def common_dir_boot(dir1,dir2,bootsteps=1000,plot='no'):
#test for a common direction using bootstrap method
#directions given as pandas Series listing mean direction/pole parameters
#this is assuming published means without input directions, so bootstrap is a little different from the one
#described in Tauxe: not a pseudoselection from specified points but drawing from fisher distribution directly.
#adds a level of abstraction which hopefully does not invalidate procedure
BDI1,BDI2=[],[]
for s in range(bootsteps):
fpars1=pmag.fisher_mean(get_fish(dir1))
fpars2=pmag.fisher_mean(get_fish(dir2))
BDI1.append([fpars1['dec'],fpars1['inc']])
BDI2.append([fpars2['dec'],fpars2['inc']])
bounds1=get_bounds(BDI1)
bounds2=get_bounds(BDI2)
#now want to check if is a pass or a fail - only pass if error bounds overlap in x,y,and z
bresult=[]
for b1,b2 in zip(bounds1,bounds2):
if (b1[0]>b2[1] or b1[1]<b2[0]):
bresult.append(0)
else: bresult.append(1)
if sum(bresult)==3: outcome='Pass'
else: outcome='Fail'
angle=pmag.angle((dir1['dec'],dir1['inc']),(dir2['dec'],dir2['inc']))
# set up plots - can run this if want to visually check what's going on.
if plot=='yes':
CDF={'X':1,'Y':2,'Z':3}
pmagplotlib.plot_init(CDF['X'],4,4)
pmagplotlib.plot_init(CDF['Y'],4,4)
pmagplotlib.plot_init(CDF['Z'],4,4)
# draw the cdfs
pmagplotlib.plotCOM(CDF,BDI1,BDI2,["",""])
pmagplotlib.drawFIGS(CDF)
return pd.Series([outcome,angle[0]],index=['Outcome','angle'])
def spitout(line):
dir1,dir2=[],[] # initialize list for d1,i1,d2,i2
dat=line.split() # split the data on a space into columns
dir1.append(float(dat[0]))
dir1.append(float(dat[1]))
dir2.append(float(dat[2]))
dir2.append(float(dat[3]))
ang= pmag.angle(dir1,dir2) #
print '%7.1f '%(ang)
return ang
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 watson_common_mean(Data1, Data2, NumSims=5000, plot='no'):
"""
Conduct a Watson V test for a common mean on two declination, inclination data sets
This function calculates Watson's V statistic from input files through Monte Carlo
simulation in order to test whether two populations of directional data could have
been drawn from a common mean. The critical angle between the two sample mean
directions and the corresponding McFadden and McElhinny (1990) classification is printed.
Required Arguments
----------
Data1 : a list of directional data [dec,inc]
Data2 : a list of directional data [dec,inc]
Optional Arguments
----------
NumSims : number of Monte Carlo simulations (default is 5000)
plot : the default is no plot ('no'). Putting 'yes' will the plot the CDF from
the Monte Carlo simulations.
"""
pars_1 = pmag.fisher_mean(Data1)
pars_2 = pmag.fisher_mean(Data2)
cart_1 = pmag.dir2cart([pars_1["dec"], pars_1["inc"], pars_1["r"]])
cart_2 = pmag.dir2cart([pars_2['dec'], pars_2['inc'], pars_2["r"]])
Sw = pars_1['k'] * pars_1['r'] + pars_2['k'] * pars_2['r'] # k1*r1+k2*r2
xhat_1 = pars_1['k'] * cart_1[0] + pars_2['k'] * cart_2[0] # k1*x1+k2*x2
xhat_2 = pars_1['k'] * cart_1[1] + pars_2['k'] * cart_2[1] # k1*y1+k2*y2
xhat_3 = pars_1['k'] * cart_1[2] + pars_2['k'] * cart_2[2] # k1*z1+k2*z2
Rw = np.sqrt(xhat_1**2 + xhat_2**2 + xhat_3**2)
V = 2 * (Sw - Rw)
# keep weighted sum for later when determining the "critical angle"
# let's save it as Sr (notation of McFadden and McElhinny, 1990)
Sr = Sw
# do monte carlo simulation of datasets with same kappas as data,
# but a common mean
counter = 0
Vp = [] # set of Vs from simulations
for k in range(NumSims):
# get a set of N1 fisher distributed vectors with k1,
# calculate fisher stats
Dirp = []
for i in range(pars_1["n"]):
Dirp.append(pmag.fshdev(pars_1["k"]))
pars_p1 = pmag.fisher_mean(Dirp)
# get a set of N2 fisher distributed vectors with k2,
# calculate fisher stats
Dirp = []
for i in range(pars_2["n"]):
Dirp.append(pmag.fshdev(pars_2["k"]))
pars_p2 = pmag.fisher_mean(Dirp)
# get the V for these
Vk = pmag.vfunc(pars_p1, pars_p2)
Vp.append(Vk)
# sort the Vs, get Vcrit (95th percentile one)
Vp.sort()
k = int(.95 * NumSims)
Vcrit = Vp[k]
# equation 18 of McFadden and McElhinny, 1990 calculates the critical
# value of R (Rwc)
Rwc = Sr - (Vcrit / 2)
# following equation 19 of McFadden and McElhinny (1990) the critical
# angle is calculated. If the observed angle (also calculated below)
# between the data set means exceeds the critical angle the hypothesis
# of a common mean direction may be rejected at the 95% confidence
# level. The critical angle is simply a different way to present
# Watson's V parameter so it makes sense to use the Watson V parameter
# in comparison with the critical value of V for considering the test
# results. What calculating the critical angle allows for is the
# classification of McFadden and McElhinny (1990) to be made
# for data sets that are consistent with sharing a common mean.
k1 = pars_1['k']
k2 = pars_2['k']
R1 = pars_1['r']
R2 = pars_2['r']
critical_angle = np.degrees(
np.arccos(((Rwc**2) - ((k1 * R1)**2) - ((k2 * R2)**2)) /
(2 * k1 * R1 * k2 * R2)))
D1 = (pars_1['dec'], pars_1['inc'])
D2 = (pars_2['dec'], pars_2['inc'])
angle = pmag.angle(D1, D2)
print "Results of Watson V test: "
print ""
print "Watson's V: " '%.1f' % (V)
print "Critical value of V: " '%.1f' % (Vcrit)
if V < Vcrit:
print '"Pass": Since V is less than Vcrit, the null hypothesis'
print 'that the two populations are drawn from distributions'
print 'that share a common mean direction can not be rejected.'
elif V > Vcrit:
print '"Fail": Since V is greater than Vcrit, the two means can'
print 'be distinguished at the 95% confidence level.'
print ""
print "M&M1990 classification:"
print ""
print "Angle between data set means: " '%.1f' % (angle)
print "Critical angle for M&M1990: " '%.1f' % (critical_angle)
if V > Vcrit:
print ""
elif V < Vcrit:
if critical_angle < 5:
print "The McFadden and McElhinny (1990) classification for"
print "this test is: 'A'"
elif critical_angle < 10:
print "The McFadden and McElhinny (1990) classification for"
print "this test is: 'B'"
elif critical_angle < 20:
print "The McFadden and McElhinny (1990) classification for"
print "this test is: 'C'"
else:
print "The McFadden and McElhinny (1990) classification for"
print "this test is: 'INDETERMINATE;"
if plot == 'yes':
CDF = {'cdf': 1}
#pmagplotlib.plot_init(CDF['cdf'],5,5)
p1 = pmagplotlib.plotCDF(CDF['cdf'], Vp, "Watson's V", 'r', "")
p2 = pmagplotlib.plotVs(CDF['cdf'], [V], 'g', '-')
p3 = pmagplotlib.plotVs(CDF['cdf'], [Vp[k]], 'b', '--')
pmagplotlib.drawFIGS(CDF)
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
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 common_dir_MM90(dir1,dir2,NumSims=5000,plot='no'):
dir1['r']=get_R(dir1)
dir2['r']=get_R(dir2)
#largely based on iWatsonV routine of Swanson-Hyell in IPMag
cart_1=pmag.dir2cart([dir1["dec"],dir1["inc"],dir1["r"]])
cart_2=pmag.dir2cart([dir2['dec'],dir2['inc'],dir2["r"]])
Sw=dir1['k']*dir1['r']+dir2['k']*dir2['r'] # k1*r1+k2*r2
xhat_1=dir1['k']*cart_1[0]+dir2['k']*cart_2[0] # k1*x1+k2*x2
xhat_2=dir1['k']*cart_1[1]+dir2['k']*cart_2[1] # k1*y1+k2*y2
xhat_3=dir1['k']*cart_1[2]+dir2['k']*cart_2[2] # k1*z1+k2*z2
Rw=np.sqrt(xhat_1**2+xhat_2**2+xhat_3**2)
V=2*(Sw-Rw)
# keep weighted sum for later when determining the "critical angle"
# let's save it as Sr (notation of McFadden and McElhinny, 1990)
Sr=Sw
# do monte carlo simulation of datasets with same kappas as data,
# but a common mean
counter=0
Vp=[] # set of Vs from simulations
for k in range(NumSims):
# get a set of N1 fisher distributed vectors with k1,
# calculate fisher stats
Dirp=[]
for i in range(int(dir1["n"])):
Dirp.append(pmag.fshdev(dir1["k"]))
pars_p1=pmag.fisher_mean(Dirp)
# get a set of N2 fisher distributed vectors with k2,
# calculate fisher stats
Dirp=[]
for i in range(int(dir2["n"])):
Dirp.append(pmag.fshdev(dir2["k"]))
pars_p2=pmag.fisher_mean(Dirp)
# get the V for these
Vk=pmag.vfunc(pars_p1,pars_p2)
Vp.append(Vk)
# sort the Vs, get Vcrit (95th percentile one)
Vp.sort()
k=int(.95*NumSims)
Vcrit=Vp[k]
# equation 18 of McFadden and McElhinny, 1990 calculates the critical
# value of R (Rwc)
Rwc=Sr-(Vcrit/2)
