')
imgout = 'invers_atmo_' + imgstd
ntt.util.delete(imgout)
iraf.set(direc=ntt.__path__[0] + '/')
_cursor = 'direc$standard/ident/cursor_sky_0'
iraf.noao.onedspec.bplot(imgstd, cursor=_cursor,
spec2=imgstd, new_ima=imgout, overwri='yes')
xxstd, ffstd = ntt.util.readspectrum(imgout)
if _grism in ['Gr13', 'Gr16']:
llo2 = np.compress((np.array(xxstd) >= 7550) & (
np.array(xxstd) <= 7750), np.array(xxstd))
llh2o = np.compress((np.array(xxstd) >= 7100) & (
np.array(xxstd) <= 7500), np.array(xxstd))
ffo2 = np.compress((np.array(xxstd) >= 7550) & (
np.array(xxstd) <= 7750), np.array(ffstd))
ffh2o = np.compress((np.array(xxstd) >= 7100) & (
np.array(xxstd) <= 7500), np.array(ffstd))
elif _grism in ['Gr11']:
llo2 = np.compress((np.array(xxstd) >= 6830) & (
np.array(xxstd) <= 7100), np.array(xxstd))
llh2o = np.compress((np.array(xxstd) >= 7100) & (
np.array(xxstd) <= 7500), np.array(xxstd))
ffo2 = np.compress((np.array(xxstd) >= 6830) & (
np.array(xxstd) <= 7100), np.array(ffstd))
ffh2o = np.compress((np.array(xxstd) >= 7100) & (
np.array(xxstd) <= 7500), np.array(ffstd))
if _grism in ['Gr13', 'Gr16', 'Gr11']:
_skyfileh2o = 'direc$standard/ident/ATLAS_H2O.fits'
_skyfileo2 = 'direc$standard/ident/ATLAS_O2.fits'
atlas_smooto2 = '_atlas_smoot_o2.fits'
atlas_smooth2o = '_atlas_smoot_h2o.fits'
_sigma = 200
ntt.util.delete(atlas_smooto2)
ntt.util.delete(atlas_smooth2o)
iraf.imfilter.gauss(_skyfileh2o, output=atlas_smooth2o, sigma=_sigma)
iraf.imfilter.gauss(_skyfileo2, output=atlas_smooto2, sigma=_sigma)
llskyh2o, ffskyh2o = ntt.util.readspectrum(atlas_smooth2o)
llskyo2, ffskyo2 = ntt.util.readspectrum(atlas_smooto2)
ffskyo2cut = np.interp(llo2, llskyo2, ffskyo2)
ffskyh2ocut = np.interp(llh2o, llskyh2o, ffskyh2o)
_scaleh2o = []
integral_h2o = []
for i in range(1, 21):
j = 0.6 + i * 0.04
_ffskyh2ocut = list((np.array(ffskyh2ocut) * j) + 1 - j)
diff_h2o = abs(_ffskyh2ocut - ffh2o)
integraleh2o = np.trapz(diff_h2o, llh2o)
integral_h2o.append(integraleh2o)
_scaleh2o.append(j)
_scaleo2 = []
integral_o2 = []
for i in range(1, 21):
j = 0.6 + i * 0.04
_ffskyo2cut = list((np.array(ffskyo2cut) * j) + 1 - j)
diff_o2 = abs(_ffskyo2cut - ffo2)
integraleo2 = np.trapz(diff_o2, llo2)
integral_o2.append(integraleo2)
_scaleo2.append(j)
sh2o = _scaleh2o[np.argmin(integral_h2o)]
so2 = _scaleo2[np.argmin(integral_o2)]
telluric_features = ((np.array(ffskyh2o) * sh2o) +
1 - sh2o) + ((np.array(ffskyo2) * so2) + 1 - so2) - 1
telluric_features = np.array([1] + list(telluric_features) + [1])
llskyo2 = np.array([1000] + list(llskyo2) + [15000])
telluric_features_cut = np.interp(xxstd, llskyo2, telluric_features)
_imgout = 'atmo_' + imgstd
data1, hdr = pyfits.getdata(imgstd, 0, header=True)
data1[0] = np.array(telluric_features_cut)
data1[1] = data1[1] / data1[1]
data1[2] = data1[2] / data1[2]
data1[3] = data1[3] / data1[3]
ntt.util.delete(_imgout)
pyfits.writeto(_imgout, np.float32(data1), hdr)
ntt.util.delete(atlas_smooto2)
ntt.util.delete(atlas_smooth2o)
ntt.util.delete(imgout)
else:
_imgout = ''
print '### telluric correction with model not possible '
return _imgout
def oned_extract(object_b_fn, object_b_fn_ec, order_def, colour):
# Import IRAF modules:
iraf.noao(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.apextract(_doprint=0)
# Check input file and reference extraction exist before proceeding:
if os.path.isfile(object_b_fn) == True:
if os.path.isfile(order_def) == True:
# Extract one dimensional spectrum from bias- and background scatter-subtracted
# normalized flat-fielded red object frame using IRAF task apall:
if colour == 'red':
parList = "r_oned_extraction_apall.par"
if os.path.isfile(parList) == True:
iraf.apall.setParList(ParList = "r_oned_extraction_apall.par")
iraf.apall(input=object_b_fn, output=object_b_fn_ec, references=order_def)
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'One dimensional spectral extraction of bias- and '
print 'background-subtracted, normalized flat-fielded '
print 'red object ' + object_b_fn
print 'to create ' + object_b_fn_ec + '.'
