<filename>coralie/coraliepipe.py
import sys
base = '../'

sys.path.append(base+"utils/Continuum")
sys.path.append(base+"utils/Correlation")
sys.path.append(base+"utils/GLOBALutils")
sys.path.append(base+"utils/OptExtract")

baryc_dir= base+'utils/SSEphem/'
sys.path.append(baryc_dir)
ephemeris='DEc403'

import matplotlib
matplotlib.use("Agg") 
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages

from pylab import *


# ceres modules
import coralieutils
import continuum
import correlation
import GLOBALutils
import Marsh

# other useful modules
import argparse
import ephem
import jplephem
from math import radians as rad
from astropy.io import fits as pyfits
import pickle
import os
import scipy
import scipy.interpolate
from scipy import interpolate

import statsmodels.api as sm
lowess = sm.nonparametric.lowess

# Recive input parameters
parser = argparse.ArgumentParser()
parser.add_argument('directorio')
parser.add_argument('-avoid_plot', action="store_true", default=False)
parser.add_argument('-dirout',default='default')
parser.add_argument('-do_class', action="store_true", default=False)
parser.add_argument('-just_extract', action="store_true", default=False)
parser.add_argument('-npools', default=1)
parser.add_argument('-o2do',default='all')
parser.add_argument('-reffile',default='default')


args   = parser.parse_args()
dirin            = args.directorio
avoid_plot       = args.avoid_plot
dirout           = args.dirout
DoClass          = args.do_class
JustExtract      = args.just_extract
npools           = int(args.npools)
object2do        = args.o2do
reffile          = args.reffile

if dirin[-1] != '/':
    dirin = dirin + '/'

if dirout == 'default':
    dirout = dirin[:-1]+'_red/'

if not os.access(dirout,os.F_OK):
    os.system('mkdir '+dirout)
if os.access(dirout+'proc',os.F_OK):
    os.system('rm -r '+dirout+'proc')
os.system('mkdir '+dirout+'proc')

f_res = open(dirout+'proc/'+'results.txt','w')

if reffile == 'default':
    reffile = dirin+'reffile.txt'

####### GLOBAL VARIABLES #####
force_pre_process  = False
force_flat_extract = False
force_thar_extract = False
force_thfp_extract = False
force_tharxc       = False
force_thar_wavcal  = False
force_thfp_wavcal  = False
force_sci_extract  = False
force_spectral_file_build = True
force_stellar_pars = False
dumpargon          = False
minlines_glob_ob   = 700
minlines_glob_co   = 500

Inverse_m          = True
use_cheby          = True
MRMS               = 100   # max rms in m/s, global wav solution

trace_degree       = 4
Marsh_alg          = 0
ext_aperture       = 3
NSigma_Marsh       = 5
NCosmic_Marsh      = 10
S_Marsh            = 0.4
N_Marsh            = 3      # grado polinomio 
min_extract_col    = 50
max_extract_col    = 2000
n_useful           = 70    # up to which order do we care?

# Number of coefficients for the global wavelength solution
ncoef_x            = 4
ncoef_m            = 6
npar_wsol = (min(ncoef_x,ncoef_m) + 1) * (2*max(ncoef_x,ncoef_m) - min(ncoef_x,ncoef_m) + 2) / 2

models_path = base+"data/COELHO_MODELS/R_40000b/"    # path to the synthetic models 
order_dir   = base+"coralie/wavcals/"  # path to reference files for the wavelength solution

#############################

# file containing the log
log = dirout+'night.log'

print "\n\n\tCoralie Euler1.2m  PIPELINE\n"
print "\tRAW data is in ",dirin
print "\tProducts of reduction will be in",dirout
print '\n'

# classification of input images according to header info
biases, ob_flats, co_flats, ob_loc, co_loc, ThAr_ref, ThFP_ref,\
        simThAr_sci,sim_FP_sci,ThAr_ref_dates,ThFP_ref_dates,obnames,\
	obnames_FP,exptimes, exptimes_FP, flats = coralieutils.FileClassify(dirin,log)

# Pre-process
if ( (( len(ob_flats) > 0) and (os.access(dirout+'FlatOb.fits',os.F_OK) == False)) or \
     (( len(co_flats) > 0) and (os.access(dirout+'FlatCo.fits',os.F_OK) == False)) or \
     (( len(flats) > 0) and (os.access(dirout+'Flat.fits',os.F_OK) == False)) or \
     (os.access(dirout+'trace.pkl',os.F_OK) == False)  or \
     (os.access(dirout+'MasterBias.fits',os.F_OK) == False)  or \
     (force_pre_process) ):
    print "\tNo previous pre-processing files or found"
    pre_process = 1
else:
    print "\tPre-processing files found, going straight to extraction"
    pre_process = 0

if (pre_process == 1):
    print "\tGenerating Master calibration frames..."
    # median combine Biases
    MasterBias, RO_bias, GA_bias = coralieutils.MedianCombine(biases,ZF=0.)
    hdu = pyfits.PrimaryHDU( MasterBias )
    if (os.access(dirout+'MasterBias.fits',os.F_OK)):
        os.remove(dirout+'MasterBias.fits')
    hdu.writeto(dirout+'MasterBias.fits')
    print "\t\t-> Masterbias: done!"

    if len(flats) > 0:
    	    # median combine list of co flats2
	    Flat,RO_flat,GA_flat = coralieutils.MedianCombine(flats,ZF=MasterBias)
	    hdu = pyfits.PrimaryHDU(Flat)
	    if (os.access(dirout+'Flat.fits',os.F_OK)):
		os.remove(dirout+'Flat.fits')
	    hdu.writeto(dirout+'Flat.fits')

    if len(ob_flats) > 0:
	    # median combine list of ob flats
	    Flat_ob, RO_ob, GA_ob = coralieutils.MedianCombine(ob_flats,ZF=MasterBias)
	    # save this file for later reference
	    hdu = pyfits.PrimaryHDU( Flat_ob )
	    if (os.access(dirout+'FlatOb.fits',os.F_OK)):
		os.remove(dirout+'FlatOb.fits')
	    hdu.writeto(dirout+'FlatOb.fits')
    else:
	Flat_ob = Flat

    if len(co_flats) > 0:
	    # median combine list of co flats
	    Flat_co,RO_co,GA_co = coralieutils.MedianCombine(co_flats,ZF=MasterBias)
	    hdu = pyfits.PrimaryHDU(Flat_co)
	    if (os.access(dirout+'FlatCo.fits',os.F_OK)):
		os.remove(dirout+'FlatCo.fits')
	    hdu.writeto(dirout+'FlatCo.fits')
    else:
	Flat_co = Flat



    print "\t\t-> Masterflats: done!"
    # Find orders & traces
    print "\tTracing echelle orders..."

    if len(ob_flats)>0 and len(co_flats)>0:
	    c_ob, nord_ob = GLOBALutils.get_them(Flat_ob, 8, trace_degree,maxords=-1,mode=1)
	    c_co, nord_co = GLOBALutils.get_them(Flat_co, 8, trace_degree,maxords=-1,startfrom=300,mode=1)
    else:
        c_all, nord_all = GLOBALutils.get_them(Flat, 5, trace_degree,maxords=-1,mode=1,nsigmas=3)
	GA_co,GA_ob = GA_flat, GA_flat
	RO_co,RO_ob = RO_flat, RO_flat

	c_ob = c_all[:22]
	c_co = c_all[22]
	i = 23
	while i < len(c_all)-1:
		c_ob = np.vstack((c_ob,c_all[i]))
		c_co = np.vstack((c_co,c_all[i+1]))
		i+=2
	nord_co, nord_ob = len(c_co),len(c_ob)
        print '\t', nord_ob, 'object orders found...'
        print '\t', nord_co, 'comparison orders found...'


    if len(ob_flats)>0 and len(co_flats)>0:
    	trace_dict = {'c_ob':c_ob,'c_co':c_co,
                  'nord_ob':nord_ob, 'nord_co':nord_co,
                  'GA_ob': GA_ob, 'RO_ob': RO_ob,
                  'GA_co': GA_co, 'RO_co': RO_co}
    else:
	trace_dict = {'c_all':c_all,'c_ob':c_ob,'c_co':c_co,
                  'nord_ob':nord_ob, 'nord_co':nord_co,'nord_all':nord_all,
                  'GA_ob': GA_ob, 'RO_ob': RO_ob,
                  'GA_co': GA_co, 'RO_co': RO_co}

    pickle.dump( trace_dict, open( dirout+"trace.pkl", 'w' ) )

else:
    trace_dict = pickle.load( open( dirout+"trace.pkl", 'r' ) )
    c_co = trace_dict['c_co']
    c_ob = trace_dict['c_ob']
    nord_ob = trace_dict['nord_ob']
    nord_co = trace_dict['nord_co']
    if 'c_all' in trace_dict.keys():
        c_all    = trace_dict['c_all']
        nord_all = trace_dict['nord_all']
    # recover GA*, RO*
    GA_ob = trace_dict['GA_ob']
    RO_ob = trace_dict['RO_ob']
    GA_co = trace_dict['GA_co']
    RO_co = trace_dict['RO_co']
    # recover flats & master bias
    if len(ob_flats)>0:
    	h = pyfits.open(dirout+'FlatOb.fits')
    	Flat_ob = h[0].data
    else:
	h = pyfits.open(dirout+'Flat.fits')
    	Flat_ob = h[0].data
    if len(co_flats)>0:
        h = pyfits.open(dirout+'Flat.fits')
        Flat_co = h[0].data
    else:
	h = pyfits.open(dirout+'Flat.fits')
    	Flat_co = h[0].data
    h = pyfits.open(dirout+'MasterBias.fits')
    MasterBias = h[0].data


if len(ob_flats)>0 and len(co_flats)>0:
	c_all = GLOBALutils.Mesh(c_ob,c_co)

print '\n\tExtraction of Flat calibration frames:'
# Extract flat spectra, object
P_ob_fits = dirout + 'P_ob.fits'
S_flat_ob_fits = dirout +'S_flat_ob.fits'
P_ob = np.zeros( Flat_ob.shape )
S_flat_ob = np.zeros((nord_ob, 3, Flat_ob.shape[1]) )

if ( os.access(P_ob_fits,os.F_OK) == False ) or ( os.access(S_flat_ob_fits,os.F_OK) == False ) or \
   (force_flat_extract):
    print "\t\tNo extracted flat object spectra found or extraction forced, extracting and saving..."
    
    print "\t\t\tWill extract",nord_ob,"orders for object fibre..."
    P_ob = GLOBALutils.obtain_P(Flat_ob,c_ob,ext_aperture,RO_ob,\
                                    GA_ob,NSigma_Marsh, S_Marsh, \
                    N_Marsh, Marsh_alg, min_extract_col,\
                    max_extract_col, npools)

    S_flat_ob  = GLOBALutils.optimal_extraction(Flat_ob,P_ob,c_ob,ext_aperture,\
                                                RO_ob,GA_ob,S_Marsh,NCosmic_Marsh,\
                                                min_extract_col,max_extract_col,npools)

    # write P_on and S_flat_ob as fits files
    if (os.access(P_ob_fits,os.F_OK)):
        os.remove( P_ob_fits )
    if (os.access(S_flat_ob_fits,os.F_OK)):
        os.remove( S_flat_ob_fits )
    
    hdu = pyfits.PrimaryHDU( P_ob )
    hdu.writeto( P_ob_fits )
    hdu = pyfits.PrimaryHDU( S_flat_ob )
    hdu.writeto( S_flat_ob_fits )

   
else:
    print "\t\tExtracted flat object spectra found, loading..."
    P_ob       = pyfits.getdata( P_ob_fits )
    S_flat_ob  = pyfits.getdata( S_flat_ob_fits )

# Extract flat spectra, comparison
P_co_fits = dirout + 'P_co.fits'
S_flat_co_fits = dirout +'S_flat_co.fits'
P_co = np.zeros( Flat_co.shape )
S_flat_co = np.zeros((nord_co, 3, Flat_co.shape[1]) )
if ( os.access(P_co_fits,os.F_OK) == False ) or ( os.access(S_flat_co_fits,os.F_OK) == False ) or (force_flat_extract):
    print "\t\tNo extracted flat comparison spectra found or extraction forced, extracting and saving..."
    
    print "\t\t\tWill extract",nord_co,"orders for comparison fibre"
    P_co = GLOBALutils.obtain_P(Flat_co,c_co,ext_aperture,RO_co,\
                                    GA_co,NSigma_Marsh, S_Marsh, \
                    N_Marsh, Marsh_alg, min_extract_col,\
                    max_extract_col, npools)
    
    S_flat_co  = GLOBALutils.optimal_extraction(Flat_co,P_co,c_co,ext_aperture,RO_co,GA_co,\
                                                S_Marsh,NCosmic_Marsh,min_extract_col,\
                                                max_extract_col,npools) 

    # write P_on and S_flat_co as fits files
    if (os.access(P_co_fits,os.F_OK)):
        os.remove( P_co_fits )
    if (os.access(S_flat_co_fits,os.F_OK)):
        os.remove( S_flat_co_fits )

    hdu = pyfits.PrimaryHDU( P_co )
    hdu.writeto( P_co_fits )
    hdu = pyfits.PrimaryHDU( S_flat_co )
    hdu.writeto( S_flat_co_fits )
   
else:
    print "\t\tExtracted flat comparison spectra found, loading..."
    P_co       = pyfits.getdata( P_co_fits )
    S_flat_co  = pyfits.getdata( S_flat_co_fits )

# Normalize flat field spectra.
S_flat_ob_n, maxvals_ob = GLOBALutils.FlatNormalize_single( S_flat_ob, mid=int(0.5*S_flat_ob.shape[2]))
S_flat_co_n, maxvals_co = GLOBALutils.FlatNormalize_single( S_flat_co, mid=int(0.5*S_flat_co.shape[2]))

print '\n\tExtraction of ThAr calibration frames:'
# Extract all ThAr files
for fsim in ThAr_ref:
    hthar = pyfits.open( fsim )
    dthar = coralieutils.OverscanTrim( pyfits.getdata( fsim ) )
    ron  = hthar[0].header['HIERARCH ESO CORA CCD RON']
    gain = hthar[0].header['HIERARCH ESO CORA CCD GAIN']
    thar_fits_ob = dirout + fsim.split('/')[-1][:-8]+'spec.ob.fits.S'
    thar_fits_co = dirout + fsim.split('/')[-1][:-8]+'spec.co.fits.S'  
    if ( os.access(thar_fits_ob,os.F_OK) == False ) or \
       ( os.access(thar_fits_co,os.F_OK) == False ) or \
       (force_thar_extract):

        print "\t\tNo previous extraction or extraction forced for ThAr file", fsim, "extracting..."
	thar_S_ob  = GLOBALutils.optimal_extraction(dthar,P_ob,c_ob,ext_aperture,ron,gain,\
                                                    S_Marsh,100.,min_extract_col,max_extract_col,npools)
	thar_S_co  = GLOBALutils.optimal_extraction(dthar,P_co,c_co,ext_aperture,ron,gain,\
                                                    S_Marsh,100.,min_extract_col,max_extract_col,npools)           
       
        # save as fits file
        if (os.access(thar_fits_ob,os.F_OK)):
            os.remove( thar_fits_ob )
        if (os.access(thar_fits_co,os.F_OK)):
            os.remove( thar_fits_co )
            
        hdu = pyfits.PrimaryHDU( thar_S_ob )
        hdu.writeto( thar_fits_ob )
        hdu = pyfits.PrimaryHDU( thar_S_co )
        hdu.writeto( thar_fits_co )
    else:
        print "\t\tThAr file", fsim, "all ready extracted, loading..."

print "\n\tWavelength solution of ThAr calibration spectra:"
# compute wavelength calibration files
sorted_ThAr_dates = np.argsort( ThAr_ref_dates )
p0_array = np.zeros( (len(ThAr_ref_dates), npar_wsol) )

for i in range(len(sorted_ThAr_dates)):
    index = sorted_ThAr_dates[i]
    wavsol_pkl   = dirout + ThAr_ref[index].split('/')[-1][:-8]+'wavsolpars.pkl'
    thar_fits_ob = dirout + ThAr_ref[index].split('/')[-1][:-8]+'spec.ob.fits.S'
    thar_fits_co = dirout + ThAr_ref[index].split('/')[-1][:-8]+'spec.co.fits.S'
    if ( os.access(wavsol_pkl,os.F_OK) == False ) or (force_thar_wavcal):
        print "\t\tComputing wavelength solution of ThAr file", ThAr_ref[index] 
        hthar = pyfits.open( ThAr_ref[index] )
        mjd, mjd0 = coralieutils.mjd_fromheader( hthar )
        thar_S_ob = pyfits.getdata( thar_fits_ob )
        thar_S_co = pyfits.getdata( thar_fits_co )

        lines_thar_ob  = thar_S_ob[:,1,:]
        iv_thar_ob     = thar_S_ob[:,2,:]
        lines_thar_co  = thar_S_co[:,1,:]
        iv_thar_co     = thar_S_co[:,2,:]

