def calflux(lfits,
sensitivity='sensfunc.fits',
extinction="lapalma_extinction.dat",
observatory='lapalma',
extinct='yes',
outfits=None,
fmt_out='cf%s',
prefix=None,
suffix=None,
in_files='tmp_in.list',
out_files='tmp_out.list'):
lfits = cklist(lfits)
if outfits is None:
outfits = [fmt_file(fmt_out, fits) for fits in lfits]
delfiles(outfits)
wfile(in_files, lfits, prefix=prefix, suffix=suffix)
wfile(out_files, outfits)
unlearn('calibrate')
# output=pref+'//@'+tlist
calibrate(input='@' + in_files,
output='@' + out_files,
sensiti=sensitivity,
extinct=extinct,
extinction=extinction,
observatory=observatory,
ignoreaps='yes')
unlearn('calibrate')
delfiles([in_files, out_files])
def combine(lfits,
outfits,
rej='crreject',
combine='average',
scale='exposure',
weight='exposure',
nlow=1,
expname='EXPTIME',
rdnoise=0.0,
gain=1.0,
suffix=None,
prefix=None,
input_files='tmp_input.list',
delete_list=True):
wfile(input_files, lfits, suffix=suffix, prefix=prefix)
delfiles(outfits)
unlearn('imcombine')
imcombine('@' + input_files,
output=outfits,
combine=combine,
reject=rej,
scale=scale,
weight=weight,
expname=expname,
rdnoise=rdnoise,
gain=gain,
nlow=nlow)
unlearn('imcombine')
if delete_list:
delfiles(input_files)
def ccdp(files,
prefix=None,
suffix=None,
input_files='tmp_input.list',
output_files='tmp_output.list',
fmt_out='c%s',
**kwargs):
# Dictionary with default parameters for ccdproc
dpar = dict(ccdtype="",
fixpix='no',
overscan='yes',
trim='yes',
zerocor='yes',
flatcor='no',
darkcor='no',
illumco='no',
fringec='no',
zero="",
illum="",
function='legendre',
order=4,
interactive='no',
readaxi='column',
trimsec=None,
biassec=None,
flat="")
# Update parameters dictionary for ccdproc if new are provided (kwargs)
dpar.update(kwargs)
# Make "files" a list, in case there is only one file
files = cklist(files)
# Write the list of input files to a temporary file
wfile(input_files, files, prefix=prefix, suffix=suffix)
# Create a list of names with the output name after ccdproc is applied
new_files_ccdproc = [fmt_out % item for item in files]
# Write the new files list to a file
wfile(output_files, new_files_ccdproc)
# Delete, if exits, the new files to be created
delfiles(new_files_ccdproc)
# Apply ccdproc
unlearn('ccdproc')
ccdproc(images='@' + input_files, output='@' + output_files, **dpar)
unlearn('ccdproc')
# Delete the temporary lists
delfiles([input_files, output_files])
def transf(lfits,
fitnames,
interp='linear',
database='database',
flux='yes',
y1='INDEF',
prefix=None,
suffix=None,
input_files='tmp_input.list',
output_files='tmp_output.list',
fmt_out='l%s',
dispaxis=None,
**kwargs):
fitnames = '.'.join(fitnames.split('.')[0:-1])
if isinstance(lfits, str) and input_files is None:
input_files = lfits
lfits = np.loadtxt(input_files, dtype=np.str, unpack=True)
else:
lfits = cklist(lfits)
wfile(input_files, lfits, prefix=prefix, suffix=suffix)
wfits = [fmt_out % fits.split('[')[0] for fits in lfits]
wfile(output_files, wfits)
delfiles(wfits)
if dispaxis is not None:
for fits in lfits:
hedit(fits, 'DISPAXIS', dispaxis, add='yes', verify='no')
unlearn('transform')
transform(input='@' + input_files,
output='@' + output_files,
fitnames=fitnames,
interp=interp,
database=database,
flux=flux,
y1=y1,
