def wl_cal(filename, outname, cals):
    print 'run wavelength calibration...'
    if os.path.isfile(outname):
        print 'file %s is already exist' % outname
    else:
        iraf.imred()
        iraf.specred()
        print 'The calibration file is listed below:'
        for i in xrange(len(cals)):
            print i, cals[i], get_fits_objname(cals[i])
        valget = 0
        if len(cals) > 1:
            valget = raw_input('Which one are you want to use?:')
            while type(eval(valget)) != int:
                valget = raw_input('input is not a int type, please reinput:')
            valget = int(valget)
        iraf.refspectra(input = filename
                , references = cals[valget], apertures = '', refaps = ''
                , ignoreaps = True, select = 'interp', sort = ''
                , group = '', time = False, timewrap = 17.0
                , override = False, confirm = True, assign = True
                , logfiles = 'STDOUT,logfile', verbose = False, answer = 'yes')
        print 'make file ' + outname + '...'
        iraf.dispcor(input = filename
                , output = outname, linearize = True, database = 'database'
                , table = '', w1 = 'INDEF', w2 = 'INDEF', dw = 'INDEF'
                , nw = 'INDEF', log = False, flux = True, blank = 0.0
                , samedisp = False, ignoreaps = True
                , confirm = False, listonly = False, verbose = True
                , logfile = '')
    print 'splot %s' % outname
    iraf.splot(images = outname)

def divflat(imagesre, flat='Flat'):
    '''Run ccdproc task to images'''

    imageslist = glob.glob(imagesre)
    imagesin = ', '.join(imageslist)

    # Load packages
    iraf.imred()
    iraf.ccdred()
    # Unlearn settings
    iraf.ccdred.ccdproc.unlearn()
    iraf.ccdred.combine.unlearn()
    # Setup and run task
    iraf.ccdred.ccdproc.ccdtype = ''
    iraf.ccdred.ccdproc.noproc = False
    iraf.ccdred.ccdproc.fixpix = False
    iraf.ccdred.ccdproc.overscan = False
    iraf.ccdred.ccdproc.darkcor = False
    iraf.ccdred.ccdproc.illumcor = False
    iraf.ccdred.ccdproc.fringecor = False
    iraf.ccdred.ccdproc.readcor = False
    iraf.ccdred.ccdproc.scancor = False
    iraf.ccdred.ccdproc.trim = False
    iraf.ccdred.ccdproc.trimsec = ''
    iraf.ccdred.ccdproc.readaxis = 'line'
    iraf.ccdred.ccdproc.zerocor = False
    iraf.ccdred.ccdproc.zero = ''
    iraf.ccdred.ccdproc.flatcor = True
    iraf.ccdred.ccdproc.flat = flat
    iraf.ccdred.ccdproc(images=imagesin)

def coroverbiastrim(lstfile):
    iraf.noao()
    iraf.imred()
    iraf.ccdred()
    x1,x2,y1,y2 = get_trim_sec()
    iraf.ccdproc(images = '@' + lstfile + '//[1]'
        , output = '%bo%bo%@' + lstfile
        , ccdtype = '', max_cache = 0, noproc = False
        , fixpix = False, overscan = True, trim = False
        , zerocor = True, darkcor = False, flatcor = False
        , illumcor = False, fringecor = False, readcor = False
        , scancor = False, readaxis = 'line', fixfile = ''
        , biassec = '[5:45,%s:%s]'%(y1,y2), trimsec = '[%s:%s,%s:%s]'%(x1,x2,y1,y2)
        , zero = 'Zero', dark = '', flat = '', illum = '', fringe = ''
        , minreplace = 1.0, scantype = 'shortscan', nscan = 1
        , interactive = False, function = 'chebyshev', order = 1
        , sample = '*', naverage = 1, niterate = 1
        , low_reject = 3.0, high_reject = 3.0, grow = 1.0)
    iraf.ccdproc(images = '%bo%bo%@' + lstfile
        , output = '%tbo%tbo%@' + lstfile
        , ccdtype = '', max_cache = 0, noproc = False
        , fixpix = False, overscan = False, trim = True
        , zerocor = False, darkcor = False, flatcor = False
        , illumcor = False, fringecor = False, readcor = False
        , scancor = False, readaxis = 'line', fixfile = ''
        , biassec = '[5:45,%s:%s]'%(y1,y2), trimsec = '[%s:%s,%s:%s]'%(x1,x2,y1,y2)
        , zero = 'Zero', dark = '', flat = '', illum = '', fringe = ''
        , minreplace = 1.0, scantype = 'shortscan', nscan = 1
        , interactive = False, function = 'chebyshev', order = 1
        , sample = '*', naverage = 1, niterate = 1
        , low_reject = 3.0, high_reject = 3.0, grow = 1.0)
    iraf.flpr()

