def main():
"""
NAME
revtest_MM1990.py
DESCRIPTION
calculates Watson's V statistic from input files through Monte Carlo simulation in order to test whether normal and reversed populations could have been drawn from a common mean (equivalent to watsonV.py). Also provides the critical angle between the two sample mean directions and the corresponding McFadden and McElhinny (1990) classification.
INPUT FORMAT
takes dec/inc as first two columns in two space delimited files (one file for normal directions, one file for reversed directions).
SYNTAX
revtest_MM1990.py [command line options]
OPTIONS
-h prints help message and quits
-f FILE
-f2 FILE
-P (don't plot the Watson V cdf)
OUTPUT
Watson's V between the two populations and the Monte Carlo Critical Value Vc.
M&M1990 angle, critical angle and classification
Plot of Watson's V CDF from Monte Carlo simulation (red line), V is solid and Vc is dashed.
"""
D1,D2=[],[]
plot=1
Flip=1
if '-h' in sys.argv: # check if help is needed
print(main.__doc__)
sys.exit() # graceful quit
if '-P' in sys.argv: plot=0
if '-f' in sys.argv:
ind=sys.argv.index('-f')
file1=sys.argv[ind+1]
f1=open(file1,'r')
for line in f1.readlines():
rec=line.split()
Dec,Inc=float(rec[0]),float(rec[1])
D1.append([Dec,Inc,1.])
f1.close()
if '-f2' in sys.argv:
ind=sys.argv.index('-f2')
file2=sys.argv[ind+1]
f2=open(file2,'r')
print("be patient, your computer is doing 5000 simulations...")
for line in f2.readlines():
rec=line.split()
Dec,Inc=float(rec[0]),float(rec[1])
D2.append([Dec,Inc,1.])
f2.close()
#take the antipode for the directions in file 2
D2_flip=[]
for rec in D2:
d,i=(rec[0]-180.)%360.,-rec[1]
D2_flip.append([d,i,1.])
pars_1=pmag.fisher_mean(D1)
pars_2=pmag.fisher_mean(D2_flip)
cart_1=pmag.dir2cart([pars_1["dec"],pars_1["inc"],pars_1["r"]])
cart_2=pmag.dir2cart([pars_2['dec'],pars_2['inc'],pars_2["r"]])
Sw=pars_1['k']*pars_1['r']+pars_2['k']*pars_2['r'] # k1*r1+k2*r2
xhat_1=pars_1['k']*cart_1[0]+pars_2['k']*cart_2[0] # k1*x1+k2*x2
xhat_2=pars_1['k']*cart_1[1]+pars_2['k']*cart_2[1] # k1*y1+k2*y2
xhat_3=pars_1['k']*cart_1[2]+pars_2['k']*cart_2[2] # k1*z1+k2*z2
Rw=numpy.sqrt(xhat_1**2+xhat_2**2+xhat_3**2)
V=2*(Sw-Rw)
#
#keep weighted sum for later when determining the "critical angle" let's save it as Sr (notation of McFadden and McElhinny, 1990)
#
Sr=Sw
#
# do monte carlo simulation of datasets with same kappas, but common mean
#
counter,NumSims=0,5000
Vp=[] # set of Vs from simulations
for k in range(NumSims):
#
# get a set of N1 fisher distributed vectors with k1, calculate fisher stats
#
Dirp=[]
for i in range(pars_1["n"]):
Dirp.append(pmag.fshdev(pars_1["k"]))
pars_p1=pmag.fisher_mean(Dirp)
#
# get a set of N2 fisher distributed vectors with k2, calculate fisher stats
#
Dirp=[]
for i in range(pars_2["n"]):
Dirp.append(pmag.fshdev(pars_2["k"]))
pars_p2=pmag.fisher_mean(Dirp)
#
# get the V for these
#
Vk=pmag.vfunc(pars_p1,pars_p2)
Vp.append(Vk)
#
# sort the Vs, get Vcrit (95th percentile one)
#
Vp.sort()
k=int(.95*NumSims)
Vcrit=Vp[k]
#
# equation 18 of McFadden and McElhinny, 1990 calculates the critical value of R (Rwc)
#
Rwc=Sr-(old_div(Vcrit,2))
#
#following equation 19 of McFadden and McElhinny (1990) the critical angle is calculated.
#
k1=pars_1['k']
k2=pars_2['k']
R1=pars_1['r']
R2=pars_2['r']
critical_angle=numpy.degrees(numpy.arccos(old_div(((Rwc**2)-((k1*R1)**2)-((k2*R2)**2)),(2*k1*R1*k2*R2))))
D1_mean=(pars_1['dec'],pars_1['inc'])
D2_mean=(pars_2['dec'],pars_2['inc'])
angle=pmag.angle(D1_mean,D2_mean)
#
# print the results of the test
#
print("")
print("Results of Watson V test: ")
print("")
print("Watson's V: " '%.1f' %(V))
print("Critical value of V: " '%.1f' %(Vcrit))
if V<Vcrit:
print('"Pass": Since V is less than Vcrit, the null hypothesis that the two populations are drawn from distributions that share a common mean direction (antipodal to one another) cannot be rejected.')
elif V>Vcrit:
print('"Fail": Since V is greater than Vcrit, the two means can be distinguished at the 95% confidence level.')
print("")
print("M&M1990 classification:")
print("")
print("Angle between data set means: " '%.1f'%(angle))
print("Critical angle of M&M1990: " '%.1f'%(critical_angle))
if V>Vcrit:
print("")
elif V<Vcrit:
if critical_angle<5:
print("The McFadden and McElhinny (1990) classification for this test is: 'A'")
elif critical_angle<10:
print("The McFadden and McElhinny (1990) classification for this test is: 'B'")
elif critical_angle<20:
print("The McFadden and McElhinny (1990) classification for this test is: 'C'")
else:
print("The McFadden and McElhinny (1990) classification for this test is: 'INDETERMINATE;")
if plot==1:
CDF={'cdf':1}
pmagplotlib.plot_init(CDF['cdf'],5,5)
p1 = pmagplotlib.plotCDF(CDF['cdf'],Vp,"Watson's V",'r',"")
p2 = pmagplotlib.plotVs(CDF['cdf'],[V],'g','-')
p3 = pmagplotlib.plotVs(CDF['cdf'],[Vp[k]],'b','--')
pmagplotlib.drawFIGS(CDF)
files,fmt={},'svg'
if file2!="":
files['cdf']='WatsonsV_'+file1+'_'+file2+'.'+fmt
else:
files['cdf']='WatsonsV_'+file1+'.'+fmt
if pmagplotlib.isServer:
black = '#000000'
purple = '#800080'
titles={}
titles['cdf']='Cumulative Distribution'
CDF = pmagplotlib.addBorders(CDF,titles,black,purple)
pmagplotlib.saveP(CDF,files)
else:
ans=input(" S[a]ve to save plot, [q]uit without saving: ")
if ans=="a": pmagplotlib.saveP(CDF,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=".")
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_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()
if not set_env.IS_WIN:
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.plot_eq_sym(FIG['eq'],DIblock,title,sym)
#if plot==0:pmagplotlib.draw_figs(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=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=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.plot_eq_sym(FIG['bdirs'],BnDIs,'Bootstrapped Eigenvectors',vsym)
if len(rDIs)>5:
BrDIs=pmag.di_boot(rDIs)
if len(nDIs)>5: # plot on existing plots
pmagplotlib.plot_di_sym(FIG['bdirs'],BrDIs,vsym)
else:
pmagplotlib.plot_eq(FIG['bdirs'],BrDIs,'Bootstrapped Eigenvectors',vsym)
if dist=='B':
if len(nDIs)> 3 or len(rDIs)>3: pmagplotlib.plot_conf(FIG['eq'],etitle,[],npars,0)
elif len(nDIs)>3 and dist!='BV':
pmagplotlib.plot_conf(FIG['eq'],etitle,[],npars,0)
if len(rDIs)>3:
pmagplotlib.plot_conf(FIG['eq'],etitle,[],rpars,0)
elif len(rDIs)>3 and dist!='BV':
pmagplotlib.plot_conf(FIG['eq'],etitle,[],rpars,0)
#if plot==0:pmagplotlib.draw_figs(FIG)
if plot==0:pmagplotlib.draw_figs(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.add_borders(FIG,titles,black,purple)
pmagplotlib.save_plots(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.save_plots(FIG,files)
else:
pmagplotlib.save_plots(FIG,files)
def main():
"""
NAME
plotdi_e.py
DESCRIPTION
plots equal area projection from dec inc data and cones of confidence
(Fisher, kent or Bingham or bootstrap).
INPUT FORMAT
takes dec/inc as first two columns in space delimited file
SYNTAX
plotdi_e.py [command line options]
OPTIONS
-h prints help message and quits
-i for interactive parameter entry
-f FILE, sets input filename on command line
-Fish plots unit vector mean direction, alpha95
-Bing plots Principal direction, Bingham confidence ellipse
-Kent plots unit vector mean direction, confidence ellipse
-Boot E plots unit vector mean direction, bootstrapped confidence ellipse
-Boot V plots unit vector mean direction, distribution of bootstrapped means
"""
dist='F' # default distribution is Fisherian
mode=1
EQ={'eq':1}
if len(sys.argv) > 0:
if '-h' in sys.argv: # check if help is needed
print main.__doc__
sys.exit() # graceful quit
if '-i' in sys.argv: # ask for filename
file=raw_input("Enter file name with dec, inc data: ")
dist=raw_input("Enter desired distrubution: [Fish]er, [Bing]ham, [Kent] [Boot] [default is Fisher]: ")
if dist=="":dist="F"
if dist=="Boot":
type=raw_input(" Ellipses or distribution of vectors? [E]/V ")
if type=="" or type=="E":
dist="BE"
else:
dist="BE"
else:
#
if '-f' in sys.argv:
ind=sys.argv.index('-f')
file=sys.argv[ind+1]
else:
print 'you must specify a file name'
print main.__doc__
sys.exit()
if '-Bing' in sys.argv:dist='B'
if '-Kent' in sys.argv:dist='K'
if '-Boot' in sys.argv:
ind=sys.argv.index('-Boot')
type=sys.argv[ind+1]
if type=='E':
dist='BE'
elif type=='V':
dist='BV'
EQ['bdirs']=2
pmagplotlib.plot_init(EQ['bdirs'],5,5)
else:
print main.__doc__
sys.exit()
pmagplotlib.plot_init(EQ['eq'],5,5)
#
# get to work
f=open(file,'r')
data=f.readlines()
#
DIs= [] # set up list for dec inc data
DiRecs=[]
pars=[]
nDIs,rDIs,npars,rpars=[],[],[],[]
mode =1
for line in data: # read in the data from standard input
DiRec={}
rec=line.split() # split each line on space to get records
DIs.append((float(rec[0]),float(rec[1]),1.))
DiRec['dec']=rec[0]
DiRec['inc']=rec[1]
DiRec['direction_type']='l'
DiRecs.append(DiRec)
# split into two modes
ppars=pmag.doprinc(DIs) # get principal directions
for rec in DIs:
angle=pmag.angle([rec[0],rec[1]],[ppars['dec'],ppars['inc']])
if angle>90.:
rDIs.append(rec)
else:
nDIs.append(rec)
if dist=='B': # do on whole dataset
title="Bingham confidence ellipse"
bpars=pmag.dobingham(DIs)
for key in bpars.keys():
if key!='n':print " ",key, '%7.1f'%(bpars[key])
if key=='n':print " ",key, ' %i'%(bpars[key])
npars.append(bpars['dec'])
npars.append(bpars['inc'])
npars.append(bpars['Zeta'])
npars.append(bpars['Zdec'])
npars.append(bpars['Zinc'])
npars.append(bpars['Eta'])
npars.append(bpars['Edec'])
npars.append(bpars['Einc'])
if dist=='F':
title="Fisher confidence cone"
if len(nDIs)>3:
fpars=pmag.fisher_mean(nDIs)
print "mode ",mode
for key in fpars.keys():
if key!='n':print " ",key, '%7.1f'%(fpars[key])
if key=='n':print " ",key, ' %i'%(fpars[key])
mode+=1
npars.append(fpars['dec'])
npars.append(fpars['inc'])
npars.append(fpars['alpha95']) # Beta
npars.append(fpars['dec'])
isign=abs(fpars['inc'])/fpars['inc']
npars.append(fpars['inc']-isign*90.) #Beta inc
npars.append(fpars['alpha95']) # gamma
npars.append(fpars['dec']+90.) # Beta dec
npars.append(0.) #Beta inc
if len(rDIs)>3:
fpars=pmag.fisher_mean(rDIs)
print "mode ",mode
for key in fpars.keys():
if key!='n':print " ",key, '%7.1f'%(fpars[key])
if key=='n':print " ",key, ' %i'%(fpars[key])
mode+=1
rpars.append(fpars['dec'])
rpars.append(fpars['inc'])
rpars.append(fpars['alpha95']) # Beta
rpars.append(fpars['dec'])
isign=abs(fpars['inc'])/fpars['inc']
rpars.append(fpars['inc']-isign*90.) #Beta inc
rpars.append(fpars['alpha95']) # gamma
rpars.append(fpars['dec']+90.) # Beta dec
rpars.append(0.) #Beta inc
if dist=='K':
title="Kent confidence ellipse"
if len(nDIs)>3:
kpars=pmag.dokent(nDIs,len(nDIs))
print "mode ",mode
for key in kpars.keys():
if key!='n':print " ",key, '%7.1f'%(kpars[key])
if key=='n':print " ",key, ' %i'%(kpars[key])
mode+=1
npars.append(kpars['dec'])
npars.append(kpars['inc'])
npars.append(kpars['Zeta'])
npars.append(kpars['Zdec'])
npars.append(kpars['Zinc'])
npars.append(kpars['Eta'])
npars.append(kpars['Edec'])
npars.append(kpars['Einc'])
if len(rDIs)>3:
kpars=pmag.dokent(rDIs,len(rDIs))
print "mode ",mode
for key in kpars.keys():
if key!='n':print " ",key, '%7.1f'%(kpars[key])
if key=='n':print " ",key, ' %i'%(kpars[key])
mode+=1
rpars.append(kpars['dec'])
rpars.append(kpars['inc'])
rpars.append(kpars['Zeta'])
rpars.append(kpars['Zdec'])
rpars.append(kpars['Zinc'])
rpars.append(kpars['Eta'])
rpars.append(kpars['Edec'])
rpars.append(kpars['Einc'])
else: # assume bootstrap
if dist=='BE':
if len(nDIs)>5:
BnDIs=pmag.di_boot(nDIs)
Bkpars=pmag.dokent(BnDIs,1.)
print "mode ",mode
for key in Bkpars.keys():
if key!='n':print " ",key, '%7.1f'%(Bkpars[key])
if key=='n':print " ",key, ' %i'%(Bkpars[key])
mode+=1
npars.append(Bkpars['dec'])
npars.append(Bkpars['inc'])
npars.append(Bkpars['Zeta'])
npars.append(Bkpars['Zdec'])
npars.append(Bkpars['Zinc'])
npars.append(Bkpars['Eta'])
npars.append(Bkpars['Edec'])
npars.append(Bkpars['Einc'])
if len(rDIs)>5:
BrDIs=pmag.di_boot(rDIs)
Bkpars=pmag.dokent(BrDIs,1.)
