コード例 #1
0
ファイル: saltclean.py プロジェクト: swj1442291549/pysalt
def saltclean(images,
              outpath,
              obslogfile=None,
              gaindb=None,
              xtalkfile=None,
              geomfile=None,
              subover=True,
              trim=True,
              masbias=None,
              subbias=False,
              median=False,
              function='polynomial',
              order=5,
              rej_lo=3,
              rej_hi=3,
              niter=5,
              interp='linear',
              clobber=False,
              logfile='salt.log',
              verbose=True):
    """SALTCLEAN will provide basic CCD reductions for a set of data.  It will 
      sort the data, and first process the biases, flats, and then the science 
      frames.  It will record basic quality control information about each of 
      the steps.
   """
    plotover = False

    #start logging
    with logging(logfile, debug) as log:

        # Check the input images
        infiles = saltio.argunpack('Input', images)

        # create list of output files
        outpath = saltio.abspath(outpath)

        #does the gain database file exist
        if gaindb:
            dblist = saltio.readgaindb(gaindb)
        else:
            dblist = []

        # does crosstalk coefficient data exist
        if xtalkfile:
            xtalkfile = xtalkfile.strip()
            xdict = saltio.readxtalkcoeff(xtalkfile)
        else:
            xdict = None
        #does the mosaic file exist--raise error if no
        saltio.fileexists(geomfile)

        # Delete the obslog file if it already exists
        if os.path.isfile(obslogfile) and clobber: saltio.delete(obslogfile)

        #read in the obsveration log or create it
        if os.path.isfile(obslogfile):
            msg = 'The observing log already exists.  Please either delete it or run saltclean with clobber=yes'
            raise SaltError(msg)
        else:
            headerDict = obslog(infiles, log)
            obsstruct = createobslogfits(headerDict)
            saltio.writefits(obsstruct, obslogfile)

        #create the list of bias frames and process them
        filename = obsstruct.data.field('FILENAME')
        detmode = obsstruct.data.field('DETMODE')
        ccdtype = obsstruct.data.field('CCDTYPE')

        #set the bias list of objects
        biaslist = filename[ccdtype == 'ZERO']
        masterbias_dict = {}
        for img in infiles:
            if os.path.basename(img) in biaslist:
                #open the image
                struct = fits.open(img)
                bimg = outpath + 'bxgp' + os.path.basename(img)

                #print the message
                if log:
                    message = 'Processing Zero frame %s' % img
                    log.message(message, with_stdout=verbose)

                #process the image
                struct = clean(struct,
                               createvar=False,
                               badpixelstruct=None,
                               mult=True,
                               dblist=dblist,
                               xdict=xdict,
                               subover=subover,
                               trim=trim,
                               subbias=False,
                               bstruct=None,
                               median=median,
                               function=function,
                               order=order,
                               rej_lo=rej_lo,
                               rej_hi=rej_hi,
                               niter=niter,
                               plotover=plotover,
                               log=log,
                               verbose=verbose)

                #write the file out
                # housekeeping keywords
                fname, hist = history(
                    level=1,
                    wrap=False,
                    exclude=['images', 'outimages', 'outpref'])
                saltkey.housekeeping(struct[0], 'SPREPARE',
                                     'Images have been prepared', hist)
                saltkey.new('SGAIN', time.asctime(time.localtime()),
                            'Images have been gain corrected', struct[0])
                saltkey.new('SXTALK', time.asctime(time.localtime()),
                            'Images have been xtalk corrected', struct[0])
                saltkey.new('SBIAS', time.asctime(time.localtime()),
                            'Images have been de-biased', struct[0])

                # write FITS file
                saltio.writefits(struct, bimg, clobber=clobber)
                saltio.closefits(struct)

                #add files to the master bias list
                masterbias_dict = compareimages(struct,
                                                bimg,
                                                masterbias_dict,
                                                keylist=biasheader_list)

        #create the master bias frame
        for i in masterbias_dict.keys():
            bkeys = masterbias_dict[i][0]
            blist = masterbias_dict[i][1:]
            mbiasname = outpath + createmasterbiasname(blist, bkeys)
            bfiles = ','.join(blist)
            saltcombine(bfiles, mbiasname, method='median', reject='sigclip', mask=False,
                        weight=False, blank=0, scale=None, statsec=None, lthresh=3,    \
                        hthresh=3, clobber=False, logfile=logfile,verbose=verbose)

        #create the list of flatfields and process them
        flatlist = filename[ccdtype == 'FLAT']
        masterflat_dict = {}
        for img in infiles:
            if os.path.basename(img) in flatlist:
                #open the image
                struct = fits.open(img)
                fimg = outpath + 'bxgp' + os.path.basename(img)

                #print the message
                if log:
                    message = 'Processing Flat frame %s' % img
                    log.message(message, with_stdout=verbose)

                #process the image
                struct = clean(struct,
                               createvar=False,
                               badpixelstruct=None,
                               mult=True,
                               dblist=dblist,
                               xdict=xdict,
                               subover=subover,
                               trim=trim,
                               subbias=False,
                               bstruct=None,
                               median=median,
                               function=function,
                               order=order,
                               rej_lo=rej_lo,
                               rej_hi=rej_hi,
                               niter=niter,
                               plotover=plotover,
                               log=log,
                               verbose=verbose)

                #write the file out
                # housekeeping keywords
                fname, hist = history(
                    level=1,
                    wrap=False,
                    exclude=['images', 'outimages', 'outpref'])
                saltkey.housekeeping(struct[0], 'SPREPARE',
                                     'Images have been prepared', hist)
                saltkey.new('SGAIN', time.asctime(time.localtime()),
                            'Images have been gain corrected', struct[0])
                saltkey.new('SXTALK', time.asctime(time.localtime()),
                            'Images have been xtalk corrected', struct[0])
                saltkey.new('SBIAS', time.asctime(time.localtime()),
                            'Images have been de-biased', struct[0])

                # write FITS file
                saltio.writefits(struct, fimg, clobber=clobber)
                saltio.closefits(struct)

                #add files to the master bias list
                masterflat_dict = compareimages(struct,
                                                fimg,
                                                masterflat_dict,
                                                keylist=flatheader_list)

        #create the master flat frame
        for i in masterflat_dict.keys():
            fkeys = masterflat_dict[i][0]
            flist = masterflat_dict[i][1:]
            mflatname = outpath + createmasterflatname(flist, fkeys)
            ffiles = ','.join(flist)
            saltcombine(ffiles, mflatname, method='median', reject='sigclip', mask=False,
                        weight=False, blank=0, scale=None, statsec=None, lthresh=3,    \
                        hthresh=3, clobber=False, logfile=logfile,verbose=verbose)

        #process the science data
        for img in infiles:
            nimg = os.path.basename(img)
            #print nimg, nimg in flatlist, nimg in biaslist
            if not (nimg in biaslist):
                #open the image
                struct = fits.open(img)
                simg = outpath + 'bxgp' + os.path.basename(img)

                #print the message
                if log:
                    message = 'Processing science frame %s' % img
                    log.message(message, with_stdout=verbose)

                #process the image
                struct = clean(struct,
                               createvar=False,
                               badpixelstruct=None,
                               mult=True,
                               dblist=dblist,
                               xdict=xdict,
                               subover=subover,
                               trim=trim,
                               subbias=False,
                               bstruct=None,
                               median=median,
                               function=function,
                               order=order,
                               rej_lo=rej_lo,
                               rej_hi=rej_hi,
                               niter=niter,
                               plotover=plotover,
                               log=log,
                               verbose=verbose)

                #write the file out
                # housekeeping keywords
                fname, hist = history(
                    level=1,
                    wrap=False,
                    exclude=['images', 'outimages', 'outpref'])
                saltkey.housekeeping(struct[0], 'SPREPARE',
                                     'Images have been prepared', hist)
                saltkey.new('SGAIN', time.asctime(time.localtime()),
                            'Images have been gain corrected', struct[0])
                saltkey.new('SXTALK', time.asctime(time.localtime()),
                            'Images have been xtalk corrected', struct[0])
                saltkey.new('SBIAS', time.asctime(time.localtime()),
                            'Images have been de-biased', struct[0])