# following equation 19 of McFadden and McElhinny (1990) the critical
# angle is calculated. If the observed angle (also calculated below)
# between the data set means exceeds the critical angle the hypothesis
# of a common mean direction may be rejected at the 95% confidence
# level. The critical angle is simply a different way to present
# Watson's V parameter so it makes sense to use the Watson V parameter
# in comparison with the critical value of V for considering the test
# results. What calculating the critical angle allows for is the
# classification of McFadden and McElhinny (1990) to be made
# for data sets that are consistent with sharing a common mean.
k1=dir1['k']
k2=dir2['k']
R1=dir1['r']
R2=dir2['r']
critical_angle=np.degrees(np.arccos(((Rwc**2)-((k1*R1)**2)
-((k2*R2)**2))/
(2*k1*R1*k2*R2)))
D1=(dir1['dec'],dir1['inc'])
D2=(dir2['dec'],dir2['inc'])
angle=pmag.angle(D1,D2)
if V<Vcrit:
outcome='Pass'
if critical_angle<5: MM90class='A'
elif critical_angle<10: MM90class='B'
elif critical_angle<20: MM90class='C'
else: MM90class='INDETERMINATE'
else:
outcome='Fail'
MM90class='FAIL'
result=pd.Series([outcome,V,Vcrit,angle[0],critical_angle,MM90class], index=['Outcome','VWatson','Vcrit','angle','critangle','MM90class'])
if plot=='yes':
CDF={'cdf':1}
#pmagplotlib.plot_init(CDF['cdf'],5,5)
p1 = pmagplotlib.plotCDF(CDF['cdf'],Vp,"Watson's V",'r',"")
p2 = pmagplotlib.plotVs(CDF['cdf'],[V],'g','-')
p3 = pmagplotlib.plotVs(CDF['cdf'],[Vp[k]],'b','--')
pmagplotlib.drawFIGS(CDF)
return result
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 watson_common_mean(Data1,Data2,NumSims=5000,plot='no'):
"""
Conduct a Watson V test for a common mean on two declination, inclination data sets
This function calculates Watson's V statistic from input files through Monte Carlo
simulation in order to test whether two populations of directional data could have
been drawn from a common mean. The critical angle between the two sample mean
directions and the corresponding McFadden and McElhinny (1990) classification is printed.
Required Arguments
----------
Data1 : a list of directional data [dec,inc]
Data2 : a list of directional data [dec,inc]
Optional Arguments
----------
NumSims : number of Monte Carlo simulations (default is 5000)
plot : the default is no plot ('no'). Putting 'yes' will the plot the CDF from
the Monte Carlo simulations.
"""
pars_1=pmag.fisher_mean(Data1)
pars_2=pmag.fisher_mean(Data2)
cart_1=pmag.dir2cart([pars_1["dec"],pars_1["inc"],pars_1["r"]])
cart_2=pmag.dir2cart([pars_2['dec'],pars_2['inc'],pars_2["r"]])
Sw=pars_1['k']*pars_1['r']+pars_2['k']*pars_2['r'] # k1*r1+k2*r2
xhat_1=pars_1['k']*cart_1[0]+pars_2['k']*cart_2[0] # k1*x1+k2*x2
xhat_2=pars_1['k']*cart_1[1]+pars_2['k']*cart_2[1] # k1*y1+k2*y2
xhat_3=pars_1['k']*cart_1[2]+pars_2['k']*cart_2[2] # k1*z1+k2*z2
Rw=np.sqrt(xhat_1**2+xhat_2**2+xhat_3**2)
V=2*(Sw-Rw)
# keep weighted sum for later when determining the "critical angle"
# let's save it as Sr (notation of McFadden and McElhinny, 1990)
Sr=Sw
# do monte carlo simulation of datasets with same kappas as data,
# but a common mean
counter=0
Vp=[] # set of Vs from simulations
for k in range(NumSims):
# get a set of N1 fisher distributed vectors with k1,
# calculate fisher stats
Dirp=[]
for i in range(pars_1["n"]):
Dirp.append(pmag.fshdev(pars_1["k"]))
pars_p1=pmag.fisher_mean(Dirp)
# get a set of N2 fisher distributed vectors with k2,
# calculate fisher stats
Dirp=[]
for i in range(pars_2["n"]):
Dirp.append(pmag.fshdev(pars_2["k"]))
pars_p2=pmag.fisher_mean(Dirp)
# get the V for these
Vk=pmag.vfunc(pars_p1,pars_p2)
Vp.append(Vk)
# sort the Vs, get Vcrit (95th percentile one)
Vp.sort()
k=int(.95*NumSims)
Vcrit=Vp[k]
# equation 18 of McFadden and McElhinny, 1990 calculates the critical
# value of R (Rwc)
Rwc=Sr-(Vcrit/2)
# following equation 19 of McFadden and McElhinny (1990) the critical
# angle is calculated. If the observed angle (also calculated below)
# between the data set means exceeds the critical angle the hypothesis
# of a common mean direction may be rejected at the 95% confidence
# level. The critical angle is simply a different way to present
# Watson's V parameter so it makes sense to use the Watson V parameter
# in comparison with the critical value of V for considering the test
# results. What calculating the critical angle allows for is the
# classification of McFadden and McElhinny (1990) to be made
# for data sets that are consistent with sharing a common mean.
k1=pars_1['k']
k2=pars_2['k']
R1=pars_1['r']
R2=pars_2['r']
critical_angle=np.degrees(np.arccos(((Rwc**2)-((k1*R1)**2)
-((k2*R2)**2))/
(2*k1*R1*k2*R2)))
D1=(pars_1['dec'],pars_1['inc'])
D2=(pars_2['dec'],pars_2['inc'])
angle=pmag.angle(D1,D2)
print "Results of Watson V test: "
print ""
print "Watson's V: " '%.1f' %(V)
print "Critical value of V: " '%.1f' %(Vcrit)
if V<Vcrit:
print '"Pass": Since V is less than Vcrit, the null hypothesis'
print 'that the two populations are drawn from distributions'
print 'that share a common mean direction can not be rejected.'
elif V>Vcrit:
print '"Fail": Since V is greater than Vcrit, the two means can'
print 'be distinguished at the 95% confidence level.'
print ""
print "M&M1990 classification:"
print ""
print "Angle between data set means: " '%.1f'%(angle)
print "Critical angle for M&M1990: " '%.1f'%(critical_angle)
if V>Vcrit:
print ""
elif V<Vcrit:
if critical_angle<5:
print "The McFadden and McElhinny (1990) classification for"
print "this test is: 'A'"
elif critical_angle<10:
print "The McFadden and McElhinny (1990) classification for"
print "this test is: 'B'"
elif critical_angle<20:
print "The McFadden and McElhinny (1990) classification for"
print "this test is: 'C'"
else:
print "The McFadden and McElhinny (1990) classification for"
print "this test is: 'INDETERMINATE;"
if plot=='yes':
CDF={'cdf':1}
#pmagplotlib.plot_init(CDF['cdf'],5,5)
p1 = pmagplotlib.plotCDF(CDF['cdf'],Vp,"Watson's V",'r',"")
p2 = pmagplotlib.plotVs(CDF['cdf'],[V],'g','-')
p3 = pmagplotlib.plotVs(CDF['cdf'],[Vp[k]],'b','--')
pmagplotlib.drawFIGS(CDF)
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
angle.py
DESCRIPTION
calculates angle between two input directions D1,D2
INPUT (COMMAND LINE ENTRY)
D1_dec D1_inc D1_dec D2_inc
OUTPUT
angle
SYNTAX
angle.py [-h][-i] [command line options] [< filename]
OPTIONS
-h prints help and quits
-i for interactive data entry
-f FILE input filename
-F FILE output filename (required if -F set)
Standard I/O
"""
if '-h' in sys.argv:
print main.__doc__
sys.exit()
if '-f' in sys.argv:
ind=sys.argv.index('-f')
file=sys.argv[ind+1]
dat=[]
f=open(file,'rU')
if '-F' in sys.argv:
ind=sys.argv.index('-F')
o=sys.argv[ind+1]
out=open(o,'w')
input=f.readlines()
for line in input:
ang=spitout(line)
out.write('%7.1f \n'%(ang))
sys.exit()
elif '-i' in sys.argv:
cont=1
while cont==1:
dir1,dir2=[],[]
try:
ans=raw_input('Declination 1: [ctrl-D to quit] ')
dir1.append(float(ans))
ans=raw_input('Inclination 1: ')
dir1.append(float(ans))
ans=raw_input('Declination 2: ')
dir2.append(float(ans))
ans=raw_input('Inclination 2: ')
dir2.append(float(ans))
except:
print "\nGood bye\n"
sys.exit()
ang= pmag.angle(dir1,dir2) # send dirs to angle and spit out result
print '%7.1f '%(ang)
else:
input = sys.stdin.readlines() # read from standard input
for line in input: # read in the data (as string variable), line by line
ang=spitout(line)
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 sb_vgp_calc(dataframe):
"""
This function calculates the angular dispersion of VGPs and corrects
for within site dispersion to return a value S_b. The input data
needs to be within a pandas Dataframe. The function also plots the
VGPs and their associated Fisher mean on a projection centered on
the mean.
