Python JLA_library.reindex_SNe方法代码示例

# 需要导入模块: import JLA_library [as 别名]
# 或者: from JLA_library import reindex_SNe [as 别名]
def compute_model(options):

    import numpy
    import astropy.io.fits as fits
    import JLA_library as JLA
    from astropy.table import Table
    from astropy.cosmology import FlatwCDM
    from scipy.interpolate import interp1d


    # -----------  Read in the configuration file ------------
    params=JLA.build_dictionary(options.config)

    # -----------  Read in the SN ordering ------------------------
    SNeList = numpy.genfromtxt(options.SNlist,
                               usecols=(0, 2),
                               dtype='S30,S200',
                               names=['id', 'lc'])
    nSNe = len(SNeList)

    for i, SN in enumerate(SNeList):
        SNeList['id'][i] = SNeList['id'][i].replace('lc-', '').replace('.list', '').replace('_smp', '')

    lcfile = JLA.get_full_path(params[options.lcfits])
    SNe = Table.read(lcfile, format='fits')

    print 'There are %d SNe' % (nSNe)

    indices = JLA.reindex_SNe(SNeList['id'], SNe)
    SNe = SNe[indices]

    redshift = SNe['zcmb']
    replace=(redshift < 0)

    # For SNe that do not have the CMB redshift
    redshift[replace]=SNe[replace]['zhel']
    print len(redshift)

    if options.raw:
        # Data from the bottom left hand figure of Mosher et al. 2014.
        # This is option ii) that is descibed above
        offsets=Table.read(JLA.get_full_path(params['modelOffset']),format='ascii.csv')
        Delta_M=interp1d(offsets['z'], offsets['offset'], kind='linear',bounds_error=False,fill_value='extrapolate')(redshift)
    else:
        Om_0=0.303 # JLA value in the wCDM model
        cosmo1 = FlatwCDM(name='SNLS3+WMAP7', H0=70.0, Om0=Om_0, w0=-1.0)
        cosmo2 = FlatwCDM(name='SNLS3+WMAP7', H0=70.0, Om0=Om_0, w0=-1.024)
        Delta_M=5*numpy.log10(cosmo1.luminosity_distance(redshift)/cosmo2.luminosity_distance(redshift))
    
    # Build the covariance matrix. Note that only magnitudes are affected
    Zero=numpy.zeros(nSNe)
    H=numpy.concatenate((Delta_M,Zero,Zero)).reshape(3,nSNe).ravel(order='F')
    C_model=numpy.matrix(H).T * numpy.matrix(H)

    date = JLA.get_date()
    fits.writeto('C_model_%s.fits' % (date),numpy.array(C_model),clobber=True) 

    return None

# 需要导入模块: import JLA_library [as 别名]
# 或者: from JLA_library import reindex_SNe [as 别名]
def compute_Cstat(options):
    """Python program to compute C_stat
    """

    import numpy
    import astropy.io.fits as fits
    from astropy.table import Table
    import JLA_library as JLA

    # -----------  Read in the configuration file ------------

    params=JLA.build_dictionary(options.config)

    # -----------  Read in the SN ordering ------------------------
    SNeList = numpy.genfromtxt(options.SNlist,
                               usecols=(0, 2),
                               dtype='S30,S200',
                               names=['id', 'lc'])
    nSNe = len(SNeList)

    for i, SN in enumerate(SNeList):
        SNeList['id'][i] = SNeList['id'][i].replace('lc-', '').replace('.list', '')

    lcfile = JLA.get_full_path(params[options.lcfits])
    SNe = Table.read(lcfile, format='fits')


    # -----------  Read in the data --------------------------

    print 'There are %d SNe in the sample' % (nSNe)

    indices = JLA.reindex_SNe(SNeList['id'], SNe)
    SNe=SNe[indices]

    C_stat=numpy.zeros(9*nSNe*nSNe).reshape(3*nSNe,3*nSNe)

    for i,SN in enumerate(SNe):
        cov=numpy.zeros(9).reshape(3,3)
        cov[0,0]=SN['dmb']**2.
        cov[1,1]=SN['dx1']**2.
        cov[2,2]=SN['dcolor']**2.
        cov[0,1]=SN['cov_m_s']
        cov[0,2]=SN['cov_m_c']
        cov[1,2]=SN['cov_s_c']
        # symmetrise
        cov=cov+cov.T-numpy.diag(cov.diagonal())
        C_stat[i*3:i*3+3,i*3:i*3+3]=cov

