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 sites, samples, specimens, or measurements
OPTIONS
-h prints help message and quits
-f FILE: specify input magic format file from magic, default='sites.txt'
supported types=[measurements, specimens, samples, sites]
-fsp FILE: specify specimen file name, (required if you want to plot measurements by sample)
default='specimens.txt'
-fsa FILE: specify sample file name, (required if you want to plot specimens by site)
default='samples.txt'
-fsi FILE: specify site file name, default='sites.txt'
-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
"""
# initialize some default variables
FIG = {} # plot dictionary
FIG['eqarea'] = 1 # eqarea is figure 1
plotE = 0
plt = 0 # default to not plotting
verbose = pmagplotlib.verbose
# extract arguments from sys.argv
if '-h' in sys.argv:
print(main.__doc__)
sys.exit()
dir_path = pmag.get_named_arg_from_sys("-WD", default_val=".")
pmagplotlib.plot_init(FIG['eqarea'],5,5)
in_file = pmag.get_named_arg_from_sys("-f", default_val="sites.txt")
in_file = pmag.resolve_file_name(in_file, dir_path)
if "-WD" not in sys.argv:
dir_path = os.path.split(in_file)[0]
#full_in_file = os.path.join(dir_path, in_file)
plot_by = pmag.get_named_arg_from_sys("-obj", default_val="all").lower()
spec_file = pmag.get_named_arg_from_sys("-fsp", default_val="specimens.txt")
samp_file = pmag.get_named_arg_from_sys("-fsa", default_val="samples.txt")
site_file = pmag.get_named_arg_from_sys("-fsi", default_val="sites.txt")
if plot_by == 'all':
plot_key = 'all'
elif plot_by == 'sit':
plot_key = 'site'
elif plot_by == 'sam':
plot_key = 'sample'
elif plot_by == 'spc':
plot_key = 'specimen'
else:
plot_by = 'all'
plot_key = 'all'
if '-c' in sys.argv:
contour = 1
else:
contour = 0
if '-sav' in sys.argv:
plt = 1
verbose = 0
if '-ell' in sys.argv:
plotE = 1
ind = sys.argv.index('-ell')
ell_type = sys.argv[ind+1]
ell_type = pmag.get_named_arg_from_sys("-ell", "F")
dist = ell_type.upper()
# if dist type is unrecognized, use Fisher
if dist not in ['F', 'K', 'B', 'BE', 'BV']:
dist = 'F'
if dist == "BV":
FIG['bdirs'] = 2
pmagplotlib.plot_init(FIG['bdirs'],5,5)
crd = pmag.get_named_arg_from_sys("-crd", default_val="g")
if crd == "s":
coord = "-1"
elif crd == "t":
coord = "100"
else:
coord = "0"
fmt = pmag.get_named_arg_from_sys("-fmt", "svg")
dec_key = 'dir_dec'
inc_key = 'dir_inc'
tilt_key = 'dir_tilt_correction'
#Dir_type_keys=['','site_direction_type','sample_direction_type','specimen_direction_type']
#
fnames = {"specimens": spec_file, "samples": samp_file, 'sites': site_file}
contribution = nb.Contribution(dir_path, custom_filenames=fnames,
single_file=in_file)
try:
contribution.propagate_location_to_samples()
contribution.propagate_location_to_specimens()
contribution.propagate_location_to_measurements()
except KeyError as ex:
pass
# the object that contains the DataFrame + useful helper methods:
table_name = list(contribution.tables.keys())[0]
data_container = contribution.tables[table_name]
# the actual DataFrame:
data = data_container.df
if plot_key != "all" and plot_key not in data.columns:
print("-E- You can't plot by {} with the data provided".format(plot_key))
return
# add tilt key into DataFrame columns if it isn't there already
if tilt_key not in data.columns:
data.loc[:, tilt_key] = None
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":
# return all where plot_key is not blank
if plot_key not in data.columns:
print('Can\'t plot by "{}". That header is not in infile: {}'.format(plot_key, in_file))
return
plots = data[data[plot_key].notnull()]
plotlist = plots[plot_key].unique() # grab unique values
else:
plotlist.append('All')
for plot in plotlist:
if verbose:
print(plot)
if plot == 'All':
# plot everything at once
plot_data = data
else:
# pull out only partial data
plot_data = data[data[plot_key] == plot]
DIblock = []
GCblock = []
# SLblock, SPblock = [], []
title = plot
mode = 1
k = 0
if dec_key not in plot_data.columns:
print("-W- No dec/inc data")
continue
# get all records where dec & inc values exist
plot_data = plot_data[plot_data[dec_key].notnull() & plot_data[inc_key].notnull()]
if plot_data.empty:
continue
# this sorting out is done in get_di_bock
#if coord == '0': # geographic, use records with no tilt key (or tilt_key 0)
# cond1 = plot_data[tilt_key].fillna('') == coord
# cond2 = plot_data[tilt_key].isnull()
# plot_data = plot_data[cond1 | cond2]
#else: # not geographic coordinates, use only records with correct tilt_key
# plot_data = plot_data[plot_data[tilt_key] == coord]
# get metadata for naming the plot file
locations = data_container.get_name('location', df_slice=plot_data)
site = data_container.get_name('site', df_slice=plot_data)
sample = data_container.get_name('sample', df_slice=plot_data)
specimen = data_container.get_name('specimen', df_slice=plot_data)
