Exemple #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))
Exemple #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))
Exemple #3
0
def main():
    """
    NAME
       revtest_MM1990.py

    DESCRIPTION
       calculates Watson's V statistic from input files through Monte Carlo simulation in order to test whether normal and reversed populations could have been drawn from a common mean (equivalent to watsonV.py). Also provides the critical angle between the two sample mean directions and the corresponding McFadden and McElhinny (1990) classification.

    INPUT FORMAT
       takes dec/inc as first two columns in two space delimited files (one file for normal directions, one file for reversed directions).

    SYNTAX
       revtest_MM1990.py [command line options]

    OPTIONS
        -h prints help message and quits
        -f FILE
        -f2 FILE
        -P  (don't plot the Watson V cdf)

    OUTPUT
        Watson's V between the two populations and the Monte Carlo Critical Value Vc.
        M&M1990 angle, critical angle and classification
        Plot of Watson's V CDF from Monte Carlo simulation (red line), V is solid and Vc is dashed.

    """
    D1, D2 = [], []
    plot = 1
    Flip = 1
    if '-h' in sys.argv:  # check if help is needed
        print(main.__doc__)
        sys.exit()  # graceful quit
    if '-P' in sys.argv: plot = 0
    if '-f' in sys.argv:
        ind = sys.argv.index('-f')
        file1 = sys.argv[ind + 1]
    f1 = open(file1, 'r')
    for line in f1.readlines():
        rec = line.split()
        Dec, Inc = float(rec[0]), float(rec[1])
        D1.append([Dec, Inc, 1.])
    f1.close()
    if '-f2' in sys.argv:
        ind = sys.argv.index('-f2')
        file2 = sys.argv[ind + 1]
        f2 = open(file2, 'r')
        print("be patient, your computer is doing 5000 simulations...")
        for line in f2.readlines():
            rec = line.split()
            Dec, Inc = float(rec[0]), float(rec[1])
            D2.append([Dec, Inc, 1.])
        f2.close()
    #take the antipode for the directions in file 2
    D2_flip = []
    for rec in D2:
        d, i = (rec[0] - 180.) % 360., -rec[1]
        D2_flip.append([d, i, 1.])

    pars_1 = pmag.fisher_mean(D1)
    pars_2 = pmag.fisher_mean(D2_flip)

    cart_1 = pmag.dir2cart([pars_1["dec"], pars_1["inc"], pars_1["r"]])
    cart_2 = pmag.dir2cart([pars_2['dec'], pars_2['inc'], pars_2["r"]])
    Sw = pars_1['k'] * pars_1['r'] + pars_2['k'] * pars_2['r']  # k1*r1+k2*r2
    xhat_1 = pars_1['k'] * cart_1[0] + pars_2['k'] * cart_2[0]  # k1*x1+k2*x2
    xhat_2 = pars_1['k'] * cart_1[1] + pars_2['k'] * cart_2[1]  # k1*y1+k2*y2
    xhat_3 = pars_1['k'] * cart_1[2] + pars_2['k'] * cart_2[2]  # k1*z1+k2*z2
    Rw = numpy.sqrt(xhat_1**2 + xhat_2**2 + xhat_3**2)
    V = 2 * (Sw - Rw)
    #
    #keep weighted sum for later when determining the "critical angle" let's save it as Sr (notation of McFadden and McElhinny, 1990)
    #
    Sr = Sw
    #
    # do monte carlo simulation of datasets with same kappas, but common mean
    #
    counter, NumSims = 0, 5000
    Vp = []  # set of Vs from simulations
    for k in range(NumSims):
        #
        # get a set of N1 fisher distributed vectors with k1, calculate fisher stats
        #
        Dirp = []
        for i in range(pars_1["n"]):
            Dirp.append(pmag.fshdev(pars_1["k"]))
        pars_p1 = pmag.fisher_mean(Dirp)
        #
        # get a set of N2 fisher distributed vectors with k2, calculate fisher stats
        #
        Dirp = []
        for i in range(pars_2["n"]):
            Dirp.append(pmag.fshdev(pars_2["k"]))
        pars_p2 = pmag.fisher_mean(Dirp)
        #
        # get the V for these
        #
        Vk = pmag.vfunc(pars_p1, pars_p2)
        Vp.append(Vk)


