Beispiel #1
0
def main():
    """
    NAME
        fishrot.py

    DESCRIPTION
        generates set of Fisher distributed data from specified distribution 

    SYNTAX
        fishrot.py [-h][-i][command line options]

    OPTIONS
        -h prints help message and quits
        -i for interactive  entry
        -k kappa specify kappa, default is 20
        -n N specify N, default is 100
        -D D specify mean Dec, default is 0
        -I I specify mean Inc, default is 90
        where:
            kappa:  fisher distribution concentration parameter
            N:  number of directions desired
    OUTPUT
        dec,  inc   


    """
    N, kappa, D, I = 100, 20., 0., 90.
    if len(sys.argv) != 0 and '-h' in sys.argv:
        print(main.__doc__)
        sys.exit()
    elif '-i' in sys.argv:
        ans = input('    Kappa: ')
        kappa = float(ans)
        ans = input('    N: ')
        N = int(ans)
        ans = input('    Mean Dec: ')
        D = float(ans)
        ans = input('    Mean Inc: ')
        I = float(ans)
    else:
        if '-k' in sys.argv:
            ind = sys.argv.index('-k')
            kappa = float(sys.argv[ind + 1])
        if '-n' in sys.argv:
            ind = sys.argv.index('-n')
            N = int(sys.argv[ind + 1])
        if '-D' in sys.argv:
            ind = sys.argv.index('-D')
            D = float(sys.argv[ind + 1])
        if '-I' in sys.argv:
            ind = sys.argv.index('-I')
            I = float(sys.argv[ind + 1])
    for k in range(N):
        dec, inc = pmag.fshdev(kappa)  # send kappa to fshdev
        drot, irot = pmag.dodirot(dec, inc, D, I)
        print('%7.1f %7.1f ' % (drot, irot))
Beispiel #2
0
def main():
    """
    NAME
        fishrot.py

    DESCRIPTION
        generates set of Fisher distributed data from specified distribution 

    SYNTAX
        fishrot.py [-h][-i][command line options]

    OPTIONS
        -h prints help message and quits
        -i for interactive  entry
        -k kappa specify kappa, default is 20
        -n N specify N, default is 100
        -D D specify mean Dec, default is 0
        -I I specify mean Inc, default is 90
        where:
            kappa:  fisher distribution concentration parameter
            N:  number of directions desired
    OUTPUT
        dec,  inc   


    """
    N,kappa,D,I=100,20.,0.,90.
    if len(sys.argv)!=0 and  '-h' in sys.argv:
        print(main.__doc__)
        sys.exit()
    elif '-i' in sys.argv:
        ans=input('    Kappa: ')
        kappa=float(ans)
        ans=input('    N: ')
        N=int(ans)
        ans=input('    Mean Dec: ')
        D=float(ans)
        ans=input('    Mean Inc: ')
        I=float(ans)
    else:
        if '-k' in sys.argv:
            ind=sys.argv.index('-k')
            kappa=float(sys.argv[ind+1])
        if '-n' in sys.argv:
            ind=sys.argv.index('-n')
            N=int(sys.argv[ind+1])
        if '-D' in sys.argv:
            ind=sys.argv.index('-D')
            D=float(sys.argv[ind+1])
        if '-I' in sys.argv:
            ind=sys.argv.index('-I')
            I=float(sys.argv[ind+1])
    for k in range(N): 
        dec,inc= pmag.fshdev(kappa)  # send kappa to fshdev
        drot,irot=pmag.dodirot(dec,inc,D,I)   
        print('%7.1f %7.1f ' % (drot,irot))
Beispiel #3
0
def main():
    """
    NAME
       foldtest.py

    DESCRIPTION
       does a fold test (Tauxe, 2010) on data

    INPUT FORMAT
       dec inc dip_direction dip

    SYNTAX
       foldtest.py [command line options]

    OPTIONS
        -h prints help message and quits
        -f FILE file with input data
        -F FILE for confidence bounds on fold test
        -u ANGLE (circular standard deviation) for uncertainty on bedding poles
        -b MIN MAX bounds for quick search of percent untilting [default is -10 to 150%]
        -n NB  number of bootstrap samples [default is 1000]
        -fmt FMT, specify format - default is svg
        -sav  save figures and quit
    INPUT FILE
    Dec Inc Dip_Direction Dip  in space delimited file

    OUTPUT PLOTS
        Geographic: is an equal area projection of the input data in
                    original coordinates
        Stratigraphic: is an equal area projection of the input data in
                    tilt adjusted coordinates
        % Untilting: The dashed (red) curves are representative plots of
                    maximum eigenvalue (tau_1) as a function of untilting
                    The solid line is the cumulative distribution of the
                    % Untilting required to maximize tau for all the
                    bootstrapped data sets.  The dashed vertical lines
                    are 95% confidence bounds on the % untilting that yields
                   the most clustered result (maximum tau_1).
        Command line: prints out the bootstrapped iterations and
                   finally the confidence bounds on optimum untilting.
        If the 95% conf bounds include 0, then a post-tilt magnetization is indicated
        If the 95% conf bounds include 100, then a pre-tilt magnetization is indicated
        If the 95% conf bounds exclude both 0 and 100, syn-tilt magnetization is
                possible as is vertical axis rotation or other pathologies
        Geographic: is an equal area projection of the input data in