def correctimages(imagesre, zero='Zero', flat='nFlat'):
    '''Run ccdproc task to correct images'''

    imageslist = glob.glob(imagesre)
    imagesin = ', '.join(imageslist)

    trimsection = str(raw_input('Enter trim section (or Hit <Enter>): '))
    trimquery = True
    if trimsection == '':
        trimquery = False

    # Load Packages
    iraf.imred()
    iraf.ccdred()
    # Unlearn Settings
    iraf.ccdred.ccdproc.unlearn()
    iraf.ccdred.combine.unlearn()
    # Setup and run task
    iraf.ccdred.ccdproc.ccdtype = ''
    iraf.ccdred.ccdproc.noproc = False
    iraf.ccdred.ccdproc.fixpix = False
    iraf.ccdred.ccdproc.overscan = False
    iraf.ccdred.ccdproc.darkcor = False
    iraf.ccdred.ccdproc.illumcor = False
    iraf.ccdred.ccdproc.fringecor = False
    iraf.ccdred.ccdproc.readcor = False
    iraf.ccdred.ccdproc.scancor = False
    iraf.ccdred.ccdproc.trim = trimquery
    iraf.ccdred.ccdproc.trimsec = trimsection
    iraf.ccdred.ccdproc.readaxis = 'line'
    iraf.ccdred.ccdproc.zerocor = True
    iraf.ccdred.ccdproc.zero = zero
    iraf.ccdred.ccdproc.flatcor = True
    iraf.ccdred.ccdproc.flat = flat
    iraf.ccdred.ccdproc(images=imagesin)

def subzero(imagesre, zero='Zero'):
    '''Run ccdproc remove Zero level noise'''

    imageslist = glob.glob(imagesre)
    imagesin = ', '.join(imageslist)

    # Load packages
    iraf.imred()
    iraf.ccdred()
    # Unlearn previouse settings
    iraf.ccdred.ccdproc.unlearn()
    iraf.ccdred.combine.unlearn()
    # setup and run task
    iraf.ccdred.ccdproc.ccdtype = ''
    iraf.ccdred.ccdproc.noproc = False
    iraf.ccdred.ccdproc.fixpix = False
    iraf.ccdred.ccdproc.overscan = False
    iraf.ccdred.ccdproc.darkcor = False
    iraf.ccdred.ccdproc.illumcor = False
    iraf.ccdred.ccdproc.fringecor = False
    iraf.ccdred.ccdproc.readcor = False
    iraf.ccdred.ccdproc.scancor = False
    iraf.ccdred.ccdproc.trim = False
    iraf.ccdred.ccdproc.trimsec = ''
    iraf.ccdred.ccdproc.readaxis = 'line'
    iraf.ccdred.ccdproc.zerocor = True
    iraf.ccdred.ccdproc.zero = zero
    iraf.ccdred.ccdproc.flatcor = False
    iraf.ccdred.ccdproc.flat = ''
    iraf.ccdred.ccdproc(images=imagesin)

def masterbias(biasre, output='Zero', combine='median', reject='minmax',
        ccdtype='', rdnoise='rdnoise', gain='gain'):
    '''run the task ccdred.zerocombine with chosen parameters

    Input:
    -------
     str biasre: regular expression to identify zero level images

    Output:
    -------
     file Zero.fits: combined zerolevel images
    '''

    biaslist = glob.glob(biasre)
    biasstring = ', '.join(biaslist)

