stare.py 26.6 KB
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"""
Last modified: 2017-03-07

Reduction of the single Obj-Sky group:
- check the Obj image quality (signal in well defined region)
- check that the exposure times of Obj and Sky are the same
- check that the number of Obj and Sky images are the same
- average the Obj images
- average the Sky images
- create Obj-Sky image
- remove bad pixel using the bad pixel mask
- straigthen the image
- divide by the masterflat (if required)
- perform optimal extraction
- use the optimal profiles with UNe lamp and do wavelength calibration
- save the C image
Reduction of all the Obj-Sky group:
- check the Obj image quality (signal in well defined region)
- check that the exposure times of Obj and Sky are the same
- check that the number of Obj and Sky images are the same
- average the Obj images
- average the Sky images
- create Obj-Sky image
- remove bad pixel using the bad pixel mask
- straigthen the image
- divide by the masterflat (if required)
- perform optimal extraction
- use the optimal profiles with UNe lamp and do wavelength calibration
- save the C image
"""


from drslib.config import CONFIG
from drslib import db, varie

from astropy import units as u
from astropy.io import ascii, fits

import warnings
from astropy.utils.exceptions import AstropyWarning
import ccdproc

import numpy as np
import math, os, subprocess, time, shutil

from collections import OrderedDict, Counter
#import matplotlib.pyplot as plt


class GBStare():
    def __init__(self, stare, group, dbconn):
        self.stare = stare
        self.group = group
        self.dbconn = dbconn
        self.quality = []
        self.messages = []
        self.starelist = []
        self.skylist = []
        self.starecorr = {}
        self.mjd = -99999

    def qualitycheck(self):
        """
        Check image's quality: check the signal in a well-defined region (only Obj).
        After this, check the number of Obj and Sky images and their exposure times.
        Discard those with exposure times different, discard other images as needed
        to have the same number of Obj and Sky.
        """

        expt_obj = []
        expt_sky = []
        signal_obj = []
        sky_time = []
        mjd_obj = []
        name_obj = []
        name_sky = []

        for frame in self.stare:

            nod = ccdproc.CCDData.read(frame, unit=u.adu)

            try: nod.header[CONFIG['KEYS']['STARE']]
            except:
                nod.header[CONFIG['KEYS']['STARE']] = raw_input('Define stare observation %s: [obj/sky]: ' % (os.path.basename(frame))).upper()
                if nod.header[CONFIG['KEYS']['STARE']] == '':
                    continue

            # check the signal in a well-defined zone
            if nod.header[CONFIG['KEYS']['STARE']] == CONFIG['OBJ']:
                expt_obj.append(nod.header[CONFIG['KEYS']['EXPTIME']])
                zone = nod.data[CONFIG['SCIENCECHECK'][0]:CONFIG['SCIENCECHECK'][1],CONFIG['SCIENCECHECK'][2]:CONFIG['SCIENCECHECK'][3]]
                mean = np.mean(zone)
                #std = np.std(zone)
                signal_obj.append(mean)

                if mean < CONFIG['NODSIGNAL']:
                    self.messages.append('Science frame %s failed quality check: signal too low (%s). It will not be reduced.' % (str(os.path.basename(frame)),str(mean)))

                else:
                    self.starelist.append(nod)
                    mjd_obj.append(nod.header[CONFIG['KEYS']['MJD']])
                    name_obj.append(os.path.basename(frame))

                    try:
                        nod.header[CONFIG['KEYS']['EXTMODE']]
                    except:
                        nod.header[CONFIG['KEYS']['EXTMODE']] = CONFIG['EXTPAIR']

                    ext = nod.header[CONFIG['KEYS']['EXTMODE']]

            else:
                expt_sky.append(nod.header[CONFIG['KEYS']['EXPTIME']])
                sky_time.append(nod.header[CONFIG['KEYS']['MJD']])
                self.skylist.append(nod)
                name_sky.append(os.path.basename(frame))

# Check if there is at least one Obj image.

        if len(self.starelist) == 0:
            print self.starelist
            self.messages.append('No Obj frame has passed the quality test (signal too low), this group will not be reduced.')
            return False