        All_Pixel_Centers = np.array([])
        All_Wavelengths   = np.array([])
        All_Orders        = np.array([])
        All_Centroids     = np.array([])
        All_Sigmas        = np.array([])
        All_Intensities   = np.array([])

        for order in range(n_useful):
            order_s = str(order)
            if (order < 10):
                order_s = '0'+str(order)
            
            thar_order_orig = lines_thar_ob[order,:]
            IV              = iv_thar_ob[order,:]
            wei             = np.sqrt( IV )
            bkg             = GLOBALutils.Lines_mBack(thar_order_orig, IV,  thres_rel=3)        
            thar_order      = thar_order_orig - bkg

            coeffs_pix2wav, coeffs_pix2sigma, pixel_centers, wavelengths,\
            rms_ms, residuals, centroids, sigmas, intensities \
                = GLOBALutils.Initial_Wav_Calibration(order_dir+'order_'+order_s+'o.iwdat',\
                                                      thar_order,order,wei,rmsmax=5000000,\
                                                      minlines=10,FixEnds=True,Dump_Argon=dumpargon,\
                                                      Dump_AllLines=True, Cheby=use_cheby)
            if (order == 35): 
                if (use_cheby):
                    Global_ZP = GLOBALutils.Cheby_eval( coeffs_pix2wav, 1023, len(thar_order) )
                else:
                    Global_ZP = scipy.polyval( coeffs_pix2wav, 0.0 )

            All_Pixel_Centers = np.append( All_Pixel_Centers, pixel_centers )
            All_Wavelengths   = np.append( All_Wavelengths, wavelengths )
            All_Orders        = np.append( All_Orders, np.zeros( len(pixel_centers) ) + order )
            All_Centroids     = np.append( All_Centroids, centroids)
            All_Sigmas        = np.append( All_Sigmas, sigmas)
            All_Intensities   = np.append( All_Intensities, intensities )

        p0 = np.zeros( npar_wsol )
        p0[0] =  (35+89) * Global_ZP 
        p1, G_pix, G_ord, G_wav, II, rms_ms, G_res = \
            GLOBALutils.Fit_Global_Wav_Solution(All_Pixel_Centers, All_Wavelengths, All_Orders,\
                                                np.ones(All_Intensities.shape), p0, Cheby=use_cheby,\
                                                maxrms=MRMS, Inv=Inverse_m,minlines=700,order0=89, \
                                                ntotal=n_useful,npix=len(thar_order),nx=ncoef_x,nm=ncoef_m)

        # Now calibrate COMPARISON orders. Use p1 above as p0
        All_Pixel_Centers_co = np.array([])
        All_Wavelengths_co   = np.array([])
        All_Orders_co        = np.array([])
        All_Centroids_co     = np.array([])
        All_Sigmas_co        = np.array([])
        All_Intensities_co   = np.array([])

        for order in range(22,n_useful):
            order_s = str(order)
            if (order < 10):
                order_s = '0'+str(order)
            
            thar_order_orig = lines_thar_co[order-22,:]
            IV              = iv_thar_co[order-22,:]
            wei             = np.sqrt( IV )
            bkg             = GLOBALutils.Lines_mBack(thar_order_orig, IV,  thres_rel=3)        
            thar_order      = thar_order_orig - bkg
            
            coeffs_pix2wav, coeffs_pix2sigma, pixel_centers, wavelengths, rms_ms, residuals, centroids, sigmas, intensities \
                = GLOBALutils.Initial_Wav_Calibration( order_dir+'order_'+order_s+'o.iwdat', thar_order, order, wei, \
                                                            rmsmax=5000000, minlines=10,FixEnds=True,Dump_Argon=dumpargon, \
                                                            Dump_AllLines=True, Cheby=use_cheby)
          
            All_Pixel_Centers_co = np.append( All_Pixel_Centers_co, pixel_centers )
            All_Wavelengths_co   = np.append( All_Wavelengths_co, wavelengths )
            All_Orders_co        = np.append( All_Orders_co, np.zeros( len(pixel_centers) ) + order )
            All_Centroids_co     = np.append( All_Centroids_co, centroids)
            All_Sigmas_co        = np.append( All_Sigmas_co, sigmas)
            All_Intensities_co   = np.append( All_Intensities_co, intensities )

        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), p1, Cheby=use_cheby,\
                                                     maxrms=MRMS, Inv=Inverse_m,minlines=500,order0=89,ntotal=n_useful,npix=len(thar_order),nx=ncoef_x,nm=ncoef_m)
           
        # end COMPARISON orders. 
        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,
                 'p1_co':p1_co, 'G_pix_co':G_pix_co, 'G_ord_co':G_ord_co, 'G_wav_co':G_wav_co, 'II_co':II_co, 'rms_ms_co':rms_ms_co,\
                     'G_res_co':G_res_co, 'All_Centroids_co':All_Centroids_co}
        pickle.dump( pdict, open( wavsol_pkl, 'w' ) )
        #print "Median sigma:", np.median( All_Sigmas )    
        p0_array[i,:] = p1

    else:
        print "\t\tUsing previously computed wavelength solution in file",wavsol_pkl
        pdict         = pickle.load(open(wavsol_pkl,'r'))
        p0_array[i,:] = pdict['p1']

p0_G  = np.median(p0_array,axis=0)

if len(ThFP_ref) > 0:
    print '\n\tExtraction of Fabry-Perot calibration frames:'
else:
    print '\n\tNo Fabry-Perot calibration images found, moving on'
# Now extract ThAr-FP images
for fsim in ThFP_ref:
    hthfp = pyfits.open( fsim )
    thfp_fits_ob = dirout + fsim.split('/')[-1][:-8]+'spec.ob.fits.S'
    thfp_fits_co = dirout + fsim.split('/')[-1][:-8]+'spec.co.fits.S'
 
    if ( os.access(thfp_fits_ob,os.F_OK) == False ) or \
       ( os.access(thfp_fits_co,os.F_OK) == False ) or \
       (force_thfp_extract):
        print "\t\tNo previous extraction or extraction forced for ThFP file", fsim, "extracting..."
        dthfp = coralieutils.OverscanTrim( pyfits.getdata( fsim ) )
        Centers = np.zeros((len(c_all),dthfp.shape[1]))
        for i in range(c_all.shape[0]):
            Centers[i,:]=scipy.polyval(c_all[i,:],np.arange(len(Centers[i,:])))
        bac = GLOBALutils.get_scat(dthfp,Centers,span=5)
        dthfp -= bac

	thfp_S_ob  = GLOBALutils.optimal_extraction(dthfp,P_ob,c_ob,ext_aperture,\
                                                    hthfp[0].header['HIERARCH ESO CORA CCD RON'],\
                                                    hthfp[0].header['HIERARCH ESO CORA CCD GAIN'],\
                                                    S_Marsh,100.,min_extract_col,max_extract_col,npools)
	thfp_S_co  = GLOBALutils.optimal_extraction(dthfp,P_co,c_co,ext_aperture,\
                                                    hthfp[0].header['HIERARCH ESO CORA CCD RON'],
                                                    hthfp[0].header['HIERARCH ESO CORA CCD GAIN'],
                                                    S_Marsh,100.,min_extract_col,max_extract_col,npools)
        # save as fits file
        if (os.access(thfp_fits_ob,os.F_OK)):
            os.remove( thfp_fits_ob )
        if (os.access(thfp_fits_co,os.F_OK)):
            os.remove( thfp_fits_co )
            
        hdu = pyfits.PrimaryHDU( thfp_S_ob )
        hdu.writeto( thfp_fits_ob )
        hdu = pyfits.PrimaryHDU( thfp_S_co )
        hdu.writeto( thfp_fits_co )
    else:
        print "\t\tFP file", fsim, "all ready extracted, loading..."

# Now calibrate the ThFP spectra with the closest ThAr spectrum
print '\n\tWavelength solution of Fabry-Perot spectra with closest ThAr spectrum:'
for fsim in ThFP_ref:
    hthfp = pyfits.open( fsim )
    mjd, mjd0 = coralieutils.mjd_fromheader(hthfp)
    im = np.argmin(np.absolute(np.array(ThAr_ref_dates) - mjd))
    wavsol_dict = pickle.load(open(dirout + ThAr_ref[im].split('/')[-1][:-8]+'wavsolpars.pkl','r'))
    thfp_fits_ob = dirout + fsim.split('/')[-1][:-8]+'spec.ob.fits.S'
    thfp_fits_co = dirout + fsim.split('/')[-1][:-8]+'spec.co.fits.S'
    wavsol_pkl_fp = dirout + fsim.split('/')[-1][:-8]+'wavsolpars.pkl'
    if ( os.access(wavsol_pkl_fp,os.F_OK) == False ) or (force_thfp_wavcal):
            print '\t\tCalibrating', fsim,'...'
	    thar_fp = pyfits.getdata(thfp_fits_ob)
	    lines_thar_ob  = thar_fp[:,1,:]
	    iv_thar_ob     = thar_fp[:,2,:]

	    All_Pixel_Centers = np.array([])
	    All_Wavelengths   = np.array([])
	    All_Orders        = np.array([])
	    All_Centroids     = np.array([])
	    All_Sigmas        = np.array([])
	    All_Intensities   = np.array([])

	    for order in range(n_useful):
		order_s = str(order)
		if (order < 10):
		    order_s = '0'+str(order)
		thar_order_orig = lines_thar_ob[order,:]
		IV              = iv_thar_ob[order,:]
		wei             = np.sqrt( IV )
		bkg             = GLOBALutils.Lines_mBack(thar_order_orig, IV,  thres_rel=3)        
		thar_order      = thar_order_orig - bkg

		coeffs_pix2wav, coeffs_pix2sigma, pixel_centers, wavelengths, rms_ms, residuals, centroids, sigmas, intensities \
		            = GLOBALutils.Initial_Wav_Calibration( order_dir+'order_'+order_s+'o.iwdat', thar_order, order, wei, \
		                                                        rmsmax=5000000, minlines=10,FixEnds=True,Dump_Argon=dumpargon, \
		                                                        Dump_AllLines=True, Cheby=use_cheby)

		All_Pixel_Centers = np.append( All_Pixel_Centers, pixel_centers )
		All_Wavelengths   = np.append( All_Wavelengths, wavelengths )
		All_Orders        = np.append( All_Orders, np.zeros( len(pixel_centers) ) + order )
		All_Centroids     = np.append( All_Centroids, centroids)
		All_Sigmas        = np.append( All_Sigmas, sigmas)
		All_Intensities   = np.append( All_Intensities, intensities )
		
	    p1, G_pix, G_ord, G_wav, II, rms_ms, G_res = \
		GLOBALutils.Fit_Global_Wav_Solution(All_Pixel_Centers, All_Wavelengths, All_Orders,\
		                                             np.ones(All_Intensities.shape), p0_G, Cheby=use_cheby,\
		                                             maxrms=100, Inv=Inverse_m, minlines=minlines_glob_ob,\
                                                             order0=89,ntotal=n_useful,npix=len(thar_order),nx=ncoef_x,nm=ncoef_m)

	    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), wavsol_dict['p1'],\
		                                                   Cheby=True,Inv=True,maxrms=100,minlines=minlines_glob_ob,\
								   order0=89,ntotal=n_useful,npix=len(thar_order),nx=ncoef_x,nm=ncoef_m)

	    pdict = {'p1':p1,'p_shift':p_shift,'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, 'p1_co':wavsol_dict['p1_co']}
	    pickle.dump( pdict, open( wavsol_pkl_fp, 'w' ) )
    else:
	print '\t\tFP spectrum', fsim, 'already calibrated, loading...'

### start of science frame reductions ###

new_list = []
new_list_obnames = []
new_list_texp = []
for i in range(len(simThAr_sci)):
    fsim = simThAr_sci[i]
    obname = obnames[i]
    texp = exptimes[i]
    if (object2do == 'all'):
        new_list.append(fsim)
        new_list_obnames.append( obname )
        new_list_texp.append( texp )

    else:
        if (obname == object2do):
            new_list.append(fsim)
            new_list_obnames.append( obname )
            new_list_texp.append( texp )

print '\n\tThe following targets will be processed:'
for nlisti in range(len(new_list)):
	print '\t\t'+new_list_obnames[nlisti]

# Does any image have a special requirement for dealing with the moonlight?
if os.access(dirin + 'moon_corr.txt', os.F_OK):
    fmoon = open(dirin + 'moon_corr.txt','r')
    moon_lns = fmoon.readlines()
    spec_moon = []
    use_moon = []
    for line in moon_lns:
        spec_moon.append(line.split()[0])
        if line.split()[1] == '0':
            use_moon.append(False)
        else:
            use_moon.append(True)
else:
    spec_moon = []
    use_moon = []

spec_moon = np.array(spec_moon)
use_moon  = np.array(use_moon)

# now extract the images
for nlisti in range(len(new_list)):	
    fsim   = new_list[ nlisti ]
    obname = new_list_obnames[ nlisti ]
    TEXP   = np.around(new_list_texp[ nlisti ])

    know_moon = False
    if fsim.split('/')[-1] in spec_moon:
        I = np.where(fsim.split('/')[-1] == spec_moon)[0]
        know_moon = True
        here_moon = use_moon[I]

    # get header h of image
    h = pyfits.open(fsim)
    print '\n'
    print "\t--> Working on image: ", fsim
    
    # get mjd and mjd0
    mjd,mjd0 = coralieutils.mjd_fromheader(h)

    #  get gain and readnoise of object 
    ronoise = h[0].header['HIERARCH ESO CORA CCD RON']
    gain    = h[0].header['HIERARCH ESO CORA CCD GAIN']
    
    # Object name

    print "\t\tObject name:",obname

    # Open file, trim, overscan subtract and MasterBias subtract
    data = h[0].data
    data = coralieutils.OverscanTrim(data)
    data -= MasterBias

    bacfile = dirout + 'BAC_' + fsim.split('/')[-1][:-4]+'fits'''
    if (os.access(bacfile,os.F_OK))== False:
        Centers = np.zeros((len(c_all),data.shape[1]))
        for i in range(c_all.shape[0]):
            Centers[i,:]=scipy.polyval(c_all[i,:],np.arange(len(Centers[i,:])))
        bac = GLOBALutils.get_scat(data,Centers,span=5)
        hdbac = pyfits.PrimaryHDU( bac )
        hdbac.writeto(bacfile)
    else:
        bac = pyfits.getdata(bacfile)
    data -= bac

    ra,dec = h[0].header['RA'],h[0].header['DEC']

    ra2,dec2 = GLOBALutils.getcoords(obname,mjd,filen=reffile)
    if ra2 !=0 and dec2 != 0:
        ra = ra2
        dec = dec2
    else:
        print '\t\tUsing the coordinates found in the image header.'

