def main():
"""
NAME
stats.py
DEFINITION
calculates Gauss statistics for input data
SYNTAX
stats [command line options][< filename]
INPUT
single column of numbers
OPTIONS
-h prints help message and quits
-i interactive entry of file name
-f input file name
-F output file name
OUTPUT
N, mean, sum, sigma, (%)
where sigma is the standard deviation
where % is sigma as percentage of the mean
stderr is the standard error and
95% conf.= 1.96*sigma/sqrt(N)
"""
if '-h' in sys.argv:
print main.__doc__
sys.exit()
if '-i' in sys.argv:
file=raw_input("Enter file name: ")
f=open(file,'rU')
elif '-f' in sys.argv:
ind=sys.argv.index('-f')
file=sys.argv[ind+1]
f=open(file,'rU')
else:
f=sys.stdin
ofile = ""
if '-F' in sys.argv:
ind = sys.argv.index('-F')
ofile= sys.argv[ind+1]
out = open(ofile, 'w + a')
data=f.readlines()
dat=[]
sum=0
for line in data:
rec=line.split()
dat.append(float(rec[0]))
sum+=float(float(rec[0]))
mean,std=pmag.gausspars(dat)
outdata = len(dat),mean,sum,std,100*std/mean
if ofile == "":
print len(dat),mean,sum,std,100*std/mean
else:
for i in outdata:
i = str(i)
out.write(i + " ")
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
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_nn'],vgp['average_lat']=rec[2],rec[3],rec[4]
slats.append(float(rec[4]))
else:
vgp['average_k'],vgp['average_nn'],vgp['average_lat']="0","0","0"
if 90.-(float(vgp['vgp_lat']))<=cutoff and float(vgp['average_k'])>=kappa and int(vgp['average_nn'])>=n: Vgps.append(vgp)
if spin==0: # do transformation to pole
ppars=pmag.doprinc(Pvgps)
for vgp in Vgps:
vlon,vlat=pmag.dotilt(float(vgp['vgp_lon']),float(vgp['vgp_lat']),ppars['dec']-180.,90.-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])
def main():
"""
NAME
pick_AC_specimens.py
DESCRIPTION
finds whether anisotropy correction yeilds more tightly
grouped intensities than uncorrected data.
picks either all corrected or all uncorrected data and
puts in pmag_specimen format file
SYNTAX
pick_AC_specimens.py [-h][-i][-fu TFILE][-fc AFILE][-F FILE]
OPTIONS
-h prints help message and quits
-i allows interactive setting of file names
-fu TFILE uncorrected pmag_specimen format file with thellier interpretations
created by thellier_magic_redo.py
-fc AFILE anisotropy corrected pmag_specimen format file
created by thellier_magic_redo.py
-fcr CRIT pmag_criteria.txt format file with acceptance criteria
-opt SIG use the optimizer_thelpars.txt file for criteria
-F FILE pmag_specimens format output file with "best" set of data
DEFAULTS
TFILE: thellier_specimens.txt
AFILE: AC_specimens.txt
FILE: pmag_specimens.txt
"""
tspec="thellier_specimens.txt"
aspec="AC_specimens.txt"
ofile="pmag_specimens.txt"
critfile="pmag_criteria.txt"
ACSamplist,Samplist,sigmin=[],[],10000
GoodSamps,SpecOuts=[],[]
P={'cdf':1}
pmagplotlib.plot_init(P['cdf'],5,5)
# get arguments from command line
if '-h' in sys.argv:
print main.__doc__
