def main():
"""
NAME
plot_2cdfs.py
DESCRIPTION
makes plots of cdfs of data in input file
SYNTAX
plot_2cdfs.py [-h][command line options]
OPTIONS
-h prints help message and quits
-f FILE1 FILE2
-t TITLE
-fmt [svg,eps,png,pdf,jpg..] specify format of output figure, default is svg
"""
fmt = 'svg'
title = ""
if '-h' in sys.argv:
print(main.__doc__)
sys.exit()
if '-f' in sys.argv:
ind = sys.argv.index('-f')
file = sys.argv[ind + 1]
X = numpy.loadtxt(file)
file = sys.argv[ind + 2]
X2 = numpy.loadtxt(file)
# else:
# X=numpy.loadtxt(sys.stdin,dtype=numpy.float)
else:
print('-f option required')
print(main.__doc__)
sys.exit()
if '-fmt' in sys.argv:
ind = sys.argv.index('-fmt')
fmt = sys.argv[ind + 1]
if '-t' in sys.argv:
ind = sys.argv.index('-t')
title = sys.argv[ind + 1]
CDF = {'X': 1}
pmagplotlib.plot_init(CDF['X'], 5, 5)
pmagplotlib.plot_cdf(CDF['X'], X, '', 'r', '')
pmagplotlib.plot_cdf(CDF['X'], X2, title, 'b', '')
D, p = scipy.stats.ks_2samp(X, X2)
if p >= .05:
print(D, p, ' not rejected at 95%')
else:
print(D, p, ' rejected at 95%')
pmagplotlib.draw_figs(CDF)
ans = input('S[a]ve plot, <Return> to quit ')
if ans == 'a':
files = {'X': 'CDF_.' + fmt}
pmagplotlib.save_plots(CDF, files)
def main():
"""
NAME
plot_2cdfs.py
DESCRIPTION
makes plots of cdfs of data in input file
SYNTAX
plot_2cdfs.py [-h][command line options]
OPTIONS
-h prints help message and quits
-f FILE1 FILE2
-t TITLE
-fmt [svg,eps,png,pdf,jpg..] specify format of output figure, default is svg
"""
fmt='svg'
title=""
if '-h' in sys.argv:
print(main.__doc__)
sys.exit()
if '-f' in sys.argv:
ind=sys.argv.index('-f')
file=sys.argv[ind+1]
X=numpy.loadtxt(file)
file=sys.argv[ind+2]
X2=numpy.loadtxt(file)
# else:
# X=numpy.loadtxt(sys.stdin,dtype=numpy.float)
else:
print('-f option required')
print(main.__doc__)
sys.exit()
if '-fmt' in sys.argv:
ind=sys.argv.index('-fmt')
fmt=sys.argv[ind+1]
if '-t' in sys.argv:
ind=sys.argv.index('-t')
title=sys.argv[ind+1]
CDF={'X':1}
pmagplotlib.plot_init(CDF['X'],5,5)
pmagplotlib.plot_cdf(CDF['X'],X,'','r','')
pmagplotlib.plot_cdf(CDF['X'],X2,title,'b','')
D,p=scipy.stats.ks_2samp(X,X2)
if p>=.05:
print(D,p,' not rejected at 95%')
else:
print(D,p,' rejected at 95%')
pmagplotlib.draw_figs(CDF)
ans= input('S[a]ve plot, <Return> to quit ')
if ans=='a':
files={'X':'CDF_.'+fmt}
pmagplotlib.save_plots(CDF,files)
def main():
"""
NAME
plot_cdf.py
DESCRIPTION
makes plots of cdfs of data in input file
SYNTAX
plot_cdf.py [-h][command line options]
OPTIONS
-h prints help message and quits
-f FILE
-t TITLE
-fmt [svg,eps,png,pdf,jpg..] specify format of output figure, default is svg
-sav saves plot and quits
"""
fmt, plot = 'svg', 0
title = ""
if '-h' in sys.argv:
print(main.__doc__)
sys.exit()
if '-sav' in sys.argv: plot = 1
if '-f' in sys.argv:
ind = sys.argv.index('-f')
file = sys.argv[ind + 1]
X = numpy.loadtxt(file)
# else:
# X=numpy.loadtxt(sys.stdin,dtype=numpy.float)
else:
print('-f option required')
print(main.__doc__)
sys.exit()
if '-fmt' in sys.argv:
ind = sys.argv.index('-fmt')
fmt = sys.argv[ind + 1]
if '-t' in sys.argv:
ind = sys.argv.index('-t')
