Example #1
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)
Example #2
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.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)
Example #3
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)
Example #4
0
def main():
    """
    NAME
        find_EI.py

    DESCRIPTION
        Applies series of assumed flattening factor and "unsquishes" inclinations assuming tangent function.
        Finds flattening factor that gives elongation/inclination pair consistent with TK03.
        Finds bootstrap confidence bounds

    SYNTAX
        find_EI.py [command line options]

    OPTIONS
        -h prints help message and quits
        -f FILE specify input file name
        -nb N specify number of bootstraps - the more the better, but slower!, default is 1000
        -sc uses a "site-level" correction to a Fisherian distribution instead
            of a "study-level" correction to a TK03-consistent distribution.
            Note that many directions (~ 100) are needed for this correction to be reliable.
        -fmt [svg,png,eps,pdf..] change plot format, default is svg
        -sav  saves the figures and quits

    INPUT
        dec/inc pairs, delimited with space or tabs

    OUTPUT
        four plots:  1) equal area plot of original directions
                      2) Elongation/inclination pairs as a function of f,  data plus 25 bootstrap samples
                      3) Cumulative distribution of bootstrapped optimal inclinations plus uncertainties.
                         Estimate from original data set plotted as solid line
                      4) Orientation of principle direction through unflattening
    NOTE: If distribution does not have a solution, plot labeled: Pathological.  Some bootstrap samples may have
       valid solutions and those are plotted in the CDFs and E/I plot.

    """
    fmt, nb = 'svg', 1000
    plot = 0
    if '-h' in sys.argv:
        print(main.__doc__)
        sys.exit()  # graceful quit
    elif '-f' in sys.argv:
        ind = sys.argv.index('-f')
        file = sys.argv[ind + 1]
    else:
        print(main.__doc__)
        sys.exit()
    if '-nb' in sys.argv:
        ind = sys.argv.index('-nb')
        nb = int(sys.argv[ind + 1])
    if '-sc' in sys.argv:
        site_correction = True
    else:
        site_correction = False
    if '-fmt' in sys.argv:
        ind = sys.argv.index('-fmt')
        fmt = sys.argv[ind + 1]
    if '-sav' in sys.argv: plot = 1
    data = numpy.loadtxt(file)
    upper, lower = int(round(.975 * nb)), int(round(.025 * nb))
    E, I = [], []
    PLTS = {'eq': 1, 'ei': 2, 'cdf': 3, 'v2': 4}
    pmagplotlib.plot_init(PLTS['eq'], 6, 6)
    pmagplotlib.plot_init(PLTS['ei'], 5, 5)
    pmagplotlib.plot_init(PLTS['cdf'], 5, 5)
    pmagplotlib.plot_init(PLTS['v2'], 5, 5)
    pmagplotlib.plotEQ(PLTS['eq'], data, 'Data')
    if plot == 0: pmagplotlib.drawFIGS(PLTS)
    ppars = pmag.doprinc(data)
    Io = ppars['inc']
    n = ppars["N"]
    Es, Is, Fs, V2s = pmag.find_f(data)
    if site_correction:
        Inc, Elong = Is[Es.index(min(Es))], Es[Es.index(min(Es))]
        flat_f = Fs[Es.index(min(Es))]
    else:
        Inc, Elong = Is[-1], Es[-1]
        flat_f = Fs[-1]
    pmagplotlib.plotEI(PLTS['ei'], Es, Is, flat_f)
    pmagplotlib.plotV2s(PLTS['v2'], V2s, Is, flat_f)
    b = 0
    print("Bootstrapping.... be patient")
    while b < nb:
        bdata = pmag.pseudo(data)
        Esb, Isb, Fsb, V2sb = pmag.find_f(bdata)
        if b < 25:
            pmagplotlib.plotEI(PLTS['ei'], Esb, Isb, Fsb[-1])
        if Esb[-1] != 0:
            ppars = pmag.doprinc(bdata)
            if site_correction:
                I.append(abs(Isb[Esb.index(min(Esb))]))
                E.append(Esb[Esb.index(min(Esb))])
            else:
                I.append(abs(Isb[-1]))
                E.append(Esb[-1])
            b += 1
            if b % 25 == 0: print(b, ' out of ', nb)
    I.sort()
    E.sort()
    Eexp = []
    for i in I:
        Eexp.append(pmag.EI(i))
    if Inc == 0:
        title = 'Pathological Distribution: ' + '[%7.1f, %7.1f]' % (I[lower],
                                                                    I[upper])
    else:
        title = '%7.1f [%7.1f, %7.1f]' % (Inc, I[lower], I[upper])
    pmagplotlib.plotEI(PLTS['ei'], Eexp, I, 1)
    pmagplotlib.plotCDF(PLTS['cdf'], I, 'Inclinations', 'r', title)
    pmagplotlib.plotVs(PLTS['cdf'], [I[lower], I[upper]], 'b', '--')
    pmagplotlib.plotVs(PLTS['cdf'], [Inc], 'g', '-')
    pmagplotlib.plotVs(PLTS['cdf'], [Io], 'k', '-')
    if plot == 0:
        pmagplotlib.drawFIGS(PLTS)
        print("Io Inc  I_lower, I_upper, Elon, E_lower, E_upper")
        print('%7.1f %s %7.1f _ %7.1f ^ %7.1f:  %6.4f _ %6.4f ^ %6.4f' %
              (Io, " => ", Inc, I[lower], I[upper], Elong, E[lower], E[upper]))
        ans = input("S[a]ve plots - <return> to quit:  ")
        if ans != 'a':
            print("\n Good bye\n")
            sys.exit()
    files = {}
    files['eq'] = 'findEI_eq.' + fmt
    files['ei'] = 'findEI_ei.' + fmt
    files['cdf'] = 'findEI_cdf.' + fmt
    files['v2'] = 'findEI_v2.' + fmt
    pmagplotlib.saveP(PLTS, files)
Example #5
0
def main():
    """
    NAME
        find_EI.py