    # Find lambda_bary/lambda_topo using JPLEPHEM
    altitude    = h[0].header['HIERARCH ESO OBS GEO ALTITUDE']
    latitude    = h[0].header['HIERARCH ESO OBS GEO LATITU']
    longitude   = h[0].header['HIERARCH ESO OBS GEO LONGIT']
    epoch       = h[0].header['HIERARCH ESO OBS EQUICAT']

    iers                    = GLOBALutils.JPLiers( baryc_dir, mjd-999.0, mjd+999.0 )
    obsradius, R0           = GLOBALutils.JPLR0( latitude, altitude)
    obpos                   = GLOBALutils.obspos( longitude, obsradius, R0 )

    jplephem.set_ephemeris_dir( baryc_dir , ephemeris )
    jplephem.set_observer_coordinates( obpos[0], obpos[1], obpos[2] )

    res         = jplephem.doppler_fraction(ra/15.0, dec, int(mjd), mjd%1, 1, 0.0)
    lbary_ltopo = 1.0 + res['frac'][0]
    bcvel_baryc = ( lbary_ltopo - 1.0 ) * 2.99792458E5

    print "\t\tBarycentric velocity:", bcvel_baryc

    res = jplephem.pulse_delay(ra/15.0, dec, int(mjd), mjd%1, 1, 0.0)   
    mbjd = mjd + res['delay'][0] / (3600.0 * 24.0)

    # Moon Phase Calculations
    gobs      = ephem.Observer()  
    gobs.name = 'Swiss1.2'  
    gobs.lat  = rad(latitude)  # lat/long in decimal degrees  
    gobs.long = rad(longitude)

    DDATE = h[0].header['HIERARCH ESO CORA SHUTTER START DATE']
    HHOUR = h[0].header['HIERARCH ESO CORA SHUTTER START HOUR']

    Mho = str(int(HHOUR))
    if len(Mho)<2:
        Mho = '0'+Mho
    mins = (HHOUR - int(Mho))*60.
    Mmi = str(int(mins))
    if len(Mmi)<2:
        Mmi = '0'+Mmi
    segs = (mins - int(Mmi))*60.
    if segs<10:
        Mse = '0'+str(segs)[:5]
    else:
        Mse = str(segs)[:6]

    gobs.date = str(DDATE[:4]) + '-' +  str(DDATE[4:6]) + '-' + str(DDATE[6:]) + ' ' +  Mho + ':' + Mmi +':' +Mse

    mephem = ephem.Moon()
    mephem.compute(gobs)
    Mcoo = jplephem.object_track("Moon", int(mjd), float(mjd%1), 1, 0.0)
    Mp   = jplephem.barycentric_object_track("Moon", int(mjd), float(mjd%1), 1, 0.0)
    Sp   = jplephem.barycentric_object_track("Sun", int(mjd), float(mjd%1), 1, 0.0)
    res  = jplephem.object_doppler("Moon", int(mjd), mjd%1, 1, 0.0)
    lunation,moon_state,moonsep,moonvel = GLOBALutils.get_lunar_props(ephem,gobs,Mcoo,Mp,Sp,res,ra,dec)
    refvel = bcvel_baryc + moonvel
    print '\t\tRadial Velocity of sacttered moonlight:',refvel

    sorted_indices    = np.argsort( np.abs( np.array(ThAr_ref_dates) - mjd ) )
    sorted_indices_FP = np.argsort( np.abs( np.array(ThFP_ref_dates) - mjd ) )

    print '\t\tExtraction:'
    # optimally and simply extract spectra
    sci_fits_ob = dirout + fsim.split('/')[-1][:-8]+'spec.ob.fits.S'
    sci_fits_co = dirout + fsim.split('/')[-1][:-8]+'spec.co.fits.S'
    sci_fits_ob_simple = dirout + fsim.split('/')[-1][:-8]+'spec.simple.ob.fits.S'
    sci_fits_co_simple = dirout + fsim.split('/')[-1][:-8]+'spec.simple.co.fits.S'
    sci_fits_bac = dirout + fsim.split('/')[-1][:-8]+'spec.simple.bac.fits.S'
    if ( os.access(sci_fits_ob,os.F_OK) == False ) or \
        ( os.access(sci_fits_co,os.F_OK) == False ) or \
        ( os.access(sci_fits_ob_simple,os.F_OK) == False ) or \
        ( os.access(sci_fits_co_simple,os.F_OK) == False ) or \
        ( os.access(sci_fits_bac,os.F_OK) == False ) or \
        (force_sci_extract):

        print "\t\t\tNo previous extraction or extraction forced for science file", fsim, "extracting..."
        sci_Ss_ob = GLOBALutils.simple_extraction(data,c_ob,ext_aperture,\
                                                  min_extract_col,max_extract_col,npools)
        sci_Ss_co = GLOBALutils.simple_extraction(data,c_co,ext_aperture,\
                                                  min_extract_col,max_extract_col,npools)
        sci_S_ob  = GLOBALutils.optimal_extraction(data,P_ob,c_ob,ext_aperture,\
                                                   h[0].header['HIERARCH ESO CORA CCD RON'],\
                                                   h[0].header['HIERARCH ESO CORA CCD GAIN'],\
                                                   S_Marsh,NCosmic_Marsh,\
                                                   min_extract_col,max_extract_col,npools)
        sci_S_co  = GLOBALutils.optimal_extraction(data,P_co,c_co,ext_aperture,\
                                                   h[0].header['HIERARCH ESO CORA CCD RON'],\
                                                   h[0].header['HIERARCH ESO CORA CCD GAIN'],\
                                                   S_Marsh,2.*NCosmic_Marsh,\
                                                   min_extract_col,max_extract_col,npools)
        sci_bac =  GLOBALutils.simple_extraction(bac,c_ob,ext_aperture,\
                                                  min_extract_col,max_extract_col,npools)       

        # save as fits file
        if (os.access(sci_fits_ob,os.F_OK)):
            os.remove( sci_fits_ob )
        if (os.access(sci_fits_co,os.F_OK)):
            os.remove( sci_fits_co )
        if (os.access(sci_fits_ob_simple,os.F_OK)):
            os.remove( sci_fits_ob_simple )
        if (os.access(sci_fits_co_simple,os.F_OK)):
            os.remove( sci_fits_co_simple )
        if (os.access(sci_fits_bac,os.F_OK)):
            os.remove( sci_fits_bac )          
        hdu = pyfits.PrimaryHDU( sci_S_ob )
        hdu.writeto( sci_fits_ob )
        hdu = pyfits.PrimaryHDU( sci_S_co )
        hdu.writeto( sci_fits_co )
        hdu = pyfits.PrimaryHDU( sci_Ss_ob )
        hdu.writeto( sci_fits_ob_simple )
        hdu = pyfits.PrimaryHDU( sci_Ss_co )
        hdu.writeto( sci_fits_co_simple )
        hdu = pyfits.PrimaryHDU( sci_bac )
        hdu.writeto( sci_fits_bac )
    else:
        print '\t\t\t'+fsim, "has already been extracted, reading in product fits files..."
        sci_S_ob = pyfits.getdata( sci_fits_ob )
        sci_S_co = pyfits.getdata( sci_fits_co )
        sci_Ss_ob = pyfits.getdata( sci_fits_ob_simple )
        sci_Ss_co = pyfits.getdata( sci_fits_ob_simple )
        sci_bac = pyfits.getdata( sci_fits_bac )
    
    fout = 'proc/'+ obname + '_' + \
        h[0].header['HIERARCH ESO CORA SHUTTER START DATE'] + '_' +\
        'UT' + fsim[-17:-9] + '_' +\
        'sp.fits'

    #Build spectra
    if ( os.access(dirout+fout ,os.F_OK) == False ) or (force_spectral_file_build):
        # initialize file that will have the spectra
        # n_useful should be nord_ob, but we still have not calibrated that bluest order -- TODO
        spec = np.zeros((11, n_useful, data.shape[1]))
        hdu = pyfits.PrimaryHDU( spec )
        hdu = GLOBALutils.update_header(hdu,'HIERARCH MJD', mjd)
        hdu = GLOBALutils.update_header(hdu,'HIERARCH MBJD', mbjd)
        hdu = GLOBALutils.update_header(hdu,'HIERARCH SHUTTER START DATE', h[0].header['HIERARCH ESO CORA SHUTTER START DATE'] )
        hdu = GLOBALutils.update_header(hdu,'HIERARCH SHUTTER START UT',  h[0].header['HIERARCH ESO CORA SHUTTER START HOUR'])
        hdu = GLOBALutils.update_header(hdu,'HIERARCH TEXP (S)',h[0].header['HIERARCH ESO OBS TEXP'])
        hdu = GLOBALutils.update_header(hdu,'HIERARCH FLUX WEIGHTED MEAN F ',h[0].header['HIERARCH ESO CORA PM FLUX TMMEAN'])
        hdu = GLOBALutils.update_header(hdu,'HIERARCH BARYCENTRIC CORRECTION (KM/S)', bcvel_baryc)
        hdu = GLOBALutils.update_header(hdu,'HIERARCH (LAMBDA_BARY / LAMBDA_TOPO)', lbary_ltopo)    
        hdu = GLOBALutils.update_header(hdu,'HIERARCH TARGET NAME', obname)
        hdu = GLOBALutils.update_header(hdu,'HIERARCH RA',h[0].header['HIERARCH ESO TEL TARG ALPHA'])
        hdu = GLOBALutils.update_header(hdu,'HIERARCH DEC',h[0].header['HIERARCH ESO TEL TARG DELTA'])
        hdu = GLOBALutils.update_header(hdu,'HIERARCH RA DEG',h[0].header['RA'])
        hdu = GLOBALutils.update_header(hdu,'HIERARCH DEC DEG',h[0].header['DEC'])
        hdu = GLOBALutils.update_header(hdu,'HIERARCH RA BARY',ra)
        hdu = GLOBALutils.update_header(hdu,'HIERARCH DEC BARY',dec)
        hdu = GLOBALutils.update_header(hdu,'HIERARCH EQUINOX',h[0].header['HIERARCH ESO OBS EQUICAT'])
        hdu = GLOBALutils.update_header(hdu,'HIERARCH OBS LATITUDE',h[0].header['HIERARCH ESO OBS GEO LATITU'])
        hdu = GLOBALutils.update_header(hdu,'HIERARCH OBS LONGITUDE',h[0].header['HIERARCH ESO OBS GEO LONGIT'])
        hdu = GLOBALutils.update_header(hdu,'HIERARCH OBS ALTITUDE',h[0].header['HIERARCH ESO OBS GEO ALTITUDE'])
        hdu = GLOBALutils.update_header(hdu,'HIERARCH TARG AIRMASS',h[0].header['HIERARCH ESO OBS TARG AIRMASS'])

        print '\t\tWavelength calibration:'
        print '\t\t\tComparision fibre is '+ h[0].header['HIERARCH ESO TPL TYPE']
        if h[0].header['HIERARCH ESO TPL TYPE'] == 'OBTH':
            # get ThAr closest in time
            indice = sorted_indices[0]
            thar_fits_ob = dirout + ThAr_ref[indice].split('/')[-1][:-8]+'spec.ob.fits.S'
            thar_fits_co = dirout + ThAr_ref[indice].split('/')[-1][:-8]+'spec.co.fits.S'
            pkl_wsol = dirout + ThAr_ref[indice].split('/')[-1][:-8]+'wavsolpars.pkl'
            print "\t\t\tUnpickling reference wavelength solution from", pkl_wsol, " ..."
            wsol_dict = pickle.load(open(pkl_wsol,'r'))
            # Extract thAr lines from comparison orders
            lines_thar_co  = sci_S_co[:,1,:]
            iv_thar_co     = sci_S_co[:,2,:]
            All_Pixel_Centers_co = np.array([])
            All_Wavelengths_co   = np.array([])
            All_Orders_co        = np.array([])
            All_Centroids_co     = np.array([])
            All_Sigmas_co        = np.array([])
            All_Intensities_co   = np.array([])
            for order in range(22,n_useful):
                order_s = str(order)
                if (order < 10):
                    order_s = '0'+str(order)
                thar_order_orig = lines_thar_co[order-22,:]
                IV              = iv_thar_co[order-22,:]
                wei             = np.sqrt( IV )
                bkg             = GLOBALutils.Lines_mBack(thar_order_orig, IV,  thres_rel=3)        
                thar_order      = thar_order_orig - bkg
		    
                coeffs_pix2wav, coeffs_pix2sigma, pixel_centers, wavelengths, rms_ms, residuals, centroids, sigmas, intensities \
                    = GLOBALutils.Initial_Wav_Calibration( order_dir+'order_'+order_s+'o.iwdat', thar_order, order, wei, \
		                                                    rmsmax=5000000, minlines=10,FixEnds=True,Dump_Argon=dumpargon, \
		                                                    Dump_AllLines=True, Cheby=use_cheby)
		
                All_Pixel_Centers_co = np.append( All_Pixel_Centers_co, pixel_centers )
                All_Wavelengths_co   = np.append( All_Wavelengths_co, wavelengths )
                All_Orders_co        = np.append( All_Orders_co, np.zeros( len(pixel_centers) ) + order )
                All_Centroids_co     = np.append( All_Centroids_co, centroids)
                All_Sigmas_co        = np.append( All_Sigmas_co, sigmas)
                All_Intensities_co   = np.append( All_Intensities_co, intensities )
	    
            # get a global solution for the lines found
            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), wsol_dict['p1_co'], Cheby=use_cheby,\
		                                             maxrms=MRMS, Inv=Inverse_m,minlines=minlines_glob_co,\
                                                             order0=89,ntotal=n_useful,npix=len(thar_order),nx=ncoef_x,nm=ncoef_m)
	    
            # get shift with respect to reference ThAr
            p_shift, pix_centers, orders, wavelengths, I, rms_ms, residuals  = \
		      GLOBALutils.Global_Wav_Solution_vel_shift(G_pix_co, G_wav_co, G_ord_co,\
		                                                   np.ones(G_wav_co.shape), wsol_dict['p1_co'],\
		                                                   Cheby=True,Inv=True,maxrms=100,minlines=minlines_glob_co,\
                                                                   order0=89,ntotal=n_useful,npix=len(thar_order),nx=ncoef_x,nm=ncoef_m)
	    
            precision = rms_ms/np.sqrt(len(I))
            good_quality = True
            if (precision > 10):
                good_quality = False
		
        else:
            indice = sorted_indices_FP[0]
            thfp_fits_co = dirout + ThFP_ref[indice].split('/')[-1][:-8]+'spec.co.fits.S'
            pkl_wsol = dirout + ThFP_ref[indice].split('/')[-1][:-8]+'wavsolpars.pkl'
            print "\t\t\tUnpickling reference wavelength solution from", pkl_wsol, " ..."
            wsol_dict = pickle.load(open(pkl_wsol,'r'))
            lines_thar_co = np.zeros(sci_Ss_co.shape)
            lines_thar_co_ref = np.zeros(sci_Ss_co.shape)
            for si in range(S_flat_co_n.shape[0]):
                JI = np.where(S_flat_co_n[si,1]>0)[0]
                lines_thar_co[si,JI] = sci_S_co[si,1,JI] / S_flat_co_n[si,1,JI]
                lines_thar_co_ref[si,JI]  = pyfits.getdata(thfp_fits_co)[si,1,JI] / S_flat_co_n[si,1,JI]
                JI1 = np.where(lines_thar_co[si]<0)[0]
                JI2 = np.where(lines_thar_co_ref[si]<0)[0]
                lines_thar_co[si,JI1] = 0.
                lines_thar_co_ref[si,JI2] = 0.
            #lines_thar_co      = sci_S_co[:,1,:] / S_flat_co_simple_n
            #lines_thar_co_ref  = pyfits.getdata(thfp_fits_co)[:,1,:] / S_flat_co_simple_n
            rv_fps = []
            for order in range(nord_co):
                I = np.where(np.isnan(lines_thar_co[order]))[0]
                lines_thar_co[order][I]=0.
                I = np.where(np.isnan(lines_thar_co_ref[order]))[0]
                lines_thar_co_ref[order][I]=0.
                try:
                    tc = GLOBALutils.fp_base(lines_thar_co[order])
                    tcr = GLOBALutils.fp_base(lines_thar_co_ref[order])
                    IJ1 = np.where(tc!=0)[0]
                    IJ2 = np.where(tcr!=0)[0]
                    tc /= np.median(tc[IJ1])
                    tcr /= np.median(tcr[IJ2])
                    rv_fp = GLOBALutils.ccf_fp(tc,tcr,wsol_dict['p1_co'],order+22,order0=89,nx=ncoef_x,nm=ncoef_m,npix=len(tc))
                except:
                    rv_fp = -999
                rv_fps.append(rv_fp)
            #plot(rv_fps,'ro')

            rv_fps = np.array(rv_fps)
            I = np.where(rv_fps!=-999)[0]
            rv_fps = rv_fps[I]
            rv_fps = GLOBALutils.sig_cli2(rv_fps,ns=3.)
            #plot(rv_fps,'ro')
            #show()
            #print np.median(rv_fps),np.sqrt(np.var(rv_fps))/np.sqrt(float(len(rv_fps)))
            fp_shift = np.median(rv_fps)
            p_sh = wsol_dict['p_shift'] * 299792458. * 1e-6
            fp_shift += p_sh
            p_shift = 1e6*fp_shift/299792458.
            print '\t\t\tFP shift = ',fp_shift[0],'+-',np.sqrt(np.var(rv_fps))/np.sqrt(float(len(rv_fps))),'m/s'
            good_quality = True

        equis = np.arange( data.shape[1] )        

        for order in range(n_useful):
            m = order + 89
            chebs = GLOBALutils.Calculate_chebs(equis, m, Inverse=Inverse_m,order0=89,ntotal=n_useful,npix=data.shape[1],nx=ncoef_x,nm=ncoef_m)
            if good_quality:
                WavSol = lbary_ltopo * (1.0 + 1.0e-6*p_shift) * (1.0/m) * GLOBALutils.Joint_Polynomial_Cheby(wsol_dict['p1'],chebs,ncoef_x,ncoef_m)
            else:
                WavSol = lbary_ltopo * (1.0/m) * GLOBALutils.Joint_Polynomial_Cheby(wsol_dict['p1'],chebs,ncoef_x,ncoef_m)
            spec[0,order,:] = WavSol
            spec[1,order,:] = sci_S_ob[order,1, :]
            spec[2,order,:] = sci_S_ob[order,2, :]
            # Flat-fielded spectrum
            fn = S_flat_ob_n[order,1,:]
            L  = np.where( fn > 0 )
            spec[3,order,:][L] = sci_S_ob[order,1,:][L] / S_flat_ob_n[order,1,:][L]
            spec[4,order,:][L] = sci_S_ob[order,2,:][L] * ( S_flat_ob_n[order,1,:][L] ** 2 )

