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
-fmt [svg,png,eps,pdf..] change plot format, default is svg
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
if '-i' in sys.argv:
file=raw_input("Enter file name for processing: ")
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 '-fmt' in sys.argv:
ind=sys.argv.index('-fmt')
fmt=sys.argv[ind+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')
pmagplotlib.drawFIGS(PLTS)
ppars=pmag.doprinc(data)
Io=ppars['inc']
n=ppars["N"]
Es,Is,Fs,V2s=find_f(data)
Inc,Elong=Is[-1],Es[-1]
pmagplotlib.plotEI(PLTS['ei'],Es,Is,Fs[-1])
pmagplotlib.plotV2s(PLTS['v2'],V2s,Is,Fs[-1])
b=0
print "Bootstrapping.... be patient"
while b<nb:
bdata=pmag.pseudo(data)
Es,Is,Fs,V2s=find_f(bdata)
if b<25:
pmagplotlib.plotEI(PLTS['ei'],Es,Is,Fs[-1])
if Es[-1]!=0:
ppars=pmag.doprinc(bdata)
I.append(abs(Is[-1]))
E.append(Es[-1])
b+=1
if b%25==0:print b,' out of ',nb
I.sort()
E.sort()
Eexp=[]
for i in I:
Eexp.append(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','-')
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= raw_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)
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
find_EI.py
DESCRIPTION
Applies 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 [-h][-i] [-f FILE]
INPUT
dec/inc pairs
OUTPUT
three 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
"""
if '-i' in sys.argv:
file=raw_input("Enter file name for processing: ")
f=open(file,'rU')
elif '-f' in sys.argv:
ind=sys.argv.index('-f')
file=sys.argv[ind+1]
f=open(file,'rU')
else:
print main.__doc__
sys.exit()
rseed,nb,data=10,5000,[]
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)
random.seed(rseed)
for line in f.readlines():
rec=line.split()
dec=float(rec[0])
inc=float(rec[1])
rec=[dec,inc,1.]
data.append(rec)
pmagplotlib.plotEQ(PLTS['eq'],data,'Data')
ppars=pmag.doprinc(data)
Io=ppars['inc']
n=ppars["N"]
Es,Is,Fs,V2s=find_f(data)
Inc,Elong=Is[-1],Es[-1]
pmagplotlib.plotEI(PLTS['ei'],Es,Is,Fs[-1])
pmagplotlib.plotV2s(PLTS['v2'],V2s,Is,Fs[-1])
b=0
print "Bootstrapping.... be patient"
while b<nb:
bdata=[]
for j in range(n):
boot=random.randint(0,n-1)
random.jumpahead(rseed)
bdata.append(data[boot])
Es,Is,Fs,V2s=find_f(bdata)
if b<25:
pmagplotlib.plotEI(PLTS['ei'],Es,Is,Fs[-1])
if Es[-1]!=0:
ppars=pmag.doprinc(bdata)
I.append(abs(Is[-1]))
E.append(Es[-1])
b+=1
if b%25==0:print b,' out of ',nb
I.sort()
E.sort()
Eexp=[]
for i in I:
Eexp.append(EI(i))
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','-')
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])
try:
raw_input("Return to save plots - <return> to quit: ")
except EOFError:
print "\n Good bye\n"
sys.exit()
files={}
files['eq']='findEI_eq.svg'
files['ei']='findEI_ei.svg'
files['cdf']='findEI_cdf.svg'
files['v2']='findEI_v2.svg'
pmagplotlib.saveP(PLTS,files)