def fitContinuum(continuuminter, tempInter, grating):
"""
Fit a continuum to the telluric correction spectrum to normalize it. The continuum
fitting regions were derived by eye and can be improved.
Results are in fit<Grating>.fits
"""
# These were found to fit the curves well by hand. You can probably improve them; feel free to fiddle around!
if grating == "K":
order = 5
sample = "20279:20395,20953:24283"
elif grating == "J":
order = 5
sample = "11561:12627,12745:12792,12893:13566"
elif grating == "H":
order = 5
sample = "*"
elif grating == "Z":
order = 5
sample = "9453:10015,10106:10893,10993:11553"
if os.path.exists("fit.fits"):
os.remove("fit.fits")
iraf.continuum(input='final_tel_no_hlines_no_norm',output='fit',ask='yes',lines='*',bands='1',type="fit",replace='no',wavescale='yes',logscale='no',override='no',listonly='no',logfiles='',inter=continuuminter,sample=sample,naverage=1,func='spline3',order=order,low_rej=1.0,high_rej=3.0,niterate=2,grow=1.0,markrej='yes',graphics='stdgraph',cursor='',mode='ql')
# Plot the telluric correction spectrum with the continuum fit.
final_tel_no_hlines_no_norm = astropy.io.fits.open('final_tel_no_hlines_no_norm.fits')[0].data
fit = astropy.io.fits.open('fit.fits')[0].data
if continuuminter or tempInter:
plt.title('Unnormalized Telluric Correction and Continuum fit Used to Normalize')
plt.plot(final_tel_no_hlines_no_norm)
plt.plot(fit)
plt.show()
def normalize(fn):
iraf.noao()
iraf.onedspec()
for i in range(len(fn)):
if fn[i][-1] == 'L':
nord = '10'
else:
nord = '7'
iraf.continuum(fn[i], fn[i], order=nord, ask='no', logfile='norm_log')
def normalize(setup, filelist):
iraf.noao()
iraf.onedspec()
for i in (filelist):
name = setup + '/' + i
iraf.continuum(name,
name,
order=1,
ask='no',
logfile=setup + '/norm_log')
def combine_normalize_images(WORK_DIR, combine=False):
if combine:
print '\n + Combine images\n'
try:
os.remove('combined_sum.fits')
except: pass
iraf.scombine(input=','.join([obj+'.ec.vh' for obj in observations[WORK_DIR]['objects']]), output='combined_sum', group='apertures', combine='sum', reject='none', Stdout="/dev/null")
# normalize spectra
# This step will require you to manually normalize all the spectra.
print '\n + Normalize spectra\n'
if combine:
try:
os.remove('combined_sum_cone_rv_echellet.fits')
except: pass
iraf.continuum(input='combined_sum', output='combined_sum_cont', type='fit', replace='no', listonly='no', functio='cheb', order=13, low_rej=2, high_rej=3, naverag=-3, niter=9, interac='no', markrej='no')
else:
for obj in observations[WORK_DIR]['objects']:
try:
os.remove(obj+'_cont.fits')
os.remove(obj+'_vh_norm.fits')
except: pass
iraf.continuum(input=obj+'.ec.vh', output=obj+'_cont', type='fit', replace='no', listonly='no', functio='cheb', order=13, low_rej=2, high_rej=3, naverag=-3, niter=9, interac='no', markrej='no', ask='yes')
iraf.sarith(input1=obj+'.ec.vh', op='/', input2=obj+'_cont', output=obj+'_vh_norm', format='multispec', Stdout="/dev/null")
#combine apertures
print '\n + Combine apertures\n'
if combine:
try:
os.remove('combined_sum_data.fits')
os.remove('combined_sum_norm.fits')
os.remove('combined_final.0001.fits')
except: pass
iraf.scombine(input='combined_sum', output='combined_sum_data', group='all', combine='sum', reject='none', Stdout="/dev/null")
iraf.scombine(input='combined_sum_cont', output='combined_sum_norm', group='all', combine='sum', reject='none', Stdout="/dev/null")
iraf.sarith(input1='combined_sum_data', op='/', input2='combined_sum_norm', output='combined_final', format='onedspec', Stdout="/dev/null")
else:
for obj in observations[WORK_DIR]['objects']:
try:
os.remove(obj+'_data1D.fits')
os.remove(obj+'_cont1D.fits')
os.remove(obj+'_1D_vh_norm.0001.fits')
os.remove(obj+'_1D_vh_norm.fits')
except: pass
iraf.scombine(input=obj+'.ec.vh', output=obj+'_data1D', group='all', combine='sum', reject='none', Stdout="/dev/null")
iraf.scombine(input=obj+'_cont', output=obj+'_cont1D', group='all', combine='sum', reject='none', Stdout="/dev/null")
iraf.sarith(input1=obj+'_data1D', op='/', input2=obj+'_cont1D', output=obj+'_1D_vh_norm', format='onedspec', Stdout="/dev/null")
def divide_smooth(input_file):
os.system("rm smoothdiv*_"+input_file)
print wave_w1,wave_w2
iraf.sarith(
input1 = "spec_" + input_file,\
op = "/",\
input2 = "master_smooth.fits",\
output = "smoothdivtemp_"+input_file,\
w1 = wave_w1,\
w2 = wave_w2,\
apertures = "*",\
bands = "",\
beams = "",\
apmodulus = 0,\
reverse = 0,\
ignoreaps = 1,\
format = "multispec",\
renumber = 1,\
offset = 0,\
clobber = 1,\
merge = 0,\
rebin = 1,\
errval = 0.0,\
verbose = 1)
### Apply continuum to remove artifacts from the division
iraf.continuum(