Parameters
-----------
dataframe : the name of the pandas.DataFrame containing the data
the data frame needs to contain these columns:
dataframe['site_lat'] : latitude of the site
dataframe['site_lon'] : longitude of the site ---- changed to site_long so that it works for a given DF
dataframe['inc_tc'] : tilt-corrected inclination
dataframe['dec_tc'] : tilt-corrected declination
dataframe['k'] : fisher precision parameter for directions
dataframe['vgp_lat'] : VGP latitude ---- changed to pole_lat so that it works for a given DF
dataframe['vgp_lon'] : VGP longitude ---- changed to pole_long so that it works for a given DF
"""
# calculate the mean from the directional data
dataframe_dirs = []
for n in range(0, len(dataframe)):
dataframe_dirs.append(
[dataframe['dec_tc'][n], dataframe['inc_tc'][n], 1.])
dataframe_dir_mean = pmag.fisher_mean(dataframe_dirs)
# calculate the mean from the vgp data
dataframe_poles = []
dataframe_pole_lats = []
dataframe_pole_lons = []
for n in range(0, len(dataframe)):
dataframe_poles.append(
[dataframe['pole_long'][n], dataframe['pole_lat'][n], 1.])
dataframe_pole_lats.append(dataframe['pole_lat'][n])
dataframe_pole_lons.append(dataframe['pole_long'][n])
dataframe_pole_mean = pmag.fisher_mean(dataframe_poles)
# calculate mean paleolatitude from the directional data
dataframe['paleolatitude'] = lat_from_inc(dataframe_dir_mean['inc'])
angle_list = []
for n in range(0, len(dataframe)):
angle = pmag.angle(
[dataframe['pole_long'][n], dataframe['pole_lat'][n]],
[dataframe_pole_mean['dec'], dataframe_pole_mean['inc']])
angle_list.append(angle[0])
dataframe['delta_mean-pole'] = angle_list
# use eq. 2 of Cox (1970) to translate the directional precision parameter
# into pole coordinates using the assumption of a Fisherian distribution in
# directional coordinates and the paleolatitude as calculated from mean
# inclination using the dipole equation
dataframe['K'] = dataframe['k'] / (
0.125 * (5 + 18 * np.sin(np.deg2rad(dataframe['paleolatitude']))**2 +
9 * np.sin(np.deg2rad(dataframe['paleolatitude']))**4))
dataframe['Sw'] = 81 / (dataframe['K']**0.5)
summation = 0
N = 0
for n in range(0, len(dataframe)):
quantity = dataframe['delta_mean-pole'][
n]**2 - dataframe['Sw'][n]**2 / np.float(dataframe['n'][n])
summation += quantity
N += 1
Sb = ((1.0 / (N - 1.0)) * summation)**0.5
plt.figure(figsize=(6, 6))
m = Basemap(projection='ortho',
lat_0=dataframe_pole_mean['inc'],
lon_0=dataframe_pole_mean['dec'],
resolution='c',
area_thresh=50000)
m.drawcoastlines(linewidth=0.25)
m.fillcontinents(color='bisque', lake_color='white', zorder=1)
m.drawmapboundary(fill_color='white')
m.drawmeridians(np.arange(0, 360, 30))
m.drawparallels(np.arange(-90, 90, 30))
plot_vgp(m, dataframe_pole_lons, dataframe_pole_lats, dataframe_pole_lons)
plot_pole(m,
dataframe_pole_mean['dec'],
dataframe_pole_mean['inc'],
dataframe_pole_mean['alpha95'],
color='r',
marker='s')
return Sb
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
revtest_MM1990.py
DESCRIPTION
calculates Watson's V statistic from input files through Monte Carlo simulation in order to test whether normal and reversed populations could have been drawn from a common mean (equivalent to watsonV.py). Also provides the critical angle between the two sample mean directions and the corresponding McFadden and McElhinny (1990) classification.
INPUT FORMAT
takes dec/inc as first two columns in two space delimited files (one file for normal directions, one file for reversed directions).
SYNTAX
revtest_MM1990.py [command line options]
OPTIONS
-h prints help message and quits
-f FILE
-f2 FILE
-P (don't plot the Watson V cdf)
OUTPUT
Watson's V between the two populations and the Monte Carlo Critical Value Vc.
M&M1990 angle, critical angle and classification
Plot of Watson's V CDF from Monte Carlo simulation (red line), V is solid and Vc is dashed.
"""
D1, D2 = [], []
plot = 1
Flip = 1
if "-h" in sys.argv: # check if help is needed
print main.__doc__
sys.exit() # graceful quit
if "-P" in sys.argv:
plot = 0
if "-f" in sys.argv:
ind = sys.argv.index("-f")
file1 = sys.argv[ind + 1]
f1 = open(file1, "rU")
for line in f1.readlines():
rec = line.split()
Dec, Inc = float(rec[0]), float(rec[1])
D1.append([Dec, Inc, 1.0])
f1.close()
if "-f2" in sys.argv:
ind = sys.argv.index("-f2")
file2 = sys.argv[ind + 1]
f2 = open(file2, "rU")
print "be patient, your computer is doing 5000 simulations..."
for line in f2.readlines():
rec = line.split()
Dec, Inc = float(rec[0]), float(rec[1])
D2.append([Dec, Inc, 1.0])
f2.close()
# take the antipode for the directions in file 2
D2_flip = []
for rec in D2:
d, i = (rec[0] - 180.0) % 360.0, -rec[1]
D2_flip.append([d, i, 1.0])
pars_1 = pmag.fisher_mean(D1)
pars_2 = pmag.fisher_mean(D2_flip)
cart_1 = pmag.dir2cart([pars_1["dec"], pars_1["inc"], pars_1["r"]])
cart_2 = pmag.dir2cart([pars_2["dec"], pars_2["inc"], pars_2["r"]])
Sw = pars_1["k"] * pars_1["r"] + pars_2["k"] * pars_2["r"] # k1*r1+k2*r2
xhat_1 = pars_1["k"] * cart_1[0] + pars_2["k"] * cart_2[0] # k1*x1+k2*x2
xhat_2 = pars_1["k"] * cart_1[1] + pars_2["k"] * cart_2[1] # k1*y1+k2*y2
xhat_3 = pars_1["k"] * cart_1[2] + pars_2["k"] * cart_2[2] # k1*z1+k2*z2
Rw = numpy.sqrt(xhat_1 ** 2 + xhat_2 ** 2 + xhat_3 ** 2)
V = 2 * (Sw - Rw)
#
# keep weighted sum for later when determining the "critical angle" let's save it as Sr (notation of McFadden and McElhinny, 1990)
#
Sr = Sw
#
# do monte carlo simulation of datasets with same kappas, but common mean
#
counter, NumSims = 0, 5000
Vp = [] # set of Vs from simulations
for k in range(NumSims):
#
# get a set of N1 fisher distributed vectors with k1, calculate fisher stats
#
Dirp = []
for i in range(pars_1["n"]):
Dirp.append(pmag.fshdev(pars_1["k"]))
pars_p1 = pmag.fisher_mean(Dirp)
#
# get a set of N2 fisher distributed vectors with k2, calculate fisher stats
#
Dirp = []
for i in range(pars_2["n"]):
Dirp.append(pmag.fshdev(pars_2["k"]))
pars_p2 = pmag.fisher_mean(Dirp)
#
# get the V for these
#
Vk = pmag.vfunc(pars_p1, pars_p2)
Vp.append(Vk)
#
# sort the Vs, get Vcrit (95th percentile one)
#
Vp.sort()
k = int(0.95 * NumSims)
Vcrit = Vp[k]
#
# equation 18 of McFadden and McElhinny, 1990 calculates the critical value of R (Rwc)
#
Rwc = Sr - (Vcrit / 2)
#
# following equation 19 of McFadden and McElhinny (1990) the critical angle is calculated.
#
k1 = pars_1["k"]
k2 = pars_2["k"]
R1 = pars_1["r"]
R2 = pars_2["r"]
critical_angle = numpy.degrees(
numpy.arccos(((Rwc ** 2) - ((k1 * R1) ** 2) - ((k2 * R2) ** 2)) / (2 * k1 * R1 * k2 * R2))
)
D1_mean = (pars_1["dec"], pars_1["inc"])
D2_mean = (pars_2["dec"], pars_2["inc"])
angle = pmag.angle(D1_mean, D2_mean)
#
# print the results of the test
#
print ""
print "Results of Watson V test: "
print ""
print "Watson's V: " "%.1f" % (V)
print "Critical value of V: " "%.1f" % (Vcrit)
if V < Vcrit:
print '"Pass": Since V is less than Vcrit, the null hypothesis that the two populations are drawn from distributions that share a common mean direction (antipodal to one another) cannot be rejected.'
