def thar_cal(object_b_fn_ec, object_b_fn_ec_w, colour):
# Import IRAF modules:
iraf.noao(_doprint=0)
iraf.onedspec(_doprint=0)
# Check input file and reference extraction exist before proceeding:
if os.path.isfile(object_b_fn_ec) == True:
# Perform dispersion correction:
iraf.dispcor(input=object_b_fn_ec, output=object_b_fn_ec_w)
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print colour.capitalize() + ' object spectrum '
print '(' + str(object_b_fn_ec) + ')'
print 'successfully wavelength calibrated in file '
print str(object_b_fn_ec_w) + '.'
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
else:
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Input frame '
print str(object_b_fn_ec)
print 'does not exist. Exiting script. '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
print ' '
sys.exit()
def dispcor_err(msfile):
'''Propagate error through wavelength rectification.
Read wavelength solution information from the FITS header and resample an
image to a linear wavelength scale.
The error is propagated by simply passing a squared .ms.fits file to
**dispcor** and taking the square root of the result. This method makes the
following assumptions:
1. The wavelength solution in the header is close enough to linear that
spline3 interpolation used by DISPCOR essentially becomes a linear
interpolation. This means the error on each output pixel is just the
quadrature sum of the errors of the input pixels that went into that
output pixel (divided by the number of pixels).
2. Any effects of fractional pixels is minimal.
'''
tmpname = 'tmp_sq{}'.format(msfile)
outputname = msfile.replace('me.fits', 'me_lin.fits')
tmpoutput = 'tmp_sq{}'.format(outputname)
pow_image(msfile, tmpname, 2.)
iraf.dispcor(tmpname, tmpoutput, linearize=True, samedisp=True)
pow_image(tmpoutput, outputname, 0.5)
return outputname
def wl_cal(filename, outname, cals):
print 'run wavelength calibration...'
if os.path.isfile(outname):
print 'file %s is already exist' % outname
else:
iraf.imred()
iraf.specred()
print 'The calibration file is listed below:'
for i in xrange(len(cals)):
print i, cals[i], get_fits_objname(cals[i])
valget = 0
if len(cals) > 1:
valget = raw_input('Which one are you want to use?:')
while type(eval(valget)) != int:
valget = raw_input('input is not a int type, please reinput:')
valget = int(valget)
iraf.refspectra(input = filename
, references = cals[valget], apertures = '', refaps = ''
, ignoreaps = True, select = 'interp', sort = ''
, group = '', time = False, timewrap = 17.0
, override = False, confirm = True, assign = True
, logfiles = 'STDOUT,logfile', verbose = False, answer = 'yes')
print 'make file ' + outname + '...'
iraf.dispcor(input = filename
, output = outname, linearize = True, database = 'database'
, table = '', w1 = 'INDEF', w2 = 'INDEF', dw = 'INDEF'
, nw = 'INDEF', log = False, flux = True, blank = 0.0
, samedisp = False, ignoreaps = True
, confirm = False, listonly = False, verbose = True
, logfile = '')
print 'splot %s' % outname
iraf.splot(images = outname)
def correlate():
scilist = glob.glob('AGN*EX.fits')
stdlist = glob.glob('STD*EX.fits')
lamps = glob.glob('LAMP*EX.fits')
for img in lamps:
hdu = pyfits.getheader(img)
if hdu['HIERARCH ESO DPR TECH'] == 'SPECTRUM':
specref = img.rsplit('.fits',1)[0]
elif hdu['HIERARCH ESO DPR TECH'] == 'MOS':
mosref = img.rsplit('.fits',1)[0]
for spec in scilist:
spec = spec.rsplit('.fits',1)[0]
iraf.refspectra(spec,references=specref,sort='',group='',confirm='no',assign='yes')
name = spec.rsplit('_EX',1)[0]+'_EL'
iraf.dispcor(spec,output=name)
for spec in stdlist:
spec = spec.rsplit('.fits',1)[0]
iraf.refspectra(spec,references=mosref,sort='',group='',confirm='no',assign='yes')
name = spec.rsplit('_EX',1)[0]+'_EL'
iraf.dispcor(spec,output=name)
def dispcor_err(msfile):
'''Propagate error through wavelength rectification.
Read wavelength solution information from the FITS header and resample an
image to a linear wavelength scale.
The error is propagated by simply passing a squared .ms.fits file to
**dispcor** and taking the square root of the result. This method makes the
following assumptions:
1. The wavelength solution in the header is close enough to linear that
spline3 interpolation used by DISPCOR essentially becomes a linear
interpolation. This means the error on each output pixel is just the
quadrature sum of the errors of the input pixels that went into that
output pixel (divided by the number of pixels).
2. Any effects of fractional pixels is minimal.
'''
tmpname = 'tmp_sq{}'.format(msfile)
outputname = msfile.replace('me.fits','me_lin.fits')
tmpoutput = 'tmp_sq{}'.format(outputname)
pow_image(msfile,tmpname,2.)
iraf.dispcor(tmpname,
tmpoutput,
linearize=True,
samedisp=True)
pow_image(tmpoutput,outputname,0.5)
return outputname
def wave_cal (images):
iraf.dispcor.glo = 'no'
iraf.dispcor.flux = 'yes'
iraf.dispcor.input = '@'+ images
iraf.dispcor.output = 'd//@' + images
iraf.lpar(iraf.dispcor)
iraf.dispcor(mode = 'h')
def red_science(image, flats, spath, object=None, arc='Arc-Red.fits',
smooth='BD284211.smooth.fits', telluric='telluric.fits',
sens='BD284211.sens', biassec=REDBIAS, trimsec=REDTRIM,
outflat='Flat-Red.fits', gain=REDGAIN, rdnoise=REDRDNOISE):
'''Full reduction of KAST science spectra on red CCD'''
# Bias subtract everything first
redbias(image, biassec=biassec, trimsec=trimsec)
redbias(flats, biassec=biassec, trimsec=trimsec)
# Create and apply flat-field
make_flat(flats, outflat, gain=gain, rdnoise=rdnoise)
iraf.ccdproc(image[0], ccdtype='', noproc=no, fixpix=no, overscan=no,
trim=no, zerocor=no, darkcor=no, flatcor=yes, illumcor=no,
fringecor=no, readcor=no, scancor=no, flat=outflat)
# Cosmic ray rejection
#iraf.lacos_spec(image[0], 'c%s' % image[0], 'cm%s' % image[0],
# gain=gain, readn=rdnoise, xorder=9, yorder=3,
# sigclip=4.5, sigfrac=0.5, objlim=1.0, niter=3)
# Extract spectrum
if object==None:
object=get_head(image, 'OBJECT')
iraf.apall('%s' % image[0], output=object, references='', interactive=yes,
find=yes, recenter=yes, resize=yes, edit=yes, trace=yes,
fittrace=yes, extract=yes, extras=yes, review=no,
background='fit', weights='variance', pfit='fit1d',
readnoise=rdnoise, gain=gain)
# Apply wavelength solution to standard
shutil.copy('%s/%s' % (spath, arc), '.')