# make sure method_codes is in plot_data
if 'method_codes' not in plot_data.columns:
plot_data['method_codes'] = ''
# get data blocks
DIblock = data_container.get_di_block(df_slice=plot_data,
tilt_corr=coord, excl=['DE-BFP'])
#SLblock = [[ind, row['method_codes']] for ind, row in plot_data.iterrows()]
# get great circles
great_circle_data = data_container.get_records_for_code('DE-BFP', incl=True,
use_slice=True, sli=plot_data)
if len(great_circle_data) > 0:
gc_cond = great_circle_data[tilt_key] == coord
GCblock = [[float(row[dec_key]), float(row[inc_key])] for ind, row in great_circle_data[gc_cond].iterrows()]
#SPblock = [[ind, row['method_codes']] for ind, row in great_circle_data[gc_cond].iterrows()]
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 len(DIblock) == 0 and len(GCblock) == 0:
if verbose:
print("no records for plotting")
continue
#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 list(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 list(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=old_div(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 list(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=old_div(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 list(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 list(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 list(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 list(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)
for key in list(FIG.keys()):
files = {}
filename = pmag.get_named_arg_from_sys('-fname')
if filename: # use provided filename
filename+= '.' + fmt
elif pmagplotlib.isServer: # use server plot naming convention
filename='LO:_'+locations+'_SI:_'+site+'_SA:_'+sample+'_SP:_'+specimen+'_CO:_'+crd+'_TY:_'+key+'_.'+fmt
elif plot_key == 'all':
filename = 'all'
if 'location' in plot_data.columns:
locs = plot_data['location'].unique()
loc_string = "_".join([loc.replace(' ', '_') for loc in locs])
filename += "_" + loc_string
filename += "_" + crd + "_" + key
filename += ".{}".format(fmt)
else: # use more readable naming convention
filename = ''
# fix this if plot_by is location , for example
use_names = {'location': [locations], 'site': [locations, site],
'sample': [locations, site, sample],
'specimen': [locations, site, sample, specimen]}
use = use_names[plot_key]
use.extend([crd, key])
for item in use: #[locations, site, sample, specimen, crd, key]:
if item:
item = item.replace(' ', '_')
filename += item + '_'
if filename.endswith('_'):
filename = filename[:-1]
filename += ".{}".format(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)
if plt:
pmagplotlib.saveP(FIG,files)
continue
if verbose:
pmagplotlib.drawFIGS(FIG)
ans=input(" S[a]ve to save plot, [q]uit, Return to continue: ")
if ans == "q":
sys.exit()
if ans == "a":
pmagplotlib.saveP(FIG,files)
continue
def main():
"""
NAME
plotdi_a.py
DESCRIPTION
plots equal area projection from dec inc data and fisher mean, cone of confidence
INPUT FORMAT
takes dec, inc, alpha95 as first three columns in space delimited file
SYNTAX
plotdi_a.py [-i][-f FILE]
OPTIONS
-f FILE to read file name from command line
-fmt [png,jpg,eps,pdf,svg] set plot file format ['svg' is default]
-sav save plot and quit
"""
fmt,plot='svg',0
if len(sys.argv) > 0:
if '-h' in sys.argv: # check if help is needed
print(main.__doc__)
sys.exit() # graceful quit
if '-fmt' in sys.argv:
ind=sys.argv.index('-fmt')
fmt=sys.argv[ind+1]
if '-sav' in sys.argv:plot=1
if '-f' in sys.argv:
ind=sys.argv.index('-f')
file=sys.argv[ind+1]
f=open(file,'r')
data=f.readlines()
else:
data=sys.stdin.readlines() # read in data from standard input
DIs,Pars=[],[]
for line in data: # read in the data from standard input
pars=[]
rec=line.split() # split each line on space to get records
DIs.append([float(rec[0]),float(rec[1])])
pars.append(float(rec[0]))
pars.append(float(rec[1]))
pars.append(float(rec[2]))
pars.append(float(rec[0]))
isign=old_div(abs(float(rec[1])),float(rec[1]))
pars.append(float(rec[1])-isign*90.) #Beta inc
pars.append(float(rec[2])) # gamma
pars.append(float(rec[0])+90.) # Beta dec
pars.append(0.) #Beta inc
Pars.append(pars)
#
EQ={'eq':1} # make plot dictionary
pmagplotlib.plot_init(EQ['eq'],5,5)
title='Equal area projection'
pmagplotlib.plotEQ(EQ['eq'],DIs,title)# plot directions
for k in range(len(Pars)):
pmagplotlib.plotELL(EQ['eq'],Pars[k],'b',0,1) # plot ellipses
files={}
for key in list(EQ.keys()):
files[key]=key+'.'+fmt
titles={}
titles['eq']='Equal Area Plot'
if pmagplotlib.isServer:
black = '#000000'
purple = '#800080'
EQ = pmagplotlib.addBorders(EQ,titles,black,purple)
pmagplotlib.saveP(EQ,files)
elif plot==0:
pmagplotlib.drawFIGS(EQ)
ans=input(" S[a]ve to save plot, [q]uit, Return to continue: ")
if ans=="q": sys.exit()
if ans=="a":
pmagplotlib.saveP(EQ,files)
else:
pmagplotlib.saveP(EQ,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