        #elif len(self.skylist) == 0:
        #    self.messages.append('There are no Sky images, this group will not be reduced.')
        #    return False



# Check exposure times: if they are different, the pipeline
# will only keep the majority of images with the same exposure time

        exp_common = Counter(expt_obj).most_common(1)[0][0]

        if Counter(expt_obj).most_common(1)[0][1] < len(expt_obj):
            #print len(self.starelist)
            self.messages.append('The Obj images have different exposure times, some of them will be skipped')
            for n in xrange(len(self.starelist)):
                if self.starelist[n].data[CONFIG['KEYS']['EXPTIME']] != exp_common:
                    self.messages.append('%s has %ss of exposure time: skipped.' % (name_obj[n],str(self.starelist[n].data[CONFIG['KEYS']['EXPTIME']]),))
                    signal_obj.pop(n)
                    self.starelist.pop(n)
                    mjd_obj.pop(n)
                    name_obj.pop(n)
            #print len(self.starelist)

        self.mjd = np.average(np.asarray(mjd_obj)) + (exp_common/(2*86400))
        sky_time = abs(np.asarray(sky_time) - self.mjd) 

# Skip sky images with exposure time different than Obj
        #print len(self.skylist)
        try:
            for n in xrange(len(expt_sky)):
                if expt_sky[n] != exp_common:
                    self.messages.append('The sky image %s has %ss of exposure time: skipped.' % (name_sky[n],str(expt_sky[n]),))
                    np.delete(sky_time,n)
                    self.skylist.pop(n)
                    name_sky.pop(n)
        except:
            pass
        #print len(self.skylist)

        #if len(self.skylist) == 0:
        #    self.messages.append('There are no Sky images with the same exposure time as the Obj images, this group will not be reduced.')
        #    return False

# Check if the number of Obj and Sky is the same, otherwise skip some images
# Obj: skip the images with lowest signal: MODIFIED - keep all the obj images, even if the sky images are fewer
#        if len(self.starelist) > len(self.skylist):
#            while len(self.starelist) > len(self.skylist):
#                worst = np.argmin(np.asarray(signal_obj))
#                self.messages.append('There are more Obj images than Sky. %s has the lowest signal: skipped.' % (name_obj[n],))
#                signal_obj.pop(worst)
#                self.starelist.pop(worst)
#                name_obj.pop(worst)

# Sky: skip the images farther temporally from the Obj: MODIFIED - keep all the obj images, even if the sky images are fewer
#        elif len(self.starelist) < len(self.skylist):
#        if len(self.starelist) < len(self.skylist):
#            while len(self.starelist) < len(self.skylist):
#                farther = np.argmax(sky_time)
#                self.messages.append('There are more Sky images than Obj. %s was observed farthest from the Obj sequence: skipped.' % (name_sky[n],))
#                np.delete(sky_time,farther)
#                self.skylist.pop(farther)
#                name_sky.pop(farther)

        if ext == CONFIG['EXTAVG']:
            self.group['stares'].extend(self.stare)

        return True


    def createObj(self,grp):
        """
        Average the Obj and the Sky images, subtract Sky from Obj.
        Bad pixel removal.
        """

        badpix = ccdproc.CCDData.read(CONFIG['BADPIX_MASK'], unit=u.adu)
        bad_mask=badpix.data
        inverse_mask=np.logical_not(bad_mask)

        #t1 = time.time()

        obj = ccdproc.Combiner(self.starelist)
        med_obj = obj.average_combine()
        med_obj.header = self.starelist[0].header
        med_obj.header[CONFIG['GAIN_EFF'][0]] = (len(self.starelist)*CONFIG['GAIN'],CONFIG['GAIN_EFF'][1])
        #print med_obj.header[CONFIG['GAIN_EFF'][0]]

        med_obj.header[CONFIG['RON_EFF'][0]] = (math.sqrt(len(self.starelist))*CONFIG['RON'],CONFIG['RON_EFF'][1])

        am = []
        for n in self.starelist:
            am.append(n.header[CONFIG['KEYS']['AM']])
        am = np.average(np.asarray(am))

