def main():
"""
NAME
goprinc.py
DESCRIPTION
calculates Principal components from dec/iinc data
INPUT FORMAT
takes dec/inc as first two columns in space delimited file
SYNTAX
goprinc.py [options] [< filename]
OPTIONS
-h prints help message and quits
-i for interactive filename entry
-f FILE, specify input file
-F FILE, specifies output file name
< filename for reading from standard input
OUTPUT
tau_1 V1_Dec, V1_Inc, tau_2 V2_Dec V2_Inc, tau_3 V3_Dec V3_Inc, N
"""
if len(sys.argv) > 0:
if '-h' in sys.argv: # check if help is needed
print main.__doc__
sys.exit() # graceful quit
if '-f' in sys.argv:
ind=sys.argv.index('-f')
file=sys.argv[ind+1]
f=open(file,'rU')
data=f.readlines()
elif '-i' in sys.argv: # ask for filename
file=raw_input("Enter file name with dec, inc data: ")
f=open(file,'rU')
data=f.readlines()
else:
#
data=sys.stdin.readlines() # read in data from standard input
ofile = ""
if '-F' in sys.argv:
ind = sys.argv.index('-F')
ofile= sys.argv[ind+1]
out = open(ofile, 'w + a')
DIs= [] # set up list for dec inc data
for line in data: # read in the data from standard input
if '\t' in line:
rec=line.split('\t') # split each line on space to get records
else:
rec=line.split() # split each line on space to get records
DIs.append((float(rec[0]),float(rec[1])))
#
ppars=pmag.doprinc(DIs)
output = '%7.5f %7.1f %7.1f %7.5f %7.1f %7.1f %7.5f %7.1f %7.1f %i' % (ppars["tau1"],ppars["dec"],ppars["inc"],ppars["tau2"],ppars["V2dec"],ppars["V2inc"],ppars["tau3"],ppars["V3dec"],ppars["V3inc"],ppars["N"])
if ofile == "":
print output
else:
out.write(output+'\n')
def find_f(data):
rad=math.pi/180.
Es,Is,Fs,V2s=[],[],[],[]
ppars=pmag.doprinc(data)
D=ppars['dec']
for f in numpy.arange(1.,.2 ,-.01):
fdata=[]
for rec in data:
U=math.atan((1./f)*math.tan(rec[1]*rad))/rad
fdata.append([rec[0],U,1.])
ppars=pmag.doprinc(fdata)
Fs.append(f)
Es.append(ppars["tau2"]/ppars["tau3"])
angle=pmag.angle([D,0],[ppars["V2dec"],0])
if 180.-angle<angle:angle=180.-angle
V2s.append(angle)
Is.append(abs(ppars["inc"]))
if EI(abs(ppars["inc"]))<=Es[-1]:
del Es[-1]
del Is[-1]
del Fs[-1]
del V2s[-1]
if len(Fs)>0:
for f in numpy.arange(Fs[-1],.2 ,-.005):
for rec in data:
U=math.atan((1./f)*math.tan(rec[1]*rad))/rad
fdata.append([rec[0],U,1.])
ppars=pmag.doprinc(fdata)
Fs.append(f)
Es.append(ppars["tau2"]/ppars["tau3"])
Is.append(abs(ppars["inc"]))
angle=pmag.angle([D,0],[ppars["V2dec"],0])
if 180.-angle<angle:angle=180.-angle
V2s.append(angle)
if EI(abs(ppars["inc"]))<=Es[-1]:
return Es,Is,Fs,V2s
return [0],[0],[0],[0]
def find_f(data):
rad=numpy.pi/180.
Es,Is,Fs,V2s=[],[],[],[]
ppars=pmag.doprinc(data)
D=ppars['dec']
Decs,Incs=data.transpose()[0],data.transpose()[1]
Tan_Incs=numpy.tan(Incs*rad)
for f in numpy.arange(1.,.2 ,-.01):
U=numpy.arctan((1./f)*Tan_Incs)/rad
fdata=numpy.array([Decs,U]).transpose()
ppars=pmag.doprinc(fdata)
Fs.append(f)
Es.append(ppars["tau2"]/ppars["tau3"])
angle=pmag.angle([D,0],[ppars["V2dec"],0])
if 180.-angle<angle:angle=180.-angle
V2s.append(angle)
Is.append(abs(ppars["inc"]))
if EI(abs(ppars["inc"]))<=Es[-1]:
del Es[-1]
del Is[-1]
del Fs[-1]
del V2s[-1]
if len(Fs)>0:
for f in numpy.arange(Fs[-1],.2 ,-.005):
U=numpy.arctan((1./f)*Tan_Incs)/rad
fdata=numpy.array([Decs,U]).transpose()
ppars=pmag.doprinc(fdata)
Fs.append(f)
Es.append(ppars["tau2"]/ppars["tau3"])
Is.append(abs(ppars["inc"]))
angle=pmag.angle([D,0],[ppars["V2dec"],0])
if 180.-angle<angle:angle=180.-angle
V2s.append(angle)
if EI(abs(ppars["inc"]))<=Es[-1]:
return Es,Is,Fs,V2s
return [0],[0],[0],[0]
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
plotdi_e.py
DESCRIPTION
plots equal area projection from dec inc data and cones of confidence
(Fisher, kent or Bingham or bootstrap).
INPUT FORMAT
takes dec/inc as first two columns in space delimited file
SYNTAX
plotdi_e.py [command line options]
OPTIONS
-h prints help message and quits
-i for interactive parameter entry
-f FILE, sets input filename on command line
-Fish plots unit vector mean direction, alpha95
-Bing plots Principal direction, Bingham confidence ellipse
-Kent plots unit vector mean direction, confidence ellipse
-Boot E plots unit vector mean direction, bootstrapped confidence ellipse
-Boot V plots unit vector mean direction, distribution of bootstrapped means
"""
dist='F' # default distribution is Fisherian
mode=1
EQ={'eq':1}
if len(sys.argv) > 0:
if '-h' in sys.argv: # check if help is needed
print main.__doc__
sys.exit() # graceful quit
if '-i' in sys.argv: # ask for filename
file=raw_input("Enter file name with dec, inc data: ")
dist=raw_input("Enter desired distrubution: [Fish]er, [Bing]ham, [Kent] [Boot] [default is Fisher]: ")
if dist=="":dist="F"
if dist=="Boot":
type=raw_input(" Ellipses or distribution of vectors? [E]/V ")
if type=="" or type=="E":
dist="BE"
else:
dist="BE"
else:
#
if '-f' in sys.argv:
ind=sys.argv.index('-f')
file=sys.argv[ind+1]
else:
print 'you must specify a file name'
print main.__doc__
sys.exit()
if '-Bing' in sys.argv:dist='B'
if '-Kent' in sys.argv:dist='K'
if '-Boot' in sys.argv:
ind=sys.argv.index('-Boot')
type=sys.argv[ind+1]
if type=='E':
dist='BE'
elif type=='V':
dist='BV'
EQ['bdirs']=2
pmagplotlib.plot_init(EQ['bdirs'],5,5)
else:
print main.__doc__
sys.exit()
pmagplotlib.plot_init(EQ['eq'],5,5)
#
# get to work
f=open(file,'r')
data=f.readlines()
#
DIs= [] # set up list for dec inc data
DiRecs=[]
pars=[]
nDIs,rDIs,npars,rpars=[],[],[],[]
mode =1
for line in data: # read in the data from standard input
DiRec={}
rec=line.split() # split each line on space to get records
DIs.append((float(rec[0]),float(rec[1]),1.))
DiRec['dec']=rec[0]
DiRec['inc']=rec[1]
DiRec['direction_type']='l'
DiRecs.append(DiRec)
# split into two modes
ppars=pmag.doprinc(DIs) # get principal directions
for rec in DIs:
angle=pmag.angle([rec[0],rec[1]],[ppars['dec'],ppars['inc']])
if angle>90.:
rDIs.append(rec)
else:
nDIs.append(rec)
if dist=='B': # do on whole dataset
title="Bingham confidence ellipse"
bpars=pmag.dobingham(DIs)
for key in bpars.keys():
if key!='n':print " ",key, '%7.1f'%(bpars[key])
if key=='n':print " ",key, ' %i'%(bpars[key])
npars.append(bpars['dec'])
npars.append(bpars['inc'])
npars.append(bpars['Zeta'])
npars.append(bpars['Zdec'])
npars.append(bpars['Zinc'])
npars.append(bpars['Eta'])
npars.append(bpars['Edec'])
npars.append(bpars['Einc'])
if dist=='F':
title="Fisher confidence cone"
if len(nDIs)>3:
fpars=pmag.fisher_mean(nDIs)
print "mode ",mode
for key in fpars.keys():
if key!='n':print " ",key, '%7.1f'%(fpars[key])
if key=='n':print " ",key, ' %i'%(fpars[key])
mode+=1
npars.append(fpars['dec'])
npars.append(fpars['inc'])
npars.append(fpars['alpha95']) # Beta
npars.append(fpars['dec'])
isign=abs(fpars['inc'])/fpars['inc']
npars.append(fpars['inc']-isign*90.) #Beta inc
npars.append(fpars['alpha95']) # gamma
npars.append(fpars['dec']+90.) # Beta dec
npars.append(0.) #Beta inc
if len(rDIs)>3:
fpars=pmag.fisher_mean(rDIs)
print "mode ",mode
for key in fpars.keys():
if key!='n':print " ",key, '%7.1f'%(fpars[key])
if key=='n':print " ",key, ' %i'%(fpars[key])
mode+=1
rpars.append(fpars['dec'])
rpars.append(fpars['inc'])
rpars.append(fpars['alpha95']) # Beta
rpars.append(fpars['dec'])
isign=abs(fpars['inc'])/fpars['inc']
rpars.append(fpars['inc']-isign*90.) #Beta inc
rpars.append(fpars['alpha95']) # gamma
rpars.append(fpars['dec']+90.) # Beta dec
rpars.append(0.) #Beta inc
if dist=='K':
title="Kent confidence ellipse"
if len(nDIs)>3:
kpars=pmag.dokent(nDIs,len(nDIs))
print "mode ",mode
for key in kpars.keys():
if key!='n':print " ",key, '%7.1f'%(kpars[key])
if key=='n':print " ",key, ' %i'%(kpars[key])
mode+=1
npars.append(kpars['dec'])
npars.append(kpars['inc'])
npars.append(kpars['Zeta'])
npars.append(kpars['Zdec'])
npars.append(kpars['Zinc'])
npars.append(kpars['Eta'])
npars.append(kpars['Edec'])
npars.append(kpars['Einc'])
if len(rDIs)>3:
kpars=pmag.dokent(rDIs,len(rDIs))
print "mode ",mode
for key in kpars.keys():
if key!='n':print " ",key, '%7.1f'%(kpars[key])
if key=='n':print " ",key, ' %i'%(kpars[key])
mode+=1
rpars.append(kpars['dec'])
rpars.append(kpars['inc'])
rpars.append(kpars['Zeta'])
rpars.append(kpars['Zdec'])
rpars.append(kpars['Zinc'])
rpars.append(kpars['Eta'])
rpars.append(kpars['Edec'])
rpars.append(kpars['Einc'])
else: # assume bootstrap
if dist=='BE':
if len(nDIs)>5:
BnDIs=pmag.di_boot(nDIs)
Bkpars=pmag.dokent(BnDIs,1.)
print "mode ",mode
for key in Bkpars.keys():
if key!='n':print " ",key, '%7.1f'%(Bkpars[key])
if key=='n':print " ",key, ' %i'%(Bkpars[key])
mode+=1
npars.append(Bkpars['dec'])
npars.append(Bkpars['inc'])
npars.append(Bkpars['Zeta'])
npars.append(Bkpars['Zdec'])
npars.append(Bkpars['Zinc'])
npars.append(Bkpars['Eta'])
npars.append(Bkpars['Edec'])
npars.append(Bkpars['Einc'])
if len(rDIs)>5:
BrDIs=pmag.di_boot(rDIs)
Bkpars=pmag.dokent(BrDIs,1.)