    # -----------  Read in the base matrix computed using salt2_stat.cc ------------

    if options.base!=None:
        C_stat+=fits.getdata(options.base)

    date = JLA.get_date()
    fits.writeto('C_stat_%s.fits' % date,C_stat,clobber=True) 

    return

# 需要导入模块: import JLA_library [as 别名]
# 或者: from JLA_library import reindex_SNe [as 别名]
def compute_Ccal(options):
    """Python program to compute Ccal
    """

    import numpy
    import astropy.io.fits as fits
    from astropy.table import Table
    import multiprocessing as mp
    import matplotlib.pyplot as plt

    # -----------  Read in the configuration file ------------

    params=JLA.build_dictionary(options.config)
    try:
        salt_prefix = params['saltPrefix']
    except KeyError:
        salt_prefix = ''

    # ---------- Read in the SNe list -------------------------

    SNeList = Table(numpy.genfromtxt(options.SNlist,
                                     usecols=(0, 2),
                                     dtype='S30,S100',
                                     names=['id', 'lc']))


    for i,SN in enumerate(SNeList):
        SNeList['id'][i]=SNeList['id'][i].replace('lc-', '').replace('.list', '').replace('_smp', '')

    # ----------  Read in the SN light curve fits ------------
    # This is used to get the SN redshifts which are used in smoothing the Jacbian

    lcfile = JLA.get_full_path(params[options.lcfits])
    SNe = Table.read(lcfile, format='fits')

    # Make sure that the order is correct
    indices = JLA.reindex_SNe(SNeList['id'], SNe)
    SNe = SNe[indices]
    if len(indices) != len(SNeList['id']):
        print "We are missing SNe"
        exit()

    # -----------  Set up the structures to handle the different salt models -------
    # The first model is the unperturbed salt model
    SALTpath=JLA.get_full_path(params['saltPath'])

    SALTmodels=JLA.SALTmodels(SALTpath+'/saltModels.list')
    nSALTmodels=len(SALTmodels)-1
    print SALTmodels, nSALTmodels

    nSNe=len(SNeList)
    print 'There are %d SNe in the sample' % (nSNe)
    print 'There are %d SALT models' % (nSALTmodels)

    # Add a survey column, which we use with the smoothing, and the redshift
    SNeList['survey'] = numpy.zeros(nSNe,'a10')
    SNeList['z'] = SNe['zhel']

    # Identify the SNLS, SDSS, HST and low-z SNe. We use this when smoothing the Jacobian
    # There is rather inelegant 
    # We still need to allow for Vanina's naming convention when doing this for the photometric sample
    for i,SN in enumerate(SNeList):
        if SN['id'][0:4]=='SDSS':
            SNeList['survey'][i]='SDSS'
        elif SN['id'][2:4] in ['D1','D2','D3','D4']:
            SNeList['survey'][i]='SNLS'
        elif SN['id'][0:3] in ['DES']:
            SNeList['survey'][i]='DES'
        elif SN['id'][0:2]=='sn':
            SNeList['survey'][i]='nearby'
        else:
            SNeList['survey'][i]='high-z'

    # -----------   Read in the calibration matrix -----------------
    Cal=fits.getdata(JLA.get_full_path(params['C_kappa']))

    # Multiply the ZP submatrix by 100^2, and the two ZP-offset submatrices by 100,
    # because the magnitude offsets are 0.01 mag and the units of the covariance matrix are mag
    size=Cal.shape[0] / 2
    Cal[0:size,0:size]=Cal[0:size,0:size]*10000.
    Cal[0:size,size:]*=Cal[0:size,size:]*100.
    Cal[size:,0:size]=Cal[size:,0:size]*100.


    # ------------- Create an area to work in -----------------------
    workArea = JLA.get_full_path(options.workArea)
    try:
        os.mkdir(workArea)
    except:
        pass

    # -----------   The lightcurve fitting --------------------------

    firstSN=True
    
    log=open('log.txt','w')

    for i,SN in enumerate(SNeList):
        J=[]
        try:
#.........这里部分代码省略.........

