im = np.argmin(np.absolute(np.array(ThAr_ref_dates) - mjd))
pkl_wsol = dirout + ThAr_ref[im].split(
'/')[-1][:-4] + 'spec.wavsolpars.pkl'
print "\t\tUnpickling wavelength solution from", pkl_wsol, " ..."
wsol_dict = pickle.load(open(pkl_wsol, 'r'))
# Apply new wavelength solution including barycentric correction
equis = np.arange(data.shape[0])
for order in range(nord1):
#order = 16
#print order
m = order + ro0
chebs = GLOBALutils.Calculate_chebs(equis, m, npix=data.shape[0],\
order0=ro0, ntotal=nord1, Inverse=Inverse_m,nx=ncoef_x,nm=ncoef_m)
WavSol = lbary_ltopo * (
1.0 / m) * GLOBALutils.Joint_Polynomial_Cheby(
wsol_dict['p1'], chebs, ncoef_x, ncoef_m)
spec[0, order, :] = GLOBALutils.ToVacuum(WavSol)
spec[1, order, :] = sci_S['chip1'][order, 1, :]
#spec[1,order,:300] = 0.
spec[2, order, :] = sci_S['chip1'][order, 2, :]
spec[3, order, :] = spec[1, order, :] / sflat['chip1'][order][::-1]
spec[4,
order, :] = spec[2, order, :] * sflat['chip1'][order][::-1]**2
ccoef = GLOBALutils.get_cont_single(spec[0, order],
spec[3, order],
spec[4, order],
ll=1.5,
lu=5,
nc=3)
ratio = np.polyval(ccoef, spec[0, order])
L = np.where(spec[1, order, :] != 0)
print('\t\tWavelength calibration:')
print("\t\t\tInstrumental drift:",(1e-6*p_shift)*299792458.0)
# Apply new wavelength solution including barycentric correction
equis = np.arange( data.shape[1] )
if len(mjds_thar) > 1:
p_shift = scipy.interpolate.splev(mjd,tck_shift)
else:
p_shift = 0.
order = ro0
ind = 0
while order < ro0 + n_useful:
oss = order - difo
m = order
chebs = GLOBALutils.Calculate_chebs(equis, m, npix=data.shape[1], order0=ro0, ntotal=n_useful, Inverse=Inverse_m,nx=ncoef_x,nm=ncoef_m)
WavSol = GLOBALutils.ToVacuum( lbary_ltopo * (1.0 + 1.0e-6*p_shift) * (1.0/float(m)) * \
GLOBALutils.Joint_Polynomial_Cheby(p_ref,chebs,ncoef_x,ncoef_m) )
spec[0,ind,:] = WavSol
spec[1,ind,:] = sci_S[oss,1]
spec[2,ind,:] = sci_S[oss,2]
fn = S_flat[oss,1,:]
L = np.where( fn == 0 )[0]
spec[3,ind,:] = spec[1,ind,:] / S_flat[oss,1,:]
spec[4,ind,:] = spec[2,ind] * ( S_flat_n[oss,1,:] ** 2 )
spec[3,ind,L] = 0.
spec[4,ind,L] = 0.