**kwargs)
unlearn('transform')
# for fits in wfits:
# hedit(fit, 'WAT2_001', 'wtype=linear label=Wavelength units=Angstroms', add='yes', verify='no')
delfiles([input_files, output_files])
def back(fits, outfits=None, fmt='b%s', **kwargs):
# sample: Lines or columns to be used in the background fits. The default "*" selects all lines or columns
# sample = '200:400,1000:1200'
dback = dict(axis=1,
low_reject=3.,
high_reject=3.,
inter='yes',
order=1,
niterat=3)
dback.update(kwargs)
if outfits is None:
outfits = fmt_file(fmt, fits)
delfiles(outfits)
unlearn('background')
background(fits, outfits, **dback)
unlearn('background')
def setair(files,
observatory='lapalma',
equinox='EPOCH',
ut='UTSTART',
input_files='tmp_input.list',
prefix=None,
suffix=None):
files = cklist(files)
wfile(input_files, files, prefix=prefix, suffix=suffix)
unlearn('setairmass')
setairmass(images='@' + input_files,
observatory=observatory,
equinox=equinox,
ut=ut)
unlearn('setairmass')
delfiles(input_files)
def rmcosmic(lfits,
xorder=5,
yorder=5,
sigfrac=2.0,
niter=5,
objlim=2,
sigclip=3.0,
fmt_in=None,
fmt_out='cr%s',
rdnoise=0.0,
gain=1.0):
# Download "lacos_spec.cl" from: http://www.astro.yale.edu/dokkum/lacosmic/lacos_spec.cl
# Save it into a directory ("lacos_directory" for our purposes)
# In your IRAF "login.cl" include the following line:
# task lacos_spec = lacos_directory/lacos_spec.cl
from iraf import lacos_spec
lfits = cklist(lfits)
fmt_in = '%s' if fmt_in is None else fmt_in
unlearn('lacos_spec')
for fits in lfits:
fits = fmt_in % fits
output = fmt_out % fits
mask = 'mask%s' % fits
delfiles([mask, output])
lacos_spec(fits,
output,
mask,
gain=gain,
readn=rdnoise,
xorder=xorder,
yorder=yorder,
sigclip=sigclip,
sigfrac=sigfrac,
objlim=objlim,
niter=niter)
delfiles(mask)
unlearn('lacos_spec')
def cdispcor(fits,
outfits,
ref_disp=None,
flux='yes',
linearize='yes',
key_ref='REFSPEC1',
add='yes',
verify='no',
**kwargs):
# To use the dispersion solution of other image, create a keyword
# in the header "REFSPEC1" to point to the file with the dispersion
# solution you want to use
if ref_disp is not None:
hedit(fits, key_ref, ref_disp, add=add, verify=verify)
delfiles(outfits)
unlearn('dispcor')
dispcor(fits, outfits, linearize=linearize, flux=flux)
unlearn('dispcor')
def unlearn_list(ltask):
ltask = cklist(ltask)
for task in ltask:
unlearn(task)
def sensf(lstd,
sensitivity='sensfunc',
standards='standards.dat',
observatory='lapalma',
graphs='srei',
caldir='onedstds$iidscal/',
extinction='lapalma_extinction.dat',
istand='yes',
isens='yes',
order=5,
logfile="",
star_names=None,
**kwargs):
# La Palma extinction directory
# http://www.ing.iac.es/Astronomy/observing/conditions/wlext.html
# Directory of the standard star file
# iraf/noao/lib/onedstds/iidscal/bd253941.dat
# iraf/noao/lib/onedstds/iidscal/bd332642.dat
# iraf/iraf/noao/lib/onedstds/oke1990/feige110.dat
# caldir = "onedstds$oke1990/"
# iraf/iraf/noao/lib/obsdb.dat -> observatory
# The ones in IRAF have very poor resolution. Better to use new ones by ESO:
# https://www.eso.org/sci/observing/tools/standards/spectra/okestandards_rev.html
# Download them from:
# ftp://ftp.eso.org/pub/stecf/standards/okestan/
# The 'f' files list wavelength ( A ), flux ( ergs/cm/cm/s/A * 10**16 ) and flux ( milli-Jy ) and bin (A)
# The file name consists of a prefix 'f' and the star name
# The 'm' files list wavelength ( A ), AB magnitude and bin (A). The file
# name consists of a prefix 'm' and the star name --> TAKE 'm' FILES FOR IRAF
# The in the current directory create a file called "standars.men" with the folling format
# Standard stars in .:
#
# mbd25d4655
# mbd33d2642
# Set the variable caldir="" and run standard (sensf)
sensfile = '%s.fits' % sensitivity
