def calibrate(namelst):
iraf.noao()
iraf.twodspec()
iraf.longslit(dispaxis=2,
nsum=1,
observatory='ca',
extinction=extpath,
caldir=stdpath)
for fitname in namelst:
outname = 'mark_' + fitname
if os.path.isfile(outname):
print('remove file ' + outname)
os.remove(outname)
iraf.calibrate(input=fitname,
output=outname,
extinct='yes',
flux='yes',
extinction=extpath,
ignoreaps='yes',
sensitivity='Sens',
fnu='no')
iraf.splot(images=outname)
iraf.flpr()
def 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 standard(namelst):
iraf.noao()
iraf.twodspec()
iraf.longslit(dispaxis=2, nsum=1, observatory=func.obs.name,
extinction=extpath, caldir=stdpath)
if os.path.isfile('Std'):
print('remove file Std')
os.remove('Std')
for std_fitsname in namelst:
stdname, stdmag, stdmagband = func.standard_star_info(std_fitsname)
print(colored('the standard star is ' + stdname, 'green'))
wid, sep = get_band_width_sep(std_fitsname)
airmas = pyfits.getval(std_fitsname, 'airmass')
exposure = pyfits.getval(std_fitsname, 'exptime')
iraf.standard(input=std_fitsname, output='Std', samestar=True,
beam_switch=False, apertures='', bandwidth=wid,
bandsep=sep, # 30.0 20.0
fnuzero=3.6800000000000E-20, extinction=extpath,
caldir=stdpath, observatory=func.obs.name, interact=True,
graphics='stdgraph', cursor='', star_name=stdname,
airmass=airmas, exptime=exposure, mag=stdmag,
magband=stdmagband, teff='', answer='yes')
if os.path.isfile('Sens.fits'):
print('remove file Sens.fits')
os.remove('Sens.fits')
iraf.sensfunc(standards='Std', sensitivity='Sens',
extinction=extpath, function='spline3', order=9)
iraf.splot('Sens')
def lris_standard(standards, xcorr=yes):
'''Extract standard stars and calculate sensitivity functions.'''
for ofile, nstd in standards:
shutil.copy(ofile, "%s.fits" % nstd)
# Extract standard
if get_head("%s.fits" % nstd, "SKYSUB"):
iraf.apall(nstd, output="", inter=yes, find=yes, recenter=yes, resize=yes,
edit=yes, trace=yes, fittrace=yes, extract=yes, extras=yes,
review=no, background="none")
else:
iraf.apall(nstd, output="", inter=yes, find=yes, recenter=yes, resize=yes,
edit=yes, trace=yes, fittrace=yes, extract=yes, extras=yes,
review=no, background="fit")
if xcorr:
# Cross correlate to tweak wavelength
iraf.xcsao("%s.ms" % nstd, templates=XCTEMPLATE,
correlate="wavelength", logfiles="%s.xcsao" % ofile)
# If a solution was found, apply shift
try:
xcsaof = open("%s.xcsao" % ofile)
shift = float(xcsaof.readlines()[6].split()[14])
except:
shift = 0.0
iraf.specshift("%s.ms" % nstd, -shift)
# Create telluric
iraf.splot("%s.ms" % nstd) # Remove telluric and absorption, save as a%s.ms
iraf.sarith("%s.ms" % nstd, "/", "a%s.ms" % nstd, "telluric.%s.fits" % nstd)
iraf.imreplace("telluric.%s.fits" % nstd, 1.0, lower=0.0, upper=0.0)
iraf.splot("telluric.%s.fits" % nstd) # Remove stellar features and resave
# Create smoothed standard
iraf.gauss("a%s.ms[*,1,1]" % nstd, "s%s.ms" % nstd, 5.0)
iraf.sarith("%s.ms" % nstd, "/", "s%s.ms" % nstd, "ds%s.ms" % nstd)
# Apply telluric correction
iraf.telluric("ds%s.ms" % nstd, "tds%s.ms" % nstd, "telluric.%s.fits" % nstd,
xcorr=no, tweakrms=no, interactive=no,
sample='4000:4010,6850:6975,7150:7350,7575:7725,8050:8400,8900:9725')
# Define bandpasses for standard star calculation
obj=get_head("%s.fits" % nstd, "OBJECT")
iraf.standard("tds%s.ms" % nstd, "%s.std" % nstd,
extinction='home$extinct/maunakeaextinct.dat',
caldir='home$standards/', observatory='Keck', interac=yes,
star_name=obj, airmass='', exptime='')
# Determine sensitivity function
iraf.sensfunc('%s.std' % nstd, '%s.sens' % nstd,
extinction='home$extinct/maunakeaextinct.dat',
newextinction='extinct.dat', observatory='Keck',
function='legendre', order=4, interactive=yes)
return
def SplotTask(self, InputFile, Fits_Folder, xmin = 'INDEF', xmax = 'INDEF', ymin = 'INDEF', ymax='INDEF'):
splot_conf = self.SplotAttributes(InputFile, Fits_Folder,xmin, xmax, ymin, ymax)
iraf.splot(**splot_conf)
return
def get_cal_spec_line(filename1, filename2):
print 'Run get_cal_spec_line, apall...'
outname = 'a' + filename1
if os.path.isfile(outname):
print outname, 'is already exist'
else:
print 'display %s 1' % filename1
iraf.display(image = filename1, frame = 1)
iraf.apall(input = filename1
, output = outname, apertures = '', format = 'multispec'
, references = filename2, profiles = '', interactive = True
, find = False, recenter = False, resize = False
, edit = False, trace = False, fittrace = False
, extract = True, extras = False, review = True
, line = 'INDEF', nsum = 10, lower = -15.0
, upper = 15.0, apidtable = '', b_function = 'chebyshev'
, b_order = 1, b_sample = '-30:-20,20:30', b_naverage = -3
, b_niterate = 0, b_low_reject = 3.0, b_high_reject = 3.0
, b_grow = 0.0, width = 5.0, radius = 10.0
, threshold = 0.0, nfind = 1, minsep = 5.0
, maxsep = 1000.0, order = 'increasing', aprecenter = ''
, npeaks = 'INDEF', shift = True, llimit ='INDEF'
, ulimit = 'INDEF', ylevel = 0.1, peak = True
, bkg = True, r_grow = 0.0, avglimits = False
, t_nsum = 10, t_step = 10, t_nlost = 3, t_function = 'spline3'
, t_order = 3, t_sample = '*', t_naverage = 1
, t_niterate = 0, t_low_reject = 3.0, t_high_reject = 3.0
, t_grow = 0.0, background = 'none', skybox = 1
, weights = 'none', pfit = 'fit1d', clean = False
, saturation = 'INDEF', readnoise = 9.4, gain = 0.35
, lsigma = 4.0, usigma = 4.0, nsubaps = 1)
print 'splot %s' % outname
iraf.splot(images = outname)
return outname
def calibrate(scifiles, extfile, observatory):
for f in scifiles:
redorblue = getredorblue(f)
iraf.unlearn(iraf.gscalibrate)
iraf.gscalibrate('et' + f[:-4] + '.fits',
sfunc='sens' + redorblue + '.fits', fl_ext=True, fl_vardq=dodq,
extinction=extfile, observatory=observatory)
if os.path.exists('cet' + f[:-4] + '.fits'):
iraf.unlearn(iraf.splot)
iraf.splot('cet' + f.replace('.txt', '.fits') + '[sci]') # just to check
def calibrate(scifiles, extfile, observatory):
for f in scifiles:
redorblue = getredorblue(f)
iraf.unlearn(iraf.gscalibrate)
iraf.gscalibrate('et' + f[:-4] + '.fits',
sfunc='sens' + redorblue + '.fits', fl_ext=True,
extinction=extfile, observatory=observatory)
if os.path.exists('cet' + f[:-4] + '.fits'):
iraf.unlearn(iraf.splot)
iraf.splot('cet' + f.replace('.txt', '.fits') + '[sci]') # just to check
def standard(namelst):
iraf.noao()
iraf.twodspec()
iraf.longslit(dispaxis=2,
nsum=1,
observatory='ca',
extinction=extpath,
caldir=stdpath)
std_fitsname = namelst[0]
stdname, stdmag, stdmagband = standard_star_info(std_fitsname)
wid, sep = get_band_width_sep(stdname)
print('<<<<<the standard star is ', stdname, '>>>>>')
print std_fitsname
if os.path.isfile('Std'):
print('remove file Std')
os.remove('Std')
iraf.standard(
input=std_fitsname,
output='Std',
samestar=True,
beam_switch=False,
apertures='',
bandwidth=wid,
bandsep=sep, # 30.0 20.0
fnuzero=3.6800000000000E-20,
extinction=extpath,
caldir=stdpath,
observatory='ca',
interact=True,
graphics='stdgraph',
cursor='',
star_name=stdname,
airmass='',
exptime='',
mag=stdmag,
magband=stdmagband,
teff='',
answer='yes')
if os.path.isfile('Sens.fits'):
print('remove file Sens.fits')
os.remove('Sens.fits')
iraf.sensfunc(standards='Std',
sensitivity='Sens',
extinction=extpath,
function='spline3',
order=15)
iraf.splot('Sens')
iraf.flpr()
def sumFibers(self, inIm, **kwargs):
print "\nSUMMING FIBERS FROM " + inIm
# delete previous summed spectra
# TODO: add attribute to class to turn imdelete verifications on/off
iraf.imdelete("a" + inIm)
# sum the fibers and view the final spectrum
iraf.gfapsum(inIm, **kwargs)
iraf.splot("a" + inIm + "[SCI,1]")
return
def linefitManual(spectrum, band):
""" Enter splot so the user can fit and subtract lorents (or, actually, any) profiles
"""
iraf.splot(images=spectrum, new_image='final_tel_no_hlines_no_norm', save_file='../PRODUCTS/lorentz_hlines.txt', overwrite='yes')
# it's easy to forget to use the 'i' key to actually write out the line-free spectrum, so check that it exists:
# with the 'tweak' options, the line-free spectrum will already exists, so this lets the user simply 'q' and move on w/o editing (too bad if they edit and forget to hit 'i'...)
