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 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 thar_ref(object_b_fn_ec, wave_ref, colour):
# Import IRAF modules:
iraf.noao(_doprint=0)
iraf.onedspec(_doprint=0)
# iraf.echelle(_doprint=0)
parList = "wavelength_calibration_refspectra.par"
if os.path.isfile(object_b_fn_ec) == True:
if os.path.isfile(wave_ref) == True:
if os.path.isfile(parList) == True:
# Assign reference spectra to input object spectra using IRAF task refspectra:
iraf.refspectra.setParList(ParList="wavelength_calibration_refspectra.par")
iraf.refspectra(input=object_b_fn_ec, references=wave_ref)
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print colour.capitalize() + ' object spectrum '
print '(' + str(object_b_fn_ec) + ') '
print 'assigned to ' + str(colour) + ' reference spectrum '
print '(' + str(wave_ref) + ') successfully. '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
else:
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Wavelength reference IRAF .par file '
print str(parList)
print 'does not exist. Exiting script. '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
print ' '
sys.exit()
else:
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'ThAr reference frame '
print str(wave_ref)
print 'does not exist. Exiting script. '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
print ' '
sys.exit()
else:
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Input frame '
print str(object_b_fn_ec)
print 'does not exist. Exiting script. '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
print ' '
sys.exit()
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 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.")
# threshold=50,shift=INDEF)
iraf.ecreidentify(images='@arcs_extracted',
reference='arcnewref.ec',
shift=INDEF,
cradius=2,
threshold=45,
refit=yes)
# ASSIGN WAVECALS TO OBJECT SPECTRA
# Assign comparison spectra to object images based on times the images were
# made, with weights for later dispersion solution assignment
iraf.refspectra('@targets_extracted',
references='@arcs_extracted',
select='interp',
sort='utmiddle',
group='observat',
time=yes,
confirm=no,
override=yes,
assign=yes)
iraf.refspectra('sig_//@targets_extracted',
references='@arcs_extracted',
select='interp',
sort='utmiddle',
group='observat',
time=yes,
confirm=no,
override=yes,
assign=yes)