def main(): """ NAME goprinc.py DESCRIPTION calculates Principal components from dec/iinc data INPUT FORMAT takes dec/inc as first two columns in space delimited file SYNTAX goprinc.py [options] [< filename] OPTIONS -h prints help message and quits -i for interactive filename entry -f FILE, specify input file -F FILE, specifies output file name < filename for reading from standard input OUTPUT tau_1 V1_Dec, V1_Inc, tau_2 V2_Dec V2_Inc, tau_3 V3_Dec V3_Inc, N """ if len(sys.argv) > 0: if '-h' in sys.argv: # check if help is needed print main.__doc__ sys.exit() # graceful quit if '-f' in sys.argv: ind=sys.argv.index('-f') file=sys.argv[ind+1] f=open(file,'rU') data=f.readlines() elif '-i' in sys.argv: # ask for filename file=raw_input("Enter file name with dec, inc data: ") f=open(file,'rU') data=f.readlines() else: # data=sys.stdin.readlines() # read in data from standard input ofile = "" if '-F' in sys.argv: ind = sys.argv.index('-F') ofile= sys.argv[ind+1] out = open(ofile, 'w + a') DIs= [] # set up list for dec inc data for line in data: # read in the data from standard input if '\t' in line: rec=line.split('\t') # split each line on space to get records else: rec=line.split() # split each line on space to get records DIs.append((float(rec[0]),float(rec[1]))) # ppars=pmag.doprinc(DIs) output = '%7.5f %7.1f %7.1f %7.5f %7.1f %7.1f %7.5f %7.1f %7.1f %i' % (ppars["tau1"],ppars["dec"],ppars["inc"],ppars["tau2"],ppars["V2dec"],ppars["V2inc"],ppars["tau3"],ppars["V3dec"],ppars["V3inc"],ppars["N"]) if ofile == "": print output else: out.write(output+'\n')
def find_f(data): rad=math.pi/180. Es,Is,Fs,V2s=[],[],[],[] ppars=pmag.doprinc(data) D=ppars['dec'] for f in numpy.arange(1.,.2 ,-.01): fdata=[] for rec in data: U=math.atan((1./f)*math.tan(rec[1]*rad))/rad fdata.append([rec[0],U,1.]) ppars=pmag.doprinc(fdata) Fs.append(f) Es.append(ppars["tau2"]/ppars["tau3"]) angle=pmag.angle([D,0],[ppars["V2dec"],0]) if 180.-angle<angle:angle=180.-angle V2s.append(angle) Is.append(abs(ppars["inc"])) if EI(abs(ppars["inc"]))<=Es[-1]: del Es[-1] del Is[-1] del Fs[-1] del V2s[-1] if len(Fs)>0: for f in numpy.arange(Fs[-1],.2 ,-.005): for rec in data: U=math.atan((1./f)*math.tan(rec[1]*rad))/rad fdata.append([rec[0],U,1.]) ppars=pmag.doprinc(fdata) Fs.append(f) Es.append(ppars["tau2"]/ppars["tau3"]) Is.append(abs(ppars["inc"])) angle=pmag.angle([D,0],[ppars["V2dec"],0]) if 180.-angle<angle:angle=180.-angle V2s.append(angle) if EI(abs(ppars["inc"]))<=Es[-1]: return Es,Is,Fs,V2s return [0],[0],[0],[0]
def find_f(data): rad=numpy.pi/180. Es,Is,Fs,V2s=[],[],[],[] ppars=pmag.doprinc(data) D=ppars['dec'] Decs,Incs=data.transpose()[0],data.transpose()[1] Tan_Incs=numpy.tan(Incs*rad) for f in numpy.arange(1.,.2 ,-.01): U=numpy.arctan((1./f)*Tan_Incs)/rad fdata=numpy.array([Decs,U]).transpose() ppars=pmag.doprinc(fdata) Fs.append(f) Es.append(ppars["tau2"]/ppars["tau3"]) angle=pmag.angle([D,0],[ppars["V2dec"],0]) if 180.-angle<angle:angle=180.-angle V2s.append(angle) Is.append(abs(ppars["inc"])) if EI(abs(ppars["inc"]))<=Es[-1]: del Es[-1] del Is[-1] del Fs[-1] del V2s[-1] if len(Fs)>0: for f in numpy.arange(Fs[-1],.2 ,-.005): U=numpy.arctan((1./f)*Tan_Incs)/rad fdata=numpy.array([Decs,U]).transpose() ppars=pmag.doprinc(fdata) Fs.append(f) Es.append(ppars["tau2"]/ppars["tau3"]) Is.append(abs(ppars["inc"])) angle=pmag.angle([D,0],[ppars["V2dec"],0]) if 180.-angle<angle:angle=180.-angle V2s.append(angle) if EI(abs(ppars["inc"]))<=Es[-1]: return Es,Is,Fs,V2s return [0],[0],[0],[0]
def main(): """ NAME scalc.py DESCRIPTION calculates Sb from VGP Long,VGP Lat,Directional kappa,Site latitude data SYNTAX scalc -h [command line options] [< standard input] INPUT takes space delimited files with PLong, PLat,[kappa, N_site, slat] OPTIONS -h prints help message and quits -f FILE: specify input file -c cutoff: specify VGP colatitude cutoff value -k cutoff: specify kappa cutoff -v : use the VanDammme criterion -a: use antipodes of reverse data: default is to use only normal -C: use all data without regard to polarity -b: do a bootstrap for confidence -p: do relative to principle axis NOTES if kappa, N_site, lat supplied, will consider within site scatter OUTPUT N Sb Sb_lower Sb_upper Co-lat. Cutoff """ coord,kappa,cutoff="0",0,90. nb,anti,boot=1000,0,0 all=0 n=0 v=0 spin=1 coord_key='tilt_correction' if '-h' in sys.argv: print main.__doc__ sys.exit() if '-f' in sys.argv: ind=sys.argv.index("-f") in_file=sys.argv[ind+1] f=open(in_file,'rU') lines=f.readlines() else: lines=sys.stdin.readlines() if '-c' in sys.argv: ind=sys.argv.index('-c') cutoff=float(sys.argv[ind+1]) if '-k' in sys.argv: ind=sys.argv.index('-k') kappa=float(sys.argv[ind+1]) if '-n' in sys.argv: ind=sys.argv.index('-n') n=int(sys.argv[ind+1]) if '-a' in sys.argv: anti=1 if '-C' in sys.argv: cutoff=180. # no cutoff if '-b' in sys.argv: boot=1 if '-v' in sys.argv: v=1 if '-p' in sys.argv: spin=0 # # # find desired vgp lat,lon, kappa,N_site data: # A,Vgps,slats,Pvgps=180.,[],[],[] for line in lines: if '\t' in line: rec=line.replace('\n','').split('\t') # split each line on space to get records else: rec=line.replace('\n','').split() # split each line on space to get records vgp={} vgp['vgp_lon'],vgp['vgp_lat']=rec[0],rec[1] Pvgps.append([float(rec[0]),float(rec[1])]) if anti==1: if float(vgp['vgp_lat'])<0: vgp['vgp_lat']='%7.1f'%(-1*float(vgp['vgp_lat'])) vgp['vgp_lon']='%7.1f'%(float(vgp['vgp_lon'])-180.) if len(rec)==5: vgp['average_k'],vgp['average_nn'],vgp['average_lat']=rec[2],rec[3],rec[4] slats.append(float(rec[4])) else: vgp['average_k'],vgp['average_nn'],vgp['average_lat']="0","0","0" if 90.-(float(vgp['vgp_lat']))<=cutoff and float(vgp['average_k'])>=kappa and int(vgp['average_nn'])>=n: Vgps.append(vgp) if spin==0: # do transformation to pole ppars=pmag.doprinc(Pvgps) for vgp in Vgps: vlon,vlat=pmag.dotilt(float(vgp['vgp_lon']),float(vgp['vgp_lat']),ppars['dec']-180.,90.-ppars['inc']) vgp['vgp_lon']=vlon vgp['vgp_lat']=vlat vgp['average_k']="0" S_B= pmag.get_Sb(Vgps) A=cutoff if v==1: thetamax,A=181.,180. vVgps,cnt=[],0 for vgp in Vgps:vVgps.append(vgp) # make a copy of Vgps while thetamax>A: thetas=[] A=1.8*S_B+5 cnt+=1 for vgp in vVgps:thetas.append(90.-(float(vgp['vgp_lat']))) thetas.sort() thetamax=thetas[-1] if thetamax<A:break nVgps=[] for vgp in vVgps: if 90.-(float(vgp['vgp_lat']))<thetamax:nVgps.append(vgp) vVgps=[] for vgp in nVgps:vVgps.append(vgp) S_B= pmag.get_Sb(vVgps) Vgps=[] for vgp in vVgps:Vgps.append(vgp) # make a new Vgp list SBs,Ns=[],[] if boot==1: for i in range(nb): # now do bootstrap BVgps=[] for k in range(len(Vgps)): ind=random.randint(0,len(Vgps)-1) random.jumpahead(int(ind*1000)) BVgps.append(Vgps[ind]) SBs.append(pmag.get_Sb(BVgps)) SBs.sort() low=int(.025*nb) high=int(.975*nb) print len(Vgps),'%7.1f %7.1f %7.1f %7.1f '%(S_B,SBs[low],SBs[high],A) else: print len(Vgps),'%7.1f %7.1f '%(S_B,A) if len(slats)>2: stats= pmag.gausspars(slats) print 'mean lat = ','%7.1f'%(stats[0])
def main(): """ NAME plotdi_e.py DESCRIPTION plots equal area projection from dec inc data and cones of confidence (Fisher, kent or Bingham or bootstrap). INPUT FORMAT takes dec/inc as first two columns in space delimited file SYNTAX plotdi_e.py [command line options] OPTIONS -h prints help message and quits -i for interactive parameter entry -f FILE, sets input filename on command line -Fish plots unit vector mean direction, alpha95 -Bing plots Principal direction, Bingham confidence ellipse -Kent plots unit vector mean direction, confidence ellipse -Boot E plots unit vector mean direction, bootstrapped confidence ellipse -Boot V plots unit vector mean direction, distribution of bootstrapped means """ dist='F' # default distribution is Fisherian mode=1 EQ={'eq':1} if len(sys.argv) > 0: if '-h' in sys.argv: # check if help is needed print main.__doc__ sys.exit() # graceful quit if '-i' in sys.argv: # ask for filename file=raw_input("Enter file name with dec, inc data: ") dist=raw_input("Enter desired distrubution: [Fish]er, [Bing]ham, [Kent] [Boot] [default is Fisher]: ") if dist=="":dist="F" if dist=="Boot": type=raw_input(" Ellipses or distribution of vectors? [E]/V ") if type=="" or type=="E": dist="BE" else: dist="BE" else: # if '-f' in sys.argv: ind=sys.argv.index('-f') file=sys.argv[ind+1] else: print 'you must specify a file name' print main.__doc__ sys.exit() if '-Bing' in sys.argv:dist='B' if '-Kent' in sys.argv:dist='K' if '-Boot' in sys.argv: ind=sys.argv.index('-Boot') type=sys.argv[ind+1] if type=='E': dist='BE' elif type=='V': dist='BV' EQ['bdirs']=2 pmagplotlib.plot_init(EQ['bdirs'],5,5) else: print main.__doc__ sys.exit() pmagplotlib.plot_init(EQ['eq'],5,5) # # get to work f=open(file,'r') data=f.readlines() # DIs= [] # set up list for dec inc data DiRecs=[] pars=[] nDIs,rDIs,npars,rpars=[],[],[],[] mode =1 for line in data: # read in the data from standard input DiRec={} rec=line.split() # split each line on space to get records DIs.append((float(rec[0]),float(rec[1]),1.)) DiRec['dec']=rec[0] DiRec['inc']=rec[1] DiRec['direction_type']='l' DiRecs.append(DiRec) # split into two modes ppars=pmag.doprinc(DIs) # get principal directions for rec in DIs: angle=pmag.angle([rec[0],rec[1]],[ppars['dec'],ppars['inc']]) if angle>90.: rDIs.append(rec) else: nDIs.append(rec) if dist=='B': # do on whole dataset title="Bingham confidence ellipse" bpars=pmag.dobingham(DIs) for key in bpars.keys(): if key!='n':print " ",key, '%7.1f'%(bpars[key]) if key=='n':print " ",key, ' %i'%(bpars[key]) npars.append(bpars['dec']) npars.append(bpars['inc']) npars.append(bpars['Zeta']) npars.append(bpars['Zdec']) npars.append(bpars['Zinc']) npars.append(bpars['Eta']) npars.append(bpars['Edec']) npars.append(bpars['Einc']) if dist=='F': title="Fisher confidence cone" if len(nDIs)>3: fpars=pmag.fisher_mean(nDIs) print "mode ",mode for key in fpars.keys(): if key!='n':print " ",key, '%7.1f'%(fpars[key]) if key=='n':print " ",key, ' %i'%(fpars[key]) mode+=1 npars.append(fpars['dec']) npars.append(fpars['inc']) npars.append(fpars['alpha95']) # Beta npars.append(fpars['dec']) isign=abs(fpars['inc'])/fpars['inc'] npars.append(fpars['inc']-isign*90.) #Beta inc npars.append(fpars['alpha95']) # gamma npars.append(fpars['dec']+90.) # Beta dec npars.append(0.) #Beta inc if len(rDIs)>3: fpars=pmag.fisher_mean(rDIs) print "mode ",mode for key in fpars.keys(): if key!='n':print " ",key, '%7.1f'%(fpars[key]) if key=='n':print " ",key, ' %i'%(fpars[key]) mode+=1 rpars.append(fpars['dec']) rpars.append(fpars['inc']) rpars.append(fpars['alpha95']) # Beta rpars.append(fpars['dec']) isign=abs(fpars['inc'])/fpars['inc'] rpars.append(fpars['inc']-isign*90.) #Beta inc rpars.append(fpars['alpha95']) # gamma rpars.append(fpars['dec']+90.) # Beta dec rpars.append(0.) #Beta inc if dist=='K': title="Kent confidence ellipse" if len(nDIs)>3: kpars=pmag.dokent(nDIs,len(nDIs)) print "mode ",mode for key in kpars.keys(): if key!='n':print " ",key, '%7.1f'%(kpars[key]) if key=='n':print " ",key, ' %i'%(kpars[key]) mode+=1 npars.append(kpars['dec']) npars.append(kpars['inc']) npars.append(kpars['Zeta']) npars.append(kpars['Zdec']) npars.append(kpars['Zinc']) npars.append(kpars['Eta']) npars.append(kpars['Edec']) npars.append(kpars['Einc']) if len(rDIs)>3: kpars=pmag.dokent(rDIs,len(rDIs)) print "mode ",mode for key in kpars.keys(): if key!='n':print " ",key, '%7.1f'%(kpars[key]) if key=='n':print " ",key, ' %i'%(kpars[key]) mode+=1 rpars.append(kpars['dec']) rpars.append(kpars['inc']) rpars.append(kpars['Zeta']) rpars.append(kpars['Zdec']) rpars.append(kpars['Zinc']) rpars.append(kpars['Eta']) rpars.append(kpars['Edec']) rpars.append(kpars['Einc']) else: # assume bootstrap if dist=='BE': if len(nDIs)>5: BnDIs=pmag.di_boot(nDIs) Bkpars=pmag.dokent(BnDIs,1.) print "mode ",mode for key in Bkpars.keys(): if key!='n':print " ",key, '%7.1f'%(Bkpars[key]) if key=='n':print " ",key, ' %i'%(Bkpars[key]) mode+=1 npars.append(Bkpars['dec']) npars.append(Bkpars['inc']) npars.append(Bkpars['Zeta']) npars.append(Bkpars['Zdec']) npars.append(Bkpars['Zinc']) npars.append(Bkpars['Eta']) npars.append(Bkpars['Edec']) npars.append(Bkpars['Einc']) if len(rDIs)>5: BrDIs=pmag.di_boot(rDIs) Bkpars=pmag.dokent(BrDIs,1.) print "mode ",mode for key in Bkpars.keys(): if key!='n':print " ",key, '%7.1f'%(Bkpars[key]) if key=='n':print " ",key, ' %i'%(Bkpars[key]) mode+=1 rpars.append(Bkpars['dec']) rpars.append(Bkpars['inc']) rpars.append(Bkpars['Zeta']) rpars.append(Bkpars['Zdec']) rpars.append(Bkpars['Zinc']) rpars.append(Bkpars['Eta']) rpars.append(Bkpars['Edec']) rpars.append(Bkpars['Einc']) title="Bootstrapped confidence ellipse" elif dist=='BV': if len(nDIs)>5: pmagplotlib.plotEQ(EQ['eq'],nDIs,'Data') BnDIs=pmag.di_boot(nDIs) pmagplotlib.plotEQ(EQ['bdirs'],BnDIs,'Bootstrapped Eigenvectors') if len(rDIs)>5: BrDIs=pmag.di_boot(rDIs) if len(nDIs)>5: # plot on existing plots pmagplotlib.plotDI(EQ['eq'],rDIs) pmagplotlib.plotDI(EQ['bdirs'],BrDIs) else: pmagplotlib.plotEQ(EQ['eq'],rDIs,'Data') pmagplotlib.plotEQ(EQ['bdirs'],BrDIs,'Bootstrapped Eigenvectors') pmagplotlib.drawFIGS(EQ) ans=raw_input('s[a]ve, [q]uit ') if ans=='q':sys.exit() if ans=='a': files={} for key in EQ.keys(): files[key]='BE_'+key+'.svg' pmagplotlib.saveP(EQ,files) sys.exit() if len(nDIs)>5: pmagplotlib.plotCONF(EQ['eq'],title,DiRecs,npars,1) if len(rDIs)>5 and dist!='B': pmagplotlib.plotCONF(EQ['eq'],title,[],rpars,0) elif len(rDIs)>5 and dist!='B': pmagplotlib.plotCONF(EQ['eq'],title,DiRecs,rpars,1) pmagplotlib.drawFIGS(EQ) ans=raw_input('s[a]ve, [q]uit ') if ans=='q':sys.exit() if ans=='a': files={} for key in EQ.keys(): files[key]=key+'.svg' pmagplotlib.saveP(EQ,files)
