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
-sav save figures and quit
INPUT FILE
Dec Inc Dip_Direction Dip in space delimited file
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 post-tilt magnetization is indicated
If the 95% conf bounds include 100, then a pre-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,plot='svg',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')
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 '-sav' in sys.argv:plot=1
if '-b' in sys.argv:
ind=sys.argv.index('-b')
min=int(sys.argv[ind+1])
max=int(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.plot_eq(PLTS['geo'],DIDDs,'Geographic')
D,I=pmag.dotilt_V(DIDDs)
TCs=numpy.array([D,I]).transpose()
pmagplotlib.plot_eq(PLTS['strat'],TCs,'Stratigraphic')
if not set_env.IS_WIN:
if plot==0:pmagplotlib.draw_figs(PLTS)
Percs=list(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)
files={}
for key in list(PLTS.keys()):
files[key]=('foldtest_'+'%s'%(key.strip()[:2])+'.'+fmt)
if plot==0:
pmagplotlib.draw_figs(PLTS)
ans= input('S[a]ve all figures, <Return> to quit ')
if ans!='a':
print("Good bye")
sys.exit()
pmagplotlib.save_plots(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 sites formatted file [default for 3.0 is sites.txt, for 2.5, pmag_sites.txt]
-fsa samples formatted file
-fsi sites formatted file
-exc use criteria to set acceptance criteria (supported only for data model 3)
-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
-sav saves plots and quits
-DM NUM MagIC data model number (2 or 3, default 3)
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
"""
if '-h' in sys.argv: # check if help is needed
print(main.__doc__)
sys.exit() # graceful quit
kappa = 0
dir_path = pmag.get_named_arg("-WD", ".")
nboot = int(float(pmag.get_named_arg("-n", 1000))) # number of bootstraps
fmt = pmag.get_named_arg("-fmt", "svg")
data_model_num = int(float(pmag.get_named_arg("-DM", 3)))
if data_model_num == 3:
infile = pmag.get_named_arg("-f", 'sites.txt')
orfile = 'samples.txt'
site_col = 'site'
dec_col = 'dir_dec'
inc_col = 'dir_inc'
tilt_col = 'dir_tilt_correction'
dipkey, azkey = 'bed_dip', 'bed_dip_direction'
crit_col = 'criterion'
critfile = 'criteria.txt'
else:
infile = pmag.get_named_arg("-f", 'pmag_sites.txt')
orfile = 'er_samples.txt'
site_col = 'er_site_name'
dec_col = 'site_dec'
inc_col = 'site_inc'
tilt_col = 'site_tilt_correction'
dipkey, azkey = 'sample_bed_dip', 'sample_bed_dip_direction'
crit_col = 'pmag_criteria_code'
critfile = 'pmag_criteria.txt'
if '-sav' in sys.argv:
plot = 1
else:
plot = 0
if '-b' in sys.argv:
ind = sys.argv.index('-b')
untilt_min = int(sys.argv[ind+1])
untilt_max = int(sys.argv[ind+2])
else:
untilt_min, untilt_max = -10, 150
if '-fsa' in sys.argv:
orfile = pmag.get_named_arg("-fsa", "")
elif '-fsi' in sys.argv:
orfile = pmag.get_named_arg("-fsi", "")
if data_model_num == 3:
dipkey, azkey = 'bed_dip', 'bed_dip_direction'
else:
dipkey, azkey = 'site_bed_dip', 'site_bed_dip_direction'