                # write FITS file
                saltio.writefits(struct, simg, clobber=clobber)
                saltio.closefits(struct)

                #mosaic the files--currently not in the proper format--will update when it is
                if not saltkey.fastmode(saltkey.get('DETMODE', struct[0])):
                    mimg = outpath + 'mbxgp' + os.path.basename(img)
                    saltmosaic(images=simg,
                               outimages=mimg,
                               outpref='',
                               geomfile=geomfile,
                               interp=interp,
                               cleanup=True,
                               clobber=clobber,
                               logfile=logfile,
                               verbose=verbose)

                    #remove the intermediate steps
                    saltio.delete(simg)
コード例 #2
0
ファイル: imred.py プロジェクト: apodemus/SALTsandbox
def imred(rawdir, prodir, cleanup=True):
    print rawdir
    print prodir

    #get the name of the files
    infile_list=glob.glob(rawdir+'*.fits')
    infiles=','.join(['%s' % x for x in infile_list])
    

    #get the current date for the files
    obsdate=os.path.basename(infile_list[0])[1:9]
    print obsdate

    #set up some files that will be needed
    logfile='imred'+obsdate+'.log'
    flatimage='FLAT%s.fits' % (obsdate)
    dbfile='spec%s.db' % obsdate

    #create the observation log
    obs_dict=obslog(infile_list)

 
    #prepare the data
    saltprepare(infiles, '', 'p', createvar=False, badpixelimage='', clobber=True, logfile=logfile, verbose=True)

    #bias subtract the data
    saltbias('pP*fits', '', 'b', subover=True, trim=True, subbias=False, masterbias='',  
              median=False, function='polynomial', order=5, rej_lo=3.0, rej_hi=5.0, 
              niter=10, plotover=False, turbo=False, 
              clobber=True, logfile=logfile, verbose=True)

    #gain correct the data
    saltgain('bpP*fits', '', 'g', usedb=False, mult=True, clobber=True, logfile=logfile, verbose=True)

    #cross talk correct the data
    saltxtalk('gbpP*fits', '', 'x', xtalkfile = "", usedb=False, clobber=True, logfile=logfile, verbose=True)

    #cosmic ray clean the data
    #only clean the object data
    for i in range(len(infile_list)):
      if obs_dict['CCDTYPE'][i].count('OBJECT') and obs_dict['INSTRUME'][i].count('RSS'):
          img='xgbp'+os.path.basename(infile_list[i])
          saltcrclean(img, img, '', crtype='edge', thresh=5, mbox=11, bthresh=5.0,
                flux_ratio=0.2, bbox=25, gain=1.0, rdnoise=5.0, fthresh=5.0, bfactor=2,
                gbox=3, maxiter=5, multithread=True,  clobber=True, logfile=logfile, verbose=True)
 
    #flat field correct the data
    flat_imgs=''
    for i in range(len(infile_list)):
        if obs_dict['CCDTYPE'][i].count('FLAT'):
           if flat_imgs: flat_imgs += ','
           flat_imgs += 'xgbp'+os.path.basename(infile_list[i])

    if len(flat_imgs)!=0:
         saltcombine(flat_imgs,flatimage, method='median', reject=None, mask=False,    \
                weight=True, blank=0, scale='average', statsec='[200:300, 600:800]', lthresh=3,    \
                hthresh=3, clobber=True, logfile=logfile, verbose=True)
         #saltillum(flatimage, flatimage, '', mbox=11, clobber=True, logfile=logfile, verbose=True)

         saltflat('xgbpP*fits', '', 'f', flatimage, minflat=500, clobber=True, logfile=logfile, verbose=True)
    else:
         flats=None
         imfiles=glob.glob('xgbpP*fits')
         for f in imfiles:
             shutil.copy(f, 'f'+f)

    #mosaic the data
    geomfile=iraf.osfn("pysalt$data/rss/RSSgeom.dat")
    saltmosaic('fxgbpP*fits', '', 'm', geomfile, interp='linear', cleanup=True, geotran=True, clobber=True, logfile=logfile, verbose=True)

    #clean up the images
    if cleanup:
           for f in glob.glob('p*fits'): os.remove(f)
           for f in glob.glob('bp*fits'): os.remove(f)
           for f in glob.glob('gbp*fits'): os.remove(f)
           for f in glob.glob('xgbp*fits'): os.remove(f)
           for f in glob.glob('fxgbp*fits'): os.remove(f)
コード例 #3
0
def imred(infile_list, prodir, bpmfile=None, gaindb = None, cleanup=True):
    #get the name of the files
    infiles=','.join(['%s' % x for x in infile_list])
    

    #get the current date for the files
    obsdate=os.path.basename(infile_list[0])[1:9]
    print obsdate

    #set up some files that will be needed
    logfile='im'+obsdate+'.log'
    flatimage='FLAT%s.fits' % (obsdate)
    dbfile='spec%s.db' % obsdate

    #create the observation log
    obs_dict=obslog(infile_list)

    with logging(logfile, debug) as log:
        log.message('Pysalt Version: '+pysalt.verno, with_header=False)
 
    #prepare the data
    saltprepare(infiles, '', 'p', createvar=False, badpixelimage='', clobber=True, logfile=logfile, verbose=True)

    for img in infile_list:
        hdu = pyfits.open('p'+os.path.basename(img), 'update')
        # for backwards compatibility  
        if not hdu[1].header.has_key('XTALK'):
            hdu[1].header.update('XTALK',1474)
            hdu[2].header.update('XTALK',1474)
            hdu[3].header.update('XTALK',1166)
            hdu[4].header.update('XTALK',1111)
            hdu[5].header.update('XTALK',1377)
            hdu[6].header.update('XTALK',1377)
        hdu.close()
        
    #bias subtract the data
    saltbias('pP*fits', '', 'b', subover=True, trim=True, subbias=False, masterbias='',  
              median=False, function='polynomial', order=5, rej_lo=3.0, rej_hi=5.0, 
              niter=10, plotover=False, turbo=False, 
              clobber=True, logfile=logfile, verbose=True)

    add_variance('bpP*fits', bpmfile)

    #gain correct the data 
    usedb = False
    if gaindb: usedb = True
    saltgain('bpP*fits', '', 'g', gaindb=gaindb, usedb=usedb, mult=True, clobber=True, logfile=logfile, verbose=True)

    #cross talk correct the data
    saltxtalk('gbpP*fits', '', 'x', xtalkfile = "", usedb=False, clobber=True, logfile=logfile, verbose=True)

    #cosmic ray clean the data
    #only clean the object data
    for i in range(len(infile_list)):
        if (obs_dict['CCDTYPE'][i].count('OBJECT') \
            and obs_dict['LAMPID'][i].count('NONE') \
            and obs_dict['INSTRUME'][i].count('RSS')):
          img='xgbp'+os.path.basename(infile_list[i])
          saltcrclean(img, img, '', crtype='edge', thresh=5, mbox=11, bthresh=5.0,
                flux_ratio=0.2, bbox=25, gain=1.0, rdnoise=5.0, fthresh=5.0, bfactor=2,
                gbox=3, maxiter=5, multithread=True,  clobber=True, logfile=logfile, verbose=True)