-sav save figures and quit
INPUT FILE
Dec Inc Dip_Direction Dip in space delimited file
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 post-tilt magnetization is indicated
If the 95% conf bounds include 100, then a pre-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,plot='svg',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')
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 '-sav' in sys.argv:plot=1
if '-b' in sys.argv:
ind=sys.argv.index('-b')
min=int(sys.argv[ind+1])
max=int(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')
if plot==0: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)
files={}
for key in PLTS.keys():
files[key]=('foldtest_'+'%s'%(key.strip()[:2])+'.'+fmt)
if plot==0:
pmagplotlib.drawFIGS(PLTS)
ans= raw_input('S[a]ve all figures, <Return> to quit ')
if ans!='a':
print "Good bye"
sys.exit()
pmagplotlib.saveP(PLTS,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
-f FILE specify input file name
-nb N specify number of bootstraps - the more the better, but slower!, default is 1000
-sc uses a "site-level" correction to a Fisherian distribution instead
of a "study-level" correction to a TK03-consistent distribution.
Note that many directions (~ 100) are needed for this correction to be reliable.
-fmt [svg,png,eps,pdf..] change plot format, default is svg
-sav saves the figures and quits
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
plot = 0
if '-h' in sys.argv:
print(main.__doc__)
sys.exit() # graceful quit
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 '-sc' in sys.argv:
site_correction = True
else:
site_correction = False
if '-fmt' in sys.argv:
ind = sys.argv.index('-fmt')
fmt = sys.argv[ind + 1]
if '-sav' in sys.argv: plot = 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')
if plot == 0: pmagplotlib.drawFIGS(PLTS)
ppars = pmag.doprinc(data)
Io = ppars['inc']
n = ppars["N"]
Es, Is, Fs, V2s = pmag.find_f(data)
if site_correction:
Inc, Elong = Is[Es.index(min(Es))], Es[Es.index(min(Es))]
flat_f = Fs[Es.index(min(Es))]
else:
Inc, Elong = Is[-1], Es[-1]
flat_f = Fs[-1]
pmagplotlib.plotEI(PLTS['ei'], Es, Is, flat_f)
pmagplotlib.plotV2s(PLTS['v2'], V2s, Is, flat_f)
b = 0
print("Bootstrapping.... be patient")
while b < nb:
bdata = pmag.pseudo(data)
Esb, Isb, Fsb, V2sb = pmag.find_f(bdata)
if b < 25:
pmagplotlib.plotEI(PLTS['ei'], Esb, Isb, Fsb[-1])
if Esb[-1] != 0:
ppars = pmag.doprinc(bdata)
if site_correction:
I.append(abs(Isb[Esb.index(min(Esb))]))
E.append(Esb[Esb.index(min(Esb))])
else:
I.append(abs(Isb[-1]))
E.append(Esb[-1])
b += 1
if b % 25 == 0: print(b, ' out of ', nb)
I.sort()
E.sort()
Eexp = []
for i in I:
Eexp.append(pmag.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', '-')
if plot == 0:
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 = 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
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_magic.py
DESCRIPTION
makes equal area projections from declination/inclination data
SYNTAX
eqarea_magic.py [command line options]
INPUT
takes magic formatted sites, samples, specimens, or measurements
OPTIONS
-h prints help message and quits
-f FILE: specify input magic format file from magic, default='sites.txt'
supported types=[measurements, specimens, samples, sites]
-fsp FILE: specify specimen file name, (required if you want to plot measurements by sample)
default='specimens.txt'
-fsa FILE: specify sample file name, (required if you want to plot specimens by site)
default='samples.txt'
-fsi FILE: specify site file name, default='sites.txt'
-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
"""
# initialize some default variables
FIG = {} # plot dictionary
FIG['eqarea'] = 1 # eqarea is figure 1
plotE = 0
plt = 0 # default to not plotting
verbose = pmagplotlib.verbose
# extract arguments from sys.argv
if '-h' in sys.argv:
print(main.__doc__)
sys.exit()
dir_path = pmag.get_named_arg_from_sys("-WD", default_val=os.getcwd())
pmagplotlib.plot_init(FIG['eqarea'],5,5)
in_file = pmag.get_named_arg_from_sys("-f", default_val="sites.txt")
full_in_file = os.path.join(dir_path, in_file)
plot_by = pmag.get_named_arg_from_sys("-obj", default_val="all").lower()
spec_file = pmag.get_named_arg_from_sys("-fsp", default_val="specimens.txt")
samp_file = pmag.get_named_arg_from_sys("-fsa", default_val="samples.txt")
site_file = pmag.get_named_arg_from_sys("-fsi", default_val="sites.txt")
if plot_by == 'all':
plot_key = 'all'
elif plot_by == 'sit':
plot_key = 'site'
elif plot_by == 'sam':
plot_key = 'sample'
elif plot_by == 'spc':
plot_key = 'specimen'
else:
plot_key = 'all'
if '-c' in sys.argv:
contour = 1
else:
contour = 0
if '-sav' in sys.argv:
plt = 1
verbose = 0
if '-ell' in sys.argv:
plotE = 1
ind = sys.argv.index('-ell')
ell_type = sys.argv[ind+1]
ell_type = pmag.get_named_arg_from_sys("-ell", "F")