    # load packages
    iraf.imred()
    iraf.ccdred()
    # unlearn settings
    iraf.imred.unlearn()
    iraf.ccdred.unlearn()
    iraf.ccdred.ccdproc.unlearn()
    iraf.ccdred.combine.unlearn()
    iraf.ccdred.zerocombine.unlearn()
    iraf.ccdred.setinstrument.unlearn()
    # setup task
    iraf.ccdred.zerocombine.output = output
    iraf.ccdred.zerocombine.combine = combine
    iraf.ccdred.zerocombine.reject = reject
    iraf.ccdred.zerocombine.ccdtype = ccdtype
    iraf.ccdred.zerocombine.rdnoise = rdnoise
    iraf.ccdred.zerocombine.gain = gain
    # run task
    iraf.ccdred.zerocombine(input=biasstring)

def scalewavelenght(calspec):
    ''' Creates a wavelenght solution for 'calspec' '''
    iraf.imred()
    iraf.specred()

    linelist = str(raw_input('Enter file with list of lines (linelists$thar.dat) : '))
    if linelist == '':
        linelist = 'linelists$thar.dat'
    iraf.specred.identify.coordlist = linelist
    iraf.specred.identify(images=calspec)

def combinebias(filename):
    iraf.noao()
    iraf.imred()
    iraf.ccdred()
    iraf.zerocombine(input = 'o//@' + filename
	, output = 'Zero', combine = 'average', reject = 'minmax'
	, ccdtype = '', process = False, delete = False
	, clobber = False, scale = 'none', statsec = ''
	, nlow = 0, nhigh = 1, nkeep = 1, mclip = True
	, lsigma = 3.0, hsigma = 3.0, rdnoise = 'rdnoise'
	, gain = 'gain', snoise = 0.0, pclip = -0.5, blank = 0.0)

def main():
	iraf.noao()
	iraf.imred()
	iraf.ccdred()
        print '=' * 20, 'Overscan', '=' * 20
	correct_overscan('spec.lst')
	print '=' * 20, 'combine bias', '=' * 20
	combine_bias('bias.lst')
	print '=' * 20, 'correct bias', '=' * 20
	correct_bias('spec_no_bias.lst', 'bias_spec.fits')
        name = os.popen('ls object*.lst').readlines()
        name = [i.split()[0] for i in name]
        for i in name:
            ntrim_flat(i)

def flatresponse(input='Flat', output='nFlat'):
    ''' normalize Flat to correct illumination patterns'''

    iraf.imred()
    iraf.ccdred()
    iraf.specred()

    iraf.ccdred.combine.unlearn()
    iraf.ccdred.ccdproc.unlearn()
    iraf.specred.response.unlearn()
    iraf.specred.response.interactive = True
    iraf.specred.response.function = 'chebyshev'
    iraf.specred.response.order = 1
    iraf.specred.response(calibration=input, normalization=input,
            response=output)

def ImportPackages():
	iraf.noao(_doprint=0)
	iraf.rv(_doprint=0)
	iraf.imred(_doprint=0)
	iraf.kpnoslit(_doprint=0)	
	iraf.ccdred(_doprint=0)
	iraf.astutil(_doprint=0)
	