def main(): """ NAME eqarea_ell.py DESCRIPTION makes equal area projections from declination/inclination data and plot ellipses SYNTAX eqarea_ell.py -h [command line options] INPUT takes space delimited Dec/Inc data OPTIONS -h prints help message and quits -f FILE -fmt [svg,png,jpg] format for output plots -ell [F,K,B,Be,Bv] plot Fisher, Kent, Bingham, Bootstrap ellipses or Boostrap eigenvectors """ FIG = {} # plot dictionary FIG["eq"] = 1 # eqarea is figure 1 fmt, dist, mode = "svg", "F", 1 plotE = 0 if "-h" in sys.argv: print main.__doc__ sys.exit() pmagplotlib.plot_init(FIG["eq"], 5, 5) if "-f" in sys.argv: ind = sys.argv.index("-f") title = sys.argv[ind + 1] f = open(title, "rU") data = f.readlines() if "-ell" in sys.argv: plotE = 1 ind = sys.argv.index("-ell") ell_type = sys.argv[ind + 1] if ell_type == "F": dist = "F" if ell_type == "K": dist = "K" if ell_type == "B": dist = "B" if ell_type == "Be": dist = "BE" if ell_type == "Bv": dist = "BV" FIG["bdirs"] = 2 pmagplotlib.plot_init(FIG["bdirs"], 5, 5) if "-fmt" in sys.argv: ind = sys.argv.index("-fmt") fmt = sys.argv[ind + 1] DIblock = [] for line in data: if "\t" in line: rec = line.split("\t") # split each line on space to get records else: rec = line.split() # split each line on space to get records DIblock.append([float(rec[0]), float(rec[1])]) if len(DIblock) > 0: pmagplotlib.plotEQ(FIG["eq"], DIblock, title) else: print "no data to plot" sys.exit() if plotE == 1: ppars = pmag.doprinc(DIblock) # get principal directions nDIs, rDIs, npars, rpars = [], [], [], [] for rec in DIblock: angle = pmag.angle([rec[0], rec[1]], [ppars["dec"], ppars["inc"]]) if angle > 90.0: rDIs.append(rec) else: nDIs.append(rec) if dist == "B": # do on whole dataset etitle = "Bingham confidence ellipse" bpars = pmag.dobingham(DIblock) for key in bpars.keys(): if key != "n" and pmagplotlib.verbose: print " ", key, "%7.1f" % (bpars[key]) if key == "n" and pmagplotlib.verbose: print " ", key, " %i" % (bpars[key]) npars.append(bpars["dec"]) npars.append(bpars["inc"]) npars.append(bpars["Zeta"]) npars.append(bpars["Zdec"]) npars.append(bpars["Zinc"]) npars.append(bpars["Eta"]) npars.append(bpars["Edec"]) npars.append(bpars["Einc"]) if dist == "F": etitle = "Fisher confidence cone" if len(nDIs) > 3: fpars = pmag.fisher_mean(nDIs) for key in fpars.keys(): if key != "n" and pmagplotlib.verbose: print " ", key, "%7.1f" % (fpars[key]) if key == "n" and pmagplotlib.verbose: print " ", key, " %i" % (fpars[key]) mode += 1 npars.append(fpars["dec"]) npars.append(fpars["inc"]) npars.append(fpars["alpha95"]) # Beta npars.append(fpars["dec"]) isign = abs(fpars["inc"]) / fpars["inc"] npars.append(fpars["inc"] - isign * 90.0) # Beta inc npars.append(fpars["alpha95"]) # gamma npars.append(fpars["dec"] + 90.0) # Beta dec npars.append(0.0) # Beta inc if len(rDIs) > 3: fpars = pmag.fisher_mean(rDIs) if pmagplotlib.verbose: print "mode ", mode for key in fpars.keys(): if key != "n" and pmagplotlib.verbose: print " ", key, "%7.1f" % (fpars[key]) if key == "n" and pmagplotlib.verbose: print " ", key, " %i" % (fpars[key]) mode += 1 rpars.append(fpars["dec"]) rpars.append(fpars["inc"]) rpars.append(fpars["alpha95"]) # Beta rpars.append(fpars["dec"]) isign = abs(fpars["inc"]) / fpars["inc"] rpars.append(fpars["inc"] - isign * 90.0) # Beta inc rpars.append(fpars["alpha95"]) # gamma rpars.append(fpars["dec"] + 90.0) # Beta dec rpars.append(0.0) # Beta inc if dist == "K": etitle = "Kent confidence ellipse" if len(nDIs) > 3: kpars = pmag.dokent(nDIs, len(nDIs)) if pmagplotlib.verbose: print "mode ", mode for key in kpars.keys(): if key != "n" and pmagplotlib.verbose: print " ", key, "%7.1f" % (kpars[key]) if key == "n" and pmagplotlib.verbose: print " ", key, " %i" % (kpars[key]) mode += 1 npars.append(kpars["dec"]) npars.append(kpars["inc"]) npars.append(kpars["Zeta"]) npars.append(kpars["Zdec"]) npars.append(kpars["Zinc"]) npars.append(kpars["Eta"]) npars.append(kpars["Edec"]) npars.append(kpars["Einc"]) if len(rDIs) > 3: kpars = pmag.dokent(rDIs, len(rDIs)) if pmagplotlib.verbose: print "mode ", mode for key in kpars.keys(): if key != "n" and pmagplotlib.verbose: print " ", key, "%7.1f" % (kpars[key]) if key == "n" and pmagplotlib.verbose: print " ", key, " %i" % (kpars[key]) mode += 1 rpars.append(kpars["dec"]) rpars.append(kpars["inc"]) rpars.append(kpars["Zeta"]) rpars.append(kpars["Zdec"]) rpars.append(kpars["Zinc"]) rpars.append(kpars["Eta"]) rpars.append(kpars["Edec"]) rpars.append(kpars["Einc"]) else: # assume bootstrap if len(nDIs) < 10 and len(rDIs) < 10: print "too few data points for bootstrap" sys.exit() if dist == "BE": if len(nDIs) >= 10: BnDIs = pmag.di_boot(nDIs) Bkpars = pmag.dokent(BnDIs, 1.0) if pmagplotlib.verbose: print "mode ", mode for key in Bkpars.keys(): if key != "n" and pmagplotlib.verbose: print " ", key, "%7.1f" % (Bkpars[key]) if key == "n" and pmagplotlib.verbose: print " ", key, " %i" % (Bkpars[key]) mode += 1 npars.append(Bkpars["dec"]) npars.append(Bkpars["inc"]) npars.append(Bkpars["Zeta"]) npars.append(Bkpars["Zdec"]) npars.append(Bkpars["Zinc"]) npars.append(Bkpars["Eta"]) npars.append(Bkpars["Edec"]) npars.append(Bkpars["Einc"]) if len(rDIs) >= 10: BrDIs = pmag.di_boot(rDIs) Bkpars = pmag.dokent(BrDIs, 1.0) if pmagplotlib.verbose: print "mode ", mode for key in Bkpars.keys(): if key != "n" and pmagplotlib.verbose: print " ", key, "%7.1f" % (Bkpars[key]) if key == "n" and pmagplotlib.verbose: print " ", key, " %i" % (Bkpars[key]) mode += 1 rpars.append(Bkpars["dec"]) rpars.append(Bkpars["inc"]) rpars.append(Bkpars["Zeta"]) rpars.append(Bkpars["Zdec"]) rpars.append(Bkpars["Zinc"]) rpars.append(Bkpars["Eta"]) rpars.append(Bkpars["Edec"]) rpars.append(Bkpars["Einc"]) etitle = "Bootstrapped confidence ellipse" elif dist == "BV": vsym = {"lower": ["+", "k"], "upper": ["x", "k"], "size": 5} if len(nDIs) > 5: BnDIs = pmag.di_boot(nDIs) pmagplotlib.plotEQsym(FIG["bdirs"], BnDIs, "Bootstrapped Eigenvectors", vsym) if len(rDIs) > 5: BrDIs = pmag.di_boot(rDIs) if len(nDIs) > 5: # plot on existing plots pmagplotlib.plotDIsym(FIG["bdirs"], BrDIs, vsym) else: pmagplotlib.plotEQ(FIG["bdirs"], BrDIs, "Bootstrapped Eigenvectors", vsym) if dist == "B": if len(nDIs) > 3 or len(rDIs) > 3: pmagplotlib.plotCONF(FIG["eq"], etitle, [], npars, 0) elif len(nDIs) > 3 and dist != "BV": pmagplotlib.plotCONF(FIG["eq"], etitle, [], npars, 0) if len(rDIs) > 3: pmagplotlib.plotCONF(FIG["eq"], etitle, [], rpars, 0) elif len(rDIs) > 3 and dist != "BV": pmagplotlib.plotCONF(FIG["eq"], etitle, [], rpars, 0) pmagplotlib.drawFIGS(FIG) # files = {} for key in FIG.keys(): files[key] = title + "_" + key + "." + fmt if pmagplotlib.isServer: black = "#000000" purple = "#800080" titles = {} titles["eq"] = "Equal Area Plot" FIG = pmagplotlib.addBorders(FIG, titles, black, purple) pmagplotlib.saveP(FIG, files) else: ans = raw_input(" S[a]ve to save plot, [q]uit, Return to continue: ") if ans == "q": sys.exit() if ans == "a": pmagplotlib.saveP(FIG, files)
def main(): """ NAME find_EI.py DESCRIPTION Applies series of assumed flattening factor and "unsquishes" inclinations assuming tangent function. Finds flattening factor that gives elongation/inclination pair consistent with TK03. Finds bootstrap confidence bounds SYNTAX find_EI.py [command line options] OPTIONS -h prints help message and quits -i allows interactive input of file name -f FILE specify input file name -nb N specify number of bootstraps - the more the better, but slower!, default is 1000 -fmt [svg,png,eps,pdf..] change plot format, default is svg INPUT dec/inc pairs, delimited with space or tabs OUTPUT four plots: 1) equal area plot of original directions 2) Elongation/inclination pairs as a function of f, data plus 25 bootstrap samples 3) Cumulative distribution of bootstrapped optimal inclinations plus uncertainties. Estimate from original data set plotted as solid line 4) Orientation of principle direction through unflattening NOTE: If distribution does not have a solution, plot labeled: Pathological. Some bootstrap samples may have valid solutions and those are plotted in the CDFs and E/I plot. """ fmt,nb='svg',1000 if '-i' in sys.argv: file=raw_input("Enter file name for processing: ") elif '-f' in sys.argv: ind=sys.argv.index('-f') file=sys.argv[ind+1] else: print main.__doc__ sys.exit() if '-nb' in sys.argv: ind=sys.argv.index('-nb') nb=int(sys.argv[ind+1]) if '-fmt' in sys.argv: ind=sys.argv.index('-fmt') fmt=sys.argv[ind+1] data=numpy.loadtxt(file) upper,lower=int(round(.975*nb)),int(round(.025*nb)) E,I=[],[] PLTS={'eq':1,'ei':2,'cdf':3,'v2':4} pmagplotlib.plot_init(PLTS['eq'],6,6) pmagplotlib.plot_init(PLTS['ei'],5,5) pmagplotlib.plot_init(PLTS['cdf'],5,5) pmagplotlib.plot_init(PLTS['v2'],5,5) pmagplotlib.plotEQ(PLTS['eq'],data,'Data') pmagplotlib.drawFIGS(PLTS) ppars=pmag.doprinc(data) Io=ppars['inc'] n=ppars["N"] Es,Is,Fs,V2s=find_f(data) Inc,Elong=Is[-1],Es[-1] pmagplotlib.plotEI(PLTS['ei'],Es,Is,Fs[-1]) pmagplotlib.plotV2s(PLTS['v2'],V2s,Is,Fs[-1]) b=0 print "Bootstrapping.... be patient" while b<nb: bdata=pmag.pseudo(data) Es,Is,Fs,V2s=find_f(bdata) if b<25: pmagplotlib.plotEI(PLTS['ei'],Es,Is,Fs[-1]) if Es[-1]!=0: ppars=pmag.doprinc(bdata) I.append(abs(Is[-1])) E.append(Es[-1]) b+=1 if b%25==0:print b,' out of ',nb I.sort() E.sort() Eexp=[] for i in I: Eexp.append(EI(i)) if Inc==0: title= 'Pathological Distribution: '+'[%7.1f, %7.1f]' %(I[lower],I[upper]) else: title= '%7.1f [%7.1f, %7.1f]' %( Inc, I[lower],I[upper]) pmagplotlib.plotEI(PLTS['ei'],Eexp,I,1) pmagplotlib.plotCDF(PLTS['cdf'],I,'Inclinations','r',title) pmagplotlib.plotVs(PLTS['cdf'],[I[lower],I[upper]],'b','--') pmagplotlib.plotVs(PLTS['cdf'],[Inc],'g','-') pmagplotlib.plotVs(PLTS['cdf'],[Io],'k','-') pmagplotlib.drawFIGS(PLTS) print "Io Inc I_lower, I_upper, Elon, E_lower, E_upper" print '%7.1f %s %7.1f _ %7.1f ^ %7.1f: %6.4f _ %6.4f ^ %6.4f' %(Io, " => ", Inc, I[lower],I[upper], Elong, E[lower],E[upper]) ans= raw_input("S[a]ve plots - <return> to quit: ") if ans!='a': print "\n Good bye\n" sys.exit() files={} files['eq']='findEI_eq.'+fmt files['ei']='findEI_ei.'+fmt files['cdf']='findEI_cdf.'+fmt files['v2']='findEI_v2.'+fmt pmagplotlib.saveP(PLTS,files)