    #mosaic the data
    #khn: attempt to use most recent previous geometry to obsdate.  
    #NOTE: mosaicing does not do this correctly
    #geomdb = open(datadir+"RSSgeom.dat",'r')
    #for geomline in geomdb:
    #    if geomline[0]=='#': continue
    #    if (int(obsdate) > int(geomline.split(' ')[0].replace('-',''))): break
    #geomfile = "RSSgeom_obsdate.dat"
    #open(geomfile,'w').write(geomline)

    geomfile=iraf.osfn("pysalt$data/rss/RSSgeom.dat")
    
    try:
       saltmosaic('xgbpP*fits', '', 'm', geomfile, interp='linear', cleanup=True, geotran=True, clobber=True, logfile=logfile, verbose=True)
    except:
       saltmosaic('xgbpP*fits', '', 'm', geomfile, interp='linear', cleanup=True, geotran=True, clobber=True, logfile=logfile, verbose=True)
    #khn: fix mosaiced VAR and BPM extensions
    #khn: fix mosaiced bpm missing some of gap
    for img in infile_list:
        filename = 'mxgbp'+os.path.basename(img)
        hdu = pyfits.open(filename, 'update')
        hdu[2].header.update('EXTNAME','VAR')
        hdu[3].header.update('EXTNAME','BPM')
        bpm_rc = (hdu[3].data>0).astype('uint8')
        zeroscicol = hdu['SCI'].data.sum(axis=0) == 0
        bpmgapcol = bpm_rc.mean(axis=0) == 1
        addbpmcol = zeroscicol & ~bpmgapcol
        addbpmcol[np.argmax(addbpmcol)-4:np.argmax(addbpmcol)] = True    # allow for chip tilt
        bpm_rc[:,addbpmcol] = 1
        hdu[3].data = bpm_rc
        hdu.writeto(filename,clobber=True)

    #clean up the images
    if cleanup:
           for f in glob.glob('p*fits'): os.remove(f)
           for f in glob.glob('bp*fits'): os.remove(f)
           for f in glob.glob('gbp*fits'): os.remove(f)
           for f in glob.glob('xgbp*fits'): os.remove(f)
コード例 #4
0
ファイル: saltclean.py プロジェクト: dr-jpk/pysalt
def saltclean(images, outpath, obslogfile=None, gaindb=None,xtalkfile=None, 
	geomfile=None,subover=True,trim=True,masbias=None, 
        subbias=False, median=False, function='polynomial', order=5,rej_lo=3,
        rej_hi=3,niter=5,interp='linear', clobber=False, logfile='salt.log', 
        verbose=True):
   """SALTCLEAN will provide basic CCD reductions for a set of data.  It will 
      sort the data, and first process the biases, flats, and then the science 
      frames.  It will record basic quality control information about each of 
      the steps.
   """
   plotover=False

   #start logging
   with logging(logfile,debug) as log:

       # Check the input images 
       infiles = saltio.argunpack ('Input',images)

       # create list of output files 
       outpath=saltio.abspath(outpath)


       #does the gain database file exist
       if gaindb:
           dblist= saltio.readgaindb(gaindb)
       else:
           dblist=[]

       # does crosstalk coefficient data exist
       if xtalkfile:
           xtalkfile = xtalkfile.strip()
           xdict = saltio.readxtalkcoeff(xtalkfile)
       else:
           xdict=None
       #does the mosaic file exist--raise error if no
       saltio.fileexists(geomfile)


       # Delete the obslog file if it already exists
       if os.path.isfile(obslogfile) and clobber: saltio.delete(obslogfile)

       #read in the obsveration log or create it
       if os.path.isfile(obslogfile):
           msg='The observing log already exists.  Please either delete it or run saltclean with clobber=yes'
           raise SaltError(msg)
       else:
           headerDict=obslog(infiles, log)
           obsstruct=createobslogfits(headerDict)
           saltio.writefits(obsstruct, obslogfile)

       #create the list of bias frames and process them
       filename=obsstruct.data.field('FILENAME')
       detmode=obsstruct.data.field('DETMODE')
       ccdtype=obsstruct.data.field('CCDTYPE')

       #set the bias list of objects
       biaslist=filename[ccdtype=='ZERO']
       masterbias_dict={}
       for img in infiles:
           if os.path.basename(img) in biaslist:
               #open the image
               struct=pyfits.open(img)
               bimg=outpath+'bxgp'+os.path.basename(img)

               #print the message
               if log:
                   message='Processing Zero frame %s' % img
                   log.message(message, with_stdout=verbose)

               #process the image
               struct=clean(struct, createvar=False, badpixelstruct=None, mult=True, 
                            dblist=dblist, xdict=xdict, subover=subover, trim=trim, subbias=False,
                            bstruct=None, median=median, function=function, order=order,
                            rej_lo=rej_lo, rej_hi=rej_hi, niter=niter, plotover=plotover, log=log,
                            verbose=verbose)

               #write the file out
               # housekeeping keywords
               fname, hist=history(level=1, wrap=False, exclude=['images', 'outimages', 'outpref'])
               saltkey.housekeeping(struct[0],'SPREPARE', 'Images have been prepared', hist)
               saltkey.new('SGAIN',time.asctime(time.localtime()),'Images have been gain corrected',struct[0])
               saltkey.new('SXTALK',time.asctime(time.localtime()),'Images have been xtalk corrected',struct[0])
               saltkey.new('SBIAS',time.asctime(time.localtime()),'Images have been de-biased',struct[0])

               # write FITS file
               saltio.writefits(struct,bimg, clobber=clobber)
               saltio.closefits(struct)

               #add files to the master bias list
               masterbias_dict=compareimages(struct, bimg, masterbias_dict, keylist=biasheader_list)

       #create the master bias frame
       for i in masterbias_dict.keys():
           bkeys=masterbias_dict[i][0]
           blist=masterbias_dict[i][1:]
           mbiasname=outpath+createmasterbiasname(blist, bkeys)
           bfiles=','.join(blist)
           saltcombine(bfiles, mbiasname, method='median', reject='sigclip', mask=False, 
                       weight=False, blank=0, scale=None, statsec=None, lthresh=3,    \
                       hthresh=3, clobber=False, logfile=logfile,verbose=verbose)

           

       #create the list of flatfields and process them
       flatlist=filename[ccdtype=='FLAT']
       masterflat_dict={}
       for img in infiles:
           if os.path.basename(img) in flatlist:
               #open the image
               struct=pyfits.open(img)
               fimg=outpath+'bxgp'+os.path.basename(img)

               #print the message
               if log:
                   message='Processing Flat frame %s' % img
                   log.message(message, with_stdout=verbose)

               #process the image
               struct=clean(struct, createvar=False, badpixelstruct=None, mult=True, 
                            dblist=dblist, xdict=xdict, subover=subover, trim=trim, subbias=False,
                            bstruct=None, median=median, function=function, order=order,
                            rej_lo=rej_lo, rej_hi=rej_hi, niter=niter, plotover=plotover, log=log,
                            verbose=verbose)

               #write the file out
               # housekeeping keywords
               fname, hist=history(level=1, wrap=False, exclude=['images', 'outimages', 'outpref'])
               saltkey.housekeeping(struct[0],'SPREPARE', 'Images have been prepared', hist)
               saltkey.new('SGAIN',time.asctime(time.localtime()),'Images have been gain corrected',struct[0])
               saltkey.new('SXTALK',time.asctime(time.localtime()),'Images have been xtalk corrected',struct[0])
               saltkey.new('SBIAS',time.asctime(time.localtime()),'Images have been de-biased',struct[0])

               # write FITS file
               saltio.writefits(struct,fimg, clobber=clobber)
               saltio.closefits(struct)

               #add files to the master bias list
               masterflat_dict=compareimages(struct, fimg, masterflat_dict,  keylist=flatheader_list)

       #create the master flat frame
       for i in masterflat_dict.keys():
           fkeys=masterflat_dict[i][0]
           flist=masterflat_dict[i][1:]
           mflatname=outpath+createmasterflatname(flist, fkeys)
           ffiles=','.join(flist)
           saltcombine(ffiles, mflatname, method='median', reject='sigclip', mask=False, 
                       weight=False, blank=0, scale=None, statsec=None, lthresh=3,    \
                       hthresh=3, clobber=False, logfile=logfile,verbose=verbose)

       #process the science data
       for img in infiles:
           nimg=os.path.basename(img)
           if not nimg in flatlist or not nimg in biaslist:
               #open the image
               struct=pyfits.open(img)
               simg=outpath+'bxgp'+os.path.basename(img)

               #print the message
               if log:
                   message='Processing science frame %s' % img
                   log.message(message, with_stdout=verbose)