        # Continuum normalized spectrum
        ron  = h[0].header['HIERARCH ESO CORA CCD RON']
        gain = h[0].header['HIERARCH ESO CORA CCD GAIN']

        wav_temp, norm_spec = continuum.NORM2( spec[0,:,:],spec[3,:,:])
        for order in range(n_useful):
            L  = np.where( spec[1,order,:] != 0 )
            spec[5,order,:][L] = norm_spec[order][L]
            nJ = np.where(np.isnan(spec[5,order])==True)[0]
            nJ2 = np.where(np.isinf(spec[5,order])==True)[0]
            spec[5,order,nJ] = 1.0
            spec[5,order,nJ2] = 1.0
            ratio              = spec[3,order,:][L] / norm_spec[order][L]  
            spec[6,order,:][L] = spec[4,order,:][L] * (ratio ** 2 )
            spec[7,order,:][L] = ratio
            #spec[8,order,:][L] = ratio * S_flat_ob_n[order,1,:][L] / np.sqrt( ratio * S_flat_ob_n[order,1,:][L] / gain + ext_aperture*2*(ron/gain)**2 + sci_bac[order,:][L] / gain )
            spec[8,order,:][L] = ratio * S_flat_ob_n[order,1,:][L] / np.sqrt( ratio * S_flat_ob_n[order,1,:][L] / gain + (ron/gain)**2 )

            spl           = scipy.interpolate.splrep(np.arange(WavSol.shape[0]), WavSol,k=3)
            dlambda_dx    = scipy.interpolate.splev(np.arange(WavSol.shape[0]), spl, der=1)
            NN            = np.average(dlambda_dx)
            dlambda_dx    /= NN
            # clean-up of CRs in continuum-normalized spectrum. Set troublesome pixels to 1
            median_cn_spectrum = np.zeros( spec[5,order,:].shape )
            median_cn_spectrum[L] = scipy.signal.medfilt( spec[5,order,:][L], 7 )
            LK = np.where(spec[8,order] == 0.)[0]
            spec[8,order,LK] = 0.000001
            LL =  np.where(spec[5,order] > 1. + 5./spec[8,order])
            LL2 = np.where(spec[5,order] < - 5./spec[8,order])
            spec[8,order,LK] = 0.
            spec[5,order,:][LL] = 1
            spec[5,order,:][LL2] = 1
            spec[5,order,:][LK] = 0
            spec[6,order,:][LL] = spec[8,order,:][LL] ** 2
            spec[6,order,:][LL2] = spec[8,order,:][LL2] ** 2

            spec[9,order,:][L] = spec[5,order,:][L] * (dlambda_dx[L] ** 1) 
            spec[10,order,:][L] = spec[6,order,:][L] / (dlambda_dx[L] ** 2)

    if (not JustExtract):

        if DoClass:
            print '\t\tSpectral Analysis:'
            # spectral analysis
            # First, query SIMBAD with the object name
            query_success = False
            sp_type_query = 'None'
            #query_success,sp_type_query = GLOBALutils.simbad_query_obname(obname)
            # Now, query SIMBAD by coordinates if above not successful
            #if (not query_success):
            #    query_success,sp_type_query = GLOBALutils.simbad_query_coords('12:00:00','00:00:00')
            #print "\t\t\tSpectral type returned by SIMBAD query:",sp_type_query

            hdu = GLOBALutils.update_header(hdu,'HIERARCH SIMBAD SPTYP', sp_type_query)
            pars_file = dirout + fsim.split('/')[-1][:-8]+'_stellar_pars.txt'

            if os.access(pars_file,os.F_OK) == False or force_stellar_pars:
                print "\t\t\tEstimating atmospheric parameters:"
                Rx = np.around(1./np.sqrt(1./40000.**2 - 1./60000**2))
                spec2 = spec.copy()
                for i in range(spec.shape[1]):
                    IJ = np.where(spec[5,i]!=0.)[0]
                spec2[5,i,IJ] = GLOBALutils.convolve(spec[0,i,IJ],spec[5,i,IJ],Rx)
                T_eff, logg, Z, vsini, vel0, ccf = correlation.CCF(spec2,model_path=models_path,npools=npools)
                line = "%6d %4.1f %4.1f %8.1f %8.1f\n" % (T_eff,logg, Z, vsini, vel0)
                f = open(pars_file,'w')
                f.write(line)
                f.close()
            else:
                print "\t\t\tAtmospheric parameters loaded from file:"
                T_eff, logg, Z, vsini, vel0 = np.loadtxt(pars_file,unpack=True)

            print "\t\t\t\tT_eff=",T_eff,"log(g)=",logg,"Z=",Z,"vsin(i)=",vsini,"vel0",vel0

        else:
            T_eff, logg, Z, vsini, vel0 = -999,-999,-999,-999,-999

        T_eff_epoch = T_eff
        logg_epoch  = logg
        Z_epoch     = Z
        vsini_epoch = vsini
        vel0_epoch  = vel0
        hdu = GLOBALutils.update_header(hdu,'HIERARCH TEFF', float(T_eff))
        hdu = GLOBALutils.update_header(hdu,'HIERARCH LOGG', float(logg))
        hdu = GLOBALutils.update_header(hdu,'HIERARCH Z', Z)
        hdu = GLOBALutils.update_header(hdu,'HIERARCH VSINI', vsini)
        hdu = GLOBALutils.update_header(hdu,'HIERARCH VEL0', vel0)

        print "\t\tRadial Velocity analysis:"
        # assign mask
        sp_type, mask = GLOBALutils.get_mask_reffile(obname,reffile=reffile,base='../data/xc_masks/')
        print "\t\t\tWill use",sp_type,"mask for CCF."

        # Read in mask
        ml, mh, weight = np.loadtxt(mask,unpack=True)
        ml_v = GLOBALutils.ToVacuum( ml )
        mh_v = GLOBALutils.ToVacuum( mh )
       
        # make mask larger accounting for factor ~2 lower res in CORALIE w/r to HARPS
        av_m = 0.5*( ml_v + mh_v )
        ml_v -= (av_m - ml_v)
        mh_v += (mh_v - av_m)
        mask_hw_kms = (GLOBALutils.Constants.c/1e3) * 0.5*(mh_v - ml_v) / av_m

        #sigma_fout = stellar_pars_dir + obname + '_' +'sigma.txt'

        disp = GLOBALutils.get_disp(obname, reffile=reffile)
        if disp == 0:
            known_sigma = False
            if vsini != -999 and vsini != 0.:
                disp = vsini
            else:
                disp = 3.
        else:
            known_sigma = True

        mask_hw_wide = av_m * disp / (GLOBALutils.Constants.c/1.0e3)
        ml_v = av_m - mask_hw_wide
        mh_v = av_m + mask_hw_wide 

        print '\t\t\tComputing the CCF...'
        cond = True
        while (cond):
            # first rough correlation to find the minimum
            vels, xc_full, sn, nlines_ccf, W_ccf = \
                    GLOBALutils.XCor(spec, ml_v, mh_v, weight, 0, lbary_ltopo, vel_width=300,vel_step=3,\
                                          spec_order=9,iv_order=10,sn_order=8,max_vel_rough=300)
            xc_av = GLOBALutils.Average_CCF(xc_full, sn, sn_min=3.0, Simple=True, W=W_ccf)

            # Normalize the continuum of the CCF robustly with lowess   
            yy = scipy.signal.medfilt(xc_av,11)
            pred = lowess(yy, vels,frac=0.4,it=10,return_sorted=False)
            tck1 = scipy.interpolate.splrep(vels,pred,k=1)
            xc_av_orig = xc_av.copy()
            xc_av /= pred
            vel0_xc = vels[ np.argmin( xc_av ) ]
            rvels, rxc_av, rpred, rxc_av_orig, rvel0_xc = vels.copy(), \
            xc_av.copy(), pred.copy(), xc_av_orig.copy(), vel0_xc

            xc_av_rough = xc_av
            vels_rough  = vels
            if disp > 30:
                disp = 30.
            vel_width = np.maximum( 20.0, 6*disp )

            vels, xc_full, sn, nlines_ccf, W_ccf =\
                    GLOBALutils.XCor(spec, ml_v, mh_v, weight, vel0_xc, lbary_ltopo, vel_width=vel_width,vel_step=0.1,\
                                          spec_order=9,iv_order=10,sn_order=8,max_vel_rough=300)

            xc_av = GLOBALutils.Average_CCF(xc_full, sn, sn_min=3.0, Simple=True, W=W_ccf)
            pred = scipy.interpolate.splev(vels,tck1)
            xc_av /= pred

            moonsep_cor = h[0].header['HIERARCH ESO OBS MOON SEP']
		
            if sp_type == 'M5':
                moon_sig = 2.5
            elif sp_type == 'K5':
                moon_sig = 3.3
            else:
                moon_sig = 4.5

            p1,XCmodel,p1gau,XCmodelgau,Ls2 = GLOBALutils.XC_Final_Fit( vels, xc_av ,\
                                  sigma_res = 4, horder=8, moonv = refvel, moons = moon_sig, moon = False)

            #ldc = CoralieUtils.get_ldc(T_eff, logg, Z, 1.0, ldfile = 'lin_coe_sloan2.dat')
            #p1R, ROTmodel = CoralieUtils.XC_Final_Fit_Rot( vels, xc_av, ldc = ldc, vsini = vsini )
            moonmatters = False
            if (know_moon and here_moon):
                moonmatters = True
                ismoon = True
                confused = False
                p1_m,XCmodel_m,p1gau_m,XCmodelgau_m,Ls2_m = GLOBALutils.XC_Final_Fit( vels, xc_av , sigma_res = 4, horder=8, moonv = refvel, moons = moon_sig, moon = True)
                moon_flag = 1
            else:
                confused = False
                ismoon = False
                p1_m,XCmodel_m,p1gau_m,XCmodelgau_m,Ls2_m = p1,XCmodel,p1gau,XCmodelgau,Ls2
                moon_flag = 0

            SP = GLOBALutils.calc_bss2(vels,xc_av,p1gau)
            #SP = bspan[0]

            #print 'Bisector span:', SP
            if (not known_sigma):
                disp = np.floor(p1gau[2])
                if (disp < 3.0): 
                    disp = 3.0
                mask_hw_wide = av_m * disp / (GLOBALutils.Constants.c/1.0e3)
                ml_v = av_m - mask_hw_wide
                mh_v = av_m + mask_hw_wide            
                known_sigma = True
            else:
                cond = False

        xc_dict = {'vels':vels,'xc_av':xc_av,'XCmodelgau':XCmodelgau,'Ls2':Ls2,'refvel':refvel,\
		       'rvels':rvels,'rxc_av':rxc_av,'rpred':rpred,'rxc_av_orig':rxc_av_orig,\
		       'rvel0_xc':rvel0_xc,'xc_full':xc_full, 'p1':p1, 'sn':sn, 'p1gau':p1gau,\
		       'p1_m':p1_m,'XCmodel_m':XCmodel_m,'p1gau_m':p1gau_m,'Ls2_m':Ls2_m,\
		       'XCmodelgau_m':XCmodelgau_m}

        moon_dict = {'moonmatters':moonmatters,'moon_state':moon_state,'moonsep':moonsep,\
		         'lunation':lunation,'mephem':mephem,'texp':h[0].header['EXPTIME']}

        pkl_xc = dirout + fsim.split('/')[-1][:-8]+obname+'_XC_'+sp_type+'.pkl'
        pickle.dump( xc_dict, open( pkl_xc, 'w' ) )

        ccf_pdf = dirout + 'proc/' + fsim.split('/')[-1][:-4] + obname + '_XCs_' + sp_type + '.pdf'

        if not avoid_plot:
            GLOBALutils.plot_CCF(xc_dict,moon_dict,path=ccf_pdf)

        SNR_5130 = np.median(spec[8,30,1000:1101] )
        airmass  = h[0].header['HIERARCH ESO OBS TARG AIRMASS']
        seeing   = h[0].header['HIERARCH ESO OBS AMBI DIMM SEEING']
        
        if sp_type == 'G2':
            if T_eff < 6000:
                A = 0.06544
                B = 0.00146
                D = 0.24416
                C = 0.00181
            else:
                A = 0.09821
                B = 0.00014
                D = 0.33491
                C = 0.00113
        elif  sp_type == 'K5':
            A = 0.05348
            B = 0.00147	
            D = 0.20695
            C = 0.00321
        else:
            A = 0.05348
            B = 0.00147	
            D = 0.20695
            C = 0.00321

        RVerr =  B + ( 1.6 + 0.2 * p1gau[2] ) * A / np.round(SNR_5130)
        depth_fact = 1. + p1gau[0]/(p1gau[2]*np.sqrt(2*np.pi))

        if depth_fact >= 1.:
            RVerr2 = -999.000
        else:
            if sp_type == 'G2':
                depth_fact = (1 - 0.62) / (1 - depth_fact)
            else:
                depth_fact = (1 - 0.59) / (1 - depth_fact)
            RVerr2 = RVerr * depth_fact
            if (RVerr2 <= 0.009):
                RVerr2 = 0.009

        if not good_quality:
            RVerr2 = np.sqrt(0.03**2 + RVerr2**2)

        BSerr = D / float(np.round(SNR_5130)) + C

        RV     = np.around(p1gau_m[1],4)  
        BS     = np.around(SP,4)   
        RVerr2 = np.around(RVerr2,4)
        BSerr = np.around(BSerr,4)

        print '\t\t\tRV = '+str(RV)+' +- '+str(RVerr2)
        print '\t\t\tBS = '+str(BS)+' +- '+str(BSerr)

        bjd_out = 2400000.5 + mbjd
        T_eff_err = 100
        logg_err = 0.5
        Z_err = 0.5
        vsini_err = 2
        XC_min = np.abs(np.around(np.min(XCmodel),2))

        SNR_5130 = np.around(SNR_5130)
        SNR_5130_R = np.around(SNR_5130*np.sqrt(2.9))

        disp_epoch = np.around(p1gau_m[2],1)
        hdu = GLOBALutils.update_header(hdu,'RV', RV)
        hdu = GLOBALutils.update_header(hdu,'RV_E', RVerr2)
        hdu = GLOBALutils.update_header(hdu,'BS', BS)
        hdu = GLOBALutils.update_header(hdu,'BS_E', BSerr)
        hdu = GLOBALutils.update_header(hdu,'DISP', disp_epoch)
        hdu = GLOBALutils.update_header(hdu,'SNR', SNR_5130)
        hdu = GLOBALutils.update_header(hdu,'SNR_R', SNR_5130_R)
        hdu = GLOBALutils.update_header(hdu,'INST', 'CORALIE')
        hdu = GLOBALutils.update_header(hdu,'RESOL', '60000')
        hdu = GLOBALutils.update_header(hdu,'PIPELINE', 'CERES')
        hdu = GLOBALutils.update_header(hdu,'XC_MIN', XC_min)
        hdu = GLOBALutils.update_header(hdu,'BJD_OUT', bjd_out)
	    
        line_out = "%-15s %18.8f %9.4f %7.4f %9.3f %5.3f   coralie   ceres   60000 %6d %5.2f %5.2f %5.1f %4.2f %5.2f %6.1f %4d %s\n"%\
                      (obname, bjd_out, RV, RVerr2, BS, BSerr, T_eff_epoch, logg_epoch, Z_epoch, vsini_epoch, XC_min, disp_epoch,\
        TEXP, SNR_5130_R, ccf_pdf)
        f_res.write(line_out)
        if (os.access( dirout + fout,os.F_OK)):
            os.remove( dirout + fout)
        hdu.writeto( dirout + fout )

    else:
        print "\t\tReading spectral file from", fout
        spec = pyfits.getdata( fout )

f_res.close()
<filename>coralie/coraliepipe.py
import sys
base = '../'

sys.path.append(base+"utils/Continuum")
sys.path.append(base+"utils/Correlation")
sys.path.append(base+"utils/GLOBALutils")
sys.path.append(base+"utils/OptExtract")

baryc_dir= base+'utils/SSEphem/'
sys.path.append(baryc_dir)
ephemeris='DEc403'

import matplotlib
matplotlib.use("Agg") 
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages

from pylab import *


# ceres modules
import coralieutils
import continuum
import correlation
import GLOBALutils
import Marsh

# other useful modules
import argparse
import ephem
import jplephem
from math import radians as rad
from astropy.io import fits as pyfits
import pickle
import os
import scipy
import scipy.interpolate
from scipy import interpolate

import statsmodels.api as sm
lowess = sm.nonparametric.lowess

# Recive input parameters
parser = argparse.ArgumentParser()
parser.add_argument('directorio')
parser.add_argument('-avoid_plot', action="store_true", default=False)
parser.add_argument('-dirout',default='default')
parser.add_argument('-do_class', action="store_true", default=False)
parser.add_argument('-just_extract', action="store_true", default=False)
parser.add_argument('-npools', default=1)
parser.add_argument('-o2do',default='all')
parser.add_argument('-reffile',default='default')


args   = parser.parse_args()
dirin            = args.directorio
avoid_plot       = args.avoid_plot
dirout           = args.dirout
DoClass          = args.do_class
JustExtract      = args.just_extract
npools           = int(args.npools)
object2do        = args.o2do
reffile          = args.reffile

if dirin[-1] != '/':
    dirin = dirin + '/'

if dirout == 'default':
    dirout = dirin[:-1]+'_red/'

if not os.access(dirout,os.F_OK):
    os.system('mkdir '+dirout)
if os.access(dirout+'proc',os.F_OK):
    os.system('rm -r '+dirout+'proc')
os.system('mkdir '+dirout+'proc')

f_res = open(dirout+'proc/'+'results.txt','w')

if reffile == 'default':
    reffile = dirin+'reffile.txt'

####### GLOBAL VARIABLES #####
force_pre_process  = False
force_flat_extract = False
force_thar_extract = False
force_thfp_extract = False
force_tharxc       = False
force_thar_wavcal  = False
force_thfp_wavcal  = False
force_sci_extract  = False
force_spectral_file_build = True
force_stellar_pars = False
dumpargon          = False
minlines_glob_ob   = 700
minlines_glob_co   = 500

Inverse_m          = True
use_cheby          = True
MRMS               = 100   # max rms in m/s, global wav solution

trace_degree       = 4
Marsh_alg          = 0
ext_aperture       = 3
NSigma_Marsh       = 5
NCosmic_Marsh      = 10
S_Marsh            = 0.4
N_Marsh            = 3      # grado polinomio 
min_extract_col    = 50
max_extract_col    = 2000
n_useful           = 70    # up to which order do we care?