#
# sort the Vs, get Vcrit (95th percentile one)
#
    Vp.sort()
    k = int(.95 * NumSims)
    Vcrit = Vp[k]
    #
    # equation 18 of McFadden and McElhinny, 1990 calculates the critical value of R (Rwc)
    #
    Rwc = Sr - (old_div(Vcrit, 2))
    #
    #following equation 19 of McFadden and McElhinny (1990) the critical angle is calculated.
    #
    k1 = pars_1['k']
    k2 = pars_2['k']
    R1 = pars_1['r']
    R2 = pars_2['r']
    critical_angle = numpy.degrees(
        numpy.arccos(
            old_div(((Rwc**2) - ((k1 * R1)**2) - ((k2 * R2)**2)),
                    (2 * k1 * R1 * k2 * R2))))
    D1_mean = (pars_1['dec'], pars_1['inc'])
    D2_mean = (pars_2['dec'], pars_2['inc'])
    angle = pmag.angle(D1_mean, D2_mean)
    #
    # print the results of the test
    #
    print("")
    print("Results of Watson V test: ")
    print("")
    print("Watson's V:           " '%.1f' % (V))
    print("Critical value of V:  " '%.1f' % (Vcrit))

    if V < Vcrit:
        print(
            '"Pass": Since V is less than Vcrit, the null hypothesis that the two populations are drawn from distributions that share a common mean direction (antipodal to one another) cannot be rejected.'
        )
    elif V > Vcrit:
        print(
            '"Fail": Since V is greater than Vcrit, the two means can be distinguished at the 95% confidence level.'
        )
    print("")
    print("M&M1990 classification:")
    print("")
    print("Angle between data set means: " '%.1f' % (angle))
    print("Critical angle of M&M1990:   " '%.1f' % (critical_angle))

    if V > Vcrit:
        print("")
    elif V < Vcrit:
        if critical_angle < 5:
            print(
                "The McFadden and McElhinny (1990) classification for this test is: 'A'"
            )
        elif critical_angle < 10:
            print(
                "The McFadden and McElhinny (1990) classification for this test is: 'B'"
            )
        elif critical_angle < 20:
            print(
                "The McFadden and McElhinny (1990) classification for this test is: 'C'"
            )
        else:
            print(
                "The McFadden and McElhinny (1990) classification for this test is: 'INDETERMINATE;"
            )
    if plot == 1:
        CDF = {'cdf': 1}
        pmagplotlib.plot_init(CDF['cdf'], 5, 5)
        p1 = pmagplotlib.plot_cdf(CDF['cdf'], Vp, "Watson's V", 'r', "")
        p2 = pmagplotlib.plot_vs(CDF['cdf'], [V], 'g', '-')
        p3 = pmagplotlib.plot_vs(CDF['cdf'], [Vp[k]], 'b', '--')
        pmagplotlib.draw_figs(CDF)
        files, fmt = {}, 'svg'
        if file2 != "":
            files['cdf'] = 'WatsonsV_' + file1 + '_' + file2 + '.' + fmt
        else:
            files['cdf'] = 'WatsonsV_' + file1 + '.' + fmt
        if pmagplotlib.isServer:
            black = '#000000'
            purple = '#800080'
            titles = {}
            titles['cdf'] = 'Cumulative Distribution'
            CDF = pmagplotlib.add_borders(CDF, titles, black, purple)
            pmagplotlib.save_plots(CDF, files)
        else:
            ans = input(" S[a]ve to save plot, [q]uit without saving:  ")
            if ans == "a": pmagplotlib.save_plots(CDF, files)
Exemple #4
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)
Exemple #5
0
def main():
    """
    NAME
       watsons_v.py

    DESCRIPTION
       calculates Watson's V statistic from input files

    INPUT FORMAT
       takes dec/inc as first two columns in two space delimited files
   
    SYNTAX
       watsons_v.py [command line options]

    OPTIONS
        -h prints help message and quits
        -f FILE (with optional second)
        -f2 FILE (second file) 
        -ant,  flip antipodal directions to opposite direction
           in first file if only one file or flip all in second, if two files 
        -P  (don't save or show plot)
        -sav save figure and quit silently
        -fmt [png,svg,eps,pdf,jpg] format for saved figure

    OUTPUT
        Watson's V and the Monte Carlo Critical Value Vc.
        in plot, V is solid and Vc is dashed.