else:
if data_model_num == 3:
orfile = 'sites.txt'
else:
orfile = 'pmag_sites.txt'
orfile = pmag.resolve_file_name(orfile, dir_path)
infile = pmag.resolve_file_name(infile, dir_path)
critfile = pmag.resolve_file_name(critfile, dir_path)
df = pd.read_csv(infile, sep='\t', header=1)
# keep only records with tilt_col
data = df.copy()
data = data[data[tilt_col].notnull()]
data = data.where(data.notnull(), "")
# turn into pmag data list
data = list(data.T.apply(dict))
# get orientation data
if data_model_num == 3:
# often orientation will be in infile (sites table)
if os.path.split(orfile)[1] == os.path.split(infile)[1]:
ordata = df[df[azkey].notnull()]
ordata = ordata[ordata[dipkey].notnull()]
ordata = list(ordata.T.apply(dict))
# sometimes orientation might be in a sample file instead
else:
ordata = pd.read_csv(orfile, sep='\t', header=1)
ordata = list(ordata.T.apply(dict))
else:
ordata, file_type = pmag.magic_read(orfile)
if '-exc' in sys.argv:
crits, file_type = pmag.magic_read(critfile)
SiteCrits = []
for crit in crits:
if crit[crit_col] == "DE-SITE":
SiteCrits.append(crit)
#break
# get to work
#
PLTS = {'geo': 1, 'strat': 2, 'taus': 3} # make plot dictionary
if not set_env.IS_WIN:
pmagplotlib.plot_init(PLTS['geo'], 5, 5)
pmagplotlib.plot_init(PLTS['strat'], 5, 5)
pmagplotlib.plot_init(PLTS['taus'], 5, 5)
if data_model_num == 2:
GEOrecs = pmag.get_dictitem(data, tilt_col, '0', 'T')
else:
GEOrecs = data
if len(GEOrecs) > 0: # have some geographic data
num_dropped = 0
DIDDs = [] # set up list for dec inc dip_direction, dip
for rec in GEOrecs: # parse data
dip, dip_dir = 0, -1
Dec = float(rec[dec_col])
Inc = float(rec[inc_col])
orecs = pmag.get_dictitem(
ordata, site_col, rec[site_col], 'T')
if len(orecs) > 0:
if orecs[0][azkey] != "":
dip_dir = float(orecs[0][azkey])
if orecs[0][dipkey] != "":
dip = float(orecs[0][dipkey])
if dip != 0 and dip_dir != -1:
if '-exc' in sys.argv:
keep = 1
for site_crit in SiteCrits:
crit_name = site_crit['table_column'].split('.')[1]
if crit_name and crit_name in rec.keys() and rec[crit_name]:
# get the correct operation (<, >=, =, etc.)
op = OPS[site_crit['criterion_operation']]
# then make sure the site record passes
if op(float(rec[crit_name]), float(site_crit['criterion_value'])):
keep = 0
if keep == 1:
DIDDs.append([Dec, Inc, dip_dir, dip])
else:
num_dropped += 1
else:
DIDDs.append([Dec, Inc, dip_dir, dip])
if num_dropped:
print("-W- Dropped {} records because each failed one or more criteria".format(num_dropped))
else:
print('no geographic directional data found')
sys.exit()
pmagplotlib.plot_eq(PLTS['geo'], DIDDs, 'Geographic')
data = np.array(DIDDs)
D, I = pmag.dotilt_V(data)
TCs = np.array([D, I]).transpose()
pmagplotlib.plot_eq(PLTS['strat'], TCs, 'Stratigraphic')
if plot == 0:
pmagplotlib.draw_figs(PLTS)
Percs = list(range(untilt_min, untilt_max))
Cdf, Untilt = [], []
plt.figure(num=PLTS['taus'])
print('doing ', nboot, ' iterations...please be patient.....')
