def wspectext():
if not os.path.exists(Path + '/test3/'):
os.mkdir(Path + '/test3/')
fs = glob(Path + '/test/' + '/sci*.fits')
for i, f in enumerate(fs):
iraf.cd(Path + '/test3/')
iraf.wspectext(f, ('sci' + str(i) + '.dat'))
def export_observation_to_txt(fits_path, txt_path):
print(' Exporting file')
for order in np.arange(1, 32, 1):
try:
iraf.wspectext(input=fits_path + '[*,' + str(order) + ',1]',
output=txt_path + '_{:.0f}.txt'.format(order),
header='no')
except Exception as e:
print(e)
pass
def export_spectrum_to_txt(WORK_DIR, suffix='_vh_norm', export_cal=False):
if export_cal:
objects = observations[WORK_DIR]['calibs']
else:
objects = observations[WORK_DIR]['objects']
for obj in objects:
print ' Exporting file:', obj+suffix
for order in np.arange(1, 31, 1):
try:
iraf.wspectext(input=obj+suffix+'.fits[*,'+str(order)+',1]', output=obj+suffix+'_order{:02.0f}.txt'.format(order), header='no')
except:
pass
def fits2ascii(input_files):
if '.list' in input_files:
input_files = [line.rstrip('\n') for line in open(input_files)]
elif len(input_files.split()) > 1:
pass
elif '.fits' in input_files:
input_files = [input_files]
for input_file in input_files:
try:
output_file = input_file.split('.fits')[0] + '.dat'
iraf.wspectext(input_file + '[*,1]', output_file, header=False)
print(input_file, ' > ', output_file)
except:
print(input_file, ' failed...')
justplot = True
if justplot == False:
print 'Extracting spectrum'
np.savetxt('.' + input_location + 'allspec', [spectrum + '[4]'],
fmt="%s")
## begin IRAFing
### Extract the 1D spectrum
iraf.scopy(spectrum + '[4]', '.' + input_location + 'allspeccal2')
iraf.wspectext('.' + input_location + 'allspeccal2',
'.' + input_location + '' + SN + '_' + date + '.txt',
header='no')
print 'Removing allspeccal2.fits'
os.remove('.' + input_location + 'allspeccal2.fits')
else:
print 'Skipping spectrum extraction'
### Correct the spectrum
corrections = glob.glob('./CorrectionFiles/*.txt')
clist = [float(os.path.basename(c)[:-4]) for c in corrections]
correction_index = np.argmin(abs(np.array(clist) - airmass))
corloc = './CorrectionFiles/' + str(clist[correction_index]) + '.txt'
print 'Correcting flux for airmass', airmass, 'using', str(
clist[correction_index]) + '.txt'
os.system("rm fluxcal_" + file_name)
os.system("mv temp.fits fluxcal_" + file_name)
### Move spectrum to reduced/
#os.system("rm " + file_path_reduced + "spec_" + file_name)
os.system("cp -f " + "fluxcal_" + file_name + " " + file_path_reduced)
########################
### Create .dat file ###
########################
### This file will later be read in for spectral matching
os.chdir(file_path_reduced)
os.system("rm fluxcal_" + file_name + ".dat")
iraf.wspectext("fluxcal_" + file_name+"[*,1,1]", "fluxcal_" + file_name + ".dat")
### rewrite it in the form #wavelength flux
spectrum = functions.read_ascii("fluxcal_" + file_name + ".dat")
spectrum = functions.read_table(spectrum)