while True:
try:
with open("final_tel_no_hlines_no_norm.fits") as f: pass
break
except IOError as e:
logging.info("It looks as if you didn't use the i key to write out the lineless spectrum. We'll have to try again. --> Re-entering splot")
iraf.splot(images=spectrum, new_image='final_tel_no_hlines_no_norm', save_file='../PRODUCTS/lorentz_hlines.txt', overwrite='yes')
def sensfunc(standards, obs):
"""
sensfunc -- Determine sensitivity and extinction functions
sensfunc standards sensitivity
"""
import glob
import os, sys
from pyraf import iraf
iraf.noao()
iraf.onedspec()
output_sens = "sens"
iraf.sensfunc(standards=standards,
sensitivity=output_sens,
extinct='/iraf/iraf/noao/lib/onedstds/ctioextinct.dat',
observatory=obs)
iraf.splot(output_sens)
def calibrate(lstfile):
stdpath = os.path.split(os.path.realpath(__file__))[0] + os.sep + 'standarddir' + os.sep
extpath = os.path.split(os.path.realpath(__file__))[0] + os.sep + 'LJextinct.dat'
iraf.noao()
iraf.twodspec()
iraf.longslit(dispaxis = 2, nsum = 1, observatory = 'Lijiang',
extinction = extpath, caldir = stdpath)
f = open(lstfile)
l = f.readlines()
f.close()
l = [tmp.split('\n')[0] for tmp in l]
for fitname in l:
stdobjname = select_std(fitname)
stdfitname = 'sensawftbo' + stdgroup[stdobjname][0]
iraf.calibrate(input = 'awftbo'+ fitname, output = 'mark_awftbo' + fitname,
extinct = 'yes', flux = 'yes', extinction = extpath,
ignoreaps = 'yes', sensitivity = stdfitname, fnu = 'no')
iraf.splot(images = 'mark_awftbo' + fitname)
def 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 combinespecs(inputre, scale='exposure', rdnoise='rdnoise', gain='gain'):
''' combine two or more spectra tha matches the input regular expression
'''
specfiles = glob.glob(specre)
specstring = ', '.join(specfiles)
print 'The following spectra will be combined: '
print specfiles
specout = str(raw_input('Enter output file name: '))
iraf.scombine.unlearn()
iraf.scombine.scale = scale
iraf.scombine.rdnoise = rdnoise
iraf.scombine.gain = gain
iraf.scombine(input=specstring, output=specout)
ask = str(raw_input('Plot output with splot? Y/N: '))
if (ask == 'y') or (ask == 'Y'):
iraf.splot.unlearn()
iraf.splot(specout)
def combinespecs(inputre, scale='exposure', rdnoise='rdnoise', gain='gain'):
''' combine two or more spectra tha matches the input regular expression
'''
specfiles = glob.glob(specre)
specstring = ', '.join(specfiles)
print 'The following spectra will be combined: '
print specfiles
specout = str(raw_input('Enter output file name: '))
iraf.scombine.unlearn()
iraf.scombine.scale = scale
iraf.scombine.rdnoise = rdnoise
iraf.scombine.gain = gain
iraf.scombine(input=specstring, output=specout)
ask = str(raw_input('Plot output with splot? Y/N: '))
if (ask == 'y') or (ask == 'Y'):
iraf.splot.unlearn()
iraf.splot(specout)
def execute(self):
"""Execute pyraf task: splot"""
log.debug("SplotETI.execute()")
# Populate object lists
xcldict = copy(self.clparam_dict)
for fil in self.file_objs:
xcldict.update(fil.get_parameter())
for par in self.param_objs:
xcldict.update(par.get_parameter())
iraf.unlearn(iraf.splot)
# Use setParam to list the parameters in the logfile
for par in xcldict:
#Stderr and Stdout are not recognized by setParam
if par != "Stderr" and par !="Stdout":
iraf.splot.setParam(par,xcldict[par])
log.fullinfo("\nSPLOT PARAMETERS:\n")
iraf.lpar(iraf.splot, Stderr=xcldict["Stderr"], \
Stdout=xcldict["Stdout"])
# Execute the task using the same dict as setParam
# (but this time with Stderr and Stdout)
iraf.splot(**xcldict)
def lris_extract(science, standards, dostandard=yes):
'''Extract and flux calibrate LRIS spectra (assuming they have been
processed into 2D images using LRIS_pipe above).'''