print "mode ",mode
for key in Bkpars.keys():
if key!='n':print " ",key, '%7.1f'%(Bkpars[key])
if key=='n':print " ",key, ' %i'%(Bkpars[key])
mode+=1
rpars.append(Bkpars['dec'])
rpars.append(Bkpars['inc'])
rpars.append(Bkpars['Zeta'])
rpars.append(Bkpars['Zdec'])
rpars.append(Bkpars['Zinc'])
rpars.append(Bkpars['Eta'])
rpars.append(Bkpars['Edec'])
rpars.append(Bkpars['Einc'])
title="Bootstrapped confidence ellipse"
elif dist=='BV':
if len(nDIs)>5:
pmagplotlib.plotEQ(EQ['eq'],nDIs,'Data')
BnDIs=pmag.di_boot(nDIs)
pmagplotlib.plotEQ(EQ['bdirs'],BnDIs,'Bootstrapped Eigenvectors')
if len(rDIs)>5:
BrDIs=pmag.di_boot(rDIs)
if len(nDIs)>5: # plot on existing plots
pmagplotlib.plotDI(EQ['eq'],rDIs)
pmagplotlib.plotDI(EQ['bdirs'],BrDIs)
else:
pmagplotlib.plotEQ(EQ['eq'],rDIs,'Data')
pmagplotlib.plotEQ(EQ['bdirs'],BrDIs,'Bootstrapped Eigenvectors')
pmagplotlib.drawFIGS(EQ)
ans=raw_input('s[a]ve, [q]uit ')
if ans=='q':sys.exit()
if ans=='a':
files={}
for key in EQ.keys():
files[key]='BE_'+key+'.svg'
pmagplotlib.saveP(EQ,files)
sys.exit()
if len(nDIs)>5:
pmagplotlib.plotCONF(EQ['eq'],title,DiRecs,npars,1)
if len(rDIs)>5 and dist!='B':
pmagplotlib.plotCONF(EQ['eq'],title,[],rpars,0)
elif len(rDIs)>5 and dist!='B':
pmagplotlib.plotCONF(EQ['eq'],title,DiRecs,rpars,1)
pmagplotlib.drawFIGS(EQ)
ans=raw_input('s[a]ve, [q]uit ')
if ans=='q':sys.exit()
if ans=='a':
files={}
for key in EQ.keys():
files[key]=key+'.svg'
pmagplotlib.saveP(EQ,files)
def main():
"""
NAME
eqarea_ell.py
DESCRIPTION
makes equal area projections from declination/inclination data
and plot ellipses
SYNTAX
eqarea_ell.py -h [command line options]
INPUT
takes space delimited Dec/Inc data
OPTIONS
-h prints help message and quits
-f FILE
-fmt [svg,png,jpg] format for output plots
-ell [F,K,B,Be,Bv] plot Fisher, Kent, Bingham, Bootstrap ellipses or Boostrap eigenvectors
"""
FIG = {} # plot dictionary
FIG["eq"] = 1 # eqarea is figure 1
fmt, dist, mode = "svg", "F", 1
plotE = 0
if "-h" in sys.argv:
print main.__doc__
sys.exit()
pmagplotlib.plot_init(FIG["eq"], 5, 5)
if "-f" in sys.argv:
ind = sys.argv.index("-f")
title = sys.argv[ind + 1]
f = open(title, "rU")
data = f.readlines()
if "-ell" in sys.argv:
plotE = 1
ind = sys.argv.index("-ell")
ell_type = sys.argv[ind + 1]
if ell_type == "F":
dist = "F"
if ell_type == "K":
dist = "K"
if ell_type == "B":
dist = "B"
if ell_type == "Be":
dist = "BE"
if ell_type == "Bv":
dist = "BV"
FIG["bdirs"] = 2
pmagplotlib.plot_init(FIG["bdirs"], 5, 5)
if "-fmt" in sys.argv:
ind = sys.argv.index("-fmt")
fmt = sys.argv[ind + 1]
DIblock = []
for line in data:
if "\t" in line:
rec = line.split("\t") # split each line on space to get records
else:
rec = line.split() # split each line on space to get records
DIblock.append([float(rec[0]), float(rec[1])])
if len(DIblock) > 0:
pmagplotlib.plotEQ(FIG["eq"], DIblock, title)
else:
print "no data to plot"
sys.exit()
if plotE == 1:
ppars = pmag.doprinc(DIblock) # get principal directions
nDIs, rDIs, npars, rpars = [], [], [], []
for rec in DIblock:
angle = pmag.angle([rec[0], rec[1]], [ppars["dec"], ppars["inc"]])
if angle > 90.0:
rDIs.append(rec)
else:
nDIs.append(rec)
if dist == "B": # do on whole dataset
etitle = "Bingham confidence ellipse"
bpars = pmag.dobingham(DIblock)
for key in bpars.keys():
if key != "n" and pmagplotlib.verbose:
print " ", key, "%7.1f" % (bpars[key])
if key == "n" and pmagplotlib.verbose:
print " ", key, " %i" % (bpars[key])
npars.append(bpars["dec"])
npars.append(bpars["inc"])
npars.append(bpars["Zeta"])
npars.append(bpars["Zdec"])
npars.append(bpars["Zinc"])
npars.append(bpars["Eta"])
npars.append(bpars["Edec"])
npars.append(bpars["Einc"])
if dist == "F":
etitle = "Fisher confidence cone"
if len(nDIs) > 3:
fpars = pmag.fisher_mean(nDIs)
for key in fpars.keys():
if key != "n" and pmagplotlib.verbose:
print " ", key, "%7.1f" % (fpars[key])
if key == "n" and pmagplotlib.verbose:
print " ", key, " %i" % (fpars[key])
mode += 1
npars.append(fpars["dec"])
npars.append(fpars["inc"])
npars.append(fpars["alpha95"]) # Beta
npars.append(fpars["dec"])
isign = abs(fpars["inc"]) / fpars["inc"]
npars.append(fpars["inc"] - isign * 90.0) # Beta inc
npars.append(fpars["alpha95"]) # gamma
npars.append(fpars["dec"] + 90.0) # Beta dec
npars.append(0.0) # Beta inc
if len(rDIs) > 3:
fpars = pmag.fisher_mean(rDIs)
if pmagplotlib.verbose:
print "mode ", mode
for key in fpars.keys():
if key != "n" and pmagplotlib.verbose:
print " ", key, "%7.1f" % (fpars[key])
if key == "n" and pmagplotlib.verbose:
print " ", key, " %i" % (fpars[key])
mode += 1
rpars.append(fpars["dec"])
rpars.append(fpars["inc"])
rpars.append(fpars["alpha95"]) # Beta
rpars.append(fpars["dec"])
isign = abs(fpars["inc"]) / fpars["inc"]
rpars.append(fpars["inc"] - isign * 90.0) # Beta inc
rpars.append(fpars["alpha95"]) # gamma
rpars.append(fpars["dec"] + 90.0) # Beta dec
rpars.append(0.0) # Beta inc
if dist == "K":
etitle = "Kent confidence ellipse"
if len(nDIs) > 3:
kpars = pmag.dokent(nDIs, len(nDIs))
if pmagplotlib.verbose:
print "mode ", mode
for key in kpars.keys():
if key != "n" and pmagplotlib.verbose:
print " ", key, "%7.1f" % (kpars[key])
if key == "n" and pmagplotlib.verbose:
print " ", key, " %i" % (kpars[key])
mode += 1
npars.append(kpars["dec"])
npars.append(kpars["inc"])
npars.append(kpars["Zeta"])
npars.append(kpars["Zdec"])
npars.append(kpars["Zinc"])
npars.append(kpars["Eta"])
npars.append(kpars["Edec"])
npars.append(kpars["Einc"])
if len(rDIs) > 3:
kpars = pmag.dokent(rDIs, len(rDIs))
if pmagplotlib.verbose:
print "mode ", mode
for key in kpars.keys():
if key != "n" and pmagplotlib.verbose:
print " ", key, "%7.1f" % (kpars[key])
if key == "n" and pmagplotlib.verbose:
print " ", key, " %i" % (kpars[key])
mode += 1
rpars.append(kpars["dec"])
rpars.append(kpars["inc"])
rpars.append(kpars["Zeta"])
rpars.append(kpars["Zdec"])
rpars.append(kpars["Zinc"])
rpars.append(kpars["Eta"])
rpars.append(kpars["Edec"])
rpars.append(kpars["Einc"])
else: # assume bootstrap
if len(nDIs) < 10 and len(rDIs) < 10:
print "too few data points for bootstrap"
sys.exit()
if dist == "BE":
if len(nDIs) >= 10:
BnDIs = pmag.di_boot(nDIs)
Bkpars = pmag.dokent(BnDIs, 1.0)
if pmagplotlib.verbose:
print "mode ", mode
for key in Bkpars.keys():
if key != "n" and pmagplotlib.verbose:
print " ", key, "%7.1f" % (Bkpars[key])
if key == "n" and pmagplotlib.verbose:
print " ", key, " %i" % (Bkpars[key])
mode += 1
npars.append(Bkpars["dec"])
npars.append(Bkpars["inc"])
npars.append(Bkpars["Zeta"])
npars.append(Bkpars["Zdec"])
npars.append(Bkpars["Zinc"])
npars.append(Bkpars["Eta"])
npars.append(Bkpars["Edec"])
npars.append(Bkpars["Einc"])
if len(rDIs) >= 10:
BrDIs = pmag.di_boot(rDIs)
Bkpars = pmag.dokent(BrDIs, 1.0)
if pmagplotlib.verbose:
print "mode ", mode
for key in Bkpars.keys():
if key != "n" and pmagplotlib.verbose:
print " ", key, "%7.1f" % (Bkpars[key])
if key == "n" and pmagplotlib.verbose:
print " ", key, " %i" % (Bkpars[key])
mode += 1
rpars.append(Bkpars["dec"])
rpars.append(Bkpars["inc"])
rpars.append(Bkpars["Zeta"])
rpars.append(Bkpars["Zdec"])
rpars.append(Bkpars["Zinc"])
rpars.append(Bkpars["Eta"])
rpars.append(Bkpars["Edec"])
rpars.append(Bkpars["Einc"])
etitle = "Bootstrapped confidence ellipse"
elif dist == "BV":
vsym = {"lower": ["+", "k"], "upper": ["x", "k"], "size": 5}
if len(nDIs) > 5:
BnDIs = pmag.di_boot(nDIs)
pmagplotlib.plotEQsym(FIG["bdirs"], BnDIs, "Bootstrapped Eigenvectors", vsym)
if len(rDIs) > 5:
BrDIs = pmag.di_boot(rDIs)
if len(nDIs) > 5: # plot on existing plots
pmagplotlib.plotDIsym(FIG["bdirs"], BrDIs, vsym)
else:
pmagplotlib.plotEQ(FIG["bdirs"], BrDIs, "Bootstrapped Eigenvectors", vsym)
if dist == "B":
if len(nDIs) > 3 or len(rDIs) > 3:
pmagplotlib.plotCONF(FIG["eq"], etitle, [], npars, 0)
elif len(nDIs) > 3 and dist != "BV":
pmagplotlib.plotCONF(FIG["eq"], etitle, [], npars, 0)
if len(rDIs) > 3:
pmagplotlib.plotCONF(FIG["eq"], etitle, [], rpars, 0)
elif len(rDIs) > 3 and dist != "BV":
pmagplotlib.plotCONF(FIG["eq"], etitle, [], rpars, 0)
pmagplotlib.drawFIGS(FIG)
#
files = {}
for key in FIG.keys():
files[key] = title + "_" + key + "." + fmt
if pmagplotlib.isServer:
black = "#000000"
purple = "#800080"
titles = {}
titles["eq"] = "Equal Area Plot"
FIG = pmagplotlib.addBorders(FIG, titles, black, purple)
pmagplotlib.saveP(FIG, files)
else:
ans = raw_input(" S[a]ve to save plot, [q]uit, Return to continue: ")
if ans == "q":
sys.exit()
if ans == "a":
pmagplotlib.saveP(FIG, 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
-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
eqarea_ell.py
DESCRIPTION
makes equal area projections from declination/inclination data
and plot ellipses
SYNTAX
eqarea_ell.py -h [command line options]
INPUT
takes space delimited Dec/Inc data
OPTIONS
-h prints help message and quits
-f FILE
-fmt [svg,png,jpg] format for output plots
-sav saves figures and quits
-ell [F,K,B,Be,Bv] plot Fisher, Kent, Bingham, Bootstrap ellipses or Boostrap eigenvectors
"""
FIG = {} # plot dictionary
FIG['eq'] = 1 # eqarea is figure 1
fmt, dist, mode, plot = 'svg', 'F', 1, 0
sym = {'lower': ['o', 'r'], 'upper': ['o', 'w'], 'size': 10}
plotE = 0
if '-h' in sys.argv:
print main.__doc__
sys.exit()
pmagplotlib.plot_init(FIG['eq'], 5, 5)
if '-sav' in sys.argv: plot = 1
if '-f' in sys.argv:
ind = sys.argv.index("-f")
title = sys.argv[ind + 1]
data = numpy.loadtxt(title).transpose()
if '-ell' in sys.argv:
plotE = 1
ind = sys.argv.index('-ell')
ell_type = sys.argv[ind + 1]
if ell_type == 'F': dist = 'F'
if ell_type == 'K': dist = 'K'
if ell_type == 'B': dist = 'B'
if ell_type == 'Be': dist = 'BE'
if ell_type == 'Bv':
dist = 'BV'
FIG['bdirs'] = 2
pmagplotlib.plot_init(FIG['bdirs'], 5, 5)
if '-fmt' in sys.argv:
ind = sys.argv.index("-fmt")
fmt = sys.argv[ind + 1]
DIblock = numpy.array([data[0], data[1]]).transpose()
if len(DIblock) > 0:
pmagplotlib.plotEQsym(FIG['eq'], DIblock, title, sym)
if plot == 0: pmagplotlib.drawFIGS(FIG)
else:
print "no data to plot"
sys.exit()
if plotE == 1:
ppars = pmag.doprinc(DIblock) # get principal directions
nDIs, rDIs, npars, rpars = [], [], [], []
for rec in DIblock:
angle = pmag.angle([rec[0], rec[1]], [ppars['dec'], ppars['inc']])
if angle > 90.:
rDIs.append(rec)
else:
nDIs.append(rec)
if dist == 'B': # do on whole dataset
etitle = "Bingham confidence ellipse"
bpars = pmag.dobingham(DIblock)
for key in bpars.keys():
if key != 'n' and pmagplotlib.verbose:
print " ", key, '%7.1f' % (bpars[key])
if key == 'n' and pmagplotlib.verbose:
print " ", key, ' %i' % (bpars[key])
npars.append(bpars['dec'])
npars.append(bpars['inc'])
npars.append(bpars['Zeta'])
npars.append(bpars['Zdec'])
npars.append(bpars['Zinc'])
npars.append(bpars['Eta'])
npars.append(bpars['Edec'])
npars.append(bpars['Einc'])
if dist == 'F':
etitle = "Fisher confidence cone"
if len(nDIs) > 3:
fpars = pmag.fisher_mean(nDIs)
for key in fpars.keys():
if key != 'n' and pmagplotlib.verbose:
print " ", key, '%7.1f' % (fpars[key])
if key == 'n' and pmagplotlib.verbose:
print " ", key, ' %i' % (fpars[key])
mode += 1
npars.append(fpars['dec'])
npars.append(fpars['inc'])
npars.append(fpars['alpha95']) # Beta
npars.append(fpars['dec'])
isign = abs(fpars['inc']) / fpars['inc']
npars.append(fpars['inc'] - isign * 90.) #Beta inc
npars.append(fpars['alpha95']) # gamma
npars.append(fpars['dec'] + 90.) # Beta dec
npars.append(0.) #Beta inc
if len(rDIs) > 3:
fpars = pmag.fisher_mean(rDIs)
if pmagplotlib.verbose: print "mode ", mode
for key in fpars.keys():
if key != 'n' and pmagplotlib.verbose:
print " ", key, '%7.1f' % (fpars[key])
if key == 'n' and pmagplotlib.verbose:
print " ", key, ' %i' % (fpars[key])
mode += 1
rpars.append(fpars['dec'])
rpars.append(fpars['inc'])
rpars.append(fpars['alpha95']) # Beta
rpars.append(fpars['dec'])
isign = abs(fpars['inc']) / fpars['inc']
rpars.append(fpars['inc'] - isign * 90.) #Beta inc
rpars.append(fpars['alpha95']) # gamma
rpars.append(fpars['dec'] + 90.) # Beta dec
rpars.append(0.) #Beta inc
if dist == 'K':
etitle = "Kent confidence ellipse"
if len(nDIs) > 3:
kpars = pmag.dokent(nDIs, len(nDIs))
if pmagplotlib.verbose: print "mode ", mode
for key in kpars.keys():
if key != 'n' and pmagplotlib.verbose:
print " ", key, '%7.1f' % (kpars[key])
if key == 'n' and pmagplotlib.verbose:
print " ", key, ' %i' % (kpars[key])
mode += 1
npars.append(kpars['dec'])
npars.append(kpars['inc'])
npars.append(kpars['Zeta'])
npars.append(kpars['Zdec'])
npars.append(kpars['Zinc'])
npars.append(kpars['Eta'])
npars.append(kpars['Edec'])
npars.append(kpars['Einc'])
if len(rDIs) > 3:
kpars = pmag.dokent(rDIs, len(rDIs))
if pmagplotlib.verbose: print "mode ", mode
for key in kpars.keys():
if key != 'n' and pmagplotlib.verbose:
print " ", key, '%7.1f' % (kpars[key])
if key == 'n' and pmagplotlib.verbose:
print " ", key, ' %i' % (kpars[key])
mode += 1
rpars.append(kpars['dec'])
rpars.append(kpars['inc'])
rpars.append(kpars['Zeta'])
rpars.append(kpars['Zdec'])
rpars.append(kpars['Zinc'])
rpars.append(kpars['Eta'])
rpars.append(kpars['Edec'])
rpars.append(kpars['Einc'])
else: # assume bootstrap
if len(nDIs) < 10 and len(rDIs) < 10:
print 'too few data points for bootstrap'
sys.exit()
if dist == 'BE':
print 'Be patient for bootstrap...'