dist = ell_type.upper()
# if dist type is unrecognized, use Fisher
if dist not in ['F', 'K', 'B', 'BE', 'BV']:
dist = 'F'
if dist == "BV":
FIG['bdirs'] = 2
pmagplotlib.plot_init(FIG['bdirs'],5,5)
crd = pmag.get_named_arg_from_sys("-crd", default_val="g")
if crd == "s":
coord = "-1"
elif crd == "t":
coord = "100"
else:
coord = "0"
fmt = pmag.get_named_arg_from_sys("-fmt", "svg")
dec_key = 'dir_dec'
inc_key = 'dir_inc'
tilt_key = 'dir_tilt_correction'
#Dir_type_keys=['','site_direction_type','sample_direction_type','specimen_direction_type']
#
fnames = {"specimens": spec_file, "samples": samp_file, 'sites': site_file}
contribution = nb.Contribution(dir_path, custom_filenames=fnames,
single_file=in_file)
# the object that contains the DataFrame + useful helper methods:
table_name = list(contribution.tables.keys())[0]
data_container = contribution.tables[table_name]
# the actual DataFrame:
data = data_container.df
# uses sample infile to add temporary site_name
# column to the specimen table
data_container = contribution.tables[table_name]
data = data_container.df
if (plot_key != "all") and (plot_key not in data.columns):
contribution.propagate_location_to_measurements()
contribution.propagate_location_to_specimens()
# add tilt key into DataFrame columns if it isn't there already
if tilt_key not in data.columns:
data.loc[:, tilt_key] = None
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":
# return all where plot_key is not blank
if plot_key not in data.columns:
print('Can\'t plot by "{}". That header is not in infile: {}'.format(plot_key, in_file))
return
plots = data[data[plot_key].notnull()]
plotlist = plots[plot_key].unique() # grab unique values
else:
plotlist.append('All')
for plot in plotlist:
if verbose:
print(plot)
if plot == 'All':
# plot everything at once
plot_data = data
else:
# pull out only partial data
plot_data = data[data[plot_key] == plot]
DIblock = []
GCblock = []
# SLblock, SPblock = [], []
title = plot
mode = 1
k = 0
if dec_key not in plot_data.columns:
print("-W- No dec/inc data")
continue
# get all records where dec & inc values exist
plot_data = plot_data[plot_data[dec_key].notnull() & plot_data[inc_key].notnull()]
if plot_data.empty:
continue
# this sorting out is done in get_di_bock
#if coord == '0': # geographic, use records with no tilt key (or tilt_key 0)
# cond1 = plot_data[tilt_key].fillna('') == coord
# cond2 = plot_data[tilt_key].isnull()
# plot_data = plot_data[cond1 | cond2]
#else: # not geographic coordinates, use only records with correct tilt_key
# plot_data = plot_data[plot_data[tilt_key] == coord]
# get metadata for naming the plot file
locations = data_container.get_name('location', df_slice=plot_data)
site = data_container.get_name('site', df_slice=plot_data)
sample = data_container.get_name('sample', df_slice=plot_data)
specimen = data_container.get_name('specimen', df_slice=plot_data)
# make sure method_codes is in plot_data
if 'method_codes' not in plot_data.columns:
plot_data['method_codes'] = ''
# get data blocks
DIblock = data_container.get_di_block(df_slice=plot_data,
tilt_corr=coord, excl=['DE-BFP'])
#SLblock = [[ind, row['method_codes']] for ind, row in plot_data.iterrows()]
# get great circles
great_circle_data = data_container.get_records_for_code('DE-BFP', incl=True,
use_slice=True, sli=plot_data)
if len(great_circle_data) > 0:
gc_cond = great_circle_data[tilt_key] == coord
GCblock = [[float(row[dec_key]), float(row[inc_key])] for ind, row in great_circle_data[gc_cond].iterrows()]
#SPblock = [[ind, row['method_codes']] for ind, row in great_circle_data[gc_cond].iterrows()]
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 len(DIblock) == 0 and len(GCblock) == 0:
if verbose:
print("no records for plotting")
continue
#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 list(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 list(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=old_div(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 list(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=old_div(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 list(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 list(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 list(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 list(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)
for key in list(FIG.keys()):
files = {}
filename = pmag.get_named_arg_from_sys('-fname')
if filename: # use provided filename
filename+= '.' + fmt
elif pmagplotlib.isServer: # use server plot naming convention
filename='LO:_'+locations+'_SI:_'+site+'_SA:_'+sample+'_SP:_'+specimen+'_CO:_'+crd+'_TY:_'+key+'_.'+fmt
else: # use more readable naming convention
filename = ''
for item in [locations, site, sample, specimen, crd, key]:
if item:
item = item.replace(' ', '_')
filename += item + '_'
if filename.endswith('_'):