sys.exit()
if '-fu' in sys.argv:
ind=sys.argv.index('-fu')
tspec=sys.argv[ind+1]
if '-fc' in sys.argv:
ind=sys.argv.index('-fc')
aspec=sys.argv[ind+1]
if '-fcr' in sys.argv:
ind=sys.argv.index('-fcr')
critfile=sys.argv[ind+1]
if '-opt' in sys.argv:
ind=sys.argv.index('-opt')
critfile='optimum_thelpars.txt'
sigcutoff=sys.argv[ind+1]
if '-F' in sys.argv:
ind=sys.argv.index('-F')
ofile=sys.argv[ind+1]
if '-i' in sys.argv:
file=raw_input(" thellier_specimnens.txt file [thellier_specimens.txt]: ")
if file!="":tfile=file
file=raw_input(" AC_specimnens.txt file [AC_specimens.txt]: ")
if file!="":afile=file
file=raw_input(" pmag_specimnens.txt file [pmag_specimens.txt]: ")
if file!="":ofile=file
# read in pmag_specimens file
Specs,file_type=pmag.magic_read(tspec)
Speclist=pmag.get_specs(Specs)
ACSpecs,file_type=pmag.magic_read(aspec)
ACspeclist=pmag.get_specs(ACSpecs)
Crits,file_type=pmag.magic_read(critfile)
keys=['specimen_int_mad','specimen_drats','specimen_fvds','specimen_b_beta','specimen_Z','specimen_md','specimen_dang']
accept={}
for crit in Crits:
if critfile!='optimum_thelpars.txt':
if crit['pmag_criteria_code']=='IE-SPEC':
for key in keys: accept[key]=float(crit[key]) # assign acceptance criteria
break
else:
if float(crit['sample_int_sigma_perc'])==float(sigcutoff):
for key in keys: accept[key]=float(crit[key])
Diff=[]
for aspec in ACSpecs:
grade,kill=pmag.grade(aspec,accept)
if grade==len(accept):
print 'AC: ',aspec["er_specimen_name"],'%i'%(1e6*float(aspec["specimen_int"]))
aint=(1e6*float(aspec["specimen_int"]))
for spec in Specs:
if spec["er_specimen_name"]==aspec['er_specimen_name']:
print 'UC: ',spec["er_specimen_name"],'%i'%(1e6*float(spec["specimen_int"]))
int=(1e6*float(spec["specimen_int"]))
Diff.append(100.*abs(aint-int)/aint)
x,s=pmag.gausspars(Diff)
print x,s
Diff.sort()
print Diff[0],Diff[-1]
pmagplotlib.plotCDF(P['cdf'],Diff,'% Difference','r')
pmagplotlib.drawFIGS(P)
raw_input()
def main():
"""
NAME
pick_AC_specimens.py
DESCRIPTION
finds whether anisotropy correction yeilds more tightly
grouped intensities than uncorrected data.
picks either all corrected or all uncorrected data and
puts in pmag_specimen format file
SYNTAX
pick_AC_specimens.py [command line options]
OPTIONS
-h prints help message and quits
-fu TFILE uncorrected pmag_specimen format file with thellier interpretations
created by thellier_magic_redo.py
-fc AFILE anisotropy corrected pmag_specimen format file
created by thellier_magic_redo.py
-fcr CRIT pmag_criteria.txt format file with acceptance criteria
-opt SIG use the optimizer_thelpars.txt file for criteria
-F FILE pmag_specimens format output file with "best" set of data
DEFAULTS
TFILE: thellier_specimens.txt
AFILE: AC_specimens.txt
FILE: TorAC_specimens.txt
"""
tspec="thellier_specimens.txt"
aspec="AC_specimens.txt"
ofile="TorAC_specimens.txt"
critfile="pmag_criteria.txt"
dir_path='.'