    DESCRIPTION
        Applies series of assumed flattening factor and "unsquishes" inclinations assuming tangent function.
        Finds flattening factor that gives elongation/inclination pair consistent with TK03.
        Finds bootstrap confidence bounds

    SYNTAX
        find_EI.py [command line options]

    OPTIONS
        -h prints help message and quits
        -i allows interactive input of file name
        -f FILE specify input file name
        -nb N specify number of bootstraps - the more the better, but slower!, default is 1000
        -sc uses a "site-level" correction to a Fisherian distribution instead
            of a "study-level" correction to a TK03-consistent distribution.
            Note that many directions (~ 100) are needed for this correction to be reliable.
        -fmt [svg,png,eps,pdf..] change plot format, default is svg
        -sav  saves the figures and quits

    INPUT
        dec/inc pairs, delimited with space or tabs

    OUTPUT
        four plots:  1) equal area plot of original directions
                      2) Elongation/inclination pairs as a function of f,  data plus 25 bootstrap samples
                      3) Cumulative distribution of bootstrapped optimal inclinations plus uncertainties.
                         Estimate from original data set plotted as solid line
                      4) Orientation of principle direction through unflattening
    NOTE: If distribution does not have a solution, plot labeled: Pathological.  Some bootstrap samples may have
       valid solutions and those are plotted in the CDFs and E/I plot.