    OPTIONAL OUTPUT FILE:
       The output file has the % untilting within the 95% confidence bounds
nd the number of bootstrap samples
    """
    kappa=0
    fmt,plot='svg',0
    nb=1000 # number of bootstraps
    min,max=-10,150
    if '-h' in sys.argv: # check if help is needed
        print(main.__doc__)
        sys.exit() # graceful quit
    if '-F' in sys.argv:
        ind=sys.argv.index('-F')
        outfile=open(sys.argv[ind+1],'w')
    else:
        outfile=""
    if '-f' in sys.argv:
        ind=sys.argv.index('-f')
        file=sys.argv[ind+1]
        DIDDs=numpy.loadtxt(file)
    else:
        print(main.__doc__)
        sys.exit()
    if '-fmt' in sys.argv:
        ind=sys.argv.index('-fmt')
        fmt=sys.argv[ind+1]
    if '-sav' in sys.argv:plot=1
    if '-b' in sys.argv:
        ind=sys.argv.index('-b')
        min=int(sys.argv[ind+1])
        max=int(sys.argv[ind+2])
    if '-n' in sys.argv:
        ind=sys.argv.index('-n')
        nb=int(sys.argv[ind+1])
    if '-u' in sys.argv:
        ind=sys.argv.index('-u')
        csd=float(sys.argv[ind+1])
        kappa=(81. / csd)**2
    #
    # get to work
    #
    PLTS={'geo':1,'strat':2,'taus':3} # make plot dictionary
    pmagplotlib.plot_init(PLTS['geo'],5,5)
    pmagplotlib.plot_init(PLTS['strat'],5,5)
    pmagplotlib.plot_init(PLTS['taus'],5,5)
    pmagplotlib.plot_eq(PLTS['geo'],DIDDs,'Geographic')
    D,I=pmag.dotilt_V(DIDDs)
    TCs=numpy.array([D,I]).transpose()
    pmagplotlib.plot_eq(PLTS['strat'],TCs,'Stratigraphic')
    if not set_env.IS_WIN:
        if plot==0:pmagplotlib.draw_figs(PLTS)
    Percs=list(range(min,max))
    Cdf,Untilt=[],[]
    pylab.figure(num=PLTS['taus'])
    print('doing ',nb,' iterations...please be patient.....')
    for n in range(nb): # do bootstrap data sets - plot first 25 as dashed red line
            if n%50==0:print(n)
            Taus=[] # set up lists for taus
            PDs=pmag.pseudo(DIDDs)
            if kappa!=0:
                for k in range(len(PDs)):
                    d,i=pmag.fshdev(kappa)
                    dipdir,dip=pmag.dodirot(d,i,PDs[k][2],PDs[k][3])
                    PDs[k][2]=dipdir
                    PDs[k][3]=dip
            for perc in Percs:
                tilt=numpy.array([1.,1.,1.,0.01*perc])
                D,I=pmag.dotilt_V(PDs*tilt)
                TCs=numpy.array([D,I]).transpose()
                ppars=pmag.doprinc(TCs) # get principal directions
                Taus.append(ppars['tau1'])
            if n<25:pylab.plot(Percs,Taus,'r--')
            Untilt.append(Percs[Taus.index(numpy.max(Taus))]) # tilt that gives maximum tau
            Cdf.append(float(n) / float(nb))
    pylab.plot(Percs,Taus,'k')
    pylab.xlabel('% Untilting')
    pylab.ylabel('tau_1 (red), CDF (green)')
    Untilt.sort() # now for CDF of tilt of maximum tau
    pylab.plot(Untilt,Cdf,'g')
    lower=int(.025*nb)
    upper=int(.975*nb)
    pylab.axvline(x=Untilt[lower],ymin=0,ymax=1,linewidth=1,linestyle='--')
    pylab.axvline(x=Untilt[upper],ymin=0,ymax=1,linewidth=1,linestyle='--')
    tit= '%i - %i %s'%(Untilt[lower],Untilt[upper],'Percent Unfolding')
    print(tit)
    print('range of all bootstrap samples: ', Untilt[0], ' - ', Untilt[-1])
    pylab.title(tit)
    outstring= '%i - %i; %i\n'%(Untilt[lower],Untilt[upper],nb)
    if outfile!="":outfile.write(outstring)
    files={}
    for key in list(PLTS.keys()):
        files[key]=('foldtest_'+'%s'%(key.strip()[:2])+'.'+fmt)
    if plot==0:
        pmagplotlib.draw_figs(PLTS)
        ans= input('S[a]ve all figures, <Return> to quit   ')
        if ans!='a':
            print("Good bye")
            sys.exit()
    pmagplotlib.save_plots(PLTS,files)
Beispiel #4
0
def main():
    """
    NAME
       foldtest_magic.py

    DESCRIPTION
       does a fold test (Tauxe, 2010) on data

    INPUT FORMAT
       pmag_specimens format file, er_samples.txt format file (for bedding)

    SYNTAX
       foldtest_magic.py [command line options]

    OPTIONS
        -h prints help message and quits
        -f sites  formatted file [default for 3.0 is sites.txt, for 2.5, pmag_sites.txt]
        -fsa samples  formatted file
        -fsi sites  formatted file
        -exc use criteria to set acceptance criteria (supported only for data model 3)
        -n NB, set number of bootstraps, default is 1000
        -b MIN, MAX, set bounds for untilting, default is -10, 150
        -fmt FMT, specify format - default is svg
        -sav saves plots and quits
        -DM NUM MagIC data model number (2 or 3, default 3)

    OUTPUT
        Geographic: is an equal area projection of the input data in
                    original coordinates
        Stratigraphic: is an equal area projection of the input data in
                    tilt adjusted coordinates
        % Untilting: The dashed (red) curves are representative plots of
                    maximum eigenvalue (tau_1) as a function of untilting
                    The solid line is the cumulative distribution of the
                    % Untilting required to maximize tau for all the
                    bootstrapped data sets.  The dashed vertical lines
                    are 95% confidence bounds on the % untilting that yields
                   the most clustered result (maximum tau_1).
        Command line: prints out the bootstrapped iterations and
                   finally the confidence bounds on optimum untilting.
        If the 95% conf bounds include 0, then a pre-tilt magnetization is indicated
        If the 95% conf bounds include 100, then a post-tilt magnetization is indicated
        If the 95% conf bounds exclude both 0 and 100, syn-tilt magnetization is
                possible as is vertical axis rotation or other pathologies

    """
    if '-h' in sys.argv:  # check if help is needed
        print(main.__doc__)
        sys.exit()  # graceful quit