def main(): """ NAME eqarea_ell.py DESCRIPTION makes equal area projections from declination/inclination data and plot ellipses SYNTAX eqarea_ell.py -h [command line options] INPUT takes space delimited Dec/Inc data OPTIONS -h prints help message and quits -f FILE -fmt [svg,png,jpg] format for output plots -sav saves figures and quits -ell [F,K,B,Be,Bv] plot Fisher, Kent, Bingham, Bootstrap ellipses or Boostrap eigenvectors """ FIG = {} # plot dictionary FIG['eq'] = 1 # eqarea is figure 1 fmt, dist, mode, plot = 'svg', 'F', 1, 0 sym = {'lower': ['o', 'r'], 'upper': ['o', 'w'], 'size': 10} plotE = 0 if '-h' in sys.argv: print main.__doc__ sys.exit() pmagplotlib.plot_init(FIG['eq'], 5, 5) if '-sav' in sys.argv: plot = 1 if '-f' in sys.argv: ind = sys.argv.index("-f") title = sys.argv[ind + 1] data = numpy.loadtxt(title).transpose() if '-ell' in sys.argv: plotE = 1 ind = sys.argv.index('-ell') ell_type = sys.argv[ind + 1] if ell_type == 'F': dist = 'F' if ell_type == 'K': dist = 'K' if ell_type == 'B': dist = 'B' if ell_type == 'Be': dist = 'BE' if ell_type == 'Bv': dist = 'BV' FIG['bdirs'] = 2 pmagplotlib.plot_init(FIG['bdirs'], 5, 5) if '-fmt' in sys.argv: ind = sys.argv.index("-fmt") fmt = sys.argv[ind + 1] DIblock = numpy.array([data[0], data[1]]).transpose() if len(DIblock) > 0: pmagplotlib.plotEQsym(FIG['eq'], DIblock, title, sym) if plot == 0: pmagplotlib.drawFIGS(FIG) else: print "no data to plot" sys.exit() if plotE == 1: ppars = pmag.doprinc(DIblock) # get principal directions nDIs, rDIs, npars, rpars = [], [], [], [] for rec in DIblock: angle = pmag.angle([rec[0], rec[1]], [ppars['dec'], ppars['inc']]) if angle > 90.: rDIs.append(rec) else: nDIs.append(rec) if dist == 'B': # do on whole dataset etitle = "Bingham confidence ellipse" bpars = pmag.dobingham(DIblock) for key in bpars.keys(): if key != 'n' and pmagplotlib.verbose: print " ", key, '%7.1f' % (bpars[key]) if key == 'n' and pmagplotlib.verbose: print " ", key, ' %i' % (bpars[key]) npars.append(bpars['dec']) npars.append(bpars['inc']) npars.append(bpars['Zeta']) npars.append(bpars['Zdec']) npars.append(bpars['Zinc']) npars.append(bpars['Eta']) npars.append(bpars['Edec']) npars.append(bpars['Einc']) if dist == 'F': etitle = "Fisher confidence cone" if len(nDIs) > 3: fpars = pmag.fisher_mean(nDIs) for key in fpars.keys(): if key != 'n' and pmagplotlib.verbose: print " ", key, '%7.1f' % (fpars[key]) if key == 'n' and pmagplotlib.verbose: print " ", key, ' %i' % (fpars[key]) mode += 1 npars.append(fpars['dec']) npars.append(fpars['inc']) npars.append(fpars['alpha95']) # Beta npars.append(fpars['dec']) isign = abs(fpars['inc']) / fpars['inc'] npars.append(fpars['inc'] - isign * 90.) #Beta inc npars.append(fpars['alpha95']) # gamma npars.append(fpars['dec'] + 90.) # Beta dec npars.append(0.) #Beta inc if len(rDIs) > 3: fpars = pmag.fisher_mean(rDIs) if pmagplotlib.verbose: print "mode ", mode for key in fpars.keys(): if key != 'n' and pmagplotlib.verbose: print " ", key, '%7.1f' % (fpars[key]) if key == 'n' and pmagplotlib.verbose: print " ", key, ' %i' % (fpars[key]) mode += 1 rpars.append(fpars['dec']) rpars.append(fpars['inc']) rpars.append(fpars['alpha95']) # Beta rpars.append(fpars['dec']) isign = abs(fpars['inc']) / fpars['inc'] rpars.append(fpars['inc'] - isign * 90.) #Beta inc rpars.append(fpars['alpha95']) # gamma rpars.append(fpars['dec'] + 90.) # Beta dec rpars.append(0.) #Beta inc if dist == 'K': etitle = "Kent confidence ellipse" if len(nDIs) > 3: kpars = pmag.dokent(nDIs, len(nDIs)) if pmagplotlib.verbose: print "mode ", mode for key in kpars.keys(): if key != 'n' and pmagplotlib.verbose: print " ", key, '%7.1f' % (kpars[key]) if key == 'n' and pmagplotlib.verbose: print " ", key, ' %i' % (kpars[key]) mode += 1 npars.append(kpars['dec']) npars.append(kpars['inc']) npars.append(kpars['Zeta']) npars.append(kpars['Zdec']) npars.append(kpars['Zinc']) npars.append(kpars['Eta']) npars.append(kpars['Edec']) npars.append(kpars['Einc']) if len(rDIs) > 3: kpars = pmag.dokent(rDIs, len(rDIs)) if pmagplotlib.verbose: print "mode ", mode for key in kpars.keys(): if key != 'n' and pmagplotlib.verbose: print " ", key, '%7.1f' % (kpars[key]) if key == 'n' and pmagplotlib.verbose: print " ", key, ' %i' % (kpars[key]) mode += 1 rpars.append(kpars['dec']) rpars.append(kpars['inc']) rpars.append(kpars['Zeta']) rpars.append(kpars['Zdec']) rpars.append(kpars['Zinc']) rpars.append(kpars['Eta']) rpars.append(kpars['Edec']) rpars.append(kpars['Einc']) else: # assume bootstrap if len(nDIs) < 10 and len(rDIs) < 10: print 'too few data points for bootstrap' sys.exit() if dist == 'BE': print 'Be patient for bootstrap...' if len(nDIs) >= 10: BnDIs = pmag.di_boot(nDIs) Bkpars = pmag.dokent(BnDIs, 1.) if pmagplotlib.verbose: print "mode ", mode for key in Bkpars.keys(): if key != 'n' and pmagplotlib.verbose: print " ", key, '%7.1f' % (Bkpars[key]) if key == 'n' and pmagplotlib.verbose: print " ", key, ' %i' % (Bkpars[key]) mode += 1 npars.append(Bkpars['dec']) npars.append(Bkpars['inc']) npars.append(Bkpars['Zeta']) npars.append(Bkpars['Zdec']) npars.append(Bkpars['Zinc']) npars.append(Bkpars['Eta']) npars.append(Bkpars['Edec']) npars.append(Bkpars['Einc']) if len(rDIs) >= 10: BrDIs = pmag.di_boot(rDIs) Bkpars = pmag.dokent(BrDIs, 1.) if pmagplotlib.verbose: print "mode ", mode for key in Bkpars.keys(): if key != 'n' and pmagplotlib.verbose: print " ", key, '%7.1f' % (Bkpars[key]) if key == 'n' and pmagplotlib.verbose: print " ", key, ' %i' % (Bkpars[key]) mode += 1 rpars.append(Bkpars['dec']) rpars.append(Bkpars['inc']) rpars.append(Bkpars['Zeta']) rpars.append(Bkpars['Zdec']) rpars.append(Bkpars['Zinc']) rpars.append(Bkpars['Eta']) rpars.append(Bkpars['Edec']) rpars.append(Bkpars['Einc']) etitle = "Bootstrapped confidence ellipse" elif dist == 'BV': print 'Be patient for bootstrap...' vsym = {'lower': ['+', 'k'], 'upper': ['x', 'k'], 'size': 5} if len(nDIs) > 5: BnDIs = pmag.di_boot(nDIs) pmagplotlib.plotEQsym(FIG['bdirs'], BnDIs, 'Bootstrapped Eigenvectors', vsym) if len(rDIs) > 5: BrDIs = pmag.di_boot(rDIs) if len(nDIs) > 5: # plot on existing plots pmagplotlib.plotDIsym(FIG['bdirs'], BrDIs, vsym) else: pmagplotlib.plotEQ(FIG['bdirs'], BrDIs, 'Bootstrapped Eigenvectors', vsym) if dist == 'B': if len(nDIs) > 3 or len(rDIs) > 3: pmagplotlib.plotCONF(FIG['eq'], etitle, [], npars, 0) elif len(nDIs) > 3 and dist != 'BV': pmagplotlib.plotCONF(FIG['eq'], etitle, [], npars, 0) if len(rDIs) > 3: pmagplotlib.plotCONF(FIG['eq'], etitle, [], rpars, 0) elif len(rDIs) > 3 and dist != 'BV': pmagplotlib.plotCONF(FIG['eq'], etitle, [], rpars, 0) if plot == 0: pmagplotlib.drawFIGS(FIG) if plot == 0: pmagplotlib.drawFIGS(FIG) # files = {} for key in FIG.keys(): files[key] = title + '_' + key + '.' + fmt if pmagplotlib.isServer: black = '#000000' purple = '#800080' titles = {} titles['eq'] = 'Equal Area Plot' FIG = pmagplotlib.addBorders(FIG, titles, black, purple) pmagplotlib.saveP(FIG, files) elif plot == 0: ans = raw_input(" S[a]ve to save plot, [q]uit, Return to continue: ") if ans == "q": sys.exit() if ans == "a": pmagplotlib.saveP(FIG, files) else: pmagplotlib.saveP(FIG, files)
def main(): """ NAME EI.py [command line options] DESCRIPTION Finds bootstrap confidence bounds on Elongation and Inclination data SYNTAX EI.py [command line options] OPTIONS -h prints help message and quits -f FILE specifies input file -p do parametric bootstrap INPUT dec/inc pairs OUTPUT makes a plot of the E/I pair and bootstrapped confidence bounds along with the E/I trend predicted by the TK03 field model prints out: Io (mean inclination), I_lower and I_upper are 95% confidence bounds on inclination Eo (elongation), E_lower and E_upper are 95% confidence bounds on elongation Edec,Einc are the elongation direction """ par=0 if '-h' in sys.argv: print main.__doc__ sys.exit() if '-f' in sys.argv: ind=sys.argv.index('-f') file=open(sys.argv[ind+1],'rU') if '-p' in sys.argv: par=1 rseed,nb,data=10,5000,[] upper,lower=int(round(.975*nb)),int(round(.025*nb)) Es,Is=[],[] PLTS={'eq':1,'ei':2} pmagplotlib.plot_init(PLTS['eq'],5,5) pmagplotlib.plot_init(PLTS['ei'],5,5) # poly_tab= [ 3.07448925e-06, -3.49555831e-04, -1.46990847e-02, 2.90905483e+00] poly_new= [ 3.15976125e-06, -3.52459817e-04, -1.46641090e-02, 2.89538539e+00] # poly_cp88= [ 5.34558576e-06, -7.70922659e-04, 5.18529685e-03, 2.90941351e+00] # poly_qc96= [ 7.08210133e-06, -8.79536536e-04, 1.09625547e-03, 2.92513660e+00] # poly_cj98=[ 6.56675431e-06, -7.91823539e-04, -1.08211350e-03, 2.80557710e+00] # poly_tk03_g20= [ 4.96757685e-06, -6.02256097e-04, -5.96103272e-03, 2.84227449e+00] # poly_tk03_g30= [ 7.82525963e-06, -1.39781724e-03, 4.47187092e-02, 2.54637535e+00] # poly_gr99_g=[ 1.24362063e-07, -1.69383384e-04, -4.24479223e-03, 2.59257437e+00] # poly_gr99_e=[ 1.26348154e-07, 2.01691452e-04, -4.99142308e-02, 3.69461060e+00] E_EI,E_tab,E_new,E_cp88,E_cj98,E_qc96,E_tk03_g20=[],[],[],[],[],[],[] E_tk03_g30,E_gr99_g,E_gr99_e=[],[],[] I2=range(0,90,5) for inc in I2: E_new.append(EI(inc,poly_new)) # use the polynomial from Tauxe et al. (2008) pmagplotlib.plotEI(PLTS['ei'],E_new,I2,1) if '-f' in sys.argv: random.seed(rseed) for line in file.readlines(): rec=line.split() dec=float(rec[0]) inc=float(rec[1]) if par==1: if len(rec)==4: N=(int(rec[2])) # append n K=float(rec[3]) # append k rec=[dec,inc,N,K] data.append(rec) else: rec=[dec,inc] data.append(rec) pmagplotlib.plotEQ(PLTS['eq'],data,'Data') ppars=pmag.doprinc(data) n=ppars["N"] Io=ppars['inc'] Edec=ppars['Edir'][0] Einc=ppars['Edir'][1] Eo=(ppars['tau2']/ppars['tau3']) b=0 print 'doing bootstrap - be patient' while b<nb: bdata=[] for j in range(n): boot=random.randint(0,n-1) random.jumpahead(rseed) if par==1: DIs=[] D,I,N,K=data[boot][0],data[boot][1],data[boot][2],data[boot][3] for k in range(N): dec,inc=pmag.fshdev(K) drot,irot=pmag.dodirot(dec,inc,D,I) DIs.append([drot,irot]) fpars=pmag.fisher_mean(DIs) bdata.append([fpars['dec'],fpars['inc'],1.]) # replace data[boot] with parametric dec,inc else: bdata.append(data[boot]) ppars=pmag.doprinc(bdata) Is.append(ppars['inc']) Es.append(ppars['tau2']/ppars['tau3']) b+=1 if b%100==0:print b Is.sort() Es.sort() x,std=pmag.gausspars(Es) stderr=std/math.sqrt(len(data)) pmagplotlib.plotX(PLTS['ei'],Io,Eo,Is[lower],Is[upper],Es[lower],Es[upper],'b-') # pmagplotlib.plotX(PLTS['ei'],Io,Eo,Is[lower],Is[upper],Eo-stderr,Eo+stderr,'b-') print 'Io, Eo, I_lower, I_upper, E_lower, E_upper, Edec, Einc' print '%7.1f %4.2f %7.1f %7.1f %4.2f %4.2f %7.1f %7.1f' %(Io,Eo,Is[lower],Is[upper],Es[lower],Es[upper], Edec,Einc) # print '%7.1f %4.2f %7.1f %7.1f %4.2f %4.2f' %(Io,Eo,Is[lower],Is[upper],Eo-stderr,Eo+stderr) pmagplotlib.drawFIGS(PLTS) files,fmt={},'svg' for key in PLTS.keys(): files[key]=key+'.'+fmt ans=raw_input(" S[a]ve to save plot, [q]uit without saving: ") if ans=="a": pmagplotlib.saveP(PLTS,files)