filename = filename[:-1]
filename += ".{}".format(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)
if plt:
pmagplotlib.saveP(FIG,files)
continue
if verbose:
pmagplotlib.drawFIGS(FIG)
ans=input(" S[a]ve to save plot, [q]uit, Return to continue: ")
if ans == "q":
sys.exit()
if ans == "a":
pmagplotlib.saveP(FIG,files)
continue
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
-sav save figures and quit
INPUT FILE
Dec Inc Dip_Direction Dip in space delimited file
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 post-tilt magnetization is indicated
If the 95% conf bounds include 100, then a pre-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, plot = 'svg', 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')
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 '-sav' in sys.argv: plot = 1
if '-b' in sys.argv:
ind = sys.argv.index('-b')
min = int(sys.argv[ind + 1])
max = int(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')
if plot == 0: 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)
files = {}
for key in PLTS.keys():
files[key] = ('foldtest_' + '%s' % (key.strip()[:2]) + '.' + fmt)
if plot == 0:
pmagplotlib.drawFIGS(PLTS)
ans = raw_input('S[a]ve all figures, <Return> to quit ')
if ans != 'a':
print "Good bye"
sys.exit()
pmagplotlib.saveP(PLTS, 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
-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, 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
plt=0
if '-sav' in sys.argv:
plt=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 plt:
pmagplotlib.saveP(FIG,files)
def main():
"""
NAME
foldtest_magic.py
DESCRIPTION
does a fold test (Tauxe, 2010) 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]
-fsi er_sites formatted file
-exc use pmag_criteria.txt to set acceptance criteria
-n NB, set number of bootstraps, default is 1000
-b MIN, MAX, set bounds for untilting, default is -10, 150
-fmt FMT, specify format - default is svg
-sav saves plots and quits
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
dir_path = '.'
infile, orfile = 'pmag_sites.txt', 'er_samples.txt'
critfile = 'pmag_criteria.txt'
dipkey, azkey = 'sample_bed_dip', 'sample_bed_dip_direction'
fmt = 'svg'
plot = 0
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 '-fmt' in sys.argv:
ind = sys.argv.index('-fmt')
fmt = sys.argv[ind + 1]
if '-sav' in sys.argv: plot = 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]
elif '-fsi' in sys.argv:
ind = sys.argv.index('-fsi')
orfile = sys.argv[ind + 1]
dipkey, azkey = 'site_bed_dip', 'site_bed_dip_direction'
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)
GEOrecs = pmag.get_dictitem(data, 'site_tilt_correction', '0', 'T')
if len(GEOrecs) > 0: # have some geographic data
DIDDs = [] # set up list for dec inc dip_direction, dip
for rec in GEOrecs: # parse data
dip, dip_dir = 0, -1
Dec = float(rec['site_dec'])
Inc = float(rec['site_inc'])
orecs = pmag.get_dictitem(ordata, 'er_site_name',
rec['er_site_name'], 'T')
if len(orecs) > 0:
if orecs[0][azkey] != "": dip_dir = float(orecs[0][azkey])
if orecs[0][dipkey] != "": dip = float(orecs[0][dipkey])
if dip != 0 and dip_dir != -1:
if '-exc' in sys.argv:
keep = 1
for key in list(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])
else:
print('no geographic directional data found')
sys.exit()
pmagplotlib.plotEQ(PLTS['geo'], DIDDs, 'Geographic')
data = numpy.array(DIDDs)
D, I = pmag.dotilt_V(data)
TCs = numpy.array([D, I]).transpose()
pmagplotlib.plotEQ(PLTS['strat'], TCs, 'Stratigraphic')
if plot == 0: pmagplotlib.drawFIGS(PLTS)
Percs = list(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(old_div(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)
if plot == 0:
pmagplotlib.drawFIGS(PLTS)
ans = input('S[a]ve all figures, <Return> to quit \n ')
if ans != 'a':
print("Good bye")
sys.exit()
files = {}
for key in list(PLTS.keys()):
files[key] = ('foldtest_' + '%s' % (key.strip()[:2]) + '.' + 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 list(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 list(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 = old_div(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 list(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 = old_div(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 list(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 list(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 list(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 list(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 list(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 = 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
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
-sc uses a "site-level" correction to a Fisherian distribution instead
of a "study-level" correction to a TK03-consistent distribution.