temp = []
for i in spectrum:
if len(i) == 2:
if functions.is_number(i[0]):
temp.append(i)
spectrum = temp
spectrum = spectrum[1:len(spectrum)-2]
output_spectrum = open("fluxcal_" + file_name + ".dat","w")
functions.write_table(spectrum,output_spectrum)
output_spectrum.close()
def wechelletxt(self, infile, outdir):
for i in range(1, self.norders + 1): #Do this for all 51 orders
inp = infile + "[*,{:d}]".format(i)
out = outdir + "/{:0>2d}.txt".format(i)
iraf.wspectext(input=inp, output=out)
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 gen_ar():
#cargo resultado de f2c
temps, pasos, intensidad = f2c(res_tem="S", step_tem=100)
x = pasos
y = temps
z = intensidad
print(x[z.index(max(z))], y[z.index(max(z))])
os.chdir("../")
# obtengo parametros para para fxcor
obfx = "aB" + str(x[z.index(max(z))])
temp = str(int(y[z.index(max(z))]))
if len(temp) == 4:
temp = "0" + temp
iraf.imdel("ss")
iraf.imdel("tem")
iraf.sarit("T" + temp, "-", 1, "ss")
iraf.sarit("ss", "*", "amortigua", "tem")
fig = plt.figure(figsize=(12, 9.0))
fig.subplots_adjust(hspace=0.4,
bottom=0.06,
top=0.94,
left=0.12,
right=0.94)
#ejecuto y ploteo fxcor
ax2 = plt.subplot2grid((4, 1), (0, 0)) # grafico la func, de correlacion
program_dir = os.getcwd() + "/"
ccf_command = program_dir + "fxcor_cursor_command.txt" #comando para extraeer los datos del grafico
interact = 1
iraf.imdel("new_ccf.txt")
iraf.fxcor(object=obfx,
templates="tem",
cursor=ccf_command,
ccftype="text",
interactive=interact) #fxcor de irad
data = asciitable.read("new_ccf.txt") #archivo de salida del fxcor
ax2.set_xlim(-2000, 2000)
plt.axhline(linewidth=2, color='r')
plt.plot(data["col1"], data["col2"], "-", color="black")
plt.axvspan(-100, 100, facecolor='grey', alpha=0.5)
#plt.axhspan(0.9, 1.00, facecolor='grey', alpha=0.5)
ax2.yaxis.set_major_locator(plt.MaxNLocator(5))
ax2.set_xlabel(r'Velocity', fontsize=18)
ax2.set_ylabel(r'Correlation', fontsize=18)
#datos para scale
iraf.imdel("scale")
iraf.imdel("tplsca")
obscale = "B" + str(x[z.index(max(z))])
temp = "T" + temp
w1 = 5900
w2 = 6490
wi = 300
iraf.imdel("scale.txt")
iraf.imdel("tplsca.txt")
iraf.scale(spec=obscale, tpl=temp, w1=w1, w2=w2, width=wi)
iraf.wspectext("scale.fits", "scale.txt", header="no")
iraf.wspectext("tplsca.fits", "tplsca.txt", header="no")
iraf.wspectext(obscale, "obscale.txt", header="no")
#ploteo resultados de f2c
ax1 = plt.subplot2grid((4, 1), (1, 0), rowspan=3)
# define grid.
xi = np.linspace(min(x), max(x), 1000)
yi = np.linspace(min(y), max(y), 1000)
# grid the data.
zi = griddata((x, y), z, (xi[None, :], yi[:, None]), method='linear')
# contour the gridded data, plotting dots at the randomly spaced data points.
CS = plt.contour(xi, yi, zi, 15, linewidths=0.5, colors='k')
CS = plt.contourf(xi, yi, zi, 15, cmap=plt.cm.jet)
cb = plt.colorbar(orientation='horizontal', pad=0.1,
aspect=30) # draw colorbar
cb.set_label('Intensity', fontsize=18)