def main(): """ NAME fishqq.py DESCRIPTION makes qq plot from dec,inc input data INPUT FORMAT takes dec/inc pairs in space delimited file SYNTAX fishqq.py [command line options] OPTIONS -h help message -f FILE, specify file on command line """ fmt,plot='svg',0 if '-h' in sys.argv: # check if help is needed print main.__doc__ sys.exit() # graceful quit elif '-f' in sys.argv: # ask for filename ind=sys.argv.index('-f') file=sys.argv[ind+1] f=open(file,'rU') data=f.readlines() DIs,nDIs,rDIs= [],[],[] # set up list for data for line in data: # read in the data from standard input if '\t' in line: rec=line.split('\t') # split each line on space to get records else: rec=line.split() # split each line on space to get records DIs.append([float(rec[0]),float(rec[1])]) # append data to Inc # split into two modes ppars=pmag.doprinc(DIs) # get principal directions for rec in DIs: angle=pmag.angle([rec[0],rec[1]],[ppars['dec'],ppars['inc']]) if angle>90.: rDIs.append(rec) else: nDIs.append(rec) # if len(rDIs) >=10 or len(nDIs) >=10: D1,I1=[],[] QQ={'unf1':1,'exp1':2} pmagplotlib.plot_init(QQ['unf1'],5,5) pmagplotlib.plot_init(QQ['exp1'],5,5) if len(nDIs) < 10: ppars=pmag.doprinc(rDIs) # get principal directions Dbar,Ibar=ppars['dec']-180.,-ppars['inc'] for di in rDIs: d,irot=pmag.dotilt(di[0],di[1],Dbar-180.,90.-Ibar) # rotate to mean drot=d-180. if drot<0:drot=drot+360. D1.append(drot) I1.append(irot) Dtit='Reverse Declinations' Itit='Reverse Inclinations' else: ppars=pmag.doprinc(nDIs) # get principal directions Dbar,Ibar=ppars['dec'],ppars['inc'] for di in nDIs: d,irot=pmag.dotilt(di[0],di[1],Dbar-180.,90.-Ibar) # rotate to mean drot=d-180. if drot<0:drot=drot+360. D1.append(drot) I1.append(irot) Dtit='Declinations' Itit='Inclinations' print drot,irot pmagplotlib.plotQQunf(QQ['unf1'],D1,Dtit) # make plot pmagplotlib.plotQQexp(QQ['exp1'],I1,Itit) # make plot else: print 'you need N> 10 for at least one mode' sys.exit() if len(rDIs)>10 and len(nDIs)>10: D2,I2=[],[] QQ={'unf2':3,'exp2':4} pmagplotlib.plot_init(QQ['unf2'],5,5) pmagplotlib.plot_init(QQ['exp2'],5,5) ppars=pmag.doprinc(rDIs) # get principal directions Dbar,Ibar=ppars['dec']-180.,-ppars['inc'] for di in rDIs: d,irot=pmag.dotilt(di[0],di[1],Dbar-180.,90.-Ibar) # rotate to mean drot=d-180. if drot<0:drot=drot+360. D2.append(drot) I2.append(irot) Dtit='Reverse Declinations' Itit='Reverse Inclinations' pmagplotlib.plotQQunf(QQ['unf2'],D2,Dtit) # make plot pmagplotlib.plotQQexp(QQ['exp2'],I2,Itit) # make plot pmagplotlib.drawFIGS(QQ) files={} for key in QQ.keys(): files[key]=key+'.'+fmt if pmagplotlib.isServer: black = '#000000' purple = '#800080' titles={} titles['eq']='Equal Area Plot' EQ = pmagplotlib.addBorders(EQ,titles,black,purple) pmagplotlib.saveP(QQ,files) elif plot==1: files['qq']=file+'.'+fmt pmagplotlib.saveP(QQ,files) else: ans=raw_input(" S[a]ve to save plot, [q]uit without saving: ") if ans=="a": pmagplotlib.saveP(QQ,files)
def main(): """ NAME find_EI.py DESCRIPTION Applies assumed flattening factor and "unsquishes" inclinations assuming tangent function. Finds flattening factor that gives elongation/inclination pair consistent with TK03. Finds bootstrap confidence bounds SYNTAX find_EI.py [-h][-i] [-f FILE] INPUT dec/inc pairs OUTPUT three plots: 1) equal area plot of original directions 2) Elongation/inclination pairs as a function of f, data plus 25 bootstrap samples 3) Cumulative distribution of bootstrapped optimal inclinations plus uncertainties. Estimate from original data set plotted as solid line """ if '-i' in sys.argv: file=raw_input("Enter file name for processing: ") f=open(file,'rU') elif '-f' in sys.argv: ind=sys.argv.index('-f') file=sys.argv[ind+1] f=open(file,'rU') else: print main.__doc__ sys.exit() rseed,nb,data=10,5000,[] upper,lower=int(round(.975*nb)),int(round(.025*nb)) E,I=[],[] PLTS={'eq':1,'ei':2,'cdf':3,'v2':4} pmagplotlib.plot_init(PLTS['eq'],6,6) pmagplotlib.plot_init(PLTS['ei'],5,5) pmagplotlib.plot_init(PLTS['cdf'],5,5) pmagplotlib.plot_init(PLTS['v2'],5,5) random.seed(rseed) for line in f.readlines(): rec=line.split() dec=float(rec[0]) inc=float(rec[1]) rec=[dec,inc,1.] data.append(rec) pmagplotlib.plotEQ(PLTS['eq'],data,'Data') ppars=pmag.doprinc(data) Io=ppars['inc'] n=ppars["N"] Es,Is,Fs,V2s=find_f(data) Inc,Elong=Is[-1],Es[-1] pmagplotlib.plotEI(PLTS['ei'],Es,Is,Fs[-1]) pmagplotlib.plotV2s(PLTS['v2'],V2s,Is,Fs[-1]) b=0 print "Bootstrapping.... be patient" while b<nb: bdata=[] for j in range(n): boot=random.randint(0,n-1) random.jumpahead(rseed) bdata.append(data[boot]) Es,Is,Fs,V2s=find_f(bdata) if b<25: pmagplotlib.plotEI(PLTS['ei'],Es,Is,Fs[-1]) if Es[-1]!=0: ppars=pmag.doprinc(bdata) I.append(abs(Is[-1])) E.append(Es[-1]) b+=1 if b%25==0:print b,' out of ',nb I.sort() E.sort() Eexp=[] for i in I: Eexp.append(EI(i)) title= '%7.1f [%7.1f, %7.1f]' %( Inc, I[lower],I[upper]) pmagplotlib.plotEI(PLTS['ei'],Eexp,I,1) pmagplotlib.plotCDF(PLTS['cdf'],I,'Inclinations','r',title) pmagplotlib.plotVs(PLTS['cdf'],[I[lower],I[upper]],'b','--') pmagplotlib.plotVs(PLTS['cdf'],[Inc],'g','-') pmagplotlib.plotVs(PLTS['cdf'],[Io],'k','-') print "Io Inc I_lower, I_upper, Elon, E_lower, E_upper" print '%7.1f %s %7.1f _ %7.1f ^ %7.1f: %6.4f _ %6.4f ^ %6.4f' %(Io, " => ", Inc, I[lower],I[upper], Elong, E[lower],E[upper]) try: raw_input("Return to save plots - <return> to quit: ") except EOFError: print "\n Good bye\n" sys.exit() files={} files['eq']='findEI_eq.svg' files['ei']='findEI_ei.svg' files['cdf']='findEI_cdf.svg' files['v2']='findEI_v2.svg' pmagplotlib.saveP(PLTS,files)
def main(): """ NAME foldtest.py DESCRIPTION does a fold test (Tauxe, 2007) on data INPUT FORMAT dec inc dip_direction dip SYNTAX foldtest.py [-h][-i][command line options] OPTIONS -h prints help message and quits -i for interactive parameter entry -f FILE OUTPUT Geographic: is an equal area projection of the input data in original coordinates Stratigraphic: is an equal area projection of the input data in tilt adjusted coordinates % Untilting: The dashed (red) curves are representative plots of maximum eigenvalue (tau_1) as a function of untilting The solid line is the cumulative distribution of the % Untilting required to maximize tau for all the bootstrapped data sets. The dashed vertical lines are 95% confidence bounds on the % untilting that yields the most clustered result (maximum tau_1). Command line: prints out the bootstrapped iterations and finally the confidence bounds on optimum untilting. If the 95% conf bounds include 0, then a pre-tilt magnetization is indicated If the 95% conf bounds include 100, then a post-tilt magnetization is indicated If the 95% conf bounds exclude both 0 and 100, syn-tilt magnetization is possible as is vertical axis rotation or other pathologies """ if '-h' in sys.argv: # check if help is needed print main.__doc__ sys.exit() # graceful quit if '-i' in sys.argv: # ask for filename file=raw_input("Enter file name with dec, inc dip_direction and dip data: ") f=open(file,'rU') data=f.readlines() elif '-f' in sys.argv: ind=sys.argv.index('-f') file=sys.argv[ind+1] f=open(file,'rU') data=f.readlines() else: print main.__doc__ sys.exit() # # get to work # PLTS={'geo':1,'strat':2,'taus':3,'ei':4} # make plot dictionary pmagplotlib.plot_init(PLTS['geo'],5,5) pmagplotlib.plot_init(PLTS['strat'],5,5) pmagplotlib.plot_init(PLTS['taus'],5,5) pmagplotlib.plot_init(PLTS['ei'],5,5) DIDDs= [] # set up list for dec inc dip_direction, dip nb=100 # number of bootstraps for line in data: # read in the data from standard input rec=line.split() # split each line on space to get records DIDDs.append([float(rec[0]),float(rec[1]),float(rec[2]),float(rec[3])]) pmagplotlib.plotEQ(PLTS['geo'],DIDDs,'Geographic') TCs,Ps,Taus,Es,Is=[],[],[],[],[] for k in range(len(DIDDs)): drot,irot=pmag.dotilt(DIDDs[k][0],DIDDs[k][1],DIDDs[k][2],DIDDs[k][3]) TCs.append([drot,irot,1.]) pmagplotlib.plotEQ(PLTS['strat'],TCs,'Stratigraphic') Percs=range(-10,110) for perc in Percs: tilt=0.01*perc TCs=[] for k in range(len(DIDDs)): drot,irot=pmag.dotilt(DIDDs[k][0],DIDDs[k][1],DIDDs[k][2],tilt*DIDDs[k][3]) TCs.append([drot,irot,1.]) ppars=pmag.doprinc(TCs) # get principal directions Taus.append(ppars['tau1']) Es.append(ppars["tau2"]/ppars["tau3"]) Is.append(ppars["inc"]) if int(10*(EI(ppars["inc"])))==int(10*Es[-1]): print EI(ppars["inc"]),Es[-1],perc Ps.append(perc) pylab.figure(num=PLTS['taus']) pylab.plot(Percs,Taus,'b-') pylab.figure(num=PLTS['ei']) pylab.plot(Es,Is,'b-') Is.sort() Eexp=[] for i in Is: Eexp.append(EI(i)) pylab.plot(Eexp,Is,'g-') Cdf,Untilt=[],[] print 'doing ',nb,' iterations...please be patient.....' for n in range(nb): # do bootstrap data sets - plot first 25 as dashed red line Es,Is=[],[] if n%50==0:print n Taus=[] # set up lists for taus PDs=pmag.pseudo(DIDDs) for perc in Percs: tilt=0.01*perc TCs=[] for k in range(len(PDs)): drot,irot=pmag.dotilt(PDs[k][0],PDs[k][1],PDs[k][2],tilt*PDs[k][3]) TCs.append([drot,irot,1.]) ppars=pmag.doprinc(TCs) # get principal directions Taus.append(ppars['tau1']) Es.append(ppars["tau2"]/ppars["tau3"]) Is.append(ppars["inc"]) if int(10*(EI(ppars["inc"])))==int(10*Es[-1]): Ps.append(perc) if n<25: pylab.figure(num=PLTS['taus']) pylab.plot(Percs,Taus,'r--') pylab.figure(num=PLTS['ei']) pylab.plot(Es,Is,'r--') Untilt.append(Percs[Taus.index(pylab.max(Taus))]) # tilt that gives maximum tau Cdf.append(float(n)/float(nb)) pylab.figure(num=PLTS['taus']) pylab.plot(Percs,Taus,'k') pylab.xlabel('% Untilting') pylab.ylabel('tau_1 (red), CDF (green)') Untilt.sort() # now for CDF of tilt of maximum tau Ps.sort() pylab.plot(Untilt,Cdf,'g') lower=int(.025*nb) upper=int(.975*nb) pylab.axvline(x=Untilt[lower],ymin=0,ymax=1,linewidth=1,linestyle='--') pylab.axvline(x=Untilt[upper],ymin=0,ymax=1,linewidth=1,linestyle='--') tit= '%i - %i %s'%(Untilt[lower],Untilt[upper],'Percent Unfolding') pylab.title(tit) print Ps[lower],Ps[upper] pmagplotlib.drawFIGS(PLTS) try: raw_input('Return to save all figures, cntl-d to quit\n') except EOFError: print "Good bye" sys.exit() files={} for key in PLTS.keys(): files[key]=('fold_'+'%s'%(key.strip()[:2])+'.svg') pmagplotlib.saveP(PLTS,files)
def main(): """ NAME foldtest_magic.py DESCRIPTION does a fold test (Tauxe, 2007) on data INPUT FORMAT pmag_specimens format file, er_samples.txt format file (for bedding) SYNTAX foldtest_magic.py [command line options] OPTIONS -h prints help message and quits -f pmag_sites formatted file [default is pmag_sites.txt] -fsa er_samples formatted file [default is er_samples.txt] -exc use pmag_criteria.txt to set acceptance criteria -n NB, set number of bootstraps, default is 500 -b MIN, MAX, set bounds for untilting, default is -10, 150 OUTPUT Geographic: is an equal area projection of the input data in original coordinates Stratigraphic: is an equal area projection of the input data in tilt adjusted coordinates % Untilting: The dashed (red) curves are representative plots of maximum eigenvalue (tau_1) as a function of untilting The solid line is the cumulative distribution of the % Untilting required to maximize tau for all the bootstrapped data sets. The dashed vertical lines are 95% confidence bounds on the % untilting that yields the most clustered result (maximum tau_1). Command line: prints out the bootstrapped iterations and finally the confidence bounds on optimum untilting. If the 95% conf bounds include 0, then a pre-tilt magnetization is indicated If the 95% conf bounds include 100, then a post-tilt magnetization is indicated If the 95% conf bounds exclude both 0 and 100, syn-tilt magnetization is possible as is vertical axis rotation or other pathologies """ kappa=0 nb=500 # number of bootstraps min,max=-10,150 dir_path='.' infile,orfile='pmag_sites.txt','er_samples.txt' critfile='pmag_criteria.txt' if '-WD' in sys.argv: ind=sys.argv.index('-WD') dir_path=sys.argv[ind+1] if '-h' in sys.argv: # check if help is needed print main.__doc__ sys.exit() # graceful quit if '-n' in sys.argv: ind=sys.argv.index('-n') nb=int(sys.argv[ind+1]) if '-b' in sys.argv: ind=sys.argv.index('-b') min=int(sys.argv[ind+1]) max=int(sys.argv[ind+2]) if '-f' in sys.argv: ind=sys.argv.index('-f') infile=sys.argv[ind+1] if '-fsa' in sys.argv: ind=sys.argv.index('-fsa') orfile=sys.argv[ind+1] orfile=dir_path+'/'+orfile infile=dir_path+'/'+infile critfile=dir_path+'/'+critfile data,file_type=pmag.magic_read(infile) ordata,file_type=pmag.magic_read(orfile) if '-exc' in sys.argv: crits,file_type=pmag.magic_read(critfile) for crit in crits: if crit['pmag_criteria_code']=="DE-SITE": SiteCrit=crit break # get to work # PLTS={'geo':1,'strat':2,'taus':3} # make plot dictionary pmagplotlib.plot_init(PLTS['geo'],5,5) pmagplotlib.plot_init(PLTS['strat'],5,5) pmagplotlib.plot_init(PLTS['taus'],5,5) DIDDs= [] # set up list for dec inc dip_direction, dip for rec in data: # read in the data from standard input if eval(rec['site_tilt_correction'])==0: dip,dip_dir=0,-1 Dec=float(rec['site_dec']) Inc=float(rec['site_inc']) for orec in ordata: if orec['er_site_name']==rec['er_site_name']: if orec['sample_bed_dip_direction']!="":dip_dir=float(orec['sample_bed_dip_direction']) if orec['sample_bed_dip']!="":dip=float(orec['sample_bed_dip']) break if dip!=0 and dip_dir!=-1: if '-exc' in sys.argv: keep=1 for key in SiteCrit.keys(): if 'site' in key and SiteCrit[key]!="" and rec[key]!="" and key!