# Number of coefficients for the global wavelength solution
ncoef_x            = 4
ncoef_m            = 6
npar_wsol = (min(ncoef_x,ncoef_m) + 1) * (2*max(ncoef_x,ncoef_m) - min(ncoef_x,ncoef_m) + 2) / 2

models_path = base+"data/COELHO_MODELS/R_40000b/"    # path to the synthetic models 
order_dir   = base+"coralie/wavcals/"  # path to reference files for the wavelength solution

#############################

# file containing the log
log = dirout+'night.log'

print "\n\n\tCoralie Euler1.2m  PIPELINE\n"
print "\tRAW data is in ",dirin
print "\tProducts of reduction will be in",dirout
print '\n'

# classification of input images according to header info
biases, ob_flats, co_flats, ob_loc, co_loc, ThAr_ref, ThFP_ref,\
        simThAr_sci,sim_FP_sci,ThAr_ref_dates,ThFP_ref_dates,obnames,\
	obnames_FP,exptimes, exptimes_FP, flats = coralieutils.FileClassify(dirin,log)

# Pre-process
if ( (( len(ob_flats) > 0) and (os.access(dirout+'FlatOb.fits',os.F_OK) == False)) or \
     (( len(co_flats) > 0) and (os.access(dirout+'FlatCo.fits',os.F_OK) == False)) or \
     (( len(flats) > 0) and (os.access(dirout+'Flat.fits',os.F_OK) == False)) or \
     (os.access(dirout+'trace.pkl',os.F_OK) == False)  or \
     (os.access(dirout+'MasterBias.fits',os.F_OK) == False)  or \
     (force_pre_process) ):
    print "\tNo previous pre-processing files or found"
    pre_process = 1
else:
    print "\tPre-processing files found, going straight to extraction"
    pre_process = 0

if (pre_process == 1):
    print "\tGenerating Master calibration frames..."
    # median combine Biases
    MasterBias, RO_bias, GA_bias = coralieutils.MedianCombine(biases,ZF=0.)
    hdu = pyfits.PrimaryHDU( MasterBias )
    if (os.access(dirout+'MasterBias.fits',os.F_OK)):
        os.remove(dirout+'MasterBias.fits')
    hdu.writeto(dirout+'MasterBias.fits')
    print "\t\t-> Masterbias: done!"

    if len(flats) > 0:
    	    # median combine list of co flats2
	    Flat,RO_flat,GA_flat = coralieutils.MedianCombine(flats,ZF=MasterBias)
	    hdu = pyfits.PrimaryHDU(Flat)
	    if (os.access(dirout+'Flat.fits',os.F_OK)):
		os.remove(dirout+'Flat.fits')
	    hdu.writeto(dirout+'Flat.fits')

    if len(ob_flats) > 0:
	    # median combine list of ob flats
	    Flat_ob, RO_ob, GA_ob = coralieutils.MedianCombine(ob_flats,ZF=MasterBias)
	    # save this file for later reference
	    hdu = pyfits.PrimaryHDU( Flat_ob )
	    if (os.access(dirout+'FlatOb.fits',os.F_OK)):
		os.remove(dirout+'FlatOb.fits')
	    hdu.writeto(dirout+'FlatOb.fits')
    else:
	Flat_ob = Flat

    if len(co_flats) > 0:
	    # median combine list of co flats
	    Flat_co,RO_co,GA_co = coralieutils.MedianCombine(co_flats,ZF=MasterBias)
	    hdu = pyfits.PrimaryHDU(Flat_co)
	    if (os.access(dirout+'FlatCo.fits',os.F_OK)):
		os.remove(dirout+'FlatCo.fits')
	    hdu.writeto(dirout+'FlatCo.fits')
    else:
	Flat_co = Flat



    print "\t\t-> Masterflats: done!"
    # Find orders & traces
    print "\tTracing echelle orders..."

    if len(ob_flats)>0 and len(co_flats)>0:
	    c_ob, nord_ob = GLOBALutils.get_them(Flat_ob, 8, trace_degree,maxords=-1,mode=1)
	    c_co, nord_co = GLOBALutils.get_them(Flat_co, 8, trace_degree,maxords=-1,startfrom=300,mode=1)
    else:
        c_all, nord_all = GLOBALutils.get_them(Flat, 5, trace_degree,maxords=-1,mode=1,nsigmas=3)
	GA_co,GA_ob = GA_flat, GA_flat
	RO_co,RO_ob = RO_flat, RO_flat

	c_ob = c_all[:22]
	c_co = c_all[22]
	i = 23
	while i < len(c_all)-1:
		c_ob = np.vstack((c_ob,c_all[i]))
		c_co = np.vstack((c_co,c_all[i+1]))
		i+=2
	nord_co, nord_ob = len(c_co),len(c_ob)
        print '\t', nord_ob, 'object orders found...'
        print '\t', nord_co, 'comparison orders found...'


    if len(ob_flats)>0 and len(co_flats)>0:
    	trace_dict = {'c_ob':c_ob,'c_co':c_co,
                  'nord_ob':nord_ob, 'nord_co':nord_co,
                  'GA_ob': GA_ob, 'RO_ob': RO_ob,
                  'GA_co': GA_co, 'RO_co': RO_co}
    else:
	trace_dict = {'c_all':c_all,'c_ob':c_ob,'c_co':c_co,
                  'nord_ob':nord_ob, 'nord_co':nord_co,'nord_all':nord_all,
                  'GA_ob': GA_ob, 'RO_ob': RO_ob,
                  'GA_co': GA_co, 'RO_co': RO_co}

    pickle.dump( trace_dict, open( dirout+"trace.pkl", 'w' ) )

else:
    trace_dict = pickle.load( open( dirout+"trace.pkl", 'r' ) )
    c_co = trace_dict['c_co']
    c_ob = trace_dict['c_ob']
    nord_ob = trace_dict['nord_ob']
    nord_co = trace_dict['nord_co']
    if 'c_all' in trace_dict.keys():
        c_all    = trace_dict['c_all']
        nord_all = trace_dict['nord_all']
    # recover GA*, RO*
    GA_ob = trace_dict['GA_ob']
    RO_ob = trace_dict['RO_ob']
    GA_co = trace_dict['GA_co']
    RO_co = trace_dict['RO_co']
    # recover flats & master bias
    if len(ob_flats)>0:
    	h = pyfits.open(dirout+'FlatOb.fits')
    	Flat_ob = h[0].data
    else:
	h = pyfits.open(dirout+'Flat.fits')
    	Flat_ob = h[0].data
    if len(co_flats)>0:
        h = pyfits.open(dirout+'Flat.fits')
        Flat_co = h[0].data
    else:
	h = pyfits.open(dirout+'Flat.fits')
    	Flat_co = h[0].data
    h = pyfits.open(dirout+'MasterBias.fits')
    MasterBias = h[0].data


if len(ob_flats)>0 and len(co_flats)>0:
	c_all = GLOBALutils.Mesh(c_ob,c_co)

print '\n\tExtraction of Flat calibration frames:'
# Extract flat spectra, object
P_ob_fits = dirout + 'P_ob.fits'
S_flat_ob_fits = dirout +'S_flat_ob.fits'
P_ob = np.zeros( Flat_ob.shape )
S_flat_ob = np.zeros((nord_ob, 3, Flat_ob.shape[1]) )

if ( os.access(P_ob_fits,os.F_OK) == False ) or ( os.access(S_flat_ob_fits,os.F_OK) == False ) or \
   (force_flat_extract):
    print "\t\tNo extracted flat object spectra found or extraction forced, extracting and saving..."
    
    print "\t\t\tWill extract",nord_ob,"orders for object fibre..."
    P_ob = GLOBALutils.obtain_P(Flat_ob,c_ob,ext_aperture,RO_ob,\
                                    GA_ob,NSigma_Marsh, S_Marsh, \
                    N_Marsh, Marsh_alg, min_extract_col,\
                    max_extract_col, npools)

    S_flat_ob  = GLOBALutils.optimal_extraction(Flat_ob,P_ob,c_ob,ext_aperture,\
                                                RO_ob,GA_ob,S_Marsh,NCosmic_Marsh,\
                                                min_extract_col,max_extract_col,npools)

    # write P_on and S_flat_ob as fits files
    if (os.access(P_ob_fits,os.F_OK)):
        os.remove( P_ob_fits )
    if (os.access(S_flat_ob_fits,os.F_OK)):
        os.remove( S_flat_ob_fits )
    
    hdu = pyfits.PrimaryHDU( P_ob )
    hdu.writeto( P_ob_fits )
    hdu = pyfits.PrimaryHDU( S_flat_ob )
    hdu.writeto( S_flat_ob_fits )

   
else:
    print "\t\tExtracted flat object spectra found, loading..."
    P_ob       = pyfits.getdata( P_ob_fits )
    S_flat_ob  = pyfits.getdata( S_flat_ob_fits )

# Extract flat spectra, comparison
P_co_fits = dirout + 'P_co.fits'
S_flat_co_fits = dirout +'S_flat_co.fits'
P_co = np.zeros( Flat_co.shape )
S_flat_co = np.zeros((nord_co, 3, Flat_co.shape[1]) )
if ( os.access(P_co_fits,os.F_OK) == False ) or ( os.access(S_flat_co_fits,os.F_OK) == False ) or (force_flat_extract):
    print "\t\tNo extracted flat comparison spectra found or extraction forced, extracting and saving..."
    
    print "\t\t\tWill extract",nord_co,"orders for comparison fibre"
    P_co = GLOBALutils.obtain_P(Flat_co,c_co,ext_aperture,RO_co,\
                                    GA_co,NSigma_Marsh, S_Marsh, \
                    N_Marsh, Marsh_alg, min_extract_col,\
                    max_extract_col, npools)
    
    S_flat_co  = GLOBALutils.optimal_extraction(Flat_co,P_co,c_co,ext_aperture,RO_co,GA_co,\
                                                S_Marsh,NCosmic_Marsh,min_extract_col,\
                                                max_extract_col,npools) 

    # write P_on and S_flat_co as fits files
    if (os.access(P_co_fits,os.F_OK)):
        os.remove( P_co_fits )
    if (os.access(S_flat_co_fits,os.F_OK)):
        os.remove( S_flat_co_fits )

    hdu = pyfits.PrimaryHDU( P_co )
    hdu.writeto( P_co_fits )
    hdu = pyfits.PrimaryHDU( S_flat_co )
    hdu.writeto( S_flat_co_fits )
   
else:
    print "\t\tExtracted flat comparison spectra found, loading..."
    P_co       = pyfits.getdata( P_co_fits )
    S_flat_co  = pyfits.getdata( S_flat_co_fits )

# Normalize flat field spectra.
S_flat_ob_n, maxvals_ob = GLOBALutils.FlatNormalize_single( S_flat_ob, mid=int(0.5*S_flat_ob.shape[2]))
S_flat_co_n, maxvals_co = GLOBALutils.FlatNormalize_single( S_flat_co, mid=int(0.5*S_flat_co.shape[2]))

print '\n\tExtraction of ThAr calibration frames:'
# Extract all ThAr files
for fsim in ThAr_ref:
    hthar = pyfits.open( fsim )
    dthar = coralieutils.OverscanTrim( pyfits.getdata( fsim ) )
    ron  = hthar[0].header['HIERARCH ESO CORA CCD RON']
    gain = hthar[0].header['HIERARCH ESO CORA CCD GAIN']
    thar_fits_ob = dirout + fsim.split('/')[-1][:-8]+'spec.ob.fits.S'
    thar_fits_co = dirout + fsim.split('/')[-1][:-8]+'spec.co.fits.S'  
    if ( os.access(thar_fits_ob,os.F_OK) == False ) or \
       ( os.access(thar_fits_co,os.F_OK) == False ) or \
       (force_thar_extract):

        print "\t\tNo previous extraction or extraction forced for ThAr file", fsim, "extracting..."
	thar_S_ob  = GLOBALutils.optimal_extraction(dthar,P_ob,c_ob,ext_aperture,ron,gain,\
                                                    S_Marsh,100.,min_extract_col,max_extract_col,npools)
	thar_S_co  = GLOBALutils.optimal_extraction(dthar,P_co,c_co,ext_aperture,ron,gain,\
                                                    S_Marsh,100.,min_extract_col,max_extract_col,npools)           
       
        # save as fits file
        if (os.access(thar_fits_ob,os.F_OK)):
            os.remove( thar_fits_ob )
        if (os.access(thar_fits_co,os.F_OK)):
            os.remove( thar_fits_co )
            
        hdu = pyfits.PrimaryHDU( thar_S_ob )
        hdu.writeto( thar_fits_ob )
        hdu = pyfits.PrimaryHDU( thar_S_co )
        hdu.writeto( thar_fits_co )
    else:
        print "\t\tThAr file", fsim, "all ready extracted, loading..."

print "\n\tWavelength solution of ThAr calibration spectra:"
# compute wavelength calibration files
sorted_ThAr_dates = np.argsort( ThAr_ref_dates )
p0_array = np.zeros( (len(ThAr_ref_dates), npar_wsol) )

for i in range(len(sorted_ThAr_dates)):
    index = sorted_ThAr_dates[i]
    wavsol_pkl   = dirout + ThAr_ref[index].split('/')[-1][:-8]+'wavsolpars.pkl'
    thar_fits_ob = dirout + ThAr_ref[index].split('/')[-1][:-8]+'spec.ob.fits.S'
    thar_fits_co = dirout + ThAr_ref[index].split('/')[-1][:-8]+'spec.co.fits.S'
    if ( os.access(wavsol_pkl,os.F_OK) == False ) or (force_thar_wavcal):
        print "\t\tComputing wavelength solution of ThAr file", ThAr_ref[index] 
        hthar = pyfits.open( ThAr_ref[index] )
        mjd, mjd0 = coralieutils.mjd_fromheader( hthar )
        thar_S_ob = pyfits.getdata( thar_fits_ob )
        thar_S_co = pyfits.getdata( thar_fits_co )

        lines_thar_ob  = thar_S_ob[:,1,:]
        iv_thar_ob     = thar_S_ob[:,2,:]
        lines_thar_co  = thar_S_co[:,1,:]
        iv_thar_co     = thar_S_co[:,2,:]

        All_Pixel_Centers = np.array([])
        All_Wavelengths   = np.array([])
        All_Orders        = np.array([])
        All_Centroids     = np.array([])
        All_Sigmas        = np.array([])
        All_Intensities   = np.array([])

        for order in range(n_useful):
            order_s = str(order)
            if (order < 10):
                order_s = '0'+str(order)
            
            thar_order_orig = lines_thar_ob[order,:]
            IV              = iv_thar_ob[order,:]
            wei             = np.sqrt( IV )
            bkg             = GLOBALutils.Lines_mBack(thar_order_orig, IV,  thres_rel=3)        
            thar_order      = thar_order_orig - bkg

            coeffs_pix2wav, coeffs_pix2sigma, pixel_centers, wavelengths,\
            rms_ms, residuals, centroids, sigmas, intensities \
                = GLOBALutils.Initial_Wav_Calibration(order_dir+'order_'+order_s+'o.iwdat',\
                                                      thar_order,order,wei,rmsmax=5000000,\
                                                      minlines=10,FixEnds=True,Dump_Argon=dumpargon,\
                                                      Dump_AllLines=True, Cheby=use_cheby)
            if (order == 35): 
                if (use_cheby):
                    Global_ZP = GLOBALutils.Cheby_eval( coeffs_pix2wav, 1023, len(thar_order) )
                else:
                    Global_ZP = scipy.polyval( coeffs_pix2wav, 0.0 )