    kappa = 0

    dir_path = pmag.get_named_arg("-WD", ".")
    nboot = int(float(pmag.get_named_arg("-n", 1000)))     # number of bootstraps
    fmt = pmag.get_named_arg("-fmt", "svg")
    data_model_num = int(float(pmag.get_named_arg("-DM", 3)))
    if data_model_num == 3:
        infile = pmag.get_named_arg("-f", 'sites.txt')
        orfile = 'samples.txt'
        site_col = 'site'
        dec_col = 'dir_dec'
        inc_col = 'dir_inc'
        tilt_col = 'dir_tilt_correction'
        dipkey, azkey = 'bed_dip', 'bed_dip_direction'
        crit_col = 'criterion'
        critfile = 'criteria.txt'
    else:
        infile = pmag.get_named_arg("-f", 'pmag_sites.txt')
        orfile = 'er_samples.txt'
        site_col = 'er_site_name'
        dec_col = 'site_dec'
        inc_col = 'site_inc'
        tilt_col = 'site_tilt_correction'
        dipkey, azkey = 'sample_bed_dip', 'sample_bed_dip_direction'
        crit_col = 'pmag_criteria_code'
        critfile = 'pmag_criteria.txt'
    if '-sav' in sys.argv:
        plot = 1
    else:
        plot = 0
    if '-b' in sys.argv:
        ind = sys.argv.index('-b')
        untilt_min = int(sys.argv[ind+1])
        untilt_max = int(sys.argv[ind+2])
    else:
        untilt_min, untilt_max = -10, 150
    if '-fsa' in sys.argv:
        orfile = pmag.get_named_arg("-fsa", "")
    elif '-fsi' in sys.argv:
        orfile = pmag.get_named_arg("-fsi", "")
        if data_model_num == 3:
            dipkey, azkey = 'bed_dip', 'bed_dip_direction'
        else:
            dipkey, azkey = 'site_bed_dip', 'site_bed_dip_direction'
    else:
        if data_model_num == 3:
            orfile = 'sites.txt'
        else:
            orfile = 'pmag_sites.txt'
    orfile = pmag.resolve_file_name(orfile, dir_path)
    infile = pmag.resolve_file_name(infile, dir_path)
    critfile = pmag.resolve_file_name(critfile, dir_path)
    df = pd.read_csv(infile, sep='\t', header=1)
    # keep only records with tilt_col
    data = df.copy()
    data = data[data[tilt_col].notnull()]
    data = data.where(data.notnull(), "")
    # turn into pmag data list
    data = list(data.T.apply(dict))
    # get orientation data
    if data_model_num == 3:
        # often orientation will be in infile (sites table)
        if os.path.split(orfile)[1] == os.path.split(infile)[1]:
            ordata = df[df[azkey].notnull()]
            ordata = ordata[ordata[dipkey].notnull()]
            ordata = list(ordata.T.apply(dict))
        # sometimes orientation might be in a sample file instead
        else:
            ordata = pd.read_csv(orfile, sep='\t', header=1)
            ordata = list(ordata.T.apply(dict))
    else:
        ordata, file_type = pmag.magic_read(orfile)

    if '-exc' in sys.argv:
        crits, file_type = pmag.magic_read(critfile)
        SiteCrits = []
        for crit in crits:
            if crit[crit_col] == "DE-SITE":
                SiteCrits.append(crit)
                #break

# get to work
#
    PLTS = {'geo': 1, 'strat': 2, 'taus': 3}  # make plot dictionary
    if not set_env.IS_WIN:
        pmagplotlib.plot_init(PLTS['geo'], 5, 5)
        pmagplotlib.plot_init(PLTS['strat'], 5, 5)
        pmagplotlib.plot_init(PLTS['taus'], 5, 5)
    if data_model_num == 2:
        GEOrecs = pmag.get_dictitem(data, tilt_col, '0', 'T')
    else:
        GEOrecs = data
    if len(GEOrecs) > 0:  # have some geographic data
        num_dropped = 0
        DIDDs = []  # set up list for dec inc  dip_direction, dip
        for rec in GEOrecs:   # parse data
            dip, dip_dir = 0, -1
            Dec = float(rec[dec_col])
            Inc = float(rec[inc_col])
            orecs = pmag.get_dictitem(
                ordata, site_col, rec[site_col], 'T')
            if len(orecs) > 0:
                if orecs[0][azkey] != "":
                    dip_dir = float(orecs[0][azkey])
                if orecs[0][dipkey] != "":
                    dip = float(orecs[0][dipkey])
            if dip != 0 and dip_dir != -1:
                if '-exc' in sys.argv:
                    keep = 1
                    for site_crit in SiteCrits:
                        crit_name = site_crit['table_column'].split('.')[1]
                        if crit_name and crit_name in rec.keys() and rec[crit_name]:
                            # get the correct operation (<, >=, =, etc.)
                            op = OPS[site_crit['criterion_operation']]
                            # then make sure the site record passes
                            if op(float(rec[crit_name]), float(site_crit['criterion_value'])):
                                keep = 0

                    if keep == 1:
                        DIDDs.append([Dec, Inc, dip_dir, dip])
                    else:
                        num_dropped += 1
                else:
                    DIDDs.append([Dec, Inc, dip_dir, dip])
        if num_dropped:
            print("-W- Dropped {} records because each failed one or more criteria".format(num_dropped))
    else:
        print('no geographic directional data found')
        sys.exit()

    pmagplotlib.plot_eq(PLTS['geo'], DIDDs, 'Geographic')
    data = np.array(DIDDs)
    D, I = pmag.dotilt_V(data)
    TCs = np.array([D, I]).transpose()
    pmagplotlib.plot_eq(PLTS['strat'], TCs, 'Stratigraphic')
    if plot == 0:
        pmagplotlib.draw_figs(PLTS)
    Percs = list(range(untilt_min, untilt_max))
    Cdf, Untilt = [], []
    plt.figure(num=PLTS['taus'])
    print('doing ', nboot, ' iterations...please be patient.....')
    for n in range(nboot):  # do bootstrap data sets - plot first 25 as dashed red line
        if n % 50 == 0:
            print(n)
        Taus = []  # set up lists for taus
        PDs = pmag.pseudo(DIDDs)
        if kappa != 0:
            for k in range(len(PDs)):
                d, i = pmag.fshdev(kappa)
                dipdir, dip = pmag.dodirot(d, i, PDs[k][2], PDs[k][3])
                PDs[k][2] = dipdir
                PDs[k][3] = dip
        for perc in Percs:
            tilt = np.array([1., 1., 1., 0.01*perc])
            D, I = pmag.dotilt_V(PDs*tilt)
            TCs = np.array([D, I]).transpose()
            ppars = pmag.doprinc(TCs)  # get principal directions
            Taus.append(ppars['tau1'])
        if n < 25:
            plt.plot(Percs, Taus, 'r--')
        # tilt that gives maximum tau
        Untilt.append(Percs[Taus.index(np.max(Taus))])
        Cdf.append(float(n) / float(nboot))
    plt.plot(Percs, Taus, 'k')
    plt.xlabel('% Untilting')
    plt.ylabel('tau_1 (red), CDF (green)')
    Untilt.sort()  # now for CDF of tilt of maximum tau
    plt.plot(Untilt, Cdf, 'g')
    lower = int(.025*nboot)
    upper = int(.975*nboot)
    plt.axvline(x=Untilt[lower], ymin=0, ymax=1, linewidth=1, linestyle='--')
    plt.axvline(x=Untilt[upper], ymin=0, ymax=1, linewidth=1, linestyle='--')
    tit = '%i - %i %s' % (Untilt[lower], Untilt[upper], 'Percent Unfolding')
    print(tit)
    plt.title(tit)
    if plot == 0:
        pmagplotlib.draw_figs(PLTS)
        ans = input('S[a]ve all figures, <Return> to quit  \n ')
        if ans != 'a':
            print("Good bye")
            sys.exit()
    files = {}
    for key in list(PLTS.keys()):
        files[key] = ('foldtest_'+'%s' % (key.strip()[:2])+'.'+fmt)
    pmagplotlib.save_plots(PLTS, files)
Beispiel #5
0
def main():
    """
    NAME
       foldtest_magic.py