input = "smoothdivtemp_" + input_file,\
output = "smoothdiv_" + input_file,\
ask = "no",\
lines = "*",\
bands = "1",\
type = "data",\
replace = 1,\
wavescale = 1,\
logscale = 0,\
override = 1,\
listonly = 0,\
logfiles = "logfile",\
interactive = 0,\
sample = "*",\
naverage = 1,\
function = "spline3",\
order = 10,\
low_reject = 30.0,\
high_reject = 30.0,\
niterate = 1,\
grow = 5.0)
def ContinuumTask(self, InputFile, OutputFile, Fits_Folder, Suffix = 'N'):
#Incase no output name is given, we generate one with the provided "Suffix" (The defaul one is N)
if OutputFile == None:
OutputFile = self.outputNameGenerator(InputFile, Suffix)
continuum_conf = self.ContinuumAttributes(InputFile, OutputFile, Fits_Folder)
#Display the equivalent command in IRAF
Command = self.printIrafCommand('continuum', continuum_conf, printindividually=False)
print '--Using command'
print Command
iraf.continuum(**continuum_conf)
def continumsub(imagefile, _order1, _order2):
# print "LOGX:: Entering `continumsub` method/function in %(__file__)s" %
# globals()
from pyraf import iraf
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.specred(_doprint=0)
toforget = ['specred.continuum']
for t in toforget:
iraf.unlearn(t)
delete('tsky.fits')
iraf.specred.continuum(imagefile, output='tsky.fits', type='difference',
interact='no', function='legendre', niterat=300, low_rej=3, high_re=2, sample='*',
order=_order1, ask='YES')
delete(imagefile)
iraf.continuum('tsky.fits', output=imagefile, type='difference',
interact='no', function='spline1', overrid='yes', niterat=10, low_rej=3, high_re=1, sample='*',
order=_order2, ask='YES')
delete('tsky.fits')
return imagefile
def icontinuum(fn):
"""
call iraf command continuum.
If out put file already exist, this function will delete the old one.
fn : fits name
type : string
return : out put file name
type : string
"""
outname = 'c' + fn
if os.path.isfile(outname):
print('remove file ' + outname)
os.remove(outname)
iraf.continuum(input=fn,
output=outname,
lines='*',
bands=1,
type='ratio',
replace='no',
wavescale='Yes',
logscale='No',
override='No',
listonly='No',
logfiles='logfile',
interactive='Yes',
sample='*',
naverage=1,
function='spline3',
order=1,
low_reject=1.5,
high_reject=0.0,
niterate=20,
grow=2.0,
markrej='Yes',
graphics='stdgraph',
cursor='',
ask='yes')
return outname # return out fit name
def normalise(input_spectrum):
### Apply normalisation with continuum
print "Fitting continuum and normalising spectrum"
os.system("rm temp.fits")
iraf.continuum(
input = input_spectrum,\
output = "temp.fits",\
lines = "*",\
bands = "*",\
type = "ratio",\
replace = 0,\
wavescale = 1,\
logscale = 0,\
override = 1,\
listonly = 0,\
logfiles = "logfile",\
interactive = 0,\
function = "spline3",\
order = 1,\
low_reject = 1.5,\
high_reject = 3.0,\
niterate = 5,\
grow = 2,\
ask = "no",)
#!/usr/bin/env python
#
# Try to normalize spectra
#
from pyraf import iraf
iraf.continuum.unlearn()
spec = str(raw_input("Enter spectra to normalize: "))
out = str(raw_input("Enter name of ouput normalized spectra: "))
iraf.continuum.function = 'spline3'
iraf.continuum.order = 1
iraf.continuum.low_reject = 3.0
iraf.continuum.high_reject = 3.0
iraf.continuum(input=spec, output=out)
bfmod = model[:-4]+'.bf.apogee.txt'
# Various outfiles, some of which will be deleted at the end
textcont = model[:-4]+'_short.txt'
fitscont = model[:-4]+'_short.fits'
fitsnocont = model[:-4]+'_nocont.fits'
convtext = model[:-4]+'.cnv'
convout = model[:-4]+'.cnv.out'
# Continuum fit the spectrum so we can use IRAF's continuum function to remove it
np.savetxt(textcont, np.array([AIR,newf]).transpose())
iraf.rspectext(input=textcont, output=fitscont, flux='No', dtype='interp')
print('iraf rspectext complete')
iraf.continuum(input=fitscont, output=fitsnocont, lines='*',
type='ratio', replace=no, wavescale=yes, logscale=no,
listonly=no, interactive=no, sample='*',
naverage=-25, function='spline3', order=5, niterate=10,
low_reject=1.5, high_reject=5.0, markrej=no, grow=0,
override=yes)
print('iraf continuum complete')
print('friendly untouched by convspec FITS saved {0}'.format(fitsnocont))
# Create a text file correctly formatted for convspec
wave, data = read_fits(fitsnocont)
np.savetxt(convtext, np.array([wave, np.zeros(len(wave)), data]).transpose())
# Run convspec, the fortran code
call([convspecexe, convtext, convspecpar])
print('completed convspec')
# Read the output from convspec
a, b = np.loadtxt(convout, unpack=True, usecols=[0,2])
apmodulus = "0",\
format = "multispec",\
renumber = 0,\
offset = 0,\
clobber = 1,\
merge = 0,\
rebin = 0,\
verbose = 1)