shutil.copy('%s/database/id%s' % (spath, arc.rstrip('.fits')), 'database')
iraf.refspec(object, references=arc, sort="", group="", override=yes,
confirm=no, assign=yes)
iraf.dispcor(object, '%s.w' % object, confirm=no, listonly=no)
# Smooth
shutil.copy('%s/%s' % (spath, smooth), '.')
iraf.sarith('%s.w' % object, '/', smooth, '%s.s' % object)
# Create and apply telluric correction
shutil.copy('%s/%s' % (spath, telluric), '.')
iraf.telluric('%s.s' % object, '%s.t' % object, telluric, xcorr=yes,
tweakrms=yes, interactive=yes, sample='6850:6950,7575:7700')
# Flux calibration
shutil.copy('%s/%s.0001.fits' % (spath, sens), '.')
iraf.calibrate('%s.t' % object, '%s.f' % object, extinct=yes, flux=yes,
extinction='onedstds$kpnoextinct.dat',
observatory='Lick', sensitivity=sens, airmass='',
exptime='')
return
def blue_science(image, spath, object=None, flat='Flat-Blue.fits',
arc='Arc-Blue.fits', smooth='BD284211.smooth.fits',
sens='BD284211.sens', biassec1=BLUEBIAS1, trimsec1=BLUETRIM1,
biassec2=BLUEBIAS2, trimsec2=BLUETRIM2, gain=BLUEGAIN,
rdnoise=BLUERDNOISE):
'''Full reduction of KAST science spectra on blue CCD'''
# Bias subtract everything first
bluebias(image, biassec1=biassec1, trimsec1=trimsec1, biassec2=biassec2,
trimsec2=trimsec2)
# Apply flat-field
shutil.copy('%s/%s' % (spath, flat), '.')
iraf.ccdproc('j%s' % image[0], ccdtype='', noproc=no, fixpix=no,
overscan=no, trim=no, zerocor=no, darkcor=no, flatcor=yes,
illumcor=no, fringecor=no, readcor=no, scancor=no,
flat=flat)
# Cosmic ray rejection
#iraf.lacos_spec('j%s' % image[0], 'cj%s' % image[0], 'cmj%s' % image[0],
# gain=gain, readn=rdnoise, xorder=9, yorder=3,
# sigclip=4.5, sigfrac=0.5, objlim=1.0, niter=3)
# Extract spectrum
if object==None:
object=get_head(image, 'OBJECT')
iraf.apall('j%s' % image[0], output=object, references='',
interactive=yes, find=yes, recenter=yes, resize=yes, edit=yes,
trace=yes, fittrace=yes, extract=yes, extras=yes, review=no,
background='fit', weights='variance', pfit='fit1d',
readnoise=rdnoise, gain=gain)
# Apply wavelength solution to standard
shutil.copy('%s/%s' % (spath, arc), '.')
shutil.copy('%s/database/id%s' % (spath, arc.rstrip('.fits')), 'database')
iraf.refspec(object, references=arc, sort="", group="", override=yes,
confirm=no, assign=yes)
iraf.dispcor(object, '%s.w' % object, confirm=no, listonly=no)
# Smooth
shutil.copy('%s/%s' % (spath, smooth), '.')
iraf.sarith('%s.w' % object, '/', smooth, '%s.s' % object)
# Flux calibration
shutil.copy('%s/%s.0001.fits' % (spath, sens), '.')
iraf.calibrate('%s.s' % object, '%s.f' % object, extinct=yes, flux=yes,
extinction='onedstds$kpnoextinct.dat',
observatory='Lick', sensitivity=sens, airmass='',
exptime='')
return
def applywavesolution(inputre, calspec):
''' apply calibration solution to science spectra '''
inputlist = glob.glob(inputre)
inputstring = ', '.join(inputlist)
outputstring = ', '.join([inp[:-5] + '_spec.fits' for inp in inputlist])
iraf.hedit.unlearn()
iraf.dispcor.unlearn()
iraf.hedit.fields = 'REFSPEC1'
iraf.hedit.value = calspec
iraf.hedit.add = True
iraf.hedit(images=inputstring)
iraf.dispcor(input=inputstring, output=outputstring)
def applywavesolution(inputre, calspec):
''' apply calibration solution to science spectra '''
inputlist = glob.glob(inputre)
inputstring = ', '.join(inputlist)
outputstring = ', '.join([inp[:-5]+'_spec.fits' for inp in inputlist])
iraf.hedit.unlearn()
iraf.dispcor.unlearn()
iraf.hedit.fields = 'REFSPEC1'
iraf.hedit.value = calspec
iraf.hedit.add = True
iraf.hedit(images=inputstring)
iraf.dispcor(input=inputstring, output=outputstring)
def apply_wav_solution(WORK_DIR, include_cal=False):
print '\n + Applying the wavelength solution\n'
iraf.unlearn('dispcor')
for i, obj in enumerate(observations[WORK_DIR]['objects']):
cal = observations[WORK_DIR]['calibs'][i]+'.ec'
# export wav calibrated spectra
print 'Using cal:', cal
for j in ['.ec', '.ec.nosky']:
#iraf.hedit(images=obj, fields='refspec1', value=observations[WORK_DIR]['calibs'][0]+'.ec', add='yes', verify='no')
iraf.refspectra(input=obj+j, referen=cal, sort='', group='', confirm='no', Stdout="/dev/null")
iraf.dispcor(input=obj+j, output=obj+j, lineari='no', verbose='yes')
if include_cal:
os.remove(cal+'_wvl.fits')
# export wav calibrated cal image
iraf.refspectra(input=cal, referen=cal, sort='', group='', confirm='no', Stdout="/dev/null")
iraf.dispcor(input=cal, output=cal+'_wvl', lineari='no', verbose='yes')
def dispcor(imlist_name, database='database'):
"""
dispcor -- Dispersion correct and resample spectra
dispcor input output [records]
database -- path in which idXXX file. ex) /data1/SN2019ein/work/SAO_Spectrum/red/20190509/arc/database/
dispcor fcdbstd.ms.fits wfcdbstd.ms.fits
"""
import glob
import os, sys
from pyraf import iraf
iraf.noao()
iraf.imred()
iraf.kpnoslit()
imlist = glob.glob(imlist_name)
imlist.sort()
for i in range(len(imlist)):
inim = imlist[i]
iraf.dispcor(input=inim,
output='w' + inim,
database='database',
linearize='no')
iraf.splot(images='w' + inim)
def blue_standard(image,
arcs,
flats,
object=None,
biassec1=BLUEBIAS1,
trimsec1=BLUETRIM1,
biassec2=BLUEBIAS2,
trimsec2=BLUETRIM2,
outflat='Flat-Blue.fits',
gain=BLUEGAIN,
rdnoise=BLUERDNOISE,
arc='Arc-Blue.fits',
caldir='home$standards/'):
'''Reduce and calibrate standard star observation with blue CCD'''
# Bias subtract everything first
bluebias(image,