    DESCRIPTION
       does a fold test (Tauxe, 2010) on data

    INPUT FORMAT
       pmag_specimens format file, er_samples.txt format file (for bedding)

    SYNTAX
       foldtest_magic.py [command line options]

    OPTIONS
        -h prints help message and quits
        -f sites  formatted file [default for 3.0 is sites.txt, for 2.5, pmag_sites.txt]
        -fsa samples  formatted file
        -fsi sites  formatted file
        -exc use criteria to set acceptance criteria (supported only for data model 3)
        -n NB, set number of bootstraps, default is 1000
        -b MIN, MAX, set bounds for untilting, default is -10, 150
        -fmt FMT, specify format - default is svg
        -sav saves plots and quits
        -DM NUM MagIC data model number (2 or 3, default 3)

    OUTPUT
        Geographic: is an equal area projection of the input data in
                    original coordinates
        Stratigraphic: is an equal area projection of the input data in
                    tilt adjusted coordinates
        % Untilting: The dashed (red) curves are representative plots of
                    maximum eigenvalue (tau_1) as a function of untilting
                    The solid line is the cumulative distribution of the
                    % Untilting required to maximize tau for all the
                    bootstrapped data sets.  The dashed vertical lines
                    are 95% confidence bounds on the % untilting that yields
                   the most clustered result (maximum tau_1).
        Command line: prints out the bootstrapped iterations and
                   finally the confidence bounds on optimum untilting.
        If the 95% conf bounds include 0, then a pre-tilt magnetization is indicated
        If the 95% conf bounds include 100, then a post-tilt magnetization is indicated
        If the 95% conf bounds exclude both 0 and 100, syn-tilt magnetization is
                possible as is vertical axis rotation or other pathologies

    """
    if '-h' in sys.argv:  # check if help is needed
        print(main.__doc__)
        sys.exit()  # graceful quit

    kappa = 0

    dir_path = pmag.get_named_arg("-WD", ".")
    nboot = int(float(pmag.get_named_arg("-n", 1000)))  # number of bootstraps
    fmt = pmag.get_named_arg("-fmt", "svg")
    data_model_num = int(float(pmag.get_named_arg("-DM", 3)))
    if data_model_num == 3:
        infile = pmag.get_named_arg("-f", 'sites.txt')
        orfile = 'samples.txt'
        site_col = 'site'
        dec_col = 'dir_dec'
        inc_col = 'dir_inc'
        tilt_col = 'dir_tilt_correction'
        dipkey, azkey = 'bed_dip', 'bed_dip_direction'
        crit_col = 'criterion'
        critfile = 'criteria.txt'
    else:
        infile = pmag.get_named_arg("-f", 'pmag_sites.txt')
        orfile = 'er_samples.txt'
        site_col = 'er_site_name'
        dec_col = 'site_dec'
        inc_col = 'site_inc'
        tilt_col = 'site_tilt_correction'
        dipkey, azkey = 'sample_bed_dip', 'sample_bed_dip_direction'
        crit_col = 'pmag_criteria_code'
        critfile = 'pmag_criteria.txt'
    if '-sav' in sys.argv:
        plot = 1
    else:
        plot = 0
    if '-b' in sys.argv:
        ind = sys.argv.index('-b')
        untilt_min = int(sys.argv[ind + 1])
        untilt_max = int(sys.argv[ind + 2])
    else:
        untilt_min, untilt_max = -10, 150
    if '-fsa' in sys.argv:
        orfile = pmag.get_named_arg("-fsa", "")
    elif '-fsi' in sys.argv:
        orfile = pmag.get_named_arg("-fsi", "")
        if data_model_num == 3:
            dipkey, azkey = 'bed_dip', 'bed_dip_direction'
        else:
            dipkey, azkey = 'site_bed_dip', 'site_bed_dip_direction'
    else:
        if data_model_num == 3:
            orfile = 'sites.txt'
        else:
            orfile = 'pmag_sites.txt'
    orfile = pmag.resolve_file_name(orfile, dir_path)
    infile = pmag.resolve_file_name(infile, dir_path)
    critfile = pmag.resolve_file_name(critfile, dir_path)
    df = pd.read_csv(infile, sep='\t', header=1)
    # keep only records with tilt_col
    data = df.copy()
    data = data[data[tilt_col].notnull()]
    data = data.where(data.notnull(), "")
    # turn into pmag data list
    data = list(data.T.apply(dict))
    # get orientation data
    if data_model_num == 3:
        # often orientation will be in infile (sites table)
        if os.path.split(orfile)[1] == os.path.split(infile)[1]:
            ordata = df[df[azkey].notnull()]
            ordata = ordata[ordata[dipkey].notnull()]
            ordata = list(ordata.T.apply(dict))
        # sometimes orientation might be in a sample file instead
        else:
            ordata = pd.read_csv(orfile, sep='\t', header=1)
            ordata = list(ordata.T.apply(dict))
    else:
        ordata, file_type = pmag.magic_read(orfile)

    if '-exc' in sys.argv:
        crits, file_type = pmag.magic_read(critfile)
        SiteCrits = []
        for crit in crits:
            if crit[crit_col] == "DE-SITE":
                SiteCrits.append(crit)
                #break


# get to work
#
    PLTS = {'geo': 1, 'strat': 2, 'taus': 3}  # make plot dictionary
    if not set_env.IS_WIN:
        pmagplotlib.plot_init(PLTS['geo'], 5, 5)
        pmagplotlib.plot_init(PLTS['strat'], 5, 5)
        pmagplotlib.plot_init(PLTS['taus'], 5, 5)
    if data_model_num == 2:
        GEOrecs = pmag.get_dictitem(data, tilt_col, '0', 'T')
    else:
        GEOrecs = data
    if len(GEOrecs) > 0:  # have some geographic data
        num_dropped = 0
        DIDDs = []  # set up list for dec inc  dip_direction, dip
        for rec in GEOrecs:  # parse data
            dip, dip_dir = 0, -1
            Dec = float(rec[dec_col])
            Inc = float(rec[inc_col])
            orecs = pmag.get_dictitem(ordata, site_col, rec[site_col], 'T')
            if len(orecs) > 0:
                if orecs[0][azkey] != "":
                    dip_dir = float(orecs[0][azkey])
                if orecs[0][dipkey] != "":
                    dip = float(orecs[0][dipkey])
            if dip != 0 and dip_dir != -1:
                if '-exc' in sys.argv:
                    keep = 1
                    for site_crit in SiteCrits:
                        crit_name = site_crit['table_column'].split('.')[1]
                        if crit_name and crit_name in rec.keys(
                        ) and rec[crit_name]:
                            # get the correct operation (<, >=, =, etc.)
                            op = OPS[site_crit['criterion_operation']]
                            # then make sure the site record passes
                            if op(float(rec[crit_name]),
                                  float(site_crit['criterion_value'])):
                                keep = 0