        med_obj.header[CONFIG['AIRMASS'][0]] = (am,CONFIG['AIRMASS'][1])


        try:
            sky = ccdproc.Combiner(self.skylist)
            med_sky = sky.average_combine()
            med_sky.header = self.skylist[0].header
            med_sky.header[CONFIG['GAIN_EFF'][0]] = (len(self.skylist)*CONFIG['GAIN'],CONFIG['GAIN_EFF'][1])
            med_sky.header[CONFIG['RON_EFF'][0]] = (math.sqrt(len(self.skylist))*CONFIG['RON'],CONFIG['RON_EFF'][1])

            sky_corrected = med_obj.data - med_sky.data
            self.messages.append('Sky subtracted.')

        except:
            exptime = med_obj.header[CONFIG['KEYS']['EXPTIME']]
            darkname = 'dark' + str(int(exptime))
            use_dark = True

            try:
                masterdark = db.extract_dbfile(self.dbconn,darkname)
            except:
                masterdark = False

            if not masterdark:
                self.messages.append('No masterdark found for this night, it will be taken from the calibration database.')
                try:
                    db.copy_dbfile(self.dbconn,darkname)
                    masterdark = db.extract_dbfile(self.dbconn,darkname)
                except:
                    for key in CONFIG['DARKLIST']:
                        try:
                            darkname = 'dark' + str(int(key))
                            db.copy_dbfile(self.dbconn,darkname)
                            masterdark = db.extract_dbfile(self.dbconn,darkname)
                            self.messages.append('No masterdark in the calibration database with the same exposure time as the flat-field. The %s sec masterdark will be used instead' % (str(int(key))))
                            break
                        except:
                            self.messages.append('There are no masterdark in the calibration database. The masterdark will not be used.')
                            use_dark = False

            if use_dark:
                mdark = ccdproc.CCDData.read(masterdark, unit=u.adu)
                sky_corrected = ccdproc.subtract_dark(med_obj,mdark,exposure_time=CONFIG['KEYS']['EXPTIME'],exposure_unit=u.second)
                sky_corrected = sky_corrected.data
                self.messages.append('There is no sky image, the masterdark has been subtracted.')
            else:
                sky_corrected = med_obj.data
                self.messages.append('There is no sky image, the object will be reduced anyway.')

        bp_corrected = varie.badpix(sky_corrected,bad_mask,inverse_mask)
        bp_corrected = np.asarray(bp_corrected,dtype='float32')
        self.messages.append('Bad pixel correction done.')

        corrected = ccdproc.CCDData(bp_corrected,unit=u.adu)
        #corrected = ccdproc.CCDData(bp_corrected)
        corrected.header = self.starelist[0].header
        corrected.header[CONFIG['RON_EFF'][0]] = (math.sqrt(2)*med_obj.header[CONFIG['RON_EFF'][0]],CONFIG['RON_EFF'][1])

        corrected.header[CONFIG['DRS_MJD'][0]] = (self.mjd,CONFIG['DRS_MJD'][1])

        corrected.header[CONFIG['KEYS']['NCOMBINE']] = len(self.starelist) + len(self.skylist)
        for n in xrange(len(self.starelist)):
            keyA = ''.join((CONFIG['SPEC_USED'][0],str(n),'OBJ'))
            mjdA = ''.join((CONFIG['SPEC_MJD'][0],str(n),'OBJ'))

            #value_keyA = self.starelist[n].header[CONFIG['KEYS']['FILENAME']]
            value_keyA = self.starelist[n].header[CONFIG['KEYS']['IMANAME']]
            value_mjdA = self.starelist[n].header[CONFIG['KEYS']['MJD']]

            corrected.header[keyA] = (value_keyA,CONFIG['SPEC_USED'][1])
            corrected.header[mjdA] = (value_mjdA,CONFIG['SPEC_MJD'][1])

        for n in xrange(len(self.skylist)):
            keyA = ''.join((CONFIG['SPEC_USED'][0],str(n),'SKY'))
            mjdA = ''.join((CONFIG['SPEC_MJD'][0],str(n),'SKY'))