elif V > Vcrit:
print '"Fail": Since V is greater than Vcrit, the two means can be distinguished at the 95% confidence level.'
print ""
print "M&M1990 classification:"
print ""
print "Angle between data set means: " "%.1f" % (angle)
print "Critical angle of M&M1990: " "%.1f" % (critical_angle)
if V > Vcrit:
print ""
elif V < Vcrit:
if critical_angle < 5:
print "The McFadden and McElhinny (1990) classification for this test is: 'A'"
elif critical_angle < 10:
print "The McFadden and McElhinny (1990) classification for this test is: 'B'"
elif critical_angle < 20:
print "The McFadden and McElhinny (1990) classification for this test is: 'C'"
else:
print "The McFadden and McElhinny (1990) classification for this test is: 'INDETERMINATE;"
if plot == 1:
CDF = {"cdf": 1}
pmagplotlib.plot_init(CDF["cdf"], 5, 5)
p1 = pmagplotlib.plotCDF(CDF["cdf"], Vp, "Watson's V", "r", "")
p2 = pmagplotlib.plotVs(CDF["cdf"], [V], "g", "-")
p3 = pmagplotlib.plotVs(CDF["cdf"], [Vp[k]], "b", "--")
pmagplotlib.drawFIGS(CDF)
files, fmt = {}, "svg"
if file2 != "":
files["cdf"] = "WatsonsV_" + file1 + "_" + file2 + "." + fmt
else:
files["cdf"] = "WatsonsV_" + file1 + "." + fmt
if pmagplotlib.isServer:
black = "#000000"
purple = "#800080"
titles = {}
titles["cdf"] = "Cumulative Distribution"
CDF = pmagplotlib.addBorders(CDF, titles, black, purple)
pmagplotlib.saveP(CDF, files)
else:
ans = raw_input(" S[a]ve to save plot, [q]uit without saving: ")
if ans == "a":
pmagplotlib.saveP(CDF, files)
def main():
"""
NAME
specimens_results_magic.py
DESCRIPTION
combines pmag_specimens.txt file with age, location, acceptance criteria and
outputs pmag_results table along with other MagIC tables necessary for uploading to the database
SYNTAX
specimens_results_magic.py [command line options]
OPTIONS
-h prints help message and quits
-usr USER: identify user, default is ""
-f: specimen input magic_measurements format file, default is "magic_measurements.txt"
-fsp: specimen input pmag_specimens format file, default is "pmag_specimens.txt"
-fsm: sample input er_samples format file, default is "er_samples.txt"
-fsi: specimen input er_sites format file, default is "er_sites.txt"
-fla: specify a file with paleolatitudes for calculating VADMs, default is not to calculate VADMS
format is: site_name paleolatitude (space delimited file)
-fa AGES: specify er_ages format file with age information
-crd [s,g,t,b]: specify coordinate system
(s, specimen, g geographic, t, tilt corrected, b, geographic and tilt corrected)
Default is to assume geographic
NB: only the tilt corrected data will appear on the results table, if both g and t are selected.
-cor [AC:CR:NL]: colon delimited list of required data adjustments for all specimens
included in intensity calculations (anisotropy, cooling rate, non-linear TRM)
unless specified, corrections will not be applied
-pri [TRM:ARM] colon delimited list of priorities for anisotropy correction (-cor must also be set to include AC). default is TRM, then ARM
-age MIN MAX UNITS: specify age boundaries and units
-exc: use exiting selection criteria (in pmag_criteria.txt file), default is default criteria
-C: no acceptance criteria
-aD: average directions per sample, default is NOT
-aI: average multiple specimen intensities per sample, default is by site
-aC: average all components together, default is NOT
-pol: calculate polarity averages
-sam: save sample level vgps and v[a]dms, default is by site
-xSi: skip the site level intensity calculation
-p: plot directions and look at intensities by site, default is NOT
-fmt: specify output for saved images, default is svg (only if -p set)
-lat: use present latitude for calculating VADMs, default is not to calculate VADMs
-xD: skip directions
-xI: skip intensities
OUPUT
writes pmag_samples, pmag_sites, pmag_results tables
"""
# set defaults
Comps=[] # list of components
version_num=pmag.get_version()
args=sys.argv
DefaultAge=["none"]
skipdirs,coord,excrit,custom,vgps,average,Iaverage,plotsites,opt=1,0,0,0,0,0,0,0,0
get_model_lat=0 # this skips VADM calculation altogether, when get_model_lat=1, uses present day
fmt='svg'
dir_path="."
model_lat_file=""
Caverage=0
infile='pmag_specimens.txt'
measfile="magic_measurements.txt"
sampfile="er_samples.txt"
sitefile="er_sites.txt"
agefile="er_ages.txt"
specout="er_specimens.txt"
sampout="pmag_samples.txt"
siteout="pmag_sites.txt"
resout="pmag_results.txt"
critout="pmag_criteria.txt"
instout="magic_instruments.txt"
sigcutoff,OBJ="",""
noDir,noInt=0,0
polarity=0
coords=['0']
Dcrit,Icrit,nocrit=0,0,0
corrections=[]
nocorrection=['DA-NL','DA-AC','DA-CR']
priorities=['DA-AC-ARM','DA-AC-TRM'] # priorities for anisotropy correction
# get command line stuff
if "-h" in args:
print main.__doc__
sys.exit()
if '-WD' in args:
ind=args.index("-WD")
dir_path=args[ind+1]
if '-cor' in args:
ind=args.index('-cor')
cors=args[ind+1].split(':') # list of required data adjustments
for cor in cors:
nocorrection.remove('DA-'+cor)
corrections.append('DA-'+cor)
if '-pri' in args:
ind=args.index('-pri')
priorities=args[ind+1].split(':') # list of required data adjustments
for p in priorities:
p='DA-AC-'+p
if '-f' in args:
ind=args.index("-f")
measfile=args[ind+1]
if '-fsp' in args:
ind=args.index("-fsp")
infile=args[ind+1]
if '-fsi' in args:
ind=args.index("-fsi")
sitefile=args[ind+1]
if "-crd" in args:
ind=args.index("-crd")
coord=args[ind+1]
if coord=='s':coords=['-1']
if coord=='g':coords=['0']
if coord=='t':coords=['100']
if coord=='b':coords=['0','100']
if "-usr" in args:
ind=args.index("-usr")
user=sys.argv[ind+1]
else: user=""
if "-C" in args: Dcrit,Icrit,nocrit=1,1,1 # no selection criteria
if "-sam" in args: vgps=1 # save sample level VGPS/VADMs
if "-xSi" in args:
nositeints=1 # skip site level intensity
else:
nositeints=0
if "-age" in args:
ind=args.index("-age")
DefaultAge[0]=args[ind+1]
DefaultAge.append(args[ind+2])
DefaultAge.append(args[ind+3])
Daverage,Iaverage,Caverage=0,0,0
if "-aD" in args: Daverage=1 # average by sample directions
if "-aI" in args: Iaverage=1 # average by sample intensities
if "-aC" in args: Caverage=1 # average all components together ??? why???
if "-pol" in args: polarity=1 # calculate averages by polarity
if '-xD' in args:noDir=1
if '-xI' in args:
noInt=1
elif "-fla" in args:
if '-lat' in args:
print "you should set a paleolatitude file OR use present day lat - not both"
sys.exit()
ind=args.index("-fla")
model_lat_file=dir_path+'/'+args[ind+1]
get_model_lat=2
mlat=open(model_lat_file,'rU')
ModelLats=[]
for line in mlat.readlines():
ModelLat={}
tmp=line.split()
ModelLat["er_site_name"]=tmp[0]
ModelLat["site_model_lat"]=tmp[1]
ModelLat["er_sample_name"]=tmp[0]
ModelLat["sample_lat"]=tmp[1]
ModelLats.append(ModelLat)
get_model_lat=2
elif '-lat' in args:
get_model_lat=1
if "-p" in args:
plotsites=1
if "-fmt" in args:
ind=args.index("-fmt")
fmt=args[ind+1]
if noDir==0: # plot by site - set up plot window
import pmagplotlib
EQ={}
EQ['eqarea']=1
pmagplotlib.plot_init(EQ['eqarea'],5,5) # define figure 1 as equal area projection
pmagplotlib.plotNET(EQ['eqarea']) # I don't know why this has to be here, but otherwise the first plot never plots...