title = sys.argv[ind + 1]
CDF = {'X': 1}
pmagplotlib.plot_init(CDF['X'], 5, 5)
pmagplotlib.plot_cdf(CDF['X'], X, title, 'r', '')
files = {'X': 'CDF_.' + fmt}
if plot == 0:
pmagplotlib.draw_figs(CDF)
ans = input('S[a]ve plot, <Return> to quit ')
if ans == 'a':
pmagplotlib.save_plots(CDF, files)
else:
pmagplotlib.save_plots(CDF, files)
def main():
"""
NAME
plot_cdf.py
DESCRIPTION
makes plots of cdfs of data in input file
SYNTAX
plot_cdf.py [-h][command line options]
OPTIONS
-h prints help message and quits
-f FILE
-t TITLE
-fmt [svg,eps,png,pdf,jpg..] specify format of output figure, default is svg
-sav saves plot and quits
"""
fmt,plot='svg',0
title=""
if '-h' in sys.argv:
print(main.__doc__)
sys.exit()
if '-sav' in sys.argv:plot=1
if '-f' in sys.argv:
ind=sys.argv.index('-f')
file=sys.argv[ind+1]
X=numpy.loadtxt(file)
# else:
# X=numpy.loadtxt(sys.stdin,dtype=numpy.float)
else:
print('-f option required')
print(main.__doc__)
sys.exit()
if '-fmt' in sys.argv:
ind=sys.argv.index('-fmt')
fmt=sys.argv[ind+1]
if '-t' in sys.argv:
ind=sys.argv.index('-t')
title=sys.argv[ind+1]
CDF={'X':1}
pmagplotlib.plot_init(CDF['X'],5,5)
pmagplotlib.plot_cdf(CDF['X'],X,title,'r','')
files={'X':'CDF_.'+fmt}
if plot==0:
pmagplotlib.draw_figs(CDF)
ans= input('S[a]ve plot, <Return> to quit ')
if ans=='a':
pmagplotlib.save_plots(CDF,files)
else:
pmagplotlib.save_plots(CDF,files)
def main():
"""
NAME
revtest_MM1990.py
DESCRIPTION
calculates Watson's V statistic from input files through Monte Carlo simulation in order to test whether normal and reversed populations could have been drawn from a common mean (equivalent to watsonV.py). Also provides the critical angle between the two sample mean directions and the corresponding McFadden and McElhinny (1990) classification.
INPUT FORMAT
takes dec/inc as first two columns in two space delimited files (one file for normal directions, one file for reversed directions).
SYNTAX
revtest_MM1990.py [command line options]
OPTIONS
-h prints help message and quits
-f FILE
-f2 FILE
-P (don't plot the Watson V cdf)
OUTPUT
Watson's V between the two populations and the Monte Carlo Critical Value Vc.
M&M1990 angle, critical angle and classification
Plot of Watson's V CDF from Monte Carlo simulation (red line), V is solid and Vc is dashed.
"""
D1, D2 = [], []
plot = 1
Flip = 1
if '-h' in sys.argv: # check if help is needed
print(main.__doc__)
sys.exit() # graceful quit
if '-P' in sys.argv: plot = 0
if '-f' in sys.argv:
ind = sys.argv.index('-f')
file1 = sys.argv[ind + 1]
f1 = open(file1, 'r')
for line in f1.readlines():
rec = line.split()
Dec, Inc = float(rec[0]), float(rec[1])
D1.append([Dec, Inc, 1.])
f1.close()
if '-f2' in sys.argv:
ind = sys.argv.index('-f2')
file2 = sys.argv[ind + 1]
f2 = open(file2, 'r')
print("be patient, your computer is doing 5000 simulations...")
for line in f2.readlines():
rec = line.split()
Dec, Inc = float(rec[0]), float(rec[1])
D2.append([Dec, Inc, 1.])
f2.close()
#take the antipode for the directions in file 2
D2_flip = []
for rec in D2:
d, i = (rec[0] - 180.) % 360., -rec[1]
D2_flip.append([d, i, 1.])
