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

    DESCRIPTION
        generates set of Fisher distribed 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=raw_input('    Kappa: ')
        kappa=float(ans)
        ans=raw_input('    N: ')
        N=int(ans)
        ans=raw_input('    Mean Dec: ')
        D=float(ans)
        ans=raw_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
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def get_fish(dir):
    """
    generate fisher distributed points according to the supplied parameters
    (includes dec,inc,n,k) in a pandas object.
    """
    tempD,tempI=[],[]
    for k in range(int(dir.n)):
        dec,inc=pmag.fshdev(dir.k)
        drot,irot=pmag.dodirot(dec,inc,dir.dec,dir.inc)
        tempD.append(drot)
        tempI.append(irot)
    return np.column_stack((tempD,tempI))
Exemple #3
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def fishrot(kappa,N,D,I): #from Pmagpy
    """
    Description: generates set of Fisher distributed data from specified distribution 
	Input: kappa (fisher distribution concentration parameter), number of desired subsamples, Dec and Inc
	Output: list with N pairs of Dec, Inc.
    """
    out_d=[]
    out=[]

    for k in range(N): 
        dec,inc= pmag.fshdev(kappa)  # send kappa to fshdev
        drot,irot=pmag.dodirot(dec,inc,D,I)
        out_d=[drot,irot]
        out.append(out_d)
    return out
def ifishrot(k=20,n=100,Dec=0,Inc=90):
    """
    Generates Fisher distributed unit vectors from a specified distribution 
    using the pmag.py fshdev and dodirot functions
    
    Parameters
    ----------
    k kappa precision parameter (default is 20) 
    n number of vectors to determine (default is 100)
    Dec mean declination of data set (default is 0)
    Inc mean inclination of data set (default is 90)
    """
    directions=[]
    for data in range(n):
        dec,inc=pmag.fshdev(k) 
        drot,irot=pmag.dodirot(dec,inc,Dec,Inc)
        directions.append([drot,irot,1.])
    return directions
Exemple #5
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def main():
    """
    NAME
        scalc_magic.py

    DESCRIPTION
       calculates Sb from pmag_results files

    SYNTAX 
        scalc_magic -h [command line options]
    
    INPUT 
       takes magic formatted pmag_results table
       pmag_result_name must start with "VGP: Site"
       must have average_lat if spin axis is reference
    
    OPTIONS
        -h prints help message and quits
        -f FILE: specify input results file, default is 'pmag_results.txt'
        -c cutoff:  specify VGP colatitude cutoff value
        -k cutoff: specify kappa cutoff
        -crd [s,g,t]: specify coordinate system, default is geographic
        -v : use the VanDammme criterion 
        -a: use antipodes of reverse data: default is to use only normal
        -C: use all data without regard to polarity
        -r:  use reverse data only
        -p: do relative to principle axis
        -b: do bootstrap confidence bounds