    """
    Flip = 0
    show, plot = 1, 0
    fmt = 'svg'
    file2 = ""
    if '-h' in sys.argv:  # check if help is needed
        print main.__doc__
        sys.exit()  # graceful quit
    if '-ant' in sys.argv: Flip = 1
    if '-sav' in sys.argv: show, plot = 0, 1  # don't display, but do save plot
    if '-fmt' in sys.argv:
        ind = sys.argv.index('-fmt')
        fmt = sys.argv[ind + 1]
    if '-P' in sys.argv: show = 0  # don't display or save plot
    if '-f' in sys.argv:
        ind = sys.argv.index('-f')
        file1 = sys.argv[ind + 1]
        data = numpy.loadtxt(file1).transpose()
        D1 = numpy.array([data[0], data[1]]).transpose()
    else:
        print "-f is required"
        print main.__doc__
        sys.exit()
    if '-f2' in sys.argv:
        ind = sys.argv.index('-f2')
        file2 = sys.argv[ind + 1]
        data2 = numpy.loadtxt(file2).transpose()
        D2 = numpy.array([data2[0], data2[1]]).transpose()
        if Flip == 1:
            D2, D = pmag.flip(D2)  # D2 are now flipped
            if len(D2) != 0:
                if len(D) != 0:
                    D2 = numpy.concatenate(D, D2)  # put all in D2
            elif len(D) != 0:
                D2 = D
            else:
                print 'length of second file is zero'
                sys.exit()
    elif Flip == 1:
        D2, D1 = pmag.flip(D1)  # peel out antipodal directions, put in D2
    #
    counter, NumSims = 0, 5000
    #
    # first calculate the fisher means and cartesian coordinates of each set of Directions
    #
    pars_1 = pmag.fisher_mean(D1)
    pars_2 = pmag.fisher_mean(D2)
    #
    # get V statistic for these
    #
    V = pmag.vfunc(pars_1, pars_2)
    #
    # do monte carlo simulation of datasets with same kappas, but common mean
    #
    Vp = []  # set of Vs from simulations
    if show == 1: print "Doing ", NumSims, " simulations"
    for k in range(NumSims):
        counter += 1
        if counter == 50:
            if show == 1: print k + 1
            counter = 0
        Dirp = []
        # get a set of N1 fisher distributed vectors with k1, calculate fisher stats
        for i in range(pars_1["n"]):
            Dirp.append(pmag.fshdev(pars_1["k"]))
        pars_p1 = pmag.fisher_mean(Dirp)
        # get a set of N2 fisher distributed vectors with k2, calculate fisher stats
        Dirp = []
        for i in range(pars_2["n"]):
            Dirp.append(pmag.fshdev(pars_2["k"]))
        pars_p2 = pmag.fisher_mean(Dirp)
        # get the V for these
        Vk = pmag.vfunc(pars_p1, pars_p2)
        Vp.append(Vk)


#
# sort the Vs, get Vcrit (95th one)
#
    Vp.sort()
    k = int(.95 * NumSims)
    if show == 1:
        print "Watson's V,  Vcrit: "
        print '   %10.1f %10.1f' % (V, Vp[k])
    if show == 1 or plot == 1:
        print "Watson's V,  Vcrit: "
        print '   %10.1f %10.1f' % (V, Vp[k])
        CDF = {'cdf': 1}
        pmagplotlib.plot_init(CDF['cdf'], 5, 5)
        pmagplotlib.plotCDF(CDF['cdf'], Vp, "Watson's V", 'r', "")
        pmagplotlib.plotVs(CDF['cdf'], [V], 'g', '-')
        pmagplotlib.plotVs(CDF['cdf'], [Vp[k]], 'b', '--')
        if plot == 0: pmagplotlib.drawFIGS(CDF)
        files = {}
        if file2 != "":
            files['cdf'] = 'watsons_v_' + file1 + '_' + file2 + '.' + fmt
        else:
            files['cdf'] = 'watsons_v_' + file1 + '.' + fmt
        if pmagplotlib.isServer:
            black = '#000000'
            purple = '#800080'
            titles = {}
            titles['cdf'] = 'Cumulative Distribution'
            CDF = pmagplotlib.addBorders(CDF, titles, black, purple)
            pmagplotlib.saveP(CDF, files)
        elif plot == 0:
            ans = raw_input(" S[a]ve to save plot, [q]uit without saving:  ")
            if ans == "a": pmagplotlib.saveP(CDF, files)
        if plot == 1:  # save and quit silently
            pmagplotlib.saveP(CDF, files)
def main():
    """
    NAME
       revtest_MM1990.py

    DESCRIPTION
       calculates Watson's V statistic from input files through Monte Carlo simulation in order to test whether normal and reversed populations could have been drawn from a common mean (equivalent to watsonV.py). Also provides the critical angle between the two sample mean directions and the corresponding McFadden and McElhinny (1990) classification.