pars_1 = pmag.fisher_mean(D1)
pars_2 = pmag.fisher_mean(D2_flip)
cart_1 = pmag.dir2cart([pars_1["dec"], pars_1["inc"], pars_1["r"]])
cart_2 = pmag.dir2cart([pars_2['dec'], pars_2['inc'], pars_2["r"]])
Sw = pars_1['k'] * pars_1['r'] + pars_2['k'] * pars_2['r'] # k1*r1+k2*r2
xhat_1 = pars_1['k'] * cart_1[0] + pars_2['k'] * cart_2[0] # k1*x1+k2*x2
xhat_2 = pars_1['k'] * cart_1[1] + pars_2['k'] * cart_2[1] # k1*y1+k2*y2
xhat_3 = pars_1['k'] * cart_1[2] + pars_2['k'] * cart_2[2] # k1*z1+k2*z2
Rw = numpy.sqrt(xhat_1**2 + xhat_2**2 + xhat_3**2)
V = 2 * (Sw - Rw)
#
#keep weighted sum for later when determining the "critical angle" let's save it as Sr (notation of McFadden and McElhinny, 1990)
#
Sr = Sw
#
# do monte carlo simulation of datasets with same kappas, but common mean
#
counter, NumSims = 0, 5000
Vp = [] # set of Vs from simulations
for k in range(NumSims):
#
# get a set of N1 fisher distributed vectors with k1, calculate fisher stats
#
Dirp = []
for i in range(pars_1["n"]):
Dirp.append(pmag.fshdev(pars_1["k"]))
pars_p1 = pmag.fisher_mean(Dirp)
#
# get a set of N2 fisher distributed vectors with k2, calculate fisher stats
#
Dirp = []
for i in range(pars_2["n"]):
Dirp.append(pmag.fshdev(pars_2["k"]))
pars_p2 = pmag.fisher_mean(Dirp)
#
# get the V for these
#
Vk = pmag.vfunc(pars_p1, pars_p2)
Vp.append(Vk)
#
# sort the Vs, get Vcrit (95th percentile one)
#
Vp.sort()
k = int(.95 * NumSims)
Vcrit = Vp[k]
#
# equation 18 of McFadden and McElhinny, 1990 calculates the critical value of R (Rwc)
#
Rwc = Sr - (old_div(Vcrit, 2))
#
#following equation 19 of McFadden and McElhinny (1990) the critical angle is calculated.
#
k1 = pars_1['k']
k2 = pars_2['k']
R1 = pars_1['r']
R2 = pars_2['r']
critical_angle = numpy.degrees(
numpy.arccos(
old_div(((Rwc**2) - ((k1 * R1)**2) - ((k2 * R2)**2)),
(2 * k1 * R1 * k2 * R2))))
D1_mean = (pars_1['dec'], pars_1['inc'])
D2_mean = (pars_2['dec'], pars_2['inc'])
angle = pmag.angle(D1_mean, D2_mean)
#
# print the results of the test
#
print("")
print("Results of Watson V test: ")
print("")
print("Watson's V: " '%.1f' % (V))
print("Critical value of V: " '%.1f' % (Vcrit))
if V < Vcrit:
print(
'"Pass": Since V is less than Vcrit, the null hypothesis that the two populations are drawn from distributions that share a common mean direction (antipodal to one another) cannot be rejected.'
)
elif V > Vcrit:
print(
'"Fail": Since V is greater than Vcrit, the two means can be distinguished at the 95% confidence level.'