='site_alpha95': if float(rec[key])<float(SiteCrit[key]): keep=0 print rec['er_site_name'],key,rec[key] if key=='site_alpha95' and SiteCrit[key]!="" and rec[key]!="": if float(rec[key])>float(SiteCrit[key]): keep=0 if keep==1: DIDDs.append([Dec,Inc,dip_dir,dip]) else: DIDDs.append([Dec,Inc,dip_dir,dip]) pmagplotlib.plotEQ(PLTS['geo'],DIDDs,'Geographic') TCs=[] for k in range(len(DIDDs)): drot,irot=pmag.dotilt(DIDDs[k][0],DIDDs[k][1],DIDDs[k][2],DIDDs[k][3]) TCs.append([drot,irot,1.]) pmagplotlib.plotEQ(PLTS['strat'],TCs,'Stratigraphic') Percs=range(min,max) Cdf,Untilt=[],[] pylab.figure(num=PLTS['taus']) print 'doing ',nb,' iterations...please be patient.....' for n in range(nb): # do bootstrap data sets - plot first 25 as dashed red line if n%50==0:print n Taus=[] # set up lists for taus PDs=pmag.pseudo(DIDDs) for perc in Percs: tilt=0.01*perc TCs=[] for k in range(len(PDs)): drot,irot=pmag.dotilt(PDs[k][0],PDs[k][1],PDs[k][2],tilt*PDs[k][3]) TCs.append([drot,irot,1.]) ppars=pmag.doprinc(TCs) # get principal directions Taus.append(ppars['tau1']) if n<25:pylab.plot(Percs,Taus,'r--') Untilt.append(Percs[Taus.index(numpy.max(Taus))]) # tilt that gives maximum tau Cdf.append(float(n)/float(nb)) pylab.plot(Percs,Taus,'k') pylab.xlabel('% Untilting') pylab.ylabel('tau_1 (red), CDF (green)') Untilt.sort() # now for CDF of tilt of maximum tau pylab.plot(Untilt,Cdf,'g') lower=int(.025*nb) upper=int(.975*nb) pylab.axvline(x=Untilt[lower],ymin=0,ymax=1,linewidth=1,linestyle='--') pylab.axvline(x=Untilt[upper],ymin=0,ymax=1,linewidth=1,linestyle='--') tit= '%i - %i %s'%(Untilt[lower],Untilt[upper],'Percent Unfolding') print tit pylab.title(tit) try: raw_input('Return to save all figures, cntl-d to quit\n') except EOFError: print "Good bye" sys.exit() files={} for key in PLTS.keys(): files[key]=('fold_'+'%s'%(key.strip()[:2])+'.svg') pmagplotlib.saveP(PLTS,files)
def main(): """ NAME fishqq.py DESCRIPTION makes qq plot from dec,inc input data INPUT FORMAT takes dec/inc pairs in space delimited file SYNTAX fishqq.py [command line options] OPTIONS -h help message -f FILE, specify file on command line -F FILE, specify output file for statistics OUTPUT: Dec Inc N Mu Mu_crit Me Me_crit Y/N where direction is the principal component and Y/N is Fisherian or not separate lines for each mode with N >=10 (N and R) """ fmt,plot='svg',0 outfile="" if '-h' in sys.argv: # check if help is needed print main.__doc__ sys.exit() # graceful quit elif '-f' in sys.argv: # ask for filename ind=sys.argv.index('-f') file=sys.argv[ind+1] f=open(file,'rU') data=f.readlines() if '-F' in sys.argv: ind=sys.argv.index('-F') outfile=open(sys.argv[ind+1],'w') # open output file DIs,nDIs,rDIs= [],[],[] # set up list for data for line in data: # read in the data from standard input if '\t' in line: rec=line.split('\t') # split each line on space to get records else: rec=line.split() # split each line on space to get records DIs.append([float(rec[0]),float(rec[1])]) # append data to Inc # split into two modes ppars=pmag.doprinc(DIs) # get principal directions for rec in DIs: angle=pmag.angle([rec[0],rec[1]],[ppars['dec'],ppars['inc']]) if angle>90.: rDIs.append(rec) else: nDIs.append(rec) # if len(rDIs) >=10 or len(nDIs) >=10: D1,I1=[],[] QQ={'unf1':1,'exp1':2} pmagplotlib.plot_init(QQ['unf1'],5,5) pmagplotlib.plot_init(QQ['exp1'],5,5) if len(nDIs) < 10: ppars=pmag.doprinc(rDIs) # get principal directions Drbar,Irbar=ppars['dec']-180.,-ppars['inc'] Nr=len(rDIs) for di in rDIs: d,irot=pmag.dotilt(di[0],di[1],Drbar-180.,90.-Irbar) # rotate to mean drot=d-180. if drot<0:drot=drot+360. D1.append(drot) I1.append(irot) Dtit='Mode 2 Declinations' Itit='Mode 2 Inclinations' else: ppars=pmag.doprinc(nDIs) # get principal directions Dnbar,Inbar=ppars['dec'],ppars['inc'] Nn=len(nDIs) for di in nDIs: d,irot=pmag.dotilt(di[0],di[1],Dnbar-180.,90.-Inbar) # rotate to mean drot=d-180. if drot<0:drot=drot+360. D1.append(drot) I1.append(irot) Dtit='Mode 1 Declinations' Itit='Mode 1 Inclinations' Mu_n,Mu_ncr=pmagplotlib.plotQQunf(QQ['unf1'],D1,Dtit) # make plot Me_n,Me_ncr=pmagplotlib.plotQQexp(QQ['exp1'],I1,Itit) # make plot if outfile!="": # Dec Inc N Mu Mu_crit Me Me_crit Y/N if Mu_n<=Mu_ncr and Me_n<=Me_ncr: F='Y' else: F='N' outstring='%7.1f %7.1f %i %5.3f %5.3f %5.3f %5.3f %s \n'%(Dnbar,Inbar,Nn,Mu_n,Mu_ncr,Me_n,Me_ncr,F) outfile.write(outstring) else: print 'you need N> 10 for at least one mode' sys.exit() if len(rDIs)>10 and len(nDIs)>10: D2,I2=[],[] QQ['unf2']=3 QQ['exp2']=4 pmagplotlib.plot_init(QQ['unf2'],5,5) pmagplotlib.plot_init(QQ['exp2'],5,5) ppars=pmag.doprinc(rDIs) # get principal directions Drbar,Irbar=ppars['dec']-180.,-ppars['inc'] Nr=len(rDIs) for di in rDIs: d,irot=pmag.dotilt(di[0],di[1],Drbar-180.,90.-Irbar) # rotate to mean drot=d-180. if drot<0:drot=drot+360. D2.append(drot) I2.append(irot) Dtit='Mode 2 Declinations' Itit='Mode 2 Inclinations' Mu_r,Mu_rcr=pmagplotlib.plotQQunf(QQ['unf2'],D2,Dtit) # make plot Me_r,Me_rcr=pmagplotlib.plotQQexp(QQ['exp2'],I2,Itit) # make plot if outfile!="": # Dec Inc N Mu Mu_crit Me Me_crit Y/N if Mu_r<=Mu_rcr and Me_r<=Me_rcr: F='Y' else: F='N' outstring='%7.1f %7.1f %i %5.3f %5.3f %5.3f %5.3f %s \n'%(Drbar,Irbar,Nr,Mu_r,Mu_rcr,Me_r,Me_rcr,F) outfile.write(outstring) pmagplotlib.drawFIGS(QQ) files={} for key in QQ.keys(): files[key]=key+'.'+fmt if pmagplotlib.isServer: black = '#000000' purple = '#800080' titles={} titles['eq']='Equal Area Plot' EQ = pmagplotlib.addBorders(EQ,titles,black,purple) pmagplotlib.saveP(QQ,files) elif plot==1: files['qq']=file+'.'+fmt pmagplotlib.saveP(QQ,files) else: ans=raw_input(" S[a]ve to save plot, [q]uit without saving: ") if ans=="a": pmagplotlib.saveP(QQ,files)
def main(): """ NAME foldtest.py DESCRIPTION does a fold test (Tauxe, 2008) on data INPUT FORMAT dec inc dip_direction dip SYNTAX foldtest.py [command line options] OPTIONS -h prints help message and quits -f FILE -u ANGLE (circular standard deviation) for uncertainty on bedding poles -b MIN MAX bounds for quick search of percent untilting [default is -10 to 150%] -n NB number of bootstrap samples [default is 1000] OUTPUT Geographic: is an equal area projection of the input data in original coordinates Stratigraphic: is an equal area projection of the input data in tilt adjusted coordinates % Untilting: The dashed (red) curves are representative plots of maximum eigenvalue (tau_1) as a function of untilting The solid line is the cumulative distribution of the % Untilting required to maximize tau for all the bootstrapped data sets. The dashed vertical lines are 95% confidence bounds on the % untilting that yields the most clustered result (maximum tau_1). Command line: prints out the bootstrapped iterations and finally the confidence bounds on optimum untilting. If the 95% conf bounds include 0, then a pre-tilt magnetization is indicated If the 95% conf bounds include 100, then a post-tilt magnetization is indicated If the 95% conf bounds exclude both 0 and 100, syn-tilt magnetization is possible as is vertical axis rotation or other pathologies """ kappa=0 nb=1000 # number of bootstraps min,max=-10,150 if '-h' in sys.argv: # check if help is needed print main.__doc__ sys.exit() # graceful quit if '-f' in sys.argv: ind=sys.argv.index('-f') file=sys.argv[ind+1] f=open(file,'rU') data=f.readlines() else: print main.__doc__ sys.exit() if '-b' in sys.argv: ind=sys.argv.index('-b') min=float(sys.argv[ind+1]) max=float(sys.argv[ind+2]) if '-n' in sys.argv: ind=sys.argv.index('-n') nb=int(sys.argv[ind+1]) if '-u' in sys.argv: ind=sys.argv.index('-u') csd=float(sys.argv[ind+1]) kappa=(81./csd)**2 # # get to work # PLTS={'geo':1,'strat':2,'taus':3} # make plot dictionary pmagplotlib.plot_init(PLTS['geo'],5,5) pmagplotlib.plot_init(PLTS['strat'],5,5) pmagplotlib.plot_init(PLTS['taus'],5,5) DIDDs= [] # set up list for dec inc dip_direction, dip for line in data: # read in the data from standard input rec=line.split() # split each line on space to get records DIDDs.append([float(rec[0]),float(rec[1]),float(rec[2]),float(rec[3])]) pmagplotlib.plotEQ(PLTS['geo'],DIDDs,'Geographic') TCs=[] for k in range(len(DIDDs)): drot,irot=pmag.dotilt(DIDDs[k][0],DIDDs[k][1],DIDDs[k][2],DIDDs[k][3]) TCs.append([drot,irot,1.]) pmagplotlib.plotEQ(PLTS['strat'],TCs,'Stratigraphic') Percs=range(min,max) Cdf,Untilt=[],[] pylab.figure(num=PLTS['taus']) print 'doing ',nb,' iterations...please be patient.....' for n in range(nb): # do bootstrap data sets - plot first 25 as dashed red line if n%50==0:print n Taus=[] # set up lists for taus PDs=pmag.pseudo(DIDDs) if kappa!=0: for k in range(len(PDs)): d,i=pmag.fshdev(kappa) dipdir,dip=pmag.dodirot(d,i,PDs[k][2],PDs[k][3]) PDs[k][2]=dipdir PDs[k][3]=dip for perc in Percs: tilt=0.01*perc TCs=[] for k in range(len(PDs)): drot,irot=pmag.dotilt(PDs[k][0],PDs[k][1],PDs[k][2],tilt*PDs[k][3]) TCs.append([drot,irot,1.]) ppars=pmag.doprinc(TCs) # get principal directions Taus.append(ppars['tau1']) if n<25:pylab.plot(Percs,Taus,'r--') Untilt.append(Percs[Taus.index(numpy.max(Taus))]) # tilt that gives maximum tau Cdf.append(float(n)/float(nb)) pylab.plot(Percs,Taus,'k') pylab.xlabel('% Untilting') pylab.ylabel('tau_1 (red), CDF (green)') Untilt.sort() # now for CDF of tilt of maximum tau pylab.plot(Untilt,Cdf,'g') lower=int(.025*nb) upper=int(.975*nb) pylab.axvline(x=Untilt[lower],ymin=0,ymax=1,linewidth=1,linestyle='--') pylab.axvline(x=Untilt[upper],ymin=0,ymax=1,linewidth=1,linestyle='--') tit= '%i - %i %s'%(Untilt[lower],Untilt[upper],'Percent Unfolding') print tit print 'range of all bootstrap samples: ', Untilt[0], ' - ', Untilt[-1] pylab.title(tit) try: raw_input('Return to save all figures, cntl-d to quit\n') except: print "Good bye" sys.exit() files={} for key in PLTS.keys(): files[key]=('fold_'+'%s'%(key.strip()[:2])+'.svg') pmagplotlib.saveP(PLTS,files)
def main(): """ NAME foldtest.py DESCRIPTION does a fold test (Tauxe, 2010) on data INPUT FORMAT dec inc dip_direction dip SYNTAX foldtest.py [command line options] OPTIONS -h prints help message and quits -f FILE file with input data -F FILE for confidence bounds on fold test -u ANGLE (circular standard deviation) for uncertainty on bedding poles -b MIN MAX bounds for quick search of percent untilting [default is -10 to 150%] -n NB number of bootstrap samples [default is 1000] -fmt FMT, specify format - default is svg OUTPUT PLOTS Geographic: is an equal area projection of the input data in original coordinates Stratigraphic: is an equal area projection of the input data in tilt adjusted coordinates % Untilting: The dashed (red) curves are representative plots of maximum eigenvalue (tau_1) as a function of untilting The solid line is the cumulative distribution of the % Untilting required to maximize tau for all the bootstrapped data sets. The dashed vertical lines are 95% confidence bounds on the % untilting that yields the most clustered result (maximum tau_1). Command line: prints out the bootstrapped iterations and finally the confidence bounds on optimum untilting. If the 95% conf bounds include 0, then a pre-tilt magnetization is indicated If the 95% conf bounds include 100, then a post-tilt magnetization is indicated If the 95% conf bounds exclude both 0 and 100, syn-tilt magnetization is possible as is vertical axis rotation or other pathologies Geographic: is an equal area projection of the input data in OPTIONAL OUTPUT FILE: The output file has the % untilting within the 95% confidence bounds nd the number of bootstrap samples """ kappa=0 fmt='svg' nb=1000 # number of bootstraps min,max=-10,150 if '-h' in sys.argv: # check if help is needed print main.__doc__ sys.exit() # graceful quit if '-F' in sys.argv: ind=sys.argv.index('-F') outfile=open(sys.argv[ind+1],'w') else: outfile="" if '-f' in sys.argv: ind=sys.argv.index('-f') file=sys.argv[ind+1] DIDDs=numpy.loadtxt(file) else: print main.