if dostandard:
lris_standard(standards)
for src in science:
bimages = iraffiles("%s_??_B.fits" % src)
rimages = iraffiles("%s_??_R.fits" % src)
joinstr = ""
for bimage in bimages:
bimage = bimage.split(".")[0]
# Find appropriate standard for this image
bstd = get_head("%s.fits" % bimage, "STDNAME")
# Extract 1D spectra
if get_head("%s.fits" % bimage, "SKYSUB"):
iraf.apall(bimage, output="", inter=yes, find=yes, recenter=yes,
resize=yes, edit=yes, trace=yes, fittrace=yes, extract=yes,
extras=yes, review=no, background="none",
reference=bstd)
else:
iraf.apall(bimage, output="", inter=yes, find=yes, recenter=yes,
resize=yes, edit=yes, trace=yes, fittrace=yes, extract=yes,
extras=yes, review=no, background="fit",
reference=bstd)
# Normalize by the standard continuua
iraf.sarith("%s.ms" % bimage, "/", "../standards/s%s.ms.fits" % bstd,
"s%s.ms" % bimage, w1=INDEF, w2=INDEF)
# Telluric correct
iraf.telluric("s%s.ms" % bimage, "ts%s.ms" % bimage,
"../standards/telluric.%s.fits" % bstd, tweakrms=yes,
interactive=yes, sample='6850:6950,7575:7700,8750:9925')
# Flux calibration
iraf.calibrate("ts%s.ms" % bimage, "fts%s.ms" % bimage, extinct=yes,
flux=yes, extinction="home$extinct/maunakeaextinct.dat",
observatory="Keck", sens="../standards/%s.sens" % bstd,
airmass='', exptime='')
joinstr += "fts%s.ms," % bimage
for rimage in rimages:
rimage = rimage.split(".")[0]
# Find appropriate standard for this image
rstd = get_head("%s.fits" % rimage, "STDNAME")
# Extract 1D spectra
if get_head("%s.fits" % rimage, "SKYSUB"):
iraf.apall(rimage, output="", inter=yes, find=yes, recenter=yes,
resize=yes, edit=yes, trace=yes, fittrace=yes, extract=yes,
extras=yes, review=no, background="none",
reference=rstd)
else:
iraf.apall(rimage, output="", inter=yes, find=yes, recenter=yes,
resize=yes, edit=yes, trace=yes, fittrace=yes, extract=yes,
extras=yes, review=no, background="fit",
reference=rstd)
# Normalize by the standard continuua
iraf.sarith("%s.ms" % rimage, "/", "../standards/s%s.ms.fits" % rstd,
"s%s.ms" % rimage, w1=5500, w2=INDEF)
# Telluric correct
iraf.telluric("s%s.ms" % rimage, "ts%s.ms" % rimage,
"../standards/telluric.%s.fits" % rstd, tweakrms=yes,
interactive=yes, sample='6850:6950,7575:7700')
# Flux calibration
iraf.calibrate("ts%s.ms" % rimage, "fts%s.ms" % rimage, extinct=yes,
flux=yes, extinction="home$extinct/maunakeaextinct.dat",
observatory="Keck", sens="../standards/%s.sens" % rstd,
airmass='', exptime='')
joinstr += "fts%s.ms," % rimage
# Combine
iraf.scombine(joinstr[:-1], "%s.ms.fits" % src, combine="average",
reject="avsigclip", w1=INDEF, w2=INDEF, dw=INDEF, nw=INDEF,
scale="median", zero="none", weight="none", sample="5450:5600",
lsigma=3.0, hsigma=3.0, gain=RCCDGAIN, rdnoise=RCCDRNOISE)
# Plot to enable final tweaks
iraf.splot("%s.ms.fits" % src)
return
import glob
# Load third-party modules
from pyraf import iraf
# choose spectra to combine
specre = str(raw_input("Enter regular expression for spectra files: "))
specfiles = glob.glob(specre)
specstring = ', '.join(specfiles)
# choose spectra output name
specout = str(raw_input("Enter output combined spectra name: "))
# unlearn previous settings
iraf.scombine.unlearn()
# setup
iraf.scombine.combine = 'median'
iraf.scombine.reject = 'sigclip'
iraf.scombine.scale = 'exposure'
iraf.scombine.rdnoise = 'rdnoise'
iraf.scombine.gain = 'gain'
# call scombine
iraf.scombine(input=specstring, output=specout)
# visualize combined spectra with splot to check
iraf.splot.unlearn()
iraf.splot(specout)
print '--- DONE ---'
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=no,
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=no,
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=no,
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=no,
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)
# Create telluric
iraf.splot("%s.ms" %
bstd) # Remove telluric and absorption, save as a%s.ms
iraf.sarith("%s.ms" % bstd, "/", "a%s.ms" % bstd,
"telluric.B.%s.fits" % bstd)
iraf.imreplace("telluric.B.%s.fits" % bstd, 1.0, lower=0.0, upper=0.0)
iraf.splot("telluric.B.%s.fits" %
bstd) # Remove stellar features and resave
iraf.splot("%s.ms" %
rstd) # Remove telluric and absorption, save as a%s.ms
iraf.sarith("%s.ms" % rstd, "/", "a%s.ms" % rstd,
"telluric.R.%s.fits" % rstd)
iraf.imreplace("telluric.R.%s.fits" % rstd, 1.0, lower=0.0, upper=0.0)
iraf.splot("telluric.R.%s.fits" %
rstd) # Remove stellar features and resave
# Create smoothed standard
iraf.gauss("a%s.ms[*,1,1]" % bstd, "s%s.ms" % bstd, 5.0)
iraf.sarith("%s.ms" % bstd, "/", "s%s.ms" % bstd, "ds%s.ms" % bstd)
iraf.gauss("a%s.ms[*,1,1]" % rstd, "s%s.ms" % rstd, 5.0)
iraf.sarith("%s.ms" % rstd, "/", "s%s.ms" % rstd, "ds%s.ms" % rstd)
# 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.%s.fits" % bstd,
xcorr=no,
tweakrms=no,
interactive=no,
sample='6850:6950,7575:7700')
iraf.telluric("ds%s.ms" % rstd,
"tds%s.ms" % rstd,
"telluric.R.%s.fits" % rstd,
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
def deimos_extract(science, standard, dostandard=yes):
'''Extract and flux calibrate DEIMOS spectra (assuming they have been
processed into 2D images using deimos_pipe above).'''
if dostandard:
deimos_standard(standard)
for source in science:
images = iraffiles("%s_??_B.fits" % source)
joinstr = ""
for image in images:
bimage = image.split(".")[0]
rimage = bimage[:-1] + "R"
update_head(bimage, "FLUX_OBJ", standard)
update_head(rimage, "FLUX_OBJ", standard)
# Extract 1D spectra
if get_head("%s.fits" % bimage, "SKYSUB"):
iraf.apall(bimage,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="none",
reference="%s_01_B" % standard)
iraf.apall(rimage,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="none",
reference="%s_01_R" % standard)
else:
iraf.apall(bimage,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="fit",
reference="%s_01_B" % standard)
iraf.apall(rimage,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="fit",
reference="%s_01_R" % standard)
# Normalize by the standard continuua
iraf.sarith("%s.ms" % bimage, "/", "s%s_01_B.ms.fits" % standard,
"s%s.ms" % bimage)
iraf.sarith("%s.ms" % rimage, "/", "s%s_01_R.ms.fits" % standard,
"s%s.ms" % rimage)
# Telluric correct
bstd = "%s_01_B" % standard
rstd = "%s_01_R" % standard
iraf.telluric("s%s.ms" % bimage,
"ts%s.ms" % bimage,
"telluric.B.%s.fits" % bstd,
tweakrms=yes,
interactive=yes,
sample='6850:6950,7575:7700')
iraf.telluric("s%s.ms" % rimage,
"ts%s.ms" % rimage,
"telluric.R.%s.fits" % rstd,
tweakrms=yes,
interactive=yes,
sample='6850:6950,7575:7700')
# Flux calibration
iraf.calibrate("ts%s.ms" % bimage,
"fts%s.ms" % bimage,
extinct=yes,
flux=yes,
extinction="home$extinct/maunakeaextinct.dat",
observatory="Keck",
sensitivity="%s.B.sens" % standard,
airmass='',
exptime='')
iraf.calibrate("ts%s.ms" % rimage,
"fts%s.ms" % rimage,
extinct=yes,
flux=yes,
extinction="home$extinct/maunakeaextinct.dat",
observatory="Keck",
sensitivity="%s.R.sens" % standard,
airmass='',
exptime='')
joinstr += "fts%s.ms,fts%s.ms," % (bimage, rimage)
# Combine
iraf.scombine(joinstr[:-1],
"%s.ms.fits" % source,
combine="average",
reject="avsigclip",
w1=INDEF,
w2=INDEF,
dw=INDEF,
nw=INDEF,
scale="none",
zero="none",
weight="none",
lsigma=3.0,
hsigma=3.0,
gain=CCDGAIN,
rdnoise=CCDRNOISE)
# Plot to enable final tweaks
iraf.splot("%s.ms.fits" % source)
return
def combine(do_cti=False, doreduce=True, doshifts=True):
if do_cti:
os.system('stis_cti --crds_update')
if doreduce:
# Defringing didn't seem to converge because of the low S/N
stistools.ocrreject.ocrreject('oc0102070_flc.fits',
'oc0102070_crc.fits')