)
print("")
print("M&M1990 classification:")
print("")
print("Angle between data set means: " '%.1f' % (angle))
print("Critical angle of M&M1990: " '%.1f' % (critical_angle))
if V > Vcrit:
print("")
elif V < Vcrit:
if critical_angle < 5:
print(
"The McFadden and McElhinny (1990) classification for this test is: 'A'"
)
elif critical_angle < 10:
print(
"The McFadden and McElhinny (1990) classification for this test is: 'B'"
)
elif critical_angle < 20:
print(
"The McFadden and McElhinny (1990) classification for this test is: 'C'"
)
else:
print(
"The McFadden and McElhinny (1990) classification for this test is: 'INDETERMINATE;"
)
if plot == 1:
CDF = {'cdf': 1}
pmagplotlib.plot_init(CDF['cdf'], 5, 5)
p1 = pmagplotlib.plot_cdf(CDF['cdf'], Vp, "Watson's V", 'r', "")
p2 = pmagplotlib.plot_vs(CDF['cdf'], [V], 'g', '-')
p3 = pmagplotlib.plot_vs(CDF['cdf'], [Vp[k]], 'b', '--')
pmagplotlib.draw_figs(CDF)
files, fmt = {}, 'svg'
if file2 != "":
files['cdf'] = 'WatsonsV_' + file1 + '_' + file2 + '.' + fmt
else:
files['cdf'] = 'WatsonsV_' + file1 + '.' + fmt
if pmagplotlib.isServer:
black = '#000000'
purple = '#800080'
titles = {}
titles['cdf'] = 'Cumulative Distribution'
CDF = pmagplotlib.add_borders(CDF, titles, black, purple)
pmagplotlib.save_plots(CDF, files)
else:
ans = input(" S[a]ve to save plot, [q]uit without saving: ")
if ans == "a": pmagplotlib.save_plots(CDF, files)
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
-n 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 '-n' in sys.argv:
ind=sys.argv.index('-n')
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.plot_eq(PLTS['eq'],data,'Data')
# this is a problem
#if plot==0:pmagplotlib.draw_figs(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.plot_ei(PLTS['ei'],Es,Is,flat_f)
pmagplotlib.plot_v2s(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.plot_ei(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.plot_ei(PLTS['ei'],Eexp,I,1)
pmagplotlib.plot_cdf(PLTS['cdf'],I,'Inclinations','r',title)
pmagplotlib.plot_vs(PLTS['cdf'],[I[lower],I[upper]],'b','--')
pmagplotlib.plot_vs(PLTS['cdf'],[Inc],'g','-')
pmagplotlib.plot_vs(PLTS['cdf'],[Io],'k','-')
if plot==0:
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]))
print("Io Inc I_lower, I_upper, Elon, E_lower, E_upper")
pmagplotlib.draw_figs(PLTS)
ans = ""
while ans not in ['q', 'a']:
ans= input("S[a]ve plots - <q> to quit: ")
if ans=='q':
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.save_plots(PLTS,files)
def main():
"""
NAME
revtest_MM1990.py
DESCRIPTION
calculates Watson's V statistic from input files through Monte Carlo simulation in order to test whether normal and reversed populations could have been drawn from a common mean (equivalent to watsonV.py). Also provides the critical angle between the two sample mean directions and the corresponding McFadden and McElhinny (1990) classification.
INPUT FORMAT
takes dec/inc as first two columns in two space delimited files (one file for normal directions, one file for reversed directions).
SYNTAX
revtest_MM1990.py [command line options]
OPTIONS
-h prints help message and quits
-f FILE
-f2 FILE
-P (don't plot the Watson V cdf)
OUTPUT
Watson's V between the two populations and the Monte Carlo Critical Value Vc.
M&M1990 angle, critical angle and classification
Plot of Watson's V CDF from Monte Carlo simulation (red line), V is solid and Vc is dashed.
"""
D1,D2=[],[]
plot=1
Flip=1
if '-h' in sys.argv: # check if help is needed
print(main.__doc__)
sys.exit() # graceful quit
if '-P' in sys.argv: plot=0
if '-f' in sys.argv:
ind=sys.argv.index('-f')
file1=sys.argv[ind+1]
f1=open(file1,'r')
for line in f1.readlines():
rec=line.split()
Dec,Inc=float(rec[0]),float(rec[1])
D1.append([Dec,Inc,1.])
f1.close()
if '-f2' in sys.argv:
ind=sys.argv.index('-f2')
file2=sys.argv[ind+1]
f2=open(file2,'r')
print("be patient, your computer is doing 5000 simulations...")
for line in f2.readlines():
rec=line.split()
Dec,Inc=float(rec[0]),float(rec[1])
D2.append([Dec,Inc,1.])
f2.close()
#take the antipode for the directions in file 2
D2_flip=[]
for rec in D2:
d,i=(rec[0]-180.)%360.,-rec[1]
D2_flip.append([d,i,1.])