print "mode ",mode
for key in Bkpars.keys():
if key!='n':print " ",key, '%7.1f'%(Bkpars[key])
if key=='n':print " ",key, ' %i'%(Bkpars[key])
mode+=1
rpars.append(Bkpars['dec'])
rpars.append(Bkpars['inc'])
rpars.append(Bkpars['Zeta'])
rpars.append(Bkpars['Zdec'])
rpars.append(Bkpars['Zinc'])
rpars.append(Bkpars['Eta'])
rpars.append(Bkpars['Edec'])
rpars.append(Bkpars['Einc'])
title="Bootstrapped confidence ellipse"
elif dist=='BV':
if len(nDIs)>5:
pmagplotlib.plotEQ(EQ['eq'],nDIs,'Data')
BnDIs=pmag.di_boot(nDIs)
pmagplotlib.plotEQ(EQ['bdirs'],BnDIs,'Bootstrapped Eigenvectors')
if len(rDIs)>5:
BrDIs=pmag.di_boot(rDIs)
if len(nDIs)>5: # plot on existing plots
pmagplotlib.plotDI(EQ['eq'],rDIs)
pmagplotlib.plotDI(EQ['bdirs'],BrDIs)
else:
pmagplotlib.plotEQ(EQ['eq'],rDIs,'Data')
pmagplotlib.plotEQ(EQ['bdirs'],BrDIs,'Bootstrapped Eigenvectors')
pmagplotlib.drawFIGS(EQ)
ans=raw_input('s[a]ve, [q]uit ')
if ans=='q':sys.exit()
if ans=='a':
files={}
for key in EQ.keys():
files[key]='BE_'+key+'.svg'
pmagplotlib.saveP(EQ,files)
sys.exit()
if len(nDIs)>5:
pmagplotlib.plotCONF(EQ['eq'],title,DiRecs,npars,1)
if len(rDIs)>5 and dist!='B':
pmagplotlib.plotCONF(EQ['eq'],title,[],rpars,0)
elif len(rDIs)>5 and dist!='B':
pmagplotlib.plotCONF(EQ['eq'],title,DiRecs,rpars,1)
pmagplotlib.drawFIGS(EQ)
ans=raw_input('s[a]ve, [q]uit ')
if ans=='q':sys.exit()
if ans=='a':
files={}
for key in EQ.keys():
files[key]=key+'.svg'
pmagplotlib.saveP(EQ,files)
def main():
"""
NAME
eqarea_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
specimens_results_magic.py
DESCRIPTION
combines pmag_specimens.txt file with age, location, acceptance criteria and
outputs pmag_results table along with other MagIC tables necessary for uploading to the database
SYNTAX
specimens_results_magic.py [command line options]
OPTIONS
-h prints help message and quits
-usr USER: identify user, default is ""
-f: specimen input magic_measurements format file, default is "magic_measurements.txt"
-fsp: specimen input pmag_specimens format file, default is "pmag_specimens.txt"
-fsm: sample input er_samples format file, default is "er_samples.txt"
-fsi: specimen input er_sites format file, default is "er_sites.txt"
-fla: specify a file with paleolatitudes for calculating VADMs, default is not to calculate VADMS
format is: site_name paleolatitude (space delimited file)
-fa AGES: specify er_ages format file with age information
-crd [s,g,t,b]: specify coordinate system
(s, specimen, g geographic, t, tilt corrected, b, geographic and tilt corrected)
Default is to assume geographic
NB: only the tilt corrected data will appear on the results table, if both g and t are selected.
-cor [AC:CR:NL]: colon delimited list of required data adjustments for all specimens
included in intensity calculations (anisotropy, cooling rate, non-linear TRM)
unless specified, corrections will not be applied
-pri [TRM:ARM] colon delimited list of priorities for anisotropy correction (-cor must also be set to include AC). default is TRM, then ARM
-age MIN MAX UNITS: specify age boundaries and units
-exc: use exiting selection criteria (in pmag_criteria.txt file), default is default criteria
-C: no acceptance criteria
-aD: average directions per sample, default is NOT
-aI: average multiple specimen intensities per sample, default is by site
-aC: average all components together, default is NOT
-pol: calculate polarity averages
-sam: save sample level vgps and v[a]dms, default is by site
-xSi: skip the site level intensity calculation
-p: plot directions and look at intensities by site, default is NOT
-fmt: specify output for saved images, default is svg (only if -p set)
-lat: use present latitude for calculating VADMs, default is not to calculate VADMs
-xD: skip directions
-xI: skip intensities
OUPUT
writes pmag_samples, pmag_sites, pmag_results tables
"""
# set defaults
Comps = [] # list of components
version_num = pmag.get_version()
args = sys.argv
DefaultAge = ["none"]
skipdirs, coord, excrit, custom, vgps, average, Iaverage, plotsites, opt = 1, 0, 0, 0, 0, 0, 0, 0, 0
get_model_lat = 0 # this skips VADM calculation altogether, when get_model_lat=1, uses present day
fmt = 'svg'
dir_path = "."
model_lat_file = ""
Caverage = 0
infile = 'pmag_specimens.txt'
measfile = "magic_measurements.txt"
sampfile = "er_samples.txt"
sitefile = "er_sites.txt"
agefile = "er_ages.txt"
specout = "er_specimens.txt"
sampout = "pmag_samples.txt"
siteout = "pmag_sites.txt"
resout = "pmag_results.txt"
critout = "pmag_criteria.txt"
instout = "magic_instruments.txt"
sigcutoff, OBJ = "", ""
noDir, noInt = 0, 0
polarity = 0
coords = ['0']
Dcrit, Icrit, nocrit = 0, 0, 0
corrections = []
nocorrection = ['DA-NL', 'DA-AC', 'DA-CR']
priorities = ['DA-AC-ARM',
'DA-AC-TRM'] # priorities for anisotropy correction
# get command line stuff
if "-h" in args:
print main.__doc__
sys.exit()
if '-WD' in args:
ind = args.index("-WD")
dir_path = args[ind + 1]
if '-cor' in args:
ind = args.index('-cor')
cors = args[ind + 1].split(':') # list of required data adjustments
for cor in cors:
nocorrection.remove('DA-' + cor)
corrections.append('DA-' + cor)
if '-pri' in args:
ind = args.index('-pri')
priorities = args[ind + 1].split(
':') # list of required data adjustments
for p in priorities:
p = 'DA-AC-' + p
if '-f' in args:
ind = args.index("-f")
measfile = args[ind + 1]
if '-fsp' in args:
ind = args.index("-fsp")
infile = args[ind + 1]
if '-fsi' in args:
ind = args.index("-fsi")
sitefile = args[ind + 1]
if "-crd" in args:
ind = args.index("-crd")
coord = args[ind + 1]
if coord == 's': coords = ['-1']
if coord == 'g': coords = ['0']
if coord == 't': coords = ['100']
if coord == 'b': coords = ['0', '100']
if "-usr" in args:
ind = args.index("-usr")
user = sys.argv[ind + 1]
else:
user = ""
if "-C" in args: Dcrit, Icrit, nocrit = 1, 1, 1 # no selection criteria
if "-sam" in args: vgps = 1 # save sample level VGPS/VADMs
if "-xSi" in args:
nositeints = 1 # skip site level intensity
else:
nositeints = 0
if "-age" in args:
ind = args.index("-age")
DefaultAge[0] = args[ind + 1]
DefaultAge.append(args[ind + 2])
DefaultAge.append(args[ind + 3])
Daverage, Iaverage, Caverage = 0, 0, 0
if "-aD" in args: Daverage = 1 # average by sample directions
if "-aI" in args: Iaverage = 1 # average by sample intensities
if "-aC" in args:
Caverage = 1 # average all components together ??? why???
if "-pol" in args: polarity = 1 # calculate averages by polarity
if '-xD' in args: noDir = 1
if '-xI' in args:
noInt = 1
elif "-fla" in args:
if '-lat' in args:
print "you should set a paleolatitude file OR use present day lat - not both"
sys.exit()
ind = args.index("-fla")
model_lat_file = dir_path + '/' + args[ind + 1]
get_model_lat = 2
mlat = open(model_lat_file, 'rU')
ModelLats = []
for line in mlat.readlines():
ModelLat = {}
tmp = line.split()
ModelLat["er_site_name"] = tmp[0]
ModelLat["site_model_lat"] = tmp[1]
ModelLat["er_sample_name"] = tmp[0]
ModelLat["sample_lat"] = tmp[1]
ModelLats.append(ModelLat)
get_model_lat = 2
elif '-lat' in args:
get_model_lat = 1
if "-p" in args:
plotsites = 1
if "-fmt" in args:
ind = args.index("-fmt")
fmt = args[ind + 1]
if noDir == 0: # plot by site - set up plot window
import pmagplotlib
EQ = {}
EQ['eqarea'] = 1
pmagplotlib.plot_init(
EQ['eqarea'], 5, 5) # define figure 1 as equal area projection
pmagplotlib.plotNET(
EQ['eqarea']
) # I don't know why this has to be here, but otherwise the first plot never plots...
pmagplotlib.drawFIGS(EQ)
if '-WD' in args:
infile = dir_path + '/' + infile
measfile = dir_path + '/' + measfile
instout = dir_path + '/' + instout
sampfile = dir_path + '/' + sampfile
sitefile = dir_path + '/' + sitefile
agefile = dir_path + '/' + agefile
specout = dir_path + '/' + specout
sampout = dir_path + '/' + sampout
siteout = dir_path + '/' + siteout
resout = dir_path + '/' + resout
critout = dir_path + '/' + critout
if "-exc" in args: # use existing pmag_criteria file
if "-C" in args:
print 'you can not use both existing and no criteria - choose either -exc OR -C OR neither (for default)'
sys.exit()
crit_data, file_type = pmag.magic_read(critout)
print "Acceptance criteria read in from ", critout
else: # use default criteria (if nocrit set, then get really loose criteria as default)
crit_data = pmag.default_criteria(nocrit)
if nocrit == 0:
print "Acceptance criteria are defaults"
else:
print "No acceptance criteria used "
accept = {}
for critrec in crit_data:
for key in critrec.keys():
# need to migrate specimen_dang to specimen_int_dang for intensity data using old format
if 'IE-SPEC' in critrec.keys() and 'specimen_dang' in critrec.keys(
) and 'specimen_int_dang' not in critrec.keys():
critrec['specimen_int_dang'] = critrec['specimen_dang']
del critrec['specimen_dang']
# need to get rid of ron shaars sample_int_sigma_uT
if 'sample_int_sigma_uT' in critrec.keys():
critrec['sample_int_sigma'] = '%10.3e' % (
eval(critrec['sample_int_sigma_uT']) * 1e-6)
if key not in accept.keys() and critrec[key] != '':
accept[key] = critrec[key]
#
#
if "-exc" not in args and "-C" not in args:
print "args", args
pmag.magic_write(critout, [accept], 'pmag_criteria')
print "\n Pmag Criteria stored in ", critout, '\n'
#
# now we're done slow dancing
#
SiteNFO, file_type = pmag.magic_read(
sitefile) # read in site data - has the lats and lons
SampNFO, file_type = pmag.magic_read(
sampfile) # read in site data - has the lats and lons
height_nfo = pmag.get_dictitem(SiteNFO, 'site_height', '',
'F') # find all the sites with height info.