# 需要导入模块: import JLA_library [as 别名]
# 或者: from JLA_library import reindex_SNe [as 别名]
def compute_bias(options):

    import numpy
    import astropy.io.fits as fits
    import JLA_library as JLA
    from astropy.table import Table
    from astropy.cosmology import FlatwCDM
    from  scipy.optimize import leastsq
    import matplotlib.pyplot as plt
    from scipy.stats import t


    # -----------  Read in the configuration file ------------
    params=JLA.build_dictionary(options.config)

    # -----------  Read in the SN ordering ------------------------
    SNeList = Table(numpy.genfromtxt(options.SNlist,
                               usecols=(0, 2),
                               dtype='S30,S200',
                               names=['id', 'lc']))
    nSNe = len(SNeList)

    for i, SN in enumerate(SNeList):
        SNeList['id'][i] = SNeList['id'][i].replace('lc-', '').replace('.list', '').replace('_smp','')

    lcfile = JLA.get_full_path(params[options.lcfits])
    SNe = Table.read(lcfile, format='fits')
    print 'There are %d SNe' % (nSNe)

    indices = JLA.reindex_SNe(SNeList['id'], SNe)
    SNe=SNe[indices]

    # Add a column that records the error in the bias correction
    SNe['e_bias'] = numpy.zeros(nSNe,'f8')

    # Read in the bias correction (see, for example, Fig.5 in B14)
    # Fit a polynomial to the data
    # Determine the uncertainties

    bias = numpy.genfromtxt(JLA.get_full_path(params['biasPolynomial']),
                                  skip_header=4,
                                  usecols=(0, 1, 2, 3),
                                  dtype='S10,f8,f8,f8',
                                  names=['sample', 'redshift', 'bias', 'e_bias'])

    if options.plot:
        fig=plt.figure()
        ax=fig.add_subplot(111)
        colour={'nearby':'b','SNLS':'r','SDSS':'g','DES':'k'}

    for sample in numpy.unique(bias['sample']):
        selection=(bias['sample']==sample)
        guess=[0,0,0]

        print bias[selection]
        plsq=leastsq(residuals, guess, args=(bias[selection]['bias'],
                                             bias[selection]['redshift'],
                                             bias[selection]['e_bias'],
                                             'poly'), full_output=1)

        if plsq[4] in [1,2,3,4]:
            print 'Solution for %s found' % (sample)

        if options.plot:
            ax.errorbar(bias[selection]['redshift'],
                    bias[selection]['bias'],
                    yerr=bias[selection]['e_bias'],
                    ecolor='k',
                    color=colour[sample],
                    fmt='o',
                    label=sample)
            z=numpy.arange(numpy.min(bias[selection]['redshift']),numpy.max(bias[selection]['redshift']),0.001)
            ax.plot(z,poly(z,plsq[0]),color=colour[sample])

        # For each SNe, determine the uncerainty in the correction. We use the approach descibed in
        # https://www.astro.rug.nl/software/kapteyn/kmpfittutorial.html
        
        # Compute the chi-sq.
        chisq=(((bias[selection]['bias']-poly(bias[selection]['redshift'],plsq[0]))/bias[selection]['e_bias'])**2.).sum()
        dof=selection.sum()-len(guess)
        print "Reduced chi-square value for sample %s is %5.2e" % (sample, chisq / dof)

        alpha=0.315 # Confidence interval is 100 * (1-alpha)
        # Compute the upper alpha/2 value for the student t distribution with dof
        thresh=t.ppf((1-alpha/2.0), dof)
        
        if options.plot and sample!='nearby':
            # The following is only valid for polynomial fitting functions, and we do not compute it for the nearby sample
            upper_curve=[]
            lower_curve=[]
            for x in z:
                vect=numpy.matrix([1,x,x**2.])
                offset=thresh * numpy.sqrt(chisq / dof * (vect*numpy.matrix(plsq[1])*vect.T)[0,0])
                upper_curve.append(poly(x,plsq[0])+offset)
                lower_curve.append(poly(x,plsq[0])-offset)

            ax.plot(z,lower_curve,'--',color=colour[sample])
            ax.plot(z,upper_curve,'--',color=colour[sample])

        # Compute the error in the bias
#.........这里部分代码省略.........

# 需要导入模块: import JLA_library [as 别名]
# 或者: from JLA_library import reindex_SNe [as 别名]
    parser = OptionParser()

    parser.add_option("-c", "--config", dest="config", default="JLA.config",
                      help="Parameter file containing the location of various JLA parameters")

    parser.add_option("-s", "--SNlist", dest="SNlist",
                      help="List of SNe")

    parser.add_option("-l", "--lcfits", dest="lcfits", default="lightCurveFits",
                      help="Key in config file pointing to lightcurve fit parameters")
    
    parser.add_option("-o", "--output", dest="output",default="sigma_mu.txt", 
                  help="Output")