biassec1=biassec1,
trimsec1=trimsec1,
biassec2=biassec2,
trimsec2=trimsec2)
bluebias(arcs,
biassec1=biassec1,
trimsec1=trimsec1,
biassec2=biassec2,
trimsec2=trimsec2)
bluebias(flats,
biassec1=biassec1,
trimsec1=trimsec1,
biassec2=biassec2,
trimsec2=trimsec2)
# Create and apply flat-field
for i in range(len(flats)):
flats[i] = 'j%s' % flats[i]
make_flat(flats, outflat, gain=gain, rdnoise=rdnoise)
iraf.ccdproc('j%s' % image[0],
ccdtype='',
noproc=no,
fixpix=no,
overscan=no,
trim=no,
zerocor=no,
darkcor=no,
flatcor=yes,
illumcor=no,
fringecor=no,
readcor=no,
scancor=no,
flat=outflat)
iraf.ccdproc('j%s' % arcs[0],
ccdtype='',
noproc=no,
fixpix=no,
overscan=no,
trim=no,
zerocor=no,
darkcor=no,
flatcor=yes,
illumcor=no,
fringecor=no,
readcor=no,
scancor=no,
flat=outflat)
# Extract spectrum of standard
if object == None:
object = get_head(image, 'OBJECT')
iraf.apall('j%s' % image[0],
output=object,
references='',
interactive=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background='fit',
weights='variance',
pfit='fit1d',
readnoise=rdnoise,
gain=gain)
# Extract arc and fit wavelength solution
reference_arc('j%s' % arcs[0], arc, 'j%s' % image[0])
# Apply wavelength solution to standard
iraf.refspec(object,
references=arc,
sort="",
group="",
override=yes,
confirm=no,
assign=yes)
iraf.dispcor(object, '%s.w' % object, confirm=no, listonly=no)
# Remove absorption features and smooth
iraf.splot('%s.w' % object, 1, 1)
iraf.gauss('temp1[*,1,1]', '%s.smooth' % object, 3.0)
iraf.sarith('%s.w' % object, '/', '%s.smooth' % object, '%s.s' % object)
# Define bandpasses for standard star calculation
iraf.standard('%s.s' % object,
'%s.std' % object,
extinction='onedstds$kpnoextinct.dat',
caldir=caldir,
observatory='Lick',
interact=yes,
star_name=object,
airmass='',
exptime='')
# Determine sensitivity function
iraf.sensfunc('%s.std' % object,
'%s.sens' % object,
extinction='onedstds$kpnoextinct.dat',
newextinction='extinct.dat',
observatory='Lick',
function='legendre',
order=3,
interactive=yes)
return
def salt_scombine(objlist):
obj = np.loadtxt(objlist, dtype="str", unpack=True, ndmin=1)
objlist1d = objlist + '1d'
objj = os.path.splitext(obj[0])[0]
if os.path.isfile(objj + 'wsf.fits'):
os.system('sed s/.fits/wsf1d.fits/ ' + objlist + ' > ' + objlist1d)
objnofits = string.split(obj[0], sep='.')[0] + 'wsf'
os.system('sed s/.fits/wsf1d_var.fits/ ' + objlist + ' > templist')
else:
os.system('sed s/.fits/ws1d.fits/ ' + objlist + ' > ' + objlist1d)
objnofits = string.split(obj[0], sep='.')[0] + 'ws'
os.system('sed s/.fits/ws1d_var.fits/ ' + objlist + ' > templist')
low_ap = float(
rt.getsh("grep -i 'low\t' database/ap" + objnofits +
" | awk '{print $3}'"))
high_ap = float(
rt.getsh("grep -i 'high\t' database/ap" + objnofits +
" | awk '{print $3}'"))
apradius = abs(low_ap) + abs(high_ap)
readnoise = 2.45 * np.sqrt(apradius)
print "ggg = %s" % readnoise
iraf.scombine.combine = 'average'
iraf.scombine.rdnoise = readnoise
iraf.scombine.reject = 'ccdclip'
iraf.scombine.scale = 'median'
outnam = ''.join(rt.header(obj[0], 'OBJECT').split()).lower()
outname = outnam + '_' + objlist
if os.path.isfile(outname + '.fits'):
os.system('rm ' + outname + '.fits')
inname = str('@' + objlist1d)
iraf.scombine(input=inname, output=outname, first='yes')
tempfile = 'temp.fits'
if os.path.isfile(tempfile):
os.system('rm ' + tempfile)
iraf.imsum('@templist', tempfile)
nspec = len(obj)
outname_var = outname + '_var'
if os.path.isfile(outname_var + '.fits'):
os.system('rm ' + outname_var + '.fits')
nspec = nspec * nspec
iraf.imarith(tempfile, '/', nspec, outname_var)
os.system('rm *temp.fits')
os.system('rm templist')
iraf.dispcor(outname_var, output='', table=outname)
outnametxt = outname + '.txt'
outname_vartxt = outname_var + '.txt'
iraf.wspectext(outname, outnametxt, header='no')
iraf.wspectext(outname_var, outname_vartxt, header='no')
rssdate = rt.header(obj[0], 'DATE-OBS')
rssdate = string.replace(rssdate, '-', '')
pastefinal = outname + '_final_' + rssdate + '.txt'
os.system('paste ' + outnametxt + ' ' + outname_vartxt + ' > ' +
pastefinal)
def wavelength_solution(targetdir,
islog,
w1,
w2,
npix,
dw,
dv):
"""
Applies wavelength solution using dispcor. Must be run in target directory.
"""
print 'Target directory is ' + targetdir
print 'Applying wavelength solution...'
if os.getcwd() != targetdir:
raise ValueError('Current working directory must be target directory!')
if os.path.exists('dimcomb.ms.fits'):
os.remove('dimcomb.ms.fits')
iraf.unlearn('dispcor')
iraf.dispcor.setParam('input','imcomb.ms.fits')
iraf.dispcor.setParam('output','dimcomb.ms.fits')
iraf.dispcor.setParam('confirm','yes')
if len(w1) == 0:
w1 = None
if len(w2) == 0:
w2 = None
if len(npix) == 0:
npix = None
if len(dw) == 0:
dw = None
if len(dv) == 0:
dv = None
if islog == 'no':
iraf.dispcor.setParam('log','no')
if None not in (w1,w2,npix,dw):
w1 = float(w1)
w2 = float(w2)
npix = float(npix)
dw = float(dw)
elif None not in (w1, w2, npix):
w1 = float(w1)
w2 = float(w2)
npix = float(npix)
dw = 'INDEF'
elif None not in (w1, w2, dw):
w1 = float(w1)
w2 = float(w2)
dw = float(dw)
npix = 'INDEF'
elif None not in (w1, dw, npix):
w1 = float(w1)
npix = float(npix)
dw = float(dw)
w2 = 'INDEF'
elif None not in (w2, dw, npix):
w2 = float(w1)
npix = float(npix)
dw = float(dw)
w1 = 'INDEF'
else:
raise ValueError('Not enough info to make a wavelength scale!')