            #value_keyA = self.starelist[n].header[CONFIG['KEYS']['FILENAME']]
            value_keyA = self.starelist[n].header[CONFIG['KEYS']['IMANAME']]
            value_mjdA = self.starelist[n].header[CONFIG['KEYS']['MJD']]

            corrected.header[keyA] = (value_keyA,CONFIG['SPEC_USED'][1])
            corrected.header[mjdA] = (value_mjdA,CONFIG['SPEC_MJD'][1])

        #Cnome = self.starelist[0].header[CONFIG['KEYS']['FILENAME']]
        Cnome = self.starelist[0].header[CONFIG['KEYS']['IMANAME']]
        #print Cnome
        #qui = Cnome.rindex('.')
        nomebase = os.path.splitext(Cnome)[0]
        if grp:
            #Cnome = '_'.join((Cnome[0:qui],'Cgrp.fits'))
            Cnome = '_'.join((nomebase,'Cgrp.fits'))
        else:
            Cnome = '_'.join((nomebase,'C.fits'))
        Cnome = os.path.join(CONFIG['TMP_DIR'],Cnome)

        #cor_fits = corrected.to_hdu()
        hdu = fits.PrimaryHDU(data=corrected.data,header=corrected.header)
        cor_fits = fits.HDUList([hdu])
        cor_fits.writeto(Cnome,clobber=True)

        return Cnome

    def reduce(self,fitsfile,slit_pos):
        """
        Straighten, divide by the masterflat, optimal extraction
        """
        # straighten

        straight = fitsfile.replace('.fits','_str.fits')
        args = [CONFIG['STRAIGHT'],fitsfile,straight,CONFIG['STRAIGHT_OPT']]

        subprocess.call(args)
        str_file = os.path.join(CONFIG['RED_STR'],os.path.basename(straight))
        try: shutil.copyfile(straight,str_file)
        except: pass

        self.messages.append('%s: orders straightened.' % str(os.path.basename(fitsfile)),)

        imstr = ccdproc.CCDData.read(straight, unit=u.adu)

        try: nspec = imstr.header[CONFIG['KEYS']['NCOMBINE']]
        except: nspec = 1

        # use only the regions of the orders
        try:
            goodmask = ccdproc.CCDData.read(CONFIG['MASK_C'], unit=u.adu)
        except:
            try:
                masterflat = db.extract_dbfile(self.dbconn,'flatstr')
            except:
                masterflat = False
            if not masterflat:
                db.copy_dbfile(self.dbconn,'flatstr')
                masterflat = db.extract_dbfile(self.dbconn,'flatstr')
                self.messages.append('No masterflat found for this night, it will be taken from the calibration database: %s' % (os.path.basename(masterflat)))
            mflat = ccdproc.CCDData.read(masterflat, unit=u.adu)
            varie.buildMaskC(mflat.data)
            self.messages.append('The extraction mask was created.')
            goodmask = ccdproc.CCDData.read(CONFIG['MASK_C'], unit=u.adu)

        gmask = goodmask.data

        roneff = imstr.header[CONFIG['RON_EFF'][0]]
        gaineff = imstr.header[CONFIG['GAIN_EFF'][0]]

        if CONFIG['USE_FLAT']['global']:
            try:
                masterflat = db.extract_dbfile(self.dbconn,'flatstr')
            except:
                masterflat = False

            if not masterflat:
                db.copy_dbfile(self.dbconn,'flatstr')
                masterflat = db.extract_dbfile(self.dbconn,'flatstr')
                self.messages.append('No masterflat found for this night, it will be taken from the calibration database: %s' % (os.path.basename(masterflat)))

            flat = ccdproc.CCDData.read(masterflat, unit=u.adu)
            froneff = flat.header[CONFIG['RON_EFF'][0]]
            fgaineff = flat.header[CONFIG['GAIN_EFF'][0]]
            meanflat = np.mean(flat.data)
            norflat = np.true_divide(flat.data,meanflat)
            with np.errstate(divide='ignore', invalid='ignore'):
                imflat = np.true_divide(imflat,norflat)

        elif CONFIG['USE_FLAT']['order']:
            try:
                masterflat = db.extract_dbfile(self.dbconn,'flatstr')
            except:
                masterflat = False