    (options, args) = parser.parse_args()

    params = JLA.build_dictionary(options.config)
    
    lcfile = JLA.get_full_path(params[options.lcfits])
    SN_data = Table.read(lcfile, format='fits')

    SN_list_long = np.genfromtxt(options.SNlist, usecols=(0), dtype='S30')
    SN_list = [name.replace('lc-', '').replace('.list', '').replace('_smp','') for name in SN_list_long]
    SN_indices = JLA.reindex_SNe(SN_list, SN_data)
    SN_data = SN_data[SN_indices]

    sigma_diag = compute_diag(SN_data)

    np.savetxt(options.output,sigma_diag, header='coh lens pecvel')

# 需要导入模块: import JLA_library [as 别名]
# 或者: from JLA_library import reindex_SNe [as 别名]
def compute_rel_size(options):
    import numpy
    import astropy.io.fits as fits
    from astropy.table import Table
    import JLA_library as JLA
    from astropy.cosmology import FlatwCDM
    import os
    
    # -----------  Read in the configuration file ------------

    params=JLA.build_dictionary(options.config)

    # ---------- Read in the SNe list -------------------------

    SNeList=numpy.genfromtxt(options.SNlist,usecols=(0,2),dtype='S30,S200',names=['id','lc'])

    for i,SN in enumerate(SNeList):
        SNeList['id'][i]=SNeList['id'][i].replace('lc-','').replace('.list','')

    # -----------  Read in the data JLA --------------------------

    lcfile = JLA.get_full_path(params[options.lcfits])
    SNe = Table.read(lcfile, format='fits')

    nSNe=len(SNe)
    print 'There are %d SNe in this sample' % (nSNe)

    # sort it to match the listing in options.SNlist
    indices = JLA.reindex_SNe(SNeList['id'], SNe)        
    SNe=SNe[indices]

    # ---------- Compute the Jacobian ----------------------
    # The Jacobian is an m by 4 matrix, where m is the number of SNe
    # The columns are ordered in terms of Om, w, alpha and beta

    J=[]
    JLA_result={'Om':0.303,'w':-1.00,'alpha':0.141,'beta':3.102,'M_B':-19.05}
    offset={'Om':0.01,'w':0.01,'alpha':0.01,'beta':0.01,'M_B':0.01}
    nFit=4

    cosmo1 = FlatwCDM(name='SNLS3+WMAP7', H0=70.0, Om0=JLA_result['Om'], w0=JLA_result['w'])

    # Varying Om
    cosmo2 = FlatwCDM(name='SNLS3+WMAP7', H0=70.0, Om0=JLA_result['Om']+offset['Om'], w0=JLA_result['w'])
    J.append(5*numpy.log10((cosmo1.luminosity_distance(SNe['zcmb'])/cosmo2.luminosity_distance(SNe['zcmb']))[:,0]))

    # varying alpha
    J.append(1.0*offset['alpha']*SNe['x1'][:,0])

    # varying beta
    J.append(-1.0*offset['beta']*SNe['color'][:,0])

    # varying M_B

    J.append(offset['M_B']*numpy.ones(nSNe))
    
    J = numpy.matrix(numpy.concatenate((J)).reshape(nSNe,nFit,order='F') * 100.)

    # Set up the covariance matrices

    systematic_terms = ['bias', 'cal', 'host', 'dust', 'model', 'nonia', 'pecvel', 'stat']

    covmatrices = {'bias':params['bias'],
                   'cal':params['cal'],
                   'host':params['host'],
                   'dust':params['dust'],
                   'model':params['model'],
                   'nonia':params['nonia'],
                   'pecvel':params['pecvel'],
                   'stat':params['stat']}


    if options.type in systematic_terms:
        print "Using %s for the %s term" % (options.name,options.type) 
        covmatrices[options.type]=options.name

    # Combine the matrices to compute the full covariance matrix, and compute its inverse
    if options.all:
        #read in the user provided matrix, otherwise compute it, and write it out
        C=fits.getdata(JLA.get_full_path(params['all']))
    else:
        C=add_covar_matrices(covmatrices,params['diag'])
        date=JLA.get_date()
        fits.writeto('C_total_%s.fits' % (date), C, clobber=True)

    Cinv=numpy.matrix(C).I


    # Construct eta, a 3n vector

    eta=numpy.zeros(3*nSNe)
    for i,SN in enumerate(SNe):
        eta[3*i]=SN['mb']
        eta[3*i+1]=SN['x1']
        eta[3*i+2]=SN['color']

    # Construct A, a n x 3n matrix
    A=numpy.zeros(nSNe*3*nSNe).reshape(nSNe,3*nSNe)

    for i in range(nSNe):
#.........这里部分代码省略.........