pars_1=pmag.fisher_mean(D1)
pars_2=pmag.fisher_mean(D2_flip)
cart_1=pmag.dir2cart([pars_1["dec"],pars_1["inc"],pars_1["r"]])
cart_2=pmag.dir2cart([pars_2['dec'],pars_2['inc'],pars_2["r"]])
Sw=pars_1['k']*pars_1['r']+pars_2['k']*pars_2['r'] # k1*r1+k2*r2
xhat_1=pars_1['k']*cart_1[0]+pars_2['k']*cart_2[0] # k1*x1+k2*x2
xhat_2=pars_1['k']*cart_1[1]+pars_2['k']*cart_2[1] # k1*y1+k2*y2
xhat_3=pars_1['k']*cart_1[2]+pars_2['k']*cart_2[2] # k1*z1+k2*z2
Rw=numpy.sqrt(xhat_1**2+xhat_2**2+xhat_3**2)
V=2*(Sw-Rw)
#
#keep weighted sum for later when determining the "critical angle" let's save it as Sr (notation of McFadden and McElhinny, 1990)
#
Sr=Sw
#
# do monte carlo simulation of datasets with same kappas, but common mean
#
counter,NumSims=0,5000
Vp=[] # set of Vs from simulations
for k in range(NumSims):
#
# get a set of N1 fisher distributed vectors with k1, calculate fisher stats
#
Dirp=[]
for i in range(pars_1["n"]):
Dirp.append(pmag.fshdev(pars_1["k"]))
pars_p1=pmag.fisher_mean(Dirp)
#
# get a set of N2 fisher distributed vectors with k2, calculate fisher stats
#
Dirp=[]
for i in range(pars_2["n"]):
Dirp.append(pmag.fshdev(pars_2["k"]))
pars_p2=pmag.fisher_mean(Dirp)
#
# get the V for these
#
Vk=pmag.vfunc(pars_p1,pars_p2)
Vp.append(Vk)
#
# sort the Vs, get Vcrit (95th percentile one)
#
Vp.sort()
k=int(.95*NumSims)
Vcrit=Vp[k]
#
# equation 18 of McFadden and McElhinny, 1990 calculates the critical value of R (Rwc)
#
Rwc=Sr-(old_div(Vcrit,2))
#
#following equation 19 of McFadden and McElhinny (1990) the critical angle is calculated.
#
k1=pars_1['k']
k2=pars_2['k']
R1=pars_1['r']
R2=pars_2['r']
critical_angle=numpy.degrees(numpy.arccos(old_div(((Rwc**2)-((k1*R1)**2)-((k2*R2)**2)),(2*k1*R1*k2*R2))))
D1_mean=(pars_1['dec'],pars_1['inc'])
D2_mean=(pars_2['dec'],pars_2['inc'])
angle=pmag.angle(D1_mean,D2_mean)
#
# print the results of the test
#
print("")
print("Results of Watson V test: ")
print("")
print("Watson's V: " '%.1f' %(V))
print("Critical value of V: " '%.1f' %(Vcrit))
if V<Vcrit:
print('"Pass": Since V is less than Vcrit, the null hypothesis that the two populations are drawn from distributions that share a common mean direction (antipodal to one another) cannot be rejected.')
elif V>Vcrit:
print('"Fail": Since V is greater than Vcrit, the two means can be distinguished at the 95% confidence level.')
print("")
print("M&M1990 classification:")
print("")
print("Angle between data set means: " '%.1f'%(angle))
print("Critical angle of M&M1990: " '%.1f'%(critical_angle))
if V>Vcrit:
print("")
elif V<Vcrit:
if critical_angle<5:
print("The McFadden and McElhinny (1990) classification for this test is: 'A'")
elif critical_angle<10:
print("The McFadden and McElhinny (1990) classification for this test is: 'B'")
elif critical_angle<20:
print("The McFadden and McElhinny (1990) classification for this test is: 'C'")
else:
print("The McFadden and McElhinny (1990) classification for this test is: 'INDETERMINATE;")
if plot==1:
CDF={'cdf':1}
pmagplotlib.plot_init(CDF['cdf'],5,5)
p1 = pmagplotlib.plot_cdf(CDF['cdf'],Vp,"Watson's V",'r',"")
p2 = pmagplotlib.plot_vs(CDF['cdf'],[V],'g','-')
p3 = pmagplotlib.plot_vs(CDF['cdf'],[Vp[k]],'b','--')
pmagplotlib.draw_figs(CDF)
files,fmt={},'svg'
if file2!="":
files['cdf']='WatsonsV_'+file1+'_'+file2+'.'+fmt
else:
files['cdf']='WatsonsV_'+file1+'.'+fmt
if pmagplotlib.isServer:
black = '#000000'
purple = '#800080'
titles={}
titles['cdf']='Cumulative Distribution'
CDF = pmagplotlib.add_borders(CDF,titles,black,purple)
pmagplotlib.save_plots(CDF,files)
else:
ans=input(" S[a]ve to save plot, [q]uit without saving: ")
