def ExpNormalize(images, outbase="_n"):
			
	# Build the list of output image names
	out = [os.path.splitext(i) for i in images]	# Split off the extension
	out = [i[0] + outbase + '.fits' for i in out]	# Paste the outbase at the end of the filename 
												# and put the extension back on
	# Get a list of exposure times.
	exp_times = [GetHeaderKeyword(i, 'exptime') for i in images]
	exp_times = [str(e) for e in exp_times]	
	
	# run imarith to do the normalization
	iraf.imarith.unlearn()
	iraf.imarith.op = '/'
	iraf.imarith.mode = 'h'

	for i in range(len(images)):
		iraf.imarith.operand1 = images[i]
		iraf.imarith.operand2 = exp_times[i]
		iraf.imarith.result = out[i]
	
		iraf.imarith()

		# update the exptime keyword		
		iraf.hedit.unlearn()
		iraf.hedit.verify='no'
		iraf.hedit.show='yes'
		iraf.hedit.update='yes'
		iraf.hedit.images=out[i]
		iraf.hedit.fields='exptime'
		iraf.hedit.value=1
		iraf.hedit.mode='h'

		iraf.hedit(Stdout=1)

	return out

def cos_clear2(filenames):
    outname = 'fake_' + filenames[0]
    if os.path.isfile(outname):
        print outname, 'is already exist'
    else:
        inname = filenames[0] + ',' + filenames[1]
        print 'runing cos_clear2, imcombine...'
        print 'make file', outname
        iraf.imcombine(input = inname
                , output = outname, headers = '', bpmasks = ''
                , rejmasks = '', nrejmasks = '', expmasks = ''
                , sigmas = '', imcmb = '$I', logfile = 'STDOUT'
                , combine = 'average', reject = 'minmax', project = False
                , outtype = 'real', outlimits = 'none', offsets = 'none'
                , masktype = 'none', maskvalue = 0, blank = 0.0
                , scale = 'exposure', zero = 'none', weight = 'none'
                , statsec = '', expname = 'EXPTIME', lthreshold = 'INDEF'
                , hthreshold = 'INDEF', nlow = 0, nhigh = 1
                , nkeep = 1, mclip = True, lsigma = 3.0
                , hsigma = 10.0, rdnoise = 'RDNOISE', gain = 'GAIN'
                , snoise = 0.0, sigscale = 0.1, pclip = -0.5, grow = 0.0)
        iraf.hedit(images = outname
                , fields = 'ncombine', value = '0', add = True
                , addonly = False, delete = False, verify = False
                , show = True, update = True)
        iraf.hedit(images = outname
                , fields = 'EXPTIME', value = '0', add = True
                , addonly = False, delete = False, verify = False
                , show = True, update = True)
    print 'display %s 1' % outname
    iraf.display(image = outname, frame = 1)
    filenames.append(outname)
    return cos_clear3(filenames)

 def omitPhotcorr(self, file):
     """
     This function will change PHOTCORR keyword value to OMIT in the header
     of given file. The file can contain wildcards i.e. multiple files can
     be given on one command..
     """
     I.hedit(file, fields='PHOTCORR', value='omit', verify='no', show='no')