iraf.normspflat(inflat='oc0102070_crc.fits',
outflat='oc0102070_nsp.fits',
do_cal='no')
iraf.imcalc(input='oc0102070_nsp.fits',
output='temp_nsp.fits',
equals='if(x .lt. 250) then 1 else im1')
iraf.imcopy('temp_nsp.fits[1][1:250,*]',
'oc0102070_nsp.fits[1][1:250,*]')
#iraf.defringe('oc0102060_flc.fits', 'oc0102070_nsp.fits', 'oc0102060_dfr.fits')
#for each image
for im in ['oc0102050_flc', 'oc0102060_flc']:
outbase = 'blue'
if im[:-4] == 'oc0102060':
outbase = 'red'
#reset the aperture table to the newer file (we maybe should check this)
pyfits.setval(im + '.fits',
'APERTAB',
value='oref$y2r1559to_apt.fits')
pyfits.setval(im + '.fits',
'SPTRCTAB',
value='oref$qa31608go_1dt.fits')
# fixpix any negative values. In principle some of this noise
# could be real, but I have found that is often not the case
hdu = fits.open(im + '.fits')
mask1 = hdu[1].data < -20
mask2 = hdu[4].data < -20
hdu.close()
fits.writeto(outbase + 'mask1.fits',
mask1.astype('i'),
clobber=True)
fits.writeto(outbase + 'mask2.fits',
mask2.astype('i'),
clobber=True)
iraf.unlearn(iraf.fixpix)
iraf.fixpix(im + '[1]', outbase + 'mask1.fits')
iraf.unlearn(iraf.fixpix)
iraf.fixpix(im + '[4]', outbase + 'mask2.fits')
# Subtract off the median value
hdu = fits.open(im + '.fits', mode='update')
hdu[1].data -= np.median(hdu[1].data)
hdu[4].data -= np.median(hdu[4].data)
readnoise1 = 1.4826 * np.median(np.abs(hdu[1].data))
readnoise2 = 1.4826 * np.median(np.abs(hdu[4].data))
# Cosmic ray reject both images using scrappy
# Make sure to treat the noise in a sensible way
crmask1, clean1 = detect_cosmics(hdu[1].data,
readnoise=readnoise1,
sigclip=5,
objlim=5,
sigfrac=0.8,
fsmode='median',
psfmodel='gaussy',
psffwhm=2.,
cleantype='idw')
crmask2, clean2 = detect_cosmics(hdu[4].data,
readnoise=readnoise2,
sigclip=5,
objlim=5,
sigfrac=0.8,
fsmode='median',
psfmodel='gaussy',
psffwhm=2.,
cleantype='idw')
hdu.flush()
hdu.close()
fits.writeto(outbase + '_crmask1.fits',
crmask1.astype('i'),
clobber=True)
fits.writeto(outbase + '_crmask2.fits',
crmask2.astype('i'),
clobber=True)
# Run fixpix on the frames
iraf.unlearn(iraf.fixpix)
iraf.fixpix(im + '[1]', outbase + '_crmask1.fits')
iraf.unlearn(iraf.fixpix)
iraf.fixpix(im + '[4]', outbase + '_crmask2.fits')
if outbase == 'red':
iraf.mkfringeflat('oc0102060_flc.fits',
'oc0102070_nsp.fits',
'oc0102070_frr.fits',
beg_scale=0.6,
end_scale=1.5,
scale_step=0.01,
beg_shift=-3.0,
end_shift=3.0,
shift_step=0.05)
iraf.defringe('oc0102060_flc.fits', 'oc0102070_frr.fits',
'oc0102060_dfr.fits')
#Run x2d on the flt frame
stistools.x2d.x2d(input='oc0102060_dfr.fits',
output=im[:-4] + 'x2d.fits')
else:
stistools.x2d.x2d(input='oc0102050_flc.fits',
output=im[:-4] + 'x2d.fits')
h = pyfits.open(im[:-4] + 'x2d.fits', mode='update')
#Replace all of the bad pixels in the image by -666 based on the DQ array
#save them to a new file
#Throw away bad reference file pixels and saturated pixels. None of the other error codes
#were in the first file so I haven't included them here, but we might want to
d = h[3].data
badpix = logical_and(
bitwise_and(d, 256) == 256,
bitwise_and(d, 512) == 512)
h[1].data[badpix] = -10000
d = h[6].data
badpix = logical_and(
bitwise_and(d, 256) == 256,
bitwise_and(d, 512) == 512)
h[4].data[badpix] = -10000
h.flush()
# Trim the images
for i in range(1, 7):
h[i].data = h[i].data[100:-100, 100:-100]
h[i].header['CRPIX1'] -= 100
h[i].header['CRPIX2'] -= 100
h.flush()
h.close()
# Combine the images
iraf.unlearn(iraf.imcombine)
iraf.imcombine(input=im[:-4] + 'x2d[1],' + im[:-4] + 'x2d[4]',
output=outbase + '_com.fits',
reject='crreject')
hdu = pyfits.open(outbase + '_com.fits')
mask = hdu[0].data == 0.0
hdu.close()
fits.writeto(outbase + '_mask.fits',
mask.astype('i'),
clobber=True)
iraf.unlearn(iraf.fixpix)
iraf.fixpix(outbase + '_com.fits', outbase + '_mask.fits')
iraf.unlearn(iraf.apall)
iraf.apall(input=outbase + '_com',
output='13dh_' + outbase,
review='no',
nsum=-500,
b_order=1,
b_function='legendre',
b_niterate=30,
b_naverage=-21,
nfind=1,
t_order=3,
background='median',
weights='variance',
skybox=100)
iraf.splot(outbase + '[SCI]')
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
extinction='mk_extinct.txt',
observatory='Keck',
ignoreaps=yes,
sensitivity='sensstar2.fits')
inputname = ptfsn1f + ',' + ptfsn2f
#Produce combined spectrum
iraf.scombine(input=inputname,
out='finalspectrum.ms',
reject='avsigclip',
scale='median',
sample='5500:6500')
l.write('calib=yes')
iraf.splot(images='finalspectrum.ms.fits')
iraf.wspectext(input='finalspectrum.ms.fits[*,1]',
output=ptfsn1_name + '.ascii',
header='NO')
iraf.wspectext(input='ptfsn1.f.fits[*,1]',
output=ptfsn1_name + '_r400.ascii',
header='NO')
iraf.wspectext(input='ptfsn2.f.fits[*,1]',
output=ptfsn1_name + '_b600.ascii',
header='NO')
l.write('finalname=' + ptfsn1_name + '.ascii')
l.close()
# Load third-party modules
from pyraf import iraf
# choose spectra to combine
specre = str(raw_input("Enter regular expression for spectra files: "))
specfiles = glob.glob(specre)
specstring = ", ".join(specfiles)
# choose spectra output name
specout = str(raw_input("Enter output combined spectra name: "))
# unlearn previous settings
iraf.scombine.unlearn()
# setup
iraf.scombine.combine = "median"
iraf.scombine.reject = "sigclip"
iraf.scombine.scale = "exposure"
iraf.scombine.rdnoise = "rdnoise"
iraf.scombine.gain = "gain"
# call scombine
iraf.scombine(input=specstring, output=specout)
# visualize combined spectra with splot to check
iraf.splot.unlearn()
iraf.splot(specout)
print "--- DONE ---"
head = pf.getheader(f)
start = head['CRVAL1']
step = head['CDELT1']
length = head['NAXIS1']
x = start + pl.arange(0,length)*step
hi = x > Lines[line][0]
low = x < Lines[line][1]
xx = hi*low
med = pl.median(data[xx])
print med
cursor = open('cursor','w')
cursor.write('%s %s 1 e\n%s %s 1 e\n'%(Lines[line][0],med,Lines[line][1],med))
cursor.close()
iraf.splot(images=f,\
cursor='cursor',\
save_file='splot.log')
# read splot.log and extract the results
myfile = open('splot.log','r')
lines = myfile.readlines()
try:
# the first log written to empty file has header
temp = string.split(string.strip(lines[3]))
results.append(float(temp[0]))
flux.append(float(temp[2]))
fwhm.append(float(temp[3]))
except:
temp = (string.split(string.strip(lines[2])))
results.append(float(temp[0]))
flux.append(float(temp[2]))
args.xmax + ' 1 1 d \n'
)
for i in args.points:
filecur.write(
i +' 1 1 g \n'
)
filecur.write(
'0 0 1 q \n'
'0 0 1 a \n'
'0 0 1 a \n'
'0 0 1 y \n'
'0 0 1 t \n'
'0 0 1 q \n'
)
iraf.splot(args.spectra[0],cursor=source+'.cur' , save_file=args.log+'.log', graphics='eps')
iraf.gflush
#print 'call:\n python2 rename.py '+ source +' '+args.log+'.log'
#print 'to plot: \n python2 plotspectra.py '+ args.spectra[0]
##move log and images
#os.rename(args.log+'.log', './logs/'+args.log+'.log')
#imagesiraf = glob.glob('sgi*.eps')
#
#for i,j in enumerate(imagesiraf):
# print j
# im = Image.open(j)
# im.save('./plots/'+source+'_'+str(i)+'.png')
nlow=1,
nhigh=1,
nkeep=2)
iraf.scombine(input=combstr,
out='combinedspec.ms',
combine='average',
reject='minmax',
scale='none',
sample='5000:7500',
nlow=0,
nhigh=1,
nkeep=1)
l.write('calib=yes')
iraf.splot(images='combinedspec_rmlow.ms.fits')
for objname in objects_pars:
if 'sn' in objname:
SNName = objects_pars[objname][0]
break
iraf.wspectext(input='combinedspec.ms.fits[*,1]',
output=SNName + '.ascii',
header='NO')
iraf.wspectext(input='combinedspec_rmlow.ms.fits[*,1]',
output=SNName + '_rmlow.ascii',
header='NO')
l.write('finalname=' + SNName + '.ascii')
l.close()
def flux_cal_new(objname, stdobjnames):
print 'run flux_cal...'