ind+=1
order+=1
ccoefs = GLOBALutils.get_cont(spec[0,:,:],spec[3,:,:])
order = ro0
ind = 0
# Apply new wavelength solution including barycentric correction
equis = np.arange(data.shape[1])
del_vels = []
for order in range(n_useful):
m = order + 37
chebs = GLOBALutils.Calculate_chebs(equis,
m,
npix=data.shape[1],
order0=37,
ntotal=n_useful,
Inverse=Inverse_m,
nx=ncoef_x,
nm=ncoef_m)
WavSol = GLOBALutils.ToVacuum(
((1. + 1.0e-6 * psh) / float(m)) *
GLOBALutils.Joint_Polynomial_Cheby(pref, chebs, ncoef_x,
ncoef_m) * lbary_ltopo)
spec[0, order, :] = WavSol
spec[1, order, :] = sci_S[order, 1, :]
if len(np.where(np.isnan(spec[1, order, :]))[0]) > 0:
spec[1, order, :] = sci_Ss[order, :]
spec[2, order, :] = 1. / (spec[1, order, :] / gain +
(ronoise / gain)**2)
blaze = flat_S_n[order] #scipy.signal.medfilt(flat_S[order],21)
spec[3, order, :] = spec[1, order, :] / blaze
spec[4, order, :] = spec[2, order, :] * blaze**2
#cont_coef = continuum.NORM_single(spec[0,order,:],scipy.signal.medfilt(spec[3,order,:],7),orden=3)
cont_coef = GLOBALutils.get_cont_single(spec[0, order],
spec[3, order],
spec[4, order],
ll=1.5,
lu=5,
"""
if rms_ms / np.sqrt(float(len(G_wav))) < 10:
good_arcs.append(thars[index])
equis = np.arange(thar_S.shape[1])
for order in range(n_useful):
m = order + 37
chebs = GLOBALutils.Calculate_chebs(equis,
m,
npix=thar_S.shape[1],
order0=37,
ntotal=n_useful,
Inverse=Inverse_m,
nx=ncoef_x,
nm=ncoef_m)
WavSol = (1.0 / m) * GLOBALutils.Joint_Polynomial_Cheby(
p1, chebs, ncoef_x, ncoef_m)
thar_data[0, order, :] = WavSol
if (os.access(thar_out, os.F_OK)):
os.remove(thar_out)
hdu = pyfits.PrimaryHDU(thar_data)
hdu.writeto(thar_out)
pdict = {'p1':p1,'mjd':mjd, 'G_pix':G_pix, 'G_ord':G_ord, 'G_wav':G_wav, 'II':II, 'rms_ms':rms_ms,\
'G_res':G_res, 'All_Centroids':All_Centroids, 'All_Orders':All_Orders, 'All_Sigmas':All_Sigmas}
pickle.dump(pdict, open(wavsol_pkl, 'w'))
else:
print "\t\tUsing previously computed wavelength solution in file", wavsol_pkl
pdict = pickle.load(open(wavsol_pkl, 'r'))
if pdict['rms_ms'] / np.sqrt(float(len(pdict['G_wav']))) < 10:
np.ones(All_Intensities.shape), p0, Cheby=use_cheby,\
maxrms=50, Inv=Inverse_m,minlines=minlines_glob,order0=93, \
ntotal=n_useful,npix=len(thar_order),nx=ncoef_x,nm=ncoef_m)
equis = np.arange(thar_S.shape[2])
for order in range(n_useful):
m = order + 93
chebs = GLOBALutils.Calculate_chebs(equis,
m,
npix=thar_S.shape[2],
order0=93,
ntotal=n_useful,
Inverse=Inverse_m,
nx=ncoef_x,
nm=ncoef_m)
WavSol = (1.0 / m) * GLOBALutils.Joint_Polynomial_Cheby(
p1, chebs, ncoef_x, ncoef_m)
thar_data[0, order, :] = WavSol
if (os.access(thar_out, os.F_OK)):
os.remove(thar_out)
hdu = pyfits.PrimaryHDU(thar_data)
hdu = GLOBALutils.update_header(hdu, 'SLITNAME', hd['SLITNAME'])
hdu.writeto(thar_out)
#for orde in np.unique(G_ord):
# I = np.where(G_ord == orde)[0]
# plot(G_wav[I],G_res[I],'o')
#show()
pdict = {'p1':p1,'mjd':mjd, 'G_pix':G_pix, 'G_ord':G_ord, 'G_wav':G_wav, 'II':II, 'rms_ms':rms_ms,\
# get ThAr closest in time
im = np.argmin(np.absolute(np.array(ThAr_ref_dates) - mjd))
pkl_wsol = dirout + ThAr_ref[im].split('/')[-1][:-4]+'spec_'+str(int(chip))+ '.wavsolpars.pkl'
print "\t\tUnpickling wavelength solution from", pkl_wsol, " ..."