def time_calibration(input_file):
    """
    Obtain the calibration for time (hjd) by pyraf and the airmass for each image. Include in the header all information.
    """
    original_path = os.getcwd()
    save_path = input_file['save_path']
    #change to save data reduction directory
    os.chdir(save_path)
    print '\n Reading the list of images ....\n'
    planet = input_file['exoplanet'] #set exoplanet name
    images = sorted(glob.glob('AB'+planet+'*.fits'))
    print images
    #include de RA,DEC and epoch of the exoplanet
    RA,DEC,epoch = input_file['RA'],input_file['DEC'],input_file['epoch']
    #obtain ST JD using iraf task and introduce in the header
    for i in range(len(images)):
        hdr = fits.getheader(images[i])
        if int(split(hdr['UT'],':')[0]) < int(hdr['timezone']):
            new_date = use.yesterday(hdr['date-obs'])
            #print images[i], new_date
        else:
            new_date = hdr['date-obs']
        year,month,day = split(new_date,'-')
        iraf.asttimes(year=year,month=month,day=day,time=hdr['loctime'],obs=input_file['observatory'])
        JD = iraf.asttimes.jd #obtain julian date
        LMST = iraf.asttimes.lmst #obtain the sideral time
        LMST = use.sexagesimal_format(LMST) #convert sideral time in sexagesimal format
        iraf.hedit(images[i],'ST',LMST,add='yes',verify='no',show='no',update='yes') #create the ST keyword in the header
        iraf.ccdhedit(images[i],'LMST',LMST,type='string') #include the mean sideral time in the header
        iraf.ccdhedit(images[i],'JD',JD,type='string') #include de julian date in the header
        #include RA, and DEC of the object in your header
        iraf.ccdhedit(images[i],"RA",RA,type="string") #include right ascention in the header
        iraf.ccdhedit(images[i],"DEC",DEC,type="string")  #include declination in the header
        iraf.ccdhedit(images[i],"epoch",epoch,type="string") #include epoch in the header
        # use.update_progress((i+1.)/len(images))
    print '\n Setting airmass ....\n'
    for i in range(len(images)):
        print '# ',images[i]
        #iraf.hedit(images[i],'airmass',airmass,add='yes')
        #iraf.hedit(images[i],'HJD',HJD,add='yes')
        iraf.setairmass.observatory = input_file['observatory']
        iraf.setairmass(images[i])
        iraf.setjd.time = 'ut'
        iraf.setjd(images[i])
    print '\n.... done.\n'
    #export information
    hjd, jd, airmass, st = [],[],[],[]
    for i in range(len(images)):
        hdr = fits.getheader(images[i])
        hjd.append(hdr['HJD'])
        jd.append(hdr['JD'])
        airmass.append(hdr['airmass'])
        st.append(hdr['st'])
    #saving the data
    data = DataFrame([list(hjd),list(jd),list(st),list(airmass)]).T
    data.columns = ['HJD','JD','ST','Airmass']
    data.to_csv('results_iraf_calibrations.csv')
    #change to workings directory
    os.chdir(original_path)
    return

def main():
    parser = argparse.ArgumentParser(description='Fixes BITPIX issue with archive data')
    parser.add_argument('filelist',nargs='+',help='Input images to process')

    args = parser.parse_args()

    for image in args.filelist:
        iraf.hedit(images=image,fields='BITPIX',value=32,update=True,show=True,addonly=True)

    def test_hedit(self):
        if os.path.exists('image.real.fits'):
            os.remove('image.real.fits')

        iraf.imarith('dev$pix', '/', '1', 'image.real', pixtype='r')
        iraf.hedit('image.real', 'title', 'm51 real', verify=False,
                   Stdout="/dev/null")
        with fits.open('image.real.fits') as f:
            assert f[0].header['OBJECT'] == 'm51 real'

def append_VHELIO(image_file,v_value): #Note v_value must be string!
    iraf.hedit(
        images = image_file,\
        fields = "VHELIO",\
        value = v_value,\
        add = 1,\
        addonly = 0,\
        delete = 0,\
        verify = 0,\
        show = 1,\
        update = 1)

    def omitPHOTCORR(self):
        """
        Sets PHOTCORR keyword to OMIT to prevent crashing.