### Then normalise this spectrum (rather than combining the normalised
### spectra from each aperture... easier!)
iraf.continuum(
input = "spec_" + file_name,\
output = "normspec_" + file_name,\
type = "ratio",\
replace = 0,\
wavescale = 1,\
logscale = 0,\
override = 1,\
listonly = 0,\
logfiles = "logfile",\
interactive = 0,\
function = "spline3",\
order = 15,\
low_reject = 5.0,\
high_reject = 0.0,\
niterate = 2,\
grow = 1,\
ask = "no",)
os.system("rm " + "master_smooth.fits")
os.system("mv " + "temp_master_smooth.fits master_smooth.fits")
### Apply continuum
os.system("rm continuum_fit.fits")
iraf.hedit(images="master_smooth",fields = "SFIT",value="",add=0,delete=1,verify=0, show=1, update=1)
iraf.continuum(
input = "master_smooth.fits",\
output = "continuum_fit.fits",\
lines = "*",\
bands = "*",\
type = "fit",\
replace = 0,\
wavescale = 1,\
logscale = 0,\
override = 1,\
listonly = 0,\
logfiles = "logfile",\
interactive = 1,\
function = "spline3",\
order = 100,\
low_reject = 5.0,\
high_reject = 5.0,\
niterate = 5,\
grow = 1,\
ask = "yes",)
os.system("mv continuum_fit.fits " + grating + "_" + dichroic + "_smooth.fits")
def contAndStack(directory='', fileTag=''):
# Function goes through all the files in the passed directory, breaks them
# into their component orders, and continuum-normalizes each order. It
# then re-combines the continuum-ed orders into a single 1-D spectrum.
# It then performs a user-assisted Doppler correction.
fluxFiles = sorted(glob.glob(directory+'/*_Flux.fits'))
spectraCount = 0
# Load the necessary IRAF packages
iraf.onedspec()
# We're going to keep track of the files for each object separately.
# Note: With the potential for multiple exposures, we may have multiple
# images to weight and stack for a given object.
fileDict = {}
for thisFile in fluxFiles:
# Reason #93 as to why IRAF sucks...80 character limit for
# file paths and names...
if len(thisFile) >80:
tempName = directory+'/a_{0}.fits'.format(spectraCount)
if len(tempName)>80:
# make the user rename their path...
print('File path/name too long: {0}'.format(thisFile))
print('Skipping this file.')
continue
os.rename(thisFile, tempName)
thisFile = tempName
# Resulting file names will use the self-reported object name
# (if any) and an index based on the alphabetical order of
# the original ("HI.xxxxx...") filename. Individual CCD files
# (ie: HI.xxxx..._[1,2,3]_Flux.fits") will be left separate.
objName = SD.GetObjectName(thisFile).strip().replace(' ','_')
if objName not in fileDict.keys():
fileDict[objName] = []
# Using a "spectraCount" ensures that if multiple exposures of
# the same object exist, they are given unique filenames for the
# combining process
starName = '{0}{1}_{2:02d}'.format(fileTag, objName, spectraCount)