wsol_dict = pickle.load(open(pkl_wsol,'r'))
# Apply new wavelength solution including barycentric correction
equis = np.arange( data.shape[1] )
for order in range(nord):
#order = 16
#print order
m = order + ro0
chebs = GLOBALutils.Calculate_chebs(equis, m, npix=data.shape[1],\
order0=ro0, ntotal=nord, Inverse=Inverse_m,nx=ncoef_x,nm=ncoef_m)
WavSol = lbary_ltopo * (1.0/m) * GLOBALutils.Joint_Polynomial_Cheby(wsol_dict['p1'],chebs,ncoef_x,ncoef_m)
spec[0,order,:] = GLOBALutils.ToVacuum(WavSol)
spec[1,order,:] = sci_S[order,1, :]
#spec[1,order,-100:] = 0.
spec[2,order,:] = sci_S[order,2, :]
spec[3,order,:] = spec[1,order,:] / sflat[order]
spec[4,order,:] = spec[2,order,:] * sflat[order] ** 2
I = np.where(spec[1,order]!=0)[0]
cont_coef,x,y = hiresutils.get_cont(spec[0,order,I],scipy.signal.medfilt(spec[3,order,I],11),n=3)
#plot(spec[0,order],sflat[order])
#show()
#cv = np.vstack((x,y)).T
#x_i,y_i = HIRESutils.bspline(cv, n=1000, degree=3, periodic=False)
#plot(x_i,y_i)
equis = np.arange(np.shape(thar_wav_Ss)[2])
order = orwa
orre = 0
while orre < nup:
m = order + oro0
chebs = GLOBALutils.Calculate_chebs(equis,
m,
order0=oro0,
ntotal=nup,
npix=len(thar_order),
Inverse=Inverse_m,
nx=ncoef_x,
nm=ncoef_m)
thar_wav_Ss[0, orre, :] = GLOBALutils.ToVacuum(
(1.0 / m) * GLOBALutils.Joint_Polynomial_Cheby(
p1, chebs, ncoef_x, ncoef_m))[::-1]
thar_wav_Ss[1, orre, :] = thar_Ss[orre][::-1]
orre += 1
order += 1
if (os.access(thar_fits_simple_wav, os.F_OK)):
os.remove(thar_fits_simple_wav)
hdu = pyfits.PrimaryHDU(thar_wav_Ss)
hdu.writeto(thar_fits_simple_wav)
#if rms_ms/np.sqrt(NL)<50:
nThAr_ref.append(thar)
nthtimes.append(thtime)
pdict = {
'p1': p1,
orders_offset = pdict['orders_offset']
orderi = 0
if orders_offset < 0:
orderi = - orders_offset
orderf = nord - 1
if orderf + orders_offset >= n_useful:
orderf = n_useful - orders_offset - 1
final = np.zeros( [11, orderf - orderi + 1, np.shape(obj_S)[2]] )
equis = np.arange( obj_S.shape[2] )
for order in range(orderi,orderf+1):
m = order + oro0
chebs = GLOBALutils.Calculate_chebs(equis, m, order0=oro0, ntotal=n_useful, npix=obj_S.shape[2], Inverse=Inverse_m,nx=ncoef_x,nm=ncoef_m)
WavSol = lbary_ltopo*(1.0/m) * GLOBALutils.Joint_Polynomial_Cheby(global1,chebs,ncoef_x,ncoef_m)
final[0,order,:] = WavSol
final[1,order,:] = obj_S[order,1,:]
final[2,order,:] = obj_S[order,2,:]
final[3,order,:] = final[1,order,:]/flat[order,1]
final[4,order,:] = final[2,order,:]*(flat[order,1]**2)
ccoef = GLOBALutils.get_cont_single(final[0,order],final[3,order],final[4,order],ll=1.5,lu=5,nc=3)
L = np.where( final[1,order] != 0 )
ratio = np.polyval(ccoef,final[0,order])
final[5,order,:] = final[3,order,:]/ratio
Inan = np.where( np.isnan(final[1,order,:]) == True )[0]
final[5,order,Inan] = 1.