                    if keep == 1:
                        DIDDs.append([Dec, Inc, dip_dir, dip])
                    else:
                        num_dropped += 1
                else:
                    DIDDs.append([Dec, Inc, dip_dir, dip])
        if num_dropped:
            print(
                "-W- Dropped {} records because each failed one or more criteria"
                .format(num_dropped))
    else:
        print('no geographic directional data found')
        sys.exit()

    pmagplotlib.plot_eq(PLTS['geo'], DIDDs, 'Geographic')
    data = np.array(DIDDs)
    D, I = pmag.dotilt_V(data)
    TCs = np.array([D, I]).transpose()
    pmagplotlib.plot_eq(PLTS['strat'], TCs, 'Stratigraphic')
    if plot == 0:
        pmagplotlib.draw_figs(PLTS)
    Percs = list(range(untilt_min, untilt_max))
    Cdf, Untilt = [], []
    plt.figure(num=PLTS['taus'])
    print('doing ', nboot, ' iterations...please be patient.....')
    for n in range(
            nboot
    ):  # do bootstrap data sets - plot first 25 as dashed red line
        if n % 50 == 0:
            print(n)
        Taus = []  # set up lists for taus
        PDs = pmag.pseudo(DIDDs)
        if kappa != 0:
            for k in range(len(PDs)):
                d, i = pmag.fshdev(kappa)
                dipdir, dip = pmag.dodirot(d, i, PDs[k][2], PDs[k][3])
                PDs[k][2] = dipdir
                PDs[k][3] = dip
        for perc in Percs:
            tilt = np.array([1., 1., 1., 0.01 * perc])
            D, I = pmag.dotilt_V(PDs * tilt)
            TCs = np.array([D, I]).transpose()
            ppars = pmag.doprinc(TCs)  # get principal directions
            Taus.append(ppars['tau1'])
        if n < 25:
            plt.plot(Percs, Taus, 'r--')
        # tilt that gives maximum tau
        Untilt.append(Percs[Taus.index(np.max(Taus))])
        Cdf.append(float(n) / float(nboot))
    plt.plot(Percs, Taus, 'k')
    plt.xlabel('% Untilting')
    plt.ylabel('tau_1 (red), CDF (green)')
    Untilt.sort()  # now for CDF of tilt of maximum tau
    plt.plot(Untilt, Cdf, 'g')
    lower = int(.025 * nboot)
    upper = int(.975 * nboot)
    plt.axvline(x=Untilt[lower], ymin=0, ymax=1, linewidth=1, linestyle='--')
    plt.axvline(x=Untilt[upper], ymin=0, ymax=1, linewidth=1, linestyle='--')
    tit = '%i - %i %s' % (Untilt[lower], Untilt[upper], 'Percent Unfolding')
    print(tit)
    plt.title(tit)
    if plot == 0:
        pmagplotlib.draw_figs(PLTS)
        ans = input('S[a]ve all figures, <Return> to quit  \n ')
        if ans != 'a':
            print("Good bye")
            sys.exit()
    files = {}
    for key in list(PLTS.keys()):
        files[key] = ('foldtest_' + '%s' % (key.strip()[:2]) + '.' + fmt)
    pmagplotlib.save_plots(PLTS, files)
Beispiel #6
0
def main():
    """
    NAME
       foldtest.py

    DESCRIPTION
       does a fold test (Tauxe, 2010) on data

    INPUT FORMAT
       dec inc dip_direction dip

    SYNTAX
       foldtest.py [command line options]

    OPTIONS
        -h prints help message and quits
        -f FILE file with input data
        -F FILE for confidence bounds on fold test
        -u ANGLE (circular standard deviation) for uncertainty on bedding poles
        -b MIN MAX bounds for quick search of percent untilting [default is -10 to 150%]
        -n NB  number of bootstrap samples [default is 1000]
        -fmt FMT, specify format - default is svg
        -sav  save figures and quit
    INPUT FILE
	Dec Inc Dip_Direction Dip  in space delimited file

    OUTPUT PLOTS
        Geographic: is an equal area projection of the input data in
                    original coordinates
        Stratigraphic: is an equal area projection of the input data in
                    tilt adjusted coordinates
        % Untilting: The dashed (red) curves are representative plots of
                    maximum eigenvalue (tau_1) as a function of untilting
                    The solid line is the cumulative distribution of the
                    % Untilting required to maximize tau for all the
                    bootstrapped data sets.  The dashed vertical lines
                    are 95% confidence bounds on the % untilting that yields
                   the most clustered result (maximum tau_1).
        Command line: prints out the bootstrapped iterations and
                   finally the confidence bounds on optimum untilting.
        If the 95% conf bounds include 0, then a post-tilt magnetization is indicated
        If the 95% conf bounds include 100, then a pre-tilt magnetization is indicated
        If the 95% conf bounds exclude both 0 and 100, syn-tilt magnetization is
                possible as is vertical axis rotation or other pathologies
        Geographic: is an equal area projection of the input data in