# plot data points.
plt.scatter(x, y, marker='o', c='b', s=5)
plt.scatter(x[z.index(max(z))],
y[z.index(max(z))],
marker='o',
color="w",
s=20)
plt.xlabel('Mass ratio')
plt.ylabel('Secondary Temperature (K)')
ax1.set_xlabel(r'Mass ratio', fontsize=18)
ax1.set_ylabel(r'Secondary Temperature (K)', fontsize=18)
ax1.set_xlim(min(pasos), max(pasos))
ax1.set_ylim(min(temps), max(temps))
ax1.yaxis.set_major_locator(plt.MaxNLocator(6))
ax1.xaxis.set_major_locator(plt.MaxNLocator(8))
ax2.text(0.03,
0.96,
"T=%s K" % y[z.index(max(z))],
ha='left',
va='top',
transform=ax2.transAxes,
fontsize=20)
ax2.text(0.04,
0.5,
"q=%s" % x[z.index(max(z))],
ha='left',
va='top',
transform=ax2.transAxes,
fontsize=20)
#plot scale
fig = plt.figure(figsize=(12, 9.0))
fig.subplots_adjust(hspace=0.4,
bottom=0.06,
top=0.94,
left=0.12,
right=0.94)
ax2 = plt.subplot2grid((4, 1), (0, 0))
data = asciitable.read("tplsca.txt") #archivo de salida del scale
#ax2.set_xlim(min(data["col1"]),max(data["col1"]))
plt.plot(data["col1"], data["col2"], "-", color="red")
data = asciitable.read("obscale.txt")
plt.plot(data["col1"], data["col2"], "-", color="black")
ax2.yaxis.set_major_locator(plt.MaxNLocator(5))
ax2.set_xlabel(r'Wavelength ($\AA$)', fontsize=18)
ax2.set_ylabel(r'Intensity', fontsize=18)
data = asciitable.read("scale.txt") #archivo de salida del scale
ax2.set_xlim(min(data["col1"]), max(data["col1"]) - 1)
plt.plot(data["col1"], data["col2"] * 100, "-", color="black")
ax2.yaxis.set_major_locator(plt.MaxNLocator(5))
ax2.set_xlabel(r'Wavelength ($\AA$)', fontsize=18)
ax2.set_ylabel(r'$I_2/I_T$ (%)', fontsize=18)
#plt.savefig('mass_temp.pdf',format="pdf", bbox_inches='tight',dpi = 300)
plt.show()
iraf.deredden(
input = "fluxcal_" + file_name,\
output = redden_name,\
value = redden,\
R = 3.1,\
type = "E(B-V)",\
apertures = "*",\
override = 1,\
uncorrect = 0,\
mode = "al")
### Create .dat file out of fits file redden_name
os.system("rm " + redden_name + ".dat")
iraf.wspectext(redden_name + "[*,1,1]", redden_name + ".dat")
spectrum = functions.read_ascii(redden_name + ".dat")
spectrum = functions.read_table(spectrum)
temp = []
for i in spectrum:
if len(i) == 2:
if functions.is_number(i[0]):
temp.append(i)
spectrum = temp
spectrum = spectrum[1:len(spectrum)-2]
output_spectrum = open(redden_name + ".dat","w")
functions.write_table(spectrum,output_spectrum)
output_spectrum.close()
def wechelletxt(self, infile, outdir):
for i in range(1, self.norders + 1): #Do this for all 51 orders
inp = infile + "[*,{:d}]".format(i)
out = outdir + "/{:0>2d}.txt".format(i)
iraf.wspectext(input=inp, output=out)
def get_RV(WORK_DIR, multi_ref=False, multi_sample=False):
# list RV ref files
if multi_ref:
rv_ref_list = glob.glob('../../*_Echelle_RVS.fits')
rv_ref_list = [rf.split('/')[-1].split('.')[0] for rf in rv_ref_list]
else:
rv_ref_list = ['solar']
# rv_ref_list = ['solar_spectra_conv']
# print ' RV ref files list:', rv_ref_list
for obj in observations[WORK_DIR]['objects']:
for rv_ref in rv_ref_list:
ts = time.time()
rvs = []
e_rvs = []
# determine barycentric RV
with open('RVS_'+obj+'_'+rv_ref+'.txt', 'w+') as file:
for order in np.arange(2, 31, 1):
try:
iraf.wspectext(input=obj+'_vh_norm.fits[*,'+str(order)+',1]', output='template.txt', header='no')
template = np.loadtxt('template.txt')
wav = template[:, 0]
mid1 = int(len(wav)/4.0)
mid2 = int(len(wav)/12.0)
wav1 = wav[mid1*1]
wav2 = wav[mid1*3]
#print 'WAV:', wav, mid1, wav1,wav2
# create multiple wav1, wav2 ranges if requested
if multi_sample:
wav_ranges = [[wav[mid2*2], wav[mid2*7]], [wav[mid2*4], wav[mid2*8]], [wav[mid2*5], wav[mid2*10]]]
else:
wav_ranges = [[wav1, wav2]]
for wav_r in wav_ranges:
iraf.unlearn('fxcor')
try:
os.remove('test.txt')
os.remove('test.log')
os.remove('test.gki')
except: pass
try:
os.remove('template.txt')
except: pass
iraf.fxcor(obj+'.ec.vh', '../../'+rv_ref, apertures=order, pixcorr='no', continuum='both',
osample='a %s %s' % (wav_r[0], wav_r[1]), rsample='a %s %s' % (wav_r[0], wav_r[1]),
interactive='no', output='test', function = 'gaussian',
apodize = 0.0, rebin = "smallest")
fields = np.loadtxt('test.txt', comments='#', dtype='str')
# print wav_r
# print fields
vrel, verr = fields[-3], fields[-1]