pmagplotlib.drawFIGS(EQ)
if '-WD' in args:
infile=dir_path+'/'+infile
measfile=dir_path+'/'+measfile
instout=dir_path+'/'+instout
sampfile=dir_path+'/'+sampfile
sitefile=dir_path+'/'+sitefile
agefile=dir_path+'/'+agefile
specout=dir_path+'/'+specout
sampout=dir_path+'/'+sampout
siteout=dir_path+'/'+siteout
resout=dir_path+'/'+resout
critout=dir_path+'/'+critout
if "-exc" in args: # use existing pmag_criteria file
if "-C" in args:
print 'you can not use both existing and no criteria - choose either -exc OR -C OR neither (for default)'
sys.exit()
crit_data,file_type=pmag.magic_read(critout)
print "Acceptance criteria read in from ", critout
else : # use default criteria (if nocrit set, then get really loose criteria as default)
crit_data=pmag.default_criteria(nocrit)
if nocrit==0:
print "Acceptance criteria are defaults"
else:
print "No acceptance criteria used "
accept={}
for critrec in crit_data:
for key in critrec.keys():
if 'sample_int_sigma_uT' in critrec.keys():
critrec['sample_int_sigma']='%10.3e'%(eval(critrec['sample_int_sigma_uT'])*1e-6)
if key not in accept.keys() and critrec[key]!='':
accept[key]=critrec[key]
#
#
if "-exc" not in args and "-C" not in args:
print "args",args
pmag.magic_write(critout,[accept],'pmag_criteria')
print "\n Pmag Criteria stored in ",critout,'\n'
#
# now we're done slow dancing
#
SiteNFO,file_type=pmag.magic_read(sitefile) # read in site data - has the lats and lons
SampNFO,file_type=pmag.magic_read(sampfile) # read in site data - has the lats and lons
height_nfo=pmag.get_dictitem(SiteNFO,'site_height','','F') # find all the sites with height info.
if agefile !="":AgeNFO,file_type=pmag.magic_read(agefile) # read in the age information
Data,file_type=pmag.magic_read(infile) # read in specimen interpretations
IntData=pmag.get_dictitem(Data,'specimen_int','','F') # retrieve specimens with intensity data
comment,orient="",[]
samples,sites=[],[]
for rec in Data: # run through the data filling in missing keys and finding all components, coordinates available
# fill in missing fields, collect unique sample and site names
if 'er_sample_name' not in rec.keys():
rec['er_sample_name']=""
elif rec['er_sample_name'] not in samples:
samples.append(rec['er_sample_name'])
if 'er_site_name' not in rec.keys():
rec['er_site_name']=""
elif rec['er_site_name'] not in sites:
sites.append(rec['er_site_name'])
if 'specimen_int' not in rec.keys():rec['specimen_int']=''
if 'specimen_comp_name' not in rec.keys() or rec['specimen_comp_name']=="":rec['specimen_comp_name']='A'
if rec['specimen_comp_name'] not in Comps:Comps.append(rec['specimen_comp_name'])
rec['specimen_tilt_correction']=rec['specimen_tilt_correction'].strip('\n')
if "specimen_tilt_correction" not in rec.keys(): rec["specimen_tilt_correction"]="-1" # assume sample coordinates
if rec["specimen_tilt_correction"] not in orient: orient.append(rec["specimen_tilt_correction"]) # collect available coordinate systems
if "specimen_direction_type" not in rec.keys(): rec["specimen_direction_type"]='l' # assume direction is line - not plane
if "specimen_dec" not in rec.keys(): rec["specimen_direction_type"]='' # if no declination, set direction type to blank
if "specimen_n" not in rec.keys(): rec["specimen_n"]='' # put in n
if "specimen_alpha95" not in rec.keys(): rec["specimen_alpha95"]='' # put in alpha95
if "magic_method_codes" not in rec.keys(): rec["magic_method_codes"]=''
#
# start parsing data into SpecDirs, SpecPlanes, SpecInts
SpecInts,SpecDirs,SpecPlanes=[],[],[]
samples.sort() # get sorted list of samples and sites
sites.sort()
if noInt==0: # don't skip intensities
IntData=pmag.get_dictitem(Data,'specimen_int','','F') # retrieve specimens with intensity data
if nocrit==0: # use selection criteria
for rec in IntData: # do selection criteria
kill=pmag.grade(rec,accept,'specimen_int')
if len(kill)==0: SpecInts.append(rec) # intensity record to be included in sample, site calculations
else:
SpecInts=IntData[:] # take everything - no selection criteria
# check for required data adjustments
if len(corrections)>0 and len(SpecInts)>0:
for cor in corrections:
SpecInts=pmag.get_dictitem(SpecInts,'magic_method_codes',cor,'has') # only take specimens with the required corrections
if len(nocorrection)>0 and len(SpecInts)>0:
for cor in nocorrection:
SpecInts=pmag.get_dictitem(SpecInts,'magic_method_codes',cor,'not') # exclude the corrections not specified for inclusion
# take top priority specimen of its name in remaining specimens (only one per customer)
PrioritySpecInts=[]
specimens=pmag.get_specs(SpecInts) # get list of uniq specimen names
for spec in specimens:
ThisSpecRecs=pmag.get_dictitem(SpecInts,'er_specimen_name',spec,'T') # all the records for this specimen
if len(ThisSpecRecs)==1:
PrioritySpecInts.append(ThisSpecRecs[0])
elif len(ThisSpecRecs)>1: # more than one
prec=[]
for p in priorities:
ThisSpecRecs=pmag.get_dictitem(SpecInts,'magic_method_codes',p,'has') # all the records for this specimen
if len(ThisSpecRecs)>0:prec.append(ThisSpecRecs[0])
PrioritySpecInts.append(prec[0]) # take the best one
SpecInts=PrioritySpecInts # this has the first specimen record
if noDir==0: # don't skip directions
AllDirs=pmag.get_dictitem(Data,'specimen_direction_type','','F') # retrieve specimens with directed lines and planes
Ns=pmag.get_dictitem(AllDirs,'specimen_n','','F') # get all specimens with specimen_n information
if nocrit!=1: # use selection criteria
for rec in Ns: # look through everything with specimen_n for "good" data
kill=pmag.grade(rec,accept,'specimen_dir')
if len(kill)==0: # nothing killed it
SpecDirs.append(rec)
else: # no criteria
SpecDirs=AllDirs[:] # take them all
# SpecDirs is now the list of all specimen directions (lines and planes) that pass muster
#
PmagSamps,SampDirs=[],[] # list of all sample data and list of those that pass the DE-SAMP criteria
PmagSites,PmagResults=[],[] # list of all site data and selected results
SampInts=[]
for samp in samples: # run through the sample names
if Daverage==1: # average by sample if desired
SampDir=pmag.get_dictitem(SpecDirs,'er_sample_name',samp,'T') # get all the directional data for this sample
if len(SampDir)>0: # there are some directions
for coord in coords: # step through desired coordinate systems
CoordDir=pmag.get_dictitem(SampDir,'specimen_tilt_correction',coord,'T') # get all the directions for this sample
if len(CoordDir)>0: # there are some with this coordinate system
if Caverage==0: # look component by component
for comp in Comps:
CompDir=pmag.get_dictitem(CoordDir,'specimen_comp_name',comp,'T') # get all directions from this component
if len(CompDir)>0: # there are some
PmagSampRec=pmag.lnpbykey(CompDir,'sample','specimen') # get a sample average from all specimens
PmagSampRec["er_location_name"]=CompDir[0]['er_location_name'] # decorate the sample record
PmagSampRec["er_site_name"]=CompDir[0]['er_site_name']
PmagSampRec["er_sample_name"]=samp
PmagSampRec["er_citation_names"]="This study"
PmagSampRec["er_analyst_mail_names"]=user
PmagSampRec['magic_software_packages']=version_num
if nocrit!=1:PmagSampRec['pmag_criteria_codes']="ACCEPT"
if agefile != "": PmagSampRec= pmag.get_age(PmagSampRec,"er_site_name","sample_inferred_",AgeNFO,DefaultAge)
site_height=pmag.get_dictitem(height_nfo,'er_site_name',PmagSampRec['er_site_name'],'T')
if len(site_height)>0:PmagSampRec["sample_height"]=site_height[0]['site_height'] # add in height if available
PmagSampRec['sample_comp_name']=comp
PmagSampRec['sample_tilt_correction']=coord
PmagSampRec['er_specimen_names']= pmag.get_list(CompDir,'er_specimen_name') # get a list of the specimen names used
PmagSampRec['magic_method_codes']= pmag.get_list(CompDir,'magic_method_codes') # get a list of the methods used
if nocrit!=1: # apply selection criteria
kill=pmag.grade(PmagSampRec,accept,'sample_dir')
else:
kill=[]
if len(kill)==0:
SampDirs.append(PmagSampRec)
if vgps==1: # if sample level VGP info desired, do that now
PmagResRec=pmag.getsampVGP(PmagSampRec,SiteNFO)
if PmagResRec!="":PmagResults.append(PmagResRec)
PmagSamps.append(PmagSampRec)
if Caverage==1: # average all components together basically same as above
PmagSampRec=pmag.lnpbykey(CoordDir,'sample','specimen')
PmagSampRec["er_location_name"]=CoordDir[0]['er_location_name']
PmagSampRec["er_site_name"]=CoordDir[0]['er_site_name']
PmagSampRec["er_sample_name"]=samp