	iraf.keywpars.setParam('ra','CAT-RA') 
	iraf.keywpars.setParam('dec','CAT-DEC')
	iraf.keywpars.setParam('ut','UT')
	iraf.keywpars.setParam('utmiddl','UT-M_E')
	iraf.keywpars.setParam('exptime','EXPTIME')
	iraf.keywpars.setParam('epoch','CAT-EPOC')
	iraf.keywpars.setParam('date_ob','DATE-OBS')
	iraf.keywpars.setParam('hjd','HJD')
	iraf.keywpars.setParam('mjd_obs','MJD-OBS')
	iraf.keywpars.setParam('vobs','VOBS')
	iraf.keywpars.setParam('vrel','VREL')
	iraf.keywpars.setParam('vhelio','VHELIO')
	iraf.keywpars.setParam('vlsr','VLSR')
	iraf.keywpars.setParam('vsun','VSUN')
	iraf.keywpars.setParam('mode','ql')
	iraf.fxcor.setParam('continu','both')
	iraf.fxcor.setParam('filter','none')
	iraf.fxcor.setParam('rebin','smallest')
	iraf.fxcor.setParam('pixcorr','no')
	iraf.fxcor.setParam('apodize','0.2')
	iraf.fxcor.setParam('function','gaussian')
	iraf.fxcor.setParam('width','INDEF')
	iraf.fxcor.setParam('height','0.')
	iraf.fxcor.setParam('peak','no')
	iraf.fxcor.setParam('minwidt','3.')
	iraf.fxcor.setParam('maxwidt','21.')
	iraf.fxcor.setParam('weights','1.')
	iraf.fxcor.setParam('backgro','0.')
	iraf.fxcor.setParam('window','INDEF')
	iraf.fxcor.setParam('wincent','INDEF')
	iraf.fxcor.setParam('verbose','long')
	iraf.fxcor.setParam('imupdat','no')
	iraf.fxcor.setParam('graphic','stdgraph')
	iraf.fxcor.setParam('interac','yes')
	iraf.fxcor.setParam('autowri','yes')
	iraf.fxcor.setParam('autodra','yes')
	iraf.fxcor.setParam('ccftype','image')
	iraf.fxcor.setParam('observa','lapalma')
	iraf.fxcor.setParam('mode','ql')
	return 0

def corhalogen(lstfile):
    iraf.noao()
    iraf.imred()
    iraf.ccdred()
    iraf.ccdproc(images = '[email protected]' + lstfile
        , output = '%ftbo%ftbo%@' + lstfile
        , ccdtype = '', max_cache = 0, noproc = False
        , fixpix = False, overscan = False, trim = False
        , zerocor = False, darkcor = False, flatcor = True
        , illumcor = False, fringecor = False, readcor = False
        , scancor = False, readaxis = 'line', fixfile = ''
        , biassec = '', trimsec = ''
        , zero = 'Zero', dark = '', flat = 'Resp', illum = '', fringe = ''
        , minreplace = 1.0, scantype = 'shortscan', nscan = 1
        , interactive = False, function = 'chebyshev', order = 1
        , sample = '*', naverage = 1, niterate = 1
        , low_reject = 3.0, high_reject = 3.0, grow = 1.0)
    iraf.flpr()

def coroverscan(filename):
    iraf.noao()
    iraf.imred()
    iraf.ccdred()
    iraf.ccdproc(images = '@' + filename + '//[1]'
    	, output = '%o%o%@' + filename
	, ccdtype = '', max_cache = 0, noproc = False
	, fixpix = False, overscan = True, trim = False
	, zerocor = False, darkcor = False, flatcor = False
	, illumcor = False, fringecor = False, readcor = False
	, scancor = False, readaxis = 'line', fixfile = ''
	, biassec = '[5:45,1:4612]', trimsec = '', zero = ''
	, dark = '', flat = '', illum = '', fringe = ''
	, minreplace = 1.0, scantype = 'shortscan', nscan = 1
	, interactive = False, function = 'chebyshev', order = 1
	, sample = '*', naverage = 1, niterate = 1
	, low_reject = 3.0, high_reject = 3.0, grow = 1.0)
    iraf.flpr()

def runapall(imagesre, gain='gain', rdnoise='rdnoise'):
    '''Extract aperture spectra for science images ...'''

    imageslist = glob.glob(imagesre)
    imagesin = ', '.join(imageslist)

    # load packages
    iraf.imred()
    iraf.ccdred()
    iraf.specred()
    # unlearn previous settings
    iraf.ccdred.combine.unlearn()
    iraf.ccdred.ccdproc.unlearn()
    iraf.specred.apall.unlearn()
    # setup and run task
    iraf.specred.apall.format = 'onedspec'
    iraf.specred.apall.readnoise = rdnoise
    iraf.specred.apall.gain = gain
    iraf.specred.apall(input=imagesin)

def findaperture(img, _interactive=False):
    # print "LOGX:: Entering `findaperture` method/function in %(__file__)s" %
    # globals()
    import re
    import string
    import os
    from pyraf import iraf
    import ntt
    iraf.noao(_doprint=0)
    iraf.imred(_doprint=0)
    iraf.specred(_doprint=0)
    toforget = ['specred.apfind']
    for t in toforget:
        iraf.unlearn(t)