               #process the image
               struct=clean(struct, createvar=False, badpixelstruct=None, mult=True, 
                            dblist=dblist, xdict=xdict, subover=subover, trim=trim, subbias=False,
                            bstruct=None, median=median, function=function, order=order,
                            rej_lo=rej_lo, rej_hi=rej_hi, niter=niter, plotover=plotover, log=log,
                            verbose=verbose)

               #write the file out
               # housekeeping keywords
               fname, hist=history(level=1, wrap=False, exclude=['images', 'outimages', 'outpref'])
               saltkey.housekeeping(struct[0],'SPREPARE', 'Images have been prepared', hist)
               saltkey.new('SGAIN',time.asctime(time.localtime()),'Images have been gain corrected',struct[0])
               saltkey.new('SXTALK',time.asctime(time.localtime()),'Images have been xtalk corrected',struct[0])
               saltkey.new('SBIAS',time.asctime(time.localtime()),'Images have been de-biased',struct[0])

               # write FITS file
               saltio.writefits(struct,simg, clobber=clobber)
               saltio.closefits(struct)

               #mosaic the files--currently not in the proper format--will update when it is
               if not saltkey.fastmode(saltkey.get('DETMODE', struct[0])):
                   mimg=outpath+'mbxgp'+os.path.basename(img)
                   saltmosaic(images=simg, outimages=mimg,outpref='',geomfile=geomfile,
                        interp=interp,cleanup=True,clobber=clobber,logfile=logfile,
                        verbose=verbose)

                   #remove the intermediate steps
                   saltio.delete(simg)
コード例 #5
0
ファイル: saltadvance.py プロジェクト: saltastro/pipetools
def saltadvance(images, outpath, obslogfile=None, gaindb=None,xtalkfile=None, 
	geomfile=None,subover=True,trim=True,masbias=None, 
        subbias=False, median=False, function='polynomial', order=5,rej_lo=3,
        rej_hi=3,niter=5,interp='linear',  sdbhost='',sdbname='',sdbuser='', password='',
        clobber=False, cleanup=True, logfile='salt.log', verbose=True):
   """SALTADVANCE provides advanced data reductions for a set of data.  It will 
      sort the data, and first process the biases, flats, and then the science 
      frames.  It will record basic quality control information about each of 
      the steps.
   """
   plotover=False

   #start logging
   with logging(logfile,debug) as log:

       # Check the input images 
       infiles = saltio.argunpack ('Input',images)
       infiles.sort()

       # create list of output files 
       outpath=saltio.abspath(outpath)

       #log into the database
       sdb=saltmysql.connectdb(sdbhost, sdbname, sdbuser, password)

       #does the gain database file exist
       if gaindb:
           dblist= saltio.readgaindb(gaindb)
       else:
           dblist=[]

       # does crosstalk coefficient data exist
       if xtalkfile:
           xtalkfile = xtalkfile.strip()
           xdict = saltio.readxtalkcoeff(xtalkfile)
       else:
           xdict=None
       #does the mosaic file exist--raise error if no
       saltio.fileexists(geomfile)


       # Delete the obslog file if it already exists
       if os.path.isfile(obslogfile) and clobber: saltio.delete(obslogfile)

       #read in the obsveration log or create it
       if os.path.isfile(obslogfile):
           msg='The observing log already exists.  Please either delete it or run saltclean with clobber=yes'
           raise SaltError(msg)
       else:
           headerDict=obslog(infiles, log)
           obsstruct=createobslogfits(headerDict)
           saltio.writefits(obsstruct, obslogfile)

       #create the list of bias frames and process them
       filename=obsstruct.data.field('FILENAME')
       detmode=obsstruct.data.field('DETMODE')
       obsmode=obsstruct.data.field('OBSMODE')
       ccdtype=obsstruct.data.field('CCDTYPE')
       propcode=obsstruct.data.field('PROPID')
       masktype=obsstruct.data.field('MASKTYP')

       #set the bias list of objects
       biaslist=filename[(ccdtype=='ZERO')*(propcode=='CAL_BIAS')]
       masterbias_dict={}
       for img in infiles:
           if os.path.basename(img) in biaslist:
               #open the image
               struct=fits.open(img)
               bimg=outpath+'bxgp'+os.path.basename(img)

               #print the message
               if log:
                   message='Processing Zero frame %s' % img
                   log.message(message, with_stdout=verbose)

               #process the image
               struct=clean(struct, createvar=True, badpixelstruct=None, mult=True, 
                            dblist=dblist, xdict=xdict, subover=subover, trim=trim, subbias=False,
                            bstruct=None, median=median, function=function, order=order,
                            rej_lo=rej_lo, rej_hi=rej_hi, niter=niter, plotover=plotover, log=log,
                            verbose=verbose)
 
               #update the database
               updatedq(os.path.basename(img), struct, sdb)

               #write the file out
               # housekeeping keywords
               fname, hist=history(level=1, wrap=False, exclude=['images', 'outimages', 'outpref'])
               saltkey.housekeeping(struct[0],'SPREPARE', 'Images have been prepared', hist)
               saltkey.new('SGAIN',time.asctime(time.localtime()),'Images have been gain corrected',struct[0])
               saltkey.new('SXTALK',time.asctime(time.localtime()),'Images have been xtalk corrected',struct[0])
               saltkey.new('SBIAS',time.asctime(time.localtime()),'Images have been de-biased',struct[0])

               # write FITS file
               saltio.writefits(struct,bimg, clobber=clobber)
               saltio.closefits(struct)

               #add files to the master bias list
               masterbias_dict=compareimages(struct, bimg, masterbias_dict, keylist=biasheader_list)

       #create the master bias frame
       for i in masterbias_dict.keys():
           bkeys=masterbias_dict[i][0]
           blist=masterbias_dict[i][1:]
           mbiasname=outpath+createmasterbiasname(blist, bkeys)
           bfiles=','.join(blist)
           saltcombine(bfiles, mbiasname, method='median', reject='sigclip', mask=False, 
                       weight=False, blank=0, scale=None, statsec=None, lthresh=3,    \
                       hthresh=3, clobber=False, logfile=logfile,verbose=verbose)

           

       #create the list of flatfields and process them
       flatlist=filename[ccdtype=='FLAT']
       masterflat_dict={}
       for img in infiles:
           if os.path.basename(img) in flatlist:
               #open the image
               struct=fits.open(img)
               fimg=outpath+'bxgp'+os.path.basename(img)

               #print the message
               if log:
                   message='Processing Flat frame %s' % img
                   log.message(message, with_stdout=verbose)

               #process the image
               struct=clean(struct, createvar=True, badpixelstruct=None, mult=True, 
                            dblist=dblist, xdict=xdict, subover=subover, trim=trim, subbias=False,
                            bstruct=None, median=median, function=function, order=order,
                            rej_lo=rej_lo, rej_hi=rej_hi, niter=niter, plotover=plotover, log=log,
                            verbose=verbose)

               #update the database
               updatedq(os.path.basename(img), struct, sdb)

               #write the file out
               # housekeeping keywords
               fname, hist=history(level=1, wrap=False, exclude=['images', 'outimages', 'outpref'])
               saltkey.housekeeping(struct[0],'SPREPARE', 'Images have been prepared', hist)
               saltkey.new('SGAIN',time.asctime(time.localtime()),'Images have been gain corrected',struct[0])
               saltkey.new('SXTALK',time.asctime(time.localtime()),'Images have been xtalk corrected',struct[0])
               saltkey.new('SBIAS',time.asctime(time.localtime()),'Images have been de-biased',struct[0])

               # write FITS file
               saltio.writefits(struct,fimg, clobber=clobber)
               saltio.closefits(struct)

               #add files to the master bias list
               masterflat_dict=compareimages(struct, fimg, masterflat_dict,  keylist=flatheader_list)

       #create the master flat frame
       for i in masterflat_dict.keys():
           fkeys=masterflat_dict[i][0]
           flist=masterflat_dict[i][1:]
           mflatname=outpath+createmasterflatname(flist, fkeys)
           ffiles=','.join(flist)
           saltcombine(ffiles, mflatname, method='median', reject='sigclip', mask=False, 
                       weight=False, blank=0, scale=None, statsec=None, lthresh=3,    \
                       hthresh=3, clobber=False, logfile=logfile,verbose=verbose)