PmagSampRec["er_citation_names"]="This study"
PmagSampRec["er_analyst_mail_names"]=user
PmagSampRec['magic_software_packages']=version_num
if nocrit!=1:PmagSampRec['pmag_criteria_codes']=""
if agefile != "": PmagSampRec= pmag.get_age(PmagSampRec,"er_site_name","sample_inferred_",AgeNFO,DefaultAge)
site_height=pmag.get_dictitem(height_nfo,'er_site_name',site,'T')
if len(site_height)>0:PmagSampRec["sample_height"]=site_height[0]['site_height'] # add in height if available
PmagSampRec['sample_tilt_correction']=coord
PmagSampRec['sample_comp_name']= pmag.get_list(CoordDir,'specimen_comp_name') # get components used
PmagSampRec['er_specimen_names']= pmag.get_list(CoordDir,'er_specimen_name') # get specimne names averaged
PmagSampRec['magic_method_codes']= pmag.get_list(CoordDir,'magic_method_codes') # assemble method codes
if nocrit!=1: # apply selection criteria
kill=pmag.grade(PmagSampRec,accept,'sample_dir')
if len(kill)==0: # passes the mustard
SampDirs.append(PmagSampRec)
if vgps==1:
PmagResRec=pmag.getsampVGP(PmagSampRec,SiteNFO)
if PmagResRec!="":PmagResults.append(PmagResRec)
else: # take everything
SampDirs.append(PmagSampRec)
if vgps==1:
PmagResRec=pmag.getsampVGP(PmagSampRec,SiteNFO)
if PmagResRec!="":PmagResults.append(PmagResRec)
PmagSamps.append(PmagSampRec)
if Iaverage==1: # average by sample if desired
SampI=pmag.get_dictitem(SpecInts,'er_sample_name',samp,'T') # get all the intensity data for this sample
if len(SampI)>0: # there are some
PmagSampRec=pmag.average_int(SampI,'specimen','sample') # get average intensity stuff
PmagSampRec["sample_description"]="sample intensity" # decorate sample record
PmagSampRec["sample_direction_type"]=""
PmagSampRec['er_site_name']=SampI[0]["er_site_name"]
PmagSampRec['er_sample_name']=samp
PmagSampRec['er_location_name']=SampI[0]["er_location_name"]
PmagSampRec["er_citation_names"]="This study"
PmagSampRec["er_analyst_mail_names"]=user
if agefile != "": PmagSampRec=pmag.get_age(PmagSampRec,"er_site_name","sample_inferred_", AgeNFO,DefaultAge)
site_height=pmag.get_dictitem(height_nfo,'er_site_name',PmagSampRec['er_site_name'],'T')
if len(site_height)>0:PmagSampRec["sample_height"]=site_height[0]['site_height'] # add in height if available
PmagSampRec['er_specimen_names']= pmag.get_list(SampI,'er_specimen_name')
PmagSampRec['magic_method_codes']= pmag.get_list(SampI,'magic_method_codes')
if nocrit!=1: # apply criteria!
kill=pmag.grade(PmagSampRec,accept,'sample_int')
if len(kill)==0:
PmagSampRec['pmag_criteria_codes']="ACCEPT"
SampInts.append(PmagSampRec)
PmagSamps.append(PmagSampRec)
else:PmagSampRec={} # sample rejected
else: # no criteria
SampInts.append(PmagSampRec)
PmagSamps.append(PmagSampRec)
PmagSampRec['pmag_criteria_codes']=""
if vgps==1 and get_model_lat!=0 and PmagSampRec!={}: #
if get_model_lat==1: # use sample latitude
PmagResRec=pmag.getsampVDM(PmagSampRec,SampNFO)
del(PmagResRec['model_lat']) # get rid of the model lat key
elif get_model_lat==2: # use model latitude
PmagResRec=pmag.getsampVDM(PmagSampRec,ModelLats)
if PmagResRec!={}:PmagResRec['magic_method_codes']=PmagResRec['magic_method_codes']+":IE-MLAT"
if PmagResRec!={}:
PmagResRec['er_specimen_names']=PmagSampRec['er_specimen_names']
PmagResRec['er_sample_names']=PmagSampRec['er_sample_name']
PmagResRec['pmag_criteria_codes']='ACCEPT'
PmagResRec['average_int_sigma_perc']=PmagSampRec['sample_int_sigma_perc']
PmagResRec['average_int_sigma']=PmagSampRec['sample_int_sigma']
PmagResRec['average_int_n']=PmagSampRec['sample_int_n']
PmagResRec['vadm_n']=PmagSampRec['sample_int_n']
PmagResRec['data_type']='i'
PmagResults.append(PmagResRec)
if len(PmagSamps)>0:
TmpSamps,keylist=pmag.fillkeys(PmagSamps) # fill in missing keys from different types of records
pmag.magic_write(sampout,TmpSamps,'pmag_samples') # save in sample output file
print ' sample averages written to ',sampout
#
#create site averages from specimens or samples as specified
#
for site in sites:
if Daverage==0: key,dirlist='specimen',SpecDirs # if specimen averages at site level desired
if Daverage==1: key,dirlist='sample',SampDirs # if sample averages at site level desired
tmp=pmag.get_dictitem(dirlist,'er_site_name',site,'T') # get all the sites with directions
tmp1=pmag.get_dictitem(tmp,key+'_tilt_correction',coords[-1],'T') # use only the last coordinate if Caverage==0
sd=pmag.get_dictitem(SiteNFO,'er_site_name',site,'T') # fish out site information (lat/lon, etc.)
if len(sd)>0:
sitedat=sd[0]
if Caverage==0: # do component wise averaging
for comp in Comps:
siteD=pmag.get_dictitem(tmp1,key+'_comp_name',comp,'T') # get all components comp
if len(siteD)>0: # there are some for this site and component name
PmagSiteRec=pmag.lnpbykey(siteD,'site',key) # get an average for this site
PmagSiteRec['site_comp_name']=comp # decorate the site record
PmagSiteRec["er_location_name"]=siteD[0]['er_location_name']
PmagSiteRec["er_site_name"]=siteD[0]['er_site_name']
PmagSiteRec['site_tilt_correction']=coords[-1]
PmagSiteRec['site_comp_name']= pmag.get_list(siteD,key+'_comp_name')
if Daverage==1:
PmagSiteRec['er_sample_names']= pmag.get_list(siteD,'er_sample_name')
else:
PmagSiteRec['er_specimen_names']= pmag.get_list(siteD,'er_specimen_name')
# determine the demagnetization code (DC3,4 or 5) for this site
AFnum=len(pmag.get_dictitem(siteD,'magic_method_codes','LP-DIR-AF','has'))
Tnum=len(pmag.get_dictitem(siteD,'magic_method_codes','LP-DIR-T','has'))
DC=3
if AFnum>0:DC+=1
if Tnum>0:DC+=1
PmagSiteRec['magic_method_codes']= pmag.get_list(siteD,'magic_method_codes')+':'+ 'LP-DC'+str(DC)
PmagSiteRec['magic_method_codes'].strip(":")
if plotsites==1:
print PmagSiteRec['er_site_name']
pmagplotlib.plotSITE(EQ['eqarea'],PmagSiteRec,siteD,key) # plot and list the data
pmagplotlib.drawFIGS(EQ)
PmagSites.append(PmagSiteRec)
else: # last component only
siteD=tmp1[:] # get the last orientation system specified
if len(siteD)>0: # there are some
PmagSiteRec=pmag.lnpbykey(siteD,'site',key) # get the average for this site
PmagSiteRec["er_location_name"]=siteD[0]['er_location_name'] # decorate the record
PmagSiteRec["er_site_name"]=siteD[0]['er_site_name']
PmagSiteRec['site_comp_name']=comp
PmagSiteRec['site_tilt_correction']=coords[-1]
PmagSiteRec['site_comp_name']= pmag.get_list(siteD,key+'_comp_name')
PmagSiteRec['er_specimen_names']= pmag.get_list(siteD,'er_specimen_name')
PmagSiteRec['er_sample_names']= pmag.get_list(siteD,'er_sample_name')
AFnum=len(pmag.get_dictitem(siteD,'magic_method_codes','LP-DIR-AF','has'))
Tnum=len(pmag.get_dictitem(siteD,'magic_method_codes','LP-DIR-T','has'))
DC=3
if AFnum>0:DC+=1
if Tnum>0:DC+=1
PmagSiteRec['magic_method_codes']= pmag.get_list(siteD,'magic_method_codes')+':'+ 'LP-DC'+str(DC)
PmagSiteRec['magic_method_codes'].strip(":")
if Daverage==0:PmagSiteRec['site_comp_name']= pmag.get_list(siteD,key+'_comp_name')
if plotsites==1:
pmagplotlib.plotSITE(EQ['eqarea'],PmagSiteRec,siteD,key)
pmagplotlib.drawFIGS(EQ)
PmagSites.append(PmagSiteRec)
else:
print 'site information not found in er_sites for site, ',site,' site will be skipped'
for PmagSiteRec in PmagSites: # now decorate each dictionary some more, and calculate VGPs etc. for results table
PmagSiteRec["er_citation_names"]="This study"
PmagSiteRec["er_analyst_mail_names"]=user
PmagSiteRec['magic_software_packages']=version_num
if agefile != "": PmagSiteRec= pmag.get_age(PmagSiteRec,"er_site_name","site_inferred_",AgeNFO,DefaultAge)
PmagSiteRec['pmag_criteria_codes']='ACCEPT'
if 'site_n_lines' in PmagSiteRec.keys() and 'site_n_planes' in PmagSiteRec.keys() and PmagSiteRec['site_n_lines']!="" and PmagSiteRec['site_n_planes']!="":
if int(PmagSiteRec["site_n_planes"])>0:
PmagSiteRec["magic_method_codes"]=PmagSiteRec['magic_method_codes']+":DE-FM-LP"
elif int(PmagSiteRec["site_n_lines"])>2:
PmagSiteRec["magic_method_codes"]=PmagSiteRec['magic_method_codes']+":DE-FM"
kill=pmag.grade(PmagSiteRec,accept,'site_dir')
if len(kill)==0:
PmagResRec={} # set up dictionary for the pmag_results table entry
PmagResRec['data_type']='i' # decorate it a bit
PmagResRec['magic_software_packages']=version_num
PmagSiteRec['site_description']='Site direction included in results table'
PmagResRec['pmag_criteria_codes']='ACCEPT'
dec=float(PmagSiteRec["site_dec"])
inc=float(PmagSiteRec["site_inc"])
if 'site_alpha95' in PmagSiteRec.keys() and PmagSiteRec['site_alpha95']!="":
a95=float(PmagSiteRec["site_alpha95"])
else:a95=180.