spectraCount += 1
# makee 'linearize' breaks a 2-d spectrum into it's component
# orders, one order per file.
makeeCall = kMakeeLinearCall + thisFile
sub.call(makeeCall, shell=True)
orderFiles = sorted(glob.glob(directory+'/*_Flux-??.fits'))
for order in orderFiles:
orderNo = order[-7:-5]
if starName == '':
fileRoot = order.split('/')[-1]
orderFileName = directory+'/temp_'+fileRoot
else:
orderFileName = directory+'/temp_'+starName+'_'+orderNo+'.fits'
# Continuum the orders
try:
iraf.continuum(input=order, output=orderFileName,
functio='chebyshev', interac='no', order=5, low_rej=3.0,
high_re=3.5, niterat=10)
except:
print('Error in continuum {0}. Skipping.'.format(order))
u.clearFile(order)
continue
u.clearFile(order)
fileDict[objName].append(orderFileName)
completedList=['H027771','H027859','H028099','H028205','H028344','H028462',\
'H028992','Hy_vB59=HD28034','Hy_vB64=HD28099','Hy_vB65=HD28205',\
'Hy_vB73=HD28344','Hy_vB92=HD28805','Hy_vB93=HD28878','Hy_vB97=HD28992',\
'vA_294','vB_173','vB_180','vB_183','vB__46','vB__49','vB__64','vB__65',\
'vB__73','vB__92','vB__93','vB__97','RHy_281','VA294','28344','HD_028344'\
'HD28394','08-U20187']
for objName in fileDict.keys():
if len(fileDict[objName]) == 0:
continue
if objName in completedList:
print('Skipping {0}'.format(objName))
continue
# Create a temporary input file list:
fpInput = open('input.dat','w')
fpWeight = open('weight.dat','w')
print('---- {0} ----'.format(objName))
for fn in fileDict[objName]:
fpInput.write(fn+'\n')
sn = SD.GetSpectraSN(fn)
fpWeight.write('{0:6.1f}\n'.format(sn))
print(' {0:40}: {1:5.1f}'.\
format(fn.split('/')[-1], sn))
fpInput.close()
fpWeight.close()
contFileName = directory+'/wc_'+objName+'.fits'
# Stack all the images/orders for this object.
iraf.onedspec.scombine(input='@input.dat', output=contFileName,\
weight='@weight.dat', combine='median', \
lthreshold=0.05, hthreshold=2.0, group="all")
# iraf.stsdas.hst_calib.tomultispec(input=contFileName, output='ms_'+contFileName)
u.clearFile('input.dat')
u.clearFile('weight.dat')
for fn in fileDict[objName]:
u.clearFile(fn)
# For now, the automated Doppler correction is good only for very small
# corrections (ie: <5km/s), so use the manual version.
dopCorVelocity, dopWLs = DCL.dopCor(contFileName, interactive=True)
# print('Doppler correction = {0:2.3f}km/s'.format(dopCorVelocity))
dopCorFilename = directory+'/D'+contFileName.split('/')[-1][1:]
iraf.dopcor(input=contFileName, output=dopCorFilename, redshift=dopCorVelocity, isveloc='yes', verbose='yes')
u.clearFile(contFileName)
return
def fitContinuum(rawFrame, grating, continuumInter, tempInter, log, over):
"""
Fit a continuum to the telluric correction spectrum to normalize it. The continuum
fitting regions were derived by eye and can be improved.
Results are in fit<Grating>.fits
"""
# These were found to fit the curves well by hand. You can probably improve them; feel free to fiddle around!
if grating == "K":
order = 5
sample = "20279:20395,20953:24283"
elif grating == "J":
order = 5
sample = "11561:12627,12745:12792,12893:13566"
elif grating == "H":
order = 5
sample = "*"
elif grating == "Z":
order = 5
sample = "9453:10015,10106:10893,10993:11553"
if os.path.exists('2_fit' + rawFrame + '.fits'):
if over:
os.remove('2_fit' + rawFrame + '.fits')
iraf.continuum(input='1_htel' + rawFrame,
output='2_fit' + rawFrame,
ask='yes',
lines='*',
bands='1',
type="fit",
replace='no',
wavescale='yes',
logscale='no',
override='no',
listonly='no',
logfiles=log,
inter=continuumInter,
sample=sample,
naverage=1,
func='spline3',
order=order,
low_rej=1.0,
high_rej=3.0,
niterate=2,
grow=1.0,
markrej='yes',
graphics='stdgraph',
cursor='',
mode='ql')
else:
logging.info(
"\nOutput exists and -over not set - skipping continuum fit to telluric correction"
)
else:
iraf.continuum(input='1_htel' + rawFrame,
output='2_fit' + rawFrame,
ask='yes',
lines='*',
bands='1',
type="fit",
replace='no',
wavescale='yes',
logscale='no',
override='no',
listonly='no',
logfiles=log,
inter=continuumInter,
sample=sample,
naverage=1,
func='spline3',
order=order,
low_rej=1.0,
high_rej=3.0,
niterate=2,
grow=1.0,
markrej='yes',
graphics='stdgraph',
cursor='',
mode='ql')
if os.path.exists('../products_fluxcal_AND_telluric_corrected/0_fit' +
rawFrame + '.fits'):
if over:
os.remove('../products_fluxcal_AND_telluric_corrected/0_fit' +
rawFrame + '.fits')
shutil.copy(
'2_fit' + rawFrame + '.fits',
'../products_fluxcal_AND_telluric_corrected/0_fit' + rawFrame +
'.fits')
else:
logging.info(
"\nOutput exists and -over not set - skipping copy of fit to products_fluxcal_AND_telluric_corrected"
)
else:
shutil.copy(
'2_fit' + rawFrame + '.fits',
'../products_fluxcal_AND_telluric_corrected/0_fit' + rawFrame +
'.fits')
if tempInter:
# Plot the telluric correction spectrum with the continuum fit.
final_tel_no_hLines_no_norm = astropy.io.fits.open('1_htel' +
rawFrame +
'.fits')[0].data
fit = astropy.io.fits.open('2_fit' + rawFrame + 'fit.fits')[0].data
plt.title(
'Unnormalized Telluric Correction and Continuum fit Used to Normalize'
)
plt.plot(final_tel_no_hLines_no_norm)
plt.plot(fit)
plt.show()
fnu = 0,\
mode = "al")
### Apply high/low rejection again to remove cosmic rays
os.system("rm temp.fits")
iraf.continuum(
input = "fluxcal_"+ file_name,\
output = "temp.fits",\
ask = "no",\
lines = "*",\
bands = "*",\
type = "data",\
replace = 1,\
wavescale = 1,\
logscale = 0,\
override = 1,\
listonly = 0,\
logfiles = "logfile",\
interactive = 0,\
sample = "*",\
naverage = 1,\
function = "spline3",\
order = 15,\
low_reject = 30.0,\
high_reject = 30.0,\
niterate = 10,\
grow = 1.0)
os.system("rm fluxcal_" + file_name)
os.system("mv temp.fits fluxcal_" + file_name)
### Move spectrum to reduced/
###
### Continuum fitting and removal different orders for different parts...
### Trial and error to get it working.
###
print 'Continuum fitting the spectra...'
iraf.continuum(input='@targets_extracted',
output='fitcont//@targets_extracted',
lines='*',
type='fit',
replace=no,
wavescale=yes,
logscale=no,
listonly=no,
interactive=no,
sample='*',
naverage=1,
function='spline3',
order=4,
niterate=20,
low_reject=1.15,
high_reject=2.5,
markrej=no,
grow=0,
override=no)
# Good fits for old 90-92 Ca H and K
#if grating == 'old':
# iraf.continuum(input='@targets_extracted',
# output='fitcont//@targets_extracted',lines='90,91,92',
# type='fit',replace=no,wavescale=yes,logscale=no,listonly=no,
def deimos_standard(standard):
'''Extract standard star and calculate sensitivit function.'''
bstd = "%s_01_B" % standard; rstd = "%s_01_R" % standard
# Extract standard
if get_head("%s.fits" % bstd, "SKYSUB"):
iraf.apall(bstd, output="", inter=yes, find=yes, recenter=yes, resize=yes,
edit=yes, trace=yes, fittrace=yes, extract=yes, extras=yes,
review=yes, background="none")
iraf.apall(rstd, output="", inter=yes, find=yes, recenter=yes, resize=yes,
edit=yes, trace=yes, fittrace=yes, extract=yes, extras=yes,
review=yes, background="none")
else:
iraf.apall(bstd, output="", inter=yes, find=yes, recenter=yes, resize=yes,
edit=yes, trace=yes, fittrace=yes, extract=yes, extras=yes,
review=yes, background="fit")
iraf.apall(rstd, output="", inter=yes, find=yes, recenter=yes, resize=yes,
edit=yes, trace=yes, fittrace=yes, extract=yes, extras=yes,
review=yes, background="fit")
# Fit the continuum
iraf.continuum("%s.ms" % bstd, output="s%s.ms" % bstd, lines=1, bands=1,
type="fit", sample="*", naverage=1, function="chebyshev",
order=15, low_reject=3.0, high_reject=5.0, niterate=10, grow=1.0,
interac=yes)
iraf.continuum("%s.ms" % rstd, output="s%s.ms" % rstd, lines=1, bands=1,
type="fit", sample="*", naverage=1, function="chebyshev",
order=15, low_reject=3.0, high_reject=5.0, niterate=10, grow=1.0,
interac=yes)
# Divide through by smoothed standard
iraf.sarith("%s.ms" % bstd, "/", "s%s.ms" % bstd, "ds%s.ms" % bstd)
iraf.sarith("%s.ms" % rstd, "/", "s%s.ms" % rstd, "ds%s.ms" % rstd)
# Create and apply telluric correction
iraf.splot("%s.ms" % bstd) # Remove telluric, save as a%s.ms
iraf.splot("%s.ms" % rstd) # Remove telluric, save as a%s.ms
iraf.sarith("%s.ms" % bstd, "/", "a%s.ms" % bstd, "telluric.B.fits")
iraf.sarith("%s.ms" % rstd, "/", "a%s.ms" % rstd, "telluric.R.fits")
iraf.imreplace("telluric.B.fits", 1.0, lower=0.0, upper=0.0)
iraf.imreplace("telluric.R.fits", 1.0, lower=0.0, upper=0.0)
iraf.telluric("ds%s.ms" % bstd, "tds%s.ms" % bstd, "telluric.B.fits",
xcorr=no, tweakrms=no, interactive=no, sample='6850:6950,7575:7700')
iraf.telluric("ds%s.ms" % rstd, "tds%s.ms" % rstd, "telluric.R.fits",