if ans=="a": pmagplotlib.save_plots(CDF,files)
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
-n 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 '-n' in sys.argv:
ind = sys.argv.index('-n')
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.plot_eq(PLTS['eq'], data, 'Data')
# this is a problem
#if plot==0:pmagplotlib.draw_figs(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.plot_ei(PLTS['ei'], Es, Is, flat_f)
pmagplotlib.plot_v2s(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.plot_ei(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.plot_ei(PLTS['ei'], Eexp, I, 1)
pmagplotlib.plot_cdf(PLTS['cdf'], I, 'Inclinations', 'r', title)
pmagplotlib.plot_vs(PLTS['cdf'], [I[lower], I[upper]], 'b', '--')
pmagplotlib.plot_vs(PLTS['cdf'], [Inc], 'g', '-')
pmagplotlib.plot_vs(PLTS['cdf'], [Io], 'k', '-')
if plot == 0:
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]))
print("Io Inc I_lower, I_upper, Elon, E_lower, E_upper")
pmagplotlib.draw_figs(PLTS)
ans = ""
while ans not in ['q', 'a']:
ans = input("S[a]ve plots - <q> to quit: ")
if ans == 'q':
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.save_plots(PLTS, files)
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()
file1_name=os.path.split(file1)[1].split('.')[0]
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()
file2_name=os.path.split(file2)[1].split('.')[0]
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.plot_cdf(CDF['cdf'],Vp,"Watson's V",'r',"")
pmagplotlib.plot_vs(CDF['cdf'],[V],'g','-')
pmagplotlib.plot_vs(CDF['cdf'],[Vp[k]],'b','--')
if plot==0:pmagplotlib.draw_figs(CDF)
files={}
if pmagplotlib.isServer: # use server plot naming convention
if file2!="":
files['cdf']='watsons_v_'+file1+'_'+file2+'.'+fmt
else:
files['cdf']='watsons_v_'+file1+'.'+fmt
else: # use more readable plot naming convention
if file2!="":
files['cdf']='watsons_v_'+file1_name+'_'+file2_name+'.'+fmt
else:
files['cdf']='watsons_v_'+file1_name+'.'+fmt
if pmagplotlib.isServer:
black = '#000000'
purple = '#800080'
titles={}
titles['cdf']='Cumulative Distribution'
CDF = pmagplotlib.add_borders(CDF,titles,black,purple)
pmagplotlib.save_plots(CDF,files)
elif plot==0:
ans=input(" S[a]ve to save plot, [q]uit without saving: ")
if ans=="a": pmagplotlib.save_plots(CDF,files)
if plot==1: # save and quit silently
pmagplotlib.save_plots(CDF,files)
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()
file1_name = os.path.split(file1)[1].split('.')[0]
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()
file2_name = os.path.split(file2)[1].split('.')[0]
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.plot_cdf(CDF['cdf'], Vp, "Watson's V", 'r', "")
pmagplotlib.plot_vs(CDF['cdf'], [V], 'g', '-')
pmagplotlib.plot_vs(CDF['cdf'], [Vp[k]], 'b', '--')
if plot == 0: pmagplotlib.draw_figs(CDF)
files = {}
if pmagplotlib.isServer: # use server plot naming convention
if file2 != "":
files['cdf'] = 'watsons_v_' + file1 + '_' + file2 + '.' + fmt
else:
files['cdf'] = 'watsons_v_' + file1 + '.' + fmt
else: # use more readable plot naming convention
if file2 != "":
files[
'cdf'] = 'watsons_v_' + file1_name + '_' + file2_name + '.' + fmt
else:
files['cdf'] = 'watsons_v_' + file1_name + '.' + fmt
if pmagplotlib.isServer:
black = '#000000'
purple = '#800080'
titles = {}
titles['cdf'] = 'Cumulative Distribution'
CDF = pmagplotlib.add_borders(CDF, titles, black, purple)
pmagplotlib.save_plots(CDF, files)
elif plot == 0:
ans = input(" S[a]ve to save plot, [q]uit without saving: ")
if ans == "a": pmagplotlib.save_plots(CDF, files)
if plot == 1: # save and quit silently
pmagplotlib.save_plots(CDF, files)