if len(nDIs) >= 10:
BnDIs = pmag.di_boot(nDIs)
Bkpars = pmag.dokent(BnDIs, 1.)
if pmagplotlib.verbose: print "mode ", mode
for key in Bkpars.keys():
if key != 'n' and pmagplotlib.verbose:
print " ", key, '%7.1f' % (Bkpars[key])
if key == 'n' and pmagplotlib.verbose:
print " ", key, ' %i' % (Bkpars[key])
mode += 1
npars.append(Bkpars['dec'])
npars.append(Bkpars['inc'])
npars.append(Bkpars['Zeta'])
npars.append(Bkpars['Zdec'])
npars.append(Bkpars['Zinc'])
npars.append(Bkpars['Eta'])
npars.append(Bkpars['Edec'])
npars.append(Bkpars['Einc'])
if len(rDIs) >= 10:
BrDIs = pmag.di_boot(rDIs)
Bkpars = pmag.dokent(BrDIs, 1.)
if pmagplotlib.verbose: print "mode ", mode
for key in Bkpars.keys():
if key != 'n' and pmagplotlib.verbose:
print " ", key, '%7.1f' % (Bkpars[key])
if key == 'n' and pmagplotlib.verbose:
print " ", key, ' %i' % (Bkpars[key])
mode += 1
rpars.append(Bkpars['dec'])
rpars.append(Bkpars['inc'])
rpars.append(Bkpars['Zeta'])
rpars.append(Bkpars['Zdec'])
rpars.append(Bkpars['Zinc'])
rpars.append(Bkpars['Eta'])
rpars.append(Bkpars['Edec'])
rpars.append(Bkpars['Einc'])
etitle = "Bootstrapped confidence ellipse"
elif dist == 'BV':
print 'Be patient for bootstrap...'
vsym = {'lower': ['+', 'k'], 'upper': ['x', 'k'], 'size': 5}
if len(nDIs) > 5:
BnDIs = pmag.di_boot(nDIs)
pmagplotlib.plotEQsym(FIG['bdirs'], BnDIs,
'Bootstrapped Eigenvectors', vsym)
if len(rDIs) > 5:
BrDIs = pmag.di_boot(rDIs)
if len(nDIs) > 5: # plot on existing plots
pmagplotlib.plotDIsym(FIG['bdirs'], BrDIs, vsym)
else:
pmagplotlib.plotEQ(FIG['bdirs'], BrDIs,
'Bootstrapped Eigenvectors', vsym)
if dist == 'B':
if len(nDIs) > 3 or len(rDIs) > 3:
pmagplotlib.plotCONF(FIG['eq'], etitle, [], npars, 0)
elif len(nDIs) > 3 and dist != 'BV':
pmagplotlib.plotCONF(FIG['eq'], etitle, [], npars, 0)
if len(rDIs) > 3:
pmagplotlib.plotCONF(FIG['eq'], etitle, [], rpars, 0)
elif len(rDIs) > 3 and dist != 'BV':
pmagplotlib.plotCONF(FIG['eq'], etitle, [], rpars, 0)
if plot == 0: pmagplotlib.drawFIGS(FIG)
if plot == 0: pmagplotlib.drawFIGS(FIG)
#
files = {}
for key in FIG.keys():
files[key] = title + '_' + key + '.' + fmt
if pmagplotlib.isServer:
black = '#000000'
purple = '#800080'
titles = {}
titles['eq'] = 'Equal Area Plot'
FIG = pmagplotlib.addBorders(FIG, titles, black, purple)
pmagplotlib.saveP(FIG, files)
elif plot == 0:
ans = raw_input(" S[a]ve to save plot, [q]uit, Return to continue: ")
if ans == "q": sys.exit()
if ans == "a":
pmagplotlib.saveP(FIG, files)
else:
pmagplotlib.saveP(FIG, 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
fishqq.py
DESCRIPTION
makes qq plot from dec,inc input data
INPUT FORMAT
takes dec/inc pairs in space delimited file
SYNTAX
fishqq.py [command line options]
OPTIONS
-h help message
-f FILE, specify file on command line
"""
fmt,plot='svg',0
if '-h' in sys.argv: # check if help is needed
print main.__doc__
sys.exit() # graceful quit
elif '-f' in sys.argv: # ask for filename
ind=sys.argv.index('-f')
file=sys.argv[ind+1]
f=open(file,'rU')
data=f.readlines()
DIs,nDIs,rDIs= [],[],[] # set up list for data
for line in data: # read in the data from standard input
if '\t' in line:
rec=line.split('\t') # split each line on space to get records
else:
rec=line.split() # split each line on space to get records
DIs.append([float(rec[0]),float(rec[1])]) # append data to Inc
# split into two modes
ppars=pmag.doprinc(DIs) # get principal directions
for rec in DIs:
angle=pmag.angle([rec[0],rec[1]],[ppars['dec'],ppars['inc']])
if angle>90.:
rDIs.append(rec)
else:
nDIs.append(rec)
#
if len(rDIs) >=10 or len(nDIs) >=10:
D1,I1=[],[]
QQ={'unf1':1,'exp1':2}
pmagplotlib.plot_init(QQ['unf1'],5,5)
pmagplotlib.plot_init(QQ['exp1'],5,5)
if len(nDIs) < 10:
ppars=pmag.doprinc(rDIs) # get principal directions
Dbar,Ibar=ppars['dec']-180.,-ppars['inc']
for di in rDIs:
d,irot=pmag.dotilt(di[0],di[1],Dbar-180.,90.-Ibar) # rotate to mean
drot=d-180.
if drot<0:drot=drot+360.
D1.append(drot)
I1.append(irot)
Dtit='Reverse Declinations'
Itit='Reverse Inclinations'
else:
ppars=pmag.doprinc(nDIs) # get principal directions
Dbar,Ibar=ppars['dec'],ppars['inc']
for di in nDIs:
d,irot=pmag.dotilt(di[0],di[1],Dbar-180.,90.-Ibar) # rotate to mean
drot=d-180.
if drot<0:drot=drot+360.
D1.append(drot)
I1.append(irot)
Dtit='Declinations'
Itit='Inclinations'
print drot,irot
pmagplotlib.plotQQunf(QQ['unf1'],D1,Dtit) # make plot
pmagplotlib.plotQQexp(QQ['exp1'],I1,Itit) # make plot
else:
print 'you need N> 10 for at least one mode'
sys.exit()
if len(rDIs)>10 and len(nDIs)>10:
D2,I2=[],[]
QQ={'unf2':3,'exp2':4}
pmagplotlib.plot_init(QQ['unf2'],5,5)
pmagplotlib.plot_init(QQ['exp2'],5,5)
ppars=pmag.doprinc(rDIs) # get principal directions
Dbar,Ibar=ppars['dec']-180.,-ppars['inc']
for di in rDIs:
d,irot=pmag.dotilt(di[0],di[1],Dbar-180.,90.-Ibar) # rotate to mean
drot=d-180.
if drot<0:drot=drot+360.
D2.append(drot)
I2.append(irot)
Dtit='Reverse Declinations'
Itit='Reverse Inclinations'
pmagplotlib.plotQQunf(QQ['unf2'],D2,Dtit) # make plot
pmagplotlib.plotQQexp(QQ['exp2'],I2,Itit) # make plot
pmagplotlib.drawFIGS(QQ)
files={}
for key in QQ.keys():
files[key]=key+'.'+fmt
if pmagplotlib.isServer:
black = '#000000'
purple = '#800080'
titles={}
titles['eq']='Equal Area Plot'
EQ = pmagplotlib.addBorders(EQ,titles,black,purple)
pmagplotlib.saveP(QQ,files)
elif plot==1:
files['qq']=file+'.'+fmt
pmagplotlib.saveP(QQ,files)
else:
ans=raw_input(" S[a]ve to save plot, [q]uit without saving: ")
if ans=="a": pmagplotlib.saveP(QQ,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)
def main():
"""
NAME
foldtest.py
DESCRIPTION
does a fold test (Tauxe, 2007) on data
INPUT FORMAT
dec inc dip_direction dip
SYNTAX
foldtest.py [-h][-i][command line options]
OPTIONS
-h prints help message and quits
-i for interactive parameter entry
-f FILE
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
if '-i' in sys.argv: # ask for filename
file=raw_input("Enter file name with dec, inc dip_direction and dip data: ")
f=open(file,'rU')
data=f.readlines()
elif '-f' in sys.argv:
ind=sys.argv.index('-f')
file=sys.argv[ind+1]
f=open(file,'rU')
data=f.readlines()
else:
print main.__doc__
sys.exit()
#
# get to work
#
PLTS={'geo':1,'strat':2,'taus':3,'ei':4} # 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_init(PLTS['ei'],5,5)
DIDDs= [] # set up list for dec inc dip_direction, dip
nb=100 # number of bootstraps
for line in data: # read in the data from standard input
rec=line.split() # split each line on space to get records
DIDDs.append([float(rec[0]),float(rec[1]),float(rec[2]),float(rec[3])])
pmagplotlib.plotEQ(PLTS['geo'],DIDDs,'Geographic')
TCs,Ps,Taus,Es,Is=[],[],[],[],[]
for k in range(len(DIDDs)):
drot,irot=pmag.dotilt(DIDDs[k][0],DIDDs[k][1],DIDDs[k][2],DIDDs[k][3])
TCs.append([drot,irot,1.])
pmagplotlib.plotEQ(PLTS['strat'],TCs,'Stratigraphic')
Percs=range(-10,110)
for perc in Percs:
tilt=0.01*perc
TCs=[]
for k in range(len(DIDDs)):
drot,irot=pmag.dotilt(DIDDs[k][0],DIDDs[k][1],DIDDs[k][2],tilt*DIDDs[k][3])
TCs.append([drot,irot,1.])