    INPUT FORMAT
       takes dec/inc as first two columns in two space delimited files (one file for normal directions, one file for reversed directions).

    SYNTAX
       revtest_MM1990.py [command line options]

    OPTIONS
        -h prints help message and quits
        -f FILE
        -f2 FILE
        -P  (don't plot the Watson V cdf)

    OUTPUT
        Watson's V between the two populations and the Monte Carlo Critical Value Vc.
        M&M1990 angle, critical angle and classification
        Plot of Watson's V CDF from Monte Carlo simulation (red line), V is solid and Vc is dashed.

    """
    D1,D2=[],[]
    plot=1
    Flip=1
    if '-h' in sys.argv: # check if help is needed
        print(main.__doc__)
        sys.exit() # graceful quit
    if '-P' in  sys.argv: plot=0
    if '-f' in sys.argv:
        ind=sys.argv.index('-f')
        file1=sys.argv[ind+1]
    f1=open(file1,'r')
    for line in f1.readlines():
        rec=line.split()
        Dec,Inc=float(rec[0]),float(rec[1])
        D1.append([Dec,Inc,1.])
    f1.close()
    if '-f2' in sys.argv:
        ind=sys.argv.index('-f2')
        file2=sys.argv[ind+1]
        f2=open(file2,'r')
        print("be patient, your computer is doing 5000 simulations...")
        for line in f2.readlines():
            rec=line.split()
            Dec,Inc=float(rec[0]),float(rec[1])
            D2.append([Dec,Inc,1.])
        f2.close()
    #take the antipode for the directions in file 2
    D2_flip=[]
    for rec in D2:
        d,i=(rec[0]-180.)%360.,-rec[1]
        D2_flip.append([d,i,1.])

    pars_1=pmag.fisher_mean(D1)
    pars_2=pmag.fisher_mean(D2_flip)

    cart_1=pmag.dir2cart([pars_1["dec"],pars_1["inc"],pars_1["r"]])
    cart_2=pmag.dir2cart([pars_2['dec'],pars_2['inc'],pars_2["r"]])
    Sw=pars_1['k']*pars_1['r']+pars_2['k']*pars_2['r'] # k1*r1+k2*r2
    xhat_1=pars_1['k']*cart_1[0]+pars_2['k']*cart_2[0] # k1*x1+k2*x2
    xhat_2=pars_1['k']*cart_1[1]+pars_2['k']*cart_2[1] # k1*y1+k2*y2
    xhat_3=pars_1['k']*cart_1[2]+pars_2['k']*cart_2[2] # k1*z1+k2*z2
    Rw=numpy.sqrt(xhat_1**2+xhat_2**2+xhat_3**2)
    V=2*(Sw-Rw)
#
#keep weighted sum for later when determining the "critical angle" let's save it as Sr (notation of McFadden and McElhinny, 1990)
#
    Sr=Sw
#
# do monte carlo simulation of datasets with same kappas, but common mean
#
    counter,NumSims=0,5000
    Vp=[] # set of Vs from simulations
    for k in range(NumSims):
#
# get a set of N1 fisher distributed vectors with k1, calculate fisher stats
#
        Dirp=[]
        for i in range(pars_1["n"]):
            Dirp.append(pmag.fshdev(pars_1["k"]))
        pars_p1=pmag.fisher_mean(Dirp)
#
# get a set of N2 fisher distributed vectors with k2, calculate fisher stats
#
        Dirp=[]
        for i in range(pars_2["n"]):
            Dirp.append(pmag.fshdev(pars_2["k"]))
        pars_p2=pmag.fisher_mean(Dirp)
#
# get the V for these
#
        Vk=pmag.vfunc(pars_p1,pars_p2)
        Vp.append(Vk)
#
# sort the Vs, get Vcrit (95th percentile one)
#
    Vp.sort()
    k=int(.95*NumSims)
    Vcrit=Vp[k]
#
# equation 18 of McFadden and McElhinny, 1990 calculates the critical value of R (Rwc)
#
    Rwc=Sr-(old_div(Vcrit,2))
#
#following equation 19 of McFadden and McElhinny (1990) the critical angle is calculated.
#
    k1=pars_1['k']
    k2=pars_2['k']
    R1=pars_1['r']
    R2=pars_2['r']
    critical_angle=numpy.degrees(numpy.arccos(old_div(((Rwc**2)-((k1*R1)**2)-((k2*R2)**2)),(2*k1*R1*k2*R2))))
    D1_mean=(pars_1['dec'],pars_1['inc'])
    D2_mean=(pars_2['dec'],pars_2['inc'])
    angle=pmag.angle(D1_mean,D2_mean)
#
# print the results of the test
#
    print("")
    print("Results of Watson V test: ")
    print("")
    print("Watson's V:           " '%.1f' %(V))
    print("Critical value of V:  " '%.1f' %(Vcrit))