            if not masterflat:
                db.copy_dbfile(self.dbconn,'flatstr')
                masterflat = db.extract_dbfile(self.dbconn,'flatstr')
                self.messages.append('No masterflat found for this night, it will be taken from the calibration database: %s' % (os.path.basename(masterflat)))
            flat = ccdproc.CCDData.read(masterflat, unit=u.adu)
            froneff = flat.header[CONFIG['RON_EFF'][0]]
            fgaineff = flat.header[CONFIG['GAIN_EFF'][0]]


        elif CONFIG['USE_FLAT']['nor']:
            try:
                masterflat = db.extract_dbfile(self.dbconn,'flatnor')
            except:
                masterflat = False

            if not masterflat:
                db.copy_dbfile(self.dbconn,'flatnor')
                masterflat = db.extract_dbfile(self.dbconn,'flatnor')
                self.messages.append('No masterflat found for this night, it will be taken from the calibration database: %s' % (os.path.basename(masterflat)))

            flat = ccdproc.CCDData.read(masterflat, unit=u.adu)
            froneff = flat.header[CONFIG['RON_EFF'][0]]
            fgaineff = flat.header[CONFIG['GAIN_EFF'][0]]
            meanflat = 1.0
            norflat = flat.data
            with np.errstate(divide='ignore', invalid='ignore'):
                imflat = np.true_divide(imflat,norflat)


        try:
            masterlamp = db.extract_dbfile(self.dbconn,'une_str')
        except:
            masterlamp = False

        if not masterlamp:
            db.copy_dbfile(self.dbconn,'une_str')
            masterlamp = db.extract_dbfile(self.dbconn,'une_str')
            db.copy_dbfile(self.dbconn,'une_calib')
            self.messages.append('No calibration lamp found for this night, it will be taken from the calibration database: %s' % (os.path.basename(masterlamp)))

        mlamp = ccdproc.CCDData.read(masterlamp, unit=u.adu)

        lroneff = mlamp.header[CONFIG['RON_EFF'][0]]
        lgaineff = mlamp.header[CONFIG['GAIN_EFF'][0]]

        # read the lines to use in the wavelength calibration
        select_lines, all_lines = varie.UNe_linelist()

        # prepare the structure for the calibrated results
        heacal = OrderedDict() # header for the calibration table
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        #stdSpectrum = np.zeros((CONFIG['N_ORD'],CONFIG['YCCD']))
        optSpectrum = np.zeros((CONFIG['N_ORD'],CONFIG['YCCD']))
        fsnr = np.zeros((CONFIG['N_ORD'],CONFIG['YCCD']))
        snr = np.zeros((CONFIG['N_ORD'],CONFIG['YCCD']))
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        all_cosmics = 0
        for x in xrange(CONFIG['N_ORD']):
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            start = x*CONFIG['W_ORD']
            end = min(start+CONFIG['W_ORD'],CONFIG['YCCD'])
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            # select only the rows wit the signal using the appropriate mask

            omask = gmask[start:end]

            order = imstr.data[start:end]

            if CONFIG['USE_FLAT']['order']:
                ordflat = flat.data[start:end]
                # divide by masterflat normalized by its average value
                meanflat = np.mean(ordflat)
                norflat = np.true_divide(ordflat,meanflat)
                with np.errstate(divide='ignore', invalid='ignore'):
                    order = np.true_divide(order,norflat)

            ordermasked = np.ma.MaskedArray(order,mask=omask)
            goodorder = np.ma.compress_rows(ordermasked)