for n in range(nboot): # 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 = np.array([1., 1., 1., 0.01*perc])
D, I = pmag.dotilt_V(PDs*tilt)
TCs = np.array([D, I]).transpose()
ppars = pmag.doprinc(TCs) # get principal directions
Taus.append(ppars['tau1'])
if n < 25:
plt.plot(Percs, Taus, 'r--')
# tilt that gives maximum tau
Untilt.append(Percs[Taus.index(np.max(Taus))])
Cdf.append(float(n) / float(nboot))
plt.plot(Percs, Taus, 'k')
plt.xlabel('% Untilting')
plt.ylabel('tau_1 (red), CDF (green)')
Untilt.sort() # now for CDF of tilt of maximum tau
plt.plot(Untilt, Cdf, 'g')
lower = int(.025*nboot)
upper = int(.975*nboot)
plt.axvline(x=Untilt[lower], ymin=0, ymax=1, linewidth=1, linestyle='--')
plt.axvline(x=Untilt[upper], ymin=0, ymax=1, linewidth=1, linestyle='--')
tit = '%i - %i %s' % (Untilt[lower], Untilt[upper], 'Percent Unfolding')
print(tit)
plt.title(tit)
if plot == 0:
pmagplotlib.draw_figs(PLTS)
ans = input('S[a]ve all figures, <Return> to quit \n ')
if ans != 'a':
print("Good bye")
sys.exit()
files = {}
for key in list(PLTS.keys()):
files[key] = ('foldtest_'+'%s' % (key.strip()[:2])+'.'+fmt)
pmagplotlib.save_plots(PLTS, 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
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 sites formatted file [default for 3.0 is sites.txt, for 2.5, pmag_sites.txt]
-fsa samples formatted file
-fsi sites formatted file
-exc use criteria to set acceptance criteria (supported only for data model 3)
-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
-sav saves plots and quits
-DM NUM MagIC data model number (2 or 3, default 3)
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
"""
if '-h' in sys.argv: # check if help is needed
print(main.__doc__)
sys.exit() # graceful quit
kappa = 0
dir_path = pmag.get_named_arg("-WD", ".")
nboot = int(float(pmag.get_named_arg("-n", 1000))) # number of bootstraps
fmt = pmag.get_named_arg("-fmt", "svg")
data_model_num = int(float(pmag.get_named_arg("-DM", 3)))
if data_model_num == 3:
infile = pmag.get_named_arg("-f", 'sites.txt')
orfile = 'samples.txt'
site_col = 'site'
dec_col = 'dir_dec'
inc_col = 'dir_inc'
tilt_col = 'dir_tilt_correction'
dipkey, azkey = 'bed_dip', 'bed_dip_direction'
crit_col = 'criterion'
critfile = 'criteria.txt'
else:
infile = pmag.get_named_arg("-f", 'pmag_sites.txt')
orfile = 'er_samples.txt'
site_col = 'er_site_name'
dec_col = 'site_dec'
inc_col = 'site_inc'
tilt_col = 'site_tilt_correction'
dipkey, azkey = 'sample_bed_dip', 'sample_bed_dip_direction'
crit_col = 'pmag_criteria_code'
critfile = 'pmag_criteria.txt'
if '-sav' in sys.argv:
plot = 1
else:
plot = 0
if '-b' in sys.argv:
ind = sys.argv.index('-b')
untilt_min = int(sys.argv[ind + 1])
untilt_max = int(sys.argv[ind + 2])
else:
untilt_min, untilt_max = -10, 150
if '-fsa' in sys.argv:
orfile = pmag.get_named_arg("-fsa", "")
elif '-fsi' in sys.argv:
orfile = pmag.get_named_arg("-fsi", "")
if data_model_num == 3:
dipkey, azkey = 'bed_dip', 'bed_dip_direction'
else:
dipkey, azkey = 'site_bed_dip', 'site_bed_dip_direction'
else:
if data_model_num == 3:
orfile = 'sites.txt'
else:
orfile = 'pmag_sites.txt'
orfile = pmag.resolve_file_name(orfile, dir_path)
infile = pmag.resolve_file_name(infile, dir_path)
critfile = pmag.resolve_file_name(critfile, dir_path)
df = pd.read_csv(infile, sep='\t', header=1)
# keep only records with tilt_col
data = df.copy()
data = data[data[tilt_col].notnull()]
data = data.where(data.notnull(), "")
# turn into pmag data list
data = list(data.T.apply(dict))
# get orientation data
if data_model_num == 3:
# often orientation will be in infile (sites table)
if os.path.split(orfile)[1] == os.path.split(infile)[1]:
ordata = df[df[azkey].notnull()]