ppars=pmag.doprinc(TCs) # get principal directions
Taus.append(ppars['tau1'])
Es.append(ppars["tau2"]/ppars["tau3"])
Is.append(ppars["inc"])
if int(10*(EI(ppars["inc"])))==int(10*Es[-1]):
print EI(ppars["inc"]),Es[-1],perc
Ps.append(perc)
pylab.figure(num=PLTS['taus'])
pylab.plot(Percs,Taus,'b-')
pylab.figure(num=PLTS['ei'])
pylab.plot(Es,Is,'b-')
Is.sort()
Eexp=[]
for i in Is: Eexp.append(EI(i))
pylab.plot(Eexp,Is,'g-')
Cdf,Untilt=[],[]
print 'doing ',nb,' iterations...please be patient.....'
for n in range(nb): # do bootstrap data sets - plot first 25 as dashed red line
Es,Is=[],[]
if n%50==0:print n
Taus=[] # set up lists for taus
PDs=pmag.pseudo(DIDDs)
for perc in Percs:
tilt=0.01*perc
TCs=[]
for k in range(len(PDs)):
drot,irot=pmag.dotilt(PDs[k][0],PDs[k][1],PDs[k][2],tilt*PDs[k][3])
TCs.append([drot,irot,1.])
ppars=pmag.doprinc(TCs) # get principal directions
Taus.append(ppars['tau1'])
Es.append(ppars["tau2"]/ppars["tau3"])
Is.append(ppars["inc"])
if int(10*(EI(ppars["inc"])))==int(10*Es[-1]):
Ps.append(perc)
if n<25:
pylab.figure(num=PLTS['taus'])
pylab.plot(Percs,Taus,'r--')
pylab.figure(num=PLTS['ei'])
pylab.plot(Es,Is,'r--')
Untilt.append(Percs[Taus.index(pylab.max(Taus))]) # tilt that gives maximum tau
Cdf.append(float(n)/float(nb))
pylab.figure(num=PLTS['taus'])
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
Ps.sort()
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')
pylab.title(tit)
print Ps[lower],Ps[upper]
pmagplotlib.drawFIGS(PLTS)
try:
raw_input('Return to save all figures, cntl-d to quit\n')
except EOFError:
print "Good bye"
sys.exit()
files={}
for key in PLTS.keys():
files[key]=('fold_'+'%s'%(key.strip()[:2])+'.svg')
pmagplotlib.saveP(PLTS,files)
def main():
"""
NAME
foldtest_magic.py
DESCRIPTION
does a fold test (Tauxe, 2007) on data
INPUT FORMAT
pmag_specimens format file, er_samples.txt format file (for bedding)
SYNTAX
foldtest_magic.py [command line options]
OPTIONS
-h prints help message and quits
-f pmag_sites formatted file [default is pmag_sites.txt]
-fsa er_samples formatted file [default is er_samples.txt]
-exc use pmag_criteria.txt to set acceptance criteria
-n NB, set number of bootstraps, default is 500
-b MIN, MAX, set bounds for untilting, default is -10, 150
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=500 # number of bootstraps
min,max=-10,150
dir_path='.'
infile,orfile='pmag_sites.txt','er_samples.txt'
critfile='pmag_criteria.txt'
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 '-b' in sys.argv:
ind=sys.argv.index('-b')
min=int(sys.argv[ind+1])
max=int(sys.argv[ind+2])
if '-f' in sys.argv:
ind=sys.argv.index('-f')
infile=sys.argv[ind+1]
if '-fsa' in sys.argv:
ind=sys.argv.index('-fsa')
orfile=sys.argv[ind+1]
orfile=dir_path+'/'+orfile
infile=dir_path+'/'+infile
critfile=dir_path+'/'+critfile
data,file_type=pmag.magic_read(infile)
ordata,file_type=pmag.magic_read(orfile)
if '-exc' in sys.argv:
crits,file_type=pmag.magic_read(critfile)
for crit in crits:
if crit['pmag_criteria_code']=="DE-SITE":
SiteCrit=crit
break
# get to work
#
PLTS={'geo':1,'strat':2,'taus':3} # make plot dictionary
pmagplotlib.plot_init(PLTS['geo'],5,5)
pmagplotlib.plot_init(PLTS['strat'],5,5)
pmagplotlib.plot_init(PLTS['taus'],5,5)
DIDDs= [] # set up list for dec inc dip_direction, dip
for rec in data: # read in the data from standard input
if eval(rec['site_tilt_correction'])==0:
dip,dip_dir=0,-1
Dec=float(rec['site_dec'])
Inc=float(rec['site_inc'])
for orec in ordata:
if orec['er_site_name']==rec['er_site_name']:
if orec['sample_bed_dip_direction']!="":dip_dir=float(orec['sample_bed_dip_direction'])
if orec['sample_bed_dip']!="":dip=float(orec['sample_bed_dip'])
break
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])
pmagplotlib.plotEQ(PLTS['geo'],DIDDs,'Geographic')
TCs=[]
for k in range(len(DIDDs)):
drot,irot=pmag.dotilt(DIDDs[k][0],DIDDs[k][1],DIDDs[k][2],DIDDs[k][3])
TCs.append([drot,irot,1.])
pmagplotlib.plotEQ(PLTS['strat'],TCs,'Stratigraphic')
Percs=range(min,max)
Cdf,Untilt=[],[]
pylab.figure(num=PLTS['taus'])
print 'doing ',nb,' iterations...please be patient.....'
for n in range(nb): # do bootstrap data sets - plot first 25 as dashed red line
if n%50==0:print n
Taus=[] # set up lists for taus
PDs=pmag.pseudo(DIDDs)
for perc in Percs:
tilt=0.01*perc
TCs=[]
for k in range(len(PDs)):
drot,irot=pmag.dotilt(PDs[k][0],PDs[k][1],PDs[k][2],tilt*PDs[k][3])
TCs.append([drot,irot,1.])
ppars=pmag.doprinc(TCs) # get principal directions
Taus.append(ppars['tau1'])
if n<25:pylab.plot(Percs,Taus,'r--')
Untilt.append(Percs[Taus.index(numpy.max(Taus))]) # tilt that gives maximum tau
Cdf.append(float(n)/float(nb))
pylab.plot(Percs,Taus,'k')
pylab.xlabel('% Untilting')
pylab.ylabel('tau_1 (red), CDF (green)')
Untilt.sort() # now for CDF of tilt of maximum tau
pylab.plot(Untilt,Cdf,'g')
lower=int(.025*nb)
upper=int(.975*nb)
pylab.axvline(x=Untilt[lower],ymin=0,ymax=1,linewidth=1,linestyle='--')
pylab.axvline(x=Untilt[upper],ymin=0,ymax=1,linewidth=1,linestyle='--')
tit= '%i - %i %s'%(Untilt[lower],Untilt[upper],'Percent Unfolding')
print tit
pylab.title(tit)
try:
raw_input('Return to save all figures, cntl-d to quit\n')
except EOFError:
print "Good bye"
sys.exit()
files={}
for key in PLTS.keys():
files[key]=('fold_'+'%s'%(key.strip()[:2])+'.svg')
pmagplotlib.saveP(PLTS,files)
def main():
"""
NAME
fishqq.py
DESCRIPTION
makes qq plot from dec,inc input data
INPUT FORMAT
takes dec/inc pairs in space delimited file
SYNTAX
fishqq.py [command line options]
OPTIONS
-h help message
-f FILE, specify file on command line
-F FILE, specify output file for statistics
OUTPUT:
Dec Inc N Mu Mu_crit Me Me_crit Y/N
where direction is the principal component and Y/N is Fisherian or not
separate lines for each mode with N >=10 (N and R)
"""
fmt,plot='svg',0
outfile=""
if '-h' in sys.argv: # check if help is needed
print main.__doc__
sys.exit() # graceful quit
elif '-f' in sys.argv: # ask for filename
ind=sys.argv.index('-f')
file=sys.argv[ind+1]
f=open(file,'rU')
data=f.readlines()
if '-F' in sys.argv:
ind=sys.argv.index('-F')
outfile=open(sys.argv[ind+1],'w') # open output file
DIs,nDIs,rDIs= [],[],[] # set up list for data
for line in data: # read in the data from standard input
if '\t' in line:
rec=line.split('\t') # split each line on space to get records
else:
rec=line.split() # split each line on space to get records
DIs.append([float(rec[0]),float(rec[1])]) # append data to Inc
# split into two modes
ppars=pmag.doprinc(DIs) # get principal directions
for rec in DIs:
angle=pmag.angle([rec[0],rec[1]],[ppars['dec'],ppars['inc']])
if angle>90.:
rDIs.append(rec)
else:
nDIs.append(rec)
#
if len(rDIs) >=10 or len(nDIs) >=10:
D1,I1=[],[]
QQ={'unf1':1,'exp1':2}
pmagplotlib.plot_init(QQ['unf1'],5,5)
pmagplotlib.plot_init(QQ['exp1'],5,5)
if len(nDIs) < 10:
ppars=pmag.doprinc(rDIs) # get principal directions
Drbar,Irbar=ppars['dec']-180.,-ppars['inc']
Nr=len(rDIs)
for di in rDIs:
d,irot=pmag.dotilt(di[0],di[1],Drbar-180.,90.-Irbar) # rotate to mean
drot=d-180.
if drot<0:drot=drot+360.
D1.append(drot)
I1.append(irot)
Dtit='Mode 2 Declinations'
Itit='Mode 2 Inclinations'
else:
ppars=pmag.doprinc(nDIs) # get principal directions
Dnbar,Inbar=ppars['dec'],ppars['inc']
Nn=len(nDIs)
for di in nDIs:
d,irot=pmag.dotilt(di[0],di[1],Dnbar-180.,90.-Inbar) # rotate to mean
drot=d-180.
if drot<0:drot=drot+360.
D1.append(drot)
I1.append(irot)
Dtit='Mode 1 Declinations'
Itit='Mode 1 Inclinations'
Mu_n,Mu_ncr=pmagplotlib.plotQQunf(QQ['unf1'],D1,Dtit) # make plot
Me_n,Me_ncr=pmagplotlib.plotQQexp(QQ['exp1'],I1,Itit) # make plot
if outfile!="":
# Dec Inc N Mu Mu_crit Me Me_crit Y/N
if Mu_n<=Mu_ncr and Me_n<=Me_ncr:
F='Y'
else:
F='N'
outstring='%7.1f %7.1f %i %5.3f %5.3f %5.3f %5.3f %s \n'%(Dnbar,Inbar,Nn,Mu_n,Mu_ncr,Me_n,Me_ncr,F)
outfile.write(outstring)
else:
print 'you need N> 10 for at least one mode'
sys.exit()
if len(rDIs)>10 and len(nDIs)>10:
D2,I2=[],[]
QQ['unf2']=3
QQ['exp2']=4
pmagplotlib.plot_init(QQ['unf2'],5,5)
pmagplotlib.plot_init(QQ['exp2'],5,5)
ppars=pmag.doprinc(rDIs) # get principal directions
Drbar,Irbar=ppars['dec']-180.,-ppars['inc']
Nr=len(rDIs)
for di in rDIs:
d,irot=pmag.dotilt(di[0],di[1],Drbar-180.,90.-Irbar) # rotate to mean
drot=d-180.
if drot<0:drot=drot+360.
D2.append(drot)
I2.append(irot)
Dtit='Mode 2 Declinations'
Itit='Mode 2 Inclinations'
Mu_r,Mu_rcr=pmagplotlib.plotQQunf(QQ['unf2'],D2,Dtit) # make plot
Me_r,Me_rcr=pmagplotlib.plotQQexp(QQ['exp2'],I2,Itit) # make plot
if outfile!="":
# Dec Inc N Mu Mu_crit Me Me_crit Y/N
if Mu_r<=Mu_rcr and Me_r<=Me_rcr:
F='Y'
else:
F='N'
outstring='%7.1f %7.1f %i %5.3f %5.3f %5.3f %5.3f %s \n'%(Drbar,Irbar,Nr,Mu_r,Mu_rcr,Me_r,Me_rcr,F)
outfile.write(outstring)
pmagplotlib.drawFIGS(QQ)
files={}
for key in QQ.keys():
files[key]=key+'.'+fmt
if pmagplotlib.isServer:
black = '#000000'
purple = '#800080'
titles={}
titles['eq']='Equal Area Plot'
EQ = pmagplotlib.addBorders(EQ,titles,black,purple)
pmagplotlib.saveP(QQ,files)
elif plot==1:
files['qq']=file+'.'+fmt
pmagplotlib.saveP(QQ,files)
else:
ans=raw_input(" S[a]ve to save plot, [q]uit without saving: ")
if ans=="a": pmagplotlib.saveP(QQ,files)
def main():
"""
NAME
foldtest.py
DESCRIPTION
does a fold test (Tauxe, 2008) on data
INPUT FORMAT
dec inc dip_direction dip
SYNTAX
foldtest.py [command line options]
OPTIONS
-h prints help message and quits
-f FILE
-u ANGLE (circular standard deviation) for uncertainty on bedding poles
-b MIN MAX bounds for quick search of percent untilting [default is -10 to 150%]
-n NB number of bootstrap samples [default is 1000]
OUTPUT
Geographic: is an equal area projection of the input data in
original coordinates
Stratigraphic: is an equal area projection of the input data in
tilt adjusted coordinates
% Untilting: The dashed (red) curves are representative plots of
maximum eigenvalue (tau_1) as a function of untilting
The solid line is the cumulative distribution of the
% Untilting required to maximize tau for all the
bootstrapped data sets. The dashed vertical lines
are 95% confidence bounds on the % untilting that yields
the most clustered result (maximum tau_1).
Command line: prints out the bootstrapped iterations and
finally the confidence bounds on optimum untilting.
