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)
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))
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
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)
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)
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)
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)
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)
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])