ordata = ordata[ordata[dipkey].notnull()]
ordata = list(ordata.T.apply(dict))
# sometimes orientation might be in a sample file instead
else:
ordata = pd.read_csv(orfile, sep='\t', header=1)
ordata = list(ordata.T.apply(dict))
else:
ordata, file_type = pmag.magic_read(orfile)
if '-exc' in sys.argv:
crits, file_type = pmag.magic_read(critfile)
SiteCrits = []
for crit in crits:
if crit[crit_col] == "DE-SITE":
SiteCrits.append(crit)
#break
# get to work
#
PLTS = {'geo': 1, 'strat': 2, 'taus': 3} # make plot dictionary
if not set_env.IS_WIN:
pmagplotlib.plot_init(PLTS['geo'], 5, 5)
pmagplotlib.plot_init(PLTS['strat'], 5, 5)
pmagplotlib.plot_init(PLTS['taus'], 5, 5)
if data_model_num == 2:
GEOrecs = pmag.get_dictitem(data, tilt_col, '0', 'T')
else:
GEOrecs = data
if len(GEOrecs) > 0: # have some geographic data
num_dropped = 0
DIDDs = [] # set up list for dec inc dip_direction, dip
for rec in GEOrecs: # parse data
dip, dip_dir = 0, -1
Dec = float(rec[dec_col])
Inc = float(rec[inc_col])
orecs = pmag.get_dictitem(ordata, site_col, rec[site_col], 'T')
if len(orecs) > 0:
if orecs[0][azkey] != "":
dip_dir = float(orecs[0][azkey])
if orecs[0][dipkey] != "":
dip = float(orecs[0][dipkey])
if dip != 0 and dip_dir != -1:
if '-exc' in sys.argv:
keep = 1
for site_crit in SiteCrits:
crit_name = site_crit['table_column'].split('.')[1]
if crit_name and crit_name in rec.keys(
) and rec[crit_name]:
# get the correct operation (<, >=, =, etc.)
op = OPS[site_crit['criterion_operation']]
# then make sure the site record passes
if op(float(rec[crit_name]),
float(site_crit['criterion_value'])):
keep = 0
if keep == 1:
DIDDs.append([Dec, Inc, dip_dir, dip])
else:
num_dropped += 1
else:
DIDDs.append([Dec, Inc, dip_dir, dip])
if num_dropped:
print(
"-W- Dropped {} records because each failed one or more criteria"
.format(num_dropped))
else:
print('no geographic directional data found')
sys.exit()
pmagplotlib.plot_eq(PLTS['geo'], DIDDs, 'Geographic')
data = np.array(DIDDs)
D, I = pmag.dotilt_V(data)
TCs = np.array([D, I]).transpose()
pmagplotlib.plot_eq(PLTS['strat'], TCs, 'Stratigraphic')
if plot == 0:
pmagplotlib.draw_figs(PLTS)
Percs = list(range(untilt_min, untilt_max))
Cdf, Untilt = [], []
plt.figure(num=PLTS['taus'])
print('doing ', nboot, ' iterations...please be patient.....')
for n in range(
nboot
): # 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 = np.array([1., 1., 1., 0.01 * perc])
D, I = pmag.dotilt_V(PDs * tilt)
TCs = np.array([D, I]).transpose()
ppars = pmag.doprinc(TCs) # get principal directions
Taus.append(ppars['tau1'])
if n < 25:
plt.plot(Percs, Taus, 'r--')
# tilt that gives maximum tau
Untilt.append(Percs[Taus.index(np.max(Taus))])
Cdf.append(float(n) / float(nboot))
plt.plot(Percs, Taus, 'k')
plt.xlabel('% Untilting')
plt.ylabel('tau_1 (red), CDF (green)')
Untilt.sort() # now for CDF of tilt of maximum tau
plt.plot(Untilt, Cdf, 'g')
lower = int(.025 * nboot)
upper = int(.975 * nboot)
plt.axvline(x=Untilt[lower], ymin=0, ymax=1, linewidth=1, linestyle='--')
plt.axvline(x=Untilt[upper], ymin=0, ymax=1, linewidth=1, linestyle='--')
tit = '%i - %i %s' % (Untilt[lower], Untilt[upper], 'Percent Unfolding')
print(tit)
plt.title(tit)
if plot == 0:
pmagplotlib.draw_figs(PLTS)
ans = input('S[a]ve all figures, <Return> to quit \n ')
if ans != 'a':
print("Good bye")
sys.exit()
files = {}
for key in list(PLTS.keys()):
files[key] = ('foldtest_' + '%s' % (key.strip()[:2]) + '.' + fmt)
pmagplotlib.save_plots(PLTS, 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
-nb N specify number of bootstraps - the more the better, but slower!, default is 1000
-sc uses a "site-level" correction to a Fisherian distribution instead
of a "study-level" correction to a TK03-consistent distribution.