If the 95% conf bounds include 0, then a pre-tilt magnetization is indicated
If the 95% conf bounds include 100, then a post-tilt magnetization is indicated
If the 95% conf bounds exclude both 0 and 100, syn-tilt magnetization is
possible as is vertical axis rotation or other pathologies
"""
kappa=0
nb=1000 # number of bootstraps
min,max=-10,150
if '-h' in sys.argv: # check if help is needed
print main.__doc__
sys.exit() # graceful quit
if '-f' in sys.argv:
ind=sys.argv.index('-f')
file=sys.argv[ind+1]
f=open(file,'rU')
data=f.readlines()
else:
print main.__doc__
sys.exit()
if '-b' in sys.argv:
ind=sys.argv.index('-b')
min=float(sys.argv[ind+1])
max=float(sys.argv[ind+2])
if '-n' in sys.argv:
ind=sys.argv.index('-n')
nb=int(sys.argv[ind+1])
if '-u' in sys.argv:
ind=sys.argv.index('-u')
csd=float(sys.argv[ind+1])
kappa=(81./csd)**2
#
# get to work
#
PLTS={'geo':1,'strat':2,'taus':3} # make plot dictionary
pmagplotlib.plot_init(PLTS['geo'],5,5)
pmagplotlib.plot_init(PLTS['strat'],5,5)
pmagplotlib.plot_init(PLTS['taus'],5,5)
DIDDs= [] # set up list for dec inc dip_direction, dip
for line in data: # read in the data from standard input
rec=line.split() # split each line on space to get records
DIDDs.append([float(rec[0]),float(rec[1]),float(rec[2]),float(rec[3])])
pmagplotlib.plotEQ(PLTS['geo'],DIDDs,'Geographic')
TCs=[]
for k in range(len(DIDDs)):
drot,irot=pmag.dotilt(DIDDs[k][0],DIDDs[k][1],DIDDs[k][2],DIDDs[k][3])
TCs.append([drot,irot,1.])
pmagplotlib.plotEQ(PLTS['strat'],TCs,'Stratigraphic')
Percs=range(min,max)
Cdf,Untilt=[],[]
pylab.figure(num=PLTS['taus'])
print 'doing ',nb,' iterations...please be patient.....'
for n in range(nb): # do bootstrap data sets - plot first 25 as dashed red line
if n%50==0:print n
Taus=[] # set up lists for taus
PDs=pmag.pseudo(DIDDs)
if kappa!=0:
for k in range(len(PDs)):
d,i=pmag.fshdev(kappa)
dipdir,dip=pmag.dodirot(d,i,PDs[k][2],PDs[k][3])
PDs[k][2]=dipdir
PDs[k][3]=dip
for perc in Percs:
tilt=0.01*perc
TCs=[]
for k in range(len(PDs)):
drot,irot=pmag.dotilt(PDs[k][0],PDs[k][1],PDs[k][2],tilt*PDs[k][3])
TCs.append([drot,irot,1.])
ppars=pmag.doprinc(TCs) # get principal directions
Taus.append(ppars['tau1'])
if n<25:pylab.plot(Percs,Taus,'r--')
Untilt.append(Percs[Taus.index(numpy.max(Taus))]) # tilt that gives maximum tau
Cdf.append(float(n)/float(nb))
pylab.plot(Percs,Taus,'k')
pylab.xlabel('% Untilting')
pylab.ylabel('tau_1 (red), CDF (green)')
Untilt.sort() # now for CDF of tilt of maximum tau
pylab.plot(Untilt,Cdf,'g')
lower=int(.025*nb)
upper=int(.975*nb)
pylab.axvline(x=Untilt[lower],ymin=0,ymax=1,linewidth=1,linestyle='--')
pylab.axvline(x=Untilt[upper],ymin=0,ymax=1,linewidth=1,linestyle='--')
tit= '%i - %i %s'%(Untilt[lower],Untilt[upper],'Percent Unfolding')
print tit
print 'range of all bootstrap samples: ', Untilt[0], ' - ', Untilt[-1]
pylab.title(tit)
try:
raw_input('Return to save all figures, cntl-d to quit\n')
except:
print "Good bye"
sys.exit()
files={}
for key in PLTS.keys():
files[key]=('fold_'+'%s'%(key.strip()[:2])+'.svg')
pmagplotlib.saveP(PLTS,files)
def main():
"""
NAME
foldtest.py
DESCRIPTION
does a fold test (Tauxe, 2010) on data
INPUT FORMAT
dec inc dip_direction dip
SYNTAX
foldtest.py [command line options]
OPTIONS
-h prints help message and quits
-f FILE file with input data
-F FILE for confidence bounds on fold test
-u ANGLE (circular standard deviation) for uncertainty on bedding poles
-b MIN MAX bounds for quick search of percent untilting [default is -10 to 150%]
-n NB number of bootstrap samples [default is 1000]
-fmt FMT, specify format - default is svg
OUTPUT PLOTS
Geographic: is an equal area projection of the input data in
original coordinates
Stratigraphic: is an equal area projection of the input data in
tilt adjusted coordinates
% Untilting: The dashed (red) curves are representative plots of
maximum eigenvalue (tau_1) as a function of untilting
The solid line is the cumulative distribution of the
% Untilting required to maximize tau for all the
bootstrapped data sets. The dashed vertical lines
are 95% confidence bounds on the % untilting that yields
the most clustered result (maximum tau_1).
Command line: prints out the bootstrapped iterations and
finally the confidence bounds on optimum untilting.
If the 95% conf bounds include 0, then a pre-tilt magnetization is indicated
If the 95% conf bounds include 100, then a post-tilt magnetization is indicated
If the 95% conf bounds exclude both 0 and 100, syn-tilt magnetization is
possible as is vertical axis rotation or other pathologies
Geographic: is an equal area projection of the input data in
OPTIONAL OUTPUT FILE:
The output file has the % untilting within the 95% confidence bounds
nd the number of bootstrap samples
"""
kappa=0
fmt='svg'
nb=1000 # number of bootstraps
min,max=-10,150
if '-h' in sys.argv: # check if help is needed
print main.__doc__
sys.exit() # graceful quit
if '-F' in sys.argv:
ind=sys.argv.index('-F')
outfile=open(sys.argv[ind+1],'w')
else:
outfile=""
if '-f' in sys.argv:
ind=sys.argv.index('-f')
file=sys.argv[ind+1]
DIDDs=numpy.loadtxt(file)
else:
print main.__doc__
sys.exit()
if '-fmt' in sys.argv:
ind=sys.argv.index('-fmt')
fmt=sys.argv[ind+1]
if '-b' in sys.argv:
ind=sys.argv.index('-b')
min=float(sys.argv[ind+1])
max=float(sys.argv[ind+2])
if '-n' in sys.argv:
ind=sys.argv.index('-n')
nb=int(sys.argv[ind+1])
if '-u' in sys.argv:
ind=sys.argv.index('-u')
csd=float(sys.argv[ind+1])
kappa=(81./csd)**2
#
# get to work
#
PLTS={'geo':1,'strat':2,'taus':3} # make plot dictionary
pmagplotlib.plot_init(PLTS['geo'],5,5)
pmagplotlib.plot_init(PLTS['strat'],5,5)
pmagplotlib.plot_init(PLTS['taus'],5,5)
pmagplotlib.plotEQ(PLTS['geo'],DIDDs,'Geographic')
D,I=pmag.dotilt_V(DIDDs)
TCs=numpy.array([D,I]).transpose()
pmagplotlib.plotEQ(PLTS['strat'],TCs,'Stratigraphic')
pmagplotlib.drawFIGS(PLTS)
Percs=range(min,max)
Cdf,Untilt=[],[]
pylab.figure(num=PLTS['taus'])
print 'doing ',nb,' iterations...please be patient.....'
for n in range(nb): # do bootstrap data sets - plot first 25 as dashed red line
if n%50==0:print n
Taus=[] # set up lists for taus
PDs=pmag.pseudo(DIDDs)
if kappa!=0:
for k in range(len(PDs)):
d,i=pmag.fshdev(kappa)
dipdir,dip=pmag.dodirot(d,i,PDs[k][2],PDs[k][3])
PDs[k][2]=dipdir
PDs[k][3]=dip
for perc in Percs:
tilt=numpy.array([1.,1.,1.,0.01*perc])
D,I=pmag.dotilt_V(PDs*tilt)
TCs=numpy.array([D,I]).transpose()
ppars=pmag.doprinc(TCs) # get principal directions
Taus.append(ppars['tau1'])
if n<25:pylab.plot(Percs,Taus,'r--')
Untilt.append(Percs[Taus.index(numpy.max(Taus))]) # tilt that gives maximum tau
Cdf.append(float(n)/float(nb))
pylab.plot(Percs,Taus,'k')
pylab.xlabel('% Untilting')
pylab.ylabel('tau_1 (red), CDF (green)')
Untilt.sort() # now for CDF of tilt of maximum tau
pylab.plot(Untilt,Cdf,'g')
lower=int(.025*nb)
upper=int(.975*nb)
pylab.axvline(x=Untilt[lower],ymin=0,ymax=1,linewidth=1,linestyle='--')
pylab.axvline(x=Untilt[upper],ymin=0,ymax=1,linewidth=1,linestyle='--')
tit= '%i - %i %s'%(Untilt[lower],Untilt[upper],'Percent Unfolding')
print tit
print 'range of all bootstrap samples: ', Untilt[0], ' - ', Untilt[-1]
pylab.title(tit)
outstring= '%i - %i; %i\n'%(Untilt[lower],Untilt[upper],nb)
if outfile!="":outfile.write(outstring)
pmagplotlib.drawFIGS(PLTS)
ans= raw_input('S[a]ve all figures, <Return> to quit ')
if ans!='a':
print "Good bye"
sys.exit()
else:
files={}
for key in PLTS.keys():
files[key]=('foldtest_'+'%s'%(key.strip()[:2])+'.'+fmt)
pmagplotlib.saveP(PLTS,files)
def main():
"""
NAME
eqarea_magic.py
DESCRIPTION
makes equal area projections from declination/inclination data
SYNTAX
eqarea_magic.py [command line options]
INPUT
takes magic formatted pmag_results, pmag_sites, pmag_samples or pmag_specimens
OPTIONS
-h prints help message and quits
-f FILE: specify input magic format file from magic,default='pmag_results.txt'
supported types=[magic_measurements,pmag_specimens, pmag_samples, pmag_sites, pmag_results, magic_web]
-obj OBJ: specify level of plot [all, sit, sam, spc], default is all
-crd [s,g,t]: specify coordinate system, [s]pecimen, [g]eographic, [t]ilt adjusted
default is geographic, unspecified assumed geographic
-fmt [svg,png,jpg] format for output plots
-ell [F,K,B,Be,Bv] plot Fisher, Kent, Bingham, Bootstrap ellipses or Boostrap eigenvectors
-c plot as colour contour
-sav save plot and quit quietly
NOTE
all: entire file; sit: site; sam: sample; spc: specimen
"""
FIG = {} # plot dictionary
FIG['eqarea'] = 1 # eqarea is figure 1
in_file, plot_key, coord, crd = 'pmag_results.txt', 'all', "0", 'g'
plotE, contour = 0, 0
dir_path = '.'