            # call optimal extraction
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            optSpectrum[x],varOptFlux,profile,x1,x2,cosmics = varie.optExtract(goodorder,gaineff,roneff,slit_pos,x)
            all_cosmics = all_cosmics + cosmics
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            olamp = mlamp.data[start:end]
            orderlamp = np.ma.MaskedArray(olamp,mask=omask)
            goodlamp = np.ma.compress_rows(orderlamp)

            extlamp = varie.extract(goodlamp, optSpectrum[x], x1, x2, profile, lgaineff, lroneff)

            if any(CONFIG['USE_FLAT'].values()) is True:
                extflat = varie.extract(norflat, optSpectrum[x], x1, x2, profile, fgaineff, froneff)
                #print extflat
                with np.errstate(divide='ignore', invalid='ignore'):
                    fsnr[x] = (np.sqrt(froneff**2 + (fgaineff*(extflat*meanflat))))/(fgaineff*(extflat*meanflat))
                    fsnr[fsnr==np.inf] = 0
                    fsnr[fsnr==-np.inf] = 0
                    fsnr = np.nan_to_num(fsnr)
                #print fsnr
            else:
                fsnr[x] = np.zeros(len(optSpectrum[x]))

            with np.errstate(divide='ignore', invalid='ignore'):
                ssnr = (np.sqrt(roneff**2 + (gaineff*nspec*optSpectrum[x])))/(gaineff*nspec*optSpectrum[x])
                snr[x] = 1.0/(np.sqrt(ssnr**2 + fsnr[x]**2))


            calib_failed, coeffs, comments = varie.UNe_calibrate(extlamp,x+32,select_lines[x+32],all_lines[x+32])

            for comment in comments:
                self.messages.append(comment)

            if calib_failed:
                self.messages.append(' *** WARNING ***')
                self.messages.append('The default wavelength calibration for the order %s will be taken from the database and as such it will not be optimal for the night.' % (str(x+32),))
                wcalib = db.extract_dbfile(self.dbconn,'une_calib')
                wlc = ccdproc.CCDData.read(wcalib, unit=u.adu)
                for key in coeffs:
                    keyword = ''.join((CONFIG['WLCOEFFS'][key][0],str(x+32)))
                    heacal[keyword] = wlc.header[keyword]

            else:
                for key in coeffs:
                    keyword = ''.join((CONFIG['WLCOEFFS'][key][0],str(x+32)))
                    heacal[keyword] = (coeffs[key],CONFIG['WLCOEFFS'][key][1])


        optSpectrum = np.asarray(optSpectrum, dtype='float32')


        self.messages.append('The spectrum %s was extracted.' % str(os.path.basename(straight)),)
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        if all_cosmics == 1050:
            self.messages.append('%s cosmics were removed (maximum iteration reached).' % str(all_cosmics),)
        else:
            self.messages.append('%s cosmics were removed.' % str(all_cosmics),)
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        redname = os.path.join(CONFIG['RED_DIR'],str(os.path.basename(fitsfile)))
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        sfx = ''.join(('_',CONFIG['MERGED'],'.fits'))
        msfx = ''.join(('_',CONFIG['UNMERGED'],'.fits'))
        calname = redname.replace('.fits',msfx)
        calname1d = redname.replace('.fits',sfx)
        #calname = redname.replace('.fits','_e2ds.fits')
        #calname1d = redname.replace('.fits','_s1d.fits')
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        heaspe = fits.Header(imstr.header)

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        if masterflat:
            heaspe[CONFIG['MASTERFLAT'][0]] = (os.path.basename(masterflat),CONFIG['MASTERFLAT'][1])
        if masterlamp:
            heaspe[CONFIG['MASTERLAMP'][0]] = (os.path.basename(masterlamp),CONFIG['MASTERLAMP'][1])

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        snr[snr==np.inf] = 0
        snr[snr==-np.inf] = 0
        snr = np.nan_to_num(snr)

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        self.messages.append('Stare image: SNR[Y band, order=73, wl=1050 nm] = %s' % (str(round(np.mean(snr[41]),2))),)
        self.messages.append('Stare image: SNR[J band, order=61, wl=1250 nm] = %s' % (str(round(np.mean(snr[29]),2))),)
        self.messages.append('Stare image: SNR[H band, order=46, wl=1650 nm] = %s' % (str(round(np.mean(snr[14]),2))),)
        self.messages.append('Stare image: SNR[K band, order=35, wl=2200 nm] = %s' % (str(round(np.mean(snr[3]),2))),)
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        for o in xrange(CONFIG['N_ORD']):
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            key_snr = ''.join((CONFIG['SNR'][0],str(o+32)))
            heaspe[key_snr] = (round(np.mean(snr[o]),2),CONFIG['SNR'][1])

        heaspe[CONFIG['WLFIT'][0]] = (CONFIG['WLFIT_FUNC'],CONFIG['WLFIT'][1])
        for hea in heacal:
            heaspe[hea] = heacal[hea]

        barycorr, hjd = varie.berv_corr(heaspe)
        heaspe[CONFIG['BERV'][0]] = (barycorr,CONFIG['BERV'][1])
        heaspe[CONFIG['HJD'][0]] = (hjd,CONFIG['HJD'][1])

