def main():
"""
NAME
gokent.py
DESCRIPTION
calculates Kent parameters from dec inc data
INPUT FORMAT
takes dec/inc as first two columns in space delimited file
SYNTAX
gokent.py [-h][-i][command line options] [< filename]
OPTIONS
-h prints help message and quits
-i for interactive filename entry
-f FILE, specify filename
< filename for reading from standard input
OUTPUT
mean dec, mean inc, Eta, Deta, Ieta, Zeta, Zdec, Zinc, 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
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])))
#
kpars=pmag.dokent(DIs,len(DIs))
print '%7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %i' % (kpars["dec"],kpars["inc"],kpars["Eta"],kpars["Edec"],kpars["Einc"],kpars["Zeta"],kpars["Zdec"],kpars["Zinc"],kpars["n"])
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
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
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
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
pyscu_calc.py
DESCRIPTION
perform the routine explained in Calvín et al. (2017)
https://doi.org/10.1016/j.cageo.2017.07.002
calculates the Small Circle Intersection from a dataset
calculates the paleodip of the beds
INPUT
spaced separated text file with header, with the next columns
site dec inc kappa dipDir dip kappa
interactive data entry using Easygui
(http://easygui.sourceforge.net/)
"""
if '-h' in sys.argv:
print(main.__doc__)
sys.exit()
infile = eg.fileopenbox(msg="Abrir archivo",
title="Control: fileopenbox",
default='')
ans_sci = eg.boolbox(msg='Do you want to calculate te SCI solution?',
title='Control: boolbox',
choices=('Yes', 'No'))
if ans_sci == False:
campos = ['Declination', 'Inclination']
ref = []
ref = eg.multenterbox(msg='Write the remagnetization direction',
title='Interactive entry of the remag. dir.',
fields=campos,
values=())
ref[0] = float(ref[0])
ref[1] = float(ref[1])
ans_A = eg.boolbox(msg='Do you want to calculate te A matrix?',
title='Control: boolbox',
choices=('Yes', 'No'))
if ans_A == True:
pregunta_matriz = 'y'
else:
pregunta_matriz = 'n'
ans_inc = eg.buttonbox(
msg='Are you looking a positive or a negative inclination',
title='Control: boolbox',
choices=('Positive', 'Negative'))
if ans_inc == 'Positive':
pregunta_inc = 'pos'
else:
pregunta_inc = 'neg'
if ans_sci == True:
nb = eg.integerbox(
msg='Number of bootstraps (default=500):',
title='Control: integerbox',
default=500,
lowerbound=0,
upperbound=1000,
)
data, site, geo, bed_d, bed_s, bed_pole, N_sites = scu.saveInputFile(
infile) #Saving in diferent list the data
tilt = scu.tilt_rot(geo, bed_s) #Calculating TILT directions
out_file = eg.filesavebox(msg="Save plots",
title="Control: filesavebox",
default='')
time_ini = time()
#Calculating BFD and A/N for a manual input remagnetization direction.
if ans_sci == False:
pregunta_remag = 'n'
BFD, A_min = scu.calAQQ2(geo, bed_s, ref)
Qmean_min = scu.fisher_mean(BFD)
N = len(BFD)
An = A_min / N
Dir_remag = [N, ref[0], ref[1], round(A_min, 3), round(An, 3)]
print('Used direction: ', 'Dec / Inc ', '(', "%.1f" % Dir_remag[1],
'/', "%.1f" % Dir_remag[2], ')', 'A/n ', "%.3f" % An)
else: #Calculating SCI_solutions, their mean (the remagnetization direction) and the rest of parameters
pregunta_remag = 'y'
SCIs = []
pgeo = scu.para_dir(geo)
pbed_pole = scu.para_dir(bed_pole)
w, f, cont = 0, 0, nb / 10
print('\nplease, be patient... calculating', nb, 'SCI_solutions')
for l in range(nb):
w += 1
dec = np.random.randint(0, 359, 1)
inc = np.random.randint(1, 89, 1)
if pregunta_inc == 'neg': inc = inc * (-1)
point = [dec[0], inc[0]]
pgeo_u = scu.selec_para_geo(pgeo)
pbed_u = scu.selec_para_pole(pbed_pole)
pQ = scu.p_calAQQ2(pgeo_u, pbed_u, point)
Qmean_min = scu.p_minA(pgeo_u, pbed_u, pQ)
SCIs.append(Qmean_min)
if w == cont:
f += 1
if f != 8:
print("%.0f" % cont)
cont += nb / 10.
else:
print('almost finished...')
cont += nb / 10.