fmt = 'svg'
verbose = pmagplotlib.verbose
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]
pmagplotlib.plot_init(FIG['eqarea'], 5, 5)
if '-f' in sys.argv:
ind = sys.argv.index("-f")
in_file = dir_path + "/" + sys.argv[ind + 1]
if '-obj' in sys.argv:
ind = sys.argv.index('-obj')
plot_by = sys.argv[ind + 1]
if plot_by == 'all': plot_key = 'all'
if plot_by == 'sit': plot_key = 'er_site_name'
if plot_by == 'sam': plot_key = 'er_sample_name'
if plot_by == 'spc': plot_key = 'er_specimen_name'
if '-c' in sys.argv: contour = 1
plots = 0
if '-sav' in sys.argv:
plots = 1
verbose = 0
if '-ell' in sys.argv:
plotE = 1
ind = sys.argv.index('-ell')
ell_type = sys.argv[ind + 1]
if ell_type == 'F': dist = 'F'
if ell_type == 'K': dist = 'K'
if ell_type == 'B': dist = 'B'
if ell_type == 'Be': dist = 'BE'
if ell_type == 'Bv':
dist = 'BV'
FIG['bdirs'] = 2
pmagplotlib.plot_init(FIG['bdirs'], 5, 5)
if '-crd' in sys.argv:
ind = sys.argv.index("-crd")
crd = sys.argv[ind + 1]
if crd == 's': coord = "-1"
if crd == 'g': coord = "0"
if crd == 't': coord = "100"
if '-fmt' in sys.argv:
ind = sys.argv.index("-fmt")
fmt = sys.argv[ind + 1]
Dec_keys = ['site_dec', 'sample_dec', 'specimen_dec', 'measurement_dec', 'average_dec', 'none']
Inc_keys = ['site_inc', 'sample_inc', 'specimen_inc', 'measurement_inc', 'average_inc', 'none']
Tilt_keys = ['tilt_correction', 'site_tilt_correction', 'sample_tilt_correction', 'specimen_tilt_correction',
'none']
Dir_type_keys = ['', 'site_direction_type', 'sample_direction_type', 'specimen_direction_type']
Name_keys = ['er_specimen_name', 'er_sample_name', 'er_site_name', 'pmag_result_name']
data, file_type = pmag.magic_read(in_file)
if file_type == 'pmag_results' and plot_key != "all": plot_key = plot_key + 's' # need plural for results table
if verbose:
print len(data), ' records read from ', in_file
#
#
# find desired dec,inc data:
#
dir_type_key = ''
#
# get plotlist if not plotting all records
#
plotlist = []
if plot_key != "all":
plots = pmag.get_dictitem(data, plot_key, '', 'F')
for rec in plots:
if rec[plot_key] not in plotlist:
plotlist.append(rec[plot_key])
plotlist.sort()
else:
plotlist.append('All')
for plot in plotlist:
if verbose: print plot
DIblock = []
GCblock = []
SLblock, SPblock = [], []
title = plot
mode = 1
dec_key, inc_key, tilt_key, name_key, k = "", "", "", "", 0
if plot != "All":
odata = pmag.get_dictitem(data, plot_key, plot, 'T')
else:
odata = data # data for this obj
for dec_key in Dec_keys:
Decs = pmag.get_dictitem(odata, dec_key, '', 'F') # get all records with this dec_key not blank
if len(Decs) > 0: break
for inc_key in Inc_keys:
Incs = pmag.get_dictitem(Decs, inc_key, '', 'F') # get all records with this inc_key not blank
if len(Incs) > 0: break
for tilt_key in Tilt_keys:
if tilt_key in Incs[0].keys(): break # find the tilt_key for these records
if tilt_key == 'none': # no tilt key in data, need to fix this with fake data which will be unknown tilt
tilt_key = 'tilt_correction'
for rec in Incs: rec[tilt_key] = ''
cdata = pmag.get_dictitem(Incs, tilt_key, coord, 'T') # get all records matching specified coordinate system
if coord == '0': # geographic
udata = pmag.get_dictitem(Incs, tilt_key, '', 'T') # get all the blank records - assume geographic
if len(cdata) == 0: crd = ''
if len(udata) > 0:
for d in udata: cdata.append(d)
crd = crd + 'u'
for name_key in Name_keys:
Names = pmag.get_dictitem(cdata, name_key, '', 'F') # get all records with this name_key not blank
if len(Names) > 0: break
for dir_type_key in Dir_type_keys:
Dirs = pmag.get_dictitem(cdata, dir_type_key, '', 'F') # get all records with this direction type
if len(Dirs) > 0: break
if dir_type_key == "": dir_type_key = 'direction_type'
locations, site, sample, specimen = "", "", "", ""
for rec in cdata: # pick out the data
if 'er_location_name' in rec.keys() and rec['er_location_name'] != "" and rec[
'er_location_name'] not in locations: locations = locations + rec['er_location_name'].replace("/",
"") + "_"
if 'er_location_names' in rec.keys() and rec['er_location_names'] != "":
locs = rec['er_location_names'].split(':')
for loc in locs:
if loc not in locations: locations = locations + loc.replace("/", "") + '_'
if plot_key == 'er_site_name' or plot_key == 'er_sample_name' or plot_key == 'er_specimen_name':
site = rec['er_site_name']
if plot_key == 'er_sample_name' or plot_key == 'er_specimen_name':
sample = rec['er_sample_name']
if plot_key == 'er_specimen_name':
specimen = rec['er_specimen_name']
if plot_key == 'er_site_names' or plot_key == 'er_sample_names' or plot_key == 'er_specimen_names':
site = rec['er_site_names']
if plot_key == 'er_sample_names' or plot_key == 'er_specimen_names':
sample = rec['er_sample_names']
if plot_key == 'er_specimen_names':
specimen = rec['er_specimen_names']
if dir_type_key not in rec.keys() or rec[dir_type_key] == "": rec[dir_type_key] = 'l'
if 'magic_method_codes' not in rec.keys(): rec['magic_method_codes'] = ""
DIblock.append([float(rec[dec_key]), float(rec[inc_key])])
SLblock.append([rec[name_key], rec['magic_method_codes']])
if rec[tilt_key] == coord and rec[dir_type_key] != 'l' and rec[dec_key] != "" and rec[inc_key] != "":
GCblock.append([float(rec[dec_key]), float(rec[inc_key])])
SPblock.append([rec[name_key], rec['magic_method_codes']])
if len(DIblock) == 0 and len(GCblock) == 0:
if verbose: print "no records for plotting"
sys.exit()
if verbose:
for k in range(len(SLblock)):
print '%s %s %7.1f %7.1f' % (SLblock[k][0], SLblock[k][1], DIblock[k][0], DIblock[k][1])
for k in range(len(SPblock)):
print '%s %s %7.1f %7.1f' % (SPblock[k][0], SPblock[k][1], GCblock[k][0], GCblock[k][1])
if len(DIblock) > 0:
if contour == 0:
pmagplotlib.plotEQ(FIG['eqarea'], DIblock, title)
else:
pmagplotlib.plotEQcont(FIG['eqarea'], DIblock)
else:
pmagplotlib.plotNET(FIG['eqarea'])
if len(GCblock) > 0:
for rec in GCblock: pmagplotlib.plotC(FIG['eqarea'], rec, 90., 'g')
if plotE == 1:
ppars = pmag.doprinc(DIblock) # get principal directions
nDIs, rDIs, npars, rpars = [], [], [], []
for rec in DIblock:
angle = pmag.angle([rec[0], rec[1]], [ppars['dec'], ppars['inc']])
if angle > 90.:
rDIs.append(rec)
else:
nDIs.append(rec)
if dist == 'B': # do on whole dataset
etitle = "Bingham confidence ellipse"
bpars = pmag.dobingham(DIblock)
for key in bpars.keys():
if key != 'n' and verbose: print " ", key, '%7.1f' % (bpars[key])
if key == 'n' and verbose: print " ", key, ' %i' % (bpars[key])
npars.append(bpars['dec'])
npars.append(bpars['inc'])
npars.append(bpars['Zeta'])
npars.append(bpars['Zdec'])
npars.append(bpars['Zinc'])
npars.append(bpars['Eta'])
npars.append(bpars['Edec'])
npars.append(bpars['Einc'])
if dist == 'F':
etitle = "Fisher confidence cone"
if len(nDIs) > 2:
fpars = pmag.fisher_mean(nDIs)
for key in fpars.keys():
if key != 'n' and verbose: print " ", key, '%7.1f' % (fpars[key])
if key == 'n' and verbose: print " ", key, ' %i' % (fpars[key])
mode += 1
npars.append(fpars['dec'])
npars.append(fpars['inc'])
npars.append(fpars['alpha95']) # Beta
npars.append(fpars['dec'])
isign = abs(fpars['inc']) / fpars['inc']
npars.append(fpars['inc'] - isign * 90.) #Beta inc
npars.append(fpars['alpha95']) # gamma
npars.append(fpars['dec'] + 90.) # Beta dec
npars.append(0.) #Beta inc
if len(rDIs) > 2:
fpars = pmag.fisher_mean(rDIs)
if verbose: print "mode ", mode
for key in fpars.keys():
if key != 'n' and verbose: print " ", key, '%7.1f' % (fpars[key])
if key == 'n' and verbose: print " ", key, ' %i' % (fpars[key])
mode += 1
rpars.append(fpars['dec'])
rpars.append(fpars['inc'])
rpars.append(fpars['alpha95']) # Beta
rpars.append(fpars['dec'])
isign = abs(fpars['inc']) / fpars['inc']
rpars.append(fpars['inc'] - isign * 90.) #Beta inc
rpars.append(fpars['alpha95']) # gamma
rpars.append(fpars['dec'] + 90.) # Beta dec
rpars.append(0.) #Beta inc
if dist == 'K':
etitle = "Kent confidence ellipse"
if len(nDIs) > 3:
kpars = pmag.dokent(nDIs, len(nDIs))
if verbose: print "mode ", mode
for key in kpars.keys():
if key != 'n' and verbose: print " ", key, '%7.1f' % (kpars[key])
if key == 'n' and verbose: print " ", key, ' %i' % (kpars[key])
mode += 1
npars.append(kpars['dec'])
npars.append(kpars['inc'])
npars.append(kpars['Zeta'])
npars.append(kpars['Zdec'])
npars.append(kpars['Zinc'])
npars.append(kpars['Eta'])
npars.append(kpars['Edec'])
npars.append(kpars['Einc'])
if len(rDIs) > 3:
kpars = pmag.dokent(rDIs, len(rDIs))
if verbose: print "mode ", mode
for key in kpars.keys():
if key != 'n' and verbose: print " ", key, '%7.1f' % (kpars[key])
if key == 'n' and verbose: print " ", key, ' %i' % (kpars[key])
mode += 1
rpars.append(kpars['dec'])
rpars.append(kpars['inc'])
rpars.append(kpars['Zeta'])
rpars.append(kpars['Zdec'])
rpars.append(kpars['Zinc'])
rpars.append(kpars['Eta'])
rpars.append(kpars['Edec'])
rpars.append(kpars['Einc'])
else: # assume bootstrap
if dist == 'BE':
if len(nDIs) > 5:
BnDIs = pmag.di_boot(nDIs)
Bkpars = pmag.dokent(BnDIs, 1.)
if verbose: print "mode ", mode
for key in Bkpars.keys():
if key != 'n' and verbose: print " ", key, '%7.1f' % (Bkpars[key])
if key == 'n' and verbose: print " ", key, ' %i' % (Bkpars[key])
mode += 1
npars.append(Bkpars['dec'])
npars.append(Bkpars['inc'])
npars.append(Bkpars['Zeta'])
npars.append(Bkpars['Zdec'])
npars.append(Bkpars['Zinc'])
npars.append(Bkpars['Eta'])
npars.append(Bkpars['Edec'])
npars.append(Bkpars['Einc'])
if len(rDIs) > 5:
BrDIs = pmag.di_boot(rDIs)
Bkpars = pmag.dokent(BrDIs, 1.)
if verbose: print "mode ", mode
for key in Bkpars.keys():
if key != 'n' and verbose: print " ", key, '%7.1f' % (Bkpars[key])
if key == 'n' and verbose: print " ", key, ' %i' % (Bkpars[key])
mode += 1
rpars.append(Bkpars['dec'])
rpars.append(Bkpars['inc'])
rpars.append(Bkpars['Zeta'])
rpars.append(Bkpars['Zdec'])
rpars.append(Bkpars['Zinc'])
rpars.append(Bkpars['Eta'])
rpars.append(Bkpars['Edec'])
rpars.append(Bkpars['Einc'])
etitle = "Bootstrapped confidence ellipse"
elif dist == 'BV':
sym = {'lower': ['o', 'c'], 'upper': ['o', 'g'], 'size': 3, 'edgecolor': 'face'}
if len(nDIs) > 5:
BnDIs = pmag.di_boot(nDIs)
pmagplotlib.plotEQsym(FIG['bdirs'], BnDIs, 'Bootstrapped Eigenvectors', sym)
if len(rDIs) > 5:
BrDIs = pmag.di_boot(rDIs)
if len(nDIs) > 5: # plot on existing plots
pmagplotlib.plotDIsym(FIG['bdirs'], BrDIs, sym)
else:
pmagplotlib.plotEQ(FIG['bdirs'], BrDIs, 'Bootstrapped Eigenvectors')
if dist == 'B':
if len(nDIs) > 3 or len(rDIs) > 3: pmagplotlib.plotCONF(FIG['eqarea'], etitle, [], npars, 0)
elif len(nDIs) > 3 and dist != 'BV':
pmagplotlib.plotCONF(FIG['eqarea'], etitle, [], npars, 0)
if len(rDIs) > 3:
pmagplotlib.plotCONF(FIG['eqarea'], etitle, [], rpars, 0)
elif len(rDIs) > 3 and dist != 'BV':
pmagplotlib.plotCONF(FIG['eqarea'], etitle, [], rpars, 0)
if verbose: pmagplotlib.drawFIGS(FIG)
#
files = {}
locations = locations[:-1]
for key in FIG.keys():
filename = 'LO:_' + locations + '_SI:_' + site + '_SA:_' + sample + '_SP:_' + specimen + '_CO:_' + crd + '_TY:_' + key + '_.' + fmt
files[key] = filename
if pmagplotlib.isServer:
black = '#000000'
purple = '#800080'
titles = {}
titles['eq'] = 'Equal Area Plot'
FIG = pmagplotlib.addBorders(FIG, titles, black, purple)
pmagplotlib.saveP(FIG, files)
elif verbose:
ans = raw_input(" S[a]ve to save plot, [q]uit, Return to continue: ")
if ans == "q": sys.exit()
if ans == "a":
pmagplotlib.saveP(FIG, files)
if plots:
pmagplotlib.saveP(FIG, files)
def main():
"""
NAME
eqarea_magic.py
DESCRIPTION
makes equal area projections from declination/inclination data
SYNTAX
eqarea_magic.py [command line options]
INPUT
takes magic formatted pmag_results, pmag_sites, pmag_samples or pmag_specimens
OPTIONS
-h prints help message and quits
-f FILE: specify input magic format file from magic,default='pmag_results.txt'
supported types=[magic_measurements,pmag_specimens, pmag_samples, pmag_sites, pmag_results, magic_web]
-obj OBJ: specify level of plot [all, sit, sam, spc], default is all
-crd [s,g,t]: specify coordinate system, [s]pecimen, [g]eographic, [t]ilt adjusted
default is geographic
-fmt [svg,png,jpg] format for output plots
-ell [F,K,B,Be,Bv] plot Fisher, Kent, Bingham, Bootstrap ellipses or Boostrap eigenvectors
-c plot as colour contour
NOTE
all: entire file; sit: site; sam: sample; spc: specimen
"""
FIG={} # plot dictionary
FIG['eq']=1 # eqarea is figure 1
in_file,plot_key,coord,crd='pmag_results.txt','all',"-1",'g'
fmt,dist,mode='svg','F',1
plotE,contour=0,0
dir_path='.'