# vrel, verr = fields[-3], 0.
if vrel == 'INDEF' or np.float(verr) > 10. or np.abs(np.float(vrel)) > 350.:
vrel = np.nan
verr = np.nan
print order, vrel, verr
file.write("%s %s %s\n" % (order, vrel, verr))
rvs.append(float(vrel))
e_rvs.append(float(verr))
except:
print_exception()
# export all values in a list
# file.write('\n')
# file.write("'rv_echelle':[" + ','.join([str(v) for v in rvs]) + "]\n")
# file.write("'e_rv_echelle':[" + ','.join([str(v) for v in e_rvs]) + "]\n")
print '%s: RV median: %s, RV mean: %s, RV sigma: %s' % (obj, np.nanmedian(rvs), np.nanmean(rvs), np.nanstd(rvs))
print 'RV fit time: '+str((time.time()-ts)/60.)+' min'
print ''
def iterate():
os.system("rm temp_norm.dat")
iraf.wspectext(
input = "temp_norm.fits",\
output = "temp_norm.dat",\
header = 0)
data_spec = transpose(loadtxt("temp_norm.dat"))
wave = arange(min(data_spec[0]),max(data_spec[0]),1)
data_flux = interpolate.splrep(data_spec[0],data_spec[1])
data_flux = interpolate.splev(wave,data_flux)
telluric_spec = transpose(loadtxt("telluric.dat"))
telluric_flux = interpolate.splrep(telluric_spec[0],telluric_spec[1])
telluric_flux = interpolate.splev(wave,telluric_flux)
### Loop through telluric scaling
telluric_scaling = []
rms_list = []
maxscale = 1.08
for scale in arange(0.0,maxscale,0.01):
scatter_list = []
for region in telluric_region_list:
temp_flux = []
temp_tel = []
for i in range(len(wave)):
if wave[i] > region[0] and wave[i] < region[1]:
temp_flux.append(data_flux[i])
temp_tel.append(telluric_flux[i])
if wave[i] > region[1]:
break
temp_flux = array(temp_flux)
temp_tel = array(temp_tel)
scatter = temp_flux / (temp_tel * scale + (1-scale))
# plt.plot(scatter)
# plt.show()
scatter_list.append(std(scatter))
telluric_scaling.append(scale)
rms_list.append(average(scatter_list))
telluric_scaling,rms_list = array(telluric_scaling),array(rms_list)
best_scale = find_min(telluric_scaling,rms_list)
if best_scale > maxscale:
best_scale = maxscale
if best_scale < 0:
best_scale = 0
print "scaling telluric by",best_scale
# plt.plot(telluric_scaling,rms_list)
# plt.show()
os.system("cp telluric.fits telluric_temp.fits")
iraf.sarith(
input1 = "telluric_temp.fits",\
op = "*",\
input2 = best_scale,\
output = "telluric_temp.fits",\
w1 = "INDEF",\
w2 = "INDEF",\
apertures = "",\
bands = "",\
beams = "",\
apmodulus = 0,\
reverse = 0,\
ignoreaps = 1,\
format = "multispec",\
renumber = 0,\
offset = 0,\
clobber = 1,\
merge = 0,\
rebin = 0,\
verbose = 0)
iraf.sarith(
input1 = "telluric_temp.fits",\
op = "+",\
input2 = 1-best_scale,\
output = "telluric_temp.fits",\
w1 = "INDEF",\
w2 = "INDEF",\
apertures = "",\
bands = "",\
beams = "",\
apmodulus = 0,\
reverse = 0,\
ignoreaps = 1,\
format = "multispec",\
renumber = 0,\
offset = 0,\
clobber = 1,\
merge = 0,\
rebin = 0,\
verbose = 0)
iraf.sarith(
input1 = "temp_spec.fits",\
op = "/",\
input2 = "telluric_temp.fits",\
output = "temp_spec",\
w1 = "INDEF",\
w2 = "INDEF",\
apertures = "",\
bands = "",\
beams = "",\
apmodulus = 0,\
reverse = 0,\
ignoreaps = 1,\
format = "multispec",\
renumber = 0,\
offset = 0,\
clobber = 1,\
merge = 0,\
rebin = 0,\
verbose = 0)
iraf.sarith(
input1 = "temp_norm.fits",\
op = "/",\
input2 = "telluric_temp.fits",\
output = "temp_norm",\
w1 = "INDEF",\
w2 = "INDEF",\
apertures = "",\
bands = "",\
beams = "",\
apmodulus = 0,\
reverse = 0,\
ignoreaps = 1,\
format = "multispec",\
renumber = 0,\
offset = 0,\
clobber = 1,\
merge = 0,\
rebin = 0,\
verbose = 0)
return best_scale
sens=sensfile,
extinct='no',
ignoreaps='yes'
) ## If extinct='yes', try extinction ='onedstds$ctioextinct.dat'
print('\n' +
'Extracting the 1D spectra from the calibrated multispec file...')
