def normalize_masterflat(WORK_DIR):
print '\n + Normalizing masterflat.\n'
try: os.remove('masterflat_norm.fits')
except: pass
iraf.apflatten(input='masterflat.fits', output='masterflat_norm.fits', referen=observations[WORK_DIR]['objects'][0], interac='no', find='no', recenter='no', resize='no', edit='no', trace='no', fittrac='no', flatten='yes', fitspec='yes', functio='spline3', order=13, sample='51:2098', niterat=5)
def produce_flat(filelist_flat, masterflat_name):
flat_directory = os.path.dirname(filelist_flat[0])
filelist_text = open("filelist.lis", "w")
for i in range(len(filelist_flat)):
filelist_text.write(filelist_flat[i] + "\n")
filelist_text.close()
if len(filelist_flat) > 1:
iraf.imcombine("@filelist.lis",
"Flat.raw.fits",
combine="average",
reject="minmax",
nlow=1,
nhigh=1,
nkeep=1)
trim = trim_remove_bias("Flat.raw.fits")
iraf.apflatten(trim,
masterflat_name,
referen="find_orders_flat",
apertures="3-50",
trace="no",
gain=0.1913,
readnoise=3.380)
else:
iraf.apflatten(filelist_flat[0],
masterflat_name,
referen="find_orders_flat",
apertures="3-50",
trace="no",
gain=0.1913,
readnoise=3.380)
os.remove("Flat.raw.fits")
os.remove(trim)
os.remove("filelist.lis")
shutil.move(masterflat_name, os.path.join(flat_directory))
print("Success! Produced file:" + masterflat_name)
# ir.apnorm1.gain = ")apnormalize.gain"
# ir.apnorm1.lsigma = ")apnormalize.lsigma"
# ir.apnorm1.usigma = ")apnormalize.usigma"
# ir.apnorm1.clean = ")apnormalize.clean"
if True: # the old, IRAF way:
#ir.apnormalize(_sflatdc+'big', _sflatdcn+'big', sample=horizsamp, niterate=1, threshold=flat_threshold, function="spline3", pfit = "fit1d", clean='yes', cennorm='no', recenter='yes', resize='yes', edit='yes', trace='yes', weights='variance', fittrace='yes', interactive=interactive, background='fit', order=3)
ir.apflatten(_sflatdc + 'big',
_sflatdcn + 'big',
sample=horizsamp,
niterate=1,
threshold=flat_threshold,
function="spline3",
pfit="fit1d",
clean='yes',
recenter='yes',
resize='yes',
edit='yes',
trace='yes',
fittrace='yes',
interactive=interactive,
order=3)
else:
mudflat = pyfits.getdata(_sflatdc + 'big.fits')
mudhdr = pyfits.getheader(_sflatdc + 'big.fits')
trace = spec.traceorders(_sflatdc + 'big.fits',
pord=2,
nord=ir.aptrace.order,
g=gain,
def correctblazefn(inflat, outflat, ref_ap=None):
#Create padded file to get aperatures on edges
flatdat = pyfits.getdata(inflat + postfn)
flathdr = pyfits.getheader(inflat + postfn)
n_big = 1400
n_base = flatdat.shape[0]
pad = (n_big - n_base) / 2
bigflat = ny.zeros([n_big, n_base])
bigflat[pad:(pad + n_base), :] = flatdat
pyfits.writeto(inflat + 'big' + postfn,
bigflat,
flathdr,
overwrite=True,
output_verify='warn')
# Flatten Iraf or otherwise
if irafapflatten:
if ref_ap == None:
ir.apflatten(inflat + 'big',
outflat + 'big',
sample=horizsamp,
niterate=1,
threshold=flat_threshold,
function="spline3",
pfit="fit1d",
clean='yes',
recenter='yes',
resize='yes',
edit='yes',
trace='yes',
fittrace='yes',
interactive=interactive,
order=3)
else:
ir.apflatten(inflat + 'big',
outflat + 'big',
references=ref_ap,
sample=horizsamp,
niterate=1,
threshold=flat_threshold,
function="spline3",
pfit="fit1d",
clean='yes',
recenter='yes',
resize='yes',
edit='yes',
trace='no',
fittrace='yes',
interactive=False,
order=3)
else:
mudflat = pyfits.getdata(inflat + 'big.fits')
mudhdr = pyfits.getheader(inflat + 'big.fits')
trace = spec.traceorders(inflat + 'big.fits',
pord=2,
nord=ir.aptrace.order,
g=gain,
rn=readnoise,
fitwidth=100)
normflat = spec.normalizeSpecFlat(mudflat * gain,
nspec=ir.aptrace.order,
traces=trace)
pyfits.writeto(outflat + 'big.fits',
normflat,
header=mudhdr,
output_verify='warn')
# Remove Padding
normflatdat = pyfits.getdata(outflat + 'big' + postfn)
normflathdr = pyfits.getheader(outflat + 'big' + postfn)
smallnormflat = normflatdat[pad:(pad + n_base), :]
smallnormflat[smallnormflat == 0] = 1.
pyfits.writeto(outflat + postfn,
smallnormflat,
normflathdr,
overwrite=True,
output_verify='warn')
str_v_mod_out = temp_dir + ('[0]' + ',' +
temp_dir).join(v_mod) + '[0]'
#iraf.apall("master_flat.fits",extract='no',nfind=58,interactive="no",find="yes")
iraf.apall(temp_dir + star_file.replace('.fits', '_boost.fits'),
extract='no',
nfind=62,
interactive="no",
find="yes",
recenter="yes")
v_mod_norm = SRP.outputnames(v, 'tnrm')
str_v_mod_flats = temp_dir + (',' + temp_dir).join(v_mod_norm) + ''
for j, x in enumerate(v_mod):
iraf.apflatten(cal_dir + 'master_flat.fits',
output=temp_dir + v_mod_norm[j],
reference=temp_dir +
star_file.replace('.fits', '_boost.fits'))
iraf.reset(use_new_imt="no")
iraf.flpr("0")
#divide each science image by its flat
iraf.imarith(str_v_mod_out, '/', str_v_mod_flats, str_v_mod)
#stack all science images
iraf.imcombine(str_v_mod_out, temp_dir + star_file, combine="sum")
iraf.reset(use_new_imt="no")
iraf.flpr("0")
str_star = star_file.replace('.fits', '.ec.fits')