xcorr=no, tweakrms=no, interactive=no, sample='6850:6950,7575:7700')
# Define bandpasses for standard star calculation
iraf.standard("tds%s.ms" % bstd, "%s.B.std" % standard,
extinction='home$extinct/maunakeaextinct.dat',
caldir='home$standards/', observatory='Keck', interac=yes,
star_name=standard, airmass='', exptime='')
iraf.standard("tds%s.ms" % rstd, "%s.R.std" % standard,
extinction='home$extinct/maunakeaextinct.dat',
caldir='home$standards/', observatory='Keck', interac=yes,
star_name=standard, airmass='', exptime='')
# Determine sensitivity function
iraf.sensfunc('%s.B.std' % standard, '%s.B.sens' % standard,
extinction='home$extinct/maunakeaextinct.dat',
newextinction='extinct.dat', observatory='Keck',
function='legendre', order=4, interactive=yes)
iraf.sensfunc('%s.R.std' % standard, '%s.R.sens' % standard,
extinction='home$extinct/maunakeaextinct.dat',
newextinction='extinct.dat', observatory='Keck',
function='legendre', order=4, interactive=yes)
return
### Apply continuum to normalise
os.system("rm norm_" + file_name)
iraf.continuum(
input = "trim_" + file_name,\
output = "norm_" + file_name,\
lines = "*",\
bands = "1",\
type = "ratio",\
replace = 0,\
wavescale = 1,\
logscale = 0,\
override = 1,\
listonly = 0,\
logfiles = "logfile",\
interactive = 0,\
sample = "*",\
naverage = 1,\
function = "spline3",\
order = 3,\
low_reject = 1.5,\
high_reject = 4.0,\
niterate = 10,\
markrej = 1,\
graphics = "stdgraph",\
cursor = "",\
ask = "no",\
mode = "ql")
#######################
### Convert to .dat ###
flat # <codecell> #create flats with different aperture widths iraf.apall(input=flat, output='flat_1', referen=flat, format='multispec', interac='no', find='no', recenter='no', resize='yes', edit='no', trace='no', fittrac='no', extract='yes', extras='no', review='no', line=2000, lower=-1, upper=1, bkg='no', nsum=-10, ylevel="INDEF", llimit=-1, ulimit=1,Stdout="/dev/null") # iraf.apall(input='tmp/masterflat.fits', output='tmp/flat_2', referen='tmp/masterflat.fits', format='multispec', interac='no', find='no', recenter='no', resize='yes', edit='no', trace='no', fittrac='no', extract='yes', extras='no', review='no', line=2000, lower=-2, upper=2, bkg='no', nsum=-10, ylevel="INDEF", llimit=-2, ulimit=2,Stdout="/dev/null") # iraf.apall(input='tmp/masterflat.fits', output='tmp/flat_3', referen='tmp/masterflat.fits', format='multispec', interac='no', find='no', recenter='no', resize='yes', edit='no', trace='no', fittrac='no', extract='yes', extras='no', review='no', line=2000, lower=-3, upper=3, bkg='no', nsum=-10, ylevel="INDEF", llimit=-3, ulimit=3,Stdout="/dev/null") # <codecell> #normalize different flats iraf.noao(_doprint=0,Stdout="/dev/null") iraf.onedspec(_doprint=0,Stdout="/dev/null") iraf.continuum(input=flat, output='flat_1_norm', lines='*', bands='*', type='ratio', wavescale='no', interac='no', sample='1:4095', functio='spline3', order=13, low_rej=2, high_rej=2, niter=10,Stdout="/dev/null") # iraf.continuum(input='tmp/flat_2', output='tmp/flat_2_norm', lines='*', bands='*', type='ratio', wavescale='no', interac='no', sample='1:4095', functio='spline3', order=13, low_rej=2, high_rej=2, niter=10,Stdout="/dev/null") # iraf.continuum(input='tmp/flat_3', output='tmp/flat_3_norm', lines='*', bands='*', type='ratio', wavescale='no', interac='no', sample='1:4095', functio='spline3', order=13, low_rej=2, high_rej=2, niter=10,Stdout="/dev/null") # <codecell> #combine normalized flats # iraf.images(_doprint=0,Stdout="/dev/null") # iraf.imutil(_doprint=0,Stdout="/dev/null") # iraf.imarith(operand1='tmp/flat_1_norm', op='*', operand2=0.43, result='tmp/flat_1_norm2',Stdout="/dev/null") # iraf.imarith(operand1='tmp/flat_2_norm', op='*', operand2=0.41, result='tmp/flat_2_norm2',Stdout="/dev/null") # iraf.imarith(operand1='tmp/flat_3_norm', op='*', operand2=0.16, result='tmp/flat_3_norm2',Stdout="/dev/null") # <codecell> iraf.imsum(input='*norm.fits', output='masterflat_norm.fits', option='sum',Stdout="/dev/null")