delfiles([sensfile, standards])
unlearn('standard')
unlearn('sensfunc')
lstd = cklist(lstd)
star_names = cklist(star_names)
if star_names is None:
star_names = [
'.'.join(os.path.basename(std).split('.')[0:-1]) for std in lstd
]
for std, star_name in zip(lstd, star_names):
standard(input=std,
output=standards,
observatory=observatory,
caldir=caldir,
extinction=extinction,
star_name=star_name,
interact=istand)
# ignorepars='yes' --> All the observations are combined into a single sensitivity function
sensfunc(standards=standards,
sensitivity=sensitivity,
extinction=extinction,
observatory=observatory,
logfile=logfile,
graphs=graphs,
interactive=isens,
order=order,
ignoreaps='yes',
**kwargs)
unlearn('standard')
unlearn('sensfunc')
def extract_aperture(fits,
line,
lower=-5,
upper=5,
b_sample='-10:-6,6:10',
outfits=None,
fmt_out='ap%s',
fmt_ap='%s.000?.fits',
database='database/',
rdnoise=0.0,
gain=1.0,
std=False,
**kwargs):
# line, nsum: The dispersion line (line or column perpendicular to the dispersion axis) and
# number of adjacent lines (half before and half after unless at the end of the image)
# used in finding, recentering, resizing, and editing operations. A line of INDEF selects
# the middle of the image along the dispersion axis. A positive nsum selects a sum of
# lines and a negative selects a median of lines.
# lower, upper: Default lower and upper aperture limits relative to the aperture center.
# These limits are used for apertures found with apfind and when defining the first
# aperture in apedit.
# b_sample = "-10:-6,6:10": Default background sample. The sample is given by a set of colon
# separated ranges each separated by either whitespace or commas. The string "*" refers to
# all points. Note that the background coordinates are relative to the aperture center and not
# image pixel coordinates so the endpoints need not be integer.
# Default parameter dictionary
dapall = dict(format='onedspec',
nsum=18,
t_function='spline3',
t_order=5,
recenter='no',
b_naverage=-50,
resize='no',
gain=gain,
readnoise=rdnoise,
interac='yes',
extras='no',
trace='no',
references="",
apertures="1",
b_function='legendre')
# Default values for standard or object
# For standard stars: background='fit', weights='variance', clean='yes'
# For objects: background='none', weights='none', clean='no'
if std:
dextra = dict(background='fit', weights='variance', clean='yes')
else:
dextra = dict(background='none', weights='none', clean='no')
dapall.update(dextra)
# Overwrite default values
dapall.update(kwargs)
# outfits name
if outfits is None:
outfits = fmt_file(fmt_out, fits)
# Delete output file
delfiles(outfits)
# Delete aperture files created from previous extractions
ap_file = fmt_file(fmt_ap, '.'.join(outfits.split('.')[0:-1]))