     OUTPUT:
         if option -b used: N,  S_B, lower and upper bounds
         otherwise: N,  S_B, cutoff
    """
    in_file='pmag_results.txt'
    coord,kappa,cutoff="0",1.,90.
    nb,anti,spin,v,boot=1000,0,1,0,0
    coord_key='tilt_correction'
    rev=0
    if '-h' in sys.argv:
        print main.__doc__
        sys.exit()
    if '-f' in sys.argv:
        ind=sys.argv.index("-f")
        in_file=sys.argv[ind+1]
    if '-c' in sys.argv:
        ind=sys.argv.index('-c')
        cutoff=float(sys.argv[ind+1])
    if '-k' in sys.argv:
        ind=sys.argv.index('-k')
        kappa=float(sys.argv[ind+1])
    if '-crd' in sys.argv:
        ind=sys.argv.index("-crd")
        coord=sys.argv[ind+1]
        if coord=='s':coord="-1"
        if coord=='g':coord="0"
        if coord=='t':coord="100"
    if '-a' in sys.argv: anti=1
    if '-C' in sys.argv: cutoff=180. # no cutoff
    if '-r' in sys.argv: rev=1
    if '-p' in sys.argv: spin=0
    if '-v' in sys.argv: v=1
    if '-b' in sys.argv: boot=1
    data,file_type=pmag.magic_read(in_file)
    #
    #
    # find desired vgp lat,lon, kappa,N_site data:
    #
    #
    #
    A,Vgps,Pvgps=180.,[],[]
    VgpRecs=pmag.get_dictitem(data,'vgp_lat','','F') # get all non-blank vgp latitudes
    VgpRecs=pmag.get_dictitem(VgpRecs,'vgp_lon','','F') # get all non-blank vgp longitudes
    SiteRecs=pmag.get_dictitem(VgpRecs,'data_type','i','T') # get VGPs (as opposed to averaged)
    SiteRecs=pmag.get_dictitem(SiteRecs,coord_key,coord,'T') # get right coordinate system
    for rec in SiteRecs:
            if anti==1:
                if 90.-abs(float(rec['vgp_lat']))<=cutoff and float(rec['average_k'])>=kappa: 
                    if float(rec['vgp_lat'])<0:
                        rec['vgp_lat']='%7.1f'%(-1*float(rec['vgp_lat']))
                        rec['vgp_lon']='%7.1f'%(float(rec['vgp_lon'])-180.)
                    Vgps.append(rec)
                    Pvgps.append([float(rec['vgp_lon']),float(rec['vgp_lat'])])
            elif rev==0: # exclude normals
                if 90.-(float(rec['vgp_lat']))<=cutoff and float(rec['average_k'])>=kappa: 
                    Vgps.append(rec)
                    Pvgps.append([float(rec['vgp_lon']),float(rec['vgp_lat'])])
            else: # include normals
                if 90.-abs(float(rec['vgp_lat']))<=cutoff and float(rec['average_k'])>=kappa: 
                    if float(rec['vgp_lat'])<0:
                        rec['vgp_lat']='%7.1f'%(-1*float(rec['vgp_lat']))
                        rec['vgp_lon']='%7.1f'%(float(rec['vgp_lon'])-180.)
                        Vgps.append(rec)
                        Pvgps.append([float(rec['vgp_lon']),float(rec['vgp_lat'])])
    if spin==0: # do transformation to pole
        ppars=pmag.doprinc(Pvgps)
        for vgp in Vgps:
            vlon,vlat=pmag.dodirot(float(vgp['vgp_lon']),float(vgp['vgp_lat']),ppars['dec'],ppars['inc'])
            vgp['vgp_lon']=vlon
            vgp['vgp_lat']=vlat
            vgp['average_k']="0"
    S_B= pmag.get_Sb(Vgps)
    A=cutoff
    if v==1:
        thetamax,A=181.,180.
        vVgps,cnt=[],0
        for vgp in Vgps:vVgps.append(vgp) # make a copy of Vgps
        while thetamax>A:
            thetas=[]
            A=1.8*S_B+5
            cnt+=1
            for vgp in vVgps:thetas.append(90.-(float(vgp['vgp_lat'])))
            thetas.sort()
            thetamax=thetas[-1]
            if thetamax<A:break
            nVgps=[]
            for  vgp in vVgps:
                if 90.-(float(vgp['vgp_lat']))<thetamax:nVgps.append(vgp)
            vVgps=[]
            for vgp in nVgps:vVgps.append(vgp)
            S_B= pmag.get_Sb(vVgps)
        Vgps=[]
        for vgp in vVgps:Vgps.append(vgp) # make a new Vgp list
    SBs=[]
    if boot==1:
        for i in range(nb): # now do bootstrap 
            BVgps=[]
            if i%100==0: print i,' out of ',nb
            for k in range(len(Vgps)):
                ind=random.randint(0,len(Vgps)-1)
                random.jumpahead(int(ind*1000))
                BVgps.append(Vgps[ind])
            SBs.append(pmag.get_Sb(BVgps))
        SBs.sort()
        low=int(.025*nb)
        high=int(.975*nb)
        print len(Vgps),'%7.1f _ %7.1f ^ %7.1f %7.1f'%(S_B,SBs[low],SBs[high],A)
    else:
        print len(Vgps),'%7.1f  %7.1f '%(S_B,A)
Exemple #6
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def main():
    """
    NAME
        EI.py [command line options]

    DESCRIPTION
        Finds bootstrap confidence bounds on Elongation and Inclination data

    SYNTAX
        EI.py  [command line options]

    OPTIONS
        -h prints help message and quits
        -f FILE specifies input file
        -p do parametric bootstrap

    INPUT
        dec/inc pairs

    OUTPUT
        makes a plot of the E/I pair and bootstrapped confidence bounds
        along with the E/I trend predicted by the TK03 field model
        prints out:
            Io (mean inclination), I_lower and I_upper are 95% confidence bounds on inclination
            Eo (elongation), E_lower and E_upper are 95% confidence bounds on elongation
            Edec,Einc are the elongation direction