    OPTIONAL OUTPUT FILE:
       The output file has the % untilting within the 95% confidence bounds
nd the number of bootstrap samples
    """
    kappa = 0
    fmt, plot = 'svg', 0
    nb = 1000  # number of bootstraps
    min, max = -10, 150
    if '-h' in sys.argv:  # check if help is needed
        print main.__doc__
        sys.exit()  # graceful quit
    if '-F' in sys.argv:
        ind = sys.argv.index('-F')
        outfile = open(sys.argv[ind + 1], 'w')
    else:
        outfile = ""
    if '-f' in sys.argv:
        ind = sys.argv.index('-f')
        file = sys.argv[ind + 1]
        DIDDs = numpy.loadtxt(file)
    else:
        print main.__doc__
        sys.exit()
    if '-fmt' in sys.argv:
        ind = sys.argv.index('-fmt')
        fmt = sys.argv[ind + 1]
    if '-sav' in sys.argv: plot = 1
    if '-b' in sys.argv:
        ind = sys.argv.index('-b')
        min = int(sys.argv[ind + 1])
        max = int(sys.argv[ind + 2])
    if '-n' in sys.argv:
        ind = sys.argv.index('-n')
        nb = int(sys.argv[ind + 1])
    if '-u' in sys.argv:
        ind = sys.argv.index('-u')
        csd = float(sys.argv[ind + 1])
        kappa = (81. / csd)**2
    #
    # get to work
    #
    PLTS = {'geo': 1, 'strat': 2, 'taus': 3}  # make plot dictionary
    pmagplotlib.plot_init(PLTS['geo'], 5, 5)
    pmagplotlib.plot_init(PLTS['strat'], 5, 5)
    pmagplotlib.plot_init(PLTS['taus'], 5, 5)
    pmagplotlib.plotEQ(PLTS['geo'], DIDDs, 'Geographic')
    D, I = pmag.dotilt_V(DIDDs)
    TCs = numpy.array([D, I]).transpose()
    pmagplotlib.plotEQ(PLTS['strat'], TCs, 'Stratigraphic')
    if plot == 0: pmagplotlib.drawFIGS(PLTS)
    Percs = range(min, max)
    Cdf, Untilt = [], []
    pylab.figure(num=PLTS['taus'])
    print 'doing ', nb, ' iterations...please be patient.....'
    for n in range(
            nb):  # do bootstrap data sets - plot first 25 as dashed red line
        if n % 50 == 0: print n
        Taus = []  # set up lists for taus
        PDs = pmag.pseudo(DIDDs)
        if kappa != 0:
            for k in range(len(PDs)):
                d, i = pmag.fshdev(kappa)
                dipdir, dip = pmag.dodirot(d, i, PDs[k][2], PDs[k][3])
                PDs[k][2] = dipdir
                PDs[k][3] = dip
        for perc in Percs:
            tilt = numpy.array([1., 1., 1., 0.01 * perc])
            D, I = pmag.dotilt_V(PDs * tilt)
            TCs = numpy.array([D, I]).transpose()
            ppars = pmag.doprinc(TCs)  # get principal directions
            Taus.append(ppars['tau1'])
        if n < 25: pylab.plot(Percs, Taus, 'r--')
        Untilt.append(Percs[Taus.index(
            numpy.max(Taus))])  # tilt that gives maximum tau
        Cdf.append(float(n) / float(nb))
    pylab.plot(Percs, Taus, 'k')
    pylab.xlabel('% Untilting')
    pylab.ylabel('tau_1 (red), CDF (green)')
    Untilt.sort()  # now for CDF of tilt of maximum tau
    pylab.plot(Untilt, Cdf, 'g')
    lower = int(.025 * nb)
    upper = int(.975 * nb)
    pylab.axvline(x=Untilt[lower], ymin=0, ymax=1, linewidth=1, linestyle='--')
    pylab.axvline(x=Untilt[upper], ymin=0, ymax=1, linewidth=1, linestyle='--')
    tit = '%i - %i %s' % (Untilt[lower], Untilt[upper], 'Percent Unfolding')
    print tit
    print 'range of all bootstrap samples: ', Untilt[0], ' - ', Untilt[-1]
    pylab.title(tit)
    outstring = '%i - %i; %i\n' % (Untilt[lower], Untilt[upper], nb)
    if outfile != "": outfile.write(outstring)
    files = {}
    for key in PLTS.keys():
        files[key] = ('foldtest_' + '%s' % (key.strip()[:2]) + '.' + fmt)
    if plot == 0:
        pmagplotlib.drawFIGS(PLTS)
        ans = raw_input('S[a]ve all figures, <Return> to quit   ')
        if ans != 'a':
            print "Good bye"
            sys.exit()
    pmagplotlib.saveP(PLTS, files)
Beispiel #7
0
def main():
    """
    NAME
       foldtest_magic.py

    DESCRIPTION
       does a fold test (Tauxe, 2010) on data

    INPUT FORMAT
       pmag_specimens format file, er_samples.txt format file (for bedding)

    SYNTAX
       foldtest_magic.py [command line options]

    OPTIONS
        -h prints help message and quits
        -f pmag_sites  formatted file [default is pmag_sites.txt]
        -fsa er_samples  formatted file [default is er_samples.txt]
        -fsi er_sites  formatted file 
        -exc use pmag_criteria.txt to set acceptance criteria
        -n NB, set number of bootstraps, default is 1000
        -b MIN, MAX, set bounds for untilting, default is -10, 150
        -fmt FMT, specify format - default is svg
        -sav saves plots and quits
    
    OUTPUT
        Geographic: is an equal area projection of the input data in 
                    original coordinates
        Stratigraphic: is an equal area projection of the input data in 
                    tilt adjusted coordinates
        % Untilting: The dashed (red) curves are representative plots of 
                    maximum eigenvalue (tau_1) as a function of untilting
                    The solid line is the cumulative distribution of the
                    % Untilting required to maximize tau for all the 
                    bootstrapped data sets.  The dashed vertical lines
                    are 95% confidence bounds on the % untilting that yields 
                   the most clustered result (maximum tau_1).  
        Command line: prints out the bootstrapped iterations and
                   finally the confidence bounds on optimum untilting.
        If the 95% conf bounds include 0, then a pre-tilt magnetization is indicated
        If the 95% conf bounds include 100, then a post-tilt magnetization is indicated
        If the 95% conf bounds exclude both 0 and 100, syn-tilt magnetization is
                possible as is vertical axis rotation or other pathologies

    """
    kappa = 0
    nb = 1000  # number of bootstraps
    min, max = -10, 150
    dir_path = '.'
    infile, orfile = 'pmag_sites.txt', 'er_samples.txt'
    critfile = 'pmag_criteria.txt'
    dipkey, azkey = 'sample_bed_dip', 'sample_bed_dip_direction'
    fmt = 'svg'
    plot = 0
    if '-WD' in sys.argv:
        ind = sys.argv.index('-WD')
        dir_path = sys.argv[ind + 1]
    if '-h' in sys.argv:  # check if help is needed
        print(main.__doc__)
        sys.exit()  # graceful quit
    if '-n' in sys.argv:
        ind = sys.argv.index('-n')
        nb = int(sys.argv[ind + 1])
    if '-fmt' in sys.argv:
        ind = sys.argv.index('-fmt')
        fmt = sys.argv[ind + 1]
    if '-sav' in sys.argv: plot = 1
    if '-b' in sys.argv:
        ind = sys.argv.index('-b')
        min = int(sys.argv[ind + 1])
        max = int(sys.argv[ind + 2])
    if '-f' in sys.argv:
        ind = sys.argv.index('-f')
        infile = sys.argv[ind + 1]
    if '-fsa' in sys.argv:
        ind = sys.argv.index('-fsa')
        orfile = sys.argv[ind + 1]
    elif '-fsi' in sys.argv:
        ind = sys.argv.index('-fsi')
        orfile = sys.argv[ind + 1]
        dipkey, azkey = 'site_bed_dip', 'site_bed_dip_direction'
    orfile = dir_path + '/' + orfile
    infile = dir_path + '/' + infile
    critfile = dir_path + '/' + critfile
    data, file_type = pmag.magic_read(infile)
    ordata, file_type = pmag.magic_read(orfile)
    if '-exc' in sys.argv:
        crits, file_type = pmag.magic_read(critfile)
        for crit in crits:
            if crit['pmag_criteria_code'] == "DE-SITE":
                SiteCrit = crit
                break