    if V<Vcrit:
        print('"Pass": Since V is less than Vcrit, the null hypothesis that the two populations are drawn from distributions that share a common mean direction (antipodal to one another) cannot be rejected.')
    elif V>Vcrit:
        print('"Fail": Since V is greater than Vcrit, the two means can be distinguished at the 95% confidence level.')
    print("")
    print("M&M1990 classification:")
    print("")
    print("Angle between data set means: " '%.1f'%(angle))
    print("Critical angle of M&M1990:   " '%.1f'%(critical_angle))

    if V>Vcrit:
        print("")
    elif V<Vcrit:
        if critical_angle<5:
            print("The McFadden and McElhinny (1990) classification for this test is: 'A'")
        elif critical_angle<10:
            print("The McFadden and McElhinny (1990) classification for this test is: 'B'")
        elif critical_angle<20:
            print("The McFadden and McElhinny (1990) classification for this test is: 'C'")
        else:
            print("The McFadden and McElhinny (1990) classification for this test is: 'INDETERMINATE;")
    if plot==1:
        CDF={'cdf':1}
        pmagplotlib.plot_init(CDF['cdf'],5,5)
        p1 = pmagplotlib.plotCDF(CDF['cdf'],Vp,"Watson's V",'r',"")
        p2 = pmagplotlib.plotVs(CDF['cdf'],[V],'g','-')
        p3 = pmagplotlib.plotVs(CDF['cdf'],[Vp[k]],'b','--')
        pmagplotlib.drawFIGS(CDF)
        files,fmt={},'svg'
        if file2!="":
            files['cdf']='WatsonsV_'+file1+'_'+file2+'.'+fmt
        else:
            files['cdf']='WatsonsV_'+file1+'.'+fmt
        if pmagplotlib.isServer:
            black     = '#000000'
            purple    = '#800080'
            titles={}
            titles['cdf']='Cumulative Distribution'
            CDF = pmagplotlib.addBorders(CDF,titles,black,purple)
            pmagplotlib.saveP(CDF,files)
        else:
            ans=input(" S[a]ve to save plot, [q]uit without saving:  ")
            if ans=="a": pmagplotlib.saveP(CDF,files)
Exemple #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 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)
Exemple #8
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)
Exemple #9
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)
Exemple #10
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)
Exemple #11
0
def main():
    """
    NAME
       watsons_v.py

    DESCRIPTION
       calculates Watson's V statistic from input files

    INPUT FORMAT
       takes dec/inc as first two columns in two space delimited files
   
    SYNTAX
       watsons_v.py [command line options]

    OPTIONS
        -h prints help message and quits
        -f FILE (with optional second)
        -f2 FILE (second file) 
        -ant,  flip antipodal directions to opposite direction
           in first file if only one file or flip all in second, if two files 
        -P  (don't save or show plot)
        -sav save figure and quit silently
        -fmt [png,svg,eps,pdf,jpg] format for saved figure

    OUTPUT
        Watson's V and the Monte Carlo Critical Value Vc.
        in plot, V is solid and Vc is dashed.

