def wavesol(arcfiles, rawpath):
for f in arcfiles:
binning = get_binning(f, rawpath)
iraf.unlearn(iraf.gsreduce)
if dobias:
bias_filename = "bias{binning}".format(binning=binning)
else:
bias_filename = ''
iraf.gsreduce('@' + f, outimages=f[:-4], rawpath=rawpath,
fl_flat=False, bias=bias_filename, fl_bias=dobias,
fl_fixpix=False, fl_over=dooverscan, fl_cut=False, fl_gmosaic=True,
fl_gsappwave=True, fl_oversize=False, fl_vardq=dodq)
# determine wavelength calibration -- 1d and 2d
iraf.unlearn(iraf.gswavelength)
iraf.gswavelength(f[:-4], fl_inter='yes', fl_addfeat=False, fwidth=15.0, low_reject=2.0,
high_reject=2.0, step=10, nsum=10, gsigma=2.0,
cradius=16.0, match=-6, order=7, fitcxord=7,
fitcyord=7)
if do_qecorr:
# Make an extra random copy so that gqecorr works. Stupid Gemini.
iraf.cp(f[:-4]+'.fits', f[:-4]+'.arc.fits')
# transform the CuAr spectrum, for checking that the transformation is OK
# output spectrum has prefix t
iraf.unlearn(iraf.gstransform)
iraf.gstransform(f[:-4], wavtran=f[:-4])
def calWave(self, arcList, **kwargs):
iraf.gswavelength(arcList, **kwargs)
# TODO: include call to gftransform to rectify arcs?
return
def wavesol(arcfiles, rawpath):
for f in arcfiles:
iraf.unlearn(iraf.gsreduce)
iraf.gsreduce('@' + f, outimages=f[:-4], rawpath=rawpath,
fl_flat=False, bias="bias",
fl_fixpix=False, fl_over=dooverscan, fl_cut=False, fl_gmosaic=True,
fl_gsappwave=True, fl_oversize=False)
# determine wavelength calibration -- 1d and 2d
iraf.unlearn(iraf.gswavelength)
iraf.gswavelength(f[:-4], fl_inter='yes', fwidth=15.0, low_reject=2.0,
high_reject=2.0, step=10, nsum=10, gsigma=2.0,
cradius=16.0, match=-6, order=7, fitcxord=7,
fitcyord=7)
if is_GS:
# Make an extra random copy so that gqecorr works. Stupid Gemini.
shutil.copy(f[:-4]+'.fits', f[:-4]+'.arc.fits')
# transform the CuAr spectrum, for checking that the transformation is OK
# output spectrum has prefix t
iraf.unlearn(iraf.gstransform)
iraf.gstransform(f[:-4], wavtran=f[:-4])
scidir= '/Users/bkirk/Documents/A851/Science/'
proc_logs='/Users/bkirk/Documents/A851/proc_logs/'
calibdir='/Users/bkirk/Documents/A851/bias_frame/'
flatdir='/Users/bkirk/Documents/A851/Flat/'
gmos.logfile="A851_cuar.log"
#At the moment it'll be easier to pick the flats by hand rather than scripting a way through to find a flat that matches the arc frame specs. These are the flats whose dimensions I'll use to cut the arc frames.
flat_mask1_660 = '%sdc_mN20011221S139_norm_flat.fits' % scidir
flat_mask1_670 = '%sdc_mN20011220S156_norm_flat.fits' % scidir
flat_mask2_660 = '%sdc_mN20011224S127_norm_flat.fits' % scidir
flat_mask2_670 = '%sdc_mN20011222S112_norm_flat.fits' % scidir
'''
#Run GSREDUCE on the arc frames, separated by different central wavelength files, and match the arc files with their respective proc_log and reference files (the reference files here are the stich & slitted flat frames that are paired to them (same mask and central wavelength). Should the x and y offsets have the same settings as the flat frames? <- There aren't any internal emisson lines to mask out in the arc. Inger's settings have different values for the different arc frames, so I may as well put them in.