ACSamplist,Samplist,sigmin=[],[],10000
GoodSamps,SpecOuts=[],[]
# get arguments from command line
if '-h' in sys.argv:
print main.__doc__
sys.exit()
if '-WD' in sys.argv:
ind=sys.argv.index('-WD')
dir_path=sys.argv[ind+1]
if '-fu' in sys.argv:
ind=sys.argv.index('-fu')
tspec=sys.argv[ind+1]
if '-fc' in sys.argv:
ind=sys.argv.index('-fc')
aspec=sys.argv[ind+1]
if '-fcr' in sys.argv:
ind=sys.argv.index('-fcr')
critfile=sys.argv[ind+1]
if '-opt' in sys.argv:
ind=sys.argv.index('-opt')
critfile='optimum_thelpars.txt'
sigcutoff=sys.argv[ind+1]
if '-F' in sys.argv:
ind=sys.argv.index('-F')
ofile=sys.argv[ind+1]
if '-i' in sys.argv:
file=raw_input(" thellier_specimnens.txt file [thellier_specimens.txt]: ")
if file!="":tfile=file
file=raw_input(" AC_specimnens.txt file [AC_specimens.txt]: ")
if file!="":afile=file
file=raw_input(" pmag_specimnens.txt file [pmag_specimens.txt]: ")
if file!="":ofile=file
# read in pmag_specimens file
tspec=dir_path+'/'+tspec
aspec=dir_path+'/'+aspec
ofile=dir_path+'/'+ofile
critfile=dir_path+'/'+critfile
Specs,file_type=pmag.magic_read(tspec)
Speclist=pmag.get_specs(Specs)
ACSpecs,file_type=pmag.magic_read(aspec)
ACspeclist=pmag.get_specs(ACSpecs)
Crits,file_type=pmag.magic_read(critfile)
keys=['specimen_int_mad','specimen_drats','specimen_fvds','specimen_b_beta','specimen_Z','specimen_md','specimen_dang']
accept={}
for crit in Crits:
if critfile!='optimum_thelpars.txt':
if crit['pmag_criteria_code']=='IE-SPEC':
for key in keys: accept[key]=float(crit[key]) # assign acceptance criteria
break
else:
if float(crit['sample_int_sigma_perc'])==float(sigcutoff):
for key in keys: accept[key]=float(crit[key])
print len(accept)
for spec in Specs:
grade,kill=pmag.grade(spec,accept)
if grade==len(accept):
if spec["er_sample_name"] not in Samplist:Samplist.append(spec["er_sample_name"])
for spec in ACSpecs:
grade,kill=pmag.grade(spec,accept)
if grade==len(accept):
if spec["er_sample_name"] not in ACSamplist:ACSamplist.append(spec["er_sample_name"])
#
for samp in Samplist:
print samp
useAC,Ints,ACInts,GoodSpecs,AC,UC,ALL,ALLInts=0,[],[],[],[],[],[],[]
sample_int_sigma_perc=""
for spec in Specs:
ThisAC=[]
if spec["er_sample_name"]==samp:
grade,kill=pmag.grade(spec,accept)
if grade==len(accept):
UC.append(spec)
print 'UC: ',spec["er_specimen_name"],'%i'%(1e6*float(spec["specimen_int"]))
Ints.append(float(spec["specimen_int"]))
if samp in ACSamplist:
for spec in ACSpecs:
if spec["er_sample_name"]==samp:
grade,kill=pmag.grade(spec,accept)
if grade==len(accept):
AC.append(spec)
print 'AC: ',spec["er_specimen_name"],'%i'%(1e6*float(spec["specimen_int"]))
ACInts.append(float(spec["specimen_int"]))
ThisAC.append(spec["er_specimen_name"])
ALLInts.append(float(spec["specimen_int"]))
ALL.append(spec)
for spec in UC:
if spec['er_specimen_name'] not in ThisAC:
ALLInts.append(float(spec["specimen_int"]))
ALL.append(spec)
if len(AC)>2:
allx,allstd=pmag.gausspars(ALLInts)
ix,istd=pmag.gausspars(Ints)
ax,astd=pmag.gausspars(ACInts)
print "Nall= ",len(ALLInts),allx,allstd,"Ni= ",len(Ints),ix,istd,"Na= ",len(ACInts),ax,astd
if astd<istd:
if astd<allstd:
for spec in AC: SpecOuts.append(spec)
print 'using AC'
else:
for spec in ALL:
SpecOuts.append(spec)
print spec['er_specimen_name'],spec['magic_method_codes'],spec['specimen_int']
print 'using ALL'
else:
if istd<allstd:
for spec in UC: SpecOuts.append(spec)
print 'using UC'
else:
for spec in ALL:
SpecOuts.append(spec)
print spec['er_specimen_name'],spec['magic_method_codes'],spec['specimen_int']
print 'using ALL'
else:
for spec in UC: SpecOuts.append(spec)
print 'No AC, using UC'
raw_input()
pmag.magic_write(SpecOuts,ofile,'pmag_specimens')
print 'thellier data assessed for AC correction put in ', ofile