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
else:
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'One dimensional extraction IRAF .par file '
print str(parList)
print 'does not exist. Exiting script. '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
print ' '
sys.exit()
# Extract one dimensional spectrum from bias- and background scatter-subtracted
# normalized flat-fielded blue object frame using IRAF task apall:
if colour == 'blue':
parList = "b_oned_extraction_apall.par"
if os.path.isfile(parList) == True:
iraf.apall.setParList(ParList = "b_oned_extraction_apall.par")
iraf.apall(input=object_b_fn, output=object_b_fn_ec, references=order_def)
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'One dimensional spectral extraction of bias- and '
print 'background-subtracted, normalized flat-fielded '
print 'blue object ' + object_b_fn
print 'to create ' + object_b_fn_s + '.'
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
else:
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'One dimensional extraction IRAF .par file '
print str(parList)
print 'does not exist. Exiting script. '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
print ' '
sys.exit()
else:
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Order definition frame '
print str(order_def)
print 'does not exist. Exiting script. '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
print ' '
sys.exit()
else:
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Input frame '
print str(object_b_fn)
print 'does not exist. Exiting script. '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
print ' '
sys.exit()
from datetime import date
import operator
import time
import traceback
from lmfit import minimize, Parameters, report_fit
#import cosmics
iraf.noao(_doprint=0,Stdout="/dev/null")
iraf.rv(_doprint=0,Stdout="/dev/null")
iraf.imred(_doprint=0,Stdout="/dev/null")
iraf.ccdred(_doprint=0,Stdout="/dev/null")
iraf.images(_doprint=0,Stdout="/dev/null")
iraf.immatch(_doprint=0,Stdout="/dev/null")
iraf.onedspec(_doprint=0,Stdout="/dev/null")
iraf.twodspec(_doprint=0,Stdout="/dev/null")
iraf.apextract(_doprint=0,Stdout="/dev/null")
iraf.imutil(_doprint=0,Stdout="/dev/null")
iraf.echelle(_doprint=0,Stdout="/dev/null")
iraf.astutil(_doprint=0,Stdout="/dev/null")
iraf.apextract.dispaxi=1
iraf.echelle.dispaxi=1
#fixes a bug with latest versions of iraf
iraf.ccdred.instrum='blank.txt'
os.environ['PYRAF_BETA_STATUS'] = '1'
# REFS NEW: ecEC59550 ecEC59758 ecEC59795 ecEC59842 ecEC59844 ecEC59864 ecEC59866 ecEC59881 ecEC59883 ecEC59885
observations = {
import os
folderroot = '/Users/lucaizzo/Documents/NOT/test/'
os.chdir(folderroot)
import numpy as np
from astropy.io import fits
from matplotlib import pyplot as plt
import shutil
import sys
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.kpnoslit(_doprint=0)
iraf.astutil(_doprint=0)
iraf.onedspec(_doprint=0)
iraf.twodspec.longslit.dispaxis = 2
#read object keywords
for file in os.listdir(os.getcwd()):
if file.endswith('.fits'):
testfile = file
hduo = fits.open(testfile)
#name targets (science & standard)
target = hduo[0].header['OBJECT']
#target2 = 'SP0644p375'
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
import os
# Delete some directories/files from previous runs.
os.system("rm -rf login.cl pyraf database uparm")
os.system("mkiraf")
# Now load IRAF
import pyraf.iraf as iraf
# Load the packages we might need.
iraf.noao(_doprint=0)
iraf.onedspec(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.apextract(_doprint=0)
iraf.unlearn(iraf.apall)
# The name of the science file.
filename = 'vega_9.3narrow.fit'
extracted_filename = 'vega_9.3narrow.ms.fits'
calibrated_filename = 'vega_9.3narrow.calib.fits'
# Delete previous results.
os.system("rm "+extracted_filename+" "+calibrated_filename)
# Make sure that the dispersion axis is in the header.
iraf.noao(_doprint=0,Stdout="/dev/null")
iraf.imred(_doprint=0,Stdout="/dev/null")
iraf.ccdred(_doprint=0,Stdout="/dev/null")
# <codecell>
#bias subtract
iraf.ccdproc(images=flat, ccdtype='', fixpix='no', oversca='no', trim='no', zerocor='yes', darkcor='no', flatcor='no', zero='masterbias',Stdout="/dev/null")
# <codecell>
#apall on a flat field
print " + Extract masterflat"
iraf.noao(_doprint=0,Stdout="/dev/null")
iraf.twodspec(_doprint=0,Stdout="/dev/null")
iraf.apextract(_doprint=0,Stdout="/dev/null")
# <codecell>
#adds DISPAXIS=1 to the header
iraf.hedit(images=flat, fields='DISPAXIS', value=1, add='yes', verify='no', update='yes',Stdout="/dev/null")
# <codecell>
iraf.unlearn('apall') #restores initial paramenters to apall
# <codecell>
shift_master(ap_ref[ccd], "database/ap%s" % ap_ref[ccd][:-5])