       #process the arc data
       arclist=filename[(ccdtype=='ARC') * (obsmode=='SPECTROSCOPY') * (masktype=='LONGSLIT')]
       for i, img in enumerate(infiles):
           nimg=os.path.basename(img)
           if nimg in arclist:
               #open the image
               struct=fits.open(img)
               simg=outpath+'bxgp'+os.path.basename(img)
               obsdate=os.path.basename(img)[1:9]

               #print the message
               if log:
                   message='Processing ARC frame %s' % img
                   log.message(message, with_stdout=verbose)


               struct=clean(struct, createvar=False, badpixelstruct=None, mult=True, 
                            dblist=dblist, xdict=xdict, subover=subover, trim=trim, subbias=False,
                            bstruct=None, median=median, function=function, order=order,
                            rej_lo=rej_lo, rej_hi=rej_hi, niter=niter, plotover=plotover, 
                            log=log, verbose=verbose)

               # write FITS file
               saltio.writefits(struct,simg, clobber=clobber)
               saltio.closefits(struct)

               #mosaic the images
               mimg=outpath+'mbxgp'+os.path.basename(img)
               saltmosaic(images=simg, outimages=mimg,outpref='',geomfile=geomfile,
                    interp=interp,cleanup=True,clobber=clobber,logfile=logfile,
                    verbose=verbose)

               #remove the intermediate steps
               saltio.delete(simg)


               #measure the arcdata
               arcimage=outpath+'mbxgp'+nimg
               dbfile=outpath+obsdate+'_specid.db'
               lamp = obsstruct.data.field('LAMPID')[i]
               lamp = lamp.replace(' ', '')
               lampfile = iraf.osfn("pysalt$data/linelists/%s.salt" % lamp)
               print arcimage, lampfile, os.getcwd()
               specidentify(arcimage, lampfile, dbfile, guesstype='rss', 
                                guessfile='', automethod='Matchlines', function='legendre',
                                order=3, rstep=100, rstart='middlerow', mdiff=20, thresh=3,
                                startext=0, niter=5, smooth=3, inter=False, clobber=True, logfile=logfile, 
                                verbose=verbose)
               try:
                   ximg = outpath+'xmbxgp'+os.path.basename(arcimage)
                   specrectify(images=arcimage, outimages=ximg, outpref='', solfile=dbfile, caltype='line',
                              function='legendre', order=3, inttype='interp', w1=None, w2=None, dw=None,
                              nw=None, blank=0.0, conserve=True, nearest=True, clobber=True,
                              logfile=logfile, verbose=verbose)
               except:
                   pass


              
       #process the science data
       for i, img in enumerate(infiles):
           nimg=os.path.basename(img)
           if not (nimg in flatlist or nimg in biaslist or nimg in arclist):
     
               #open the image
               struct=fits.open(img)
               if struct[0].header['PROPID'].count('CAL_GAIN'): continue
               simg=outpath+'bxgp'+os.path.basename(img)
   

               #print the message
               if log:
                   message='Processing science frame %s' % img
                   log.message(message, with_stdout=verbose)


               #Check to see if it is RSS 2x2 and add bias subtraction
               instrume=saltkey.get('INSTRUME', struct[0]).strip()
               gainset = saltkey.get('GAINSET', struct[0])    
               rospeed = saltkey.get('ROSPEED', struct[0])    
               target = saltkey.get('OBJECT', struct[0]).strip()
               exptime = saltkey.get('EXPTIME', struct[0])
               obsmode = saltkey.get('OBSMODE', struct[0]).strip()
               detmode = saltkey.get('DETMODE', struct[0]).strip()
               masktype = saltkey.get('MASKTYP', struct[0]).strip()
  
               
               xbin, ybin = saltkey.ccdbin( struct[0], img)
               obsdate=os.path.basename(img)[1:9]
               bstruct=None
               crtype=None
               thresh=5 
               mbox=11 
               bthresh=5.0,
               flux_ratio=0.2 
               bbox=25 
               gain=1.0 
               rdnoise=5.0 
               fthresh=5.0 
               bfactor=2
               gbox=3 
               maxiter=5
    
               subbias=False
               if instrume=='RSS' and gainset=='FAINT' and rospeed=='SLOW':
                   bfile='P%sBiasNM%ix%iFASL.fits' % (obsdate, xbin, ybin)
                   if os.path.exists(bfile):
                      bstruct=fits.open(bfile)
                      subbias=True
                   if detmode=='Normal' and target!='ARC' and xbin < 5 and ybin < 5:
                       crtype='edge' 
                       thresh=5 
                       mbox=11 
                       bthresh=5.0,
                       flux_ratio=0.2 
                       bbox=25 
                       gain=1.0 
                       rdnoise=5.0 
                       fthresh=5.0 
                       bfactor=2
                       gbox=3 
                       maxiter=3
    
               #process the image
               struct=clean(struct, createvar=True, badpixelstruct=None, mult=True, 
                            dblist=dblist, xdict=xdict, subover=subover, trim=trim, subbias=subbias,
                            bstruct=bstruct, median=median, function=function, order=order,
                            rej_lo=rej_lo, rej_hi=rej_hi, niter=niter, plotover=plotover, 
                            crtype=crtype,thresh=thresh,mbox=mbox, bbox=bbox,      \
                            bthresh=bthresh, flux_ratio=flux_ratio, gain=gain, rdnoise=rdnoise, 
                            bfactor=bfactor, fthresh=fthresh, gbox=gbox, maxiter=maxiter,
                            log=log, verbose=verbose)

               

               #update the database
               updatedq(os.path.basename(img), struct, sdb)

               #write the file out
               # housekeeping keywords
               fname, hist=history(level=1, wrap=False, exclude=['images', 'outimages', 'outpref'])
               saltkey.housekeeping(struct[0],'SPREPARE', 'Images have been prepared', hist)
               saltkey.new('SGAIN',time.asctime(time.localtime()),'Images have been gain corrected',struct[0])
               saltkey.new('SXTALK',time.asctime(time.localtime()),'Images have been xtalk corrected',struct[0])
               saltkey.new('SBIAS',time.asctime(time.localtime()),'Images have been de-biased',struct[0])

               # write FITS file
               saltio.writefits(struct,simg, clobber=clobber)
               saltio.closefits(struct)

               #mosaic the files--currently not in the proper format--will update when it is
               if not saltkey.fastmode(saltkey.get('DETMODE', struct[0])):
                   mimg=outpath+'mbxgp'+os.path.basename(img)
                   saltmosaic(images=simg, outimages=mimg,outpref='',geomfile=geomfile,
                        interp=interp,fill=True, cleanup=True,clobber=clobber,logfile=logfile,
                        verbose=verbose)

                   #remove the intermediate steps
                   saltio.delete(simg)

               #if the file is spectroscopic mode, apply the wavelength correction
               if obsmode == 'SPECTROSCOPY' and masktype.strip()=='LONGSLIT':
                  dbfile=outpath+obsdate+'_specid.db'
                  try:
                     ximg = outpath+'xmbxgp'+os.path.basename(img)
                     specrectify(images=mimg, outimages=ximg, outpref='', solfile=dbfile, caltype='line', 
                              function='legendre', order=3, inttype='interp', w1=None, w2=None, dw=None,
                              nw=None, blank=0.0, conserve=True, nearest=True, clobber=True, 
                              logfile=logfile, verbose=verbose)
                  except Exception, e:
                     log.message('%s' % e)


       #clean up the results
       if cleanup:
          #clean up the bias frames
          for i in masterbias_dict.keys():
               blist=masterbias_dict[i][1:]
               for b in blist: saltio.delete(b)

          #clean up the flat frames
          for i in masterflat_dict.keys():
               flist=masterflat_dict[i][1:]
               for f in flist: saltio.delete(f)
コード例 #6
0
def imred(rawdir, prodir, cleanup=True):
    print rawdir
    print prodir