iraf.imred()
iraf.kpnoslit()
stdname, stdmag, stdmagband = get_std_name(stdobjnames[0])
print 'the standard star is ' + stdname
stdmag = float(stdmag)
stdairmass, stdexptime = get_fits_airmass_exptime(stdobjnames[0])
scripath = sys.argv[0]
tempindex = scripath.rfind(os.sep)
scripath = scripath[:tempindex]
extpath = scripath + os.sep + 'LJextinct.dat'
calpath = scripath + os.sep + 'standarddir' + os.sep
outname1 = 'std' + stdobjnames[0]
stdobjname = ''
for tempstdname in stdobjnames:
stdobjname = stdobjname + tempstdname + ','
stdobjname = stdobjname[:-1]
if os.path.isfile(outname1):
print 'file %s is already exist' % outname1
else:
print 'run standard...'
print 'make file %s ...' % outname1
iraf.standard(input = stdobjname
, output = outname1, samestar = True, beam_switch = False
, apertures = '', bandwidth = 30.0, bandsep = 20.0
, fnuzero = 3.6800000000000E-20, extinction = extpath
, caldir = calpath, observatory = ')_.observatory'
, interact = True, graphics = 'stdgraph', cursor = ''
, star_name = stdname, airmass = stdairmass, exptime = stdexptime
, mag = stdmag, magband = stdmagband, teff = '', answer = 'yes')
outname2 = 'sens' + stdobjname
if os.path.isfile(outname2):
print 'file %s is already exist' % outname2
else:
print 'run sensfunc...'
print 'make file %s ...' % outname2
iraf.sensfunc(standards = outname1
, sensitivity = outname2, apertures = '', ignoreaps = True
, logfile = 'logfile', extinction = ')_.extinction'
, newextinction = extpath, observatory = 'bao', function = 'spline3'
, order = 6, interactive = True, graphs = 'sr'
, marks = 'plus cross box', colors = '2 1 3 4', cursor =''
, device = 'stdgraph', answer = 'yes')
outname3 = 'c' + objname
if os.path.isfile(outname3):
print 'file %s is already exist' % outname3
else:
print 'run calibrate...'
print 'make file %s ...' % outname3
iraf.calibrate(input = objname
, output = outname3, extinct = True, flux = True
, extinction = extpath, observatory = 'bao', ignoreaps = True
, sensitivity = outname2, fnu = False)
final_outname = 'mark_' + objname
if os.path.isfile(final_outname):
print 'file %s is already exist'
else:
print 'run scopy'
print 'make file %s ...' % final_outname
iraf.scopy(input = outname3
, output = final_outname, w1 = 'INDEF', w2 = 'INDEF'
, apertures = '', bands = 1, beams = '', apmodulus = 0
, format = 'multispec', renumber = False, offset = 0
, clobber = False, merge = False, rebin = True, verbose = False)
print 'splot %s' % final_outname
iraf.splot(images = final_outname)
return final_outname
def promptLine(specFile, specType):
'''
Helper function - prompts user to measure a line in splot and to provide the
identity of the measured line, finding both the rest and measured wavelengths.
This and other helper functions are the only locations where values are
hard-coded, making for easier customizability.
Provides shortcuts for certain lines, allowing for high precision for rest
wavelengths.
Args:
specFile absolute path to the spectrum file
specType type of spectrum to be measured; either 'sky' or 'science'
Returns:
restW rest wavelength of one measured line in the spectrum
userW user-measured wavelength of one line in the spectrum, or:
[empty] only if user wants to skip line measuring (because e.g. the
spectrum has no good lines)
'''
##Obtain measurement of a single line to get spectrum's redshift.
#set up names and files
specPath, __ = split(specFile)
logFile = join(specPath, 'splot.log')
#run splot to let user measure 1 line
print('\n*****In the splot window, measure one line with a known rest ' \
'wavelength, then press q. If there are no lines, just press q.')
iraf.splot(images=specFile, save_file=logFile)
##Prompt user for the identity of the measured line
while (True): #continue until the user gives valid input
if specType == 'science': #user is measuring actual emission lines
try: #ask for valid input
key = eval(
input('*****If you measured a line, enter:\n'
'0 for no lines - skip this spectrum\n'
'1 for H-alpha \n'
'2 for OIII \n'
'3 for OII \n'
'the wavelength of another line\n'))
key = float(key) #trigger a Name/ValueError if not a float
except (NameError, ValueError, SyntaxError):
print(
'Invalid input - please enter 0, 1, 2, 3, or a wavelength:\n'
)
continue
if key > 3 and key < 100: continue
#set the wavelength based on input key
#it would be nice if Python had a switch-statement...
if key == 0: return ('', '', 1) #user wants to skip
elif key == 1: restW = 6562.82 #H-alpha
elif key == 2: restW = 5006.84 #OIII
elif key == 3: restW = 3727.53 #OII blend
else: restW = key
break
elif specType == 'sky': #user is measuring sky emission lines
try: #ask for valid input
key = eval(
input('*****If you measured a line, enter:\n'
'0 to skip\n'
'1 for Hg 4358\n'
'2 for Hg 5460\n'
'3 for O I 5577\n'
'4 for Na D 5891\n'
'5 for O I 6300\n'
'6 for O I 6363\n'
'the wavelength of another line\n'))
key = float(key) #trigger a Name/ValueError if not a float
except (NameError, ValueError, SyntaxError):
print(
'Invalid input - please enter a number key or a wavelength:\n'
)
continue
#set the wavelength based on input key
if key == 0: return ('', '', 1)
elif key == 1: restW = 4358.33 #Hg
elif key == 2: restW = 5460.74 #Hg
elif key == 3: restW = 5577.35 #OI
elif key == 4: restW = 5891.94 #Na D
elif key == 5: restW = 6300.23 #OI
elif key == 6: restW = 6363.88 #OI
else: restW = key
break
#read the measured wavelength from the splot log file
entry = readSplotLog(logFile)
userW = entry[0]
skip = 0
return (userW, restW, skip)
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 check_spectra(WORK_DIR, ext):
print "\n + Visually check spectra before proceeding\n"
for obj in observations[WORK_DIR]['objects']:
iraf.splot(obj+ext)
start = head['CRVAL1']
step = head['CDELT1']
length = head['NAXIS1']
x = start + pl.arange(0,length)*step
HJD.append(float(head['HJD']))
SNR = []
# calculate snr using splot for every region.
for reg in enumerate(['Ha','red','yellow','Hb','He2','blue']):
# write to cursor file
#os.remove('cursor')
cfile = file('cursor','w')
s = "%s 0.001 1 m\n%s 0.001 1 m\n" % (regions[reg[1]][0],regions[reg[1]][1])
cfile.write(s)
cfile.close() # do this otherwise get freaky bugs
out = iraf.splot(images=spec.strip(),cursor='cursor',Stdout=1)
SNR.append(read_out(out))
print reg, SNR[0]
# Now print output
s = [' '.join('%s'%i for i in SNR)]
#print s
output.write(str(head['HJD'])+' ' + s[0]+'\n')
output.close()
# run bplot
#iraf.bplot(images='@speclist',cursor='cursor')
#HJDfile = open('HJD.dat','w')
#for d in HJD:
iraf.unlearn('gfapsum')
iraf.unlearn('gsstandard')
# ---------- Sensitivity function (after reducing standard star) ---------- #
std = np.loadtxt(ic.lst_std, dtype=str)
if (std.size > 1):
raise ValueError("Please check if there is only one image for the standard star.")