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]
pmagplotlib.plot_init(FIG['eq'],5,5)
if '-f' in sys.argv:
ind=sys.argv.index("-f")
in_file=dir_path+"/"+sys.argv[ind+1]
if '-obj' in sys.argv:
ind=sys.argv.index('-obj')
plot_by=sys.argv[ind+1]
if plot_by=='all':plot_key='all'
if plot_by=='sit':plot_key='er_site_name'
if plot_by=='sam':plot_key='er_sample_name'
if plot_by=='spc':plot_key='er_specimen_name'
if '-c' in sys.argv: contour=1
if '-ell' in sys.argv:
plotE=1
ind=sys.argv.index('-ell')
ell_type=sys.argv[ind+1]
if ell_type=='F':dist='F'
if ell_type=='K':dist='K'
if ell_type=='B':dist='B'
if ell_type=='Be':dist='BE'
if ell_type=='Bv':
dist='BV'
FIG['bdirs']=2
pmagplotlib.plot_init(FIG['bdirs'],5,5)
if '-crd' in sys.argv:
ind=sys.argv.index("-crd")
coord=sys.argv[ind+1]
if coord=='g':coord="0"
if coord=='t':coord="100"
if '-fmt' in sys.argv:
ind=sys.argv.index("-fmt")
fmt=sys.argv[ind+1]
Dec_keys=['site_dec','sample_dec','specimen_dec','measurement_dec','average_dec']
Inc_keys=['site_inc','sample_inc','specimen_inc','measurement_inc','average_inc']
Tilt_keys=['tilt_correction','site_tilt_correction','sample_tilt_correction','specimen_tilt_correction']
Dir_type_keys=['','site_direction_type','sample_direction_type','specimen_direction_type']
Name_keys=['er_specimen_name','er_sample_name','er_site_name','pmag_result_name']
data,file_type=pmag.magic_read(in_file)
if file_type=='pmag_results' and plot_key!="all":plot_key=plot_key+'s' # need plural for results table
if pmagplotlib.verbose:
print len(data),' records read from ',in_file
#
#
# find desired dec,inc data:
#
dir_type_key=''
#
# get plotlist if not plotting all records
#
plotlist=[]
if plot_key!="all":
for rec in data:
if rec[plot_key] not in plotlist:
plotlist.append(rec[plot_key])
plotlist.sort()
else:
plotlist.append('Whole file')
for plot in plotlist:
DIblock=[]
GCblock=[]
SLblock,SPblock=[],[]
tilt_key=""
mode=1
for rec in data: # find what data are available
if plot_key=='all' or rec[plot_key]==plot:
if plot_key!="all":
title=rec[plot_key]
else:
title=plot
if coord=='-1':title=title+' Specimen Coordinates'
if coord=='0':title=title+' Geographic Coordinates'
if coord=='100':title=title+' Tilt corrected Coordinates'
dec_key,inc_key,tilt_key,name_key,k="","","","",0
while dec_key=="" and k<len(Dec_keys):
if Dec_keys[k] in rec.keys() and rec[Dec_keys[k]]!="" and Inc_keys[k] in rec.keys() and rec[Inc_keys[k]]!="":
dec_key,inc_key =Dec_keys[k],Inc_keys[k]
k+=1
k=0
while tilt_key=="" and k<len(Tilt_keys):
if Tilt_keys[k] in rec.keys():tilt_key=Tilt_keys[k]
k+=1
k=0
while name_key=="" and k<len(Name_keys):
if Name_keys[k] in rec.keys():name_key=Name_keys[k]
k+=1
k=1
while dir_type_key=="" and k<len(Dir_type_keys):
if Dir_type_keys[k] in rec.keys():dir_type_key=Dir_type_keys[k]
k+=1
if dec_key!="":break
if tilt_key=="":tilt_key='-1'
if dir_type_key=="":dir_type_key='direction_type'
for rec in data: # pick out the data
if (plot_key=='all' or rec[plot_key]==plot) and rec[dec_key].strip()!="" and rec[inc_key].strip()!="":
if dir_type_key not in rec.keys() or rec[dir_type_key]=="":rec[dir_type_key]='l'
if tilt_key not in rec.keys():rec[tilt_key]='-1' # assume specimen coordinates unless otherwise specified
if coord=='-1':
DIblock.append([float(rec[dec_key]),float(rec[inc_key])])
SLblock.append([rec[name_key],rec['magic_method_codes']])
elif rec[tilt_key]==coord and rec[dir_type_key]=='l' and rec[dec_key]!="" and rec[inc_key]!="":
if rec[tilt_key]==coord and rec[dir_type_key]=='l' and rec[dec_key]!="" and rec[inc_key]!="":
DIblock.append([float(rec[dec_key]),float(rec[inc_key])])
SLblock.append([rec[name_key],rec['magic_method_codes']])
elif rec[tilt_key]==coord and rec[dir_type_key]!='l' and rec[dec_key]!="" and rec[inc_key]!="":
GCblock.append([float(rec[dec_key]),float(rec[inc_key])])
SPblock.append([rec[name_key],rec['magic_method_codes']])
if len(DIblock)==0 and len(GCblock)==0:
if pmagplotlib.verbose: print "no records for plotting"
sys.exit()
if pmagplotlib.verbose:
for k in range(len(SLblock)):
print '%s %s %7.1f %7.1f'%(SLblock[k][0],SLblock[k][1],DIblock[k][0],DIblock[k][1])
for k in range(len(SPblock)):
print '%s %s %7.1f %7.1f'%(SPblock[k][0],SPblock[k][1],GCblock[k][0],GCblock[k][1])
if len(DIblock)>0:
if contour==0:
pmagplotlib.plotEQ(FIG['eq'],DIblock,title)
else:
pmagplotlib.plotEQcont(FIG['eq'],DIblock)
else: pmagplotlib.plotNET(FIG['eq'])
if len(GCblock)>0:
for rec in GCblock: pmagplotlib.plotC(FIG['eq'],rec,90.,'g')
if plotE==1:
ppars=pmag.doprinc(DIblock) # get principal directions
nDIs,rDIs,npars,rpars=[],[],[],[]
for rec in DIblock:
angle=pmag.angle([rec[0],rec[1]],[ppars['dec'],ppars['inc']])
if angle>90.:
rDIs.append(rec)
else:
nDIs.append(rec)
if dist=='B': # do on whole dataset
etitle="Bingham confidence ellipse"
bpars=pmag.dobingham(DIblock)
for key in bpars.keys():
if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(bpars[key])
if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(bpars[key])
npars.append(bpars['dec'])
npars.append(bpars['inc'])
npars.append(bpars['Zeta'])
npars.append(bpars['Zdec'])
npars.append(bpars['Zinc'])
npars.append(bpars['Eta'])
npars.append(bpars['Edec'])
npars.append(bpars['Einc'])
if dist=='F':
etitle="Fisher confidence cone"
if len(nDIs)>2:
fpars=pmag.fisher_mean(nDIs)
for key in fpars.keys():
if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(fpars[key])
if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(fpars[key])
mode+=1
npars.append(fpars['dec'])
npars.append(fpars['inc'])
npars.append(fpars['alpha95']) # Beta
npars.append(fpars['dec'])
isign=abs(fpars['inc'])/fpars['inc']
npars.append(fpars['inc']-isign*90.) #Beta inc
npars.append(fpars['alpha95']) # gamma
npars.append(fpars['dec']+90.) # Beta dec
npars.append(0.) #Beta inc
if len(rDIs)>2:
fpars=pmag.fisher_mean(rDIs)
if pmagplotlib.verbose:print "mode ",mode
for key in fpars.keys():
if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(fpars[key])
if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(fpars[key])
mode+=1
rpars.append(fpars['dec'])
rpars.append(fpars['inc'])
rpars.append(fpars['alpha95']) # Beta
rpars.append(fpars['dec'])
isign=abs(fpars['inc'])/fpars['inc']
rpars.append(fpars['inc']-isign*90.) #Beta inc
rpars.append(fpars['alpha95']) # gamma
rpars.append(fpars['dec']+90.) # Beta dec
rpars.append(0.) #Beta inc
if dist=='K':
etitle="Kent confidence ellipse"
if len(nDIs)>3:
kpars=pmag.dokent(nDIs,len(nDIs))
if pmagplotlib.verbose:print "mode ",mode
for key in kpars.keys():
if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(kpars[key])
if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(kpars[key])
mode+=1
npars.append(kpars['dec'])
npars.append(kpars['inc'])
npars.append(kpars['Zeta'])
npars.append(kpars['Zdec'])
npars.append(kpars['Zinc'])
npars.append(kpars['Eta'])
npars.append(kpars['Edec'])
npars.append(kpars['Einc'])
if len(rDIs)>3:
kpars=pmag.dokent(rDIs,len(rDIs))
if pmagplotlib.verbose:print "mode ",mode
for key in kpars.keys():
if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(kpars[key])
if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(kpars[key])
mode+=1
rpars.append(kpars['dec'])
rpars.append(kpars['inc'])
rpars.append(kpars['Zeta'])
rpars.append(kpars['Zdec'])
rpars.append(kpars['Zinc'])
rpars.append(kpars['Eta'])
rpars.append(kpars['Edec'])
rpars.append(kpars['Einc'])
else: # assume bootstrap
if dist=='BE':
if len(nDIs)>5:
BnDIs=pmag.di_boot(nDIs)
Bkpars=pmag.dokent(BnDIs,1.)
if pmagplotlib.verbose:print "mode ",mode
for key in Bkpars.keys():
if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(Bkpars[key])
if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(Bkpars[key])
mode+=1
npars.append(Bkpars['dec'])
npars.append(Bkpars['inc'])
npars.append(Bkpars['Zeta'])
npars.append(Bkpars['Zdec'])
npars.append(Bkpars['Zinc'])
npars.append(Bkpars['Eta'])
npars.append(Bkpars['Edec'])
npars.append(Bkpars['Einc'])
if len(rDIs)>5:
BrDIs=pmag.di_boot(rDIs)
Bkpars=pmag.dokent(BrDIs,1.)
if pmagplotlib.verbose:print "mode ",mode
for key in Bkpars.keys():
if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(Bkpars[key])
if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(Bkpars[key])
mode+=1
rpars.append(Bkpars['dec'])
rpars.append(Bkpars['inc'])
rpars.append(Bkpars['Zeta'])
rpars.append(Bkpars['Zdec'])
rpars.append(Bkpars['Zinc'])
rpars.append(Bkpars['Eta'])
rpars.append(Bkpars['Edec'])
rpars.append(Bkpars['Einc'])
etitle="Bootstrapped confidence ellipse"
elif dist=='BV':
if len(nDIs)>5:
BnDIs=pmag.di_boot(nDIs)
pmagplotlib.plotEQ(FIG['bdirs'],BnDIs,'Bootstrapped Eigenvectors')
if len(rDIs)>5:
BrDIs=pmag.di_boot(rDIs)
if len(nDIs)>5: # plot on existing plots
pmagplotlib.plotDI(FIG['bdirs'],BrDIs)
else:
pmagplotlib.plotEQ(FIG['bdirs'],BrDIs,'Bootstrapped Eigenvectors')
if dist=='B':
if len(nDIs)> 3 or len(rDIs)>3: pmagplotlib.plotCONF(FIG['eq'],etitle,[],npars,0)
elif len(nDIs)>3 and dist!='BV':
pmagplotlib.plotCONF(FIG['eq'],etitle,[],npars,0)
if len(rDIs)>3:
pmagplotlib.plotCONF(FIG['eq'],etitle,[],rpars,0)
elif len(rDIs)>3 and dist!='BV':
pmagplotlib.plotCONF(FIG['eq'],etitle,[],rpars,0)
pmagplotlib.drawFIGS(FIG)
#
files={}
for key in FIG.keys():
files[key]=title.replace(" ","_")+'_'+'eqarea'+'.'+fmt
if pmagplotlib.isServer:
black = '#000000'
purple = '#800080'
titles={}
titles['eq']='Equal Area Plot'
FIG = pmagplotlib.addBorders(FIG,titles,black,purple)
pmagplotlib.saveP(FIG,files)
else:
ans=raw_input(" S[a]ve to save plot, [q]uit, Return to continue: ")
if ans=="q": sys.exit()
if ans=="a":
pmagplotlib.saveP(FIG,files)
def main():
"""
NAME
scalc_magic.py
DESCRIPTION
calculates Sb from pmag_results files
SYNTAX
scalc_magic -h [command line options]
INPUT
takes magic formatted pmag_results table
pmag_result_name must start with "VGP: Site"
must have average_lat if spin axis is reference
OPTIONS
-h prints help message and quits
-f FILE: specify input results file, default is 'pmag_results.txt'
-c cutoff: specify VGP colatitude cutoff value
-k cutoff: specify kappa cutoff
-crd [s,g,t]: specify coordinate system, default is geographic
-v : use the VanDammme criterion
-a: use antipodes of reverse data: default is to use only normal
-C: use all data without regard to polarity
-r: use reverse data only
-p: do relative to principle axis
-b: do bootstrap confidence bounds
OUTPUT:
if option -b used: N, S_B, lower and upper bounds
otherwise: N, S_B, cutoff
"""
in_file='pmag_results.txt'
coord,kappa,cutoff="0",1.,90.