iraf.scopy('allspeccal[0]',
'allspeccal2',
format='onedspec',
w1=4000,
w2=8000,
rebin='no')
print('\n' + '1D spectrum output...')
iraf.wspectext('allspeccal2.0001', obj + '_' + date + '.txt', header='no')
print('\n' + 'Tidying up...')
a = glob.glob('./*')
for f in a:
if 'allspec' in f:
os.remove(f)
if apply_flux_correction != 'n':
s = np.loadtxt('./ManualExtractionFluxCorrections.txt',
unpack=True,
usecols=(0, 1))
file_to_use = obj + '_' + date + '.txt'
print('\n' + 'Using ' + file_to_use + ' as reference...')
low_reject = 1.5,\
high_reject = 4.0,\
niterate = 10,\
markrej = 1,\
graphics = "stdgraph",\
cursor = "",\
ask = "no",\
mode = "ql")
#######################
### Convert to .dat ###
#######################
os.system("rm norm_" + file_name + ".dat")
iraf.wspectext("norm_" + file_name +"[*,1,1]", "norm_" + file_name + ".dat")
spectrum = functions.read_ascii("norm_" + file_name + ".dat")
spectrum = functions.read_table(spectrum)
temp = []
for i in spectrum:
if len(i) == 2:
if functions.is_number(i[0]):
temp.append(i)
spectrum = temp
spectrum = spectrum[1:len(spectrum)-2]
output_spectrum = open("norm_" + file_name + ".dat","w")
functions.write_table(spectrum,output_spectrum)
output_spectrum.close()
def salt_scombine(objlist):
obj = np.loadtxt(objlist, dtype="str", unpack=True, ndmin=1)
objlist1d = objlist + '1d'
objj = os.path.splitext(obj[0])[0]
if os.path.isfile(objj + 'wsf.fits'):
os.system('sed s/.fits/wsf1d.fits/ ' + objlist + ' > ' + objlist1d)
objnofits = string.split(obj[0], sep='.')[0] + 'wsf'
os.system('sed s/.fits/wsf1d_var.fits/ ' + objlist + ' > templist')
else:
os.system('sed s/.fits/ws1d.fits/ ' + objlist + ' > ' + objlist1d)
objnofits = string.split(obj[0], sep='.')[0] + 'ws'
os.system('sed s/.fits/ws1d_var.fits/ ' + objlist + ' > templist')
low_ap = float(
rt.getsh("grep -i 'low\t' database/ap" + objnofits +
" | awk '{print $3}'"))
high_ap = float(
rt.getsh("grep -i 'high\t' database/ap" + objnofits +
" | awk '{print $3}'"))
apradius = abs(low_ap) + abs(high_ap)
readnoise = 2.45 * np.sqrt(apradius)
print "ggg = %s" % readnoise
iraf.scombine.combine = 'average'
iraf.scombine.rdnoise = readnoise
iraf.scombine.reject = 'ccdclip'
iraf.scombine.scale = 'median'
outnam = ''.join(rt.header(obj[0], 'OBJECT').split()).lower()
outname = outnam + '_' + objlist
if os.path.isfile(outname + '.fits'):
os.system('rm ' + outname + '.fits')
inname = str('@' + objlist1d)
iraf.scombine(input=inname, output=outname, first='yes')
tempfile = 'temp.fits'
if os.path.isfile(tempfile):
os.system('rm ' + tempfile)
iraf.imsum('@templist', tempfile)
nspec = len(obj)
outname_var = outname + '_var'
if os.path.isfile(outname_var + '.fits'):
os.system('rm ' + outname_var + '.fits')
nspec = nspec * nspec
iraf.imarith(tempfile, '/', nspec, outname_var)
os.system('rm *temp.fits')
os.system('rm templist')
iraf.dispcor(outname_var, output='', table=outname)
outnametxt = outname + '.txt'
outname_vartxt = outname_var + '.txt'
iraf.wspectext(outname, outnametxt, header='no')
iraf.wspectext(outname_var, outname_vartxt, header='no')
rssdate = rt.header(obj[0], 'DATE-OBS')
rssdate = string.replace(rssdate, '-', '')