sitedat=pmag.get_dictitem(SiteNFO,'er_site_name',PmagSiteRec['er_site_name'],'T')[0] # fish out site information (lat/lon, etc.)
lat=float(sitedat['site_lat'])
lon=float(sitedat['site_lon'])
plong,plat,dp,dm=pmag.dia_vgp(dec,inc,a95,lat,lon) # get the VGP for this site
if PmagSiteRec['site_tilt_correction']=='-1':C=' (spec coord) '
if PmagSiteRec['site_tilt_correction']=='0':C=' (geog. coord) '
if PmagSiteRec['site_tilt_correction']=='100':C=' (strat. coord) '
PmagResRec["pmag_result_name"]="VGP Site: "+PmagSiteRec["er_site_name"] # decorate some more
PmagResRec["result_description"]="Site VGP, coord system = "+str(coord)+' component: '+comp
PmagResRec['er_site_names']=PmagSiteRec['er_site_name']
PmagResRec['pmag_criteria_codes']='ACCEPT'
PmagResRec['er_citation_names']='This study'
PmagResRec['er_analyst_mail_names']=user
PmagResRec["er_location_names"]=PmagSiteRec["er_location_name"]
if Daverage==1:
PmagResRec["er_sample_names"]=PmagSiteRec["er_sample_names"]
else:
PmagResRec["er_specimen_names"]=PmagSiteRec["er_specimen_names"]
PmagResRec["tilt_correction"]=PmagSiteRec['site_tilt_correction']
PmagResRec["pole_comp_name"]=PmagSiteRec['site_comp_name']
PmagResRec["average_dec"]=PmagSiteRec["site_dec"]
PmagResRec["average_inc"]=PmagSiteRec["site_inc"]
PmagResRec["average_alpha95"]=PmagSiteRec["site_alpha95"]
PmagResRec["average_n"]=PmagSiteRec["site_n"]
PmagResRec["average_n_lines"]=PmagSiteRec["site_n_lines"]
PmagResRec["average_n_planes"]=PmagSiteRec["site_n_planes"]
PmagResRec["vgp_n"]=PmagSiteRec["site_n"]
PmagResRec["average_k"]=PmagSiteRec["site_k"]
PmagResRec["average_r"]=PmagSiteRec["site_r"]
PmagResRec["average_lat"]='%10.4f ' %(lat)
PmagResRec["average_lon"]='%10.4f ' %(lon)
if agefile != "": PmagResRec= pmag.get_age(PmagResRec,"er_site_names","average_",AgeNFO,DefaultAge)
site_height=pmag.get_dictitem(height_nfo,'er_site_name',site,'T')
if len(site_height)>0:PmagResRec["average_height"]=site_height[0]['site_height']
PmagResRec["vgp_lat"]='%7.1f ' % (plat)
PmagResRec["vgp_lon"]='%7.1f ' % (plong)
PmagResRec["vgp_dp"]='%7.1f ' % (dp)
PmagResRec["vgp_dm"]='%7.1f ' % (dm)
PmagResRec["magic_method_codes"]= PmagSiteRec["magic_method_codes"]
if PmagSiteRec['site_tilt_correction']=='0':PmagSiteRec['magic_method_codes']=PmagSiteRec['magic_method_codes']+":DA-DIR-GEO"
if PmagSiteRec['site_tilt_correction']=='100':PmagSiteRec['magic_method_codes']=PmagSiteRec['magic_method_codes']+":DA-DIR-TILT"
PmagSiteRec['site_polarity']=""
if polarity==1: # assign polarity based on angle of pole lat to spin axis - may want to re-think this sometime
angle=pmag.angle([0,0],[0,(90-plat)])
if angle <= 55.: PmagSiteRec["site_polarity"]='n'
if angle > 55. and angle < 125.: PmagSiteRec["site_polarity"]='t'
if angle >= 125.: PmagSiteRec["site_polarity"]='r'
PmagResults.append(PmagResRec)
if noInt!=1 and nositeints!=1:
for site in sites: # now do intensities for each site
if plotsites==1:print site
if Iaverage==0: key,intlist='specimen',SpecInts # if using specimen level data
if Iaverage==1: key,intlist='sample',PmagSamps # if using sample level data
Ints=pmag.get_dictitem(intlist,'er_site_name',site,'T') # get all the intensities for this site
if len(Ints)>0: # there are some
PmagSiteRec=pmag.average_int(Ints,key,'site') # get average intensity stuff for site table
PmagResRec=pmag.average_int(Ints,key,'average') # get average intensity stuff for results table
if plotsites==1: # if site by site examination requested - print this site out to the screen
for rec in Ints:print rec['er_'+key+'_name'],' %7.1f'%(1e6*float(rec[key+'_int']))
if len(Ints)>1:
print 'Average: ','%7.1f'%(1e6*float(PmagResRec['average_int'])),'N: ',len(Ints)
print 'Sigma: ','%7.1f'%(1e6*float(PmagResRec['average_int_sigma'])),'Sigma %: ',PmagResRec['average_int_sigma_perc']
raw_input('Press any key to continue\n')
er_location_name=Ints[0]["er_location_name"]
PmagSiteRec["er_location_name"]=er_location_name # decorate the records
PmagSiteRec["er_citation_names"]="This study"
PmagResRec["er_location_names"]=er_location_name
PmagResRec["er_citation_names"]="This study"
PmagSiteRec["er_analyst_mail_names"]=user
PmagResRec["er_analyst_mail_names"]=user
PmagResRec["data_type"]='i'
if Iaverage==0:
PmagSiteRec['er_specimen_names']= pmag.get_list(Ints,'er_specimen_name') # list of all specimens used
PmagResRec['er_specimen_names']= pmag.get_list(Ints,'er_specimen_name')
PmagSiteRec['er_sample_names']= pmag.get_list(Ints,'er_sample_name') # list of all samples used
PmagResRec['er_sample_names']= pmag.get_list(Ints,'er_sample_name')
PmagSiteRec['er_site_name']= site
PmagResRec['er_site_names']= site
PmagSiteRec['magic_method_codes']= pmag.get_list(Ints,'magic_method_codes')
PmagResRec['magic_method_codes']= pmag.get_list(Ints,'magic_method_codes')
kill=pmag.grade(PmagSiteRec,accept,'site_int')
if nocrit==1 or len(kill)==0:
b,sig=float(PmagResRec['average_int']),""
if(PmagResRec['average_int_sigma'])!="":sig=float(PmagResRec['average_int_sigma'])
sdir=pmag.get_dictitem(PmagResults,'er_site_names',site,'T') # fish out site direction
if len(sdir)>0 and sdir[-1]['average_inc']!="": # get the VDM for this record using last average inclination (hope it is the right one!)