if agefile != "":
AgeNFO, file_type = pmag.magic_read(
agefile) # read in the age information
Data, file_type = pmag.magic_read(
infile) # read in specimen interpretations
IntData = pmag.get_dictitem(Data, 'specimen_int', '',
'F') # retrieve specimens with intensity data
comment, orient = "", []
samples, sites = [], []
for rec in Data: # run through the data filling in missing keys and finding all components, coordinates available
# fill in missing fields, collect unique sample and site names
if 'er_sample_name' not in rec.keys():
rec['er_sample_name'] = ""
elif rec['er_sample_name'] not in samples:
samples.append(rec['er_sample_name'])
if 'er_site_name' not in rec.keys():
rec['er_site_name'] = ""
elif rec['er_site_name'] not in sites:
sites.append(rec['er_site_name'])
if 'specimen_int' not in rec.keys(): rec['specimen_int'] = ''
if 'specimen_comp_name' not in rec.keys(
) or rec['specimen_comp_name'] == "":
rec['specimen_comp_name'] = 'A'
if rec['specimen_comp_name'] not in Comps:
Comps.append(rec['specimen_comp_name'])
rec['specimen_tilt_correction'] = rec[
'specimen_tilt_correction'].strip('\n')
if "specimen_tilt_correction" not in rec.keys():
rec["specimen_tilt_correction"] = "-1" # assume sample coordinates
if rec["specimen_tilt_correction"] not in orient:
orient.append(rec["specimen_tilt_correction"]
) # collect available coordinate systems
if "specimen_direction_type" not in rec.keys():
rec["specimen_direction_type"] = 'l' # assume direction is line - not plane
if "specimen_dec" not in rec.keys():
rec["specimen_direction_type"] = '' # if no declination, set direction type to blank
if "specimen_n" not in rec.keys(): rec["specimen_n"] = '' # put in n
if "specimen_alpha95" not in rec.keys():
rec["specimen_alpha95"] = '' # put in alpha95
if "magic_method_codes" not in rec.keys():
rec["magic_method_codes"] = ''
#
# start parsing data into SpecDirs, SpecPlanes, SpecInts
SpecInts, SpecDirs, SpecPlanes = [], [], []
samples.sort() # get sorted list of samples and sites
sites.sort()
if noInt == 0: # don't skip intensities
IntData = pmag.get_dictitem(
Data, 'specimen_int', '',
'F') # retrieve specimens with intensity data
if nocrit == 0: # use selection criteria
for rec in IntData: # do selection criteria
kill = pmag.grade(rec, accept, 'specimen_int')
if len(kill) == 0:
SpecInts.append(
rec
) # intensity record to be included in sample, site calculations
else:
SpecInts = IntData[:] # take everything - no selection criteria
# check for required data adjustments
if len(corrections) > 0 and len(SpecInts) > 0:
for cor in corrections:
SpecInts = pmag.get_dictitem(
SpecInts, 'magic_method_codes', cor,
'has') # only take specimens with the required corrections
if len(nocorrection) > 0 and len(SpecInts) > 0:
for cor in nocorrection:
SpecInts = pmag.get_dictitem(
SpecInts, 'magic_method_codes', cor, 'not'
) # exclude the corrections not specified for inclusion
# take top priority specimen of its name in remaining specimens (only one per customer)
PrioritySpecInts = []
specimens = pmag.get_specs(SpecInts) # get list of uniq specimen names
for spec in specimens:
ThisSpecRecs = pmag.get_dictitem(
SpecInts, 'er_specimen_name', spec,
'T') # all the records for this specimen
if len(ThisSpecRecs) == 1:
PrioritySpecInts.append(ThisSpecRecs[0])
elif len(ThisSpecRecs) > 1: # more than one
prec = []
for p in priorities:
ThisSpecRecs = pmag.get_dictitem(
SpecInts, 'magic_method_codes', p,
'has') # all the records for this specimen
if len(ThisSpecRecs) > 0: prec.append(ThisSpecRecs[0])
PrioritySpecInts.append(prec[0]) # take the best one
SpecInts = PrioritySpecInts # this has the first specimen record
if noDir == 0: # don't skip directions
AllDirs = pmag.get_dictitem(
Data, 'specimen_direction_type', '',
'F') # retrieve specimens with directed lines and planes
Ns = pmag.get_dictitem(
AllDirs, 'specimen_n', '',
'F') # get all specimens with specimen_n information
if nocrit != 1: # use selection criteria
for rec in Ns: # look through everything with specimen_n for "good" data
kill = pmag.grade(rec, accept, 'specimen_dir')
if len(kill) == 0: # nothing killed it
SpecDirs.append(rec)
else: # no criteria
SpecDirs = AllDirs[:] # take them all
# SpecDirs is now the list of all specimen directions (lines and planes) that pass muster
#
PmagSamps, SampDirs = [], [
] # list of all sample data and list of those that pass the DE-SAMP criteria
PmagSites, PmagResults = [], [
] # list of all site data and selected results
SampInts = []
for samp in samples: # run through the sample names
if Daverage == 1: # average by sample if desired
SampDir = pmag.get_dictitem(
SpecDirs, 'er_sample_name', samp,
'T') # get all the directional data for this sample
if len(SampDir) > 0: # there are some directions
for coord in coords: # step through desired coordinate systems
CoordDir = pmag.get_dictitem(
SampDir, 'specimen_tilt_correction', coord,
'T') # get all the directions for this sample
if len(CoordDir
) > 0: # there are some with this coordinate system
if Caverage == 0: # look component by component
for comp in Comps:
CompDir = pmag.get_dictitem(
CoordDir, 'specimen_comp_name', comp, 'T'
) # get all directions from this component
if len(CompDir) > 0: # there are some
PmagSampRec = pmag.lnpbykey(
CompDir, 'sample', 'specimen'
) # get a sample average from all specimens
PmagSampRec["er_location_name"] = CompDir[0][
'er_location_name'] # decorate the sample record
PmagSampRec["er_site_name"] = CompDir[0][
'er_site_name']
PmagSampRec["er_sample_name"] = samp
PmagSampRec[
"er_citation_names"] = "This study"
PmagSampRec["er_analyst_mail_names"] = user
PmagSampRec[
'magic_software_packages'] = version_num
if nocrit != 1:
PmagSampRec[
'pmag_criteria_codes'] = "ACCEPT"
if agefile != "":
PmagSampRec = pmag.get_age(
PmagSampRec, "er_site_name",
"sample_inferred_", AgeNFO,
DefaultAge)
site_height = pmag.get_dictitem(
height_nfo, 'er_site_name',
PmagSampRec['er_site_name'], 'T')
if len(site_height) > 0:
PmagSampRec[
"sample_height"] = site_height[0][
'site_height'] # add in height if available
PmagSampRec['sample_comp_name'] = comp
PmagSampRec[
'sample_tilt_correction'] = coord
PmagSampRec[
'er_specimen_names'] = pmag.get_list(
CompDir, 'er_specimen_name'
) # get a list of the specimen names used
PmagSampRec[
'magic_method_codes'] = pmag.get_list(
CompDir, 'magic_method_codes'
) # get a list of the methods used
if nocrit != 1: # apply selection criteria
kill = pmag.grade(
PmagSampRec, accept, 'sample_dir')
else:
kill = []
if len(kill) == 0:
SampDirs.append(PmagSampRec)
if vgps == 1: # if sample level VGP info desired, do that now
PmagResRec = pmag.getsampVGP(
PmagSampRec, SiteNFO)
if PmagResRec != "":
PmagResults.append(PmagResRec)
PmagSamps.append(PmagSampRec)
if Caverage == 1: # average all components together basically same as above
PmagSampRec = pmag.lnpbykey(
CoordDir, 'sample', 'specimen')
PmagSampRec["er_location_name"] = CoordDir[0][
'er_location_name']
PmagSampRec["er_site_name"] = CoordDir[0][
'er_site_name']
PmagSampRec["er_sample_name"] = samp
PmagSampRec["er_citation_names"] = "This study"
PmagSampRec["er_analyst_mail_names"] = user
PmagSampRec[
'magic_software_packages'] = version_num
if nocrit != 1:
PmagSampRec['pmag_criteria_codes'] = ""
if agefile != "":
PmagSampRec = pmag.get_age(
PmagSampRec, "er_site_name",
"sample_inferred_", AgeNFO, DefaultAge)
site_height = pmag.get_dictitem(
height_nfo, 'er_site_name', site, 'T')
if len(site_height) > 0:
PmagSampRec["sample_height"] = site_height[0][
'site_height'] # add in height if available
PmagSampRec['sample_tilt_correction'] = coord
PmagSampRec['sample_comp_name'] = pmag.get_list(
CoordDir,
'specimen_comp_name') # get components used
PmagSampRec['er_specimen_names'] = pmag.get_list(
CoordDir, 'er_specimen_name'
) # get specimne names averaged
PmagSampRec['magic_method_codes'] = pmag.get_list(
CoordDir,
'magic_method_codes') # assemble method codes
if nocrit != 1: # apply selection criteria
kill = pmag.grade(PmagSampRec, accept,
'sample_dir')
if len(kill) == 0: # passes the mustard
SampDirs.append(PmagSampRec)
if vgps == 1:
PmagResRec = pmag.getsampVGP(
PmagSampRec, SiteNFO)
if PmagResRec != "":
PmagResults.append(PmagResRec)
else: # take everything
SampDirs.append(PmagSampRec)
if vgps == 1:
PmagResRec = pmag.getsampVGP(
PmagSampRec, SiteNFO)
if PmagResRec != "":
PmagResults.append(PmagResRec)
PmagSamps.append(PmagSampRec)
if Iaverage == 1: # average by sample if desired
SampI = pmag.get_dictitem(
SpecInts, 'er_sample_name', samp,
'T') # get all the intensity data for this sample
if len(SampI) > 0: # there are some
PmagSampRec = pmag.average_int(
SampI, 'specimen', 'sample') # get average intensity stuff
PmagSampRec[
"sample_description"] = "sample intensity" # decorate sample record
PmagSampRec["sample_direction_type"] = ""
PmagSampRec['er_site_name'] = SampI[0]["er_site_name"]
PmagSampRec['er_sample_name'] = samp
PmagSampRec['er_location_name'] = SampI[0]["er_location_name"]
PmagSampRec["er_citation_names"] = "This study"
PmagSampRec["er_analyst_mail_names"] = user
if agefile != "":
PmagSampRec = pmag.get_age(PmagSampRec, "er_site_name",
"sample_inferred_", AgeNFO,
DefaultAge)
site_height = pmag.get_dictitem(height_nfo, 'er_site_name',
PmagSampRec['er_site_name'],
'T')
if len(site_height) > 0:
PmagSampRec["sample_height"] = site_height[0][
'site_height'] # add in height if available
PmagSampRec['er_specimen_names'] = pmag.get_list(
SampI, 'er_specimen_name')
PmagSampRec['magic_method_codes'] = pmag.get_list(
SampI, 'magic_method_codes')
if nocrit != 1: # apply criteria!
kill = pmag.grade(PmagSampRec, accept, 'sample_int')
if len(kill) == 0:
PmagSampRec['pmag_criteria_codes'] = "ACCEPT"
SampInts.append(PmagSampRec)
PmagSamps.append(PmagSampRec)
else:
PmagSampRec = {} # sample rejected
else: # no criteria
SampInts.append(PmagSampRec)
PmagSamps.append(PmagSampRec)
PmagSampRec['pmag_criteria_codes'] = ""
if vgps == 1 and get_model_lat != 0 and PmagSampRec != {}: #
if get_model_lat == 1: # use sample latitude
PmagResRec = pmag.getsampVDM(PmagSampRec, SampNFO)
del (PmagResRec['model_lat']
) # get rid of the model lat key
elif get_model_lat == 2: # use model latitude
PmagResRec = pmag.getsampVDM(PmagSampRec, ModelLats)
if PmagResRec != {}:
PmagResRec['magic_method_codes'] = PmagResRec[
'magic_method_codes'] + ":IE-MLAT"
if PmagResRec != {}:
PmagResRec['er_specimen_names'] = PmagSampRec[
'er_specimen_names']
PmagResRec['er_sample_names'] = PmagSampRec[
'er_sample_name']
PmagResRec['pmag_criteria_codes'] = 'ACCEPT'
PmagResRec['average_int_sigma_perc'] = PmagSampRec[
'sample_int_sigma_perc']
PmagResRec['average_int_sigma'] = PmagSampRec[
'sample_int_sigma']
PmagResRec['average_int_n'] = PmagSampRec[
'sample_int_n']
PmagResRec['vadm_n'] = PmagSampRec['sample_int_n']
PmagResRec['data_type'] = 'i'
PmagResults.append(PmagResRec)
if len(PmagSamps) > 0:
TmpSamps, keylist = pmag.fillkeys(
PmagSamps) # fill in missing keys from different types of records
pmag.magic_write(sampout, TmpSamps,
'pmag_samples') # save in sample output file
print ' sample averages written to ', sampout
#
#create site averages from specimens or samples as specified
#
for site in sites:
if Daverage == 0:
key, dirlist = 'specimen', SpecDirs # if specimen averages at site level desired
if Daverage == 1:
key, dirlist = 'sample', SampDirs # if sample averages at site level desired
tmp = pmag.get_dictitem(dirlist, 'er_site_name', site,
'T') # get all the sites with directions
tmp1 = pmag.get_dictitem(
tmp, key + '_tilt_correction', coords[-1],
'T') # use only the last coordinate if Caverage==0
sd = pmag.get_dictitem(
SiteNFO, 'er_site_name', site,
'T') # fish out site information (lat/lon, etc.)
if len(sd) > 0:
sitedat = sd[0]
if Caverage == 0: # do component wise averaging
for comp in Comps:
siteD = pmag.get_dictitem(tmp1, key + '_comp_name', comp,
'T') # get all components comp
if len(
siteD
) > 0: # there are some for this site and component name
PmagSiteRec = pmag.lnpbykey(
siteD, 'site', key) # get an average for this site
PmagSiteRec[
'site_comp_name'] = comp # decorate the site record
PmagSiteRec["er_location_name"] = siteD[0][
'er_location_name']
PmagSiteRec["er_site_name"] = siteD[0]['er_site_name']
PmagSiteRec['site_tilt_correction'] = coords[-1]
PmagSiteRec['site_comp_name'] = pmag.get_list(
siteD, key + '_comp_name')
if Daverage == 1:
PmagSiteRec['er_sample_names'] = pmag.get_list(
siteD, 'er_sample_name')
else:
PmagSiteRec['er_specimen_names'] = pmag.get_list(
siteD, 'er_specimen_name')
# determine the demagnetization code (DC3,4 or 5) for this site
AFnum = len(
pmag.get_dictitem(siteD, 'magic_method_codes',
'LP-DIR-AF', 'has'))
Tnum = len(
pmag.get_dictitem(siteD, 'magic_method_codes',
'LP-DIR-T', 'has'))
DC = 3
if AFnum > 0: DC += 1
if Tnum > 0: DC += 1
PmagSiteRec['magic_method_codes'] = pmag.get_list(
siteD,
'magic_method_codes') + ':' + 'LP-DC' + str(DC)
PmagSiteRec['magic_method_codes'].strip(":")
if plotsites == 1:
print PmagSiteRec['er_site_name']
pmagplotlib.plotSITE(EQ['eqarea'], PmagSiteRec,
siteD,
key) # plot and list the data
pmagplotlib.drawFIGS(EQ)
PmagSites.append(PmagSiteRec)
else: # last component only
siteD = tmp1[:] # get the last orientation system specified
if len(siteD) > 0: # there are some
PmagSiteRec = pmag.lnpbykey(
siteD, 'site', key) # get the average for this site
PmagSiteRec["er_location_name"] = siteD[0][
'er_location_name'] # decorate the record
PmagSiteRec["er_site_name"] = siteD[0]['er_site_name']
PmagSiteRec['site_comp_name'] = comp
PmagSiteRec['site_tilt_correction'] = coords[-1]
PmagSiteRec['site_comp_name'] = pmag.get_list(
siteD, key + '_comp_name')
PmagSiteRec['er_specimen_names'] = pmag.get_list(
siteD, 'er_specimen_name')
PmagSiteRec['er_sample_names'] = pmag.get_list(
siteD, 'er_sample_name')
AFnum = len(
pmag.get_dictitem(siteD, 'magic_method_codes',
'LP-DIR-AF', 'has'))
Tnum = len(
pmag.get_dictitem(siteD, 'magic_method_codes',
'LP-DIR-T', 'has'))
DC = 3
if AFnum > 0: DC += 1
if Tnum > 0: DC += 1
PmagSiteRec['magic_method_codes'] = pmag.get_list(
siteD, 'magic_method_codes') + ':' + 'LP-DC' + str(DC)
PmagSiteRec['magic_method_codes'].strip(":")
if Daverage == 0:
PmagSiteRec['site_comp_name'] = pmag.get_list(
siteD, key + '_comp_name')
if plotsites == 1:
pmagplotlib.plotSITE(EQ['eqarea'], PmagSiteRec, siteD,
key)
pmagplotlib.drawFIGS(EQ)
PmagSites.append(PmagSiteRec)
else:
print 'site information not found in er_sites for site, ', site, ' site will be skipped'
for PmagSiteRec in PmagSites: # now decorate each dictionary some more, and calculate VGPs etc. for results table
PmagSiteRec["er_citation_names"] = "This study"
PmagSiteRec["er_analyst_mail_names"] = user
PmagSiteRec['magic_software_packages'] = version_num
if agefile != "":
PmagSiteRec = pmag.get_age(PmagSiteRec, "er_site_name",
"site_inferred_", AgeNFO, DefaultAge)
PmagSiteRec['pmag_criteria_codes'] = 'ACCEPT'
if 'site_n_lines' in PmagSiteRec.keys(
) and 'site_n_planes' in PmagSiteRec.keys() and PmagSiteRec[
'site_n_lines'] != "" and PmagSiteRec['site_n_planes'] != "":
if int(PmagSiteRec["site_n_planes"]) > 0:
PmagSiteRec["magic_method_codes"] = PmagSiteRec[
'magic_method_codes'] + ":DE-FM-LP"
elif int(PmagSiteRec["site_n_lines"]) > 2:
PmagSiteRec["magic_method_codes"] = PmagSiteRec[
'magic_method_codes'] + ":DE-FM"
kill = pmag.grade(PmagSiteRec, accept, 'site_dir')
if len(kill) == 0:
PmagResRec = {
} # set up dictionary for the pmag_results table entry
PmagResRec['data_type'] = 'i' # decorate it a bit
PmagResRec['magic_software_packages'] = version_num
PmagSiteRec[
'site_description'] = 'Site direction included in results table'
PmagResRec['pmag_criteria_codes'] = 'ACCEPT'
dec = float(PmagSiteRec["site_dec"])
inc = float(PmagSiteRec["site_inc"])
if 'site_alpha95' in PmagSiteRec.keys(
) and PmagSiteRec['site_alpha95'] != "":
a95 = float(PmagSiteRec["site_alpha95"])
else:
a95 = 180.