    iraf.specred.databas = 'database'
    iraf.specred.dispaxi = 2
    iraf.specred.apedit.thresho = 0

    dv = ntt.dvex()
    grism = ntt.util.readkey3(ntt.util.readhdr(img), 'grism')
    if _interactive:
        _interac = 'yes'
        _edit = 'yes'
    else:
        _interac = 'no'
        _edit = 'no'
    if os.path.isfile('database/ap' + re.sub('.fits', '', img)):
        ntt.util.delete('database/ap' + re.sub('.fits', '', img))
    xx = iraf.specred.apfind(img, interac=_interac, find='yes', recenter='yes', edit=_edit, resize='no',
                             aperture=1, Stdout=1, nfind=1, line=dv['line'][grism], nsum=50, mode='h')
    try:
        for line in open('database/ap' + re.sub('.fits', '', img)):
            if "center" in line:
                center = float(string.split(line)[1])
    except:
        center = 9999
    return center

def combine_flat(lstfile):
    iraf.noao()
    iraf.imred()
    iraf.ccdred()
    iraf.flatcombine(input = 'tbo//@' + lstfile
    	, output = 'Halogen', combine = 'average', reject = 'crreject'
    	, ccdtype = '', process = False, subsets = False
    	, delete = False, clobber = False, scale = 'mode'
    	, statsec = '', nlow = 1, nhigh = 1, nkeep = 1
    	, mclip = True, lsigma = 3.0, hsigma = 3.0
    	, rdnoise = 'rdnoise', gain = 'gain', snoise = 0.0
    	, pclip = -0.5, blank = 1.0)
    script_path = os.path.split(os.path.realpath(__file__))[0]
    iraf.twodspec()
    iraf.longslit(dispaxis = 2, nsum = 1, observatory = 'observatory'
        , extinction = script_path + os.sep + 'LJextinct.dat'
        , caldir = script_path + os.sep + 'standarddir' + os.sep, interp = 'poly5')
    iraf.response(calibration = 'Halogen'
    	, normalization = 'Halogen', response = 'Resp'
    	, interactive = True, threshold = 'INDEF', sample = '*'
    	, naverage = 1, function = 'spline3', order = 25
    	, low_reject = 10.0, high_reject = 10.0, niterate = 1
    	, grow = 0.0, graphics = 'stdgraph', cursor = '')

def masterflat(flatre, output='Flat', combine='median', reject='sigclip',
               scale='mode', rdnoise='rdnoise', gain='gain'):
    '''combine flat images with the task ccdred.flatcombine

    Input:
    -------
     str: flatre - regular expression to bias files in the current directory

    Output:
    -------
     file: Flat.fits - combined flat field images
    '''

    flatlist = glob.glob(flatre)
    flatstring = ', '.join(flatlist)

    # load packages
    iraf.imred()
    iraf.ccdred()
    # unlearn settings
    iraf.imred.unlearn()
    iraf.ccdred.unlearn()
    iraf.ccdred.ccdproc.unlearn()
    iraf.ccdred.combine.unlearn()
    iraf.ccdred.flatcombine.unlearn()
    iraf.ccdred.setinstrument.unlearn()
    # setup task
    iraf.ccdred.flatcombine.output = output
    iraf.ccdred.flatcombine.combine = combine
    iraf.ccdred.flatcombine.reject = reject
    iraf.ccdred.flatcombine.ccdtype = ''
    iraf.ccdred.flatcombine.process = 'no'
    iraf.ccdred.flatcombine.subsets = 'yes'
    iraf.ccdred.flatcombine.scale = scale
    iraf.ccdred.flatcombine.rdnoise = rdnoise
    iraf.ccdred.flatcombine.gain = gain
    iraf.ccdred.flatcombine(input=flatstring)

def extractSpectra():
    """
    Extract 1D spectra using IRAF interactively

    Interpolate across the two arcs either side to
    get the most accurate wavelength solution

    TODO: Finish docstring
          Add method of using super arc for inital
          identify
    """
    # load IRAF from the location of the login.cl file
    here = os.getcwd()
    os.chdir(loginCl_location)
    from pyraf import iraf
    os.chdir(here)
    time.sleep(2)