std0 = std.item(0)
# Sum the fibers
iraf.imdelete('astxeqxbrg@'+ic.lst_std)
iraf.gfapsum('stxeqxbrg'+std0, combine='sum', fl_inter='no')
iraf.splot('astxeqxbrg'+std0+'[sci,1]')
# Plot the sum spectra
spec_sum, hd = fits.getdata('astxeqxbrg'+std0+'.fits', ext=1, header=True)
w = hd['CRVAL1'] + np.arange(len(spec_sum))*hd['CD1_1']
fig, ax = plt.subplots(1, 1, figsize=(7,5))
plt.subplots_adjust(left=0.16, right=0.96, bottom=0.13, top=0.91)
plt.suptitle("Sum of spectra", x=0.55, ha='center', y=0.97, va='top',
fontsize=16.0, fontweight='bold')
ax.set_xlabel(r"Wavelength [${\rm \AA}$]", fontsize=12.0)
ax.set_ylabel("Counts", fontsize=12.0)
# ax.set_yscale('log')
ax.tick_params(axis='both', labelsize=12.0)
ax.plot(w, spec_sum, color='C0', linewidth=1.5, alpha=0.8)
#gt = x > 6400.0
#lt = x < 6600.0
# Hydrogen Beta
gt = x > 4700.0
lt = x < 4900.0
continuum = pl.median(data[gt*lt])
# write to cursor file
cfile = open('cursor','w')
#s = "4676 %s 1 e\n4696 %s 1 e\n" % (continuum,continuum)
s = "4850 %s 1 e\n4870 %s 1 e\n" % (continuum,continuum)
#s = "6553 %s 1 e\n 6573 %s 1 e\n" % (continuum,continuum)
cfile.write(s)
iraf.splot(images=spec.strip(),cursor='cursor')
# run bplot
#iraf.bplot(images='@speclist',cursor='cursor')
HJDfile = open('HJD.dat','w')
for d in HJD:
HJDfile.write('%s\n'%d)
HJDfile.close()
# get the output. Tune this grep command for the wavelength
#os.system('grep 65 splot.log > rv.dat')
head = pf.getheader('fec2117_%04d.fits'%i)
start = head['CRVAL1']
step = head['CDELT1']
length = head['NAXIS1']
x = start + pl.arange(0,length)*step
hi = x > 4640
low = x < 4730
xx = hi*low
med = pl.median(data[xx])
print med
cursor = open('cursor','w')
cursor.write('4640 %s 1 k\n4730 %s 1 k\n'%(med,med))
cursor.close()
iraf.splot(images='fec2117_%04d.fits'%i,\
cursor='cursor',\
save_file='splot.log')
# read splot.log and extract the results
myfile = open('splot.log','r')
lines = myfile.readlines()
try:
# the first log written to empty file has header
temp = string.split(string.strip(lines[3]))
results.append(float(temp[0]))
fwhm.append(float(temp[5]))
except:
temp = (string.split(string.strip(lines[2])))
results.append(float(temp[0]))
fwhm.append(float(temp[5]))
# remove the log file
print ' ' print ':::::::::::::::::::::::::::::::::::::::::::::::::::' print 'PLOTTING EXTRACTED SPECTRA:' print ':::::::::::::::::::::::::::::::::::::::::::::::::::' print 'The extracted, wavelength-calibrated spectrum ' print str(object_b_ec_w) print 'is now plotted at order ' + str(order) + ' for the user' print 'specified wavelength at ' + str(wvlength) + 'A:' print ':::::::::::::::::::::::::::::::::::::::::::::::::::' print ' ' plot_order = str(-(int(order)) + 86) iraf.noao(_doprint=0) iraf.onedspec(_doprint=0) iraf.splot(images=object_b_ec_w, line=plot_order) # ::::::::::::::::::::::::::::::: # STEP 8: REMOVE TEMPORARY FILES: # ::::::::::::::::::::::::::::::: print ' ' print ' ' print ':::::::::::::::::::::::::::::::::::::::::::::::::::' print 'TEMPORARY FILE REMOVAL: ' print ':::::::::::::::::::::::::::::::::::::::::::::::::::' print 'Temporary files clean up: ' print ':::::::::::::::::::::::::::::::::::::::::::::::::::' print ' '
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$kpnoextinct.dat',
newextinction='extinct.dat',
observatory='Lick',
function='legendre',
order=3,
interactive=yes)
return
def combine(do_cti=False, doreduce=True, doshifts=True):
if do_cti:
os.system('stis_cti --crds_update')
if doreduce:
# Defringing didn't seem to converge because of the low S/N
stistools.ocrreject.ocrreject('oc0102070_flc.fits','oc0102070_crc.fits')
iraf.normspflat(inflat='oc0102070_crc.fits',outflat='oc0102070_nsp.fits', do_cal='no')
iraf.imcalc(input='oc0102070_nsp.fits', output='temp_nsp.fits', equals='if(x .lt. 250) then 1 else im1')
iraf.imcopy('temp_nsp.fits[1][1:250,*]', 'oc0102070_nsp.fits[1][1:250,*]')
#iraf.defringe('oc0102060_flc.fits', 'oc0102070_nsp.fits', 'oc0102060_dfr.fits')
#for each image
for im in ['oc0102050_flc','oc0102060_flc']:
outbase = 'blue'
if im[:-4] == 'oc0102060':
outbase = 'red'
#reset the aperture table to the newer file (we maybe should check this)
pyfits.setval(im +'.fits','APERTAB',value='oref$y2r1559to_apt.fits')
pyfits.setval(im +'.fits', 'SPTRCTAB', value='oref$qa31608go_1dt.fits')
# fixpix any negative values. In principle some of this noise
# could be real, but I have found that is often not the case
hdu = fits.open(im+ '.fits')
mask1 = hdu[1].data < -20
mask2 = hdu[4].data < -20
hdu.close()
fits.writeto(outbase+'mask1.fits', mask1.astype('i'), clobber=True)
fits.writeto(outbase+'mask2.fits', mask2.astype('i'), clobber=True)
iraf.unlearn(iraf.fixpix)
iraf.fixpix(im+'[1]', outbase+'mask1.fits')
iraf.unlearn(iraf.fixpix)
iraf.fixpix(im+'[4]', outbase+'mask2.fits')
# Subtract off the median value
hdu = fits.open(im+ '.fits', mode='update')
hdu[1].data -= np.median(hdu[1].data)
hdu[4].data -= np.median(hdu[4].data)
readnoise1 = 1.4826 * np.median(np.abs(hdu[1].data))
readnoise2 = 1.4826 * np.median(np.abs(hdu[4].data))
# Cosmic ray reject both images using scrappy
# Make sure to treat the noise in a sensible way
crmask1, clean1 = detect_cosmics(hdu[1].data, readnoise=readnoise1,
sigclip=5, objlim=5, sigfrac=0.8,
fsmode='median', psfmodel='gaussy',
psffwhm=2., cleantype='idw')
crmask2, clean2 = detect_cosmics(hdu[4].data, readnoise=readnoise2,
sigclip=5, objlim=5, sigfrac=0.8,
fsmode='median', psfmodel='gaussy',
psffwhm=2., cleantype='idw')
hdu.flush()
hdu.close()
fits.writeto(outbase + '_crmask1.fits', crmask1.astype('i'), clobber=True)
fits.writeto(outbase + '_crmask2.fits', crmask2.astype('i'), clobber=True)
# Run fixpix on the frames
iraf.unlearn(iraf.fixpix)
iraf.fixpix(im+'[1]', outbase+'_crmask1.fits')
iraf.unlearn(iraf.fixpix)
iraf.fixpix(im+'[4]', outbase+'_crmask2.fits')
if outbase=='red':
iraf.mkfringeflat('oc0102060_flc.fits', 'oc0102070_nsp.fits', 'oc0102070_frr.fits',
beg_scale=0.6, end_scale=1.5, scale_step=0.01,
beg_shift=-3.0, end_shift=3.0,shift_step=0.05)
iraf.defringe('oc0102060_flc.fits', 'oc0102070_frr.fits', 'oc0102060_dfr.fits')
#Run x2d on the flt frame
stistools.x2d.x2d(input='oc0102060_dfr.fits',output=im[:-4]+'x2d.fits')
else:
stistools.x2d.x2d(input='oc0102050_flc.fits',output=im[:-4]+'x2d.fits')
h = pyfits.open(im[:-4]+'x2d.fits', mode='update')
#Replace all of the bad pixels in the image by -666 based on the DQ array
#save them to a new file
#Throw away bad reference file pixels and saturated pixels. None of the other error codes
#were in the first file so I haven't included them here, but we might want to
d = h[3].data
badpix = logical_and(bitwise_and(d,256) == 256,bitwise_and(d,512) == 512)
h[1].data[badpix] = -10000
d = h[6].data
badpix = logical_and(bitwise_and(d,256) == 256,bitwise_and(d,512) == 512)
h[4].data[badpix] = -10000
h.flush()
# Trim the images
for i in range(1,7):
h[i].data = h[i].data[100:-100, 100:-100]
h[i].header['CRPIX1'] -= 100
h[i].header['CRPIX2'] -= 100
h.flush()
h.close()
# Combine the images
iraf.unlearn(iraf.imcombine)
iraf.imcombine(input=im[:-4]+'x2d[1],'+im[:-4]+'x2d[4]', output=outbase+'_com.fits',
reject='crreject')
hdu = pyfits.open(outbase +'_com.fits')
mask = hdu[0].data == 0.0
hdu.close()
fits.writeto(outbase+'_mask.fits', mask.astype('i'), clobber=True)
iraf.unlearn(iraf.fixpix)
iraf.fixpix(outbase+'_com.fits', outbase+'_mask.fits')
iraf.unlearn(iraf.apall)
iraf.apall(input=outbase+'_com',output='13dh_'+outbase, review='no',
nsum = -500, b_order = 1,
b_function='legendre',b_niterate=30, b_naverage = -21,
nfind=1,t_order=3,background='median',weights='variance',
skybox=100 )
iraf.splot(outbase+'[SCI]')
def deimos_extract(science, standard, dostandard=yes):
'''Extract and flux calibrate DEIMOS spectra (assuming they have been
processed into 2D images using deimos_pipe above).'''