sitedat = pmag.get_dictitem(
SiteNFO, 'er_site_name', PmagSiteRec['er_site_name'],
'T')[0] # fish out site information (lat/lon, etc.)
lat = float(sitedat['site_lat'])
lon = float(sitedat['site_lon'])
plong, plat, dp, dm = pmag.dia_vgp(
dec, inc, a95, lat, lon) # get the VGP for this site
if PmagSiteRec['site_tilt_correction'] == '-1':
C = ' (spec coord) '
if PmagSiteRec['site_tilt_correction'] == '0':
C = ' (geog. coord) '
if PmagSiteRec['site_tilt_correction'] == '100':
C = ' (strat. coord) '
PmagResRec["pmag_result_name"] = "VGP Site: " + PmagSiteRec[
"er_site_name"] # decorate some more
PmagResRec[
"result_description"] = "Site VGP, coord system = " + str(
coord) + ' component: ' + comp
PmagResRec['er_site_names'] = PmagSiteRec['er_site_name']
PmagResRec['pmag_criteria_codes'] = 'ACCEPT'
PmagResRec['er_citation_names'] = 'This study'
PmagResRec['er_analyst_mail_names'] = user
PmagResRec["er_location_names"] = PmagSiteRec[
"er_location_name"]
if Daverage == 1:
PmagResRec["er_sample_names"] = PmagSiteRec[
"er_sample_names"]
else:
PmagResRec["er_specimen_names"] = PmagSiteRec[
"er_specimen_names"]
PmagResRec["tilt_correction"] = PmagSiteRec[
'site_tilt_correction']
PmagResRec["pole_comp_name"] = PmagSiteRec['site_comp_name']
PmagResRec["average_dec"] = PmagSiteRec["site_dec"]
PmagResRec["average_inc"] = PmagSiteRec["site_inc"]
PmagResRec["average_alpha95"] = PmagSiteRec["site_alpha95"]
PmagResRec["average_n"] = PmagSiteRec["site_n"]
PmagResRec["average_n_lines"] = PmagSiteRec["site_n_lines"]
PmagResRec["average_n_planes"] = PmagSiteRec["site_n_planes"]
PmagResRec["vgp_n"] = PmagSiteRec["site_n"]
PmagResRec["average_k"] = PmagSiteRec["site_k"]
PmagResRec["average_r"] = PmagSiteRec["site_r"]
PmagResRec["average_lat"] = '%10.4f ' % (lat)
PmagResRec["average_lon"] = '%10.4f ' % (lon)
if agefile != "":
PmagResRec = pmag.get_age(PmagResRec, "er_site_names",
"average_", AgeNFO, DefaultAge)
site_height = pmag.get_dictitem(height_nfo, 'er_site_name',
site, 'T')
if len(site_height) > 0:
PmagResRec["average_height"] = site_height[0][
'site_height']
PmagResRec["vgp_lat"] = '%7.1f ' % (plat)
PmagResRec["vgp_lon"] = '%7.1f ' % (plong)
PmagResRec["vgp_dp"] = '%7.1f ' % (dp)
PmagResRec["vgp_dm"] = '%7.1f ' % (dm)
PmagResRec["magic_method_codes"] = PmagSiteRec[
"magic_method_codes"]
if PmagSiteRec['site_tilt_correction'] == '0':
PmagSiteRec['magic_method_codes'] = PmagSiteRec[
'magic_method_codes'] + ":DA-DIR-GEO"
if PmagSiteRec['site_tilt_correction'] == '100':
PmagSiteRec['magic_method_codes'] = PmagSiteRec[
'magic_method_codes'] + ":DA-DIR-TILT"
PmagSiteRec['site_polarity'] = ""
if polarity == 1: # assign polarity based on angle of pole lat to spin axis - may want to re-think this sometime
angle = pmag.angle([0, 0], [0, (90 - plat)])
if angle <= 55.: PmagSiteRec["site_polarity"] = 'n'
if angle > 55. and angle < 125.:
PmagSiteRec["site_polarity"] = 't'
if angle >= 125.: PmagSiteRec["site_polarity"] = 'r'
PmagResults.append(PmagResRec)
if polarity == 1:
crecs = pmag.get_dictitem(PmagSites, 'site_tilt_correction', '100',
'T') # find the tilt corrected data
if len(crecs) < 2:
crecs = pmag.get_dictitem(
PmagSites, 'site_tilt_correction', '0',
'T') # if there aren't any, find the geographic corrected data
if len(crecs) > 2: # if there are some,
comp = pmag.get_list(
crecs,
'site_comp_name').split(':')[0] # find the first component
crecs = pmag.get_dictitem(
crecs, 'site_comp_name', comp,
'T') # fish out all of the first component
precs = []
for rec in crecs:
precs.append({
'dec': rec['site_dec'],
'inc': rec['site_inc'],
'name': rec['er_site_name'],
'loc': rec['er_location_name']
})
polpars = pmag.fisher_by_pol(
precs) # calculate average by polarity
for mode in polpars.keys(
): # hunt through all the modes (normal=A, reverse=B, all=ALL)
PolRes = {}
PolRes['er_citation_names'] = 'This study'
PolRes[
"pmag_result_name"] = "Polarity Average: Polarity " + mode #
PolRes["data_type"] = "a"
PolRes["average_dec"] = '%7.1f' % (polpars[mode]['dec'])
PolRes["average_inc"] = '%7.1f' % (polpars[mode]['inc'])
PolRes["average_n"] = '%i' % (polpars[mode]['n'])
PolRes["average_r"] = '%5.4f' % (polpars[mode]['r'])
PolRes["average_k"] = '%6.0f' % (polpars[mode]['k'])
PolRes["average_alpha95"] = '%7.1f' % (
polpars[mode]['alpha95'])
PolRes['er_site_names'] = polpars[mode]['sites']
PolRes['er_location_names'] = polpars[mode]['locs']
PolRes['magic_software_packages'] = version_num
PmagResults.append(PolRes)
if noInt != 1 and nositeints != 1:
for site in sites: # now do intensities for each site
if plotsites == 1: print site
if Iaverage == 0:
key, intlist = 'specimen', SpecInts # if using specimen level data
if Iaverage == 1:
key, intlist = 'sample', PmagSamps # if using sample level data
Ints = pmag.get_dictitem(
intlist, 'er_site_name', site,
'T') # get all the intensities for this site
if len(Ints) > 0: # there are some
PmagSiteRec = pmag.average_int(
Ints, key,
'site') # get average intensity stuff for site table
PmagResRec = pmag.average_int(
Ints, key,
'average') # get average intensity stuff for results table
if plotsites == 1: # if site by site examination requested - print this site out to the screen
for rec in Ints:
print rec['er_' + key + '_name'], ' %7.1f' % (
1e6 * float(rec[key + '_int']))
if len(Ints) > 1:
print 'Average: ', '%7.1f' % (1e6 * float(
PmagResRec['average_int'])), 'N: ', len(Ints)
print 'Sigma: ', '%7.1f' % (
1e6 * float(PmagResRec['average_int_sigma'])
), 'Sigma %: ', PmagResRec['average_int_sigma_perc']
raw_input('Press any key to continue\n')
er_location_name = Ints[0]["er_location_name"]
PmagSiteRec[
"er_location_name"] = er_location_name # decorate the records
PmagSiteRec["er_citation_names"] = "This study"
PmagResRec["er_location_names"] = er_location_name
PmagResRec["er_citation_names"] = "This study"
PmagSiteRec["er_analyst_mail_names"] = user
PmagResRec["er_analyst_mail_names"] = user
PmagResRec["data_type"] = 'i'
if Iaverage == 0:
PmagSiteRec['er_specimen_names'] = pmag.get_list(
Ints, 'er_specimen_name') # list of all specimens used
PmagResRec['er_specimen_names'] = pmag.get_list(
Ints, 'er_specimen_name')
PmagSiteRec['er_sample_names'] = pmag.get_list(
Ints, 'er_sample_name') # list of all samples used
PmagResRec['er_sample_names'] = pmag.get_list(
Ints, 'er_sample_name')
PmagSiteRec['er_site_name'] = site
PmagResRec['er_site_names'] = site
PmagSiteRec['magic_method_codes'] = pmag.get_list(
Ints, 'magic_method_codes')
PmagResRec['magic_method_codes'] = pmag.get_list(
Ints, 'magic_method_codes')
kill = pmag.grade(PmagSiteRec, accept, 'site_int')
if nocrit == 1 or len(kill) == 0:
b, sig = float(PmagResRec['average_int']), ""
if (PmagResRec['average_int_sigma']) != "":
sig = float(PmagResRec['average_int_sigma'])
sdir = pmag.get_dictitem(PmagResults, 'er_site_names',
site,
'T') # fish out site direction
if len(sdir) > 0 and sdir[-1][
'average_inc'] != "": # get the VDM for this record using last average inclination (hope it is the right one!)
inc = float(sdir[0]['average_inc']) #
mlat = pmag.magnetic_lat(
inc) # get magnetic latitude using dipole formula
PmagResRec["vdm"] = '%8.3e ' % (pmag.b_vdm(
b, mlat)) # get VDM with magnetic latitude
PmagResRec["vdm_n"] = PmagResRec['average_int_n']
if 'average_int_sigma' in PmagResRec.keys(
) and PmagResRec['average_int_sigma'] != "":
vdm_sig = pmag.b_vdm(
float(PmagResRec['average_int_sigma']), mlat)
PmagResRec["vdm_sigma"] = '%8.3e ' % (vdm_sig)
else:
PmagResRec["vdm_sigma"] = ""
mlat = "" # define a model latitude
if get_model_lat == 1: # use present site latitude
mlats = pmag.get_dictitem(SiteNFO, 'er_site_name',
site, 'T')
if len(mlats) > 0: mlat = mlats[0]['site_lat']
elif get_model_lat == 2: # use a model latitude from some plate reconstruction model (or something)
mlats = pmag.get_dictitem(ModelLats, 'er_site_name',
site, 'T')
if len(mlats) > 0:
PmagResRec['model_lat'] = mlats[0][
'site_model_lat']
mlat = PmagResRec['model_lat']
if mlat != "":
PmagResRec["vadm"] = '%8.3e ' % (
pmag.b_vdm(b, float(mlat))
) # get the VADM using the desired latitude
if sig != "":
vdm_sig = pmag.b_vdm(
float(PmagResRec['average_int_sigma']),
float(mlat))
PmagResRec["vadm_sigma"] = '%8.3e ' % (vdm_sig)
PmagResRec["vadm_n"] = PmagResRec['average_int_n']
else:
PmagResRec["vadm_sigma"] = ""
sitedat = pmag.get_dictitem(
SiteNFO, 'er_site_name', PmagSiteRec['er_site_name'],
'T') # fish out site information (lat/lon, etc.)
if len(sitedat) > 0:
sitedat = sitedat[0]
PmagResRec['average_lat'] = sitedat['site_lat']
PmagResRec['average_lon'] = sitedat['site_lon']
else:
PmagResRec['average_lon'] = 'UNKNOWN'
PmagResRec['average_lon'] = 'UNKNOWN'
PmagResRec['magic_software_packages'] = version_num
PmagResRec["pmag_result_name"] = "V[A]DM: Site " + site
PmagResRec["result_description"] = "V[A]DM of site"
PmagResRec["pmag_criteria_codes"] = "ACCEPT"
if agefile != "":
PmagResRec = pmag.get_age(PmagResRec, "er_site_names",
"average_", AgeNFO,
DefaultAge)
site_height = pmag.get_dictitem(height_nfo, 'er_site_name',
site, 'T')
if len(site_height) > 0:
PmagResRec["average_height"] = site_height[0][
'site_height']
PmagSites.append(PmagSiteRec)
PmagResults.append(PmagResRec)
if len(PmagSites) > 0:
Tmp, keylist = pmag.fillkeys(PmagSites)
pmag.magic_write(siteout, Tmp, 'pmag_sites')
print ' sites written to ', siteout
else:
print "No Site level table"
if len(PmagResults) > 0:
TmpRes, keylist = pmag.fillkeys(PmagResults)
pmag.magic_write(resout, TmpRes, 'pmag_results')
print ' results written to ', resout
else:
print "No Results level table"
def 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
fishqq.py
DESCRIPTION
makes qq plot from dec,inc input data
INPUT FORMAT
takes dec/inc pairs in space delimited file
SYNTAX
fishqq.py [command line options]
OPTIONS
-h help message
-f FILE, specify file on command line
-F FILE, specify output file for statistics
-sav save and quit [saves as input file name plus fmt extension]
-fmt specify format for output [png, eps, svg, pdf]
OUTPUT:
Dec Inc N Mu Mu_crit Me Me_crit Y/N
where direction is the principal component and Y/N is Fisherian or not
separate lines for each mode with N >=10 (N and R)
"""
fmt,plot='svg',0
outfile=""
if '-h' in sys.argv: # check if help is needed
print main.__doc__
sys.exit() # graceful quit
elif '-f' in sys.argv: # ask for filename
ind=sys.argv.index('-f')
file=sys.argv[ind+1]
f=open(file,'rU')
data=f.readlines()
if '-F' in sys.argv:
ind=sys.argv.index('-F')
outfile=open(sys.argv[ind+1],'w') # open output file
if '-sav' in sys.argv: plot=1
if '-fmt' in sys.argv:
ind=sys.argv.index('-fmt')
fmt=sys.argv[ind+1]
DIs,nDIs,rDIs= [],[],[] # set up list for data
for line in data: # read in the data from standard input
if '\t' in line:
rec=line.split('\t') # split each line on space to get records
else:
rec=line.split() # split each line on space to get records
DIs.append([float(rec[0]),float(rec[1])]) # append data to Inc
# split into two modes
ppars=pmag.doprinc(DIs) # get principal directions
for rec in DIs:
angle=pmag.angle([rec[0],rec[1]],[ppars['dec'],ppars['inc']])
if angle>90.:
rDIs.append(rec)
else:
nDIs.append(rec)
#
if len(rDIs) >=10 or len(nDIs) >=10:
D1,I1=[],[]
QQ={'unf1':1,'exp1':2}
pmagplotlib.plot_init(QQ['unf1'],5,5)
pmagplotlib.plot_init(QQ['exp1'],5,5)
if len(nDIs) < 10:
ppars=pmag.doprinc(rDIs) # get principal directions
Drbar,Irbar=ppars['dec']-180.,-ppars['inc']
Nr=len(rDIs)
for di in rDIs:
d,irot=pmag.dotilt(di[0],di[1],Drbar-180.,90.-Irbar) # rotate to mean
drot=d-180.
if drot<0:drot=drot+360.
D1.append(drot)
I1.append(irot)
Dtit='Mode 2 Declinations'
Itit='Mode 2 Inclinations'
else:
ppars=pmag.doprinc(nDIs) # get principal directions
Dnbar,Inbar=ppars['dec'],ppars['inc']
Nn=len(nDIs)
for di in nDIs:
d,irot=pmag.dotilt(di[0],di[1],Dnbar-180.,90.-Inbar) # rotate to mean
drot=d-180.
if drot<0:drot=drot+360.