        :note: find a proper fix for this.
        :note: Change from iraf to pyfits.
        """
        for raw in glob.glob('*_raw.fits'):
            iraf.hedit(images=raw + '[0]', fields='PHOTCORR', value='OMIT',
                       add=self.no, addonly=self.no, delete=self.no,
                       verify=self.no, show=self.yes, update=self.yes)

def main():
    print('FLAG 1================')
    checkfilepath = os.path.split(os.path.abspath(__file__))[0] + os.sep + 'objcheck.lst'
    dct = filedict(checkfilepath)
    fitlst = findmarkfit(os.getcwd())
    for fitname in fitlst:
        print(fitname)
    for fitname in fitlst:
        fit = pyfits.open(fitname)
        oldsname = fit[0].header['sname']
        newsname = dct[str(oldsname)]
        iraf.hedit(images = fitname, fields = 'sname',
                value = newsname, add = 'yes', delete = 'no',
                verify = 'no', show = 'yes', update = 'yes')

def prepare_files(list_files, resultpath, readoutnoise):
    # copies data to output directory, trim data files and mask files to contain only good exposure parts, normalize masks, normalize images by masks. Yields exposure-normalized, trimmed sky images.
    for datafile in list_files[:]:
	    #prepare directories and copy image to resultpath
	    filename=os.path.basename(datafile)
	    obsid=os.path.basename(os.path.dirname(datafile))
	    imagepath=os.path.join(resultpath,obsid)
	    image=os.path.join(imagepath,filename)
	    if not os.access(imagepath,os.F_OK): os.makedirs(imagepath)
	    print image
	    shutil.copy(datafile,imagepath)
	    os.chdir(imagepath)
	    iraf.cd(imagepath)
	    # add readoutnoise keyword to header:
	    iraf.hedit(images=filename, fields="RDNOISE", value=str(readoutnoise), addonly="yes", verify="no")

def wcs_transfer(file1,file2,extensions="1,2,3,4"):

    if type(extensions) is str:
        extensions = [int(x) for x in extensions.split(',')]

    template = "%s[%i]" 

    for ext in extensions:
        for kw in WCS_keywords:
            #print file1,file2,ext,kw,
            iraf.images.imutil.imgets(template % (file1,ext),kw)
            var = iraf.images.imutil.imgets.value
            #print var
            if var != '0':
                iraf.hedit(template % (file2,ext), fields=kw, value=var,
                        add=True, verify=False, update=True, show=True)

def checkRN(filename):
  imageHeader = fits.open(filename)[0]
  hasDateObsKey = True
  dateString = ""
  
  try:
    dateString = imageHeader.header['DATE-OBS']
  except KeyError:
    hasDateObsKey = False
      

  year = dateString[:4]
  
  if (hasDateObsKey and year=='2011'):
    iraf.hedit(filename,'RDNOISE',26,add='yes',verify='no')
    print(" ")

def applywavesolution(inputre, calspec):
    ''' apply calibration solution to science spectra '''

    inputlist = glob.glob(inputre)
    inputstring = ', '.join(inputlist)
    outputstring = ', '.join([inp[:-5]+'_spec.fits' for inp in inputlist])

    iraf.hedit.unlearn()
    iraf.dispcor.unlearn()

    iraf.hedit.fields = 'REFSPEC1'
    iraf.hedit.value = calspec
    iraf.hedit.add = True
    iraf.hedit(images=inputstring)

    iraf.dispcor(input=inputstring, output=outputstring)

def edit_header (input1,input2):
    abrir1 = open(input1,'r')
    abrir2 = open(input2,'r')
    for line in abrir1:
        linea = abrir2.readline().split('\n')[0]
        line = line.split('\n')[0]
        iraf.hedit.images = line
        iraf.hedit.fields = 'REFSPEC1'
        iraf.hedit.add  = 'yes'
        iraf.hedit.value = linea
        iraf.hedit.show = 'yes'
        iraf.hedit.ver = 'no'
        iraf.hedit(mode='h')

    abrir1.close()
    abrir2.close()

def continuumReduce(imageName,imageHaName):

  avgScaleFactor = contsubfactor

  print("The scale factor is "+str(avgScaleFactor))
  
