p_ref = dct['p1']
mjds_thar,shifts = [],[]
print('\n\tDetermination of instrumental drift during the night...')
temps = []
for i in range(len(ThAr_ref_dates)):
wavsol_pkl = dirout + ThAr_ref[i].split('/')[-1][:-4]+'wavsolpars.pkl'
hthar = pyfits.open( ThAr_ref[i] )
npix = hthar[0].data.shape[1]
temps.append(float(hthar[0].header['HIERARCH CAHA GEN AMBI PRES']))
mjd, mjd0 = cafeutils.mjd_fromheader2( hthar )
mjds_thar.append(mjd)
dct = pickle.load(open(wavsol_pkl,'r'))
p_shift, pix_centers, orders, wavelengths, I, rms_ms, residuals = \
GLOBALutils.Global_Wav_Solution_vel_shift(dct['All_Pixel_Centers'], dct['All_Wavelengths'], dct['All_Orders'],\
np.ones(len(dct['All_Orders'])), p_ref, order0=ro0, npix=npix,\
Cheby=use_cheby, ntotal=n_useful, maxrms=200, Inv=Inverse_m, minlines=500, nx=ncoef_x,nm=ncoef_m)
shifts.append(p_shift[0])
mjds_thar,shifts,temps = np.array(mjds_thar),np.array(shifts),np.array(temps)-temps[0]
shv = (1e-6*shifts)*299792458.0
if len(mjds_thar) > 1:
tck_v = scipy.interpolate.splrep(mjds_thar,shv,k=1)
tck_shift = scipy.interpolate.splrep(mjds_thar,shifts,k=1)
temp = np.arange(mjds_thar[0],mjds_thar[-1],0.0001)
mjdsh_thar = (mjds_thar - int(temp.min())) * 24.
thour = (temp - int(temp.min())) * 24.
thours = (new_times - int(temp.min())) * 24.
pp = PdfPages(dirout + 'proc/ThAr_shifts.pdf')
plot(mjdsh_thar,shv,'ro')
for thar in ThAr_ref:
h = pyfits.open(thar)
mjd, mjd0 = pucherosutils.mjd_fromheader(h)
hdth = pyfits.getheader(thar)
thtimes.append(mjd)
wavsol_pkl = dirout + 'ThAr' + hdth['DATE-OBS'][:10] + '_' + hdth[
'DATE-OBS'][11:13] + '-' + hdth['DATE-OBS'][14:16] + '-' + hdth[
'DATE-OBS'][17:] + '.wavsolpars.pkl'
wavsol = pickle.load(open(wavsol_pkl, 'r'))
G_pix = wavsol['G_pix']
G_wav = wavsol['G_wav']
G_ord = wavsol['G_ord']
oro0 = wavsol['oro0']
p_shift, pix_centers, orders, wavelengths, I, rms_ms, residuals = \
GLOBALutils.Global_Wav_Solution_vel_shift(G_pix, G_wav, G_ord,\
np.ones(G_wav.shape), p1_ref, Cheby=True, Inv=True, \
maxrms=150,minlines=250,order0=oro0,ntotal=nup,\
npix=h[0].data.shape[0],nx=ncoef_x,nm=ncoef_m)
thshifts.append(p_shift)
f.write(thar + '\t' + str(thtime) + '\t' +
str((1e-6 * p_shift) * 299792.458) + '\n')
#print p_shift
thshifts, thtimes = np.array(thshifts), np.array(thtimes)
Is = np.argsort(thtimes)
thtimes, thshifts = thtimes[Is], thshifts[Is]
if len(thtimes) > 3:
thshifts = (1e-6 * thshifts) * 299792.458
thtck = scipy.interpolate.splrep(thtimes, thshifts, k=3, s=0)
ejeje = np.arange(thtimes[0], thtimes[-1], 0.0001)
ejeyy = scipy.interpolate.splev(ejeje, thtck, der=0)
#if not avoid_plot:
print "\n\tDetermination of Instrumental drift along the night:"
for i in range(len(good_arcs)):
hthar = pyfits.open(good_arcs[i])
hour = mikeutils.get_hour(float(hthar[ih].header['UT-TIME']))
thar_fits_simple = dirout + 'MIKE_' + hthar[ih].header[
'UT-DATE'] + '_' + hour + '.ThAr.spec.simple.fits'
length = pyfits.getdata(thar_fits_simple).shape[1]
hour = mikeutils.get_hour(float(hthar[ih].header['UT-TIME']))
wavsol_pkl = dirout + 'MIKE_' + hthar[ih].header[
'UT-DATE'] + '_' + hour + '.wavsolpars.pkl'
pdict = pickle.load(open(wavsol_pkl, 'r'))
p1, sh_centers, sh_orders, sh_wavelengths, sh_I, sh_rms_ms, sh_residuals = \
GLOBALutils.Global_Wav_Solution_vel_shift(pdict['G_pix'],\
pdict['G_wav'], pdict['G_ord'],\
np.ones(len(pdict['G_ord'])), pref,\
minlines=250, maxrms=150,order0=37, ntotal=n_useful,\
Cheby=use_cheby, Inv=Inverse_m, npix=length,nx=ncoef_x,nm=ncoef_m)
pshs.append(p1)
arc_mjds.append(pdict['mjd'])
pshs, arc_mjds = np.array(pshs), np.array(arc_mjds)
tck_sh = scipy.interpolate.splrep(arc_mjds, pshs, k=1)
### start of science frame reductions ###
print '\n\tThe following targets will be processed:'
new_list = []
new_list_obnames = []
new_list_texp = []
for i in range(len(science)):
fsim = science[i]
obname = obnames[i]
p0[0] = (25 + 49) * Global_ZP
p1_co, G_pix_co, G_ord_co, G_wav_co, II_co, rms_ms_co, G_res_co = \
GLOBALutils.Fit_Global_Wav_Solution(All_Pixel_Centers_co, All_Wavelengths_co, All_Orders_co,\
np.ones(All_Intensities_co.shape), p0, Cheby=True,\
maxrms=MRMS, Inv=Inverse_m,minlines=400,order0=49, \
ntotal=nord_co,npix=len(thar_order),nx=ncoef_x,nm=ncoef_m)
#shifts = np.array(shifts)
#shifts = FEROSutils.sigma_clip(shifts)
#the_sh = np.around(shifts.mean(),1)
#error_sh = np.around(np.sqrt(np.var(shifts)/float(len(shifts)-1)),1)
#print 'Shifts (per order):', the_sh, '+-', error_sh
p_shift, pix_centers, orders, wavelengths, I, rms_ms, residuals = \
GLOBALutils.Global_Wav_Solution_vel_shift(All_Pixel_Centers_co, All_Wavelengths_co, All_Orders_co,\
np.ones(All_Intensities_co.shape), wsol_dict['p1_co'],\
Cheby=True,Inv=True,maxrms=MRMS,minlines=minlines_glob_co,\
order0=49,ntotal=nord_co,npix=len(thar_order),nx=ncoef_x,nm=ncoef_m)
fout = 'proc/' + fsim.split('/')[-1][:-4] + 'sp.fits'
spec = np.zeros((11, nord_ob, data.shape[0]))
equis = np.arange(data.shape[0])
for order in range(nord_ob):
m = order + 49
chebs = GLOBALutils.Calculate_chebs(equis,
m,
Inverse=Inverse_m,
order0=49,
ntotal=nord_ob,
npix=data.shape[1],
nx=ncoef_x,
nm=ncoef_m)