    #get the name of the files
    infile_list = glob.glob(rawdir + '*.fits')
    infiles = ','.join(['%s' % x for x in infile_list])

    #get the current date for the files
    obsdate = os.path.basename(infile_list[0])[1:9]
    print obsdate

    #set up some files that will be needed
    logfile = 'imred' + obsdate + '.log'
    flatimage = 'FLAT%s.fits' % (obsdate)
    dbfile = 'spec%s.db' % obsdate

    #create the observation log
    obs_dict = obslog(infile_list)

    #prepare the data
    saltprepare(infiles,
                '',
                'p',
                createvar=False,
                badpixelimage='',
                clobber=True,
                logfile=logfile,
                verbose=True)

    #bias subtract the data
    saltbias('pP*fits',
             '',
             'b',
             subover=True,
             trim=True,
             subbias=False,
             masterbias='',
             median=False,
             function='polynomial',
             order=5,
             rej_lo=3.0,
             rej_hi=5.0,
             niter=10,
             plotover=False,
             turbo=False,
             clobber=True,
             logfile=logfile,
             verbose=True)

    #gain correct the data
    saltgain('bpP*fits',
             '',
             'g',
             usedb=False,
             mult=True,
             clobber=True,
             logfile=logfile,
             verbose=True)

    #cross talk correct the data
    saltxtalk('gbpP*fits',
              '',
              'x',
              xtalkfile="",
              usedb=False,
              clobber=True,
              logfile=logfile,
              verbose=True)

    #cosmic ray clean the data
    #only clean the object data
    for i in range(len(infile_list)):
        if obs_dict['CCDTYPE'][i].count(
                'OBJECT') and obs_dict['INSTRUME'][i].count('RSS'):
            img = 'xgbp' + os.path.basename(infile_list[i])
            saltcrclean(img,
                        img,
                        '',
                        crtype='edge',
                        thresh=5,
                        mbox=11,
                        bthresh=5.0,
                        flux_ratio=0.2,
                        bbox=25,
                        gain=1.0,
                        rdnoise=5.0,
                        fthresh=5.0,
                        bfactor=2,
                        gbox=3,
                        maxiter=5,
                        multithread=True,
                        clobber=True,
                        logfile=logfile,
                        verbose=True)

    #flat field correct the data
    flat_imgs = ''
    for i in range(len(infile_list)):
        if obs_dict['CCDTYPE'][i].count('FLAT'):
            if flat_imgs: flat_imgs += ','
            flat_imgs += 'xgbp' + os.path.basename(infile_list[i])

    if len(flat_imgs) != 0:
        saltcombine(flat_imgs,flatimage, method='median', reject=None, mask=False,    \
               weight=True, blank=0, scale='average', statsec='[200:300, 600:800]', lthresh=3,    \
               hthresh=3, clobber=True, logfile=logfile, verbose=True)
        #saltillum(flatimage, flatimage, '', mbox=11, clobber=True, logfile=logfile, verbose=True)

        saltflat('xgbpP*fits',
                 '',
                 'f',
                 flatimage,
                 minflat=500,
                 clobber=True,
                 logfile=logfile,
                 verbose=True)
    else:
        flats = None
        imfiles = glob.glob('xgbpP*fits')
        for f in imfiles:
            shutil.copy(f, 'f' + f)

    #mosaic the data
    geomfile = iraf.osfn("pysalt$data/rss/RSSgeom.dat")
    saltmosaic('fxgbpP*fits',
               '',
               'm',
               geomfile,
               interp='linear',
               cleanup=True,
               geotran=True,
               clobber=True,
               logfile=logfile,
               verbose=True)

    #clean up the images
    if cleanup:
        for f in glob.glob('p*fits'):
            os.remove(f)
        for f in glob.glob('bp*fits'):
            os.remove(f)
        for f in glob.glob('gbp*fits'):
            os.remove(f)
        for f in glob.glob('xgbp*fits'):
            os.remove(f)
        for f in glob.glob('fxgbp*fits'):
            os.remove(f)
コード例 #7
0
ファイル: science_red.py プロジェクト: crawfordsm/saltdes
def science_red(rawdir, prodir, imreduce=True, specreduce=True, bpmfile=None, calfile=None, lampfile='Ar', automethod='Matchlines', skysection=[800,1000], cleanup=True):
    print rawdir
    print prodir

    #get the name of the files
    infile_list=glob.glob(rawdir+'P*.fits')
    infiles=','.join(['%s' % x for x in infile_list])
    

    #get the current date for the files
    obsdate=os.path.basename(infile_list[0])[1:9]
    print obsdate

    #set up some files that will be needed
    logfile='spec'+obsdate+'.log'
    flatimage='FLAT%s.fits' % (obsdate)
    dbfile='spec%s.db' % obsdate

    #create the observation log
    obs_dict=obslog(infile_list)

 
    if imreduce:   
      #prepare the data
      saltprepare(infiles, '', 'p', createvar=False, badpixelimage='', clobber=True, logfile=logfile, verbose=True)

      #bias subtract the data
      saltbias('pP*fits', '', 'b', subover=True, trim=True, subbias=False, masterbias='',  
              median=False, function='polynomial', order=5, rej_lo=3.0, rej_hi=5.0, 
              niter=10, plotover=False, turbo=False, 
              clobber=True, logfile=logfile, verbose=True)

      add_variance('bpP*fits', bpmfile)

      #gain correct the data
      saltgain('bpP*fits', '', 'g', usedb=False, mult=True, clobber=True, logfile=logfile, verbose=True)

      #cross talk correct the data
      saltxtalk('gbpP*fits', '', 'x', xtalkfile = "", usedb=False, clobber=True, logfile=logfile, verbose=True)

 
      #flat field correct the data
      flat_imgs=''
      for i in range(len(infile_list)):
        if obs_dict['CCDTYPE'][i].count('FLAT'):
           if flat_imgs: flat_imgs += ','
           flat_imgs += 'xgbp'+os.path.basename(infile_list[i])

      if 0: #len(flat_imgs)!=0:
         saltcombine(flat_imgs,flatimage, method='median', reject=None, mask=False,    \
                weight=False, blank=0, scale=None, statsec='[200:300, 600:800]', lthresh=3,    \
                hthresh=3, clobber=True, logfile=logfile, verbose=True)
         saltillum(flatimage, flatimage, '', mbox=11, clobber=True, logfile=logfile, verbose=True)

         saltflat('xgbpP*fits', '', 'f', flatimage, minflat=0.8, allext=False, clobber=True, logfile=logfile, verbose=True)
      else:
         flats=None
         imfiles=glob.glob('xgbpP*fits')
         for f in imfiles:
             shutil.copy(f, 'f'+f)

      #cosmic ray clean the data
      #only clean the object data
      for i in range(len(infile_list)):
        if obs_dict['CCDTYPE'][i].count('OBJECT') and obs_dict['INSTRUME'][i].count('RSS'):
          img='fxgbp'+os.path.basename(infile_list[i])
          saltcrclean(img, img, '', crtype='edge', thresh=5, mbox=11, bthresh=5.0,
                flux_ratio=0.2, bbox=25, gain=1.0, rdnoise=5.0, fthresh=5.0, bfactor=2,
                gbox=3, maxiter=5, multithread=True,  clobber=True, logfile=logfile, verbose=True)

      #mosaic the data
      geomfile=iraf.osfn("pysalt$data/rss/RSSgeom.dat")
      saltmosaic('fxgbpP*fits', '', 'm', geomfile, interp='linear', cleanup=True, geotran=True, clobber=True, logfile=logfile, verbose=True)

      #clean up the images
      if cleanup:
           for f in glob.glob('p*fits'): os.remove(f)
           for f in glob.glob('bp*fits'): os.remove(f)
           for f in glob.glob('gbp*fits'): os.remove(f)
           for f in glob.glob('xgbp*fits'): os.remove(f)
           for f in glob.glob('fxgbp*fits'): os.remove(f)