pastefinal = outname + '_final_' + rssdate + '.txt'
os.system('paste ' + outnametxt + ' ' + outname_vartxt + ' > ' +
pastefinal)
for ec in objects:
source_fits1 = source_dir + ec + '.ec.fits'
source_fits2 = source_dir + ec + '.ec.vh.fits'
date_f = obs_key.split('_')[-1]
if not os.path.isfile(source_fits1):
print(ec + ' - no data (' + date_f + ')')
continue
if not os.path.isfile(source_fits2):
print(ec + ' - no vh (' + date_f + ')')
continue
os.system('rm temp.txt')
iraf.wspectext(input=source_fits2 + '[*,15,1]',
output='temp.txt',
header='no')
s = np.loadtxt('temp.txt')
wvl = s[:, 0]
if wvl[1] - wvl[0] == 1:
print(ec + ' - no wvl (' + date_f + ')')
raise SystemExit
obs_metadata = Table.read(data_dir + 'star_data_all.csv')
obs_metadata = obs_metadata[obs_metadata['odkdaj'] == 'nova']
_go_to_dir('Binaries_spectra')
copyfile(data_dir + 'star_data_all.csv', 'star_data_all.csv')
for star in [
finalcomb.append(file)
print('.')
print('.')
print('Combining frames...')
print('.')
print('.')
file1 = open('listascombine', 'w')
file1.writelines(["%s\n" % item for item in finalcomb])
file1.close()
#
iraf.scombine('@listascombine', 'temp_quick.fits')
iraf.scopy('temp_quick.fits', target + '_quick.fits', w1='3800', w2='9000')
iraf.wspectext(target + '_quick.fits', target + '_spec.txt', header='no')
iraf.wspectext(target + '_quick.fits', target + '_spec.csv', header='no')
#mv final file in a dedicated folder
shutil.copy(target + '_quick.fits', './output/.')
shutil.copy(target + '_spec.txt', './output/.')
shutil.copy(target + '_spec.csv', './output/.')
#
#remove temp FILES
os.remove('lapalmaextinct.dat')
os.remove(target + '_spec.txt')
os.remove(target + '_spec.csv')
for file in os.listdir(os.getcwd()):
if file.endswith('fits'):
os.remove(file)
elif file.startswith('lista'):
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()
def remove_molecule(molecule,spec_name,outspec_name):
telluric_region = eval("TELLURIC_REGION_"+molecule)
telluric_region_list = string.split(telluric_region,",")
for n in range(len(telluric_region_list)):
region = telluric_region_list[n][1:]
region = string.split(region,"-")
temp = []
for i in region:
temp.append(float(i))
telluric_region_list[n] = temp
### Copy telluric line spectra to working directory
telluric_fits = grating + "_"+molecule+".fits"
os.system("cp -f "+program_dir+"telluric_fits/" + telluric_fits + " telluric.fits")
os.system("rm temp_norm*")
os.system("rm temp_spec*")
os.system("cp -f "+spec_name+" temp_spec.fits")
iraf.continuum(
input = spec_name,\
output = "temp_norm.fits",\
lines = "*",\
bands = "*",\
type = "ratio",\
replace = 0,\
wavescale = 1,\
logscale = 0,\
override = 1,\
listonly = 0,\
logfiles = "logfile",\
interactive = 0,\
sample = "*",\
naverage = 1,\
function = "spline3",\
order = 20,\
low_reject = 2.0,\
high_reject = 5.0,\
niterate = 5,\
grow = 2.0,\
ask = "yes")
##################
### Find shift ###
##################
shift = run_fxcor("telluric.fits","temp_norm.fits",telluric_region)
os.system("rm telluric.dat")
iraf.dopcor(
input = "telluric.fits",\
output = "telluric.fits",\
redshift = shift,\
isvelocity = 1,\
add = 1,\
dispersion = 1,\