print 'User input w1 = {}, w2 = {}, dw = {}, nw = {}'.format(w1,w2,dw,npix)
iraf.dispcor.setParam('w1',w1) # Starting wavelength
iraf.dispcor.setParam('w2',w2) # Ending wavelength
iraf.dispcor.setParam('nw',npix) # Number of output pixels
iraf.dispcor.setParam('dw',dw) # Wavelength interval per pixel
elif islog == 'yes':
iraf.dispcor.setParam('log','yes')
# Leave everything black ecept dv
if None not in (w1,w2,dv):
w1 = float(w1)
w2 = float(w2)
dv = float(dv)
print 'Constant dv = %.3f km/s' % dv
from scipy.constants import c
c = c / 1e3
dwlog = -np.log10(1 - dv / c)
dw = dwlog * (w2 - w1) / (np.log10(w2) - np.log10(w1))
else:
raise ValueError('Need w1, w2 and dv!')
print 'User input w1 = {}, w2 = {}, dv = {}, nw = INDEF'.format(w1,w2,dv)
iraf.dispcor.setParam('w1',w1) # Starting wavelength
iraf.dispcor.setParam('w2',w2) # Ending wavelength
iraf.dispcor.setParam('nw','INDEF') # Number of output pixels
iraf.dispcor.setParam('dw',dw) # Wavelength interval per pixel
iraf.dispcor()
return None
verify="no",
show="yes",
update="yes")
print('Starting dispersion correction')
iraf.dispcor(
input="J105829_250_3500_4_Grism_7_2015_06_17_yf170018bfcap.fits",
output="J105829_250_3500_4_Grism_7_2015_06_17_yf170018bfcapw.fits",
linearize="yes",
database="database",
table="",
w1="3000",
w2="8000",
dw="INDEF",
nw="INDEF",
log="no",
flux="yes",
blank=0.,
samedisp="no",
ignoreaps="no",
confirm="yes",
listonly="no",
verbose="no",
logfile="yes")
#os.system("ls *apbfcw.fits> air.in")
#iraf.asthedit(' @air.in /home/aries/Music/Atsoa2018/airmass.dat')
iraf.standard(
input="J105829_250_3500_4_Grism_7_2015_06_17_yf170018bfcapw.fits",
#!/usr/bin/env python
#
# Associate wavelenght solution to science spectra
#
import glob
from pyraf import iraf
calspec = str(raw_input("Enter calibrated spectra: "))
inputre = str(raw_input("Enter regular expression for science spectra: "))
inputlist = glob.glob(inputre)
iraf.hedit.unlearn()
iraf.dispcor.unlearn()
iraf.hedit.fields = 'REFSPEC1'
iraf.hedit.value = calspec
iraf.hedit.add = True
for img in inputlist:
iraf.hedit(images=img)
iraf.dispcor(input=img, output=img[:-5] + '_spec')
print '--- DONE ---'
def reduce_data(filelist_new, filelist_masterflats, filelist_masterthars):
if len(filelist_new) == 0:
print("Please, choose science files to reduce first!")
elif len(filelist_masterflats) == 0:
print("Please, choose masterflat files first!")
elif len(filelist_masterthars) == 0:
print("Please, choose Thorium-Argon reference files first!")
else:
print("Starting reduction process...")
extracted_science_files = []
current_directory = os.getcwd()
years = [
'{:04d}'.format(year) for year in range(2005,
date.today().year + 1)
]
month = ['{:02d}'.format(mon) for mon in range(1, 13)]
for i in range(len(filelist_new)):
trim = trim_remove_bias(filelist_new[i])
chosen_masterflat = None
dummyI = trim.replace(".trim_will_be_removed.fits", ".dummyI.fits")
dummyII = trim.replace(".trim_will_be_removed.fits",
".dummyII.fits")
if os.path.exists(dummyI):
os.remove(dummyI)
if os.path.exists(dummyII):
os.remove(dummyII)
for y in years:
for mon in month:
date_check_1 = y + "_" + mon
date_check_2 = y + "-" + mon
if ((date_check_1 in trim)
or (date_check_2 in trim)) == True:
for masterflat in filelist_masterflats:
if ((date_check_1 in masterflat)
or (date_check_2 in masterflat)) == True:
chosen_masterflat = masterflat
if chosen_masterflat is None:
print("Masterflat File not found!!")
continue
iraf.imarith(trim, "/", chosen_masterflat, dummyI)
iraf.apscatter(dummyI,
output=dummyII,
interactive="No",
apertures="1-51",
references="find_orders.fits")
clean = trim.replace(".trim_will_be_removed.fits", ".clean.fits")
mask = trim.replace(".trim_will_be_removed.fits", ".mask.fits")
mean_image = iraf.imstat(dummyII,
Stdout=1,
fields="mean",
format="no")
print(mean_image)
if float(mean_image[0]) > 5000:
array, header = cosmics.fromfits(dummyII)
sigclip = 50.0
objlim = 50.0
c = cosmics.cosmicsimage(array,
gain=0.368,
readnoise=3.7,
sigclip=sigclip,
sigfrac=10.0,
objlim=objlim)
c.run(maxiter=0)
cosmics.tofits(clean, c.cleanarray, header)
cosmics.tofits(mask, c.mask, header)
elif 5000 > float(mean_image[0]) > 1000:
array, header = cosmics.fromfits(dummyII)
sigclip = 20.0
objlim = 20.0
c = cosmics.cosmicsimage(array,
gain=0.368,
readnoise=3.7,
sigclip=sigclip,
sigfrac=4.0,
objlim=objlim)
c.run(maxiter=2)
cosmics.tofits(clean, c.cleanarray, header)
cosmics.tofits(mask, c.mask, header)
else:
array, header = cosmics.fromfits(dummyII)
sigclip = 5.0
objlim = 5.0
c = cosmics.cosmicsimage(array,
gain=0.368,
readnoise=3.7,
sigclip=sigclip,
sigfrac=1.8,
objlim=objlim)
c.run(maxiter=2)
cosmics.tofits(clean, c.cleanarray, header)
cosmics.tofits(mask, c.mask, header)
extract = trim.replace(".trim_will_be_removed.fits",
".extracted.fits")
extracted_science_files.append(extract)
iraf.apall(clean,
output=extract,
references="find_orders",
profiles="find_orders",
apertures="1-51")
# iraf.apall(clean, output=extract, references= "find_orders", profiles= "find_orders", apertures = "3-50" )
# iraf.apall(clean, output=extract, references= "find_orders", profiles= "find_orders", apertures = "3-50" )
os.remove(dummyI)
os.remove(dummyII)
os.remove(trim)
write_reftable(filelist_new, filelist_masterthars)
thar_directory = os.path.dirname(filelist_masterthars[0])
if thar_directory == os.path.dirname(filelist_new[0]):
pass
else:
remove_file(thar_directory, "science.lis")
remove_file(thar_directory, "reftable.dat")
shutil.move("science.lis", thar_directory)
shutil.move("reftable.dat", thar_directory)
os.chdir(thar_directory)
iraf.refspectra("@science.lis",
references="reftable.dat",
ignoreaps="Yes",
sort="",
group="",
override="yes",
confirm="no",
assign="yes")
iraf.dispcor("@science.lis", output="@science.lis")
if thar_directory == os.path.dirname(filelist_new[0]):
pass
else:
remove_file(current_directory, "science.lis")
remove_file(current_directory, "reftable.dat")
shutil.move("science.lis", current_directory)
shutil.move("reftable.dat", current_directory)
os.chdir(current_directory)
normalize_and_merge(extracted_science_files)
print("Success! Reduction process complete.")