verbose = 1,\
mode = "ql")
trim_spectra("synthetic.fits")
print "Fitting continuum and normalising synthetic spectrum"
iraf.continuum(
input = "synthetic",\
output = "n_synthetic",\
lines = "*",\
bands = 1,\
type = "ratio",\
replace = 0,\
wavescale = 1,\
logscale = 0,\
override = 1,\
listonly = 0,\
logfiles = "logfile",\
interactive = 0,\
function = "spline3",\
order = 15,\
low_reject = 2.0,\
high_reject = 0.0,\
niterate = 5,\
grow = 1,\
ask = "no")
os.system("mv -f n_synthetic.fits synthetic.fits")
#################
### Run fxcor ###
#################
lowrej = 15.0
highrej = 25.0
nit = 2
os.system("rm cray_" + im_slice + "_" + file_name)
iraf.continuum(
input = "dispcorr_" + im_slice + "_" + file_name,\
output = "cray_" + im_slice + "_" + file_name,\
ask = "no",\
lines = "*",\
bands = "*",\
type = "data",\
replace = 1,\
wavescale = 1,\
logscale = 0,\
override = 1,\
listonly = 0,\
logfiles = "logfile",\
interactive = 0,\
sample = "*",\
naverage = 1,\
function = "spline3",\
order = 15,\
low_reject = lowrej,\
high_reject = highrej,\
niterate = nit,\
grow = 1.0)
iraf.hedit(images="cray_" + im_slice + "_" + file_name,fields="SFIT",value="1",add=0,delete=1,verify=0,show=1,update=1)
iraf.hedit(images="cray_" + im_slice + "_" + file_name,fields="SFITB",value="1",add=0,delete=1,verify=0,show=1,update=1)
### Apply normalisation with continuum
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")
file_name[k] + "_blue.fits",
star_folders[i] + "/reduction/" +
file_name[k] + "_blue_d.fits",
analyse['CCF_RVC'][k],
isveloc="yes")
iraf.dopcor(star_folders[i] + "/reduction/" +
file_name[k] + "_red.fits",
star_folders[i] + "/reduction/" +
file_name[k] + "_red_d.fits",
analyse['CCF_RVC'][k],
isveloc="yes")
iraf.continuum(star_folders[i] + "/reduction/" +
file_name[k] + "_blue_d.fits",
star_folders[i] + "/reduction/" +
file_name[k] + "_blue_dn.fits",
order=80,
nit=7,
low_rej=1.0,
high_rej=3,
interactive="no",
wavescale="no")
iraf.continuum(star_folders[i] + "/reduction/" +
file_name[k] + "_red_d.fits",
star_folders[i] + "/reduction/" +
file_name[k] + "_red_dn.fits",
order=80,
nit=7,
low_rej=1.0,
high_rej=3,
interactive="no",
wavescale="no")
norm_blue.append(file_name[k] + "_blue_dn.fits")
pyfits.writeto("REGION_Amp5_resto0.fits", imagen_noC, header)
from pyraf import iraf
from iraf import onedspec, continuum, imutil, imarith
############# CONTINUUM###############
iraf.continuum(input="REGION_1_ORIG_resto0.fits",
output="REGION_1_ORIG_CONT.fits",
lines="*",
band="*",
type='fit',
replace='no',
interac='no',
sample='1056:1309',
naverag='3',
functio='legendre',
order='4',
low_rej='3',
high_rej='3',
niterate='10',
ask='no')
iraf.continuum(input="REGION_2_ORIG_resto0.fits",
output="REGION_2_ORIG_CONT.fits",
lines="*",
band="*",
type='fit',
replace='no',
interac='no',
def remove_molecule(molecule,spec_name,outspec_name):
telluric_region = eval("TELLURIC_REGION_"+molecule)
telluric_region_list = string.split(telluric_region,",")
for n in range(len(telluric_region_list)):
region = telluric_region_list[n][1:]
region = string.split(region,"-")
temp = []
for i in region:
temp.append(float(i))
telluric_region_list[n] = temp
### Copy telluric line spectra to working directory
telluric_fits = grating + "_"+molecule+".fits"
os.system("cp -f "+program_dir+"telluric_fits/" + telluric_fits + " telluric.fits")
os.system("rm temp_norm*")
os.system("rm temp_spec*")
os.system("cp -f "+spec_name+" temp_spec.fits")
iraf.continuum(
input = spec_name,\