# 需要导入模块: import JLA_library [as 别名]
# 或者: from JLA_library import reindex_SNe [as 别名]
def compute_nonIa(options):
    """Pythom program to compute the systematic unsertainty related to
    the contamimation from Ibc SNe"""

    import numpy
    import astropy.io.fits as fits
    from astropy.table import Table, MaskedColumn, vstack
    import JLA_library as JLA

    # The program computes the covaraince for the spectroscopically confirmed SNe Ia only
    # The prgram assumes that the JLA SNe are first in any list
    # Taken from C11

    # Inputs are the rates of SNe Ia and Ibc, the most likely contaminant

    # Ia rate - Perett et al.
    # SN Ibc rate - proportional to the star formation rate - Hopkins and Beacom
    # SN Ib luminosity distribution. Li et al + bright SN Ibc Richardson

    # The bright Ibc population
    # d_bc = 0.25     # The offset in magnitude between the Ia and bright Ibc
    # s_bc = 0.25     # The magnitude spread
    # f_bright = 0.25 # The fraction of Ibc SN that are bright

    # Simulate the characteristics of the SNLS survey
    # Apply outlier rejection
    # All SNe that pass the cuts are included in the sample

    # One then has a mixture of SNe Ia and SNe Ibc
    # and the average magnitude at each redshift is biased. This
    # is called the raw bias. One multiplies the raw bias by the fraction of
    # objects classified as SNe Ia*

    # The results are presented in 7 redshift bins defined in table 14 of C11
    # We use these results to generate the matrix.
    # Only the SNLS SNe in the JLA sample are considered.
    # For the photometrically selected sample and other surveys, this will probably be different
    # JLA compute this for the SNLS sample only

    # We assume that the redshift in this table refers to the left hand edge of each bin

    z_bin = numpy.array([0.0, 0.1, 0.26, 0.41, 0.57, 0.72, 0.89, 1.04])
    raw_bias = numpy.array([0.0, 0.015, 0.024, 0.024, 0.024, 0.023, 0.026, 0.025])
    f_star = numpy.array([0.0, 0.00, 0.06, 0.14, 0.17, 0.24, 0.50, 0.00])

    # The covaraiance between SNe Ia in the same redshift bin is fully correlated
    # Otherwise, it is uncorrelated

    # -----------  Read in the configuration file ------------

    params = JLA.build_dictionary(options.config)

    SNeList = numpy.genfromtxt(options.SNlist,
                               usecols=(0, 2),
                               dtype='S30,S200',
                               names=['id', 'lc'])

    nSNe = len(SNeList)
    for i, SN in enumerate(SNeList):
        SNeList['id'][i] = SNeList['id'][i].replace('lc-', '').replace('.list', '')

    lcfile = JLA.get_full_path(params[options.lcfits])
    SNe = Table.read(lcfile, format='fits')

    # Add a bin column and a column that specified of the covariance is non-zero
    SNe['bin'] = 0
    SNe['eval'] = False

    # make order of data (in SNe) match SNeList
    
    indices = JLA.reindex_SNe(SNeList['id'], SNe)
    SNe = SNe[indices]

    # Identify the SNLS SNe in the JLA sample

    for i, SN in enumerate(SNe):
        if SN['source'][0] == 'JLA' and SN['name'][0][2:4] in ['D1', 'D2', 'D3', 'D4']:
            SNe['eval'][i] = True

    # Work out which redshift bin each SNe belongs to
    # In numpy.digitize, the bin number starts at 1, so we subtract 1
    SNe['bin'] = numpy.digitize(SNe['zhel'], z_bin)-1

    # Build the covariance matrix

    C_nonIa = numpy.zeros(nSNe*3*nSNe*3).reshape(nSNe*3, nSNe*3)

    # It is only computes the covariance for the spectroscopically confirmed SNLS SNe
    # We assume that covariance between redshift bins is uncorrelated

    for i in range(nSNe):
        bin1 = SNe['bin'][i]
        for j in range(nSNe):
            bin2 = SNe['bin'][j]
            if SNe['eval'][j] and SNe['eval'][i] and bin1 == bin2:
                C_nonIa[3*i, 3*j] = (raw_bias[bin1] * f_star[bin1])*(raw_bias[bin2] * f_star[bin2])

    date = JLA.get_date()

    fits.writeto('C_nonIa_%s.fits' % date, numpy.array(C_nonIa), clobber=True)
#.........这里部分代码省略.........