    """
    fmt,nb='svg',1000
    plot=0
    if '-i' in sys.argv:
        file=input("Enter file name for processing: ")
    if '-h' in sys.argv:
        print(main.__doc__)
        sys.exit() # graceful quit
    elif '-f' in sys.argv:
        ind=sys.argv.index('-f')
        file=sys.argv[ind+1]
    else:
        print(main.__doc__)
        sys.exit()
    if '-nb' in sys.argv:
        ind=sys.argv.index('-nb')
        nb=int(sys.argv[ind+1])
    if '-sc' in sys.argv:
        site_correction = True
    else:
        site_correction = False
    if '-fmt' in sys.argv:
        ind=sys.argv.index('-fmt')
        fmt=sys.argv[ind+1]
    if '-sav' in sys.argv:plot=1
    data=numpy.loadtxt(file)
    upper,lower=int(round(.975*nb)),int(round(.025*nb))
    E,I=[],[]
    PLTS={'eq':1,'ei':2,'cdf':3,'v2':4}
    pmagplotlib.plot_init(PLTS['eq'],6,6)
    pmagplotlib.plot_init(PLTS['ei'],5,5)
    pmagplotlib.plot_init(PLTS['cdf'],5,5)
    pmagplotlib.plot_init(PLTS['v2'],5,5)
    pmagplotlib.plotEQ(PLTS['eq'],data,'Data')
    if plot==0:pmagplotlib.drawFIGS(PLTS)
    ppars=pmag.doprinc(data)
    Io=ppars['inc']
    n=ppars["N"]
    Es,Is,Fs,V2s=pmag.find_f(data)
    if site_correction:
        Inc,Elong=Is[Es.index(min(Es))],Es[Es.index(min(Es))]
        flat_f = Fs[Es.index(min(Es))]
    else:
        Inc,Elong=Is[-1],Es[-1]
        flat_f = Fs[-1]
    pmagplotlib.plotEI(PLTS['ei'],Es,Is,flat_f)
    pmagplotlib.plotV2s(PLTS['v2'],V2s,Is,flat_f)
    b=0
    print("Bootstrapping.... be patient")
    while b<nb:
        bdata=pmag.pseudo(data)
        Esb,Isb,Fsb,V2sb=pmag.find_f(bdata)
        if b<25:
            pmagplotlib.plotEI(PLTS['ei'],Esb,Isb,Fsb[-1])
        if Esb[-1]!=0:
            ppars=pmag.doprinc(bdata)
            if site_correction:
                I.append(abs(Isb[Esb.index(min(Esb))]))
                E.append(Esb[Esb.index(min(Esb))])
            else:
                I.append(abs(Isb[-1]))
                E.append(Esb[-1])
            b+=1
            if b%25==0:print(b,' out of ',nb)
    I.sort()
    E.sort()
    Eexp=[]
    for i in I:
       Eexp.append(pmag.EI(i))
    if Inc==0:
        title= 'Pathological Distribution: '+'[%7.1f, %7.1f]' %(I[lower],I[upper])
    else:
        title= '%7.1f [%7.1f, %7.1f]' %( Inc, I[lower],I[upper])
    pmagplotlib.plotEI(PLTS['ei'],Eexp,I,1)
    pmagplotlib.plotCDF(PLTS['cdf'],I,'Inclinations','r',title)
    pmagplotlib.plotVs(PLTS['cdf'],[I[lower],I[upper]],'b','--')
    pmagplotlib.plotVs(PLTS['cdf'],[Inc],'g','-')
    pmagplotlib.plotVs(PLTS['cdf'],[Io],'k','-')
    if plot==0:
        pmagplotlib.drawFIGS(PLTS)
        print("Io Inc  I_lower, I_upper, Elon, E_lower, E_upper")
        print('%7.1f %s %7.1f _ %7.1f ^ %7.1f:  %6.4f _ %6.4f ^ %6.4f' %(Io, " => ", Inc, I[lower],I[upper], Elong, E[lower],E[upper]))
        ans= input("S[a]ve plots - <return> to quit:  ")
        if ans!='a':
           print("\n Good bye\n")
           sys.exit()
    files={}
    files['eq']='findEI_eq.'+fmt
    files['ei']='findEI_ei.'+fmt
    files['cdf']='findEI_cdf.'+fmt
    files['v2']='findEI_v2.'+fmt
    pmagplotlib.saveP(PLTS,files)