    # make a list of the science images to be analysed
    templist = g.glob('i_s*')
    # import IRAF packages for spectroscopy
    iraf.imred(_doprint=0)
    iraf.kpnoslit(_doprint=0)
    # apall parameters
    iraf.apall.setParam('format', 'multispec')
    iraf.apall.setParam('interac', 'yes')
    iraf.apall.setParam('find', 'yes')
    iraf.apall.setParam('recen', 'yes')
    iraf.apall.setParam('resize', 'yes')
    iraf.apall.setParam('trace', 'yes')
    iraf.apall.setParam('fittrac', 'yes')
    iraf.apall.setParam('extract', 'yes')
    iraf.apall.setParam('extras', 'yes')
    iraf.apall.setParam('review', 'yes')
    iraf.apall.setParam('line', 'INDEF')
    iraf.apall.setParam('nsum', '12')
    iraf.apall.setParam('lower', '-6')
    iraf.apall.setParam('upper', '6')
    iraf.apall.setParam('b_funct', 'chebyshev')
    iraf.apall.setParam('b_order', '1')
    iraf.apall.setParam('b_sampl', '-25:-15,15:25')
    iraf.apall.setParam('b_naver', '-100')
    iraf.apall.setParam('b_niter', '0')
    iraf.apall.setParam('b_low_r', '3')
    iraf.apall.setParam('b_high', '3')
    iraf.apall.setParam('b_grow', '0')
    iraf.apall.setParam('width', '10')
    iraf.apall.setParam('radius', '10')
    iraf.apall.setParam('threshold', '0')
    iraf.apall.setParam('nfind', '1')
    iraf.apall.setParam('t_nsum', '10')
    iraf.apall.setParam('t_step', '10')
    iraf.apall.setParam('t_nlost', '3')
    iraf.apall.setParam('t_niter', '7')
    iraf.apall.setParam('t_funct', 'spline3')
    iraf.apall.setParam('t_order', '3')
    iraf.apall.setParam('backgro', 'fit')
    iraf.apall.setParam('skybox', '1')
    iraf.apall.setParam('weights', 'variance')
    iraf.apall.setParam('pfit', 'fit1d')
    iraf.apall.setParam('clean', 'yes')
    iraf.apall.setParam('saturat', SATURATION)
    iraf.apall.setParam('readnoi', RDNOISE)
    iraf.apall.setParam('gain', GAIN)
    iraf.apall.setParam('lsigma', '4.0')
    iraf.apall.setParam('usigma', '4.0')
    iraf.apall.setParam('nsubaps', '1')
    iraf.apall.saveParList(filename="apall.pars")

    # make reference arc for reidentify
    if '.' in args.refarc:
        args.refarc = args.refarc.split('.')[0]
    refarc = "a_s_{}_t.fits".format(args.refarc)
    refarc_out = "a_s_{}_t.ms.fits".format(args.refarc)

    # loop over all the the spectra
    for i in range(0, len(templist)):
        hdulist = fits.open(templist[i])
        prihdr = hdulist[0].header
        target_id = prihdr['CAT-NAME']
        spectrum_id = int(templist[i].split('_')[2].split('r')[1])
        if args.ds9:
            os.system('xpaset fuckingds9 fits < {}'.format(templist[i]))
        # extract the object spectrum
        print("[{}/{}] Extracting spectrum of {} from image {}".format(i+1, len(templist), target_id, templist[i]))
        print("[{}/{}] Check aperture and background. Change if required".format(i+1, len(templist)))
        print("[{}/{}] AP: m = mark aperture, d = delete aperture".format(i+1, len(templist)))
        print("[{}/{}] SKY: s = mark sky, t = delete sky, f = refit".format(i+1, len(templist)))
        print("[{}/{}] q = continue".format(i+1, len(templist)))
        iraf.apall(input=templist[i])
        print("Spectrum extracted!")
        # find the arcs either side of the object
        arclist = []
        arc1 = "a_s_r{0:d}_t.fits".format(spectrum_id-1)
        arc2 = "a_s_r{0:d}_t.fits".format(spectrum_id+1)
        arc1_out = "a_s_r{0:d}_t.ms.fits".format(spectrum_id-1)
        arc2_out = "a_s_r{0:d}_t.ms.fits".format(spectrum_id+1)
        # predict the arc names
        print("\nPredicting arcs names...")
        print("Arc1: {}".format(arc1))
#.........这里部分代码省略.........