Note that many directions (~ 100) are needed for this correction to be reliable.
-fmt [svg,png,eps,pdf..] change plot format, default is svg
-sav saves the figures and quits
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
plot=0
if '-i' in sys.argv:
file=input("Enter file name for processing: ")
if '-h' in sys.argv:
print(main.__doc__)
sys.exit() # graceful quit
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 '-sc' in sys.argv:
site_correction = True
else:
site_correction = False
if '-fmt' in sys.argv:
ind=sys.argv.index('-fmt')
fmt=sys.argv[ind+1]
if '-sav' in sys.argv:plot=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')
if plot==0:pmagplotlib.drawFIGS(PLTS)
ppars=pmag.doprinc(data)
Io=ppars['inc']
n=ppars["N"]
Es,Is,Fs,V2s=pmag.find_f(data)
if site_correction:
Inc,Elong=Is[Es.index(min(Es))],Es[Es.index(min(Es))]
flat_f = Fs[Es.index(min(Es))]
else:
Inc,Elong=Is[-1],Es[-1]
flat_f = Fs[-1]
pmagplotlib.plotEI(PLTS['ei'],Es,Is,flat_f)
pmagplotlib.plotV2s(PLTS['v2'],V2s,Is,flat_f)
b=0
print("Bootstrapping.... be patient")
while b<nb:
bdata=pmag.pseudo(data)
Esb,Isb,Fsb,V2sb=pmag.find_f(bdata)
if b<25:
pmagplotlib.plotEI(PLTS['ei'],Esb,Isb,Fsb[-1])
if Esb[-1]!=0:
ppars=pmag.doprinc(bdata)
if site_correction:
I.append(abs(Isb[Esb.index(min(Esb))]))
E.append(Esb[Esb.index(min(Esb))])
else:
I.append(abs(Isb[-1]))
E.append(Esb[-1])
b+=1
if b%25==0:print(b,' out of ',nb)
I.sort()
E.sort()
Eexp=[]
for i in I:
Eexp.append(pmag.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','-')
if plot==0:
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= 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_magic.py
DESCRIPTION
makes equal area projections from declination/inclination data
SYNTAX
eqarea_magic.py [command line options]
INPUT
takes magic formatted sites, samples, specimens, or measurements
OPTIONS
-h prints help message and quits
-f FILE: specify input magic format file from magic, default='sites.txt'
supported types=[measurements, specimens, samples, sites]
-fsp FILE: specify specimen file name, (required if you want to plot measurements by sample)
default='specimens.txt'
-fsa FILE: specify sample file name, (required if you want to plot specimens by site)
default='samples.txt'
-fsi FILE: specify site file name, default='sites.txt'
-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
"""
# initialize some default variables
FIG = {} # plot dictionary
FIG["eqarea"] = 1 # eqarea is figure 1
plotE = 0
plt = 0 # default to not plotting
verbose = pmagplotlib.verbose
# extract arguments from sys.argv
if "-h" in sys.argv:
print main.__doc__
sys.exit()
dir_path = pmag.get_named_arg_from_sys("-WD", default_val=os.getcwd())
pmagplotlib.plot_init(FIG["eqarea"], 5, 5)
in_file = pmag.get_named_arg_from_sys("-f", default_val="sites.txt")
full_in_file = os.path.join(dir_path, in_file)
plot_by = pmag.get_named_arg_from_sys("-obj", default_val="all").lower()
spec_file = pmag.get_named_arg_from_sys("-fsp", default_val="specimens.txt")
samp_file = pmag.get_named_arg_from_sys("-fsa", default_val="samples.txt")
site_file = pmag.get_named_arg_from_sys("-fsi", default_val="sites.txt")
if plot_by == "all":
plot_key = "all"
elif plot_by == "sit":
plot_key = "site"
elif plot_by == "sam":
plot_key = "sample"
elif plot_by == "spc":
plot_key = "specimen"
else:
plot_key = "all"
if "-c" in sys.argv:
contour = 1
else:
contour = 0
if "-sav" in sys.argv:
plt = 1
verbose = 0
if "-ell" in sys.argv:
plotE = 1
ind = sys.argv.index("-ell")
ell_type = sys.argv[ind + 1]
ell_type = pmag.get_named_arg_from_sys("-ell", "F")
dist = ell_type.upper()
# if dist type is unrecognized, use Fisher
if dist not in ["F", "K", "B", "BE", "BV"]:
dist = "F"
if dist == "BV":
FIG["bdirs"] = 2
pmagplotlib.plot_init(FIG["bdirs"], 5, 5)
crd = pmag.get_named_arg_from_sys("-crd", default_val="g")
if crd == "s":
coord = "-1"
elif crd == "t":
coord = "100"
else:
coord = "0"
fmt = pmag.get_named_arg_from_sys("-fmt", "svg")
dec_key = "dir_dec"
inc_key = "dir_inc"
tilt_key = "dir_tilt_correction"
# Dir_type_keys=['','site_direction_type','sample_direction_type','specimen_direction_type']
#
fnames = {"specimens": spec_file, "samples": samp_file, "sites": site_file}
contribution = nb.Contribution(dir_path, custom_filenames=fnames, single_file=in_file)