D1.append(drot)
I1.append(irot)
Dtit='Mode 1 Declinations'
Itit='Mode 1 Inclinations'
Mu_n,Mu_ncr=pmagplotlib.plotQQunf(QQ['unf1'],D1,Dtit) # make plot
Me_n,Me_ncr=pmagplotlib.plotQQexp(QQ['exp1'],I1,Itit) # make plot
#print Mu_n,Mu_ncr,Me_n, Me_ncr
if outfile!="":
# Dec Inc N Mu Mu_crit Me Me_crit Y/N
if Mu_n<=Mu_ncr and Me_n<=Me_ncr:
F='Y'
else:
F='N'
outstring='%7.1f %7.1f %i %5.3f %5.3f %5.3f %5.3f %s \n'%(Dnbar,Inbar,Nn,Mu_n,Mu_ncr,Me_n,Me_ncr,F)
outfile.write(outstring)
else:
print 'you need N> 10 for at least one mode'
sys.exit()
if len(rDIs)>10 and len(nDIs)>10:
D2,I2=[],[]
QQ['unf2']=3
QQ['exp2']=4
pmagplotlib.plot_init(QQ['unf2'],5,5)
pmagplotlib.plot_init(QQ['exp2'],5,5)
ppars=pmag.doprinc(rDIs) # get principal directions
Drbar,Irbar=ppars['dec']-180.,-ppars['inc']
Nr=len(rDIs)
for di in rDIs:
d,irot=pmag.dotilt(di[0],di[1],Drbar-180.,90.-Irbar) # rotate to mean
drot=d-180.
if drot<0:drot=drot+360.
D2.append(drot)
I2.append(irot)
Dtit='Mode 2 Declinations'
Itit='Mode 2 Inclinations'
Mu_r,Mu_rcr=pmagplotlib.plotQQunf(QQ['unf2'],D2,Dtit) # make plot
Me_r,Me_rcr=pmagplotlib.plotQQexp(QQ['exp2'],I2,Itit) # make plot
if outfile!="":
# Dec Inc N Mu Mu_crit Me Me_crit Y/N
if Mu_r<=Mu_rcr and Me_r<=Me_rcr:
F='Y'
else:
F='N'
outstring='%7.1f %7.1f %i %5.3f %5.3f %5.3f %5.3f %s \n'%(Drbar,Irbar,Nr,Mu_r,Mu_rcr,Me_r,Me_rcr,F)
outfile.write(outstring)
files={}
for key in QQ.keys():
files[key]=file+'_'+key+'.'+fmt
if pmagplotlib.isServer:
black = '#000000'
purple = '#800080'
titles={}
titles['eq']='Equal Area Plot'
EQ = pmagplotlib.addBorders(EQ,titles,black,purple)
pmagplotlib.saveP(QQ,files)
elif plot==1:
pmagplotlib.saveP(QQ,files)
else:
pmagplotlib.drawFIGS(QQ)
ans=raw_input(" S[a]ve to save plot, [q]uit without saving: ")
if ans=="a": pmagplotlib.saveP(QQ,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
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
angle.py
DESCRIPTION
calculates angle between two input directions D1,D2
INPUT (COMMAND LINE ENTRY)
D1_dec D1_inc D1_dec D2_inc
OUTPUT
angle
SYNTAX
angle.py [-h][-i] [command line options] [< filename]
OPTIONS
-h prints help and quits
-i for interactive data entry
-f FILE input filename
-F FILE output filename (required if -F set)
Standard I/O
"""
out=""
if '-h' in sys.argv:
print main.__doc__
sys.exit()
if '-F' in sys.argv:
ind=sys.argv.index('-F')
o=sys.argv[ind+1]
out=open(o,'w')
if '-i' in sys.argv:
cont=1
while cont==1:
dir1,dir2=[],[]
try:
ans=raw_input('Declination 1: [ctrl-D to quit] ')
dir1.append(float(ans))
ans=raw_input('Inclination 1: ')
dir1.append(float(ans))
ans=raw_input('Declination 2: ')
dir2.append(float(ans))
ans=raw_input('Inclination 2: ')
dir2.append(float(ans))
except:
print "\nGood bye\n"
sys.exit()
ang= pmag.angle(dir1,dir2) # send dirs to angle and spit out result
print '%7.1f '%(ang)
elif '-f' in sys.argv:
ind=sys.argv.index('-f')
file=sys.argv[ind+1]
input=numpy.loadtxt(file)
else:
input = numpy.loadtxt(sys.stdin.readlines(),dtype=numpy.float) # read from standard input
if len(input.shape)>1: # list of directions
dir1,dir2=input[:,0:2],input[:,2:]
else: dir1,dir2=input[0:2],input[2:]
angs=pmag.angle(dir1,dir2)
for ang in angs: # read in the data (as string variable), line by line
print '%7.1f'%(ang)
if out!="":out.write('%7.1f \n'%(ang))
def main():
"""
NAME
angle.py
DESCRIPTION
calculates angle between two input directions D1,D2
INPUT (COMMAND LINE ENTRY)
D1_dec D1_inc D1_dec D2_inc
OUTPUT
angle
SYNTAX
angle.py [-h][-i] [command line options] [< filename]
OPTIONS
-h prints help and quits
-i for interactive data entry
-f FILE input filename
-F FILE output filename (required if -F set)
Standard I/O
"""
out = ""
if '-h' in sys.argv:
print main.__doc__
sys.exit()
if '-F' in sys.argv:
ind = sys.argv.index('-F')
o = sys.argv[ind + 1]
out = open(o, 'w')
if '-i' in sys.argv:
cont = 1
while cont == 1:
dir1, dir2 = [], []
try:
ans = raw_input('Declination 1: [ctrl-D to quit] ')
dir1.append(float(ans))
ans = raw_input('Inclination 1: ')
dir1.append(float(ans))
ans = raw_input('Declination 2: ')
dir2.append(float(ans))
ans = raw_input('Inclination 2: ')
dir2.append(float(ans))
except:
print "\nGood bye\n"
sys.exit()
ang = pmag.angle(dir1,
dir2) # send dirs to angle and spit out result
print '%7.1f ' % (ang)
elif '-f' in sys.argv:
ind = sys.argv.index('-f')
file = sys.argv[ind + 1]
input = numpy.loadtxt(file)
else:
input = numpy.loadtxt(sys.stdin.readlines(),
dtype=numpy.float) # read from standard input
if len(input.shape) > 1: # list of directions
dir1, dir2 = input[:, 0:2], input[:, 2:]
else:
dir1, dir2 = input[0:2], input[2:]
angs = pmag.angle(dir1, dir2)
for ang in angs: # read in the data (as string variable), line by line
print '%7.1f' % (ang)
if out != "": out.write('%7.1f \n' % (ang))
def main():
"""
NAME
revtest_MM1990.py
DESCRIPTION
calculates Watson's V statistic from input files through Monte Carlo simulation in order to test whether normal and reversed populations could have been drawn from a common mean (equivalent to watsonV.py). Also provides the critical angle between the two sample mean directions and the corresponding McFadden and McElhinny (1990) classification.
INPUT FORMAT
takes dec/inc as first two columns in two space delimited files (one file for normal directions, one file for reversed directions).
SYNTAX
revtest_MM1990.py [command line options]
OPTIONS
-h prints help message and quits
-f FILE
-f2 FILE
-P (don't plot the Watson V cdf)
OUTPUT
Watson's V between the two populations and the Monte Carlo Critical Value Vc.
M&M1990 angle, critical angle and classification
Plot of Watson's V CDF from Monte Carlo simulation (red line), V is solid and Vc is dashed.
"""
D1, D2 = [], []
plot = 1
Flip = 1
if '-h' in sys.argv: # check if help is needed
print(main.__doc__)
sys.exit() # graceful quit
if '-P' in sys.argv: plot = 0
if '-f' in sys.argv:
ind = sys.argv.index('-f')
file1 = sys.argv[ind + 1]
f1 = open(file1, 'r')
for line in f1.readlines():
rec = line.split()
Dec, Inc = float(rec[0]), float(rec[1])
D1.append([Dec, Inc, 1.])
f1.close()
if '-f2' in sys.argv:
ind = sys.argv.index('-f2')
file2 = sys.argv[ind + 1]
f2 = open(file2, 'r')
print("be patient, your computer is doing 5000 simulations...")
for line in f2.readlines():
rec = line.split()
Dec, Inc = float(rec[0]), float(rec[1])
D2.append([Dec, Inc, 1.])
f2.close()
#take the antipode for the directions in file 2
D2_flip = []
for rec in D2:
d, i = (rec[0] - 180.) % 360., -rec[1]
D2_flip.append([d, i, 1.])
pars_1 = pmag.fisher_mean(D1)
pars_2 = pmag.fisher_mean(D2_flip)
cart_1 = pmag.dir2cart([pars_1["dec"], pars_1["inc"], pars_1["r"]])
cart_2 = pmag.dir2cart([pars_2['dec'], pars_2['inc'], pars_2["r"]])
Sw = pars_1['k'] * pars_1['r'] + pars_2['k'] * pars_2['r'] # k1*r1+k2*r2
xhat_1 = pars_1['k'] * cart_1[0] + pars_2['k'] * cart_2[0] # k1*x1+k2*x2
xhat_2 = pars_1['k'] * cart_1[1] + pars_2['k'] * cart_2[1] # k1*y1+k2*y2
xhat_3 = pars_1['k'] * cart_1[2] + pars_2['k'] * cart_2[2] # k1*z1+k2*z2
Rw = numpy.sqrt(xhat_1**2 + xhat_2**2 + xhat_3**2)
V = 2 * (Sw - Rw)
#
#keep weighted sum for later when determining the "critical angle" let's save it as Sr (notation of McFadden and McElhinny, 1990)
#
Sr = Sw
#
# do monte carlo simulation of datasets with same kappas, but common mean
#
counter, NumSims = 0, 5000
Vp = [] # set of Vs from simulations
for k in range(NumSims):
#
# get a set of N1 fisher distributed vectors with k1, calculate fisher stats
#
Dirp = []
for i in range(pars_1["n"]):
Dirp.append(pmag.fshdev(pars_1["k"]))
pars_p1 = pmag.fisher_mean(Dirp)
#
# get a set of N2 fisher distributed vectors with k2, calculate fisher stats
#
Dirp = []
for i in range(pars_2["n"]):
Dirp.append(pmag.fshdev(pars_2["k"]))
pars_p2 = pmag.fisher_mean(Dirp)
#
# get the V for these
#
Vk = pmag.vfunc(pars_p1, pars_p2)
Vp.append(Vk)
#
# sort the Vs, get Vcrit (95th percentile one)
#
Vp.sort()
k = int(.95 * NumSims)
Vcrit = Vp[k]
#
# equation 18 of McFadden and McElhinny, 1990 calculates the critical value of R (Rwc)
#
Rwc = Sr - (old_div(Vcrit, 2))
#
#following equation 19 of McFadden and McElhinny (1990) the critical angle is calculated.
#
k1 = pars_1['k']
k2 = pars_2['k']
R1 = pars_1['r']
R2 = pars_2['r']
critical_angle = numpy.degrees(
numpy.arccos(
old_div(((Rwc**2) - ((k1 * R1)**2) - ((k2 * R2)**2)),
(2 * k1 * R1 * k2 * R2))))
D1_mean = (pars_1['dec'], pars_1['inc'])
D2_mean = (pars_2['dec'], pars_2['inc'])
angle = pmag.angle(D1_mean, D2_mean)
#
# print the results of the test
#
print("")
print("Results of Watson V test: ")
print("")
print("Watson's V: " '%.1f' % (V))
print("Critical value of V: " '%.1f' % (Vcrit))
if V < Vcrit:
print(
'"Pass": Since V is less than Vcrit, the null hypothesis that the two populations are drawn from distributions that share a common mean direction (antipodal to one another) cannot be rejected.'
)
elif V > Vcrit:
print(
'"Fail": Since V is greater than Vcrit, the two means can be distinguished at the 95% confidence level.'