def efoscspec1Dredu(files, _interactive, _ext_trace, _dispersionline, liststandard, listatmo0, _automaticex,
                    _verbose=False):
    # print "LOGX:: Entering `efoscspec1Dredu` method/function in
    # %(__file__)s" % globals()
    import ntt

    try:        import pyfits
    except:     from astropy.io import fits as pyfits

    import re
    import string
    import sys
    import os
    import numpy as np

    os.environ["PYRAF_BETA_STATUS"] = "1"
    _extinctdir = 'direc$standard/extinction/'
    _extinction = 'lasilla2.txt'
    _observatory = 'lasilla'
    import datetime

    now = datetime.datetime.now()
    datenow = now.strftime('20%y%m%d%H%M')
    MJDtoday = 55927 + (datetime.date.today() - datetime.date(2012, 01, 01)).days
    dv = ntt.dvex()
    scal = np.pi / 180.
    _gain = ntt.util.readkey3(ntt.util.readhdr(
        re.sub('\n', '', files[0])), 'gain')
    _rdnoise = ntt.util.readkey3(
        ntt.util.readhdr(re.sub('\n', '', files[0])), 'ron')
    std, rastd, decstd, magstd = ntt.util.readstandard(
        'standard_efosc_mab.txt')
    objectlist = {}
    for img in files:
        hdr = ntt.util.readhdr(img)
        img = re.sub('\n', '', img)
        ntt.util.correctcard(img)
        _ra = ntt.util.readkey3(hdr, 'RA')
        _dec = ntt.util.readkey3(hdr, 'DEC')
        _object = ntt.util.readkey3(hdr, 'object')
        _grism = ntt.util.readkey3(hdr, 'grism')
        _filter = ntt.util.readkey3(hdr, 'filter')
        _slit = ntt.util.readkey3(hdr, 'slit')
        dd = np.arccos(np.sin(_dec * scal) * np.sin(decstd * scal) + np.cos(_dec * scal) *
                       np.cos(decstd * scal) * np.cos((_ra - rastd) * scal)) * ((180 / np.pi) * 3600)
        if min(dd) < 100:
            _type = 'stdsens'
        else:
            _type = 'obj'
        if min(dd) < 100:
            ntt.util.updateheader(
                img, 0, {'stdname': [std[np.argmin(dd)], '']})
            ntt.util.updateheader(
                img, 0, {'magstd': [float(magstd[np.argmin(dd)]), '']})

        if _type not in objectlist:
            objectlist[_type] = {}
        if (_grism, _filter, _slit) not in objectlist[_type]:
            objectlist[_type][_grism, _filter, _slit] = [img]
        else:
            objectlist[_type][_grism, _filter, _slit].append(img)

    from pyraf import iraf
    iraf.noao(_doprint=0)
    iraf.imred(_doprint=0)
    iraf.specred(_doprint=0)
    iraf.imutil(_doprint=0)
    toforget = ['imutil.imcopy', 'specred.sarith', 'specred.standard']
    for t in toforget:
        iraf.unlearn(t)
    iraf.specred.verbose = 'no'
    iraf.specred.dispaxi = 2
    iraf.set(direc=ntt.__path__[0] + '/')
    sens = {}
    print objectlist
    outputfile = []
    if 'obj' in objectlist.keys():
        tpe = 'obj'
    elif 'stdsens' in objectlist.keys():
        tpe = 'stdsens'
    else:
        sys.exit('error: no objects and no standards in the list')