# the object that contains the DataFrame + useful helper methods:
table_name = contribution.tables.keys()[0]
data_container = contribution.tables[table_name]
# the actual DataFrame:
data = data_container.df
# uses sample infile to add temporary site_name
# column to the specimen table
data_container = contribution.tables[table_name]
data = data_container.df
if (plot_key != "all") and (plot_key not in data.columns):
data = contribution.propagate_name_down(plot_key, table_name)
# add tilt key into DataFrame columns if it isn't there already
if tilt_key not in data.columns:
data.loc[:, tilt_key] = None
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":
# return all where plot_key is not blank
if plot_key not in data.columns:
print 'Can\'t plot by "{}". That header is not in infile: {}'.format(plot_key, in_file)
return
plots = data[data[plot_key].notnull()]
plotlist = plots[plot_key].unique() # grab unique values
else:
plotlist.append("All")
for plot in plotlist:
if verbose:
print plot
if plot == "All":
# plot everything at once
plot_data = data
else:
# pull out only partial data
plot_data = data[data[plot_key] == plot]
DIblock = []
GCblock = []
# SLblock, SPblock = [], []
title = plot
mode = 1
k = 0
if dec_key not in plot_data.columns:
print "-W- No dec/inc data"
continue
# get all records where dec & inc values exist
plot_data = plot_data[plot_data[dec_key].notnull() & plot_data[inc_key].notnull()]
if plot_data.empty:
continue
# this sorting out is done in get_di_bock
# if coord == '0': # geographic, use records with no tilt key (or tilt_key 0)
# cond1 = plot_data[tilt_key].fillna('') == coord
# cond2 = plot_data[tilt_key].isnull()
# plot_data = plot_data[cond1 | cond2]
# else: # not geographic coordinates, use only records with correct tilt_key
# plot_data = plot_data[plot_data[tilt_key] == coord]
# get metadata for naming the plot file
locations = data_container.get_name("location", df_slice=plot_data)
site = data_container.get_name("site", df_slice=plot_data)
sample = data_container.get_name("sample", df_slice=plot_data)
specimen = data_container.get_name("specimen", df_slice=plot_data)
# make sure method_codes is in plot_data
if "method_codes" not in plot_data.columns:
plot_data["method_codes"] = ""
# get data blocks
DIblock = data_container.get_di_block(df_slice=plot_data, tilt_corr=coord, excl=["DE-BFP"])
# SLblock = [[ind, row['method_codes']] for ind, row in plot_data.iterrows()]
# get great circles
great_circle_data = data_container.get_records_for_code("DE-BFP", incl=True, use_slice=True, sli=plot_data)
if len(great_circle_data) > 0:
gc_cond = great_circle_data[tilt_key] == coord
GCblock = [[float(row[dec_key]), float(row[inc_key])] for ind, row in great_circle_data[gc_cond].iterrows()]
# SPblock = [[ind, row['method_codes']] for ind, row in great_circle_data[gc_cond].iterrows()]
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, 90.0, "g")
if len(DIblock) == 0 and len(GCblock) == 0:
if verbose:
print "no records for plotting"
continue
# 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 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 * 90.0) # Beta inc
npars.append(fpars["alpha95"]) # gamma
npars.append(fpars["dec"] + 90.0) # Beta dec
npars.append(0.0) # Beta inc
if len(rDIs) > 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 * 90.0) # 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 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, 1.0)
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, 1.0)
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)
for key in FIG.keys():
files = {}
filename = pmag.get_named_arg_from_sys("-fname")
if filename:
filename += "." + fmt
else:
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)
if plt:
pmagplotlib.saveP(FIG, files)
continue
if verbose:
pmagplotlib.drawFIGS(FIG)
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)
continue
def main():
"""
NAME
foldtest_magic.py
DESCRIPTION
does a fold test (Tauxe, 2010) 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]
-fsi er_sites formatted file
-exc use pmag_criteria.txt to set acceptance criteria
-n NB, set number of bootstraps, default is 1000
-b MIN, MAX, set bounds for untilting, default is -10, 150
-fmt FMT, specify format - default is svg
-sav saves plots and quits
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
dir_path='.'