def red_standard(image, arcs, flats, object=None, biassec=REDBIAS,
trimsec=REDTRIM, outflat='Flat-Red.fits', gain=REDGAIN,
rdnoise=REDRDNOISE, arc='Arc-Red.fits',
caldir='home$standards/'):
'''Reduce and calibrate standard star observation with red CCD'''
# Bias subtract everything first
redbias(image, biassec=biassec, trimsec=trimsec)
redbias(arcs, biassec=biassec, trimsec=trimsec)
redbias(flats, biassec=biassec, trimsec=trimsec)
# Create and apply flat-field
make_flat(flats, outflat, gain=gain, rdnoise=rdnoise)
iraf.ccdproc(image[0], ccdtype='', noproc=no, fixpix=no, overscan=no,
trim=no, zerocor=no, darkcor=no, flatcor=yes, illumcor=no,
fringecor=no, readcor=no, scancor=no, flat=outflat)
arcimages=','.join(arcs)
iraf.ccdproc(arcs, ccdtype='', noproc=no, fixpix=no, overscan=no,
trim=no, zerocor=no, darkcor=no, flatcor=yes, illumcor=no,
fringecor=no, readcor=no, scancor=no, flat=outflat)
# Extract spectrum of standard
if object==None:
object=get_head(image, 'OBJECT')
iraf.apall(image[0], output=object, references='', interactive=yes,
find=yes, recenter=yes, resize=yes, edit=yes, trace=yes,
fittrace=yes, extract=yes, extras=yes, review=no,
background='fit', weights='variance', pfit='fit1d',
readnoise=rdnoise, gain=gain)
# Extract arc and fit wavelength solution
iraf.imarith(arcs[0], '+', arcs[1], 'Arc-Sum.fits')
reference_arc('Arc-Sum.fits', arc, image[0])
# Apply wavelength solution to standard
iraf.refspec(object, references=arc, sort="", group="", override=yes,
confirm=no, assign=yes)
iraf.dispcor(object, '%s.w' % object, confirm=no, listonly=no)
# Remove absorption features and smooth
iraf.splot('%s.w' % object, 1, 1)
iraf.gauss('temp1[*,1,1]', '%s.smooth' % object, 3.0)
iraf.sarith('%s.w' % object, '/', '%s.smooth' % object, '%s.s' % object)
# Create and apply telluric correction
iraf.sarith('%s.w' % object, '/', 'temp1', 'telluric')
iraf.splot('telluric', 1, 1)
iraf.telluric('%s.s' % object, '%s.t' % object, 'telluric', xcorr=no,
tweakrms=no, interactive=no, sample='6850:6950,7575:7700')
# Define bandpasses for standard star calculation
iraf.standard('%s.t' % object, '%s.std' % object,
extinction='onedstds$kpnoextinct.dat', caldir=caldir,
observatory='Lick', interact=yes, star_name=object,
airmass='', exptime='')
# Determine sensitivity function
iraf.sensfunc('%s.std' % object, '%s.sens' % object,
extinction='onedstds$kpnoextinct.dat',
newextinction='extinct.dat', observatory='Lick',
function='legendre', order=3, interactive=yes)
return
#!/usr/bin/env python
#
# Associate wavelenght solution to science spectra
#
import glob
from pyraf import iraf
calspec = str(raw_input("Enter calibrated spectra: "))
inputre = str(raw_input("Enter regular expression for science spectra: "))
inputlist = glob.glob(inputre)
iraf.hedit.unlearn()
iraf.dispcor.unlearn()
iraf.hedit.fields = 'REFSPEC1'
iraf.hedit.value = calspec
iraf.hedit.add = True
for img in inputlist:
iraf.hedit(images=img)
iraf.dispcor(input=img, output=img[:-5]+'_spec')
print '--- DONE ---'
def red_science(image,
flats,
spath,
object=None,
arc='Arc-Red.fits',
smooth='BD284211.smooth.fits',
telluric='telluric.fits',
sens='BD284211.sens',
biassec=REDBIAS,
trimsec=REDTRIM,
outflat='Flat-Red.fits',
gain=REDGAIN,
rdnoise=REDRDNOISE):
'''Full reduction of KAST science spectra on red CCD'''
# Bias subtract everything first
redbias(image, biassec=biassec, trimsec=trimsec)
redbias(flats, biassec=biassec, trimsec=trimsec)
# Create and apply flat-field
make_flat(flats, outflat, gain=gain, rdnoise=rdnoise)
iraf.ccdproc(image[0],
ccdtype='',
noproc=no,
fixpix=no,
overscan=no,
trim=no,
zerocor=no,
darkcor=no,
flatcor=yes,
illumcor=no,
fringecor=no,
readcor=no,
scancor=no,
flat=outflat)
# Cosmic ray rejection
#iraf.lacos_spec(image[0], 'c%s' % image[0], 'cm%s' % image[0],
# gain=gain, readn=rdnoise, xorder=9, yorder=3,
# sigclip=4.5, sigfrac=0.5, objlim=1.0, niter=3)
# Extract spectrum
if object == None:
object = get_head(image, 'OBJECT')
iraf.apall('%s' % image[0],
output=object,
references='',
interactive=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background='fit',
weights='variance',
pfit='fit1d',
readnoise=rdnoise,
gain=gain)
# Apply wavelength solution to standard
shutil.copy('%s/%s' % (spath, arc), '.')
shutil.copy('%s/database/id%s' % (spath, arc.rstrip('.fits')), 'database')
iraf.refspec(object,
references=arc,
sort="",
group="",
override=yes,
confirm=no,
assign=yes)
iraf.dispcor(object, '%s.w' % object, confirm=no, listonly=no)
# Smooth
shutil.copy('%s/%s' % (spath, smooth), '.')
iraf.sarith('%s.w' % object, '/', smooth, '%s.s' % object)
# Create and apply telluric correction
shutil.copy('%s/%s' % (spath, telluric), '.')
iraf.telluric('%s.s' % object,
'%s.t' % object,
telluric,
xcorr=yes,
tweakrms=yes,
interactive=yes,
sample='6850:6950,7575:7700')
# Flux calibration
shutil.copy('%s/%s.0001.fits' % (spath, sens), '.')