__doc__ sys.exit() if '-fmt' in sys.argv: ind=sys.argv.index('-fmt') fmt=sys.argv[ind+1] if '-b' in sys.argv: ind=sys.argv.index('-b') min=float(sys.argv[ind+1]) max=float(sys.argv[ind+2]) if '-n' in sys.argv: ind=sys.argv.index('-n') nb=int(sys.argv[ind+1]) if '-u' in sys.argv: ind=sys.argv.index('-u') csd=float(sys.argv[ind+1]) kappa=(81./csd)**2 # # get to work # PLTS={'geo':1,'strat':2,'taus':3} # make plot dictionary pmagplotlib.plot_init(PLTS['geo'],5,5) pmagplotlib.plot_init(PLTS['strat'],5,5) pmagplotlib.plot_init(PLTS['taus'],5,5) pmagplotlib.plotEQ(PLTS['geo'],DIDDs,'Geographic') D,I=pmag.dotilt_V(DIDDs) TCs=numpy.array([D,I]).transpose() pmagplotlib.plotEQ(PLTS['strat'],TCs,'Stratigraphic') pmagplotlib.drawFIGS(PLTS) Percs=range(min,max) Cdf,Untilt=[],[] pylab.figure(num=PLTS['taus']) print 'doing ',nb,' iterations...please be patient.....' for n in range(nb): # do bootstrap data sets - plot first 25 as dashed red line if n%50==0:print n Taus=[] # set up lists for taus PDs=pmag.pseudo(DIDDs) if kappa!=0: for k in range(len(PDs)): d,i=pmag.fshdev(kappa) dipdir,dip=pmag.dodirot(d,i,PDs[k][2],PDs[k][3]) PDs[k][2]=dipdir PDs[k][3]=dip for perc in Percs: tilt=numpy.array([1.,1.,1.,0.01*perc]) D,I=pmag.dotilt_V(PDs*tilt) TCs=numpy.array([D,I]).transpose() ppars=pmag.doprinc(TCs) # get principal directions Taus.append(ppars['tau1']) if n<25:pylab.plot(Percs,Taus,'r--') Untilt.append(Percs[Taus.index(numpy.max(Taus))]) # tilt that gives maximum tau Cdf.append(float(n)/float(nb)) pylab.plot(Percs,Taus,'k') pylab.xlabel('% Untilting') pylab.ylabel('tau_1 (red), CDF (green)') Untilt.sort() # now for CDF of tilt of maximum tau pylab.plot(Untilt,Cdf,'g') lower=int(.025*nb) upper=int(.975*nb) pylab.axvline(x=Untilt[lower],ymin=0,ymax=1,linewidth=1,linestyle='--') pylab.axvline(x=Untilt[upper],ymin=0,ymax=1,linewidth=1,linestyle='--') tit= '%i - %i %s'%(Untilt[lower],Untilt[upper],'Percent Unfolding') print tit print 'range of all bootstrap samples: ', Untilt[0], ' - ', Untilt[-1] pylab.title(tit) outstring= '%i - %i; %i\n'%(Untilt[lower],Untilt[upper],nb) if outfile!="":outfile.write(outstring) pmagplotlib.drawFIGS(PLTS) ans= raw_input('S[a]ve all figures, <Return> to quit ') if ans!='a': print "Good bye" sys.exit() else: files={} for key in PLTS.keys(): files[key]=('foldtest_'+'%s'%(key.strip()[:2])+'.'+fmt) pmagplotlib.saveP(PLTS,files)
def main(): """ NAME eqarea_magic.py DESCRIPTION makes equal area projections from declination/inclination data SYNTAX eqarea_magic.py [command line options] INPUT takes magic formatted pmag_results, pmag_sites, pmag_samples or pmag_specimens OPTIONS -h prints help message and quits -f FILE: specify input magic format file from magic,default='pmag_results.txt' supported types=[magic_measurements,pmag_specimens, pmag_samples, pmag_sites, pmag_results, magic_web] -obj OBJ: specify level of plot [all, sit, sam, spc], default is all -crd [s,g,t]: specify coordinate system, [s]pecimen, [g]eographic, [t]ilt adjusted default is geographic, unspecified assumed geographic -fmt [svg,png,jpg] format for output plots -ell [F,K,B,Be,Bv] plot Fisher, Kent, Bingham, Bootstrap ellipses or Boostrap eigenvectors -c plot as colour contour -sav save plot and quit quietly NOTE all: entire file; sit: site; sam: sample; spc: specimen """ FIG = {} # plot dictionary FIG['eqarea'] = 1 # eqarea is figure 1 in_file, plot_key, coord, crd = 'pmag_results.txt', 'all', "0", 'g' plotE, contour = 0, 0 dir_path = '.' fmt = 'svg' verbose = pmagplotlib.verbose if '-h' in sys.argv: print main.__doc__ sys.exit() if '-WD' in sys.argv: ind = sys.argv.index('-WD') dir_path = sys.argv[ind + 1] pmagplotlib.plot_init(FIG['eqarea'], 5, 5) if '-f' in sys.argv: ind = sys.argv.index("-f") in_file = dir_path + "/" + sys.argv[ind + 1] if '-obj' in sys.argv: ind = sys.argv.index('-obj') plot_by = sys.argv[ind + 1] if plot_by == 'all': plot_key = 'all' if plot_by == 'sit': plot_key = 'er_site_name' if plot_by == 'sam': plot_key = 'er_sample_name' if plot_by == 'spc': plot_key = 'er_specimen_name' if '-c' in sys.argv: contour = 1 plots = 0 if '-sav' in sys.argv: plots = 1 verbose = 0 if '-ell' in sys.argv: plotE = 1 ind = sys.argv.index('-ell') ell_type = sys.argv[ind + 1] if ell_type == 'F': dist = 'F' if ell_type == 'K': dist = 'K' if ell_type == 'B': dist = 'B' if ell_type == 'Be': dist = 'BE' if ell_type == 'Bv': dist = 'BV' FIG['bdirs'] = 2 pmagplotlib.plot_init(FIG['bdirs'], 5, 5) if '-crd' in sys.argv: ind = sys.argv.index("-crd") crd = sys.argv[ind + 1] if crd == 's': coord = "-1" if crd == 'g': coord = "0" if crd == 't': coord = "100" if '-fmt' in sys.argv: ind = sys.argv.index("-fmt") fmt = sys.argv[ind + 1] Dec_keys = ['site_dec', 'sample_dec', 'specimen_dec', 'measurement_dec', 'average_dec', 'none'] Inc_keys = ['site_inc', 'sample_inc', 'specimen_inc', 'measurement_inc', 'average_inc', 'none'] Tilt_keys = ['tilt_correction', 'site_tilt_correction', 'sample_tilt_correction', 'specimen_tilt_correction', 'none'] Dir_type_keys = ['', 'site_direction_type', 'sample_direction_type', 'specimen_direction_type'] Name_keys = ['er_specimen_name', 'er_sample_name', 'er_site_name', 'pmag_result_name'] data, file_type = pmag.magic_read(in_file) if file_type == 'pmag_results' and plot_key != "all": plot_key = plot_key + 's' # need plural for results table if verbose: print len(data), ' records read from ', in_file # # # find desired dec,inc data: # dir_type_key = '' # # get plotlist if not plotting all records # plotlist = [] if plot_key != "all": plots = pmag.get_dictitem(data, plot_key, '', 'F') for rec in plots: if rec[plot_key] not in plotlist: plotlist.append(rec[plot_key]) plotlist.sort() else: plotlist.append('All') for plot in plotlist: if verbose: print plot DIblock = [] GCblock = [] SLblock, SPblock = [], [] title = plot mode = 1 dec_key, inc_key, tilt_key, name_key, k = "", "", "", "", 0 if plot != "All": odata = pmag.get_dictitem(data, plot_key, plot, 'T') else: odata = data # data for this obj for dec_key in Dec_keys: Decs = pmag.get_dictitem(odata, dec_key, '', 'F') # get all records with this dec_key not blank if len(Decs) > 0: break for inc_key in Inc_keys: Incs = pmag.get_dictitem(Decs, inc_key, '', 'F') # get all records with this inc_key not blank if len(Incs) > 0: break for tilt_key in Tilt_keys: if tilt_key in Incs[0].keys(): break # find the tilt_key for these records if tilt_key == 'none': # no tilt key in data, need to fix this with fake data which will be unknown tilt tilt_key = 'tilt_correction' for rec in Incs: rec[tilt_key] = '' cdata = pmag.get_dictitem(Incs, tilt_key, coord, 'T') # get all records matching specified coordinate system if coord == '0': # geographic udata = pmag.get_dictitem(Incs, tilt_key, '', 'T') # get all the blank records - assume geographic if len(cdata) == 0: crd = '' if len(udata) > 0: for d in udata: cdata.append(d) crd = crd + 'u' for name_key in Name_keys: Names = pmag.get_dictitem(cdata, name_key, '', 'F') # get all records with this name_key not blank if len(Names) > 0: break for dir_type_key in Dir_type_keys: Dirs = pmag.get_dictitem(cdata, dir_type_key, '', 'F') # get all records with this direction type if len(Dirs) > 0: break if dir_type_key == "": dir_type_key = 'direction_type' locations, site, sample, specimen = "", "", "", "" for rec in cdata: # pick out the data if 'er_location_name' in rec.keys() and rec['er_location_name'] != "" and rec[ 'er_location_name'] not in locations: locations = locations + rec['er_location_name'].replace("/", "") + "_" if 'er_location_names' in rec.keys() and rec['er_location_names'] != "": locs = rec['er_location_names'].split(':') for loc in locs: if loc not in locations: locations = locations + loc.replace("/", "") + '_' if plot_key == 'er_site_name' or plot_key == 'er_sample_name' or plot_key == 'er_specimen_name': site = rec['er_site_name'] if plot_key == 'er_sample_name' or plot_key == 'er_specimen_name': sample = rec['er_sample_name'] if plot_key == 'er_specimen_name': specimen = rec['er_specimen_name'] if plot_key == 'er_site_names' or plot_key == 'er_sample_names' or plot_key == 'er_specimen_names': site = rec['er_site_names'] if plot_key == 'er_sample_names' or plot_key == 'er_specimen_names': sample = rec['er_sample_names'] if plot_key == 'er_specimen_names': specimen = rec['er_specimen_names'] if dir_type_key not in rec.keys() or rec[dir_type_key] == "": rec[dir_type_key] = 'l' if 'magic_method_codes' not in rec.keys(): rec['magic_method_codes'] = "" DIblock.append([float(rec[dec_key]), float(rec[inc_key])]) SLblock.append([rec[name_key], rec['magic_method_codes']]) if rec[tilt_key] == coord and rec[dir_type_key] != 'l' and rec[dec_key] != "" and rec[inc_key] != "": GCblock.append([float(rec[dec_key]), float(rec[inc_key])]) SPblock.append([rec[name_key], rec['magic_method_codes']]) if len(DIblock) == 0 and len(GCblock) == 0: if verbose: print "no records for plotting" sys.exit() if verbose: for k in range(len(SLblock)): print '%s %s %7.1f %7.1f' % (SLblock[k][0], SLblock[k][1], DIblock[k][0], DIblock[k][1]) for k in range(len(SPblock)): print '%s %s %7.1f %7.1f' % (SPblock[k][0], SPblock[k][1], GCblock[k][0], GCblock[k][1]) if len(DIblock) > 0: if contour == 0: pmagplotlib.plotEQ(FIG['eqarea'], DIblock, title) else: pmagplotlib.plotEQcont(FIG['eqarea'], DIblock) else: pmagplotlib.plotNET(FIG['eqarea']) if len(GCblock) > 0: for rec in GCblock: pmagplotlib.plotC(FIG['eqarea'], rec, 90., 'g') if plotE == 1: ppars = pmag.doprinc(DIblock) # get principal directions nDIs, rDIs, npars, rpars = [], [], [], [] for rec in DIblock: angle = pmag.angle([rec[0], rec[1]], [ppars['dec'], ppars['inc']]) if angle > 90.: rDIs.append(rec) else: nDIs.append(rec) if dist == 'B': # do on whole dataset etitle = "Bingham confidence ellipse" bpars = pmag.dobingham(DIblock) for key in bpars.keys(): if key != 'n' and verbose: print " ", key, '%7.1f' % (bpars[key]) if key == 'n' and verbose: print " ", key, ' %i' % (bpars[key]) npars.append(bpars['dec']) npars.append(bpars['inc']) npars.append(bpars['Zeta']) npars.append(bpars['Zdec']) npars.append(bpars['Zinc']) npars.append(bpars['Eta']) npars.append(bpars['Edec']) npars.append(bpars['Einc']) if dist == 'F': etitle = "Fisher confidence cone" if len(nDIs) > 2: fpars = pmag.fisher_mean(nDIs) for key in fpars.keys(): if key != 'n' and verbose: print " ", key, '%7.1f' % (fpars[key]) if key == 'n' and verbose: print " ", key, ' %i' % (fpars[key]) mode += 1 npars.append(fpars['dec']) npars.append(fpars['inc']) npars.append(fpars['alpha95']) # Beta npars.append(fpars['dec']) isign = abs(fpars['inc']) / fpars['inc'] npars.append(fpars['inc'] - isign * 90.) #Beta inc npars.append(fpars['alpha95']) # gamma npars.append(fpars['dec'] + 90.) # Beta dec npars.append(0.) #Beta inc if len(rDIs) > 2: fpars = pmag.fisher_mean(rDIs) if verbose: print "mode ", mode for key in fpars.keys(): if key != 'n' and verbose: print " ", key, '%7.1f' % (fpars[key]) if key == 'n' and verbose: print " ", key, ' %i' % (fpars[key]) mode += 1 rpars.append(fpars['dec']) rpars.append(fpars['inc']) rpars.append(fpars['alpha95']) # Beta rpars.append(fpars['dec']) isign = abs(fpars['inc']) / fpars['inc'] rpars.append(fpars['inc'] - isign * 90.) #Beta inc rpars.append(fpars['alpha95']) # gamma rpars.append(fpars['dec'] + 90.) # Beta dec rpars.append(0.) #Beta inc if dist == 'K': etitle = "Kent confidence ellipse" if len(nDIs) > 3: kpars = pmag.dokent(nDIs, len(nDIs)) if verbose: print "mode ", mode for key in kpars.keys(): if key != 'n' and verbose: print " ", key, '%7.1f' % (kpars[key]) if key == 'n' and verbose: print " ", key, ' %i' % (kpars[key]) mode += 1 npars.append(kpars['dec']) npars.append(kpars['inc']) npars.append(kpars['Zeta']) npars.append(kpars['Zdec']) npars.append(kpars['Zinc']) npars.append(kpars['Eta']) npars.append(kpars['Edec']) npars.append(kpars['Einc']) if len(rDIs) > 3: kpars = pmag.dokent(rDIs, len(rDIs)) if verbose: print "mode ", mode for key in kpars.keys(): if key != 'n' and verbose: print " ", key, '%7.1f' % (kpars[key]) if key == 'n' and verbose: print " ", key, ' %i' % (kpars[key]) mode += 1 