# 需要导入模块: import JLA_library [as 别名]
# 或者: from JLA_library import reindex_SNe [as 别名]
def compute_nonIa(options):
    """Pythom program to compute the systematic unsertainty related to
    the contamimation from Ibc SNe"""

    import numpy
    import astropy.io.fits as fits
    from astropy.table import Table, MaskedColumn, vstack
    import JLA_library as JLA

    # The program computes the covaraince for the spectroscopically confirmed SNe Ia only
    # The prgram assumes that the JLA SNe are first in any list
    # Taken from C11

    # Inputs are the rates of SNe Ia and Ibc, the most likely contaminant

    # Ia rate - Perett et al.
    # SN Ibc rate - proportional to the star formation rate - Hopkins and Beacom
    # SN Ib luminosity distribution. Li et al + bright SN Ibc Richardson

    # The bright Ibc population
    # d_bc = 0.25     # The offset in magnitude between the Ia and bright Ibc
    # s_bc = 0.25     # The magnitude spread
    # f_bright = 0.25 # The fraction of Ibc SN that are bright

    # Simulate the characteristics of the SNLS survey
    # Apply outlier rejection
    # All SNe that pass the cuts are included in the sample

    # One then has a mixture of SNe Ia and SNe Ibc
    # and the average magnitude at each redshift is biased. This
    # is called the raw bias. One multiplies the raw bias by the fraction of
    # objects classified as SNe Ia*

    # The results are presented in 7 redshift bins defined in table 14 of C11
    # We use these results to generate the matrix.
    # Only the SNLS SNe in the JLA sample are considered.
    # For the photometrically selected sample and other surveys, this will probably be different
    # JLA compute this for the SNLS sample only

    # We assume that the redshift in this table refers to the left hand edge of each bin

    # -----------  Read in the configuration file ------------

    params = JLA.build_dictionary(options.config)
    

    data=numpy.genfromtxt(JLA.get_full_path(params['classification']),comments="#",usecols=(0,1,2),dtype=['float','float','float'],names=['redshift','raw_bias','fraction'])
    z_bin=data['redshift']
    raw_bias=data['raw_bias']
    f_star=data['fraction']
    
    # The covaraiance between SNe Ia in the same redshift bin is fully correlated
    # Otherwise, it is uncorrelated

    # -----------  Read in the configuration file ------------

    params = JLA.build_dictionary(options.config)

    SNeList = numpy.genfromtxt(options.SNlist,
                               usecols=(0, 2),
                               dtype='S30,S200',
                               names=['id', 'lc'])

    for i, SN in enumerate(SNeList):
        SNeList['id'][i] = SNeList['id'][i].replace('lc-', '').replace('.list', '').replace('_smp','')

    lcfile = JLA.get_full_path(params[options.lcfits])
    SNe = Table.read(lcfile, format='fits')

    # Add a bin column and a column that specifies if the covariance needs to be computed
    SNe['bin'] = 0
    SNe['eval'] = False

    # make the order of data (in SNe) match SNeList
    indices = JLA.reindex_SNe(SNeList['id'], SNe)
    SNe = SNe[indices]

    nSNe = len(SNe)
    # Identify the SNLS SNe in the JLA sample
    # We use the source and the name to decide if we want to add corrections for non-Ia contamination
    # Identify the DESS SNe in the DES sample.
    for i, SN in enumerate(SNe):
        try:
            # If the source keyword exists
            if (SN['source'] == 'JLA' or SN['source'] == 'SNLS_spec') and SN['name'][2:4] in ['D1', 'D2', 'D3', 'D4']:
                SNe['eval'][i] = True
            elif (SN['source']== 'SNLS_photo') and (SN['name'][2:4] in ['D1', 'D2', 'D3', 'D4'] or (SN['name'][0:2] in ['D1', 'D2', 'D3', 'D4'])):
                SNe['eval'][i] = True
        except:
            # If the source keyword does not exist
            if SN['name'][0:3]=="DES":
                SNe['eval'][i] = True

    print list(SNe['eval']).count(True)
    # Work out which redshift bin each SNe belongs to
    # In numpy.digitize, the bin number starts at 1, so we subtract 1 -- need to check...
    SNe['bin'] = numpy.digitize(SNe['zhel'], z_bin)-1 

    # Build the covariance matrix
    C_nonIa = numpy.zeros(nSNe*3*nSNe*3).reshape(nSNe*3, nSNe*3)
#.........这里部分代码省略.........