Note that many directions (~ 100) are needed for this correction to be reliable.
-fmt [svg,png,eps,pdf..] change plot format, default is svg
-sav saves the figures and quits
INPUT
dec/inc pairs, delimited with space or tabs
OUTPUT
four plots: 1) equal area plot of original directions
2) Elongation/inclination pairs as a function of f, data plus 25 bootstrap samples
3) Cumulative distribution of bootstrapped optimal inclinations plus uncertainties.
Estimate from original data set plotted as solid line
4) Orientation of principle direction through unflattening
NOTE: If distribution does not have a solution, plot labeled: Pathological. Some bootstrap samples may have
valid solutions and those are plotted in the CDFs and E/I plot.
"""
fmt, nb = 'svg', 1000
plot = 0
if '-h' in sys.argv:
print(main.__doc__)
sys.exit() # graceful quit
elif '-f' in sys.argv:
ind = sys.argv.index('-f')
file = sys.argv[ind + 1]
else:
print(main.__doc__)
sys.exit()
if '-nb' in sys.argv:
ind = sys.argv.index('-nb')
nb = int(sys.argv[ind + 1])
if '-sc' in sys.argv:
site_correction = True
else:
site_correction = False
if '-fmt' in sys.argv:
ind = sys.argv.index('-fmt')
fmt = sys.argv[ind + 1]
if '-sav' in sys.argv: plot = 1
data = numpy.loadtxt(file)
upper, lower = int(round(.975 * nb)), int(round(.025 * nb))
E, I = [], []
PLTS = {'eq': 1, 'ei': 2, 'cdf': 3, 'v2': 4}
pmagplotlib.plot_init(PLTS['eq'], 6, 6)
pmagplotlib.plot_init(PLTS['ei'], 5, 5)
pmagplotlib.plot_init(PLTS['cdf'], 5, 5)
pmagplotlib.plot_init(PLTS['v2'], 5, 5)
pmagplotlib.plotEQ(PLTS['eq'], data, 'Data')
if plot == 0: pmagplotlib.drawFIGS(PLTS)
ppars = pmag.doprinc(data)
Io = ppars['inc']
n = ppars["N"]
Es, Is, Fs, V2s = pmag.find_f(data)
if site_correction:
Inc, Elong = Is[Es.index(min(Es))], Es[Es.index(min(Es))]
flat_f = Fs[Es.index(min(Es))]
else:
Inc, Elong = Is[-1], Es[-1]
flat_f = Fs[-1]
pmagplotlib.plotEI(PLTS['ei'], Es, Is, flat_f)
pmagplotlib.plotV2s(PLTS['v2'], V2s, Is, flat_f)
b = 0
print("Bootstrapping.... be patient")
while b < nb:
bdata = pmag.pseudo(data)
Esb, Isb, Fsb, V2sb = pmag.find_f(bdata)
if b < 25:
pmagplotlib.plotEI(PLTS['ei'], Esb, Isb, Fsb[-1])
if Esb[-1] != 0:
ppars = pmag.doprinc(bdata)
if site_correction:
I.append(abs(Isb[Esb.index(min(Esb))]))
E.append(Esb[Esb.index(min(Esb))])
else:
I.append(abs(Isb[-1]))
E.append(Esb[-1])
b += 1
if b % 25 == 0: print(b, ' out of ', nb)
I.sort()
E.sort()
Eexp = []
for i in I:
Eexp.append(pmag.EI(i))
if Inc == 0:
title = 'Pathological Distribution: ' + '[%7.1f, %7.1f]' % (I[lower],
I[upper])
else:
title = '%7.1f [%7.1f, %7.1f]' % (Inc, I[lower], I[upper])
pmagplotlib.plotEI(PLTS['ei'], Eexp, I, 1)
pmagplotlib.plotCDF(PLTS['cdf'], I, 'Inclinations', 'r', title)
pmagplotlib.plotVs(PLTS['cdf'], [I[lower], I[upper]], 'b', '--')
pmagplotlib.plotVs(PLTS['cdf'], [Inc], 'g', '-')
pmagplotlib.plotVs(PLTS['cdf'], [Io], 'k', '-')
if plot == 0:
pmagplotlib.drawFIGS(PLTS)
print("Io Inc I_lower, I_upper, Elon, E_lower, E_upper")
print('%7.1f %s %7.1f _ %7.1f ^ %7.1f: %6.4f _ %6.4f ^ %6.4f' %
(Io, " => ", Inc, I[lower], I[upper], Elong, E[lower], E[upper]))
ans = input("S[a]ve plots - <return> to quit: ")
if ans != 'a':
print("\n Good bye\n")
sys.exit()
files = {}
files['eq'] = 'findEI_eq.' + fmt