            All_Pixel_Centers = np.append( All_Pixel_Centers, pixel_centers )
            All_Wavelengths   = np.append( All_Wavelengths, wavelengths )
            All_Orders        = np.append( All_Orders, np.zeros( len(pixel_centers) ) + order )
            All_Centroids     = np.append( All_Centroids, centroids)
            All_Sigmas        = np.append( All_Sigmas, sigmas)
            All_Intensities   = np.append( All_Intensities, intensities )

        p0 = np.zeros( npar_wsol )
        p0[0] =  (35+89) * Global_ZP 
        p1, G_pix, G_ord, G_wav, II, rms_ms, G_res = \
            GLOBALutils.Fit_Global_Wav_Solution(All_Pixel_Centers, All_Wavelengths, All_Orders,\
                                                np.ones(All_Intensities.shape), p0, Cheby=use_cheby,\
                                                maxrms=MRMS, Inv=Inverse_m,minlines=700,order0=89, \
                                                ntotal=n_useful,npix=len(thar_order),nx=ncoef_x,nm=ncoef_m)

        # Now calibrate COMPARISON orders. Use p1 above as p0
        All_Pixel_Centers_co = np.array([])
        All_Wavelengths_co   = np.array([])
        All_Orders_co        = np.array([])
        All_Centroids_co     = np.array([])
        All_Sigmas_co        = np.array([])
        All_Intensities_co   = np.array([])

        for order in range(22,n_useful):
            order_s = str(order)
            if (order < 10):
                order_s = '0'+str(order)
            
            thar_order_orig = lines_thar_co[order-22,:]
            IV              = iv_thar_co[order-22,:]
            wei             = np.sqrt( IV )
            bkg             = GLOBALutils.Lines_mBack(thar_order_orig, IV,  thres_rel=3)        
            thar_order      = thar_order_orig - bkg
            
            coeffs_pix2wav, coeffs_pix2sigma, pixel_centers, wavelengths, rms_ms, residuals, centroids, sigmas, intensities \
                = GLOBALutils.Initial_Wav_Calibration( order_dir+'order_'+order_s+'o.iwdat', thar_order, order, wei, \
                                                            rmsmax=5000000, minlines=10,FixEnds=True,Dump_Argon=dumpargon, \
                                                            Dump_AllLines=True, Cheby=use_cheby)
          
            All_Pixel_Centers_co = np.append( All_Pixel_Centers_co, pixel_centers )
            All_Wavelengths_co   = np.append( All_Wavelengths_co, wavelengths )
            All_Orders_co        = np.append( All_Orders_co, np.zeros( len(pixel_centers) ) + order )
            All_Centroids_co     = np.append( All_Centroids_co, centroids)
            All_Sigmas_co        = np.append( All_Sigmas_co, sigmas)
            All_Intensities_co   = np.append( All_Intensities_co, intensities )

        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), p1, Cheby=use_cheby,\
                                                     maxrms=MRMS, Inv=Inverse_m,minlines=500,order0=89,ntotal=n_useful,npix=len(thar_order),nx=ncoef_x,nm=ncoef_m)
           
        # end COMPARISON orders. 
        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,
                 'p1_co':p1_co, 'G_pix_co':G_pix_co, 'G_ord_co':G_ord_co, 'G_wav_co':G_wav_co, 'II_co':II_co, 'rms_ms_co':rms_ms_co,\
                     'G_res_co':G_res_co, 'All_Centroids_co':All_Centroids_co}
        pickle.dump( pdict, open( wavsol_pkl, 'w' ) )
        #print "Median sigma:", np.median( All_Sigmas )    
        p0_array[i,:] = p1

    else:
        print "\t\tUsing previously computed wavelength solution in file",wavsol_pkl
        pdict         = pickle.load(open(wavsol_pkl,'r'))
        p0_array[i,:] = pdict['p1']

p0_G  = np.median(p0_array,axis=0)

if len(ThFP_ref) > 0:
    print '\n\tExtraction of Fabry-Perot calibration frames:'
else:
    print '\n\tNo Fabry-Perot calibration images found, moving on'
# Now extract ThAr-FP images
for fsim in ThFP_ref:
    hthfp = pyfits.open( fsim )
    thfp_fits_ob = dirout + fsim.split('/')[-1][:-8]+'spec.ob.fits.S'
    thfp_fits_co = dirout + fsim.split('/')[-1][:-8]+'spec.co.fits.S'
 
    if ( os.access(thfp_fits_ob,os.F_OK) == False ) or \
       ( os.access(thfp_fits_co,os.F_OK) == False ) or \
       (force_thfp_extract):
        print "\t\tNo previous extraction or extraction forced for ThFP file", fsim, "extracting..."
        dthfp = coralieutils.OverscanTrim( pyfits.getdata( fsim ) )
        Centers = np.zeros((len(c_all),dthfp.shape[1]))
        for i in range(c_all.shape[0]):
            Centers[i,:]=scipy.polyval(c_all[i,:],np.arange(len(Centers[i,:])))
        bac = GLOBALutils.get_scat(dthfp,Centers,span=5)
        dthfp -= bac

	thfp_S_ob  = GLOBALutils.optimal_extraction(dthfp,P_ob,c_ob,ext_aperture,\
                                                    hthfp[0].header['HIERARCH ESO CORA CCD RON'],\
                                                    hthfp[0].header['HIERARCH ESO CORA CCD GAIN'],\
                                                    S_Marsh,100.,min_extract_col,max_extract_col,npools)
	thfp_S_co  = GLOBALutils.optimal_extraction(dthfp,P_co,c_co,ext_aperture,\
                                                    hthfp[0].header['HIERARCH ESO CORA CCD RON'],
                                                    hthfp[0].header['HIERARCH ESO CORA CCD GAIN'],
                                                    S_Marsh,100.,min_extract_col,max_extract_col,npools)
        # save as fits file
        if (os.access(thfp_fits_ob,os.F_OK)):
            os.remove( thfp_fits_ob )
        if (os.access(thfp_fits_co,os.F_OK)):
            os.remove( thfp_fits_co )
            
        hdu = pyfits.PrimaryHDU( thfp_S_ob )
        hdu.writeto( thfp_fits_ob )
        hdu = pyfits.PrimaryHDU( thfp_S_co )
        hdu.writeto( thfp_fits_co )
    else:
        print "\t\tFP file", fsim, "all ready extracted, loading..."

# Now calibrate the ThFP spectra with the closest ThAr spectrum
print '\n\tWavelength solution of Fabry-Perot spectra with closest ThAr spectrum:'
for fsim in ThFP_ref:
    hthfp = pyfits.open( fsim )
    mjd, mjd0 = coralieutils.mjd_fromheader(hthfp)
    im = np.argmin(np.absolute(np.array(ThAr_ref_dates) - mjd))
    wavsol_dict = pickle.load(open(dirout + ThAr_ref[im].split('/')[-1][:-8]+'wavsolpars.pkl','r'))
    thfp_fits_ob = dirout + fsim.split('/')[-1][:-8]+'spec.ob.fits.S'
    thfp_fits_co = dirout + fsim.split('/')[-1][:-8]+'spec.co.fits.S'
    wavsol_pkl_fp = dirout + fsim.split('/')[-1][:-8]+'wavsolpars.pkl'
    if ( os.access(wavsol_pkl_fp,os.F_OK) == False ) or (force_thfp_wavcal):
            print '\t\tCalibrating', fsim,'...'
	    thar_fp = pyfits.getdata(thfp_fits_ob)
	    lines_thar_ob  = thar_fp[:,1,:]
	    iv_thar_ob     = thar_fp[:,2,:]

	    All_Pixel_Centers = np.array([])
	    All_Wavelengths   = np.array([])
	    All_Orders        = np.array([])
	    All_Centroids     = np.array([])
	    All_Sigmas        = np.array([])
	    All_Intensities   = np.array([])

	    for order in range(n_useful):
		order_s = str(order)
		if (order < 10):
		    order_s = '0'+str(order)
		thar_order_orig = lines_thar_ob[order,:]
		IV              = iv_thar_ob[order,:]
		wei             = np.sqrt( IV )
		bkg             = GLOBALutils.Lines_mBack(thar_order_orig, IV,  thres_rel=3)        
		thar_order      = thar_order_orig - bkg

		coeffs_pix2wav, coeffs_pix2sigma, pixel_centers, wavelengths, rms_ms, residuals, centroids, sigmas, intensities \
		            = GLOBALutils.Initial_Wav_Calibration( order_dir+'order_'+order_s+'o.iwdat', thar_order, order, wei, \
		                                                        rmsmax=5000000, minlines=10,FixEnds=True,Dump_Argon=dumpargon, \
		                                                        Dump_AllLines=True, Cheby=use_cheby)

		All_Pixel_Centers = np.append( All_Pixel_Centers, pixel_centers )
		All_Wavelengths   = np.append( All_Wavelengths, wavelengths )
		All_Orders        = np.append( All_Orders, np.zeros( len(pixel_centers) ) + order )
		All_Centroids     = np.append( All_Centroids, centroids)
		All_Sigmas        = np.append( All_Sigmas, sigmas)
		All_Intensities   = np.append( All_Intensities, intensities )
		
	    p1, G_pix, G_ord, G_wav, II, rms_ms, G_res = \
		GLOBALutils.Fit_Global_Wav_Solution(All_Pixel_Centers, All_Wavelengths, All_Orders,\
		                                             np.ones(All_Intensities.shape), p0_G, Cheby=use_cheby,\
		                                             maxrms=100, Inv=Inverse_m, minlines=minlines_glob_ob,\
                                                             order0=89,ntotal=n_useful,npix=len(thar_order),nx=ncoef_x,nm=ncoef_m)

	    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), wavsol_dict['p1'],\
		                                                   Cheby=True,Inv=True,maxrms=100,minlines=minlines_glob_ob,\
								   order0=89,ntotal=n_useful,npix=len(thar_order),nx=ncoef_x,nm=ncoef_m)

	    pdict = {'p1':p1,'p_shift':p_shift,'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, 'p1_co':wavsol_dict['p1_co']}
	    pickle.dump( pdict, open( wavsol_pkl_fp, 'w' ) )
    else:
	print '\t\tFP spectrum', fsim, 'already calibrated, loading...'

### start of science frame reductions ###

new_list = []
new_list_obnames = []
new_list_texp = []
for i in range(len(simThAr_sci)):
    fsim = simThAr_sci[i]
    obname = obnames[i]
    texp = exptimes[i]
    if (object2do == 'all'):
        new_list.append(fsim)
        new_list_obnames.append( obname )
        new_list_texp.append( texp )

    else:
        if (obname == object2do):
            new_list.append(fsim)
            new_list_obnames.append( obname )
            new_list_texp.append( texp )

print '\n\tThe following targets will be processed:'
for nlisti in range(len(new_list)):
	print '\t\t'+new_list_obnames[nlisti]

# Does any image have a special requirement for dealing with the moonlight?
if os.access(dirin + 'moon_corr.txt', os.F_OK):
    fmoon = open(dirin + 'moon_corr.txt','r')
    moon_lns = fmoon.readlines()
    spec_moon = []
    use_moon = []
    for line in moon_lns:
        spec_moon.append(line.split()[0])
        if line.split()[1] == '0':
            use_moon.append(False)
        else:
            use_moon.append(True)
else:
    spec_moon = []
    use_moon = []

spec_moon = np.array(spec_moon)
use_moon  = np.array(use_moon)

# now extract the images
for nlisti in range(len(new_list)):	
    fsim   = new_list[ nlisti ]
    obname = new_list_obnames[ nlisti ]
    TEXP   = np.around(new_list_texp[ nlisti ])

    know_moon = False
    if fsim.split('/')[-1] in spec_moon:
        I = np.where(fsim.split('/')[-1] == spec_moon)[0]
        know_moon = True
        here_moon = use_moon[I]

    # get header h of image
    h = pyfits.open(fsim)
    print '\n'
    print "\t--> Working on image: ", fsim
    
    # get mjd and mjd0
    mjd,mjd0 = coralieutils.mjd_fromheader(h)

    #  get gain and readnoise of object 
    ronoise = h[0].header['HIERARCH ESO CORA CCD RON']
    gain    = h[0].header['HIERARCH ESO CORA CCD GAIN']
    
    # Object name

    print "\t\tObject name:",obname

    # Open file, trim, overscan subtract and MasterBias subtract
    data = h[0].data
    data = coralieutils.OverscanTrim(data)
    data -= MasterBias

    bacfile = dirout + 'BAC_' + fsim.split('/')[-1][:-4]+'fits'''
    if (os.access(bacfile,os.F_OK))== False:
        Centers = np.zeros((len(c_all),data.shape[1]))
        for i in range(c_all.shape[0]):
            Centers[i,:]=scipy.polyval(c_all[i,:],np.arange(len(Centers[i,:])))
        bac = GLOBALutils.get_scat(data,Centers,span=5)
        hdbac = pyfits.PrimaryHDU( bac )
        hdbac.writeto(bacfile)
    else:
        bac = pyfits.getdata(bacfile)
    data -= bac

    ra,dec = h[0].header['RA'],h[0].header['DEC']

    ra2,dec2 = GLOBALutils.getcoords(obname,mjd,filen=reffile)
    if ra2 !=0 and dec2 != 0:
        ra = ra2
        dec = dec2
    else:
        print '\t\tUsing the coordinates found in the image header.'

    # Find lambda_bary/lambda_topo using JPLEPHEM
    altitude    = h[0].header['HIERARCH ESO OBS GEO ALTITUDE']
    latitude    = h[0].header['HIERARCH ESO OBS GEO LATITU']
    longitude   = h[0].header['HIERARCH ESO OBS GEO LONGIT']
    epoch       = h[0].header['HIERARCH ESO OBS EQUICAT']

    iers                    = GLOBALutils.JPLiers( baryc_dir, mjd-999.0, mjd+999.0 )
    obsradius, R0           = GLOBALutils.JPLR0( latitude, altitude)
    obpos                   = GLOBALutils.obspos( longitude, obsradius, R0 )

    jplephem.set_ephemeris_dir( baryc_dir , ephemeris )
    jplephem.set_observer_coordinates( obpos[0], obpos[1], obpos[2] )

    res         = jplephem.doppler_fraction(ra/15.0, dec, int(mjd), mjd%1, 1, 0.0)
    lbary_ltopo = 1.0 + res['frac'][0]
    bcvel_baryc = ( lbary_ltopo - 1.0 ) * 2.99792458E5

    print "\t\tBarycentric velocity:", bcvel_baryc

    res = jplephem.pulse_delay(ra/15.0, dec, int(mjd), mjd%1, 1, 0.0)   
    mbjd = mjd + res['delay'][0] / (3600.0 * 24.0)

    # Moon Phase Calculations
    gobs      = ephem.Observer()  
    gobs.name = 'Swiss1.2'  
    gobs.lat  = rad(latitude)  # lat/long in decimal degrees  
    gobs.long = rad(longitude)

    DDATE = h[0].header['HIERARCH ESO CORA SHUTTER START DATE']
    HHOUR = h[0].header['HIERARCH ESO CORA SHUTTER START HOUR']

    Mho = str(int(HHOUR))
    if len(Mho)<2:
        Mho = '0'+Mho
    mins = (HHOUR - int(Mho))*60.
    Mmi = str(int(mins))
    if len(Mmi)<2:
        Mmi = '0'+Mmi
    segs = (mins - int(Mmi))*60.
    if segs<10:
        Mse = '0'+str(segs)[:5]
    else:
        Mse = str(segs)[:6]

    gobs.date = str(DDATE[:4]) + '-' +  str(DDATE[4:6]) + '-' + str(DDATE[6:]) + ' ' +  Mho + ':' + Mmi +':' +Mse

    mephem = ephem.Moon()
    mephem.compute(gobs)
    Mcoo = jplephem.object_track("Moon", int(mjd), float(mjd%1), 1, 0.0)
    Mp   = jplephem.barycentric_object_track("Moon", int(mjd), float(mjd%1), 1, 0.0)
    Sp   = jplephem.barycentric_object_track("Sun", int(mjd), float(mjd%1), 1, 0.0)
    res  = jplephem.object_doppler("Moon", int(mjd), mjd%1, 1, 0.0)
    lunation,moon_state,moonsep,moonvel = GLOBALutils.get_lunar_props(ephem,gobs,Mcoo,Mp,Sp,res,ra,dec)
    refvel = bcvel_baryc + moonvel
    print '\t\tRadial Velocity of sacttered moonlight:',refvel

    sorted_indices    = np.argsort( np.abs( np.array(ThAr_ref_dates) - mjd ) )
    sorted_indices_FP = np.argsort( np.abs( np.array(ThFP_ref_dates) - mjd ) )

    print '\t\tExtraction:'
    # optimally and simply extract spectra
    sci_fits_ob = dirout + fsim.split('/')[-1][:-8]+'spec.ob.fits.S'
    sci_fits_co = dirout + fsim.split('/')[-1][:-8]+'spec.co.fits.S'
    sci_fits_ob_simple = dirout + fsim.split('/')[-1][:-8]+'spec.simple.ob.fits.S'
    sci_fits_co_simple = dirout + fsim.split('/')[-1][:-8]+'spec.simple.co.fits.S'
    sci_fits_bac = dirout + fsim.split('/')[-1][:-8]+'spec.simple.bac.fits.S'
    if ( os.access(sci_fits_ob,os.F_OK) == False ) or \
        ( os.access(sci_fits_co,os.F_OK) == False ) or \
        ( os.access(sci_fits_ob_simple,os.F_OK) == False ) or \
        ( os.access(sci_fits_co_simple,os.F_OK) == False ) or \
        ( os.access(sci_fits_bac,os.F_OK) == False ) or \
        (force_sci_extract):

        print "\t\t\tNo previous extraction or extraction forced for science file", fsim, "extracting..."
        sci_Ss_ob = GLOBALutils.simple_extraction(data,c_ob,ext_aperture,\
                                                  min_extract_col,max_extract_col,npools)
        sci_Ss_co = GLOBALutils.simple_extraction(data,c_co,ext_aperture,\
                                                  min_extract_col,max_extract_col,npools)
        sci_S_ob  = GLOBALutils.optimal_extraction(data,P_ob,c_ob,ext_aperture,\
                                                   h[0].header['HIERARCH ESO CORA CCD RON'],\
                                                   h[0].header['HIERARCH ESO CORA CCD GAIN'],\
                                                   S_Marsh,NCosmic_Marsh,\
                                                   min_extract_col,max_extract_col,npools)
        sci_S_co  = GLOBALutils.optimal_extraction(data,P_co,c_co,ext_aperture,\
                                                   h[0].header['HIERARCH ESO CORA CCD RON'],\
                                                   h[0].header['HIERARCH ESO CORA CCD GAIN'],\
                                                   S_Marsh,2.*NCosmic_Marsh,\
                                                   min_extract_col,max_extract_col,npools)
        sci_bac =  GLOBALutils.simple_extraction(bac,c_ob,ext_aperture,\
                                                  min_extract_col,max_extract_col,npools)       