)
print("")
print("M&M1990 classification:")
print("")
print("Angle between data set means: " '%.1f' % (angle))
print("Critical angle of M&M1990: " '%.1f' % (critical_angle))
if V > Vcrit:
print("")
elif V < Vcrit:
if critical_angle < 5:
print(
"The McFadden and McElhinny (1990) classification for this test is: 'A'"
)
elif critical_angle < 10:
print(
"The McFadden and McElhinny (1990) classification for this test is: 'B'"
)
elif critical_angle < 20:
print(
"The McFadden and McElhinny (1990) classification for this test is: 'C'"
)
else:
print(
"The McFadden and McElhinny (1990) classification for this test is: 'INDETERMINATE;"
)
if plot == 1:
CDF = {'cdf': 1}
pmagplotlib.plot_init(CDF['cdf'], 5, 5)
p1 = pmagplotlib.plot_cdf(CDF['cdf'], Vp, "Watson's V", 'r', "")
p2 = pmagplotlib.plot_vs(CDF['cdf'], [V], 'g', '-')
p3 = pmagplotlib.plot_vs(CDF['cdf'], [Vp[k]], 'b', '--')
pmagplotlib.draw_figs(CDF)
files, fmt = {}, 'svg'
if file2 != "":
files['cdf'] = 'WatsonsV_' + file1 + '_' + file2 + '.' + fmt
else:
files['cdf'] = 'WatsonsV_' + file1 + '.' + fmt
if pmagplotlib.isServer:
black = '#000000'
purple = '#800080'
titles = {}
titles['cdf'] = 'Cumulative Distribution'
CDF = pmagplotlib.add_borders(CDF, titles, black, purple)
pmagplotlib.save_plots(CDF, files)
else:
ans = input(" S[a]ve to save plot, [q]uit without saving: ")
if ans == "a": pmagplotlib.save_plots(CDF, 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:
# get all records with this dec_key not blank
Decs = pmag.get_dictitem(odata, dec_key, '', 'F')
if len(Decs) > 0:
break
for inc_key in Inc_keys:
# get all records with this inc_key not blank
Incs = pmag.get_dictitem(Decs, inc_key, '', 'F')
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] = ''
# get all records matching specified coordinate system
cdata = pmag.get_dictitem(Incs, tilt_key, coord, 'T')
if coord == '0': # geographic
# get all the blank records - assume geographic
udata = pmag.get_dictitem(Incs, tilt_key, '', 'T')
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:
# get all records with this name_key not blank
Names = pmag.get_dictitem(cdata, name_key, '', 'F')
if len(Names) > 0:
break
for dir_type_key in Dir_type_keys:
# get all records with this direction type
Dirs = pmag.get_dictitem(cdata, dir_type_key, '', 'F')
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.plot_eq(FIG['eqarea'], DIblock, title)
else:
pmagplotlib.plot_eq_cont(FIG['eqarea'], DIblock)
else:
pmagplotlib.plot_net(FIG['eqarea'])
if len(GCblock) > 0:
for rec in GCblock:
pmagplotlib.plot_circ(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.plot_eq_sym(
FIG['bdirs'], BnDIs, 'Bootstrapped Eigenvectors', sym)
if len(rDIs) > 5:
BrDIs = pmag.di_boot(rDIs)
if len(nDIs) > 5: # plot on existing plots
pmagplotlib.plot_di_sym(FIG['bdirs'], BrDIs, sym)
else:
pmagplotlib.plot_eq(
FIG['bdirs'], BrDIs, 'Bootstrapped Eigenvectors')
if dist == 'B':
if len(nDIs) > 3 or len(rDIs) > 3:
pmagplotlib.plot_conf(FIG['eqarea'], etitle, [], npars, 0)
elif len(nDIs) > 3 and dist != 'BV':
pmagplotlib.plot_conf(FIG['eqarea'], etitle, [], npars, 0)
if len(rDIs) > 3:
pmagplotlib.plot_conf(FIG['eqarea'], etitle, [], rpars, 0)
elif len(rDIs) > 3 and dist != 'BV':
pmagplotlib.plot_conf(FIG['eqarea'], etitle, [], rpars, 0)
if verbose:
pmagplotlib.draw_figs(FIG)
#
files = {}
locations = locations[:-1]
for key in FIG.keys():
if 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 plot 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.add_borders(FIG, titles, black, purple)
pmagplotlib.save_plots(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.save_plots(FIG, files)
if plt:
pmagplotlib.save_plots(FIG, files)
def main():
"""
NAME
fishqq.py
DESCRIPTION
makes qq plot from dec,inc input data
INPUT FORMAT
takes dec/inc pairs in space delimited file
SYNTAX
fishqq.py [command line options]
OPTIONS
-h help message
-f FILE, specify file on command line
-F FILE, specify output file for statistics
-sav save and quit [saves as input file name plus fmt extension]
-fmt specify format for output [png, eps, svg, pdf]
OUTPUT:
Dec Inc N Mu Mu_crit Me Me_crit Y/N
where direction is the principal component and Y/N is Fisherian or not
separate lines for each mode with N >=10 (N and R)
"""
fmt,plot='svg',0
outfile=""
if '-h' in sys.argv: # check if help is needed
print(main.__doc__)
sys.exit() # graceful quit
elif '-f' in sys.argv: # ask for filename
ind=sys.argv.index('-f')
file=sys.argv[ind+1]
f=open(file,'r')
data=f.readlines()
if '-F' in sys.argv:
ind=sys.argv.index('-F')
outfile=open(sys.argv[ind+1],'w') # open output file
if '-sav' in sys.argv: plot=1
if '-fmt' in sys.argv:
ind=sys.argv.index('-fmt')
fmt=sys.argv[ind+1]
DIs,nDIs,rDIs= [],[],[] # set up list for data
for line in data: # read in the data from standard input
if '\t' in line:
rec=line.split('\t') # split each line on space to get records
else:
rec=line.split() # split each line on space to get records
DIs.append([float(rec[0]),float(rec[1])]) # append data to Inc
# split into two modes
ppars=pmag.doprinc(DIs) # get principal directions
for rec in DIs:
angle=pmag.angle([rec[0],rec[1]],[ppars['dec'],ppars['inc']])
if angle>90.:
rDIs.append(rec)
else:
nDIs.append(rec)
#
if len(rDIs) >=10 or len(nDIs) >=10:
D1,I1=[],[]
QQ={'unf1':1,'exp1':2}
pmagplotlib.plot_init(QQ['unf1'],5,5)
pmagplotlib.plot_init(QQ['exp1'],5,5)
if len(nDIs) < 10:
ppars=pmag.doprinc(rDIs) # get principal directions
Drbar,Irbar=ppars['dec']-180.,-ppars['inc']
Nr=len(rDIs)
for di in rDIs:
d,irot=pmag.dotilt(di[0],di[1],Drbar-180.,90.-Irbar) # rotate to mean
drot=d-180.
if drot<0:drot=drot+360.
D1.append(drot)
I1.append(irot)
Dtit='Mode 2 Declinations'
Itit='Mode 2 Inclinations'
else:
ppars=pmag.doprinc(nDIs) # get principal directions
Dnbar,Inbar=ppars['dec'],ppars['inc']
Nn=len(nDIs)
for di in nDIs:
d,irot=pmag.dotilt(di[0],di[1],Dnbar-180.,90.-Inbar) # rotate to mean
drot=d-180.
if drot<0:drot=drot+360.
D1.append(drot)
I1.append(irot)
Dtit='Mode 1 Declinations'
Itit='Mode 1 Inclinations'
Mu_n,Mu_ncr=pmagplotlib.plot_qq_unf(QQ['unf1'],D1,Dtit) # make plot
Me_n,Me_ncr=pmagplotlib.plot_qq_exp(QQ['exp1'],I1,Itit) # make plot
#print Mu_n,Mu_ncr,Me_n, Me_ncr
if outfile!="":
# Dec Inc N Mu Mu_crit Me Me_crit Y/N
if Mu_n<=Mu_ncr and Me_n<=Me_ncr:
F='Y'
else:
F='N'
outstring='%7.1f %7.1f %i %5.3f %5.3f %5.3f %5.3f %s \n'%(Dnbar,Inbar,Nn,Mu_n,Mu_ncr,Me_n,Me_ncr,F)
outfile.write(outstring)
else:
print('you need N> 10 for at least one mode')
sys.exit()
if len(rDIs)>10 and len(nDIs)>10:
D2,I2=[],[]
QQ['unf2']=3
QQ['exp2']=4
pmagplotlib.plot_init(QQ['unf2'],5,5)
pmagplotlib.plot_init(QQ['exp2'],5,5)
ppars=pmag.doprinc(rDIs) # get principal directions
Drbar,Irbar=ppars['dec']-180.,-ppars['inc']
Nr=len(rDIs)
for di in rDIs:
d,irot=pmag.dotilt(di[0],di[1],Drbar-180.,90.-Irbar) # rotate to mean
drot=d-180.
if drot<0:drot=drot+360.
D2.append(drot)
I2.append(irot)
Dtit='Mode 2 Declinations'
Itit='Mode 2 Inclinations'
Mu_r,Mu_rcr=pmagplotlib.plot_qq_unf(QQ['unf2'],D2,Dtit) # make plot
Me_r,Me_rcr=pmagplotlib.plot_qq_exp(QQ['exp2'],I2,Itit) # make plot
if outfile!="":
# Dec Inc N Mu Mu_crit Me Me_crit Y/N
if Mu_r<=Mu_rcr and Me_r<=Me_rcr:
F='Y'
else:
F='N'
outstring='%7.1f %7.1f %i %5.3f %5.3f %5.3f %5.3f %s \n'%(Drbar,Irbar,Nr,Mu_r,Mu_rcr,Me_r,Me_rcr,F)
outfile.write(outstring)
files={}
for key in list(QQ.keys()):
files[key]=file+'_'+key+'.'+fmt
if pmagplotlib.isServer:
black = '#000000'
purple = '#800080'
titles={}
titles['eq']='Equal Area Plot'
EQ = pmagplotlib.add_borders(EQ,titles,black,purple)
pmagplotlib.save_plots(QQ,files)
elif plot==1:
pmagplotlib.save_plots(QQ,files)
else:
pmagplotlib.draw_figs(QQ)
ans=input(" S[a]ve to save plot, [q]uit without saving: ")
if ans=="a": pmagplotlib.save_plots(QQ,files)
def main():
"""
NAME
specimens_results_magic.py
DESCRIPTION
combines pmag_specimens.txt file with age, location, acceptance criteria and
outputs pmag_results table along with other MagIC tables necessary for uploading to the database
SYNTAX
specimens_results_magic.py [command line options]
OPTIONS
-h prints help message and quits
-usr USER: identify user, default is ""
-f: specimen input magic_measurements format file, default is "magic_measurements.txt"
-fsp: specimen input pmag_specimens format file, default is "pmag_specimens.txt"
-fsm: sample input er_samples format file, default is "er_samples.txt"
-fsi: specimen input er_sites format file, default is "er_sites.txt"
-fla: specify a file with paleolatitudes for calculating VADMs, default is not to calculate VADMS
format is: site_name paleolatitude (space delimited file)
-fa AGES: specify er_ages format file with age information
-crd [s,g,t,b]: specify coordinate system
(s, specimen, g geographic, t, tilt corrected, b, geographic and tilt corrected)
Default is to assume geographic
NB: only the tilt corrected data will appear on the results table, if both g and t are selected.
-cor [AC:CR:NL]: colon delimited list of required data adjustments for all specimens
included in intensity calculations (anisotropy, cooling rate, non-linear TRM)
unless specified, corrections will not be applied
-pri [TRM:ARM] colon delimited list of priorities for anisotropy correction (-cor must also be set to include AC). default is TRM, then ARM
-age MIN MAX UNITS: specify age boundaries and units
-exc: use exiting selection criteria (in pmag_criteria.txt file), default is default criteria
-C: no acceptance criteria
-aD: average directions per sample, default is NOT
-aI: average multiple specimen intensities per sample, default is by site
-aC: average all components together, default is NOT
-pol: calculate polarity averages
-sam: save sample level vgps and v[a]dms, default is by site
-xSi: skip the site level intensity calculation
-p: plot directions and look at intensities by site, default is NOT
-fmt: specify output for saved images, default is svg (only if -p set)
-lat: use present latitude for calculating VADMs, default is not to calculate VADMs
-xD: skip directions
-xI: skip intensities
OUPUT
writes pmag_samples, pmag_sites, pmag_results tables
"""
# set defaults
Comps=[] # list of components
version_num=pmag.get_version()
args=sys.argv
DefaultAge=["none"]
skipdirs,coord,excrit,custom,vgps,average,Iaverage,plotsites,opt=1,0,0,0,0,0,0,0,0
get_model_lat=0 # this skips VADM calculation altogether, when get_model_lat=1, uses present day
fmt='svg'
dir_path="."
model_lat_file=""
Caverage=0
infile='pmag_specimens.txt'
measfile="magic_measurements.txt"
sampfile="er_samples.txt"
sitefile="er_sites.txt"
agefile="er_ages.txt"
specout="er_specimens.txt"
sampout="pmag_samples.txt"
siteout="pmag_sites.txt"
resout="pmag_results.txt"
critout="pmag_criteria.txt"
instout="magic_instruments.txt"
sigcutoff,OBJ="",""
noDir,noInt=0,0
polarity=0
coords=['0']
Dcrit,Icrit,nocrit=0,0,0
corrections=[]
nocorrection=['DA-NL','DA-AC','DA-CR']
priorities=['DA-AC-ARM','DA-AC-TRM'] # priorities for anisotropy correction
# get command line stuff
if "-h" in args:
print(main.__doc__)
sys.exit()
if '-WD' in args:
ind=args.index("-WD")
dir_path=args[ind+1]
if '-cor' in args:
ind=args.index('-cor')
cors=args[ind+1].split(':') # list of required data adjustments
for cor in cors:
nocorrection.remove('DA-'+cor)
corrections.append('DA-'+cor)
if '-pri' in args:
ind=args.index('-pri')
priorities=args[ind+1].split(':') # list of required data adjustments
for p in priorities:
p='DA-AC-'+p
if '-f' in args:
ind=args.index("-f")
measfile=args[ind+1]
if '-fsp' in args:
ind=args.index("-fsp")
infile=args[ind+1]
if '-fsi' in args:
ind=args.index("-fsi")
sitefile=args[ind+1]
if "-crd" in args:
ind=args.index("-crd")
coord=args[ind+1]
if coord=='s':coords=['-1']
if coord=='g':coords=['0']
if coord=='t':coords=['100']
if coord=='b':coords=['0','100']
if "-usr" in args:
ind=args.index("-usr")
user=sys.argv[ind+1]
else: user=""
if "-C" in args: Dcrit,Icrit,nocrit=1,1,1 # no selection criteria
if "-sam" in args: vgps=1 # save sample level VGPS/VADMs
if "-xSi" in args:
nositeints=1 # skip site level intensity
else:
nositeints=0
if "-age" in args:
ind=args.index("-age")
DefaultAge[0]=args[ind+1]
DefaultAge.append(args[ind+2])
DefaultAge.append(args[ind+3])
Daverage,Iaverage,Caverage=0,0,0
if "-aD" in args: Daverage=1 # average by sample directions
if "-aI" in args: Iaverage=1 # average by sample intensities
if "-aC" in args: Caverage=1 # average all components together ??? why???
if "-pol" in args: polarity=1 # calculate averages by polarity
if '-xD' in args:noDir=1
if '-xI' in args:
noInt=1
elif "-fla" in args:
if '-lat' in args:
print("you should set a paleolatitude file OR use present day lat - not both")
sys.exit()
ind=args.index("-fla")
model_lat_file=dir_path+'/'+args[ind+1]
get_model_lat=2
mlat=open(model_lat_file,'r')
ModelLats=[]
for line in mlat.readlines():
ModelLat={}
tmp=line.split()
ModelLat["er_site_name"]=tmp[0]
ModelLat["site_model_lat"]=tmp[1]
ModelLat["er_sample_name"]=tmp[0]
ModelLat["sample_lat"]=tmp[1]
ModelLats.append(ModelLat)
get_model_lat=2
elif '-lat' in args:
get_model_lat=1
if "-p" in args:
plotsites=1
if "-fmt" in args:
ind=args.index("-fmt")
fmt=args[ind+1]
if noDir==0: # plot by site - set up plot window
import pmagplotlib
EQ={}
EQ['eqarea']=1
pmagplotlib.plot_init(EQ['eqarea'],5,5) # define figure 1 as equal area projection
pmagplotlib.plotNET(EQ['eqarea']) # I don't know why this has to be here, but otherwise the first plot never plots...
pmagplotlib.drawFIGS(EQ)
if '-WD' in args:
infile=dir_path+'/'+infile
measfile=dir_path+'/'+measfile
instout=dir_path+'/'+instout
sampfile=dir_path+'/'+sampfile
sitefile=dir_path+'/'+sitefile
agefile=dir_path+'/'+agefile
specout=dir_path+'/'+specout
sampout=dir_path+'/'+sampout
siteout=dir_path+'/'+siteout
resout=dir_path+'/'+resout
critout=dir_path+'/'+critout
if "-exc" in args: # use existing pmag_criteria file
if "-C" in args:
print('you can not use both existing and no criteria - choose either -exc OR -C OR neither (for default)')
sys.exit()
crit_data,file_type=pmag.magic_read(critout)
print("Acceptance criteria read in from ", critout)
else : # use default criteria (if nocrit set, then get really loose criteria as default)
crit_data=pmag.default_criteria(nocrit)
if nocrit==0:
print("Acceptance criteria are defaults")
else:
print("No acceptance criteria used ")
accept={}
for critrec in crit_data:
for key in list(critrec.keys()):
# need to migrate specimen_dang to specimen_int_dang for intensity data using old format
if 'IE-SPEC' in list(critrec.keys()) and 'specimen_dang' in list(critrec.keys()) and 'specimen_int_dang' not in list(critrec.keys()):
critrec['specimen_int_dang']=critrec['specimen_dang']
del critrec['specimen_dang']
# need to get rid of ron shaars sample_int_sigma_uT
if 'sample_int_sigma_uT' in list(critrec.keys()):
critrec['sample_int_sigma']='%10.3e'%(eval(critrec['sample_int_sigma_uT'])*1e-6)
if key not in list(accept.keys()) and critrec[key]!='':
accept[key]=critrec[key]
#
#
if "-exc" not in args and "-C" not in args:
print("args",args)
pmag.magic_write(critout,[accept],'pmag_criteria')
print("\n Pmag Criteria stored in ",critout,'\n')
#
# now we're done slow dancing
#
SiteNFO,file_type=pmag.magic_read(sitefile) # read in site data - has the lats and lons
SampNFO,file_type=pmag.magic_read(sampfile) # read in site data - has the lats and lons
height_nfo=pmag.get_dictitem(SiteNFO,'site_height','','F') # find all the sites with height info.