# get to work
#
    PLTS = {'geo': 1, 'strat': 2, 'taus': 3}  # make plot dictionary
    pmagplotlib.plot_init(PLTS['geo'], 5, 5)
    pmagplotlib.plot_init(PLTS['strat'], 5, 5)
    pmagplotlib.plot_init(PLTS['taus'], 5, 5)
    GEOrecs = pmag.get_dictitem(data, 'site_tilt_correction', '0', 'T')
    if len(GEOrecs) > 0:  # have some geographic data
        DIDDs = []  # set up list for dec inc  dip_direction, dip
        for rec in GEOrecs:  # parse data
            dip, dip_dir = 0, -1
            Dec = float(rec['site_dec'])
            Inc = float(rec['site_inc'])
            orecs = pmag.get_dictitem(ordata, 'er_site_name',
                                      rec['er_site_name'], 'T')
            if len(orecs) > 0:
                if orecs[0][azkey] != "": dip_dir = float(orecs[0][azkey])
                if orecs[0][dipkey] != "": dip = float(orecs[0][dipkey])
            if dip != 0 and dip_dir != -1:
                if '-exc' in sys.argv:
                    keep = 1
                    for key in list(SiteCrit.keys()):
                        if 'site' in key and SiteCrit[key] != "" and rec[
                                key] != "" and key != 'site_alpha95':
                            if float(rec[key]) < float(SiteCrit[key]):
                                keep = 0
                                print(rec['er_site_name'], key, rec[key])
                        if key == 'site_alpha95' and SiteCrit[
                                key] != "" and rec[key] != "":
                            if float(rec[key]) > float(SiteCrit[key]):
                                keep = 0
                    if keep == 1: DIDDs.append([Dec, Inc, dip_dir, dip])
                else:
                    DIDDs.append([Dec, Inc, dip_dir, dip])
    else:
        print('no geographic directional data found')
        sys.exit()
    pmagplotlib.plotEQ(PLTS['geo'], DIDDs, 'Geographic')
    data = numpy.array(DIDDs)
    D, I = pmag.dotilt_V(data)
    TCs = numpy.array([D, I]).transpose()
    pmagplotlib.plotEQ(PLTS['strat'], TCs, 'Stratigraphic')
    if plot == 0: pmagplotlib.drawFIGS(PLTS)
    Percs = list(range(min, max))
    Cdf, Untilt = [], []
    pylab.figure(num=PLTS['taus'])
    print('doing ', nb, ' iterations...please be patient.....')
    for n in range(
            nb):  # do bootstrap data sets - plot first 25 as dashed red line
        if n % 50 == 0: print(n)
        Taus = []  # set up lists for taus
        PDs = pmag.pseudo(DIDDs)
        if kappa != 0:
            for k in range(len(PDs)):
                d, i = pmag.fshdev(kappa)
                dipdir, dip = pmag.dodirot(d, i, PDs[k][2], PDs[k][3])
                PDs[k][2] = dipdir
                PDs[k][3] = dip
        for perc in Percs:
            tilt = numpy.array([1., 1., 1., 0.01 * perc])
            D, I = pmag.dotilt_V(PDs * tilt)
            TCs = numpy.array([D, I]).transpose()
            ppars = pmag.doprinc(TCs)  # get principal directions
            Taus.append(ppars['tau1'])
        if n < 25: pylab.plot(Percs, Taus, 'r--')
        Untilt.append(Percs[Taus.index(
            numpy.max(Taus))])  # tilt that gives maximum tau
        Cdf.append(old_div(float(n), float(nb)))
    pylab.plot(Percs, Taus, 'k')
    pylab.xlabel('% Untilting')
    pylab.ylabel('tau_1 (red), CDF (green)')
    Untilt.sort()  # now for CDF of tilt of maximum tau
    pylab.plot(Untilt, Cdf, 'g')
    lower = int(.025 * nb)
    upper = int(.975 * nb)
    pylab.axvline(x=Untilt[lower], ymin=0, ymax=1, linewidth=1, linestyle='--')
    pylab.axvline(x=Untilt[upper], ymin=0, ymax=1, linewidth=1, linestyle='--')
    tit = '%i - %i %s' % (Untilt[lower], Untilt[upper], 'Percent Unfolding')
    print(tit)
    pylab.title(tit)
    if plot == 0:
        pmagplotlib.drawFIGS(PLTS)
        ans = input('S[a]ve all figures, <Return> to quit  \n ')
        if ans != 'a':
            print("Good bye")
            sys.exit()
    files = {}
    for key in list(PLTS.keys()):
        files[key] = ('foldtest_' + '%s' % (key.strip()[:2]) + '.' + fmt)
    pmagplotlib.saveP(PLTS, files)
def main():
    """
    NAME
       foldtest_magic.py

    DESCRIPTION
       does a fold test (Tauxe, 2010) on data

    INPUT FORMAT
       pmag_specimens format file, er_samples.txt format file (for bedding)

    SYNTAX
       foldtest_magic.py [command line options]

    OPTIONS
        -h prints help message and quits
        -f pmag_sites  formatted file [default is pmag_sites.txt]
        -fsa er_samples  formatted file [default is er_samples.txt]
        -fsi er_sites  formatted file 
        -exc use pmag_criteria.txt to set acceptance criteria
        -n NB, set number of bootstraps, default is 1000
        -b MIN, MAX, set bounds for untilting, default is -10, 150
        -fmt FMT, specify format - default is svg
        -sav saves plots and quits
    
    OUTPUT
        Geographic: is an equal area projection of the input data in 
                    original coordinates
        Stratigraphic: is an equal area projection of the input data in 
                    tilt adjusted coordinates
        % Untilting: The dashed (red) curves are representative plots of 
                    maximum eigenvalue (tau_1) as a function of untilting
                    The solid line is the cumulative distribution of the
                    % Untilting required to maximize tau for all the 
                    bootstrapped data sets.  The dashed vertical lines
                    are 95% confidence bounds on the % untilting that yields 
                   the most clustered result (maximum tau_1).  
        Command line: prints out the bootstrapped iterations and
                   finally the confidence bounds on optimum untilting.
        If the 95% conf bounds include 0, then a pre-tilt magnetization is indicated
        If the 95% conf bounds include 100, then a post-tilt magnetization is indicated
        If the 95% conf bounds exclude both 0 and 100, syn-tilt magnetization is
                possible as is vertical axis rotation or other pathologies

