stare.py 30 KB
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"""
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():
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    def __init__(self, stare, group, dbconn, dbnight):
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        self.stare = stare
        self.group = group
        self.dbconn = dbconn
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        self.dbnight = dbnight
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        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.
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        Discard those with exposure times different, (discard other images as needed
        to have the same number of Obj and Sky --> NOT ANYMORE).
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        """

        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)

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        self.mjd = np.average(np.asarray(mjd_obj)) + (exp_common/(2.0*86400.0))
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        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
        """
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        dbreduced = {}
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        # straighten

        straight = fitsfile.replace('.fits','_str.fits')
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        args = [CONFIG['STRAIGHT'],fitsfile,straight]
        args.extend(CONFIG['STRAIGHT_OPT'])
        # search for shift defined in the straighten options in config.py
        dy = True
        for opt in CONFIG['STRAIGHT_OPT']:
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            try:
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                dy = opt.rindex('DY=')
                ypos = int(opt[dy-2:])
                shift = CONFIG['SHIFT_Y'] + ypos
                dy = False
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            except:
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                pass

        if dy:
            shift = db.extract_dbfile(self.dbconn,'shiftY')
            if not shift:
                cal_flat = db.extract_dbfile(self.dbconn,'flat')
                if cal_flat:
                    mflat = ccdproc.CCDData.read(cal_flat, unit=u.adu)
                    shift = varie.shiftY(mflat.data)
                    db.insert_dbfile(self.dbconn,'shiftY',shift)
                else:
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                    db.copy_dbfile(self.dbconn,'shiftY')
                    shift = db.extract_dbfile(self.dbconn,'shiftY')
                    if not shift:
                        shift = CONFIG['SHIFT_Y']

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            shiftY = [''.join(('DY=',str(shift - CONFIG['SHIFT_Y'])))]
            #print shiftY
            args.extend(shiftY)
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        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'):
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                    #fsnr[x] = (np.sqrt(froneff**2 + (fgaineff*(extflat*meanflat))))/(fgaineff*(extflat*meanflat))
                    fsnr[x] = np.true_divide(np.sqrt(froneff**2 + (fgaineff*(extflat*meanflat))),fgaineff*(extflat*meanflat))
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                    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'):
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                #ssnr = (np.sqrt(roneff**2 + (gaineff*nspec*optSpectrum[x])))/(gaineff*nspec*optSpectrum[x])
                ssnr = np.true_divide(np.sqrt(roneff**2 + (gaineff*nspec*optSpectrum[x])),gaineff*nspec*optSpectrum[x])
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                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)

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            keyfail = ''.join((CONFIG['CAL_FAILED'][0],str(x+32)))

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            if calib_failed:
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                heacal[keyfail] = (False,CONFIG['CAL_FAILED'][1])
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                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:
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                heacal[keyfail] = (True,CONFIG['CAL_FAILED'][1])
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                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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        heaspe[CONFIG['KEYS']['FILENAME']] = str(os.path.basename(calname))
        heaspe[CONFIG['KEYS']['IMANAME']] = str(os.path.basename(calname))
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        try:
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            heaspe[CONFIG['MASTERFLAT'][0]] = (os.path.basename(masterflat),CONFIG['MASTERFLAT'][1])
        except:
            heaspe[CONFIG['MASTERFLAT'][0]] = ('None',CONFIG['MASTERFLAT'][1])
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        try:
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            heaspe[CONFIG['MASTERLAMP'][0]] = (os.path.basename(masterlamp),CONFIG['MASTERLAMP'][1])
        except:
            heaspe[CONFIG['MASTERLAMP'][0]] = ('None',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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        snry = round(np.mean(snr[41]),2)
        snrj = round(np.mean(snr[29]),2)
        snrh = round(np.mean(snr[14]),2)
        snrk = round(np.mean(snr[3]),2)

        self.messages.append('Stare image: SNR[Y band, order=73, wl=1050 nm] = %s' % (str(snry)),)
        self.messages.append('Stare image: SNR[J band, order=61, wl=1250 nm] = %s' % (str(snrj)),)
        self.messages.append('Stare image: SNR[H band, order=46, wl=1650 nm] = %s' % (str(snrh)),)
        self.messages.append('Stare image: SNR[K band, order=35, wl=2200 nm] = %s' % (str(snrk)),)
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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]

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

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        #tbhdu = fits.BinTableHDU.from_columns([c1, c2, c3, c4],header=heaspe)
        tbhdu = fits.BinTableHDU.from_columns([c1, c2, c3, c4])
        prihdu = fits.PrimaryHDU(data=None, header=heaspe)
        hdulist = fits.HDUList([prihdu, tbhdu])
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        calname = os.path.join(CONFIG['RED_DIR'],calname)
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        #tbhdu.writeto(calname,clobber=True)
        hdulist.writeto(calname,clobber=True)
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        try: obj_name = heaspe[CONFIG['KEYS']['OBJECT']]
        except: obj_name = 'NONE'

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        if CONFIG['S1D']:
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            heaspe[CONFIG['KEYS']['FILENAME']] = str(os.path.basename(calname1d))
            heaspe[CONFIG['KEYS']['IMANAME']] = str(os.path.basename(calname1d))
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            #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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            rid = varie.random_id(12)

            dbreduced['s1d'] = {'slit':slit_pos, 'path':calname1d, 'snry':snry, 'snrj':snrj, 'snrh':snrh, 'snrk':snrk, 'type':'s1d', 'name':obj_name, 'id':rid}

        rid = varie.random_id(12)
        dbreduced['ms1d'] = {'slit':slit_pos, 'path':calname, 'snry':snry, 'snrj':snrj, 'snrh':snrh, 'snrk':snrk, 'type':'ms1d', 'name':obj_name, 'id':rid}
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        if imstr.header[CONFIG['KEYS']['EXTMODE']] == CONFIG['EXTPAIR']:
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            return calname, straight, dbreduced 
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        elif 'grp' in fitsfile:
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            return calname, straight, dbreduced
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        return calname, False, dbreduced
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    def pair_process(self):
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        #reduced = ','.join(map(os.path.basename,self.stare))
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        stamp = time.time()
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        #db.insert_dbnight(self.dbnight, reduced, stamp)
        db.insert_dbnight(self.dbnight, self.stare, stamp)
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        warnings.simplefilter('ignore', category=AstropyWarning)
        if self.qualitycheck():
            obj = self.createObj(False)
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            calib, straight, dbreduced = self.reduce(obj,CONFIG['C_POS'])

            try:
                db.insert_dbreduced(self.dbnight, dbreduced['s1d'], stamp)
            except:
                pass
            db.insert_dbreduced(self.dbnight, dbreduced['ms1d'], stamp)


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            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)
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            calib, straight, dbreduced = self.reduce(obj,CONFIG['C_POS'])
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            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)
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            calib, straight, dbreduced = self.reduce(obj,CONFIG['C_POS'])
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            if straight:
                os.remove(obj)
                os.remove(straight)
        self.group.clear()

        return