list_ap_files = glob(ap_file)
delfiles(list_ap_files)
# Delete aperture database for this file
root_name_ap = '.'.join(outfits.split('.fit')[0:-1])
db_files = glob(os.path.join(database, root_name_ap))
delfiles(db_files)
# When using optimum extraction sometimes the spectrum is zero
unlearn('apall')
apall(input=fits,
output=outfits,
line=line,
lower=lower,
upper=upper,
b_sample=b_sample,
**dapall)
unlearn('apall')
def flatccdres(flat_files,
flat='flat.fits',
nflat='nflat.fits',
cflat='cflat.fits',
iccd='no',
iresponse='yes',
combine='average',
reject='avsigclip',
ccdtype="",
process='no',
fixpix='no',
overscan='yes',
zerocor='yes',
trim='yes',
darkcor='no',
flatcor='no',
illumco='no',
fringec='no',
function='legendre',
corder=5,
rorder=18,
readaxi='column',
biassec=None,
zero=None,
trimsec=None,
low_reject=3,
high_reject=3,
scale='mode',
rdnoise=0.0,
gain=1.0,
lflats=None,
prefix=None,
suffix=None):
unlearn('flatcombine')
unlearn('ccdproc')
unlearn('response')
delfiles(flat)
if lflats is not None:
wfile(flat_files, lflats, prefix=prefix, suffix=suffix)
flatcombine('@' + flat_files,
output=flat,
combine=combine,
reject=reject,
ccdtype=ccdtype,
scale=scale,
process=process,
rdnoise=rdnoise,
gain=gain)
delfiles(cflat)
ccdproc(images=flat,
output=cflat,
ccdtype=ccdtype,
fixpix=fixpix,
overscan=overscan,
trim=trim,
zerocor=zerocor,
darkcor=darkcor,
flatcor=flatcor,
illumco=illumco,
fringec=fringec,
zero=zero,
function=function,
order=corder,
interactive=iccd,
readaxi=readaxi,
trimsec=trimsec,
biassec=biassec)
# Dispersion goes up-down
# hedit(cfout,'DISPAXIS', 2, add='yes', verify='no')
delfiles(nflat)
response(cflat,
cflat,
nflat,
interac=iresponse,
order=rorder,
low_reject=low_reject,
high_reject=high_reject)
unlearn('flatcombine')
unlearn('ccdproc')
unlearn('response')
interact=sys.argv[8]
############################
#
# unlearn all parameters
#
#########################
print "\n Unlearning the following tasks:\n"
print "gemini\n"
unlearn ("gemini")
print "gnirs\n"
unlearn ("gnirs")
print "niri\n"
unlearn ("niri")
print "f2\n"
unlearn ("f2")
print "gemtools\n"
unlearn ("gemtools")
print "geomap\n"
unlearn ("geomap")
print "ERROR: too many objects! Include one only. Bye!\n"
else:
print "So using", len(drk), "dark,", len(flt), "flat and", len(
obj), "objects images, with object's rootname:", rname
getout = raw_input(
"Is everything ok? Press 'enter' to continue, 'e' to exit.\t")
# option to exit
if getout == 'e' or getout == 'E':
print "\nexiting then... Bye!\n"
raise SystemExit
# preparing header fields (input of imagetyp, exptime/darktime)
print "> preparing the image headers"
#l.write("\n> preparing the image headers")
unlearn(iraf.ccdhedit)
iraf.ccdhedit("Dark*.fit", parameter="imagetyp", value="dark")
iraf.ccdhedit("Flat*.fit", parameter="imagetyp", value="flat")
iraf.ccdhedit(rname + "_*.fit", parameter="imagetyp", value="object")
for im in glob.glob('*.fit*'):
unlearn(iraf.imgets)
iraf.imgets(im, param="inttime")
iraf.hedit(im,
fields="exptime",
value=iraf.imgets.value,
add="no",
addonly="yes",
delete="no",
verify="no",
show="no",
update="yes")
def runCrosscorr(flist, reference, input_pars):
""" Use cross-correlation to compute shifts.
Input and reference will ONLY be singly-drizzled images.