files['ei'] = 'findEI_ei.' + fmt
files['cdf'] = 'findEI_cdf.' + fmt
files['v2'] = 'findEI_v2.' + fmt
pmagplotlib.saveP(PLTS, files)
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
-sav save figures and quit
INPUT FILE
Dec Inc Dip_Direction Dip in space delimited file
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 post-tilt magnetization is indicated
If the 95% conf bounds include 100, then a pre-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, plot = 'svg', 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')
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 '-sav' in sys.argv: plot = 1
if '-b' in sys.argv:
ind = sys.argv.index('-b')
min = int(sys.argv[ind + 1])
max = int(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')
if plot == 0: 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)
files = {}
for key in PLTS.keys():
files[key] = ('foldtest_' + '%s' % (key.strip()[:2]) + '.' + fmt)
if plot == 0:
pmagplotlib.drawFIGS(PLTS)
ans = raw_input('S[a]ve all figures, <Return> to quit ')
if ans != 'a':
print "Good bye"
sys.exit()
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]
-fsi er_sites formatted file
-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
-sav saves plots and quits
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'
dipkey, azkey = 'sample_bed_dip', 'sample_bed_dip_direction'
fmt = 'svg'
plot = 0
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 '-sav' in sys.argv: plot = 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]
elif '-fsi' in sys.argv:
ind = sys.argv.index('-fsi')
orfile = sys.argv[ind + 1]
dipkey, azkey = 'site_bed_dip', 'site_bed_dip_direction'
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][azkey] != "": dip_dir = float(orecs[0][azkey])
if orecs[0][dipkey] != "": dip = float(orecs[0][dipkey])
if dip != 0 and dip_dir != -1:
if '-exc' in sys.argv:
keep = 1
for key in list(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')
if plot == 0: pmagplotlib.drawFIGS(PLTS)
Percs = list(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(old_div(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)
if plot == 0:
pmagplotlib.drawFIGS(PLTS)
ans = input('S[a]ve all figures, <Return> to quit \n ')
if ans != 'a':
print("Good bye")
sys.exit()
files = {}
for key in list(PLTS.keys()):
files[key] = ('foldtest_' + '%s' % (key.strip()[:2]) + '.' + fmt)
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]
-fsi er_sites formatted file
-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
-sav saves plots and quits
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'
dipkey,azkey='sample_bed_dip','sample_bed_dip_direction'
fmt='svg'
plot=0
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 '-sav' in sys.argv:plot=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]
elif '-fsi' in sys.argv:
ind=sys.argv.index('-fsi')
orfile=sys.argv[ind+1]
dipkey,azkey='site_bed_dip','site_bed_dip_direction'
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][azkey]!="":dip_dir=float(orecs[0][azkey])
if orecs[0][dipkey]!="":dip=float(orecs[0][dipkey])
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')
if plot==0: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)
if plot==0:
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)