        # save as fits file
        if (os.access(sci_fits_ob,os.F_OK)):
            os.remove( sci_fits_ob )
        if (os.access(sci_fits_co,os.F_OK)):
            os.remove( sci_fits_co )
        if (os.access(sci_fits_ob_simple,os.F_OK)):
            os.remove( sci_fits_ob_simple )
        if (os.access(sci_fits_co_simple,os.F_OK)):
            os.remove( sci_fits_co_simple )
        if (os.access(sci_fits_bac,os.F_OK)):
            os.remove( sci_fits_bac )          
        hdu = pyfits.PrimaryHDU( sci_S_ob )
        hdu.writeto( sci_fits_ob )
        hdu = pyfits.PrimaryHDU( sci_S_co )
        hdu.writeto( sci_fits_co )
        hdu = pyfits.PrimaryHDU( sci_Ss_ob )
        hdu.writeto( sci_fits_ob_simple )
        hdu = pyfits.PrimaryHDU( sci_Ss_co )
        hdu.writeto( sci_fits_co_simple )
        hdu = pyfits.PrimaryHDU( sci_bac )
        hdu.writeto( sci_fits_bac )
    else:
        print '\t\t\t'+fsim, "has already been extracted, reading in product fits files..."
        sci_S_ob = pyfits.getdata( sci_fits_ob )
        sci_S_co = pyfits.getdata( sci_fits_co )
        sci_Ss_ob = pyfits.getdata( sci_fits_ob_simple )
        sci_Ss_co = pyfits.getdata( sci_fits_ob_simple )
        sci_bac = pyfits.getdata( sci_fits_bac )
    
    fout = 'proc/'+ obname + '_' + \
        h[0].header['HIERARCH ESO CORA SHUTTER START DATE'] + '_' +\
        'UT' + fsim[-17:-9] + '_' +\
        'sp.fits'

    #Build spectra
    if ( os.access(dirout+fout ,os.F_OK) == False ) or (force_spectral_file_build):
        # initialize file that will have the spectra
        # n_useful should be nord_ob, but we still have not calibrated that bluest order -- TODO
        spec = np.zeros((11, n_useful, data.shape[1]))
        hdu = pyfits.PrimaryHDU( spec )
        hdu = GLOBALutils.update_header(hdu,'HIERARCH MJD', mjd)
        hdu = GLOBALutils.update_header(hdu,'HIERARCH MBJD', mbjd)
        hdu = GLOBALutils.update_header(hdu,'HIERARCH SHUTTER START DATE', h[0].header['HIERARCH ESO CORA SHUTTER START DATE'] )
        hdu = GLOBALutils.update_header(hdu,'HIERARCH SHUTTER START UT',  h[0].header['HIERARCH ESO CORA SHUTTER START HOUR'])
        hdu = GLOBALutils.update_header(hdu,'HIERARCH TEXP (S)',h[0].header['HIERARCH ESO OBS TEXP'])
        hdu = GLOBALutils.update_header(hdu,'HIERARCH FLUX WEIGHTED MEAN F ',h[0].header['HIERARCH ESO CORA PM FLUX TMMEAN'])
        hdu = GLOBALutils.update_header(hdu,'HIERARCH BARYCENTRIC CORRECTION (KM/S)', bcvel_baryc)
        hdu = GLOBALutils.update_header(hdu,'HIERARCH (LAMBDA_BARY / LAMBDA_TOPO)', lbary_ltopo)    
        hdu = GLOBALutils.update_header(hdu,'HIERARCH TARGET NAME', obname)
        hdu = GLOBALutils.update_header(hdu,'HIERARCH RA',h[0].header['HIERARCH ESO TEL TARG ALPHA'])
        hdu = GLOBALutils.update_header(hdu,'HIERARCH DEC',h[0].header['HIERARCH ESO TEL TARG DELTA'])
        hdu = GLOBALutils.update_header(hdu,'HIERARCH RA DEG',h[0].header['RA'])
        hdu = GLOBALutils.update_header(hdu,'HIERARCH DEC DEG',h[0].header['DEC'])
        hdu = GLOBALutils.update_header(hdu,'HIERARCH RA BARY',ra)
        hdu = GLOBALutils.update_header(hdu,'HIERARCH DEC BARY',dec)
        hdu = GLOBALutils.update_header(hdu,'HIERARCH EQUINOX',h[0].header['HIERARCH ESO OBS EQUICAT'])
        hdu = GLOBALutils.update_header(hdu,'HIERARCH OBS LATITUDE',h[0].header['HIERARCH ESO OBS GEO LATITU'])
        hdu = GLOBALutils.update_header(hdu,'HIERARCH OBS LONGITUDE',h[0].header['HIERARCH ESO OBS GEO LONGIT'])
        hdu = GLOBALutils.update_header(hdu,'HIERARCH OBS ALTITUDE',h[0].header['HIERARCH ESO OBS GEO ALTITUDE'])
        hdu = GLOBALutils.update_header(hdu,'HIERARCH TARG AIRMASS',h[0].header['HIERARCH ESO OBS TARG AIRMASS'])

        print '\t\tWavelength calibration:'
        print '\t\t\tComparision fibre is '+ h[0].header['HIERARCH ESO TPL TYPE']
        if h[0].header['HIERARCH ESO TPL TYPE'] == 'OBTH':
            # get ThAr closest in time
            indice = sorted_indices[0]
            thar_fits_ob = dirout + ThAr_ref[indice].split('/')[-1][:-8]+'spec.ob.fits.S'
            thar_fits_co = dirout + ThAr_ref[indice].split('/')[-1][:-8]+'spec.co.fits.S'
            pkl_wsol = dirout + ThAr_ref[indice].split('/')[-1][:-8]+'wavsolpars.pkl'
            print "\t\t\tUnpickling reference wavelength solution from", pkl_wsol, " ..."
            wsol_dict = pickle.load(open(pkl_wsol,'r'))
            # Extract thAr lines from comparison orders
            lines_thar_co  = sci_S_co[:,1,:]
            iv_thar_co     = sci_S_co[:,2,:]
            All_Pixel_Centers_co = np.array([])
            All_Wavelengths_co   = np.array([])
            All_Orders_co        = np.array([])
            All_Centroids_co     = np.array([])
            All_Sigmas_co        = np.array([])
            All_Intensities_co   = np.array([])
            for order in range(22,n_useful):
                order_s = str(order)
                if (order < 10):
                    order_s = '0'+str(order)
                thar_order_orig = lines_thar_co[order-22,:]
                IV              = iv_thar_co[order-22,:]
                wei             = np.sqrt( IV )
                bkg             = GLOBALutils.Lines_mBack(thar_order_orig, IV,  thres_rel=3)        
                thar_order      = thar_order_orig - bkg
		    
                coeffs_pix2wav, coeffs_pix2sigma, pixel_centers, wavelengths, rms_ms, residuals, centroids, sigmas, intensities \
                    = GLOBALutils.Initial_Wav_Calibration( order_dir+'order_'+order_s+'o.iwdat', thar_order, order, wei, \
		                                                    rmsmax=5000000, minlines=10,FixEnds=True,Dump_Argon=dumpargon, \
		                                                    Dump_AllLines=True, Cheby=use_cheby)
		
                All_Pixel_Centers_co = np.append( All_Pixel_Centers_co, pixel_centers )
                All_Wavelengths_co   = np.append( All_Wavelengths_co, wavelengths )
                All_Orders_co        = np.append( All_Orders_co, np.zeros( len(pixel_centers) ) + order )
                All_Centroids_co     = np.append( All_Centroids_co, centroids)
                All_Sigmas_co        = np.append( All_Sigmas_co, sigmas)
                All_Intensities_co   = np.append( All_Intensities_co, intensities )
	    
            # get a global solution for the lines found
            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), wsol_dict['p1_co'], Cheby=use_cheby,\
		                                             maxrms=MRMS, Inv=Inverse_m,minlines=minlines_glob_co,\
                                                             order0=89,ntotal=n_useful,npix=len(thar_order),nx=ncoef_x,nm=ncoef_m)
	    
            # get shift with respect to reference ThAr
            p_shift, pix_centers, orders, wavelengths, I, rms_ms, residuals  = \
		      GLOBALutils.Global_Wav_Solution_vel_shift(G_pix_co, G_wav_co, G_ord_co,\
		                                                   np.ones(G_wav_co.shape), wsol_dict['p1_co'],\
		                                                   Cheby=True,Inv=True,maxrms=100,minlines=minlines_glob_co,\
                                                                   order0=89,ntotal=n_useful,npix=len(thar_order),nx=ncoef_x,nm=ncoef_m)
	    
            precision = rms_ms/np.sqrt(len(I))
            good_quality = True
            if (precision > 10):
                good_quality = False
		
        else:
            indice = sorted_indices_FP[0]
            thfp_fits_co = dirout + ThFP_ref[indice].split('/')[-1][:-8]+'spec.co.fits.S'
            pkl_wsol = dirout + ThFP_ref[indice].split('/')[-1][:-8]+'wavsolpars.pkl'
            print "\t\t\tUnpickling reference wavelength solution from", pkl_wsol, " ..."
            wsol_dict = pickle.load(open(pkl_wsol,'r'))
            lines_thar_co = np.zeros(sci_Ss_co.shape)
            lines_thar_co_ref = np.zeros(sci_Ss_co.shape)
            for si in range(S_flat_co_n.shape[0]):
                JI = np.where(S_flat_co_n[si,1]>0)[0]
                lines_thar_co[si,JI] = sci_S_co[si,1,JI] / S_flat_co_n[si,1,JI]
                lines_thar_co_ref[si,JI]  = pyfits.getdata(thfp_fits_co)[si,1,JI] / S_flat_co_n[si,1,JI]
                JI1 = np.where(lines_thar_co[si]<0)[0]
                JI2 = np.where(lines_thar_co_ref[si]<0)[0]
                lines_thar_co[si,JI1] = 0.
                lines_thar_co_ref[si,JI2] = 0.
            #lines_thar_co      = sci_S_co[:,1,:] / S_flat_co_simple_n
            #lines_thar_co_ref  = pyfits.getdata(thfp_fits_co)[:,1,:] / S_flat_co_simple_n
            rv_fps = []
            for order in range(nord_co):
                I = np.where(np.isnan(lines_thar_co[order]))[0]
                lines_thar_co[order][I]=0.
                I = np.where(np.isnan(lines_thar_co_ref[order]))[0]
                lines_thar_co_ref[order][I]=0.
                try:
                    tc = GLOBALutils.fp_base(lines_thar_co[order])
                    tcr = GLOBALutils.fp_base(lines_thar_co_ref[order])
                    IJ1 = np.where(tc!=0)[0]
                    IJ2 = np.where(tcr!=0)[0]
                    tc /= np.median(tc[IJ1])
                    tcr /= np.median(tcr[IJ2])
                    rv_fp = GLOBALutils.ccf_fp(tc,tcr,wsol_dict['p1_co'],order+22,order0=89,nx=ncoef_x,nm=ncoef_m,npix=len(tc))
                except:
                    rv_fp = -999
                rv_fps.append(rv_fp)
            #plot(rv_fps,'ro')

            rv_fps = np.array(rv_fps)
            I = np.where(rv_fps!=-999)[0]
            rv_fps = rv_fps[I]
            rv_fps = GLOBALutils.sig_cli2(rv_fps,ns=3.)
            #plot(rv_fps,'ro')
            #show()
            #print np.median(rv_fps),np.sqrt(np.var(rv_fps))/np.sqrt(float(len(rv_fps)))
            fp_shift = np.median(rv_fps)
            p_sh = wsol_dict['p_shift'] * 299792458. * 1e-6
            fp_shift += p_sh
            p_shift = 1e6*fp_shift/299792458.
            print '\t\t\tFP shift = ',fp_shift[0],'+-',np.sqrt(np.var(rv_fps))/np.sqrt(float(len(rv_fps))),'m/s'
            good_quality = True

        equis = np.arange( data.shape[1] )        

        for order in range(n_useful):
            m = order + 89
            chebs = GLOBALutils.Calculate_chebs(equis, m, Inverse=Inverse_m,order0=89,ntotal=n_useful,npix=data.shape[1],nx=ncoef_x,nm=ncoef_m)
            if good_quality:
                WavSol = lbary_ltopo * (1.0 + 1.0e-6*p_shift) * (1.0/m) * GLOBALutils.Joint_Polynomial_Cheby(wsol_dict['p1'],chebs,ncoef_x,ncoef_m)
            else:
                WavSol = lbary_ltopo * (1.0/m) * GLOBALutils.Joint_Polynomial_Cheby(wsol_dict['p1'],chebs,ncoef_x,ncoef_m)
            spec[0,order,:] = WavSol
            spec[1,order,:] = sci_S_ob[order,1, :]
            spec[2,order,:] = sci_S_ob[order,2, :]
            # Flat-fielded spectrum
            fn = S_flat_ob_n[order,1,:]
            L  = np.where( fn > 0 )
            spec[3,order,:][L] = sci_S_ob[order,1,:][L] / S_flat_ob_n[order,1,:][L]
            spec[4,order,:][L] = sci_S_ob[order,2,:][L] * ( S_flat_ob_n[order,1,:][L] ** 2 )