Qend = pmag.dokent(SCIs, 1.)
ref = [Qend['dec'], Qend['inc']]
BFD, A_min = [], []
BFD, A_min = scu.calAQQ2(geo, bed_s, ref)
An = A_min / N_sites
distance = [
] #This is the angle between the BFD and the remagnetization direction, for each site
for dato in BFD:
distance_site = scu.ang2point(dato, ref)
distance.append(distance_site)
paleobed = scu.paleo_dip(tilt, bed_s, BFD)
api = scu.cal_api(geo, bed_s)
api2 = scu.cal_api2(geo, bed_s)
out_inter = scu.inter(api2, site)
mr = len(out_inter)
mp = N_sites * (N_sites - 1.) / 2.
mr_mp = round(mr / mp, 2)
#joining the data in a unique list
out_main = []
if pregunta_remag == 'y':
Dir_remag = [
N_sites,
round(Qend['dec'], 1),
round(Qend['inc'], 1),
round(Qend['Eta'], 1),
round(Qend['Zeta'], 1),
round(Qend['Edec'], 1),
round(Qend['Einc'], 1),
round(Qend['Zdec'], 1),
round(Qend['Zinc'], 1),
round(An, 3),
round(A_min, 3), mr_mp, nb
]
print(
'\nKent mean remagnetization direction (Kent, 1982; Tauxe et al., 1991)',
'\nDec / Inc: ', "%.1f" % Dir_remag[1], "/", "%.1f" % Dir_remag[2])
print('A/n: ', "%.3f" % An, 'mr/mp: ', "%.2f" % mr_mp)
print('Eta_95, dec, inc:', "%.1f" % Dir_remag[3],
"%.1f" % Dir_remag[5], "%.1f" % Dir_remag[6])
print('Zeta_95, dec, inc', "%.1f" % Dir_remag[4],
"%.1f" % Dir_remag[7], "%.1f" % Dir_remag[8])
for i in range(len(site)):
site_main = [
site[i][0], data[i][1], data[i][2], data[i][3], data[i][4],
data[i][5], data[i][6], data[i][7],
"%.1f" % tilt[i][0],
"%.1f" % tilt[i][1],
"%.1f" % BFD[i][0],
"%.1f" % BFD[i][1],
"%.0f" % bed_s[i][0],
"%.2f" % api[i],
"%.0f" % ((paleobed[i][0] + 90) % 360),
"%.0f" % paleobed[i][1],
"%.1f" % distance[i]
]
out_main.append(site_main)
#saving the files
header_main = [
'Site', 'Dec_BBC', 'Inc_BBC', 'a95', 'k', 'DipDir', 'Dip', 'k_bed',
'Dec_ATBC', 'Inc_ATBC', 'Dec_BFD', 'Inc_BFD', 'SC_Strike',
'SC_Api_angle', 'Paleo_DipDir', 'Paleo_dip', 'Ref_BFD_angle'
]
name_main = out_file + '_main'
scu.save_out_file(header_main, out_main, name_main)
header_inter = ['Dec', 'Inc', 'Site_i', 'Site_j']
name_inter = out_file + '_inter'
scu.save_out_file(header_inter, out_inter, name_inter)
if pregunta_remag == 'y':
header_Ref = [
'N', 'Dec', 'Inc', 'Eta', 'Zeta', 'Dec_E', 'Inc_E', 'Dec_Z',
'Inc_Z', 'A/n', 'Asum', 'mr_mp', 'number of SCI_solutions'
]
name_Ref = out_file + '_Ref'
scu.save_out_file(header_Ref, [Dir_remag], name_Ref)
header_SCIs = ['Dec', 'Inc']
name_SCIs = out_file + '_SCIs'
scu.save_out_file(header_SCIs, SCIs, name_SCIs)
if pregunta_matriz == 'y':
print('\n\nTo be patient! To calculate the matrix spend some min')
mat = scu.cal_matriz(geo, bed_s)
header_matriz = [
'Dec', 'Inc', 'x_eqarea', 'y_eqarea', 'x_eq_normalized',
'y_eq_normalized', 'A', 'A/n'
]
name_matriz = out_file + '_mat'
scu.save_out_file(header_matriz, mat, name_matriz)
time_fin = time()
time_tot = time_fin - time_ini
print('\nExecution time: ', round(time_tot, 1), 'seg')
print(
'\nYou can use pyscu_draw.py and pyscu_draw_labels.py to print your results'
)
print('Good bye')
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
gokent.py
DESCRIPTION
calculates Kent parameters from dec inc data
INPUT FORMAT
takes dec/inc as first two columns in space delimited file
SYNTAX
gokent.py [options]
OPTIONS
-h prints help message and quits
-i for interactive filename entry
-f FILE, specify filename
-F FILE, specifies output file name
< filename for reading from standard input
OUTPUT
mean dec, mean inc, Eta, Deta, Ieta, Zeta, Zdec, Zinc, 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])))
#
kpars = pmag.dokent(DIs, len(DIs))
output = "%7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %7.1f %i" % (
kpars["dec"],
kpars["inc"],
kpars["Eta"],
kpars["Edec"],
kpars["Einc"],
kpars["Zeta"],
kpars["Zdec"],
kpars["Zinc"],
kpars["n"],
)
if ofile == "":
print output
else:
out.write(output + "\n")