if dostandard:
deimos_standard(standard)
for source in science:
images = iraffiles("%s_??_B.fits" % source)
joinstr = ""
for image in images:
bimage = image.split(".")[0]; rimage = bimage[:-1] + "R"
update_head(bimage, "FLUX_OBJ", standard)
update_head(rimage, "FLUX_OBJ", standard)
# Extract 1D spectra
if get_head("%s.fits" % bimage, "SKYSUB"):
iraf.apall(bimage, output="", inter=yes, find=yes, recenter=yes,
resize=yes, edit=yes, trace=yes, fittrace=yes, extract=yes,
extras=yes, review=no, background="none",
reference="%s_01_B" % standard)
iraf.apall(rimage, output="", inter=yes, find=yes, recenter=yes,
resize=yes, edit=yes, trace=yes, fittrace=yes, extract=yes,
extras=yes, review=no, background="none",
reference="%s_01_R" % standard)
else:
iraf.apall(bimage, output="", inter=yes, find=yes, recenter=yes,
resize=yes, edit=yes, trace=yes, fittrace=yes, extract=yes,
extras=yes, review=no, background="fit",
reference="%s_01_B" % standard)
iraf.apall(rimage, output="", inter=yes, find=yes, recenter=yes,
resize=yes, edit=yes, trace=yes, fittrace=yes, extract=yes,
extras=yes, review=no, background="fit",
reference="%s_01_R" % standard)
# Normalize by the standard continuua
iraf.sarith("%s.ms" % bimage, "/", "s%s_01_B.ms.fits" % standard,
"s%s.ms" % bimage)
iraf.sarith("%s.ms" % rimage, "/", "s%s_01_R.ms.fits" % standard,
"s%s.ms" % rimage)
# Telluric correct
bstd = "%s_01_B" % standard; rstd = "%s_01_R" % standard
iraf.telluric("s%s.ms" % bimage, "ts%s.ms" % bimage,
"telluric.B.%s.fits" % bstd, tweakrms=yes,
interactive=yes, sample='6850:6950,7575:7700')
iraf.telluric("s%s.ms" % rimage, "ts%s.ms" % rimage,
"telluric.R.%s.fits" % rstd, tweakrms=yes,
interactive=yes, sample='6850:6950,7575:7700')
# Flux calibration
iraf.calibrate("ts%s.ms" % bimage, "fts%s.ms" % bimage, extinct=yes,
flux=yes, extinction="home$extinct/maunakeaextinct.dat",
observatory="Keck", sensitivity="%s.B.sens" % standard,
airmass='', exptime='')
iraf.calibrate("ts%s.ms" % rimage, "fts%s.ms" % rimage, extinct=yes,
flux=yes, extinction="home$extinct/maunakeaextinct.dat",
observatory="Keck", sensitivity="%s.R.sens" % standard,
airmass='', exptime='')
joinstr += "fts%s.ms,fts%s.ms," % (bimage, rimage)
# Combine
iraf.scombine(joinstr[:-1], "%s.ms.fits" % source, combine="average",
reject="avsigclip", w1=INDEF, w2=INDEF, dw=INDEF, nw=INDEF,
scale="none", zero="none", weight="none", lsigma=3.0,
hsigma=3.0, gain=CCDGAIN, rdnoise=CCDRNOISE)
# Plot to enable final tweaks
iraf.splot("%s.ms.fits" % source)
return
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=no, 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=no, 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=no, 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=no, 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)
# Create telluric
iraf.splot("%s.ms" % bstd) # Remove telluric and absorption, save as a%s.ms
iraf.sarith("%s.ms" % bstd, "/", "a%s.ms" % bstd, "telluric.B.%s.fits" % bstd)
iraf.imreplace("telluric.B.%s.fits" % bstd, 1.0, lower=0.0, upper=0.0)
iraf.splot("telluric.B.%s.fits" % bstd) # Remove stellar features and resave
iraf.splot("%s.ms" % rstd) # Remove telluric and absorption, save as a%s.ms
iraf.sarith("%s.ms" % rstd, "/", "a%s.ms" % rstd, "telluric.R.%s.fits" % rstd)
iraf.imreplace("telluric.R.%s.fits" % rstd, 1.0, lower=0.0, upper=0.0)
iraf.splot("telluric.R.%s.fits" % rstd) # Remove stellar features and resave
# Create smoothed standard
iraf.gauss("a%s.ms[*,1,1]" % bstd, "s%s.ms" % bstd, 5.0)
iraf.sarith("%s.ms" % bstd, "/", "s%s.ms" % bstd, "ds%s.ms" % bstd)
iraf.gauss("a%s.ms[*,1,1]" % rstd, "s%s.ms" % rstd, 5.0)
iraf.sarith("%s.ms" % rstd, "/", "s%s.ms" % rstd, "ds%s.ms" % rstd)
# 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.%s.fits" % bstd,
xcorr=no, tweakrms=no, interactive=no, sample='6850:6950,7575:7700')
iraf.telluric("ds%s.ms" % rstd, "tds%s.ms" % rstd, "telluric.R.%s.fits" % rstd,
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$extinct/maunakeaextinct.dat',
newextinction='extinct.dat', observatory='Keck',
function='legendre', order=4, interactive=yes)
return
def red_standard(image, arcs, flats, object=None, biassec=REDBIAS,
trimsec=REDTRIM, outflat='Flat-Red.fits', gain=REDGAIN,
rdnoise=REDRDNOISE, arc='Arc-Red.fits',
caldir='home$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
def lris_extract(science, standards, dostandard=yes):
'''Extract and flux calibrate LRIS spectra (assuming they have been
processed into 2D images using LRIS_pipe above).'''