    """
    Flip=0
    show,plot=1,0
    fmt='svg'
    file2=""
    if '-h' in sys.argv: # check if help is needed
        print main.__doc__
        sys.exit() # graceful quit
    if '-ant' in  sys.argv: Flip=1
    if '-sav' in sys.argv: show,plot=0,1 # don't display, but do save plot
    if '-fmt' in sys.argv: 
        ind=sys.argv.index('-fmt')
        fmt=sys.argv[ind+1]
    if '-P' in  sys.argv: show=0 # don't display or save plot
    if '-f' in sys.argv:
        ind=sys.argv.index('-f')
        file1=sys.argv[ind+1]
        data=numpy.loadtxt(file1).transpose()
        D1=numpy.array([data[0],data[1]]).transpose()
    else:
        print "-f is required"
        print main.__doc__
        sys.exit()
    if '-f2' in sys.argv:
        ind=sys.argv.index('-f2')
        file2=sys.argv[ind+1]
        data2=numpy.loadtxt(file2).transpose()
        D2=numpy.array([data2[0],data2[1]]).transpose()
        if Flip==1:
            D2,D=pmag.flip(D2) # D2 are now flipped
            if len(D2)!=0:
                if len(D)!=0: 
                    D2=numpy.concatenate(D,D2) # put all in D2
            elif len(D)!=0:
                D2=D
            else: 
                print 'length of second file is zero'
                sys.exit()
    elif Flip==1:D2,D1=pmag.flip(D1) # peel out antipodal directions, put in D2
#
    counter,NumSims=0,5000
#
# first calculate the fisher means and cartesian coordinates of each set of Directions
#
    pars_1=pmag.fisher_mean(D1)
    pars_2=pmag.fisher_mean(D2)
#
# get V statistic for these
#
    V=pmag.vfunc(pars_1,pars_2)
#
# do monte carlo simulation of datasets with same kappas, but common mean
# 
    Vp=[] # set of Vs from simulations
    if show==1:print "Doing ",NumSims," simulations"
    for k in range(NumSims):
        counter+=1
        if counter==50:
            if show==1:print k+1
            counter=0
        Dirp=[]
# get a set of N1 fisher distributed vectors with k1, calculate fisher stats
        for i in range(pars_1["n"]):
            Dirp.append(pmag.fshdev(pars_1["k"]))
        pars_p1=pmag.fisher_mean(Dirp)
# get a set of N2 fisher distributed vectors with k2, calculate fisher stats
        Dirp=[]
        for i in range(pars_2["n"]):
            Dirp.append(pmag.fshdev(pars_2["k"]))
        pars_p2=pmag.fisher_mean(Dirp)
# get the V for these
        Vk=pmag.vfunc(pars_p1,pars_p2)
        Vp.append(Vk)
#
# sort the Vs, get Vcrit (95th one)
#
    Vp.sort()
    k=int(.95*NumSims)
    if show==1:
        print "Watson's V,  Vcrit: " 
        print '   %10.1f %10.1f'%(V,Vp[k])
    if show==1 or plot==1:
        print "Watson's V,  Vcrit: " 
        print '   %10.1f %10.1f'%(V,Vp[k])
        CDF={'cdf':1}
        pmagplotlib.plot_init(CDF['cdf'],5,5)
        pmagplotlib.plotCDF(CDF['cdf'],Vp,"Watson's V",'r',"")
        pmagplotlib.plotVs(CDF['cdf'],[V],'g','-')
        pmagplotlib.plotVs(CDF['cdf'],[Vp[k]],'b','--')
        if plot==0:pmagplotlib.drawFIGS(CDF)
        files={}
        if file2!="":
            files['cdf']='watsons_v_'+file1+'_'+file2+'.'+fmt
        else:
            files['cdf']='watsons_v_'+file1+'.'+fmt
        if pmagplotlib.isServer:
            black     = '#000000'
            purple    = '#800080'
            titles={}
            titles['cdf']='Cumulative Distribution'
            CDF = pmagplotlib.addBorders(CDF,titles,black,purple)
            pmagplotlib.saveP(CDF,files)
        elif plot==0:
            ans=raw_input(" S[a]ve to save plot, [q]uit without saving:  ")
            if ans=="a": pmagplotlib.saveP(CDF,files) 
        if plot==1: # save and quit silently
            pmagplotlib.saveP(CDF,files)
Exemple #12
0
def spitout(kappa):
    dec, inc = pmag.fshdev(kappa)  # send kappa to fshdev
    print('%7.1f %7.1f ' % (dec, inc))
    return
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)
Exemple #14
0
def spitout(kappa):
    dec,inc= pmag.fshdev(kappa)  # send kappa to fshdev
    print '%7.1f %7.1f ' % (dec,inc)
    return