#GSREDUCE calls GSAPPWAVE at the end so it's already done. GSREDUCE has options which include mosaic'ing and cutting; the mosaic'ing does so with interpolation on. This produces arc frames that are in MEF format and are transformed and have a prefix 't' to them
#### mask 1 ####
#central wavelength of 660nm:
arc_mask1_660 = [line.strip() for line in open('arc_mask1_660')]
for i in arc_mask1_660:
current_arc = i
iraf.gsreduce(inimages=rawdir+current_arc, fl_bias = 'no', fl_flat = 'no', mdfdir=proc_logs, refimage=flat_mask1_660)
#central wavelength of 670nm:
arc_mask1_670 = [line.strip() for line in open('arc_mask1_670')]
for i in arc_mask1_670:
iraf.gsreduce(inimages='@CuAr_b600_520_1.txt',
flatim='b600_520_norm_flat_1.fits',
fl_bias=no,
fl_crspec=no)
iraf.gsreduce(inimages='@CuAr_b600_525_1.txt',
flatim='b600_525_norm_flat_1.fits',
fl_bias=no,
fl_crspec=no)
#log?
l.write('reduced=yes')
#Wavelength calibrate ARCs in 2d
iraf.gswavelength(inimages='gs//@CuAr_b600_520_1.txt')
iraf.gswavelength(inimages='gs//@CuAr_b600_525_1.txt')
#log?
l.write('arcs=yes')
#Applying Wavelength Calibration to Science Images
iraf.gstransform(inimages='gs//@object_b600_520_1.txt',
wavtran='gs//@CuAr_b600_520_1.txt')
iraf.gstransform(inimages='gs//@object_b600_520_2.txt',
wavtran='gs//@CuAr_b600_520_1.txt')
iraf.gstransform(inimages='gs//@object_b600_520_3.txt',
wavtran='gs//@CuAr_b600_520_1.txt')
iraf.gstransform(inimages='gs//@object_b600_525_1.txt',
# Reduce the arc image
iraf.gsreduce(rawdir + arc,
rawpath=rawdir,
bias=rawdir + bias,
fl_fixpix='yes',
flat='flat.fits',
fl_over='no')
iraf.gsreduce(rawdir + arc_star,
rawpath=rawdir,
bias=rawdir + bias_star,
fl_fixpix='yes',
flat='flat_star.fits',
fl_over='no')
# Fit wavelength solution
iraf.gswavelength('gs' + arc, fl_inter='no')
iraf.gswavelength('gs' + arc_star, fl_inter='no')
# Reduce standard star
iraf.gsreduce(rawdir + star,
rawpath=rawdir,
bias=rawdir + bias_star,
fl_fixpix='yes',
flat='flat_star.fits',
fl_over='no',
fl_gscrrej='yes',
fl_vardq='yes')
iraf.gstransform('gs' + star,
wavtraname='gs' + arc_star.replace('.fits', ''))
iraf.gsskysub('tgs' + star, long_sample='150:200')
iraf.gsextract('stgs' + star)
# Reduce the arc frames
logger.info("Reducing arc frames..")
for filename in arc_filenames:
reduce_arc_result = iraf.gsreduce("g{0}".format(filename),
fl_over=False, fl_trim=True, fl_bias=False, fl_dark=False,
fl_flat=False, fl_cut=True, fl_gsappwave=True, yoffset=5.0, Stdout=1)
log_iraf_result(reduce_arc_result)
logger.info("Running gswavelength..")
#gswavelength_result = iraf.gswavelength("gsg{0}".format(filename),
# fl_inter="NO", nsum=5, step=5, function='chebyshev', order=6,
# fitcxord=5, fitcyord=4, Stdout=1)
gswavelength_result = iraf.gswavelength("gsg{0}".format(filename),
fl_inter="NO", nsum=5, step=5, function="chebyshev", order="6",
fitcxord=5, fitcyord=4, Stdout=1, niterate=10, low_reject=1.5,
high_reject=1.5, fl_dbwrite="YES", fl_overwrite="yes")
log_iraf_result(gswavelength_result)
# Apply transformations
logger.info("Applying wavelength transformations to arc..")