if dostandard:
lris_standard(standards)
for src in science:
bimages = iraffiles("%s_??_B.fits" % src)
rimages = iraffiles("%s_??_R.fits" % src)
joinstr = ""
for bimage in bimages:
bimage = bimage.split(".")[0]
# Find appropriate standard for this image
bstd = get_head("%s.fits" % bimage, "STDNAME")
# Extract 1D spectra
if get_head("%s.fits" % bimage, "SKYSUB"):
iraf.apall(bimage,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="none",
reference=bstd)
else:
iraf.apall(bimage,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="fit",
reference=bstd)
# Normalize by the standard continuua
iraf.sarith("%s.ms" % bimage,
"/",
"../standards/s%s.ms.fits" % bstd,
"s%s.ms" % bimage,
w1=INDEF,
w2=INDEF)
# Telluric correct
iraf.telluric("s%s.ms" % bimage,
"ts%s.ms" % bimage,
"../standards/telluric.%s.fits" % bstd,
tweakrms=yes,
interactive=yes,
sample='6850:6950,7575:7700,8750:9925')
# Flux calibration
iraf.calibrate("ts%s.ms" % bimage,
"fts%s.ms" % bimage,
extinct=yes,
flux=yes,
extinction="home$extinct/maunakeaextinct.dat",
observatory="Keck",
sens="../standards/%s.sens" % bstd,
airmass='',
exptime='')
joinstr += "fts%s.ms," % bimage
for rimage in rimages:
rimage = rimage.split(".")[0]
# Find appropriate standard for this image
rstd = get_head("%s.fits" % rimage, "STDNAME")
# Extract 1D spectra
if get_head("%s.fits" % rimage, "SKYSUB"):
iraf.apall(rimage,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="none",
reference=rstd)
else:
iraf.apall(rimage,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="fit",
reference=rstd)
# Normalize by the standard continuua
iraf.sarith("%s.ms" % rimage,
"/",
"../standards/s%s.ms.fits" % rstd,
"s%s.ms" % rimage,
w1=5500,
w2=INDEF)
# Telluric correct
iraf.telluric("s%s.ms" % rimage,
"ts%s.ms" % rimage,
"../standards/telluric.%s.fits" % rstd,
tweakrms=yes,
interactive=yes,
sample='6850:6950,7575:7700')
# Flux calibration
iraf.calibrate("ts%s.ms" % rimage,
"fts%s.ms" % rimage,
extinct=yes,
flux=yes,
extinction="home$extinct/maunakeaextinct.dat",
observatory="Keck",
sens="../standards/%s.sens" % rstd,
airmass='',
exptime='')
joinstr += "fts%s.ms," % rimage
# Combine
iraf.scombine(joinstr[:-1],
"%s.ms.fits" % src,
combine="average",
reject="avsigclip",
w1=INDEF,
w2=INDEF,
dw=INDEF,
nw=INDEF,
scale="median",
zero="none",
weight="none",
sample="5450:5600",
lsigma=3.0,
hsigma=3.0,
gain=RCCDGAIN,
rdnoise=RCCDRNOISE)
# Plot to enable final tweaks
iraf.splot("%s.ms.fits" % src)
return
def lris_standard(standards, xcorr=yes):
'''Extract standard stars and calculate sensitivity functions.'''
for ofile, nstd in standards:
shutil.copy(ofile, "%s.fits" % nstd)
# Extract standard
if get_head("%s.fits" % nstd, "SKYSUB"):
iraf.apall(nstd,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="none")
else:
iraf.apall(nstd,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="fit")
if xcorr:
# Cross correlate to tweak wavelength
iraf.xcsao("%s.ms" % nstd,
templates=XCTEMPLATE,
correlate="wavelength",
logfiles="%s.xcsao" % ofile)
# If a solution was found, apply shift
try:
xcsaof = open("%s.xcsao" % ofile)
shift = float(xcsaof.readlines()[6].split()[14])
except:
shift = 0.0
iraf.specshift("%s.ms" % nstd, -shift)
# Create telluric
iraf.splot("%s.ms" %
nstd) # Remove telluric and absorption, save as a%s.ms
iraf.sarith("%s.ms" % nstd, "/", "a%s.ms" % nstd,
"telluric.%s.fits" % nstd)
iraf.imreplace("telluric.%s.fits" % nstd, 1.0, lower=0.0, upper=0.0)
iraf.splot("telluric.%s.fits" %
nstd) # Remove stellar features and resave
# Create smoothed standard
iraf.gauss("a%s.ms[*,1,1]" % nstd, "s%s.ms" % nstd, 5.0)
iraf.sarith("%s.ms" % nstd, "/", "s%s.ms" % nstd, "ds%s.ms" % nstd)
# Apply telluric correction
iraf.telluric(
"ds%s.ms" % nstd,
"tds%s.ms" % nstd,
"telluric.%s.fits" % nstd,
xcorr=no,
tweakrms=no,
interactive=no,
sample='4000:4010,6850:6975,7150:7350,7575:7725,8050:8400,8900:9725'
)
# Define bandpasses for standard star calculation
obj = get_head("%s.fits" % nstd, "OBJECT")
iraf.standard("tds%s.ms" % nstd,
"%s.std" % nstd,
extinction='home$extinct/maunakeaextinct.dat',
caldir='home$standards/',
observatory='Keck',
interac=yes,
star_name=obj,
airmass='',
exptime='')
# Determine sensitivity function
iraf.sensfunc('%s.std' % nstd,
'%s.sens' % nstd,
extinction='home$extinct/maunakeaextinct.dat',
newextinction='extinct.dat',
observatory='Keck',
function='legendre',
order=4,
interactive=yes)
return
def viewSpec(self, image):
iraf.splot(image)
return
def MeasureWidthsInter(spectraFilename, lineFilename):
# Function: MeasureWidthsInter(spectraFilename, lineData, apBoundaries)
#
# Inputs: spectraFilename : The absolute path of the .fits file containing the spectrum to measure
# lineFilename: A file containing information on lines to measure
# Outputs: EQWidths - A list of the measured widths, each tuple of the form:
# (wl, ion, ep, gf, c6, eqw, meas. wl, fwhm (gaussian)).
#
# Desription: This function calls the iraf 'splot' task, and asks the user to manually
# measure each line in the passed list. The splot log file is read and the actual
# line equivalent width is assumed to be the last measurement in the log file.
objectName, numApertures, apertureSize, apBoundaries = SD.ReadSpectraInfo(
spectraFilename)
lineData, unused = getLinesForApertures(lineFilename, apBoundaries)
EQWidths = []
badLines = []
dopplerCount = 0
dopplerCheck = 0.0
for [wl, ion, ep, gf, c6, solarEqw, comment] in lineData:
contentAps = [
apBoundaries.index(x) for x in apBoundaries if x[0] <= wl <= x[1]
]
if len(contentAps) == 0:
print(
'Wavelength ({0:8.3f}) for {1:4.1f} line not covered by spectra. Continuing.'
.format(wl, ion))
continue
wlUpper = wl + k.HalfWindowWidth
wlLower = wl - k.HalfWindowWidth
print(
'Measuring {0:4.1f} line: {1:8.3f} (displaying first of {2:1d} orders)'
.format(ion, wl, len(contentAps)))
iraf.onedspec()
iraf.splot(spectraFilename,
line=contentAps[0] + 1,
band=1,
xmin=wlLower + 1.,
xmax=wlUpper + 1.,
save_file=k.SplotLogFilename,
options='nosysid, auto')
# iraf.splot(spectraFilename, line=contentAps[0]+1, band=1, xmin=wlLower+1., xmax=wlUpper+1., ymin=k.SpecFluxMin, ymax=k.SpecFluxMax, save_file=k.SplotLogFilename, options='nosysid, auto')
try:
with open(k.SplotLogFilename) as filePointer:
# Assume that the desired fit is the _last_ fit made.
# It is important for the blended fit ('d' option) that the
# actual line is the last one selected in the blend
dataLine = filePointer.readlines()[-1].split()
# splot data line is: line center, cursor loc. (cont. level), flux(?),
# eqw, core depth from cursor, gfwhm, lfwhm
lineCenter = float(dataLine[0])
print(
'Measured line: {0:8.3f} at: {1:5.1f} mAngstroms (FWHM = {2:5.4f})'
.format(wl,
float(dataLine[3]) * 1000.0, float(dataLine[5])))
EQWidths.append([
wl, ion, ep, gf, c6,
float(dataLine[3]) * 1000.0, lineCenter,
float(dataLine[5])
])
thisDoppler = ((lineCenter - wl) / wl) * k.SpeedofLight
dopplerCheck += thisDoppler
dopplerCount += 1
os.remove(k.SplotLogFilename)
except (IOError, ValueError):
# IOError - No line is fit (no log was created)
# ValueError - Log output from splot is in unrecognizeable format
badLines.append([wl, ion, k.unknownReason, k.unknownStr])
print('No line measured for wl: {0:8.3f}.'.format(wl))
return EQWidths, badLines, dopplerCount, dopplerCheck
# 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)
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