if agefile !="":AgeNFO,file_type=pmag.magic_read(agefile) # read in the age information
Data,file_type=pmag.magic_read(infile) # read in specimen interpretations
IntData=pmag.get_dictitem(Data,'specimen_int','','F') # retrieve specimens with intensity data
comment,orient="",[]
samples,sites=[],[]
for rec in Data: # run through the data filling in missing keys and finding all components, coordinates available
# fill in missing fields, collect unique sample and site names
if 'er_sample_name' not in list(rec.keys()):
rec['er_sample_name']=""
elif rec['er_sample_name'] not in samples:
samples.append(rec['er_sample_name'])
if 'er_site_name' not in list(rec.keys()):
rec['er_site_name']=""
elif rec['er_site_name'] not in sites:
sites.append(rec['er_site_name'])
if 'specimen_int' not in list(rec.keys()):rec['specimen_int']=''
if 'specimen_comp_name' not in list(rec.keys()) or rec['specimen_comp_name']=="":rec['specimen_comp_name']='A'
if rec['specimen_comp_name'] not in Comps:Comps.append(rec['specimen_comp_name'])
rec['specimen_tilt_correction']=rec['specimen_tilt_correction'].strip('\n')
if "specimen_tilt_correction" not in list(rec.keys()): rec["specimen_tilt_correction"]="-1" # assume sample coordinates
if rec["specimen_tilt_correction"] not in orient: orient.append(rec["specimen_tilt_correction"]) # collect available coordinate systems
if "specimen_direction_type" not in list(rec.keys()): rec["specimen_direction_type"]='l' # assume direction is line - not plane
if "specimen_dec" not in list(rec.keys()): rec["specimen_direction_type"]='' # if no declination, set direction type to blank
if "specimen_n" not in list(rec.keys()): rec["specimen_n"]='' # put in n
if "specimen_alpha95" not in list(rec.keys()): rec["specimen_alpha95"]='' # put in alpha95
if "magic_method_codes" not in list(rec.keys()): rec["magic_method_codes"]=''
#
# start parsing data into SpecDirs, SpecPlanes, SpecInts
SpecInts,SpecDirs,SpecPlanes=[],[],[]
samples.sort() # get sorted list of samples and sites
sites.sort()
if noInt==0: # don't skip intensities
IntData=pmag.get_dictitem(Data,'specimen_int','','F') # retrieve specimens with intensity data
if nocrit==0: # use selection criteria
for rec in IntData: # do selection criteria
kill=pmag.grade(rec,accept,'specimen_int')
if len(kill)==0: SpecInts.append(rec) # intensity record to be included in sample, site calculations
else:
SpecInts=IntData[:] # take everything - no selection criteria
# check for required data adjustments
if len(corrections)>0 and len(SpecInts)>0:
for cor in corrections:
SpecInts=pmag.get_dictitem(SpecInts,'magic_method_codes',cor,'has') # only take specimens with the required corrections
if len(nocorrection)>0 and len(SpecInts)>0:
for cor in nocorrection:
SpecInts=pmag.get_dictitem(SpecInts,'magic_method_codes',cor,'not') # exclude the corrections not specified for inclusion
# take top priority specimen of its name in remaining specimens (only one per customer)
PrioritySpecInts=[]
specimens=pmag.get_specs(SpecInts) # get list of uniq specimen names
for spec in specimens:
ThisSpecRecs=pmag.get_dictitem(SpecInts,'er_specimen_name',spec,'T') # all the records for this specimen
if len(ThisSpecRecs)==1:
PrioritySpecInts.append(ThisSpecRecs[0])
elif len(ThisSpecRecs)>1: # more than one
prec=[]
for p in priorities:
ThisSpecRecs=pmag.get_dictitem(SpecInts,'magic_method_codes',p,'has') # all the records for this specimen
if len(ThisSpecRecs)>0:prec.append(ThisSpecRecs[0])
PrioritySpecInts.append(prec[0]) # take the best one
SpecInts=PrioritySpecInts # this has the first specimen record
if noDir==0: # don't skip directions
AllDirs=pmag.get_dictitem(Data,'specimen_direction_type','','F') # retrieve specimens with directed lines and planes
Ns=pmag.get_dictitem(AllDirs,'specimen_n','','F') # get all specimens with specimen_n information
if nocrit!=1: # use selection criteria
for rec in Ns: # look through everything with specimen_n for "good" data
kill=pmag.grade(rec,accept,'specimen_dir')
if len(kill)==0: # nothing killed it
SpecDirs.append(rec)
else: # no criteria
SpecDirs=AllDirs[:] # take them all
# SpecDirs is now the list of all specimen directions (lines and planes) that pass muster
#
PmagSamps,SampDirs=[],[] # list of all sample data and list of those that pass the DE-SAMP criteria
PmagSites,PmagResults=[],[] # list of all site data and selected results
SampInts=[]
for samp in samples: # run through the sample names
if Daverage==1: # average by sample if desired
SampDir=pmag.get_dictitem(SpecDirs,'er_sample_name',samp,'T') # get all the directional data for this sample
if len(SampDir)>0: # there are some directions
for coord in coords: # step through desired coordinate systems
CoordDir=pmag.get_dictitem(SampDir,'specimen_tilt_correction',coord,'T') # get all the directions for this sample
if len(CoordDir)>0: # there are some with this coordinate system
if Caverage==0: # look component by component
for comp in Comps:
CompDir=pmag.get_dictitem(CoordDir,'specimen_comp_name',comp,'T') # get all directions from this component
if len(CompDir)>0: # there are some
PmagSampRec=pmag.lnpbykey(CompDir,'sample','specimen') # get a sample average from all specimens
PmagSampRec["er_location_name"]=CompDir[0]['er_location_name'] # decorate the sample record
PmagSampRec["er_site_name"]=CompDir[0]['er_site_name']
PmagSampRec["er_sample_name"]=samp
PmagSampRec["er_citation_names"]="This study"
PmagSampRec["er_analyst_mail_names"]=user
PmagSampRec['magic_software_packages']=version_num
if nocrit!=1:PmagSampRec['pmag_criteria_codes']="ACCEPT"
if agefile != "": PmagSampRec= pmag.get_age(PmagSampRec,"er_site_name","sample_inferred_",AgeNFO,DefaultAge)
site_height=pmag.get_dictitem(height_nfo,'er_site_name',PmagSampRec['er_site_name'],'T')
if len(site_height)>0:PmagSampRec["sample_height"]=site_height[0]['site_height'] # add in height if available
PmagSampRec['sample_comp_name']=comp
PmagSampRec['sample_tilt_correction']=coord
PmagSampRec['er_specimen_names']= pmag.get_list(CompDir,'er_specimen_name') # get a list of the specimen names used
PmagSampRec['magic_method_codes']= pmag.get_list(CompDir,'magic_method_codes') # get a list of the methods used
if nocrit!=1: # apply selection criteria
kill=pmag.grade(PmagSampRec,accept,'sample_dir')
else:
kill=[]
if len(kill)==0:
SampDirs.append(PmagSampRec)
if vgps==1: # if sample level VGP info desired, do that now
PmagResRec=pmag.getsampVGP(PmagSampRec,SiteNFO)
if PmagResRec!="":PmagResults.append(PmagResRec)
PmagSamps.append(PmagSampRec)
if Caverage==1: # average all components together basically same as above
PmagSampRec=pmag.lnpbykey(CoordDir,'sample','specimen')
PmagSampRec["er_location_name"]=CoordDir[0]['er_location_name']
PmagSampRec["er_site_name"]=CoordDir[0]['er_site_name']
PmagSampRec["er_sample_name"]=samp
PmagSampRec["er_citation_names"]="This study"
PmagSampRec["er_analyst_mail_names"]=user
PmagSampRec['magic_software_packages']=version_num
if nocrit!=1:PmagSampRec['pmag_criteria_codes']=""
if agefile != "": PmagSampRec= pmag.get_age(PmagSampRec,"er_site_name","sample_inferred_",AgeNFO,DefaultAge)
site_height=pmag.get_dictitem(height_nfo,'er_site_name',site,'T')
if len(site_height)>0:PmagSampRec["sample_height"]=site_height[0]['site_height'] # add in height if available
PmagSampRec['sample_tilt_correction']=coord
PmagSampRec['sample_comp_name']= pmag.get_list(CoordDir,'specimen_comp_name') # get components used
PmagSampRec['er_specimen_names']= pmag.get_list(CoordDir,'er_specimen_name') # get specimne names averaged
PmagSampRec['magic_method_codes']= pmag.get_list(CoordDir,'magic_method_codes') # assemble method codes
if nocrit!=1: # apply selection criteria
kill=pmag.grade(PmagSampRec,accept,'sample_dir')
if len(kill)==0: # passes the mustard
SampDirs.append(PmagSampRec)
if vgps==1:
PmagResRec=pmag.getsampVGP(PmagSampRec,SiteNFO)
if PmagResRec!="":PmagResults.append(PmagResRec)
else: # take everything
SampDirs.append(PmagSampRec)
if vgps==1:
PmagResRec=pmag.getsampVGP(PmagSampRec,SiteNFO)
if PmagResRec!="":PmagResults.append(PmagResRec)
PmagSamps.append(PmagSampRec)
if Iaverage==1: # average by sample if desired
SampI=pmag.get_dictitem(SpecInts,'er_sample_name',samp,'T') # get all the intensity data for this sample
if len(SampI)>0: # there are some
PmagSampRec=pmag.average_int(SampI,'specimen','sample') # get average intensity stuff
PmagSampRec["sample_description"]="sample intensity" # decorate sample record
PmagSampRec["sample_direction_type"]=""
PmagSampRec['er_site_name']=SampI[0]["er_site_name"]
PmagSampRec['er_sample_name']=samp
PmagSampRec['er_location_name']=SampI[0]["er_location_name"]
PmagSampRec["er_citation_names"]="This study"
PmagSampRec["er_analyst_mail_names"]=user
if agefile != "": PmagSampRec=pmag.get_age(PmagSampRec,"er_site_name","sample_inferred_", AgeNFO,DefaultAge)
site_height=pmag.get_dictitem(height_nfo,'er_site_name',PmagSampRec['er_site_name'],'T')
if len(site_height)>0:PmagSampRec["sample_height"]=site_height[0]['site_height'] # add in height if available
PmagSampRec['er_specimen_names']= pmag.get_list(SampI,'er_specimen_name')
PmagSampRec['magic_method_codes']= pmag.get_list(SampI,'magic_method_codes')
if nocrit!=1: # apply criteria!
kill=pmag.grade(PmagSampRec,accept,'sample_int')
if len(kill)==0:
PmagSampRec['pmag_criteria_codes']="ACCEPT"
SampInts.append(PmagSampRec)
PmagSamps.append(PmagSampRec)
else:PmagSampRec={} # sample rejected
else: # no criteria
SampInts.append(PmagSampRec)
PmagSamps.append(PmagSampRec)
PmagSampRec['pmag_criteria_codes']=""
if vgps==1 and get_model_lat!=0 and PmagSampRec!={}: #
if get_model_lat==1: # use sample latitude
PmagResRec=pmag.getsampVDM(PmagSampRec,SampNFO)
del(PmagResRec['model_lat']) # get rid of the model lat key
elif get_model_lat==2: # use model latitude
PmagResRec=pmag.getsampVDM(PmagSampRec,ModelLats)
if PmagResRec!={}:PmagResRec['magic_method_codes']=PmagResRec['magic_method_codes']+":IE-MLAT"
if PmagResRec!={}:
PmagResRec['er_specimen_names']=PmagSampRec['er_specimen_names']
PmagResRec['er_sample_names']=PmagSampRec['er_sample_name']
PmagResRec['pmag_criteria_codes']='ACCEPT'
PmagResRec['average_int_sigma_perc']=PmagSampRec['sample_int_sigma_perc']
PmagResRec['average_int_sigma']=PmagSampRec['sample_int_sigma']
PmagResRec['average_int_n']=PmagSampRec['sample_int_n']
PmagResRec['vadm_n']=PmagSampRec['sample_int_n']
PmagResRec['data_type']='i'
PmagResults.append(PmagResRec)
if len(PmagSamps)>0:
TmpSamps,keylist=pmag.fillkeys(PmagSamps) # fill in missing keys from different types of records
pmag.magic_write(sampout,TmpSamps,'pmag_samples') # save in sample output file
print(' sample averages written to ',sampout)
#
#create site averages from specimens or samples as specified
#
for site in sites:
if Daverage==0: key,dirlist='specimen',SpecDirs # if specimen averages at site level desired
if Daverage==1: key,dirlist='sample',SampDirs # if sample averages at site level desired
tmp=pmag.get_dictitem(dirlist,'er_site_name',site,'T') # get all the sites with directions
tmp1=pmag.get_dictitem(tmp,key+'_tilt_correction',coords[-1],'T') # use only the last coordinate if Caverage==0
sd=pmag.get_dictitem(SiteNFO,'er_site_name',site,'T') # fish out site information (lat/lon, etc.)