    """
    par=0
    if '-h' in sys.argv:
        print main.__doc__
        sys.exit()
    if '-f' in sys.argv:
        ind=sys.argv.index('-f')
        file=open(sys.argv[ind+1],'rU')
    if '-p' in sys.argv: par=1
    rseed,nb,data=10,5000,[]
    upper,lower=int(round(.975*nb)),int(round(.025*nb))
    Es,Is=[],[]
    PLTS={'eq':1,'ei':2}
    pmagplotlib.plot_init(PLTS['eq'],5,5) 
    pmagplotlib.plot_init(PLTS['ei'],5,5) 
#    poly_tab= [  3.07448925e-06,  -3.49555831e-04,  -1.46990847e-02,   2.90905483e+00]
    poly_new= [  3.15976125e-06,  -3.52459817e-04,  -1.46641090e-02,   2.89538539e+00]
#    poly_cp88= [ 5.34558576e-06,  -7.70922659e-04,   5.18529685e-03,   2.90941351e+00]
#    poly_qc96= [  7.08210133e-06,  -8.79536536e-04,   1.09625547e-03,   2.92513660e+00]
#    poly_cj98=[  6.56675431e-06,  -7.91823539e-04,  -1.08211350e-03,   2.80557710e+00]
#    poly_tk03_g20= [  4.96757685e-06,  -6.02256097e-04,  -5.96103272e-03,   2.84227449e+00]
#    poly_tk03_g30= [  7.82525963e-06,  -1.39781724e-03,   4.47187092e-02,   2.54637535e+00]
#    poly_gr99_g=[  1.24362063e-07,  -1.69383384e-04,  -4.24479223e-03,   2.59257437e+00]
#    poly_gr99_e=[  1.26348154e-07,   2.01691452e-04,  -4.99142308e-02,   3.69461060e+00]
    E_EI,E_tab,E_new,E_cp88,E_cj98,E_qc96,E_tk03_g20=[],[],[],[],[],[],[]
    E_tk03_g30,E_gr99_g,E_gr99_e=[],[],[]
    I2=range(0,90,5)
    for inc in I2:
        E_new.append(EI(inc,poly_new)) # use the polynomial from Tauxe et al. (2008)
    pmagplotlib.plotEI(PLTS['ei'],E_new,I2,1)
    if '-f' in sys.argv:
        random.seed(rseed)
        for line in file.readlines():
            rec=line.split()
            dec=float(rec[0])
            inc=float(rec[1])
            if par==1:
                if  len(rec)==4:
                    N=(int(rec[2]))  # append n
                    K=float(rec[3])  # append k
                    rec=[dec,inc,N,K]
                    data.append(rec)
            else:
                rec=[dec,inc]
                data.append(rec)
        pmagplotlib.plotEQ(PLTS['eq'],data,'Data')
        ppars=pmag.doprinc(data)
        n=ppars["N"]
        Io=ppars['inc']
        Edec=ppars['Edir'][0]
        Einc=ppars['Edir'][1]
        Eo=(ppars['tau2']/ppars['tau3'])
        b=0
        print 'doing bootstrap - be patient'
        while b<nb:
            bdata=[]
            for j in range(n):
                boot=random.randint(0,n-1)
                random.jumpahead(rseed)
                if par==1:
                    DIs=[]
                    D,I,N,K=data[boot][0],data[boot][1],data[boot][2],data[boot][3]
                    for k in range(N):
                        dec,inc=pmag.fshdev(K)
                        drot,irot=pmag.dodirot(dec,inc,D,I)
                        DIs.append([drot,irot])
                    fpars=pmag.fisher_mean(DIs)
                    bdata.append([fpars['dec'],fpars['inc'],1.])  # replace data[boot] with parametric dec,inc    
                else:
                    bdata.append(data[boot])
            ppars=pmag.doprinc(bdata)
            Is.append(ppars['inc'])
            Es.append(ppars['tau2']/ppars['tau3'])
            b+=1
            if b%100==0:print b
        Is.sort()
        Es.sort()
        x,std=pmag.gausspars(Es)
        stderr=std/math.sqrt(len(data))
        pmagplotlib.plotX(PLTS['ei'],Io,Eo,Is[lower],Is[upper],Es[lower],Es[upper],'b-')
#        pmagplotlib.plotX(PLTS['ei'],Io,Eo,Is[lower],Is[upper],Eo-stderr,Eo+stderr,'b-')
        print 'Io, Eo, I_lower, I_upper, E_lower, E_upper, Edec, Einc'
        print '%7.1f %4.2f %7.1f %7.1f %4.2f %4.2f %7.1f %7.1f' %(Io,Eo,Is[lower],Is[upper],Es[lower],Es[upper], Edec,Einc)
#        print '%7.1f %4.2f %7.1f %7.1f %4.2f %4.2f' %(Io,Eo,Is[lower],Is[upper],Eo-stderr,Eo+stderr)
    pmagplotlib.drawFIGS(PLTS)
    files,fmt={},'svg'
    for key in PLTS.keys():
        files[key]=key+'.'+fmt 
    ans=raw_input(" S[a]ve to save plot, [q]uit without saving:  ")
    if ans=="a": pmagplotlib.saveP(PLTS,files) 
Exemple #7
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
    