inc=float(sdir[0]['average_inc']) #
mlat=pmag.magnetic_lat(inc) # get magnetic latitude using dipole formula
PmagResRec["vdm"]='%8.3e '% (pmag.b_vdm(b,mlat)) # get VDM with magnetic latitude
PmagResRec["vdm_n"]=PmagResRec['average_int_n']
if 'average_int_sigma' in PmagResRec.keys() and PmagResRec['average_int_sigma']!="":
vdm_sig=pmag.b_vdm(float(PmagResRec['average_int_sigma']),mlat)
PmagResRec["vdm_sigma"]='%8.3e '% (vdm_sig)
else:
PmagResRec["vdm_sigma"]=""
mlat="" # define a model latitude
if get_model_lat==1: # use present site latitude
mlats=pmag.get_dictitem(SiteNFO,'er_site_name',site,'T')
if len(mlats)>0: mlat=mlats[0]['site_lat']
elif get_model_lat==2: # use a model latitude from some plate reconstruction model (or something)
mlats=pmag.get_dictitem(ModelLats,'er_site_name',site,'T')
if len(mlats)>0: PmagResRec['model_lat']=mlats[0]['site_model_lat']
mlat=PmagResRec['model_lat']
if mlat!="":
PmagResRec["vadm"]='%8.3e '% (pmag.b_vdm(b,float(mlat))) # get the VADM using the desired latitude
if sig!="":
vdm_sig=pmag.b_vdm(float(PmagResRec['average_int_sigma']),float(mlat))
PmagResRec["vadm_sigma"]='%8.3e '% (vdm_sig)
PmagResRec["vadm_n"]=PmagResRec['average_int_n']
else:
PmagResRec["vadm_sigma"]=""
sitedat=pmag.get_dictitem(SiteNFO,'er_site_name',PmagSiteRec['er_site_name'],'T') # fish out site information (lat/lon, etc.)
if len(sitedat)>0:
sitedat=sitedat[0]
PmagResRec['average_lat']=sitedat['site_lat']
PmagResRec['average_lon']=sitedat['site_lon']
else:
PmagResRec['average_lon']='UNKNOWN'
PmagResRec['average_lon']='UNKNOWN'
PmagResRec['magic_software_packages']=version_num
PmagResRec["pmag_result_name"]="V[A]DM: Site "+site
PmagResRec["result_description"]="V[A]DM of site"
PmagResRec["pmag_criteria_codes"]="ACCEPT"
if agefile != "": PmagResRec= pmag.get_age(PmagResRec,"er_site_names","average_",AgeNFO,DefaultAge)
site_height=pmag.get_dictitem(height_nfo,'er_site_name',site,'T')
if len(site_height)>0:PmagResRec["average_height"]=site_height[0]['site_height']
PmagSites.append(PmagSiteRec)
PmagResults.append(PmagResRec)
if len(PmagSites)>0:
Tmp,keylist=pmag.fillkeys(PmagSites)
pmag.magic_write(siteout,Tmp,'pmag_sites')
print ' sites written to ',siteout
else: print "No Site level table"
if len(PmagResults)>0:
TmpRes,keylist=pmag.fillkeys(PmagResults)
pmag.magic_write(resout,TmpRes,'pmag_results')
print ' results written to ',resout
else: print "No Results level table"
def sb_vgp_calc(dataframe):
"""
This function calculates the angular dispersion of VGPs and corrects
for within site dispersion to return a value S_b. The input data
needs to be within a pandas Dataframe. The function also plots the
VGPs and their associated Fisher mean on a projection centered on
the mean.
Parameters
-----------
dataframe : the name of the pandas.DataFrame containing the data
the data frame needs to contain these columns:
dataframe['site_lat'] : latitude of the site
dataframe['site_lon'] : longitude of the site ---- changed to site_long so that it works for a given DF
dataframe['inc_tc'] : tilt-corrected inclination
dataframe['dec_tc'] : tilt-corrected declination
dataframe['k'] : fisher precision parameter for directions
dataframe['vgp_lat'] : VGP latitude ---- changed to pole_lat so that it works for a given DF
dataframe['vgp_lon'] : VGP longitude ---- changed to pole_long so that it works for a given DF
"""
# calculate the mean from the directional data
dataframe_dirs=[]
for n in range(0,len(dataframe)):
dataframe_dirs.append([dataframe['dec_tc'][n],
dataframe['inc_tc'][n],1.])
dataframe_dir_mean=pmag.fisher_mean(dataframe_dirs)
# calculate the mean from the vgp data
dataframe_poles=[]
dataframe_pole_lats=[]
dataframe_pole_lons=[]
for n in range(0,len(dataframe)):
dataframe_poles.append([dataframe['pole_long'][n],
dataframe['pole_lat'][n],1.])
dataframe_pole_lats.append(dataframe['pole_lat'][n])
dataframe_pole_lons.append(dataframe['pole_long'][n])
dataframe_pole_mean=pmag.fisher_mean(dataframe_poles)
# calculate mean paleolatitude from the directional data
dataframe['paleolatitude']=lat_from_inc(dataframe_dir_mean['inc'])
angle_list=[]
for n in range(0,len(dataframe)):
angle=pmag.angle([dataframe['pole_long'][n],dataframe['pole_lat'][n]],
[dataframe_pole_mean['dec'],dataframe_pole_mean['inc']])
angle_list.append(angle[0])
dataframe['delta_mean-pole']=angle_list
# use eq. 2 of Cox (1970) to translate the directional precision parameter
# into pole coordinates using the assumption of a Fisherian distribution in
# directional coordinates and the paleolatitude as calculated from mean
# inclination using the dipole equation
dataframe['K']=dataframe['k']/(0.125*(5+18*np.sin(np.deg2rad(dataframe['paleolatitude']))**2
+9*np.sin(np.deg2rad(dataframe['paleolatitude']))**4))
dataframe['Sw']=81/(dataframe['K']**0.5)
summation=0
N=0
for n in range(0,len(dataframe)):
quantity=dataframe['delta_mean-pole'][n]**2-dataframe['Sw'][n]**2/np.float(dataframe['n'][n])
summation+=quantity
N+=1
Sb=((1.0/(N-1.0))*summation)**0.5
plt.figure(figsize=(6, 6))
m = Basemap(projection='ortho',lat_0=dataframe_pole_mean['inc'],lon_0=dataframe_pole_mean['dec'],resolution='c',area_thresh=50000)
m.drawcoastlines(linewidth=0.25)
m.fillcontinents(color='bisque',lake_color='white',zorder=1)
m.drawmapboundary(fill_color='white')
m.drawmeridians(np.arange(0,360,30))
m.drawparallels(np.arange(-90,90,30))
plot_vgp(m,dataframe_pole_lons,dataframe_pole_lats,dataframe_pole_lons)
plot_pole(m,dataframe_pole_mean['dec'],dataframe_pole_mean['inc'],
dataframe_pole_mean['alpha95'],color='r',marker='s')
return Sb
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
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
angle.py
DESCRIPTION
calculates angle between two input directions D1,D2
INPUT (COMMAND LINE ENTRY)
D1_dec D1_inc D1_dec D2_inc
OUTPUT
angle
SYNTAX
angle.py [-h][-i] [command line options] [< filename]
OPTIONS
-h prints help and quits
-i for interactive data entry
-f FILE input filename
-F FILE output filename (required if -F set)
Standard I/O
"""
out = ""
if "-h" in sys.argv:
print main.__doc__
sys.exit()
if "-F" in sys.argv:
ind = sys.argv.index("-F")
o = sys.argv[ind + 1]
out = open(o, "w")
if "-i" in sys.argv:
cont = 1
while cont == 1:
dir1, dir2 = [], []
try:
ans = raw_input("Declination 1: [ctrl-D to quit] ")
dir1.append(float(ans))
ans = raw_input("Inclination 1: ")
dir1.append(float(ans))
ans = raw_input("Declination 2: ")
dir2.append(float(ans))
ans = raw_input("Inclination 2: ")
dir2.append(float(ans))
except:
print "\nGood bye\n"
sys.exit()
ang = pmag.angle(dir1, dir2) # send dirs to angle and spit out result
print "%7.1f " % (ang)
elif "-f" in sys.argv:
ind = sys.argv.index("-f")
file = sys.argv[ind + 1]
input = numpy.loadtxt(file)
else:
input = numpy.loadtxt(sys.stdin.readlines(), dtype=numpy.float) # read from standard input
if len(input.shape) > 1: # list of directions
dir1, dir2 = input[:, 0:2], input[:, 2:]
else:
dir1, dir2 = input[0:2], input[2:]
angs = pmag.angle(dir1, dir2)
for ang in angs: # read in the data (as string variable), line by line
print "%7.1f" % (ang)
if out != "":
out.write("%7.1f \n" % (ang))