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        waves = np.zeros((CONFIG['N_ORD'],CONFIG['YCCD']))
        for o in xrange(CONFIG['N_ORD']):
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            waves[o] = varie.wcalib(heaspe,o)

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        #spefits = fits.PrimaryHDU(optSpectrum,header=heaspe)
        #wavefits = fits.ImageHDU(waves,name='WAVE')
        #snrfits = fits.ImageHDU(snr,name='SNR')
        #results = fits.HDUList([spefits,wavefits,snrfits])
        #calname = os.path.join(CONFIG['RED_DIR'],calname)
        #results.writeto(calname,clobber=True)
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        orders=np.arange(CONFIG['N_ORD'])+32
        c1 = fits.Column(name='ORDER', format='I', array=orders)
        c2 = fits.Column(name='WAVE', format=''.join((str(CONFIG['YCCD']),'D')), unit='nm', array=waves)
        c3 = fits.Column(name='FLUX', format=''.join((str(CONFIG['YCCD']),'D')), array=optSpectrum)
        c4 = fits.Column(name='SNR', format=''.join((str(CONFIG['YCCD']),'D')), array=snr)
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        tbhdu = fits.BinTableHDU.from_columns([c1, c2, c3, c4],header=heaspe)
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        calname = os.path.join(CONFIG['RED_DIR'],calname)
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        tbhdu.writeto(calname,clobber=True)

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        if CONFIG['S1D']:
            #s1d = varie.create_s1d(optSpectrum,snr,heaspe)
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            s1d, startval = varie.create_s1d(optSpectrum,heaspe)
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            heaspe['CRPIX1'] = (1.,'Reference pixel')
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            heaspe['CRVAL1'] = (startval,'Coordinates at reference pixel')
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            heaspe['CDELT1'] = (CONFIG['S1D_STEP'],'Coordinates increment per pixel')

            heaspe['CTYPE1'] = ('Nanometers','Units of coordinates')
            heaspe['BUNIT'] = ('Relative Flux','Units of data values')

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            s1dfits = fits.PrimaryHDU(s1d,header=heaspe)
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            calname1d = os.path.join(CONFIG['RED_DIR'],calname1d)
            s1dfits.writeto(calname1d,clobber=True)

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        if imstr.header[CONFIG['KEYS']['EXTMODE']] == CONFIG['EXTPAIR']:
            return calname, straight

        elif 'grp' in fitsfile:
            return calname, straight

        return calname, False

    def pair_process(self):
        warnings.simplefilter('ignore', category=AstropyWarning)
        if self.qualitycheck():
            obj = self.createObj(False)
            calib, straight = self.reduce(obj,CONFIG['C_POS'])
            if straight:
                os.remove(obj)
                os.remove(straight)

        #self.stare[:] = []
        self.starelist[:] = []
        self.skylist[:] = []

        return

    def group_process(self):
        warnings.simplefilter('ignore', category=AstropyWarning)
        self.pair_process()
        self.stare = self.group['stares']
        #print self.stare
        if self.qualitycheck():
            obj = self.createObj(True)
            calib, straight = self.reduce(obj,CONFIG['C_POS'])
            if straight:
                os.remove(obj)
                os.remove(straight)
        self.group.clear()

        return

    def ingroup_process(self):
        warnings.simplefilter('ignore', category=AstropyWarning)
        self.pair_process()
        self.stare = self.group['stares']
        if self.qualitycheck():
            obj = self.createObj(True)
            calib, straight = self.reduce(obj,CONFIG['C_POS'])
            if straight:
                os.remove(obj)
                os.remove(straight)
        self.group.clear()

        return