    #set up the name of the images
    if specreduce:
       for i in range(len(infile_list)):
           if obs_dict['OBJECT'][i].upper().strip()=='ARC':
               lamp=obs_dict['LAMPID'][i].strip().replace(' ', '')
               arcimage='mfxgbp'+os.path.basename(infile_list[i])
               #lampfile=iraf.osfn("pysalt$data/linelists/%s.salt" % lamp)

               specidentify(arcimage, lampfile, dbfile, guesstype='rss', 
                  guessfile='', automethod=automethod,  function='legendre',  order=3, 
                  rstep=100, rstart='middlerow', mdiff=50, thresh=2, niter=5, smooth=3,
                  inter=True, clobber=True, logfile=logfile, verbose=True)

               specrectify(arcimage, outimages='', outpref='x', solfile=dbfile, caltype='line', 
                   function='legendre',  order=3, inttype='interp', w1=None, w2=None, dw=None, nw=None,
                   blank=0.0, clobber=True, logfile=logfile, verbose=True)
     

    objimages=''
    for i in range(len(infile_list)):
       if obs_dict['CCDTYPE'][i].count('OBJECT') and obs_dict['INSTRUME'][i].count('RSS'):
          if objimages: objimages += ','
          objimages+='mfxgbp'+os.path.basename(infile_list[i])

    if specreduce:
      #run specidentify on the arc files

      specrectify(objimages, outimages='', outpref='x', solfile=dbfile, caltype='line', 
           function='legendre',  order=3, inttype='interp', w1=None, w2=None, dw=None, nw=None,
           blank=0.0, clobber=True, logfile=logfile, verbose=True)


    return
コード例 #8
0
ファイル: imred.py プロジェクト: enricokotze/polsalt
def imred(infilelist,
          prodir,
          bpmfile=None,
          crthresh='',
          gaindb=None,
          cleanup=True):
    #get the name of the files
    infiles = ','.join(['%s' % x for x in infilelist])

    #get the current date for the files
    obsdate = os.path.basename(infilelist[0])[1:9]
    print "Observation Date: ", obsdate

    #set up some files that will be needed
    logfile = 'im' + obsdate + '.log'
    flatimage = 'FLAT%s.fits' % (obsdate)
    dbfile = 'spec%s.db' % obsdate

    #create the observation log
    #    obs_dict=obslog(infilelist)

    verbose = True

    with logging(logfile, debug) as log:
        log.message('Pysalt Version: ' + pysalt.verno, with_header=False)

        #prepare the data

        for img in infilelist:
            hdu = pyfits.open(img)

            # for backwards compatibility
            hdu = remove_duplicate_keys(hdu)
            if not 'XTALK' in hdu[1].header:
                hdu[1].header['XTALK'] = 1474
                hdu[2].header['XTALK'] = 1474
                hdu[3].header['XTALK'] = 1166
                hdu[4].header['XTALK'] = 1111
                hdu[5].header['XTALK'] = 1377
                hdu[6].header['XTALK'] = 1377

            img = os.path.basename(img)

            hdu = prepare(hdu, createvar=False, badpixelstruct=None)
            if not cleanup: hdu.writeto('p' + img, clobber=True)

            hdu = bias(hdu,
                       subover=True,
                       trim=True,
                       subbias=False,
                       bstruct=None,
                       median=False,
                       function='polynomial',
                       order=5,
                       rej_lo=5.0,
                       rej_hi=5.0,
                       niter=10,
                       plotover=False,
                       log=log,
                       verbose=verbose)
            if not cleanup: hdu.writeto('bp' + img, clobber=True)

            # put windowed data into full image
            exts = len(hdu)
            if exts > 7:
                rows, cols = hdu[1].data.shape
                cbin, rbin = [
                    int(x) for x in hdu[0].header['CCDSUM'].split(" ")
                ]
                ampsecO = hdu[1].header["AMPSEC"].strip("[]").split(",")
                ampsecE = hdu[7].header["AMPSEC"].strip("[]").split(",")
                rO = int((float(ampsecO[1].split(":")[0]) - 1.) / rbin)
                rE = int((float(ampsecE[1].split(":")[0]) - 1.) / rbin)
                keylist = ['BIASSEC', 'DATASEC', 'AMPSEC', 'CCDSEC', 'DETSEC']
                oldlist = [
                    hdu[1].header[key].strip("[]").split(",")[1]
                    for key in keylist
                ]
                newlist = 2 * ['1:' + str(int(0.5 + 4102 / rbin))
                               ] + 3 * [str(int(rbin / 2)) + ':4102']

                for amp in range(6):
                    hduO = hdu[amp + 1].copy()
                    hdu[amp + 1].data = np.zeros((4102 / rbin, cols))
                    hdu[amp + 1].data[rO:rO + rows] = hduO.data
                    hdu[amp + 1].data[rE:rE + rows] = hdu[amp + 7].data
                    hdu[amp + 1].update_header
                    for k, key in enumerate(keylist):
                        hdu[amp+1].header[key] = \
                            hdu[amp+1].header[key].replace(oldlist[k],newlist[k])
                del hdu[7:]
                hdu[0].header['NSCIEXT'] = 6

            badpixelstruct = saltio.openfits(bpmfile)
            hdu = add_variance(hdu, badpixelstruct)

            #gain correct the data
            if gaindb:
                usedb = True
                dblist = saltio.readgaindb(gaindb.strip())
            else:
                usedb = False
                dblist = ''
            hdu = gain(hdu,
                       mult=True,
                       usedb=usedb,
                       dblist=dblist,
                       log=log,
                       verbose=verbose)
            if not cleanup: hdu.writeto('gbp' + img, clobber=True)

            #cross talk correct the data
            hdu = xtalk(hdu, [], log=log, verbose=verbose)

            #cosmic ray clean the data
            #only clean the object data
            if crthresh == '':
                thresh = 5.0
                if hdu[0].header['GRATING'].strip() == 'PG0300': thresh = 7.0
            else: thresh = crthresh

            if hdu[0].header['CCDTYPE']=='OBJECT' and \
                hdu[0].header['LAMPID']=='NONE' and \
                hdu[0].header['INSTRUME']=='RSS':
                if crthresh != False:
                    log.message('Cleaning CR using thresh={}'.format(thresh))
                    hdu = multicrclean(hdu,
                                       crtype='edge',
                                       thresh=thresh,
                                       mbox=11,
                                       bthresh=5.0,
                                       flux_ratio=0.2,
                                       bbox=25,
                                       gain=1.0,
                                       rdnoise=5.0,
                                       fthresh=5.0,
                                       bfactor=2,
                                       gbox=3,
                                       maxiter=5,
                                       log=log,
                                       verbose=verbose)
                    for ext in range(13, 19):
                        hdu[ext].data = hdu[ext].data.astype('uint8')
                    hdu[0].header.add_history(
                        'CRCLEAN: multicrclean, thresh = ', thresh)
                else:
                    hdu[0].header.add_history('CRCLEAN: None')
            hdu.writeto('xgbp' + img, clobber=True)
            hdu.close()

    #mosaic the data
    #khn: attempt to use most recent previous geometry to obsdate.
    #NOTE: mosaicing does not do this correctly
    #geomdb = open(datadir+"RSSgeom.dat",'r')
    #for geomline in geomdb:
    #    if geomline[0]=='#': continue
    #    if (int(obsdate) > int(geomline.split(' ')[0].replace('-',''))): break
    #geomfile = "RSSgeom_obsdate.dat"
    #open(geomfile,'w').write(geomline)

    geomfile = iraf.osfn("pysalt$data/rss/RSSgeom.dat")

    try:
        saltmosaic('xgbpP*fits',
                   '',
                   'm',
                   geomfile,
                   interp='linear',
                   cleanup=True,
                   geotran=True,
                   clobber=True,
                   logfile=logfile,
                   verbose=True)
    except:
        saltmosaic('xgbpP*fits',
                   '',
                   'm',
                   geomfile,
                   interp='linear',
                   cleanup=True,
                   geotran=True,
                   clobber=True,
                   logfile=logfile,
                   verbose=True)
    #khn: fix mosaiced VAR and BPM extensions
    #khn: fix mosaiced bpm missing some of gap
    for img in infilelist:
        filename = 'mxgbp' + os.path.basename(img)
        hdu = pyfits.open(filename, 'update')
        hdu[2].header['EXTNAME'] = 'VAR'
        hdu[3].header['EXTNAME'] = 'BPM'
        bpm_rc = (hdu[3].data > 0).astype('uint8')
        zeroscicol = hdu['SCI'].data.sum(axis=0) == 0
        bpmgapcol = bpm_rc.mean(axis=0) == 1
        addbpmcol = zeroscicol & ~bpmgapcol
        addbpmcol[np.argmax(addbpmcol) -
                  4:np.argmax(addbpmcol)] = True  # allow for chip tilt
        bpm_rc[:, addbpmcol] = 1
        hdu[3].data = bpm_rc
        hdu.writeto(filename, clobber=True)