    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 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
        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='svg'
    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 '-b' in sys.argv:
        ind=sys.argv.index('-b')
        min=float(sys.argv[ind+1])
        max=float(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')
    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)
    pmagplotlib.drawFIGS(PLTS)
    ans= raw_input('S[a]ve all figures, <Return> to quit   ')
    if ans!='a':
        print "Good bye"
        sys.exit()
    else:
        files={}
        for key in PLTS.keys():
            files[key]=('foldtest_'+'%s'%(key.strip()[:2])+'.'+fmt)
        pmagplotlib.saveP(PLTS,files)
Exemple #8
0
def main():
    """
    NAME
       foldtest.py

    DESCRIPTION
       does a fold test (Tauxe, 2008) on data

    INPUT FORMAT
       dec inc dip_direction dip

    SYNTAX
       foldtest.py [command line options]

    OPTIONS
        -h prints help message and quits
        -f FILE
        -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]
    
    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
    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')
        file=sys.argv[ind+1] 
        f=open(file,'rU')
        data=f.readlines()
    else:
        print main.__doc__
        sys.exit()
    if '-b' in sys.argv:
        ind=sys.argv.index('-b')
        min=float(sys.argv[ind+1])
        max=float(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)
    DIDDs= [] # set up list for dec inc  dip_direction, dip
    for line in data:   # read in the data from standard input
        rec=line.split() # split each line on space to get records
        DIDDs.append([float(rec[0]),float(rec[1]),float(rec[2]),float(rec[3])])
    pmagplotlib.plotEQ(PLTS['geo'],DIDDs,'Geographic')
    TCs=[]
    for k in range(len(DIDDs)):
        drot,irot=pmag.dotilt(DIDDs[k][0],DIDDs[k][1],DIDDs[k][2],DIDDs[k][3])
        TCs.append([drot,irot,1.])
    pmagplotlib.plotEQ(PLTS['strat'],TCs,'Stratigraphic')
    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=0.01*perc
            TCs=[]
            for k in range(len(PDs)):
                drot,irot=pmag.dotilt(PDs[k][0],PDs[k][1],PDs[k][2],tilt*PDs[k][3])
                TCs.append([drot,irot,1.])
            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)
    try:
        raw_input('Return to save all figures, cntl-d to quit\n')
    except:
        print "Good bye"
        sys.exit()
    files={}
    for key in PLTS.keys():
        files[key]=('fold_'+'%s'%(key.strip()[:2])+'.svg')
    pmagplotlib.saveP(PLTS,files)
Exemple #9
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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]
        -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
    
    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'
    fmt='svg'
    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 '-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] 
    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]['sample_bed_dip_direction']!="":dip_dir=float(orecs[0]['sample_bed_dip_direction'])
                    if orecs[0]['sample_bed_dip']!="":dip=float(orecs[0]['sample_bed_dip'])
            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')
    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)
    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 #10
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def main():
    """
    NAME
        scalc.py

    DESCRIPTION
       calculates Sb from VGP Long,VGP Lat,Directional kappa,Site latitude data

    SYNTAX 
        scalc -h [command line options] [< standard input]
    
    INPUT 
       takes space delimited files with PLong, PLat,[kappa, N_site, slat]
    