if len(sd)>0:
sitedat=sd[0]
if Caverage==0: # do component wise averaging
for comp in Comps:
siteD=pmag.get_dictitem(tmp1,key+'_comp_name',comp,'T') # get all components comp
if len(siteD)>0: # there are some for this site and component name
PmagSiteRec=pmag.lnpbykey(siteD,'site',key) # get an average for this site
PmagSiteRec['site_comp_name']=comp # decorate the site record
PmagSiteRec["er_location_name"]=siteD[0]['er_location_name']
PmagSiteRec["er_site_name"]=siteD[0]['er_site_name']
PmagSiteRec['site_tilt_correction']=coords[-1]
PmagSiteRec['site_comp_name']= pmag.get_list(siteD,key+'_comp_name')
if Daverage==1:
PmagSiteRec['er_sample_names']= pmag.get_list(siteD,'er_sample_name')
else:
PmagSiteRec['er_specimen_names']= pmag.get_list(siteD,'er_specimen_name')
# determine the demagnetization code (DC3,4 or 5) for this site
AFnum=len(pmag.get_dictitem(siteD,'magic_method_codes','LP-DIR-AF','has'))
Tnum=len(pmag.get_dictitem(siteD,'magic_method_codes','LP-DIR-T','has'))
DC=3
if AFnum>0:DC+=1
if Tnum>0:DC+=1
PmagSiteRec['magic_method_codes']= pmag.get_list(siteD,'magic_method_codes')+':'+ 'LP-DC'+str(DC)
PmagSiteRec['magic_method_codes'].strip(":")
if plotsites==1:
print(PmagSiteRec['er_site_name'])
pmagplotlib.plotSITE(EQ['eqarea'],PmagSiteRec,siteD,key) # plot and list the data
pmagplotlib.drawFIGS(EQ)
PmagSites.append(PmagSiteRec)
else: # last component only
siteD=tmp1[:] # get the last orientation system specified
if len(siteD)>0: # there are some
PmagSiteRec=pmag.lnpbykey(siteD,'site',key) # get the average for this site
PmagSiteRec["er_location_name"]=siteD[0]['er_location_name'] # decorate the record
PmagSiteRec["er_site_name"]=siteD[0]['er_site_name']
PmagSiteRec['site_comp_name']=comp
PmagSiteRec['site_tilt_correction']=coords[-1]
PmagSiteRec['site_comp_name']= pmag.get_list(siteD,key+'_comp_name')
PmagSiteRec['er_specimen_names']= pmag.get_list(siteD,'er_specimen_name')
PmagSiteRec['er_sample_names']= pmag.get_list(siteD,'er_sample_name')
AFnum=len(pmag.get_dictitem(siteD,'magic_method_codes','LP-DIR-AF','has'))
Tnum=len(pmag.get_dictitem(siteD,'magic_method_codes','LP-DIR-T','has'))
DC=3
if AFnum>0:DC+=1
if Tnum>0:DC+=1
PmagSiteRec['magic_method_codes']= pmag.get_list(siteD,'magic_method_codes')+':'+ 'LP-DC'+str(DC)
PmagSiteRec['magic_method_codes'].strip(":")
if Daverage==0:PmagSiteRec['site_comp_name']= pmag.get_list(siteD,key+'_comp_name')
if plotsites==1:
pmagplotlib.plotSITE(EQ['eqarea'],PmagSiteRec,siteD,key)
pmagplotlib.drawFIGS(EQ)
PmagSites.append(PmagSiteRec)
else:
print('site information not found in er_sites for site, ',site,' site will be skipped')
for PmagSiteRec in PmagSites: # now decorate each dictionary some more, and calculate VGPs etc. for results table
PmagSiteRec["er_citation_names"]="This study"
PmagSiteRec["er_analyst_mail_names"]=user
PmagSiteRec['magic_software_packages']=version_num
if agefile != "": PmagSiteRec= pmag.get_age(PmagSiteRec,"er_site_name","site_inferred_",AgeNFO,DefaultAge)
PmagSiteRec['pmag_criteria_codes']='ACCEPT'
if 'site_n_lines' in list(PmagSiteRec.keys()) and 'site_n_planes' in list(PmagSiteRec.keys()) and PmagSiteRec['site_n_lines']!="" and PmagSiteRec['site_n_planes']!="":
if int(PmagSiteRec["site_n_planes"])>0:
PmagSiteRec["magic_method_codes"]=PmagSiteRec['magic_method_codes']+":DE-FM-LP"
elif int(PmagSiteRec["site_n_lines"])>2:
PmagSiteRec["magic_method_codes"]=PmagSiteRec['magic_method_codes']+":DE-FM"
kill=pmag.grade(PmagSiteRec,accept,'site_dir')
if len(kill)==0:
PmagResRec={} # set up dictionary for the pmag_results table entry
PmagResRec['data_type']='i' # decorate it a bit
PmagResRec['magic_software_packages']=version_num
PmagSiteRec['site_description']='Site direction included in results table'
PmagResRec['pmag_criteria_codes']='ACCEPT'
dec=float(PmagSiteRec["site_dec"])
inc=float(PmagSiteRec["site_inc"])
if 'site_alpha95' in list(PmagSiteRec.keys()) and PmagSiteRec['site_alpha95']!="":
a95=float(PmagSiteRec["site_alpha95"])
else:a95=180.
sitedat=pmag.get_dictitem(SiteNFO,'er_site_name',PmagSiteRec['er_site_name'],'T')[0] # fish out site information (lat/lon, etc.)
lat=float(sitedat['site_lat'])
lon=float(sitedat['site_lon'])
plong,plat,dp,dm=pmag.dia_vgp(dec,inc,a95,lat,lon) # get the VGP for this site
if PmagSiteRec['site_tilt_correction']=='-1':C=' (spec coord) '
if PmagSiteRec['site_tilt_correction']=='0':C=' (geog. coord) '
if PmagSiteRec['site_tilt_correction']=='100':C=' (strat. coord) '
PmagResRec["pmag_result_name"]="VGP Site: "+PmagSiteRec["er_site_name"] # decorate some more
PmagResRec["result_description"]="Site VGP, coord system = "+str(coord)+' component: '+comp
PmagResRec['er_site_names']=PmagSiteRec['er_site_name']
PmagResRec['pmag_criteria_codes']='ACCEPT'
PmagResRec['er_citation_names']='This study'
PmagResRec['er_analyst_mail_names']=user
PmagResRec["er_location_names"]=PmagSiteRec["er_location_name"]
if Daverage==1:
PmagResRec["er_sample_names"]=PmagSiteRec["er_sample_names"]
else:
PmagResRec["er_specimen_names"]=PmagSiteRec["er_specimen_names"]
PmagResRec["tilt_correction"]=PmagSiteRec['site_tilt_correction']
PmagResRec["pole_comp_name"]=PmagSiteRec['site_comp_name']
PmagResRec["average_dec"]=PmagSiteRec["site_dec"]
PmagResRec["average_inc"]=PmagSiteRec["site_inc"]
PmagResRec["average_alpha95"]=PmagSiteRec["site_alpha95"]
PmagResRec["average_n"]=PmagSiteRec["site_n"]
PmagResRec["average_n_lines"]=PmagSiteRec["site_n_lines"]
PmagResRec["average_n_planes"]=PmagSiteRec["site_n_planes"]
PmagResRec["vgp_n"]=PmagSiteRec["site_n"]
PmagResRec["average_k"]=PmagSiteRec["site_k"]
PmagResRec["average_r"]=PmagSiteRec["site_r"]
PmagResRec["average_lat"]='%10.4f ' %(lat)
PmagResRec["average_lon"]='%10.4f ' %(lon)
if agefile != "": PmagResRec= pmag.get_age(PmagResRec,"er_site_names","average_",AgeNFO,DefaultAge)
site_height=pmag.get_dictitem(height_nfo,'er_site_name',site,'T')
if len(site_height)>0:PmagResRec["average_height"]=site_height[0]['site_height']
PmagResRec["vgp_lat"]='%7.1f ' % (plat)
PmagResRec["vgp_lon"]='%7.1f ' % (plong)
PmagResRec["vgp_dp"]='%7.1f ' % (dp)
PmagResRec["vgp_dm"]='%7.1f ' % (dm)
PmagResRec["magic_method_codes"]= PmagSiteRec["magic_method_codes"]
if PmagSiteRec['site_tilt_correction']=='0':PmagSiteRec['magic_method_codes']=PmagSiteRec['magic_method_codes']+":DA-DIR-GEO"
if PmagSiteRec['site_tilt_correction']=='100':PmagSiteRec['magic_method_codes']=PmagSiteRec['magic_method_codes']+":DA-DIR-TILT"
PmagSiteRec['site_polarity']=""
if polarity==1: # assign polarity based on angle of pole lat to spin axis - may want to re-think this sometime
angle=pmag.angle([0,0],[0,(90-plat)])
if angle <= 55.: PmagSiteRec["site_polarity"]='n'
if angle > 55. and angle < 125.: PmagSiteRec["site_polarity"]='t'
if angle >= 125.: PmagSiteRec["site_polarity"]='r'
PmagResults.append(PmagResRec)
if polarity==1:
crecs=pmag.get_dictitem(PmagSites,'site_tilt_correction','100','T') # find the tilt corrected data
if len(crecs)<2:crecs=pmag.get_dictitem(PmagSites,'site_tilt_correction','0','T') # if there aren't any, find the geographic corrected data
if len(crecs)>2: # if there are some,
comp=pmag.get_list(crecs,'site_comp_name').split(':')[0] # find the first component
crecs=pmag.get_dictitem(crecs,'site_comp_name',comp,'T') # fish out all of the first component
precs=[]
for rec in crecs:
precs.append({'dec':rec['site_dec'],'inc':rec['site_inc'],'name':rec['er_site_name'],'loc':rec['er_location_name']})
polpars=pmag.fisher_by_pol(precs) # calculate average by polarity
for mode in list(polpars.keys()): # hunt through all the modes (normal=A, reverse=B, all=ALL)
PolRes={}
PolRes['er_citation_names']='This study'
PolRes["pmag_result_name"]="Polarity Average: Polarity "+mode #
PolRes["data_type"]="a"
PolRes["average_dec"]='%7.1f'%(polpars[mode]['dec'])
PolRes["average_inc"]='%7.1f'%(polpars[mode]['inc'])
PolRes["average_n"]='%i'%(polpars[mode]['n'])
PolRes["average_r"]='%5.4f'%(polpars[mode]['r'])
PolRes["average_k"]='%6.0f'%(polpars[mode]['k'])
PolRes["average_alpha95"]='%7.1f'%(polpars[mode]['alpha95'])
PolRes['er_site_names']= polpars[mode]['sites']
PolRes['er_location_names']= polpars[mode]['locs']
PolRes['magic_software_packages']=version_num
PmagResults.append(PolRes)
if noInt!=1 and nositeints!=1:
for site in sites: # now do intensities for each site
if plotsites==1:print(site)
if Iaverage==0: key,intlist='specimen',SpecInts # if using specimen level data
if Iaverage==1: key,intlist='sample',PmagSamps # if using sample level data
Ints=pmag.get_dictitem(intlist,'er_site_name',site,'T') # get all the intensities for this site
if len(Ints)>0: # there are some
PmagSiteRec=pmag.average_int(Ints,key,'site') # get average intensity stuff for site table
PmagResRec=pmag.average_int(Ints,key,'average') # get average intensity stuff for results table
if plotsites==1: # if site by site examination requested - print this site out to the screen
for rec in Ints:print(rec['er_'+key+'_name'],' %7.1f'%(1e6*float(rec[key+'_int'])))
if len(Ints)>1:
print('Average: ','%7.1f'%(1e6*float(PmagResRec['average_int'])),'N: ',len(Ints))
print('Sigma: ','%7.1f'%(1e6*float(PmagResRec['average_int_sigma'])),'Sigma %: ',PmagResRec['average_int_sigma_perc'])
input('Press any key to continue\n')
er_location_name=Ints[0]["er_location_name"]
PmagSiteRec["er_location_name"]=er_location_name # decorate the records
PmagSiteRec["er_citation_names"]="This study"
PmagResRec["er_location_names"]=er_location_name
PmagResRec["er_citation_names"]="This study"
PmagSiteRec["er_analyst_mail_names"]=user
PmagResRec["er_analyst_mail_names"]=user
PmagResRec["data_type"]='i'
if Iaverage==0:
PmagSiteRec['er_specimen_names']= pmag.get_list(Ints,'er_specimen_name') # list of all specimens used
PmagResRec['er_specimen_names']= pmag.get_list(Ints,'er_specimen_name')
PmagSiteRec['er_sample_names']= pmag.get_list(Ints,'er_sample_name') # list of all samples used
PmagResRec['er_sample_names']= pmag.get_list(Ints,'er_sample_name')
PmagSiteRec['er_site_name']= site
PmagResRec['er_site_names']= site
PmagSiteRec['magic_method_codes']= pmag.get_list(Ints,'magic_method_codes')
PmagResRec['magic_method_codes']= pmag.get_list(Ints,'magic_method_codes')
kill=pmag.grade(PmagSiteRec,accept,'site_int')
if nocrit==1 or len(kill)==0:
b,sig=float(PmagResRec['average_int']),""
if(PmagResRec['average_int_sigma'])!="":sig=float(PmagResRec['average_int_sigma'])
sdir=pmag.get_dictitem(PmagResults,'er_site_names',site,'T') # fish out site direction
if len(sdir)>0 and sdir[-1]['average_inc']!="": # get the VDM for this record using last average inclination (hope it is the right one!)
inc=float(sdir[0]['average_inc']) #
mlat=pmag.magnetic_lat(inc) # get magnetic latitude using dipole formula
PmagResRec["vdm"]='%8.3e '% (pmag.b_vdm(b,mlat)) # get VDM with magnetic latitude
PmagResRec["vdm_n"]=PmagResRec['average_int_n']
if 'average_int_sigma' in list(PmagResRec.keys()) and PmagResRec['average_int_sigma']!="":
vdm_sig=pmag.b_vdm(float(PmagResRec['average_int_sigma']),mlat)
PmagResRec["vdm_sigma"]='%8.3e '% (vdm_sig)
else:
PmagResRec["vdm_sigma"]=""
mlat="" # define a model latitude
if get_model_lat==1: # use present site latitude
mlats=pmag.get_dictitem(SiteNFO,'er_site_name',site,'T')
if len(mlats)>0: mlat=mlats[0]['site_lat']
elif get_model_lat==2: # use a model latitude from some plate reconstruction model (or something)
mlats=pmag.get_dictitem(ModelLats,'er_site_name',site,'T')
if len(mlats)>0: PmagResRec['model_lat']=mlats[0]['site_model_lat']
mlat=PmagResRec['model_lat']
if mlat!="":
PmagResRec["vadm"]='%8.3e '% (pmag.b_vdm(b,float(mlat))) # get the VADM using the desired latitude
if sig!="":
vdm_sig=pmag.b_vdm(float(PmagResRec['average_int_sigma']),float(mlat))
PmagResRec["vadm_sigma"]='%8.3e '% (vdm_sig)
PmagResRec["vadm_n"]=PmagResRec['average_int_n']
else:
PmagResRec["vadm_sigma"]=""
sitedat=pmag.get_dictitem(SiteNFO,'er_site_name',PmagSiteRec['er_site_name'],'T') # fish out site information (lat/lon, etc.)
if len(sitedat)>0:
sitedat=sitedat[0]
PmagResRec['average_lat']=sitedat['site_lat']
PmagResRec['average_lon']=sitedat['site_lon']
else:
PmagResRec['average_lon']='UNKNOWN'
PmagResRec['average_lon']='UNKNOWN'
PmagResRec['magic_software_packages']=version_num
PmagResRec["pmag_result_name"]="V[A]DM: Site "+site
PmagResRec["result_description"]="V[A]DM of site"
PmagResRec["pmag_criteria_codes"]="ACCEPT"
if agefile != "": PmagResRec= pmag.get_age(PmagResRec,"er_site_names","average_",AgeNFO,DefaultAge)
site_height=pmag.get_dictitem(height_nfo,'er_site_name',site,'T')
if len(site_height)>0:PmagResRec["average_height"]=site_height[0]['site_height']
PmagSites.append(PmagSiteRec)
PmagResults.append(PmagResRec)
if len(PmagSites)>0:
Tmp,keylist=pmag.fillkeys(PmagSites)
pmag.magic_write(siteout,Tmp,'pmag_sites')
print(' sites written to ',siteout)
else: print("No Site level table")
if len(PmagResults)>0:
TmpRes,keylist=pmag.fillkeys(PmagResults)
pmag.magic_write(resout,TmpRes,'pmag_results')
print(' results written to ',resout)
else: print("No Results level table")