"""
shift_dict = {}
iraf.set(imtype='fits')
# Run 'crossdriz' with proper inputs
iraf.unlearn('crossdriz')
iraf.unlearn('shiftfind')
iraf.crossdriz.dinp = input_pars['dinp']
iraf.crossdriz.dref = input_pars['dref']
iraf.crossdriz.pad = input_pars['pad']
iraf.crossdriz.margin = input_pars['margin']
iraf.crossdriz.tapersz = input_pars['tapersz']
iraf.crossdriz.mintheta = 0.0
iraf.crossdriz.maxtheta = 0.0
iraf.crossdriz.stptheta = 0.1
iraf.shiftfind.xshift = 0.
iraf.shiftfind.xshift = 0.
iraf.shiftfind.boxsize = 'INDEF'
iraf.shiftfind.fwhm = input_pars['xc_fwhm']
iraf.shiftfind.ellip = input_pars['xc_ellip']
iraf.shiftfind.pa = input_pars['xc_pa']
iraf.shiftfind.fitbox = input_pars['xc_fitbox']
iraf.shiftfind.kellip = yes
iraf.shiftfind.kpa = yes
iraf.shiftfind.tempdir = "tmp$"
print('#\n# Performing Cross-correlation with CROSSDRIZ \n#')
print(' Reference image : ', reference)
xcorr_root = 'tweak_xcorr'
xcorr_sroot = 'xcorr_shifts'
outname = define_output_name(reference)
shift_dict['order'] = [outname]
shift_dict[outname] = [0., 0., 0., 1.0]
indx = 0
for img in flist:
print(' Single input : ', img)
xcorr_indx = "%03d" % indx
xcorr_base = xcorr_root + xcorr_indx
xcorr_shift = xcorr_sroot + xcorr_indx + '.txt'
# Insure that products from previous runs are deleted
xcorr_list = irafglob.irafglob(xcorr_base + '*')
for file in xcorr_list:
removeFile(file)
iraf.crossdriz(image=img, refimage=reference, basename=xcorr_base)
# Now, run shiftfind to compute the shifts
removeFile(xcorr_shift)
print('#\n# Computing shift using SHIFTFIND \n#')
iraf.shiftfind(cclist=xcorr_base, outfile=xcorr_shift)
# Read shifts from shiftfind file...
xcfile = open(xcorr_shift, 'r')
xclines = xcfile.readlines()
xcol = 2
ycol = 4
for xcl in xclines:
if xcl[0] == '#' or xcl.strip() == '': continue
xcl = xcl.split()
outname = define_output_name(xcl[0])
shift_dict[outname] = (-float(xcl[xcol]), -float(xcl[ycol]), 0.0,
1.0)
xcfile.close()
# Clean up intermediate CROSSDRIZ products
if input_pars['clean'] == yes:
removeFile(xcorr_shift)
xcorr_list = irafglob.irafglob(xcorr_base + '*')
for file in xcorr_list:
removeFile(file)
# Used for indexing xcorr_base rootname for all inputs
indx += 1
if img != reference:
outname = define_output_name(img)
shift_dict['order'].append(outname)
return shift_dict
iraf.gemini.gnirs.nsheaders.instrument ='f2'
iraf.gemini.gnirs.nsheaders.instrument ='f2'
iraf.gemini.gnirs.nsheaders.instrument ="f2"
iraf.gemini.gnirs.nsheaders.instrument ="f2"
iraf.gemini.gnirs.nsheaders.logfile ="f2.logfile.log"
iraf.gemini.gnirs.nsheaders ('f2',logfile="f2.logfile.log")
iraf.gemini.gnirs.nsheaders ('f2',logfile="f2.logfile.log")
iraf.gemini.gnirs.nsheaders ('f2',logfile="f2.logfile.log")
print "\n Unlearning the following tasks:\n"
print "hselect\n"
unlearn("hselect")
print "gemseeing\n"
unlearn("gemseeing")
print "phot\n"
unlearn("phot")
print "datapars"
unlearn("datapars")
print "centerpars\n"
unlearn("centerpars")
print "fitskypars\n"
unlearn("fitskypars")
print "photpars\n"
unlearn("photpars")