rpars.append(kpars['dec']) rpars.append(kpars['inc']) rpars.append(kpars['Zeta']) rpars.append(kpars['Zdec']) rpars.append(kpars['Zinc']) rpars.append(kpars['Eta']) rpars.append(kpars['Edec']) rpars.append(kpars['Einc']) else: # assume bootstrap if dist == 'BE': if len(nDIs) > 5: BnDIs = pmag.di_boot(nDIs) Bkpars = pmag.dokent(BnDIs, 1.) if verbose: print "mode ", mode for key in Bkpars.keys(): if key != 'n' and verbose: print " ", key, '%7.1f' % (Bkpars[key]) if key == 'n' and verbose: print " ", key, ' %i' % (Bkpars[key]) mode += 1 npars.append(Bkpars['dec']) npars.append(Bkpars['inc']) npars.append(Bkpars['Zeta']) npars.append(Bkpars['Zdec']) npars.append(Bkpars['Zinc']) npars.append(Bkpars['Eta']) npars.append(Bkpars['Edec']) npars.append(Bkpars['Einc']) if len(rDIs) > 5: BrDIs = pmag.di_boot(rDIs) Bkpars = pmag.dokent(BrDIs, 1.) if verbose: print "mode ", mode for key in Bkpars.keys(): if key != 'n' and verbose: print " ", key, '%7.1f' % (Bkpars[key]) if key == 'n' and verbose: print " ", key, ' %i' % (Bkpars[key]) mode += 1 rpars.append(Bkpars['dec']) rpars.append(Bkpars['inc']) rpars.append(Bkpars['Zeta']) rpars.append(Bkpars['Zdec']) rpars.append(Bkpars['Zinc']) rpars.append(Bkpars['Eta']) rpars.append(Bkpars['Edec']) rpars.append(Bkpars['Einc']) etitle = "Bootstrapped confidence ellipse" elif dist == 'BV': sym = {'lower': ['o', 'c'], 'upper': ['o', 'g'], 'size': 3, 'edgecolor': 'face'} if len(nDIs) > 5: BnDIs = pmag.di_boot(nDIs) pmagplotlib.plotEQsym(FIG['bdirs'], BnDIs, 'Bootstrapped Eigenvectors', sym) if len(rDIs) > 5: BrDIs = pmag.di_boot(rDIs) if len(nDIs) > 5: # plot on existing plots pmagplotlib.plotDIsym(FIG['bdirs'], BrDIs, sym) else: pmagplotlib.plotEQ(FIG['bdirs'], BrDIs, 'Bootstrapped Eigenvectors') if dist == 'B': if len(nDIs) > 3 or len(rDIs) > 3: pmagplotlib.plotCONF(FIG['eqarea'], etitle, [], npars, 0) elif len(nDIs) > 3 and dist != 'BV': pmagplotlib.plotCONF(FIG['eqarea'], etitle, [], npars, 0) if len(rDIs) > 3: pmagplotlib.plotCONF(FIG['eqarea'], etitle, [], rpars, 0) elif len(rDIs) > 3 and dist != 'BV': pmagplotlib.plotCONF(FIG['eqarea'], etitle, [], rpars, 0) if verbose: pmagplotlib.drawFIGS(FIG) # files = {} locations = locations[:-1] for key in FIG.keys(): filename = 'LO:_' + locations + '_SI:_' + site + '_SA:_' + sample + '_SP:_' + specimen + '_CO:_' + crd + '_TY:_' + key + '_.' + fmt files[key] = filename if pmagplotlib.isServer: black = '#000000' purple = '#800080' titles = {} titles['eq'] = 'Equal Area Plot' FIG = pmagplotlib.addBorders(FIG, titles, black, purple) pmagplotlib.saveP(FIG, files) elif verbose: ans = raw_input(" S[a]ve to save plot, [q]uit, Return to continue: ") if ans == "q": sys.exit() if ans == "a": pmagplotlib.saveP(FIG, files) if plots: pmagplotlib.saveP(FIG, files)
def main(): """ NAME eqarea_magic.py DESCRIPTION makes equal area projections from declination/inclination data SYNTAX eqarea_magic.py [command line options] INPUT takes magic formatted pmag_results, pmag_sites, pmag_samples or pmag_specimens OPTIONS -h prints help message and quits -f FILE: specify input magic format file from magic,default='pmag_results.txt' supported types=[magic_measurements,pmag_specimens, pmag_samples, pmag_sites, pmag_results, magic_web] -obj OBJ: specify level of plot [all, sit, sam, spc], default is all -crd [s,g,t]: specify coordinate system, [s]pecimen, [g]eographic, [t]ilt adjusted default is geographic -fmt [svg,png,jpg] format for output plots -ell [F,K,B,Be,Bv] plot Fisher, Kent, Bingham, Bootstrap ellipses or Boostrap eigenvectors -c plot as colour contour NOTE all: entire file; sit: site; sam: sample; spc: specimen """ FIG={} # plot dictionary FIG['eq']=1 # eqarea is figure 1 in_file,plot_key,coord,crd='pmag_results.txt','all',"-1",'g' fmt,dist,mode='svg','F',1 plotE,contour=0,0 dir_path='.' if '-h' in sys.argv: print main.__doc__ sys.exit() if '-WD' in sys.argv: ind=sys.argv.index('-WD') dir_path=sys.argv[ind+1] pmagplotlib.plot_init(FIG['eq'],5,5) if '-f' in sys.argv: ind=sys.argv.index("-f") in_file=dir_path+"/"+sys.argv[ind+1] if '-obj' in sys.argv: ind=sys.argv.index('-obj') plot_by=sys.argv[ind+1] if plot_by=='all':plot_key='all' if plot_by=='sit':plot_key='er_site_name' if plot_by=='sam':plot_key='er_sample_name' if plot_by=='spc':plot_key='er_specimen_name' if '-c' in sys.argv: contour=1 if '-ell' in sys.argv: plotE=1 ind=sys.argv.index('-ell') ell_type=sys.argv[ind+1] if ell_type=='F':dist='F' if ell_type=='K':dist='K' if ell_type=='B':dist='B' if ell_type=='Be':dist='BE' if ell_type=='Bv': dist='BV' FIG['bdirs']=2 pmagplotlib.plot_init(FIG['bdirs'],5,5) if '-crd' in sys.argv: ind=sys.argv.index("-crd") coord=sys.argv[ind+1] if coord=='g':coord="0" if coord=='t':coord="100" if '-fmt' in sys.argv: ind=sys.argv.index("-fmt") fmt=sys.argv[ind+1] Dec_keys=['site_dec','sample_dec','specimen_dec','measurement_dec','average_dec'] Inc_keys=['site_inc','sample_inc','specimen_inc','measurement_inc','average_inc'] Tilt_keys=['tilt_correction','site_tilt_correction','sample_tilt_correction','specimen_tilt_correction'] Dir_type_keys=['','site_direction_type','sample_direction_type','specimen_direction_type'] Name_keys=['er_specimen_name','er_sample_name','er_site_name','pmag_result_name'] data,file_type=pmag.magic_read(in_file) if file_type=='pmag_results' and plot_key!="all":plot_key=plot_key+'s' # need plural for results table if pmagplotlib.verbose: print len(data),' records read from ',in_file # # # find desired dec,inc data: # dir_type_key='' # # get plotlist if not plotting all records # plotlist=[] if plot_key!="all": for rec in data: if rec[plot_key] not in plotlist: plotlist.append(rec[plot_key]) plotlist.sort() else: plotlist.append('Whole file') for plot in plotlist: DIblock=[] GCblock=[] SLblock,SPblock=[],[] tilt_key="" mode=1 for rec in data: # find what data are available if plot_key=='all' or rec[plot_key]==plot: if plot_key!="all": title=rec[plot_key] else: title=plot if coord=='-1':title=title+' Specimen Coordinates' if coord=='0':title=title+' Geographic Coordinates' if coord=='100':title=title+' Tilt corrected Coordinates' dec_key,inc_key,tilt_key,name_key,k="","","","",0 while dec_key=="" and k<len(Dec_keys): if Dec_keys[k] in rec.keys() and rec[Dec_keys[k]]!="" and Inc_keys[k] in rec.keys() and rec[Inc_keys[k]]!="": dec_key,inc_key =Dec_keys[k],Inc_keys[k] k+=1 k=0 while tilt_key=="" and k<len(Tilt_keys): if Tilt_keys[k] in rec.keys():tilt_key=Tilt_keys[k] k+=1 k=0 while name_key=="" and k<len(Name_keys): if Name_keys[k] in rec.keys():name_key=Name_keys[k] k+=1 k=1 while dir_type_key=="" and k<len(Dir_type_keys): if Dir_type_keys[k] in rec.keys():dir_type_key=Dir_type_keys[k] k+=1 if dec_key!="":break if tilt_key=="":tilt_key='-1' if dir_type_key=="":dir_type_key='direction_type' for rec in data: # pick out the data if (plot_key=='all' or rec[plot_key]==plot) and rec[dec_key].strip()!="" and rec[inc_key].strip()!="": if dir_type_key not in rec.keys() or rec[dir_type_key]=="":rec[dir_type_key]='l' if tilt_key not in rec.keys():rec[tilt_key]='-1' # assume specimen coordinates unless otherwise specified if coord=='-1': DIblock.append([float(rec[dec_key]),float(rec[inc_key])]) SLblock.append([rec[name_key],rec['magic_method_codes']]) elif rec[tilt_key]==coord and rec[dir_type_key]=='l' and rec[dec_key]!="" and rec[inc_key]!="": if rec[tilt_key]==coord and rec[dir_type_key]=='l' and rec[dec_key]!="" and rec[inc_key]!="": DIblock.append([float(rec[dec_key]),float(rec[inc_key])]) SLblock.append([rec[name_key],rec['magic_method_codes']]) elif rec[tilt_key]==coord and rec[dir_type_key]!='l' and rec[dec_key]!="" and rec[inc_key]!="": GCblock.append([float(rec[dec_key]),float(rec[inc_key])]) SPblock.append([rec[name_key],rec['magic_method_codes']]) if len(DIblock)==0 and len(GCblock)==0: if pmagplotlib.verbose: print "no records for plotting" sys.exit() if pmagplotlib.verbose: for k in range(len(SLblock)): print '%s %s %7.1f %7.1f'%(SLblock[k][0],SLblock[k][1],DIblock[k][0],DIblock[k][1]) for k in range(len(SPblock)): print '%s %s %7.1f %7.1f'%(SPblock[k][0],SPblock[k][1],GCblock[k][0],GCblock[k][1]) if len(DIblock)>0: if contour==0: pmagplotlib.plotEQ(FIG['eq'],DIblock,title) else: pmagplotlib.plotEQcont(FIG['eq'],DIblock) else: pmagplotlib.plotNET(FIG['eq']) if len(GCblock)>0: for rec in GCblock: pmagplotlib.plotC(FIG['eq'],rec,90.,'g') if plotE==1: ppars=pmag.doprinc(DIblock) # get principal directions nDIs,rDIs,npars,rpars=[],[],[],[] for rec in DIblock: angle=pmag.angle([rec[0],rec[1]],[ppars['dec'],ppars['inc']]) if angle>90.: rDIs.append(rec) else: nDIs.append(rec) if dist=='B': # do on whole dataset etitle="Bingham confidence ellipse" bpars=pmag.dobingham(DIblock) for key in bpars.keys(): if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(bpars[key]) if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(bpars[key]) npars.append(bpars['dec']) npars.append(bpars['inc']) npars.append(bpars['Zeta']) npars.append(bpars['Zdec']) npars.append(bpars['Zinc']) npars.append(bpars['Eta']) npars.append(bpars['Edec']) npars.append(bpars['Einc']) if dist=='F': etitle="Fisher confidence cone" if len(nDIs)>2: fpars=pmag.fisher_mean(nDIs) for key in fpars.keys(): if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(fpars[key]) if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(fpars[key]) mode+=1 npars.append(fpars['dec']) npars.append(fpars['inc']) npars.append(fpars['alpha95']) # Beta npars.append(fpars['dec']) isign=abs(fpars['inc'])/fpars['inc'] npars.append(fpars['inc']-isign*90.) #Beta inc npars.append(fpars['alpha95']) # gamma npars.append(fpars['dec']+90.) # Beta dec npars.append(0.) #Beta inc if len(rDIs)>2: fpars=pmag.fisher_mean(rDIs) if pmagplotlib.verbose:print "mode ",mode for key in fpars.keys(): if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(fpars[key]) if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(fpars[key]) mode+=1 rpars.append(fpars['dec']) rpars.append(fpars['inc']) rpars.append(fpars['alpha95']) # Beta rpars.append(fpars['dec']) isign=abs(fpars['inc'])/fpars['inc'] rpars.append(fpars['inc']-isign*90.) #Beta inc rpars.append(fpars['alpha95']) # gamma rpars.append(fpars['dec']+90.) # Beta dec rpars.append(0.) #Beta inc if dist=='K': etitle="Kent confidence ellipse" if len(nDIs)>3: kpars=pmag.dokent(nDIs,len(nDIs)) if pmagplotlib.verbose:print "mode ",mode for key in kpars.keys(): if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(kpars[key]) if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(kpars[key]) mode+=1 npars.append(kpars['dec']) npars.append(kpars['inc']) npars.append(kpars['Zeta']) npars.append(kpars['Zdec']) npars.append(kpars['Zinc']) npars.append(kpars['Eta']) npars.append(kpars['Edec']) npars.append(kpars['Einc']) if len(rDIs)>3: kpars=pmag.dokent(rDIs,len(rDIs)) if pmagplotlib.verbose:print "mode ",mode for key in kpars.keys(): if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(kpars[key]) if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(kpars[key]) mode+=1 rpars.append(kpars['dec']) rpars.append(kpars['inc']) rpars.append(kpars['Zeta']) rpars.append(kpars['Zdec']) rpars.append(kpars['Zinc']) rpars.append(kpars['Eta']) rpars.append(kpars['Edec']) rpars.append(kpars['Einc']) else: # assume bootstrap if dist=='BE': if len(nDIs)>5: BnDIs=pmag.di_boot(nDIs) Bkpars=pmag.dokent(BnDIs,1.) if pmagplotlib.verbose:print "mode ",mode for key in Bkpars.keys(): if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(Bkpars[key]) if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(Bkpars[key]) mode+=1 npars.append(Bkpars['dec']) npars.append(Bkpars['inc']) npars.append(Bkpars['Zeta']) npars.append(Bkpars['Zdec']) npars.append(Bkpars['Zinc']) npars.append(Bkpars['Eta']) npars.append(Bkpars['Edec']) npars.append(Bkpars['Einc']) if len(rDIs)>5: BrDIs=pmag.di_boot(rDIs) Bkpars=pmag.dokent(BrDIs,1.) if pmagplotlib.verbose:print "mode ",mode for key in Bkpars.keys(): if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(Bkpars[key]) if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(Bkpars[key]) mode+=1 rpars.append(Bkpars['dec']) rpars.append(Bkpars['inc']) rpars.append(Bkpars['Zeta']) rpars.append(Bkpars['Zdec']) rpars.append(Bkpars['Zinc']) rpars.append(Bkpars['Eta']) rpars.append(Bkpars['Edec']) rpars.append(Bkpars['Einc']) etitle="Bootstrapped confidence ellipse" elif dist=='BV': if len(nDIs)>5: BnDIs=pmag.di_boot(nDIs) pmagplotlib.plotEQ(FIG['bdirs'],BnDIs,'Bootstrapped Eigenvectors') if len(rDIs)>5: BrDIs=pmag.di_boot(rDIs) if len(nDIs)>5: # plot on existing plots pmagplotlib.plotDI(FIG['bdirs'],BrDIs) else: pmagplotlib.plotEQ(FIG['bdirs'],BrDIs,'Bootstrapped Eigenvectors') if dist=='B': if len(nDIs)> 3 or len(rDIs)>3: pmagplotlib.plotCONF(FIG['eq'],etitle,[],npars,0) elif len(nDIs)>3 and dist!='BV': pmagplotlib.plotCONF(FIG['eq'],etitle,[],npars,0) if len(rDIs)>3: pmagplotlib.plotCONF(FIG['eq'],etitle,[],rpars,0) elif len(rDIs)>3 and dist!='BV': pmagplotlib.plotCONF(FIG['eq'],etitle,[],rpars,0) pmagplotlib.drawFIGS(FIG) # files={} for key in FIG.keys(): files[key]=title.replace(" ","_")+'_'+'eqarea'+'.'+fmt if pmagplotlib.isServer: black = '#000000' purple = '#800080' titles={} titles['eq']='Equal Area Plot' FIG = pmagplotlib.addBorders(FIG,titles,black,purple) pmagplotlib.saveP(FIG,files) else: ans=raw_input(" S[a]ve to save plot, [q]uit, Return to continue: ") if ans=="q": sys.exit() if ans=="a": pmagplotlib.saveP(FIG,files)