output = "temp_norm.fits",\
lines = "*",\
bands = "*",\
type = "ratio",\
replace = 0,\
wavescale = 1,\
logscale = 0,\
override = 1,\
listonly = 0,\
logfiles = "logfile",\
interactive = 0,\
sample = "*",\
naverage = 1,\
function = "spline3",\
order = 20,\
low_reject = 2.0,\
high_reject = 5.0,\
niterate = 5,\
grow = 2.0,\
ask = "yes")
##################
### Find shift ###
##################
shift = run_fxcor("telluric.fits","temp_norm.fits",telluric_region)
os.system("rm telluric.dat")
iraf.dopcor(
input = "telluric.fits",\
output = "telluric.fits",\
redshift = shift,\
isvelocity = 1,\
add = 1,\
dispersion = 1,\
flux = 0,\
verbose = 0)
iraf.wspectext(
input = "telluric.fits",\
output = "telluric.dat",\
header = 0)
######################################
### Iterative telluric subtraction ###
######################################
def iterate():
os.system("rm temp_norm.dat")
iraf.wspectext(
input = "temp_norm.fits",\
output = "temp_norm.dat",\
header = 0)
data_spec = transpose(loadtxt("temp_norm.dat"))
wave = arange(min(data_spec[0]),max(data_spec[0]),1)
data_flux = interpolate.splrep(data_spec[0],data_spec[1])
data_flux = interpolate.splev(wave,data_flux)
telluric_spec = transpose(loadtxt("telluric.dat"))
telluric_flux = interpolate.splrep(telluric_spec[0],telluric_spec[1])
telluric_flux = interpolate.splev(wave,telluric_flux)
### Loop through telluric scaling
telluric_scaling = []
rms_list = []
maxscale = 1.08
for scale in arange(0.0,maxscale,0.01):
scatter_list = []
for region in telluric_region_list:
temp_flux = []
temp_tel = []
for i in range(len(wave)):
if wave[i] > region[0] and wave[i] < region[1]:
temp_flux.append(data_flux[i])
temp_tel.append(telluric_flux[i])
if wave[i] > region[1]:
break
temp_flux = array(temp_flux)
temp_tel = array(temp_tel)
scatter = temp_flux / (temp_tel * scale + (1-scale))
# plt.plot(scatter)
# plt.show()
scatter_list.append(std(scatter))
telluric_scaling.append(scale)
rms_list.append(average(scatter_list))
telluric_scaling,rms_list = array(telluric_scaling),array(rms_list)
best_scale = find_min(telluric_scaling,rms_list)
if best_scale > maxscale:
best_scale = maxscale
if best_scale < 0:
best_scale = 0
print "scaling telluric by",best_scale
# plt.plot(telluric_scaling,rms_list)
# plt.show()
os.system("cp telluric.fits telluric_temp.fits")
iraf.sarith(
input1 = "telluric_temp.fits",\
op = "*",\
input2 = best_scale,\
output = "telluric_temp.fits",\
w1 = "INDEF",\
w2 = "INDEF",\
apertures = "",\
bands = "",\
beams = "",\
apmodulus = 0,\
reverse = 0,\
ignoreaps = 1,\
format = "multispec",\
renumber = 0,\
offset = 0,\
clobber = 1,\
merge = 0,\
rebin = 0,\
verbose = 0)
iraf.sarith(
input1 = "telluric_temp.fits",\
op = "+",\
input2 = 1-best_scale,\
output = "telluric_temp.fits",\
w1 = "INDEF",\
w2 = "INDEF",\
apertures = "",\
bands = "",\
beams = "",\
apmodulus = 0,\
reverse = 0,\
ignoreaps = 1,\
format = "multispec",\
renumber = 0,\
offset = 0,\
clobber = 1,\
merge = 0,\
rebin = 0,\
verbose = 0)
iraf.sarith(
input1 = "temp_spec.fits",\
op = "/",\
input2 = "telluric_temp.fits",\
output = "temp_spec",\
w1 = "INDEF",\
w2 = "INDEF",\
apertures = "",\
bands = "",\
beams = "",\
apmodulus = 0,\
reverse = 0,\
ignoreaps = 1,\
format = "multispec",\
renumber = 0,\
offset = 0,\
clobber = 1,\
merge = 0,\
rebin = 0,\
verbose = 0)
iraf.sarith(
input1 = "temp_norm.fits",\
op = "/",\
input2 = "telluric_temp.fits",\
output = "temp_norm",\
w1 = "INDEF",\
w2 = "INDEF",\
apertures = "",\
bands = "",\
beams = "",\
apmodulus = 0,\
reverse = 0,\
ignoreaps = 1,\
format = "multispec",\
renumber = 0,\
offset = 0,\
clobber = 1,\
merge = 0,\
rebin = 0,\
verbose = 0)
return best_scale
best_scaling = 1.0
while best_scaling > 0.05:
best_scaling = iterate()
os.system("cp temp_spec.fits "+outspec_name)