final[6,order,:] = final[4,order,:]*(ratio**2)
hdu = GLOBALutils.update_header(hdu,'HIERARCH DEC BARY',dec)
hdu = GLOBALutils.update_header(hdu,'HIERARCH EQUINOX',h[ih].header['EQUINOX'])
hdu = GLOBALutils.update_header(hdu,'HIERARCH OBS LATITUDE',h[ih].header['SITELAT'])
hdu = GLOBALutils.update_header(hdu,'HIERARCH OBS LONGITUDE',h[ih].header['SITELONG'])
hdu = GLOBALutils.update_header(hdu,'HIERARCH OBS ALTITUDE',h[ih].header['SITEALT'])
hdu = GLOBALutils.update_header(hdu,'HIERARCH TARG AIRMASS',h[ih].header['AIRMASS'])
psh = scipy.interpolate.splev(mjd,tck_sh)
# Apply new wavelength solution including barycentric correction
equis = np.arange( data.shape[1] )
del_vels = []
for order in range(n_useful):
m = order + 37
chebs = GLOBALutils.Calculate_chebs(equis, m, npix=data.shape[1], order0=37, ntotal=n_useful, Inverse=Inverse_m,nx=ncoef_x,nm=ncoef_m)
WavSol = GLOBALutils.ToVacuum(((1.+1.0e-6*psh)/float(m)) * GLOBALutils.Joint_Polynomial_Cheby(pref,chebs,ncoef_x,ncoef_m) * lbary_ltopo)
spec[0,order,:] = WavSol
spec[1,order,:] = sci_S[order,1, :]
if len( np.where(np.isnan(spec[1,order,:])) [0]) > 0:
spec[1,order,:] = sci_Ss[order,:]
spec[2,order,:] = 1. / ( spec[1,order,:] / gain + (ronoise/gain)**2 )
blaze = flat_S_n[order] #scipy.signal.medfilt(flat_S[order],21)
spec[3,order,:] = spec[1,order,:] / blaze
spec[4,order,:] = spec[2,order,:] * blaze ** 2
#cont_coef = continuum.NORM_single(spec[0,order,:],scipy.signal.medfilt(spec[3,order,:],7),orden=3)
cont_coef = GLOBALutils.get_cont_single(spec[0,order],spec[3,order],spec[4,order],ll=1.5,lu=5,nc=3)
ratio = np.polyval(cont_coef, spec[0,order,:])
L = np.where( spec[1,order,:] != 0 )[0]
spec[5,order,:][L] = spec[3,order,:][L] / ratio[L]
spec[6,order,:][L] = spec[4,order,:][L] * (ratio[L] ** 2 )
spec[7,order,:][L] = ratio[L]
final = np.zeros([11, orderf - orderi + 1, np.shape(obj_S)[2]])
equis = np.arange(obj_S.shape[2])
for order in range(orderi, orderf + 1):
m = order + oro0
chebs = GLOBALutils.Calculate_chebs(equis,
m,
order0=oro0,
ntotal=n_useful,
npix=obj_S.shape[2],
Inverse=Inverse_m,
nx=ncoef_x,
nm=ncoef_m)
WavSol = lbary_ltopo * (1.0 /
m) * GLOBALutils.Joint_Polynomial_Cheby(
global1, chebs, ncoef_x, ncoef_m)
final[0, order, :] = WavSol
final[1, order, :] = obj_S[order, 1, :]
final[2, order, :] = obj_S[order, 2, :]
final[3, order, :] = final[1, order, :] / flat[order, 1]
final[4, order, :] = final[2, order, :] * (flat[order, 1]**2)
ccoef = GLOBALutils.get_cont_single(final[0, order],
final[3, order],
final[4, order],
ll=1.5,
lu=5,
nc=3)
L = np.where(final[1, order] != 0)