    OPTIONS
        -h prints help message and quits
        -f FILE: specify input file
        -c cutoff:  specify VGP colatitude cutoff value
        -k cutoff: specify kappa cutoff
        -v : use the VanDammme criterion 
        -a: use antipodes of reverse data: default is to use only normal
        -C:  use all data without regard to polarity
        -b: do a bootstrap for confidence
        -p: do relative to principle axis
    NOTES
        if kappa, N_site, lat supplied, will consider within site scatter
    OUTPUT
        N Sb  Sb_lower Sb_upper Co-lat. Cutoff
    """
    coord,kappa,cutoff="0",0,90.
    nb,anti,boot=1000,0,0
    all=0
    n=0
    v=0
    spin=1
    coord_key='tilt_correction'
    if '-h' in sys.argv:
        print main.__doc__
        sys.exit()
    if '-f' in sys.argv:
        ind=sys.argv.index("-f")
        in_file=sys.argv[ind+1]
        f=open(in_file,'rU')
        lines=f.readlines()
    else:
        lines=sys.stdin.readlines()
    if '-c' in sys.argv:
        ind=sys.argv.index('-c')
        cutoff=float(sys.argv[ind+1])
    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 '-a' in sys.argv: anti=1
    if '-C' in sys.argv: cutoff=180. # no cutoff
    if '-b' in sys.argv: boot=1
    if '-v' in sys.argv: v=1
    if '-p' in sys.argv: spin=0
    #
    #
    # find desired vgp lat,lon, kappa,N_site data:
    #
    A,Vgps,slats,Pvgps=180.,[],[],[]
    for line in lines:
        if '\t' in line:
            rec=line.replace('\n','').split('\t') # split each line on space to get records
        else:
            rec=line.replace('\n','').split() # split each line on space to get records
        vgp={}
        vgp['vgp_lon'],vgp['vgp_lat']=rec[0],rec[1]
        Pvgps.append([float(rec[0]),float(rec[1])])
        if anti==1:
            if float(vgp['vgp_lat'])<0:
                vgp['vgp_lat']='%7.1f'%(-1*float(vgp['vgp_lat']))
                vgp['vgp_lon']='%7.1f'%(float(vgp['vgp_lon'])-180.)
        if len(rec)==5:
            vgp['average_k'],vgp['average_n'],vgp['average_lat']=rec[2],rec[3],rec[4]
            slats.append(float(rec[4]))
        else: 
            vgp['average_k'],vgp['average_n'],vgp['average_lat']="0","0","0"
        if 90.-(float(vgp['vgp_lat']))<=cutoff and float(vgp['average_k'])>=kappa and int(vgp['average_n'])>=n: Vgps.append(vgp) 
    if spin==0: # do transformation to pole
        ppars=pmag.doprinc(Pvgps)
        for vgp in Vgps:
            vlon,vlat=pmag.dodirot(float(vgp['vgp_lon']),float(vgp['vgp_lat']),ppars['dec'],ppars['inc'])
            vgp['vgp_lon']=vlon  
            vgp['vgp_lat']=vlat  
            vgp['average_k']="0"
    S_B= pmag.get_Sb(Vgps)
    A=cutoff
    if v==1:
        thetamax,A=181.,180.
        vVgps,cnt=[],0
        for vgp in Vgps:vVgps.append(vgp) # make a copy of Vgps
        while thetamax>A:
            thetas=[]
            A=1.8*S_B+5
            cnt+=1
            for vgp in vVgps:thetas.append(90.-(float(vgp['vgp_lat'])))
            thetas.sort()
            thetamax=thetas[-1]
            if thetamax<A:break
            nVgps=[]
            for  vgp in vVgps:
                if 90.-(float(vgp['vgp_lat']))<thetamax:nVgps.append(vgp)
            vVgps=[]
            for vgp in nVgps:vVgps.append(vgp) 
            S_B= pmag.get_Sb(vVgps)
        Vgps=[]
        for vgp in vVgps:Vgps.append(vgp) # make a new Vgp list
    SBs,Ns=[],[]
    if boot==1:
      for i in range(nb): # now do bootstrap 
        BVgps=[]
        for k in range(len(Vgps)):
            ind=random.randint(0,len(Vgps)-1)
            random.jumpahead(int(ind*1000))
            BVgps.append(Vgps[ind])
        SBs.append(pmag.get_Sb(BVgps))
      SBs.sort()
      low=int(.025*nb)
      high=int(.975*nb)
      print len(Vgps),'%7.1f %7.1f  %7.1f %7.1f '%(S_B,SBs[low],SBs[high],A)
    else:
      print len(Vgps),'%7.1f  %7.1f '%(S_B,A)
    if  len(slats)>2:
        stats= pmag.gausspars(slats)
        print 'mean lat = ','%7.1f'%(stats[0])