def main(): """ NAME scalc_magic.py DESCRIPTION calculates Sb from pmag_results files SYNTAX scalc_magic -h [command line options] INPUT takes magic formatted pmag_results table pmag_result_name must start with "VGP: Site" must have average_lat if spin axis is reference OPTIONS -h prints help message and quits -f FILE: specify input results file, default is 'pmag_results.txt' -c cutoff: specify VGP colatitude cutoff value -k cutoff: specify kappa cutoff -crd [s,g,t]: specify coordinate system, default is geographic -v : use the VanDammme criterion -a: use antipodes of reverse data: default is to use only normal -C: use all data without regard to polarity -r: use reverse data only -p: do relative to principle axis -b: do bootstrap confidence bounds OUTPUT: if option -b used: N, S_B, lower and upper bounds otherwise: N, S_B, cutoff """ in_file='pmag_results.txt' coord,kappa,cutoff="0",1.,90. nb,anti,spin,v,boot=1000,0,1,0,0 coord_key='tilt_correction' rev=0 if '-h' in sys.argv: print main.__doc__ sys.exit() if '-f' in sys.argv: ind=sys.argv.index("-f") in_file=sys.argv[ind+1] if '-c' in sys.argv: ind=sys.argv.index('-c') cutoff=float(sys.argv[ind+1]) if '-k' in sys.argv: ind=sys.argv.index('-k') kappa=float(sys.argv[ind+1]) if '-crd' in sys.argv: ind=sys.argv.index("-crd") coord=sys.argv[ind+1] if coord=='s':coord="-1" if coord=='g':coord="0" if coord=='t':coord="100" if '-a' in sys.argv: anti=1 if '-C' in sys.argv: cutoff=180. # no cutoff if '-r' in sys.argv: rev=1 if '-p' in sys.argv: spin=0 if '-v' in sys.argv: v=1 if '-b' in sys.argv: boot=1 data,file_type=pmag.magic_read(in_file) # # # find desired vgp lat,lon, kappa,N_site data: # # # A,Vgps,Pvgps=180.,[],[] VgpRecs=pmag.get_dictitem(data,'vgp_lat','','F') # get all non-blank vgp latitudes VgpRecs=pmag.get_dictitem(VgpRecs,'vgp_lon','','F') # get all non-blank vgp longitudes SiteRecs=pmag.get_dictitem(VgpRecs,'data_type','i','T') # get VGPs (as opposed to averaged) SiteRecs=pmag.get_dictitem(SiteRecs,coord_key,coord,'T') # get right coordinate system for rec in SiteRecs: if anti==1: if 90.-abs(float(rec['vgp_lat']))<=cutoff and float(rec['average_k'])>=kappa: if float(rec['vgp_lat'])<0: rec['vgp_lat']='%7.1f'%(-1*float(rec['vgp_lat'])) rec['vgp_lon']='%7.1f'%(float(rec['vgp_lon'])-180.) Vgps.append(rec) Pvgps.append([float(rec['vgp_lon']),float(rec['vgp_lat'])]) elif rev==0: # exclude normals if 90.-(float(rec['vgp_lat']))<=cutoff and float(rec['average_k'])>=kappa: Vgps.append(rec) Pvgps.append([float(rec['vgp_lon']),float(rec['vgp_lat'])]) else: # include normals if 90.-abs(float(rec['vgp_lat']))<=cutoff and float(rec['average_k'])>=kappa: if float(rec['vgp_lat'])<0: rec['vgp_lat']='%7.1f'%(-1*float(rec['vgp_lat'])) rec['vgp_lon']='%7.1f'%(float(rec['vgp_lon'])-180.) Vgps.append(rec) Pvgps.append([float(rec['vgp_lon']),float(rec['vgp_lat'])]) if spin==0: # do transformation to pole ppars=pmag.doprinc(Pvgps) for vgp in Vgps: vlon,vlat=pmag.dodirot(float(vgp['vgp_lon']),float(vgp['vgp_lat']),ppars['dec'],ppars['inc']) vgp['vgp_lon']=vlon vgp['vgp_lat']=vlat vgp['average_k']="0" S_B= pmag.get_Sb(Vgps) A=cutoff if v==1: thetamax,A=181.,180. vVgps,cnt=[],0 for vgp in Vgps:vVgps.append(vgp) # make a copy of Vgps while thetamax>A: thetas=[] A=1.8*S_B+5 cnt+=1 for vgp in vVgps:thetas.append(90.-(float(vgp['vgp_lat']))) thetas.sort() thetamax=thetas[-1] if thetamax<A:break nVgps=[] for vgp in vVgps: if 90.-(float(vgp['vgp_lat']))<thetamax:nVgps.append(vgp) vVgps=[] for vgp in nVgps:vVgps.append(vgp) S_B= pmag.get_Sb(vVgps) Vgps=[] for vgp in vVgps:Vgps.append(vgp) # make a new Vgp list SBs=[] if boot==1: for i in range(nb): # now do bootstrap BVgps=[] if i%100==0: print i,' out of ',nb for k in range(len(Vgps)): ind=random.randint(0,len(Vgps)-1) random.jumpahead(int(ind*1000)) BVgps.append(Vgps[ind]) SBs.append(pmag.get_Sb(BVgps)) SBs.sort() low=int(.025*nb) high=int(.975*nb) print len(Vgps),'%7.1f _ %7.1f ^ %7.1f %7.1f'%(S_B,SBs[low],SBs[high],A) else: print len(Vgps),'%7.1f %7.1f '%(S_B,A)
def main(): """ NAME eqarea_ell.py DESCRIPTION makes equal area projections from declination/inclination data and plot ellipses SYNTAX eqarea_ell.py -h [command line options] INPUT takes space delimited Dec/Inc data OPTIONS -h prints help message and quits -f FILE -fmt [svg,png,jpg] format for output plots -sav saves figures and quits -ell [F,K,B,Be,Bv] plot Fisher, Kent, Bingham, Bootstrap ellipses or Boostrap eigenvectors """ FIG={} # plot dictionary FIG['eq']=1 # eqarea is figure 1 fmt,dist,mode,plot='svg','F',1,0 sym={'lower':['o','r'],'upper':['o','w'],'size':10} plotE=0 if '-h' in sys.argv: print main.__doc__ sys.exit() pmagplotlib.plot_init(FIG['eq'],5,5) if '-sav' in sys.argv:plot=1 if '-f' in sys.argv: ind=sys.argv.index("-f") title=sys.argv[ind+1] data=numpy.loadtxt(title).transpose() if '-ell' in sys.argv: plotE=1 ind=sys.argv.index('-ell') ell_type=sys.argv[ind+1] if ell_type=='F':dist='F' if ell_type=='K':dist='K' if ell_type=='B':dist='B' if ell_type=='Be':dist='BE' if ell_type=='Bv': dist='BV' FIG['bdirs']=2 pmagplotlib.plot_init(FIG['bdirs'],5,5) if '-fmt' in sys.argv: ind=sys.argv.index("-fmt") fmt=sys.argv[ind+1] DIblock=numpy.array([data[0],data[1]]).transpose() if len(DIblock)>0: pmagplotlib.plotEQsym(FIG['eq'],DIblock,title,sym) if plot==0:pmagplotlib.drawFIGS(FIG) else: print "no data to plot" sys.exit() if plotE==1: ppars=pmag.doprinc(DIblock) # get principal directions nDIs,rDIs,npars,rpars=[],[],[],[] for rec in DIblock: angle=pmag.angle([rec[0],rec[1]],[ppars['dec'],ppars['inc']]) if angle>90.: rDIs.append(rec) else: nDIs.append(rec) if dist=='B': # do on whole dataset etitle="Bingham confidence ellipse" bpars=pmag.dobingham(DIblock) for key in bpars.keys(): if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(bpars[key]) if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(bpars[key]) npars.append(bpars['dec']) npars.append(bpars['inc']) npars.append(bpars['Zeta']) npars.append(bpars['Zdec']) npars.append(bpars['Zinc']) npars.append(bpars['Eta']) npars.append(bpars['Edec']) npars.append(bpars['Einc']) if dist=='F': etitle="Fisher confidence cone" if len(nDIs)>3: fpars=pmag.fisher_mean(nDIs) for key in fpars.keys(): if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(fpars[key]) if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(fpars[key]) mode+=1 npars.append(fpars['dec']) npars.append(fpars['inc']) npars.append(fpars['alpha95']) # Beta npars.append(fpars['dec']) isign=abs(fpars['inc'])/fpars['inc'] npars.append(fpars['inc']-isign*90.) #Beta inc npars.append(fpars['alpha95']) # gamma npars.append(fpars['dec']+90.) # Beta dec npars.append(0.) #Beta inc if len(rDIs)>3: fpars=pmag.fisher_mean(rDIs) if pmagplotlib.verbose:print "mode ",mode for key in fpars.keys(): if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(fpars[key]) if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(fpars[key]) mode+=1 rpars.append(fpars['dec']) rpars.append(fpars['inc']) rpars.append(fpars['alpha95']) # Beta rpars.append(fpars['dec']) isign=abs(fpars['inc'])/fpars['inc'] rpars.append(fpars['inc']-isign*90.) #Beta inc rpars.append(fpars['alpha95']) # gamma rpars.append(fpars['dec']+90.) # Beta dec rpars.append(0.) #Beta inc if dist=='K': etitle="Kent confidence ellipse" if len(nDIs)>3: kpars=pmag.dokent(nDIs,len(nDIs)) if pmagplotlib.verbose:print "mode ",mode for key in kpars.keys(): if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(kpars[key]) if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(kpars[key]) mode+=1 npars.append(kpars['dec']) npars.append(kpars['inc']) npars.append(kpars['Zeta']) npars.append(kpars['Zdec']) npars.append(kpars['Zinc']) npars.append(kpars['Eta']) npars.append(kpars['Edec']) npars.append(kpars['Einc']) if len(rDIs)>3: kpars=pmag.dokent(rDIs,len(rDIs)) if pmagplotlib.verbose:print "mode ",mode for key in kpars.keys(): if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(kpars[key]) if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(kpars[key]) mode+=1 rpars.append(kpars['dec']) rpars.append(kpars['inc']) rpars.append(kpars['Zeta']) rpars.append(kpars['Zdec']) rpars.append(kpars['Zinc']) rpars.append(kpars['Eta']) rpars.append(kpars['Edec']) rpars.append(kpars['Einc']) else: # assume bootstrap if len(nDIs)<10 and len(rDIs)<10: print 'too few data points for bootstrap' sys.exit() if dist=='BE': print 'Be patient for bootstrap...' if len(nDIs)>=10: BnDIs=pmag.di_boot(nDIs) Bkpars=pmag.dokent(BnDIs,1.) if pmagplotlib.verbose:print "mode ",mode for key in Bkpars.keys(): if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(Bkpars[key]) if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(Bkpars[key]) mode+=1 npars.append(Bkpars['dec']) npars.append(Bkpars['inc']) npars.append(Bkpars['Zeta']) npars.append(Bkpars['Zdec']) npars.append(Bkpars['Zinc']) npars.append(Bkpars['Eta']) npars.append(Bkpars['Edec']) npars.append(Bkpars['Einc']) if len(rDIs)>=10: BrDIs=pmag.di_boot(rDIs) Bkpars=pmag.dokent(BrDIs,1.) if pmagplotlib.verbose:print "mode ",mode for key in Bkpars.keys(): if key!='n' and pmagplotlib.verbose:print " ",key, '%7.1f'%(Bkpars[key]) if key=='n' and pmagplotlib.verbose:print " ",key, ' %i'%(Bkpars[key]) mode+=1 rpars.append(Bkpars['dec']) rpars.append(Bkpars['inc']) rpars.append(Bkpars['Zeta']) rpars.append(Bkpars['Zdec']) rpars.append(Bkpars['Zinc']) rpars.append(Bkpars['Eta']) rpars.append(Bkpars['Edec']) rpars.append(Bkpars['Einc']) etitle="Bootstrapped confidence ellipse" elif dist=='BV': print 'Be patient for bootstrap...' vsym={'lower':['+','k'],'upper':['x','k'],'size':5} if len(nDIs)>5: BnDIs=pmag.di_boot(nDIs) pmagplotlib.plotEQsym(FIG['bdirs'],BnDIs,'Bootstrapped Eigenvectors',vsym) if len(rDIs)>5: BrDIs=pmag.di_boot(rDIs) if len(nDIs)>5: # plot on existing plots pmagplotlib.plotDIsym(FIG['bdirs'],BrDIs,vsym) else: pmagplotlib.plotEQ(FIG['bdirs'],BrDIs,'Bootstrapped Eigenvectors',vsym) if dist=='B': if len(nDIs)> 3 or len(rDIs)>3: pmagplotlib.plotCONF(FIG['eq'],etitle,[],npars,0) elif len(nDIs)>3 and dist!='BV': pmagplotlib.plotCONF(FIG['eq'],etitle,[],npars,0) if len(rDIs)>3: pmagplotlib.plotCONF(FIG['eq'],etitle,[],rpars,0) elif len(rDIs)>3 and dist!='BV': pmagplotlib.plotCONF(FIG['eq'],etitle,[],rpars,0) if plot==0:pmagplotlib.drawFIGS(FIG) if plot==0:pmagplotlib.drawFIGS(FIG) # files={} for key in FIG.keys(): files[key]=title+'_'+key+'.'+fmt if pmagplotlib.isServer: black = '#000000' purple = '#800080' titles={} titles['eq']='Equal Area Plot' FIG = pmagplotlib.addBorders(FIG,titles,black,purple) pmagplotlib.saveP(FIG,files) elif plot==0: ans=raw_input(" S[a]ve to save plot, [q]uit, Return to continue: ") if ans=="q": sys.exit() if ans=="a": pmagplotlib.saveP(FIG,files) else: pmagplotlib.saveP(FIG,files)