iraf.calibrate('%s.t' % object,
'%s.f' % object,
extinct=yes,
flux=yes,
extinction='onedstds$kpnoextinct.dat',
observatory='Lick',
sensitivity=sens,
airmass='',
exptime='')
return
def extractSpectra():
"""
Extract 1D spectra using IRAF interactively
Interpolate across the two arcs either side to
get the most accurate wavelength solution
TODO: Finish docstring
Add method of using super arc for inital
identify
"""
# load IRAF from the location of the login.cl file
here = os.getcwd()
os.chdir(loginCl_location)
from pyraf import iraf
os.chdir(here)
time.sleep(2)
# make a list of the science images to be analysed
templist = g.glob('i_s*')
# import IRAF packages for spectroscopy
iraf.imred(_doprint=0)
iraf.kpnoslit(_doprint=0)
# apall parameters
iraf.apall.setParam('format', 'multispec')
iraf.apall.setParam('interac', 'yes')
iraf.apall.setParam('find', 'yes')
iraf.apall.setParam('recen', 'yes')
iraf.apall.setParam('resize', 'yes')
iraf.apall.setParam('trace', 'yes')
iraf.apall.setParam('fittrac', 'yes')
iraf.apall.setParam('extract', 'yes')
iraf.apall.setParam('extras', 'yes')
iraf.apall.setParam('review', 'yes')
iraf.apall.setParam('line', 'INDEF')
iraf.apall.setParam('nsum', '12')
iraf.apall.setParam('lower', '-6')
iraf.apall.setParam('upper', '6')
iraf.apall.setParam('b_funct', 'chebyshev')
iraf.apall.setParam('b_order', '1')
iraf.apall.setParam('b_sampl', '-25:-15,15:25')
iraf.apall.setParam('b_naver', '-100')
iraf.apall.setParam('b_niter', '0')
iraf.apall.setParam('b_low_r', '3')
iraf.apall.setParam('b_high', '3')
iraf.apall.setParam('b_grow', '0')
iraf.apall.setParam('width', '10')
iraf.apall.setParam('radius', '10')
iraf.apall.setParam('threshold', '0')
iraf.apall.setParam('nfind', '1')
iraf.apall.setParam('t_nsum', '10')
iraf.apall.setParam('t_step', '10')
iraf.apall.setParam('t_nlost', '3')
iraf.apall.setParam('t_niter', '7')
iraf.apall.setParam('t_funct', 'spline3')
iraf.apall.setParam('t_order', '3')
iraf.apall.setParam('backgro', 'fit')
iraf.apall.setParam('skybox', '1')
iraf.apall.setParam('weights', 'variance')
iraf.apall.setParam('pfit', 'fit1d')
iraf.apall.setParam('clean', 'yes')
iraf.apall.setParam('saturat', SATURATION)
iraf.apall.setParam('readnoi', RDNOISE)
iraf.apall.setParam('gain', GAIN)
iraf.apall.setParam('lsigma', '4.0')
iraf.apall.setParam('usigma', '4.0')
iraf.apall.setParam('nsubaps', '1')
iraf.apall.saveParList(filename="apall.pars")
# make reference arc for reidentify
if '.' in args.refarc:
args.refarc = args.refarc.split('.')[0]
refarc = "a_s_{}_t.fits".format(args.refarc)
refarc_out = "a_s_{}_t.ms.fits".format(args.refarc)
# loop over all the the spectra
for i in range(0, len(templist)):
hdulist = fits.open(templist[i])
prihdr = hdulist[0].header
target_id = prihdr['CAT-NAME']
spectrum_id = int(templist[i].split('_')[2].split('r')[1])
if args.ds9:
os.system('xpaset fuckingds9 fits < {}'.format(templist[i]))
# extract the object spectrum
print("[{}/{}] Extracting spectrum of {} from image {}".format(i+1, len(templist), target_id, templist[i]))
print("[{}/{}] Check aperture and background. Change if required".format(i+1, len(templist)))
print("[{}/{}] AP: m = mark aperture, d = delete aperture".format(i+1, len(templist)))
print("[{}/{}] SKY: s = mark sky, t = delete sky, f = refit".format(i+1, len(templist)))
print("[{}/{}] q = continue".format(i+1, len(templist)))
iraf.apall(input=templist[i])
print("Spectrum extracted!")
# find the arcs either side of the object
arclist = []
arc1 = "a_s_r{0:d}_t.fits".format(spectrum_id-1)
arc2 = "a_s_r{0:d}_t.fits".format(spectrum_id+1)
arc1_out = "a_s_r{0:d}_t.ms.fits".format(spectrum_id-1)
arc2_out = "a_s_r{0:d}_t.ms.fits".format(spectrum_id+1)
# predict the arc names
print("\nPredicting arcs names...")
print("Arc1: {}".format(arc1))
print("Arc2: {}".format(arc2))
# setup a reference filename for the arc conditions in database
reffile = templist[i].split('.fits')[0]
# extract the arcs
print("\nExtracting arcs under the same conditions...")
if os.path.exists(arc1):
iraf.apall(input=arc1,
reference=reffile,
recente="no",
trace="no",
backgro="no",
interac="no")
print("Arc1 {} extracted".format(arc1))
arclist.append(arc1_out)
else:
print("\n\nArc1 {} FILE NOT FOUND\n\n".format(arc1))
if os.path.exists(arc2):
iraf.apall(input=arc2,
reference=reffile,
recente="no",
trace="no",
backgro="no",
interac="no")
print("Arc2 {} extracted".format(arc2))
arclist.append(arc2_out)
else:
print("\n\nArc2 {} FILE NOT FOUND\n\n".format(arc2))
# get a list of the extracted arcs and objects
spectrum_out = "i_s_r{0:d}_t.ms.fits".format(spectrum_id)
if i == 0:
# extract the master reference arc
print("\nExtracting master arc {} under the same conditions...".format(refarc))
iraf.apall(input=refarc,
reference=reffile,
recente="no",
trace="no",
backgro="no",
interac="no")
print("Reference arc {} extracted".format(refarc))
# identify the lines in it
print("\nIdentify arc lines:")
print("Enter the following in the splot window")
print("\t:thres 500")
print("\t:order 1, max = 3")
print("\tfwidth 2")
print("Select 3-5 arc lines from line atlas")
print("Press 'm' to mark, then enter wavelength")
print("Then press 'l' to automatically ID the other lines")
print("Press 'f' to fit the dispersion correction")
print("Use 'd' to remove bad points, 'f' to refit")
print("'q' from fit, then 'q' from identify to continue\n")
iraf.identify(images=refarc_out, coordlist=lineList_location)
# use the refarc to ID all the subsequent arcs
for arc in arclist:
print("\nReidentifying arclines from {}".format(arc))
iraf.reidentify(reference=refarc_out, images=arc)
# add the refspec keywords to the image header for dispcor
# refspec_factor tells IRAF how to interpolate the arcs
refspec_factor = round((1./len(arclist)), 1)
for i in range(0, len(arclist)):
refspec = "{} {}".format(arclist[i].split(".fits")[0], refspec_factor)
print("REFSPEC{}: {}".format(i+1, refspec))
iraf.hedit(images=spectrum_out,
fields="REFSPEC{}".format(i+1),
value=refspec,
add="yes",
verify="no",
show="yes")
print("Headers updated!\n")
# apply the dispersion correction
print("Applying the dispersion correction")
iraf.dispcor(input=spectrum_out,
output=spectrum_out,
lineari="yes",
databas="database",
table="")
print("Correction applied!")