nb,anti,spin,v,boot=1000,0,1,0,0
coord_key='tilt_correction'
rev=0
if '-h' in sys.argv:
print main.__doc__
sys.exit()
if '-f' in sys.argv:
ind=sys.argv.index("-f")
in_file=sys.argv[ind+1]
if '-c' in sys.argv:
ind=sys.argv.index('-c')
cutoff=float(sys.argv[ind+1])
if '-k' in sys.argv:
ind=sys.argv.index('-k')
kappa=float(sys.argv[ind+1])
if '-crd' in sys.argv:
ind=sys.argv.index("-crd")
coord=sys.argv[ind+1]
if coord=='s':coord="-1"
if coord=='g':coord="0"
if coord=='t':coord="100"
if '-a' in sys.argv: anti=1
if '-C' in sys.argv: cutoff=180. # no cutoff
if '-r' in sys.argv: rev=1
if '-p' in sys.argv: spin=0
if '-v' in sys.argv: v=1
if '-b' in sys.argv: boot=1
data,file_type=pmag.magic_read(in_file)
#
#
# find desired vgp lat,lon, kappa,N_site data:
#
#
#
A,Vgps,Pvgps=180.,[],[]
VgpRecs=pmag.get_dictitem(data,'vgp_lat','','F') # get all non-blank vgp latitudes
VgpRecs=pmag.get_dictitem(VgpRecs,'vgp_lon','','F') # get all non-blank vgp longitudes
SiteRecs=pmag.get_dictitem(VgpRecs,'data_type','i','T') # get VGPs (as opposed to averaged)
SiteRecs=pmag.get_dictitem(SiteRecs,coord_key,coord,'T') # get right coordinate system
for rec in SiteRecs:
if anti==1:
if 90.-abs(float(rec['vgp_lat']))<=cutoff and float(rec['average_k'])>=kappa:
if float(rec['vgp_lat'])<0:
rec['vgp_lat']='%7.1f'%(-1*float(rec['vgp_lat']))
rec['vgp_lon']='%7.1f'%(float(rec['vgp_lon'])-180.)
Vgps.append(rec)
Pvgps.append([float(rec['vgp_lon']),float(rec['vgp_lat'])])
elif rev==0: # exclude normals
if 90.-(float(rec['vgp_lat']))<=cutoff and float(rec['average_k'])>=kappa:
Vgps.append(rec)
Pvgps.append([float(rec['vgp_lon']),float(rec['vgp_lat'])])
else: # include normals
if 90.-abs(float(rec['vgp_lat']))<=cutoff and float(rec['average_k'])>=kappa:
if float(rec['vgp_lat'])<0:
rec['vgp_lat']='%7.1f'%(-1*float(rec['vgp_lat']))
rec['vgp_lon']='%7.1f'%(float(rec['vgp_lon'])-180.)
Vgps.append(rec)
Pvgps.append([float(rec['vgp_lon']),float(rec['vgp_lat'])])
if spin==0: # do transformation to pole
ppars=pmag.doprinc(Pvgps)
for vgp in Vgps:
vlon,vlat=pmag.dodirot(float(vgp['vgp_lon']),float(vgp['vgp_lat']),ppars['dec'],ppars['inc'])
vgp['vgp_lon']=vlon
vgp['vgp_lat']=vlat
vgp['average_k']="0"
S_B= pmag.get_Sb(Vgps)
A=cutoff
if v==1:
thetamax,A=181.,180.
vVgps,cnt=[],0
for vgp in Vgps:vVgps.append(vgp) # make a copy of Vgps
while thetamax>A:
thetas=[]
A=1.8*S_B+5
cnt+=1
for vgp in vVgps:thetas.append(90.-(float(vgp['vgp_lat'])))
thetas.sort()
thetamax=thetas[-1]
if thetamax<A:break
nVgps=[]
for vgp in vVgps:
if 90.-(float(vgp['vgp_lat']))<thetamax:nVgps.append(vgp)
vVgps=[]
for vgp in nVgps:vVgps.append(vgp)
S_B= pmag.get_Sb(vVgps)
Vgps=[]
for vgp in vVgps:Vgps.append(vgp) # make a new Vgp list
SBs=[]
if boot==1:
for i in range(nb): # now do bootstrap
BVgps=[]
if i%100==0: print i,' out of ',nb
for k in range(len(Vgps)):
ind=random.randint(0,len(Vgps)-1)
random.jumpahead(int(ind*1000))
BVgps.append(Vgps[ind])
SBs.append(pmag.get_Sb(BVgps))
SBs.sort()
low=int(.025*nb)
high=int(.975*nb)
print len(Vgps),'%7.1f _ %7.1f ^ %7.1f %7.1f'%(S_B,SBs[low],SBs[high],A)
else:
print len(Vgps),'%7.1f %7.1f '%(S_B,A)
def main():
"""
NAME
eqarea_ell.py
DESCRIPTION
makes equal area projections from declination/inclination data
and plot ellipses
SYNTAX
eqarea_ell.py -h [command line options]
INPUT
takes space delimited Dec/Inc data
OPTIONS
-h prints help message and quits
-f FILE
-fmt [svg,png,jpg] format for output plots
-sav saves figures and quits
-ell [F,K,B,Be,Bv] plot Fisher, Kent, Bingham, Bootstrap ellipses or Boostrap eigenvectors
"""
FIG={} # plot dictionary
FIG['eq']=1 # eqarea is figure 1
fmt,dist,mode,plot='svg','F',1,0
sym={'lower':['o','r'],'upper':['o','w'],'size':10}
plotE=0
if '-h' in sys.argv:
print main.__doc__
sys.exit()
pmagplotlib.plot_init(FIG['eq'],5,5)
if '-sav' in sys.argv:plot=1
if '-f' in sys.argv:
ind=sys.argv.index("-f")
title=sys.argv[ind+1]
data=numpy.loadtxt(title).transpose()
if '-ell' in sys.argv:
plotE=1
ind=sys.argv.index('-ell')
ell_type=sys.argv[ind+1]
if ell_type=='F':dist='F'
if ell_type=='K':dist='K'
if ell_type=='B':dist='B'
if ell_type=='Be':dist='BE'
if ell_type=='Bv':
dist='BV'
FIG['bdirs']=2
pmagplotlib.plot_init(FIG['bdirs'],5,5)
if '-fmt' in sys.argv:
ind=sys.argv.index("-fmt")
fmt=sys.argv[ind+1]
DIblock=numpy.array([data[0],data[1]]).transpose()
if len(DIblock)>0:
pmagplotlib.plotEQsym(FIG['eq'],DIblock,title,sym)
if plot==0:pmagplotlib.drawFIGS(FIG)
else:
print "no data to plot"
sys.exit()
if plotE==1:
ppars=pmag.doprinc(DIblock) # get principal directions
nDIs,rDIs,npars,rpars=[],[],[],[]
for rec in DIblock:
angle=pmag.angle([rec[0],rec[1]],[ppars['dec'],ppars['inc']])
if angle>90.:
rDIs.append(rec)
else:
nDIs.append(rec)
if dist=='B': # do on whole dataset
etitle="Bingham confidence ellipse"
bpars=pmag.dobingham(DIblock)
for key in bpars.keys():
if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(bpars[key])
if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(bpars[key])
npars.append(bpars['dec'])
npars.append(bpars['inc'])
npars.append(bpars['Zeta'])
npars.append(bpars['Zdec'])
npars.append(bpars['Zinc'])
npars.append(bpars['Eta'])
npars.append(bpars['Edec'])
npars.append(bpars['Einc'])
if dist=='F':
etitle="Fisher confidence cone"
if len(nDIs)>3:
fpars=pmag.fisher_mean(nDIs)
for key in fpars.keys():
if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(fpars[key])
if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(fpars[key])
mode+=1
npars.append(fpars['dec'])
npars.append(fpars['inc'])
npars.append(fpars['alpha95']) # Beta
npars.append(fpars['dec'])
isign=abs(fpars['inc'])/fpars['inc']
npars.append(fpars['inc']-isign*90.) #Beta inc
npars.append(fpars['alpha95']) # gamma
npars.append(fpars['dec']+90.) # Beta dec
npars.append(0.) #Beta inc
if len(rDIs)>3:
fpars=pmag.fisher_mean(rDIs)
if pmagplotlib.verbose:print "mode ",mode
for key in fpars.keys():
if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(fpars[key])
if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(fpars[key])
mode+=1
rpars.append(fpars['dec'])
rpars.append(fpars['inc'])
rpars.append(fpars['alpha95']) # Beta
rpars.append(fpars['dec'])
isign=abs(fpars['inc'])/fpars['inc']
rpars.append(fpars['inc']-isign*90.) #Beta inc
rpars.append(fpars['alpha95']) # gamma
rpars.append(fpars['dec']+90.) # Beta dec
rpars.append(0.) #Beta inc
if dist=='K':
etitle="Kent confidence ellipse"
if len(nDIs)>3:
kpars=pmag.dokent(nDIs,len(nDIs))
if pmagplotlib.verbose:print "mode ",mode
for key in kpars.keys():
if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(kpars[key])
if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(kpars[key])
mode+=1
npars.append(kpars['dec'])
npars.append(kpars['inc'])
npars.append(kpars['Zeta'])
npars.append(kpars['Zdec'])
npars.append(kpars['Zinc'])
npars.append(kpars['Eta'])
npars.append(kpars['Edec'])
npars.append(kpars['Einc'])
if len(rDIs)>3:
kpars=pmag.dokent(rDIs,len(rDIs))
if pmagplotlib.verbose:print "mode ",mode
for key in kpars.keys():
if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(kpars[key])
if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(kpars[key])
mode+=1
rpars.append(kpars['dec'])
rpars.append(kpars['inc'])
rpars.append(kpars['Zeta'])
rpars.append(kpars['Zdec'])
rpars.append(kpars['Zinc'])
rpars.append(kpars['Eta'])
rpars.append(kpars['Edec'])
rpars.append(kpars['Einc'])
else: # assume bootstrap
if len(nDIs)<10 and len(rDIs)<10:
print 'too few data points for bootstrap'
sys.exit()
if dist=='BE':
print 'Be patient for bootstrap...'
if len(nDIs)>=10:
BnDIs=pmag.di_boot(nDIs)
Bkpars=pmag.dokent(BnDIs,1.)
if pmagplotlib.verbose:print "mode ",mode
for key in Bkpars.keys():
if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(Bkpars[key])
if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(Bkpars[key])
mode+=1
npars.append(Bkpars['dec'])
npars.append(Bkpars['inc'])
npars.append(Bkpars['Zeta'])
npars.append(Bkpars['Zdec'])
npars.append(Bkpars['Zinc'])
npars.append(Bkpars['Eta'])
npars.append(Bkpars['Edec'])
npars.append(Bkpars['Einc'])
if len(rDIs)>=10:
BrDIs=pmag.di_boot(rDIs)
Bkpars=pmag.dokent(BrDIs,1.)
if pmagplotlib.verbose:print "mode ",mode
for key in Bkpars.keys():
if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(Bkpars[key])
if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(Bkpars[key])
mode+=1
rpars.append(Bkpars['dec'])
rpars.append(Bkpars['inc'])
rpars.append(Bkpars['Zeta'])
rpars.append(Bkpars['Zdec'])
rpars.append(Bkpars['Zinc'])
rpars.append(Bkpars['Eta'])
rpars.append(Bkpars['Edec'])
rpars.append(Bkpars['Einc'])
etitle="Bootstrapped confidence ellipse"
elif dist=='BV':
print 'Be patient for bootstrap...'
vsym={'lower':['+','k'],'upper':['x','k'],'size':5}
if len(nDIs)>5:
BnDIs=pmag.di_boot(nDIs)
pmagplotlib.plotEQsym(FIG['bdirs'],BnDIs,'Bootstrapped Eigenvectors',vsym)
if len(rDIs)>5:
BrDIs=pmag.di_boot(rDIs)
if len(nDIs)>5: # plot on existing plots
pmagplotlib.plotDIsym(FIG['bdirs'],BrDIs,vsym)
else:
pmagplotlib.plotEQ(FIG['bdirs'],BrDIs,'Bootstrapped Eigenvectors',vsym)
if dist=='B':
if len(nDIs)> 3 or len(rDIs)>3: pmagplotlib.plotCONF(FIG['eq'],etitle,[],npars,0)
elif len(nDIs)>3 and dist!='BV':
pmagplotlib.plotCONF(FIG['eq'],etitle,[],npars,0)
if len(rDIs)>3:
pmagplotlib.plotCONF(FIG['eq'],etitle,[],rpars,0)
elif len(rDIs)>3 and dist!='BV':
pmagplotlib.plotCONF(FIG['eq'],etitle,[],rpars,0)
if plot==0:pmagplotlib.drawFIGS(FIG)
if plot==0:pmagplotlib.drawFIGS(FIG)
#
files={}
for key in FIG.keys():
files[key]=title+'_'+key+'.'+fmt
if pmagplotlib.isServer:
black = '#000000'
purple = '#800080'
titles={}
titles['eq']='Equal Area Plot'
FIG = pmagplotlib.addBorders(FIG,titles,black,purple)
pmagplotlib.saveP(FIG,files)
elif plot==0:
ans=raw_input(" S[a]ve to save plot, [q]uit, Return to continue: ")
if ans=="q": sys.exit()
if ans=="a":
pmagplotlib.saveP(FIG,files)
else:
pmagplotlib.saveP(FIG,files)