    """
    kappa=0
    nb=1000 # number of bootstraps
    min,max=-10,150
    dir_path='.'
    infile,orfile='pmag_sites.txt','er_samples.txt'
    critfile='pmag_criteria.txt'
    dipkey,azkey='sample_bed_dip','sample_bed_dip_direction'
    fmt='svg'
    plot=0
    if '-WD' in sys.argv:
        ind=sys.argv.index('-WD')
        dir_path=sys.argv[ind+1]
    if '-h' in sys.argv: # check if help is needed
        print main.__doc__
        sys.exit() # graceful quit
    if '-n' in sys.argv:
        ind=sys.argv.index('-n')
        nb=int(sys.argv[ind+1])
    if '-fmt' in sys.argv:
        ind=sys.argv.index('-fmt')
        fmt=sys.argv[ind+1]
    if '-sav' in sys.argv:plot=1
    if '-b' in sys.argv:
        ind=sys.argv.index('-b')
        min=int(sys.argv[ind+1])
        max=int(sys.argv[ind+2])
    if '-f' in sys.argv:
        ind=sys.argv.index('-f')
        infile=sys.argv[ind+1] 
    if '-fsa' in sys.argv:
        ind=sys.argv.index('-fsa')
        orfile=sys.argv[ind+1] 
    elif '-fsi' in sys.argv:
        ind=sys.argv.index('-fsi')
        orfile=sys.argv[ind+1] 
        dipkey,azkey='site_bed_dip','site_bed_dip_direction'
    orfile=dir_path+'/'+orfile
    infile=dir_path+'/'+infile
    critfile=dir_path+'/'+critfile
    data,file_type=pmag.magic_read(infile)
    ordata,file_type=pmag.magic_read(orfile)
    if '-exc' in sys.argv:
        crits,file_type=pmag.magic_read(critfile)
        for crit in crits:
             if crit['pmag_criteria_code']=="DE-SITE":
                 SiteCrit=crit
                 break
# get to work
#
    PLTS={'geo':1,'strat':2,'taus':3} # make plot dictionary
    pmagplotlib.plot_init(PLTS['geo'],5,5)
    pmagplotlib.plot_init(PLTS['strat'],5,5)
    pmagplotlib.plot_init(PLTS['taus'],5,5)
    GEOrecs=pmag.get_dictitem(data,'site_tilt_correction','0','T')
    if len(GEOrecs)>0: # have some geographic data
        DIDDs= [] # set up list for dec inc  dip_direction, dip
        for rec in GEOrecs:   # parse data
            dip,dip_dir=0,-1
            Dec=float(rec['site_dec'])
            Inc=float(rec['site_inc'])
            orecs=pmag.get_dictitem(ordata,'er_site_name',rec['er_site_name'],'T')
            if len(orecs)>0:
                    if orecs[0][azkey]!="":dip_dir=float(orecs[0][azkey])
                    if orecs[0][dipkey]!="":dip=float(orecs[0][dipkey])
            if dip!=0 and dip_dir!=-1:
                if  '-exc' in  sys.argv:
                    keep=1
                    for key in SiteCrit.keys():
                        if 'site' in key  and SiteCrit[key]!="" and rec[key]!="" and key!='site_alpha95':
                            if float(rec[key])<float(SiteCrit[key]): 
                                keep=0
                                print rec['er_site_name'],key,rec[key]
                        if key=='site_alpha95'  and SiteCrit[key]!="" and rec[key]!="":
                            if float(rec[key])>float(SiteCrit[key]): 
                                keep=0
                    if keep==1:  DIDDs.append([Dec,Inc,dip_dir,dip])
                else:
                                DIDDs.append([Dec,Inc,dip_dir,dip])
    else:
        print 'no geographic directional data found'
        sys.exit()
    pmagplotlib.plotEQ(PLTS['geo'],DIDDs,'Geographic')
    data=numpy.array(DIDDs)
    D,I=pmag.dotilt_V(data)
    TCs=numpy.array([D,I]).transpose()
    pmagplotlib.plotEQ(PLTS['strat'],TCs,'Stratigraphic')
    if plot==0:pmagplotlib.drawFIGS(PLTS)
    Percs=range(min,max)
    Cdf,Untilt=[],[]
    pylab.figure(num=PLTS['taus'])
    print 'doing ',nb,' iterations...please be patient.....'
    for n in range(nb): # do bootstrap data sets - plot first 25 as dashed red line
            if n%50==0:print n
            Taus=[] # set up lists for taus
            PDs=pmag.pseudo(DIDDs)
            if kappa!=0:
                for k in range(len(PDs)):
                    d,i=pmag.fshdev(kappa)
                    dipdir,dip=pmag.dodirot(d,i,PDs[k][2],PDs[k][3])
                    PDs[k][2]=dipdir
                    PDs[k][3]=dip
            for perc in Percs:
                tilt=numpy.array([1.,1.,1.,0.01*perc])
                D,I=pmag.dotilt_V(PDs*tilt)
                TCs=numpy.array([D,I]).transpose()
                ppars=pmag.doprinc(TCs) # get principal directions
                Taus.append(ppars['tau1'])
            if n<25:pylab.plot(Percs,Taus,'r--')
            Untilt.append(Percs[Taus.index(numpy.max(Taus))]) # tilt that gives maximum tau
            Cdf.append(float(n)/float(nb))
    pylab.plot(Percs,Taus,'k')
    pylab.xlabel('% Untilting')
    pylab.ylabel('tau_1 (red), CDF (green)')
    Untilt.sort() # now for CDF of tilt of maximum tau
    pylab.plot(Untilt,Cdf,'g')
    lower=int(.025*nb)     
    upper=int(.975*nb)
    pylab.axvline(x=Untilt[lower],ymin=0,ymax=1,linewidth=1,linestyle='--')
    pylab.axvline(x=Untilt[upper],ymin=0,ymax=1,linewidth=1,linestyle='--')
    tit= '%i - %i %s'%(Untilt[lower],Untilt[upper],'Percent Unfolding')
    print tit
    pylab.title(tit)
    if plot==0:
        pmagplotlib.drawFIGS(PLTS)
        ans= raw_input('S[a]ve all figures, <Return> to quit  \n ')
        if ans!='a':
            print "Good bye"
            sys.exit()
    files={}
    for key in PLTS.keys():
        files[key]=('foldtest_'+'%s'%(key.strip()[:2])+'.'+fmt)
    pmagplotlib.saveP(PLTS,files)