# normalize the spectrum using continuum
normspec_out = "{}n.ms.fits".format(spectrum_out.split('.ms')[0])
iraf.continuum(input=spectrum_out,
output=normspec_out,
logfile="logfile",
interac="yes",
functio="spline3",
order="5",
niterat="10",
markrej="yes")
print("\n\n")
def blue_science(image,
spath,
object=None,
flat='Flat-Blue.fits',
arc='Arc-Blue.fits',
smooth='BD284211.smooth.fits',
sens='BD284211.sens',
biassec1=BLUEBIAS1,
trimsec1=BLUETRIM1,
biassec2=BLUEBIAS2,
trimsec2=BLUETRIM2,
gain=BLUEGAIN,
rdnoise=BLUERDNOISE):
'''Full reduction of KAST science spectra on blue CCD'''
# Bias subtract everything first
bluebias(image,
biassec1=biassec1,
trimsec1=trimsec1,
biassec2=biassec2,
trimsec2=trimsec2)
# Apply flat-field
shutil.copy('%s/%s' % (spath, flat), '.')
iraf.ccdproc('j%s' % image[0],
ccdtype='',
noproc=no,
fixpix=no,
overscan=no,
trim=no,
zerocor=no,
darkcor=no,
flatcor=yes,
illumcor=no,
fringecor=no,
readcor=no,
scancor=no,
flat=flat)
# Cosmic ray rejection
#iraf.lacos_spec('j%s' % image[0], 'cj%s' % image[0], 'cmj%s' % image[0],
# gain=gain, readn=rdnoise, xorder=9, yorder=3,
# sigclip=4.5, sigfrac=0.5, objlim=1.0, niter=3)
# Extract spectrum
if object == None:
object = get_head(image, 'OBJECT')
iraf.apall('j%s' % image[0],
output=object,
references='',
interactive=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background='fit',
weights='variance',
pfit='fit1d',
readnoise=rdnoise,
gain=gain)
# Apply wavelength solution to standard
shutil.copy('%s/%s' % (spath, arc), '.')
shutil.copy('%s/database/id%s' % (spath, arc.rstrip('.fits')), 'database')
iraf.refspec(object,
references=arc,
sort="",
group="",
override=yes,
confirm=no,
assign=yes)
iraf.dispcor(object, '%s.w' % object, confirm=no, listonly=no)
# Smooth
shutil.copy('%s/%s' % (spath, smooth), '.')
iraf.sarith('%s.w' % object, '/', smooth, '%s.s' % object)
# Flux calibration
shutil.copy('%s/%s.0001.fits' % (spath, sens), '.')
iraf.calibrate('%s.s' % object,
'%s.f' % object,
extinct=yes,
flux=yes,
extinction='onedstds$kpnoextinct.dat',
observatory='Lick',
sensitivity=sens,
airmass='',
exptime='')
return
def red_standard(image,
arcs,
flats,
object=None,
biassec=REDBIAS,
trimsec=REDTRIM,
outflat='Flat-Red.fits',
gain=REDGAIN,
rdnoise=REDRDNOISE,
arc='Arc-Red.fits',
caldir='home$standards/'):
'''Reduce and calibrate standard star observation with red CCD'''
# Bias subtract everything first
redbias(image, biassec=biassec, trimsec=trimsec)
redbias(arcs, biassec=biassec, trimsec=trimsec)
redbias(flats, biassec=biassec, trimsec=trimsec)
# Create and apply flat-field
make_flat(flats, outflat, gain=gain, rdnoise=rdnoise)
iraf.ccdproc(image[0],
ccdtype='',
noproc=no,
fixpix=no,
overscan=no,
trim=no,
zerocor=no,
darkcor=no,
flatcor=yes,
illumcor=no,
fringecor=no,
readcor=no,
scancor=no,
flat=outflat)
arcimages = ','.join(arcs)
iraf.ccdproc(arcs,
ccdtype='',
noproc=no,
fixpix=no,
overscan=no,
trim=no,
zerocor=no,
darkcor=no,
flatcor=yes,
illumcor=no,
fringecor=no,
readcor=no,
scancor=no,
flat=outflat)
# Extract spectrum of standard
if object == None:
object = get_head(image, 'OBJECT')
iraf.apall(image[0],
output=object,
references='',
interactive=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background='fit',
weights='variance',
pfit='fit1d',
readnoise=rdnoise,
gain=gain)
# Extract arc and fit wavelength solution
iraf.imarith(arcs[0], '+', arcs[1], 'Arc-Sum.fits')
reference_arc('Arc-Sum.fits', arc, image[0])
# Apply wavelength solution to standard
iraf.refspec(object,
references=arc,
sort="",
group="",
override=yes,
confirm=no,
assign=yes)
iraf.dispcor(object, '%s.w' % object, confirm=no, listonly=no)
# Remove absorption features and smooth
iraf.splot('%s.w' % object, 1, 1)
iraf.gauss('temp1[*,1,1]', '%s.smooth' % object, 3.0)
iraf.sarith('%s.w' % object, '/', '%s.smooth' % object, '%s.s' % object)
# Create and apply telluric correction
iraf.sarith('%s.w' % object, '/', 'temp1', 'telluric')
iraf.splot('telluric', 1, 1)
iraf.telluric('%s.s' % object,
'%s.t' % object,
'telluric',
xcorr=no,
tweakrms=no,
interactive=no,
sample='6850:6950,7575:7700')
# Define bandpasses for standard star calculation
iraf.standard('%s.t' % object,
'%s.std' % object,
extinction='onedstds$kpnoextinct.dat',
caldir=caldir,
observatory='Lick',
interact=yes,
star_name=object,
airmass='',
exptime='')
# Determine sensitivity function
iraf.sensfunc('%s.std' % object,
'%s.sens' % object,
extinction='onedstds$kpnoextinct.dat',
newextinction='extinct.dat',
observatory='Lick',
function='legendre',
order=3,
interactive=yes)
return
#FROM NOW -> iraf
iraf.noao.twodspec.apextract
iraf.apall('spec*.fits')
#assign dispersion solution - from now IRAF
shutil.copy('./arc/database/idarc1.ms','./database/.')
for file in os.listdir(os.getcwd()):
if file.endswith('ms.fits'):
iraf.hedit(file,fields="REFSPEC1",value="arc1.ms",add='yes',ver='no',show='yes')
#wavelength calinration
for file in os.listdir(os.getcwd()):
if file.endswith('ms.fits'):
iraf.dispcor(file, 'd'+file)
#flux calibration
shutil.copy('./stds/sens.0001.fits','.')
shutil.copy('./stds/lapalmaextinct.dat','.')