gstransform_arc_result = iraf.gstransform("gsg{0}".format(filename),
wavtraname="gsg{0}".format(filename), Stdout=1)
log_iraf_result(gstransform_arc_result)
logger.info("Doing wavelength transformations, sky and extraction:")
for object_filename in object_filenames:
logger.info("Working on filename {}".format(object_filename))
logger.info("Applying wavelength transformations to object..")
fl_bias=no)
iraf.gsreduce(inimages='@object_b600_2.txt',
flatim='b600_norm_flat_2.fits',
fl_bias=no)
iraf.gsreduce(inimages='@CuAr_r400_2.txt',
flatim='r400_norm_flat_2.fits',
fl_bias=no)
iraf.gsreduce(inimages='@CuAr_b600_2.txt',
flatim='b600_norm_flat_2.fits',
fl_bias=no)
#log?
l.write('reduced=yes')
#Wavelength calibrate ARCs in 2d
iraf.gswavelength(inimages='gs//@CuAr_r400_1.txt')
iraf.gswavelength(inimages='gs//@CuAr_b600_1.txt')
iraf.gswavelength(inimages='gs//@CuAr_r400_2.txt')
iraf.gswavelength(inimages='gs//@CuAr_b600_2.txt')
#log?
l.write('arcs=yes')
#Applying Wavelength Calibration to Science Images
iraf.gstransform(inimages='gs//@object_r400_1.txt',
wavtran='gs//@CuAr_r400_1.txt')
iraf.gstransform(inimages='gs//@object_b600_1.txt',
wavtran='gs//@CuAr_b600_1.txt')
iraf.gstransform(inimages='gs//@object_b600_2.txt',
wavtran='gs//@CuAr_b600_2.txt')
iraf.gstransform(inimages='gs//@object_r400_2.txt',
# ---------- Wavelength solution ---------- #
# Extract the arc
flatref = iraf.type(ic.lst_flat, Stdout=1)[0].strip()
iraf.imdelete('g@'+ic.lst_arc)
iraf.imdelete('rg@'+ic.lst_arc)
iraf.imdelete('erg@'+ic.lst_arc)
for arc in iraf.type(ic.lst_arc, Stdout=1):
arc = arc.strip()
iraf.gfreduce(arc, rawpath=ic.rawdir, fl_extract='yes', recenter='no',
trace='no', reference='erg'+flatref, fl_bias='no',
fl_over='yes', fl_trim='yes', mdffile=ic.nmdf, mdfdir='./',
slits='both', fl_fluxcal='no', fl_gscrrej='no',
fl_wavtran='no', fl_skysub='no', fl_inter='no')
iraf.sleep(10.0)
# ----- Measure the wavelength solution ----- #
for arc in iraf.type(ic.lst_arc, Stdout=1):
arc = arc.strip()
iraf.gswavelength('erg'+arc, fl_inter='yes',
nlost=20, ntarget=15, threshold=25,
coordlis='gmos$data/GCALcuar.dat')
# Printing the running time
print('--- %s seconds ---' %(time.time()-start_time))
path = os.path.basename(arc["path"])
iraf.unlearn("gireduce")
iraf.unlearn("gsflat")
iraf.unlearn("gemextn")
iraf.gsreduce(path,
bias="master_bias.fits",
fl_fixpix=False,
fl_flat=False,
fl_oversize=False)
# Do wavelength calibration on arc frames.
iraf.unlearn("gswavelength")
iraf.gswavelength("gs{}".format(path), **gswavelength_kwds)
# Reduce all science frames.
science_frames = association[(association["obsclass"] == "science") \
+ ((association["obstype"] == "OBJECT") * (association["obsclass"] == "partnerCal"))]
# Load template spectrum.
template_disp, template_flux, _ = np.loadtxt("Atlas.Arcturus.372_926nm.txt",
skiprows=1).T
template_disp *= 10.0
for frame in science_frames:
path = os.path.basename(frame["path"])
iraf.unlearn("gireduce")
def reduce_stdstar(rawdir, rundir, caldir, starobj, stdstar, flat,
arc, twilight, starimg, bias, overscan, vardq):
"""
Reduction pipeline for standard star.