    for setup in objectlist[tpe]:
        extracted = []
        listatmo = []
        if setup not in sens:
            sens[setup] = []
        if tpe == 'obj':
            print '\n### setup= ', setup, '\n### objects= ', objectlist['obj'][setup], '\n'
            for img in objectlist['obj'][setup]:
                #              hdr=readhdr(img)
                print '\n\n### next object= ', img, ' ', ntt.util.readkey3(ntt.util.readhdr(img), 'object'), '\n'
                if os.path.isfile(re.sub('.fits', '_ex.fits', img)):
                    if ntt.util.readkey3(ntt.util.readhdr(re.sub('.fits', '_ex.fits', img)), 'quality') == 'Rapid':
                        ntt.util.delete(re.sub('.fits', '_ex.fits', img))
                imgex = ntt.util.extractspectrum(img, dv, _ext_trace, _dispersionline, _interactive, 'obj',
                                                 automaticex=_automaticex)
                if not os.path.isfile(imgex):
                    sys.exit('### error, extraction not computed')
#.........这里部分代码省略.........

def sensfunction(standardfile, _function, _order, _interactive):
    # print "LOGX:: Entering `sensfunction` method/function in %(__file__)s" %
    # globals()
    import re
    import os
    import sys
    import ntt
    import datetime

    try:       import pyfits  #  added later
    except:    from astropy.io import fits as pyfits

    from pyraf import iraf
    import numpy as np

    MJDtoday = 55927 + (datetime.date.today() - datetime.date(2012, 01, 01)).days
    iraf.noao(_doprint=0)
    iraf.imred(_doprint=0)
    iraf.specred(_doprint=0)
    toforget = ['specred.scopy', 'specred.sensfunc', 'specred.standard']
    for t in toforget:
        iraf.unlearn(t)
    iraf.specred.scopy.format = 'multispec'
    iraf.specred.verbose = 'no'
    hdrs = ntt.util.readhdr(standardfile)
    try:
        _outputsens = 'sens_' + str(ntt.util.readkey3(hdrs, 'date-night')) + '_' + \
                      str(ntt.util.readkey3(hdrs, 'grism')) + '_' + str(ntt.util.readkey3(hdrs, 'filter')) + '_' + \
                      re.sub('.dat', '', ntt.util.readkey3(
                          hdrs, 'stdname')) + '_' + str(MJDtoday)
    except:
        sys.exit('Error: missing header -stdname- in standard ' +
                 str(standardfile) + '  ')

    _outputsens = ntt.util.name_duplicate(standardfile, _outputsens, '')
    if os.path.isfile(_outputsens):
        if _interactive.lower() != 'yes':
            ntt.util.delete(_outputsens)
        else:
            answ = raw_input(
                'sensitivity function already computed, do you want to do it again [[y]/n] ? ')
            if not answ:
                answ = 'y'
            if answ.lower() in ['y', 'yes']:
                ntt.util.delete(_outputsens)

    if not os.path.isfile(_outputsens):
        iraf.set(direc=ntt.__path__[0] + '/')
        _caldir = 'direc$standard/MAB/'
        _extinctdir = 'direc$standard/extinction/'
        _observatory = 'lasilla'
        _extinction = 'lasilla2.txt'
        refstar = 'm' + \
            re.sub('.dat', '', pyfits.open(standardfile)
                   [0].header.get('stdname'))
        _airmass = ntt.util.readkey3(hdrs, 'airmass')
        _exptime = ntt.util.readkey3(hdrs, 'exptime')
        _outputstd = 'std_' + str(ntt.util.readkey3(hdrs, 'grism')) + '_' + \
                     str(ntt.util.readkey3(hdrs, 'filter')) + '.fits'
        ntt.util.delete(_outputstd)
        ntt.util.delete(_outputsens)
        iraf.specred.standard(input=standardfile, output=_outputstd, extinct=_extinctdir + _extinction,
                              caldir=_caldir, observa=_observatory, star_nam=refstar, airmass=_airmass,
                              exptime=_exptime, interac=_interactive)
        iraf.specred.sensfunc(standard=_outputstd, sensitiv=_outputsens, extinct=_extinctdir + _extinction,
                              ignorea='yes', observa=_observatory, functio=_function, order=_order,
                              interac=_interactive)

        data, hdr = pyfits.getdata(standardfile, 0, header=True)  # added later
        data1, hdr1 = pyfits.getdata(
            _outputsens, 0, header=True)  # added later
        ntt.util.delete(_outputsens)  # added later
        pyfits.writeto(_outputsens, np.float32(data1), hdr)  # added later
    return _outputsens