  #configure hedit
  iraf.hedit.add='yes'
  iraf.hedit.verify='no'
  
  iraf.imarith(imageName+'.fits',"*",avgScaleFactor,imageName+"_scaled.fits")
  iraf.imarith(imageHaName+'.fits',"-",imageName+"_scaled.fits",imageHaName+"cs.fits")
  iraf.hedit(imageHaName+'cs.fits','Rscale',avgScaleFactor) 
  
  #clear up superfluous images
  for f in (imageName+'_scaled.fits'):
    silentDelete(f)

  print('The continuum subtracted image is '+ imageHaName+'cs.fits')

def AvThAr(ThAr1,ThAr2,operator,ThArAv1,ThArAv):
	
	# check that the ARCSs are really the right ones before combining
	if pf.open(ThAr1+".fits")[0].header['OBJECT'] != pf.open(ThAr2+".fits")[0].header['OBJECT']:
		return 1
	
	# Do imarith while keeping FITS extensions
	# first make a sum of the two images -> ThArAv1
	# then divide ThArAv1 / 2 to get the average
	iraf.mscred.mscarith(ThAr1,operator,ThAr2,ThArAv1,verbose='yes')
	iraf.mscred.mscarith(ThArAv1,"/",2,ThArAv,verbose='yes')
	
	iraf.hedit(images=ThAr1+"A.fits[0]",fields='THARCOMB',value='xxx',add='yes',verify='no',show='yes')
	iraf.hedit(images=ThAr1+"A.fits[0]",fields='THARCOMB',value='Combined ThAr from (%s+%s)/2' % (ThAr1,ThAr2),add='yes',verify='no',show='yes')
	
	os.system('mv %s.fits uncombinedarcs/' % ThAr1)
	os.system('mv %s.fits uncombinedarcs/' % ThAr2)
	os.system('rm -rf %s.fits' % ThArAv1)
	
	return 0

def cor_airmass(lstfile):
    f = open(lstfile)
    l = f.readlines()
    f.close()
    l = [tmp.split('\n')[0] for tmp in l]
    fitlst = ['awftbo' + tmp for tmp in l]
    for fitname in fitlst:
        if os.path.isfile(fitname):
            fit = pyfits.open(fitname)
            objname = fit[0].header['object'].replace('_', ' ').split()[0]
            print(fitname + ' ' + objname)
            objname_new = find_normal_objname(objname)
            if len(objname_new) == 0:
                objname_new = raw_input('please input object name:')
            radec = findradec(objname_new)
            if len(radec) == 0:
                radec = raw_input('please input ra dec of objname:')
                radec = radec.split()
            fitextnum = len(fit)
            fit.close()
            for lay in range(fitextnum):
                airold = iraf.hselect(images = fitname + '[%i]' % lay, fields = 'airmass', expr = 'yes', Stdout = 1)
                airold = float(airold[0])
                print(fitname + ' ' + objname + ' ' + str(lay) + ' airmass old: ' + str(airold))
                fitnamelay = fitname + '[%i]' % lay
                iraf.hedit(images = fitnamelay, fields = 'airold', 
                    value = airold, add = 'yes', addonly = 'yes', delete = 'no', 
                    verify = 'no', show = 'yes', update = 'yes')
                iraf.hedit(images = fitnamelay, fields = 'sname', 
                    value = objname_new, add = 'yes', addonly = 'yes', delete = 'no', 
                    verify = 'no', show = 'yes', update = 'yes')
                iraf.hedit(images = fitnamelay, fields = 'RA', 
                    value = radec[0], add = 'yes', addonly = 'yes', delete = 'no', 
                    verify = 'no', show = 'yes', update = 'yes')
                iraf.hedit(images = fitnamelay, fields = 'DEC', 
                    value = radec[1], add = 'yes', addonly = 'yes', delete = 'no', 
                    verify = 'no', show = 'yes', update = 'yes')
                iraf.twodspec()
                stdpath = os.path.split(os.path.realpath(__file__))[0] + os.sep + 'standarddir' + os.sep
                iraf.longslit(dispaxis = 2, nsum = 1, observatory = 'Lijiang', 
                    extinction = 'onedstds$LJextinct.dat', caldir = stdpath)
                iraf.setairmass(images = fitnamelay,
                    observatory = 'Lijiang', intype = 'beginning', 
                    outtype = 'effective', ra = 'ra', dec = 'dec', 
                    equinox = 'epoch', st = 'lst', ut = 'date-obs', 
                    date = 'date-obs', exposure = 'exptime', airmass = 'airmass', 
                    utmiddle = 'utmiddle', scale = 750.0, show = 'yes', 
                    override = 'yes', update = 'yes')
                print('name airmass_new airmass_old')
                iraf.hselect(fitnamelay, fields = '$I,airmass,airold', 
                             expr = 'yes')