Parameters
----------
rawdir: string
Directory containing raw images.
rundi: string
Directory where processed files are saved.
caldir: string
Directory containing standard star calibration files.
starobj: string
Object keyword for the star image.
stdstar: string
Star name in calibration file.
flat: list
Names of the files containing flat field images.
arc: list
Arc images.
twilight: list
Twilight flat images.
starimg: string
Name of the file containing the image to be reduced.
bias: list
Bias images.
"""
iraf.set(stdimage='imtgmos')
iraf.gemini()
iraf.gemtools()
iraf.gmos()
#iraf.unlearn('gemini')
#iraf.unlearn('gmos')
iraf.task(lacos_spec='/storage/work/gemini_pairs/lacos_spec.cl')
tstart = time.time()
#set directories
iraf.set(caldir=rawdir) #
iraf.set(rawdir=rawdir) # raw files
iraf.set(procdir=rundir) # processed files
iraf.gmos.logfile='logfile.log'
iraf.cd('procdir')
# building lists
def range_string(l):
return (len(l)*'{:4s},').format(*[i[-9:-5] for i in l])
iraf.gemlist(range=range_string(flat), root=flat[0][:-9],
Stdout='flat.list')
iraf.gemlist(range=range_string(arc), root=arc[0][:-9],
Stdout='arc.list')
#iraf.gemlist(range=range_string(star), root=star[0][:-4],
# Stdout='star.list')
iraf.gemlist(range=range_string(twilight),
root=twilight[0][:-9], Stdout='twilight.list')
iraf.gfreduce.bias = 'caldir$'+bias[0]
#######################################################################
#######################################################################
### Star reduction #
#######################################################################
#######################################################################
#
# Flat reduction
#
iraf.gfreduce(
'@flat.list', slits='header', rawpath='rawdir$', fl_inter='no',
fl_addmdf='yes', key_mdf='MDF', mdffile='default', weights='no',
fl_over=overscan, fl_trim='yes', fl_bias='yes', trace='yes', t_order=4,
fl_flux='no', fl_gscrrej='no', fl_extract='yes', fl_gsappwave='no',
fl_wavtran='no', fl_novl='no', fl_skysub='no', reference='',
recenter='yes', fl_vardq=vardq)
iraf.gfreduce('@twilight.list', slits='header', rawpath='rawdir$',
fl_inter='no', fl_addmdf='yes', key_mdf='MDF',
mdffile='default', weights='no',
fl_over=overscan, fl_trim='yes', fl_bias='yes', trace='yes',
recenter='no',
fl_flux='no', fl_gscrrej='no', fl_extract='yes', fl_gsappwave='no',
fl_wavtran='no', fl_novl='no', fl_skysub='no',
reference='erg'+flat[0], fl_vardq=vardq)
#
# Response function
#
for i, j in enumerate(flat):
j = j[:-5]
iraf.imdelete(j+'_response')
iraf.gfresponse('erg'+j+'.fits', out='erg'+j+'_response',
skyimage='erg'+twilight[i], order=95, fl_inter='no',
func='spline3',
sample='*', verbose='yes')
# Arc reduction
#
iraf.gfreduce(
'@arc.list', slits='header', rawpath='rawdir$', fl_inter='no',
fl_addmdf='yes', key_mdf='MDF', mdffile='default', weights='no',
fl_over=overscan, fl_trim='yes', fl_bias='yes', trace='no',
recenter='no',
fl_flux='no', fl_gscrrej='no', fl_extract='yes', fl_gsappwave='no',
fl_wavtran='no', fl_novl='no', fl_skysub='no', reference='erg'+flat[0],
fl_vardq=vardq)
# Finding wavelength solution
# Note: the automatic identification is very good
#
for i in arc:
iraf.gswavelength('erg'+i, function='chebyshev', nsum=15, order=4,