for file in os.listdir(os.getcwd()):
if file.startswith('dspec'):
iraf.calibrate(file, 'f'+file)
#combine spectra
finalcomb = []
for file in os.listdir(os.getcwd()):
if file.startswith('fds'):
iraf.apall(input=filename,
find="No",
recenter="No",
resize="No",
interactive="Yes")
# Make sure that the dispersion axis is in the header.
iraf.hedit(images=[filename], fields=["DISPAXIS"], value=["1"], add="Yes")
iraf.identify(filename[:-5],
coordli=home + "/Downloads/HgNe(1).dat",
section="line 105 125",
crval=crval,
cdelt=dispersion,
fwidth=5)
# Tell the extracted spectrum what the wavelength solutions are.
iraf.hedit(images=[extracted_filename],
fields=["REFSPEC1"], \
value=[filename], add="Yes")
iraf.refspec(input=extracted_filename,
referen=filename[:-5],
sort='',
group='',
confirm='no')
iraf.dispcor(input=extracted_filename, output=calibrated_filename)
# Plot the extracted spectrum?
iraf.splot(calibrated_filename)
def blue_standard(image, arcs, flats, object=None, biassec1=BLUEBIAS1,
trimsec1=BLUETRIM1, biassec2=BLUEBIAS2,
trimsec2=BLUETRIM2, outflat='Flat-Blue.fits', gain=BLUEGAIN,
rdnoise=BLUERDNOISE, arc='Arc-Blue.fits',
caldir='home$standards/'):
'''Reduce and calibrate standard star observation with blue CCD'''
# Bias subtract everything first
bluebias(image, biassec1=biassec1, trimsec1=trimsec1, biassec2=biassec2,
trimsec2=trimsec2)
bluebias(arcs, biassec1=biassec1, trimsec1=trimsec1, biassec2=biassec2,
trimsec2=trimsec2)
bluebias(flats, biassec1=biassec1, trimsec1=trimsec1, biassec2=biassec2,
trimsec2=trimsec2)
# Create and apply flat-field
for i in range(len(flats)):
flats[i]='j%s' % flats[i]
make_flat(flats, outflat, gain=gain, rdnoise=rdnoise)
iraf.ccdproc('j%s' % image[0], ccdtype='', noproc=no, fixpix=no,
overscan=no, trim=no, zerocor=no, darkcor=no, flatcor=yes,
illumcor=no, fringecor=no, readcor=no, scancor=no,
flat=outflat)
iraf.ccdproc('j%s' % arcs[0], ccdtype='', noproc=no, fixpix=no,
overscan=no, trim=no, zerocor=no, darkcor=no, flatcor=yes,
illumcor=no, fringecor=no, readcor=no, scancor=no,
flat=outflat)
# Extract spectrum of standard
if object==None:
object=get_head(image, 'OBJECT')
iraf.apall('j%s' % image[0], output=object, references='', interactive=yes,
find=yes, recenter=yes, resize=yes, edit=yes, trace=yes,
fittrace=yes, extract=yes, extras=yes, review=no,
background='fit', weights='variance', pfit='fit1d',
readnoise=rdnoise, gain=gain)
# Extract arc and fit wavelength solution
reference_arc('j%s' % arcs[0], arc, 'j%s' % image[0])
# Apply wavelength solution to standard
iraf.refspec(object, references=arc, sort="", group="", override=yes,
confirm=no, assign=yes)
iraf.dispcor(object, '%s.w' % object, confirm=no, listonly=no)
# Remove absorption features and smooth
iraf.splot('%s.w' % object, 1, 1)
iraf.gauss('temp1[*,1,1]', '%s.smooth' % object, 3.0)
iraf.sarith('%s.w' % object, '/', '%s.smooth' % object, '%s.s' % object)
# Define bandpasses for standard star calculation
iraf.standard('%s.s' % object, '%s.std' % object,
extinction='onedstds$kpnoextinct.dat', caldir=caldir,
observatory='Lick', interact=yes, star_name=object,
airmass='', exptime='')
# Determine sensitivity function
iraf.sensfunc('%s.std' % object, '%s.sens' % object,
extinction='onedstds$kpnoextinct.dat',
newextinction='extinct.dat', observatory='Lick',
function='legendre', order=3, interactive=yes)
return
assign=yes)
iraf.refspectra('@arcs_extracted',
references='@arcs_extracted',
select='match',
confirm=no,
override=yes,
assign=yes)
# APPLY DISPERSION SOLUTION TO OBJECT SPECTRA
# Apply the dispersion solution to object spectra based on the previously-
# assigned comparison spectra
iraf.dispcor(input='@targets_extracted',
output='@targets_extracted',
flux=no,
linearize=no,
log=no,
verbose=yes,
samedisp=no)
iraf.dispcor(input='sig_//@targets_extracted',
output='sig_//@targets_extracted',
flux=no,
linearize=no,
log=no,
verbose=yes,
samedisp=no)
# Just as a check to the dispersion solution.
iraf.dispcor(input='@arcs_extracted',
output='z//@arcs_extracted',
interac='yes',
find='yes',
nfind=1,
recente='yes',
resize='yes',
background='fit')
print('\n' +
'Editing the header to point to the wavelength calibration...')
iraf.hedit(images=n + '.ms',
addonly='yes',
fields='REFSPEC1',
value='arc.ms')
print('\n' + 'Applying the dispersion correction...')
iraf.dispcor(input=n + '.ms', output=n + '.w')
print('\n' + 'Wavelength calibrated spectrum output as ' + n +
'.w.fits')
print('\nBeginning flux calibration...')
## create the speclist but importing all fits files in folder but rejecting the sensfunc file
file_list = glob.glob('./*.fits')
file_list.sort()
final = []
for j in range(len(file_list)):
if 'fits.w.fits' in file_list[j]:
final.append(file_list[j])
np.savetxt('./speclist', final, fmt="%s")
# Delete previous results.
os.system("rm "+extracted_filename+" "+calibrated_filename)
# Make sure that the dispersion axis is in the header.
iraf.hedit(images=[filename], fields=["DISPAXIS"], value=["1"], add="Yes")
# Run the spectral extraction program.
iraf.apextract.setParam("dispaxis", "1")
iraf.apall(input=filename, find="No", recenter="No", resize="No")
# Now identify the spectral lines in the arc lamps. Need to replace l1 l2
# with the range of rows that have the spectral lines.
iraf.identify(filename, section="line 265 285")
# Tell the extracted spectrum what the wavelength solutions are.
iraf.hedit(images=[extracted_filename], fields=["REFSPEC1"], \
value=[filename], add="Yes")
iraf.dispcor(input=extracted_filename, output=calibrated_filename)
# Plot the extracted spectrum?
iraf.splot(calibrated_filename)
update = 1)
count = count + 1
### Run dispcor to calibrate the object spectrum according to the dispersion
### solution
print "Using dispcor to apply the dispersion solution to the object image"
os.system("rm dispcorr_" + im_slice + "_" + file_name)
iraf.dispcor(
input = "t" + im_slice + "_" + string.split(file_name,".")[0] + ".ms.fits",\
output = "dispcorr_" + im_slice + "_" + file_name,\
linearize = "no",\
database = "database",\
log = 0,\
flux = 1,\
samedisp = 0,\
ignoreaps = 0,\
confirm = 0,\
listonly = 0,\
verbose = 1)
iraf.hedit(images="dispcorr_" + im_slice + "_" + file_name,fields="SFIT",value="1",add=0,delete=1,verify=0,show=1,update=1)
iraf.hedit(images="dispcorr_" + im_slice + "_" + file_name,fields="SFITB",value="1",add=0,delete=1,verify=0,show=1,update=1)
### Apply high rejection to remove cosmic rays, dead pixels, etc.
print "Applying high rejection"
### Be more lenient if apertures will be combined
if combine_aps == "true":
lowrej = 50.0