        # Continuum normalized spectrum
        ron  = h[0].header['HIERARCH ESO CORA CCD RON']
        gain = h[0].header['HIERARCH ESO CORA CCD GAIN']

        wav_temp, norm_spec = continuum.NORM2( spec[0,:,:],spec[3,:,:])
        for order in range(n_useful):
            L  = np.where( spec[1,order,:] != 0 )
            spec[5,order,:][L] = norm_spec[order][L]
            nJ = np.where(np.isnan(spec[5,order])==True)[0]
            nJ2 = np.where(np.isinf(spec[5,order])==True)[0]
            spec[5,order,nJ] = 1.0
            spec[5,order,nJ2] = 1.0
            ratio              = spec[3,order,:][L] / norm_spec[order][L]  
            spec[6,order,:][L] = spec[4,order,:][L] * (ratio ** 2 )
            spec[7,order,:][L] = ratio
            #spec[8,order,:][L] = ratio * S_flat_ob_n[order,1,:][L] / np.sqrt( ratio * S_flat_ob_n[order,1,:][L] / gain + ext_aperture*2*(ron/gain)**2 + sci_bac[order,:][L] / gain )
            spec[8,order,:][L] = ratio * S_flat_ob_n[order,1,:][L] / np.sqrt( ratio * S_flat_ob_n[order,1,:][L] / gain + (ron/gain)**2 )

            spl           = scipy.interpolate.splrep(np.arange(WavSol.shape[0]), WavSol,k=3)
            dlambda_dx    = scipy.interpolate.splev(np.arange(WavSol.shape[0]), spl, der=1)
            NN            = np.average(dlambda_dx)
            dlambda_dx    /= NN
            # clean-up of CRs in continuum-normalized spectrum. Set troublesome pixels to 1
            median_cn_spectrum = np.zeros( spec[5,order,:].shape )
            median_cn_spectrum[L] = scipy.signal.medfilt( spec[5,order,:][L], 7 )
            LK = np.where(spec[8,order] == 0.)[0]
            spec[8,order,LK] = 0.000001
            LL =  np.where(spec[5,order] > 1. + 5./spec[8,order])
            LL2 = np.where(spec[5,order] < - 5./spec[8,order])
            spec[8,order,LK] = 0.
            spec[5,order,:][LL] = 1
            spec[5,order,:][LL2] = 1
            spec[5,order,:][LK] = 0
            spec[6,order,:][LL] = spec[8,order,:][LL] ** 2
            spec[6,order,:][LL2] = spec[8,order,:][LL2] ** 2

            spec[9,order,:][L] = spec[5,order,:][L] * (dlambda_dx[L] ** 1) 
            spec[10,order,:][L] = spec[6,order,:][L] / (dlambda_dx[L] ** 2)

    if (not JustExtract):

        if DoClass:
            print '\t\tSpectral Analysis:'
            # spectral analysis
            # First, query SIMBAD with the object name
            query_success = False
            sp_type_query = 'None'
            #query_success,sp_type_query = GLOBALutils.simbad_query_obname(obname)
            # Now, query SIMBAD by coordinates if above not successful
            #if (not query_success):
            #    query_success,sp_type_query = GLOBALutils.simbad_query_coords('12:00:00','00:00:00')
            #print "\t\t\tSpectral type returned by SIMBAD query:",sp_type_query

            hdu = GLOBALutils.update_header(hdu,'HIERARCH SIMBAD SPTYP', sp_type_query)
            pars_file = dirout + fsim.split('/')[-1][:-8]+'_stellar_pars.txt'

            if os.access(pars_file,os.F_OK) == False or force_stellar_pars:
                print "\t\t\tEstimating atmospheric parameters:"
                Rx = np.around(1./np.sqrt(1./40000.**2 - 1./60000**2))
                spec2 = spec.copy()
                for i in range(spec.shape[1]):
                    IJ = np.where(spec[5,i]!=0.)[0]
                spec2[5,i,IJ] = GLOBALutils.convolve(spec[0,i,IJ],spec[5,i,IJ],Rx)
                T_eff, logg, Z, vsini, vel0, ccf = correlation.CCF(spec2,model_path=models_path,npools=npools)
                line = "%6d %4.1f %4.1f %8.1f %8.1f\n" % (T_eff,logg, Z, vsini, vel0)
                f = open(pars_file,'w')
                f.write(line)
                f.close()
            else:
                print "\t\t\tAtmospheric parameters loaded from file:"
                T_eff, logg, Z, vsini, vel0 = np.loadtxt(pars_file,unpack=True)

            print "\t\t\t\tT_eff=",T_eff,"log(g)=",logg,"Z=",Z,"vsin(i)=",vsini,"vel0",vel0

        else:
            T_eff, logg, Z, vsini, vel0 = -999,-999,-999,-999,-999

        T_eff_epoch = T_eff
        logg_epoch  = logg
        Z_epoch     = Z
        vsini_epoch = vsini
        vel0_epoch  = vel0
        hdu = GLOBALutils.update_header(hdu,'HIERARCH TEFF', float(T_eff))
        hdu = GLOBALutils.update_header(hdu,'HIERARCH LOGG', float(logg))
        hdu = GLOBALutils.update_header(hdu,'HIERARCH Z', Z)
        hdu = GLOBALutils.update_header(hdu,'HIERARCH VSINI', vsini)
        hdu = GLOBALutils.update_header(hdu,'HIERARCH VEL0', vel0)

        print "\t\tRadial Velocity analysis:"
        # assign mask
        sp_type, mask = GLOBALutils.get_mask_reffile(obname,reffile=reffile,base='../data/xc_masks/')
        print "\t\t\tWill use",sp_type,"mask for CCF."

        # Read in mask
        ml, mh, weight = np.loadtxt(mask,unpack=True)
        ml_v = GLOBALutils.ToVacuum( ml )
        mh_v = GLOBALutils.ToVacuum( mh )
       
        # make mask larger accounting for factor ~2 lower res in CORALIE w/r to HARPS
        av_m = 0.5*( ml_v + mh_v )
        ml_v -= (av_m - ml_v)
        mh_v += (mh_v - av_m)
        mask_hw_kms = (GLOBALutils.Constants.c/1e3) * 0.5*(mh_v - ml_v) / av_m

        #sigma_fout = stellar_pars_dir + obname + '_' +'sigma.txt'

        disp = GLOBALutils.get_disp(obname, reffile=reffile)
        if disp == 0:
            known_sigma = False
            if vsini != -999 and vsini != 0.:
                disp = vsini
            else:
                disp = 3.
        else:
            known_sigma = True

        mask_hw_wide = av_m * disp / (GLOBALutils.Constants.c/1.0e3)
        ml_v = av_m - mask_hw_wide
        mh_v = av_m + mask_hw_wide 

        print '\t\t\tComputing the CCF...'
        cond = True
        while (cond):
            # first rough correlation to find the minimum
            vels, xc_full, sn, nlines_ccf, W_ccf = \
                    GLOBALutils.XCor(spec, ml_v, mh_v, weight, 0, lbary_ltopo, vel_width=300,vel_step=3,\
                                          spec_order=9,iv_order=10,sn_order=8,max_vel_rough=300)
            xc_av = GLOBALutils.Average_CCF(xc_full, sn, sn_min=3.0, Simple=True, W=W_ccf)

            # Normalize the continuum of the CCF robustly with lowess   
            yy = scipy.signal.medfilt(xc_av,11)
            pred = lowess(yy, vels,frac=0.4,it=10,return_sorted=False)
            tck1 = scipy.interpolate.splrep(vels,pred,k=1)
            xc_av_orig = xc_av.copy()
            xc_av /= pred
            vel0_xc = vels[ np.argmin( xc_av ) ]
            rvels, rxc_av, rpred, rxc_av_orig, rvel0_xc = vels.copy(), \
            xc_av.copy(), pred.copy(), xc_av_orig.copy(), vel0_xc

            xc_av_rough = xc_av
            vels_rough  = vels
            if disp > 30:
                disp = 30.
            vel_width = np.maximum( 20.0, 6*disp )

            vels, xc_full, sn, nlines_ccf, W_ccf =\
                    GLOBALutils.XCor(spec, ml_v, mh_v, weight, vel0_xc, lbary_ltopo, vel_width=vel_width,vel_step=0.1,\
                                          spec_order=9,iv_order=10,sn_order=8,max_vel_rough=300)

            xc_av = GLOBALutils.Average_CCF(xc_full, sn, sn_min=3.0, Simple=True, W=W_ccf)
            pred = scipy.interpolate.splev(vels,tck1)
            xc_av /= pred

            moonsep_cor = h[0].header['HIERARCH ESO OBS MOON SEP']
		
            if sp_type == 'M5':
                moon_sig = 2.5
            elif sp_type == 'K5':
                moon_sig = 3.3
            else:
                moon_sig = 4.5

            p1,XCmodel,p1gau,XCmodelgau,Ls2 = GLOBALutils.XC_Final_Fit( vels, xc_av ,\
                                  sigma_res = 4, horder=8, moonv = refvel, moons = moon_sig, moon = False)

            #ldc = CoralieUtils.get_ldc(T_eff, logg, Z, 1.0, ldfile = 'lin_coe_sloan2.dat')
            #p1R, ROTmodel = CoralieUtils.XC_Final_Fit_Rot( vels, xc_av, ldc = ldc, vsini = vsini )
            moonmatters = False
            if (know_moon and here_moon):
                moonmatters = True
                ismoon = True
                confused = False
                p1_m,XCmodel_m,p1gau_m,XCmodelgau_m,Ls2_m = GLOBALutils.XC_Final_Fit( vels, xc_av , sigma_res = 4, horder=8, moonv = refvel, moons = moon_sig, moon = True)
                moon_flag = 1
            else:
                confused = False
                ismoon = False
                p1_m,XCmodel_m,p1gau_m,XCmodelgau_m,Ls2_m = p1,XCmodel,p1gau,XCmodelgau,Ls2
                moon_flag = 0

            SP = GLOBALutils.calc_bss2(vels,xc_av,p1gau)
            #SP = bspan[0]

            #print 'Bisector span:', SP
            if (not known_sigma):
                disp = np.floor(p1gau[2])
                if (disp < 3.0): 
                    disp = 3.0
                mask_hw_wide = av_m * disp / (GLOBALutils.Constants.c/1.0e3)
                ml_v = av_m - mask_hw_wide
                mh_v = av_m + mask_hw_wide            
                known_sigma = True
            else:
                cond = False

        xc_dict = {'vels':vels,'xc_av':xc_av,'XCmodelgau':XCmodelgau,'Ls2':Ls2,'refvel':refvel,\
		       'rvels':rvels,'rxc_av':rxc_av,'rpred':rpred,'rxc_av_orig':rxc_av_orig,\
		       'rvel0_xc':rvel0_xc,'xc_full':xc_full, 'p1':p1, 'sn':sn, 'p1gau':p1gau,\
		       'p1_m':p1_m,'XCmodel_m':XCmodel_m,'p1gau_m':p1gau_m,'Ls2_m':Ls2_m,\
		       'XCmodelgau_m':XCmodelgau_m}

        moon_dict = {'moonmatters':moonmatters,'moon_state':moon_state,'moonsep':moonsep,\
		         'lunation':lunation,'mephem':mephem,'texp':h[0].header['EXPTIME']}

        pkl_xc = dirout + fsim.split('/')[-1][:-8]+obname+'_XC_'+sp_type+'.pkl'
        pickle.dump( xc_dict, open( pkl_xc, 'w' ) )

        ccf_pdf = dirout + 'proc/' + fsim.split('/')[-1][:-4] + obname + '_XCs_' + sp_type + '.pdf'

        if not avoid_plot:
            GLOBALutils.plot_CCF(xc_dict,moon_dict,path=ccf_pdf)

        SNR_5130 = np.median(spec[8,30,1000:1101] )
        airmass  = h[0].header['HIERARCH ESO OBS TARG AIRMASS']
        seeing   = h[0].header['HIERARCH ESO OBS AMBI DIMM SEEING']
        
        if sp_type == 'G2':
            if T_eff < 6000:
                A = 0.06544
                B = 0.00146
                D = 0.24416
                C = 0.00181
            else:
                A = 0.09821
                B = 0.00014
                D = 0.33491
                C = 0.00113
        elif  sp_type == 'K5':
            A = 0.05348
            B = 0.00147	
            D = 0.20695
            C = 0.00321
        else:
            A = 0.05348
            B = 0.00147	
            D = 0.20695
            C = 0.00321

        RVerr =  B + ( 1.6 + 0.2 * p1gau[2] ) * A / np.round(SNR_5130)
        depth_fact = 1. + p1gau[0]/(p1gau[2]*np.sqrt(2*np.pi))

        if depth_fact >= 1.:
            RVerr2 = -999.000
        else:
            if sp_type == 'G2':
                depth_fact = (1 - 0.62) / (1 - depth_fact)
            else:
                depth_fact = (1 - 0.59) / (1 - depth_fact)
            RVerr2 = RVerr * depth_fact
            if (RVerr2 <= 0.009):
                RVerr2 = 0.009

        if not good_quality:
            RVerr2 = np.sqrt(0.03**2 + RVerr2**2)

        BSerr = D / float(np.round(SNR_5130)) + C

        RV     = np.around(p1gau_m[1],4)  
        BS     = np.around(SP,4)   
        RVerr2 = np.around(RVerr2,4)
        BSerr = np.around(BSerr,4)

        print '\t\t\tRV = '+str(RV)+' +- '+str(RVerr2)
        print '\t\t\tBS = '+str(BS)+' +- '+str(BSerr)

        bjd_out = 2400000.5 + mbjd
        T_eff_err = 100
        logg_err = 0.5
        Z_err = 0.5
        vsini_err = 2
        XC_min = np.abs(np.around(np.min(XCmodel),2))

        SNR_5130 = np.around(SNR_5130)
        SNR_5130_R = np.around(SNR_5130*np.sqrt(2.9))

        disp_epoch = np.around(p1gau_m[2],1)
        hdu = GLOBALutils.update_header(hdu,'RV', RV)
        hdu = GLOBALutils.update_header(hdu,'RV_E', RVerr2)
        hdu = GLOBALutils.update_header(hdu,'BS', BS)
        hdu = GLOBALutils.update_header(hdu,'BS_E', BSerr)
        hdu = GLOBALutils.update_header(hdu,'DISP', disp_epoch)
        hdu = GLOBALutils.update_header(hdu,'SNR', SNR_5130)
        hdu = GLOBALutils.update_header(hdu,'SNR_R', SNR_5130_R)
        hdu = GLOBALutils.update_header(hdu,'INST', 'CORALIE')
        hdu = GLOBALutils.update_header(hdu,'RESOL', '60000')
        hdu = GLOBALutils.update_header(hdu,'PIPELINE', 'CERES')
        hdu = GLOBALutils.update_header(hdu,'XC_MIN', XC_min)
        hdu = GLOBALutils.update_header(hdu,'BJD_OUT', bjd_out)
	    
        line_out = "%-15s %18.8f %9.4f %7.4f %9.3f %5.3f   coralie   ceres   60000 %6d %5.2f %5.2f %5.1f %4.2f %5.2f %6.1f %4d %s\n"%\
                      (obname, bjd_out, RV, RVerr2, BS, BSerr, T_eff_epoch, logg_epoch, Z_epoch, vsini_epoch, XC_min, disp_epoch,\
        TEXP, SNR_5130_R, ccf_pdf)
        f_res.write(line_out)
        if (os.access( dirout + fout,os.F_OK)):
            os.remove( dirout + fout)
        hdu.writeto( dirout + fout )

    else:
        print "\t\tReading spectral file from", fout
        spec = pyfits.getdata( fout )

f_res.close()
# ceres modules # other useful modules # Recive input parameters ####### GLOBAL VARIABLES ##### # max rms in m/s, global wav solution # grado polinomio # up to which order do we care? # Number of coefficients for the global wavelength solution # path to the synthetic models # path to reference files for the wavelength solution ############################# # file containing the log # classification of input images according to header info # Pre-process # median combine Biases # median combine list of co flats2 # median combine list of ob flats # save this file for later reference # median combine list of co flats # Find orders & traces # recover GA*, RO* # recover flats & master bias # Extract flat spectra, object # write P_on and S_flat_ob as fits files # Extract flat spectra, comparison # write P_on and S_flat_co as fits files # Normalize flat field spectra. # Extract all ThAr files # save as fits file # compute wavelength calibration files # Now calibrate COMPARISON orders. Use p1 above as p0 # end COMPARISON orders. #print "Median sigma:", np.median( All_Sigmas ) # Now extract ThAr-FP images # save as fits file # Now calibrate the ThFP spectra with the closest ThAr spectrum ### start of science frame reductions ### # Does any image have a special requirement for dealing with the moonlight? # now extract the images # get header h of image # get mjd and mjd0 #  get gain and readnoise of object # Object name # Open file, trim, overscan subtract and MasterBias subtract # Find lambda_bary/lambda_topo using JPLEPHEM # Moon Phase Calculations # lat/long in decimal degrees # optimally and simply extract spectra # save as fits file #Build spectra # initialize file that will have the spectra # n_useful should be nord_ob, but we still have not calibrated that bluest order -- TODO # get ThAr closest in time # Extract thAr lines from comparison orders # get a global solution for the lines found # get shift with respect to reference ThAr #lines_thar_co      = sci_S_co[:,1,:] / S_flat_co_simple_n #lines_thar_co_ref  = pyfits.getdata(thfp_fits_co)[:,1,:] / S_flat_co_simple_n #plot(rv_fps,'ro') #plot(rv_fps,'ro') #show() #print np.median(rv_fps),np.sqrt(np.var(rv_fps))/np.sqrt(float(len(rv_fps))) # Flat-fielded spectrum # Continuum normalized spectrum #spec[8,order,:][L] = ratio * S_flat_ob_n[order,1,:][L] / np.sqrt( ratio * S_flat_ob_n[order,1,:][L] / gain + ext_aperture*2*(ron/gain)**2 + sci_bac[order,:][L] / gain ) # clean-up of CRs in continuum-normalized spectrum. Set troublesome pixels to 1 # spectral analysis # First, query SIMBAD with the object name #query_success,sp_type_query = GLOBALutils.simbad_query_obname(obname) # Now, query SIMBAD by coordinates if above not successful #if (not query_success): #    query_success,sp_type_query = GLOBALutils.simbad_query_coords('12:00:00','00:00:00') #print "\t\t\tSpectral type returned by SIMBAD query:",sp_type_query # assign mask # Read in mask # make mask larger accounting for factor ~2 lower res in CORALIE w/r to HARPS #sigma_fout = stellar_pars_dir + obname + '_' +'sigma.txt' # first rough correlation to find the minimum # Normalize the continuum of the CCF robustly with lowess #ldc = CoralieUtils.get_ldc(T_eff, logg, Z, 1.0, ldfile = 'lin_coe_sloan2.dat') #p1R, ROTmodel = CoralieUtils.XC_Final_Fit_Rot( vels, xc_av, ldc = ldc, vsini = vsini ) #SP = bspan[0] #print 'Bisector span:', SP