def manual_offset(config, sci, total_sci_ext):

	#any other files not specifically mentioned were within 1 angstrom of 5577 so I didn't give them any changes
	if frame in ('N20011220S155.fits', 'N20011220S158.fits', 'N20011220S159.fits'):
		big_offset = -1.5

	elif frame in ('N20020114S113.fits', 'N20020114S116.fits','N20020114S117.fits'):
		big_offset = -11

	elif frame in ('N20020108S097.fits', 'N20020108S098.fits', 'N20020108S100.fits', 'N20020108S101.fits'):
		big_offset = -7.5

	else:
		big_offset = 0.0


	for i in range(2,total_sci_ext+1):
  		slit_header = pyfits.getheader(sky, i)
   		crval1 = slit_header['CRVAL1']
    		iraf.hedit('%s[%d]' % (sky,i), 'CRVAL1', crval1 + big_offset, update='yes', verify='no')
      		iraf.hedit('%s[%d]' % (sci,i), 'CRVAL1', crval1 + big_offset, update='yes', verify='no')

def header_correction(config, fits, sky, sky_nofits, fits_number, offset_value):

	#getting the MDF information; neccessary for length of second for-loop
	mdf_sky = pyfits.getdata(sky, 'MDF')
	mdf_sci = pyfits.getdata(sci, 'MDF')

	
	#get CRVAL1 from header and add the offset for all extensions in the sky and science frames
	#The range will start where the MEF sci extensions start, 3, and ends 1 before bc the way python loops work
	for i in range(3,len(mdf_sci)-1): #fix this to the offset value - don't hardcode it
		
		#this is loading the mdf for the specific file and then extracting the specific CRVAL1 for each slit
		sky_header = pyfits.getheader(sky, i)
		sci_header = pyfits.getheader(sci, i)
		crval1_sky= sky_header['CRVAL1']
		crval1_sci = sci_header['CRVAL1']
		print '%s %s slit %d' % (config, fits, i)
		print 'Sky CRVAL1: ', crval1_sky
		print 'Science CRVAL1: ', crval1_sci

		#the new CRVAL1 value will be equal to the offset_value (dlambda) plus the original CRVAL1
		iraf.hedit('%s[%d]' % (sky,i), 'CRVAL1', (crval1_sky+offset_value), update='yes', ver='no')
		iraf.hedit('%s[%d]' % (sci,i), 'CRVAL1', (crval1_sci+offset_value), update='yes', ver='no')	

def run_scldark(input):
    iraf.image(_doprint=0)
    iraf.image.imutil(_doprint=0)
    scl=float(input[1])/float(input[2])
#    print input[1],scl
    iraf.imarith(input[0],"*",scl,input[3],divzero="0.0",hparams="",pixtype="",calctype="",verbose="no",noact="no")
    iraf.hedit(input[3],"EXPTIME",input[1], add="yes", addonly="yes", delete="no", verify="no", show="no", update="yes")
    iraf.hedit(input[3],"DARKTIME",input[1], add="yes", addonly="yes", delete="no", verify="no", show="no", update="yes")
    iraf.hedit(input[3],"EXPOSURE","", add="no", addonly="no", delete="yes", verify="no", show="no", update="yes")