flux = 0,\
verbose = 0)
iraf.wspectext(
input = "telluric.fits",\
output = "telluric.dat",\
header = 0)
######################################
### Iterative telluric subtraction ###
######################################
def iterate():
os.system("rm temp_norm.dat")
iraf.wspectext(
input = "temp_norm.fits",\
output = "temp_norm.dat",\
header = 0)
data_spec = transpose(loadtxt("temp_norm.dat"))
wave = arange(min(data_spec[0]),max(data_spec[0]),1)
data_flux = interpolate.splrep(data_spec[0],data_spec[1])
data_flux = interpolate.splev(wave,data_flux)
telluric_spec = transpose(loadtxt("telluric.dat"))
telluric_flux = interpolate.splrep(telluric_spec[0],telluric_spec[1])
telluric_flux = interpolate.splev(wave,telluric_flux)
### Loop through telluric scaling
telluric_scaling = []
rms_list = []
maxscale = 1.08
for scale in arange(0.0,maxscale,0.01):
scatter_list = []
for region in telluric_region_list:
temp_flux = []
temp_tel = []
for i in range(len(wave)):
if wave[i] > region[0] and wave[i] < region[1]:
temp_flux.append(data_flux[i])
temp_tel.append(telluric_flux[i])
if wave[i] > region[1]:
break
temp_flux = array(temp_flux)
temp_tel = array(temp_tel)
scatter = temp_flux / (temp_tel * scale + (1-scale))
# plt.plot(scatter)
# plt.show()
scatter_list.append(std(scatter))
telluric_scaling.append(scale)
rms_list.append(average(scatter_list))
telluric_scaling,rms_list = array(telluric_scaling),array(rms_list)
best_scale = find_min(telluric_scaling,rms_list)
if best_scale > maxscale:
best_scale = maxscale
if best_scale < 0:
best_scale = 0
print "scaling telluric by",best_scale
# plt.plot(telluric_scaling,rms_list)
# plt.show()
os.system("cp telluric.fits telluric_temp.fits")
iraf.sarith(
input1 = "telluric_temp.fits",\
op = "*",\
input2 = best_scale,\
output = "telluric_temp.fits",\
w1 = "INDEF",\
w2 = "INDEF",\
apertures = "",\
bands = "",\
beams = "",\
apmodulus = 0,\
reverse = 0,\
ignoreaps = 1,\
format = "multispec",\
renumber = 0,\
offset = 0,\
clobber = 1,\
merge = 0,\
rebin = 0,\
verbose = 0)
iraf.sarith(
input1 = "telluric_temp.fits",\
op = "+",\
input2 = 1-best_scale,\
output = "telluric_temp.fits",\
w1 = "INDEF",\
w2 = "INDEF",\
apertures = "",\
bands = "",\
beams = "",\
apmodulus = 0,\
reverse = 0,\
ignoreaps = 1,\
format = "multispec",\
renumber = 0,\
offset = 0,\
clobber = 1,\
merge = 0,\
rebin = 0,\
verbose = 0)
iraf.sarith(
input1 = "temp_spec.fits",\
op = "/",\
input2 = "telluric_temp.fits",\
output = "temp_spec",\
w1 = "INDEF",\
w2 = "INDEF",\
apertures = "",\
bands = "",\
beams = "",\
apmodulus = 0,\
reverse = 0,\
ignoreaps = 1,\
format = "multispec",\
renumber = 0,\
offset = 0,\
clobber = 1,\
merge = 0,\
rebin = 0,\
verbose = 0)
iraf.sarith(
input1 = "temp_norm.fits",\
op = "/",\
input2 = "telluric_temp.fits",\
output = "temp_norm",\
w1 = "INDEF",\
w2 = "INDEF",\
apertures = "",\
bands = "",\
beams = "",\
apmodulus = 0,\
reverse = 0,\
ignoreaps = 1,\
format = "multispec",\
renumber = 0,\
offset = 0,\
clobber = 1,\
merge = 0,\
rebin = 0,\
verbose = 0)
return best_scale
best_scaling = 1.0
while best_scaling > 0.05:
best_scaling = iterate()
os.system("cp temp_spec.fits "+outspec_name)