infile,orfile='pmag_sites.txt','er_samples.txt'
critfile='pmag_criteria.txt'
dipkey,azkey='sample_bed_dip','sample_bed_dip_direction'
fmt='svg'
plot=0
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 '-fmt' in sys.argv:
ind=sys.argv.index('-fmt')
fmt=sys.argv[ind+1]
if '-sav' in sys.argv:plot=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]
elif '-fsi' in sys.argv:
ind=sys.argv.index('-fsi')
orfile=sys.argv[ind+1]
dipkey,azkey='site_bed_dip','site_bed_dip_direction'
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)
GEOrecs=pmag.get_dictitem(data,'site_tilt_correction','0','T')
if len(GEOrecs)>0: # have some geographic data
DIDDs= [] # set up list for dec inc dip_direction, dip
for rec in GEOrecs: # parse data
dip,dip_dir=0,-1
Dec=float(rec['site_dec'])
Inc=float(rec['site_inc'])
orecs=pmag.get_dictitem(ordata,'er_site_name',rec['er_site_name'],'T')
if len(orecs)>0:
if orecs[0][azkey]!="":dip_dir=float(orecs[0][azkey])
if orecs[0][dipkey]!="":dip=float(orecs[0][dipkey])
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])
else:
print 'no geographic directional data found'
sys.exit()
pmagplotlib.plotEQ(PLTS['geo'],DIDDs,'Geographic')
data=numpy.array(DIDDs)
D,I=pmag.dotilt_V(data)
TCs=numpy.array([D,I]).transpose()
pmagplotlib.plotEQ(PLTS['strat'],TCs,'Stratigraphic')
if plot==0: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
pylab.title(tit)
if plot==0:
pmagplotlib.drawFIGS(PLTS)
ans= raw_input('S[a]ve all figures, <Return> to quit \n ')
if ans!='a':
print "Good bye"
sys.exit()
files={}
for key in PLTS.keys():
files[key]=('foldtest_'+'%s'%(key.strip()[:2])+'.'+fmt)
pmagplotlib.saveP(PLTS,files)
def main():
"""
NAME
plotdi_a.py
DESCRIPTION
plots equal area projection from dec inc data and fisher mean, cone of confidence
INPUT FORMAT
takes dec, inc, alpha95 as first three columns in space delimited file
SYNTAX
plotdi_a.py [-i][-f FILE]
OPTIONS
-f FILE to read file name from command line
-fmt [png,jpg,eps,pdf,svg] set plot file format ['svg' is default]
-sav save plot and quit
"""
fmt, plot = 'svg', 0
if len(sys.argv) > 0:
if '-h' in sys.argv: # check if help is needed
print(main.__doc__)
sys.exit() # graceful quit
if '-fmt' in sys.argv:
ind = sys.argv.index('-fmt')
fmt = sys.argv[ind + 1]
if '-sav' in sys.argv: plot = 1
if '-f' in sys.argv:
ind = sys.argv.index('-f')
file = sys.argv[ind + 1]
f = open(file, 'r')
data = f.readlines()
else:
data = sys.stdin.readlines() # read in data from standard input
DIs, Pars = [], []
for line in data: # read in the data from standard input
pars = []
rec = line.split() # split each line on space to get records
DIs.append([float(rec[0]), float(rec[1])])
pars.append(float(rec[0]))
pars.append(float(rec[1]))
pars.append(float(rec[2]))
pars.append(float(rec[0]))
isign = old_div(abs(float(rec[1])), float(rec[1]))
pars.append(float(rec[1]) - isign * 90.) #Beta inc
pars.append(float(rec[2])) # gamma
pars.append(float(rec[0]) + 90.) # Beta dec
pars.append(0.) #Beta inc
Pars.append(pars)
#
EQ = {'eq': 1} # make plot dictionary
pmagplotlib.plot_init(EQ['eq'], 5, 5)
title = 'Equal area projection'
pmagplotlib.plotEQ(EQ['eq'], DIs, title) # plot directions
for k in range(len(Pars)):
pmagplotlib.plotELL(EQ['eq'], Pars[k], 'b', 0, 1) # plot ellipses
files = {}
for key in list(EQ.keys()):
files[key] = key + '.' + fmt
titles = {}
titles['eq'] = 'Equal Area Plot'
if pmagplotlib.isServer:
black = '#000000'
purple = '#800080'
EQ = pmagplotlib.addBorders(EQ, titles, black, purple)
pmagplotlib.saveP(EQ, files)
elif plot == 0:
pmagplotlib.drawFIGS(EQ)
ans = input(" S[a]ve to save plot, [q]uit, Return to continue: ")
if ans == "q": sys.exit()
if ans == "a":
pmagplotlib.saveP(EQ, files)
else:
pmagplotlib.saveP(EQ, files)