fl_inter='no', nlost=5, ntarget=20, aiddebug='s', threshold=5,
section='middle line')
#
# Apply wavelength solution to the lamp 2D spectra
#
iraf.gftransform('erg'+i, wavtran='erg'+i, outpref='t', fl_vardq=vardq)
##
## Actually reduce star
##
iraf.gfreduce(
starimg, slits='header', rawpath='rawdir$', fl_inter='no',
fl_addmdf='yes', key_mdf='MDF', mdffile='default', weights='no',
fl_over=overscan, fl_trim='yes', fl_bias='yes', trace='no',
recenter='no',
fl_flux='no', fl_gscrrej='no', fl_extract='no', fl_gsappwave='no',
fl_wavtran='no', fl_novl='yes', fl_skysub='no', fl_vardq=vardq)
iraf.gemcrspec('rg{:s}'.format(starimg), out='lrg'+starimg, sigfrac=0.32,
niter=4, fl_vardq=vardq)
iraf.gfreduce(
'lrg'+starimg, slits='header', rawpath='./', fl_inter='no',
fl_addmdf='no', key_mdf='MDF', mdffile='default',
fl_over='no', fl_trim='no', fl_bias='no', trace='no',
recenter='no',
fl_flux='no', fl_gscrrej='no', fl_extract='yes',
fl_gsappwave='yes',
fl_wavtran='yes', fl_novl='no', fl_skysub='yes',
reference='erg'+flat[0][:-5], weights='no',
wavtraname='erg'+arc[0][:-5],
response='erg'+flat[0][:-5]+'_response.fits',
fl_vardq=vardq)
#
# Apsumming the stellar spectra
#
iraf.gfapsum(
'stexlrg'+starimg, fl_inter='no', lthreshold=400.,
reject='avsigclip')
#
# Building sensibility function
#
iraf.gsstandard(
('astexlrg{:s}').format(starimg), starname=stdstar,
observatory='Gemini-South', sfile='std', sfunction='sens',
caldir=caldir)
#
# Apply flux calibration to galaxy
#
#
##iraf.imdelete('*****@*****.**')
#
##iraf.gscalibrate('*****@*****.**',sfunction='sens.fits',fl_ext='yes',extinct='onedstds$ctioextinct.dat',observatory='Gemini-South',fluxsca=1)
#
##
## Create data cubes
##
#
#
##for i in objs:
## iraf.imdelete('d0.1cstexlrg'+i+'.fits')
## iraf.gfcube('cstexlrg'+i+'.fits',outpref='d0.1',ssample=0.1,fl_atmd='yes',fl_flux='yes')
#
##
## Combine cubes
##
#
#
##iraf.imdelete('am2306-721r4_wcsoffsets.fits')
##iraf.imcombine('d0.1cstexlrgS20141113S00??.fits[1]',output='am2306-721r4_wcsoffsets.fits',combine='average',reject='sigclip',masktype='badvalue',lsigma=2,hsigma=2,offset='wcs',outlimits='2 67 2 48 100 1795')
#
tend = time.time()
print('Elapsed time in reduction: {:.2f}'.format(tend - tstart))
iraf.gfreduce('@arc'+str(m)+'.list', slits='header', rawpath='rawdir$', fl_inter='no',
fl_addmdf='yes', key_mdf='MDF', mdffile='default', weights='no',
fl_over='yes', fl_trim='yes', fl_bias='no', trace='no', recenter='no',
fl_flux='no', fl_gscrrej='no', fl_extract='yes', fl_gsappwave='no',
fl_wavtran='no', fl_novl='no', fl_skysub='no', reference='erg'+flat[m][0],
fl_vardq='no', bias='caldir$'+bias[m][0])
#
# Finding wavelength solution
# Note: the automatic identification is very good
#
for m in range(len(star)):
for i in arc[m]:
iraf.gswavelength('erg'+i, function='chebyshev', nsum=15, order=4,
fl_inter='no', nlost=5, ntarget=20, aiddebug='s', threshold=5,
section='middle line')
#
# Apply wavelength solution to the lamp 2D spectra
#
for m in range(len(star)):
for i in arc[m]:
# (B) Fiz fl_vardq-, pois mudei acima tambem --------
iraf.imdelete('terg'+i+'.fits')
iraf.gftransform('erg'+i, wavtran='erg'+i, outpref='t', fl_vardq='no')
##
## Actually reduce star
##