    #clean up the images
    if cleanup:
        for f in glob.glob('p*fits'):
            os.remove(f)
        for f in glob.glob('bp*fits'):
            os.remove(f)
        for f in glob.glob('gbp*fits'):
            os.remove(f)
        for f in glob.glob('xgbp*fits'):
            os.remove(f)
コード例 #9
0
def specred(rawdir, prodir, imreduce=True, specreduce=True, calfile=None, lamp='Ar', automethod='Matchlines', skysection=[800,1000], cleanup=True):
    print rawdir
    print prodir

    #get the name of the files
    infile_list=glob.glob(rawdir+'*.fits')
    infiles=','.join(['%s' % x for x in infile_list])
    

    #get the current date for the files
    obsdate=os.path.basename(infile_list[0])[1:9]
    print obsdate

    #set up some files that will be needed
    logfile='spec'+obsdate+'.log'
    flatimage='FLAT%s.fits' % (obsdate)
    dbfile='spec%s.db' % obsdate

    #create the observation log
    obs_dict=obslog(infile_list)

 
    if imreduce:   
      #prepare the data
      saltprepare(infiles, '', 'p', createvar=False, badpixelimage='', clobber=True, logfile=logfile, verbose=True)

      #bias subtract the data
      saltbias('pP*fits', '', 'b', subover=True, trim=True, subbias=False, masterbias='',  
              median=False, function='polynomial', order=5, rej_lo=3.0, rej_hi=5.0, 
              niter=10, plotover=False, turbo=False, 
              clobber=True, logfile=logfile, verbose=True)

      #gain correct the data
      saltgain('bpP*fits', '', 'g', usedb=False, mult=True, clobber=True, logfile=logfile, verbose=True)

      #cross talk correct the data
      saltxtalk('gbpP*fits', '', 'x', xtalkfile = "", usedb=False, clobber=True, logfile=logfile, verbose=True)

      #cosmic ray clean the data
      #only clean the object data
      for i in range(len(infile_list)):
        if obs_dict['CCDTYPE'][i].count('OBJECT') and obs_dict['INSTRUME'][i].count('RSS'):
          img='xgbp'+os.path.basename(infile_list[i])
          saltcrclean(img, img, '', crtype='edge', thresh=5, mbox=11, bthresh=5.0,
                flux_ratio=0.2, bbox=25, gain=1.0, rdnoise=5.0, fthresh=5.0, bfactor=2,
                gbox=3, maxiter=5, multithread=True,  clobber=True, logfile=logfile, verbose=True)
 
      #flat field correct the data
      flat_imgs=''
      for i in range(len(infile_list)):
        if obs_dict['CCDTYPE'][i].count('FLAT'):
           if flat_imgs: flat_imgs += ','
           flat_imgs += 'xgbp'+os.path.basename(infile_list[i])

      if len(flat_imgs)!=0:
         saltcombine(flat_imgs,flatimage, method='median', reject=None, mask=False,    \
                weight=True, blank=0, scale='average', statsec='[200:300, 600:800]', lthresh=3,    \
                hthresh=3, clobber=True, logfile=logfile, verbose=True)
         saltillum(flatimage, flatimage, '', mbox=11, clobber=True, logfile=logfile, verbose=True)

         saltflat('xgbpP*fits', '', 'f', flatimage, minflat=500, clobber=True, logfile=logfile, verbose=True)
      else:
         flats=None
         imfiles=glob.glob('cxgbpP*fits')
         for f in imfiles:
             shutil.copy(f, 'f'+f)

      #mosaic the data
      geomfile=iraf.osfn("pysalt$data/rss/RSSgeom.dat")
      saltmosaic('fxgbpP*fits', '', 'm', geomfile, interp='linear', cleanup=True, geotran=True, clobber=True, logfile=logfile, verbose=True)

      #clean up the images
      if cleanup:
           for f in glob.glob('p*fits'): os.remove(f)
           for f in glob.glob('bp*fits'): os.remove(f)
           for f in glob.glob('gbp*fits'): os.remove(f)
           for f in glob.glob('xgbp*fits'): os.remove(f)
           for f in glob.glob('fxgbp*fits'): os.remove(f)


    #set up the name of the images
    if specreduce:
       for i in range(len(infile_list)):
           if obs_dict['OBJECT'][i].upper().strip()=='ARC':
               lamp=obs_dict['LAMPID'][i].strip().replace(' ', '')
               arcimage='mfxgbp'+os.path.basename(infile_list[i])
               lampfile=iraf.osfn("pysalt$data/linelists/%s.txt" % lamp)

               specidentify(arcimage, lampfile, dbfile, guesstype='rss', 
                  guessfile='', automethod=automethod,  function='legendre',  order=5, 
                  rstep=100, rstart='middlerow', mdiff=10, thresh=3, niter=5, 
                  inter=True, clobber=True, logfile=logfile, verbose=True)

               specrectify(arcimage, outimages='', outpref='x', solfile=dbfile, caltype='line', 
                   function='legendre',  order=3, inttype='interp', w1=None, w2=None, dw=None, nw=None,
                   blank=0.0, clobber=True, logfile=logfile, verbose=True)
     

    objimages=''
    for i in range(len(infile_list)):
       if obs_dict['CCDTYPE'][i].count('OBJECT') and obs_dict['INSTRUME'][i].count('RSS'):
          if objimages: objimages += ','
          objimages+='mfxgbp'+os.path.basename(infile_list[i])

    if specreduce:
      #run specidentify on the arc files

      specrectify(objimages, outimages='', outpref='x', solfile=dbfile, caltype='line', 
           function='legendre',  order=3, inttype='interp', w1=None, w2=None, dw=None, nw=None,
           blank=0.0, clobber=True, logfile=logfile, verbose=True)


    #create the spectra text files for all of our objects
    spec_list=[]
    for img in objimages.split(','):
       spec_list.extend(createspectra('x'+img, obsdate, smooth=False, skysection=skysection, clobber=True))
    print spec_list
 
    #determine the spectrophotometric standard
    extfile=iraf.osfn('pysalt$data/site/suth_extinct.dat')

    for spec, am, et, pc in spec_list:
        if pc=='CAL_SPST':
           stdstar=spec.split('.')[0]
           print stdstar, am, et
           stdfile=iraf.osfn('pysalt$data/standards/spectroscopic/m%s.dat' % stdstar.lower().replace('-', '_'))
           print stdfile
           ofile=spec.replace('txt', 'sens')
           calfile=ofile #assumes only one observations of a SP standard
           specsens(spec, ofile, stdfile, extfile, airmass=am, exptime=et,
                stdzp=3.68e-20, function='polynomial', order=3, thresh=3, niter=5,
                clobber=True, logfile='salt.log',verbose=True)
    

    for spec, am, et, pc in spec_list:
        if pc!='CAL_SPST':
           ofile=spec.replace('txt', 'spec')
           speccal(spec, ofile, calfile, extfile, airmass=am, exptime=et, 
                  clobber=True, logfile='salt.log',verbose=True)
           #clean up the spectra for bad pixels
           cleanspectra(ofile)